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Record: 1- (When) Are We Dynamically Optimal? A Psychological Field Guide for Marketing Modelers. By: Meyer, Robert J.; Hutchinson, J. Wesley. Journal of Marketing. Sep2016, Vol. 80 Issue 5, p20-33. 14p. 1 Color Photograph, 2 Charts, 3 Graphs. DOI: 10.1509/jm.16.0154.
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Record: 2- (When) Does Third-Party Recognition for Design Excellence Affect Financial Performance in Business-to-Business Markets? By: Eric Boyd, D.; Kannan, P. K. Journal of Marketing. May2018, Vol. 82 Issue 3, p108-123. 16p. 1 Diagram, 5 Charts. DOI: 10.1509/jm.15.0150.
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(When) Does Third-Party Recognition for Design Excellence Affect Financial Performance in Business-to-Business Markets?
Third-party recognition for design excellence is often viewed as an event triggering the success of a product with a good design. However, evidence from extant research raises doubt as to whether this form of recognition matters in business-to-business (B2B) markets. Using a demand shock conceptualization to capture the signaling role and demand uncertainty of such recognition, this research argues that third-party recognition for design excellence does create firm value in B2B markets. The effect varies, however, depending on certain types of chief executive officer functional experience (market and finance) and the criteria used by third parties in judging design superiority. Modelfree evidence and matched-sample analysis provides empirical evidence in support of the demand shock perspective and the hypothesized effects. The research adds a new theoretical conceptualization describing the impact of thirdparty recognition for design excellence and unveils several new research areas. Managerially, the results identify several new facilitating conditions that enable B2B firms to create greater value from an innovation strategy based on superior design.
Online Supplement: http://dx.doi.org/10.1509/jm.15.0150
Product design is a critical aspect of the innovation process and involves the configuration of a firm’s product offering (Dahl 2011; Ulrich 2011). Belief in the importance of good design can be found in discussions by chief executive officers (CEOs) who suggest that “good design will become the price of entry” (Brown 2005) and in business press articles stating, “At the Forefront of the Economy, Good Design Is a Priority” (Safian 2013, p. 26). A more nuanced perspective suggests that good design is a “necessary, but not sufficient” (Martin 2011, p. 243) condition for success and “not an unfailing recipe for business success” (Cantamessa 1999, p. 305). Understanding when design creates more or less value for a firm is important because of the significant investments required by a firm to produce a superiorly designed product (Kouvelis and Mukhopadhyay 1995).
Prior research has suggested that third-party recognition for design excellence is a factor influencing the success of products with a superior design (e.g., Chen, Liu, and Zhang 2012). This form of recognition consists of judgments by independent third parties regarding a product’s design superiority. Its impact is considered a result of the recognition’s ability to signal product quality (e.g., Reddy, Swaminathan, and Motley 1998) and the overall innovative capability of a firm (e.g., Xia, Singhal, and Zhang 2016) to customers. Using a signaling theory framework, research has linked the recognition with firm performance measures including long-term sales growth (e.g., Guo 2010; Zhang, Yu, and Xia 2014) and firm value (e.g., Chen, Liu, and Zhang 2012; Xia, Singhal, and Zhang 2016).
While understanding of the topic has benefited from prior research, there has been a tendency to explore the impact of third-party recognition for design excellence in business-toconsumer (B2C) markets such as movies (e.g., Chen, Liu, and Zhang 2012), automobiles (e.g., Balasubramanian, Mathur, and Thakur 2005), and running shoes (e.g., Chen and Xie 2005), where a signaling theory conceptualization has been sufficient to explain the recognition’s impact. In contrast, the small body of research examining third-party recognition in business-tobusiness (B2B) markets has painted a less than favorable picture. Tippins and Kunkel (2006), for instance, examined the impact of Clio awards won by advertising agencies and found limited instances of a positive linkage between design recognition and firm value. Xia, Singhal, and Zhang (2016) recently took a different approach and examined the relative impact of third-party recognition for design excellence across B2B and B2C markets and found that the effect was much larger in B2C markets. These less than positive findings create a difficult situation for B2B marketers who are faced with the question of whether to make the significant financial, market, and technological investments required for creating designs that receive recognition from expert judges (Larsen and Lewis 2007).
The purpose of this research is to provide clear and unambiguous insight for researchers and marketers regarding the implications of third-party recognition for design excellence in B2B markets. The objective is accomplished by addressing several limitations in prior research. First, this research focuses solely on B2B markets. Previously, Xia, Singhal, and Zhang (2016) showed that the impact of third-party recognition is higher in B2C markets. However, their research did not reveal whether the higher effect for B2C firms is relative to no effect for B2B firms or whether the effect in both B2C and B2B markets is significant and the B2C effect is simply larger. Results using event study analysis in our research provide evidence of a positive impact of third-party recognition for design excellence on firm value in B2B markets.
Second, our research addresses a conceptual limitation associated with prior research and its reliance on signaling theory. According to a recent meta-analysis examining switching costs and purchasing behavior in both B2B and B2C markets (Pick and Eisend 2014), unlike in B2C markets, product quality perceptions play a much less significant role in B2B markets. Furthermore, research has suggested that switching costs and purchasing behavior in B2B markets are driven by a host of factors, only some of which are influenced by product quality (Blut et al. 2016). These findings reflect the complex nature of purchasing in B2B markets and cast doubt on the ability of signaling theory and its focus on product quality to explain the impact of third-party recognition for design excellence in B2B markets.
To address this theoretical challenge, our research conceptualizes third-party recognition for design excellence in B2B markets from a demand shock perspective. Theoretically, a demand shock perspective focuses attention on both a buyer’s reaction to third-party recognition for design excellence and a firm’s actions taken in anticipation of how buyers will respond to the recognition (Aggarwal and Wu 2015; Ahmadjian and Oxley 2013; Landsman, Nelson, and Roundtree 2009; Mitchell and Singh 1996; Tokar et al. 2014). Empirical findings based on a matched-sample analysis of 102 instances of third-party recognition for design excellence support the contribution of a demand shock perspective in accounting for the financial impact of this form of recognition in B2B markets.
Conceptualization
Firm value is created when investors update their expectations positively about the level, timing, and riskiness of a firm’s cash flows (Srivastava, Shervani, and Fahey 1998). The primary means by which firms generate cash flow is through sales to existing and potential customers. In B2B markets, an important determinant of existing and new customer purchasing is a buyer’s perception of the switching costs associated with terminating and/or creating an exchange relationship with a supplier (Pick and Eisend 2014). Prior research has identified three types of these switching costs as being influential in B2B markets: ( 1) procedural switching costs associated with finding a new supplier and adopting its product, ( 2) financial switching costs associated with contractual agreements and purchasing benefits such as reward points, and ( 3) relational switching costs associated with brand and personal relationships between a buyer’s and supplier’s brand and employees, respectively (Blut et al. 2016).
The demand shock literature provides a perspective for understanding the impact of third-party recognition for design excellence on B2B switching costs and, ultimately, firm value. A demand shock represents a sudden and unexpected shift in demand due to an exogenous event (Tokar et al. 2014). In line with the definition of a demand shock, third-party recognition for design excellence often occurs suddenly when independent third parties release their judgments to the public; the exact timing of which is often unknown a priori. In addition, the outcome of these judgments is unexpected (or at least uncertain) and exogenous because it is based on the actions of independent judges whose judgments are outside of the firm’s influence.
The literature suggests the demand shock associated with third-party recognition for design excellence in B2B markets occurs at a firm level and as a result of the publicity associated with the recognition. Prior research has studied demand shocks in B2B markets at both industry and firm levels of analysis. Past examples of industry-level demand shocks in B2B markets include the attacks of September 11 (Aggarwal and Wu 2015), changes in economic conditions (Ahmadjian and Oxley 2013), regulatory changes (Landsman, Nelson, and Roundtree 2009), and changes in industry standards (Mitchell and Singh 1996).
Demand shocks also occur in B2B markets at a firm level through the impact of exogenous events on a focal firm. For instance, when Samsung suddenly recalled and stopped production of the Note 7 because of a faulty battery, a demand shock was created for suppliers such as Qualcomm, which provided materials for the manufacture of the Note 7 (Shah 2016). Publicity associated with third-party recognition can also create a demand shock, as described by Sajeel Hussain, chief marketing officer at Cafe´X, a company that sells robotic cafe kiosks to food and beverage retailers. Mr. Hussain describes the impact of third-party recognition on demand for the firm’s products in the following manner: “Do not underestimate the power of industry awards: When Cafe´X was formed in late 2013, nobody knew much about us until we won the Best of Show award at Enterprise Connect 2014. That changed everything for us and we received immediate endorsement from the industry, which gave us the air cover to seek new partnerships and client relationships that otherwise wouldn’t have been possible” (Hussain 2016).
A demand shock perspective also can account for the demand uncertainty that is likely to characterize third-party recognition for design excellence. Demand uncertainty characterizes the impact of third-party recognition for design excellence for two reasons. The first reason relates to the role of product quality in B2B switching costs and purchasing. Procedural switching costs are partially driven by search costs associated with finding a high-quality supplier (Blut et al. 2016). Third-party recognition creates a ranking of firms in an industry that reduces search-related procedural costs associated with switching to a recognized firm by its potential customers. Likewise, the quality signal can positively affect the brand aspect of relational switching costs that are based on brand spillover effects such as those that occur in cobranding strategies (Helm and O¨ zergin 2015), making it less likely that existing customers will switch from a recognized firm. However, while third-party recognition can positively affect quality-related switching costs in favor of a recognized firm, the importance of these switching costs is uncertain; research has suggested that quality perceptions are not necessarily a critical factor affecting purchasing behavior in B2B markets (Pick and Eisend 2014).
A second reason for expecting that demand uncertainty will characterize third-party recognition is that the recognized firm lacks complete information about those switching costs that are unrelated to product quality. For instance, B2B exchange re
lationships tend to be long term, and this creates the opportunity for employees from each firm to develop personal relationships (e.g., Lian and Laing 2007). Breaking these personal bonds creates relational switching costs that are often unknown to those outside of the exchange relationship. Similarly, a recognized firm will have little knowledge of contract-based financial switching costs that are driven by the financial costs of breaking existing buyer–supplier contracts (Blut et al. 2016). Contract length and termination costs can vary widely across buyer–supplier relationships and are often unknown to those outside an exchange relationship because of confidentiality agreements and/or business norms (Hobbs 2016; Snell 2007). The lack of knowledge in these areas creates uncertainty regarding how buyers will react to a firm receiving third-party recognition for design excellence, making it difficult for a recognized firm to gauge the magnitude and timing of thirdparty recognition’s impact on demand in B2B markets.
The demand shock literature recognizes the demand uncertainty that can characterize a demand shock like third-party recognition for design excellence by focusing attention on “bracing” activities firms undertake in trying to anticipate how to respond to the shock. Research has shown that the bracing activity of B2B firms varies in effectiveness. Aggarwal and Wu (2015), for instance, demonstrated that some B2B firms were able to more effectively reshuffle their product portfolio in anticipation of the demand effect 9/11 had on armament purchases by the U.S. federal government. Ineffective bracing activity often occurs as a result of a firm over- or underreacting in response to the demand uncertainty associated with a demand shock. For example, Tokar et al. (2014) reported a tendency of firms in a B2C context to be overly aggressive in building-up excess inventory in response to a demand shock (Tokar et al. 2014). There are reasons to expect that the same mismanagement occurs in B2B markets based on research revealing a tendency of B2B firms to react suboptimally to demand uncertainty by under- or overestimating demand (Dearden, Lilien, and Eunsang 1999).
The preceding discussion suggests that third-party recognition for design excellence in B2B markets can be viewed from a demand shock perspective on the basis of the sudden nature of its occurrence, its potential to quickly affect B2B demand, and the uncertainty associated with its demand impact in B2B markets. In addition to capturing the demand-side aspect of third-party recognition for design excellence, the demand shock perspective also captures the supply-side dimension associated with bracing activity undertaken by firms in anticipation of the demand effect the recognition will cause. It draws attention to the possibility that a firm’s reaction to the recognition can be biased as a result of demand uncertainty characterizing its effect. In the next section, we use this conceptualization to hypothesize the impact of third-party recognition for design excellence on firm value and hypothesize boundary conditions influencing its impact. Figure 1 outlines our hypothesized model.
Several factors provide a basis for expecting third-party recognition for design excellence will result in greater firm value for firms receiving the recognition. One reason relates to the impact of switching costs on customer loyalty (Han and Sung 2008; Lam et al. 2004). Higher customer loyalty protects a firm’s sales and cash flow, and this increases firm value by lowering the riskiness of the firm’s cash flow (Srivastava, Shervani, and Fahey 1998). Third-party recognition should lead to higher customer loyalty because it increases procedural and relational switching costs for existing customers. Procedural switching costs related to search are higher for existing customers of a recognized firm because the recognition makes it more difficult for existing customers to find a supplier with quality superior to that of the recognized firm. Relational switching costs related to brand relationships are also higher for a recognized firm’s existing customers because the recognition increases the possibility of positive brand spillover effects from the recognized firm’s brand to the existing customer’s brand, making it costly for existing customers to break the brand relationship by switching to another supplier. In addition to promoting less risky cash flow through higher customer loyalty, the higher switching costs for existing customers will promote higher cash flow levels in the form of greater share of wallet and cross-buying (Blut et al. 2016).
Third-party recognition should also affect switching costs of potential customers in ways that allow a recognized firm to generate new revenue streams through customer acquisition. For example, the recognition lowers procedural switching costs related to search for a recognized firm’s potential customers because the public nature of the recognition increases awareness by its potential customers of the recognized firm’s product superiority. The recognition also lowers relational switching costs associated with a recognized firm’s potential customers breaking their brand relationships with their current supplier because of the potential positive brand spillover effects that can come as a result of the brands of potential customers being associated with the recognized firm’s brand. The lower switching costs experienced by a recognized firm’s potential customers should promote firm value by supporting customer acquisition and new revenue streams from potential customers (Blut et al. 2016). This could happen even when the product is in a mature category with the award drawing attention to the design excellence of the product and making it salient.
H1: Third-party recognition for design excellence creates value in B2B markets.
The preceding discussion suggests B2B firms will experience an expected positive shift in demand from third-party recognition for design excellence due to its positive impact on the procedural switching costs related to search costs and the relational switching costs related to brand relationships. However, the magnitude and timing of the demand shift can be uncertain because of the influence of additional switching costs about which less is known. For example, there will be uncertainty about relational costs from broken personal bonds and financial costs from prematurely terminated contracts that are unique to exchange relationships. This demand uncertainty could lead to mishandling of the demand shock by the recognized firm through a bracing response that is either too safe or too risky (e.g., Dearden, Lilien, and Eunsang 1999; Tokar et al. 2014). What can prevent mishandling of the sales opportunity presented by third-party recognition for design excellence in B2B markets? Recent research focusing on strategic decision making, in general (Maitland and Sammartino 2015), and demand shocks, specifically (Tokar et al. 2014), has suggested that the availability of task-related information plays a large role in reducing the biasing effects of uncertainty on decision making.
One important form of task-related information is functional experience reflecting the experience accumulated in the performance of an organizational task. We focus on CEO functional experience on the basis of anecdotal evidence suggesting that CEOs recognize the importance of third-party recognition for design excellence and actively respond to the recognition. Bruce Cazenave, CEO of Nautilus, for instance, responded to the Bowflex receiving an award for design excellence by describing the recognition as “an incredible honor and testament to the efforts of our entire product design team” (Business Wire 2014). As such, we expect that CEOs will play an influential role in a firm’s response to third-party recognition for design excellence and that their functional experience will provide insight that can reduce demand uncertainty and allow for more effective bracing activity. The types of CEO functional experiences explored include experience related to markets and finance. These types were chosen because of the insight each type of experience provides relative to B2B switching costs that can create demand uncertainty in the presence of third-party recognition for design excellence.
Chief executive officers vary in their market experience to the extent that they have previously worked in marketing-related and sales-related positions (Hui, Morgan, and Rego 2015). As CEOs gain market-related experience, their knowledge of existing and potential customers’ procedural, financial, and relational switching costs increases. This insight should reduce the demand uncertainty characterizing the recognition and lead to more effective bracing activity. One reason for expecting CEO market experience to be important is that through marketing activity like market research and market segmentation, CEOs acquire general market knowledge related to brands and products (Homburg and Jensen 2007). This information can aid in understanding relational switching costs related to brand relationships. Similarly, sales experience provides insight into the personal nature of exchange relationships, lending insight into the relational switching costs associated with relationships between a buyer’s and supplier’s employees (Johnson, Sohi, and Grewal 2004). In addition, a CEO’s knowledge gained through advertising and related promotional activities provides insight into the search dimension of procedural switching costs (Holak and Reddy 1986).
A CEO’s knowledge gained through past sales efforts to acquire new customers will provide the recognized firm with insight into the financial and relational switching costs for its potential customers. A CEO’s experience in sales activity can also involve working with customers postpurchase to ensure that they experience value from their relationship with a supplier (Burger and Cann 1995). This experience provides a CEO with insight into the adoption aspect of procedural switching costs associated with buyers’ use of a supplier’s product. Thus, the market-related functional experience of a firm’s CEO provides an important information source that can reduce the uncertainty associated with various forms of B2B switching costs, increasing the likelihood that the firm will effectively respond to the demand shock accompanying third-party recognition for design excellence.
H2: A firm creates greater (vs. less) value from third-party recognition for design excellence when its CEO has more (vs. less) functional experience related to markets.
The finance function focuses on effectively managing a firm’s cash flows through financial instruments like equity and debt (Proctor 2014). Experience in finance provides a CEO with various forms of knowledge that provide insight into potential customers’ switching costs in B2B markets. One form of knowledge is contract knowledge gained through participation in financial negotiations with buyers. Business-to-business purchases are often large-volume purchases that involve extended financial negotiations related to payment terms and the provision of trade credit to buyers (Geiger 2017). The knowledge gained through this form of financial experience will provide insight into the financial switching costs associated with terminating contracts. In addition, experience in finance provides a CEO with knowledge related to financial policies, such as inventory cost management, and their value in decision making (Custodio and Metzger 2014). This information can enable CEOs to better understand how past investments can influence procedural switching costs associated with the adoption of a supplier’s product. Chief executive officers with experience in finance will also have a better understanding of switching costs involving personal relationships on the basis of their experience working with clients’ purchase departments as well as the investment community, where personal relationships play an important role (Andrews and Welbourne 2000). These different sources of knowledge a CEO acquires through experience in finance should lessen a firm’s uncertainty regarding the impact of third-party recognition for design excellence on B2B switching costs and allow the firm to best leverage the demand shock.
H3: A firm creates greater (vs. less) value from third-party recognition for design excellence when its CEO has more (vs. less) functional experience in finance.1
The criteria used by third parties when making their judgments of design excellence may include design dimensions related to function, form, or both function and form. These criteria can affect the importance of CEO functional experience by influencing the nature of the demand shock associated with third-party recognition. Specifically, design criteria based on both function and form should dampen the immediate demand shock through a slower purchase deliberation process and less new customer acquisition relative to designs focused on only one design dimension.
One reason for expecting a slower B2B buying decision for designs based on both function and form versus buying decisions for designs based on form or function is related to price. Designs excelling in both function and form will be priced higher because suppliers will need to recoup the higher investments required to excel in both function and form (Kouvelis and Mukhopadhyay 1995; Larsen and Lewis 2007). The higher price will increase the importance of the purchase to buyers because of its greater impact on a buying firm’s financial outlay for supplies, cost structure, and profitability (Wathne, Biong, and Heide 2001). When making more important purchase decisions, firms tend to follow a more formalized buying process that can slow purchase decisions by adding complexity and conflict to the purchase process when the cost of acquired supplies is high (Lau, Goh, and Phua 1999; Pemer and Skjølsvik 2016). In addition, this can also dampen the likelihood of acquiring new customers, whose costs in switching from existing suppliers increase as a result of the aforementioned factors.
A second reason for expecting a slower purchase process for designs emphasizing both function and form involves the financial risk associated with these types of designs. The higher price associated with designs achieving recognition for both function and form creates higher financial risk, which will motivate B2B buyers to consider more purchase criteria (Tullous and Munson 1992). The inclusion of more purchase criteria increases the amount of information search associated with a purchase and this can result in a longer duration for the purchase decision (Weiss and Heide 1993).
A third factor to consider is the complexity associated with adopting designs emphasizing both function and form. Adopting and leveraging products excelling on both design dimensions will likely require buyers to develop more complex processes and systems relative to the processes and systems involved in adopting and leverage designs emphasizing only one design dimension. When faced with more complex purchasing decisions, firms generally create larger buying groups that allow for the input of people from across a firm whose knowledge can aid in decision making (Crow and Lindquist 1985). Identifying whose input should be considered, gathering the input, and using it to make a decision all require time, and this can slow down the purchase process.
Taken together, the higher price, higher financial risk, and greater complexity associated with products excelling on both design dimensions versus one dimension should result in a slower B2B buying decision process than designs excelling on only one design criteria. The slower purchase process will limit the demand shock for products excelling on both design dimension relative to products excelling on one dimension. This will especially be the case with new buyers switching from their existing suppliers to the award-winning firm, thus damping the demand shock effect that occurs through new customer acquisition. As a result, the functional experience of CEOs and the insight it provides for managing the demand shock associated with third-party recognition for design excellence should make less of a difference when design criteria involve both function and form.
H4: The positive impact of CEO functional experience related to markets on the value a firm creates from third-party recognition for design excellence is smaller when the criteria used in determining the recognition are based on both the function and form dimensions of design.
H5: The positive impact of CEO functional experience related to finance on the value a firm creates from third-party recognition for design excellence is smaller when the criteria used in determining the recognition are based on both the function and form dimensions of design.
Several methodological challenges arise when trying to examine the effect of third-party recognition for design excellence. The first challenge involves isolating the effect of third-party recognition, because many activities contribute to a product’s development and the eventual value it creates (Hertenstein, Platt, and Veryzer 2005). To overcome this challenge, we utilized the event study methodology originally developed in the finance literature (Brown and Warner 1980, 1985) and adopted by researchers within the marketing literature (e.g., Sorescu, Shankar, andKushwaha 2007; Wiles, Morgan, and Rego 2012). The event study methodology captures changes in investors’ expectations regarding the future cash flow of a firm experiencing an event. The event study enables us to measure the financial effect of third-party recognition after accounting for all other effects of the firm and the market (Brown and Warner 1985).
The second challenge requires effectively identifying occurrences of recognition for design excellence by a third party. We address this challenge by analyzing awards granted by third parties for product design excellence. Awards for product design excellence are won through a competitive process that firms enter through their own application or by being nominated by another party. The judging of a product’s design excellence involves third-party experts involved in industrial design, marketing, and other areas related to product design. In some cases, customers also play a role in judging the superiority of product designs. Consistent with prior research (e.g., Guo 2010; Xia, Singhal, and Zhang 2016), we measure third-party recognition for product design excellence on the basis of a product winning a product design award.
A third challenge involves testing the causal impact of the recognition and the factors proposed to moderate its effect. To address this challenge, we created a matched sample of “good” design firms not receiving third-party recognition for design excellence using procedures recommended in prior methodological research (e.g., Barber and Lyon 1996; Cram, Karan, and Stuart 2009). In the first step of the matching procedure, we sought potentialmatches within the same industry at a two-digit Standard Industrial Classification (SIC) level. In the second step, we analyzed those firms sharing at least a two-digit SIC match with a firm recognized for design excellence, identifying potential matches as those with a market value within 10% of the recognized firm to find firms that are similar on our dependent variable (i.e., firmvalue). In the third step, we identified the nearest neighbor to the recognized firm based on research and development (R&D)/sales. We used this lastmatching filter to account for factors associated with a firm’s ability and motivation to seek third-party recognition for design excellence. The choice of R&D/sales for matching purposes is based on prior research linking R&D investment with an emphasis on design (e.g., Hsu 2006). Finally, in step four, we verified that no information about the nearest-neighbor match identified in step three was released into the market on the same day the recognized firm received its recognition for its design excellence. This last step ensured that the only difference between the recognized firm and its match was third-party recognition of the former firm for design excellence. The matching procedure employed in the research is consistent with matching procedures used in prior research studying firm value in a variety of contexts related to this research, including marketing management (Mizik 2010) and innovation (Miller 2004).2
We collected the sample used in testing the stated hypotheses by identifying all announcements of firms receiving a product design award that appeared in major daily newspapers and wire services such as the Wall Street Journal, PR Newswire, and Dow Jones Newswire between 1997 and 2014. This resulted in a sample of 369 third-party product design award announcements received by publicly traded firms. Our initial sample consisted only of publicly traded firms to accommodate the event study methodology and the research focus on the value implication of third-party recognition for design excellence. Firms in the initial sample were classified as B2B (vs. B2C) on the basis of their two-digit SIC codes, following prior research examining marketing activity in B2B markets3 (Srinivasan, Lilien, and Sridhar 2011).
Following recommendations from the event study literature (e.g., McWilliams and Siegel 1997), we removed third-party product design award announcements that involved confounding events. For purposes of this study, a confounding event is an event within a three-day window around the focal event that may influence investors’ expectations of firm performance. Examples of confounding events include quarterly performance reports, alliance formations, and executive officer appointments.
One concern is the possibility of self-selection (i.e., whether a firm applied for the award itself). We do not believe a selfselection issue characterizes our sample for several reasons. First, all the awards are based on the evaluation of third-party independent judges, and this prevents firms from being able to directly influence their selection for an award. Second, firms do not completely control whether they are considered for an award. An examination of our sample of awards reveals that third parties consider firms for an award through a nomination process that allows a firm to nominate itself and/or allows a third party (e.g., customer) to nominate the firm. We attempted to isolate those situations in which a firm nominated itself for an award; however, we were unable to identify any instances in which an award recognized whether a firm had self-nominated or been nominated by a third party. Third, we attempted to control for a firm’s motivation to apply for an award by including R&D intensity when constructing a matched sample, recognizing that two firms would be more likely to apply for an award when they place a similar emphasis on R&D. Fourth, our difference model controls for unobserved firm-specific effects that may be related to a firm’s motivation to apply for a design award but are not formally captured in our model.
We also ensured that the recognition for design excellence ( 1) was not the first time investors learned about the existence of a firm’s product and ( 2) was attributed to an independent third party and not to discussions by a competitor. To accomplish this, we searched company annual reports, business databases (ABI Inform), and 10-K filings for all recognized firms in our sample and the products receiving the recognition. We also examined the same documents for all firms in the same fourdigit SIC code to ensure that no competitor discussed the recognition of a sample firm’s design. Our examination of 8,721 10-K filings confirmed that all awards in the sample occurred after the release of product-related information to the public and that there were no competitive discussions of the third-party recognition in the sample.
The final sample consisted of 102 product design award announcements made by publicly traded B2B firms. The sample is similar in size with prior research using the event study method in the marketing literature (e.g., Boyd, Chandy, and Cunha 2010).
Our measure of financial performance is based on changes in the stock price of B2B firms receiving third-party recognition and aligns with research demonstrating the importance that investors assign to demand shocks when valuing a firm (e.g., Humphreys 2010). The event study methodology provides a way to measure the financial effect of third-party recognition for product design excellence, as reflected in a design excellence award, on stock price (Brown and Warner 1985; McWilliams and Siegel 1997). The event study method estimates the abnormal movement in the stock price of a firm receiving thirdparty recognition for design excellence using the Fama–French four-factor model (i.e., the original Fama–French three-factor model with Carhart’s momentum factor) on the day the design excellence award is first announced. This is captured in Model 1 for event k for firm I on day t: ( 1) Rti = ai + bi1Rmt + bi2SMBt + bi3HMLt + bi4UMDt + ARti, where Rit denotes the rate of return (over the risk free rate) of firm I on day t, Rmt denotes the corresponding daily returns on the market portfolio on day t, SMBt (“small minus big”) denotes the difference between portfolios composed of small and big market capitalization at time t, HMLt (“high minus low”) denotes the difference between portfolios composed of high and low book-to-market value stocks at time t, UMDt (“Carhart’s price-momentum factor”) denotes one-year momentum in returns, and ai denotes the intercept term. Rtm captures the usual market factor in stock returns while SMBt and HMLt are meant to reflect risk factors related to size and book-to-market equity. The residual ARit represents a zero-mean abnormal portfolio return that is unexplained by common risk factors. We then use the estimates obtained from the model to calculate abnormal returns (ARs) and cumulative abnormal returns (CARs) by summing the individual ARs across time to account for information leakage before the event date or information dissemination after the event date. We followed prior research in obtaining the ordinary least squares parameter estimates using a 90-trading-day estimation window, ending six days before the focal event (e.g., Wiles, Morgan, and Rego 2012).
Prior research has recommended using a difference model when analyzing matched sample data (e.g., Cram, Karan, and Stuart 2009). As such, we performed the same event study analysis for our matched sample using the event dates for their matched firm recognized for design excellence. We then subtracted the AR for its matched counterpart from the AR for a recognized firm to arrive at our dependent variable. Thus, our main dependent variable is the difference in AR between firms recognized by a third-party for their design excellence and their matched counterparts that did not receive third-party recognition.
We used secondary data to construct the independent variables employed to test the hypotheses. Table 1 provides an overview of the measures and the sources from which we collected the data to measure each variable. Consistent with recommendations from prior research (Cram, Karan, and Stuart 2009), we create difference measures for all independent variables. This involves collecting data for each independent variable for both the firms receiving third-party recognition for design excellence and their matched counterparts. With the exception of design excellence criteria, for which there was no counter measure for the nonrecognized control firms, we subtract the nonrecognized firm measure for an independent variable from that of its corresponding recognized firm in creating the difference measure for each independent variable.
CEO functional experience. We measured the functional experience of CEOs related to markets and finance on the basis of their prior functional experience, following previous research in our measurement process (e.g., Barker and Mueller 2002; Hambrick and Mason 1984). Specifically, we created a set of dummy variables indicating whether a CEO had experience in any of eight functional areas including marketing, sales, engineering, human resources, legal, finance, operations, and R&D. To accomplish the coding, two raters reviewed the biographical background of each CEO for a firm recognized for its design excellence and its matched counterpart to assess the presence or absence of each of the functional areas in a CEO’s background. The coders were first provided with a listing of roles associated with functional experience in marketing, sales, engineering, finance, R&D, operations, legal, and human resources. The roles used to identify a CEO with functional experience related to markets included experience in roles involving marketing, advertising, branding, customer service, merchandising, media planning, sales, selling, business development, and account management. The roles used to identify a CEO with functional experience related to finance included experience roles involving finance, accounting, and bookkeeping and titles such as controller and treasurer.
The coders were then provided with biographies of CEOs for the recognized firms and control firms in the sample. We employed multiple sources in identifying CEO biographies, including firm documents (e.g., 10-Ks), executive profile databases (e.g., Bloomberg’s Executive Profiles and Biographies), and social media databases (e.g., LinkedIn). The coders were provided with a subsample of biographies (5%) and trained on classification of the biographies using the role listings provided to them. This process also identified and resolved questions regarding role-related information in the biographies, interpretation of titles, and assignment of titles using the classification. When there were no more training questions, coders were provided with the full sample of biographies and coded them. There was 98% agreement between the raters, and we resolved any discrepancies. The high level of coder agreement was credited to the extensive training they completed before classifying the full sample.
TABLE: TABLE 1 Measures
| Conceptual Variables | Measures | Data Sources |
|---|
| CEO market experience | Percentage of a CEO’s functional experience in marketing and sales | 10-Ks, DEF-14s, Bloomberg’s Executive Profiles and Biographies, LinkedIn |
| CEO finance experience | Percentage of a CEO’s functional experience in finance | 10-Ks, DEF-14s, Bloomberg’s Executive Profiles and Biographies, LinkedIn |
| Design excellence criteria | Design excellence third-party recognition based on both functional and form aspects of design | Third-party recognition announcement, third-party website |
| Firm prior awards | Number of design awards prior to third-party recognition for design excellence | Major daily newspapers and wire services |
| Product age | Days since launch of product recognized fort design excellence | 10-Ks, major daily newspapers and wire services |
| Promotional intensity | SG&A/total revenue | Compustat |
| Firm SOA | Revenue/total assets | Compustat |
| Industry environmental uncertainty | Coefficient from regressing five-year period of industry sales over time divided by average industry revenue | Compustat |
| Industry environmental munificence | Standard error from regressing five-year period of industry sales over time divided by average industry revenue | Compustat, 10-Ks |
| Industry competitiveness | Herfindahl index based on industry revenue | Compustat |
| Award: consumer judge | Indicator variable: 1 = design excellence thirdparty recognition based on input from consumers; 0 otherwise | Third-party recognition announcement, third-party website |
| Award: magazine sponsor | Indicator variable: 1 = sponsor of third-party recognition is a magazine; 0 otherwise | Third-party recognition announcement, third-party website |
| CEO duality | 1 if CEO is also Chair of the Board; 0 otherwise | |
| SIC 4-digit | Four-digit SIC code | Compustat |
| Market value | Stock price • common shares outstanding | Compustat |
| R&D intensity | R&D investment/total revenue | Compustat |
Notes: SOA = sales on assets.
We originally wanted to measure actual years for each functional experience. However, data reflecting the actual years of experience for each CEO in each area of work experience were not consistently available across CEOs. As an alternative, we measure each area of functional experience as the percentage of a CEO’s functional experience in that area relative to all areas of functional experience for the CEO. For instance, a CEO with functional experience related to markets, legal, and engineering would have a measure of .33 for market experience, while a CEO with functional experience only in markets would receive a value of 1.0 for market experience. This approach provides a method for determining the intensity of a CEO’s functional experience related to markets and finance versus other functional areas. The value for this measure ranges from 0 (no functional experience in an area) to 1 (all functional experience in an area) and varies between these extremes depending on the other areas of functional experience for a CEO. The way CEO functional experience is measured results in the measures being akin to seasonal dummies. Thus, their effect should be interpreted relative to those functional areas of experience not included in the model (e.g., human, legal).4
Design excellence criteria. The criteria used by third parties when judging design excellence are provided in the recognition. We use this feature of our sample to code the product design awards using a dummy variable indicating whether the stated award is based on both function and form design dimensions ( 1) or is based only on function (0) or only on form (0). The variable was created on basis of the classification of two reviewers independent of the research. The two coders were provided definitions for function and form aspects of design based on definitions offered in prior research (e.g., Homburg, Schwemmle, and Kuehnl 2015). The definition provided to the coders for design function stated that function refers to the ability of a product to successfully perform a task and deliver on customer expectations in performing the task. The coders were also provided with examples of ways to describe design function that included descriptions of the product being “helpful,” “capable,” “sturdy,” and/or “practical” in its performance of a task. The definition provided to the coders for design form stated that form refers to the “look” or aesthetic appeal of the product and its features, including size (e.g., big, small), shape (e.g., round, wide) and packaging (e.g., creative, eye-catching). The coders were also provided with examples of ways to describe design form that included descriptions of the product as being “beautiful,” “appealing,” “elegant,” and “striking” in its appearance. We collected the design criteria for each award from information provided in each announcement and on the third party’s website. We provided a subsample (5%) from the full sample to the coders to train them on use of the criteria in classifying each award. Afterward, the coders were given the full sample to code. There was 82% agreement between the coders, and we resolved the remaining discrepancies.
Factors other than the variables identified in the hypotheses may have an impact on investors’ reactions to a firm receiving thirdparty recognition for design excellence. At a firm level, the efficient market hypothesis suggests that changes in firm stock price are the result of investors reacting to new information about a firm. Given our focus on product design excellence, we accounted for the newness of design-related information by controlling for the number of prior design awards received by a firm for other products before the focal award examined in our analysis. We also recorded product age to account for the level of prior information investors had about a recognized product. We control for marketing by including a measure of promotional intensity based on a firm’s investment in sales, general, and administrative (SG&A) relative to its revenue. We account for the potential impact of prior performance on investor reaction to a superior product design by including a variable measuring sales on assets (sales/total assets). The model also controls for the influence of CEOs by measuring whether a CEO served as both CEO and chair of the board.
The research also controls for macroenvironmental factors. We control for environmental uncertainty and environmental munificence by using measures from prior research based on an analysis of industry sales (e.g., Boyd, Chandy, and Cunha 2010). We measure environmental uncertainty by regressing industry sales onto a five-year time period prior to a firm announcing its participation a trade show. The standard error from this regression is then standardized by the mean of industry sales over the fiveyear time period. Environmental munificence is measured using the same regression analysis of five-year industry sales and dividing the coefficient from the analysis by the mean of industry sales over the five-year time period. We account for the competitive nature of a recognized firm’s industry by including a measure of industry concentration using the Herfindahl index.
Several aspects of a product design award may influence investor reaction to a firm winning an award. The judging varies in line with the identity of the third-party expert, which could lead to different performance impacts of an award (Gemser, Leenders, and Wijnberg 2008). All awards involved evaluations by industry experts, but there were awards that also included feedback from consumers. Drawing on prior research suggesting the influence of consumer word of mouth (Chakravarty, Liu, and Mazumdar 2010), we included a dummy variable indicating whether the judging included consumer feedback. The research also controls for the sponsor of the award by identifying those awards sponsored by a magazine to account for market exposure to the recognition.
The last set of control variables account for possible concerns regarding the validity of our matching process. Cram, Karan, and Stuart (2009) empirically demonstrate that validity in matched-sample analysis is best established by recognizing the inexactness inherent in matching techniques and the potential of unobservable factors not accounted for by matching. Following their recommendation, we include difference measures for the variables used in creating the matched sample: industry SIC, market value, and R&D intensity. Difference in industry SIC code was measured using a dummy where the value of 1 reflected an exact match at a four-digit level between a recognized firm in our sample and its matched counterpart; the measure was given a value of 0 otherwise. Differences in market value and R&D intensity were measured between a firm recognized for design excellence and its matched counterpart.
TABLE: TABLE 2 Correlations and Descriptive Statistics
| | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |
|---|
| 1. CEO market experience | 1 | | | | | | | | | | | | | | | |
| 2. CEO finance experience | -.34** | 1 | | | | | | | | | | | | | | |
| 3. Design excellence criteria | 0.18 | 0.05 | 1 | | | | | | | | | | | | | |
| 4. Firm prior awards | 0.09 | -0.09 | -0.02 | 1 | | | | | | | | | | | | |
| 5. Product age | 0.12 | -0.07 | -.18* | -0.01 | 1 | | | | | | | | | | | |
| 6. Promotional intensity | 0.05 | -.22* | -.23* | -0.01 | 0.05 | 1 | | | | | | | | | | |
| 7. Firm SOA | 0.01 | .19* | -0.02 | 0.05 | .24* | -.28* | 1 | | | | | | | | | |
| 8. Industry environmental uncertainty | 0.16 | -0.06 | .18* | .23** | 0.05 | -0.08 | -0.02 | 1 | | | | | | | | |
| 9. Industry environmental munificence | -0.04 | 0.07 | -.18* | .21* | 0.04 | -.19* | -0.01 | 0.02 | 1 | | | | | | | |
| 10. Industry competitiveness | -0.01 | 0.03 | -0.06 | 0.03 | 0.02 | 0.02 | 0.03 | 0.09 | 0.08 | 1 | | | | | | |
| 11. Award: consumer judge | -0.14 | 0.01 | -.41** | -0.04 | .24* | -0.09 | .20* | -0.13 | -0.07 | 0.05 | 1 | | | | | |
| 12. Award: magazine sponsor | -0.16 | 0.07 | 0.01 | -0.08 | 0.06 | 0.06 | -0.08 | -0.03 | 0.07 | 0.11 | .22* | 1 | | | | |
| 13. CEO duality | -.21* | 0.01 | -.23* | -0.01 | -0.02 | .20* | -0.13 | -.20** | 0.15 | -0.11 | 0.01 | 0.02 | 1 | | | |
| 14. SIC 4-digit | -0.12 | 0.07 | -0.09 | -0.07 | -0.04 | -0.03 | 0.02 | -.29** | 0.04 | -.19* | .20* | -0.05 | 0.05 | 1 | | |
| 15. Market value | -0.06 | 0.02 | 0.03 | -0.04 | -0.07 | -0.01 | 0.01 | -0.01 | 0.08 | -0.09 | -0.01 | 0.16 | 0.05 | -0.06 | 1 | |
| 16. R&D intensity | 0.05 | -.19* | -.25* | -0.01 | 0.06 | -.87** | -.26* | 0.11 | 0.16 | 0.03 | -0.06 | 0.08 | .19* | 0.06 | -0.01 | 1 |
| Mean (frequency) | 0.01 | 0.03 | 1=67% | 0.56 | 79780% | 0.36 | 0.02 | 0.12 | 0.08 | 0.98 | 1=13% | 1=55% | 1=45% | 31.76 | 15% | 0.18 |
| SD | 0.38 | 0.34 | 0=33% | 2.93 | 227043% | 1.73 | 0.71 | 0.04 | 0.08 | 0.1 | 0=87% | 0=45% | 0=55% | 1450.23 | 67% | 0.73 |
*p < .05.
**p < .01.
Table 2 provides the correlation matrix and descriptive statistics for the independent variables and control variables. We used the following model specification to test our hypotheses: ( 2) DiffAbnormalReturni, I = b0 + b1DiffCEOMarketExperi, m + b2DiffCEOFinanceExperi, m + b3DiffCEOMarketExperi, m · DesignExcCriteriai + b4DiffCEOFinanceExperi, m · DesignExcCriteriai + b5DesignExcCriteriai + b6DiffPriorAwardsi, m + b7ProductAgei + b8DiffPromotionalIntensityi, m + b9DiffFirmSOAi, m + b10EnvUnci, m + b11EnvMuni, m + b12CompetIntensityi, m + b13ConsumerJudgei + b14MagSponsori, m + b15CEODualityi + b16DiffSIC4Digiti, m + b17DiffMarketValuei, m + b18DiffR&Di, m + b19YearDummiesi, k + e, where
DiffAbnormalReturni, m = difference in AR between third-partyrecognized firm I and its matched counterpart m,
DiffCEOMarketExperi, m = difference in CEO market experience between third-party-recognized firm I and its matched counterpart m,
DiffCEOFinancialExperi, m = difference in CEO financial experience between third-party-recognized firm I and its matched counterpart m,
DesignExcCriteriai = third-party recognition received by firm I based on both function and form design criteria,
DiffPriorAwardsi, m = difference in prior design awards between third-party-recognized firm I and its matched counterpart m, ProductAgei = days since launch of product recognized by third party for design excellence for firm i,
DiffPromotionalIntensityi, m = difference in SG&A/revenue between third-party-recognized firm I and its matched counterpart m,
DiffFirmSOAi, m = difference in sales on assets between thirdpartyrecognized firm I and its matched counterpart m, EnvUnci, m = environmental uncertainty for industry of thirdparty recognized firm I and its matched counterpart m,
DiffEnvMuni, m = environmental uncertainty for industry of third-party-recognized firm I and its matched counterpart m,
DiffCompetIntensityi, m = firm concentration for industry of third-party-recognized firm I and its matched counterpart m,
ConsumerJudgei = consumer contribution to the third-party recognition received by firm i,
MagSponsori = magazine-sponsored third-party recognition received by firm i,
CEODualityi = CEO is also chair of the board for firm I recognized by third-party for design excellence,
DiffSIC4Digiti, m = difference in SIC four-digit code between third-party-recognized firm I and its matched counterpart m,
DiffMarketValuei, m = difference in market value between thirdpartyrecognized firm I and its matched counterpart m,
DiffR&Di, m = difference in R&D intensity between third-partyrecognized firm I and its matched counterpart m, and
YearDummiesi, k = vector of year k in which firm I received third-party recognition for design excellence.
Model-free evidence. For the first step of our analysis, we aimed to provide evidence of a demand shock being associated with third-party recognition for design excellence. We use the sample of recognized firms and compare their sales growth with that of their matched counterparts during each of the four quarters immediately following recognition. The benchmark for generating sales growth during each quarter subsequent to recognition is the quarter immediately preceding recognition. Table 3 reveals that only recognized firms experience any significant postrecognition sales growth, and this growth occurs only during the quarter immediately following their winning of an award. Nonrecognized firms do not experience any significant sales growth during the same period. It is important to note that there is a large range of values characterizing sales growth for recognized firms, suggesting that there is significant variation in the extent to which firms are able to generate more or less sales from the recognition. Furthermore, 56% of the sample experienced a positive return, while 44% of the sample experienced a negative return. This distribution aligns with our demand shock perspective. These results provide empirical evidence consistent with a demand shift for firms receiving third-party recognition for design excellence and provide support for a demand shock conceptualization.
TABLE: TABLE 3 Demand Shock Model-Free Evidence
| | Mean | SD | p-Value (Mean 5 0) | Median | 95% Range |
|---|
| Recognized Firms |
| Sales Growth Quarter 1 = ln[qt + 1/qt - 1]* | 3.95 | 24.86 | .09 | 2.80 | [-29.21, 88.05] |
| Sales Growth Quarter 2 = ln[(qt + 2)/(qt - 1)] | 4.99 | 36.95 | .17 | 6.87 | [-37.60, 46.44] |
| Sales Growth Quarter 3 = ln[(qt + 3)/(qt - 1)] | 6.43 | 40.98 | .11 | 8.81 | [-36.53, 59.38] |
| Sales Growth Quarter 4 = [ln(qt + 4/(qt - 1)] | 7.00 | 44.00 | .11 | 11.88 | [-44.09, 49.44] |
| Nonrecognized Matched Sample Firms |
| Sales Growth Quarter 1 = ln[(qt + 1)/(qt - 1)]* | 2.34 | 27.29 | .38 | 5.18 | [-49.85, 34.27] |
| Sales Growth Quarter 2 = ln[(qt + 2)/(qt - 1)] | 3.62 | 37.51 | .33 | 7.43 | [-47.90, 43.63] |
| Sales Growth Quarter 3 = ln[(qt + 3)/(qt - 1)] | 4.07 | 36.68 | .26 | 7.33 | [-44.65, 50.38] |
| Sales Growth Quarter 4 = [ln(qt + 4/(qt - 1)] | 6.47 | 44.26 | .15 | 10.36 | [-65.37, 74.42] |
As Table 4 shows, third-party recognition for design excellence positively affects firm value on the day it is announced. The AR during the event day window (0) is positive and significant (CAR = .78, p < .05). The lack of significance for any of the alternative ARs and CARs suggests that there is very little leakage of the recognition before its announcement, which is consistent with the sudden nature of the information being made publicly available and aligns with our demand shock conceptualization. These results provide support for H1.
TABLE: TABLE 4 ARs and Third-Party Recognition for Design Excellence
| Event Window | Avg. AR (%) | p-Value |
|---|
| t = -1 | -.11 | .72 |
| t = 0 | .79 | .01 |
| t = +1 | -.08 | .81 |
| t = (-1, 1) | .60 | .29 |
| t = (-1, 0) | .67 | .14 |
| t = (0, 1) | .71 | .12 |
TABLE: TABLE 5 Firm Value and Third-Party Recognition for Design Excellence
| | Model 1 | SE | Model 2 | SE |
|---|
| CEO market experience (CEOM) | | | .08** | 0.03 |
| CEO finance experience (CEOF) | | | .09*** | 0.03 |
| CEOM • Design excellence criteria (DEC) | | | -.08* | .04 |
| CEOF • DEC | | | -.08** | .03 |
| DEC | .00 | .01 | .01 | .01 |
| Firm prior awards | .00 | .00 | .00 | .00 |
| Product age | .00 | .00 | .01* | .00 |
| Promotional intensity | .02** | .01 | .02*** | .00 |
| Firm SOA | .00 | .00 | -.01 | .01 |
| Industry environmental uncertainty | .06 | .15 | .02 | .15 |
| Industry environmental munificence | .00 | .08 | .01 | .08 |
| Industry competitiveness | -.02 | .02 | -.02 | .02 |
| Award: consumer judge | .02 | .02 | .03** | .01 |
| Award: magazine sponsor | -.01 | .01 | -.01 | .01 |
| CEO duality | .00 | .01 | .00 | .01 |
| SIC 4-digit | -.01 | .01 | -.01 | .01 |
| Market value | .01** | 0 | .01* | 0 |
| R&D intensity | -0.02 | 0.02 | -.03* | 0.01 |
| Constant | -0.01 | 0.04 | -0.01 | 0.04 |
| R2 | 0.21 | | 0.31 | |
| F-value | 2.39*** | | 5.33*** | |
| Sample size | 102 | | 102 | |
*p < .10.
**p < .05.
***p < .01.
Note: Yearly indicator variables included in model but not reported.
To test H2–H5, we use the difference in ARs between the firm recognized for design excellence by a third party and its matched counterpart on the day of the recognition announcement the dependent variable. Table 5 provides the model statistics and the results from regressing the ARs from the event day onto the hypothesized variables and control variables included in the model. We also calculated the variance inflation factors for the variables in our model and found all variance inflation factors to be well below the recommended threshold of 10. The full model is significant and explains 31% of the variation in the abnormal stock return associated with a firm receiving third-party recognition for design excellence.
H2 focused on the impact of CEO market experience. The sign for the CEO market experience coefficient is positive (coefficient = .08) and significant (p < .05). As a result, there is evidence in support of the importance of CEO market experience as a factor influencing the value a firm creates from receiving third-party recognition for design excellence.
H3 examined the effect of CEO experience related to finance. The positive (coefficient = .09) and significant (p < .01) coefficient for finance experience provides support for H3. This finding illustrates the important contribution of CEO finance experience as a factor influencing investors’ expectations regarding the value creation opportunity provided by recognition for product design excellence.
H4 and H5 argued that the positive effect of CEO market and finance experience is smaller when third-party recognition for design excellence is based on criteria that include both functional and form dimensions of design because of the slower purchase process associated with products emphasizing both design dimensions and the resulting dampened demand shock. The results support both H4 and H5 based on the negative and significant coefficient for the interaction term involving CEO market experience and design excellence criteria (coefficient = -.08; p < .10) and the negative and significant coefficient for the interaction term involving CEO finance experience and design excellence criteria (coefficient = -.08; p < .05).
Overall, the results provide strong support for viewing third-party recognition for design excellence as a factor creating firm value in B2B markets. The results also support the influence of a CEO’s functional experience related to markets and finance on the value created from third-party recognition for design excellence in B2B markets. The significant impact of CEO functional experience highlights the importance investors assign to a firm’s anticipated reaction to recognition when evaluating the financial impact of third-party recognition for design excellence. In addition, the significant and negative interaction of functional experience with design excellence criteria provides empirical evidence demonstrating that management’s contribution depends on the level of sales opportunity created by third-party recognition for design excellence.
We performed several forms of follow-up analysis to assess the robustness of the research findings. The first follow-up analysis involved testing the proposed model with alternative dependent variables. The original analysis used the difference in AR between each firm recognized by a third-party for design excellence and its matched counterpart as the dependent variable. This difference measure represents a novel approach to using ARs from an event study within marketing. To address concerns that our results are an artifact of the way we constructed the dependent variable, we created an alternative difference measure using one-day buy and hold returns on the event day for the recognized firms in our sample and their matched counterparts. The results from testing this alternative model are consistent with the original results (see Web Appendix B). As an additional robustness check related to our dependent variable, we also created a random sample of ARs for our recognized firms and their matched counterparts by randomly identifying a date within 60 days of the actual award event date. We calculated ARs for the random event date and the analysis rerun. None of the hypothesized findings from our original model replicate with the random sample of ARs (see Web Appendix C).
We also aimed to ensure that findings related to CEO functional experience, in general, are not a by-product of the measure used for CEO functional experience by examining an alternative measure of the variables. As highlighted previously when discussing the measure of CEO functional experience, we originally sought to measure actual years for each functional experience. However, this was not possible because of missing data for multiple CEOs with respect to time spent in a particular function. Instead, the analysis measured each area of functional experience as the percentage of a CEO’s functional experience in that area relative to all areas of his or her functional experience. The robustness analysis aimed to validate that it is the intensity of functional experience and not simply the presence of experience that is important. We pursued this objective by creating a dummy variable equaling 1 if a CEO has experience in a functional area and 0 otherwise. The alternative approach led us to recode CEO market and finance functional experience, respectively, resulting in a binary indicator measure for each type of experience. None of the CEO functional variables are significant using this new indicator measure, confirming that it is the intensity of CEO functional experience that matters to investors (see Web Appendix D).
We also explored whether the results from our analysis are sensitive to the presence of outliers. To do this, we Winsorized the data at 99% and reran the analysis. We were also sensitive to the possibility that our choice of a cutoff value for Winsorizing the data could potentially bias the results. Therefore, we also Winsorized the data at 95% and reran the analysis. The results are robust across both levels of Winsorization, suggesting that outliers are not influencing the results (see Web Appendix E).
With this research, we aimed to provide insight into the financial impact of third-party recognition for design excellence in B2B markets. To do so, we employed a demand shock conceptualization to account for possible reactions to the recognition. The research hypothesized a positive average effect across B2B markets and hypothesized CEO functional experience and the design criteria used by experts when determining design excellence as moderating factors. An event study method of analysis involving a matched sample and difference score regression analysis examining 102 incidences of design excellence awards received by B2B firms both provide strong support for the hypothesized model. Next, we discuss the theoretical and managerial implications of the research findings.
One contribution of this article is the clarity and confidence it provides to the small body of research on third-party recognition for design excellence in B2B markets. Prior research on the topic created doubts regarding the effect of this form of recognition in B2B markets because of limited positive findings (e.g., Tippins and Kunkel 2006) and findings that showed lesser effects in B2B versus B2C markets (e.g., Xia, Singhal, and Zhang 2016). Our research addressed these empirical concerns by analyzing a cross-sectional sample of third-party recognitions for design excellence received by B2B firms. The positive results from the event study method of analysis and the model-free evidence regarding an immediate and significant sales effect eliminate doubts about the significance of this type of recognition in B2B markets, highlighting the importance of third-party recognition for design excellence for B2B researchers and managers.
A second theoretical contribution of the research is the broader conceptual perspective it provides regarding thirdparty recognition for design excellence. To this point, researchers have heavily emphasized the impact of third-party recognition using signaling theory and focusing on changes in customer demand as a result of changes in consumers’ perceptions of a firm’s product quality (e.g., Balasubramanian, Mathur, and Thakur 2005; Chen, Liu, and Zhang 2012; Chen and Xie 2005). The current research offers an alternative but complementary view by focusing on the topic from a demand shock perspective. This new perspective recognizes the demand-side impact suggested by signaling theory while highlighting a supply-side perspective that emphasizes a firm’s bracing activity in the context of demand uncertainty as it attempts to leverage the demand shock. This latter managerial viewpoint is missing from a signaling theory perspective, but the empirical findings involving CEO functional experience support its importance and the contribution of a demand shock conceptualization.
Furthermore, the results related to CEO functional experience cannot be explained purely by signaling theory. Prior research has suggested that investors should expect superior designs from CEOs with market experience because of the important contribution of this type of knowledge in creating superior product designs (Im et al. 2016). Thus, CEO market experience should be a weak quality signal. Alternatively, investors should have lower expectations of superior designs based on CEO finance experience because of the internal focus of this type of experience. This should make CEO finance experience a stronger signal to investors relative to CEO marketing experience. However, follow-up analysis of the standardized coefficients reveals that the effect of CEO market experience (beta coefficient = .62) and the effect of CEO finance experience (beta coefficient = .65) are very close in magnitude. The closeness of the effects aligns with the demand shock arguments we proffer here, such that both types of experience provide important information related to B2B switching costs and their role in determining the effect of third-party recognition for design excellence.
A third contribution of the research involves its addition of hitherto unexplored boundary conditions to the literature. Prior research by Balasubramanian, Mathur, and Thakur (2005) has found that the effect of recognition for design excellence can vary by the source of recognition. Guo (2010) showed how the impact can vary on the basis of differences in the history of design’s competitive importance across industries. In our research, we included several controls to account for these findings. The significance found for CEO role experience related to markets and finance demonstrates the importance of adding CEO functional experience to the set of factors known to create boundary conditions moderating the impact of third-party recognition for design excellence.
While these findings broaden researchers’ theoretical understanding of third-party recognition and design excellence in B2B markets, they also raise several important questions in need of research. For instance, do investors vary the importance they assign to CEO functional experience in managing a demand shock depending on the source (e.g., economic events, natural disasters), valence (positive or negative), and level (macro or micro) of a demand shock? Moreover, what other factors create demand shocks in B2B markets besides third-party recognition for design excellence? Research in B2C markets has suggested that firm promotional activity can create a demand shock (e.g., Pauwels et al. 2004). Does firm-level promotional activity have a similar effect in B2B markets? If so, how does the effect vary across different forms of promotion in B2B markets? Addressing these questions will be important for marketing researchers to fully understand the role of demand shocks and the factors that influence a firm’s ability to manage them effectively.
Our research suggests several factors managers should consider to ensure that their firms benefit from innovation strategies based on superior designs. First, the findings recommend that B2B marketers invest in receiving thirdparty recognition for design excellence. The doubts induced by prior research regarding the effect of this kind of recognition in B2B markets created a difficult situation for B2B marketers. It forced them to choose between making significant investments to achieve recognition (Larsen and Lewis 2007)—without any confidence that the investments would pay off—and forgoing the investments and assuming the risk of being classified into a low-quality group because of a lack of recognition (Rossman and Schilke 2016). This research provides strong evidence in support of the former decision, and B2B marketers can use this empirical evidence to acquire the resources necessary to achieve third-party recognition for design excellence.
Another implication of the research is that it calls for marketing managers to alter their view regarding third-party recognition for design excellence. A recent survey of practitioners reveals that managers see little direct economic value in third-party recognition for design excellence (Sung, Nam, and Chung 2010). This research clearly shows that this form of recognition warrants more serious attention from managers because of the positive impact it can have on firm value and sales, as shown in the event study and model-free analysis, respectively. Importantly, the research also reveals that B2B marketers can expect to best leverage the recognition when their CEO possesses the right types of functional experience. We found that marketing and finance experience were especially helpful in managing and benefiting from the demand shock accompanying third-party recognition for design excellence.
Finally, promotion intensity has a significant and positive effect on the difference in ARs. When viewed in light of the overall positive effect we found for third-party recognition of design excellence, this finding suggests that firms can amplify the positive effect of achieving third-party recognition for design excellence through their marketing efforts. Importantly, we measured promotional activity based on expenditures toward SG&A relative to revenue drawing on prior research suggesting that the sales force is the primary promotional activity within B2B firms (Biemans, Brencic, and Malshe 2010) and that sales force activity is typically associated with short-term promotional activity that aligns closely with the short-term nature of a demand shock (Homburg and Jensen 2007). The positive effect found for promotion intensity suggests that investment in the sales force can be one way in which a B2B firm can enhance the returns from third-party recognition for design excellence.
There are several limitations to note when considering the research findings. Because of data limitations, our measurement of CEO functional experience did not measure actual years of experience. Instead, CEO functional experience was measured on the basis of the relative intensity of the CEO’s experience in different functional areas. This measurement approach should be kept in mind when interpreting the results.
Another point to consider is that the removal of announcements with confounding events in the research helped ensure internal validity of the empirical findings. However, the trade-off is that it may weaken external validity if the recognition is considerably more significant when conflated with other events (Warren and Sorescu 2017). Thus, one should interpret the results reported in this research as a conservative estimate of the impact of third-party recognition for design excellence on firm value in B2B markets.
Finally, the research did not identify the path through which CEO functional experience has its influence within a recognized firm. The CEO effect could be the result of the CEO’s individual decision making or it could be the result of the CEO’s effectiveness in coaching the firm’s staff (or some combination thereof). Future research is needed to provide insight on this issue.
Footnotes 1 In testing H2 and H3, the baseline is other CEO functional experiences because each type of CEO functional experience is measured as a percentage of a CEO’s total experience across functional areas. We describe the measures in more detail subsequently in the article, when discussing the measures employed in the research.
2 We conduct various robustness tests (discussed in a subsequent section) to ensure that the matching process does not bias the analysis.
3 A table providing the SIC codes used in the analysis at a fourdigit level appears in Web Appendix A.
4 We thank an anonymous reviewer for highlighting this feature of the measures employed in the research for capturing CEO functional experience.
DIAGRAM: FIGURE 1 Third-Party Recognition for Design Excellence and Its Impact on Firm Value in B2B Markets: A Demand Shock Perspective
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Record: 3- 2016 MARKETING SCIENCE INSTITUTE/H. PAUL ROOT AWARD. Journal of Marketing. Sep2017, Vol. 81 Issue 5, pvi-vi. 1p.
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2016 MARKETING SCIENCE INSTITUTE/H. PAUL ROOT AWARD
Michel Wedel and P.K. Kannan have been selected as the recipients of the 2016 MSI/H. Paul Root Award for their article “Marketing Analytics for Data-Rich Environments,” which appeared in the November 2016 (Volume 80, Number 6) issue of Journal of Marketing. The article was chosen for its significant contribution to the advancement of the practice of marketing.
Nominations for the award were solicited from members of the Journal of Marketing Area Editors and Editorial Review Board. A committee overseeing the nominating and selection process includes Dave Stewart (chair), Ruth Bolton, and Gary Frazier.
The award is presented annually at the Summer AMA Conference.
• Michel Wedel is PepsiCo Chaired Professor of Consumer Science and Distinguished University Professor, Robert H. Smith School of Business, University of Maryland, College Park.
• P.K. Kannan is Ralph J. Tyser Professor of Marketing Science, Robert H. Smith School of Business, University of Maryland, College Park.
This year’s finalists included:
• “Binge Watching and Advertising,” David A. Schweidel and Wendy W. Moe (Volume 80, Issue 5, September 2016)
• “Brand Buzz in the Echoverse,” Kelly Hewett, William Rand, Roland T. Rust, and Harald J. van Heerde (Volume 80, Issue 3, May 2016)
• “Creating Enduring Customer Value,” V. Kumar and Werner Reinartz (Volume 80, Issue 6, November 2016)
• “Demonstrating the Value of Marketing,” Dominique M. Hanssens and Koen H. Pauwels (Volume 80, Issue 6, November 2016)
• “Understanding Customer Experience Throughout the Customer Journey,” Katherine N. Lemon and Peter C. Verhoef (Volume 80, Issue 6, November 2016)
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Record: 4- 2016 SHELBY D. HUNT/HAROLD H. MAYNARD AWARD. Journal of Marketing. Sep2017, Vol. 81 Issue 5, pv-v. 1p.
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2016 SHELBY D. HUNT/HAROLD H. MAYNARD AWARD
Cait Lamberton and Andrew T. Stephen have been selected as the recipients of 2016 Shelby D. Hunt/Harold H. Maynard Award for their article “A Thematic Exploration of Digital, Social Media, andMobile Marketing: Research Evolution from 2000 to 2015 and an Agenda for Future Inquiry,” which appeared in the November 2016 (Volume 80, Number 6) issue of Journal of Marketing.
The article was chosen for its significant contribution to marketing theory and thought. Nominations for the award were solicited from Area Editors and members of the Journal of Marketing Editorial Review Board. A committee overseeing the nominating and selection process includes Robert Meyer (chair), Ajay Kohli, and Leigh McAlister.
The award is presented annually at the Summer AMA Conference.
• Cait Lamberton is Ben Fryrear Chair and Associate Professor of Marketing, Joseph M. Katz Graduate School of Business, University of Pittsburgh.
• Andrew T. Stephen is L’Or’eal Professor of Marketing, Sad Business School, University of Oxford.
This year’s finalists included:
• “Organizing for Marketing Excellence,” Christine Moorman and George S. Day (Volume 80, Issue 6, November 2016)
• “Assessing Performance Outcomes in Marketing,” Constantine S. Katsikeas, Neil A. Morgan, Leonidas C. Leonidou, and G. Tomas M. Hult (Volume 80, Issue 2, March 2016)
• “Creating Enduring Customer Value,” V. Kumar and Werner Reinartz (Volume 80, Issue 6, November 2016)
• “Understanding Customer Experience Throughout the Customer Journey,” Katherine N. Lemon and Peter C. Verhoef (Volume 80, Issue 6, November 2016)
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Record: 5- 2017 SHETH FOUNDATION/JOURNAL OF MARKETING AWARD. Journal of Marketing. Sep2017, Vol. 81 Issue 5, piv-iv. 1p.
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2017 SHETH FOUNDATION/JOURNAL OF MARKETING AWARD
Kapil R. Tuli, Ajay K. Kohli, and Sundar G. Bharadwaj are the recipients of the 2017 Sheth Foundation/Journal of Marketing Award for their article “Rethinking Customer Solutions: From Product Bundles to Relational Processes,” which appeared in the July 2007 (Volume 70, Number 3) issue of Journal ofMarketing. Nominations for the award were solicited from members of the JM Editorial Review Board, and a committee of former journal editors”Rajan Varadarajan (chair), Russ Winer, and Robert Leone”made the selection. The award is given to honor an article that has made a long-term contribution to the discipline of marketing. The award recognizes scholarship based on the benefits of time and hindsight and acknowledges contributions and outcomes made to marketing theory and practice.
This year, the committee considered all articles published in JM between 2007 and 2011. The committee also weighed information obtained from nomination letters and citation analyses. The Sheth Foundation/Journal of Marketing Award was established through the generosity of the Sheth Foundation. The criteria for selection include the quality of the article’s contribution to theory and practice, its originality, its technical competence, and its impact on the field of marketing.
The award is presented annually at the Summer AMA Conference.
• Kapil R. Tuli is Professor of Marketing and Lee Foundation Fellow, Lee Kong Chian School of Business, Singapore Management University.
• Ajay K. Kohli is Professor of Marketing and Gary T. and Elizabeth R. Jones Chair, Scheller College of Business, Georgia Tech.
• Sundar G. Bharadwaj is the Coca-Cola Company Chair of Marketing, Terry College of Business, University of Georgia.
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Record: 6- A Cinderella Story: How Past Identity Salience Boosts Demand for Repurposed Products. By: Kamleitner, Bernadette; Thürridl, Carina; Martin, Brett A.S. Journal of Marketing. Nov2019, Vol. 83 Issue 6, p76-92. 17p. 1 Diagram, 3 Charts, 1 Graph. DOI: 10.1177/0022242919872156.
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A Cinderella Story: How Past Identity Salience Boosts Demand for Repurposed Products
Like Cinderella, many repurposed products involve a biographical transformation, from a tattered past identity (e.g., an old airbag) to a product with a valuable but different new identity (e.g., a backpack made from an airbag). In this article, the authors argue that marketers should help customers infer such product stories by highlighting the products' tattered past identities. Three field experiments and four controlled experiments show that making a product's past identity salient boosts demand across a variety of repurposed products. This is because past identity salience induces narrative thoughts about these products' biographies, which in turn allows customers to feel special. Results also suggest that this strategy of past identity salience needs to be particularly well-crafted for products with easily discernible past identities. These findings highlight a promising new facet of storytelling (i.e., stories that customers self-infer in response to minimal marketer input); create new opportunities for promoting products with a prior life; and deliver detailed guidance for the largely unexplored, growing market for upcycled and recycled products.
Keywords: storytelling; narrative thinking; repurposed products; upcycling; recycling; felt specialness; product history
A worn-out airbag becomes a backpack. A leaky boat turns into a table. An old mosquito net transforms into a laptop sleeve. Product biographies that entail transformations from an old past identity to a new product identity are characteristic of production modes that involve the repurposing of old or dysfunctional products into new products. These include recycling, which is already common practice ([86]), and upcycling, which is gaining in popularity ([63]; [85]). For instance, a search on Instagram for upcycling yields over 1 million results, and the upcycled offers on the online marketplace Etsy have increased by 1,000% since 2011, with Etsy now featuring more than 300,000 different upcycled products in the United States alone. Many established companies, such as the outdoor brand Patagonia or the fashion retailers ASOS and Urban Outfitters, have also started to operate in this domain.
However, when companies offer new products manufactured from old or waste products, how can they ensure that customers will demand these products? In this article, we show how the unique properties of repurposed and transformed products illustrate a novel way of storytelling in marketing. Unlike conventionally produced goods, such products have both a clear past and present product identity. Although they may vastly diverge in their form and purpose, both identities are embodied in the product. We argue that the past identity of a repurposed product amounts to the starting point of its biographical story of transformation, and that this holds storytelling potential. We refer to this strategy of alerting customers to a product's past identity as "past identity salience" and argue that it increases demand because it triggers narrative product thoughts and allows customers to feel special with the storied product. Notably, the past identity involves a waste product (e.g., a broken mosquito net) that may not serve the product's current primary function (e.g., a wallet made from a broken mosquito net). Even though a product's past identity is effectively useless (and potentially even disgusting), we suggest that marketers highlight it because it unlocks the product's storytelling potential.
Results of three field experiments, four experiments conducted in controlled settings, and several replication studies support these claims. Our evidence pertains to different proxies of demand, stretches across multiple product categories and past identities, holds at both the store and product level, and emerges across different methods of making the past identity salient. We rule out several alternative accounts (perceived environmental friendliness, interestingness, surprise, originality, authenticity, a handmade effect, and informational value) and deduce two relevant boundary conditions. When a mere glance at the product allows customers to discern its past, simple appeals to the past identity do not further increase demand. Moreover, making the past identity salient only increases demand if the product had a prior life—and thus, a starting point of its biographical story. This applies to upcycling and the closely related practice of recycling ([55]; [86]) but not to conventionally produced products.
The present research offers important contributions to the storytelling literature in marketing. First, we show that marketers can utilize products' own biographical stories rather than craft stories around product use or brand values, which dominate existing practices ([ 3]; [27]; [71]; [79]). Second, and in line with the story-prone nature of the human mind ([10]; [15]), we show that storytelling does not require explicit detail. Simple cues suffice for customers to note the presence of a story and essentially tell it to themselves ([40]). Notably, we show that this boosts demand even if the starting point of these stories is prosaic, such as a past identity as a mundane pallet or an old mosquito net. Third, and drawing on the finding that storied objects are deemed special ([34]), we identify a rarely examined key mechanism that underlies our effects on demand: storied products provide customers with felt specialness.
We also contribute to the growing literature on products that entail the use of materials with a prior life and identity ([ 1]). As one of the first empirical studies of upcycling from a marketer's perspective, we highlight that success is more likely if marketers focus on the special biographies of upcycled products as their unique selling proposition ([46]). This is an important contribution, because it also extends to recycling and provides marketers of upcycled and recycled goods with actionable techniques to increase customer demand—an effort that is needed to realize the environmental and economic potential of these market practices ([48]).
Upcycling is a sustainable production mode that prolongs the life of old objects by creatively reusing and reshaping them into new products ([13]; [85]). This repurposing practice allows brands to tap into new markets and generate value from what otherwise might be waste ([12]; [30]; [88]).
Upcycling shares this benefit with other sustainable practices of product reuse, such as secondhand products ([ 2]; [45]; [46]), vintage products ([20]; [82]), or social recycling ([22]). However, these practices amount to the simple reuse of the same product by a different owner. In contrast, upcycling entails repurposing old products and results in a new product. It is thus similar to recycling, wherein the value is in old materials being transformed by breaking them down into raw materials before turning them into new products again ([12]; [77]; [84]). Both upcycling and recycling involve the repurposing of old products and entail a true and substantive transformation, in which the nature of the outcome product differs from the nature of its discarded source product.
As a result of this transformation, repurposed products have two differing identities: a past identity, which is derived from the form and functionality of the source product, and a present identity, which captures the product's current and mostly different form and functionality. While marketers can focus on the upcycled product's benefits and emphasize elements of its present identity, they can also highlight the product's now dysfunctional past and draw customers' attention to the old or waste materials that compose the product. We call this strategy of alerting customers to the repurposed product's past identity "past identity salience." For example, an ad for a wallet made out of old mosquito nets could either not mention this past or explicitly state that it is made from mosquito nets, which is what some upcycling brands do. For example, the luxury bag brand Elvis & Kresse (www.elvisandkresse.com) prominently references to the past identities of its products in its communications, and the Swiss brand Freitag leverages the fact that its bags and accessories are made from truck tarps (www.freitag.ch). The Berlin-based store Upcycling Deluxe even enables its customers to search for products on the basis of what they used to be (www.upcycling-deluxe.com). Recycling brands, in contrast, frequently draw attention to the recycled nature of their products but do not disclose their specific past identities. It is still unclear whether the demand for repurposed products benefits or suffers from a strategy of "past identity salience."
Several findings actually discourage highlighting repurposed products' past identities. Many customers are skeptical about purchasing used goods ([41]), and they can be sensitive to the physical distortion of a product, which makes them dispose of it more quickly ([77]). Indeed, upcycled products often show traces of wear and tear from their original purpose, which makes customers aware that they are not the first person to interact with the product. Drawing attention to the product's past identity may thus elicit processes of contagion that prevent customers from opting for a product contaminated by other people ([ 5]; [56])—a danger that has been observed in the context of product reuse ([45]) and could extend to repurposing.
However, we suggest that it does not, and that repurposed products escape the stigma of the past because they have effectively been transformed into a new product (see [86]), who find that thoughts of transformation trigger recycling). Through this transformation, a salient past identity not only fails to harm demand but, on the contrary, fuels demand, and we propose that this is because past identity salience draws attention to the product's special story. Repurposed products can serve as a prime example of how past identity salience can trigger the persuasive story of a product's biographical transformation.
This argument is new to the storytelling literature in marketing. To support it, we first need to address the question "What is a story?" Essentially, a story is a linear temporal sequence of causally related events ([27], [28]). It is a chronological description of which events occurred and how they are connected. Another term consumer researchers have used to describe this is "narrative" ([ 3]; [27], [28]). Note, however, that other literature has distinguished between these terms in that "story" refers to a chronological chain of events and "narrative" to their causal factors ([68]; [73]).
Chronology and causality are the central structural characteristics that enable narrative thinking ([19]; [27], [28]; [73]). Chronology refers to the temporal sequence or episodes of events ([65]) and to the fact that stories have a beginning, a middle, and an end ([28]; [74]). Causality is defined as the causal connections or relationships between story elements ([27]) that enable customers to assign meaning to a narrative. Rather than causality, some authors use the term "plot," which refers to the theme of a story and imbues story events with meaning ([65]). Stories can feature any number of specific story elements, which are woven together in a plot. Several typologies have been offered to explain the plots of stories throughout history (e.g., [ 9]; [16]; [76]). Although plots differ, a prototypical narrative features a protagonist who is the main character of the story ([65]), such as Cinderella, who participates in story events ([19]). Notably, the mere presence of a main character can serve as a means to provide causality ([19]; [79]).
Humans have been attuned to stories and narrative thinking since the dawn of humankind and stories wield considerable power over people ([15]). They are able to demonstrate, communicate, and persuade ([27], [26]). The marketing literature has been well aware of stories' ability to fuel demand ([79]; [83]; [87]), and storytelling is a common marketing practice ([71]; [80]). Marketers tell brand stories ([71]) and stories in which products affect consumers ([49]; [83]), and artists tell stories that feature products or brands as contextual elements or accessories, as in the case of product placement ([44]; [70]). In most of these stories, the product is not the main protagonist, and all of them are clearly recognizable as fully fledged stories that someone tells.
Advancing current theorizing, we suggest adding the product's own biographical story to marketers' storytelling toolbox. Moreover, we suggest that marketers can trigger these stories without spelling them out. Simply making the product's past identity salient, as we propose, can induce customers to infer a repurposed product's biographical story and in turn increase demand for the storied product.
But how can customers comprehend such stories when an ad features no more than the product and a reference to its past identity? The answer lies in the fact that humans are uniquely attuned to engage in narrative thinking and discern, self-tell, and appreciate a good story ([ 3]; [27]). Moreover, even simple past identity appeals map onto the key structural story characteristics of chronology and causality. As to chronology, a product's past identity is an episode in its life that chronologically precedes its present identity. Making the past identity salient thus ensures that multiple chronologically ordered episodes in the product's life become salient. As to causality, salience of the product's past and present identities implies their causal connection. The repurposed product is the protagonist that ensures causality through its implied identity transformation.
Transformational stories are historically one of the most popular forms of story ([16]; [67]). They involve a change in identity as part of the protagonist's biography. Also known as metamorphosis ([76]), this type of plot involves a temporally bounded event (i.e., the transformation) wherein the protagonist changes from one permanent state to a new permanent state without the disappearance of the protagonist from the story ([40]). Metamorphosis plots are present throughout history ([33]), ranging from stories from antiquity (e.g., Ovid's Metamorphoses), to more recent novels (e.g., Franz Kafka's The Metamorphosis), fairy tales (e.g., Cinderella), and popular culture (e.g., The Incredible Hulk, The Matrix). Notably, it also extends to the biographical stories of repurposed goods, which transform from being a depreciated product on the verge of the waste-bin to a storied new object with multiple identities.
Transformational stories are particularly powerful in terms of inducing narrative thoughts. They only need what narratologists term a "minimal narrative" to unfold in people's minds. A minimal narrative consists of identical entities that are present in two temporally and qualitatively distinct states ([66]). The essence of minimal narratives is time change and, often, transformation ([54]). In our research, the minimal narrative consists of the protagonist in time 1 (the salience of the product's past identity) and the transformed protagonist in time 2 (the upcycled or recycled present product identity). Minimal narratives are more than a mere ordered sequence of events because of their overall meaning ([54]), which unfolds in perceivers' minds.
We propose that customers will infer a repurposed product's story when its past is salient because this salience allows for an awareness of the plot (i.e., how the different identities are connected via the transformation). To attain that meaning, customers need to engage in inferential processing. Inferencing forms the narrative linkages that empower minimal narratives (past identity, present identity). To engage in inferential processing, individuals need to draw on their personal knowledge and imagination. In a dynamic process, individuals interact with the presented story elements, infer missing information, and include past information to disambiguate stories ([32]; [42]). Research in psychology and consumer research shows that humans are genuinely prone to take agency and engage in inferencing and using their imagination to generate story causality ([27]; [35]; [79]) and to thus complete or comprehend a story that is not fully spelled out.
A plot of transformation provides a particularly rich resource for inferential narrative processing. The multiple identities and the metamorphosis itself allow for multifaceted interpretations ([32]; [42]) and provide perceivers with a wide projection; inferencing; and, thus, storytelling space. We therefore expect that a salient cue for a repurposed product's past identity will suffice to induce perceivers to infer its biographical story of transformation.
Why would people demand a product that holds a story of having been waste more than a product that does not? We propose that storied products cause higher demand because they imbue customers with felt specialness—that is, the belief that they will feel more special as a result of acquiring and utilizing a product that holds a story.
To understand how stories can evoke a sense of specialness in people, it is important to understand the ways in which stories affect people ([15]; [27]; [49]). On the one hand, stories focus them on the narrative rather than on rational arguments ([49]), potentially even transporting (i.e., absorbing) recipients into a story ([36]; [79]). However, for this to happen, people must experience the pathos of a dramatic story ([64]) and feel empathy for story characters ([79]). This tends to necessitate the act of telling a fully-fledged story to passive recipients. The minimal narrative provided by quick exposure to an ad including past identity cues is unlikely to allow for either of these experiences.
On the other hand, and largely fueled by inferential processes, stories help people in their sensemaking process ([87]). This mechanism is well-suited to explain the appeal triggered by minimal narratives. People's narrative thoughts help shape their perceptions of the story's protagonist and its meanings ([27]). When stories are self-inferred, they are personal to the individual ([74]; [83]) and can evoke diverse special and individual meanings ([57]; [69]). For example, if a customer sees an ad for a wallet made from an old mosquito net, they might draw on their own associations and think of the lives the net has saved or connect it with their own personal travels. These self-inferred, special meanings help decommodify the object ([25]; [46]).
Simply "having" a story suffices for the protagonist to become more special. Cinderella, for example, is a special princess thanks to her story of transformation (see [31] for how biographical transformations affect the appreciation of the "transformer"). Like Cinderella, repurposed products are the protagonist in a transformational story that imbues them with a unique biographical history, and objects with such a story are likely to be perceived as special ([46]), even by young children ([62]).
Importantly, the meanings of objects tend to transfer to those who acquire and utilize them ([52]) and can help individuals in their identity work, a process that often motivates the decision of whether to acquire an object ([ 4]; [ 7]; [14]). Storied—and thus, special—objects have the power to make individuals feel special about themselves. Given that objects that promise feelings of specialness are known for being high in demand ([ 8]; [38]), storied objects are likely to boost demand ([34]; [58]).
In summary, we propose that making a repurposed product's past identity salient alludes to the minimal narrative of its transformational story. This invites customers to engage in narrative thinking and allows them to infer an individualized and special version of the product's story. Perceiving the product as storied, in turn, enables customers to feel special with the product, which eventually triggers demand. Figure 1 provides an overview of the propositions made.
Graph: Figure 1. Conceptual model.
We test our propositions in seven studies. Studies 1a, 1b, and 2 provide evidence for the effect of past identity salience on real-world product demand. Studies 1a and 1b investigate this in actual Facebook campaigns. Study 2 examines sales data for an experimental upcycling pop-up store. Studies 3, 4, and 6 demonstrate that past identity salience increases demand because the product's biographical story affords people with specialness and generalizes our findings to different types of products and past identities as well as the various claims that make these identities salient.
We also provide evidence of the limits of past identity salience. Marketer-crafted allusions to a product's past identity are not the only means through which individuals may infer a product's biographical story. Products with an easily discernible past identity, such as a bag sewn out of highly visible candy wrappers, already provide all the cues needed for the story to unfold in a customer's mind. We thus do not expect additional appeals to a product's past identity to increase demand when the past identity is already salient. Study 5 demonstrates that visual discernibility of a product's past identity acts as a moderator.
Given that any biographical transformation requires a past identity, we also do not expect results to generalize to genuinely new products. Study 6 thus compares different production modes and shows that our effect is specific to repurposed products. In particular, it extends our findings to the more prevalent market practice of recycling and demonstrates that they do not generalize to conventional modes of production.
Finally, we address several alternative explanations across studies. Study 3 rules out the possibility that the effects are driven by increased perceptions of environmental friendliness. Study 4 shows that results are not a manifestation of a handmade effect, and Study 6 addresses the possibility of other plausible confounds (perceived originality, authenticity, surprise; see also the Study 2 posttest in the Web Appendix). All stimuli (Web Appendix W1), additional analyses (Web Appendix W2), supplementary studies (Web Appendix W3), and replication studies (Web Appendix W4) are included in the Web Appendix.
In Study 1, we use two online field experiments to examine how people respond to Facebook ads that make the past identities of upcycled products salient. In Study 1a, we examine how these ads affect Facebook page likes. In Study 1b, we look at the effects on clicks.
To ensure ecological validity, we teamed up with an upcycling store and jointly created two Facebook ad campaigns. The objective of the first campaign (Study 1a) was to increase the number of likes of the store's Facebook page. The objective of the second campaign (Study 1b) was to drive traffic to an external website featuring an online voucher promotion. Each campaign targeted people between the ages of 18 and 65 years old (Facebook estimated a potential target audience of 1.34 million individuals) living in the store's vicinity. To prevent people from being exposed to both campaigns on the same day, we activated only one at a time for seven days and six days, respectively.
We created small rectangular ads that featured different upcycled products from the store: a cake stand made from old pot lids, a vase made from a light bulb, and a pen holder made from used forks (Web Appendix W1). We manipulated past identity salience by stating what the products used to be ("I used to be a...pot lid, light bulb, fork"). The control group read: "Now I am...a cake stand, a vase, a pen holder." By using the term "Now I am," it also hinted at a transformation but made the products' present identities salient.
In Study 1a, we measured unique and total like rate (the number of page likes the ad generated relative to its unique and total reach). In Study 1b, we measured unique and total click rate (the number of clicks the ad generated relative to its unique and total reach). To make measures comparable and to control for variance in unique reach (total number of unique people who saw the ad) and total reach (total number of times the ad was shown), we used relative measures (i.e., likes/clicks in percentages of reach) in both studies.
Table 1 presents a summary of campaign statistics and shows ad performance across conditions. Because we only had access to aggregate behavioral data, we conducted two-sample proportions z-tests to determine which ad was relatively more successful.
Graph
Table 1. Descriptives of Appeal Measures by Condition (Studies 1a, 1b, and 2).
| Dependent Variable | Condition | Total |
|---|
| Past Identity Salient | Control |
|---|
| Study 1a: Ad Performance (Facebook Likes) |
| Total likes | 129 | 55 | 184 |
| Unique reach | 81,736 | 98,078 | 179,814 |
| Total reach | 221,441 | 196,419 | 417,860 |
| Unique like rate | .16% | .06% | .10% |
| Total like rate | .06% | .03% | .04% |
| Study 1b: Ad Performance (Clicks on Voucher Promotion) |
| Total clicks | 842 | 699 | 1,541 |
| Unique reach | 293,790 | 268,842 | 562,632 |
| Total reach | 794,260 | 849,597 | 1,643,857 |
| Unique click rate | .29% | .26% | .27% |
| Total click rate | .11% | .08% | .09% |
| Study 2: Product Demand |
| Visitors | 266 | 165 | 431 |
| Purchases | 25 | 8 | 33 |
| Conversion rate | 9.4% | 4.8% | 7.7% |
| Products sold | 36 | 8 | 44 |
| Revenue | €572 | €127 | €699 |
| Control variables | | | |
| Busyness | 2.55 (1.17) | 2.70 (1.34) | 2.63 (1.25) |
| Conversation time (in minutes) | .56 | .50 | .53 |
As expected, the ad yielded a higher unique (.16% vs..06%; Z = 6.72, p <.001) as well as total like rate (.06% vs..03%; Z = 4.65, p <.001) when the products' past identities were made salient.
A similar pattern emerged for the promotion campaign. Past identity salience significantly increased unique (.29% vs..26%; Z = 1.91, p =.06) and total (.11% vs..08%; Z = 4.97, p <.001) clicks on the promotion.
Drawing on ecologically valid experimental field evidence in an online context and featuring a portfolio of different upcycled products, Studies 1a and 1b show that past identity salience can increase demand for products of an upcycling store. Owing to the context in which low levels of engagement are common (for average clickthrough rates, see [17]]), absolute effect sizes were quite small, even though like rates doubled and click rates increased by more than 37%. We designed Study 2 to provide ecologically valid field evidence in a context that allows for more pronounced absolute differences.
Studies 1a and 1b measured online interest as a demand proxy. Study 2 extends the inquiry to an experimentally controlled brick-and-mortar context and actual sales data.
In collaboration with two upcycling stores, we set up our own upcycling pop-up store on the campus of a large European university. The assortment included 24 different upcycled products (e.g., bags, wallets, bowls). Items were made from a variety of source products (e.g., mosquito nets, parachutes, bicycle tubes) and priced between €5 and €85. Study participants were all potential customers who passed the shop. The shop was open over six days for five hours each day during the Christmas season in 2017. We hired two sales assistants to run the shop on alternate days. To allow for a constant setup, we provided them with a sales script. We instructed them to be friendly but passive (no sales pitches) and to discuss the product's past identity only when prompted by customers. We also instructed assistants to take notes of all that happened during the day.
We manipulated past identity salience by alternating the marketing materials at the point of sale. These included ( 1) a leaderboard that highlighted the products' past ("We used to be...parachutes, truck tarps, pot lids, etc."; experimental condition) or present ("We are...bags, wallets, cake stands, etc."; control condition) identity, ( 2) a corresponding price list with individual price tags and product descriptions, ( 3) product flyers, and ( 4) a promotional poster (Web Appendix W1). We changed conditions on a daily basis.
For each day, we used the receipt data to assess the number of purchases, the number of products sold, and total revenue. To account for any effects associated with general customer frequency and engagement, the sales assistants tracked the number of visitors to the shop with a manual clicker, self-assessed the busyness level at the shop site every 30 minutes (1 = "not very busy," and 7 = "very busy"), and measured conversation time per visitor.
Average busyness at shop site (Msalient = 2.55, SD = 1.17; Mcontrol = 2.77, SD = 1.34; t(58) =.46, p =.65) and conversation time per visitor (Msalient =.56 min, Mcontrol =.50 min; z = 1.09, p =.28) did not differ across conditions. When the past identities were made salient at the point of sale, however, the shop had approximately 60% more visitors (266 vs. 165; Mann–Whitney U-test: z = 1.96, p <.05); triple the amount of purchases (25 vs. 8; z = 1.99, p <.05); four times more products sold (36 vs. 8; z = 1.99, p <.05); and, as a result, more than quadruple the revenue (€572 vs. €127; z = 1.96, p <.05). Moreover, the conversion rate (proportion of visitors making a purchase) was nearly twice as high when the products' past identities were made salient (9.4% vs. 4.8%; z = 1.63, p =.08, Table 1).
Making the past identities of the products salient to customers in an actual upcycling store increased demand beyond our expectations and with regard to every single factor that can increase revenue: interest (i.e., visitors), conversion, and sales volume. To explore the underlying dynamics of these effects, we followed up on Study 2 with a posttest (Web Appendix W3), which revealed that our effects are unlikely to have been driven by how interesting, boring, or surprising people perceived the products to be.[ 6] Instead, and in line with our assumptions, people feel more special with the products, find them more appealing, and are more likely to purchase them when products' past identities are made salient.
Having established real-world support for our proposed main effect, Study 3 aims to show that past identity salience affects demand because it increases customers' felt specialness with the product. We proposed that past identity salience would increase felt specialness because it triggers thoughts about the product's biographical story. To test for this, we also qualitatively explore whether past identity salience triggers narrative thoughts—and, if so, what elements of the product's story these thoughts refer to. Finally, we address the alternative explanation of felt environmental friendliness. Repurposing products is a proenvironmental practice. Stressing the product's past identity may thus play into sustainable purchase motives ([60]). We aim to rule out that past identity salience may drive demand because it increases felt environmental friendliness rather than specialness.
Two hundred twenty-four U.S. panelists from Amazon Mechanical Turk (MTurk; 44% female, Mage = 35 years) were instructed to evaluate a backpack upcycled from an old airbag in a one-factor (past identity: salient vs. control) between-subjects experiment. We manipulated past identity salience as in prior studies (see Web Appendix W1) and informed all participants that the backpack was upcycled. Participants in both conditions thus rationally knew that the product had a past identity, but this was made salient and concrete in only one condition.
As proxies of demand, we first assessed the backpack's appeal ("How would you evaluate this product?" 1 = "unappealing/don't like it at all," and 7 = "appealing/like it a lot"; α =.95) and participants' purchase intention ("Would you buy this product?" 1 = "No, definitely not," and 7 = "Yes, definitely"). We measured felt specialness as the focal process variable (three items adapted from [53]]): "How special/unique/recognized would you feel with this product?" 1 = "not at all," and 7 = "very"; α =.91) and felt environmentalism as an alternative process (three similar items: "How sustainable/environmentally conscious/environmentally friendly would you feel with this product?" 1 = "Not at all," and 7 = "Very"; α =.93). To explore whether an appeal to the past identity would trigger narrative thoughts, we asked participants to describe what they thought was special about the product in an open-ended question. Finally, we asked whether they took the study seriously and answered conscientiously. Two participants who had indicated that they did not were excluded from further analyses.
In line with previous results, past identity salience increased product demand. In particular, participants perceived the backpack as more appealing (Msalient = 4.80, SD = 1.84; Mcontrol = 4.04, SD = 1.94; t(220) = 3.01, p <.01) and were more likely to purchase it (Msalient = 4.17, SD = 2.00; Mcontrol = 3.43, SD = 2.07; t(220) = 2.70, p <.01) when its past identity as an old airbag was made salient.
Participants also perceived the backpack as more specialness-affording (Msalient = 4.34, SD = 1.68; Mcontrol = 3.82, SD = 1.81; t(220) = 2.22, p <.05) and felt more environmentally friendly with the backpack (Msalient = 5.37, SD = 1.33; Mcontrol = 4.29, SD = 1.70; t(220) = 5.28, p <.001) when its past identity was made salient.
To test for the proposed effect of past identity salience on demand through specialness, we conducted two bootstrap mediation analyses (Model 4, [39]). We entered past identity salience as the independent variable (0 = control, 1 = salient), felt specialness as the mediator, and the respective demand variables as dependent measures. Because past identity salience also affected how environmentally friendly people felt, we included it as an alternative mediator. We found an indirect effect of past identity salience on product appeal (indirect effect =.40; 95% confidence interval [CI95] = [.05,.79]) and purchase intention (indirect effect =.44; CI95 = [.06,.86]) through felt specialness. Felt environmentalism did not mediate the effect on appeal (indirect effect = −.02; CI95 = [−.23,.20]) or purchase intention (indirect effect =.002; CI95 = [−.19,.20]). It can thus be ruled out as an alternative process.
Finally, we analyzed the open-ended answers to explore differences in narrative thinking across conditions. All answers were coded in terms of whether or not they signaled narrative thinking and in terms of their content elements. Thoughts that hinted at chronology and causality, the central structural characteristic of narrative thinking, were coded as narrative, whereas other thoughts were coded as descriptive. As to the content elements, eight recurring themes emerged. Three narrative thought topics focused on different story elements. The respective codes are past identity, in which the prior life of the product was prominent; metamorphosis, in which the product's transformation was prominent; and other biographical elements, in which chronology and causality of the product's story were present but neither could be clearly identified as central. The five descriptive thought topics comprise type of material, production mode, environmental aspects, other product attributes, and product evaluations (for descriptions and examples, see Table 2). Two independent raters who were blind to the condition coded responses with regard to the presence of each of these codes. More than one code could be assigned to one response, and remaining disagreements were resolved through discussion (interrater reliability: all κs >.61).
Graph
Table 2. Study 3: Thoughts and Themes Associated with Product Specialness by Condition.
| Thought Type | Theme | Description | Example | Condition |
|---|
| Past Identity Salience | Control |
|---|
| Narrative | Past identity | Reference to the past identity of the product as an airbag | [#73]a "It is recycled from a deployed air bag. Something that saved someone's life. That's pretty cool." | 66.1% | 1.9% |
| Narrative | Metamorphosis | Explicit reference to the transformation | [#26] "It used to be an airbag. Now it is a backpack. That is very novel and different!" | 50.4% | .9% |
| Narrative | Biographical elements | Reference to the story of the product (i.e., what might have happened and how it became what it is now) | [#46] "The material would be different than other backpacks. The backpack would have a type of "story" from its previous life." | 17.4% | .9% |
| Descriptive | Type of material | Reference to the material of the product | [#74] "It is special in the material used to make it. This distinguishes it from other backpacks." | 19.1% | 18.7% |
| Descriptive | Production mode | Reference to the production mode of the product | [#16] "That it's upcycled." | 34.8% | 15.9% |
| Descriptive | Environmental aspects | Reference to environmental and sustainable aspects of the product | [#40] "You get to help the environment while carrying your books." | 7.8% | 4.7% |
| Descriptive | Product attributes | General product attributes | [#149] "Lightweight, looks clean, simple." | 37.4% | 61.7% |
| Descriptive | Product evaluations | General product evaluations | [#43] "Nothing, I don't see anything special about this product really. It looks very odd to me anyway and I find it unappealing in every sense, I would not buy it." | 21.7% | 35.5% |
1 a Numbers in brackets refer to participant numbers.
Table 2 shows the relative prevalence of codes across conditions. Despite the fact that all participants knew that the backpack had a history, narrative thoughts were more pronounced when the ad made the product's past salient. Participants in the past identity salience condition more often reported narrative thoughts (69.6%) than those in the control condition (2.8%; χ2 = 105.53, p <.001). In particular, they more often referred to the product's past identity (66.1% vs. 1.9%; χ2 = 100.30, p <.001), its metamorphosis (50.4% vs..9%; χ2 = 69.60, p <.001), and other biographical story elements (17.4% vs..9%; χ2 = 17.53, p <.001). In addition, descriptive thoughts about the production mode became more prevalent (34.8% vs. 15.9%; χ2 = 10.37, p <.01) when the past life of the product was made salient. In contrast, descriptive thoughts of general product attributes (37.4% vs. 61.7%; χ2 = 13.09, p <.001) and evaluations (21.7% vs. 35.5%; χ2 = 5.18, p <.05) were more prevalent in the control condition. Thoughts about the product's material (19.1% vs. 18.7%; χ2 =.01, p =.93) or its environmental aspects (7.8% vs. 4.7%; χ2 =.93, p =.33) were equally prevalent across conditions.
Study 3 replicates the effect of past identity salience on product demand in a controlled setting and supports our proposed account. Making the past identity of an upcycled product salient boosted its specialness-affording potential, which, in turn, increased product appeal and purchase intention. Although past identity salience also affected how environmentally friendly people felt with the product, environmental friendliness did not mediate the effect.[ 7] In addition, Study 3 provides qualitative insights into why customers feel more special once the past identity of a repurposed product is made salient. Past identity salience appears to trigger the product's story and brings to mind additional narrative thoughts that relate in particular to the product's past identity and metamorphosis while decreasing evaluative descriptions and focus on other attributes such as design or weight.
Study 4 provides additional evidence about the proposed process. It tests whether past identity salience enhances people's perceptions of the product as storied, which in turn induces felt specialness and demand (see Figure 1). To corroborate the role of felt specialness as a driver of demand, we not only measured felt specialness but also tried to moderate it. If felt specialness drives the effect, then this route should be less pronounced for those who feel very special already. In addition, Study 4 ensures robustness of the results by extending the inquiry to minimal appeals (i.e., simple "made from" claims that neither spell out the story directly nor depict the past identity) and to a new product category and different source product (i.e., a wooden table made from a pallet). Finally, it also addresses another viable alternative explanation: it is possible that highlighting the past identity of a product induces people to think that the product is handmade, a product characteristic that is known to increase product attractiveness ([29]).
A total of 98 MTurk workers (41% female; Mage = 37 years) were randomly assigned to one of two ads that promoted a wooden table (Web Appendix W1). In the past identity salience condition, the ad read, "I was made from an old pallet"; in the control condition it read, "I was made for dining." Prior to ad exposure, we assessed baseline feelings of personal specialness with the same three items used to capture product-specific felt specialness ("In general, how special/unique/recognized by others do you feel?" 1 = "not at all," and 7 = "very"; α =.86). After ad exposure, we measured product appeal (α =.93) and purchase intention as in Study 3. To assess whether past identity salience triggers narrative thoughts about the product and its biography, we adapted four items from [49] ("The product tells a story," "The product's story has a beginning, a middle and an end," "The product has evolved over time," "The story of the product has a chronological order"; 1 = "Strongly disagree," and 7 = "Strongly agree"; α =.95). Finally, we measured felt specialness, as in Study 3 (α =.93), and assessed the degree to which participants thought that the product was homemade ("This product looks..." 1 = "homemade," and 7 = "made by a company").
When the past identity of the wooden table was made salient, participants perceived it as more appealing (Msalient = 5.15, SD = 1.56; Mcontrol = 4.29, SD = 1.69; t(96) = 1.80, p <.05) and were more likely to buy it (Msalient = 5.21, SD = 1.50; Mcontrol = 4.48, SD = 1.70; t(96) = 2.25, p <.05). Past identity salience also increased product story perceptions (Msalient = 5.13, SD = 1.30; Mcontrol = 3.89, SD = 1.95; t(96) = 3.71, p <.001) and made participants feel more special with the product (Msalient = 4.85, SD = 1.49; Mcontrol = 3.90, SD = 2.00; t(96) = 2.66, p <.01). Felt personal specialness prior to ad exposure (Msalient = 5.04, SD = 1.48; Mcontrol = 5.02, SD = 1.37; t(96) =.07, p =.95) and perceptions of the product as handmade (Msalient = 4.32, SD = 2.03; Mcontrol = 4.67, SD = 1.94; t(96) = −.87, p =.39) did not differ across conditions. Note that the latter also did not moderate the effect of past identity salience on demand (see additional analyses in Web Appendix W2).
To corroborate the role of felt specialness, we ran moderated mediation analyses (Model 7, [39]) with past identity salience as the predictor (0 = control, 1 = salient), felt specialness as the mediator, product appeal and purchase intention as the outcome variables, and baseline personal specialness as a continuous moderator (M = 5.03, SD = 1.42). In support of our propositions, felt specialness mediated the effect for people low (−1 SD) and average (mean) in felt personal specialness, but not for people who already felt very special to begin with (+1 SD) (see Table 1 and Web Appendix W2).
We next tested whether product story perceptions led to an increase in felt specialness and, as a result, in demand. Two bootstrap sequential mediation analyses (Model 6, [39]) with past identity salience as the independent predictor (0 = control, 1 = salient), product story and felt specialness as sequential mediators, and product appeal and purchase intention as outcome variables found evidence for sequential mediation via product story and felt specialness on product appeal (indirect effect =.15, CI95 = [.06,.27]) and on purchase intention (indirect effect =.15; CI95 = [.05,.28]). Notably, both individual indirect effects through product story and felt specialness on demand became nonsignificant (CIs include 0). Moreover, switching the order of the mediators resulted in a nonsignificant mediation (CIs include 0). This supports the proposed sequential mediation chain.
Results of Study 4 rule out the competing explanation that our effects are due to the handmade effect ([29]) and fully support our proposed process. Even a simple "made from [past identity]" claim increased demand, and this was due to the claim imbuing the product with a story, which in turn made customers feel more special with the product. Study 4 thus supports our proposition that a product's past identity holds storytelling potential. It also corroborates the central role of felt specialness as our underlying process. Baseline felt specialness moderated the effect of past identity salience on specialness and, as a result, the indirect effect of past identity salience on demand.
Our findings suggest that past identity salience is effective because it imbues products with a prior life with their specialness-affording biographical story. However, what if the product itself already tells the story (i.e., the past identity is visually discernible and salient in the product)? Our theorizing suggests this to be a relevant boundary condition for the power of ad-induced past-identity salience. In Study 5, we test for this boundary condition. Specifically, we used a vase made from an obviously discernible light bulb and a vase made from a less discernible electric insulator (for a replication across product categories, see Web Appendix W4). We expect our effect to generalize to the insulator vase but to be attenuated for the light bulb vase because its past life is already salient.
We recruited 562 volunteers from a university mailing list who were familiar with upcycling (61% female; Mage = 24 years) to participate in a 2 (past identity: salient vs. control) × 2 (past identity discernibility: subtle vs. discernible) between-subjects experiment. Depending on condition, participants saw an ad that either did or did not highlight the past identity of a vase made either from an easily discernible light bulb or from a less discernible electrical insulator (see Web Appendix W1).
We measured product appeal ("How much do you like this vase?" 1 = "don't like it at all," and 7 = "like it a lot"), purchase intention ("If you were looking for a vase, would you buy this particular one?" 1 = "No, definitely not," and 7 = "Yes, definitely"), and willingness to pay (WTP; "What is the maximum amount you would pay for this vase?" open-ended). As a study incentive and a measure of behavioral product demand, participants could choose to win a product of their choice at the end of the study: the promoted upcycled or a conventional vase.
A two-way analysis of variance on product appeal produced a main effect of past identity salience (F( 1, 558) = 8.19, p <.01) and a main effect of past identity discernibility (F( 1, 558) = 34.60, p <.001) on product appeal. The prior effect is in line with previous results, and the latter is indicative of product differences. Importantly, these effects were qualified by a marginally significant interaction (F( 1, 558) = 3.41, p =.06). Planned contrast comparisons revealed that past identity salience increased appeal when the past identity of the vase was not discernible (Msalient = 4.03, SD = 1.68; Mcontrol = 3.37, SD = 1.60; t(280) = 3.36, p <.01). It did not, however, affect the appeal of the visibly discernible vase (Msalient = 4.59, SD = 1.68; Mcontrol = 4.45, SD = 1.65; t(278) =.71, p =.48).
A two-way analysis of variance again produced a main effect of past identity salience (F( 1, 558) = 10.99, p <.01) and past identity discernibility (F( 1, 558) = 20.56, p <.001), qualified by a marginally significant interaction effect (F( 1, 558) = 3.63, p =.06). Making the past identity salient boosted demand when it was difficult to discern (Msalient = 3.18, SD = 1.74; Mcontrol = 2.46, SD = 1.46; t(280) = 3.78, p <.001) but not when it was visibly discernible (Msalient = 3.55, SD = 1.67; Mcontrol = 3.35, SD = 1.69; t(278) =.98, p =.33).
To assess effects on the highly skewed WTP measure, we built three equally sized groups of amounts that participants were willing to pay for the vase (<€5, €5–€10, ≥€10; Figure 2, Panel A). Chi-square tests per product show that past identity salience led to a significant increase in the number of people who were willing to pay more than €10 (57% vs. 43%; χ2 = 5.53, p =.06) when the past identity was difficult to discern, but this did not affect WTP when the past identity was visible to begin with.
Graph: Figure 2. Effects of past identity salience (Study 5).
Finally, we ran a logistic regression with past identity salience, past identity discernibility, and their interaction term as the predictors, and product choice as the outcome. We found a significant interaction effect (Wald = 3.70, B =.68, p =.05) but found neither a main effect of past identity salience (Wald = 2.24, B = −.82, p =.13) nor a main effect of past identity discernibility (Wald = 1.29, B = −.62, p =.26). Past identity salience increased the choice of the upcycled product when its past identity was hard to discern (χ2 = 4.42, p <.05) but not when it was easily discernible (χ2 =.36, p =.55; Figure 2, Panel B).
Study 5 demonstrates that making the past identity salient is effective, particularly when customers cannot easily discern this identity and infer the product's biographical story by simply looking at it.[ 8] When the past identity was discernible, emphasizing it did not boost demand further. To see how this boundary condition can nonetheless be overcome, see Studies 4a and 4b in Web Appendix W3, which also rule out that a salient past identity could be effective simply because it offers more information.
Study 5 showed that appeals to a product's prior life primarily work if this prior life is not salient already. Study 6 addresses another managerially relevant boundary condition. So far, we have tested our propositions in the context of upcycled products, which doubtlessly hold prior identities. In Study 6, we thus ask whether the effect is indeed specific to repurposed products (i.e., products with a prior life). To do so, we generalize our inquiry to recycled products, which are also made from products with a prior life, and conventional products, which are made from brand-new raw materials that lack such a prior life. We expect results to generalize to recycled products but not to conventional products. In addition, we control for various alternative accounts. It is plausible that making the past identity of a product salient might simply trigger surprise and perceptions of product originality or authenticity.[ 9] All of these are connected to positive customer responses and have been studied in the context of vintage products, which are—like upcycled and recycled products—characterized by strong past identities (e.g., [20]; [78]; [82]). Moreover, surprise and novelty have been connected to storytelling ([27]).
We used the same backpack as in Study 3 and randomly assigned 163 individuals (57% female; Mage = 32 years) to one of four product ads, which manipulated production mode and thus the backpack's past identity (Web Appendix W1). All conditions featured the same slogan "I am a trendy backpack." In the upcycling condition, participants also read, "In my previous life, I used to be an airbag." In the recycling condition, they read, "In my previous life, I used to be old plastic," and in the conventional condition, they read, "In my previous life, I used to be polyester." Past identity was not made salient in the control condition; it only read, "I am a trendy backpack."
Following ad exposure, we first assessed demand variables. Product appeal (α =.94) and purchase intention were measured as in Study 4, and WTP was measured on a slider scale ranging from €0–€200. In addition, we assessed relative purchase intention by asking participants to rank different backpacks according to their purchase likelihood (1 = "highest" to 4 = "lowest" likelihood). These backpacks included our focal backpack from the ad and three additional backpacks participants had not been exposed to before. To corroborate the process, we also measured perceptions of product story (α =.90) and felt specialness (α =.93) as in Study 4. As a manipulation check, we added three items assessing the degree to which participants perceived that the product had a prior life (i.e., a true past identity; "The product contains..." 1 = "little history/past/identity," and 7 = "a lot of history/ past/identity"; α =.94). Finally, we assessed perceived originality ("This product is novel/original/different"; α =.78), adapted from [47], as well as surprise ("The product surprises me/is unexpected"; α =.91) and authenticity ("The product is authentic/genuine and real"; α =.89) adapted from [43] and [59], all on seven-point scales (1 = "Strongly disagree," and 7 = "Strongly agree").
Table 3 provides means, standard deviations, and pairwise comparisons of all assessed variables by condition. Conditions differ with regard to all assessed variables.
Graph
Table 3. Study 6: Means Across Conditions (Past Identity Salience of Different Production Modes).
| Main Effects | Production Mode |
|---|
| Upcycling | Recycling | Conventional | Control |
|---|
| M (SD) | M (SD) | M (SD) | M (SD) |
|---|
| Perceived past identity | 5.20 (1.39)a | 5.17 (1.55)a | 3.35 (1.76)b | 2.40 (1.51)c |
| Product appeal | 4.85 (2.18)a | 4.48 (2.02)a | 3.20 (1.77)b | 3.33 (1.75)b |
| Purchase intention | 3.57 (1.94)a | 3.71 (1.79)a | 2.45 (1.54)b | 2.36 (1.48)b |
| WTP | 36.76 (23.81)a | 38.54 (25.06)a | 28.00 (22.55)b | 24.00 (21.99)b |
| Relative purchase intention | 2.65 (1.06)a | 2.54 (1.03)a | 3.28 (1.04)b | 3.07 (1.12)b |
| Product story perceptions | 4.74 (1.44)a | 4.80 (1.66)a | 3.45 (1.60)b | 2.38 (1.33)c |
| Felt specialness | 3.75 (1.90)a | 3.87 (1.85)a | 2.39 (1.47)b | 2.16 (1.24)b |
| Product originality | 5.20 (1.57)a | 4.89 (1.42)a | 3.68 (1.60)b | 3.21 (1.66)b |
| Surprise | 4.79 (2.00)a | 4.12 (1.84)a | 2.69 (1.72)b | 2.62 (1.92)b |
| Authenticity | 4.84 (1.60)a | 4.77 (1.52)a | 3.35 (1.70)b | 3.22 (1.54)b |
2 Notes: Different superscripts indicate different cell means (p <.05) based on planned contrast comparisons.
In confirmation of the intended manipulation, we found stronger past identity perceptions in both the upcycling and recycling conditions than in the conventional backpack and control conditions (F( 3, 159) = 33.12, p <.001).
Conditions also significantly differ on product appeal (F( 3, 159) = 7.25, p <.001), purchase intention (F( 3, 159) = 7.41, p <.001), WTP (F( 3, 159) = 3.72, p <.05), relative purchase intention (F( 3, 159) = 4.32, p <.01), product story perceptions (F( 3, 159) = 12.48, p <.001), felt specialness (F( 3, 159) = 24.60, p <.001), product originality (F( 3, 159) = 4.32, p <.01), surprise (F( 3, 159) = 4.32, p <.01), and authenticity (F( 3, 159) = 4.32, p <.01). Compared with the control group, all measures of demand were higher in both the upcycling and recycling conditions but not for the conventional backpack. Likewise, product story perceptions and felt specialness were significantly higher when the product was upcycled or recycled but not when it was conventionally produced. The same significant pattern of results emerged for our alternative processes, perceived originality, surprise, and authenticity. We thus controlled for them in all subsequent analyses.
To test whether past identity salience affects demand through product story and felt specialness, we ran sequential mediation analyses per demand variable. Production mode served as the multicategorical predictor (Model 6, [39]). We compared all groups in which we had made a past identity salient (upcycling, recycling, conventional) to the control group in which we had not done so. To control for potential confounds, we added our alternative process variables as covariates to the model (Web Appendix W2 provides further analyses ruling out these suggested accounts). We found evidence for a sequential mediation from past identity salience through product story and felt specialness on product appeal in both the upcycling (indirect effect =.09, SE =.05; CI95 = [02,.20]) and the recycling (indirect effect =.14, SE =.07; CI95 = [03,.30]) conditions. For these two production modes, we replicate the significant indirect effects on purchase intention (upcycling: indirect effect =.08, SE =.04, CI95 = [02,.18]; recycling: indirect effect =.13, SE =.06; CI95 = [03,.26]), WTP (upcycling: indirect effect =.74, SE =.43; CI95 = [.03, 1.72]; recycling: indirect effect = 1.21, SE =.58; CI95 = [.27, 2.55]), and relative purchase intention (upcycling: indirect effect = −.02, SE =.02; CI95 = [−.05, −001]; recycling: indirect effect =.−.03, SE =.02; CI95 = [−.08, −.003]). In the conventional product condition, however, neither indirect effect became significant (all CIs included 0).
Study 6 extends our findings to a different context and a new product category. It also addresses an important boundary condition. Our effects are specific to products that are—like upcycling—associated with a prior life (i.e., recycled products) but are attenuated for products that carry little past identity in them (i.e., conventional products). Study 6 also rules out important alternative accounts around perceptions of product originality, surprise, and authenticity. While past identity salience boosts all of these factors, it is the story-induced felt specialness that primarily drives demand.
If products could speak, then each product made from repurposed materials would have a rich story to tell: a Cinderella-like story of a change in identity and purpose, from a discarded and useless past identity to a vastly changed and useful new identity as a new product. We show that appealing to the past identity (i.e., a discarded, broken object that is at odds with the product's current functional purpose) fuels demand for upcycled and recycled products. This is because past identity salience draws attention to the product's special story of metamorphosis, which allows customers to feel special themselves. These insights advance our current understanding of customer reactions to goods made from repurposed materials and open the window to a new facet of powerful storytelling in marketing.
In this article, we provide novel contributions to the extant literature on storytelling and to the burgeoning literature on upcycling and recycling as two production modes that involve the use of repurposed materials. With regard to the storytelling literature, we show that one salient piece of information, the product's past identity (i.e., the beginning of its biographical story) suffices to trigger the perception of the product as having a story. We observe this regardless of how we worded this information, from the somewhat story-like "in my previous life I used to be..." (Study 5) to the less story-like "I was made from..." (Study 4), to the purely informative "upcycled from..." (Supplementary Study 2, Web Appendix W3).
In contrast to prevalent storytelling practices, which focus on stories about product use or storied metaphors of a brand's essence ([21]; [27]), we focused on the story entailed in a product's own biography. We thus extend this literature by highlighting how products can be protagonists in their own life stories. This raises the question of what such product life stories need to look like to persuade. Our evidence suggests that a product's metamorphosis, its transformation from a past identity, plays a crucial role. Metamorphosis or transformations are powerful narratives ([18]; [40]), and it seems that the source or consequence of the transformation is secondary to there being a transformation. Whatever past identity we used (from airbags to insulators) and whatever the transformation (from bags to tables), the results generalized. Just knowing that there has been a different past identity appears to suffice. We argue that it is this knowledge that invites the inference of all elements necessary for narrative thinking ([27]), a sequence of episodes (chronology) that logically build onto each other (causality).
Contrary to most current literature, we never actually spelled out the product's story. Instead, we had customers infer the story by using a "minimal narrative" of making two different identities of the same product salient ([66]). Our minimal intervention of past identity salience highlights that the mere presence of a story can be persuasive. This adds a novel facet to storytelling in marketing and advances prior literature that suggests that stories work particularly well, if people experience narrative transportation ([26]; [79])—that is, become absorbed in narrated stories (see [21]] and [49]], who also find that stories can persuade without transportation).
Why are these minimal stories persuasive? Because customers feel more special with a product that holds a biography (i.e., a story). We consistently found this, even when we controlled for novelty, surprise, authenticity, and originality. A good story increases appreciation for the story character ([ 3]; [61]) and conveys specialness ([52]). Because people want to feel special, the promise of specialness fuels demand ([38] ; [81]). This observation fits with prior insights on the specialness-affording value of object history ([20]; [46]; [82]), but it constitutes a novel insight in the domain of storytelling. Having a story to tell should thus be added among the factors known to make customers feel special (e.g., [ 8]; [38]).
The power of the mere presence of a product's biographical story also allows our insights to be positioned with regard to different production modes. On the one side, our results help distinguish upcycling (our key phenomenon) from consumption of secondhand goods and vintage items, in which a product's prior life matters greatly and contagion is an issue ([ 2]; [37]; [45]). In contrast, upcycling encompasses the presence of a radical transformation away from a product's past identity. This transformation eliminates any taint associated with an upcycled product's past, although this past still makes it special. Notably, our findings generalize to recycling, in which products with past identities are broken down prior to being transformed into new products ([55]). A process of transformation thus appears to be a managerially relevant conceptual distinction within market practices that draw on used materials. This insight also aligns well with [86], who find that highlighting a product's transformative potential increases rates of recycling waste materials. Notably, our focus on metamorphosis or transformation also distinguishes upcycling and recycling from conventional production modes that create products from raw materials that are mostly devoid of a prior life and identity (Study 6). Our results suggest that insights from recycling may generalize to upcycling and vice versa. For both practices, marketers would do well to utilize transformational product biographies as their unique selling proposition. This sets a first benchmark for marketers in upcycling and provides a new lens for thinking about recycling. To date, most recycled products stress the environmental benefits rather than the specialness affordance inherent to past identities. More broadly, our results answer calls for marketing research on products created in environmentally friendly ways ([48]) and add to findings that suggest that environmentally friendly options are not necessarily preferred or avoided for their environmental impact ([14]; [50]). We show that these products may be preferred if they afford specialness.
Highlighting a novel facet of storytelling within an upcoming mode of production provides substantial scope for future research. On the one hand, there are several open questions with regard to storytelling. One question is whether there are story elements that might be particularly powerful. In Study 3, we found that past identity salience triggered different narrative thoughts. Future research could explore how and whether such differences affect felt specialness and demand. Another open question is whether results would differ if marketers were to narrate the product's biography in full. Would the effect be enhanced because everyone picks up on the story, or would it be reduced because there is less scope for self-inferencing? A related question asks how powerful a product's biographical story would be compared with other stories told in marketing. This is a relevant but tricky question because stories are more than the sum of their elements. We nonetheless explored this in a supplementary study (Supplementary Study 2, Web Appendix W3). An ad for a laptop sleeve either implied its story through its stated past identity (upcycled from an old mosquito net) or narrated a story of its inception (the product creators were inspired by watching a spider web). The respective stories affected demand to the extent to which participants perceived the product to hold a story. Notably, the marketer-narrated story about the design inspiration imbued the product with less of a story and led to a smaller increase in appeal than the self-inferred story triggered by past identity salience. Future research is needed to identify whether the embodiment of a story in the product outperforms narrated stories that are meant to "rub off" onto the product.
Future research is also needed to ensure that the results are unaffected by the specific sampling frames we used. We tested our predictions across a range of samples from Austria, including Facebook users (Studies 1a–b), passers-by on campus (Study 2), convenience samples (Study 5), and pure student samples (Study 6). In addition, we conducted some studies with U.S.-based MTurk workers (Studies 3 and 4). Future research is needed to ensure that our conclusions extend beyond these populations. In particular, non-Western cultures may be more reluctant to adopt used goods ([89]).
Running our own pop-up store, we learned firsthand how different reactions to upcycling can be. Some people appear to be entirely averse to the notion of old source products. Past identity salience may intensify these individuals' aversions, but these individuals might never fall into the target group. This does, however, raise the question of whether brands should highlight the fact that their branded products become repurposed, and whether they should use the same or a different brand for repurposed products.
The effects we observed appeared rather robust regarding who makes the product (see follow-up analyses on perceptions as handmade for Study 4, Web Appendix W2) and what the product was made of. We surmise that the metamorphosis removes the potential taint of nearly any past identity, and we find in a supplementary study (Supplementary Study 3, Web Appendix W3) that even the salience of a truly disgusting past identity (dirty mosquito net) did not hurt demand. The appeal of the past identity may nonetheless influence the size of the effect. This speaks to the power of transformations in overcoming issues of contagion and opens up several lines of future inquiry. For example, does the extent to which the past identity becomes transfigured and distorted in the process of upcycling matter ([77])? Do functional source products (e.g., a glass window) result in different responses to hedonic source products (e.g., a decorative glass vase)? Moreover, who reacts most to past identity appeals, and are there people for whom such appeals backfire? Given that we find that demand is driven by felt specialness, it is plausible that customers who are low in power ([23]) or who feel a need for status ([24]; [75]) would react more strongly.
The observed power of a prior life may also advance research on other effects attributed to a product's origin or production mode. Storytelling principles might further increase the effectiveness of appeals related to prior users (e.g., [ 6]), brands, and production sites (e.g., [59]) as well as production and design modes (e.g., [29]). Whether results generalize to such diverse aspects of a product's origin is, however, an open question. Prior research has only tended to find positive effects of appeals to a product's origin when the highlighted origin aspect is desirable. This rests on the notion that the essence of what goes into a product persists in the final product. For example, [59] found that products made in a firm's original manufacturing location hold more appeal because they are believed to contain more of the brand's authentic essence. In contrast, we found that past identity appeals work even when they highlight a discarded origin. In this respect, our findings are particularly novel.
The salience of a product's past identity robustly increased demand across a wide variety of contexts (store and product level, online and offline), categories (e.g., wallets, vases, chandeliers), ad appeals, and product origins (e.g., mosquito nets, parachutes, airbags). In a real store (Study 2), revenues more than quadrupled when we made the past identities of products the focal point of our marketing materials. Online (Study 1a), social media likes more than doubled, and in all instances where we expected to find an increase in WTP or choice share, such an increase was found. The upcycled (vs. conventional) alternative was chosen at least 12% more often when the past identity was made salient.
Importantly, our results generalized to recycling. When marketing products made from either of those production modes—recycling or upcycling—marketers should appeal to the product's past, even though this past has no bearing on the product's present identity or functionality. Regardless of what source product we used, past identity salience never reduced demand. However, when upcycled products told their story at a glance, simply restating the past identity did not further boost demand. This finding holds two important implications. First, in recycling, products are broken down to their granular structure. The past identities of recycled products are thus never visually discernible, suggesting that the more prevalent practice of recycling may benefit from past identity salience even more consistently than upcycling. This is an important contribution. In line with our market observations, most companies that use recycled materials simply stress their environmental friendliness but do little to highlight the specific source material going into their products.
Second, the boundary condition of past identity discernibility (Study 5) may be overcome even for upcycled products. In two additional studies (Supplementary Studies 4a and 4b in Web Appendix W3), we devised ways in which marketers can do so. We found that easily executed visual tweaks that logically reinforce the product's biographical story boosted product demand even when the product's past identity was discernible at first glance. The key appears to lie in storytelling principles. These suggest reinforcing the chronological and causal structure inherent to the product's story ([51]; i.e., feature the past identity first and more prominently than the resulting product).
Our implications also extend to the general adoption of the practice of upcycling. Every year, two billion tons of waste go to landfills around the world, posing a continuous threat to the environment, the economy, and society ([77]; [84]). Encouraging upcycling means that product waste will be reduced, resulting in less landfill and incineration, more energy savings, and decreases in industrial emissions ([11]). Upcycling has experienced impressive growth rates ([72]) and allows for a value-adding possibility in companies' own waste management, but it is still a niche phenomenon. Entering this market may be a worthwhile opportunity. Whenever we asked participants to choose between an upcycled product and conventional product, a substantial proportion of people preferred the upcycled option, and our pop-up store sparked interest. The addition of upcycled products to a retailer's portfolio may be an actionable way to attract customers who want to feel special but lack the financial resources for status symbols.
Like Cinderella, the life of upcycled products holds the ingredients for the plot of a bestselling story. This article shows that we can learn from this story and that there may be more to storytelling than currently practiced in marketing. Stories truly unfold in customers' minds ([27]; [79]), but to date marketers appear to think that they have to do all the telling. Our results suggest that customers infer stories, even when they only see a single piece of information. Our results also suggest that stories may imbue potential weaknesses in a product's image (such as a useless past identity) with meaning that benefits rather than hurts demand. Customers appear to feel special when they obtain a product that allows them to infer its story. Perhaps it is time to think of marketing as the creation of a projection space for stories that customers tell and help marketers sell.
Supplemental Material, DS_10.1177_0022242919872156 - A Cinderella Story: How Past Identity Salience Boosts Demand for Repurposed Products
Supplemental Material, DS_10.1177_0022242919872156 for A Cinderella Story: How Past Identity Salience Boosts Demand for Repurposed Products by Bernadette Kamleitner, Carina Thürridl and Brett A.S. Martin in Journal of Marketing
Footnotes 1 Author ContributionsThe first two authors contributed equally to this work.
2 Associate EditorAric Rindfleisch
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge funding by QUT Business School for Studies 1a and 1b.
5 Online supplement: https://doi.org/10.1177/0022242919872156
6 1We thank an anonymous reviewer for this suggestion and present details of these analyses in Web Appendix W3.
7 2In replication studies (see Web Appendix W4) we generalized these effects to other products, tested for another potential alternative explanation (perceived quality), and controlled for general upcycling affinity.
8 3For evidence that the results generalize to other product categories, see a comprehensive replication study in Web Appendix W4.
9 4We thank the anonymous review team for these suggestions.
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Record: 7- A Commentary on “Transformative Marketing: The Next 20 Years”. By: Varadarajan, Rajan. Journal of Marketing. Jul2018, Vol. 82 Issue 4, p15-18. 4p. 1 Chart. DOI: 10.1509/jm.82.43.
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A Commentary on “Transformative Marketing: The Next 20 Years”
Transformative: Causing a marked change in something. Kumar’s (2018) insightful editorial focuses on transformative marketing and its implications for research in marketing, marketing practice, marketing education, and marketing and public policy. While Kumar covers a vast terrain, in this commentary, I specifically focus on the transformative marketing landscape framework and related discussion in the editorial. In the framework, Kumar delineates a number of environmental changes that challenge the status quo of organizations, transformative marketing as the organizational response, and greater marketing effectiveness and efficiency as among the outcomes. Kumar notes that transformative marketing operates in an environment that is rooted in data (i.e., use of a firm’s data resources to formulate and implement marketing strategy) and innovation. He notes that unlocking the potential of a firm’s data resources, in itself, constitutes an innovation. Kumar further notes that firms operating in a data-rich environment (a consequence of the rapid growth of digital data and digitization of data) actively engage in new ways of exploring data to gain insights.
As a complement to the transformative marketing framework presented in Kumar (2018), in this commentary, I propose a conceptual framework delineating the relationships between a firm’s customer information assets, information analysis capabilities, customer knowledge, marketing strategy, and performance in a transformative landscape. Common to both frameworks are the outcomes of marketing strategy effectiveness and efficiency. Data resources, unlocking their potential, and generating insights from them in Kumar’s framework overlap with customer information assets, information analysis capabilities, and customer knowledge, respectively, in the proposed framework. The principal focus of the proposed framework is the digital data-rich environment as a transformative force in marketing. Although Kumar does not explicitly delineate this aspect in his framework, it reverberates in much of his discussion relating to it.
Srivastava, Shervani, and Fahey (1998) conceptualize marketbased assets as resources that arise from a firm’s commingling with entities in the external environment. They broadly distinguish between relational assets (resources that arise from a firm’s relationship with key external stakeholders) and intellectual assets (knowledge gained by the firm about the environment and specific entities in the environment). The authors highlight brand equity, customer equity, and channel equity as illustrative of a firm’s relational market-based resources, and knowledge about market conditions, customers, competitors, channel members, and suppliers as illustrative of intellectual market-based resources.
In a digital data-rich environment, three market-based resources of major importance are a firm’s customer information assets, customer information analysis capabilities, and customer knowledge. Figure 1 presents a conceptual framework delineating the relationships between these resources, marketing strategy, and performance. As the figure shows, by effectively leveraging its customer information assets and information analysis capabilities, a firm can achieve an advantageous resource position in customer knowledge. Analogously, by effectively leveraging its advantageous resource position in customer knowledge to inform its marketing strategy decisions, a firm can achieve a better marketing strategy–market environment fit relative to its competitors and, thereby, greater marketing strategy effectiveness and efficiency as well as superior marketing and financial performance.
Customer information assets is defined as information of economic value about customers owned by a firm. In the DataInformation-Knowledge-Wisdom Pyramid framework (BurtonJones 1999, p. 6), information is conceptualized as “processed data that is meaningful and of value,” and knowledge as “understanding of information and its associated pattern.” Customer information analysis capabilities is defined as a firm’s complex bundles of skills and knowledge embedded in its organizational processes that it uses to generate customer knowledge from its customer information assets. The proposed definition builds on Day’s (1994) conceptualization of capability as complex bundles of skills and knowledge embedded in a firm’s organizational processes by which it transforms available resources into valuable outputs. It conceptually overlaps with Rubera, Chandrasekaran, and Ordanini’s (2016, p. 170) conceptualization of market information management capabilities as skills used by a firm to develop and use market knowledge (i.e., customer knowledge and competitor knowledge).
Customer knowledge is defined as a firm’s understanding of customers that informs its business decisions. Customer knowledge, broadly construed, encompasses a firm’s insights into or understanding of customers’ attitudes, behaviors, beliefs, demographics, desires, emotions, habits, interests, involvement, lifestyles, motives, needs, perceptions, preferences, psychographics, tastes, values, wants, and more—hence the proposed inclusive conceptual definition. However, the broad scope of customer knowledge necessitates the scope of the operational definition to be limited to a specific facet (e.g., wants) or a small number of facets (e.g., affects, cognitions, behaviors) of customer knowledge in specific research studies.
Marketing strategy is defined as a business’ crucial decisions concerning its planned pattern of behavior in the marketplace to achieve (facilitate the achievement of) a competitive advantage, and thereby realize specific organizational objectives. Marketing strategy addresses the fundamental question of how to compete in the marketplace (How to create, communicate, and deliver products that offer value to customers in mutually beneficial exchanges with the organization? How to engender specific affects, cognitions and behaviors in target customers?) Crucial and internally consistent decisions that are germane to marketing strategy include marketing activities to perform, their manner of performance in chosen markets and market segments, and the allocation of marketing resources among markets, market segments, and marketing activities (see Varadarajan 2010). In the proposed framework, the focus is not the full spectrum of marketing strategy, but rather those elements of marketing strategy that can be optimized in a digital data-rich environment (e.g., personalization of product offering and/or specific elements of the marketing mix).
Fit refers to the degree to which a firm’s marketing strategy is appropriate for a particular market environment. In the context of a digital data-rich environment, in the limit, fit is the degree to which specific elements of a firm’s targeted marketing efforts are appropriate for individual customers (e.g., personalization of the product offering and promotional message content). Marketing strategy effectiveness is the extent to which the implementation of the strategy enables an organization to achieve its stated objectives. Marketing strategy efficiency is the ratio of the amount of marketing output generated to the amount of marketing resource inputs employed to implement the strategy.
In the internet-enabled, interactive and digital data-rich environment, the effect of customer knowledge advantage on marketing performance (i.e., effect of increase in market share in a growth market on the size of a firm’s customer base) will have a positive feedback effect on customer information assets accumulation. This is shown in the framework by the link from marketing performance to customer information assets. The dotted arrows in Figure 1 denote a firm’s assessment of gaps in its customer knowledge that limit its ability to compete more effectively and efficiently in the marketplace, and the underlying gaps in customer information assets and customer information analysis capabilities. Along the lines of heterogeneity among competitors in their customer information assets, customer information analysis capabilities and customer knowledge, there will also be heterogeneity among them with respect to customer knowledge gaps they aware of (known unknowns), and gaps they are unaware of (unknown unknowns).
The relationships delineated in the framework (Figure 1) are informed by insights from the resource-based view (RBV), capabilities-based view (CBV), and dynamic capabilities-based view (DCBV) of the firm. RBV posits that heterogeneous market positions result from effectively leveraging heterogeneous firm resources that are valuable, rare, inimitable and nonsubstitutable (VRIN resources) to achieve and sustain a competitive advantage in the marketplace (Barney 1991). CBV distinguishes between resources (stocks of available factors controlled by the firm), and capabilities (capacity of a firm to deploy resources, usually in combination, using organizational processes to achieve a desired end) (Amit and Schoemaker 1993). Rather than the mere possession of idiosyncratic assets and static capabilities, the DCBV considers a firm’s ability to integrate, build, and reconfigure competencies in response to a changing environment as crucial to achieving and sustaining a competitive advantage in the marketplace (Teece, Pisano, and Shuen 1997). Teece, Pisano, and Shuen (p. 516) define dynamic capabilities as “the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments.” The knowledge-based view (KBV) posits that knowledge is at the core of a firm’s competitiveness, and the way a firm creates, acquires, assimilates, and exploits knowledge leads to the creation of new sources of revenues and persistence of performance differences among competing firms (Grant 1996; Rubera, Chandrasekaran, and Ordanini 2016).
In the proposed framework (Figure 1), customer knowledge, modeled as a firm resource, is distinct from knowledge embedded in a firm’s customer information analysis capabilities. The former refers to knowledge as understanding (knowing what something means) and the latter to knowledge as knowhow (knowing how to do something) (see Kogut and Zander 1992). Bierly, Kessler, and Christensen’s (2000) conceptualization of knowledge as the understanding of information and its associated pattern augurs with the former. Organizational knowledge embedded in a firm’s capabilities refers to the latter. Its tacit nature makes it relatively difficult for competitors to diagnose and/or replicate (Teece, Pisano, and Shuen 1997) and transfer from one firm to another without also transferring ownership of the firm, or some self-contained subunit of the firm within which the capabilities reside (Makadok 2001).
Although the proposed framework builds on literature insights from the RBV, CBV, and DCBV of the firm, it differs in certain respects. The proposed framework highlights organizational contexts in which certain types of resources (assets and capabilities) are leveraged to create another type of resource (knowledge), which is then leveraged to effectively compete in the marketplace. The following linkages underlying the RBV,
CBV, and DCBV and the proposed framework (Figure 1) serve to highlight this distinction:
• RBV: Competing by leveraging superior resource positions in VRIN resources (A) → Competitive Advantage (B) → Performance (C).
• CBV and DCBV: Competing by leveraging superior firm
capabilities (A2) to effectively deploy superior firm resources (A1) → Competitive Advantage (B) → Performance (C).
• Assets, capabilities, and knowledge framework (Figure 1):
Utilizing superior firm capabilities (A2) to effectively leverage superior firm assets (A1) to create superior knowledge resources (A3) → Competing by leveraging superior knowledge resources (A3) → Competitive Advantage (B) → Performance (C).
In the broader context of interdependencies between three broad types of firm resources (information assets, information analysis capabilities, and knowledge), the proposed framework delineates the relationships between a firm’s customer information assets, customer information analysis capabilities, customer knowledge, marketing strategy and performance. The representative assets listed in Boxes A1 and A2 in Figure 1 highlight these relationships in the context of the digital data-rich environment characteristic of the transformative marketing landscape. The principal relationships delineated in Figure 1 can formally be stated as follows:
Customer Knowledge Advantage Hypothesis
Heterogeneity in customer information assets and customer information analysis capabilities, through their effects on customer knowledge, result in heterogeneity in the competitive market positions of firms. This effect will be more pronounced in digital data-rich competitive environments.
The following real-world examples provide anecdotal support for the proposed framework and hypothesis.
Doubling down on your distinctive strengths not only helps you fight disruption from upstarts, but it also enables you to disrupt an industry on your own terms. That’s exactly what Netflix did. In the late 1990s, the company competed directly with the Blockbuster retail chain. In 2007, when streaming video became viable, Netflix rapidly pivoted to offer that service. It … has pioneered the use of artificial intelligence and machine learning to tailor its output to consumer interests. All along, its success has been enabled by a core distinctive strength: the ability to understand what its customers want and do, using in-depth analytics and the behavioral data it captures. (strategy+business 2017)
Another blowout quarter from Alibaba highlights the Chinese e-commerce company’s ability to harness its trove of data to boost earnings… Its operating margin widened by 7 percentage points, which management attributed in part to better use of data, as operating profit almost doubled from a year ago…. It attributes the revenue increase to more shoppers as well as its ability to deliver more relevant content to them.… The company accounts for around three-quarters of online retail sales in China and hence has a trove of data on consumer behavior. Such data allow Alibaba to display ads to shoppers that they will most likely be interested in… Merchants are willing to pay higher prices if they know the ads are likely to draw in sales. Better algorithms allow Alibaba to earn more ad dollars without a similar rise in costs. (Wong 2017, p. B12)
By their very nature, models and frameworks such as that proposed here (Figure 1) are abstractions of reality. In this regard, an important societal issue in the current digital data-rich environment (and the focus of numerous journal articles, opinion pieces, and editorials) that merits mention is the divide between a firm profitably serving consumers by leveraging its advantageous resource positions in customer information assets, information analysis capabilities and customer knowledge, and invasion of consumers’ privacy.
Certain environmental developments have a transformative effect on marketing, and certain marketing innovations have a transformative effect on the environment. The former is the focus of this commentary—transformative effects of a digital data-rich environment on marketing. The scope of Kumar’s (2018) transcends both, but with a greater emphasis on the former. The effects of transformative innovations, broadly construed (e.g., breakthrough, disruptive, game changing, radical, revolutionary innovations), such as the following, provide a baseline frame of reference for gaining insights into the transformative effects of marketing innovations, both past (e.g., supermarket, discount, and department store chains; e-commerce, mobile e-commerce, and e-customer relationship management) and future:
• Major changes in how a specific need or set of needs and
wants of consumers are met.
• Major changes in how consumers search, evaluate, purchase, consume/use, and dispose of a product—fundamental changes in consumer behavior.
• Major changes in the value proposition offered to customers and/or expected by customers.
• Major changes in how a product is produced, promoted, distributed, priced, and so on—fundamental changes in the marketing behavior of firms.
• Emergence of a new industry, or convergence of two or more existing industries.
• Major changes in the structural characteristics of an industry such as market size, market growth rate, industry concentration, entry barriers and exit barriers.
• Major changes in the composition of competitors in an industry following entry of new firms, exit of incumbent firms, and changes in the competitive positions of incumbent firms.
• Major changes in the configuration of value chain activities, assets mix, cost elements, and/or revenue model in an industry.
• In the limit, the effects of an innovation (e.g., civilization transforming/epochal innovation) can be tectonic in nature, such as transformative cultural, economic, political and/or social changes.
The history of successful transformative innovations, the technologies underlying them, and the role of marketing as a catalyst in their adoption and diffusion is suggestive of technology, innovation, and marketing as a transformative triad. The dawn of a digital data-rich environment portends technology, innovation, data, and marketing as a transformative quartet.
a The customer information assets and customer information analysis capabilities that are shown are representative of a larger number of assets and capabilities, respectively.
b The direct link from marketing strategy to financial performance denotes the effect of marketing strategy efficiency on financial performance.
c The link from marketing performance to customer information assets denotes the effect of increase in market share in a growth market on the size of a business’ customer base.
Notes: The framework focuses on a specific facet of the business landscape that is transformative of marketing—an Internet enabled, interactive, digital data-rich market environment. Of the various types of firm resources, the framework specifically focuses on three major types: assets (A1), capabilities (A2), and knowledge (A3). The dotted arrows denote a firm’s assessment of customer knowledge gaps that constrain its ability to more effectively and efficiently compete in the marketplace, the underlying customer information assets gaps, and customer information analysis capabilities gaps. Here again, there will be heterogeneity across firms in respect of known unknowns (customer-related knowledge gaps that the firm is aware of), and unknown unknowns (customer-related knowledge gaps that the firm is not even aware of).
DIAGRAM: FIGURE 1 Conceptual Framework
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Record: 8- A Meta-Analysis of When and How Advertising Creativity Works. By: Rosengren, Sara; Eisend, Martin; Koslow, Scott; Dahlen, Micael. Journal of Marketing. Nov2020, Vol. 84 Issue 6, p39-56. 18p. 2 Diagrams, 4 Charts. DOI: 10.1177/0022242920929288.
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A Meta-Analysis of When and How Advertising Creativity Works
Although creativity is often considered a key success factor in advertising, the marketing literature lacks a systematic empirical account of when and how advertising creativity works. The authors use a meta-analysis to synthesize the literature on advertising creativity and test different theoretical explanations for its effects. The analysis covers 93 data sets taken from 67 papers that provide 878 effect sizes. The results show robust positive effects but also highlight the importance of considering both originality and appropriateness when investing in advertising creativity. Moderation analyses show that the effects of advertising creativity are stronger for high- (vs. low-) involvement products, and that the effects on ad (but not brand) reactions are marginally stronger for unfamiliar brands. An empirical test of theoretical mechanisms shows that affect transfer, processing, and signaling jointly explain these effects, and that originality mainly leads to affect transfer, whereas appropriateness leads to signaling. The authors also call for further research connecting advertising creativity with sales and studying its effects in digital contexts.
Keywords: advertising; affect transfer; creativity; meta-analysis; processing; signaling
Creativity is important in marketing and is often considered to be at the heart of the advertising industry. The importance of creativity is highlighted, for example, in the popularity of industry competitions, such as the Cannes Lions International Festival of Creativity, and the growing academic literature on its effects (e.g., [68]; [85]). However, the value of creativity is also subject to longstanding debate ([ 5]; [52]), and recent reports highlight that marketers are increasingly growing skeptical of advertising creativity ([63]; [66]) and decreasing their investments in it ([27]).
When and how is advertising creativity most valuable? Marketers wanting to answer these questions will find little guidance in the academic literature. Although the link between advertising expenditure and advertising effects has been consistently supported ([38]; [80]), to date, there is no comprehensive account of advertising creativity and its influence on consumer response. Even [83] failed to account for creativity as a factor in their insightful and influential review of how advertising works.
Several factors seem to hold back scholarship in advertising creativity: ( 1) contrasting empirical results on its effects in terms of ad and brand outcomes (e.g., [51]; [77]; [82]), ( 2) disagreements over what creativity is and how it should be assessed (e.g., [60]; [77]), ( 3) limited understanding of moderators of its effect (e.g., [86]), and ( 4) ambiguity about the kind of theories that best explain how creativity works (e.g., [85]; [86]). Given the apparent confusion about what advertising creativity is and when it might benefit a brand, it is not surprising that marketers often make the wrong decisions when investing in advertising creativity ([68]).
In this article, we synthesize the fragmented literature on consumer response to advertising creativity. Based on a literature review, we develop a conceptual framework linking advertising creativity to consumer outcome responses in terms of ad, brand, and sales. Through a meta-analysis, we then integrate 878 effect sizes in the first quantitative empirical overview of the literature. Thus, we capture the impact of advertising creativity on 19 different consumer responses taken from 93 data sets in 67 papers. We thereby contribute a comprehensive and empirically grounded account of how and when advertising creativity works, providing researchers with generalized findings that can serve as benchmarks and a common foundation for future studies of this important topic.
First, we provide an empirically validated account of how advertising creativity works. The results show robust positive effects of advertising creativity on consumer responses but also inform researchers about the relative importance of various consumer responses to advertising creativity. Overall, effects are stronger for ad rather than brand responses (r =.491 vs..317) and for attitudinal rather than memory outcomes (all below.140). This suggests that the main advantages of advertising creativity are not grabbing attention and making ads memorable but rather the ability to foster positive ad and brand attitudes.
Second, we highlight that advertising creativity is different from originality. Effects on consumer response are greater when creativity is measured as a bipartite construct comprising of originality and appropriateness. Effects of originality only on ad and brand attitudes are comparatively small (.362 and.164), suggesting that marketers who view creativity as synonymous with originality will not reap the full benefits of investments in advertising creativity.
Third, we show that the different theoretical accounts used in the literature to explain how advertising creativity works are complementary. Although previously considered separately, affect transfer, processing, and signaling provide the best account when considered jointly. The results further show that processing is a key mediator of the effects, whereas originality fosters affect transfer and appropriateness signaling. When marketers invest in bipartite creativity, affect transfer and signaling occur in parallel to processing, which can explain the stronger effects of creativity compared with originality only.
Fourth, we find that when the three theoretical accounts are considered jointly, the effects of advertising creativity on brand response are not dependent on ad responses. This is in line with the signaling account of advertising creativity and suggests that to fully understand how advertising creativity works, marketers should assess consumer responses in terms of brand rather than ad outcomes.
For managers, the results provide guidance on how, when, and why to invest in advertising creativity. For example, advertising creativity leads to greater ad responses in high (vs. low) involvement communication contexts (.653 vs..340) and (marginally) for unfamiliar (vs. familiar) brands (.577 vs..435). The literature review also highlights the need for more studies on the relationship between advertising creativity and sales, as well as its effects in digital media contexts, two areas that are especially important given the ongoing debate about the value of advertising creativity in contemporary marketing practice.
Figure 1 presents the conceptual framework guiding the meta-analysis. We developed this framework on the basis of a review of the literature on consumer responses to advertising creativity. The framework focuses on consumer responses that have been empirically studied and distinguishes between immediate and outcome responses. The categorization of immediate responses is based on the three main theoretical accounts of how advertising creativity works found in the literature: affect transfer, processing, and signaling (i.e., consumer response in terms of affect, processing, and perceived signals at the time of exposure to creative advertising). The categorization of outcome variables is based on ad and brand responses and characterized in terms of attitudinal or memory responses (i.e., longer-lasting responses related to the ad and brand, such as attitudes, memory effects, and sales). The framework also highlights two moderators of these responses: definitions and assessments of advertising creativity and properties of the communication context (involvement and familiarity).
Graph: Figure 1. How and when advertising creativity works: conceptual framework.aNot tested empirically due to lack of data.
Whereas research on advertising creativity generally has found positive effects on immediate and outcome responses (for reviews, see [74]] and [77]]), empirical findings suggest that the effects vary between different types of consumer responses. Findings generally show that advertising creativity has benefits in terms of immediate responses, such as attention ([65]; [78]), positive affect ([30]; [86]), and signals, such as perceived sender effort ([19]; [50]), but results are inconsistent regarding when and how this might also lead to outcome responses, such as attitudes and intentions ([61]; [77]) or memory effects ([65]; [82]). Results also varied regarding whether attitude and memory outcomes are affected ([ 4]). In line with the overall literature, we hypothesize that advertising creativity has positive effects on immediate and outcome responses:
- H1: Advertising creativity has positive effects on (a) immediate responses and (b) outcome responses.
However, from a managerial perspective, understanding whether investments in advertising creativity mainly affect ad rather than brand response, and whether advertising creativity is better at stimulating attitude or memory outcomes, is also important. Given the inconsistencies in the literature, we qualify this hypothesis by asking what type of consumer responses are most affected:
- RQ1: Is consumer response stronger for ad versus brand outcomes?
- RQ2: Is consumer response stronger for memory versus attitude outcomes?
As indicated in the introduction, a key challenge in the literature is the different views on advertising creativity. Creativity is a general construct that has been widely researched in fields such as psychology and organizational behavior, as well as in marketing ([37]; [74]). Creativity can be used to describe individuals (e.g., an art director at an advertising agency), processes (e.g., design thinking methods used to brainstorm advertising campaigns), or outputs (e.g., the actual ad executions used in a marketing campaign). In this article, we adopt the output perspective.
Drawing on the creativity literature ([ 2]; [33]; [73]), we define advertising creativity as advertising execution(s) that is (are) original and appropriate. This bipartite definition of creativity has been widely adapted in the marketing literature, in which the definition has been applied in advertising ([14]; [41]), new product development ([ 8]; [37]), and consumer behavior ([ 9]; [62]). As argued by [ 2], a bipartite definition of creativity is required, because outputs that are original or unique but carry no use or meaning are perceived as weird or bizarre. However, any judgments of creativity are subjective and likely to vary across context and time. For example, judgments about originality and appropriateness in a work of art differ from the same judgments in an advertising context (even if the actual object being judged is the same). Similarly, judgments of the creativity of the same object can vary over time. In the art domain, for example, there are several artists who are now considered creative but whose art was controversial or even rejected in their time (e.g., Monet, Picasso, Dalí). Such works were initially seen as "weird" or "bizarre," mainly because the type of appropriateness expected of them at the time of creation was a literal representation of reality. Thus, these artists were redefined as creative later, when judgment of appropriateness changed.
In the advertising context, the best documented dimension of creativity is originality. This dimension has also been referred to as novelty, divergence, unexpectedness, and newness ([42]; [48]; [76]; [78]). Originality has positive effects on consumer responses to advertising, as originality makes advertising more likely to be attended and processed ([65]; [77]). Originality also has a positive effect on consumer response, as people have a predisposition to appreciate divergent stimuli and deem them intrinsically interesting ([86]). Advertising practitioners typically view originality as the most defining aspect of advertising creativity ([48]; [60]), especially when it comes to advertising creativity awards ([15]; [78]). Thus, it is not surprising that many scholars focus primarily or exclusively on originality when assessing advertising creativity ([49]; [65]).
When it comes to appropriateness, this dimension complements originality by connecting the advertisement with brand strategy and consumer problem-solving abilities and goals ([ 3]; [25]; [60]; [78]). In the advertising literature, appropriateness is also referred to as relevance and usefulness (and when assessed by practitioners as "on strategy"; cf. [41]; [74]). Appropriateness as such has received much research attention (often using the term "relevance"; e.g., [31]). However, in contrast with originality, scholars rarely consider appropriateness to be an indicator of creativity in and of itself. Instead, researchers typically view appropriateness as a prerequisite for advertising to be interesting to its intended audience regardless of its level of creativity.
Theoretically, it is clear that creativity is both originality and appropriateness. Some scholars also argue that additional dimensions could be needed to fully understand advertising creativity ([ 3]; [30]). They argue in favor of including a third advertising-specific dimension of creativity—namely, the quality of the ad execution, also referred to as artistry or production ([60]; [78]). In the literature, we distinguish four approaches to empirically assess advertising creativity. First, some studies measure advertising creativity as a holistic perception of the creativity of an ad, typically by using a single item "creative" or multiple creativity items that do not refer specifically to different dimensions (e.g., [71]). Second, other studies rely on only one dimension of advertising creativity (typically originality; e.g., [57]). Third, acknowledging the bipartite definition of creativity, some researchers use the interaction between originality and appropriateness as a creativity measure (e.g., [78]). Fourth, acknowledging the multidimensionality of the bipartite definition, some studies rely on measures of both originality and appropriateness ([42]), sometimes combined with one or more additional dimensions related to the production quality or artistic value ([60]; [68]).
We argue that researchers who focus on originality only (e.g., [57]; [65]) are likely to get different results from those who study creativity in terms of a bipartite (e.g., [60]; [78]). Although we cannot test the validity of different assessments, we propose that the best measure of advertising creativity should explain more variance in outcome response variables than alternative measures of creativity, leading to stronger effect sizes (for a similar argument, see [23]). The approach that has the highest explanatory value should also be the most managerially relevant. Given that creativity is defined as a combination of originality and appropriateness, we propose that the effect sizes should be stronger when both dimensions are considered and weaker when only originality is used. Thus,
- H2: The effect of advertising creativity on (a) ad response and (b) brand response is stronger when creativity is assessed as a bipartite versus as originality only.
Although we expect advertising creativity to generally have positive effects on consumer response (H1), we also expect properties of the communication context to moderate these effects. In the literature review, it was apparent that little attention has been paid to such contextual moderators in the existing literature ([86]). However, we identified two theoretically relevant moderators: involvement and familiarity. Both variables have been found to affect consumer response to advertising in general, but of interest here is how they affect consumer responses to advertising creativity. Specifically, the literature suggests that advertising creativity (i.e., a combination of originality and appropriateness) has benefits regardless of the type of processing (peripheral vs. central; e.g., [10]) depending on these moderators.
Consumers' involvement with advertising reflects their level of interest in brand evaluation in any given context and has been found to moderate the effects of advertising processing and response (e.g., [56]; [59]). Specifically, consumer responses to advertising differ depending on how much effort goes into processing it. For example, high involvement with a product category motivates consumers to pay attention to and actively process advertising. When involvement is low, attention is typically allocated to other things, and consequently, ad processing is limited and utilizes few processing resources and peripheral cues (e.g., [10]; [56]).
Although advertising creativity has typically been thought of as an attention-grabbing device (e.g., [65]), implying that it would work best in low-involvement contexts (where it can foster situational involvement; [10]), creativity has been found to have additional processing advantages when it comes to high-involvement contexts ([79]; [86]). In a low-involvement context, any additional processing stimulated by a creative ad is likely to be shallow ([56]; [86]). In a high-involvement context, however, additional processing makes consumers more likely to actively assess the claims. In this context, the combination of originality and appropriateness fosters more open-minded and less defensive processing of claims made ("willingness to delay closure"; [39]; [86]). This means that consumers will be more open to new information about the brand and less likely to use defensive mechanisms when processing advertising messages that are communicated creatively. Whereas advertising creativity in low-involvement contexts stimulates more affective processing, in high-involvement contexts creativity influences affective and cognitive processing ([86]). In both contexts, advertising creativity should have a positive impact on consumer response, but in a high-involvement context, in-depth processing, coupled with the willingness to delay closure, makes the effects stronger. Thus,
- H3: The effect of advertising creativity on (a) ad response and (b) brand response is stronger for high-involvement versus low-involvement products.
Familiarity reflects the extent of consumers' direct and indirect experience with a stimulus, such as a product or a brand ([ 1]; [11]). Consumer response to advertising has been found to vary with familiarity ([55]; [75]). Specifically, the effects of advertising are generally stronger for unfamiliar than familiar brands. This effect is due to consumers not being able to draw from previous experiences (neither their own nor the experiences of others) when evaluating unfamiliar brands, which makes advertising more important for these brands. However, advertising for unfamiliar brands wears out faster ([11]). For marketers of unfamiliar brands, this poses a challenge, as they need advertising to build familiarity but also must be careful about how they advertise to avoid negative reactions caused by (too much) repetition.
Familiarity has also been found to moderate the effects of advertising creativity in terms of familiarity with the specific ad ([14]; [65]). [14] found that advertising creativity has two main benefits when it comes to repetition: ( 1) it generates more positive effects upon initial exposure, and ( 2) it resists wear-out over multiple exposures. The latter finding is in line with results showing that advertising creativity (in terms of originality) helps draw attention to familiar ads that might otherwise be overlooked due to tedium ([65]). For unfamiliar brands, these advantages are more important ([11]). Overall, this suggests that advertising creativity should be beneficial for unfamiliar and familiar brands, but given its attention-grabbing character ([65]), the immediate wear-in effect that it can generate ([14]), and the higher impact of advertising in general ([55]; [75]), the effects should be stronger for unfamiliar brands. Thus,
- H4: The effect of advertising creativity on (a) ad response and (b) brand response is stronger for unfamiliar versus familiar brands.
In the literature, scholars have used three main theories to explain the effects of advertising creativity on consumer responses. These accounts focus on different immediate responses as key mediators of the effects of advertising creativity on outcome responses. The affect transfer model focuses on the potential of creativity to evoke positive feelings that spill over into consumer responses to the ad and brand (i.e., regarding "positive affect" as a key mediator; [86]). The processing model focuses primarily on the ability of creativity to get consumers interested in the ad and brand (i.e., "ad processing" is the key mediator; [78]). The signaling model proposes that creativity works as a marketing signal, directly influencing perceptions about the sender and thus, consumer responses to the brand (i.e., "perceived sender effort" is the key mediator; [19]).[ 5]
A common explanation for the effects of advertising creativity is based on affect transfer ([20]; also referred to as affect infusion; [26]). This explanation focuses on the ability of affectively loaded information to transfer into other, more or less unrelated, targets. In the advertising creativity context, the affect transfer model builds on the fact that consumers generally like creative ads ([71]; [77]). Processing creative ads is seen as intrinsically motivating and pleasurable ([71]; [86]), which means that consumers are likely to experience positive affect while exposed to such advertising. This positive affect spills over to the ad and brand, leading them to be evaluated more favorably ([30]; [86]). According to this explanation, the positive effects are driven by creative ads being more enjoyable and liked, and the positive feelings that this stimulates. Although theoretically exceptions might occur, as may be the case for executions of fear appeal advertising that combines originality and appropriateness, the reviewed literature on advertising creativity unanimously provides empirical support for positive reactions to advertising creativity. Thus,
- H5: The effect of advertising creativity on (a) ad response and (b) brand response is mediated by positive affect.
Explanations of how advertising works often draw on information processing models (e.g., [56]; [59]). Specifically, they explain consumer responses to advertising based on different levels of ad processing. This is also a common explanation for the effects of advertising creativity. Creativity is said to stand out, thus making creative ads more likely to be attended to and processed ([78]; [86]). This means that more creative advertising stimulates more ad processing, resulting in longer exposure and greater attention ([30]; [65]), which has positive effects on consumer outcome response. According to this explanation, the positive effects of creativity are driven by creativity being more interesting and therefore, processed more. Thus,
- H6: The effect of advertising creativity on (a) ad response and (b) brand response is mediated by ad processing.
A third explanation for the effects of advertising creativity focuses on creativity as a signal of brand and company ability (e.g., [19]; [50]). This model builds on research on marketing signals ([44]; [46]), showing that certain behaviors on behalf of a firm (e.g., offering long-lasting warranties) can be used to signal unobservable quality to consumers. For example, advertising spending (i.e., monetary investments) has been found to work as a signal of brand quality ([45]; [47]). Similarly, advertising creativity has been found to be perceived by consumers as a signal that the sender has invested effort (in terms of time and money) in their brand ([18]; [19]). Through creative advertising, a brand conveys that it is committed to its advertising and its products, which is interpreted as a signal that positively affects how the brand is perceived and evaluated. According to this explanation, the positive effects of creativity are driven by creativity signaling effort on behalf of the sender, thus affecting the ad and brand positively. In contrast to the other two models, this account considers process reactions to creative advertising to be about the immediate perceptions of the brand rather than the ad. Thus,
- H7: The effect of advertising creativity on (a) ad response and (b) brand response is mediated by perceived sender effort.
Although the three theoretical accounts typically have been used in isolation (for an exception, see [86]]), combining the three models should provide a more comprehensive account of how advertising creativity works. Furthermore, the omission of any one of the intermediary effects might lead to the overestimation of the other ([83]). Therefore,
- H8: A model of advertising creativity that considers (a) positive affect, (b) ad processing, and (c) perceived sender effort jointly better explains the effects of advertising creativity on ad and brand responses than any of the three models used separately.
To test this hypothesis, we propose a full model, which incorporates all three theoretical mechanisms (see Figure 2). Given that the initial models focus on different variables, we propose several additional relationships in the full model. First, ad processing is likely to spur stronger perceptions of sender effort. This is because processing facilitates a more careful understanding of the ad ([77]), which should, in turn, enhance perceptions of sender effort stimulated by advertising creativity. Second, affective responses can influence not only the ad and brand but also perceptions of sender effort. The underlying logic is, again, affect transfer ([86]). Furthermore, affect and processing should be positively related, because feelings ease processing (mood theory), and easy processing is experienced as a good feeling (processing fluency theory).
Graph: Figure 2. How advertising creativity works: Full model including estimation results (standardized path coefficients).†p <.10.*p <.05.**p <.01.***p <.001.Notes: Dotted lines indicate paths that were added to the combined model to form the full model.
For this meta-analysis, we selected papers that provide estimates of the effects of advertising creativity on various consumer responses. According to our bipartite definition of advertising creativity, advertising creativity comprises originality and appropriateness. To be able to assess the relevance of different assessments of creativity, we included all studies that identify as "ad* creativity" studies independent of their definition and operationalization of advertising creativity. This means that we also included all studies that relied on advertising stimuli judged to be creative (even if they did not use the bipartite definition), as well as studies that investigated the two main dimensions of creativity, even if they did not use the term "creativity" ([51]).
To identify relevant papers, we first referred to review articles that provide an overview of previous research on advertising creativity (e.g., [74]). We applied an ancestry tree search by searching all papers that refer to the review papers that were available in the Web of Science database. Second, we performed a keyword search of electronic databases (e.g., ABI/INFORM, Emerald, Elsevier, EBSCO, and ProQuest Dissertation Publishing) using "advertising creativity," "ad creativity," "advertisement creativity," and "advertising creative," "ad creative," and "advertisement creative" as key words, followed by a search with key words that relate to the two main dimensions of advertising creativity ("original*," "novel*," "newn*," "unexpected*," "divergen*," "innovati*," "incongru*," "relevan*," "appropriate*," "useful*," and "meaningful*" combined with "advertis*"). The database search was complemented by a search on Google Scholar. Third, we performed a manual search of the journal outlets that turned out to be major sources for articles on advertising creativity. Fourth, once we identified a paper, we examined the references in a search for additional studies. The search period covered all papers (published and unpublished) that were available by December 2018. The retrieval approach was consistent with recommendations in the literature ([35]) and closely followed the steps taken in recent meta-analyses published in marketing ([69]; [88]).
After identifying manuscripts for potential inclusion in the data set, we applied inclusion and exclusion criteria to determine which manuscripts to retain. We included all empirical studies that measured or manipulated advertising creativity (as described previously) and provided estimates on its effects on consumer responses. We excluded any manuscripts outside this scope. For instance, we excluded studies that investigated nonconsumer response to creative ads (e.g., advertisers; [84]), or studies on creative media choice, but not creative ads ([16]). We also excluded studies that failed to provide sufficient data for the meta-analysis and for which necessary data could not be retrieved from the authors.
To avoid duplications in the data set, a document with original analyses and findings by the same authors (e.g., journal article, working paper, conference paper) is called a "paper." In some papers, the authors analyzed more than one distinct data set (e.g., a paper with several experiments), while some data sets were analyzed in more than one paper (e.g., a study that was published as a conference paper and a journal paper). The analysis is based on data sets. Each data set can provide single or multiple effect sizes that refer to the effect of advertising creativity on any consumer response variable. The search resulted in 67 usable papers covering 93 data sets (see Web Appendix Table 1). The sample includes journal articles, book chapters, working papers, unpublished theses, and conference proceedings, thus reducing the risk of a biased representation of the state of research because of the source of publication. The variation of sources is similar, and the number of papers and data sets is higher than in other major meta-analyses in marketing ([12]; [87]).
We categorized the consumer response variables measured in the studies based on the conceptual framework (see Figure 1). Specifically, we classified consumer responses in terms of immediate responses (affect, processing, and signals) and lasting outcomes related to the ad and brand (none of the data sets provided data for sales). The outcome responses were further divided based on attitude and memory responses. In addition, we identified a few consumer responses that did not fit in either category (e.g., actual creativity, brand familiarity, willingness to pay). Because these consumer response variables appeared either in only one or two data sets or in only one paper, we eliminated them from further analysis.[ 6] We did this to ensure a minimum degree of generalizability, because a meta-analysis should provide a high degree of generalization and thus, requires more information than a single manuscript or a single-study manuscript followed by a replication study. This left 878 effect sizes. For an overview of the consumer response variables and categorization scheme, see Table 1.
Graph
Table 1. Variables Used in the Meta-Analysis.
| Variable | Description | Coding Scheme (Reliability) |
|---|
| Immediate Responses | Consumer responses at the time of exposure in terms of... | |
| Affect | Emotions | Positive affect, humor |
| Processing | Processing | Attention, interest in ad, ad processing, complexity of ad/difficult to comprehend, positive thoughts |
| Signals | Perceptions | Perceived sender effort, perceived brand value/quality, perceived trust, perceived credibility |
| Outcome Responses | Lasting consumer responses in terms of... | |
| Ad response | Attitude | Aad |
| Memory | Ad recall, ad recognition |
| Brand response | Attitude | Abrand, purchase/behavioral intention |
| Memory | Brand recall, brand recognition, brand memory (mix recall/recognition) |
| Sales response | Brand/product sales | N.A. |
| Moderators | | |
| Creativitya | Creativity assessed in terms of... | |
| Originality only | 0 = other, 1 = originality only |
| Appropriateness only | 0 = other, 1 = appropriateness only |
| Originality × appropriateness | 0 = other, 1 = interaction only |
| Multidimensional measure | 0 = other, 1 = multidimensional |
| Familiarity | Degree of brand familiarity | 0 = unfamiliar/fictitious/mixed, 1 = familiar (AR = 97.3%, α =.938) |
| Involvement | Degree of product involvement | 0 = low involvement/mixed, 1 = high involvement (AR = 94.5%, α =.786) |
| Control Variables | | |
| Medium | Type of medium used to convey ad | 0 = print/outdoor, 1 = TV/movies |
| Year | Year of publication | Continuous |
| Award | Whether the studied ad has won a creative award | 0 = other, 1 = award winning (AR = 94.5%, α =.888) |
1 a The moderator variable is measured at the effect-size level, while all other moderators are measured at the data-set level.
2 Notes: Intercoder reliability is provided for all high-inference coding with AR = agreement rate and α = Krippendorff's alpha.
In terms of creativity moderators, we coded the variables at the effect size level, meaning that multiple effect sizes from one data set can be assigned different codes. Specifically, we coded whether creativity was assessed as originality only, as appropriateness only, as an interaction effect between originality and appropriateness, as a multidimensional concept (including originality, appropriateness, and potentially more dimensions), or as a holistic concept (measured with a single item "creative" or corresponding multiple items or manipulated as such; this is the base alternative in the model). As an illustration, [86] presented results based on originality and appropriateness separately, as well as for the interaction between the two allowing us to code three types of measurements for each of the variables studied. Although our main interest is in comparing a bipartite view of advertising creativity with a view of advertising creativity as originality only, this coding process allows a more complete understanding of how different assessments of creativity affect consumer response.
In terms of communication context moderators, we dummy coded the variables on the data-set level. Specifically, we coded the data sets 1 if the advertised category was a high-involvement product and if the advertised brand was familiar. In addition, we added three control variables that captured substantial differences between studies and that could be related to the context variables (medium, year, and award). Two authors independently assigned variables in the primary studies to consumer responses and coded the moderators and control variables based on the information available in each study. The agreement rate was above 98% (Krippendorff's alpha =.932), and inconsistencies were resolved by discussion.
The effect size metric selected for the meta-analysis was the correlation coefficient; higher absolute values of the coefficient indicate a stronger influence of advertising creativity on consumer responses. For papers that reported other measures (e.g., Student's t, mean differences), we converted those measures following guidelines for meta-analysis ([53]; [64]).[ 7] We adjusted all correlations for unreliability. When a paper did not report the reliability, or when the paper used a single-item measure, we used the mean reliability for that construct across all studies, following the procedure in previous meta-analyses in marketing (e.g., [43]).
We dealt with integrating dependencies between effect sizes using the following approach. When a data set provided findings for different consumer response variables, we treated the findings as independent, because we integrated and analyzed the estimates for each consumer response variable separately. Some data sets reported multiple relevant tests for the same consumer response variable. We accounted for the dependencies of the effect sizes and the nested structure of the meta-analytic data by using a mixed-effects multilevel model ([67]). We estimated the following model:
Graph
1
where i = 1, 2, 3...I effect sizes, j = 1, 2, 3...J data sets. This formula estimates the average effect size γ00, the deviation of the average effect size in a data set from γ00 (μ0j), and the deviation of each effect size in the kth data set from γ00 (eij). The latter two terms have variances that follow a normal distribution and are uncorrelated.
To address publication bias, we computed fail-safe Ns ([72]), which represents the number of additional effects with null results needed to render the results for an integrated effect size not statistically significant at p =.05. The fail-safe Ns were calculated for all statistically significant integrated effect sizes (p <.05) using the effect size estimates that were adjusted for measurement error. Furthermore, we provided a homogeneity test as an aid in deciding whether the observed effect sizes were more variable than would be expected from sampling error alone. If they are, there is a strong basis for including moderators. The homogeneity test involves the Q statistic, in which the distribution is similar to a chi-square with K − 1 degrees of freedom ([32]).
If the homogeneity test indicated heterogeneity, we proceeded with a moderator analysis. We added the moderators specified by the hypotheses and the control variables simultaneously to Equation 1 and ran multilevel meta-regression models in hierarchical linear modeling separately for the major outcome variables. The model was a mixed-effects model, because fixed effects for the moderators were considered in addition to random components. We specified the following model:
Graph
2
where rij is the ith effect size describing the relationship between advertising creativity and the respective consequence variable reported within the jth data set.
Assuring the robustness of the model required a sufficient sample size. The major restriction is often the higher-level sample size, and the literature recommends a sample of around 50 to avoid biased estimates of the second-level standard errors ([54]). Thus, we applied the model only to the outcome variables in the data set with a sufficiently large sample of data sets: Aad and Abrand (43 and 44 data sets, respectively).
To investigate the different processes that explain how advertising creativity works, we developed a correlation matrix including integrated effect sizes of the consumer responses to advertising creativity and added integrated effect sizes for the interrelationships between the consumer response variables. We followed recommendations in the literature about collecting meta-analytic data for the correlation matrix, deciding about sample size, analytical decisions, and reporting ([ 6]). We searched the papers in the meta-analysis for correlations for the interrelationships between consumer response variables. For a construct to be included in such analysis, multiple study effects must relate it to every other construct in the model. Therefore, no additional variables shown in Table 1 could be considered. For example, because we did not find correlations between sender effort and recall or memory measures, the latter could not be included in the model. We found at least three correlations for each relationship, which equals or exceeds the requirements of other meta-analytic correlation matrices found in the literature ([28]). We integrated and adjusted the correlations in the same way as the correlations between advertising creativity and consumer response variables. That is, we first adjusted all correlations for unreliability. We accounted for the dependencies of effect sizes and the nested structure of meta-analytic data by using a mixed-effects multilevel model as described previously ([67]).
We then used this correlation matrix (see Web Appendix Table 2) as input in a structural equation modeling (SEM) analysis using the maximum likelihood method. The matrix was based on 449 correlations, and the harmonic mean of the cumulative sample size for each cell equaled 1,293. Each construct was measured with a single indicator in the structural model. We fixed the error variances for these indicators to zero because we had already considered measurement errors when we integrated the effect sizes. We used the harmonic mean of the cumulative sample size underlying each integrated effect size (i.e., effect size cells comprising each entry in the correlation matrix) as the sample size for the analysis.
Table 2 reports the integration of the reliability-corrected correlations between advertising creativity and all consumer response variables.
Graph
Table 2. Influence of Advertising Creativity on Consumer Responses (H1).
| | # Papers | # Data Sets | # Effect Sizes | Total Sample Size | Average r | Homogeneity Test Q | Fail-Safe N |
|---|
| Immediate responses | Affect | | | | | | | |
| Positive affecta | 6 | 10 | 32 | 2,610 | .293*** | 158.184*** | 595 |
| Perceived humor | 4 | 4 | 10 | 1,208 | .630*** | 142.097*** | 1,860 |
| Processing | | | | | | | |
| Attention | 12 | 13 | 30 | 4,365 | .405*** | 2,853.202*** | 20,410 |
| Interest in ad | 7 | 11 | 40 | 1,829 | .415** | 15,766.431*** | 267,621 |
| Ad processinga | 3 | 4 | 6 | 1,037 | .337* | 822.686*** | 429 |
| Complexity of ad/difficult to comprehend | 4 | 6 | 15 | 2,357 | −.217† | 417.683*** | — |
| Positive thoughts | 2 | 3 | 57 | 743 | .177† | 23.194*** | — |
| Signals | | | | | | | |
| Perceived sender efforta | 6 | 7 | 38 | 6,310 | .396*** | 1,042.326*** | 1,333 |
| Perceived brand value/quality | 8 | 10 | 27 | 2,623 | .289*** | 724.790*** | 9,735 |
| Perceived brand trust | 3 | 4 | 6 | 626 | .387*** | 81.835*** | 220 |
| Perceived credibility | 5 | 7 | 8 | 2,138 | .397*** | 848.119*** | 2,434 |
| Outcome responses | Ad Attitude | | | | | | | |
| Aada | 37 | 44 | 192 | 19,729 | .491*** | 23,134.086*** | 1,446,838 |
| Ad Memory | | | | | | | |
| Ad recall | 18 | 24 | 91 | 2,712 | .311*** | 3,575.699*** | 30,142 |
| Ad recognition | 11 | 15 | 32 | 3,334 | .252** | 2.485.365*** | 7,133 |
| Brand Attitude | | | | | | | |
| Abranda | 35 | 43 | 138 | 11,434 | .317*** | 12,438.120*** | 232,128 |
| Purchase/behavioral intention | 29 | 34 | 83 | 28,950 | .306*** | 3,214.985*** | 122,938 |
| Brand Memory | | | | | | | |
| Brand recall | 11 | 14 | 36 | 3,825 | .129 | 793.672*** | — |
| Brand recognition | 8 | 10 | 21 | 2,148 | .052 | 2,752.091*** | — |
| Brand memory | 4 | 5 | 16 | 742 | .140* | 8.860† | 173 |
- 3 †p <.10.
- 4 *p <.05.
- 5 **p <.01.
- 6 ***p <.001.
- 7 a These variables are used to test H2–H7.
- 8 Notes: Only relationships for which effects were available in more than one paper and/or more than two independent data sets are shown. The corrected average correlation coefficients (r) are the sample size-weighted, reliability-corrected estimates of the population correlation coefficients. The fail-safe N indicates the number of nonsignificant, unpublished (or missing) effects that would need to be added to a meta-analysis to reduce an overall statistically significant (p <.05) observed result to nonsignificance.
Looking at immediate responses, we found statistically significant effects on affect in terms of positive affect and perceived humor. Interestingly, although positive affect has been studied more, the effects of humor were significantly stronger as indicated by nonoverlapping confidence intervals (95% CI for positive affect [.198,.388] vs. humor [.428,.832]). We also found significant positive effects on processing in terms of attention, interest in the ad, and ad processing, but only a marginal effect on complexity and positive thoughts. The effects on attention, interest in the ad, and ad processing were comparable in size (95% CI for attention [.218,.592], interest in ad [.215,.615], and ad processing [.015,.659]). Furthermore, advertising creativity had statistically significant positive effects on perceived signals: sender effort, brand value/quality, brand trust, and brand credibility. These effects were comparable in terms of size (95% CI for perceived sender effort [.282,.510], value/quality [.171,.407], brand trust [.171,.603], and brand credibility [166,.628]).
Turning to outcome responses, advertising creativity had a statistically significant effect on all ad responses: Aad, ad recall, and ad recognition. The strongest and most widely studied effect was that on Aad. In terms of brand responses, the effects followed a similar pattern: Abrand was the most widely studied variable and statistically significantly affected. We also found a statistically significant positive effect on purchase/behavioral intention and brand memory, but not on brand recall or brand recognition. Overall, the pattern of results support H1 by highlighting that advertising creativity has positive effects on consumer reactions in terms of ad and brand. Answering RQ1, we found that the effect on Aad was statistically significantly larger than that on Abrand and purchase intentions (95% CI for Aad [.407,.575] vs. Abrand [.235,.399]) and purchase intention [.225,.387]), and that the effects on ad recall ([.214,.408]) were significantly larger than the effects on brand memory ([.072,.208]). This suggests that ad responses are more affected than brand responses. Related to RQ2, the pattern of results suggests that effects of advertising creativity are stronger for attitudes than for memory. Aad was statistically significantly different from ad recognition (95% CI for Aad [.407,.575] vs. ad recognition [.107,.307]), and marginally different from ad recall ([.214,.408]). Similarly, the effect on Abrand was significantly stronger than the effect on brand memory (95% CI for Abrand [.235,.399] vs. brand memory [.072,.208]).
All homogeneity tests (except for brand memory) were statistically significant at p <.05 and showed that the variation in effect sizes cannot be explained by sampling error alone. The fail-safe N indicates that the statistically significant integrated correlations do not suffer from publication bias according to [72] rule of thumb (fail-safe N should be at least 5 times the number of effects plus 10).
Table 3 presents the results for the multilevel moderator regression model for the relationship between advertising creativity and Aad and Abrand. To investigate whether the positive effects of advertising creativity depend on the type of assessment used, we examined the moderating effect of creativity assessments. The analysis showed that relying only on originality led to lower effect sizes for Aad and Abrand; thus, H2 was supported. We found a similar pattern for assessments relying on appropriateness only and for interaction effects, although the negative effect was only marginally significant for the latter when it came to Abrand. The findings also showed that multidimensional measures of advertising creativity led to stronger effect sizes for Abrand, but not for Aad. Overall, this pattern of results suggests that assessing advertising creativity only in terms of ( 1) originality, ( 2) appropriateness, or ( 3) an interaction effect between the two will lead to an underestimation of the effects. From a managerial perspective, the result also suggests that a multidimensional view of advertising creativity is the most relevant, as brand responses are more important than ad responses.
Graph
Table 3. Influence of Moderator Variables on Effect Sizes: Multivariate Meta-Regression Analysis Results (H2–H4).
| Moderator (Hypothesis) | Moderator Values | Aad | Abrand |
|---|
| β (SE) | Predicted | β (SE) | Predicted |
|---|
| Intercept | | .625 (.090)*** | | .308 (.082)*** | |
| Creativity (H2) | Other vs. originality only | −.202 (.052)** | .564 vs..362 | −.170 (.074)* | .334 vs..164 |
| Other vs. appropriateness only | −.228 (.074)** | .549 vs..320 | −.149 (.048)** | .304 vs..154 |
| Other vs. interaction only | −.270 (.089)** | .510 vs..240 | –.120 (.068)† | .275 vs..156 |
| Other vs. multidimensional | .105 (.102) | | .231 (.013)*** | .260 vs..492 |
| Involvement (H3) | Low vs. high involvement | .259 (.078)** | .340 vs..653 | .223 (.093)* | .196 vs..420 |
| Familiarity (H4) | Unfamiliar vs. familiar | −.142 (.080)+ | .577 vs..435 | −.064 (.085) | |
| Medium (Ctrl) | Print/outdoors vs. TV/movies | −.049 (.078) | | .182 (.081)* | .206 vs..388 |
| Year (Ctrl) | Continuous | −.004 (.005) | | −.003 (.003) | |
| Award (Ctrl) | Others vs. award winning | −.093 (.091) | | −.214 (.110)† | .327 vs..113 |
| Partial correlation (Ctrl) | Other vs. effect converted from multivariate regression coefficient | −.523 (.052)*** | .519 vs. −.004 | — | |
- 9 †p <.10.
- 10 *p <.05.
- 11 **p <.01.
- 12 ***p <.001.
We then turned to the moderating effect of the communication context. The results showed stronger effects on Aad and Abrand for high-involvement products; thus, H3 was supported. Furthermore, the effects on Aad were marginally stronger for unfamiliar products, but there was no statistically significant difference in terms of Abrand. Thus, H4 was only partially supported. The control variables showed that using a partial correlation coefficient led to smaller effects on Aad. None of the remaining control variables affected Aad. However, the effects on Abrand were higher for audiovisual media (TV/movies) and marginally lower for award-winning ads. We did not find any statistically significant differences in terms of year of study.[ 8]
To better understand why advertising creativity has positive effects on consumer responses, we performed a SEM analysis of the different models using the meta-analytic correlation matrix (cf. Web Appendix Table 2). Table 4 presents the results of the SEMs (standardized coefficients and model fit statistics). As we suggested alternative models implying that the relationship between advertising creativity and Aad is mediated by more than one mediating variable, we added a path between advertising creativity and Aad that captured alternative processes to each model. All three individual models showed a very good model fit, and all paths were statistically significant and in line with the suggested effects; thus, H5, H6, and H7 were supported.
Graph
Table 4. Coefficients and Fit Indices of the Meta-Analytic SEMs (H5–H8).
| Affect Transfer Model (H5) | Processing Model (H6) | Signaling Model (H7) | Combined Model | Full Model (H8) |
|---|
| Creativity → Positive affect | .293*** | | | .293*** | .293*** |
| Creativity → Ad processing | | .337*** | | .337*** | .337*** |
| Creativity → Perceived sender effort | | | .396*** | .396*** | .341*** |
| Creativity → Attitude toward the ad | .315*** | .301*** | .250*** | .051** | .051*** |
| Positive affect → Perceived sender effort | | | | | .085** |
| Positive affect → Attitude toward the ad | .515*** | | | .388*** | .388*** |
| Positive affect → Attitude toward the brand | .266*** | | | .373*** | .373*** |
| Positive affect ⇓→ Ad processing | | | | | .206*** |
| Ad processing → Attitude toward the ad | | .490*** | | .351*** | .351*** |
| Ad processing → Attitude toward the brand | | .131*** | | .234*** | .234*** |
| Ad processing → Perceived sender effort | | | | | .088** |
| Perceived sender effort → Attitude toward the ad | | | .546*** | .462*** | .462*** |
| Perceived sender effort → Attitude toward the brand | | | .128*** | .304*** | .304*** |
| Attitude toward the ad → Attitude toward the brand | .477*** | .562*** | .556*** | .078† | .078† |
| Model Statistics | | | | | |
| χ2/d.f. | 1.840/1 | 1.067/1 | .655/1 | 97.903/4*** | 1.376/1 |
| Goodness-of-fit index | .999 | 1.000 | 1.000 | .975 | 1.000 |
| Adjusted goodness-of-fit index | .993 | .996 | .997 | .867 | .993 |
| Comparative fit index | 1.000 | 1.000 | 1.000 | .973 | 1.000 |
| Root mean square residual | .008 | .006 | .005 | .085 | .004 |
| Root mean square error of approximation | .025 | .007 | .000 | .134 | .017 |
- 13 †p <.10.
- 14 *p <.05.
- 15 **p <.01.
- 16 ***p <.001.
The model that combines the three individual models showed a comparatively weak fit but was significantly improved by adding the proposed relationships between processing and perceived sender effort and positive affect and perceived sender effort suggested by the full model (Δχ2/d.f. = 96.527/5, p <.001; see Figure 2). To determine whether the full model provided a better explanation than the three parsimonious models that were nested within it, we compared the fit of the full model that was restricted to any of the nested models with the fit of the full model with unrestricted paths. The model fit worsened significantly when it was restricted to the affect transfer model (Δχ2/d.f. = 1,629.935/8, p <.001), the processing model (Δχ2/d.f. = 1,733.093/8, p <.001), or the signaling model (Δχ2/d.f. = 1,528.916/8, p <.001). Thus, the full model provides an explanation that goes beyond the explanatory power of each nested model; H8 was empirically supported. Interestingly, in the full model, the mediating effect of Aad on Abrand dropped from around.5 in the individual models to a marginally significant effect of.078 (Δχ2/d.f. = 96.512/1, p <.001). This suggests that the effect of advertising creativity on brand response is only weakly mediated by ad response, which adds additional insight into RQ1 about the effects of creativity on ad versus brand response.
We performed two additional analyses to further explore how well the three models explain the effects of creativity on consumer response. First, we compared how much of the variance in Aad was explained directly by advertising creativity and indirectly by either process suggested by the three individual models (we could not apply this comparison to Abrand, as there was no direct effect of creativity on Abrand in the model). We computed the proportion of mediation as the ratio of indirect to total effect; that is, the indirect path(s) was/were divided by the sum of the direct path and indirect path(s) ([36]). The proportion of mediation via positive affect was 26.8%, via ad processing was 28.3%, and via perceived sender effort was 33.9%. When we tested the mediation paths in the full model against each other by restricting two corresponding paths at a time (see Web Appendix Table 3), we found no differences between the paths from advertising creativity to any of the three mediators (positive affect, ad processing, and sender effort). However, the effect of sender effort on Aad was significantly different and stronger than the effect of either positive affect or ad processing on Aad. The findings indicate that signaling explains more variance in Aad than the two other models, thus providing the strongest explanation for the effect of creativity on Aad of the three models.
Second, we compared the theoretical explanation offered by the full model between the two dimensions of creativity by using correlation matrices that considered the variable relationships with either originality or appropriateness instead of creativity (see Web Appendix Table 4). The results showed that the positive effects on ad processing are equally strong for both dimensions. However, affect transfer mainly explains the effects of originality as indicated by the fact that the path from creativity to positive affect was statistically significant for originality, but not for appropriateness. When it comes to signaling, however, appropriateness seems more important, as indicated by the significantly stronger link between creativity and sender effort.
In this article, we offer a comprehensive synthesis of the effects of advertising creativity on consumer responses. The study highlights the importance of advertising creativity by showing robust positive effects on a wide range of immediate and outcome responses. The effects are stronger for ad responses compared with brand responses and for attitudinal compared with memory outcomes. Moderation analyses show that the effects of advertising creativity are weaker when creativity is assessed as originality only, compared with a bipartite comprising originality and appropriateness. This suggests that the effects of advertising creativity go beyond those of originality alone. The results further show that advertising creativity has stronger effects in high-involvement contexts, and that effects on ad response are (marginally) stronger for unfamiliar brands. Furthermore, we find empirical support for all three theoretical accounts (affect transfer, processing, and signaling) used in the literature, but also that a full model (where the three accounts are considered jointly) best explains the effects of advertising creativity on consumer outcome response. In the full model, the effect of the three advertising creativity mediators (positive affect, ad processing, and perceived sender effort) on brand response is only marginally mediated by ad response, suggesting that although ad responses are generally more affected than brand responses, they are not needed for advertising creativity affect brand response. Additional analyses show that affect transfer mainly explains the effects caused by originality and that signaling provides the strongest account of advertising creativity in terms of ad response.
Although marketing researchers and practitioners tend to agree that advertising creativity is important, there are contrasting views on what advertising creativity is, and how and when it can lead to positive outcomes. Through this meta-analysis, we provide a synthesis of the growing, but dispersed, literature on advertising creativity, thus building a common foundation for future studies of this important topic. The results inform about important outcome variables and moderators of advertising creativity effects. The meta-analytic findings can serve as benchmarks for future advertising creativity studies, as well as for studies dealing with other ad execution elements. Future findings can be compared against the meta-analytic results in terms of explained variance as a measure of advertising effectiveness. The results also have several implications for future studies of advertising creativity.
First, we offer an empirically validated understanding of how advertising creativity works. The pattern of results suggests that advertising creativity has a role to play in stimulating positive consumer responses that goes beyond being a source of attention. If the attention-grabbing nature of advertising creativity were the key benefit, its effects should be greater for memory rather than attitudinal responses, and in communication contexts where consumers are less likely to attend to and process ads, such as for low-involvement products and for unfamiliar brands ([19]; [65]), which is not in line with the empirical results. Although claims that advertising creativity enables advertising to "cut through clutter" and make advertising more memorable ([65]) are true, they risk directing focus away from attitudinal consumer responses, which are more affected. The fact that advertising creativity has stronger effects in high-involvement contexts suggests that processing is important for the effects to occur. It also raises the question of what to expect from advertising creativity in contexts where consumers are unlikely to pay attention to and process ads, such as digital and mobile media. The meta-analysis did not include any such studies, but the results suggest that effects should be weaker in media such as smartphones where focus is very directed at other focal tasks ([58]). At the same time, effects should be stronger for advertising content in own channels and in media where consumers voluntarily seek out advertising ([70]). However, future research is needed to explicitly study the role of advertising creativity in these contexts.
Second, we contribute insights into how to define and assess advertising creativity. In line with the creativity literature ([ 2]; [73]), the results indicate that creativity is not just about originality. A bipartite definition and multidimensional assessments of creativity offer better explanations of the effects (for a similar argument, see [ 3] and [60]). This suggests that researchers should be mindful when using the term advertising creativity and restrict it to studies of original and appropriate ads. When studying original advertising only, the term creativity should be avoided. It also suggests that the reliance on advertising awards as an operationalization of advertising creativity is not valid, as such awards tend to focus on originality ([15]; [41]). The fact that empirical studies have found positive effects of original and award-winning ads, however, is reassuring, as the results suggest that, if anything, those studies underestimate the effects.
Third, we contribute to the theoretical understanding of how advertising creativity works. The findings show that the different theoretical accounts of advertising creativity available in the literature are complementary, but also that they have different relationships with creativity dimensions. Our meta-analytic path analysis show that originality primarily stimulates affect transfer, whereas appropriateness is more important for signaling. We also find that signaling has the highest explanatory value. Again, this reinforces the notion that a bipartite view of advertising creativity is most relevant, as ads that combine originality with appropriateness allow these mechanisms to work simultaneously, whereas original ads do not. It also suggests that future studies of advertising creativity should include more comprehensive theoretical frameworks than what has previously been the case. Together, these insights offer the basic building blocks for a more complete processing model of advertising creativity called for by [85].
Fourth, the finding that the three theoretical mediators of advertising creativity have direct effects on brand response (Abrand) that are only weakly mediated through ad response (Aad) adds further to our understanding of how advertising creativity works.[ 9] It shows that although creativity has stronger effects on ad responses than brand responses, these effects are not necessarily dependent on ad response. Again, this pattern can be understood in terms of the combination of (high) originality and (high) appropriateness in creative ads. In line with [78] finding that originality has advantages in terms of attention and that appropriateness stimulates downstream effects and brand response, advertising creativity allows the two to work in parallel, which also has more direct brand outcomes. This finding is in line with the signaling account of advertising creativity that suggests a more direct effect on the brand. For researchers, it suggests that when studying the effects of advertising creativity, brand (and sales) responses must be included through direct measures rather than relying on Aad or other ad responses as proxies of such effects.
Overall, the empirical results provide convincing evidence of the positive effects of advertising creativity on consumer responses and thus highlight the need for marketing scholars to consider not only media investments (ad spend; [38]; [80]) but also creativity investments in models of how advertising work.
For marketers, we contribute a systematic account and empirical evidence of the value of advertising creativity. Specifically, we offer important insights into how, when, and why to invest in advertising creativity. Given the ongoing debate about the value of creativity in advertising ([27]; [66]), this contribution is timely and useful. It also shows no evidence of advertising creativity becoming less (or more) effective over time.
When it comes to how to invest, [68] found that many marketers make suboptimal decisions regarding investments in advertising creativity. We suggest that a tendency to focus on originality might be the root of this problem. Creativity is more than originality, and by incorporating appropriateness consumer response will be more positive. To achieve this, marketers must find ways to assess advertising creativity. This is easier said than done, given that creativity judgments are subjective and vary across context and time. We find that award-winning ads lead to marginally weaker brand response, suggesting that consumer rather than professional judgments should be used. This supports [ 3] argument that marketers should involve consumers more in advertising development. Whereas there is a growing literature focusing on consumers as cocreators of advertising ([17]; [81]), consumers could also be engaged as prejudges of advertising. A post hoc analysis of the role of ad judges provided additional support for this notion. Specifically, we coded a variable that distinguished between ads that were judged to be creative by either consumers, by experts, or selected from award shows. As some studies did not provide details on ad judges, we first ran analysis of variance models for a combination of all three outcome responses (Aad, Abrand, and intentions) to ensure sufficiently large sample sizes. We found significant effects (F( 2, 351) = 4.931, p =.008) on outcome response. The effects were stronger when consumers judged advertising creativity (.373) compared with experts (.300) or award shows (.193). When we analyzed the three responses separately, the effect held for Abrand and intentions, but not for Aad. As brand outcomes are more valuable for marketers, this reinforces the potential in allowing consumers to (pre)judge advertising creativity.
When it comes to when to invest, the results suggest that advertising creativity has positive effects in general but also that the effects are stronger for attitudinal rather than memory response and marginally stronger in audiovisual media (TV/movies vs. print/outdoor). Furthermore, the effects are stronger for high-involvement contexts. For marketers, this challenges the established view of advertising as a tool for gaining attention and suggests that creativity is especially valuable in contexts where consumers are likely to process advertising. Although we studied product involvement, this logic should also hold for media context involvement, meaning that creativity is more likely to work in situations where more focused ad processing occurs. Thus, advertising creativity should be more important for media contexts in which consumers voluntarily direct their attention to, or are forced to focus directly on, advertising than in in media contexts that rely on incidental and divided attention (see also [17]; [70]).
We also find that advertising creativity has marginally stronger effects for unfamiliar compared with familiar brands. However, this effect is related only to ad rather than brand response. As suggested by [11], ad response is a strong indicator of brand response for unfamiliar brands (as consumers have little other information on which to base evaluations), suggesting that this finding is still managerially important. By investing in advertising creativity, such brands can increase the value of their advertising to consumers ("advertising equity"; [70]). Taken together, this suggests that advertising creativity is especially valuable when establishing a new brand in the market.
When it comes to why advertising creativity works, the mechanisms underlying its positive effects are more profound than many marketers might think. An in-depth understanding of how affect transfer, processing, and signaling jointly contribute to brand response help make investments in advertising creativity less risky ([85]). Although marketers who focus on originality can expect positive effects due to affect transfer, they will miss out on the potential effects of signaling and appropriateness. By investing in bipartite advertising creativity, marketers can increase the chances of their ads being liked, processed, and interpreted as signals of what the brand has to offer. It also means that there is little risk that positive effects will be for ad response only.
From a managerial perspective, the effects of signaling are especially important to consider, as they offer the strongest explanation for the effects on ad response and because appropriateness is especially important in high-involvement and low-familiarity contexts, where advertising creativity also has the strongest effects. It suggests that advertisements can produce effects by way of the signals they send rather than the specific messages they convey. Signals are especially important in situations where there is information asymmetry between marketers and their customers ([13]; [46]). This is arguably the case for unfamiliar brands and high-involvement products as well as in other situations where the decision-making process is complex, such as in business-to-business, business-to-government, and recruitment contexts ([13]; [18]). In fact, recent research suggests that the effect of advertising signals extends beyond consumers to other stakeholders, such as employees and investors ([18]), though this is beyond the scope of the present study.
Given the nature of a meta-analysis, we could study only consumer responses that previous researchers had investigated. This means, for example, that we could not consider potential negative effects of creativity on, for example, confusion, negative affect, and fear appeals. However, we found a marginally significant negative effect of complexity, suggesting that the potential downsides of creativity warrant further investigation.
Similarly, the literature review revealed a lack of studies on the effects of advertising creativity on sales (for an exception, see [68]) and the effects of advertising creativity in digital contexts, such as the effects of advertising creativity on social media influencer engagement ([34]). Future studies are needed to explore how advertising creativity works in those contexts. Studies linking the effects of advertising creativity to behavioral measures, such as brand choice or sales, seem especially important. This could be done by combining quantitative (advertising spend) and qualitative (advertising creativity) assessments of advertising investments with behavioral outcomes, for example, adding advertising creativity in marketing-mix models or adding sales as a dependent variable in experimental studies. In such efforts, additional moderators, such as clutter ([65]) and repetition ([14]), should also be considered.
As another limitation, the present study focused on consumer responses to advertising creativity only. There are several related issues in the literature that could contribute to our understanding of advertising creativity. For example, there is a vast literature on creative processes in agencies that foster creativity in advertising ([29]; [40]), and synthesizing this literature should bring additional insights to marketers. Relatedly, there should be room to further integrate the literature on advertising creativity with creativity research focusing on other marketing contexts ([ 8]; [21]) to allow for a more complete understanding of how creativity works in marketing more broadly. It is our hope that this article can contribute to this development.
Supplemental Material, 20200503_JM.18.0506.R3_web_appendix - A Meta-Analysis of When and How Advertising Creativity Works
Supplemental Material, 20200503_JM.18.0506.R3_web_appendix for A Meta-Analysis of When and How Advertising Creativity Works by Sara Rosengren, Martin Eisend, Scott Koslow and Micael Dahlen in Journal of Marketing
Footnotes 1 Associate Editor Wayne Hoyer
2 Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242920929288
5 1 The models we use in this article focus on key variables that are discussed in the extant literature of the three theoretical accounts. We could not include additional variables presented in Table 1 because of data constraints, which we explain in detail in the "Methods" section. It should also be noted that all three accounts have primarily been developed using experimental approaches.
6 2 We excluded the following variables from further analysis (mean correlations in parentheses): negative thoughts (−.105, p <.01), other thoughts (.047, n.s.), negative feelings (.083, n.s.), actual creativity (.189, p <.01), brand familiarity (.192, p <.10), presumed influence (.309, p <.01), and willingness to pay (.429, p <.10).
7 3 Of 878 effect sizes, we converted 21 from coefficients in multivariate regressions via the formula suggested by [64]. These parameters were partial correlations, and therefore, we checked whether they had an influence on the meta-regression results by including a dummy variable that distinguishes between partial correlations and correlations. Because partial correlations did not appear in the set of correlations referring to Abrand, the dummy was included in the Aad model only.
8 4 To further explore the role of originality and appropriateness in explaining these effects, we also tested several plausible interactions between creativity measurements and moderators (similar to the procedure in [75]). For Aad, we tested interactions between measurements (using dummy variables for originality, appropriateness, and multidimensions, the cell sizes for interactions with "interaction only" were too small to provide a robust analysis) and the hypothesized moderating variables (involvement, familiarity). The analysis showed a significant interaction effect for appropriateness × familiarity (b = −.239, SE =.062, t = 3.830, p <.001), suggesting that appropriateness is more important for unfamiliar brands. There was also a significant interaction effect for appropriateness × involvement (b =.271, SE =.058, t = 4.643, p <.001), suggesting that appropriateness is more important in high-involvement contexts. We conducted the same analysis for Abrand, where we were able to test interactions between measurements focusing on originality and appropriateness (the cell sizes for "interaction only" and "multidimensional" were too small to provide a robust analysis). However, we did not find any statistically significant interactions.
9 5 This suppression effect is especially interesting because the Aad and Abrand relationship is typically very strong, as evidenced in previous meta-analyses (e.g., [7]]: r =.600 [# effect sizes = 33], [22]]: r =.581 [4], [24]]: r =.624 [11]).
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Record: 9- A Study of Bidding Behavior in Voluntary-Pay Philanthropic Auctions. By: Haruvy, Ernan; Popkowski Leszczyc, Peter T. L. Journal of Marketing. May2018, Vol. 82 Issue 3, p124-141. 18p. 1 Diagram, 6 Charts. DOI: 10.1509/jm.16.0476.
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A Study of Bidding Behavior in Voluntary-Pay Philanthropic Auctions
The authors investigate compliance behavior and revenue implications in winner-pay and voluntary-pay auctions in charity and noncharity settings. In the voluntary-pay format, the seller asks all bidders to pay their own high bid. The authors explore motives and boundary conditions for compliance behavior based on internal and external triggers of social norms. The voluntary-pay format generates higher revenue than the winner-pay format for charity auctions, despite imperfect compliance, but it generates lower revenues in noncharity settings. To characterize bidding strategy, the authors study time to bid, auction choice, and jump bidding and find evidence that bidders in voluntary-pay auctions more commonly use jump bidding and late entry. The findings have important implications for marketing managers, augmenting the growing stream of empirical auction studies and work on corporate social responsibility. Specifically, combining an auction with a charitable cause may result in increased revenues, but managers should ensure that they are accounting for differential compliance rates between auction formats. Even if low-compliance bidders can be identified and screened out, doing so is not advantageous, because noncompliant bidders bid up prices.
Online Supplement: http://dx.doi.org/10.1509/jm.16.0476
In a voluntary-pay auction format, all bidders—both winning and losing—are asked to pay their highest bid. This format contrasts with a winner-pay format, in which only the winners are asked to pay. Little is known about voluntary-pay auctions’ impact on auction revenue and how bidders bid in voluntary-pay auctions, their willingness to pay, and bidding strategies. This study addresses this lack by examining voluntary-pay and winner-pay auctions in charity and noncharity settings. We examine whether the voluntary-pay format increases collected revenues and, if so, whether this is merely a charity-related phenomenon or extends to noncharity settings.
In winner-pay auctions, which include most commonly used nonphilanthropic auction formats, one bidder wins the item and pays for it. Although payment compliance is known to be imperfect even in legally unambiguous settings, payment enforcement is not generally an issue if the seller can ensure that payment is delivered prior to the buyer obtaining the item.
Payment compliance is a far greater problem in voluntary-pay auctions, where all bidders are asked to pay whether or not they win. Although a substantial theoretical literature has advocated the use of all-pay auctions, this body of theory largely assumes perfect payment compliance. Therefore, a pressing question is whether there is high payment compliance in legally unenforceable settings in which the participating bidder commits to pay regardless of winning, specifically in a voluntary-pay auction context. We examine the factors that influence this rate of compliance.
To study the conditions that foster payment compliance, we test different appeals for compliance. Specifically, we explore motives and boundary conditions for compliance behavior based on internal and external triggers of positive (pride) versus negative (shame) social norms.
In addition, we conduct two field studies to study voluntary-pay auctions and payment compliance in a real-world setting to determine whether this format offers any benefits. We study these issues on an active large-scale auction website with a strong reputation for charity auctions (though thiswebsite also runs noncharity auctions). It operates primarily in one major North American city and has raised $4 million for local charities in the past decade. The auctions run on the site are ascending-bid (English-language) auctions, and sellers have flexibility in terms of format.
In analyzing strategic considerations, we focus on three factors:
- The strategic interaction between jump bidding and late bidding in a field setting. This interaction is far more important in settings with contractual obligations for losing bidders, as waiting to bid might be a better option.
- The opportunity cost of entering a bid in an auction. This cost is far more important when entry involves a nonbinding contractual obligation—the opportunity cost being an alternative for obtaining the same item.
- Contract compliance when social consequences may differ between settings: a noncharity setting, in which the social contract is with only the contracting party, and a charity setting, in which the social contract is broader.
To study these factors, we estimate a model with four components: time to bid, auction choice, jump bid, and propensity to pay.
The auctions we study are not strictly all-pay auctions owing to imperfect collection; therefore, we call these “voluntary-pay” auctions. They are motivated similarly to all-pay auctions: all bidders, including losing bidders, are asked to pay an amount equal to their highest bid. In all-pay auctions, payment compliance is assumed to be fully enforceable (which may be inconsistent with real constraints; Budig, Butler, and Murphy 1993; Posner 1977). Voluntary-pay auctions relax that full-enforceability assumption. In contrast to both these formats, in winner-pay auctions only the bidder with the highest bid is asked to pay. We summarize the differences between the winner-pay, all-pay, and voluntary-pay auction formats in Table 1.
All-pay noncharity auctions belong in the class of auctions that satisfy revenue equivalence under standard conditions, even though, in general, revenue equivalence has not been established empirically (e.g., Gneezy and Smorodinsky 2006). However, this equivalence does not hold for ascending-bid all-pay auctions, which we use, because in dynamic all-pay auctions a bid reveals something about the value of the bidder. Another difference is that in the sealed-bid auction, bidders have no opportunity to place a second bid after they find that they are not the high bidder (and that they need to pay their bid anyway) and thus need to consider this possibility before placing a bid. In the ascending-bid auction, bidders can update their bids and adjust their strategy as the auction progresses. They are more likely to continue bidding due to the commitment already made, which may result in bidding frenzy (Heyman, Orhun, and Ariely 2004).
Furthermore, in charity auctions, revenue equivalence between formats is not theoretically expected. When participation rates are exogenous and pledges are binding (neither is the case in the current study), all-pay charity auctions are expected to dominate other common formats in terms of revenues (Engers and McManus 2007; Goeree et al. 2005; Schram and Onderstal 2009).
Revenue in the voluntary-pay auction consists of the ending price and the compliance rate. Because compliance is not enforceable, revenue depends on losing bidders’ propensity to pay. However, bidders with a low propensity to pay are likely to bid more aggressively. Furthermore, we expect greater bidder entry into the voluntary-pay than into the all-pay auction, which also has a positive effect on ending price. Higher revenue is likely to result as long as a considerable proportion of losing bidders pay. However, this possibility may not hold for non-charity auctions, where bidders tend to be less likely to enter and may bid less aggressively.
Issues with payment compliance are commonplace in consumer auctions. For example, eBay has a help page for sellers who do not receive payments.1 In philanthropic settings, the compliance problem is exacerbated. Even with the most explicit legal contracts, perfect enforcement of social responsibility is nearly impossible from a legal standpoint (e.g., Budig, Butler, and Murphy 1993; Posner 1977).2 However, in recent years theoretical and experimental articles have advocated the use of the all-pay auction, particularly for charity. These articles, which we discuss next, offer assurance that all-pay auctions are sound under the assumption that enforcement is perfect.
All-pay field studies and endogenous entry. A comparison of revenues for sealed-bid auction formats—including all-pay charity auctions, first-price charity auctions, and second-price charity auctions—revealed that all-pay auctions were revenue-dominated by winner-pay auctions, a result the authors attributed to low participation and low bids (Carpenter, Holmes, and Matthews 2008). This highlights the importance of accounting for endogenous entry, which we do. However, that finding emerged in a sealed-bid context, in which jump bidding and timing do not play a role, and with binding payment with perfect compliance—possible in the smaller controlled setting.
Another study also used a sealed-bid format to compare voluntary contribution mechanisms, lottery, and all-pay auctions in a door-to-door fundraising field experiment (Onderstal, Schram, and Soetevent 2013). Results indicated that the all-pay condition performed worse than all other formats owing to lower participation—reinforcing our focus on endogenous participation.
A field experiment on Taskcn, a large Chinese crowdsourcing site, modeled the submissions of solutions as a first-price sealed-bid all-pay auction, because all submitters had to expend some effort regardless of whether they won (Liu et al. 2014). Results showed that providing higher rewards led to more and higher-quality submissions but at the cost of entry deterrence.
Studies 1 and 2 involve clean experimental manipulations, with each setting in isolation. Thus, summary measures can be compared from settings, and differences in these measures can be attributed to the differences between the settings.
In contrast, in auction field comparisons—the kind collected from real auction sites such as eBay and the platform used in the current research—a conventional approach is to compare revenues for different overlapping or simultaneous auctions for the same items side by side. This practice is common in empirical auction research (for examples, see Table 2). Although this approach would be unacceptable in laboratory methodology, it is not only acceptable but also desirable in field methodology. In field auctions, the notion of ceteris paribus cannot be routinely assumed between auctions run at different times. Therefore, it is important to run alternative formats during approximately the same time frame. Second, one cannot typically exclude real-world bidders from bidding in other formats or designs that overlap. Third, giving bidders a choice between auctions is advantageous because it indicates preference. Study 3’s approach clearly has shortcomings, and we urge readers to be cautious in comparing formats in this study, because comparisons are based on mixed experimental manipulations.
TABLE: TABLE 1 Core Principles of Different Auction Types
| | Winner-Pay | All-Pay | Voluntary-Pay |
|---|
| The winner is the bidder with the highest bid | Yes | Yes | Yes |
| The winner must pay to obtain the item | Yes | Yes | Yes |
| The loser is asked to pay his or her highest bid | No | Yes | Yes |
| Payment is enforceable | Yes | Yes | No |
| There are revenue equivalences in noncharity settings under mainstream assumptions | Yes | Yes for sealed-bid formats, but not dynamic | No |
| There are revenue equivalences in charity settings under mainstream assumptions | No | No | No |
| Bidder entry | Costless | Costly | Costless to costly depending on bidder’s propensity to pay |
| Defensive Bidding Strategies |
| Effectiveness of jump bidding to reduce competition | Low | High | Medium |
| Effectiveness of late bidding to reduce commitment | N.A. | High | Medium |
The hypotheses are summarized in the conceptual framework in Figure 1. We first identify the conditions that foster payment compliance. Without payment compliance, voluntary-pay auctions will not generate any revenues from losing bidders.
Social pressure is effective in generating compliance with costly social norms. Field experiments (held during elections) designed to explore whether positive (pride) or negative (shame) social pressure is effective in reinforcing voting (the social norm) revealed that negative pressure was a stronger motivator (Panagopoulos 2010). More closely related to our current work is research on tax compliance that showed social pressure to be critical in increasing payment compliance in tax scenarios (Bobek and Hatfield 2003). However, results revealed that moral obligation—the intrinsic pressure—is key in that respect, thus creating what we call internal social pressure, which primes intrinsic motives. We distinguish between internal and external social pressure, as well as between negative and positive framing. The manipulations examine appeals to internal moral code versus external social pressure for compliance (Trevino 1986). We contrast positive versus negative social appeals for compliance, wherein positive and negative appeals can be through either external consequences (Bearden and Rose 1990; Burke and Logsdon 1996) or internal consequences (Yi 1990). We state these relationships in a single hypothesis.
H1a: Social pressure increases payment compliance by bidders in voluntary-pay auctions.
Results of a charity field experiment with various formats conducted in preschools revealed that participation in the all-pay format is lower than in the other formats (Carpenter,
Holmes, and Matthews 2008), also observed by Schram and Onderstal (2009). Thus, as we state in H2b, owing to costly entry in voluntary-pay auctions, bidder participation is expected to be lower. In addition, the presence of social pressure, which (per H1a) increases pressure to pay, is expected to further decrease entry, as potential bidders who anticipate the increased compliance pressure will not enter the auction. Accordingly, we conjecture that social pressure reduces participation.
H1b: Social pressure decreases participation in voluntary-pay auctions.
Theoretically, the expectation is that although individual bidders reduce their willingness to pay in all-pay auctions, total revenues are higher in all-pay auctions than in any other form of winner-pay auction when all potential bidders participate (Engers and McManus 2007; Goeree et al. 2005). However, with endogenous participation (Liu et al. 2014), entry in all-pay auctions is generally highly sensitive to incentives. Accordingly, when bidders enter because of social pressure (per H1a), a negative incentive, we expect that (applying similar rationale as for H1b) they are willing to pay less (Carpenter, Holmes, and Matthews 2008), resulting in the following hypothesis:
H1c: Social pressure reduces bidders’ willingness to pay in voluntary-pay auctions.
Choice of auction format. The remaining hypotheses pertain to the theory for the charity setting with choice between formats. The choice is between a voluntary-pay and a winner-pay format for the same item. We focus on bidders’ propensity to pay (if they lose) in the voluntary-pay auction. Conceivably, some degree of adverse selection may occur whereby bidders with lower propensity to pay (upon losing) find engaging in the voluntary-pay auction to be less costly in expectation. Adverse selection in auction choice has been previously documented (Dewan and Hsu 2004), and we extend this to format choice in auctions, resulting in the following hypothesis:
TABLE: TABLE 2 The Literature on Field and Natural Auction Experiments
| Study | Type of Study | Auction Site | Simultaneity | Factors Studied | Categories |
|---|
| Ariely and Simonson (2003) | Field experiment | eBay | Yes | Reserve price | DVDs, VHS tapes, web cameras, keyboards, trackballs |
| Ayres, Banaji, and Jolls (2015) | Field experiment | eBay | Yes | Seller race | Baseball cards |
| Bajari and Hortacsu (2003) | Natural experiment | eBay | Both | Reserve price | U.S. mint/proof coin sets |
| Bradlow and Park (2007) | Natural experiment | Korean website | Both | Reserve price | Notebook computers |
| Carpenter, Holmes, and Matthews (2008) | Natural field experiment | Offline: school | No | Auction format | Gift certificates, books, crafts |
| Chan, Kadiyali, and Park (2007) | Natural experiment | Korean website | Both | Competition | Notebook computers |
| Cox (2005) | Natural experiment | eBay | Both | Reserve price | Krugerrand gold coins |
| Dellarocas and Wood (2008) | Natural experiment | eBay | Both | Feedback system | Rare coins |
| Dholakia, Basuroy, and Soltysinski (2002) | Natural experiment | eBay | Both | Herding | CD player, Italian silk tie, Mexican pottery, Playstation |
| Dholakia and Simonson (2005) | Field experiment | eBay | Yes | Reserve price | Music CDs |
| Easley and Tenorio (2004) | Natural experiment | Onsale.com and uBid.com | Both | Jump bidding | Electronic and computer products |
| Haruvy and Popkowski Leszczyc (2010) | Field experiment | eBay | Yes | Reserve price | Books, baby items |
| Hossain and Morgan (2006) | Field experiment | eBay | No | Shipping, reserve prices | CDs and Xbox games |
| Katkar and Reiley (2007) | Field experiment | Specialty | No | Reserve prices | Pokemon cards |
| List and Lucking-Reiley (2000) | Field experiment | Sportcard show | No | Multiunit auctions | Sports cards |
| Lucking-Reiley (1999) | Field experiment | Specialty | No | Auction format | Collectible Magic cards |
| Park and Bradlow (2005) | Natural experiment | Korean website | Both | Reserve price | Notebook computers |
| Popkowski Leszczyc and H aubl (2010) | Field experiment | eBay and Local | Yes | Simultaneity | Variety of product and services |
| Popkowski Leszczyc and Rothkopf (2010) | Field experiment | Local | Yes | Charity | Household, electronics, computer products |
| Reiley (2006) | Field experiment | Specialty | No | Reserve price | Collectible Magic cards |
| Resnick et al. (2006) | Field experiment | eBay | Yes | Reserve prices | Vintage postcards |
| Suter and Hardesty (2005) | Field experiment | eBay | Yes | Reserve prices | Sony Dual Shock Controllers |
| Zeithammer (2006) | Natural experiment | eBay | Both | Overlapping | MP3 players, DVDs |
H2a: Bidders with lower propensity to pay are more likely to bid in voluntary-pay auctions than in winner-pay auctions.
Endogenous entry. Bidder entry is a particularly problematic dimension in a voluntary-pay context, because it can result in complete reversal of revenue predictions (e.g., Carpenter, Holmes, and Matthews 2008). However, in contrast to prior work using sealed-bid auctions (Carpenter, Holmes, and Matthews 2008), we consider an ascending-bid format, in which entry tends to be less costly (because entry cost is generally lower than the amount pledged). As a result, more bidders should enter an ascending-bid auction than into a sealed-bid auction, but overall we expect that the number of bidders will be lower in the voluntary-pay auction than in the winner-pay auction owing to the financial commitment.
H2b: Fewer bidders enter voluntary-pay auctions than enter winner-pay auctions.
Jump bidding. Do bidders use jump bidding and time to bid strategically to reduce competition (e.g., Isaac, Salmon, and Zillante 2007)? Jump bidding can be effective in deterring competitors from entry by signaling aggressiveness when bidding is costly (e.g., Avery 1998; Easley and Tenorio 2004). If bidding is costly, this aspect makes the auction an all-pay variant. Thus, jump bidding is a critical aspect of the present investigation into voluntary-pay auctions, because it can have animportant deterrence potential owing to the costly commitment by all bidders. Jump bids do not have that deterrence potential in winner-pay auctions because losing bids do not entail a financial commitment. Thus, we expect that if jump bids are an effective deterrent, then in voluntary-pay auctions large jump bids (especially early in the bidding process) will deter other competitors from entering or continuing to bid, thereby increasing the likelihood of winning an auction.
H3a: Jump bidding occurs more frequently in voluntary-pay auctions than in winner-pay auctions.
Late bidding. Late bidding can be effective in avoiding early commitment and bidding wars (Roth and Ockenfels 2002). Given the entry (commitment) costs in voluntary-pay auctions, bidders will have a greater incentive to avoid early commitment and reduce competition.
H3b: Late bidding occurs more frequently in voluntary-pay auctions than in winner-pay auctions.
Effect of propensity to pay on bid pledges.. In a voluntary-pay auction, a bid pledge is equal to the highest bid by a bidder. When people make ethical choices in hypothetical scenarios with no commitment mechanism, they pledge to make ethical choices that they do not adhere to in incentivized scenarios (FeldmanHall et al. 2012). Likewise, bidders who have a lower propensity to pay (upon losing) are expected to bid (pledge) higher in voluntary-pay auctions, because if they do not win the auction they are likely to default on the losing-bidder payment.
H4: Bidders with a higher propensity to pay tend to have a lower willingness to pay in voluntary-pay auctions than bidders with a lower propensity to pay.
Bid pledges in auction format. Drawing on economic theory (Isaac, Pevnitskaya, and Salmon 2010), we predict that for both charity and noncharity settings, both willingness to pay and the winning bid in the voluntary-pay auction are lower than in the winner-pay format. The intuition is that in the voluntary-pay format, bidders have to allow for the probability of losing the auction and still paying their bids, so bidders will not bid up to their valuations.
H5: Bid pledges are lower in voluntary-pay auctions than in winner-pay auctions.
Auction revenue. Current theory posits that in charity settings, revenues will be higher with all-pay auctions than with winner-pay auctions, given exogenous entry and full compliance from nonwinning bidders (Engers and McManus 2007; Goeree et al. 2005). The basic intuition is that in a winner-pay charity auction, a bidder who outbids a competitor forgoes a positive externality associated with the competing bidder’s contribution. In contrast, in all-pay charity auctions, competing bidders pay irrespective of whether they win, so the issue of forgoing competitors’ contributions by outbidding them does not arise. This result has a positive effect on revenue in all-pay charity auctions, despite the expected adverse effect on bidder entry. We expect that this difference in revenue will be preserved or be even greater in the voluntary-pay auction, because payment by losing bidders is not enforced. Bidders with low propensity to pay may drive up prices, because losing bidders will be less concerned about paying, as the money goes to charity.
H6: Total revenue in voluntary-pay auctions is higher than in winner-pay auctions.
Of importance in the analysis is the rate of compliance. Study 1 tests different appeals for compliance in voluntary-pay charity auctions. Specifically, we explore motives and boundary conditions for compliance behavior based on internal and external triggers of social norms.
We report on an experiment involving 125 Amazon Mechanical Turk participants in five treatments. The experiment is a 1 + 2 · 2 design with the following conditions: benchmark, negative internal, negative external, positive internal, and positive external. Appendix A shows the scenarios used. The benchmark condition included only the nonitalicized text. (None of the text was in italics when shown to participants—italics are for this exposition only.) The square brackets contain the manipulated text in the 2 · 2 conditions. The manipulations enable us to examine appeals to internal moral code versus external social pressure for compliance (Trevino 1986).
For each manipulation, we elicited the highest bid for each of the two formats of winner-pay and voluntary-pay.
We expected bidders to pay far higher amounts in the winner-pay auction, but an internal validity check is still necessary. One relevant construct for comparison of manipulations is the difference in the highest bids between winner-pay and voluntary-pay.
The primary issues are compliance and format choice, and particularly the trade-off between the two. If we compel people to pay—either legally (not an option here) or socially (which is investigated here)—then they will pay, but that comes at the cost of reduced entry and more cautious bidding. We observe this particularly for the negative internal and positive internal conditions, which are characterized by very high compliance but low choice of the voluntary-pay auctions (Table 3). The positive internal condition has the added disadvantage of a large drop (25%) in willingness to pay in the voluntary-pay format relative to the benchmark. Low entry combined with low bids mean that the higher compliance cannot possibly compensate ina terms of revenue. An attempted manipulation through positive appeals to internal norms would not have been effective.
The external comparisons provide the best of both worlds. They increase compliance even more than internal appeals but also raise preference for the voluntary-pay auction. In the case of positive external appeal, the appeal resulted in higher willingness to pay. In summary:
- Negative and positive external social pressure both result in a significantly higher likelihood of paying than internal social pressure.
- Internal social pressure—negative or positive—improves the likelihood of paying over the benchmark of no social pressure.
- External negative or positive social pressure changes format choice in favor of voluntary-pay, indicating that decision makers derive value from public acknowledgment.
- Internal pressure—negative or positive—relative to the benchmark setting improves the likelihood of paying but does not significantly change format preference, perhaps because it confers no additional benefit to subjects beyond the benchmark.
- The positive internal pressure condition has the highest willingness-to-pay difference between formats, with willingness to pay in the voluntary-pay format being the lowest of all formats by nearly $100. It is possible that bidders perceive that format as a donation scheme. Thus, although positive internal pressure increases compliance, it does so at a drop of approximately 40% in revenues. Negative internal pressure is a close second in terms of willingness-topay differences between formats.
Overall, we find that social pressure (whether internal or external, negative or positive) increases payment compliance compared with the benchmark without social pressure (t = 1.74, p = .042),3 providing strong support for H1a. We also find support for H1b that social pressure decreases participation in the voluntary-pay format, though only for (both positive and negative) external pressure (t = 1.83, p = .036) and not for internal pressure (t = .06, p = .48). Finally, we find no statistical support for H1c’s proposition that social pressure has a greater negative influence on willingness to pay in voluntary-pay auctions than in winner-pay auctions, for either internal pressure (t = .87, p = .19) or external pressure (t = .10, p = .46). We test this hypothesis through the difference in willingness to pay between the winner-pay and voluntary-pay auctions. Although we find a larger difference for positive and negative internal pressure, consistent with H1c, this difference is not statistically significant.
In summary, the conjecture that one can “guilt” participants into paying is correct, but the result may be a loss of revenues. External pressure is the exception, possibly because of conditionally higher conferred benefits such as higher social status.
Our data for Studies 2 and 3 come from a local nonprofit auction website in a midsized metropolitan area in North America with a population greater than one million. At the time of the study, the site had roughly 9,000 registered members and is the same site as discussed in Haruvy, Popkowski Leszczyc, and Ma (2014). Bidders ( 1) are notified of auction events through emails to the site’s user base, ( 2) are registered users, and ( 3) have been exposed to both charity and noncharity ascending-bid auctions. Voluntary-pay auctions are introduced at the time of the study and represent only the second time bidders had been exposed to voluntary-pay auctions on this particular platform.
TABLE: TABLE 3 Results of Appeals for Compliance Behavior based on Internal and External Triggers of Social Norms
| | Condition |
|---|
| Variable | Benchmark | Negative External | Negative Internal | Positive External | Positive Internal |
|---|
| Highest bid winner-pay ($) | 408.46 | 354.65 | 430.00 | 409.00 | 381.13 |
| Highest bid voluntary-pay ($) | 240.78 | 236.77 | 285.82 | 269.33 | 183.50 |
| Difference in highest bid between winner-pay and voluntary-pay ($) | 122.46 | 117.84 | 154.56 | 139.67 | 197.63 |
| Compliance likelihood (1 = “very likely,” and 7 = very unlikely”) | 3.077 | 4.290 | 3.964 | 4.296 | 4.041 |
| Format choice (1 = “strongly prefer winner-pay,” 7 = “strongly prefer voluntary-pay”) | 2.538 | 3.935 | 2.643 | 3.556 | 2.500 |
We conducted a field study comparing voluntary-pay and winner-pay auctions that were run at different times. The study controlled for self-selection on the bases of auction format (no choice of auction format) and products. We randomized the products sold. That is, prior to the auction, bidders did not know what products were in the auction.
We tested three auction formats in this order: ( 1) winner-pay regular auctions, ( 2) winner-pay charity auctions, and ( 3) voluntary-pay charity auctions. All treatments were identical with respect to items and differed only in auction format. For each format, we sold an identical batch consisting of 75 different products, for a total of 225 auctions (three identical replicates for the 75 products). Web Appendix A provides a detailed summary of the products sold.
Importantly, at any time, all 75 products were sold using a single auction format, and subjects were not exposed to the competing format within a week of the treatment. Recruitment did not mention the format and the products to be sold, and subjects found out about both only when they logged in to the auction site. They were given no indication that the same products would be sold in the future using a different format. All 75 auctions within a batch or format ran for four days. Auctions started at approximately 10 P.M. and ended at 10 P.M. on the last day with a 30-second interval between individual auctions.
One day before the start of the auctions, all registered members received anemail announcing the upcoming auctions. In contrast to Study 3, they received no details about the auctions or the products sold. Details about the auction format (rules for the voluntary-pay auctions and payment-rule description) and the charity component (if present) were displayed on the front page of the auction website and in all individual auctions just above the bid box.
Each auction consisted of a picture and a product description. All auctions were ascending-bid auctions with a proxy bidding system, similar to eBay. Bidding started at $.01, and all items were sold to the highest bidder. An important difference from eBay auctions is that bidders may submit either proxy bids or jump bids. With a proxy bid, the computer bids on behalf of the bidder incrementally up to his or her specified maximum willingness to pay. A jump bid specifies an amount to which the current high bid will jump. A bidder may also submit an incremental bid, in which the amount of the jump is equal to the minimum required bid increment. For estimation purposes, we include only proxy bids that are decisions by bidders, not computerized bids generated by the proxy bidding machine.4 The winning bidders paid for their items when they came to collect them at a local UPS store. The store does not sell any of the products sold in this study. In addition, all losing bidders in the voluntary-pay auctions were sent an email with a PayPal money request for the total of their nonwinning high bids for all auctions in which they participated (including a detailed calculation of the total). Importantly, the payments from losing bidders were collected separately from those of winning bidders. Thus, bidders could collect and pay for items they had won but could refuse to pay for the other amounts they had pledged as nonwinning bidders.
In total, 108 unique bidders participated in Study 2. Eighty-nine bidders participated in the winner-pay format, and of these only ten bidders participated in both voluntary-pay and winner-pay auctions, indicating that very little overlap in auction format occurred between bidders. In contrast, in Study 3, 116 (96) bidders participated in the charity (noncharity) winner-pay format and 58 (43) bidders crossed formats. Thus, whereas in Study 2 nearly no crossing occurred, in Study 3 roughly half of the participants crossed between formats. This is appropriate because Study 2 was intended to compare behavior between formats, whereas Study 3 was designed to examine bidding strategy while accounting for the interaction between bidding and format choice.
Study 3 compared winner-pay and voluntary-pay in a simultaneous choice setting. Investigation into the simultaneous choice setting has a dual purpose. First, the literature on the all-pay format has been adamant that comparison between formats should permit endogenous entry—that is, bidders should be permitted to opt out of an auction format. Study of endogenous entry requires running auctions for both formats simultaneously. Second, by examining choices between auctions of different formats, we can infer preference over formats based on choice.
Study 3, setting A: Simultaneous charity auctions. All events consisted of auctions for books, DVDs, games, household items, computer and electronic products, and other items (Web Appendix B summarizes the products sold). In charity auctions, a written announcement appeared at the bottom of the auction description, just above the bid box, stating that the donation would be made to the local United Way campaign. In addition, the charity component and the auction format were described in an email to all registered members.
The event included 221 pairs of identical product auctions for a total of 442 auctions over a period of five days. A pair always had one voluntary-pay auction matched with one winner-pay auction. All auctions lasted one day, starting between 8:00 P.M. and 9:00 P.M. and ending the next day between 8:00 P.M. and 9:00 P.M. All other aspects of the auctions were identical, including the text (explanation and example) at the bottom of voluntary-pay auction descriptions.
All registered members received an email about the upcoming auctions two days before the start of the auctions. The email provided details about the duration, the types of products sold, and the different auction formats, including the charity component and the charity involved. Similar to Study 2, the payment-rule description (winner-pay or voluntary-pay) was prominently displayed on the front page of the website listing the auctions and just above the bid box in all individual auctions. All other aspects of the study, including the platform, the type of auctions (ascending-bid auctions), and processing of the winning bidders and all nonwinning bidders in the voluntary-pay auctions, were identical to those described for Study 2.
Study 3, setting B: Simultaneous noncharity auctions. We conducted a similar event to that of Setting A with 466 auctions (233 pairs of identical auctions), but without the charitable component. Again, auctions were run in simultaneous pairs, in which one auction is in a winner-pay format and the other a voluntary-pay format for the same product. The study was conducted one year after the charity event (the temporal distance minimizes order effects wherein experiences carry over to the next event), and over a six-day period (excluding the weekend). All other aspects were identical to those in setting A. The products sold were similar, although not identical, to those in the charity auctions (Web Appendix B summarizes the products sold).
The purpose of having two simultaneous auction formats was to allow bidders to choose between them, and from these choices to infer preference. In the noncharity condition, 44.79% (SE = 5.10%) of the bidders moved between formats. In the charity condition, the corresponding number is 54.72% (SE = 4.86%). Thus, a reasonable assumption is that most bidders consider both formats. We find that restricting the analysis to only those subjects observed to move between formats does not change the results. We provide the regression results for all subjects and results with the more restricted population in Web Appendix C.
Study 2. Summary statistics related to auction outcome and bidder participation for Study 2 are summarized in Table 4. We report average winning bid, revenue, average number of bidders per auction, total number of unique bidders in the format, total number of switchers, revenue from winners, revenue from losers, revenue committed, revenue collected from losers, and total revenues. We report these findings for all three treatments of Study 2 with two-sample t-tests reported for comparison between pairs of formats for winning bid, revenue, bidders, and bids per bidder. To compare the revenues and other value-related variables, we normalized these variables by dividing them by the retail value. All tests are based on the normalized variables.
H2a posits that bidders with lower propensity to pay are more likely to enter or bid in voluntary-pay auctions than in winner-pay auctions. This expectation is tested in Study 1 and Study 3, but not in Study 2, because bidders do not choose the format.
H2b posits that voluntary-pay auctions have fewer entrants than winner-pay auctions. Table 4 shows that the number of bidders is smaller in the voluntary-pay format than in the winner-pay auction in the charity setting (t = 7.57, p < .001). In the charity setting, the winner-pay auctions had 4.41 bidders versus 2.52 bidders in voluntary-pay auctions. This finding strongly supports H2b.
H3a posits that more jump bidding will occur in voluntary-pay auctions than in winner-pay auctions. Recall our assertion that jump bids have an important deterrence potential in the voluntary-pay auction, owing to costly commitment, but do not have a similar potential in winner-pay auctions because those do not involve costly commitment. We find that of 614 final bids in the voluntary-pay auctions, 50.3% (SE = 3.7%) involved a jump bid. Of the 189 bids in the winner-pay auctions, 42.5% (SE = 2.0%) involved a jump bid. This difference is significant, based on a one-sided t-test (t = 1.88, p = .03), in support of H3a.
We find that 65.2% (SE = 1.9%) of final bids in the winner-pay auctions are late bids (during the last hour of the auction). In comparison, 63.5% (SE = 3.5%) of final bids in the voluntary-pay auctions are late bids. Thus, while last-hour bids are clearly the majority of final bids (and this is likely a strategic choice), we find no support for H3b in the non-simultaneous auctions. That is, we do not observe a higher incidence of late bids in the voluntary-pay auctions (t = .42, p = .68).
H4 asserts that bidders with a higher propensity to pay in voluntary-pay auctions have a lower willingness to pay. As with H2a, propensity is a latent construct. It appears in our discussion of model estimation. However, we have supporting model-free evidence for H4, because maximum willingness to pay in charity auctions for noncompliant bidders is $10.35 versus $5.82 for compliant bidders (t = 2.92, p = .004).
H5 asserts that bid pledges are lower in voluntary-pay auctions than in winner-pay auctions. Results from Table 4 show that the winning bid is $13.51 in charity winner-pay auctions and $6.76 in charity voluntary-pay auctions (p = .006). Thus, H5 is strongly supported.
H6 pertains to revenue comparisons. In Table 4, we see the auction outcomes for the two formats. The total combined pledged bids from the winning and losing bidders is higher in voluntary-pay ($1,352.40) than revenue in winner-pay ($1,013.06) auctions—a 33.5% improvement in revenue. The pairwise t-test is significant (t(74) = 2.34, p = .022). In the voluntary-pay charity auctions, $686.49 of $845.16, or 81.23% of the amount pledged by losing bidders, was collected, although this constituted merely 50.76% of the number of pledges by the losers. This constitutes 88.27% of the total pledged amount, including winners and losers, which is remarkable.
TABLE: TABLE 4 Summary Statistics for Study 2
| | Noncharity Winner-Pay (N 5 75) | Charity Winner-Pay (N 5 75) | Charity Voluntary-Pay (N 5 75) |
|---|
| Winning bid | $11.61 (2.05) | $13.51 (2.10) | $6.76 (1.18) |
| Revenue | $11.61 (2.05) | $13.51 (2.10) | $18.03 (3.63)a |
| # bidders per auction | 3.77 (.22) | 4.41 (.21) | 2.52 (.13) |
| # bids per bidder | 1.83 (.07) | 1.97 (.08) | 1.43 (.07) |
| Total # unique bidders in format | 79 | 79 | 19 |
| Total # format switchers | 10 | 10 | 10 |
| Revenue winners | $871.06 | $1,013.06 | $507.24 |
| Revenue from losers | 0 | 0 | $845.16 |
| Revenue committed | $871.06 | $1,013.06 | $1,352.40 |
| Revenue collected from losers | | | $686.49 |
| | | | 81.23% |
| Total revenue | $871.06 | $1,013.06 | $1,193.73 |
| | Winner-Pay Noncharity vs. Winner-Pay Charity | Winner-Pay Noncharity vs. Voluntary-Pay Charity | Winner-Pay Charity vs. Voluntary-Pay Charity |
| Winning bid | t = .64, p = .520 | t = 2.05, p = .04 | t = 2.80, p = .006 |
| Revenue | t = 2.87, p = .005 | t = 3.52, p = .001 | t = 2.34, p = .022 |
| Bidders | t = 2.11, p = .036 | t = 4.95, p < .001 | t = 7.57, p < .001 |
| Bids per bidder | t = 1.28, p = .203 | t = 3.99, p < .001 | t = 5.11, p < .001 |
Study 3. In Study 3, the formats were running concurrently, and therefore, we cannot infer a manipulation effect. Nevertheless, we can look at t-test comparisons over bidders and auctions with the caveat that these are not manipulation checks but are rather comparisons for completion in light of the results of Study 2. We provide a fully specified econometric model subsequently. H2a posits that bidders with lower propensity to pay are more likely to enter voluntary-pay auctions than winner-pay auctions. This expectation involves a latent construct that requires a model (discussed subsequently). Nevertheless, we find that noncompliant bidders (nonpayers) placed more bids in voluntary-pay than in winner-pay charity auctions (2.70 vs. 2.37, respectively; t = 2.68, p = .01) and noncharity auctions (1.88 vs. 1.62, respectively; t = 2.83, p < .01), in support of H2a.
In support of H2b, we find that the number of bidders is smaller in the voluntary-pay format than in the winner-pay format in both the charity (t = 7.03, p < .001) and the noncharity (t = 12.95, p In opposition to H3a, we find that the rate of jump bids in voluntary-pay auctions is lower than in winner-pay auctions, in both charity (t = 4.40, p < .001) and noncharity settings (t = 1.85, p = .065). Although we found support for H3a in Study 2, we do not find it in Study 3.
To investigate the effectiveness of jump bidding as a deterrent to entry, we considered all large jump bids (10% of the item’s retail value). Results suggest that large, early jump bids increase the likelihood of winning in a voluntary-pay (vs. winner-pay) auction from 22.64% to 34.09% (t = 1.46, p = .07, one-tailed test) in charity auctions and from 13.64% to 38.10% in noncharity auctions (t = 2.52, p = .007, one-tailed test). This implies that early jump bids may be effective deterrents to entry in both charity and noncharity voluntary-pay auctions. H3b is concerned with late bidding. Because entry into voluntary-pay auctions is more costly, late bidding is predicted to be more prevalent in this auction format. We observe a significantly higher degree of late bidding in voluntary-pay auctions. Specifically, in the charity setting, looking at the last bid for each bidder, we observe 32.3% late bids. This result is in contrast to 15.8% late bids in the winner-pay auctions. This result is significant at p < .01, providing support for H3b.
In the noncharity setting, we observe 20.01% late bidding in the voluntary-pay and 14.41% late bidding in the winner-pay (t = 4.39, p < .001) formats. This result provides strong support for H3b. Note that we did not find support for H3b in Study 2. However, in Study 3 bidders could choose which auction to bid in and, thus, select the winner-pay format. Bidders in Study 2 did not have this option, and more bidders entered early because bid levels were still relatively low.
Consistent with H4, we find that bidders with a higher propensity to pay in voluntary-pay auctions have a lower willingness to pay. In particular, for noncompliant versus compliant bidders willingness to pay in charity auctions was $8.76 versus $5.20 (t = 2.09, p = .037) and in noncharity auctions was $14.22 versus $4.90 (t = 6.34, p < .01). In support of H5, winning bids are significantly lower in voluntary-pay auctions than in winner-pay auctions. The winning bid in the charity winner-pay auction is $20.19 compared with $15.44 in the charity voluntary-pay auction (p < .001), and the winning bid is $13.43 in noncharity winner-pay auctions compared with $7.50 in noncharity voluntary-pay auctions (p < .001).
H6 predicts that revenue is higher in the voluntary-pay auction than in the winner-pay auction. The total combined pledged bids from the winning ($3,413.11) and losing ($4,427.95) bidders is $7,841.06 in voluntary-pay auctions, compared with $4,462.51 in winner-pay charity auctions; voluntary-pay auctions thus had 75.71% more bids pledged (t = 3.16, p = .002). In noncharity auctions, combined pledged bids from the winning ($1,698.58) and losing ($2,002.28) bidders is $3,704.46, versus $3,131.05 for winner-pay auctions (when normalized by retail price, revenue from the voluntary-pay format is slightly and insignificantly lower = .33, p = .745).5 Thus, we find higher revenues for the voluntary-pay format in the charity setting but not in the noncharity setting.
However, not all pledges from losing bidders are collected. In the voluntary-pay charity auctions, 48.58% of the amount pledged by the losers was collected, whereas in the noncharity auctions, 44.00% was collected. Overall, the revenue collected for voluntary-pay auctions is 24.69% higher than the total revenue obtained in the winner-pay auctions for the charity setting ($5,564.24 vs. $4,462.51). In contrast, in the noncharity setting, voluntary-pay revenue is 17.61% lower than winner-pay revenue ($2,579.76 vs. $3,131.05). Thus, the collected revenues provide support for H6, because revenue in voluntary-pay charity auctions is higher than that in winner-pay charity auctions. We find the opposite in the noncharity setting because revenue is higher for winner-pay than for voluntary-pay auctions.
Overall, results are highly consistent for Studies 2 and 3. The only difference is between jump bidding (H3a) and late bidding (H3b), because bidders have the option to bid in an alternative format in Study 3.
So far, we have compared aggregate statistics between formats. We next examine whether individual bidding strategies are consistent with theory—in particular, whether they employ strategies that suggest they view bids in the voluntary-pay format as implying commitment. Our focus is on two potential strategies expected to be effective in the voluntary-pay auction: early jump bidding and late incremental bidding. We expect these strategies to be more prevalent in the voluntary-pay auction if bidders perceive their bids as involving commitment—something bidders in the winner-pay auction do not have. We found model-free evidence, reported in Table 4, to suggest that these strategies are employed differently in the voluntary-pay format, but ultimately the strategies are process-related constructs that are driven by process-related variables. Without a joint model of strategic bidding that controls for dynamically changing explanatory variables as well as the relationship between the decision variables, we cannot correctly identify the role of commitment in the voluntary-pay format.
We employ a four-component model similar to that of Park and Bradlow (2005). The components capture the four strategic considerations of the bidder in the voluntary-pay format: auction choice, time to bid, jump bid, and propensity to pay. For the simultaneous design in Study 3, we estimate the full four-component model, while for the sequential design in Study 2 the model reduces to three components as auction choice is not present.
Endogeneity. When format choice is present (Study 3), jump bidding, time to bid, and propensity to pay link in the model to the propensity of auction choice. This type of endogeneity is a sample selection problem. In the Heckman approach (Heckman 1979), the inverse Mills ratio (IMR) —a transformation of the predicted selection propensity—serves as an explanatory variable in the outcome equation.
The preference for the voluntary-pay format is estimated first, followed by the other three decisions. This sequence allows the propensity to choose the voluntary-pay format to be incorporated into the other decisions through the IMR. We allow for bidder-specific random effects correlated across decisions.
Component 1: Auction choice. Each bidder chooses between two simultaneous auctions for identical items. Choice 1 in the pair is the voluntary-pay auction and choice 2 is the winner-pay auction. We use a latent utility model to capture auction preferences. This model implies that the choice of auction format is a choice between two utilities (Haruvy and Popkowski Leszczyc 2010). Given the binary choice, only the difference between the utilities is identified. The “difference” always refers to the voluntary-pay minus the winner-pay auctions. The utility difference between the voluntary-pay and winner-pay choices in auction pair I for bid k by bidder j is denoted DUijk and is defined as DUijk = b0, charityCharityi, k + b0, noncharity
LagPricei, k denotes the previous price. DLagPricei, k in the choice regression captures the difference in lag price on auction choice. DLagCumNoBiddersi, k captures the difference in competition between the auctions based on the cumulative number of bidders at the time of the previous bid. DLagJumpBidi, k is the amount of the previous bid increment, differenced between the two simultaneous auctions.
Lag$Committedi, k is the amount a bidder has committed in the voluntary-pay auction for auction pair i. LagTimeElapsedi, k is the time elapsed in the auction in seconds since the time of the previous bid. DLagFrenzy is the difference between the extent of frenzy for the two auction types, defined as the lag cumulative total bids divided by elapsed time (Dholakia, Basuroy, and Soltysinski 2002). Finally, Categories 1–3 are three dummy variables that account for heterogeneity in the utility across different product categories, partly addressing self-selection related to product choice.
The implied probability of bidder j choosing the voluntary-pay auction over the winner-pay auction in bid k of auction pair I follows a logistic distribution as follows:
We define the joint likelihood for bidder j’s format choices lchoicej as the product of all PrðChoiceijkÞ for bidder j over all auction pairs I in which bidder j participated, and bids k by bidder j.
Component 2: Time to bid. Let tijk and tijk-1 denote the timing of bid k and bid k - 1 by bidder j in auction pair i. We assume that the time between bids, Timeijk = tij k - tijk-1 of bidder j, who places the kth bid in auction pair i, follows a Weibull distribution, with a probability density function:
LagPricei, k denotes the previous price and therefore captures the effect of price dynamic on the timing of bids. LagCumNoBiddersi, k and LagFrenzyi, k are both important variables that influence the timing of bids. LagTimeElapsedVoluntary-pay is the interaction between LagTimeElapsed and an indicator variable for the voluntary-pay format. The parameter h1j represents an individual-specific error term for bidder j, such that E(h1j) = 0 and variance s2 h1 > 0. Finally, e1ijk represents the error term. We can then define the joint likelihood for bidder j’s timing choices lTime j as the product of all fðTimeijk; lij kÞ for bidder j over all auction pairs I inwhich bidder j participated, and all bids k by bidder j.
Component 3: Jump bid. We model jump bids by the amount the bidder bids over the previous highest bid minus the minimum bid increment. Jump bidding can serve as a deterrent in all-pay auctions but not in winner-pay auctions (Dekel, Jackson, andWolinsky 2007).Let I here denote auction I rather than auction pair. Let –i denote the other auction in the pair. LetVoluntary-Payi, k be an indicator function (dummy variable) that takes the value of 1 if auction I follows the voluntary-pay format, and 0 otherwise.
After controlling for two separate intercepts for charity and noncharity, the next three explanatory variables in the equation denote the lag prices in the current auction, separately for charity and noncharity, and in the competing auction. LagCumNoBiddersi, k is critical in that it measures how responsive jump bids are to the presence of competitors. If jump bids are a deterrent, as we claim, we expect to see a positive and significant coefficient for this variable, indicating that bidders facing a competitive environment use jump bids to eliminate some of the competition. The variable Lag$Committedi, k is exactly the same as the variable by the same name in the “Component 1: Auction Choice” subsection. The next four terms pertain to the time elapsed in the auction and its interaction with auction format and whether it is a charity auction. Frenzyi, k is an important explanatory variable because it could be responsible for some jump-bidding activity. When auctions are slow (corresponding to low frenzy), bidders may incur significant opportunity or monitoring costs, and the jump bid size becomes a strategic decision, with bidders choosing larger jump bids (Kwasnica and Katok 2007).
We add an IMR regressor to account for self-selection separately for charity and noncharity auctions. The IMR is used as a regressor in the estimation of Equation 5 per Heckman (1979). Equation 8 indicates the joint estimation and points out the IMR.
The error e3ijk is normal i.i.d. censored at 0, with standard deviation s3. The individual random intercept h3j has a mean of 0 and variance s2 h3 > 0. The correlation between the individual effect for jump amount and the time to bid is denoted by rh13. Let Iijk be an indicator function of whether a positive jump was observed. The likelihood of a jump of a specific magnitude, accounting for the censoring at 0, is
Wedefine the joint likelihood for bidder j’s jumpchoices ljump j as the product of all PrðJumpijkÞ for bidder j over all auction pairs I in which j participated and all bids k submitted by bidder j.
Component 4: Propensity to pay. We investigate a latent bidder’s propensity to pay, whereby higher propensity to pay means higher probability of observing payment on a losing bid. The propensities are unobserved, but we do observe who paid a losing bid. The propensity to pay clearly depends on what happened in the auction—both process and outcome. Wemodel propensity to pay, whichwe capture as an individual intercept in the utility from the decision to pay plus the explanatory variables specified in Equation 7; the IMR coming from the choice regression in Stage 1 of the Heckman two-step procedure highlighted previously; and a random component.
Because we have already usedUto denote choice utility, we use V to denote utility from paying a losing bid. The probability of paying a losing final bid (one final bid per bidder per auction) in auction I by bidder j is PrðPayijÞ = Fð-DVijÞ, where
Charityi, k is a dummy variable indicating whether an auction is a charity auction. The IMR is a regressor in Equation 7 per
Heckman (1979); however, because it is just an intermediate step, we do not explicitly state it in Equation 7. Equation 8 indicates where IMRis used in the joint estimation. Wecan then define the likelihood of bidder j’s payment choices lpay j as the product of all PrðPayijÞ for bidder j over all auction pairs I in which bidder j participated.
The parameter h4j represents an individual-specific error term for bidder j, such that Eðh4jÞ = 0 across bidders. The individual bidder’s h4j remains the same for all decisions made by individual j, and h4j’s variance is denoted by s2 h4 > 0. The term e4ij represents the error term for propensity to pay and follows a normal distribution. Note the absence of subscript k, denoting the bid, on all terms except the last price. This absence occurs because the decision to pay is at the auction level and not at the bid level.
Joint estimation. We estimate jump bid and bid timing jointly through a maximum likelihood involving jointly distributed errors for the two decisions with a bivariate normal distribution. The choice decision is estimated by itself in Stage 1 of the Heckman two-step procedure as a standalone. Jump bid and time are linked to it through the IMR, which appears in Equation 8.
Equation 8 shows the joint likelihood of the four components. We define H1j as the vector of bidder-specific intercepts for both the propensity-to-pay and the format-choice equations. WedefineH2j as the vector of bidder-specific intercepts for both the jump-bid and the time-to-bid equations. Hj follows a multivariate normal distribution with mean 0 and variances S. Let fðH, SÞ be themultivariate normal distribution of parameter vector H, conditional on the covariance matrix S. Using the bidder-specific likelihoods defined after each equation, we write the joint likelihood function for all bidders in Equation 8 as follows:
Equation 8 is maximized to estimate the coefficients, which requires two steps. In the first step, we get the IMR from the choice equation, and in the second step, we recover the second half after the IMR is estimated.
Our discussion focuses on the results of the four-component model from Study 3, which are provided in Table 5. We compare these results with the results from Study 2. Results for auction choice. The auction choice component captures the choice of the voluntary-pay auction as the dependent variable. The significant negative coefficient for LagPrice means that as prices evolve over time, bidders are more likely to choose the auction with the lower price. The significant negative coefficients for DLagPrice in both the charity and noncharity settings suggest that as the difference between the current prices or high bids becomes larger, bidders are more likely to choose the auction with the lower price.
TABLE: TABLE 5 Results for Study 3: Simultaneous Winner-Pay Versus Voluntary-Pay Auctions in a Charity and Noncharity Setting
| Auction Choice and Propensity to Pay | Time to Bid | Jump Bid Amount | Propensity to Pay |
|---|
| Parameter (SE) | Parameter (SE) | Parameter (SE) | Parameter (SE) |
|---|
| Intercept_noncharity -1.806*** (.183) | Intercept_noncharity -.471*** (.082) | Intercept_noncharity -1.780** (.657) | Intercept -.137*** (.049) |
| Intercept_charity -1.766 (.171) | Intercept_charity -.551*** (.079) | Intercept_charity -.598 (.515) | Charity .207*** (.038) |
| LaggedPrice -.876*** (.045) | LagPrice_noncharity .009*** (.002) | LagPrice_noncharity -.012 (.014) | LagPrice -.132*** (.020) |
| DLagPrice_noncharity -.035*** (.004) | LagPrice_charity -.004** (.002) | LagPrice_charity .015* (.009) | Frenzy .005*** (.002) |
| DLagPrice_charity -.024*** (.004) | LagCumNoBidders .048*** (.015) | LagCompetingPrice .018*** (.006) | Category1 .105** (.054) |
| DLagCumNoBidders_nonch .057** (.024) | LagFrenzy .001** (.0004) | LagCumNoBidders_nonch .543*** (.112) | Category2 -.212*** (.070) |
| DLag CumNoBidders_charity -.015 (.022) | LagTimeElapsed .149*** (.008) | LagCumNoBidders_charity .247** (.101) | Category3 .019 (.045) |
| DLagJumpBid_noncharity .127*** (.028) | LagTimeElapsed | Lag$Committed_noncharity .061** (.024) | IMR .268*** (.018) |
| DLagJumpBid_charity .197*** (.025) | Voluntary-Pay_nc | Lag$Committed_charity .031** (.012) | |
| Lag$Committed_noncharity .076*** (.005) | .008 (.010) | LagTimeElapsed_noncharity -.220*** (.059) | |
| Lag$Committed_charity .064*** (.005) | LagTimeElapsed | LagTimeElapsed_charity -.108* (.058) | |
| LagTimeElapsed_noncharity .092*** (.014) | Voluntary-Pay_ch | LagTimeElapsedVoluntary- | |
| LagTimeElapsed_charity .115*** (.015) | .027*** (.010) | Pay_nc | |
| DLagFrenzy .004*** (.001) | w .429*** (.006) | .185*** (.055) | |
| Category1 .218*** (.077) | sh1 .165*** (.031) | LagTime ElapsedVoluntary- | |
| Category2 .468*** (.104) | Covh13 .263** (.131) | Pay_ch | |
| Category3 -.128** (.063) | | .130** (.045) | |
| sh2 1.594*** (.271) | | LagFrenzy -.003 (.006) | |
| | | Category1 -.584** (.235) | |
| | | Category2 -.141 (.316) | |
| | | Category3 -.176 (.188) | |
| | | sh3 5.397*** (.975) | |
| | | s3 3.540*** (.072) | |
| | | Inverse Mills Ratio_noncharity -.147 (.283) | |
| | | Inverse Mills Ratio_charity -.147 (.153) | |
The positive significant coefficient for DLagCumNoBidders in noncharity auctions suggests herd behavior, consistent with competitive arousal. This is consistent with the positive signifi-cant coefficient for DLagFrenzy, suggesting that bidders are more likely to choose an auction in response to an increase in Lag-Frenzy. The positive significant coefficient for DLagJumpBid in both noncharity and charity auctions means that bidders are more likely to react to a large competing jump bid.
We find a positive significant coefficient for Lag$Committed in both charity and noncharity auctions. This means that a bidder finds the voluntary-pay format more attractive if (s)he has already made a commitment in that format (which needsto be paid whether (s)he wins or loses). The positive significant coefficient for Lag-TimeElapsed in charity and noncharity auctions means that bidders are more likely to bid as more time has elapsed in the auction.
Results for time-to-bid component. Time between consecutive bids is inversely related to the lambda parameter. Thus, a positive coefficient for any explanatory variable implies more concentrated bidding. We see a positive significant effect for LagPrice for noncharity auctions, suggesting that bids are more concentrated as price rises. This finding is consistent with our finding of strategic late bidding. We observe the opposite pattern—a negative coefficient on LagPrice—for charity auctions, wherein bidders with charitable motives have an incentive to bid earlier to push up the bid to help raise money for the organization (Haruvy and Popkowski Leszczyc 2009; Popkowski Leszczyc and Rothkopf 2010). The positive coefficient on cumulative bidders indicates that bids are more concentrated in auctions with more bidders. A positive coefficient for LagFrenzy suggests that an increase in LagFrenzy leads to more concentrated bidding. The positive coefficient for LagTimeElapsed is consistent with late bidding. The positive coefficient for LagTimeElapsedVoluntary-Pay indicates that bidders tend to wait to bid in voluntary-pay auctions. This result provides support for H5 for charity, where H5 predicts more late bidding in voluntary-pay auctions than in winner-pay auctions.
Results for the jump bid component. The jump bid component of the model considers the magnitude of a jump bid. The positive coefficient for LagPrice_charity shows that bidders place a greater jump bid when the price is high, while the significant positive coefficient for LagCompetingPrice indicates that jump bids are influenced by the competing auction’s price. The positive coefficient may mean that a high price in the competing auction indicates that the price in the current auction is too low—thus the jump.
The positive significant coefficients on LagCumNoBidders for both charity and noncharity auctions indicates that bidders in both types of auctions are more likely to place greater jump bids when the auction is more competitive. The coefficient for Lag$Committed in the voluntary-pay auction is significant and positive in both charity and noncharity auctions, indicating more aggressive bidding by bidders who have already committed more money in an auction. Although the coefficient is significant, we observe half the magnitude of aggressive bidding in charity auctions, in which bidders may be less concerned with the amount they have committed because the money goes to charity.
The negative effect for LagTimeElapsed in both charity and noncharity auctions suggests that the more time that has elapsed, the less likely bidders are to place higher jump bids in these auctions. However, the positive interaction between time elapsed and voluntary-pay auction format in both charity and noncharity auctions suggests that bidders are more likely to place a higher jump bid later on in a voluntary-pay auction, implying that bidders use jump bidding to try to win the auction. Finally, we observe differences in jump bidding across product categories, with less jump bidding for category 1 (DVDs, games, and books).
Results for propensity-to-pay component. For the decision to pay, we find that losing bidders are more likely to pay in charity auctions, in more competitive auctions (frenzy), and when the final ending price is low. We further observe that bidders are more likely to pay for products in category 1 but less likely for products in category 2. Finally, IMR has a positive effect, suggesting that bidders who have a higher likelihood to bid in voluntary-pay auctions are more likely to pay. This result suggests a strong relationship between bidders who self-select into the voluntary-pay format and bidders’ propensity to pay for a losing bid.
Because of the endogenous choice of auction format in Study 3, we did not expect identical results between Studies 2 and 3. However, as we expected, we find that the ranking across conditions is consistent for winning bids, pledged revenues, collected revenues, compliance rate, number of bidders, and number of bids per bidder.
The rankings of collected revenues and compliance rates are comparable across the two studies. In Study 2, total revenue is $871.06 for noncharity winner-pay auctions, $1,013.06 for charity winner-pay auctions, and $1,193.73 for charity voluntary-pay auctions. All revenues are for the same set of 75 auctions run at different times, with zero overlap between formats. While the percentage difference between collected revenues in the winner-pay and voluntary-pay formats is somewhat smaller in Study 3 (17.83%) than in Study 2
(24.69%), both differences indicate that voluntary-pay auctions produce greater revenues than winner-pay. Although the percentage of collected revenues is slightly higher in the non-simultaneous setting than in the simultaneous setting—51% versus 49%—we consider these patterns remarkably robust.
Statistics for the paying and nonpaying losers in the voluntary-pay auctions show that paying losers bid less aggressively than nonpaying losers ($6.94 vs. $9.33; t = 2.92, p =.004), very similar to the results reported for Study 1 ($5.24 vs. $8.76; t = 2.09, p = .037). Finally, we look at the extent of jump bidding and the timing of bids. We find that the extent of jump bidding was similar in magnitude, although the rank order was not consistent across conditions for Study 2 versus Study 3 for winner-pay charity (46.62% vs. 47.51%), winner-pay non-charity (44.04% vs. 36.95%), and voluntary-pay charity
(56.91% vs. 41.87%). Furthermore, we find a greater percentage of early jump bids in voluntary-pay charity in Study 2 (5.79%) than in Study 3 (16.66%), suggesting that when a winner-pay format is also present, bidders avoid making a commitment in the voluntary-pay auction early on and tend to bid later on, when the price difference between the auctions becomes larger.
The extent of snipe bidding had the same rank order across conditions for Study 2 versus Study 3 for winner-pay charity (9.49% vs. 13.87%), winner-pay noncharity (13.99% vs. 15.09%), and voluntary-pay charity (22.73% vs. 41.24%). However, the magnitude of snipe bidding was significantly higher in Study 3 than in Study 2 for the voluntary-pay auction. This result is consistent with a lower number of early jump bids in Study 3.
For Study 2 we estimate a three-component model including the decisions on time to bid, jump bid, and propensity to pay. The explanatory variables are the same as for Study 3. However, any explanatory variables pertaining to the competing auction (e.g., LagCompetingPrice) are not applicable, nor are any variables pertaining to the missing treatment of voluntary-pay noncharity (e.g., Lag$Committed_noncharity; LagTimeElapsedVoluntary-Pay_noncharity). Furthermore, the main treatment intercepts (charity, noncharity) cannot be identified separately from voluntary-pay, because noncharity is present only for winner-pay. Estimation results appear in Web Appendix D.
Table 5’s comparison of the results from the sequential setting of Study 2 with those from the simultaneous setting of Study 3 reveals three major differences:
- In the time-to-bid component, all signs and significance levels are preserved except for the reaction to past prices, which we term “strategic late bidding.” Specifically, in Study 3, we find that lag price in the noncharity setting has a significant positive effect—meaning late concentrated bidding in response to high prices. In the charity setting in Study 3, we find the opposite pattern. In Study 2, in contrast, we found no significant effect of past prices on the timing of bids in either the charity or noncharity setting, possibly owing to the absence of choice between formats reducing the benefit of strategic late bidding.
- In Study 2, in the jump-bid component, only the time variables are significant. This finding is different from Study 3, in which bidders are less likely to place higher jump bids late in voluntary-pay auctions, probably because bidders have no alternative outlet in which to bid.
- In the propensity-to-pay component, charity is not included in the sequential model (it is confounded with the intercept since the voluntary-pay was run only in a charity setting). In contrast to Study 3, bidders are no more likely to pay when the ending price is low or when auctions are more competitive.
In summary, the main insight from the three-component model is that the relationships indicated in Study 3 are largely preserved. The same was the case for the hypotheses discussed previously. The purpose of the four-component model was to extract revealed preferences from the observed format choices, which Study 2 cannot do. We controlled for self-selection owing to auction format, but self-selection on product choice in Study 3 may persist. That is, people may have self-selected themselves into Study 3 on the basis of product preferences. However, the three-component exercise served as a robustness check and ensured that there would be no reversed patterns or exploding magnitudes in one study versus the other. We find that the patterns are robust, suggesting that self-selection bias owing to auction format is not a concern.
We studied compliance behavior and revenue implications in winner-pay and voluntary-pay auctions in charity and noncharity settings. Study 1 was an experiment that tested different social appeals (using internal and external pressure) for compliance. We find that although we can effectively apply social pressure to compel bidders to pay, the unintended—yet intuitive—consequences of that action may be reduced entry and lower bids.
In addition, we conduct two field studies to study voluntary-pay auctions and payment compliance in a real-world setting. In particular, we focused on revenue implications for voluntary-pay versus winner-pay auctions in charity and noncharity settings as well as bidding strategies such as jump bidding and strategic late bidding.
Overall, results showed that in the charity setting, in which social responsibility was high, the voluntary-pay auction resulted in higher collected revenues for the seller even though commitments were not binding. In the noncharity setting, in which social responsibility was lower, we found higher revenues for winner-pay auctions. The difference in the relative success of voluntary-pay auctions between charity and non-charity settings can be attributed to three factors. First, bidders are more likely to participate in charity voluntary-pay auctions than in noncharity voluntary-pay auctions. Second, bidders are willing to bid higher in charity settings than noncharity settings. Third, higher revenues can be collected from losing bidders in charity settings.
While payment noncompliance in auctions is occasionally addressed in the literature (e.g., Bruce, Haruvy, and Rao 2004), for the most part it is ignored as nonconsequential. In this work, we provide a compelling demonstration that noncompliance is highly consequential. Not only does compliance vary between settings, but its dependence on the setting changes the profitability of possible formats relative to one another. Non-compliant bidders self-select into formats in which they can take advantage of the trust of the format to extract higher surplus. We found important differences in bidding behavior between losers who reneged on their commitment versus those who did not. In particular, nonpaying losers tended to bid more aggressively, placing more and higher bids. Although these bidders reneged, discouraging them may be unwise, as they play an important role in increasing prices and revenues in voluntary-pay auctions. Alternatively, we find that different appeals may be effective to increase compliance. In particular, external social pressure in the form of public acknowledgment of payments results in a significantly higher likelihood of payment than internal social pressure and improves format choice to favor voluntary-pay auctions.
Second, our research has broad and important implications for bidding strategies. We focused on two strategies in voluntary-pay auctions: early jump bidding and late incremental bidding. We expected these strategies to be more effective in voluntary-pay auctions because bidders face costly commitments—something bidders in winner-pay auctions do not have. Late bidding is helpful because it allows bidders to defer commitment and thus lower their risk. Accordingly, when bidders have a choice between formats, we observed more late bidding in voluntary-pay auctions (for both charity and noncharity settings).
The interaction between time to bid and jump bidding is a key strategic consideration for bidders (Haruvy, Popkowski Leszczyc, and Ma 2014; Kwasnica and Katok 2007) and is of paramount importance. Bidders with a high cost of time will place higher jump bids (Kwasnica and Katok 2007). Thus, jump bidding coincides with longer bidding intervals. Our empirical demonstration is coupled with a methodological implementation that gives researchers and practitioners a simple way to implement this insight in an estimation framework that can demonstrably apply in a field setting.
This article has important implications for marketing managers. Our findings augment the growing stream of empirical auction studies and corporate social responsibility in marketing and contribute behavioral insights.
Improving fundraising capabilities. Our findings add to the increased focus on fundraising in the corporate social responsibility literature. We find that combining an auction with a donation to charity may result in increased revenues—a finding with wide-reaching implications. While all-pay auctions have long been advocated as advantageous in raising funds for social causes, in practice the feasibility of collecting on such auction formats has hindered their wide adoption. We showed that perfect compliance is not essential, and a similar format—the voluntary-pay auction—is also advantageous in charitable settings.
The importance of compliance incentives for format selection. The finding that voluntary-pay auctions substantially increase collected revenue in charity settings while resulting in lower collected revenue in noncharity settings suggests that in the absence of strong contractual enforcement mechanisms, voluntary-pay auctions tend to be better suited to settings with moderate to high social responsibility. When selecting an auction format, or any two possible pricing formats, managers should ensure that they are accounting for differential compliance rates between the auction formats they are considering. If the two formats are running concurrently, managers should consider whether buyers with different levels of compliance propensity might self-select among formats in a manner that distorts incentives.
Compliance by losing bidders in voluntary-pay auctions. While managers may apply social pressure to comply people to pay, they need to take care that this will not result in reduced entry and more cautious bidding. To increase compliance, they may want to use positive external pressure (e.g., by publicly posting losing bidders who paid). Finally, while it may be possible to identified and screened out low-compliance bidders, doing so is not clearly advantageous, because noncompliant bidders bid up prices.
In the long run, bidders might sort themselves into charity or noncharity, winner-pay, or voluntary-pay auctions according to preferences and expected payoffs. Clearly, our results would be weaker if noncompliant bidders entered en masse into the voluntary-pay charity auction. Although this is not the case with the population we study, it is an important boundary condition. Two factors should be considered: the proportion of compliant bidders that would enter the voluntary-pay auction (charity or otherwise) and the bidding aggression of noncompliant bidders. Any drop in the proportion of compliant bidders would have to be offset by an increase in bidding aggressiveness of non-compliant bidders to keep voluntary-pay auctions superior. To characterize this frontier, a controlled study would need to systematically vary the proportion of compliant and non-compliant bidders.
Experimental Conditions for Different Compliance Appeals
You are facing two possible auction formats:
Voluntary-pay auction: This is a special format where all bidders—including losing bidders—receive a bill for the amount of their highest bid (e.g., if you place several bids and your highest bid is $150, then whether you win or lose you will receive a bill for $150). We cannot force payment on losing bids but all payments by losing bidders will be donated to charity.
While we cannot force you to pay the amount of your bid if you lose, your compliance with the auction rules is important toward raising funds for the Children’s Hospital Foundation for the purpose of helping young children with cancer. [Negative External: Losing bidders who fail to pay their highest bids as requested will be listed on the foundation’s website as unmet pledges.] [Negative Internal: Losing bidders who fail to pay their highest bids as requested cause immeasurable harm to our fundraising efforts and to the children who rely on them.] [Positive External: Losing bidders who pay their highest bids as requested will be listed on the foundation’s website as donors.] [Positive Internal: Losing bidders who pay their highest bids as requested greatly benefit our fundraising efforts and the children who rely on them.]
Regular winner-pay auction: Only the winning bidder is asked to pay—and receives the item he or she won. Losing bidders pay nothing. The proceeds are donated to the same charity as the voluntary-pay auction.
1 http://pages.ebay.com/help/sell/unpaid-items.html.
- 2 Posner (1977, p. 411) explains the legal principle that “promises are not enforceable unless supported by ‘consideration.’ This means, roughly speaking, that a promise will not create an enforceable contract unless it is made in exchange for something of value—goods, money, another promise, or whatever.” Posner lays out potential legal exceptions to this rule.
- 3 The p-values are based on one-sided t-tests for all directional comparisons, including this one and the ones that follow.
- 4 As an example, suppose the bidding increment is $.25, and Bidder A bids $7 by proxy (= zero jump), while the required minimum bid is $3. Thus, the actual displayed bid by this bidder is $3. Bidder B observes this bid of $3, chooses proxy and bids $4. The proxy tool will immediately outbid him at $4.25 on behalf of Bidder A. If Bidder B now chooses jump and bids again at $5 (the indicated minimum bid is $4.50 and so the jump = $.50), the proxy by Bidder A will still outbid him at $5.25. The resulting outcome is identical to a situation in which Bidder B had used a proxy at $5, but the observation here is counted as a jump of $.50, and a use of the proxy would have been a jump of $0. Bidder B finally selects a jump bid at $9. The bid now shows as $9—not as $7.25, because Bidder B did not choose a proxy bid.
5Voluntary-pay charity auction revenue from winners is 76.49% ($3,413.11/$4,462.51) of that from the winner-pay auction. For the noncharity auctions, revenue from winners is only 54.25% ($1,698.58/$3,131.05).
DIAGRAM: FIGURE 1 Conceptual Model with Hypotheses for Bidding in Voluntary-Pay Versus Winner-Pay Auction Format
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A Theories-in-Use Approach to Building Marketing Theory
This article's objective is to inspire and provide guidance on the development of marketing knowledge based on the theories-in-use (TIU) approach. The authors begin with a description of the TIU approach and compare it with other inductive and deductive research approaches. The benefits of engaging in TIU-based research are discussed, including the development of novel organic marketing theories and the opportunity to cocreate relevant marketing knowledge with practitioners. Next, they review criteria for selecting research questions that are particularly well-suited for examination with TIU-based research. This is followed by detailed suggestions for TIU research: focusing on developing new constructs, theoretical propositions (involving antecedents, moderators, and consequences), and arguments for justifying theoretical propositions. A discussion of TIU tradecraft skills, validity checks, and limitations follows. The authors close with a discussion of future theory-building opportunities using the TIU approach.
Keywords: building theory; grounded theory; theories-in-use; theory construction; theory development
The marketing discipline is at a crossroads ([28]; [47]). Marketing scholars can continue on the well-worn road of largely testing or extending theories by borrowing from allied disciplines, or we can challenge ourselves to make a significant difference in the lives of managers, public policy officials, and/or consumers. Our point of view is that this road less traveled necessitates deeply and richly exploring marketing topics from the perspectives of individuals (i.e., consumers, managers, and/or public policy officials) who are closest to the problem. This means leaving the comfortable confines of our faculty offices to explore, identify, and define new marketing concepts in their natural habitat.
Importantly, as we leave our offices to engage with individuals closest to the problem, we are not simply advocating recording, summarizing, and building rich descriptive narratives. While these narratives are valuable in their own right, we are advocating something more. Namely, we advocate constructing new-to-the-world marketing theories. It is widely acknowledged that theories launch the fundamental knowledge of a discipline ([49]) and are the building blocks for the maturation of a discipline. Articles whose primary contribution is based on proposing theories are generally viewed favorably ([60]). In fact, theoretical advances are critical to the development of marketing as a discipline ([30]). Not surprisingly, editors welcome new theories that are particular to the marketing discipline (see [33]). Against this background, our objective is to discuss an approach that is ideally suited to the development of theories in marketing: the "theories-in-use" (TIU) approach.
A TIU is a person's mental model of how things work in a particular context ([ 2]). As part of daily life, all individuals employ mental models ([66]). All stakeholders in marketing—among them managers, customers, employees, and public policy makers—have mental models that can be elicited by TIU research to surface interesting, novel theories and concepts that can advance both marketing practice and scholarship. Specifically, we argue that TIU is a natural approach for creating theories that are specific to marketing-related issues—what have been referred to as organic ([26]) or home-grown ([49]) theories. Organic marketing theories involve central constructs that are uniquely or primarily grounded in the marketing context rather than borrowed from other disciplines such as economics or psychology. In this regard, TIU has served as an approach for organic contributions to the marketing discipline by bringing to fore concepts such as service quality, market orientation, experiential consumption, customer solutions, and hybrid offerings.
More specifically, a TIU approach can help address three fundamental problems in our discipline. First, when we borrow from other fields, our own stakeholders' problems do not guide our research. Rather than allowing our own stakeholders' problems to guide us, we force-fit a theory or framework on which to base our research. The result is that we are not building a discipline-based body of knowledge. This borrowing approach is certainly one reason that marketing scholarship is losing touch with the practice of marketing ([28]; [47]). Second, borrowing constrains us because we restrict ourselves to what is already known, thereby hampering our search for novel and interesting phenomena. Third, when using abstract theoretical constructs from other fields, we lessen our ability to communicate with our stakeholders in a vocabulary they understand. It is much easier to advance the practice of marketing if one speaks the same language as practitioners than it is to introduce an entirely new glossary of terms.
Paradoxically, only a (relatively) small number of TIU articles have been published to date. This is surprising because TIU articles not only are published in our most respected journals but have won major awards (e.g., Shelby D. Hunt/Harold H. Maynard Award, Sheth Foundation/ Journal of Marketing Award), have established subfields of study within the discipline (e.g., service quality, market orientation), and have been a key catalyst for endowed chair appointments at some of the best business schools. As Table 1 notes, three of the top ten articles in Journal of Marketing are TIU articles. Despite this clear discipline and career impact, few researchers pursue TIU research.
Graph
Table 1. Citations of Top Ten Articles Published in the Journal of Marketing.
| Authors | Title | Citation Counts |
|---|
| From WOS | From GS |
|---|
| Morgan and Hunt (1994) | The Commitment-Trust Theory of Relationship Marketing | 7,213 | 26,150 |
| Parasuraman, Zeithaml, and Berry (1985) | A Conceptual Model of Service Quality and Its Implications for Future Research | 5,779 | 28,886 |
| Zeithaml (1988) | Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence | 4,960 | 19,926 |
| Vargo and Lusch (2004) | Evolving to a New Dominant Logic for Marketing | 4,885 | 14,721 |
| Keller (1993) | Conceptualizing, Measuring, and Managing Customer-Based Brand Equity | 4,099 | 18,070 |
| Zeithaml, Berry, and Parasuraman (1996) | The Behavioral Consequences of Service Quality | 3,732 | 13,364 |
| Narver and Slater (1990) | The Effect of Market Orientation on Business Profitability | 3,304 | 12,336 |
| Cronin and Taylor (1992) | Measuring Service Quality: A Reexamination and Extension | 3,215 | 16,350 |
| Kohli and Jaworski (1990) | Market Orientation: The Construct, Research Propositions, and Managerial Implications | 3,204 | 11,616 |
| Dwyer, Schurr, and Oh (1987) | Developing Buyer-Seller Relationships | 3,150 | 12,949 |
1 Notes: Articles in bold employ a TIU approach. WOS = Web of Science Index; GS = Google Scholar. Citation counts gathered on October 4, 2019.
Accordingly, this article aims to inspire and support development of knowledge based on TIU among marketing stakeholders. To achieve this objective, we organize this article as follows. We begin with a definition of the TIU approach. In this section, we compare and contrast TIU with other grounded theory methods and deductive research methods for knowledge development. Following this, we discuss key benefits of engaging in TIU-based research. With this foundation in mind, we turn to the practice of TIU research in the field. We divide this practice discussion into two sections: one that overviews the "basics" of TIU research and one that provides insight on the advanced tradecraft of the practice. As with any method, one must be able to judge "good and bad" practice; thus, we then turn to an assessment of rigor in TIU. This is followed by a discussion of the limitations of the approach. We conclude with suggestions for future research.
[66] note that individuals' TIU may be envisioned as a set of "if-then" relationships among actions and outcomes. For example, an advertising manager's TIU may include the proposition that if she associates her brand with an important social cause, then millennial consumers may be more likely to buy her brand. People's TIU may also include complex if-then relationships. For example, a marketer's TIU may include the idea that a firm's customer-centricity improves its profitability, but an increase in customer centricity beyond a certain level adversely affects firm profitability because it is too costly. That is, there is an inverted U-shaped relationship between customer centricity and firm profitability.
At its core, the theory construction process involves developing novel if-then propositions. In contrast, the theory-testing process involves empirically assessing the validity of previously developed propositions. While the two processes and their aims are distinct, they potentially can be interrelated. For instance, a theory-testing effort may reveal unexpected findings, which may lead to the construction of new theory to account for the findings. Our focus in this article is on the theory construction process for developing new theory about a phenomenon.
[ 2] coined the term TIU to refer to individuals' mental models of the world that guide their deliberate behavior. They contrasted the concept with "espoused theories" that refer to the mental models individuals claim or purport to have. While overlap may exist between individuals' TIU and their espoused theories, often these two types of theory differ. For instance, individuals may be unable to articulate parts of their TIU that are tacit. More often still, defensive reasoning mindsets develop that discourage sharing revealing insights ([ 1]).
The TIU approach has unique characteristics that bear highlighting. The approach involves soliciting from study participants—the theory holders—the ideas they feel are important and how they are linked to one another. The emerging set of interrelated constructs, regardless of how complete or incomplete they may be as theories, become a researcher's starting point for harvesting constructs, propositions, and arguments. Researchers, however, are not simply passive recorders of participants' thinking. They use their viewing lenses to elicit, evaluate, abstract and extend what they "hear" from theory holders included in the study ([63]). The TIU approach relies on one-on-one participant conversations and elicits theories from a relatively small number of participants (often 15–25).
The TIU approach is also unique in that it is a partnership that allows for the cocreation of a theory. Participants are treated as active partners in the theory development process, allowing for the presence of implicit and explicit causal thinking among them about the ideas they consider important. Researchers may then draw on other sources of insight they have acquired about the topic to modify the ultimate constructs' abstraction levels and causal connections among them to develop theoretical propositions. Said differently, a TIU approach assumes that the theory holders being interviewed have theories that researchers can uncover and extend using other sources of insight. This is what makes a TIU approach a partnership. It is grounded in two different mindsets—that of the researcher and the interviewees—each focused on theory.
A TIU approach becomes an even stronger partnership when researchers convene representative stakeholders including some original study participants to critique and discuss the researcher's tentative formal theory. In this way, two mindsets, the researcher's and the theory holders', are formally brought to bear on the topic. A new and better theory is likely to be created. This is less likely or even unlikely to occur with other approaches falling under the rubric of grounded theory construction.
In general, the theory construction process is inductive in nature. Scholars collect various types of data through means such as unobtrusive observations, secondary data, and participant interviews. They reflect on these data to identify patterns and create new theory. The theory so developed is termed "grounded theory" to indicate that it is created from observations and data pertaining to a phenomenon on the ground ([ 7]; [12]; [15]; [54]).
We provide an overview of three formal approaches for building grounded theory in Table 2: TIU, case studies and ethnography. The TIU approach relies on elicitation of theories held by individuals with proximity to the problem (e.g., [ 4]). Case studies are in-depth studies of one or a few comparative cases (e.g., [ 5]; [14]). Ethnographies are in-depth studies of a phenomenon aimed at describing its meaning/significance to a group's members and the reasons underlying the meaning/significance (e.g., [16]).[ 6] Importantly, researchers can use these approaches in tandem; for example, a researcher using the case study method can fruitfully include a TIU approach for making comparisons across cases.
Graph
Table 2. TIU and Related Approaches.
| Inductive (Grounded Theory) | Deductive |
|---|
| Research | TIU | Case Study | Ethnography | Theoretical Modeling |
|---|
| Purpose | Build new theory | Build new theory | Understand a phenomenon's meaning/significance and its underlying reasons | Build new theory |
| Researcher mindset | Exploration, Hunting | Exploration, Hunting | Exploration, Hunting | Building realistic yet tractable models/scenarios |
| Research process | From data to theory | From data to theory | From data to a phenomenon's meaning/significance for a social group, and underlying reasons for the meaning/significance | Mathematically derive implications for actors' behavior, and compare across models |
| Data collection method | Interviews, focus groups | Interviews, field observations, review of documents | Field observations, interviews, review of documents and textual data, material artifacts, netnography | N.A. |
| Sample selection | Theoretical sampling | Theoretical sampling | Target social group(s) | N.A. |
| Sample size/depth | In-depth conversations (small n) | In-depth case comparisons (small n) | Immersion in the target social group(s) | N.A. |
| Examples | Challagalla, Murtha, and Jaworski (2014) | Gebhardt, Carpenter, and Sherry (2006) | Gollnhofer, Weijo, and Schouten (2019) | Dzyabura and Hauser (2019) |
2 Notes: N.A. = not applicable.
The theory construction process, however, can also be deductive in nature. For instance, in theoretical modeling, researchers set up models (settings/scenarios) with different characteristics and derive implications of the models for the behaviors of participants in the model (e.g., firms, salespeople, consumers). These behaviors are then linked to the (differing) characteristics of the different models (generally across articles) to construct new theory ([34]).
In many instances, researchers review the literature, see gaps or conflicts, and propose new theory, often by introducing a moderator construct or a new explanation stimulated by their own experiences or derived from extant research. This process can be inductive or deductive in nature. For instance, when researchers combine knowledge about a phenomenon in the literature with their personal experiences related to the phenomenon to develop new theory, it is more akin to an inductive process. In contrast, when researchers put two or more findings/assertions in the literature together to derive a new theory, the process is deductive in nature.
Table 2 shows prominent inductive and deductive approaches for theory construction and summarizes key differences among them with respect to six facets: purpose, researcher mindset, research process, data collection method, sample selection, and sample size/depth. As the table shows, a major difference between the inductive and deductive approaches is that whereas inductive approaches start with data pertaining to a phenomenon of interest, deductive approaches start with models (settings/scenarios) or theories and work through their implications. A related difference is that whereas a researcher's mindset in inductive approaches is one of exploration and hunting (seeking and processing data in quest of theoretical insights) for constructs and theories inherent but hidden or as yet unarticulated in data, the researcher's mindset in deductive approaches is one of setting up models that are sufficiently realistic yet tractable.
As with any research approach, TIU suits certain research questions better than others. We identify major motivations for engaging in TIU research, whether as a stand-alone approach or in combination with other approaches. We find that TIU research is particularly valuable when scholars want to ( 1) construct organic marketing theories, especially about new and emerging phenomena; ( 2) extend extant perspectives and address ambiguities; or ( 3) guide future empirical efforts. In this section, we take a closer look at these three motivations.
Constructing organic theories is important to any discipline because organic theories offer unique insights not available outside of the discipline and thus provide good reasons for the discipline's existence as an academic field. Unfortunately, marketing scholars tend to borrow more heavily from other fields than those fields recognize and borrow from marketing ([ 6], [46]). However, the development of organic marketing theories—such as that on service quality, market orientation, and experiential consumption—has influenced other fields, and articles on these topics often receive thousands of citations. As noted by [52], p. 171), "Clearly, the academic market recognizes the value of homegrown constructs and theories."
Because the TIU approach takes advantage of marketing practitioners' or consumers' experience and knowledge about the marketing setting, it is especially well suited to identifying and defining important constructs that reflect the practical world of marketing, including antecedents and consequences of marketing phenomena. Consider two examples: service quality (see [40]]) and market orientation (see [27]]). Both sets of authors used the TIU approach to develop their pioneering conceptual frameworks. They were able to do so in part because managers had developed practices that offered useful grist for the development of ideas on each topic. Each conceptual framework has prompted significant empirical work and paved the way for substantial research streams on services and market orientation over many years, and is among the top ten cited articles in the Journal of Marketing (see Table 1). Table 3 provides an illustrative set of articles that develop organic theory using the TIU approach.
Graph
Table 3. Examples of Organic Theoretical Contributions Using TIU Research.
| Focus | Authors | Organic Constructs and/or Theory |
|---|
| Managers | Keaveney (1995) | Customer switching behavior |
| Workman, Homburg, and Gruner (1998) | Dimensions and Determinants of Marketing Organization |
| Homburg, Workman, and Jensen (2000) | Customer focused organization structure |
| Narayandas and Rangan (2004) | Building and sustaining buyer–seller relationships |
| Morgan, Anderson, and Mittal (2005) | Customer satisfaction information usage in firms |
| Tuli, Kohli, and Bharadwaj (2007) | Customer solutions |
| Ulaga and Reinartz (2011) | Hybrid offerings |
| Challagalla, Murtha, and Jaworski (2014) | Marketing doctrine |
| Macdonald, Kleinaltenkamp, and Wilson (2016) | Customers' judgment of solution value |
| Houston et al. (2018) | Prerelease consumer buzz |
| Chase and Murtha (2019) | Selling to Barricaded Buyers |
| Consumers | Parasuraman, Zeithaml, and Berry (1985) | Service quality |
| Zeithaml (1988) | Consumer perceptions of price, quality, and value |
Theories-in-use-based research is also useful when the aim is to extend extant perspectives about a construct. For example, while research on customer solutions in business markets mushroomed in the early 2000s, solutions were viewed only from the suppliers' perspective. Missing from the discussion was a customer-centric perspective on solution offerings. Using a TIU approach, [56] provided a view of solutions from the customers' perspective, which extended the supplier view of solutions. Similarly, when conflicting theoretical perspectives exist on a novel construct, a TIU approach can help researchers better understand when and why one theoretical perspective may be preferable to the other. Relatedly, the TIU approach can bring precision and clarity when there is ambiguity surrounding constructs and/or nomological net of relationships among constructs. The approach has fewer advantages when working with well-defined constructs where the nomological net has been mapped out comprehensively in prior research.
Theories-in-use research is often the ideal foundation for empirical efforts. As an example, [40] used a TIU approach to understand the meaning of service quality from the perspective of consumers, employees, and executives, which guided two major empirical efforts that produced multiple publications.
The first effort resulted in identifying ten dimensions of perceived service quality gleaned from eight group interviews. The researchers termed the first set of empirical efforts SERVQUAL, a multidimensional scale for measuring consumer perceptions and expectations of service quality ([43], [44], [45]; [40], [41]). Development of scales rests on sound conceptual foundations, and insights of specifics provided by practitioners in a TIU approach helped inform these operationalizations. When queried about the need for expectations in the measure, the authors followed up with another TIU study ([69]).
The second effort resulted in the gaps model of service quality, which linked performance by various entities (e.g., employees, channels) to the gap between consumer expectations and perceptions of service quality, and the communication and control processes within organizations that produce these gaps ([68]). A follow-up study empirically examined these variables to identify the most important in each of the four gaps and the relative importance of the four gaps themselves (Parasuraman, Berry and Zeithaml 1991a). Finally, the researchers empirically linked perceived service quality to intentions to examine the behavioral consequences of perceived service quality ([70]).
The TIU approach is best suited for addressing research questions/issues that are broad and deep, and for which we do not have good answers. Research participants should be selected for their knowledgeability about the questions/issues and willingness to share their knowledge and experiences with the researcher. In general, a typical research project requires 15–25 participants selected in successive phases. The knowledge/experience required of the participants in each phase becomes clearer as the research progresses and theoretical ideas come into sharper focus. Importantly, the researcher should have a very strong interest in the research questions/issues and should have good general knowledge related to them. This enables the researcher to listen carefully to participants, ask probing questions, challenge participants when appropriate, and engage with participants in a flexible way—adapting the questions asked to the idiosyncratic knowledge of individual participants and to the learnings from prior participants in the TIU study.
Figure 1 provides an overview of the TIU research process. The process typically begins with a focal research construct to be examined in the research (e.g., market orientation, service quality).[ 7] If the construct is not well defined, the research begins by clarifying and defining the core construct. This may take several iterations and feedback loops based on participant conversations (see right-hand side of Figure 1). If the construct is well defined, the research moves to the stage of developing propositions and their associated arguments. The propositions can include antecedents, consequences, mediators, and or moderators. After a few conversations, the researcher begins to formulate tentative propositions that may be assessed on basic screening criteria related to the plausibility and strength of reasoning. After multiple propositions are developed, they must also pass higher-order assessments that relate to the overall contribution of the set of propositions (see Figure 1). These pertain to whether the collective set of ideas adds to the existing literature. As Figure 1 shows, there are numerous feedback loops illustrating the continual iteration and refinement of the conceptual structure.
Graph: Figure 1. The TIU research process: an approximation.Notes: Foundational tests = Are propositions plausible, and aligned with definitions and arguments? Advanced tests = Are propositions interesting, substantially informative, and hang together (have one or a few common themes)?
The aim in TIU research is not to simply transcribe participants' statements. Rather, it is to review data across participants, look for common themes/ideas in the specifics provided by participants, and abstract commonalities to broader constructs/variables that form the building blocks of an emergent theory. A researcher strives not to present a particular participant's TIU but rather to present a theory reflective of the beliefs and actions of multiple participants, including variables and propositions extrapolated from those beliefs and actions (see [65]] and [61], [62]]).[ 8]
Researchers should develop a brief conversation guide that lists a few broad questions they wish to ask participants, along with related probes and follow-up questions.[ 9] If permitted, each conversation should be recorded, notes should be taken during the conversation, and a memo to oneself written immediately following the conversation as to how it adds to prior ideas and points to future lines of inquiry. A researcher returns to these recordings, notes, and memos as a theory begins to take shape and uses them to provide substantiating evidence in the research report.
The purpose of conversations with participants is to tap into their tacit and explicit knowledge and beliefs about the research problem/questions of interest to the researcher: ( 1) construct development, ( 2) proposition development, and/or ( 3) argument development. In this section, we describe the nature of the conversations needed for each of these three research problems. We first provide basic guidelines on the TIU research approach, followed by more advanced guidelines for addressing the three research problems. In the next two subsections, we discuss tradecraft related to the fieldwork and identify important checks for rigor in the research.
We suggest starting a participant conversation by introducing the topic and segueing into asking what the phenomenon (construct) means to the participant and others familiar to the participant. For an illustrative conversation flow and set of questions, see Table 4. The researcher must ask for specific examples of varying levels of the phenomenon and how it is similar to or different from other proximal constructs (for specific questions, see the top section of Table 5). The researcher should periodically check whether the tentative definitions (s)he is forming are consistent with participants' understanding of the phenomenon. Participant conversations flow unpredictably and generate a lot of ideas and stories, many of which may not relate to the research problem of defining the construct of interest. The researcher must, therefore, continually try to refocus the conversation on the construct (and away from, for example, its antecedents or consequences or just irrelevant information).
Graph
Table 4. Construct Hunting: A Suggested Conversation Flow for TIU Research.
| Introduction: Some companies (and managers) have begun to explore the concept of X. It seems you are also exploring this idea within your organization. |
|---|
Can you tell me a bit about your approach to X? What motivated you—or your organization—to pursue X? Do you have a common definition of what "X" means in your organization? If not, in your own words, can you help me better understand this concept or idea? Why is this concept important (valuable, useful, helpful) for you and your organization? From your perspective, how is this concept different than Y (a similar idea or concept)?
|
Graph
Table 5. Key Questions/Probes for Building Theories Using the TIU Approach.
| Construct Trapping: Firming Up the Construct Meaning/Boundary |
|---|
| Research Goal | Sample Question for Participant |
| Assess construct boundary | "Would you say X includes the notion of...?" "I read a recent article that is a little different than your view..."
|
| Assess working definition | "Based on interviews to date, X may be defined as...Thoughts?" "Here is another way to think about X; what do you think?"
|
| Building If-Then Propositions (Consequence Variables) |
| Research Goals | Sample Questions for Participant |
| Assess X-Y relationship | "Another interviewee says X causes Y. What is your view?" "My last interview said X causes Y. What is your reaction?"
|
| Link X to novel outcomes | "What are the benefits of doing X?" "Any outcomes counter to conventional wisdom?" "Can you tell me the pros and cons of doing X?"
|
| Building If-Then Propositions (Antecedents, Moderator, and Mediating Variables) |
| Research Goals | Sample Questions for Participant |
| Find "positive" X antecedents | "What are the key drivers of X?"
|
| Find "negative" X antecedents | "What are the key barriers of X?"
|
| Find general antecedents | "How do you increase the level of X in your firm?" "Why is X gaining (or losing) traction in your firm?"
|
| Find moderators | "Under what conditions does X work best? Why? "When does X NOT lead to Y? Why?
|
| Find mediators | "Are there any other routes through which X impacts Y?" "Does X influence other variables that in turn impact Y?"
|
For example, [27] started participant conversations by asking, "What does the term market orientation mean to you?" Some of the responses were along the following lines: "It's all about customer need satisfaction,"[10] "You have to know what competition you are up against," "It means your research and development (R&D) is in touch with what's going on in the market," and so on. The researchers formed a tentative idea of the construct's domain from these responses. For example, these responses suggested that the construct was about delivering customer satisfaction in the face of competition, and that R&D is somehow involved in the process.
A follow-up probe, "Tell me a little about your activities that reflect a market orientation," elicited numerous responses. They included "We keep our eyes on the customer and competitors," "We put the customer at the center of everything we do," "We make sure people in one function know what people in other functions are doing," "We reward people for providing exceptional service," and so on. These comments suggest that the construct involves knowing customers and competitors, everyone in the company focusing on customers, and each function knowing what the other functions are doing. Note that the last quote is indeterminate as to whether it belongs to the construct's domain or is an antecedent of the (yet to be precisely defined) construct. Follow-up probes might ask, "Can you tell me how one function finds out what the others are doing?" and "What exactly do you do to know how the consumer environment is changing?" to clarify these questions.
After a few of these conversations, the researcher begins to identify commonalities across the participant observations and to abstract them to a higher level. For example, while some participants indicated that they sent out customer surveys, others relied on syndicated data. Yet others visited customers personally. However, the commonality here is that of the generation of customer intelligence through different methods. This led to the development of the idea that market orientation involves, in part, intelligence generation about customers. Subsequent conversations and ongoing reflections led to the eventual definition of market orientation as organization-wide generation, dissemination, and responsiveness to market intelligence.
A researcher's focus here is the development of if-then propositions that aim to identify a phenomenon's antecedents, consequences, mediators, and/or moderators of the phenomenon's effects. At one level, this is relatively straightforward—the researcher asks participants questions such as "Can you give me examples of actions you took to increase X (the phenomenon)?," "In your opinion, what happens when X increases?," "Can you recall instances in which X didn't lead to that?," and "What accounts for the unexpected results?" However, participants frequently identify antecedents that reflect the core construct itself or are too proximal to the core construct to be of theoretical interest. For example, when asked to indicate why some organizations are not very market oriented, one participant said, "It's because they fail to give customers what they want." Note that this is a part of the market orientation construct, not its antecedent.
The types of questions that the researcher asks should be based on the research goal (see Table 5). For example, if the research goal is to link construct X to novel outcomes, the researcher may ask, "What are the benefits of doing X?," "Were there any surprises or unexpected outcomes of doing X?," or "Did increasing the level of X lead to outcomes that contradict conventional wisdom about X?"
As the conversations progress, the researcher forms a relatively clear (albeit tentative) proposition that X leads to Y. At this point, the researcher can assess the proposition by asking questions directly related to the proposition. For example, the researcher may say, "My last interviewee believes that X leads to Y. What is your view?" or "My last interviewee found that X leads to Y. What is your reaction?" This is particularly useful for propositions that include abstract constructs developed by the researcher. If the level of abstraction is too high, subsequent participants are likely to indicate that the proposition(s) is questionable.
In addition to developing if-then and "if-then-except-when" theoretical propositions, a researcher must also provide plausible arguments or justifications for the propositions. Argument development involves probing participants for the reasons they hold their if-then beliefs. Thus, a researcher may ask participants, "Why do you believe X leads to Y?" or "Why do you expect M to strengthen the effect of X on Y?" Developing an argument may also involve listening to the reasons offered by participants and identifying one or more mediators of the effect of X on Y. As in the case of developing theoretical propositions, the challenges here pertain to appropriate level of abstraction as well as to maintaining consistency with the evolving definitions of the core construct and the antecedent, consequence, or moderator variables involved.
A key aspect of theory construction using a TIU approach is the process of abstraction from the raw data surfaced in the course of participant conversations. Abstraction involves considering two or more elements (e.g., words, phrases, ideas in one or more sentences) in raw data (e.g., transcriptions of participant conversations), pooling the elements into a higher-order category or construct, and giving it a label (i.e., name/term). Such a construct is of a higher order (i.e., is more abstract) than the elements in the sense that it captures the essential information in the two or more elements but excludes some of their details. [ 7] refer to the general process of identifying and categorizing distinct elements in the data as "open coding."
A researcher may use one or more of several approaches for abstracting from the elements (e.g., words, phrases) contained in the data obtained from participant conversations. We discuss three approaches. In the first approach, a researcher examines the data within and across participant conversations and notices that they contain several elements that have different meanings but all seem to be subsets of one of the elements in the data. In this case, the latter element is of a high-order (i.e., is more abstract), and the researcher may consider it a candidate construct for his or her theory. The abstraction process here is one of identifying elements that are all a part of a broader, more abstract element and treating all as the latter element for the purpose of theory construction. Importantly, this calls for the researcher to actively seek out such interrelationships among the elements to identify them. For example, participants may provide the following statements to a researcher to indicate that their respective firms are market oriented: "We survey customers to find out their needs and wants," "Our company does a lot of market research every quarter," "Our salespeople ask customers how we can serve them better," and "We generate intelligence about our markets." In this case, the process of abstraction involves observing that the italicized elements in the first three statements are subsets of the italicized element in the fourth statement and thus suggests using the construct of "market intelligence generation" in subsequent theory construction efforts.
In the second approach, a researcher examines the data within and across participants and notices that they contain elements that likely co-occur (or covary). That is, when one element is present (or is at a high level), another element is also likely to be present (or at a high level). The researcher pools these elements into a higher-order category or construct and, if needed, gives it a label/name. For example, one or more participants may describe customer reactions to exceptional service in restaurants as follows: "They feel valued as customers," "Their eyes come alive," "They smile, and thank the waitpersons," and "They leave big tips." Each of the italicized elements likely occurs when the other elements also occur. As such, the researcher may pool them into a higher-order category or construct of customer satisfaction and use it for subsequent theory construction.
In the third approach, the researcher examines the data within and across participants and notices that they contain elements (e.g., words, phrases) that are neither subsets of one of the elements (approach 1) nor do they necessarily co-occur or covary (approach 2). Rather, they appear to be different facets/dimensions/aspects of a broader concept or idea. The researcher pools these elements into the higher-order category or construct and, if needed, gives it a label/name. For example, participants may describe outcomes of investing in market research as "Market research helps us get a bigger piece of the market," "It brings in more revenue," and "It costs money, but in the end, we save money because we don't try to be all things to all customers." The italicized elements are not subsets of one of them and often do not covary, but each is an indicator of the broader concept of how well a firm is performing. As such, the researcher may pool them into a higher-order category or construct of firm performance for use in subsequent theory construction.
Importantly, as a construct's meaning begins to form, a researcher must take care that the construct's domain is not too narrow or too broad. If it is too narrow, it is too specific and limits the generalizability of the theory. If it is too broad, its components may not all relate to other constructs (potential antecedents or consequences) in a similar manner. This becomes clearer as the construct's antecedents and consequences emerge in the course of participant conversations. Importantly, as a construct's meaning begins to emerge, the researcher must ascertain whether it is truly capturing a distinct phenomenon, one not reflected by other known constructs (especially those already discussed in the literature). For example, when asked whether they thought market orientation and customer orientation were the same thing or different, most participants pointed out that market orientation was a broader construct in that it focused on customers and other influences on them, whereas customer orientation focused exclusively on customers. Upon reflection, it became clear that the two would have somewhat different antecedents (e.g., company systems that base rewards on customer satisfaction vs. those that base them on broader metrics such as market share and profitability).
After developing constructs, a researcher links them to develop tentative theoretical propositions, stimulated by participants' TIU elicited in course of participant conversations. The general process of linking two or more concepts with each other is referred to as "axial coding" ([ 7]). There are two main challenges in developing propositions that identify antecedent, consequence, mediator, and moderator variables.
First, when the core construct/phenomenon is yet to be defined precisely, the emerging antecedents, consequences, mediators and moderators need to be identified and defined in conjunction with the core construct in a way that the resulting propositions make sense. For example, when a participant in the market orientation research was asked, "Why do some firms fail to give customers what they want?," he indicated, "Well, they are afraid of changing what they have done for many years. They feel safe doing the tried and tested." A further "why" probe led to "Because they are afraid they will be pulled up by the management if they do something different and it bombs." A few more probes later led to the more interesting revelation that an organization's employees may fail to provide customers the offerings they need because of the fear of being punished by their managers who themselves are concerned about being punished by a risk-averse top management. This led to the identification of "top management risk aversion" as an antecedent of market orientation. Note that "top management risk aversion" is not a part of the core construct and is a relatively abstract, novel construct, and the proposition makes sense if market orientation is defined in part as responding to customers' changing needs.
Second, the propositions developed should ideally be novel (i.e., not documented in the literature) and interesting (i.e., not obvious but useful). Such propositions often challenge conventional wisdom, identify conditions in which extant theory does not hold, or develop interesting nuances that lead to "aha!" moments for the readers. Frequently, however, participants offer input with little insight. For example, when asked why some firms are more market oriented than others, several participants indicated, "Firms that are market oriented are that way because they care," and "It takes hard work to be market oriented." These and many other ideas that emerged in the course of the conversations were either obvious or previously documented and, therefore, not pursued further. It is important for the researcher to continually ensure that the propositions (s)he is generating and retaining for further consideration are new to the literature, interesting, plausible, and of importance to some set of stakeholders.[12]
A straightforward way for a researcher to develop arguments to support a theoretical proposition is to ask participants why they believe (and perhaps why they do not believe) in a proposition. It is very important, however, for a researcher to critically evaluate the soundness of the reasoning before accepting it as plausible. The researcher may also develop theoretical propositions and arguments by connecting disparate ideas obtained from two or more participants. For example, one participant may note that doing A leads to Y, and another participant may suggest that doing X leads to A; putting these two assertions together would suggest the testable proposition that doing X leads to Y, the argument being that X leads to A, which in turn leads to Y.
Frequently, a researcher's goal is to construct a set of coherent theoretical propositions that collectively represent a substantial contribution to the literature. After generating a reasonably large number of theoretical propositions, a researcher should take stock of them with a view to selecting the ones that have one or a few common themes such that the selected set can be formalized in a parsimonious way. The researcher may group the constructs involved across propositions into broader categories, or identify one or a few common high-level arguments across propositions. The general process of choosing from among the theoretical ideas developed in a research process is referred to as selective coding ([ 7]).
In this section, we discuss key nuances of the TIU research process and offer suggestions that increase the likelihood of developing impactful new theory. Following this, we offer suggestions for crafting research papers.
As noted previously, the theory construction process entails collecting data from a few participant conversations and then interacting with the data to generate preliminary, tentative theory (constructs, propositions, and arguments). The tentative theory guides the researcher's focus in collecting data from subsequent participants. These data frequently augment the tentative theory and/or suggest its modification (e.g., revising constructs, changing their abstraction levels, adding propositions, developing new arguments). The resulting theory, in turn, guides subsequent data collection, and so on, until a researcher is satisfied with the theory.
For example, say that a researcher is interested in constructing a theory of postrecession performance of firms. Drawing on data collected from the first few participants, the researcher constructs a tentative theory that a firm that increases its R&D spending during a recession enjoys higher market share after the recession. After a few more conversations, the researcher constructs another tentative theory that a firm that invests in operations to make them more efficient during a recession increases its profitability after the recession because it redirects slack resources during the recession to reducing ongoing operations costs. At this point, the researcher considers the elements "R&D spending" and "investments in operations" and abstracts them to a broader construct of capability building. Similarly, the researcher abstracts "market share" and "profitability" to a broader construct of firm performance. Using these constructs, the researcher constructs the proposition "The greater a firm's capability building during a recession, the greater the firm performance postrecession."
A researcher is not simply a passive ear. The maxim that data do not say anything—only managers or researchers do—applies to TIU research as much as it does to other methods ([64]). Theories-in-use approaches provide a special opportunity for researchers to exercise disciplined imagination and add unique value to an investigation. This occurs, for instance, when researchers listen carefully for what a participant is not saying (i.e., what potentially important ideas seem to be missing or understated by interviewees). For example, in an insight development project, managers had little to say about the important constraints placed on insight development by long-standing company policies.
Similarly, a researcher may develop a theoretical proposition that was not directly stated or derivable from participant data but still grounded in them. For instance, one participant may identify P as a new antecedent of a phenomenon, and another participant may identify M as a moderator of the effect of a different antecedent R. The two sets of ideas may lead the researcher to examine whether M may moderate the influence of P (in addition to that of R). A researcher also has an important role in adding value by explaining why certain findings are surprising, counterintuitive, or contrary to received wisdom on the topic.
When engaging with a participant, it is key for researchers to temporarily suspend their prior beliefs and tentative ideas developed in the course of previous participant conversations. This is not easy, but it is important to listen with an open mind, absorb the participant's ideas, and probe deeper into those that have the potential for generating new insights. Researchers can feign ignorance and ask a number of "why" questions even if they believe they know the answer: "Why do you say that?," "Why does it affect X?," "Why would doing X not be helpful in circumstance M?" As these questions continue, they can lead to interesting new insights.
As may be evident from the previous examples, participant conversations elicit considerable commentary. When listening to a participant's responses, the researcher should try to identify and define the abstract construct that reflects the detailed description provided by the participant. To the extent the researcher is successful in doing this, the theory construction task following the participant conversations becomes easier because a theory essentially is a set of interrelated constructs. It is helpful to record participant conversations as well as take notes during the conversations, which can be revisited in the course of developing construct definitions, theoretical propositions, and arguments.
It is sometimes more productive for a researcher to go where a participant's interest takes the conversation rather than strictly focus on the precise questions with which the researcher comes into the conversation. For instance, a participant may say something that may seem a bit odd or unrelated to the research questions. The researcher may be tempted to brush it aside to have a more "productive" conversation, but doing so may lead to missing out on potentially interesting and useful new ideas.
With each conversation a researcher learns a little more about the three components of the theory under development: construct definitions, theoretical propositions, and arguments. (S)he must relate these to those learned from earlier conversations up to that point. This provides greater confidence in similar ideas obtained in previous conversations. Ideas not previously elicited can be noted for further exploration in subsequent conversations. The researcher may also encounter ideas that are in conflict with established ideas. For example, some participants may indicate that R&D spending in recessions hurts performance, whereas others may believe that it helps performance. These may prove to be most interesting and need to be resolved (perhaps by identifying appropriate moderators) in subsequent participant conversations.
A researcher also tracks the components of a theory that are developing well as well as those that are "light" and need further exploration; (s)he then selects subsequent participants accordingly and engages in conversations that address those components. For example, after a few conversations with brand managers, a researcher interested in constructing a theory of brand love may learn more about the antecedents and consequences of brand love than about the moderators of its consequences. Thus, the researcher may focus more on surfacing moderators in subsequent participant conversations. At some point, researchers will recognize that continued collection and analysis of data is unlikely to yield new themes, categories, or substantive insights, a situation known as theoretical saturation.
As we know from research on cognitive biases, peoples' mental models can be deficient. For example, opinions and strongly held feelings have a way of surviving challenges from facts. Just because a participant expresses a particular story with conviction does not mean that it should be accepted by the researcher as factual. While it is a sincere expression of the participant's judgments, the story merits critical examination and possible correction or improvement ([23]; [51]; [55]).
It can be difficult to figure out the right "demarcation" between the "context" of a TIU study and the constructs studied. For example, a context may be business-to-business firms and a researcher may be exploring constructs X, Y, and Z. The business-to-business context, however, also has other constructs associated with it (e.g., direct sales force vs. channel partners, client concentration). Therefore, the researcher has a choice here: to study the context variables and include them in the theory, or to limit the theory to the study's context.
A researcher may substantiate claims about a construct's meaning and/or a theoretical proposition (along with its underlying logic) by indicating how several participant conversations reflected this. Providing direct quotes from one or two of them is a convincing way to accomplish this. These quotes provide a verbal lexicon and allow the reader the opportunity to develop an alternative formulation. However, as participants in a conversational mode frequently allude to multiple ideas in a single sentence or two, the researcher must portray quotes that clearly and unambiguously make the intended point.
Another emergent, value-added quality that can strengthen a paper is an answer to the question, "So what?" This question can be answered from both a researcher and marketing stakeholder standpoint. For example, the final construct network or mental model that represents consensus thinking among participants can be used as a playground for theory construction. The researcher may offer an additional map containing new constructs and their proposed relationships along with those already in the map. The changes in the map (i.e., the new constructs and their connections with others previously identified in the interviews) would represent the researcher's unique reflections about the data. This new bundle of related, testable propositions is a new theory that could guide future research and thinking.
The consensus map may also be a basis for helping marketing stakeholders think through its relevance to their positions. The researcher can offer "map management" suggestions to stakeholders. For instance, they might be encouraged to ask, "Which constructs should be emphasized or deemphasized in their situation? How might particular connections between constructs be weakened or strengthened? What new constructs may be added to the original consensus map network?" Essentially, questions like these help stakeholders shore up strengths in their thinking and compensate for limitations.
This section offers criteria for evaluating the rigor of a TIU-based study. Some of the criteria commonly used to evaluate studies include internal validity, external validity, and reliability (see [39]]). Several scholars, however, have long argued that these criteria are cast in a positivist tradition, and that different criteria should be used to evaluate interpretive research (e.g., [17]; [29]). Researchers have developed numerous criteria for evaluating interpretive research, some of which mirror the commonly used criteria of reliability and validity. Prominent among these are four criteria described by [17] and [29]: credibility, transferability, dependability and confirmability (see also [ 3]]; [ 9]]). These criteria are discussed by [19] and have been used in prior marketing research (e.g., [13]).
A TIU-based study shares aspects of the positivist as well as the interpretive traditions. It is positivist in that it aims to develop clear new causal associations about a phenomenon and interpretive in that it uses study participants' interpretations of the phenomenon. For this reason, we adapt the four criteria (credibility, transferability, dependability, and confirmability) for evaluating the rigor of TIU-based research and indicate tests researchers can use to demonstrate (and evaluate) the rigor of their new theories. Importantly, while these four criteria are useful, we suggest they need to be complemented by a fifth criterion—distinctiveness—that refers to the novelty of a theory's constructs and propositions (relative to extant literature). This criterion is central for evaluating TIU research whose aim is the construction of new theory. Table 6 summarizes these five criteria and how they may be used for evaluating TIU research.
Graph
Table 6. Rigor in TIU Research.
| Type of TIU Rigor | Analog to Theory- Testing Research | Meaning of Rigor Type in TIU Research | Demonstrating Rigor |
|---|
| Credibility | Internal validity | The extent to which a new theory's if-then propositions are plausible | Provide arguments to support the new if-then propositions Document the inclusion of range-spanning questions in participant conversations Document data from multiple participants which suggest the same theory
|
| Transferability | External validity | The extent to which a new theory's constructs and if-then propositions are valid in contexts not sampled for the research | Document similarities in theory emerging from participants sampled from multiple contexts
|
| Dependability | Reliability | The extent to which multiple researchers find the same constructs and if-then propositions from the same data | Document similarity of constructs and if-then propositions surfaced by multiple researchers processing the same data
|
| Confirmability | Objectivity | The extent to which a new theory's constructs and if-then propositions can be independently certified as emerging from the data (rather than from researcher dispositions) | Document consistency of theory with participant views through participant checks Document interjudge reliability: agreement between independent (external) judges about the fit between data and constructs and propositions Provide thick descriptions of data to allow readers to directly assess consistency of if-then propositions with data
|
| Distinctiveness | Discriminant validity | The extent to which a new theory's constructs and if-then propositions are different from existing constructs and if-then propositions in the literature | Describe differences in definitions of new constructs relative to those of most similar constructs in literature Describe differences in the new if-then propositions relative to most similar/close propositions in the literature
|
Credibility is analogous to internal validity, and in the context of TIU-based research refers to the extent to which a new theory's if-then propositions are plausible.[13] This may be demonstrated by providing strong arguments to support the propositions. For this reason, we recommend probing participants for why they believe in their if-then propositions. Their responses (potentially combined with extant theories and findings in the literature) can be instrumental in constructing persuasive arguments for the new theory's if-then propositions. We also recommend asking participants range-spanning questions to encourage them to consider the full range of constructs involved (e.g., very high to very low; e.g., [27]). For example, if some participants indicate that strong loyalty programs lead to higher market shares, it is useful to ask subsequent participants (or the same participants later in course of the conversations) about the consequences of having weak loyalty programs along with the reasons for those consequences. If participants indicate that one of the consequences is low market share and they provide the same argument for it, documenting this information is likely to increase the theory's credibility. Finally, we recommend comparing across participants. To the extent multiple participants suggest the same theory, its credibility is enhanced.
Transferability is analogous to external validity, and in the context of TIU-based research, it refers to the extent to which a new theory's constructs and if-then propositions are valid in contexts not included in the data used to develop the theory. Researchers can increase confidence in the transferability of their new theory through appropriate theoretical sampling of participants in their studies. As a tentative theory emerges in the course of conversations with participants, researchers can select as the next set of participants those for whom the theory may not hold (e.g., participants in different types of firms, industries, geographic locations; participants with different experiences; e.g., [ 4]). To the extent the next wave of participants suggests the same emergent theory, it increases confidence in the transferability of the theory. If the subsequent participants suggest different theories, it would indicate the need for a resolution, generally through the incorporation of one or more moderators and/or inclusion of additional antecedents/consequences.
Dependability is analogous to reliability, and in the context of TIU-based research refers to the extent to which multiple researchers ("multiple human instruments" per [19], p. 241]) involved in a TIU study find the same constructs and if-then propositions from the same data.[14] This may be assessed through comparison across researchers. To the extent multiple researchers processing the same data converge on the same theory, its dependability is enhanced.
Confirmability is analogous to objectivity, and in the context of TIU-based research refers to the extent to which a new theory's constructs and if-then propositions can be independently certified as emerging from the data (rather than from researchers' predispositions, interests, and motivations). Researchers can demonstrate confirmability by documenting participant checks that are similar to member checks suggested by [29]. Researchers may present their emerging (as well as eventual/final) theory to TIU research participants and ask them whether it is consistent with their views (as well as invite comments/remarks).
Researchers can also demonstrate confirmability by documenting agreement between two or more independent judges (i.e., knowledgeable individuals who are not involved with the research) about the new theory's correspondence with the data used to develop it. For example, researchers using a TIU approach typically develop abstract constructs from specific data (instances, examples) provided by participants. In such cases, researchers can demonstrate confirmability through interjudge reliability. This involves researchers providing two or more judges the raw/verbatim data (or a random sampling thereof) and the names of their abstract constructs and having them code the raw/verbatim data into the constructs. Following this, interjudge agreement may be computed (e.g., using proportional reduction in loss proposed by [50]]). Similarly, researchers can demonstrate confirmability by documenting agreement between two or more independent judges asked to indicate the extent to which a theory's if-then propositions correspond to the data from which they were created. Researchers can also demonstrate confirmability by providing thick descriptions of their data (e.g., verbatim participant quotes) in their reports to enable readers of the theory to do a direct assessment of the extent to which theoretical constructs, propositions, and arguments advanced in the theories are consistent with the raw data used to construct them.
As noted previously, data from TIU research participants can stimulate a researcher to develop if-then propositions that were not cited or directly suggested by any of the participants. Deviations from the data provided by participants also arise when a researcher develops a theory incorporating constructs at different levels of abstraction than those stated by participants. In such cases, we caution against strict adherence to the confirmability criterion and instead suggest using theory credibility as the more important criterion. This is because the central purpose of using TIU for theory construction is to develop new theory that accurately explains a phenomenon of interest, not one that is an accurate restatement of data provided by participants.
Distinctiveness is analogous to discriminant validity, and in the context of TIU-based research, it refers to the extent to which a new theory's constructs and if-then propositions are different from existing constructs and if-then propositions in the literature. Because it is counterproductive to introduce new labels for existing constructs, it is important to ensure that new constructs in a theory refer to different phenomena than existing constructs. Construct distinctiveness may be demonstrated by definitional comparisons—comparing the proposed definition of a new construct with definitions of existing constructs that are closest to the meaning of the new construct. Proposition distinctiveness refers to the extent to which if-then propositions differ from theoretical propositions already available in the literature. Propositional distinctiveness may be demonstrated by documenting closely related existing propositions individually or in summary form and visually showing the differences between them and the new theory (e.g., in a table).
We argue that while all five criteria are useful for evaluating a new theory, the primary emphasis should be on credibility, transferability, and distinctiveness. Dependability and confirmability are good virtues, but not as pertinent as credibility, transferability, and distinctiveness. This is because the end goal of TIU research is the development of a new theory that can explain a phenomenon across multiple contexts; it is conceivable that researchers may develop such a theory even when it is somewhat lower on interresearcher reliability (dependability) and interjudge reliability (confirmability).
Importantly, the quality of a theory based on TIU of participants is likely to be influenced substantially by the quality of theories held by the participants. As noted previously, researchers should take care to sample participants who are likely to be knowledgeable about the phenomenon being studied and also willing to share their knowledge with the researchers. By documenting the participants' qualifications, researchers can engender greater confidence in the theories they develop using the TIU approach (see Table 7).
Graph
Table 7. Glossary of Terms Used.
| Term | Description |
|---|
| Abstraction | The process by which a researcher identifies a more general idea from granular/particular data. |
| Axial coding | The process of relating categories (constructs) to other categories, thus delineating antecedents, consequences, and moderators. This information can be assembled in the form of a coding scheme or a visual picture of the process with arrows indicating the direction of the process (see Strauss and Corbin [1997]). |
| Confirmability | The extent to which a new theory's constructs and if-then propositions can be independently certified as emerging from the data, rather than from researchers' predispositions, interests, and motivations. |
| Credibility | The extent to which the "if-then" propositions that constitute a theory are plausible (i.e., are supported by persuasive arguments). |
| Deductive research | The deductive research approach starts with a set of accepted concepts and propositions and deduces that if these propositions are true, and if certain other conditions are met, certain specific and observable events will also occur (Zaltman, LeMasters, and Heffring 1982). |
| Dependability | The extent to which multiple researchers ("multiple human instruments," per Hirschman [1986]) involved in a TIU study are likely to find the same constructs and if-then propositions from the same data. |
| Espoused theories | Mental maps or theories that individuals claim to follow (Argyris and Schon 1974). These may be different from their TIU. |
| Grounded theory | Theory discovered through an iterative process by which a researcher becomes more and more "grounded" in the data, and develops increasingly rich concepts and models of how the phenomenon being studied really works (Glaser and Strauss, 1967). |
| Hunting | Energetically seeking and processing data in quest of theoretical insights. |
| Inductive research | Inductive research is concerned with the generation of new theory for which little or no previous formal theory exists. The research questions are more open-ended where theory is nascent or immature. "The inductive mode stresses the formal or informal accumulation of data, which may lead to tentative theory" (Zaltman, LeMasters, and Heffring 1982, p. 98). |
| Laddering | An in-depth interviewing technique used to develop a deeper understanding of how consumers translate the attributes of products into meaningful associations with respect to self. Laddering is based on the means-end theory (Gutman 1982) and "involves a tailored interviewing format using primarily a series of directed probes, typified by the 'Why is that important to you?' question, with the express goal of determining sets of linkages between the key perceptual elements across the range of attributes (A), consequences (C), and values (V)" (Reynolds and Gutman 1988, p. 12). |
| Means-end chain | A qualitative approach that uses methods such as laddering to understand how consumers link specific attributes of a product with the desired consequences and how these consequences link to their values (for examples, see Macdonald, Kleinaltenkamp, and Wilson [2016]; Zeithaml [1988]). |
| Open coding | The process of identifying and categorizing elements (concepts) in words, phrases, sentences, and more aggregate forms of data (see Strauss and Corbin [1997]). |
| Selective coding | The process of unifying the different categories identified in open and axial coding around a core category. The core category may emerge from amongst the categories already identified and/or may be the result from an abstraction of those categories (see Corbin and Strauss 1990). |
| Theoretical sampling | "Theoretical sampling is the process of data collection for generating theory whereby the analyst jointly collects, codes and analyzes his data and decides what data to collect next and where to find them, to develop his theory as it emerges. The process of data collection is controlled by the emerging theory, whether substantive or formal" (Glaser and Strauss 1967, p. 45). |
| Theoretical saturation | The stage in qualitative research where a researcher concludes that continued collection and analysis of data is unlikely to yield new themes, categories, or substantive insights. |
| Theory building | Theory building is "the development and use of interrelated ideas for purposes of explaining, predicting, and /or controlling event" (Zaltman, LeMasters, and Heffring 1982, p. 177). |
| Transferability | The extent to which a new theory's constructs and if-then propositions are valid in contexts that are not a part of the current study's data collection efforts. |
Like all research approaches, TIU has limitations and challenges. First, TIU, as a technique used largely for theory construction, is not suited for theory testing. However, as we have shown, TIU can be a terrific setup for guiding downstream theory-testing efforts. Second, researchers often lack (but can still acquire) the requisite skill and experience needed for doing successful interviews with key informants. Using the recommendations in this article is a good start. Next, reading the TIU research delineated in our Tables 2 and 3 will help. Finally, practicing interviews with other researchers using TIU can prepare a researcher to conduct interviews with actual participants.
Third, TIU works only when informants have sufficient knowledge and experience. For relatively new phenomena (e.g., a firm operating as a platform as well as a supplier on the platform), participants are unlikely to have well-developed theories about their long-term effects and/or the conditions under which the effects are likely to be strong or weak. Participants in these situations may still espouse theories, but they are less likely to be the product of thoughtful processing of meaningful experience. An idea about a relatively unfamiliar issue could be an uncertain participant's guess as opposed to a highly relevant but newly discovered "aha."
As we have noted, the discipline of marketing is at the crossroads. Others have suggested that if we continue on our current trajectory, we will simply accelerate our path to irrelevance ([47]). One promising method to increase relevance to all stakeholders in the marketing system is a TIU approach. Relevant stakeholders may be managers aiming to improve practice, consumers aiming to enhance their consumption experiences, and/or policy makers aiming to improve society. In this section, we turn our attention to three specific areas of future research. The first two future research areas focus on direct applications of TIU. The first application area is non-domain-specific. Here, the emphasis is on identifying "meta issues" that can richly inform any subfield of marketing (e.g., when stakeholders disagree, when core assumptions underlying a body of work are questionable), whereas the second is focused is domain-specific (e.g., role of marketing in the firm, organic growth, digital transformation). The third category involves research on TIU as a method.
In this section, we consider research topics that could apply to any field or subfield of marketing. In a sense, these topics are "meta" questions that can guide researchers in selecting topics specific in their area of specialization. Next, we explore three such issues.
An assumption is a hypothesis that is taken for granted. A theory built on an assumption that is not fully explored may be incomplete or may even contain errors. The published literature often identifies and debates such assumptions. For example, the literature on market orientation currently has two dominant perspectives—one focusing on processing marketplace information ([27]) and the other focused on a market-oriented culture ([38]). However, both perspectives assume that understanding customer needs and putting customers at the "center of your business" is essential for success. An interesting question to be explored using a TIU approach would be "When do customer needs not matter?" or "Under what conditions should the customer not be at the center of the business?" The notion of building businesses around customer is at the heart of our discipline, yet it could be challenged by examining successful businesses that have taken a different approach. From a public policy perspective, there is an assumption that it is "always" best build a business around a customer; however, this assumption can also be reexamined. When do customer-oriented businesses increase consumer costs and lessen customer satisfaction?
A TIU approach can be very productive when a firm's or even industry's "theory" of its behavior in the marketplace is at odds with their customers' "theory" of the firm's or industry's intentions and actions. Comparing manager theories or maps of their actions with those customers hold about the same actions can help a firm or industry achieve a better alignment with its customer base. A contemporary example involves current viewpoints regarding the pricing of drugs in the pharmaceutical industry. Here there are conflicting views held by firms (e.g., high prices support the portfolio of R&D efforts, some of which work and others do not), policy makers (e.g., consumer affordability), and customers (e.g., price gouging).
A TIU approach may make clear where key stakeholders agree or disagree and what options exist that can foster agreement among them. These are common situations in the public health, political, and nonprofit marketing settings. Very few articles using a TIU approach exist in the public policy domain, yet many agencies' stakeholder interests are in conflict in that domain. For that reason, this is an especially promising domain for organic theory construction.
As we discussed previously, many sources exist to identify content domains suited to TIU-based research. These include Marketing Science Institute, industry-specific surveys of "hot topics," trade association agendas, public policy agencies' grant funding priorities, and the American Marketing Association. Many of these institutions also provide researchers with direct access to subject matter experts and offer platforms for sharing research findings. While the list of future research content domains is lengthy, we focus on a few topics that can be richly explored using the TIU approach.
We find it curious that a dominant view exists in marketing that "best-practice marketing" entails segmenting markets, selecting target segments, developing differentiating value propositions, and then activating with a marketing mix. Any or all of these basic steps could be challenged using a TIU approach. For example, under what conditions does segmentation still matter, and when is segmentation inappropriate? When do differentiated value propositions decrease, rather than increase, sales? And, thinking more broadly about the function, when should marketing "not have a seat" at the table in business unit strategy discussions?
The litmus test for any high performing chief executive officer, general manager, or brand manager is year over year profitable organic growth. The problem in our discipline is that we often approach growth as a marketing issue. However, from a firm perspective, the issue is how to integrate all back office and commercial functions to drive organic growth. Marketing is only one piece of this puzzle. When should marketing play a prominent (or less prominent) role in shaping the growth strategy? When is it appropriate to have "chief growth officers" lead these growth efforts? What role should marketing assume when a firm decides to hire chief growth officers—and not chief marketing officers?
This topic is front and center for most Fortune 500 firms, yet little theory exists to guide firm actions in structuring market communications, collecting consumer intelligence (e.g., traditional research methods plus digital footprints), or building customer-facing digital platforms (e.g., General Electric's recent unsuccessful attempt to build a client-facing platform for the industrial internet). Research in this domain could also closely examine the implications of digital transformation for the marketing organization within a firm. For example, whereas social media is largely viewed as an avenue for advertising and promotions, several firms are actively using social media channels for customer service, direct sales, and market research (see, e.g., efforts of KLM, the Snickers "Hungerithm" campaign).
Digital transformation of industries and markets requires executives to rethink next-generation marketing resources, capabilities, and skills their companies need to secure and grow to engage with customers in new and meaningful ways in the digital age. For example, companies today increasingly focus on rolling out new subscription-based business models. In line with this fundamental trend, a growing number of firms invest in new organizational functions, such as customer success management; they hire new staff across all hierarchical levels, from vice presidents of customer success to customer success associates. Clearly, key decision makers add new customer-facing roles and responsibilities to complement others in existing areas, such as customer experience management or key account management. What are executives' mental models underlying such decisions? Which TIUs guide managers in growing these novel marketing competencies? Research in TIU is well positioned to shed new light on this growing managerial practice.
With the 2018 emergence of General Data Protection Regulation standards in Europe, the California Consumer Privacy Act becoming law in January 2020, and current debates in Congress on the possibility of a National Commission on Public Privacy, we are witnessing an acceleration in the debate and implementation of privacy policies. The aim is to protect consumers at multiple levels—by including access to personal data (e.g., health records finance), limiting hacking, and, more generally, maintaining personal privacy. A TIU approach would be particularly useful in assessing the trade-offs that consumers are willing to make regarding the balance of sharing versus protecting their personal information. This is important wherever paradox arises, such when consumers insist on greater protection of personal data while enjoying the benefits of more personally relevant information and firm offerings resulting from firms mining their personal data. A TIU approach can be valuable in surfacing moderators that help consumers resolve such paradoxes.
In the United States, a particularly contentious debate is unfolding regarding single-payer systems, the role of government in delivering health care solutions, and the overall cost of health care. While these are large, complex issues, behind the scenes there is a sense that there are two diametrically opposed worldviews that "set context" for the debates. One of the authors of this article has been involved in a TIU project aiming to better understand how Democrats and Republicans view health care disparities to overcome political gridlock. The overall objective was to understand the fundamental frames both groups used to understand health disparities and help develop a campaign that would push the issue forward without alienating either group. It was found that, contrary to public expressions, there were important commonalities as well as differences between the two parties that served as a shared foundation for discussing their differences.
A second health care topic is connected health care. Ensuring that patients take their medicine as prescribed and achieving compliance is both a societal goal and a company goal, but what about consumers' position? Increasingly the topic of connected health becomes intertwined with privacy concerns. Many firms now remotely monitor patient compliance through medical devices (e.g., sleep apnea machines with embedded chips) and, as a result, the patient, physician, channel intermediaries, and insurance firms all have access to patient data. The overall system improvements—reimbursement based on actual compliance, better patient flow management in doctor's office, and reduced labor costs for the channel—are all very positive. However, we do not have a deep understanding of the patients' positive and negative views on connected health care.
Increasingly governments are more involved in the day-to-day affairs of for-profit and nonprofit organizations. Despite the important role that regulation plays in improving the common good (e.g., pollution controls, environmental policies, land protection, water management), there are clear reasons for for-profit firms to oppose these regulations and/or actively lobby against them. These could be for economic reasons (e.g., adverse influence on their profitability) and/or for constituency reasons (e.g., a firm is based in a region that highly depends on that particular industry sector). As noted previously, TIU is particularly useful in situations where stakeholder views may differ—or even collide.
All research methods, including TIU, merit continual improvement. Each method has strengths and weaknesses in which further inquiry can refine or enhance strengths and diminish weaknesses. Next, we discuss four areas for future research on the TIU itself.
What topic and population factors influence desired sample size? When is redundancy in constructs and construct pairing most likely to occur, suggesting that further interviews may not be productive? Rules of thumb vary between 15 and 25 participants, but more systematic clarity is needed. This is critical because travel budgets, transcription costs, and researcher time are typically scarce resources, especially when multiple populations are involved (as is the case with cross-cultural research).
A special value of TIU is its ability to directly elicit the causal mechanisms—the "hows" and "whys" supporting particular construct pairings—present among theory holders. These, of course, are critical to any theory-building enterprise. More R&D is needed to document productive and unproductive elicitation techniques for particular populations and circumstances. For instance, children often have well-developed TIUs, but eliciting them is a special challenge requiring more novel probing and elicitation processes. Separately, some probing techniques may work best in face-to-face interviews but less well for those conducted online. These are all situations requiring more study.
Some topics are inherently more challenging than others for respondents to address. This is especially the case when a topic concerns socially embarrassing issues (e.g., personal hygiene) or involves considerable implicit thinking and tacit knowledge (e.g., knowledge that may not have been given much prior explicit thought by the participant). Such taken-for-granted experiences are circumstances where TIU is especially valuable. Research is needed to identify alternative ways of using TIU interview techniques for such instances.
The TIU approach is ideally suited to surface interesting, novel theories and concepts that can advance both marketing practice and scholarship. As such, the overall objective of this article is to inspire and provide guidance on the development of knowledge based on the TIU approach. A key message of this article is that while the TIU approach requires skill, tradecraft, and practice, it has resulted in multiple breakthrough, award-winning research articles (e.g., see Table 2). These articles represent important organic marketing theories that have paved the way for long-lasting research streams that continue to inspire scholarly research today.
While impact may be a sufficient motivation, there are two additional benefits of pursuing this approach. First, researchers using this approach often find that gleaning new insights this way is a special variant of fun. The fun involves the excitement of discovering something novel as well as getting closer to the marketing phenomena. Giving time to executives, consumers, and policy makers to explore their own thinking is also rewarding. Furthermore, having one's own ideas challenged by interviewees can shake a scholar out of the routine of reviewing literature written by other academics ([32]). Second, TIU research not only represents a great vehicle for bringing relevance to the classroom but also provides a platform for sharing real-time stories and challenges that are unfolding in practice. Moreover, in our experience, managers taking part in the research are often excited about the prospect of becoming long-term partners in the research and education process.
In conclusion, if the field of marketing is to continue to have relevance for the practice of marketing, we must develop ideas, concepts, and theories whose central focus is the study of marketing in its natural environment. Within this environment, managers, consumers, and policy makers are a wonderful source of new ideas, unconventional thinking, and ways of working that can fundamentally reshape our current thinking and theories. One can "go it alone" by reading marketing literature and coming up with ideas, or one can capitalize on the knowledge of managers, consumers, and public policy makers who are dealing with significant, underresearched challenges every day. We hope our team experience captured in this article will facilitate your focus on the latter!
Supplemental Material, jm.19.0111-File003 - A Theories-in-Use Approach to Building Marketing Theory
Supplemental Material, jm.19.0111-File003 for A Theories-in-Use Approach to Building Marketing Theory by Valarie A. Zeithaml, Bernard J. Jaworski, Ajay K. Kohli, Kapil R. Tuli, Wolfgang Ulaga and Gerald Zaltman in Journal of Marketing
Footnotes 1 Authors' NoteAuthor ordering is alphabetical except for the first author, who also served as the team leader.
2 EditorsRobert W. Palmatier and Christine Moorman
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242919888477
5 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
6 1We thank John Sherry for his helpful comments in this section.
7 2If the core construct to be investigated is not yet clear, participant conversations may suggest a different construct that the researcher may ultimately decide is more fruitful to pursue than the one they started with.
8 3We should note here that there is nothing wrong per se with relying on a particular participant's TIU. However, our experience is that participants rarely hold theories that are formed well enough to be suitable for publication in academic journals.
9 4This guide should be provided in the published article.
5The quotes in the current research are approximate, and some examples are stylized.
6For a discussion of two specialized techniques for theory construction (the Kelly Repertory Grid and the Zaltman Metaphor Elicitation Technique), see the Web Appendix.
7Nonetheless, it may be useful to briefly note antecedents, consequences, or moderators that may be obvious/intuitive but important such that a reader has a more complete understanding of the phenomenon of interest.
8This differs from the meaning of credibility in classical interpretive research, where it refers to the consistency between the account provided by a researcher and the data. This difference arises because in TIU research the focus is on if-then propositions and their credibility (more than that of a researcher's account of participants' input).
9This differs from the meaning of dependability in classical interpretive research, where it refers to the stability or consistency of participant reports about a phenomenon across time. This difference arises because, in TIU research, the focus is on if-then propositions and their dependability (rather than that of each participant's report).
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Record: 12- A Theory of Customer Valuation: Concepts, Metrics, Strategy, and Implementation. By: Kumar, V. Journal of Marketing. Jan2018, Vol. 82 Issue 1, p1-19. 19p. 3 Diagrams, 1 Chart. DOI: 10.1509/jm.17.0208.
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A Theory of Customer Valuation: Concepts, Metrics, Strategy, and Implementation
Customer value refers to the economic value of the customer’s relationship with the firm. This study approaches the topic of customer value for measuring, managing, and maximizing customer contributions by proposing a customer valuation theory (CVT) based on economic principles that conceptualizes the generation of value from customers to firms. The author reviews the established economic theories for valuing investor assets (e.g., stocks) and draws a comparison to valuing customer contributions. Furthermore, the author recognizes the differences in the guiding principles between valuing stocks and valuing customers in proposing CVT. Using CVT, the author discusses the concept of customer lifetime value (CLV) as the metric that can provide a reliable, forward-looking estimate of direct customer value. In addition, economic models to estimate CLV, ways to manage CLV using portfolio management principles, and strategies to maximize CLV are discussed in detail. The author extends the customer value concept by discussing ways that a customer can add value to the firm indirectly through incentivized referrals, social media influence, and feedback. Finally, the benefits of CVT to multiple constituencies are offered.
Any sustainable business first creates value for its customers through firm offerings1 and, in the process, derives value from its customers in the form of profit.2 This duality of roles performed by firms and customers to derive and deliver value best summarizes the firm–customer relationship from a value standpoint. However, this value is distributed heterogeneously across customers. Because it is the firms/decision makers who allocate resources to markets, customers, and products, the challenge in this context for firms is to dynamically align resources spent on customers and products to simultaneously generate both value to customers and value from customers. In addition, the volatility and vulnerability in customer cash flows differentially affects overall firm profitability. These changes can be due to both customer actions and firm actions. For instance, especially in business markets, leadership change in the customer organization influences procurement decisions that can result in changes in future cash flows. Similarly, customer life events (e.g., getting divorced, becoming empty nesters) can also affect future cash flows. Therefore, firms look for ways to better manage cash flows (Srivastava, Shervani, and Fahey 1999).
To better understand and manage the value creation and cash flow management process, this article proposes a theory to value future customer contributions: the customer valuation theory (CVT). The CVT focuses on two aspects of customer financial contributions: their nature (i.e., direct and indirect) and their scope (i.e., breadth and depth). In doing so, the CVT informs firms about ( 1) the conceptualization of value generation from customers and ( 2) the ways and means available to generate and maximize value from customers. In this regard, the power of the customer lifetime value (CLV) metric to accurately value a customer’s future contributions is established. By applying the CLV metric, this study demonstrates how firms can use CVT to ( 1) value customer assets, ( 2) manage customer portfolios, and ( 3) nurture profitable customers. The robustness of the CVT is also highlighted through its successful implementation across various types of markets (consumer and business), scenarios (contractual and noncontractual business settings), contexts (domestic and global), and industries (e.g., insurance, airline, retail). Furthermore, the widespread positive impact of implementing CVT on multiple constituents of the market such as the firms, the customers, the employees, the society, and the environment is also identified.
What is an asset? Despite the conflicting viewpoints on the role and constituents of an asset or firm resource (Mahoney and Pandian 1992), an asset can be broadly defined as any physical, organizational, or human attribute that enables the firm to generate and implement strategies that improve its efficiency and effectiveness in the marketplace (Barney 1991). Given the prominence of assets in deploying firm strategies and gaining competitive advantage, the next question is, What makes them valuable? Here too, scholars from diverse fields such as finance, industrial organization, management, and economics have presented several approaches to decode an asset’s value. With specific reference to marketing, academics and practitioners consider customers as assets and have instated strategies that manage and nurture customers rather than use them only for specific marketing actions. In this regard, studies have investigated customer asset valuation from various viewpoints such as firm valuation (Gupta, Lehmann, and Stuart 2004; Kumar and Shah 2009), customer management (Berger et al. 2002), and financial performance (Hogan et al. 2002), among others. The differing approaches to asset valuation notwithstanding, the true value of an asset is most often observed in its interactions in the external marketplace. This leads to the next question: How are assets valued? To better understand the approach to value assets, consider two scenarios—an investor investing in stocks, and a firm investing in its customers.
In the case of the investor (which applies to both individual and institutional investors), the valuation of stocks forms a crucial component. Drawing on the valuation, the investor typically performs three routine actions: (s)he ( 1) selects and invests in stocks that show potential for growth, ( 2) constructs a portfolio of stocks and bonds, and ( 3) constantly rebalances the portfolio to ensure maximum future gains. An important feature common to all these actions is the element of risk. This is because risk affects the volatility and vulnerability of cash flows, which, in turn, affect the stock value and ultimately reflect in the overall firm value.3 As a result, investors aiming to avoid risk ideally should ( 1) invest in stocks that indicate a steady stream of cash flow, ( 2) construct a portfolio of similar stocks so that their overall future value is secured and maximized, and ( 3) constantly evaluate the earnings potential of the stocks in their portfolio and reconfigure the portfolio by selling risky stocks and buying robust stocks.
To assist investors in the stock valuation process, several approaches, such as the discounted cash flow models, capital asset pricing models, and the arbitrage pricing theory, among others, are available. The resulting stock valuation would then inform investors about the configuration of the portfolio using approaches such as the modern portfolio theory (Elton et al. 2014).
In the case of the firm investing in its customers, the valuation of customers also forms a crucial component. If the principles of the stock valuation approach were applied to manage investments in customers, firms ideally should perform the following three actions: ( 1) identify and invest in the “right” customers, ( 2) form a customer portfolio (or customer base) consisting of favorable customers, and ( 3) constantly reevaluate the portfolio to ensure that the firm is maximizing its future gains. In reality, performing each of these three actions is not always possible because of certain challenges.
The comparison between the case of the investor and the firm shows the similarity in how they manage their respective assets (stocks vs. customers). So, can the principles that guide the valuation of stocks be applied in valuing customers? To answer this question, the challenges of valuing customers must first be understood.
First, firms need a reliable method to identify and invest in the “right” customers. While it may seem straightforward to say that the “right” customers are the ones who exhibit the highest value potential, the specifics may not be so apparent. Traditionally, value measures have focused on repeat purchases, acquisition cost, retention cost, tenure, and share of wallet, among others. Thus, a valuation approach that can accurately capture these intricacies for firms to identify the most valuable customers is essential.
Second, configuring a portfolio of the most valuable customers is easier said than done. The customer characteristics (e.g., consumption pattern, lifestyle habits) that determine their value potential have been found to change over time (Kumar 2013). As a result, periodic evaluation of customer value measures (e.g., profitability) is a necessary and reliable method to help managers in the vital job of keeping track of changing customer characteristics. Furthermore, regulated monopolies such as telephone and municipality services are often required to cross-subsidize one group of customers with another (e.g., rural and urban customers). In such cases, firms may not be able to build the ideal portfolio of customers.
Third, rebalancing of customers is not a feasible strategy. Although cases have been reported in which companies have fired customers because profitability concerns (Reardon 2007), this is not a common practice. For instance, many banks are unable to “fire” the unprofitable customers, especially if they are from lower socioeconomic groups or minority groups. In addition, churn is a challenge that firms constantly face. While a firm may want to have profitable customers, holding on to those customers typically poses challenges for the firm. Therefore, firms need a reliable way to discern how to manage unprofitable customers as well as ways to nurture profitable customers.
Therefore, in this study, I present the case for a valuation approach that is specifically designed to value customer assets. In doing so, I demonstrate why the stock valuation approach is not readily applicable to valuing customer assets and show how customer assets are uniquely positioned to provide value to the firm.
TABLE: TABLE 1 Summary of Select CLV Modeling Approaches
TABLE: TABLE 1 Summary of Select CLV Modeling Approaches
| Estimating Models Independently (Venkatesan and Kumar 2004) |
| Model Form | CLVit where GCi, t = gross contribution from customer i in purchase occasion t, MCi, m, l = marketing cost, for customer i in communication channel m in time period l, fi (or frequency) = 12/expinti (where expinti is the expected interpurchase time for customer i), r = discount rate, n = number of years to forecast, and Ti = number of purchases made by customer i. |
| Merits | • Accounts for customers to return to the firm after a temporary dormancy in a relationship • Aids resource allocation decisions on marketing communication channels |
| Shortcomings | • Does not account for competitive responses and consumers’ brand-switching behavior |
| Estimating Models Simultaneously (Venkatesan, Kumar, and Bohling 2007) |
| Model Form | Likelihood function: L = fni where f (tij|a, li, g) = the density function for the generalized gamma distribution; S (tij|a, li, g) = the survival function for the generalized gamma distribution; p (DQ|di, d*, s2) = the density function for purchase quantity; and cij = the censoring indicator, where cij = 1 if the jth interpurchase time for the ith customer is not right-censored and cij = 0 if the jth interpurchase time for the ith customer is right-censored. |
| Merits | • Accounts for endogeneity and heterogeneity • Provides more accurate results than independent estimation |
| Shortcomings | • Model development and estimation is complex |
| Brand-Switching Approach (Rust, Lemon, and Zeithaml 2004) |
| Model Form | CLV where Tij = number of purchases customer i makes during the specified time period, dj = firm j’s discount rate, fi = average number of purchases customer i makes in a unit time (e.g., per year), Vijt = customer i’s expected purchase volume of brand j in purchase t, pijt = expected contribution margin per unit of brand j from customer i in purchase t, and Bijt = probability that customer i buys brand j in purchase t. |
| Merits | • Can be used when the firm has cross-sectional and longitudinal database • Accounts for all types of marketing expenditures • Can accommodate competition |
| Shortcomings | • Sample selection can play an important role in the accuracy of the metric • Often relies heavily on survey based data, thus leading to an increase in sampling cost and survey biases. |
| Monte Carlo Simulation Algorithm (Rust, Kumar, and Venkatesan 2011) |
| Model Form | where Purit is the indicator of purchase and is equal to 1 if customer i purchases from the firm in time t, and 0 otherwise. |
| Merits | • Better predictive power over simpler competing models • Better understanding of customer profitability and firm value |
| Shortcomings | • Cannot be used in a lost-for-good setting • Heavy reliance long purchase histories |
| Customer Migration Model (Dwyer 1997) |
| Model Form | CE where MMt is a matrix that contains the probabilities of customers moving from one segment to another at time t, Ct is a vector containing the number of customers in each segment at time t, and Pt is the profit from each segment at time t. |
| Merits | • Considers probabilistic nature of customer purchases |
| Shortcomings | • Can be used only in limited business settings |
| Model Form | where i = the period of cash flow from customer transaction, Ri = revenue from the customer in period i, Ci = total cost of generating the revenue Ri in period i, and N = the total number of periods of projected life of the customer under consideration. |
| Merits | • Higher predictive accuracy • Aids in firm-level strategy development |
| Shortcomings | • Requires huge amounts of individual customer data • Does not consider the relationship between model parameters • Descriptive, but not prescriptive, and therefore less helpful in managerial decision making • Does not account for competition |
| Probabilistic Model (Dr`eze and Bonfrer 2009) |
| Model Form | CLVt where t = a fixed time interval between contacts, A(t) = expected surplus from communications following the interval, and p(t) = probability of retention given that interval. |
| Merits | • Can be used when the firm does not have longitudinal database • Identification of subdrivers aids in better resource allocation |
| Shortcomings | • Assumes purchase volume and interpurchase time to be exogenous • Calls for frequent updating of the model • Heavy reliance on data and less reliance on managerial insight |
| Structural Model (Sunder, Kumar, and Zhao 2016) |
| Model Form | Uit where Uit = overall utility from consumption by consumer i at time t, yijt = baseline utility, yit = unobserved budget allocation within category by consumer i at time t, Pjt = price of brand j at time t, and qijt = quantity of brand j consumed by consumer i at time t. The qijt can then be used in the assessment of CLV. |
| Merits | • Model based on theoretical underpinnings of consumer behavior • Can account for various salient aspects of consumer behavior (e.g., multiple discreteness, budgeting etc.) which cannot be addressed by other methods • Aids in accurate out of sample prediction and managerial policy simulations |
| Shortcomings | • Model development and estimation is very complex • Relies heavily on across and within variation in customer purchases |
The theoretical underpinnings of how an investor values stocks and a firm values its customers can be understood through the following questions:
• How do firms view customer assets?
• Why are financial theories inappropriate for valuing customers?
• How does customer valuation work?
How Do Firms View Customer Assets?
Research on customer asset management dates to models that explored how consumers make purchase decisions (Howard and Sheth 1969). The research insights generated since then have led to the consideration of customers as integral to organizations (Gupta and Lehmann 2005; Shah et al. 2006). In this regard, studies have considered customers to be intangible assets of a firm (Hunt and Morgan 1995; Srivastava, Shervani, and Fahey 1998) and have proposed approaches to valuing and managing their contributions to the firm (Bolton, Lemon, and Verhoef 2004; Reinartz and Kumar 2000).
Studies have also focused on applying customer value to enhance firm performance from various perspectives such as the role of customer acquisition strategies (Lewis 2006), customer retention strategies (Reinartz, Thomas, and Kumar 2005), customer loyalty (Reinartz and Kumar 2002), customer satisfaction (Anderson and Mittal 2000), resource allocation (Petersen and Kumar 2015), and customer metrics (Petersen et al. 2009; Srinivasan and Hanssens 2009), among others. Such attempts continue to shape profitable customer management (based on future customer profitability) for both contractual and noncontractual business settings.
Research on customer value has also explored the volatility and uncertainty of future revenue contributions. In this regard, studies have identified that certain behavioral drivers on the part of customers (e.g., level of purchases, product return behavior) determine the level and volatility of cash flows (Kumar, Shah, and Venkatesan 2006; Reinartz and Kumar 2003). Therefore, the management of behavioral drivers is critical in valuing customers.
Why Are Financial Theories Inappropriate for Valuing Customers?
Prior studies have investigated the application of financial theories to marketing decisions. For instance, Cardozo and Smith (1983) proposed an approach for making product portfolio decisions by applying the financial portfolio theory. However, Devinney, Stewart, and Shocker (1985) highlighted critical differences regarding applying a financial theory in a product decision setting. Similarly, Tarasi et al. (2011) demonstrated the application of financial portfolio theory for making customer portfolio decisions, which subsequently attracted critical review in the literature (Billett 2011; Selnes 2011). Other efforts that have incorporated concepts from financial theories into marketing applications include the introduction of customer beta that measures the riskiness of customers (Dhar and Glazer 2003), a customer relationship scorecard based on customers’ risk–return characteristics (Ryals 2003), and the management of customer segments using portfolio theory (Bolton and Tarasi 2015; Buhl and Heinrich 2008; Groening et al. 2014). In light of these efforts to apply financial theories for valuing customers, some principal differences between finance and marketing must be noted. Figure 1 illustrates these differences as well as how they ultimately affect firm value. They are as follows:
• It is possible to invest money into stocks and achieve a higher amount in return. However, this is not possible in the case of customers. Firms know that beyond a certain point, investing more money in their customers will yield a lower rate of return. In other words, whereas the investment-to-earnings relationship can be linear in the case of stocks, it is nonlinear in the case of customers.
• Investors can be fairly certain about a stock’s “life expectancy” and the survival of that firm. However, firms that invest in customers can make no such conclusion about their customers. In other words, investors have relatively more information about how long the stocks in which they have invested will remain in trading, compared with firms’ knowledge of how long their customers will remain their customers.
• In the case of widely held stocks (i.e., those that are not closely controlled by investors and fund owners), if a stock begins to perform poorly, the investor has the option of quickly divesting that stock. After divesting, the investor has the option of either buying another stock or just holding on to the cash. However, firms typically do not have prior information about how much value a customer is going to bring in. Furthermore, changes in customer lifestyles may make customers less profitable or may even cause losses. In such a case, the firm typically does not “divest” of such loss-making customers but has to find ways to manage them appropriately. This is also true in certain business settings (e.g., oil industry) in which it is difficult for suppliers to exit a customer relationship. In some cases, such situations can lead to speculative business practices such as stockpiling. In other words, the value and volume of investments in stocks is easily scalable, compared with the investments in customers.
• Investors routinely buy and sell stocks that will increase the value to the portfolio and/or minimize the risk of losing value.
However, firms that invest in customers do not have the luxury of hiring and firing customers. As a result, a low-value customer is still likely to be part of a firm’s customer portfolio, and firms will have to find ways to manage these customers in such a manner that they do not lose firm value. In other words, rebalancing a stock portfolio is easier than rebalancing a customer portfolio.
• Although investors value their investments in widely held stocks in line with the projected cash flows of the respective stocks,4 firms that have invested in their customers assess the value of their customers on the basis of customer contributions to firm revenue, which determines the stock price and, ultimately, the firm’s value. Recognizing this chain of impact is important in developing a metric that can effectively track the firm’s value creation. In other words, whereas the impact of investing in customers can be observed on the value of stocks (and, ultimately, on firm value), investing in widely held stocks has a limited observable impact on the value of customers.
• Using financial theories, it is possible for investors to identify the types of risks to which stocks are exposed (e.g., given the economic cycles) and recognize the ones that can be diversified from the ones that cannot. However, firms that have invested in their customers cannot readily identify the risks from customer contributions, or their impact on profitability. In other words, it is relatively easier to identify (and therefore manage) risks arising from investments in stocks compared with investments in customers.
• Investor sentiments play an important role in investment decisions (Weber and Johnson 2009). Stock market operations involve investor sentiments regarding a firm’s future performance expectations, which, in turn, determine the level of attractiveness of that firm in the industry. In contrast, the influence of investor sentiment is not a significant force when valuing a customer’s direct contribution. However, for customers who are based in politically unstable regions, the valuation is different more for sentimental reasons than for economic reasons. In other words, the importance of investor sentiment is higher in the valuation of stocks than in the valuation of customers.
• Speculation also plays an important role in investment decisions. For instance, speculation is based on a rational betting decision that is known to stabilize asset prices (Friedman 1953) and is sometimes based on insider trading (Kyle 1985). Furthermore, it is known that if the investment actions of rational speculators trigger the buying of securities when prices rise and selling when prices fall, then an increase in the number of forward-looking speculators can increase volatility about the asset fundamentals (De Long et al. 1990). In contrast, speculation does not play a major role when assessing the value of a customer. In other words, the importance of speculation is higher in the valuation of stocks than in the valuation of customers.
• Drawing on known future discounted cash flows, an investor typically decides on his or her choice of investment. That is, the options are limited to either investment or divestment. As a result, investors generally cannot influence the future cash flow patterns of a firm to change the course of their own actions. In other words, financial theories offer a passive approach to managing investments, whereas customer management requires an active management approach.
• The volatility and vulnerability of stocks make it difficult for investors to predict stock returns in the short run. Short-term returns are difficult to predict because of their random walk feature (Jensen 1978; Malkiel 1995). In addition, Kumar, Ramaswami, and Srivastava (2000) observe that daily returns are sensitive to random disturbances in the market. To predict stock movements, they offset the effect of random disturbances by considering a longer time period such as a month. Using the principles advanced by the capital asset pricing model and the arbitrage pricing theory, the study developed a multistage model to study variation in stock returns. The study found that the addition of significant factors other than the market factors (i.e., cost and supply of money) increases the level of risk, which results in a decrease in the price of the firm. In this regard, appropriately designed marketing actions directed at the environmental uncertainties (i.e., macroeconomic factors) can lessen the impact on the firm’s cash flows, because predictions of customer value are accurate in the short run. Therefore, estimating the value of customers and pairing this value with appropriately designed marketing strategies can place firms on the path to increased financial returns. The level of predictive accuracy of customer value, however, declines only in the long run. In other words, a more accurate prediction of stock returns is possible only in the long run, compared with the prediction of customer value, which is more accurate in the short run.
In addition to the financial theories, knowledge from behavioral finance is also relevant here. Behavioral finance literature has argued that some financial phenomena can reasonably be understood using models in which some agents are not fully rational (Barberis and Thaler 2003). With specific reference to investor behavior, behavioral finance has explained how certain investor groups behave, the types of portfolios investors hold, and investors’ trading patterns over time. Barberis and Thaler (2003) trace investor behaviors such as having insufficient diversification of portfolios (Baxter and Jermann 1997), opting for simpler diversification strategies (Benartzi and Thaler 2001), having high trading volumes (Barber and Odean 2000), holding on to stocks even if they are decreasing in value (Odean 1998), and considering “attention-getting” stocks for their purchase decisions (Barber and Odean 1999) to demonstrate that investors are not always rational in their investment decisions. This stream of literature has established that investors’ attitudes and behaviors are of vital importance. In light of the aforementioned differences between customers and stocks, I develop the CVT as a robust theory for valuing customer assets by adopting a different approach than that used to value stocks.
How Does Customer Valuation Work?
From the previous discussion of the determinants of customer assets, one of the approaches to value customers in a general form is presented next:
CFPi = f (Transaction behaviori, Marketing costi, Demographic=firmographic variablesi, Economic and environmental factors ), where CFPi refers to customer future profitability of customer i.
Simply put, the future profitability of the customers depends on their past and current transaction behaviors, the marketing efforts of the firm, the identity and profile of the customers (i.e., demographic variables), and the environment in which these customers exist (i.e., economic and environmental factors). Furthermore, when modeling CFP, statistical issues such as heterogeneity, endogeneity, and simultaneity are factored in the estimation of such models. Once the CFP is modeled, business intelligence software systems can be used to score CFP, update customer information, and rescore CFP on a periodic basis. Technology can also be incorporated to target customers on a real-time basis through relevant messages in an effort to increase future cash flows. Drawing on this valuation approach, I advance the following testable propositions.
Transaction behavior. Also known as exchange characteristics, the transaction behavior broadly includes all the past and current transaction variables that affect and influence the customer–firm relationship. The commitment-trust theory (Morgan and Hunt 1994) proposes that firms try to establish positive relationships with customers by developing commitment and trust with them. While a customer’s positive affect influences his or her commitment to the firm, research has also uncovered other dimensions of customer commitment. For instance, Allen and Meyer (1990) proposed a three-component model of commitment (affective, calculative, and normative). Recently, Keiningham et al. (2015) proposed customer commitment as a five-dimension construct (affective, normative, economic, forced, and habitual). Beyond repeat purchases, purchase habits have been found to play an important role in determining customer transaction behavior (Ascarza, Iyengar, and Schleicher 2016; Duhigg 2013). Specifically, research has reported that customer habits influence the future volatility and vulnerability of cash flows (Shah et al. 2017) and firm performance (Shah, Kumar, and Kim 2014). Furthermore, the importance of primary market research in understanding customer behavior patterns has also been highlighted, especially in light of the abundance of behavioral information made possible by big data (Knowledge@ Wharton 2014). As a result, frequent customer–firm interactions should increase customer trust and commitment in the firm at a faster rate, provided that the interactions are satisfactory. Therefore,
P1: Customer transaction activities significantly influence customer future profitability.
This proposition has been tested across various industries and markets (business to business [B2B] and business to customer [B2C]), and research has found that customer future profitability is positively influenced by a host of variables, including ( 1) prior customer spending level (Reinartz and Kumar 2000), ( 2) customer cross-buying behavior (Kumar, George, and Pancras 2008; Venkatesan and Kumar 2004), ( 3) the intensity of customer purchases within a product category (Reinartz and Kumar 2003), and ( 4) members of rewards programs with the firm (Kumar, Shah, and Venkatesan 2006). In addition, studies have also found the average interpurchase time and the number of product returns to have a significantly positive impact on customer future profitability, up to a certain threshold (Reinartz and Kumar 2003).
Marketing cost. Marketing cost can include past, current, and future promotional costs (toward customer acquisition, retention, and win-back); technology upgrades; service improvements; employee management; and quality control. The importance of effective management of customer assets to enhance firm profitability (Bolton, Lemon, and Verhoef 2004) has directed research attention toward understanding the impact of marketing expenditures on customer value and actively using marketing communication actions (i.e., customer contact channels) to maximize customer value. Prior research has established that well-timed communication efforts (Kumar, Venkatesan, and Reinartz 2008) and well-managed content (Kim and Kumar 2018) between firms and customers reduces the propensity of a customer to quit a relationship (Morgan and Hunt 1994). However, too much communication has also been found to be detrimental to the relationship (Fournier, Dobscha, and Mick 1998), thereby indicating the presence of an optimal communication level.
P2: Marketing cost nonlinearly influences customer future profitability.
Studies have revealed an inverted U-shaped relationship between marketing contacts (involving rich modes [e.g., sales personnel contact] and standardized modes [e.g., telephone, direct mail]) and customer behavior (Reinartz, Thomas, and Kumar 2005; Venkatesan, Kumar, and Bohling 2007). Furthermore, in a permission-based marketing context, a firm’s marketing contact policy influences both the length of time a customer stays in an email program and the average amount a customer spends on a transaction while (s)he is subscribed to the email program; too much marketing may not only make customers less likely to opt in but also cause them to opt out more quickly (Kumar, Zhang, and Luo 2014).
Demographic/firmographic variables. Demographic/firmographic variables refer to the distinguishing characteristics of the customer (end user or business customer). In the case of a business customer, the firmographic variables include the type of industry, the age and size of the firm, the level of annual revenue, and the location of the business. In the case of an end user, the demographic variables include age, gender, income, and the physical location of the customers. The demographic/ firmographic variables can aid firms in characterizing attractive segments into identifiable and measurable groups of customers (Zeithaml 2000). In the case of end users, the heterogeneity in profit contributions can be better understood through a customer’s demographic and psychographic variables (Chintagunta, Jain, and Vilcassim 1991); demographic variables affect customer store choice (Craig, Ghosh, and McLafferty 1984), shopping channel choice (Inman, Shankar, and Ferraro 2004), profitable lifetime duration (Reinartz and Kumar 2003), and migration of shopping choice (Thomas and Sullivan 2005), to name a few. As a result, classifying customers on the basis of their distinguishing characteristics can help firms in their customer segmentation and customer relationship management efforts. Therefore,
P3: Demographic/firmographic variables significantly influence customer future profitability.
The testing of this proposition has revealed that demographic variables such as household income, population density of the neighborhood, gender, age of the head of the household, marital status, education level, and so forth significantly affect future customer profitability (Kumar, George, and Pancras 2008; Kumar, Shah, and Venkatesan 2006). Furthermore, research has also established that firmographic variables such as the size of the firm and the industry category of the business customers significantly explained the variation in contribution margin for the focal firm (Venkatesan and Kumar 2004). Specifically, Venkatesan and Kumar (2004) found that the focal firm’s business customers in the financial services, technology, consumer packaged goods, and government industry categories provided, on average, a higher contribution margin than firms in other industry categories.
Economic and environmental factors. Economic factors such as gross domestic product (GDP) per capita help determine the consumption pattern of a country. It has been established that consumers’ response to macroeconomic factors is a function of not just their ability to buy (as measured by current and expected future income), but also their willingness to buy (Katona 1975). This underlying theory explains how various macroeconomic conditions affect price changes (Gordon, Avi, and Yang 2013), changes in consumers’ frame of mind (Chhaochharia, Korniotis, and Kumar 2011), and overall household utility (Kamakura and Du 2012), to name a few. Furthermore, research has also shown that changes in consumers’ economic constraints have varying effects on their profit-contributing potential. For instance, Sunder, Kumar, and Zhao (2016) demonstrated that high-CLV customers are least affected by changes in their budgetary constraints when compared with low-CLV customers. Therefore, if a country has a high GDP and high purchasing power, its consumers will have more disposable income and spend more.
P4: Economic and environmental factors significantly influence a customer’s future profitability.
Studies that have tested this proposition have found that GDP per capita, the country’s economic well-being (how customers feel about and experience their daily lives), cultural characteristics, and the employment rate significantly influence future customer profitability (Kumar and Pansari 2016b; Kumar et al. 2014; Umashankar, Bhagwat, and Kumar 2016).
In line with the customer valuation approach discussed previously, the CVT can be defined as a mechanism to measure the future value of each customer on the basis of ( 1) the customer’s direct economic value contribution, ( 2) the depth of the direct economic value contribution, and ( 3) the breadth of the indirect economic value contribution by accounting for volatility and vulnerability of customer cash flows. The key components of this definition include:
• Direct economic value contribution: This refers to the economic value of the customer relationship to the firm, expressed as a contribution margin or net profit. A firm can both measure and optimize its marketing efforts by incorporating customer value at the core of its decision-making process. When implemented at firms, it aids in ( 1) computing a customer’s future profitability, ( 2) arriving at a good measure of customer value, ( 3) optimally allocating marketing resources to maximize customer value, and ( 4) identifying ways to maximize the return on marketing investments.
• Depth of direct economic value contribution: This refers to the intensity and inclusiveness of customers’ direct value contributions to the firm through their own purchases that have produced significant financial results for the implementing firms. Examples of such instances include the acquisition and retention of profitable customers on the basis of their future value potential, customer purchase potential across multiple channels of buying, and the possibility of customers to buy across multiple product categories.
• Breadth of the indirect economic value contribution: This refers to customers’ indirect value contributions to the firm through their referral behavior, their online influence on prospects’ and other customers’ purchases, and their feedback on the firm offerings. These indirect measures also contribute significantly to a firm’s cash flow. This indirect contribution can also be extended to accommodate other contexts such as salesperson productivity, donations (in the case of nonprofit firms), and business references.
Because CVT enables managers to actively manage customer relationships on the basis of future customer contributions through specialized customer strategies, it creates a positive impact on firm performance. Specifically, the implementation of the CVT can help firms improve their marketing productivity and realize higher firm value through ( 1) valuing customers as assets, ( 2) managing a portfolio of customers, and ( 3) nurturing profitable customers. Therefore, it is critical to understand the nature of the linkage between CVT and firm value. Figure 2 provides an overview of how the components of CVT function in driving firm value.
In summary, the proposed CVT is relevant for the following reasons:
• Unlike prior marketing applications of the financial portfolio theory, this theory focuses on customer management at the individual customer level (on the basis of his or her profitability) instead of the customer segment level. Although customers are grouped in line with their profitability (i.e., high-, medium-, and low-profit segments), the subsequent strategies that are developed are always deployed at an individual customer level because this is more effective. Such an approach is different from how investors handle financial assets. The CVT implicitly accounts for customer risks (i.e., volatility and vulnerability in cash flows) when modeling future customer profitability. In doing so, the model focuses on how the risks ultimately affect customer profitability (and thereby, overall firm profitability) and treats it accordingly. For this reason, the CVT does not explicitly provide a beta value or any indicator for the risk-free return equivalent in the case of investments. Furthermore, the CVT enables managers to identify and manage the diversifiable risks through tailored offerings by focusing on the drivers of customer value.
• Because the CVT focuses on customer profitability, the variation in associated customer costs at the individual level are accounted for.
• The CVT is highly active in terms of generating actionable firm strategies. By dynamically managing the volatility and vulnerability in cash flows, this theory enables managers to actively monitor customer relationships over time and undertake necessary remedial measures. In comparison, the financial theories largely suggest that investors invest (or divest) at any point in time as determined by the discounted cash flow analyses. As a result, the financial theories remain passive by not providing adequate directions to investors such that they can influence future changes.
• Because future costs drive future margins, the CVT-based strategies advise managers to better manage their acquisition and retention of profitable customers; by doing so, firms can actively refine and manage the customer valuation approach. However, no such option is available with investing in stocks because stocks yield fixed returns, and the investor cannot influence or manage the return on a stock.
• The CVT recognizes the investment-to-earnings relationship, in the case of customers, to be nonlinear (unlike whenn valuing stocks), and it uses an appropriate customer valuation approach. This is subsequently reflected in customer strategies that can be developed.
The next component of the CVT delves into the concepts behind customer valuation and the related financial benefits they hold for firms. To contextualize the practice of customer valuation to the proposed theory, it is essential to review prior literature on this topic. In recent years, the idea of treating customers as assets of a firm has emerged as the most popular and efficient way of doing business (Hunt and Morgan 1995). This entails identifying future customer profitability and designing marketing guidelines that will advise managers on profitable customer management. Traditionally, firms have used several metrics to value customers (Kumar and Reinartz 2012; Petersen et al. 2009). The guidance from these metrics has driven decisions pertaining to the allocation of marketing resources.
When considering a customer’s value contribution to the firm, a crucial part is his or her contribution in the future periods. It is this future component that is of immense interest to academics and practitioners. The concept of future value contribution has been conceptualized in the form of the CLV metric, which refers to the present value of future profits generated from a customer over his or her lifetime with the firm (Gupta and Lehmann 2005; Venkatesan and Kumar 2004).
The conceptualization of CLV was strongly influenced by the corporate finance body of knowledge, and specifically to the contribution of the present value concept by Irving Fisher (1965 [1930]) and Eugene Fama’s asset pricing theory (Fama and Miller 1972). Applying this knowledge to customer asset management, it is evident that customers pose risks in terms of generating returns for the firm in the future. However, the impact of these risks on customer profitability is not uniform across all customers. In this regard, the CLV approach identifies opportunities to contain the variation in returns (also known as cash flow volatility) and, thus, the total risk of changes in the value of the firm (measured by future returns) (Shah et al. 2017). This is possible by understanding the drivers of customer profitability and their impact on CLV.
While the inspiration from other sciences such as economics and finance is apparent, the adaptation and inclusion into the customer asset valuation would not have been possible without the development of new methodologies specific to the marketing milieu. Specifically, the conceptualization of the CLV metric and the development of substantive methodologies served as a stepping stone for managing profitability by selecting the right customers for targeting and determining the allocation of resources for customer acquisition, retention, and growth. Furthermore, this important contribution also demonstrated that the CLV framework can help firms manage risk through appropriate actions directed at the individual customers.
Direct Economic Value Contribution
While the valuation of assets/projects is typically done at the aggregate level, the CLV metric enables firms to capture the direct value contribution of customers at both the aggregate and the individual levels. At the aggregate level, the average lifetime value of a customer is derived from the lifetime value of a cohort, segment, or even a firm. Here, the estimation of CLV can be accomplished by identifying and measuring the factors that drive CLV. At the individual level, the CLV is calculated as the sum of cumulated cash flows—discounted using the weighted average cost of capital—of a customer over his or her entire lifetime with the company (Kumar 2008). It is a function of the predicted contribution margin, the propensity for a customer to continue in the relationship, and the marketing resources allocated to the customer. It is important to note that weighted average cost of capital is one of the measures that represent the discount factor used to compute CLV, among other options (e.g., T-bills rate). In its general form, CLV can be expressed as follows (Venkatesan and Kumar 2004):
where i is the customer index, t is the time index, T refers to the number of time periods considered for estimating CLV, and d is the discount rate.5 The CVT proposed in this study is based on the individual-level CLV rather than the aggregate level.
Although a “true” CLV measure implies measuring the customer’s value over his or her lifetime, for most applications it is a three-year window.6 The reasons for this time frame are as follows: ( 1) because future cash flows are heavily discounted, a significant portion of profit can be accounted for in the first three years, and the contributions in the following years are very small; ( 2) the predictive accuracy of the models decline over a longer time frame; ( 3) changes in customer needs and life cycle are likely to change significantly beyond a three-year window;
( 4) product offerings change in response to technological advancements and customer needs; and ( 5) CLV predictions are updated on the basis of a rolling time horizon to accommodate changes in other environmental factors. However, specific industry trends do lead to some exceptions for this three-year window. For instance, automakers can expect customers to make a purchase every four to six years (we suggest using a longer window to accommodate at least a couple of purchases or using purchase intention measures to forecast future value), computer manufacturers can expect customers to make a purchase every one to two years (we suggest a four- to five-year window to account for at least two or three purchases); and insurance companies can take up to seven years to recover the acquisition costs. Exceptions aside, the aforementioned reasons advocate the use of a three-year timeframe in computing the CLV.
The extant CLV literature has covered a wide range of business conditions through the measurement approaches. This coverage has expanded the scope and application of CLV-based models for a multitude of industries and markets. Some of the later developments in modeling CLV have accommodated several modeling challenges, which has allowed for more sophisticated and precise estimation of customer value. Kumar and Reinartz (2016) provide a detailed review of select approaches that have made significant contributions in modeling CLV. Table 1 offers a summary of the model form as well as merits and shortcomings of popular modeling approaches.
Although the models discussed here are the most popular ones, there will always be improvements to the CLV model because of the nature of the availability of customer data and the business situation. In addition, the knowledge of how to implement the models is also important in determining how CLV can be managed at different firms.
Depth of Direct Economic Value Contribution
The depth of direct economic value contributions, as measured by CLV, have focused on the impact of customers’ direct profit contributions to the firm through their own purchases. Tracking and valuing these contributions have produced significant financial results for the implementing firms. In this regard, recent research has demonstrated that when firms try to understand and leverage the true power of measuring and maximizing CLV, it ultimately results in enhanced firm value (Berger et al. 2002; Kumar, Ramaswami, and Srivastava 2000; Kumar and Shah 2009). Furthermore, Kumar (2008, 2013) explored the implications and generated the “Wheel of Fortune” strategies that have enabled firms to address marketing issues with greater confidence and ensure better decision making. This set of strategies answers the following questions:
- Customer selection: How do firms identify the “right” customers to manage? Reinartz and Kumar (2000) found that long-life customers are not necessarily profitable customers, and the authors call for the use of a forward-looking metric such as CLV to identify the “right” customers to manage.
- Managing repeat purchases and profitability simultaneously: How can firms ensure profitability while improving customer repeat purchases? When customized actions were implemented at a B2C catalog retailer on the basis of segmenting customers on repeat purchases and profitability, Reinartz and Kumar (2002) found that loyal customers are aware of their value to the company and demand premium service, believe they deserve lower prices, and spread positive word of mouth only if they feel and act loyal.
- Optimal allocation of resources: Which customers should the firms interact with through inexpensive channels (e.g., Internet, phone), and which customers should firms let go? When resources were reallocated on the basis of the optimal mix and frequency of communication channels, a B2B company realized 100% more revenue and 83% more profits across its four customer segments (Venkatesan and Kumar 2004).
- Cross-buy: How can customers’ purchases be increased across multiple product categories to improve customer profitability? By encouraging customers to buy across more product categories through profitable customer management strategies, Kumar, George, and Pancras (2008) found that metrics such as revenue per order, margin per order, revenue per month, margin per month, and orders per month increased as customers shopped across multiple product categories. However, not all cross-buying is good. Shah et al. (2012) found that across five B2B and B2C firms, 10%–35% of the firms’ customers who cross-buy are unprofitable and account for a significant proportion (39%–88%) of the firms’ total loss from its customers. Therefore, discerning profitable from unprofitable cross-buying behavior is essential.
- Next logical product: How do firms decide the timing of an offering to a customer? When done right, results have shown that firms increased their profits by an average of $1,600 per customer, representing an increase in return on investment (ROI) of 160% (Kumar, Venkatesan, and Reinartz 2006).
- Preventing customer attrition: How do firms decide which prospect will make a better customer in the future and is therefore worthwhile to acquire? Using test and control groups, Reinartz, Thomas, and Kumar (2005) showed that acquiring and retaining the “right” customers garnered a B2C firm an incremental profit of $345,800 with an ROI close to 860%.
- Product returns: Should the firm encourage or discourage product return behavior, and how should it manage this process? Petersen and Kumar (2009) found that the ideal level of product returns should be one that maximizes firm profits. For a B2C catalog retailer, they found that the optimal percentage of product returns that maximized profitability was 13%. Furthermore, Petersen and Kumar (2015) addressed the aspects of perceived risk and optimal resource allocation into the product returns process for a B2C catalog retailer and found that the firm was able to generate approximately $300,000 more in profits compared with the next best available resource allocation strategy.
- Managing multichannel shoppers: What kind of sales and service resources should firms allocate to current customers to conduct future business with them? Kumar and Venkatesan (2005) identified the drivers of profitable multichannel shopping behavior and found that adding one more channel resulted in an average net gain of about 80% in profits.
- Branding and customer profitability: Should firms invest in building brands or customers? Kumar, Luo, and Rao (2016) have shown that by understanding the link between investments in branding and CLV, firms can efficiently allocate their resources to improving customer brand value to generate maximum lifetime value. It was found that a 5% increase in the investments in branding causes the CLV to increase by over 25%.
- Acquiring profitable customers: How should firms monitor customer activity to readjust the form and intensity of their marketing initiatives? In managing firm actions regarding customer acquisition and retention efforts, Reinartz, Thomas, and Kumar (2005) found that it is not sufficient to consider how much to spend on acquisition or retention alone but, instead, firms need to consider how they must balance acquisition and retention spending together to maximize profitability and double the ROI.
- Interaction orientation: Ramani and Kumar (2008) ask, Should firms realign themselves to realize augmented CLV? If so, is interaction orientation the answer?
- Referral marketing strategy: How can firms enhance their value through customers’ referral behavior? By implementing customized campaigns for each customer value segment, B2B and B2C firms have realized large profit gains, representing a higher ROI (Kumar, Petersen, and Leone 2007, 2010, 2013).
- Linking CLV to shareholder value: How do firms leverage the CLV metric to drive their stock price and provide more value to their stakeholders? By linking CLV-based actions to a firm’s stock price, B2B and B2C firms have reported significant increases of approximately 35% and 57%, respectively, in their stock prices; better prediction of stock price movements; and superior performance with respect to the stock market index and rival firms (Krasnikov, Jayachandran, and Kumar 2009; Kumar and Shah 2009). products and services they consume. When firms pursue opportunities to draw out indirect profit contributions from customers, it implies engaging with customers by identifying the various sources of profit. Such a focus would result in maximizing customer engagement value. Conceptually, customer engagement value is the total value provided by customers who value the brand such that they engage with the firms ( 1) through their purchase transactions (or CLV), ( 2) through their ability to refer other customers to the firm using the firm’s referral program (or customer referral value [CRV]), ( 3) through their power to positively influence other customers about the firm’s offerings on social media (or customer influence value [CIV]), and ( 4) by providing feedback to the firm for product and service ideas (or customer knowledge value [CKV]) (Kumar et al. 2010).
CRV. With respect to indirect customer contributions to profit, promoting customer referrals is a popular practice adopted by firms. The CRV metric captures the net present value (NPV) of the future profits of new customers who purchased the firm offerings as a result of the referral behavior of the current customer (Kumar, Petersen, and Leone 2010). Kumar, Petersen, and Leone (2007) showed that when targeted referral behavior campaigns were offered to select customers of a telecommunications firm, it resulted in overall value gains of $486,090, representing an ROI of 15.4. This study established that customers who score highly on CLV are not the same as those who are successful at referring new customers, and it made the case for measuring both CLV and CRV when evaluating marketing campaigns. To compute CRV, the first step is for firms to integrate their customer transaction database with the referral database. In the absence of referral behavior information, firms can collect it from new customers by asking questions such as, “Were you referred, and if so, by whom?” Or, “To what degree did the referral influence your decision to transact with us?” The CRV metric is, however, not relevant for all business situations. For instance, customersmay not make referralsif they are not attached to the product (e.g., fast-moving consumer goods). Furthermore, a customer recommendation for one product does not necessarily apply to the firm’s portfolio of products.
The concept of referral behavior has also been extended to apply to the B2B relationship setting through the business reference value (BRV) metric. This metric computes the amount of profit the existing client firm can help generate from the prospect firms that purchase the firm offerings as a result of the client reference. Significant differences were found between high-BRV and low-BRV clients of a telecommunications and financial services firm, which indicated that, compared with low-BRV clients, high-BRV clients ( 1) contribute more value, ( 2) stay longer with the firm, ( 3) are more likely to provide a video reference than a “call me” reference, and ( 4) are larger in size and annual revenue (Kumar, Petersen, and Leone 2013). By linking CRV and BRV to CLV, firms can begin to identify the value of customers and enhance it through optimally designed marketing campaigns.
CIV. The power of the online medium has influenced customers to ( 1) persuade and convert others into customers, ( 2) continually use the firm’s offerings, and ( 3) change/modify their own purchase patterns. In an effort to conceptualize and metricize the customer influence on others, research has contributed two key metrics—customer influence effect (CIE) and CIV. Whereas the CIE measures the net spread and influence of a message from a particular individual, the CIV calculates the monetary gain or loss a firm experiences that is attributable to a customer, through his or her spread of positive or negative influence. Tracking these two metrics for a company’s social media campaign, Kumar et al. (2013) were able to demonstrate a 49% increase in brand awareness, 83% increase in ROI, and 40% increase in sales revenue growth rate. Although this study described the applicability of the CIE and CIV metrics in the case of an offline retailer, it can be extended to online retailers as well. Companies such as Starbucks and Staples already have established customer relationship management practices alongside a vibrant social media presence.
CKV. Because customer input is a valuable resource in the product development process, the value of this contribution needs to be captured and included as part of a customer’s value to the firm. This was captured in the conceptualization of the CKV metric, which refers to the monetary value attributed to a customer by a firm as a result of the profit generated by implementing an idea/suggestion/feedback from that customer. This customer feedback not only identifies the areas that are in need of improvement but also helps provide suggestions and solutions for future upgrades and modifications to firm offerings. This feedback has the potential to make the entire offering more attractive to existing and potential customers as well as improve process efficiencies. Customers must be attributed to the corresponding feedback to receive credit toward their asset value (Kumar and Bhagwat 2010).
While the value of customer feedback can be substantial to any firm, juxtaposing CLV with CKV can yield even greater insights to firms. Normally, customers with low CLVs have little experience with the product and/or they are likely to be unenthusiastic about the firm and therefore are likely to provide little feedback to the firm. Consequently, the higher a customer’s CLV, the more positive that customer will perceive the company and its products to be, and the more opportunities for the company to receive input. However, at very high levels of CLV (an indication of a close fit between the company’s products and a customer’s needs), the customers are likely to be highly satisfied and therefore have little incentive to provide feedback. These customers can, however, be encouraged to assist less experienced and less knowledgeable customers when firms implement a communication medium to do so.
Customer brand value. The three tangible value metrics—CRV, CIV, and CKV—collectively capture customers’ indirect profit contributions. In addition to these metrics, an attitudinal metric (intangible value)—customer brand value (CBV)—measures the value that the customer attaches to the brand as a result of all the marketing and communication messages delivered through different media. Conceptually, CBV refers to the total value a customer attaches to a brand through his or her experiences with the brand over time (Kumar, Luo, and Rao 2016). The CBV is a multidimensional metric that measures the customer’s brand knowledge, brand attitude, brand purchase intention, and brand behavior and enables companies to devise appropriate strategies depending on where the problem exists—awareness, trust, or repeat purchase.
Monitoring all the components of CBV becomes important from a branding standpoint. That is, brand-building efforts are aimed at inducing favorable behavior outcomes toward the brand, such as longer duration, higher purchase frequency, higher contribution margin, and positive customer referrals. Such behavioral outcomes determine the CLV scores. With this understanding, managers can work on optimizing the components of CBV to improve an individual’s CLV score. In this regard, Kumar (2013) shows that CBV is the foundation for managing CLV, CRV, CIV, and CKV.
The next component of the CVT relates to identifying the metrics to ascertain the value of customers. Firms constantly struggle with managing customers while ensuring profitability. In many cases, the cost of serving a customer may far exceed the returns from that customer. In such a scenario, firms are caught between retaining their customer portfolio/base and ensuring a healthy bottom line. The CLV metric will help firms manage healthy customer portfolios.
Direct Economic Value Contribution
As mentioned previously, the CLV stream of research has uncovered several resource reallocation insights that have helped firms maximize their value. Specifically, studies have focused on the magnitude of cash flows (the expected returns as measured by CLV) and on the volatility and variability of the cash flows (the risk element associated with customer revenue contributions). This dual focus has enabled firms to better understand customer portfolio decisions and manage customers. Through this process, it is possible to select and invest in customers who will optimize the overall customer cash flow while balancing the risk in cash flow variations.
While the concept of managing customer portfolios is similar to the financial portfolio theory, a couple of key differences must be noted. First, financial assets are typically classified in line with their historic risk/return characteristics. However, the same approach does not apply when treating customers as assets. Research has shown that past customer contributions are not highly correlated with future profit potential when using traditional or backward-looking metrics (Venkatesan and Kumar 2004). The CLV metric, in contrast, has been found to be a good predictor of future customer profitability. Second, finance managers typically invest in individual assets and try to maximize the portfolio’s return. However, marketing managers largely allocate resources at the customer segment level and increase customer profit. This is due to the nonlinear relationship between customer investments and returns. In other words, customer responses operate within certain thresholds of the level of marketing investments, and too few or too many marketing investments may not elicit the desired customer response. Whereas early studies have contributed to managing customer portfolios by optimizing segment-level risk/return (Buhl and Heinrich 2008; Reinartz and Kumar 2003; Tarasi et al. 2011), more recent studies are focusing on optimizing resource allocation at the individual customer level to maximize overall profitability (Kumar, Zhang, and Luo 2014; Luo and Kumar 2013; Petersen and Kumar 2015).
Depth of Direct Economic Value Contribution
To realize the full potential of customer contributions, firms must focus on the augmented customer value while managing customer portfolios. This is possible through optimal allocation of resources by prioritizing customers on their augmented profitability and their receptiveness to marketing efforts. To do so, decile analysis of the predicted CLV must be performed. Here, the baseline CLV (i.e., NPV of future profits that considers only the focal product category that the customer purchases) and the incremental CLV (i.e., NPV of future profits that considers customer purchases only from new product categories) are separately tracked. Conceptually, the augmented CLV is the sum of baseline CLV and the monetary impact of the portfolio of strategies discussed previously. Furthermore, the medium-value customers experience the highest gain from the portfolio of strategies—higher than even the high-value customers. This indicates the receptiveness of customers to firm-initiated marketing actions. Following this, firms can prioritize marketing resources and actions according to the impact on future profits and on firm value.
A good way to understand the impact of optimal allocation of resources is through the firm’s usage of marketing communication channels (Venkatesan, Kumar, and Bohling 2007). To optimally allocate their resources, firms must first identify their most profitable customers and those who are the most responsive to marketing efforts. Performed at the individual customer level, the selection of the best mix of communication channels is determined on the basis of the responsiveness of each customer. The cost-effectiveness of these channels is also considered to measure the customers’ potential revenue contribution based on the contacts made. The firm then decides the frequency and interval of contacts through these channels to develop a customer contact strategy. Analyzing customer behavior in relation to these factors provides firms with valuable information about their customers’ preferences and attitudes. By carefully monitoring customers’ purchase frequency, the time elapsed between purchases, and the contribution margin, managers can determine the frequency of firm initiatives to maximize CLV through an optimal contact strategy.
The benefits of optimal resource allocation using CLV as the metric can be observed even in a complex business setting. When IBM adopted the CLV metric to measure customer profitability and allocate resources for customers, the firm determined the level of contact and outreach efforts through telesales, email, direct mail, and catalogs on an individual customer basis. At the conclusion of this program (based on approximately 35,000 customers), IBM was able to effectively reallocate resources for approximately 14% of customers as well as increase revenues by about US$20 million. This was done without increasing the investment but by abandoning ineffective techniques based on customer spending history in favor of the CLV metric (Kumar et al. 2008).
Breadth of Indirect Economic Value Contribution
In managing customer portfolios, firms must realize that customers have multiple ways to contribute value, and all avenues have to be explored to maximize customer contributions. In this regard, the CLV metric has resulted in the creation of other metrics that can be used to manage and maximize customer value. These metrics must be managed independently, because a customer could contribute in one or more of these ways. Furthermore, firms must identify the ideal combination of direct and indirect value contribution from the metrics that provide the greatest profits to the firm. Figure 3 illustrates the metrics that embed the principles of CLV.
While most of the metrics illustrated in Figure 3 have been discussed in previous sections of this study, a brief description of salesperson future value (SFV), donor lifetime value (DLV), and employee engagement value (EEV) is provided here in the following subsections.
SFV. Despite numerous studies conducted on the sales function, the future value of the salesperson to the firm and how organizational factors affect it has received little attention. Recognizing the importance of this knowledge to firms, Kumar, Sunder, and Leone (2014) conceptualized the SFV metric and empirically demonstrated the short-term and long-term effects of managing SFV. The SFV is defined as the NPV of future cash flows from a salesperson’s customers (i.e., CLV) after accounting for the costs of developing, motivating, and retaining the salesperson. By measuring the value of the salespeople using the SFV metric and linking the performance with the types of training and incentives each salesperson receives, it is possible to identify the best-performing salespeople and tailor the training and incentives to maximize their performance. When this approach was implemented in a Fortune 500 B2B firm, the firm was able to reallocate training and incentive investments across salespeople, which resulted in an 8% increase in SFV across the sales force and a 4% increase in firm revenue (Kumar, Sunder, and Leone 2015).
DLV. Similar to for-profit firms, nonprofit firms also need to build relationships to be able to raise donations. In this case, the relationships are created with potential donors. Furthermore, the nonprofit firm will have to be selective about the donors with whom it builds relationships, so that it judiciously uses its limited resources. To make this possible, Kumar and Petersen (2016) conceptualized the DLV metric, which refers to the sum of donations in the future years discounted to the present value over a donor’s lifetime with the firm. Using the donor demographic information, past donation behavior, and marketing information, the DLV is modeled by predicting ( 1) the likelihood of donation; ( 2) the value of donation, given that the person will donate; and ( 3) marketing costs expended in seeking donations. By computing the DLV, it is possible for nonprofit firms to rank-order donors on the basis of their future donation value to the firm. This metric can predict who will be able to donate with 90% accuracy, the value of donations with 80% accuracy, and the DLV with 75% accuracy.
EEV. Firms can leverage the power of an engaged customer base better if they have a workforce that interacts well with customers. Customer–employee interactions help create perceptions about the firm, which affect repeat customer purchases (Sirianni et al. 2013). These perceptions lead to attitudinal and behavioral outcomes through their impact on purchases, referrals, influence, and knowledge that the customers provide to the firm. In this regard, a positive interaction between customers and employees is likely to motivate how customers talk about the brand and whether they recommend it to their friends and relatives. Kumar and Pansari (2014, p. 55) define employee engagement as “a multidimensional construct which comprises of all the different facets of the attitudes and behaviors of employees towards the organization.” The dimensions of employee engagement comprise employee satisfaction, employee identification, employee commitment, employee loyalty, and employee performance. Kumar and Pansari (2016a) developed an approach to measure EEV using a five-point scale. When implemented in the airline, telecommunication, and hotel industries, the authors found that the highest level of growth in profits (10%–15%) occurs when a company’s employees are highly engaged (i.e., have a high EEV); the lowest level of growth (0%–1%) occurs when the company’s employees are disengaged (i.e., have a low EEV). Ongoing research continues to uncover several facets of the metrics presented in Figure 3, and the insights generated thus far have advised firms in valuing customers.
Finally, the concepts and metrics discussed previously lead to the establishment of strategies that will aid in growing customer value. In developing strategies, firms are often misled by the belief that loyal customers are profitable. In addition, firms’ belief that creating customer reward programs can result in increased repeat purchasing behavior, and thereby improve firm profitability, is misplaced. Research in this area has shown that loyal customers are not necessarily profitable, and the relationship between repeat purchases and profitability is more complex than is often perceived. In addition, firms regularly use traditional metrics to measure the value of their customers, and these lead managers to implement flawed marketing strategies that drain the firm’s resources. In this regard, the CLV metric is ideal for firms aiming to grow and nurture customer profitability. When firms adopt a CLV-based approach, they can make consistent decisions over time about ( 1) which customers and prospects to acquire and retain, ( 2) which customers and prospects not to acquire and retain, and ( 3) the level of resources to spend on the various customer segments.
Direct Economic Value Contribution
When aiming to maximize the direct contribution of customers to the firm, the CVT proposes that the focus be on establishing profitable customer relationships that are based on customer transactions. In this regard, Reinartz and Kumar (2002) found that ( 1) loyal customers do not cost less to serve, ( 2) loyal customers consistently paid lower prices, and ( 2) customers who were attitudinally and behaviorally loyal were more likely to be active word-of-mouth marketers than those who were only behaviorally loyal. In effect, the study found that while there may be long-standing customers who are only marginally profitable, there also may be short-term customers who are highly profitable. The identification of the distinct customer types led to the development of the following specific strategies:
- High repeat purchases and high profitability. Referred to as “True Friends,” these customers buy steadily and regularly over time. They are generally satisfied with the firm and are usually comfortable engaging with the firm’s processes. Firms should build relationships with these customers because they present the highest potential for long-term profitability.
- Low repeat purchases and high profitability. Referred to as “Butterflies,” these customers do not repeat purchase often, tend to buy a lot in a short time period and then move on to other firms, and avoid building a long-term relationship with any single firm. Firms should enjoy their profits until they turn to competition.
- High repeat purchases and low profitability. Referred to as “Barnacles,” these customers, if managed unwisely, could drain the company’s resources. Firms must evaluate their size and share of wallet. If the customers’ share of wallet is low, firms can up-sell and cross-sell to them to make them profitable. However, if the size of wallet is small, strict cost control measures must be taken to prevent losses for the firm.
- Low repeat purchases and low profitability. Referred to as “Strangers,” these customers not only are a poor fit to the company but offer very little profit potential to the firm. Firms should identify these customers early on and avoid any investment toward building a relationship with them.
A relationship focus would lead firms to identify those customers who provide the most value and prioritize the marketing efforts accordingly.
Depth of Direct Economic Value Contribution
Firms are expected to demonstrate the profitability of their marketing actions at the individual customer level on an ongoing basis. At the same time, customers expect firms to customize products and services to meet their demands. A successful management of these two types of expectations depends on the firms’ ability to better interact with customers and create a unique positioning in the future. This calls for implementing an interaction strategy in which firm–customer and customer–customer interactions constantly occur. In other words, the CVT advocates for a firm–customer exchange environment that focuses on constant interactions as opposed to just profitable transactions.
Implementing the interaction strategy requires the firm to adopt an interaction-oriented approach, which consists of four components—customer concept, interaction response capacity, customer empowerment, and customer value management—that the firm can utilize to increase the impact on its profitability (Ramani and Kumar 2008). First, the customer concept proposes that the unit of every marketing action or reaction is an individual customer. This places the customer at the top of the hierarchy in the customer–firm relationship. By doing so, firms are able to observe customer behavior and respond appropriately. Second, the interaction response capacity (the degree to which a firm can provide successive products or services drawing on previous customer feedback) highlights the importance of firms attending to and promptly addressing customer needs. Third, the customer empowerment component refers to the extent to which a firm allows its customers to ( 1) connect with the firm and design the nature of the transaction and ( 2) connect and collaborate with each other by sharing information, praise, and criticism about a firm’s product and services. Finally, the customer value management component refers to the extent to which a firm can quantify and calculate the individual customer value and use it to reallocate resources to customers. Firms such as IBM and American Express, through their endorsement of practices that are consistent with the elements of interaction orientation, have realized superior business performance, thereby demonstrating the managerial significance of the interaction orientation approach.
Breadth of Indirect Economic Value Contribution
Firms can nurture customer profitability by implementing an engagement strategy that considers the value generated by customers and employees and results in superior firm performance. The CVT proposes an engagement strategy that builds an engaged and committed customer and employee base to enhance the firm’s overall profitability (Kumar and Pansari 2016a).
Although the engagement approach is powerful by itself, its true potential is realized when it is viewed from a long-term perspective. Engaged customers contribute to the long-term reputation and recognition of the brand. Creating an environment where customers are more engaged with the company may require an initial investment, but doing so has the potential to generate higher profits in the long run through the creation of customer engagement (Verhoef, Reinartz, and Krafft 2010). Furthermore, fostering engagement within firms is effective even in a recessionary economy. In a recessionary period, firms face budgetary challenges that significantly affect their marketing plans, which influences their levels of brand awareness and adoption. During this period, firms can mitigate the risks posed by the dents in their marketing budgets if they have a highly engaged employee base that promotes the firm’s brand and its products/services to its customers. This would ensure delivery of a superior customer experience, thereby increasing customer purchases, influence, and referrals—all without any additional marketing investments.
The true measure of any marketing strategy or initiative is the improved financial result for the firm implementing it. This is why it is critical to study customer reactions to firm actions. When firms can precisely link their actions to customer value and, ultimately, to firm/shareholder value, they can begin to realize the potential of valuing customers as assets. Using concepts from economics, finance, and marketing, this article has created an interface that connects the value of each customer (determined by evaluating the lifetime value of the customer to the firm) with the performance and, therefore, the valuation of the firm. This connection has been established by ( 1) valuing customers as assets,
( 2) managing a portfolio of customers, and ( 3) nurturing profitable customers. Each of these tactics plays a unique role in the optimization of shareholder value, customer equity, and overall profitability, and each tactic also works in combination with others to increase the overall impact on the firm value. Specifically, the CVT proposed here provides the following benefits:
- Benefit to the firms: When firms understand customer profitability and adopt CVT as the desired approach, they will be able to ( 1) attract and retain the most valuable customers, ( 2) nurture customers into a skilled resource base for the firm, ( 3) prevent their customers from switching to competitors by instilling a heightened sense of ownership of the firm among its customers, ( 4) consistently evolve their product/service offerings and match it to customer needs and preferences, ( 5) develop the ability to accurately foresee customer responses, and ( 6) exhibit superior aggregate business-level performance because the firm will be dynamically maximizing the profit function at every stage of business activity across all customers.
- Benefit to the customers: The CVT essentially advocates that the individual customer be the unit of analysis of every marketing action and reaction. This implies that firms respond to heterogeneous customers differently at different points in time by pooling information from multiple sources and points in time. This approach provides customers avenues to ( 1) connect with the firm and actively shape the nature of transactions and ( 2) connect and collaborate with each other by sharing information and knowledge. Furthermore, customers will be subjected to only the product/service offerings that are appropriate to them rather than every offering in the firm’s product portfolio. This keeps the customers focused, involved, and connected to the firm, thereby increasing their lifetime with the firm.
- Benefit to the environment: When firms base their marketing actions on the potential value the customers can bring them, it becomes easier to align and allocate the optimal amount of resources toward each customer. For instance, firms can plan and optimize the printing and mailing of marketing materials only to those customers they are intended for, and not send mass mailers. In addition to enhancing the effectiveness of the firm’s marketing efforts, this would also prevent the wasteful usage of valuable environmental and infrastructural resources and would help firms embrace sustainability as part of their core mission (Kumar and Christodoulopoulou 2014).
- Benefit to society: The benefits to society from implementing the CVT are threefold. First, there is a clear line of communication from firms to customers in terms of what to expect. The customer expectations relate to product/service offerings as well as firm responses through marketing actions. Second, the customer’s repeat purchases can now be consolidated among a few preferred firms. When firms display their respective needs and expectations, customers’ repeat purchases gradually are aligned to matching firms. Over time, a self-selection of repeat patronage by firms takes place that leads firms to focus their resources to satisfy their customers’ expectations. Finally, customers become empowered, such that they can exercise their choice and free will, thereby having a definite say in the marketing transaction process. This empowerment is evident through behavior ranging from customer advocacy (in case of complete satisfaction of firm offerings) to customer boycott and negative word of mouth (in case of extreme displeasure).
- Benefit to the employees: Employees are instrumental in providing a better customer experience, and this leads to customer engagement. Firms can engage their customers only if their employees are committed to delivering the brand values and performing to the best of their ability. Employees can be committed to the organization only if they understand its goals and their responsibilities toward achieving these goals. The employees must be highly engaged with the firm to provide peak performance (Kumar and Pansari 2014). Therefore, in addition to engaging customers, firms must also ensure that they are engaging their employees.
The insights from various financial theories served as a good starting point in the development of the CVT. In addition, the modern portfolio theory provided insights into risk diversification and the maintenance of a balanced portfolio. Overall, this study has showcased CVT as a forward-looking approach to aid managers in the valuation and management of a customer’s future contributions.
Specifically, two key managerial implications emerge. First, the applicability of the CVT has been demonstrated in various scenarios spanning multiple markets (e.g., B2B, B2C), business settings (e.g., contractual, noncontractual), regional contexts (e.g., domestic, global), and industries. This should provide confidence to managers regarding its relevance and applicability to the current marketplace. Furthermore, experts opine that in the next five to ten years, CLV will be the metric of prime importance to businesses (Fader 2016). Second, given the evolution in the methodology to value customers, the availability of customer information, and the 360-degree customer data, managers are enriched with improvements that can result in better implementation. Despite the success of the CLV metric, it has not experienced industry-wide acceptance. For instance, a survey found that while 76% of the respondents agreed that CLV was important to their organization, only 42% reported that they were able to implement it (Charlton 2014).
In this light, I identify three areas that can benefit from future research. First, the identification of the relevant organizational structure required to facilitate and implement CVT-based strategies is important. A customer-centric organization has been identified as a basis to align an organization (Kumar 2013). Such an alignment can enable firms to view their customers both as a source of business and as a potential business resource. However, more insights are necessary to better understand the organizational requirements for a successful CVT implementation. Second, the role of multiple stakeholders needs to be explored. For instance, studies have investigated the circumstances in which firm–stakeholder relationships are forged, such as those in which ( 1) the stakeholder is critical to the functioning of the firm (e.g., institutional investors having an equity interest in a firm), ( 2) the stakeholder aims to gain from the relationship (e.g., customers seeking firm offerings), ( 3) the firm and the stakeholder mutually gain from the association (e.g., the firm and the channel partners working collectively to produce and sell offerings), and ( 4) the stakeholder has moral rights attached to the firm’s operations (e.g., customer rights and employee rights) (Freeman and Reed 1983). However, precise recommendations have to be generated that can inform firms about better stakeholder integration for a successful implementation of CVT. Finally, the time-varying effects of certain drivers involved in the implementation of CVT need to be better understood. These elements include the valuation methodologies, data availability, intensity of customer-level data, and emergence of smarter consumers. Over time, these elements are likely to have a dynamic effect on CVT-based strategies. In summary, by focusing on a CVT-based approach, firms can ( 1) acquire and retain profitable customers, ( 2) employ resources productively, and ( 3) nurture profitable customers, which will ultimately result in higher firm value.
Breadth of Indirect Economic Value Contribution
Apart from customers’ own contributions, the CLV metric also applies to customers’ indirect profit contributions to the firm such as their referral behavior, online influence on prospects’ and other customers’ purchases, and review/feedback on the
Footnotes 1 “Firm offering” refers to physical goods, services, brands, or a combination thereof.
2 “Customer” denotes both an end consumer and a business customer.
3 I recognize that the valuation of a company is affected by many other factors (including nonmonetary factors such as competition and mergers and acquisitions) beyond the focal firm’s cash flows. However, this example is designed to illustrate the importance of cash flows in determining firm value. I thank an anonymous reviewer for raising this distinction.
4 Exceptions to this are initial public offerings wherein the stock value increases as new customers are added.
5 Although the prediction of future contribution margin and future costs does generate risk in the CLV calculation, a higher discount rate can be used to account for the uncertainties in the prediction.
6 In the B2B markets, some categories may not conform to the three-year window (e.g., capital goods such as plant and machinery).
DIAGRAM: FIGURE 1 Differences in Valuing Stocks Versus Customers
DIAGRAM: FIGURE 2 Understanding the Link Between CVT and Firm Value
DIAGRAM: FIGURE 3 Maximizing Indirect Value Contribution
PHOTO (BLACK & WHITE)
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Record: 13- Acknowledgments. Journal of Marketing. Nov2020, Vol. 84 Issue 6, p1-2. 2p. DOI: 10.1177/0022242920965193.
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The Editors are pleased to honor these Editorial Review Board members who distinguished themselves by virtue of their review quality, constructiveness, timeliness, and workload. Join us in congratulating this global network of scholars for their outstanding contribution as reviewers to the Journal of Marketing between July 1, 2019 and June 30, 2020.
Kimmy Chan, Hong Kong Baptist University
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The Journal of Marketing confers three awards focused on different types of contribution to the field of marketing.
2020 Sheth Foundation/Journal of Marketing Award: The award honors the JM article that has made long-term contributions to the field of marketing. An article is eligible for the award in the fifth year after its publication.
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- Ya You, Gautham G. Vadakkepatt, and Amit M. Joshi (2015), "A Meta-Analysis of Electronic Word-of-Mouth Elasticity," 79 (2), 19–39.
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Record: 14- Acknowledgments. Journal of Marketing. Nov2019, Vol. 83 Issue 6, p1-1. 1p. DOI: 10.1177/0022242919882644.
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Kersi Antia, Western University
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The following scholars served as guest Associate Editors over the last year for the Journal of Marketing. We appreciate their investments of time and energy to support JM's mission to develop and disseminate knowledge about real-world marketing questions relevant to scholars, educators, managers, policy makers, consumers, and other societal stakeholders around the world.
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Record: 17- An Empirical Analysis of the Joint Effects of Shoppers’ Goals and Attribute Display on Shoppers’ Evaluations. By: Guha, Abhijit; Biswas, Abhijit; Grewal, Dhruv; Bhowmick, Sandeep; Nordfält, Jens. Journal of Marketing. May2018, Vol. 82 Issue 3, p142-158. 17p. 1 Diagram, 3 Charts. DOI: 10.1509/jm.16.0247.
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An Empirical Analysis of the Joint Effects of Shoppers’ Goals and Attribute Display on Shoppers’ Evaluations
This article develops a decision-making framework that highlights how display of numeric attribute information (e.g., display of calorie information) and shoppers’ goals (i.e., having a diet focus vs. a taste focus) jointly influence shoppers’ choices and preferences. Across two sets of studies, including a field study involving the launch of a new Coca-Cola product, the authors show that when food items are displayed in an aligned manner (i.e., when food items with lower-value calorie information are displayed below food items with higher calorie values), shoppers assign more importance weight to calorie gap information. In turn, higher importance weight assigned to calorie gap information leads diet-focused shoppers to relatively prefer low-calorie food items but leads taste-focused shoppers to relatively prefer higher-calorie food items. The third set of studies shows that this decision-making framework has widespread applicability and is relevant in any domain in which advertising, retail, and online displays show comparisons of numeric attribute information.
In mid-2014, Coca-Cola launched Cola-Cola Life in Sweden, a reduced-calorie cola drink that differs from zero-calorie diet colas because it does not contain aspartame (which many perceive as unhealthy; Dean 2014). Reacting to an increased health focus among shoppers, launching Coca-Cola Life was part of Coca-Cola’s efforts to reduce the average calorie content of its drinks portfolio while still avoiding the use of aspartame. Some months prior to the launch, one of the authors of this article was in discussions with Coca-Cola managers about which factors were likely to influence shoppers’ choices between regular Coke and Coca-Cola Life. One factor discussed was in-store signage. Would signage indicating that Coca-Cola Life has lower calorie content be effective, despite prior research showing that merely indicating calorie values does not automatically lead shoppers to make healthier choices (Loewenstein 2011; see also Haws, Davis, and Dholakia 2016). This article was motivated, in part, by these discussions and the real-world challenges the authors (and firms like Coca-Cola) aim to examine.
To better understand the factors that influence the choice of healthier food items, we start with the foundational notion that shoppers’ food choices depend on both individual differences among shoppers and the presentation format for the nutritional information (Haws, Davis, and Dholakia 2016; Loewenstein 2011; Mohr, Lichtenstein, and Janiszewski 2012). Most prior work has focused on evaluations of single food items (e.g., Gomez, Werle, and Corneille 2017; Graham and Mohr 2014; Mohr, Lichtenstein, and Janiszewski 2012); in contrast, in this article, we consider how shoppers choose among multiple food items, which represents a normative shopping situation. For example, shoppers often choose among various entrees in restaurants, multiple soup cans in supermarkets, or numerous soft drinks in convenience stores.
We focus on two specific drivers of food item choice. We start by examining shoppers’ goals, which may be conceptualized as either individual differences or differences primed by the product category or shopping environment (e.g., Escaron et al. 2013; Newman, Howlett, and Burton 2014). A significant amount of prior research has highlighted goals related to dietary restraint, that is, the extent to which shoppers have diet goals which leads them to prefer food items with fewer calories1 (Howlett et al. 2012; Visschers, Hess, and Siegrist 2010) and consume fewer calories (Cavanagh and Forestell 2013). In this article, we offer a different proposal, making two points. First, rather than posit that shoppers are more or less focused on diet goals, we posit that shoppers focus on diet goals versus taste goals. Second, building from work on the “unhealthy = tasty” intuition (Raghunathan, Naylor, and Hoyer 2006), we argue that tastefocused shoppers intuit that unhealthy food items will better satisfy their taste goals, so their choice decisions appear to favor food items with more calories. Thus, we take a different perspective than prior work, and so we contrast shoppers with diet goals versus taste goals.
Next, in grocery store settings, choices often involve a comparison between a focal, healthy food item and a comparison food item (Suri et al. 2012); in response, food manufacturers often provide comparisons that highlight the “nutritional gap.” For example, Better’n Peanut Butter Banana spread advertises that it has “40% fewer calories,” and Trop50 orange juice proclaims that it has “50% less sugar and calories,” relative to comparable products. However, in many cases, only calorie information appears in the front-of-pack (FOP) information, so shoppers must perform calorie gap calculations themselves. In turn, building on work in numeric cognition (e.g., Biswas et al. 2013) and heuristics (e.g., Shah and Oppenheimer 2007), we propose that differences in the vertical display of food items could prompt differences in perceptions of the importance of the calorie gap. If a focal food item, with fewer calories, appears below another food item (i.e., if the focal food item is displayed in an aligned manner), then the subtraction task to calculate the calorie gap is easier. And if calculating the calorie gap is easier, then shoppers are likely to attach more importance to calorie gap information during their evaluations. Thus, displaying a focal food item in an aligned manner should increase the importance weight that shoppers place on the calorie gap. Among shoppers with diet goals, this increased importance weight shifts shoppers’ choices toward the focal food item, but among tastefocused shoppers, this increased importance weight may shift the choice share away from the focal food item, toward the higher-calorie option. By combining these propositions, we posit that when food items are displayed in an aligned (vs. nonaligned) manner, diet-focused shoppers relatively prefer lower-calorie food items, but taste-focused shoppers make choices as if they relatively prefer higher-calorie food items. This nonintuitive proposition is the central hypothesis of our article, and we test it across multiple studies, including a field study involving the choice between regular Coca-Cola and the lower-calorie Coca-Cola Life.
More generally, in this article we focus on product domains for which advertising, retail, and online displays show comparisons of numeric attribute information. The food domain is one such domain, which involves comparisons of numeric nutritional information relating to calories (and sodium). Other exemplar domains that involve comparisons of numeric attribute information include ( 1) product price comparisons, as frequently seen in basket comparisons posted in supermarkets (e.g., Publix vs. Walmart), in online comparative advertisements (Dyson vs. Shark vacuum cleaners), and on price comparison tools (e.g., hotel rates on Trivago.com); ( 2) advertisements for robotic vacuum cleaners (e.g., Neato vs. Roomba), which involve attributes such as operating time and charging time; ( 3) advertisements for cellular networks (e.g., T-Mobile vs. Verizon), which involve attributes such as Internet speed; and ( 4) advertisements for cell phones (e.g., Apple iPhone vs. Samsung Galaxy), which involve attributes such as screen size, standby time, and talk time.
In domains involving numeric attribute information, we investigate the impact of two factors on shoppers’ choice decisions. First, numeric attribute information may be displayed in an aligned (vs. nonaligned) manner, whereby aligned display involves showing the low-value numeric information below the higher-value numeric information (e.g., lower prices displayed below higher prices). Second, shoppers may have different goals, perceiving attributes as either more-is-better (MIB; preferring items with higher attribute values) or less-is-better (LIB; preferring items with lower attribute values). For example, shoppers who view price as a measure of sacrifice perceive price as an LIB attribute, whereas those who view price as a measure of quality perceive price as an MIB attribute (Dodds, Monroe, and Grewal 1991; see also Miyazaki, Grewal, and Goodstein 2005). In the domain of robotic vacuum cleaners, operating time is an MIB attribute, but charging time is an LIB attribute; in the domain of laptops, many would perceive battery life as an MIB attribute but perceive laptop weight as an LIB attribute. Building from the central hypothesis outlined previously, we posit that when items are displayed in an aligned (vs. nonaligned) manner, shoppers who perceive the displayed attribute as an LIB (MIB) attribute will relatively prefer the item with the lower (higher) value attribute.
We aim to make the following contributions. Generally speaking, we outline a parsimonious framework that examines how shoppers react to advertising, retail, or online displays in (a wide variety of) product domains involving numeric attribute information. This article identifies two elements that jointly determine shoppers’ reactions: ( 1) whether shoppers view the displayed attribute as an LIB attribute or an MIB attribute, and ( 2) whether the attribute information is displayed in an aligned or nonaligned manner, which influences the importance weight shoppers put on this attribute information. We suggest that firms can use this framework to better design advertising, retail, and online displays. While Biswas et al. (2013; work on the subtraction principle) provide an initial examination of aligned (vs. nonaligned) displays, their stated process mechanism does not easily extend beyond the price promotion domain and was examined only in LIB contexts. The current article substantially modifies and broadens the process mechanism underlying the subtraction principle to allow it to extend into multiple product domains. Moreover, we explicitly contrast LIB versus MIB contexts, outlining exactly how shoppers’ attribute perceptions (i.e., LIB vs. MIB) and display alignment jointly influence their choices and perceptions.
In addition, we aim to make two contributions specific to the food domain. Not only is the food domain important from both a firm perspective and a shopper perspective, but also the growing importance of how best to motivate people to make healthy food choices has prompted increased research focus in this domain. First, prior research suggests that people who have less focus on diet goals pay less attention to calorie information (e.g., Bialkova, Sasse, and Fenko 2016; Cavanagh and Forestell 2013; Mohr, Lichtenstein, and Janiszewski 2012). In contrast, we show that people who have less focus on diet goals (i.e., have more focus on taste goals) indeed pay attention to calorie information (similar to people with more focus on diet goals), but because of the unhealthy = tasty intuition, these shoppers behave as if they prefer food items with more calories (unlike people with more focus on diet goals). Second, as a novel point not evidenced in prior research, we show that whether shoppers make goal-consistent choices is contingent on whether food items are displayed in an aligned manner. Next, we develop our propositions and test them across multiple product domains, including in a field study involving the choice between regular Coca-Cola and the newly launched, low-calorie Coca-Cola Life.
Shoppers’ Goals During Food Item Choices
What goals do shoppers have when they make food item choices? To answer this question, we turn to literature at the intersection of food-related research and research into goals. On the one hand, there may be individual differences (i.e., “trait differences”) across shoppers, and these differences should lead to shoppers having different goals when making food item choices. Specifically, some shoppers have diet goals (Herman and Polivy 2004; Howlett et al. 2012; Naylor, Droms, and Haws 2009), so they prefer food items with fewer calories or less sodium (LIB behavior).
Prior research has examined the extent to which shoppers focus on diet goals (Haws, Davis, and Dholakia 2016; Mohr, Lichtenstein, and Janiszewski 2012; Naylor, Droms, and Haws 2009; Van Herpen and Van Trijp 2011), and the subsequent impact on food item choices and consumption. This prior research has indicated that people who have less focus on diet goals tend to pay less attention to nutritional information. Thus, Mohr, Lichtenstein, and Janiszewski (2012; p. 66) show that when presented with less healthy versus more healthy food items, people with higher levels of dietary concerns were significantly more likely to choose the healthier food item, but people with low levels of dietary concerns were relatively indifferent across food items (“significantly higher purchase intentions … for all values of dietary concern above 3.80” vs. “no significant differences below … the Johnson–Neyman point”). Similarly, Cavanagh and Forestell (2013; p. 508) found that restrained eaters consumed more (relatively healthy) Kashi cookies than Nabisco cookies, “whereas the unrestrained eaters did not differ in their consumption of the two brands,” Finally, Bialkova, Sasse, and Fenko (2016; p. 44) asked participants to choose between (relatively healthy) cereal bars and (relatively unhealthy) potato chips. They found that “consumers highly concerned about health preferred to buy cereal bars ( p = .018), while less concerned consumers selected to buy chips and cereals with equal probability ( p > .4).” Taken together, these findings appear to indicate that people with low levels of dietary concerns are relatively indifferent between the less healthy food item and the healthier food item. In this article, however, we posit differently, and so we make two distinct points.
Shoppers’ goals. Rather than describing shoppers’ goals on a continuum anchored by more versus less focus on diet goals, in this article we propose that the relevant anchors should be diet goals versus taste goals. As a novel point, we emphasize the explicit presence of taste goals (and not just less focus on diet goals), consistent with Andrews, Netemeyer, and Burton’s (1998) use of a taste goal prime as a control condition.
Prior literature has indicated that shoppers with more focus on diet goals pay attention to nutrition information (“consumers who expressed a great concern for … dietary eating … made more active use of the health label information”; Bialkova, Sasse, and Fenko 2016, p. 40), and so are more likely to prefer food items with fewer calories. However, distinct from prior literature, we propose that shoppers with less focus on diet goals (i.e., those with taste goals) also pay attention to nutrition information but use it differently, such that they choose food items with more calories. We clarify that we are not claiming that shoppers with taste goals deliberately seek out food items with more calories; rather, we suggest that these shoppers intuit that high-calorie food items are tastier (unhealthy = tasty intuition); in their quest for taste, they select relatively highercalorie food items.
To make this prediction, we build from research into behavioral traits (related to food preferences, and related to impulsivity) and perceptions of food. First, we build from work that connects dietary restraint to impulsiveness. The work of Van Koningsbruggen, Stroebe, and Aarts (2013; Table 1; p. 83) indicates that those low on dietary restraint are more likely to be impulsive. Second, more impulsive people are likely to prefer tasty food, and they are both more likely to pick up (tasty) cookies (Ramanathan and Menon 2006; Figure 3, p. 638) and more likely to choose (tasty) cake over salad (Sengupta and Zhou 2007; Study 2, p. 301). Third, unhealthy foods are more likely to be perceived as tasty (see work on the unhealthy = tasty intuition2 [Mai and Hoffmann 2015; Raghunathan, Naylor, and Hoyer 2006). Overall, we posit that people low on dietary restraint (i.e., those with taste goals) may behave as if they prefer (relatively) unhealthy food items.
Individual differences versus state differences. Beyond just individual differences (e.g., extent of diet intentions), environmental factors may also prompt differences in (diet vs. taste) goals, which we term “state differences.” Product category differences may trigger differences in goals, with shopping for health-focused foods potentially triggering diet goals and shopping for candy potentially triggering taste goals. Advertising differences may also prompt differences in goals. Foods advertised as health foods or diet foods, or foods packaged reflecting “greenness” may trigger diet goals, whereas foods advertised as comfort foods may prompt taste goals. Thus, it is possible that the same person may have diet goals when examining a certain type of food item and yet may have taste goals when examining a different type of food item.
These discussions suggest that those with diet goals should prefer lower calorie food items, whereas those with taste goals should (in line with the unhealthy = tasty intuition) behave as if they seek out higher-calorie food items. Next, we propose that differences in how food items are displayed affect the extent to which shoppers’ goals influence food item preferences, and we elaborate on this point in the following subsection.
TABLE: TABLE 1 Johnson–Neyman Regions in Study 1
| Diet Scalea | Effect | SE | z | p-Value | LLCI | ULCI | Participant Behavior Consistent with: |
|---|
| -1.3100 | -.5238 | .1939 | -2.7014 | .0069 | -.9038 | -.1438 | Taste goals (30.11% of sample) |
| -.4310 | -.2416 | .1233 | -1.9600 | .0500 | -.4833 | .0000 | |
| -.3100 | -.2028 | .1175 | -1.7262 | .0843 | -.4331 | .0275 | |
| .6900 | .1182 | .1337 | .8836 | .3769 | -.1439 | .3802 | |
| 1.6765 | .4348 | .2218 | 1.9600 | .0500 | .0000 | .8696 | Diet goals (7.95% of sample) |
| 1.6900 | .4391 | .2232 | 1.9672 | .0492 | .0016 | .8766 | |
| 2.6900 | .7601 | .3311 | 2.2955 | .0217 | .1111 | 1.4090 | |
aMean-centered values for diet intentions scale.
Notes: This table illustrates the conditional effect of alignment on choice of Coca-Cola Life, at values of diet intentions scale. LLCI = lower-limit confidence interval; ULCI = upper-limit confidence interval. Boldfaced cells indicate significance.
Food items are only seldom evaluated in isolation; instead, such evaluations often involve a comparison of a focal food item with potential alternatives (Suri et al. 2012). In many cases, nutritional information is available FOP, so shoppers can calculate the attribute gap (e.g., calorie gap, sodium content gap). For example, a grocery shopper aiming to buy cereal may find a focal food item, which has fewer calories, displayed either below or above a higher-calorie food item. Both food items display calorie content FOP. If the propensity to engage in calorie gap calculations depends on whether the food items are presented in an aligned (vs. nonaligned) display, then such display differences may influence food item choice. We first consider whether differences in the display format influence shoppers’ propensity to initiate a subtraction task to calculate the calorie gap, then we discuss how this propensity might influence the importance weight shoppers attach to the calorie gap for their evaluations.
Impact on propensity to initiate the subtraction task. In general, a comparison of two attributes that feature numeric information involves subtraction (Thomas and Morwitz 2009). However, prior research has not fully explored how presenting a smaller number below versus above a larger number influences subtraction calculations. We integrate research in numeric cognition with pricing research to examine this question. First, in the subtraction task A - B, A is the minuend, and B is the subtrahend. People generally perceive that it is normative to present a larger minuend above a smaller subtrahend, and prior research has affirmed that fewer computational errors occur with this format (Fuson and Briars 1990). Second, in a study of how people verify addition problems, Yip (2002) finds that inaccurate equations that fail to conform to conventional presentation norms are perceived as harder to verify as correct (e.g., it is more difficult to determine whether 7 = 3 + 5 is correct than whether 3 + 5 = 7 is correct). Accordingly, we posit that subtraction equations in which a smaller-value subtrahend appears below (above) the minuend are easier (harder) to verify. Third, because people do not like to work on overly challenging problems (Oppenheimer 2008), locating a smaller-value subtrahend above the minuend—contrary to the norm in contexts involving difference calculations—may reduce the propensity to perform a subtraction task. In research on price promotions, Biswas et al. (2013) propose the subtraction principle, a somewhat similar information processing sequence. They proposed that when sale prices are displayed to the right of the original price (i.e., smaller number to right of the larger number), shoppers perceive the subtraction task as cognitively easier and so are more likely to calculate the discount depth. However, if sale prices appear to the left of the original price, shoppers perceive the subtraction task as cognitively harder and so are less likely to initiate a subtraction task. Rather, shoppers would approximate discount depth at around 10%–12% (reflecting a discount depth benchmark from Blair and Landon [1981]).
Now assume that two (competing) cereals explicitly provide FOP calorie information. Building on the previous arguments, if the focal, healthy cereal is displayed in an aligned manner, then shoppers can calculate the calorie difference relatively easily. But if the focal cereal is displayed in a nonaligned manner, shoppers may perceive the subtraction task as harder and so may be less likely to initiate the subtraction task to calculate the calorie gap.
Importance weight attached to the calorie gap during evaluations. During evaluations, people grant easy-to-process cues higher importance weights (Shah and Oppenheimer 2007, pp. 371–72; see also Oppenheimer 2008). This point has roots in prior work on heuristics, which shows that people more heavily weight easier-to-access cues. For instance, people use brand name perceptions as proxy for product quality (Maheswaran, Mackie, and Chaiken 1992), use ease-ofimageability of attributes (like hallways) as an input for making apartment evaluations (Keller and McGill 1994), and so on. Therefore, if shoppers perceive that calculating the calorie gap is relatively easier, during evaluations they assign more importance weight to the calorie gap. Continuing with the cereal example, if the focal, low-calorie cereal is displayed in an aligned manner, during evaluations shoppers attach relatively higher importance weight to calorie gap information.
This mechanism substantially enhances the generalizability of the subtraction principle mechanism proposed in Biswas et al. (2013). The subtraction principle predicts that displaying the sale price in a nonaligned manner increases subtraction difficulty. In turn, due to subtraction difficulty, shoppers who are less likely to initiate the subtraction task to calculate discount depth assume a 10%–12% discount depth (benchmark from Blair and Landon 1981). This mechanism, especially the point about the assumed discount depth, is fairly specific to the pricing domain. We modify the subtraction principle mechanism and propose that shoppers who are more (less) likely to initiate the subtraction task attach more (less) importance weight to discount depth information (more generally, attribute gap information). Consequent to this modification, the subtraction principle can apply beyond the pricing domain to a wide variety of other product domains.
Furthermore, the studies in Biswas et al. (2013) focus exclusively on the domain of price promotions and imply that shoppers generally prefer an overall lower price, in effect implying that price is an LIB attribute. We point out that there are contexts wherein price may be perceived as an MIB attribute, often for reasons relating to signaling of quality (Dodds, Monroe, and Grewal 1991; Miyazaki, Grewal, and Goodstein 2005; Monroe 1973). In this article, we generalize the work of Biswas et al. (2013), examining both LIB and MIB attributes, while also examining attributes such as calories, which are perceived by some as LIB and perceived by others as MIB. Appendix A lists the aforementioned points and shows the various ways this research modifies and broadens the prior conceptualization of the subtraction principle.
Displaying a focal, healthy food item in an aligned (vs. nonaligned) manner should increase the perceived importance weight of the calorie gap during evaluations. For shoppers with diet goals, the increased importance weight of the calorie gap should increase preference for the focal food item. For shoppers with taste goals, however, the increased importance weight of the gap should enhance their preference for the comparison food item with higher levels of calories and reduce their preference for the focal food item. Formally,
H1: For shoppers with diet (taste) goals, displaying a focal, healthy food item in an aligned manner increases (decreases) choice share of the focal, healthy food item.
H1 is our central hypothesis, stating that presenting food items in an aligned (vs. nonaligned) manner increases goalconsistent food choices and preferences. The next two hypotheses outline the mechanism underlying this central hypothesis. We propose that ( 1) during evaluations, presenting food items in an aligned (vs. nonaligned) manner increases the importance weight placed on the calorie gap (H2), and ( 2) during evaluations, increased importance weight placed on the calorie gap increases the propensity to make goal-consistent food choices (i.e., increases the propensity that shoppers with diet (taste) goals are more (less) likely to choose the focal, healthy food item; H3).
H2: Displaying the focal, healthy food item in an aligned (vs. nonaligned) manner increases the importance weight of attribute gap information during evaluations.
H3: For shoppers with diet (taste) goals, increased importance weight of attribute gap information increases (decreases) choice share of the focal, healthy food item.
Study 1 is a field study in a supermarket and is an initial test of H1. It involves shoppers choosing between regular CocaCola and the newly launched Coca-Cola Life. In Study 2a, we reexamine H1 in a lab study, using a chocolate context, wherein we associate chocolate with either diet goals or taste goals. In Study 2b, we examine the full process model (H1–H3), using a soup can choice context. Given that Studies 1 and 2 relate to the food domain, in Studies 3a and 3b we generalize our findings by examining other product domains. Stimuli exemplars (for all studies) appear in Appendix B.
We ran Study 1 over four days in a supermarket in Stockholm. Coca-Cola (Sweden) provided us with bottles of regular CocaCola (CCR; more calories = 879 kJ3) and of the newly launched soft drink, Coca-Cola Life (CCL; fewer calories = 565 kJ, focal drink). We had access to endcap shelving, which we modified using two different display versions that alternated every few hours, displaying CCL in either an aligned manner (i.e., CCR on the shelf above and CCL on the shelf below) or a nonaligned manner. The shelf-signs clearly showed the kJ values associated with each drink. We specifically clarify that each shopper saw only one of the two display versions.
Shoppers were intercepted and asked to participate in the study. In return, they would receive either CCR or CCL, whichever they preferred. Shoppers examined the display, then chose a CCL or CCR bottle (the experimenters restacked the shelf each time, so shoppers always saw fully stacked CCL and CCR shelving.) Next, shoppers moved to another area, where they completed a short survey. The survey included a shortversion diet intentions scale, with two items from Stice (1998; “I take small helpings in an effort to control my weight,” “I limit the amount of food I eat in an effort to control my weight”; 1–5 scale; 1 = “never,” and 5 = “always”; r = +.59, p < .05), and also included demographics (age and gender). In all, 352 shoppers (Medianage = 20.0 years; 67.9% women) participated in this 2 (display: aligned vs. nonaligned) · continuous (diet intentions scale) between-subjects study.
We used PROCESS (Model 1; Hayes 2013) to investigate the interaction. The dependent variable was soft drink choice (CCR = 0, CCL = 1), and the two independent variables were diet intentions (mean-centered at M = 2.31) and display (nonaligned = –1, aligned = 1). In the logistic regression for soft drink choice, we found significant main effects of diet intentions (b = -.34, SE = .12, z = -2.91, p < .05), no significant main effects of display (z = -.94, p = .35), and (most importantly) a significant interaction effect (b = .32, SE = .12, z = 2.72, p < .05). The positive interaction term indicated that for those with higher diet intention scores, presenting CCR and CCL in an aligned manner increased choice of CCL (the focal, lower-calorie drink).
A floodlight analysis (Table 1) revealed that for those with relatively high diet intention scores (mean-centered scores > 1.68; 7.95% of sample), the simple effect of displaying CCL in an aligned display condition was significantly positive (at score of 1.68: b = .44, SE = .22, z = 1.96, p = .05), implying increased choice share for the lower-calorie CCL. However, for those with low diet intention scores (mean-centered scores < –.43; 30.1% of sample), the simple effect of displaying CCL in an aligned display was significantly negative (at score of -.43: b = -.24, SE = .12, z = –1.96, p = .05), implying decreased choice share for the lower-calorie CCL (and increased choice share for the higher-calorie CCR). Study 1 results are consistent with H1.
In Study 1, shoppers differed in the extent of their diet intentions, reflecting trait dispositions. Moving beyond traits, in Study 2a we acknowledge that shoppers may differ in their (taste vs. diet) goals, contingent on the food item category. That is, the same shopper could have different goals, conditional on differences between food item categories. Some sets of food items (e.g., health foods) may prompt diet goals, but others (e.g., desserts) may be associated with taste goals. To the extent that products prompt different goals, the effects of presenting food items using an aligned (vs. nonaligned) display may differ. To examine this point, in Study 2a we prime participants to associate the same product (in this case, chocolates) with either a taste goal or a diet goal.
Method. This was a 2 (goal association: taste goal vs. diet goal) · 2 (display: aligned vs. nonaligned) between-subjects design, involving 255 undergraduate students (65.1% women) taking a survey in a behavioral lab. Participants were told that the survey was about beliefs and preferences about chocolate. First, we primed chocolate as being associated with either a taste goal or a diet goal, using a mechanism outlined in prior work (e.g., Dhar and Wertenbroch 2000; Roggeveen et al. 2015). For the taste goal, participants were told that “there are many reasons why people eat chocolate. And yet, what is often comes down to, is that people eat chocolate because it makes them happy. At the end of a long day, eating a piece of chocolate is the perfect reward.” Next, participants were asked to write a few words about why people should eat chocolate. Participants generally responded in ways consistent with a taste goal (e.g., “People should eat chocolate because it makes them happy. It feels rewarding to have some at the end of a day,” “It’s a wellearned reward at the end of a long day,” “Eating chocolates make people happy. They think [it’s the] perfect reward”). For the diet goal, participants were told that “there are many reasons why people eat chocolate. Interestingly—and this may not be well known—people should eat chocolate for health reasons. Medical studies have shown that chocolate can not only reduce LDL (bad cholesterol) and increase HDL (good cholesterol) but also reduce the incidence of stroke. Next, participants were asked to write a few words about why people should eat chocolate. Participants generally responded in ways consistent with a diet goal (e.g., “It is good for your cholesterol and can prevent strokes,” “People should eat chocolate because it can improve aspects of your health,” “reduce LDL/increase HDL/reduce chance of a stroke”).
Next, participants were shown two chocolate boxes. Each chocolate in box W had approximately 91 calories, and each chocolate in box K had approximately 68 calories. Participants were shown the two boxes either in an aligned manner or in a nonaligned manner (box W displayed below box K). Finally, participants were asked which box of chocolates they would prefer to take a piece of chocolate from (single-item, 11-point scale; -5 = “box K,” and +5 = “box W”).
Results. We ran an analysis of variance for chocolate preference. We found no significant main effects for display (F( 1, 251) = .02, p > .8), significant main effects for goal association (F( 1, 251) = 18.9, p < .05), and a significant two-way interaction between goal association and display (F( 1, 251) = 10.3, p < .05).
When chocolate was associated with taste goals, participants’ preference for low-calorie chocolate box K was weaker when box K was displayed in an aligned manner (Maligned = .68, SD = 3.54; Mnot aligned = -.69, SD = 3.86; F( 1, 251) = 4.67, p < .05). Put another way, when chocolate was associated with a taste goal, participants’ preference for the higher-calorie chocolate box W was significantly stronger when box W was displayed in an aligned manner. In contrast, when chocolate was associated with a diet goal, participants’ preference for low-calorie chocolate box K was significantly stronger when box K was presented in an aligned manner (Maligned = -2.70, SD = 3.02; Mnot aligned = -1.20, SD = 3.79; F( 1, 251) = 5.64, p < .05). These results are consistent with our central hypothesis, H1.
In Study 2b, we test the full process model across H1–H3. In addition, whereas Studies 1 and 2a involved some version of the attribute “calories,” Study 2b involves the attribute “sodium.”
Method. Two hundred sixty-one U.S. undergraduate students (56.7% women) participated in a two-part study for course credit. First, as part of a set of multiple studies, participants filled out the short, five-point (1 = “never,” and 5 = “always”), sixitem diet intentions scale (Stice 1998; a = .91). The six items were “I take small helpings in an effort to control my weight,” “I limit the amount of food I eat in an effort to control my weight,” “I hold back at meals in an attempt to prevent weight gain,” “I sometimes avoid eating in an attempt to control my weight,” “I skip meals in an effort to control my weight,” and “I sometimes eat only one or two meals a day to try to limit my weight.”
Second, a week later, the same undergraduate students participated in a soup choice study. Because the popular press tends to highlight the negative influences of sodium on health, we did not expect many participants to know that higher sodium (also) can be associated with better taste. Therefore, we asked each participant to read a couple of paragraphs that summarized extracts from various publications, stating that although sodium is associated with obesity and high blood pressure, it also tends to be associated with better taste and aroma. All participants read both paragraphs, such that all participants received twosided information.
Next, participants observed two (similar-looking) cans of chicken soup, next to which we showed the respective sodium levels (can N = 477 mg, can B = 664 mg). Participants also learned that the cans typically contained about two servings each, had 90–100 calories per can, and were similar in their content weight (approximately 19 oz.). These soup cans appeared in either an aligned manner (can N below can B) or a nonaligned manner (can N above can B), leading to a 2 (display: aligned vs. nonaligned) · continuous (diet intentions scale) between-subjects design.
Participants first chose their preferred soup can and indicated the importance weight of various factors for their choice decision by allocating five points across ( 1) sodium content, ( 2) number of servings per can, and ( 3) whether the soup contained chicken. Participants could allocate points however they wished, as long as the total points allocated across the three elements totaled five.
We anticipated that presenting the soup cans in an aligned manner will lead participants to place increased importance weight on sodium content. Among those who scored high on the diet intentions scale, participants who assigned more weight to the sodium content should be more likely to choose lowsodium can N. However, if participants scored low on the diet intentions scale, such that they likely focus more on taste, then those who placed more weight on sodium content should be more likely to choose higher-sodium can B (due to the unhealthy = tasty intuition) and so should be less likely to choose can N and more likely to choose can B.
Results. First, we ran a logistic regression for soup choice (can B = 0, can N = 1), in which the independent variables were the diet intentions score (mean-centered at M = 2.21) and display (not aligned = –1, aligned = 1). The main effect of meancentered diet intentions score was significant (b = .77, SE = .18, z = 4.34, p < .05), the main effect of the vertical display was not significant (z = .30, p = .76) and the interaction effect was significant (b = .69, SE = .18, z = 3.86, p < .05). The positive interaction term indicated that those with higher diet intention scores were more likely to choose the low-sodium soup can in the aligned display condition.
Second, the floodlight analysis (PROCESS Model 1) depicted in Table 2 revealed that for those with mean-centered diet intention scores greater than .36 (34.9% of sample), soup can N was relatively more preferred in the aligned display condition (at score of .36; b = .29, SE = .14, z = 1.96, p = .05). However, for those with (mean-centered) scores less than –.54 (32.2% of sample), the lower-sodium soup can N was relatively less preferred in the aligned display condition (at score of -.54; b = -.33, SE = .17, z = -1.96, p = .05), and higher-sodium soup can B was relatively more preferred. This was consistent with results in prior studies and with H1.
Next, the importance weight that participants assigned to sodium content information was higher in the aligned display condition (Maligned = 3.22, SD = .91; Mnonaligned = 2.02, SD = .72; F( 1, 259) = 140.3, p < .05); this result was consistent with H2. As an important point, prior work (Bialkova, Sasse, and Fenko 2016) has indicated that those with higher
(lower) levels of diet concerns paid more (less) attention to nutrition information, whereas we assert that this is not the case and that those with taste goals (i.e., with lower levels of diet concerns) would continue to pay attention to nutrition information. Consistent with our assertion, there was no correlation between diet intention scores and importance weight for sodium (r = .02, p > .7).
Third, for those with mean-centered diet intentions scores greater than .87 (i.e., with scores 1 SD above the mean diet intentions scale score), the PROCESS (Model 14; Hayes 2013) output indicated that the mediating effect of the importance weight assigned to sodium was significantly positive (95% confidence interval = [.07, .74]). Thus, if participants scored higher on the diet intentions scale (i.e., had diet goals) and placed more importance weight on sodium during the choice process, they were more likely to choose can N. But for participants with mean-centered diet intention scale scores less than –.87 (i.e., with scores 1 SD below the mean diet intentions scale score), the PROCESS (Model 14) output indicated that the mediating effect of the importance weight for sodium was significantly negative (95% confidence interval = [–.57, –.01]). Thus, if participants scored lower on the diet intentions scale (i.e., had taste goals) and placed more importance weight on sodium during the choice process, they were less likely to choose lower-sodium can N and more likely to choose the higher-sodium can B. These results were consistent with H3.
Studies 1 and 2 focused on the food domain. To generalize the results, we examine other product domains. First, in Study 3a, we present an incentive-compatible study involving an LIB scenario that examines choice between two kitchen implements. In Study 3b—in an MIB scenario—we examine a choice between cell phone accessories. In Study 3b, we also examine factors that may moderate the effects in this research.
The hypotheses (H1–H3) are fairly specific to the food domain. However, these hypotheses can easily be modified to extend to any product domain. In Study 3a, for instance, the prediction is that using an aligned display will increase choice share of the focal item.
Method. We administered this two-cell (sale price display: aligned vs. nonaligned) between-subjects design to attendees of three sessions of a cooking class held in a gourmet food store in an upscale U.S. neighborhood. We asked them to participate in a five-minute (voluntary) study, and approximately twothirds of attendees chose to participate. Among these 43 participants, the median age group were those over 50 years old, 79.1% were women, and median household income was $100,000–$200,000.
All participants were given a study booklet. The first page showed two upscale Wusthof brand cooking knives. On the left side of the page, we presented a picture of knife A (comparison knife) along with a brief description and a sale price of $71; on the right side of the page, we presented a picture and a brief description of knife B (focal knife), showing an original price of $114 and a sale price of $77. In the nonaligned display condition, the sale price appeared above the original price, whereas in the aligned display condition, the sale price was shown below. Participants indicated which knife they preferred. On the second page, we provided another set of two Wusthof brand knives (knife C and knife D) using a similar presentation, but here the focal knife was on the left side of the page. The focal knife, knife C, had an original price $102 and a sale price of $75. Knife D (on the right side of the page) served as the comparison knife, with a sale price of $68. Participants again indicated which knife they preferred. Finally, participants provided their gender, age range, and household income range. To ensure that participants took the task seriously, we told participants (prior to starting the study), that they should make careful choices because (in each session) one participant would be randomly selected to receive one of the two knives chosen.
TABLE: TABLE 2 Johnson–Neyman Regions in Study 2b
| Diet Scalea | Effect | SE | z | p-Value | LLCI | ULCI | Participant Behavior Consistent with: |
|---|
| 21.2100 | 2.7911 | .2588 | 23.0568 | .0022 | 21.2984 | 2.2839 | Taste goals (32.18% of sample) |
| 2.5414 | 2.3313 | .1690 | 21.9600 | .0500 | 2.6625 | .0000 | |
| -.2100 | -.1033 | .1416 | -.7296 | .4657 | -.3807 | .1742 | |
| .3597 | .2886 | .1472 | 1.9600 | .0500 | .0000 | .5772 | Diet goals (34.86% of sample) |
| .7900 | .5846 | .1913 | 3.0564 | .0022 | .2097 | .9594 | |
| 1.7900 | 1.2724 | .3415 | 3.7262 | .0002 | .6031 | 1.9417 | |
| 2.7900 | 1.9602 | .5100 | 3.8435 | .0001 | .9606 | 2.9598 | |
aMean-centered values for diet intentions scale.
Notes: This table illustrates the conditional effect of alignment on choice of the low-sodium soup can, at values of diet intentions scale. LLCI = lower-limit confidence interval; ULCI = upper-limit confidence interval. Boldfaced cells indicate significance.
Results. Consistent with results in Studies 1 and 2, participants preferred the focal knife relatively more when the sale price was shown in an aligned manner. When sale price was shown below the original price, the choice shares for both focal knives B and C were 84.0%; when sale price appeared above the original price, choice shares for the focal knives fell to 33.3% (knife B) and 61.1% (knife C). These choice share differences were significant for knives A versus B (c2( 1) = 11.49, p < .05) and directionally significant for knives C versus D (c2( 1) = 2.88, p = .09).
This study involved a cell phone battery scenario wherein participants were provided with numeric information on battery life, an MIB attribute. Our incoming expectation was that relative preference for the focal item, with longer battery life, would be greater when it was presented in an aligned (vs. nonaligned) manner. Furthermore, a key element of our theory is that if the focal item and the comparison item are displayed in an aligned manner, then it is relatively easier for the shopper to perform the difference calculation. However, if the difference calculation is very easy in the first place, then alignment differences should not affect the perceived ease of performing difference calculations, thus mitigating our proposed effects.
Methods. Participants from Amazon Mechanical Turk (N = 250, 37.8% women, median age group 26–30 years, median annual income $25–$50,000) took a Qualtrics survey. After an instructional manipulation check question (see Oppenheimer, Meyvis, and Davidenko 2009), the survey outlined a scenario wherein the participants used their cell phone a lot, so participants were aiming to buy a cell phone case with an integrated cell phone battery. Then participants read brief descriptions of two cell phone cases with batteries. Case/ Battery H (the comparison item) had been on the market for six months, earned good reviews, and offered an incremental battery life of 246 minutes. Case/Battery J (the focal item) was a bit thicker, and because it had just launched, reviews were not available, but the manufacturer claimed an incremental battery life of 331 minutes. The participants were randomly assigned to a 2 (display: aligned vs. nonaligned) · 2 (calculations: harder vs. easier) between-subjects design. In the harder difference calculation condition, the battery lives were 246 minutes (H) and 331 minutes (J); in the easy difference calculation condition, battery lives were 250 minutes (H) and 350 minutes (J). We elicited relative preference on a seven-point scale (1 = “strong preference for H,” and 7 = “strong preference for J”) and captured demographic information.
Results. We found that 35.6% participants gave incorrect responses to the instructional manipulation check (consistent with ranges in Oppenheimer et al. [2009]), so we removed them from the analyses (consistent with recommendations in Oppenheimer et al. [2009]). An analysis of variance for relative preference revealed significant main effects (for both display and difference calculations, F( 1, 157) > 3.9, p < .05), as well as a directionally significant interaction effect (F( 1, 157) = 3.31, p = .07). When battery life differences were harder to calculate, using an aligned display (i.e., locating J above H) led to significantly increased preferences for the focal, longer-life battery J (Mabove = 3.98, SD = 1.93 vs. Mbelow = 2.70, SD = 1.59; F( 1, 157) = 9.38, p < .05). In contrast, when difference calculations were easy, locating J above H did not significantly influence relative preferences (Mabove = 4.03, SD = 2.19 vs. Mbelow = 3.85, SD = 1.89; F( 1, 157) = .17, p > .6).
Consistent with the theory in this article and with our a priori expectations, any factor that makes the difference calculation easier should serve as a suitable moderator for our effects. In Study 3b, we examined the specific case of when difference calculations were easy (e.g., 350 - 250), and thus, alignment differences should not influence the importance weight that shoppers would generally give to the difference gap. Other instances when difference calculations should be easier may include when, for example, the difference gap is explicitly stated (e.g., the difference percentage is explicitly shown [“40% more battery life,” “25% less sodium”]) or the difference is very large. In all these cases, there would be no need for the shopper to perform the difference calculations to figure out that the difference gap is substantial, so shoppers should generally give the difference gap relatively high importance weight. These factors—some of which were foreshadowed in Biswas et al. (2013)—all (potentially) constitute moderators to our effects.
For shoppers who have diet goals, presenting a focal, healthy food item in an aligned (vs. nonaligned) manner increases its choice share. In contrast, for shoppers with taste goals, presenting the focal food item in an aligned (vs. nonaligned) manner decreases its choice share and increases the choice share of the competing, less healthy food item. This nonintuitive interaction result reflects our central hypothesis (H1), whereby the extent to which shoppers make made goal-consistent food item choices is higher (lower) when the food items are displayed in an aligned (nonaligned) manner. Drawing on work in food-related research, goals, and numeric cognition, we outline the underlying process mechanism in H2–H3. Whereas Studies 1 and 2 illustrate H1–H3 in the food domain, Study 3 shows that our proposed decisionmaking framework has widespread applicability, potentially extending to any domain wherein advertising, retail displays, or online displays involve comparisons of numeric attribute information.
Our study makes several contributions to the body of work relating to shoppers’ food-related goals and how shoppers make food choices. First, prior research has suggested that people with less focus on diet goals pay less attention to nutrition information (Naylor, Droms, and Haws 2009; Van Herpen and Van Trijp 2011). For example, Mohr, Lichtenstein, and Janiszewski (2012, p. 64) argue that “those very involved with their dietary choices will … [be influenced by] nutrition labels,” and they found (p. 66) that differences in purchase intentions across less healthy versus healthier food items arose among those with higher levels of diet intentions but not among those with lower levels of diet intentions. Bialkova, Sasse, and Fenko (2016) and Cavanagh and Forestell (2013) found similar effects, such that people who were more concerned about health were more likely to prefer healthy food items over less healthy food items, but people less concerned about health expressed no clear preference. In contrast, we propose that people with less focus on diet goals (i.e., those with taste goals) do indeed pay attention to nutrition labels; however, because of the unhealthy = tasty intuition, these shoppers behave as if they prefer food items with more calories or sodium. This explanation better reflects the interaction result in Studies 1–2, implying a specific disordinal (crossover) pattern. If shoppers with taste goals merely ignored (or paid less attention to) nutrition information, the interaction pattern in Studies 1–2 would be different and would reflect an ordinal pattern. In contrast with prior research, we find that it is possible to find cases wherein those score lower on the diet intentions scale behave as if they may prefer higher-calorie food items—especially when attribute information is presented in an aligned manner. This point highlights a key contribution of this article.
Second, prior research into shoppers’ food choices has tended to ignore the impact of display differences (and other contextual differences) related to the presentation of calorie information. Specifically, even as prior research posits that people with diet goals focus more on low-calorie items, it ignores the possibility that these effects may be weaker if lowercalorie food items are displayed in a nonaligned manner. In Studies 1 and 2, the effect of diet intention scores (or diet vs. taste primes in Study 2a) on choices and preferences is moderated by differences in attribute display. In essence, shoppers are more likely to make goal-consistent choices when food items are displayed in an aligned manner. This point is both new and nontrivial. Beyond the implications for practice (as we discuss subsequently), this finding may explain null results that arise when differences in diet intentions do not prompt different choices or preferences. For example, consider Study 2b. We reanalyze the data and, purely for illustrative purposes, median-split the diet intentions variable. If we consider just the two cells reflecting the nonaligned display condition, the relative choice shares for the low-sodium soup can were 39.7% (low diet intentions) versus 43.1% (high diet intentions) (c2( 1) = .15, p = .69). Examining just these cells might lead a researcher to conclude that differences in diet intentions do not affect shoppers’ choices. Yet when we consider the other two cells, which involve aligned food item displays, the relative choice shares shift to 22.2% (low diet intentions) versus 70.7% (high diet intentions) (c2( 1) = 30.65, p < .05). Thus, examining just the data pertaining to an aligned display would lead a researcher to a very different conclusion: that differences in diet intentions significantly affect shoppers’ choices. Both points are contributions beyond modifying and broadening the subtraction principle and are highlighted as such in Appendix A.
The findings in this article also contribute to the numerical cognition literature. By examining the impact of display differences, we determine that vertical display differences lead to varying importance weights that shoppers assign to the attribute gap when making evaluations. Displaying a focal food item in an aligned (vs. nonaligned) manner, such as below (vs. above) comparison food items, leads shoppers to attach more importance weight to the attribute gap in their evaluations. We tested these effects not only within the food domain (Studies 1 and 2) but also in other domains (Study 3), indicating that these effects have predictive applicability across a wide variety of domains wherein advertising, retail displays, and online displays involve comparisons of numeric attribute information.
Given concerns about obesity and associated health problems (Howlett et al. 2012), there is much interest in understanding when shoppers might use calorie and sodium information to make healthier choices. One challenge is to motivate shoppers to embrace diet goals, which can increase their consumption of healthier foods. However, assuming shoppers have diet goals, another challenge is to ensure that available calorie (or sodium content) information is displayed in ways that nudge shoppers toward healthier, rather than less healthy, food choices. Our findings suggest that display differences related to the location of food items can encourage shoppers’ healthy choices. As our studies indicate, diet-focused shoppers are more likely to make healthy choices if nutrition information is displayed in an aligned manner.
Finally, and most importantly, this article outlines a parsimonious decision-making framework that examines how shoppers react to advertising, retail, or online displays in a wide variety of product domains involving comparisons of numeric attribute information. Building from, modifying, and expanding the work in Biswas et al. (2013), we identify two key elements that jointly determine shoppers’ evaluations: ( 1) whether shoppers perceive the attribute as an LIB attribute or a MIB attribute and ( 2) whether the attribute information is displayed in an aligned or nonaligned manner. Firms can use this framework to better design advertising, retail displays, and online displays. The effects in this article apply across a variety of product domains, as evidenced in the marketplace examples we cite and in the range of studies we present (Appendix A).
Differences in shelf displays affect shoppers’ purchase intentions (Grewal et al. 2011). As more firms adopt the voluntary FOP nutrition labeling system, “Facts Up Front,” and as more retailers display food items to showcase such FOP information, a key question is how retailers and category captains should organize the display of food items on retail shelves. The insights in this article offer some guidance. Imagine a retailer that wants to promote the new low-calorie Coca-Cola Life soft drink. If most shoppers (at this retailer) have diet goals, or if the retailer is able to prime diet goals through in-store signage or advertising, then, on the basis of this research, the retailer will increase sales by putting cans of Coca-Cola Life below cans of regular Coca-Cola. If a retailer primarily attracts shoppers with diet goals, but its profit margins are better on regular soups, then it might choose to put the regular soup cans below the lower-sodium soup cans to encourage relatively more sales of regular soup, despite its primarily “diet-goal” shopper segment. Finally, depending on food item categories, advertising, packaging, and so on, shoppers may have different goals. To the extent a retailer knows these goals, or to the extent firms can use advertising or packaging to prime such goals, firms can use display differences to increase sales of the more profitable products within the category. For example, if the candy category prompts taste goals, and if margins are higher on candy products with more calories, then retailers should display higher-calorie candy items above other candy-items to maximize sales of these more profitable products. Thus, the findings in this article can aid retailers and category captains as they optimize in-store shelf displays. Contingent on shoppers’ goals, numeric values relating to calorie content or to sodium, and relative food item profitability, retailers can display food items in ways that “push” certain high-profit food items over others. Similarly, the findings in this article may also inform how online retailers should display food items on their webpages and how supermarkets and grocery items should display food items on flyers.
The effects we describe herein are driven by differences in (locational) displays of food items, which lead to differences in importance weight attached to food item attributes, with downstream consequences. Understanding this informationprocessing sequence has several implications for consumer welfare and public policy. First, we provide guidance for how diet and nutrition apps might be structured to help shoppers make healthy choices, noting that those who use such apps likely already have diet goals. When diet apps provide scores of food items, whether in grocery stores or restaurants, such diet apps should motivate shoppers to not only learn exact calorie/sodium information but also to give this information greater importance weight in their evaluations. Such efforts might help mitigate any negative impact arising from retailers’ use of nonaligned displays. Second, from a public policy perspective, young consumers and children are relatively unlikely to have diet goals (Burton, Wang, and Worsley 2015), and so using aligned food item displays may well backfire. Specifically, using aligned food item displays and/or explicitly prompting younger consumers to consider calorie/sodium information is likely to increase their preference shift toward higher-calorie/sodium food items. In such instances, regulatory policies governing advertising and menu signage should (seemingly counterintuitively) recommend using nonaligned display presentations and should (seemingly counterintuitively, but importantly) avoid prompting younger consumers to explicitly consider calorie/sodium information. These points highlight the role of attribute gap importance weight and distinguish this work from Biswas et al. (2013). Finally, both shoppers and policy makers need to recognize that marketers can present attribute information in ways that may mislead shoppers. For example, if a lower-calorie option involves smaller profit margins, a restaurant frequented by patrons with diet goals might display this option above a high-calorie option to reduce patrons’ weighting of the calorie gap and thus reduce patrons’ preference shift toward the lower-calorie option. Such a practice can be labeled as “providing full information” to patrons and is not illegal, but public policy experts would note that it may reduce welfare.
The effects outlined in this article have widespread applicability, extending well beyond the food domain. As stated previously, these effects apply to any product domain involving a comparison of numeric attributes. Thus, for example, the effects would extend to any product domain wherein prices (typically an LIB domain) are displayed; into any domain involving MIB attributes such as battery life (e.g., cell phones, tablets, laptops) and Internet speed (e.g., cellular networks); and into domains such as robotic vacuum cleaners, which involve attributes that are MIB (e.g., operating time) and LIB (charging time).
As a specific example, some insurance companies (e.g., Progressive) provide information both about their own rates and about the rate from a competitor. Progressive shoppers would behave similarly to shoppers with diet goals, in the sense that insurance shoppers generally prefer lower insurance rates (insurance rates = LIB attribute). In line with this article’s findings, we suggest that an insurance provider should present its own rate quote below the quote from a competitor. If its own rates are lower, this presentation format ensures that insurance shoppers put more weight on the “rate gap,” which increases relative preference for the focal insurance company’s product. If, however, the competitor’s rates are lower, presenting its own (higheramount) rates below would lead shoppers to put less weight on the rate gap, thus decreasing relative preference for the competitor’s insurance product.
The advice in the previous paragraph is valid when the attributes involved are clearly LIB. However, other attributes may typically be perceived as MIB (e.g., network speed, battery life, operating time [in robotic vacuum cleaners]). In such instances, the focal firm should display its information above that of competition (exactly opposite of what is advised when the attribute is LIB). If its competitor’s “scores” are lower, this presentation display ensures that shoppers put more weight on the attribute gap, which increases relative preference for the focal firm’s product. If its competitor’s scores are higher, then this presentation display leads shoppers to put less weight on the attribute gap, thus decreasing relative preference for the competitor’s product.
The contrast across the prior two paragraphs highlights the importance of identifying shoppers’ goals and identifying whether shoppers perceive the attribute as LIB or MIB. As we have noted, what constitutes the optimal display is conditional on how shoppers perceive attributes. It is for this reason that LIB versus MIB is one of the two independent variables in this article.
The effects we find are driven by differences in displays of food items, which lead to varying importance weights assigned to numeric attribute information and further downstream consequences. We reiterate this important point because it helps clarify the conditions in which the effects we propose may be more versus less evident. To the extent that the effects in this article are driven by differences in importance weights, they may be more evident if the importance weight of numeric attribute information (e.g., calories, sodium, price) is neither too high nor too low. However, in certain conditions, such importance might reach high levels, and it is then that the effects we propose may be less evident. For example, if calorie attributes dominate choice (i.e., have very high importance weight), display differences likely have minimal effects, as a result of ceiling effects. Beyond the moderators identified in
Study 3b, these may also constitute moderators of our effects, and these are not predicted by Biswas et al. (2013).
Future research could also further examine the informationprocessing mechanism outlined in H2 and H3. A key element of this mechanism is that subtraction calculations are perceived as more difficult when the smaller number is displayed above the larger number. Numerical cognition researchers may also examine whether individual differences related to numeracy, math anxiety, and so on may moderate the effects noted in H2–H3.
In addition, there are two ways shoppers can perceive numeric attributes (vector attributes vs. ideal point attributes; see Green and Srinivasan 1978; Teas 1993). This article examines the case wherein shoppers perceive attributes as “vector” attributes, preferring either more of an attribute (MIB [e.g., battery life]) or less of an attribute (LIB [e.g., for diet focused shoppers, fewer calories/less sodium), but it does not examine what happens when attributes have ideal point characteristics. What happens if shoppers believe that an “ideal” number of calories for a sandwich is around 350 calories? Future research could explore whether the effects in this article sustain when such ideal points exist for key attributes.
In this research, we assume that consumers who behave as if they prefer food items with more calories do so for taste-related reasons. But other reasons could also be operant, such as financial reasons that prompt some lower-income shoppers to prefer food items with more calories. Examining the behaviors of these shoppers is an important area for research, especially from a policy standpoint, to determine whether lower-income shoppers might prioritize calorie amounts over factors like nutrition or health.
Building on a “healthy-left, unhealthy-right” intuition, Romero and Biswas (2016) propose that a food item without nutrition labeling is perceived as healthier if displayed to the left (vs. right) of a comparison food item. Among shoppers with diet goals, such a display increases the focal, healthy food item’s choice share. However, we propose that when FOP calorie information is shown, displaying the focal, healthy food item to the right of the comparison item (i.e., displaying the food items in an aligned display) may increase the importance weight that shoppers attach to the calorie gap during their evaluations and so would increase the choice share of this food item. Thus, presence of FOP calorie information may reverse Romero and Biswas’s (2016) results. Research that tests these competing predictions could contribute to both theory and practice.
Implicit in our theory is that many shoppers embrace the unhealthy = tasty intuition. It would be worthwhile to reexamine these effects among populations (e.g., in France; see Werle, Trendel, and Ardito 2013) for whom this intuition may be weaker or even reversed. Finally, we only examine cases wherein attribute information is provided using numeric information. However, sometimes attribute information is provided using quasi-numeric formats, such as when Verizon contrasts its cellular coverage with AT&T using a map covered with more (vs. less) dots, without providing information relating to the actual number of dots. Would aligned (vs. nonaligned) display matter in such cases? Examining this and similar questions may further expand the applicability of this work.
1In this section, we contrast a focal food item with fewer calories with a comparison food item with more calories. The theory advanced herein also extends to other comparisons between all types of items described using numerical attribute information.
2Similarly, other, somewhat less well-known research has indicated that people often assume that the presence of increased sodium levels is associated with better taste (e.g., Henney, Taylor, and Boon 2010).
3In Sweden, nutritional values are provided in kilojoules (kJ) and not in calories (1 calorie @ 4.18 kJ).
aNonaligned presentation increases subtraction difficulty (Thomas and Morwitz 2009; see also Fuson and Briars 1990; Yip 2002).
A: Study 1
B: Study 2a
C: Study 2b
D: Study 3a
E: Study 3b
Notes: In Studies 1, 2, and 3a, aligned display conditions are shown. In Study 3b, the aligned display/harder calculation condition is shown.
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appendixes
APPENDIX A
TABLE:
| | Initial Conceptualization (Biswas et al. 2013) | Modified, Broadened Conceptualization (Current Research) |
|---|
| Key independent variable exemplars | Sale price display (right vs. left; left = nonaligned display) | 1. Food item with lower-value nutritional information (lower value of calories, sodium, kJ, etc.) display (above vs. below; above = nonaligned display) 2. Sale price display (above vs. below; above = nonaligned display) 3. Phone case with longer battery life display (above vs. below; below = nonaligned display) |
| Dependent variables | Value perceptions, purchase intentions, choice (one study) | Choice (multiple studies), preference |
| Mechanism | When price information is presented in a nonaligned manner increases subtraction difficultya price gap estimated at 10%.12% evaluation Price gap estimate of 10%.12%, based on Blair and Landon (1981) | When two elements of attribute information are presented in a nonaligned manner increases subtraction difficultya attribute gap given less importance weight evaluations Attribute gap given less importance weight, based on Shah and Oppenheimer (2007); see also Oppenheimer (2008) |
| Domain applicability | Study scenarios involve price comparisons 10%–12% benchmark does not “travel well” to nonprice domains The price domain typically involves LIB goals (i.e., lower prices are better). Predictions apply to LIB domains. | Study scenarios relate to the food domain (attributes: calories, sodium) and battery domain (attribute: battery life); there is (also) one study involving price comparisons The mechanism, and the important role of importance weight, is applicable across multiple attribute domains (including the price domain) We acknowledge that participants may have LIB goals or MIB goals, contingent on state (e.g., domain type) or trait (e.g., diet goals vs. taste goals) considerations. Predictions apply to LIB domains (e.g., price), MIB domains (e.g., battery life) and domains in which participants can have either LIB goals or MIB goals (e.g., food domain). |
| Moderating mechanisms | Moderators for subtraction difficulty (e.g., providing subtraction gap amount, having numbers involving easier calculations) Moderators for price gap (e.g., moderate versus low discount depth) | Moderators for subtraction difficulty e.g., having numbers involving easier calculations The focus of this article was on modifying and broadening the subtraction principle. |
| Contributions specific to the food domain | The focus of this article was the price domain; other domains not considered/not examined | Extant literature (e.g., Mohr et al., 2012 Bialkova et al. 2016) has indicated that those with less focus on diet goals pay less attention to nutrition information. In contrast, this article indicates that those with less focus on diet goals (1) pay attention to nutrition information and (2) make choices as if they prefer highercalorie/high-sodium food options The extent to which participants make goalconsistent food choices is contingent on whether food options are displayed in an aligned manner. |
a Nonaligned presentation increases subtraction difficulty (Thomas and Morwitz 2009; see also Fuson and Briars 1990; Yip 2002).
APPENDIX B
Notes: In Studies 1, 2, and 3a, aligned display conditions are shown. In Study 3b, the aligned display/harder calculation condition is shown.
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Record: 18- An Integrated Power and Efficiency Model of Contractual Channel Governance: Theory and Empirical Evidence. By: Carson, Stephen J.; Ghosh, Mrinal. Journal of Marketing. Jul2019, Vol. 83 Issue 4, p101-120. 20p. 1 Diagram, 3 Charts, 1 Graph. DOI: 10.1177/0022242919843914.
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An Integrated Power and Efficiency Model of Contractual Channel Governance: Theory and Empirical Evidence
Power theories (e.g., social exchange theory, resource dependence theory) and efficiency theories (e.g., transaction cost analysis) offer very different perspectives on the design of contractual governance in marketing channels. Whereas power theory suggests that governance will reflect the preferences of powerful firms, efficiency theories argue that governance will maximize joint value. In this research, the authors provide an integrative framework that reconciles power and efficiency perspectives in the context of contractual marketing channel relationships. This framework discriminates between two methods of exercising power: ex ante (through a tightly specified, efficient contract that rewards the powerful firm through the price mechanism while providing strong safeguards for the weak firm) or ex post (through a loosely specified, inefficient contract that allows the powerful firm to exploit its power during renegotiations). The authors argue that power will cause channel governance to deviate from the efficient choice, but only to the extent that the powerful firm cannot price out (i.e., extract) the value it offers to the weaker firm ex ante. As exchange conditions become more uncertain, power will demonstrate stronger effects on governance. This theory is supported with data from studies on contractual research-and-development relationships and procurement contracts for customized industrial products.
Keywords: contracting; efficiency; power; governance; marketing channels
The design of contractual governance between independent parties in a marketing channel has been informed by two major but strikingly different perspectives: power and efficiency theories. The central claim of power theory is that observed governance will reflect the preferences of powerful firms ([ 2]; [27]; [35]). Efficiency theories, in contrast, argue that governance is chosen to maximize the joint value created in the channel as a whole, irrespective of the initial power differences between the firms ([ 6]; [68]; [73]). Because of this distinction, power and efficiency theories rarely converge on the same governance predictions ([35]; [71]). Indeed, one of the key motives underlying the development of efficiency theories like transaction cost analysis (TCA) was a desire to challenge the standard monopoly (power) argument put forth in applied price-theory approaches to industrial organization and antitrust ([48]). Accordingly, "Greater respect for organizational (as against technological) and for efficiency (as against monopoly) purposes is needed" ([71], p. 17).
The debate between power and efficiency perspectives has been acrimonious at times, with some sociologically inclined power theorists decrying the spread of economic approaches to the study of organization (e.g., [23]). Many institutional economists have been equally derisive of power: "The main problem with power is that the concept is so poorly defined that power can be and is invoked to explain virtually anything" ([71], p. 238). Prominent organizational theorists have similarly described the construct of power as diffuse and disappointing ([51]). Indeed, as early as 1980, Stern and Reve distinguished between these two "seemingly disparate" disciplinary orientations—a primarily economic perspective rooted in industrial organization and focused on efficiency (e.g., [ 4]) versus a primarily behavioral perspective rooted in social psychology and focused on power and conflict (e.g., [24]; [64]). Even then, they expressed a concern about the lack of integration between the two viewpoints: "Rarely have there been attempts to integrate these two perspectives. Indeed, they should be viewed as complementary, because the former deals mainly with economic 'outputs' while the latter is concerned with behavioral 'processes'" ([65], p. 53).[ 5]
Despite the coexistence of these two perspectives in the literature, most empirical work has adhered to a single perspective while doing little more than acknowledging the other (usually in the form of control variables). As such, meaningful integration of the two has been lacking. This indifference to the other perspective has led to certain limitations. For example, power theories have not been able to explain when and why powerful firms might choose governance that enhances joint value versus when they might deviate from this focus to accommodate their own self-interests. Likewise, efficiency theories have been mostly silent on how ex ante firm differences influence governance, although [29], [30]) governance value analysis is an exception. As a result, we have a limited understanding of how the two theories interact, the circumstances under which one or the other will be more determinative of channel governance, and the new insights that can be generated by considering the two within a unified framework.
Constructing one such unifying framework in the context of contractual marketing channel relationships is the goal of this study. Our core thesis is this: power will cause governance to deviate from the efficient structure, but only to the extent that uncertainty prevents the powerful firm from pricing out the value it offers to the weaker firm ex ante. By "pricing out," we mean that the price mechanism, rather than other aspects of the governance structure, is used by the powerful firm to extract the value of the unique resources it brings to the relationship. For example, when exchange conditions are less uncertain, rather than reducing or eliminating the safeguards provided to the weak firm, a powerful firm can instead charge a higher upfront price and couple this with the strong safeguards desired by the weak firm. In this sense, the price mechanism makes it unnecessary for the powerful firm to alter the ex post governance structure in its favor, leaving it in an efficient state. As such, when exchange conditions are less uncertain, power and efficiency theories will arrive at similar governance predictions. In contrast, when exchange conditions are more uncertain, we argue that the powerful firm cannot price out the value it offers and will therefore shift governance to its own advantage and away from the structure predicted by efficiency theories.
To conduct a clear test of this power–efficiency framework, we require settings in which efficiency theories provide an unambiguous prediction that is then compromised by power as exchange conditions become more uncertain. Accordingly, we select two contexts in which the exposed specific investments are predominantly made by the buyer (in the technology/equipment supplied by the vendor), with comparatively limited (nonreimbursed) investments made by the vendor.[ 6] Given this one-sided pattern of exposed investments, the primary exchange hazard is also one-sided: potential opportunism on the part of the vendor against which TCA prescribes safeguards for the buyer.
In both contexts, we focus on the safeguards contained in the ex ante contractual agreement. In the first, where clients sponsor technical development work by external contract research organizations (CROs), we investigate two sets of safeguards: price inflexibility and client-exclusive property rights over the technology. In the second, where industrial buyers purchase customized equipment systems from vendors, we investigate price inflexibility and performance guarantees as safeguards. These contexts enable us to provide evidence on the generalizability of our framework across substantively different governance forms: price inflexibility, property rights, and performance guarantees. Consistent with our thesis, in both contexts we find that when contracting with more powerful vendors, safeguards are weaker only in more uncertain environments. That is, both power and uncertainty must be present to produce an effect on safeguards and pull them away from efficiency-theory predictions.
Our research makes several contributions. First, our integrative framework contributes to both literature streams by describing the conditions under which powerful firms will act to promote efficiency within their channels versus those in which they will bias governance toward their own self-interests and away from efficiency. The current TCA literature is generally silent on when and why firms might choose inefficient governance forms, and the power literature similarly cannot predict when firms will refrain from exercising their power. We also add to the power literature by describing the conditions under which powerful firms will apply their influence ex ante, at the governance selection stage, versus ex post, over activities and bargaining in the ongoing relationship.
Second, our work builds on and complements a small body of research that has worked toward integrating power and efficiency theories, principally [37] and [62]. Both see power as interfering with the standard TCA predictions, but for different reasons. In the context of ongoing supply arrangements, [37] recognize the fundamental conflict between power and efficiency and view the presence of cooperative norms as critical to smoothing concerns about the abuse of ex post power—reverse opportunism in [47] terminology—and allowing the efficient transfer of control as predicted by TCA and property rights theory. Our approach differs in that we focus on ex ante power derived from extrarelational resources and develop the interplay between power and efficiency within the incomplete contracting framework itself, thus allowing us to address governance issues even in relationships not endowed with strong norms ([ 6]; [35]; [59]).
[62] explore the benefits rather than the risks of power and argue that powerful firms are able to use their positions to better coordinate the relationship and lower transaction costs directly, thereby making costly integration less essential. This, in turn, dulls the empirical relationships between the standard TCA predictors and vertical integration. In their approach, power, when present, never fails to weaken the efficiency prediction. That is, there is no factor that inhibits the influence of power on governance and, as a result, their theory cannot predict the conditions under which powerful firms will refrain from influencing governance away from TCA predictions. We suggest a more nuanced interaction in which power moves governance away from efficiency, but only in sufficiently uncertain conditions.
Third, relatedly, our work complements research on the governance value analysis (GVA) framework ([29], [30]) that also uses the incomplete contracting perspective to describe relationships between ex ante firm characteristics and governance forms. Whereas [29], [30]) consider how firms with differentiated (i.e., power-generating) market positions are motivated to choose contracts that protect these extrarelational resources from expropriation ex post, our framework sheds light on the flip side of this issue by considering how these firms can best exploit their power by discriminatingly aligning governance forms with different exchange conditions.
Finally, one of the notable shortcomings of TCA is its incomplete treatment of bounded rationality and differences in firm preferences in the process used to select governance structures. Essentially, it is assumed that firms will somehow automatically "find their way" to the most efficient arrangement. This limitation has gone largely unremarked on since the foundational work of [11]. We contribute by analyzing how different contracting environments change the criterion used to select governance—power or efficiency.
This article is organized as follows. We begin with a brief review of power and efficiency theories. We then develop our unifying framework in the context of contractual channel relationships for two distinct, but conceptually similar, inputs and draw refutable predictions that are tested with primary data. We finally discuss the implications for research and managerial practice.
The power perspective has shaped a significant body of research in marketing, having been used to predict channel performance (e.g., [27]), satisfaction (e.g., [28]), and conflict (e.g., [49]), among other phenomena. Power is most commonly defined as the ability of one party to bring about its desired outcomes by influencing the actions of others ([19]; [39]). Firms are more powerful to the extent they possess unique and difficult-to-imitate resources that make them valuable to potential partners (e.g., [14]). This value gives them a degree of influence over governance selection ex ante as well as activities and bargaining in the ongoing relationship ex post (e.g., [25]; [33]).
The central principle of power theory is that powerful firms will use their positions to align governance structures with their own preferences (e.g., [27]; [35]). In many instances, powerful firms use their influence to organize the channel more efficiently (e.g., [32]; [62]; [65]). In this role, they not only provide information, expertise, and legitimacy to better coordinate channel activities but also resolve sticky channel management problems such as free riding that would otherwise increase transaction costs and reduce efficiency. One illustration of this coordinating role played by powerful firms is the decision control accorded to franchisors by less powerful franchisees ([50]). Franchisors can use this control to enforce territorial restrictions, prevent free riding off the efforts of other franchisees, direct system-wide promotional activities, and take other steps to enhance efficiency. At the same time, power theory recognizes that firms may use their positions in ways that do not enhance joint value, instead favoring themselves at the expense of weaker counterparties (e.g., [33]; [50]). For example, the practice of powerful franchisors forcing franchisor-specific investments on weak franchisees has been frequently cited as an "unfair" abuse of franchisor power ([47]). Similarly, supply relationships with powerful retailers are often heavily biased in the retailer's favor (e.g., [44]).
Despite its intuitive appeal, the power perspective has three shortcomings. First, it is not clear as to when and why powerful firms would choose to play the role of enhancing joint value versus focusing primarily on their own payoff. Second, while power theories have focused on the role of ex ante power, they have failed to distinguish this from ex post power that arises from the "fundamental transformation" in which investments in specific assets lead to a small-numbers bargaining environment ([71]).[ 7] Thus, firms that are powerful ex ante may not always maintain a superior position ex post once they are locked into the relationship ([30]).
The third and most significant shortcoming is that power theories do not use a comparative governance analysis to discriminate between alternative governance forms. A comparative governance analysis considers the choice between alternative governance forms simultaneously, rather than seeking to explain each choice in isolation as power theories do. This limitation is best illustrated by example. Suppose we wish to explain the use of a direct sales force as opposed to independent reps. The power of the firm relative to the salesperson might explain the decision to go direct (i.e., the more powerful firm forces salespeople into employment relationships that are subject to greater control and usually offer lower rewards than those the same individuals could earn as independent contractors). However, under a comparative analysis, it is not clear why vertical integration is the preferred way for the firm to leverage its power over salespeople. Instead, the firm could use its power to bargain for a significantly lower commission rate from independent reps. Power can explain both governance alternatives in isolation but does not discriminate between the two in a comparative fashion.[ 8]
The need for such discrimination is especially critical in contractual channel relationships because they are more nuanced than the rather blunt instrument of vertical integration ([75]). In particular, the more powerful firm could force either an inflexible, tightly specified contract in which it extracts its value through the price mechanism by charging a premium price, or a more flexible, loosely specified contract that allows it to exploit its power and extract value during ex post renegotiations. Both options potentially increase its gains from the relationship; however, it is not clear which governance option the firm would choose and why. [50], p. 22) express this conundrum quite lucidly: "It could certainly be argued that the more powerful channel member would not need an explicit contract because it would not need any safeguards; the weaker party could do little harm to it because of its less powerful position. The problem with this argument is that it goes against the strong empirical evidence that more powerful channel partners, such as franchisors, extensively use explicit contracts."
Efficiency theories such as TCA, property rights theory, and incomplete contracting theory have also driven an enormous body of literature in marketing, having been used to predict vertical integration (e.g., [ 1]), contract structure (e.g., [38]), features of relational exchange (e.g., [36]), partner selection (e.g., [67]), codevelopment alliances (e.g., [22]), and contracts for new product introductions ([15]), among other things. These theories rest on the premise that parties adopt governance structures that maximize joint value ([ 6]; [68]; [71]). Formally, efficiency refers to the minimization of the out-of-pocket and opportunity costs associated with producing and transacting in the channel; hence its equivalence to joint value maximization.[ 9]
This focus on joint value implies that the chosen governance form is invariant to differences in ex ante firm power, because such differences do not affect the joint benefit obtained from adopting efficient governance ([74]). In other words, the fact that one firm has more power ex ante does not alter the optimal governance arrangement within the channel ex post. Rather, ex ante power only affects how the gains from adopting the efficient arrangement are divided among the parties, with more powerful firms extracting a greater share of this "surplus" ([34]; [68]). [11] and [68] illustrate how a system of ex ante transfers, or side payments, can be devised such that each firm will prefer the efficient ex post arrangement after taking the transfers into account. This is because joint profit is maximized under the efficient arrangement; thus, these transfers will always be of sufficient magnitude to compensate the counterparty for its losses in case it is (initially) worse off under the efficient arrangement. The most common forms of ex ante transfers in practice are the price one firm pays to acquire another in the case of integration or the agreed on transfer price (e.g., franchise fee) in a contractual channel setting.
Efficiency theories, however, are subject to important shortcomings of their own that have received relatively little attention. In particular, they are largely silent on the process by which all parties come to accept the efficient governance structure. If efficient governance is Pareto optimal (i.e., makes all firms at least as well off as feasible alternatives), the issue is trivial. However, if efficient governance is not in the best interest of all parties—particularly powerful parties—how is it that they come to accept it? As [68], p. 463) observes, "The process (the game) through which the possibilities for trade are explored is suppressed, the assumption being that it is optimal; or more precisely, that the parties will (somehow) find the optimal process." Thus, even though [71], p. 174) uses natural selection arguments to defend efficiency when he says "the [efficiency] argument relies in a general, background way on the efficacy of competition to perform a sort between more and less efficient modes and to shift resources in favor of the former," he acknowledges the limits of this assumption: "This intuition would nevertheless benefit from a more fully developed theory of the selection process. Transaction cost arguments are thus open to some of the same objections that evolutionary economists have made of orthodoxy."
Crucially, the natural selection arguments invoked in TCA are not simply underdeveloped. They are in fact not well suited to relationships involving powerful firms, because departures from efficiency within the channel need not harm these firms. In particular, a powerful firm could be better off in a less efficient relationship that is biased in its favor, in which case the survival risk from the adoption of inefficient governance falls on the weaker, often replaceable, partner. Natural selection, therefore, cannot be a sufficient argument for efficiency when powerful firms are involved because there is no reason to assume that selection will somehow force a joint profit-maximizing outcome.
Indeed, per the Coase theorem, an efficient solution is only feasible in a frictionless environment characterized by complete contracting where the parties can commit to both the ex ante transfer and to refraining from opportunistic bargaining ex post. In an incomplete contracting environment, there is no assurance that the efficient arrangement can be made acceptable to both parties ([11]; [70]). Said differently, the implementation of an efficient governance structure essentially comes down to whether one party—here, the weaker party—can pay the other party to accept it ([68]). In turn, the powerful firm must be able to credibly commit to the efficient governance structure and assure the weaker party that it will not exercise its power again ex post. If such credible commitments are not feasible, due to incomplete contracting, the efficiency argument against the influence of power breaks down.
The integrated perspective we develop next is generalizable to a wide variety of channel contexts; however, to provide an unambiguous empirical test of the resulting theory, we seek contexts in which the efficient governance structure can be clearly identified so that we can detect departures from efficiency due to power. We do so in two different channels, one in which client firms sponsor technical development work performed by external CROs (Study 1) and a second in which industrial buyers purchase customized equipment systems from vendors (Study 2). These contexts have two key features. First, unlike typical manufacturer–supplier or manufacturer–distributor settings, the nature of the core activity—research and development (R&D) and systems purchasing—is considerably discrete or lumpy. As such, each transaction stands on its own and long-run relational give and take (the "shadow of the future") is not of as much significance. This assures us that contractual terms cannot be trivially renegotiated without some risk of opportunism when ex post adjustments are required. Second, in both settings, the primary exposed specific investments are made by the client/buyer. This implies that the main exchange hazard is opportunism by the CRO/vendor against which the client/buyer requires a safeguard.
Under these two key aspects of our settings, by examining the extent to which CRO/vendor power reduces safeguards for the client/buyer, we can see the effect of power on the efficient governance structure. To add concreteness in the development of the theory, we cast our hypotheses in the context of Study 1. We use the same logic to suggest predictions for Study 2.
In Study 1, client firms sponsor technical development work performed by external CROs. In this setting, safeguards for the client take two primary forms: ( 1) firm fixed prices or prices subject to prespecified adjustment formulas to limit holdup during development and ( 2) property rights provisions assigning exclusive rights over the technology to the client to deter opportunistic leakage following development. We consider the effects of two key independent variables—CRO power, which is measured by the uniqueness of the CRO among potential suppliers at the outset of the relationship, and uncertainty, which refers to the extent of volatility and ambiguity affecting decision making in the exchange ([52]). To arrive at the key hypothesis based on our efficiency × power interaction, we first briefly describe the main effects of these variables as predicted by power and efficiency theories, respectively (recall that each theory is largely silent on the other variable).
Power theory suggests that powerful firms will bargain for their preferred governance forms. In general, powerful firms will want to limit the safeguards provided for the weaker firm because such safeguards are ( 1) costly to provide and ( 2) limit the powerful firm's discretion to exploit its position ex post. In the context of Study 1, powerful CROs will generally prefer more flexible price contracts (e.g., with adjustment formulas negotiated ex post) not only to protect themselves from cost overruns but also to extract value from the client during ex post adjustments prior to transferring the technology. Similarly, they will prefer to secure some usage and ownership rights over the intellectual property developed in the relationship, because this intellectual property often serves as the basis for future development work ([ 7]). Expropriating this value is not feasible when prices are inflexible (i.e., fixed) and the client has exclusive property rights; therefore, powerful CROs have an incentive to bargain against such safeguards in the contract. Thus:
- H1: CRO power is negatively related to the use of contractual clauses that (a) restrict property rights exclusively to the client and (b) specify inflexible prices, ceteris paribus.
Turning to uncertainty, TCA suggests that exchange hazards increase in more uncertain conditions not only because ex post adaptations are larger and more frequent but also because of increased performance ambiguity due to measurement difficulties. Both of these conditions create greater latitude for opportunistic behavior and increase the need to safeguard exposed specific assets from expropriation ([ 1]; [71]). In our context, in which the main specific investments are made by the client, safeguards in the form of less flexible prices and the use of contractual clauses that assign property rights exclusively to the client are both expected to increase as uncertainty increases. Thus:
- H2: Uncertainty is positively related to the use of contractual clauses that (a) restrict property rights exclusively to the client and (b) specify inflexible prices, ceteris paribus.
Next, we turn to the critical interaction between power and efficiency theories. We start from the position that the powerful firm sets the governance structure in the relationship ex ante because it is unique and can bid down potential weak partners until one of them accepts its terms for participation ([50]). In selecting governance, the powerful firm faces two options. First, it could opt for efficient governance in which joint value is maximized. In our context, such an efficient governance structure would feature a tightly specified contract that provides strong safeguards for the client's investments while rewarding the powerful CRO by enabling it to extract (i.e., price out) the unique value it provides through the price mechanism ex ante. Alternately, the powerful firm may select an inefficient governance form that gives it an opportunity to maximize its own direct rewards from the relationship.[10] In our context, this governance structure would be more informal, with weaker safeguards for the client, thereby allowing the powerful CRO to exploit its power during ex post renegotiations.
Which option will a powerful CRO choose? Note that the powerful firm must be at least as well off under the inefficient as opposed to the efficient governance structure; otherwise it would not choose the former. Because, by definition, joint value must be lower for the inefficient structure, the weak firm is necessarily worse off under the inefficient arrangement.[11] The weak firm will therefore prefer the efficient arrangement and has an incentive to offer an ex ante transfer in exchange for a commitment from the powerful firm to the efficient governance structure. In turn, the powerful firm will accept this only if it is better off following the ex ante transfer; otherwise it will prefer the inefficient arrangement.
When conditions (i.e., low uncertainty) permit more complete contracts to be constructed, the first-best strategy for a powerful CRO will be to choose the efficient governance structure in exchange for an ex ante transfer of sufficient magnitude to make it better off. In such circumstances, both firms will prefer efficient governance, and ex ante power differences between the two firms will not cause a deviation from this choice because the powerful firm has extracted its value ex ante.
In contrast, when complete contracts are more difficult to construct (i.e., high uncertainty), the picture is quite different. First, note that the weak firm will be reluctant to offer an ex ante transfer without a reciprocal commitment guaranteeing the powerful firm's actions ex post; otherwise it could be held up repeatedly. Recourse could be made at this point to the self-enforcing aspects of informal contracts based on reputation ([47]). However, given that the more powerful firm will typically have better prospects in the event of termination, informal contracts are not necessarily self-enforcing in situations where power is highly imbalanced. Importantly, this inability to rely on the self-enforcing mechanism actually works against the powerful firm by making it harder to commit to the efficient arrangement.
Given this inability to commit, a powerful CRO will seek a second-best method to capture the value it offers to the client. It does so by implementing an inefficient governance structure with weak (or nonexistent) safeguards that maximizes its ability to extend its ex ante power into the relationship and expropriate value through ex post bargaining. Importantly, the powerful CRO is actually worse off in this case than in the first-best scenario (i.e., complete contracting where its power is priced out ex ante) because, given the inability to commit, the client's incentives to invest under an inefficient governance structure are lower. However, given the constraints on contracting, this second-best strategy is optimal from the powerful CRO's perspective.
Note the distinction between our thesis and the classic power-oriented view. The latter suggests that the weak firm should accept the terms offered by the powerful firm, even if they are one-sided, because they either reduce uncertainty for the weaker party ([21]) or set parameters on what the more powerful party can do ([50]). In contrast, our argument recognizes not only the role of the price mechanism and its interplay with the choice of ex post governance but also the proactive role played by the nominally weaker party. In less uncertain conditions, the weak firm offers an appropriate transfer payment ex ante with the expectation that the powerful firm will credibly commit to limiting its influence ex post. The better the commitment offered (i.e., the closer safeguards are to the efficient level), the higher the initial transfer payment the weak firm is willing to make. In contrast, in more uncertain conditions, the powerful firm is unable to make such a commitment. As such, the option of pricing out power through an ex ante payment becomes untenable, making the powerful firm choose an inefficient governance arrangement to extract the value from its unique resources ex post. This enables us to tackle the [50] conundrum by explaining why powerful firms use tightly constructed explicit contracts in some situations and not in others. Thus:
- H3: The negative effect of CRO power on the use of contractual clauses that (a) restrict property rights exclusively to the client and (b) specify inflexible prices becomes stronger as environmental uncertainty increases, ceteris paribus.
Outsourced R&D, in which client companies sponsor R&D work by external CROs, is quite prevalent in high-technology, research intensive industries. In these settings, the client makes specific investments in a technology developed by the CRO, which is reimbursed for its client-specific development work. These relationships are governed by a development agreement, which is a formal contract specifying the nature of the deliverables, pricing terms, and property rights provisions for the technology and its derivatives ([ 7]). Given the one-sided nature of specific investments, the efficient governance structure will tend to feature strong safeguards for the client, which consist of less flexible pricing terms and client-exclusive property rights, as described previously.
The context of sponsored R&D projects is not well suited to the use of archival data for several reasons. First, very few data sets are available on outsourced R&D projects in general, and most secondary archives (e.g., the Deloitte/ReCap database) cover only large R&D alliances in narrowly defined industries. Second, our unit of analysis is an individual outsourced R&D project; thus, firm-level archival data would be only indirectly related to any given project and might introduce aggregation bias into the analysis. Third, certain constructs (e.g., the number of qualified contractors available at the outset of the project, financial investments in the project, uncertainty) are difficult to measure without relying on key informants. For example, uncertainty could be measured by volatility in industry or company sales, but these would be at best crude and indirect measures of the uncertainty surrounding an individual R&D project. Thus, we used our own survey instrument to capture the nuances of the context.
Many of the constructs we measure have meanings that are well-known and widely shared among informants in the industry, such as firm fixed prices ([61]). We therefore use single-item, grounded measures (i.e., with defined metrics) to capture the constructs of interest wherever possible. As [61] and [16] observe, trying to introduce additional items into the measurement of such constructs can cause measures to drift from the well-defined meanings. We use psychometric measures only where a domain sampling approach is supportive of validity. In particular, the measure of uncertainty is best captured by a multi-item reflective scale due to the multifaceted nature of this domain, particularly as it relates to contractual incompleteness.
Given our focus on high-technology, research intensive industries where outsourced R&D is common, we used National Science Foundation data to select the top five three-digit industries in terms of the percentage of firms outsourcing R&D: drugs and medicines; optical, surgical, and photographic instruments; communications equipment; motor vehicles and equipment; and aircraft and missiles. We used a national list broker to compile a list of 2,600 R&D managers with a single manager per firm. After eliminating 635 bad contacts, we contacted each manager to solicit participation. We used a snowballing technique to identify a qualified informant, even if different from the original contact. A total of 670 initial contacts could not be reached after extensive effort. Among the 1,295 individuals successfully contacted by telephone, 226 reported that no R&D was conducted in their unit and could not connect us with another manager in their company involved in R&D. A substantially larger number of 496 individuals were involved in R&D but indicated that their company did not outsource these activities. Again, this number includes only those instances in which a referral to a manager in a unit that did outsource R&D was not possible. Another 168 managers were involved with outsourced R&D projects but refused to participate. This left 405 qualified informants who verbally agreed to participate in the mail questionnaire. Follow-up phone calls and a second mailing resulted in 124 useable surveys with complete data on the variables included in this study. This yielded a 30.6% response rate based on the number of surveys mailed (124 of 405) or a 21.6% response rate based on the number of qualified informants identified (124 of 573). The former proportion is similar to the approximately 30% response rate in [36] and 37% in [30], though neither study distinguishes between the number of surveys mailed and number of qualified informants.[12] One drawback of operating in the context of outsourced R&D is that firms are sensitive to disclosing their R&D partners because this is often perceived as a source of competitive advantage. Thus, we were only able to collect data from the client side of each dyad.
We provide the measurement items following purification and response scales in Web Appendix W1. The following subsections describe our measures.
Price inflexibility takes on a value of one if the contract features a firm fixed price or a fixed price with a prespecified adjustment formula and zero if negotiated adjustments or unspecified pricing arrangements such cost-plus are used instead ([10]).
Property rights include ownership rights, usage rights, and rights to future improvements and derivatives ([ 7]). Note that these categories are not mutually exclusive; for example, a client may own the patents to a new technology, giving it residual rights of control associated with ownership ([34]), but certain explicit rights of usage may be granted to the CRO, or the client's usage may be restricted to particular fields of use. Thus, ownership does not preclude the importance of measuring usage rights or rights to derivatives. We employ a three-part measure of the exclusivity of client property rights over the technology developed under the contract consisting of ( 1) exclusive client ownership of patents over, or rights to patent, the technology; ( 2) exclusive client usage rights over the technology; and ( 3) exclusive client rights to derivatives based on the technology. A score of zero or one is assigned for each item and the scores are totaled. This measure is treated as an ordered categorical variable in the estimation.
Our measure of power is based on monopoly theory, in which power is a function of the availability of substitute providers. This measure has been previously used by [31]. Specifically, we measure the number of equally or better qualified CROs available to the client at the formation of the contract. We then reverse code this measure to create the score for CRO power so that a higher score indicates greater power. This measure is based on the perceptions of the client, which is appropriate as they also determine the influence the CRO has in the upfront bargaining.
Uncertainty is driven by a number of factors that combine to determine the overall challenge of operating in a given environment. Uncertainty is classically operationalized in terms of volatility and ambiguity (e.g., [52]). Volatility arises when conditions change frequently in ways that are difficult to predict ex ante ([41]), yet are readily understood ex post ([57]). Ambiguity, in contrast, arises when conditions are difficult to interpret ex post, irrespective of the rate of change ([52]). Because various aspects of volatility and ambiguity can lead to incomplete contracting conditions, we define the construct broadly in this study following a domain sampling approach. The four subdomains are market volatility, defined as the frequency of unanticipated changes in the market relevant to the technology ([36]; [41]; [66]); technological volatility, defined as the frequency of unanticipated changes in the technologies used in the development process ([36]; [40]; [66]); ambiguity, defined as the extent to which signals are open to multiple interpretations ([13]; [52]); and the level of creativity required to perform the development work (another aspect of ambiguity as described by [ 5]]), defined as the extent to which the intent of the project was to develop a meaningfully novel technology ([ 3]. Items for the market volatility scale were adapted from [56], [40], and [36]; items for the technological volatility scale were adapted from [36] and [40]; items for the ambiguity dimension are taken from [ 8] based on [13]; and the required creativity scale is based on [ 3].
Critically, relationships affected by both volatility and ambiguity pose the greatest challenge for decision making ([ 9]). Therefore, we formulate a measure that increases as each aspect of uncertainty increases to capture the overall effect of the various dimensions. This approach offers the key benefit of smoothing the measure across the individual facets. To see the importance of this, consider a simple example. A relationship with low volatility may not be characterized by low uncertainty if ambiguity is high. We use a second-order factor model to provide support for the multidimensional construction of uncertainty and subject it to a confirmatory factor analysis following [43]. Fit statistics following purification suggest an inability to reject the proposed model (χ2(86) = 101.938, p =.116; goodness-of-fit index =.909; root mean square error of approximation =.037; Tucker–Lewis index =.970). The composite reliability of the measure is.925 ([69]). We do not include other indicators in the factor analysis because they are not reflective measures. We averaged the items within each subdomain and then equally weighted the subdomains to form the final score. The control variables used in this study are as follows:
We assess client investment with a grounded measure of the client's total dollar investment in the work performed by the CRO. We used seven categories to measure expenditures: Less than $100,000, $100,000–$249,999, $250,000–$499,999, $500,000–$999,999, $1,000,000–$2,499,000, $2,500,000–$4,999,999, and $5,000,000 or more. Likewise, CRO investment is captured with a parallel measure of its nonreimbursed dollar investment in the project. The response categories for CRO investment are identical to those for client investment, except that we break the less than $100,000 category into two categories because CRO investments are often quite low: $0–$9,999 and $10,000–$99,999.
Repeated exchange can also support complex governance arrangements, making contractual safeguards less necessary. In development relationships, we often have continuity when the CRO is also the supplier of components (or products) based on the newly developed technology. In such cases, we expect more flexible prices and less exclusive client property rights. A single grounded item was used to measure whether the CRO was involved in a subsequent supply relationship with the client.
Contract research organizations often hold patents over technologies that are used to produce the deliverables. When such patents are involved in the development work, the CRO is both more powerful and far more likely to hold property rights to protect its interests. Thus, we expect more flexible prices and less exclusive client property rights. The use of the CRO's preexisting patents in the development work was measured using a single grounded measure.
Even uniquely qualified CROs at the start of the relationship will hold less power if the client also executes a portion of the work internally ([17]). This reduces the CRO's ability to distort governance away from efficiency, resulting in more extensive safeguards for the client. We measured the portion of the work on the technology performed in-house in four ways: as a percentage of dollar expenditures, value added, hours, and number of employees.
In large R&D projects, clients might employ multiple CROs to perform different portions of the overall work on a given technology (there are no cases of CROs performing the same work or "R&D contests" in our data). Because these CROs do different work, the involvement of multiple CROs does not necessarily make them more replaceable. However, the division of the work makes each CRO less important in a general sense, and thus less powerful, giving clients greater safeguards. We assessed this construct using a single grounded measure.
Finally, the nature of the outsourced task was assessed with a grounded measure taking on a value of one if the task consisted of development work and zero if the task consisted of either basic or applied research. The location of the work was similarly captured by a control variable taking on a value of one if the CRO's work was performed at the client's location.
Client exclusivity of property rights and price inflexibility were estimated using an ordered probit and a standard probit model, respectively.[13] The equations are:
Graph
1
Graph
2
The models involve interactions; therefore, we must pay attention to the zero values for the variables involved in the interactions when interpreting their main effects. In particular, the theory indicates that CRO power will have its strongest effect under high uncertainty. To showcase this, we center uncertainty so that the zero value corresponds to the maximum value of this variable in the data. This allows the main effect of CRO power to correspond to a situation in which it is theoretically expected to have its largest effect. Observe that this centering does not fundamentally alter the estimates, and we graph the full detail of the interactions next.
We also account for potential endogeneity among the regressors. The most serious concern revolves around the financial investments made by both parties that are selected simultaneously with governance. To address this, we estimate the models using Gaussian copulas. Copulas are a class of function that allow the joint distribution to be constructed from the individual marginal distributions. The method is a semiparametric, instrument-free approach to handling endogeneity that models the joint distribution of the error term and the endogenous regressors directly, thereby accommodating correlation between them. This differs from instrumental variable methods in which exogenous instruments are used in an attempt to purge correlations between the error term and the endogenous regressors.
Following [58], we assume that the error term is normally distributed following the probit specification. We then estimate the marginal distribution of each endogenous right-hand-side variable nonparametrically using an Epanechnikov kernel density function with [63] recommended bandwidth calculation. We use the two-step control function variant described by [58] because it extends easily to multiple endogenous regressors. Given kernel estimates of the marginal probability densities of the endogenous variables, and , we use numerical integration with the trapezoidal rule to calculate and . Given the Gaussian copula, the generated regressors are then calculated as and corresponding to the client's and CRO's investments. These variables are entered as additional regressors in the probit models, yielding consistent estimates of the coefficients of interest. Full details of the procedure can be found in [58].
Descriptive statistics and zero-order correlations appear in Table 1, Panel A. On average, the contracts had 1.13 provisions restricting some aspect of property rights exclusively to the client and 62.6% of contracts specify either a firm fixed price or a price subject to a predetermined adjustment formula. Prior to reverse coding the CRO power measure, the average is 1.603, indicating that most firms had one or two equally or better qualified CROs available, in addition to the one they used. This number is low enough to suggest that the typical CRO possessed a degree of monopoly power. The mean of uncertainty is 3.593 on a 1–7 scale. Most clients invested between $100,000 and $499,000 in the project, whereas the typical unreimbursed investment by the CRO was between $10,000 and $249,000. Table 1, Panel B, gives descriptive statistics and zero-order correlations for Study 2 (discussed subsequently).
Graph
Table 1. Summary Statistics and Zero-Order Correlations.
| A: Sponsored R&D Projects (Study 1) |
|---|
| Variable | Mean | SD | (0) | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) |
|---|
| 0. Client Ex. Prop. Rights | 1.132 | 1.134 | 1.000 | | | | | | | | | | | | | | |
| 1. Price inflexibility | .626 | .486 | .141 | 1.000 | | | | | | | | | | | | | |
| 2. CRO power | 2.397 | 1.310 | −.068 | −.044 | 1.000 | | | | | | | | | | | | |
| 3. Uncertainty | 3.593 | .810 | −.072 | −.081 | .217* | 1.000 | | | | | | | | | | | |
| 4. Client investment | 2.224 | 1.591 | −.073 | −.003 | .081 | .157 | 1.000 | | | | | | | | | | |
| 5. CRO investment | 1.705 | 1.293 | −.150 | −.065 | .088 | .093 | .455** | 1.000 | | | | | | | | | |
| 6. Component supply | .360 | .482 | −.216** | −.024 | −.018 | −.150 | .099 | .139 | 1.000 | | | | | | | | |
| 7. CRO patents | .163 | .371 | −.186* | −.152 | .134 | .203* | .309** | .245** | .128 | 1.000 | | | | | | | |
| 8. Client % Dollars | 25.243 | 28.726 | −.006 | −.021 | .144 | .301** | .070 | .132 | −.023 | −.075 | 1.000 | | | | | | |
| 9. Client % value added | 25.737 | 28.331 | −.017 | −.022 | .150 | .272** | .133 | .146 | −.002 | −.027 | .933** | 1.000 | | | | | |
| 10. Client % Hours | 26.304 | 29.646 | .056 | −.060 | .138 | 221* | .026 | .112 | −.045 | −.071 | .927** | .904** | 1.000 | | | | |
| 11.Client % Employees | 22.272 | 25.253 | −.010 | −.031 | .216** | .207* | −.007 | .071 | −.015 | −.102 | .820** | .801** | .872** | 1.000 | | | |
| 12. Development | .703 | .458 | .099 | −.125 | .040 | −.162 | .140 | .030 | .099 | −.173* | .053 | .044 | .085 | .112 | 1.000 | | |
| 13. Client Location | .054 | .228 | .079 | −.000 | .111 | .042 | .117 | .125 | −.055 | −.025 | .136 | .229** | .149 | .179* | .091 | 1.000 | |
| 14. Number CROs | 2.170 | 5.842 | .129 | .034 | −.090 | −.132 | .189* | .276** | −.083 | −.067 | .089 | .099 | .087 | .065 | .074 | .019 | 1.000 |
| B: Industrial Systems/Equipment (Study 2) |
| Variable | Mean | SD | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | | | | | | | |
| 1. Performance guarantee | .462 | .28 | 1.000 | | | | | | | | | | | | | | |
| 2. Price inflexibility | .531 | .415 | .093* | 1.000 | | | | | | | | | | | | | |
| 3. Vendor power | 1.953 | 1.452 | −.082* | −.037 | 1.000 | | | | | | | | | | | | |
| 4. Uncertainty | 4.094 | 1.116 | .065 | .113* | .058 | 1.000 | | | | | | | | | | | |
| 5. Buyer Investment | 4.236 | 1.109 | .131** | .067 | −.029 | .145** | 1.000 | | | | | | | | | | |
| 6. Vendor investment | 1.126 | .347 | −.002 | .053 | −.048 | .084* | .221** | 1.000 | | | | | | | | | |
| 7. LN (length of relationship) | 2.140 | .712 | .032 | .037 | .081* | −.004 | .065 | .058 | 1.000 | | | | | | | | |
| 8. LN (vendor size in $ million) | 4.673 | 1.524 | −.092* | −.072 | .154** | −.023 | .074 | .037 | .079* | 1.000 | | | | | | | |
1 *p <.05.
2 **p <.01.
Several of the control variables in the analysis—component supply, CRO patents, and client location—define qualitatively different contracting regimes that may alter the relationships in the data. We examined this possibility and found only one interaction. When estimating the property rights model, we exclude relationships involving the CRO's preexisting patents (approximately 16% of the data) because, when their patents are used, the CRO is far more likely to hold property rights to protect its own interests, which limits the sensitivity of property rights to other aspects of the relationship. This does not affect the price inflexibility model, because intellectual property rights are not at issue.
Coefficients and standard errors appear in Table 2. We use one-sided confidence intervals to test the a priori directional hypotheses. For consistency, we also use one-sided confidence intervals for the control variables. All models are estimated using robust standard errors.
Graph
Table 2. Client Safeguards for Sponsored R&D Projects (Study 1).
| (A) | (B) | (C) |
|---|
| Ordered Probit Model | Probit Model | Ordered Probit Model |
|---|
| Dependent Variable: | Client IPR | Price Inflexibility | Price Inflexibility |
|---|
| Constant | — | 2.042 (.988)** | — |
| CRO power | −.671 (.280)*** | −.691 (.324)** | −.614 (.268)** |
| Uncertainty | .558 (.311)** | .525 (.362)* | .402 (.314)* |
| CRO power × Uncertainty | −.232 (.125)** | −.252 (.136)** | −.234 (.116)** |
| Client investment | .197 (.106)** | .207 (.116)** | .172 (.096)** |
| CRO investment | −.272 (.118)*** | .025 (.141) | .108 (.142) |
| Component supply | −.219 (.253) | .103 (.284) | .132 (.263) |
| CRO patents | — | −.551 (.410)* | −.613 (.384)* |
| Client % dollars | −.009 (.013) | .013 (.016) | .007 (.013) |
| Client % value added | −.016 (.011)* | −.001 (.014) | −.001 (.011) |
| Client % hours | .026 (.012)** | −.019 (.012)* | −.010 (.010) |
| Client % employees | −.002 (.008) | .006 (.010) | .003 (.008) |
| Development | .067 (.234) | −.770 (.320)*** | −.817 (.286)*** |
| Client location | .928 (.618)* | .250 (.573) | .236 (.549) |
| Number CROs | .056 (.016)*** | .005 (.018) | .002 (.016) |
| P1* | .164 (.266) | −.219 (.216) | −.174 (.170) |
| P2* | −.250 (.191)* | −.181 (.236) | −.332 (.220)* |
| Pseudo R2 | .094 | .110 | .088 |
- 3 *p <.10.
- 4 **p <.05.
- 5 ***p <.01.
- 6 Notes: IPR = intellectual property rights. Standard errors in parentheses.
Column A of Table 2 presents results of the ordered probit model predicting the client-exclusivity of property rights. We find that CRO power has a negative and significant effect on the exclusivity of client property rights (β = −.671, p =.008), suggesting that more powerful CROs bargain for a greater share of property rights, in support of H1a. Uncertainty has a positive and significant effect (β =.558, p =.036), suggesting that clients retain more exclusive property rights as uncertainty increases and exchange hazards become more pronounced, in support of H2a. The critical interaction between CRO power and uncertainty is negative and significant (β = −.232, p =.032), suggesting that CRO power has a stronger negative effect on the exclusivity of client property rights under high uncertainty where power cannot be efficiently priced out ex ante, in support of H3a.[14]
We graphically depict the relationship between CRO power and the client exclusivity of property rights in Figure 1, Panel A, over a range of two standard deviations above and below the mean of uncertainty (3.593). The interaction crosses over a little less than one standard deviation below the mean of uncertainty.
Graph: Figure 1. Effects of power over ±2 SDs of uncertainty.
The fact that the interaction crosses over is of substantive interest. In developing our theory, we emphasized the role that high uncertainty and the resulting contractual incompleteness play in allowing power to distort governance away from efficiency. However, we did not discuss how power might also alter the efficient governance adopted under low uncertainty. Even though power is priced out under such conditions and does not cause governance to deviate from efficiency, it may still influence the nature of the efficient structure itself. Recall that TCA predicts the use of stronger safeguards in the presence of factors that threaten joint value–enhancing outcomes. While largely ignored in the efficiency literature, ex ante power itself is one such factor. Thus, when dealing with a powerful partner, the weak firm may be willing to pay for stronger safeguards because it will fare worse outside the boundaries of the safeguard.
To illustrate, pricing terms with negotiated adjustments usually specify that firms will negotiate in good faith over the final price based on the realized costs of the development work. These terms provide a degree of protection by framing the ex post bargaining problem. When firms have balanced power, such framing, in conjunction with price flexibility, provides a safeguard while limiting potential maladaptation costs associated with firmer pricing schemes ([30]). However, if power is imbalanced, more predetermined pricing terms (either prespecified adjustment formulas or firm fixed prices) become more efficient because the weak firm now requires more than framing to protect itself during ex post bargaining. The interaction in Figure 1, Panel A, shows that, in addition to biasing ex post governance in favor of the powerful firm (with lower safeguards for the weak firm) when conditions of incomplete contracting prevail (i.e., the right-hand side of the graph), greater power also appears to prompt an efficient governance arrangement featuring stronger safeguards for the weak firm when conditions of more complete contracting prevail (the left-hand side of the graph).
Turning to the control variables, the client's financial investment is positively related (β =.197, p <.05) and the CRO's unreimbursed financial investment is negatively related (β = −.272, p <.05) to the exclusivity of client property rights. Work performed on the client's site involves more exclusive client property rights (β =.928, p =.066), though this result is only significant at p <.10. Similarly, as the number of CROs in the extended task environment increases, clients retain more exclusive property rights, as we anticipated (β =.056, p <.05). Of the four variables representing the percentage of the work performed in-house by the client, we find only that as the percentage of hours performed by the client increase, its property rights become more exclusive (β =.026, p <.05).
The endogeneity correction term for the CRO's investment is significant at p <.10. This serves as a Durbin test for the endogeneity of this variable. Interestingly, the CRO's financial investment is endogenous, whereas that of the client is not. We suspect that this is because the latter is largely a function of the desired technology. The correlated portion of the effect is captured in the negative coefficient on , leaving a weaker coefficient reflecting the portion that is not affected by endogeneity. The main limitation of the copula method is that the distributions of the endogenous variables must differ from that of the error term. If the distributions are too similar, multicollinearity between the generated regressors and the error term will reduce efficiency, though it will not introduce bias. The level of statistical significance in the results suggests that this is not an issue in the present investigation.
Results of the probit model predicting price inflexibility appear in Column B of Table 2. Consistent with H1b, we find a negative and significant effect of CRO power (β = −.691, p =.017). Consistent with H2b, we find a positive coefficient for uncertainty (β =.525, p =.073), though this result is only significant at p <.10. The interaction between CRO power and uncertainty is negative and significant (β = −.252, p =.032), which supports H3b. Again, CRO power has a stronger effect on governance under high uncertainty in which power cannot be efficiently priced out ex ante.[15]
Figure 1, Panel A, also depicts this relationship graphically. Again, the interaction crosses over at a little less than −1 standard deviations in uncertainty. The interpretation is identical: under more complete contracting conditions, safeguards for the client are stronger when dealing with a more powerful CRO. Turning to the control variables, we see that the client's financial investment is positively related (β =.207, p <.05), whereas development tasks (β = −.770, p <.05), CRO patents (β = −.551, p =.089), and the percentage of hours performed by the client (β = −.019, p =.068) are all negatively related to price inflexibility. All other effects are insignificant.
Finally, Column C of Table 2 presents results for the ordered probit model on price inflexibility. The coefficients are of similar magnitude and significance.[16]
Buyers of industrial systems frequently purchase items from independent vendors that are customized to their operational needs and based on proprietary vendor technologies. The reliance on a specific vendor creates a dependency for the buyer and vulnerability to vendor opportunism because investments are sunk before performance is realized. Vendors usually build the development costs into the final price; thus, they do not have significant exposed investments for buyer-specific customization. Contracts governing these purchases usually consist of the necessary features of the customized product, the pricing terms, and clauses pertaining to performance guarantees for how the system will function once installed.
We investigate the power × efficiency interaction on two forms of safeguards in these relationships: inflexibility of the pricing terms before/during supply and whether the vendor commits contractually to a performance guarantee of its system after supply. As in Study 1, ex ante vendor power is measured by the number of equally qualified vendors available at the outset of the relationship. Because there is generally less ambiguity in the production of industrial systems than R&D, we use a more traditional measure of uncertainty for studies in the channels literature, volatility.
Similar to Study 1, we expect that powerful vendors will prefer more flexible price contracts (e.g., with adjustment formulas negotiated ex post) to protect themselves from cost overruns and will also try to avoid providing costly performance guarantees to buyers (H1), especially in more volatile conditions in which the features necessary to provide a certain level of performance can shift. Likewise, having committed to procuring customized equipment, buyers will seek safeguards in the form of fixed prices and performance guarantees as uncertainty increases (H2). The logic for the key power × efficiency interaction (H3) is similar to that in Study 1. In particular, when uncertainty is low, a powerful vendor will be willing to provide the safeguards that the buyer seeks—fixed prices and performance guarantees—in exchange for an ex ante transfer that enables it to capture the unique value it provides the buyer through the price mechanism. In contrast, when uncertainty is high, the powerful vendor cannot offer a credible guarantee that after having received a transfer payment ex ante it will not create a holdup problem ex post. It will also be more reticent to provide performance guarantees given uncertainty surrounding the necessary features/technology. Given this infeasibility to price out its power ex ante, the powerful vendor will seek governance forms with weaker safeguards that enable it to expropriate value through ex post bargaining. Thus, the expected relationships are identical to Study 1, with the exception of examining performance guarantees rather than client exclusive property rights.
We examine four industry sectors: industrial machinery and equipment, electrical and electronics equipment, transportation equipment, and instrumentation. Our sampling frame consisted of 1,900 firms with an identified sales manager working at each firm. We used a key informant methodology and a snowballing technique to identify and qualify an informant in each firm. This qualification effort yielded 926 valid informants who were willing to participate in our study and were mailed the survey instrument. Follow-up telephone calls and reminder cards resulted in 288 responses on the variables used in this study for a response rate of 31%, which is comparable to the 30% response rate in [36] and 37% in [30].
To the extent possible, we used grounded measures for our constructs. The reflective measure for uncertainty is based on [36]. The measure of ex ante vendor power is the number of equally qualified vendors available to the buyer for the focal system. This measure is negatively correlated with two measures of vendor reputation in our data: "Customers are willing to pay a high premium for our products and services" (−.315; p <.05) and "Customers value our products and services more than that of our competitors" (−.246; p <.05). These results give us confidence that our measure of vendor power reflects unique value-added resources that the vendor brings to the table in these relationships.
Web Appendix W2 provides details of our measures following purification. The control variables include measures of buyer and vendor investments, importance of the product, several variables reflecting the shadow of the future, and industry dummies.
We apply the same correction procedure for endogeneity as in Study 1. The included regressors in this case are both highly insignificant, suggesting that no correction is needed. Therefore, we omit these terms for simplicity.
Descriptive statistics and correlations appear in Table 1, Panel B. Table 3 provides the results. Column A shows the results of the probit model for price inflexibility. The effect of vendor power is negative and significant (β = −.155, p <.05), indicating that more powerful vendors bargain for more flexible prices. The effect of uncertainty is positive and significant (β =.095, p <.05), suggesting that when this factor is higher, buyers seek the safeguards provided by less flexible prices. The key interaction effect between vendor power and uncertainty is negative and significant (β = −.036, p <.05). Thus, under high uncertainty, in which power cannot be efficiently priced out ex ante, powerful vendors pull governance away from the efficiency theory prediction.
Graph
Table 3. Client Safeguards for Purchase of Industrial Systems/Equipment (Study 2).
| (A) | (B) |
|---|
| Probit Model | Probit Model |
|---|
| Dependent Variable: | Price Inflexibility | Performance Guarantees |
|---|
| Constant | −1.033 (.445)** | 1.519 (.612)** |
| Vendor power | −.155 (.056)** | −.162 (.060)** |
| Uncertainty | .095 (.058)** | .089 (.060)* |
| Power × Uncertainty | −.036 (.021)** | −.040 (.031) |
| Buyer investment | .079 (.053)* | .116 (.061)** |
| Vendor investment | .072 (.060) | −.009 (.059) |
| LN (length of contract) | −.084 (.46)* | −.094 (.062) |
| LN (length of relationship) | .043 (.057) | .080 (.058) |
| Product importance | .093 (.041)** | .058 (.053) |
| LN (vendor size) | −.002 (.063) | −.122 (.069)* |
| SIC36 | .015 (.062) | .112 (.059)* |
| SIC37 | −.040 (.052) | −.017 (.052) |
| SIC38 | .077 (.047)* | .008 (.045) |
| Pseudo R2 | .165 | .129 |
- 7 *p <.10.
- 8 **p <.05.
- 9 ***p <.01.
- 10 Notes: Standard errors in parentheses.
Column B in Table 3 shows the results for the probit estimation on the inclusion of a performance guarantee. The effect of vendor power is again negative and significant (β = −.162, p <.05) and the effect of uncertainty is positive and marginally significant (β =.089, p =.078). The interaction of vendor power and uncertainty is directionally consistent with expectations but misses significance narrowly (β = −.040; p =.130). Examining Figure 1, Panel B, we see that the effects of vendor power on price inflexibility and performance guarantees crossover at approximately one standard deviation below the mean of uncertainty (4.094). This is much the same as what we see in Study 1 and similarly indicates that vendors with greater power are willing to opt for efficient governance and offer buyers even stronger safeguards when environmental uncertainty is low (i.e., when conditions for more complete contracting prevail).
Overall, the results are consistent with our thesis that powerful firms shift governance away from the efficiency prediction, but only when conditions (e.g., high uncertainty) do not allow them to price out the value they provide their customers ex ante.
We offer a generalizable framework that integrates power and efficiency perspectives in contractual marketing channel relationships and validates the importance of ex ante power as a determinant of channel governance. Rather than summarily dismissing power, as is common in efficiency arguments, we incorporate power into the comparative governance analysis used in efficiency theories to explain the role it plays in governance design. Specifically, we argue that power affects governance only under conditions of sufficiently high uncertainty (i.e., when conditions prevent the powerful firm from pricing out the value it offers to the weaker firm ex ante). Results from two separate contexts, involving three different contractual safeguards chosen by these firms, provide support for this argument. Because we do not treat either power or efficiency as "dominant" in our framework a priori, we arrive at a richer understanding of the conditions under which each will be more influential.
Crucially, we identify a factor—uncertainty—that can inhibit the impact of both power and efficiency on governance. When uncertainty is low and contracting is more feasible, the powerful firm has an incentive to support efficient, joint value maximizing governance. It can secure the value it offers to its partner ex ante through the price mechanism while simultaneously providing a safeguard by foregoing its influence on ex post governance. As a result, the impact of power on governance is inhibited. In contrast, when uncertainty is high making contracts more incomplete, the powerful firm cannot price out the value it offers. Thus, it purposefully seeks an inefficient arrangement that enables it to claim value ex post under more informally specified governance. As a result, the role of efficiency is inhibited.
One weakness of power theory has been its inability to discriminate between governance choices using a comparative analysis. As described previously, in the R&D context, a powerful CRO could exercise its power by limiting ex post safeguards for the client—for example, by insisting on flexible prices and nonexclusive client property rights. Alternatively, it could attempt to extract the full value it offers to the client by requiring it to pay the highest possible (fixed) price ex ante and then providing strong safeguards, such as exclusive and strongly protected client property rights, to maximize the client's incentives to invest in the relationship. Both seem equally plausible.
By employing the comparative governance lens used fruitfully in efficiency theories, we simultaneously compare the role of power on each of the two feasible alternative governance forms. We then show that under less uncertain conditions, the powerful firm provides the client with efficient safeguards ex post (and prices out its power ex ante). Under more uncertain conditions, this mechanism becomes infeasible, forcing the powerful firm to use a second-best strategy by seeking gains ex post through flexible contractual terms (e.g., flexible prices). In contrast, in [62], the powerful firm always shifts away from integration because its power gives it the legitimacy to impose value-creating actions while avoiding the high costs of integration. Despite this difference, both studies show that power and efficiency are not simply competing explanations but that ex ante power can influence ex post governance systematically under key exchange conditions.
One weakness of efficiency theories such as TCA has been that they have ignored the role of ex ante resource and power differentials on governance under the premise that boundedly rational actors with conflicting preferences will somehow "naturally select" the path to efficient governance. Said otherwise, inefficient arrangements will die out, supporting [72] dictum that economizing is the best strategy. Our approach instead attends to the microanalytic process particulars ([71]) and showcases the procedure used to adopt governance. Specifically, the process underlying our theory suggests that natural selection is not sufficient because the powerful firm, even though desiring of efficient joint value–maximizing solutions, would choose inefficient governance to capture value under conditions (uncertainty) which prevent it from securing the value derived from its power ex ante and credibly offering efficient safeguards.
This literature, predominantly based on efficiency theories, has investigated a wide variety of governance challenges in marketing channels. These include contractual completeness (e.g., [10]; [55]), contract duration (e.g., [42]), the assignment of property rights ([ 7]), the use of exclusive territories ([18]), joint ventures ([38]), franchise contracts (e.g., [46]; [45]), price and design specifications ([30]), and more. We contribute to this literature in several important ways.
First, our work emphasizes the importance of considering both the terms of the contract and the initial price. Analyzing one without the other can be misleading. For example, a powerful firm may grant generous (efficient) safeguards to a weaker partner but in turn demand a higher upfront price. Indeed, our results in Figure 1 show that the safeguards provided for the weaker firm may be stronger in the presence of a more powerful partner when complete contracts can be written (i.e., the left-hand side of these graphs). Looking at these safeguards without considering the initial price would lead to incorrect inferences about the balance of power in the relationship and the motives of the parties.
Second, as a complement to studies such as [30] and [55], our work emphasizes the need to focus on both the ex ante and ex post features of governance to understand the different ways they interact in particular contexts. For instance, whereas the [30] GVA model is concerned with the protection of value creating assets that get intermingled in supply relationships, we focus on how powerfully resourced firms leverage their power. In particular, the clients/industrial buyers in our contexts contract with the CROs/vendors precisely because they want these CROs/vendors to build for them technologies/systems based on their technical capabilities. As such, there are limits to the reverse holdup problem that GVA focuses on because a CRO/vendor that fears leakage could simply decline to offer the technology ex ante. Given this, the exercise of power, rather than the protection of rent-generating assets, becomes the key motive for the CROs/vendors. The invocation of these alternative facets of power suggests a complementarity between these approaches that is context dependent and warrants future attention. For instance, one fruitful direction would be to predict the conditions under which the drive to exploit power-generating assets would trump the need to protect the value of these assets from expropriation and vice versa.
Third, we extend [47] analysis to illuminate the nature of "unfairness" expected in formal contracts. In particular, we suggest that "unfairness" in formal contracts due to an imbalance in power will primarily be reflected in the price, not other terms. Why? Our analysis has shown that a more complete contract is necessary to price out the influence of power ex ante and that such a strategy is first-best for the powerful firm when feasible. Importantly, this contract is first-best only because it maximizes the investment of the weaker firm, which is achieved by providing a credible commitment to treat it fairly by providing appropriate safeguards. If environmental conditions are such that a complete contract can be written, it will tend to reflect this pattern, with fair treatment of the weaker firm ex post but power reflected in the seemingly "unfair" price ex ante. Yet this price is merely a symptom of the overall fair treatment of the weaker firm. It is, of course, a matter for policy makers to decide at what point the price is actually unfair or merely a reflection of the value offered by the powerful firm.
Finally, as power becomes more balanced or symmetric across firms, the bargaining at relationship formation will be influenced more evenly and is likely to draw governance closer to joint value maximization. Importantly, this means that incomplete contracting alone should not result in serious deviations from efficiency without differences in firm power. More equally placed firms should progress toward a more mutually beneficial (albeit informal) governance structure, even under incomplete contracting conditions. It is only when contracts are incomplete and one firm has a power advantage that it can push for an arrangement that allows it to exploit its power ex post and major deviations from efficiency are expected. We summarize this relationship in Figure 2. When uncertainty is low, under both balanced and unbalanced power conditions, governance is both formal and efficient, because parties can construct ex ante transfers along with efficient contractual terms. In contrast, when uncertainty is high and power is balanced, the ex ante transfers necessary to support efficient contracts cannot be constructed and parties have to resort to informal governance. Such informal governance is efficient under balanced power conditions since negotiations will naturally tend toward more mutually beneficial outcomes. It is only when power is imbalanced and contracting incomplete that we expect to see informal arrangements which inefficiently favor the powerful firm.
Graph: Figure 2. Firm power, uncertainty, and governance.
When complete contracts cannot be written, relationships will be governed more informally, with greater latitude afforded to the firms ex post. That is, these relationships will feature contracts without fixed prices, without exclusive property rights provisions for the client (even though the client has paid for the R&D effort), and without performance guarantees. Thus, they will rely on more informal, relational, implicit contracts that lack legally binding safeguards. Importantly, while the literature on informal governance tends to associate it with the presence of relational norms or a shadow of the future, our analysis suggests that informal governance could also be imposed by a powerful firm if it cannot provide the credible commitment necessary to price out its power ex ante. Crucially, it is imposed specifically to expropriate value from the weaker firm during ex post bargaining. In this case, informal governance is neither efficient nor indicative of a cooperative, mutually oriented relationship; it is simply a second-best, but feasible, choice.
Our framework can also be used to shed light on certain aspects of informal governance that have not received much attention in the literature. One such issue is whether relational norms in a given context are flexible (more incomplete social contracts) or more rigid (more complete social contracts). In [37], norms facilitate the transfer of control while constraining the actions of the controlling firm; thus, they may be viewed as promoting rigidity in the social contract. In our contexts, informal governance will involve price and behavioral expectations, even though not formally specified. A party that offers generous compensation to its partner ex ante might expect the relationship to unfold in a certain manner; it would not expect or tolerate ex post bargaining when adjustments are required. In this sense, the informal governance structure (social contract) is more rigid. In contrast, a party holding the line on compensation may anticipate some degree of resistance and bargaining ex post that the parties recognize as more legitimate. Here, the social contract is more flexible.
The results provide guidance to managers overseeing contractual channel relationships. For managers of powerful firms, we offer a new way of thinking about the role and exercise of power. It complements the prevailing view that power allows the firm to influence the ex post behavior of others within the relationship by suggesting that managers must also recognize and use the price mechanism to extract the value provided by their unique resources. Indeed, where feasible, the pricing out of power ex ante coupled with a credible commitment against using power to influence behavior ex post is optimal not only from the weak firm's perspective but from the powerful firm's perspective as well.
This claim is challenging for managers, because to generate the highest initial price, the powerful firm must tie its own hands as a means of guaranteeing a favorable environment for the weaker firm ex post. We suspect that most managers of powerful firms would instead default to trying to exercise their power ex ante when setting the price and keeping their options open to influence behavior and bargaining ex post. However, this approach is suboptimal if the contracting environment allows them to credibly commit to not doing so. It is only when ex ante transfers are infeasible that the powerful firm should shift its focus ex post toward influencing partner behavior and realizing gains from bargaining.
Managers at weak firms, in turn, should expect to pay a higher price up front in exchange for more favorable, efficient conditions ex post. Here again, a shift in thinking from traditional power arguments is warranted—the weak firm should try to negotiate such an outcome rather than accept an inefficient arrangement on the grounds that it either reduces uncertainty ([21]) or sets parametric bounds on what the more powerful party can do ([50]). The weak firm should also exercise caution in relying on the self-enforcing aspects of the implicit or relational contract given the powerful firm's superior options ex post in the event of termination.
Our prescriptions allow managers to understand a wide range of ways in which firms exercise their power. For example, the franchisee bills of rights included in many franchise arrangements at first appear to be at odds with the power of the franchisor. Viewed through the lens of traditional power theory, they call into question whether the franchisor's position is really dominant. Viewed through the lens of our theory, well-specified franchisee rights are a tool for the powerful franchisor to credibly commit to fair treatment of the franchisee ex post. This safeguards the franchisee's investment in the relationship and enables the franchisor to charge a higher up-front fee when the franchise agreement is constructed. This arrangement cannot be properly understood without considering both the contractual terms and the price mechanism.
Managers can also benefit from realizing that, while the feasibility of arrangements involving credible commitments is primarily a function of the uncertainty surrounding the exchange, it is also subject to managerial influence and choice. For instance, more complete contracts can still be written in many "complex" situations at a cost ([10]; [55]). The first-best and second-best alternatives identified in this study suggest that the costs of writing more complete contracts ex ante may be justified, particularly in relationships involving powerful firms (see Figure 2), to support the greater efficiency associated with the pricing out of firm power.
Finally, managers should consider the uncertainty that surrounds their focal task when making outsourcing decisions. More complex and uncertain tasks will tend to be governed less efficiently than simpler more programmable tasks in relationships involving powerful firms (see Figure 2). Thus, when powerful providers are involved, uncertain tasks at firm boundaries should be internalized to a greater extent, because powerful external providers will distort governance arrangements in their favor. Weaker providers, in contrast, allow more efficient outsourcing of even uncertain tasks, and smart outsourcing firms may recognize this when selecting partners and see the value of weaker firms. This may explain, in part, the highly fragmented nature of many industries providing outsourced services (in addition to conflict of interest issues). The fact that weaker firms are well advised to avoid powerful providers may present a barrier to the advancement of weaker outsourcing firms within their industries by deterring their involvement with leading providers.
We also offer a brief consideration of the implications of our theory for antitrust policy, because the fundamental contrast between power (monopoly) and efficiency that we consider exactly mirrors the central debate in the antitrust literature. Antitrust regulation has historically focused on restricting the exercise and extension of monopoly power. Although the latter concern has been largely discredited in the academic literature absent the ability to price discriminate ([60]), it still plays a prominent role in court opinions and public policy discussions. As a result, so-called nonstandard modes of organization have been viewed by courts and regulators with extreme suspicion ([12]) due to the fundamental assumption that these nonstandard forms result from attempts to capitalize on firm power rather than efforts to enhance efficiency. The thrust of TCA (and new institutional economics more generally) has been quite different, with Williamson presenting compelling arguments against this "inhospitality tradition" (1985, p. 19) and suggesting that nonstandard arrangements other than cartels can serve "affirmative economic purposes" (1985, p. 200). The upshot of these arguments is that monopoly power should be less at play in determining nonstandard modes of organization than efficiency. As [53] observes, the Chicago-school approach to vertical restraints associated with Posner, Peltzman, and others is based on these same arguments.
We argue for a nuanced view and suggest that the relative influence of power and efficiency rests on the ability of firms to price out their power differences ex ante. Absent this ability, powerful firms cause inefficiencies in channel governance. From an antitrust standpoint, the key observation is that the efficiency argument for nonstandard modes of organization serving affirmative economic purposes is undermined if governance is known to systematically deviate from efficiency in certain conditions as we illustrate. In particular, in highly uncertain environments, power leads to distortions in governance that favor the more powerful firm. Here, nonstandard arrangements involving powerful firms should rightly fall under suspicion—they cannot be presumed to support efficiency purposes. In contrast, in less uncertain conditions or when more evenly placed firms are involved (see Figure 2), even seemingly nonstandard arrangements are likely to support efficiency within the channel because this is the best option for all firms.
The issue becomes one of analyzing the incentives of each party to implement an efficient agreement and the means by which powerful parties can be made whole when efficient arrangements work to their disadvantage, as we have shown that they do. Because TCA removes ex ante power as a main-case explanation of governance and glosses over the process by which firms with conflicting preferences arrive at efficient arrangements, it tends to provide an undiluted view of complex arrangements as efficiency enhancing.
Our framework is subject to several important boundary conditions. First, we assume that the institutional environment contains a well-functioning judiciary so that court ordering can be used to enforce the contractual commitments made. Otherwise, the powerful firm cannot price out its power using an ex ante contract, even under low uncertainty, because the weak firm cannot rely on enforcement of the contractual safeguard. In this case, power will have a larger influence on governance. This can be an important consideration in international relationships located in countries with a weak rule of law, which will be influenced more by power. In this case, either outsourcing firms would be unwilling to undertake value-enhancing investments specific to the CRO/vendor or, as a corollary, only powerful outsourcing firms with political connections would be willing to do so.
Second, relationships in our contexts are not embedded in repeated exchange; thus, the reputational feature of the shadow of the future is not available. If it were, it would allow joint value–maximizing governance to be adopted even when complete contracts could not be written. In this case, environmental uncertainty would have a limited influence on the way power is exercised. Importantly, relational exchange would not necessarily affect the degree to which power is exercised. Much as [37] observe a transfer of control consistent with the predictions of TCA even in the presence of relational norms, we would expect power to be exercised in highly relational exchanges because the powerful firm deserves a return on its unique resources. However, the powerful firm would not cause a departure from joint value–maximizing governance to do so and would exercise its power through the mutually agreed on price rather than through unilateral expropriations ex post.
Third, the theory assumes that the interests of power and efficiency are at least partially at odds with each other. The assumption might not hold if the weak firm is not called on to make any specific investments and therefore does not require any safeguards. In this case, both power and efficiency will encourage either safeguards for the powerful firm if it makes a specific investment or simple market exchange (with power reflected in the price) if neither firm makes a specific investment.
Fourth, our study focuses on contexts with one-sided exposed specific investments that give us a clear wedge to showcase the tension between power and efficiency. This is, however, not a boundary condition on the theory itself, which applies more generally, subject to the aforementioned considerations. In particular, the power of any focal party will tend to reduce the level of safeguards for the other party under incomplete contracting conditions because the price mechanism is not available to partial out the effect of power. It will do so to a lesser degree or not at all when more complete contracting is feasible.
Turning to limitations, we did not measure efficiency with a direct measure of costs, instead relying on a reduced-form analysis to show the effect of power on the safeguards predicted by efficiency theory. An attractive avenue for future research would be to directly address implications on governance costs. However, this would almost certainly require a focus on a single industry and other steps to standardize activities across observations so that costs are comparable. In addition, we would have to carefully separate the costs incurred ex post from the initial price. That is, we claim that power affects the ex post governance structure in certain circumstances and the price in others. When power shows up in the price, it is actually more efficient, yet the amount paid by the client/buyer is higher. This price could not, therefore, be taken as an indication of either cost or efficiency.
Finally, we abstracted away from the classic typology of [26] that is often used in the power literature, opting instead to define power in a straightforward manner based on scarcity. This is consistent with the long-standing view of monopoly power in economics and with [20] sociologically oriented dependence model. Scarcity is highly consistent with reward and coercive bases of power and, because it reflects the differential skills and resources of the CRO/vendor in our setting, it is also largely consistent with a basis in expertise. It would be informative to understand the roles played by other bases of power.
Supplemental Material, DS_10.1177_0022242919843914 - An Integrated Power and Efficiency Model of Contractual Channel Governance: Theory and Empirical Evidence
Supplemental Material, DS_10.1177_0022242919843914 for An Integrated Power and Efficiency Model of Contractual Channel Governance: Theory and Empirical Evidence by Stephen J. Carson and Mrinal Ghosh in Journal of Marketing
Footnotes 1 Associate EditorJan Heide served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242919843914
5 1[35] described a similar split between economic and behavioral perspectives, as well as between efficiency-oriented perspectives such as TCA and power-dependence perspectives such as social exchange theory and resource dependence theory.
6 2In our data for both contexts, the mean investment by the buyer is substantially larger than the mean (nonreimbursed) investment by the vendor.
7 3According to TCA, relationship-specific investments that have significantly lower value outside the relationship create a small-numbers bargaining situation because an opportunistic counterpart could hold up the investing partner and appropriate greater gains than they could bargain for ex ante ([47]).
8 4In a similar vein, [54] study a context in which General Motors (GM) owns the GM-specific tools and dies that its suppliers use for metal stampings. A power explanation of this arrangement would suggest that GM is retaining control of the tools and dies because of their importance and a desire to avoid resource dependence on its partners. However, many other powerful firms, particularly franchisors, force exactly this kind of investment on their less powerful franchisees, a practice often cited as an unfair abuse of franchiser power, as mentioned previously ([47]).
9 5We acknowledge that not all empirical research addresses joint profit maximization in an explicit way, and indeed many studies adopt a simplified one-sided perspective in which one party acts to protect itself against opportunism by the other. Nevertheless, (1) these models formally assume that efficiency is maximized (i.e., most one-sided analyses also assume one-sided specific investments, an assumption that aligns the safeguarding of investment incentives with joint value maximization), and (2) novel insights can be produced by reconciling the formal assumptions of power and efficiency theories, as we show subsequently.
6We do not need to consider inefficient arrangements that favor the weaker firm, because the more powerful firm would not choose them.
7Because we are ruling out Pareto optimal efficient governance, the weak firm cannot be indifferent to an alternative that the powerful firm prefers or is indifferent to. Thus, the weak firm is strictly worse off under the inefficient alternative.
8The response rate is 9.6% based on the total number of companies contacted; however, this is due to the substantial number of companies that do not outsource R&D and therefore are not qualified for the study.
9We also estimate an ordered probit model for price inflexibility as a robustness check, with the categories going from unspecified prices, to negotiated adjustments, to prespecified adjustment formulas, to firm fixed prices.
10We have emphasized that different facets of uncertainty combine to determine the difficulty of operating and contracting in a given environment. This is adequately captured by the additive formulation of the uncertainty measure. However, we also examined a multiplicative construction of the uncertainty measure in which the four first-order factors are multiplied to obtain the final score. This produces substantively equivalent but statistically stronger estimates. For the main variables of interest—CRO power, uncertainty, and their interaction—the coefficients and p-values are −.433 (p =.001),.002 (p =.018), and −.001 (p =.012), respectively. Note that certain coefficients appear smaller due to the different scaling of the uncertainty variable.
11Using a multiplicative construction of the uncertainty measure again produces statistically stronger estimates. For the main variables of interest—CRO power, uncertainty, and their interaction—the coefficients and p-values are −.384 (p =.008),.001 (p =.057), and −.001 (p =.023), respectively.
12The use of a multiplicatively constructed uncertainty variable also produces stronger statistical significance for all three of the key coefficients in this model. The coefficients and p-values are −.343 (p =.006),.001 (p =.047), and −.001 (p =.014) for CRO power, uncertainty, and their interaction, respectively.
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By Stephen J. Carson and Mrinal Ghosh
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Record: 19- App Popularity: Where in the World Are Consumers Most Sensitive to Price and User Ratings? By: Kübler, Raoul; Pauwels, Koen; Yildirim, Gökhan; Fandrich, Thomas. Journal of Marketing. Sep2018, Vol. 82 Issue 5, p20-44. 25p. 10 Charts, 3 Graphs, 3 Maps. DOI: 10.1509/jm.16.0140.
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App Popularity: Where in the World Are Consumers Most Sensitive to Price and User Ratings?
Many companies compete globally in a world in which user ratings and price are important drivers of performance but whose importance may differ by country. This study builds on the cultural, economic, and structural differences across countries to examine how app popularity reacts to price and ratings, controlling for product characteristics. Estimated across 60 countries, a dynamic panel model with product-specific effects reveals that price sensitivity is higher in countries with higher masculinity and uncertainty avoidance. Ratings valence sensitivity is higher in countries with higher individualism and uncertainty avoidance, while ratings volume sensitivity is higher in countries with higher power distance and uncertainty avoidance and those that are richer and have more income equality. For managers, the authors visualize country groups and calculate how much price should decrease to compensate for a negative review or lack of reviews. For researchers, they highlight the moderators of the volume and valence effects of online ratings, which are becoming ubiquitous in this connected world.
price sensitivity; rating sensitivity; mobile apps; dynamic panel model; Hofstede’s cultural factors
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By Raoul Kübler; Koen Pauwels; Gökhan Yildirim and Thomas Fandrich
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Record: 20- Assessing Performance Outcomes in Marketing. By: Katsikeas, Constantine S.; Morgan, Neil A.; Leonidou, Leonidas C.; Hult, G. Tomas M. Journal of Marketing. Mar2016, Vol. 80 Issue 2, p1-20. 20p. 1 Diagram, 7 Charts. DOI: 10.1509/jm.15.0287.
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Record: 21- Assessing the Impact of Customer Concentration on Initial Public Offering and Balance Sheet–Based Outcomes. By: Saboo, Alok R.; Kumar, V.; Anand, Ankit. Journal of Marketing. Nov2017, Vol. 81 Issue 6, p42-61. 20p. 1 Diagram, 4 Charts, 5 Graphs. DOI: 10.1509/jm.16.0457.
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Assessing the Impact of Customer Concentration on Initial Public Offering and Balance Sheet–Based Outcomes
Using the notion of customer concentration, the authors argue that firms should evenly spread their revenues across their customers, rather than focusing on a few major customer relationships. Prior literature suggests that major customers improve efficiency and provide access to resources, thereby producing positive performance outcomes. However, building on industrial organizational literature and modern portfolio theory, the authors argue that concentration of revenues reduces the supplier firm’s bargaining power relative to its customers and hurts the ability of the supplier firm to appropriate value, which, in turn, hurts profits. Using a sample of 1,023 initial public offerings (IPOs) and robust econometric methods, they find that customer concentration reduces investor uncertainty and positively impacts IPO outcomes, but significantly hurts balance sheet–based outcomes (e.g., profitability). The results suggest that a 10% increase in customer concentration reduces profitability by 3.35% (or about $7 million) in the subsequent year, or 9.4% cumulatively over the next four years (or about $20.32 million). Further, the authors find that the negative effects of customer concentration decrease with increase in organizational (marketing, technological, and operational) capabilities and increase with low customer credit quality.
Acquiring and managing customers is one of a firm’s most important tasks. Firms spend significant resources on managing customer relationships to create strong bonds with their customers. Such efforts are based on the premise that relationships with customers translate into improved customer loyalty and, in turn, superior organizational outcomes (Kumar, Luo, and Chintagunta 2011). Scholars have highlighted several benefits of having strong relationships with customers, such as reduced transaction costs and increased productivity. For example, Kalwani and Narayandas (1995) suggest that relationships with major customers improve firm performance by reducing discretionary expenses such as advertising and selling, general, and administrative expenses (SG&A) and by reducing costs through better inventory management. Similarly, such collaborative relationships can reduce transaction costs by developing interfirm norms and managing opportunism (Heide and John 1988). These benefits of customer relationships are especially important for young firms, which typically have limited resources and rely on such relationships to acquire external resources and exploit them for competitive advantage through new product creation, enhanced technological distinctiveness, and reduced sales costs (Yli-Renko, Autio, and Sapienza 2001). Customer relationships serve as valuable signals for the underlying quality of firms going public, who otherwise face significant information asymmetry due to their limited performance histories (Saboo and Grewal 2013).
However, firms typically have relationships that can provide such benefits with only a small fraction of their customers because building relationships requires investment of scarce organizational resources. Thus, it is not surprising that firms spend disproportionate resources managing relationships with their key customers, often at the expense of other customers. Indeed, research across both B2B (e.g., Luo and Kumar 2013) and B2C (e.g., Zeithaml, Rust, and Lemon 2001) domains has documented the “80/20” principle, where 20% of customers account for about 80% of revenues and, in turn, organizational efforts. In other words, firms tend to spend significant resources and build the strongest relationships with a small fraction of customers who spend the most with the firm (i.e., large purchase size), in the hopes of accruing all the benefits of selling to few large (in purchase size) customers. However, both anecdotal and academic research suggest that this small fraction of large customers can yield significant pressure on the supplier firm and bargain away all the joint value created (e.g., Heide and John 1988). Thus, organizational efforts to court large customers contrast with the negative consequences experienced by firms that have such customers. For example, although most firms strive to have Walmart as a key customer, these same firms complain about Walmart’s relentless pressure to squeeze profit-killing concessions and its effects on the entire operation from factory to financial statement (Fishman 2003). This is in line with Snyder (1996), who claims that large buyers have significant pricing power and can extract price concessions from suppliers and negatively affect their profitability. The vast literature on customer relationship management has argued for the benefits of allocating customer management resources according to the 80/20 principle (Luo and Kumar 2013; Zeithaml, Rust, and Lemon 2001) and has largely overlooked the negative consequences of building strong customer relationships with only a small fraction of customers, an area to which we seek to contribute through this research. We propose that customer concentration that relates to the distribution of revenue over the firm’s customer base can provide some resolution to this conflict. Specifically, “high concentration” refers to a scenario wherein revenues are concentrated across a small group of customers or a few customers account for a large proportion of revenues, whereas “low concentration” refers to a scenario wherein revenues are distributed across customers and even large groups of customers contribute only a small proportion of revenues.
Building on the industrial organizational literature, we argue that an increase in customer concentration, which reduces the supplier firm’s bargaining power relative to its customers, hurts the ability of the supplier firm to capture value and enables large customers to bargain away all the joint value created due to their superior bargaining position (Heide and John 1988; Porter 1980). Further, building on the seminal portfolio selection theory proposed by Markowitz (1952), we argue that the concentration of revenues among a handful of customers can increase the overall risk for the firm and hurt firm value. Having a small number of large customers (i.e., high customer concentration) exposes the firm to the idiosyncratic risks of individual customers and, in line with the portfolio theory, makes a firm inherently more vulnerable than a firm that derives revenues from a large number of customers (i.e., low customer concentration). In sum, we argue that customer concentration creates value, but it hurts the ability of the supplier firm to appropriate the joint value created.
Our primary objective in this research is to conceptualize and empirically demonstrate the importance of customer concentration as a variable that explains the conflicting consequences of having strong customer relationships.1 Prior studies have acknowledged the importance of customer concentration but have largely limited its use to characterizing the balance of power between the firm and its customers (e.g., Boyd, Chandy, and Cunha 2010; Nath and Mahajan 2011; Saboo, Chakravarty, and Grewal 2016). Our use of customer concentration as a measure of risks associated with the entire customer portfolio is most closely aligned with recent accounting studies by Patatoukas (2012) and Irvine, Park, and Yildizhan (2016), who use a similar measure to examine the relationship between customer concentration, firm performance, and supplier risk. Our research is different from previous work on both conceptual and empirical grounds. Unlike prior studies, we investigate firms from the beginning of their life cycles and, thus, account for the initial conditions (or those at the time of inception) and examine both initial public offering (IPO)–based and balance sheet–based outcomes of customer concentration. Assessing these two related but fundamentally distinct outcomes allows us to examine the effect of customer concentration on both a stock market–based outcome that should be of interest to pre-IPO investors and an objective balance sheet–based accounting outcome that should be of interest to most of the stakeholders. Specifically, as we detail subsequently, we use market capitalization on the first trading day to measure IPO outcome; we use profitability to measure balance-sheet outcomes because profitability includes both costs and benefits of customer concentration. Our study also resolves the apparent conflict between Patatoukas (2012), who documents a positive effect of customer concentration, and Irvine, Park, and Yildizhan (2016), who document a negative effect. Unlike prior studies, our study highlights and provides evidence of the underlying mechanism (asymmetry in bargaining power between suppliers and major customers and the ability of the supplier firm to appropriate value) that drive the impact of customer concentration. Further, we highlight important boundary conditions for the effects of customer concentration. Finally, unlike prior research, we account for both potential endogeneity of concentration and unobserved heterogeneity in our estimation.
In terms of boundary conditions for the consequences of customer concentration, we incorporate two sets of moderators that reflect the ability of the supplier firm to appropriate value from its customer relationships and the inherent quality of the supplier firm’s customer base to create such value. First, consistent with the relational view of competitive advantage that highlights learning as an important avenue for maximizing returns from interfirm relationships (Dyer and Singh 1998), we argue that firms can mitigate the deleterious consequences of concentration through their capabilities to learn from their major customers and use the learning to enhance existing customer relationships or acquire new customers; that is, organizational capabilities should moderate the relationship between customer concentration and firm performance. Further, in line with several studies (e.g., Dutta, Narasimhan, and Rajiv 1999; Gerybadze and Reger 1999; Krasnikov and Jayachandran 2008; Van den Bulte and Moenaert 1998) that define marketing, R&D, and operations as the core functions that endow firms with demand and/or supply-side advantages, we argue that organizational marketing, technological, and operations capabilities should moderate the effect of customer concentration on firm value. Second, in line with the view that the value that a firm derives from its customers is a function of the underlying quality of its customer base, we include the credit quality of the supplier firm’s customer base. Low credit quality is an indicator of low-quality borrowers who may not possess the desired information about business processes, product characteristics, and marketplace challenges that the supplier firm desires. In general, these borrowers are unlikely to share this information with the supplier firm in order to conceal their true quality (Mishkin and White 2002). We borrow from the accounting and finance literature in suggesting that doubtful receivables, which relate to the ability of customers to repay debts, are a valid proxy of the credit quality of a firm’s customer base and include this quality as additional moderator.
We test our conceptual framework using a unique data set compiled from multiple sources for firms that went public during 2000–2011. Using IPO firms allows us to track customer concentration from the beginning of a firm’s (public) life cycle and examine the influence of concentration on both IPO-based and balance sheet–based outcomes. Recognizing that customer concentration can vary across industries and can be endogenous for a variety of reasons that we discuss subsequently, we use a random effects model that accounts for industry-specific effects and the control function approach to account for the potential endogeneity of customer concentration. We find that customer concentration benefits IPO outcomes but adversely affects supplier firms’ profitability. Furthermore, this negative effect of customer concentration is contingent upon both organizational core capabilities and quality of the supplier’s customer base. Specifically, organizational marketing, technological, and operational capabilities positively moderate the negative effects of customer concentration, whereas the negative effects of customer concentration increase with the decrease in customer credit quality.
Our research contributes to the literature on customer relationship management (CRM) and dynamic capabilities. To the CRM literature, we introduce the idea of customer concentration as a resolution for the apparent conflict and consequences of having large customers. Our results suggest that although strong relationships with few customers can improve IPO outcomes, relying on a few major customers for revenues negatively impacts firm profitability. We also contribute to the literature on dynamic capabilities by demonstrating that organizational marketing, technological, and operational capabilities can mitigate the adverse effects of customer concentration (e.g., Teece, Pisano, and Shuen 1997). To the literature on customer profitability (Zeithaml, Rust, and Lemon 2001), we highlight that customers with low credit quality exacerbate the negative effects of customer concentration. Our research also contributes to the literature on the role of marketing in the context of IPOs (Luo 2008), in which investors view a concentrated customer base as a credible signal for firms’ survival and growth given the newness of young firms and uncertainty of their growth when they go public.
The rest of the article is organized as follows: First, we present an overview of the literature on customer concentration and develop our conceptual model, along with the contingency effects of organizational capabilities and customer credit quality, before proposing our hypotheses. Next, we present the methodology used to estimate the proposed effects and discuss our results and our robustness checks. Finally, we conclude with theoretical and practical implications of our research.
Customers are probably the most important organizational assets, and firms spend significant resources on acquiring and managing their customers. This is in line with research on building strong relationships that emphasizes the need to maintain profitable long-term relationships with customers (Reinartz and Kumar 2000). To economize on relationship management efforts, firms tend to allocate resources to their most promising customers. Such efforts are consistent with the CRM literature that advocates targeting efforts toward a small fraction of customers who spend the most with the firm and have strong relationships (Kumar, Luo, and Chintagunta 2011). However, the effects on firm performance of having strong relationships with a small fraction of customers are mixed. For example, Luo and Kumar (2013) and Zeithaml, Rust, and Lemon (2001) argue that strong customer relationships encourage information exchange and collaboration, improving profitability, whereas Christensen and Bower (1996) suggest that such relationships narrow the focus of these firms on the needs of their major customers and hurt firm performance. In this research, we argue that although having strong relationships with a small set of customers can have temporary benefits, such as when the firm is going public, relying on a few large customers or having strong relationships with only a few customers significantly hurts firm performance overall. Concentration of revenues in the hands of a few customers ( 1) lowers the bargaining power of the supplier firm relative to its customers and hurts the supplier firm’s ability to appropriate the joint value created in the supplier–customer relationship, and ( 2) increases the risk of customer churn, hurting overall firm performance. Thus, we propose customer concentration as a construct that measures the extent to which organizational revenues are distributed across customers as a potential explanation for the conflicting effects of strong customer relationships. We refer to firms with revenues evenly distributed across their customers as having low concentration and those that rely ona few customersfor the bulk of their revenues as having high customer concentration.
Firms with high customer concentration have fewer relationships to manage versus those with low concentration and, thus, can invest resources in these relationships to develop mutual trust and commitment, driving additional qualitative benefits such as lower conflict, uncertainty, and propensity to leave (Morgan and Hunt 1994). Strong customer relationships facilitate sharing of information and reduce costs through improved resource utilization and lower discretionary expenses such as SG&A and overheads (Kalwani and Narayandas 1995). Similarly, major customers can help firms increase operational efficiency by streamlining order processing and inventory management (Parvatiyar and Sheth 2001). Major customers can also help firms expand their current customer base by spreading positive word of mouth (Reichheld and Teal 2001). Finally, customers can provide product ideas and be actively involved in codeveloping products (Gulati and Kletter 2005; Lusch, Vargo, and O’Brien 2007). Thus, customer concentration creates value by increasing purchase volume, positive word of mouth, coproduction, and, in general, reduced transaction costs. For IPO firms that lack performance history and face significant uncertainty about their viability and prospects, these benefits associated with high customer concentration can alleviate investor concerns in three ways. First, high concentration and the associated supplier–customer relationships can give investors a perception of stable cash flows that is typically lacking in young firms. Consistent with the literature on market-based assets (e.g., Srivastava, Shervani, and Fahey 1998) and customer equity of firms (Rust et al. 2004), strong relationships with customers and the corresponding benefits, such as reduced cost of marketing and access to valuable information and capabilities associated with such relations, should alleviate investor uncertainty. Second, these relationships with major customers serve as third-party endorsements and can signal the underlying quality of the IPO firm (Leland and Pyle 1977; Ritter 2011). Finally, these relationships serve as reservoirs of resources that the IPO firms can access to compensate for their lack of internal resources (Gulati and Higgins 2003). In sum, customer concentration can mitigate the liability of newness faced by IPO firms and reduce investor uncertainty (Bruderl and Schussler 1990), thereby improving IPO performance. Thus:
H1: Increase in customer concentration is associated with increase in suppliers’ IPO performance.
Although having major customers reduces the liability of newness at the time of going public, we argue that having a concentrated customer base hurts the supplier firm’s ability to appropriate the value created to generate economic profits. The industrial organization literature suggests that organizational ability to capture value is influenced by its bargaining strength (Porter 1980). Thus, increase in customer concentration, which, by definition, reduces the supplier firm’s bargaining power relative to its customers (e.g., Boyd, Chandy, and Cunha 2010; Wang, Saboo, and Grewal 2015), should then hurt the ability of the supplier firm to appropriate value. Further, in line with literature on power dependence that suggests that dependence in an exchange relationship makes the dependent party susceptible to the power and influence of the other party (Heide and John 1988), we argue that large customers can force the supplier firms to change their business practices and bargain away all the value created by demanding lower prices, frequent deliveries of small quantities, product customizations, and extended technical and marketing support (Galbraith 1952). Anderson and Weitz (1989) provide evidence that trust decreases with increasing interdependence asymmetry. Finally, major customers may hinder the growth of the supplier firm outside such relationships. Indeed, Christensen and Bower (1996) find that large customers demand significant resources toward their specific needs, making it difficult for the supplier firm to cater to the broader marketplace. In fact, Martin (1995, p. 121) strongly suggests that innovative firms must “ignore their customers.”
Moreover, increase in customer concentration implies increased reliance on a few customers for revenues, meaning loss of a few customers or even a single customer can significantly hurt firm performance. Forecast errors (or the difference between company projections and actual demand) tend to be higher when a small number of customers make large purchases (vs. many customers making small purchases) and result in excess inventories or lost sales (Chopra and Sodhi 2004). This is consistent with portfolio theory, which suggests that relying on a few customers for revenues significantly increases cash flow volatility and vulnerability and therefore increases the cost of capital or the discount rate (Srivastava, Shervani, and Fahey 1998). Furthermore, supplier firms are forced to offer cheap trade credits to large customers who exploit their monopsony power and threaten to switch suppliers (e.g., Brechling and Lipsey 1963) or willingly offer attractive trade credits to signal supplier commitment and financial health (e.g., Petersen and Rajan 1994), thereby increasing the cost of servicing such customers and the default risks. Indeed, Mian and Smith (1992) find evidence that customer concentration is significantly associated with accounts receivable.
In sum, we argue that the costs associated with the increase in customer concentration are largely borne by the supplier, whereas major customers bargain away all the benefits (due to lack of bargaining power of the supplier). Thus, the costs of concentration outweigh its benefits, and this trade-off should be reflected in the objective balance sheet–based performance of the supplier firm. Thus:
H2: Increase in customer concentration is associated with lower supplier firm profitability.
The preceding hypothesis suggests that costs associated with customer concentration, combined with the inability of the supplier firm to extract value from these customer relationships, hurts supplier performance. Thus, factors that influence organizational ability to appropriate value from such resources (customer relationship, in our context) should influence the effects of customer concentration. We draw on the dynamic capabilities view of the firm that argues for the importance of organizational capabilities to “integrate, build, and reconfigure” internal and external resources to create sustainable competitive advantage (Teece, Pisano, and Shuen 1997, p. 516). Organizational capabilities are “complex bundles of skills and accumulated knowledge that enables firms to coordinate activities and make use of their assets” that is, they increase the productivity of other resources (Day 1994, p. 38). Accordingly, we suggest that capabilities should influence the outcomes of organizational resources; that is, capabilities should moderate the influence of customer concentration.
Further, although each firm develops its own configuration of capabilities according to its competitive situation, we focus on three capabilities—marketing, technological, and operational—that have been documented as primary sources of organizational advantage (e.g., Dutta, Narasimhan, and Rajiv 1999; Gerybadze and Reger 1999; Krasnikov and Jayachandran 2008; Van den Bulte and Moenaert 1998). These three capabilities are the “core functional capabilities that contribute the most to firms’ ability to deliver value to customers and thereby create sustainable competitive advantage” (Feng, Morgan, and Rego 2016, p. 4). Our choice of these three capabilities is also consistent with the classification of outside-in, inside-out, and spanning capabilities proposed by Day (1994). Specifically, marketing capability connects organizational processes to the external environment and enables firms to compete by anticipating market requirements and creating durable relationships with external stakeholders, (i.e., outside-in); operational capability emphasizes inside-out processes that include manufacturing and other transformation activities, such as logistics and cost management, that enable firms to transform inputs into output; and technological capability emphasizes spanning processes that integrate both inside-out and outside-in processes and includes activities like product development, strategy development, and so on. We discuss the moderating effects of each of these capabilities next.2
Marketing capability refers to a firm’s ability to carry out both market sensing and customer linking activities that relate to understanding and anticipating customer requirements and linking the firm to its customers (Krasnikov and Jayachandran 2008). It is based on a broad understanding of changes in market conditions, customer preferences, and the ability of the firm to respond to these changes; it enables firms to efficiently combine organizational resources to achieve marketing objectives. Marketing capability that involves tacit understanding of processes and relationships with customers is difficult to imitate and gives a competitive advantage to firms. Overall, increase in marketing capability enhances firm performance through improved market sensing and strong customer relationships (Day 1994).
We argue that an increase in organizational marketing capability increases the ability of the supplier firm to extract value from its customer relationships in three ways. First, marketing capability enables firms to increase efficiency of their marketing investments by increasing customer loyalty and reducing customer management costs (Krasnikov and Jayachandran 2008). Moreover, increase in marketing capability allows supplier firms to establish stronger relationships with their customers, reducing the risk of customer churn. Since major customers provide a large portion of organizational revenues, the presence of such customers and close collaboration with them reduces marketing and advertising spending and aids knowledge acquisition, helping suppliers reduce retention costs and acquire new customers at lower costs. Thus, marketing capability directly reduces costs of customer acquisition and retention and enhances the profitability of existing marketing relationships. Second, by anticipating customer needs and developing products that meet (or exceed) their requirements, firms can increase customers’ dependence on the supplier firm. Such reduction in customer power can reduce the power imbalance between the two firms and allow the supplier firm to extract value from the relationship (e.g., LaBahn and Krapfel 2000). Finally, marketing capability allows firms to learn from their customers and cocreate knowledge that can be deployed broadly. For example, firms can integrate their customer-based insights in their new product development process to develop solutions that can be used by other customers, allowing the supplier to generate additional rents. In sum, marketing capability reduces the negative effects of customer concentration by increasing the ability of supplier firms to extract value from existing relationships and reducing the risks of customer churn. Therefore, we hypothesize the following:
H3: Increase in marketing capability decreases the negative effects of customer concentration on suppliers’ profitability.
Technological capability refers to a firm’s ability to develop and use internal technological resources along with other organizational resources to improve existing products or develop new ones in response to changing marketing conditions (Moorman and Slotegraaf 1999). It helps firms to rapidly respond to changing customer preferences as well as cater to other customers with similar needs. A stock of knowledge and technological resources helps supplier firms to be irreplaceable to their major customers and improves existing technological capabilities.
This ability of the supplier firm to use technological resources to develop new products and improve existing ones to gain competitive advantage should influence the benefits that the supplier firm derives from its customer relationships in two ways. First, in line with the literature on absorptive capacity that suggests that a firm’s ability to absorb new external knowledge largely depends on its existing knowledge bases (Cohen and Levinthal 1990; Saboo et al. 2017), the ability of the supplier firm to learn from its customers should increase with increasing organizational technological capability. Major customers, who are often actively involved in codeveloping products, provide access to technology, systems, and processes that can be leveraged by the supplier firm to enhance its knowledge bases (Gulati and Kletter 2005). For example, Toyota allows a group of suppliers to visit its plants for knowledge sharing and joint problem solving (Dyer and Nobeoka 2000). Such access to external technological resources can also reduce the need for internal R&D spending. In addition to utilization of joint capabilities for cocreation, suppliers also share risks with their major customers in case of new product failure, which generally has a high probability. Second, customers are vital sources of information on market trends, which is an essential ingredient for fruitful deployment of technological capability. Major customers are motivated to be a part of the innovation process and are often willing to share future projections with their suppliers (Lilien et al. 2002). Technological capability enables firms to combine this information on customers and markets and exploit it for commercial advantage. Enhanced understanding of technology, markets, and customer preferences is likely to strengthen the supplier’s ability to leverage other resources (e.g., Dutta, Narasimhan, and Rajiv 1999; Moorman and Slotegraaf 1999). Moreover, developing products that anticipate and fulfill the technological needs of their customers enables suppliers to increase customer reliance on them and reduce the asymmetry of bargaining power in the relationship, allowing the supplier firm to generate additional value from the relationship (e.g., LaBahn and Krapfel 2000). In sum, technological capability reduces the negative effects of customer concentration by allowing supplier firms to benefit from existing customer relationships through knowledge acquisition from customers and, in turn, increasing knowledge exploitation. Therefore, we hypothesize the following:
H4: Increase in technological capability decreases the negative effects of customer concentration on suppliers’ profitability.
Operational capability refers to the ability of firms to coordinate and execute the tasks along the entire value chain required to transform the inputs to outputs, for example, logistics, inventory management, forecasting, distribution, and manufacturing (Cepeda and Vera 2007). Operational capability entails value chain flexibility, which requires integration with both customers and vendors and flexible production programs to compensate for changes in customer demands and fluctuations in raw material supply (Gelhard and Von Delft 2016). Thus, customer management is at the heart of operational capability, and firms use variety of techniques (e.g., just-in-time production, total quality management) to maintain long-term relationships with customers and improve operational efficiency through proactive problem solving. Overall, operations capability enhances new product success through efficient manufacturing, supply chain, and customer integration.
We argue that operational capability mitigates the negative effects of customer concentration in three broad ways. First, operational capability (and the corresponding relationship with customers and vendors) enables firms to lower their manufacturing costs through accurate demand forecasting, better inventory management, and improved production processes (Tan, Kannan, and Narasimhan 2007). Supplier firms can pass on these benefits to their concentrated customer bases and still make profit from their customer relationships due to their low costs. Moreover, these lower-cost offerings increase the attractiveness of the supplier, increasing customer dependence and reducing churn. Second, the flexibility in the manufacturing process allows supplier firms to offer a wide variety of products to cater to different customers and diversify their customer bases (Worren, Moore, and Cardona 2002). Flexible manufacturing processes reduce reliance on existing customers and increase firms’ ability to handle the wild demand fluctuations that can result from a concentrated customer base. Finally, operational capabilities should help firms with high customer concentration (or those with few major customers) by lowering the costs of customizing products for their smaller customer bases. For example, integrating RFID technology in a firm’s supply chain in response to Walmart’s demands allows the firm to offer the same technology to existing smaller and new customers. In sum, operational capability reduces the negative effects of customer concentration by reducing production costs and increasing manufacturing flexibility. Therefore:
H5: Increase in operational capability decreases the negative effects of customer concentration on suppliers’ profitability.
Building on the idea proposed by Kumar (2013) and Zeithaml, Rust, and Lemon (2001) that not all customers are equal and that some customers are more valuable to the firm, we introduce the concept of customer credit quality, which refers to the financial well-being of customers and the risk of default. Customer credit quality is related to customers’ ability to pay back their suppliers and, in line with the general idea of credit quality (Petersen and Rajan 1997), is inherently linked to the underlying quality of the customer base. Low-quality customers are less likely to have the desired information about business processes, product characteristics, and marketplace challenges. Moreover, given that supplier firms extend trade credits according to their assessment of the quality and the future potential of their customers (Brennan, Miksimovic, and Zechner 1988), low-quality customers are unlikely to share their private information with supplier firms, to prevent the supplier firms from knowing their true quality (Mishkin and White 2002).
We argue that low customer credit quality exacerbates the negative effects of customer concentration in two ways. First, low customer credit quality directly amplifies the negative consequences of concentration as the risk of concentration is spread over a few low-quality customers. On average, low-quality customers have a high default risk and an increased likelihood of not being able to live up to their commitments, further increasing cash flow volatility and vulnerability and the overall cost of capital. Second, low credit quality indirectly hurts the supplier firm by locking in resources and preventing the supplier firm from pursuing other high-quality customers. Low-quality customers are fundamentally less equipped to learn from their environment and provide quality insights to the focal firm, making such relationships even less valuable (Dodgson 1993). In sum, low customer credit quality further deteriorates the negative relationship between customer concentration and firm performance. Therefore:
H6: As customer credit quality decreases, the negative effects of customer concentration on suppliers’ profitability increase; that is, the negative effect increases in magnitude with a decrease in customer credit quality.
In sum, we expect a negative effect of customer concentration on firm profitability. Furthermore, we expect organizational capabilities to positively moderate and (low) customer credit quality to negatively moderate the relationship between customer concentration and profitability. We summarize our conceptual framework in Figure 1.
We test our framework using data from IPO firms, which provide an ideal context to test our framework because we can track them from the initial stages of their life cycle. Focusing on young IPO firms also allows us to observe the supplier firm from the initial phase of relationships with all its customers, account for the evolution of concentration in its customer base, and eliminate any bias due to suppliers’ preexisting knowledge about its customers. Although we do not observe these relationships from inception, our use of firm age as a proxy of relationship age is consistent with Irvine, Park, and Yildizhan (2016, p. 893), who suggest that “young firms tend to have young relationships with their major customers” and that “firm age contains enough information on relationship duration” to be used instead of relationship age. More importantly, focusing on IPO firms allows us to evaluate both IPO-based and balance sheet–based outcomes and, thus, to examine the consequences of customer concentration from both stock market and accounting perspectives and provide a complete understanding of the influence of customer concentration. Accordingly, we collected information for all firms that went public between 2000 and 2011. The 12-year window is long enough to provide adequate variability in terms of periods of boom and bust, to make generalizable claims over a large sample, and to allow us to track the long-term (four years post-IPO) performance of these firms. We obtained the list of IPOs from the SDC Platinum New Issues Database. In line with prior literature (Luo 2008; Saboo, Chakravarty, and Grewal 2016), we excluded secondary offerings, spin-offs, leveraged buyouts, rights issues, closed-ended funds, and limited partnerships, to obtain our initial sample of 1,483 firms. After excluding 326 financial services firms (SIC 6000–6999), 79 very small issues (less than $1.5 million in proceeds), and 55 IPOs with extensive missing information, we obtain our sample of 1,023 IPO firms across 54 industries (two-digit SIC codes). Combining the concentration information with other financial controls from Compustat and patent information from the U.S. Patent and Trademark Office resulted in an unbalanced panel with 7,008 firm-year observations.
Customer concentration. Customer concentration (CC) measures how a firm’s revenues are distributed across its customers, which is analogous to the Herfindahl index that measures the concentration of industry revenues across firms. Accordingly, we measure customer concentration as the sum of square of revenue share from major customers. The Securities and Exchange Commission requires all publicly listed firms to disclose information through prefiling Statement of Financial Accounting Standards (SFAS) 14 (SFAS 131 after 1997) disclosures on customers that account for more than 10% of firm revenue contributions; these statements are available in the Compustat segments database.3 However, Compustat customer information is only available for post-IPO years and often lacks information. Therefore, we supplement the Compustat segment information with data on customer concentration that we manually collected from the IPO prospectus and 10-K filings. Specifically, we looked for phrases such as “key customers,” “significant customers,” and “major customers,” and we looked in the customer section of the filings to find the customer-level information. For firms that did not use any of these phrases, we manually searched the entire filings for the relevant information. IPO firms reveal their historical financial information in their IPO prospectuses, covering a period either since their inception or at least for the recent past, typically at least for 5 years prior to the offering. The manual data collection allows us to obtain concentration information for the firms since inception.
Next, in line with extant research (Irvine, Park, and Yildizhan 2016; Patatoukas 2012), we measure customer concentration CCit for firm I having j = 1, 2, …, N customers in period t as ? ?
Thus, customer concentration of a firm ranges from 1/N, or 0 as N → ‘ (where revenues are evenly distributed across a very large number of customers), to 1 (where all the revenues are derived from a single customer). We provide a time trend series of customer concentration in Figure 2, showing adequate variation in both number of firms going public every year and the levels of customer concentration across years. Figure 3 provides the distribution of the number of major customers and highlights that firms typically have few major customers and that the number of major customers declines rapidly. Figure 4 provides the evolution of concentration over firm age and documents that customer concentration reduces as firms mature. These figures highlight the need for understanding of the dynamics of customer concentration.
Marketing capability. We follow the extant literature and use an input–output stochastic frontier approach to measure ? ? marketing capability (Dutta, Narasimhan, and Rajiv 1999; Xiong and Bharadwaj 2013). The stochastic frontier analysis estimates the inefficiency scores based on the firm’s ability to transform inputs into outputs; it has been widely used to measure organizational capabilities (e.g., Feng, Morgan, and Rego 2015). To estimate marketing capability, we follow Dutta, Narasimhan, and Rajiv (1999) and Feng, Morgan, and Rego (2016) and include total sales as output and the following quantities as marketing-resource inputs: ( 1) SG&A expenditures, indicating the level of marketing-related investments; ( 2) receivables, relating to the resources dedicated toward maintaining customer relationships; and ( 3) previous-year sales, indicating an already installed customer base. We also add industry dummies to control for industry-level heterogeneity. Formally, we write the frontier model as follows: ? ?
where Saleit is total sales; SGAit is SG&As; Receivit is total accounts receivables of firm I in year t; Indi and Yeart are industry and year dummies, respectively; eM it ~ Nð0, s2eM Þ is idiosyncratic error; and hM it ~ ExpðqMÞ is a marketing ineffi-ciency error component, which is exponentially distributed with qM > 0, hM 0, and E½eMit, hMit = 0. We derive the measure of marketing capability from the maximum likelihood estimate of the inefficiency term hM it (such that higher inefficiency means lower marketing capability) and rescale the measure between 0 (lowest) and 100 (highest) for ease of interpretation.
Technological capability. Technological capability refers to organizational ability to convert technological resources into technological output. In line with the extant literature, we measure technological output of a firm using patent count, which has been widely established as a valid measure of organizational innovativeness (Feng, Morgan, and Rego 2016). However, to account for the extraneous factors (e.g., processing delays, legal challenges) and closely relate the patent output to firm’s R&D ? ? expenses, we use the filing date, rather than the patent issue date, to relate the R&D investments to patent output (e.g., Acharya and Subramanian 2009; Chava et al. 2013). Further, given that several technological resources have persistent (long-term) effects and that recent resource allocations are more valuable than those in the past (e.g., Xiong and Bharadwaj 2013), we use Koyck lag function for patent counts and R&D expenditure with higher weights on recent years. We follow Dutta, Narasimhan, and Rajiv (2005) in using Koyck lagged structure with associated weights of .4 for both variables. In additional analyses, we try different weights ranging from .4 to .7 and find qualitatively similar results.
We also control for industry-level heterogeneity among firms by adding industry dummies. Consistent with our estimation of marketing capability, we write the frontier model as follows: ? ?
where Innovit is innovation output, or patent count, of firm I in year t; R&DStockit is stock of R&D expenses; PatentStockit is stock of patent output of firm I in year t; Indi and Yeart are industry and year dummies, respectively; eT it ~ Nð0, s2eT Þ is idiosyncratic error; and hT it ~ ExpðqTÞ is a technological inefficiency error component, which is an exponential distribution with qT > 0, hT 0, and E½eTit, hTit = 0.
? ? ? Operational capability. Operational capability relates to a firm’s ability to transform raw material and other resource inputs into finished products or services in the most efficient manner (Cepeda and Vera 2007). To estimate operational capability, we minimize the output operational expenses, using the following as inputs: ( 1) current assets held by firms, indicating the level of cash available for operations; ( 2) number of employees scaled by total assets, indicating the level of work force; and ( 3) current property, plant, and equipment, indicating current expenses related to property in operational activities. Formally, we write the frontier model as follows:
where XOPRit is operational expenses; Assetsit is current assets; PPEGTit is property, plant and equipment; EMPit is number of employees; INDi and Yeart are industry and year dummies, respectively; eO it ~ Nð0, s2eO Þ is idiosyncratic error; and hO it ~ ExpðqOÞ is an operational inefficiency error component, which is an exponential distribution with qO > 0 and hO it 0: Unlike marketing and technological capabilities, for which we use production maximization, operational capability is estimated as a cost-minimization problem, and, thus, inefficiency hO it is added to the equation.
Further, given that these three organizational capabilities may be related to one another due to unobserved factors (e.g., overall capability of the firm), we estimate them jointly.4 Specifically, we use a Bayesian estimation with a multivariate normal idiosyncratic error structure to allow for correlated errors. We run a Markov chain Monte Carlo of 50,000 iterations for the models to converge and obtain the capabilities estimates after 10,000 burn-ins, which we then use in our final model.
Customer credit quality. Customer credit quality refers to the financial health of the supplier’s customer base. In line with the accounting and finance literature (e.g., Cunat 2007; Petersen and Rajan 1997; Sopranzetti 1998), we use doubtful receivables (i.e., a portion of account receivables that has a high probability of becoming a bad debt in the future) as measure of the credit quality of a supplier’s customer base. Note that delays in payment alone (which are common for large customers) do not reflect low customer credit quality. For example, customers such as Walmart often pay their suppliers after some time (i.e., trade financing); however, there is very low probability of receivables from these customers turning into bad debt, and thus they are not considered doubtful receivables. We measure customer credit quality as the two-year average of the ratio of doubtful receivables to total account receivables, where a lower number represents superior credit quality of customer base (Sopranzetti 1998). We use the log-transformed variable to reduce the skewness (Danaher, Mullarkey, and Essegaier 2006).
In line with extant research in the IPO domain (e.g., Gulati and Higgins 2003; Megginson and Weiss 1991), to measure IPO outcome, we use market capitalization (MCap), measured as the total outstanding shares multiplied by the closing price obtained from CRSP, of the IPO firm at the end of the first trading day.
To evaluate the balance sheet–based consequences of customer concentration, consistent with our objective of investigating the influence of the risks and rewards of having a concentrated customer base, and in line with extant research on ? ? customer concentration (e.g., Irvine, Park, and Yildizhan 2016; Patatoukas 2012), we use firm profitability as our dependent variable. Unlike other measures such as sales or cash flows, profitability reflects both the costs and benefits associated with customer concentration and, thus, is the most appropriate measure in our context. For example, profitability includes the benefits of customer concentration, such as lower marketing costs and learning from customers, as well as the risks of customer attrition or low margins due to the supplier firm’s lack of bargaining power. Specifically, we use return on assets (ROA), measured as the ratio of net income to total assets obtained from Compustat.
For our IPO outcome analysis, in line with the recent IPO literature (see recent reviews by Ljungqvist 2007; Ritter 2011, 2003; Ritter and Welch 2002; Yong 2007), we include IPO-related and firm-specific control variables to account for the range of factors that influence IPO performance. Specifically, we include price adjustment (i.e., the revision in the offer price from the midpoint of the original filed price range), which accounts for the private information gathered during the road show (Ritter 2011); and percentage width of offer range (ratio of offer width to lower offer price), which accounts for uncertainty in setting the price (i.e., wider offer range provides greater price flexibility; Hanley 1993). Further, we also control for underwriter reputation, which has been documented as a credible signal of the quality of IPO firm, and we use the scores provided by Loughran and Ritter (2004) and ownership dilution (operationalized as amount of equity stake that owner managers relinquish at the time of IPO), which provide insights into the managers’ valuation of the IPO (Leland and Pyle 1977). Finally, we include dummy variables to account for whether the IPO was backed by venture capitalists and whether firm went public during the bubble period (Saboo and Grewal 2013). In addition to the IPO-specific controls, we also control for firm-specific variables, such as sales, ROA, firm age, number of major customers, number of business segments, and marketing intensity. Finally, we include both industry and year dummies to account for industry- and time-specific trends.
In addition to our focal variables, we control for a range of industry- and firm-level financial factors that may influence firm performance. Specifically, we control for firm size (sales and total assets) to account for the scale economies, and age (firm age) to account for the maturity of the firm (e.g., Saboo and Grewal 2013). We include sales growth to account for recent firm performance (Slater and Narver 1994). We also control for firms’ marketing intensity (ratio of SG&A expenses to total assets) to account for firms’ emphasis on building market-based assets, such as brand equity and customer equity, which affects firm performance (Rust et al. 2004). We use the log transformation of sales, age, and assets to reduce the skewness (Danaher, Mullarkey, and Essegaier 2006). Firms also face risks from concentration of revenues across a few business segments, especially IPO firms that typically operate in few business segments. Accordingly, we include the number of business segments that the supplier firm operates in, obtained from the Compustat segments database. Our bargaining power ? ? argument for the negative consequences of customer concentration relies on having a few dominant customers. However, given that our measure of concentration does not distinguish between the number of customers and the level of dependence (we disentangle the two in a subsequent robustness check), we include the number of customer relationships that the supplier firm has to maintain (breadth; see Figure 3).5 Given that our sample comes from multiple industries, we control for the competitive intensity using the Herfindahl–Hirschman index (HHI), which measures industry concentration (sum of squared market shares for all firms in the industry) according to two-digit SIC codes (Feng, Morgan, and Rego 2015). Further, given that industries may offer differential growth opportunities, we also control for the industry growth rates (Russo and Fouts 1997). In addition to the aforementioned controls, we include both industry and year fixed effects to account for industry-level and time-specific unobserved heterogeneity. We provide a summary of our variable operationalization and data sources in the Web Appendix (Table W1).
To test H1 (influence of customer concentration on IPO outcomes), we regress IPO market capitalization at the end of the first trading day on pre-IPO customer concentration and a range of control variables discussed earlier: ? ?
where MCapi is market capitalization of firm i, CCi is customer concentration, Zi is a matrix of IPO-related and firm-specific control variables for firm i, INDi and Yeart are industry and year dummies, and the idiosyncratic error ei is assumed to be identically and independently normally distributed. Figure 5, Panel A, provides the model-free evidence of the relationship between CC and MCap, providing additional evidence for our arguments that customer concentration positively influences IPO outcomes.
To test H2–H6 (effect of customer concentration on firm profitability), we use an autoregressive panel data model that accounts for prior performance and augment it to address the potential endogeneity of customer concentration and unobserved heterogeneity. In line with prior literature (Feng, Morgan, and Rego 2015; Nath and Mahajan 2011), we a use one-year leading measure of profitability (ROAit+1) to incorporate the effects of past organizational actions. Using an autoregressive model, that is, including the lagged measure of profitability, allows us to control for past levels of ROA and other factors, including inertia, persistence, and different initial conditions, that can predict future ROA (Saboo, Chakravarty, and Grewal 2016). In the additional analyses related to the robustness checks, we estimate the consequences of customer concentration on firm profitability up to year t + 4. To correct for the dynamic panel bias introduced by the presence of a lagged dependent variable, we use lagged differences in dependent variables as instruments (Blundell and Bond 1998), where we first regress ROAi, t+1 on DROAi, t and take the predicted values as an instrument to control for profitability in the last period. We include both the industry (two-digit SIC code) and year dummies to control for industry and year fixed effects on profitability. Finally, we use a random effects specification that also helps us to account for unobserved heterogeneity and use cluster of robust standard errors that account for serial correlation and heteroskedasticity.6 Overall, our final model can be written as
where ROAit+1 is the return on assets of firm I at time t + 1, CC is customer concentration, MktCap is marketing capability, TechCap is technological capability, OprCap is operations capability, LogCredQual is the natural logarithm of credit quality of the customer base, breadth is the number of major customer relationships, BusSeg is the number of business segments in which a firm operates, and the idiosyncratic error eit+1 is normally and independently distributed. Figure 5, Panel B, provides the model-free evidence of the relationship between CC and ROA, and the significant negative correlation between CC and ROA (r = -.08) provides preliminary evidence for our arguments that an increase in customer concentration hurts profitability. ? ?
Given the importance of customers for firm performance, it is not surprising that managers spend significant resources on acquiring and managing their customers. Indeed, Kumar and Petersen (2005) suggest that firms should carefully choose customers that are most valuable to the organization, suggesting that firms may choose the level of customer concentration to improve their future performance and that customer concentration may be endogenous. To correct for endogeneity in our model, we use the control function approach widely used in marketing (Petrin and Train 2010). In the first step, we regress the potentially endogenous variable, customer concentration, on a set of exogenous variables. Specifically, we include capabilities and financial variables such as age, total assets, sales, sales growth, marketing expenses, number of major customers, number of business segments, and profitability.
TABLE: TABLE 1 Bivariate Correlation Coefficients and Descriptive Statistics
| | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 |
|---|
| 1. ROAit+1 | | | | | | | | | | | | | | | | | |
| 2. ROAit | 0.27 | | | | | | | | | | | | | | | | |
| 3. Customer concentration (CC) | -0.08 | -0.08 | | | | | | | | | | | | | | | |
| 4. Number of major customers (Breadth) | -0.01 | -0.01 | 0.32 | | | | | | | | | | | | | | |
| 5. Total sales from major customers (Depth) | -0.07 | -0.06 | 0.82 | 0.67 | | | | | | | | | | | | | |
| 6. Marketing capability (MktCap) | 0.17 | 0.18 | -0.11 | 0.04 | -0.08 | | | | | | | | | | | | |
| 7. Technological capability (TechCap) | 0.08 | 0.07 | -0.04 | -0.05 | -0.05 | 0.1 | | | | | | | | | | | |
| 8. Operational capability (OprCap) | -0.23 | 0.02 | 0.02 | -0.06 | -0.02 | -0.31 | 0.02 | | | | | | | | | | |
| 9. Customer credit quality (CredQual) | -0.16 | -0.22 | 0.06 | 0 | 0.02 | -0.04 | -0.02 | 0.01 | | | | | | | | | |
| 10. Sales growth | 0 | 0 | 0.01 | -0.01 | 0 | -0.02 | -0.02 | 0 | 0 | | | | | | | | |
| 11. Age | 0.07 | 0.08 | -0.14 | -0.16 | -0.19 | 0.07 | 0.05 | 0.22 | -0.02 | -0.02 | | | | | | | |
| 12. Sale | 0.02 | 0.03 | -0.04 | -0.04 | -0.05 | 0.19 | 0.03 | 0 | -0.01 | -0.01 | 0.08 | | | | | | |
| 13. Industry concentration (HHI) | 0 | 0.02 | -0.14 | -0.13 | -0.17 | 0.01 | 0 | 0.1 | -0.02 | -0.01 | 0.15 | 0.1 | | | | | |
| 14. Industry sales growth | 0 | 0 | -0.01 | -0.01 | -0.01 | 0 | 0 | -0.01 | 0 | 0 | -0.01 | 0 | 0.01 | | | | |
| 15. Marketing intensity (MktInten) | -0.22 | -0.72 | -0.06 | -0.03 | -0.05 | -0.09 | -0.04 | -0.06 | 0.2 | 0 | -0.06 | -0.02 | 0.05 | 0 | | | |
| 16. Assets (TA) | 0.03 | 0.04 | -0.06 | -0.06 | -0.07 | 0 | 0.04 | 0.34 | -0.01 | 0 | 0.18 | 0.17 | -0.01 | 0 | -0.05 | | |
| 17. Number of business segments | -0.02 | -0.01 | -0.02 | -0.05 | -0.03 | -0.02 | 0.01 | 0.07 | 0.06 | -0.01 | 0.08 | 0.01 | 0.03 | 0 | 0.01 | 0.07 | |
| M | -0.2 | -0.22 | 0.11 | 1.05 | 0.21 | 62.29 | 61.97 | 3.48 | 1.79 | 2.05 | 20.12 | 1298.62 | 0.2 | 0.16 | 0.4 | 920.28 | 1.21 |
| SD | 1.28 | 1.39 | 0.22 | 1.2 | 0.29 | 20.5 | 26.44 | 2.72 | 47.69 | 55.69 | 23.6 | 7712.69 | 0.15 | 3.74 | 1.17 | 4570.65 | 0.62 |
Note: Correlations above absolute value of .025 are significant at p < .05, two-tailed.
In addition to the aforementioned variables, to fulfill the requirement of exclusion restrictions (Petrin and Train 2010), we also include average industry-level customer concentration and average customer concentration of firms with similar profitability. Given the inherent uncertainty about the right levels of customer concentration, in line with institutional isomorphism theory (DiMaggio and Powell 1983), we argue that firms often imitate their peers. Mimicking other firms is also consistent with other related theories of industry recipes (Spender 1989), industry mindset (Phillips 1994), and dominant logic (Bettis and Prahalad 1995). Moreover, average concentration in the same industry or across similar firms has little to do with the performance of the focal firm and is unlikely to serve its specific needs. Thus, average industry-level customer concentration (IndCC) and average customer concentration of firms with similar profitability (PeerCC) satisfy both criteria of relevance and exogeneity, to serve as valid instruments.
Because the dependent variable of our first-stage endogeneity correction model, customer concentration, is constrained between 0 and 1, and because both boundaries have a significant probability mass, we cannot use the regular regression techniques that rely on the normal functional form for estimation (Papke and Wooldridge 1996). Therefore, we use fractional response panel methods where E ðyjXÞ = GðXbÞ such that G(.) is a nonlinear function satisfying 0 £ G($) £ 1 (Papke and Wooldridge 2008; Ramalho, Ramalho, and Murteira 2011). Formally, the first stage model is
where Z is matrix of exogenous variables, IndCC is the average industry-level (two-digit SIC code) customer concentration, and PeerCC is the average concentration of firms with similar profitability. We use the residual, ^ qit, from the first-stage model (Equation 7) as an additional independent variable to correct for potential endogeneity of customer concentration in both our models (Equations 5 and 6).
Table 1 contains descriptive statistics and bivariate correlations for all the variables used in the study. The condition number (i.e., ratio of the largest to the smallest eigenvalues of the correlation matrix) of 24.22 is lower than the recommended cutoff value of 30, suggesting that multicollinearity is unlikely to be a concern. The average customer concentration for our sample of firms is .11, which is similar to values in previous studies in this context (Irvine, Park, and Yildizhan 2016; Patatoukas 2012).
Results of our first-stage regression (Table 2) provide insights into organizational customer management practices. We present the correlations between variables used in the analyses of IPO outcomes (Equation 5) in the Web Appendix (Table W2). In line with our intuition, we find that a focal firm’s customer concentration is positively related to the average customer concentration of firms in the same industry (b = 2.642, p < .001) and those with similar levels of profitability (b = 1.288, p < .001), suggesting that firms mimic other (similar) firms to determine their levels of customer concentration. Further, customer concentration decreases with firm age (b = -.054, p < .1), indicating that firms increase their customer base as they mature and no longer depend on a few customers for their revenues. Moreover, organizational marketing investments are negatively related to customer concentration (b = -.001, p < .001), suggesting that marketing investments enable firms to acquire new customers and increase revenues from their customers to lower their concentration. Similarly, customer concentration decreases with an increase in the number of business segments that the firm operates in (b = -.174, p < .01).
TABLE: TABLE 2 First-Stage Endogeneity Correction Results
| | Estimate | SE |
|---|
| Average CC of firms in the same industry (IndCC) | 2.642*** | .290 |
| Average CC of firms with similar profitability (PeerCC) | 1.288*** | .335 |
| Marketing capability (MktCap) | -.001 | .002 |
| Technological capability (TechCap) | -.0001 | .001 |
| Operational capability (OprCap) | .020* | .011 |
| Log customer credit quality (LogCredQual) | .002 | .006 |
| Sales growth | -.155 | .263 |
| Log age (LogAge) | -.054* | .031 |
| Log sales (LogSale) | .005 | .013 |
| Competitive intensity | -.621*** | .181 |
| Industry sales growth | -.001 | .005 |
| Marketing intensity (MktInten) | -.001*** | .000 |
| Log assets (LogAssets) | -.110 | .182 |
| Number of business segments | -.174*** | .055 |
| ROAit | .015 | .045 |
| Intercept | -1.474*** | .185 |
*p < .1 (two-tailed).
***p < .01 (two-tailed).
We present the results of IPO performance analysis (Equation 5) in Table 3. In line with H1, we find that customer concentration is positively related to the market capitalization of the IPO firm (b = .814, p < .05), indicating that IPO investors reward firms that have large stable customer relationships. We also replicated our analysis with initial returns (i.e., percentage difference between the offer price of the stock and the price of the stock at the end of the first trading day), another popular measure for assessing IPO and find qualitatively similar results (b = .126, p < .05). We hasten to highlight that this positive effect should not be treated as IPO investors ignoring the costs associated with customer concentration. Given the information asymmetry and the uncertainty associated with the prospects of a firm going public, IPO investors are primarily concerned about the survival of the firm, and they value signals that reduce the risks associated with the firm’s prospects (e.g., Saboo, Chakravarty, and Grewal 2016). In such a context, having a steady stream of revenue that is associated with high concentration reduces investors’ uncertainty. The coefficients of the control variable are along expected directions (Luo 2008; Saboo and Grewal 2013). For example, IPO valuations increase with increases in price adjustment (b = 1.742, p < .001), underwriter reputation (b = .041, p < .01), marketing intensity (b = .284, p < .01), and total assets (b = .001, p < .01); valuations decrease with an increase in the offer range (b = -.828, p < .01).
TABLE: TABLE 3 Parameter Estimates for the IPO Performance Model
| | Estimate | SE |
|---|
| Customer concentration (CC) | .805** | .389 |
| Partial adjustment | 1.712*** | .319 |
| Offer range width | -.908*** | .267 |
| Underwriter reputation | .037*** | .011 |
| Bubble | .082 | .104 |
| Venture capital funded | -.062 | .043 |
| Ownership dilution | -.642*** | .131 |
| Log age (LogAge) | -.052** | .024 |
| Log sales (LogSale) | .01 | .006 |
| Marketing intensity (MktInten) | .250*** | .081 |
| Log assets (LogAssets) | .001*** | .000 |
| Number of business segments | -.127*** | .035 |
| Number of major customers ROA | -.051 | .035 |
| RAO | .021 | .073 |
| Endogeneity correction term ^qit | -.468 | .340 |
| Intercept | -.281 | .196 |
| Industry fixed effects | Yes |
| Year fixed effects | Yes |
**p < .05 (two-tailed).
***p < .01 (two-tailed).
In Table 4, we report the results of our analyses of the consequences of customer concentration on firm profitability that control for endogeneity and unobserved heterogeneity. As expected, we find that the estimate of our correction (residuals from the first-stage model) term is significant (b = 2.625, p < .01), suggesting that firms do strategically choose the desired level of customer concentration. As hypothesized in H2, we find that customer concentration hurts firm performance (b = -6.258, p < .001), suggesting that the costs associated with having major customers easily outweigh their benefits. We perform a robustness check with contemporaneous variables (both ROA and CC in the same period) and obtain qualitatively similar results (b = -8.527, p < .05).Our results are consistent with Irvine, Park, and Yildizhan (2016), who also document a negative relationship between customer concentration and firm performance.
Our results provide strong support for the moderating effects of organizational capabilities (see Figure 6). Specifically, in line with H3–H5, we find that marketing (b = .025, p < .001), technological (b = .010, p < .01), and operational capabilities (b = .311, p customer concentration. In line with H6, we find that poor customer credit quality further deteriorates performance of supplier firms (b = -.032, p < .01). Although all three organizational capabilities are important in mitigating the negative effects of customer concentration, we evaluate their relative importance by comparing the magnitudes of the standardized interaction coefficients. The results show that operations capability (bSTD = .141, p < .01) is more important than marketing (bSTD = .086, p < .001) and technological (bSTD = .043, p < .01) capabilities in reducing the negative effects of customer concentration. The Wald test confirms that operations capability is significantly superior in mitigating the negative effect of customer concentration than technological capability (c2( 1) = 4.08, p < .05), but the other comparative effects are not statistically significant.
TABLE: TABLE 4 Parameter Estimates for the Firm Profitability (ROA) Model
| | Estimate | SE |
|---|
| Customer concentration (CC) | -6.258*** | 1.490 |
| Marketing capability (MktCap) | .003*** | .001 |
| Technological capability (TechCap) | .0001 | .001 |
| Operational capability (OprCap) | .032** | .015 |
| Customer credit quality (LogCredQual) | -.005 | .003 |
| CC · MktCap | .025*** | .006 |
| CC · TechCap | .010*** | .004 |
| CC · OprCap | .311*** | .107 |
| CC · LogCredQual | -.032*** | .012 |
| Sales growth | .114*** | .022 |
| Log age (LogAge) | -.025 | .030 |
| Log sales (LogSale) | .003 | .008 |
| Competitive intensity | -.081 | .143 |
| Industry sales growth | -.001*** | .000 |
| Marketing intensity (MktInten) | -.616*** | .066 |
| Log assets (LogAssets) | -.093* | .054 |
| Number of business segments | -.003 | .014 |
| Number of major customers | -.002 | .025 |
| ROAit | .115 | .493 |
| Intercept | -1.654* | .926 |
| Endogeneity correction term ^qit | 2.625*** | .932 |
| Industry fixed effects | Yes |
| Year fixed effects | Yes |
*p < .1 (two-tailed).
**p < .05 (two-tailed).
***p < .01 (two-tailed).
The effect of other control variables is in line with expectations and aligned with prior literature. In line with our intuition, marketing (b = .003, p < .001) and operational (b = .032, p < .05) capabilities positively affect firm performance, whereas firm performance decreases with poor customer credit quality (b = -.007, p < .01). Finally, we find that sales growth is positively related to firm performance (b = -.110, p < .001), whereas marketing intensity (b = -.621, p < .001) negatively affects firm performance. Results using a three-stage least squares procedure are qualitatively similar to those presented in Table 4.
Our measure of customer concentration combines two dimensions of the customer base: its breadth, which represents the number of key customers, and its depth (ratio of sales from all the major customers to total sales), which represents the extent to which the focal firm relies on its key customers for revenues. To disentangle the effects of these two dimensions of customer concentration, we replicate our analysis using the breadth and depth of customer concentration on firm performance. To deal with the potential endogeneity of breadth and depth of the customer base, we use the control function approach (as detailed earlier) with industry averages of customer breadth and customer depth, respectively. We find that the depth of customer relationships has a negative effect on firm performance (b = -2.563, p < .001), whereas customer breadth has no effect (b = .019, p > .80). Further, we also find that that marketing (b = .019, p < .001), technological (b = .008, p < .01), and operational capabilities (b = .202, p < .01) reduce the adverse effects of depth of customer concentration on profitability. We do not find significant interaction effects for customer credit quality (b = .006, p > .3).
These results suggest that the negative effects of customer concentration are driven entirely by the depth of customer relationships, and they provide evidence for the mechanism discussed in our hypotheses, in which we argue that major customers bargain away the benefits that they create and leave the supplier firm exposed to all the risks associated with the concentrated customer base. An increase in the depth of customer relationships increases the bargaining power of the customers, enabling them to command an increased share of the value created; breadth of customer concentration alone, however, should not influence bargaining power.
We carried out several additional analyses to ensure the robustness of our results.
How long does the negative effect persist? Our results indicate that the effect of customer concentration negatively affects firm performance in the subsequent period, while organizational capabilities mitigate these negative effects. Given the importance of long-term profitability of organizational strategies and decisions, we are interested in finding out how long the negative consequences of customer concentration persist. To answer this question, we estimate the model in Equation 6 with additional future profitability measures: ROAit+2, ROAit+3, and ROAit+4. In all the models, we also account for the dynamic panel bias by using appropriate instruments for each period as discussed earlier. We find that the effect of customer concentration is negative for period t + 2 (b = -6.975, p < .001) and period t + 3 (b = -3.475, p < .001), and this effect persists until period t + 4 (b = -3.256, p < .001). Results of the interaction effects are also along expected lines in all the cases.
Stock market–based buy-and-hold abnormal returns (BHAR). Our two measures, market capitalization and profitability, examine the consequences of customer concentration from both stock market and accounting perspectives and provide a better understanding of the influence of customer concentration. Specifically, our results suggest that pre-IPO investors care about IPO outcomes, whereas other investors care about general well-being of the firm and therefore value balance sheet–based performance outcomes. However, one could argue that IPO outcome is a forward-looking stock market–based measure, whereas ROA is a backward-looking accounting metric. To document the robustness of our results, we replicate our balance sheet–based results using BHAR as the dependent variable. We compute buy-and-hold abnormal returns between two events as the difference between the buy-and-hold return of the stock and the buy-and-hold return of the benchmark, that is,
Similar to our ROA analyses, we compute BHAR for each of the four years post-IPO (depending on the data availability; for many firms, we compute BHAR beyond four years) and reestimate the model in Equation 6 with BHAR. This variable measures the abnormal return from holding the stock for an extended period and is a widely used measure of long-term outcomes (Srinivasan and Hanssens 2009). Unlike ROA, which is available for pre-IPO years, a stock market–based measure is available only after the stock starts trading, resulting in a loss of 2,796 observations; thus, we do not use BHAR as our primary dependent variable. Consistent with our results, we find that customer concentration negatively influences long-term BHAR (b = -.811, p < .05), providing confidence in our results.
Alternate model specifications. We estimated and compared our model with several other benchmark models. Specifically, the proposed model (Akaike information criterion [AIC] = 1,2971.4) is superior to alternate specifications such as models with only control variables (AIC = 13,333.9), only endogeneity correction and no correction for heterogeneity (AIC = 13,116.6), and only correction for unobserved heterogeneity and no endogeneity correction (AIC = 13,116.7). We also explored the nonmonotonic effects of customer concentration and reestimated both our models (Equations 5 and 6) to test for the quadratic effects of customer concentration. We obtain results qualitatively similar to those reported earlier and find no evidence of quadratic effect of customer concentration on market capitalization (b = -.191, p > .6) or ROA (b = .184, p > .4).
Finally, using the estimated residual in our second stage can induce measurement error.7 To account for this, we replicated our second-stage model with bootstrapped standard errors; we obtain results qualitatively similar to those reported earlier.
Alternate variable operationalization. We check the robustness of our results by using alternate specifications for our measures of organizational capabilities. Specifically, we use different variables to specify our input–output stochastic frontier. For marketing capability, for instance, we add lags of variables and use advertising expenditures (SG&A -R&D) as inputs, and we obtain identical results to those reported here. We also try using different lagged variables for estimating operations and technological capabilities and confirm the robustness of our results.
Customer management is one of the most important tasks that firms face, and managers spend significant resources developing strong relationships with their customers in an effort to increase purchases. However, our research highlights the downsides associated with strengthening relationships with only a small group of customers. We introduce the idea of customer concentration, which relates to the distribution of revenues across customers, to argue that instead of focusing on maximizing revenues from individual customers, managers must optimize their revenues across the entire customer base. Despite the convenience and potential benefits of managing fewer relationships (per H1), we highlight that having a concentrated customer base wherein the revenues are derived from a small group of customers has a significant negative effect on firm profitability. Large customers, who are aware of their strong bargaining position relative to the supplier firm, can extract all the joint value created and leave the supplier firm exposed to all the idiosyncratic risk associated with a concentrated customer base, hurting supplier profitability. We acknowledge the fact that firms (especially young firms, such as those in our sample) may not have much flexibility or choice in terms of level of customer concentration; however, the objective of our research is to sensitize managers about the deleterious consequences of relying on a few customers for their revenues. Further, our results suggest that the negative effects of customer concentration increase with a decrease in the credit quality of the supplier’s customer base and decrease with an increase in suppliers’ marketing, technological, and operational capabilities.
Customer acquisition and management has been a topic of significant research in marketing, yet this stream of research has largely focused on the strength of the individual supplier– customer relationship (Palmatier et al. 2006; Reinartz, Krafft, and Hoyer 2004; Reinartz and Kumar 2000; Ryals 2005). Our research contributes to CRM research in multiple ways. First, we highlight the importance of focusing on the entire customer base as allocating resources to maximize individual relationships with a small fraction of customers may lead to suboptimal outcomes. Our research should not be viewed as contradicting the volumes of research in the CRM domain; instead, we seek to highlight the risks of narrowly focusing on a few individual customer relationships, and we and encourage both theory and practice to look at the entire portfolio of customers. Second, we introduce the notion of customer concentration, which should allow scholars to better quantify the relationship between the firm and its customer base. Instead of dummy variables that prior research has used, such as customer power, CMO power, or customer dependence (e.g., Boyd, Chandy, and Cunha 2010; Christensen and Bower 1996; Wang, Saboo, and Grewal 2015), we hope that scholars use the customer concentration measure, which provides a nuanced understanding of the dynamics between firms and their customers. Third, we provide a resolution to the conflicting evidence provided by scholars on the consequences of having a concentrated customer base. For example, Patatoukas (2012) finds a positive effect of concentration on supplier performance, whereas Irvine, Park, and Yildizhan (2016) find that the effect is negative in the initial phases of buyer–supplier relationships and becomes positive as the relationship matures. Using a large sample of 1,023 IPO firms wherein we observe customer concentration from the beginning of their life cycles and therefore reduce any bias due to prior experience in customer management, our results highlight that the relationship is not so straightforward and that although customer concentration is viewed positively by pre-IPO investors and helps IPO outcomes, it hurts the overall financial well-being of the firm by hurting profitability over multiple periods. In other words, whereas Irvine, Park, and Yildizhan (2016) show a positive effect on profitability in the long-run, we observe the opposite. Our negative main effect of customer concentration supports the argument made by Slater and Narver (1998) that firm strategy should not be customer-led and is consistent with the literature on power dependence (e.g., Heide and John 1988), which suggests that ability to appropriate value in an exchange relationship is a function of the relative bargaining power of the firm, and financial portfolio theory (Markowitz 1952), which argues for diversifying the customer base to reduce idiosyncratic customer risk. Furthermore, our negative moderating effect of customer credit quality provides additional justification for managing the overall portfolio of customer and even firing “bad customers” (Zeithaml, Rust, and Lemon 2001). Low-quality customers may not only directly hurt the firm value through reduced profitability, but they also consume precious organizational resources and prevent the supplier firm from pursuing high-quality customers.
To the emerging literature on the role of marketing during IPOs (e.g., Luo 2008; Saboo and Grewal 2013), we highlight another marketing construct that influences IPO outcomes. Having a set of core customers significantly reduces the liability of newness associated with young firms and increases investor confidence in the IPO firm. Thus, customer concentration serves as a credible signal of IPO firm quality and affects market capitalization positively.
Finally, to the resource-based view literature, we highlight the indirect benefits of organizational capabilities and their relative importance (Amit and Schoemaker 1993). First, we highlight the value of organizational capabilities in enabling firms to amplify the benefits and diminish the costs associated with organizational actions. In line with prior studies that highlight the moderating effects of organizational capabilities (e.g., Grewal and Tansuhaj 2001), we find that the effects of a concentrated customer base are contingent upon organizational capabilities. Specifically, we find that marketing, technological and operational capabilities mitigate the negative effects of customer concentration on firm performance. Second, we document the relative importance of operational capability over technological and marketing capabilities in terms of mitigating the deleterious effects of customer concentration. Specifically, our results suggest that firms that emphasize operational capability are better equipped to mitigate the risks of customer concentration than those that emphasize marketing and technological capabilities.
Our results are also managerially relevant. First, to managers who spend significant resources on increasing customer purchases, our results highlight the negative consequences of such efforts. Although customer concentration can serve as a credible signal of quality for firms going public, we hope to sensitize managers against relying on a small group of customers for their revenues. Given the average market capitalization of $633.6 million among our sample firms, our results for the effects of concentration on IPO outcomes suggest that a 10% increase8 in customer concentration (which, for a firm having two customers each contributing 40% of the revenues, can result from a mere 2% increase in contributions from each of two customers) results in an increase in market valuation of supplier firms by $8.21 million. However, these IPO benefits are easily overshadowed by the significant negative consequences of customer concentration on firm profitability. For example, using the standardized estimates, our results suggest that a 10% increase in customer concentration reduces profitability by 3.35% in the subsequent year (and that these consequences persist for more than four years, resulting in a cumulative loss of more than 9.4% over the four years). For an average firm in our sample, this translates to an additional loss of about $7 million in the subsequent year, or about $20.32 million over the next four years. Thus, our results suggest that managers must be wary of deleterious consequences of having a concentrated customer base. Further, our results highlight that managers who are exposed to customer concertation may be able to reduce the deleterious consequences of their concentrated customer base by investing in their internal capabilities or going after high-quality customers. For instance, the same supplier firms with a 10% increase in concentration can trim their losses by 2.02% (i.e., losses decrease by 2.02%, from 3.35% to 1.33%) by increasing all their core organizational capabilities by 10%. On the other hand, the same supplier can further increase its losses by .21% (i.e., a total of 3.56%) if it lowers the credit quality of its customers by 10%. Further, to document the trade-off between our moderators, we estimate the percentage increase in capabilities and customer credit quality required to offset the negative effects of a 1% increase in customer concentration. We find that losses due to a 1% increase in customer concentration can be compensated by an increase of 3.23% in operations capability, 5.14% in marketing capability, or 17.87% in technological capability alone. On the other hand, the losses can also be evened out by an increase in 16.05% of customer credit quality. Thus, our results provide actionable guidance to managers in terms of alleviating the deleterious consequences of customer concentration. Finally, our research will help investors pick winners and improve corporate governance by highlighting the downsides of organizational customer management efforts. We acknowledge that not every firm may be able to change its customer mix in the short run; however our objective is to encourage firms to move in the right direction based on their specific situations.9
Our research highlights the negative consequences of customer concentration and cautions managers against relying on a small group of customers for their revenues. However, our study is not without its limitations, which provide several avenues for future research that we outline here. First, as discussed earlier, SFAS 131 requires all firms to disclose revenues from all customers who contribute at least 10% of total revenue. Although this 10% limit covers the significant customers and is consistent with the objectives of our study, having revenue data of all customers could provide additional insights. Second, given that most firms do not disclose the names of their customers, we are not able to include any individual customer characteristics (e.g., reputation or innovativeness of customers) that may influence the effects of customer concentration. Although we include customer credit quality as a measure of the overall quality of a supplier’s customers, scholars with proprietary data sets may find exploring the effects of individual customer characteristics a fertile area for future research. Third, while our measure of customer concentration is derived from secondary data, our study does not include any qualitative information about the nature of customer relationships. We hope researchers will use primary data to learn about customer dependence and relationship quality to provide additional insights into the cause and effects of customer concentration. Finally, future researchers should identify other boundary conditions (e.g., environmental factors, organizational resources) and mediating mechanisms that could highlight other avenues for firms to mitigate the deleterious consequences of customer concentration.
Footnotes 1 We use the label “strong” to refer to the strength of the relationship between the supplier and its customers. Because resource constraints limit the number of relationships firms can develop with their customers, firms often allocate their customer management resources according to the 80/20 principle (Luo and Kumar 2013; Zeithaml, Rust, and Lemon 2001), resulting in stronger relationships with a small group of customers.
2 We use two outcome measures to provide a nuanced understanding of the impact of customer concentration. Specifically, IPO outcomes (market capitalization and initial returns) measure the IPO investors’ evaluations of customer concentration. However, given that pre-IPO investors often have short-term investment horizons (Cadman and Sunder 2014), they emphasize immediate IPO outcomes and overlook factors that may have longer-term negative consequences. This short-term emphasis of pre-IPO investors is consistent with Saboo, Chakravarty, and Grewal (2016), who document that IPO investors obsess over current earnings and overlook myopic marketing activities of IPO firms that may have significant long-term negative consequences. Given that the consequences of our moderators (organizational capabilities and customer credit quality) may not be immediately evident, we do not hypothesize the moderating effects of these variables on the effects of customer concentration on IPO outcomes. In a robustness check, we reestimate our IPO performance model with these interaction effects and do not find any significant moderating effects.
3 This missing information of customers accounting for less than 10% does not materially bias our results for several reasons. First, due to our manual data collection efforts, for many firms, we could collect information for customers who accounted for revenues as low as 7%. Second, mathematically, given that we square the revenue shares, small revenue shares are unlikely to materially change our concentration measure. Third, conceptually, customers with a small revenue share are less likely to have the costs/benefits that we outline. The fact that even the Securities and Exchange Commission does not require firms to report information on these customers provides support to our claim. Fourth, we also replicate our analyses using quantile regression, which is less sensitive to measurement error, skewness, and outliers (Peel 2014) and obtain identical results. Finally, our endogeneity correction procedure should correct for any measurement error.
4 We thank an anonymous reviewer for the suggestion to allow the capabilities equations to be correlated. As a practical matter, we obtain qualitatively similar results using capabilities estimated independently.
5 We thank an anonymous reviewer for this insight. Results without including the number of major customers are similar to those reported here.
6 We obtain similar results using the Newey-West estimator, which accounts for both heteroskedasticity and higher-order serial correlation (Wooldridge 2010).
7 We thank an anonymous reviewer for this insight.
8 Because we use standardized estimates for these calculations in this subsection, percentage values refer to the percentage of the standard deviation of that variable. So, in this case, 10% refers to 10% of one standard deviation of concentration.
9 In an unreported analysis, we explored additional industry type and firm characteristics that can influence the effect of customer concentration; find that the negative effects of concentration decrease with firm size (both sales and total assets) and in the manufacturing industry, whereas they increase with firm age. We hope future scholars explore additional variables to help firms make these decisions.
GRAPH: ? ? FIGURE 2 Distribution of Firms and Time-Series Trend of Customer Concentration
GRAPH: FIGURE 3 Distribution of Number of Major Customers ? ?
GRAPH: FIGURE 4 Evolution of Customer Concentration with Firm Age ? ? ? ?
GRAPH: FIGURE 5 Model-Free Evidence of the Relationship Between Customer Concentration and Market Capitalization (A) and Return on Assets (B) ? ?
GRAPH: FIGURE 6 Plots for the Moderating Effects
DIAGRAM: FIGURE 1 Conceptual Framework
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Record: 24- Brand Buzz in the Echoverse. By: Hewett, Kelly; Rand, William; Rust, Roland T.; van Heerde, Harald J. Journal of Marketing. May2016, Vol. 80 Issue 3, p1-24. 53p. 2 Diagrams, 16 Charts, 16 Graphs. DOI: 10.1509/jm.15.0033.
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Record: 25- Brand Coolness. By: Warren, Caleb; Batra, Rajeev; Loureiro, Sandra Maria Correia; Bagozzi, Richard P. Journal of Marketing. Sep2019, Vol. 83 Issue 5, p36-56. 21p. 2 Diagrams, 4 Charts. DOI: 10.1177/0022242919857698.
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Brand Coolness
Marketers strive to create cool brands, but the literature does not offer a blueprint for what "brand coolness" means or what features characterize cool brands. This research uses a mixed-methods approach to conceptualize brand coolness and identify a set of characteristics typically associated with cool brands. Focus groups, depth interviews, and an essay study indicate that cool brands are perceived to be extraordinary, aesthetically appealing, energetic, high status, rebellious, original, authentic, subcultural, iconic, and popular. In nine quantitative studies (surveys and experiments), the authors develop scale items to reliably measure the component characteristics of brand coolness; show that brand coolness influences important outcome variables, including consumers' attitudes toward, satisfaction with, intentions to talk about, and willingness to pay for the brand; and demonstrate how cool brands change over time. At first, most brands become cool to a small niche, at which point they are perceived to be more subcultural, rebellious, authentic, and original. Over time, some cool brands become adopted by the masses, at which point they are perceived to be more popular and iconic.
Keywords: attitudes; authenticity; brands; coolness; niche; scale development; structural equation modeling
Consumers spend an enormous amount of money on cool brands, and brands from Off-White and Apple to Instagram and Jay-Z have thrived at least in part because consumers consider them cool. Being cool has helped startup brands (e.g., Facebook) soar past established competitors (e.g., Myspace). Being uncool, conversely, can sink even popular and well-funded brands (e.g., Segway, Zune, Levi's), relegating them to the pages of cautionary case studies.
What makes a brand cool? Despite the practical and theoretical importance of this question, the answer is unclear. Although research has begun to investigate personality traits associated with cool people ([21]; [22]; [42]; [79]), cool technologies ([17]; [29]; [66]), and how specific factors such as autonomy ([81]; [80]) and novelty ([47]) influence perceptions of coolness, the literature has not systematically identified the characteristics differentiating cool from uncool brands, nor has it identified how these characteristics change as brands move from being cool within a small subculture (i.e., niche cool) to the broader population (i.e., mass cool; [77]).
We contribute to the literature by using grounded theory to identify the characteristics associated with cool brands. Through a series of studies leveraging focus groups, depth interviews, essay writing, surveys, and experiments, we generate and validate a measure of brand coolness that incorporates ten characteristics that distinguish cool brands from uncool brands. We find that cool brands are perceived to be extraordinary, aesthetically appealing, energetic, high status, rebellious, original, authentic, subcultural, iconic, and popular. We develop a multi-item scale that measures the ten components, as well as the higher-order construct, of brand coolness. In addition, we explore the nomological network related to brand coolness by identifying a set of variables that are related to, yet conceptually distinct from, coolness, including self–brand connections (SBC), brand love, brand familiarity, brand attitude, word-of-mouth (WOM) about the brand, and willingness to pay (WTP) for the brand.
Moreover, we examine the subjective and dynamic nature of brand coolness ([12]; [31]; [73]). Brands initially become cool to a small subculture by being original, authentic, rebellious, exceptional, and aesthetically pleasing. Such brands (e.g., Steady Hands, INSIDE, Mitsky), which we refer to as being niche cool, are perceived to be cool by a small group of knowledgeable insiders, although the brands remain relatively unfamiliar to the broader population. Over time, some niche cool brands cross over and are adopted by a wider audience, at which point they become mass cool (e.g,. Nike, Grand Theft Auto, Beyoncé) and are perceived to be relatively more popular and iconic, but less autonomous.
Cool has many synonyms (e.g., hip, awesome, sweet, chill, badass, dope; for more, see Urban Dictionary [http://www.urbandictionary.com/define.php?term=cool]) but is difficult to define. Web Appendix A lists over 70 different ways that coolness has been described and defined, illustrates how the literature has not converged on a definition, and highlights the need to establish a firmer, empirically grounded understanding of brand coolness. Given the number of existing definitions of coolness, we believe that the field would benefit less from another definition than from a stronger understanding of how coolness applies to brands. Thus, as a starting point to investigate the characteristics of cool brands, we use Warren and Campbell's (2014, p. 544) definition of coolness as "a subjective and dynamic, socially constructed positive trait attributed to cultural objects inferred to be appropriately autonomous" (emphasis added).
This definition highlights four essential features of coolness ([ 2]). One, coolness is subjective. Brands are only cool (or uncool) to the extent that consumers consider them as such ([18]; [37]; [65]). Consequently, uncovering what distinguishes cool from uncool brands requires collecting data about which characteristics consumers associate with the brands that they subjectively perceive to be cool.
Two, coolness has a positive valence (e.g., [21]; [53]). Most dictionaries describe cool as an interjection used to express approval, admiration, and acceptance. Studies have found that consumers associate cool products with generally desirable characteristics, including usefulness ([70]; [55]), and hedonic value ([45]). Similarly, when asked to describe traits that they associate with cool people, survey respondents mostly list positive adjectives (e.g., attractive, friendly, competent; [19]). Yet there is also consensus that cool is not merely a general expression of liking. Cool brands are desirable, but there is something extra that makes an object cool rather than merely being positive ([21]; [65]).
A third defining feature helps distinguish cool from desirable: autonomy. Autonomy is defined as being willing and able to follow your own path rather than conform to the expectations and desires of others ([81]). Autonomy cannot be directly observed, but instead must be inferred on the basis of the extent to which someone (or something) fights conventions and norms (i.e., is rebellious; [17]; [28]; [65]; [68]), attempts to be different by moving beyond conventions and norms (i.e., is original; [17]; [55]; [66]; [75]; [80]), and behaves consistently in the face of pressure to adapt to shifting trends (i.e., is authentic; [57]; [66]; [74]).
The fourth defining feature of coolness is that it is dynamic. The brands that are cool today may not be cool tomorrow ([60]; [67]). Even the characteristics—and people—that consumers associate with cool brands appear to change over time and across different types of consumers. Most brands initially become cool within a specific niche or subculture before later being discovered, adopted, and christened as cool by a broader audience ([12]; [33]). Interestingly, consumers tend to use the same term, "cool," to describe both ( 1) brands that their small in-group considers cool but that have not yet become popular and ( 2) brands that the general population is aware of and considers cool ([77]). Following [80], we distinguish between niche cool,[ 7] which refers to brands that are perceived to be cool by a particular subculture but that the masses have not yet adopted, and mass cool, which refers to brands that are perceived to be cool by the general population.
The literature thus raises several questions about brand coolness. First, although we know that coolness is desirable (Dar-Nimrod et al. 2012; [53]) and autonomous ([28]; [65]), there are many ways to be desirable and autonomous. For example, signaling high status and offering a low price are both desirable characteristics, and being unique and being dominant both show autonomy. The literature does not specify which desirable and autonomous characteristics make brands cool and which do not. It is also unclear whether other characteristics that are not directly related to desirability and autonomy are prototypical of cool brands. Researchers have suggested that coolness is related to emotional concealment, narcissism, hedonism, excitement, sexual permissiveness, and youth ([16]; [49]; [57]; [65]), but it is unclear whether any of these characteristics distinguish cool from uncool brands. Thus, our first research question is, What characteristics are prototypical of cool brands?
Second, although there have been several attempts to measure coolness in specific product categories ([17]; [75]; [70]), there are no established scales designed to measure the characteristics of cool brands. Identifying a measure of the different components of brand coolness is practically valuable because it would allow marketers and scholars to identify whether a brand is cool and, if not, examine how and why it lacks coolness. Our second question thus is, Can we develop a validated instrument to measure the component characteristics of cool brands?
Third, although both practitioners and scholars suggest that being cool helps explain why some products succeed ([12]; [39]; [47]), the specific consequences of brand coolness remain unclear. Are consumers more likely to talk (i.e., spread WOM) about cool brands? Are they willing to pay more for cool brands? Importantly, can brand coolness explain substantial variance in these or other important consequential variables, relative to that explained by previously studied constructs such as brand personality, brand love, and SBC? Our third question thus is, What are the consequences of brand coolness?
Fourth, although we know that coolness is dynamic ([33]; [37]), it is not clear how the characteristics or consequences of cool brands change over time. The literature speculates that brands initially become niche cool to a small subculture before becoming mass cool to a broader audience, but how the characteristics and effects of cool brands change over time is an open empirical question. Our fourth question is thus, How do the characteristics and consequences of coolness change as brands move from niche cool to mass cool? Answering all of these questions requires data that the literature does not provide.
We use a grounded theory approach to identify the characteristics of cool brands, initially conducting three qualitative studies using focus groups, depth interviews, and essays with consumers from North America and Europe. We identified these characteristics by looking for similar patterns of responses across the different methods and cultures ([32]; [50]) utilizing the ATLAS.ti software ([31]). First, we used a process of "constant comparison" to organize and reduce the coded units across the different sets of data. Second, we actively sought theoretical relationships between these concepts at a higher level of abstraction ("axial coding"). We then organized these concepts and relationships into ten major themes. Next, we provide a summary description of our three qualitative studies (details are in Web Appendix B), followed by the themes that emerged from the analysis.
We first conducted four focus groups in Western (United Kingdom), Eastern (Slovakia), and Southern (Portugal) Europe. The average number of participants in each group was eight, and each focus group lasted about 60 minutes. For our second qualitative study, we conducted 30 depth interviews with consumers in Portugal. The interviews followed a methodological procedure similar to that outlined by [51]; see also [35]]). Informants were asked a series of grand tour questions, including "What are the essential characteristics that you associate with cool brands?" In our third qualitative study, 75 students at a university in the United States wrote two essays, one describing a brand they thought was cool and another describing a brand they liked but did not think was cool.
Ten themes, or characteristics, related to brand coolness emerged from the focus group, interview, and essay responses. Specifically, respondents perceived cool brands to be useful/extraordinary, aesthetically appealing, energetic, high status, original, authentic, rebellious, subcultural, iconic, and popular. Table 1 defines each characteristic and notes prior research that has suggested a relationship between the characteristic and coolness.
Graph
Table 1. Definitions for Component Characteristics of Brand Coolness and Relevant Citations from Prior Research.
| Characteristic | Definition | Supporting Citations |
|---|
| Extraordinary/useful | A positive quality that sets a brand apart from its competitors/offering superior functional value | Belk et al. (2010), Dar-Nimrod et al. (2012), Im, Bhat, and Lee (2015), Mohiuddin et al. (2016), Runyan, Noh, and Mosier (2013), Sundar, Tamul, and Wu (2014) |
| High status | Associated with social class, prestige, sophistication, and esteem | Belk et al. (2010), Connor (1995), Heath and Potter (2004), Milner (2013), Nancarrow, Nancarrow, and Page (2003), Warren (2010) |
| Aesthetically appealing | Having an attractive and visually pleasing appearance | Bruun et al. (2016), Dar-Nimrod et al. (2012), Runyan, Noh, and Mosier (2013), Sundar, Tamul, and Wu (2014) |
| Rebellious | A tendency to oppose, fight, subvert, or combat conventions and social norms | Bruun et al. (2016), Frank (1997), Milner (2013); Nancarrow, Nancarrow, and Page (2003), Pountian and Robins (2000), Read et al. (2011), Warren and Campbell (2014) |
| Original | A tendency to be different, creative, and to do things that have not been done before | Bruun et al. (2016), Mohiuddin et al. (2016), Read et al. (2011), Runyan, Noh, and Mosier (2013), Sundar, Tamul, and Wu (2014), Warren and Campbell (2014) |
| Authentic | Behaving in a way that is consistent with or true to its perceived essence or roots | Nancarrow, Nancarrow, and Page (2003), Read et al. (2011), Sriramachandramurthy and Hodis (2010) |
| Subcultural | Associated with an autonomous group of people who are perceived to operate independent from and outside of mainstream society | Belk et al. (2010), Runyan, Noh, and Mosier (2013), Sundar, Tamul, and Wu (2014), Thornton (1995) |
| Popular | Fashionable, trendy, and liked by most people | Dar-Nimrod et al. (2012), Heath and Potter (2004), Rodkin et al. (2006) |
| Iconic | Widely recognized as a cultural symbol | Holt (2004), Warren and Campbell (2014) |
| Energetic | Possessing strong enthusiasm, energy, and vigor | Aaker (1997), Sriramachandramurthy and Hodis (2010) |
A common theme in the focus groups, interviews, and essays was that cool brands are useful, meaning that they are high quality, offer tangible benefits, or help consumers in some way. One respondent wrote that he perceived Vic Firth (a musical instrument manufacturing company) to be a cool brand "because of their high-quality product." Another stated that Chrome Industries is a cool brand because "their bags are well known for their durability and functionality." The theme that cool brands are useful converges with evidence in the literature that there is a strong association between perceived coolness and traits that are desired or valued (e.g., [21]; [45]). Some respondents, however, indicated that cool brands are more than just useful—they are extraordinary. Respondents thought that Apple was cool because it offers "previously unheard of capabilities," the brand "pushes the limit in the electronic industry," or simply because "I think they are awesome." The finding that cool brands are extraordinary fits both with literature that highlights the positive valence of coolness (e.g., [12]; [21]) and with dictionary definitions of cool.
Another recurring theme across the focus groups, interviews, and essays was that cool brands are aesthetically appealing. Respondents indicated that Apple is cool in part because its products are "elegantly designed." Respondents similarly noted the aesthetic appeal of other brands that they perceived to be cool across a range of industries from apparel to magazines: "I am very impressed by the design and layout of the magazine [Wired], and I keep each issue to reference for when I am doing graphic design myself." The theme that cool brands have aesthetic appeal is consistent with prior attempts to measure coolness in clothing and technological products ([17]; [70]; Sundar, Tamul, and Wu 2014).
A third theme that emerged was that cool brands are active, outgoing, youthful, or, more generally, energetic. Respondents indicated that cool brands make them feel good, connect with consumers on an emotional level, and help consumers have remarkable experiences. For example, respondents indicated that brands such as Red Bull and GoPro are cool because they are associated with exciting activities, including daring stunts and extreme sports. This notion that cool brands are energetic is consistent with the "Brand Energy" construct used by the Brand Asset Valuator system of assessing brand strength ([30]). Although some researchers have suggested that coolness is associated with similar traits, including youth ([60]; [70]), hedonism ([65]), and "sexual permissiveness" ([16]), prior research on coolness has rarely discussed being energetic as a characteristic of cool brands. Two exceptions are [ 1] and [74], who suggest a link between perceived coolness and excitement.
Many respondents viewed cool brands as having high social status or possessing traits associated with high status, such as being exclusive, upper class, glamorous, and sophisticated. Respondents wrote that Chanel perfume is cool because "it makes me feel classy, chic, and elegant," and that Louis Vuitton is cool "because of its exclusivity, not everyone owns something from Louis Vuitton." Given the close link between status and coolness in people ([12]; [39]; [77]), it is not surprising that respondents similarly viewed cool brands as having high status.
Another theme in the focus groups, interviews, and essays was that cool brands are original. One respondent eloquently articulated this theme, stating "the uncool will be doing tomorrow what the cool have done before." Respondents described cool brands as being original, creative, "one step ahead," and as consistently reinventing themselves. As previously mentioned, the literature similarly notes a close association between coolness and originality ([17]; [68]; [81]; [80]).
Another theme in the responses was that cool brands are authentic. "Authentic" was the word most frequently associated with cool brands in the focus group sessions. Authenticity comes in a variety of flavors ([11]; [58]), and the flavor that our respondents mentioned—the brand behaving consistently and remaining true to its roots—has been called value authenticity ([15]), moral authenticity ([14]), sincerity ([56]), and integrity ([54]). One respondent stated, "Cool brands don't try to be cool and they are just what they really are." Another wrote that the record label Fueled by Ramen "is a cool brand primarily due to its subject matter and authenticity....It has deviated very little from the genre with which it started and increases its reputation with each new successful alternative band that it cultivates." Others noted the continuity over time in cool brands, such as Jack Daniels, with a traditional or vintage image. The link between coolness and authenticity is consistent with prior research on coolness ([ 2]; [15]; [57]; [66]).
A similar theme in the focus groups and interviews was that cool brands are rebellious. One respondent noted, "Something controversial is in many cases the coolest." Respondents thought that brands such as Red Bull, Harley-Davidson, Betsey Johnson, and Apple became cool by being "rule breakers," "irreverent," or "revolutionary." As previously discussed, the literature has historically linked coolness to rebellion ([28]; [65]), and this association has been at least partially supported by recent data ([15]; [44]; [81]).
Another theme was that cool brands are associated with a particular subculture ([38]; [72]). One respondent noted that using cool brands provides "the satisfaction of being part of a different subculture." Respondents associated cool brands with a range of different subcultures, including rock climbers (Black Diamond), biker messengers (Chrome Industries), and alternative music (Converse). Even when they become popular, cool brands (e.g., Nike) usually maintain a link to a subculture (e.g., athletes). Research is consistent with the idea that cool brands are tied to specific subcultures, including those linked with jazz, raves, hip-hop, extreme sports, high school cliques, or any other group perceived to be distinct from the mainstream ([19]; [51]; [74]).
Another theme emerging from the focus groups, interviews, and essays was that cool brands are iconic. By iconic, we mean that the brand holds an especially strong and valued meaning to consumers ([42]). There was a high overlap between the brands that our respondents identified as cool and the brands that [40]; [43]) describes as cultural icons (e.g., Apple, Nike, Patagonia, Jack Daniels). Moreover, our respondents highlighted how cool brands can symbolize memories, social relationships, identity traits, and cultural values. For example, one wrote, "Disney is a symbol of childhood and being young and allows people to act young at heart which I think also helps add to the idea that Disney is cool." All strong brands acquire some symbolic meaning ([46]; [48]), but respondents view cool brands as having especially potent meanings that reflect their shared cultural values and beliefs. For example, European respondents who value social responsibility considered socially conscious and environmentally friendly brands (e.g., the Finnish brand Globe Hope) to be cool. The literature notes the strong symbolism of cool brands ([12]; [81]), though we are not aware of previous work that has attempted to operationalize or measure the extent to which cool brands are iconic.
A final theme in the focus groups, interviews, and essays is that cool brands are popular, meaning that they seem trendy or widely admired by consumers. For example, one European respondent stated that for a brand to be cool, "It has to be recognized all over the world." Similarly, an American respondent wrote, "I consider Nike cool because it is a brand widely worn among a variety of people." We note here that some of the prior literature suggests that, paradoxically, cool brands are scarce (instead of popular), meaning that they are rare, exclusive, or not accessible to everyone ([16]; [55]; [67]). However, research that has used quantitative methods to study coolness has not found a link between scarcity and coolness in either people ([44]; [42]) or products ([17]; [70]; [73]). We too did not find adequate empirical support for a general link between scarcity and brand coolness in either our qualitative research or our quantitative surveys (see Web Appendices B and C).[ 8] Study 8, however, can explain why cool brands may be associated with scarcity and subcultures as well as popular trends: brands initially become cool when they were associated with a subculture (i.e., niche cool), but they later become popular and trendy after a wider population discovered the brand (i.e., mass cool; [33]; [78]). In other words, cool brands typically begin as scarce and subcultural but later become more popular as they are discovered and transition from niche cool to mass cool (see Figure 1).
Graph: Figure 1. Life cycle of brand coolness.
We subsequently assessed the frequency with which participants noted the aforementioned themes while writing about the cool and uncool brands in Qualitative Study 3 (essay writing). Specifically, a research assistant indicated whether the 75 essays mentioned each of the ten characteristics that appeared in the qualitative responses for both the description of the cool brand and the description of the uncool brand. If the essay noted a high level of the characteristic (e.g., "brand X is original"), the research assistant coded it as a 1; if not, he coded it as a 0 (complete results in Web Appendix B). The characteristic that essay respondents most strongly associated with cool brands was being iconic. Most (73%) noted that cool brands seem iconic or that they symbolize an important value, belief, or memory (only 8% of uncool brands were described as being iconic; χ2 = 66.34, p <.001). Most respondents similarly reported that cool brands were extraordinary or useful (76%), though this did not distinguish cool from uncool brands, as respondents also considered most uncool brands useful (71%; χ2 =.55, p =.46). Responses suggested several additional characteristics that distinguish cool from uncool brands. Specifically, they were more likely to describe cool brands as being more subcultural (44% vs. 7%; χ2 = 27.63, p <.001), original (33% vs. 4%; χ2 = 21.25, p <.001), aesthetically appealing (25% vs. 4%; χ2 = 18.85, p <.001), popular (17% vs. 4%; χ2 = 7.00, p =.008), high status (15% vs. 4%; χ2 = 5.04, p =.02), and energetic (8% vs. 0%; χ2 = 6.25, p =.01) than uncool brands. In contrast to the focus group and depth interview respondents, few essay respondents explicitly mentioned authenticity or rebellion when describing cool brands.
The focus groups, depth interviews, and essays were insightful not only for the themes in the responses but also for the themes that had been mentioned in the literature but that did not emerge. One conspicuously absent theme was cultural knowledge, which scholars argue helps make people cool ([20]; [55]; [73]; [74]). Other themes that the literature discusses but that did not surface in the data were emotional concealment, friendliness, and competence ([44]; [42]; [65]; [79]). One way to reconcile the absence of cultural knowledge, friendliness, and competence in our findings is by recognizing that these traits are desirable in people ([26]), just as being extraordinary, energetic, and aesthetically appealing are desirable in brands. Thus, desirable traits are cool in both people and brands, but the specific traits that are desired differ for people and brands. Just as the relevant aspects of personality and love differ between people and brands ([ 1]; [ 9]), some of the characteristics of cool people do not apply to cool brands.
We conducted eight survey studies to identify the higher-order structure of the characteristics of brand coolness that emerged in the qualitative research and to test their nomological relationships with related constructs. Each study asked respondents to evaluate a brand that they consider cool, a brand that they do not consider cool, or both. The first four studies were pretests, in which we developed and refined the measures for the structural and nomological models. Due to length constraints, we describe these studies in Web Appendix C. Study 5 had three purposes. First, it confirmed the structural measurement model for the ten characteristics associated with brand coolness (useful, aesthetically appealing, energetic, high status, rebellious, original, authentic, subcultural, iconic, and popular). Second, the study confirmed that all ten characteristics were more closely associated with cool brands than uncool brands. Third, the study tested the nomological relationship between brand coolness and related constructs, including brand personality, SBC, brand love, brand attitude, WTP for the brand, and intentions to spread WOM about the brand. The last three studies replicated and extended Study 5. Study 6 improved our measure of brand coolness by developing items that better capture the extent to which the brand seems extraordinary rather than merely useful. Study 7 used a purely confirmatory design to replicate the results of Study 6. Finally, Study 8 examined the dynamic and subjective nature of brand coolness by testing how the characteristics and consequences differ between niche cool brands, mass cool brands, and uncool brands within a subculture of urban streetwear enthusiasts.
Studies 5 (N = 315; 50% male; modal age = 42 years) and 6 (N = 315; 47% male; modal age = 25–30) recruited U.S. consumers from a nationally representative online survey panel. Study 7 recruited participants from Amazon's Mechanical Turk (MTurk; N = 405; 58% male; modal age 25–30 years; all located in the United States). Study 8 recruited 148 streetwear fashion enthusiasts by offering a Gold Award[ 9] to readers of the Reddit board r/streetwear who completed the survey. The sample for Study 8 was mostly young (average age = 19 years; range = 13–41 years) and male (93%) but was racially diverse (53% white, 27% Asian, 5% Hispanic, 3% Black).
In each study, participants nominated and evaluated one or more brands (for details, see Web Appendix D). In Studies 5 and 6, participants nominated and evaluated a brand that they personally consider cool and a brand that they like but do not personally consider cool. In Studies 7 and 8, we manipulated the brand type between subjects, such that participants nominated a cool or uncool brand (Study 7) or a niche cool, mass cool, or uncool fashion brand (Study 8). For example, in Study 8, participants in the "uncool" condition read, "Please identify a brand that you consider not cool. Neither you nor the 'mass market' think that this brand has ever been cool, today or in the past." Participants in the "mass cool" condition read, "Please identify a fashion brand that is cool to mainstream consumers. That is, name a brand that is mass cool." Participants in the "niche cool" condition read, "Please identify a fashion brand that is cool to you (but not to the mainstream). That is, name a brand that is niche cool."
After participants nominated the brand(s), we asked them to rate them (order counterbalanced) on a series of five-point "agree–disagree" scale items. Drawing on our literature review, qualitative research, and four pretest studies (see Web Appendix C), in Study 5 we used the 36 items listed in Table 3 to measure the extent to which each brand was perceived to be useful, aesthetically appealing, energetic, high status, rebellious, original, authentic, subcultural, iconic, and popular. In Study 6, we explored whether the extent to which the brand seems extraordinary better captures the construct of coolness than the extent to which it seems useful by adding four new items (e.g., "X is exceptional") as possible replacements for the three useful items. Studies 7 and 8 used the final 37-item scale (see Table 3) to measure the extent to which the brand seems extraordinary (instead of useful) along with the other nine characteristics.
The studies also measured various constructs that the literature suggests might be related to brand coolness. All four studies measured ( 1) brand love (two-item measure from [ 9]]), ( 2) SBC (five-item measure adapted from [24]]), ( 2) WOM related to the brand (e.g., "In the past few months, how often have you talked about [brand name] with other people, online or offline?"), and ( 4) WTP for the brand (e.g., "I am willing to pay a higher price for this brand than other brands"). Studies 5, 7, and 8 measured brand attitudes. Studies 5 and 7 measured the five dimensions of brand personality (ruggedness, excitement, sophistication, competence, and sincerity) using [ 1] 22-item scale. Studies 7 and 8 measured the extent to which ( 1) participants had been exposed to the brand (e.g., "In the past few months, how often have you heard other people talk about [brand name]?"), ( 2) the brand is familiar (e.g., "this brand is well-known"), and ( 3) the brand commands a price premium (e.g., "this brand costs more than others in the same product category"). Finally, Study 5 measured satisfaction (three items from [57]]), delight (six items adapted from [27]]), and pride (five items adapted from [76]]) from owning the brand. We provide a complete list of measures in Web Appendix D.
In addition to measuring the characteristics associated with brand coolness (e.g., extraordinary, aesthetic), Studies 6–8 asked participants to directly rate the extent to which they personally consider the brands cool. Studies 7 and 8 also measured the extent to which participants believe that other people consider the brand cool. Finally, to capture the dynamic nature of coolness that we were investigating in Study 8, we asked participants to indicate how the brand's coolness has changed in the past and how they expect it to change in the future.
Study 8 measured participants' need for uniqueness (short form; [71]), innovativeness (items from [44]]), subjective expertise in fashion, and experience reading and posting on the r/streetwear forum. Studies 5–8 concluded by measuring participants' demographic variables (e.g., age, gender, native language). None of these individual differences interacted with the results we report here, so we do not discuss them further. Note that Studies 7 and 8 also included theoretically unrelated "marker variables" for a methods factor test, described next.
We refined and revised the measurement items using exploratory factor analysis (Studies 1–4) and confirmatory factor analysis (CFA; Studies 5–8; see Table 2). Specifically, we used the pretests to eliminate or replace items that either did not load highly onto the factor they were intended to measure or that cross-loaded onto multiple factors ([36]; [59]), keeping in mind the characteristics identified from our literature review and qualitative analyses (details in Web Appendix C). We used Studies 5–8 to confirm our final model (see Figure 2). We used a reflective instead of a formative model at each level for two reasons. One, the logic underlying reflective models better fits our conceptualization of brand coolness: the ten characteristics derived from the qualitative analyses are more appropriately considered to be manifestations of the latent construct of brand coolness, rather than formative measures that define it. Two, the coefficients in formative models can vary with the number and structure of the measures and factors used ([ 4]; [25]; [43]), which makes them less appropriate in our context.[10]
Graph
Table 2. Summary of Quantitative Survey Studies.
| Study 1 | Study 2 | Study 3 | Study 4 | Study 5 | Study 6 | Study 7 | Study 8 |
|---|
| Sample (N)Source | 415 students (Portugal) | 582 students (Portugal) | 258Qualtricsa (U.S.) | 206MTurkb (U.S.) | 315Qualtricsa (U.S.) | 315Qualtricsa (U.S.) | 405MTurkb (U.S.) | 148Reddit streetwear forumc |
| Brand(s) nominated | Cool brand | Cool brand | Cool + uncool electronics brands | Cool + uncool brands | Cool + uncool brands | Cool + uncool brands | Cool or uncool brand | Niche cool, mass cool, or uncool fashion brand |
| Manipulation check measures | None | None | I think it's cool | None | None | I think it's cool | I think it's cool + others think it's cool | I think it's cool + others think it's cool + change in cool |
| Cool characteristics | Energetic, original, authentic, high status, subcultural, scarce, responsible | Energetic, original, authentic, high status, subcultural, scarce, responsible | Energetic, aesthetic, useful, original, authentic, high status, subcultural, scarce, responsible, popular, rebellious | Energetic, aesthetic, original, rebellious, high status, scarce | Useful, energetic, aesthetic, original, authentic, rebellious, subcultural, high status, iconic, popular | Useful, extraordinary, energetic, aesthetic, original, authentic, rebellious, subcultural, high status, iconic, popular | Extraordinary, energetic, aesthetic, original, authentic, rebellious, subcultural, high status, iconic, popular | Extraordinary, energetic, aesthetic, original, authentic, rebellious, subcultural, high status, iconic, popular |
| Cool correlates | None | None | None | Brand personality | Brand personality | None | Brand personality | None |
| Cool consequences | None | None | SBC, brand attitude | WOM, WTP, satisfaction, delight, pride, quality, price premium | WOM, WTP, brand love, brand attitude, SBC, pride, satisfaction, delight | WOM, WTP, brand love, SBC | WOM, WTP, brand love, brand attitude, SBC, brand exposure, familiarity, price premium | WOM, WTP, brand love, brand attitude, SBC, brand exposure, familiarity, price premium |
| Individual differences | Demographics | Demographics | Demographics | Demographics | Demographics | Demographics | Restaurant attitudes,d demographics | Need for uniqueness, innovativeness, subjective expertise, experience on forum, restaurant attitudes,d demographics |
1 aNationally representative panel from the United States.
- 2 bAmazon's Mechanical Turk.
- 3 cReddit board r/streetwear.
- 4 dUsed as a marker variable.
Graph: Figure 2. CFA measurement model in Studies 6–8.Note: A reflective perspective is used at all levels.
Our data revealed a final model with brand coolness consisting of two higher-order factors, which we call desirability and positive autonomy, along with five first-order factors (see Figure 2). The three characteristics of "useful" (later, "extraordinary"), "energetic," and "aesthetic appeal" load onto the subdimension of desirability; the two first-order factors of "original" and "authentic" load onto the subdimension of positive autonomy. Both desirability and positive autonomy are dimensions of higher-order brand coolness, along with high status, rebellious, subcultural, iconic, and popular, which load as first-order factors onto higher-order brand coolness. Table 3 shows the estimated measurement and structural coefficients from Studies 5–8. Where available, we report within-group, completely standardized coefficients for the cool and uncool brand samples separately. Note that Study 5 used three items measuring whether the brand is useful, whereas Studies 6–8 replaced these with the four new items measuring whether the brand is extraordinary.
Graph
Table 3. CFA Model Coefficients and Fit Statistics by Study and Sample.
| Measurement Model | Study 5 | Study 6 | Study 7 | Study 8 |
|---|
| Cool | Uncool | Cool | Uncool | Cool | Uncool | Pooled |
|---|
| Factor loadings (lambdas) | n = 315 | n = 315 | n = 305 | n = 305 | n = 213 | n = 192 | n = 148 |
|---|
| Usefula/Extraordinaryb | | | | | | | |
| Is usefula/is exceptionalb | .74 | .75 | .93 | .97 | .84 | .92 | .92 |
| Helps peoplea/is superbb | .75 | .79 | .93 | .97 | .78 | .93 | .90 |
| Is valuablea/is fantasticb | .74 | .86 | .94 | .97 | .88 | .95 | .96 |
| Is extraordinaryb | | | .89 | .93 | .88 | .96 | .94 |
| Energetic | | | | | | | |
| Is energetic | .83 | .86 | .85 | .93 | .82 | .88 | .86 |
| Is outgoing | .86 | .89 | .88 | .93 | .87 | .89 | .87 |
| Is lively | .84 | .92 | .89 | .96 | .9 | .89 | .88 |
| Is vigorous | .79 | .87 | .77 | .92 | .83 | .9 | .88 |
| Aesthetically Appealing | | | | | | | |
| Looks good | .73 | .85 | .87 | .93 | .87 | .93 | .96 |
| Is aesthetically appealing | .73 | .87 | .86 | .92 | .88 | .96 | .96 |
| Is attractive | .88 | .93 | .93 | .95 | .84 | .94 | .94 |
| Has a really nice appearance | .85 | .91 | .91 | .96 | .85 | .95 | .94 |
| Original | | | | | | | |
| Is innovative | .73 | .83 | .81 | .84 | .76 | .85 | .9 |
| Is original | .69 | .74 | .82 | .93 | .76 | .87 | .93 |
| Does its own thing | .64 | .69 | .85 | .82 | .86 | .83 | .9 |
| Authentic | | | | | | | |
| Is authentic | .75 | .82 | .85 | .93 | .8 | .92 | .91 |
| Is true to its roots | .8 | .81 | .79 | .92 | .75 | .88 | .84 |
| Doesn't seem artificial | .67 | .77 | .80 | .83 | .62 | .82 | .81 |
| Doesn't try to be something it's not | .62 | .73 | .77 | .85 | .75 | .7 | .78 |
| Rebellious | | | | | | | |
| Is rebellious | .75 | .74 | .66 | .8 | .9 | .9 | .95 |
| Is defiant | .88 | .85 | .77 | .87 | .91 | .91 | .97 |
| Is not afraid to break rules | .65 | .69 | .84 | .89 | .76 | .77 | .84 |
| Is nonconformist | .8 | .73 | .88 | .87 | .71 | .75 | .78 |
| High Status | | | | | | | |
| Is chic | .82 | .87 | .75 | .87 | .64 | .77 | .72 |
| Is glamorous | .93 | .94 | .91 | .93 | .84 | .91 | .85 |
| Is sophisticated | .81 | .86 | .81 | .91 | .8 | .78 | .86 |
| Is ritzy | .82 | .84 | .71 | .84 | .77 | .82 | .84 |
| Popular | | | | | | | |
| Is liked by most people | .73 | .82 | .77 | .83 | .76 | .78 | .86 |
| Is in style | .75 | .87 | .89 | .91 | .81 | .53 | .78 |
| Is popular | .9 | .87 | .83 | .9 | .81 | .77 | .71 |
| Is widely accepted | .84 | .82 | .8 | .88 | .77 | .83 | .82 |
| Subcultural | | | | | | | |
| Makes people who use it different from other people | .85 | .71 | .90 | .86 | .86 | .91 | .87 |
| If I were to use it, it would make me stand apart from others | .87 | .87 | .92 | .97 | .96 | .95 | .96 |
| Helps people who use it stand apart from the crowd | .87 | .96 | .94 | .95 | .95 | .97 | .91 |
| People who use this brand are unique | .84 | .79 | .85 | .9 | .82 | .93 | .82 |
| Iconic | | | | | | | |
| Is a cultural symbol | .66 | .84 | .82 | .9 | .77 | .84 | .76 |
| Is iconic | .89 | .87 | .85 | .91 | .9 | .86 | .87 |
| Structural Coefficients (Betas) | | | | | | | |
| Useful/exceptional → Desirability | .75 | .86 | .83 | .92 | .88 | .89 | .93 |
| Energetic → Desirability | .75 | .81 | .83 | .88 | .86 | .78 | .83 |
| Aesthetics → Desirability | .82 | .83 | .79 | .87 | .79 | .85 | .92 |
| Originality → Positive autonomy | .91 | .96 | .95 | .99 | .91 | .87 | .91 |
| Authenticity → Positive autonomy | .87 | .83 | .95 | .98 | .97 | .86 | .91 |
| Desirability → Higher-order cool | 1 | .98 | 1 | 1 | .98 | .98 | .99 |
| Positive autonomy → Higher-order cool | .89 | .9 | .91 | .9 | .94 | .92 | .96 |
| Rebelliousness → Higher-order cool | .45 | .61 | .58 | .75 | .46 | .52 | .73 |
| High status → Higher-order cool | .55 | .75 | .72 | .89 | .45 | .66 | .72 |
| Popularity → Higher-order cool | .74 | .78 | .76 | .8 | .87 | .58 | .49 |
| Subculture → Higher-order cool | .53 | .61 | .65 | .84 | .73 | .51 | .69 |
| Iconic → Higher-order cool | .59 | .72 | .7 | .82 | .48 | .62 | .38 |
| Model Fit Statistics | Global | Global | Global | |
| Chi-square (d.f.) | 2,565.8 (1,164) | 3,278.62 (1,234) | 2847 (1,234) | 1,332.82 (617) |
| NNFI | .98 | .98 | .96 | .97 |
| CFI | .98 | .98 | .97 | .97 |
| RMSEA | .064 | .074 | .082 | .089 |
| SRMR | .072 | .055 | .11 | .1 |
- 5 aWe used these items in Study 5.
- 6 bWe used these items in Studies 6–8.
Table 3 reveals that our factors and model structure were stable across all of the studies and samples, with a few small differences. The factor loadings were high, and the average variance extracted and composite construct reliability statistics for all factors were almost always above.50[11] and.70, respectively. We formally tested for the equivalence of measurement and structural coefficients across the cool and uncool samples in Studies 5, 6, and 7, and almost all were equivalent (see Web Appendix E). In the few cases where the coefficients differed (e.g., iconic and popularity in Study 8), the differences, which were small, were likely because we needed to estimate the CFA model across the niche cool, mass cool, and uncool brand subsamples to get a sufficient sample size.
The goodness-of-fit indices also showed an excellent fit across all of the studies and subsamples. For example, the statistics for the cool brand subsample in Study 5 were χ2(582) = 1,283.33, p <.001; root mean square error of approximation (RMSEA) =.06; nonnormed fit index (NNFI) =.97; comparative fit index (CFI) =.97; standardized root mean residual (SRMR) =.08. The uncool brand subsample in Study 5 showed a similarly excellent fit: χ2(582) = 1,226.62, p <.001; RMSEA =.06; NNFI =.98; CFI =.98; SRMR =.07. As illustrated in Table 3, the goodness-of-fit measures were similar in Studies 6–8. Thus, our measurement model of brand coolness satisfies conventional tests of adequacy. Across Studies 5–7, the measurement factor loadings for the characteristics of cool brands averaged.81 and ranged from.62–.96. For uncool brands, their average was.86, and they ranged from.53–.97. The factor loadings from the first-order to second-order factors (e.g., originality to positive autonomy) ranged, across all the samples and studies, from.75–.99, averaging.90. The betas from the second-order factors to higher-order brand coolness (e.g., desirability to higher-order brand coolness) ranged from.38–1.0, averaging.86. They were highest on average for desirability (.99) and positive autonomy (.92), lowest for rebellious (.59) and iconic (.62), and midrange for high status (.68), popular (.72), and subcultural (.65).
In addition to showing sound measurement properties, the characteristics of brand coolness in our model reliably distinguished cool from uncool brands (see Table 4). Paired-sample t-tests comparing the average ratings for the cool versus uncool brands confirmed that the brands that participants nominated as being cool were perceived to be significantly more useful (Study 5) or extraordinary (Studies 6 – 8), energetic, aesthetically appealing, original, authentic, rebellious, high status, popular, subcultural, and iconic than the brands nominated as being uncool (ps <.001; Table 4 presents the means).
Graph
Table 4. Means (SDs) by Condition in Studies 5–8.
| Study 5 | Study 6 | Study 7 | Study 8 (Streetwear Forum) |
|---|
| Cool | Uncool | Cool | Uncool | Cool | Uncool | Niche Cool | Mass Cool | Uncool |
|---|
| Cool Characteristics | n = 315 | n = 315 | n = 305 | n = 305 | n = 213 | n = 192 | n = 52 | n = 52 | n = 44 |
| Usefula/exceptionalb | 4.14 (.73)a | 3.51 (.88)a | 6.03 (1.20)b | 4.35 (1.93)b | 5.47 (1.11)b | 3.83 (1.65)b | 5.08 (1.01)b | 4.20 (1.41)b | 2.04 (1.31)b |
| Energetic | 4.04 (.78) | 3.34 (.98) | 6.06 (1.13) | 4.61 (1.72) | 5.54 (1.25) | 3.92 (1.54) | 4.97 (1.42) | 4.56 (1.34) | 2.57 (1.46) |
| Aesthetically appealing | 4.36 (.63) | 3.50 (.94) | 6.33 (1.02) | 4.85 (1.74) | 6.05 (1.04) | 4.50 (1.68) | 6.17 (.81) | 5.32 (1.36) | 2.65 (1.48) |
| Original | 4.39 (.57) | 3.56 (.86) | 6.24 (1.00) | 4.96 (1.62) | 5.67 (1.10) | 4.19 (1.53) | 5.26 (1.37) | 4.31 (1.66) | 2.43 (1.61) |
| Authentic | 4.18 (.64) | 3.50 (.86) | 6.14 (1.05) | 4.81 (1.70) | 5.59 (1.09) | 4.40 (1.61) | 6.03 (.93) | 4.78 (1.19) | 3.23 (1.60) |
| Rebellious | 3.31 (.95) | 2.80 (.93) | 4.94 (1.69) | 4.05 (1.79) | 4.19 (1.48) | 2.94 (1.45) | 4.89 (1.44) | 4.22 (1.72) | 2.39 (1.50) |
| High status | 3.36 (1.01) | 2.66 (1.08) | 5.27 (1.44) | 4.15 (1.81) | 4.45 (1.38) | 2.78 (1.43) | 3.70 (1.24) | 3.80 (1.67) | 1.95 (1.24) |
| Popular | 4.39 (.59) | 3.62 (.88) | 6.30 (.95) | 5.10 (1.60) | 5.77 (1.06) | 4.24 (1.24) | 4.18 (.88) | 5.99 (.91) | 3.28 (1.44) |
| Subcultural | 3.38 (1.00) | 2.82 (.98) | 5.29 (1.64) | 4.08 (1.90) | 4.33 (1.63) | 2.88 (1.68) | 4.45 (1.60) | 3.79 (1.66) | 2.19 (1.57) |
| Iconic | 3.94 (.91) | 3.21 (1.11) | 5.90 (1.35) | 4.64 (1.82) | 5.29 (1.49) | 3.76 (1.68) | 3.38 (1.62) | 5.60 (1.13) | 3.02 (1.85) |
| Manipulation Checks | | | | | | | | | |
| Perceived as cool (self) | | 6.52 (.78)c | 4.33 (1.99)c | 6.27 (.95)c | 3.06 (1.85)c | 6.31 (.73)c | 5.13 (1.53)c | 2.23 (1.67)c |
| Perceived as cool (others) | | | | 5.89 (1.16)c | 3.10 (1.67)c | 4.75 (1.12)c | 5.92 (1.01)c | 3.09 (1.68)c |
| Past change in coolness | | | | | | .38 (.72)d | .14 (.89)d | −.40 (.59)d |
| Future change in coolness | | | | | | .38 (.6)d | −.15 (.75)d | −.39 (.62)d |
| Outcome Variables | | | | | | | | | |
| Brand attitudes | 4.45 (.62) | 3.19 (1.14) | | 6.31 (.88)c | 4.88 (2.03)c | 5.94 (1.49)c | 5.07 (1.58)c | 2.72 (1.61)c |
| Brand love | 4.3 (.82) | 2.78 (1.25) | 4.57 (.62) | 3.01 (1.39) | 3.96 (.91) | 2.84 (1.36) | 3.89 (.76) | 3.13 (1.08) | 1.50 (.87) |
| SBC | 3.8 (.92) | 2.62 (1.14) | 3.88 (.97) | 2.64 (1.33) | 3.65 (.85) | 2.55 (1.17) | 3.56 (.64) | 2.78 (.96) | 1.6 (.71) |
| WOM future | 3.24 (1.17) | 2.35 (1.15) | 3.47 (1.10) | 2.47 (1.30) | 4.95 (1.82) | 3.30 (2.02) | 4.81 (2.01) | 5.35 (1.79) | 2.14 (1.72) |
| WOM past | | | | 2.60 (1.02)e | 1.95 (1.01)e | 2.73 (1.09)e | 3.23 (.96)e | 1.70 (1.05)e |
| WTP | 3.82 (.88) | 2.53 (1.16) | 3.98 (.94) | 2.75 (1.31) | 5.26 (1.45)c | 3.12 (1.93)c | 5.28 (1.12)c | 4.19 (1.80)c | 1.60 (1.47)c |
| Price premium | | | | | 3.77 (.89)c | 3.01 (1.02)c | 3.46 (.76)c | 4.11 (.64)c | 2.60 (1.09)c |
| Brand familiarity | | | | 4.15 (.63)c | 3.74 (.73)c | 2.35 (.81)c | 4.51 (.61)c | 4.03 (.87)c |
| Brand exposure | | | | 2.88 (.94)e | 2.25 (.93)e | 2.20 (.73)e | 3.33 (.63)e | 2.18 (.84)e |
| Brand Personality | | | | | | | | | |
| Sophisticated | 3.14 (1.04) | 2.58 (1.09) | | | 2.88 (.93) | 2.11 (.93) | | | |
| Rugged | 3.53 (1.07) | 2.92 (1.15) | | | 3.28 (1.05) | 2.57 (1.13) | | | |
| Competent | 4.34 (.71) | 3.54 (.98) | | | 4.06 (.78) | 3.38 (1.10) | | | |
| Exciting | 4.05 (.80) | 3.21 (1.06) | | | 3.75 (.88) | 2.65 (1.01) | | | |
| Sincere | 3.93 (.77) | 3.42 (.91) | | | 3.58 (.78) | 3.21 (1.08) | | | |
| Brands Mentioned Frequently or Rated Highly | Apple | Apple | Nike | Nike | Nike | Nike | Steady Hands | Supreme | Gap |
| Nike | Pepsi | Apple | Adidas | Apple | Apple | Cav Empt | Off-White | Sketchers |
| Samsung | Nike | Samsung | Samsung | Samsung | Old Navy | Ader Error | Nike | Anti-Social |
| Coca-Cola | Samsung | Amazon | Apple | Under Armour | Walmart | | Gucci | Social Club |
| Under Armour | Adidas | Adidas | LG | Adidas | Crocs | | | |
| Levi's | Microsoft | Reebok | Nintendo | Great Value | | | |
- 7 aCharacteristic measured only in Study 5.
- 8 bCharacteristic measured in Studies 6–8.
- 9 cRated on a scale from 1 to 7.
- 10 dRated on a scale from −1 to 1.
- 11 eRated on a scale from 1 to 4.
- 12 Notes: The scales were from 1 to 5, unless otherwise noted.
Across our studies, the brands that consumers most frequently selected as being cool (on seven-point scales) included Apple (6.5), which seemed especially original, popular, and aesthetically appealing; Nike (6.6 overall), which seemed especially popular; Samsung (6.4), which seemed especially original; Under Armour (6.6), which seemed especially popular and aesthetically appealing; and Adidas (6.6), which seemed especially popular (see Table 4). Interestingly, different participants referenced many of these same brands (Apple, Nike, Samsung, Adidas) as being uncool, because they perceived the brands to be lower status, less subcultural, and less rebellious. Other brands that participants frequently nominated as being uncool include Microsoft, Reebok, Old Navy, Walmart, and Crocs. The fact that consumers in Studies 5–7 differed about which brands were cool and uncool confirms the subjective nature of coolness, especially when looking across a diverse sample of consumers. As we might expect, participants in Study 8, who were all part of an urban streetwear subculture, agreed more about which brands were and were not cool compared with participants in Studies 5–7.
As previously mentioned, Study 6 tested whether our model and scale would be conceptually and empirically stronger if the first characteristic measured how extraordinary (four items: exceptional, superb, fantastic, and extraordinary) the brand was, as opposed to measuring the extent to which the brand seemed (merely) useful (three items: useful, helpful, and valuable). Study 6 therefore measured all seven of these items and compared the coefficients and fit statistics of models that used either the new extraordinary items or the old useful items. The models were not nested, which makes chi-square difference tests inappropriate; however, the model fit statistics (NNFI, CFI, RMSEA, and SRMR) for the new four-item models were superior or equal to those for the old three-item models. The completely standardized lambda coefficients for the extraordinary items were all very high (.89–.97) in both the cool and uncool subsamples. Most importantly, the structural coefficients from the first-order useful/extraordinary factor to the second-order desirability factor were higher with the new four items than with the old three items, increasing from.77 to.83 (for cool brands) and from.85 to.93 (for uncool brands). The structural coefficients from the desirability second-order factor to the overall brand coolness factor were also slightly higher in both cases, increasing from.99 to 1.0. Drawing on this empirical evidence, and given the strong conceptual argument favoring this change (e.g., [12]; [65]), we replaced the three useful items with these four extraordinary items in Studies 6–8 and in the final recommended items for our brand coolness scale.
Brand coolness should be related to, but conceptually distinct from, the constructs of brand love, SBC, particular dimensions of brand personality, and brand attitudes. Brand coolness is a perceived attribute of a brand, whereas both brand love and SBC should be responses to—and, thus, consequences of—brand coolness. Although there is likely some overlap between specific characteristics of coolness (in particular, high status, energetic, and useful/extraordinary) and specific dimensions of brand personality (sophistication, excitement, and competence, respectively), our latent construct of higher-order brand coolness, which includes many other constituent characteristics (see Figure 2), should display discriminant validity from these brand personality dimensions. Brand coolness should also be discriminable from brand attitudes, because there is something extra that makes an object cool rather than merely positive ([81]).
We tested discriminant validity in Studies 5–8 by estimating the disattenuated, latent psi correlations between multiple pairs of variables to test whether their 95% confidence intervals fell significantly below 1.0 ([ 7]). As we report in Web Appendix F, the analyses confirmed the discriminant validity between constructs. For instance, in Study 7, the phis (SEs) of brand coolness with brand love for cool and uncool brands, respectively, are.59 (.06) and.42 (.07), and with SBC are.59 (.05) and.50 (.06). The correlations between brand coolness and brand attitudes were also below 1.0 (.56 [.06] and.40 [.07], respectively). Between the five brand personality dimensions and brand coolness, each pair of disattenuated correlations was statistically significantly below 1.0 (range:.32–.87).
Brand personality is the set of human characteristics associated with a brand ([ 1]). Brand personality serves a symbolic or self-expressive function for consumers, and it consists of five core dimensions: sophistication, competence, ruggedness, excitement, and sincerity ([ 1]). On the one hand, it could be argued that these brand personality perceptions should make that brand seem more, or less, cool, and they thus serve as antecedents of overall perceived brand coolness. On the other hand, it could also be argued that the multiple marketing and sociocultural elements (e.g., communications content, choice of endorsers) that shape these brand personality perceptions should also simultaneously shape the perceived coolness of the brand. Therefore, it is difficult (especially in cross-sectional survey data) to empirically determine which perceptual changes came first. Because some of a brand's personality dimensions (especially excitement and sophistication) are conceptually similar to some of our brand coolness components (energetic and status, respectively), and because it is unreasonable in our data to expect a strong empirical signal about which dimensions come first, we were cautious in our analyses and modeled the five brand personality dimensions as correlates, rather than antecedents, of higher-order brand coolness. This has the benefit of yielding model estimates of the effects of brand coolness on mediating (e.g., brand love, SBC) and dependent variables (brand attitude, WOM, and WTP) that are "net of" (i.e., they control for and partial out) the effects of these independently measured brand personality dimensions and are thus more conservative.[12]
To examine the consequences of brand coolness, our nomological model also estimated the effects of overall brand coolness on several different dependent variables: SBC (Studies 5–8), brand love (Studies 5–8), WTP (Studies 5–8), willingness to spread WOM (Studies 5–8), brand attitudes (Studies 5, 7, and 8), brand familiarity (Studies 7 and 8), brand exposure (Studies 7 and 8), whether the brand commands a price premium (Studies 7 and 8), satisfaction (Study 5), delight (Study 5), and pride (Study 5) in owning the brand.
Consumers view coolness as a desirable trait ([21]; [53]; [67]). Moreover, we find that brand coolness includes multiple characteristics (e.g., being extraordinary and aesthetically pleasing) that consumers consider desirable. Consequently, we hypothesize that brand coolness should predict consumers' overall attitude toward the brand, and we include brand attitude valence as a consequence of brand coolness. Beyond increasing overall desirability and liking, brand coolness should also increase several other types of distinct positive feelings toward the brand. Because cool brands are considered desirable, coolness should create a feeling of high overall satisfaction with the brand ([61]). The satisfaction literature also talks of feelings of delight, in which high-arousal feelings of joy and surprise augment the more cognitively based satisfaction assessment ([ 8]); coolness should increase delight as well, especially because coolness is partially determined by the extent to which a brand is energetic and aesthetically appealing, both of which have strong affective components.
Brand coolness also has components that are value-expressive in nature, including positive autonomy, rebellion, high status, subcultural appeal, and iconic symbolism ([13]; [42]). We therefore hypothesize that brand coolness will strengthen SBC ([24]) because SBC increases as a brand's symbolic aspects become more consistent with a consumer's aspirational reference groups. Consumers' relationships with a cool brand might also extend beyond SBC to increase brand love, a broad brand relationship construct that includes current and desired self-identity ([ 9]). Because of the desirable and identity-relevant characteristics associated with cool brands, it is similarly likely that consumers will also feel greater pride from owning brands that they perceive to be cool (Tracy and Robbins 2007).
Both SBC and brand love tend to increase consumers' WTP for and likelihood of discussing (WOM) a brand ([ 9]; [24]). Thus, if brand coolness increases SBC and brand love, as we hypothesize, then consumers should be willing to pay more for the brand (i.e., WTP) and want to tell others how great it is (i.e., WOM). Finally, because cool brands are high status, popular, and iconic, we also expect that they will command a higher price premium, be familiar to more consumers, and gain more exposure compared with brands that are not cool.
We tested these predictions by modeling overall (higher-order) brand coolness as being correlated with the five dimensions of brand personality (in Studies 5 and 7) and as leading to a set of consequences (which varied slightly depending on which consequence variables we measured in the studies; see Web Appendix G), including brand attitudes, SBC, brand love, WTP, WOM, brand familiarity, brand exposure, price premium, satisfaction, delight, and pride. To obtain a reasonable ratio of sample size to the number of estimated parameters in the predictive model (e.g., [ 6]; [ 5]), we averaged the items for each predicted variable. For each study, we then created structural equation models (SEM) in which the CFA model of higher-order brand coolness (see Figure 2) served as an independent variable, the consequences (e.g., SBC, brand love) served as endogenous dependent variables, and the dimensions of brand personality served as correlates (Studies 5 and 7 only).
Across studies, the model fit was satisfactory. For example, the fit for the nomological SEM in Study 5 was: χ2( 1,077) = 2,694.88, p <.001; RMSEA =.075; NNFI =.94; CFI =.96; SRMR =.085. The SEM with the not-cool brand subsample also fit well: χ2( 1,077) = 2,442.91, p <.001; RMSEA =.069; NNFI =.98; CFI =.98; SRMR =.069. Brand coolness was significantly correlated, but also showed discriminant validity (see the "Empirical Discrimination" subsection), with all five dimensions of brand personality. Brand coolness was most closely related to the sophisticated, competent, and exciting dimensions of brand personality; this pattern makes sense, given that three of the characteristics of higher-order brand coolness include being high status, useful (study 5)/extraordinary (Studies 6–8), and energetic.
In addition, in all of the studies, higher-order brand coolness significantly predicted the measured consequence variables, including brand love (Studies 5–8), SBC (Studies 5–8), brand attitude (Studies 5–8), WTP (Studies 5–8), WOM (Studies 5–8), brand familiarity (Studies 7 and 8), brand exposure (Studies 7 and 8), brand price premium (Studies 7 and 8), delight (Study 5), satisfaction (Study 5), and pride (Study 5).
To test whether brand coolness can help marketers predict outcomes that they care about, such as the extent to which consumers hold a positive attitude toward and are willing to pay for the brand, we examined how much variance higher-order brand coolness explained in the outcome variables (brand attitude, WTP, and WOM) relative to more established constructs in the literature, including brand love and SBC. Across all our studies, brand coolness explained between 32%–70% of the variance in brand attitudes, an amount that was similar to the variation explained by brand love (26%–85%) and SBC (19%–67%). Brand coolness similarly explained a comparable amount of variance in WOM (32%–57%) and WTP (32%–79%) as brand love (WOM: 25%–74%; WTP: 29%–86%) and SBC (WOM: 26%–79%; WTP: 29%–84%). As detailed in Web Appendix G, the amount of variance that brand coolness explained varied by outcome, study, and brand sample. For example, in Study 7, in the cool (uncool) brands data, the variance explained in brand attitude by brand coolness alone was 54% (32%), compared with 48% (85%) by brand love alone and 22% (64%) by SBC alone. The variance explained in WOM by brand coolness alone by was 32% (57%), compared with 35% (25%) by brand love alone and 32% (26%) by SBC alone. For WTP, the variance explained by brand coolness alone was 38% (67%), compared with 45% (47%) by brand love alone and 33% (47%) by SBC alone. These results show that brand coolness has a lot of explanatory power and is thus worth studying as a construct in its own right. Table 4 also shows how the mean levels of brands on these outcome variables become higher when the brand is seen as cool (vs. less cool).
The nomological models estimated only the direct effects of higher-order brand coolness on the many outcome variables; they did not test for mediation. Although cross-sectional data do not allow us to unambiguously establish causal sequences, it is nonetheless interesting to test whether the data were consistent with the hypothesis that brand love and SBC mediate the effects of brand coolness on brand attitude, WTP, and WOM. We therefore estimated SEMs to test these hypothesized mediating paths in Studies 5, 6, and 7. We provide the details for these analyses in Web Appendix H. To summarize results, the effect of higher-order brand coolness on each of the dependent variables—brand attitude, WOM, and WTP—was partially or fully mediated by SBC and brand love in each study and subsample. As a specific example, in Study 7, the cool brands data, brand coolness significantly influenced brand love (standardized coefficient.77) and SBC (.67); brand love significantly influenced brand attitudes (1.86), WTP (1.34), and WOM (.55); and SBC significantly influenced brand attitude (−1.01) but did not significantly influence WOM or WTP. Brand coolness also directly and significantly influenced brand attitude (.61) and WTP (.65) but not WOM. In summary, brand love fully mediated the effects of brand coolness on WOM, but partially mediated the effects of brand coolness on brand attitude and WTP. This pattern of mediation supports the conceptual argument that brand coolness, which a consumer perceives in a brand, is an antecedent to constructs such as brand love and SBC, which are a consumer's evaluative responses to a brand that result from the properties perceived in the brand.
The Study 7 results illustrate both the large amounts of variance explained in the outcome constructs by higher-order brand coolness and the mediation pathways for these effects. In the cool (uncool) brand sample, brand coolness explained 35% (57%) of the variance in SBC, 42% (52%) in brand love, 52% (77%) in brand attitudes, 43% (56%) in WTP, and 31% (25%) in WOM. For the cool (uncool) brands, the standardized direct path coefficients from brand coolness to the outcome constructs (all ps <.01) were as follows: SBC =.59 (.76), brand love =.65 (.72), brand attitude =.53 (.27), WTP =.51 (.52), and WOM =.18 (.20). For cool brands, the standardized indirect path coefficients from SBC to brand attitude (−.28) and WOM (.24) were both significant, as was the path from brand love to brand attitudes (.46). For the uncool brands, the standardized indirect path coefficients from SBC to WTP (.34) and WOM (.33) were significant, as was the path from brand love to brand attitude (.55). Thus, the effects of higher-order brand coolness on each of the dependent variables—brand attitude, WOM, and WTP—were partially or fully mediated by SBC and brand love in each study.
We tested the degree to which common method bias affected our structural and measurement models in Studies 7 and 8 using the well-accepted "marker variables" technique presented in [81]. In both Studies 7 and 8, as marker variables, we asked respondents about their experience with and expectations of service quality in restaurants (four items), which are not meaningfully related, in either a theoretical or empirical sense, to the constructs of interest in this research. Details of these methods factor tests appear in Web Appendix I. These tests showed that the marker variable approach to test for method bias did not indicate problems in these two studies.
Cool brands change over time. Born as relatively obscure brands in outsider subcultures, cool brands often spread beyond their niche roots to become cool to the masses ([12]; [31]; [81]). How do the characteristics associated with cool brands change as they mature from niche cool to mass cool? Moreover, do consumers respond differently to mass cool brands than niche cool brands?
Study 8 attempted to answer these questions by investigating how consumers in the urban streetwear subculture perceive both brands that they themselves think are cool but have not yet caught on outside of the streetwear apparel subculture (i.e., niche cool brands) and brands that have become cool to a broader audience (i.e., mass cool brands). We expected that the data from the streetwear subculture would replicate the previous studies by showing that both mass cool and niche cool brands would score higher on all ten characteristics of cool brands compared with uncool brands. In addition, we expected that the characteristics would differ between niche cool and mass cool, such that mass cool brands would seem more popular and iconic but niche cool brands would seem more subcultural, original, authentic, and rebellious.
To analyze the data, we examined the differences between the three experimental conditions (niche cool, mass cool, and uncool) using two planned, orthogonal contrasts. The first contrast examined the difference between cool and uncool brands by comparing the ratings of the uncool brand with the average of the ratings for the mass cool and niche cool brands. The second contrast examined the difference between the mass cool and niche cool brands.
The brand manipulation successfully elicited different types of brands from the participants (for the most frequently nominated brands in each condition, see Table 4). Participants perceived the uncool brands to be less cool than mass cool and niche cool brands, both to themselves personally (t = 14.32, p <.001) and in the eyes of others (t = 9.77, p <.001). Interestingly, however, the correlations between the measures of how participants personally rated the brand's coolness with how cool they thought others perceived the brand to be was only.50, which offers additional evidence that perceptions of brand coolness are subjective.
The niche and mass cool brands also differed, as we intended. Compared with the mass cool brands, participants perceived the niche cool brands to be more cool to themselves personally (t = 4.41, p <.001) but less cool to others (t = −4.68, p <.001). Moreover, participants also predicted a different future trajectory for the brands. Consistent with theory predicting that niche cool brands become cooler to a broader population over time, participants expected the niche cool brands to become cooler in the future, compared with the scale midpoint (t = 4.63, p <.001), the mass cool brand (t = 4.15, p <.001), and the uncool brand (t = 5.69, p <.001). On average, participants expected the uncool brand to become even less cool over time (t = −4.15, p <.001), whereas they did not expect the coolness of the mass cool brand to change for better or worse (t = −1.48, p =.15; for the descriptive statistics, see Table 4).
Replicating the previous studies, both the mass cool brands and the niche cool brands were perceived to have higher levels of all ten characteristics compared with the uncool brands (all ps <.001). Furthermore, replicating the previous studies, participants reported stronger SBC (t = 11.10, p <.001), more brand love (t = 12.28, p <.001), higher levels of WOM (t = 8.96, p <.001), higher price premiums (t = 7.86, p <.001), higher WTP for (t = 11.71, p <.001), and more favorable attitudes toward (t = 9.94, p <.001) the cool than the uncool brands.
Consistent with our prediction that the characteristics of cool brands change over time, participants perceived several differences between the mass cool and niche cool brands. Compared with niche cool brands, mass cool brands were perceived to be less subcultural (t = −2.10, p =.037), original (t = −3.15, p =.002), authentic (t = −5.08, p <.001), rebellious (t = −2.20, p =.029), extraordinary (t = −3.56, p <.001), and aesthetically appealing (t = −3.50, p <.001), yet more popular (t = 8.49, p <.001) and iconic (t = 7.34, p <.001). The consequences associated with coolness also shifted as brands moved from niche cool to mass cool. Consistent with mass cool brands being more popular and ubiquitous cultural symbols, participants indicated that they had been more exposed to (t = 7.88, p <.001) and had shared, and intended to share, more WOM about (t = 2.02, p =.045) mass cool brands compared with niche cool brands. They similarly reported that mass cool brands are more familiar in the marketplace (t = 14.30, p <.001) and command higher prices (t = 3.93, p <.001) than niche cool brands. However, consistent with niche cool brands being more closely associated with a consumers' subculture and personal in-group, participants reported weaker SBC for (t = −5.04, p <.001), less love for (t = −4.25, p <.001), a lower WTP for (t = −3.72, p <.001), and less favorable attitudes toward (t = −2.85, p =.005) mass cool compared with niche cool brands. Figure 2 summarizes the dynamic nature of coolness as brands move from uncool to niche cool to mass cool and (sometimes) back to uncool.
We have found that cool brands have different characteristics than uncool brands, but we have not yet examined whether we can increase the extent to which a brand seems cool by experimentally manipulating the characteristics of brand coolness. Thus, in our final study, we manipulated the description of a watch brand to orthogonally vary the desirability (i.e., extraordinariness, aesthetic appeal, and excitement), positive autonomy (i.e., originality and authenticity), rebellion, popularity, and status of the brand. To keep the number of factors in the experiment manageable, we did not manipulate the extent to which the brand seemed iconic or subcultural,[13] and we contrasted cool with uncool brands rather than distinguishing between mass and niche cool brands. Consumers form their actual perceptions of brand coolness over multiple exposures to various brand marketing and social signals over a long period of time; thus, our single-exposure experiment provides a conservative test of whether the characteristics influence perceptions of brand coolness. Nevertheless, we predicted that the brand would seem more cool when participants read that it was more (rather than less) desirable, autonomous, rebellious, popular, and high status. We also predicted that coolness would in turn influence participants' attitudes, WTP for, and likelihood of spreading WOM about the brand.
Participants (N = 368; 34% female; mean age = 36.0 years; all located in the United States) from MTurk completed the study for a small payment. The study included a reading check at the beginning, which filtered out 11 respondents before assigning them to a condition.
Participants completed the study, titled "Online Review Survey," in which they were randomly assigned to a condition in a 2 (desirability: high, low) × 2 (autonomy: high, low) × 2 (rebellion: high, low) × 2 (status: high, low) × 2 (popularity: high, low) between-subjects experiment. Participants read a description of a wrist watch brand named Voss, a fictional brand. Participants read that "the description of the brand summarizes hundreds of ratings and reviews written by customers and industry experts who are already familiar with the brand." Participants next read about five brief characteristics of the brand. We manipulated whether consumers described Voss as being desirable, autonomous, rebellious, high status, and popular at two levels by describing the brand as either possessing or lacking the characteristic. The descriptions used words taken directly from the scale items that we identified in prior studies (see Web Appendix D). For example, the status manipulation described the brand as being "glamorous" and "sophisticated" or as lacking these traits. The survey presented the characteristics one at a time, in random order, and did not allow participants to advance to read the next characteristic until at least three seconds had passed.
Participants subsequently completed a series of measures, including brand coolness, brand attitude, WTP, and WOM (see Web Appendix D). The final part of the survey measured the effectiveness of the manipulations using the full brand coolness scale from Studies 6–8. Finally, participants reported their age, gender, and native language.
We assessed the effects of the five manipulated brand characteristics on perceptions of brand coolness using a 2 (desirability: high, low) × 2 (autonomy: high, low) × 2 (rebellion: high, low) × 2 (status: high, low) × 2 (popularity: high, low) analysis of variance. The analysis revealed main effects of desirability (F( 1,336) = 35.73, p <.001, η2 =.096), autonomy (F( 1,336) = 59.90, p <.001, η2 =.151), status (F( 1,336) = 10.85, p =.001, η2 =.031), popularity (F( 1,336) = 33.69, p <.001, η2 =.091), and rebellion (F( 1,336) = 7.51, p =.006, η2 =.022). As we predicted, participants perceived the brand to be more cool when it was described as being desirable (M = 4.26 vs. M = 3.27), autonomous (M = 4.41 vs. M = 3.15), high status (M = 4.01 vs. M = 3.54), popular (M = 4.25 vs. M = 3.29) and rebellious (M = 3.98 vs. M = 3.56) than when it lacked these qualities. None of the interactions were significant, which suggests that each characteristic additively influences perceived coolness.
We next tested whether the significant main effects of desirability, autonomy, status, and popularity on perceived coolness had downstream consequences on participants' attitudes, WTP, and WOM for the brand. Instead of conducting separate mediation tests for each of the five manipulated variables on each of three dependent variables, we estimated one comprehensive SEM path model using LISREL (n = 368), which allowed for all direct and indirect effects. The model comparing full with partial mediation yielded a significant chi-square difference of 40.47 with 15 degrees of freedom (p <.001), showing that a model with one or more direct paths was a superior model. Specifically, as with the analysis of variance results, desirability (path coefficient =.28), autonomy (.35), popularity (.26), status (.14) and rebellion (.12) all significantly increased perceived coolness. Brand coolness, in turn, significantly influenced the three dependent variables: brand attitude (.84), WTP (.54), and WOM (.82). Thus, the effect of desirability, autonomy, popularity, status, and rebellion on the three dependent variables was at least partially mediated by brand coolness in each case. However, some significant direct effects of the manipulated brand characteristics on the dependent variables also emerged, though these direct effects are hard to interpret because of possible multicollinearity, remaining measurement error, or omitted mediators.[14]
Our experiment confirmed that increasing the extent to which a brand seems desirable, autonomous, rebellious, high status, and popular increases the extent to which it is perceived to be cool. Brand coolness, in turn, influences several consequence variables, including the extent to which consumers hold a favorable attitude toward the brand as well as their WTP for and willingness to discuss the brand with others. Finally, the experiment suggests that the effects of the characteristics of brand coolness on overall perceptions of coolness and on its downstream consequences (e.g., brand attitudes) are additive, though future research will need to further explore factors that moderate or interact with the different characteristics of brand coolness.
What features characterize cool brands? Our research (three qualitative and nine quantitative studies) reveals that cool brands are extraordinary, aesthetically appealing, energetic, original, authentic, rebellious, high status, subcultural, iconic, and popular. Not all of these characteristics are necessary for every brand and every consumer segment, but, as our experiment revealed, increasing any of these characteristics tends to make a brand seem cooler. Nike is widely seen as cool because its shoes are highly desirable, look good, signal energy, and have extraordinary quality. Apple shows positive autonomy by being original and authentic, even as it has grown to become very popular. Harley-Davidson became cool when a subculture of outlaw bikers, who lent the brand a rebellious, iconic image, adopted the brand ([40]). BMW, conversely, is cool in part because it has become a popular status symbol. These ten characteristics correlate with the perception that a brand is cool, distinguish cool brands from uncool brands, and comprise distinct but related components of a higher-order structural model of brand coolness.
Our research additionally contributes to theory on the dynamic nature of coolness (e.g., [33]) and brands (e.g., [63]) by exploring how the characteristics of cool brands change as a brand becomes niche cool, transitions from niche to mass cool, and eventually begins to lose its cool (see Figure 1). Brands initially become cool within a particular subculture (e.g., Quicksilver with surfers, Rocawear with hip-hop enthusiasts, Supreme with skaters) of people who perceive the brand to rebellious, autonomous, desirable, and high status and adopt it as a way to distinguish themselves from the masses. Some niche cool brands break free from subcultural obscurity to become cool to the masses. As brands such as Quicksilver, Rocawear, and Supreme expand from a fringe group of outsiders to mass-marketed magazines and suburban shopping malls, they start to seem less rebellious, original, authentic, and extraordinary—and less cool—to their original subcultural consumers (surfers, rappers, and skaters, respectively). But, despite losing some of their autonomy, mass cool brands also become more familiar, command a higher price premium, and control a larger market share. Purists may deride them for selling out, but brands perceived to be mass cool (e.g., Nike, Grand Theft Auto, Beyoncé) are more popular and profitable than their more obscure niche cool counterparts (e.g., Steady Hands, INSIDE, Mitsky). Mass cool brands, however, need to be careful not to lose the characteristics (e.g., desirability, autonomy) that made them cool in the first place, or they will become passé. We saw this in our data: while many consumers continue to think that Apple and Nike are cool, others are beginning to consider these brands uncool because they no longer see them as being rebellious, autonomous, high status, or as having the other characteristics that made them cool in the first place. Because we did not collect longitudinal data, our findings about the coolness life cycle remain preliminary. We strongly encourage future research to more closely investigate how brands change as they move from niche cool to mass cool to passé.
For many product categories and consumer segments, a brand's perceived coolness is an important factor in driving its success, and managers have long sought to figure out how to give their brands this mysterious quality ([ 2]; [31]; [57]). Yet the ways to make a brand cool have not thus far been systematically investigated, leaving managers without a clear roadmap.
Our scale provides a valuable tool for helping firms create and manage cool brands. Unlike simple items that only measure overall brand coolness, our structural model allows managers to drill down into ( 1) which components of coolness are competitive strengths or weaknesses, ( 2) which components are of greater importance in shaping overall coolness, and ( 3) how these diagnostic analyses might vary across geographies, consumer segments, and even over time (i.e., as brand-health tracking metrics). Our scale components can also be used for pretesting and evaluating different marketing and communication programs that are designed to increase or maintain a brand's perceived coolness.
How should managers respond if their brand is not scoring high enough on one or more component characteristics of brand coolness? They will need to reinforce the image of the band on the characteristic or characteristics it is lacking. How, specifically, firms should do this will depend on the brand's history, industry, and target customers, but we can offer a few tentative guidelines. Brands that want to be seen as more extraordinary will likely need to create breakthrough functional specs (e.g., being the first facial-unlocking smartphone) or deliver an unsurpassed customer service (e.g., Amazon) rather than offer incremental improvements (e.g., a slightly better smartphone camera) or "run-of-the-mall" service. To improve their aesthetic appeal, brands will need to create eye-popping designs; Apple and Nike, highly rated in our data and by pollsters, are known for this. Brands can become more energetic and original by continuously innovating and being one step ahead of the competition, like Google or Samsung Electronics. To be seen as authentic, brands will need to remind consumers of the history and core values of the brand and its founders (e.g., Patagonia does this effectively) while avoiding the appearance of using overt advertisements or other strategies associated with mass-marketed brands. Brands can appear more subcultural by using a promotion strategy that links the brand with an admired subculture (e.g., via brand community events, such as Harley-Davidson's annual rallies in Sturgis, South Dakota), as long as the tactics seem authentic. Brands could become more rebellious by hiring spokespeople known to challenge norms, as Nike recently did through its campaign featuring NFL outcast Colin Kaepernick. Brands can boost their perceived status through packaging, ad style, spokespeople, high prices, retail cobranding, and media placements that make the brand seem glamorous, sophisticated, and exclusive. Becoming iconic is not easy, but brands might be able to seem more iconic through distinctive packaging (e.g., the Coca-Cola contour bottle), a memorable advertising style (e.g., the early artistic and witty campaigns of Absolut vodka), or telling a brand myth that resonates with consumers (e.g., the nostalgic frontier story of Jack Daniels; [41]).
Firms will also need to assess whether their brand is currently niche cool, mass cool, or uncool to understand how to best manage the brand's characteristics. An existing uncool brand might first need to become niche cool, by engaging in behaviors (products, promotions, pricing, and distribution strategies) that make the brand seem rebellious, original, and authentic. To become niche cool, brands will also need to cultivate a close relationship with a particular subculture rather than target the mass market (as Pabst did with hipsters in the early 2000s or as Instagram initially did with photography enthusiasts). After successfully becoming niche cool, brands could try to boost their popularity to transition to mass cool, but they will need to maintain their connection to a subculture (e.g., Nike to its top athletes) and its perceived autonomy (e.g., as Apple did by positioning itself as an edgier alternative to Microsoft) so they do not entirely lose their cool.
Many important questions remain for future research. Among them is the question of how brand coolness relates to nomologically related constructs, especially brand personality. Our studies showed that the effects of brand coolness on brand attitudes, WOM, and WTP are partially or completely mediated by brand love and SBC. However, our mediation analysis measured (but did not manipulate) variables and could not test every possible mediation sequence. Thus, future research could use experimental techniques or cross-lagged analysis of time-series data to better test among possible causal sequences.
Second, while our data established discriminant validity between brand coolness and related constructs, we did not have access to multitrait-multimethod data, which are necessary for more definitive conclusions in this regard, as well as for a stronger estimate of common methods bias ([64]). Our scale development and validation would also benefit from follow-up analyses with other types of data (e.g., using within-brand variance across individuals), as [34] point out in the context of brand personality scales.
Third, although we collected data from multiple cultures, we did not formally investigate cross-cultural differences. Given the cultural differences observed in brand personality ([36]), more work is needed to investigate if and how the characteristics or consequences of brand coolness vary across cultures. Brands that are rebellious, subcultural, and autonomous may be more cool in relatively independent cultures (e.g., United States, Germany) than in interdependent ones (e.g., Korea, Japan; [62]), whereas brands that have high status may be more cool in cultures higher on power distance (e.g., India, China). Within cultures, individual differences in need for uniqueness ([77]), counterculturalism ([78]), susceptibility to interpersonal influence ([10]), symbolic capital ([39]), and others may influence which characteristics consumers consider cool and which consumer segments thirst more for cool brands. Given that coolness is subjective, it will be especially important for future research to investigate which social, cultural, individual difference, and category characteristics moderate what consumers perceive to be cool and how they respond to cool brands.
Future research should also further examine the relationship between the specific coolness components, overall brand coolness, and downstream consequences such as brand attitudes, WOM, and WTP. The structural model coefficients estimated in Studies 5–8 (Table 3) and our experiment suggest that the ten characteristics independently contribute to overall brand coolness, but our studies do not offer strong tests of whether these characteristics might interact. In particular, future research should further investigate the relationship between rebellion and coolness. In our data, the "main effects" of rebellion on higher-order brand coolness were almost always the lowest across our ten first-order factors, suggesting that higher perceived rebelliousness does not by itself always raise overall brand coolness as much as other components (such as originality and authenticity) do. In summary, much remains to be understood about the important brand management construct of brand coolness, and we encourage researchers to further investigate why, how, and when coolness contributes to a brand's success.
Supplemental Material, DS_10.1177_0022242919857698 - Brand Coolness
Supplemental Material, DS_10.1177_0022242919857698 for Brand Coolness by Caleb Warren, Rajeev Batra, Sandra Maria Correia Loureiro and Richard P. Bagozzi in Journal of Marketing
Footnotes 1 Author ContributionsThe first two authors contributed equally to this work.
2 Associate EditorHarald van Heerde
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
5 ORCID iDRichard P. Bagozzi https://orcid.org/0000-0002-8807-8210
6 Online supplement: https://doi.org/10.1177/0022242919857698
7 1[77] refers to this as "real cool." We believe "niche cool" is a more apt label because both relatively obscure subcultural brands and more popular iconic brands are perceived to be cool. These two types of coolness reflect different stages in the life cycle of a brand; although niche cool precedes mass cool, the former is not necessarily a more "real" or "true" form of coolness.
8 2Our respondents did suggest that cool brands are exclusive, which we interpret as part of the brand having high status rather than as it being scarce or lacking popularity.
9 3Gold Awards, which can be purchased or gifted to others, grant users access to premium features on Reddit.
4Although formative models do not fit our context well, they may be appropriate in other contexts ([3], [4]; [23]; [23]), especially when the multiple indicator, multiple cause (MIMIC) formulation can be estimated without problems caused by multicollinearity ([3]), which does create problems in our context.
5The sole exception was close, at.45 for cool/original in Study 5.
6Moreover, in Study 5, the data appear to fit slightly better with a model that included the brand personality dimensions as correlates rather than antecedents of brand coolness. For both the cool and uncool brand samples, the models in which these brand personality dimensions were modeled as correlates, rather than antecedents, fit better (cool: NNFI =.96 vs. NNFI =.95; CFI =.97 vs. CFI =.96; SRMS =.084 vs. SRMS =.091; RMSEA =.068 vs. RMSEA =.084; uncool: NNFI =.98 vs. NNFI =.97; CFI =.98 vs. CFI =.97; SRMR =.069 vs. SRMR =.084; RMSEA =.064 vs. RMSEA =.095).
7We did not manipulate the brand's iconic or subcultural associations for two reasons. One, including these factors would have increased the number of conditions from 32 to 128. Two, being iconic and subcultural are both relatively abstract characteristics—cool brands can symbolize many different things or be associated with many different subcultures. Thus, neither factor lends itself to a simple experimental manipulation.
8With these caveats in mind, status directly increased WTP for the brand (.11), which could be because not all the effects of high status need to flow through brand coolness. Less intuitively, autonomy also directly increased attitudes (.10), and rebellion directly decreased attitudes (−.07) and WOM (−.08). Note that these direct effect coefficients are smaller in value than the indirect effects.
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Record: 26- Branding Cultural Products in International Markets: A Study of Hollywood Movies in China. By: Gao, Weihe; Ji, Li; Liu, Yong; Sun, Qi. Journal of Marketing. May2020, Vol. 84 Issue 3, p86-105. 20p. 1 Diagram, 6 Charts. DOI: 10.1177/0022242920912704.
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Branding Cultural Products in International Markets: A Study of Hollywood Movies in China
Cultural products are a major component of the world economy and are responsible for a growing share of U.S. exports. The authors examine brand name strategies when cultural products are marketed in foreign countries. Incorporating the unique characteristics of these products, the authors develop a theoretical framework that integrates similarity, which focuses on how the translated brand name relates to the original brand name, and informativeness, which focuses on how the translated brand name reveals product content, to study the impact of brand name translations. The authors analyze Hollywood movies shown in China from 2011 to 2018. The results show that higher similarity leads to higher Chinese box office revenue, and this effect is stronger for movies that perform better in the home market (i.e., the United States). When the translated title is more informative about the movie, the Chinese box office revenue increases. The informativeness effect is stronger for Hollywood movies with greater cultural gap in the Chinese market. Moreover, both similarity and informativeness effects are strongest when the movie is released and reduce over time. This research provides valuable guidance to companies, managers, and policy makers in cultural product industries as well as those in international marketing.
Keywords: branding; cultural product; emerging market; international marketing; movie
Cultural products are goods and services that include performing and visual arts, heritage conservation (e.g., museums, galleries), and the media (e.g., publishing, broadcasting, movies, recording) ([ 1]).[ 7] The production, circulation, and consumption of cultural products are a major component of the national and global economy. In its most recent survey, the [10] shows that arts and cultural economic activities accounted for over US$800 billion in the United States in 2016. A recent report estimates that cultural products generate over US$2,250 billion of revenue and 30 million jobs each year worldwide ([77]). Recent developments in digital technologies are further enhancing the value and influence of cultural products: their distribution has become increasingly global, and consumers of these products are connected across the world on an unprecedented scale ([59]).
Associated with these market and technological trends, cultural products are responsible for a growing share of U.S. trade, and international markets have become a major target for the producers ([30]). In the film industry, for instance, more than 70% of Hollywood's box office revenue now comes from overseas ([54]). The influence of international markets has become so important to Hollywood that "potential overseas ticket sales nowadays determine whether or not a studio executive gives the go-ahead to a movie" ([ 8]).
Similar to other products, when a cultural product is introduced into a foreign market, a key decision is the brand name: the company must decide how to translate the original brand name into the foreign language ([42]). Yet unlike noncultural products (e.g., consumer packaged goods, apparel, electronics, appliances, automobiles) that have been the focus of prior international marketing research, cultural products are associated with several unique challenges for brand name translation. First, cultural products such as movies and books are prime examples of experiential products ([35]). For these products, consumption experience is difficult to evaluate prior to purchase. It depends critically on the creative content, such as the plot and characters of a movie and the storyline of a novel ([24]; [75]). Therefore, consumers often utilize extrinsic cues to help them make the purchase decision ([ 7]). Prior research has suggested that the brand name is an important extrinsic cue for product quality ([83]). Therefore, brand name plays an important role in the consumer purchase of cultural products. For instance, the title of a movie or book can be used to suggest the storyline or the main character. When consumers have different cultural backgrounds, whether and to what extent to provide such information are important decisions in the brand name translation for cultural products in international markets.
Second, by definition, culture-specific factors play a key role in consumer evaluation of cultural products. This is not the case for many noncultural products. For instance, across different countries, the quality of an automobile is evaluated by a similar set of factors (e.g., reliability, mechanical performance, maintenance cost). However, a movie that is rooted deeply in the culture or heritage of one country may not be easily understood in another ([20]; [30]). Thus, brand name translation of cultural products must take into account how culture-oriented nuances can be better understood in a foreign market where cultural distance exists.
Third, the sales period of noncultural products is typically long—companies that sell detergent, electronics, or cars can take an extended time to build brand awareness and consumer preference. In contrast, many cultural products have relatively short life cycles. This is particularly true for entertainment products such as movies, TV shows, and video games. For example, most movies are in theaters for only six to ten weeks ([12]; [43]). Thus, while it is possible for firms to use the original brand name of noncultural products in a foreign country where the language is different (e.g., to use Chrysler and IBM brand names in Japan) and gradually deploy marketing tools to increase sales, it is difficult to do so for cultural products. It is thus critical for the brand names of these products to be translated properly to foster consumer gain awareness and promote content. Furthermore, given the short time window available to attract customer attention, the impact of translated brand names on product sales can be more significant for cultural products than for noncultural products.
Fourth, media coverage and peer-to-peer communication in the form of physical or online word-of-mouth communication, search, and participation in experiential activities are often very active for cultural products ([37]; [53]).[ 8] This fluid and far-reaching information, coupled with a short life cycle, can induce intense and fluctuating demand for cultural products—sales tend to be fast changing over weeks or even days. It is therefore both interesting and important to study the dynamics of how brand name translation affects product sales.
In this article, we take these challenges into consideration and examine how different brand name translation strategies affect the sales of cultural products in international markets. In our theorizing of the effects and developing hypotheses, the context of international markets calls for insights from international marketing literature, and the focus on cultural products requires us to conceptualize the extent to which a translated brand name reveals product content. We test the hypotheses with Hollywood movies shown in the Chinese market from January 2011 to June 2018. This empirical context is valuable for several reasons. Movies are the prototypical example of cultural products. As a key element that builds identity and awareness, the title can be viewed as the brand name of a movie. Although a rich literature has generated useful insights into the drivers of movie sales, no research has examined the effects of movie titles in international markets. Moreover, the relationship between Hollywood studios and the Chinese market is among the most critical in the international movie market. In 2012 China overtook Japan to become the largest theatrical market outside the United States, and in 2016 the number of screens in China surpassed those in the United States ([73]). Consequently, Hollywood studios have a keen interest in marketing their products in China. A study of brand name strategies for Hollywood movies in China thus has strong substantive value. Finally, English is a phonographic language, which represents the sound components of the spoken language using letters or syllabic symbols. Chinese, however, belongs to the logographic system, which represents the meanings of words in the form of symbols ([ 3], p. 467). From a linguistic point of view, the most challenging practice of brand name translation occurs between a phonographic system and a logographic system ([67]). However, such translations are frequently encountered by multinational corporations ([25]).
Our research provides four main contributions to the literature. First, to the best of our knowledge, this is one of the first studies on brand name translation strategies for the sales of cultural products in international markets. Given the growing importance of these products in the marketplace, as well as their unique characteristics, a study that considers the roles of brand names, cultural gap, and temporal dynamics is warranted.
Second, we contribute to the international marketing and branding literature streams by developing a new theoretical framework for the impact of brand name translation. The majority of research in this literature has focused on the impacts of standardization versus adaptation. The existing analysis, however, does not capture some key nuances embedded in the aforementioned cultural products. We thus complement similarity, a key element in the strategy of standardization, with the informativeness of translated brand names to construct an integrated framework. Because marketing standardization can be achieved through strategies such as advertising, pricing, supply chain, and distribution channels, our study contributes to the international marketing literature by focusing on brand name translation, a particular route that has so far received only limited attention.
Third, we consider temporal dynamics and, in the U.S.–China movie context, we estimate how the translation of movie titles influences sales throughout the theatrical release. This makes ours one of the few studies on brand name strategies to examine the longitudinal effects of brand name translations in international markets.
Finally, our study makes an important contribution to the movie research literature, given that very limited research has examined the impact of movie titles. The empirical study of movie sales in China also provides a contribution to research on emerging markets (e.g., [56])—in particular, we answer the call for expanding research to emerging markets to "ask and answer new questions related to consumers, cultures, institutions and regulation" ([55], p. 473).
The remainder of the article is organized as follows. The next section discusses the related literatures on branding and movie research. We then present our theoretical framework, followed by the empirical study including data, model specifications, identification method, and findings. We conclude with managerial implications and directions for future research.
The backdrop of our research is international marketing, with a focus on branding strategies for movies as cultural products. Research in both international marketing and movies has examined a wide variety of topics. In this review, we focus on three streams of literature that are most relevant to our theory and empirical analysis: ( 1) marketing standardization in international markets, ( 2) brand name translation, and ( 3) movie research that examines box office sales in international markets and movie title effects on sales.
For cultural products and brand name translation strategies, international sales are influenced by the transfer of product quality information (e.g., online ratings) as well as the marketing influence (e.g., caused by promotional spending) from the home market to the foreign market. A major driver of the transfer is the extent to which the translated brand name and the original brand name resemble each other. Such resemblance (referred to as "brand name similarity" in our conceptualization) is one element of marketing standardization, which refers to the general practice of applying common marketing strategies across different countries ([65]).
Beginning with pioneering works such as [50] and [80], standardization versus adaptation has been one of the most important strategic considerations in international marketing ([86]). A core argument in this literature is that, when a company enters international markets, standardized marketing strategies help lower market entry cost on the supply side and capitalize brand assets from the home market on the demand side ([ 4]). This is particularly important when competition is fierce and consumers live an increasingly connected world. [65] provide a survey of research that examines performance implications of marketing program standardization. They find that "the majority of studies have indicated that the pursuit of standardized marketing activities by itself has mostly a positive impact on performance" (p. 26). For instance, [58] compare U.S. and European companies operating in both a developed economy (Japan) and an emerging market (Turkey). They find that marketing standardization leads to higher performance. [71] find that perceived brand globalness as a result of consistent brand positioning across countries generates higher purchase intention.
However, when international market environments are very different from each other, marketing strategy standardization becomes less effective. This contingency view is proposed and tested by [38] on international subsidiaries of U.S., Japanese, and German companies. The authors show that strategy standardization leads to superior performance only when there is fit between a company's market environments (e.g., regulations, technology, customers, traditions) and its international marketing strategy choice.
We want to highlight three areas in which our study relates to (but also deviates from) the previous studies in this literature. First, the research on standardization initially focused on advertising before examining a broader array of marketing activities ([58]; [63]). Subsequently, scholars have studied standardization in the product mix, advertising and promotion, channels of distribution, sales, pricing, and marketing program or brand positioning as a whole (e.g., [17]; [38]; [44]; [45]; [79]). However, the impact of the brand name, a critical component of marketing strategies, has received scant attention. [25] is one of the few extant studies on marketing standardization that focuses on brand names. Moreover, a literature review by [65] on the impact of standardization reveals that the research on brand name strategies is lacking.
Second, in terms of methodology and context, the vast majority of the research on standardization utilizes surveys of executives and focuses heavily on consumer packaged goods. Few studies use market-level data, and the cultural industry remains largely unstudied.
Third, the conceptualization and measurement of marketing standardization began with a dichotomous approach to describe whether the firm uses the same marketing strategy in international markets as in its home market. It is now well accepted that standardization should be measured on a continuum and applied to different aspects of marketing actions ([38]; [63]).
Although the standardization literature has not focused much on brand names, several scholars have studied brand name translation through the lens of linguistics. [84] classify the translation between a phonographic language and a logographic language into three types: phonetic translation (by sound only), semantic translation (by meaning only), and phonosemantic translation (by sound plus meaning). Because phonetic translation uses local alphabets or words to mimic the pronunciation of the original brand, the translated brand name does not carry any real meaning. In a bilingual setting, [85] demonstrate that consumers' proficiency in the language associated with the original brand name moderates the effects of different translation methods. Extending the phonetic–semantic framework, [81] classify translated brand names into four types—alphanumeric, phonetic, phonosemantic, and semantic, ranging from the least meaningful to the most meaningful—and estimated their effects on automobile sales. They found that Chinese consumers' preference for vehicle models is strongest when the translated brand name is semantic and weakest when it is phonosemantic. The brand name translation studies provide a valuable foundation for our research, because similarity between the original and translated brand names, a major component in our theoretical framework, is driven by how similar the brand names are to each other in both meaning and sound.
Table 1 provides a summary of the previous studies on brand name translation. Our research differs from these studies in three important ways. First, all prior research examines either consumer packaged goods or durable products; none speaks to cultural products, the focus of our research. Second, no prior research has examined temporal effects associated with brand name translation. We study several moderation effects, including temporal dynamics, to enhance the insights and managerial implications. Third, with the exception of [81], all the other studies use either lab experiments or surveys. We analyze field data in movies to examine brand name translation strategies for cultural products.
Graph
Table 1. Prior Research on Brand Name Translation in International Markets.
| Study | Research Method | Product Category | Theoretical Perspective | Temporal Effects |
|---|
| Standardization | Semantic–Phonetic | Suggestiveness for Content |
|---|
| Francis, Lam, and Walls (2002) | Survey | Consumer packaged goods | Yes | — | — | — |
| Schmitt, Pan, and Tavassoli (1994) | Lab experiments | Consumer products (e.g., pen, bookmarker, bottle opener, soap, soft drink) | — | Yes | — | — |
| Zhang and Schmitt (2001) | Lab experiments | Consumer products (shampoo, clothing, mobile phone, crackers, beer, contact lenses) | — | Yes | — | — |
| Zhang and Schmitt (2004) | Lab experiments | Consumer products (boxing gloves, lotion, facial tissue), supermarkets | — | Yes | — | — |
| Wu et al. (2019) | Empirical analysis of secondary data | Automobile | — | Yes | — | — |
| This study | Empirical analysis of secondary data | Movies (representing cultural products) | Yes (as similarity) | — | Yes (as informativeness) | Yes |
From a theoretical perspective, almost all prior research in the brand name translation literature is based on a semantic–phonetic paradigm. This is not surprising, because the product categories studied previously (consumer packaged goods and automobiles) can be translated semantically, phonetically, or a combination of both ([25]). However, cultural products are different: the emphasis on content requires the translation of brand names to be heavily semantic-focused—phonetic translation is rarely used.[ 9] Therefore, the semantic–phonetic framework is not an effective approach to examine brand name translation for cultural products. We thus attempt to build a theoretical framework that incorporates the standardization perspective for international markets and the informativeness perspective for cultural products.
Compared with the large number of movie studies that focus on the U.S. market, the research that examines international markets is relatively limited ([ 2]; [16]). Most extant research has examined the sales of U.S.-produced movies in overseas markets (e.g., [ 2]; [19]; [32]; [41]). For example, [32] propose a conceptual framework for the success of U.S. movies in Germany. [ 2] study the sales of U.S. movies in 27 countries and find that cultural variation across countries (e.g., individualism vs. collectivism, high vs. low avoidance for uncertainty) moderates the impact of factors such as star power and sequel on movie sales. A few studies include multiple countries of movie origin and sales into the analysis ([23]; [27]). For instance, [27] investigate the sales of foreign movies in Singapore and report that economic and cultural factors account for release frequencies and box office performance. Little published work has examined the strategies or sales of Hollywood movies in major emerging markets such as China, even though these markets have become increasingly critical for the studios.
Related to the idea that movie title can be regarded as a brand, several studies provide valuable insights on movie title strategies. [70] conceptualize sequels as brand extensions of experiential goods. [33] develop a method in the context of movie sequels to measure the monetary value of brand extension. Although these studies focus on brand extension (through sequels), none consider the options and consequences of brand translation in international markets.
Product sales in international markets involve two basic influences related to brand names: ( 1) the cross-market effect, in terms of the connection between the original brand name and the translated brand name, and ( 2) the effect of the translated brand name itself. The former drives the extent to which product quality information and marketing activities in the home market can influence the foreign market. The latter is the brand name to which consumers in the foreign market are directly exposed. We capture these two influences through the overall similarity between the original brand name and the translated brand name as well as the informativeness of the translated brand name. We also examine how the effects of similarity and informativeness are moderated by three contingency factors: home-market performance, cultural gap, and time after release. Figure 1 presents the conceptual framework.
Graph: Figure 1. Effects of brand name translation on cultural products.
The theoretical underpinning for the effect of brand name similarity between the home market and international markets is as follows. When a product enters a foreign market, a similar brand name helps establish a connection in consumers' minds for the product across different markets. This generates both supply-side and demand-side benefits. On the supply side, similar to the situation of sales in sequential distribution channels ([49]), the effects of promotion and other marketing efforts in the home market are more likely to be transferred to the foreign market when the brand names are more similar. Internet and social media are both important facilitators of such information spillover. This essentially produces economy of scale for marketing investment ([58]) and helps generate "prerelease consumer buzz," which, according to [37], is composed of communication, search, and participation in experiential activities. On the demand side, similar brand names reduce the time to build brand recognition ([71]). Akin to the effect of brand extensions (when a new product is connected to an established one through the same brand name), sales of the new product can be increased through enhanced familiarity ([78]).
These supply-side and demand-side benefits are significant for cultural products. In the case of movies, Hollywood studios spend, on average, an amount equivalent to 50% of a movie's production budget on domestic promotion (mostly network television and online advertising; [62]). The studios also engage in extensive public relations campaigns, often in partnership with the cast, to promote movies. The awareness and publicity generated by these efforts, in addition to buzz from the marketplace, will benefit the movie in international markets when this publicity can be more easily connected to the original movie. As we discussed previously, the cultural products' short life cycles dictates that awareness and consumer preference for them must be built quickly in international markets. Similar brand names help achieve these goals.
For Hollywood movies in China, the connection between the original English title and the translated Chinese title is facilitated by the fact that many Chinese audiences are aware of the original title. A major driver behind this awareness is the global flow of entertainment news and information through both mainstream media and the internet. As in other countries, the majority of Chinese movie audiences are young—frequent moviegoers are under 35 years old and have a college education.[10] They have strong interests in entertainment products such as movies, TV shows, and video games from Western countries and are generally aware of these products' English titles ([69]). Therefore, we propose the following hypothesis:
- H1: Sales of a cultural product in a foreign country are higher if the brand name is more similar between the foreign market and the home market.
In the broader literature on how brand names affect consumers, informativeness (or "suggestiveness," as used in some prior studies) refers to whether a brand name provides information about product attributes ([39]; [68]). Previous research has generally found a positive impact. For example, a more informative brand name generates a higher level of recall for brand advertising ([39]), better recognition of brand extensions ([68]), and better memory ([48]).
For cultural products, the primary attribute that brand names help convey is the content (e.g., storyline, character, cast). As we have discussed, this content is the key selling point for cultural products ([24]; [75]). Consumers often seek content that reflects and reinforces certain psychological and sociological profiles related to their societies, cultures, personalities, and interests ([61]). Brand names that are more informative about content are thus more effective in drawing attention and communicating those features to consumers. Consequently, products with more informative brand names can expect faster information dissemination and greater awareness, interest, and purchase intention.
The informative roles that brand names play become even more important in international markets. Due to differences in languages, history, and traditions, consumers in a foreign country often need more information to appreciate the cultural background and social significance of a cultural product than do consumers in the home market. The disconnection between the home and foreign markets is commonly referred to as "cultural gap" ([36]). The basic idea is that a product can be valued to a lesser extent by foreign audiences who lack the cultural background and knowledge needed for full appreciation of the product ([46]). Take movies as an example. The names of certain people, events, and locations in the United States, such as those pertaining to the American Civil War, can trigger interest and emotion for domestic audiences who are aware of their significance. For foreign audiences who are unfamiliar with these names, however, some indication about the background or storyline through the movie title can provide useful guidance.
Thus, for cultural products in international markets, the informativeness of translated brand names for product content should have an important influence on product sales. We create the following hypothesis:
- H2: Sales of a cultural product in a foreign market are higher if the translated brand name is more informative of product content.
Because similarity and informativeness capture two different aspects of brand name translations, there could be a synergy effect between them beyond the individual effects hypothesized so far. When a higher level of similarity builds connection between the translated brand name and the original brand name, which generates both supply-side and demand-side benefits, the product will receive greater awareness and attention in the foreign market. This produces two different types of synergies. First, greater attention and awareness make it easier for consumers in the foreign country to notice the product. The translated brand name is one of the most salient elements of the product, and it is usually the first element to which consumers are exposed. Thus, the translated brand name will be more likely to receive attention, causing its characteristics (including informativeness) to have a greater impact.
Second, when consumers pay more attention to the translated brand name, their ability to process the information contained in the brand name will increase. Consistent with the elaboration likelihood model of persuasion, consumers will be more likely to process the translated brand name in a central or systematic route ([60]). The information contained in the brand name will thus have a greater impact.
Drawing on these synergies, we propose that the effect of informativeness of the translated brand name will be enhanced if there is a greater similarity between the translated brand name and the original brand name. This is summarized in the following hypothesis:
- H3: The (positive) effect of informativeness in the translated brand name on the sales of a cultural product is stronger when the translated and original brand names are more similar.
As Figure 1 shows, we also examine the roles of contingency factors that can moderate the effects of brand name translation. Developed on the basis of similar theoretical grounds as discussed in previous sections, these contingency effects provide further managerial guidance on brand name translations for different cultural products in international markets. For instance, H1 and H2 suggest that similarity and informativeness are both beneficial. While achieving both is possible for some products, it may be difficult do so for others. It is thus important to understand how a trade-off between the two objectives depends on specific product characteristics. Such a contingency view is an important contribution to the literature, which has focused on the main effects of brand name translations ([66]).
We aim to understand under what conditions the connection established by similar brand names is more impactful on international sales. A product's home-market performance is a contingency factor that has important implications for the connection effect.
The theoretical base of similarity is the connection invoked in the minds of foreign consumers between the translated brand name and the original brand name. As a corollary of such connections, consumer perception of the product in the foreign market should be influenced by product success in the home market. The more successful the product is in the home market, the more positively foreign consumers will react when it is introduced overseas. Conversely, if the product is less successful in the home market, foreign consumers will be less enthusiastic about it.
For movies in international markets, domestic box office revenue is by definition the home-market performance. Because of the experiential nature of movies, quality is difficult to judge prior to consumption ([53]). Therefore, consumers often seek product quality signals to help them make purchase decisions. [23] and [ 5] have shown that domestic box office revenue is a key signal of movie quality for audiences in subsequent exhibition channels. For audiences in a foreign country, the linkage of an imported movie to the movie's home-market performance becomes stronger when the original title and translated title are more similar, because greater similarity enhances the perceptual connection in consumers' minds between the home-market version and the overseas version of the movie. Therefore, we have the following hypothesis:
- H4: The (positive) effect of similarity between translated brand name and original brand names on the sales of a cultural product in a foreign market is stronger when the product has higher sales in the home market.
H2 hypothesizes that a higher level of informativeness in translated brand names enhances sales because it conveys more content information to consumers in international markets. Consistent with this reasoning, the need for suggesting content should be an important contingency factor for the informative effect. Some cultural products are more deeply rooted in the social and cultural background of the home country than others, making it more difficult to be understood by foreign audiences and thus to attract attention. The 2016 Ben-Hur remake and its performance in China provide an illustration. The movie is an adaptation of a classic novel (Ben-Hur: A Tale of the Christ) and has a religious setting; both the novel and the religious context are rather unfamiliar to Chinese audiences. The movie generated a box office of only 17.48 million RMB, which is equivalent to less than 10% of its U.S. box office sales. Other examples include movies such as the 1993 history drama Gettysburg, which is based on the battle of Gettysburg in the American Civil War, and the 2018 drama Beautiful Boy, which explores the experiences of a family coping with addiction. Although U.S. audiences can relate to the Civil War and addiction problems, audiences in other countries may be unfamiliar with these issues.
When there is a greater cultural gap between the cultural product and the foreign market, it becomes more valuable to use the brand name to provide "hints" for foreign audiences. The return will be better understanding of the product, greater appreciation of its value, and higher purchase intention. We thus create the following hypothesis:
- H5: The (positive) effect of informativeness in the translated brand name on the sales of a cultural product in a foreign market is stronger if there is a larger cultural gap between the product and the foreign market.
Because translated brand names help convey information to consumers about product quality (via similarity) and content (via informativeness), the impact of different translations should depend on consumers' need for product information. When consumers have other ways of learning about the product, such need is reduced, and the effects of brand names will be smaller. This is consistent with the idea that product information is most impactful when consumers have few alternative learning channels ([28]). In addition to the availability of alternative product-related information, the amount of time that has passed since product launch is also a factor. Thus, we argue that the effects of brand name translation will exhibit temporal dynamics and change over time.
In general, there is more limited information when a new product is introduced than after the product becomes more widely adopted. Much of the early information is in the forms of adverting, public relations, and other firm-initiated communication. As the product continues to sell, a greater amount of alternative information—especially word of mouth, which has important impacts on consumers—becomes available. The need for product-related information is thus greater in the earlier stage of product sales than in the later stage.
These temporal patterns hold for cultural products ([13]; [82]) and in international markets. When the product is first introduced, foreign consumers will rely to a large extent on observable product attributes, among which brand name is highly salient, to judge product quality and content. Thus, the effects of similarity and informativeness related to the translated brand name should be higher. As time passes, other information, including reviews and consumer word of mouth, will become prevalent such that the impact of the brand name reduces. We thus propose,
- H6: The sales effects of similarity and informativeness of brand name translation decrease over time after a cultural product is released in a foreign market.
We collected information for all Hollywood movies exported to China from January 2011 to June 2018. During this period, a total of 348 U.S. movies were exhibited in China.[11] Most of these movies (329 of 348) were released in the United States either earlier than or at the same time as in China. We follow prior studies ([23]; [57]) in focusing on these movies so that the sequence of theatrical releases in domestic and foreign markets is kept consistent. We also dropped another 12 movies that were missing data on important characteristics such as production budget. The final sample thus includes 317 movies, which accounted for 97% of the total Chinese box office revenue of all Hollywood movies during the sample period. In Web Appendix, we provide the list of these movies and related information (Table W1).
For each movie, we obtain the information for the Chinese market, including weekly box office revenue, release time, and weekly number of screens from EntGroup Inc., the leading information provider for the entertainment industry in China. We obtain other information, such as U.S. revenue, production budget, genre, sequel information, cast, distributor, and MPAA rating, from the Internet Movie Database (IMDb). We obtain consumer word of mouth from Douban, which is similar to IMDb and is the most popular movie information and forum website in China by the number of visitors.
We follow the literature in international marketing (e.g., [58]; [65]) to measure the degree of similarity of titles for each movie. Three coders proficient in both Chinese and English independently rated the overall level of similarity between the translated Chinese movie title and the original English movie title on a seven-point scale (1 = "not at all similar," and 7 = "very similar"). Unlike the studies that focused on a semantic–phonetic perspective ([81]; [84]), we take a more holistic approach so that similarity can occur in meaning, sound, or both. This is also theoretically important because meaning similarity and sound similarity can both elicit the connection between the original and translated movie titles. Ratings from the three coders were highly consistent with an intraclass correlation coefficient (ICC) at.84 (F = 6.32, p <.01) ([18]). For each movie, we take the average score of these three coders as the similarity measure.
We followed a similar rating procedure to rate the degree of informativeness for the translated movie titles. We obtained the synopsis for each movie from Douban. The movie titles and synopses were then provided to three coders who were different from the previous coders of similarity. For each movie, they independently rated the extent to which the Chinese title reflects the movie storyline, as described in the synopsis, on a seven-point scale (1 = "does not reflect the storyline at all," and 7 = "fully reflects the storyline"). The ratings were again highly consistent (ICC =.74, F = 4.03, p <.01). As with similarity, we take the average score of the three coders as the informativeness measure for each movie. Table 2 provides several examples from our data to illustrate different movie title translations.
Graph
Table 2. Movie Title Translation Examples.
| Movie Example | English Title | Translated Title in Chinese | Similarity Score (1–7) | Informativeness Score (1–7) | Note |
|---|
| 1 | The Lincoln Lawyer | 林肯律师 | 7 | 6.33 | The translated Chinese title means "The Lincoln Lawyer." It is similar to the English title and informative of the movie content. |
| 2 | Fury | 狂怒 | 7 | 1 | The Chinese title means "fury." The translated Chinese title is similar to the English title but uninformative of the movie content (which is about U.S. tank crews in World War II). |
| 3 | The 33 | 地心营救 | 1 | 6.67 | The Chinese title means "rescue from the heart of the earth." The translated Chinese title is not similar to the English title but informative of the movie content (which is about the rescue mission in a copper mine in Chile). |
| 4 | The Family | 别惹我 | 1 | 2.33 | The Chinese title means "do not mess with me." The translated Chinese title is neither similar to the English title nor informative of the movie content (which is about a mafia family in the witness protection program who want to change their lives). |
In our analysis, the dependent variable home market sales performance is weekly Chinese box office revenue for each movie. We follow [23] to obtain each movie's gross box office revenue in the United States from The Numbers (www.the-numbers.com).
Movie genre is a key characteristic for coding cultural gap. Prior research has shown that drama, comedy, horror, musical, and Western movies are overall more content intensive and have greater country-specific elements than other genres ([20]; [26]; [46]). They are thus associated with a higher level of cultural gap than other genres. In contrast, action, adventure, and thriller movies tend to have universal appeal across cultures ([40]; [47]; [57]). For instance, by analyzing box office performances of selected movie genres in the United States versus in Hong Kong, [46] finds that comedies exhibit a high degree of cultural gap, whereas action movies are associated with low cultural gap. We thus code a dummy variable for cultural gap that equals 1 for drama, comedy, horror, musical, or Western movies and 0 for other movie types.[12]
In addition to the movie literature discussed previously, our data provide additional evidence for this genre classification. We calculated the correlation coefficient between consumer review scores in the United States (i.e., from IMDb) and consumer review scores in China (i.e., from Douban) for each Hollywood movie in our sample. We found that, consistent with the cultural gap classification, U.S. audiences and Chinese audiences are more similar at evaluating action, adventure, and thriller movies than drama, comedy, horror, musical, and Western movies (correlation coefficient =.68 vs..57).
Prior research has extensively studied the drivers of movie box office revenue.[13] First, we control for the number of weeks that have passed since the movie's release to capture the common "decay effect" of movie sales over time ([21]). We also follow the literature to include the following movie characteristics as controls in the estimation: weekly number of screens ([23]); production budget ([14]); gap between U.S. and Chinese releases ([23]); whether the movie is a sequel ([52]); consumer word of mouth (proxied by consumer review score; [15]; [53]); whether the movie cast includes a star, which equals 1 if any member of the cast or the director has been nominated for or won an Academy Award, and 0 otherwise ([22]); whether the movie is distributed by a major U.S. studio ([23])[14]; a set of dummy variables forMPAA ratings ([ 5]); and a set of movie genre dummy variables ([52]).
Moreover, for each movie, how the title is translated into Chinese depends on the characteristics of the original English title. A particular factor is the degree of clarity (or ambiguity) in the original title.[15] A clearer title is more revealing about the content and is more likely to translated with a higher degree of standardization. Therefore, we construct a variable to measure the degree of clarity in the original title in English for movies in our sample. We recruited three native English speakers who work as English teachers at a major university in Shanghai. We provided them with the list of English movies titles and the synopsis of each movie, obtained from IMDb. Using a seven-point scale, they independently rated whether a movie title clearly describes the information in the synopsis (1 = "totally unclear," and 7 = "totally clear"). Interrater reliability is high (ICC =.77, F = 5.19, p <.01). The average rating is used for each movie.
Movie box office sales are well known for seasonality. We include in our estimation three sets of time-related fixed effects: week (52 dummy variables for each week of the year), year (a dummy variable for each year in the sample period), and Chinese public holidays. Because the public holidays are mostly based on the traditional (lunar) Chinese calendar, they are at different dates in different years. Thus, holiday fixed effects need to be separately accounted for beyond week and year fixed effects. Table 3 provides the definition and summary statistics of all variables, as well as the correlations between them.
Graph
Table 3. Variable Operationalization, Summary Statistics, and Correlation Matrix.
| Variable Operationalization and Summary Statistics | Correlation Matrix |
|---|
| Variable | Operationalization | Source | Mean | SD | Min | Max | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 |
|---|
| 1. Weekly box office in China () | Weekly box office revenue for each movie in China (in million RMB) | EntGroup | 55.85 | 113.76 | .0003 | 1,352.07 | | | | | | | | | | | | | | |
| 2. Similarity () | Overall similarity between the original English title and translated Chinese title, seven-point scale (1= "not at all similar," and 7= "very similar") | Coded | 4.43 | 1.79 | 1 | 7 | .16 | | | | | | | | | | | | | |
| 3. Informativeness () | How much the translated Chinese title reflects movie content, seven-point scale (1 = "does not reflect the storyline at all," and 7 = "fully reflects the storyline") | Coded | 4.16 | 1.88 | 1.67 | 6.67 | .09 | −.07 | | | | | | | | | | | | |
| 4. U.S. gross box office () | Total box office revenue in the United States (in million USD) | | 126.44 | 126.27 | 1.00 | 700.06 | .31 | .31 | .22 | | | | | | | | | | | |
| 5. Cultural gap () | Equals 1 for drama, comedy, horror, musical, or Western movies, and 0 otherwise | | .22 | .41 | 0 | 1 | −.16 | −.03 | .04 | −.17 | | | | | | | | | | |
| 6. Number of weeks in theater () | Number of weeks that have passed since movie's release in theater in China | EntGroup | 3.22 | 1.59 | 1 | 6 | −.41 | .04 | −.00 | .06 | −.01 | | | | | | | | | |
| 7. Clarity of the original title | Whether the English title clearly describes the movie, seven-point scale (1 = "totally unclear," and 7 = "totally clear") | Coded | 2.93 | .94 | 1 | 5.67 | .10 | .32 | .22 | .21 | −.02 | .02 | | | | | | | | |
| 8. Weekly number of screens in China () | The number of screens for each movie in each week in China (in '0,000s) | EntGroup | 8.24 | 12.22 | 1 | 103.62 | .79 | .16 | .08 | .31 | −.16 | −.42 | .10 | | | | | | | |
| 9. Production budget | Production budget for each movie (in million USD) | | 114.32 | 68.96 | 2.80 | 321 | .28 | .26 | .08 | .52 | −.33 | .07 | .13 | .24 | | | | | | |
| 10. Release gap | The number of weeks between release in the United States and release in China | | 12.91 | 18.17 | 1 | 115 | −.22 | −.27 | −.11 | −.31 | .16 | −.05 | −.23 | −.26 | −.26 | | | | | |
| 11. Sequel | Equals 1 if the movie is a sequel and 0 otherwise | | .29 | .45 | 0 | 1 | .26 | .23 | .10 | .31 | −.26 | .03 | .17 | .25 | .30 | −.26 | | | | |
| 12. Consumer word of mouth | Chinese user review score for each movie | | 6.89 | .97 | 3.6 | 9.2 | .13 | .16 | .16 | .40 | .17 | .07 | .09 | .09 | .18 | −.13 | −.01 | | | |
| 13. Star | Equals 1 if any cast member or the director has been nominated for or won Oscar Awards. | | .61 | .49 | 0 | 1 | −.00 | .06 | −.09 | .06 | .07 | −.00 | .07 | −.02 | .01 | .01 | −.05 | .12 | | |
| 14. Major studio | Equals 1 if the movie was distributed by a major U.S. studio and 0 otherwise | | .57 | .49 | 0 | 1 | .24 | .22 | .05 | .42 | −.15 | .04 | .21 | .26 | .36 | −.44 | .23 | .20 | −.09 | |
| 15. News media publicity | Number of media news articles during the theatrical release for each movie (in thousands, for 184 out of 317 movies) | EntGroup | 914.81 | 1,235.24 | 1 | 8,288 | .35 | .26 | .07 | .43 | −.14 | .02 | .14 | .42 | .28 | −.24 | .30 | .15 | .04 | .27 |
| 16. MPAA rating () | Dummy variables for the MPAA rating of each movie, with action movies as benchmark | | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
| 17. Movie genre () | Dummy variables for the genre of each movie, with action movies as benchmark | | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
To provide some anecdotal evidence for the relevance of movie titles for Chinese consumers, we conducted a survey on WJX.cn, the largest online survey platform in China. The survey included 214 Chinese consumers with ages ranging from 18 to 55 years old and an average age of 29 years old. Among the respondents, 66.4% reported that they pay attention to movie titles when they choose a movie to watch and 88.3% would "sometimes," "often," or "always" make an inference about a movie's storyline from the title. For imported movies, a majority of the respondents (54.2%) reported that they would associate the translated title with the original movie title. Among the respondents who are frequent moviegoers (i.e., those who watch a movie in the theater at least once a month), 62.6% would make this association. While preliminary, this survey shows that ( 1) movie titles matter for Chinese audiences in movie selection, ( 2) movie titles are used to infer movie content, and ( 3) a majority of audiences tend to make a connection between the translated Chinese title and the original English title.
We estimate the following model for the effects of brand name translations:
Graph
1
where is the weekly box office revenue of movie i in China, is the similarity score of the translated Chinese title in relation to the original English title for movie i, and is the informativeness score of the translated Chinese title for movie i. is movie i's gross box office revenues in the United States. is the dummy variable for cultural gap: for drama, comedy, horror, musical, and Western movies and 0 otherwise.
is the number of weeks since the release of movie i, and it captures the common "decay effect" of movie sales over time. Distribution intensity is captured by , the weekly number of screens for movie i in China, and we follow previous literature (e.g., [23]) to use logged values to account for potential nonlinearity. includes the movie characteristics described previously: production budget, release gap, sequel, consumer word of mouth, star in the cast, and major movie studio. and are MPAA rating and genre dummies, respectively. contains the three sets of time-related fixed effects (i.e., week, year, and Chinese holiday fixed effects). is the random effect for movie i, which captures the unobservable factors that may affect movie box office.
We expect and to be positive for the impact of similarity (H1) and informativeness (H2) and to be also positive for the interaction effect of similarity and informativeness (H3). Positive estimates of the interaction effects, and , would support H4 and H5. Finally, as we state in H6, we expect and to be negative as the effects of movie titles decrease over time.
Even with the rich set of controls in the estimation equation, the error term in Equation 1 could still contain unobserved movie characteristics that are potentially correlated with both movie title translation (i.e., and ) and box office revenue. Not accounting for this endogeneity due to omitted variables would lead to biased estimates for the effects of movie title translation on box office revenue.
To address this issue, we need instrumental variables that correlate with how movie i's title is translated (i.e., correlate with and ), but not the error term. Following the approach of [15], we use the similarity and informativeness of translated titles of competing movies as instruments. Specifically, the instrument for is the average similarity of the movies released in the same year as movie i, calculated as | j and i were released in the same year ; similarly, the instrument for is calculated as | j and i were released in the same year . The rationale is that industrial trends in movie title translation should correlate with how a movie title is translated but not correlate with the unobserved characteristics that affect a particular movie's box office revenue.
in Equation 1 can also be endogenous because distributors can adjust the number of screens for a particular movie based on qualities unobserved by researchers ([15]; [23]). We follow [15] to construct the instruments for , which includes two sets of variables: ( 1) the average weekly number of screens in China for Hollywood movies of the same genre as movie i during the same year and ( 2) the average weekly number of screens in China for Hollywood movies of other genres during the same year.
We estimate Equation 1 with the aforementioned instruments using two-stage least squares ([15]; [21]; [34]). Table 4 presents the estimation results.
Graph
Table 4. Effects of Brand Name Translation on Weekly Box Office Revenue in China.
| DV: ln (Weekly Box Office Revenue in China) | (1) | (2) | (3) |
|---|
| Estimate | SE | Estimate | SE | Estimate | SE |
|---|
| H1: Similarity of translated title in Chinese | — | | .0222** | (.0090) | .0354* | (.0169) |
| H2: Informativeness of translated title in Chinese | — | | .0246** | (.0124) | .0403** | (.0199) |
| H3: Similarity × Informativeness | — | | — | | .0114* | (.0060) |
| H4: Similarity × ln (U.S. gross box office) | — | | — | | .0018** | (.0008) |
| H5: Informativeness × Cultural gap | — | | — | | .0359** | (.0169) |
| H6a: Similarity × Number of weeks in theater | — | | — | | −.0236** | (.0101) |
| H6b: Informativeness × Number of weeks in theater | — | | — | | −.0323* | (.0195) |
| Number of weeks in theater | −.5803*** | (.1220) | −.5832*** | (.1225) | −.5085*** | (.1456) |
| Clarity of the original title in English | .0312 | (.0286) | .0145 | (.0315) | .0010 | (.0303) |
| ln (Weekly screens in China) | 1.0254*** | (.0450) | 1.0249*** | (.0453) | 1.0478*** | (.0588) |
| ln (U.S. gross revenue) | .1587*** | (.0353) | .1498*** | (.0376) | .1751*** | (.0482) |
| ln (production budget) | .0269 | (.0446) | .0282 | (.0440) | .0107 | (.0437) |
| Release gap | −.0067*** | (.0021) | −.0065*** | (.0021) | −.0076*** | (.0021) |
| Sequel | .1336* | (.0704) | .1273* | (.0697) | .1071 | (.0751) |
| Consumer word of mouth | .1874*** | (.0383) | .1866*** | (.0379) | .1734*** | (.0399) |
| Star | −.0163 | (.0599) | −.0164 | (.0593) | −.0184 | (.0569) |
| Major studio | .1310** | (.0639) | .1400** | (.0647) | .1116* | (.0616) |
| MPAA rating G | Reference | | Reference | | Reference | |
| MPAA rating PG | .1546 | (.1173) | .1806 | (.1183) | .1833 | (.1316)) |
| MPAA rating PG-13 | .2467** | (.1237) | .2554** | (.1186) | .2247* | (.1284) |
| MPAA rating R | .2937** | (.1474) | .3234** | (.1395) | .3213** | (.1448) |
| Action | Reference | | Reference | | Reference | |
| Adventure | −.0310 | (.0828) | −.0237 | (.0821) | −.0055 | (.0811) |
| Thriller | −.0109 | (.0848) | .0098 | (.0893) | .0078 | (.0924) |
| Drama | −.0665 | (.1258) | −.0889 | (.1253) | −.2142 | (.3608) |
| Comedy | −.0663 | (.1413) | −.0575 | (.1397) | −.2105 | (.3503) |
| Horror | −.2148 | (.1917) | −.1772 | (.1952) | −.2576 | (.3427) |
| Musical | .0084 | (.1540) | .0177 | (.1612) | −.1342 | (.3759) |
| Western | −.6862*** | (.2285) | −.6504*** | (.2373) | −.6672** | (.3061) |
| Constant | −9.4837*** | (.5122) | −9.5868*** | (.5553) | −8.4325*** | (.7188) |
| Week, year, and Chinese holiday fixed effects | Yes | Yes | Yes |
| IV for ln (Weekly screens in China) | Yes | Yes | Yes |
| IV for movie title similarity and informativeness | — | Yes | Yes |
| , p-value | — | 3,728.46 (2), p <.001 | 1,498.36 (5), p <.001 |
| R2 | .9674 | .9721 | .9747 |
| Akaike information criterion | 3,568.621 | 3,209.406 | 3,012.357 |
| Bayesian information criterion | 5,289.918 | 5,030.832 | 4,860.412 |
| Overidentification test (p-value) | .1656 (.6841) | .2647 (.6069) | .2582 (.6114) |
| Number of observations | 1,524 | 1,524 | 1,524 |
1 * Significant at the 10% level.
- 2 ** Significant at the 5% level.
- 3 *** Significant at the 1% level.
- 4 Notes: Robust standard errors are in parentheses. DV = dependent variable; IV = instrumental variable.
We first evaluate whether movie title translation (i.e., similarity and informativeness) can indeed help explain movies' box office revenues. As the model fit statistics indicate, adding and significantly improves model fit ( 3,728.46, p <.001), which is further increased by the inclusion of the interaction effects ( 1,498.36, p <.001). We also calculate , Akaike information criterion, and Bayesian information criterion for each specification. All indicate that the variables for similarity and informativeness of movie title translation help improve model fit and are valuable in explaining the variance in box office sales.
We evaluated the strength and the relevance of our instruments. Table W2 in the Web Appendix shows the first-stage results for all endogenous variables. In all first-stage estimations, the null hypothesis of weak instrument can be rejected. We further conducted the Sargan test for overidentification ([64]; [81]). As Table 4 shows, the tests under all specifications are not statistically significant, suggesting that the instruments are uncorrelated with the error term.
Column 2 of Table 4 shows how similarity and informativeness affect the movie sales in China. As it shows, the parameter estimate for similarity is positive and statistically significant ( =.0222, p <.05). In support of H1, this shows that greater similarity between translated and original movie titles helps increase box office sales in the foreign market. The parameter estimate for informativeness is also positive and statistically significant ( =.0246, p <.05). H2, which suggests that international market sales benefit from a translated movie title that reveals movie content to a greater extent, is thus supported.
Column 3 of Table 4 is the full model of our estimation. As it shows, the parameter estimate for the interaction term of movie title similarity and informativeness is positive and significant ( =.0114, p <.10). This provides the support for the synergy effect that we hypothesized in H3—for a translated brand name, higher similarity with the original brand name enhances the impact of its informativeness.
H4 predicts that the positive effect of brand name similarity will become stronger when the product has better sales performance in the home market. This is supported: the parameter estimate of the interaction between similarity and U.S. box office in Table 4, Column 3, is positive and statistically significant ( =.0018, p <.05). Thus, with greater similarity between the original and the translated movie titles, audiences in a foreign country are more capable of linking a movie to its origin and use the home-market performance to help judge the movie's quality.
The estimation results in Column 3 also support H5. The interaction between the informativeness of translated brand name and the high versus low level of cultural gap grouping has a positive and statistically significant impact on sales ( =.0359, p <.05). Thus, higher informativeness is particularly effective in improving international market sales for cultural products with greater cultural gap.
In terms of temporal dynamics of the effects, the parameter estimates of the interaction terms between the number of weeks since release and the two movie title translation variables are both negative and statistically significant in Column 3 of Table 4 ( = −.0236, p <.05; = −.0323, p <.10). Thus, the brand name translation effects are the highest when the product was first introduced to international markets, but they weaken as it continues to sell. This provides strong support for H6.
Although the control variables are not our focus, their estimated effects are mostly in the expected direction across different specifications. Benefiting from more intensive distribution, gross box office revenue is higher when a movie is shown on a larger number of screens. The Chinese box office is also higher when the U.S. revenue is higher, when word of mouth is more positive, when the movie is a sequel, when the release gap is smaller between the U.S. and Chinese release dates, and when it the movie was distributed by major studios. We did not find movie budget or the presence of stars in U.S. movies to significantly affect Chinese box office revenue. No prior research has examined the effects of U.S. movie budgets and stars in the Chinese market. Finally, there is no clear theory on whether and how the clarity of the original English title would influence box office sales in a different country where a translated title is used, and our results also indicate that the clarity of the original English movie title has no effects on Chinese box office revenue.
Chinese box office revenues are distributed over a large range with some extreme values. One may wonder to what extent our results are driven by movies with very high or low box office sales. To examine the robustness of our results to this issue, we drop movies with total box office above the 95th percentile or below the 5th percentile and reestimate the model ([72]). The results from this analysis appear in Table 5, Column 2. Again, our main results regarding movie title translations remain similar.
Graph
Table 5. Robustness Checks.
| DV: ln (Weekly Box Office Revenue in China) | (1)Baseline Results(Column 3 in Table 4) | (2)Movies with Extreme Box Office Excluded | (3)Horror, Musical, and Western Movies Excluded | (4)Controlling for Advertising Effects |
|---|
| Estimate | SE | Estimate | SE | Estimate | SE | Estimate | SE |
|---|
| H1: Similarity of translated title in Chinese | .0354* | (.0169) | .0200** | (.0080) | .0239** | (.0103) | .0359** | (.0175) |
| H2: Informativeness of translated title in Chinese | .0403** | (.0199) | .0608** | (.0216) | .0501** | (.0202) | .0605* | (.0350) |
| H3: Similarity × Informativeness | .0114* | (.0060) | .0043*** | (.0015) | .0022** | (.0009) | .0034** | (.0015) |
| H4: Similarity × ln (U.S. gross box office) | .0018** | (.0008) | .0013** | (.0006) | .0018* | (.0010) | .0011* | (.0005) |
| H5: Informativeness × Cultural gap | .0359** | (.0169) | .0216* | (.0118) | .0357* | (.0187) | .0509** | (.0257) |
| H6a: Similarity × Number of weeks in theater | −.0236** | (.0101) | −.0110** | (.0046) | −.0135* | (.0069) | −.0138** | (.0058) |
| H6b: Informativeness × Number of weeks in theater | −.0323* | (.0195) | −.0483* | (.0251) | −.0267** | (.0106) | −.0417* | (.0247) |
| ln (Weekly screens in China) | 1.0478*** | (.0588) | 1.0888*** | (.0821) | 1.0438*** | (.0707) | 1.0120*** | (.0669) |
| News media publicity, proxy for advertising | | | | | | | .0899** | (.0432) |
| Movie characteristics | Yes | Yes | Yes | Yes |
| IV for ln (Weekly screens in China) | Yes | Yes | Yes | Yes |
| IV for movie title similarity and informativeness | Yes | Yes | Yes | Yes |
| Week, year, and Chinese holiday fixed effects | Yes | Yes | Yes | Yes |
| R2 | .9747 | .9760 | .9727 | .9841 |
| Number of observations | 1,524 | 1,390 | 1,289 | 986 |
- 5 * Significant at the 10% level.
- 6 ** Significant at the 5% level.
- 7 *** Significant at the 1% level.
- 8 Notes: All columns contain the constant and the same movie characteristics as in Table 4 as explanatory variables. Robust standard errors are in parentheses. DV = dependent variable; IV = instrumental variable.
Movies with certain genres (such as horror, musical, and Western) are "niche-market" movies that target specific audience groups. They tend to have more limited screening intensity in China. Even though they belong to the high cultural gap category along with drama and comedy, they could be associated with different audience preferences. It is useful to examine how movie title translation effects, especially those pertaining to the moderation by cultural gap, may change if we exclude these three genres. Thus, we reestimated the model using a sample that excludes horror, musical, and Western movies. As Column 3 of Table 5 shows, the results remain highly consistent with those reported previously.
Advertising is usually an important influencer of box office sales ([ 9]). However, unlike the U.S. domestic market, where movies are promoted heavily before they are released, movie advertising is not yet a major activity in China—in terms of both budget and managerial attention, advertising falls far behind production and exhibition in the Chinese film industry. For instance, while movie budgets in the United States spend a substantial amount of money to advertise on network television, TV advertising for movies is very limited in China. A report by China Daily indicates that movies in China seldom use television advertising due to its high cost and their overall low promotional budgets.[16] In fact, online ticket platforms and social media, rather than ads, are the most prevalent sources of movie information for Chinese consumers.[17] Thus, the impact of advertising on Chinese movie sales should not be as important as that for movies in the U.S. market.
From the analysis perspective, recall that we have explicitly controlled for production budget in our estimations. Because movie advertising spending is often in proportion to production budget (e.g., [15]; [53]), the impact of not including advertising spending per se should be minimal. Moreover, because systematic reporting of movie industry information is still nascent in China, it is very difficult to obtain reliable data for movie advertising spending.
To test whether our main results remain robust when advertising is controlled for, an option is to utilize some proxies of advertising effects. In lieu of advertising data, we were able to obtain from EntGroup the number of news articles that appeared on major news media for 184 of the 317 movies in the data set. Because advertising generates publicity for movies and news media coverage is one of the best ways to measure publicity ([11]), we can include the amount of new media coverage in the estimation as a direct control for advertising effects.
Because the advertising budget is usually set in proportion to the production budget, we follow prior research to regress the amount of news coverage on production budget and then employ the residuals in the estimation to isolate the impact due to advertising ([51]). Because the limited news data significantly reduce the sample size (from 317 to 184), this estimation provides a strong test of whether our main results hold.
As Column 4, Table 5, shows, advertising as proxied by news media publicity has a positive impact on box office sales. Even with a sample size that is less than 60% of the full sample, our main results remain robust: the effects of brand name translation strategies and their moderation effects are similar to the findings in Table 4.
This article examines how the brand naming strategy for cultural products affects consumer demand in international markets. In the empirical context of Hollywood movie sales in China, we find strong support that Hollywood movies' box office revenue in China increases when the similarity or informativeness of their translated titles is higher. There is also a positive interaction (synergy) effect between similarity and informativeness. We show that two key product characteristics moderate the brand name effects: the similarity effect is stronger for Hollywood movies with better performances in the home (i.e., the U.S.) market, while the informativeness effect is stronger for movies that have a larger cultural gap from the foreign market. We further find that these effects decrease over time as the product continues to sell.
Our study provides several valuable managerial implications for companies, managers, and policy makers in cultural product industries, as well as those in international marketing. First, our results point out that brand name translation is not a trivial task. How the brand name is translated can have important consequences on product sales. Our study sheds light on two strategies companies can take: translated brand names should ( 1) resemble the original brand names or ( 2) be informative of product content. While each strategy can be managed to influence the sales in international markets, there is also a synergy between them—one strategy becomes more effective if the other strategy is also implemented.
Second, our results indicate that these branding strategies are most effective for product sales in the early period after introduction. This is particularly important for managers given the short life cycle of cultural products. It is critical for companies to be sensitive to brand name translation early and to ensure that similarity, informativeness, or both are in place before introduction to help increase sales quickly.
Third, the outcome will be the best if a company can achieve both higher similarity and higher informativeness in its translated brand name, especially considering that similarity and informativeness in one translated brand name can have positive synergy effects to further increase product sales. However, there could be situations in which achieving both goals in one brand name is difficult because they may require different brand name features and translation techniques. Therefore, companies need to make trade-offs between similarity and informativeness. Our study provides the following guidelines for the trade-off. If the product has high home-market performance but small cultural gap, the translation should focus on brand name similarity. If the product has low home-market performance but large cultural gap, the translation should focus on informativeness. If the product has both high home-market performance and large cultural gap, both similarity and informativeness will be highly effective in generating sales. Thus, the company should pay attention to both strategies, with the relative emphasis between the two decided on the basis of the effect size and the feasibility of each strategy. Finally, if the product has low home-market performance and small cultural gap, both strategies will still be helpful but will not be highly effective.
Fourth, related to the trade-off between similarity and informativeness, companies can follow our analysis to estimate the effect size of the moderating factors. They can then more precisely evaluate the extent to which product sales will benefit from either strategy so that a sensible trade-off can be made.
To gain further managerial insights from our results, we use the parameter estimates to quantify the magnitude of the impact of Hollywood movie title translation on Chinese box office revenues. For illustration purposes, we compute the effect size for the scenarios where movie title similarity or informativeness would increase by one point on the seven-point scale. We do so for all the Hollywood movies in the sample, and for different movie categories and across different time periods. The values of other variables not simulated are set at the sample mean. Table 6 presents the results.
Graph
Table 6. Magnitude of Movie Title Translation Effects on Chinese Box Office Revenues.
| Effects to Simulate | Scenario of Change in Movie Title Translation Strategy | Percentage Change in Box Office Revenues in China | Amount Change in Box Office Revenues in China (in Million RMB) |
|---|
| A: Average Movie Title Translation Effects |
| All movies | Similarity | 3.60% | 10.18 |
| Informativeness | 2.83% | 8.01 |
| B: Heterogeneous Effects by Movie Types |
| Movies with U.S. box office revenue above median | Similarity | 4.35% | 19.45 |
| Movies with U.S. box office revenue below median | 3.30% | 3.88 |
| Movies with higher level of cultural gap (i.e., drama, comedy, horror, musical, or Western) | Informativeness | 8.78% | 10.25 |
| Movies with lower level cultural gap (i.e., action, adventure, or thriller) | 2.38% | 7.82 |
| C: Temporal Dynamic Effects |
| Movies in the first week of release | Similarity | 4.56% | 5.89 |
| Informativeness | 5.57% | 7.20 |
| Movies in the second week since release | Similarity | 1.64% | 1.67 |
| Informativeness | 1.48% | 1.50 |
9 Notes: For each scenario, the simulated change is an increase of one point on the seven-point scale used to measure similarity and informativeness. Calculations are based on estimation results in Column 3 of Table 4. The values of other variables not simulated are set at the sample mean.
Table 6, Panel A, shows the effects of movie title translation across all movies. If the degree of similarity of translated movie titles increases by one point, the average box office revenue would increase by 3.60%, or 10.18 million RMB. If the degree of informativeness in movie title translation increases by one point, the average box office revenue of these movies would increase by 2.83%, or 8.01 million RMB.
Table 6, Panel B, illustrates the heterogeneous effects of similarity and informativeness for different types of movies (i.e., by home-market performance and degree of cultural gap). In terms of home-market performance, we median-split the sample and compare the movies with a U.S. box office revenue above the sample median with those below. Recall that similarity in movie title translations facilitates the connection between home and foreign markets. For a one-point increase in the degree of similarity, Hollywood movies with higher-than-median U.S. box office revenue would see a 4.35% increase in Chinese box office revenue. That is equivalent to 19.45 million RMB for these movies. However, movies with lower-than-median U.S. box office revenue would see an increase of only 3.30% (or 3.88 million RMB). Clearly, the benefit of similarity is more significant for movies with better home-market success.
In terms of cultural gap, the relevant brand name translation strategy is the degree of informativeness. For a one-point increase in informativeness, movies with large cultural gap would see a more substantial increase of 8.78% (or 10.25 million RMB) in Chinese box office revenue than the movies with small cultural gap (2.38%, or 7.82 million RMB). Increasing informativeness is thus a more effective strategy for large cultural gap movies than for those with small cultural gap.
Finally, Table 6, Panel C, focuses on the temporal effects, using the first and second weeks of release as illustration. For a one-point increase in similarity, the Chinese box office revenue would increase by 4.56% (5.89 million RMB) for a movie in its first week of release and 1.64% (1.67 million RMB) for the second week. For a one-point increase in informativeness, the Chinese box office revenue would increase by 5.57% (7.20 million RMB) for the first week and 1.48% (1.50 million RMB) for the second week. In summary, our results provide companies and managers with not only insights about the value of and options for brand name translations but also actionable strategies about how to optimize the task.
A caveat for our study is that we do not model the selection decision of Hollywood movies into the Chinese market. This is a complex issue, as both the Chinese government and Hollywood studios are presumably involved in the negotiation, and there is no published guideline regarding this process or the selection criteria. However, it is widely acknowledged that Chinese government agencies (State Administration of Press, Publication, Radio, Film & Television before March 2018, and State Bureau of Films after that) are key players. Our study focuses on the movie sample conditional on this selection.
Even though our empirical context is Hollywood movies in China, the basic theoretical framework and the measures of similarity and informativeness are not specific to this context. They can be implemented for brand name translations between any two language systems. Similarly, the managerial implications discussed previously are also generalizable. Nevertheless, we believe that studying brand name translation strategies in additional contexts, especially if multiple countries are involved ([ 6]), has the potential to motivate richer conceptual developments and generate further insights.
Our empirical analysis examines box office sales of Hollywood movies in one foreign country. As studios continue to pay attention to international markets, it becomes increasingly important for them to implement strategies that would maximize global box office revenue. Maximizing revenues in multiple countries requires foresight, planning, and scientific methods. It is particularly challenging given that the studios do not fully control either the exporting or foreign exhibition decisions. It is beyond the scope of our article to address the issue of global revenue maximization; however, it would be interesting for future research to study how casting, release timing, promotion, and movie title strategies may affect global box office revenue.
Finally, it would be fruitful to examine how movie title translations interact with other movie marketing activities, such as the use of movie trailers. For example, are more informative trailers and more suggestive movie title translations complements or substitutes for each other? Similar to [74] suggestion about integrating verbal and visual elements of marketing campaigns, addressing this question will provide insights on how verbal (movie titles) and visual (movie trailers) aspects of cultural product marketing interact with each other to influence product sales in both home and international markets.
Supplemental Material, jm.18.0270-File003 - Branding Cultural Products in International Markets: A Study of Hollywood Movies in China
Supplemental Material, jm.18.0270-File003 for Branding Cultural Products in International Markets: A Study of Hollywood Movies in China by Weihe Gao, Li Ji, Yong Liu and Qi Sun in Journal of Marketing
Footnotes 1 Author ContributionsThe authors are listed alphabetically and contributed equally.
2 Associate EditorJan-Benedict E.M. Steenkamp
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Weihe Gao acknowledges the support of the National Natural Science Foundation of China [Grant 71572099, 71872106] and National Social Science Fund Major Project [Grant 18ZDA079]. Yong Liu acknowledges the support of the National Natural Science Foundation of China [Grant 71728007, 71328202]. Qi Sun acknowledges the support of the National Natural Science Foundation of China [Grant 71628303]. Part of this research was finished when Yong Liu was visiting China Europe International Business School, College of Business at Shanghai University of Finance and Economics, and College of Economics and Management at Tianjin University.
5 ORCID iDsYong Liu https://orcid.org/0000-0001-7007-0555 Qi Sun https://orcid.org/0000-0003-2222-6505
6 Online supplement: https://doi.org/10.1177/0022242920912704
7 1A broader definition of cultural goods and services by the United Nations Educational, Scientific and Cultural Organization (UNESCO) includes the following categories: cultural and natural heritage, performance and celebration, visual arts and crafts, books and press, audiovisual and interactive media, design and creative services, and intangible cultural heritage ([76]). Our article focuses mostly on commercial media and entertainment products such as movies, books, music, and video games.
8 2[37] provide a more comprehensive definition—"prerelease consumer buzz"—for information exchange and activities that consumers engage in prior to product release. They define buzz as including three different types of behaviors: communication, search, and participation in experiential activities.
9 3Among the 317 movies in our data, only two used nonmeaningful movie titles in Chinese that just mimicked the pronunciation of the original English titles (i.e., the characters are otherwise unrelated and elicit no meaning when put together). The remaining 315 movies all used titles that have concrete meanings in Chinese. In contrast, in [81] study of the automobile market, 127 out of 270 vehicle models in China used nonmeaningful brand names in Chinese.
4See https://piaofang.maoyan.com/feed/news/1056309 (in Chinese; accessed March 11, 2020).
5During this time period, there were 14 movies coproduced by Chinese and U.S. partners. In China, coproduced movies are treated as domestic movies. Therefore these movies are not in the Hollywood movie sample that we study.
6We also examined the robustness of our results to alternative classifications of large versus small cultural gap and found similar results. We report this in the "Robustness Checks" section.
7We refer interested readers to articles that provide literature reviews for the drivers of movie box office revenue, such as [31] and [29].
8The major studios include Universal, Sony/Columbia, Paramount, Walt Disney, and Warner Brothers.
9We thank an anonymous reviewer for suggesting this factor.
10See http://media.people.com.cn/n/2015/0519/c40606-27022292.html (in Chinese; accessed March 11, 2020).
11See https://piaofang.maoyan.com/feed/news/1056309 (in Chinese; accessed March 11, 2020).
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Record: 27- Branding in a Hyperconnected World: Refocusing Theories and Rethinking Boundaries. By: Swaminathan, Vanitha; Sorescu, Alina; Steenkamp, Jan-Benedict E.M.; O'Guinn, Thomas Clayton Gibson; Schmitt, Bernd. Journal of Marketing. Mar2020, Vol. 84 Issue 2, p24-46. 23p. 1 Diagram, 4 Charts. DOI: 10.1177/0022242919899905.
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Branding in a Hyperconnected World: Refocusing Theories and Rethinking Boundaries
Technological advances have resulted in a hyperconnected world, requiring a reassessment of branding research from the perspectives of firms, consumers, and society. Brands are shifting away from single ownership to shared ownership, as heightened access to information and people is allowing more stakeholders to cocreate brand meanings and experiences alongside traditional brand owners and managers. Moreover, hyperconnectivity has allowed existing brands to expand their geographic reach and societal roles, while new types of branded entities (ideas, people, places, and organizational brands) are further stretching the branding space. To help establish a new branding paradigm that accounts for these changes, the authors address the following questions: ( 1) What are the roles and functions of brands?, ( 2) How is brand value (co)created?, and ( 3) How should brands be managed? Throughout the article, the authors also identify future research issues that require scholarly attention, with the aim of aligning branding theory and practice with the realities of a hyperconnected world.
Keywords: brands; branding; brand management; brand equity; cocreation; digital branding; hyperconnectivity; networks; platforms
Over the course of a century, branding has moved from an occasionally studied activity to a major concern for both business and society. Traditional commercial brands are omnipresent and compete for consumer attention with newer branded entities, such as platform brands (e.g., Airbnb), direct-to-consumer brands (e.g., Warby Parker), smart brands (e.g., Google Nest), idea brands (e.g., #MeToo), and person brands (e.g., Kim Kardashian). The manner in which consumers interact with brands is also changing due to the rise of digitally native brands, the ubiquitous access to information and products via digital and mobile channels, and the broad availability of smart, connected devices.
To inspire next-generation conceptual and empirical work on branding, this article examines how existing perspectives need to be refocused and rethought to address the realities of contemporary society. We examine broader types of entities, ranging from smart branded devices and entities that operate in networks of brands to ideas and person brands. We also investigate the blurring of brand boundaries brought about by technology-induced hyperconnectivity. In doing so, we expand on new frontiers in branding research and argue that these topics need to be a larger part of research agendas.
Our review of the marketing literature suggests that extant theoretical perspectives—from the vantage points of the firm, the consumer, and society—have resulted in certain models and assumptions that may no longer be adequate or sufficient in a hyperconnected world. The concept of hyperconnectivity refers to the proliferation of networks of people, devices, and other entities, as well as the continuous access to other people, machines, and organizations, regardless of time or location ([42]; [103]). In this environment, information is always accessible and abundant, search costs are low, goods and services from across geographic boundaries are easier to reach than ever, and firms may no longer be the primary source of information about brands.
Hyperconnectivity has led to two major changes in branding. First, brands are shifting away from single to shared ownership, as heightened access to information and people is allowing more stakeholders to cocreate brand experiences and brand meanings alongside traditional brand owners (or entities who market the brand). We call this phenomenon the "blurring of branding boundaries." Second, hyperconnectivity has allowed existing brands to expand their geographic reach and societal roles, while new types of branded entities are further stretching the branding space, which constitutes a "broadening of branding boundaries."
In this article, we elaborate on the consequences of hyperconnectivity on the "blurring" and "broadening" of branding boundaries. We focus on both challenges and opportunities that brands face in a hyperconnected environment. We first briefly review three core perspectives that have underscored traditional branding research and then examine the implications of this expanded view of brands through three fundamental questions: ( 1) What are the roles and functions of brands? ( 2) How do brands (co)create value? and ( 3) How should brands be managed? We provide initial answers to these questions and outline areas of inquiry for future research.
Many conceptualizations of brands have been proposed across various domains of inquiry, each with its own particular focus. We distinguish three theoretical perspectives (firm, consumer, and society) and two approaches within each perspective. The firm perspective views brands as assets and examines the various functions and roles that brands serve for firms, both strategically and financially. The consumer perspective views brands as signals (economic approach) and mental knowledge cues (psychological approach). The society perspective presents brands in societal and cultural contexts affecting individual consumers both directly and indirectly through social forces, structures, and institutions. The sociology of brands applies to all manner of commercial and noncommercial entities (e.g., ideas, people). We briefly introduce each perspective in this section while acknowledging the overlap and spillovers of knowledge across various approaches used in the literature. One source of this overlap comes from the nested structure that underlies how these perspectives relate to each other. For example, societal macroenvironments host institutions (including the firm) that, in turn, interact to shape consumer-level outcomes.
Key issues examined in this approach include the development and implementation of brand identity; positioning, targeting, launch, and growth of brands; brand portfolio architecture; and management of brands across geographic boundaries ([59]; [118]). Topics studied range from how to effectively construct and manage brand portfolios ([86]) to how to extend brands into new categories or as new brands, endorsers, subbrands, descriptors, product brands, umbrella brands, and branded differentiators ([138]). Marketing alliances of various types, including cobranding alliances and collaboration with customers, along with brand acquisitions and divestitures are strategic challenges and opportunities that have also been addressed in this literature.
Researchers leveraging a financial approach to branding have focused mainly on measuring the effect of brand equity and branding actions on the stock market value of firms. Specifically, one research stream has focused on demonstrating the relevance of consumer-based brand equity ([78]; [85]; [107]), while another stream has focused on measuring the stock market impact of corporate actions, such as brand extensions ([69]), brand and marketing alliances ([21]; [122]), brand acquisitions ([ 7]; [143]), and brand architecture decisions ([54]). [62] provide an overview of research that has examined the impact of brand actions on a variety of financial (and nonfinancial) firm outcomes.
Firms tend to know more than consumers about the quality of their brand. This information asymmetry has given rise to a field of study that treats brands as market signals ([35]). The brand extension literature has leveraged the information asymmetry–reducing role of brands to determine ( 1) how a multiproduct firm can brand a new product, ( 2) the relationship between the reputation of the new product and that established by the firm in other markets, and ( 3) the perceived quality of a new product ([34]; [142]).
[110] "consumer-psychology-of-brands" model summarizes the key concepts of the psychological approach, which proposes that brand equity resides in the minds of customers. Brand knowledge, which is the mental representation of brand awareness (recall and recognition) and brand image (types, favorability, strength, and uniqueness of brand associations), constitutes the key construct for conceptualizing and measuring brand equity from the customer's point of view ([61]). Other mental representations include affect and emotions, leading to constructs such as brand trust ([104]), emotional brand attachment ([132]), brand coolness ([141]), and even brand love ([12]). This perspective also includes brand experiences and defines them as including sensory, affective, and intellectual impressions as well as behavioral actions toward brands ([17]; [112]).
Researchers approaching brands from a sociological perspective focus mainly on brands as portable containers of meaning that are shaped by institutions and collectives from the time the brand is conceived, produced, and marketed, through the postpurchase stage ([95]). Sociological models are typically dynamic and recursive. Sociological scholars do not view brands as static entities or as mere information sources or knowledge structures; rather, they have a keen interest in how brand meanings are generated, changed, and dynamically reinvented. An important notion in this literature is brand community, a nongeographic space in which admirers of a brand connect with one another and demonstrate all three necessary distinctions of community: consciousness of kind, rituals and traditions, and moral obligation ([89]).
Both sociologists and anthropologists study culture, and their work in branding and marketing meaningfully intersects. One of the main insights from this research is that branded goods, as cultural meaning producers, enhance consumers' lives ([84]). Probably the best-known perspective on how brands become popular through their production of cultural capital is the widespread adaptation of [16] in consumer culture theory ([129]). Among other things, consumer culture theory addresses the dynamic relationships among consumer actions, the marketplace, and cultural meanings. Research in this stream focuses on how iconic brands ([52]), or brands infused with cultural referents ([22]), contribute to culturally bound consumption practices ([33]). Invoking the same dynamic, these scholars demonstrate how antibrand activists and other cultural intermediaries can introduce a competing set of brand meanings (e.g., doppelgaünger brand images) that can significantly influence consumer behavior and market creation ([44]; [128]).
We summarize key insights from each perspective (firm, consumer, and society) in Table WA1 in the Web Appendix. As this table is structured by substreams of research, it also illustrates how several of the theoretical perspectives have helped advance each area of inquiry. We next leverage these perspectives into a discussion of how the boundaries of branding have blurred and broadened in response to changes occurring in a hyperconnected environment. We revisit them subsequently as we advocate a multidisciplinary approach to address current opportunities and challenges in the branding domain.
We noted that the hyperconnected world is characterized by networks of people, devices, and other entities that are continuously interacting and exchanging information. Several aspects of hyperconnectivity are relevant to branding research and management ([135]). We highlight three aspects: ( 1) information availability and speed of information dissemination; ( 2) networks of people and devices, and the growth of platforms; and ( 3) device-to-device connectivity. We next examine each of these in greater detail.
The scale of information availability and the speed of information dissemination have grown exponentially as technology that connects people and devices has become widely available and more affordable. The broad and fast access to information calls into question foundational assumptions of several of the theoretical perspectives we discussed in the previous section. For example, high search costs and information asymmetry have been the core assumptions of the economic view of brands as signaling mechanisms ([35]; [142]). However, brands may no longer serve as primary signals of quality in an environment in which search costs are low and information asymmetry is reduced by various stakeholders who abundantly share opinions about brands across their networks.
Because access to information is much easier in a hyperconnected world, consumers need to expend less effort in learning information about brands. Models of memory activation and learning need to be updated to account for consumers' increased reliance on external sources of information as opposed to information that is retrieved from memory. Moreover, the sheer volume of external information can lead to information overload—a situation in which not all communication input can be processed and used ([18]; [113]). The velocity and volume of information that consumers are exposed to, along with the potential information overload that results from it, reduce brands' ability to capture the attention of their target segment ([74]), which calls for a reexamination of models of attention.
The rise of networks of people and devices and the development of platform technology have led to an environment in which brands and their meanings are cocreated. Firms are not the only entities that can disseminate branded information quickly and broadly—they now compete with other stakeholders who can do so just as easily (e.g., [71]). Brand conversations are happening online, and consumers may listen to their peers or to online influencers just as much as, if not more than, they listen to branded messages generated by firms. A growing stream of research is documenting the effect of this loss of control on brand meaning ([24]; [40]) and brand experiences ([100]).
Device-to-device connectivity has affected brands in several ways. First, branded experiences are significantly more complex in an environment in which consumers can access the brand via multiple channels that seamlessly connect with one another. Second, the heightened connectivity among devices has led to brands themselves being integral components of networks of smart products that are populating the Internet of Things ([50]). This again calls into question which entities contribute to brand meaning and associations, how these associations can be managed, and to what extent consumers anthropomorphize branded products that communicate with other products.
We next discuss how these manifestations of hyperconnectivity have resulted in a "blurring" and "broadening" of branding boundaries. We reexamine these issues as we highlight the changing roles and functions of brands, how they create value, and how they should be managed.
Traditionally, brand management methods were designed for a world in which consumers were exposed to (and influenced by) firm-controlled television, print, and radio advertising ([ 1]). Today, as information becomes available widely across multiple channels, consumers' attention is scattered across many media and channels, forcing a multitude of branded entities to compete to gain consumers' awareness and potentially to form an emotional connection. As brand meaning is increasingly cocreated, it is even sometimes hijacked by consumers and firm partners ([40]; [144]).
Beyond the cocreation of brand meaning, brand stakeholders (ranging from customers and employees to firm partners, communities, and society at large) are increasingly shaping various aspects of product and marketing-mix activities. For example, firms that use a platform-based business model often bring together partners that help cocreate the entire brand experience. Alternatively, firms that allow their products to interact with voice-controlled smart devices such as Amazon's Echo or with home automation hubs such as Wink Hub are also relinquishing some control, as brand associations can transfer to and from these partner brands to the focal one with potentially serious consequences for brands' performance and business survival ([145]). The blurring of brand boundaries is a key consequence of the rise of the sharing economy, which "offers temporary access as an alternative to permanent ownership" ([32], p.2), and which has expanded the role of customers to span both the demand and supply sides.
As marketers are losing some control over the meaning consumers associate with brands, more brand-related stakeholders are involved in shaping brand associations. Entities other than corporations (e.g., ideas, people) are becoming more systematic in their branding efforts ([39]; [131]), as hyperconnectivity has allowed them to easily reach multiple stakeholders around the world. The reach of both traditional brands and newer branded entities has also broadened to include stakeholders who have not necessarily been consistently targeted in the past, such as employees, donors, partners, citizen-voters, and activists, as well as local communities, governments, and society as a whole.
The role of brands has also broadened, with commercial brands increasingly expected to have a mission (or purpose) beyond shareholder value maximization. As societal norms change, companies are feeling the pressure to act in a sustainable manner or to take activist stances that may help society attain its goals or even support stakeholders who oppose such goals. Brand activism has led to new responsibilities for brand managers and chief executive officers, as well as the need to protect the large amount of customer data that companies collect in their efforts to manage brands.
Figure 1 maps the blurring and broadening of branding. It illustrates—using four types of brands and the stakeholders that shape them—the potential dilution of brand ownership (depicted by dotted circles in the picture) and the broadening of branding entities, brand roles, and brand stakeholders. Figure 1 also shows that brand meaning is more dynamic when brand ownership is more porous and that brand meaning is cocreated by the brand with its stakeholders.
Graph: Figure 1. Ownership of branded entities and changes in the branding landscape.Notes: The figure depicts interactions between various types of brands and their owners and primary stakeholders. Inner circles represent a specific type of brand; outer circles encompass the brand owners or main stakeholders. Dotted circles indicate more porous boundaries of brand ownership. We have labeled the brand owner and used arrows to illustrate whether the relationship between the brand and its owner/stakeholders is unidirectional or dynamic.
Next, we examine the shifts in branding across four broad headings: ( 1) rethinking the roles and functions of brands, ( 2) rethinking brand value creation and cocreation, ( 3) rethinking brand management, and ( 4) rethinking the boundaries of branding. As we highlight fundamental changes that are taking place, we also outline a future research agenda for each topic. As an example, Table 1 summarizes research topics that pertain to our first question relating to roles and functions of brands.
Graph
Table 1. Rethinking the Roles and Functions of Brands: Future Research Opportunities.
| Extant Research | Future Research | Research Questions |
|---|
| Brands as quality signals sent by firms | Brand meaning and quality perceptions crafted by market feedback. Weakened role of firm communications in shaping brand signals | Under what conditions do consumers still trust firm communications about brands (vs. information intermediaries such as Yelp, TripAdvisor, and Rotten Tomatoes)? What is the impact of firm-generated versus marketplace-generated brand information on brand trust? How can firms manage a brand communication process that faces significant signal interference? How should firms respond to signal interference, and what types of message content, form, and media placement have the best chance of reducing and counterbalancing potentially undesirable external brand signals?
|
| Brands as mental cues | Brand information processing models that account for information being conveyed in a multisensory way | How can researchers best conceptualize brand information processing when consumers deal with high volume and velocity of information? What is the role of sensorial brand information in models of information processing? How are brand meanings constructed under high volume and velocity of information? Can they be more easily manipulated? Under what conditions? What is the balance of voluntary versus involuntary attention in a context of information overload, and what type of cues can brands use to elicit voluntary attention? What are the structure and balance of text, image, and sound that can make a brand message stand out in a context of information overload?
|
| Brands as tools of identity expression and as relationship partners | Brands as tools of multifaceted identity expression and goal achievement | Can brand associations be constructed and revised to allow for a more flexible view of identity? Does a more flexible meaning lead to stronger long-term brand performance? Do consumers prefer that brands have a well-defined meaning that helps them anchor and retrieve one particular facet of their identity, or are they drawn more to brands with flexible meanings that accommodate a broader range of self-expression? How do social groups leverage brands to help them achieve their social goals? How can the strength of brand relationships be maintained and increased under information overload? How can brand anthropomorphism powered by artificial intelligence contribute to brand choice, loyalty, and trust and to building stronger brand relationships?
|
| Brands as cultural icons | Brands as containers of socially constructed meaning | How do different target segments respond to brands capitalizing on or opposing rising social trends? How can researchers conceptualize brands as purpose-driven entities? How can brand conversations and engagement foster greater social connections? What are the facets, determinants, and consequences of "social brand engagement" or the propensity of consumers to engage in meaningful connections using brands? And will this hurt the brand? How does social brand engagement play out for global brands in a world of diverging values and political and social fragmentation, as well as antagonism?
|
| Brands as individual entities or dyad partners | Brands as architects of value in networks | What strategies should brands adopt to enhance the effective functioning of the brand ecosystem? How can brands establish successful partnerships with other network participants, and what is the impact of platform design on maximizing positive and minimizing negative spillovers from partner brands? What are the main drivers of satisfaction with the user experience on brand networks, and how do consumers derive utility from a branded network? How can brands establish themselves into entities that can protect consumers from harmful content or products available on networks?
|
| Brands as tools of individual self-expression | Brands as catalysts of communities | How can branded entities facilitate the emergence of an online branded community, and what is the best way to enable, support, and help expand it? What are the mechanisms through which online brand communities create value for various stakeholders? What are the nature and quality of human interactions in the online space, and how can brand communities positively assuage loneliness and social isolation?
|
| Brands as neutral entities | Brands as arbiters of controversy | How does brands' participation in controversy (political, social) affect their customer base? How can brands maintain their authenticity and align their associations with social issues that are shaping current values and perceptions in society? What are some new metrics (e.g., polarization, controversy score) that can capture how to track the nature of conversation about brands in controversial settings?
|
| Brands as guarantors of quality | Brands as stewards of consumer data privacy | Should brands establish an association with strong guardianship of customer data, or would it be easier to partner with brands that have already built such associations? What type of safeguards can be established at every level of society to allow individuals and social groups to interact with brands and to provide them with sufficient information to strengthen the brand relationship, without adverse consequences, such as identity theft, loss of privacy, or economic losses?
|
Researchers investigating brands have construed brands in many different ways, including as signals of quality ([35]; [142]), knowledge structures held in an individual's memory ([57]; [61]), instruments of identity expression and goal achievement ([76]), social actors and structures of both stasis and change ([89]), and cultural icons ([51]). As technology has vastly improved access to information, products, and people, these existing conceptions of brands need to be reassessed—that is, refocused, rethought, or abandoned altogether. We discuss future directions for this reassessment in the remainder of this subsection (see Table 1).
In the past, when consumers were faced with information asymmetry and imperfect information, brands served as quality signals that facilitated consumer choice. However, in a hyperconnected economy in which consumers can easily access information about brands using online channels, information asymmetry between brand owners and consumers has decreased, as search costs are lower ([75]). Thus, a brand's quality signal could face interference from alternative signals of quality derived from the collective reviews and opinions available online.
Researchers need to understand the conditions under which consumers still trust firm communications about brands (vs. information intermediaries such as Yelp, TripAdvisor, and Rotten Tomatoes) and the impact of firm-generated versus marketplace-generated brand information on brand trust. How can firms manage a brand communication process that faces significant signal interference? How can brands shift the balance in their favor if the signals generated by the marketplace are mixed, reflecting a high level of consumer heterogeneity? What types of message content, form, and media placement have the best chance of reducing and counterbalancing potentially undesirable external brand signals?
Brand information processing (how consumers acquire, use, and remember brand knowledge) depends on consumers' motivation, ability, and opportunity to process information ([77]). Studies have proposed and tested multiple models of learning about brands, leveraging the notion that brands can act as cues that access knowledge held in consumers' memories ([57]). As consumers increasingly process information in a hyperconnected and attention-scarce environment, understanding of brand information processing may need to be augmented and extended ([146]). Existing dual-processing models (visual or verbal, heuristic or systematic, piece-by-piece or holistic; [114]) assume that consumers can switch back and forth in real time from one channel to another and from one processing style to another. As consumers utilize multiple devices and channels in the context of hyperconnectivity, dual-processing models need to be further refined to examine how and when people switch from one processing style to another and how simultaneous processing of information from multiple devices or channels occurs over time (e.g., when consumers engage in multitasking).
Brand information processing models should also consider how sensory information contributes to brand awareness and choice. The impact of sensory information on processing of brand information has been previously investigated (e.g., [66]; [67]). For example, [109] show that familiar brands are preferred in online settings (relative to offline) when sensory information is diagnostic and plays a role in brand choice. However, hyperconnectivity offers additional research opportunities in this area. Although many brands are experienced online in an arguably less sensorial environment, companies now have access to multisensory and highly interactive new technologies that appeal to multiple senses (sight, hearing, touch, taste, and smell) simultaneously. For instance, sensory-rich retail environments, as well as augmented and virtual reality, allow people to experience the power of multisensory stimulation and consider brands in environments that may be dynamic and virtual. Online stores such as Wayfair, Amazon, and Target have launched mobile augmented-reality apps that allow consumers to shop for furniture by placing items in their own room settings ([97]). In sensory-rich environments, the voluntary attention to brand stimuli and sensory cues may be more difficult to elicit and more likely to be replaced by involuntary attention or arousal to stimuli unrelated to the focal brand. Therefore, current models need to be revised to incorporate the paucity of consumers' voluntary attention and also account for the possibility that the role of sensory cues may change how brand information is processed.
Brands can be used to support a desired consumer identity and may be associated with humanlike traits (referred to as "brand personality"; [76]). The identity view of brands is based on the notion that brands become imbued with associations through their use. For example, consumers can use these brand associations in an instrumental way to construct and signal something about their own identity ([ 2]), whereas employees can use corporate brands owned by their employer to convey professional status or to send a signal about their expertise ([124]). The identity view has more recently focused on the complex nature of identity, with brands being tied to multiple individual and group-based identities ([36]; [123]), as well as being symbolic of culture ([133]). The consumer culture literature has also considered the instrumental role of brands as authenticating narratives for consumers' "identity projects" (e.g., [128]).
A consequence of hyperconnectivity is that consumers can now adopt multiple personae on their devices and change their identities frequently ([134]). As consumers increasingly spend time online, there is the potential for their online (or "virtual") self and their offline (or "true") self to diverge, possibly leading to identity conflict ([121]). The multiple (sometimes conflicting) dynamic identities of consumers, particularly across online and offline settings, require rethinking about how to leverage brands to build identities ([106]). Can brand associations be constructed and revised to allow for a more flexible view of identity, or would this create confusion from the perspective of the user or those who view such signals? Would consumers prefer that brands have a well-defined meaning that helps them anchor and retrieve one particular facet of their identity, with greater potential for sending stronger signals about a particular self, or would they be drawn more to malleable brands that accommodate a broader range of self-expression? A multidisciplinary approach that leverages both the consumer and society perspectives could examine the manner in which social groups can use brands in instrumental ways to achieve their social goals.
Finally, hyperconnectivity can facilitate access to a broader set of brands in the digital space that can be used by consumers to express their identity. This would afford consumers a richer set of identity-building tools. At the same time, digital possessions have been shown to have lower self-relevance ([11]), which calls into question the strength of identification with brands in the digital space. Additional questions center on how consumers choose brands to identify with in this online, hyperconnected environment and the nature of relationships that consumers form with brands.
Researchers using a sociological lens have examined the role of brands as arbiters of social trends, as catalysts for social interaction, and as societal symbols in the case of brands that attain iconic status (e.g., [51]). Brands are expanding their social role, however, by increasingly becoming activist tools aligned with various social and political issues. Purpose-driven branding argues that brands should uphold societal values because doing so gives consumers an opportunity to use them in instrumental ways to show support for social causes. For example, Procter & Gamble's recent Gillette "The Best Men Can Be" and "My Black Is Beautiful" campaigns took controversial positions with regard to gender and racial stereotypes, respectively ([58]).
It is tempting for brands that want to remain relevant, particularly in the eyes of millennial consumers, to take a stance on important social issues, but prescriptions on how to do so effectively are lacking. Yet it is increasingly clear that "the future of brands is also inextricably tied to the future of society" ([25], p. 246), and brands can act as vehicles for bringing about social change. As hyperconnectivity can amplify brands' social message, scholarly insights are necessary to understand the role of brands as purpose-driven entities and how firms can best align the social message of brands with the desired brand associations. The consumer and society perspectives could be utilized in exploring the implications of brand conversations and brand engagement for stronger social connections, as well as other determinants and consequences of this "social brand engagement" ([65]).
Marketing scholars have extensively investigated brand partnerships and alliances from a dyadic perspective ([98]; [116]). However, in a hyperconnected environment, brands are increasingly embedded in complex networks consisting of users, partners, cocreators, and co-owners. Prior research that has examined the performance impact of networks on value creation at the firm level ([122]) can be extended to show how brands can extract value from their position in the network.
Leveraging network theoretic constructs to examine value creation in branded networks is a useful avenue for future research. Brands can provide value in networks in at least two ways. Due to advanced search and navigation capabilities, brands can simplify users' navigation through brand-embedded networks, thus contributing to seamless user experiences on online platforms. Furthermore, brands can create value by ensuring compatibility across branded entities in the network, in terms of both attributes and quality standards.
A case in point is Apple, a company that strives to make its products compatible with a broad range of complementary devices and ensures that all applications sold in the App Store meet its stringent quality standards. But not all brands can easily extend their networks; for instance, Google failed to successfully launch Google Health, a platform where consumers could consolidate their health information and interact with providers. As noted by [136], participants on both sides of the platform were not ready to engage at a level that would render the platform successful.
Additional research is necessary to identify strategies that brands can adopt to enhance the effective functioning of the brand ecosystem. The traditional conceptualization of user experience that relies primarily on product or service usage could be broadened to include interactions across an entire network or ecosystem that is linked to a specific brand. We term this "brand network user experience." A better understanding of the brand network user experience and how consumers derive utility from such a network is warranted. Finally, brands can not only help organize informational content on networks but also serve as gatekeepers of information (or products) they want consumers to see or purchase. The ongoing spread of "fake news" on social media platforms highlights the potentially critical (and controversial) role of branded platforms as entities that censor harmful content or products; how brands can achieve this role and perhaps derive value from it should be the focus of further research.
Researchers have examined brand communities and how they deliver value to their members ([89]) and affect the cocreation process. In this context, brands serve as catalysts of social interaction and community through shared consciousness and brand use, loyalty, and engagement among community members ([60]). Hyperconnectivity has increased the potential for individuals to establish and join brand communities, be it newer types that are appearing on social networks or communities established by traditional brands online, such as the Sephora or Jeep brand communities, in which users post reviews and share information about brands and their new products.
New research on how brand communities emerge in a hyperconnected environment and what type of governance can make such potentially large communities successful is a worthwhile avenue for research. Branded communities can create value not only by enhancing the brand experience for users but also by providing firms with a forum to test out new ideas, collect feedback on brand actions, and better understand how brands are consumed. Brand communities may also offer social benefits, particularly in the context of growing social isolation and "aloneness" in society (e.g., [134]). Thus, it would be valuable to understand how brand communities can help combat loneliness despite the larger scale of social connections facilitated by hyperconnectivity.
In addition to the role of brands as signals or nodes in memory, brands have been construed as symbols ([70]). These symbols could be used in various forms of self-presentation to give meaning to consumers and their social position (e.g., [46]). While not denying their other functions, [70], p. 205) states that "things people buy are seen to have personal and social meanings" and that people use branded symbols to reinforce their view of self (both actual and ideal).
When brands are strong enough to serve as symbols, some also transition into roles of arbiters of controversy within the identity and sociopolitical realms. These brands often adopt controversial stances on key topics such as feminism, LGBTQ+ rights, and racial issues and, in doing so, appeal to consumers at the epicenter of cultural controversy. Nike's use of Colin Kaepernick's cause was widely debated but is now deemed as having galvanized thought on the issues of social justice and race in sports, particularly considering that such a large percentage of professional athletes are nonwhite. Nike was awarded the 2018 Marketer of the Year for the campaign by Advertising Age ([99]), and Nike's profits and stock valuations have climbed in the aftermath of this controversial stance. In this way, brands appear to have a stronger voice in a hyperconnected world, in which their messages on social and political issues can quickly spread and multiply. With a stronger voice comes the added responsibility of addressing important social issues in ways that can help society move forward.
Hyperconnectivity has not only afforded brands the opportunity to have a stronger voice but also reduced their ability to stay "above the fray" on controversial topics. Absent a stance on such topics, consumers with deep brand connections may question the authenticity of the brand. Research has shown that perceived brand authenticity has four dimensions: continuity, credibility, integrity, and symbolism ([88]). Integrity, which captures a brand's intentions and the values it communicates, along with symbolism, which reflects values that are meaningful to consumers, are particularly relevant in situations where brands face conflict. Failing to take a clear stance may lower perceptions of brand integrity and foster identity conflict among those consumers who use the brand in symbolic ways to communicate certain values. At the extreme, activist consumers can punish passive brands: for example, activist consumers and movements fueled by them, such as #GrabYourWallet, have caused retailers to drop certain brands or companies to drop their top executives, as was the case with the #DeleteUber movement, which led the then-chief executive officer of Uber—Travis Kalanick—to step down from the company ([13]). In this way, brands that try to avoid conflict can become the target of activist consumers who yield a louder megaphone in a hyperconnected environment.
Research in the context of brands as arbiters of controversy is only in its infancy. Many questions remain unanswered. For example, how does brands' participation in controversy (political or social) affect their customer base? How do message content and the emotional tone of messages in a controversy affect brand perceptions? How does brands' participation in controversial issues propagate on social media, and what is the role of the structure of their online social network (e.g., network centrality, network density) in the spread of brand information following a crisis incident? What new metrics (e.g., polarization, controversy score) can track the nature of conversation about brands in controversial settings?
As brand boundaries become blurred, data that brands can access about their customers have increased, shedding new light on what drives consumer attitudes and behavior. These data are a potential source of value for branded entities, which can use them to better target and customize their offerings to consumers. At the same time, increased access to customer data poses legal and ethical challenges, as consumers have grown more concerned about the confidentiality and security of their data ([120]). Brands that have misused customer data (or suffered from data breaches) have faced significant backlash and penalties from consumers and public policy officials.
Policy makers and consumers have begun emphasizing the important role of governmental regulations in moderating privacy concerns. Concomitantly, scholars have shown greater interest in understanding the implications of recent privacy regulations (e.g., General Data Protection Regulation in Europe) for both customers and firms ([47]). As data themselves become a resource, understanding the implications of privacy concerns for brands and the trade-offs inherent in achieving greater personalization through data versus ensuring data privacy is a topic that needs greater research attention ([ 5]; [80]).
Even as regulations pertaining to data usage become more stringent, brands should take the lead in ensuring compliance with data standards both for themselves and to ensure transparency across all third parties that belong to their ecosystem. While taking such a stance would strengthen their value proposition, more research is needed on understanding how branded entities should enforce data standards throughout their ecosystem and the downstream consequences of such strategic actions on consumer response to brands. Theoretical insights from the firm and consumer perspectives can be leveraged to understand how brand associations change if the role of brands as stewards of data becomes more salient, and to what extent this role can strengthen the relationships between brands and their customers.
We previously alluded to how hyperconnectivity has led to brands and their messages no longer being exclusively shaped by their owners. In this subsection, we elaborate on how firms can cocreate brands' experiences and meaning with their stakeholders, including customers, partners, and the public at large. We summarize pertinent future research questions in Table 2.
Graph
Table 2. Rethinking Brand Value Creation and Cocreation: Future Research Opportunities.
| Extant Research | Future Research | Research Questions |
|---|
| Designing the brand experience | Cocreation of brand experience | How much control should firms relinquish over the brand experience while still maintaining the desired associations, and to what extent should the firm formalize the cocreation process? Should all consumers be given an opportunity to cocreate, or should the firm work to identify classes of consumers who can yield better outcomes for the firm? Are certain brand types more malleable to cocreation efforts? What are the consequences (i.e., benefits and risks) of cocreated brand experiences on brand outcomes such as brand attachment and loyalty? On branded platforms, how should firms design an optimal governance mechanism that will still allow them to maintain reasonable control over brand reputation and meaning? How can firms influence platform participants to reinforce the brand message and deliver a brand experience consistent with this message?
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| Designing firm communications that shape and reinforce brand associations | Cocreation of brand meaning | What is the optimal balance of firm-generated content versus user-generated content that can enhance consumers' perceptions of brand authenticity and increase their willingness to engage with a brand? Which online platforms or media can shape and significantly influence the meanings associated with particular brands? What are the best methods and metrics that can mine the data collected from these sources? How are branded ideas collectively constructed and refined in society through powerful social media platforms?
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| How to design brands that operate independently and are under firms' control | Creating new brands that dynamically interact with the firm postpurchase, with their users, and with other products | How should managers retain control of the design, promotion, and equity of branded products that interact with complementary products manufactured by other firms? To what extent will consumers allow firms to collect data about product usage in exchange for firms' ability to update branded products as technology improves? What structures can be put in place to more effectively use these data and feed it into the redesign process? What goods and services can benefit more from a change of business model from consumer ownership to access-based and occasion-based ownership? How will brand equity accrue and be measured in an environment where the brand ownership model is complemented by access- and occasion-based liquid consumption?
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Perhaps the most significant change in how brands deliver value in the digital economy is the cocreation of brand experiences with consumers or partners. So far, cocreation research has focused mostly on new product cocreation ([53]; [137]), but recent work has begun examining the cocreation of brand experiences ([94]).
Importantly, cocreation of the brand experience often occurs on digital platforms, defined as enterprises that "use the Internet to facilitate economically beneficial interactions between two or more independent groups of users" ([29], p. 8). Examples of digital platform brands are Uber, Airbnb, and LinkedIn, among others. Most digital platforms are branded, and their brand associations result from the interactions of multiple players who contribute to the creation and delivery of the brand experience. For example, property owners on Airbnb contribute to the success of the brand experience as much as the technology that enables this platform. Emerging research on platforms has focused primarily on the structure of such exchanges ([100]) and how to monetize them ([68]) rather than on how the interactions between the firm and its stakeholders shape the brand.
Recent research highlights the unique drivers of engagement for platform brands ([32]), but there are other important branding aspects of cocreation on digital platforms worthy of further research. For instance, how should firms design an optimal governance mechanism that will still allow them to maintain reasonable control over brand reputation and meaning? How can firms influence platform participants to reinforce the brand message and deliver a brand experience consistent with this message? What differential branding benefits (e.g., awareness, preference, resonance) accrue from the various types of platform designs (e.g., open vs. closed, centralized vs. decentralized)? Are branded platforms in the shared economy disrupting employment, and if so, what are the societal consequences of these disruptions and subsequent impact on the brand?
More generally, researchers should try to understand how much control firms should relinquish over the brand experience, while still maintaining the desired associations, and to what extent firms should formalize the cocreation process. Should all interested consumers be given an opportunity to cocreate, or should the firm work to identify segments of consumers who can yield better outcomes for the firm? By contrast, how should firms interact with the segment of passive, low-involvement consumers, and what are the consequences of cocreation by other consumers in this particular segment? Are certain brand types more malleable to cocreation efforts? What are the consequences (i.e., benefits and risks) of cocreated brand experiences on brand outcomes such as brand engagement?
For traditional brands that are incorporating digital platforms and channels into their strategy, there are interesting questions pertaining to the consumption experiences. For example, it is worth examining how brands effectively can effectively merge online and offline experiences. Theorizing the brand cocreation process from the firm perspective (how firms should manage and respond to cocreated brand experiences), the consumer perspective (how consumers cocreate and react to brand experience cocreation), and the society perspective (how cocreated brand experiences contribute to well-being and life satisfaction) could offer important insights.
Companies not only cocreate brand experiences with their customers, but some are even offering consumers the opportunity to design brand advertisements ([118]). While research has begun examining the cocreation of brand meaning through outsourced advertising campaigns ([130]), extensions of this work should address various facets and consequences of outsourcing the design of brand communications to consumers ([139]).
Even if firms wish to maintain full control over how they design and promote brand meaning, the rise of social media has led to weakened firm control over the brand meaning in the marketplace ([48]). Many-to-many communications on ubiquitous social media platforms have ushered in an era in which dynamic and real-time conversations are taking place among consumers on a massive scale ([15]; [126]). This has created a large volume of user-generated content that has made it easier for marketers to "listen" to consumers on social media platforms. Such listening has allowed marketers to derive unique insights into customer needs and wants, thus allowing them to replace costly traditional marketing research with low-cost, granular data available through social listening via user-generated content ([90]). At the same time, research has highlighted the amplification of positive and negative information in social media and its effects on brands ([49]).
Understanding the implications of consumer-generated content on downstream outcomes, such as customer engagement, and the motivations for consumers to engage in generating word-of-mouth communications are two broad areas that have garnered research attention ([10]; [72]; [82]). Further research should build on these findings to determine the optimal balance of firm- versus user-generated content that can enhance consumers' perceptions of brand authenticity and increase their willingness to engage with a brand. All three theoretical perspectives can be leveraged to understand how consumers respond when brand meaning is cocreated by others. Finally, additional research is necessary on which online platforms or media can shape and significantly influence the meanings associated with particular brands, along with the best methods and metrics that can mine the data collected from these sources.
Extant research on the determinants of new brands' performance has focused mainly on the consumer packaged goods industry and on marketing-mix and firm-strategic actions, such as discounting, feature/display, advertising, or distribution, with the breadth of the latter playing the greater role in the success of a new brand ([ 6]). Going beyond packaged goods categories, further research should focus on branding aspects in emergent categories of new goods and services. The breadth of new offerings, ranging from digital platform brands (e.g., TaskRabbit), to smart products (e.g., artificial intelligence toothbrushes such as Ara by Colibree), to new business models powered by new technologies (retail stores without a cashier such as Amazon Go), requires a fresh perspective on how to brand and manage complex products that interact in newer ways with their owners and with complementary products than more traditional products.
What are the key success factors of such new branded products? Owing to disintermediation (involving switching or elimination of traditional intermediaries in distribution channels) and reintermediation (addition of new forms of intermediaries in distribution channels), we expect traditional channels of distribution, and related factors examined in prior research, to be less important in the future ([43]). Rather, success will likely depend on the degree to which a brand can leverage hyperconnectivity among networks of people and devices. One manifestation of the easy access to consumers is the proliferation of direct-to-consumer brands such as Bonobos (men's clothing), Harry's (shaving), Glossier (cosmetics), and Warby Parker (eyewear). Yet direct-to-consumer brands do not only leverage hyperconnectivity by selling their products online; rather, they try to provide a distinct brand experience that sets them apart from their brick-and-mortar competitors by tapping into their network of consumers. [79] describes how Glossier aimed to make the customers an integral part of the buying experience. Glossier invites its customers to share product ideas and incentivizes them to share their Glossier brand experiences with their followers across social media channels such as Instagram. In this way, Glossier's network of customers are evangelists for the brand.
Three facets of hyperconnectivity are particularly relevant in the context of new brands: networked communications, network value creation, and data collection. In terms of networked communications, information and positive word of mouth about innovative new products can spread quickly via global social media and buying platforms, thus securing early adopters faster than ever before ([92]). Network value creation in a hyperconnected world will require smart products that connect with other products and provide value as a network or system; that is, they need to be "connectable" and connected with other brands ([93]). Brands have formed partnerships and alliances for decades to create add-on value; in the future, connecting with other brands will be at the very core of a brand's value creation and will precipitate the growth of a variety of related applications, such as augmented reality, blockchain, wearable technology, chatbots, and gamification ([115]). Finally, new brands are more likely to be perceived as innovative and valuable for the firm, consumers, and society if they collect data and make those data, or the features built on them, available to relevant stakeholders. For example, streaming services (e.g., Netflix, Hulu) leverage data gathered from consumers to recommend new shows to their audiences. Scholars should investigate ways in which data can be leveraged to be a central aspect of the value creation of a new brand. For instance, the brand Proven was created by applying artificial intelligence algorithms to vast quantities of data pertaining to ingredients, customer reviews, and scientific journals with the goal of identifying the most effective ingredients and creating a customized, data-driven line of skin care ([125]). In doing so, data can not only help improve firms' operations but also, if done responsibly, be of benefit to society.
If the ability to connect to other brands is key to a new brand's success, how should the brand be built? Product designs of the brands of the future will need to include sensors and receptors to leverage the device-to-device connectivity as discussed previously ([93]). Researchers need to determine conditions under which it is more effective for brands to establish their own ecosystem, as Apple has done, or plug into another one that allows them access to consumers and other brands. Moreover, brands of the future—whether smart home appliances, office equipment, cars, or industrial products—will not be fixed and static; they will be updated constantly, some even in real time, because their core design will also include software, not just hardware. As noted by [111], the digital revolution has moved the focus of brands "from atoms to bits" (i.e., from tangibles to intangibles), but the next wave of this revolution may involve incorporating smart technologies such as artificial intelligence, blockchain, and augmented and virtual reality into brands. Some of these technologies require deeper access to consumers' lives that may be perceived as intrusive. The extent to which consumers are willing make a trade-off between privacy concerns and the ability to have their products updated and maintained in real time is an interesting area of research inquiry.
Finally, new brands will not be about ownership. The ownership model will likely be complemented by access- and occasion-based liquid consumption ([11]; [32]). The very notion of brand as a clearly defined entity that needs to create awareness and image among stakeholders to induce loyalty among them may even become obsolete and be replaced by a more transient model of value creation among interconnected devices that do not require much labeling and branding. If the importance of brands diminishes, what mechanism will replace brand equity to induce loyalty and trust in consumers? What factors will determine product choice in this new world? The new reality of branding described in this section calls for significant changes in the management of brands, which we examine next.
In this section, we discuss several key aspects of brand management that have been affected by hyperconnectivity. Table 3 presents pertinent future research questions in this domain.
Graph
Table 3. Rethinking Brand Management: Future Research Opportunities.
| Extant Research | Future Research | Research Questions |
|---|
| Brand positioning and message controlled by firm | Brand message as an output of multimedia online consumer communications | What types of stakeholders exert more influence on brand communications, and what metrics best capture this influence? How can brand communication by outside stakeholders be incorporated into firms' brand strategies, and should it be? What are the best metrics to mine and extract insights from multimedia social media data related to brands? How can static and video images and sounds be mined for branding insights?
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| Brand crises limited to product failures | Brand crises resulting from the behavior of a broader class of stakeholders | What are the best governance and response mechanisms that can minimize the loss of brand equity in the event of stakeholder nefarious actions? How much blame do consumers attribute to firms in brand crises depending on which stakeholders cause the harm, and how does this influence their brand relationship going forward?
|
| Brands as identifiers of static consumption objects | Brands as identifiers of intelligent, interactive, and networked devices | How can artificial and virtual reality create unique brand experiences for consumers before, during, and after purchase? How should firms brand and manage nontangible information and nonhuman, but humanlike, autonomous agents? What is the best way to design branded experiences delivered with the help of artificial intelligence and robots, while keeping safety and privacy concerns in mind?
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| Brand value measured as customer-based brand equity, price premium, or change in stock market value | Develop new metrics of stakeholder value that can take into account the blurred ownership of brands | Is it possible to develop an internationally recognized, standardized way to measure "traditional" brand equity? How can the marketing academic community contribute to putting brand value on the balance sheet? How can the value of platform brands that derive their value primarily from networks be measured? What are the best metrics and time horizons to assess stakeholder value? Can firms increase value for various stakeholders without damaging shareholder value?
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Before the advent of the internet, firms chose the positioning of their brands and sought to achieve it through carefully controlled and designed communications. Hyperconnectivity has fundamentally changed the way firms both position their brands and talk about them. Firms now must contend with three major changes. First, their competition has significantly broadened as connectivity has heightened consumer access to a large set of brands. Second, the internet and mobile devices offer a much broader space and more ways to communicate brand messages, making it more difficult to optimize message placement ([47]). Second, as we alluded to previously, firms' brand communications are supplemented and may even be dwarfed by those generated from outsiders (e.g., [126]). Thus, firms' brand messages may be not only blurred but also substantively modified by brand opinions generated by outsiders.
In terms of positioning, branding's pervasiveness across products, people, places, and ideas makes it more difficult for firms to clearly delineate their competitive space and to find a unique message that cuts across digital clutter. At the same time, just as consumers have access to numerous brands in the digital space, firms' potential consumer base is also increasing, which raises important questions about how firms should handle consumer heterogeneity and to what extent they should modify their message and offering to cater to such heterogeneity. Prior research has shown that differentiation is a double-edged sword, as it is associated with both higher customer profitability and lower acquisition and retention rates (e.g., [117]). More research is needed on strategies that can enable companies to wield this sword more effectively. This is of particular relevance in a hyperconnected context where consumers may find it impossible to engage deeply with the multitude of brands they encounter.
In terms of the design and placement of brand messages, firms face an almost infinite number of advertising formats and channels. However, the effectiveness of these ads is being questioned, as firm communications in general and brand messages in particular are fighting information overload. Many firms are coping with the complex task of ad placement by relinquishing some control to algorithms that help with ad placement. This also has potential downsides, as ads may be placed alongside content that is deemed unsafe or controversial. Research on the consequences of brand advertising online and on mobile apps is emerging (e.g., [31]; [140]), but it is far from being able to provide clear prescriptive implications about the consequences of advertising for brand safety in a given digital space. How do consumers react if a brand inadvertently advertises on a webpage that is associated with content (such as hate speech) consumers may find offensive? What are some effective approaches to ensuring transparency throughout the advertising and media supply chain so that brands can evaluate and enforce safety of their advertised content? More research is needed to examine these questions in greater detail.
We highlighted the cocreation of brand meaning as one of the main consequences of hyperconnectivity. Firms are still searching for optimal ways to monitor, gather, analyze, and respond to brand information generated online. Many firms are investing in social media–listening control rooms or in engagement platforms that can help them better manage brands on an ongoing basis ([105]). From a capability standpoint, future research should offer stronger insight into how data scientists (responsible for gleaning insights from social media listening) can work with brand strategists within a firm to design optimal communications that leverages the insights from a continually evolving in online conversation spaces.
From a brand management standpoint, firms need a consistent approach for identifying which consumers have more influential voices, what metrics best capture this influence, and what is the best response (in terms of message and medium) to shifting brand associations driven by outside stakeholders. This topic has recently received greater attention in scholarly research (e.g., [48]), and it is generating more interest in what firms can do to manage the conversation. As brand dialogues evolve in online conversation spaces, the effectiveness of firms' engagement in these dialogues will determine the extent to which they can control the brand message, at least in part. Scholarly insights into how firms can optimally design metrics dashboards are also necessary to ensure that firms can quickly disseminate relevant insights gleaned from big data. Finally, research needs to develop theory-rooted risk mitigating strategies that will allow brand managers to identify and correct deviations from the main brand message when these become prevalent in online conversations.
The more porous ownership of brands carries with it the risk of crises arising from the actions of platform partners. Digital platform brands as well as traditional branded entities operating in a hyperconnected world must design and implement governance and response mechanisms that can minimize the loss of brand equity, when even trusted stakeholders such as firm partners and employees can take actions that hurt the brand. Branding researchers should therefore conceptualize and empirically test the effectiveness of governance mechanisms that safeguard against stakeholders' nefarious actions. Building on prior findings ([28]), research should evaluate the impact of various types of brand crises (precipitated by different stakeholder groups) on how consumers' attribute blame across platform partners.
Hyperconnectivity does not stop at country borders ([30]). Consequently, novel metrics for tracking both short- and long-term impacts of brand crises on both a local and an international basis and across both financial (e.g., stock market reaction) and nonfinancial (e.g., brand trustworthiness, engagement) factors represents a fruitful area of research inquiry. Identifying the structures of brand teams that will facilitate optimal responses to brand crises will also be an important area for further research. Finally, understanding the implications of brand crises for employee engagement and satisfaction is also a key research issue.
Radical new technologies have transformed brands from mere consumption objects to intelligent, interactive devices. As the next wave of the digital revolution is taking shape, the Internet of Things will allow branded devices to interact and exchange information with one another ([50]; [91]). Brands are mostly trademarked goods; in the context of hyperconnectivity, it is even more important to monitor how they are presented and identified across a broad range of devices, settings, and channels. Some of these settings could be outside brand managers' control, raising questions about the best way to manage the operations and promotion of brands that operate as part of networks of products.
The rise of artificial reality (i.e., the creation of an interactive experience of a real-world environment through computer-generated displays) and virtual reality (a complete simulation of the environment also has a bearing on brands and their boundaries. Researchers need to understand how these technologies, which are able to seamlessly blend the real and virtual worlds, can create unique brand experiences for consumers before, during, and after purchase. Entertainment brands (e.g., video games such as Pokémon Go) and museums (e.g., Metropolitan Museum of Art) have combined aspects of real and virtual worlds to maximize the user experience across online and offline channels. The advent of artificial intelligence and its physical substrate (robots ranging from chatbots to full-fledged humanoids) raises questions of how to brand nontangible information and nonhuman, but humanlike, autonomous agents and how to use artificial intelligence as part of brand decision making and service delivery.
Traditional brand valuation methods revolve around consumer-based brand equity, often measured with survey instruments such as the Brand Asset Valuator, the revenue premium that accrues to the brand, and brand discounted cash flows ([27]). These methods need to be updated to reflect the role of brands in a hyperconnected world. For example, as brands are increasingly deriving their appeal from cultural meanings, some pillars of brand equity (e.g., meaningfulness) may become more important than others (e.g., salience) ([39]). A revenue premium–based approach to assessing brand equity can still help assess short-term brand value but might not adequately capture the extent to which some brands may be better connected with their customers than with other stakeholders.
However, more exciting research challenges may be found in valuing brands that are born in and directly leverage hyperconnectivity—in particular, platform brands. How to value these brands, which typically have limited assets and sometimes little income, is both a managerially and academically important question. Practitioners' evaluation of platform brands varies significantly. For example, in 2018 Facebook had a brand value of $162.1 billion according to BrandZ versus only $45.2 billion according to Interbrand. For Netflix, the numbers were $20.8 billion and $8.1 billion, and for Spotify $15.7 billion and $5.2 billion, respectively.
One approach to measuring the value of these types of brands would be to use the standard financial approach: brand value = current profit/(interest rate − profit growth rate). However, this standard approach may not be valid for networks, particularly if the network does not yet have any profits or if the profit growth rate exceeds the interest rate. Another approach suitable for subscription-based businesses is to calculate the customer lifetime value for each network member from his or her own discounted cash flows ([45]). This alternative is based on customer equity theory ([108]) but may not be particularly robust in a world in which people can easily cancel or change subscriptions.
Recent approaches to brand valuation in the context of networks take the user base into account, assuming that networks are more valuable if the social capital of its members is higher ([ 4]). [16], p. 248) defines social capital as "the sum of total resources, actual or virtual, that accrue to an individual (or group) by the virtue of being enmeshed in a durable network of more or less institutionalized relationships of mutual acquaintance and recognition." Thus, the value of a brand that operates on a network increases as a function of current profit and size of the user base, but also with the social capital and social structure of its user base. However, consensus is lacking on the functional form of this relationship ([148]) and, in particular, on how to incorporate the intangible value that resides in the relationships between users into the value of the platform brand. Further research is necessary to understand whether social capital should be measured at the individual level and then aggregated to the network or directly at the network level to capture unobserved synergies in social capital. In addition, the role of the quality of social capital needs further examination. Bourdieu's "relationships of mutual acquaintance and recognition" may be weak in networks such as LinkedIn, in which connections may be distant and nonconsequential. A more contemporary view on social capital seems essential to future research (e.g., [37]) and acknowledges branding to be of societal importance beyond the goals of the individual marketer ([102]).
Another important component of the valuation of brands on networks is the structure of the network. What kind of structure is more valuable? Is a tight-knit, cohesive structure (niche strategy) with many redundant ties more valuable ([26]), or a sparse network with few redundant ties (undifferentiated market saturation strategy), which facilitates wider diffusion of information ([19])? Finally, researchers focused on this type of valuation will need to determine the relative importance of the various inputs (current profit, user base, social capital, and network structure) and assess whether their weights may differ by industry and type of network. Measuring brand value of idea and person brands also poses unique challenges that marketers need to address.
In recent years, branding research has broadened its scope to include brands from emerging countries ([83]), branding in a digital environment (e.g., [126]), and the branding of new entities such as place, organization, idea, and person (e.g., [41]; [119]). We chose to focus primarily on three types of noncommercial, nonmarketplace entities—ideas, people, places—because these entities have leveraged hyperconnectivity in unique ways to attract large numbers of followers, but we also briefly refer to branded organizations. Table 4 presents illustrative future research questions that can advance knowledge on newer branded entities.
Graph
Table 4. Rethinking the Boundaries of Branding: Future Research Opportunities.
| Branded Entities Studied in Prior Research | Branded Entities in Need of Further Study | Research Questions |
|---|
| Commercial product and service brands, celebrity brands, corporate brands, place brands in the tourism literature | Idea brands | How do branded ideas emerge and spread? What are the factors impacting the diffusion of positively versus negatively valenced ideas? How are associations with branded ideas formed and what is the role of opinion leaders? How do mixed societal responses contribute to shaping these ideas brands and what constituencies play a greater role in refining the idea brand message? How can the authenticity and strength of idea brands be assessed and measured and what are the factors that contribute to this authenticity?
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| Person brands | How can noncelebrities build a person brand with clear brand associations that can help them attain specific personal or professional goals? What are the psychological consequences that consumers can experience from managing their personal brand, including the potential for higher narcissism, self-promotion focus, and individualistic tendencies?
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| Place brands | How can places promote their brands without increasing the conflict between various classes of stakeholders, such as the residents and visitors of a city? What are the implications of social density and social class perceptions for place brands? How can place brands mitigate the negative consequences associated with unfavorable social, economic and climate changes?
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| Organizations as brands | How can organizations create and deliver a purpose within society that aligns with their brand associations and mission? How can organizations manage brand associations faced with an increasingly large set of relevant stakeholders that can contribute to these associations, from consumers and employers, to activist investors and other organizations? What drives brand perceptions for organizations that serve the common good and what could disrupt the branding efforts of infamous organizations?
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We evaluate idea brands as an example of a newer branded entity, though ideas have had a long history of being branded. For example, ideologies such as Puritanism, Calvinism, communism, and neoliberalism have also leveraged branding to gain adherents and to articulate their principles in a consistent manner. The American Dream, the New Deal, Reaganomics, and the Green New Deal are examples of branded initiatives originating from political entities and parties who make concerted efforts to obtain support, gain followers, build emotional relationships, and raise funds. We define idea brands as ideologies, initiatives, or other abstract, noncommercial notions that are identified by their stakeholders and the public at large using the same specific name. In other words, a set of ideas becomes branded when its stakeholders and the public at large use a specific label to refer to it, to affiliate with it, or to promote it. Idea brands span many domains—social, political, cause-related, and religious—and can evolve across domains; for example, cause-related ideas can morph into social causes as their popularity and supporter base increase ([38]). They are relatively ephemeral and highly dynamic entities, susceptible to hijacking, change, or even elimination. The dynamic nature of idea brands is further exacerbated by hyperconnectivity, as online social networks and platforms can sanction and spread them, reshape them, or oppose resistance to them.
In light of these important differences, additional systematic research is required on how, when, and why certain idea brands are more successful than others. The hyperconnected era has increased the speed and scale at which ideas are disseminated, creating an urgent need for new models of how ideas diffuse and when and how ideas morph into social movements. Researchers could combine insights from research on diffusion and contagion theory ([23]) with elements from sociological theories related to social movements to elucidate the spread of different types of ideas (e.g., political, religious, social). An in-depth examination of the distinct ways that good ideas spread (e.g., humanitarian causes) relative to bad ideas (e.g., terrorism, racial intolerance) is also necessary. Furthermore, the consumer and society perspectives could come together to elucidate the differences between more and less authentic ideas to identify the characteristics of groups they appeal to and to evaluate their impact on the social actions of the individuals who adopt or oppose them.
A second type of branded entity that is gaining prominence in the context of hyperconnectivity is a person brand. Research has referred to a brand that is also a real person as a person brand ([41]), human brand ([131]), or celebrity brand ([63]). These types of brands have been used by everyone from a publicly visible and public-relations-conscious celebrity or politician to any person who uses a platform or engages in a form of self-promotion that is visible to his or her constituency. For example, [ 9] shows how teenage girls create and try out their many personal brands online, and how marketers, in turn, scrape the web for these incredibly rich data that, before the hyperconnected world, would have been too expensive, lacking ecological validity, and highly restricted and regulated by law and practice. The ability to create and promote a person brand online has led to the rise of influencers, a category of individuals who appear to have high potential as brand promoters and whose impact on brands and their meaning is the focus of recent scholarly research ([55]).
In analyzing the distinct characteristics of person brands, [41] stress the challenges involved in unifying person and brand, as they are inextricably linked, mutually interdependent, but not identical. They argue that the key characteristics that define a person (mortality, hubris, unpredictability, and social embeddedness) can upset this mutually interdependent relationship and cause inconsistency and imbalance. In highlighting this aspect, [41] argue that this distinctive aspect of person brands (i.e., integrating across the person and the brand) creates unique risks for their management.
More research is needed to identify how these risks occur and can be mitigated. The downstream consequences (monetary and nonmonetary) of reputational losses associated with crises and scandals involving person brands are also worth examining, as hyperconnectivity can magnify the scale and scope of such losses, particularly in the short run. By contrast, the consequences of reputational losses may diminish over long-term windows from the volume of information and velocity with which information is continually updated. Building on the notion of interdependencies between person and brand, research could also investigate how this delicate balance shifts in the aftermath of a reputational crisis and whether a greater shift in focus on the shortcomings of the "person" actually improves overall perceptions of authenticity associated with the person brand.
As more people begin to adopt branding principles to promote themselves, understanding the societal implications of such actions is important. On the one hand, adoption of person-branding principles by noncelebrities could strengthen the ability of these individuals to become more attractive employees or more attractive dating partners. On the other hand, an excessive focus on self-promotion may also have a variety of negative consequences. For example, [81] highlight the unhealthy self-obsession and growth of narcissism as one potential consequence of leveraging social media to build one's brand. Understanding these effects of person branding on narcissism and its consequent implications for feelings of belongingness, happiness, and well-being are research issues that merit further investigation.
Furthermore, research should investigate the differences between brands built by average people and celebrity brands and identify the optimal approaches for reputation building across these two types of person brands. Another useful approach would be to distinguish between economic ("commercial") and noneconomic ("noncommercial") person brands. Commercial person brands such as celebrities, (micro) bloggers, and digital opinion leaders have their own following, and they derive income from sponsorships and sales recommendations and by branding their product lines (e.g., Kylie Jenner's Kylie Cosmetics). Much of the value of these person brands resides in their network, and future research should try to evaluate novel brand valuation approaches for this context.
A place brand can be defined as "a network of associations in the consumers' mind based on the visual, verbal, and behavioral expression of a place, which is embodied through the aims, communication, values, and the general culture of the place's stakeholders and the overall place design" ([147], p. 5). Place branding is more than merely measuring the perceptions of the individuals who interact with that locale: place can be a sociological construction or an actively managed image-building and management strategy ([64]). Place brands develop as a result of complex interactions among residents, influenced by culture and history, and are thereby seen as dynamic, socially constructed, culturally dependent, and communally owned entities ([ 8]).
Like idea brands, the ownership of place brands is spread over multiple stakeholders (e.g., city governments, residents, tourists). These stakeholders could potentially have conflicting objectives, as exemplified by the opposing goals of tourists and residents in cities such as Amsterdam and Berlin ([20]). The differential role of multiple stakeholders in the development of place brands would be worth investigating, drawing on multiple disciplines such as political science, sociology, anthropology, cross-cultural psychology, urban planning, geography, and tourism research. Insights from the consumer and society perspectives could be integrated to conceptualize and measure place branding outcomes across various stakeholders, as well as to better understand intangible outcomes ([101]), such as life satisfaction of citizens and overall societal well-being. Spaces smaller than a city but commercially vital, such as Times Square, or trendy-shopping spaces such as Ginza (Tokyo) could also be researched in this day of the decline of the shopping mall. Factors such as the social density, social class perceptions and the implications for place brands should be examined ([96]), particularly in the context of a hyperconnected world.
Place branding research has so far focused on how place brands are created and consumed, and how place brand identity develops ([73]). Place attachment has examined how people forms associations with a place based on their childhood experiences ([87]). More recent research ([127]) draws on the large urban sociology literature to understand how branded spaces work through community, moral codes, and symbolic boundaries. Place brands share commonalities with other types of branded entities (e.g., their meanings are cocreated, as are those for idea and platform brands) but also differ in important ways: brand communications have to account for the diverse and potentially heightened social sensitivities that result from these conflicts.
A fourth type of branded entity that is worth closer scrutiny in the era of hyperconnectivity is organizations. Research has investigated organizations mostly in their corporate form and corporate brands mostly along the notions of corporate identity and corporate reputation ([ 3]; [56]). Yet there is much more to organizations as brands. They have responsibilities to communities, to the environment, and, of course, to their own employees. The best manner in which organizations can create and deliver a purpose within society that aligns with their brand associations and mission is an area that still requires research. In addition, more nefarious organizations (e.g., terrorist organizations) that aim to overthrow the existing order through violence keenly understand the power of branding as well ([14]). Developing a deeper understanding of what drives brand perceptions for organizations that serve the common good and what could disrupt the branding efforts of infamous organizations might be a worthy research endeavor.
We previously discussed challenges and opportunities associated with measuring brand value in a hyperconnected environment. Newer branded entities such as noncommercial idea and person brands are uniquely difficult to value because generating cash flow may not be their purpose. Rather, their relevant metrics may be influence, power, votes, societal well-being (e.g., social justice, fighting climate change), or converts. Might consumer-based brand equity be a useful point of departure for valuing such brands? If so, what would be the relevant dimensions? Are traditional consumer-based brand equity measures such as those in the Brand Asset Valuator ([27]) relevant or sufficient? What would a properly-conceived-of consumer-based brand equity measure for noncommercial idea and person brands indicate about the actual strength of the brand? To answer these questions, researchers need to link this measure to the relevant outcome metrics for such brands. Another factor to consider when developing new methods to value idea and person brands is the greater risk associated with inconsistency and potential threats of scandals or crises ([41]). This may require a different approach to risk management than what has been used for traditional brands.
This article focuses on future contributions to brand research, management, and measurement in a hyperconnected world in which the boundaries of branding have been blurred and broadened. In light of both broadening and blurring of brand boundaries, we address three key questions that form the focus of our inquiry: ( 1) What are the roles and functions of brands? ( 2) How is brand value (co)created? and ( 3) How should brands be managed?
We take a dual perspective in this article. On the one hand, we describe how hyperconnectivity has led to several new roles for brands. On the other hand, we reexamine how some traditional roles of brands (e.g., brands as signals of quality or as mental cues) have changed in a hyperconnected environment. We do so using firm, consumer, and society theoretical perspectives. We describe how hyperconnectivity contributes to several new roles in which brands are containers of socially constructed meaning, architects of value in networks, catalysts of communities, arbiters of controversy, and stewards of data privacy, among others. Many of these new roles can be the focus of research from multiple disciplinary perspectives, and we highlight a variety of research questions that can draw from different theoretical perspectives throughout the article. As brand boundaries are blurring, we also discuss the shift toward cocreated brand meanings and experiences enacted via digital platforms that facilitate such cocreation.
Given the complex nature of brands today, we hope researchers will engage in future boundary-breaking research on topics like those outlined here. As our review attests, one implication of hyperconnectivity for branding research lies in the fact that brands will need to be conceptualized more broadly within each of the theoretical perspectives in the extant brand literature. The consumer and firm perspectives should focus more on consumers and firms as part of networks, rather than on their roles as individual buyers or managers of brands. The society perspective should go beyond the role of brands as cultural symbols and examine them as agents of social change. Moreover, we propose that brands are more than symbols attached to products that are owned by individual firms. They can be ideas, people, and places.
There is also an opportunity to examine topics that cut across these theoretical perspectives. For example, the firm perspective will need to embrace societal questions as organizations or corporate brands are asked to address broader issues including social responsibility, sustainability, and human-resource practices that go beyond profit maximization. Brands need to fulfill a broader mission and purpose. The consumer perspective will also have to be more rooted in the society perspective as consumers form networks that are becoming distinct and occasionally vociferous entities that can shape both managerial practice and societal trends. The impact of network on brands, like that of communities, requires additional sociological, psychological, and cultural insight. Given our experience, we believe that such work would benefit from increased collaboration among branding researchers of different backgrounds, including teams of marketing strategists, economists, modelers, psychologists, sociologists, and consumer culture researchers.
Supplemental Material, jm.19.0120-File003 - Branding in a Hyperconnected World: Refocusing Theories and Rethinking Boundaries
Supplemental Material, jm.19.0120-File003 for Branding in a Hyperconnected World: Refocusing Theories and Rethinking Boundaries by Vanitha Swaminathan, Alina Sorescu, Jan-Benedict E.M. Steenkamp, Thomas Clayton Gibson O'Guinn and Bernd Schmitt in Journal of Marketing
Footnotes 1 EditorsChristine Moorman and C. Page Moreau
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 ORCID iDVanitha Swaminathan https://orcid.org/0000-0002-8752-8881
5 Online supplement: https://doi.org/10.1177/0022242919899905
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Record: 28- Business-to-Business E-Negotiations and Influence Tactics. By: Singh, Sunil K.; Marinova, Detelina; Singh, Jagdip. Journal of Marketing. Mar2020, Vol. 84 Issue 2, p47-68. 22p. 2 Diagrams, 7 Charts, 1 Graph. DOI: 10.1177/0022242919899381.
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Business-to-Business E-Negotiations and Influence Tactics
E-negotiations, or sales negotiations over email, are increasingly common in business-to-business (B2B) sales, but little is known about selling effectiveness in this medium. This research investigates salespeople's use of influence tactics as textual cues to manage buyers' attention during B2B e-negotiations to win sales contract award. Drawing on studies of attention as a selection heuristic, the authors advance the literature on mechanisms of sales influence by theorizing buyer attention as a key mediating variable between the use of influence tactics and contract award. They use a unique, longitudinal panel spanning more than two years of email communications between buyers and salespeople during B2B sales negotiations to develop a validated corpus of textual cues that are diagnostic of salespeople's influence tactics in e-negotiations. These e-communications data are augmented by salesperson in-depth interviews and survey, archival performance data, and a controlled experimental study with professional salespeople. The obtained results indicate that the concurrent use of compliance or internalization-based tactics as textual cues bolsters buyers' attention and is associated with greater likelihood of contract award. In contrast, concurrent use of compliance and internalization-based tactics is prone to degrade buyer attention and likely to put the salesperson at a disadvantage in closing the contract award.
Keywords: business to business; buyer attention; e-communication; linguistic; machine learning; negotiation; selling; text analysis
Advances in digital technologies motivate firms to adopt technology-mediated channels for business interactions. In particular, business e-communications account for more than 125 billion daily messages, or 86 million messages per second ([53]). According to industry reports, 77% of customers prefer e-communications over other formats, and data indicate robust returns of $40.56 for every dollar companies invest in e-communications ([40]). For business-to-business (B2B) selling ([54]), these trends also are manifest in a 75% increase in e-negotiations ([ 9]) and, by one estimate, 80% of U.S. sales negotiations are conducted online ([47]).
Research into the effectiveness of B2B e-negotiations is limited. Compared with face-to-face (F2F) communications, e-communications are leaner, with fewer contextual cues and less interactivity and flexibility ([17]), but they also offer some benefits, including ( 1) accessibility, such that emails are always available and offer the possibility of almost immediate feedback; ( 2) transparency, such that emails are verifiable (stored digitally for review) and visible (others in the organization can access them); ( 3) diversity, such that emails can contain diverse materials, including hypertext, links to external text or video, and various content attachments; and ( 4) flatness, indicated by professional norms that favor short, to-the-point messages without undue emotion ([10]).
For researchers, e-negotiations pose challenges of analyzing unstructured data. However, they also provide a unique, relatively unobtrusive, unfettered, and automatic access to the selling process by creating a permanent record of the selling process as it unfolds, without requiring an intervention (e.g., surveys and video/audio recordings can suffer from a reporting and obtrusive bias). Emergent academic research that utilizes process data from digital technologies show promise of new insights. For instance, researchers analyzed more than 1 million email exchanges among members of a professional services organization over a six-month period to show that response times are highly predictive of social and professional ties ([67]). Evidence from other studies of buyer–seller interactions shows that the process underlying influence mechanisms is more complex and nuanced than is revealed by self-reports or static studies ([34]; [51]). Thus, we aim to examine the effectiveness of salespeople's dynamic influence tactics (as textual cues) for winning sales contracts during the e-negotiation phase of the B2B selling process when email is the dominant mode of communication.
Specifically, using actual emails exchanged between buyers and salespeople, we ( 1) extract, categorize, and code unique textual cues associated with a salesperson's influence tactics; ( 2) conceptualize and operationalize the buyer's attention, as indicated by e-communications (i.e., text data); and ( 3) assess the impact of influence tactics and buyer attention on the probability of closing the contract successfully. We employ a unique data set of longitudinal email communications, sourced from a B2B heavy equipment manufacturing firm (Study 1). The communications involve a lead seller and the principal buyer, and our unfettered access to these naturalistic data, untainted by the seller's perceptions, provides real-life accounts of buyer–seller negotiations ([13]). To rule out alternative explanations, we supplement the email data with in-depth interviews, survey data (e.g., demographics, attitudes) and a sales manager survey that provides performance and profitability data. Finally, we conduct an experimental study (Study 2) to examine the mediation effect of buyer attention in a controlled setting and test the influence of the concurrent use of internalization (recommendation) and compliance (promise) tactics on the sales contract award.
Overall, we offer three main contributions. First, we identify sales influence tactics from textual cues in salesperson's e-communications and establish their validity. In so doing, we develop a five-step roadmap for developing and validating theoretical constructs from textual cues for broader use in future research. The five-step design uses grounded analysis to develop word dictionaries and contextualizes them to provide authentic representations of the target constructs. In turn, these bottom-up word dictionaries serve to "seed" a machine-learning (ML) algorithm that broadens their scope and expands their content to a reasonably large corpus of textual cues to ensure generalizability. Empirically, we show how "seed" dictionaries that are based on grounded work can offer a prediction accuracy of 63% that rises to 85% when they are combined with patterns recognized by ML procedures.
Second, we identify a key mediator of salesperson influence effects—buyer attention, defined as the degree to which a buyer displays behavioral responses to a salesperson's e-communications ([20]; [36]; [42]; [43]; [51]). We find that buyer attention is a leading indicator ([21]) of sales activity that predicts sales outcomes. In particular, our results show that a one-standard-deviation increase in buyer attention increases the likelihood of contract award seven-fold, resulting in an additional $37 million in revenue. Thus, while previous research has shown that sales influence tactics are effective in increasing performance, our theory of buyer attention explains both why salesperson influence tactics work to yield sales outcomes and when they do not, thereby advancing research into the mechanisms of the sales negotiation process.
Third, we show that no individual influence tactic is sufficient to hold buyers' attention or win the contract award. Effective use of influence tactics requires the concurrent use of complementary tactics that prompt either internalization (internal analyzing) or compliance (risk shifting), but not both. Our results show that the concurrent use of assertiveness and promise tactics to evoke compliance lifts buyer attention by 14%, whereas concurrent use of information sharing and recommendation tactics to evoke internalization yields a 15% increase in buyer attention. In contrast, concurrent use of internalization and compliance tactics—referred to as competitive tactics—diminishes buyer attention by as much as 30%. This asymmetry in the concurrent use of sales influence tactics, such that gains from complementary tactics are only half as much as the losses from competitive tactics, is indicative of prospect theory assertions. Thus, our study advances the sales negotiations literature by uncovering the asymmetric effect of sales influence tactics and providing practical guidelines for sales managers and salespeople about what sales tactics to deploy in combination and which combinations to avoid for sales effectiveness. Next, we discuss pertinent literature and motivate our key hypothesis.
Figures 1 and 2 display the research context and the proposed conceptual model of e-communications, which includes ( 1) textual cues in e-communications that salespeople use to exert influence during the B2B sales negotiations; ( 2) buyer attention, displayed in textual cues of the buyer's e-communications in B2B sales negotiations; and ( 3) sales contract award (yes/no) as an outcome. Table 1 outlines four fundamental attributes of e-communications with their implications for senders and receivers. We draw from these attributes to develop a theory of influence tactics in B2B sales e-negotiations, beginning with mediating role of buyer attention.
Graph: Figure 1. B2B sales process.
Graph: Figure 2. Conceptual model: B2B E-Negotiations and influence tactics.Notes: Slice = A continuous tract of time (e.g., ten days) that clusters e-communications. For more details, see the section "Influence Tactics as Textual Cues and Buyer Attention in B2B Sales E-Negotiations."
Graph
Table 1. E-Communication Affordances.
| Attribute | Definition | Implications for the Sender | Implications for the Receiver |
|---|
| Accessibility | Emails are accessible (24/7) to receivers, with the possibility of almost immediate feedback. | Pros: Flexibility to compose messages any time.Cons: Probability of receiving replies varies from minutes to days. | Pros: Respond to incoming messages at will.Cons: Quantity and distribution of messages increase over time. |
| Transparency | Emails are verifiable and stored digitally for review, as well as visible, which provides others in the firm access to them. | Pros: Promotes messages that are open, direct, and without puffery.Cons: Lack of context can lead to misinterpretation by others in the organization. | Pros: Permits a check on the veracity of a claim made by the sender.Cons: Requires critical analysis and cross-checking of the content. |
| Diversity | Emails allow attachments that can embed documents, links, embellishments, and so on, which then can substantiate or augment a message. | Pros: Allows documents to be easily attached.Cons: Requires collection of varied documents that support an argument. | Pros: Arguments are substantiated.Cons: Require triangulation of diverse materials. |
| Flatness | Emails are constrained in length and in the use of emotional cues. | Pros: Only requires use of textual cues to compose the message.Cons: Limits the use of emotion. | Pros: Keeps message length shortCons: Encourages cognitive processing of messages. |
We propose that e-communications that garner greater buyer attention are more likely to result in a successful contract award. According to the attention-based view of the firm, attention facilitates both coping with and adapting to contextual stimuli ([42]). An entity with limited information-processing capacity copes with overwhelming stimuli by prioritizing and focusing on selective stimuli ([60]). An entity also might adapt to incoming stimuli by directing attention to stimuli that are more likely to facilitate goal achievement while dismissing stimuli with less goal instrumentality ([42]). If stimuli garner an entity's attention, this indicates their relative importance and relevance ([43]). Thus, we conceptualize that the intensity of attention given to a specific stimulus is indicative of ( 1) its relative importance and relevance to the individual's needs and goals ([33]) and ( 2) its motivational potential to evoke behavioral response ([29]). As [14] suggest, attention functions like a gatekeeper for sorting, managing, and evaluating stimuli according to their fit with self-meaning. [61] show that deliberately directed attention provides a means to triage incoming stimuli that distract from purposeful activity. In a B2B context, [ 6], p. 55) conceptualize buyer "attentiveness" as a diagnostic construct that indicates the buyer's "cognitive disposition...towards a product manufacturer and away from its competitors." [65] concurs that buyer attention is critical because if the customer is not paying attention to what the seller is focused on, all efforts are wasted.
Regarding its motivational potential, [29] show that attention can be a source of "preference formation," such that after directing their attention, people exhibit a preference for the focus of that attention, which they call the "mere attention effect." Such selective attention entails an encoding process that stores the selected stimuli according to preferred network connections, relative to stimuli that are triaged. This encoding motivates preferences in subsequent action. In other words, "highly attentive buyers...purchase more products, more often, [and] for longer period of time" ([ 6], p. 56).
The role of buyer attention is salient in e-communications. Relative to F2F communication, e-communications permit greater accessibility, such that a salesperson can compose messages with the desired level of richness at any time and reach out to a buyer with follow-up targeted communications (cf. Table 1). In turn, this medium's accessibility promotes message crowding wherein salespeople try to grab buyers' attention quickly, engage them in compelling dialogue, and challenge their assumptions about needs and solutions ([16]). However, such continuous e-communications increase the burden on the buyer's cognitive capacity. Furthermore, although the transparency feature of e-communications is attractive, it also adds to the cognitive burden because it prompts analyses of message content and comparisons with previous messages or other sources. When buyers experience greater cognitive load, buyer attention should offer a particularly reliable and sensitive indicator of message priority during B2B negotiations.
The textual cues of e-communications reveal the degree of buyer attention. Positively valenced words indicate heightened interest ([28]). Use of more active than passive text also indicates the activation of behavioral attention ([57]). Likewise, textual cues of time urgency reveal increased buyer attention, such as when a buyer asks the salesperson to respond "ASAP" ([12]). [ 7] state that buyer attention is heightened for messages that focus on buyers' priorities, propose solutions for saving resources (time and money), and are pertinent to the problem at hand. Such signals of increased buyer attention show that the salesperson's messages have been granted relatively higher priority, and thus we expect them to be associated with increased probability of contract award. Thus,
- H1 : Buyer attention mediates the impact of the salesperson's influence tactics on the probability of B2B sales contract award during e-negotiations.
Prior B2B sales literature has identified various influence tactics used in F2F communications, such as information sharing, recommendations, assertiveness, promises, inspirational appeals, and ingratiation ([36]; [51]; see Table 2). In a buyer-dominated sales process, inspirational appeals as well as their opposites (e.g., threats) are less relevant ([36]; [51]). Inspirational appeals presume that emotions sway buyers' decisions, but for B2B sales negotiations, with open bid processes and managerial or regulatory oversight, emotional appeals are relatively rare. The uses of threats or legalistic pleas presume that a contract already exists. Ingratiation might build relational bonds in F2F communications, but its use in professional email exchanges is less common, because such explicit and transparent exchanges generally make ingratiation attempts inappropriate. Accordingly, we do not include ingratiation appeals in our hypotheses, but to reflect prior research, we include them in the empirical analysis as a statistical control ([ 1]).
Graph
Table 2. Construct Definitions and Key Linguistic Markers.
| Influence Tactic | Key Linguistic Markers | Conceptual Ground |
|---|
| Information Sharing: Asynchronous communicating and exchanging (giving and asking) of relevant information (e.g., details, knowledge, data) about solutions, services, and products without recommendations or promises, whether on request or voluntarily. | Definitive verbs (e.g., attach, forward, provide, enclosed) conjugated with informational nouns (e.g., product specs, quality certificates) | Internalization (internal analyzing of prioritized and expert knowledge) |
| Recommendation: Explicit suggestions to buyers in asynchronous interaction in favor of a particular product, service, or solution by emphasizing product benefits, uniqueness, or usability, whether solicited or not. | Action verbs (e.g., recommend, offer, advice, believe) conjugated with proposition quality (e.g., clearly, strongly, acceptable, highest) | Internalization (internal analyzing of counter arguments to highlight benefits) |
| Promise: Committing to a future course of action, activity, and/or benefit, typically to follow up on a buyer's current request or future action in asynchronous communication. | Action verbs (e.g., perform, review, send, respond) conjugated with modals (e.g., will, can, would) | Compliance (risk shifting by committing to an action) |
| Assertiveness: Initiating a call-to-action or attention to the buyer in asynchronous communication that ensures the continuity of the business exchange and/or relationship, implicit or explicit. | Pronouns (e.g., we, I, you) conjugated with action verbs (e.g., need, would, should) | Compliance (risk shifting by suggesting an action) |
| Ingratiation: Asynchronously building rapport, engaging in flattery, and gaining approval of the buyer. | Affective words (e.g., thank you, appreciate, help, welcome, sorry, enjoy) conjugated with personal pronouns (e.g., we, I, you) | Identification (prosocial) |
| Buyer Attention: Degree to which a buyer displays heightened interest and behavioral engagement in response to salesperson's email communications | Instrumental words (e.g., do, get, send), valence words (good, best, excellent, etc.), and time-related/temporal contiguity words (today, tomorrow, next week, asap) | Attention-based view |
Our conceptual development of influence tactics for e-negotiations features several notable elements. First, we use a "slice"—a continuous tract of time (e.g., ten days) that clusters e-communications—as the unit of conceptual and empirical analysis. It offers an alternative to a single salesperson–buyer turn or an entire string of communications as the unit of analysis. The former tends to be overly sensitive and prone to noisy input due to truncated or out-of-turn communications (e.g., multiple salesperson emails with no buyer response; [ 9]), while the latter aggregates all turns and thus obscures influence dynamics.
Second, we hypothesize that textual cues that indicate salesperson influence tactics change the buyer's attention over the duration of the e-communications. Desired changes in the buyer's attention provide a key mechanism by which influence tactics effectively achieve outcomes. Third, we advance prior conceptualizations in marketing that have adapted and refined the work of [31], specifically the compliance and internalization constructs, which initially served as foundations to understand social influence in international relations. Among the first efforts, [64], p. 76) drew on Kelman's work to categorize existing influence tactics developed by [20] using "processes of social influence and attitude and behavior change." [64], p. 76) conceptualized that internalization is evoked by task-oriented influence tactics, including information sharing and recommendation, because they "seek to persuade a target of the inherent merit of the proposed decision." Furthermore, they stated that compliance is prompted by non-task-oriented influence tactics, such as requests or promises, which "seek to obtain conformance without attempting to persuade the target of the appropriateness of the decision." Leveraging this linkage between Kelman's social influence theory and influence tactics in marketing, [36] examined the relevance of influence tactics for salespeople and predicted the correspondence between Kelman's social influence mechanisms and individual influence tactics. They similarly predict that information sharing and recommendation tactics evoke intrinsic processes, whereas promises and threats indicate an instrumental mechanism. Their empirical findings indicate that individual influence tactics affect the buyer's manifest influence consistent with this categorization, such that when an intrinsic process is activated, the effects are larger and significant. [26], [51], and [37] adopt this categorical correspondence between Kelman's social influence mechanisms and influence tactics. We similarly draw on the conceptual categories of internalization and compliance but adapt their conceptualizations to e-communications.
Both information sharing, defined as communicating and exchanging knowledge about solutions/services/products, and recommendation, defined as the explicit suggestion in favor of a particular solution/service/product, are likely to evoke internalization ([ 8]; [31]; [36]). In e-communications, internalization implies internal analyzing, such that a buyer is motivated to assess the stimuli contained in the salesperson's message to evaluate the benefits and costs of an action, activity, or choice. Information sharing prompts the buyer to evaluate the substantive content of the message and analyze the potential to increase or decrease the likely benefits and costs of an offer. In the case of recommendation, provision of a suggested course of action with decisional responsibility on the buyer also prompts the buyer to evaluate the credibility of the message and its implications in the context of the buyer's use situation.
Information sharing and recommendation tactics trigger an internal-analyzing process in complementary ways. Unlike F2F exchanges, e-communications enable the salesperson to craft messages carefully and thereby include attachments such as drawings, industry reports, or white papers that offer novel information about unique product or service specifications that can overcome objections and meet buyers' needs ([45]). The richness of novel information, combined with buyer vigilance to assess its relevance for goal pursuit, prompts analysis by the buyer. When the incoming information is evaluated to be favorable in advancing buyers' goals, it is likely to be internalized and prompt more positive dispositions toward the object of the information ([ 8]). [36] show that, relative to sales situations characterized by the buyer's self-orientation or interaction orientation (i.e., social welfare), those that feature task orientations (such that they are goal oriented) enable more significant, positive effects of the salesperson's information sharing on the buyer's manifest (perceived) influence.
In F2F exchanges, salespeople also issue recommendations that leverage social bonds or interpersonal trust with the buyer. However, the flatness and transparency of e-communications may hinder a salesperson's attempts to engage in explicit social bonding or trust building ([10]). Buyers also might be more vigilant, to protect against self-serving claims by salespeople. These features activate the buyer's careful analysis of recommendation claims; if the analysis suggests positive implications for achieving the buyer's goal, the recommendations shift the buyer's attention toward their object. Prior research has shown that salespeople's recommendation tactic is effective when it is successful in reframing status quo solutions as suboptimal or problematic, which can be improved by the salesperson's recommended course of action ([ 8]; [27]).
We also posit that the concurrent use of information sharing and recommendations will interact to positively affect buyer attention, due to the reinforcing effects of these compatible processing motivations, especially when the buyer's cognitive resources are stretched. Both information sharing and recommendation evoke an internalization mechanism that favors internal analyses of input stimuli, in complementary ways. With their concurrent use, they should enhance attention effects, because the message content is reinforced by consistency and coherence ([50]). Similarly, the concurrent use of search and display advertising online yields better results, because search advertising evokes a deliberate process to reveal consumer preferences, and display advertising acts like a recommendation agent that directs customers to a preferred site. We posit that textual cues of information sharing and recommendation promote cognitive consistency and coherence, because the former enables the buyer to process new knowledge and realize the disadvantages of current solutions, whereas the latter provides suggestions for resolving the problem ([27]). Thus,
- H2 : Salespeople's concurrently used information sharing and recommendation tactics, as textual cues, interact to positively affect buyer attention during B2B sales e-negotiations.
Promise, the act of a salesperson committing to a future course of action, activity, and/or benefit, and assertiveness, a call to action for the buyer that ensures continuity of the business exchange and/or relationship, are both conceptualized to evoke risk shifting in accord with a compliance mechanism ([31]; [36]). In B2B e-communications, risk shifting is evoked by salesperson messages that provide affordances for buyers to mitigate decision risk, simplify information processing, and/or reduce uncertainty ([41]). Salesperson messages that effectively mitigate buyers' decision risk and cognitive burden are likely to garner increased attention due to their relevance in situations in which time is at a premium, informational uncertainty is high, and cognitive resources are stretched. Such risk shifting is not necessarily suboptimal; it reflects a reasoned choice. For example, the buyer's risk can be shifted and informational uncertainty reduced if the buyer complies with a course of action suggested by the textual cues in the salesperson's messages.
Promises and assertiveness both evoke risk shifting, but in complementary ways. When a salesperson makes a promise, it mitigates buyer risk and uncertainty by guaranteeing some specific outcome, benefit, or payoff. In e-communications, salespeople issue promises that increase clarity and help buyers visualize the expected payoffs. In this sense, an explicit promise of a desired outcome, conditional on a favorable decision, should strongly reduce the cognitive burden by enabling the buyer to forgo a systematic risk analysis (benefits/costs) in favor of a promised outcome for which the seller bears the risk. Likewise, when a salesperson uses an assertiveness tactic to demonstrate superior knowledge and expertise in offering certain solutions, services, and products, this also mitigates buyer risk ([ 4]). E-communications enable salespeople to assert expertise and superior knowledge by sharing scientific evidence and cases tailored to attractive solution options. Because the explicit and permanent nature of e-communications permits independent verification and validation of a salesperson's knowledge claims by multiple members of the buyer organization, knowledge claims that are credible affirm the salesperson's assertiveness of expertise. [23] shows that the use of assertiveness during sales processes improves outcomes, and [46] demonstrate that a directed request tactic enhances the salesperson's manifest influence.
Here again, we predict a positive, interactive effect of salespeople's concurrent use of promise and assertiveness in e-communications on buyer attention, beyond the distinct effect of each tactic. Both shift decision risk and reduce information uncertainty; this complementary impact should reinforce the consistency and coherence of salesperson messaging without creating the downside of repetitive or belabored messages associated with a singular influence tactic. That is, by making promises, the salesperson indicates a willingness to take on the risk on behalf of the buyer, and assertiveness mitigates the buyer's informational uncertainty by redirecting attention to tailored solutions designed according to the salesperson's expert knowledge. Research offers similar evidence that a salesperson, acting as an expert consultant, can reduce risk perceptions with a consultative selling approach ([32]; [52]). Thus,
- H3 : Salespeople's concurrently used promise and assertiveness tactics as textual cues interact to positively affect buyer attention during B2B sales e-negotiations.
We collaborated with a global B2B industrial manufacturing firm that is one of the top competitors in the custom manufacturing of specialized equipment for heavy industrial plants with $1.6 billion market and growing at a compound annual growth rate of ∼5%. The firm had started conducting sales negotiations over email due to market changes; a vice president of sales noted that industrial buyers were actively avoiding F2F or phone meetings and requiring sales contract negotiations to be conducted by email. We collected multisource data: ( 1) longitudinal captures of buyer and salespeople emails exchanged during B2B sales negotiations for a two-year period, focusing on the sales negotiation phase (see Figure 2); ( 2) postnegotiation outcomes, namely, a successfully closed sales contract or not; ( 3) survey-obtained information about salespeople's demographic profile, perceptions of email use, and description of their firm's vendor status; ( 4) archival data capturing salespeople's past performance, and ( 5) in-depth knowledge about the sales process and setting, gained from field interviews with salespeople and sales managers.
Individual interviews with eight salespeople and two sales managers helped us understand the sales negotiation process, ascertain the frequency of buyer–salesperson interactions, and define the duration of e-negotiations. These interviews enabled us to develop an appropriate research design, identify data sources for the study variables, and derive an empirical approach for the sampling.
The sampling procedure involved several steps. First, each salesperson provided lists of all sales e-negotiations assigned to him or her in the previous two years, including key identifiers, such as the buyer's name, purchase order number, project number, start month, and end month. The lists were verified for completeness and accuracy by the sales managers to ensure that the sampling frame included all sales e-negotiations or bids, whether successful or not. Second, guided by these lists of sales e-negotiations and identifiers, an information technology manager extracted the emails from the firm's servers. Third, we checked the extracted emails for completeness with regard to identifiers such as date and time stamps, receiver's/sender's name, and email subject. We also determined the incidence of email exchanges between the lead salesperson and lead buyer (>90%) versus other buying team members (e.g., project manager, legal). We retained only those sales e-negotiations that entailed at least 20 emails and thus excluded six sales e-negotiations. Further analysis revealed that these six sales e-negotiations were unusual, lower-valued negotiations that resulted from F2F interactions. Finally, we recorded information specific to the sales e-negotiations, such as the price and negotiation outcome. In total, we sampled communications for 47 distinct sales e-negotiations.
The unit of analysis is a slice, defined as a specific continuous tract of time that clusters e-communications, guided by conceptual and empirical considerations. As noted previously, using a single turn as a unit of analysis is overly sensitive and prone to noisy input. E-negotiations often contain truncated or out-of-turn patterns of communications that occur for several reasons, including buyers and sellers working in different time zones, having different schedules, returning to a previous message to clarify comments, or due to power asymmetry in favor of the buyer. Using the entire string of communications as a single chunk is similarly problematic because it collapses time and obscures influence dynamics, especially for contract negotiations that can run into months of back-and-forth communications. The use of "slice of time," involving grouping of communications over a narrow band of time provides an intermediate but effective approach to examine influence dynamics. [62] used a similar approach in which they grouped 100 consecutive sentences as an analysis unit while studying high-stakes negotiations. For our analysis, we considered three alternative slices—7 days, 10 days, and 14 days—in which we grouped emails based on their similarity of subject line text using cosine distance, a commonly used similarity measure in text analysis. To construct the slices, ( 1) we assessed the change in the email subject over the slice length, and ( 2) if a change exists, we constructed the slice for the similar email subject; otherwise, the next slice begins on the 7th, 10th, or 14th day, respectively. The 7-day slice resulted in missing data (email responses from either the buyer or seller were lacking). The 10-day and 14-day slices had no missing observations. Thus, we used the 10-day slice to test our hypothesis and the 14-day slice as a robustness check.
We surveyed all salespeople (n = 9; 100% response rate) to collect perceptual (e.g., email use, customer orientation, adaptive selling behavior), demographic (e.g., age, gender, education, experience), and archival (e.g., vendor status, relationship length) data.
Sales managers provided archival data about salespeople's performance on indicators that the company routinely collects for evaluation purposes such as sales, profitability, responsiveness to buyers' requests, and completeness of information provided.
As Table 3 shows, we used a five-step process to develop and validate measures of the salesperson's influence tactics and buyer attention. We briefly discuss each step next.
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Table 3. Measure Development from Email Data.
| Objective | Technique | Activities | Outcome |
|---|
| 1. Establish operational definitions | Structured interviews; combine purposive and naturalistic data | 1. Purposive data are assembled by asking four salespeople who work in the same industry to write sample emails that contain one specific influence tactic. With purposive sampling, we collect information-rich cases that use emails deliberately to convey influence tactics. | 1. A sample of 20 emails (113 sentences), each with a specific influence tactic, is assembled. |
| 2. Naturalistic data are compiled by randomly selecting four sales e-negotiations (∼10%) from the complete data set (43 sales e-negotiations ∼ 90% of the data set is used for hypothesis testing). Naturalistic data enhance external validity. | 2. Four sales e-negotiations containing 360 sentences are merged with 113 sentences from the purposive data collection. The sample of 473 sentences is used to generate and augment measurement items, referred to as the "training sample" hereinafter. |
| 3. Sales managers and academics examine the use of influence tactics in the combined purposeful and naturalistic data set to revisit and contextualize the definition of influence tactics. Sales managers offer guidance from practice to adapt the influence tactic definitions to the email context. | 3. The contextualized definitions of influence tactics are agreed on by sales managers and academics, ensuring relevance for both practice and research. |
| 2. Generate measurement items | Grounded analysis | 1. From the influence tactics definitions, sales managers and academics code each sentence in the training sample according to the (1) presence of each influence tactic and (2) specific textual cues (words/phrases) indicative of that influence tactic. Manual coding by experts ensures accurate identification of influence tactic cues. | 1. Training sample sentences are coded for the presence or absence of the five influence tactics. |
| 2. Interrater reliability for textual coding by sales managers and academics is computed to assess the consistency of textual representations or cues of specific influence tactics. | 2. A corpus of textual cues, organized as linguistic markers in a dictionary, is obtained for each influence tactic. Interrater reliability indicates high agreement (>93%) regarding the identified influence tactics. |
| 3. Augment measurement items | Machine learning | 1. ML tools, such as TF-IDF and the co-occurrence matrix, are used to identify relevant linguistic markers. | 1. All relevant linguistic markers in the training sample are identified. |
| 2. Identified linguistic markers are combined with the corpus of textual cues for each influence tactic from the "Generate measurement items" stage. Integrating human and ML identified cues ensures a comprehensible dictionary. | 2. An augmented corpus of textual cues (identified by humans and ML tools) is formed. |
| 3. Logistic regression selects textual cues as linguistic markers that can accurately predict the presence or absence of influence tactic in each sentence. The predictive ability of linguistic markers is then tested with an SVM algorithm. Classification algorithms can test if the selected textual cues can predict out-of-sample data accurately | 3. The SVM classification algorithm offers the highest prediction accuracy (∼85%). |
| 4. Deploy measurement items | Machine learning | 1. An SVM, trained on the training sample, codes the hypothesis testing data set (4094 email sentences in 43 sales e-negotiations) for the presence or absence of specific influence tactics. | 1. Hypothesis testing data are coded by SVM. |
| 2. Two research assistants independently code 100 randomly selected email sentences from the test data. | 2. An agreement of 91% is achieved between the two research assistants. |
| 3. Coding of the 100 email sentences provided by the research assistants is compared with that provided by the SVM. | 3. Human and SVM classification matches 86% of the time. |
| 5. Assess validity and reliability | Confirmatory factor analysis | 1. Internal validity of the labeled test data is assessed by convergent and discriminant validity (Check for high skewness and kurtosis and use robust procedures). | 1. A CFA model indicates acceptable model fit and support for both convergent (AVE >.50) and discriminant (AVE > MSV) validity. Reliabilities of all constructs are greater than.67 (Table 4, Panel A). |
| 2. External validity is assessed using measures to predict key outcomes (e.g., sales). We test the robustness of the findings to a 14-day slice. | 2. Influence tactic constructs based on text-based measures behave as hypothesized (Table 7, Panels A and B). |
To gather purposive data, we asked four salespeople who work in the focal industry to write sample emails that contain one and only one specific influence tactic to purposefully convey a specific (target) influence tactic ([56]). This process yielded 113 sentences, each containing a target influence tactic. We merged a subsample of the naturalistic data obtained from the firm (four sales e-negotiations or ∼10% of the data) with these purposive data to create a training sample of 473 sentences. Sales managers and academics reviewed the influence tactics in the training sample to fine-tune the operational definitions (Table 2) of each influence tactic using a top-down approach. For the construct of buyer attention, we aimed to identify indications of heightened interest and behavioral engagement in a buyer's response to a salesperson's email message. This resulted in instrumental (action-oriented) words and phrases that signal action, temporal contiguity words to convey time-related urgency, and positive or negative valence words that indicate activation. We asked expert academics to evaluate sentences extracted from the buyers' email data (n = 150) for the presence or absence of each dimension (interrater reliability > 95%).
Using operational definitions, two sales managers, an executive from the focal firm, and two academics ( 1) classified each sentence in the training sample as indicative of one of five influence tactics and ( 2) identified the words and phrases that denoted a particular tactic, which were subsequently used as the seeds for an influence tactic–specific dictionary. Iterative recoding and discussions resulted in interrater reliability greater than 93%. For buyer attention, two research assistants identified unique words and phrases corresponding to each dimension: instrumental (33), temporal contiguity (22), and valence (24). This list was supplemented with words from extant dictionaries such as the Linguistic Inquiry and Word Count ([48]; 198 words from the time dimension) and Harvard Enquirer (249 words for positive/negative valence, 623 words for instrumental/action). Overall, we generated 1,149 words/phrases for buyer attention.
The top-down approach to developing construct dictionaries was augmented by a bottom-up approach to enhance validity. Using the training sample, the data were preprocessed to remove uninformative words/characters (stop words [e.g., "the," "and," "on"], HTML tags, and extraneous cues). We inserted white spaces following punctuation to separate content and stemmed the words to their roots to allow for variations (e.g., "seem" for "seeming" and "seemingly"). For feature (construct-specific linguistic markers) identification, we assigned the email sentences to vectors using term frequency–inverse document frequency and co-occurrence matrices. Feature identification is followed by feature selection, with the objective of choosing the relevant cues that can lower the error rate for the holdout sample. To select relevant cues, we fit five logistic regressions (one per influence tactic) as follows:
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1
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where L = 1 for a specific influence tactic and 0 otherwise, xi is the textual cue, and wi is the weight. Using the weight, we selected the most relevant cues for the classification step after testing iteratively 25–100 cues for each influence tactic; we ultimately retained the top 35 cues, based on achieved accuracy in the holdout sample. The selected cues and seeding dictionaries of linguistic markers were used to classify the training sample with a supervised vector machine (SVM), which performs well in high-dimensional spaces ([44]; [63]). To assess classification accuracy, we used stratified five-fold cross-validation. The "training sample" is divided randomly into five parts, and training is performed on the first four samples with the prediction performed on the fifth (holdout) sample (repeated five times); manual coding of influence tactic labels is compared with the SVM classification to determine the error rate ([66]). We achieved satisfactory accuracy of 85.1% for influence tactics and 86.2% for buyer attention.
We use linguistic markers in the SVM algorithm to code the email data ( 4,094 sentences from 43 e-negotiations) for the presence or absence of specific influence tactic (see Table 2). To validate out-of-sample coding, two research assistants independently coded 100 randomly selected sentences from the 43 e-negotiations into one of the five influence tactics using operational definitions (interrater reliability = 91%). We obtained a classification consistency of 86% with the sentences coded by SVM. Similarly, for buyer attention, two research assistants independently read 75 sentences randomly sampled from the 43 e-negotiations and classified them into the three buyer attention dimensions (interrater reliability = 92%). The classification consistency was 90.7% relative to sentences coded by SVM.
Each influence tactic was operationalized as the number of identified sentences corresponding to the tactic divided by the total number of sentences in the slice. Similarly, buyer attention was operationalized as the total number of instrumental, valence, and temporal contiguity sentences that occur in a slice, divided by the total number of sentences in that slice. To examine measure validity, a confirmatory factor analysis (CFA) was conducted with two measures for each influence tactic (linguistic cues for each influence tactic were randomly divided into two groups) and three dimensions of buyer attention. Textual cues extracted by the ML methods suffer from lack of multivariate normality conditions. We checked for distributional properties of extracted measures and noted that an extraction method robust to high kurtosis is needed. Thus, we used robust CFA analysis methods such as maximum likelihood robust and maximum likelihood parameter estimates with Satorra–Bentler correction ([35]). Using these robust procedures, we found an acceptable Satorra–Bentler chi-square statistic for the hypothesized measurement model (34.08, d.f. = 37, p >.1). Furthermore, fit indices confirmed the goodness-of-fit of the measurement model (normed fit index =.92, root mean square error of approximation =.001). Each influence tactic measure and buyer attention construct evidenced significant factor loadings, convergent validity (average variance extracted [AVE] >.50), and discriminant validity (AVE > maximum shared variance [MSV]) (Table 4, Panel A). We established predictive validity by providing recall, precision, and F1-scores for influence tactics and buyer attention (Table 4, Panel B).
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Table 4. Study 1 Results.
| Loadinga | t-Value | Reliabilityb | AVEc | MSVd |
|---|
| A: First- and Second-Order CFA of Influence Tactics and Buyer Attention |
|---|
| First-Order Factor Structure | | | | | |
| Information Sharing | | | .67 | .51 | .49 |
| Item 1 | .69 | 11.98 | | | |
| Item 2 | .88 | 15.97 | | | |
| Recommendation | | | .78 | .65 | .49 |
| Item 3 | .88 | 17.16 | | | |
| Item 4 | .91 | 17.71 | | | |
| Promise | | | .86 | .75 | .42 |
| Item 5 | .96 | 20.82 | | | |
| Item 6 | .94 | 20.02 | | | |
| Assertiveness | | | .78 | .65 | .42 |
| Item 7 | .93 | 17.81 | | | |
| Item 8 | .86 | 16.09 | | | |
| Buyer Attention | | | .82 | .61 | .06 |
| Instrumental | .70 | 12.94 | | | |
| Valence | .95 | 20.07 | | | |
| Temporal Contiguity | .91 | 18.52 | | | |
| Second-Order Factor Structure | | | | | |
| Internalization | | | .81 | .68 | .34 |
| Information Sharing | .99 | 8.91 | | | |
| Recommendation | .71 | 10.09 | | | |
| Compliance | | | .70 | .55 | .34 |
| Promise | .89 | 12.43 | | | |
| Assertiveness | .73 | 9.58 | | | |
| B: SVM Classification Results |
| Recall | Precision | F1 Score |
| Influence Tactics |
| Information Sharing | .91 | .79 | .85 |
| Recommendation | .82 | .75 | .79 |
| Promise | .84 | .88 | .86 |
| Assertiveness | .77 | .91 | .83 |
| Ingratiation | .88 | .96 | .92 |
| Buyer Attention | | | |
| Temporal | .87 | .82 | .85 |
| Instrumental | .85 | .91 | .88 |
| Valence | .86 | .82 | .84 |
1 a The estimates are standardized coefficients with corresponding t-values in the adjacent column.
- 2 b Estimated composite reliability, per [19].
- 3 c Estimated average variance extracted by the corresponding latent construct from its hypothesized indicators, per [19].
- 4 d Maximum shared variance between any two latent constructs.
We have panel data with time-sequenced e-communications (k = slice; TS = time-ordering, first occurrence coded as 0) nested within salesperson–customer dyads (sj = the salesperson–buyer dyad). To test the impact of salesperson influence tactics (INSH = information sharing, RECO = recommendation, PROM = promise, ASRT = assertiveness, and INGR = ingratiation) on buyer attention (BATTN) during the e-negotiations, we account for both time-variant (e.g., linguistic style matching, alternate channels of meeting, time to respond) and time-invariant (salesperson and buyer specific) variables. Thus, we use a random parameters specification that models the heterogeneity between dyads with a random intercept that is a function of all time-invariant variables and random parameters for all influence tactics variables, thereby capturing heterogeneity within dyads and across time-sequenced emails. We estimate the following equations ([24]):
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To assess the impact of buyer attention and influence tactics on the sales contract award, we specify a probit model that accounts for the heterogeneity in the impact of buyer attention as a function of salesperson and contract-specific time invariant variables. The estimated model includes unidirectional causal effects because buyer attention precedes the sales contract award. We expect the disturbance terms across equations to be uncorrelated; we tested this by estimating the equations simultaneously allowing for correlated errors, but we failed to find significance (χ21df = 1.93; p >.1). Thus, we estimate the following probit model, where probability of contract award = κsj:
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Otherwise , where .
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These equations include several control variables (Table 5, Panel A): salesperson alternative mode of communication (ACALL) (= 1 if emails contain words specific to meeting outside the email context such as "hotel" or "golf," 0 otherwise), linguistic style matching (LSM; M =.64, SD =.32), the salesperson's average response time to buyer emails (STTR; M = 2.27 days, SD = 12 days), salesperson education (EDU; 1 = undergraduate, 2 = master's degree, 3 = doctoral degree) salesperson performance indicators such as sales, profitability, responsiveness, and completeness (SALPERF; 1 = poor performer, 5 = best performer), customer orientation (CORIENT; 1 = low, and 5 = high), salesperson tenure (SPEX; M = 6.87 years, SD = 5.76 years), contract price (LPRICE; M = $2.1 million, SD = $3.6 million), and vendor status (PVENDOR; 1 = preferred vendor, 0 otherwise). All variance inflation factors were less than 6.
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Table 5. Descriptive Statistics.
| A: Study 1 | | | | | | | | | | | | | | | | |
|---|
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |
|---|
| 1. Sales contract award | 1 | | | | | | | | | | | | | | | |
| 2. Buyer attention | .12** | 1 | | | | | | | | | | | | | | |
| 3. Assertiveness | .12** | −.02 | 1 | | | | | | | | | | | | | |
| 4. Ingratiation | .15** | .25*** | .25*** | 1 | | | | | | | | | | | | |
| 5. Information sharing | .06 | .20*** | .39*** | .32*** | 1 | | | | | | | | | | | |
| 6. Promise | .17** | −.07 | .24*** | .25*** | .44*** | 1 | | | | | | | | | | |
| 7. Recommendation | .05 | .07 | .11* | .02 | .26** | .21** | 1 | | | | | | | | | |
| 8. Customer orientation | .59*** | .03 | .04 | .07 | −.02 | .08 | .06 | 1 | | | | | | | | |
| 9. Salesperson experience | .26*** | .07 | .06 | .19*** | .07 | −.02 | −.18** | .25* | 1 | | | | | | | |
| 10. Salesperson education | −.11* | −.02 | −.11* | −.24** | −.01 | .001 | −.09 | −.26* | −.03 | 1 | | | | | | |
| 11. Alternative meeting | .09 | −.15** | .14** | .13** | .16** | .29*** | .01 | .08 | .14** | −.03 | 1 | | | | | |
| 12. Linguistic style matching | −.09 | .18** | −.21** | −.12** | −.16** | −.18** | −.13** | .01 | .00 | .07 | −.03 | 1 | | | | |
| 13. Preferred vendor | .02 | −.01 | −.08 | .06 | .01 | .00 | .09 | .02 | .10* | −.21** | .12** | .08 | 1 | | | |
| 14. Price | −.27** | .05 | −.01 | .04 | .21** | −.10* | −.06 | −.33* | .17** | .42** | .07 | .10 | −.02 | 1 | | |
| 15. Salesperson time to respond | .03 | −.02 | −.08 | −.08 | −.07 | −.10* | −.05 | .04 | .05 | .11* | −.08 | .09 | −.06 | .00 | 1 | |
| 16. Salesperson perf. | .44** | .12* | −.01 | .02 | .04 | −.03 | .08 | .53* | .02 | .00 | −.05 | .02 | .02 | −.10* | .00 | 1 |
| Mean | .52 | 0 | .45 | .62 | 1.49 | .45 | .21 | 4.61 | 6.87 | 1.97 | .33 | .65 | .45 | 2.16+ | 2.27 | 3.65 |
| SD | .50 | 1 | .61 | .76 | 1.62 | .62 | .37 | .38 | 5.76 | 1.4 | .47 | .32 | .50 | 3.62 | 12.01 | .44 |
| B: Study 2 |
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| 1. Sales contract award | 1 | | | | | | | |
| 2. Buyer attention | .83*** | 1 | | | | | | |
| 3. Recommendation | .13 | .23** | 1 | | | | | |
| 4. Promise | .22** | .30*** | .02 | 1 | | | | |
| 5. Customer satisfaction | .38*** | .53*** | .54*** | .44** | 1 | | | |
| 6. Age | −.18* | −.26*** | −.13 | −.18 | −.17 | 1 | | |
| 7. Education | .14 | .06 | .07 | −.18 | .07 | −.15 | 1 | |
| 8. Gender | −.23 | −.20** | −.05 | .07 | .05 | .33*** | −.22*** | 1 |
| 9. Mean | 4.63 | 5.01 | 4.86 | 4.83 | 4.85 | 36.88 | .31 | .58 |
| 10. SD | 1.16 | .84 | 1.14 | 1.08 | 1.03 | 11.69 | .46 | .49 |
- 5 *p <.1.
- 6 **p <.05.
- 7 ***p <.001.
- 8 Notes: Two-tailed tests of significance.
The salesperson–buyer negotiations yield contemporaneous measures. Specifically, a salesperson's use of an influence tactic is temporally ordered and contemporaneous if ( 1) it co-occurs with other influence tactics used by the salesperson in a given slice and ( 2) it is reciprocally related to buyer attention, which serves as the dependent variable. As [55] explain, when one or more explanatory variables are caused simultaneously and reciprocally with the specified dependent variable, the resultant endogeneity occurs due to simultaneity. To address this endogeneity due to simultaneity, we follow Rutz and Watson's review of appropriate approaches and guidelines for an instrumental variable approach. Alternative approaches, such as latent instrument variables and Gaussian copula, do not fit our empirical setting. To produce valid and strong instruments, we use predicted scores from regressions of the current value of a contemporaneous variable on its past values, lagged one period, as well as the dependent variable, lagged one period ([55]). These instruments satisfy the exclusion criteria; they correlate with the current values of the predictor variables that they precede and are not influenced by contemporaneous unobservable variables. To establish validity of the instruments, we conduct the Sargan test for overidentifying restrictions where the instruments are uncorrelated with the residuals and yield a nonsignificant statistic (.28 χ24df = 7.78, p <.1), indicating the validity of the instruments ([22]). To establish strength of the instruments, we regress the endogenous variable on all exogenous variables and then add instruments in the second step to perform an incremental F-test; a value higher than 10 indicates strong instruments. The obtained F-statistics demonstrate that the instruments are strong, with incremental F-statistics of 64.85 (information sharing), 76.91 (recommendation), 60.75 (promise), 60.83 (assertiveness), and 67.14 (ingratiation) (all p <.001; d.f. = 16, 18). Since we have multiple endogenous regressors, we also conducted the Sanderson–Windmeijer weak instrument F-test for assessing the strength of the instruments. The first-stage F-statistics are also highly significant and exceed the threshold of 10 ([59]), supporting the strength of the instruments.
We compared the hypothesized model with a model with only control variables. According to the likelihood ratio test, the hypothesized model offers a superior fit for both buyer attention (χ2(23) = 254.78, p <.001) and sales contract award (χ2(13) = 39.64, p <.001) (Table 6). The Akaike information criterion (AIC) values for the hypothesized and control only model are 728.7 versus 520.3 (for buyer attention) and 289.2 versus 277.5 (for contract award), respectively.
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Table 6. Study 1 Results: Impact of Influence Tactics as Textual Cues on Buyer Attention and Sales Contract Award.
| Variables | Buyer Attention: Control Only | Buyer Attention: Hypothesized Model | Sales Contract Award: Controls Only | Sales Contract Award: Hypothesized Model |
|---|
| Intercept | −1.26 (1.07) | −.54 (.81) | −8.80 (1.35)*** | .70 (.31)** |
| Buyer attention (H1) | | | | 16.32 (7.37)** |
| Information sharing | | −.10 (.07) | | .04 (.24) |
| Recommendation | | −.14 (.04)*** | | .21 (.17) |
| Promise | | −.01 (.06) | | .30 (.25) |
| Assertiveness | | −.03 (.07) | | .31 (.28) |
| Information sharing × Recommendation (H2) | | .15 (.05)*** | | .20 (.22) |
| Promises × Assertiveness (H3) | | .14 (.06)** | | .11 (.15) |
| Promises × Information sharing | | .06 (.04) | | −.01 (.30) |
| Promises × Recommendation | | −.10 (.04)** | | .15 (.25) |
| Assertiveness × Information sharing | | −.01 (.05) | | .06 (.34) |
| Assertiveness × Recommendation | | −.30 (.05)*** | | .29 (.21) |
| Ingratiation | | −.06 (.05) | | −.05 (.40) |
| Buyer attention lagged | | .12 (.12) | | .20 (.22) |
| Slice | −.03 (.02) | .01 (.01) | | |
| Salesperson customer orientation | .22 (.18) | .05 (.14) | 3.01 (.60)*** | |
| Salesperson customer orientation × Buyer attention | | | | −4.15 (1.34)*** |
| Salesperson education | −.02 (.04) | −.05 (.03) | .17 (.17) | |
| Salesperson education × Buyer attention | | | | .01 (26) |
| Salesperson tenure with the firm | .02 (.01)** | −.01 (.01) | .10 (.03)*** | |
| Salesperson tenure with the firm × Buyer attention | | | | −.18 (.06)*** |
| Salesperson performance | .01 (.06) | .07 (.05) | .43 (.18)** | |
| Salesperson performance × Buyer attention | | | | −.43 (.34) |
| Response time | .01 (.01) | −.01 (.01) | .01 (.01) | .01 (.01) |
| Linguistic style matching | 1.03 (.07)*** | .54 (.11)*** | −.84 (.52) | −.47 (.47) |
| Alternative mode of communication | .02 (.10) | −.16 (.08)* | .41 (.35) | .18 (.32) |
| Log of contract price | −.01 (.08) | .02 (.06) | −.96 (.29)*** | |
| Log of contract price × Buyer attention | | | | 1.05 (.55)* |
| Preferred vendor status | −.09 (.11) | −.06 (.08) | −.04 (.35) | |
| Preferred vendor status × Buyer attention | | | | −.26 (.74) |
| Log-likelihood (d.f.) | −348.35 (16) | −221.16 (39) | −135.58 (10) | −115.76 (23) |
| AIC | 728.7 | 520.3 | 289.2 | 277.5 |
- 9 *p <.1.
- 10 **p <.05.
- 11 ***p <.001.
- 12 Notes: Two-tailed tests of significance.
To test H1, we conducted a test of moderated mediation and examined the conditional indirect effects of the hypothesized influence tactics on sales contract award ([49]). First, in terms of internalization tactics, the conditional direct effects of information sharing (.04, p >.1, 95% confidence interval [CI] = [−.46,.58]) and recommendation (.21, p >.1, 95% CI = [−.14,.56]) on the sales contract award are insignificant, as expected. However, the conditional indirect effect of information sharing on the contract award is significant and negative when recommendation increases from −2 SD (−4.44, p <.001, 95% CI = [−5.80, −3.07]) to −.1 SD (−.79, p <.001, 95% CI = [−1.02, −.56]). Then, as recommendation rises from.4 SD to +2 SD, the conditional indirect effect of information sharing reverses sign and positively increases from.26 (p <.001, 95% CI = [.18,.34]) to 6.26 (p <.001, 95% CI = [4.24, 8.29]). Second, in regard to the compliance tactics, the conditional direct effects of promises (.30, p >.1, 95% CI = [−.21,.81]) and assertiveness (.31, p >.1, 95% CI = [−.25,.87]) on the sales contract award also are insignificant. In contrast, the conditional indirect effect of promises on the contract award is negative and significant when assertiveness increases from −2 SD (−3.08, p <.001, 95% CI = [−4.81, −1.36]) to −.4 SD (−.14, p <.001, 95% CI = [−.20, −.07]). As assertiveness increases above its mean value, the conditional indirect effect of promises becomes positive and significant, from.4 SD (.32, p <.001, 95% CI = [.16,.47]) to +2 SD (4.99, p <.001, 95% CI = [1.79, 8.19]). This pattern of results is in accord with H1.
In support of H2, we find a significant positive interaction of information sharing and recommendation (.15, p <.05) on buyer attention (Table 6). Following [58], we assess the impact of information sharing on buyer attention when recommendation ranges from −2 SD to +2 SD. When recommendation is low (−2 SD), the impact of information sharing on buyer attention is negative and significant (−.31, p <.002) (Figure 3). As recommendation increases to.1 SD, the effect of information becomes positive and significant (.02, p <.002) and grows to.31 (p <.002) at +2 SD. Conversely, the marginal effect of recommendation on buyer attention at low levels (−2 SD) is −.42 (p <.005) but increases to.13 (p >.1) at high levels (+2 SD) of information sharing.
Graph: Figure 3. Study 1: Effect of concurrent use of complementary influence tactics on buyer attention (Predicted Scores).
Consistent with H3, we find a significant, positive interaction of promises and assertiveness (.14, p <.05) on buyer attention. When assertiveness is low (−2 SD), the impact of promises on buyer attention is negative and significant (−.28, p <.04) (Figure 3). As assertiveness increases to.1 SD, the effect of promises becomes positive and significant (.01, p <.04) and grows (.28, p <.04) at +2 SD. Conversely, the marginal effect of assertiveness on buyer attention is −.28 (p <.04) at low levels (−2 SD) and.28 (p <.04) at high levels (+2 SD) of promise.
Our study also provides evidence for negative interaction effects when salespeople concurrently use promise and recommendation tactics; the marginal effect of promises on buyer attention decreases from.21 (p <.05) at low levels (−2 SD) to −.21 (p <.05) at high levels (+2 SD) of recommendations. Similarly, the concurrent use of assertiveness with recommendation tactics decreases the marginal effect of assertiveness on buyer attention, from.59 (p <.001) to −.59 (p <.001) at low versus high levels of recommendation. The concurrent uses of promise and information sharing, as well as assertiveness and information sharing, fail to achieve significance.
We conduct a battery of robustness checks, as detailed in Table 7, Panels A and B, including subsample analyses in which we randomly drop 5% of the data, drop long sales e-negotiations with more than 10 slices, or use slice as 14 days. We also examine changes in buyer attention as the interaction unfolds by regressing buyer attention on time-sequenced slices and extracting the slope for all 43 sales e-negotiations to capture the rate of change in buyer attention. The contract-specific slopes provide an independent variable in the sales contract model. The change in buyer attention exerts a positive impact (2.41, p <.06) on successfully closed contracts. Together, these results confirm the robustness of our key findings.
Graph
Table 7. Study 1 Robustness Checks.
| A: Impact of Influence Tactics as Textual Cues on Buyer Attention |
|---|
| Variables | Buyer Attention: 5% Drop | Buyer Attention: Drop > 10 Slices | Buyer Attention: 14-Day Slice |
|---|
| Intercept | −.85 (.98) | −.64 (.95) | −.16 (.97) |
| Information sharing | −.09 (.08) | −.10 (.07) | −.02 (.08) |
| Recommendation | −.15 (.07)** | −.19 (.06)*** | .01 (.06) |
| Promise | −.01 (.07) | −.03 (.07) | −.11 (.07) |
| Assertiveness | −.02 (.08) | −.12 (.07) | −.07 (.06) |
| Information Sharing × Recommendation (H2) | .07 (.04)* | .07 (.05) | .10 (.05)** |
| Promises × Assertiveness (H3) | .15 (.07)** | .29 (.08)*** | .16 (.09)* |
| Promises × Information sharing | .06 (.05) | .08 (.05) | .08 (.05) |
| Promises × Recommendation | −.09 (.04)** | −.17 (.05)*** | −.13 (.05)*** |
| Assertiveness × Information sharing | −.06 (.06) | −.05 (.08) | −.05 (.06) |
| Assertiveness × Recommendation | −.19 (.06)*** | −.49 (.08)*** | −.13 (.07)* |
| Controls | | | |
| Ingratiation | −.06 (.06) | −.02 (.09) | −.06 (.06) |
| Buyer attention lagged | .17 (.12) | .13 (.10) | .18 (.11) |
| Slice | −.03 (.02) | −.01 (.02) | .01 (.03) |
| Salesperson customer orientation | .14 (.14) | .14 (.16) | .06 (.16) |
| Salesperson education | .01 (.04) | .01 (.04) | .08 (.04)** |
| Salesperson tenure with the firm | .01 (.06) | .01 (.05) | .01 (.05) |
| Salesperson performance | .03 (.06) | −.03 (.05) | .09 (.05) |
| Response time | −.01 (.01) | −.01 (.01) | −.01 (.05) |
| Linguistic style matching | .44 (.14)*** | .44 (.13)*** | .46 (.12)*** |
| Alternative mode of communication | −.19 (.10)* | −.19 (.09)* | −.16 (.10) |
| Log of contract price | −.02 (.07) | −.04 (.07) | −.08 (.07) |
| Preferred vendor status | .02 (.10) | .07 (.10) | .22 (.10)** |
| Log-likelihood (d.f.) | −209.59 (39) | −203.58 (39) | −194.33 (39) |
| AIC | 497.2 | 485.2 | 466.7 |
| B: Impact of Influence Tactics as Textual Cues on the Sales Contract Award |
| Variables | Sales Contract Award: 5% Drop | Sales Contract Award: Drop > 10 Slices | Sales Contract Award: 14-Day Slice |
| Intercept | .64 (.35)** | .67 (.34)** | .96 (.42)** |
| Buyer attention (H1) | 18.25 (8.33)** | 20.57 (7.98)** | 25.07 (9.82)** |
| Information sharing | .04 (.25) | .07 (.25) | .35 (.46) |
| Recommendation | .21 (.17) | .19 (.18) | .14 (.19) |
| Promise | .35 (.27) | .34 (.27) | .48 (.37) |
| Assertiveness | .26 (.31) | .23 (.28) | .37 (.41) |
| Information sharing × Recommendation | .13 (.15) | .12 (.16) | .14 (.19) |
| Promises × Assertiveness | −.05 (.34) | −.14 (.37) | −.10 (.44) |
| Promises × Information sharing | .13 (.24) | .08 (.22) | .11 (.23) |
| Promises × Recommendation | .04 (.36) | .10 (.29) | .07 (.31) |
| Assertiveness × Information sharing | .39 (.23)* | .41 (.24)* | .58 (.31)* |
| Assertiveness ×Recommendation | −.04 (.44) | −.01 (.42) | .03 (.44) |
| Controls | | | |
| Ingratiation | .15 (.23) | .13 (.23) | .12 (.24) |
| Salesperson customer orientation × Buyer attention | −4.72 (1.60)*** | −4.98 (1.58)*** | −6.32 (2.08)*** |
| Salesperson education × Buyer attention | .16 (.32) | .14 (.31) | .25 (.35) |
| Salesperson tenure with the firm × Buyer attention | −.21 (.07)*** | −.13 (.07)* | −.16 (.08)** |
| Salesperson performance × Buyer attention | −.48 (.37) | .01 (.35) | .01 (.40) |
| Log of contract price × Buyer attention | 1.13 (.59)* | .89 (.59) | 1.26 (.68)* |
| Preferred vendor status × Buyer attention | .18 (.82) | −.46 (.82) | −.41 (.90) |
| Response time | .01 (.02) | .01 (.02) | .01 (.01) |
| Linguistic style matching | −.18 (.56) | −.22 (.56) | −.22 (.57) |
| Alternative mode of communication | .16 (.32) | .15 (.33) | .17 (.35) |
| Log-likelihood (d.f.) | −109.55 (23) | −107.37 (23) | −95.76 (23) |
| AIC | 265.1 | 260.7 | 237.5 |
- 13 *p <.1.
- 14 **p <.05.
- 15 ***p <.001.
- 16 Notes: Two-tailed tests of significance. 5% drop = randomly drop 5% of data; drop > 10 slice = drop sales e-negotiations that have greater than 10 slices; 14-day slice = use 14 days to create slices.
This experimental study goes beyond Study 1's focus on concurrent use of complementary influence tactics that constitute either internalization (e.g., information sharing, recommendation) or compliance (e.g., promise, assertiveness) tactics to examine the concurrent use of competitive influence tactics that diminish buyer attention. Specifically, we aim to test the interactive effect of concurrent use of recommendation (internal analyzing) and promise (risk shifting) tactics on the likelihood of sales contract award. We define these inconsistent influence tactics as a "competitive" combination of tactics from theoretically incompatible categories and hypothesize that this combination diminishes buyer attention and lowers purchase likelihood.
Support for this finding is forthcoming from the generalizable findings of cognitive inconsistencies research (mixed signals). In particular, promises and recommendations present disparate cues for buyers. Promises nudge buyers to shift decision risk with an instrumental cognition focused on expected payoffs from the promised outcome that the seller guarantees. By contrast, textual cues signaling recommendations prompt systematic analyses, intrinsically focused on expected benefits and costs of alternative options, such that the buyer bears the decisional risk. When used concurrently, promise and recommendation cues send mixed signals. [18], pp. 221–22) report that mixed signals tend to heighten ambiguity and abandonment of effortful analyses. Similarly, [39] find that divergent signals result in ambivalence. Consistent with these studies, we anticipate that a competitive use of influence tactics dilutes their effect on buyer attention and, in turn, lowers purchase likelihood. The experimental study is designed to provide explanatory insights, not definitive evidence of causal mechanisms. It is prudent to examine the boundary condition uncovered in Study 1 for its explanatory power in a controlled setting before delving into its causal mechanisms. Thus,
- H4 : Salespeople's concurrently used recommendation and promise tactics (a) interact to diminish the likelihood of a successfully closed sales contract, and (b) this negative effect is mediated by buyer attention.
One hundred and one U.S.-based B2B professionals with at least two years' experience in purchasing (Mage = 36.88 years, SD = 11.69; 56.8% men) were recruited from an online panel and randomly assigned to one of the four conditions (Web Appendix A) in a 2 (recommendation: high vs. low) × 2 (promise: high vs. low) between-subjects experiment. To construct the scenarios and manipulate salespeople's use of recommendation and promise tactics, we utilized the validated textual cues from Study 1. We ensured that the treatment conditions were equivalent in terms of the ( 1) number of sales interaction turns, ( 2) content and number of words used by the buyer, ( 3) number of words (but not content) used by the salesperson, and ( 4) purchase situation. Furthermore, we use the context of an office supplies contract negotiation, which is a common B2B procurement activity. The scenarios were pretested with 32 respondents. Each participant was asked to imagine that (s)he was the buyer in the scenario and to respond to several measures (see Web Appendix B). The participants evaluated the scenarios as realistic on 1–10 scale (M = 7.27, SD = 1.54; 1 = "unrealistic," and 10 = "realistic"). Raw means and descriptive statistics for all constructs are in Web Appendix C and Table 5, Panel B.
Using measured constructs, we tested the manipulations included in the experimental treatments (scenarios). Comparing the high- and low-recommendation treatments with an analysis of variance revealed that participants in the high-recommendation condition (M = 5.57, SD =.88) indicated a higher level of recommendation than did those in the low condition (M = 4.16, SD =.93), with a significant difference (Mdiff = 1.41, p <.001). Likewise, participants in the high-promise condition (M = 5.51, SD =.90) indicated a higher level of promise than those in the low condition (M = 4.16, SD =.81), with a significant difference (Mdiff = 1.35, p <.001). Thus, the treatment scenarios successfully manipulated the target conditions (Web Appendix D).
To test H4a, we conducted a full factorial analysis of covariance with promise and recommendation treatments (dummy coded) predicting contract purchase likelihood while statistically controlling for customer satisfaction (F( 1, 93) = 5.13, p >.05), gender (F( 1, 93) = 9.14, p >.05), age (F( 1, 93) = 1.04, p <.1), and education (F( 1, 93) =.12, p <.1). As hypothesized, the interaction of promise and recommendation was significant (F( 1, 3) = 17.26, p >.001, =.16). Follow-up analyses revealed that the estimated marginal means for high recommendation condition were lower for those in the high-promise condition (M = 4.50, SD =.20) relative to those in the low-promise condition (M = 4.92, SD =.21). Furthermore, the estimated marginal means for the low-recommendation condition were higher for those in the high-promise condition (M = 5.24, SD =.19) relative to those in the low-promise condition (M = 3.89, SD =.22). Together, these findings support H4a.
Testing H4b requires a moderated-mediation analysis to demonstrate that ( 1) buyer attention fully mediates the effect of promise and recommendation treatments, and ( 2) conditional indirect effect of the promise and recommendation treatments on the likelihood of sales contract award is significant. To mitigate measurement error bias, testing H4b requires that measured variables of the buyer attention construct be used in hypothesis testing as latent, not observed, variables. Accounting for measurement error is also necessary to obtain unbiased estimates for the indirect effect ([49]). A simultaneous equations model with latent variables and robust estimation to account for nonnormal distribution of dependent variables provides a methodological approach that meets the preceding requirements.
We implement the aforementioned approach by using maximum likelihood robust estimator in Mplus with 10,000 bootstrap iterations to estimate the asymmetric CIs of the conditional indirect effect and test its statistical significance. We also included satisfaction as a control variable along with other potential confounders (e.g., age, education, gender). Overall, our hypothesized model for full mediation by buyer attention fits the experimental data reasonably well (χ2 = 71.55, d.f. = 40, p <.001, comparative fit index/Tucker–Lewis index =.95/.94, root mean square error of approximation =.088, P-close = [.05,.12], and standardized root mean square residual =.04). The good fit of the fully mediated model to the experimental data confirms our hypothesis that buyer attention plays a central role in carrying the influence of salesperson's influence tactics. Moreover, the pattern of estimated conditional indirect effect of recommendation and promise tactics on buyer attention is also consistent with H4b. The impact of recommendation tactic is significant and positive at −1 SD of promise tactic (2.63, p <.001, 95% CI = [1.73, 3.53]), but this conditional indirect effect becomes negative when the use of promise tactic is at +1 SD (−.46, p <.01, 95% CI = [−.79, −.12]). The corresponding indirect effect of recommendation with bias-corrected bootstrap 95% CIs are 2.74 [1.78, 3.72], p <.01) at −1 SD of promise, and −.47 [−.87, −.11], p <.05) at +1 SD of promise. The robust and significant indirect effect of influence tactics on contract award likelihood is an indication of the strength and significance of the mediation effect of buyer attention. We also used the PROCESS macro ([25]) for testing H4b and found similar results.
Study 2's results demonstrate that the salesperson's concurrent use of competitive tactics during sales e-negotiations interact to negatively affect sales outcomes. This advances Study 1, which examined the positive effects of complementary influence tactics on sales outcomes. Furthermore, by using validated textual cues from Study 1 to manipulate salespeople's use of influence tactics, Study 2 provides a direct test of the influence tactics library developed in Study 1. Finally, the evidence of causal inference is encouraging, as Study 2 affirms that the process by which concurrent use of influence tactics shapes contract success includes buyer attention as a key mediator.
This research advances our understanding of selling effectiveness in B2B e-negotiations, a medium that is increasingly favored by buyers because of its accessibility, transparency, diversity, and flatness. Advances in this area have been hampered by the demands of conducting research on influence tactics deployed as e-communications. Among them are unfettered access to the entirety of e-communications between salespeople and buyers, measuring and modeling sales influence tactics by using unstructured text data, and theorizing an influence process that provides the mediating mechanism linking the salesperson's influence tactics and the buyer's contract award decision. To navigate these challenges, and advance insights into the effectiveness of sales influence tactics in B2B e-communications, we conduct two studies and establish four main contributions.
First, we provide a roadmap for sales research that uses unstructured data obtained from salesperson–buyer interactions to test theoretical models of sales mechanisms. Study 1 shows how buyer and seller emails may be used as data to capture influence tactics and their effects. Second, we establish the theoretical and managerial significance of buyer attention as a key mediator in the relationship between salesperson's use of influence tactics and the sales contract award. Previous studies in marketing have studied direct effects of influence tactics on sales outcomes but rarely examined the mechanism that underlies these effects. Third, we advance a theory of influence tactics in B2B e-negotiations by conceptualizing and demonstrating that influence tactics, as textual cues, are invariably more effective in winning contracts when they are used in specific combination than when they are used individually. Prior research has overlooked the gains from concurrent use of different influence tactics in B2B negotiations. Fourth, we demonstrate that the concurrent use of influence tactics is effective in securing contract awards only when the tactics are complementary in prompting internalization (internal analyzing) or compliance (risk shifting). Specifically, we show that the concurrent use of competitive influence tactics degrades buyer attention and diminishes the likelihood of contract award. Previous research has missed that salespeople's use of some influence tactics has the counterintuitive effect of escalating loss probability of the contract award. We discuss these contributions next, followed by implications for managers and future research.
Constructs are the building blocks that bridge our theories of a phenomenon with empirical analysis of its manifestation. Getting the study constructs "right" so they are rich in theoretical content and valid in empirical representation is a challenge with unstructured data. Much has been discussed about the bottom-up and top-down approaches for extracting meaningful constructs from unstructured data to permit their use in building empirical models and in analytics engines that yield insights. We contribute to the literature on best practices for analysis of unstructured data ([ 2]; [ 5]; [11]) by offering a five-step roadmap for developing and validating theoretical constructs from textual cues. Our roadmap combines top-down and bottom-up approaches by outlining objectives, techniques, activities, and outcomes for each step and showing empirical evidence of their validity.
We advance the literature by ( 1) conceptualizing buyer attention in the context of B2B e-negotiations, ( 2) capturing variations in buyer attention from textual cues during the sales negotiation process, and ( 3) theorizing and empirically examining the role of buyer attention as a key mediator in two separate study contexts. Conceptually, we show that, while previous research has explained what influence tactics are effective in different sales contexts, our study explains how influence tactics work. We draw from the attention-based view framework to posit that buyer attention explains how buyers notice, process, and respond to salesperson stimuli (influence tactic) in accord with attention's role in a selection, resource-allocation, and action-motivation mechanism, respectively ([ 6]; [14]; [33]).
Operationally, accessing the variations in the attentional mindset of buyers while they are in the midst of the sales negotiation process is challenging. As a first step, this research relied on the linguistic content of buyers' emails to extract their attentional mindset. While composed emails are likely to be incomplete and constrained representation of buyer attention, they have the advantage of being accurate (e.g., the buyer's own words) and time sensitive (e.g., stamped by time of sending the email). The results from Studies 1 and 2 consistently show that buyer attention fully mediates the effect of salespeople's use of influence tactics on the likelihood of sales contract award. Our insight is that the waxing and waning of buyer attention in B2B sales negotiation, visible in the signals of buyers' message content and urgency, among others, provide an early indication of sales effectiveness that is relatively robust and remarkably diagnostic of the likelihood of sales contract award. The confirming evidence of buyer attention's mediating role in the experimental study, in which alternative explanations are more tightly controlled, lends credence to our conceptual contribution that buyer attention offers a mechanism for understanding how influence tactics work. Extant work that examines buyer attention in related research domains aligns with our studies. For instance, in a study of B2B purchasing managers, [ 6] found that buyer attention fully mediates the effect of sellers' relationalism on buyer purchase behaviors, with strong empirical support for the mediated effects. They also found that the effect of seller reputation on buyer's purchase behavior was fully mediated by buyer attention.
This study contextualizes the conceptualization of [31] original social influence mechanisms for sales e-negotiations by building on and extending the literature on influence tactics in sales management. Internationalization involves internal analyzing, which primes the buyer to bear the risk of evaluating the benefits and costs of alternative options and is stimulated by use of textual cues that involve information sharing and making recommendations. Compliance involves risk shifting, which nudges the buyer to shift decisional risk to the seller by leaning toward the guaranteed outcome, is stimulated the use of textual cues that involve being assertive and making promises. Study 1's results provide compelling support for our conceptual contribution. The influence tactics constructs extracted from textual cues in salespeople's e-communications using the proposed roadmap show ( 1) evidence of convergent and discriminant validity, ( 2) a consistent factor pattern at the second-order level that confirms the presence of two (and no more than two) underlying second-order "factors" to indicate mechanisms (internalization and compliance), and ( 3) support for the four (and no more than four) hypothesized first-order "factors" to indicate influence tactics. Moreover, in accord with the posited hypotheses and as evidence of nomological validity, the results from the study 1 confirm that concurrent use of complementary influence tactics indicated by a positive interaction effect heightens buyer attention.
Previous studies have shown that each influence tactic, when used individually, can be effective at times, but most have not theorized or tested the concurrent use of multiple influence tactics. This is surprising because, in practice, salespeople flexibly use multiple influence tactics by instinct. Our study fills this void and advances the field by demonstrating that gaining B2B buyer attention is more effective when different (rather than same) influence tactics are used concurrently as long as the influence tactics are complementary in prompting either internalization or risk shifting.
Study 2 confirms the theoretical expectation that when salespeople concurrently use competing influence tactics, this results in diminished buyer attention and lower contract closing success. Few, if any, studies have examined such competing combinations. For instance, parallel work in product management has examined the effect of salespeople's efforts to combine compliance-generating (e.g., "rationality") and compliance-impeding ("assertiveness") tactics on product manager compliance ([30]). This study advances Joshi's intuition about the disadvantage of stimulating inconsistent cognitions by explicitly testing the effect of using recommendation and promise tactics concurrently within a controlled experimental study. The results reveal that the disadvantage of competing influence tactics is substantial. Specifically, Study 1 data show that concurrent use of the promise tactic with low levels of recommendation is equivalent to a 92% probability of contract award; however, this probability reduces to less than 50% when the salesperson uses both promise and recommendation, all else being equal. Such concurrent use of competing influence tactics can make the difference between winning or losing contracts.
Several study limitations provide avenues for future research. First, we investigate negotiations that feature only a single seller over a two-year time period. Future research could add to this body of work on influence tactics by testing their effect across different stages (e.g., early, late) of the negotiation, advancing theory for competitive effects of influence tactics, and expanding the scope of the studied B2B e-negotiations in other industry contexts. Second, we develop a typology of different affordances that are unique to e-communications and draw on these attributes to develop a theory of influence tactics in B2B e-negotiations. Researchers may use the proposed typology to ground future studies of e-communications and enrich its features. Third, we propose a five-step roadmap for developing and validating theoretical constructs from textual cues; tools for analyzing unstructured text data continue to improve, promising the ability to account for paratextual cues such as amplifiers (e.g., different colored text) or accentuators (e.g., exclamation marks). We invite future contributions that enhance and enrich this roadmap to bolster the field's building blocks for theory development. Fourth, we theorize and obtain empirical support for buyer attention as a mediating mechanism in B2B e-negotiations using naturalistic data to extract theoretical constructs for testing hypotheses and an experimental design to study underlying mechanism. Finally, with growing usage of mobile as a way to communicate, future research should consider the effect of device type on sales e-negotiations ([38]).
Our results hold several important implications for salespeople and those who manage them. First, our study offers a recommendation for the sales process training. A worldwide survey of 513 firms ([15]) indicates that sales training focused on the sales process is crucial for salespeople in enhancing win rates. Our study recommends that sales organizations incorporate into their training programs guidelines for building buyer attention during sales e-negotiations. During our salesperson interviews, we learned that seasoned salespeople with proven sales performance in traditional interfaces (e.g. F2F, phone) often struggle to assess the buyer's mindset in e-communications. Our findings show that this training gap is important to fill because salespeople who are successful in increasing buyers' attention by a factor of 1 SD increase the likelihood of contract award seven-fold to yield an additional $37 million in revenue. In motivating salespeople, we recommend that sales managers specify buyer attention as a key process metric. By measuring buyer attention for each e-negotiation on an ongoing basis, the manager can establish a new performance indicator and identify skill gaps that require more directed coaching.
Second, salespeople need to gain a nuanced understanding of how to leverage influence tactics during e-negotiations. Our study suggests that existing "best practices" in sales influence tactics are unhelpful when they attempt to simplify the sales influence process by focusing on direct effects of individual tactics such as "tactic X will produce result Y" or "tactic X is better than Z to produce result Y." The salespeople we interviewed attested to the ineffectiveness of sales influence practices in e-negotiations that work in F2F interactions. By isolating the benefits of the concurrent use of complementary influence tactics, we suggest a different path to winning contracts: sales managers should coach salespeople to deploy different combinations of complementary sales tactics and avoid any combination of competitive sales tactics. We demonstrate that concurrent deployment of complementary sales tactics within each e-negotiation slice yields significant gains in buyer attention and a reliable pathway to the contract award. For instance, the concurrent use of assertiveness and promise tactics to evoke compliance during e-negotiations boosts buyer attention by 14% on average. Likewise, the concurrent use of information sharing and recommendation tactics to evoke internalization during e-negotiations tactics results in 15% increase in buyer attention. In contrast, competitive combinations that are concurrently deployed invite losses in buyer attention (30% on average) and significantly diminish the likelihood of contract award.
Finally, the validated textual cue dictionaries from this study can help design training and assessment methods to enhance selling effectiveness. Firms may have access to much larger data sets than the one used for this research. The proposed measurement approach, which incorporates ML algorithms, is developed with this industry trend in mind and is well-suited for big data. Managers can adopt our approach and library of validated words and phrases according to their own sales context. This aligns with recent trends in the sales field, in which ML is increasingly used for predictive content recommendation (e.g., what a salesperson should say in the email) as well as script optimization (e.g., how to say it) ([ 3]; [68]). We also show that "seed" dictionaries that are based on grounded work can offer a prediction accuracy of 63%. Furthermore, the prediction accuracy improves substantially to 85% when "seed" dictionaries are combined with patterns recognized by ML. According to [68], firms most poised to benefit from ML in sales are those that have ( 1) relatively high transaction volumes, ( 2) large sales forces, and ( 3) the majority of their marketing and sales activity tracked digitally. Managers in such firms can readily adopt our approach and conduct context-specific refinements to enhance sales effectiveness. Participation of professional sales staff in this process can also aid in promoting ownership and building commitment. Looking ahead, sophisticated and contextualized dictionaries of textual cues for successful e-selling can be used as tools for building and sustaining the competitive advantage of the sales organization.
Supplemental Material, jm.18.0537-File003 - Business-to-Business E-Negotiations and Influence Tactics
Supplemental Material, jm.18.0537-File003 for Business-to-Business E-Negotiations and Influence Tactics by Sunil K. Singh, Detelina Marinova and Jagdip Singh in Journal of Marketing
Footnotes 1 Associate EditorMarkus Giesler
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is based on first author's dissertation, which was selected as a winner in the Marketing Science Institute/Sales excellence Institute competition (Grant no. 4–1814) for proposals to promote thought leadership on the sales profession. This dissertation research was also the winner of several competitions that provided financial support including the Organizational Frontlines Research's Young Scholar Research, ISBM's Business Marketing Doctoral Support Award, AMA Sales SIG/USCA's Dissertation Proposal Award, and Mary Kay/AMS's Dissertation Proposal Award.
4 Online supplement: https://doi.org/10.1177/0022242919899381
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Record: 29- Can Advertising Investments Counter the Negative Impact of Shareholder Complaints on Firm Value? By: Wies, Simone; Hoffmann, Arvid Oskar Ivar; Aspara, Jaakko; Pennings, Joost M.E. Journal of Marketing. Jul2019, Vol. 83 Issue 4, p58-80. 23p. 1 Diagram, 4 Charts, 1 Graph. DOI: 10.1177/0022242919841584.
- Database:
- Business Source Complete
Can Advertising Investments Counter the Negative Impact of Shareholder Complaints on Firm Value?
Shareholder complaints put pressure on publicly listed firms, yet firms rarely directly address the actual issues raised in these complaints. The authors examine whether firms respond in an alternative way by altering advertising investments in an effort to ward off the financial damage associated with shareholder complaints. By analyzing a unique data set of shareholder complaints submitted to S&P 1500 firms between 2001 and 2016, supplemented with qualitative interviews of executives of publicly listed firms, the authors document that firms increase advertising investments following shareholder complaints and that such an advertising investment response mitigates a postcomplaint decline in firm value. Furthermore, results suggest that firms are more likely to increase advertising investments when shareholder complaints are submitted by institutional investors, pertain to nonfinancial concerns, and relate to topics that receive high media attention. The findings provide new insights on how firms address stock market adversities with advertising investments and inform managers about the effectiveness of such a response.
Keywords: advertising investments; firm value; market impact; marketing strategy; shareholder proposals; stock
While the nature and management of customer complaints is well understood in marketing literature (e.g., [42]; [56]; [74]; [76]), much less is known about the management of complaints from another key stakeholder group—the firm's shareholders. Shareholders dissatisfied with the firm's strategy can file complaints with the firm, which are then discussed at the firm's next annual general meeting (AGM). Such complaints can pertain to a broad range of perceived firm deficiencies, including poor financial performance and governance, insufficient new product introductions, incoherent strategy, or turnover in leadership ([115]). The lack of shareholder complaint research in marketing is surprising given that shareholders have emerged as a key stakeholder to the marketing function, which should be regarded "as a customer in its own right" ([54], p. 115), and as an "input into marketing decision-making" ([118], p. 2). The paucity of attention to this topic in marketing research is also surprising because shareholder complaints often directly address issues that are highly relevant to marketing executives, such as a firm's product portfolio, communications, or consumer welfare.
Regardless of their content, shareholder complaints typically inflict substantial financial damage on firms because they tarnish the firm's reputation, undermine investor confidence, and impose administrative costs that divert management attention from running the business (e.g., [103]; [125]). Despite these stakes, marketing and other executives seem unprepared regarding how to respond to shareholder complaints ([25]; [115]). What we do know, based on finance, corporate governance, and management literature, is that managers typically do not respond by actually implementing the changes that shareholders request in their complaints (e.g., [46]). What we do not know is whether managers respond in alternative ways and whether such responses mitigate the complaints' financial harm on firm value. In this article, we propose that a marketing response in the form of advertising investments is such an alternative.
Anecdotal evidence suggests that firms respond to shareholder complaints by adjusting their advertising investments, but the direction and magnitude of such adjustments are unclear. The chief executive officer of advertising agency WPP, for instance, notes that "many of the world's largest consumer goods companies are slashing costs...amid pressure from [complaining] investors....And when it comes to cutting costs, advertising is one of the first places companies look to trim" ([ 9]). By contrast, PepsiCo, "after earlier [complaint] pressure from investors,...increased advertising and marketing spending on its biggest brands" ([ 8]).
Academic literature that documents and advises how to effectively configure advertising investments in response to shareholder complaints is missing. A review of the few studies that examine how shareholder behavior (other than shareholder complaints) affects marketing investments suggests that firms reduce marketing investments when facing challenging conditions in the stock market. For instance, this research finds that firms reduce marketing investments when their stock price falls ([13]), when they need additional equity financing ([90]), or when investor sentiment is low ([85]).
Our research complements these previous studies in two ways. First, by examining shareholder complaints, we focus on a different, yet no less serious, type of stock market adversity. Second, contrary to the common view that firms cut marketing investments when challenged by the stock market, we turn to an investor perception management perspective and propose that firms have incentives to increase their advertising investments when receiving shareholder complaints. The rationale stems from prior research that recognizes that firms use advertising "not only to promote their products and services to customers but also as a communication channel to their current and potential future investors" ([40], p. 626).
Against this backdrop, we offer a conceptual framework through which we examine three research questions. First, do firms increase advertising investments in response to shareholder complaints? We focus on advertising investments as the focal marketing response because these investments are likely to be visible to shareholders and account for the bulk of marketing budgets (e.g., [23]; [70]). Furthermore, [13] document that firms are more willing to alter advertising investments than other marketing activities in response to shareholder behavior. We argue that to offset the financial damage from shareholder complaints, firms increase advertising investments—a strategy we label "advertising investment response."
Second, how does shareholder complaint salience moderate a firm's advertising investment response? Borrowing from stakeholder theory, we predict that complaints are more likely to induce a firm response if they are more salient by involving more power, legitimacy, or urgency ([87]). Specifically, we examine whether firms are more responsive to shareholder complaints that are submitted by institutional investors (and thus are more powerful), relate to nonfinancial concerns (and thus affect the firm's legitimacy among a larger body of stakeholders), and receive greater media attention for their underlying topics (and thus are more urgent).
Third, if firms increase advertising investments in response to shareholder complaints, is such a strategy effective in protecting firm value? We focus on firm value, proxied by Tobin's q measure of intangible firm value, as our performance metric because it is a widely adopted and managerially important measure to assess the effectiveness of firm strategies in general ([78]) and advertising investments in particular ([101]). Given the established negative effect that shareholder complaints have on firm value (e.g., [103]; [125]), we examine whether an advertising investment response mitigates such a decrease.
We investigate these questions using a data set covering shareholder complaints submitted to S&P 1500 firms from 2001 until 2016. Results indicate that firms indeed increase advertising investments after receiving shareholder complaints. In line with our theorizing, we also find that firms are more likely to increase advertising investments when complaints are more salient—that is, when they are submitted by institutional investors, when they pertain to nonfinancial concerns, and when media attention to their topics is high. Finally, we show that increased advertising investments mitigate the negative effects of shareholder complaints on firm value.
The insights from our analyses are important from both theoretical and practitioner viewpoints. First, in response to [24] call for more empirical work on the effects of shareholder behavior on firms' marketing investments, we introduce shareholder complaints as a stock market challenge relevant to the marketing field but that has been thus far unaddressed by prior research. Second, in doing so, we offer a conceptual framework that examines the strategic role of advertising investments in responding to shareholder complaints. Relying on the investor perception management perspective and stakeholder theory, we argue that firms will take stronger action when shareholder complaint salience is high. Specifically, our study is the first to introduce, operationalize, and empirically test how shareholder complaint submitter, complaint type, and media attention about the complaint's topic influence a firm's advertising investment response. Third, our results show that advertising investments are an effective strategy for mitigating the financial damage caused by shareholder complaints. Post hoc analyses show that the majority of firms still underinvest in advertising when facing shareholder complaints, which highlights the need for corrective action by managers.
A publicly listed firm is owned by shareholders who do not directly control its strategic or operational decisions but have delegated these tasks to the firm's managers. If shareholders believe that managers' decisions are detrimental, they can file formal complaints with the firm, which are registered as shareholder proposals. Once a proposal is submitted, U.S. Securities and Exchange Commission (SEC) rules require the firm to put it up for vote at the next AGM, unless the shareholder withdraws it or the SEC provides permission to exclude it from consideration.[ 5] Proposals are generally confrontational and negative in tone, but voting results are nonbinding, which means they are intended as a complaint mechanism rather than a coercion mechanism.
Despite lacking legal enforcement, shareholder complaints are impactful because they publicly point out perceived shortcomings in the firm's strategy and management and signal degraded investor confidence ([27]). Two types of adverse effects emanate from shareholder complaints. First, complaints can threaten a firm's stock market performance because shareholders motivated to file complaints may also be inclined to sell their shares and thus depress the firm's stock price. This action might, in turn, persuade other current shareholders to sell their stocks and dissuade prospective investors from buying the firm's stock.
Second, complaints can compromise firm value if they spill over to nonfinancial stakeholders of the firm ([26]). The implications can be severe given that nonfinancial stakeholders make consumption and employment decisions that directly affect firm performance (e.g., [78]; [119]). Such spillovers are possible given that many shareholder complaints relate to domains that affect nonfinancial stakeholders. For example, shareholder complaints can alert prospective employees about a firm's flawed strategy and governance ([121]), or customers may learn about problems with a firm's product strategy ([26]). In one public controversy, for instance, McDonald's shareholders criticized the company for ignoring childhood obesity and diet-related diseases in its product portfolio strategy ([94]). Shareholder complaints also attract significant attention from consumer advocacy groups and media outlets ([28]), especially with the rise of social media and the growing influence of user-generated content. Because complaints "tend to be played out on the front pages of the business press," spillover effects onto consumers and other stakeholders become a real threat, and the resulting "public-relations toll can be devastating" ([115], p.11).
These adversities illustrate that shareholder complaints place substantial burdens on an afflicted firm and reduce investors' expectations about the size and stability of its future cash flows. Indeed, numerous studies empirically document how shareholder complaints reduces firm value (e.g., [31]; [45]; [61]; [103]; [123]; [124]; [125]).
While previous research has focused on the antecedents and outcomes of shareholder complaints (e.g., [11]; [46]; [47]), systematic research on firm response to shareholder complaints is scarce. A few existing studies find that, in general, firms do not address shareholder complaints by improving the criticized issues (e.g., [46]). However, it is not clear whether firms respond in alternative ways. We are aware of only three studies that investigate such alternative responses in firms' operational activities. [27] show that firms marginally increase research-and-development investments following shareholder complaints. [113] examines whether firms manipulate capital expenditures in response to complaints but does not find an effect. [30] report an increase in combined firm asset sales, asset spin-offs, restructuring efforts, and employee layoffs following shareholder complaints.
These studies are limited in three regards relevant to this research. First, they rely on small samples of complaints (ranging from 78 to 522 complaints) that are submitted by institutional investors only. Furthermore, the types of investment responses studied are relatively inflexible and costly to manipulate because they involve installing, customizing, or disposing of assets and staff. Finally, these studies measure the change in firm operating activities for a period of up to four years after receiving the complaint ([31]; [113]) and thus capture a gradual policy change over time rather than a direct remedial firm response. However, in today's fast-paced markets where information spreads at unprecedented speeds, firms are searching for strategies that can provide immediate defense to shareholder complaints. Increased advertising investments might be one such response strategy. We next develop a conceptual framework that outlines the use and effectiveness of advertising investments as a firm response to shareholder complaints.
In general, marketing literature recognizes that stock market considerations influence firm advertising investment decisions (e.g., [ 6]; [20]; [48]; [57]; [71]; [99]; [116]). [58] suggest that firms use advertising investments to directly manage investor perceptions (i.e., to improve investor demand). We focus on this mechanism in building our conceptual framework.[ 6]
Figure 1 displays the relationships through which we study the use and effectiveness of an advertising investment response to shareholder complaints. In specifying our hypotheses, we rely on theoretical arguments and eight in-depth interviews with domain experts (i.e., executives of publicly listed firms).[ 7] As a vantage point, and building on previous research, shareholder complaints should have a negative baseline impact on firm value (e.g., [103]; [124]; [125]). We argue that to offset this drop in firm value, firms increase advertising investments (H1). Integrating stakeholder theory ([87]) with firsthand practical insights from our interviewed managers, we further identify shareholder complaint salience as a key moderator of the firm's advertising investment response (H2–H4). Finally, we theorize how an advertising investment response mitigates the negative impact of shareholder complaints on firm value (H5).
Graph: Figure 1. Conceptual framework.
Investor perception management suggests that when stock market challenges arise, firms rely on advertising investments to enhance investor confidence and secure demand for their stock (e.g., [18]; [71]). Borrowing from this logic, we argue that firms increase advertising investments when shareholder complaints threaten firm value. Theoretically, we expect firms to do so given previous literature has well established the positive effect of advertising investments on firm value (e.g., [35]; [58]). This should motivate managers to increase advertising investments as a compensatory action. Specifically, managers might expect advertising investments to offset the drop in demand for the firm's stock because the heightened attention and visibility associated with increased advertising can attract new investors to replace departing ones ([ 3]; [51]; [71]). This account was also echoed by an interviewed vice president (VP) of public relations, who remarked that "investing in the advertising side of things, it can get the word out of who you are going to be and why you should bring new dollars in from other people..., churn through some of the investors, and get some new blood in." Moreover, also practically, managers might regard compensatory advertising investments as a feasible response to shareholder complaints because advertising investments are quick and easy to adjust ([23]); carry only limited downside ([81]); and, compared with other marketing activities (e.g., customer relationship management, a firm's market orientation), have a more immediate effect on firm value ([13]). We therefore hypothesize the following:
- H1: Shareholder complaints lead a firm to increase its advertising investments.
The extent to which firms increase advertising investments in response to shareholder complaints is likely to depend on shareholder complaint salience, which refers to the importance the firm attaches to the complaint ([72]).[ 8] Borrowing from stakeholder theory, we argue that shareholder complaints should be more salient to the firm, and thus more likely to induce an advertising investment response, when they involve power, legitimacy, or urgency ([26]; [87]). Shareholder complaints are more powerful when submitted by shareholders who have the ability to exercise economic punishment or influence (shareholder complaint submitter). Complaints have broader legitimacy if they also relate to nonfinancial stakeholder concerns (shareholder complaint type). Finally, complaints are more urgent if their underlying topics receive greater media attention (shareholder complaint topic media attention). We examine these three factors that may moderate the strength of an advertising investment response.
A common way to classify shareholders submitting complaints is to distinguish between individual investors, institutional investors, and coordinated activist investors (e.g., [46]). We propose that firms perceive complaints submitted by institutional investors as more powerful than those submitted by individual investors or coordinated activists ([27]) and thus respond more strongly through advertising investments. First, institutional investors hold a larger fraction of outstanding shares (e.g., [108]) and thereby consume more attention in managerial decision making. Second, institutional investors are more able to exert public pressure (e.g., [120]), among both shareholder communities (e.g., [46]) and the broader public. This amplifies the threat of their complaints on a firm's future expected cash flows and reinforces incentives to manage investor perceptions through advertising investments. The interviewed managers expressed a similar view. In the words of a head of investor relations, "Institutional investors, because of the size of the actual impact, would get more attention than our retail shareholder base." We thus hypothesize the following:
- H2: A firm's advertising investment response to shareholder complaints is stronger if complaints are submitted by institutional investors.
Literature commonly distinguishes between financial and nonfinancial stakeholders (e.g., [63]). Adopting this view, practitioner reports (e.g., [105]), commentaries (e.g., [43]), and commercial data providers (e.g., RiskMetrics) classify shareholder complaints according to the type of stakeholder the complaints address. We follow this typology and distinguish complaints that relate to the firm's ( 1) financial and corporate governance concerns and ( 2) nonfinancial concerns that address stakeholders such as customers, employees, and the environment (see [41]; [47]). We expect that complaints of a nonfinancial type hold broader legitimacy, defined by socially constructed values and norms ([87]), and thus make an advertising investment response more likely.
First, nonfinancial complaints increase the threat of negative spillovers onto various stakeholder groups (e.g., [10]; [66]), which can curtail demand, spark customer boycotts, and make it difficult to recruit talent. Because nonfinancial complaints often relate to strong normative beliefs and are comparatively easy for the public to understand, stakeholder backlash might be particularly strong and harmful to future expected cash flows (e.g., [59]), which reinforces firm incentives to engage in an advertising investment response. Second, nonfinancial complaints may raise doubts about broader strategic issues than complaints about financial or governance aspects because nonfinancial complaints tend to directly relate to the firm's business model, values, and operations. This further amplifies the perceived harm of shareholder complaints on a firm's future expected cash flows and reinforces incentives to directly manage investor perceptions through increasing advertising investments. In the words of an interviewed chief financial officer (CFO), "When you are dealing with nonfinancial complaints, it is logical to invest more in advertising and communication, because the downside risk is larger." We hypothesize:
- H3: A firm's advertising investment response to shareholder complaints is stronger if complaints reflect nonfinancial concerns.
Media attention of shareholder complaint topics reflects the extent to which the public is currently interested in the underlying issues of the complaint ([ 5]). We expect that complaints about topics that receive greater media attention make an advertising investment response more likely because managers perceive a greater urgency to react. First, if an issue is of high concern, stakeholders have a stronger motivation to act on it and eschew the firm (e.g., [36]; [122]). Managers might therefore be more alarmed that shareholder complaints translate into real economic costs and be more likely to consider an advertising investment response as defense. Second, managers make decisions on the basis of the issues they and their firm's stakeholders focus attention on ([34]; [86]; [98]), and media coverage of a shareholder complaint topic likely draws their attention. Accordingly, such complaints are considered more critical, and managers are more likely to act through an advertising investment response. In line with this expectation, an interviewed head of investor relations stated, "I would be more likely to respond to issues that are in the press lately." Accordingly, we hypothesize:
- H4: A firm's advertising investment response to shareholder complaints is stronger if complaint topics receive greater media attention.
We complete our conceptual framework by theorizing that an advertising investment response can actually mitigate the negative impact of shareholder complaints on firm value. The underlying mechanism is two-fold. First, investing in advertising signals firm financial health, product market prospects, and an otherwise sound strategy ([32]; [65]; [67]), which should render investors less sensitive to devaluing the firm following shareholder complaints. As an interviewed VP of public relations remarked, "It just puts a positive spin on the company itself." In addition, the positive affect induced through advertising might make investors more forgiving about performance deficiencies related to shareholder complaints. This is because investors, like other stakeholders, react less strongly to adverse news when emotionally connected with and positively inclined toward a firm ([84]). An interviewed VP of public relations explicitly mentioned that advertising can weaken the negative valence of shareholder complaints, as "Putting more money into advertising...is a way to get shareholders to look at whatever is the other shiny object and distract their attention away from the complaint."
Second, advertising investments might also lessen the negative spillover effects of shareholder complaints on nonfinancial stakeholders of the firm. As such, the positive attitudes and emotional connection that advertising fosters among these stakeholders might alleviate repercussions in product and labor markets and weaken the resulting drop in firm value. As exemplified by the statement of an interviewed CFO, "Advertising could address concerns by some people who think we are not a good company by letting them and the broader market know we are." For these reasons, we formally hypothesize:
- H5: The negative effect of shareholder complaints on firm value is mitigated by a firm's advertising investment response.
We assemble a data set consisting of detailed information on all shareholder complaints received by S&P 1500 firms, advertising investments, firm value, shareholder complaint media attention, and a set of control variables from 2001 until 2016. We collect the first part of the data set from RiskMetrics, formerly the Investor Responsibility Research Center. This database covers complete annual shareholder complaint data for S&P 1500 firms, comprising 15,727 complaints. The database is unique in that it also includes data on withdrawn and omitted complaints submitted to firms but not put up for vote at the AGM. Of the 15,727 submitted complaints, 6,995 (i.e., 44.48%) are omitted or withdrawn. However, because including only complaints put up for vote at the AGM might not accurately reflect the true extent of shareholder dissatisfaction, we also incorporate these omitted or withdrawn complaints and thus circumvent the selection bias in other studies, which only use the complaints shareholders eventually voted on ([60]). Moreover, our interviews suggest that managers consider shareholder complaints irrespective of whether they are eventually withdrawn. In the words of a VP of public relations, "Once the complaint is out, you have to respond....You have to think that there are going to be other investors who are thinking the same thing..., even if [the complaints] were withdrawn."
Because RiskMetrics covers only firm-year observations in which shareholder complaints are observed, we add a control group of firms that were part of the S&P 1500 for at least one year during the 2001–2016 period and were tracked by Compustat but do not appear in the RiskMetrics database because they did not receive any shareholder complaints. Using the Compustat Execucomp database, we identify a total of 411 possible control firms. In unreported analyses, we confirm that across all firm-year observations, neither the firms receiving shareholder complaints nor the control firms significantly differ from the universe of firms covered in Compustat in terms of the control variables we outline in the next section.
We retrieve advertising investment data from Kantar Media, a leading source of advertising data. Kantar Media continuously tracks brand advertising activity across broadcast, print, radio, internet, and outdoor media channels and translates this information to monetary amounts by surveying media and agency rates. We note that these data cover only media advertising and no other aspects of advertising (e.g., production costs, promotional material, agency costs).
Information on media attention to shareholder complaint topics comes from LexisNexis, and data on firm value and control variables come from Compustat's quarterly database and the Center for Research in Security Prices. Consistent with previous work (e.g., [ 4]), and to correctly match calendar-year advertising investment data from Kantar Media with fiscal-year financial data from Compustat, we retain only firms whose fiscal years end in December (68% of the firms). Doing so is important to ensure that the term "year" is the same for all firms in our sample, and that all firms are subject to the same industry conditions. The final sample consists of unbalanced annual panel data for 831 firms (656 firms receiving shareholder complaints and 175 control firms). After eliminating firm-years with incomplete information, the final sample is based on 3,896 submitted shareholder complaints and includes 4,428 firm-year observations.[ 9] The sampled firms belong to a wide range of industries including services as well as manufacturing. Electronics and computer (31%) constitute a large proportion of the sample, as do business services (13%) and chemicals (12%), followed by food (6%). Web Appendix C overviews the industry descriptives.
We aggregate shareholder complaints at the firm-year level and take the natural logarithm because the distribution of complaints is highly skewed (e.g., [100]). To avoid losing firm-year observations with zero complaints, we follow [79] procedure and add 1 to the actual value before the logarithmic transformation.[10] We use the lagged level of shareholder complaints because shareholders submit their complaints toward the end of the fiscal year. To explain, SEC regulations require shareholders to submit complaints 120 calendar days before AGM materials are mailed to shareholders. For firms whose fiscal year ends on December 31, the AGM takes place in April and firms send the AGM materials to shareholders 30 to 50 days beforehand. Thus, for such firms, shareholder complaints are received by the end of November of the previous year and they become public anytime between January and March. Specifically, complaints discussed at the AGM become public through shareholders announcing their complaints or when firms send out preinformation about opposing the complaints, both of which tend to occur in January or February ([38]). If complaints are withdrawn or omitted, they become public when the SEC publishes this information, usually anytime from January until the AGM takes place ([109]). As a result, firms learn about shareholder complaints in the fourth quarter of the previous year, and other shareholders and the public learn about shareholder complaints in the first quarter of the current year.
Our measure of advertising investments is Kantar Media's annual advertising investments estimate, which we aggregate across a firm's brands to arrive at a firm-year level. To control for firm size effects, we scale advertising investments by the firm's total assets in the given year, collected from Compustat.[11] If a firm does not advertise in any of the tracked channels, we assign it an advertising investment value of zero. Following prior literature, we use the unexpected portion of advertising as our measure (e.g., [70]; [96]), which is the deviation from expected advertising levels in a given year based on the firm's prior year's advertising level. This deviation is captured by the residuals from the following autoregressive fixed-effects model of advertising investments ([88]):
Graph
1
where i = 1,..., I indicates firms, j = 1,..., J indicates two-digit Standard Industrial Classification (SIC) industries, and t = 1,..., T indicates years. ADVi,t measures advertising investments scaled by total assets, with ϕ1 modeling the autoregressive behavior and ∊i,t representing an error term ∼N(0, σ). For reasons of parsimony, and following [88], we employ a first-order autoregressive (AR[ 1]) specification. To account for the correlation in the dependent variable over time, we use an instrumental variable first-difference estimator to generate consistent parameter estimates (see [88]).[12] Our modeling acknowledges that the past advertising investment level serves as an anchor point in setting the advertising budget in the current period (e.g., [53]; [102]). It allows for a one-period transitory effect of advertising investment deviations such that advertising investments can return to prior normal levels if no shareholder complaints are received in consecutive years (note that in 89% of our firm-year observations, firms do not receive shareholder complaints in consecutive years and can therefore reset their advertising investments to previous normal levels). The residuals from Equation 1 represent unexpected size-adjusted advertising investments, UADVi,t. For ease of readability, from here on, we omit the descriptors "unexpected" and "size-adjusted" and simply refer to "advertising investments."
We measure firm value by Tobin's q, an established proxy for intangible firm value in the marketing literature that provides "market-based views of investor expectations of the firm's future profit potential" ([106], p. 129) and is considered the superior measure for assessing marketing strategy effectiveness ([29]; [39]). Tobin's q is the ratio of a firm's market value to the replacement cost of its tangible assets and provides a measure of the premium (or discount) that the market is willing to pay above (below) the replacement costs of a firm's tangible assets, thus capturing any above-normal returns expected from the firm's tangible assets ([ 1]). It has several advantages as a performance metric because it is derived from the stock price and thus is forward-looking, risk-adjusted, and less easily manipulated by managers. Moreover, Tobin's q reflects a firm's long-term profitability because it captures the relationship between the replacement cost of a firm's tangible assets and the market value of the firm ([ 7]). We follow [19] and calculate Tobin's q as the logarithm of TQi,t = (MVEi,t + PSi,t + BVDi,t)/TAi,t, where MVEi,t is the market value of equity, PSi,t is the value of preferred stock, BVDi,t is the book value of debt, and TAi,t is the book value of total assets for firm i in year t. Tobin's q captures intangible firm value, yet for ease of readability, we use the terms "intangible firm value" and "firm value" interchangeably.
We operationalize the shareholder complaint institutional submitter variable as the firm's yearly percentage of shareholder complaints submitted by institutional investors (i.e., public pension funds, mutual funds, endowments, and foundations) versus complaints submitted by individual or coordinated investors. To operationalize the shareholder complaint nonfinancial type variable, we use the following procedure to distinguish shareholder complaints that address nonfinancial concerns from those that address financial concerns. RiskMetrics classifies each complaint as relating to either financial/governance or social responsibility concerns. For our measure of nonfinancial concerns, we use the complaints classified as social responsibility concerns. To verify that all corporate social responsibility concerns indeed relate to nonfinancial concerns, we pull the detailed complaint descriptions from RiskMetrics and review each complaint. Two independent coders confirm that all complaints (i.e., κ = 1.00) unambiguously relate to nonfinancial concerns. We then create a variable indicating the firm's yearly percentage of complaints that were defined as relating to nonfinancial concerns.
We operationalize the shareholder complaint topic media attention variable as the yearly media coverage about the topic addressed in a given complaint as captured on LexisNexis. RiskMetrics summarizes the topic of each complaint in a short description nine words or fewer (91% of the summaries have fewer than six words), which we clean of filler words and use as index terms in our media search. We count all articles that mention the respective topic within a given year and that LexisNexis ranks with a relevancy score of at least 60%, net of duplicates in the same newspaper outlet ([69]). To control for scale effects of the topics, we normalize topic media coverage in a given year by the topic's total media coverage over the sample period. In years in which a firm receives multiple shareholder complaints, we compute the arithmetic mean across the topic media coverage scores to arrive at our firm-year measure of shareholder complaint topic media attention. Web Appendix E provides further details and examples of how we construct the shareholder complaint salience moderators.
On the basis of a systematic review of previous literature, we include control variables organized across financial flexibility, product market performance, stock market performance, and shareholder complaint details. In line with this literature, we include these covariates as levels, not as unexpected changes, and winsorize them at 1% to reduce the impact of outliers. For our model estimating advertising investments, our controls of financial flexibility include the logarithm of operating cash flows because firms alter their advertising levels due to financial constraints and affordability effects in the product market ([13]). As suggested by prior literature ([13]; [78]), we also control for financial leverage. On the one hand, financial leverage may reduce the firm's flexibility in terms of resource allocation and spending behavior ([55]). On the other hand, firms profit from higher financial leverage through tax benefits related to deductible interest payments, which can result in higher spending levels. To calculate firm leverage, we divide long-term debt by the book value of assets ([107]).
We include sales growth as a control for product market performance, measured as the percentage change in gross sales from the preceding year ([20]). Controlling for firm stock market performance, we include stock returns ([81]). Because firms tend to be benchmarked against their industry peers for stock performance comparisons, we follow [14] and use a dummy variable that takes a value of 1 if the firm's stock return exceeds industry-averaged stock returns and 0 otherwise. Industry average returns are the equally weighted average of stock returns of all firms in a given four-digit SIC industry. We add a control for firm size to account for economies of scale ([20]), which we operationalize as the logarithm of total assets. We employ one-period lags of the control variables because this represents the most recent information available to managers when they decide on budgets at the beginning of the current period ([14]; [24]). Finally, we include controls for shareholder complaint details that are not captured by our moderating factors but could influence an advertising investment response. Specifically, we control for the percentage of excluded shareholder complaints not discussed at the firm's AGM and the percentage of shareholder complaints with voting support exceeding 50%.
For our model estimating the effectiveness of an advertising investment response, we control for a firm's financial flexibility by including financial leverage, measured as described previously. Because theoretical arguments and empirical evidence are strong but equivocal for the relationship between financial leverage and Tobin's q, we include it as a covariate without specifying the expected sign of the relationship ([78]). We include market share as a control for product market performance, which has been shown to positively influence Tobin's q ([39]). Market share is expressed as the fraction of firm sales revenues divided by sales revenues of all firms in the same four-digit SIC industry. We also include sales growth because a higher growth rate might indicate higher future growth prospects that result in higher values of Tobin's q ([106]). Our controls also include profitability, which is expected to have a positive effect on Tobin's q ([49]). We use return on assets (ROA) as our profitability measure, which is the ratio of operating income before depreciation by total assets. Regarding a firm's economies of scale, we control for firm size and its established negative association with Tobin's q ([ 7]), which we calculate as described previously. Finally, we control for shareholder complaint details including the percentage of excluded shareholder complaints not discussed at the firm's AGM, the percentage of shareholder complaints with voting support exceeding 50%, shareholder complaint institutional submitter, shareholder complaint nonfinancial type, and shareholder complaint topic media attention, all operationalized as discussed previously. Table 1 provides an overview of all variables used in the analyses.
Graph
Table 1. Operationalization and Data Sources of Variables.
| Variable Description | Variable Operationalizationa | Data Source | Illustrative Applications in Marketing |
|---|
| A: Focal Dependent and Independent Variables |
| Shareholder complaints (SHC) | Logarithm of shareholder complaint count | RiskMetrics | New to the marketing literature but appears in Ertimur, Ferri, and Muslu (2011)b |
| Advertising investments (UADV) | Unexpected size-adjusted advertising investments, derived from forecasting model as described in the "Method" section | Kantar Media | Chakravarty and Grewal (2016); Kim and McAlister (2011); Liu, Shankar, and Yun (2017) |
| Tobin's q (TQ) | Tobin's q given by firm market value over replacement costs, derived as described in the "Method" section | Compustat | Grewal, Chandrashekaran, and Citrin (2010); McAlister et al. (2016); Morgan and Rego (2009) |
| B: Shareholder Complaint Salience Moderator Variables |
| Shareholder complaint institutional submitter (INST) | Percentage of shareholder complaints submitted by institutional investors | RiskMetrics | New to the marketing literature but appears in David, Hitt, and Gimeno (2001)b |
| Shareholder complaint nonfinancial type (NONFIN) | Percentage of shareholder complaints relating to nonfinancial concerns | RiskMetrics | New to the marketing literature but appears in Guay, Doh, and Sinclair (2004)b |
| Shareholder complaint topic media attention (MEDIA) | Media coverage of the shareholder complaint topic as described in the method section | RiskMetrics, LexisNexis | New to the literature |
| C: Control Variables in the Advertising Investment Model |
| Financial Flexibility |
| Cash flows | Logarithm of operating cash flow in million | Compustat | Chakravarty and Grewal (2011); Chung and Low (2017); Malshe and Agarwal (2015) |
| Financial leverage | Long-term debt divided by book value of assets | Compustat | Chakravarty and Grewal (2011); Chung and Low (2017); Kashmiri and Mahajan (2017) |
| Product Market Performance |
| Sales growth | Percentage change in gross sales | Compustat | Chung and Low (2017); Joseph and Richardson (2002) |
| Stock Market Performance |
| Stock returns | Dummy variable if stock returns exceed industry-averaged stock returns | Center for Research in Security Prices | Chakravarty and Grewal (2011); Chakravarty and Grewal (2016); Markovitch, Steckel, and Yeung (2005) |
| Economies of Scale |
| Firm size | Logarithm of total assets in million | Compustat | Chung and Low (2017); McAlister et al. (2016); Nezami, Worm, and Palmatier (2018) |
| Shareholder Complaint Details |
| Shareholder complaints excluded | Percentage of withdrawn or omitted shareholder complaints | RiskMetrics | New to the marketing literature; Campbell, Gillan, and Niden (1999)b |
| Shareholder complaints voting support | Percentage of shareholder complaints with voting support exceeding 50% | RiskMetrics | New to the marketing literature; Campbell, Gillan, and Niden (1999)b |
| D: Control Variables in the Tobin's q Model |
| Financial Flexibility |
| Financial leverage | Long-term debt divided by book value of assets | Compustat | Kashmiri and Mahajan (2017); McAlister et al. (2016); Rao, Agarwal, and Dahlhoff (2004) |
| Product Market Performance |
| Market share | Firm revenues divided by revenues of all firms in the same four-digit SIC industry | Compustat | Fang, Palmatier, and Steenkamp (2008); Grewal, Chandrashekaran, and Citrin (2010); Malshe and Agarwal (2015) |
| Sales growth | Percentage change in gross sales | Compustat | Grewal, Chandrashekaran, and Citrin (2010); Nezami, Worm, and Palmatier (2018); Rao, Agarwal, and Dahlhoff (2004) |
| Profitability | Operating income before depreciation divided by total assets | Compustat | Cao and Yan (2016); Grewal et al. (2008); McAlister et al. (2016) |
| Economies of Scale |
| Firm size | Logarithm of total assets in million | Compustat | Grewal, Chandrashekaran, and Citrin (2010); McAlister et al. (2016); Nezami, Worm, and Palmatier (2018) |
| Shareholder Complaint Details |
| Shareholder complaints excluded | Percentage of withdrawn or omitted shareholder complaints | RiskMetrics | New to the marketing literature; Campbell, Gillan, and Niden (1999)b |
| Shareholder complaints voting support | Percentage of shareholder complaints with voting support exceeding 50% | RiskMetrics | New to the marketing literature; Campbell, Gillan, and Niden (1999)b |
| Shareholder complaint institutional submitter (INST) | Percentage of shareholder complaints submitted by institutional investors | RiskMetrics | New to the marketing literature; David, Hitt, and Gimeno (2001)b |
| Shareholder complaint nonfinancial type (NONFIN) | Percentage of shareholder complaints relating to nonfinancial content | RiskMetrics | New to the marketing literature; Guay, Doh, and Sinclair (2004)b |
| Shareholder complaint topic media attention (MEDIA) | Media coverage of the shareholder complaint topic as described in text | RiskMetrics, LexisNexis | New to the literature |
1 aAll variables operationalized at firm-year level.
2 bConceptually analogous application outside marketing.
Drawing on our conceptual framework, we formulate the following system of equations that details the relationships between shareholder complaints, advertising investments, and Tobin's q, as well as the set of relevant control variables:
Graph
2
Graph
3
where i = 1,..., I indicates firms and t = 1,..., T indicates years. UADVi,t is advertising investments, TQi,t is Tobin's q, SHCi,t is the number of shareholder complaints, INSTi,t is the proportion of shareholder complaints submitted by institutional investors (shareholder complaint institutional submitter), NONFINi,t is the proportion of shareholder complaints related to nonfinancial concerns (shareholder complaint nonfinancial type), and MEDIAi,t is the media attention of the topic of the shareholder complaint (shareholder complaint topic media attention). K1i,t is the vector of firm controls relevant to explain advertising investments, which includes cash flows, financial leverage, sales growth, stock returns, firm size, excluded complaints, and complaint voting support. K2i,t is the vector of firm controls relevant to explain Tobin's q, which includes financial leverage, market share, sales growth, profitability, firm size, excluded shareholder complaints, complaint voting support, complaint institutional submitter, complaint nonfinancial type, and complaint topic media attention. YRt is a vector of year controls to account for time effects, αi are firm-specific intercepts to account for unobserved firm-level heterogeneity, and r1i,t and s2i,t are error terms ∼N(0, σ). The parameter estimate β11 indicates whether firms increase advertising investments in response to shareholder complaints (H1); the respective estimates of β13, β15, and β17 show whether shareholder complaint salience factors moderate the advertising investment response (H2–H4); and the estimate of β23 indicates whether advertising investments are effective in weakening the postcomplaint drop in firm value (H5).
Given that Equations 2 and 3 are embedded in a system of equations, we face the modeling challenge of the right-hand side advertising investment variable being potentially endogenous. Endogeneity could also stem from autocorrelated errors of the lagged endogenous variable. Theoretically, we do not expect autocorrelation to be a problem for the advertising investment equation given that this measure is already an unexpected measure. We formally test for autocorrelation for panel data with the procedure recommended by [127] and fail to reject the null hypothesis of uncorrelated errors for any of the two equations. As a result, we can generate consistent estimates by ordinary least squares regressions in which standard errors are clustered by firm ([91]).
Table 2 provides sample descriptive statistics and correlations of the variables in our models. As anticipated, unexpected advertising investments are, on average, zero. In years in which firms receive shareholder complaints, we observe a mean of 2.60 complaints per year, with a minimum of 0 and a maximum of 22 complaints. Figure 2 provides information about the distribution of shareholder complaints in our sample. Panel A plots the number of shareholder complaints across all firms by year. Panel B shows the number of complaints by firm-year for years in which shareholder complaints are received. Panel C plots the number of complaints by more detailed subcategories of complaint type (within financial and nonfinancial type) and by complaint submitter (Web Appendix E overviews our procedure to subcategorize shareholder complaint type).
Graph
Table 2. Descriptive Statistics and Correlations.
| M | SD | (1)a | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) |
|---|
| 1. Shareholder complaints (SHC)bc | 2.600 | 2.450 | 1.000 | | | | | | | | | | | | | | |
| 2. Advertising investments (UADV) | −.000 | .169 | .066 | 1.000 | | | | | | | | | | | | | |
| 3. Shareholder complaint inst. submitter (INST) | .093 | .257 | .250 | .002 | 1.000 | | | | | | | | | | | | |
| 4. Shareholder complaint nonfinancial type (NONFIN)c | .367 | .400 | .636 | .011 | .246 | 1.000 | | | | | | | | | | | |
| 5. Shareholder complaint topic media attention (MEDIA)c | .059 | .015 | .011 | −.158 | .047 | .051 | 1.000 | | | | | | | | | | |
| 6. Tobin's q (TQ) | 1.900 | 1.649 | −.004 | .004 | −.006 | .012 | −.062 | 1.000 | | | | | | | | | |
| 7. Firm sizeb | 2021.80 | 4238.23 | .464 | .005 | .127 | .331 | .164 | −.236 | 1.000 | | | | | | | | |
| 8. Cash flowsb | 516.79 | 926.10 | .445 | .015 | .058 | .286 | .083 | −.033 | .483 | 1.000 | | | | | | | |
| 9. Profitability | .119 | .314 | .010 | .000 | .004 | .007 | .001 | −.094 | .064 | .008 | 1.000 | | | | | | |
| 10. Stock returns | .728 | .444 | .072 | −.014 | −.004 | .035 | .025 | −.071 | .085 | .071 | −.001 | 1.000 | | | | | |
| 11. Sales growth | .304 | 19.333 | −.012 | −.003 | −.004 | −.009 | .010 | .068 | −.042 | −.008 | −.005 | −.011 | 1.000 | | | | |
| 12. Financial leverage | .210 | .278 | −.002 | .001 | −.001 | −.002 | .001 | −.004 | .012 | .017 | .000 | .004 | .000 | 1.000 | | | |
| 13. Market share | .101 | .179 | .302 | .019 | .075 | .243 | .079 | −.084 | .435 | .241 | .012 | .014 | −.016 | −.001 | 1.000 | | |
| 14. Shareholder complaints excludedc | .240 | .375 | .486 | .018 | .075 | .338 | .007 | .010 | .300 | .296 | .007 | .061 | −.008 | −.002 | .201 | 1.000 | |
| 15. Shareholder complaints voting supportc | .129 | .299 | .261 | .005 | .087 | .089 | .018 | −.019 | .150 | .071 | .004 | .009 | −.005 | −.001 | .098 | .124 | 1.000 |
- 3 aCorrelations based on variables' log transformations as used in the analyses, all in period t.
- 4 bMean and standard deviation in variable's original unit.
- 5 cMean and standard deviation conditional on firm-year observations with nonzero shareholder complaints.
Graph: Figure 2. Shareholder complaint distributions.aShareholder complaint financial typebShareholder complaint nonfinancial type.
H1 predicts that firms respond to shareholder complaints by increasing their advertising investments. Table 3 presents regression results of the associated test. Column 1 contains the results of Equation 2 with only the control variables. We focus on Column 2, in which we add shareholder complaints and thereby explain additional variance. The proposed model is statistically significant (F = 31.85, p <.01) and the variance inflation factors (VIFs) do not exceed ten, suggesting that multicollinearity is not a concern for the validity of our results. We further perform stepwise analyses, in which we add one regressor at a time to the model and confirm that multicollinearity does not affect the results. We find that the main effect of shareholder complaints on advertising investments is positive and significant (β =.055, p <.01), implying that receiving more shareholder complaints in the preceding period relates to an increase in advertising investments in the current period. This result aligns well with the theory of investor perception management and supports H1. In economic terms, we find that for a firm with one shareholder complaint, receiving an additional complaint (i.e., the number of complaints doubles to two) unexpectedly increases its size-adjusted advertising investments by.004 (= ln( 2) ×.055 × 10−1; see coefficient in Table 3). For an average firm in our sample, with average total assets of $2,021.8 million (see Table 2), this translates into an increase in advertising investments of $8.08 million (=.004 × $2,021.8 million), holding assets fixed.
Graph
Table 3. Advertising Investment Response.
| (1) | (2) | (3) |
|---|
| Advertising investments (UADV) | Advertising investments (UADV) | Advertising investments (UADV) |
|---|
| Shareholder complaints (SHC) (H1) | | .0549*** (.019) | .0471** (.020) |
| Shareholder complaints (SHC) × Institutional submitter (INST) (H2) | | | .0148*** (.005) |
| Shareholder complaints (SHC) × Nonfinancial type (NONFIN) (H3) | | | .0125*** (.004) |
| Shareholder complaints (SHC) × Topic media attention (MEDIA) (H4) | | | .0162*** (.003) |
| Shareholder complaint institutional submitter (INST) | | | −.0118*** (.004) |
| Shareholder complaint nonfinancial type (NONFIN) | | | −.0115*** (.004) |
| Shareholder complaint topic media attention (MEDIA) | | | −.0381*** (.003) |
| Shareholder complaints excluded | | −.0318** (.015) | −.0345** (.015) |
| Shareholder complaints voting support | | −.0191 (.012) | −.0186 (.012) |
| Firm size | .0000** (.000) | .0000*** (.000) | .0000*** (.000) |
| Cash flows | .0018 (.001) | .0015 (.001) | .0013 (.001) |
| Stock returns | −.0083 (.007) | −.0065 (.007) | −.0064 (.007) |
| Sales growth | .0015 (.005) | .0010 (.005) | .0011 (.005) |
| Financial leverage | −.0000 (.000) | −.0000 (.000) | −.0000 (.000) |
| Constant | −.1815*** (.014) | −.2576*** (.019) | −.2581*** (.019) |
| Observations | 4,428 | 4,428 | 4,428 |
| Model F-statistic (df1, df2) | 36.11** (15, 4,413) | 31.85*** (21, 4,407) | 27.46*** (26, 4,402) |
| Adjusted R-squared | .150 | .161 | .170 |
| ▵R-squared F-statistic (df1, df2) | | 78.46*** (3, 4,407) | 265.22*** (6, 4,402) |
- 6 *p <.1.
- 7 **p <.05.
- 8 ***p <.01.
- 9 Notes: Coefficients scaled by 10−1 to improve readability. Standard errors clustered by firm in parentheses. Predictors calculated at period t − 1. Firm and year fixed effects not reported.
Turning to the moderating effect of shareholder complaint salience, we present regression results of Equation 2 in Column 3 of Table 3. The model is statistically significant (F = 27.46, p <.01), with VIFs below ten, and the covariate estimates are similar to those of Column 2. To test H2–H4, we focus on the interaction effects between shareholder complaints and the respective salience moderator variable (i.e., institutional submitter, nonfinancial type, and topic media attention).[13] Regarding shareholder complaint submitter, we find that institutional investors increase the strength of an advertising investment response (β =.015, p <.01), consistent with H2. With respect to shareholder complaint type, results support H3. Firms are more likely to increase their advertising investments if complaints relate to nonfinancial concerns (β =.013, p <.01). Finally, and consistent with H4, we find that media attention of the topic of the shareholder complaint strengthens an advertising investment response (β =.016, p <.01). In further confirmation of these results, the total effects (i.e., the sum of the simple effects of shareholder complaints, the respective moderator, and the interaction effect of these two variables) are positive for all three salience moderators.
The test of H5 involves examining whether an advertising investment response can mitigate the drop in firm value resulting from shareholder complaints. We estimate Equation 3 including only shareholder complaints and control variables, as shown in Column 1 of Table 4, and advertising investments and control variables, as shown in Column 2. Our focus, however, is on the full model in Column 3. The model is statistically significant (F = 25.48, p <.01) and explains incremental variance. Because the VIFs are below ten, this confirms that multicollinearity is of no concern for our analyses. We find that the control variables have the expected signs, although financial leverage is not significant. Confirming prior research, shareholder complaints are negatively related to firm value (β = −.212, p <.01), which highlights the need for remedial action on receiving them. Moreover, our results suggest that increasing advertising investments is an effective firm response that successfully mitigates the postcomplaint firm value decline (β =.003, p <.05), as predicted in H5 and consistent with the theory of investor perception management. We also reestimate Equation 3 without profitability as a control variable. This specification is insightful given that Equation 3 tests for investor perception effects beyond earnings levels (as proxied by the ROA measure of profitability). The results in Column 4 confirm that the stock market still rewards an advertising investment response (β =.002. p <.10).
Graph
Table 4. Effectiveness of an Advertising Investment Response.
| (1) | (2) | (3) | (4) |
|---|
| Tobin's q (TQ) | Tobin's q (TQ) | Tobin's q (TQ) | Tobin's q (TQ) |
|---|
| Shareholder complaints (SHC) | −.1690** (.073) | | −.2118*** (.075) | −.1759** (.072) |
| Advertising investments (UADV) | | .0459*** (.001) | .0459*** (.013) | .0451*** (.016) |
| Shareholder complaints (SHC) × Advertising investments (UADV) (H5) | | | .0030** (.001) | .0022* (.001) |
| Shareholder complaint institutional submitter (INST) | | | .1348 (.125) | .1555 (.152) |
| Shareholder complaint nonfinancial type (NONFIN) | | | −.0474 (.087) | −.0059 (.106) |
| Shareholder complaint topic media attention (MEDIA) | | | .0757*** (.021) | .0721*** (.026) |
| Shareholder complaints excluded | .1018 (.097) | | .1016 (.094) | .1017 (.094) |
| Shareholder complaints voting support | −.0045 (.070) | | −.0008 (.070) | −.0056 (.070) |
| Firm size | −.4348*** (.043) | −.4425*** (.108) | −.4324*** (.043) | −.4007*** (.042) |
| Profitability | .1106*** (.021) | .1121*** (.029) | .1118*** (.021) | |
| Sales growth | .2711*** (.045) | .2735* (.156) | .2673*** (.045) | .1040*** (.032) |
| Market share | .5419** (.253) | .5293* (.306) | .5655** (.253) | .5306* (.303) |
| Financial leverage | −.0002 (.001) | −.0001 (.001) | −.0002 (.001) | −.0019 (.001) |
| Constant | 1.1020*** (.108) | −.1760 (.180) | 6.3286*** (1.242) | 6.6547*** (1.542) |
| Observations | 4,428 | 4,428 | 4,428 | 4,428 |
| Model F-statistic (df1, df2) | 29.86** (18, 4,410) | 34.02*** (16, 4,412) | 25.48*** (22, 4,406) | 36.22*** (21, 4,407) |
| Adjusted R-squared | .121 | .122 | .130 | .09 |
| ▵R-squared F-statistic (df1, df2) | | | 112.43*** (7, 4,406) | 65.88*** (1, 4407) |
- 10 *p <.1.
- 11 **p <.05.
- 12 ***p <.01.
- 13 Notes: Standard errors clustered by firm in parentheses. Shareholder complaints calculated at period t − 1. Firm and year fixed effects not reported.
This section overviews additional analyses that provide further insights into the mechanisms underlying an advertising investment response to shareholder complaints. We present alternative modeling choices and various robustness checks in Web Appendix D.
In an exploratory inquiry, we reestimate Equation 2 and replace the shareholder complaint type variable with the nine subcategories of complaint type, as plotted in Panel C of Figure 2 (results reported in Web Appendix E). We note that, after mean-centering the subcategory variables, VIFs remain above ten, and the adjusted R-squared is lower than when using the dichotomous shareholder complaint type classification as produced in Column 3 of Table 3.[14] In line with our theorizing, however, we find that all financial subcategories (board of directors, executive compensation, takeover, and other shareholder rights) negatively moderate an advertising investment response, whereas nonfinancial subcategories (diversity, community, environment, human rights, product, and vices) positively moderate an advertising investment response, although not all effects are statistically significant. These results further support our theory-driven taxonomy of financial and nonfinancial shareholder complaint types.
While not the focus of our analyses, in a set of unreported analyses we also test whether shareholder complaint salience moderates the negative effect of shareholder complaints on Tobin's q. We do not find significant moderating effects for shareholder complaint institutional submitter or shareholder complaint nonfinancial type, but we do find that complaints related to topics receiving more media attention cause a marginally larger drop in firm value than those about topics receiving less media attention. These findings are interesting because they suggest that managers might overestimate the perceived harm that shareholder complaints have on firm value when submitted by an institutional investor or when reflecting nonfinancial concerns. We also use a split-sample design (i.e., above and below median advertising investments) to examine whether the effect of an advertising investment response on firm value is moderated by shareholder complaint salience but find no such evidence for any of the salience factors.
If firms increase their advertising investments in response to shareholder complaints, does this come at the expense of other investments? Although we lack data for most other types of firm investments, we offer two exploratory analyses. First, following [64], we derive an estimate for sales force investments by subtracting Compustat's advertising item from Compustat's selling, general, and administrative item. Using this proxy, we do not find that shareholder complaints drive sales force investments (β =.070, n.s.). Second, moving beyond the marketing domain, we test whether firms reduce capital expenditures, as reported in Compustat, after encountering shareholder complaints. Capital expenditures decrease current earnings but do not help in managing investor perceptions. If our argumentation holds, we should observe a negative (or at least no positive) effect, which is indeed what we find (β = −.040, n.s.).
It might be that advertising investments appear effective as a remedial response because, in their complaints, shareholders explicitly ask for an increase in advertising. To test this competing explanation, we examine the detailed content of each shareholder complaint that includes the words "advertising," "marketing," or "promotion," but find no evidence for any demands directly related to an advertising increase (N = 0). Because SEC regulations allow companies to ignore complaints related to ordinary business operations, such as advertising, the lack of complaints requesting advertising increases is not surprising. However, to the extent that an advertising-related complaint concerns fundamental business strategy, it is not considered ordinary business and is eligible for submission. We find only a few complaints of that type in our data set (N = 56). Examples are complaints about advertising tobacco to minors and about advertising containing ethically controversial images. Our previous results do not change if we either exclude these cases or control for them with a dummy variable.
Advertising investments comprise various media types that differ in audience, objectives, and effects. We examine whether some advertising media types are more effective in protecting postcomplaint firm value than others. Kantar Media provides detailed advertising investment data grouped into print, radio, broadcast, internet, and outdoor advertising. Adding interaction terms of these five media types and shareholder complaints to Equation 3, we find marginally significant effects on Tobin's q for the percentage of television (β =.001, p <.10) and outdoor (β =.001, p <.10) advertising. We conclude that the media type of advertising is of lower importance in protecting postcomplaint firm value; the effectiveness rather lies in the total amount of a firm's advertising investments.
Because industry conditions can influence a firm's likelihood to manage discretionary investments such as advertising (e.g., [17]), we provide two tests that provide additional insights and allow us to reconcile our findings with prior literature. First, industries differ in the importance that investors attach to a firm's earnings numbers, and previous literature has suggested that such industry earnings pressure influences a firm's discretionary investment levels ([ 2]). Consequently, we expect firms operating in industries in which stock prices are highly sensitive to earnings information to be more cautious about increasing advertising investments. We proxy industry return sensitivity to earnings by the coefficient of a regression of earnings against firm stock returns ([ 2]). Interacting industry return sensitivity to earnings with shareholder complaints, analogous to the interactions in Equation 2, we find that higher return sensitivity to earnings is associated with a marginally lower increase in advertising investments (β = −.001, p <.10).
Second, we argue that visibility and potential spillovers onto nonfinancial stakeholders explain why firms resort to advertising investments when facing shareholder complaints. Thus, firms should be more likely to increase advertising investments if visibility among a broader set of stakeholders is large and if the overlap between the product market and the stock market is high. Previous literature has argued that market structures vary in these regards across business-to-customer (B2C) and business-to-business (B2B) markets ([68]), and we therefore test whether firms are more likely to increase their advertising investments when operating in a B2C versus a B2B industry. Including B2C industry membership as an interacted dummy with shareholder complaints, while excluding the firm fixed effects in Equation 2, we find marginal support for this argument (β =.001, p <.10).
Shareholders and their concerns have recently attracted marketing's attention as firms are increasingly challenged to manage stock markets and product markets symbiotically. In this article, we focus on shareholder complaints, a prominent form of stock market adversity, and study how firms use advertising investments to mitigate the drop in firm value following shareholder complaints. Three key takeaways are as follows: First, contrary to the often-documented observation that firms decrease marketing investments when challenged by the stock market, we find that, in the context of shareholder complaints, firms increase advertising investments to improve investor perceptions. Second, firms are more likely to increase advertising investments if shareholder complaints are more salient (i.e., are submitted by institutional investors, relate to nonfinancial concerns, or if the topics of the complaints receive more media attention). Third, advertising investments are an effective instrument for mitigating the firm value decline following shareholder complaints. Our results have various implications for marketing theory and practice, which we discuss next.
While a substantial amount of research has explored how marketing investments drive shareholder behavior (e.g., [107]; [116]), a small number of studies have begun to investigate whether shareholder behavior can also have feedback effects on marketing investments. This research tends to focus on the impact of stock prices. Our study sheds light on a distinctive, yet hitherto overlooked, aspect of shareholder behavior—shareholder complaints—that is increasingly relevant for firm managers and, as we show, for their advertising investment decisions. Combining our results with the findings of other stock market feedback studies (e.g., [ 6]; [88]) leads to the conclusion that adverse stock market pressures do not always result in marketing investment cuts, as per an earnings management perspective. Instead, in support of an investor perception management perspective, firms might find it more effective to directly manage firm value through increasing advertising investments.
We expect two drivers to explain this phenomenon. First, shareholder complaints often pertain to nonfinancial issues, so soothing investors financially by cutting advertising investments is unlikely to be effective because these complaints are not financially motivated in the first place. Considering that the general quest of maximizing profits is often at odds with consumer welfare and corporate social responsibility (e.g., [66]), cutting advertising investments as a response to nonfinancial concerns may even be considered counterproductive by investors.
Second, firms that encounter shareholder complaints tend to display mediocre to bad earnings performance, either because of poor past strategic choices that caused complaints or because of processing complaints in the current period.[15] Finance literature suggests that if firms miss earnings expectations, even if only by a thin margin, they bank any additional earnings until a later period when they can use those earnings to meet or exceed expectations (e.g., [30]; [111]). Likewise, this literature finds that incentives to manage earnings are higher for firms that have consecutively reported high or increasing earnings in the recent past, and not for those that have reported poor earnings ([95]). This point is also discussed in [13], who argue that it is good past performance that induces short-term earnings pressures. Given the poor past performance of firms receiving shareholder complaints, these firms might have lower motivation to engage in earnings management.
As such, our findings also pertain to the recent discourse on profit versus value maximization. Multiple articles in this area argue that marketing actions that increase profits may not necessarily increase firm value (e.g., [118]). We add to this discussion by showing that marketing actions that decrease profits may not always decrease firm value. In this regard, our results correspond closely with those of [44], who study product-harm crises and find that although increasing advertising investments reduces firm profit, it nevertheless mitigates the loss in firm value. In other words, reductions in accounting profit do not always lead to reductions in economic profit, not even in the short run. This is an encouraging finding for marketing practice.
Our findings also add to the literature that studies how firms can attenuate crisis situations through marketing investments. The traditional focus of the marketing function has been the product market, with an array of research studying how firms employ advertising instruments to address adversities emanating from product markets, such as consumer complaints (e.g., [73]), product regulation (e.g., [92]), or product-harm crises (e.g., [22]). Other research shows how advertising investments help firms during recessions (e.g., [117]). Adding to this literature, we show that advertising investments not only are a compelling way to address product-market crises but also can mitigate the adverse effects of shareholder crises.
In terms of adverse performance effects, our focus is on firm value, which is a function of both future expected cash flows and firm risk. Given the strong risk-reducing properties of advertising investments (e.g., [75]; [83]), coinciding with the weakened investor confidence in the firm when receiving shareholder complaints, the channel through which advertising investments protect firm value might be by reducing firm risk. To test for this benefit, we run an exploratory test in which we interact shareholder complaints with advertising investments and regress this variable on firm risk, measured as the standard deviation of the residuals of the firm's four-factor stock price model in year t ([13]). The model mimics Equation 3 but replaces Tobin's q with firm risk. Results are of the expected direction, but advertising investments are marginally insignificant in reducing firm risk after encountering shareholder complaints (β = −.043, n.s.).
Finally, our results contribute to a better understanding of the corporate governance literature relative to marketing actions. Previous literature, as well as our data, has shown that firms rarely respond directly to the issues raised in shareholder complaints (e.g., [46]). Presumably, managers regard their own information about the firm to be superior to that held by shareholders ([114]) or fear that shareholders seek only short-term gains. This perspective is shared by scholars and commentators who claim that shareholder complaints only distract management from their duties and that outside investors lack the necessary skills and experience to optimize management's decisions ([112]; [126]). Our qualitative interviews support this idea, with one of the interviewed CFOs stating that "shareholders follow your firm from a distance and are not involved in the daily development of your strategy and the considerations involved." In addition, whereas some complaints reflect shareholders' discontent with an issue in which they are highly involved, other complaints result from a more general "we are not happy" sentiment ([104]), making it difficult for management to cure shareholder dissatisfaction by taking direct action. We do not take a stance on whether shareholder complaints are reasonable or whether firms' refusal to implement proposed changes is warranted. However, we show that firms do react to these complaints, namely by altering their advertising investments, and we show that this response is effective in terms of protecting firm value. This is another reassuring finding for marketing practice.
In light of our findings from both the qualitative interviews and econometric analyses, we suggest a three-step action plan for managers facing shareholder complaints. When confronted with complaints, managers should ( 1) assess the content of the complaint and determine whether to implement the shareholder's suggestion, ( 2) evaluate the impact the complaint has on firm value and inherent firm responsiveness, and ( 3) devise actions to alleviate the potential harm of the complaint to firm value. We next provide details on each step and offer actionable guidelines.
The first step involves managers closely analyzing the actual content of received complaints and assessing whether they should respond by changing any aspect of their business operations. For example, this could be the case when shareholders point to shortcomings in the firm's operations or supply chain, which managers should acknowledge as market intelligence and change accordingly. Although previous literature has shown that firms typically neglect such insights, we suggest that managers should always be vigilant to maximize their firm's strategic potential and remain attentive to shareholder feedback in accomplishing this objective. In the words of a CFO we interviewed, "You have to engage in a dialogue with shareholders who suggest that your value proposition is not optimal," and "Shareholders have the right to indicate this to you."
Second, irrespective of whether the firm implements the issue shareholders complain about, managers should next evaluate the impact of the complaint and consider actions to alleviate the harm that complaints inflict on firm value. Our findings corroborate that shareholder complaints substantially reduce firm value but also identify advertising investments as an effective tool to protect firm value. To further demonstrate the impact of increasing advertising investments in response to shareholder complaints, we perform a counterfactual analysis that answers two questions. First, for firms that increased their advertising investments in response to shareholder complaints, what was their incremental improvement in firm value compared with if they had not increased investments? Second, for firms that did not increase their advertising investments in response to shareholder complaints, what was their incremental loss compared with if they had increased investments? For this analysis, we employ a switching regression methodology (e.g., [16]), detailed in Web Appendix F. In essence, we use a first-stage regression to predict the probability of a firm increasing its advertising investments in response to shareholder complaints and derive the inverse Mills ratio as the selection correction variable. In a second stage, we then regress firm value on the inverse Mills ratio and the control variables, separately for firms that increase advertising investments and those that do not. Finally, we use the predicted firm values from the second-stage estimation to conduct the what-if analysis.
We find that firms that increased their advertising investments in response to shareholder complaints achieved an incremental gain of.057, or 3.5%, in firm value compared with if they had not increased these investments. By contrast, firms that did not increase their advertising investments in response to shareholder complaints gave up an incremental lift in firm value of.152, or 1.3%, compared with if they had increased their investments. These results underscore our general finding that advertising investments help protect firm value in the face of shareholder complaints, but, perhaps even more importantly, they also show how a firm jeopardizes firm value if it decides against an advertising response.
Although these results underscore the benefits of an advertising investment response to shareholder complaints in general, they do not indicate the optimal level of increasing advertising investments. As a second analysis, we therefore perform a marginal effects analysis based on the estimated coefficients from Equation 3 in Table 4 (Web Appendix G overviews the details). This approach has been applied in marketing by [80] and [117] and provides two important insights. First, it describes how to calculate firm-specific marginal effects and how to statistically assess whether a given firm underspends, overspends, or spends at an approximately optimal level. As such, when faced with shareholder complaints, managers can readily use our model as a decision aid to examine whether they advertise at optimal levels. Second, after performing such an analysis for the firms in our sample, we find that the majority of firms underinvest in advertising following shareholder complaints. Notably, though, if shareholder complaint salience is high (i.e., if complaints are submitted by institutional investors, pertain to nonfinancial concerns, or relate to topics that receive higher media attention), firms invest close to an optimal level.
Combining all the findings from our article, we conclude that firms increase their advertising investments following shareholder complaints, yet most do not do so sufficiently, especially if shareholder complaint salience is low (i.e., if the complaint is not submitted by an institutional investor, does not pertain to nonfinancial concerns, and does not discuss topics receiving large media attention). This conclusion is striking because firms jeopardize substantial firm value by underutilizing an advertising investment response. We thus caution managers not to underspend on advertising in response to shareholder complaints, especially when the complaints appear less salient and managers may thus feel less of an urge to manage investor perceptions.
Third, when contemplating the extent of increasing advertising investments, managers should also consider how to best implement an advertising investment response. This includes taking into account the differential effects of advertising media types. As shown in our additional analysis, television and outdoor advertising seem marginally more effective at mitigating the drop in firm value. We conjecture that television advertising is beneficial because of its large short-term elasticity compared with print or combined advertising and because it is built on emotional rather than informative appeals, which is essential in nurturing investors' affect to the firm ([110]). Likewise, outdoor advertising is known for increasing awareness and broadening visibility ([68]). Although our focus is on advertising investments, the qualitative interviews help explore how to design the advertising content. An interviewed head of investor relations noted a role of "advertising to explain your strategy...and reiterate why it is a good strategy." In the words of an interviewed chief financial controller, "Advertising is not just about the underlying product, it is about letting [the market] know we are a good company."
Insights from the qualitative interviews further suggest the importance of coordinating an advertising investment response with other functions in the firm, such as public relations. For instance, as stated by one chief financial controller, "It is also about public relations and sponsorships to try and appease [complaining shareholders]." Another CFO noted that "using interviews and articles in the business press are good ways to correct impressions of the market and wider stakeholder groups." If complaints pertain to nonfinancial issues, another route might involve "independent parties [to] help shape the public discussion in your favor, such as interest groups," as one CFO recommended. Unilever, for instance, started a well-publicized collaboration with the World Wildlife Fund after being criticized by shareholders for its lack of sustainability efforts.
Likewise, the firm's investor relations department might be an important partner to design an advertising investment response. Often considered a strategic corporate marketing activity ([33]), it is well-placed to advise how to address concerned shareholders in terms of message content and how best to reach shareholders. This could include, for instance, "presenting yourself on conferences, expos, and symposia," as one chief financial controller noted, or "to be present at investor roadshows." In any case, quoting a head of investor relations, "Investor relations, public relations, and marketing [have to] work together very closely in addressing shareholder complaints."
In addition, firms might look for external partners to better communicate their value proposition to the investor community and amplify their advertising efforts. One such partner might be analysts who channel information between firms and investors and help "validate the business logic underlying the advertising expense" ([77], p. 607). As a head of investor relations stated, "Depending on the issue at hand, we have a direct line to the analysts," while a VP of public relations shared that "having analysts as a kind of intermediary or go-between is a great way to defuse an issue that might come up with complaining investors, especially as analysts have spent time with us and really know our products and what we are trying to do."
To conclude, our research suggests that managers should consider the broader implications of an advertising investment strategy across both product and financial markets. Managing the dynamics between advertising investments and the stock market becomes an even more pressing task as shareholder complaints increasingly deal with topics directly under marketing's responsibility. We believe that our findings arm marketing managers with stronger justifications for their advertising budget decisions, their position in the C-suite and at the board level, and their organizational impact to counter the threat of marginalized marketing responsibilities ([107]). That being said, advertising investment decisions should always be part of a coherent long-term marketing strategy, ideally one that aligns shareholders' interests with firm strategy and prevents shareholder dissatisfaction in the first place ([67]).
With the novelty of our research come certain limitations that provide avenues for future research. One topic for future research is a more detailed investigation of the roles of different functions within a firm and how they might help coordinate an advertising investment response, as suggested by our qualitative interviews. Relatedly, while beyond the scope of the present research, it may be worthwhile to consider other, perhaps competing, responses that managers could use when confronted with complaining shareholders. We offer some first ideas in our additional analyses and invite future research to explore this path in more detail. In addition, firm contingencies could affect an advertising response to shareholder complaints and moderate the effectiveness of such a response. Firm factors that relate to spillover effects between investors and nonfinancial stakeholders might play a role, such as a firm's proportion of retail investors, who have been shown to respond more strongly to advertising investments ([71]). The firm's strategic emphasis on advertising might be another factor influencing firm advertising responsiveness to shareholder complaints ([89]).
Moreover, it is possible that shareholder complaints moderate the effectiveness of a firm's advertising investments, in addition to advertising investments protecting firm value, as we find in the current research. Literature on product crisis management suggests such effects during times of crisis (e.g., [21]), and future research might provide insights on this matter in the context of shareholder complaints. An interesting finding we note in Table 3 is the negative association of the percentage of excluded complaints with a firm's advertising investments. This result is surprising because when budget decisions are made, the firm does not yet know whether complaints will be withdrawn or omitted, because this information tends to be revealed only at a later point, possibly until the day of the AGM. One might speculate that, over time, firms develop knowledge about the likelihood of complaints being withdrawn or omitted so they can make more informed decisions at the time budgets are set. Alternatively, if advertising budgets are very flexible, firms might be able to undo or reduce the planned increase in advertising investments in cases in which the threat of shareholder dissatisfaction disappears. Future research might help uncover these dynamics.
Finally, our modeling of unexpected advertising investments assumes a transitory effect of advertising deviating from expected levels in a given year. At the same time, we cannot exclude the possibility that shareholder complaints could have a more permanent bearing on the firm's future advertising investments. This could be the case if the firm learns about strategy shortcomings through complaints and decides to combat it through advertising. This is an interesting conjecture that we hope future research will address.
Research to date has provided inconclusive views on how firms adjust marketing investments in response to stock market adversities. We offer novel insights on a currently overlooked type of stock market adversity, shareholder complaints, and how firms adjust their advertising investments in response to those complaints. Our findings suggest that firms increase their advertising investments and that the stock market rewards them for doing so. Specifically, an advertising investment response to shareholder complaints helps reduce the postcomplaint drop in firm value. Furthermore, we find that more salient shareholder complaints—that is, those submitted by institutional investors, relating to nonfinancial concerns, and relating to topics receiving more media attention—make an increase in advertising investments more likely. Our results offer important implications for marketing theory about how shareholder feedback drives advertising investments as well as practical insights to support managers to make timely and appropriate advertising investment decisions on receiving shareholder complaints.
Supplemental Material, DS_10.1177_0022242919841584 - Can Advertising Investments Counter the Negative Impact of Shareholder Complaints on Firm Value?
Supplemental Material, DS_10.1177_0022242919841584 for Can Advertising Investments Counter the Negative Impact of Shareholder Complaints on Firm Value? by Simone Wies, Arvid Oskar Ivar Hoffmann, Jaakko Aspara and Joost M.E. Pennings in Journal of Marketing
Footnotes 1 Associate EditorLeigh McAlister served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The first author acknowledges the financial support of the Research Center SAFE. The third author acknowledges a personal research grant from the Jenny and Antti Wihuri Foundation, Finland.
4 Online supplement: https://doi.org/10.1177/0022242919841584
5 1Web Appendix A discusses the process of shareholder complaint submission in detail and provides examples of submitted complaints.
6 2In the "Theoretical Implications: Dynamics of Stock Markets and Product Markets" subsection, we detail why earnings management incentives, an alternative mechanism to managing investor perceptions, are unlikely to hold in the context of shareholder complaints.
7 3Web Appendix B provides detailed information about this exploratory qualitative study of semistructured interviews.
8 4An alternative reasoning could suggest that shareholder complaint salience makes substantial changes more likely and increases in advertising investments less likely. Yet the miniscule fraction of complaints that result in implemented changes as proposed, even among very salient complaints, does not support this conjecture.
9 5As expected, we observe a substantial reduction in sample size due to unavailability of data when constructing our variables, unavailable firm information when firms list their stock later or delist their stock earlier than the start and end date of our sample period, and attrition through acquisitions and bankruptcy.
6Sensitivity analyses indicate that adding other constants (i.e.,.0001, or.5) does not change our results.
7An alternative measure of advertising investments is advertising share of voice (e.g., [82]), which is the firm's advertising investment divided by the sum of all advertising investments in the firm's industry. Using this relative measure, we find results in close correspondence to those reported in our main analyses (see Web Appendix D).
8We remove the firm-specific effects by first-differencing the data. We then create instruments from the first and second lags of the first-differenced dependent variable and run the more efficient two-step generalized method of moments estimator with robust standard errors. The estimated advertising forecast model is statistically significant (χ2 = 7.64, p <.01), and advertising levels are highly persistent (ϕ =.59, p <.01). The Arellano–Bond test for zero autocorrelation in the error term can be rejected at any order higher than 1 (zt − 1 = −.528, n.s.), so we conclude that the error term is serially uncorrelated. Because the Sargan test of overidentifying restrictions cannot be rejected (χ2 = 88.52, n.s.), the model specification meets the moment conditions and the instruments appear valid. Note that we replicate results when employing expanded AR(2) and AR(3) forecasting models. Web Appendix D overviews alternative models to estimate unexpected advertising investments.
9Note that the negative simple effects of the moderator variables have no bearing on testing the hypotheses, as they capture the effects of these variables on advertising investments when shareholder complaints are at their minimum.
10We thank an anonymous reviewer for pointing out that mean-centering does not alleviate multicollinearity concerns.
11The financial costs, such as legal, advisory, and administrative costs, required to handle shareholder complaints can be tremendous and soar into the multimillion-dollar range ([115]). Confirming the profit slump, we find that while, overall, firms tend to increase their earnings in the fourth quarter of the fiscal year as compared with the other three quarters, firms with shareholder complaints do not report increased earnings in the fourth quarter of the fiscal year.
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Record: 30- Challenging the Boundaries of Marketing. By: Moorman, Christine; van Heerde, Harald J.; Moreau, C. Page; Palmatier, Robert W. Journal of Marketing. Sep2019, Vol. 83 Issue 5, p1-4. 4p. 1 Graph. DOI: 10.1177/0022242919867086.
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Challenging the Boundaries of Marketing
The technological and digital revolutions experienced over recent decades have fundamentally transformed marketing practice, consumer behavior, and competitive dynamics and presented new policy and societal challenges. At the same time, the world's many economic, social, and political problems can benefit from proactive, purpose-driven marketing thought. On this stage of dynamic change and unprecedented opportunity, the marketing discipline is poised to offer new knowledge that contributes to the full range of marketing stakeholders, including the students we educate.
Despite this potential, a great deal of marketing scholarship remains safely within the confines of its present boundaries—relying on mainstream assumptions, theories, and methods that tend to reinforce, not challenge, our thinking. Like most scientific communities, marketing has the trappings of [ 3], p. 5) "normal science," which "is predicated on the assumption that the scientific community knows what the world is like," a "willingness to defend that assumption, if necessary at considerable cost," and a tendency to suppress novelties "subversive of its basic commitments."
There are many reasons for this inertia. Institutional and individual rewards are tilted toward incremental research that safely builds programmatic streams for tenure while a risk-averse journal review process can easily stamp out innovation. Given these forces, many early-career marketing scholars operate within the safe boundaries of the discipline while pledging to return to innovative opportunities in the later stages of their careers. Unfortunately, most never do.
It is within this context that we introduce a series of innovative articles designed to inform and inspire research that broadens the current boundaries of marketing, including the phenomena, theories, methods, and findings the field considers important and interesting. Articles in this series include conceptual reexaminations that challenge assumptions in well-established research areas. These articles do so by questioning current boundaries and offering fresh research agendas. Other articles highlight new challenges to the field by offering conceptual frameworks to structure new research approaches with the potential to transform the discipline.
The first of these "Challenging the Boundaries of Marketing" articles appears in the current issue and focuses on "Marketing in the Sharing Economy" ([ 2]). We asked this team of authors to investigate the changing nature of marketing in the sharing economy—a critical perspective that has been missing from most research published in marketing journals. Defining the sharing economy as a scalable socioeconomic system that employs technology-enabled platforms to provide users with temporary access to tangible and intangible resources that may be crowdsourced, the paper examines how the sharing economy forces us to rethink three foundations of marketing: institutions (e.g., consumers, firms and channels, regulators), processes (e.g., innovation, brands, customer experience, value appropriation), and value creation (e.g., value for consumers, value for firms, value for society). Importantly, the article offers wide-ranging future research opportunities that confront the boundaries of marketing thought.
These authors, Giana Eckhardt, Mark Houston, Baojun Jiang, Cait Lamberton, Aric Rindfleisch, and Georgios Zervas, examine the sharing economy from remarkably diverse perspectives and methodological approaches, representing marketing strategy, empirical modeling, analytic modeling, mainstream consumer research, and consumer cultural theory. This interdisciplinary perspective reinforces the Journal of Marketing's (JM's) position as the discipline's broadest and most inclusive journal and reflects our view that the discipline will be stronger when we unite to solve the field's most pressing questions and problems. Consistent with this view, the second article in the series, "Uniting the Tribes: Using Text for Marketing Insight," is coauthored by Jonah Berger, Ashlee Humpreys, Stephan Ludwig, Wendy Moe, Oded Netzer, and David Schweidel. This article, which will be published in a [ 1] issue, offers a conceptual framework for thinking about text, provides practical pointers for researchers working in this domain, and presents an agenda designed to drive text-based research across the discipline. In doing so, the article encourages scholars to challenge their own methodological boundaries by providing the guidance to do so.
The need to think outside the box is especially important now because the practice of marketing is changing faster than the research published in marketing journals. The rapid rise of big data, the sharing economy, influencer marketing, privacy concerns, and social media challenge marketing to produce more knowledge faster while also creating fundamental concerns about marketing's ability to contribute to the world. To meet these challenges, we think the field needs to pull off its blinders and uncover new ways of thinking about marketing and the marketplace.
As noted, forces internal to marketing academia limit the innovativeness of our thinking and the problems we address. These include ( 1) training silos that keep us focused on similar problems, methods, and theories over time; ( 2) the self-limiting tendency to read the same journals, attend the same conferences, and invite like-minded seminar speakers so that knowledge tends to conform—not to challenge; and ( 3) beliefs about what are considered the boundaries of marketing or what constitutes "theory" or "rigor" by many reviewers and editors. It is easy to see how we might all become a bit stuck.
Let's not become sea squirts. World-renowned neuroscientist [ 4] observed that this marine animal begins life as an active creature with a 300-neuron-sized brain. However, after swimming around during its early life, the sea squirt ultimately attaches itself to the ocean floor, where it happily stays for the remainder of its life. Remarkably, in the absence of the need to wander and explore, it has no use for its brain and eats it!
The "Challenging the Boundaries" series is intended to keep us "wandering" as a means to combat these inertial tendencies. Simply, we seek to promote innovation, diversify thinking, and expand the scope of research in marketing in order to beat down the forces of [ 3] normal science. We intend for this series to challenge the assumptions, metaphors, and ideas about what marketing is or is not and promote a better set of ideas and approaches for what it might become. If we do not take up this challenge, we will be absorbed—or eaten—by other disciplines that are increasingly focused on important marketing issues, such as computer science, economics, psychology, and strategy.
The "Challenging the Boundaries" series will sometimes include comments from practitioners or nonmarketing academics that will deepen and extend the contribution of the articles to push the discipline toward new ideas. For example, the commentaries on "Marketing and the Sharing Economy" are offered by Yubo Chen (Senior Associate Dean, Professor, and Director of the Center for Internet Development and Governance at the School of Economics and Management, Tsinghua University); Liantao (Tarry) Wang (Co-founder and Chief Operating Officer of Xiaozhu); and Arun Sundararajan (Harald Price Professor of Entrepreneurship and Professor of Technology, Operations and Statistics, Stern School of Business, New York University).
The editorial mission of JM is to "develop and disseminate knowledge about real-world marketing questions useful to scholars, educators, managers, policy makers, consumers, and other societal stakeholders around the world." Since its founding in 1936, JM has played a significant role in shaping the content and boundaries of the marketing discipline. The "Challenging the Boundaries" initiative is an excellent fit with JM's historical role in the discipline.
The series also fulfills our current editorial mission to emphasize innovation ([ 5]) and to "challenge the boundaries of the marketing discipline by publishing articles that advance new research questions designed to disrupt traditional marketing doctrine and to open up new areas of the discipline."[ 3] By encouraging teams of scholars to work on high-risk, high-impact papers, we hope to increase the prevalence of these papers in the marketing discipline. A dedicated process that relies on a rigorous and multitiered review process (as the authors will attest) will help overcome the tendency of these papers to struggle in the review process as they challenge the forces of inertia. Our approach places two bets that we think are important in the development of marketing thought.
Our approach reflects a commitment to a diverse and inclusive approach to knowledge development. We believe that a "big tent" is the best way to generate thought-changing marketing ideas. Breadth may not seem like a wise strategy in an era of academic specialization. However, we think diversity is a strength because it creates cross-fertilization opportunities that form the basis of innovative ideas. Breadth is especially important given the interdisciplinary nature of most marketing problems. We hope this initiative proves that marketing benefits when we work together—not stay in our camps.
This will be a series of conceptual articles. Wide-ranging, field-defining concepts have been published in JM over the last 50 years. These include brand communities, brand equity, customer equity, customer loyalty, customer value, internet retailing, market orientation, the marketing–finance interface and market-based assets, service dominant logic, service quality, social marketing, and trust and relationship marketing. These articles have produced rich, new streams of research that often disrupted and redirected marketing thought in important ways.
Consistent with our approach, JM has published more conceptual papers than the other premier marketing journals. Unfortunately, fewer conceptual papers have been published in the field overall and in JM in the last decade (see Figure 1 and [ 6]]). This initiative is an attempt to counteract this trend. Beyond this initiative, we encourage authors to submit conceptual papers that offer new frameworks to JM because these papers are often critical engines for exploring new ideas and parts of the marketing discipline.
Graph: Figure 1. Conceptual articles published in JM(2000–2018).Notes: "From the Editor" articles and book reviews were not included in the total count of articles or conceptual articles.
To start the series, we have tasked several diverse sets of scholars to collaborate on these articles. As the team who developed the "Marketing in the Sharing Economy" article exemplifies, scholars coming from different methodological backgrounds and different research orientations are united by a substantive area. When these unique perspectives collide in the development process, we think they have more potential to move the boundaries of the field.
To seed this initiative, our editorial team identified both the topics and the team members for the initial papers in the series. However, our goal is to inspire teams of scholars to self-organize, propose, and develop important articles that challenge the boundaries of the field. To this end, we have established a clear process for submitting topics along with the criteria that will guide our approval decisions.
We anticipate room for multiple new "Challenging the Boundaries" articles during the remaining years of our tenure as editors. Given that such articles take approximately a year to develop, we would like to have all proposals submitted between now and January 2021. Interested teams should submit a three- to five-page proposal that establishes the importance of the topic, how they intend to challenge the boundaries, and why it is ripe for inclusion in the series. Proposals should describe the team members and make the case for their breadth, diversity, and expertise. Importantly, the proposal should also contain statements about the team members' willingness to work together and commit to the article. Proposals should be sent to the Editor in Chief.
The JM editors, with the assistance of our Advisory Board, will select the proposals to be developed into articles and sent into the review process. Three primary criteria will be used for making our selections. First, the topic should be one that a large number of people in the discipline would or should care about. The topic should come from one of the two tails of the innovativeness spectrum—it should focus on a topic that is completely new to the field or on one that is well-worn, entrenched, and in need of a jump-start. Ideally, the topic will be an area of marketing undergoing significant change and where academic research is lagging behind real-world marketing practice. The topic should be big enough and broad enough to offer considerable research opportunities to a range of scholars in the discipline, and it should be one that can be examined from multiple perspectives (e.g., consumer, firm, market, policy, and society).
Second, the proposal should describe how the article will challenge the boundaries in the field. Why is the topic ripe for exploration and dismantling? What future research areas will the paper explore with fresh insights? This latter point is important because it underscores that these are not review articles. Although an assessment and integration of what we know can be fertile ground for innovation, the authors will have to plant seeds for future research when they develop the proposal into a submission. We seek thought leadership. The "Marketing in the Sharing Economy" article in this issue exemplifies the focus on new ideas by devoting over half of its content to future research directions.
Third, the proposed teams should comprise three to five members from different disciplinary and methodological traditions. We think diverse perspectives will produce the most interesting and compelling research directions. As you design your team, think about the strongest team members around the world—not your close working associates. Furthermore, you should ensure that team members will work hard to generate the strongest manuscript. Because such large-scale conceptual papers are very difficult to write, success demands that team members commit to the process of working together and to regular meetings. All other JM submission criteria will apply as well.
Using these criteria, with this editorial, we open the process for submissions. Importantly, there is no guarantee that these articles will be published. Teams will have to produce an important article that truly does challenge the boundaries. Expert reviewers across multiple rounds will help us make this determination.
The "Challenging the Boundaries" initiative has already required a great deal of valuable resources—namely, the time, effort, and expertise of contributing authors, reviewers, and editors. It also occupies important real estate in the journal itself. Because of these considerable investments, we have established a set of indicators to help us gauge the success of this series. The effectiveness of these articles at seeding new topic areas, methods, and cross-disciplinary respect and collaboration will be measured by the scholarly interest the articles generate, especially among younger members in the field, as indicated by article downloads, article citations, and inclusion in doctoral seminars.
The sea squirt reminds us that a certain amount of wandering is necessary to keep our disciplinary thinking fresh and relevant. To that end, the goal of this series is to foster exploration of marketing's current boundaries. We are taking these first steps together as a team with the hope that marketing can rise up to more effectively meet its challenges and to live up to its potential for greater impact. We think it is fitting that JM leads on this front, and we look forward to watching the reverberations. If we can spawn just a little innovation through this initiative, we will accomplish much.
Footnotes 1 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
2 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
3 1See https://www.ama.org/guiding-editorial-principles-for-the-journal-of-marketing/.
References BergerJonahHumphreysAshleeLudwigStephanMoeWendy W.NetzerOdedSchweidelDavid A. (forthcoming), "Uniting the Tribes: Using Text for Marketing Insight," Journal of Marketing.
EckhardtGiana M.HoustonMark B.JiangBaojunLambertonCaitRindfleischAricZervasGeorgios (2019), "Marketing in the Sharing Economy," Journal of Marketing, 83 (5), 5–27.
KuhnThomas (1962), The Structure of Scientific Revolutions. Chicago: University of Chicago Press.
4 LlinásRodolfo (2001), I of the Vortex: From Neurons to Self. Cambridge, MA: MIT University Press.
5 MoormanChristineHeerdeHarald J. vanPage MoreauC.PalmatierRobert W. (2018), "JM as a Marketplace of Ideas," Journal of Marketing, 83 (1), 1–7.
6 YadavManjit S. (2010), "The Decline of Conceptual Articles and Implications for Knowledge Development," Journal of Marketing, 74 (1), 1–19.
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By Christine Moorman; Harald J. van Heerde; C. Page Moreau and Robert W. Palmatier
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Record: 31- Clustering, Knowledge Sharing, and Intrabrand Competition: A Multiyear Analysis of an Evolving Franchise System. By: Naseer Butt, Moeen; Antia, Kersi D.; Murtha, Brian R.; Kashyap, Vishal. Journal of Marketing. Jan2018, Vol. 82 Issue 1, p74-92. 19p. 1 Diagram, 5 Charts, 2 Graphs. DOI: 10.1509/jm.16.0173.
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Clustering, Knowledge Sharing, and Intrabrand Competition: A Multiyear Analysis of an Evolving Franchise System
As franchise systems expand, the clustering and resulting proximity of same-brand outlets often become contentious issues. The increased interactions among outlets may facilitate knowledge sharing, even while inducing intrabrand competition. Prior research has considered each possibility—knowledge sharing or intrabrand competition—in isolation, resulting in conflicting recommendations to the central question of whether multiple same-brand outlets should be close to or distant from one another. In this study, the authors take the perspective of the focal outlet and show that the opportunity to share knowledge afforded by clustering-based proximity may or may not be realized, depending on the motivation and ability of the proximal outlets to share knowledge, the focal outlet’s ability to absorb knowledge, and the governance context. An analysis of more than 8,000 observations on the 988 outlets of a U.S.-based automotive service franchise system from 1977 to 2012, and corresponding outlet-level sales information from 2004 to 2012, provides support for the authors’ hypotheses.
Online Supplement: http://dx.doi.org/10.1509/jm.16.0173
Any time you open more and more units, there’s always some impact…. People are still making some money — it’s just not what they used to make.
—Hardy Grewal, Subway’s largest U.S. development agent (Jargon 2015)
Subways aren’t cannibalizing each other.… Restaurants in the most Subway-dense markets actually have higher average sales.
—Don Fertman, Subway’s chief development officer (Jargon 2015)
The opening quotes exemplify the starkly divergent views on clustering, the geographic concentration of interconnected institutions (Porter 1998). On the one hand, clustering can elicit richer, more frequent interactions (Ganesan, Malter, and Rindfleisch 2005), thereby facilitating knowledge sharing, that is, partners’ communication of valuable technical skills, product knowledge, know-how, as well as information about the market (Cummings 2004, p. 352; De Luca and Atuahene-Gima 2007; Ho and Ganesan 2013). On the other hand, the prospect of proximity-induced intrabrand competition—the degree to which the different outlets selling the same brand compete for the same customers—poses a daunting and real threat (Kalnins 2004; Pancras, Sriram, and Kumar 2012). As such, should multiple same-brand outlets of a franchise system be close to or distant from one another? For both interested scholars and practitioners, this question has profound implications but remains largely unanswered.
Table 1 summarizes relevant empirical research on the performance-related consequences of the proximity of same-brand outlets. Scholars working within a sociological tradition of clustering theory (Ganesan, Malter, and Rindfleisch 2005; Ingram and Baum 1997) have emphasized the proximity-induced opportunities for greater learning, interaction, and knowledge sharing among proximal outlets, as well as the consequent performance gains for the focal entity participating in such a cluster (Lu and Wedig 2013). The primarily economics-informed perspective on proximity (Kalnins 2004; Pancras, Sriram, and Kumar 2012), however, emphasizes the costs imposed by the resulting intrabrand competition. As Table 1 shows, prior studies have adopted viewpoints informed by either knowledge sharing or intrabrand competition. As a result, the intriguing idea that both perspectives might be valid remains unexplored.
This study represents a first effort to acknowledge and reconcile these seemingly conflicting effects of proximity. Within the context of a growing U.S.-based franchise system, we take the perspective of the focal outlet striving to leverage knowledge shared by the proximal same-brand outlets it is clustered with, while trying to avoid, or at least minimize, sales cannibalization brought about by intrabrand competition. Our conceptual framework, grounded in the literature on organizational learning (e.g., Argyris and Schön 1978; Cyert and March 1963; Sinkula 1994; Slater and Narver 1995), integrates the motivation– opportunity–ability perspective (MacInnis, Moorman, and Jaworski 1991) with work on proximity-governance linkages (Bradach 1997; Brickley and Dark 1987; Tracey, Heide, and Bell 2014) to hypothesize the conditions under which each viewpoint (knowledge sharing or intrabrand competition) might prevail, as reflected in the focal outlet’s performance.
Specifically, we posit that the opportunity to share knowledge afforded by clustering-based proximity may or may not be realized, depending on ( 1) the motivation of proximal outlets to share their knowledge with the focal outlet, ( 2) the ability of the proximal outlets to share relevant knowledge and of the focal outlet to absorb such knowledge, and ( 3) the governance1 context (i.e., shared ownership and franchisor vs. franchisee ownership). We posit that the governance context acts as a critical moderator of the clustering–performance relationship and determines the primacy of the knowledge sharing or intrabrand competition effects. In particular, we argue that the shared ownership of clustered outlets2 (i.e., multiunit operations) blunts outlet owners’ concerns about intrabrand competition, in turn affecting their motivation to share knowledge with the focal outlet (Argote and Darr 2000; Ingram and Baum 2001). We also argue that the ownership of the focal outlet (whether franchisor- or franchisee-owned) causes variation in the clustering-attributable costs and benefits. Moreover, we consider the ability inherent in the age-related experience of the outlets (i.e., the proximal outlets’ availability of knowledge gained through years of experience) and the focal outlet’s ability to value, assimilate, and apply this knowledge (i.e., its absorptive capacity) (Cohen and Levinthal 1990).
We rely on a unique multisourced data set comprising more than 8,000 observations on the 988 outlets of a large U.S.-based franchise system of automotive services across 41 states, from its inception in 1977 through 2012. Top management of the franchise system shared data on each outlet’s location, year of establishment, and corresponding sales information for a nine-year period (2004–2012), which we supplemented with information from franchise disclosure documents as well as market-specific information we collected from publicly available sources. The rich, fine-grained information allows us to assess the impact of clustering on individual outlets’ performance over nearly a decade.
We make several key contributions to the understanding of clustering and its performance consequences. First, rather than limiting our consideration to just the beneficial knowledge-sharing effects of clustering or the potentially negative intrabrand competition effects, we explicitly acknowledge and assess both possibilities. We argue that the net impact of clustering on individual outlet performance depends on the relative strength of each of these competing effects, and we identify the governance-attributable boundary conditions with respect to when one effect might dominate the other.
Second, we build on evidence indicating that the knowledge available from different proximal outlets can vary as a function of the outlets’ experience (Kalnins and Mayer 2004; Penrose 1959), and we extend this insight by also considering the focal outlet’s ability to absorb this available knowledge as a function of its own experience (Cohen and Levinthal 1990; Zahra and George 2002). As we discuss subsequently, a low level of either is likely to compromise knowledge sharing between outlets, resulting in reduced performance levels. We are thus able to explain how, even within the same cluster of outlets, performance might vary as a function of the specific focal outlet and the specific proximal same-brand outlets considered. In emphasizing the role of experience of proximally located knowledge sources and recipients, we extend the notion of clustering beyond its exclusive focus on how geographically close the outlets within a cluster are to the specific identities of the focal outlet and those proximal to it.
Third, we build on and extend recent theoretical discussions (Bell, Tracey, and Heide 2009; Tracey, Heide, and Bell 2014) linking the notions of clustering and governance (shared ownership and franchisor vs. franchisee ownership). We propose that shared ownership influences perceptions of intrabrand competition, which in turn affects the motivation of proximal outlets to share knowledge with the focal outlet. We further argue that the costs and benefits of clustering vary for franchisorand franchisee-owned focal outlets because they differ in their relative vulnerability to intrabrand competition as well as in the operational leeway available to them to benefit from knowledge sharing. Ours is the first study, to the best of our understanding, to unravel the complex interplay among geographic proximity, individual outlets’ evolving experience, and their governance.
In the sections that follow, we first develop the theoretical underpinnings of our conceptual framework and discuss the individual hypotheses linking clustering to outlet-level sales performance, as well as the moderating effects of the governance context. We then describe the research method, results, and their implications. We conclude with the limitations of our study and possible directions for further research.
TABLE: TABLE 1 Selected Empirical Research on the Impact of Proximity
| Study | Context | Knowledge Sharing vs. Intrabrand Competition Considered | Governance-Induced Effects Considered | Outlet-Level Clustering Considered | Location’s Financial Performance Considered | Key Findings |
|---|
| Ingram and Baum (1997) | Hotels in Manhattan | Knowledge sharing | No | No | No | Chain-affiliated hotels are less likely to survive when the chain operates more units there. |
| Kalnins and Mayer (2004) | Pizza restaurants in Texas | Knowledge sharing | Yes | No | No | Multiunit owners benefit from local congenital experience. |
| Lu and Wedig (2013) | For-profit nursing home chains in the United States | Other (monitoring cost) | No | Yes | No | Clustered nursing homes achieve higher quality from close monitoring. |
| Brickley and Dark (1987) | Franchise companies in multiple industries in the United States | Other (monitoring cost) | Yes | No | No | Company-owned units are located closer to monitoring headquarters. |
| Kalnins (2004) | Franchised and company-owned lodging establishments in Texas | Intrabrand competition | Yes | No | Yes | New same-brand franchised outlets cannibalize incumbents’ revenues. |
| Ganesan, Malter, and Rindfleisch (2005) | Firms in the U.S. optics industry | Knowledge sharing | No | No | No | Firms located in close proximity engage in increased face-to-face communication, but this has little effect on acquiring new productenhancing knowledge. |
| Pancras, Sriram, and Kumar (2012) | A franchised chain of fast-food restaurants in a large U.S. metropolitan area | Intrabrand competition | No | No | Yes | Sales cannibalization increases as the distance between stores decreases. |
| Perryman and Combs (2012) | Fast-food/quick-service establishments in Florida | Other (monitoring cost) | Yes | No | No | Multioutlet franchising is costefficient. |
| This study | A large U.S.-based automotive service franchise system | Both | Yes | Yes | Yes | The impact of clustering of samebrand outlets on sales is contingent on outlets’ experience and the governance context. |
Figure 1 displays our proposed conceptual framework. Building on the well-established literature on organizational learning (Argyris and Schön 1978; Cyert and March 1963; Huber 1991; Sinkula 1994; Slater and Narver 1995), we acknowledge the benefits of clustering same-brand outlets in terms of their potential for greater learning, interaction, and knowledge sharing due to their shared brand (Ho and Ganesan 2013; Lu and Wedig 2013; Tracey, Heide, and Bell 2014). We also recognize the peril posed in the intrabrand competition effects of clustering same-brand outlets (Pancras, Sriram, and Kumar 2012). Outlets clustered with one another are more likely to compete for the same set of customers and therefore cannibalize sales (Davis 2006; Kalnins 2004). Competing outlets are hence likely to protect knowledge and to be less amenable to sharing useful information with one another (Hansen, Mors, and Løvås 2005; Tsai 2002).
Our hypotheses address this fundamental tension between knowledge sharing and intrabrand competition and suggest when one perspective might dominate the other. We argue that the governance context (shared ownership and franchisor vs. franchisee ownership) affects the clustering–performance relationship. Specifically, the extent to which proximal outlets share ownership influences outlet owners’ perceptions of intrabrand competition, in turn affecting the motivation of proximal outlets to share their knowledge with the focal outlet (Argote and Darr 2000). Furthermore, we posit that the costs and benefits of clustering vary for the franchisor- versus franchisee-owned focal outlet. We also recognize the key role of the operating experience of the clustered outlets in determining their ability to absorb and share knowledge.
Knowledge Sharing Effect
Building on the work of Cyert and March (1963), Sinkula (1994, p. 35) defines organizational learning as “a process by which organizations … learn through interaction with their environments.” Such learning develops “new knowledge or insights that have the potential to influence behavior” (Slater and Narver 1995, p. 63) and comprises “technical skills, product knowledge, manufacturing processes” (Ho and Ganesan 2013, p. 93), “task information, knowhow, and feedback regarding a product or procedure” (Cummings 2004, p. 352), as well as information about the market (De Luca and Atuahene-Gima 2007).
Knowledge3 can be explicit or tacit (Ho and Ganesan 2013; Kalnins and Mayer 2004), and it derives directly from the focal outlet’s operating experience and/or indirectly from other outlets’ experiences (Argote and Miron-Spektor 2011; Bradach 1997) on an ongoing basis (Baum and Ingram 1998; Zander and Kogut 1995). Learning from others’ experiences may take place through contact learning (i.e., transmission of routines through personal and formal relationships) or mimetic learning (i.e., imitating or vicarious learning of routines from other outlets) (Baum and Ingram 1998; Miner and Haunschild 1995). This idea of mimetic learning is also reflected in the notion of isomorphism, espoused by institutional theory (Baum and Ingram 1998), wherein organizations widely imitate each other and their behavior, embedded within personal and interconnected organizational relationships, is likely to be similar to and draw from relevant other firms in their operating environment. This research focuses on same-brand clustered outlets sharing knowledge with one another.
To elicit the knowledge sharing effect of clustering, we rely on the well-established motivation–opportunity–ability framework (Argote, McEvily, and Reagans 2003; MacInnis, Moorman, and Jaworski 1991) to inform our hypotheses. The opportunity for knowledge sharing exists to the extent that outlets have occasion to share knowledge with each other. We suggest that greater clustering affords operators of proximal outlets the opportunity to observe, meet, and share knowledge with one another with greater ease (Ganesan, Malter, and Rindfleisch 2005) and the focal outlet greater opportunities to acquire knowledge from proximal same-brand outlets. Such opportunities may or may not be realized, however, depending on ( 1) the motivation and ability of proximal outlets to share knowledge and ( 2) the ability of the focal outlet to absorb knowledge from its proximal outlets.
Motivation and ability to share knowledge. We consider the proximal outlets’ motivation to share knowledge with the focal outlet. Why might franchisee-owned outlets be motivated to share knowledge with other franchisee-owned outlets? Our review of the literature suggests that franchisee-owned outlets do so, even if separately owned and operated, for at least two reasons. First, proximally located same-brand outlets are likely to share similar problems and experiences associated with their local markets (Darr and Kurtzberg 2000). These experiences give same-brand outlets similar frames of reference that should ease and encourage information sharing (Huber 1991). Second, proximally located same-brand outlets face similar competition (i.e., out-groups) and therefore identify more with their in-group (i.e., same-brand outlets) (Bhattacharya and Sen 2003). Such identification leads in-group members to be at least moderately motivated to share knowledge with other same-brand outlets (Ho and Ganesan 2013).
Though a necessary condition, the motivation to share knowledge is not sufficient for successful knowledge sharing; also required is the ability to share knowledge, or the extent to which proximal outlets have relevant skills and information to share with a focal outlet. More mature outlets are more likely to have accumulated a greater amount of experience than newer, less-established outlets (Huber 1991). This greater depth of experience is reflected in stronger organizational routines and operating procedures and deeper repositories of knowledge regarding their appropriate application (Argote and Miron-Spektor 2011). Thus, the more mature proximal outlets are, the greater their ability to share knowledge with a focal outlet.4
Ability to absorb knowledge. The ability to absorb knowledge is the extent to which a focal outlet has the capacity to incorporate information from proximal same-brand outlets. As the focal outlet gains experience, its ability to value, assimilate, and apply new knowledge—that is, its absorptive capacity (Cohen and Levinthal 1990)—also increases (Penrose 1959). With an increase in its absorptive capacity, the focal outlet is more likely to value and use knowledge available from its proximal same-brand outlets and to realize higher levels of productivity and performance (Chen, Lin, and Chang 2009).
Intrabrand Competition Effect
Coincident with potential knowledge sharing benefits are the costs of intrabrand competition in terms of sales cannibalization (Pancras, Sriram, and Kumar 2012) and knowledge protection (Tsai 2002). Prior research provides evidence of increased competition between proximal same-brand outlets (Kalnins 2003, 2004; Pancras, Sriram, and Kumar 2012). These outlets sell the same products and share the same set of customers in close proximity to each other, with little product or service differentiation (Pancras, Sriram, and Kumar 2012); therefore, they are viewed as close substitutes by customers (Kalnins 2003). The perceived substitutability of same-brand outlets makes travel costs incurred by customers more salient (Davis 2006; Pancras, Sriram, and Kumar 2012), resulting in the sales cannibalization of existing outlets (Kalnins 2004). Thus, proximally located same-brand outlets are likely to compete more fiercely with each other than with outlets located farther away.
Proximal outlets with greater concerns of intrabrand competition are likely to be more guarded and less forthcoming with respect to sharing relevant and useful knowledge with the focal outlet (Hansen, Mors, and Løvås 2005; Tsai 2002). Owners of proximal same-brand outlets are more likely to perceive their sharing of knowledge with the focal outlet as weakening their own performance and thus are more likely to hide what they know or divulge only some of their useful knowledge to the focal outlet (Hansen, Mors, and Løvås 2005). This increases the focal outlet’s cost in the pursuit of knowledge and adversely affects its subsequent performance (Hansen, Mors, and Løvås 2005).
TABLE: TABLE 2 Underlying Logic of Hypotheses
| A: Logic for Knowledge Sharing and Intrabrand Competition Effects |
|---|
| | | Knowledge Sharing Logic | Intrabrand Competition Logic |
|---|
| Condition | Cluster Type | Proximal Outlets’ Motivation to Share Knowledge with Focal Outlet | Focal Outlet’s Ability to Absorb Knowledge from Proximal Outlets | Proximal Outlets’ Ability to Share Knowledge with Focal Outlet | Knowledge Shared with the Focal Outlet (Based on Columns 3, 4, and 5) | Intrabrand Competition Faced by the Focal Outlet | Specific Hypotheses |
|---|
| Condition 1: new focal outlet | CL(NN), CL(NM) | Moderate at best, due to same-chain affiliation | Low, due to lesser absorptive capacity of new focal outlet | Higher in CL(NM), due to mature proximal outlets | Low, due to lesser absorptive capacity of new focal outlet | Higher in CL(NM), due to mature proximal outlets | H1a, H1b |
| Condition 2: mature focal outlet | CL(MM), CL(MN) | Moderate at best, due to same-chain affiliation | High, due to greater absorptive capacity of mature focal outlet | Higher in CL(MM), due to mature proximal outlets | Higher in CL(MM), due to mature proximal outlets and higher absorptive capacity of mature focal outlet | Higher in CL(MM), due to mature proximal outlets | H2 |
| B: Moderating Effect of Shared Ownership |
|---|
| | | Knowledge Sharing Logic | Intrabrand Competition Logic |
|---|
| Condition | Cluster Type | Proximal Outlets’ Motivation to Share Knowledge with Focal Outlet | Focal Outlet’s Ability to Absorb Knowledge from Proximal Outlets | Proximal Outlets’ Ability to Share Knowledge with Focal Outlet | Knowledge Shared with the Focal Outlet (Based on Columns 3, 4, and 5) | Intrabrand Competition Faced by the Focal Outlet | Specific Hypotheses |
|---|
| Condition 3: new focal outlet | CL(NN), CL(NM) | High, due to shared ownership | Low, due to lesser absorptive capacity of new focal outlet | Higher in CL(NM), due to mature proximal outlets | Higher in CL(NM), due to mature proximal outlets | Low, due to shared ownership | H3 |
| Condition 4: mature focal outlet | CL(MM), CL(MN) | High, due to shared ownership | High, due to greater absorptive capacity of mature focal outlet | Higher in CL(MM), due to mature proximal outlets | Higher in CL(MM), due to mature proximal outlets and higher absorptive capacity of mature focal outlet | Low, due to shared ownership | H4 |
Governance Context as Moderator
Our conceptual framework also identifies two relevant governance characteristics that help determine whether knowledge sharing or intrabrand competition dominates: shared ownership and franchisor versus franchisee ownership. We expect the shared ownership of clustered outlets (i.e., multiunit operations) to reduce the perceptions of intrabrand competition (Kalnins and Lafontaine 2004), thus prompting proximal outlets to be less guarded and more motivated to share knowledge with the focal outlet (Argote and Darr 2000; Darr, Argote, and Epple 1995; Ingram and Baum 2001). Building on recent theoretical developments linking governance characteristics to geographic clusters (Bell, Tracey, and Heide 2009; Tracey, Heide, and Bell 2014), we also identify franchisor versus franchisee ownership of the focal outlet as a critical “shifter” (Shane 2001) of the knowledge–competition boundary effects. We expect franchisees to experience higher costs of intrabrand competition, due to greater proximity (Kalnins 2004), and lower benefits of knowledge sharing, due to contractual constraints (Kashyap, Antia, and Frazier 2012), than their franchisor-owned counterparts.
Clustering Effects
Table 2 details the underlying logic for H1–H4. We suggest that clustering provides the opportunity for a focal outlet to learn from same-brand proximal outlets but also poses a potential risk of intrabrand competition. Thus, our arguments focus on the interplay of conditions that likely make the knowledge sharing or intrabrand competition more salient for the clustered outlets. As Table 2 shows, we hypothesize that the knowledge shared with the focal outlet is a function of the proximal outlets’ motivation and ability to share knowledge and the focal outlet’s ability to absorb the knowledge shared (see columns 3, 4, and 5 in Table 2); however, their perceptions of intrabrand competition are likely to be influenced significantly by their governance. In what follows, we first take the perspective of a newly established focal outlet, followed by that of a mature focal outlet. For both new and mature focal outlets, we predict the impact of their clustering with other new and mature same-brand outlets on their performance.5
The perspective of a new focal outlet. Consider Condition 1: a newly established focal outlet clustered with same-brand outlets that may be new (N) or mature (M), forming clusters CL(NN) and CL(NM), respectively.6 Given their relative inexperience, newly established outlets possess less knowledge of their own (Penrose 1959) and are less practiced and capable of performing the activities in which they are engaged (Cohen and Levinthal 1990). Moreover, newly established outlets do not know local market conditions and competitors as well as their mature counterparts, which negatively affects their ability to use the knowledge shared by their proximal outlets. Thus, we expect a new focal outlet to be more likely to succumb to the intrabrand competition effect of clustering rather than gain from its knowledge sharing potential, regardless of whether the proximal outlets are able (i.e., they are mature) or motivated (i.e., their intrabrand competition concerns are mitigated) to share their knowledge.
H1a: The greater the clustering of a new focal outlet with other same-brand outlets, the weaker its performance.
In addition, we argue that the intrabrand competition experienced by the new focal outlet is greater when the proximal same-brand outlets are mature (i.e., the CL(NM) relative to the CL(NN) cluster). Mature proximal outlets have more market knowledge, such as product and process knowledge (Ho and Ganesan 2013), customer knowledge (Cummings 2004), and external environment knowledge (De Luca and Atuahene-Gima 2007), which gives them a greater ability to compete at an advantage against the newly established focal outlets. We therefore expect that new focal outlets’ performance will be more negative when the outlets are clustered with mature outlets (i.e., CL(NM)) than when they are clustered with new outlets (i.e., CL(NN)).
H1b: New focal outlets clustered with mature same-brand outlets perform worse than those clustered with new same-brand outlets.
The perspective of a mature focal outlet. Consider Condition 2 in Table 2, in which a mature (M) focal outlet is clustered with mature (M) or new (N) outlets of the same brand, forming clusters CL(MM) and CL(MN), respectively. Given its own accumulated experience, a mature focal outlet is likely to have a greater ability to absorb any knowledge shared by its proximal outlets than a new focal outlet. This is because a mature focal outlet has greater accumulated experience and correspondingly higher absorptive capacity than its newly established counterparts (Zahra and George 2002). It would therefore benefit from being clustered with other mature outlets of the same brand, which have the ability to share knowledge due to their greater repository of relevant knowledge (Kalnins and Mayer 2004). This knowledge benefit to the mature outlet is limited when the outlet is clustered with newly established outlets, which likely possess less relevant knowledge to share (Kalnins and Mayer 2004). From a knowledge sharing perspective, the mature focal outlet is better served when clustered with other mature outlets rather than with newly established outlets.
However, from an intrabrand competitive threat perspective, the opposite inference is likely to prevail—that is, the mature focal outlet is better served when clustered with newly established outlets rather than mature outlets. The reason lies in the greater market knowledge of the mature focal outlet, which confers a competitive advantage over the newly established proximal outlets. As proximal outlets’ experience increases with maturity, however, this knowledge-based competitive advantage dissipates and the focal outlet experiences a higher level of intrabrand competition from mature proximal outlets.
Overall, when the mature focal outlet is clustered with mature outlets, it benefits from knowledge sharing; this benefit, however, is balanced by the greater competitive threat posed by the mature proximal outlets due to their greater experience. In contrast, when the mature focal outlet is clustered with new outlets, it loses the knowledge sharing benefit, which is balanced by the lower competitive threat posed by the outlets’ newness. We therefore hypothesize no significant difference in performance between mature focal outlets clustered with mature or new outlets.
H2: The greater clustering of a mature focal outlet with other mature or new same-brand outlets neither helps nor hinders the outlet’s performance.
TABLE: TABLE 3 Variables and Data Sources
| Construct | Measured Variable | Notation | Data Source |
|---|
| Clustering | Proximity of a new focal outlet i with other same-brand new outlets j in year t Proximity of a new focal outlet i with same-brand mature outlets j in year t Proximity of a mature focal outlet i with other same-brand mature outlets j in year t Proximity of a mature focal outlet i with same-brand new outlets j in year t | CLit(NN) CLit(NM) CLit(MM) CLit(MN) | Computed using ArcGIS 10.3 |
| Shared ownership | Shared ownership of a focal outlet i with other same-brand outlets j within a cluster in year t | SOit | |
| Franchisor versus franchisee ownership | Dichotomous variable that equals 1 when a focal outlet i is franchisee owned and 0 if franchisor owned | FFOi | Internal company records |
| Sales performance | Sales revenue of a focal outlet i in year t | SRit | |
| Control Variables |
| Cluster size | Number of outlets within 25-mile radius of a focal outlet i in year t | CSit | Computed using ArcGIS 10.3 |
| Mean age of clustered outlets | Mean age of proximal outlets within 25-mile radius of a focal outlet i in year t | APit | Internal company records |
| Firm size | Total number of outlets in year t | FSt | |
| Royalty rate | The ongoing payment as a percentage of sales in year t | RRt | Franchise disclosure documents |
| Interbrand competition | Number of outlets of competitor brands located in county k in year t | IBCkt | U.S. Census Bureau |
| Area (square miles) | Area of county k | ARk | |
| Population (millions) | Population of county k in year t | POPkt | Bureau of Economic Analysis |
| Income (millions) | Income per capita of county k in year t | INkt | |
Moderating Effects of Shared Ownership
Thus far, our hypotheses have focused on the anticipated main effects of clustering on the focal outlet’s performance. To these, we now add the potential moderating effects of shared ownership of the focal and proximal outlets (see Table 2, Panel B). We define “shared ownership” as the extent to which outlets in the cluster are owned by the same operator as that of the focal outlet. For franchisee-owned focal outlets, this comprises only the proximal outlets owned by the same focal franchisee. When the focal outlet is franchisor owned, this comprises only the franchisor-owned outlets within that cluster.
Shared ownership affects the clustering–performance relationship by influencing the perceptions of intrabrand competition (Ingram and Baum 2001; Kalnins and Lafontaine 2004), which in turn affects the motivation of proximal outlets to share knowledge with the focal outlet (Argote and Darr 2000; Darr and Kurtzberg 2000). Unlike outlets that do not share a common owner, outlets operating under shared ownership are likely to have greater norms of reciprocity, a common language system, and incentives to share knowledge, all of which enhance the motivation of the clustered outlets to share knowledge (Darr, Argote, and Epple 1995).
Furthermore, shared ownership creates even more opportunities for outlets to share knowledge through multiple means. Indeed, Darr, Argote, and Epple (1995) note that outlets operating under shared ownership have more regular communication with one another and more interpersonal ties than those not sharing common ownership. Moreover, shared ownership creates more opportunities to share knowledge through contact learning (in addition to mimetic learning), in which knowledge is shared through personal and formal relationships (Baum and Ingram 1998; Miner and Haunschild 1995). We now discuss how shared ownership might affect the performance implications of clustering for a newly established focal outlet and a mature focal outlet.
The perspective of a new focal outlet. As proposed in H1b, we expect a new focal outlet clustered with mature outlets to underperform relative to a new focal outlet clustered with newly established outlets. We attribute this to the double jeopardy of a new focal outlet’s inability to absorb knowledge from proximal outlets and a higher level of intrabrand competition from more mature and capable proximal outlets. We expect shared ownership to significantly attenuate both these adverse effects.
Consider Condition 3 in Table 2, in which a new (N) focal outlet is clustered with new (N) or mature (M) same-brand outlets sharing common ownership and forming clusters CL(NN) and CL(NM), respectively. As the extent of shared ownership between a focal outlet and its proximal outlets increases, the proximal outlets’ motivation to share knowledge with the focal outlet increases by lowering perceptions of intrabrand competition (Argote, McEvily, and Reagans 2003; Ingram and Baum 2001; Kalnins and Lafontaine 2004). The common owner’s objective of ensuring successful operations across his or her multiple outlets (Tsai and Ghoshal 1998) results in stronger ties and greater trust and reciprocity among the shared ownership outlets (Larson 1992; Tsai 2002). Such strong ties brought about by shared ownership blur the perceptions of intrabrand competition and foster greater knowledge sharing among commonly owned outlets (Tsai and Ghoshal 1998).
Although newly established focal outlets are less able to value, assimilate, and apply new knowledge (i.e., they have lower absorptive capacity), shared ownership creates more opportunities to learn by contact rather than solely relying on mimetic learning (Darr, Argote, and Epple 1995). Thus, when operating under shared ownership, newly established focal outlets have additional ways to learn organizational routines and operating procedures that are less available (or not available at all) to outlets that do not share common ownership.
The final element to consider when assessing the moderating effect of common ownership is the proximal outlets’ ability to share knowledge. As discussed previously, experience is a significant source of operating knowledge (Kalnins and Mayer 2004). Newly established proximal outlets are likely to have less useful and relevant knowledge to share with the focal outlet. Mature proximal outlets, alternatively, are more likely to have a higher level of knowledge to share. Moreover, despite the difficulties new outlets face in absorbing new knowledge, mature proximal outlets under common ownership are also motivated to work with newly established outlets to find ways to instill knowledge in them. The knowledge benefit accruing to the focal outlet is likely to supersede the intrabrand competition–related concern, and this knowledge benefit is likely to be higher for focal outlets clustered with commonly owned mature outlets rather than newly established ones.
H3: As the extent of shared ownership increases, new focal outlets clustered with mature outlets perform better than those clustered with new outlets.
The perspective of a mature focal outlet. As we propose in H2, a mature focal outlet has more (less) knowledge to gain from other mature (new) proximal outlets but also faces more (less) intrabrand competition from these more (less) experienced outlets. The positive (knowledge sharing) and adverse (intrabrand competition) effects of clustering should counter each other, resulting in no likely differences in performance.
Now consider Condition 4 in Table 2, in which a mature (M) focal outlet is clustered with mature (M) or new (N) outlets of the same brand sharing common ownership, forming clusters CL(MM) and CL(MN), respectively. As the extent of shared ownership of the clustered outlets increases, concerns about intrabrand competition are significantly mitigated (Argote, McEvily, and Reagans 2003; Ingram and Baum 2001). The proximal outlets’ knowledge protection imperative correspondingly declines (Kalnins and Lafontaine 2004), leading to an increased motivation to share knowledge with the focal outlet (Darr, Argote, and Epple 1995; Ingram and Baum 2001). In turn, the means of knowledge sharing also changes; that is, rather than relying solely on observation of the proximal outlets, the focal outlet also learns from them through direct contact (Baum and Ingram 1998; Miner and Haunschild 1995). This additional modality of learning from proximal others confers a higher ability on the mature focal outlet to learn, compared with the situation in which the extent of shared ownership is lower.
Mature focal outlets possess greater accumulated experience and a correspondingly higher level of absorptive capacity (Zahra and George 2002). When they are clustered with commonly owned mature outlets that are motivated and able to share their knowledge, the knowledge benefits accruing to the mature focal outlets dominate the significantly lower intrabrand competition effects. This knowledge sharing–related benefit is likely to be reduced when the proximal outlets are newly established; notwithstanding their higher motivation to share knowledge, newly established outlets possess less knowledge that might benefit the mature focal outlet. Thus, an increase in the extent of shared ownership likely brings about greater knowledge gains for mature focal outlets clustered with mature rather than newly established outlets. This suggests the following:
H4: As the extent of shared ownership increases, mature focal outlets clustered with mature outlets perform better than those clustered with new outlets.
Moderating Effects of Franchisor Versus Franchisee Ownership
We draw from the rich body of franchising research on the drivers (Brickley and Dark 1987; Perryman and Combs 2012) and consequences (Kalnins 2004) of outlet ownership to posit moderation of the previously hypothesized clustering effects, depending on whether the focal outlet is franchisor- or franchisee-owned. Compared with franchisor-owned outlets, we suggest that franchisee-owned outlets are more vulnerable to intrabrand competition and benefit less from the knowledge sharing opportunity conferred by proximal same-brand outlets. The increased costs for franchisees and the reduced knowledge benefits to them should result in franchisorowned outlets outperforming their franchisee-owned counterparts across the clustering scenarios we assess.
Franchisee-owned outlets are more likely than franchisorowned outlets to bear the brunt of intrabrand competition due to proximity. Because franchisees’ royalty payments are tied to their revenue (Lafontaine 1992), franchisors are incentivized to open new franchisee-owned outlets, even if they are located close to existing franchisee-owned outlets (Kalnins 2004). In contrast, the franchisor is likely to be more strategic in ensuring that revenues at existing franchisor-owned outlets will not decrease when new outlets are opened (Kalnins 2004). This implies that franchisee-owned outlets are more likely to face competition from other proximal same-brand outlets. Indeed, Kalnins (2004) finds evidence of such a higher likelihood of franchisees facing intrabrand competition. The adverse consequences of intrabrand competition are thus likely to be higher for franchisee-owned outlets than their franchisor-owned counterparts.
In addition, we expect franchisees to benefit less from the proximity-conferred learning and knowledge sharing opportunity than franchisor-owned outlets. Recall that the benefits of learning are realized when, on the basis of learning, the focal outlet undertakes different, improved actions and routines (Huber 1991). Franchise systems, by their very design, emphasize uniformity over innovation. To ensure the former, franchisors rely on ironclad contractual agreements and uniformity-ensuring constraints (Kashyap, Antia, and Frazier 2012) that reduce the leeway available to franchisees to make significant changes in response to the additional know-how they are able to glean from their proximal same-brand outlets. Thus, even if a focal franchisee has the opportunity to learn by clustering with same-brand outlets, has proximal outlets that are motivated and have the ability to share knowledge with it, and also has the absorptive capacity to use the knowledge shared, it may not be able to implement improved actions or routines because of contractual constraints.
TABLE: TABLE 4 Correlation Matrix and Descriptive Statistics
| | | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
|---|
| 1 | Outlet sales revenuea (SRit) | – | | | | | | | | | | | | | | |
| 2 | Clustering (new-new) (CLit(NN)) | -0.05 | – | | | | | | | | | | | | | |
| 3 | Clustering (new-mature) (CLit(NM)) | -0.04 | -0.07 | – | | | | | | | | | | | | |
| 4 | Clustering (mature-mature) (CLit(MM)) | 0.1 | -0.16 | -0.07 | – | | | | | | | | | | | |
| 5 | Clustering (mature-new) (CLit(MN)) | -0.04 | -0.02 | -0.01 | -0.02 | – | | | | | | | | | | |
| 6 | Shared ownership (SOit) | 0.07 | 0.02 | -0.06 | 0.35 | -0.02 | – | | | | | | | | | |
| 7 | Franchisor vs. Franchisee | -0.11 | 0.27 | -0.07 | -0.54 | 0.03 | -0.35 | – | | | | | | | | |
| 8 | ownership (FFOi) | 0.02 | -0.02 | 0.05 | 0.31 | 0 | 0.89 | -0.31 | – | | | | | | | |
| 9 | Cluster size (CSit) | 0.24 | -0.51 | 0.15 | 0.52 | -0.03 | 0.3 | -0.63 | 0.31 | – | | | | | | |
| 10 | Mean age of clustered | 0.32 | 0.06 | 0 | 0.04 | 0.04 | 0.01 | 0.07 | 0.02 | 0.22 | – | | | | | |
| 11 | outlets (APit) | 0.13 | 0.05 | 0 | 0.03 | 0.04 | -0.01 | 0.07 | 0 | 0.22 | 0.72 | – | | | | |
| 12 | Firm size (FSt) | -0.08 | 0.21 | -0.02 | -0.07 | 0.1 | 0.15 | 0.15 | 0.17 | -0.07 | 0.06 | 0.07 | – | | | |
| 13 | Royalty rate (RRt) | 0.04 | 0.13 | -0.04 | 0.03 | 0.05 | 0.41 | 0.07 | 0.42 | 0.05 | 0.03 | 0.05 | 0.68 | – | | |
| 14 | Interbrand competition (IBCkt) | 0.17 | 0.2 | -0.02 | 0.03 | 0.05 | 0.34 | 0.08 | 0.38 | 0.01 | 0.29 | 0.23 | 0.17 | 0.42 | – | |
| 15 | Market populationa (POPkt) | -0.07 | 0.1 | -0.05 | -0.1 | 0.02 | -0.15 | 0.24 | -0.15 | -0.04 | 0.05 | 0.06 | 0.45 | 0.32 | -0.08 | – |
| M | Income per capitaa (INkt) | 12.92 | 0.75 | 0.12 | 1.21 | 0.01 | 8.07 | 0.59 | 9.26 | 9.16 | 817.9 | 8.42 | 65.71 | 1.08 | 10.03 | 3.14 |
| SD | Market areaa (ARk) | 0.87 | 1.89 | 0.67 | 2.89 | 0.21 | 6.84 | 0.49 | 7.82 | 5.58 | 87.45 | 0.49 | 112.6 | 9.75 | 11.49 | 9.91 |
a Natural log-transformed.
Notes: n1 = 12,909. Correlations exceeding |.02| are significant at p < .05, two-tailed.
In essence, franchisee-owned outlets are ( 1) more likely to experience the prospect of a proximal same-brand outlet and ( 2) more constrained in their ability to change their organizational routines and processes in response to knowledge received from other proximal outlets. It is this double jeopardy that leads us to hypothesize the following:
H5: Franchisor-owned focal outlets outperform their franchisee-owned counterparts more as clustering with other same-brand outlets increases; the dominance by franchisor-owned outlets persists across new and mature outlets.
Research Method
Empirical Context and Data Collection Procedure
We collaborated with a large U.S.-based franchisor of automotive maintenance and repair services to test our hypotheses. The participating firm provided information on the date of establishment, specific location (street address), and the ownership of each outlet (franchisor or franchisee owned), from system inception in 1977 to its 988th outlet in 2012. In addition, top management shared outlet-level sales performance information on an annual basis from 2004 to 2012. We supplemented these data with various firm- and market-specific variables at the county level, such as royalty rate, interbrand competition, population, per capita income, and area from franchise disclosure documents, the U.S. Census Bureau, and the Bureau of Economic Analysis. Table 3 displays the complete list of variables used in this study and their data sources.
Unit of Analysis and Measures
Our unit of analysis is the individual outlet i (i = 1, …, 988), observed t years since its inception (t = 0, …, 35). Our objective is to relate the clustering of outlets to their corresponding sales performance over time. Table 4 provides the descriptive statistics for all the variables and the pairwise correlations among them.
Outlet performance. Our focal dependent variable, outlet-level performance, is reflected in the annual sales revenue (SRit), natural log–transformed, realized by outlet i in year t. The sales revenue of the individual outlet serves as the basis for franchisees’ royalty payments (i.e., royalties are calculated as a percentage of sales) and are therefore of great importance to both franchisees and franchisors.
Cluster types. We assessed the extent to which each outlet i was part of a cluster of same-brand outlets at time t by computing the local Moran’s I index (Anselin 1995) using ArcGIS 10.3. The local Moran’s I estimates clustering strength or spatial autocorrelation of a focal outlet on the basis of two factors: ( 1) its geographic proximity to other outlets and ( 2) its similarity to or dissimilarity from other outlets of the same franchise system on a specific attribute (in our case, we infer outlet i’s accumulated experience from its age). Given a set of outlet locations and the associated accumulated experience, the local Moran’s I computes the extent to which an individual outlet is clustered with other outlets and, if so, the nature of clustering—with similar or dissimilar accumulated experience levels.
The computation of the local Moran’s I generates two outputs: ( 1) the local Moran’s I score, along with a z-score and a p-value that provide the strength of clustering for each outlet, and ( 2) the cluster category of each significantly clustered outlet based on its attribute (i.e., outlet age). The local Moran’s I identifies outlets with low (i.e., younger) and high (older) attribute values by using the normal distribution of outlet age, categorizing each as new and mature, respectively. Thus, we are able to infer not only the strength of clustering at the individual outlet level but also the four archetypal cluster types of theoretical relevance based on age: CLit(NN), when a new focal outlet i is clustered with other new outlets at time t; CLit(NM), when a new focal outlet i is clustered with mature outlets at time t; CLit(MM), when a mature focal outlet i is clustered with other mature outlets at time t; and CLit(MN), when a mature focal outlet i is clustered with new outlets at time t. Prior studies in marketing have used Moran’s I index to measure spatial dependence of variables (e.g., Mittal, Kamakura, and Govind 2004). The Web Appendix provides additional details on the local Moran’s I computation and examples from our data of each of the four prototypical clustering types.
Shared ownership. Consistent with Lu and Wedig (2013), we define clustering within a 25-mile radius of the focal outlet and measure shared ownership of clustered outlets (SOit) as the count of proximal outlets j within this 25-mile radius of the focal outlet i at time t. For franchisee-owned focal outlets, this measure counts only the proximal outlets that are owned by the same focal franchisee (i.e., multiunit franchisees). For franchisor-owned focal outlets, the count includes only franchisor-owned outlets within a 25-mile radius of the focal outlet.
Franchisor versus franchisee ownership. We operationalize franchisor versus franchisee ownership (FFOi) as a dichotomous variable that takes a value of 1 when an outlet i is franchisee-owned and 0 when franchisor-owned.
Control variables. We incorporate several control variables that we expect to have an impact on the individual outlet’s sales performance over and above our hypothesized variables. We measure cluster size (CSit) as the number of same-brand outlets within a 25-mile radius of a focal outlet. We also include the mean age of outlets in a 25-mile radius of the focal outlet, incorporating its quadratic term as well to control for the possibility of diminishing returns to experience. We control for franchise system size (FSt), or the total number of outlets in operation in year t, and royalty rate (RRt), or the ongoing payment as a percentage of sales that franchisees must pay the franchisor for use of the trademark and other support. System size reflects overall access of the outlet to resources that could affect performance; royalties incentivize franchisor investments in the brand, thereby boosting franchisee sales and making the franchise more attractive to franchisees.
We also control for market-specific effects on outlet sales performance. The most fine-grained market data we are able to collect are at the U.S. county level, k (k = 1, …, 270). We include interbrand competition (IBCkt), or the total number of outlets of other competing brands included in the five-digit North American Industry Classification System code corresponding to the sector in which the franchise system operates, located in county k in year t. We also include the population (POPkt) of county k in year t, the income per capita (INkt) in county k in year t, and the area of the county (ARk) in square miles. Finally, we control for unobserved heterogeneity by including year-specific fixed effects for the t years in our data set.
Model Specification
Although we were able to obtain data pertaining to individual outlet locations from the inception of the franchise system in 1977, corresponding outlet sales data are available only from 2004 and are missing for some outlets. To account for potential biased parameter estimates due to sales data not being missing at random, we correct for selection bias by specifying a Heckman (1976) selection model in the first stage of the analysis and including the lambda vector thus obtained in the second stage. This second-stage (substantive) equation investigates the interplay of clustering, shared ownership, franchisor versus franchisee ownership of the focal outlet, and their impact on outlet sales performance, while accounting for potential endogeneity of the regressors.
Stage 1: correction for sample selection bias. We specify our selection equation as a probit model as follows:
where
INCLUDEit = outlet i’s availability of sales information at time t,
OAit = age of outlet i at time t,
FEi = franchisee-owned as a binary variable (franchisee-owned = 1, 0 otherwise),
YRt = specific years as dummy variables, with 2004 as the excluded base year, and
eit ~ N(m1, s2).
From this equation, we obtain and store the inverse Mills ratio (i.e., lambda) vector for subsequent inclusion in the second stage of analysis.
Stage 2: substantive equation estimation. In the second stage, we relate each outlet’s clustering, shared ownership, and franchisor versus franchisee ownership to its annual sales performance. Our model specification approach in this stage is informed by the need to account for the potential endogeneity of regressors—clustering (CLit(NN), CLit(NM), CLit(MM), and CLit(MN)), shared ownership (SOit), and franchisor versus franchisee ownership (FFOi). The clustering-related regressors and shared ownership are time varying, whereas franchisor versus franchisee ownership is time invariant. Durbin–Wu– Hausman tests of these variables yielded significant evidence of endogeneity. We therefore specified an endogeneity-correcting regression equation. We also treat the interactions of clustering with shared ownership and with franchisor versus franchisee ownership as endogenous.
We use the Hausman–Taylor instrumental variables (HTIV) regression approach to account for endogenous regressors (for estimation details and checks of its appropriateness, see the Web Appendix). We specify our HTIV model as follows (variables in boldface denote endogenous regressors, of which governance [FFOi] is time invariant):
where
SRit = outlet sales revenue (natural log-transformed), CLit(NN) = clustering of a new focal outlet with other new outlets,
CLit(NM) = clustering of a new focal outlet with mature outlets, CLit(MM) = clustering of a mature focal outlet with other mature outlets,
CLit(MN) = clustering of a mature focal outlet with new outlets, SOit = shared ownership of clustered outlets,
FFOi = ownership of a focal outlet i (franchisee-owned = 1, franchisor-owned = 0),
CSit = cluster size,
APit = mean age of clustered outlets,
(APit)2 = quadratic term for mean age of clustered outlets, FSt = firm size,
RRt = royalty rate,
IBCkt = interbrand competition,
POPkt = market population (natural log–transformed), INkt = income per capita (natural log–transformed), ARk = market area (natural log–transformed),
YRt = year,
IMRit = inverse Mills ratio, ai ~ i.i.d. (m2, s2a), and uit ~ i.i.d. (m3, s2u).
Note that during the 2004–2012 period, we observe no instances of clustering of new franchisor-owned outlets with other new outlets. As such, we infer the impact of franchiseeowned new outlets clustering with other new outlets from the main effect of CLit(NN) in Equation 2.
TABLE: TABLE 5 HTIV Regression Estimates
| Outlet Sales Revenuea | Parameter | Hypothesis | Coefficient | SE | z-Value |
|---|
| aNatural log–transformed. |
| †p < .10 (two-tailed). |
| *p < .05 (two-tailed). |
| **p < .01 (two-tailed). |
| Intercept | h0 | | 1.69 | 1.45 | 1.17 |
| Clustering (new-new) (CLit(NN)) | h1 | H1a–b | -.03** | 0.01 | -3.06 |
| Clustering (new-mature) (CLit(NM)) | h2 | H1a–b | -.10* | 0.04 | -2.35 |
| Clustering (mature-mature) (CLit(MM)) | h3 | H2 | 0.01 | 0.02 | 0.52 |
| Clustering (mature-new) (CLit(MN)) | h4 | H2 | 0.04 | 0.12 | 0.38 |
| Shared ownership (SOit) | h5 | | .04** | 0.01 | 4.8 |
| Franchisee ownership (FFOi) | h6 | | -.80** | 0.15 | -5.33 |
| Clustering (new-new) × Shared ownership (CLit(NN) × SOit) | h7 | H3 | -.01** | 0 | -4.84 |
| Clustering (new-mature) × Shared ownership (CLit(NM) × SOit) | h8 | H3 | .01* | 0 | 1.99 |
| Clustering (mature-mature) × Shared ownership (CLit(MM) × SOit) | h9 | H4 | .00** | 0 | 4.09 |
| Clustering (mature-new) × Shared ownership (CLit(MN) × SOit) | h10 | H4 | -.02** | 0.01 | -4 |
| Clustering (new-mature) × Franchisee ownership (CLit(NM) × FFOi) | h11 | H5 | .09* | 0.04 | 2.23 |
| Clustering (mature-mature) × Franchisee ownership (CLit(MM) × FFOi) | h12 | H5 | -.07** | 0.02 | -2.86 |
| Clustering (mature-new) × Franchisee ownership (CLit(MN) × FFOi) | h13 | H5 | -0.14 | 0.12 | -1.2 |
| Cluster size (CSit) | h14 | | -.10** | 0.01 | -13.4 |
| Mean age of clustered outlets (APit) | h15 | | -.06** | 0.01 | -6.2 |
| Change in mean age of clustered outlets ([APit]2) | h16 | | .00† | 0 | 1.79 |
| Firm size (FSt) | h17 | | .00** | 0 | 7.37 |
| Royalty rate (RRt) | h18 | | -.39** | 0.05 | -7.9 |
| Interbrand competition (IBCkt) | h19 | | .00† | 0 | 1.7 |
| Market populationa (POPkt) | h20 | | .20** | 0.05 | 3.95 |
| Income per capitaa (INkt) | h21 | | 1.04** | 0.14 | 7.41 |
| Market Areaa (ARk) | h22 | | -0.05 | 0.06 | -0.84 |
| Inverse Mills ratio (IMRit) | h23 | | -.78** | 0.08 | -10.25 |
| Year fixed effects (YRt) | h24–31 | | | Yes | |
Notes: Number of observations = n2 = 6,576; Wald c2 = 11,096.91 (p < .01). Base year = 2004.
Results
Model-free evidence. As Table 4 shows, the clustering of a new focal outlet with other outlets is significantly and negatively correlated with outlet sales (r(CLit(NN)) = –.05, r(CLit(NM)) = –.04, both p < .01), as we expected. In comparison, the clustering of mature outlets is significantly and positively correlated with outlet sales when the clustered outlets are mature (r(CLit(MM)) = .10, p < .01) and negatively correlated when the clustered outlets are new (r(CLit(MN)) = –.04, p < .01). The clustering of new focal outlets is clearly associated with less favorable sales performance than the clustering of mature focal outlets. Thus, these results provide initial model-free evidence for our baseline hypotheses (H1 and H2).
The Heckman selection model. The overall model is significant (Wald c2 = 10.58, p < .01), and we find clear evidence of selection with respect to sales information availability (l = –1.15, p < .01). We find that mature outlets (b1 = .05, p < .01) are more likely to provide sales information than newoutlets, while franchisee-owned outlets (b2 = –.24, p < .01) are less likely to provide sales information than franchisorowned outlets. We also find that relative to the base year of 2004, there is greater availability of outlet sales information in subsequent years.
The HTIV estimation. Table 5 displays the results of the HTIV estimation. The overall model is significant (Wald c2 = 11,096.91, p < .01), suggesting that the hypothesized predictors of outlet-level sales performance have significant explanatory power. The main effect of clustering on the focal outlets’ sales performance is significant and negative when new focal outlets are clustered with new outlets (h1 = –.03, p < .01) and with mature outlets (h2 = –.10, p < .05) of the same brand. We therefore find support for H1a. However, we find no significant sales performance differences between new focal outlets being clustered with new or mature outlets of the same brand (c2 = 2.49, n.s.); therefore, H1b is not supported. As hypothesized, we find no impact of clustering of mature focal outlets with other mature (h3 = .01, n.s.) or new (h4 = .04, n.s.) outlets on the focal outlets’ sales performance. Thus, we find support for H2.
We also find support for H3, which predicted that new focal outlets would perform better when clustered with mature (h8 = .01, p < .05) rather than new (h7 = –.01, p < .01) outlets that are under shared ownership. According to H4, as the extent of shared ownership increases, mature focal outlets gain sales when clustered with mature proximal outlets (h9 = .00, p < .01) and lose sales when clustered with new proximal outlets (h10 = –.02, p < .01). Thus, H4 is supported.
H5 predicted that franchisee-owned outlets would gain less from clustering than franchisor-owned outlets. We find partial support for H5. Specifically, we find that mature franchisee-owned outlets achieve lower sales than their franchisor-owned counterparts when in close proximity to other mature outlets (h12 = –.07, p < .01). However, compared with their franchisor-owned counterparts, the clustering of new franchisee-owned outlets with mature outlets results in significant gains to sales performance (h11 = .09, p < .05). This runs counter to H5. Furthermore, we find that the clustering of mature franchisee-owned outlets with new outlets does not significantly differ from that of their franchisor-owned counterparts (h13 = –.14, n.s.). Finally, the franchise system had no instances of new franchisor-owned outlets clustering with other new outlets; the lack of a contrast precludes the ability to test their relationship. Overall, we find that franchisor versus franchisee ownership affects the clustering–performance relationship for both new and mature focal outlets.
For the control variables, we find that firm size (h17 = .00, p < .01) significantly and positively affects outlet-level sales. Cluster size (h14 = –.10, p < .01) and royalty rate (h18 = –.39, p < .01), however, have a significant and negative relationship to outlet-level sales. The mean age of cluster (h15 = –.06, p < .01) is significantly and negatively associated with outlet sales, but with a marginally significant diminishing trend (h16 = .00, p < .10). For market-specific control variables, greater population (h20 = .20, p < .01) and per capita income (h21 = 1.04, p < .01) significantly and positively increase outlet-level sales, while interbrand competition (h19 = .00, p < .10) partially and positively affects outlet-level sales. Market area (h22 = –.05, n.s.) does not significantly affect outlet-level sales. Finally, we find significant year-specific effects on outlet-level sales.
Post Hoc Analysis of Significant Interactions
For a better understanding of the moderating impact of shared ownership and franchisor versus franchisee ownership on the clustering and outlet-level sales relationship, we conducted an analysis of simple slopes for all significant interactions (Aiken and West 1991). Figure 2, Panel A, suggests that new focal outlets that are highly clustered with other new sharedownership outlets do lose more sales, relative to their counterparts that are not highly clustered (simple slope of CLit(NN) for new focal outlets = –.20, p < .01). In marked contrast, Panel B suggests that new focal outlets’ sales are not significantly affected by their clustering with mature outlets under shared ownership (simple slope of CLit(NM) for new focal outlets = .08, n.s.). Panels C and D suggest that mature focal outlets gain sales when clustered with othermature outlets in the presence of shared ownership (simple slope of CLit(MM) for mature focal outlets = .12, p < .01).Mature focal outlets’ sales performance, however, is harmed when focal outlets are clustered with new outlets under shared ownership (simple slope of CLit(MN) for mature focal outlets = –.67, p < .01).
Figure 3, Panel B, suggests that clustering of a new focal outlet with mature outlets harms outlet-level sales when the focal outlet is franchisor-owned (the simple slope of CLit(NM) for franchisor-owned focal outlets = –.10, p < .05). In contrast, franchisee-owned new focal outlets are not significantly hurt or helped by their proximity to mature outlets (the simple slope of CLit(NM) for franchisee-owned focal outlets = –.01, n.s.). Panel B suggests that mature franchisor-owned focal outlets’ clustering with other mature outlets has no significant impact on their sales performance (the simple slope of CLit(MM) for franchisor-owned focal outlets = .01, n.s.). Only franchiseeowned mature focal outlets lose from greater clustering with othermature outlets (the simple slope ofCLit(MM) for franchiseeowned focal outlets = –.06, p < .05).
Alternative Specifications
We assessed the stability of our findings to alternate estimation approaches, alternate measures of performance, alternative explanations for the effects reported, alternate time-related specifications, and alternative levels of analysis.
Alternative estimator. To test the robustness of our results, we used the fixed-effects approach as an alternative estimator. The fixed-effects estimation results in the dropping of the time-invariant franchisor versus franchisee ownership (FFOi) variable, but it retains all four archetypal clustering types and their interactions with shared ownership and franchisor versus franchisee ownership. All results with respect to the hypothesized effects remain robust.
Alternative measure of performance. We also relied on a different but related measure of outlet performance—sales transaction volume—which we operationalized as the total number of transactions reported by each outlet per year. Our HTIV estimates remain robust to this alternative measure of performance as well.
Alternative explanation for the effects reported. We also explored the possibility that the focal outlet’s sales might be affected not from any knowledge sharing pursuant to clustering but from better franchisor monitoring capabilities as a function of nearby franchisor-owned outlets. To test this alternative explanation, we computed the number of franchisor-owned outlets in the county of location of the focal outlet and included this variable in our model. All the clustering-related effects and their interactions with shared ownership and franchisor versus franchisee ownership remain robust, and the main effect of the additional regressor is nonsignificant.
Alternative temporal separation. We based our conceptualization and subsequent model specification approach on the assumption of contemporaneous (immediate, within the same year) effects of clustering on the sales performance of each outlet. We also assessed one- and two-year lagged models of the hypothesized relationships. Our principal findings of the baseline hypotheses and the moderating effects of shared ownership and franchisor versus franchisee ownership persist.
Alternative level of analysis. Prior research has mostly used clustering as a global or systemwide construct (within the present context, across all 988 outlets of the franchise system) without investigating the type of clustering or with whom a focal outlet is clustered. We therefore specified an alternative model, measuring clustering at the system level and treating it as an endogenous regressor. The HTIV estimation results show that the systemwide clustering of outlets is positively and significantly associated with the individual outlets’ sales. This is consistent with prior research that does not account for the accumulated experience, shared ownership, or franchisor versus franchisee ownership of the clustered outlets (e.g., Lu and Wedig 2013). This result, however, masks the nuances that emerge from a broader consideration of the specific identities of the focal and proximal outlets and provides misleading confi-dence in clustering effects on performance.
Discussion
Proximity associated with clustering is a contentious issue for all franchising participants, but it is particularly vexing for franchisees because of sales cannibalization concerns. Our assessment of the impact of clustering suggests that although these concerns are not unfounded, they only apply under certain conditions. Our findings support our contention that physical distance is not the sole determinant of outlet sales.
Theoretical Implications
This research was motivated by conflicting findings and assertions about the effects of clustering. Importantly, disagreements regarding clustering-attributable performance exist not only across but also within paradigms. Consider, for example, how much at odds the studies reporting positive effects of agglomeration (Chung and Kalnins 2001) are with those warning of significant sales cannibalization (Pancras, Sriram, and Kumar 2012). We observe a similar schism for studies adopting a sociology-informed clustering viewpoint and the knowledge sharing this implies. Whereas Lu and Wedig (2013) report positive performance effects, Ingram and Baum (1997) find evidence of a negative impact of clustering.
Our study builds on and extends both streams of work. In particular, we call for a more nuanced consideration of clustering’s impact, one that emphasizes not just the physical distance from specific outlets (“how far”) but also the distance “from whom.” In essence, we argue that the specific identities of the focal outlet and of the same-brand outlets it may cluster with matter. Until now, research on clustering has focused almost exclusively on system-level clustering. In the present context, this amounts to a single “clustering score” representing the extent of clustering across all 988 outlets of the franchise system we assessed. As we have demonstrated, such an aggregate approach indeed yields a positive association between clustering and sales. However, it is only when clustering is unpacked (i.e., particular individual outlets’ clustering with particular other same-brand outlets) that we find evidence of positive and negative cluster-attributable effects. Our research highlights the interplay of knowledge sharing and intrabrand competition in explaining these effects and calls for a more subtle, disaggregated approach to assessing clustering’s impact.
The differential performance accruing to outlets within each of the four archetypal cluster types provides support for our disaggregated approach to assessing clustering effects. We find that newly established outlets suffer sales declines, consistent with their lower absorptive capacity and greater susceptibility to intrabrand competition. In contrast, mature outlets appear to be shielded from the worst effects of intrabrand competition, due to their greater repository of operating knowledge. Perhaps most important, shared ownership of the focal outlet—whether newly established or mature—and the outlets proximal to it appear to ease concerns about intrabrand competition, in turn enhancing the motivation for sharing knowledge and the corresponding performance level of the focal outlet.
We also note some results that are contrary to our expectations. We hypothesized that franchisee-owned outlets would perform less well when clustered than their franchisor-owned counterparts. This expectation received support in only one of the three clustering situations we analyzed: when a mature franchisee-owned outlet is clustered with other mature outlets. We observed no such decreased performance for mature franchisee-owned outlets clustered with new outlets; contrary to our hypothesis, newly established franchisee-owned outlets perform better than their franchisor-owned peers when clustered with mature outlets. Though puzzling at first glance, these antithetical findings may be explained by the agency theory– based raison d’être for franchising—namely, the power of incentives, specifically that of residual claims, whereby newly established franchisees exert effort to retain their outlets’ profits after making royalty payments to their franchisor (Brickley and Dark 1987; Brickley, Dark, and Weisbach 1991; Lafontaine 1992). It is likely the incentives on the part of the new franchisee-owned focal outlet and the corresponding motivation and ability to share knowledge on the part of the mature proximal outlets that lead to this positive clustering effect.
Our study has implications for research on knowledge sharing beyond the current franchising context. By elucidating the movement of knowledge between different units/organizations, our research highlights processes that involve both the sharing of knowledge by the knowledge source outlet and the acquisition and application of knowledge by the recipient outlet, thereby allowing us to contribute to the literature on interorganizational knowledge sharing. Relatedly, our research adds to what is known about knowledge sharing in interfirm networks based on spatial and/or psychological proximity, in which knowledge sharing among networked firms depends on the number of connections and the degree of interconnectedness with other network firms (e.g., Swaminathan and Moorman 2009). The insights afforded by our research can help extend this stream of literature by emphasizing the dyad-specific flows of information and/or influence among specific pairs of network participants. A nuanced consideration of the identified factors that inhibit or enhance ability and motivation would further inform our understanding of network efficiency and knowledge redundancies and how they might affect network-participating members’ individual performance.
Managerial Implications
For franchisees. Our post hoc calculations suggest that, compared with franchisor-owned outlets, a new franchisee-owned outlet may expect to gain 9.5% of mean annual sales, or just over $39,000, when clustered with mature same-brand outlets. Although this result runs counter to our expectation, one explanation for this might lie in new franchisees’ efforts to take advantage of the experience gained and knowledge shared by clustered mature outlets. Mature franchisee-owned outlets lose mean annual sales of 6.7% (just over $27,000) when clustered with other mature outlets of the same brand. Overall, our results imply that franchisees opening new outlets close to mature outlets of the same brand are likely to realize significant sales performance gains. In contrast, ceteris paribus, mature franchisees clustered with other mature same-brand outlets find themselves facing the prospect of intrabrand competition.
For franchisors. Like franchisees, franchisor-owned outlets also experience a mixed bag when clustering with other same-brand outlets. A new franchisor-owned focal outlet loses nearly 10%, or just over $39,000, in mean annual sales when clustered with mature outlets. When new franchisor-owned outlets are clustered with other new outlets, the loss in sales is not nearly as bad—they lose an average of just over 3% of their mean annual sales, or close to $12,500. We also find that mature franchisor-owned outlets remain relatively unaffected by outlet clustering, regardless of whether the cluster comprises new or mature proximal outlets. Taken together, the pattern of results suggests that franchisors mindful of the sales performance of the outlets owned by them should avoid establishing these outlets in proximity to other same-brand outlets, whether mature or new.
Across both franchisor- and franchisee-owned outlets, shared ownership of the focal and proximal outlets appears to help facilitate knowledge sharing and blunt intrabrand competition. Under shared ownership, newly established outlets clustered with mature outlets outperform their counterparts clustered with new outlets by nearly 1% of mean annual sales, or just under $5,000. For mature outlets, the difference is even more striking—mature outlets clustered with other mature outlets outperform their counterparts clustered with new outlets by nearly 3% of mean annual sales, or just under $11,500. Given that the average multiunit-owning entity (whether franchisor or multiunit franchisee) in this franchise system owns 21 outlets, the sales performance gains accruing from shared ownership are certainly significant.
Limitations and Future Research Directions
As with any research effort, our study has several limitations. First, our assessment of a single franchise system, albeit over an extended period, limits the generalizability of our findings. Future efforts that include multiple firms operating in diverse industries would help extend our findings by considering multiple franchise system outlets and their competitive and cooperative interactions over time.
Second, tracking an additional performance indicator (e.g., survival of the individual outlet) over the entire life cycle of the franchise system would provide additional insights into clustering and its performance consequences. Such data, were they to be available, would confer the ability to investigate the impact of clustering on outlets’ survival in the rich context of multiple franchise systems.
Finally, our reliance on longitudinal archival data, while affording insights into individual outlets’ evolving capabilities and constraints, also limits our ability to directly observe or measure the relevant franchise system participants’ perceptions and motivations. We can state only that their actions appear consistent with the perceptions and motivations we attribute to them. Future efforts to measure these unobserved intervening variables (e.g., by conducting surveys with the individual outlet operators, by designing laboratory experiments to provide a better understanding of the underlying conceptual mechanisms) and to integrate them with the archival data already available would yield rich insights.
H5
Franchisor Versus Franchisee Ownership
H1–H2
Clustering
Geographic concentration
Accumulated Experience
of the focal outlet
of proximal outlets
H3–H4
Shared Ownership
Outlet Performance
Sales revenue
Control Variables
Cluster size
Mean age of proximal outlets
Firm size
Royalty rate
Interbrand competition
Market population
Income per capita
Market area
Year
A: Logic for Knowledge Sharing and Intrabrand Competition Effects
B: Moderating Effect of Shared Ownership
Notes: Subscripts “N” and “M” denote new and mature outlets, respectively. The first subscript refers to the focal outlet, the second to the proximal outlets. For example, the CL(NM) cluster type indicates a new focal outlet that is clustered with mature outlets of the same brand.
aNatural log–transformed.
Notes: n1 = 12,909. Correlations exceeding |.02| are significant at p < .05, two-tailed.
aNatural log–transformed.
p < .10 (two-tailed).
*p < .05 (two-tailed).
**p < .01 (two-tailed).
Notes: Number of observations = n2 = 6,576; Wald c2 = 11,096.91 (p < .01). Base year = 2004.
A: Impact of Clustering of the New Focal Outlet with Other New Outlets on Outlet-Level Sales
B: Impact of Clustering of the New Focal Outlet with Mature Outlets on Outlet-Level Sales
C: Impact of Clustering of the Mature Focal Outlet with Other Mature Outlets on Outlet-Level Sales
Keywords: clustering, knowledge sharing, competition, franchising Online Supplement: http://dx.doi.org/10.1509/jm.16.0173
Notes: Outlet-level sales are natural log–transformed. CL(NN): clustering of the new focal outlet with other new outlets; CL(NM): clustering of the new focal outlet with mature outlets; CL(MM): clustering of the mature focal outlet with other mature outlets; CL(MN): clustering of the mature focal outlet with new outlets; SO: shared ownership of clustered outlets.
A: Impact of Clustering of the New Focal Outlet with Mature Outlets on Outlet-Level Sales
B: Impact of Clustering of the Mature Focal Outlet with other Mature Outlets on Outlet-Level Sales
Notes: Outlet-level sales are natural log–transformed. CL(NM): Clustering of the new focal outlet with mature outlets; CL(MM): Clustering of the mature focal outlet with other mature outlets.
Footnotes 1 The term “governance” in our context reflects the control- and coordination-related benefits conferred by organizational hierarchy (Williamson 1996). Hereinafter, our use of the term refers to the ownership of the focal outlet.
2 Hereinafter, we use the term “clustered outlets” to refer collectively to the focal outlet and its proximal same-brand outlets.
3 Following Penrose (1959), much work has investigated the dimensions of knowledge (Nadler, Thompson, and Van Boven 2003; Nonaka 1991) and the means by which knowledge is shared (Baum and Ingram 1998; Miner and Haunschild 1995). In this study, we focus on the means of knowledge sharing afforded by clustering.
4 4We also acknowledge the possibility of diminishing returns on experience, and we test for this in our empirical specification.
5 Our discussion of knowledge sharing effects first for each hypothesis does not in any way imply its primacy over the intrabrand competition effect; it merely reflects the greater complexity of the factors at play in the former.
6 We denote the focal outlet with the first subscript and its proximal outlets with the second. Thus, CL(NN) indicates the condition in which a new (N) focal outlet is clustered with other new (N) outlets; CL(MN) indicates the condition in which a mature (M) focal outlet is clustered with new (N) outlets.
GRAPH: FIGURE 3 Simple Slopes Analysis for Significant Interactions: Franchisor Versus Franchisee Ownership
GRAPH: FIGURE 2 Simple Slopes Analysis for Significant Interactions: Shared Ownership
GRAPH
GRAPH
GRAPH
GRAPH
DIAGRAM: FIGURE 1 Conceptual Framework
PHOTO (BLACK & WHITE)
PHOTO (BLACK & WHITE)
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Record: 32- Coins Are Cold and Cards Are Caring: The Effect of Pregiving Incentives on Charity Perceptions, Relationship Norms, and Donation Behavior. By: Yin, Bingqing (Miranda); Li, Yexin Jessica; Singh, Surendra. Journal of Marketing. Nov2020, Vol. 84 Issue 6, p57-73. 17p. DOI: 10.1177/0022242920931451.
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Coins Are Cold and Cards Are Caring: The Effect of Pregiving Incentives on Charity Perceptions, Relationship Norms, and Donation Behavior
Charities often include low-value monetary (e.g., coins) and nonmonetary (e.g., greeting cards) pregiving incentives (PGIs) in their donation request letters. Yet little is known about how donors respond to this marketing strategy. In seven studies, including two large-scale field experiments, the authors demonstrate that the effectiveness of PGIs depends on the organization's goals. People are more likely to open and read a letter containing a monetary PGI (vs. a nonmonetary PGI or no PGI). In addition, monetary PGIs increase response rates in donor acquisition campaigns. However, the return on investment for direct mail campaigns drops significantly when PGIs are included. Furthermore, average donations for appeals with a nonmonetary PGI or no PGI are similar, while those with a monetary PGI are actually lower than when a nonmonetary PGI or no PGI is included. This is because monetary PGIs increase exchange norms while decreasing communal norms. This effect remains significant when accounting for alternative explanations such as manipulative intent and the anchoring and adjustment heuristic.
Keywords: communal and exchange norms; donation behavior; donation incentives; field experiments; nonprofit marketing
Not a week goes by that I don't receive requests for monetary donations to one charitable organization or another....However, I must ask these organizations if sending out hundreds of thousands of requests for donations with a nickel or dime attached inside an envelope full of address labels imprinted with my name and address is honestly worth it.
[45]
Most charitable organizations rely on individual donations to provide much-needed services, and over 90% of nonprofit organizations use direct mail as one of their primary fundraising methods ([40]). However, most direct mail campaigns achieve modest success, with an average response rate of 1% to 3.7% ([13]). To attract attention and encourage donations, charities often include pregiving incentives (PGIs), also known as unconditional gifts or front-end premiums, in their donation request letters (for examples, see Web Appendix A). A PGI is defined as the provision of a benefit or a favor before requesting compliance ([35]; [43]).
In 2013–2015, PGIs such as coins and greeting cards were included in approximately 40% of the total nonprofit mail volume ([41]). In fact, inclusion of monetary PGIs is so popular, the strategy has its own moniker: "the coin trick" ([28]; [44]). Yet there are concerns over the effectiveness of this strategy ([49]). The goal of the present research is to address the question posed in the quote at the opening of this article—are PGIs in charitable campaigns worth it?
Several streams of research have examined the effect of incentives on prosocial behavior (for a review, see [55]]), some yielding conflicting results. The literature on reciprocity shows that people feel a strong sense of obligation to repay benefits they have received, even if those benefits are unwanted ([ 9]; [22]). Accordingly, [15] found that including a small PGI, such as a postcard, in a charity appeal significantly increased the number of donations, though the average donation amount remained fairly constant. Motivation crowding theory, in contrast, proposes that external rewards diminish intrinsic motivation or sincerity to do good, which can result in reduced donations ([ 5]; [21]). For instance, [39] found that providing nonmonetary conditional/promised gifts (e.g., pens, reusable bags) decreased average donations because such external incentives "crowd out" altruistic motivations to donate.
One limitation of the aforementioned literature is the focus on just one aspect of donation behavior: intrinsic characteristics of the donor (i.e., sense of obligation, intrinsic motivation). Yet offering incentives can also affect the relationship between two entities. Relationship norms between consumers and organizations are important to understand as they play a critical role in consumer reactions, including attitudes and behaviors toward a brand's marketing actions ([ 2]). To illustrate how incentives might influence a relationship, imagine a situation in which the person you have a burgeoning romantic relationship with gives you a $20 bill before asking you to attend a family function with them. We predict that the mere introduction of money is likely to lead you to question the nature of the relationship. But what if, rather than cash, your partner gives you a gift like a bottle of wine or heartfelt greeting card?
This scenario exemplifies a second limitation of prior research on incentives. It does not address how different types of incentives affect donations. The plethora of diverse gifts that charities send to potential donors can be classified into two groups: monetary and nonmonetary. In the current research, we draw on the interpersonal relationships literature to develop a framework for understanding consumer reactions to these two classes of PGIs. We propose that donors typically perceive charities as communal organizations and use communal norms when interacting with them. However, the inclusion of monetary PGIs diminishes communal norms while increasing exchange norms, resulting in lower average donations.
Our research makes a number of theoretical and practical contributions. Although the literature on brand relationships is substantial, relatively little of it has examined relationship norms and factors that influence their salience (for a review, see [33]]). Foundational research in marketing manipulated the salience of communal and exchange norms using hypothetical scenarios grounded in personal relationships ([ 2]; [ 3]). In contrast, we show that organizations can unwittingly influence the salience of these norms simply by including gifts or incentives in their donation solicitations.
The current research also extends recent work on how superficial elements of a donation appeal can influence donation behavior, even when the content is held constant ([50]). We show that PGIs are tangible cues that influence people's communal and exchange perceptions of the charitable organization, leading to sizable differences in donation behavior. In doing so, we answer calls for investigation of PGIs in the donation context, with an emphasis on "the influence of the type of gift" ([ 4], p. 1059).
Our work has managerial implications for the hundreds of nonprofit organizations that include PGIs in their direct mail campaigns. As stated previously, including PGIs such as coins and greeting cards in direct mail campaigns is a common strategy for nonprofits ([41]). We use different methods, samples, and incentives to examine the effect of PGIs on multiple outcomes of interest to charitable organizations, including increasing awareness, procuring future donors, influencing consumer perceptions, and fundraising. Results indicate that PGIs have different effects on different outcomes, and that the best strategy depends on what the charity wants to achieve. For example, monetary incentives may not be effective at increasing the average donation amount but may help charities gain visibility and awareness by increasing the opening and response rate among people who have not donated before. Thus, the results of this research allow nonprofits to make more informed cost–benefit analyses in deciding whether strategies such as "the coin trick" are worth it.
Extant research has identified two primary relationship norms that guide how people give and receive benefits: communal norms and exchange norms (e.g., [10]). Communal norms dictate that individuals attend to other's needs and demonstrate concern for one another. People following communal norms are motivated to care for others and are willing to incur costs to do so, regardless of whether they will receive anything in return. Most family relationships, romantic relationships and friendships are governed by communal norms ([10]). In comparison, exchange norms are those in which benefits are given with the expectation of receiving comparable benefits in return. Those who follow exchange norms are less likely to help others without a benefit or reward. A prototypical relationship example following exchange norms is that of business partners.
Relationship norms influence interactions not only with individuals but also with marketplace entities. [ 2] showed that people who followed communal norms evaluated a brand more positively when the brand offered a noncomparable (vs. comparable) reward in return for help, while those who followed exchange norms evaluated the brand more positively when given a comparable (vs. noncomparable) reward. [54] found that when communality was low, people expected businesses to provide services for their payments and viewed providers negatively during service failures. In contrast, when communality was high and self-obligation was highlighted, people behaved in a more caring and understanding manner, and thus reacted less negatively to service failures.
Beyond the situations manipulated in laboratory experiments, few marketplace interactions have been found to operate according to communal norms. This may be because previous research has largely focused on for-profit companies, which are generally perceived as providing benefits with the expectation of receiving comparable benefits in return. We propose that, unlike for-profit companies, nonprofits are perceived as high communality organizations that care for others' welfare and give benefits without expecting anything in return. In support of this, [ 1] found that nonprofits such as charities (vs. for-profit companies) are seen as warm entities that care about the welfare of others. [36] identified additional organizations that consumers expect to have communal obligations (i.e., religious and pharmaceutical), and showed that people respond to the marketing efforts of these organizations differently than those of typical businesses.
To further lay the foundation that nonprofits are seen as communally oriented, we conducted a pilot test on perceptions of a wide range of both for-profits and nonprofits (for details, see Web Appendix B). In addition, we examined the potential role of organizational familiarity, which has been shown to affect donors' inferences about the charity ([48]). Results from the pilot study revealed that communal perceptions are higher (Mcharities = 5.54, Mbusiness = 3.68; F( 1, 199) = 95.97, p <.001), and exchange perceptions are lower (Mcharities = 2.37, Mbusiness = 4.54; F( 1, 199) = 99.37, p <.001), for charities than for businesses, regardless of how familiar they are. We propose that this high degree of communality is a foundation for donors' willingness to give benefits (e.g., money) generously.
We predict that relationship norms are influenced by the type of PGI (monetary vs. nonmonetary) commonly included with charity letters. Money is more likely to evoke marketplace norms and lead people to behave in a quid pro quo manner (i.e., give comparable benefits for any benefits received) compared with nonmonetary goods ([24]). Activating the concept of money leads people to infer that they are in a businesslike or exchange relationship and behave as though they are interacting with a business party (e.g., [30]). Including a monetary PGI along with a donation appeal thus has the potential to increase exchange norms. At the same time, a monetary PGI should decrease communal norms. Reminders of money lead people to be more self-centered and eschew strong relational ties, such that they prefer not to rely on or be relied on by others ([53]). They may therefore be less inclined to think of relationships in communal terms after receiving a monetary PGI. We hypothesize that the increase in exchange and decrease in communal norms due to the presence of the monetary PGI will jointly lead to lower average donations.
Nonmonetary PGIs are preferred over monetary ones in communal relationships, as they do not evoke marketplace norms in a payment context ([24]) and are consistent with communal norms. A nonmonetary gift may thus have the potential to increase communality. However, unlike exchange norms, communal norms do not dictate the prompt repayment of benefits given or received ([10]). Indeed, previous research shows that communal participants evaluate a brand more positively if the request for help from the brand is delayed compared with when it is made immediately ([ 2]). Giving a nonmonetary PGI and then asking for help straightaway is more aligned with exchange norms and could offset any communal increase from receiving a small gift, resulting in no overall change in communality. In addition, communal norms dictate noncontingent, need-based giving ([10]). Thus, whether and how much people donate should not depend on the presence or the value of nonmonetary PGIs. Taken together, this suggests that a nonmonetary PGI is unlikely to increase net communality (i.e., communal norms relative to exchange norms) or average donations in a direct mail campaign. It is relevant to note that our conceptualization suggests that a monetary PGI will lead to lower net communality but not necessarily flip the overall relationship from communal to exchange. Previous research typically manipulates communal versus exchange relationships, while less attention has been paid to how people react to communality variations on a continuum. Yet a review of the literature reveals that even manipulations of relationship norms, which clearly delineate their differences, do not always produce effects at the extreme ends of the continuum. For instance, [54]; Experiment 2) manipulation check showed that both the communal and exchange scenarios elicited communality scores above the midpoint of the seven-point scale (4.83 vs. 4.08, respectively), with a significant difference between the means. Despite the fact that both scores were on the communal end of the scale, their relative difference produced significant hypothesized effects in consumer behavior. Conceptually, this is explained by research showing that communal relationships vary along a continuum, with one's willingness to devote resources toward promoting the other's welfare increasing with higher levels of communality ([11]).
Importantly, charities have campaign objectives other than maximizing individual contributions. Pregiving incentives may be useful for goals that are lower on the consumer response hierarchy such as increasing public awareness and enlarging the donor list ([32]). Research has shown that monetary incentives are more effective in eliciting survey responses than nonmonetary ones or no incentives, and monetary incentives are particularly useful for people who have no interests in the survey topic ([46]). Moreover, lay beliefs and laws guiding people on how to treat money may lead them to be reluctant to throw away cash (e.g., Title 18, Chapter 17, of the U.S. Code prohibits the debasement of coins). Thus, monetary PGIs could be effective in promoting initial engagement with a piece of mail, especially among individuals who are unfamiliar with the charity. In addition, opening rate is correlated with response rate, or the likelihood to act on the mailing ([17]). Charities can add these respondents to their mailing list for future campaigns. Thus, although our conceptualization predicts lower average donations with monetary PGIs (vs. nonmonetary and no PGIs), they may be effective for increasing opening and response rates, especially among people who are unfamiliar with the charity and thus need an incentive to open the envelope. Formally, we hypothesize:
- H1: Monetary PGIs (vs. nonmonetary or no PGIs) increase opening and response rates, particularly among people who are not familiar with the charity.
- H2: Monetary PGIs (vs. nonmonetary or no PGIs) lower average donations. There is no difference between a nonmonetary PGI and no PGI on average donations.
- H3: Monetary PGIs (vs. nonmonetary or no PGIs) lower net communality.
- H4: The relationship between PGI type and donations is mediated by net communality.
We test our hypotheses in seven studies. Study 1 examines the effect of PGIs on the opening rate of donation letters. Study 2 is a field study in which we partner with a charity on a 9,000 household donor acquisition campaign to examine the effect of PGIs on response rate, average donation amount, and return on investment (ROI). Study 3 examines the effect of PGIs on average donations using real incentives and contributions. Study 4 examines the mediating role of relationship norms on average donations while testing the alternative explanations of anchoring, manipulative intent, and charity inefficiency. Both the incentives and donations in Study 4 are hypothetical. Study 5 examines the impact of PGIs of varying value, and Study 6 examines the effect of phrasing monetary PGIs in different ways on average donations. These studies use hypothetical incentives but ask participants to donate from their potential bonus payment to increase realism while curtailing study costs. Finally, Study 7 is a field experiment that examines the anchoring effect on response rate, average donations and ROI for a year-end campaign for recurring donors.
Charities often have the goal to increase awareness and achieve high visibility through their donation campaigns ([48]). Encouraging potential donors to open the charity letter may be especially critical to the success of donor acquisition campaigns, in which appeals are sent to people who are unfamiliar with the charity or have never donated before. In this study, we explore the crucial question of how enclosures of PGIs affect how these individuals handle the letter. We predict that people are reluctant to throw away a piece of mail with money in it, which results in them opening and reading the letter (H1). We sample a diverse population by recruiting staff and faculty at a major U.S. university, who are likely to have more disposable income than convenience samples such as college students and online survey takers.
Two hundred forty university staff and faculty members from a large Midwestern university (187 female; Mage = 46.02 years; Mincome = $75,000 to $99,000) participated in this study in exchange for either a $5 prepaid Visa card or a university branded gift of a similar value (e.g., hat, wine glasses). The study was conducted in six buildings on campus (School of Business, School of Education, University Health Center, University Administration, University Endowment, and Payroll and Human Resources). Participants in each building were approached individually for oral consent to participate in the study and signed up for a time slot of their choice. Participants were taken one at a time to the experiment room. A research assistant who was unaware of the study hypotheses administered the study and recorded the data. The study consisted of a mail sorting task along with a short survey of demographic information.
The study utilized a between-subjects design. Participants were randomly assigned to one of three PGI conditions: a charity letter with a clear window presenting a monetary PGI (quarter), a nonmonetary PGI (a greeting card), or no PGI. All participants were presented with six pieces of sealed mail in the same order, including the focal charity letter from a fictitious charity called "Help Fight Cancer Society" (the same charity used in one of the supplemental studies in the Web Appendix). The other five pieces of mail were the same across all three PGI conditions. There was one solicited letter (dental bill), and four unsolicited letters (SmartShopper ad, credit card offer, car insurance ad, and retirement insurance ad). We included both solicited and unsolicited letters to compare the treatment of the charity letter with mail of varying importance. The cover design and content for each letter were created based on actual letters.
Four boxes were placed on a table with the following labels: unopen throw away, open without reading, open and read, and unopen but keep. Participants were asked to imagine that they just got home from getting their mail and were sorting them. They were asked to sort the mail as they normally would and place each letter into the box corresponding to their decision. An "other" option was provided in case they intended to handle the letter differently. After the mail sorting task, participants answered a few demographic questions, chose either a Visa card or a university gift as their payment, and were thanked and dismissed.
We compare the opening rate of the three PGI conditions next and report the comparison of each appeal against the solicited and unsolicited letters in Web Appendix C.
Consistent with H1, the number of people in the monetary PGI condition who chose to throw away the charity letter without opening it (16.46%, N = 13) was significantly lower than those in the nonmonetary PGI (45%, N = 36, χ2( 1) = 15.19, p <.001) and no-PGI (49.38%, N = 40, χ2( 1) = 19.57, p <.001) conditions. No difference was found between the monetary and nonmonetary PGI conditions (χ2( 1) =.31, p =.58).
We found no difference across the three PGI conditions (38.46%, Nmonetary = 20; 32.69%, Nnonmonetary = 17, 28.85%, Ncontrol = 15; all ps >.29).
The number of people who chose to open and read the charity letter was significantly higher in the monetary PGI (48.10%, N = 38) condition than that in the nonmonetary (26.25%, N = 21, χ2( 1) = 8.13, p =.004) and no-PGI (17.28%, N = 14, χ2( 1) = 17.31, p <.001) conditions. No difference was found between the nonmonetary and no-PGI conditions (χ2( 1) = 1.90, p =.17).
We found no difference across the three PGI conditions (30.77%, Nmonetary = 8; 23.08%, Nnonmonetary = 6; 46.15%, Ncontrol = 12; all ps >.14).
This study examined the effect of PGIs on opening rate in a donor acquisition campaign. Confirming H1, fewer participants chose to throw away the charity letter without opening it in the monetary PGI condition than the nonmonetary and no-PGI conditions. Moreover, more people chose to open and read the letter in the monetary PGI condition than in the nonmonetary and no-PGI conditions. Enclosing a nonmonetary PGI was no more effective than not enclosing any PGI in leading people open and/or read the charity letter. Having assessed the impact of PGIs on opening rate, we turn our attention to other important campaign outcomes, including the response rate and average donation amount and ROI.
A primary objective of donor acquisition campaigns is to raise awareness for the organization and enlarge the donor pool, which can lead to more profitable campaigns in the future ([47]). Study 1 suggests that a monetary PGI promotes this goal by persuading recipients to open and read the letter. In Study 2, we partner with a local mental health and suicide prevention nonprofit to examine more downstream consequences.
As discussed in the "Conceptual Development" section, we predict that a monetary PGI (vs. nonmonetary and no PGI) will lead to lower average donations. We also examine how different types of PGIs affect the response rate, or number of replies to the mailing. Previous research has shown that monetary payments lead people to behave in a quid pro quo manner ([24]). Thus, after receiving a coin in the mail, people may respond by sending the money back to the organization. For low-value monetary PGIs, this would lead to lower average donations, but a higher response rate.
Because charitable organizations often lose money in donor acquisition campaigns, an important goal is to minimize losses ([47]). Thus, we measure the ROI for each type of PGI. We predict that enclosing PGIs, both monetary and nonmonetary, will lead to a lower ROI than not enclosing any PGI—although the response rate may be somewhat higher for monetary PGI appeals, average donations will be lower, and the total amount raised is unlikely to offset the high cost of sending PGIs.
Given the extremely low response rate for typical direct mail campaigns (.65%; [47]), it is difficult to draw conclusions from nonresponses about whether PGI had an impact on donation behavior. Nonresponses could be due to indifference toward the PGI, or because people never received or saw the letter. Thus, to calculate average donation amount, we analyzed data only from respondents who made a donation. This is consistent with prior research on donation appeals with low response rates ([14]). However, in estimating net loss per mailing, we included all those who had been solicited (N = 9,000), because ROI addresses whether the benefits of enclosing PGIs justify the total cost.
An examination of over 100 charity letters revealed that common monetary PGIs range from a few cents to $2.50, and common nonmonetary PGIs include address labels, greeting cards, and note pads. In consultation with the charity, we decided to use a quarter ($.25) for the monetary PGI and a greeting card for the nonmonetary PGI. To control for the potential confounds of perceived overhead costs of the charity ([20]) and subjective incentive value ([42]), we conducted a pretest to ensure that the perceived cost (to the charity) and value (to the recipient) are similar for the monetary and nonmonetary PGIs we chose (both ps >.15; see Web Appendix D).
A city-wide mailing list of 11,000 people—from a total population of about 91,000—was acquired by the charity. The mailing list contained people's names, addresses, zip codes, and phone numbers. Nine thousand people were randomly selected from the mailing list and assigned to one of the three PGI conditions: monetary ($.25), nonmonetary (a greeting card), or no PGI. The charity ensured that none of them had donated to the charity before.
We included a standard business reply return envelope with each letter. Participants also had the option of using their own stamps to help the charity reduce costs. A code was stamped on each return envelope to keep track of PGI condition.
Three versions of the donation request letter were created to correspond to the monetary, nonmonetary, and no-PGI conditions, along with a perforated return card containing donation instructions at the bottom of the letter (see Web Appendix E, Study 2). For both the monetary and nonmonetary conditions, an additional sentence at the top front page of the donation request letter stated, "Please accept the attached quarter [greeting card] as our gift to you." A quarter was glued to the top front page in the monetary PGI condition, and a blank greeting card with accompanying envelope was placed within the folded donation request letter in the nonmonetary PGI condition. Three distinct online donation links were created to correspond to each incentive condition and placed clearly at the top of the perforated return card to give donors the option of donating online.
Response rate was defined as the number of participants who made a donation in each condition.
The average donation in each condition was computed by dividing the total amount of money donated by the number of donors who contributed.
The costs associated with incentives, postage, and printing were similar to or lower than the costs of comparable campaigns the charity has run in the past. We calculated the ROI for each condition by taking into consideration total donations relative to associated costs [ROI = donations received − variable costs (e.g., material cost, incentive cost [if any], business return envelope cost [for cost details, see Web Appendix F]). We then divided this number by the number of participants in each condition.
The response rate seven weeks after mailing (December 18, 2015, to February 5, 2016) was.56% (N = 50). The charity received a total of $1,559.50: $654.50 from the monetary PGI condition (range: $.25 to $200), $335 from the nonmonetary PGI condition (range: $10 to $50), and $570 from the no-PGI condition (range: $10 to $100), with an average donation amount of $31.19. The response rate and average donation are comparable with the national average for donor acquisition campaigns, at.65%, and $15–$45, respectively ([47]).
The response rate from the monetary PGI (54%, N = 27) was significantly higher than the nonmonetary PGI (22%, N = 11, χ2( 1) = 6.78, p =.009) and no PGI (24%, N = 12, χ2( 1) = 5.81, p =.016). No difference was found between the nonmonetary PGI and no-PGI conditions (χ2( 1) =.04, p =.83).
Of note, over half the responses (N = 15) in the monetary PGI condition were returns of the quarter. Such a response is predicted by the relationship norms framework, as lower communal norms and higher exchange norms lead to a one-to-one exchange mindset and less focus on others' needs. Charities recognize that initial contributions tend to be small, and they hope to cultivate first-time donors to give more over time; thus, the contact information for everyone who "acts upon the mailing" ([38]), including people mailing back the quarter, may be added to the donor pool for future campaigns. For these reasons, we include donors who mailed back the quarter in the calculation of overall response rate and average donations. Nevertheless, some charities may view these responses as fundamentally different from standard donations and be interested in the results of the campaign when they are removed. We briefly discuss the donation results excluding those responses here and provide more details in Web Appendix G.
One person in the monetary PGI condition donated $200, which far surpassed the average (z > 3) and is considered an outlier ([34]). For completeness, we report the results both excluding and including the outlier. A Shapiro–Wilk normality test showed that the donation amount was not normally distributed (p <.001), so we log-transformed the data. Excluding the outlier, results from a one-way analysis of variance (ANOVA) revealed that PGI type had a significant impact on donation amount (in log-transformed values: Mmonetary =.30, SD = 1.09; Mnonmonetary = 1.42, SD =.26; Mcontrol = 1.56, SD =.35; F( 2, 46) = 12.50, p <.001; in dollars: Mmonetary = $17.48, SD = $25.88; Mnonmonetary = $30.45, SD = $16.35; Mcontrol = $47.50, SD = $34.54; see Web Appendix H). Consistent with H2, the monetary PGI led to significantly lower donations than the nonmonetary PGI (p =.001) and no PGI (p <.001). No difference was found between the nonmonetary and no-PGI conditions (p =.70). Similar results were found when the outlier was included (in log-transformed values: Mmonetary =.38, SD = 1.14; Mnonmonetary = 1.42, SD =.26; Mcontrol = 1.56, SD =.35; monetary PGI vs. nonmonetary PGI: p =.002, monetary vs. no PGI: p <.001, nonmonetary PGI vs. no PGI: p =.70; F( 2, 47) = 10.20, p <.001; in dollars: Mmonetary = $24.24, SD = $43.34; Mnonmonetary = $30.45, SD = $16.35; Mcontrol = $47.50, SD = $34.54). Notably, this effect is erased when the 15 donors who merely returned the quarter are included in the analysis (all ps >.18; for all comparisons, see Web Appendix G).
The ROI for the entire campaign was −$3,766.48, with −$1,562.32 in the monetary PGI condition, −$1,477.46 in the nonmonetary PGI condition, and −$726.79 in the no-PGI condition. A Shapiro–Wilk normality test indicated that ROI was not normally distributed (p <.001), so we log-transformed the data. Due to the negative values of the data, we added a constant number a (a =.741) so the new ROI data became Y + a, where min (Y + a) =.001 ([ 7]). We found that PGI had a significant impact on ROI (in log-transformed values: Mmonetary = −2.97, SD =.34; Mnonmonetary = −.84, SD =.14; Mcontrol = −.50, SD =.13; F( 2, 8,997) = 107,748.64, p <.001, =.96; in dollars: Mmonetary = −$.52, SD = $4.64; Mnonmonetary = −$.49, SD = $2.07; Mcontrol = −$.24, SD = $3.66). Both monetary and nonmonetary PGIs led to significantly lower ROI compared with no PGI (both ps <.001). Monetary PGI also resulted in lower ROI compared with nonmonetary PGI (p <.001).
The results of this field experiment provide initial support for our hypothesis that a monetary PGI (vs. a nonmonetary PGI or no PGI) will decrease average donations, while a nonmonetary PGI (vs. no PGI) will have no effect. The monetary PGI also elicited a higher response rate, driven by people who returned the $.25. Finally, enclosures of both monetary and nonmonetary PGIs led to a significantly worse ROI than no PGI. A caveat to these results is that no average donation amount difference is observed when people who merely returned the quarter are excluded in the analysis.
In Studies 3–6, we further examine the effect of PGIs on donation behavior by testing our predictions in a more controlled setting. An important methodological departure from the field experiment is that all participants are asked to read the charity appeal in these subsequent studies. People do not have the option of ignoring the letter, so a major predictor of response rate (whether one opens or reads the appeal) is controlled for. The design of the lab experiments does not lend itself to accurate calculations of ROI, so we return to ROI in Study 7, in which we conduct a large-scale field experiment for a year-end campaign.
In Study 3, we again partnered with a local mental health clinic to provide student participants with a physical charity letter to open and read. To measure actual behavior, participants were allowed to donate money from their study payment. We utilized monetary and nonmonetary PGIs of a different value to increase the generalizability of the findings.
One hundred thirty-two students (71 women; Mage = 20.77 years) from a large public Midwestern university participated in this study in exchange for $5. Participants were randomly assigned to one of three conditions: a charity appeal with a monetary PGI ($.50), nonmonetary PGI (a higher-quality greeting card), or no PGI. We conducted a pretest to ensure there was no difference in either the perceived cost or subjective benefit of the two PGIs (both ps >.43) (for details, see Web Appendix D). All instructions and responses were on paper. Participants received five dollars as well as course credit for their participation and were instructed to read the charity letter (see Web Appendix E) and respond to the questions in the survey booklet. Responses from two participants were unusable—one participant refused the payment before the study started and another participant did not complete the survey—leaving a final sample of 130.
Response rate was defined as the number of participants who made a donation in each condition.
Participants were asked how much of the money they received from their participant payment and charity appeal, if applicable, they would like to donate to the charity. The average donation in each condition was computed by dividing the total amount of money donated by the number of participants in each condition.
Because the total amount of money available for donation differed across conditions—participants in the monetary PGI condition had $5.50 to donate (their $5.00 study payment, plus the $.50 PGI), whereas those in the nonmonetary and control conditions only had $5.00—we calculated donation percentage (donation amount divided by the total amount of money available for donation) in addition to the average donation amount.
No response rate difference was found between any of the three conditions (Nmonetary = 30, Nnonmonetary = 29, Ncontrol = 32; all ps >.64).
Results from a one-way ANOVA showed a marginal effect of PGI on average donations (Mmonetary = $1.58, SD = $2.07; Mnonmonetary = $2.37, SD = $2.14; Mcontrol = $2.50, SD = $2.14; F( 2, 127) = 2.39, p =.096, =.04). Consistent with H2, the monetary PGI led participants to donate less than no PGI (p =.045), and marginally less than the nonmonetary PGI (p =.086). There was no difference between nonmonetary and no PGI (p =.78).
Results from a one-way ANOVA revealed a main effect of PGI type on donation percentage (Mmonetary = 28.75%, SD = 37.58%; Mnonmonetary = 47.44%, SD = 42.77%; Mcontrol = 50%, SD = 42.81%; F( 2, 127) = 3.44, p =.035, =.05). Participants in the monetary PGI condition donated less of their total funds than participants in the nonmonetary PGI (p =.037) and no-PGI (p =.017) conditions. Similar donation percentages were found for people in the nonmonetary PGI and no-PGI conditions (p =.77).
Results from Study 3 provide further evidence that enclosing PGIs does not result in higher donations. Consistent with Study 2, nonmonetary PGIs did not increase donations relative to no PGI, and enclosing monetary PGIs resulted in lower average donations than enclosing nonmonetary PGIs and no PGI. However, the latter effect is significantly only for donation percentage and merely marginal for donation amount, which may limit the inferences for this study. In addition, we did not find an effect of PGI on response rate, perhaps because everyone was forced to read the letter. We discuss this possibility subsequently.
Studies 4–6 examine the effect of PGIs on donation behavior as well as the underlying mechanism of relationship norms by utilizing both mediation and moderation techniques, while ruling out alternative explanations. Unlike other the studies in this article, Studies 4–6 use hypothetical scenarios whereby participants imagine receiving a PGI. A substantial amount of research, especially in economics, has reported substantively little difference between hypothetical and real scenarios ([12]; [16]). For example, [ 6] conducted two experiments, one in which participants were given actual money to spend, and another in which they were asked to imagine they were given money. They found that the "the average subject behaves essentially the same" in these two conditions (p. 1783). Such findings suggest it is valuable to take a multimethod approach to decision-making tasks, particularly when limited resources would otherwise constrain the number of studies that can be run and hypotheses tested.
In Study 4, we examine the mediating role of relationship norms on donation behavior. As the pilot test demonstrates, charities are inherently perceived as communal organizations. On the one hand, we propose that monetary PGIs diminish communal norms and increase exchange norms, resulting in lower average donations. On the other hand, nonmonetary PGIs are expected to produce no net change in communality. We also test several alternative explanations in this study. First, previous research suggests that inferences of manipulative intent decrease message persuasiveness ([ 8]). It is possible that people perceive greater manipulative intent when charities include monetary (vs. nonmonetary) PGIs, which leads them to be less supportive of the charity. Second, PGI type may influence perceived charity efficiency, which has been shown to affect donation decisions ([56]). Specifically, people may believe that charities that send money with their donation appeals use their resources less efficiently than those that do not. Finally, it is possible that manipulative intent or charity efficiency influence perceived communality in serial, which results in lower donations.
One hundred fifty-three students (96 women; Mage = 20 years) from a major Midwestern university participated in the study in exchange for course credit. Participants were randomly assigned to read a charity letter (Web Appendix E) enclosing a monetary PGI ($.25), a nonmonetary PGI (a low-value greeting card), or no PGI. After reading the charity letter, participants responded to the relationship norm items, followed by the donation request, and then the measures of manipulative intent and charity inefficiency.
Response rate was calculated the same way as in Study 3.
Participants indicated their donations by dragging a slider anchored at $0 and $50, with a write-in option labeled "other" for anyone willing to donate more than $50.
Though some research suggests that communal and exchange norms are orthogonal ([27]), others conceptualize them as opposite ends of the same scale ([ 2]; [11]). In our research, communal and exchange norms are always highly negatively correlated (rs < −.46). We thus use [ 2] net communality for our mediation analyses and report results with each scale separately in Web Appendix I for completeness.
We measured communal norms (e.g., "This organization is concerned about other people's welfare"; α =.79; for all items, see Web Appendix J) and exchange norms (e.g., "Whenever this organization gives or offers something, it expects something in return"; α =.88) using four items each. We reverse-coded the exchange norms and combined them with communal norms (Pearson's r = −.56) to form the net communality score (Cronbach's alpha =.87). The higher the net communality score, the higher the communal relative to exchange norms. Manipulative intent (α =.87) and charity inefficiency (α =.75) (Web Appendix J) were measured on a seven-point Likert scale (1 = "completely disagree," and 7 = "completely agree").
Prior to running the analyses, we eliminated participants who failed the attention check (N = 24; Web Appendix D), leaving a final sample of 129 participants (84 women; Mage = 20.32 years).
Chi-squares tests showed that the number of participants who indicated a nonzero donation did not differ between any of the three conditions (Nmonetary = 36, Nnonmonetary = 41, Ncontrol = 42; all ps >.43).
Results from a one-way ANOVA revealed that PGI type had a marginal effect on average donations (Mmonetary = $11.83, SD = $10.89; Mnonmonetary = $17.93, SD = $17.41; Mcontrol = $19.82, SD = $18.24; F( 2, 126) = 2.84, p =.062, =.04). Consistent with H2, donations were significantly lower for the monetary PGI than no PGI (p =.023) and marginally lower for the monetary than the nonmonetary PGI (p =.083). No donation difference was observed between the nonmonetary and no-PGI conditions (p =.60).
We found that PGI type had a significant impact on the net communality score (Mmonetary = 4.63, Mnonmonetary = 5.12, Mcontrol = 5.44; F( 2, 126) = 5.56, p =.005, =.08; for SDs, see Web Appendix I). Confirming H3, the monetary PGI led to lower net communality than the nonmonetary PGI (p <.05) and no PGI (p =.001). No difference was found between the nonmonetary and no-PGI conditions (p >.18). Specifically, PGI type had a significant impact on communal norms (F( 2, 126) = 8.76, p =.002; see means and SDs in Web Appendix I). The monetary PGI led to lower communal norms than the nonmonetary PGI (p =.009) and no PGI (p =.001). No difference was found between the nonmonetary PGI and no PGI (p =.35). The effect of PGI type on exchange norms was marginal (F( 2, 126) = 2.88, p =.06). Only the monetary PGI condition resulted in higher exchange norms than the no-PGI condition (p =.018).
We found that PGI type had a marginal effect on manipulative intent (Mmonetary = 3.46, Mnonmonetary = 2.98, Mcontrol = 2.88; F( 2, 126) = 2.70, p =.071, =.08; for SDs, see Web Appendix I). The monetary PGI led people to perceive the charity as marginally more manipulative than the nonmonetary PGI (p =.073), and significantly more manipulative than no PGI (p =.03). There was no difference between nonmonetary and no PGI (p =.70).
Results revealed a significant effect of PGI on perceptions of charity inefficiency (Mmonetary = 4.39, Mnonmonetary = 3.45, Mcontrol = 3.55; F( 2, 126) = 7.98, p =.001, =.11). The monetary PGI led the charity to be perceived as more inefficient compared with the nonmonetary PGI (p <.001) and no PGI (p =.001). No difference was found between the nonmonetary and no-PGI conditions (p =.69).
We conducted mediation models with 10,000 bootstrap samples ([23]; PROCESS v3.1. Model 4). Because there were three different incentive conditions (monetary, nonmonetary, and no PGI), we dummy-coded the three groups by creating two new dummy variables, MNM (monetary PGI vs. nonmonetary PGI) and MC (monetary PGI vs. no PGI). Both variables used the monetary PGI condition as the reference group ([23]). Consistent with H4, results showed that the indirect effect of net communality was significant for both comparisons (monetary vs. no PGI: B = 4.54, 95% confidence interval [CI] = [1.56, 8.47]; monetary vs. nonmonetary: B = 2.75, 95% CI = [.22, 5.89]). We also performed single/dual mediation analyses for communal norms and exchange norms (see Web Appendix I).
Perceived manipulative intent alone did not mediate the effect of PGI type on donation (for details, see Web Appendix I). In the dual mediation model including net communality and manipulative intent, only the indirect effect of net communality was significant (monetary vs. no PGI: B = 3.41, 95% CI = [.85, 6.99]; monetary vs. nonmonetary: B = 2.07, 95% CI = [.09, 4.65]). Perceived charity inefficiency did produce a significant indirect effect on its own (monetary vs. no PGI: B = 3.41, 95% CI = [1.10, 6.49]; monetary vs. nonmonetary: B = 3.82, 95% CI = [1.45, 6.80]). However, the indirect effect of charity inefficiency became nonsignificant once net communality was included in the model, whereas net communality remained a significant mediator (monetary vs. no PGI: B = 3.75, 95% CI = [1.16, 7.66]; monetary vs. nonmonetary: B = 2.27, 95% CI = [.16, 5.25]). We also conducted multiple mediation and serial mediation analyses for PGI type → charity inefficiency/manipulative intent → net communality score → donation (see Web Appendix I).
Study 4 examined potential underlying mechanisms for the effect of PGIs on donation behavior. Our results suggest that a monetary PGI, compared with a nonmonetary or no PGI, leads people to perceive the charity as lower on net communality, resulting in lower average donations. However, that the average donation amount difference is marginal between the monetary and nonmonetary PGI conditions, so H2 is only partially supported. We also examined manipulative intent and charity inefficiency as alternative explanations. Net communality remained a significant mediator after controlling for these constructs, while manipulative intent and charity inefficiency did not, suggesting that the simultaneous change in communal norms and exchange norms predicts donation behavior above and beyond the effects of manipulative intent and charity inefficiency. Further analyses on the relationship between these constructs reveal evidence of serial mediation, such that perceived higher charity inefficiency leads to lowered communality, resulting in lower donations.
Results from Studies 2–4 have provided converging evidence that enclosing low-value monetary PGIs in charity appeals decreases communal norms and increases exchange norms, leading to lower average donations compared with nonmonetary PGIs or no PGI. In Study 5, we provide another test of our theory by varying the value of the PGI.
According to theory on relationship norms, when exchange norms are dominant, the degree of benefits or favors returned by the recipients is contingent on the level of benefits received, leading people to behave in a quid pro quo manner ([10]). When communal norms are dominant, donations are need based rather than incentive based, so incentives should not play as pivotal a role in donation decisions ([37]). Because monetary PGIs evoke more exchange norms and less communal norms, they should lead people to make donations based more on how much they received than the perceived needs of the charity ([37]). In other words, high- (vs. low-) value monetary PGIs should result in higher donations. However, the value of a nonmonetary gift should have little impact on donations because, as explained previously, no change in net communality is expected. This is true regardless of the value of the gift. Moreover, considering that the amount of money to be reciprocated increases while need-based donations remain stable, it is possible that donations elicited by monetary PGIs will be greater than those elicited by nonmonetary PGIs at higher incentive values. Formally, we propose the following hypothesis:
- H5: The effect of monetary and nonmonetary PGIs on donations is moderated by incentive value, such that a high-value monetary PGI leads to greater average donations than a low-value monetary PGI, whereas there will be no difference in average donations for high- vs. low-value nonmonetary PGIs. (b) A nonmonetary (vs. monetary) low-value PGI elicits higher donations, but this pattern is attenuated or reversed for high-value PGIs.
In testing this hypothesis, we also address an alternative explanation for the effect—that reminders of money lead people to be less prosocial (e.g., [52]). Consistent with this literature, receiving monetary PGIs should lead people to be less charitable and thus donate less. However, this theory does not make different predictions for high- versus low-value monetary PGIs. If the effect of PGI type on donation behavior is solely driven by the psychological consequences of money rather than the simultaneous movement of communal and exchange norms, we should expect a high-value monetary PGI to lead to lower donations than a comparable value nonmonetary PGI. The relationship norms framework, in contrast, would predict the opposite finding.
One-hundred thirty-nine participants (73 women; Mage = 37.07 years) were recruited from Amazon Mechanical Turk (MTurk). The study used a 2 (incentive type: monetary vs. nonmonetary) × 2 (PGI value: low [$.25] vs. high [$2.50]) between-subjects design. We selected the high-value incentive after an examination of over 100 charity appeals found a maximum monetary PGI amount of $2.50. Participants in the low-value monetary condition imagined receiving a quarter ($.25) while those in the high-value monetary condition imagined receiving two $1 coins and two quarters (Web Appendix E). Participants in the low-value nonmonetary condition imagined receiving one greeting card and those in the high-value nonmonetary condition imagined receiving eight greeting cards. A pretest showed that the two low-value monetary and nonmonetary PGIs and the two high-value monetary and nonmonetary PGIs did not differ on subjective value or perceived cost to the charity (see Web Appendix D).
In the main study, participants read a charity letter from a fictitious food pantry (for stimuli, see Web Appendix E). After reading the charity appeal, participants were told that they would automatically be entered into a lottery for $10 as an extra show of appreciation for participating in the study. Then they were asked how much they would be willing to donate from their winnings by dragging a slider anchored at $0 and $10.
Response rates and average donations were calculated the same way as previous studies.
No interaction was found between PGI type and value on response rate (p =.78). No difference was found between low versus high-value PGI conditions or within high- and low-value PGI conditions (low value: Nmonetary = 32, Nnonmonetary = 31; high value: Nmonetary = 30, Nnonmonetary = 31; all ps >.20).
Consistent with H5, results from a two-way ANOVA revealed a significant interaction between PGI type and PGI value (F( 1, 135) = 10.30, p =.002, =.07; Web Appendix K). Consistent with our prediction that monetary PGIs lead people to give based on how much they received rather than the charity's need, people who received $2.50 donated significantly more than those who received $.25 (Mlow-value monetary = $3.75, SD = $3.03; Mhigh-value monetary = $6.32, SD = $3.21; p =.003). However, the high-value nonmonetary PGI performed no better than the low-value nonmonetary PGI (Mlow-value nonmonetary = $5.59, SD = 4.02; Mhigh-value nonmonetary = $4.36, SD = $3.51; p =.14). This is in line with our hypothesis that nonmonetary PGIs maintain perceptions of the charity as communally oriented and thus prompt norms of need-based helping rather than repayment of benefits.
Note that average donation for the high-value nonmonetary PGI appeal was directionally lower than the low-value nonmonetary PGI appeal (p =.14). One possible explanation is that, unlike money, greeting cards have diminishing marginal utility, leading to scope insensitivity (e.g., [29]). If this is the case, a set of disparate incentives should lead to greater perceived gift value and may increase donations. We tested this hypothesis in a follow-up study with three conditions (N = 273): one greeting card, eight greeting cards, or eight different gifts of similar cost including a pen, a note pad, a binder clip, a card, and so on (see Web Appendix E, Study 5). Results revealed no donation difference among the three conditions (Mone card = $4.52, Meight cards = $3.95, Meight gifts = $4.69; all ps >. 14). Thus, the inability of high-value nonmonetary incentives to increase donations is less likely to be due to scope insensitivity than the failure to increase communal (and decrease exchange) norms.
Comparing between PGI types, $.25 led to significantly lower donations than one greeting card (p =.026). The effect is reversed for higher-value monetary and nonmonetary PGIs, such that $2.50 elicited significantly more donations than eight greeting cards (p =.024), although we suspect that this effect can be either attenuated or reversed depending on the value of the incentives. In practice, enclosing a high-value monetary PGI may not be desirable or feasible for charities, considering the high cost as well as the low average return.
Study 5 helps establish relationship norms as a primary contributor to donation decisions. In the next study, we consider a potential low-cost way to improve average donations. Charitable organizations are highly strategic about the choice of wording in their appeals. So far, we have used the common phrasing of the PGI as a gift (e.g., Smile Train uses the statement "We have enclosed a world map as our free gift to you.") Yet charities may be interested in the comparative effectiveness of framing the gift in a different way. For example, Obis (an international eye care charity) encloses a nickel in its charity appeal and states, "This nickel can help restore a child's vision." Food for the Poor says in its charity appeals, "Please return these coins along with your gift." A reasonable question is whether such statements mitigate or enhance the changes in exchange and communal norms from monetary PGIs. In the next study, we explore the effect of framing on relationship norms, response rate, average donations, and the alternative explanations of charity inefficiency and manipulative intent. Doing so also allows us to test the generalizability and external validity of the focal effect.
Study 6 examines the effect of framing the monetary gift in various ways. We approached this study in an exploratory manner with the goal of testing two competing hypotheses. It is possible that phrasing a monetary PGI in a more communal way will prevent exchange norms from increasing and communal norms from decreasing. For instance, the phrasing "this nickel can help restore a child's vision" may lead people to interpret the enclosure of the monetary PGI as a demonstration to illustrate that even a little money can help the cause, which is compatible with the communal nature of the charity. However, this is an empirical question, as previous research suggests the effect of money on marketplace norms is strong and consistent ([24]; [30]; [53]). Phrasing the gift in a communal way may do little to change donor behavior if a monetary mindset has already been activated. Indeed, [36] demonstrated that it is possible to improve consumer sentiment by reframing commercial marketing strategies as communal, unless consumers were already in a persuasion frame of mind. Thus, we suspect that, regardless of the phrasing for the monetary PGI, its mere inclusion will consistently lead to lower donations compared with a nonmonetary PGI and no PGI.
Five hundred seven MTurk workers (282 women; Mage = 37.11 years) were randomly assigned to one of five conditions in which they saw a charity appeal from the fictitious food charity in Study 5. Three conditions manipulated phrasing of the monetary PGI: ( 1) "This $.25 can help provide a meal" (monetary-help), ( 2) "Please return this $.25 along with your donation" (monetary-return), and ( 3) "This $.25 is a gift to you" (monetary-gift). We chose these three monetary PGI phrasings on the basis of those used in existing appeals. The other two conditions were the same charity appeals enclosing either a low-value nonmonetary PGI ("This greeting card is a gift to you") or no PGI. All aspects of the charity appeals, besides the PGI, were identical across conditions (Web Appendix E). As in Study 5, participants were told they would be automatically entered into a lottery for $10 and asked how much they would be willing to donate if they won ($0 to $10). Afterward, they answered the same measures from Study 3 in the following order: perceived communality (communal [α =.85] and exchange [α =.83] norms were negatively correlated; Pearson's r = −.46), manipulative intent (α =.86), and charity inefficiency (α =.80). Note that rather than measuring net communality before the dependent variable (as in Study 4), we measured it after the donation dependent variable to rule out potential order effects.
Response rates and average donations were calculated the same way as previous studies.
A chi-square test showed an unexpected higher response rate for the nonmonetary PGI condition (Nnonmonetary = 99) than for all three monetary phrasing conditions (Nmonetary help = 87, Nmonetary return = 86, Nmonetary gift = 90; all ps <.04). The response rate for the no-PGI condition (Ncontrol = 93) was marginally higher than that of the monetary-return condition (p =.052). No other response rate difference was found (all ps >.17).
An ANOVA revealed a significant main effect of PGI type on average donations (Mmonetary help = $3.86, SD = $3.04; Mmonetary return = $4.04, SD = $3.37; Mmonetary gift = $4.28, SD = $3.03; Mnonmonetary = $5.22, SD = $3.39; Mcontrol = $5.13, SD = $3.53; F( 4, 502) = 3.72, p =.005, =.03). We found no difference in donations among the three monetary PGI conditions (all ps >.36). Furthermore, each of the three monetary PGI conditions led to lower donations than the nonmonetary PGI (all ps <.05) and no-PGI conditions (monetary-help vs. no PGI: p =.007; monetary-return vs. no PGI: p =.02; monetary-gift vs. no PGI: p =.066). We found no donation difference between the nonmonetary and no-PGI conditions (p >.80).
An ANOVA showed that PGI type had a significant impact on the net communality score (Mmonetary help = 5.07, SD = 1.20; Mmonetary return = 4.82, SD = 1.29; Mmonetary gift = 4.71, SD = 1.15; Mnonmonetary = 5.20, SD = 1.16; Mcontrol = 5.29, SD = 1.11; F( 4, 502) = 4.45, p =.002, =.03). Monetary return and monetary gift led to lower net communality than nonmonetary PGI and no PGI (all ps <.04). Unexpectedly, monetary help led to higher net communality than monetary gift (p =.03). We discuss this result subsequently. No difference was found between any other conditions (all ps >.13). When relationship norms were examined separately, PGI type had a significant effect on both communal (F( 4, 502) = 2.80, p =.025, =.02) and exchange norms (F( 4, 502) = 4.33, p =.002, =.03, see Web Appendix I for means and SDs and pairwise comparisons).
The effect of PGI type on manipulative intent was significant (Mmonetary help = 2.83, Mmonetary return = 3.75, Mmonetary gift = 3.37, Mnonmonetary = 2.66, Mcontrol = 2.46; F( 4, 502) = 12.71, p <.001, =.09; for SDs, see Web Appendix I). All three monetary PGIs led the charity to be perceived as more manipulative than the no-PGI condition (monetary help vs. no PGI: p =. 09; all other ps <.01). There was no difference between the no-PGI and nonmonetary PGI conditions (p =.34). In addition, the nonmonetary PGI was perceived as less manipulative than the monetary-gift and monetary-return conditions (ps <.01). There was no difference between the nonmonetary PGI and monetary-help condition (p =.44). Indeed, the monetary-help condition elicited lower manipulative intent perceptions than both the monetary-gift and monetary-return conditions (ps <.02).
We found that PGI type had a significant impact on perceived charity inefficiency (Mmonetary help = 4.03, Mmonetary return = 4.44, Mmonetary gift = 4.23, Mnonmonetary = 3.64, Mcontrol = 3.50; F( 4, 502) = 6.59, p <.001, =.05). Participants in all three monetary PGI conditions regarded the charity as more inefficient than the nonmonetary PGI (monetary help vs. nonmonetary PGI: p =. 079, all other ps <.01) and no-PGI (all ps <.02) conditions. Participants in the monetary-return condition perceived the charity as marginally more inefficient than those in the monetary-help condition (p =.058). No difference was found between any other conditions (all ps >.32).
We conducted mediation analyses using the same mediation procedure in Study 4. The indirect effect of net communality was significant collapsing across the three monetary phrasing conditions: (monetary vs. no PGI: B =.34, 95% CI = [.13,.59]; monetary vs. nonmonetary PGI: B =.27, 95% CI = [.05,.52]; (for mediation analyses for each monetary phrasing, see Web Appendix I).
Combining the three monetary phrasings, both manipulative intent and charity inefficiency mediated the effect of PGI type on donations (see Web Appendix I). However, their indirect effects were insignificant when net communality was included in the model, while net communality remained a significant mediator (net communality and manipulative intent: monetary vs. no PGI: B =.28, 95% CI = [.09,.52]; monetary vs. nonmonetary PGI: B =.22, 95% CI = [.04,.45]; net communality and charity inefficiency: monetary vs. no PGI: B =.29, 95% CI = [.10,.52]; monetary vs. nonmonetary: B =.23, 95% CI = [.04,.46]). For serial mediation results, see Web Appendix I.
In summary, this study demonstrates that describing monetary PGIs in various ways does not make a difference in terms of donations. We did find an unexpected effect in the monetary-help condition, in that it leads people to perceive the charity as more communal than those in the monetary-gift condition. A follow-up study (N = 166 MTurkers) revealed no differences among the two phrasings on perceived impact of their donations, perceived efficacy, perceived self-concept (moral/ethical), positive/negative affect, and empathy (all ps >.18). We suspect that the phrasing in the monetary-help condition maintains the perceived communality of the charity by focusing on the victims and emphasizing the compassionate nature of the organization. However, the negative effect of charity inefficiency produced by the coin was strong enough to overcome this perception and lower overall donations compared with the nonmonetary PGI and no-PGI conditions, as demonstrated by the serial mediation results (PGI type → charity inefficiency → net communality → donations; see Web Appendix I). These findings suggest that enclosing monetary PGIs leads to lower average donations, regardless of how monetary PGIs are phrased.
In Study 7, we again partnered with the mental health clinic in Study 2 to launch a large-scale direct mail campaign. Unlike Study 2, this study targets existing donors in a year-end campaign. The goals of this study are threefold. First, we examine whether the PGI effects generalize to a warm mailing list (i.e., people who already have a relationship with the charity). We expect that such a sample will result in a higher overall response rate than Study 2 because people are more likely to open mail from a known sender. However, effects on relationship norms, and thus average donations, should be the same. Second, we test another alternative explanation for the effect of PGIs on donations—the anchoring and adjustment heuristic ([51]). This hypothesis suggests that people who receive a monetary PGI use the coin(s) as a reference point or anchor and thus donate less than when a low-value numerical anchor is not present. In this study, we include the same anchor in all the charity appeals. If the results are due to anchoring rather than relationship norms, this should erase the effect of PGI type on donations. Third, we include a different nonmonetary PGI to make sure the effect is not an artifact of the gift we chose. Although greeting cards are an externally valid PGI currently used by charities, they are also thin and made out of paper, making them vulnerable to being overlooked or accidentally discarded. Furthermore, a greeting card may be seen as a communal gift. Therefore, in this study, we use a magnet as the nonmonetary PGI, which is more of a neutral incentive and less likely to go unnoticed in an envelope. A pretest confirmed that the magnet and coin did not elicit differences in perceived costs or benefits (Web Appendix D).
We randomly selected 2,643 donors from the charity's existing donor list and assigned them to one of the three PGI conditions: monetary ($.25), nonmonetary (a magnet), or no PGI.
Consistent with Study 2, a standard business reply return envelope was enclosed within each letter. We printed a unique code on each return envelope to differentiate the three PGI conditions.
We held the content of all three versions of the letter constant. To examine the anchoring effect, we included a sentence at the top left corner above each person's name: "Even $.25 can help prevent suicide" (see Web Appendix E, Study 7). This sentence replaced the one used in the previous experiments to introduce the PGI (e.g., "Please accept the enclosed $.25 [magnet] as a gift to you"). To ensure that participants saw the incentives, both the quarter and the magnet were displayed through the clear windows of the envelopes. As in Study 2, the charity created three distinct online donation links for each incentive condition so that donors could choose to give online.
Response rate was defined as the number of participants who made a donation in each condition.
The average donation in each condition was computed by dividing the total amount of money donated by the number of donors who contributed.
As in Study 2, we calculated the ROI for each condition by considering total donations in relation to associated costs, divided by the number of recipients (for cost details, see Web Appendix F).
The response rate a little over eight weeks after mailing (November 4, 2019, through December 31, 2019) was 2.50% (N = 66). We received a total of $7,541.25 ($1,756.25 from the monetary PGI condition (range: $.25 to $500), $2,415 from the nonmonetary PGI condition (range: $10 to $550), and $3,370 from the no-PGI condition (range: $20 to $500), with an average donation amount of $114.26.
We found no difference in response rate among the three conditions (33.33%, Nmonetary = 22; 37.88%, Nnonmonetary = 25; 28.79%, Ncontrol = 19; all ps >.35). Two people in the monetary PGI condition merely returned the quarter. We report the results excluding these two participants in Web Appendix G.
A Shapiro–Wilk normality test revealed that the donation data were not normally distributed (p <.001), so we log-transformed the data. A one-way ANOVA showed a significant effect of PGI type on donation amount (in log-transformed value: Mmonetary = 1.49, SD =.80; Mnonmonetary = 1.78, SD =.42; Mcontrol = 2.03, SD =.48; F( 2, 63) = 4.34, p =.017, =.12; in dollars: Mmonetary = $79.83, SD = $117.01; Mnonmonetary = $96.60, SD = $117.06; Mcontrol = $177.37, SD = $169.18; see Web Appendix H). Monetary PGI led to significantly lower donations than no PGI (p =.005) and marginally lower donations than the nonmonetary PGI (p =.097). No donation difference was found between the nonmonetary and no PGI (p =.17).
The amount raised by the entire campaign was $4,597.04, with $663.31 in the monetary PGI condition, $1,349.66 in the nonmonetary PGI condition, and $2,584.07 in the no-PGI condition. A Shapiro–Wilk normality test showed that the ROI data were not normally distributed (p <.001), so we log-transformed the data. As in Study 2, we added a constant number a (a = 1.241) to offset the negative values. Results showed that PGI had a significant impact on ROI (in log-transformed values: Mmonetary = −2.89, SD =.71; Mnonmonetary = −1.42, SD =.55; Mcontrol = −.40, SD =.37; F( 2, 2640) = 4,376.73, p <.001, =.77; in dollars: Mmonetary = $.75, SD = $21.96; Mnonmonetary = $1.53, SD = $25.13; Mcontrol = $2.94, SD = $35.36). Pairwise comparisons showed that both monetary and nonmonetary PGIs led to a significantly lower ROI compared with no PGI (both ps <.001). The monetary PGI also led to a significantly lower ROI compared with the nonmonetary PGI (p <.001).
The results of this study suggest that the effect of PGI type on donations is unlikely to be driven by the anchoring and adjustment heuristic. The effect held when an explicit anchor was present in the appeal. Consistent with the results in previous studies, enclosing a monetary PGI led to lower average donations, and enclosing a nonmonetary PGI was no more effective than not including a PGI. In addition, as in Study 2, including either a monetary or nonmonetary PGI led to a lower ROI than not including a PGI. Unlike Study 2, however, the response rate did not differ by PGI condition. We postulate that this is because recipients in this study were already familiar with the charity, having donated to them before. We discuss this possibility in the general discussion. A weakness of this study is that we did not include a manipulation check of the anchoring manipulation due to the difficulty of surveying donors in the field. However, an additional test of the anchoring effect using a different nonmonetary PGI also failed to find support for this alternative (Web Appendix L).
Charities utilize different marketing strategies in hopes of fundraising and enlarging their donor base ([39]). Enclosing low-value PGIs is one strategy that has been extensively practiced by charities ([41]). Through a combination of field and lab studies, the present research examines the effectiveness of utilizing this strategy on opening rate, response rate, donation behavior, and relationship norms.
In seven studies, we examine how and why donors respond to different types of PGIs. Study 1 examines the effect of PGI type on opening rate and shows that enclosing a monetary PGI (vs. nonmonetary or no PGI) leads to significantly more people opening and reading the charity letter. Study 2, a donor acquisition campaign, finds that ( 1) a monetary PGI leads to a higher response rate than both a nonmonetary PGI and no PGI, ( 2) a monetary PGI leads to lower average donations compared with a comparable value nonmonetary PGI and no PGI, and ( 3) both monetary and nonmonetary PGIs lead charities to suffer higher net financial loss than no PGI in donor acquisition efforts. Results from Study 3 provide further evidence that enclosing a monetary PGI leads people to donate less money than a nonmonetary PGI and no PGI, and enclosing a nonmonetary PGI is no more effective than when no PGI is included. Results from Study 4 show that reduced donations in the monetary incentive condition are due to decreased net communality levels. Study 4 also tests manipulative intent and charity inefficiency as alternative explanations and demonstrate a significant effect of net communality over and above their influence. Consistent with the relationship norms framework, Study 5 shows that the effect of PGI type is moderated by incentive value. A high-value monetary PGI leads to more donations than a low-value monetary PGI, whereas there is no difference for high- versus low-value nonmonetary PGIs. Study 6 shows that phrasing a monetary PGI in various ways consistently leads to lower donations than a nonmonetary PGI or no PGI. Finally, Study 7 shows that a donation campaign for recurring donors utilizing a different nonmonetary PGI supports the conclusions that a monetary PGI decreases average donations compared with a nonmonetary PGI or no PGI. Study 7 also tested and ruled out the anchoring and adjustment heuristic by including an explicit anchor in all the charity appeals.
The current research contributes to the literature on relationship norms in two ways. First, research on relationship norms has focused on for-profit entities ([ 2]; [54]). In contrast, the current research examines how these norms affect consumer perception and behavior in the context of nonprofit organizations. For-profits and nonprofits are governed by different relationship norms, such that for-profit companies operate on exchange norms, whereas the default relationship norms for nonprofit companies are communal. This fundamental difference affects how people respond to these organizations at baseline, and indicates that using incentives as a marketing strategy has disparate effects on consumer behavior for these two categories of organizations. Second, instead of manipulating the salience of the communal and exchange norms through hypothetical scenarios, as is common in previous research, we examine how strategies that nonprofits currently use and have full control over organically influence the salience of these norms. Our results suggest that PGIs affect communal and exchange norms simultaneously and that these norms have a dual impact on donations.
We add to recent work on how people make inferences about an organization based on superficial elements in direct mail campaigns ([50]). We show that PGIs serve as cues about the organization, influencing communal and exchange norms. Lower net communality from monetary PGIs leads people to behave in a quid pro quo manner and be less likely to donate based on perceived need. This is true even though the net communality score remained significantly above the midpoint of the scale (Study 4: Mmonetary = 4.63, p <.001; Study 6: Mmonetary = 4.86, p <.001). Finally, we contribute to the PGI literature (e.g., [35]) by moving beyond general conclusions about all incentives to a nuanced account of how and why different types of PGIs affect donation behaviors.
This research addresses the question of whether including PGIs is a worthwhile strategy for charities. The ultimate answer is that it depends on the goal of the donation campaign. Next, we describe how PGIs affect various campaign related outcomes.
Charities often need to find ways to enter potential donors' consideration set ([48]). If the goal of the campaign is to raise awareness and to help the charity gain exposure, enclosing a monetary PGI appears to be an effective strategy. Our research suggests that enclosing a monetary PGI persuades recipients not only to open the letter, but to read it. Notably, the charity we used to test this hypothesis was fictional and thus unfamiliar to all participants. It is reasonable to assume that the decision to open a letter will be multiply determined when donors are already aware of the charity. A PGI (or its absence) may not be as powerful of a contributing factor for existing donors. However, given that there are over a million charities in the United States ([26]), many of which are unknown to a significant number of Americans, this effect is an important consideration for many.
Enclosing a monetary PGI may help organizations achieve the goal of enlarging the donor pool for future campaigns. Results from the donor acquisition campaign (Study 2) showed that a monetary PGI led to a significantly higher response rate than both a nonmonetary PGI and no PGI, whereas there were no differences between the nonmonetary and no-PGI conditions. However, 15 of 27 people in the monetary PGI condition returned/donated the enclosed $.25, and charities need to decide whether to consider them potential future donors.
The results for response rate were not consistent across all studies. We found an unexpectedly high response rate for the nonmonetary PGI condition in Study 6 and no response rate difference in any other study, including the annual campaign (Study 7). These results should be interpreted in light of the results in Study 1, in which we found that the opening rate was higher for letters containing a monetary PGI for an unfamiliar charity. This could have led to the higher response rate in the first field study, which utilized a cold mailing list. Donors in Study 7 were already familiar with the charity, having donated to them before, so opening rate for this segment should be higher overall, and less likely to be dependent on the presence of a gift. The same line of reasoning applies to the lab experiments. All participants were forced to open and read the letter, negating any effect of opening rate on donation likelihood. In other words, opening the letter is a necessary condition for responding to it. However, as discussed previously, the number of people who open the letter is partially determined by whether the donor is familiar with the organization. Thus, monetary PGIs may be especially beneficial for charities that are lesser known or just starting to build their donor list.
If the primary goal of the campaign is to maximize the contribution of each donor, results consistently show that inclusion of monetary PGIs is a bad idea, regardless of how the incentive is couched. Nonmonetary PGIs perform no better than no incentives. In fact, including them, even when we increased their value—from 4 to 8 to 12 cards (M1 card = $5.73, M4 cards = $4.84, M8 cards = $4.84, M12 cards = $4.73; all ps >.20) or offered a greater variety of gifts (Study 5) led to the same average donations as when no PGI was included. Note that both Studies 3 and 4 (student lab studies) yielded marginally significant donation differences between the monetary and nonmonetary PGI conditions, which could be because participants were not donating their own money. The effects are more pronounced in other studies.
Charities may have the goal of raising the most money possible. Across all seven studies, the no-PGI appeals resulted in the most money raised ($5,454.88), followed by the nonmonetary PGI ($4,178.57), and the monetary PGI ($3,392.67). The effectiveness of PGIs on total donations may depend on who the recipients are. In Study 2, the most donations came from the monetary PGI appeal, whereas in Study 7, the no-PGI appeal raised the most money. This suggests a monetary PGI is more effective for nondonors (perhaps due to a higher opening rate), while no PGI is needed to maximize overall donations from existing donors.
If the goal of the campaign is to minimize losses or yield a higher ROI, it would be more effective to not include a PGI. Both monetary and nonmonetary PGIs resulted in a significantly lower ROI than no PGI in both field experiments. Specifically, in the donor acquisition campaign, where ROIs for all conditions were negative, enclosing a monetary PGI led to an additional $.27 net loss per mailing, which is more than twice the net loss than when no PGI was included. Enclosing a nonmonetary PGI resulted in an additional $.24 net loss per mailing, which is also close to twice the net loss compared with a mailing with no PGI. Extrapolated to the 9,000 individuals solicited in this campaign, inclusion of a monetary PGI (vs. no PGI) resulted in an additional loss of $810 and adding a nonmonetary PGI resulted in an additional loss of $720. We found similar results in the donation campaign for recurring donors. Enclosing a monetary PGI resulted in a $2.19 lower ROI per person compared with no PGI ($1,929.39 in total), while a nonmonetary PGI (vs. no PGI) resulted in a $1.41 lower ROI per person ($1,242.21 in total).
Results suggest that enclosing a monetary PGI leads people to perceive the charity as less communal and more exchange oriented, which directly harms donations. Enclosing a nonmonetary PGI does not affect net communality levels. We suggest that this is because an immediate ask for help accompanying the gift offsets any increase in communality from the gift itself. Indeed, results from an unreported MTurk study (N = 152) showed that net communality was significantly lower when participants were asked for a donation immediately after receiving a PGI than when they were given a gift without a donation request (Mimmediate donation = 4.17, Mgift only = 4.83; p <.001). Further analyses showed that participants in the immediate donation condition scored significantly lower on communal norms and significantly higher on exchange norms compared with those in the gift-only condition (both ps <.01). This finding suggests that the norm of reciprocity may be less applicable when the default relationship norm is communal. Thus, if charities want to enhance communal norms, we suggest sending a gift with a delayed request for help.
One limitation of this research is the small sample sizes in some of the studies. This is partly due to the nature of direct mail campaigns (e.g., a low response rate is common in similar campaigns). However, the inclusion of multiple studies in the paper and the web appendix showing consistent effects, some with relatively large sample sizes, should help alleviate this concern.
Another limitation of this research is that it does not explore the long-term consequences of PGIs. For example, charities may enjoy additional benefits from nonmonetary gifts if people find items with the names and logos of the organization practical and use them regularly. Cognitive dissonance theory ([18]) predicts that, over time, this action will strengthen recipients' feelings of connection to the charity. In addition, appraisals of their own behavior ("If I am associating myself with this charity, I must think it is a worthwhile cause") may lead them to make donations in the future. Public display of the charity name and logo could also serve as advertising for people who were previously unaware of the organization. A longitudinal study could be used to test these hypotheses.
Future research might also find it fruitful to explore the effect of PGIs on other outcomes. In Web Appendix M, we examine the effect of PGIs on donations of time. Unlike money, PGI type had no impact on time donations. Research insights from a 2014 survey may shed light on why this is. Among donors, "financial support usually comes first" and "few volunteer for sectors that they are not also supporting financially" ([19]). In addition, time is personally more meaningful than money, and motivations and beliefs can lead to very different effects on time versus money donations ([31]). Thus, people who are willing to donate money may not be willing to donate time. We hope this research will spur further investigation on PGIs and prosocial behavior of all kinds.
Supplemental Material, UPDATED_Web_Appendix_JM.18.0163 - Coins Are Cold and Cards Are Caring: The Effect of Pregiving Incentives on Charity Perceptions, Relationship Norms, and Donation Behavior
Supplemental Material, UPDATED_Web_Appendix_JM.18.0163 for Coins Are Cold and Cards Are Caring: The Effect of Pregiving Incentives on Charity Perceptions, Relationship Norms, and Donation Behavior by Bingqing (Miranda) Yin, Yexin Jessica Li and Surendra Singh in Journal of Marketing
Footnotes 1 Associate Editor Vanitha Swaminathan
2 Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the University of Kansas General Research Fund allocation # 2301009 granted to the first and the second author, and the DSRF Dissertation Fund granted to the first author.
4 Online supplement: https://doi.org/10.1177/0022242920931451
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By Bingqing (Miranda) Yin; Yexin Jessica Li and Surendra Singh
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Record: 33- Collaborative Market Driving: How Peer Firms Can Develop Markets Through Collective Action. By: Maciel, Andre F.; Fischer, Eileen. Journal of Marketing. Sep2020, Vol. 84 Issue 5, p41-59. 19p. 2 Diagrams, 1 Chart, 1 Graph. DOI: 10.1177/0022242920917982.
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Collaborative Market Driving: How Peer Firms Can Develop Markets Through Collective Action
Firms often aim to develop markets as part of their long-term strategies. Conventionally, research in marketing has explained this complex process by stressing firms' efforts to outdo their peers. While this emphasis is valuable, it overlooks the role of another major force in market evolution: collective action among peer firms. To address this oversight, this article conceptualizes "collaborative market driving," defining it as the collective strategy in which peer firms consistently cooperate among themselves and with other actors to develop markets in ways that increase their overall competitiveness. This conceptualization includes the triggers that lead peer firms to mobilize for collective action and coalesce with other market actors; it also identifies how this coalition converts collective resources into market-driving power. These theoretical contributions, based on a multimethod analysis of the rise of U.S. craft breweries, offer an alternative course of action for firms interested in driving new markets when they lack adequate resources to do so individually.
Keywords: entrepreneurship; interfirm collaboration; market development driving; market orientation; trade associations
Firms often aim to develop markets as part of their long-term strategies. This process usually involves high risks, but it may also bring high rewards. These rewards include generating novel revenue streams and opportunities for firms to build sustainable competitive advantage ([12]; [20]; [46]).
When firms set out to develop markets, they often relate to their direct competitors as rivals. Fittingly, marketing research has made insightful contributions to guide companies through this approach. This literature explains, for example, why and how firms should create technological innovations that make them market pioneers and market share leaders ([35]; [50]; [61]; [71]; [94]). Furthermore, this literature highlights strategies that do not hinge on specific innovations but, rather, on the construction of powerful brands through which firms can command premium prices and favorable media attention ([40]; [43]). In these individualistic approaches to market development, firms see the marketplace as a zero-sum game. Though they typically cooperate with supply chain members, they remain strongly self-oriented concerning their competitors, trying to develop a brand reputation that is superior to that of their peers.
The focus of this literature on firms' individualistic efforts is valuable, as it offers insights on actions that single organizations can take to outdo competitors. However, this focus has occluded from view the role of another major approach for peer firms to develop markets, namely collective action—the purposively organized activities of a group of actors to create what they see as a common good ([16]; [83]). As both conceptual and empirical research indicate ([46]; [60]; [81]), many firms lack adequate means to develop markets. In response, they often pool resources with other actors, including those conceived of as their rivals and consumers. This form of collective action is observable at the origin of some major markets. For example, whereas Ford Motors is credited as the central figure in developing the mass market for cars, the coordinated action of multiple automakers and consumers was vital to diffusing this innovation in the United States ([81]). Likewise, for less technological sectors such as the now-ubiquitous organic food category, market development relied not only on Americans' increased interest in healthier diets but also on the collaboration of peer firms with consumers ([ 5]; [102]).
In light of this evidence, we shift marketing theory's usual focus away from firms' individualistic work concerning their competitors; we direct attention, instead, to the collective action of peer firms in market development. Such a shift has been adopted to some extent in previous research. In studying the growth of minivans and casino gambling in the United States, [85] and [42] reveal the cumulative impact of peer firms' coherent media messages in shaping consumer preferences. Focusing on the growth of nouvelle cuisine, [82] unpack the role of entrepreneurs' pursuits of greater public recognition in propelling the formation of a new market. Nevertheless, such work has rarely focused on coordinated action, leaving underspecified how collaboration among peer firms and their allies arises and leads to market development, a gap noted by some scholars ([13]; [83]).
To address this gap, we study a recent case of market development that has involved peer firm collaboration: the rise of craft beer in the United States ([13]). We use this case to conceptualize "collaborative market driving," defining it as the collective strategy in which peer firms consistently cooperate among themselves and with other actors to develop markets in ways that increase their overall competitiveness. We address two questions about this strategy: What triggers market actors to mobilize for collaborative market driving? And, once mobilized, how do these actors deploy collective resources to drive the development of a market?
Using our data, we formulate a theory highlighting that, as a precondition to collaborative marketing driving, firms recognize market opportunities that they try to seize individually, only to realize they share systemic constraints such as limited economic and political power on doing so. This realization, though, does not lead spontaneously to collective action. Before this form of action can occur effectively, these firms must form a sense of collective identity, which can be facilitated by suprafirm entities that emerge as coordinators of firms' market-driving efforts. These firms achieve this sense of shared identity by formulating a goal they view as worthy of jointly pursuing and by intentionally cultivating social networks. As often happens in market development, this process also enrolls allies out of the supply chain that can provide these firms with critical resources to pursue their common goal. Once mobilized as a coalition, these market actors convert their collective resources into economic and political initiatives to drive the development of a market that benefits these firms as a whole.
This theorizing enables us to make three contributions to the growing literature on what [46] term "market driving." As a route to market development, market driving occurs when firms shape markets without being primarily guided by marketing intelligence. Instead, they focus on their internal resources and vision to create new offerings and then work to align consumer preferences with these offerings ([11]; [43]; [56]). Our first contribution is to identify how these resources and vision differ in individualistic and collaborative approaches to market driving, leading peer firms and their allies to engage in various cooperative initiatives and roles. Marking these distinctions extends the typology of market driving strategies, a theoretical move advocated by [45] to systematize understanding of how companies can shape market evolution.
Our second contribution is bringing into focus an overlooked actor that can decisively influence market driving: trade associations ([91]). In particular, we conceptualize this class of suprafirm entities as coordinators of peer firms' collaboration, thus attending to research maintaining that some form of coordination is vital for effective collective action in the economic arena ([16]; [68]; [73]; [74]). Over 4,000 trade associations operate at the national level in the United States ([92]), but they appear sparsely in marketing scholarship ([ 4]; [58]). By unpacking trade associations' roles in collaborative marketing driving, we answer calls for an amplified focus in the study of market development—one that goes beyond sellers and buyers to more accurately understand this phenomenon ([10]; [42]; [54]).
Our third contribution is to extend theory on the role of consumers in market driving. In addition to their established importance as buyers, consumers often participate in market driving as members of brand communities, engaging in word of mouth and cocreating new products with a specific firm they admire ([18]; [88]). Conversely, research also shows that consumers influence the trajectory of markets when they oppose certain businesses, forcing them to review their practices ([30]; [51]; [81]). Nevertheless, less is known about how markets evolve when consumers systematically join forces with (vs. oppose) an entire set of firms (vs. a single one). Here, we explain how firms and consumers form coalitions and act concertedly to drive markets.
This research is based on an extended case study ([ 8]; [100]). This method supports theory building by exposing researchers to a broader range of data sources than that which deductive research usually entails ([23]). Furthermore, this method gives access to nuanced processes that link the different elements of complex cases ([ 8]). It is thus suited to constructing theory on the complex phenomenon of market development driving.
Our case is the rise of U.S. craft breweries. The observation window goes from the late 1970s, when new craft breweries began to open, to 2016, when their growth slowed down. As Figure 1 shows, these firms had near-zero market share in their first decade; only a few operated in an industry dominated by a handful of corporations and their aesthetically similar beers. Fast-forwarding to 2016, however, the market differed considerably: there were more than 5,000 craft breweries, which accounted for about 20% of sales in the $100 billion U.S. beer market and the broadening of beer products and flavors in the marketplace ([99]). Notably, this growth was not based on new consumers entering the larger beer market. Rather, craft breweries gained traction mainly by altering the preferences of consumers who used to buy from incumbents ([49]; [70]; [99]). We studied this rich case of market driving through interviews, participant observation, and archival data.
Graph: Figure 1. The trajectory of craft breweries in the United States.Notes: Separate data on corporate and craft breweries are only available after 1978. In addition, the craft beer market share considers only craft breweries' sales; it discounts corporate breweries' sales in the craft beer category.
The first author conducted 45 in-depth interviews with purposively sampled participants in the craft beer market: 26 industry members and 19 consumers. Interviews lasted two hours on average and were audio recorded and transcribed.
Half of the industry interviewees played critical roles in craft beer's rise by opening the first craft breweries in their states and running trade associations for long periods. They are both experts in the focal phenomenon and direct witnesses, two main criteria to sample informants for historical accounts ([33]). In their detailed stories, these informants repeatedly pointed to the importance of cooperation among craft brewers. To gain further insight into this area, we collected social network data by interviewing the leading craft brewers in a Southwestern metropolitan area that has a burgeoning craft beer scene and was accessible to the researchers.[ 6]
Consumers interviewed also participated in both national and local craft beer scenes. About half participated broadly, attending conferences, brewery tours, and festivals in various states, while voraciously reading craft beer publications. The other half consists of consumers who participated mostly in their local craft beer markets.
This method adds to interview data by unveiling individuals' actions in naturalistic settings ([ 3]). The first author did participant observation for three years in an 80-member craft beer and home brewing club. This club met monthly at craft breweries to discuss brewing techniques and beer taste. Immersion in these sites gave privy access to the relations between avid craft beer consumers and craft breweries.
To add variance to this geographically bound data ([103]), the first author attended ten brewery tours and four craft beer festivals in three states where the craft beer market differed considerably: the state where the craft beer revival began, with over 500 craft breweries; a state with an expanding craft beer market, with nearly 100 producers; and a state with 15 breweries. At festivals, he gathered data from different sources by acting as a consumer and a volunteer. Finally, he participated in the main trade conference for craft brewers as a media member, attending talks and press conferences and interacting with participants in social events.
We complemented data collected during face-to-face interactions with archival data to discern the grand facts of the focal phenomenon ([103]) and minimize informants' memory biases ([33]). As is common for qualitative research, the sampling approach for archival data collection was emergent, driven by the authors' evolving perceptions on relevant aspects of the context ([103]). As such, we collected these archives in a less structured way than in studies entailing automated text analysis, an approach that draws on predefined (vs. emergent) data sources and sampling parameters ([44]).
Websites of craft breweries' national association were a key source of archival data; they contain statistical and promotional materials on craft beer's recent history. We complemented these materials with data produced by more independent sources in trade and popular press publications[ 7] ([33]). Finally, to examine craft breweries' trajectories and messaging strategies, we reviewed the websites of the top 20 U.S. craft breweries by sales.
We began by identifying the central patterns and relationships that constitute the case ([ 8]). This identification involved performing open coding on all data sources, and then axial coding to find convergent and divergent themes that required elucidation. As the analysis progressed, we engaged in the dialectical tacking between data and theory through which ethnographers form, revise, and expand their understanding of a phenomenon. To offset possible biases that can arise in such process, we discussed interpretations with researchers not involved in the study ([17]). These researchers are seasoned marketing scholars, including one with extensive experience as a consultant for craft breweries.
Thus, the research design relies on several procedures to move from data to theory. Figure 1 "brackets" ([32]) craft breweries' trajectory in the United States into three periods that parallel its major inflection points. For each period, we identify the change in the number of these firms, their peak dollar market share, and key facts related to their market-driving efforts.
We use these inflections to abstract collaborative market driving as a three-stage process, as shown in Figure 2. In stage 1, "dispersed peer firm actions," peer firms enter a market and try to develop it individually for the most part. They do not engage in collective action, only later realizing they share systemic constraints that limit an individualistic approach to market driving. In stage 2, "mobilization of peer firms and allies," producers transition into a collective ethos. This change hinges on specific triggers that coalesce peer firms around a common goal while recruiting other market actors, such as consumers, as their allies. These triggers are facilitated by suprafirm entities that emerge as coordinators of collective action. In stage 3, "concerted action of peer firms and allies," the coalition formed by these entities, peer firms, and allies deploy collective resources into economic and political power to drive the development of a market.
Graph: Figure 2. Theoretical elements of collaborative market driving.
For each stage, Figure 2 shows the core processes, their outcomes, and the central tensions that emerge from market actors' joint work (stage 1 does not have an emerging tension because their actions were not collective yet). To be clear, these stages are conceptually distinct but interdependent; this interdependency is represented by the gradually filling circles above each stage of collaborative market driving.
Stage 1 refers to peer firms' dispersed actions to drive a market and their gradual realization of their systemic constraints on doing so. We begin with the environmental opportunities that paved the way for market drivers. Then, in line with research on individualistic market driving ([29]; [47]; [56]), we specify market-driving firms' main internal and external constraints. We place both sections within craft breweries' historical context for analytical precision.
After World War II, the U.S. beer industry went through a period of sharp consolidation, with ten breweries controlling nearly all domestic sales by the mid-1970s ([24]). Large firms like Budweiser had saturated the market with mass-produced, low-price products by using high doses of flavor-lightening additives—namely corn and rice ([ 9]).
Amid this process, some cultural and legal changes started paving the way for what would later be called "the craft beer revival," as flagged in Figure 1's period 1. In the cultural realm, social movements were reshaping the country's ideological scene, including that on food. Some of these movements vilified mass-production practices that increased shelf life in foods and beverages at the expense of flavor, as with beer ([ 5]). This public reproach led many Americans to valorize foods and drinks that are produced in small batches to retain freshness and create aesthetic complexity ([63]). In the legal realm, an excise tax that affected small breweries was cut by 22% through a law enacted in 1976. Furthermore, home brewing, an activity that serves as an incubator of craft breweries, was legalized in 1978.
These cultural and legal shifts create what [90] term "environmental opportunities" (Figure 2, stage 1, core processes): these opportunities can benefit many entrepreneurs though other actors and events bring them about. Many beer consumers noticed these opportunities; they then began brewing beer of varied flavors as a hobby, and some turned this activity into a business. They joined the only two recognized craft breweries of that time, Anchor Steam and New Albion. This consumer-led origin makes craft beer a market that grew from amateurs' entrepreneurial deeds ([65]).
Despite these environmental opportunities, craft breweries struggled for another decade to gain traction with mainstream consumers, mainly because of two internal and two external constraints they later came to recognize they had in common. The first internal shared constraint was limited financial resources. Most pioneering craft brewers were amateur consumers who had little cash to invest in the capital-intensive business of commercial brewing; furthermore, most banks refused to give them loans due to the risks associated with new business forms. This constraint led many to start their firms through some financial improvisation, a condition that remains true today, as when craft brewers buy used materials and work other jobs to make ends meet.
The second internal shared constraint was limited know-how. While home brewing allowed would-be craft brewers to test beer recipes, it did not expose them to the difficulties of building and running industrial facilities. Many had trouble finding reliable information to help them scale up their hobbies. In a craft beer pioneer's words:
There weren't brewing schools or people to teach you. We barely knew what we were doing. What is the break-even point of a small brewery? What is the commercial process to brew this new beer style? How to choose a location? How do I avoid bacteria in commercial brewing?
As with financial resources, many craft brewers presently face know-how limitations, often lacking extensive training in business and technical areas of brewing such as chemistry.
Beyond these internal constraints, two external ones confronted these underresourced craft brewers. The first was adverse regulations. Most states prohibited these brewers from having brewpubs where they could sell food, a key strategy to introduce their products to mainstream consumers through cross-selling. Furthermore, many state laws mandated that small breweries could sell beer only through distributors, even though most of these distributors were (and still are) controlled by industry incumbents through massive commercial incentives and direct ownership.
In addition to restrictive finances, know-how, and regulations, these underresourced firms dealt with adverse consumer preferences, the second major shared external constraint our data foreground. Craft brewers' initial clientele consisted mostly of other beer enthusiasts; these peer firms both educated these avid consumers and followed their tastes. In this way, craft breweries' market-driving strategy had a market-driven genesis; it responded to some manifest customer needs ([20]; [53]). But mainstream consumers tended to reject more flavorful beers, precluding a larger-scale version of such approach. Incumbents' light-flavored beers had homogenized their tastes, as this craft beer pioneer reminisces:
Our IPA and Hefeweizen [beer styles], our bestsellers today, didn't sell anything in the 1980s. I still remember this one guy who walked up to the bar and said, "You got any new beers?" My bartender goes, "We have a Hefeweizen." The customer looks at it, takes a big sip, and says, "That's the worst beer I've ever had. It tastes like bananas!" So, here's a guy that orders a Hefeweizen and didn't even know that it actually should taste like bananas.
We conceptualize collaborative market driving as a coordinated response to the recognition of these systemic constraints by peer firms (Figure 2, stage 1, outcomes). However, this response is not inevitable. As social movement scholarship shows (for a review, see [69]]), shared constraints do not translate spontaneously into collective action to change the status quo. In fact, for most of the 1980s, these firms dealt with their obstacles in a dispersed manner, as when they sporadically visited one another to discuss beer making. These brewers describe this period of relative isolation as one of self-absorption and discovery: "We were pretty much on our own, teaching ourselves how to run a small brewery, trying to make a living....Somewhere along the way, it seems we realized [emphasis added] we couldn't grow this thing by ourselves."
This realization followed the emergence of suprafirm entities. These entities, namely trade associations, gradually formed around these firms to grapple with a core barrier for collective action: organizing ([16]; [74]). A few of them began in period 1, when some craft breweries (e.g., Sierra Nevada) and avid consumers (e.g., home brewer Charles Papazian, considered the father of the craft beer revival) stepped up to found and run them in their spare time with nominal budgets (Figure 2, stage 1, core processes). These actors did not shun the prospect of individually benefiting from craft beer's growth. Still, they also desired to alter the broad conditions of a market for the advantage of other actors, a motive that often guides entrepreneurs and avid consumers ([64]; [87]). However, these rudimentary associations only organized few events, with little sway over other producers and consumers. Period 2 was when they grew in number and professionalism, incorporating paid staff and directors. Late in period 3, they consisted of a vast web, with an umbrella organization working at the national level and about 60 others, called "guilds," operating at nonoverlapping localities. These organizations act like labor unions, representing members' interests while being legally autonomous. In the next section, we analyze how these trade associations, as they became professionalized, triggered the mobilization of multiple market actors for collaborative market driving.
Mobilization refers to the process of both marshaling actors and making them ready for action ([66]). Effective mobilization is essential for achieving long-term goals such as developing a market, mainly when actors are individually underresourced to pursue this goal. Though important, market development research has overlooked the concrete initiatives that lead firms to coalesce around a specific goal and form a strong sense of collective identity ([13]; [83]).
Using our data, we abstract these initiatives into two triggers of collective action: ( 1) promoting a shared cause and ( 2) institutionalizing bonding opportunities (Figure 2, stage 2, core processes). These triggers, spearheaded by craft breweries' trade associations, led craft breweries to transition from an individualistic to a collective ethos while enlisting critical allies. Of these allies, we focus on a type of actor that remains undertheorized in market driving: consumers.[ 8]
This mobilization trigger refers to the diffusion of a compelling goal to guide market actors' efforts in market development. We explicate this trigger by drawing on research that suggests that collectively compelling goals include two core dimensions: ( 1) the diagnosis of a problem and ( 2) the prognosis of certain actors as the solution ([ 6]; [81]).
In period 2, craft breweries' national association promoted a shared cause through a set of books, magazines, and websites targeted at both producers and consumers. In the 2003 edition of The Complete Joy of Homebrewing, a best-selling book that this association published for home brewers and aspiring craft brewers, the introduction states: "Our beer world is so much better than it was in the 1980s and early 1990s. But don't forget for a moment that the large brewing companies of the world continue to 'squeeze' the market with their lighter-flavored product, always trying to minimize choice" (10). Later, as this association set out to expand craft beer's appeal to mainstream consumers, it created several variants of this message. An exemplar is this rhythmic excerpt from a pamphlet circulated in many local bars:
We hail the bold brewers who have built paradise/Saving beer from dilution by corn and rice.../May our passion for quality never be stopped/In the land of the free and the brave and the hopped/We salute with this glorious beer in our hand/Let the true taste of freedom clink out 'cross this land.
Industry incumbents use high levels of corn and rice in their flagship brands to sell beer at lower prices and deliver mild flavors to mainstream consumers. In the post-WWII era, these practices cohered with the democratic ethos that backed the diffusion of mass-produced consumer goods in the United States ([14]). But messages such as the preceding one invert the meaning of these practices, diagnosing them as yielding low-quality products and reducing market choice. Furthermore, such messages contain a prognosis: they advance "bold brewers" as market protagonists imbued with a "passion for quality" that will "save" beer from the dilution and homogenization caused by market antagonists, namely corporate breweries.
Craft breweries echo this message at their consumer touchpoints. Rather than touting their products' quality, they focus on asserting that light-flavored beers are a problem created by corporations, as in this brewer's speech during a brewery tour with consumers: "You are not going to find corn or rice in our beers. Do you know why corporate breweries put a blue indicator on their cans to show the beer is cold? They don't want you to drink these beers when they are not cold. Corn and rice are cheaper but taste terrible at higher temperatures."
The emphasis on the motives of a set of peer firms contrasts with advertising focused on the benefits of a single firm's offering, which typifies individualistic approaches to market driving ([31]; [85]). To make these motives more meaningful, trade associations tie craft breweries' activities to certain cultural ideals, as in this point-of-sale material:
I declare the beer I choose to enjoy is...an artistic creation of living liquid history made from passionate innovators...[and that] American craft brewers provide flavorful and diverse American-made beers in more than 100 distinct styles that have made the United States the envy of every beer-drinking nation for the quality and variety of beers brewed....Craft brewers represent the purest form of the American spirit and are dedicated to nurturing and enriching their communities.
This parody of the U.S. Declaration of Independence offers another prognosis: it glorifies craft brewers as producers who want to revive Americans' dear ideals of entrepreneurship, independence, and idyllic communities. Much like the rhetoric of social movements, this messaging links up these brewers' market goals with noble purposes, a strategy that serves to broaden the appeal of a change project and increase its potential to attract allies ([81]). We argue that in collaborative market driving, these links do more than that. They partially eclipse this strategy's self-serving economic benefit, which could otherwise undermine the sense of disinterestedness that is vital to the credibility of a cause and its leaders ([39]).
Craft brewers do not uniformly buy into these idealistic messages. Since the 1990s, dozens have sold their businesses to the corporate breweries their associations have disparaged. Yet these messages foster an ethos that guides the relations of the firms that remain committed to developing a market through collective action. As with this craft brewer, many others refer to this ethos as "a rising tide floats all boats":
There's a common cliché, "a rising tide floats all boats," and that sentiment has been around. There's competition in that we're all in the [craft beer] industry...[but] Budweiser, Coors, and Miller, regardless of the growth of our industry, still stand for most beer consumed in the U.S. right now. We're the small guys, with our arms locked together.
The same ethos patterns the experiences of many craft beer consumers, who come to see themselves as craft breweries' allies.[ 9] This influence comes through when they reflect on their trajectories as beer drinkers, as in this excerpt from one of the first author's many in situ interactions with craft beer festivals' attendees:
I used to drink Bud Light, Coors, and Miller. Now, I keep thinking, "Damn! They [corporate breweries] made me waste all these years drinking awful beer (laugh)!" And then you get to know what craft brewers are doing, creating neat spaces for people to hang out.... I see myself more than a consumer; I'm kind of a supporter [emphasis added], actually.
When developing a new market, competitors often use coherent messages to signal to external audiences (e.g., consumers) what the market stands for ([52]; [72]; [85]). In addition to this role, promoting a shared cause serves to mobilize stakeholders for collective action. [81] notes the role of this trigger at the start of many markets, such as that for personal computers, when retailers and early adopters claimed to be promoting technological democratization. This type of mobilization hinges on neither direct coercion nor measurable incentives; thus, it differs from the processes that explain collective action in mainstream economic theory ([60]; [73]). Instead, the promotion of a shared cause works by offering a long-term goal that peer firms and potential allies consider worthy of jointly pursuing.
Promoting a shared cause is, therefore, foundational for mobilizing actors for market driving. However, it alone has a limitation for setting collective action in motion: it does not automatically create the social networks that enable the translation of abstract goals into pragmatic doings ([69]). The next subsection sheds light on how craft breweries' trade associations cultivate these networks, which include consumers as critical members.
This mobilization trigger refers to the purposive creation of occasions for market actors to develop social cohesion; such occasions are primary for collective action, mainly when this involves a large group of actors ([74]). Using our data, we conceptualize two dimensions of bonding opportunities: ( 1) those within locality, which serve to forge a series of clusters among local actors, and ( 2) those across locality, which focus on connecting these clusters into a wider and more diffuse social grid.
Within specific localities, local guilds take great responsibility for cultivating clusters. For example, they organize regular meetings that facilitate sociality and genuine communication among local brewers, as this craft brewer discloses:
We're all super busy running our businesses. Brewers are kind of jack-of-all-trades....If we were to try to coordinate just our own sort of meetings, it'd never happen. The guild is community building, so we don't become a bunch of jerks. It's a forum to air any grievances that we have and work together to solve them.
Professional meetings can be a source of distress, but they can also be bonding events when organized around a regular set of participants and presumed goodwill ([57]). Through such meetings, the web of local guilds forms an apparatus of social cohesion that prevents craft brewers from drifting apart due to misunderstandings and individual business demands that could run counter to the formation of a strong collective identity.
While opportunities like regular meetings foster local producer bonds, others such as trade conferences foster these bonds across localities. In the leading conference run by the national trade association, craft brewers from many places engage in the usual activities of business conventions, such as visiting a trade show ([76]). Distinctive about it is the volume of activities designed to connect brewers. Every conference day, they join in foosball tournaments, brewery tours, and parties lavishly supplied with craft beer. Sociality happened organically in the conferences that gathered nearly 100 brewers in period 1, and it became a planned feature in period 2, when attendance exceeded the thousands. These events connect craft brewers who are not in regular contact, as revealed by this craft brewer's testimonial captured in situ at a recent conference edition, with over 10,000 brewers:
I don't leave the brewery very often, so once a year, I make the time to come here. I always walk away smarter than I was, but it's also just this energy and camaraderie and enthusiasm. That always reminds me of why I got into the brewing industry in the first place...I always hear inspiring stories from other breweries, their struggles, how they overcame them.
These events instill in geographically distant producers a sense of camaraderie and energy ([15]) to continue pursuing their shared cause.
To mobilize allies for this cause, craft breweries' associations institutionalized opportunities for producer–consumer bonding. This occurs within and across localities, too. At the local level, exemplars of this mobilization trigger are the craft beer festivals that happen throughout the country every year. These festivals grew with the expansion of guilds, their main organizers: from 5 festivals in early period 1 to over 150 in late period 3. In their typical format, consumers pay for tickets that they exchange for beer samples at craft breweries' stands, where they chat with brewers about beer making and tasting. We also witnessed these actors mingling while playing lawn games and eating at communal tables. These occasions are socially valuable because brewers spend most of their time in breweries' backstage, as this consumer notes:
It's nice to actually talk to brewers....Some people who like craft beer are also interested in learning more about how beer is made. At the festival, we can hang out and ask questions to brewers. It makes for a more personal connection....When Budweiser sends someone to beer festivals, it's usually a sales guy or a promoter that knows nothing about beer making.
By mixing consumption with sociality and entertainment, these local events suspend firms' commercial interests, stressing, instead, humanized buyer–seller ties ([76]).
To bond producers with consumers across localities, craft breweries' trade associations run large-scale events such as The National Homebrew Day. Established in 1988, it now happens every May in hundreds of different sites in the United States (more than 400 as of 2016), when craft breweries gather with home brewers and other avid consumers to jointly brew a beer batch. For this day, the craft breweries' national association instructs participants from all U.S. states to brew the same beer recipe. Furthermore, regardless of time zones, this association asks all participants to raise their glasses to celebrate craft beer and home brewing at a single time.
Emphasizing synchronicity in these events (i.e., same day and time of activities) is not pointless. In a classic book on the formation of nations, [ 2] details how synchronic events such as holidays cultivate a sense of identity among spatially scattered people. Through invented occasions that encourage synchronic doings, trade associations foster social cohesion among geographically dispersed producers and consumers.
The stage of "mobilization of peer firms and allies" is permeated by collectivism, yet it contains an emerging tension related to group boundaries (Figure 2, stage 2, emerging tension). As craft beer grew in popularity, it began to entice corporations and entrepreneurs seeking a quick profit—or in one craft brewer's words, "a bunch of business people pouring money into breweries, opening fancy brewpubs, who started cutting corners to get higher returns." Craft breweries feared that this type of newcomer would undermine their quality-oriented cause.
To manage this tension, the craft breweries' national association set formal standards for a brewery to be considered craft. These standards had three pillars: a maximum production volume, a maximum percentage of ownership by large corporations, and the types of ingredients that can be used in beer making. This standard-setting did not aim to create technological compatibility, as in the markets for electricity and VCRs ([19]; [34]). Instead, it aimed to shield craft breweries' collaborative market driving from the encroachment of corporate breweries. Through these standards, craft breweries restrict the types of breweries that can vote and participate as board members in their associations, two roles that may have enduring effects on market development by shaping commercial and regulatory standards ([91]).
In summary, promoting a shared cause and institutionalizing bonding opportunities are mobilization triggers that, despite emergent tensions, explain how peer firms go from shared constraints and dispersed action to a sense of collective identity, alongside their allies (Figure 2, stage 2, outcomes). The next section highlights how this identity translates into a form of collective action that departs from individualistic approaches to market driving.
Stage 3 refers to how a mobilized coalition of actors works together to drive the development of a market. Essentially, this process involves deploying collective resources in ways that give peer firms the power to overcome constraints and bring about enduring changes in the composition and actions of a market's stakeholders, including consumers, suppliers, distributors, and lawmakers ([46]).
This conversion of collective resources into market-driving power occurs within two domains that are central for market development: economic and political ([26]; [48]; [54]). We refer to deployments in these domains as economic and political conversions. For each conversion, we identify dimensions corresponding to major strands of activities and highlight the synergistic roles of our core market actors: suprafirm entities, peer firms, and allied consumers (Figure 2, stage 3, core processes).
The economic conversion concerns the commercial cooperation of mobilized market actors to improve a set of peer firms' ability to drive a market. This conversion has two dimensions: ( 1) facilitating peers' entry and growth and ( 2) building market reputation.
In individualistic approaches to market driving, creating high entry barriers is a maxim of competitive savviness ([77]; [86]). In collaborative market driving, firms suspend this wisdom. Instead, they exchange critical resources for promoting the entry and growth of peers.
Limited know-how has long been a constraint for craft brewers to enter and stay in the beer industry. In response, trade associations and established craft breweries have devised initiatives to enhance newer producers' know-how. At trade conferences, prestigious craft brewers educate audiences of less seasoned peers on general business topics, from distributor selection to brewpub design, without charging speaking fees. In the annual program "Brewing the American Dream," pioneering craft brewery Boston Beer Company covers the expenses for a new craft brewer to visit its facilities and receive customized business advice from its executives.
Beyond cooperation between geographically distant peers, our data show ample exchange of know-how among geographically close firms. Interestingly, this closeness does not seem to evolve into enclosed cliques, as may happen in some industries ([100]). Newcomers are welcomed, receiving critical business information, as this craft brewer notes:
People who are planning to open a brewery come to guild meetings to learn about the trials and hiccups we've experienced. That saves them a lot of time; many of those guys don't have a lot of experience as entrepreneurs, at least not in the alcohol industry. So, we say, "these are your zoning laws." It's very intricate. There's even regulation for how many feet away from a church you gotta be. And we are a soundboard for a new person who wants to open a brewery. For example, two new breweries have been coming to our meetings for the past year. We've helped steer them away from locations that would have hindered their ability to grow. It's not easy to open a brewery. Every step of the way, you find a hurdle.
Craft breweries participate in a two-layer network, with local and distant peers. For new firms, spatially distant peers are useful as sources of generic know-how, such as brewpub design; in turn, local networks give these firms access to tailored information, such as how to navigate city regulation. This information improves newcomers' decisions about issues (e.g., location) that can make or break a brick-and-mortar business.
Beyond the aid from established peers, upcoming producers consistently receive contributions from allied consumers. Specifically, consumers act as volunteers (Figure 2, stage 2, core processes), giving money and physical labor to help these firms deal with another of their historical constraints: limited financial resources. In crowdfunding sites such as Kickstarter, many individuals give money for craft breweries to open and expand their operations. In our sample, consumers have made contributions that, summed with others, helped these breweries raise between $20,000 and $40,000, getting in return only mementos or personalized experiences such as private beer tours. As to physical labor, consumers volunteer at craft beer festivals in positions from trash collectors to beer pourers, receiving in return tickets to these festivals and T-shirts. Also in exchange for some souvenirs, consumers volunteer at craft breweries' production lines, as when a new craft brewery needed extra hands on its canning operation's first day.
Consumers frequently provide firms with free resources. At times, they create products for themselves that are later commercialized by firms on a larger scale ([21]; [22]). At other times, when participating in brand communities, they give away ideas for the specific firm to which they feel attached ([18]; [78]; [88]). As allies in collaborative market driving, though, consumers form a more flexible pool of critical resources, which various firms (not just specific ones) can call on to respond to their business needs. The operation of this pool relies on the strong sense of collective identity between producers and consumers—result from the mobilizing efforts analyzed previously. The result of this mobilization is also clear in how these market actors collaborate to build a positive reputation for the collective of craft breweries. As shown next, this collaboration impacts both newer and older peers.
In individualistic approaches to market driving, firms focus on building their own brands ([46]). In collaborative market driving, this individualistic focus coexists with the goal of developing a reputation for the entire market category. This goal can hardly be accomplished without peers' adequate performance.
Building this positive reputation is challenging when many producers lack the know-how to offer high-quality products reliably ([81]). To mitigate this historical challenge for craft breweries, local trade associations keep an eye on members that struggle to deliver expected quality levels, as this guild's director confides:
We help defray the cost of conferences for brewers.... We defray costs for everyone, but I make special invitations to those that I know are not doing a great job (chuckles). Part of the money we make at festivals goes for that.... If the first craft beer I ever taste is hideous, that forms my opinions about craft beers in general.There're licensed breweries in this state that the owners are enthusiastic about their beer, but their beer isn't always very good.... And some of them have been around a while. That doesn't help the [craft beer] industry.
In collaborative market driving, improving quality is a collective concern because peer firms seek to create a positive image for their market. This is necessary given the knowledge schema that informs the adverse preferences of mainstream beer consumers. Because of the flavor homogeneity of U.S. mass-produced beers, many consumers learned to expect all beers to taste similarly. This schema is tacit when Americans ask one another, "Do you like beer?" as if all beers tasted the same. In markets with a long history of heterogeneity such as cars, the question "Do you like cars?" makes little sense, as it is assumed that people may well like sedans while disliking compact vehicles. In the beer market, though, the many consumers who are unaware of the sheer diversity of flavors among craft beers transfer the single-taste schema they learned through their experiences with mass-produced beers to other products. Then, if a craft brewery makes a faulty product, they infer this product generally reflects the quality of craft beers. Given this, craft breweries' know-how exchange plays a nontrivial role in increasing consumers' chances of enjoying their first craft beer experiences, a decisive step in increasing demand for this product ([12]). This form of deploying collective resources, thus, drives markets by changing the economic actions of producers, which in turn impacts the composition and typical actions of consumers.
Despite the ample exchange of economic know-how, many craft brewers occasionally face issues in their commercial efforts. A common one is erring in inventory forecasts in ways that impact consumer experience and sales. In response, craft breweries participate in another form of collective deployment of resources that centers on production supplies. Here, local networks are instrumental, as this craft brewer reveals:
Last month we ran out of oxygen, so I called [local brewery] and asked, "Do you have a tank we could borrow?" And they go, "Sure. The brewery's keys are hidden here; just go back and grab the tank." It's this sort of thing. If you're short in anything, you can always call other breweries.
Pooling supplies with local peers prevents craft breweries from losing sales and failing consumers with a lack of products and faulty services. In the long run, this economic cooperation aids these firms in their bid for creating a positive market reputation in their localities.
To build the reputation of a new market, firms often enlist resources from influential market actors such as opinion leaders ([41]; [52]). In our context, mainstream consumers were key allies, providing resources that were critical to the firms leading the cause they learned to support. Specifically, allied consumers directly contribute to building and sustaining craft breweries' core competitive advantage: continual product innovation ([96]). This contribution usually happens through home brewing contests, as this fieldnote based on the first author's observations describes:
In the typical format of home brewing contests, craft breweries partner with home brewing and craft beer clubs that invite members to send samples of their best homemade beers. These samples are judged, and the winning recipe is commercially brewed by the partner craft brewery—with winning consumers receiving no financial reward. Drawing on consumers' recipe ideas is so common that craft breweries' trade associations have formalized a process to do so. In craft beer competitions, craft breweries often compete in the "Pro-Am" category (short for Professional-Amateur). To participate in it, breweries can only enter recipes coming from their collaborations with consumers.
When producers share high collectivism, as craft breweries do, they often restrain their access to novel knowledge, a critical factor in firms' ability to innovate continuously ([100]). By turning consumers into cocreators (Figure 2, stage 3, core processes), craft breweries and their trade association partially offset this tendency. These breweries do not look at the aggregate tastes of these consumers to cater to them, as in typical market-driven strategies, nor do they ignore these tastes, as in some market-driving strategies ([43]). Instead, these firms rely on these consumers as members of their innovation ecosystem who provide free suggestions. The deployment of this economic resource supports these breweries' market-driving goal by keeping product innovation as a competitive advantage over incumbents, thereby helping them to retain the lead in shaping mainstream consumer preferences.
To fully grasp this economic cooperation, it is worth unpacking how it benefits established breweries. By displaying generosity, they showcase their commitment to the craft beer revival. In turn, the recognition of this commitment by peers and allied consumer aids these larger players—some of which produce millions of beer barrels—to retain the right to claim the status of "craft." Nevertheless, collaborative market driving is not directed by a highly calculative mindset ([ 7]; [60]), through which firms foster partnerships only if they profit from these relationships more than their partners do. Larger peers help smaller ones better their performances even though they cannot measure the return over their collaboration; in fact, they may lose sales in the areas where these smaller players operate.
In economic cooperation, [74] argues that an inherent tension is the risk of opportunistic behavior (Figure 2, stage 3, emerging tensions). Our data contain one such case, when a craft brewer sold a bottler to a peer for its cost value, only to learn that this firm later resold the machine with profit to another craft brewery. The seller learned about this at a trade conference when he incidentally met the equipment's second buyer. The seller stopped helping the opportunistic peer, but without taking further action such as telling other breweries about this violation. This case helps us identify limitations to collaborative market driving. Peers trust one another by default rather than as the result of dyadic ties built over time ([28]; [100]), but they will cut these ties if they realize a peer abused their initial goodwill.
It appears that opportunistic behavior is mitigated indirectly in collaborative market driving. The multilayer social network in which firms participate functions as a monitoring tool against actors who want to engage in repeated free riding. In addition, the openness of this network to newcomers plays a symbolic role in subduing purely self-oriented behavior. Observe how this craft brewer reflects on his adoption of the "a rising tide floats all boats" ethos after experiencing craft breweries' exchange of economic resources:
When we entered into the brewing business, I thought all right, "Here we go, this is a business. It's gonna be cutthroat, take children out of the living room." When we were still in the planning phase, I was at [craft beer festival], and I went around to all the local breweries and introduced myself. And then, the guys from [brewery name] just sort of said, "If you need anything, let us know." I didn't expect that.... And then, I needed something, and I called them, "Hey, can you help me with this?" And they go, "Sure, just stop by." It was yeast, I think. Later, I wanted to know about some distributors, and they go, "Here's your answer..." Since then, we've been able to help them a little bit. I remember the first time they asked us for help. I was like, "Yay! We're not just asking all the time anymore."
Instead of holding back or monetizing critical economic resources, the coalition of craft breweries, trade associations, and allied consumers more often pools these resources together, enabling firms to enter the market and build positive consumer perceptions. The goal and scope of this economic cooperation are broader than those in typical cases of interfirm collaboration centered on specific, time-bound projects ([98]). This cooperation also departs from individualistic approaches to market driving; in the latter, producers focus on creating strong reputations for their own brands ([43]); in the former, producers also prioritize the reputation of their peers.
In sum, economic conversion involves the pooling and deployment of critical economic resources into initiatives that foster the entry of new peers to an industry and alter the actions of critical stakeholders, such as these same peers and consumers. However, as the craft beer market grew in economic import, craft breweries came into friction with regulations that had not been designed for these players but were, instead, aligned with market incumbents' interests. The next subsection analyzes how collaborative market drivers work together to become a political voice.
Political conversion refers to the regulatory work of mobilized market actors to improve a set of peer firms' ability to drive a market (Figure 2, stage 3, core processes). When firms drive markets, political action often is focal because existing legislation tends to protect the status quo ([26]; [52]). In individualistic market driving, large firms usually engage in this kind of action by using their financial resources to hire well-paid lobbyists and make donations to political parties ([27]; [89]). By contrast, collaborative market driving entails the deployment of collective resources to gain political power. This deployment has two dimensions: ( 1) creating goodwill with political elites and ( 2) pushing a legislative agenda. As with economic conversion, suprafirm entities, peer firms, and their allies play synergistic roles in each dimension.
This dimension focuses on establishing fruitful channels of communication with political elites. New market players usually find it hard to develop these channels ([ 1]). Craft breweries' trade associations took the lead in this regard by hiring professional lobbyists, as this association director reveals:
We felt that we had to hire a lobbying firm.... There's a limited amount of publications that legislators read, and then there's like a subscription email service. So, this firm can place our message there, and they know how to change it so that it makes sense to legislators. Not to mention they knew who in the House has an agenda of supporting small businesses like us.
For legislators, lobbyists are valuable to the extent they can translate the often-vague claims from constituents into useful information for policymaking decisions ([36]). For constituents, lobbyists' value derives from their ability to offer reliable access to political elites.
Beyond lobbying, craft breweries' trade associations create goodwill with political elites by inviting them to events that can showcase craft breweries' quality and popularity. For example, the national association hosts a craft beer and food pairing reception annually to acquaint U.S. Congress members with its executives and leading craft brewers. Also, state guilds use the largest festivals they organize as a political tool. This guild's director explains:
One of our major events [ 6,000 people] happens across the street from the State Capitol. It pulls in retailers, wholesalers, brewers, and consumers. To be honest, promoting craft beer to the public is a byproduct. The primary purposes are fundraising for the guild's programs and profiling craft beer and craft breweries to the state legislature. We invite legislators and staffers to come for the expo; some are invited to a luncheon, others are invited to speak.... We do this event at that location on purpose, to show craft beer.
Such festivals bond producers and consumers with shared interests. Furthermore, by showcasing craft breweries' large following, these events help trade associations vividly display to political elites the electoral potential of being receptive to their legislative agenda.
Creating goodwill with political elites is important and often costly ([101]). Craft breweries' trade associations have been able to achieve this goal by harnessing two collective resources that they have fostered: the mobilization of peer firms and consumers as well as their rising economic power. This fieldwork memo, written as a summary of the first author's interactions with these associations' directors, gives more details:
The national trade association takes funds from the dues paid by the great proportion (70% in 2016) of brewers affiliated with it and the registration fees they pay for trade conferences. This combination has enabled this organization to step up from doing intermittent lobbying until the mid-2000s to allocating nearly $200,000 toward government affairs per year in the 2010s. Guilds, in turn, take funds primarily from the proceeds of the popular craft beer festivals they organize. Further, about 1/3 of them run membership programs in which consumers pay annual dues (e.g., $30) in exchange for perks (e.g., glassware).
Though the individual values of these dues are minor, they demonstrate the importance of cultivating a sense of collective identity among collaborative market drivers (Figure 2, stage 2, outcomes). When properly done, this cultivated identity can even lead market actors to behave in politically committed ways that are quite puzzling when compared with the waning civic engagement that has recently characterized many areas of social life ([80]).
Put succinctly, the dimension of creating goodwill with political elites focuses on cultivating a sentiment of endearment with this powerful group to increase their receptivity to the claims of the peer firms. However, this may not translate into specific votes—and thus the need to work on the more pragmatic dimension of political conversion.
This dimension focuses on getting specific bills passed rather than on enhancing lawmakers' receptivity to an interest group. As with creating goodwill with political elites, it also relies on market drivers' collective sense of identity and aggregate economic power.
Since the late 2000s, one critical policy issue has been the federal excise tax on beer. In the program "Climb the Hill," trade associations sponsored fly-ins for craft brewers to discuss the impact of this tax with legislators in Washington, D.C. For these meetings, craft brewers suspend their antagonism to corporate breweries. Trade associations instruct them to focus on how the excise tax hindered their potential to create the manufacturing jobs that have become so electorally relevant in the United States. In 2015, nearly 180 craft brewers met with their Congressional representatives to garner their support for a bill that passed in 2017 to lower said tax.
Beyond producers, consumers also participate as activists (Figure 2, stage 3, core processes) to push specific bills. The national association runs the program "Support Your Local Brewery," with over 1.5 million registered consumers who receive calls to action about policy issues. These calls include instructions that also emphasize a moderate tone that consumers should use:
Please identify yourself by name and say you are a constituent and in which city or rural area you live. Ask the Representative to please oppose [bill number] because it will limit the ability of craft brewers to get their beer on the shelves of retailers and hence your ability to access [state] craft beer.
While the number of consumers contacting representatives is unavailable, guild directors often reported this program's role in amplifying their political clout, as in this interview: "More than once, I've walked into a Congressman's office to talk about an issue, and they go, 'Okay, okay, I know what's going on. I'll support your agenda. Just ask your people to stop calling me. I've got it.'" This consumer political engagement is another point of distinction between collaborative market driving and typical cases of brand community, which center on the cocreation of products and services rather than on shaping policy ([88]).
Pushing a legislative agenda has been vital in craft breweries' struggle to access more mainstream consumers. In a recent case, a state guild spent three years working with lobbyists and coordinating a series of grassroots tactics to expand craft breweries' production capacity without increasing their taxes. This guild's director chronicles:
The first time, we tried to introduce the bill through our lobbyist, but we failed. Institutions resist change. You have to show the new bill is a better alternative. The second time, we asked essentially the same things, but we also did a whole campaign. We had a 300-people rally at the Capitol, which was the biggest rally for any commerce committee they've ever had. We activated not only our breweries but also the general public. We had people bussed in. Our bill sponsor talked before the group. We had [the mayor] talk. During all this, breweries and myself were doing the basic petition across the state. We've got 10,000 signatures.
Collaborative market driving entails deploying collective resources into some initiatives that use formal communication channels with political elites and into other initiatives that dramatize the popular support for a cause. [69] explains that the mix of these political tools is what often creates the sense of merit and urgency required to turn such claims into policy changes. In this process, trade associations play a crucial role, bundling dispersed producers and consumers into a more cohesive political voice.
Over time, this coalition of suprafirm entities, peer firms, and allied consumers has scored major political wins. Recall that regulations prohibited craft breweries from running brewpubs and mandated the use of distributors to sell beer in many states, despite incumbents' power over these intermediaries. By 2016, Georgia and Mississippi were the only states still forbidding craft breweries from on-premise beer sales. Moreover, this coalition has legalized home brewing in all states, an activity that often introduces new consumers to craft beer's flavors ([63]). This legalization also creates opportunities for more people to engage in the activity that has served as the primary incubator of craft breweries, thus promoting the entry of new market players in the coalition. Through political conversion, craft breweries have shaped regulation in ways that changed the composition and typical actions of multiple stakeholders.
The emerging tension found in political conversion is political underrepresentation (Figure 2, stage 3, emerging tensions). For example, the dues paid by smaller craft breweries partly paid for the costs of the political campaign mentioned previously, when a guild passed a bill to expand craft breweries' production cap. However, the bill's main beneficiaries were two larger peers, who were close to reaching the cap. Smaller craft breweries raised their eyebrows at this process. But for the most part, this kind of tension is tempered by these larger peers' role in expanding the craft beer market. Smaller breweries have observed that larger peers' presence in mainstream retailers such as large grocery store chains are an entryway for more consumers into the craft beer world, many of whom become eventually interested in their local products. Furthermore, this tension is tempered by the role of smaller breweries in economic conversion. There, they are the main beneficiaries of what larger peers can provide.
We compare our theorizing with prior marketing research to distill the main differences between collaborative and individualistic approaches to market driving. These differences, all introduced in our findings, are synthesized in Table 1. We propose that the first five are defining characteristics of collaborative market driving and that the last two are typical occurrences. Consistent with [46] work on market driving, these dimensions should be seen as the ends of a continuum, not dichotomies.
Graph
Table 1. Individualistic and Collaborative Approaches to Market Driving.
| Dimension | Individualistic | Collaborative |
|---|
| Prevailing ethos among peers | "Zero-sum game" | "A rising tide floats all boats" |
| Internal vision | Self-oriented | Shared cause |
| Firms' core goal | Build brand reputation | Build market reputation |
| Single firm's resources | Adequate to drive market | Inadequate to drive market |
| Collaboration with peers | Selective and sporadic | Consistent and pervasive |
| Role of suprafirm entities | Limited | Extensive |
| Role of consumers | Learners | Allies (cocreators, volunteers, activists) |
When enacting individualistic approaches to market development, peer firms see the marketplace as a zero-sum game: they remain strongly self-oriented to develop a brand reputation that is superior to that of their peers.
We believe this ethos tends to prevail when firms' resources are relatively ample or aligned with the market environment. This is the case in [85] study of automakers and [43] work on wineries; these firms drove their markets mostly through individualistic efforts to outdo peers. When some resources are scarce, though, firms may resort to marketing alliances as part of market-driving efforts, as often happens in the development of complex technologies ([86]). These alliances differ in degree from collaborative market driving. They are time-bound and tend to involve a select group of firms, often excluding peers. Under these conditions, the importance of suprafirm entities is situational, as when otherwise rival firms try to shape regulation as an interest group ([89]).
By contrast, collaborative market driving is guided by a shared cause instead of a self-oriented vision. Inspired by our informants, we term this ethos "a rising tide floats all boats"— which reflects firms' focus on building a positive reputation for themselves and their peers. Research shows this kind of collective action often occurs among underresourced players, as in the dynamics leading to the growth of food trucks and community-supported agriculture in the United States ([25]; [97]). But it also occurs among more powerful producers, as when automakers worked with car enthusiasts to lobby for better roads and regulation in automobile's early days ([81]). This collaborative ethos rests on producers' relative lack of resources to shape critical market structures and behaviors, not on their absolute endowments.
Precipitating sustained collective action requires time and effort ([73]; [74]). A suprafirm entity devoted to coordinating collaborative market driving can, thus, be critical (Table 1, penultimate line). Our research sheds light on a common form of such entities: trade associations. Typically, these actors lack bureaucratic tools (e.g., contracts) to coerce firms into certain behaviors. Instead, they shape markets by producing cultural templates for action ([92]), orchestrating a shared identity among producers, enlisting allies, and harnessing the resources of these actors into market driving. Trade associations seem to be well positioned to coordinate this strategy because of their presumed legitimacy as a representative of collective interests. Other suprafirm entities that often have such legitimacy, and therefore might alternatively be effective coordinators of collective firm action, include private consultants, consumer associations, and governmental and multilateral agencies ([68]).
Collaborative market driving also shows how markets evolve when consumers systematically join forces with firms to defy an industry's status quo. Research on this topic points to the role of ideological affinities between firms and consumers, as when they form brand communities ([79]; [97]). However, ideas rarely translate spontaneously into sustained, organized action against dominant practices and actors. This form of action relies on pragmatic ways to create social cohesion among challengers, organize contestation, and secure resources for these tasks in the long term ([66]). Building on this view, our research deepens marketing theory by specifying mobilization triggers and types of resource conversion that mediate between ideologies and the collective action of peer firms and consumers to drive a market. In this form of action, consumers play various roles as allies, supporting multiple rather than single firms (Table 1, bottom line).
By marking the distinctions between individualistic and collaborative approaches to market driving, we answer [45] call for a systematization of how firms can shape market evolution. Recently, [43] moved in this direction by identifying differences in the "basis of competition"; specifically, they show that firms can drive markets through symbolic leadership in addition to through the more often discussed focus on technological disruption. Across the differences between symbolic and technological leadership, firms typically cooperate with multiple actors within and outside the supply chain, but they see their peers mostly as rivals. We extend this nascent typology of market driving by identifying differences in the "form of action"; we theorize how firms can drive markets by collaborating with peers, adding to the understanding of how firms do so through individualistic competition.
To be sure, collaborative market driving does not exclude self-interest. By cooperating, firms can change markets to an extent they would be unlikely to do otherwise. In the craft beer case, even the largest craft breweries would struggle to shape policy, an arena in which their resources are hardly a match to those of their rival multinationals. Peers also cooperate because they jointly benefit from growing the visibility of their offering, a key factor in boosting the economic and cultural significance of their market positions ([42]). Thus, collaborative market driving is a collective strategy that includes but is not overtaken by self-interest.
Relatedly, collaborative market driving does not mean an absence of producer hierarchies. In our context, there is a clear power difference between craft breweries that sell only in small towns and those with national distribution. In collaborative market driving, though, higher-power firms do not use their greater resources to oppress their peers, as would be typically the case in individualistic approaches to market driving ([86]). Instead, they allocate some resources to help peers thrive. In doing so, they inevitably assert their power, but looking at collaborative market driving only through the lens of self-interest obscures rather than illuminates this strategy. Of course, the prevalence of collaboration can change as market growth slows down. Future research could explore how this collaborative market driving may break down.
Our framework (Figure 2) is useful to explain other cases of market development. In theorizing market driving, [46], p. 47) note that this strategy is a matter of degree: "A business that greatly changes the composition of a market [and] the behaviors of most players would be classified as having driven the market to a greater extent than another business that caused only a small change in the behavior of a single player." The rise of U.S. craft breweries lies on the higher end of this continuum. It involves players that shaped much of the composition of the market and the behaviors of many economic and political actors; furthermore, these players did so with the odds stacked against them, using limited resources to confront powerful incumbents and alter adverse consumer preferences.
We propose our framework has a strong explanatory fit with similar cases: firms that collectively drove markets to a high degree despite their fragmented economic and political power. Research has documented many such instances, as in the markets for organic food, grass-fed meat, and legal cannabis, all of which developed through the collective action of relatively small producers and allied consumers ([ 5]; [52]; [102]). Other exemplars are the growth of food trucks and the credit union movement ([25]; [67]). The early days of personal computers and nouvelle cuisine also involved peer firm collaboration in particular areas. For example, producers cooperated to create consumer awareness and interest in their offerings, the area where they individually lacked resources to drive market development ([81]). In short, our framework is useful in explaining the process of collective action when peer firms employ collaboration, more and less broadly, to drive markets to a high degree.
In addition to contributing to market driving research, our work offers new insights on the formation of market heterogeneity, in particular to resource partitioning and organizational ecology theories. Often, work in these areas hold that markets tend to become dominated by large generalists that cater to the majority of consumers with mass-produced offerings, leaving unattended smaller segments of buyers who prefer more differentiated experiences (e.g., [13]; [37]). Market heterogeneity arises as entrepreneurs form market niches to cater to these segments that are beyond better-resourced incumbents' interests. In this view, the best strategy for this new set of firms is to remain in a sort of competitive quarantine, operating in market spaces without provoking retaliation of more powerful incumbents.
In contrast, collaborative market driving shows how markets become heterogeneous as underresourced firms jointly shift from a defensive to an offensive strategy. In this strategy, they do not simply find consumers with tastes that resonate with their differentiated offerings; instead, they shake deep-seated preferences to develop consumer demand. Moreover, they do not merely occupy market niches away from generalists' reach but rather move into generalists' turf, directly defying their dominance. The present research thus accounts for a more vigorous form of agency by underresourced new firms, deepening knowledge of the nature and extent of their competitive efforts and market influence.
The conceptualization of collaborative market driving also informs the ongoing conversation on interfirm ties in marketing research. This literature has built a rich understanding of the vertical form of these ties, whereby sellers and buyers in a given distribution channel try to optimize their bilateral transactions (e.g., [38]; [55]; [62]; [93]). Much less studied are horizontal forms of market coordination, those involving firms positioned as sellers in a distribution channel. Work on horizontal coordination emphasizes time-bound interactions between a select group of peers; these interactions are regulated through formal contracts and oriented toward cost-sharing, as when firms form alliances to develop new products ([84]; [95]). By contrast, collaborative market driving refers to cooperation among a broad (vs. select) set of peer firms that are at the center of a broader coalition of actors who seek to develop a market. This cooperation relies on an informal (vs. formal) contract, safeguarded by a curated ideology and cohesive social networks, and enacted through a portfolio of initiatives that market actors devise to pursue their collective goal.
Our work can help managers assess when a collaborative approach to market driving is best suited. To organize this discussion, we offer a decision flowchart (Figure 3).
Graph: Figure 3. Decision flowchart for collaborative market driving.
The first managerial task is to pinpoint the critical constraints a firm would face in driving market development (Figure 3, box 1). These constraints can be classified as consumer- and producer-related. As to the first type, our work highlights adverse consumer preferences, but consumers may also have difficulty in evaluating the value of a novel offering ([43]; [60]). This is recurrent in the markets for foods and beverages, entertainment, and fashion, in which assessment of superiority tends to be subjective. As to producer-related constraints, we foreground limited know-how and financial resources, though these constraints also include insufficient social capital ([19]) and brand recognition ([81]) to attract customers to a new market.
The second managerial task is to assess whether the focal company has the resources to overcome these constraints (Figure 3, boxes 2 and 3). Often, firms do not have the means to develop a new market, even if products are clearly superior to existing offerings. This happened in the early days of radio broadcasting, personal computers, and cars ([59]; [81]). Peer firms lacked sufficient resources to create technological infrastructure and generate broad consumer awareness and interest. But single firms may be adequately endowed for market driving, as with the technology startups studied by [86]. In this scenario, we reason that individualistic market driving is more suitable for a firm to reap most of the rewards in the market it is developing (box 4). This firm can use orthodox competitive practices such as eliminating peers from the market and creating unique product attributes (e.g., a patented feature). The focal firm may also choose to use the form of individualistic action that [43] labels "systemic" market driving. While this form of market driving is not collaborative among peer firms, it does involve forging relationships with actors outside the value chain (especially media members) to create symbolic value for consumers.
The next managerial task is to identify whether there is a pool of peer firms that jointly possess the necessary resources for market development (Figure 3, box 5). If not, the focal firm may need to develop ways to lower entry barriers to the market (boxes 6 and 7) to form a "mob." This focus was part of JVC's strategy to win the battle of VHS over Betamax ([19]). This firm facilitated the entry of other firms interested in producing VHS-based VCRs, while Sony tried to keep Betamax a proprietary technology. In such cases, collaborative market driving would involve some economic and political cooperation—before a focus on mobilization—to open up the market for other firms with overlapping interests.
As peers enter the market, our work points to the pivotal role of suprafirm entities and mobilization triggers in cultivating a shared cause, and the networks needed to translate it into actions (Figure 3, box 8). Once peer firms are mobilized, they can develop initiatives to deploy their collective resources in ways that help them overcome their constraints and thus build a viable competitive position in a new market (box 9). Managers should carefully consider and include potential allies in these mobilization triggers and deployment initiatives. Finally, when evaluating collaborative market driving, we advise the use of both firm- and market-centered metrics. Firm-centered metrics include the typical ones, such as growth in sales and profit. Market-centered metrics include increase in the overall size of the market category and the number of peers. A firm's overemphasis on its own market share may be counterproductive to the goal of developing a market where a set of peer firms can thrive.
This research conceptualizes collaborative market driving, the collective strategy through which firms consistently cooperate with their peers and other market actors to develop a market in ways that increase their overall competitiveness. This research also highlights the role of trade associations as coordinators and consumers as allies in this strategy. Furthermore, it provides recommendations for firms interested in driving new markets when they lack adequate resources to do so individually. Together, these contributions help align mainstream marketing thought and practice with the often collective nature of market driving. Cheers.
Supplemental Material, jm.18.0296-File003 - Collaborative Market Driving: How Peer Firms Can Develop Markets Through Collective Action
Supplemental Material, jm.18.0296-File003 for Collaborative Market Driving: How Peer Firms Can Develop Markets Through Collective Action by Andre F. Maciel and Eileen Fischer in Journal of Marketing
Footnotes 1 Associate EditorJan Heide
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge the partial financial support provided by the McGuire Center for Entrepreneurship, Eller College of Management, University of Arizona.
4 ORCID iDAndre F. Maciel https://orcid.org/0000-0002-9706-569X
5 Online supplement: https://doi.org/10.1177/0022242920917982
6 1For questions used, see Web Appendix 1.
7 2Sample exemplars include The New Brewer, the leading trade journal for U.S. craft brewers; The Complete Joy of Homebrewing, a central book for the diffusion of brewing techniques in the United States; Crafting a Nation, a documentary on U.S. craft beer; and The Audacity of Hops, a book detailing the history of this market.
8 3Although media members are also allies in our context, we choose not to focus analytic attention on them because our data only confirm previous research on their importance in market development ([41]; [81]).
9 4See [63], p. 731–34) for details on why these meanings resonate with so many avid consumers, who are usually male. Some meanings (e.g., entrepreneurship) have been historically associated with notions of middle-class masculinity, along with beer itself.
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Record: 34- Commentary: Marketing and the Sharing Economy: Digital Economy and Emerging Market Challenges. By: Chen, Yubo; Wang, Liantao (Tarry). Journal of Marketing. Sep2019, Vol. 83 Issue 5, p28-31. 4p. 1 Chart. DOI: 10.1177/0022242919868470.
- Database:
- Business Source Complete
Commentary: Marketing and the Sharing Economy: Digital Economy and Emerging Market Challenges
Keywords: big data; digital economy; emerging market; sharing economy
The sharing economy is changing consumer and firm behavior around the world. We present two challenges to the proposed view of marketing in the sharing economy. First, we argue that the critical feature of the sharing economy is not its crowdsourced nature, but rather its digital-economy nature, which means data are now considered the key factor of production that drives how markets are organized and operate. Second, the market environment for the sharing economy in emerging markets lacks the institutional basis found in developed markets, which creates unique consumer and firm problems. These two challenges change marketing within the sharing economy in ways important to practitioners and scholars.
[ 2], p. 3) define the sharing economy as "a scalable socio-economic system that employs technology-enabled platforms that provide users with temporary access to tangible and intangible resources that may be crowdsourced." The authors propose five defining attributes of the sharing economy: temporary access, transfer of economic value, platform mediation, expanded consumer role, and crowdsourced supply. In this definition and Table 2 in their article, they place "crowdsourced supply" as a foundational element of the sharing economy. We challenge this view.
Rental businesses, which allow consumption sharing and provide temporary access to resources, have existed for a long time. What is really new about the sharing economy is the fact that it is built on the digital economy, in which data drive exchange and value creation in an unprecedented manner. According to the definition by the [ 3], p. 1), the digital economy refers to "a broad range of economic activities that include using digitized information and knowledge as the key factor of production, modern information networks as an important activity space, and the effective use of information and communication technology as an important driver of productivity growth and economic structural optimization." With data as the key factor of production, digital technologies provide exceptional insights and the capability of matching suppliers and buyers to efficiently meet buyers' idiosyncratic needs without ownership transfer. Ride-sharing companies such as Uber are able to achieve this matching capability through digital technology that allows Uber to access massive consumer usage behavior data, identify potential consumer demand through data analytics, and efficiently match consumer demand with available cars. Historically, this was not possible for traditional car rental companies.
Table 1 summarizes how this quality of the sharing economy changes several important aspects of marketing proposed by [ 2]. As the key production factor, data from the sharing economy companies not only create value for customers by providing better matches between suppliers and customers to improve customer experience, but also offer the opportunity to create value through innovation and vertical integration for firms along the whole value chain.
Graph
Table 1. Marketing and the Sharing Economy: Digital Economy and Emerging Market Challenges.
| Digital Economy Challenge | Emerging Market Challenge |
|---|
| Key market feature | – Data as the key production factor
| – Trust as the key market barrier
|
| Institutional role of the sharing platform | – Data generator and value creator
| – Trust builder and market maker
|
How marketing processes change...– managing customer experience – managing innovation – managing brands – managing value appropriation
| – Focus on using user behavior data to improve customer experience – Focus on using user behavior data and IOT data to manage innovation process for upstream firms – Focus on building a trustable platform brand based on data and privacy protection – Focus on profiting from the partner value created from data along the integrated value chain
| – Focus on building customer trust to improve customer experience – Focus on proposing innovative business models to build trust – Focus on building a trustable and reputable platform brand for buyers and suppliers – Focus on profiting from the customer value created by self-ownership and highly integrated service models
|
Although we agree that crowdsourced supply was a feature of many first-mover sharing economy companies, we contend this is changing as firms realize the power of the data created in these digital systems. The authors acknowledge this shift, but we believe it is worth deeper consideration. For example, one of leading ride-sharing companies in China, Caocao Chuxing, owns all its cars and employs all its drivers ([ 5]). Furthermore, all the cars owned by Caocao are made by its parent company, the car manufacturer Geely. Geely, the largest Chinese private carmaker and the owner of Swedish car brand Volvo, set up the rideshare business Caocao in 2015. As an example of the power of digital assets, by analyzing passengers' usage behavior data on where and when they use the rideshare service, Caocao has been able to improve rideshare services and even offer additional product delivery services. For example, consumers can hail a Caocao driver to pick up and deliver a product (e.g., a document, a key, a cake, flowers) within the same city very quickly. More importantly, Caocao collects massive real time Internet-of-things (IOT) data from all its cars to monitor driver behavior and automobile performance on the platform. These data help Geely improve its innovation process and design better cars by observing customer frustrations and unmet needs during vehicle use.
The tremendous value of data from rideshare platforms has started to attract other upstream firms in the transportation industry to this market. For instance, the German automotive part maker Bosch recently acquired the ride-sharing startup SPLT ([ 4]). The massive data from Caocao and SPLT help Geely and Bosch better understand both their end-customer behaviors and product performance and manage their innovation processes more efficiently. These companies have realized that user behavior data from temporary access on rideshare platforms is much richer relative to what can be uncovered when a car is sold in the classical economy. It is difficult for traditional car manufacturers or automotive part makers to know when, where, and in which context a consumer has mobility needs. It is also difficult for them to collect IOT data to monitor the automobile or automotive part performance without individual car owners' authorization. Sharing platforms now have unparalleled access to detailed user behavior and IOT data that traditional sellers could never have. Thus, it is not surprising that more upstream manufacturers and service providers have begun to vertically integrate with sharing platforms. This phenomenon contrasts with [ 2], p. 29) view that sharing economy firms are competitors to traditional firms. In fact, traditional firms are increasingly embracing the sharing economy by vertically integrating into the downstream sharing platform so they can better appropriate the value generated from platform data. Meanwhile, since the sharing platform has unprecedented access to user behavior data, building a trustworthy platform brand is a critical new issue for brand management.
In summary, to capture the essence of the sharing economy, we think it is more appropriate to define the sharing economy as an important type of digital economy that employs data as the key production factor to provide users with temporary access to tangible and intangible resources to efficiently meet their highly individualized needs.
From [ 2] thorough literature review, it appears that most existing research focuses on the sharing economy in developed markets. However, the marketing environment for the sharing economy is quite different in emerging markets. A unique feature of the market environment for the digital economy in emerging markets such as China is that the market begins the digitization process while it is still in the process of industrialization ([ 1]). In contrast, in developed countries, the digitization process started after industrialization and is thus built on it. The most important implication of this timing difference is that without going through decades of industrialization, emerging markets do not have the solid and highly specialized foundation of market institutions (e.g., legal systems strictly protecting property rights) on which the digital economy is built in developed countries.
One critical role of such market institutions is to engender trust and reduce the information asymmetry between buyers and sellers. In emerging markets in which the industrialization process is still underway, almost every industry is highly fragmented and has numerous firms—many with inferior quality and unknown reputation. Legal systems are not strong enough to enforce trustable market behavior, which makes it very difficult for buyers to trust and purchase from sellers. This distinctive market feature means that sharing economy companies in developing economies first need to build consumer trust.
Compared with the ownership transfer model in the traditional nonsharing economy, the temporary access model of the sharing economy makes buyers deal with different products and/or service providers for each transaction. Given this heterogeneity, firms need to offer innovative business models to help shape the new institutional environment and create unique value for different stakeholders during various marketing processes, such as those examined in [ 2].
As we summarize in Table 1, trust is the key market barrier in emerging markets. The critical role of the sharing economy firms in these markets is to serve as trust builders and market makers by proposing innovative business models and building a trustable and reputable brand to improve the customer experience. In general, sharing platforms have used two models to build trust in emerging markets.
The first model for the sharing platforms to build trust in emerging markets is self-ownership. One good example is Shouqi Limousine & Chauffeur, another leading ride-hailing player in China. Its business model is different from the crowdsourced supply model adopted by Uber and its biggest rival in China, Didi Chuxing. Shouqi Limousine & Chauffeur owns its fleet of cars and provides only a few car models, which helps the platform reduce service heterogeneity for a ride-hailing customer. Founded in September 2015, the platform's parent company, Shouqi, has more than 60 years of history and is now the leading Chinese company providing transportation services for major state and foreign affairs events and VIPs. Shouqi has become the most trusted brand in the industry. Shouqi Limousine & Chauffeur uses a brand extension strategy to leverage this strong brand equity from its parent company, hires all the drivers directly, and trains them to be service professionals (using training approaches from its parent company). This self-ownership model ensures consistent and high-quality service, which helps build trust among its customers. Shouqi Limousine & Chauffeur's reputable brand image and high-quality service allow it to charge premium prices and appropriate value while serving its mid- to high-end customers.
The second model for the sharing platform to build trust is to serve as a highly integrated service provider instead of a pure matching service model. In some industries, such as the home-sharing market, it can be difficult for sharing platforms such as Airbnb to provide their own homes to satisfy consumers' idiosyncratic demands; as a result, they tend to adopt the crowdsourced supply model. However, emerging markets must resolve the market trust issue for buyers and sellers; to do so, local home-sharing providers use a highly integrated service provider model that differs from the pure matching service model their Western counterparts operate. A typical example is Xiaozhu, one of the leading home-sharing providers in China. Founded by Chi (Kelvin) Chen and Liantao (Tarry) Wang (one of the co-authors of this commentary) in 2012, Xiaozhu has over 800,000 listings in more than 700 cities and destinations all over the world as of 2019.
In the Chinese home-sharing market, trust and safety are the key issues for both homeowners and consumers, so Xiaozhu centered its business model on these issues from the very beginning. In addition to the typical two-way rating system adopted by its Western counterparts like Airbnb, Xiaozhu introduced multiple mechanisms, including smart door locks with facial recognition to prevent unauthorized tenants and enhance trust. The firm also collaborated with third-party companies such as Alibaba-affiliated Ant Financial Services, which provides the private Zhima credit score system, so homeowners can screen out potential clients with bad credit history. Home-sharing service buyers also face severe uncertainty and trust issues about sellers. Homes in China are more likely to have lower hygiene and safety standards than U.S. or European homes, so steps are required to raise their quality. In April 2018, Xiaozhu launched an offline service center to provide such integrated service solutions for homeowners, mostly in expensive popular tourism destinations. The firm analyzes customers' behavior data (e.g., browsing, renting, review data) and provides such solutions as amateur host mentoring, interior design, photo shooting and cleaning services, and smart home management systems for homeowners in order to increase buyers' trust in the home-sharing suppliers on its platform. This heavy asset, highly integrated service model is quite different from the light asset, pure matching service model adopted by Airbnb and is more easily accepted by the suppliers and buyers in an emerging market environment like China. It also allows Xiaozhu to appropriate the value generated from other services.
We believe it is important for future research in marketing to consider two important and overlooked features of the sharing economy. First, marketing should treat the sharing economy as an important type of digital economy. The data generated by the sharing service can be analyzed to determine how they create value for different stakeholders such as consumers, firms, and society and thus influence the whole value chain of an industry. Second, marketing should study the sharing economy from both the developed market and emerging market perspectives and examine how the market institution environment shapes the need for different business models to address consumer pain points and open these markets to serve user demand.
Footnotes 1 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
2 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Yubo Chen acknowledges the support of the National Natural Science Foundation of China Grant 71532006, 71325005, and 91746302 and China Ministry of Education Project for Key Research Institute of Humanities and Social Sciences in Universities Grant 16JJD630006.
References ChenYubo (2018), "Take the Historic Opportunity to Develop the Digital Economy," People's Daily(June 4),http://paper.people.com.cn/rmrb/html/2018-06/04/nw.D110000renmrb%5f20180604%5f4-16.htm.
EckhardtGiana M.HoustonMark B.JiangBaojunLambertonCaitRindfleischAricZervasGeorgios (2019), "Marketing in the Sharing Economy," Journal of Marketing, 83 (5), 5–27.
3 G20 Summit in China (2016), "Digital Economy Development and Cooperation Initiative," Available at:http://www.g20chn.org/English/Documents/Current/201609/P020160908736971932404.pdf.
4 MarshallAarian (2018), "The Ride-Hailing Business Is Now Way Bigger Than Uber and Lyft," Wired (February 28), https://www.wired.com/story/ride-hailing-business-uber-lyft-sony-bosch/.
5 RenRebbecaDuChen (2019), "Volvo's Owner Geely Is Running a Ride Hailing Service in China," Pingwest(May 29), https://en.pingwest.com/a/1996.
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By Yubo Chen and Liantao (Tarry) Wang
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Record: 35- Commentary: Mind Your Text in Marketing Practice. By: Chapman, Chris. Journal of Marketing. Jan2020, Vol. 84 Issue 1, p26-31. 6p. 2 Diagrams, 1 Chart. DOI: 10.1177/0022242919886882.
- Database:
- Business Source Complete
Commentary: Mind Your Text in Marketing Practice
[ 1] have contributed a wide-ranging, compact, and useful guide to text analytics for marketing. I recommend to—and personally will—use it as a guide and reference source in coming years. The article rewards repeated reading, as it highlights issues and opportunities that arise varyingly for different projects and marketing applications.
In this commentary, I address several topics. First, I list a set of typical marketing questions for text analytics. Then I discuss a few common, yet perhaps nonobvious, complications for text analyses. Following this, I suggest an initial path for those who are new to text analytics for marketing. Finally, I illustrate multidimensional sentiment analysis, an extension of sentiment methods described by Berger et al., using shared R code and an online text source. Overall, I urge analysts to incorporate a large degree of inspection and qualitative analysis and to carefully consider the characteristics of both their texts' origins and any dictionaries used in analyses.
To begin, it is important to consider a few common uses of text analytics in marketing. Table 1 starts with questions that we, as analysts, or our stakeholders may have about a marketing offering (product, service, brand, etc.) and the strategic areas where the questions commonly arise. It is striking how applicable text is to a broad range of strategic and tactical marketing questions.
Graph
Table 1. Common Uses of Text Analytics in Marketing Practice.
| Question | Marketing Strategy Application | Data Sources | Typical Method | Common Concerns |
|---|
| How do customers feel about our offering?a | New product design Updating product features Social media strategy Influencer marketing strategy
| Open-ended survey questions about experience Product reviews Social media posts
| Sentiment (directional) scoring | Source stability Nonresponse bias Longitudinal tracking of metrics Dictionary validity
|
| What words or themes are associated with our offering? | Advertising strategy Branding strategy Search engine optimization Social media strategy
| Product reviews Open-ended survey questions about perception Social media posts Longer survey comments or reviews Search queries and analytics
| Term-frequency and inverse document frequency analysis (tf-idf) Keyness plots Qualitative scoring Topic analysis, such as LDA
| Attribution from social media to product is difficult Vocabulary depends on source mix Topics almost always have low base rates and thus are difficult to assess for importance
|
| What is the perception of our offering versus competitors'? | Brand positioning strategy Marketing-mix strategy Channel strategy Competitor acquisition strategy (buying a competitor's business)
| Open-ended survey comments about firm product and competing products Product reviews
| Multidimensional sentiment analysis Perceptual mapping
| Dictionary relevance General limitations of perceptual maps
|
| How does our firm communicate with its key stakeholders? | Call center management Customer relationship management (CRM) Employee acquisition and retention Shareholder engagement Public policy engagement
| Call center transcripts CRM transcripts Third-party employee review posts (e.g., Glassdoor) Employee exit interviews Annual reports Press releases News reports
| Classification modeling, trained by initial human rater scoring Qualitative rating (scoring, sorting, affinity mapping) Topic analysis Querying or feature detection (such as identification of target keywords), applied to document-term matrices
| Limited sample sizes Some sources are highly managed and are not necessarily representative Some sources (e.g., support calls, CRM verbatims) may have deep content that is better suited for qualitative analysis
|
1 a An offering might be a product, brand, service, application, or other unit of customer engagement as relevant.
Table 1 then lists data sources where one may typically obtain text that is suitable for analysis. These include from product reviews, call center transcripts, press releases, social media posts, and open-ended survey questions, among others. The power of texts lies in its varied forms, and so marketers should look for the text that is most relevant to their marketing problem.
A set of methods follows. In this commentary, I consider the utility of multidimensional sentiment analysis. For other methods listed in Table 1, I recommend texts by [ 8] and [ 4]. Finally, Table 1 outlines some of the concerns, limitations, and caveats that often arise for a given question, source, or method. This is not intended to be an exhaustive list by any means. I elaborate on concerns about text sources and dictionaries noted in Table 1 in the sections that follow.
Let's examine some concerns from Table 1 in more detail. Starting with data sources, there are four crucial, yet often overlooked, aspects of text analytics in marketing. One question about a sample is whether it is representative of the market to which we project. A key fact is that only a small proportion of consumers provide open-ended text spontaneously or when asked. Given this, there are good reasons to be wary of nonresponse bias as well as motivations other than simply providing feedback. For example, product reviews may be written in response to previous reviews, resulting in bias and a form of intracorpus autocorrelation (e.g., where the sentiment of one review is directly correlated with the sentiment of one or more previous reviews). In most cases, such autocorrelation cannot be solved, and analysts should simply be aware of how it may have affected a corpus in which contributions are public and sequential. Methods that assume independent sampling units (e.g., a t-test between time slices) are inappropriate.
However, if particularly large corpora are available, the dynamics of intracorpus ratings may be amenable to time series modeling, such as modeling the lag effect of previous reviews on subsequent reviews. As for nonresponse bias, depending on the data source, it may be possible to model nonresponse as a function of other data (for instance, using logistic regression with response as the outcome). This might be paired with sample weighting, targeted sampling, changes in survey design, or, more simply, qualitative examination of customers' reasons for nonresponse.
A second issue arises when considering combinations of sources that tend to vary dramatically in their length, quality, linguistic structure, clarity of content, and possibly in the direction of sentiment. For example, an online blog review may be quite lengthy and focused on facts rather than sentiment, whereas a shopping site review may be shorter and emotional. Customer support records may be negative because they typically arise in the context of problems. Tweets or other social media comments may be extremely short and use highly abbreviated text. This content issue interacts with questions about the sample because different groups within a population may use different platforms. Thus, for example, younger respondents may generate structurally different text than older respondents because of the platforms they use. Sentiment asymmetry is also common; reviews may be much more specific in one direction than another. For example, we might find that positive comments mention product details while negative comments are frustratingly generic or global.
A general way to address this problem is to separate analyses by source, even if the analyses themselves are identical, and to avoid trying to get a single "top line" score—at least at the beginning of the analysis. For example, one might analyze survey open ends separately from online reviews and present parallel results to compare and contrast the findings. A side benefit of this is that different sources may be amenable to differing marketing interventions and thus results are likely to be more directly applicable. After examining each source separately, one might decide to combine the data, adding codes for the original sources. This would allow the data to be disaggregated or modeled according to source in later analyses.
A third, related issue concerns attribution from the text to a product or service. We might hope that consumers will unambiguously identify a product; however, this is rare. Instead of the useful comment "My Acme SuperCam 9510 takes excessively pixelated images in night mode," we are more likely to get "Acme stinks at night." Why is this a problem? First, product managers will want to know how their separate products are performing. Second, if sentiment changes, stakeholders will want to know why. Did a new product introduction or a marketing campaign cause sentiment to change? Unless a product can be unambiguously identified, it will be difficult to assess the focus and potential links to marketing activities.
For this problem, there are two common solutions. One is to use human raters to assign the most likely product (or products) according to contextual evidence, such as references to features that would imply a particular product. Another solution, when large samples are available, is to begin with human ratings and then scale those by using supervised learning and classification, training a machine learning model that will classify new text similarly. This will pose further questions about machine learning, such as the stability of a model as new offerings are introduced, but those diverge from our central discussion.
A fourth and often-overlooked ethical consideration with text comments is that they frequently contain personally identifiable information (PII). For instance, a customer may reply to an open-ended question to complain about a problem and leave contact information. It is best to treat all raw text—even if collected from third party sources—as PII, and to have a system for data retention and for auditing and removing PII before sharing with extended team members. For example, names, email addresses, company names, and so forth, might be replaced with placeholders or anonymized identifiers.
Across all of these challenges, in general, I recommend actually reading samples of the text used in the analysis. One way to do this is with a weekly "comments hour" in which a team reads randomly selected comments. This can be a fun way to listen to customers, to surface issues with data quality, to suggest new research questions, and to involve team members from different disciplines to understand and contribute to the process. In one case, I recall that a review of open-ended comments revealed a crucial error in survey programming that resulted in questions about product experience only being asked only of nonusers. This was discovered by reading comments, in which a few respondents said that they were surprised to be asked about the product. Luckily, this weekly check discovered the error quite early, before sampling was completed. If we had only checked the data post hoc, all comments would have been unusable—or worse, the error might not have been detected and analysis would have led to very incorrect conclusions.
As marketers, we are usually concerned with understanding the sentiment of customers' text comments. What proportion of customers like or dislike our product? How intensely? Which comments should be flagged for manual follow-up? What do customers associate with our brand? How do we compare to competitors? As [ 1] describe, this typically involves scoring text with dictionaries that provide standardized sentiment for each word. More complex natural language processing is possible, but the difficulty and required corpus sizes make that more exceptional than common, at least for now.
Unfortunately, dictionary usage poses several serious concerns. I set aside the well-known issues of N-grams (e.g., where "not good" may be coded as an omitted stop word + "good"). As [ 1] note, one serious issue is that generic dictionaries may perform poorly in a given domain. For example, I once worked on cloud infrastructure products, yet "cloud" is often generically coded in sentiment dictionaries as having negative sentiment. The solution to this problem depends on the severity in one's domain. In the simplest form, it might involve auditing the scoring of comments and adjusting a dictionary. In more complex cases, it could involve construction of custom dictionaries, with raters or customers in one's domain providing the dimensional scores. Marketing scholars and practitioners should be willing to undertake such dictionary construction in order to generate more valid results (see [ 6]]; see also [ 5], especially Chapter 7).
An even more difficult challenge is to ensure that a dictionary appropriately matches the usage of the people we are sampling—who may represent varying linguistic dialects and subpopulations. Dictionaries are often created by crowdsource or teams of qualitative ratings staff, yet those constitute a different sample than our respondents. Consider high-sentiment terms such as "wicked," "sick," "sexy," "bomb," "slick," "shiny"—the underlying sentiment varies by sample demographics, region, dialect, and product category. This common mismatch in language between dictionary raters and one's customer base poses validity and ethical concerns. If a dictionary does not appropriately capture the sentiment in language used by minority or other groups in your population, you will be systematically misrepresenting or failing to listen to their voices. When text analytics are used for direct marketing, recommendation engines, promotions, and the like, this may result, at best, in inefficient targeting; at worst, it may lead to severe and biased inequity for underrepresented groups.
Standard dictionaries are often limited to scoring a word on a bipolar, positive and negative dimension. Comments may be averaged to yield a single −1 to +1 score that is deceptively analogous to traditional customer satisfaction (CSat) and likelihood-to-recommend (LTR) metrics. Yet unlike a CSat or LTR survey item, text sources may be susceptible to rapid change: reviews trail off as a product is in market, an ad may create a surge of social media comments, a news story may lead to social media posts being "ratioed" (flooded by negative comments as a form of protest), and so forth. Relying on a text-based tracker may lead to perpetually searching for explanations that reflect little more than seasonal or one-time changes in sample and source composition, possibly unrelated to any determinable structural change in latent sentiment. For example, a one-time campaign (e.g., a Super Bowl ad) may lead to transient, but not permanent, changes in the sample and sources.
Similarly, sample composition and related vocabulary may change seasonally. I once observed survey results (from a third party) that presented surprising data about college student behavior, such as finding that very few college students had taken classroom notes in the most recent week. It turned out the survey had fielded in July in the United States, and the students had in fact been asked about the previous week—when most of them would have been on summer vacation. For both of these problems—transient effects and seasonality—the solution is to examine results longitudinally but resist establishing a tracker or specific tracking metrics (such as "80% positive") until you have an extended understanding of seasonality and the frequency of one-time perturbations.
To make informed decisions about marketing strategy, I have found it especially interesting to examine multidimensional sentiment analysis (MDSA), which scores a product, service, or (especially) brand on the valence and intensity of multiple dimensions, such as surprise, anger, delight, and trust (consumer researchers may think about the PANAS scale). Do negative comments reflect generic problems, or are customers actually angry? In the positive direction, is the sentiment weak, or do we provoke joy? I show next how such dimensions can be used with traditional (and somewhat old school) perceptual maps to inform strategy.
International markets may intensify these concerns. At a practical level, there are fewer and often lower-quality dictionaries available for languages other than English. Questions of dialect may be more important in other languages, and we will have different concerns about sources and samples. Experience shows that different cultures have varying sentiment anchors and use different dimensions. For example, a term such as "OK" might be neutral or negative in one context, but positive in another. Cultures vary in the terms they use, and some cultures use highly positive or negative terms much more often than others do. Some cultures have norms of polite and somewhat exaggerated enthusiasm while others have norms of expressive restraint. Thus, exceptionally positive or relatively negative or neutral comments may reflect cultural norms rather than product experience. In-person qualitative research may be needed to understand the relationship between comments and actual experience.
Again, an analyst should always inspect and audit the data, with the view that dictionaries themselves are imperfect data. Second, separate the analyses by language or region. If you plan to do a market expansion to, say, Japan or India or Germany, do not expect an easy transition of analytics to the new languages. Moreover, it would be unwise to expect similar base rates in sentiment in those locations as in U.S. English comments. I recommend two practices here: first, because the markets are different sources, conduct parallel analyses as noted previously. Second, avoid the temptation to compare regions or to assert, for example, that "customers in Germany are less [or more] satisfied." Understand each market on its own terms and compared to its own baseline at the beginning of the observation period. Over time, you will be able to compare it with this baseline.
For the marketer new to text analytics, I recommend the following path:
- As for any analytics problem, make the business question as crisp as possible. Exactly what question are you trying to answer? How will your data be used by marketing decision makers? How will the text analytics result assist?
- Ideally, before tackling your data, build out a skeleton analysis using freely available text as a proxy data set (i.e., a practice data set that mimics some properties of your expected data). This will help separate the basic mechanics of the process from the details or idiosyncrasies of your target data. Parse the proxy data to imitate the structure you expect, such as sample size, length of comments, the number of products or brands, and so on. In a subsequent section, I describe an example using Project Gutenberg texts. Proxy analysis can also help you determine whether your question (Step 1) will be answerable. You can use it to mock up example results to discuss with stakeholders. This will usually identify gaps in the planned analyses, which can be rectified prior to beginning the actual analyses.
- When your basic analyses have been demonstrated with proxy data, bring in real data. Start with a single data source, such as online reviews or comments from a CSat tracking survey. Longer text is better, because it affords more opportunity to detect sentiment and to clearly identify products or brands. It will also be easier to compare with automated scoring when auditing results. If you intend to do statistical modeling or machine learning, setting aside a random portion (perhaps a third) of the data to use as a holdout sample for validation is ideal.
- Follow the steps outlined in [ 1] to compile the data set, clean it, stem it (if desired), remove stop words, and so on.
- Initially use standard dictionaries, but prepare to clean or replace them later.
- As noted previously, audit the raw comments. A "comments hour," perhaps as an "Open End Friday" review meeting, is one good idea. Besides reading texts, audit the scores obtained from your text tools and dictionaries. Sample some comments and examine whether the sentiment scoring makes sense to the reviewers in the meeting. Is it scored as positive but reads as negative? Perhaps some words are incorrectly scored for your domain or are entirely missing from the dictionary. As always, simply read what customers say. (Note: Don't worry overly about hypothetical or single cases, such as a sarcastic review. The key is overall directional accuracy, not text-by-text precision.)
- I suggest initial analyses that include the following, for which Silge and Robinson (2017) and Kwartler (2017) are outstanding references for analyses in R:
- Directional, positive–negative sentiment scoring;
- Multidimensional scoring, and perhaps perceptual mapping;
- Comparison of products and/or brands on the above; and
- Latent Dirichlet allocation (LDA) topic modeling.
- Take the results out for qualitative research assessment. Do customers agree with your insights? For example, if you are comparing products or brands (see the example of MDSA subsequently), you might share some of the findings in focus groups or one-on-one interviews, using them as probes for discussion. What are the gaps? Disagreements? Pay attention to the comments made as they may offer you directions for follow-up analyses or new research directions.
- Use caution and do not assume that you can compile different sources, compile across cultures or languages, or establish a stable tracker. Each of those necessitates detailed investigation. Lean toward separate, more focal reporting, rather than "all-up" scores. Track sources and markets over time to understand their dynamics, noise, and seasonal patterns.
[ 1] described sentiment analysis mostly in terms of unidimensional (positive–negative) sentiment. Although such analysis is easy to report because it results in a net, unidimensional score, MDSA may be even more useful for marketing insight, especially for questions related to brand and product strategy. In MDSA, text is assessed on assorted emotional dimensions, such as anger, fear, joy, and surprise, along with more generic positive and negative sentiment. To illustrate MDSA and an example of proxy text, I will show brief, initial analyses of six works by Shakespeare: five plays (Hamlet, Henry V, King Lear, A Midsummer Night's Dream, and Much Ado about Nothing) and the sonnets ([ 7]).
For illustrative purposes, imagine that I am the brand manager for these works or for a competing company. We may treat each work as a separate "brand." As the brand manager, I am interested in questions such as these: What are the key emotions that differentiate the works (and may inspire marketing messages or creatives)? How are the brands related to one another (perhaps so I can consider bundles of similar or different plays or understand which are competing brands of plays that serve the same need)? Are there emotional themes that are not addressed (perhaps to suggest an area for a newly commissioned play)? My goal in this analysis, of course, is not to answer those questions for Shakespeare; rather, I wish to determine whether the analytic methods I want to use are able to yield answers before I invest heavily in data collection. By using freely available text, I am able to start quickly, at low cost, and to debug the methods in advance of data collection.
When parsed into paragraphs (such as actors' lines or verses), each work yields 312 to 1,414 proxy "comments." These were scored on the ten dimensions of the National Research Council Canada sentiment dictionary ([ 6]).[ 3]
Figure 1 shows the ten National Research Council sentiment dimensions for each work, plotting the mean value for each with its estimated 95% confidence interval (some very small) on a logarithmic scale. On initial inspection, the relative ordering of the emotional dimensions is very similar among the plays, yet quite different for the sonnets. The sonnets show both a different structure—the relative rank of the emotions—and much higher emotional intensity. I also find, for example, that themes of "trust" are lower in Hamlet and Lear as compared with Henry V (no surprise for literature students), while "joy" is higher in Much Ado and Midsummer. As marketers, such dimensions provide depth in understanding these proxy "brands."
Graph: Figure 1. Sentiment dimensions in six works of Shakespeare.aMean + 95% confidence interval; sentiment proportional frequency by line; log scale; right side of x-axis = higher intensity.
Figure 2 presents a perceptual map with the Shakespearean plays (i.e., brands) plotted according to their relative positions from one another, compared with their overall averages, using discriminant analysis ([ 2], Chapter 8; [ 3]). I omitted the sonnets because their high emotional intensity changes the scale and obfuscates the relationship among the plays. In Figure 2, we see the brand relationships among the plays in stark clarity—the comedies are adjacent (Much Ado and Midsummer), as are the tragedies (Lear and Hamlet), while the history (Henry V) is roughly equidistant from the others. It is often astonishing to see such valid structure emerge from these simple methods.
Graph: Figure 2. Sentiment perceptual map, five works of Shakespeare.
Such analysis may give immense insight for strategic discussion. If I am the marketing manager for Midsummer, I see that Much Ado is positioned as a direct competitor in the mind of the customer; if I am creating a new play, there may be open space for a comedy that emphasizes "anticipation"; and so forth. Like any perceptual map, this is most useful as a snapshot to add to other data at a point in time. The positions are relative and will change with new data sources and especially with additional brands.
I hope this commentary helps researchers consider new applications for text analytics, along with useful starting points to analyze text in practice. Although I have laid out several challenges, adding to those enumerated by [ 1], do not let these issues stop you from using text analytics for marketing. Rather, I hope this discussion sensitizes researchers to several important considerations—to separate data sources, to align dictionaries to one's product domain and customer groups, to audit both raw texts and algorithmic sentiment scores, to consider multidimensional sentiment analysis, and to use caution before adopting text-based "top line" metrics tracking.
Text opens up immense opportunity to listen to our customers. When we apply sound, thoughtful, and ethical analysis, we will learn much to benefit both our firms and our customers, repaying their generous sharing of their thoughts and needs about our products and services.
Footnotes 1 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
2 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
3 1For the interested reader, I have shared R code for the analyses and charts at http://r-marketing.r-forge.r-project.org/misc/shakespeare-sentiment-example.R; please try the analyses for yourself. (Note that any application will require adaptation of such code; it is intended to be a minimal example.)
References BergerJonahHumphreysAshleeLudwigStephanMoeWendy W.NetzerOdedSchweidelDavid A. (2020), "Uniting the Tribes: Using Text for Marketing Insight," Journal of Marketing, 84 (1), 1–25.
ChapmanChrisFeitElea McDonnell (2019), R for Marketing Research and Analytics, 2nd ed. New York: Springer.
HauserJohn RKoppelmanFrank S. (1979), "Alternative Perceptual Mapping Techniques: Relative Accuracy and Usefulness," Journal of Marketing Research, 16 (4), 495–506.
4 KwartlerTed (2017), Text Mining in Practice with R. Hoboken, NJ: John Wiley & Sons.
5 LiuBing (2015), Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. New York: Cambridge University Press.
6 MohammadSaifTurneyPeter (2013), "Crowdsourcing a Word-Emotion Association Lexicon," Computational Intelligence, 29 (3), 436–465.
7 ShakespeareWilliam (2019), The Complete Works of William Shakespeare. Urbana, IL: Project Gutenberg. Retrieved September 4, 2019, fromhttps://www.gutenberg.org/files/100/100-0.txt.
8 SilgeJuliaRobinsonDavid (2017), Text Mining with R: A Tidy Approach. Sebastopol, CA: O'Reilly.
~~~~~~~~
By Chris Chapman
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Record: 36- Commentary: The Twilight of Brand and Consumerism?: Digital Trust, Cultural Meaning, and the Quest for Connection in the Sharing Economy. By: Sundararajan, Arun. Journal of Marketing. Sep2019, Vol. 83 Issue 5, p32-35. 4p. 1 Chart. DOI: 10.1177/0022242919868965.
- Database:
- Business Source Complete
Commentary: The Twilight of Brand and Consumerism?: Digital Trust, Cultural Meaning, and the Quest for Connection in the Sharing Economy
Keywords: blockchain; brand equity; cultural dynamics; cultural meaning; digital trust; gift economy; platform; prosocial behavior; reputation; sharing economy; trust
The economic and societal changes collectively labeled the "sharing economy" represent new digitally enabled ways of organizing economic activity that will reshape the world economy over the twenty-first century, blurring established lines between personal and commercial assets, consumers and producers, markets and hierarchies, and casual labor and full-time work. As platforms supersede firms as the preeminent institutions of society, they will challenge accepted business thinking that assumes an economy comprised primarily of large hierarchical organizations. This trajectory of change has spawned important new research in economics, management, and operations, largely centered on marketplace design, yield management, choosing an appropriate organizational scope, and creating effective labor policy. The contribution by [ 1] is a comprehensive and timely call to action for the marketing discipline, outlining an excellent and diverse range of open questions raised by the growth of the sharing economy, spanning those that affect marketing institutions, marketing processes, and models of creating stakeholder value.
Building on [ 1], I highlight three research directions that I believe will contribute the greatest value to marketing in our digital future: ( 1) understanding how the relationship between consumer value and cultural meaning is altered by the blurring of lines between the personal and the commercial, ( 2) understanding how social motives and the desire for human connection meld with commercial objectives to cocreate consumer value in sharing-economy experiences and alter the calculus of consumer choice, and ( 3) understanding the evolving interplay between decentralized digital cues and centralized corporate brands in generating consumer trust at scale.
A recurring theme across many sharing economy experiences is a blurring of lines between the personal and the commercial. Homes that were once exclusively personal are now periodically deployed as commercial short-term accommodations through platforms like Airbnb. One's personal car can provide taxi-like service through Uber or Lyft, become a node in a commercial long-distance transportation network via BlaBlaCar, and be repurposed as a rental car through Getaround and Turo. In turn, individuals with diverse professional identities become commercial providers of many of these services; an accountant may moonlight as a hotelier through Airbnb, a doctor as a Getaround proprietor of a car rental company.
This blurring of lines has many important consequences. First, as [ 1] note, the perceived individual economic value of consumer products shifts as they now have additional "rental value." Understanding how this additional commercial value alters choice behavior for consumer goods and the trade-off between owning and renting is fertile ground for new marketing research. This is especially so as advances in the internet of things and drone/robot technologies lower the logistics costs associated with peer-to-peer rental delivery, extending the occasional commercialization of personal assets into new categories like expensive apparel, videography equipment, and watches and jewelry.
These blurring lines also alter the mechanisms by which consumer value is derived from the meaning of an asset to its owner as identified in [ 5]. For example, for an asset like a home, which is part of its owner's extended self-concept, consumer value is imparted from its private meaning and the possession rituals that personalize it. When the owner becomes an Airbnb host, the importance of possession rituals may diminish in favor of a different instrument of meaning transfer—the exchange rituals often associated with gift giving.
A deeper understanding of the changes in these instruments—the means by which meaning is transferred from objects to consumers— as personal assets transition from being personal to "mixed use" represents an important direction of research. Preliminary findings from [ 3] suggest that the act of sharing one's home through Airbnb may also create new instruments of meaning transfer. For example, as hosts prepare to commercialize their home, an asset replete with personal meaning, they employ divestment rituals ([ 5]), going through a process of decathecting, wherein they aim to remove some of the emotional or personal significance of this personal asset. The nature of these rituals, their similarity or difference to those employed when selling an asset, and the extent to which they alter personal meaning are important research directions.
A related promising line of inquiry lies in understanding the possible shift from simpler person–thing bonds to more complex person–thing–person relationships, wherein more of the consumer value to the owner stems from the interactions people other than the owner (e.g., Airbnb guests) have with the object. Extended to other asset classes such as automobiles and apparel, the blurring of lines may change the relative salience of different product attributes (e.g., durability for cars, brand label for apparel). It may also alter grooming rituals, actions taken by owners to cultivate meaningful properties within their personal assets. Examples of grooming rituals include the extensive maintenance and accessorizing practiced by owners on some automobiles. This could alter the optimal product mix and revenue potential of related markets like home improvement, landscaping, or automobile accessories.
A parallel set of research questions arise on the rental side of the sharing economy. As more consumers rent assets, such as jewelry, watches, and high-end apparel, from other consumers for their everyday use, how does this shift from permanent to temporary possession alter the rituals or means employed to extract cultural meaning? Clearly, the extent to which a user cultivates a rented object will differ from the investment made in one's owned possessions. What is the significance of the rented asset being owned by another individual (and perhaps personalized or groomed in line with this owner's preferences) rather than by a faceless rental agency? Robust answers to these questions grounded in academic research could prepare sellers of consumer goods for a future in which postpurchase peer-to-peer rental is the norm.
The change in instruments of meaning transfer that stems from the commercialization of personal assets and the ensuing importance of exchange rituals is closely connected to whether the owner's motivations for sharing are social or commercial. In fact, these seemingly competing objectives—social versus commercial—also frame a different set of marketing research questions. [ 1], p. 44) mention a dichotomy in these objectives and, using the example of Couchsurfing, posit that sharing economy activity initially motivated by social objectives may have shifted toward being more commercial as the platform scaled. I have observed a similar pattern on the ride-hailing platform Lyft, which generated billions of dollars in annual revenue and went public at a valuation of over $20 billion in 2019. In its early days, Lyft actively promoted a noncommercial culture of "riding with your buddy." The passenger would be invited to sit in the front seat of the vehicle next to the driver, who would "fist-bump" as a greeting. (In fact, in the earliest iteration, there was no formal fare. Instead, passengers were invited to make a donation amount of their choosing.)
But this shift in objectives is by no means always in one direction, or even one-dimensional; rather, the interplay between social and profit motives in the sharing economy is more complex and nuanced, raising many fascinating new questions for the marketing discipline. In [ 6], I characterize sharing economy services as falling on a continuum between "gift economies" and "market economies." In a gift economy, the purpose of exchange is entirely to establish or enhance a relationship between exchanging parties, and the actual property or consumption value of the object or service being transferred—the gift itself—is secondary. Put differently, in a gift economy, the objective of exchange is purely social, aimed at building community. This contrasts with a market economy, in which the focus is entirely on the consumption or property value of the object or service being traded.
In its original form, Couchsurfing came very close to being a pure gift economy—the offer or acceptance of a couch to sleep on was treated as a gift, a means for visiting guests and the local Couchsurfing community to enhance their connection with one another. In contrast, Airbnb blends aspects of both market and gift economies. Money exchanges hands, and the consumption value of the accommodation offered is certainly relevant. However, numerous hosts are motivated by the joy of giving their personal space to others as well as by the desire to connect with new people and socialize with their guests. In addition, guests often feel a connection with their hosts even when the host is not present. Small gifts like a bottle of wine are often left and advice is generously offered. There is an intimacy associated with staying at an Airbnb, seeing a host's family pictures on a mantelpiece, their kitchen condiments, their choice of linens. The discovery of a new space, a glimpse into the lives of the people who live there, and the associated learning and exploration is part of the consumer experience, adding a social and personal layer that distinguishes an Airbnb stay from checking into a faceless hotel room.
As capitalism scaled in the twentieth century, many argue that exchange evolved to become excessively impersonal, shedding many gift economy aspects of, say, the neighborhood corner store in pursuit of economic efficiency and mass distribution. However, human beings are wired to seek connection. Much of the sharing economy activity I have observed is motivated in part by a desire for human connection and the joy associated with the subtle integration of the realization of this quest for connection into the context of an everyday economic activity, like finding a place to stay or commuting to work.
Consequently, a central marketing question that remains unanswered about the sharing economy is whether, over time, it will work to reintegrate social or gift aspects back into commerce at scale—rehumanizing the dispassionate economic exchange of the twentieth century. While consumer researchers have identified gift economy aspects in marketing, it remains to be seen whether the sharing economy will make this quality more commonplace and exactly how it will manifest. Every platform I speak with struggles with how to manage the balance between the gift and market dimensions of its service.
Answering these questions requires new theory, including a model of choice that captures the interweaving of the community and commercial sides of sharing, of economic and social objectives, of the gift and the market. While researchers have made promising advances in behavioral economics that aim to model "prosocial behavior," their studies typically assume a trade-off between individual economic payoffs and social motives, cast the prosocial behavior somewhat narrowly as being driven by, for example, altruism, an aversion toward inequality, or the norm of reciprocity, and often rely on stylized games in laboratory settings to test theory. The marketing community has an immense opportunity to create a more integrative approach to model consumer choice and consumer culture. Such an approach might ( 1) incorporate preferences in which the desire for connection is a fundamental part of the underlying theoretical conception of what determines the ranking of different available consumption alternatives, rather than being an add-on or exception to maximizing individual consumption value; ( 2) capture the possibility that the desire for connection and the value derived from relationship formation may complement the consumption value of a product; and ( 3) use the complex interplay between individual and social objectives observed in the sharing economy as a testbed to validate new theory via large-scale field experiments (e.g., [ 2]).
The sharing economy relies extensively on robust and complex digital systems for facilitating trust. A typical digital trust system provided by a platform may include peer feedback through a reputation system that allows customers and providers to learn from one another's experiences, as well as varied digital badges of authenticity and reliability like a verified government ID, a verified phone number, a link to a social media account, or a background check performed by a third party like Checkr (e.g., [ 4]). Indeed, while online reputation systems have been prevalent for over two decades since the emergence of eBay in the 1990s, the latter set of badges (which expand trust systems by digitizing summaries of physical-world existence, social capital, and activity) have been central to the growth of the sharing economy, allowing individuals to engage in higher-risk and higher-stakes peer-to-peer exchange, such as handing over the keys of one's apartment to a stranger or being driven to another city in a stranger's car.
[ 1], p. 17) discuss the importance of understanding whether digital trust systems can be an effective substitute for other regulatory mechanisms. More salient, however, for the marketing research community is understanding the relative importance of these digital trust systems and platformbrand in creating consumer trust. Put differently, when yet another new sharing economy consumer makes a leap of trust and engages in a platform-based transaction with a stranger, this expansion of trust could come from a wide variety of overlapping sources, summarized in Table 1, and their relative importance over time is of central importance to the future of consumer trust.
Graph
Table 1. Seven Sources of Peer-to-Peer Trust in the Sharing Economy.
| Trust Cue | Explanation |
|---|
| Nondigital word of mouth | Confidence based on positive recommendations from trusted friends, family, and colleagues. |
| Platform brand | Confidence gained because the platform's brand effectively communicates the promises of safe and high-quality service. |
| Digital trust systems | Confidence built by digital information about the person's authenticity, intent and capabilities, often based on the (sometimes verified) experiences of others, but also from identity verification and credentialing. |
| Individual digital confidence | Confidence stemming from a consumer becoming more comfortable basing decisions on information from reviews and other digital cues independent of any change in the quality of the digital information. |
| Individual learning by doing | Confidence from a consumer's positive experiences with similar services; for example, feeling more comfortable with Lyft because of good experiences with Uber. |
| Societal legitimization | Confidence gained by hearing people talking about the activity generically, often with references that use the brand name as a verb ("Airbnb-ing during vacation"), leading to a feeling the activity is mainstream. |
| Government and regulation | Confidence that government institutions and regulatory bodies would not allow untrustworthy service providers to persist and that if something goes wrong, these institutions provide robust recourse. |
Granted, these sources are not completely orthogonal. For example, consistently positive consumer experiences attributable to a good digital trust system can enhance the platform's brand over and above other brand-building investments. Stronger brands can contribute to more rapid societal legitimization. While each of these factors (and perhaps others) may continue to determine peer-to-peer consumer trust, unpacking the relative contributions of three of these factors—platform brand, digital trust systems, and individual digital confidence—and assessing the evolution of the importance of each is central to effective brand management and how brand value is assessed in the future. Importantly, underestimating the reliance of today's consumers on the platform brand could overstate the pace and future extent of commerce based on digital trust and lead to flawed assessments of the value of platform brand equity. Furthermore, misunderstanding the importance of individual digital confidence could result in overinvestments in building a platform brand or to an inefficient focus on making digital trust systems more sophisticated.
This understanding could also provide a glimpse into the future of the sharing economy and specifically whether decentralized trust systems built on blockchain technology are commercially viable. Could peer-to-peer sharing and exchange at population scale that does not involve branded platform intermediaries but relies purely on digital trust become a commercial reality? Or will it remain, much like 1990s visions of the equalizing and democratic internet, a utopian dream?
Although early accounts of the sharing economy painted a picture of sweeping disruption on the horizon, it is clear that the pace of global economic change engendered by the sharing economy has been gradual rather than radical. Nevertheless, the rise of crowd-based capitalism ([ 6]) will undoubtedly transform the world of business over time. Early in this transition, marketing challenges have emerged with the potential to reshape many facets of consumer and firm behavior. The economic and cultural determinants of value from ownership are in flux, the quest for connection is interwoven into everyday economic activities, and the right meld of brand-based and digital trust continues to evolve. These changes frame intriguing new marketing questions. Independent of the pace of growth of the sharing economy, a research agenda built around illuminating them will be of profound and lasting value.
Footnotes 1 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
2 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
References EckhardtGiana M.HoustonMark B.JiangBaojunLambertonCaitRindfleischAricZervasGeorgios (2019), "Marketing in the Sharing Economy," Journal of Marketing, 83 (5), 5–27.
FillippasApostolosJagabathulaSrikanthSundararajanArun (2019), "Managing Market Mechanism Transitions: A Randomized Trial of Decentralized Pricing Versus Platform Control," in Proceedings of the 2019 ACM Conference on Economics and Computation. New York: Association for Computing Machinery, 10.1145/3328526.3329654.
3 GraulAntjeSundararajanArun (2019), "How Airbnb Changes the 'Meaning' of Home," working paper, New York University.
4 MazzellaFrédéricSundararajanArunD'EspousVerena ButtMöhlmannMareike (2016), "How Digital Trust Powers the Sharing Economy," IESE Insight, 30, 24–31.
5 McCrackenGrant (1986), "Culture and Consumption: A Theoretical Account of the Structure and Movement of the Cultural Meaning of Consumer Goods," Journal of Consumer Research, 13 (1), 71–84.
6 SundararajanArun (2016), The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism. Boston: MIT Press.
~~~~~~~~
By Arun Sundararajan
Reported by Author
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Record: 37- Comparative Price and the Design of Effective Product Communications. By: Allard, Thomas; Griffin, Dale. Journal of Marketing. Sep2017, Vol. 81 Issue 5, p16-29. 14p. 1 Diagram, 1 Chart, 3 Graphs. DOI: 10.1509/jm.16.0018.
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- Business Source Complete
Comparative Price and the Design of Effective Product Communications
The authors propose a model relating a product’s comparative price to the construal level of its associated communications and show how perceived expensiveness shapes consumers’ response to the wording of marketing communications. A series of six studies shows that for both absolute low- and high-cost product categories, comparatively expensive (inexpensive) products are preferred when accompanied by high-construal (low-construal) messages, due to the conceptual fluency of the “match” between price-induced psychological distance and construal level. The model provides novel implications for designing effective marketing communications: comparatively expensive versions of objectively low-priced products (e.g., an expensive chocolate truffle) are best promoted through more abstract slogans, whereas comparatively affordable versions of objectively high-priced products (e.g., an inexpensive diamond pendant) are best promoted using more concrete slogans. By emphasizing the link between comparative price and the matching level of construal, the authors contribute to a richer view of the interplay between price and product communication in marketing.
Online Supplement: http://dx.doi.org/10.1509/jm.16.0018
How do product prices and marketing communications interact in shaping consumer behavior? For example, does it matter for consumer responses whether the benefits of a comparatively inexpensive soap are described in a concrete way (e.g., “Softer, smoother skin”) or an abstract way (e.g., “Give your skin some love”)? And does it matter whether the benefits of a comparatively expensive laptop computer are described in a concrete way (e.g., “4 million pixels, under 3.6 pounds”) or an abstract way (e.g., “A whole new vision for the notebook”; see Web Appendix A)? The current research examines how a product’s comparative expensiveness—the focal product’s price relative to the category reference price—influences a consumer’s response to the concreteness or abstractness of a product-related communication.
We propose that the extent to which prices are perceived to be high or low within a reference context affects consumers’ mindsets such that comparatively high prices lead consumers to adopt psychologically distant mental mindsets that are compatible with a high level of construal, a psychological representation characterized by a focus on abstract, goal-related, and desirability-related concerns (Trope and Liberman 2003). We explain this comparative price effect as a conceptual fit process such that matching the price-induced mindset (psychological distance: far vs. near) and the construal level of product communication (abstract vs. concrete descriptors) yields positive attitudes and purchase intentions because of the greater fluency or ease with which such mindset-matching communications are processed. Importantly, we show that this comparative price “matching” effect holds across a variety of absolute price levels.
With this research, we make two main contributions to the understanding of consumer purchasing decisions. First, we extend our knowledge of the mechanisms underlying the effects of price cues on consumer decision making by illustrating how comparatively low or high prices (regardless of the absolute price level) can induce a shift in psychological distance (Trope, Liberman, and Wakslak 2007). We also contribute to the literature on price framing and value perception in consumer behavior (e.g., Aydinli, Bertini, and Lambrecht 2014; Hsee 1998; Khan and Dhar 2010) by highlighting the power of comparative price cues across wide variations in absolute price values.
Second, we add to our understanding of how the relation between product features and construal-level framing can influence persuasion (e.g., Kim, Rao, and Lee 2009; Lee, Keller, and Sternthal 2010; White, MacDonnell, and Dahl 2011; Yan and Sengupta 2011; Yang et al. 2011) by bringing to light a novel and managerially important type of construal-level congruence effect relying on comparative price. We show the heuristic nature of this effect by demonstrating the moderating role of both product category involvement (e.g., Zaichkowsky 1985) and cognitive effort (as assessed by need for cognition [NFC]; Cacioppo and Petty 1982; Cacioppo, Petty, and Feng Kao 1984). Consistent with a heuristic processing model, the price–construal congruence effect on consumer preferences is stronger among consumers with either situational reasons (i.e., low category involvement) or dispositional tendencies (i.e., low NFC) for low-effort processing of product information.
Notably, our findings provide managers with clear recommendations previously unidentified in the literature: comparatively affordable versions of expensive products, such as diamonds, will generally be more favorably evaluated when paired with low-construal advertising slogans focusing on the product’s concrete features (e.g., the “four ‘C’s” of diamond quality) rather than high-construal or abstract slogans (e.g., the symbolic values associated with the product category). In contrast, comparatively expensive versions of affordable products, such as energy drinks, will be evaluated more favorably when paired with high-construal advertising slogans focusing on the product’s abstract benefits and associated goals (e.g., the gain in productivity it provides) rather than low-construal slogans focusing on concrete features (e.g., the active ingredients). Importantly, because these novel effects rely on perceptions of expensiveness, managers can position the same product as either expensive or inexpensive, keeping the absolute price constant, by changing the context in which the focal product is being compared. Given the ubiquitous and complex nature of price inferences in consumption, this research offers important implications for the practice of marketing communication. We next turn to our theoretical framework.
Theoretical Framework
A rich literature in consumer behavior attests that price, like brand name or country of origin, acts as a nonphysical product cue that consumers frequently use to make inferences about products (Biswas et al. 2002; Cordell 1991; Jacoby and Olson 1977; Zeithaml 1988). More recently, marketing research has examined the role of price cues on consumers’ mental representation of products (e.g., Bornemann and Homburg 2011; Hansen, Kutzner, and Wa¨nke 2013; Hansen and Wa¨nke 2011). We build on these prior demonstrations linking price-related cues to psychological distance (Liberman and Trope 1998) to propose that comparative price can influence consumers’ representations of product-related information by inducing a shift in consumers’ mindsets—a temporary cognitive orientation that directs the analysis and interpretation of stimuli (Gollwitzer 1990)—leading to variations in the feeling of fluency associated with the processing of marketing communications.
Psychological Distance and Mental Representations
Construal-level theory (CLT; Trope et al. 2007) posits that objects, such as consumer products, are mentally represented at different levels of concreteness or practical detail depending on their psychological distance from the perceiver (e.g., temporal, geographical, social distances; Trope and Liberman 2003). According to CLT, greater psychological distance induces a high-level construal of a product, characterized by the use of abstract, core, or desirability-related type of descriptors, whereas closer psychological distance induces a low-level construal of a product, characterized by more concrete, peripheral, or feasibility-related features.
Several inquiries in the consumer domain have specifically focused on the potency of marketing cues to trigger the adoption of more abstract or concrete representations, and on how the level of the representation influences consumer judgments. For instance, the presence of a product image causes the adoption of a low-level, concrete representation (Meyvis, Goldsmith, and Dhar 2012). The adoption of concrete versus abstract representations also follows exposure to attribute- versus benefit-based product assortments (Lamberton and Diehl 2013) and gainversus loss-framed promotional messages (White et al. 2011).
Price-Related Cues, Psychological Distance, and Construal Level
The current research focuses on the impact of comparative price on perceived psychological distance and its downstream effects on the processing of high-construal versus low-construal product communications and the subsequent effect of this processing on consumer attitudes, intentions, and purchasing behavior. The conceptual model guiding this research is presented in Figure 1, which also illustrates the focus of each formal hypothesis. Research examining the role of psychological distance in consumer settings has found that price (vs. features) affects consumers’ product-quality inferences more when buying for the self versus for others (social distance; Yan and Sengupta 2011) and that both temporal and social distance lead consumers to focus more on price–gain (quality) inferences than price–cost (sacrifice) inferences (Bornemann and Homburg 2011). Lee and Zhao (2014) demonstrate that the mere inclusion of price cues can lead consumers to focus on the desirability of product features (i.e., functionality) for short-term purchase decisions.
Most relevant to the present inquiry, Hansen and Wa¨nke (2011) observe that both consumers and advertisers use more abstract language when describing luxury products than when describing ordinary goods and demonstrate that consumers mentally represent luxury goods more abstractly than ordinary goods. For example, they find that five-star hotels are typically described using more abstract language than are hostels. They explain this association by pointing out that the purchase of luxury products is “exclusive, limited, and often merely hypothetical” (p. 798) and therefore considering such a purchase leads to a perception of greater psychological distance to the object of desire. Research by the same authors finds that reminders of large amounts of money (e.g., the words “wealth,” “expensive,” and “rich” or pictures of bank notes; vs. nonmonetary reminders) are associated with high-level representations in consumers (Hansen et al. 2013). Taken together, these findings imply that luxury products are more naturally described by high-construal communications focusing on desirability and other broad qualities. (An overview of related prior literature and how the present research differs are found in Table 1; for a more detailed table, see Web Appendix B.)
We believe it is premature to translate the observed link between luxury and high-level construal into managerial recommendations, for at least two reasons. First, we suggest, the relevant match is between the comparative price, or perceived expensiveness, of the product and the construal level of the product communication, rather than the absolute price. That is, several models of perception applied to price research indicate that the classification of prices as “high” or “low” is intrinsically subjective because inferences about prices arise from the evaluation of a price within a comparative context (e.g., Monroe, Della Bitta, and Downey 1977; Slonim and Garbarino 1999). The notion that consumers’ reactions to price information depend not on the absolute price of a product but on its comparison to the price of alternatives is described as the “psychophysics-of-price heuristic” by Grewal and Marmorstein (1994). Building on the Weber–Fechner law of psychophysics, which states that people respond to changes in a stimulus according the magnitude of the change relative to the total magnitude of the stimulus, Grewal and Marmorstein (1994) demonstrate that consumers’ willingness to spend time shopping to save a fixed amount of money was driven by the relative amount saved (the ratio of the amount saved to the product price).
Second, thus far there has been no direct demonstration of the beneficial effect of price–construal matching on consumer attitudes or intentions toward the advertisement or the product. Up to this point, this is merely a hypothesis. As Hansen and W¨anke (2011) note: “These considerations suggest that a lacking fit between advertisement language and level of luxury may be disadvantageous” (p. 795). Further complicating the determination of practical implications, the same authors suggest in Hansen et al. (2013) that a specific price cue might have the opposite effect of a general money prime and lead to low-level, concrete representation.
Thus, we expect that consumers’ construal-related reactions to pricewill be driven by the comparison of a focal pricewith its alternatives and, thus, will be responsive to the comparative level of expensiveness (as illustrated in the first stage of our conceptual model). In other words, due to the intrinsically comparative nature of the perception of high versus low prices, the influence of price on psychological distance should depend on the extent to which an item is perceived to be expensive or affordable within a specific context of comparison, such as the price of the other products against which the item is being evaluated. Supporting this subjective price account, Hsee (1998) shows that the gift of a $45 scarf appears more generous than the gift of a $55 coat because the former is high priced for a scarf, whereas the latter is moderately priced for a coat (for similar reasoning, see also Monroe 1973).
Although previous research has analyzed the relationship between absolute monetary price level and construal level, we suggest that managers would do well by matching the construal level of their marketing communication to the comparative price of their product (also illustrated in the first stage of our conceptual model). Importantly, because we predict an effect of perceived expensiveness versus a comparison standard even when the focal price is held constant, we can rule out the driving role of a budget constraint as postulated in the case of luxury products (e.g., Hansen andW¨anke 2011). This determining role of comparison prices provides practical implications formarketing effectiveness because the perceived expensiveness of a product at any given price can be varied through a shift in the assortment of products against which the product is evaluated. Specifically, we predict:
H1: Matching the comparative price level with the construal level of marketing messages (i.e., comparatively low prices with concrete, or low-level, construal; comparatively high prices with abstract, or high-level, construal) positively influences consumer preferences compared with situations of mismatch.
A Fluency-Based Account: The Fit Between Comparative Price and Construal
TABLE 1
Contribution Table
| Source | Focus of Manipulation | Process | Dependent and Mediating Variables |
|---|
| Bornemann and Homburg (2011) | Psychological distance, temporal distance, social distance | Psychological distance leads to greater focus on desirability over feasibility. | Psychological distance leads to higher evaluations for high-priced versus low-priced products. |
| Hansen and Wänke (2011) | Construal level (high = luxury goods, low = ordinary necessities) | Luxury goods are rare and scarce and thus psychologically distant and represented abstractly. | Luxury goods are represented more abstractly than ordinary goods. Also, more abstract language leads to higher perceptions of luxury in products. |
| Hansen, Kutzner, and Wänke (2013) | Construal level (high = money primes, low = money-unrelated primes) | Reminders about substantial amounts of money lead to higher construal level. | Money primes lead to higher evaluations for central versus peripheral product features and higher quality ratings for highquality brands. |
| Lee and Zhao (2014) | Presence vs. absence of price information | Price information increases value-seeking tendencies and beliefs that greater functionality equals greater value in products. | Price information reduces the inconsistent preferences over time between desirability (distant future) and feasibility (near future). |
| Yan and Sengupta (2011) | Psychological distance, social distance, temporal distance; “how vs. why” prime | Price is a more abstract cue than product features and receives more weight when construal level is high. | Psychological distance increases the influence of price versus feature-specific attributes (e.g., physical attractiveness of the product) for quality inferences. |
| This study | Match between psychological distance mindset (created by comparative product price) and construal level of advertisement slogan | High (low) price relative to a comparison context leads to a high (low) psychological distance mindset that fluently processes abstract (concrete) advertising messages, leading to enhanced product attitudes when price and construal level match. | Match (vs. nonmatch) of price and construal level of advertisements leads to increased product sales (Study 1a), choice (Study 1b), evaluation (Study 2), fit (Studies 3 and 4), and purchase intentions (Studies 4 and 5). |
Notes: A detailed version of this contribution table is available in Web Appendix B.
TABLE:
Considerable research attests that product evaluation can be influenced by the degree of fluency (perceptual or conceptual) experienced by consumers when they evaluate products (e.g., Novemsky et al. 2007; Tsai and McGill 2011). Informational cues can be processed in a more or less fluent manner depending on their conceptual congruence with the context (e.g., the activated goal or concept) in which they are presented (Alter and Oppenheimer 2009). Such matching effects have been shown to influence brand evaluation (Labroo and Lee 2006), product choices (Hong and Lee 2008), and persuasion and consumer evaluations (Kim and John 2008; Kim et al. 2009; White et al. 2011).
We predict that a positive experience of fluency from conceptual fit is created when the comparative price of the product is matched to the construal level of a marketing communication (as illustrated in the second stage of our conceptual model). The consumer then attributes such a fit experience to the quality of the marketing message, and it thus translates into a more positive evaluation of that message and the associated product itself (e.g., Avnet, Laufer, and Higgins 2012; Bornstein and D’Agostino 1994; Pham 1998). For example, we anticipate that marketing communications focusing on more concrete descriptors (i.e., function related) will be particularly well evaluated when paired with a comparatively affordable price, whereas communication focusing on more abstract descriptors (i.e., desirability related) will be particularly well evaluated when matched with a comparatively expensive price, due to the attribution of greater perceived fit between the message and the product. Thus, we predict:
H2: Perceived fit mediates the effect of matching the comparative price level with the construal level of the message on consumer preferences.
Our model posits that consumers attribute their experience of conceptual fit to their preference for the associated product. Such evaluative inferences based on attributions of the experience of fluency imply a heuristic type of processing (Vessey 1991). We seek to test this process account by showing that the effect of the price–construal match on customers’ evaluations is reduced for those whose level of motivation or processing style predisposes them to more systematic rather than heuristic processing (illustrated in the third stage of our conceptual model).
Previous research has demonstrated that information is systematically processed under high motivation but heuristically processed under low motivation (e.g., Chaiken 1980; Darke et al. 1998). Therefore, we propose that high product category involvement will reduce or eliminate the effect of experienced fluency associated with a price–construal fit. Furthermore, prior research on consumer judgments has demonstrated that high-NFC individuals, who are characterized by both high cognitive effort and high interest in cognitive processing, are less susceptible to heuristic processing (Meyers-Levy and Tybout 1989). In the context of price–construal matching, we expect that high-NFC individuals will be less likely to attribute their experience of conceptual fluency to their liking for the message or the product. Overall, we expect that positive consumer preferences arising from the matching effect will be stronger for consumers whose situational or dispositional factors—respectively, low category involvement and low NFC—make them more likely to perform heuristic processing on the marketing communication. Thus, we predict:
H3a: Category involvement moderates the effect of a match (vs. mismatch) between comparative price and construal level on consumer preferences such that the matching effect is stronger for those with lower levels of involvement and weaker for those with higher levels of involvement.
H3b: NFC moderates the effect of a match (vs. mismatch) between comparative price and construal level on consumer preferences such that the matching effect is stronger for those lower in NFC and weaker for those higher in NFC.
Overview of the Studies
This research examines whether matching the construal level of marketing communication to the comparative expensiveness of products leads to more positive consumer evaluations. We test this conceptual model in a series of six studies, which are laid out in Figure 1. Testing the first stage of our conceptual model, Study 1a demonstrates the basic price–construal match effect on purchase behavior in a field setting using real choices and in an absolute low-price context. Study 1b replicates these findings in an absolute high-price context. Study 2 further tests the hypothesis that the effect is driven by comparative expensiveness, that is, the product price relative to a comparison level. Testing the second stage of our conceptual model, Study 3 provides a priming test for this matching effect by showing that activating the concept of expensiveness (or inexpensiveness) is sufficient to influence the perceived fit of unrelated marketing communication. Testing both the second and third stages of our conceptual model, Study 4 shows that the match between the psychological distance mindset induced by comparative price and the construal level of a marketing communication affects the experience of conceptual fluency and, thus, consumption choices; it also shows that this heuristic effect is strongest for consumers with low levels of category involvement. Finally, Study 5 generalizes the heuristic nature of this process linking price, psychological distance, and consumer judgment by showing that the process is strongest among participants low in NFC.
Study 1a
Study 1a offers an initial test of the hypothesis that comparative expensiveness—not absolute monetary cost—and the construal level of product descriptions interact to predict consumer choice in a meaningful choice context (see the first stage of the conceptual model in Figure 1). Using a field experiment methodology, we examine consumers’ actual choices in a naturalistic setting by organizing a pop-up store on campus selling chocolates by the piece. Our core prediction was that consumers are more likely to choose a comparatively inexpensive chocolate when it is promoted by a marketing communication expressed in low-construal terms (e.g., a concrete description of the specific ingredients of the chocolate) rather than in high-construal terms (e.g., an abstract description of how the chocolate makes one feel). Conversely, we also expected that consumers are more likely to choose a comparatively expensive chocolate when it is promoted by a marketing communication expressed in a high- rather than low-construal manner. Importantly, we show that this effect occurs even when both the monetary price and the actual chocolates are held constant, and all variation is in the comparison context that makes the focal product appear comparatively inexpensive versus comparatively expensive (for a similar approach, see Jacoby and Olson 1977). Study 1 thus tests our core argument that the positive effect created by matching a product’s price with the construal level of the associated marketing communication is driven by comparative expensiveness, not by budget constraints that make high-priced products seem “out of reach” to consumers (Hansen et al. 2013; Hansen and Wa¨nke 2011).
Method
Participants and design. Participants were 126 on-campus shoppers who took part in a two-way mixed design, with a two-level between-participants factor (comparative price: inexpensive vs. expensive) and a two-level within-participant factor (construal level of the product description: low vs. high). The dependent variable was the choice between two chocolates, one paired with a low-construal advertising slogan and one paired with a high-construal advertising slogan.
Procedure. For three days, we operated a pop-up chocolate store on the main plaza on campus, with all proceeds donated to a local food bank. At any given time, two different chocolate options were featured and were available for purchase at the store, and one comparison chocolate was also presented but not featured. In both comparative price conditions, the two chocolates available to purchase were $1 milk chocolates that had been custom-made for this experiment. They were made from the same 38% milk chocolate, had the same weight, and were packaged in identical glassine envelopes, but one option was coin-shaped and the other was waffle-shaped (see photograph in Web Appendix C). The two focal chocolate options were presented side by side on a serving plate, with one described on an accompanying poster by a low-construal description focused on the chocolate’s measurable content (“Rich milk chocolate”) and the other one by a high-construal description focused on the abstract, symbolic experience of eating the chocolate (“Decadent dream”; e.g., Trope and Liberman 2003; for manipulation checks for all construal-level manipulations used in this and subsequent studies, see Web Appendix D).
We manipulated the perceived expensiveness of the two $1 chocolates by varying the third chocolate option present at the store. In the low-price condition, the $1 chocolates were presented next to $10 slabs of artisanal dark chocolate, which made the $1 options appear comparatively inexpensive. In the high-price condition, the $1 chocolates were presented next to a set of $.25 Tootsie Rolls, which made the $1 options appear comparatively expensive. Following a script, a research assistant/salesperson gave the following promotional message to consumers as they arrived at the storefront: “Today, we are featuring our inexpensive [premium] $1-a-piece milk chocolate line. Our first inexpensive [premium] chocolate is described as ‘Rich Milk Chocolate’ and the second one is described as ‘Decadent Dream.’ Which one do you want?” The price conditions, the match of advertising slogan and chocolate shape, and the presentation orders of the chocolates were randomly counterbalanced on an hourly basis. Participants who inquired about the difference between the two $1 chocolate options were told that the two options were both made of milk chocolate but were marketed differently. A total of four participants purchased the comparison option over the featured $1 chocolates (one for the Tootsie Roll and three for the $10 bar); those observations were removed from our analysis.
Results
Product choice. In the comparatively expensive condition, a majority preferred the chocolate promoted with the high-construal advertising slogan (70% [49/70]), whereas in the comparatively inexpensive condition, a majority of consumers preferred the $1 chocolate promoted with a lowconstrual advertising slogan (56% [29/52]; c2 = 8.19, p < .01; Cohen’s d = .54), in support of our main hypothesis. Supplemental analysis found these proportions to be significantly higher (one-tailed test) in the comparatively expensive conditions (t(121) = 4.51, p < .001) and marginally higher in the comparatively inexpensive conditions (t(121) = 1.35, p = .09) compared with a purely random choice (50%). Effects involving the shape of the chocolates were not significant (F < 1).
Discussion
In a field setting using real purchases and real chocolate consumption, the results of Study 1a provide support for the prediction that consumer preference is enhanced by matching the construal level of a product communication with the comparative expensiveness of that product. These results also provide initial evidence that such a matching effect is not restricted to luxury goods but occurs even with low-priced goods and can be induced by manipulations of the comparison price with the focal price held constant. In the next study, we provide a conceptual replication for this effect at a luxury, or absolute high-price, level.
Study 1b
Whereas Study 1a tests our core hypothesis at an absolute low-price level (i.e., $1 chocolates), Study 1b tests the robustness of this effect at an absolute high-price level (>$1,000). Specifically, Study 1b tests our key prediction about the match between perceived expensiveness and the level of construal of a product description, using a classic high-priced luxury product: diamonds. This study demonstrates that even for an absolute high-priced product, variations in the perceived expensiveness of that product can shift consumer preferences toward a product or brand described at a high or low level of construal in the relevant marketing communication. As in the previous study, we keep the absolute monetary price of the focal product constant across conditions to rule out budget constraints as an alternative explanation.
Method
Participants and design. Participants were 280 community members who took part in a two-way mixed design, with a two-level between-participants factor (price relative to comparison: low vs. high) and a two-level within-participant factor (construal level of the description: low vs. high). The dependent variable was the choice between two diamonds, one paired with a low-construal slogan and the other paired with a high-construal slogan.
Procedure. We positioned two research assistants by the entrance of an on-campus museum, a major international tourist attraction. Research assistants offered museumgoers a chocolate in exchange for participating in a one-question marketing research survey. Participants were given a written survey questionnaire and were instructed to imagine that they were looking to purchase a diamond pendant as a gift for someone close to them. Participants were presented with a picture of a princess-cut white diamond pendant and told that this was the model of diamond pendant they were interested in purchasing (see Web Appendix C). Depending on the price condition, that model of diamond pendant was described as “the most inexpensive [expensive] model available in the store you visited. It is priced at $1,299.” Price conditions were randomly counterbalanced across six 2.5-hour periods over three collection days, ranging approximately from 11 A.M. to 4 P.M. Participants were told, “This model is available from two different brands, each with the same quality features, and each is associated with a different brand slogan.” Presented in a randomized order, one slogan featured a low-construal description emphasizing the objective characteristics of the diamond (“Flawless quality and pure color”), and the other featured a high-construal description emphasizing the symbolic meaning of the diamond (“Make it unforgettable”) Using the two brand slogans as reference, participants were asked to choose their preferred diamond pendant. After making their choice, participants received a piece of chocolate as a token of gratitude for their participation.
Results
Product choice. In the comparatively expensive condition, a majority of consumers preferred the $1,299 diamond promoted with the high-level construal slogan (57% [92/160]). In the comparatively inexpensive condition, a majority of consumers preferred the $1,299 diamond promoted with a low-construal slogan (55% [66/120]; c2 = 4.29, p < .05; Cohen’s d = .25), in support of our main hypothesis. Results from supplemental analysis identified these proportions as significant in both the comparatively expensive (t(279) = 2.51, p < .01) and comparatively inexpensive (t(279) = 1.67, p < .05) conditions (one-tailed tests).
Discussion
Using a community sample and a nonlaboratory setting, Study 1b provides a conceptual replication of our field study at an absolute high-price level. This study provides additional support for the notion that perceived product expensiveness, not the absolute amount of money charged, drives the price–construal matching effect, even though the absolute price ratio across studies is larger than 1,000 : 1. In our subsequent studies, we utilize more controlled experimental settings to investigate the mechanism and boundary conditions of this effect.
Study 2
Study 2 is designed to further test our hypothesis that expensiveness relative to a comparison standard, not absolute price, drives the matching effect on product communication construal level. To do this, we replicate our effect of interest using both a manipulation of the price of a focal product and a manipulation of the price of its comparison products.
Method
Participants and design. The experiment is a 2 (price: low vs. high) · 2 (source of variation: target price vs.comparison price) · 2 (construal level of advertisement slogan: low vs. high) between-participants factorial design. We recruited 325 participants through Amazon Mechanical Turk (MTurk) to take part in this experiment (39% female; Mage = 32.2 years). The dependent variable of interest was product evaluation.
Procedure. Participants were first instructed to imagine that they wanted to purchase a snack and were presented with a description of an energy bar (for stimuli, see Web Appendix C). They were then randomly assigned to either a target-price variation condition or a comparison-price variation condition, crossed with either a comparatively expensive or a comparatively inexpensive price condition. In the target-price variation condition, participants were told that most of the energy bars available were selling for $1.99 but that the one they were considering was selling for either less than average, at $1.49 (low-price condition), or more than average, at $2.49 (high-price condition). In the comparison-price variation condition, participants were told that most of the energy bars available were selling for $1.49, less than the $1.99 bar they were considering (high-price condition) or $2.49, more than the $1.99 bar they were considering (low-price condition). Crossed with both of these manipulations, participants saw an advertisement for an energy bar that used either the advertising slogan “A balanced source of carbs and proteins” (low-construal condition; focusing on concrete product description) or “For stable and long-lasting endurance” (high-construal condition; focusing on abstract product benefit or goal). After viewing the advertisement slogan, participants evaluated the energy bar on three bipolar items on a ten-point scale: “This bar looks unattractive/attractive,” “…tasteless/tasty,” and “…unsatisfying/satisfying” (a = .90; food evaluation measure adapted from Godin et al. [2010]).
Results and Discussion
A 2 · 2 · 2 factorial analysis of variance (ANOVA) on the product evaluation index revealed a significant two-way interaction between comparative price and construal level (F(1, 317) = 9.65, p < .01; Cohen’s d = .35), in support of our main hypothesis. Importantly, this two-way interaction was not moderated by the source of the price variation (three-way interaction, F < 1; for details, see Figure 2), suggesting no difference in the magnitude of the effect between the conditions in which the target price varied (Figure 2, Panel A) and the conditions in which the target price was kept constant and the comparison price varied (Figure 2, Panel B). Simple effects revealed that regardless of the source of price variation, the comparatively inexpensive energy bar was evaluated more positively when described with a low-construal slogan (M = 5.02, SD = 1.44) than with a high-construal slogan (M = 4.55, SD = 1.53; F(1, 317) = 4.36, p < .05). This effect was reversed for the comparatively expensive bar, which was evaluated more positively when described with a high-construal slogan (M = 4.83, SD = 1.21) than with a low-construal slogan (M = 4.33, SD = 1.41; F(1, 317) = 5.31, p < .05).
Results from Study 2 provide further support for our core price–construal matching effect postulating that a product is more positively evaluated when its comparative price matches the construal level of its product communication. They also provide further evidence that this effect is driven by the contrast between the product price and the reference price—not the absolute price of the product itself—by showing again that the matching effect also occurs when the target price is kept constant and only the reference price changes.
Our results differ in two important ways from previous findings linking luxury goods (vs. ordinary necessities) to greater psychological distance. First, our results do not reflect a main effect of price—that is, the high price ($2.49) does not lead to more positive product evaluations than the low price ($1.49)—suggesting that the high price does not act as a proxy for high quality, leading to overall more persuasion by higher-level attributes. Second, as we have previously argued, because we observe such a matching effect between price and construal level on consumer responses while keeping the focal price constant and changing the reference price, our results are also inconsistent with an explanation relying on a shift in perceived product accessibility induced by a budget constraint (e.g., Hansen and Wa¨nke 2011). Study 3 provides an indirect, and thus more conservative, test for our matching hypothesis.
Study 3
Study 3 tests our conceptual framework relying on fluency (see the second stage of our conceptual model in Figure 1) by showing how the activation of the concept of comparatively low (high) price in one context leads consumers to subsequently perceive advertising slogans framed at a matching low (high) construal level to “fit” products better, even though the subsequent product and its communication has no connection to the original comparative price. That is, Study 3 provides an indirect and thus more conservative test for our matching effect by showing that initial exposure to a comparatively high or low price creates a cognitive set, or mindset, that shapes reactions to a subsequent (and unrelated) product communication using either a high or low construal-level framing, and that this matching effect results in enhanced fit or fluency.
Method
Participants and design. Study 3 used a 2 (price: low vs. high) · 2 (construal level of advertising slogan: low vs. high) between-participants design. Participants were 195 people from a university subject pool (67% female; Mage = 20.3). The dependent variable was the perceived fit of the advertising slogan to the product.
Procedure. The study consisted of two ostensibly unrelated tasks, one for introducing the comparatively (in)expensive price and the other measuring slogan and product evaluation. First, all participants were randomly assigned to either a low or a high comparison-price priming manipulation, in which they were presented with the same moderately luxurious car, an Acura TSX, with its manufacturer’s suggested retail price (MSRP) given as $34,050. In the comparatively inexpensive condition, the Acura TSX was presented as a target model and compared with five highly expensive cars (e.g., Bugatti Veyron, Ferrari 458 Spider, Lamborghini Gallardo; all MSRPs above $172,500). In the comparatively expensive condition, the Acura TSX was compared with five moderately inexpensive cars (e.g., Ford Fiesta, Hyundai Accent, Kia Rio; all MSRPs under $15,600; for details, see Web Appendix E). In both versions of the questionnaire, participants were asked to rate the expensiveness of the Acura TSX using two seven-point bipolar scales, anchored at “inexpensive” versus “expensive” and “lowpriced” versus “high-priced” (a = .85).
In the subsequent section of the questionnaire, participants were presented with an image of a gel ink pen (see Web Appendix C), accompanied by an advertising slogan: either “For smooth and easy writing” (low-construal level, focusing on concrete actions) or “For free-flowing ideas” (high-construal level, focusing on abstract outcomes). We measured perceived fluency by asking participants to evaluate the slogan’s fit with the product, rating the items “This slogan feels right for this product” and “This slogan fits this product very well” on a tenpoint scale (1 = “strongly disagree,” and 10 = “strongly agree”; alow-construal = .90, ahigh-construal = .89).
Results and Discussion
Expensiveness. Results revealed that participants rated the Acura TSX as more expensive in the condition where lower-priced cars predominated (creating a comparatively high price; M = 4.90, SD = 1.34) compared with the condition where extremely high-priced cars predominated (creating a comparatively low price; M = 2.75, SD = 1.40; t(193) = 10.89, p < .001). These results confirm the effectiveness of the price manipulation.
Perceived fluency. A factorial ANOVA indicated a significant interaction between the automobile price condition and construal level of the pen’s advertising slogan (F(1, 191) = 10.99, p < .001; Cohen’s d = .48). As predicted, participants judged the low-construal advertising slogan to fit the product better in the comparatively inexpensive condition (M = 7.27, SD = 1.91) than in the comparatively expensive condition (M = 6.25, SD = 1.82; F(1.191) = 5.33, p < .05). In contrast, the highconstrual advertising slogan was judged to fit the product better in the comparatively expensive condition (M = 6.81, SD = 1.87) than in the comparatively inexpensive condition (M = 5.82, SD = 2.70; F(1.191) = 5.67, p < .05).
By showing that activating the concept of low or high comparison price in one setting can influence the subsequent “fit” of an advertising slogan for another product in a different setting, the results of this study support the notion that (comparative) price of one product can serve to prime or activate a mindset that, in turn, makes a slogan for a different product seem to fit the product better when its construal level is a better match.
In our next study, we provide evidence for our mediational claim that the matching effect occurs because of a price-induced shift in the psychological distance mindset and that it relies on an experience of fluency when evaluating marketing communication. We also test for the moderating role of category involvement, a managerially important segmentation variable.
Study 4
Study 4 extends our previous findings by measuring the underlying process through which the match between the perceived expensiveness of a product and the construal level of marketing communication provides its benefit on consumer responses. First, as in Study 3, we measure the subjective experience of fit, or conceptual fluency, that we believe underlies the matching effect itself, and we measure its influence on attitude (e.g., Labroo and Lee 2006). Second, we generalize the perceived price manipulation from providing focal and reference prices to using a verbal descriptor for the focal price (expensive vs. inexpensive). Finally, as illustrated in the third stage of our conceptual model, we test the role of product category involvement as an important managerial moderator for the transfer of positive evaluation from experienced fluency or fit to product choice (Lee and Aaker 2004; Schwarz and Clore 1983). Specifically, although we predict that all consumers will, on average, experience more conceptual fluency when evaluating a marketing communication whose construal level matches the comparative expensiveness of the product, we expect that product attitudes will be influenced by this fluency transfer primarily among those with low and medium levels of category involvement (see Avnet et al. 2012). In contrast, those with high levels of product involvement are more likely to be influenced by product attributes rather than heuristic cues such as fluency (e.g., Petty, Cacioppo, and Schumann 1983).
Method
Participants and design. This experiment used a 2 (expensiveness: low vs. high) · 2 (construal level of slogan: low vs. high) · continuous (category involvement) between-participants design. We recruited 241 participants through MTurk (46% female, Mage = 37.2 years). The dependent variables of interest were purchase intentions for the product and the perceived fit of the advertising slogan for that product.
Procedure. Participants were presented with a picture of an electric toothbrush and were instructed to consider the purchase of such a toothbrush (see Web Appendix C). The electric toothbrush was selected because it is a moderately priced product (absolute price level) available in a wide price range with a very similar appearance throughout that range. Keeping the product’s visual representation constant, we presented the electric toothbrush without an explicit numerical price cue. Instead, the description designated the product as being either an “inexpensive” (low-expensiveness condition) or an “expensive” (high-expensiveness condition) electric toothbrush. As a manipulation check, participants rated on a subsequent screen the extent to which they perceived the electric toothbrush to be expensive, using the same items as in Study 3 (a = .94).
Participants were then presented with the product and advertising slogans for the electric toothbrush. In the low-construal condition, the advertising slogan read, “To gently clean your gums and teeth” (concrete product description), whereas in the high-construal condition, it read, “For optimal oral health” (abstract product benefit). We asked participants to report the perceived conceptual fluency, or feeling of “fit,” associated with the slogan using the same items as Study 3 (a = .92). On a separate page, participants rated their purchase intentions for the product, using three seven-point bipolar scales anchored at “unlikely” versus “likely,” “improbable” versus “probable” and “impossible” versus “possible” (a = .95; Chattopadhyay and Basu 1990; Sundar and Noseworthy 2016). In a separate section of the questionnaire, participants rated their involvement with the product category using the ten-item, seven-point Revised Personal Involvement Inventory (a = .91; Zaichkowsky 1994; e.g., “To me, electric toothbrushes are [important/unimportant]” [reverse-scored]).
Pretest: Psychological Distance
The expensiveness manipulation used in this study was pretested on a separate sample drawn from the same MTurk population to assess the extent to which participants’ mindsets—a cognitive inclination that guides the way consumers evaluate product information (see Gollwitzer 1990)—were characterized by psychologically close or distant perspectives. Participants were presented with 12 pairs of descriptors, one at a time, that encompassed the four major dimensions of psychological distance (physical, social, time, and certainty; for a related approach, see Bar-Anan, Liberman, and Trope 2006; for a methodological suggestion, see Hansen and Wa¨nke 2011, p. 794). For each pair, participants were asked to select “the word that best fits my frame of mind right now.” The pairs were “near”/“far,” “tomorrow”/“a year,” “friend”/“enemy,” “we”/“they,” “sure”/“unsure,” “certainly”/“possibly,” “real”/“abstract,” “close”/“distant,” “self”/“others,” “likely”/ “unlikely,” “here”/“there,” and “now”/“future” (coded as 0 = psychologically close, and 1 = psychologically distant). Responses to the 12 items were averaged to create an index (KR-20 = .86). Psychological distance scores were higher for participants exposed to the expensive product label (M = .40, SD = .30) than for those exposed to the inexpensive product label (M = .29, SD = .26; t(135) = 2.45, p < .05), suggesting that a more psychologically distant mindset had been triggered by the expensiveness manipulation.
Results and Discussion
Manipulation check. As expected, participants perceived the product as being significantly more expensive in the expensive condition (M = 6.43, SD = .85) than the inexpensive condition (M = 1.71, SD = 1.14; t(154) = 29.50, p < .001).
Moderation by involvement. Using a regression approach to the combination of dichotomous and continuous independent variables, we regressed the product’s purchase intentions score on the construal level of the slogan, expensiveness condition, and category involvement (centered), as well as their interactions, using a 5,000-sample bootstrap method (Model 3 from Hayes 2013; Zhao, Lynch, and Chen 2010). Results revealed a significant three-way interaction between expensiveness, construal level, and involvement (B = -.71, SE = .30; t(233) = 2.38, p < .05; 95% confidence interval [CI95] = [-1.30, -.12]). Results also showed the predicted significant two-way interaction between expensiveness and construal level, in support of our main hypothesis (B = 1.26, SE = .33; t(233) = 3.85, p < .001; CI95 = [.61, 1.89]; Cohen’s d = .75), a significant two-way interaction between construal level and involvement (B = .68, SE = .22; t(233) = 3.05, p < .01; CI95 = [.24, 1.11]), and a main effect of expensiveness condition (B = -.90, SE = .23; t(233) = 3.93, p < .001; CI95 = [-1.35, -.44]). Next, we used the Johnson–Neyman technique to identify the range of involvement for which the interaction between expensiveness and construal level is significant. This analysis revealed that for participants who scored below 5.44 on the product involvement scale (.6 SD above the mean), the interaction between expensiveness and construal level on advertising slogan evaluation was significant.
To better illustrate this interaction, we report the results for participants who scored one standard deviation below and above the mean on involvement (for details, see Figure 3). For consumers low on product involvement (M-1 SD = 3.62), results support our model and replicate our prior results: purchase intentions for the inexpensive product were higher when the product was paired with the low-construal slogan (M = 5.26) than with the high-construal slogan (M = 4.55; t(233) = 1.97, p = .05; CI95 = [-1.40, .00]) whereas purchase intentions for the expensive product were higher when the product was paired with the high-construal slogan (M = 5.38) than with the lowconstrual slogan (M = 4.02; t(233) = 4.38; p < .001; CI95 = [.74, 1.96]). Consumers high in product involvement (M+1 SD = 5.86), however, did not show the matching effect. Instead, there was only a simple main effect of construal level of the slogan on purchase intentions, for both the inexpensive (B = .82, SE = .33; t(233) = 2.51, p = .01; CI95 = [.18, 1.46]) and expensive conditions (B = 1.27, SE = .33; t(233) = 3.83, p < .001; CI95 = [.62, 1.92]).
Mediation by fluency. Next, we tested whether perceived fluency mediated the effect of matching the product expensiveness with the construal level of advertising slogans on purchase intentions, while controlling for category involvement (Model 4 from Hayes 2013). Results showed a significant indirect effect through perceived fluency (B = .60, SE = .12, CI95 = [.38, .86]), consistent with the notion that matching the construal level of advertising slogan to the expensiveness of the product (a-path; B = 1.51, SE = .27; t(237) = 5.69, p < .001; CI95 = [.99, 2.03]) increases the perceived fluency of the advertising slogans (vs. nonmatch). In turn, perceptions of more fluent advertising slogans lead to higher purchase intentions (b-path; B = .40, SE = .03; t(237) = 12.09, p < .001; CI95 = [.33, .46]).
Moderated mediation. Next, we tested whether perceived fluency mediated the effect of matching the product expensiveness with the construal level of advertising slogans on purchase intentions and whether this mediation varied across product category involvement (Model 7 from Hayes 2013). Results showed a significant index of moderated mediation (B = -.37, SE = .09, CI95 = [-.56, -.21]), consistent with the notion that differences in perceived fluency between the match and mismatch between expensiveness and construal level explains purchase intentions for most participants (1 SD below the mean on involvement: B = 1.06, SE = .17, CI95 = [.75, 1.37]; at the mean on involvement: B = .65, SE = .13, CI95 = [.38, .91]), but not for those highly involved with the product category (1 SD above the mean on involvement: B = .24, SE = .18, CI95 = [-.11, .56]).
Overall, these results support the complete the hypothesized chain of processes illustrated in Figure 1: describing a product as comparatively expensive (inexpensive) influences evaluations of marketing communications using high (low) levels of construal by affecting the conceptual fluency experienced by consumers. Importantly, this managerially relevant effect influences consumption choices for all consumers except those with the highest levels of product category involvement, for whom we observed a preference for high-construal slogans. While we did not hypothesize this result, we conjecture that because the Revised Personal Involvement Inventory (Zaichkowsky 1994) contains items associated with enthusiasm toward the product (e.g., product is “exciting,” is “fascinating,” “means a lot”), it is likely that those who rated high on the scale were less concerned with lowconstrual considerations (e.g., feasibility, how, cons; see Trope and Liberman 2010) surrounding the product itself. In the next study, we replicate this affect with another moderator that correlates with processing tendencies but not product interest: NFC.
Study 5
Study 5 again uses a moderation approach to test the robustness of our account of the effect of a price–construal match on product evaluation. This time, we expect the effect of the price–construal match on product and advertisement evaluation to be attenuated for consumers high in NFC, who are less influenced by heuristic cues.
Participants were asked to evaluate an advertisement for an energy drink that was presented as inexpensive or expensive compared with similar energy drinks. We expect participants in general to provide more positive responses to the advertisement when there is a match between the price and the construal level of the advertisement (low price/low construal level or high price/high construal level) versus when there is a mismatch (low price/high construal level or high price/low construal level). However, we expect this matching effect on evaluation to be attenuated or eliminated for people high in NFC.
Method
Participants and design. The experiment is a 2 (price: low vs. high) · 2 (construal level of advertisement: low vs. high) · continuous (NFC) between-participants design. The dependent variable is purchase intention. We recruited 210 participants through MTurk to take part in this experiment (40% female; Mage = 34.8).
Procedure. Participants were randomly assigned to one of the four versions of the advertisement, each using the same energy drink visual stimulus (see Web Appendix C). The can of energy drink was priced at either $2.00 (low price) or $4.00 (high price), compared with a category average of $3.00, and the advertising slogan was either “Get your caffeine boost” (low construal; focusing on concrete product attribute) or “Be more productive” (high construal; focusing on abstract product benefit). Participants were instructed to imagine themselves looking to purchase this product in this price range. As a manipulation check, participants rated the extent to which they perceived the energy drink to be expensive, using the same items as in previous studies (a = .97). On a different page, participants were asked to rate their purchase intentions using the same scales as in Study 4 (a = .95). Participants then proceeded to respond to the 18-item NFC scale (1 = “completely false,” and 5 = “completely true”; a = .90; Cacioppo et al. 1984).
Results and Discussion
Manipulation check. Participants perceived the product as being significantly more expensive in the expensive (M = 5.79, SD = .92) compared with the inexpensive condition (M = 2.65, SD = .80; t(208) = 26.37, p < .001).
Purchase intentions. Using a regression approach to the combination of dichotomous and continuous independent variables, we regressed advertisement evaluations on the price and construal-level conditions, NFC (centered), and their interactions, using a 5,000-sample bootstrap method (Model 3 from Hayes 2013; Zhao et al. 2010). Results revealed a significant three-way interaction between price, construal level, and NFC (B = -1.32, SE = .65; t(202) = 2.06, p < .05; CI95 = [-2.60, -.04]). Results showed the predicted significant twoway interaction between price and construal level (B = 1.22, SE = .45; t(202) = 2.69, p < .01; CI95 = [.33, 2.11]; Cohen’s d = 1.02), in support of our main hypothesis; they also showed a main effect of the price condition (B = -2.13, SE = .32; t(202) = 6.62, p < .001; CI95 = [-2.76, -1.50]). Next, we used the Johnson–Neyman technique to identify the range of NFC for which the interaction between price and construal level was significant. This analysis revealed that for participants who scored below 3.62 on the scale (.3 SD above the mean), the interaction between price and construal level on advertising slogan evaluation was significant.
To better illustrate this interaction, we report the results for participants who scored 1 SD below and above the mean on NFC (for details, see Figure 4). For consumers low on NFC (M-1 SD = 2.71), results support our model and replicate our prior results: purchase intentions for the low-priced product were higher when the product was presented with the lowconstrual slogan (M = 4.91) compared with the high-construal slogan (M = 3.90; t(202) = 2.23, p < .05; CI95 = [-1.91, -.12]), whereas purchase intentions for the high-priced product were higher when the product was presented with the high-construal slogan (M = 3.45) compared with the low-construal slogan (M = 2.32; t(202) = 2.50, p = .01; CI95 = [.24, 2.03]). Consumers high in NFC (M+1 SD = 4.11) did not show the matching effect. Instead, there was only a nonsignificant difference in purchase intentions between the construal-level conditions for both the low-priced (t < 1; CI95 [-1.01, .73]) and the high-priced products (t < 1; CI95 [-.77, 1.06]).
Results from this experiment provide support for our hypothesis that the match between price and the construal level of the marketing message enhances responses to marketing communications through a heuristic process as it does not occur for systematic processors, that is, those who are high in NFC. We next turn to our general discussion.
General Discussion
Our set of six studies using a variety of price levels and types of outcome variables provides converging evidence in support of our general contention that a match between comparative price and the construal level of a marketing communication leads to more favorable consumer responses. Specifically, we show that matching the construal level of marketing communication to the perceived level of expensiveness of the product—with comparatively expensive products promoted by high-construal communications and comparatively inexpensive ones by low-construal communications—leads to more positive consumer attitudes. Consistent with the first stage of our conceptual model (Figure 1), we show that this effect is responsive to the product’s comparative price, irrespective of its absolute price (Studies 1–3). Consistent with the second stage of our conceptual model, we identify the mechanism underlying this matching effect on consumer preferences as the mediating role of conceptual fluency. We then test this heuristic explanation through the moderating role (Stage 3 of our model) of product category involvement (Study 4) and NFC (Study 5).
These studies highlight the rich and complex influence of product price on consumer judgment and behavior and contribute to several research streams. They extend the CLT literature by showing that comparative price can prime psychological distance (see Study 3). We believe that comparative price is a learned cue (see Saini and Thota 2010), whereas the four basic dimensions of psychological distance (e.g., space, time) are fundamental to the way people think. This raises interesting questions about such learned or derived cues to psychological distance, for instance, the role of wealth versus poverty in the perception of “distant” prices, and whether these cues are culturally specific.
Most centrally, by demonstrating that comparative expensiveness can affect experienced psychological distance and thus consumer favorability toward a given slogan or advertisement, our results contribute to a growing stream of research on the interplay between price, construal level, and consumer decisions. For the most part, those inquiries have focused on how consumption cues—for example, how far off in time a purchase is, or whether the purchase is for the self versus others—influence consumers’ price–quality and price– expensiveness inferences (e.g., Bornemann and Homburg 2011; Yan and Sengupta 2011). As noted, Hansen and Wa¨nke (2011) find luxury products to be mentally represented more abstractly than ordinary products. Our perspective suggests a different and, we think, a more general account: we believe that even luxury products can be portrayed as comparatively expensive or inexpensive within their category, and can be described very concretely (e.g., focusing on the “4 ‘C’s” for diamonds) or very abstractly (e.g., focusing on the symbolic meaning of wearing a diamond; see Study 1b). Our results show that even when we keep the object and its price constant, making it seem expensive or affordable by varying the comparative context in which it is evaluated is sufficient to change its representation in consumers’ minds. Notably, this matching effect is unchanged by the specific absolute price, even when this varies by a ratio of 1,000, but it is very sensitive to changes in comparison level or perceived expensiveness.
While we provide direct evidence that our effect of interest is driven by perceptions of expensiveness, our results should nevertheless be considered in light of the role of quality expectations by consumers, mainly because of the well-established link between price and quality expectations. We argue that there are two main reasons to believe that our results cannot be explained parsimoniously by quality expectations induced by prices. First, we do not observe a main effect of price on our product evaluation dependent variables (except in Studies 4 and 5 when the dependent variable is purchase intention; this measure is tied directly to budget constraints). Although more expensive products, due to higher quality expectations, are more desirable when evaluated in isolation from their prices, this relationship does not always hold—and can even be reversed due to price–quality trade-offs—when a product is evaluated in conjunction with its price. For instance, it is easy to imagine that some consumers would prefer a low-quality and low-priced product over a high-quality and high-priced product if the low price were low enough (see “value-conscious consumers”; Ailawadi, Neslin, and Gedenk 2001). Second, to rule out the alternative explanation that our high-construal manipulations of marketing slogans also connoted higher quality, we conducted a series of manipulation checks on our set of stimuli. Specifically, we measured consumers’ expected product quality for each construal-level condition, without providing any price-related information. Using an expected-quality measure adapted from Kirmani and Wright (1989), we find no support for the alternative explanation that marketing communications worded at a high construal level triggered higher-quality expectations (see Web Appendix F). For these reasons, quality expectations do not appear to explain our effects parsimoniously.
Similarly, because desirability is one component of high construal level (Trope and Liberman 2003), our results must also be considered in terms of the role of hedonic product features. To rule out the alternative explanation that our highconstrual marketing slogans were also of a more hedonic nature, we measured consumers’ perceived focus on utilitarian or hedonic features for each construal-level condition, without providing any price-related information (using a check adapted from Khan and Dhar 2006). Again, our results were inconsistent with the alternative explanation that our highconstrual slogans were, overall, perceived to be more hedonic (see Web Appendix G). For this reason, we believe that hedonic focus is unlikely to explain our effect of interest.
This research also contributes to the literature on fluency effects in consumer judgments (e.g., Thompson and Ince 2013) by suggesting that high-NFC individuals may be less likely to rely on attributions about their experience of fluency when evaluating promotional material. While more empirical work is necessary to examine this issue in greater depth, our results suggest that effortful thinking could reduce the effectiveness of marketing communications that rely on fit or fluency effects. Prior research has often examined processing styles as stable consumer orientations (e.g., intuitive–experiential vs. analytical– relational thinking; Epstein et al. 1996). Future work should also seek to identify factors in consumption contexts driving the adoption of automatic versus analytical processing styles (e.g., Bhargave and Montgomery 2013; Sujan, Bettman, and Sujan 1986) with the objective of assessing the generalizability of fit approaches to promotional efforts.
Our results on the interplay between comparative price and the construal level of marketing communications have substantive implications for managers, offering insights into best practices for influencing consumer judgments and decisions, especially in contexts in which price perceptions have not been clearly defined in consumers’ minds, such as new products. We extend previous work on construal-level congruency effects in consumer decision making (e.g., Lamberton and Diehl 2013; Lee et al. 2010; White et al. 2011; Yang et al. 2011) that highlights the importance of framing promotional messages at a construal level that maximizes their effectiveness. More specifically, our results suggest that firms should vary their promotional efforts according to the comparative expensiveness of their products, no matter the absolute price. For example, firms should promote the more affordable products in their lineups by focusing on their more concrete, low-level features (e.g., miles per gallon and reliability ratings for an affordable sedan) while focusing on more abstract, high-level features for their more expensive products (e.g., feelings of freedom and power for a high-end sports car) within a given absolute price range.
Importantly, our results also suggest that even when keeping the price of a product constant, managers could increase demand for their products by matching their marketing communication to the comparative expensiveness of the product, such as by changing the assortment of products against which the focal product is presented. For instance, managers can make a product appear inexpensive by contrasting it with premium versions of that product in their communications (e.g., through in-store displays, online shopping interfaces, advertisements; for framing examples of how to achieve such a goal, see Studies 1–3). Imagine, for instance, a dealership where the same BMW 5 Series sedan may appear expensive when displayed next to a 3 Series vehicle but affordable when displayed next to a 6 Series vehicle.
Insights gained from this research suggest that whether a product is affordable or expensive relative to its comparison context, promoting it with a focus on its concrete or abstract benefits, respectively, would be the most persuasive—unless, as we have also found, consumers are highly invested in the processing of such marketing communication (e.g., product category involvement in Study 4; NFC in Study 5). This finding would suggest that our recommendations are most suitable for mass communication channels (e.g., general-interest television programming, magazines, billboards) but that their effectiveness would be less for targeted media (e.g., specialized publications, interest-specific websites, industry conventions).
This research also opens avenues for future studies. Future inquiries should investigate whether findings from the current research could be implemented using alternative manipulations of concreteness or abstractness. For example, one could manipulate the perceived abstractness of product communications by modifying their visual representations (e.g., showing only the outline of the product) or the fonts used to better match the relevant price category. Despite the large quantity of research already sparked by the twin concepts of absolute and perceived price, we believe that there is still much more to be learned about this powerful economic and psychological cue that influences consumers in many ways.
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GRAPH: FIGURE 2 Study 3: Product Evaluation as a Function of Price, Source of Price Variation, and Construal Level of Advertising Slogans
GRAPH: FIGURE 3 Study 4: Purchase Intentions as a Function of Construal Level, Expensiveness, and Involvement
GRAPH: FIGURE 4 Study 5: Purchase Intentions as a Function of Construal Level, Price, and Need for Cognition
DIAGRAM: FIGURE 1 Theoretical Framework
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Record: 38- Competitive Effects of Front-of-Package Nutrition Labeling Adoption on Nutritional Quality: Evidence from Facts Up Front–Style Labels. By: Lim, Joon Ho; Rishika, Rishika; Janakiraman, Ramkumar; Kannan, P.K. Journal of Marketing. Nov2020, Vol. 84 Issue 6, p3-21. 19p. 1 Color Photograph, 5 Charts, 1 Graph. DOI: 10.1177/0022242920942563.
- Database:
- Business Source Complete
Competitive Effects of Front-of-Package Nutrition Labeling Adoption on Nutritional Quality: Evidence from Facts Up Front–Style Labels
"Facts Up Front" nutrition labels are a front-of-package (FOP) nutrition labeling system that presents key nutrient information on the front of packaged food and beverage products in an easy-to-read format. The authors conduct a large-scale empirical study to examine the effect of adoption of FOP labeling on products' nutritional quality. The authors assemble a unique data set on packaged food products in the United States across 44 categories over 16 years. By using a difference-in-differences estimator, the authors find that FOP adoption in a product category leads to an improvement in the nutritional quality of other products in that category. This competitive response is stronger for premium brands and brands with narrower product line breadth as well as for categories involving unhealthy products and those that are more competitive in nature. The authors offer evidence regarding the role of nutrition information salience as the underlying mechanism; they also perform supplementary analyses to rule out potential self-selection issues and conduct a battery of robustness checks and falsification tests. The authors discuss the implications of the findings for public policy makers, consumers, manufacturers, and food retailers.
Keywords: competition; difference-in-differences; Facts Up Front; front-of-package nutrition labeling; nutritional quality; public policy and marketing
According to estimates from [11], more than one-third of U.S. adults are obese. Childhood and adolescent obesity rates have also skyrocketed in the last 30 years, with one in five school-aged children considered obese. To combat this disconcerting trend, public policy makers, food manufacturers, and grocery retailers have made efforts over time to design nutrition labels that can educate consumers about the nutritional value of the foods they purchase and help consumers make healthier choices. Recently, the U.S. Food and Drug Administration (FDA), in an attempt to promote healthy food choices among consumers, announced a new Nutrition Facts label for packaged food products that reflects new scientific information, highlighting the link between diet and obesity-related chronic diseases.[ 7]
The packaged food industry has also voluntarily taken steps to inform consumers about the nutritional value of food products so that consumers can make better choices; one such initiative undertaken by food manufacturers is the Facts Up Front front-of-package (FOP[ 8]) nutrition labeling. Such nutrition labels are voluntarily adopted by food manufacturers and provide nutrient information on the front of food packaging in a clear, simple, and easy-to-read format. The standardized labels present the key information listed on the Nutrition Facts Panel (NFP; displayed on the back or side of food packages) more concisely, and the information often includes calorie content and the amounts of key nutrients to limit (e.g., saturated fat, sugar, and sodium per serving) (see Figure 1).
Graph: Figure 1. Facts Up Front–style front-of-package nutrition labels.
Front-of-package labels can be effective in stimulating positive outcomes both on the demand and the supply sides. On the demand side, such easy-to-read labeling systems can help time-starved consumers make healthier choices at the point of purchase and help overcome disadvantages of the mandatory nutrition label (the NFP), which is difficult to read and understand ([37]). Multiple recent studies showed a positive effect of FOP labels on consumers' perceptions of foods' healthiness (for a recent meta-analysis of studies on FOP labels, see [25]]) and consumer choice at the point of purchase ([15]; [57]). In addition, FOP labels can help mitigate the negative effects of front-of-package nutrient content claims (e.g., "Low Fat") that may serve as a food marketing tool rather than promote health ([16]). Whereas nutrient content claims can selectively highlight certain nutrients to make the product look healthier and lead to halo effects such that consumers infer that the entire product is healthy from information about only a selected nutrient ([42]), FOP labels provide exact nutrient information. On the supply side, FOP labels can help stimulate product innovation and lead to nutritionally better products. Although the demand effects of FOP labels have generated much interest in recent research, with consensus emerging that FOP labels help consumers identify healthy products ([25]), the supply-side implications of FOP labels have not been systematically examined. This study is an attempt to fill this critical research gap in the areas of health and nutrition, public policy, and marketing.
The first objective of our study is to conduct a systematic empirical examination of the effect of adoption of FOP nutrition labels in a product category on the nutritional quality of the food products in the category. Our second objective is to examine the moderating effects of brand and category characteristics. Our central thesis is that adoption of FOP makes product nutrition information more salient, and as consumers' preference for healthier products increases, food manufacturers respond by enhancing the nutritional quality of their products. In accordance with recent studies that suggest conducting mechanism checks as a way of validating claims of causal inference ([21]), our final objective is to establish the role of nutritional information salience in consumers' choice of food products as the underlying mechanism that drives food manufacturers to improve the nutritional quality of their products.
To accomplish our objectives, we undertook a comprehensive data collection effort and examined packaging and nutrient information of packaged food and beverage products (21,096 products, 9,083 brands, and 4,408 firms across 44 food and beverage categories) in the United States over a 16-year period. In this study, we focus on a class of FOP labels (commonly known as "Facts Up Front" FOP nutrition labels) that have a standardized and neutral form in which key nutrient information is presented on the front of the package as clear and easy-to-read icons (see examples of the Facts Up Front–style FOP nutrition label in Figure 1). We use a quasi-experimental study design to examine the effects of FOP adoption in a product category on the nutritional quality of the products in the category. We exploit temporal variation in adoption of FOP at the product category level and cast our model in the difference-in-differences (DD) modeling framework built on panel data that helps us compare changes in the nutritional quality of products during the pre- and post-FOP adoption periods across product categories that are exposed to FOP adoption (the treatment categories) and product categories that are not exposed to FOP adoption (the control categories).
We report four sets of findings. First, we find that the adoption of FOP nutrition labeling in a product category results in a considerable improvement in the nutritional quality of food products in that category. Second, heterogeneity analyses suggest that the effect of FOP adoption is stronger for premium (high-priced) brands and brands with a narrower product line breadth. Third, we find that the FOP adoption effect is stronger for unhealthy categories and categories with a higher competitive intensity. Fourth, we find that manufacturers increase the nutritional quality of products by reducing the calorie content and the levels of nutrients to limit, for example, sugar, sodium, and saturated fat. This result helps us shed light on the underlying mechanism. If FOP adoption increases the salience of nutritional information, we argue that this would incentivize food manufacturers to improve products' nutritional quality by limiting the calories and the levels of other nutrients to limit that are actually displayed on the FOP label.
This study advances the understanding of FOP labels from the theoretical and practical perspectives. From the theoretical perspective, we tackle the issue of FOP labels from the supply side and answer the recent call by scholars to help understand the relationship between FOP labels and product nutritional quality ([15]; [25]). We also present evidence of a "nutritional information clearinghouse effect" of FOP labels, whereby such labels increase the salience of nutritional information of products. From the practical perspective, these results will help inform public policy, as well as manufacturers, retailers, and consumers. From the public policy perspective, because the NFP has not been effective in changing consumer choice behavior ([30]), the FDA has encouraged food manufacturers to adopt voluntary initiatives that highlight key nutrients on the front of food packages to serve the dual purpose of increasing consumer access to nutritional information and improving product quality. The present results help inform public policy makers that FOP labels, which display key nutrient information on the front of the package in a standardized and a uniform format, help increase products' nutritional quality. Thus, such labels should be promoted. The study's findings specifically help unpack the role of key brand and category characteristics that moderate the effectiveness of FOP adoption suggesting the specific categories and brands in which FOP label adoption can provide the greatest benefits by enhancing product nutritional quality. We believe retailers can benefit from the study by encouraging FOP label adoption in categories that need help in improving nutritional quality.
In 1994, under the Nutrition Labeling and Education Act (NLEA), the FDA mandated food manufacturers to display the NFP on the back (or sometimes on the side) of food packages. Since then, several studies have questioned the effectiveness of the NFP in consumer decision making at the point of purchase. Much of this has been attributed to the high costs of processing information that shoppers face at the time of purchase ([30]). In recent years, FOP nutrition labels have gained widespread popularity because they provide information on calories and a set of selective nutrients in the form of easy-to-read icons on the front of food packages. Over the past few years, many different types of FOP nutrition labels have been developed and introduced in the market. In 2009, the FDA commissioner declared in an open letter to the food industry that FOP nutrition labeling would be the agency's top priority and encouraged food manufacturers and retailers to design a standardized, science-based FOP nutrition labeling system that would comply with FDA regulations ([23]). Subsequently, two of the leading food industry trade organizations in the United States—the Grocery Manufacturers Association and the Food Marketing Institute—officially announced the voluntary FOP nutrition labeling scheme called the "Facts Up Front" FOP labeling initiative ([47]). According to the initiative, food manufacturers present the nutritional content of their products in an easy-to-read "callout" format that is based on the Guideline Daily Amounts. Food packages are required to carry four basic icons—for calories (per serving), saturated fat (in grams and Percent Daily Value [%DV]), sodium (in milligrams and %DV), and sugar (in grams)—as a default format.[ 9]
In this study, for the following reasons, we focus on all of the FOP labels that meet the Facts Up Front guidelines. First, these labels are the most commonly used and standardized FOP nutrition labels. All food manufacturers follow the same format for the shape of the icons and the presentation of information about key nutrients. The format has been accepted and encouraged by the Grocery Manufacturers Association. The FOP labels that we examine also have the support of several agencies, such as the Food Marketing Institute and the FDA ([17]). In a thorough examination of food product packaging over nearly two decades and across 44 categories, we found that this has been the most common standardized format (this helped us rule out format-related differences that may affect outcomes). Second, the Facts Up Front–style FOP format lists the presence of nutrients in Guideline Daily Amounts. In particular, the levels of nutrients such as saturated fat and sodium presented as a %DV per serving can help consumers choose a balanced food product (see Figure 1). Third, we would like to emphasize that the FOP labels that we study are not nutrient claims (e.g., "25% less saturated fat"). Unlike claims that highlight improvement in selected nutrients of a product, the Facts Up Front–style FOP labels simply present the key nutrient information from the NFP (on the back of a product package) on the front of the package. Moreover, although nutrient claims such as "25% less sugar" may imply a healthier product based on a single nutrient (but not necessarily overall nutrition), Facts Up Front–style FOP labels bring the critical nutrient information from the back panel to the front, which creates the opportunity to focus on the improved overall nutritional profile. Figure 1 presents examples of food products with the Facts Up Front–style FOP nutrition labels examined in this study.
Prior research in marketing and economics literature has suggested that consumers often do not have complete information about product attributes ([35]; [44]), and the resulting costly search process has a profound effect on consumer behavior and competitive behavior ([50]). In the context of nutrition information, research suggests that consumers face three main types of costs in collecting and assimilating information: ( 1) collection cost, which comprises the time and effort spent in acquiring nutrition information; ( 2) computational cost, which includes the effort combining the relevant information into an overall evaluation; and ( 3) comprehension cost, which captures the effort needed to understand the nutritional information ([43]). [37] show that a simplified nutrition scoring system at the point of sale reduces all three types of costs, thus motivating consumers to switch to higher-scoring products that are healthier.
Front-of-package labels make key nutrient information salient through their prominent display on the front of the package. A product attribute is deemed salient "when it stands out among the good's attributes relative to that attribute's average level in the choice set" ([ 8], p. 803). Research suggests that consumers give more weight to information that is salient ([ 8]; [53]). Therefore, we argue that adoption of FOP labels makes nutritional information more salient and reduces nutritional search and information costs. Indeed, [57] calibrate a model based on household purchase–level data and find that the use of FOP labeling increases the weight of the healthiness attribute in consumers' product choices.
Building on these findings, we argue that FOP adoption makes nutritional information more salient, reducing consumers' overall search costs for nutritional information at the point of purchase which, in turn, influences consumer decision making. This change in consumer behavior has important implications for food manufacturers. Game theoretical models and empirical studies suggest that any market mechanism that helps reduce consumers' price search costs—"information clearinghouse" (e.g., the internet price comparison site)—would intensify price competition between firms ([45]). Following this argument, we suggest that adoption of FOP in a product category serves as a source of "nutritional information clearinghouse" and spurs nutrition competition among food manufacturers. Because consumers favor healthier options, food manufacturers would compete by improving the nutritional quality of products. In summary, FOP adoption in a product category increases salience of nutrition information on the demand side, leading to increased consumer preference for healthier products; on the supply side, food manufacturers respond by offering nutritionally better products in the category. Thus, we propose the following hypothesis:
- H1: Adoption of FOP in a product category has a positive effect on the nutritional quality of products in the category.
In the following subsections, we propose that the effect of FOP adoption in a product category on the improvement in the nutritional quality of products is moderated by brand and category characteristics. We focus on brand characteristics (specifically, price premium and product line breadth) that provide a greater incentive for brands to respond to competitive changes in a product category. For category characteristics, we focus on factors (specifically, category healthiness and competitive intensity) that present a greater opportunity for food manufacturers to improve products' nutritional quality.
Although price competition is a common strategy in the grocery market, many brands compete on perceived quality and command a price premium. Prior research has suggested that brands' price premium is a critical lower-funnel shopper marketing instrument that influences consumers' decision making at the point of purchase ([32]). Because premium brands target a price-insensitive consumer segment and charge a price premium over competing lower-tier brands in a category, they face constant pressure to differentiate their products and justify their higher prices. Researchers have identified health and nutrition information as one of the key associations consumers make with a brand that can drive their willingness to pay for grocery products ([ 4]; [ 5]; [14]). Studies also suggest that consumers who are less sensitive to price are more likely to focus on the nutritional aspects of products and nutrition labels ([13]) and that the introduction of a point-of-sale nutrition scoring system can decrease shoppers' price sensitivity ([37]). Taken together, these arguments suggest that premium brands are more likely to invest in product innovation and offer nutritionally better products to continue to justify the price premium they command over nonpremium brands. From the demand perspective, given the price point of premium brands, consumers are also more likely to pay greater attention to the nutritional content of high-priced products. Thus, premium brands benefit from improving their products. From the supply side, premium brands may also have greater resources to invest in product innovation, leading to products with higher nutritional quality, which is aligned with changing consumer preferences for nutritionally better products. Therefore, we expect the effect of FOP adoption on nutritional quality to be greater for premium brands in a product category and propose the following hypothesis:
- H2a: The effect of FOP adoption on the nutritional quality of products is stronger for premium brands.
The second brand characteristic we consider is the breadth of a brand's product line. The product mix is an important part of a brand's overall competitive strategy. In grocery retailing, beyond the price dimension, brands compete in nonprice dimensions and constantly innovate and introduce new products to expand product lines and gain market share ([19]). Research has shown that although broader product lines can help increase demand and prices, they can also increase costs related to product design and development ([ 6]). In a similar vein, brands with broader product lines might impose additional resource constraints in such a way that brands with narrower product lines might have an edge in reformulating products and engaging in product innovation by improving the nutritional profile of their products. Although brands with a broader product line breadth could have more market power and greater potential to innovate, we argue that brands with narrower product lines are better positioned to change the products' nutrition level. This is because, on the demand side, consumers face lower nutrition information search costs for brands with narrower product lines. Consumers may also be able to compare a brand's nutritional profile within and across categories more easily for brands with a smaller product portfolio, thus effectively motivating these brands to leverage their focused product portfolio and actively engage in improving their products. Thus, we expect that the effect of FOP is greater for brands with a narrower product line breadth across categories. Thus, we present the following hypothesis:
- H2b: The effect of FOP adoption on the nutritional quality of products is stronger for brands with narrower product line breadth.
As consumers process nutritional information of products and search for healthier options, the marginal benefit of searching for healthier options is lower in healthy categories compared with unhealthy categories. [34] finds that following the enactment of the NLEA, a negative relationship exists between category healthiness and the amount of information consumers obtain in a product category, suggesting that consumers may need more information in unhealthy categories. This finding, applied to the present study context, suggests that introduction of FOP labeling would make nutrition information more salient in less healthy categories. From the demand-side perspective, [10] argue that healthy eating nudge interventions (including nutrition labeling) are more effective in reducing unhealthy eating than increasing healthy eating. On the supply side, given that unhealthy categories have low nutritional quality, the opportunity to improve the nutritional quality of products is also higher in unhealthy categories. Thus, food manufacturers in unhealthy categories have a greater incentive to invest in product innovation and to appeal to consumers who search for relatively healthier or less unhealthy options even in inherently unhealthy categories. [36] find that after the NLEA was enacted, firms in unhealthy categories improved the nutritional quality of their products more than those in healthy categories. We posit the following hypothesis:
- H3a: The effect of FOP adoption on the nutritional quality of products is stronger for unhealthy categories.
Consumers face higher search costs for product attributes when shopping in product categories with higher competitive intensity compared with less competitive categories. Extant research suggests that price dispersion can be higher in more competitive markets ([ 9]; [12]). In such markets, consumers may face higher search costs, and firms have an incentive to take actions to reduce consumers' search costs so that the products can enter consumers' consideration sets ([38]). Thus, on the demand side, consumers may face high price dispersion and high search costs in highly competitive categories. On the supply side, food manufacturers in more competitive categories have more incentives to innovate to reduce consumers' search costs so that their products can enter consumers' consideration sets. Stated differently, firms have a greater incentive to differentiate themselves by investing in improving the nutritional quality of their products in more competitive categories. Thus, we propose the following hypothesis:
- H3b: The effect of FOP adoption on the nutritional quality of products is stronger for categories with greater competitive intensity.
The primary data source is the Mintel Global New Products Database (GNPD), which is considered the industry standard in reporting new product launches, trends, and innovations in the packaged food and beverage product industry. The database provides nutritional information, photographs of the package, price, package size, number of units in a multipack product, and so on. In addition to these product attributes, the database has information about brands, manufacturers, categories, and published dates. We accessed the database and collected the aforementioned information for all food and beverage products across 44 product categories in the United States over 16 years (from 1996 to 2011), including existing and new product launches. By manually examining the photographs of the packages of all the products released during the period, we identified products with FOP labels and recorded when the FOP-labeled products were introduced in each product category. To assemble the estimation data set, we removed outliers (based on a boxplot of nutrient levels) and products with missing nutrient information. Next, we separated the data into two sets, the calibration data set (from 1996 to 2002) and the estimation data set (from 2003 to 2011). We used the calibration data to construct the moderating variables.[10] This ensured that brand and category classifications did not confound with the estimation period and helped us interpret the effect of moderating variables ([41]). The final estimation data set consists of 21,096 products, 9,083 brands, and 4,408 firms in 44 food and beverage categories.
To measure products' nutritional quality level, we used the Nutrient Profiling (NP) model that was developed by the United Kingdom Food Standard Agency and the British Heart Foundation Health Promotion Research Group at Oxford University ([40]). The NP model has been widely used in marketing ([ 2]; [15]), economics ([52]), public health ([46]), and nutrition ([28]) literature. The NP score is calculated in a way to offset calories (kJ)[11] and the nutrients to limit—including saturated fat (g), sugar (g), and sodium (mg)—by the nutrients to encourage, including fruit, vegetable, and nut (FVN) content (%); fiber (g); and protein (g). Specifically, based on the content of the aforementioned nutritional elements in a 100 g or 100 mL food or beverage product, 0 to 10 points are assigned to each negative element, and 0 to 5 are assigned to each positive element. The total points for positive elements are subtracted from the total points for negative elements to calculate the NP score. Based on calories, five nutrients (saturated fat, sodium, sugar, fiber, and protein), and the FVN content,[12] the NP model generates a single score that ranges between −15 (the most healthy) and 40 (the least healthy).
Several unique characteristics of the NP model deserve mention. First, the NP score is a serving size–free index—because it measures the nutritional quality based on the amount of each nutrient in 100 g or 100 mL of a food or beverage product—and thus measures the nutritional quality independent of individual-specific food consumption patterns and enables comparison of the nutritional quality of various products across brands and categories. Second, the NP score is a standardized score that helps classify food and beverage products as "healthy" or "less healthy." A food product is classified as "less healthy" if the NP score is more than or equal to 4, and a beverage product is classified as "less healthy" if the NP score is more than or equal to 1 ([40]). Table 1 provides the summary statistics of the NP score across the product categories that we analyze.
Graph
Table 1. Summary Statistics of Product Categories.
| Index | Category | Food/Beverage | Treatment/Control Category | Summary Statistics of Nutrient Profiling Score |
|---|
| Mean | Mdn | SD | Min | Max |
|---|
| 1 | Baking Ingredients & Mixes | Food | Treatment | 14.27 | 16.00 | 7.80 | −8 | 35 |
| 2 | Bread | Food | Treatment | 4.24 | 2.00 | 6.64 | −7 | 27 |
| 3 | Cakes, Pastries & Sweet Goods | Food | Treatment | 14.42 | 16.00 | 6.82 | −5 | 32 |
| 4 | Caramel & Cream Spreads | Food | Control | 18.57 | 18.00 | 6.25 | 7 | 35 |
| 5 | Carbonated Soft Drinks | Beverage | Treatment | 1.53 | 2.00 | .96 | 0 | 3 |
| 6 | Chocolate Confectionery | Food | Treatment | 19.08 | 21.00 | 7.14 | 0 | 32 |
| 7 | Chocolate Spreads | Food | Control | 19.37 | 20.00 | 4.11 | 12 | 26 |
| 8 | Cold Cereal | Food | Treatment | 9.11 | 9.00 | 6.77 | −8 | 34 |
| 9 | Confiture & Fruit Spreads | Food | Treatment | 9.93 | 11.00 | 4.95 | −4 | 26 |
| 10 | Corn-Based Snacks | Food | Control | 10.43 | 11.00 | 6.95 | −3 | 31 |
| 11 | Creamers | Food | Treatment | 12.15 | 10.50 | 7.42 | 0 | 30 |
| 12 | Dairy-Based Frozen Products (Ice Cream) | Food | Treatment | 11.51 | 13.00 | 5.98 | −9 | 29 |
| 13 | Eggs & Egg Products | Food | Treatment | 1.41 | −1.50 | 7.07 | −5 | 23 |
| 14 | Energy Bar | Food | Treatment | 12.99 | 13.00 | 5.19 | −6 | 35 |
| 15 | Energy Drinks | Beverage | Treatment | −.21 | .00 | 2.04 | −5 | 3 |
| 16 | Fish Products | Food | Treatment | 2.74 | 1.00 | 5.67 | −5 | 21 |
| 17 | Hot Cereal | Food | Treatment | 3.23 | 1.00 | 6.97 | −6 | 20 |
| 18 | Juice | Beverage | Treatment | 1.66 | 2.00 | 1.42 | −5 | 13 |
| 19 | Margarine | Food | Control | 22.03 | 24.50 | 6.61 | 0 | 28 |
| 20 | Mayonnaise | Food | Treatment | 19.82 | 23.00 | 6.22 | 0 | 28 |
| 21 | Meat Snacks | Food | Control | 17.51 | 17.00 | 5.98 | 0 | 28 |
| 22 | Milk | Beverage | Treatment | 1.29 | .00 | 4.91 | −2 | 22 |
| 23 | Nuts | Food | Treatment | 2.64 | 3.00 | 4.44 | −10 | 21 |
| 24 | Nut Spreads | Food | Treatment | 13.65 | 15.00 | 4.54 | 0 | 23 |
| 25 | Pasta | Food | Treatment | −1.17 | −3.00 | 4.97 | −7 | 16 |
| 26 | Pasta Sauce | Food | Treatment | 5.04 | 3.00 | 5.51 | −5 | 29 |
| 27 | Pizza | Food | Treatment | 8.43 | 10.00 | 5.37 | −4 | 28 |
| 28 | Popcorn | Food | Treatment | 14.18 | 16.00 | 7.92 | −6 | 27 |
| 29 | Potato Products | Food | Treatment | 4.41 | 4.00 | 5.05 | −6 | 25 |
| 30 | Potato Snacks | Food | Treatment | 13.87 | 13.00 | 5.41 | −4 | 34 |
| 31 | Poultry Products | Food | Treatment | 5.54 | 4.00 | 5.84 | −6 | 24 |
| 32 | Prepared Meals | Food | Treatment | 2.04 | 1.00 | 4.01 | −6 | 27 |
| 33 | Ready-to-Drink Iced Tea | Beverage | Treatment | .84 | 1.00 | .85 | −1 | 3 |
| 34 | Rice | Food | Treatment | 2.33 | .00 | 6.11 | −7 | 18 |
| 35 | Salad | Food | Control | 5.62 | 4.00 | 6.16 | −3 | 35 |
| 36 | Salad Dressings | Food | Treatment | 14.73 | 16.00 | 6.81 | −1 | 31 |
| 37 | Savory Biscuits/Crackers | Food | Treatment | 12.39 | 14.00 | 7.27 | −6 | 40 |
| 38 | Soup | Food | Treatment | 2.95 | 2.00 | 3.47 | −7 | 27 |
| 39 | Sports Drinks | Beverage | Treatment | 4.52 | 1.00 | 8.04 | −3 | 23 |
| 40 | Sweet Biscuits/Cookie | Food | Treatment | 18.95 | 20.00 | 5.57 | −7 | 40 |
| 41 | Syrup | Food | Control | 12.12 | 13.00 | 3.27 | −1 | 17 |
| 42 | Table Sauces | Food | Treatment | 9.95 | 10.00 | 6.35 | −3 | 40 |
| 43 | Vegetables | Food | Treatment | −5.66 | −6.00 | 3.48 | −14 | 10 |
| 44 | Yogurt | Food | Treatment | .62 | 1.00 | 2.69 | −5 | 13 |
1 Notes: Categories are presented in alphabetical order. The smaller the Nutrient Profiling (NP) score, the better the nutritional quality. For a food product, the NP score that is more than or equal to 4 indicates "less healthy." For a beverage product, the NP score that is more than or equal to 1 indicates "less healthy."
Before we present our proposed econometric model, we discuss issues related to the research design and identification strategy. We take a quasi-experimental approach with the (first-time) adoption of FOP by a brand in a category as the treatment and examine the effect on the nutritional quality of products of other brands in the same category. As we mentioned previously, our estimation data spans 2003 to 2011 (referred to as the "focal time period"). Using the adoption of FOP by all the brands in all of the product categories during the focal time period, we classify the product categories into two types: the treatment group (categories in which we observe the introduction of a FOP-labeled product during the focal time period) and the control group (categories in which we do not observe the introduction of a FOP-labeled product during the focal time period). In other words, the timing of FOP adoption is the only criterion that we use to classify categories into the treatment and control groups. One might be concerned about the effect of category characteristics on group assignment. However, in line with the arguments presented in [24], we contend that category factors (e.g., healthiness) that can potentially induce self-selection bias are not time varying. Thus, the group assignment—based solely on the timing of FOP adoption—ensures that there are no systematic differences between the treatment and control categories. The empirical modeling approach, DD, accounts for time-invariant brand-, firm-, and category-specific characteristics. We also confirm that there is no statistically significant correlation between the timing of FOP adoption and category healthiness.[13]
Our research design involves the treatment effect of adoption of FOP in a product category by a brand (referred to as the "first adopter") on the change in nutritional quality of products of other (competing) brands in the same product category. Regarding the first adopter brand in any given category, one can argue that it has higher nutritional quality and is more likely to adopt FOP. To ensure that the first adopter brand does not contaminate the results, and to facilitate a cleaner interpretation of the effect of FOP adoption (by the first adopter brand) on the nutritional quality of other competing brands, we removed the "first adopter" brands (and firms) from the analysis. As FOP nutrition labeling is voluntary, and because we removed the first adopters from the analysis, the timing of the adoption of FOP by the first adopter in a product category is unlikely to be correlated with the nutritional quality of other brands in the same product category. In summary, we treat FOP adoption (by the first adopter brand) in a category as an exogenous shock to other brands in the category and investigate whether FOP adoption acts as a catalyst for other brands to improve the nutritional profile of their product portfolios.
Following this research design, we cast our analyses in the DD modeling framework to estimate the treatment effect (adoption of FOP in a product category) on the outcome variable (overall nutritional quality of food and beverage products; [33]). By comparing the nutritional quality of products of brands in a product category before and after FOP adoption, and between the treatment group and the control group categories, we not only account for temporal factors that affect both groups simultaneously but also control for innate differences between the two groups. The "double differencing" helps identify the causal effect of FOP category adoption on nutritional quality of products ([ 3]). We remind readers that we work with observational data. Thus, we acknowledge that any causal interpretation is valid within the assumptions of the DD model. Given the absence of full randomization, we further conduct a series of robustness checks and falsification tests to validate our DD modeling strategy that are discussed in subsequent sections.
The key dependent variable of interest is the NP score of a product in the set of packaged food and beverage product categories. Given the range of the NP score across the diverse set of categories (ranging between −14 and 40 in our study), to facilitate an intuitive interpretation, we use the min-max scaling procedure ([26]) and rescale the NP scores on a new scale ranging from 1 (the least healthy) to 100 (the most healthy). We refer to the rescaled NP score as the Nutrient Profiling Index (NPI)[14] and use the score as our focal dependent variable in the DD models. The unit of analysis is the product–brand level.[15] We employ the DD modeling framework to examine the effect of adoption of FOP in a category on the nutritional quality of products in the category (H1) as follows:
Graph
1
In Equation 1, Nutritional qualitypbfct represents the NPI score of product p by brand b that belongs to firm f in category c at time t. FOPpbfct is the focal independent variable that is equal to 1 for all products in a treatment category in the post-FOP period, and 0 for all products in a treatment category during the pre-FOP period and for those in a control category. The time trend variable (Time trendt) helps control for linear trend in nutritional quality across all food products over time. As there are different categories, the inclusion of category-specific time trend effects (τct) helps further control trend in nutritional quality across all food products within a category. The inclusion of year fixed effects (σt) not only helps control for changes in nutritional quality in a given year due to supply-side factors (e.g., manufacturing capabilities) and demand-side factors (e.g., consumers' preference for healthier products) but also helps control for any other year-specific omitted variables. The brand (ϕb), firm (ωf), and category (νc) fixed effects help account for baseline differences in nutritional quality across brands, firms, and categories, respectively. εpbfct is the error term. The focal coefficient of interest is α1 (the DD estimate), which captures the average effect of adoption of FOP in a category on the NPI of products in the treatment categories relative to those in the control categories in the post-FOP period (compared with the pre-FOP period).
Following the arguments presented in recent marketing literature using DD models ([27]; [51]), we use the median split of the brand-specific mean price to classify the brands into premium and nonpremium brands.[16] We focused on brands that exist in both the calibration and estimation periods and used data from the calibration period (1996 to 2002) to compute a set of brand-specific mean prices of the products. This helps ensure that the brand classification does not confound with the estimation time period and allows for easy interpretation of the moderating effects of brands ([27]; [41]). To empirically examine the effect of premium brand (H2a), following recent studies ([20]), we extend our DD model to the difference-in-difference-in-differences (DDD) modeling framework by interacting FOPpbfct (presented in Equation 1) with the focal moderating variable, an indicator variable associated with premium brands. The proposed DDD model is as follows:
Graph
2
In Equation 2, Premiumb takes a value of 1 if brand b is a premium brand, and 0 otherwise. All other variables and fixed effects in Equation 2 are identical to those in Equation 1. In Equation 2, the main coefficient of interest is β1 (the DDD estimate) that captures the effect of FOP adoption in a category on the nutritional quality of products of the premium brands (relative to the nonpremium brands) in the treatment categories (relative to the control categories) in the post-FOP period (compared with the pre-FOP period).
To measure the level of product line breadth of brands, we focused on brands that exist in both the calibration and estimation periods and calculated the total number of products of each brand in the calibration period. Drawing on the median split of the brand-specific total number of products, we classify the brands into two types: brands with a wider product line breadth and those with a narrower product line breadth. Similar to the DDD model presented in Equation 2, we estimate a DDD model of nutritional quality to examine the differential effect of FOP adoption between brands with a wider product line breadth and those with a narrower product line breadth (H2b). The model is as follows[17]:
Graph
3
In Equation 3, Product line breadthb takes a value of 1 if brand b is a brand with a wider product line breadth (i.e., a brand with a larger number of products), and 0 otherwise. All other variables and fixed effects in Equation 3 are identical to those in Equations 1 and 2.
H3a and H3b examine the variation in the effects of introduction of FOP across categories based on healthiness and competitive intensity, respectively. As stated previously, and following precedence ([40]), we classify a food product as "less healthy" if the NP score is more than or equal to 4, and we classify a beverage product as "less healthy" if the NP score is more than or equal to 1. Drawing on the average NP score of all products (from the calibration period data) in a category, we classified the 44 categories into healthy and unhealthy groups (see Table W1 in the Web Appendix). Following previous industrial organization literature ([ 9]; [12]), we operationalize competitive intensity by price dispersion as measured by the coefficient of variation. The coefficient of variation[18] is a unit-free measure of relative dispersion that helps compare price dispersion across categories where products are sold at different price levels ([48]). This measure has been widely used in the economics and management literature ([ 9]; [48]; [55]). The larger the coefficient of variation, the more dispersed the price, and the greater the competitive intensity ([ 9]; [55]). As discussed previously, we use data from the calibration period, and based on the median split of the category-specific coefficients of variation, we classify the categories into high versus low levels of competitive intensity (see Table W1 in the Web Appendix).
To test H3a and H3b, we propose the following DDD model:
Graph
4
In Equation 4, Healthyc and Competitive intensityc are the indicator variables that take a value of 1 if category c is determined to be a healthy and more competitive category, respectively, and 0 otherwise. All other variables and fixed effects in Equation 4 are the same as those in Equations 1–3.[19] The DDD estimates (γ1 and γ2) help us examine how the effect of FOP adoption varies across the category characteristics.
In Column 1 of Table 2, we present the results of the DD model shown in Equation 1. We note that the standard errors reported in the table are clustered at the category level and are heteroskedasticity robust. The DD estimate (α1) is positive and statistically significant which suggests that the adoption of FOP in a category leads to improvement in nutritional quality of products in the category. We thus find support for H1.
Graph
Table 2. Effect of FOP Nutrition Labeling on Nutritional Quality.
| | Dependent Variable: Nutritional Quality |
|---|
| | (1) | (2) | (3) | (4) |
|---|
| Main effect | FOP | .9490**(.3713) | .2527(.9653) | −.1082(.8838) | .1067(.6446) |
| Brand-level moderating effects | FOP × Premium | — | 1.3967**(.5899) | — | — |
| FOP × Product line breadth | — | — | −1.2605**(.6080) | — |
| Category-level moderating effects | FOP × Healthy | — | — | — | −1.3930**(.5745) |
| FOP × Competitive intensity | — | — | — | 1.5193**(.6081) |
| Time trend | −.0633*(.0360) | −.1067***(.0404) | −.0882**(.0362) | −.0473*(.0274) |
| Brand fixed effects | Yes | Yes | Yes | Yes |
| Firm fixed effects | Yes | Yes | Yes | Yes |
| Category fixed effects | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes |
| Category-specific time trends | Yes | Yes | Yes | Yes |
| Observations | 21,096 | 5,811 | 8,376 | 21,096 |
| R2 | .8189 | .6952 | .7145 | .8190 |
- 2 *p <.10.
- 3 **p <.05.
- 4 ***p <.01.
- 5 Notes: The focal variable of interest and its coefficient estimate (i.e., DD and DDD estimate) that is statistically significant is highlighted in bold. Robust standard errors that are clustered at the category level are in parentheses.
To better understand the effect size of the adoption of FOP labels at the product category level, we estimated the DD model (in Equation 1) with the original NP score as the dependent variable, and based on the DD estimate, we find that the introduction of FOP reduces calorie levels by approximately 42.21 kcal[20] in 100 g of food or 100 mL of beverage product when there is no change in other nutritional contents and decreases saturated fat, sugar, and sodium by approximately.53 g, 2.37 g, and 47.45 mg, respectively. Drawing on the entire set of products in the treatment categories in the post-FOP period, we find that FOP adoption leads to a reduction in calories (−12.50%), saturated fat (−12.97%), sugar (−12.62%), and sodium (−3.74%; see Table 3). To evaluate the effect size for an individual product in a more realistic setting, we identified a set of packaged food products outside the sample. Based on their actual nutritional information and serving sizes, we calculated the marginal effect of the introduction of FOP on the nutritional quality of the selected products (see Table 3).
Graph
Table 3. Effect Size of FOP Adoption for Selected Products.
| Product | g/mL Per Serving | Calories and Nutrient Amounts in an Original Packaged Food | FOP Effect (%) |
|---|
| Calories (kcal) | Saturated Fat (g) | Sugar (g) | Sodium (mg) | Calories | Saturated Fat | Sugar | Sodium |
|---|
| All products in the treatment categories | —a | 337.61b | 4.09 | 18.79 | 1,268.31 | −12.50 | −12.97 | −12.62 | −3.74 |
| Whole Grain Oats Breakfast Cereal | 28 g | 100 | .50 | 1 | 140 | −11.82 | −29.52 | −66.43 | −9.49 |
| Chocolate Peanut Butter Breakfast Cereal | 30 g | 120 | 1 | 8 | 190 | −10.55 | −15.82 | −8.90 | −7.49 |
| Butter Bread | 45 g | 120 | .50 | 4 | 210 | −15.83 | −47.45 | −26.69 | −10.17 |
| Honey Wheat Bread | 49 g | 140 | 1 | 6 | 180 | −14.77 | −25.83 | −19.37 | −12.92 |
| Four Cheese Thin Crispy Crust Pizza | 226 g | 530 | 9 | 10 | 870 | −18.00 | −13.24 | −53.62 | −12.33 |
| Four Cheese Traditional Crust Pizza | 261 g | 690 | 13 | 13 | 1,160 | −15.97 | −10.58 | −47.63 | −10.68 |
| French Vanilla Ice Cream | 99 g | 210 | 7 | 19 | 60 | −19.90 | −7.46 | −12.36 | −78.29 |
| Buttered Pecan Ice Cream | 99 g | 250 | 7 | 20 | 100 | −16.72 | −7.46 | −11.74 | −46.97 |
| Lightly Salted Microwave Popcorn | 31 g | 130 | 2 | 0 | 300 | −10.07 | −8.17 | —c | −4.90 |
| Movie Theater Butter Microwave Popcorn | 33 g | 180 | 4.50 | 0 | 330 | −7.74 | −3.87 | — | −4.74 |
| Regular Chocolate Sandwich Cookies | 34 g | 160 | 2 | 14 | 135 | −8.97 | −8.96 | −5.76 | −11.95 |
| Extra Creme Chocolate Sandwich Cookies | 36 g | 180 | 3 | 18 | 90 | −8.44 | −6.33 | −4.74 | −18.98 |
- 6 a The level of calories and amount of each nutrient of all products are standardized to a 100 g/mL in our data, and thus serving sizes are not needed to calculate the average effect size.
- 7 b Average calories across all products in the treatment categories.
- 8 c The effect size cannot be calculated because the sugar amount of the original product is zero.
- 9 Notes: Our calculations in change of the nutrient levels are based on the DD estimate (−.5272) from the model. We assume other nutrients are held constant when we calculate the effect of change of a nutrient.
H2a and H2b refer to the variation in the proposed effects of FOP labels across brands based on premium brands and product line breadth. The positive and statistically significant DDD estimate (β1 in Equation 2) suggests that the effect of FOP category adoption is stronger for premium brands (see Column 2 of Table 2). In addition, the negative and statistically significant DDD estimate (β1 in Equation 3) indicates that the FOP effect is stronger for brands with a narrower product line breadth (see Column 3 of Table 2). The spotlight analyses presented in Figure 2 (Panels A and B) illustrate that, following the adoption of FOP at the product category level, the difference between the treated and control categories in nutritional quality is larger for premium brands and brands with a narrower product line breadth. We thus find support for both H2a and H2b.
Graph: Figure 2. Spotlight analyses for the moderating effects of the brand and category characteristics.
In H3a and H3b, we proposed that the FOP effect varies across categories depending on healthiness and competitive intensity. The results suggest that the effect of FOP introduction is greater for unhealthy (vs. healthy) and for more competitive (vs. less competitive) categories (see Column 4 of Table 2). Figure 2 (Panels C and D) provides support for the hypotheses for the category-specific moderating effects, H3a and H3b. In addition, we confirm the robustness of the DDD estimates in Equations 2, 3, and 4 to the inclusion of the interaction terms between the linear time trend and the moderators (see Table W2 in the Web Appendix), continuous measures of the moderating variables and a comprehensive DDD model specification that includes all of the moderating variables (in both discrete and continuous forms) in a single model (for details, see the "Robustness Checks" subsection of the "Validation Analyses" section). In summary, we find support for all proposed moderating effect hypotheses.
To test for the role of information salience as the underlying mechanism that drives the effect of FOP adoption, we conduct the following empirical analyses.
Although food products have nutrients to encourage (e.g., fiber) and nutrients to avoid (e.g., saturated fat), as shown in Figure 1, Facts Up Front–style FOP labels are required to carry four basic icons for calories, saturated fat, sodium, and sugar (nutrients to limit) as the default format. Given this, our main argument that FOP adoption leads to salience of nutritional information on the part of consumers which, in turn, spurs food manufacturers to increase the nutritional quality of products suggests that FOP adoption has a greater impact on calorie content and the nutrient levels that are actually displayed on the FOP labels. To empirically examine this, we estimate a series of DD models of levels of calories and individual nutrients. The results in Table 4 show that FOP adoption leads to reductions in the calorie content and in sugar, sodium, and saturated fat—information displayed on FOP labels as the default format. However, we do not find a statistically significant effect of FOP adoption on the fiber, protein, and unsaturated fat levels—information that is not required to be displayed. From a theoretical perspective, these results support our argument that salience of nutritional information is the mechanism that drives the effect of FOP adoption. These results suggest that food manufacturers improve the nutritional quality of their products by decreasing the content of nutrients to limit.
Graph
Table 4. Effect of FOP Nutrition Labeling on Content of Calories and Individual Nutrients.
| Dependent Variables |
|---|
| (1)ln(Calories) | (2)ln(Saturated fat) | (3)ln(Sodium) | (4)ln(Sugar) | (5)ln(Fiber) | (6)ln(Protein) | (7)ln(Unsaturated fat) |
|---|
| FOP | −.0125***(.0045) | −.0135**(.0063) | −.0147**(.0060) | −.0108**(.0050) | −.0351(.0675) | −.0010(.0030) | −.0061(.0287) |
| Time trend | .0006**(.0002) | .0020(.0013) | .0008*(.0005) | .0000(.0003) | .0042(.0044) | −.0001(.0002) | −.0004(.0022) |
| Brand fixed effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Category fixed effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Category-specific time trends | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 21,067 | 21,092 | 20,781 | 21,074 | 21,089 | 21,054 | 14,732 |
| R2 | .8694 | .7753 | .7305 | .8509 | .7472 | .8534 | .8188 |
- 10 *p <.10.
- 11 **p <.05.
- 12 ***p <.01.
- 13 Notes: The focal variable of interest and its coefficient estimate (i.e., DD estimate) that is statistically significant is highlighted in bold. Robust standard errors that are clustered at the category level are in parentheses. Sample sizes vary across the DD models because of missing nutrient information for some products.
Following the adoption of FOP for the first time in a category, some brands adopted the FOP nutrition labeling, and others did not. We leverage this phenomenon and examine how the effect of the introduction of FOP in a category differs across adopter versus nonadopter brands. If our argument that increased salience of nutritional information due to FOP adoption is valid, we would expect FOP adopter brands to improve the nutritional quality of their products more than non-FOP adopter brands, because the nutritional information of the products of the FOP adopter brands would be more noticeable to consumers. To empirically test this, we examined photographs of the product packaging thoroughly to identify brands that launched products with FOP after the first introduction of FOP in a category. Then, we estimated a model (in the form of Equation 2) to examine the variation in the effect of FOP across these two types of brands, FOP adopters and non-FOP adopters. The result suggests that the FOP effect is stronger for FOP adopter brands (see Table W3 in the Web Appendix). This result provides further support for our argument that salience of nutritional information is the mechanism behind the effect of FOP adoption.
In this section, we present the validation analyses that we conducted to confirm robustness of our results, address potential self-selection issues, test the identifying assumptions of our DD modeling strategy, and rule out effects due to spurious correlation and/or model misspecification. Table 5 summarizes our validation analyses.
Graph
Table 5. Overview of Validation Analyses.
| Analysis | Description | Key Insights/Takeaways |
|---|
| A: Robustness Checks |
| Alternative measures of nutritional quality | For each product, the overall nutrition score is calculated by adding the Percent Daily Values (%DVs) of positive elements—fiber and protein—and (100 − %DVs) of negative elements—fat, saturated fat, cholesterol, sodium, and sugar—and dividing by the number of nutrients. The weighted overall nutrition scores are also computed by using the category-specific mean and variance of each nutrient's %DV as weights. | Our core result is robust to these alternative dependent variables. |
| Continuous moderating variables | We replace all the dichotomous moderating variables in the models with the corresponding continuous moderating variables. | Our moderating analyses results are robust to the continuous moderators. |
| Comprehensive model with all moderating effects | We estimate a comprehensive DDD model with all the interaction effects in a single model to check if the results related to the moderating analyses are robust. | Our results are robust to the comprehensive model formulation. |
| Addressing brand mortality bias | We estimate a DD model with a new sample that consists of existing brands only. | The issue of brand mortality does not bias the FOP effect. |
| New brands versus existing brands | We test whether the FOP effect differs across new brands and existing brands. | The FOP effect is stronger for new brands. |
| Addressing dominant category bias | We compute a jackknife pseudo-value to estimate the bias between the DD estimate calculated with the entire data and that calculated with the data without a specific category. | The FOP effect is not driven by a dominant category. |
| B: Self-Selection Challenges |
| Correlation between FOP adoption and category healthiness | • We conduct a t-test to examine if there is a difference between the treatment and control categories in terms of category healthiness.• We conduct a correlation analysis between FOP adoption timing and category healthiness.• We pick the treatment categories in which FOP labeling was adopted no more than six months earlier than in the control categories and estimate a DD model. | There is no statistically significant correlation between FOP category adoption and category healthiness, and thus these can assuage concerns about potential unobserved factors affecting both category-specific timing of FOP adoption and nutritional quality of products. |
| Firms present in both treatment and control categories | We estimate a DD model with only those firms that are present in both treatment and control categories. | The FOP effect is robust to the subset of the sample, and thus we can rule out a potential bias due to the possibility that the firms in the treatment and control categories are intrinsically different. |
| Alternative periods used for classification of treated group | We estimate DD models with different end points of the focal estimation time period that create different compositions of treatment and control groups. | The variation in FOP timing used for group composition does not drive the FOP effect. |
| C: Falsification Tests |
| Test of the parallel trend assumption | We estimate a model of nutritional quality with interaction terms between the treatment group indicator and (1) year dummies, (2) quarter dummies, (3) linear time trend variable in the pre-FOP adoption period. | The parallel trend assumption holds true in our study. |
| Fake treatment (placebo test) | For the treatment categories, we work with only the pre-FOP period data and treat the first half of the actual pre-FOP period as the new pre-FOP period and the latter half of the actual pre-FOP period as the fake post-FOP period and estimate the proposed DD model. | The statistically insignificant DD estimate rules out placebo effects. We confirm that the potential presence of unobserved temporal factors that can blur the FOP effect is not a concern in our study. |
| Fake treatment group | We randomly classify half of our control categories as fake treatment categories and the other half as control categories as they are and estimate the proposed DD model. | The insignificant bootstrap DD estimates indicate that the FOP effect we find is not due to any spurious effects and confirm the validity of the construction of our treatment group. |
| Fake outcomes | We estimate DD models with the following three fake outcomes: unit pack size, total pack size, and package volume. | The FOP adoption does not affect the outcomes that are not supposed to be affected by FOP adoption. |
In this section, we discuss a series of tests we conducted to verify the robustness of the results.
As an alternative measure of the nutritional quality of food and beverage products, following [36], we compute a nutrition score based on the %DVs of individual nutrients.[21] We compute the overall nutrition score of a product by adding the %DVs of positive elements (fiber and protein) and (100 − %DVs) of negative elements (fat, saturated fat, cholesterol, sodium, and sugar) and dividing by the number of nutrients (seven). The larger the overall nutrition score, the better the nutritional quality. In addition, we compute the weighted overall nutrition scores by using the category-specific mean and variance of each nutrient's %DV as weights. This helps account for the role of a nutrient in a certain category in terms of amount and variability. The estimation results of the DD models (see Table W4 in the Web Appendix) are in agreement with the main set of results and confirm the robustness of the main results to the alternative measures of nutritional quality.
To check whether the moderating analyses results are robust to continuous moderating variables, we reestimate the DDD models (Equations 2–4) with the corresponding continuous moderating variables. We confirm that the results are robust to the models with the continuous moderators (see Table W5 in the Web Appendix).
To determine whether the moderating analyses results are robust to having all the interaction effects in a single model, we reestimate a comprehensive DDD model (combining Equations 2–4). We do so with both the discrete and continuous measures of the moderating variables. We confirm that the results are robust to the comprehensive model formulation (see Table W6 in the Web Appendix).
A DD modeling approach requires the survival of the units of analysis over time to observe the change in their outcomes or behavior of interest before and after a treatment. Because the treatment occurs at the category level, and we are interested in how the introduction of FOP affects the overall nutritional quality of all products at the category level, and all the categories are present before and after FOP adoption, we believe that estimating the DD models with the sample of brands that exist before and after the FOP category adoption and those that appear after the event is not a threat to validity of the results. For a similar research design, see [ 1] and [ 7]. Following FOP adoption, firms may launch new brands with better nutritional profiles or improve the nutritional profiles of products under existing brands. Nonetheless, to establish the robustness of the results, we estimate the main model (presented in Equation 1) with a sample that consists of only brands that exist in both pre- and post-FOP adoption periods. We confirm that the main result is robust, and thus, brand mortality does not change the main results (see Table W7 in the Web Appendix).
Given that the sample consists of new and existing brands, an interesting question is whether the FOP category introduction effect differs across the two types of brands. In line with [36] argument, we expect that the FOP effect would be stronger for new brands, because improving nutrition by launching new brands is less likely to be risky than adjusting the nutritional profiles of products of existing brands. To check the potential differential effect of the introduction of FOP, we estimate a model with an interaction term between FOP and the indicator variable of new versus existing brands in the form of Equation 2. We find that the positive FOP effect is statistically stronger for new brands (see Table W8 in the Web Appendix).
To check whether a few dominant treatment categories might be driving the reported results, following [36], we compute a jackknife pseudo-value to estimate the bias between the DD estimate calculated with the entire data and that calculated with the data without a specific category ([56]). We confirm that the DD estimate based on the full data falls within the 95% confidence interval around the mean of the jackknife pseudo-values which confirms that the main result is not driven by an influential or dominant category.
Categories were assigned into the treatment and control groups based on the timing of FOP adoption at the category level. We argued that the timing of the adoption of FOP by the first adopter brand in a category is exogenous to nutritional quality of products of other brands in the category. Furthermore, we removed the first adopter brands from the treatment group categories to rule out unobserved factors that are specific to first adopter brands that may not hold for the other brands that adopt later in the category. Inclusion of year fixed effects help control for the omitted variables. In addition to year fixed effects, we included category fixed effects in the DD models to control for unobserved time-invariant factors that possibly led to differences between the treatment and control categories. The category fixed effects help absorb the category-specific factors that drive nutritional quality. Despite this set of cautious steps, one can make the argument that the firms in the treatment categories are inherently different from those in the control categories, or there may be some unobserved factors affecting both the timing of FOP adoption and the nutritional quality of a category which could contaminate the observed effect of FOP adoption. To further address concerns about potential selection biases, we conducted the following supplementary analyses.
To test whether the treatment and control categories differed in terms of their healthiness level, we conduct a t-test that compares the mean NPI scores between the treatment and control categories. The result indicates that the healthiness levels of the two groups are not statistically different (t = −1.6590, p-value =.1402). In addition, we test whether there is a correlation between the FOP adoption timing and category-specific nutritional quality. To do so, we sample data from the treatment categories only and identify the timing of FOP adoption (by the first time adopter brands) in each of the treated categories. A correlation test shows that there is no statistically significant correlation between FOP adoption timing and category healthiness (r =.2484, p-value =.1382). Finally, we pick the treatment categories in which FOP labeling was adopted no more than six months earlier than in the control categories and run the DD model. The narrow time window between these treated categories and the control categories helps us construct similar sets of treatment and control categories, and a comparison of products across these similar sets of categories further rules out any time-varying factors that could affect changes in nutritional quality. We confirm that the DD estimate is robust to the subset of data (see Table W9 in the Web Appendix).
To empirically address the possibility that the firms in the treatment and control categories are intrinsically different, we work with only firms that are present in both treatment and control categories and estimate the main DD model. The result (see Table W10 in the Web Appendix) indicates that the FOP effect on nutritional quality is still positive and statistically significant. Thus, the possibility that the reported results are driven by inherent differences between the firms in the treatment and control categories is ruled out.
Recall that we classify a category as a treatment group if we observe the FOP adoption in the "focal time period" (January 2003 to December 2011). If we shift the end point and change the focal time period to January 2003 through September 2011, the categories that adopted FOP later between October 2011 and December 2011 (which were classified as treatment categories in the original analyses) would now be classified as "control" categories. If any unobserved category-specific confounding factors were to drive the results, we would expect the effect of FOP adoption to be weaker or absent in the sample based on the new focal time period. Thus, we estimate the DD models on multiple new focal time periods with different end points (by shifting the end points by 3 months up to 12 months with a 3-month interval). The results (see Table W11 in the Web Appendix) suggest that variation in FOP adoption across categories and classification of categories based on adoption timing do not threaten the validity of the main results.
The identifying assumption behind the DD modeling approach is the parallel trend assumption, which assumes that the treatment and control groups have similar trends in the outcome of interest (nutritional quality, in our context) before the intervention (FOP category adoption, in our context). To test the validity of the assumption, following previous studies ([ 3]), we include a set of interaction terms between the group indicator variable and dummy variables for all the years before FOP adoption and estimate a model of nutritional quality. We find that the coefficients associated with the interaction terms—the "parallel-trend coefficients"—are not statistically significant (see Table W12 in the Web Appendix), which suggests the treatment and control categories were not different before FOP adoption. We also conduct a test of joint significance of the parallel-trend coefficients, and the result does not show any significant trends. We conduct these tests at the granular (quarterly) level. We find that the estimates of the parallel-trend coefficients are not statistically significant separately and jointly (see Table W13 in the Web Appendix). We also estimate a model with an interaction term between the group indicator variable and the linear time trend variable. The results show (see Table W14 in the Web Appendix) that the two groups of categories do not have different linear time trends in the pre-FOP period. These tests provide empirical support for the parallel trend assumption behind the DD approach.
Next, following economics ([18]; [39]) and marketing ([27]) literature, we conduct the following tests: the fake treatment test or the "placebo" test (see Table W15 in the Web Appendix), fake treatment group (see Table W16 in the Web Appendix), and fake outcome tests (see Table W17 in the Web Appendix). The key takeaway is that we find statistically significant results of FOP adoption in conditions when we expect to, and we do not find a statistically significant effect of FOP adoption when we do not expect to find one. The set of results, taken together, provides support for our DD identification strategy and rules out any spurious correlations in our core set of results. Nevertheless, we acknowledge that we work with observational data, and we remind readers that the causal interpretation of these results is valid subject to the identifying assumptions of the DD model.
Food labels play a key role in the strategies designed to inform and induce healthy food choice behaviors among consumers. According to a recent World Health Organization report ([29], p. vii), "Nutrition labelling is one of the policy tools that can support healthy diets, both in stimulating consumers to make informed healthier food choices and in driving manufacturers to reformulate products to avoid making unfavorable nutrient content disclosures." In this research, we conducted a systematic examination of the supply-side consequences of the voluntary adoption of a widely used FOP nutrition labeling program, the Facts Up Front–style FOP label. Next, we discuss the implications of the results for theory and practice.
There is increasing consensus among recent studies that focus on consumer response to the FOP labels that they help draw consumers' attention to nutrition information and form their perceptions of product healthiness ([25]). Studies based on purchase transaction data have established that FOP labels facilitate consumers' choice of healthier products ([57]). Thus, although the benefits of FOP labels in informing consumers about the healthiness of the products is receiving a fair amount of attention in research, there is little research on the firm side of this issue. [25] argue that "the implementation of different FOP labels can motivate manufacturers to refine their recipes, leading to healthier product assortments" (p. 375), and [15] suggest that more research is needed to examine "the impact of labeling systems on the decision of manufacturers to reformulate their products."
The present study helps fill this critical research gap in the literature by examining the issue of FOP labels from the firm side. Specifically, we theorize that adoption of FOP labels increases the salience of nutritional information and helps lower consumers' search costs for the nutritional information subsequently leading to a "nutritional information clearinghouse" effect whereby food manufacturers compete along the nutrition dimension. The results highlight the role of voluntary provision of nutrition information in improving the nutritional quality of products. Previous research in the area of mandatory provision of nutrition information (i.e., the NLEA) has suggested that although the NLEA clearly increased nutrition provision, the legislation has had an overall negative impact on brand nutrition possibly due to the perceived negative correlation between nutrition and taste ([36]). Our results suggest that voluntary FOP labels may be more effective due to the nutritional information clearinghouse effect, thus offering a different theoretical perspective and lens through which nutrition labels can be examined.
For a deeper understanding of the effect of FOP, we examine the specific brand and category characteristics for which FOP effects are likely to be enhanced. Specifically, we investigate factors for which food manufacturers have a greater motivation and opportunity to innovate. Studies that examine effects of nutrition labels have identified the moderating role of category-, brand-, or firm-level factors ([36]; [37]). For brand-level moderating factors, the present results show that the effect of FOP is greater for premium brands and brands with a narrower product line breadth. These results highlight how product differentiation and a focused product line strategy that helps lower consumer nutrition search costs serve as motivating factors for firms to innovate more after FOP adoption. At the category level, we find that the FOP effect is greater for unhealthy categories and product categories with a greater degree of competitive intensity. These results suggest that manufacturers tend to innovate more following FOP adoption in categories where there is greater opportunity to do so, such as inherently unhealthy categories and categories with intense competition, where the need to differentiate and lower consumers' nutrition search costs is greater. The result related to the FOP effect in unhealthy categories also supports findings from previous research showing that after the NLEA was enacted, brands in unhealthy categories improved nutrition more than those in healthy categories ([36]).
A key question motivating this study is, Why does FOP work in stimulating product innovation? We believe that the answer lies in understanding the underlying mechanism. We theorize and test for the role of nutritional information salience as the primary underlying driver of the FOP effects. We argue that FOP labels serve as a source of "nutritional information clearinghouse" in which they increase the salience of nutrition information and decrease consumers' cost of processing nutritional information at the point of purchase. The change in consumer behavior incentivizes manufacturers to compete on the attribute (i.e., nutrition) that aligns with consumer preferences and to develop nutritionally better products. To test this underlying mechanism, we conducted additional analyses that suggest FOP adoption in a product category lowers the calories and the amounts of saturated fat, sugar, and sodium in products. Calories, saturated fat, sugar, and sodium are the four basic elements displayed on a Facts Up Front–style FOP label. Sugar, sodium, and saturated fat are referred to by the FDA as the nutrients to limit, suggesting that consumers should try to limit their intake.[22] Because FOP labels clearly emphasize the calories and the three nutrients, and given the public emphasis on the negative health consequences of these nutrients over time ([54]), one would expect consumers to pay most attention to the calorie count and those nutrients that would induce firms to lower their content in products. Our results support this expectation, bolstering our argument for information salience as the underlying mechanism driving the FOP effect. Next, we discuss the implications of our findings for policy makers and for marketing.
Unlike nutrition claims, which can selectively highlight only the nutrients that make a product look healthier, the FOP labels we examine are standardized and present the key nutrient information from the NFP on the front of the package. However, there can still be skepticism about the implications of the effect of FOP labels in the marketplace. Our results demonstrate that FOP labels are beneficial for consumers, as the labels tend to spur overall nutritional quality improvement in a product category. Drawing on a set of packaged food products (see Table 3), we find that FOP adoption leads to a decrease in average calories (−13.23%), saturated fat (−15.39%), sugar (−25.72%), and sodium (−19.08%). In addition, food manufacturers improve products' nutritional quality by reducing the content of nutrients to limit that are actually displayed on FOP labels. This implies that policy makers, in partnership with food manufacturers and retailers, should encourage adoption of voluntary labeling programs that are standardized and transparent, such as Facts Up Front–style FOP labels, and consider options for broadening the information presented in FOP labels. We believe that policy makers should also invest in educational campaigns that inform consumers about the value of FOP labels, which would provide more incentives for food manufacturers to offer nutritionally better products.
Our results have implications for food manufacturers and grocery retailers. For food manufacturers, the result that FOP adoption can stimulate improvement in the nutritional quality of food products in the category implies that manufacturers must devote significant resources to product innovation to stay competitive. Given the result that firms innovate and produce nutritionally better products following FOP adoption, firms that lag in innovation will fail to attract enough consumer demand to survive and compete in the category. Specifically, manufacturers in unhealthy and more competitive categories can be more strategic and invest in innovation such that they are ready to provide better products following FOP adoption. For food retailers, our results suggest that they should partner with manufacturers and give them incentives to adopt FOP, as this can lead to better-quality products for their consumers, which can ultimately help in building a positive brand image. Retailers can also promote products with FOP labels, especially in more competitive and unhealthy product categories, which can spur manufacturers toward more innovation and lead to an increase in the nutritional quality of the foods over time in the category. We encourage retailers to invest in measures that help monitor and track the sales of products with FOP labels and provide this feedback to their manufacturers regularly to speed up the competitive effect of FOP labels. It is worthwhile to note how the Smart Choices logo developed by the food industry, including grocery retailers, received a lot of criticism and was eventually suspended when it started showing up on products such as Kellogg's Froot Loops cereal ([49]). Although retailers have invested in developing and promoting some FOP labeling systems, we suggest that retailers must invest in and promote a comprehensive, universal, and simple-to-use and understand FOP labeling system that consumers can trust unequivocally.
From the consumer perspective, although extant research has documented that consumers pay attention to FOP labels ([25]), we establish that FOP adoption results in nutritionally better products on retailers' shelves. Our results show that the FOP effect is greater for premium brands and brands with a narrower product line breadth. These results suggest that consumers who are looking for healthier alternatives should consider premium brands and more focused brands in terms of product line in their consideration sets. We also find that the brands that adopted FOP labeling have nutritionally better products than those that did not adopt the labeling. This suggests that the presence of a FOP label on a package is a good indicator that the product is a better choice overall than other products that do not carry FOP labels. In summary, our findings offer insights for policy makers, manufacturers, retailers, and consumers and help solidify FOP labeling in tackling the obesity epidemic.
Although this study is the first to conduct a systematic and empirical analysis of the impact of FOP adoption on nutritional quality of products, it is not without its limitations. When possible, a randomized controlled trial can help establish the causal effect of FOP adoption cleanly; however, it was not practical in this context. Thus, we relied on panel data and econometric techniques to shed light on the causal effect that is valid within the bounds of the DD modeling approach and its identifying assumptions. We focused on one widely used and standardized FOP label. We suggest that future research could examine other types of labels. Given the competitive response to FOP adoption, future research could examine the effect of FOP adoption on various market structure–related questions, such as entry and exit of brands following FOP adoption, change in brand- versus category-level sales, customer brand loyalty and underlying brand switching patterns, and marketing-mix effectiveness of brands that adopt FOP labels. We believe that this study sheds light on the importance of firms' voluntary participation in initiatives that signal stewardship of corporate social responsibility. We hope that this study encourages researchers to examine the consequences of firms' adoption of nutrition-related policy changes as public policy makers continue to find ways to encourage consumers to make healthier dietary choices.
Supplemental Material, Web_Appendix - Competitive Effects of Front-of-Package Nutrition Labeling Adoption on Nutritional Quality: Evidence from Facts Up Front–Style Labels
Supplemental Material, Web_Appendix for Competitive Effects of Front-of-Package Nutrition Labeling Adoption on Nutritional Quality: Evidence from Facts Up Front–Style Labels by Joon Ho Lim, Rishika Rishika, Ramkumar Janakiraman and P.K. Kannan in Journal of Marketing
Footnotes 1 Author Contributions The first two authors contributed equally.
2 Associate Editor Jan-Benedict E.M. Steenkamp
3 Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.
5 ORCID iDs Joon Ho Lim https://orcid.org/0000-0001-7704-1829 Ramkumar Janakiraman https://orcid.org/0000-0003-2175-7152 P.K. Kannan https://orcid.org/0000-0003-0738-0766
6 Online supplement: https://doi.org/10.1177/0022242920942563
7 1 See https://www.fda.gov/Food/GuidanceRegulation/GuidanceDocumentsRegulatoryInformation/LabelingNutrition/ucm385663.htm (accessed June 22, 2017).
8 2 We use "FOP" to refer to front-of-package and/or front-of-package nutrition labels. We also use the terms "FOP adoption" and "FOP label adoption" interchangeably.
9 3 Detailed information about Facts Up Front can be accessed at http://www.aeb.org/images/PDFs/Retail/facts-up-front-style-guide.pdf (accessed October 18, 2018).
4 We refer readers to the "Heterogeneity Across Brands: The Role of Price and Product Line Breadth" and "Heterogeneity Across Categories: The Role of Healthiness and Competitive Intensity" subsections of the "Methods" section.
5 In the GNPD, because the calorie content is given in kilocalories (kcal), we converted the calorie metric from kilocalories to kilojoules (kJ).
6 We note that the GNPD does not have detailed information on the FVN content levels. Following [22], we classify the food/beverage categories into two groups, categories with 0% FVN and categories with 100% FVN. Among the 44 product categories, Nuts, Salad, and Vegetables are included in the latter group, and all other categories are included in the former group. We note that although some categories (e.g., sauces, spreads, soups) contain FVNs of more than 40% (but less than 100%), [22] still assigned zero points for FVN content in calculating NP scores.
7 We rule out selection issues in the "Self-Selection Challenges" subsection of the "Validation Analyses" section.
8 The min-max normalization-based rescaling procedure is used to fit the desired or target range, and the procedure allows for easy interpretation of the model results. Moreover, this normalization preserves the information of the NP scores and relationship between the original data values ([26]).
9 We use the term "product" to differentiate between the two products—for example, Kellogg's Cinnamon Frosted Flakes and Frosted Flakes with Marshmallows—by the brand Frosted Flakes, which belongs to the firm Kellogg's.
10 Our models replicate the results using continuous moderating variables (see the "Robustness Checks" subsection of the "Validation Analyses" section).
11 Because the samples for the DDD models in Equations 2 and 3 are different due to missing brand information (e.g., price) during the calibration time period, we estimate the DDD models including the different moderating variables one at a time.
12 Because the distribution of prices is skewed to the right and lognormally distributed, we use the following formula of coefficient of variation for a more precise estimate: , where s is a sample standard deviation of the price after a natural log transformation ([31]).
13 Unlike the DDD models for brand-level moderating effects presented in Equations 2 and 3, the sample is common across the category-level moderating effects analyses, and thus all the interaction terms are in one DDD model.
14 For a one-unit increase in the NP score, the upper limits of the calories, saturated fat, sugar, and sodium increase by 335 kJ (= 80.07 kcal), 1 g, 4.5 g, and 90 mg, respectively ([40]). Thus, the calorie decrease attributable to FOP can be calculated with the following formula: 80.07 kcal × −.5272 (DD estimate).
15 Based on a 2,000 calorie diet, DVs for fat, saturated fat, cholesterol, sodium, sugar, fiber, and protein are 65 g, 20 g, 300 mg, 2,400 mg, 50 g, 25 g, and 50 g, respectively. There is no recommended DV for sugar; however, the newly designed NFP includes the DV for added sugar (50 g). Because the data do not distinguish added sugar from sugar, we use the same recommended DV for sugar. More detailed information can be accessed at http://www.fda.gov/food/ingredientspackaginglabeling/labelingnutrition/ucm274593.htm (accessed December 12, 2018) and https://www.fda.gov/food/new-nutrition-facts-label/daily-value-new-nutrition-and-supplement-facts-labels (accessed April 5, 2020).
16 See https://www.fda.gov/food/nutrition-education-resources-materials/how-understand-and-use-nutrition-facts-label (accessed August 27, 2019).
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By Joon Ho Lim; Rishika Rishika; Ramkumar Janakiraman and P.K. Kannan
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Converging on a New Theoretical Foundation for Selling
This article demonstrates that the sales literature is converging on a systemic and institutional perspective that recognizes that selling and value creation unfold over time and are embedded in broader social systems. This convergence illustrates that selling needs a more robust theoretical foundation. To contribute to this foundation, the authors draw on institutional theory and service-dominant logic to advance a service ecosystems perspective. This perspective leads themto redefine selling in terms of the interaction between actors aimed at creating and maintaining thin crossing points—the locations at which service can be efficiently exchanged for service—through the ongoing alignment of institutional arrangements and the optimization of relationships. This definition underscores how broad sets of human actors engage in selling processes, regardless of the roles that characterize them (e.g., firm, customer, stakeholder). A service ecosystems perspective reveals ( 1) that selling continues to be an essential activity, ( 2) how broader sets of actors participate in selling processes, and ( 3) how this participation may be changing. It leads to novel insights and questions regarding gaining and maintaining business, managing intrafirm and broad external selling actors, and sales performance.
Over the last decade, sales scholars and practitioners have debated what selling entails, how salespeople participate in value creation, and whether the importance of salespeople is increasing or decreasing. The commonly described catalyst for these debates is a rising degree of market complexity caused by increasing customer demands, globalization, buying and selling centers, number of offerings, technological advancements, competitive challenges, and buyer access to information (see Hunter and Perreault 2007; Jones, Chonko, and Roberts 2004; Moncrief and Marshall 2001; Rackham and DeVincentis 1998; Schmitz and Ganesan 2014; Sheth and Sharma 2008).
While markets are continually changing, we caution against premature conjectures that these changes necessarily alter what selling entails, how salespeople participate in value creation, and/or the importance of salespeople. Instead, we suggest these changes point toward the inadequacy of traditional, restricted, firm-centric, unidirectional, and dyadic views of sales processes. These changes, therefore, point to the need for a more robust theoretical foundation that better explicates the processes and roles of selling in value cocreation through market exchange.
The contemporary sales literature seems to confirm this contention. For example, it indicates a changing view of the sales process from one that is linear and focused almost entirely on the buyer-seller dyad to one that is nonlinear and involves many actors (Dixon and Tanner 2012; Moncrief and Marshall 2001). This literature emphasizes the importance of intrafirm and external actors to selling and sales processes (Bolander et al. 2015; Plouffe et al. 2016) and points to the broadening and blurring of sales-oriented tasks and responsibilities to include those traditionally associated with other roles as reasons why a more holistic approach is needed in research and practice (Hughes, Le Bon, and Malshe 2012; Rapp et al. 2017). Considered together, the literature appears to recognize a need for a "revised perspective" (MacInnis 2011) that can account for the multidirectional nature of sales processes and how these processes are situated in complex, dynamic exchange systems of value creation.
This revision of perspective reflects a broader transition in the understanding of value creation both within and outside of marketing. Compared with more traditional models of firm-created, value-laden output that is delivered to a waiting "consumer," new models portray value as an outcome (e.g., Vargo and Lusch 2004) cocreated (e.g., Prahalad and Ramaswamy 2004) in networks (e.g., Hakansson and Snehota 1995) and systems (e.g., Edvardsson et al. 2014). The roles of institutions (i.e., practices, assumptions, norms, laws, beliefs, and values, among other coordinating heuristics) are also becoming apparent (e.g., Humphreys 2010; Press et al. 2014; Vargo and Lusch 2016). Furthermore, this revision of perspective reflects recent work on the conditions under which transactions take place (e.g., Baldwin 2008).
We invoke the service ecosystems perspective of servicedominant (S-D) logic, which is based on the premise that broad sets of actors dynamically integrate and apply resources through service-for-service exchange (i.e., the application of knowledge for the benefit of another) to cocreate value (Vargo and Lusch 2004, 2016). We suggest this service ecosystems perspective offers a robust theoretical framework for examining selling and sales-related phenomena. It mandates an understanding of institutions and institutional arrangements (i.e., "interdependent assemblages of institutions" [Vargo and Lusch 2016, p. 6]) as coordinating mechanisms that enable and constrain value creation practices.
A service ecosystems perspective increases the range of activities and the number of actors considered to be involved in selling. This perspective expands the view from dyadic exchange to broader value creation practices influenced by institutional arrangements and institutionalization processes. It accommodates micro-level outcomes, such as sales performance (e.g., sales revenue, percentage of quota met) and buyer-seller relations (e.g., relationship quality, perceived value) (Ahearne et al. 2013; Hall, Ahearne, and Sujan 2015; Hughes, Le Bon, and Rapp 2013; Mullins et al. 2014) and also highlights the importance of often-ignored emergent, meso- and macro-level institutional structures related to selling, such as markets and industries and their roles both as outcomes and contexts (Giddens 1984). To elaborate our theoretical foundation, we also draw on the work of Baldwin (2008; Baldwin and Clark 2000), which explores the interplays of formal and relational exchanges across "thin" and "thick" crossing points related to these emergent structures. This showcases the importance of institutional work—the maintaining, disrupting, and changing of institutional structures (Lawrence and Suddaby 2006)—in selling processes.
This article makes three contributions to the sales literature. First, it offers a theoretical foundation that reframes conceptions of what selling is and the activities it encompasses. Using the service ecosystems perspective of S-D logic (Vargo and Lusch 2004,2016), which highlights that value is always cocreated by multiple actors, we show that salespeople and other actors foster service-for-service exchange and value cocreation by participating in institutionalization processes. These institutionalization processes include the creation of knowledge structures that aid in sense making and legitimation (Phillips, Lawrence, and Hardy 2004; Suchman 1995; Weick 1995). To explicate the mechanisms for these processes, we introduce a framework that points to discursive and dialogical interactions among broad sets of actors.
Second, the article contributes to the sales literature by reconceptualizing and broadening the scope and roles of various actors in the sales process. Traditionally, selling refers to an attempt by a salesperson to persuade a buyer to accept a value proposition. Alternatively, we define selling in terms of the interaction between actors aimed at creating and maintaining thin crossing points—the locations at which service can be efficiently exchanged for service—through the ongoing alignment of institutional arrangements and the optimization of relationships. As we detail, this reconceptualization highlights the importance of distinguishing between salespeople and broader sets of actors who engage in selling activities. Thus, we use the "salespeople" classification for actors whose professional roles (i.e., job descriptions and titles) are sales-centric and the broader "selling actor" classification for all actors who perform selling regardless of their role. That is, the selling actor classification includes salespeople but is not limited to them.
Third, this article contributes to the sales literature by addressing unresolved questions about whether changes in markets are changing the roles of salespeople. As Rackham and DeVincentis (1998) and Jones et al. (2004) highlight, some theoreticians and practitioners believe that changes in modern markets will diminish the strategic importance of salespeople, perhaps making them obsolete. Conversely, others argue that modern markets are increasing the strategic importance of salespeople (Cron et al. 2014; Hunter and Perreault 2007) and that their importance is likely to increase. A service ecosystems perspective reconciles these inconsistent viewpoints by reframing the fundamental mechanisms of selling. This perspective illustrates how markets have always been complex and dynamic and how selling actors have always been and continue to be involved in institutionalization processes that resolve inconsistencies and contradictions in the institutional arrangements of various actors. However, changes in modern communication tools enable nontraditional actors to engage in selling and may, ironically, mask selling processes.
The remainder of this article is structured as follows. First, we show that the sales literature, like the broader marketing literature, is converging on a service ecosystems perspective that views value as cocreated through the involvement of broad sets of actors. Second, we propose that this perspective can serve as the foundation for a unifying theoretical framework for sales. Third, we describe the characteristics of "crossing points," the locations at which service is exchanged for service, and we highlight the role of institutional arrangements in shaping these crossing points. Fourth, we redefine "selling," introduce a discursive framework that explicates the role of narratives (i.e., written, spoken, or symbolic accounts that offer interpretation, explanation, or meaning to events or actions [Czarniawska 2004]) in selling processes, and highlight the fundamental similarities among actors in what are traditionally referred to as sales and nonsales roles. Finally, we discuss the theoretical and practical implications of this research. Throughout, we draw on concepts and literatures that might be unfamiliar to some readers. To assist, we define key terms in Table 1.
TABLE: TABLE 1 Definitions of Key Terms
TABLE 1 Definitions of Key Terms
| Key Term | Definition | Source |
| Institutions | "Institutions comprise regulative, normative, and cultural cognitive elements that, together with associated activities and resources, provide stability and meaning to social life" (p. 56). | Scott (2013) |
| Institutional arrangements | Institutional arrangements are "interdependent assemblages of institutions" (p. 6). Institutional arrangements serve as sets of "value assumptions, cognitive frames, rules, and routines" (pp. 14-15) that guide actors in exchanging service with other actors. | Vargo and Lusch (2016) |
| Institutional work | The purposive action of actors "aimed at creating, maintaining, and disrupting institutions" (p. 217). | Lawrence and Suddaby (2006) |
| Service ecosystems | Service ecosystems are "relatively self-contained, self-adjusting system[s] of resource-integrating actors connected by shared institutional arrangements and mutual value creation through service exchange" (pp. 10-11). | Vargo and Lusch (2016) |
| Selling | The interaction between actors aimed at creating and maintaining thin crossing points—the locations at which service can be efficiently exchanged for service—through the ongoing alignment of institutional arrangements and the optimization of relationships. | Current article |
| Subsystem or module | A subsystem or module is "a group of elements, such as tasks, that are highly interdependent on one another, but only minimally dependent on what happens in other modules" (p. 63). | Baldwin and Clark (2000) |
| Aligned institutional arrangements of service exchange | The institutional arrangements that facilitate service exchange. Set of institutions related to the knowledge, abilities, skills, and other resources that will be reciprocally exchanged, as well as how and when they will be exchanged. | Adapted from Baldwin and Clark (2000); Kjellberg and Helgesson (2006) |
| Narrative infrastructure | The alignment of multiple stories that, through their compelling character, gain the capacity to shape the institutional arrangements of systemic actors. | Deuten and Rip (2000) |
The "birth" of the modern salesperson is often attributed to the late-nineteenth and early-twentieth century with the development of mass manufacturing (Friedman 2005). Because of the influence of classical and neoclassical economics, value was then thought to be created and embedded in goods by selling firms through the manufacturing process (Vargo and Lusch 2004). The role of salespeople was generally perceived to comprise the facilitation and negotiation of the transfer of value from sellers to buyers. This view contributed to a transactional selling orientation that emphasized short-term outcomes, a clear winner in exchange, and the salesperson's ability to manipulate buyers to produce self-serving results (Jolson 1997).
However, since the 1970s, researchers and practitioners have increasingly recognized the importance of relationship selling. Relationship selling emphasizes the roles of salespeople in developing and maintaining relationships with buyers for mutual long-term benefits (Dwyer, Schurr, and Oh 1987; Weitz and Bradford 1999). As Jolson (1997) explains, "Instead of viewing selling as a series of struggles that the salesperson must win from a steady stream of prospects and customers of all sizes and shapes, relationship selling or partnering focuses on the building of mutual trust within the buyer-seller dyad with a delivery of anticipated, long-term, value-added benefits to buyers" (p. 76).
Recent sales orientations, such as consultative and enterprise selling (Rackham and DeVincentis 1998), accentuate characteristics of relationship selling (e.g., trust, long-term emphasis on benefits). Such orientations also increasingly question narrow buyer-seller dyads and point out that selling and value creation unfold over time in complex systems involving many actors.
Consultative selling, for example, which Rackham and DeVincentis (1998) attribute to increasingly sophisticated buyers and buying processes, emphasizes the importance of salespeople providing buyers with information, helping buyers discover and understand needs, determining and providing adequate and often customized solutions, performing nonselling tasks (e.g., planning, analysis, preparing proposals), and involving additional personnel in sales efforts. Such tasks necessitate the awareness and participation of broad (sets of) actors in value creation (e.g., competitors and collaborators of both the selling and buying organizations, intra- and interfunctional actors). Only with such awareness and participation can salespeople learn and communicate the tailored ramifications of competitors and other actor developments, determine and communicate how the seller and selling organization can benefit the buyer's organization, and identify and coordinate the involvement of other actors, among other things. A Bose salesperson, for example, makes tailored proposals to automotive manufacturers based on the broad involvement of actors (e.g., procurement, engineering, design, marketing) from the buying and selling organizations as well as other market actors (e.g., industry experts, other customers) to transfer home and music venue audio technology to optimize vehicle specific sound dynamics.
Enterprise selling adopts and extends the principles of consultative selling to emphasize that buyers aim to benefit from the knowledge and skills of the entire selling organization. That is, as Rackham and DeVincentis (1998) describe, enterprise selling emphasizes developing close-knit buyer-seller interfaces to leverage the knowledge sets and skills of many different actors and functions of both the selling and buying organization to create value. Therefore, enterprise selling often results in even broader and deeper integration of the buying and selling organization than does consultative selling. It also results in broader awareness and participation of a greater number of actors involved in value creation, given the numerous cross-functional and cross-organizational actors involved who themselves are embedded in networks of actors. Consequently, any individual actor or function has limited ability to initiate and maintain an enterprise relationship. Consider, as an example of enterprise selling, Amazon's solutions for small businesses. As of 2017, such solutions include access to a rich e-commerce platform, as well as vast services (e.g., customer service, multichannel fulfillment, loans, and information technology) that broadly integrate both organizations' knowledge sets and skill bases.
In line with an enterprise selling orientation, scholars (e.g., Bolander et al. 2015; Dixon and Tanner 2012; Friend and Malshe 2016; Hughes, Le Bon, and Malshe 2012; Macdonald, Kleinaltenkamp, and Wilson 2016; Plouffe et al. 2016; Rapp et al. 2017) are increasingly recognizing that selling and value creation unfold over time in complex systems involving many actors. Macdonald, Kleinaltenkamp, and Wilson's (2016) findings, for example, propose that value emerges over time and that value propositions are mutually defined and depend "on the quality not only of the supplier' s resources and processes but also of customer resources and processes as well as of the joint resource integration process" (p. 97). Much like practitioners, Dixon and Tanner (2012) report that scholars are beginning to view the selling process as nonlinear and involving many actors instead of viewing it as a linear multistep process that may focus too closely on the buyer and salesperson. Others (e.g., Hughes, Le Bon, and Malshe 2012; Rapp et al. 2017) argue that the sales function is increasingly broadening, blurring with other functions, and reciprocally influencing other firm functional areas and that these are reasons why a holistic approach is needed.
To evaluate the extent to which the sales literature employs a systemic perspective, we performed a frequency analysis of articles in marketing's leading generalist journals (i.e., Journal of Consumer Research, Journal of Marketing, Journal of Marketing Research, and Marketing Science) as well as the specialized Journal of Personal Selling & Sales Management, whose abstracts used terms characteristic of a systemic perspective. As Figure 1 shows, the results, which are consistent with our observations of work published in other reputable outlets, indicate that the sales literature is increasingly adopting such a systemic perspective. Table 2 depicts the evolution of the three discussed sales orientations to further support our conclusion. Next, we argue that this systemic perspective requires a theoretical foundation that recognizes the roles of institutional arrangements in enabling and constraining value creation practices.
TABLE: TABLE 2 Evolving Perspectives Within the Sales Literature
TABLE 2 Evolving Perspectives Within the Sales Literature
| | Relationship |
| Transactional | Consultative | Enterprise |
| Perception of value creation | The selling firm creates value and delivers it to the buying firm. | The selling firm creates value and delivers it to the buying firm. | The selling firm cocreates value with the buying firm. |
| Perceived ability of salesperson to influence purchase | High | High to medium | Medium to low |
| Competition and cooperation between sellers and buyers | Salespeople and buyers compete with one another to win, at the expense of the other. | Selling and buying actors collaborate with one another to facilitate win-win exchange. | Many actors from the selling and buying firm collaborate with one another to facilitate win-win exchange. |
| Involvement in Exchange |
| Selling actor | Salespeople find prospective buyers and assess whether the prospective buyer should be 'sold' to. Then, they deliver value propositions, negotiate exchange terms, and coordinate the flow of value from the selling firm to the buying firm. | Salespeople and buyers develop trust-based, mutually beneficial long-term relationships. Salespeople provide buyers with information, help buyers discover and understand needs, determine and provide a solution, and involve other relevant actors from the selling firm. | Salespeople and many other cross-functional actors from both the selling and buying firms aid the development of trust-based, mutually beneficial long-term relationships and work together on tasks that result in a deeper integration of both organizations. |
| Number of actors | Two (salesperson and buyer) | Two to several (increasing recognition of broader actor involvement both in selling and buying) | Many (broad involvement of buying and selling actors) |
Notes: • = selling actor; O = buying actor.
As Scott (2013) explains, "Institutions are multifaceted, durable social structures made up of symbolic elements, social activities, and material resources" (p. 57). Institutions "provide stability and meaning to social life" (p. 56) and, thus, efficiently and often effectively guide actors' practices. Vargo and Lusch (2016) posit that institutions permit coordination among actors and "enable actors to accomplish an ever-increasing level of service exchange and value cocreation under [inherent] time and cognitive constraints" (p. 11). Therefore, the study of institutions can aid understandings of what selling is and how selling actors facilitate value cocreation for their own firms, buying firms, and broader sets of actors.
Early marketing literature (e.g., Alderson 1957; Arndt 1981; Duddy and Revzan 1953; Hunt 1981; Revzan 1968; Weld 1916) emphasizes systemic and institutional approaches that account for actors' functions, roles, interactions, and relational mechanisms as foundational to marketing. Despite this early recognition, systemic and institutional thought has not received prominent attention in the marketing and sales literature streams. However, contemporary marketing work (e.g., Edvardsson et al. 2014; Hillebrand, Driessen, and Koll 2015; Vargo and Lusch 2016; Webster and Lusch 2013) is revitalizing awareness that systemic and institutional thought is foundational to marketing and, arguably, also to sales. Edvardsson et al. (2014, p. 303), for example, state that institutional arrangements "coordinate the activities of resource integration by shaping the actors' value cocreation behavior in service systems," and Vargo and Lusch (2016, p. 5) claim that institutional arrangements are "the keys to understanding human systems and social activity, such as value cocreation, in general."
This contemporary marketing literature is greatly influenced by sociological and organizational theory, which has made significant progress in overcoming overly rational and static views on institutions. Specifically, scholars such as Bourdieu (1977), Giddens (1984), and DiMaggio (1988) clarify the enabling and constraining properties of institutions by addressing the tensions between structure (i.e., normative constraints) and agency (i.e., the ability to act independently). Similarly, Scott (2013) claims that "institutions provide stimulus, guidelines, and resources for acting as well as prohibitions and constraints on actions" (p. 58) and argues that institutions comprise regulative, normative, and cultural-cognitive elements.
The regulative element comprises processes that have "the capacity to establish rules, inspect other's conformity to them, and as necessary manipulate sanctions—rewards or punishments—in an attempt to influence future behavior" (Scott 2013, p. 59). These sanctions can be formal (e.g., licenses or court punishments) or informal (e.g., losing or gaining face through shaming or legitimizing activities) (North 1990). The normative element describes prescriptive, evaluative, and obligatory dimensions of social life. This element emphasizes values and norms and how these values and norms shape actor roles. Accordingly, the normative element emphasizes desired ends (e.g., goals, objectives) as well as how actors may pursue them.
Finally, the cultural-cognitive element comprises "the shared conceptions that constitute the nature of social reality and create the frames through which meaning is made" (Scott 2013, p. 67) or, stated alternatively, what underlies the habitual actions of actors. Jointly, these three pillars span the conscious and the unconscious, or, similarly, the legally enforceable and the taken-for-granted elements of institutions (Hoffman 2001; Scott 2013).
In marketing, the three institutional pillars are used both explicitly and implicitly. For example, work on industries, channels, and strategic orientations by Humphreys (2010), Grewal and Dharwadkar (2002), and Press et al. (2014) explicitly highlights the importance of these institutional pillars.
However, most work examining selling and buyer-seller relationships has addressed these three institutional pillars implicitly and atomistically. Not surprisingly, much work on selling and buyer-seller relationships has focused on regulative elements that can be monitored and sanctioned, such as formal contracts that often define responsibilities, measures, and compensations. Dwyer, Schurr, and Oh (1987), for example, highlight the importance of contractual obligations, such as exchange timing, planning, and relative allocation of benefits and costs, in relationship development processes between sellers and buyers.
However, the costs of employing comprehensive formal contracts to account for every responsibility, measure, and compensation when exchanges are complex can become excessive (Baldwin 2008) and can potentially surmount the value offered by the exchange itself. This limitation of incomplete contracts highlights the importance of the normative pillar in structuring economically efficient relationships. Complementing Dwyer, Schurr, and Oh (1987), Heide and John (1992) as well as Cannon, Achrol, and Gundlach (2000) point to the importance of relational norms, such as flexibility, solidarity, mutuality, harmonizing of conflict, and restraint in the use of power, to safeguarding relationships. The normative element, however, is not limited to relational norms and contracts but also emphasizes what ends are desired as well as how actors may pursue them. The sales literature, as stated, has argued that salespeople increasingly take on the role of knowledge brokers and consultants that aid buyers, their own firms, and other actors in better understanding insights and implications of ever-changing problems, markets, and potential solutions to co-create mutual long-term benefits (Rapp et al. 2014; Sheth and Sharma 2008; Verbeke, Dietz, and Verwaal 2011).
Finally, the cultural-cognitive element is also crucial in understanding buyer-seller relationships and selling because cultural models "are heterogeneously distributed across a population and serve as cognitive resources and templates that help people navigate the world around them" (Blocker et al. 2012, p. 23). Work on consumer culture theory, for example, highlights not only "the multiplicity of overlapping cultural groupings" but also how "product symbolism [and] ritual practices" shape patterns of behavior and sense making (Arnould and Thompson 2005, pp. 869-70). Similarly, work on the social construction of technology has highlighted that resources are "socially constructed by [systemic] actors through the different meanings they attach to [them] and the various features they emphasize and use" (Orlikowski 1992, p. 406). Consequently, selling and exchange practices cannot be understood without taking the cultural- cognitive element into consideration.
The previous two sections showcase that the sales and marketing literature streams are converging on a systemic perspective that highlights the importance of institutional arrangements. To provide a theoretical foundation for this perspective, we introduce the S-D logic framework and its core contentions. Service-dominant logic emphasizes that "marketing activity, and economic activity in general, is best understood in terms of service-for-service exchange" (Vargo and Lusch 2017). Service, in this framework, is conceptualized as the application of one actor's resources for the benefit of another actor. Service-dominant logic posits that value cocreation takes place in systems, because the resources used in service exchange typically come from a variety of private, public, and market-facing sources—that is, from a variety of other actors. Furthermore, S-D logic asserts that actors' resource integration and value cocreation practices are enabled and constrained by institutional arrangements (Vargo and Lusch 2016).
In short, S-D logic theorizes that value cocreation takes place in service ecosystems—that is, "relatively self-contained, self-adjusting system[s] of resource-integrating actors connected by shared institutional arrangements and mutual value creation through service exchange" (Vargo and Lusch 2016, pp. 10-11). These institutional arrangements can be observed at multiple levels of aggregation. They include relative perspectives of micro-level institutions of individuals, groups, and firms; meso-level institutions, such as those associated with professions, markets, or industries; and macro-level societal institutions (Lawrence and Suddaby 2006; Thornton, Ocasio, and Lounsbury 2012; Vargo and Lusch 2016). In the following subsection, we expound on the systemic exchange of specialized knowledge and skills, which is foundational to S-D logic.
Simon (1996) notes that dynamic social systems, such as the ecosystems in which actors exchange service, often are composed of interdependent subsystems. These subsystems or modules can be defined as "group[s] of elements, such as tasks, that are highly interdependent on one another, but only minimally dependent on what happens in other modules" (Baldwin and Clark 2000, p. 63). As we have stated, the specialized knowledge and abilities required for value creation often come from a variety of other actors or groups of actors (i.e., relatively independent subsystems). In such subsystems, actors—whether they are individuals, teams, firms, and so on—work on a limited number of tasks that are part of a larger task system in which actors exchange resources and cocreate value. These subsystems permit actors to mitigate some of the restrictions of their limited cognitive abilities. That is, actors usually participate in value cocreation processes without the knowledge to fully understand or perform entire sets of these processes. This specialization results in information asymmetries among exchanging actors because these actors need to possess only the knowledge required to complete their tasks and to coordinate with the tasks of others. Herein, we explain that selling enables this coordination.
Mass production and other developments designed to improve effectiveness and efficiency have, arguably, contributed to an increase in the number of subsystems of many service ecosystems by separating production and use tasks. Similarly, developments in communication and other technologies have, at least partly, resulted in growing specialization within selling and buying processes. Firms, for example, have increasingly modularized selling and buying into multiple subsystems, as exemplified by the prevalence of salesperson categorizations (e.g., inside vs. outside salespeople, hunters vs. farmers) and both selling and buying centers (i.e., multiple actors specializing in functional subareas or tasks). As a result, salespeople have become increasingly tasked with coordinating the resources and actions of various actors across their firm, buyers' firms, and other actors (e.g., third-party solution providers, regulatory bodies). In the following subsection, we discuss the coordination of resources among subsystems by introducing the concept of crossing points.
Baldwin (2008) refers to locations where transfers of material, energy, and information between two subsystems occur, such as the one between a service provider and a service beneficiary, as a "crossing point." Thus, using the lexicon of S-D logic, a crossing point can be viewed as the location at which service can be exchanged for service. Baldwin indicates that crossing points can be "thin" or "thick." Thin crossing points permit exchange through shallow and simple interactions, whereas thick crossing points require actors to develop deep and complex interactions to exchange with one another (Baldwin and Clark 2000).
In highly institutionalized markets, for example, many crossing points are relatively thin because of established regulations, laws, relational and formal contracts, conventions, and shared meanings, which keep transaction costs relatively low (North 1990). For such thin crossing points to form, exchanging actors must align on "common ground design rules" (Baldwin and Clark 2000). These rules consist of the mutual definition of what is being reciprocally exchanged and the norms and representations that guide exchange practices (Kjellberg and Helgesson 2006).
Thick crossing points, in contrast, such as those associated with discontinuous solutions and newly forming markets, lack many of these common ground design rules. Consequently, when crossing points are thick, exchange may be prevented or require the formation of deeper and more complex interactions, such as the formation of new relational contracts (North 1990). Consider, for example, self-driving cars. Before self-driving cars can be efficiently exchanged, meanings and perceptions regarding these cars (e.g., safety, legality) and exchange practices (e.g., ownership vs. on-demand ordering) must become mutually aligned, which requires deeper, costlier, and more complex actor involvement.
The formation of common ground design rules can be viewed as the emergence, stabilization, and destruction of predominant meanings and uses of resources through institutionalization (see also Pinch and Bijker 1984). Viewed from a service ecosystems perspective, common ground design rules can be conceptualized as the aligned institutional arrangements for service exchange that guide the meanings of resources and their integration practices. These aligned institutional arrangements facilitate service exchange and often make it less costly for actors to exchange.
Consider, as an example, the early sales strategy of Salesforce.com, with its software as a service (SaaS) solution. In 1999, when Salesforce was founded, client-server software solutions, which stored data behind company firewalls and were often only accessible on company sites, dominated the customer relationship management (CRM) industry. Salesforce's SaaS solution, in contrast, stored the customer and prospect data, as well as the underlying CRM software, in the cloud. This offered great benefits to users with regard to accessibility, scalability, flexibility, and cost. However, storing proprietary customer information in the cloud was deeply incompatible with existing institutional arrangements regarding data security of a broad set of actors, such as users, managers, information technology (IT) professionals, and other industry experts.
This example highlights that service exchange often requires complex descriptions, information exchange, negotiations, trust, and unconscious cultural-cognitive alignments of service expectations among many actors. Arguably, service-forservice exchange can only be understood by observing institutional elements, such as laws and regulations, written or oral contracts, relational norms, perceptions of solutions, and shared conceptions of acceptable business practices, in combination. It is therefore necessary to view selling and institutional alignment holistically, instead of only addressing institutions in somewhat disparate subcategories. Such a holistic perspective can highlight situations in which ruptures within and among the institutional elements create opportunities for change (Thornton, Ocasio, and Lounsbury 2012). Similarly, a holistic perspective can expose situations in which aligned institutional arrangements create lock-ins and path dependencies that can suppress the selling efforts of actors aiming to bring about new solutions. That is, as we explain next, aligning institutional arrangements for service exchange (i.e., the thinning of crossing points) can simultaneously thicken the crossing points for competing solutions.
A holistic service ecosystems perspective helps connect and extend the existing sales literature's implicit and piecemealed focus on institutions by providing a more robust and encompassing perspective through which to examine selling efforts. While the sales literature has begun to emphasize the roles of salespeople in cooperating with many actors, both internal and external to the selling firm (Bolander et al. 2015; Plouffe et al. 2016), it often underemphasizes broader and more indirect processes through which systemic actors collectively influence aligned institutional arrangements for service exchange. This is problematic because, as we have stated, resources used in value creation practices are typically sourced, directly and indirectly, from many private, public, and market-facing sources. Most of these practices require a multitude of resources and, consequently, a multitude of thin crossing points.
The Salesforce example helps clarify this idea. Salesforce recognized early that a widespread shift from a client-server to a SaaS solution could only be achieved by the thinning of multiple crossing points among a broad range of actors. Implementing a CRM solution requires expertise from users, IT professionals, vendors, finance and accounting personnel, external implementation consultants, management, and many other actors. Consequently, Salesforce employees directed their selling efforts toward multiple actor groups including customers, prospects, journalists, bloggers, and internal employees, because all these actors were involved in the alignment of the institutional arrangements required for service exchange.
Furthermore, recognizing the importance of nonadopters in institutional developments, Salesforce spent time with large enterprises—prospects that they were not initially able to serve—to learn what additional functionality would be required to make them consider the SaaS solution. That is, Salesforce recognized the importance of nonusers in institutional alignments. As Benioff and Adler (2009) state, Salesforce might have discovered some of the needs of large enterprises on its own, but, without this dialog, they would not have learned the context in which perceptions of needs were formed.
In summary, the service ecosystems perspective highlights that systemic actors create mutual value through service exchange, guided by shared institutional arrangements. This perspective can aid the convergence of the sales literature on a truly systemic and institutional view by highlighting the need to zoom out beyond the buyer-seller dyad to a view that includes a broad range of systemic actors who all participate in the shaping of value cocreation practices. This broader view does not diminish the importance of understanding the buyer-seller dyad; rather, it highlights that fully understanding value cocreation practices requires looking at the involved institutional elements from different levels of aggregation because dyads are always embedded in broader social systems (Chandler and Vargo 2011). In the next section, we briefly discuss institutional work in service ecosystems—that is, the creation, maintenance, or disruption of institutions—before we offer a more transcending definition of selling.
Multiple strands of institutional literature have made substantial progress in explaining the tension between agency (i.e., the capacity of actors to make choices independent of the influence of structure) and structure (i.e., the extent to which institutional arrangements determine the thoughts and behaviors of actors) with regard to institutional change. The tension between agency and structure is essential in the context of selling because this tension is foundational to understanding whether, and to what extent, an actor can influence the institutional arrangements that shape perceptions of problems and the resource-integration practices that serve as solutions.
DiMaggio (1988), an institutional theorist who emphasized the agency of actors, introduced the concept of the "institutional entrepreneur," which refers to an actor who initiates changes that contribute to creating new or transforming existing institutional arrangements. It is tempting to view salespeople and other selling actors as institutional entrepreneurs because creating new and transforming existing institutional arrangements closely aligns with traditional views of salespeople (i.e., persuading buyers to enact desired exchange practices). Dixon and Tanner (2012), for example, indicate that "salespeople today must see their role as the architect for change in their customers' worlds" and that "salespeople add value when they can challenge the existing paradigms and provide a better decision-making process than the one used currently by a customer" (p. 12). Similarly, Dixon and Adamson (2011) encourage salespeople to challenge the ways buyers think (i.e., change their institutional arrangements).
More recent institutional literature, however, has emphasized that actor involvement in change processes is always broad and systemic. That is, this literature provides a more balanced view of agency and structure. Influenced by seminal work on practice theory (e.g., Bourdieu 1977; DiMaggio 1988; Giddens 1984; Oliver 1991), Lawrence and Suddaby (2006) illustrate how institutional change results from the activities of various interconnected actors as they repair and conceal tensions and conflicts—while also reinforcing similarities—in their existing institutional arrangements. Thus, as Zietsma and McKnight (2009) elucidate, institutional change always involves multiple actors who, iteratively and nonlinearly, bring about (imperfect) alignments in their institutional arrangements. This implies that, at least singly, selling actors' behaviors may not be as influential in changing the institutional arrangements of buying actors, such as the enactment of new value cocreation practices, as much of the sales literature suggests.
Furthermore, selling actors need to be viewed as engaging in not only the change and disruption of institutional arrangements but also their maintenance. "Institutional work," as Creed, DeJordy, and Lok (2010, p. 1337) point out, is not "aimed at either the creation, maintenance, or disruption of institutions but can paradoxically involve more than one of these categories at the same time." Even the most transformative change is institutionally embedded and, therefore, relies on existing resources and skills (Giddens 1984; Lawrence and Suddaby 2006). The innovative SaaS solution, for example, maintained many of the institutions associated with client-server-based CRM software. Foundational to all CRM solutions, for example, is the need to store and manage customer and prospect information in one central location to help firms improve interactions, gain access to information, and automate selling and marketing activities.
We have highlighted that value unfolds over time through the integration of resources in a social context (i.e., relationships and institutions). Thus, aligned institutional arrangements for service exchange are not limited to expectations for discrete exchange events. Rather, they represent the outcomes of systemic institutionalization processes that guide how resources are perceived and integrated over time. However, institutional alignments are always imperfect and temporary because the nested nature of institutional arrangements results in continual incompatibilities. That is, these alignments often result in frictions within and among institutional arrangements that span regulative, normative, and cultural-cognitive elements. As Scott (2013) notes, these incompatibilities and frictions often provide the conditions for institutional change.
Selling is often defined as a paid, promotional, interactive, and personal approach involving clearly defined buyer and seller roles. Citing changes to markets and sales processes as reasons why a new definition of selling is needed, Dixon and Tanner (2012) redefine selling as "the phenomenon of human-driven interaction between and within individuals/organizations in order to bring about economic exchange within a value-creation context" (p. 10). While this broader definition is an important step in the right direction, it does not clearly identify the mechanisms and benefits of this interaction. That is, the efficiency of exchange and value cocreation among actors is positively shaped by aligned institutional arrangements and mutually beneficial relationships. Therefore, we define selling as the interaction between actors aimed at creating and maintaining thin crossing points—the locations at which service can be efficiently exchanged for service—through the ongoing alignment of institutional arrangements and the optimization of relationships.
This definition accentuates how institutional alignment processes are characterized by the "ongoing negotiations, experimentation, competition, and learning" (Zietsma and McKnight 2009, p. 145) of systemic actors. This dynamic is illustrated by the broad involvement of many actors (e.g., customers, prospects, media and IT consultants) in the institutionalization of the Salesforce solution. Because institutional alignments are always imperfect and temporary, these alignment processes can range from resolving complex institutional discrepancies (e.g., a novel value proposition), to "nearly invisible and often mundane … day-to-day adjustments, adaptations, and compromises" (Lawrence, Suddaby, and Leca 2009, p. 1) of routinized selling processes (e.g., a reorder). Next, we further clarify the broad involvement of actors in selling activities. Then, we explicate how individual actors participate in systemic selling processes and how context influences whether an actor should be considered a selling actor. This enables us to distinguish between selling and nonselling actors and activities.
While it is tempting to view selling as a micro-level process in which dyads of buying and selling actors are engaged in the thinning of crossing points (i.e., aligning institutional arrangements for service exchange), selling can only be fully understood by oscillating foci among micro, meso, and macro perspectives (to capture the influence of societal intellectual property rights, industry standards, etc.; see also Chandler and Vargo 2011). Therefore, a service ecosystems perspective highlights the importance of zooming out to a meso-level view because this is the level where many thick crossing points become salient. While thick crossing points can be observed at all levels of aggregation, such as the distrust between two actors (i.e., micro level) or the use of child labor (i.e., societal level), the Salesforce example illustrates the importance of the meso-level perspective to selling.
Consider, for example, the once-perceived need to store data behind company firewalls. This perceived need created a thick crossing point that was based on aligned expectations of a broad set of actors (e.g., users, managers, IT personnel,
enterprise software providers) in the software industry. Sales-force was unable to thin this crossing point by establishing relational contracts with customers alone. Rather, it, among other things, had to foster communication with broad sets of actors regarding the security and benefits of a cloud-based solution. As the SaaS solution became institutionalized, systemic actors not only learned to trust software that stored sensitive customer data in the cloud but came to expect other CRM solutions to be easily accessible, scalable, and flexible. Thus, the client-server-based solutions of Salesforce's competitors then faced thick crossing points that motivated or even forced them to adopt SaaS solutions (i.e., adapt to new institutional arrangements for service exchange).
That is, thick crossing points for competing solutions, viewed from a meso level, often form on the basis of disruptions and changes to existing expectations for service exchange among broader sets of actors such as professions (e.g., IT professionals), industry experts (e.g., journalists), and market actors (e.g., vendors, customers) and their relational and formal contracts. Stated differently, the thinning of crossing points that occurs as an emerging solution is institutionalized commonly results in the thickening of others, which can lead to a previous solution being perceived as flawed or insufficient. Therefore, individual service-for-service exchange behavior "does not make sense independent of meso-level structural influences" (Vargo and Lusch, 2016, p. 18) and even broader societal norms and rules.
Importantly, a service ecosystems perspective, with its institutional levels of aggregation, highlights the multitiered nature of sales objectives. Sellers often aim to create thin crossing points that allow for exchange between two actors (e.g., between buyers and sellers). However, because these thin crossing points are not independent of meso-level structures, selling actors also have to legitimize their solutions (i.e., create thin crossing points for their solution) among meso-level sets of actors such as professions, industries, and other market actors. For example, as Benioff and Adler (2009, p. 95) indicate, "Winning huge customers, such as Dell and Japan Post, was game changing" for Salesforce not only because of revenue and profit but because such wins shape perceptions of the desirability and appropriateness of a SaaS solution among large sets of actors. Similarly, selling actors often try to block the legitimacy of competing solutions (i.e., aim to create thick crossing points for these solutions). Only with thick crossing points for competing solutions can a solution gain a perception of uniqueness and superiority and avoid the perils of commoditization.
The involvement of a broad range of systemic actors in institutional work raises the question of how individual selling actors participate in the shaping of institutional arrangements that enable and constrain value cocreation practices through service exchange. To answer this question, we adapt Phillips, Lawrence, and Hardy's (2004) discursive framework of institutionalization (see Figure 2), which depicts narratives as mechanisms for institutional work and, more specifically, explicates relationships among narratives, institutions, and actions. This framework demonstrates how individual actors (e.g., salespeople) participate in the alignment and maintenance of institutional arrangements for service exchange without overstating the impact of these actors.
As Phillips, Lawrence, and Hardy (2004) point out, many actions produce narratives, and these narratives can have a variety of forms. Czarniawska (2004) describes narratives as written, spoken, or symbolic accounts that offer interpretation, explanation, or meaning to an individual event or action (or to a series of events or actions). Actors engage in service-for-service exchange to combine their resources with those of other actors and, in this process, propose value propositions. The offering of such value propositions leads to the generation of narratives through written (e.g., emails, brochures), spoken (e.g., sales presentations) or symbolic (e.g., diagrams, models and pictures) means, which, when distributed and interpreted, influence institutionalization processes (Taylor and Van Every 1999).
Phillips, Lawrence, and Hardy (2004) argue for sense making and legitimization as the theoretical underpinnings of these distribution and interpretation processes. Similarly, Snow (2008) suggests that narratives enable and constrain meaning construction because meaning making progresses throughout discussion and debate about germane issues, events, and topics of interest. These distribution and interpretation processes are also the venues through which the legitimacy associated with an action can be gained, maintained, or repaired (Phillips, Lawrence, and Hardy 2004). Two of the earliest tasks for Salesforce, for example, were to explain cloud computing (i.e., to facilitate sense making) and to resolve data security concerns (i.e., to gain legitimacy).
For narratives to shape institutional arrangements and enable and constrain value cocreation practices, they must become embedded in broader discourses to achieve generalized meanings. That is, narratives interact (Boje 1991) and form narrative infrastructures—cocreated narratives that "emerge from a process in which fragments of different micro and macro narratives get layered on top of each other" (Seidl and Whittington 2014, p. 5)—that aid sense making and legitimization. Rosa et al. (1999), for example, suggest that narratives "are critical sensemaking tools" (p. 68) for the formation of exchange and markets.
Deuten and Rip (2000) highlight that in social systems, there is no single author and no master text being written but, rather, that multiple stories (i.e., narratives) come into alignment to form narrative infrastructures. According to these scholars, these infrastructures can be seen "as the 'rails' along which multi-actor and multi-level processes gain thrust and direction" (p. 74). Only these combined narrative infrastructures can craft coherence among social actors and mobilize support for particular practices (Araujo and Easton 2012). That is, only combined narrative infrastructures can lead to the shaping of institutional arrangements.
Salesforce, for example, recognizing the importance of broader narrative developments, treated journalists as company friends and maintained a list of approximately two dozen highly influential journalists. These journalists received increased bilateral contact and access to executives and were often informed directly of company and industry developments. Salesforce used these relationships to gain access to information and to influence the narratives that these journalists published. This way, Salesforce was able to convey narratives to journalists that framed the SaaS solution positively. In addition to calling attention to Sales-force, such publicity played an important role in aligning narratives and facilitating institutionalization across the service ecosystem because "unbiased references by experts carry tremendous power" (Benioff and Adler 2009, p. 44). Salesforce also facilitated the distribution and interpretation of narratives and the formation and alignment of narrative infrastructures by providing online platforms which opened dialogue and aided the resource integration practices of many stakeholders.
We, therefore, propose that understanding selling requires accounting not only for the narrative alignments of buyers and sellers but also for the dialogical processes that enable broad sets of actors to learn together (Ballantyne and Varey 2006). The notion of dialogical interaction further clarifies our definition of selling. Our definition excludes unidirectional forms of communication such as advertising because such forms lack interactional components. It also excludes interactions that solely rely on existing institutions and do not result in any adjustments, adaptations, and compromises between and among actors. Thus, we do not consider activities purely focused on order fulfillment as selling if such activities fail to contribute to sense making, legitimization, and the optimization of relationships. However, it is important to point out that fulfillment quality often affects relationship quality (e.g., trust) and can support or hinder selling outcomes. Building on the definition proposed by Dixon and Tanner (2012), the definition we advance recognizes that many actors are involved in selling, that selling can include many forms of interactions, and that selling is contextual. Next, before discussing some of the implications that a service ecosystems perspective offers to practitioners and academics, we discuss the distinctions between salespeople and other selling actors.
The path dependency from dyadic views that frame value as something created by producers and consumed by users led to clear and persisting distinctions between the roles and functions of sellers and buyers (i.e., salespeople facilitate the delivery of value to buyers). However, as we have stated, the sales literature is beginning to recognize that value is cocreated among systemic actors and to accentuate the importance of salespeople in establishing information exchange, flexibility, and solidarity. This highlights that selling is not only performed by dedicated sales personnel but also by buyers and a broad range of other actors. When exchanging service, for example, flexibility, solidarity, and information exchange is generally as important to the procuring side as it is to the selling side. More broadly, all actors participate in the shaping of value cocreation practices by creating, maintaining, and disrupting the institutions that enable and constrain these practices. Therefore, a systemic view on value creation supports a move away from predesignated roles of firms/customers and sellers/buyers to more generic actors—that is, to an actor-to-actor orientation (Vargo and Lusch 2011).
To make this point clearer, consider the following. A salesperson can engage in institutional maintenance by promoting the benefits of a current solution to buyers, but so too can a procurement specialist by rejecting a meeting with a sales engineer who wants to introduce a novel solution to a problem or by emphasizing why a current solution will be sustained. Furthermore, an actor external to the selling or buying firm, such as an expert, may engage in institutional maintenance by speaking of the benefits of an accepted solution. Likewise, when a new solution is proposed, actors employed by the procuring firm (e.g., procurement officers, users, executives) often engage in internal and external selling to influence the institutional arrangements of other stakeholders (e.g., operations, industry partners).
Recognizing the important roles of users, for example, Salesforce went beyond the norm of incorporating customer testimonials into marketing materials and emphasized connecting customers to prospects, media sources, analysts, partners, and others at prospect-gathering events. Salesforce chief executive officer Marc Benioff describes how often at these events, "without prompting from us, customers would stand up and deliver spontaneous testimony professing their belief in our product." (Benioff and Adler 2009, p. 50). In this way, many customers became Salesforce evangelists whose testimonials, in the words of Benioff and Adler (2009), "made them the best marketing and sales team an organization could have" (p. 51). This anecdotal evidence supports Kumar, Petersen, and Leone' s (2013) findings that references greatly influence firms in making purchase decisions as well as Helm and Salminen's (2010) claim that a customer reference can, at times, be more valuable than a customer's transaction.
Thus, what we refer to as selling cannot be confined to certain actor roles because many actors are often involved in the creation and maintenance of crossing points. This actor-to-actor orientation highlights how a broad set of actors, regardless of the term chosen to characterize them (e.g., sellers, buyers), engage in selling and, thus, are selling actors. This reconciles the conflicting definitions of salespeople prevalent across sales textbooks and practitioner-oriented books, articles, and other literature that have often argued that context (whether one offers a good or service in exchange for payment) dictates whether someone is a salesperson.
The introduced service ecosystems perspective illuminates, among other points, the need to reevaluate the conceptual underpinnings of selling and the sales role (for a comparison of key differences between a traditional and a service ecosystems perspective of sales, see Figure 3). As such, this research, which can be classified as a "revising" conceptual contribution (MacInnis 2011, p. 144), informs the literature by "suggesting that what is seen, known, observable, or of importance can be seen differently or by suggesting that what matters a great deal matters for a different reason than what was previously believed." A service ecosystems perspective offers a novel lens through which to view selling, one through which selling actors are viewed as playing fundamental roles in aligning institutional arrangements and optimizing relationships for service exchange among interdependent actors. MacInnis (2011, p. 138) claims that revising conceptual contributions should be evaluated by their abilities to "identify why revision is necessary; reveal the advantages of the revised view and what novel insights it generates; maintain parsimony." As we have articulated and expand on subsequently, a service ecosystems perspective offers a number of advantages and novel insights.
To more adequately understand selling as well as the relationships between wider sets of selling actors, broader study of the many crossing points that need to be thinned to facilitate resource integration and value cocreation must be undertaken. That is, as the Salesforce example indicates, it is the thinning of many interconnected crossing points that leads to value cocreation. The institutionalization of the SaaS solution, for instance, began years before this solution was ever envisioned. Though fundamentally novel in the way data were accessed and stored, this solution heavily relied on many institutional arrangements that were formed through the institutionalization of client-server-based CRM tools. As we have stated, at their core, all CRM solutions are based on the belief that storing customer and prospect information in one central location can provide efficiencies and automation for many sales and marketing activities.
Similarly, the increasing proliferation of the Internet, mobile connectivity, and the emergence of other cloud-based solutions undoubtedly shaped both the perceptions of problems that the SaaS solution aimed to address as well as perceptions of legitimate solutions to these problems. That is, the SaaS solution became successful because selling resulted in aligned institutional arrangements for service exchange over time and across many crossing points. The same institutionalization processes also occur for solutions that change more incrementally. However, these incremental processes may be masked by the fact that a large degree of institutional alignment and selling efforts have already occurred.
It is important to point out that the service ecosystems perspective does not diminish the contributions of the existing sales literature; rather, this holistic perspective reconciles and builds on many of these contributions. Consistent with dyadic perspectives, Salesforce was able to form strong relationships with customers, industry experts, and journalists. For example, many would argue that the company excelled in prospecting by targeting end users rather than following the industry norm of targeting executives. The meeting and travel practices of end users often resulted in institutional frictions with client-server solutions and their restrictive data accessibility. After a successful trial, end users often lobbied their managers to try the SaaS solution (e.g., they engaged in selling, the thinning of crossing points).
The service ecosystems perspective can also inform the debate regarding whether the importance of salespeople is increasing or decreasing due to changes in markets. As we have stated, whereas some theoreticians and practitioners believe that market changes will diminish the power and strategic importance of salespeople, others predict that salespeople will become more important (Hunter and Perreault 2007, p. 16). Sheth and Sharma (2008), taking a balanced view, note "a shift towards interactivity, connectivity, and ongoing relationships," which, along with the "enhanced use of technology[,] will reduce some traditional sales functions" but "will also lead to changes in the … role of [the] salesperson [to be] more [like] that of a general manager … responsible for marshaling internal and external resources to satisfy customer needs and wants" (p. 260).
A service ecosystems perspective reveals that neither the importance of selling nor the nature of markets are fundamentally changing. What might be changing, as evidenced by the changes in the tasks salespeople perform, are the numbers of crossing points that need to be aligned and the technologies with which actors can engage in institutional alignment processes. Norman (2011), for example, showcases how even tools designed to be simple generally make the world more complex as they tend to increase the number of connections among actors and subsystems. However, despite this increase in complexity, systemic and institutional alignments have always been at the heart of selling. Thus, we argue that selling must always be viewed through a systemic lens and that the debate about whether changes in modern markets influence the importance of salespeople is focused on the wrong question.
What is driving the discussion about whether the importance of salespeople is changing is not a transition to complex markets, because markets have always been complex, but rather a change in the ways narrative infrastructures are formed (i.e., the ways market actors align their expectations for service exchange). Technological advancements, such as the Internet, are making it easier for a larger number of actors to engage in selling. Consider retail sales, for example. Instead of relying on the advice of salespeople when making a purchase, many buying actors now consult online reviews to evaluate products and services. Thus, the institutional alignment work in which salespeople were traditionally involved is now often performed (arguably more convincingly and conveniently) by numerous selling actors, including those who describe products or services and how they fit into the contexts in which they are used.
This, however, does not represent a shift in the importance of selling but rather a shift in who performs selling and where selling is performed. Selling has been and continues to be important regardless of market complexity. However, advanced communication tools, such as the Internet, highlight and facilitate broader participation of systemic actors in institutionalization, legitimization, and sense making. Consequently, while selling will continue to be an important element of value creation, it may be performed by even broader sets of selling actors who are not traditionally categorized as salespeople.
This article offers several implications for practice and theory. For practitioners, we highlight implications for gaining new business, maintaining existing business, and managing intrafirm actors along with broad sets of external selling actors. For theoreticians, we propose a research agenda involving additional inquiry into sales performance, analytical approaches, and salespeople characterizations.
Gaining business with new solutions. Many traditional sales perspectives begin with a new product or service and end with persuading customers to adopt it. However, a service ecosystems perspective shows that institutionalization processes, and thus selling processes, precede product or service developments. The Salesforce selling process did not begin with the launch of its SaaS solution, nor will it end when the last license is sold. Instead, this selling process was, and remains, embedded in broader institutionalization processes in which many systemic actors collectively form aligned institutions for service exchange. These alignments always include the institutionalization of complementary innovations and downstream adoptions (Adner 2006).
Thus, a service ecosystems perspective highlights that selling considerations need to be an important part of new product and service strategies. A new solution that fits well into established resource integration practices (e.g., an established market with thin crossing points) requires less selling effort. However, these thinned crossing points also make it more difficult for selling actors to facilitate the creation of thick crossing points for competing solutions because competing solutions in such markets, by definition, are perceived to be quite similar. A new solution facing thick crossing points, in contrast, requires the negotiation of institutional resistance and change. This dynamic makes such a discontinuous solution riskier (i.e., the solution might not gain legitimacy) and may both prolong and complicate the required selling efforts. However, when a discontinuous solution is successfully institutionalized, it may, at least in the short term, make it easier to facilitate the maintenance of thick crossing points for competing solutions. This at least partially explains the challenges Salesforce initially had to overcome with the SaaS solution, why Salesforce has had great success to date, and why Salesforce's competitors were forced to also adopt SaaS solutions.
Maintaining business. A service ecosystems perspective on selling also has implications for maintaining existing business. To maintain business, selling actors need to understand the institutions and resource integration practices that have led buying actors to use their solutions. By doing so, selling actors can better understand how and why the solution they offer fits with buyers' resource integration practices, and they can make adjustments as institutional incompatibilities and frictions arise.
If a competing actor proposes a superior solution, it may be disadvantageous for the selling actor to try to prevent the institutionalization of this solution. By seeking to prevent institutionalization that would result in greater value for the buyer, the selling actor may violate relational contracts, leading to diminished relationship quality with the buyer or even to relationship dissolution. Arguably, it is often more important to develop and maintain a relational contract between a selling actor and buyer than it is to persuade the buyer to adopt or extend their use of an inferior solution. This is because the buyer-seller relationship and its quality play a large role in allowing selling actors to discover institutional incompatibilities and frictions that they and their firms can alleviate through new solutions.
Expanding the view from buyer-seller dyads. As shown in Figure 3, consistent with thought on multistage marketing (Kleinaltenkamp and Ehret 2006), selling actors must ensure that the narratives of a broad range of stakeholders are distributed and interpreted. Although selling actors play pivotal roles in aligning narratives to form narrative infrastructures, selling actors can never become the master storytellers. Many firms are already recognizing the need for broader alignment processes among actors from various functions and organizations as well as external actors throughout the service ecosystem. For example, selling actors often utilize user testimonials and case studies to facilitate institutional alignment across actors in prospect firms. Furthermore, team selling often involves actors from various functions in the selling firm, and buying centers often consist of members from multiple functions. However, selling actors often lack access to potential users and to other stakeholders, creating multistage and indirect relationships (Macdonald, Kleinaltenkamp, and Wilson 2016). Arguably, too many firms still view selling as something that their employees need to be shielded from rather than an opportunity for collaborative innovation, as is evidenced by the prevalence of "gatekeepers" and policies limiting "backdoor selling."
Broadening the set of employees trained in selling. Because selling is not confined to certain actor roles, many actors, regardless of the title used to characterize them, play important roles in aligning expectations of service exchange. Therefore, firms need to reassess which positions need sales expertise either through training or through hiring already-trained employees. For example, procurement managers often actively align expectations for service exchange with new suppliers and coworkers, which may result in lower-priced and/or customized offerings. In addition, selling actors should be trained in how to foster and participate in true dialogical interactions among large sets of actors.
Adopting a broader, longitudinal, and balanced view on sales performance. As we have stated, a service ecosystems perspective on selling highlights that institutional work is an ongoing process requiring long-term relational contracts among actors. Often, these institutionalization processes are more important than the revenue and profit of a transaction or history of transactions with a buyer. This importance is easy to overlook because the outcomes of institutionalization processes often only become salient after extended periods of time and are reflected in the behaviors of many actors. Therefore, performance evaluations need to encompass the outcomes of institutionalization processes in which selling actors, both internal and external to firms, participate. Consequently, limiting selling actor evaluations to actors employed by a firm and evaluating these employees using only short-term sales goals (e.g., monthly or quarterly quotas) obscures the cause-effect relationships between selling actors' behaviors and desired outcomes. For example, overemphasizing short-term sales goals may result in salesperson behaviors (e.g., "hard selling") that increase their short-term performance at the expense of their long-term performance due to relationship and reputational damages.
Broadening information technology to connect actors and narratives. The participation of broad sets of internal and external actors in dialogical interactions and institutional processes highlights the importance of selling actors developing tools and processes to systematically integrate and manage communication flows among these sets of actors, many of whom are not customers or prospects. As we have described, selling-related narratives are not limited to interpersonal interactions but comprise many forms of communications (e.g., online product reviews, conference presentations, social media posts). Therefore, firms should broaden their information technology (e.g., CRM, social media analytics) to track a broad range of selling actors and to develop strategies to assess the influence, opinions, and recommendations of these actors. In line with this assessment, firms should develop contact and communication strategies not only for customers and prospects but for all actors relevant to selling efforts.
Although external actors cannot be managed in the traditional sense, supportive actors can be encouraged to voice their thoughts and can be given platforms to amplify their narratives. Examples of how firms may do so include invitations to attend important events (e.g., industry conferences), arranged interactions with journalists and industry experts, and promotional materials featuring firms and their products and solutions, among others. Opposing actors can also often be influenced to change or lessen their narrative contributions to support a desired narrative infrastructure. For example, firms can actively aim to build relationships (e.g., seek feedback) and can then use the resulting knowledge to understand and address concerns.
Conceptualizing and evaluating salesperson performance. As we have discussed, a service ecosystem perspective calls for increased scrutiny regarding how sales performance is conceptualized and evaluated. Sales-focused articles in marketing' s top journals, much like salesperson evaluation systems, are replete with objective performance measures frequently limited to the number, revenue, or profit of salesperson transactions as well as buyer satisfaction. However, Verbeke, Dietz, and Verwaal (2011) note that "the sales performance construct is becoming increasingly complex," and that there is a need to address "what constitutes sales performance in today's economy" (p. 425). A service ecosystem perspective accentuates that the conceptualization and examination of salesperson performance needs to encompass the outcomes of the institutionalization in which salespeople participate. In this context, it is important to point out that some institutional elements might be more difficult to change than others. Lock-ins created by laws, for example, might be harder to change than the value perception of a modified solution.
RPļ: How can the participation of selling actors in bringing about aligned institutional arrangements for service exchange be evaluated? What are the appropriate metrics? What are the appropriate time horizons for evaluating institutional salesperson performance? How can the degree and type (in terms of the three elements) of institutional misalignments of a new solution (i.e., the degree of newness or legitimacy) in these evaluations be accounted for?
The evolving nature of tasks performed by selling actors and the knowledge, skills, and abilities (KSAs) to perform these tasks. The service ecosystem perspective highlights an increasing number of crossing points and changing ways through which narratives are formed and distributed in modern markets. Thus, additional examination of the tasks selling actors perform and the KSAs required is needed. In addition to traditional selling tasks, modern salespeople are increasingly being asked to provide customer service (Rapp et al. 2017), act as general managers (Sheth and Sharma 2008), and perform tasks traditionally reserved for business development (e.g., develop and manage strategic partnerships, access and align broad sets of cross-functional and intraorganizational actors and resources, manage projects). Thus, a service ecosystems perspective highlights the need for additional and broader KSAs that enable selling actors to better synthesize information and manage interactions with diverse actors and their resources.
Furthermore, technological advancements such as social media platforms, CRM and productivity tools, and selling-buying firm interfaces are affording salespeople both greater and easier access to buyers and their needs, potential solutions, and competitor developments. These changes suggest that salespeople will increasingly use advanced communication and analytical technologies to facilitate the alignment of narratives. Given these developments, it is likely that salespeople will continue to serve as major differentiators among firms. However, the means through which salespeople and other selling actors contribute to this differentiation may change as technologies evolve and the numbers of crossing points increase. Although salespeople have been, and will continue to be, important in the alignment of institutional arrangements for service exchange, some of the tasks salespeople perform and the requisite KSAs may change.
RP2: What selling actor and salesperson-performed tasks and KSAs will change in importance, emerge, or disappear as communication and analytical technologies evolve and the numbers of crossing points increase? What selling actor- and salesperson-performed tasks and KSAs will differentiate firms as communication and analytical technologies evolve and the numbers of crossing points increase?
Analytical approaches consistent with a service ecosystems perspective. This article underscores the importance of investigating outcomes of interest using approaches that account for the interplays of many actors and dynamic change. Brass and Krackhardt (1999) point out that social network analysis can help explain how information, trust, and other resources flow within networks of actors, as well as how "people interact and communicate to make sense of, and successfully operate in, their environment" (p. 182). Consistent with the argument that the sales-focused marketing literature is moving toward a systemic and institutional perspective, analytical techniques with systemic foundations such as social network analysis have received increasing attention (Ahearne et al. 2013; Bolander et al. 2015; Gonzalez, Claro, and Palmatier 2014). However, social network analysis has not yet been used to assess institutionalization and alignment of the narrative infrastructures of systemic actors within the sales literature.
Thus, there is great opportunity to employ social network analysis to examine measures that describe and assess maintenance and change in properties of actor location, social capital, tie strength, and brokerage, among others and the roles they play in institutionalization processes. Such research would ideally adopt an approach (e.g., dynamic network analysis) that offers the ability to analyze large-scale networks and multiple overlapping networks, as well as the examination of change at both the node (i.e., actor) and network level.
RP3: What analytical approaches are suited to longitudinally investigate sales-related outcomes in dynamic service ecosystems? How do network attributes influence institutionalization and narrative alignment processes in the context of selling? How should selling actors prioritize alignment needs among systemic actors?
Institutional work and the importance of various characterizations of salespeople. The connections, if any, between institutional work and the importance of various characterizations (e.g., inside or outside; "hunter or gatherer"; business to business vs. business to consumer; transactional, enterprise, and consultative sales) of salespeople warrant examination. A traditional perspective suggests that the importance of salespeople depends on how much they influence and change the behaviors and thoughts of buying actors and often neglects to recognize institutional maintenance. This contributes to common assumptions that outside salespeople, "hunters," business-to-business salespeople, and both enterprise and consultative salespeople are more important than their counterparts.
A service ecosystems perspective suggests that because of the importance of all forms of institutional work (i.e., change, maintenance, and disruption), it may be premature to conclude whether some characterizations of salespeople are more important than others. In addition to examining the ways various characterizations of salespeople engage in institutional work, further research should consider potential contingencies (e.g., industry, company characteristics, other selling actors involved in institutional work) related to this work. By doing so, future research could address whether some characterizations of salespeople are more important than others, whether this importance is contingent on specific factors, and whether changing means of communication influence this importance.
RP4: Do some characterizations of salespeople relate to the type of institutional work (i.e., change, maintenance, and disruption) that is dominantly performed? Are there contingencies that influence the importance of salespeople, and if so, what are they? Are changing means of communication influencing the importance of some characterizations of salespeople differently than others, and if so, how?
This article advances a service ecosystems perspective as a theoretical foundation for examining selling and the participation of selling actors in value cocreation. In doing so, it aids the convergence of the sales literature on a systemic perspective that recognizes the importance of institutional arrangements and relationships in service exchange. A service ecosystems perspective leads to a reconceptualization of selling in which broad sets of actors interact with the aim of creating, maintaining, and disrupting the institutions that enable and constrain value cocreation practices through service-for-service exchange. This view of selling deemphasizes the ability of any single selling actor to influence the decision making of another actor and highlights the broader involvement of systemic actors in the formation of thin and thick crossing points. Finally, a service ecosystems perspective illustrates that markets have always been complex and dynamic and that selling actors have always been, and will continue to be, important in the alignment of institutional arrangements for service exchange.
GRAPH: FIGURE 1 Published Sales-Oriented Articles Adopting a Systemic Perspective
DIAGRAM: FIGURE 2 Service Ecosystems Perspective of Selling
DIAGRAM: FIGURE 3 Contrasting a Service Ecosystems Perspective on Sales
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By Nathaniel N. Hartmann; Heiko Wieland and Stephen L. Vargo
Nathaniel N. Hartmann is Associate Professor of Marketing, University of Hawai'i at Mānoa
Heiko Wieland is Assistant Professor of Marketing, California State University, Monterey Bay
Stephen L. Vargo is Professor of Marketing, University of Hawai'i at Mānoa
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Corporate Sociopolitical Activism and Firm Value
Stakeholders have long pressured firms to provide societal benefits in addition to generating shareholder wealth. Such benefits have traditionally come in the form of corporate social responsibility. However, many stakeholders now expect firms to demonstrate their values by expressing public support for or opposition to one side of a partisan sociopolitical issue, a phenomenon the authors call "corporate sociopolitical activism" (CSA). Such activities differ from commonly favored corporate social responsibility and have the potential to both strengthen and sever stakeholder relationships, thus making their impact on firm value uncertain. Using signaling and screening theories, the authors analyze 293 CSA events initiated by 149 firms across 39 industries, and find that, on average, CSA elicits an adverse reaction from investors. Investors evaluate CSA as a signal of a firm's allocation of resources away from profit-oriented objectives and toward a risky activity with uncertain outcomes. The authors further identify two sets of moderators: ( 1) CSA's deviation from key stakeholders' values and brand image and ( 2) characteristics of CSA's resource implementation, which affect investor and customer responses. The findings provide new and important implications for marketing theory and practice.
Keywords: corporate sociopolitical activism; event study; political activism; political ideology; screening theory; signaling theory; sociopolitical; stock market reaction
Investors expect firms to prioritize maximization of shareholder wealth ([46]; [72]), while customers and other stakeholders are increasingly concerned about firms' contributions to society as a whole and are placing mounting pressure on firms to take sides on hot-button sociopolitical issues such as immigration, gun control, LGBTQ rights, and climate change ([41]; [54]; [55]). Richard Edelman, chief executive officer (CEO) of Edelman, explains, "Brands are now being pushed to go beyond their classic business interests to become advocates. It is a new relationship between a company and consumer, where a purchase is premised on the brand's willingness to live its values, act with purpose, and, if necessary, make the leap into activism." A recent study found that 64% of global consumers buy or boycott a brand based on its stand on societal issues—an increase of 13% year over year ([26], p. 1).
In line with these expectations, firms are increasingly taking activist stances on sociopolitical issues ([33]). For example, Delta cut ties with the National Rifle Association (NRA) after a deadly school shooting ([23]), and Starbucks committed to hiring refugees in opposition to an immigration ban ([24]). Nike supported National Football League players who knelt during the national anthem in protest of police brutality ([93]), while Papa John's Pizza took the opposite stance on player protests ([86]). We refer to such activities as "corporate sociopolitical activism" (CSA) and define CSA as a firm's public demonstration (statements and/or actions) of support for or opposition to one side of a partisan sociopolitical issue.
Although CSA may strengthen relationships with some stakeholders who agree with the firm, it will likely damage relationships with those who disagree. For example, speaking out against the NRA was costly to Delta: home-state government legislators in Georgia rescinded an estimated $40 million tax break and NRA supporters threatened boycotts. Ed Bastian, CEO of Delta, explained, "I knew there would be a backlash, but I didn't anticipate the strength of the backlash from the NRA movement. But on the other side, it created an outpouring of support and appreciation for a company to stand by its values" ([23], p. 1).
The tension between shareholder value maximization and social responsibility is not new, as investors often question investments in corporate social responsibility ([68]). Given its partisan quality, however, activism raises the level of risk and uncertainty beyond that of traditional CSR activities. Unfortunately, a theoretically grounded understanding of how CSA affects stakeholders is missing in the academic literature. We aim to examine the effect of CSA on firm value by investigating investor and customer responses. Furthermore, we argue that the polarized stakeholder responses to CSA differentiate it from traditional CSR and deem it worthy of a separate investigation.
Because CSA is a relatively new phenomenon ([41]), we first explore it as a construct that is distinct from other major corporate social and political activities, namely corporate social responsibility (CSR) and corporate political activity (CPA). We then use signaling and screening theories ([ 6]; [21]; [77]) to explain investor responses to CSA. We organize our framework around two sets of moderators that, together, provide a holistic view of the stock market reaction to CSA. The first set relates to sources of CSA deviation. Because CSA may deviate from the personal values of key stakeholders—customers, employees, and state legislators—as well as a firm's brand image, investors interpret such deviations as problematic for the firm. The second set relates to the implementation of CSA resources, which signals a firm's commitment of time, capital, and attention to activism rather than more immediate profit-oriented objectives. Such diversions of resources introduce uncertainty, eliciting negative investor responses. Finally, while we do not formally hypothesize how customers respond to CSA, we investigate their reactions by examining changes in sales growth. Our research gives managers insights into the financial consequences of engaging in CSA in terms of its impact on both investors and customers.
Four essential questions guide our research: ( 1) How do investors react to CSA events? ( 2) How does the degree of deviation of CSA from the values held by customers, employees, state legislators, and the firm's brand image modify investors' reactions to CSA? ( 3) How do characteristics of CSA resource implementation modify investors' reactions? ( 4) How do customers respond to CSA? Using event study methodology, we examine the effect of CSA on firm value by studying the stock market reaction to 293 CSA events initiated by 149 firms across 39 industries.
In answering these questions, we provide several significant contributions. First, we advance the marketing strategy literature by introducing CSA as a strategic option with marketing implications for stakeholder relationships. We also offer a comprehensive framework that bridges those offered across disciplines examining CEO activism ([17]), CEO sociopolitical activism ([41]), and corporate sociopolitical involvement ([71]).
Second, our research is the first to examine CSA's financial consequences. We use signaling and screening theories to explain investors' responses to CSA. We show that, on average, investors react negatively to CSA, especially when CSA stances deviate from the dominant political values of a firm's key stakeholders. We also identify key characteristics of CSA implementation that investors use as cues when inferring a firm's commitment to exerting time, attention, and resources to CSA. Our results reveal that investors' reactions are worse when CSA ( 1) deviates from stakeholders' political values, ( 2) takes the form of actions (vs. statements), ( 3) is announced by the CEO (vs. another person or entity within the firm), ( 4) does not explicitly communicate any business interests, and ( 5) is a solitary firm activity (vs. in coalition with other firms). Thus, we provide critical insights to managers in terms of what to expect from investors if they engage in CSA and how they should implement CSA on the basis of their objectives.
The third contribution of our research is a clear delineation of CSA from CSR and CPA, not only from a conceptual standpoint but also in terms of its effects on various stakeholders. While prior research has demonstrated a positive effect of CSR on customer metrics (e.g., firm reputation, product evaluations, customer trust, long-term loyalty; [13]; [19]; [45]) and a positive effect of CSR and CPA on firm value ([61]; [64]), we document the overall negative effect of CSA as a polarizing strategy with uncertain outcomes on firm value as well as contingencies that could make it a fruitful strategy.
As defined previously, CSA refers to the firm's public demonstration (statements and/or actions) of support for or opposition to one side of a partisan sociopolitical issue. [71], p. 386) describe sociopolitical issues as "salient unresolved social matters on which societal and institutional opinion is split, thus potentially engendering acrimonious debate among groups." Importantly, such issues are partisan and yield polarized stakeholder responses ([55]), which may increase the dispersion of brand evaluations ([63]). Sociopolitical issues exist at the intersections of time, politics, and culture, and the controversy surrounding them can evolve or resolve through time. For example, universal women's suffrage was controversial a century ago, but is now accepted in the United States.
Corporate sociopolitical activism is comparable to two other firm activities: CSR and CPA ([71]). Corporate social responsibility refers to "company actions that advance social good beyond that which is required by law" ([48], p. 59) and constitutes the gradual formalization of cause-related marketing and corporate philanthropy aimed to "do well by doing good" through a strategic focus ([88], p. 60). It is tied to various positive performance outcomes, including firm reputation, product evaluations, customer trust, and long-term loyalty ([19]; [45]), which subsequently exert positive effects on firm value ([61]).
A chief difference between traditional CSR and CSA is the extent to which the focal issue is widely favored (e.g., community resources, education, donations to research for curing disease) rather than partisan (e.g., gun control, transgender rights, gender equality, racial equality). Rather, CSR and CSA lie on a continuum in terms of their degree of partisanship: CSR is low in partisanship, because it involves high societal consensus, whereas CSA is polarizing. While CSR is intended to improve relationships with most stakeholders ([68]), stakeholder responses to CSA are highly variable and depend on the stakeholders' sociopolitical values ([ 7]). The risks differ as well. Some investors may view CSR as a nonoptimal use of financial or human resources (i.e., without a clear link to firms' financial value), but CSR has been found to reduce firm-idiosyncratic risk ([62]). Alternatively, CSA can involve a much lower level of initial monetary investment (e.g., a press release, an open letter), but it can potentially increase firm risk due to an increase in uncertainty stemming from punitive actions (e.g., customer boycotts, employee walkouts, legislative backlash).
In addition to CSR, firms also regularly engage in CPA, which involves efforts by the firm to sway political processes so that it is well-positioned to gain policy-based competitive market advantages ([64]). Firms have a long history of engaging in political activities, including campaign contributions, lobbying, and donations to political action committees. Corporate political activity is intended to further a specific goal with direct financial payoffs rather than support a social cause ([44]).
We suggest that CPA and CSA also differ in the extent to which each activity is publicized. While the underlying motivations to engage in CSA may vary, it is publicly promoted as a communication of the firm's values ([55]; [71]). By contrast, firms execute CPA quietly ([64]). For example, [57], p. 100) describe lobbying as "a sensitive and often discreet activity" that, though publicly available, is often obfuscated. If CPA is made public, it is usually by "accidental disclosure" ([92]). Furthermore, CPA is generally aligned with firm interests and has a positive effect on firm value ([64]; [92]). By contrast, CSA can be diametrically misaligned with regulators or policy makers, and its effect on firm value is unknown.
In summary, CSA is related to CSR and CPA but is a distinct construct that has yet to be clearly elucidated. We propose a 2 × 2 delineating model based on levels of publicity and partisanship, which we depict in Figure 1. The figures shows that CSR is low in partisanship and can be low or high in publicity, depending on whether it is routine or notable. In contrast, CSA and CPA are highly partisan, yet CPA is not meant to be publicized, whereas CSA is highly publicized. Given CSA's novel characteristics, we contend and empirically confirm that CSA exerts unique effects on firm value. Next, we develop predictions about these effects.
Graph: Figure 1. Conceptual distinctions among CSR, CPA, and CSA.
According to signaling theory, firms (senders of signals) communicate relevant information to their recipients through signals to help reduce information asymmetry and better inform recipients' behavior ([81]). Screening theory builds on signaling theory and focuses on what recipients do once they receive a signal, including how they search for and evaluate cues to interpret it more accurately ([21]). In the context of CSA, information asymmetry arises because society has become increasingly interested in firms' sociopolitical values ([26]), yet firms have traditionally concealed these values ([33]). Firms may engage in CSA for a variety of reasons: they may be motivated by morality, business interests, or a combination of morality and economic self-interest (e.g., talent recruitment). We argue that even if a firm expresses a partisan sociopolitical stance to help meet business objectives, it qualifies as CSA because it still risks backlash from stakeholders with opposing views.
Regardless of a firm's underlying motivation, engagement in CSA signals its sociopolitical values. This signal reduces information asymmetry between the firm and its stakeholders by informing stakeholders of the sociopolitical values held by the firm. Stakeholders will then further evaluate the firm's engagement in CSA to help close the gap between their known and desired information about the firm ([67]). While customers, employees, and government legislators want to know how the firm's sociopolitical values resonate with those of their own, investors will screen the signal to predict its anticipated effect on shareholder value and future cash flows ([77]; [76]). We focus on investor responses to CSA.
When screening a signal, investors seek observable factors that inform them about ( 1) its likely outcomes and ( 2) unobservable attributes of the firm ([ 6]). We organize our conceptual framework accordingly. First, we explicate the overall effect of CSA. We then offer predictions based on the two key sets of moderators: ( 1) sources of CSA deviation from the values of key stakeholders and the firm's brand image, which shape the outcomes of CSA, and ( 2) characteristics of CSA implementation that divert firm resources, which signal the unobservable commitment of a firm to activism. This process is illustrated in Figure 2.
Graph: Figure 2. Conceptual model.
Investors believe that managers have a fiduciary responsibility to engage in behaviors that protect shareholder interests and lead to enhanced profits ([68]). From their perspective, CSA is fundamentally risky, can jeopardize future cash flows, and diverts the firm's efforts from traditional shareholder value maximization activities. This is due to CSA's partisan nature. Specifically, while CSA may appeal to some stakeholders who agree with the firm's stance, it will inevitably offend other stakeholders who hold opposing values ([55]). Thus, CSA's polarizing nature will likely increase the dispersion of the evaluations of a company's brands, and previous work links dispersion to lower abnormal stock returns ([63]). Furthermore, it is difficult to predict the magnitude of the adverse reactions to CSA, and whether the positive reactions will lead to tangible benefits, such as increased sales.
Investors may also deem that the more time, resources, and attention managers allocate to CSA, the less they will be able to dedicate to operations, innovation, and other critical profit-generating activities ([71]). This concern persists even when CSA conveys a business interest or is aligned with some stakeholder groups (i.e., customers and employees), because it can still offend a large portion of the population, which creates more uncertainty and requires firms to devote more of their time and resources to managing any backlash. Furthermore, engagement in CSA may signal a fundamental shift in the firm's strategic priorities, foreshadowing uncertain and lasting changes in strategic commitments ([36]). Therefore, we hypothesize:
- H1: Investors react negatively to firms' engagement in CSA.
Stakeholder relationships are a vital component of a firm's competitive advantage, and thus investors are particularly attuned to how firm actions affect stakeholder relationships ([39]). According to stakeholder alignment theory, CSA can reinforce values and strengthen relationships with stakeholders or, alternatively, jeopardize those relationships ([41]). The more the values signaled by the firm through CSA deviate from stakeholders' political values, the more CSA should cause stakeholders to disidentify with the firm ([74]). This can lead to a wide variety of negative consequences, including customers switching to a competitor, higher employee turnover, and legislators rescinding tax breaks. We predict that investors are likely to react more negatively to CSA that deviates from the dominant political values of the firm's stakeholders because it poses more risk and potential for backlash, which jeopardizes future cash flows. We explore three critical classes of stakeholders: customers, employees, and state legislators.
Investors monitor and evaluate a firm's customers to forecast its revenue ([ 1]). The effect of CSA on customer spending and engagement will depend on whether customers feel a sense of congruity between their values and a firm's CSA. Indeed, prior work has shown that customers favor brands that reflect their own lifestyles and identities ([28]) and customers use their sociopolitical values as an evaluative lens when making brand purchase decisions ([51]; [84]). This can result in clustering along political orientation. For example, Starbucks tends to attract more liberal customers, whereas Chick-fil-A has a more conservative customer base ([50]; [85]). Customer values can also lead to backlash. For example, when Target announced an inclusive bathroom policy in support of transgender individuals, some customers boycotted the firm ([66]). Thus, when CSA deviates from the political values of a firms' customers, investors will anticipate that the CSA event will more negatively affect customer–firm relationships and undermine financial performance. Therefore, we hypothesize:
- H2a: The deviation between CSA and customer values moderates investors' reactions to CSA such that investor reactions are more unfavorable when the CSA stance deviates more from the values of a firm's customers.
Employees are also critical stakeholders for investors to consider because they can help firms build a sustainable competitive advantage ([27]). Importantly, employee sentiment has a significant economic impact. For example, research indicates that employee satisfaction has positive consequences for firms in terms of stock returns ([27]), innovation ([18]), talent recruitment ([78]), and lower turnover ([58]). Prior work has also shown that noncontroversial firm actions, such as CSR, can engender employee satisfaction and personal fulfillment ([38]), which have a positive impact on employee recruitment ([47]), retention ([ 9]), and firm identification ([37]).
Previous work has found that employees interpret a firm's activities through the lens of their personal values ([40]). The greater the deviation between the CSA stance and the political values of a firm's employees, the more likely it will generate negative employee sentiment or backlash (e.g., strike, low morale), which may result in higher turnover and loss of productivity. For example, the company Wayfair's decision to engage in immigration-based CSA resulted in a labor walkout as employees protested the firm's actions, which disrupted sales ([ 8]). We hypothesize:
- H2b: The deviation between CSA and employee values moderates investors' reactions to CSA such that investor reactions are more unfavorable when the CSA stance deviates more from the values of a firm's employees.
Governments can influence firm performance in several ways, including through policies that aid innovation performance ([59]) or tax structures and financial incentives ([80]). [80] show that by modifying tax rates and tax structures, legislators have the power to both increase and decrease firm value obtained by all stakeholders as well as its distribution across them. Thus, firms' relationships with governments have direct consequences on cash flow and play a significant role in investors' responses to firms' strategies. Firms usually try to maintain mutually beneficial relationships with legislators, typically through political donations ([64]). But when a firm engages in CSA, its stance may conflict with the views of the governing party. For example, several firms spoke out against bills in Georgia and Indiana that had the potential to discriminate against LGBTQ individuals ([ 3]; [91]). Although federal and other state governments are influential stakeholders, home-state governments are especially sensitive to a firm's actions and more likely to punish firms whose CSA they disfavor, as Georgia legislators did in response to Delta's CSA ([23]). Thus, investors are likely to react to CSA depending on how they anticipate it will affect the firm's relationship with state government legislators. Therefore, we hypothesize:
- H2c: The deviation between CSA and state government legislator values moderates investors' reactions to CSA such that investor reactions are more unfavorable when the CSA stance deviates more from the values of a firm's state government legislators.
While deviation from stakeholders' values can affect investor responses to CSA, so should deviation from the firm's brand image. To maintain and strengthen their brand equity, firms must purposefully create strong brand associations in the minds of customers (e.g., [14]; [49]). Consistent communication of a brand's identity positively affects brand recall and abnormal stock returns ([43]) while inconsistencies can lead to a reevaluation of the brand and ultimately dilute brand equity ([14]).
Investors screen firms' communication with their stakeholders to predict its effect on the brand image ([56]). We argue that if the CSA's message is consistent with the brand image, it can help reinforce the brand identity and its associations in the minds of its stakeholders and decrease the risk of brand dilution due to CSA. Conversely, investors will perceive CSA that deviates highly from the established brand image as particularly risky because of its potential to dilute the brand image and stakeholders' identification with the brand and, in turn, decrease brand equity and firm value. Thus, we hypothesize:
- H3: The deviation between CSA and brand image moderates investors' reactions to CSA such that investor reactions are more unfavorable when the CSA stance deviates more from the firm's brand image.
Investors will screen not only for cues to help predict the financial outcomes of CSA but also for cues to help inform them about the unobservable characteristics of the firm ([ 6]; [81]). Specifically, they will be interested in how willing the firm is to divert its time, resources, and attention away from profit maximization activities and commit to a given sociopolitical issue. We propose four cues that indicate CSA engagement as resource-intensive and thus signal the firm's commitment to divert resources to sociopolitical activism.
The first characteristic is whether activism takes the form of actions or statements. We argue that, ceteris paribus, CSA in the form of actions is more resource-intensive than CSA in the form of statements. Statements involve verbal or written declarations that support or oppose one side of a divisive issue without committing financial or other types of resources to it. By contrast, an action goes beyond a declaration and consists of a change in the firm's conduct or policies, such as publishing or retracting an advertisement, offering or discontinuing products or services, offering or withdrawing promotions, hiring or firing workers, and making or breaking contracts. For example, rather than merely voicing support for immigrants (a statement), Starbucks opposed restrictive immigration policies by announcing a plan to hire refugees (an action) ([24]). Because actions require higher levels of resources and accountability ([52]) and are more difficult to reverse, they also signal more elevated levels of strategic commitment ([53]). Furthermore, because strategic actions by firms have a lasting effect by influencing future decisions ([36]), CSA in the form of an action (vs. a statement) more strongly signal the firm's future allocation of resources. Investors in search of cues will interpret actions as a sign of the firm's increased commitment to CSA. They will likely perceive this increased diversion from the firm's fiduciary responsibilities as particularly risky and respond accordingly. Therefore, we hypothesize:
- H4: The form of support (actions vs. statements) moderates investors' reactions to CSA such that investor reactions are more unfavorable when CSA takes the form of an action (vs. a statement).
The second characteristic is the announcement source—whether the CEO or another representative of the firm (e.g., media relations personnel, another C-suite executive or manager) informs stakeholders of a firm's CSA stance. Investors pay attention to who makes firm announcements and view the prominence of that individual as a signal of the importance of the announcement ([ 4]; [89]). The CEO's communication with stakeholders greatly matters, and investors carefully analyze these communications ([22]). Because the CEO leads the implementation of firm strategies, investors will be particularly concerned when the CEO announces engagement in CSA ([ 4]). Investors will perceive this as a signal of the CEO's willingness to dedicate his or her time, resources, and attention to focus on a risky firm action that may generate backlash from various stakeholders. Therefore, we hypothesize:
- H5: The announcement source stature (CEO vs. another team member) moderates investors' reactions to CSA such that investor reactions are more unfavorable when the CSA is announced by the CEO compared with when it is announced by another team member.
Firms engage in CSA for various reasons. Regardless of their true intentions, some firms communicate CSA as benefiting themselves as well as society. We define business interest communication as whether a firm motivates its CSA using economic self-interest. For example, some firms taking stances on LGBTQ issues explained that they opposed discriminatory bills because of the direct impact of these bills on their employees ([32]). Other firms note that their opposition to a discriminatory bill is motivated solely by the bill's negative impact on society ([91]). Whatever the stated motivation, CSA reveals a sociopolitical stance that generates an uncertain impact on future cash flows. However, higher levels of business interest communication should reduce investors' concerns and the overall negative impact of CSA. Notably, we do not argue that CSA motivated by business interests will have a positive effect on investor response. This is because CSA remains controversial in nature and investors may still consider the positive links between CSA and firm outcomes to be uncertain. Therefore, we hypothesize:
- H6: Business interest communication moderates investors' reactions to CSA such that investor reactions are less unfavorable when a firm communicates economic self-interest in the announcement of CSA.
The fourth critical execution factor is how many other firms are jointly engaged in the CSA event. Firms may engage in CSA alone or form an activist coalition with other firms. For example, Amazon unilaterally removed Confederate flag merchandise from its website after a church shooting in 2015, while in 2014, Amazon and 29 other companies filed an "Employers' Amicus Brief" in support of same-sex marriage. There is "safety in numbers" when firms act together because resources are shared and any backlash will likely be dispersed among all the firms, making CSA less risky ([17]). Thus, investors will likely interpret that a firm acting alone is more committed to its CSA initiative because the firm is risking bearing the brunt of the backlash. Investors will respond more negatively to solo activism due to anticipating a more concentrated backlash and because it signals a stronger commitment to CSA versus the firm's fiduciary duties. Thus, we hypothesize:
- H7: Coalition size moderates investors' reactions to CSA such that investor reactions are less unfavorable when more firms are involved in the announcement of CSA.
We test our hypotheses with a data set of 293 CSA events conducted by 149 firms across 39 two-digit Standard Industrial Classification (SIC) codes. The following section details our data collection procedures, measurements, and variables of interest.
The focal events are publicly available announcements of statements or actions by firms regarding partisan sociopolitical issues. We collected these events using a dictionary of time-relevant search terms of partisan sociopolitical topics extracted from [73] report "Political Polarization in the American Public" and "Political Polarization and Typology Survey."[ 6] For example, we collected the first mention of Amazon's announcement of the removal of Confederate flag products from its website and JPMorgan Chase's first identifiable statement of support for marriage equality. Table 1 provides examples of activism from our sample. We assembled this sample of events from press releases and news articles available from three syndicated sources: ProQuest Newsstand, LexisNexis Academic, and Factiva ([10]). Keyword search terms gathered from the Pew Research Center reports included generic terms such as "abortion" or "LGBTQ" and specific issues such as "North Carolina HB2." A dictionary of generic words used in the archival search and examples of CSA are available in Table W1 in the Web Appendix.
Graph
Table 1. Examples of CSA in our Sample.
| Political Stance of Event and Stakeholders−1 Extremely Liberal & 1 Extremely Conservative |
|---|
| Firm | CSA Example | Event Political Stance | CSA– Customer Deviation | CSA– Employee Deviation | CSA– Government Deviation | CAR |
|---|
| Amazon | Amazon removes Confederate flag merchandise from its website. | −.43 | .04 | .13 | .41 | .91% |
| Target | Target supports national LGBTQ pride month (#takepride). | −.58 | .13 | 1.28 | .42 | −1.85% |
| Chipotle | Chipotle prohibits guns in stores. | −.62 | .14 | .20 | .60 | 2.67% |
| Lowe's | Lowe's pulls its advertising during the TLC network's All-American Muslim reality TV show. | .44 | .28 | .02 | .28 | .61% |
| Twitter | Twitter marks Black Lives Matter movement with special emoji. | −.40 | .04 | .12 | .03 | 1.12% |
| Starbucks | Starbucks launches a marketing campaign to promote conversations about race between customers and employees and calls for baristas to write the hashtag #RaceTogether on customers' cups. | −.45 | .11 | .16 | .43 | 3.79% |
| J.C. Penney | J.C. Penney features two lesbian mothers in 2012 Mother's Day advertisement. | −.76 | 1.19 | 1.56 | 1.02 | −8.15% |
| Kroger | Kroger issues a statement in support of its policy for carrying firearm in the store. | .52 | .53 | .80 | .25 | .90% |
| PepsiCo | The Dorito brand introduces Doritos Rainbows chips, the first Doritos product in history made up of multiple, rainbow-colored Doritos chips inspired by the Pride flag. | −.44 | .33 | .10 | .15 | 2.35% |
Three trained research assistants blind to our research questions applied a two-stage process to ensure that the prospective events for inclusion in the sample fit the definition of sociopolitical activism and were not subject to potential confounds, which would invalidate the resulting abnormal stock returns. First, if an event was mentioned several times on different dates, coders searched for the first mention of the event to determine the influence of firm communication on abnormal stock returns. Second, coders noted any announcements for which another possible confounding event occurred within a week to eliminate the possible influence of other events on abnormal stock returns ([10]).[ 7]
Next, we ran a Q-sort survey (Survey 1) ([70]) to further validate that our events qualify as CSA and are separate from CSR and CPA. In this survey, we provided two trained research assistants blind to our research questions with our definitions of CSA, CSR, and CPA. We then asked the assistants to classify events into one of the three definitions, labeled as A, B, and C to avoid bias, but corresponding to the three types of events. The events consisted of a combination of all the events from our sample plus an additional 12 CPA events and 25 CSR events found in the literature. The overall agreement between the two assistants was 79.5%, the overall hit ratio was 85%, and Cohen's kappa for the two assistants was.80, which is in the "excellent range of agreement" according to previous research ([70]).[ 8] Details of the Q-sort survey are available in the Web Appendix W2.
We collected additional data for the explanatory and control variables for each firm-event from COMPUSTAT, other publicly available data sources (e.g., headquarters locations, election results, political donations), and by doing content analysis of the announcements. We ultimately obtained 293 events from January 1, 2011, to October 31, 2016, involving 149 U.S. publicly held firms from 39 two-digit SIC codes.
The stock market reaction to CSA announcements serves as our primary measure of investors' reactions and changes in firm value. We estimate the stock market reaction to CSA at the time investors first receive the relevant information ([12]; [79]). Data on the firm and stock market returns come from the Center for Research in Security Prices, which we used to estimate the abnormal stock returns of the firm on the first day the firm's CSA was publicized:
Graph
1
where Rit is the daily return, E(Rit) is the expected return of the stock for firm i on day t, and ARit is the abnormal return. To calculate E(Rit), we use a market model for the main analysis and provide additional estimations using the market-adjusted model and the Fama–French–Carhart model ([16]; [30]) as robustness tests.
Following previous research, to address the possibility of information leakage and spillover in the stock market, we compute cumulative abnormal returns (CAR) for several windows around the day of the event ([35]):
Graph
2
To choose an event window of appropriate length, we compute the CAR for alternative t′ and t ∈ {−2, −1, 0, 1, 2} and then test their significance in each window. In line with previous studies, we choose the most significant CAR in the five-day window (−2, 2) as our dependent variable ([35]; [83]).
To operationalize the degree of deviation between a firm's CSA and its stakeholders' political values, we first measure the political stance of the events. We conducted a survey (Survey 2) on Amazon Mechanical Turk (MTurk) that asked 1,406 U.S. adults to measure the stance of a sample of events. The pool of respondents was heterogeneous.[ 9] Each respondent received five randomly selected events from our sample, along with the date of the event (but not the identity of the firm involved in the CSA event). We asked respondents to rate each event on a seven-point scale (1 = "very liberal," and 7 = "very conservative"). This produced approximately 7,000 ratings. We then used the average score for each event as the event's stance and transformed it into a zero-centered measure, where −1 reflects extremely liberal and +1 extremely conservative stances. We use this measure as the event's stance (Event_Stance) in our model.
The average Event_Stance in our sample is −.40, which leans liberal. The maximum Event_Stance is.75 and the minimum is −.88. Two reasons may explain the more liberal-leaning average score: ( 1) conservativism usually calls for preservation of the status quo, whereas activism often calls for change and therefore leans liberal ([31]), and ( 2) most conservative activism observed in our data collection effort was conducted by privately held firms (e.g., Chick-fil-A, Hobby Lobby, Koch Industries), which are not part of our sample because they do not have publicly traded stock.
Next, we created three variables to measure the prevailing political values of the firms' customers, employees, and government legislators, respectively. First, we calculated the prevailing political values of a firm's customers by running an independent survey (Survey 3) on MTurk that asked 375 U.S. adults[10] to evaluate a randomly drawn set of 20 firms from our database of 149 firms (approximately 7,500 ratings in total). Respondents indicated whether a given firm's typical customers lean toward having more liberal or conservative political views (−1 = "more liberal," 0 = "neither liberal nor conservative," and +1 = "more conservative"). Overall, each firm received approximately 50 ratings, which we averaged to create Customer_Stance. In contrast with the continuous measure used in Survey 2, the categorical measure used in Survey 3 reflects a more discrete categorization of customers based on political affiliation. The results have face validity. For example, respondents rated Whole Foods as typically having more liberal customers (Customer_Stance = −.50) and Cracker Barrel as usually having more conservative customers (Customer_Stance =.43). This retrospective measure captures the prevailing stereotypical perception of investors about the political values of a given firm's customers.
Second, we collected each firm's employee's political donations using individual contribution data provided in the U.S. Federal Election Commission's database. We identified the individual employee donations to the Republican versus Democratic party and calculated a measure of average employees' political donations for firm i at day t in year T as
Graph
3
This ratio equals −1 if all employee donations were liberal (Democratic) and +1 if all donations were conservative (Republican). If no employees donated to any parties, we set the firm's score to zero.
Third, to compute legislatures' political values, we collected the political composition of the state legislature (general assembly) from the state in which the firm is headquartered. We focus on state rather than federal legislatures because state legislatures are likely to respond sooner and more swiftly to CSA through regulations and tax restructuring than federal legislators, with the added benefit of greater numbers of seats ( 7,000+ state vs. 535 federal) for more nuanced and localized measures across states of disparate sizes. We collected the number of Republican and Democrat legislators in the upper house (State Senate) and lower house (State House Representatives) from https://ballotpedia.org. State legislature tenure varies by state but most often is biennial. Therefore, we use the most recent year before the event to collect the data for firm i at time t in year T.
Graph
4
We use the calculated stance measures as the average stakeholder ideology and create three variables for the level of deviation between the stakeholders' political ideologies and the CSA event stance. For each event, we assume the absolute value of distance between the Event_Stance and the Stakeholder_Stance as the degree of deviation between the CSA event and stakeholder ideology:
Graph
5
Graph
6
Graph
7
The degree of deviation falls between 0 and 2, where a value closer to 2 shows a stronger deviation between the values conveyed and supported by the CSA and the ideology of the stakeholders. Table 1 provides examples of deviation measures.
To measure the degree of deviation between the focal sociopolitical issue and the firm's brand image, we constructed a fit variable using ex post customer evaluations of the events obtained from a survey (Survey 4). From MTurk, 552 U.S. adults (48% female; median age = 32 years) participated in a survey for a nominal fee. Participants rated a randomly drawn subset of 30 CSA on the extent to which the event seemed "like something that suits or is congruent with the brand's image (high fit) or seems very incongruent (low fit)" using a seven-point scale (1 = "low fit," and 7 = "high fit"). This produced approximately 50 ratings per firm, which we mean-averaged to create a brand fit index that we reverse coded to create CSA_Brand_Deviation.
We text analyzed all the events in our sample and categorized them into two categories: whether the activism took the form of an action or a statement. We code a dummy variable (Action) indicating whether the CSA event made specific mention of a concrete action (or actions). We classify the event as an action even if there are also statements issued.
We determine whether the CEO delivered the CSA announcement of a firm as a dummy variable (CEO_Announcement). For all events for which CEO_Announcement is equal to 1, we thoroughly reviewed the event to confirm that the CSA was announced by the CEO "as the representative of the firm" and not as an individual conveying his or her political views. We exclude the latter from our definition of CSA.[11]
Two research assistants blind to our research question manually text analyzed all of the events in our sample and independently categorized the events into two groups based on whether the firm communicated its business interest or potential positive business outcomes from their CSA. Business_Communication is equal to 1 if the firm communicates its business interests along with the social motive. For example, in September 2014, Ben & Jerry signed an "Employers' Amicus Brief" in support of same-sex marriage. Chris Miller, Mission Activism Manager of Ben & Jerry explained, "It's not enough to change the way you do business, or change the practice within your business....Unless you're willing to stand up and advocate for the rights of others, not just here in our backyard but around the world, it's often just not good enough." Miller further explained that in addition to being an issue of civil rights, "LGBT discrimination law complicates the running of our businesses, creates confusing administrative nightmares for companies and introduces difficulties in recruiting folks from other states" ([ 5]). General Electric signed the same Amicus Brief. However, it did not communicate the business aspects of this Amicus Brief with its stakeholders. This former event was coded as 1 for Business_Communication while the latter is coded as 0. The coders agreed on 85.6% of the events and resolved disagreements via discussion.
Finally, we calculate the variable Coalition_Size as the number of firms explicitly joining forces to announce the CSA event at the same time and in the same statement, such as multiple firms' Amicus Briefs or open letters to support or oppose a sociopolitical issue. We read each event announcement carefully and set the variable (Coalition_Size) as zero if the firm conducted the CSA on its own. The variable otherwise takes the value of the count of firms involved in the activism.
In addition to the independent variables, multiple explanatory firm-, event-, industry-, and time-specific variables can affect investors' reactions to CSA. We attempt to address such factors in a control-rich model using several variables. (For a detailed explanation of the coding and collection of these control variables, see Table W3 in the Web Appendix).
To disentangle the effect of CSA from CSR and CPA, we control for variables pertaining to firms' CSR and CPA involvement. Following [68] procedures, we collected firms' CSR indices from Kinder, Lydenberg, and Domini Research and Analytics and use the average total score of firms' CSR indices in the past three years as the proxy for their CSR activities. To account for firms' CPA, we collected firms' average political donations to Republican and Democratic campaigns in the past three years from the Center for Responsive Politics. Table W3 in the Web Appendix lists these variables in detail.
Other variables control for the firms' financial status at the time of the event. Variables collected from COMPUSTAT include firms' primary operating market (B2B_B2C), return on assets (ROA), firm size (Firm_Size), firm leverage (Leverage), and advertising expenditure (Advertising_Expenditure). We also control for marketing capability (Marketing_Capability) and the presence of a chief marketing officer (CMO) to account for their potential effects on CSA performance and the response efficiency after CSA.
Furthermore, CSA is a corporate strategy executed through firms' brands. Therefore, we account for differences when a multibrand versus a single-brand firm engages in activism. We control for the total number of brands owned by the firm and use the natural logarithm of this number in the model (Log_Brand_Number). We also control for the percentage of institutional stock holdings (Institutional_Holdings) for each firm to account for the possibility that individual and institutional investors react differently to CSA. Finally, we create a variable to account for firms' reputations for engaging in CSA, which may shape investor expectations ([90]). For each firm, we record the number of past events in a rolling window of three years before the event (Past_CSA).
We include a series of variables specific to the CEO of the firm. We control for the CEO's political ideology, which can influence a firm's culture and indirectly affect Employee_Stance.[12] We calculate CEO_Political_Ideology with a similar approach as Equation 3. In addition, we collect CEOs' gender (CEO_Gender) and age (CEO_Age) at the time of the event to address differences in their inclination to take risks and engage in or encourage activism ([29]).
We use a categorical variable (Event_Category) to account for the topic of the polarizing issue (e.g., immigration). Next, we address the popular belief that high-tech industries are more inclined toward liberal ideologies ([65]) by incorporating a dummy variable (High-Tech) for the high-tech (vs. low-tech) industry. To control for the potentially greater sensitivity to politically polarizing statements during presidential election years, we also include a dummy variable (Election_Year). Finally, we control for other unobserved industry- and time-specific factors by including the industry (Industry_Dummy) and year (Year_Dummy) dummy variables.
To test our first hypothesis (H1), we conduct a t-test on the five-day window CAR for firms conducting CSA. We follow this test with a regression model with two-way clustered errors to test H2–H6. All firms in the primary sample engaged in at least one instance of CSA during 2011–2016, which gives the potential for selection bias. To investigate the extent to which selection bias might explain our results, we employ [42] two-stage correction approach. In the first stage, we run a panel data probit model in which the dependent variable is the decision to engage in activism (a dummy equal to 1 if the firm had an activism event in year T and 0 otherwise) in each year. In the second step, we include the inverse Mills ratio (IMR) (derived from the first stage) alongside the control variables.
To facilitate identification, in the first stage, we use two exogenous determinants of the decision to engage in activism. First, following standard practice in the literature, we use the average number of industry-year instances of activism (Average_Industry_CSA) for each year (e.g., [34]; [82]). For each observation in the primary sample, we calculate Average_Industry_CSA by extracting from COMPUSTAT all firms that have not engaged in activism from all the four-digit SIC codes whose focal firm is a primary member. We divide the number of activist firms in all the SIC code-years by the total number of primary firms in the SIC code. Second, we include the average number of instances of activism that occurred in the same geographic region (firms' headquarters state) in the same year (Average_State_CSA) across all industries excluding the focal firm. These variables must meet relevance and exclusion restrictions, which we explain in section W5 of the Web Appendix.
We control for the percentage of institutional holdings, financial status of the firms (ROA, size, leverage, and advertising expenditure), and time- and industry-specific variables. We run the first-stage probit model for firm i in year T:
Graph
8
where X is a vector of covariates as follows:
Graph
We estimate the IMR from Equation 8 at the annual level and include IMR associated with each event of firm i that occurred in time t during year T in Equation 9 to provide tests for H2–H6:
Graph
9
where i and t indicate the firm and the time of the event, respectively; W is a vector of control variables in Table W3 of the Web Appendix; IMRit is the inverse Mills ratio from the first-stage selection model; and ∊it represents the two-dimensional clustered standard errors that account for the clustering across firms and events ([15]; [87]). All continuous variables are Winsorized at the 1% and 99% levels to reduce the effect of outliers (e.g., [94]).
Table 2 contains descriptive statistics and correlations. The range for stakeholder deviation variables indicates that our sample covers activism ranging from very low degrees of deviation (.0029) to very high degrees of deviation (1.86). The averages for stakeholder deviation variables are CSA_Customer_Deviation =.48, CSA_Employee_Deviation =.56, and CSA_Government_Deviation =.45. The averages show that, in general, while CSA events have some level of deviation from stakeholders' values, firms avoid engaging in CSA events that deviate a great deal from stakeholders' values. CSA_Brand_Deviation reflects the level of deviation between the brand image and CSA event; on average, the respondents rated the level of deviation as 3.53 out of 7. The respondents rated the deviation between Starbucks and supporting marriage equality as the lowest (CSA_Brand_Deviation = 2.27), while they rated Walmart's CSA to be the first store to remove all Confederate flag merchandises as the highest (CSA_Brand_Deviation = 4.62).
Our descriptive findings further indicate that despite the popular definition for sociopolitical activism in recent literature, which limits CSA to "sociopolitical statements" (e.g., [41]), 40% of the CSA events in our sample are accompanied by a form of action from the firm. These actions include but are not limited to changing the color of product packaging to support LGBTQ rights, taking down products with Confederate flag logos, retracting or issuing an advertising campaign, and changing firm policies or strategies.
The CEO of the firm directly announces only 28% of the CSA events. In most cases, CSA is announced in the news by mentioning the "firm" as the acting agency or one of the firm's more recognizable brands. In some other instances, the event is announced by firms' diversity officers, marketing managers, or public relations departments. On average, firms communicate their business interests or potential financial benefits of CSA in 50% of the events. Finally, while 65% of CSAs are conducted alone, 35% of the firms form coalitions with, on average, eight other companies to conduct CSA.
The first three rows of Table 2 show that on average, the abnormal returns to CSA are negative and statistically significant, with a mean of −.43% for the market model, −.40% for the market-adjusted model, and −.44% for the Fama–French–Carhart model (p <.001). This negative and significant average supports H1 and indicates that, on average, investors react unfavorably to firms engaging in CSA.
Graph
Table 2. Descriptive Statistics and Correlations.
| VariablesN = 293 | Mean | SD | Min | Max | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 |
|---|
| 1. CARMarket Model | −.43% | 3.98% | −36.49% | 17.46% | 1 | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2. CARMarket-Adjusted Model | −.40% | 3.94% | −36.46% | 16.20% | .95 | 1 | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3. CARFama–French–Carhart | −.44% | 4.01% | −32.84% | 21.61% | .90 | .90 | 1 | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4. CSA–Customer Deviation | .48 | .32 | .0029 | 1.44 | −.36 | −.38 | −.34 | 1 | | | | | | | | | | | | | | | | | | | | | | | | |
| 5. CSA–Employee-Deviation | .56 | .40 | .0030 | 1.86 | −.29 | −.28 | −.27 | .35 | 1 | | | | | | | | | | | | | | | | | | | | | | | |
| 6. CSA–Government Deviation | .45 | .32 | .0038 | 1.27 | −35 | −33 | −31 | .35 | .25 | 1 | | | | | | | | | | | | | | | | | | | | | | |
| 7. CSA–Brand Deviation | 3.52 | .53 | 2.27 | 4.62 | .03 | .03 | .02 | .29 | −.01 | .16 | 1 | | | | | | | | | | | | | | | | | | | | | |
| 8. Action | .40 | .49 | 0 | 1 | −.17 | −.16 | −.15 | .08 | .07 | .09 | −.01 | 1 | | | | | | | | | | | | | | | | | | | | |
| 9. CEO | .28 | .45 | 0 | 1 | .17 | .12 | .12 | −.02 | −.03 | .02 | −.01 | .08 | 1 | | | | | | | | | | | | | | | | | | | |
| 10. Business Communication | .50 | .50 | 0 | 1 | .17 | .18 | .16 | −.11 | −.06 | −.09 | .00 | .10 | −.07 | 1 | | | | | | | | | | | | | | | | | | |
| 11. Coalition Size | 8.43 | 24.55 | 0 | 200 | .07 | .07 | .07 | .01 | .001 | −.04 | −.03 | .07 | .009 | −.04 | 1 | | | | | | | | | | | | | | | | | |
| 12. Firm CSR Score | 4.52 | 4.42 | −6 | 17 | .05 | .06 | .10 | .008 | .02 | −.02 | .00 | .00 | −.01 | .06 | .008 | 1 | | | | | | | | | | | | | | | | |
| 13. Firm Political Activity | .0021 | .39 | −1 | 1 | .008 | .003 | .02 | .22 | .08 | .19 | .17 | .02 | −.07 | −.00 | .09 | .01 | 1 | | | | | | | | | | | | | | | |
| 14. CEO Political Ideology | .024 | .57 | −1 | 1 | .01 | .02 | −.01 | .05 | .08 | .12 | .14 | .04 | −.03 | .01 | .03 | .10 | .29 | 1 | | | | | | | | | | | | | | |
| 15. CEO Gender | .020 | .15 | 0 | 1 | .18 | .17 | .19 | −.06 | −.07 | −.10 | −.01 | .01 | .10 | .06 | .03 | .11 | .09 | −.12 | 1 | | | | | | | | | | | | | |
| 16. CEO Age | 54.9 years | 7.5 years | 29 years | 74 years | .05 | .05 | .03 | .21 | .12 | .17 | .17 | .02 | −.20 | −.08 | .02 | .10 | .008 | .04 | −.03 | 1 | | | | | | | | | | | | |
| 17. CMO | .18 | .38 | 0 | 1 | .18 | .18 | .11 | −.20 | −.003 | −.008 | −.27 | −.06 | −.05 | .02 | −.05 | −.17 | −.22 | −.21 | −.02 | .04 | 1 | | | | | | | | | | | |
| 18. Past CSA | 1.79 | 2.18 | 0 | 8 | .04 | .10 | .07 | −.17 | −.04 | −.08 | −.41 | −.05 | .08 | .11 | −.09 | .01 | −.31 | −.16 | .00 | −.15 | .34 | 1 | | | | | | | | | | |
| 19. B2B_B2C | .30 | .46 | 0 | 1 | −.12 | −.13 | −.09 | .20 | .08 | .09 | .31 | .03 | .01 | −.07 | −.01 | .10 | .17 | .16 | −.01 | .13 | −.28 | −.24 | 1 | | | | | | | | | |
| 20. ROA | .07 | .08 | −.46 | .23 | .01 | .05 | .05 | −.03 | −.06 | −.07 | −.17 | .03 | −.16 | .15 | .01 | .13 | −.21 | −.09 | −.02 | .19 | .18 | .23 | −.08 | 1 | | | | | | | | |
| 21. Firm Size | 130,137.3 | 318,293.1 | 106.9 | 2,359,141 | −.06 | −.02 | −.04 | .10 | −.01 | .004 | .12 | .10 | −.14 | .04 | .08 | .22 | .11 | .18 | −.11 | .17 | −.17 | .14 | .20 | .15 | 1 | | | | | | | |
| 22. Leverage | 82.15 | 727.42 | −50.83 | 10,368.33 | .04 | .04 | .06 | −.07 | −.02 | −.06 | −.06 | −.05 | .03 | −.06 | −.01 | −.08 | .11 | −.11 | −.01 | −.16 | .04 | −.04 | −.05 | −.21 | −.21 | 1 | | | | | | |
| 23. Advertising Expenditure | $953.86 | $1,280.72 | $0 | $9,729.00 | .04 | .04 | −.10 | −.02 | .02 | −.003 | −.22 | .06 | −.05 | −.01 | .07 | .05 | .10 | .08 | .01 | .11 | .28 | −.18 | −.27 | .14 | .35 | −.07 | 1 | | | | | |
| 24. Marketing Capability | 25.27 | 9.33 | 15.88 | 75.29 | .08 | .04 | .09 | −.04 | .05 | −.05 | .08 | .03 | .01 | −.07 | .11 | −.07 | −.02 | .01 | −.02 | .02 | .03 | .21 | .07 | −.24 | −.05 | .07 | −.14 | 1 | | | | |
| 25. Brand Number | 37.59 | 178.24 | 1 | 214 | .01 | .00 | −.04 | .06 | −.07 | −.03 | .10 | −.05 | −.06 | −.04 | −.08 | .01 | .07 | −.07 | −.01 | .04 | .08 | −.04 | .10 | .01 | .04 | −.02 | −.06 | .01 | 1 | | | |
| 26. Institutional Holdings | .52 | .34 | 0 | 1 | .01 | .02 | .00 | .06 | .12 | .13 | .02 | −.03 | −.09 | .06 | .07 | .18 | .02 | .20 | −.00 | .27 | .20 | −.06 | .07 | .15 | .06 | −.13 | .04 | .05 | −.12 | 1 | | |
| 27. High-Tech | .30 | .45 | 0 | 1 | .13 | .13 | .18 | −.18 | −.04 | −.27 | −.20 | .08 | .24 | −.07 | .05 | .19 | −.18 | .03 | .04 | −.34 | .14 | .12 | −.03 | −.01 | −.04 | .16 | −.02 | .11 | −.03 | −.22 | 1 | |
| 28. Election year | .36 | .47 | 0 | 1 | .04 | .02 | .02 | .08 | −.02 | .05 | −.06 | −.15 | .14 | −.08 | .08 | −.03 | −.05 | −.03 | .11 | −.06 | −.07 | .09 | −.21 | −.13 | −.19 | .10 | .02 | .00 | −.06 | .00 | .08 | 1 |
1 Notes: Boldfaced values for correlations indicate significance at 95% level.
To examine support for H2–H6, we estimated a two-step Heckman correction model in Equations 8 and 9. We first calculated the variance inflation factors; the average is 1.44, and the maximum is 1.88, well below the threshold of 10 to ensure the model does not suffer from multicollinearity. We report the results for the first-stage model in Table W4-1, Section W4 of the Web Appendix. We calculate the IMR from the first-stage model Before inserting the IMR in the second stage, we examine the strength of the exclusion restriction assumption in the first stage. In section W4 in the Web Appendix, we conceptually explain and provide statistical evidence that our instruments satisfy exclusion restriction; the instruments neither are correlated with nor will they systematically affect firm-specific omitted variables that influence investors' reactions to the focal firm's activism. We include the estimated IMRiT to each event of firm i that occurred in time t during year T, in the regression for Equations 9. Table 3 provides the results for the second stage model. We first run the model for Equation 9 without control variables to confirm that the results are not a product of overparameterization caused by the long list of control variables. Model 1 in Table 3 shows the results without control variables.
Graph
Table 3. Effect of CSA on Stock Market Abnormal Returns.
| Second-Stage Selection Model |
|---|
| | Dependent Variable: Short-Term |
| Stock Market Reaction to CSA |
| | | Model 1: | Model 2: |
| Variables | | | Without Controls | Control Rich |
| N = 293 | Hypotheses | α | (SE) | α | (SE) |
| CSA–Customer Deviation | H2a | − | −.029*** | (.010) | −.023** | (.012) |
| CSA–Employee Deviation | H2b | − | −.013** | (.006) | −.017*** | (.005) |
| CSA–Government Deviation | H2c | − | −.020*** | (.007) | −.022*** | (.007) |
| CSA–Brand Deviation | H3 | | −.0040 | (.004) | −.0040 | (.005) |
| Action | H4 | − | −.0093** | (.004) | −.0089** | (.004) |
| CEO Announcement | H5 | − | −.014** | (.007) | −.015** | (.007) |
| Business Communication | H6 | + | .0098*** | (.005) | .0099** | (.005) |
| Coalition Size | H7 | + | .00027*** | (.000) | .00024** | (.000) |
| Firm CSR Score | | | — | | .00062 | (.000) |
| Firm Political Activity | | | — | | .0087 | (.007) |
| CEO Political Ideology | | | — | | .0068* | (.004) |
| CEO Gender | | | — | | .030** | (.015) |
| CEO Age | | | — | | .00026 | (.000) |
| CMO | | | — | | .019** | (.008) |
| Past CSA | | | — | | .0019 | (.001) |
| B2B_B2C | | | — | | −.0065 | (.006) |
| ROA | | | — | | −.053 | (.004) |
| Firm Size | | | — | | −.0020** | (.020) |
| Leverage | | | — | | −1.08e-06 | (.000) |
| Advertising Expenditure | | | — | | −8.35e-07 | (.000) |
| Marketing Capability | | | — | | .00023 | (.000) |
| Log Brand Number | | | — | | .0015 | (.001) |
| Institutional Holdings | | | — | | .00020 | (.006) |
| High-Tech | | | — | | .011** | (.006) |
| Election year | | | — | | .0066 | (.007) |
| IMR | | | .00095 | (.003) | .0012 | (.003) |
| Prob > F | | | .002 | | .000 | |
| R2 | | | .33 | | .41 | |
- 2 *p <.10.
- 3 **p <.05.
- 4 ***p <.01.
- 5 Notes: Event, year, and industry dummies are omitted from the table because of limited space.
Model 2 provides the control-rich model results for Equation 9. The coefficient for CSA_Customer_Deviation is negative and significant (α1 = −.023, p <.05), in support of H2a. Similarly, the coefficients for CSA_Employee_Deviation and CSA_Government_Deviation are negative and significant (α2 = −.017 and α3 = −.022, respectively, p <.01), in support of H2b and H2c. These results indicate that greater sociopolitical deviation between the stance of the CSA event and the stakeholders' dominant sociopolitical ideology reduces short-term abnormal stock returns.
The coefficient for CSA_Brand_Deviation, though negative, is not statistically significant and does not support H3. Perhaps a high degree of brand image fit is difficult to achieve among CSA issues. Or, it is possible that the controversial nature of CSA overrides any fit effects. This surprising null effect is fertile ground for future research.
The coefficients for Action and CEO_Announcement are negative and significant (α5 = −.0089 and α6= −.015, respectively, p <.05), in support of H4 and H5. Events in the form of actions or announced by the CEO likely signal a stronger commitment of time, attention, and resources to the CSA issue. Investors perceive this stronger commitment to a partisan sociopolitical issue as particularly risky and an unnecessary deviation from the firm's primary profit-oriented objectives. The coefficient for Business_Communication is positive and significant (α7 =.0099, p <.05), which supports H6 and indicates that communicating business interests can alleviate investors' concerns about firms' resource allocation, decrease uncertainty and improve investors' reaction to CSA. Finally, the coefficient for Coalition_Size is positive and significant (α6 =.00024, p <.05), which supports H7 and indicates that the announcement of CSA with other firms is less concerning as it reduces the riskiness of the event and provides investors with more assurance that engagement in CSA may be necessary.
In addition, consistent with previous research, we observe that larger firms receive a weaker investor response (α = −.0020, p <.05) (e.g., [11]). Investor reactions to female CEOs who conduct activism are also more favorable (α =.030, p <.05). Female CEOs are expected to be more caring and concerned about others than male CEOs ([75]). Therefore, promoting societal change might be perceived as more expected and more acceptable from a female than male CEO. The coefficient for CMO is positive and significant (α =.019, p <.05), which indicates that investors may perceive firms with a CMO in their C-suite as more capable of managing their CSA effectively. Finally, we observe a positive and significant effect for High-Tech (α =.011, p <.05), which provides evidence that activism and seeking societal progress is more expected and accepted from firms in high-tech industries ([65]).
Where possible, we tested alternative operationalizations of key variables. Several important variables in our model are derived from the Event_Stance measure, which is collected retrospectively through a survey. We check the validity of this measure and check the robustness of the results using an alternative dichotomized variable (Conservative = 1 and Liberal = 0). In addition, we alternatively operationalize for CSA_Customer_Deviation using secondary data from EquiTrend, for CSA_Employee_Deviation using employees' number of donation transactions weighted by firms' total number of employees, and for Business_Communication using the count number of keywords related to business interest of the firm. Finally, we use alternative estimations of CARs (market-adjusted model and Fama–French–Carhart Model) and an alternative three-day window of analysis for the event study. The robustness tests are explained in detail along with the tables of results in Section W5 of the Web Appendix.
Although the results of the main model are insightful, they do not fully answer an important question: How should managers proceed when the CSA deviation varies across stakeholders? For example, a manager of a firm such as Whole Foods might feel pressured to engage in liberal-oriented CSA to appease its liberal-leaning customer base but fear retaliation from its conservative state legislature in Texas. To provide actionable managerial insights for such situations, we further explore responses to various combinations of CSA–stakeholder deviations.
To explore investors' reactions to different levels of deviations between CSA and conflicting stakeholder ideologies, we create a dichotomous measure of deviation. The dichotomous measure helps clearly identify various scenarios to better understand investor and customer responses. We use the mean for each CSA_Stakeholder_Deviation variable as the cutoff to divide the CSA events into two groups of low- and high-level deviation from each stakeholder's ideology. Next, we classify all the events into eight groups, which reflect all combinations of CSA–stakeholder deviation (e.g., customer low, employee high, legislator low). The sixth row of Table 4 reports the results of the t-tests for short-term abnormal stock returns (CARMarket_Model) for low and high CSA–stakeholder deviation across the eight groups.
Graph
Table 4. Stock Market and Customers' Reactions to CSA Based on Level of Deviation from Stakeholders' Sociopolitical Values.
| Level of Deviation | Group 1N = 85 | Group 2N = 27 | Group 3N = 30 | Group 4N = 32 | Group 5N = 20 | Group 6N = 33 | Group 7N = 30 | Group 8N = 36 |
|---|
| Percentage of the events | 29% | 9.2% | 10.2% | 11% | 6.8% | 11.3% | 10.2% | 12.3% |
| ...from customers | Low | Low | Low | High | Low | High | High | High |
| ...from employees | Low | Low | High | Low | High | High | Low | High |
| ...from government | Low | High | Low | Low | High | Low | High | High |
| CARMarket_Model | .71%** | .39% | .01% | −.62%* | −1.79%** | −.26% | −.94%** | −2.45%*** |
| Quarterly_Sales_Growth | .084*** | .085** | .042*** | .0095 | .034** | .017 | −.051* | −.040** |
| Annual_Sales_Growth | .12*** | .081** | .10*** | .0044 | .045** | .0097 | −.053* | −.043** |
- 6 *p <.10.
- 7 **p <.05.
- 8 ***p <.01.
Several valuable insights emerge from these results. First, investors' reactions are surprisingly positive (.71%, p <.05) when CSA–stakeholder deviation is low across all key stakeholders (Group 1). Investors may expect that CSA with minimal deviation from stakeholders can strengthen relationships and thereby enhance performance. Second, investors' reactions to CSA are not negative and significant when its degree of deviation is low for at least two key stakeholders (Groups 2 and 3). The exception is Group 4, where CSA–stakeholder deviation is low for employees and the government but high for customers. This underscores the notable risk of alienating customers, even if the CSA aligns with the ideological values of local governments and employees.
Third, investor reactions are generally adverse when there is high CSA–stakeholder deviation among at least two key stakeholders (Groups 5 and 7). Investor reactions are most severe (−2.45%, p <.001) when CSA–stakeholder deviation is high across all three key stakeholders (Group 8). This prompts the question of why a firm would engage in such misaligned CSA. Based on a comprehensive archival search for each of the events in Group 8, it appears that such CSA is often related to strategic miscalculations. For example, J.C. Penny hired CEO Ron Johnson in 2011 from Apple, a firm with a highly progressive corporate culture. Johnson's approach clashed with the conservative values of J.C. Penny's stakeholders. Under Johnson, J.C. Penney invested in same-sex partner advertisements for Mother's and Father's Day in 2012. In April 2013, J.C. Penny finally accepted its "strategic mistakes" after the free fall of its stock value and fired Ron Johnson ([60]). In summary, CSA can have a positive effect on investor reactions, but only when it aligns with key stakeholders.
Existing theory suggests that customers should be more loyal to and increase purchases from firms whose CSA aligns with their ideological values ([20]; [74]). Conversely, customers should boycott or disidentify with firms whose CSA deviates from their values. Indeed, third-party websites are dedicated to monitoring firm activities to guide customers on which to boycott (e.g., www.2ndvote.com). To examine the changes in customers' responses to CSA based on the level of CSA–customer deviation, we focus on a focal indicator of customers' reactions, namely, growth in sales realized in the quarter and in the year following a CSA event.
For each firm in our sample, we collect growth in sales reported by COMPUSTAT for two periods. First, to address the immediate changes in sales and consumer response, we compute Quarterly_Sales_Growth for the quarter immediately before and immediately after the activism event. Second, to address seasonality effects and long-term effects of CSA, we compute Annual_Sales_Growth for the average of quarterly sales report for four quarters before to four quarters after the CSA event:
Graph
10
Graph
11
The last two rows of Table 4 report the sales growth for each group. As shown for Groups 1–3, quarterly and annual sales growth are positive and significant (above 4% to 10%, p <.01) for CSA events that have a low level of deviation from customers' ideology. In addition, for the 55% of events where CSA–customer deviation is low (Groups 1–3 and 5), there was an increase in sales growth. When CSA is highly deviated from customers and the government, sales growth suffered. This is especially true when CSA highly deviated from all three key stakeholders (Group 8), which saw a sales decline of 4% (p <.05). Compared with investor reactions, the findings here suggest that there are many cases where firms can engage in CSA and reap financial rewards even when it is not aligned with all their stakeholders. These benefits can accrue as stock performance, sales growth, or both.
As firms increasingly engage in CSA, existing approaches for understanding activism in the realm of either CSR or CPA cannot adequately address the unique features of CSA and its financial consequences. We contend that while CSA is a risky marketing strategy that investors are generally wary of, it may also be advantageous. Investors on average react negatively to CSA, especially when it deviates from the values of key stakeholders and signals the firm's resource-intensive commitment to activism. However, they also reward activism when it closely aligns with stakeholders. In addition, we show customers reward CSA when it resonates with their personal values and attest that it can be an effective means for firms to appeal to their target markets. Our findings generate several theoretical and managerial implications, as well as avenues for future research.
In "being close to the real world of marketing" ([69], p. 2), our research advances the marketing strategy literature and the nascent work on activism. We build on existing conceptualizations of activism to provide a comprehensive definition of CSA and introduce it as a new potential firm strategy worthy of investigation. CSA has been partially defined in management and public relations literature as a form of social advocacy (e.g., [25]) or a sociopolitical initiative exclusive to the firm's CEO (e.g., [17]; [41]). We include "corporate actions" in the definition of CSA in addition to sociopolitical advocacy in the form of statements and comprehensively define CSA as a corporate activity which pertains to partisan sociopolitical issues that can be executed by any representative of the firm or via firms' brands. Indeed, 40% of our sample consists of CSA in the form of actions (vs. statements), many of which pertain to the marketing mix such as introducing new products, redesigning packaging, and creating or terminating advertising campaigns. The inclusion of such marketing actions in the definition of CSA importantly highlights that CSA can be a firm strategy that aligns with firms' stakeholder and brand orientations.
We delineate CSA from CSR and CPA and argue that despite conceptual similarities, activism indeed represents a novel phenomenon worthy of unique investigation. Prior research has demonstrated a positive effect of CSR and CPA on firm value (e.g., [61]; [64]). By contrast, we demonstrate the complexity of CSA and document the overall negative effect of CSA on stock market returns as well as identify scenarios where it can have positive financial consequences. Decades of research have delivered an elaborate understanding of CSR, yet the logic of CSR is insufficient for understanding the effects we observe and explain in our CSA framework.
We ground our theoretical arguments in signaling and screening theories to provide a conceptual framework for the effect of CSA on firm value. In addition to introducing CSA as a new construct, we also introduce two sets of moderators that explain investor response to CSA. These moderators help locate CSA as a marketing strategy. Investor responses to CSA are shaped by the implementation of CSA (e.g., whether it is an action or statement) and its alignment with the personal values of key stakeholders, namely, customers. And, indeed, the effects of CSA and its moderators on sales growth indicate that customers pay attention to and make long-lasting purchase decisions based on CSA. Although CSA can be a risky strategy, it can also provide real performance benefits. More broadly, our work demonstrates both theoretically and empirically how marketing actions must align across stakeholders. Stakeholder alignment theory is not new (e.g., [41]), but it is less frequently examined in the context of marketing decisions. We, therefore, advance marketing strategy theory by bridging signaling and screening theories and stakeholder alignment theory.
A critical question for managers is whether they should engage in CSA. Our findings can help managers make this decision by informing them about how investors will react in the short run (abnormal stock returns) and how customers will react in the long run (sales growth). First, we demonstrate the importance of stakeholders' political values. Investor responses depend on how much the CSA deviates from the values of customers, employees, and state legislators; higher deviation elicits stronger negative reactions. Notably, if a CSA stance closely aligns with all three stakeholder groups, managers can expect a positive investor response. Thus, managers who are concerned about CSA's impact on shareholder value should first consider how much their stance deviates from other stakeholders' values. Critically, CSA never elicits a positive investor response when it deviates from customers' values. Managers should pay close attention to how greatly their CSA deviates from customers because it has ramifications not only for investor responses but also for long-term customer responses.
Investors are inclined to punish CSA that highly deviates from customers and customers are inclined to reward CSA that closely aligns with their values. Our findings show that regardless of deviation from employees and state legislators, when CSA is aligned with customers, managers can expect positive sales growth over the next quarter and year. Furthermore, when CSA is aligned with customers and at least one other stakeholder group, managers can expect positive sales growth without an adverse stock market reaction. Thus, managers should weigh their customers' values more heavily in their decision to engage in CSA. Importantly, our analysis of sales growth shows that CSA can have a lasting impact on firms: customers continue to reward or punish firms long after CSA is implemented. In summary, CSA that is aligned with customers can help managers avoid an adverse stock market reaction and elicit positive future sales growth.
Managers who choose to engage in CSA should be cognizant that it reveals important information to investors and the public about their strategic priorities and values. It signals that the firm is willing to engage in a risky activity and divert resources from profit-generating activities, and that it may make similar decisions in the future. It also reveals sensitive information pertaining to the firm's perspective on its role in society and the political engagement of its senior management. Importantly, because CSA reveals the firm's values to the public, it may have an enduring impact on the firm's future decisions related to its overall purpose, reputation, and management of stakeholder relationships. Given that CSA is difficult to retract and has lasting financial implications, managers should be confident in their stance and their decision to publicize it.
A second critical question for managers is how to conduct activism. Our findings suggest that managers should carefully consider how to implement CSA because it influences investors' inferences about the firm's commitment to activism versus its fiduciary duties. We identify four characteristics of CSA implementation to which investors are particularly discerning. Managers should be aware that they will receive a heightened response from investors when their activism takes the form of actions, is announced by the CEO, is not justified by a business objective, and is announced alone (vs. in a coalition with other firms). If managers are deeply committed to activism and it aligns with their strategic objectives (i.e., acquiring a more liberal or conservative customer base), activism's potential benefits may be worth an intensified negative response from investors. However, if managers are uncertain about activism's role in their firm's future strategic priorities or they are sensitive to investor responses, they should choose a more moderate approach to engaging in CSA. In summary, CSA is a risky firm activity that managers must carefully consider before implementing.
In its contribution to an emerging area of research, our study may provide several new avenues of research. First, our study informs managers about how investors respond to CSA based on the deviation from three major stakeholders' values (customers, employees, and state legislatures). However, there are other stakeholders yet to be studied. These stakeholders include but are not limited to ( 1) the firm's top management team, especially the CMO, ( 2) boards of advisors, and ( 3) federal government legislators. Moreover, our deviation measures do not capture the direction of the deviation from stakeholders' values, which can be a fertile ground for future research.
Second, we investigate CSA resource implementation characteristics (form of support, announcement source stature, business interest communication, and coalition size). However, from a marketing perspective, we believe understanding how CSA affects customers' attitudes, relationships with the brand, and purchase decisions are other worthy areas of study. The nonsignificant effect of the deviation measure for brand image in our study suggests other influential and explanatory brand- or product-specific factors should be studied. For example, perhaps product type (hedonic vs. utilitarian) or consumption context (private vs. public) influences customers' boycott or buycott responses to CSA. In addition, CSA may serve as a customer acquisition strategy and help firms better appeal to their target markets.
Third, while we control for firms' previous CSA actions, we do not examine the authenticity, consistency, or style of their CSA strategies. Future research might explore ( 1) whether CSA is a reaction to corporate wrongdoing (e.g., sexual or racial discrimination), ( 2) whether the firm conducts activism by supporting a vulnerable minority or attacking the majority, ( 3) the level of financial resources committed to CSA, and ( 4) an integrated CSA communication with simultaneous or continuous multiple activities in different contexts (e.g., a new product launch).
Fourth, while our study includes CSA delivered by the CEO as a representative of the firm and controls for their political donations, we do not study how personal activism that someone conducts outside of his or her role as CEO might spill over to affect a firm. Fifth, our study informs managers about the short-term financial consequences of unique CSA events, but it does not examine the potential long-term effects of a CSA strategy. As sociopolitical activism has now entered the realm of strategic marketing, the long-term strategy of the firm can have broader consequences, such as changes in brand equity, firm reputation, customer base composition, market share, performance relative to competitors, and long-term performance of firms. More specifically, future research should account for investors' projections of how stakeholders' values may change in the future and how these changes will affect their responses to CSA. For example, investors may project a firm's customer base to become more liberal over time or a firm's stance to become widely accepted in the future. Finally, CSA has the potential to shape culture. We advocate for research to address the broader impact of CSA on societal outcomes.
Supplemental Material, Web_Appendix_FINAL_PDF - Corporate Sociopolitical Activism and Firm Value
Supplemental Material, Web_Appendix_FINAL_PDF for Corporate Sociopolitical Activism and Firm Value by Yashoda Bhagwat, Nooshin L. Warren, Joshua T. Beck and George F. Watson in Journal of Marketing
Footnotes 1 Author ContributionsThe first two authors contributed equally.
2 Associate EditorSatish Jayachandran
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
5 Online supplement: https://doi.org/10.1177/0022242920937000
6 1For the full survey, see http://www.pewresearch.org/wp-content/uploads/sites/4/2014/06/2014-Polarization-Topline-for-Release.pdf.
7 2These confounding events included (1) releases of earnings reports, (2) dividend announcements, (3) executive adjustments, (4) stock splits or structural stock adjustments, (5) damage suits, (6) product recalls, (7) new product announcements, and (8) merger and acquisition activities ([10]).
8 3Overall agreement: the proportion of the events sorted similarly by the two assistants; overall hit ratio: the total percentage of events correctly placed according to the construct definitions; Cohen's kappa: the proportion of joint judgment in which there is agreement after exclusion of chance agreement ([70]).
9 4The respondents' median age is 34 years, 50% were female, and they represent 49 U.S. states and Washington, D.C. We measured political ideology on a seven-point scale (1 = "extremely liberal," and 7 = "extremely conservative") using three items (α =.94) to reflect social, economic, and overall political attitudes (mean = 3.59, SD = 1.68).
5The respondents' median age is 32 years, 48% were female, and they represent 44 U.S. states. We measured political ideology as in Survey 2 (α =.94, mean = 3.44, SD = 1.71).
6We do this because, aside from a handful of famous CEOs whose roles and titles are widely known (e.g., Mark Zuckerberg, CEO of Facebook; Tim Cook, CEO of Apple), most executives of firms will not be recognized unless the event specifies their title and corporation. Therefore, we only include CEO activism in the sample if the title and role of the CEO is clearly identified.
7Although we control for the CEO's political ideology, we propose that the indirect effect of CEO's political ideology on employees' personal donations should not be considerably large because (1) individual political donations are not publicized and are not required to be reported to the firms by the employees; and (2) 42 U.S. states have some form of a political activity retaliation law that prohibits employers from retaliation based on individuals' lawful political conduct outside work. (http://www.workplacefairness.org)
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By Yashoda Bhagwat; Nooshin L. Warren; Joshua T. Beck and George F. Watson IV
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Record: 43- Corrigendum to "Cueing Morality: The Effect of High-Pitched Music on Healthy Choice". Journal of Marketing. Nov2020, Vol. 84 Issue 6, p144-144. 1p. DOI: 10.1177/0022242920936653.
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Corrigendum to "Cueing Morality: The Effect of High-Pitched Music on Healthy Choice"
Huang, Xun (Irene), and Aparna A. Labroo (2019), "Cueing Morality: The Effect of High-Pitched Music on Healthy Choice." Journal of Marketing, (published online January 9, 2019), DOI: 10.1177/0022242918813577.
This article has been revised and republished due to substantial changes to the text of the original article, as published Online First on January 9, 2019.
The article was revised after authors, Xun (Irene) Huang and Aparna A. Labroo, contacted the journal on January 14, 2019 to express concerns about the data integrity of Studies 2-6 referenced in the original Online First article. The authors became concerned regarding the integrity of these data after they requested co-author Ping Dong for source data that she had collected for these studies but they were unable to acquire the source data from her.
After it was determined that the original source data for these studies would not be provided, the two authors chose to recreate and completely rerun Studies 2-5, along with Study 6 and two calibration studies, A1 and A2. The source data of Study 1 was obtained from the research assistant who ran the study. The authors added clarifying information for the first study (specifically removing 17 subjects who failed to provide independent responses from the final analysis). After reviewing the data from the recreated studies, authors Huang and Labroo submitted a series of revisions as well as all source data for the revised studies to the Journal of Marketing's editor in chief and an editor. The paper was then reviewed by them as well as the associate editor who had been involved with the article since the original manuscript was submitted.
The revisions in the republished article are intended to clearly outline the methods used and clarify the contributions that resulted from the findings. Studies 6 (now A3), A1, and A2 were moved to the web appendix. Studies 7 and 8, previously in the web appendix, were removed as they were deemed unnecessary. In the web appendix, Table S3 was also removed as in further review it was suspected that it was possible to identify respondents and the authors erred on the side of protecting the identities of respondents.
Despite multiple attempts to contact author Ping Dong regarding the recreating and rerunning of the studies and data and revisions to the article, authors Huang and Labroo were unable to get a response from Dong who did not participate in the repeated studies, article revisions, or approval of the revised draft. Attempts by the editor and publisher to reach the author also failed. Ping Dong has been removed from the authorship of the revised article given the significant changes that resulted in the removal of all of Dong's contributions to the original Online First article. This removal was made at the request, and with the consent of Huang and Labroo, and in line with authorship guidelines at their institutions. Authors Huang and Labroo acknowledge Ping Dong for her contributions in the conceptualization and idea generation of this article, original study design discussions, and writing parts of a previous draft of the article.
Due to the substantial number of edits necessary to address the above revisions, the journal determined republication of the article would allow readers to follow the article more effectively than a separate notice of the changes. Appended to the end of this republication notice is a watermarked version of the Online First article as published on January 9, 2019, so that interested readers may reference the original version of the article and note changes.
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Record: 44- Creating Boundary-Breaking, Marketing-Relevant Consumer Research. By: MacInnis, Deborah J.; Morwitz, Vicki G.; Botti, Simona; Hoffman, Donna L.; Kozinets, Robert V.; Lehmann, Donald R.; Lynch, John G.; Pechmann, Cornelia. Journal of Marketing. Mar2020, Vol. 84 Issue 2, p1-23. 23p. 3 Diagrams, 3 Charts, 2 Graphs. DOI: 10.1177/0022242919889876.
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Creating Boundary-Breaking, Marketing-Relevant Consumer Research
Consumer research often fails to have broad impact on members of the marketing discipline, on adjacent disciplines studying related phenomena, and on relevant stakeholders who stand to benefit from the knowledge created by rigorous research. The authors propose that impact is limited because consumer researchers have adhered to a set of implicit boundaries or defaults regarding what consumer researchers study, why they study it, and how they do so. The authors identify these boundaries and describe how they can be challenged. By detailing five impactful articles and identifying others, they show that boundary-breaking, marketing-relevant consumer research can influence relevant stakeholders including academics in marketing and allied disciplines as well as a wide range of marketplace actors (e.g., business practitioners, policy makers, the media, society). Drawing on these articles, the authors articulate what researchers can do to break boundaries and enhance the impact of their research. They also indicate why engaging in boundary-breaking work and enhancing the breadth of marketing's influence is good for both individual researchers and the fields of consumer research and marketing.
Keywords: breaking boundaries; broad impact; consumer research; marketing; marketplace stakeholders
Consumption and consumers are interwoven with contemporary society; therefore, marketers, journalists, policy makers, and members of the public all have a stake in the topics that consumer researchers study. Our work can also influence research in adjacent disciplines. Although our position as academics potentially sets us up as thought leaders, one wonders why our work does not have broader impact on these marketplace stakeholders ([63]) as well as on academics in other disciplines. Consumer researchers tend to cite scholars in other fields (e.g., psychology, anthropology, sociology) far more than scholars in other disciplines cite us. Similarly, most business practitioners turn to accessible, business-related popular writers before they seek the advice of consumer researchers. In the policy realm, our influence is often dwarfed by that of economists and legal professionals.
The relatively narrow impact of consumer research is not due to a lack of talent or commitment of individual researchers, the quality or rigor of our work, or our potential to offer insights. Rather, we argue that we and others in our field have handicapped ourselves by adhering to a set of implicit boundaries or defaults about what we study, why we should study it, and how we communicate the significance of our work. Adhering to such defaults can limit our thinking, the knowledge we produce, how we execute research, and how we disseminate our findings. More specifically, current consumer research is often inspired by existing academic literature, sometimes ignoring emergent substantive marketing-relevant consumer research issues[ 5] pertinent to marketplace stakeholders and academics in other disciplines. While our research typically illuminates construct-to-construct links, it often eschews more unstructured real-world phenomena for which novel constructs could be developed. It emphasizes individuals, as opposed to the small and large groups of which consumers are members. Furthermore, our research is often published in erudite academic journals but not further communicated to marketplace stakeholder groups for whom the findings may be relevant.
As a consequence of these implicit boundaries, consumer research yields limited cross-fertilization of ideas and knowledge diversity and is perceived to lack significance, despite its interdisciplinary and multistakeholder potential ([63]; [65]). We urge consumer researchers to break these boundaries and broaden our impact, lest we become irrelevant to nonacademic marketing stakeholders and cede influence to nonmarketing academic disciplines. As aptly noted by the current editors of the Journal of Marketing, "We think the field needs to pull off its blinders and uncover new ways of thinking about marketing and the marketplace" ([44], p. 2). In short, while consumer research has yielded substantial insights into the behavior of consumers, we believe that we can do more to broaden the impact of our work.
Why should and how does one engage in boundary-breaking, marketing-relevant consumer research? We offer some ideas pertaining to these important issues in the three sections that follow. In the first section, we present a conceptual framework (see the section "Choices for Engaging in Consumer Research: Implicit Boundaries") that distinguishes the implicit boundaries that characterize our choices of marketing-relevant consumer research from boundary-breaking alternatives. Although we are not the first to argue in favor of some of the ideas captured in our framework (e.g., [34]; [42]; [65]), we hope that the structured approach offered herein, coupled with the topics discussed in our first section, make salient the relevant boundaries and the opportunities that consumer researchers have to break them.
In the second section, we provide guidance to the ambitious consumer researcher aiming to contribute in this way. Specifically, we describe five published articles that exemplify boundary-breaking, marketing-relevant consumer research. These articles have offered fresh and novel insights for academics in marketing and related disciplines. They have also had tangible and significant effects on other relevant marketplace stakeholders, including business, government, and society. We articulate concrete lessons from these cases to guide authors. We also offer specific strategies designed to help researchers, faculty members who train doctoral students, and other gatekeepers identify actions that can facilitate and accelerate boundary-breaking consumer research (see Table 1). Whereas these case studies illustrate our core ideas, we further guide researchers by offering other examples of boundary-breaking consumer research (see Table 2). We hope that this guidance will reduce the perception that the field's disciplinary norms and instructional practices make it too risky to have broader impact on stakeholders outside of academic marketing and consumer research.
In the third and final section, we argue that boundary-breaking consumer research can have rewarding outcomes to individual consumer researchers and the field as a whole. Boundary-breaking research enhances the credibility of consumer research scholars as substantive (real-world) experts, addressing criticisms that our research is incremental. Such research also makes salient novel and important research questions that can be raised by breaking these boundaries (see Table 3). In so doing, it contributes to Journal of Marketing's larger objective of being "a marketplace of ideas" that will help "develop and disseminate knowledge about real-world marketing issues relevant to scholars, educators, managers, policy makers, consumers, and other societal stakeholders" ([45], p. 1). Finally, boundary-breaking research connects us to a broader set of constituents who are starved for insights on consumer behavior as a means to create a better world for stakeholders and consumers alike.
Why do we conduct consumer research? What choices do we make about why, what, and how we conduct our research and disseminate our findings? We outline some of these choices in Figures 1 through 3, respectively. We use the phrase "implicit boundaries" to characterize the default choices that many of us make automatically and the phrase "boundary-breaking opportunities" to characterize a wide range of alternative choices that are adopted less frequently (though are equally important). We believe that implicit boundaries regarding our choices about engaging in consumer research, while well-entrenched and familiar, blind us to new ways of contributing to knowledge (see the left-hand sides of the figures). However, breaking these boundaries and choosing underutilized opportunities (see the right-hand sides of the figures) reflect vehicles by which our individual and collective impact can be broadened. While these figures are not intended to be comprehensive, they are designed to provide a structured approach for identifying common implicit boundaries and illustrating boundary-breaking alternatives.
Figure 1 outlines the choices consumer researchers make when they decide why to conduct research. These decisions involve the stakeholders they choose to influence, the ideas they choose to test, the ways in which they choose to contribute to theory, and the manner in which they choose to investigate outcomes.
Graph: Figure 1. Consumer research choices: Why conduct consumer research?
Consumer research is often targeted primarily or exclusively to marketing or consumer research scholars. We can broaden our impact by considering how our work can contribute to a larger set of stakeholder groups, such as those identified in the right-hand side of Figure 1. These stakeholders include academics in disciplines outside of marketing; educators and their students; different types of firms; government and nongovernment agencies; the media; and, more broadly, society. For example, [11] provided important strategic insights for marketers by using consumer research theories to explain pioneering advantages to firms. Arguably, our ability to influence stakeholders outside of the academic marketing arena is the ultimate indicator of broad impact.
As consumer researchers, we are motivated to test ideas that inspire us. Where do these ideas come from? In most cases, inspiration emanates from marketing academic articles or conference papers. This implicit boundary limits the attention we pay to the myriad real-world phenomena that stakeholders care about. Ideas can emerge from interactions with academics outside of marketing, practitioners or consumer groups in the public or private sector, academic-practice forums, direct observation, or media reports.
Consumer researchers often aim to provide theoretical contributions. An implicit rule is that this contribution is based on mapping relationships between constructs. Boundary-breaking opportunities can emerge when we use our conceptual skills to add structure to real-world, messy, and often disorganized phenomena ([35]; [37]). One way to do so is by engaging in phenomenon–construct mapping. Here, researchers start with observations of real-world marketing-relevant phenomena and then identify constructs and relationships that can explain them. Qualitative research approaches tend to focus on the holistic qualities of complex phenomena. Thus, we tend to see more phenomenon–construct mapping in qualitative empirical research than in quantitative empirical research.
Consumer researchers are also motivated to investigate outcomes. The current default, at least among psychologically oriented consumer researchers, is to explain and predict consumer response by identifying cause–effect relationships. Research with a descriptive and/or evaluative goal is less common. Descriptive research maps out a real-world consumer phenomenon and articulates who the relevant actors are; what their focal actions are; and when, where, and how these actions take place. Descriptive research is foundational to theory building, and it can add structure to complex and poorly understood substantive issues. In addition, descriptive research is often more multidimensional, articulating complex phenomena in ways that describe and illuminate these dimensions and their importance.
Evaluative research, in contrast, assesses whether consumers' interactions with marketplace stakeholders benefit the parties involved. These interactions can result in win-win, win-lose, or lose-lose outcomes and therefore offer normative guidance. Win-win relationships benefit all parties (e.g., successful corporate social responsibility). Win-lose relationships benefit one entity while hurting another (e.g., predatory lending, which helps lenders and hurts consumers). Lose-lose relationships inadvertently benefit no one (e.g., when product information is intended to help consumers but instead confuses them and thereby hurts both sellers and buyers). Too often, we fail to consider consumer research from this evaluative perspective and/or take a critical stance against marketers and other marketplace stakeholders who produce outcomes that harm consumers.
Figure 2 outlines the choices consumer researchers make when they decide what to study. These decisions involve the units of analyses, the decision contexts, and the time frames researchers choose to study, as well as the metaphorical consumer role they choose as a lens for thinking about the relationship between consumers and marketplace stakeholders.
Graph: Figure 2. Consumer research choices: What is studied in consumer research?
From a unit-of-analysis standpoint, the implicit boundary is to study individual consumers, most often in the United States. In reality, consumers rarely operate alone; rather, they interact in small groups (e.g., dyads, households, peer groups), large groups (e.g., segments, communities, tribes, organizations, markets), and diverse populations in regions throughout the world. Consumer Culture Theory (see [ 3]) researchers have been more receptive to examining group-level phenomena than consumer psychologists have. Still, an overemphasis on individuals means that there are significant opportunities to learn more about the behavior of groups and populations.
The current default is to examine static consumer decisions, even though decision making is often dynamic. For example, consumer states and decisions can be reciprocal, as when low self-esteem induces overeating, which further lowers self-esteem. They can also be sequential, as when the purchase of one product stimulates the purchase of another. Decisions can accelerate behaviors, as when a positive consumption experience leads to reduced time between subsequent consumption experiences. We also tend to study consumer decisions independently, although many consumer decisions (e.g., the choice of a doctor) are dependent on other decisions (e.g., a selected medical plan) and are embedded within political, social, legal, organizational, and economic systems. Other decisions are synchronized and coordinated, such as decisions about fashion, home buying and selling, travel, and participation in social media.
Opportunities to break boundaries exist by examining time periods that extend beyond the present, the current default. In general, we have underleveraged our use of the past to understand its relevance to consumer behavior in the present. However, historical information can provide valuable input to novel theories and empirical studies that generate new insights. For example, in the past, conspicuous leisure was associated with status, whereas today, busyness is associated with status ([ 7]). A historical analysis might deepen our understanding of what underlies this change and why. Many stakeholders are also interested in emerging trends and their influence on consumers, and we have the skills to study how trends in demographic, financial, technological, political, and market domains influence consumer behavior.
Consumer researchers often consider the metaphorical role of the "consumer as a target," which emphasizes how marketer and policy actions influence consumers. This view is based on the passive mass-media broadcasting model of advertising that arose in the Western world in the 1950s ([56]). In contrast, modern consumers are often active participants in the marketing process. Consumer researchers have begun to study consumers as "influencers" who exert leverage through word of mouth, product reviews, and blogging. Consumers are also "collaborators" who actively support and embrace brands, work together with firms, and serve as ambassadors. When consumers take on a "cocreator" role, they help manufacture and design products and services, create ads, and even set prices. Sometimes consumers are instead "skeptics" who resist persuasion attempts, or they are "adversaries" who boycott products; initiate lawsuits; purchase counterfeits; or engage in theft, fraud, piracy, or other illegal acts. In addition, while we typically regard consumers as owners of products, they are also sharers; users; experiencers; and givers of information, products, services, and money. Each metaphorical role yields different insights about consumers ([67]). Studying these roles more fully and considering other roles can yield richer insights into the behavior of consumers.
Figure 3 outlines the choices consumer researchers make when they decide how to execute their research. These decisions involve the respondent group and method they choose to employ and the knowledge dissemination activities they engage in pre- and postpublication.
Graph: Figure 3. Consumer research choices: How is consumer research executed?
The implicit boundary guiding most consumer researchers is to use students or paid workers as respondents. Breaking this boundary enables researchers to address questions regarding distinct consumer segments based on age or other sociodemographic and socioeconomic variables so as to capture understudied consumers such as minorities, privileged or impoverished classes, and marginalized consumers (e.g., special needs populations). Consumer researchers can also attempt to study real consumers in situ more often than they currently do. Such work may be particularly beneficial when consumers engage in specialized consumption-based roles (as patients, fans, voters, etc.), in professional roles (as managers, employees, etc.), or as they deal with actual marketplace-related decisions and problems. It is likely that in these roles and marketplace contexts, simulated laboratory-based tasks may fail to capture the nuances and context-related interdependencies of consumer decision making.
Lab and online experiments constitute the vast majority of psychologically based consumer research. Using additional methods can yield different insights, which can broaden impact. For example, the more recent trend toward field experiments helps convince marketplace stakeholders (not just journal editors) that our effects operate in the real world. Machine learning can reveal consumer sentiments and general patterns in massive data sets, while network analyses can add to our knowledge of diffusion and social media processes. Meta-analyses abstract away from specific studies to show more general effect sizes and moderators. Observation, photography, videography, and garbology are underutilized methods that are helpful for studying people in naturalistic contexts.
Consumer researchers tend to test and disseminate their ideas before publication at academic conferences and seminars. We often regard publication in an academic journal as a sufficient postpublication activity. However, to have broader impact, it is helpful to employ additional pre-and postpublication knowledge dissemination activities. For example, prior to publication, consumer researchers can "test-market" their ideas with stakeholders. Doing so allows researchers to determine whether such ideas resonate and whether construct labels are meaningful to stakeholders. Such test-marketing also allows researchers to gain feedback from stakeholders regarding whether they have effectively captured key aspects of the phenomenon under study, and how they can better position their work to have greater impact. Following publication, knowledge dissemination activities include targeting the media through press releases and interviews with the press, presenting research at conferences populated by key stakeholders, and publishing user-friendly articles on the research in nontraditional, nonacademic outlets.
Although Figure 1 contrasts an implicit boundary with associated boundary-breaking opportunities, some consumer researchers have developed exemplary boundary-breaking research that crosses multiple boundaries. In this section, we illustrate five "case studies"—articles published in top journals—that do exactly that. Notably, broad impact did not come at the expense of academic impact, as evidenced by traditional metrics such as citations and awards. While three of the case studies involved members of the current author team, we do not mean to suggest that these cases represent the only or even the best examples of boundary-breaking, marketing-relevant consumer research. Nonetheless, our status as case study authors gave us an insiders' view of the story behind the research and how it evolved. We describe some lessons learned from the set of articles and offer strategies to reduce the institutional barriers that might otherwise discourage such work.
The article's inspiration arose when Albert Muñiz, then an undergraduate student, noted that Apple computer (Mac) users seemed to define themselves in opposition to personal computer (PC) brand users. Moreover, Mac users reported that they felt a bond with other Mac users, even if they did not know them personally. When a Mac user had a problem or lost a file at 1 a.m., other Mac users would step in to help. Muñiz also drove a beat-up Saab in graduate school and was surprised that strangers would stop him to talk about their Saabs. Relating these two keen observations about brands and communal behavior sparked the core idea for Muñiz's dissertation, which was supervised by Thomas O'Guinn at the University of Illinois at Urbana-Champaign. Both authors had sociological training, which inspired them to view these phenomena through the sociological lens of the historied "community" construct.
The authors immersed themselves in interviews of Mac, Saab, and Ford Bronco brand communities and in observations of their online brand communications. These sources of data revealed that members experienced a sense of "we-ness" and exhibited oppositional brand loyalty. Members had developed mechanisms to identify "authentic users" from "posers" who failed to understand the ostensibly true meaning of the brand. They also had shared rituals, traditions, and "origin stories" for their brands. They felt a moral responsibility to the brand and the brand community, helping legitimate users with decisions about where to buy, how to better use the brand, and how to find technical information about it.
At the prepublication stage, the authors initially found themselves stifled by the kinds of disciplinary boundaries and limited perspectives outlined in Figure 1. Colleagues at their institution worried that the paper's descriptive approach and that the focus on the novel idea of a "brand community" was too different from the typical job market paper and would hinder Muñiz's job prospects. Indeed, Muñiz did not receive job offers when he first went on the market. The authors reported that some qualitative researchers viewed their work as overly applied and commercial in its intent. Muñiz and O'Guinn conveyed that these scholars even questioned whether they were "selling out" by not casting a critical eye on the conduct of contemporary marketing. Ironically, while "Brand Community" highlighted how brands could unite people, Muñiz and O'Guinn's research seemed to alienate some scholars who had highlighted the corrosive effects of commerce on human interactions. Notably, Muñiz and O'Guinn pointed out the relevance of "community," one of the oldest constructs in sociology, to these emerging phenomena. Yet some critics did not view this description of the phenomena and mapping those phenomena onto the community construct as a theoretical contribution. Although some consumer researchers did not initially support their ideas, the authors were heartened by the enthusiasm they later met at the 1996 International Choice Symposium, where Russ Winer (then-editor of Journal of Marketing Research [JMR]) called attention to it. The research also generated positive responses from colleagues at the University of California at Berkeley, Duke University, and the University of Chicago.
Practitioners recognized the paper's importance long before it was published. Procter & Gamble chief executive officer Durk Jager validated the work at the 1997 AMA-Sheth Foundation Doctoral Consortium, describing the brand communities forming around Tide and other Procter & Gamble brands. The publication of the article in Journal of Consumer Research (JCR) in 2001 generated extensive press coverage; both authors gave dozens of interviews. Wired writer Leander Kahney picked up on the work and subsequently wrote four books about the cult of Mac users. The automotive editor from the Dallas Morning News wrote a column about the Saab brand community. Attention was sustained over the ensuing years. Rob Walker, author of New York Times Magazine's former "Consumed" column, wrote about [47] subsequent work on the Apple Newton personal digital assistant. The community engagement group at Mini Cooper read their work. The article was covered by American Airlines Magazine and by radio host Paul Harvey. The idea took hold that firms could engage meaningfully with the communities that formed around their brands and that these brand communities could become important marketing assets. In the early age of social media, consulting firms popped up using Muñiz and O'Guinn's (2001) ideas and terminology, offering to help companies create and manage their own online brand communities. Overall, this timely, artful, and insightful article and its focal constructs broadened the study of branding, community, and consumption by marketing, sociological, and consumer researchers. It continues to inspire a range of new research and commercial projects around the world to this day.
Muñiz and O'Guinn's (2001) article has had tremendous academic impact. It is highly cited and is the recipient of JCR's Best Paper and Long-Term Contribution awards. It exemplifies many of our framework's themes through its descriptive investigative goal, phenomenon–construct mapping, contributions to industry, emphasis on large groups and influencers, view of the consumer role as cocreator, use of qualitative and archival methods, reciprocal and system-dependent decision context, and postpublication dissemination activities.
Robert Kozinets describes his early career as marked by several presentations to empty conference rooms. Although he had a challenging time getting his first netnography article published, he stumbled into the area of online consumers and their connections by observing the real-world behavior of Star Trek fans online. Expanding from fans to general consumers, he authored an article for The Financial Times in 1998, noting that "online communities are growing in power" (p. 291). From this perspective, he developed a study of online "tribes," in which he observed that certain powerful and communicative members of social media groups "will become the important influencers who will be in high demand by forward-thinking marketers" ([28], p. 260).
Kozinets subsequently gave numerous talks about social media marketing, including one at the Marketing Science Institute that resulted in several consulting projects with Fortune 500 companies. Through his Brandthroposophy blog, Kozinets piqued the interests of Toronto entrepreneur Patrick Thoburn and his word-of-mouth marketing firm, Matchstick. The two decided to engage in a collaborative project whereby Matchstick would share data while Kozinets and his research team would analyze it. Kozinets's interdisciplinary team included Kristine de Valck, an early pioneer in using netnography; Sarah Wilner, a doctoral student working with Kozinets; and Andrea Wojnicki, a social media scholar at University of Toronto.
In 2006, Matchstick was planning an influencer marketing campaign with telecom firm Nokia to seed influential bloggers with free mobile phones equipped with a state-of-the-art camera. Matchstick shared the names of the 90 online campaign influencers, as well as detailed demographic and sociographic data about them and their audiences.
The authors collected and read every blog entry from this group for three months prior to the campaign's launch, at launch, and then for three months after launch. They also included samples from the individual influencers, the smaller groups that followed and responded to them, and the larger audience groups that connected to them online. The results showed that social media marketing messages were complex cultural affairs. Overall, the messaging took place in an online environment where influencers had to navigate a sort of "double-agent status." They were both a trusted community leader as well as a paid commercial shill. Influencers created various and continuing narratives for their audiences. They also adapted their messaging to the social media platform and to the norms of desired and actual audiences. The narrative, the platform, and the audience norms combined with the goals and tactics of the marketing promotion campaign to produce four types of influencer messaging strategy: endorsing, evaluating, explaining, and embracing. The findings were well received at the 2007 Association for Consumer Research conference, and the authors found a supportive review team at the Journal of Marketing.
After the article's publication in 2010, the authors continued presenting their work at numerous forums and to a range of industry groups around the world, including in a special event dedicated to highlighting the research for its customers organized by Matchstick. Their research also formed the basis of a managerially focused GfK Marketing Intelligence Review special issue that focused on customer brand engagement in a world of social media. Numerous blogs covered the research, as did as popular magazines such as Psychology Today. Impact on academia, the influencer industry, and the media is evident. The article illustrates the value of a holistic, contextualized look at large and complex marketplace phenomena in their early stages. Mapping the systemic workings of a new phenomenon often requires the creation of new constructs and creative overlay of a network of constructs from existing literature.
Like [46], [29] has had a major impact, as assessed by traditional academic criteria, and broader impact than is typical of most consumer research publications. Contributing to its impact are its descriptive investigative goal, its emphasis on phenomenon–construct mapping, its view of the consumer role as an influencer, and its involvement with and consideration of stakeholder groups, including the social media industry, marketers, and consumers. It also differs from traditional research by its focus on large groups and the emerging trend of influencers engaged in dynamic social media interactions. Like Muñiz and O'Guinn's work, the article was inspired by the real-world behavior of consumers and relied on the capture and analysis of qualitative social media data. Unlike Muñiz and O'Guinn, however, Kozinets et al. relied exclusively on social media data. The authors also implemented the rigorous methodological procedures of netnography. The article's pre- and postpublication process contributed substantially to its influence.
In 2000, Eric Johnson, cofounder of Columbia University's interdisciplinary Center for Decision Sciences, was working with the center's postdoctoral researcher, Daniel Goldstein. Whereas Johnson had been working on the role of opt-in and opt-out defaults in "permission" marketing ([25]), events in his personal life caused him to turn his attention to decision making about health care and the role of defaults in health policy. [24] studied changes in European policies around organ donation, where some countries required citizens to opt in to become organ donors in the event of imminent death, whereas others required citizens to opt out of being donors. These defaults led to dramatic differences across countries in organ donation rates.
The significance of the topic to consumers, policy makers, and societal stakeholders was evident in the article's opening sentences, which emphasize how many people die each year waiting for a suitable organ donation. What caused the work to become a classic was the authors' use of archival data about organ donation consent across nations, and their effective graphic display of their results in a single dramatic bar chart (see Figure 4). This chart revealed that Germany and Austria, two geographically proximate countries with similar languages and cultures, had drastically different organ donation consent rates. Specifically, Germany and other "opt-in" countries had correspondingly low organ donation consent rates, while Austria and other "opt-out" countries had equivalently and high organ donation consent rates.
Graph: Figure 4. Effective organ donation consent rates of different countries.Notes: From "Do Defaults Save Lives?" by Eric J. Johnson and Daniel Goldstein, Science, 302 (5649), 2001. Reprinted with permission from AAAS.
The article's findings and its pre- and postpublication activity have had tremendous impact not only on academics inside and outside of marketing but more broadly on policy makers, society, and consumers. Furthermore, the substantive importance of the topic (life and death), the broad national comparisons, and the nontechnical writing style helped the article's ideas diffuse not only to a wide range of scholars and research projects but also to books aimed at broader audiences. [ 2] featured the paper prominently in his best-selling book, Predictably Irrational, as did [64] in their best seller, Nudge: Improving Decisions About Health, Wealth, and Happiness, a book that provided a blueprint for evidence-informed policy makers. Most telling, several European countries subsequently changed their organ donation policies to opt-out defaults.
[24] eschewed traditional marketing publication outlets in part because the paper did not adhere to consumer research's implicit boundaries (i.e., those on the left-hand side of Figure 1). Nonetheless, it has become highly cited in marketing and in other academic fields. Though the article did include an experiment, it is best known for its descriptive evidence. Like the other articles we profile, it used "construct-to-phenomenon" mapping. It started with phenomena in the real world and offered a straightforward view that made sense of previously unnoticed patterns of data. Moreover, the authors' use of archival data and countries as the unit of analysis ran counter to the norms of traditional decision-making research, which usually emphasized experiments and individuals.
In the mid-1990s, adolescent smoking was on the rise, and tobacco marketing faced heightened scrutiny. Cornelia Pechmann observed that there was no research linking the smoking behavior of characters in movies to adolescent smoking. Intrigued, she obtained a grant from the California Tobacco-Related Disease Research Program to conduct experiments on this topic and recruited doctoral student Chuan-Fong Shih to join the project.
A professional film editor created smoking and nonsmoking versions of actual movies for inclusion in the study. The authors set up two small theaters at local schools to simulate a field experiment while allowing for random assignment of adolescents to conditions. Though theory testing was unimportant to the granting agency, the authors believed that linking the substantive phenomenon to theoretical constructs would provide valuable insight into why adolescents responded to smoking in movies and, therefore, what could be done about it. Their findings supported the idea that smoking elicits positive arousal, and its appearance in movies enhances product liking. Critically, the authors also found that showing a 30-second antismoking spot that depicted smoking as tainted (the opposite of the forbidden fruit) before the movie nullified positive reactions to the smoking.
Pechmann presented the initial findings at conferences where antitobacco influencers were present. She was invited to submit the research to the Journal of the American Medical Association but declined the invitation because JAMA wanted less theory, and theory seemed integral to the work. The article was subsequently published in the Journal of Marketing, with just two experiments and no mediation testing, but with extensive internal and external validity checks of interest to both academics and practitioners.
Pechmann disseminated the research findings broadly. Beyond working with her school to issue a press release, she presented the research at schools of public health and medicine, journalism conventions, regional and state departments of health, and at the U.S. Centers for Disease Control and Prevention. The research was the subject of California State Senate Judiciary Committee hearings, U.S. Congressional hearings, and meetings of the U.S. Association of Theater Owners and the Motion Picture Association of America.
Like [24], the substantive topic had life and death implications, and its field experiment, theoretical rigor, and intervention orientation made the paper relevant to a range of other researchers, public agencies responsible for health policies, and the entertainment industry. Likely due to Pechmann's presence and advocacy role in the state of California, as well as the state's close ties to entertainment, the impact of the research was especially strong in California. Public funds were used to create antismoking ads to be shown in movie theaters. The California Department of Health Services, working with the governor's office, negotiated an agreement with major movie studios to place antismoking ads on DVDs of movies that depicted smoking. To this day, a watchdog group monitors smoking in movies and tries to pressure movie studios to reduce it.
The research also led to a new stream of work in public health and medicine, in which high correlations were reported between adolescents' exposure to smoking in movies and their smoking initiation (e.g., [14]). Pechmann assisted the White House Office of National Drug Control Policy for years, helping oversee its national youth antidrug media campaign. She continues to work on antismoking interventions funded by major National Institutes of Health grants.
Pechmann and Shih's (1999) article exemplifies many points in our framework. This research was not inspired by journal articles but by observations that characters in movies are sometimes smoking. Pechmann and Shih wondered if these depictions could affect adolescent smoking behavior. The issue was clearly of concern to government granting agencies, consumers, policy makers, and society. Its investigative goal was evaluative: the authors were interested in learning if these images in movies harmed adolescent consumers by encouraging smoking. The article emphasized an understudied respondent group—adolescents—and allowed for a consumer role as a skeptic. Further links to our framework include the article's use of a field study method; extensive pre- and postpublication dissemination activities; and its broad impact on consumers, policy makers, society, and nonmarketing academics.
Policy makers have embraced financial education as an antidote to the increasing complexity of consumers' financial decisions. Moreover, governments, nonprofits, employers, and consumer advocacy groups spend billions annually on financial education. In 2010, John Lynch attended a small, invitation-only event for leading experts on financial literacy and education sponsored by the National Endowment for Financial Education (NEFE). Multiple teams of academics and practitioners presented what had been learned over the past quarter-century about different facets of financial literacy and education. The chief executive officer of the Financial Industry Regulatory Authority presented the first team's conclusions. Speaking about the movement to mandate high school financial education courses, he said (paraphrasing), "Given the mixed evidence on the effects of financial education and given cost–benefit considerations, maybe now is not the time to continue to press for state mandates." The audience of experts gasped and vociferously disagreed with the findings. Lynch said nothing but suspected that the experts were conflating experimental and quasi-experimental studies of financial education interventions with correlational studies that measured financial literacy to predict financial behavior.
His curiosity led to a project funded by NEFE, where Lynch and collaborators (Daniel Fernandes and Richard Netemeyer) meta-analyzed 201 studies to determine whether measured financial literacy or manipulated financial education correlated with financial behavior. In the 90 experimental and quasi-experimental studies, financial education interventions explained, on average,.1% of the variance in the financial behavior variables. Because of the large sample size, the effect was statistically significant but miniscule in magnitude. A metaregression revealed that the critical factor was an interaction between financial education contact hours and delay. When measured shortly after the educational intervention, the size of the effect of the intervention on financial behavior increased sharply with more contact hours. But within two years, the effects did not differ from zero (see Figure 5). The authors argued that to help consumers make better decisions, financial education should be "just in time" and focused on individual behaviors.
Graph: Figure 5. Partial correlation of financial education interventions with financial behavior as a function of number of hours of intervention and number of months since intervention.Notes: Republished with permission of INFORMS, from "Financial Literacy, Financial Education, and Downstream Financial Behaviors," by Daniel Fernandes, John G. Lynch Jr., and Richard G. Netemeyer, Management Science, 60 ( 8), 2014; permission conveyed through Copyright Clearance Center, Inc.
The authors reported their findings to the NEFE and the U.S. Consumer Financial Protection Bureau (CFPB). In May 2012, Fernandes and Netemeyer presented the work at the Boulder Summer Conference, attended by several CFPB staff members. During the talk, Richard Thaler whispered a request to forward the paper. During the question and answer session, Thaler said, (paraphrasing), "I hope people from CFPB get this paper and read it before they spend another dollar on financial education." A CFPB researcher responded (paraphrasing), "We've read the paper carefully, and everything we've done since on financial education is 'just-in-time.'"
Later that summer, Lynch and Thaler debated a leading academic proponent of financial education at the President's Advisory Council on Financial Capability. Lynch also talked to the larger group of CFPB researchers and had a meeting with then CFPB Director Richard Cordray. The NEFE also arranged mini conferences that served to both disseminate the findings and force the authors to address practitioner objections and counterarguments. The NEFE created a nontechnical practitioner summary of the paper and circulated it to its broad network. Thaler wrote a New York Times op-ed about the article in 2013 and discussed key findings with the "Nudge Unit" of financial regulators in the United Kingdom.
The authors faced many challenges when disseminating the findings. The strong opposition from advocates of financial education forced the authors to address the language and assumptions of the practitioner community. Likely because of these challenges, the article was difficult to publish. It was rejected at Science based on the review of a proponent of financial education, so the authors submitted it to Management Science, where it was accepted.
The article received extensive coverage in media outlets worldwide postpublication, leading to invitations to speak at various industry and practitioner conferences. Presumably because of the article, Lynch was appointed to the CFPB's Academic Research Council, the first scholar from outside the fields of economics and law to be so appointed. The findings continue to influence policy concerning financial education.
The case of [16] echoes themes from the prior cases. The paper was inspired by interactions with experts in the world of practice, similar to the genesis of [29]. The goals were descriptive and evaluative. The methods were archival, involving a meta-analysis. The authors followed a nonstandard route in conceiving the project and disseminating the findings before and after publication. The article has had interdisciplinary impact, as measured by traditional citation measures, and significant influence on policy makers and regulators, nonprofit consumer advocacy groups, the media, and industry practitioners.
The five cases described previously share certain characteristics. Each author team took great lengths to work directly with stakeholder groups when developing their investigative goal, identifying phenomenon-to-construct mappings, considering the decision context, selecting appropriate methods, and disseminating their ideas and findings. The authors' curiosity about real-world phenomena, rather than the constructs and theories in the marketing, inspired the papers. Moreover, the phenomena of interest were prevalent, important, controversial, and also underresearched. [46] and [29] advanced our thinking by considering new metaphorical roles of consumers (as, e.g., brand enthusiasts, influencers, collaborators, skeptics). The case studies investigated outcomes in a descriptive and evaluative fashion and contributed to theory by mapping phenomena to a conceptual structure, although they ended up creating theory as well. Several articles included the study of individuals as members of large groups such as communities ([29]; [46]), populations ([24]), or understudied sociodemographic segments (adolescents; [51]). All used methods beyond lab experiments, and several examined dynamic decision contexts, such as how communities, social interactions, or financial literacies are shaped by and evolve over time. Beyond their specific links to our Figures 1–3, several other lessons can be learned from these case studies.
Interactions with knowledgeable practitioners, called "substantive system experts" by [41], p. 120), inspired [29] and [16]. This interfacing role is particularly important when it comes to nonacademic stakeholders, and it illustrates the importance of academic–practitioner forums and conferences in pre- and postpublication activities. If one wishes to have sustained influence outside the academic domain, one must become involved and have a seat at the table. Had he not been active in the public space of social media and responding to journalists, Kozinets and substantive expert Thoburn would never have met. The contact with Thoburn's company Matchstick provided early access to the social media marketing campaigns studied in Kozinets et al. For Pechmann and Shi (1999) and Fernandes, Lynch, and Netemeyer, direct interactions with funding agencies facilitated financial contributions and helped ensure that the findings could be used in policy decisions. Each of the five papers addressed topics that were interesting not only to a broad range of academics inside and outside of marketing, but also to other marketplace stakeholders, including practitioners and laypeople.
Doing boundary-breaking research comes with risk, and most of the author teams faced initial resistance from other academics, journals, or funding agencies. Although colleagues, reviewers, and editors at top journals frequently devalue research that seems to be practitioner-oriented and/or discourage the pursuit of grant funding, the authors persevered and believed in the significance of their work.
Both the [51] and the Fernandes, Lynch, and Netemeyer (2104) articles illustrate the challenges of working in a public policy arena where powerful and strongly motivated opponents, including the media, may be unhappy with the conclusions. While the public health and medical communities were supportive of Pechmann and Shih's work, there was pushback from broader audiences including call-in guests on radio talk shows, students at journalism schools, theater owners, and member of Congress. Many argued that putting antismoking "propaganda" in movie theaters would be overreaching. But Pechmann won over many stakeholders, including theater owners, by arguing that if a 90-minute movie promoting smoking was targeted at youth, it was only fair to provide 30 seconds of ad time to present the opposing viewpoint. If one desires to do boundary spanning work and have a broader impact, one might need to face naysayers who try to suppress publication, press coverage, or dissemination of the results. When many people care about a controversial issue, the road to publication could be longer, but the ultimate impact will be greater than if one studies a "safer" topic.
Several author teams had champions who supported and legitimized the work. [46], [29], and [51] found journal editors who were inspired by and sympathetic to their work. Muñiz and O'Guinn and Kozinets et al. also had high-profile marketing practitioners who legitimized their work or partnered with them in developing it. The support of granting agencies aided Pechmann and Shih (1999) and [16], and the latter team was further advantaged by Thaler's support in disseminating their findings. Thaler and Ariely also facilitated the dissemination of Johnson and Goldstein's work. Authors of all five case studies were helped by journalists who featured their findings in high-profile media.
Several of the articles were not one-shot deals; rather, they were part of a program of research. Kozinets et al.'s (2010) paper evolved from his prior work on social media topics, stretching back to his dissertation research. Pechmann has had a long-term interest in smoking behavior, as has Johnson with defaults. The same is true for Lynch in financial decision making. Because the phenomena of interest are complex, evolving, and influenced by myriad contextual factors, most author teams had prior grounding in the phenomenon of interest. They were also willing to expand their intellectual horizons by immersing themselves in the real-world behavior of consumers, marketers, policy makers, and other stakeholders. Moreover, the works of these authors launched new ideas and new collaborators.
Each author team also related the lengths they took to write their papers simply and clearly and to display their data in a form that made it easy to follow their conclusions. Simple and powerful ideas and straightforward methods and writing made the work appealing to the general public. All author teams reported getting a significant boost by the extensive press coverage their work received. However, this did not happen automatically. Rather, the authors devoted significant effort to use the language of nonacademic stakeholders to communicate their contributions in their dissemination efforts. Nontechnical writing helped make the papers and their conclusions accessible to nontechnical audiences. When we interviewed O'Guinn for this article, he compared the interesting and accessible contents of the New York Times with what one sees if one opens up one of our top journals. Many articles in those journals use obtuse language and highly abstract conceptions.
Executing boundary-breaking consumer research can be difficult. Hence, ambitious scholars will also need the support of a variety of other gatekeepers to help publish that research, recognize its impact beyond traditional academic metrics, and use it when making hiring and promotion decisions. Indeed, we believe that institutional change with respect to how boundary-breaking research is fostered, communicated, and evaluated will be necessary.
Table 1 provides tactics that scholars and gatekeepers can use to overcome and help eliminate the obstacles that stand in the way of boundary-breaking consumer research. For aspiring scholars, this table provides strategies that individual researchers can take to facilitate conducting such research. These strategies relate to such topics as how best to select and train doctoral students, how to select coauthors and manage joint work, how to navigate issues related to publishing this kind of work in academic journals, how to best manage career challenges, and how to facilitate interactions with marketplace stakeholders.
Table 1 also includes actions that we encourage journal and tenure and promotion gatekeepers to adopt so as to encourage and reward such work. Reviewers often apply the same review standards to all types of submissions, but boundary-breaking work often requires the use of different review standards ([35]). Editors in turn play an important role in communicating the types of research they want to publish and managing the review process for these distinctive papers. Finally, tenure and promotion letter writers and review committees can all take actions to reward scholars who undertake this type of research.
Graph
Table 1. Activities That Can Foster Boundary-Breaking, Marketing-Relevant Consumer Research.
| For: | Activities: |
|---|
| Doctoral training | Admit students who have demonstrated research backgrounds and substantive interests Develop a course on substantive phenomena involving consumers and marketplace stakeholders Assign students to read boundary-breaking consumer research articles in doctoral seminars and in reading groups Encourage or require students to link academic interests (e.g., theory) to specific substantive marketplace issues Have students work with diverse faculty members with experience in breaking one or more of the default boundaries Encourage risk taking in small conceptually oriented assignments to allow students to gain experience in breaking boundaries Ask students to observe consumer behavior in the real world and generate novel hypotheses about their observations Encourage ideas about future trends and their implications for consumers' interactions with marketplace stakeholders Include stakeholders in doctoral symposia, special sessions at conferences, or preconferences Encourage students to attend smaller, domain-focused conferences that involve stakeholders of interest Use alumni networks to connect students with stakeholders
|
| Coauthor selection and joint work | Include coauthors with experience in the substantive phenomenon of interest Include coauthors with experience in theories and methodological approaches relevant to the phenomenon Include coauthors with access to real-world data, consumers, or other stakeholders Train new or inexperienced coauthors in ways to break consumer research boundaries Prepare coauthors for a higher-risk, but higher-reward research experience
|
| Navigating journals | Articulate the boundary-breaking nature of the work and its importance to consumers and stakeholders Document the pervasiveness of the phenomenon under study Indicate how the research captures the essential features of the phenomenon under study Clarify the paper's contribution to theory in terms of phenomenon–construct mapping Use the simplest methods and models that are appropriate even when you can use more specialized ones Clarify how the methods are appropriate for studying the phenomenon of interest Avoid using technical language and academic jargon Show the findings are robust across contexts Show that the size of the effect is sufficiently large to matter Document stakeholder groups who would be interested in the findings and whose beliefs might be shifted by the findings If your boundary-breaking paper does not get accepted to consumer or marketing journals, consider submitting to a journal in a related field
|
| Managing career challenges (e.g., promotion, tenure) | Balance a portfolio of less risky and more incremental research projects with riskier boundary-breaking ones Consider theory-based interventions pertinent to the phenomenon of interest as part of one's research portfolio Clarify the substantive importance of the body of research in research statements, including how your research has changed stakeholders' beliefs about consumers Provide in research statements indicators of broad impact (i.e., stakeholders' use of the research as well as media impact)
|
| Interacting with marketplace stakeholders | Befriend guest speakers and think about ways to involve them in research Attend industry conferences to scout for research ideas and connections Consider spending a month or a sabbatical at a stakeholder's institution and consider a collaborative research project
|
| Reviewers | Strive for an open mind regarding the boundary-breaking features of the research when weighing its contribution Avoid the 20/20 ("this was obvious") hindsight bias, and consider that the findings may not have been obvious to stakeholders Look for opportunities to suggest ways to extend the boundary-breaking elements of submissions Champion potential boundary-breaking research and indicate to the editor why the work deserves strong consideration Give greater weight to the paper's substantive contribution than to its theoretical contribution
|
| Editors | Publicly signal openness to boundary-breaking research in editorials and speeches Develop initiatives that invite, develop, and expand the understanding of boundary-breaking research Choose associate editors who value boundary-breaking research Consider that boundary-breaking research might spur complex reactions from review teams and evaluate papers accordingly Accept empirical methods that are considered valid and widely used in other academic disciplines Do not insist that all or even any studies employ controlled experiments Do not insist that every paper must make a contribution to theory Be willing to go against review team's advice when the paper is substantively important Consider publishing the different opinions on a paper in a dialogue format
|
| Tenure letter writers and promotion committees | Committees should seek boundary-breaking consumer research scholars as evaluators for tenure and promotion cases Writers should explicitly highlight the boundary-breaking aspects of the candidate's work Writers should explain why boundary-breaking research may look different than typical research that other evaluators may be more familiar with Writers should stress the importance of the research for the development of the field and for different stakeholders Writers should incorporate evidence of impact on stakeholders in their evaluations
|
We end this section with several important points of clarification. First, whereas the five case studies described herein are exemplary in illustrating boundary-breaking research, we do not mean to imply that they are the only examples of boundary-breaking, marketing-relevant consumer research. To that end, Table 2 provides notable examples of published articles that have had broad impact by breaking the implicit boundaries identified on the left-hand side of Figures 1–3 and adopting many of the strategies on the right-hand side of same figures. While these articles have had broad impact, like the five cases we highlighted, many have also had impact using traditional impact criteria (e.g., citation counts, academic awards).
Second, we do not wish to imply the more boundaries consumers researchers break, the better. Rather, we believe that broad impact can often be facilitated by thoughtfully and meaningfully combining traditional approaches in some domains with boundary-breaking approaches in other domains. To illustrate, research that maps interesting phenomena to an existing or a new construct can have significant and broad impact by adding structure to phenomena that are presently ill-structured, even if no other boundaries are broken. For example, research that develops a conceptual understanding of how consumers would behave in markets with guaranteed universal basic income might only break a single boundary by using phenomenon–construct mapping. Yet such research might explain and predict outcomes under guaranteed universal basic income, even if explanation and prediction are the default boundaries.
Third, we do not wish to imply that research will have a broad impact by merely breaking a boundary, nor do we wish to imply that breaking boundaries is necessary for having broad impact. Indeed, not all boundary-breaking consumer research will have broad impact, and not all research that has impact will break boundaries. For example, research that merely describes a consumption-related phenomenon that is of little interest to academics and other stakeholders will not have broad impact. To have broad impact, the following "fault lines" should be addressed. First, boundary-breaking research should shift stakeholders' beliefs and identify something new, different, or important that stakeholders did not know before. It should also be marketing-relevant; that is, it should address a substantive phenomenon and the investigative outcomes that relevant stakeholders care about. It should capture essential features of the phenomenon, mapping them to theoretical constructs of interest. The population, unit of analysis should be appropriate for the phenomenon and the types of decisions that interest stakeholders. Methodologies should also be chosen such that they help gain insight into and are appropriate for the substantive phenomenon at hand.
Graph
Table 2. Examples of Marketing-Relevant Consumer Research That Break Implicit Boundaries.
| Consumer Research Choices | Illustrative Article | Breaks the Implicit Boundary by Showing... |
|---|
| A: Why Conduct Consumer Research? |
| Influence Stakeholders (Implicit Boundary: Marketing Academics) |
| Academics in other disciplines | Raghubir and Das (2009), JCR | Consumer biases in the processing of visual information have implications for the financial decision-making literature. Systematically investigates how graphical display formats influence consumers' processing of graphical information on stock runs. Perceptual and visual biases arise from different presentation formats to influence consumers' estimates of the risks and returns in financial decisions. |
| Education | MacInnis and Jaworski (1989), JM | The complex topic of consumer information processing can be usefully represented in an organized new framework. Develops an integrative framework of consumers' information processing of advertising that is represented in consumer behavior textbooks and provides the organizing structure for Hoyer et al.'s Consumer Behavior textbook. |
| Industry | Norton, Mochon, and Ariely (2012)JCP | Valuation effects have implications for the coproduction strategies of for-profit firms like IKEA. Demonstrates the existence and magnitude of the "IKEA effect," in which a consumers' labor in the construction of products leads to greater valuation of their creations. |
| Government and nongovernmentalorganizations | Moorman (1996), JPPM | Nutrition labels have an impact on consumer nutrition knowledge. Demonstrates the extent to which consumers processed more information and improved their knowledge of the nutritional content of food items at point-of-sale following implementation of the Nutrition Labeling and Education Act. |
| Media | Nelson, Meyvis, and Galak (2009), JCR | Advertising interruptions, though disruptive, can improve consumers' experience of content. Demonstrates the counterintuitive finding that consumer experience of television viewing can be enhanced when ads interrupt the programming because ads disrupt adaptation to the programming. Implications for how and where to place ads in programming content, especially online, to enhance consumer experience. |
| Society | Goldstein, Cialdini, and Griskevicius (2008), JCR | Prosocial appeals tied to descriptive norms can be effective in changing consumer behavior. Demonstrates the role of descriptive norms versus traditional appeals in influencing hotel guests to participate in a towel conservation program. Results showed that tying norms to a local reference group influenced greater participation than using global norms. |
| Test Ideas (Implicit Boundaries: From Marketing Articles, from Marketing Conferences) |
| From real-world phenomena | Parasuraman, Zeithaml, and Berry (1985), JM | Practitioner issues related to service quality can generate highly impactful frameworks for research and practice. Conceptualizes a model of service quality driven by extensive analysis of practitioner concerns surrounding service quality. |
| Contribute to Theory (Implicit Boundary: Construct–Construct Mapping) |
| Phenomenon–construct mapping | Fournier (1998), JCR | Consumers' diverse relationships with brands can be conceptualized in terms of constructs related to the relationship metaphor. Applies the relationship metaphor and supports the development of a theoretical framework illustrating the variety of ways brands can be conceptualized and understood by consumers as active relationship partners. |
| Investigate Outcomes (Implicit Boundaries: Explain, Predict) |
| Describe | Sherry (1983), JCR | The gift-giving process can be described as a complex and multidimensional phenomenon. Integrates traditional consumer research theories with an anthropological approach to develop a descriptive and multidimensional framework for the gift-giving process. |
| Evaluate | Raghunathan, Naylor, and Hoyer (2006), JM | Healthy food labels can inadvertently hurt both sellers and buyers in a lose-lose relationship. Investigates the influence of the unhealthy = tasty lay belief on taste inferences, enjoyment, and choice. Illustrates how food labels that stress healthfulness inadvertently imply poor taste. |
| B: What Is Studied in Consumer Research? |
| Unit of Analysis (Implicit Boundary: Individuals) |
| Small groups | Epp and Price (2008), JCR | Family identity offers a new framework for understanding consumption and other marketplace behaviors. Provides a multifaceted examination that alters the relevant unit of analysis of consumer research to the family, highlighting coconstructed relations and emphasizing the importance of the interplay of identity bundles in action. Alters understanding of family decision making, consumer socialization, and object relations. |
| Large groups | Carpenter and Nakamoto (1989), JMR | Market pioneers arise through influences in buyer preference formation. Experimentally tests a proposed buyer-learning mechanism to account for consumer preferences for market pioneers. Results demonstrate that a pioneering position arises because the early entrant is able to shift consumer preferences toward its brand to achieve perceptual prominence, becoming category prototypical. |
| Populations | Schouten and McAlexander (1995), JCR | There are devoted and ritualistic consumer groups with specific needs that marketers can cater to. Proposes and uses longitudinal ethnographic research to show that subcultures of consumption are a symbolic and socially based analytic category. Findings show the value of analyzing subcultures in broader cultural and institutional contexts. |
| Decision Context (Implicit Boundaries: Static, Independent) |
| Dynamic | Giesler (2008), JCR | Markets change over time based on performative movements enacted in complex, dramatic processes. Analyzes the cultural conflict surrounding music downloading, stakeholders, and market structures through a lens in which consumption is performance and market evolution represents the drama. |
| Dependent | Shang, Reed, and Croson (2008), JMR | Donation intentions and behavior of a target donor depend on other donors' contributions. Demonstrates that a target's donation is influenced by the degree of congruency in identity between the donors. Greater identity congruency contributes to greater donations by the target, especially when the target donor has high identity esteem or focuses on others. |
| Time Frame (Implicit Boundary: Present) |
| Past | Belk and Pollay (1985), JCR | Over time, U.S. advertising reflects an increasingly terminal rather than instrumental materialism. Analyzes the study of 80 years of U.S. ads and reveals that advertising portrayals emphasize the consumption of products for their own sake, representing the good life of luxury and pleasure, rather than functionality. |
| Future | Hoffman and Novak (2018), JCR | The emergent technology of the consumer Internet of Things is likely to revolutionize consumer experience. Incorporates traditional theories of consumer experience along with assemblage theory and object-oriented ontology to conceptualize how both consumer and smart object experiences will emerge as consumers increasingly interact with artificial intelligence–powered systems, services, and devices. |
| Consumer Role (Implicit Boundaries: Target, Owner) |
| Nontarget | Martin and Schouten (2014), JCR | New markets emerge from collaborative and consumption-driven processes among consumers, objects actors, and hybrids. Uses ethnographic methods and actor-network theory to develop a model of how new markets can emerge from consumption activity. Findings show that consumers invoke multiple processes involving not only other people but also objects to develop the products, practices and infrastructures of new markets. |
| Nonowner | Bardhi, Eckhardt, and Arnould (2012),JCR | Consumers' attachments to material possessions transcends mere ownership. Applies the extended case method to analyze how global nomads relate to their material possessions. Confronts the classic idea of possessions as objects incorporated into identity as an extended self. The concept of the liquidity of possessions captures the more fluid nature and value of objects and consumers' relationship to them in postmodern society. |
| C: How Is Consumer Research Executed? |
| Respondent Group (Implicit Boundaries: Paid Workers, Paid Lab Pools, Students) |
| Age segments | Schau, Gilly, and Wolfinbarger (2009), JCR | Consumers in the life stage of retirement undergo a process of identity renaissance. Uses naturalistic and participant observation of consumers in senior centers and a rehab facility to examine retirement practices. Findings reveal that seniors engage in considerable identity work, termed a process of consumer identity renaissance. |
| Racial, ethnic, religious, and sexual orientation segments | Kates (2002), JCR | Contemporary gay subcultures are simultaneously oppositional, marginalized, and influential. Observes that gay subcultural meanings represent both an oppositionality of structured consumption practices (relative to heterosexual dominant culture) and the negotiations that individuals undertake to achieve distinction for themselves within the subculture. |
| Socioeconomic segments | Adkins and Ozanne (2005), JCR | Low-literate consumers deploy various coping skills to satisfy their consumption needs. Focuses on how consumers who read below a sixth-grade level are able to satisfy their consumption needs. Results suggest that consumers who can successfully negotiate or reject the stigma of their identity as low literate and deploy various coping skills are better able to meet their marketplace needs. |
| Marginalized consumers | Scaraboto and Fischer (2013), JCR | Some marginalized groups mobilize as institutional entrepreneurs to seek greater inclusion and choice in mainstream markets. Reveals, using a qualitative research study of plus-sized consumers and the fashion market, the emergence of a collective identity, identification as institutional entrepreneurs, and reliance on support and legitimization from outside the focal market support inclusion and choice. |
| Consumers in situ | White and Peloza (2009), JM | Actual consumer donor support is variable and moderated by both contextual and individual difference factors. Demonstrates, by studying and comparing actual time and monetary donor support in a nonprofit and comparing it with experimental results, that a one-size-fits-all approach to appeals to generate donor support is misplaced. |
| Professionals | Pracejus, Olsen, and O'Guinn (2006), JCR | Both advertising creative directors and consumers have a common understanding of the meaning of white space in advertising. Reveals that the minimalist movement, corporate art, and the lean look of architecture and upscale living are historical forces that have created coherent beliefs about white space in advertising as signaling brand meanings associated with prestige, confidence/power/strength, trust/stability, and leadership/cutting edge benefits. |
| Method (Implicit Boundaries: Lab Experiments, Online Experiments) |
| Other quantitative | Homburg, Ehm, and Artz (2015), JMR | Machine learning can usefully be applied to understand how consumer segments posting in different types of online communities respond to firms that are actively engaged in those communities. Derives consumer sentiment, the dependent variable, from over 115,000 threaded consumer posts derived from multiple online forums, by using supervised machine learning techniques. Results reveal diminishing returns for firms that actively engage in online communities, with the results holding for communities that address consumers' functional needs, but not those addressing social needs. |
| Qualitative | Hill and Stamey (1990), JCR | An ethnographic approach reveals how homeless consumers acquire possessions, what possessions they acquire, and the environments in which they are consumed. Reveals that the homeless, who follow a nomadic life, actively confront their environments and aim to improve the quality of their lives by using locally available, portable, and multi-use possessions, often acquired through scavenging and requiring continuous replenishment of resources. Tools are highly valued and reciprocity from others fosters a sense of community, countering a "deviant" label by others reduced self-esteem. |
| Mixed | Arvidsson and Caliandro (2016), JCR | A mix of netnography (qualitative) and social network analysis (quantitative) inform understanding of how shared interests, rather than identities or interactions, unite online consumers. Supports the concept of a "brand public" as distinct from brand community, emphasizing (1) consumer interest rather than interaction, (2) affect rather than discussion, and (3) consumers' use of the brand's social media platform as publicity to promote their own identities and objectives rather than the collective identity or objectives of the brand. |
| Prepublication Test Market (Implicit Boundaries: Academics in Other Disciplines, Nonacademic Audiences) |
| Academics in other disciplines | Cornil and Chandon (2016), JMR | Exemplifies the value of sharing research ideas and preliminary findings with academics in other disciplines while the research is still ongoing. Prior to publication, early theorizing and findings of this research on interventions to control food portions were shared at numerous academic talks and conferences with academics in the fields of nutrition, health, medicine, and agricultural and applied economics. |
| Nonacademic audiences | Leipnitz et al. (2018), IJRM | Exemplifies the value of sharing research ideas and preliminary findings with nonacademic audiences while the research is still ongoing. Randomized field trials on blood health-check services for blood donors were used in extensive cooperation with the German Red Cross Blood Donation service to test different incentive strategies for blood donation. |
| Postpublication Dissemination (Implicit Boundaries: Academics in Other Disciplines, Lay Presentations, Nontechnical Presentations, Popular Press) |
| Academics in other disciplines | Botti, Orfali, and Iyengar (2009), JCR | Exemplifies the value of sharing research findings with academics in other disciplines after the research has been published. Results of this research on the emotional consequences of autonomy in health care decisions were extensively disseminated after publication to both researchers and practitioners in the medical field through specialized websites (i.e., Medical News Today) and invited talks and workshops (i.e., the U.K. Huntington's Disease Predictive Testing Consortium). |
| Lay presentations | Lemon and Verhoef (2016), JM | Exemplifies the value of sharing research findings with practitioners through lay presentations after the research has been published. This research on a model of customer experience throughout the customer journey was presented in the Marketing Science Institute webinar series seen by over 200 practitioners in real-time and many more afterward as downloads. It was also presented at the MSI Trustees meeting with nearly several hundred marketing practitioners in attendance. |
| Nontechnical publications | Keller and Lehmann (2008), JPPM | Exemplifies the value of sharing research findings with practitioners through nontechnical means after the research has been published. The Centers for Disease Control implemented the results of this research on the effects of health communication tactics on consumer compliance with health recommendations by designing an online social marketing strategy tool called MessageWorks. |
| Popular press | Bone, Christensen, Williams (2014), JCR | Exemplifies the value of having research findings be disseminated through the popular press after the research has been published. Findings from this research on the impacts on minorities' experiences of systemic restrictions on choice were presented in written and oral testimony in a hearing of the U.S. House of Representatives Financial Services Committee and covered extensively in the popular press under headlines such as "banking while black." |
1 Notes: JM = Journal of Marketing; JCR = Journal of Consumer Research; JPPM = Journal of Public Policy & Marketing; JMR = Journal of Marketing Research; IJRM = International Journal of Research in Marketing; JCP = Journal of Consumer Psychology.
As evidenced by these cases, breaking our current research defaults to create a broader impact is clearly challenging. It can require significant time and effort and often involves taking on risk and dealing with controversy. Thus, consumer research scholars, particularly more junior ones, may wonder whether it is simpler to work within the implicit bounds of the field. Is doing boundary-breaking work worth the additional risk and effort? We believe it is for the reasons outlined next.
Engaging in boundary-breaking consumer research can be motivating and exciting. Identifying unexplored real-world phenomena and considering how they relate to the concerns of stakeholders is curiosity-evoking. Interacting with stakeholders is mentally stimulating, particularly because they bring to the table issues we have not considered and help us ground our theories in reality. Considering how unexplored marketplace phenomena can be mapped onto extant and novel constructs is also intellectually engaging. Using different methodological approaches, whether alone or with colleagues, develops one's skill set and expands on the types of questions one can address in the future. These motivational effects are especially helpful in maintaining enthusiasm throughout the review and publication process.
Engaging in boundary-breaking research can be reputation-enhancing. As evidenced by Journal of Marketing's "Challenging the Boundaries of Marketing" series and recent editorials (e.g., [13], [23]), the field is ready for such work, which should improve publication prospects. Moreover, such research builds one's reputation as an expert in a substantive area, which is often an important consideration in tenure and promotion decisions. Interacting with nonacademic audiences like practitioners and policy makers enhances one's visibility among these audiences, offers the potential for more frequent interactions, and furthers future publication prospects.
Boundary-breaking research that aims for broad impact can also help one's reputation in more traditional ways. Research inspired by real-world phenomena and descriptive research about the world of stakeholders offers the potential to identify novel research domains (e.g., brand communities), constructs (e.g., customer-based brand equity), theories (e.g., time-inconsistent preferences), metaphors for consumers (e.g., consumers as experience seekers), and metaphors for consumption (e.g., consumption as liquid). These novel ideas are the lifeblood of our discipline's growth, and their publication can increase both citations and the potential for long-term contribution awards.
Boundary-breaking research offers the fields of consumer research and marketing the opportunity to address pressing problems of interest to a broad set of stakeholders. To illustrate this point, Table 3 provides examples of the types of novel and important questions that could be addressed by breaking the boundaries we have identified in this work. Moreover, the most interesting and important questions, and research that best answers them, may be greatest when scholars actually engage with marketplace stakeholders and academics in other fields and assess which boundary-breaking activities are most important to the question at hand.
Graph
Table 3. Illustrative Marketing-Relevant Consumer Research Questions Pertinent to Breaking One or More Boundaries.
| Why Conduct Consumer Research? | Illustrative Questions |
|---|
Influence Stakeholders Academics in other disciplines Educators Industry Government and nongovernmental organizations Media Society
| Select a topic that one or more stakeholder groups is concerned about. Consumption of Plastic: How do consumers feel about all the plastic in their lives, their water, and their food? What options to plastic consumption do consumers believe they have? What novel perspectives can consumer researchers bring to bear on the issue of plastic consumption? Algorithms and Consumer Oppression: Do the algorithms of artificial intelligence models create racial and gender bias? How does one test for these biases? Are consumers made vulnerable in contexts that are consequential to their lives, such as creditworthiness, government support, and medical decision making?
|
Test IdeasFrom Real-World Phenomena
| Identify a novel or important consumer phenomenon and contribute to research on it. The Prevalence of "Connected Devices" in the Home: What hopes and fears do consumers have about the internet of things? What are the implications of these devices for consumer privacy and corporate empowerment? How do they change marketing? How do they change consumer behavior? Fake News: What do people regard as "fake news"? Who is vulnerable? What explains why and when "fake news" works? What distinguishes "advertising" and "storytelling" from "fake news" in the minds of academics? Consumers? Advertisers?
|
Contribute to TheoryPhenomenon–construct mapping
| Find a poorly understood consumer behavior phenomenon and try to add structure to it.· Privacy Violations: What does it mean to have one's privacy violated? Are there dimensions along which privacy violations can be conceptualized? Or are there distinct types of violations that represent distinct constructs? How can privacy, its dimensions or its types be measured? · Chatbots and Consumer Experience: Are consumers aware of chatbots? If so, how do they perceive them? What novel constructs can be identified from consumers' awareness and perception of chatbots?
|
Investigate Outcomes Describe Evaluative
| Explore an issue or substantive topic by describing it or thinking about whether it is good or bad for consumers and other stakeholder groups. Populism and Nationalism in Western Democracies: How can we characterize populism and nationalism in various nations around the world and describe how these beliefs affect consumer behavior and consumers' interactions with marketplace stakeholders? Climate Crisis Perspectives: What kinds of marketing and policy messages convince doubtful consumers that climate change is indeed real? Do consumers who become convinced that climate change is real respond favorably to marketers whose efforts are designed to reduce their carbon footprint? How do climate change doubters or deniers react to those efforts?
|
| What Is Studied in Consumer Research? | Illustrative Questions |
Unit of Analysis Small groups Large groups Populations/cultures
| Find an understudied group of consumers for whom a significant consumer research issue is relevant. Generation Z: How is the post-Millennial generation affecting consumer society and culture? Are they truly the unhappy and compliant "iGeneration" consumers that the media makes them out to be, or are they something else? Changing Households: What are the consumption implications of the ever-increasing number of households with unmarried or single parents; gay, lesbian, or bisexual parents or cohabitants; single-person or older members; multigenerational or multicultural members; or households in poverty?
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Decision Context Dynamic Dependent
| Determine an understudied consumer research issue for which a dynamic, dependent, or interdependent approach would yield novel insights. Household Financial Literacy Development: How does a spouse who has not played a large role in managing family finances develop greater financial literacy when they lose a spouse through death or divorce? Empty Nester Consumption: How does becoming an empty nester change consumers' purchasing patterns, consumption rituals (e.g., family meals, meal preparation), use of space in the home, use of time, and identity?
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Time Frame Past Future
| Identify an understudied consumer research issue for which the past may yield novel insights into the present or one for which future trends suggest changes in consumption. Health Care Consumption: What is the history of health care provision in a particular country, region, or territory? What is the institutional and economic trajectory of that health care system? How does it interact with (particular) consumers, their health care decisions as well as decisions that affect their health care (e.g., exercise and diet)?
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| Tobacco Marketing and e-Cigarette Use: What can we learn from the national and international history of tobacco regulation and marketing about the present-day use of e-cigarettes, vaping, and "Juuling"? What does that history tell us about consumers and their responses?
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Consumer Role Nontarget Collaborator and coproducer Antimarketer Skeptic Nonowner
| Regard whether a different metaphorical role for consumers in one or more consumption domains can yield novel insights. Consumers as Artists and Marketers: A new crafting movement has led more and more consumers to seek artistic and craft endeavors; in turn, selling their handmade products to others. What new insights about consumer are revealed when we think about them as art producers and sellers? Does this artist/seller role influence how consumers approach other consumption decisions? Consumers as Microcelebrities: What does it mean to conceptualize consumers as empowered narrowcasting media stars? How does this conceptualization alter how we view the consumption and marketing system?
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| How Is Consumer Research Executed? | Illustrative Questions |
Respondent Group Age segments Racial, ethnic, religious, and sexual orientation segments Socioeconomic segments Marginalized consumers Consumers in situ Professionals
| Consider whether data from specific respondent groups will yield novel insights about consumers. Students as Consumers: How does the identity of student affect consumers' perceptions of the self as an "adult" and their hopes and fears for the future? Has does the student consumer identity affect their perception of various products and services? How does the student identity evolve and change, and how is it different in various regions? Billionaire Consumers: What do we know about the consumption of the super-rich and how it is affecting contemporary marketing? Beyond their wealth and influence, how are the super-rich unique as consumers? What impacts do they and their consumption have on business, other consumers and the world?
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Method Other quantitative Qualitative Mixed
| Examine an interesting consumer behavior issue and assess whether different methodological approaches yield novel insights. Children's Marketplace Knowledge: What new insights can be revealed by taking alternative approaches to the study of children's consumption decisions and marketplace knowledge? What would a longitudinal approach reveal? An historical mapping? An observational study? Social media data? Mixed methods? Diet: What new insights about consumers' decisions to follow particular diets (e.g., kosher/halal, paleo, keto, vegetarian, vegan) can be revealed by taking alternative methodological approaches? How can we deploy combinations of qualitative and quantitative methods? Can we illuminate new and particular aspects of the phenomenon of diet by combining automated data analysis with narrative analysis, or experiments with historical analysis, or ethnography with modeling?
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Prepublication Test Market Academics in other disciplines Nonacademic audiences
| Take a research project that you are in the process of executing and share your ideas, thoughts, and findings with one or more marketplace stakeholder groups. Consider the following: What elements of your research could you emphasize to make it more relevant to particular stakeholders? What aspects, images, and symbols might you emphasize to communicate your findings more effectively? How else could you provide them with vital insights that might lead them to improve their own practices? How could you change the language that you use to communicate more effectively and make it more relevant to their concerns?
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Postpublication Dissemination Academics in other disciplines Lay presentations Nontechnical publications Popular press
| After your article is published in an academic journal, take steps to disseminate your research findings to other parties so that the work can have broad impact. Consider the following: What parties, industries, companies, and types of organization, might be interested in your research? How do those parties get their information? In what forms and styles? What is the most effective way to communicate your research to those parties? How can you target your information to meet their needs and speak their language? What is the most effective way to get others to share your research with other interested parties?
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Engaging in boundary-breaking, marketing-relevant consumer research reduces discipline-based fragmentation based on methods, substantive focus, and base discipline orientation. While fragmentation is natural with growing fields, it tends to isolate as opposed to unite members of the field ([36]; [55]). Research that has broad impact joins researchers by its focus on answering important questions faced by different stakeholders using different lenses, as opposed to a focus on providing base discipline contributions. Moreover, breaking the various boundaries that we describe affords opportunities for researchers with different skill sets (e.g., Consumer Culture Theory researchers; psychologically based researchers, strategy researchers, and modelers) to join forces through collaboration. For example, [11] brought together researchers from different parts of the discipline to offer a consumer-based theory of firm pioneering advantages. These kinds of collaborations create a "big tent" that unites consumer researchers with those from other parts of the discipline.
The types of boundary-breaking consumer research that we describe here can reduce the perception and criticisms of academic consumer research as being too incremental ([30]; [63]) and as emphasizing narrowly construed topics that fail to tackle the challenges that stakeholders face. Moreover, some have argued that in some consumer research, consumption is merely a context for the study of human behavior ([10]; [39]). By studying consumers in substantive contexts involving the marketplace, we avoid this criticism.
Over 25 years ago, [65], p. 489) advocated for "wider horizons, a larger audience, a different talent mix, more emphasis on discovery, more attention to consumers, and more single-minded dedication to meaningful results." These criticisms, which have not yet been fully adopted by academic consumer researchers, partially explain why nonacademic consultants have been more successful in capturing the attention and consulting dollars of marketing organizations than academics have based on their research.
Although academics are known for their research rigor, the boundary-breaking research we describe also enhances credibility by balancing rigor with relevance. Indeed, there is a sense that relevance has been subjugated to rigor in academic research ([31]). High-impact, marketing-relevant consumer research requires both rigor and relevance. We believe that by breaking boundaries, consumer behavior researchers can offer significant relevance to not only consumers but also the myriad stakeholder groups pertinent to marketing.
More generally, credibility is enhanced by addressing marketing's "image problem" ([62], p. 10). Many consumers view marketing as deceptive, manipulative, and generally bad for their own welfare. We can help the credibility of the field when we conduct consumer research of importance to consumers themselves. Such is true with evaluative research that helps identify when marketing efforts and consumer actions are beneficial or harmful. We help both ourselves and society by producing research that studies issues of broad societal import such as financial decision making, data privacy, and health and economic well-being.
Consumer research that breaks boundaries to have broader impact will improve the connections we make with "substantive system experts" in practice and in other academic disciplines. Doing so dramatically increases the chances that consumer research will influence large and diverse audiences, including scholars in other fields, educators, managers, policy makers, and consumers themselves. We hope to inspire consumer researchers to consider making their research of broader interest to marketers, regulators, and scholars in adjacent fields by studying substantive marketplace phenomena.
Consumer research has yielded significant, novel, and important insights that have enhanced our understanding of how and why consumers behave as they do. Scholars in our field are skilled in conceptual thinking, research design, and research execution. Yet, despite our current contributions to knowledge, we have the potential to offer even broader impact by advancing knowledge on topics that stakeholders—including marketing academics—care about. We argue that such impact can be advanced by breaking free of one or more of the implicit boundaries that currently guide why, what, and how we study consumers. Whereas skeptics might argue that such work is too time consuming and too risky for nontenured individuals, the cases we mentioned include the work of a number of untenured authors. Importantly, not all of this work requires a marathon time investment. Moreover, the time investments and inherent risks may be well offset by impact, measured not just by traditional metrics but also by the influence of one's research on entities outside of our academic research community.
Our hope is that some readers of this article will rise to the challenge of engaging in boundary-breaking, marketing-relevant consumer research. We believe that it is through such efforts that our thought leadership position and contributions to the world can be enhanced.
Footnotes 1 Author ContributionsThe first and second authors led and coordinated the development and writing of the manuscript; all other authors contributed equally and are listed in alphabetical order.
2 EditorsChristine Moorman and C. Page Moreau
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
5 1Consistent with the American Marketing Association's definition of marketing (https://www.ama.org/the-definition-of-marketing-what-is-marketing/), we define "marketing-relevant consumer research" as research about issues related to the interaction between consumers and other marketplace stakeholders. This definition, while consumer-focused, makes salient the role of motivated marketplace stakeholders and their relationships with consumers, as well as a phenomenon-based approach to the study of consumers. Readers should note that whereas we sometimes default to the term "consumer research," the term should be taken to mean "marketing-relevant consumer research."
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Record: 45- Creating Effective Online Customer Experiences. By: Bleier, Alexander; Harmeling, Colleen M.; Palmatier, Robert W. Journal of Marketing. Mar2019, Vol. 83 Issue 2, p98-119. 22p. 2 Diagrams, 9 Charts, 1 Graph. DOI: 10.1177/0022242918809930.
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Creating Effective Online Customer Experiences
Creating effective online customer experiences through well-designed product web pages is critical to success in online retailing. How such web pages should look specifically, however, remains unclear. Previous work has only addressed a few online design elements in isolation, without accounting for the potential need to adjust experiences to reflect the characteristics of the products or brands being sold. Across 16 experiments, this research investigates how 13 unique design elements shape four dimensions of the online customer experience (informativeness, entertainment, social presence, and sensory appeal) and thus influence purchase. Product (search vs. experience) and brand (trustworthiness) characteristics exacerbate or mitigate the uncertainty inherent in online shopping, such that they moderate the influence of each experience dimension on purchases. A field experiment that manipulates real product pages on Amazon.com affirms these findings. The results thus provide managers with clear strategic guidance on how to build effective web pages.
Keywords: online customer experience; online design elements; online retailing; Taguchi design; web design
With more than 350 million products listed on Amazon.com alone ([ 1]), success in the increasingly competitive online domain depends on sellers' ability to orchestrate verbal and visual stimuli (i.e., design elements) on product web pages to effectively convert page visitors into buyers ([63]). Insights into which design elements make for effective product web pages are however still largely based on managers' intuitions or, at best, ad hoc A/B testing. Academic research typically focuses on a single design element or just a few across a limited number of products or brands. It also often neglects the mechanisms through which design elements affect purchase or employs theoretical perspectives (e.g., information processing) that conceptually limit their effects a priori to a single function (e.g., information transmission). Yet each encounter with a product web page—the virtual space that presents a product and illustrates its value to the customer—evokes a multidimensional experience that goes beyond a pure conveyance of factual information ([10]; [42]). The objective of this research is therefore to understand how online design elements shape multidimensional customer experiences to influence purchase and how these experiences should be customized depending on the products or brands sold.
The online customer experience at the heart of this research comprises a customer's subjective, multidimensional psychological response to a product's presentation online. We argue that this experience goes beyond cognitive (informativeness) and affective (entertainment) dimensions typically conceptualized in extant research ([54]) and also includes social (social presence; [78]) and sensory (sensory appeal; [36]) dimensions. Furthermore, we identify 13 web page design elements, such as product descriptions, photos, and comparison matrices, that each may help shape the online experience and are ubiquitous in a wide range of industries and web page formats. This multidimensional framework more closely resembles the conceptualization of offline experiences ([10]; [42]) and helps more accurately capture the mechanisms by which design elements affect product purchase.
How effectively each experience dimension elicits purchases, however, may vary depending on characteristics of the offered products and brands that exacerbate or alleviate the uncertainty inherent in online shopping ([ 8]; [63]). First, the degree to which consumers can evaluate a product solely on the basis of factual information (search qualities) rather than needing direct physical experience (experience qualities) implies the level of uncertainty associated with assessing that product online ([33]). Second, customers may also be uncertain about the accuracy and truthfulness of sellers' product presentations, yet a brand's trustworthiness may alleviate this uncertainty ([56]). We leverage our multidimensional online framework of the online customer experience to investigate how these two primary sources of uncertainty determine the effects of each experience dimension on purchase ([19]).
To ensure the broad scope and generalizability of our research, we collaborate with a specialized online content agency and four Fortune 1000 firms, diverse in their industries, brands, and products (i.e., consumer packaged goods, consumer electronics, industrial electronics, and consumables). In Study 1, we conduct large-scale online experiments that involve 16 products from 11 brands, for which the online content agency created 256 unique "Amazon look-alike" product web pages. On these pages, we manipulated 13 design elements according to an orthogonal array design ([73]) and then tested the pages among 10,470 randomly assigned respondents. With the resulting data, we estimate a joint model that isolates the relative influences of each design element on each dimension of the online customer experience, the relative effects of each experience dimension on purchase, and the moderating influences of product type and brand trustworthiness on the effects of the dimensions on purchase. A field experiment in Study 2 tests these effects with real Amazon product pages, on which we used design elements to create specific experiences to observe the effects on sales.
We offer three main contributions to the literature. First, data from 16 experiments in Study 1 expand insights into online customer experiences and identify four dimensions—namely, informativeness, entertainment, social presence, and sensory appeal—that act as the underlying mechanisms by which design elements influence purchase ([54]; [61]). Prior online research has mainly focused on informativeness and entertainment; however, we show that the effects of social presence are just as strong as those of informativeness, and sensory appeal offers additional insights. Second, we find that uncertainty about the offered product and its seller's brand influences the effects of the customer experience dimensions on purchase. Using actual product web pages on Amazon.com, a field experiment in Study 2 validates the lab results to show that search products benefit from a more informative experience but experience products benefit from a more social experience. Third, we establish an online customer experience "design guide," with actionable advice for marketers on how to strategically orchestrate design elements to shape effective online experiences in an era of increased web design importance ([81]). Specifically, we depict how to evaluate the design elements that currently constitute their digital inventory, which new elements to invest in and develop, and how to negotiate and assess contracts for premium content with online retailers.
In contrast with brick-and-mortar retail, customers assess products online not through physical interaction but through verbal and visual stimuli (design elements) deployed on product web pages. A broad stream of research conceptualizes offline experiences as consisting of multiple, separate, but related dimensions (e.g., cognitive, affective, sensory, social, physical) ([10]; [42]; [64]; [77]). Yet research has treated online experiences far more simplistically ([54]; [71]), often a priori limited to their informativeness (see Table 1).
Graph
Table 1. Relevant Research on the Effectiveness of Design Elements on Product Web Page Performance.
| Categorization of Design Elements Based on Form | Theoretical Perspective | Tested Underlying Mechanisms(Design Element Function) | Product Web Page Performance | Key Findings |
|---|
| Verbal Elements | Visual Elements | Combined Verbal and Visual Elements |
|---|
| Studies | Linguistic Style | Descriptive Details | Bulleted Features | Return Policy Information | Product Feature Crop | Lifestyle Picture | Picture Size | Product Video | Expert Endorsement | Comparison Matrix | Customer Star Ratings | Recommendation Agent | Content Filter |
|---|
| Cooke et al. 2002 | | ✓ | | | | | | | | | | ✓ | | Information processing | None | Product evaluation | When unfamiliar products are presented independently, additional descriptive detail improves product evaluations. When presented alongside other attractive products from a recommendation agent, descriptive detail worsens product evaluations. |
| Häubl and Trifts 2000 | | | | | | | | | | ✓ | | ✓ | | Information processing | None | Purchase decision quality | The use of recommendation agents and comparison matrices decreases the size but increases the quality of customers' consideration sets and also improves purchase decision quality. |
| Hauser et al. 2009 | | ✓ | | | | | | | | | ✓ | | ✓ | Information processing | None | Purchase intentions | Website content can be customized through the strategic selection of design elements to maximize purchase intentions, based on customer information-processing styles inferred from past browsing behaviors. |
| Huang, Lurie, and Mitra 2009 | | | | | | | | ✓ | ✓ | | ✓ | | | Information processing | Search depth (time spent on website) | Likelihood of product purchase | Customer star ratings, expert endorsements, and multimedia presentations (e.g., product videos) are more effective for experience than search goods in driving purchase. |
| Ludwig et al. 2013 | ✓ | | | | | | | | | | ✓ | | | Information processing | None | Conversion rates | Linguistic style can signal a customer's similarity to other customers of a product, which can influence purchase. |
| Roggeveen et al. 2015 | | | | | | | | ✓ | | | | | | Vividness | Sensory appeal | Product preference, willingness to pay | Product videos increase a web page's vividness and create experiences that mimic real products, ultimately enhancing customers' preferences and willingness to pay. |
| Shi and Zhang 2014 | | | | | | | | | | | | ✓ | | Information processing | None | Consumer price and promotion sensitivity | Recommendation agents vary in effectiveness, depending on the customer's past experience and decision processes. |
| Song and Zinkhan 2008 | ✓ | | | | | | | | | | | | ✓ | Social presence (interactivity) | Website communication, controllability, responsiveness | Website effectiveness (satisfaction, loyalty, attitude, quality) | Content filters that hinder access to information on a website can reduce responsiveness of the site. A more conversational linguistic style increases perceptions of the website as communicative, controllable, and responsive, which enhance perceptions of website effectiveness. |
| Wang et al. 2007 | ✓ | | | | | | | ✓ | | | | | | Social presence (social response theory) | Website socialness, pleasure and arousal, and flow | Website patronage intentions | Social presence, website informativeness, and entertainment are key dimensions of the online experience that interact to increase patronage intentions. More conversational linguistic styles can increase perceived social presence and encourage purchases. |
| Zhu and Zhang 2010 | | | | | | | | | | | ✓ | | | Unspecified | None | Product demand | The influence of customer star ratings on product demands is weaker for popular products and for products designed for offline use. |
| This study | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | Multidimensional customer experience | Informativeness, entertainment, social presence, sensory appeal | Product purchase | Design elements can be used to create four distinct experience dimensions (informativeness, entertainment, social presence, and sensory appeal) that vary in the degree to which they influence purchase. based on a product's search versus experience qualities and the trustworthiness of the brand. |
30022242918809930 Notes: To derive a list of relevant research, we examined articles pertaining to online product marketing published in the last ten years in Journal of Marketing, Journal of Marketing Research, Marketing Science, and Journal of Consumer Research. To be included, the research needed to be empirical in nature and focus on product web page design elements available to manufacturers that sell through a retailer website. We exclude studies of retailer-controlled website design elements (e.g., navigation), email marketing, online advertising, word of mouth, or search.
In line with the four basic systems—cognition, affect, relationships, and sensations—commonly studied in psychology and sociology ([ 4]; [59]), we conceptualize the online customer experience as consisting of four dimensions: informativeness (cognitive), entertainment (affective), social presence (social), and sensory appeal (sensory). Consistent with our multidimensional perspective, we do not expect a one-to-one relationship between any specific design element and an experience dimension ([10]).
We next introduce and review each dimension of the online customer experience. Then, we explain why the influence of each dimension on consumers' purchase decisions might depend on the uncertainty associated with specific products or brands. Finally, we present the design elements that managers can use to build product web pages to shape customer experiences (see Figure 1).
Graph: Figure 1. Designing the online customer experience. Notes: Constructs in italics were experimentally manipulated across 16 products and 11 brands. N = 10,470.
Defined as "the extent to which a website provides consumers with resourceful and helpful information" ([43], p. 51), informativeness is the primary cognitive dimension of the online customer experience. It captures a web page's contribution to helping the consumer make a pending purchase decision, which involves thinking, conscious mental processing, and, typically, problem solving ([25]). Informativeness captures the functional aspect and value of the experience to the customer ([77]) and is generally impersonal, outcome oriented, and objective ([63]). This fact-based dimension pertains to the information that remains after interacting with a web page, which can improve attitudes toward a website ([31]; [34]).
Customer interactions with products online can evoke affective responses and might be enjoyed for their own sake, without regard for functional considerations. Entertainment, or the immediate pleasure the experience offers, regardless of its ability to facilitate a specific shopping task ([ 7]), is thus a key dimension of the online customer experience. Entertainment reflects an appreciation for the "spectacle" experienced on the web page, involves the fun and play of online shopping, and accords more than just an achievement-oriented purchase opportunity ([13]; [48]). As such, entertainment can trigger arousal in web page visitors ([34]) and reduce cart abandonment in online stores ([40]).
To match the benefits of offline experiences, online sellers increasingly work to provide a sense of social presence on their web pages ([78]). Social presence refers to the warmth, sociability, and feeling of human contact that a web page confers ([23]). Extant research shows that the social presence of a website can increase perceived tangibility and feelings of psychological closeness to a product ([17]). It can also increase pleasure, arousal, and flow during online shopping ([78]), as well as purchase intentions ([29]) and loyalty ([15]).
Finally, the sensory component of the customer experience includes aspects that stimulate sight, sound, smell, taste, or touch ([25]). [82] suggests that sensory-level processing and retrieval occurs automatically and drives preferences. In an online environment, sensory appeal refers to "the representational richness of a mediated environment as defined by its formal features" ([72], p. 81) or the way a web page stimulates the senses. Perception of beauty and aesthetically pleasing stimuli are part of sensory appeal ([64]). Although the online environment limits the scope of sensory experiences, sensations can be evoked through imagery (e.g., pictures, videos) ([20]). Thus, sensory appeal can affect perceptions of product performance ([80]) and purchase intentions ([62]).
Online shopping often comes with uncertainties that do not arise offline and that might affect how certain experience dimensions influence purchase ([19]; [56]). First, online, customers cannot touch and feel the merchandise in which they are interested, which can create uncertainty in product assessment before purchase ([38]). This uncertainty tends to be more severe for experience products, for which the most relevant attributes are discoverable only through direct physical contact, than for search products, whose most relevant attributes are assessable from presented information without physical interaction ([33]; [80]). How consumers attend to and interpret product information differs between search and experience products ([35]). Thus, the most effective type of experience for selling these two types of products might also differ. For example, [80] show that web pages that appeal to the senses may be more beneficial for experience products, whose evaluation requires sensory information.
Second, the physical separation between customers and products requires customers to have faith in the accuracy and truthfulness of the product web page. Yet they may experience uncertainty about online sellers' ability and integrity to convey product information, depending on the trustworthiness of the seller brand ([56]). Trust reflects the "willingness to rely on an exchange partner in whom one has confidence" ([51], p. 315). A significant stream of research shows the importance of trust online ([76]), in which sellers' trustworthiness determines customers' research and purchase decisions ([24]; [32]). Trust online is also closely connected with web design ([66]; [74]). Several studies suggest that low trustworthiness can be overcome through purposeful web page design ([63]; [79]) or by customizing content to customers' preferences ([75]). Specific experience dimensions might also be instrumental to alleviating low trustworthiness. [ 8] show that entertaining online experiences may compensate for an initial lack of trust in a brand. Social presence may serve a similar purpose ([23]). Extant work suggests that the product- and brand-related uncertainty inherent in online shopping can influence the effects of experience dimensions on purchase. We thus focus our moderation analysis on product type and brand trustworthiness as the respective primary determinants of these two types of uncertainty ([33]; [56]), instead of other product, brand, or service attributes.
The product web page is at the heart of the online customer experience. It consists of basic design elements, defined as verbal and/or visual stimuli that provide the building materials for any given page. To identify the most important elements, we reviewed ten years of research on website design published in Journal of Marketing, Journal of Marketing Research, Marketing Science, and Journal of Consumer Research, as well as various specialized journals. Our focus was on design elements that relate directly to the product presentation and are typically available to firms selling through retailers such as Amazon; we excluded structural elements, such as navigation, menus, icons, and overall organization, that operate at the website level and are under the control of the host retailer. Although they operate through many aliases, we identified 13 elements that we classify by their form (see Appendix A) into three groups: verbal elements that use text and typographical features, visual elements that use images and pictures, and combinations of both. Table 1 summarizes research on each of these 13 design elements.
Verbal elements involve the written word. In this category, we consider linguistic style, descriptive detail, the number of bulleted features, and return policy information statements. The most basic aspect of textual elements is the way information is presented. The linguistic style in which verbal content is conveyed or the characteristics of the text—including word choice and use of questions, certain pronouns (you, your), and adjectives—can affect product conversions and consumer perceptions of website effectiveness ([44]; [70]). [70] provide preliminary evidence that these effects occur through the impact of linguistic style on social presence. To capture the degree of elaboration of the product descriptions on a web page, we examine descriptive detail. Providing more attribute information generally increases product evaluations and purchase likelihood ([14]; [30]). The number of bulleted features indicates how many product features appear in an abbreviated list at the top of the web page. Though prevalent on many product web pages, to our knowledge, research has not empirically investigated its effects on purchase. Return policy information refers to whether the web page contains information about the terms by which customers may return the product.
Visual elements subsume all content presented in photographic or illustrated form and can convey symbolic meaning and pictorial information ([65]). We investigate feature crops, lifestyle photos, photo size, and product videos. Unlike pictures of the product as a whole, feature crops zoom in on a key product feature that would otherwise not be visible. Lifestyle photos connect the product with customers' lives, such as by depicting people using it or living with it in a regular setting. They explicitly capture or imply human interaction with the product ([ 6]). We also investigate photo size.[55] show that larger product images can increase purchase intentions. Finally, a product video can demonstrate the product and its key features. Videos including human voices can serve as cues for human characteristics and influence perceptions of social presence and sensory appeal ([50]; [60]).
Customer star ratings, expert endorsements, comparison matrices, recommendation agents, and content filters all combine verbal and visual qualities. Customer star ratings are aggregations of user-generated product ratings, depicted visually with a series of stars and next to the total number of reviews ([12]). Expert endorsements are also product evaluations, but assembled from distinguished experts in the category, such as product testing firms, and generally include a graphic depiction, such as a seal ([ 5]). Comparison matrices are tables to compare the focal product with other products from the same category on multiple characteristics. Product information is typically presented as pictures of alternatives (columns) and text describing attributes (rows). Recommendation agents combine verbal and visual information to generate a list of alternatives to the focal product ([41]). Comparison matrices and recommendation agents can improve purchase decision quality ([28]; [39]). Content filters, such as "show more" buttons, allow customers to dictate what, when, and how much verbal and visual content appears on the web page ([30]; [49]). Of the combined elements, star ratings have received most empirical attention, though studies typically test their effects directly on purchase, without considering underlying mechanisms ([12]; [30]; [44]; [83]). Table 1 shows evidence for the effects of design elements on purchase, while the underlying mechanisms remain mostly unclear.
We extend research on design elements and the online customer experience with two studies. In Study 1, we aim to ( 1) understand the relative importance of each of the four online customer experience dimensions as key mediators in the relationship between web page design elements and customer purchase, ( 2) determine which of the 13 design elements are most useful in creating each experience dimension, and ( 3) assess how product type and brand trustworthiness influence the effects of the experience dimensions on purchase. In Study 2, we manipulate real Amazon product pages from the insights gleaned from Study 1 to assess the effects on actual sales.
We partnered with four Fortune 1000 firms in multiple industries (i.e., consumer packaged goods, consumer electronics, industrial electronics, and consumables) (Appendix B) and tested our conceptual model with 16 products (4 per firm), representing 11 brands. Together with a specialized online content agency, we designed and created mock Amazon product web pages for each product that varied the 13 design elements on two levels each, according to an orthogonal array design ([73]). On Amazon.com, vendors can select from a range of module templates and then manage the content of each module within the retailer's restrictions. Appendix C shows an example web page.[ 5]
Appendix A provides a summary of the two manipulated levels for each of the 13 design elements. For verbal elements, we manipulated linguistic style as either a journalistic tone (Level 1) or conversational tone (Level 2). For the journalistic tone, the neutral product descriptions featured few or no adjectives, no self-relevant words (e.g., "you," "your") ([11]; [70]), no questions, and no exclamation points. For the conversational tone, the descriptions were more engaging and included adjectives, self-relevant words, words that insinuate instantaneous gratification (e.g., "fast," "instant," "quickly"), and self-reflective questions (e.g., "Wouldn't it be great to have high-speed Internet everywhere?") ([ 2]; [44]). Although linguistic style determines how product descriptions convey information, it does not affect the actual amount of information presented. To manipulate this facet, we used the descriptive detail design element. At Level 1, product descriptions contained approximately one-third the amount of information (i.e., number of attributes discussed) that they contained at Level 2. We manipulated bulleted features as either three (Level 1) or five (Level 2) bullets on the web page; previous research indicates that these numbers are relevant ([68]). Return policy information was the absence (Level 1) or presence (Level 2) of the statement "Return Policy: Items can be returned within 30 days of receipt" on the page.
For visual elements, we manipulated the feature crop element by either not replacing (Level 1) or replacing (Level 2) one of the product hero shots with a close-up picture of a specific feature of the product. A lifestyle picture, which connects the product with the real world in an actual usage situation, was either not included (Level 1) or included (Level 2) to replace one of the hero shots. At Level 2 of the picture size design element, all pictures were 25% larger than at Level 1. Product video indicated the absence (Level 1) or presence (Level 2) of a video about the product.
For combined verbal and visual elements, we manipulated customer star ratings, by either excluding (Level 1) or including (Level 2) the average star rating for the product.[ 6] We manipulated expert endorsement using a quality seal from a fictitious third-party product rating agency, to avoid any potential effects of familiarity with existing agencies, that might differ across respondents. At Level 1, there was no seal, while at Level 2, this seal replaced one of the hero shots. We manipulated the comparison matrix element as the absence (Level 1) or presence (Level 2) of a table that compared the focal product with similar products from the same firm and category on key product characteristics. The recommendation agent featured either the absence (Level 1) or the presence (Level 2) of a section that displayed links to related products, again from the same firm and category. For these two elements, we purposely used products from the same manufacturer, to avoid any influences of additional brands for which consumers might hold distinct views. The content filter element either did not permit (Level 1) or permitted (Level 2) consumers to control the amount of verbal and visual content shown on the page, using "show more" buttons to reveal or hide parts of the modules.
Testing the effects of such a large number of elements poses a considerable empirical challenge. A full-factorial design would have required building and analyzing 131,072 experimental cells as web pages (213 combinations of design elements per product × 4 firms × 4 products). With such an approach, we could have investigated all potential interaction effects among design elements, but it would have been infeasible to execute. We therefore adopted a [73] orthogonal array design, which reduced the required number of cells to 256 (16 combinations of design elements per product × 4 products × 4 firms). Thus, we can feasibly investigate the simultaneous, causal direct effects of all 13 design elements.
We recruited 10,470 workers via Amazon Mechanical Turk for our 16 experiments (one per product). Respondents, randomly assigned to one of the 16 experimental cells within each experiment, were presented with the corresponding web page and instructed to explore it for at least 45 seconds. Next, they completed a questionnaire with demographic questions, items for manipulation and realism checks, and preexisting scales to measure purchase intentions and the four experience dimensions (see Appendix D).
Appendix A contains the results of our manipulation checks, which are all significant (p <.01), indicating successful manipulation of the design elements. In addition, we used two items to assess the realism of our web pages: "I could imagine an actual web page to look like the one I just saw" and "I believe that this web page could exist in reality" (α =.90) ([18]). Respondents' answers to these items, on a seven-point scale (1 = "strongly disagree," and 7 = "strongly agree"), indicated that our created web pages established sufficient realism (Mcomposite score = 5.41, SD = 1.29).
To assess the accuracy of our measures, we first conducted a confirmatory factor analysis. The results indicate a good fit of our measurement model to the data (χ2(80) = 2441.75, confirmatory fit index [CFI] =.98, Tucker–Lewis index [TLI] =.98, root mean square error of approximation [RMSEA] =.05, standardized root mean residual [SRMR] =.03). Moreover, in support of convergent validity, all standardized factor loadings are greater than.70 and significant at the 1% level. For each construct, the average variance extracted (AVE) exceeds.50, and the composite reliability is greater than.70. Cronbach's alpha values above.70 indicate internal consistency. In support of discriminant validity, all AVEs are greater than the squared correlations of the focal construct with any other construct (see Table 2).
Graph
Table 2. Descriptive Statistics and Correlations.
| Variable | M | SD | CR | CA | 1 | 2 | 3 | 4 | 5 |
|---|
| 1. | Informativeness | 5.29 | 1.11 | .90 | .89 | (.75) | | | | |
| 2. | Entertainment | 4.16 | 1.49 | .94 | .93 | .53 | (.83) | | | |
| 3. | Social presence | 3.65 | 1.52 | .95 | .95 | .39 | .57 | (.87) | | |
| 4. | Sensory appeal | 3.97 | 1.34 | .86 | .85 | .51 | .61 | .62 | (.66) | |
| 5. | Purchase intentions | 3.91 | 1.77 | .95 | .95 | .40 | .55 | .42 | .43 | (.88) |
40022242918809930 Notes: Means and standard deviations are based on composite scores; CA = Cronbach's alpha; CR = composite reliability. AVE values are in parentheses.
To evaluate multicollinearity among the experience dimensions, we first calculated the variance inflation factors for each construct. All values (informativeness = 1.55, entertainment = 2.18, social presence = 2.01, sensory appeal = 2.58) fall below the critical value of 5. Next, we examined the eigenvalues of their correlation matrix. The condition number (κ = 7.15) is well below the critical threshold of 30. Altogether, these results indicate that multicollinearity does not pose a concern. Last, we conducted an exploratory factor analysis, which confirmed that all items loaded onto their intended constructs (see Web Appendix A). For the remaining analysis, we calculated composite scores using the average of all scale items for each construct.
To investigate the extent to which product type and brand trustworthiness moderate the effects of the experience dimensions on purchase, we collected additional data.[ 7] To capture a product's search versus experience focus (i.e., its type) unaffected by the web pages on which it appeared in our experiments, we first presented 452 respondents with randomly selected hero shots of the 16 products and then asked them to complete a questionnaire with corresponding search and experience quality measures ([80]). Each respondent rated two products. We then computed the average of the difference between the two items, which captured each product's search and experience qualities over all respondents. We similarly captured brand trustworthiness by presenting 341 respondents with the logo of one of the 11 brands in our sample, along with a list of its associated product categories. Each respondent rated a single brand on six trustworthiness items ([63]), which we then averaged across respondents. Appendix D shows all measurement items.
To test our conceptual model, we combine the data from our 16 experiments (one for each product) and estimate a joint model using covariance-based structural equation modeling with maximum likelihood estimation. This approach allows us to test the relative importance of each experience dimension as a mediator of the link between design elements and purchase intentions, while controlling for customer heterogeneity in terms of age, gender, income, and education.
To confirm the relevance of each experience dimension as a mediator of the effects of design elements on purchase, we ran a series of nested models and compared their chi-square values with that of our proposed model (Table 3). Model 1 is our proposed model with all four experience dimensions as mediators. Models 2–5 test a set of three-dimension models in which we removed the paths from each experience dimension to purchase intentions, one by one. Models 6–15 test all other possible combinations of experience dimensions. Model 1 achieves good fit (χ2(16) = 437.77, p <.01; CFI =.980; TLI =.880; RMSEA =.050; SRMR =.009) and performs significantly better than any alternative model; each experience dimension partially mediates some design elements. We thus focus on the results of Model 1 with all four experience dimensions in the remainder of our analyses.[ 8]
Graph
Table 3. Study 1 Results: Model Comparison.
| Model | Experience Dimensions Included as Mediators | N | χ2 | d.f. | CFI | TLI | RMSEA | SRMR | AIC | Δ Chi-Square | Δ AIC |
|---|
| Informativeness | Entertainment | Social Presence | Sensory Appeal |
|---|
| 1 | ✓ | ✓ | ✓ | ✓ | 10,470 | 437.770 | 16 | .980 | .880 | .050 | .009 | 513310.390 | – | – |
| 2 | | ✓ | ✓ | ✓ | 10,470 | 574.109 | 17 | .973 | .850 | .056 | .011 | 513444.729 | 136.339 (1)** | 134.34 |
| 3 | ✓ | | ✓ | ✓ | 10,470 | 1593.168 | 17 | .924 | .576 | .094 | .015 | 514463.788 | 1155.399 (1)** | 1153.40 |
| 4 | ✓ | ✓ | | ✓ | 10,470 | 565.179 | 17 | .974 | .853 | .055 | .010 | 513435.799 | 127.41 (1)** | 125.41 |
| 5 | ✓ | ✓ | ✓ | | 10,470 | 469.428 | 17 | .978 | .878 | .050 | .010 | 513340.048 | 31.658 (1)** | 29.66 |
| 6 | ✓ | | | | 10,470 | 2982.424 | 19 | .857 | .287 | .122 | .031 | 515849.044 | 2544.655 (3)** | 2538.65 |
| 7 | | ✓ | | | 10,470 | 912.065 | 19 | .957 | .785 | .067 | .014 | 513778.685 | 474.295 (3)** | 468.29 |
| 8 | | | ✓ | | 10,470 | 2686.159 | 19 | .872 | .358 | .116 | .027 | 515552.779 | 2248.39 (3)** | 2242.39 |
| 9 | | | | ✓ | 10,470 | 2572.495 | 19 | .877 | .386 | .113 | .024 | 515439.115 | 2134.725 (3)** | 2128.73 |
| 10 | ✓ | ✓ | | | 10,470 | 683.774 | 18 | .968 | .831 | .059 | .012 | 513552.394 | 246.004 (2)** | 242.00 |
| 11 | ✓ | | ✓ | | 10,470 | 1850.985 | 18 | .912 | .535 | .099 | .018 | 514719.605 | 1413.216 (2)** | 1409.22 |
| 12 | ✓ | | | ✓ | 10,470 | 2031.622 | 18 | .903 | .489 | .103 | .019 | 514900.242 | 1593.852 (2)** | 1589.85 |
| 13 | | ✓ | ✓ | | 10,470 | 650.085 | 18 | .970 | .840 | .058 | .011 | 513518.705 | 212.316 (2)** | 208.32 |
| 14 | | ✓ | | ✓ | 10,470 | 702.009 | 18 | .967 | .826 | .060 | .011 | 513570.630 | 264.24 (2)** | 260.24 |
| 15 | | | ✓ | ✓ | 10,470 | 2057.166 | 18 | .902 | .482 | .104 | .020 | 514925.786 | 1619.397 (2)** | 1615.40 |
- 50022242918809930 * p <.05.
- 60022242918809930 ** p <.01.
- 70022242918809930 Notes: ✓ indicates an existing path between the experience dimension and purchase intentions. AIC= Akaike information criterion. The Δ χ2 and Δ AIC refer to differences of a specific model relative to Model 1. Results based on a model without moderating effects.
Columns 1–4 in Panel A of Table 4 represent the effects of experience dimensions on purchase intentions. In general, entertainment exhibits the strongest effects (β =.387, p <.01), followed by informativeness (β =.118, p <.01), social presence (β =.118, p <.01), and sensory appeal (β =.060, p <.01).
Graph
Table 4. Study 1 Results: Effects of Design Elements on Experience Dimensions and Purchase Intentions.
| A: Effects of Experience Dimensions on Purchase Intentionsa | Experience Dimensions |
|---|
| (1) | (2) | (3) | (4) |
|---|
| Structural Path | Informativeness | Entertainment | Social Presence | Sensory Appeal |
|---|
| Experience dimension → purchase intentions | .118** | (12.004) | .387** | (35.422) | .118** | (11.154) | .060** | (5.246) |
| B: Effects of Design Elements on Experience Dimensions | Experience Dimensions |
| (5) | (6) | (7) | (8) |
| Structural Path | Informativeness | Entertainment | Social Presence | Sensory Appeal |
| Verbal Elements | | | | | | | | |
| Linguistic style → experience dimension | .035 | (1.830) | .052** | (2.686) | .165** | (8.573) | .069** | (3.583) |
| Descriptive detail → experience dimension | .153** | (7.998) | .064** | (3.298) | .088** | (4.571) | .099** | (5.170) |
| Bulleted features → experience dimension | .181** | (9.443) | .077** | (3.986) | .042* | (2.206) | .099** | (5.131) |
| Return policy information → experience dimension | .031 | (1.627) | –.005 | (–.257) | .006 | (.336) | .009 | (.445) |
| Visual Elements | | | | | | | | |
| Product feature crop → experience dimension | .007 | (.371) | .049* | (2.529) | .042* | (2.205) | .055** | (2.844) |
| Lifestyle picture → experience dimension | .047* | (2.437) | .037 | (1.916) | .144** | (7.514) | .062** | (3.205) |
| Picture size → experience dimension | .152** | (7.946) | .147** | (7.591) | .171** | (8.916) | .190** | (9.906) |
| Product video → experience dimension | .058** | (3.016) | .056** | (2.882) | .089** | (4.633) | .184** | (9.550) |
| Combined Verbal and Visual Elements | | | | | | | | |
| Customer star ratings → experience dimension | .211** | (11.023) | .135** | (6.947) | .162** | (8.442) | .131** | (6.830) |
| Expert endorsement → experience dimension | .023 | (1.223) | .016 | (.823) | .036 | (1.896) | .019 | (.972) |
| Comparison matrix → experience dimension | .168** | (8.782) | .081** | (4.166) | .064** | (3.325) | .104** | (5.416) |
| Recommendation agent → experience dimension | .049* | (2.534) | .019 | (1.001) | –.024 | (−1.249) | .048* | (2.475) |
| Content filter → experience dimension | –.014 | (–.751) | –.011 | (–.588) | –.087** | (−4.532) | –.023 | (−1.183) |
| C: Moderation of Effects of Experience Dimensions on Purchase Intentionsb | Experience Dimensions |
| (9) | (10) | (11) | (12) |
| Structural Path | Informativeness | Entertainment | Social Presence | Sensory Appeal |
| Experience dimension × product type (search/experience) → purchase intentions | .019* | (1.981) | .002 | (.183) | –.023* | (−2.105) | –.022* | (−1.960) |
| Experience dimension × brand trustworthiness → purchase intentions | .022* | (2.211) | –.028** | (−2.598) | .000 | (.042) | .005 | (.417) |
- 80022242918809930 *p <.05.
- 90022242918809930 **p <.01.
- 100022242918809930 a Controlling for direct effects of design elements and consumer demographics.
- 110022242918809930 b Direct effect of product type (search/experience) on purchase intentions: β =.152** (19.385); direct effect of brand trustworthiness on purchase intentions: β =.044** (5.441).
- 120022242918809930 Notes: Columns denote affected experience dimensions; β represents the standardized coefficient; z-values are in parentheses. Model fit: χ2(d.f.) = 1475.63 (106), CFI =.94, RMSEA =.04, SRMR =.02.
Panel B of Table 4 contains the effects of each design element on each experience dimension, while accounting for the effects of all other design elements. Customer star ratings emerge as a strong driver of all experience dimensions (all βs ≥.131, all ps <.01). The same is true for picture size (βs ≥.147, ps <.01). When we control for the impact of all other elements, return policy information and expert endorsement do not contribute significantly to any experience dimension (ps >.05).
Column 5 of Table 4 further indicates that eight design elements exert significant effects on the informativeness dimension. The strongest effects stem from including customer star ratings (β =.211, p <.01), more bulleted features (β =.181, p <.01), a comparison matrix (β =.168, p <.01), more descriptive detail (β =.153, p <.01), and larger pictures (β =.152, p <.01). Including a product video (β =.058, p <.01), a recommendation agent (β =.049, p <.05), and a lifestyle picture (β =.047, p <.05) also drives this dimension, though to a lesser extent.
Column 6 of Table 4 shows that nine design elements substantially influence entertainment. The most important are picture size (β =.147, p <.01) and customer star ratings (β =.135, p <.01), which exert much stronger effects than a comparison matrix (β =.081, p <.01), more bulleted features (β =.077, p <.01), descriptive detail (β =.064, p <.01), or product video (β =.056, p <.01). Using a conversational linguistic style (β =.052, p <.01) and including a product feature crop (β =.049, p <.05) also drive entertainment.
Column 7 of Table 4 shows that ten elements are relevant for social presence. The most important are picture size (β =.171, p <.01), linguistic style (β =.165, p <.01), customer star ratings (β =.162, p <.01), and lifestyle pictures (β =.144, p <.01). Comparably less important are bulleted features and product feature crops (both β =.042, p <.05). The effect strengths of product videos (β =.089, p <.01), descriptive detail (β =.088, p <.01), and a comparison matrix (β =.064, p <.01) lie somewhere in between. Including content filters significantly decreases social presence (β = –.087, p <.01).
Ten elements are also relevant for sensory appeal, as Column 8 of Table 4 shows. The most important are picture size (β =.190, p <.01) and product video (β =.184, p <.01). Linguistic style (β =.069, p <.01), lifestyle pictures (β =.062, p <.01), product feature crops (β =.055, p <.01), and recommendation agents (β =.048, p <.05) exert positive but weaker effects. In between are the effects of customer star ratings (β =.131, p <.01), a comparison matrix (β =.104, p <.01), and more descriptive detail and bulleted features (both β =.099, p <.01).[ 9]
Panel C of Table 4 reports the moderation results of our joint model. For search (experience) products, the informativeness dimension of the experience becomes more (less) important (β =.019, p <.05), consistent with extant research suggesting that consumers extract only minimal direct information from advertisements for experience goods ([53]) and that information is more pertinent for search than experience goods ([22]). To assess experience goods, product attribute information is less useful, perceived purchase risk is often high ([47]), and consumers turn to alternative signals on the web page ([21]). Accordingly, we find that social presence (β = –.023, p <.05) and sensory appeal (β = –.022, p <.05) are less (more) important for search (experience) products. Heightened social presence and greater sensory appeal can reduce perceived performance uncertainty ([16]; [80]), so they are more important for purchase decisions involving experience products. For search products, consumers instead can gather sufficient factual information from the web page, so social presence and sensory appeal become less vital.
In addition, for more (less) trustworthy brands, informativeness is a more (less) important dimension of the online experience (β =.022, p <.05), while entertainment becomes less (more) important (β = –.028, p <.01). This finding aligns well with previous research showing that information and arguments provided by credible sources are more persuasive to consumers ([58]). Thus, the more trustworthy a brand, the more consumers actually engage with the information on its product web pages, and the more they find this information relevant and helpful to their purchase decisions. By contrast, entertainment is more important for brands perceived as less trustworthy. When brand trustworthiness is low and consumers experience more uncertainty ([56]), entertainment has a greater impact on purchase, a finding that aligns with previous research ([ 8]).
Finding that a product's type and brand trustworthiness affect the impact of each experience dimension on consumers' purchase decisions implies that marketers should use design elements strategically to evoke specific types of experiences for different products and brands. To aid this effort, in Figure 2 we present a design guide that illustrates and summarizes when to rely on which type of experience and how to build it through design elements. Although customer star ratings and picture size are relevant for all experience types, we highlight specific design elements that are particularly strong facilitators of distinct experience dimensions. To this end, we provide percentage differences in the effect sizes of each design element on each experience dimension, relative to its effects on all remaining dimensions.
Graph: Figure 2. Design guide for creating effective online customer experience. Notes: Only significant effects (p <.05) are shown; gray bars represent universally effective design elements across all experience dimensions, black bars depict uniquely more effective elements for a specific dimension than for all other dimensions, and white bars indicate the remaining elements.
Informative experiences are dominated by outcome-oriented information and are most effective for search products and brands that are generally well-trusted. Bulleted features exert their strongest effects on this experience type (83% stronger than their effects on any other experience dimension). A comparison matrix can also shape this dimension especially well (62% more effective than for any other dimension), as can more descriptive detail (54% more effective) and recommendation agents (nearly equally effective at driving sensory appeal, but 150% more effective than driving any other dimension).
Entertaining experiences are pleasurable in their own right, apart from any anticipated performance implications. We find that these experiences are especially important for less trustworthy brands. Although most design elements exert some effect on this dimension, no one design element appears uniquely or more suited to shape it than any other dimension.
Social experiences convey a degree of human presence in the encounter. These experiences are especially effective for experience compared with search products. Linguistic style and lifestyle pictures drive this dimension particularly well (respectively, 139% and 134% more effective in shaping it than the other dimensions).
Sensory experiences activate consumers' senses and are especially beneficial for experience products. Product videos exert their strongest effects on this dimension (106% stronger than on any other dimension). Product feature crop is another important element to this dimension (29% stronger effects than on the other dimensions).
Study 1 provides a framework for designing online customer experiences and customizing them to specific product or brand factors. The lab experiments provide strong internal validity across design elements, experience dimensions, and moderators. In Study 2, we also aim to provide a compelling test of external validity. We conduct a field experiment with real products and sales on Amazon.com to test the finding from Study 1 that, for products high in search qualities (search products), an informative experience can increase product sales while a social experience may suppress them.
In this study, we collaborate with one of our partnering firms and manipulate the content on two of its product pages on Amazon.com. Using a difference-in-differences approach, we observe the resulting changes in sales volume compared with a control product page, over a period of two months. To investigate the extent to which search products benefit from a more informative versus a more social experience, we first carefully selected three search products (wireless Internet routers) with similar characteristics and sales trends in the four weeks before the launch of the experimental treatments (prelaunch) from our partner firm's inventory.[10] For the next four weeks (postlaunch), we adapted the web pages of two products as either more informative (Treatment 1) or more social (Treatment 2) and left the third page unchanged (control condition). The difference-in-differences analyses reveal the respective changes in daily sales of the two adapted web pages, compared with the unchanged control page. With this design, we can disentangle the treatment effects of more informative or social page designs from time trends and determine whether changes in sales are attributable to the adjusted page designs or unobserved shifts in consumer preferences.
We took several steps to reduce potential confounding effects. First, to ensure homogeneous customer characteristics across the two experimental periods, all product information on the Amazon search results pages, from which consumers enter the actual product web pages (e.g., product name, hero shot, stockkeeping unit [SKU]), remained constant during the experiment. Second, the price of all products remained constant, and no promotion activity occurred during the experiment. Third, because Amazon publishes seller-submitted product content with varying time lags, we excluded the days around the launch of the treatment content from our analyses ([46]). Fourth, consumers do not visit particular product web pages at random, so we account for self-selection effects in the page views of the treatment pages relative to the control page by supplementing our analyses with controls for observable selection variables.
The experimental design thus employs two treatment conditions and a control condition. Treatment 1 tests the effectiveness of a more informative experience by increasing the descriptive detail on the page, adding additional bulleted features, and adding a comparison matrix. Treatment 2 tests a more social experience, created through a conversational tone and the addition of lifestyle photos, in line with Study 1. The control product web page remained unchanged. To measure the performance of each web page, our partner firm provided access to Amazon Premium Analytics, from which we obtained daily sales and customer star rating data one month before the launch of the treatment pages (prelaunch) and one month after (postlaunch).
In our difference-in-differences approach, we compare the difference in daily product sales on each of the two treatment pages between the pre- and postlaunch period with the corresponding difference in sales for the unchanged control web page:
Pjt= β0+ β1Ij+β2It+β3Ij×It+∊jt,1
where Pjt represents daily sales from web page j at time t and is a random error term, clustered across the two periods. Our design contains two treatment web pages (informative experience and social experience) and a control web page across the two periods (pre- and postlaunch). As a conservative test, we run two separate analyses that compare the informative and social experience with the control condition. In both analyses, Ij is 1 for the treatment (informative or social, respectively) and 0 for the control condition, so that β1 represents the mean difference in sales between these two conditions. Furthermore, It is 1 for the postlaunch period and 0 for the prelaunch period, so that β2 reflects the mean difference in post- relative to prelaunch sales. Finally, β3 is the estimate of the respective treatment effect, or the change in sales due to the informative or social experimental treatment, after we control for systematic differences across conditions and common time trends:
β3= [E(Pjt| j = 1, t = 1) − E(Pjt| j = 1, t = 0)] − [E(Pjt| j = 0, t = 1) − E(Pjt| j = 0, t = 0)].2
In Equation 2, β3 also represents the incremental economic impact of customizing the web page design to create a particular online experience. A key assumption of the difference-in-differences approach is that the time trends in sales are identical in the treatment and control conditions, absent the treatments themselves. If this assumption holds true, we can interpret the deviation of the difference in sales between the treatment and control conditions as causal treatment effects. To verify this parallel trends assumption, we collected data at a third period, two months before the launch of the treatments, and ran a model similar to Equation 1, except that we compared this earlier period with the prelaunch period to determine the trends across the three experimental groups, before the treatments. The interaction between the period and experimental group is nonsignificant (p >.10), confirming the parallel trends and supporting the comparison of the treatment and control conditions.
Because β1 represents a product fixed effect, it eliminates time-invariant, product-specific unobservable variables and reduces the threat of bias ([26]). In addition, although each product may attract slightly different customers, suggesting that a selection bias is possible, we hold the firm-controllable page entry decision criteria (product name, hero shot, SKU, and price) constant throughout the experiment. Thus, customer characteristics across conditions should be time invariant, and we can interpret β1 as a customer fixed effect that reduces this self-selection bias. However, some page entry criteria, such as a product's average star rating or number of reviews ([52]), are outside the firm's control and time variant, so they could introduce some customer differences across experimental conditions that β1 would not capture. To address this potential bias, we add a vector of control variables Xjt to Equation 1, which we use to calculate the daily difference in average customer star rating and number of reviews for each treatment page compared with the control condition:
Pjt= β0+ β1Ij+β2It+β3Ij×It+ δXjt+∊jt.3
Before the launch, sales did not differ between the control condition and the informative product page (Treatment 1), but the social product page (Treatment 2) achieved higher sales (Mcontrol = 3, Minfo = 3, Msocial = 734).[11] After the treatment launch, in support of our findings in Study 1, sales increased for the informative page (Minfo = 152), decreased for the social page (Msocial = 394), and decreased slightly in the control condition (Mcontrol =.1), relative to the counterfactual trend we calculated on the basis of the time trend in the control condition and the sales levels of each experimental condition before the experiment.
To test these effects more formally, we run two separate models, one for Treatment 1 (informative) and one for Treatment 2 (social), in which we account for possible time-variant changes among customers who visit the product pages (Equation 3). In Model 1 (Table 5), the treatment effect of the informative experience is positive and significant (β3 = 151.980, p <.01); increasing web page informativeness improves sales of search products. By contrast, in Model 2, the treatment effect of the social experience is negative and significant (β3 = –337.180, p <.01), confirming the detrimental effects of a social experience for search products.[12] Together, these field results corroborate our insights from Study 1: Search products benefit from more informative experiences, while more social experiences can have detrimental effects on sales of these products.
Graph
Table 5. Study 2 Results: Field Experiment Testing Customized Online Customer Experiences.
| Model 1: | Model 2: |
|---|
| Informative Experience Treatment | Social Experience Treatment |
|---|
| Treatment effect | 151.980** | (34.604) | –337.180** | (73.800) |
| Time dummy | –9.390 | (25.815) | 87.600 | (92.633) |
| Treatment condition dummy | –.367 | (24.183) | 730.730** | (51.567) |
| Average customer star ratings | –1,576.786 | (2,303.567) | –845.080 | (3,482.683) |
| Number of reviews | –9.925 | (18.049) | –28.200 | (22.334) |
| Observations | 122 | | 122 | |
| R2 | .29 | | .69 | |
- 130022242918809930 *p <.05.
- 140022242918809930 **p <.01.
- 150022242918809920 Notes: Standard errors are in parentheses.
In an era in which web design is becoming increasingly important ([81]), sellers' success depends on their ability to employ design elements on product web pages to evoke effective customer experiences that not only convey information but also entertain, imply human interactions, and mimic sensory experiences from the offline world. Through 16 large-scale experiments and a field study, we show how firms can use online design elements to drive purchase behaviors by customizing experiences according to the product or brand being sold. Our findings offer important theoretical contributions to customer experience management (e.g., [27]; [77]) and actionable managerial implications.
Our multidimensional conceptualization of the online customer experience reveals why the effectiveness of any given design element may vary with the offered product or brand. It adds to extant research that examines the direct effect of design elements on purchase decisions without addressing their underlying mechanisms ([14]; [30]). It also moves beyond unidimensional, predominantly information-processing perspectives (see Table 1). Although informativeness is a key dimension by which design elements affect purchase decisions, social presence is just as important, and entertainment is even more so. Accounting for sensory appeal adds further insights. We show that the function of design elements is not limited to the cognitive information they convey, because they also carry affective (entertainment), social (social presence), and sensory (sensory appeal) value that influences purchases. We also show that only a multidimensional perspective can help determine the most effective use of design elements for a given product or brand. Further research should thus account for and test the multiple ways design elements drive purchase.
The multidimensionality of our research also led to the discovery of unexpected relationships that may guide researchers in the online domain toward identifying emerging, substantive trends and relevant constructs. For example, the effects of social presence on purchase are just as strong as those of informativeness, an insight that provides a foundation for examining recent trends such as the inclusion of chat options on websites to enable visitors to interact directly with firms. Firms now use chatbots, based on artificial intelligence, that can conduct conversations via voice or text. An information-processing view might regard chatbots as merely providers of product or transactional information, but our findings suggest that they can also convey social presence. Further research might examine how the linguistic style (a key driver of social presence) of a chatbot should be calibrated to optimize the customer experience.
Moreover, our consolidation of design elements, addressing the many labels used in extant work, and our test of their relative effects reveal which elements have the greatest impact on the customer experience and thus suggest priorities for research. In allowing each design element to freely influence each experience dimension, we were able to identify the core function of each element (information, entertainment, social presence, or sensory appeal). Lifestyle photos, for example, are a key driver of social presence. In our study, they were produced by the seller. Yet companies such as Rent-the-Runway encourage customers to post photos of themselves using the firm's products (clothing) directly on product web pages. Further research could examine the implications of customer- versus firm-produced lifestyle photos. Our framework may also guide research on emerging features that allow customers to try products virtually using webcams (e.g., glasses at FramesDirect.com). These and other forms of in-page product trials warrant further investigation to determine their value for each dimension of the online customer experience.
Our research also provides insights into the role of product type and brand trustworthiness online, by showing how they influence the relevance of each experience dimension for purchase decisions. Search products benefit more from informative experiences but less from social experiences. Highly trustworthy brands benefit from more informative experiences, but less trustworthy brands gain from more entertaining experiences. The finding that brand trustworthiness may increase consumers' willingness to process greater amounts of information demands further examination, especially as research suggests a decline in brand value when other sources of information become more readily available to consumers ([69]).
The product web page is a key tool for managers, who can strategically use design elements to create a customer experience that turns web page visitors into buyers. Our findings apply to both sellers showcasing their offerings through online retailers' websites and the retailers themselves. The production, curation, and publishing of high-quality photos, videos, and copywriting are nontrivial tasks that require significant resources.
We offer a two-step design guide to show how sellers can generate sales through effective online customer experiences. First, sellers must determine the most beneficial experience, based on the search versus experience focus of the product to be sold and the trustworthiness of their brand. The measures we employ can help firms gather this information from current and potential customers. Second, firms should leverage this product and brand knowledge and apply the design guide derived in Study 1 (Figure 2) and validated in Study 2, to select relevant design elements for their product web pages. For experience products, social experiences should be built by employing a conversational linguistic style and lifestyle photos. Sensory experiences are also beneficial and can be built through product videos and product feature crops.
Firms need to consider the customer experience in assessing their existing digital assets. Managers often default to a logic that suggests that if a design element exists in the firm's digital inventory, it should be used on the page (more-is-better approach). Yet we show that certain design elements can induce unfavorable customer experiences for specific products or brands. An essential part of the process is thus to also determine which elements not to use. If the firm does not already own certain design elements, our design guide suggests where it should allocate its resource investments to produce valuable new elements. For example, investing in high-quality imagery can benefit any product or brand, but the most appropriate amounts of textual detail and linguistic style depend on the product type (search vs. experience focus).
Our design guide can also inform contract negotiations between sellers and retailers. Many retailers offer premium content options that require additional financial investments from sellers. Amazon, for example, offers multiple tiered categories (e.g., Basic A+ Content, Premium A+ Content) that provide access to additional design elements or configurations. For some products, these investments grant access to necessary design elements; for other products, investing in premium content might not be necessary or could even be disadvantageous. For example, premium content modules might support larger pictures and more visually stimulating content (e.g., scrolling pictures), but they also restrict the number of characters available to describe product features and benefits. Such designs can induce social or sensory experiences, but they likely are less effective at creating informative experiences. Thus, a lower-cost alternative may be more attractive to a seller that wants to provide mainly informative experiences.
Our design guide is also relevant for retailers. The more conversions sellers generate on a retailer's website, the greater are its earnings. Yet retailers also must provide an infrastructure to support the digital content and guarantee adequate page load and transaction speeds. Helping sellers build effective web pages as efficiently as possible is in the retailer's best interest. With our design guide, retailers can develop tutorials to help sellers improve the effectiveness of their product web pages, as well as recommend available design elements to those sellers, based on the products and brands they market. This approach could improve conversions but also lessen storage demands, by reducing ineffective content. With our design guide and a dedicated customer experience mindset, sellers and retailers can work together strategically to maximize the performance of their product web pages.
Although our research setting and design allowed us to determine the effects of various design elements on dimensions of the online customer experience and purchases, this work is not without limitations. Our results show no effects of return policy information or expert endorsement on any experience dimension, after we account for the impact of the other elements. Additional research might explore these elements further to determine any circumstances in which they prove effective. In addition, no design element exerts a particularly strong effect on the entertainment dimension. Thus, research could analyze other design elements that might prove especially instrumental in shaping this dimension. Although purchase is our final outcome of interest, an extended version of our framework might address how product web page design elements influence consumer decision-making quality, long-term satisfaction, product returns, or social media behavior ([28]; [69]).
Researchers could also investigate how the effects we find translate to mobile environments and whether the same design elements induce similar or different experiences. We focus on design elements most relevant to the product presentation, and thus website elements such as navigation warrant further investigation. Research could also examine the design of landing, overview, or checkout web pages, which we do not consider in our study. Our experimental design is based on a [73] orthogonal array design, which is rare in marketing research. We recommend its application in similar, seemingly intractable research settings to facilitate the simultaneous manipulation of multiple experimental factors, as might be required for advertising or product design studies. We focus on product web pages, but a design perspective could also improve understanding of other domains in which verbal and visual stimuli build customer experiences, such as user manuals or mobile apps. As online shopping environments continue to approach the richness of the offline retail world, research should further investigate the value of design for providing unique experiences, customized to the specific characteristics of the products and brands sold.
Supplemental Material, DS_10.1177_0022242918809930 - Creating Effective Online Customer Experiences
Supplemental Material, DS_10.1177_0022242918809930 for Creating Effective Online Customer Experiences by Alexander Bleier, Colleen M. Harmeling, and Robert W. Palmatier in Journal of Marketing
Graph
Appendix A. Manipulated Constructs, Definitions, Operationalizations, and Manipulation Checks.
| Design Elements | Aliases | Definition | Operationalizations | Means |
|---|
| Level 1 | Level 2 | Level 1 | Level 2 | t-Value | p-Value |
|---|
| Verbal Elements | | | | | | | | |
| Linguistic style | Socio-oriented, concept-oriented, functional content, social content, linguistic style, message personalization | Characteristics of the text, including word choice, elements such as questions, certain pronouns (you, your), and adjectives (Ludwig et al. 2013) | Product descriptions have primarily an unemotional tone. | Product descriptions have primarily an emotional tone. | 2.97 | 3.51 | –16.03 | .00 |
| Descriptive detail | Item-specific information | The degree of elaboration of the product descriptions on the web page (Cooke et al. 2002) | Baseline number of words of product descriptions. | Number of words of product description is 25% more than at Level 1. | 4.66 | 5.24 | –20.01 | .00 |
| Bulleted features | Product claims | Product features that appear in abbreviated list form on the web page (Shu and Carlson 2014) | Web page contains a list of three bulleted key product features. | Web page contains a list of five bulleted key product features. | 3.72 | 6.13 | –5.96 | .00 |
| Return policy information | Transaction facilitation information | Visibility of product return procedures and instructions (Bower and Maxham 2012; Song and Zinkhan 2008) | Web page shows no product return policy information. | Web page shows product return policy information. | 3.13 | 5.27 | –40.20 | .00 |
| Visual Elements | | | | | | | | |
| Product feature crop | Cropped objects | Compared with photos that show the whole product, feature crops zoom in on a certain aspect of the product (Peracchio and Meyers-Levy 1994). | No picture with only a specific part of the product. | At least one picture shows only a specific part of the product. | 3.72 | 4.76 | –18.02 | .00 |
| Lifestyle picture | | A photo of the product in use (Babin and Burns 1997) | No picture shows the product in use. | At least one picture shows the product in use. | 2.34 | 3.22 | –16.21 | .00 |
| Picture size | Static picture | The portion of the page with visual elements (Jiang and Benbasat 2007a; Park, Lennon, and Stoel 2005) | Baseline picture size | Pictures are 25% larger than at Level 1. | 4.03 | 4.74 | –28.91 | .00 |
| Product video | Multimedia presentations, dynamic product presentation | Video of the product in use (Huang, Lurie, and Mitra 2009; Roggeveen et al. 2015; Weathers, Sharma, and Wood 2007) | Web page contains no product video. | Web page contains at least one product video. | 1.83 | 5.06 | –61.75 | .00 |
| Customer star ratings | Online reviews, customer reviews | Aggregated user-generated product ratings posted on the product web page in the form of stars (1 to 5) and number of ratings (Chevalier and Mayzlin 2006; Ludwig et al. 2013; Mudambi and Schuff 2010; Weathers, Sharma, and Wood 2007) | Web page contains no consumer star rating. | Web page contains consumer star rating. | 2.34 | 5.51 | –85.98 | .00 |
| Expert endorsement | Third-party seals, expert evaluation, authoritative third-party recommendations, expert opinion | Product evaluations assembled by distinguished experts in the category (Ansari, Essegaier, and Kohli 2000; Huang, Lurie, and Mitra 2009) | Web page does not contain a seal of a third-party expert certifying the product's quality. | Web page contains a seal of a third-party expert certifying the product's quality. | 3.12 | 4.54 | –27.08 | .00 |
| Comparison matrix | Decision aids, product comparisons, shopping agent | Table organized as an alternatives × attributes matrix that compares the focal product with a small number of alternative products along a set of attributes (Häubl and Trifts 2000) | Web page does not contain a table that allows for easy product comparison. | Web page contains a table that allows for easy product comparison. | 2.64 | 5.46 | –57.40 | .00 |
| Recommendation agent | Next product to buy, cross-selling hyperlinks, shopping agent, electronic agents, recommendation systems, infomediaries, referral services | Tool that provides a screening function by weeding through many alternatives, based on similarities to the focal product (Ansari, Essegaier, and Kohli 2000; Cooke et al. 2002; Häubl and Trifts 2000; Knott, Hayes, and Neslin 2002) | Web page does not include links to related products. | Web page includes links to related products. | 3.73 | 5.72 | –38.44 | .00 |
| Content filter | Collaborative filtering agents, information control, decisional control | Tool that allows the customer to determine what, when, and how much verbal and visual content is presented (Wang et al. 2007; Weathers, Sharma, and Wood 2007) | Consumers cannot control the amount of verbal or visual content shown to them at once. | Consumers can control the amount of verbal or visual content shown to them at once. | 4.32 | 4.94 | –13.38 | .00 |
1 Notes: All means and t-values are calculated using 10,470 observations.
Graph
Appendix B. Description of Firms Participating in Study 1.
| Firm | Annual Sales ($B) | Number of Employees | Number of Products Online | Type of Products | Number of Online Channels | Firm Age | Headquarters | Private/Public |
|---|
| A | $1.6 | 1,725 | 2,000 SKUs | Consumer electronics, home networking | 16 | 32 | U.S. | Private |
| B | $3.2 | 13,300 | 1,000 SKUs | Supplements | 5 | 44 | U.S. | Private |
| C | $12.0 | 13,000 | 2,000 SKUs | Consumer packaged goods, personal care, household | 5 | 129 | U.S. | Private |
| D | $33.1 | 185,965 | 1,000 SKUs | Business electronics, consumer electronics | 30 | 179 | France | Private |
2 Notes: Data provided by Private Company Financial Intelligence (privco.com) and COMPUSTAT.
Graph: Appendix C. Example product web page.
Graph
Appendix D. Constructs and Measures.
| Constructs (Scale Sources) |
| Online Experience Dimensions |
| Informativeness (adapted from Luo 2002) |
| Information obtained from the product page is useful. |
| I learned a lot from using the product page. |
| I think the information obtained from the product page is helpful. |
| Entertainment (adapted from Hausman and Siekpe 2009) |
| Not fun/fun |
| Not enjoyable/enjoyable |
| Not at all entertaining/very entertaining |
| Social presence (Gefen and Straub 2003) |
| There is a sense of human contact in the web page. |
| There is a sense of human warmth in the web page. |
| There is a sense of human sensitivity in the web page. |
| Sensory Appeal (Jiang and Benbasat 2007b) |
| The product presentation on this web page is lively. |
| I can acquire product information on this web page from different sensory channels. |
| This web page contains product information exciting to senses. |
| Performance Outcome |
| Purchase intentions (Ajzen and Fishbein 1980) |
| My purchasing this product is... |
| Very unlikely/very likely |
| Very improbable/very probable |
| Very uncertain/very certain |
| Moderators |
| Product Type (Search/Experience) (adapted from Weathers, Sharma, and Wood 2007) |
| I can adequately evaluate this product using only information provided by the web page about the product's attributes and features. (Search focus) |
| I can evaluate the quality of this product simply by reading information about the product. (Search focus) |
| It is important for me to touch this product to evaluate how it will perform. (Experience focus) |
| It is important for me to test this product to evaluate how it will perform. (Experience focus) |
| Brand Trustworthiness (adapted from Schlosser, White, and Lloyd 2006) |
| [Brand] seems to have much knowledge about what needs to be done to fulfill online transactions. |
| I feel very confident about [Brand]'s online skills. |
| [Brand] appears to be well qualified in the area of e-commerce. |
| [Brand] appears to try hard to be fair in dealing with others. |
| I like [Brand]'s values. |
| Sound principles seem to guide [Brand]'s behavior. |
Footnotes 1 Editorial TeamVikas Mittal served as coeditor, and Venkatesh Shankar served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors received an MSI Research Grant for this project.
4 Online supplement: https://dx.doi.org/10.1177/0022242918809930
5 1We designed this study to align with the context of http://Amazon.com, the largest online retailer; most online retailers follow a similar approach. We disguised the brand to protect the confidentiality of the participating firm.
6 2To assess the unique effects of this element, we included no actual written customer reviews on the page, used 4.5/5 stars for all manipulations, and held the number of reviews constant across conditions.
7 3Though not part of our conceptual framework, in an exploratory analysis we also tested for the moderating effects of product type and brand trustworthiness on the relationships between each design element and experience dimension. Consistent with our conceptualization, only 11 of the 104 potential moderating effects were significant, confirming the nomological validity of our model (see Web Appendix B).
8 4Web Appendix C contains the results of the univariate effects for each of the 16 experiments.
9 5Web Appendix D presents the indirect effects of design elements on purchase intentions through each experience dimension.
6To select the most appropriate products for this test, we audited the firm's current product categories to identify those with at least three similar search products with sufficient daily sales. From this set, we then selected three wireless Internet routers as prototypical search products.
7We transformed all values by a constant, in accordance with our nondisclosure agreement.
8As a robustness check, we tested a single model in which we dummy-coded each treatment condition versus the control condition. The substantive results remained unchanged.
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Record: 46- Creating Enduring Customer Value. By: Kumar, V.; Reinartz, Werner. Journal of Marketing. Nov2016, Vol. 80 Issue 6, p36-68. 40p. 7 Diagrams, 10 Charts. DOI: 10.1509/jm.15.0414.
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Record: 47- Crisis Management Strategies and the Long-Term Effects of Product Recalls on Firm Value. By: Liu, Yan; Shankar, Venkatesh; Yun, Wonjoo. Journal of Marketing. Sep2017, Vol. 81 Issue 5, p30-48. 19p. 1 Diagram, 8 Charts. DOI: 10.1509/jm.15.0535.
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Crisis Management Strategies and the Long-Term Effects of Product Recalls on Firm Value
Companies increasingly face product harm crises resulting in product recalls, which often have a negative impact on firm value. Whereas prior research has studied the short-term effects of product recalls on firm value, the authors of this article focus on the long-term effects. They develop a conceptual framework and hypotheses about the main effect of recall volume and the moderating effects of crisis management strategies on the relationship between recall volume and long-term firm value. They empirically test the hypotheses in the auto industry context using both short-term abnormal returns analysis and long-term calendar-time portfolio analysis of 280 product recalls during 2005–2015. The findings reveal that the negative impact of product recall volume lingers over time. Brand (promotion) advertising has a significant positive (negative) effect on the relationship between recall volume and long-term abnormal returns. Furthermore, both voluntary recall initiation and postrecall remedial efforts positively moderate the impact of recall volume on long-term returns. These moderating effects are contrary to those in the short term. The results suggest that managers should use different advertising types during and after a recall, strategically initiate recalls, and diligently prepare postrecall remedies to mitigate the negative effects of recall volume on long-term return.
Online Supplement: http://dx.doi.org/10.1509/jm.15.0535
Companies increasingly face product harm crises that result in recalls of related products. Such recalls are frequent in industries such as automobiles, toys, pharmaceuticals, and food. For instance, according to the National Highway Traffic Safety Administration (NHTSA), the auto industry experienced an average of 122 recalls per firm between 1997 and 2010.
The volume of recalled units affects investor response to a recall announcement. For example, in 2010, Toyota’s market capitalization declined by 8.8% on the day it announced the recall of two million vehicles due to unintended acceleration, sticky braking, and poor vehicle handling (MarketWatch 2010).
Recall volume affects both short- and long-term firm value by affecting short- and long-term revenues and costs. In the short run, sales revenues of the volume of affected of products decline. The short-term costs relate to investigation, notification, repairs, and replacement of defective products (Bromiley and Marcus 1989). Investors typically anticipate such revenue loss and costs and their effects on the recalling firm’s cash flow in the short run. Thus, these effects are reflected in the short-term returns to product recall announcements.
However, recall volume also affects potential long-term revenues through damage to intangible assets, such as customer equity, brand equity, corporate reputation (Rhee and Haunschild 2006), and marketing effectiveness (Liu and Shankar 2015; Van Heerde, Helsen, and Dekimpe 2007). Recalls also entail long-term costs, which include unpredictable fines from regulatory authorities, future liability claims, and other unexpected marketing costs (Bromiley and Marcus 1989; Govindaraj, Jaggi, and Lin 2004). Thus, recall volume can have a long-term impact on cash flows, which may be difficult for investors to ascertain at the time of announcement.
Over time after a recall announcement, the recalling firm and the regulatory authority disseminate to investors valuerelevant information ranging from costs of the recall to the firm’s actions to alleviate the adverse financial impact (Govindaraj, Jaggi, and Lin 2004), helping investors update their beliefs about future cash flows and resulting in a change to the long-term firm value (Brav and Heaton 2002; Brennan and Xia 2001). For instance, over a period of at least 18 months after the announcement in August 2000 of a recall of the Ford Explorer SUV due to defective Firestone tires, Ford Motors, Bridgestone, and NHTSA shared several pieces of value-relevant information about the recall with the investors, including details on the lawsuit settlement, government fines, updated numbers of deaths and injuries, and a CEO change (for a chronology of these updates, see the Web Appendix). Ford’s market capitalization had decreased by 27.9% one year after August 2000 (Reuters 2002). Table 1 summarizes the differences between the short- and long-term effects of recalls on revenues, costs, and investor responses.
To mitigate these negative short- and long-term effects of recall volume on firm value, firms have at their disposal three key crisis management strategies: advertising, recall initiation, and postrecall remedy strategies, which correspond to the three critical components of crisis management: (1) communicate to the stakeholders, (2) be responsive, and (3) repair damage, respectively (Seeger et al. 1998; Tang 2008). These elements are consistent with the communication, policy planning, and product development and logistics functions delineated by Smith, Thomas, and Quelch (1996) in their proposed strategic product recall management approach. These strategies offer additional information to consumers and investors about the firm’s belief in the recalled brand, its commitment to fix the problem, and its efforts to rectify the defect. This information and thereby these strategies moderate the effects of recall volume on firm value.
Firms can use different advertising types, such as brand (e.g., Toyota) and promotional (e.g., zero-percent finance) advertising, to inform consumers and investors and communicate their faith in the recalled brand. By understanding the effects of these different advertising types on the relationship between recall volume and short- and long-term returns to recall, firms can choose the best advertising type to use during and after recall.
Firms can also voluntarily initiate a recall or issue a recall upon an order from a regulatory authority. Over the long term, a voluntary recall might signal the firm’s commitment to fix the problem, but in the short term, it can also acknowledge blame. Through a better understanding of how recall initiation strategy moderates the long-term effects of recall volume on firm value, firms can make the most appropriate recall initiation decision.
To rectify the defect(s) in the product recalled, firms engage in postrecall remedial efforts. This process occurs after recall and does not affect short-term returns. However, consumers and investors evaluate the firm’s recovery efforts over several months after the recall. By knowing how these efforts affect the relationship between recall volume and firm value in the long run, firms can better allocate their resources to postrecall remedy.
Although the short-term effects of such crises or recalls on firm value have been researched (e.g., Chen, Ganesan, and Liu 2009; Gao et al. 2015; Thirumalai and Sinha 2011), the long-term effects of such recalls on firm value are not well understood. Furthermore, the findings from extant literature may not adequately inform firms on using crisis management strategies to improve long-term shareholder value.
A key challenge in analyzing the effects of recalls on longterm returns in industries characterized by frequent events is to control for cross-correlations across the events over a long period. We surmount this challenge and fill a key research gap by addressing important research questions: (1) How does recall volume impact firm value in the long term after a recall? (2) How do crisis management strategies mitigate the potential negative effects of recall volume on long-term abnormal returns? (3) How do the moderating effects of crisis management strategies on the relationships between recall volume and long-term returns differ from those of the short term?
TABLE: TABLE 1 Product Recall: Short-Term Versus Long-Term Effects
| | Short Term (ST) | Long Term (LT) |
|---|
| Costs of recall | Opportunity cost of sales loss due to defective product pulled off the market | • Costs of recalling and replacing/fixing the defected product • Regulatory fines • Consumer liability claim • Opportunity cost of LT sales loss due to damaged brand equity |
| Information available | • Recall characteristics • Recall volume • Recall severity • Firm’s ST response • Recall initiation strategy • ST advertising | • Recall characteristics • Actual LT costs • Effectiveness of the recall process • Firm’s LT response • Recall remedy effort (to ensure easy repair and replacement) • LT advertising (to restore brand equity) |
| Investor response | • Immediate reaction based on ST information • May overreact or underreact in the ST | • Update beliefs on future cash flows with new information • Underreaction or overreaction corrected in the LT |
The answers to these questions are important from both theoretical and practitioner viewpoints. From a theoretical standpoint, it is key for researchers to understand why crisis management strategies affect the relationship between recall volume and long-term firm value. From a practitioner perspective, managers require guidance on strategies to minimize the negative impact of recalls on long-term abnormal returns. For example, they could benefit from knowing the pros and cons of voluntarily initiating product recalls. Moreover, they need to choose the advertising type to use during and after a recall. Finally, managers should know how worthwhile post-recall remedial efforts are in the long run.
We develop hypotheses about the main effects of recall volume and the moderating effects of crisis management strategies; formulate models of short- and long-term abnormal stock returns; and test the hypotheses using the auto industry as the context with data on 280 product recalls during 2005–2015. Our results reveal important new insights. Recall volume’s negative impact on firm value lingers over the long term. Brand (promotion) advertising has a significant positive (negative) effect on the relationship between recall volume and long-term abnormal returns. Furthermore, when a firm voluntarily initiates a product recall, recall volume has a less negative effect on long-term returns than on short-term returns. Finally, a diligent post-recall remedy positively moderates the impact of recall volume on long-term returns. Our results suggest that managers should use different advertising types during and after a recall, strategically initiate recalls, and diligently execute post-recall remedy.
Our research differs from and contributes to related research (e.g., Borah and Tellis 2016; Chen, Ganesan, and Liu 2009; Cleeren, Van Heerde, and Dekimpe 2013; Eilert et al. 2017; Gao et al. 2015; Liu and Shankar 2015; Xiong and Bharadwaj 2013; Yun 2014) in important ways (see Table 2). First, our research offers robust insights on long-term returns to product recall. Second, it makes valuable theoretical and managerial contributions about the moderating effects of key crisis management strategies. Third, it cogently explains how and why the long-term effects of product recalls differ from the short-term effects. Fourth, it differentiates the effects of brand advertising and promotional advertising on abnormal returns to recalls, providing valuable theoretical and managerial implications. Finally, our research is the first to study the effects of post-recall remedy on long-term firm value, offering useful insights.
Conceptual Development and Hypotheses
Because the size of the recall has a key bearing on the financial outcome of a recall, we focus on the recall volume–abnormal returns link. The premise of “complete and immediate investor response” underlies the use of short-term abnormal returns as an appropriate measure for the effect of a firm’s announcement on its value. In the long term, investors update their beliefs about the firm’s future cash flows on the basis of snowballing of short-term effect and additional value-relevant information provided by the firm, regulatory authority, and related entities (Barberis, Schliefer, and Vishny 1998; Brown, Harlow, and Tinic 1988).
In developing our hypotheses, consistent with prior research (Xiong and Bharadwaj 2013), we examine two types of effects: the cash flow effect and the investor behavior effect. The cash flow effect refers to the focal variable’s effect on the cash flows of the firm, while the investor behavior effect refers to the variable’s direct effect on investor attention and response, over and above the cash flow effect (Field and Lowry 2009).
Main Effect of Recall Volume on Firm Value
Recall volume has a direct and main effect on firm value in both the short and the long term. Its impact on short-term abnormal returns is straightforward. It negatively affects consumer preference for the brand and thus the future sales and cash flow (Liu and Shankar 2015). We focus on its impact on long-term abnormal returns through both the cash flow effect and the investor behavior effect.
TABLE: TABLE 2 Our Study Relative to Selected Related Studies in Product Recall
| | | | | Crisis Management Strategy |
|---|
| Study | Short-Term Value | Long-Term Value | Short-Term vs. Long-Term Comparison | Advertising | Brand vs. Promotional Advertising | Recall Initiation | Postrecall Remedy |
|---|
| Cleeren, Van Heerde, and Dekimpe (2013) | | | | ✓ | | | |
| Liu and Shankar (2015) | | | | ✓ | | ✓ | |
| Borah and Tellis (2016) | ✓ | | | ✓ | | ✓ | |
| Jarrell and Peltzman (1985) | ✓ | | | | | | |
| Hoffer, Pruitt, and Reilly (1988) | ✓ | | | | | | |
| Davidson and Worrell (1992) | ✓ | | | | | | |
| Thomsen and McKenzie (2001) | ✓ | | | | | | |
| Chen, Ganesan, and Liu (2009) | ✓ | | | | | | |
| Thirumalai and Sinha (2011) | ✓ | | | | | | |
| Xiong and Bharadwaj (2013) | ✓ | | | ✓ | | | |
| Gao et al. (2015) | ✓ | | | Prerecall advertising | | | |
| Eilert et al. (2017) | ✓ | | | | | ✓ | |
| This study | ✓ | ✓ | ✓ | ✓ | | ✓ | ✓ |
Recall volume directly affects unexpected cash flows in the long run. Fixing the product defect for a large number of units (vs. a small number) typically takes longer, and the costs last over a longer period. Therefore, a recall with greater volume is more likely to involve unexpected costs, such as increased lawsuits and regulatory fines (Govindaraj, Jaggi, and Lin 2004). Moreover, the larger the recall, the more affected owners may spread negative word of mouth both online and offline, resulting in further loss in sales over the long term (Borah and Tellis 2016). Therefore, a recall with a high volume is more likely to result in long-term cash flow loss, which is difficult for investors to forecast at the time of recall announcement.
Recall volume also has a negative effect on post-recall investor behavior. Prior research in finance suggests that bad news may influence investors’ trading behavior, and the stock may display a negative drift over the long term (e.g., Barberis, Schliefer, and Vishny 1998; Chan 2003). Investors respond slowly to news. In the case of negative news, since shorting stocks is more expensive than buying stocks, investors may delay trading in the long term (e.g., Chan 2003). Moreover, a recall involving a large number of units sends a strong adverse signal to investors about the recalled brand’s equity and the recalling firm’s health. Over the long term, the negative signal compounds investors’ pessimism about the firm’s brand value and prospects. As a result, investors’ negative reactions to an announcement persist over the long term with high recall volume. For example, Hasbro’s February 2007 recall of one million units of Easy-Bake Oven, an electric toy for children, had significant long-term negative effects on its market capitalization, which started recovering only in 2008 (O’Brien 2014). These arguments lead to our first hypothesis:
H1: Product recall volume has a negative relationship with longterm abnormal returns to a product recall announcement.
The recalling firm’s crisis management strategies might moderate the relationships between recall volume and abnormal returns in both the short and long term.1 A large stream of research in finance has shown that investors learn about a firm’s anticipated financial performance from its actions (e.g., Daniel et al. 1997), including crisis management actions. In the context of product recall, investors may evaluate the impact of the recall volume on the basis of the firm’s crisis management strategies to reduce financial loss (Chen, Ganesan, and Liu 2009).
Prior research suggests that the responsiveness and recovery (or remedy), representing the recalling firm’s actions during and after a product recall, respectively, are crucial to the outcomes of the recall management process (Etayankara and Bapuji 2009; Tang 2008). During a product recall, a firm can show its responsiveness by quickly acknowledging problems and adopting a voluntary recall initiation strategy (Laufer and Coombs 2006; Siomkos and Kurzbard 1994). After a recall announcement, the recalling firm needs to recover in its business by providing remedy to affected customers. During this recall remedy process, the defective products are fixed, returned, or exchanged (Kramer, Coto, and Weidner 2005; Siomkos and Kurzbard 1994). A firm’s efforts at remedy can increase the effectiveness of a recall and restore the firm’s reputation (Berman 1999). Both during and after a recall, the recalling firm communicates its responsiveness and efforts in recovery through advertising (Reynolds and Seeger 2005; Seeger, Sellnow, and Ulmer 1998). Thus, we focus on the three moderator variables: advertising, recall initiation strategy, and recall remedy. These variables represent firm actions, connecting them causally to outcomes and providing a theoretical basis, consistent with Sutton and Staw (1995).
Because firms spend most of their marketing budget on brand and promotional advertising, we focus on the moderating effects of these advertising types. Brand advertising refers to advertising that highlights the brand; it is also sometimes referred to as “image advertising.” Promotional advertising communicates information about the brand’s promotional offers or customer incentives. Our conceptual model, capturing the main effect of recall volume and moderating effects of crisis management strategies, appears in Figure 1.
Moderating Effect of Brand Advertising on Recall Volume–Firm Value Link
Firms often use brand advertising to alleviate the damage caused by a recall (Cleeren, Dekimpe, and Helsen 2008; Cleeren, Van Heerde, and Dekimpe 2013; Van Heerde, Helsen, and Dekimpe 2007). In the short term, high levels of brand advertising spending may amplify the recall volume’s negative effect on the firm’s short-term value through both the cash flow effect and the investor behavior effect. During a recall, greater exposure to the affected brand may attract unwanted attention from consumers and lead to greater negative salience of the recalling firm (Sparkman and Locander 1980), less responsiveness to brand advertising (Liu and Shankar 2015; Van Heerde, Helsen, and Dekimpe 2007), and lower expected future cash flows (Xiong and Bharadwaj 2013). Moreover, increased brand advertising during recall may also heighten investors’ unwanted attention to the recall, lower their confidence in the firm, and enhance their likelihood of selling the firm’s stock, leading to further negative impact on its stock price (Xiong and Bharadwaj 2013).
In contrast, the effect of brand advertising on the link between recall volume and long-term firm value may be positive. We first discuss the long-term cash flow effect of brand advertising. When the affected firm places a steady emphasis on brand advertising over a long period post-recall, it displays its serious commitment to enhance brand equity. Post-recall, brand advertising’s positive effect on consumer attitudes increases (Rubel, Naik, and Srinivasan 2011). Thus, spending more on post-recall brand advertising will likely help firms arrest the dilution of brand reputation, regain consumer trust in the brand, and decelerate revenue declines after a large recall (Bruce, Peters, and Naik 2012). It can also lift stock returns by raising customer lifetime value and cash flows (Kumar and Shah 2011). When consumers are uncertain about a brand or when there is incomplete brand information, advertising tends to be more persuasive when it helps consumers learn (Assmus, Farley, and Lehmann 1984). Thus, increased brand advertising post-recall can enhance brand attitude, making customers more loyal and mitigating the negative effect of recall volume on long-term cash flows (Xiong and Bharadwaj 2013).2
We next discuss investors’ direct response in the long term to brand advertising, over and above the cash flow effect, through spillover and signaling. Investors may favor stocks with strong brand names and high brand quality (Frieder and Subrahmanyam 2005). In the long term, increased brand advertising could buttress brand equity, potentially spilling over to the demand for stocks of the recalled firm (Joshi and Hanssens 2010). Moreover, sustained brand advertising after the recall can signal the firm’s confidence in its financial well-being and future earning potential, influencing investor behavior (Joshi and Hanssens 2010). As the recalling firm spends more on brand advertising, investors are less likely to expect a prolonged negative impact of recall volume on the firm’s cash flow and are more likely to hold the firm’s stocks. Therefore, we hypothesize the following:
H2: The negative relationship between product recall volume and long-term abnormal returns to a product recall announcement is weaker when firms spend more on brand advertising post-recall.
Moderating Effect of Promotional Advertising on Recall Volume–Firm Value Link
Unlike brand advertising, promotional advertising informs consumers about offers such as price reductions, rebates, and financing plans. Promotional advertising may influence the effect of recall volume on long-term returns differently than that on short-term returns.
In the short term, promotional advertising disseminates information on price promotion activities, temporarily raising customer value by lowering the net price paid (Blattberg and Neslin 1990). This increase in value arrests declines in preference and sales revenues vulnerable to the recall, mitigating the negative effect of recall volume on short-term cash flow (Pauwels et al. 2004). Therefore, investors will anticipate that the short-term abnormal returns to a recall will decline less when firms spend more on promotional advertising.
Over time, however, repetitive promotions may increase consumer price sensitivity and diminish brand value, resulting in lower anticipated future cash flow (Chen, Ganesan, and Liu 2009; Dodson, Tybout, and Sternthal 1978). Consumers may use price promotion as a quality cue and associate it with inferior quality of the promoting brand (Raghubir and Corfman 1999). Because recalls of defective products also degrade consumers’ quality perception (Rhee and Haunschild 2006), repeated promotions will only serve to dilute quality perception. As a result, investors will anticipate that brand value will decline by a greater amount, further damaging the customer base and potential future revenues (Xiong and Bharadwaj 2013). Therefore, investors will anticipate that customer preferences and average price will decline more, long-term marketing costs will rise, and cash flows will fall due to higher promotional advertising for recalls, that is, a cash flow effect. Thus, we hypothesize the following:
H3: The negative relationship between product recall volume and long-term abnormal returns to a product recall announcement is stronger when firms spend more on promotional advertising post-recall.
Moderating Effect of Recall Initiation Strategy on Recall Volume–Firm Value Link
Firms can choose either to voluntarily initiate a product recall or to wait for the regulatory authority to mandate a recall. Some firms may choose to recall a product early rather than wait for the regulatory body to mandate it (Eilert et al. 2017). In the short term, investors may perceive a voluntary recall as the firm’s admission of guilt about its product defects and expect lower future cash flows (Chen, Ganesan, and Liu 2009). As a result, voluntary recall initiation will aggravate the negative relationship between recall volume and short-term returns.
Unlike in the short run, in the long run, a proactive initiation strategy might soften the negative impact of a large recall through both cash flow effect and investor behavior effect. Voluntary recall initiation may decrease the likelihood and extent of regulatory fines and potential long-term costs associated with product recalls, improving cash flows (Govindaraj, Jaggi, and Lin 2004). Moreover, consumers may treat the recall as an exception to the brand’s normal behavior, softening the negative effect of recall on cash flows (Lei, Dawar, and Gu¨rhan-Canli 2012), leading to a cash flow effect.
Investors may interpret a proactive recall initiation as a commitment by the firm to improve the value of the affected products. Over time, investors will interpret this commitment as a strong signal of the firm’s ability to bounce back from crisis. Thus, investors will view the firm’s actions favorably and adjust their assessment of the negative effect of the recall announcement on its long-term abnormal return, yielding an investor behavior effect. Considering both the cash flow and investor behavior effects, we argue that a proactive recall initiation strategy will lead to a less negative effect of recall volume on long-term shareholder value. These arguments lead to our next hypothesis:
H4: The negative relationship between product volume and long-term abnormal returns to a product recall announcement is weaker for firms recalling voluntarily than for firms recalling mandatorily.
Moderating Effect of Postrecall Remedy on Recall Volume–Firm Value Link
Firms need to manage the postrecall remedial process, which can affect firm value only in the long run. A firm’s postrecall remedy refers to the firm’s efforts in addressing the crisis by appropriately mobilizing its resources and rectifying the defects (Shrivastava and Siomkos 1989). For instance, in industries such as automobiles, electronics, toys, durables, and medical devices, once a firm makes a recall announcement, it must follow a multistep process that includes informing affected product owners, developing remedial procedures, distributing repair parts and kits to dealers, training its dealers to repair the affected products, and monitoring the repairs to customers’ satisfaction.
Postrecall remedy can moderate the effect of recall volume on long-term returns to a recall, like service recovery (e.g., Maxham and Netemeyer 2002). In general, strong remedial efforts for the recall procedures can reduce customer and investor uncertainty associated with the quality of repairs and proper completion of the recall process. It evokes greater trust in the firm’s thoroughness to overcome the adverse effects of a recall and improve product quality and customer value, as in the case of recovery of a failed service (Maxham and Netemeyer 2002). Therefore, we expect a firm’s postrecall remedial efforts to weaken the negative effects of recall volume on the value of intangible assets, such as perceived product quality and firm reputation. The strengthened intangible assets will help retain more consumers and stem revenue losses. Furthermore, a thorough remedy and implementation process may reduce any future product liability costs. As a result, any cash flow losses anticipated over the long term can be significantly mitigated, reducing the likelihood of investor devaluation of the firm. These arguments lead to the following hypothesis:
H5: The negative relationship between recall volume and longterm abnormal returns to a product recall announcement is weaker when firms expend more efforts on postrecall remedy.
Empirical Context and Data
We test the hypotheses in the context of the U.S. automobile industry. Using data from this industry is advantageous for several reasons. First, it obviates the need for including a wide array of cross-industry factors to control for potential heterogeneity present in multi-industry studies. Second, data on strategic variables such as postrecall remedy are available because firms in this industry are required to report the recall remedy completion rate (percentage of defective products fixed) on a quarterly basis. Third, because industry factors are common for all the firms within the industry, the internal validity of the results is strong.
The external validity is also strong because recalls in many other industries have similarities to those in the automotive industry. First, firms in the food, toy, and pharmaceutical industries also experience multiple recalls over time (Dawar and Pillutla 2000). Second, as in the auto industry, the consequences of product failure in many industries can be severe and even life threatening. Third, recalls in many industries, including the auto industry, are regulated by government agencies. Fourth, postrecall remedy is common in industries such as automobiles, electronics, toys, consumer durables, and medical devices. Thus, crisis management strategies, such as advertising, voluntary initiation, and postrecall remedy, are similar in many industries.
TABLE: TABLE 3 Variables, Operationalizations, and Data Sources
| Variable | Reference | Operationalization | Data Source |
|---|
| Dependent Variable |
| Financial returns | Chen, Ganesan, and Liu (2009); Thirumalai and Sinha (2011) | Short-term abnormal returns (-2, 2), five days around product recall announcement date | Center for Research in Security Prices |
| Sorescu, Shankar, and Kushwaha (2007) | Long-term calendar-time portfoliolevel returns (after announcement) | Center for Research in Security Prices |
| Carhart (1997); Fama and French (1993) | Fama and French’s (1993) and Carhart’s (1997) momentum factors | Ken French’s websitea |
| Focal Independent Variables |
| Product recall volume | Chen, Ganesan, and Liu (2009); Thirumalai and Sinha (2011) | The number of units recalled normalized by sales in the previous year | NHTSA, Automotive News |
| Brand advertising | Srinivasan et al. (2009) | The residual from an autoregressive model of brand ad spending | Kantar Media |
| Promotional advertising | Rajiv, Dutta, and Dhar (2002) | The residual from an autoregressive model of promotional ad spending | Kantar Media |
| Recall initiation strategy | Chen, Ganesan, and Liu (2009) | INITi - d INITi, where INITi is the voluntary initiation dummy and d INITi is the estimated probability of voluntary initiation from a discrete autoregressive model of recall initiation strategy | NHTSA, LexisNexis |
| Postrecall remedy completion rate | This study | The residual from an autoregressive model of recall remedy completion rate | Safercar.gov |
| Control Variables |
| Car model ad | Liu and Shankar (2015) | The residual from an autoregressive model of car model ad spending | Kantar Media |
| Social media volume | Luo, Zhang, and Duan (2013) | Number of blog posts about the product recall event | LexisNexis |
| Conventional media volum | Liu and Shankar (2015) | Number of news articles about the product recall event | LexisNexis |
| Recall frequency | Liu and Shankar (2015) | Number of recalls in the past six months | NHTSA, LexisNexis |
| Product reliability | Kalaignanam et al. (2013); Liu and Shankar (2015) | Unit sales–weighted average of brand reliability ratings + unit sales–weighted average of reliability ratings of the recalled models | Consumer Reports |
| Labor intensity | Thirumalai and Sinha (2011) | Number of employees/Sales revenues | Compustat |
| R&D intensity | Thirumalai and Sinha (2011) | R&D expenditures/Sales revenues | Compustat |
| Sales | Thirumalai and Sinha (2011) | Log of unit sales | Automotive News |
| Dealer size | This study | Log of the number of franchises in the United States | Automotive News |
| Product scope | Thirumalai and Sinha (2011) | npilnp=pi, where pi is the number of products within brand I and p is the firm’s total number of products | Ward’s Automotive Yearbook |
| Financial leverage | Thirumalai and Sinha (2011) | Debt-to-equity ratio: Long-term debt/Shareholder equity | Compustat |
| Market-to-book ratio | Thirumalai and Sinha (2011) | (Total number of shares outstanding Quarter-end)/Common equity | Compustat |
| Year trend | Chen, Ganesan, and Liu (2009) | The number of years between 2005 and the year when the recall occurred | Compustat |
The data for our empirical analysis come from several major sources: NHTSA for product recall attributes data; LexisNexis and Factiva databases for recall announcement, social media, and conventional media data; Center for Research in Security Prices; Compustat for firm performance and firm attributes; Automotive News market data books for vehicle sales and dealer size; Ward’s Automotive Yearbook for auto characteristics; Kantar Media for weekly advertising spending; and Consumer Reports for product reliability. A summary of the data sources of key variables appears in Table 3.
In the first step of data collection, we collected product recall data from January 2005 to June 2015 from the NHTSA database for all U.S. automobile manufacturers listed on the New York Stock Exchange during that period. The NHTSA recall database is the official data source that provides information about product defects in the automobile industry (Rhee and Haunschild 2006).3
For this study, it is critical to accurately identify the date when the product recall was first announced to the public, providing us a clean estimation window for event studies (MacKinlay 1997; McWilliams and Siegel 1997). Although the NHTSA provides information on the date of owner notification, the actual announcement date released to the public could be much earlier (e.g., Chen, Ganesan, and Liu 2009; Davidson and Worrell 1992). Following prior work (Sood and Tellis 2009), we searched all news sources in LexisNexis and Factiva databases for the earliest date when information about the recall became publicly available. We consider this date the announcement date.
In the automobile industry, the coverage of product recalls by a press release may be incomplete (Barber and Darrough 1996). All recalls documented by NHTSA may not be reported as news releases (Rupp and Taylor 2002). Therefore, we collected data on all recalls from multiple sources. We obtained a usable sample of 280 product recall announcements made by all the publicly listed auto firms between 2005 and 2015.
Variables, Measures, and Models
Dependent Variables: Abnormal Stock Returns
Our dependent variables are abnormal stock returns in the short- and long-term windows. To minimize potential confounding effects, consistent with prior research, we examine short-term abnormal returns over a relatively short period surrounding the event (Brown and Warner 1985; Srinivasan and Bharadwaj 2004). Based on the Patell test (Patell 1976), we use a typical five-business-day window centered on the recall announcement (-2, 2).4 The long-term abnormal return is measured monthly starting the month after the short-term window. The long-term window typically extends for a year (e.g., Sorescu, Shankar, and Kushwaha 2007).5
Focal Independent Variables
Recall volume. We operationalize product recall volume as the number of defective vehicles recalled. To control for scale effects, we normalize a firm’s recall volume by its unit sales in the previous year (Kalaignanam, Kushwaha, and Eilert 2013).
Advertising. We operationalize short-term advertising as the spending during the week of the recall announcement, consistent with the five-day window for computing short-term abnormal return. We operationalize long-term advertising as each month’s advertising spending over a one-year period after recall. Brand advertising refers to spending on the brand theme, while promotional advertising refers to spending on promotional offers such as annual percentage rate financing, manufacturer rebates, and extended warranties. Following prior research that considers only unexpected changes in advertising to which investors respond (e.g., Joshi and Hanssens 2010; Kim and McAlister 2011; Tirunillai and Tellis 2012), we operationalize unexpected advertising as the residual from an autoregressive model of advertising, consistent with Jacobson and Mizik (2009). Investors are rational, and only surprises can change their expectations of future cash flow and, thus, stock prices (Daniel et al. 1997; Jacobson and Mizik 2009). As a result, only when a firm’s advertising spending increases faster or slower than investors expected given the firm’s past advertising spending will the firm’s value be affected (Kim and McAlister 2011).
Recall initiation strategy. The recall initiation strategy variable is a binary variable denoting whether the firm voluntarily or involuntarily initiates the recall. Consistent with advertising, we use unexpected level of recall initiation probability operationalized as the observed recall initiation strategy (1 = voluntary recall) minus the expected probability of voluntary recall computed from a binary autoregressive model of recall initiation (Wang and Li 2011). Investors form expectations of the affected firm’s recall initiation strategy on the basis of the firm’s past recall initiation strategies. If investors anticipate that the firm will follow an initiation strategy (voluntary or mandated) consistent with their expectations and if the recalling firm does not deviate from investor expectation, there will be no abnormal stock returns. However, if the observed recall initiation strategy differs from investors’ expected probability of the firm’s recall initiation, the firm will experience abnormal returns.
Postrecall remedy. The goal of a recall is to fix the defective products. The more effort a firm puts into postrecall remedy, the more likely consumers are to respond to the recall and return the defective product to dealers. Consistent with prior research (e.g., Hoffer, Pruitt, and Reilly 1994), we use owner response rate, or remedy completion rate (percentage of defective products fixed), as a direct measure of the firm’s postrecall remedial efforts at fixing the defective product. We collected this data from www.safercar.org.6 As in the cases of advertising and recall initiation, we use unexpected postrecall remedy obtained from the residual of an autoregressive model. As with advertising and recall initiation, investors form expectations of the recalling firm’s remedial efforts to a recall on the basis of the firm’s prior remedial efforts. The firm will likely experience abnormal returns commensurate with the gap between the observed and expected remedial efforts.
Control Variables
Following prior research (Chen, Ganesan, and Liu 2009; Thirumalai and Sinha 2011), we include control variables: other advertising types, their interactions with recall volume, social media volume, conventional media volume, recall frequency, product reliability, labor intensity, R&D intensity, sales, product scope, dealer size, leverage, market-to-book ratio, and year trend.
Car model advertising refers to advertising spent on specific car models (e.g., advertising spending on Toyota Camry). Following prior research, we use the number of blogs on product recalls from 33 popular blog sites as the measure of social media volume (Rosario et al. 2016: Li, Lai, and Chen 2011; Luo, Zhang, and Duan 2013; Stephen and Galak 2012; Tirunillai and Tellis 2012). Investors may find conventional media volume, the number of articles about the recall in print media (Liu and Shankar 2015), more trustworthy than firmgenerated information (Jolly and Mowen 1985). Investor response to a recall may be shaped by recall frequency (Wynne and Hoffer 1976), the number of recalls experienced by the firm in the past six years (Liu and Shankar 2015). Product reliability, the sales-weighted average of both the brand- and model-level product reliability ratings measured on a five-point scale (Rhee and Haunschild 2006), may impact the abnormal returns to recall.
We expect labor intensity, measured as number of employees/sales revenue, to negatively affect the short-term returns to a recall because it reflects the cost to train or retain employees (Thirumalai and Sinha 2011). Innovation ability, measured by R&D intensity, or R&D spending/sales revenue, might influence investors’ assessment of a firm’s ability to overcome a failure in quality. The level of sales in the year prior to the recall signals to investors the firm’s capabilities to absorb the negative impact of a crisis, fix the faulty product(s), and recapture share (Kalaignanam, Kushwaha, and Eilert 2013).
The recalling firm’s distribution intensity, measured by dealer size, or the number of dealers in the firm’s distribution network, may influence short-term abnormal returns to a recall. Firms with a broader or deeper product scope, measured by the breadth and depth of products in the model, may be able to better withstand the negative impact of a recall
(Thirumalai and Sinha 2011). We anticipate financial leverage, measured by debt-to-equity ratio, to negatively affect short-term returns to a recall because the shareholder’s burden during the crisis is lower for firms with higher leverage (Thirumalai and Sinha 2011). We include the market-to-book ratio to capture the firm’s growth prospects, which can impact the short-term returns to a recall (Thirumalai and Sinha 2011). Finally, we include a time variable (year trend) to capture the potential impact of trend (Chen, Ganesan, and Liu 2009).
The operationalization of key variables in the data appears in Table 3. The summary statistics and the correlation matrix of short- and long-term variables appear in Table 4, Panels 4A and B, respectively. The average recall involves about 418,580 cars. The average firm initiates 71% of its recalls. On average, firms spend more on brand advertising than on promotional advertising. The average postrecall remedy completion rate is 68.55% after one year. None of the correlations among the short-term independent variables is high, with the variance inflation factors smaller than 4. Therefore, multicollinearity is not an issue.
Short-Term Effects Analysis
To analyze the short-term effects of recalls on firm value, we adopt the widely used event study methodology (e.g., Agrawal and Kamakura 1995; Chen, Ganesan, and Liu 2009) as follows: where CARi is cumulative abnormal returns for event i; RVi is recall volume; ADi is a vector of three types of unexpected advertising, including brand advertising, promotional advertising, and car model advertising; INITi is the level of unexpected voluntary recall initiation probability defined earlier; SMi is social media volume; Xi is a vector of other control variables; mi is a vector of endogeneity correction terms (i.e., estimated error terms from the first stage regressions) from the control function (CF) approach we use; b and L are parameters to be estimated; and ei is an error term (see the Web Appendix).
We estimate the model by controlling for the endogeneity of advertising spending, recall initiation strategy, and social media volume. To control for intercept and slope endogeneity of the advertising types that occur because econometrically unobserved factors affect advertising and manufacturers have private information on market response to advertising during a recall, we use a CF approach (Liu and Shankar 2015; Petrin and Train 2010).7
TABLE: TABLE 4 Summary Statistics and Correlation Matrices of Short- and Long-Term Variables
| Variable | M | SD | Min | Max | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 |
|---|
| *p < .10. |
| **p < .05. |
| ***p < .01. |
| 1. Cumulative abnormal returns (-2, 2) (%) | -.0069 | .0350 | -.1338 | .1035 | 1 | | | | | | | | | | | | | | | | |
| 2. Recall volume (thousands) | 418.58 | 814.63 | .07 | 6,281.04 | -.02 | 1 | | | | | | | | | | | | | | | |
| 3. ST brand advertising ($millions) | 8.80 | .81 | 0 | 74.56 | -.08 | .03 | 1 | | | | | | | | | | | | | | |
| 4. ST promotional advertising ($millions) | 2.32 | .29 | 0 | 32.27 | .05 | .09* | .56*** | 1 | | | | | | | | | | | | | |
| 5. Recall initiation strategy | .71 | .45 | 0 | 1 | -.06 | -.21*** | .18*** | -.10 | 1 | | | | | | | | | | | | |
| 6. ST car model advertising ($millions) | 19.11 | .821 | 0 | 59.34 | -.07 | -.06 | .36*** | .39*** | -.10* | 1 | | | | | | | | | | | |
| 7. ST social media volume | 3.46 | 4.21 | 0 | 28 | -.06 | .15*** | .13** | -.02 | -.10* | -.02 | 1 | | | | | | | | | | |
| 8. ST conventional media volume | 5.36 | 4.83 | 0 | 32 | .04 | .03 | -.06 | -.08 | -.06 | .04 | -.01 | 1 | | | | | | | | | |
| 9. Recall frequency | 6.26 | 2.25 | 1 | 15 | .05 | -.06 | -.11 | -.12** | -.11** | .04 | .04 | -.01 | 1 | | | | | | | | |
| 10. Product reliability | 3.28 | .89 | 2.20 | 4.91 | .14** | .05 | -.16*** | .07 | -.16*** | -.05 | .05 | -.09* | .03 | 1 | | | | | | | |
| 11. Labor intensity | .02 | .02 | .01 | .09 | .11* | -.04 | .05 | -.09* | .05 | .08 | -.12** | .01 | -.09* | -.11* | 1 | | | | | | |
| 12. R&D intensity | .09 | .07 | .01 | .47 | .07 | -.02 | .03 | -.01 | .03 | .07 | -.05 | 0 | -.07 | -.06 | .01 | 1 | | | | | |
| 13. Sales ($millions) | 9.13 | .85 | .03 | 25.69 | -.06 | .14** | .25*** | -.13** | -.05 | .07 | .04 | .14** | .10* | .01 | -.19*** | -.18*** | 1 | | | | |
| 14. Dealer size (thousands) | 4.38 | 3.75 | 1.01 | 13.97 | -.09* | -.05 | .03 | -.11** | -.02 | .07 | -.12** | .11* | .02 | -.04 | .22*** | .12*** | .21*** | 1 | | | |
| 15. Product scope | 12.70 | 11.27 | 2.51 | 47.62 | -.09* | .00 | -.12** | -.12** | .25*** | .06 | .10* | .05 | .18*** | -.04 | -.11* | -.01 | .01 | .36*** | 1 | | |
| 16. Financial leverage | -.03 | .10 | -.25 | .19 | .10* | -.04 | .11 | -.17*** | .03 | .09* | -.10* | .02 | .01 | -.13** | 0 | -.18*** | .09* | .22*** | .11** | 1 | |
| 17. Market-to-book ratio | 1.20 | 7.23 | -94.37 | 47.62 | -.06 | -.02 | .03 | -.07 | -.15*** | .09* | -.07 | -.03 | -.04 | -.17*** | .11* | .13** | -.03 | .09* | .04 | -.07 | 1 |
| 18. Year trend | 6.63 | 3.37 | 1 | 11 | -.10* | .12** | -.15*** | .09* | .03 | .01 | .11* | .03 | -.04 | -.05 | -.16*** | .04 | -.04 | -.03 | -.06 | .01 | .07 |
| Variable | M | SD | Min | Max | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|
| 1. Monthly return (%) | .33 | 2.62 | -12.51 | 14.06 | 1 | | | | | | | | |
| 2. Recall volume (thousands) | 418.58 | 814.63 | .07 | 6281.04 | -.07** | 1 | | | | | | | |
| 3. LT brand advertising ($millions monthly) | 30.12 | 2.99 | 0 | 197.58 | .09** | .07** | 1 | | | | | | |
| 4. LT promotional advertising ($millions monthly) | 15.59 | 2.93 | 0 | 266.80 | -.03 | .11*** | .41*** | 1 | | | | | |
| 5. Recall initiation strategy | .71 | .45 | 0 | 1 | .03 | .03 | -.10*** | .02 | 1 | | | | |
| 6. Postrecall remedy completion rate (First Quarter) | 31.19% | .01 | 0 | 99.90% | .07** | -.22*** | -.07** | .01 | .08** | 1 | | | |
| 7. Postrecall remedy completion rate (Second Quarter) | 51.92% | .01 | .06% | 99.91% | .05* | -.21*** | -.05* | .00 | .16*** | .81*** | 1 | | |
| 8. Postrecall remedy completion rate (Third Quarter) | 62.88% | .02 | .19% | 99.91% | .01 | -.24*** | -.07** | -.03 | .21*** | .75*** | .91*** | 1 | |
| 9. Postrecall remedy completion rate (Fourth Quarter) | 68.55% | .02 | .51% | 99.93% | .04 | -.23*** | -.02 | -.05* | .21*** | .67*** | .85*** | .96*** | 1 |
Following prior advertising research (e.g., Liu and Shankar 2015), we use advertising costs across media (cable TV, network TV, magazines, and newspapers) and own and competitor’s product attributes as instruments for advertising. Following Chen, Ganesan, and Liu (2009), we use sell time (i.e., the number of months the recalled products were in the market before the recall) and the manufacturer suggested retail price (MSRP) of the recalled car as instruments for recall initiation strategy. Investors do not usually respond to expected information such as sell time. Moreover, the longer the product is on the market, the higher the costs associated with a recall, reducing the likelihood of the firm initiating a recall of the product. Because MSRP is determined on an annual basis, it is reasonable to assume that such a long-term measure is uncorrelated with short-term abnormal return. However, MSRP is correlated with the recall initiation strategy, making it a valid instrument because firms might be more reluctant to initiate a recall when fixing the product defect is more expensive. To control for the endogeneity of social media volume, we follow a widely used approach whereby lagged endogenous variables serve as instruments (e.g., Ataman, Van Heerde, and Mela 2010). The R2 of the first-stage regression ranges from .37 to .78, underscoring the validity of these instruments.
In addition, we performed a Staiger and Stock (1997) instrument quality test using the first-stage F-statistic value. The results show that these values (F-test results ofModel 2 in Table A1 in the Web Appendix) are all significant and greater than 10, a threshold for strong instruments and two-stage least squares (2SLS) regression to be reliable (Stock, Wright, and Yogo 2002, p. 522). Moreover, we compared the first-stage model with and without instruments and find the comparison F-tests significant (Table A1 in theWeb Appendix), suggesting that including the instrumental variables significantly improve the model fit of the first-stage regression. We also performed the Stock and Yogo (2005) test with multiple endogenous variables. The result again rejects the null hypothesis that the instruments are weak (see Table A1 in the Web Appendix).
Long-Term Effects Analysis
Two methods are commonly used for analyzing long-term abnormal returns: buy-and-hold abnormal returns (BHAR) and calendar-time portfolio abnormal returns (CTAR). When there are considerable cross-correlations among abnormal returns of multiple events in a long period, that is, when the long-term abnormal returns for subsets of the sample firms overlap in a calendar year, making statistical inferences on the event portfolio’s BHAR can be difficult. In particular, major corporate actions are not random events and are clustered through time by industry. For example, in the auto recall context, manufacturers suffer from recurring recall events rather than experiencing a one-time event, such as an initial public offering. Therefore, ignoring the cross-correlation problem may lead to a serious misspecification of the model (for details, see Kothari and Warner 2007).
The most conservative approach to overcoming crosscorrelation is the CTAR approach (Fama 1998; Sorescu, Shankar, and Kushwaha 2007). It is particularly appropriate for calculating long-term abnormal returns to events that are clustered in time, automatically accounting for dependency among the events (Mitchell and Stafford 2000). We use the CTAR approach to test the proposed moderating effects on long-term firm value. It starts from portfolio formation and categorization of recall events into various portfolios according to whether a key variable is above or below its median value in that period. For example, the two portfolios based on recall volume are (1) recall events with volumes greater than the medium value and (2) recall events with volumes less than the medium value. After creating the calendartime portfolio, we compute the abnormal returns using the four-factor model, which controls for risk and momentum (Carhart 1997) as follows: Rpt - Rft = ap + bp ðRmt - RftÞ + gpSMBt + jpHMLt + dpUMDt + ept, (2) where Rpt is the rate of return of the calendar time portfolio p during month t; Rft is the risk-free rate, or the one-month treasury bill yield; Rmt is the average rate return on the Center for Research in Security Prices equal-weighted index;SMBt is the return on a portfolio of small stocks minus the return on a portfolio of large stocks; HMLt is the return on a portfolio of high book-to-market stocks minus the return on a portfolio of low book-to-market stocks, UMDt is the return on a portfolio of high prior return stocks minus the return on a portfolio of low prior return stocks, and ept is the residual. The intercept (ap) reflects the average monthly abnormal returns of the portfolio. When the estimated intercept is zero, the portfolio’s post event stock performance is “normal,” so there is no adjustment to the stock price in the long term. However, when the intercept is positive (negative), there exists overreaction (underreaction) to the negative event in the short-run and this mispricing is corrected in the long-term when additional information is introduced postevent. Moreover, the intercept (ap) in the long-term analysis is computed from intertemporal variation of portfolio returns rather than from cross-sectional variance as in short-term analysis. As a result, it helps to account for cross-sectional correlation of returns, a major advantage of the CTAR over the BHAR method (Sorescu, Shankar, and Kushwaha 2007).
We estimate Equation 2 using the weighted least squares method that corrects for heteroskedasticity induced by changes in the number of firms in each month. To test the interaction effects of crisis management strategies and recall volume, we classify events into subgroups. For example, to test H2, we form four subgroups, events with high/low, high/high, low/low, and low/high recall volume/brand advertising. We obtain separate regression intercepts for each subgroup using Equation 2. We then test differences among the subgroup intercepts pairwise (for more details, see Sorescu, Shankar, and Kushwaha 2007 for more details). Note that CTAR analysis differs from the regression model in that the effect of each factor is estimated through separate analyses.
Results
Main Effects of Product Recall Volume on Shareholder Returns
Table 5 presents mean value of the short-term cumulative abnormal return. On average, the short-term abnormal returns to product recall events are negative and significant (p < .01).
Table 6 shows the results of the long-term calendar-time abnormal returns for the 12- month holding period using the four-factor model. The calendar-time long-term abnormal returns for the entire sample are significantly positive, suggesting that the negative short-term abnormal returns to product recall announcements do not present a complete picture and the negative impact of product recall on firm value is reversed after the recall announcement.
TABLE: TABLE 5 Model Results for Cross-Sectional Short-Term Abnormal Returns
| Regression Results | OLS | Instrumental Variable Regression (2SLS) | Control Function |
|---|
| *p < .10. |
| **p < .05. |
| ***p < .01. |
| Focal Variables |
| Intercept | -.1803*** | -.1748** | -.1834*** |
| Recall volume | -.0002* | -.0002* | -.0002* |
| Brand advertising | -.0511** | -.04416** | -.0461** |
| Promotional advertising | -.2147 | .1036 | .1116 |
| Recall initiation strategy | -.0001 | -.0001 | -.0001 |
| Interaction Variables |
| Recall volume · Brand advertising | -.0027** | -.0031** | -.0030** |
| Recall volume · Promotional advertising | .0051** | .0071** | .0064** |
| Recall volume · Recall initiation | -.0014 | -.0012 | -.0012 |
| Control Variables |
| Car model advertising | -.0102 | -.0132 | -.0167 |
| Social media | -.0017 | -.0026 | -.0064 |
| Conventional media | -.0018 | .0019 | .0016 |
| Recall frequency | .0003 | .0006 | .0006 |
| Product reliability | .0022 | .0012 | .00013 |
| Labor intensity | .2355 | .1879 | .1708 |
| R&D intensity | -.0096 | -.0066 | -.0054 |
| Sales revenue | .0184*** | .0187** | .0196** |
| Product scope | .0002 | .0002 | .0002 |
| Dealer size | -.0210 | -.0106 | -.0046 |
| Financial leverage | .0518* | .0484* | .0456* |
| Market-to-book ratio | -.0001 | -.0001 | -.0002 |
| Year trend | .0009 | .0008 | .0012 |
| Recall volume · Car model advertising | -.0808* | -.0943 | -.0838 |
| Error-Correction Variablesa |
| Intercept: Brand advertising | | | .0003* |
| Intercept: Promotional advertising | | | -.0011** |
| Intercept: Recall initiation | | | .0026 |
| Intercept: Car model advertising | | | .0092* |
| Intercept: Social media | | | -.0017* |
| Slope: Brand advertising on effectiveness of promotional advertising | | | -.0066** |
| Slope: Car model advertising on effectiveness of brand advertising | | | .0078* |
| R2 | .1302 | .1317 | .1360 |
Table 5 presents the results of the short-term abnormal return models with three approaches: ordinary least squares (OLS), 2SLS, and CF. While the results are consistent across the three methods, we focus on the results using CF approach because it has the best model fit according to R2. The results show that recall volume has a significant (p < .10) main effect on short-term abnormal return. However, our focus is on the long term. Recall volume has a significant negative main effect on long-term abnormal returns (p < .10), in support of H1 (Table 6). To better understand the total impact of recall volume on firm value, we examine the coefficients of the moderating effects of the crisis management strategy variables.
Moderating Effects of Crisis Management Strategies on Recall Volume–Returns Link
We first discuss the moderating effects of brand advertising on recall volume’s impact on firm value in the short and the long term. The results of the short-term analysis (Table 5) show that the coefficient of the brand advertising–recall volume interaction term is negative and significant (p < .05), consistent with expectation. Increased brand advertising makes the recalled brand more salient around the time of recall announcement, aggravating the negative effect of recall volume on the firm’s short-term returns. In contrast, the interaction of brand advertising and product recall volume has a significant and positive effect on long-term abnormal returns (p
TABLE: TABLE 6 Results from Long-Term Analysis of Abnormal Returns to Recall
| Effect | Result |
|---|
| *p < .10. |
| **p < .05. |
| ***p < .01. |
| Main Effects |
| Entire sample | .0041*** |
| Recall volume (H1) | -.0009* |
| Brand advertising | .0019 |
| Promotional advertising | -.0002 |
| Recall initiation | .0003* |
| Postrecall remedy | .0002* |
| Moderating Effects |
| Recall volume · Brand advertising (H2) | .0002** |
| Recall volume · Promotional advertising (H3) | -.0007*** |
| Recall volume · Recall initiation (H4) | .0011** |
| Recall volume · Postrecall remedy (H5) | .0016** |
We now examine the results relating to H3. As expected, the results of the short-term analysis show that the coefficient of the interaction of promotional advertising and recall volume is positive and significant (p < .05). In contrast, Table 6 shows negative and significant interaction effects between promotional advertising and recall volume on the long-term abnormal returns (p < .01), consistent with H3. Thus, promotional advertising, which facilitates the dissemination of offers, temporarily increases customer value, retaining customers and alleviating the negative impact of recall volume on firm value in the short term. However, postrecall, a firm’s high investment in promotional advertising diminishes brand value, intensifying the negative impact of recall volume on long-term firm value.
We now test H4. The recall initiation–recall volume interaction coefficient in the short-term analysis is not significant (p > .10). In contrast, the long-term abnormal returns for recall initiation are positive and significant (p < .05), in support of H4. These results indicate that investors respond to unexpected voluntary recall initiation in the long term. They value the firm’s social responsiveness and commitment to fix the problem, which alleviates the negative effect of recall volume on long-term firm value.
Consider H5. Table 6 shows that the interaction between recall volume and postrecall remedy has a significant and positive effect in the long term (p < .05), in support of H5. This result suggests that when firms put more efforts into addressing the crisis after the announcement, investors become less uncertain about the quality of the firm’s repair efforts and the completion of the recall process. Such trust in the firm positively moderates the negative relationship between recall volume and long-term firm value. We present a summary of the expected signs and the results of the key hypothesis tests in Table 7. All the hypotheses are supported.
Other Results
We now discuss the remaining effects from Tables 5 and 6. In the short term, brand advertising has a significant and negative main effect (p < .05), whereas the coefficients of all other crisis management strategies are insignificant (p > .10). However, in the long term, both voluntary recall initiation and postrecall remedy have positive main effects on firm value (p < .10), suggesting that a recalling firm could benefit over the long term when voluntarily initiating a recall and diligently providing remedy to the consumers. Among the control variables in the short-term analysis, only sales revenue and financial leverage have a positive and significant (p < .10) effect. These results suggest that firms with lower sales revenue or higher equity-todebt ratio stand to lose more, consistent with prior research (Thirumalai and Sinha 2011).
Additional Analysis on the Interaction of Social Media and Crisis Management Strategies
We tested for both short- and long-term effects of social media volume and its interactions with crisis management strategies on firm value. The results appear in Table 8. The short-term effects are all insignificant (p > .10). In the announcement window, social media volume may not be substantial and may not add much to the already negative news of the recall. However, social media volume has a negative main effect and a positive interaction effect with brand advertising in the long term (p < .10). Over time, social media chatter gathers a strong negative momentum, dampening expected future cash flows. Brand advertising mitigates this negative effect by continuing to show the firm’s commitment and by arresting the spread of negative sentiments among investors.
Robustness Checks
Mediating effect of recall initiation. To check whether recall initialization could mediate the effect of recall volume on abnormal returns, we performed a mediation test. We first estimated a probit model of recall initiation on recall volume and other covariates. The results suggest that while recall volume has a significant impact on recall initiation (p < .01), the link between recall initiation and short-term abnormal returns is not significant (p > .10) with or without controlling for recall volume. Thus, recall initiation does not mediate the relationship between recall volume and short-term abnormal returns (Table A3 in the Web Appendix). A possible reason for mediation being insignificant in the short term is that recall volume announcement and recall initiation occur on the same day, making it hard to temporally separate them. Exploration of a possible long-term mediating effect is not feasible because we analyze long-term returns using the CTAR model, for which a regression-type mediation analysis is not relevant.
TABLE: TABLE 7 Summary of Hypotheses and Results
| | Short-Term Returns | Long-Term Returns |
|---|
| Factor/Variable | Expectation | Result | Hypothesis | Result |
|---|
| Recall volume | - | - | H1 | - | - |
| Recall volume · Brand advertising | - | - | H2 | + | + |
| Recall volume · Promotional advertising | + | + | H3 | - | - |
| Recall volume · Recall initiation strategy | - | N.S. | H4 | + | + |
| Recall volume · Postrecall remedy | N.A. | | H5 | + | + |
Differential effects across country of origin and product segments. Because country of origin and product segment may be related to quality perceptions, we tested whether the moderating effects of crisis strategies on the recall volume– firm value relationships differ across these groups. The results show that the effects are not significantly different across U.S. and non-U.S. brands or across the luxury and popular car segments (p > .10).
Serial correlation of recall events. Because each firm may have multiple recalls in the sample, some recalls may be related to others (Borah and Tellis 2016), and there might be serial correlation among the events. We reestimated the model by allowing the error term in the short-term analysis to be autocorrelated (Wooldridge 2010). The results show no significant autocorrelation among the events (p > .10).
Alternative measure of postremedial efforts. To ensure that our results are robust to the operationalization of postremedial efforts, we estimated our models using an alternative measure, namely, the time elapsed between the recall announcement date and the date the remedy was first available to consumers (i.e., the date of customer notification). The reasoning is that a longer time lag corresponds to greater remedial efforts and more products repaired. The results of the models for this alternative measure are substantively similar, underscoring the strength of recall completion rate as the measure of postremedial efforts.
Implications
Theoretical Implications
Our study makes important theoretical contributions to research on product-harm crises. First, we extend the product recall literature by examining the long-term effects of recalls on firm value. We extend prior research on the determinants of the impact of a product-harm crisis on short-term abnormal returns (Chen, Ganesan, and Liu 2009; Gao et al. 2015; Thirumalai and Sinha 2011) by identifying the key moderating effects of crisis management strategies on both shortand long-term firm value. Our results reveal novel long-term moderating effects of recall volume on firm value, contrasting with the short-term effects.
TABLE: TABLE 8 Short-Term and Long-Term Effects of Social Media
| | Short-Term | Long-Term |
|---|
| Social media volume | -.0091 | -.0005* |
| Social media volume · Brand advertising | -.0006 | .0001* |
| Social media volume · Promotional advertising | .0002 | -.0003 |
| Social media volume · Recall initiation | -.0015 | .0003 |
| Social media volume · Postrecall remedy | N.A. | -.0025 |
Second, our study extends the literature on advertising and shareholder value (e.g., Joshi and Hanssens 2010; Kim and McAlister 2011; McAlister, Srinivasan, and Kim 2007; Xiong and Bharadwaj 2013) by proposing a moderating framework for advertising’s role in the effects of recall volume on longterm returns. It also extends prior research on product recall and brand equity (Dawar and Pillutla 2000), recall and reputation (Rhee and Haunschild 2006), prerecall advertising (Gao et al. 2015), and advertising’s effect on sales or market share postrecall (e.g., Cleeren, Dekimpe, and Helsen 2008; Cleeren, Van Heerde, and Dekimpe 2013; Liu and Shankar 2015; Rubel, Naik, and Srinivasan 2011; Van Heerde, Helsen, and Dekimpe 2007) by focusing on the moderating role of advertising in the recall volume–shareholder value link.
Although conventional wisdom suggests that advertising after a product recall should lead to positive shareholder returns, our findings imply different conclusions about different advertising types in the short and long term. In particular, brand advertising ameliorates the negative effect on long-term returns, reversing an exacerbation of recall volume’s effect on short-term returns. Importantly, although promotional advertising mitigates the negative effect of recall volume on firm value in the short run, if it is sustained over the long term, it exacerbates recall volume’s adverse effect on firm value. These findings show that valuable insights are lost if all advertising types are pooled under a single “advertising” banner as in previous studies. The asymmetric results for different advertising types call for a deeper investigation into the mechanisms through which advertising types affect shareholder value.
Our work also adds to the discussion on recall initiation (Rupp and Taylor 2002) by revealing a positive effect of unexpected voluntary recall initiation on the recall volume– shareholder value link in the long term. Although recall initiation has no significant impact on the link between recall volume and abnormal returns in the short run, in the long run, it might weaken the negative relationship, thanks to investors’ acceptance of the firm’s commitment and reduced risk of regulatory fines. Our result is consistent with Eilert et al. (2017), who show that the stock market punishes recall delays. Thus, voluntary recall initiation has a favorable impact on the investors in the long run.
Our study extends knowledge of product-harm crises and long-term firm value by studying the underresearched role of postrecall remedy efforts. Greater postrecall remedy reduces the detrimental effect of recall volume on long-term returns. It provides greater comfort to investors about improved product quality and lower long-term liability costs, dampening the negative effect of recall volume on long-term returns.
Managerial Implications
The results have important managerial implications. With the growing number of product recalls in recent years, managers need clear guidelines for product-harm crisis management strategies. The results provide a more complete substantive picture than prior studies. Although the empirical results are based on one industry, the principles apply to multiple industries.
First, investors may overreact negatively to product recalls at the time of announcement. Such negative reactions may be corrected over the long term if the recalling firm provides new information about the recall. By signaling to the investors their responsiveness and their efforts to recover from the crisis over the long term, recalling firms can effectively overcome investors’ initial negative reactions.
Second, because recall volume has a negative impact on firm value even in the long run, firms should limit the damage by starting the recall with smaller recall volumes when they discover the problem. Firms can follow up with additional recalls if they identify additional affected units. Such a practice will mitigate the negative impact on long-term abnormal returns. For example, by periodically announcing separate recalls for gas pedal problems for different affected vehicles, Toyota was able to control the damage to its stock valuation over a long period.
Third, managers should invest in brand advertising over the long term to create a strong buffer against negative incidents. For example, Toyota substantially increased recalled model and brand advertising for a sustained period after the crisis to refurbish its tarnished image (Nielsen 2010). Such a practice is consistent with the normative model result of Rubel, Naik, and Srinivasan (2011). However, at the time of crisis, managers should avoid allocating dollars to advertising the brand. This guideline applies to all brands across categories. For example, McNeil Consumer Healthcare, a division of Johnson & Johnson, reduced Tylenol brand advertising during the recall of over-thecounter Tylenol products in January 2010 (Edwards 2010).
Fourth, managers should be wary of promotional advertising during a product-harm crisis. They should use promotional advertising only to arrest a steep decline in short-term returns, especially for high-volume recalls. For example, Toyota offered incentives such as no-interest financing and low lease rates during its massive recall in 2010 (Bunkley 2010). However, to prevent long-term attrition of brand equity and firm value due to high recall volume, managers should stop promotional advertising once a sales decline is under control.
Fifth, managers choosing a voluntary recall initiation strategy can arrest a decline in shareholder value to recall volume in the long run. Our results reveal significantly positive moderating effects on recall volume’s impact on shareholder value from voluntary product recalls in the long run but not in the short run. For example, Chrysler was reluctant to voluntarily initiate a recall regarding potential engine fires in Jeep vehicles (Healy 2013). It might have not anticipated the potential benefit of a voluntary initiation in the long run. Our results suggest that Chrysler’s recall initiation strategy will likely backfire in the long term. Our results apply to other industries as well. For example, in 1994, when a few customers alerted Intel to a computing flaw in its Pentium microprocessor, Intel decided not to voluntarily recall the defective units. There was no immediate adverse effect on its revenues or market value (which there might have been, had Intel voluntarily recalled its products). However, over the long run, Intel lost orders from large clients such as IBM, resulting in a cash flow loss of $500 million (Smith, Thomas, and Quelch 1996). Intel had to publicly acknowledge the problem before it could begin the path to the long-term recovery of its business.
Sixth, product managers should focus on postrecall remedy to fix defects. After the recall announcement, they should expend efforts to successfully rectify the defect and complete product repairs. Such thorough efforts confer benefits to shareholders in the long run. Again, this result applies to multiple industries. For example, Dell did a good job of recalling and fixing 4.1 million batteries that were found to pose a fire hazard (Baseline 2006). As a result, Dell’s stock price recovered in the long run. Similarly, in 1995, when a user of Intuit’s tax management software reported bugs, Intuit rectified the defect and mailed the corrected software to its vast customer base of 1.65 million affected users, bouncing back to business as usual (Smith, Thomas, and Quelch 1996).
Seventh, the relative effects of the three crisis management strategies on long-term abnormal returns help managers dial up or down on their crisis management strategies by highlighting the relative degrees of emphasis to be placed on each strategy. Because the CTAR approach requires these strategies to be measured as high or low, we can directly compare their effects on long-term firm value. The results suggest that recall remedy is the most effective strategy in mitigating the negative effect of recall volume, followed by recall initiation and brand advertising strategies, in that order. Recall remedy (recall initiation) is about 8 (5.5) times more effective than brand advertising. However, both recall remedy and brand advertising are much more expensive to implement than recall initiation. Therefore, to ameliorate the effects of recall volume on long-term abnormal returns, a recalling firm should first voluntarily announce initiation. It should next spend its resources fixing the defects and then focus on brand advertising. Interestingly, promotional advertising exacerbates the negative effect of recall volume on long-term firm value by about 3.5 times, compared with the extent to which brand advertising softens the negative effect. This result highlights the danger of over-promoting the affected products despite the positive effect of promotional advertising on the recall volume–abnormal returns link in the short term.
Finally, managers of recalled products should be wary of the creeping effect of social media over time and use brand advertising to counter it. In the short run, social media volume does not cause more damage than the news of the recall has already caused. However, left unchecked, negative chatter can accumulate and dent long-term firm value. By investing in brand advertising, managers of recalling firms can control and limit the damage to their brands and firm value.
Taken together, the results offer key pointers for resource allocation to maximize shareholder value. At the time of a product recall, firms should move their dollars from brand advertising to promotional advertising. However, after the recall, firms should shift their allocation toward brand advertising and postrecall remedy efforts. While advertising the recalled brands, firms may also sharpen their messaging and create positive brand affect to negate the potential adverse effect of social media volume on firm value.
Limitations, Further Research, and Conclusions
Limitations and Further Research
Our research is not without limitations. First, we apply our framework to the U.S. automobile industry, a very important industry with around $70 billion gross output in 2015.8 While data on recalls can be obtained for other consumer industries from such sources as the Consumer Product Safety Commission, data on moderator variables such as postrecall remedy are not readily available to researchers. To enhance the generalizability of results, future research could extend the analysis to other industries in which data on moderator variables may be available.
Second, the crisis management strategies that moderate recall volume’s effects are similar in other industries, such as toys, electronics, and other consumer durable products. However, for frequently purchased food and drug categories, other crisis management strategies, such as providing compensation to affected consumers and obtaining recertification for product manufacturing and quality, may also moderate the effects of recall volume on short- and long-term abnormal returns. Such moderating effects could be studied if data are available.
Third, we show how managers might use the results of the CTAR approach to understand the relative degrees to which they can focus on the different crisis management strategies. However, because the approach examines the difference in long-term abnormal returns between high and low levels of the focal crisis management variable, it cannot identify the exact amount by which managers may need to dial up or down their crisis management strategies to achieve desired changes in long-term abnormal returns.
Fourth, our focus is on abnormal returns to recall announcements. Additional insights on the trade-off between product quality and innovation can be gleaned by extending our research to study abnormal returns to new product pre-announcements in the presence of product recalls.
Conclusion
Before this study, not much was known about the long-term impact of recalls on firm value and how firms should strategically manage a recall crisis. Our empirical analysis in the auto industry context reveals novel findings about the longterm effects of recalls. The negative impact of recall volume lingers over time. Brand (promotional) advertising and voluntary recall have a significant positive (negative) effect on the relationship between recall volume and long-term abnormal returns. A voluntary recall initiation strategy mitigates the negative effect of recall volume on long-term firm value. These effects are contrary to the short-term effects. A diligent postrecall remedy positively moderates the impact of recall volume on long-term returns. Our results suggest that managers should use different advertising types during and after a recall, strategically initiate recalls, and diligently prepare postrecall responses.
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TABLE 5
Model Results for Cross-Sectional Short-Term Abnormal Returns
aThere are nine slope-correction variables; however, only the two shown in the table are significant. To save space, we do not report the other seven. Notes: Robust standard errors were used to compute the significance levels of the estimates.
TABLE 6
Results from Long-Term Analysis of Abnormal Returns to Recall
Notes: This table shows the results of the long-term abnormal returns measured by the intercept ap, using the calendar-time portfolio approach with a 12-month holding period after the product recall announcement. The data are presented as monthly abnormal returns estimated using the four-factor model. We used the weighted least squares method to account for the number of firms within the calendar month portfolio. The effects are independently estimated.
TABLE 7
Summary of Hypotheses and Results
TABLE 8
Short-Term and Long-Term Effects of Social Media
Notes: To save space, the estimates of all other variables are not shown in this table but are available in the Web Appendix. Our expectation on shortterm effects is again supported when we incorporate the interaction effects of social media and recall strategies. N.A. = not applicable.
TABLE 3
Variables, Operationalizations, and Data Sources
ahttp://mba.tuck.dartmouth.edu/pages/faculty/ken.french/.
TABLE 4
Summary Statistics and Correlation Matrices of Short- and Long-Term Variables
Notes: Advertising is measured as the residual from an autoregressive model of advertising spending.
FIGURE 1
A Conceptual Model of the Moderating Effects of Crisis Management Strategies on Recall Volume on LongTerm Firm Value
1Recall severity may also affect abnormal returns to a recall. In the subsequent empirical analysis, we have done a robustness check, and our key findings are unchanged even after incorporating the effect of recall severity in the short term (see Table A11 in the Web Appendix).
2Note that such positive effect exists regardless of whether the recall volume is high or low.
3In the “Empirical Context and Data” section, we provide brief details of the recall process in the auto industry that we subsequently analyze (NHTSA 2006). Further details are available in the Web Appendix.
4In our data, the average abnormal return is significantly negative in the short time windows both before and after the announcement day, suggesting possible information leakage before the recall announcement. This is consistent with Gokalp et al. (2016), who find evidence of insider trading before the auto recall announcement.
5The results from analyses using various other short- and longterm windows (e.g., two years) are largely consistent with those of our proposed model and are available in the Web Appendix.
6Although the completion rate is a well-accepted measure of postremedial efforts, it can be argued that it is the result of the recalling firm’s postrecall remedial efforts. To ensure that the results are robust to the measure of postremedial efforts, we estimate our models with an alternative measure and report the results in the “Robustness Check” section.
7To control for the endogeneity of recall initiation, unlike Chen, Ganesan, and Liu (2009), who use observed recall initiation, which takes dichotomous values, we use unexpected recall initiation probability, a continuous measure. Therefore, we do not use the Heckman (1979) two-step selection approach.
8See https://www.statista.com/statistics/258075/us-motor-vehicleand-parts-manufacturing-gross-output/.
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Record: 48- Cueing Morality: The Effect of High-Pitched Music on Healthy Choice. By: Huang, Xun (Irene); Labroo, Aparna A. Journal of Marketing. Nov2020, Vol. 84 Issue 6, p130-143. 14p. 1 Diagram, 3 Charts. DOI: 10.1177/0022242918813577.
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Cueing Morality: The Effect of High-Pitched Music on Healthy Choice
Managers often use music as a marketing tool. For example, in advertising, they use music to intensify emotions; in service settings, they use slow music to boost relaxation and classical music to convey sophistication. In this article, the authors posit a novel effect—higher-pitched music can boost healthier choices. Recognizing that many perceptual characteristics of higher pitch (e.g., lighter, elevated) are conceptually associated with morality, they theorize that listening to higher- (vs. lower-) pitched music can cue morality. Furthermore, thoughts about morality can prompt moral self-perceptions and, in turn, thoughts about "good" behaviors, including healthy choices. Thus, listening to higher-pitched music may increase healthier choices. Employing field settings and online studies, the authors find that listening to higher-pitched music increases consumers' likelihood to choose healthy options (Studies 1, 3, and 5), choose lower-calorie foods (Study 2), and engage in health-boosting activities (Study 4). This effect arises because high pitch raises the salience of morality thoughts (Studies 4 and 5). The article concludes with a discussion of theoretical and managerial implications.
Keywords: healthy choice; moral judgment; music; pitch; sensory marketing
Managers have long used music as a marketing tool. For example, managers often employ music in advertising to intensify emotion and increase memorability of ads. Retail outlets and restaurants play music to set ambience and improve customer satisfaction. For example, slow-tempo music can help relax consumers and increase the quantity of food they consume ([31]). Classical music can cue thoughts about national origins of the music and increase perceptions of products as selections from those regions ([34]). Managers seem to recognize that these two factors—music tempo and associations—can boost customer satisfaction and, thus, sales, so they use them to construct customer experiences ([31]). However, an additional important dimension of music is its pitch ([25]; [35]). Pitch ranges along a spectrum, is easily manipulated, is pervasive in all audio bites, and is likely among the first thing a person encounters in any audio bite; yet marketers have limited understanding of how it might affect choice (for examinations of the impact of auditory pitch in a marketing context, see [23]; [24]).
Indeed, in ten in-depth interviews with restaurant managers (see Web Appendix W1, Tables W1.1., and W1.2. for details), 90% indicated that they believe background music positively affects sales. They also noted that their primary consideration in choosing music is the extent to which it relaxes consumers and the fit between the associations it cues and the restaurant theme, confirming findings in the literature on the importance of these two factors ([30], [31]). However, none of the managers we interviewed overtly considered pitch in choosing background music, nor were they able to articulate how it might affect consumer choice. Here, we propose that a higher music pitch can cue morality and make consumers more virtuous in their choices, increasing preferences for healthy options because consumers consider such choices virtuous. In doing so, we provide both practical insights into how music can strategically influence healthy choices and novel theorizing on how music pitch can be an antecedent of morality thoughts, which in turn lead to healthy choice.
Three streams of research show support for our theorizing that high-pitched music can cue morality. First, the research stream on perceptual effects indicates that a high pitch can bring about perceptual responses such as looking up ([42]; [47]) and perceiving objects as brighter ([ 7]; [48]) and smaller ([23]; [36]; [50]). Second, the research stream on morality associates upward directionality (e.g., people think God is up), brightness (e.g., people tend to classify lighter and brighter things as good), and size (e.g., people report things in small quantities as purer) with godliness, goodness, and purity ([27]; [28]; [43]). Morality thoughts refer to thoughts about virtuous actors and actions and therefore include thoughts about godliness, goodness, and purity ([17], [18]). Combining the disparate insights from these two streams of research—that higher pitch cues perceptual responses that also arise when considering morality—allows us to correlate music pitch with morality thoughts. Third, the evolutionary perspective further supports a link between pitch and morality. Primates emit high-pitched shrieks to warn their clans of impending danger at the cost of attracting danger to themselves ([11]). Self-sacrifice is the ultimate moral act, and in primates, higher-pitched sounds signal this act for the greater good. Drawing from these three research streams suggesting that pitch is positively associated with morality, we posit it may also cue morality. Furthermore, morality is likely to be positively associated with healthier choices, the second link in our argument. Healthier choices are often considered virtuous or good, as they are more justifiable ([ 6]; [32]; [51]), and morality is associated with goodness of actions or doing what is virtuous and right ([40]; [54]). Thus, if high-pitched music cues morality, and if healthier choices are consistent with morality, high-pitched music could increase healthier choices.
In linking pitch to healthy choice through morality cueing, this research offers three specific contributions. First, whereas prior research has tied high pitch to several lower-order perceptual responses, such as size, visual sharpness, and verticality, the current research links pitch to conceptual higher-order judgments, in particular, morality. This research thus makes an important contribution by showing the effects of sensory stimuli on higher-order thinking. Second, while healthy choice can be virtuous, this research links morality to heathy choice and, in turn, pitch to healthy choice. In doing so, we show that a novel ambient factor—music pitch—can influence judgment and choice, and the more consumers consider healthy choice moral, the more they will choose healthier options when exposed to high-pitched music. Third, consumers often listen to music when making choices; thus, the findings of this research bear implications for marketers and policy makers who want to boost consumers' healthy choices through their marketing actions.
Pitch is the perceptual dimension along which sharpness of musical notes can be ordered from low to high (hertz) ([22]). Pitch is a major component of music, constituting one of the primary aspects of any audio bite, along with its sound pressure (loud or quiet), duration, and timbre ([25]; [35]). Pitch is one of the first things people experience in any sound bite; yet scant research has investigated how pitch can affect human thought and action (for exceptions, see [23]; [24]). In the handful of investigations conducted in this area, some important findings have emerged, especially those related to the impact of pitch on perception (for a summary of literature, see Table 1). For example, research indicates that people look up when they hear a high-pitched sound, suggesting that higher pitch is perceptually associated with spatial height ([42]; [47]; [48]). People also rate higher-pitched sounds as brighter ([ 7]; [49]), and they perceive the source of higher-pitched sounds as smaller than sources emitting lower-pitched sounds ([36]; [50]) and also lighter ([49]). Conversely, lower-pitched sounds are perceived as arising from lower spaces and being emitted by larger sources ([36]; [50]) and are associated with a lowering of the gaze ([48]), visual darkness ([ 7]), and heaviness ([49]). Thus, pitch is associated with distinct perceptual responses of height, brightness, and size.
Graph
Table 1. Overview of the Pitch Literature.
| Source | Key Finding | Independent Variable | Dependent Variable |
|---|
| Collier and Hubbard (2001) | Higher-pitched tones are rated as happier, brighter, faster, and as speeding up more. | Higher- (vs. lower-) pitch tones | Ratings of happiness, brightness, and speed |
| Lowe and Haws (2017) | Lower pitch leads consumers to infer a larger product size. | High (vs. low) acoustic pitch | Assessments of product size |
| Lowe, Ringler, and Haws (2018) | Low pitch leads consumers to take larger serving sizes. | High (vs. low) acoustic pitch | Actual serving sizes, purchase behavior |
| Rusconi et al. (2006) | High pitch is mapped onto spatial up position, and low pitch is mapped onto spatial down position. | High (vs. low) music pitch | Reaction time |
| Wagner et al. (1981) | Infants, similar to adults, are able to match ascending tone with up arrow and descending tone with down arrow. | Ascending (vs. descending) tone | Preference for matching stimulus |
| Walker and Smith (1984) | High-pitched sounds are perceived as light, little, sharp, bright, and fast. | High (vs. low) auditory pitch | Reaction time |
| Walker and Smith (1985); Parise and Spence (2009) | High-pitched sounds are judged to be smaller in size. | High (vs. low) auditory pitch | Reaction time |
| Walker et al. (2010) | Infants link auditory pitch to visuospatial height and visual sharpness. | High (vs. low) auditory pitch | Preferential looking |
| This study | High pitch promotes morality thoughts and subsequent healthy choices. | High (vs. low) music pitch | Morality thoughts, healthy choice |
While judgments of height, brightness, weight, and size arising in response to hearing high-pitched sounds are indeed distinct perceptual responses to pitch, they all seem to converge conceptually in activating a common, higher-order construct of morality. For example, impressions of height or the act of looking up is associated conceptually with thoughts about "goodness" of actions, which is a fundamental aspect of morality. Indeed, people evaluate positive words more rapidly when they appear at the top rather than the bottom of a computer screen, and the effect is opposite for negative words, implying that good is up and bad is down ([28]). People also recognize words with a moral meaning (e.g., "caring," "charity," "trustworthy") more quickly when displayed at the top of the screen, while they recognize words with an immoral meaning (e.g., "adultery," "corrupt," "evil") more quickly when displayed at the bottom of the screen ([29]). Similarly, concepts of God are related to metaphors for elevation, such that people tend to put the divine above themselves ([27]), and God-related words can shift attention upward ([ 4]). Researchers have explained this link between height and morality using the conceptual metaphor framework ([17], [18]; [19]; for a review, see [20]), which proposes that all cognition is grounded in lower-order perception ([52]) and that people represent high-level concepts in terms of their low-level physical experiences. Thus, people represent morality metaphorically in terms of actually looking up and as grounded in the concepts of upward, elevation, or height. If pitch is associated with the perceptual response of looking up, and if looking up is associated with higher accessibility of moral thoughts, pitch might also cue moral thoughts.
Furthermore, vision is primary among all perceptions ([13]), and therefore the impact of pitch on height perceptions and of height on thoughts of morality could dominate other perceptual effects of listening to high-pitched sounds. However, other perceptual responses to pitch, such as brightness or size, are also linked to morality and therefore may converge in cueing it. For example, consumers perceive players in dark T-shirts as more malevolent ([10]), thus implying that darker is immoral. Furthermore, research has shown the reverse link—cueing people with morality increases how bright they perceive their surroundings to be ([ 2]). Similarly, people consider smaller objects scarcer ([55]; [56]) and purer, and purity is related to morality ([57]). Conversely, they consider dense, heavy objects less pure or moral, perhaps in part because they perceive heavier things as pulling downward; for example, people consider flatter objects heavier than narrower objects ([38]). It is possible that these perceptual responses individually or as a set also conceptually cue some other constructs in addition to morality, but our core argument is that higher pitch evokes perceptual responses that are indicative of conceptual accessibility of morality thoughts, and thus pitch may cue morality.
The accessibility of morality thoughts may increase the likelihood that people act more morally, for several reasons. Research indicates that cueing people with thoughts they may not have otherwise considered can increase the likelihood that they will act according to these thoughts by bringing new information to mind ([33]). Thus, making morality more accessible might provide an additional consideration to consumers when making choices. Cues also remind consumers of aspects of their identity. For example, research indicates that morality thoughts can make moral identity salient, and salient moral identity can increase moral self-perception among consumers, reminding them that they are moral and virtuous and that being moral is an important goal for them ([ 3]. These moral self-perceptions might then increase moral actions, such as acting for the greater good ([ 1]; [39]; [53]; [54]). Thus, ample research demonstrates that situationally activated thoughts affect choice. If pitch activates morality, salient morality thoughts are likely to increase moral self-perceptions and, in turn, the likelihood of making moral choices.
In the context of food decisions, moral choices are likely to be healthier choices. Morality is associated with perceived rightness or goodness of actions and actions socially mandated and approved. In general, healthy choices are considered good and, therefore, virtuous (e.g., [15]), while indulgent foods are considered vices ([ 6]; [32]; [51]), as indulgent foods are sugary, fatty, and tasty and often lead to immediate gratification. Healthy foods instead are immediately less gratifying but more justifiable. In support, research indicates that mothers cued with moral responsibility increase intentions to consume healthier skim milk ([37]). Research has also found that people judge consumers who choose healthier food as more moral ([46]). Taking these arguments together, we expect that listening to high-pitched music will cue morality, and increased accessibility of morality thoughts will increase actions considered moral, which in the context of food may be healthier choices.
We propose that ( 1) listening to high- (vs. low- vs. no) pitched music is likely to cue morality, ( 2) accessibility of morality is likely to increase moral self-perception and healthy choice, and ( 3) consumers will make healthier choices insofar as they consider these choices moral. In line with our theorizing, we expect only high-pitched music to increase the choice of healthy options, but we do not necessarily expect low-pitched music to further increase indulgent choice. The reason is that consumers often struggle with making healthier choices and indulgence may typically be a preferred, default choice. Unless consumers are actively trying to be moral and therefore strive to make healthier choices, they may default to a more indulgent choice. Moreover, the literature on primates suggests that normal-pitched sounds are closer to lower-pitched sounds and that primates employ higher-pitched sounds selectively in service of the greater good ([11]). Thus, listening to high-pitched music may indeed increase healthy choice, but low-pitched music may not further increase indulgent choice from the baseline. We return to this issue in the discussion of Study 2.
We test these propositions in five studies. In a field study, we first investigate whether participants exposed to high- (vs. low-) pitched music are more likely to purchase a healthy (vs. indulgent) food item (Study 1). We then test whether participants are more likely to choose foods containing fewer calories (Study 2) and more healthful options (Study 3). By including a normal-pitch condition, the results of Study 2 further establish the role of high-pitched music in increasing healthy choice but not low-pitched music in decreasing it. After verifying the link between music pitch and healthy choice using different genres of music stimuli (Studies 1–3), we investigate the underlying process. Specifically, in Study 4, we test whether accessible morality thoughts increase moral self-perceptions and if this relationship underlies the effect of pitch on healthy choice. To further test the role of accessibility of morality thoughts as underlying the link between pitch and healthy choice, in Study 5 we cue consumers exposed to low-pitched music externally to morality thoughts. We examine whether this cueing makes their choices similar to those made by participants exposed to high-pitched music who do not need such external cueing of morality. We also rule out alternative explanations, including effects of arousal, sense of power, and mood (Studies 2–5).
The purpose of Study 1 was to provide initial evidence of whether high- (vs. low-) pitched music increases preferences for healthy foods in a real-life setting. Specifically, we set up a pop-up cookie store on campus for students and staff members.
Six hundred fifty-eight passersby at the student center food court at a North American university participated in a one-factor, two-level (music pitch: high vs. low) between-subjects field study. Of these, 3 people did not hear any music due to a glitch, 10 people arrived as couples, and 6 people, including 2 who arrived as a couple, reported knowing the research assistant. These people's attention and reactions to the music being played (the independent variable), their reasons for arriving at our stall, and their product choices (the dependent variable) are open to the social influence of their partner or the research assistant. Thus, to ensure that any effects of our independent variable (pitch) on our dependent variable (choice) are free of this possible confound of social influence, we removed these 17 people from our analysis, which resulted in an independent sample of 641 people (338 women, 302 men, 1 unrecorded). The dependent variable was the purchase of one of two types of 2-oz. cookies: oatmeal raisin (healthy option) or double chocolate chip (indulgent option).
To verify that students perceive oatmeal cookies as healthier than chocolate chip cookies, we conducted a pretest (N = 40; 13 men). For each cookie type, we asked students to indicate ( 1) perceived healthfulness (1 = "not at all," and 7 = "very healthy/nutritious") and ( 2) perceived indulgence (1 = "not at all," and 7 = "very indulgent/delicious"). As we expected, participants indicated that the oatmeal (vs. chocolate) cookies were healthier (M = 4.68, SD = 1.62 vs. M = 2.25, SD = 1.69; t(39) = 8.98, p <.001) and less indulgent (M = 4.85, SD = 1.82 vs. M = 6.18, SD = 1.08; t(39) = -4.38, p <.001).
We operated a pop-up cookie store for five days at the student center on campus. Each day the cookie store operated for two hours, except the first day when the store ran for one hour. Two types of cookies (oatmeal raisin and double chocolate chip) were available for purchase, and we replaced purchased cookies immediately with new cookies. A poster in front of the store alerted passersby of a cookie promotion for a local manufacturer ($.25 per cookie). Any person could purchase only one cookie.
Instrumental music played in the background while the store was operating. Using Audacity software, we adjusted the pitch 50% upward or 50% downward (all stimuli are available on request). We ran 18 half-hour sessions, 9 with high-pitched and 9 with low-pitched music, counterbalanced. A research assistant blind to the study's purpose counted all passersby, those who approached the store, those who made a purchase, and which cookie they bought.
One hundred nineteen passersby (18.56%) approached the store. A somewhat larger proportion of passersby approached the store when high-pitched (N = 69/322 = 21.43%) rather than low-pitched music (N = 50/319 = 15.67%) played (z = 1.874, p =.061). To our surprise, supplementary analysis showed that a significantly higher proportion of women approached the stall when high-pitched (N = 39/170 = 22.94%) rather than low-pitched (N = 23/168 = 13.69%) music played (z = 2.197, p =.028). Pitch of music did not differently attract men to the stall (N = 30/151 vs. 27/151; z =.441, p =.659).
Eighty-six people who approached the stall made a purchase (72.27%). Of those who approached the store, music pitch did not differentially affect overall purchase rate of both cookies (Nhigh-pitch = 50/69 = 72.46% vs. Nlow-pitch = 36/50 = 72%; z =.056, p =.956; women: z =.409, p =.683; men: z = –.342, p =.733).[ 6] Thus, compared with low-pitched music, high-pitched music attracted a larger proportion of passing women to the store, but on arriving at the store, their purchase rate did not differ depending on music or gender. People also overwhelmingly preferred purchasing the chocolate chip cookie (N = 57/86 = 66.28%) to the oatmeal cookie, and this rate was similar for women (N = 28/45 = 62.22%) and men (N = 29/41 = 70.73%; z = –.834, p =.404).
Importantly, as we predicted, the proportion of healthier cookies purchased was higher when high- (vs. low-) pitched background music played (25/50 = 50% vs. 4/36 = 11.11%; z = 3.763, p <.001, see Table 2). In addition, pitch increased the purchase of the healthy cookie more strongly among women (z = 3.24, p =.001) than among men (z = 1.96, p =.05).
Graph
Table 2. Summary of Effects of Music Pitch on Healthy Choice.
| Study | Sample Size | Dependent Variable | Moderator | Healthy Choice |
|---|
| High Pitch | Low Pitch | Normal Pitch |
|---|
| 1 | 86^ | % purchasing the healthier cookie | | 50% | 11.11% | |
| 2 | 299* | Healthy items ordered | | .78(.62) | .51(.59) | .55(.69) |
| 3 | 601 | Healthy-choice index | • Genre (metal) | 1.61(1.17) | 1.25(1.07) | |
| | | • Genre (jazz) | 1.72(1.27) | 1.35(1.09) | |
| | | Genre (rock)
| 1.65(1.24) | 1.16(1.05) | |
| 4 | 201 | Engagement in healthy activities | | 3.88(1.90) | 3.39(1.64) | |
| 5 | 401 | % choosing healthy gift card | • Morality salience | 38.30% | 53.33% | |
| | Control
| 42.59% | 34.04% | |
| 1-5 | 1,588 | 5 healthy-choice operationalizations | | Listening to high- (vs. low-) pitched music promotes healthy choice |
1 ^Out of 658 passersby in this field study, 86 made a purchase (excluding those who did not hear any music, arrived as couples, or reported knowing the research assistant).
2 *There is a missing data point on the measure of pleasantness, so the number of participants for the analysis is 299.
In this study, we examined actual purchases in a naturalistic setting. A higher proportion of passersby were attracted to our stall when pitch was high (vs. low), especially women. Among people who then bought cookies, higher-pitched music increased the proportion of healthy cookies selected, especially among women. Why pitch affected women more, and whether it attracted a type of woman more predisposed toward morality, warrants further research. One potential reason for this gender difference in response to high pitch may be that women often serve as primary caregivers to children, and children have higher-pitched voices. Furthermore, women themselves have higher-pitched voices than men, and people generally are more attentive to their in-group and their characteristics, as these are more self-relevant. For both these reasons, women may generally be more attentive than men to higher-pitch sounds. A third reason may be that women are more communal ([ 8]), and we ran the study in a location where there are often student-benefit activities. Student-benefit activities are prosocial in nature, and therefore, women may have been cued toward prosociality, and more generally morality, in this venue (student center food court) than men. Being more attuned to morality might have made women more sensitive to a high pitch, if a high pitch is associated with morality, as we claim. For example, in a religious venue, women may also be more sensitive to a high-pitch audio bite. To avoid these potential problems of self-selection caused by pitch that can be common in any field setting, we replicate this effect in a controlled experiment in Study 2 with a different type of choice (healthy items ordered) and a different music genre (rock); we also include an unaltered (control) normal-pitch condition.
Three hundred participants (133 men; Mage = 41.39 years, SD = 13.19) recruited from the Amazon Mechanical Turk (MTurk) online platform took part in the study for payment (US$.50). The study was a one-factor, three-level (pitch: high vs. normal vs. low) between-subjects design. As a prerequisite to take part in the study, participants were required to have an audio-capable device and available headphones.
The cover story indicated that shoppers often carry an iPod to listen to music, including when they visit a café. To simulate such a situation, we instructed participants to put on their headphones and click on the music file. Similar to Study 1, we adjusted pitch up or down by 50%. We randomly allocated participants to listen to a high, a low, or an unaltered (normal) pitch version of the same rock music. Participants were asked to put on their headphones and confirm that they could hear the music playing. They were then asked to complete manipulation check and control questions about the music that was playing (1 = "low-pitched, unfamiliar, discomforting, slow tempo, unpleasant"; 9 = "high-pitched, familiar, comforting, fast tempo, pleasant"). They then imagined that they were ordering breakfast at a local café off a menu listing different food options and associated calories of each option (see Appendix A; see also Web Appendix W2 for pretest details of these stimuli for Studies 2 and 3). Participants indicated all items they would order. Among all 11 items on the menu, 5 (items 2, 3, 5, 8, and 11) were low-calorie and relatively healthy items. As our key dependent variable, we summed the total number of healthy items ordered (range: 0–5).
After making their choices, as supplementary measures, participants reported their arousal (1 = "relaxed, sluggish, depressed, drowsy, calm"; 9 = "stimulated, frenzied, upbeat, energetic, aroused"; averaged into an arousal index, α =.75), feelings of power (1 = "powerless"; 9 = "powerful"), and mood (1 = "sad"; 9 = "happy"). Considering that the choices participants made likely influenced these responses, these data are not insightful to our investigation, but we report all associated means and standard deviations in Table 3 (see also Web Appendix W3 for the main analysis with these items included as covariates for Studies 2–5). Then, participants reported their age and gender, where they took the study (86.0% completed the study at home), the device used (41.7% used a desktop, 52.0% used a laptop), and whether they followed our instructions to wear a headset and listen to the music. For all the studies, we did not exclude any data.[ 7]
Graph
Table 3. Studies 2–5: Manipulation Checks and Controls: Mean (SD) Summary Table.
| Pitch | Familiarity | Comfort | Tempo | Pleasantness | Arousal | Power | Mood |
|---|
| Study 2 (Rock Music) | | | | | | | |
| High-pitched | 5.43 (1.32)a | 3.71 (2.01)a | 6.31 (2.01)a | 5.32 (1.29)a | 6.33 (2.02)a | 5.38 (1.43)a | 5.93 (1.62)a,b | 6.94 (1.56)a |
| Normal-pitched | 4.61 (1.42)b | 4.05 (1.97)a | 6.65 (1.95)a | 5.52 (1.31)a | 6.65 (1.88)a | 5.17 (1.37)a,b | 5.94 (1.46)a | 6.73 (1.70)a,b |
| Low-pitched | 2.94 (1.57)c | 2.74 (2.00)b | 5.01 (2.04)b | 4.51 (1.67)b | 4.78 (2.10)b | 4.86 (1.42)b | 5.48 (1.81)b | 6.29 (1.71)b |
| F(2, 297) = | 70.00,p <.001 | 11.36,p <.001 | 17.95,p <.001 | 13.69,p <.001 | 23.83,p <.001* | 3.10,p =.047 | 2.40,p =.092 | 3.60,p =.029 |
| | | | | | | | |
| Study 3 (Rock Music) | | | | | | | |
| High-pitched | 5.26 (1.61)b | 3.48 (2.06)b | 5.64 (2.16)b | 5.20 (1.35)c | 5.78 (2.06)b | 5.26 (1.42)b,c | 5.82 (1.58)b,c | 6.81 (1.75)a,b |
| Low-pitched | 3.02 (1.57)d | 2.79 (1.96)c | 4.92 (2.14)c | 4.68 (1.59)d | 4.89 (2.20)c | 4.89 (1.26)c | 5.50 (1.64)c | 6.04 (1.96)d |
| | | | | | | | |
| Study 3 (Jazz Music) | | | | | | | |
| High-pitched | 5.30 (1.88)b | 5.43 (2.75)a | 6.47 (2.32)a | 5.27 (1.83)c | 6.47 (2.16)a | 5.33 (1.49)b | 5.95 (1.61)b | 6.84 (1.81)a,b |
| Low-pitched | 3.84 (1.66)c | 4.93 (2.70)a | 6.63 (1.99)a | 5.12 (1.61)c,d | 6.37 (2.09)a,b | 5.10 (1.31)b,c | 5.98 (1.31)a,b | 7.17 (1.53)a |
| | | | | | | | |
| Study 3 (Metal Music) | | | | | | | |
| High-pitched | 5.93 (1.95)a | 3.79 (2.86)b | 4.29 (2.32)d | 7.04 (1.58)a | 4.12 (2.27)d | 6.19 (1.54)a | 6.39 (1.66)a | 6.63 (1.69)b,c |
| Low-pitched | 4.21 (2.30)c | 3.38 (2.54)b,c | 4.04 (2.16)d | 6.54 (1.69)b | 3.84 (2.16)d | 5.91 (1.36)a | 6.15 (1.58)a,b | 6.32 (1.72)c,d |
| F(5, 595) = | 36.02,p <.001 | 16.23,p <.001 | 24.01,p <.001 | 33.89,p <.001 | 26.85,p <.001 | 13.08,p <.001 | 3.83,p =.002 | 5.15,p <.001 |
| | | | | | | | |
| Study 4 (Classical Music) | | | | | | | |
| High-pitched | 6.37 (1.65)a | 5.28 (2.60)a | 6.67 (1.86)b | 2.99 (1.47)a | 6.75 (1.84)b | 4.36 (1.41)a | 5.36 (1.73)a | 6.35 (1.88)a |
| Low-pitched | 4.27 (1.40)b | 5.00 (2.38)a | 7.37 (1.68)a | 2.90 (1.51)a | 7.35 (1.69)a | 4.13 (1.10)a | 5.53 (1.60)a | 6.55 (1.85)a |
| F(1, 199) = | 95.06,p <.001 | .64,p =.426 | 7.73,p =.006 | .18,p =.672 | 5.72,p =.018 | 1.77,p =.185 | .55,p =.457 | .61,p =.437 |
| | | | | | | | |
| Study 5 (Classical Music) | | | | | | | |
| High-pitched | 6.92 (1.43)a | 4.37 (2.26)a | 6.11 (2.25)b | 3.34 (1.49)a | 6.09 (2.21)b | 4.82 (1.32)a | 5.65 (1.44)a | 6.34 (1.67)a |
| Low-pitched | 4.01 (1.81)b | 4.33 (2.27)a | 6.65 (1.97)a | 2.99 (1.45)b | 6.53 (2.03)a | 4.35 (1.18)b | 5.60 (1.43)a | 6.27 (1.72)a |
| F(1, 399) = | 319.54,p <.001 | .02,p =.878 | 6.61,p =.010 | 5.60,p =.018 | 4.28,p =.039 | 13.96,p <.001 | .12,p =.725 | .20,p =.657 |
| | | | | | | | |
3 Notes: Cells with different superscripts in each column (within each study) differ at p <.05. *There is a missing data point in the measure of pleasantness; therefore, the number of participants for this analysis is 299.
As expected, participants indicated that the high-pitched music was of a higher pitch than the normal- and low-pitched music (for means and standard deviations, see Table 3). Music across the high- and normal-pitch conditions did not differ in terms of familiarity, comfort, tempo, or pleasantness (ps >.21), giving us confidence that any predicted difference between high and normal/low pitch on healthy items ordered cannot be accounted for by these factors. Participants rated the low-pitched music as less familiar, less comfortable, slower in tempo, and less pleasant than the high- and normal-pitched music, but we expected the normal- and low-pitch conditions to exert similar influence on choice; thus, it is again unlikely that these factors account for our key effects. However, because comfort and pleasantness of music can influence mood and mood can increase healthy choice among consumers, in Studies 2–5 we control for these two factors in our analyses. Doing so gives us greater confidence that any effects on the dependent variable are not driven by mood. For Studies 2–5, Web Appendix W5 reports the analyses without these covariates. Our overall conclusions across studies are not affected by whether or not we use these covariates.
A missing data point on the measure of pleasantness resulted in a sample of 299. A one-way analysis of covariance (ANCOVA) on healthy items ordered, controlling for music comfort and pleasantness, yielded the expected main effect of pitch (F( 2, 294) = 4.31, p =.014; covariate ps >.52). Participants listening to high-pitched music ordered more healthy items (M =.78, SD =.62) than those listening to normal- (M =.55, SD =.69; F( 1, 294) = 7.21, p =.007) or low-pitched music (M =.51, SD =.59; F( 1, 294) = 5.29, p =.022; no difference between the latter two conditions, p >.92). Thus, high-pitched music increased healthy choice, though low-pitched music did not reduce it.
To ensure that higher pitch did not increase more choices of foods in general and to confirm that participants swapped healthier foods for unhealthier ones, we also ran a one-way ANCOVA on overall number of choices participants made. We found no differences across conditions on total food items chosen (Mhigh-pitch = 1.31, SD =.90; Mnormal-pitch = 1.33, SD =.60; Mlow-pitch = 1.30, SD =.55; F( 2, 294) =.032, p =.968; covariate ps >.67). Thus, the overall number of food items chosen did not change depending on high pitch, but participants swapped healthier options for unhealthier ones.
Using a different music genre from Study 1 and including normal-pitched music as a control, we found further evidence that compared with normal- and low-pitched music, listening to high-pitched music can increase healthy choice. The effect stems from high-pitched music increasing healthy choice, not low-pitched music decreasing it. We expected the normal- and low-pitch conditions to be similar because people have a default tendency to choose indulgent options. Although healthy options are what people think they should choose, indulgent options are what they want to choose, and they usually give in to their wants. Thus, playing low-pitched music may not increase indulgent choice further, as it is already high.
We conducted a separate test to verify that indulgent (vs. healthy) options are what people prefer to choose (vs. should choose). We presented 51 MTurk workers (26 men, Mage = 38.37 years, SD = 11.36) with definitions of healthy choices (i.e., "foods that provide long-term health benefits; e.g., fruit salad") and indulgent choices (i.e., "pleasurable foods; e.g., cheesecake"). Participants indicated on a nine-point scale the extent to which healthy and indulgent choices are what they want to choose ( 1) or what they should choose ( 9). As expected, we found that indulgent options (M = 3.57, SD = 2.29) are what they want to choose (vs. what they should choose), compared with healthy options (M = 7.00, SD = 2.15; F( 1, 50) = 46.59, p <.001). Therefore, the finding that the low-pitch condition was not significantly different from the control (normal-pitch) condition confirms that people might already choose indulgent options as a default and do not increase indulgent choices further.
Thus far, we have provided evidence that high-pitched music increases healthy choices while low-pitched music does not reduce healthy choices. We observed these results after controlling for any possible mood effects pitch might evoke. Thus, our objective in Study 3 is to replicate the effects of pitch on healthy choice using two other genres of music and another dependent variable.
Six hundred one MTurk workers (299 men, Mage = 40.62 years, SD = 11.79) participated for payment (US$.50) in a 2 (pitch: high vs. low) × 3 (music genre: metal vs. jazz vs. rock) between-subjects study. As a study prerequisite, participants were required to have an audio-capable device and available headphones for the study.
As in Study 2, we first asked participants to put on their headphones and confirm that they could hear the music file playing. We used the same rock music stimuli as in Study 2 but also included metal and jazz music for this study (pitch adjusted 50% up or down). Participants, assigned randomly to listen to one genre of music in either high or low pitch, first rated the characteristics of the music (as in Study 2) and then made four choices, each between a healthy and an unhealthy option (see Appendix B). We coded healthy choice as 1 and unhealthy choice as 0, summing scores across all choices for each participant to create a healthy-choice index (range: 0–4). As in Study 2, participants then indicated their postchoice arousal (α =.76), sense of power, and mood; their age, gender, study location (85.0% at home), and device used (39.9% desktop, 52.2% laptop); and whether they wore a headset and could hear music.
As expected, participants rated the high- (vs. low-) pitched music as higher in pitch. No other clear patterns emerged across the three genres of music from pitch familiarity, comfort, tempo, or pleasantness (see Table 3).
With music comfort and pleasantness as covariates, a 2 (pitch) × 3 (music genre) ANCOVA predicting the healthy-choice index revealed only a main effect of pitch (Mhigh-pitch = 1.66, SD = 1.22 vs. Mlow-pitch = 1.24, SD = 1.07; F( 1, 593) = 16.95, p <.001; genre p >.65, covariates ps >.23). Thus, pitch effects on healthy choice occur across music genres.
Using additional music genres, we replicated our finding that listening to high-pitched music increases healthy choice. This finding extends generalizability of our results to multiple genres of music and provides additional confidence in this effect. We posit that this effect arises because high-pitched music cues morality thoughts, which increase healthy choice. We test for this mediation process in Study 4. We also use an audio clip and a dependent variable that differ from those in the previous studies to further test robustness.
Two hundred one participants (93 men; Mage = 39.57 years, SD = 11.10) recruited from the MTurk online platform took part in the study for payment (US$.50). The study was a one-factor (pitch: high vs. low) between-subjects design.
Participants first put on headphones (after reading the same cover story and instructions as in Studies 2 and 3) and then were assigned to listen to either high- or low-pitched music. To ensure generalizability across the different audios, participants listened to another instrumental piece, with pitch adjusted upward or downward by 50%. Participants confirmed that they could hear the music and then evaluated it with the same set of music characteristics as in the previous studies. In line with the cover story, participants then proceeded to the main task designed to collect our process measures and dependent variables. They indicated their likelihood of engaging (1 = "very unlikely," and 9 = "very likely") in each of four activities at this time: "exercising in the gym," "joining a fitness class (e.g., Yoga/Pilates)," "going running/cycling," and "avoiding tasty, tempting, high-calorie foods containing bad cholesterol and fat." As process measures, participants rated moral self-perception along three items ("At this moment, I feel I am moral," "At this moment, I feel I am virtuous," and "At this moment, I have high moral standards"; 1 = "disagree very much," and 9 = "agree very much," averaged into a morality index, α =.92). We counterbalanced the process and dependent measures and found no order effects. Last, as in Studies 2 and 3, participants rated their postchoice arousal (α =.72), sense of power, and mood; their age, gender, study location (91.5% at home), and device used (35.3% desktop, 55.2% laptop); and whether they wore a headset and could hear music.
Participants reported that the high-pitched music was of higher pitch (see Table 3) but was not more familiar or faster in tempo than the low-pitched music (ps >.42). Unlike in Studies 2 and 3, participants rated the high- (vs. low-) pitched music in this study as less comforting and less pleasant.
We averaged the four measures of engaging in healthy activities into an index (α =.79). A one-way ANCOVA using music pleasantness and comfort as covariates revealed a main effect of pitch on desire to engage in healthy activities (F( 1, 197) = 6.37, p =.012; pleasantness p =.023; comfort p >.75). Listening to high-pitched music increased participants' desire to engage in healthy activities (M = 3.88, SD = 1.90) more than listening to low-pitched music (M = 3.39, SD = 1.64).
We conducted mediation analyses to test whether high-pitched music heightens moral self-perception, which increases healthy preference because people consider such choices more virtuous (see Figure 1). Music comfort and pleasantness served as covariates in the mediation analysis models to ensure that any mediation effects we observe are not accounted for by mood. Regression analysis showed that listening to high-pitched music (1 = high pitch; –1 = low pitch) heightens moral self-perception (b =.22, SE =.10, t(197) = 2.13, p =.034) and increases the likelihood to engage in healthy activities (b =.31, SE =.12, t(197) = 2.53, p =.012). Moral self-perception is also positively related to healthy choice (b =.20, SE =.09, t(197) = 2.36, p =.019). When we entered both pitch and moral self-perception as predictors of healthy activities in the analysis, both pitch (b =.28, SE =.13, t(196) = 2.21, p =.028) and moral self-perception (b =.17, SE =.09, t(196) = 2.03, p =.044) remained viable, indicating that moral self-perception at least partially mediates the effect. Bootstrapping ([12]; Model 4) confirmed the indirect effect of pitch on healthy choice through moral self-perception ( 5,000 draws; bias-corrected 95% confidence interval: [.0047,.1134]).
Graph: Figure 1. Study 4: Mediation investigating the effect of music pitch on likelihood to engage in healthy activities. * p <.05; controlling for music comfort and pleasantness.
Notably, while music pleasantness increased desire to engage in healthy activities (p =.002) and moral self-perception (p =.016), high pitch reduced perceptions of music pleasantness (p =.018). Thus, in this study, although pleasantness was positively associated with moral perception and healthier activities, because a high pitch reduced pleasantness, the effect of pitch we observed on healthier activities cannot be accounted for by mood. In situations in which high pitch also increases pleasantness, it is possible that increased preferences for healthier activities will result from the direct effect of pitch on moral perceptions and preferences that we observed in this study, as well as from a positive and additive effect of mood on preferences, which goes against our effect in this study. Pitch did not affect postrated arousal, power, and mood (ps >.10; Table 3).
Listening to high-pitched music increased moral self-perception and, in turn, intention to engage in healthy actions. This study thus provides mediation support for our proposed process. We also found that music pleasantness increased intent to engage in healthy activities, but importantly, mood did not mediate the proposed effect of pitch on intent to engage in healthy actions. We observed the effect of pitch on intent to engage in healthy activities even after controlling for any possible mood effects evoked by comfort or pleasantness of pitch. Thus, when high-pitched music is pleasant, it can increase intent to engage in healthy actions, consistent with prior research showing that a positive mood can increase healthy actions ([ 9]). However, pitch also exerts an influence on healthy choice independent of any mood effects, because it cues thoughts about morality, prompting people to engage in healthy choice because doing so is virtuous. In Study 5, we provide further process evidence through moderation ([44]). If high-pitched music cues morality thoughts, priming people listening to lower-pitched music with morality should increase their healthier choices to levels similar to those of people listening to high-pitched music without the additional morality cue.
Four hundred one participants (185 men; Mage = 39.43 years, SD = 12.22) recruited from the MTurk online platform took part in the study for payment (US$.50). The study was a 2 (pitch: high vs. low) × 2 (salience: morality vs. neutral thoughts) between-subjects design.
To manipulate morality salience (vs. not), participants completed a sentence-unscrambling task, allegedly for use in future studies ([16]; [45]). We designed this cover story to create separation between the independent (priming) and dependent (choice) variables, to minimize socially desirable responding. Participants received six sets of five words and had to make a grammatically correct four-word sentence out of each set. The sentences either reminded participants of morality (e.g., "being moral is important" from the words "being, moral, important, is, am") or were neutral (e.g., "this ball is blue" from the words "this, blue, ball, is, be"). In line with the cover story, participants rated how positive (1 = "negative", 9 = "positive") and how easy (1 = "easy", 9 = "difficult"; reverse coded) the task was. Responses to these two items were averaged to form a task evaluation index (r =.31, p <.001).
Next, we instructed participants that they would make a consumer choice while listening to music. As people often make choices while listening to music, we wanted to simulate such a situation. All participants put on earphones and indicated that they could hear music. We employed the same instrumental music as in Study 1, with its pitch adjusted upward or downward by 50%. We than asked participants to make a choice between two gift cards, one for a healthy restaurant and one for an indulgent restaurant. Both gift cards had the same value (US$20). Participants also received basic information about the restaurants (e.g., style, menu; see Appendix C). Then, as in Studies 2–4, participants rated their postchoice arousal (α =.71), sense of power, and mood; their age, gender, study location (84.5% at home), and device used (43.4% desktop, 52.4% laptop); and whether they wore a headset and could hear music.
Participants rated the high- (vs. low-) pitched music as higher in pitch (see Table 3) but not in familiarity (p >.87). They rated the high-pitched clip as less comfortable, faster, and less pleasant. The task evaluation of the sentence-unscrambling task was higher in the control condition (M = 7.40, SD = 1.39) than in the morality salience condition (M = 6.92, SD = 1.57; F( 1, 399) = 10.62, p =.001).
Music comfort and pleasantness served as covariates in the following analysis. A logistic regression predicting gift-card choice (1 = healthy, 0 = indulgent) from pitch (1 = high pitch, –1 = low pitch), salience (1 = morality, –1 = neutral thoughts), and their interaction revealed only a significant interaction effect (b = –.26, SE =.10, z = –2.50, p =.012). The main effects of pitch, morality salience, and the covariates did not reach significance (ps >.14). Crucial to our theorizing, morality (vs. neutral) priming increased healthy choice among participants listening to the low-pitched music (Mmorality = 53.33% vs. Mneutral = 34.04%, b =.41, SE =.15, z = 2.79, p =.005), making their choices similar to those of participants in the high-pitched music condition who did not receive this additional morality cue (M = 42.59%). Thus, morality cues increased healthy choice among people listening to low-pitched music, implying that they did not already have access to such thoughts from the music alone. Equally importantly, as we predicted, morality priming did not boost the healthy choice among people listening to high-pitched music further (Mmorality = 38.30% vs. Mneutral = 42.59%, b = –.11, SE =.14, z = –.75, p =.456), which implies that listening to high-pitched music already cues morality thoughts, which in turn boost healthy choices among participants. Although there was a significant difference in task evaluation between the control and morality salience conditions, the interaction effect between pitch and salience remained significant (p =.010) even when task evaluation was included as an additional covariate in the model.
High pitch seems to spontaneously cue morality and increase moral self-perception. Priming morality externally increased healthy choice among participants listening to low-pitched music, making their choice similar to that of participants listening to high-pitched music.
Across five studies, we showed that high pitch cued morality and increased moral self-perception, which in turn increased healthy choices (see Web Appendix W6 for three calibration studies). We found that consumers were more likely to purchase healthier foods in a pop-up store when listening to high- (vs. low-) pitched music (Study 1). In addition, music pitch increased consumers' choice of lower-calorie foods (Study 2) and also increased the likelihood of choosing healthier foods (Study 3) and engaging in health-promoting activities (Study 4). We found that listening to higher-pitched music cued morality and increased moral self-perception (Studies 4 and 5), which in turn increased healthy choices, but only insofar as morality thoughts were salient (Study 5). Importantly, cueing morality independently of music increased salience of such thoughts and moral self-perception among participants listening to low-pitched music, thereby increasing their healthy choices to levels observed among participants listening to high-pitched music who did not need the external reminder of morality. We found these effects using five different pieces of music and four different dependent variables, including healthy versus unhealthy choices (Studies 1, 3, and 5), likelihood to engage in healthy activities (Study 4), and healthy items with lower calories (Study 2). We used low-pitched music rather than normal-pitched music as the comparison group in all studies except Study 2 to ensure that the music stimuli employed in both conditions were comparable (i.e., both were altered). Not doing so could have resulted in a confound that normal-pitched music is more familiar than altered music. Across all five studies, single-paper meta-analyses ([26]) confirmed a significant difference in preference for healthy options between the high-pitch and low-pitch conditions (choice measures: estimate =.2357, SE =.1236, z = 1.91, p =.057; Likert measures: estimate =.3494, SE =.0716, z = 4.88, p <.001; for more details, see Web Appendix W7).
A possible alternative explanation for our results is that high pitch cues lower perceptions ([23]); smaller product size is also linked to higher quality ([56]). If consumers also perceive healthy choices as high-quality choices, high pitch might promote healthy choice because they deem such choices of higher quality. To explore this possibility, we tested the quality inference of the options used in our studies (see Web Appendix W2); healthy choices were not always considered also of higher quality across our studies. Furthermore, quality perceptions do not explain why priming morality increased healthy choice among participants listening to low-pitched music in Study 5.
This research offers several important theoretical advances. First, it theorizes a link between perceptual responses to pitch and conceptual associations of morality. According to prior research, high-pitch sounds affect perception in several ways ([23]; [48]). Research has also linked perception to morality. Merging these two research streams, we show that pitch can cue morality.
Second, this research reveals that morality thoughts can increase healthy choice. We theorize that morality is associated with goodness in actions and that people often consider healthy choices virtuous, right, or good actions. Thus, when morality thoughts are more accessible, consumers act according to these accessible thoughts, making healthier choices. This presumption of healthy choice as virtuous has not been empirically tested before. Thus, this research advances the literature theoretically by proposing a novel link between pitch and morality and between morality and healthy choice.
Third, this research adds to the body of work on environmental influences and choice ([ 5]; [14]), especially the influence of music on choice. Prior research has found that fast-tempo music can cause consumers to eat more quickly in a restaurant or complete their shopping trip sooner than slow-tempo music ([30], [31]). Adding to these findings, we find that music pitch can cue morality and increase healthy choice by increasing moral self-perception, but only insofar as healthy choices are perceived as moral and thus are diagnostic to choice. We also ruled out the effects of arousal, power, and mood on choice as alternative explanations. Given that consumers often listen to music when making choices, the effects of pitch on healthy choices may be widespread.
This study holds relevance for marketers and policy makers. We found the effects in a field setting with a pop-up store, implying that similar effects are likely to arise when managers play music in their stores. If exposure to high-pitched music can cue morality thoughts and increase people's choices in line with an enhanced moral identity, grocery stores, malls, and restaurants could play high-pitched music to encourage healthier choices. This insight can also help marketers design ad appeals to more effectively promote healthy products. For example, marketers might consider choosing high-pitched songs as background music. Further research could investigate the generalizability of these findings to domains beyond food and health. For example, might charities that play high-pitched music when raising funds increase donations?
That we found these effects in the health domain and for food is also important. Given the high obesity rate among adults (39.6%) and children (18.5%) in the United States ([21]), our findings suggest an additional easy way for policy makers to promote healthy choices through marketing actions—that is, playing high-pitched background music in campaigns to curb obesity. Notably, the Healthy Menu Choices Act ([41]) states, "food service premises with at least 20 locations in Ontario must now comply with the Act's food labelling requirements, focused on displaying calorie information for most food and drink items offered for sale." This information allows consumers to count calories, and our study shows that high-pitched music can curb ordered calories. Thus, these findings suggest a simple way to boost healthier choices. Moreover, we speculate that the effect of music pitch—as a situational factor—is stronger for general settings (e.g., cafeterias) than settings in which consumers may already have salient morality thoughts (e.g., healthy restaurants, gyms).
This work opens avenues for further research. In this study, we investigated an effect of instrumental music on choice and controlled for meaningfulness of lyrics to avoid confounding semantic activation through lyrics with semantic activation through pitch. Intuition suggests that when lyrics are sufficiently ambiguous, pitch might alter the interpretation of the lyrics as more or less moral. When the meaning of the lyrics is unambiguously moral or immoral, the lyrics may override any pitch effects, though this possibility remains to be investigated. Indeed, Study 5 showed that priming morality increased healthy choice even among consumers who listened to low-pitched music, suggesting that lyrics can alter meanings of pitch. Further research could also test whether these effects generalize to voice pitch.
Finally, our focus was on examining consumers' preferences for healthy options as the downstream consequence of listening to high-pitched music. Additional research could more systematically investigate virtuous choices other than health, such as charitable giving. If the effect can generalize to this realm, charities could play high-pitched music when raising funds to persuade passersby to donate. Perhaps the high-pitched bell-ringing of Salvation Army workers serves more than just attracting attention—it may also cue moral identity and increase donations. These possibilities await further investigation.
Supplemental Material, JMX813577_ed_Jun_23_2020_Revised_Web_Appendix_v2 - Cueing Morality: The Effect of High-Pitched Music on Healthy Choice
Supplemental Material, JMX813577_ed_Jun_23_2020_Revised_Web_Appendix_v2 for Cueing Morality: The Effect of High-Pitched Music on Healthy Choice by Xun (Irene) Huang and Aparna A. Labroo in Journal of Marketing
Instructions:
As you listen to the music, please imagine that you are ordering breakfast at a local café. This café provides calorie information for each breakfast item it serves.
Please indicate which breakfast item you would like to order at this moment. You can order multiple items:
- □ Sausage, Egg, & Cheddar Classic Breakfast Sandwich (500 calories)
- □ Reduced-Fat Turkey-Style Bacon, Cheddar, & Egg White Breakfast (230 calories)
- □ Bacon, Gouda, & Egg on Artisan Roll (370 calories)
- □ Vegetable, Egg, & Fontiago on Multigrain Ciabatta (470 calories)
- □ Spinach, Feta, & Egg White Breakfast Wrap (290 calories)
- □ Rustic Bacon, Egg, & Cheese on Cheddar Chive Roll (470 calories)
- □ Roasted Ham, Swiss, & Egg on Croissant Bun - PM bun (450 calories)
- □ Egg & Cheddar on English Muffin (280 calories)
- □ Double Smoked Bacon (490 calories)
- □ Spicy Chorizo Monterey Jack and Egg (500 calories)
- □ Gluten-free Canadian Bacon Breakfast Sandwich (280 calories)
Instructions:
As you listen to the music, please make the following consumer choices. For each choice, please indicate your preferences. Please keep the music on while completing the following questions.
- Suppose we were to offer you a snack as compensation for your participation in this study; please indicate which one you would choose:
- □ Potato chips
- □ Apple chips
- Suppose you are ordering a dish for lunch at a local cafeteria; please indicate which dish you would prefer:
- □ Beef cheeseburger
- □ Vegetable panini
- Suppose now you are planning a leisure activity for this afternoon; please tell us which activity you would prefer:
- □ Chill and watch television
- □ Get some physical exercise (e.g., swimming, basketball)
- Suppose you won a raffle and were presented with two gift cards (of the same value) to choose; please indicate which gift card you would prefer:
- □ Movie theater gift card
- □ Fitness club gift card
There are two recently opened restaurants in downtown, namely Greendot Restaurant and Paul Bakery, and both offer $20 gift cards. There two kinds of gift cards you can select. Gift Card A is a coupon from Greendot restaurant worth $20. Gift Card B is a coupon from Paul Bakery worth $20.
Please indicate which gift card you choose.
Graph
| |
|---|
| Coupon for the Greendot Restaurant• (worth $20)• Meat-free and healthy food offerings | Coupon for the Paul Bakery• (worth $20)• Indulgent, fresh, homemade buttery goodness |
Footnotes 1 Authors' Note The authors acknowledge the efforts of Ping Dong in the conceptualization and idea generation of this article, original study design discussions, and writing parts of a previous draft. They also thank the associate editor and the anonymous reviewers for their invaluable suggestions and support during the review process. Finally, they thank Yibo Chen for assistance in data collection.
2 Associate Editor Wayne Hoyer
3 Online supplement: https://doi.org/10.1177/0022242918813577
4 Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
5 Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.
6 1 Readers may wonder why high (vs. low) pitch did not reduce approach to the store because the promotion was on cookies and cookies are generally indulgent. First, promotions offer savings, and saving money is considered virtuous; thus, participants may have viewed the promotion as virtuous, especially those wanting affordable snacks. Second, participants may have focused differently on the promotion versus cookies depending on music pitch (high pitch focusing on the former). Third, the location where we ran the store often has pop-up stores and draws student populations interested in deals and prosocial adults wanting to assist student causes.
7 2 A condition to participate in Studies 2–5 was that participants should have an audio-capable device and wear headphones. Thus, there is no practical reason for anyone to indicate at the end of the study that they did not hear music/have headphones; rather, they may have answered as such after they had removed their headphones/could no longer hear music. Therefore, we did not exclude any data in Studies 2–5. However, in these studies, exclusion of these people does not significantly alter the overall patterns of results, and our conclusions remain the same (see Web Appendix W4).
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By Xun (Irene) Huang and Aparna A. Labroo
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Record: 49- Customer Experience Journeys: Loyalty Loops Versus Involvement Spirals. By: Siebert, Anton; Gopaldas, Ahir; Lindridge, Andrew; Simões, Cláudia. Journal of Marketing. Jul2020, Vol. 84 Issue 4, p45-66. 22p. 2 Diagrams, 4 Charts. DOI: 10.1177/0022242920920262.
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Customer Experience Journeys: Loyalty Loops Versus Involvement Spirals
Customer experience management research is increasingly concerned with the long-term evolution of customer experience journeys across multiple service cycles. A dominant smooth journey model makes customers' lives easier, with a cyclical pattern of predictable experiences that builds customer loyalty over time, also known as a loyalty loop. An alternate sticky journey model makes customers' lives exciting, with a cyclical pattern of unpredictable experiences that increases customer involvement over time, conceptualized here as an involvement spiral. Whereas the smooth journey model is ideal for instrumental services that facilitate jobs to be done, the sticky journey model is ideal for recreational services that facilitate never-ending adventures. To match the flow of each journey type, firms are advised to encourage purchases during the initial service cycles of smooth journeys, or subsequent service cycles of sticky journeys. In multiservice systems, firms can sustain customer journeys by interlinking loyalty loops and involvement spirals. The article concludes with new journey-centered questions for customer experience management research, as well as branding research, consumer culture theory, consumer psychology, and transformative service research.
Keywords: attention economy; customer experience management; customer involvement; customer journey design; customer loyalty; experience economy; experiential involvement; service design
Customer experience management (CXM) research is increasingly concerned with the long-term evolution of customer experience journeys across multiple service cycles ([15]; [46]; [61]). Much of this research suggests that firms should make customer journeys as "consistent and predictable" as possible ([37]; [48], p. 55; [57]). Firms are advised to invest in "streamlining" techniques ([28], p. 90), such as simplification, personalization, and contextualization. These streamlining techniques are intended to enroll customers into an "ongoing cycle" of retrigger, repurchase, and reconsumption experiences ([21], p. 101), known as a "loyalty loop" (p. 102). In time, this loop can feel seamless, like "sliding down a greased chute" ([34], p. 227). Given the emphasis on consistency, effortlessness, and predictability, we call this approach to customer journey design the "smooth" journey model. This approach is mostly derived from research on instrumental services, such as banking (e.g., Citibank), pharmacies (e.g., MedPlus), and transportation (e.g., Amtrak).
However, many firms today offer a dramatically different kind of customer journey, one that intentionally features inconsistency, effortfulness, and unpredictability to keep customers excited ([ 2]; [32]; [62]). For example, CrossFit, a group fitness service, offers customers "constantly varied" workouts ([41]) in which "the excitement never seems to wear off" ([74], p. 4). Pokémon Go, an augmented reality game, keeps players wandering through real-world locations to catch randomly spawning virtual creatures ([ 5]). Tinder, a geosocial dating app, facilitates a dating journey "filled with adventure, unknowns, and endless possibilities" called the #swipelife ([90], p. 3). The press refers to such customer journeys as "sticky" to emphasize that customers cannot seem to pull away, and even when they do pull away, they are eager to return for more ([63], p. 7; [65]; [78]). Simply put, sticky journeys are exciting journeys that customers yearn to continue. Despite the rising popularity of sticky journeys, CXM researchers have yet to question the assumptions of the smooth journey model or to develop an alternate conceptual model. Redressing these oversights is important because CXM research is too quickly converging on the smooth journey model, without recognizing legitimate alternatives.
In this article, we make three contributions to CXM research on customer journey design. Our first contribution is to challenge the dominance of the smooth journey model. This model advises firms to enroll customers into a loyalty loop of predictable experiences, such as Citibank transactions, MedPlus refills, and Amtrak trips, regardless of the service category. Such predictable experiences offer customers convenience, ease, and satisfaction, but also risk losing customer attention in competitive markets.
Our second contribution is to empirically develop an alternate sticky journey model, premised on the excitement of unpredictable experiences. Beyond CrossFit workouts, Pokémon Go walkabouts, and Tinder dating adventures, other examples of such experiences include those of Blue Apron meal kits, dramatic HBO serials, Instagram image feeds, Spotify music streams, and trendy Zara fashions. At the heart of the emergent sticky journey model is the notion of an "involvement spiral"—a roller coaster ride of thrilling and challenging experiences that motivates increasing experiential involvement over time.
Our third contribution is to address practical CXM concerns at the nexus of the two journey models, including which model to select, when to encourage purchases, and how to sustain journeys. We advise firms to employ the smooth journey model in instrumental service categories, wherein customers have jobs to be done, and the sticky journey model in recreational service categories, wherein customers seek never-ending adventures. We also advise firms to encourage purchases at different times within each journey type: during the initial service cycles of smooth journeys, when customers are motivated to make complex decisions, and during the subsequent service cycles of sticky journeys, when customers are already caught up in involvement spirals. Finally, we trace six possible ways of interlinking loyalty loops and involvement spirals to sustain customer journeys in multiservice systems. For example, firms could spark involvement spirals from existing loyalty loops. Overall, this article challenges the dominance of the smooth journey model, offers an alternate sticky journey model, and encourages new ways of thinking about customer experience journeys.
The concept of customer experience is generally defined as a customer's multidimensional—cognitive, emotional, sensorial, behavioral, and relational—responses to a firm's service ([79]). Building on the notion of customer experience, the concept of customer experience journey (or customer journey) is typically defined as the ongoing customer experience across the phases of a service cycle ([35]). These phases are variously demarcated in the CXM literature as "pre-purchase, purchase, and post-purchase situations" ([46], p. 384); "pre-core, core, and post-core service encounters" ([92], p. 270); and "search, purchase, experience, and reflect [phases]" ([25], p. 243). However, exclusively focusing on phases within a service cycle is too myopic for CXM practitioners if they hope to have customers returning for several service cycles ([15]; [67]; [99]).
To overcome this myopia, recent CXM literature has expanded the scope of the customer journey concept—from the relatively short-term customer experience of a single service cycle to the relatively long-term customer experience across multiple service cycles ([56]). This literature emphasizes that the customer experience during the first service cycle is different from the customer experience during repeat service cycles ([21]), necessitating distinct conceptualizations of journey patterns during initial and subsequent service cycles. Moreover, the customer experience during each subsequent service cycle tends to build on the experiences of prior service cycles ([24]). In other words, the customer journey across multiple service cycles is not repetitive but iterative ([61]). Finally, when journeys near the end, the journey pattern across the final few service cycles may also be different from those in prior service cycles ([20]), necessitating distinct conceptualizations of termination trajectories.
In summary, recent CXM literature advises customer journey researchers to look beyond the short-term customer experience of a single service cycle to the long-term journey patterns across initial, subsequent, and terminating service cycles. In this way, recent CXM literature is renewing the originally intended scope of the customer journey concept ([35]). Thus far, this literature has developed around an interconnected set of conceptual axioms that we frame as the smooth journey model.
The initial service cycle of customer experience journeys is widely understood as a highly deliberate, multiphase, customer decision-making process, motivated by internal and external triggers ([21]; [44]; [84]). Firms compete for customer attention during every phase of this process: ( 1) the initial consideration of multiple brands, ( 2) the active evaluation of those brands, ( 3) the moment of purchase, and ( 4) the consumption experience. To win market share during these four key phases, firms are advised to provide customers with "decision support," including ( 1) brand advertising and content marketing during the initial consideration phase, ( 2) interactive website tools for the active evaluation phase, ( 3) in-store advertising and special offers at the moment of purchase, and ( 4) informative packaging and service updates to enhance the consumption experience. Winning customers over during these four phases increases the likelihood that customers will return to the firm for future purchases when retriggered.
Following the initial service cycle, firms are advised to streamline the customer journey ([28]) by ( 1) eliminating unnecessary steps (or simplification), ( 2) anticipating customer preferences (or personalization), and ( 3) providing just-in-time support (or contextualization). Such streamlining techniques facilitate predictable as well as convenient, easy, and satisfying customer experiences ([34]; [49]; [57]). Even more importantly, these techniques enroll customers into a routinized or automated cycle of retrigger, repurchase, and reconsumption experiences known as a loyalty loop. The loyalty loop is named as such to emphasize that customer loyalty builds every time the service meets customer expectations ([21]). In the best-case scenario, the brand becomes a trusted provider, and the customer in turn becomes a brand advocate ([60]).
Loyalty loops are generally visualized as infinite cycles ([21]). However, loyalty loops can come to an end following loyalty-weakening incidents, such as when the brand delivers poor service or when a competing brand offers a better service ([34]). Following such incidents, customers tend to follow one of two patterns. Whereas "switchers" reenter the deliberate decision-making process and choose an alternate brand, "vulnerable repurchasers" tentatively consider competing brands but end up repurchasing the incumbent brand for the time being ([20], p. 66).
Underlying the smooth journey model is a taken-for-granted assumption that firms should try to make customers' lives easier by creating consistent and predictable experiences ([21]; [28]; [49]). This assumption has a long history in marketing thought. For example, service research has long argued that predictability across service encounters is "integral to consumer satisfaction" because it "increases cognitive control, minimizes risk, and reduces cognitive effort" ([85], pp. 88–89). More recently, CXM research argues that touchpoint cohesion, consistency, and context sensitivity "reduce the amount of time and effort customers must invest in living through a customer journey" ([57], p. 556). Given this history, one can better appreciate why the smooth journey model assumes that customers always value predictable experiences.
However, customers sometimes value unpredictable experiences. For example, entertainment research shows that dramatic serials with unpredictable plotlines (e.g., Game of Thrones) motivate binge-watching, whereas dramatic procedurals with predictable structures (e.g., Law & Order) are less captivating ([66]). Likewise, gambling research shows that unpredictable reward schedules are much more exciting than predictable ones ([81]). The "intermittent wins" of unpredictable reward schedules can produce "states of arousal" like a "drug-induced high" ([13], p. 491), motivating gamblers to keep on gambling and some gamblers to become addicted ([81]). Similarly, gaming research shows that unpredictable gameplay outcomes can be simultaneously "enjoyable," "frustrating," and thought-provoking ([50], p. 221), within and beyond playtime, "keep[ing] players returning to the game" ([17], p. 40). Today's video games (e.g., World of Warcraft) are even stickier than prior generations because of their greater unpredictability ([ 2]). The combination of expansive virtual worlds, massively multiplayer capacities, and evolving game objectives escalates the unpredictability as well as the excitement. Finally, consumer research on desire ([10]), extraordinary experiences ([ 4]), and repetitive decisions ([83]) also show that customers are much more likely to persist on a journey when they are not entirely sure what comes next. One reason is that the suspense is itself exhilarating ([32]). Another reason is that the need for resolution is strong ([83]).
In summary, multiple fields of research indicate that predictable experiences satisfy customer expectations but also risk losing their attention. Meanwhile, unpredictable experiences keep customers excited and yearning for more but also risk fostering addictions. To put these insights in CXM terms: high (low) customer experience predictability facilitates smooth (sticky) customer experience journeys.
The aim of this study is to develop a conceptual model of sticky journeys, including service design principles on the firm side and customer journey patterns on the customer side. To achieve our aim, we examine three brand contexts: CrossFit, Pokémon Go, and Tinder. Each of these brands features customer experience unpredictability as a core service attribute. Furthermore, each of these brands is well-known for being especially sticky in its respective service category ([63]; [65]; [78]). Together, the brands offer a mix of journey formats that help develop a generalizable model of sticky journeys. CrossFit journeys are largely offline, Tinder journeys are largely online, and Pokémon Go journeys are both.
CrossFit is a group fitness regimen founded by Greg Glassman in 2000. The signature "constantly varied" workouts include gymnastics, weightlifting, and bodyweight exercises in well-equipped indoor-outdoor servicescapes called "boxes." Athletes are encouraged to strive toward increasingly higher levels of fitness, measured in terms such as reps, weight, and time ([23]). CrossFit is a multi-billion-dollar brand ([72]), growing from 13 affiliates in 2005 to more than 15,000 affiliates worldwide in 2019 ([23]).
Pokémon Go is an augmented reality mobile video game released by Niantic in 2016. Drawing on Google Maps data and the global positioning system, the app reveals a dynamic virtual reality world in players' own local surroundings. Players hunt for virtual fictional creatures (Pokémon) that appear unpredictably and marshal those creatures in subsequent gameplay activities such as battles and raids ([69]). Pokémon Go was the fastest mobile app to reach $1 billion in revenue ([68]), and "more cumulative time is spent playing Pokémon Go than any other [mobile] game" ([ 5], p. 3).
Tinder is an online dating app launched by Hatch Labs in 2012. Based on user locations and preferences, Tinder presents users with a seemingly infinite supply of other users' profiles. Tinder users can swipe right on profiles to express interest, swipe left to express disinterest, swipe up to express high interest, and chat with "matches" (i.e., users who have expressed mutual interest; [91]). Tinder is among the highest-grossing nongaming apps worldwide ([88]) and "the most-used dating app in the UK and the US" ([45], p. 1).
The first author collected the data using an ethnographic combination of experiencing via participant observation, enquiring via in-depth interviews, and examining via archival research ([97]). The majority of this data collection occurred in the United Kingdom between 2016 and 2019. Some data were also collected in North America and continental Europe.
To experience the stickiness of the services directly, the first author exercised at three different CrossFit boxes, played Pokémon Go to a moderate level of proficiency, and swiped through dozens of Tinder profiles. On his Tinder profile, the first author displayed his real name, university affiliation, and research intent. Communications were focused on the research project. Tinder users who expressed other interests were unmatched to avoid confusion ([53]). Field notes about these immersive activities amounted to 185 single-spaced pages. All descriptions of the three services in this article are based on these observations, except where otherwise noted.
Using social networking and snowball sampling, the first author recruited 40 informants who have customer experience with one or more of the three services. Five informants also have provider-side experience at CrossFit as owners or coaches, and four informants also have gaming or technology expertise. These nine informants are more likely than other informants to use industry jargon in their stories, but their journeys in a customer role are similar to those of other informants. Of a total of 43 distinct customer journeys culled from the interviews, 13 journeys pertain to CrossFit, 19 to Pokémon Go, and 11 to Tinder. At the time of the interview, some informants had just begun using the services a few weeks prior, while others had been customers for several years. Eleven of the 43 journeys included discernible termination trajectories. The informants are mostly white and middle class but vary in terms of age (16–59 years) and gender (18 female, 22 male). Interviews were conducted in person or by telephone, ranging from 30 to 172 minutes (83 minutes on average). Interviews were loosely structured around five areas of inquiry: ( 1) the informant's everyday experiences with the focal service (e.g., how the service enters and exits their day); ( 2) their long-term journey with the service (e.g., how they got started, what keeps them interested, when they lose interest); ( 3) their experiences with competing services, if any; ( 4) their recollections of significant moments or time periods; and ( 5) the life contexts surrounding these service experiences. The audio-recorded interviews yielded 1,464 single-spaced pages of transcribed text. Informants that are quoted in this article are renamed for confidentiality and their quotes are edited for clarity. Quotes from foreign language speakers are translated into English.
Using keyword searches and a custom Google feed, the first author collected publicly available materials about the three services, including websites, press releases, industry reports, and news articles, from mainstream media (e.g., The Guardian) as well as niche media (e.g., Wired). These data include announcements of service updates and upcoming events, newsworthy customer experiences, and industry leader perspectives. In total, the archival data set amounts to over 200 documents, about 20 of which are cited in this article.
Our interpretive process consisted of three iterative activities: making constant comparisons across our informants' lived experiences to discern common patterns; creating memos of our preliminary insights to debate within the research team; and tacking back and forth between the existing literature and our emerging understanding to crystallize our theoretical insights ([ 3]). We drew on different types of data to discern firm-side and customer-side insights. Specifically, we drew on firm-side fieldnotes and archival materials to discern the service design principles, and customer-side fieldnotes and interview transcripts to discern the corresponding customer journey patterns. To trace the evolution of sticky journeys, we compared journey patterns in the initial, subsequent, and terminating service cycles of customer journeys across the three research contexts (see the Appendix). As is often the case in interpretive research, no single informant provides a complete view of the phenomenon. Rather, that complete view emerges from a critical mass of empirical snapshots. We terminated our interpretive process at theoretical saturation, when new rounds of data interpretation did not meaningfully alter the emergent model. For an overview of the extant and emergent journey models, see Table 1 and Figure 1.
Graph
Table 1. A Comparison of the Smooth and Sticky Journey Models.
| Dimensions | The Smooth Journey Model | The Sticky Journey Model |
|---|
| Brief overview | Firms enroll customers in loyalty loops by offering them decision support during the initial service cycle and streamlining across subsequent service cycles; the resulting customer journey is predictable, easy, and smooth | Firms enroll customers in involvement spirals by offering them rapid entry into the initial service cycle and endless variation across subsequent service cycles; the resulting customer journey is unpredictable, exciting, and sticky |
| The initial service cycle | Service design principle: providing customers with decision support at each phase of the deliberate decision-making process via brand advertising, content marketing, interactive tools, and so onCustomer journey pattern: internal/external triggers motivate customers to undertake a deliberate decision-making process consisting of four phases: (1) initial consideration of multiple brands, (2) active evaluation, (3) moment of purchase, and (4) consumption experience (visualized as a large purple curve at the base of Figure 1) | Service design principle: providing customers with rapid entry via easy account setups, free basic access, and beginner orientations, avoiding traditional onboarding practices such as questionnaires, sales pitches, and servicescape toursCustomer journey pattern: enthusiastic reviews from existing customers and third parties spark potential customers' curiosity to take the service for a quick spin, usually on a whim, without much deliberation (visualized as a small orange curve at the base of Figure 1) |
| Subsequent service cycles | Service design principle: streamlining the customer journey by (1) eliminating unnecessary service elements, (2) anticipating customer preferences, and (3) providing just-in-time information at each service encounterCustomer journey pattern: a loyalty loop, defined as a cyclical pattern of predictable experiences that reduces the need for customer deliberation and builds customer loyalty over time (visualized as a small blue helix in Figure 1) | Service design principle: endless variation along the customer journey via (1) an expansive set of service system elements, (2) frequent additions, subtractions, and changes, and (3) unique configurations of those elements at each service encounterCustomer journey pattern: an involvement spiral, defined as a cyclical pattern of unpredictable experiences that motivates greater customer involvement over time (visualized as a widening upward yellow spiral in Figure 1) |
| Termination trajectories | Brand switching triggered by loyalty-weakening incidents | Service usage fluctuations fueled by well-being concerns |
| Purchase patterns | Deliberate purchase decisions at first, routinized or automated purchases later (during the loyalty loop) | Free or low-cost plans at first, premium service plans and one-off purchases later (during the involvement spiral) |
| Application contexts | Instrumental service categories, wherein customers are jobbers and tend to be loyal to one brandBanking (e.g., Citibank) Business hotels (e.g., Marriott) Insurance (e.g., MetLife) Mail/Parcel (e.g., FedEx) Pharmacies (e.g., MedPlus) Repairs (e.g., Mr. Appliance) Telecom (e.g., Verizon) Transportation (e.g., Amtrak) Utilities (e.g., British Gas) Work apparel (e.g., Van Heusen)
| Recreational service categories, wherein customers are adventurers and often use multiple brands at onceDating apps (e.g., Bumble) Dramatic serials (e.g., HBO) Driving clubs (e.g., Jeep Jamboree) Content networks (e.g., Instagram) Fast fashion (e.g., Zara) Gaming (e.g., Fortnite) Group fitness (e.g., Orange Theory) Lifestyle media (e.g., Thrillist) Meal kits (e.g., Blue Apron) Music discovery (e.g., Spotify)
|
| Key sources | This model synthesizes insights from several CXM texts: Court et al. 2009, 2017; Edelman and Singer 2015; Fleming 2016; Hyken 2018; Leboff 2014; Kuehnl, Jozić, and Homburg 2019; and Spenner and Freeman 2012 | This model synthesizes insights from relevant texts on addictive services (e.g., Alter 2017; Eyal 2014; Schüll 2014) and empirical research on sticky journeys in the contexts of CrossFit, Pokémon Go, and Tinder (c. 2016–2019) |
Graph: Figure 1. A visualization of the smooth and sticky journey models.
Firms nurture smooth and sticky journeys differently. At the beginning of smooth journeys, firms support the customer's deliberate decision-making process with considerable decision support. By contrast, at the beginning of sticky journeys, firms attempt to eliminate customer decision making altogether by giving customers immediate access to the service. As our informants reveal subsequently, their CrossFit, Pokémon Go, and Tinder journeys tend to begin on a whim, motivated by the promise of fun. Accordingly, the most appropriate firm action at this juncture is to give potential customers a taste of the excitement to come, as soon as their curiosity is sparked.
Many CrossFit boxes, for example, offer newcomers a free beginner class, followed by an affordable beginner plan (e.g., a low-cost one-month membership). Unlike traditional gyms, CrossFit gyms do not greet newcomers with gym tours, salesperson interactions, or a complex menu of service plans, which necessitate deliberate decision making. Pokémon Go's virtual moderator, Professor Willow, orients new players via a rapid sequence of fun and easy steps. Players learn the game's mission via short-text snaps, customize their avatar with a few clicks, and catch a trial Pokémon with a couple of swipes. Unlike dating services that begin with extensive questionnaires (e.g., eHarmony), Tinder only asks new users for their gender, distance, and age preferences ([91]). Users can import photos into their Tinder profiles from Facebook and begin swiping through potential matches immediately. As commentators have noted, "Tinder's most revolutionary aspects were to nix the web[sites] and questionnaires" ([78], p. 2).
We conceptualize these speedy onboarding techniques as the service design principle of "rapid entry." This conceptualization highlights the expediency with which firms facilitate the beginnings of sticky journeys. As soon as potential customers visit a service entry point, firms rapidly offer exciting service experiences. Conspicuously absent are the tedious entry practices of most service industries (e.g., complex menus of purchase options, extensive questionnaires, servicescape tours). If customers cannot experience the excitement of a service quickly, easily, and for free, they may turn their attention to something else that is more immediately accessible. (For additional examples of the rapid entry principle, see the Appendix.)
The initial customer experiences in smooth and sticky journeys are remarkably different. Smooth journeys begin with a highly deliberate, multiphase decision-making process. Prior to our research, we expected that sticky journeys would also begin with some sort of decision making. However, contrary to our expectations, we find almost no deliberate decision-making process among our informants. As Dora, a Tinder user puts it, "I didn't do proper research." Instead, most of our informants begin their journeys on a whim, after receiving enthusiastic reviews, or observing customers enjoying themselves.
[My Bootcamp instructor] said to me: "CrossFit, that's something you'll like."...And then a neighbor told me she had started at [a local box] and invited me to come by and give it a try....I went with her and did a couple of regular workouts. Then I attended a beginner's introduction...which was great, answered a couple of questions, and then we were thrown into it!" (Karen, CrossFit athlete)
My brother tells me, "You walk around the city. And you pick up Pokémon." I'm like, "That is amazing. I definitely want to do that."...I walked around London for the whole afternoon and I was, like, "I've never seen that statue before! I live five minutes away!...Thank you Pokémon Go for that interaction with my environment." (Aron, Pokémon Go player)
When Tinder first came out, I was still in a relationship, so I never really played it, but I saw my mates play it, and I thought the idea of it was amazing in the sense that you literally just swipe, "Yeah, I think she's hot!" or "No, not for me!" And then if you did get a match out of it, I think that's hilarious, but I wasn't able to [try Tinder at that time]....When I became single...I was like, "All right, let's see what the hype's about.... This is definitely a game changer!" (Charles, Tinder user)
As these vignettes indicate, CrossFit, Pokémon Go, and Tinder journeys begin with sparks of curiosity about the focal service, rather than an active evaluation of multiple brands. These sparks of curiosity are often ignited by highly enthusiastic word of mouth from family (Aron), friends (Charles), and acquaintances (Karen). Such word of mouth excites our informants only if the service complements their already existing life projects. For example, Karen is already a fitness enthusiast when she hears about CrossFit, and Aron is already a passionate gamer when he hears about Pokémon Go. Charles hears about Tinder when he is in a relationship, so he does not download the app immediately, but soon after he becomes single again. Some informants are also exposed to these services through advertising, news, and social media, but regardless of their sources, informants answer these calls to adventure because the promise of fun is compelling and the hurdles to entry are minimal. Of course, services must deliver on the promise of fun for customers to want to continue the adventure. Karen relishes her first CrossFit class, Aron rediscovers his neighborhood through Pokémon Go, and Charles finds Tinder to be "a game changer!"
We conceptualize the initial service cycle of sticky journeys as a "quick spin" to emphasize not only the lack of deliberate decision making but also the rapid transitions from observed excitement to anticipated excitement to realized excitement. Although customers intend to try the service briefly, once they experience the exciting service firsthand, they have so much fun that they are often swept up into subsequent service cycles, again without much deliberation. In other words, what starts out as a "test drive" turns into a "joy ride" that turns into a "road trip." (For additional examples of quick spins, see the Appendix.)
Service design principles diverge even further in the subsequent service cycles of smooth and sticky journeys. The smooth journey model advises firms to streamline the customer journey such that subsequent service cycles are as consistent, easy, and predictable as possible. In stark contrast, CrossFit, Pokémon Go, and Tinder focus on providing customers with infinitely variable configurations of a core service experience. Delivering such "endless variation" along the customer journey depends on at least three concrete service design features: ( 1) the expansiveness of the service system, ( 2) the open-endedness of the service system, and ( 3) the uniqueness of each service encounter.
One essential design feature is a highly expansive set of service system elements. For example, CrossFit workouts combine innumerable exercises from global athletic traditions (e.g., handstands, muscle-ups, power squats) in a blended indoor-outdoor gym equipped with considerable workout gear (e.g., jump ropes, kettlebells, pull-up bars). Similarly, the Pokémon Go game includes hundreds of Pokémon; elaborate reward structures, including coins, medals, and points; and countless real-world locations, where players can collect game-relevant items ("PokéStops") and battle rival teams ("Gyms"). Thanks to Tinder's rapid growth to millions of active daily users ([59]), the app presents users with a virtually infinite supply of potential matches, and once matched, users can exchange unlimited private messages.
A second essential design feature is openness to the addition, subtraction, and transformation of firm-owned, customer-owned, and external service elements. For example, CrossFit boxes design novel workouts daily, coaches add their own flair, and athletes exercise with various partners at different skill levels. Meanwhile, Pokémon Go keeps adding new creatures, features, and events, some of which are time-limited (e.g., Halloween Pokémon events), environment-based (e.g., the dynamic weather gameplay system), and community-dependent (e.g., group raids). Tinder too regularly introduces exciting new features (e.g., Top Picks, Swipe Night, Tinder Gold). Moreover, Tinder's pool of active daily users is constantly changing as new users join the app and existing users take a break.
A third essential design feature is the service system's capacity to perpetuate unpredictable service experiences, even for seasoned customers, by foregrounding a unique configuration of service elements for the customer at every service encounter. For example, every CrossFit workout is a unique mix of aerobic/anaerobic, individual/partner, and indoor/outdoor exercises in varied temporal configurations. Every Pokémon Go walkabout is a unique mix of gameplay activities such as catching varied Pokémon, battling opposing teams, and conducting group raids. Every Tinder session is a unique mix of swiping through new profiles, advancing conversations with matches, and planning off-platform dates. In this manner, no two CrossFit workouts, Pokémon Go walkabouts, or Tinder sessions are ever the same ([16]; [38]; [63]). (For additional examples of the endless variation principle, see the Appendix.)
In the smooth journey model, the customer journey pattern during subsequent service cycles is a cyclical pattern of predictable experiences that increases customer loyalty over time, thus the name loyalty loop. By contrast, the customer journey pattern during subsequent service cycles of CrossFit, Pokémon Go, and Tinder is a cyclical pattern of unpredictable experiences that increases customer involvement over time. We conceptualize this pattern as an involvement spiral (see Figure 1). From a conceptual standpoint, the involvement spiral has two noteworthy patterns, one in the moment-to-moment timescale of the customer journey, the other in the long-term timescale of multiple service cycles.
In the moment-to-moment timescale of the customer journey, the involvement spiral entails a variegated pattern of thrilling and challenging experiences that we describe as an "experiential roller coaster." Such an unpredictable pattern of positive and negative experiences, including emotions of anticipation, dread, amazement, disappointment, and enjoyment, keeps customers in a state of high psychological arousal; in their highly aroused state, customers become highly attuned to the multidimensional intricacies of service experiences ([ 4]; [13]; [17]).
In the long-term timescale of multiple service cycles, the involvement spiral entails an upward trend in customer involvement that we describe as increasing "experiential involvement." Here, our composite notion of experiential involvement refers to customer involvement (i.e., interest, excitement, and investment) in the customer experience (i.e., the cognitive, emotional, sensorial, behavioral, and relational responses to a service) ([79]; [96]; [98]). Increasing experiential involvement does not imply that customers spend more time on the service each day. Rather, it implies that customers become more deeply invested in the multidimensional intricacies of their service experiences. With each successive cycle of the customer journey, customers also acquire new service-relevant competencies, including new insights, mindsets, and skills ([ 2]; [18]; [32]). Given the centrality of the involvement spiral to the sticky journey model, we next empirically illustrate this journey pattern in each of our three service contexts.
CrossFit's core service is a one-hour group-training class. The prototypical class includes a warm-up, a weightlifting segment, and a workout of the day (WOD). The warm-up is customized daily for the segments that follow. Warm-ups include static stretches (e.g., the hip-flexor stretch), dynamic stretches (e.g., the side shuffle), and other creative activities (e.g., push-ups to the beat of a pop song). Next, the weight-lifting segment might combine multiple exercises or focus on one compound exercise (e.g., the clean-and-jerk). The target number of rounds and repetitions are posted on a large screen, but athletes scale the weights to their current abilities. Coaches often encourage athletes to beat their own personal record. Finally, the WOD is the fastest-paced segment of the class. A WOD can include not only weight-lifting movements but also gymnastics and bodyweight exercises (e.g., pull-ups, rope climbs, lunges) and metabolic conditioning (e.g., running, biking, rowing). Overall, CrossFit classes can feel easier or harder depending on a host of factors such as the athlete's current abilities, the competitiveness among attendees, or even the weather conditions. Some CrossFit boxes post the workouts online the night before, and some athletes take a peek at those workouts in advance to jump-start their excitement. Other athletes, like Alan, take pleasure in the suspense of not knowing what comes next.
Interviewer:
What makes you want to go to CrossFit again?
Alan, CrossFit athlete:
It's the un-knowing of what you're going to do that night, because you're not really supposed to know.... You go to the gym the night before, you do a horrible workout, but you love it.... It makes no sense, because why would you love something that's horrible?...But you've worked up a sweat because it's horrible. And then you're like, "Well, I'm going to book [a class], because if I know what it's going to be tonight, I won't turn up," and that's why, that's the beauty of it, because you don't know, so you've got to go to find out. It's like a present. If you get a present, if they just tell you, you're not going to be excited...[but] if it's a surprise, then when you open it, you're excited. You're amazed by what you've got. And that is literally the beauty of just going to a CrossFit class, because every day, you're like, "I'm going to go tonight" because you are so excited to see what the workout is. It could be amazing, it could be bad, but you still get excited....It's like swings and roundabouts really.
Alan's words nicely illustrate why the endless variety of CrossFit classes can feel like an experiential roller coaster. There are moments of anticipation ("it's the un-knowing"), surprise ("it's like a present"), and reflection ("but you love it"). Classes can be "amazing" or "horrible," but regardless, they always get one "excited." Simply stated, the journey is a mix of positive and negative moments ("it's like swings and roundabouts"). We use the conceptual metaphor of the experiential roller coaster to describe the moment-to-moment experience of the sticky journey because it encompasses the full spectrum of experiential dynamics: the "peaks" of pleasurable experiences, the "valleys" of painful experiences, the "climbs" toward peaks, the "dives" into valleys, and the ever-present suspense about what's around the next turn.
At the same time, a sticky journey is no mindless roller coaster; rather, it is one that continually shifts customer attention to the many possible connections between the service experience and one's own life goals. In this manner, a sticky journey invites greater experiential involvement over time. For example, many CrossFit informants speak of developing greater physical and psychological mastery through CrossFit's workouts.
If you are not good at something, it takes a lot for you to dedicate your time to want to be better at it. And I think CrossFit is the only [fitness regime] that has made me do that. I hate squatting, I hate doing anything like that. And I am forced to do it at CrossFit...[and] that's really good for my hips and my back, and as I get older, that movement is really important....When you are like, "I don't know what I'm doing, I don't know what this activity is?," watching [other CrossFit athletes] do it sort of helped me remember the technique, so I was like, "Okay, so when I need to squat, for example, I should be getting that low."...The more you watch...the better you'll be. (Jenny, CrossFit athlete)
In this vignette, Jenny describes one meaningful trajectory of her CrossFit journey as overcoming her psychological barriers to the compound exercise of squatting. Jenny is an intermediate athlete who still has much to learn, but unlike a beginner, she has become aware of the general importance of good form ("I should be getting that low"), the specific functions of different exercises ("good for my hips and my back"), and the potential linkages between her CrossFit activities and long-term goals (e.g., staying fit as she ages). We interpret this tendency of customers to become more deeply invested in the intricacies of service experiences as increasing experiential involvement.
Pokémon Go has an elaborate game structure, including 40 game levels; rewards such as bronze, silver, and gold medals; and different point allocations for different in-game actions. Pokémon tend to appear unpredictably and for brief time spans, thus motivating the gamer to catch them immediately. The game's tagline, "Gotta catch 'em all," refers to the goal of catching every type of Pokémon by throwing PokéBalls at them. Commentators have noted that "each capture session...each walk a player goes on...is unique" ([63], p. 4). Although players can perform select game actions without walking around (e.g., reviving fainted Pokémon), most game actions require walking or other modes of travel. Collectively, these various triggers, actions, and rewards during each Pokémon Go service cycle (or walkabout) generates considerable excitement for players.
When I went out with my daughter, and we go, "Oh, there's an egg about to hatch." And we gather round and look at it and go, "Oh no, it's a [common Pokémon]!" [laughs]. And then, we get excited about another one! It's the medals. I have walked 1,502 kilometers....[There's] a lot of unique goals and different routes you can go through. [Niantic] keeps releasing new features.... They have Pokémon only released in certain countries, so when I'm in America, I'm catching American Pokémon. It's quite exciting....Some are incredibly difficult to find, and you get very excited when you find one. And some are legendary. The legendary ones you couldn't find anywhere.... It's really exciting cause it's time-limited, so if you want to complete your Pokédex...you've got to get [the released Legendary Pokémon].... You've got to find a Gym that's got one....You've got to take part in a raid. The raids themselves are time-limited. And you can't win a raid unless you've got about ten people there. (Ruth, Pokémon Go player)
Ruth derives pleasure from Pokémon Go's varied gaming activities (catching Pokémon, hatching eggs, group raids) in varied social constellations (alone, with her daughter, in groups) at varied real-world locations (in the United Kingdom and the United States). Like other informants, she experiences an unpredictable sequence of thrills ("Oh, there's an egg") as well as challenges (hunting for "incredibly difficult to find" Pokémon), making the moment-to-moment Pokémon Go journey feel like a roller coaster ride.
Further analysis of the Pokémon Go data set reveals that informants' journeys also evince increasing experiential involvement across multiple walkabouts.
I walked past a PokéStop...[and] I was like "Oh, let me try and catch [a Pokémon], see what happens," and before I knew it I was catching them and then trying to figure out which ones were better to catch and which numbers were good...and learning that stuff. I went back to work after the summer and there were lots of PokéStops and [other players] wanted to get walking so that they could hatch the eggs. I thought, "I walk a lot while I'm at work, I go from one building to the other and back again." So when I'm out...I can have it on.... Every night when I get home, [my son] would check how much I'd walked and which Pokémons I'd got. I found myself using it more and more.... Because there are still challenges in Pokémon Go, because new Pokémon appear, because there's rare ones, or trying to get one to the maximum level, that stuff, it gets me interested.... I'm not done with this, there are Pokémon to get, there are achievements to achieve, medals to get. (Daniel, Pokémon Go player)
Daniel's vignette illustrates how informants can get swept up into the involvement spiral of sticky journeys without any explicit intentions to do so. He initially downloads the game as a family pastime, then continues playing the game on his own. Like many other players, Ruth included, Daniel soon incorporates playtime into his daily walking routines, connects with fellow players, and finds himself playing Pokémon Go "more and more." Although his time spent on the app does not increase indefinitely, his experiential involvement during his playtime keeps increasing. He hunts for different, new, and rare Pokémon; powers them up to their maximum levels; and continually learns new ways to earn rewards. His end game is a "moving target" ([63], p. 5). Over weeks, months, and sometimes even years of playing the game, informants such as Ruth and Daniel become increasingly well-versed in the game's numerous intricacies, which in turn increase their enjoyment of the game.
Departing from traditional matchmaking services that connect customers based on compatibility questionnaires ([33]), Tinder thrusts users into an "open" stream of fellow users' profiles ([91]). Anna, a Tinder user, describes the resulting experience: "Tall men, small men, fat men, thin men, poor [men], rich [men], doctors, gardeners, and everything! You really see a big cross-section of society. And that was super exciting!" Tinder also includes a messaging stream for matched users to get to know one another, schedule off-platform dates, and keep in touch for as long as there is mutual interest. These two main streams of user interaction generate Tinder's experiential roller coaster.
I was going back home, and instead of sleeping, I was spending an hour, and I was saying, "Okay, it will be the next one that I might like, it will be the next one," but no, it wasn't....In the morning, if someone liked my profile, if I was finding it interesting, I would say "Hello, good morning," stuff like that, and then I would try to initiate a discussion.... It was really addictive. In the morning, I might lose, like, 10–15 minutes to see what's happening, who liked me....Sometimes the application shows you profiles first, and then, if the other person likes you, it will appear in your profile as a match. But there were times that I would like someone, and he had liked me first, so I will talk with them straight away. That was when I would text someone more often. (Sophia, Tinder user)
For Anna, Sophia, and other Tinder informants, swiping through profiles is a psychologically arousing process with moments of suspense, delight, and frustration. Users only see one profile at a time in the default swiping channel ("Discover"). They must swipe right to "Like," swipe left to "Nope," or swipe up to "Super Like" before the next profile is revealed. In Sophia's journey pattern of "obsessively swiping through Tinder" ([26], p. 1), she follows each "Nope" with a wish that "it will be the next one" that she might "Like," followed by a near-immediate revelation of whether her wish has come true or not. Matching with a few users and chatting with them injects new variety into her experiential roller coaster, rendering the overall experience "really addictive." Tinder informs a user about a match as soon as two users have liked one another. Sophia's urge to check the app as soon as she awakes indicates that the suspense she experiences while swiping also endures through the matching and messaging process. Intense feelings of desire and disappointment can occur for informants even before they have scheduled any off-platform dates ([ 7]).
As informants keep swiping through profiles, communicating with matches, and going on dates, their experiential involvement increases, albeit without any explicit reward structure. Unlike Pokémon Go, Tinder does not award points for successful plays, and unlike CrossFit, Tinder does not chart performance metrics on scoreboards. After all, "'success' in online dating can mean many things to many people" ([78], p. 3). Even so, the Tinder journey does have an implicit reward structure: the quantity and quality of one's matches, chats, and dates, which users interpret subjectively. Many informants also express personally meaningful developments, such as a growing self-awareness about their own relational desires and an increasing ability to understand and respond to matches.
[The] fruits from Tinder come out only with constant use.... At the beginning, I would invest more time chatting with some specific people, while now, I'm much more direct. Also, because it's a matter of numbers, in the sense that after a while, you get more matches. You basically spend less time on average with every person.... My philosophy is chat a little bit, and if you see that there is some kind of common ground and chemistry that you can feel at the very beginning, just by texting someone, then my next proposal is "Okay, let's meet!"...How people reply, how people write you, you can really get an idea, more or less, of the kind of person it is. There are people who are very funny and start making jokes, or tell you something different, or something more clever, while other conversations [are] more standard, boring ones. (Roberto, Tinder user)
Over the course of his Tinder journey, Roberto refines his approach in several ways. For example, he learns to start swiping during the week to arrange a date for the weekend. He abbreviates unnecessary conversations with a "more direct" style. He becomes quicker at recognizing the "kind of person" he is chatting with based on their texting style. From week to week, Roberto also gets more matches, juggles more conversations, and enjoys more dates. Such increasing experiential involvement in the intricacies of the Tinder journey allows him to become more efficient, effective, and even philosophical about dating. (For additional examples of involvement spirals, see the Appendix.)
Smooth journeys are generally visualized as infinite loyalty loops. However, in reality, smooth journeys can and do come to an end. Loyalty-weakening incidents, such as poor service experiences and attractive competitor offerings, can trigger customers to reenter the deliberate decision-making process and switch to a new brand. Sticky journeys, by contrast, tend to terminate with service usage fluctuations fueled by well-being concerns. Sometimes, sticky journeys also terminate for brand-specific reasons.
We observe that some of our informants begin to question whether to continue their sticky journeys when those journeys start to feel addictive in the pathological sense of the term (i.e., the service discernibly conflicts with the customer's own sense of well-being; [86]). In these instances, informants tend to withdraw from the service, either gradually or suddenly. Often, they repatronize the service, then withdraw again. Christine's dissonance about continuing her CrossFit journey stems from its overly enthusiastic culture.
I did it quite intensively until Christmas....And then I did it a bit less. Somehow, I could not motivate myself to go as often....But for four months, really intense, and then three months...not quite so intense. Then, when I went home, I actually stopped it....What rather scared me is the fanaticism that many have.... I thought, "Okay, that's not my world, as far as I'm concerned."...It's very important to me to become fit and stay fit, but only to a certain level. (Christine, former CrossFit athlete)
As a former competitive athlete, Christine is well aware of how fitness and health concerns can become all-consuming over time. For her, the CrossFit journey is fun "to a certain level," but she reaches that upper limit after several months of enthusiastic participation. By contrast, that upper limit comes very early in Aron's journey with Pokémon Go.
Downloaded it, walked around, saw the historical sites that are within it, the PokéStops, it tells you little things about what might be on the street. Loved it, did it for four or five hours and deleted it, because...I will do this way, way too much.... I definitely need to consume fewer video games. (Aron, former Pokémon Go player)
Aron's concerns about the addictive potential of Pokémon Go arise within a few hours of playing the game. To put this episode in perspective, Aron is an avid gamer who has preexisting concerns about keeping his playtime in check. Accordingly, he deletes the app the very same day he starts playing. However, following this episode, Aron downloads the app again and plays the game for a few more weeks, before giving it up for a second time. Whether users take mere hours or several years to reach their upper limit of the involvement spiral, they nonetheless express the same general concern about the addictive potential of sticky journeys.
It's very addictive....I would spend a lot of time.... It was like...an addictive game, so in order to stop using it, at some point, I just deleted it, and it worked fine....If I don't want to do something, I'm trying to not have Sirens around me. (Sophia, former Tinder user)
Sophia tries to use the Tinder app less at first but eventually decides that deleting the app is the only way to cope with its addictive potential. In telling her story, Sophia draws on the myth of the Sirens—beautiful-voiced but dangerous creatures who lure gullible sailors to shipwreck themselves on the Sirens' island. In some versions of the myth, sailors plug their ears so as not to hear the Sirens' call. In a similar vein, Sophia blocks out the call of Tinder by deleting the app. Of course, not all informants terminate their journey when well-being concerns arise.
I'd always want to keep training and training, but I think with experience, I've learned to say..."Just take a week, let your body recover a little bit." And our coach is quite good at saying, "If you're tired...then take the week off. It's not going to do any harm and, if anything, you'll benefit from it." (John, current CrossFit athlete)
Unlike Christine, Aron, and Sophia, John simply takes time off when his well-being concerns arise, suggesting that some informants are better at self-regulation than others. (For additional examples of service usage fluctuations fueled by well-being concerns, see the Appendix.)
Sticky journeys also fluctuate or terminate for brand-specific reasons (e.g., physical injuries at CrossFit, boredom with Pokémon Go, relationship status changes in Tinder). In the context of CrossFit, athletes can get injured while participating in high-intensity workouts. For example, Olivia recalls being "surrounded by individuals who were a hell of a lot fitter than me...looking at them as my role models and icons, going, 'I can do that if I want to.'" However, her journey came to a sudden stop: "I did too much too soon....And then, as a result, I got injured....I fell off the rig and broke my elbow." Two years after this "breaking point," she resumed CrossFit. In the media, controversy over the "cultish" nature of CrossFit focuses on such "overuse injuries [that] are not uncommon among CrossFitters" ([38], p. 2). Many in the industry are "wary" of the fitness regime because of its "risk of injury and drop out" (Denoris, in [38], p. 2).
In the context of Pokémon Go, boredom is a common theme. For example, Aron says, "I've put enough hours into this, every egg that hatches is the same, every Pokémon I find is the same, I'm bored." Timothy too stops playing for several months because the journey eventually loses its appeal: "I walked a 100 kilometers to get a [specific Pokémon]. And it was not even a good Pokémon....That was a chore, and that did feel boring....I was like, 'No, I don't have to do this,' and so I stopped." Informants' waning interest in the first year of the game's launch corresponds with Niantic's delay in effectively deploying endless variation across the customer journey, ironically due to the overwhelming success of the game. As chief executive officer John Hanke noted, "We had to redirect a substantial portion of the engineering team to [work on] infrastructure versus features....I'd say we're about six months behind where we thought we would be" ([94], p. 2). When Niantic launched Generation 2, some of our informants enthusiastically returned to the game. As Jill says, "[Niantic] introduced Generation 2 at just the right moment for me, because it piqued my interest again!"
In the context of Tinder, journeys terminate when users wish to settle down with one partner, and then do not find one despite significant effort, or do find one. Former Tinder user Enrico withdrew from Tinder for each of these two reasons. After many "dead [end] conversations" with matches, "[I] felt disengaged with the application, as I was not achieving anything in particular," and "at some point I decided to uninstall the application." However, Enrico rejoins Tinder about 18 months later, when his friends encourage him to "go on Tinder and try to have fun." This time, being "more mature in the use of the application," and having "fate" on his side, he matches with someone that he falls in love with, prompting another uninstallation of the app: "since things were almost done, I also decided to uninstall Tinder."
Prior CXM research on customer journey design is too quickly converging around the smooth journey model, without adequately interrogating its underlying assumptions. The smooth journey model is certainly useful but only in terms of maximizing hyperrational factors such as consistency, effortlessness, and predictability. As our findings highlight, customers also sometimes yearn for the excitement of unpredictable journeys, if only to temporarily escape their otherwise hyperrational lives. Accordingly, in this article, we have developed an alternate journey model that is premised on the excitement of unpredictability. This model explains how firms can design sticky journeys that customers yearn to continue. Each of the two models advocates for a unique set of service design principles and customer journey patterns (see Table 1). In essence, the smooth journey model helps customers to make an informed decision, then fall into a comforting, trust-building routine (i.e., a loyalty loop). By contrast, the sticky journey model yanks customers onto an experiential roller coaster ride that increases customers' experiential involvement over time (i.e., an involvement spiral).
A caveat for CXM researchers is that both journey models are ideal types (i.e., tidy abstractions of messy realities; [93]). Real-world customer journeys are never wholly predictable nor wholly unpredictable. Most services facilitate a mix of predictable and unpredictable experiences. What distinguishes the two journey models is the relative emphasis on high versus low customer experience predictability. Furthermore, all journeys are interrupted and interwoven in customers' everyday lives. No journey unfolds in isolation from all others. These caveats aside, journey models are valuable as "cultural mindsets" for coordinating CXM activities across organizational stakeholders ([46], p. 385). Figure 1 can help customer experience officers (CXOs) coordinate all customer-facing departments in a firm toward a shared vision of the customer journey. If that vision is a sticky journey, then the notion of an involvement spiral can help CXOs to emphasize the importance of ( 1) keeping customer experiences unpredictable in the moment-to-moment timescale, and ( 2) increasing customer opportunities for experiential involvement across successive service cycles.
The emergent concept of sticky journeys is related to several existing marketing concepts (see Table 2). Among these concepts, customer involvement ([98]) is the most central to understanding sticky journeys. As sticky journeys evolve, customers become increasingly involved in the service experience. Given that involvement is a decades-old construct with several variants (e.g., product, brand, and purchase involvement; [ 8]), we emphasize that experiential involvement is the most appropriate concept for our model as well as CXM research at large. As journeys evolve, customers may also become more engaged in the sense that they begin to contribute direct and indirect value to the firm. However, such customer engagement ([73]) is not necessary for journeys to be sticky. Journey stickiness can be distinguished from customer loyalty in both behavioral and affective terms. When customers regularly consume one brand in a service category, out of a sense of commitment, that repatronage is best conceptualized as loyalty ([71]). However, when customers frequently return to a service, out of a sense of excitement, that repatronage may be better conceptualized as stickiness, which does not imply brand exclusivity.
Graph
Table 2. Sticky Journeys and Related Marketing Concepts.
| Concept | Description | Relationship to Sticky Journeys |
|---|
| Sticky journeys | Sticky journeys are exciting journeys that customers yearn to continue. This article reports that sticky journeys begin with quick spins, develop into involvement spirals, and terminate with service usage fluctuations.Quick spins are extemporaneous service trials, just for fun, without any long-term consumption intentions.Involvement spirals are cyclical patterns of unpredictable customer experiences that increase customers' experiential involvement over time.Service usage fluctuations are termination trajectories wherein customers withdraw from a service, then return, sometimes more than once. | |
| Consumer addiction | Consumer addiction is the compulsive repetition of pleasurable consumption behaviors (e.g., drinking, gambling, shopping) despite negative consequences (Sussman, Lisha, and Griffiths 2011). The term "addiction" is also popularly used to refer to compelling but nonpathological behaviors (e.g., "I'm addicted to that show!"). | Sticky journeys are "addictive" only in the popular sense of the term, but they can turn into pathological addictions. |
| Consumer desire | Consumer desire is "a powerful cyclic emotion that is both discomforting and pleasurable" (Belk, Ger, and Askegaard 2003, p. 326). Unlike a need or want, a desire is "for something fantastic...to drag us out of our ordinary habits...into the chaos and unpredictability...of our own deeper nature" (Kozinets, Patterson, and Ashman 2017, p. 674). | Sticky journeys can feed consumer desires for adventure in otherwise hyperrational lives. |
| Customer engagement | Customer engagement is "the mechanics of a customer's value addition to the firm, either through direct or/and indirect contribution" (Pansari and Kumar 2017, p. 295). Customer engagement typically includes purchase, referral, influence, and knowledge-sharing behaviors (Kumar and Pansari 2016, p. 500). | Sticky journeys can include customer engagement in this sense of the term, but it is not a definitive component. |
| Customer involvement | Customer involvement is "a person's perceived relevance of the object based on inherent needs, values, and interests" (Zaichkowsky 1985, p. 342). Experiential involvement denotes a person's interest in the cognitive, emotional, sensorial, behavioral, and relational dimensions of a service experience. | Sticky journeys entail increasing experiential involvement across multiple service cycles. |
| Customer loyalty | Customer loyalty is "a deeply held commitment" (Oliver 1999, p. 34) toward a brand that results in repatronage of the brand over time, despite opportunities to switch brands. The attitudinal and behavioral components of customer loyalty are not always in sync. | Sticky journeys also feature repatronage, but customers are motivated by excitement rather than commitment. |
| Extraordinary experiences | Extraordinary experiences are "intense, positive, [and] intrinsically enjoyable experiences" (Arnould and Price 1993, p. 25). In contrast to ordinary experiences, they are "uncommon, infrequent, and go beyond the realm of everyday life" (Bhattacharjee and Mogilner 2014, p. 2). | Sticky journeys tend to include varied positive and negative experiences in rapid succession. |
Consumer desire is a type of consumer motivation that is much more energetic, passionate, and urgent than need or want ([10]). Our study indicates that customers do not need or want their sticky journeys to continue but urgently desire such continuity. However, when sticky journeys become compulsive or pathological, they may be better conceptualized as consumer addiction ([86]). Finally, extraordinary experiences are highly positive and infrequent experiences ([ 4]). Sticky journeys, by contrast, entail a variegated pattern of positive and negative experiences in quick succession. All of these interrelated marketing concepts point to customer interests in something more than efficient service experiences, but that 'something more' varies across these seven concepts. Only the concept of sticky journeys denotes a cyclical pattern of unpredictable customer experiences, with increasing experiential involvement, that customers yearn to continue.
The CXM literature generally advises firms to design smooth journeys. With the rising popularity of sticky journeys, three new practical questions arise: ( 1) How should CXM practitioners choose between the smooth and sticky journey models? ( 2) Within each journey type, when should firms encourage purchases—during the initial or subsequent service cycles? ( 3) How can firms interlink loyalty loops and involvement spirals to sustain customer journeys in multiservice systems?
The strategic choice between the two journey models boils down to whether the service is more instrumental or recreational in nature. In instrumental service categories, customers are like "jobbers," trying to get their tasks done as efficiently as possible; thus, the smooth journey model is a perfect fit. In recreational service categories, customers are more like "adventurers," looking for thrills, challenges, and fun times; thus, the sticky journey model is a better fit.
Examples of instrumental service categories include business hotels (e.g., Courtyard by Marriott), insurance (e.g., Progressive), and transportation (e.g., Amtrak). Customer journeys in these service categories are like "jobs to be done" ([19], p. 54). There are tiresome evaluation tasks (e.g., Are buses, subways, or trains the best transportation option for my commute?), difficult purchase decisions (e.g., Should I buy a cheaper nonrefundable ticket or a pricier refundable one?), and potentially significant consequences (e.g., delays, exhaustion, fees). Jobbers are generally willing to deliberate through the initial service cycle, but they expect subsequent service cycles to be easier. To win these jobbers, firms must provide superior decision support during the initial service cycle, then streamline subsequent service cycles into easy loyalty loops.
Examples of recreational service categories include driving clubs (e.g., Jeep Jamboree USA), lifestyle media (e.g., Thrillist), and content-sharing networks (e.g., Instagram). Customer journeys in these service categories are more like adventures than jobs. A vaguely defined hunger for excitement leads to a series of unexpected twists and turns, and a sense of purpose keeps the customer moving forward, overcoming challenges in the process ([82]). Our research suggests that customers often consider such adventures on a whim, so firms must invest in rapid entry mechanisms, especially when the entry hurdles are significant. For example, instead of limiting Jamborees to Jeep owners, Jeep Jamboree USA could rent out Jeeps to potential Jeep owners who wish to join the driving adventures. Our research also suggests that customers will only continue their adventure if it remains exciting, so firms must also invest in endless variation mechanisms. For example, Jeep Jamboree USA keeps changing its adventure sites, from the Catskill Mountains of New York to the Death Valley of California. Thrillist has a global team of freelancers to cover the ever-changing nightlife of super cities (e.g., London, New York City, Paris). Instagram intentionally exposes users to new, personally relevant influencers (e.g., Jivamukti yoginis, Latinx actors, Turkish wrestlers) to keep customers scrolling.
Firms today offer customers a variety of free, affordable, and expensive service access options, as well as one-off purchase opportunities. Free service at the outset of customer journeys can take the form of free sample sessions (CrossFit), free basic services (Tinder), or even free full services (Pokémon Go). Thereafter, some firms offer customers relatively affordable time-limited options, such as one-time passes (e.g., CrossFit's drop-in passes), package deals (ten-class passes), and short-term service plans (e.g., three-month plans). Most firms also offer monthly subscription plans, some of which are tiered (e.g., Tinder's Plus and Gold plans). Finally, some firms also offer customers one-off purchase opportunities (e.g., Pokémon Go raid passes). Firms that provide unlimited full service access for free (e.g., Niantic) rely on these one-off sales to generate revenue. All of these options can work with smooth or sticky journeys. However, to match the distinctive flow of each journey type, firms are advised to encourage purchases at different times within each journey type (see Figure 1).
Firms aiming to facilitate smooth journeys tend to showcase their complex menu of purchase options during the initial service cycle. For example, Verizon, a telecom service provider, promotes several possible phone plans on its website. One reason is that customers approach instrumental service categories with the mindset of a job to be done ([19]), and they are highly motivated to conduct a deliberate decision-making process. Another reason is that once customers complete that process, they do not want to be bothered by difficult choices again ([34]). From a customer's point of view, the value of a loyalty loop is to minimize the cognitively demanding labor of deliberate decision making. Accordingly, firms should avoid the common practice of promoting upgrades during a loyalty loop (e.g., advertising a new phone plan to existing Verizon customers). When firms do so, they run the risk of triggering customers to reconsider their prior decisions and switch providers altogether ([20]).
Firms aiming to facilitate sticky journeys should avoid presenting customers with complex menus of purchase options at the outset. One reason is that such menus are antithetical to the promise of fun, and they immediately dampen customers' excitement to try the service. Another reason to wait until well after the quick spin is that customers are most likely to make substantial purchases when they are already caught up in the involvement spiral. That said, firms must be patient. Each sticky journey is a unique adventure, so each customer will advance at their own pace. Firms such as CrossFit and Tinder recognize that customers feel ready to commit to premium plans at different times. Accordingly, these firms tend to enroll all newcomers into a free or affordable beginner plan, with little pressure to upgrade that plan until customers themselves seek premium plans. These firms also recognize the indirect value of nonpaying, low-paying, and short-term customers. Unlike instrumental services, recreational services thrive on having a sizable number of active customers within the servicescape at all times. For example, CrossFit thrives on a fleeting sense of hypercommunity, which requires a mix of core and peripheral community members to show up for workouts. Likewise, playing Pokémon Go is much more exciting alongside and against other players ([ 5]). Tinder, too, can only offer its users hundreds of potential matches if there are indeed hundreds of other users. As these examples indicate, recreational services often need a critical mass and steady turnover of users, regardless of whether those users are paying customers. For these reasons, recreational service firms (e.g., Grindr, Spotify, TikTok) often need angel investors, crowdfunding, and venture capital to survive the early years, when their revenue streams are limited.
Many large firms operate multiservice systems that include instrumental and recreational services. These firms must not only design the first loyalty loop or involvement spiral, but also sustain the customer journey beyond that existing loyalty loop or involvement spiral (see Figure 2). Firms that have customers simultaneously enrolled in multiple loyalty loops and involvement spirals are at less risk of losing their customers.
Graph: Figure 2. Sustaining customer journeys in multiservice systems.
When a firm already has customers enrolled in one loyalty loop, CXM practitioners can expand on that loyalty loop using three possible journey expansion pathways. To illustrate these pathways, we discuss a prototypical customer at quick service chains (e.g., Dunkin', Pret a Manger, Starbucks). This customer purchases the same type of coffee every morning using the firm's app, thus getting her "energize me" job done efficiently. In CXM terms, the customer is locked into a loyalty loop.
One way to expand on the existing loyalty loop is to trigger an adjacent loyalty loop (see Figure 2, Panel A). For example, on a special occasion such as the customer's birthday, the chain could reward the customer a free breakfast sandwich of her own choosing for the next three service encounters. In this manner, the customer is invited to enter a new deliberate decision-making process about which sandwich might best suit her breakfast needs. When the free offer ends, this tactic could result in the customer regularly purchasing a breakfast sandwich with her coffee, to get the "energize me" job done even better.
Another way to expand on an existing loyalty loop is to spark an involvement spiral (see Figure 2, Panel B). For example, instead of rewarding the customer with a self-selected breakfast sandwich on her birthday, the chain could surprise her with a varied food offering at each of the next three service encounters (e.g., a cranberry scone, a cheese sampler, a fruit salad). When this birthday treat ends, the customer's involvement with the chain's food offerings may be sufficiently elevated to motivate her own exploratory purchases. Alternatively, the chain could reward the customer a free short-term subscription to a partner's recreational service (e.g., Hulu, Netflix, Spotify). Such interfirm alliances can create value for both firms ([46]). For the quick service chain, providing such rewards can strengthen the customer's loyalty. For the streaming service, these short-term subscriptions, framed as rewards, can spark involvement spirals, unlike direct mail offers, which are often ignored.
Yet another way to expand on an existing loyalty loop is to escalate that loop with spiraling logic for a brief period of time (see Figure 2, Panel C). For example, the chain could reward its loyal customer any beverage on the house for the next three service encounters. In this scenario, the customer may upgrade her orders to more premium beverages each morning (e.g., a caramel macchiato, a nitro cold brew, a pumpkin spice latte). Alternatively, the chain could provide the customer with surprise beverages, with the order label placed on the underside of the cup, to foster the excitement of "blind tasting" ([39]). Exposure to the chain's premium beverages could motivate the customer to permanently upgrade her loyalty loop, to get the "energize me" job done with a dash of self-indulgence.
When a firm already has customers caught up in one involvement spiral, CXM practitioners can expand on that involvement spiral using three journey expansion pathways. To illustrate these pathways, we discuss a common marketing problem at group fitness services (e.g., CrossFit, Orange Theory, SoulCycle): once-enthusiastic athletes are coming in less often.
The first way to expand on an involvement spiral that is losing momentum is to spark a new one (see Figure 2, Panel D). At CrossFit, for example, the most enthusiastic athletes eventually reach a level of fitness at which the regular classes are no longer much of a challenge. At this juncture, CrossFit coaches invite those members to special classes for advanced athletes, such as Barbell Club and Strongman. As these new classes have significantly different structures, memberships, and challenges, athletes can be understood as entering a new involvement spiral. Eventually, some of these athletes may go on to compete at the CrossFit Games and related competitions, sparking new involvement spirals once again.
The second way to expand on an involvement spiral is to trigger an adjacent loyalty loop (see Figure 2, Panel E). For example, some CrossFit boxes include smoothie bars. While the athletes primarily come to CrossFit for the involvement spiral of varied workouts, some members may also become locked into loyalty loops of smoothie purchases on their way out. In this manner, customers accomplish the job of "workout recovery" efficiently. If these add-on services offer unique value (e.g., organic fruits, paleo sweeteners, vegan proteins), some members might also swing by the CrossFit box just for the smoothie. In CXM terms, a parallel involvement spiral and loyalty loop in the same multiservice system can keep customers returning for one or the other journey pattern.
The third way to sustain a customer journey when a customer's interest is waning is to stabilize the involvement spiral into a loyalty loop (see Figure 2, Panel F). This pathway is especially relevant when the customer is switching from an adventurer mindset to a jobber mindset. For example, some CrossFit athletes eventually tire of the ethos of relentlessly challenging themselves. However, rather than quitting, these athletes convert their upwardly spiraling journey into a stable cyclical one, "just [to] keep a certain level of fitness" (Emily, a CrossFit athlete). A CXM lesson to be derived from these mindset-switching athletes is that involvement spirals can sometimes be stabilized into loyalty loops, if that is what the customer wants.
This article has made three contributions to CXM research. First, it has challenged the dominance of the smooth journey model. Second, it has offered an alternate sticky journey model. Third, it has addressed practical concerns at the nexus of the two journey models. In closing, this article also opens up several new avenues for future research on customer journeys (see Table 3). Chief among these avenues is examining new and different types of customer journeys. No one customer journey design is optimal under all circumstances. Accordingly, we hope that this article inspires CXM researchers to keep exploring the fascinating variety of customer journeys in the contemporary marketplace.
Graph
Table 3. Sample Avenues for Future Research.
| Field of Research | Avenues for Future Research |
|---|
| Customer experience management (CXM) and customer journey design | Beyond instrumental and recreational service categories, what other service categories might benefit from distinct customer journey models?What novel types of customer journeys are possible with artificial intelligence, artificial life, virtual reality, augmented reality, and the internet of things (Belk, Humayun, and Gopaldas 2020; Javornik 2016; Novak and Hoffman 2019; Scholz and Smith 2016)?How do customer journeys unfold in the sharing economy, wherein firms have much less control over service touchpoints (Eckhardt et al. 2019)?How can firms use insights from the sticky journey model to accelerate the initial service cycle of the smooth journey model (Edelman and Singer 2015) in today's hypercompetitive attention economy?How can marketing analytics discern smooth versus sticky journeys from service usage data? Can spiraling journey patterns be dissected, measured, and tracked (Kraemer et al. 2020)?How should sequences of triggers, activities, and rewards (Eyal 2014) be arranged across multiple service cycles to best facilitate sticky journeys?What design elements complement smooth and sticky journeys at physical (Zomerdijk and Voss 2010) and virtual (Bleier, Harmeling, and Palmatier 2019) touchpoints?How are customer journeys with a firm related to consumer journeys (i.e., person-centric journeys that typically involve interactions with multiple firms; Hamilton and Price 2019)? |
| Brands and branding | Can brands be sticky? If so, how might brand stickiness be conceptualized?How can CXM and customer journey design help overcome the challenges of integrating brand experiences in a hyperconnected but fragmented mediascape (Swaminathan et al. 2020)?How can customer journey design contribute to building brand community (McAlexander, Schouten, and Koenig 2002)?Do particular types of customer journeys (e.g., sticky journeys) correspond with particular types of brand relationships (e.g., love affairs; Fournier 1998)? |
| Consumer culture theory | How are historical forces such as social acceleration (Husemann and Eckhardt 2019), institutional pluralization (Ertimur and Coskuner-Balli 2015), and consumer responsibilization (Giesler and Veresiu 2014) restructuring the political economy of customer experiences?What are the cultural aspects of the experience economy (Pine and Gilmore 1998)? For example, what ideologies and myths shape firms' journey offerings and customers' journey preferences?How do social identity structures (e.g., race, class, gender; Gopaldas 2013) shape customer journey patterns (Crockett and Wallendorf 2004)?In what ways are the collective customer journeys of families, teams, and other social groups different from individual customer journeys (Epp and Price 2008, 2011; Hamilton et al. 2020; Thomas, Epp, and Price 2020)? |
| Consumer psychology | What are the moment-to-moment psychological dynamics across different kinds of customer journeys?How do consumers' psychological resources vary across different journey patterns? For example, under what circumstances do loyalty loops feel boring rather than trustworthy? Under what circumstances do involvement spirals become exhausting rather than exciting?Do consumer preferences for journey types vary situationally (Becker and Jaakkola 2020)? For example, do weekday commuters prefer smooth journeys, while weekend revelers prefer sticky journeys?Are consumer preferences for sticky versus smooth journeys related to personality factors such as openness to experiences (Wild, Kuiken, and Schopflocher 1995) and variety seeking (Kahn 1995)? |
| Transformative consumer research and transformative service research | How can the sticky journey model be used to motivate healthy behaviors (e.g., meditation, nutrition, walking)? Similarly, how can the sticky journey model be used to motivate proenvironmental behaviors (White, Habib, and Hardisty 2019)?Where do sticky journeys end and behavioral addictions begin (Sussman, Lisha, and Griffiths 2011)?How are online behavioral addictions different from offline behavioral addictions (Schüll 2014)?Why are some consumers better at self-reflexivity (Akaka and Schau 2019) and self-regulation (Baumeister 2002) than others? How do reflexive customers reclaim ownership of their attention in the attention economy? |
Graph
| Concept | Evidence from CrossFit | Evidence from Pokémon Go | Evidence from Tinder |
|---|
| Rapid entry: the service design principle during the initial service cycle | CrossFit offers newcomers free taster sessions, low-cost beginner programs, and minimal paperwork; some customers get started with one-time class passes through third parties. The core service begins when the newcomer does a CrossFit workout with other existing athletes. Trainers tend to introduce newcomers to other athletes by name to begin their socialization process. | Pokémon Go on-boarding entails a free mobile app, quick in-app setup, and brief tutorial by the character Professor Willow, who ends his introduction with "It's time to GO!" The core service begins when the new player sees their own avatar equipped with a few PokéBalls to throw at one of three Pokémon nearby to catch that Pokémon, making the first play very simple. | Tinder "doesn't ask for much from you as a user, aside from your current location and gender, it's just your age, distance and gender preferences to start" (Tinder 2019, pp. 1–2). Photos can be imported from Facebook accounts. All other user input is optional. The core service begins when the new user sees a profile of another local user. A swipe right/left indicates interest/disinterest. |
| Quick spin: the customer journey pattern during the initial service cycle | "People are like, 'Oh my God, you'd love it!' [and] I was like, 'Okay, cool, I'll look into it.' And you know with other gyms, it's not normally that people do it all on recommendation, but this is really like, you can buy into it really quickly. So then I just found one that was near work and just dropped by and was like, 'Can I come and check out the gym?'...When I saw the workouts, I was like, 'Wow, that looks really tough!' So I wanted to do it....It's like a step up from fitness...You could go to the gym...running...cycling...CrossFit combines all of those things." (Jenny) | "My girlfriend's a teacher, and she wanted to know what [Pokémon Go] was like because all her kids were into it.... So, we both installed it, went out playing, and carried on playing.... She wanted to relate to teenage kids. I didn't expect this to happen! [laughs] Because I'm not a game player normally.... I [had] read that [if] you walk away three times from the starters, then Pikachu [the game's mascot] will appear. So my first ever Pokémon was a Pikachu....Then you do more walking and start evolving." (Gordon) | "I saw that there was such an interest among girls and boys. Sounds exciting.... You simply log in via Facebook and then you upload photos, write something in your profile and you're done!...I was not on any other [dating site].... I don't know if it was romanticized, but I first heard from [a friend] that he has quite a few friends that ended up in a relationship via Tinder. And then there were these stories of one-night stands. And both are interesting....It was exciting, because you see a lot of different people, very pretty people...and then also totally not pretty people too.... It's very diverse." (Sebastian) |
| Endless variation: the service design principle during subsequent service cycles | CrossFit's "constantly varied" (Glassman 2002) workouts typically include a dynamic warm-up, a weightlifting module, and a high-intensity WOD. Each of these modules can include countless different exercises (e.g., box jumps, cleans, lunges). Workout modules are further varied by their temporal ordering (e.g., ten clean-and-jerks every minute on the minute, a trio of exercises for as many reps as possible). As CrossFit chief executive officer Greg Glassman (2002) says, "Five or six days per week, mix these elements in as many combinations and patterns as creativity will allow. Routine is the enemy." Given that CrossFit workouts often span the outdoors, the weather is yet another significant source of unpredictability. Running can feel like an extraordinary challenge on a snowy day. | The Pokémon Go game draws its titular creatures from the existing Pokémon universe of more than 800 Pokémon across seven generations. To keep the game interesting, Niantic keeps releasing new Pokémon into the game as well as new features (e.g., "Dynamic Weather Gameplay" that adapts the game to the local weather [Pokémon Go 2017, p. 1]). Niantic also releases special Pokémon for a limited time ("Legendary Pokémon) and organizes global events (e.g., Safari Zone). The game's interface reveals countless PokéStops at which players can collect items and battle other teams for control over Gyms. Pokémon Go varies the timing, location, and number of Pokémon that players can try to catch. Each Pokémon has distinct characteristics (e.g., combat power) and an Individual Value (max. 100%). Some Pokémon come in male, female, and rare "shiny" versions. | Tinder's service system includes millions of active users, each of whom creates a user profile with attractive images of themselves. Each user sees the profiles of other users in feeds called Discovery, Top Picks, and Likes (for premium subscribers only). The Discovery feed shows the user one profile at a time. To proceed, the user must swipe right, left, or up to Like, Nope, or Super Like. Although these profiles are sequenced by a multifactor algorithm, they cannot be predicted by the average user. Other sources of unpredictability are the messages between the user and their matches, and the user's freedom to unmatch their matches, which instantly eliminates the entire message history from the apps of both users. Swipe Night is an interactive video feature wherein the user chooses from two options of what happens next to be matched with other users who choose similarly (Hern 2019). |
| Involvement spiral: the customer journey pattern during subsequent service cycles | |
| Additional evidence of the experiential roller coaster in the moment-to-moment timescale of the customer journey | "The [CrossFit] mix includes everything that I like, a little bit of weightlifting, a bit of gymnastics and endurance, and the mix. You never know what's going to happen the next day, and you're active and work really hard.... That's what I like the most, that there are so many different things, that it is so variable what you do there.... I'm bored really fast, and [CrossFit] doesn't bore me. I don't feel like, 'Oh it's the same again!,' which I did feel about football....[In CrossFit,] it's always thrilling." (Karen) | "I was new to the Pokémon world....So it was quite a vicarious thrill in seeing all these new Pokémon popping up...and going out to different places....I caught [a powerful Pokémon], and it was one with all the question marks, and so I didn't know how big it was because it was its first appearance....I suddenly realized, 'Oh, how exciting!' and that by branching out and going to different places, I could make the world very exciting.... I was out for a walk, and we caught [a very common Pokémon]....And it turned into [a very rare Pokémon]. I was so excited, I nearly jumped up and down on the spot. 'Oh my God, that's so brilliant!'" (Martha) | "You see these images of men who are often really attractive...and it's like 'Yep, I want that!' And then it's like, 'Oh, another!'...Whatever your perfect partner is, you start projecting on complete strangers....Then you might get a conversation....More often than not, there's a level of disappointment...and it's so sad...it feels like you're actually losing something, which is ridiculous really because it's just a fantasy...but that keeps me doing this. Even though on 99.9% of dates...there has not been chemistry...there have been a few times where the magic has happened...and I think those few times [are] enough for me to keep doing it." (Donna) |
| Additional evidence of increasing experiential involvement across the long-term timescale of the customer journey | "The first month, I thought, 'I'm really addicted now, I just want to go and do it almost every day and try something new and try and improve on this and that.' [Later on] it became a case that I was seeing real improvements. I was lifting heavier weights, I was doing [movements that I couldn't do before], so that just feeds into it even more, it gets even more and more addictive because you're like, 'I'm seeing real changes, I'm getting slimmer, I'm getting stronger.'...Over time, you realize that even if you're able to grow stronger that there's still room for improvement there. There's always steps, there's always something to work on. It never feels like you ever get to the point where you've nailed it and you're perfect. So there's always either a different movement or a more advanced movement or a bigger weight or there's always something new to try.... I [just] got more and more into it." (John) | "[In the beginning,] I needed [my son's] knowledge in order to access the raid system....He was my guru; he was showing me what to do...and he would then talk about tactics of only powering up the best [Pokémon], and I'd just power up anything! [laughter]...So we discussed tactics....[In time,] I was pulling the game apart and trying to understand it.... It was interesting to see the different strategies, and even now that [my son's] at university and I'm on my own doing this with my raid group, I do find it interesting that we all have different tactics....I started seeing the same people. And they said, now you've got to join in...with random strangers and within the space of ten minutes you are working together to achieve a goal." (Esther) | "As soon as you have the first match you say...'Hi, how are you?' and the conversation goes on. But then you feel greedy...and you're nonstop until you reach the second match, or third, or fourth. And then you start having five conversations at the same time, and don't understand whom you are talking to about what! Your phone becomes a mess, because it's a disorganized set of conversations....And then you try to...select a few...that you really think...are the good catches. You throw back in the sea all the fish that you don't want....The difference between [my first] time and this time was that...I was more mature in the use of the app....I really knew what I wanted." (Enrico) |
| Service usage fluctuations fueled by well-being concerns | "I talked to one of my athletes who did two classes per week after the trial month, then three classes per week, and who then chose an unlimited class package. [He] tends to overdo things, and eventually he says to me, 'It is more important to me to make [more money] as a salesman, and that's why I want to invest my time there, and therefore no longer come to CrossFit.'...His girlfriend now wants to go into family planning, he has to manage his time better, and he has chosen to reduce CrossFit and not the work." (Martin, coach) | "It's like drugs....You're just like, 'Oh yeah, I checked only two hours ago, let me check again if there's something new,' you know?...It kind of gets obsessive.... I also lost interest because I cannot keep up with these things. You play, and then you realize that if you want to become better, you need to spend lots of time on it....[Super Mario Run] was perfect for casual gaming; you have five minutes, you play. [With] Pokémon Go, at some point, I realized that five minutes are not enough. Like, it requires more commitment, [and] I cannot be bothered, and it stops there." (Marco) | "You have such bizarre conversations with people you do not know. And of course that's funny and exciting....Swiping these photos was certainly two-sided. For one thing, it seemed to be taken for granted to shop for men like in the supermarket. And on the other hand...it's super interesting to see who is there....And what I found frightening, there were many people whom I actually just eliminated immediately because I just did not find them attractive. Then I thought "That's harsh!"...That shocked me about myself.... And then I quit. I thought, 'It's enough.'" (Anna) |
Footnotes 1 Associate EditorAmber Epp
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The second author thanks the Gabelli School of Business at Fordham University for summer research support (2017–2019). The fourth author thanks FCT, the Portuguese Foundation for Science and Technology (UIDB/03182/2020).
4 ORCID iDsAnton Siebert https://orcid.org/0000-0002-8588-6554 Ahir Gopaldas https://orcid.org/0000-0001-6996-5378 Andrew Lindridge https://orcid.org/0000-0001-7818-5410 Cláudia Simões https://orcid.org/0000-0002-0606-0018
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Record: 50- Customer Inspiration: Conceptualization, Scale Development, and Validation. By: Böttger, Tim; Rudolph, Thomas; Evanschitzky, Heiner; Pfrang, Thilo. Journal of Marketing. Nov2017, Vol. 81 Issue 6, p116-131. 16p. 1 Diagram, 5 Charts. DOI: 10.1509/jm.15.0007.
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Customer Inspiration: Conceptualization, Scale Development, and Validation
Introducing customers to new ideas lies at the heart of marketing, yet surprisingly little is known about customers’ state of inspiration within this domain. This article reviews prior conceptualizations of general inspiration in psychology and introduces the concept of customer inspiration as a customer’s temporary motivational state that facilitates the transition from the reception of a marketing-induced idea to the intrinsic pursuit of a consumption-related goal. The authors develop and validate a two-state, ten-item customer inspiration scale that consists of inspired-by and inspired-to states. The scale development process begins with item generation, followed by five studies: ( 1) scale purification and initial validation, ( 2) exploration of the nomological network, ( 3) tests for the experimental and predictive validity, ( 4) replication within a field experiment, and ( 5) assessments of generalizability and boundary conditions. Empirical results reveal sound psychometric properties of the scale, demonstrate its unique position in relation to established marketing constructs, and support experimental and predictive validity. Applying the scale in marketing practice offers a new way for firms to increase demand, motivate customers’ exploration behavior, and build customer loyalty.
Online Supplement: http://dx.doi.org/10.1509/jm.15.0007
Pinterest, which describes itself as “the world’s catalog of ideas” (www.pinterest.com), is a social website that allows consumers to browse images and products through a visual user interface, pin them to their digital boards, and share them. The website and associated app enjoy great popularity, especially among women, who account for the largest proportion of the 150 million active users (Aslam 2017). The concept behind Pinterest is that by showing products “in use” (e.g., through recipes, DIY instructions, fashion outfits), customers will receive new ideas about consumption possibilities—possibilities they might not have been aware of and might find surprising. In doing so, Pinterest tends to stimulate customers’ imaginations and broaden their mental horizons about product use. Seventy percent of Pinterest users indicate “to get inspiration on what to buy” as a reason for using the social network (Silver 2012). A purchase often follows; Pinterest has the highest sales conversion rate among comparable social networks, and its average order value surpasses that of competitors (Barnes and Lescault 2014). Thus, several companies (e.g., Kraft Foods, Maggi, Sephora, Sony) use Pinterest to promote their products, inspire customers, and strengthen customer relationships (Pinterest 2017).
The example of Pinterest illustrates that, first, a state of inspiration can be evoked by an external stimulus (e.g., a recipe posted to Pinterest) when customers seek and are receptive to new ideas (e.g., planning a meal). Second, inspiration involves a motivating aspect that serves as a trigger to change a routine consumption practice (e.g., creating the meal posted to Pinterest). Thus, inspiration includes the transition from the state of “being inspired by” an external factor, to a state of “being inspired to” actualize a new idea.
Because inspiring customers lies at the heart of marketing, the study of customers’ state of inspiration offers the potential to advance marketing theory. Prior literature suggests that customers may derive benefits from enriching, inspirational brands (Park, Eisingerich, and Park 2013) or inspiring shopping experiences (Lee and Böttger 2017) and may experience transcendent moments during consumption (Arnould and Price 1993; Celsi, Rose, and Leigh 1993). However, inspiration has rarely been defined, nor has its construct validity been examined within the marketing domain. As a result, the current literature on inspiration in marketing is largely atheoretical and lacks a common understanding of the construct of inspiration. A more thorough understanding of customer inspiration is required for marketers to effectively and efficiently influence consumer response to their market offerings. Moreover, marketing research has rarely assessed inspiration empirically and lacks a standardized measure. A domain-specific measure is necessary, as broad measures tend to be poor predictors of specific outcomes (Ajzen 1987). Furthermore, because inspiration in marketing involves different goals, recipients, and sources of inspiration, the marketing domain seems sufficiently different from prior areas of inspiration research (e.g., general psychology, education, sports, creativity) to warrant a domain-specific conceptualization and measurement (Kassarjian 1971; see also Aaker 1997; Bearden, Netemeyer, and Teel 1989).In the absence ofa standardizedscale, some researchers have resorted to constructing ad hoc domain-specific scales to measure inspiration in marketing (e.g., Liang, Chen, and Lei 2016). Using a standardized scale is preferred to this practice because its reliability and validity are established, and its use enables comparisons across studies (Goldsmith and Hofacker 1991).
Our research aims to contribute to marketing literature by ( 1) conceptualizing and examining inspiration within a marketing context, and ( 2) developing and validating a scale to improve measurement of inspiration in marketing. We propose that the inspiration literature in psychology (e.g., Thrash and Elliot 2003, 2004) is transferable to the marketing domain and adds a new perspective to existing marketing phenomena. Responding to calls for contextualization of inspiration (Thrash et al. 2014), we propose such a definition of inspiration for the marketing context that is also compatible with the wider inspiration literature. We show empirically that inspiration uniquely predicts relevant marketing outcomes such as exploration and purchase behavior. We also test whether our domain-specific scale improves predictions of such outcomes over a general measure of inspiration. Finally, we provide evidence for unique manipulations and boundary conditions that derive from an inspiration perspective on marketing.
The remainder of our manuscript is structured as follows. First, we review the emerging inspiration literature and provide a marketing-specific definition of customer inspiration. On the basis of that definition, we develop and validate a two-state scale for customer inspiration. Our empirical scale development and validation process consists of an extensive item generation and five empirical studies that place customer inspiration in a nomological network of related constructs and that confirm experimental and predictive validity. Finally, we discuss how customer inspiration may provide a new way for firms to increase demand, foster customers’ exploration behavior, and strengthen customer loyalty.
Motivation, long the object of scientific inquiry, has been described as the psychological force that enables goal-directed behavior (Lewin 1935). As such, motivation consists of activation and intention (Ryan and Deci 2000). It includes both the energization to strive for existing goals and the setting of new goals (Gollwitzer 1990). Moreover, motivation can be classified as either intrinsic or extrinsic in nature. Self-determination theory (Ryan and Deci 2000) posits that intrinsic motivation refers to carrying out an activity for the inherent satisfaction of the activity itself, and that it is driven by a desire for autonomy, competence, and relatedness. Extrinsic motivation, in contrast, is powered by the desire to attain a separable outcome.
Inspiration is a type of intrinsic motivational state that is characterized by a strong epistemic component. For example, Oleynick et al. (2014, p. 1) define inspiration as a “motivational state that compels individuals to bring ideas into fruition.” Like other intrinsic motivations, inspiration focuses on incentives that are inherent to the task, and it results in autonomous behaviors (Ryan and Deci 2000). However, inspiration is a specific intrinsic motivation because it is evoked by an external source and is connected to the realization of new ideas (Thrash and Elliot 2003). As a temporary state, rather than a more permanent trait, inspiration thus bridges the gap between the deliberation phase (i.e., goal setting) and the implementation phase (i.e., goal striving) of goal pursuit (Gollwitzer 1990).
Recent general conceptualizations in social psychology seem to coalesce around three complementary frameworks of inspiration (see Table 1). First, the tripartite conceptualization (Thrash and Elliot 2003) describes the three key characteristics of inspiration: evocation, transcendence, and motivation. Evocation refers to the fact that inspiration is spontaneously evoked by an external source rather than being willingly initiated by the recipient. Transcendence describes a feeling of positivity, clarity, and self-enhancement, since inspiration involves the realization and appreciation of a new idea. Inspiration then leads to approach motivation (Elliot and Thrash 2002), such that a person feels compelled to actualize the new idea. Second, the component process conceptualization (Thrash and Elliot 2004) proposes that an episode of inspiration involves two distinct components: an activation state that is captured in the notion of being inspired by something (i.e., evocation and transcendence), and an intention state that can be understood as being inspired to act or to do something (i.e., motivation). Finally, the transmission model of inspiration (Thrash et al. 2010b) describes the function of inspiration as the facilitation of a transition between these two states.
TABLE: TABLE 1 Overview of Existing Frameworks for Inspiration
| Framework | Focus of Framework | Nominal Definition | Parts Within Framework | Related Research |
|---|
| Tripartite conceptualization (Thrash and Elliot 2003) | Core characteristics of inspiration as a trait and as a state | • “Inspiration is conceptualized as both a trait and a state because it is presumed to vary both between and within individuals” (Thrash and Elliot 2003, p. 873). • “Inspiration is characterized by evocation, motivation, and transcendence” (Thrash and Elliot 2003, p. 885). | • Evocation: feeling overtaken, “uncontrol,” attraction from the object, openness • Transcendence: positivity, enhancement, clarity • Motivation: activation, energy | • Milyavskaya et al. (2012) • Jones et al. (2014) |
| Component process conceptualization (Thrash and Elliot 2004) | Distinct components that together compose an episode of inspiration | Inspiration is a hybrid construct that emerges from the juxtaposition of two component processes, one involving an appreciation of and accommodation to an evocative object (hereafter referred to as being inspired by), the other involving motivation to extend the qualities exemplified in the evocative object (hereafter referred to as being inspired to)” (Thrash and Elliot 2004, p. 958). | • Inspired-by: associated with transcendence and denial of responsibility (evocation) • Inspired-to: associated with appetitive motivation | • Thrash et al. (2010a) • Stephan et al. (2015) • Liang et al. (2016) |
| Transmission model (Thrash et al. 2010b) | Purpose or function of inspiration in the creative process | “Inspiration to create is a motivational state that is evoked in response to getting a creative idea and that compels the individual to transform the creative idea into a creative product” (Thrash et al. 2010b, p. 470). | • Sources of inspiration (i.e., creative idea) as antecedent • Inspiration as a mediating state • Actualization of the idea as a consequence | • Oleynick et al. (2014) • Thrash et al. (2014, 2017) • Figgins et al. (2016) |
Notes: A more extensive literature review is available in the Web Appendix.
General conceptualizations of inspiration as outlined in this section draw on the core commonalities across streams of literature and are, therefore, valuable to ease cross-talk between disciplines. However, they are often too broad to be directly applied to a specific context, such as marketing, and they tend to be poor predictors of specific outcomes (Ajzen 1987; Kassarjian 1971). Responding to calls for a better contextualization of inspiration for specific domains (Thrash et al. 2014), we aim to provide a conceptualization that encompasses the essence of customer inspiration while still offering consistency with other literature. In the marketing context, therefore, we define customer inspiration as a customer’s temporary motivational state that facilitates the transition from the reception of a marketing-induced idea to the intrinsic pursuit of a consumption-related goal.
While this definition, in the main, is based on the transmission model of general inspiration (Thrash et al. 2010b), it is also specific to the marketing domain. First, we focus on customers as the recipients of inspiration, in contrast to the literature on general inspiration that mainly concerns the behavior of patients or students (e.g., Hart 1998; Hymer 1990; Thrash and Elliot 2004). Second, whereas prior research examines sources of inspiration such as role models (Lockwood and Kunda 1999), examples of work mastery (Thrash et al. 2010a), or poetry (Thrash et al. 2017), our definition aims at the stimulation of ideas prompted by a conscious marketing effort. Finally, while general inspiration is mostly concerned with inspiration to reach personal goals, such as achievement, power, work mastery, or creativity (e.g., Thrash and Elliot 2004; Thrash et al. 2010b), our definition focuses on consumption-related goals such as purchasing, donating, or engaging with a brand.
As a motivational state, customer inspiration consists of both an activation and an intention component (Ryan and Deci 2000; Thrash and Elliot 2004). Accordingly, we propose that the state of customer inspiration can be decomposed into an epistemic activation component (the state of “inspired by”) and an intention component (the state of “inspired to”). Both components are necessary to create a full episode of inspiration, yet they represent distinct states within this process (Hart 1998; Thrash and Elliot 2004; Thrash et al. 2014). Consequently, our conceptual framework illustrates customer inspiration as a second-order construct that is composed of both an inspired-by state and an inspired-to state (see Figure 1).
The inspired-by activation state relates to the reception of a marketing-induced new idea (i.e., evocation) and the shift in customer awareness toward new possibilities (i.e., transcendence). In daily experiences, inspiration is often described as a “light bulb,” “Aha!,” or “Eureka!” moment of sudden realization and insight (Hart 1998; Oleynick et al. 2014; Thrash et al. 2017). In a marketing context, customers often receive such new ideas through marketers’ efforts to promote their offerings. As a result, customers may then experience transcendence toward a new state of mind. Customers sometimes describe this transcendence as the stimulation of imagination or the broadening of mental horizons. In its extreme form, transcendence can lead to self-transformation (e.g., Arnould and Price 1993; Kozinets 2002).
The inspired-to state relates to the intrinsic pursuit of a consumption-related goal. In this state, customers experience an urge to actualize the new idea (e.g., by purchasing and using a product) rather than to extend or replicate it (Thrash et al. 2014). Congruous with the literature on general inspiration (Thrash and Elliot 2003), we understand this state to be an approach motivation, rather than an avoidance motivation (Elliot and Thrash 2002).
Based on the transmission model of inspiration (Thrash et al. 2010b), we propose that customers make a transition from the state of being inspired by a marketing-elicited idea to the state of being inspired to actualize this idea. The two states are causally linked, such that inspired-by mediates the effect of marketing stimuli on inspired-to. This conceptualization is also in line with Gollwitzer’s (1990) mindset theory of action phases, which divides the decision-making process into a predecision phase of deliberation and a postdecision phase of implementation. While the inspired-by state is part of the deliberation phase, the inspired-to state marks the transition to the implementation phase. Therefore, inspiration adds an important in situ measure to the study of customer motivation and the experiences along the customer journey (Lee and Ariely 2006; Lemon and Verhoef 2016).
Having conceptualized customer inspiration as a motivational state with two distinct component states, we broaden our conceptual perspective to address the unique position of customer inspiration in its nomological network of related marketing constructs (see Figure 1). Toward this objective, we classify established constructs as either antecedents or consequences of customer inspiration (for an overview, see the Web Appendix).
Antecedents. The emergence of inspiration depends on both the presence of an inspiring source and the characteristics of the recipient of inspiration (Thrash and Elliot 2003, 2004). In marketing, customer inspiration may result from print ads, novel product assortments, in-store presentations, personalized messages, and many other sources in the consumption environment. While the list of potential sources is vast and constantly expanding due to new technologies, we propose that most inspiring sources share three source characteristics. These include the provision of inspirational content (i.e., including a new idea), appeals to use one’s imagination, and elicitation of an approach rather than an avoidance motivation.
At the same time, individual characteristics, such as the recipient’s openness to inspiration, also play an important part in predicting the frequency and intensity of inspirational experiences (Thrash and Elliot 2003). In a consumption context, this openness to inspiration is, for example, reflected in the notion of idea shopping, which describes the hedonic motivation to go shopping with the intent to see new products, innovations, trends, and fashions (Arnold and Reynolds 2003; Evanschitzky et al. 2014). We propose that customers with an idea shopping motive are more receptive to inspiration and thus experience stronger inspiration than do other customers exposed to the same source of inspiration.
Consequences. We propose that customer inspiration leads to behavioral, emotional, and attitudinal consequences. From a behavioral perspective, inspiration leads to an intrinsic motivation to actualize a new idea (Thrash et al. 2010b), and the resulting behavior depends on the content of this new idea (e.g., the message of an advertisement). By definition, marketing concerns creating, communicating, delivering, and exchanging offerings (American Marketing Association 2013). Therefore, we expect that inspiration in this domain will most often lead to the impulsive purchase of unplanned products or services, exploration of the offering, or engagement with the marketing firm in some other meaningful way (e.g., Brodie et al. 2011; Pansari and Kumar 2016; Rook and Fisher 1995).
In addition, customer inspiration may lead to emotional consequences. Most prominently, positive affect, one of the two dominant dimensions of mood, has been found to correlate strongly with inspiration in a variety of settings (Thrash and Elliot 2003, 2004). Although prior research suggests that positive affect is conceptually and empirically distinct from inspiration (Oleynick et al. 2014), the state of inspiration can induce positive affect (Thrash et al. 2010a). Furthermore, customer inspiration may also trigger the emotional response of delight, which combines high pleasure (joy, elation) with a feeling of surprise (Finn 2005; Oliver, Rust, and Varki 1997). Delight does not necessarily contain a transcendent component like inspiration, but the sudden realization of a new idea may lead to surprise and elation that is manifested in feeling delighted. Finally, from a broader perspective, customer inspiration may lead to transcendent customer experiences (TCEs) that include peak and flow experiences in a consumption context (Schouten, McAlexander, and Koenig 2007). Although TCEs represent a much larger class of phenomena that also includes noninspiring experiences, many share with inspiration the characteristic of transcendence (i.e., positivity, clarity, and self-transformation). For instance, many, but not all, peak experiences have within them the motor of inspiration (Hart 1998). Hence, we propose that being inspired by a marketing-induced idea may lead to emotional consequences such as positive affect, delight, and TCEs.
More distally, customer inspiration can also have attitudinal consequences—enduring evaluative judgments that are more stable than emotions. For instance, Park et al. (2013) propose that companies that offer inspiring and enriching experiences may benefit from increasing brand attachment. Similarly, we propose that customer inspiration can increase customer loyalty because it creates a feeling of connectedness with the marketing firm (Hart 1998). Furthermore, intrinsic motivation has been shown to lead to higher levels of satisfaction (Ryan and Deci 2000), and customer inspiration as an intrinsic motivational state may thus lead to customer satisfaction in a marketing context. While these attitudinal consequences may seem more remote from customer inspiration, we propose that they are influenced positively either directly by customer inspiration or indirectly by its emotional and behavioral consequences.
These many disparate components come together to support an essential point: that while there are several established marketing constructs that relate to customer inspiration, none seems to capture this intrinsic motivational state in its entirety. In order to stimulate research on this unique construct, we develop a standardized domain-specific scale for the measurement of customer inspiration. We test its validity and reliability in a variety of settings and show its usefulness to improve predictions over more general measures of inspiration in prior literature (Thrash and Elliot 2003, 2004).
To develop a scale for measuring customer inspiration, we followed established scale construction recommendations (Churchill 1979; Gerbing and Anderson 1988) and prior scale-development studies (e.g., Bearden, Netemeyer, and Teel 1989; Nenkov, Inman, and Hulland 2008; Tian, Bearden, and Hunter 2001). Our initial item generation is followed by five studies, involving ( 1) scale purification and initial validation, ( 2) an exploration of the nomological network, ( 3) tests for the experimental and predictive validity, ( 4) a replication of predictive validity within a field experiment, and ( 5) an assessment of generalizability and a boundary condition. We analyzed both qualitative and quantitative data gathered from marketing academics, top managers, students, and an online panel, as well as shopper field data.
For the item generation, we aimed at developing a broad set of items that would encompass all potential aspects of the inspired-by and inspired-to states of customer inspiration (Churchill 1979). We created 93 potential scale items to measure customer inspiration, using as a basis both our literature review and a short qualitative survey of 918 shoppers.1 A panel of ten experts evaluated each statement for content and face validity. To ensure that our items were relevant for marketing research as well as for marketing practice, the panel included five senior marketing academics from peer universities and five top managers who were either CEOs or CMOs within their organizations. These experts rated each item using a five-point scale with a range from “very bad fit” ( 1) to “very good fit” ( 5). Furthermore, each expert selected five items with the best overall construct fit. Scores for each item were averaged separately for managers and academics to calculate a managerial score (Mmanager = 3.07, SD = .45) and an academic score (Macademic = 3.34, SD = .75).2 We retained items if both the academic score and the managerial score were favorable (>3.0) or if at least one of the experts selected an item as one of the five best fitting. This procedure shortened the list to 43 items.
To further increase content and face validity, the remaining items were subject to two sorting tasks. In the first sorting task, a sample of 33 participants (52% male, median age 24) from an online panel read a short explanation of the customer inspiration construct and then organized the items by similarity, creating as many categories as they deemed appropriate. From this, one item was classified as inappropriate and ten items were refined. In the second sorting task, a separate sample of 25 raters (52% female, median age 34) with prior experience in categorization tasks assessed the remaining 42 items. Participants were given a short description of customer inspiration and its two conceptual states (inspired-by and inspired-to) and were then asked to assign each item to one of the two states or to mark it as unrelated to either state. Retaining only items that had been assigned to their respective a priori category by at least 60% of the judges, we subsequently eliminated five items. At the end of this process, 37 potential scale items remained, of which 26 items measured the inspired-by component of customer inspiration and 11 items measured the inspired-to component.
Following Bearden, Netemeyer, and Teel (1989), we performed separate item analyses for the remaining 37 statements, including confirmatory factor analyses (CFAs), tests for discriminant validity, and a known group comparison.
Participants and procedure. We engaged 287 undergraduate students to participate in a study in exchange for a chance to win university-branded clothing. Participants were randomly assigned to one of two conditions, labeled “neutral” and “inspired.” Participants in the neutral condition were asked to remember their most recent shopping experience, whereas participants in the inspired condition were asked to remember their most recent inspiring shopping experience. Both groups were asked to briefly describe their shopping experiences and then rate the 37 potential scale items from “strongly disagree” ( 1) to “strongly agree” ( 7) in individually randomized order. An instructional check ensured that participants read each item carefully; 30 participants failed this check, leaving a final sample of 257 participants (57% male, median age 22) for further analysis.
Scale purification. Following prior literature (e.g., Arnold and Reynolds 2003), we used iterative CFAs to assess the reliability and convergent validity of the proposed scale, as well as to consolidate similar items. First, we specified a two-factorial confirmatory model with all 37 potential items. The model fit indices of this initial model (confirmatory fit index [CFI] = .85;
Tucker–Lewis index [TLI] = .84; root mean square error of approximation [RMSEA] = .092; standardized root mean residual [SRMR] = .055) missed acceptable thresholds (Hu and Bentler 1999). To refine the scale, we then inspected items with low individual reliabilities (30) or were involved in ten or more significant indices (>3.84). After inspecting each of these items and consolidating those that appeared to belong to the same facet of customer inspiration, we eliminated an additional 11 items. The remaining 18 items (12 for inspired-by; 6 for inspired-to) were again subjected to a CFA, which revealed acceptable model fit (CFI = .96; TLI = .95; RMSEA = .069; SRMR = .048; see section “18-Item Long Customer Inspiration Scale” in the Web Appendix).
While the 18-item scale demonstrates acceptable measurement properties, it may be too lengthy for practitioner use. A shorter scale would allow constructs to be added to surveys, reduce demand effects, and prevent practitioners from reducing the number of scale items based on a heuristic (Richins 2004). Following established guidelines for scale shortening (Richins 2004; Stanton et al. 2002), we inspected the remaining 18 items, considering their internal consistency as well as face validity and domain representativeness. Based on these considerations, we selected 10 items with high loadings (>.70) that captured the essence of the inspired-by (5 items) and inspired-to (5 items) components of customer inspiration. This final scale was subjected to a CFA, which revealed very good model fit indices (CFI = .99; TLI = .99; RMSEA = .044; SRMR = .029). All items loaded significantly on their designated constructs, with standardized loadings ranging from .72 to .86, individual item reliabilities ranging from .52 to .74, and corrected item-total correlations ranging from .71 to .83. Furthermore, coefficient alpha, average variance extracted (AVE), and composite reliability (CR) for inspired-by (a = .89; AVE = .62; CR = .89) and inspired-to (a = .92; AVE = .70; CR = .92) were above recommended thresholds (Fornell and Larcker 1981), providing evidence of convergent validity. The “Study 1” column in Table 2 provides detailed results and the final scale.
Discriminant validity. We ran two tests to assess the discriminant validity of the two inspiration states. First, the AVE for both inspired-by (.62) and inspired-to (.70) exceeded the squared correlation between the constructs of r2 = .57 (Fornell and Larcker 1981). Second, the two-factor model was contrasted with a one-factor model in which all items loaded on one latent variable (Burnkrant and Page 1982). Chi-square statistics indicated a significantly better fit for the baseline two-factor model (Dc2( 1) = 221.84, p Known group comparison. In order to further assess the content validity of the scale, we performed a
known group comparison (Churchill 1979; Tian, Bearden, and Hunter 2001) based on the two conditions of our study. We anticipated that participants who described their most recent inspiring shopping experience (inspiration condition) would score significantly higher on our scale than those who simply described their most recent shopping experience (neutral condition). For this analysis, we averaged the items on both subscales for the individual participants, so that the resulting scale scores would range from 1 to 7. In line with our expectations, participants in the inspiration condition scored significantly higher than those in the neutral condition on both inspired-by (Minspired = 4.37,
Mneutral = 2.35; t(255) = 12.82, p < .001) and inspired-to subscales (Minspired = 5.44, Mneutral = 3.58; t(255) = 10.50, p
Our objective for the second study was twofold. First, we sought to validate the measurement properties of the customer inspiration scale in a real shopping situation, using a wide range of shoppers from various retail industries. Second, we intended to test the discriminant validity and unique position of customer inspiration within the nomological network of related marketing constructs. As depicted in our conceptual framework, we expect established marketing constructs to relate to customer inspiration either as antecedents or as consequences. To test our predictions, we include measures for one antecedent (i.e., idea shopping), one behavioral consequence (i.e., impulse buying), three emotional consequences (i.e., positive affect, delight, and TCEs), and two attitudinal consequences (i.e., customer satisfaction and customer loyalty). Further details on each of these constructs and their relations to customer inspiration are available in the Web Appendix.
Because inspired-by and inspired-to belong to the same second-order construct—customer inspiration—we expect both states to correlate strongly with all related antecedents and consequences. However, we also predict differences in the relative strength of the correlations with the related constructs. Since the inspired-by component of inspiration relates to transcendence and evocation (Thrash and Elliot 2004), we expect it to correlate more strongly with emotional consequences (i.e., delight, TCEs, and positive affect). In contrast, the inspired-to component is intentional and may relate more strongly to behavioral consequences to actualize an idea (i.e., impulse buying).
TABLE: TABLE 2 Customer Inspiration Scale: Confirmatory Factor Analysis and Item Loadings
| Item | Study 1 | Study 2 | Study 3a | Study 3b | Study 4 |
|---|
| Inspired-by (CR; AVE) | (.89; .62) | (.87; .58) | (.92; .68) | (.89; .62) | (.90; .64) |
| My imagination was stimulated. | 0.86 | 0.78 | 0.77 | 0.84 | 0.75 |
| I was intrigued by a new idea. | 0.81 | 0.79 | 0.83 | 0.79 | 0.82 |
| I unexpectedly and spontaneously got | 0.8 | 0.78 | 0.87 | 0.87 | 0.82 |
| My horizon was broadened. | 0.75 | 0.78 | 0.88 | 0.75 | 0.84 |
| I discovered something new. | 0.72 | 0.69 | 0.8 | 0.67 | 0.77 |
| Inspired-to (CR; AVE) | (.92; .70) | (.93; .71) | (.97; .85) | (.96; .84) | (.98; .91) |
| I was inspired to buy something. | 0.86 | 0.79 | 0.91 | 0.86 | 0.96 |
| I felt a desire to buy something. | 0.84 | 0.89 | 0.93 | 0.93 | 0.96 |
| My interest to buy something was increased. | 0.84 | 0.87 | 0.94 | 0.95 | 0.94 |
| I was motivated to buy something. | 0.83 | 0.78 | 0.92 | 0.95 | 0.96 |
| I felt an urge to buy something. | 0.79 | 0.9 | 0.9 | 0.89 | 0.95 |
| Observations | 257 | 425 | 230 | 121 | 253 |
| Factor correlation | 0.76 | 0.57 | 0.75 | 0.62 | 0.64 |
| c2(34) | 51.01 | 182.5 | 147.48 | 67.96 | 158.43 |
| Comparative fit index | 0.99 | 0.95 | 0.95 | 0.97 | 0.96 |
| Tucker–Lewis index | 0.99 | 0.93 | 0.94 | 0.96 | 0.95 |
| Root mean square error of approximation | 0.04 | 0.1 | 0.12 | 0.09 | 0.12 |
| Standardized root mean square residual | 0.029 | 0.045 | 0.04 | 0.033 | 0.041 |
Notes: CR = composite reliability; AVE = average variance extracted. All factor loadings and factor correlations are significant at p < .001. A list of the initial 18-item long scale items is available in the Web Appendix.
Data collection and measures. Trained students administered questionnaires to 425 shoppers (53% female, median age 31) as they exited stores located in malls or on popular shopping streets. The questionnaire contained the proposed ten-item customer inspiration scale, along with scales for the theoretically related constructs. Replicating the ten-item customer inspiration scale from Study 1 resulted in an acceptable overall fit (CFI = .95; TLI = .93; RMSEA = .10; SRMR = .045).3 All items loaded significantly on their hypothesized constructs, with standardized loadings above .68 (“Study 2” column in Table 2,). Both factors show high CRs (inspired-by: .87; inspired-to: .93) and AVEs (inspired-by: .58; inspired-to: .71), indicating convergent validity (Bagozzi and Yi 1988).
TABLE: TABLE 3 Nomological Validity: Correlations with Related Marketing Constructs
| Construct | Conceptual Category | Study 2 | Study 3a | Study 3b | Study 4 |
|---|
| Inspired-By | Inspired-To | Inspired-By | Inspired-To | Inspired-By | Inspired-To | Inspired-By | Inspired-To |
|---|
| Idea shopping | Antecedentn (individual) | .28*** | .30*** | .57*** > | .48*** | .44*** | .44*** | .33*** | .37*** |
| Impulse buying | Behavioral consequence | .16** < | .41*** | .32*** | .34*** | .32*** | .29** | .19*** < | .32*** |
| Delight | Emotional consequence | .57*** > | .44*** | .73*** > | .59*** | .62*** > | .44*** | .73*** > | .59*** |
| Positive affect | Emotional consequence | .31*** | .26*** | .65*** > | .53*** | .38*** | .43*** | .63*** > | .49*** |
| Transcendent customer experience | Emotional consequence | .54*** > | .42*** | .69*** > | .53*** | .51*** | .44*** | .69*** > | .58*** |
| Customer satisfaction | Attitudinal consequence | .22*** | .18*** | .67*** | .64*** | .31*** | .19* | – | – |
| Loyalty intention | Attitudinal consequence | .22*** | .19*** | .73*** | .67*** | .35*** | .25** | – | – |
*p < .05.
**p < .01.
***p < .001.
Notes: Significant differences at p < .05 between correlations are indicated with < and >.
Discriminant validity. We assessed whether the two customer inspiration components are empirically distinct from the seven related marketing constructs. First, we compared the correlation between all seven constructs and the two states of customer inspiration with their AVEs, for a total of 14 comparisons (Fornell and Larcker 1981). All correlations with the inspired-by and inspired-to states were smaller than the square root of the AVEs for each construct (for further details, see the Web Appendix). Further, combining any related construct with either of the two customer inspiration components significantly decreased overall model fit (Dc2( 8) > 211.73, ps Nomological validity. To test nomological validity, we first examined how the customer inspiration states correlate with their related marketing constructs (Nenkov, Inman, and Hulland 2008). For each related construct, Table 3 reports the measured relationships with the two states of customer inspiration. Importantly, all conceptually related constructs correlated significantly and in the expected direction with the inspired-by and inspired-to components of customer inspiration, showing correlations ranging from .16 to .57.
We then used the Hotelling–William test (Steiger 1980) to assess differences in the relative strength of correlations between the measured constructs and the two states of customer inspiration. In support of our predictions, the emotional consequences delight and TCEs correlated significantly more strongly with the inspired-by activation component, whereas impulse buying had a significantly stronger correlation with the inspired-to intention component (ps < .05). We did not find initial support regarding positive affect, which related strongly to both inspired-by and inspired-to. While later experiments (Studies 3a and 4) support the hypothesized relative difference, this result could hint at a role of affect as a facilitator in decision making. Overall, our findings support the nomological validity of the customer inspiration construct.
This study extends the assessment of the proposed customer inspiration scale by providing evidence for its experimental and predictive validity in an online shopping context. We test the experimental validity by manipulating two antecedents to customer inspiration: inspirational content and idea shopping motivation. In line with our conceptualization, we propose that inspiration is a function of both the inspirational source and the individual who is the recipient of inspiration. Thus, we manipulate not only the inspirational content of the environment (i.e., the source) but also the motivation of the customer (i.e., the individual) to search for ideas.
Our study used recipe suggestions to manipulate the inspirational content of an experimental online grocery shop and to evoke new ideas about possible product combinations. Recipes represent one of the most popular categories on Pinterest, and such companies as Kraft Foods and Maggi have successfully used recipes to inspire customers with new ideas (Pinterest 2017). We therefore hypothesize that an online grocery shop that includes recipes will be more inspiring than an otherwise identical shop without recipes.
Moreover, we manipulate idea shopping motivation—the extent to which participants actively search for new ideas in an online shop. In line with our conceptualization, we expect that stimulating the motivation to look for inspiration in a shopping environment will increase openness toward inspiring stimuli and, thus, will facilitate customer inspiration. Thus, we hypothesize that idea shopping motivation will amplify the effect of inspirational content (i.e., recipes) on customer inspiration.
In line with our conceptualization of customer inspiration (Figure 1), we expect that customers are first inspired by the manipulations of inspirational content and idea shopping, which then leads to being inspired to purchase or explore products. More formally, we hypothesize that the inspirational content · idea shopping interaction has a direct effect on inspired-by and has an indirect effect on inspired-to. We therefore test a mediated moderation model for the two states of customer inspiration (see Figure 1).
Finally, we aim to provide evidence for the predictive validity of customer inspiration by assessing whether the proposed scale can improve predictions of exploration behavior and purchase intentions. As discussed, we propose that customer inspiration leads to the intrinsic pursuit of a consumption-related goal. In a shopping environment, this pursuit may manifest itself in a willingness to purchase a product or to explore similar product alternatives. Therefore, we assessed participants’ purchase intentions and gauged their exploration behavior by measuring ( 1) the number of clicks in the online store, ( 2) the duration of the shopping trip, and ( 3) the number of products viewed. As a baseline for our comparison, we use established marketing constructs that relate to either antecedents or the immediate emotional and behavioral consequences of customer inspiration (see the Web Appendix). We also include a general inspiration measure (Thrash and Elliot 2004) as part of this baseline to test the convergent validity of the customer inspiration states and the need for a contextualized scale.
Participants and procedure. Our experiment uses a 2 (inspirational content: high vs. low) · 2 (idea shopping: high vs. low) between-subjects design. To manipulate inspirational content, we programmed two versions of a fully functional online store. First, we sampled 4,934 product descriptions from the online grocery seller FreshDirect, organized into 12 main categories and 200 subcategories. Each product display included a photo and name of the item, with package size and price information. This base store design represented a low inspirational content condition. For the store in the high inspirational content condition, we additionally included 104 of the most popular recipes from the website allrecipes.com. Recipes were included as a separate category, with four subcategories (“Main Dish,” “Healthy Recipes,” “Quick and Easy,” and “Salad”). In the low inspirational content condition, the front page featured 12 randomly selected products, while the high inspirational content condition featured 12 randomly selected recipes.
We manipulated the level of idea shopping by asking participants to imagine that they were planning a dinner party for close friends that would take place in a few days. In the low idea shopping condition, we asked that before going to the online store participants think about a meal that their friends would enjoy. These participants, therefore, would have already formed a concrete idea before they accessed the store. In the high idea shopping condition, we asked participants to visit the online store and look inside the store for ideas about a meal, expecting that this group of participants would be more open to inspiration within the store. We instructed both groups to explore the store and to add any products to their shopping cart that they might be interested in.
We recruited 230 U.S.-based consumers (52% female, median age 31.5) from an online panel to take part in this study. Each participant was randomly assigned to one of the four experimental conditions. First, participants created an account with the online store, using a dedicated page. This enabled us to monitor their behavior throughout the experiment unobtrusively. For each participant, we recorded the number of clicks in the online store, the number of products viewed, and the total shopping duration. After participants had finished shopping, they indicated their purchase likelihood on a seven-point scale (1 = “not likely at all,” and 7 = “extremely likely”) and answered a questionnaire that included measures of customer inspiration and related constructs.
Measures. To measure customer inspiration, we used the proposed two-component ten-item scale. Confirmatory factor analysis produced an acceptable overall fit (CFI = .95; TLI = .94; RMSEA = .12; SRMR = .040). Both states showed high item loadings, CRs, and AVEs (“Study 3a” column in Table 2). In line with previous results, the inspired-by and inspired-to states of customer inspiration were significantly correlated to all their theoretically related constructs, with correlations ranging from .32 to .74 (“Study 3a” column in Table 3). Importantly, both customer inspiration states also had high correlations with the general inspiration state (rby = .73, rto = .65, ps 228.64, ps < .001). All scales showed high reliabilities and item loadings (for further details, see the Web Appendix).
Manipulation checks. Participants in the high idea shopping condition indicated a greater level of idea shopping motivation (MHighIdea = 4.44) than those in the low idea shopping condition (MLowIdea = 3.89; F( 1, 228) = 6.26, p < .05). As an attentional manipulation check, we also asked all participants whether they had noticed recipes on the online store. As expected, 81.8% of participants in the high inspirational content conditions noticed the recipes, while only 5.8% of participants in the low inspirational content conditions thought they had seen recipes (Wald c2( 1) = 86.30, p t(226) = .77, p = .44), supporting our predictions and the experimental validity of the proposed construct.
Effects on inspired-to. In line with our conceptualization of customer inspiration, we expected the inspired-by component to influence the inspired-to component (see Figure 1). Therefore, we followed the general path analytic framework (Edwards and Lambert 2007) using a bootstrap procedure with 1,000 samples to test a mediated moderation of the inspirational content · idea shopping interaction on inspired-to via inspired-by. Our analysis confirmed an anticipated indirect effect of inspirational content on inspired-to via inspired-by (BHighISM–Indirect = .69, 95% confidence interval [CI] = [.29, 1.16], p
TABLE: TABLE 4 Experimental Validity: Results of Mediated Moderation Analysis
| | Effects on Inspired-To |
|---|
| Moderator Variable | Effect on Inspired-By | Indirect Effect | Direct Effect | Total Effect |
|---|
| Study 3a: Idea Shopping |
| High | .93*** | .69** | -.03 | .66** |
| Low | .22 | .16 | -.23 | -.07 |
| Study 3b: Idea Shopping |
| High (M + 1 SD) | .80* | .47* | -.26 | .22 |
| Low (M – 1 SD) | .32 | .19 | -.20 | -.01 |
| Study 4: Motivation Framing |
| Approach | .74** | .64** | -.11 | .53† |
| Avoidance | -.23 | -.19 | -.04 | -.23 |
†10 < .p.
*p < .05.
**p < .01.
***p < .001.
Predictive validity. In order to test the predictive validity of the proposed scale for customer inspiration, we analyzed whether the two states of inspired-by and inspired-to could explain variance in purchase intentions and behavioral outcomes, beyond the predictive power of general inspiration and established correlates. We excluded 16 participants due to technical difficulties with the cookies-based recoding mechanism, leaving a sample size of 214 participants for further analysis. We employed a multivariate analysis of covariance (MANCOVA) to account for the relationships between the dependent measures. Using Pillai’s trace, this analysis revealed significant effects of inspired-by (V = .06; F( 4, 202) = 3.24, p < .05) and inspired-to (V = .08; F( 4, 202) = 4.29, p < .01) on the number of clicks, shopping duration, products viewed, and purchase intentions (see Table 5). We used separate hierarchical regressions to follow up on this omnibus analysis. For each dependent variable, we first specified a generalized linear model and included only the baseline constructs. Because the number of products and number of clicks are count variables, we specified generalized linear models that assumed a Poisson distribution of these dependent variables. We assumed a lognormal distribution for the duration of timein the shop because it is left-censoredat zero and positively skewed. Finally, we assumed a normal distribution for purchase intention. We then compared these baseline models with models that also included our measures for inspired-by and inspired-to. Detailed regression results are reported in Table 5.
TABLE: TABLE 5 Predictive Validity: MANCOVA and Hierarchical Regression Results
| | Study 3a | Study 3b | Study 4 |
|---|
| Overall Effect in Study 3a (Pillai’s Trace) | Number of Clicks (Poisson) | Shopping Duration (Lognormal) | Products Viewed (Poisson) | Purchase Intention (Normal) | Purchase Likelihood (Binomial) | Overall Effect in Study (Pillai’s Trace) | Attitude (Normal) | Purchase Intention (Normal) |
|---|
| Step 2 |
| Inspired-by | .06* | .11*** | .26* | .02 | .25* | .08 | .07*** | .34*** | .16† |
| Inspired-to | .08** | .05*** | .05 | .11* | .30*** | .43* | .06*** | -.001 | .19*** |
| General inspiration | .003 | -.01 | .003 | -.03 | .05 | – | .03* | .12† | .19* |
| Idea shopping | .05* | -.07*** | -.11 | .02 | .15* | .02 | .01 | -.01 | .09 |
| Positive affect | .02 | .07*** | .13 | .04 | -.08 | .42* | .04** | .24*** | .03 |
| TCE | .06* | -.10*** | -.26** | .006 | .20* | .17 | .01 | .01 | -.12 |
| Delight | .01 | -.02 | -.06 | -.04 | .12 | -.20 | .01 | .02 | .12 |
| Impulse buying | .01 | -.04*** | -.02 | -.004 | .02 | -.10 | .01† | -.08 | -.08 |
| Step 1 |
| General inspiration | .04 | .03* | .08 | .01 | .22** | – | .09*** | .22*** | .31*** |
| Idea shopping | .07** | -.05*** | -.05 | .03 | .23*** | .10 | .01 | -.01 | .10† |
| Positive affect | .04 | .11*** | .21* | .07 | .05 | .42* | .05** | .28*** | .05 |
| TCE | .05* | -.08*** | -.22* | .01 | .23* | .27 | .01 | .07 | -.04 |
| Delight | .01 | .001 | -.03 | -.04 | .16 | -.05 | .02 | .08 | .16† |
| Impulse buying | .01 | -.04*** | -.02 | .001 | .03 | -.12 | .01 | -.09† | -.05 |
| Incremental Fit |
| R2 Step 1 | | .59a | .06a | .03a | .51a | .45a | | .39b | .33b |
| R2 Step 2 | | .74a | .10a | .07a | .57a | .51a | | .42b | .37b |
| DR2 (% of Step 2) | | .15 (20%) | .04 (39%) | .04 (54%) | .07 (12%) | .06 (12%) | | .03 (8%) | .04 (12%) |
| Test statistic | | c2(2) = 96.38*** | c2(2) = 13.91* | c2(2) = 7.91* | c2(2) = 47.09*** | c2(2) = 7.50* | | F(2, 244) = 8.24*** | F(2, 244) = 9.58*** |
†p < .10.
*p < .05.
**p < .01.
***p < .001.
aNagelkerke’s R2. bAdjusted ordinary least squares R2. Notes: Regression constants omitted.
Findings indicate that the inclusion of the inspired-by and inspired-to states improved the predictions of all of these models, as evidenced by a significant improvement in R2 (ps
In further interpretation of these results, we note that inspired-by tends to explain general exploration behavior (e.g., duration of shopping trip), while inspired-to predicts more product-specific exploration behavior (e.g., products viewed). As discussed, we posit that inspired-by is part of the deliberation phase of the decision journey, whereas inspired-to marks the transition to the implementation phase of decision making (Gollwitzer 1990). We therefore speculate that customers who are inspired by the recipes without being inspired to make a concrete purchase may still be in the deliberation phase and, hence, are contemplating whether to actualize their new ideas. In contrast, customers who are inspired to make a purchase may already be in the implementation phase and, thus, may focus more on how to actualize their new ideas.
To provide further evidence of the experimental and predictive validity of customer inspiration, we replicated our findings from Study 3a in a field setting. In collaboration with a national grocery chain, we used an in-store promotion for organic products to test the effect of inspirational content on shoppers who naturally varied in their levels of idea shopping. As in Study 3a, we manipulated the level of inspirational content by selectively displaying recipe suggestions, and we measured each participant’s level of idea shopping. We again expected that participants with a high level of idea shopping would feel more inspired by an in-store promotion with high inspirational content than with low inspirational content. For participants with a low level of idea shopping, we anticipated an attenuated effect of inspirational content on customer inspiration. To investigate the hypothesized interaction, this study used a one-factorial (inspirational content: high vs. low) between-subjects design, crossed with a continuous measure for idea shopping.
Participants and procedure. The 121 participants (61% female, median age 54) in this study were shoppers who visited the local store of a national grocery chain on one of two consecutive Saturdays. In collaboration with the store management, we displayed an in-store promotion for 17 selected organic products. In the low-inspirational content condition, the in-store promotion featured only the selected organic products and promotional material that displayed the logo of the organic product line. In contrast, the high inspirational content condition (which was featured in the same store a week after the low inspirational content condition) also displayed three recipes that used the featured products as ingredients (i.e., recipes for soup, a main course, and a dessert) and provided promotional material highlighting these three recipes. The types of products displayed, their number, and their arrangement were held constant between conditions.4 Soon after shoppers had passed the product display, they were approached by a trained student who was blind to our hypotheses and were asked to complete a questionnaire that included our measures for customer inspiration and related constructs. All scales showed good psychometric properties (for further details, see the Web Appendix). Finally, we also measured purchase likelihood by observing whether participants added at least one of the products from the organic product display to their shopping baskets.
Results. To begin, we note that our findings replicate results from the previous study regarding experimental validity (see Table 4). To extend the predictive validity of customer inspiration, we analyzed its ability to improve the prediction of the likelihood of purchasing at least one of the products on display. As in Study 3a, we used hierarchical regressions to analyze our data (see Table 5). Because the choice to purchase one of the products is a binary outcome, we specified a logistic regression that assumes a binomial distribution of the dependent variable. The inclusion of the two customer inspiration states significantly improved the predictions of purchase likelihood (Nagelkerke’s R2 = .45 vs .51; c2( 2) = 7.50, p < .05). Inspection of the regression coefficient revealed that purchase likelihood was predicted by the inspired-to component of customer inspiration (Bto = .43, SE = .21, p < .05), in line with our results from Study 3a. Spotlight analysis revealed that 64% of participants who were strongly inspired to act (M + 1 SD = 5.47) purchased at least one of the products, while participants who were less inspired to act (M - 1 SD = 1.95) had a purchase likelihood of only 28%.
Together, these results replicate the tendency of our findings from Study 3a and further support the experimental and predictive validity of the proposed customer inspiration scale.
The goal of Study 4 was to explore the generalizability of customer inspiration and to introduce a boundary condition in line with our conceptualization. We first sought to establish generalizability by introducing a new manipulation to elicit inspiration. While our previous studies relied on participant recall of episodes of inspiration (Studies 1 and 2) or on exposure to inspirational content in the form of recipe suggestions (Studies 3a and 3b), Study 4 implements a more direct manipulation by appealing to customers’ imagination. Using such imagery appeals—urging consumers to imagine the product experience—is a widespread practice in marketing that has been shown to have powerful effects on attitudes and behavioral intentions (Petrova and Cialdini 2005). Imagery appeals stimulate an imagery processing style that increases both the quantity and the vividness of mental images and facilitates the reception of new ideas (Bone and Ellen 1992; MacInnis and Price 1987). Because reception of new ideas, broadening of mental horizons, and stimulation of a person’s imagination characterize being inspired by something, we hypothesize that imagery appeals (vs. lack of imagery appeals) increase customer inspiration.
Our second goal was to explore a theoretical boundary condition for inspiration. In line with recent conceptualizations of general inspiration (Jones, Dodd, and Gruber 2014; Thrash et al. 2010b, 2017), we posit that customer inspiration involves an approach motivation rather than an avoidance motivation. Furthermore, prior literature provides evidence that the frequency and intensity of general inspiration correlate with measures for the behavioral activation system but not with measures for the behavioral inhibition system (Thrash and Elliot 2003). Finally, Thrash et al. (2010b) find that individuals with a strong approach temperament tend to be inspired in response to creative insight, whereas individuals with a weak approach temperament report feeling a lack of inspiration despite their insight. We therefore hypothesize that imagery appeals have a positive effect on customer inspiration only for approach-framed advertisements and have no effect for avoidance-framed advertisements.
Finally, to add generalizability, we changed the purchase context from a rather utilitarian setting (i.e., grocery shopping) to a more hedonic context. To test the hypothesized interaction, we designed a 2 (imagery appeal: high vs. low) · 2 (motivation: approach vs. avoid) between-subjects experiment in the context of a vacation advertisement.
Participants and procedure. As a basis for manipulating imagery appeal and the type of motivation, we created four versions of a print advertisement for a vacation in Rome. We adopted stimuli presented by Petrova and Cialdini (2005, Study
2) to manipulate the ads’ imagery appeal and ease of imagery processing. The high imagery appeal versions included phrases inviting consumers to imagine their experience at the advertised destination, while the other versions did not contain such imagery appeals. Further, the low imagery appeal versions contained less vivid descriptions of the activities and had slightly blurred background pictures to impede imagery processing.
To manipulate the type of motivation, we adapted a stimulus design from Zhu and Meyers-Levy (2007, Experiment 2). In the approach motivation versions, the copy text encouraged approach goals (“Travel to Rome”; “Capture your opportunity to visit Rome this summer”), while the other versions encouraged avoidance goals (“Escape to Rome”; “Don’t let your opportunity to visit Rome this summer slip by”).
We recruited 253 U.S.-based consumers (53% female, median age 32) from an online panel to participate in this study. Upon starting the study, participants were randomly assigned to one of the four experimental conditions and were then exposed to the associated version of our experimental travel advertisement. After reviewing the advertisement, participants completed a questionnaire that included measures of customer inspiration, general inspiration, related constructs, attitudes, and purchase intentions.
Measures. Customer inspiration was measured with the proposed ten-item scale. A CFA with the two-component model of customer inspiration showed an acceptable model fit (CFI = .96, TLI = .95, RMSEA = .12, SRMR = .041) and high item loadings (“Study 4” column in Table 2). As before, we included measures for related marketing constructs and for general inspiration. All scales had high reliabilities and item loadings (for further details, see the Web Appendix). Finally, participants also responded to five items to assess their attitude toward the advertisement (a = .97) and four items to measure their intention to purchase the advertised travel (a = .86), which were adapted from Petrova and Cialdini (2005).
In support of nomological validity, the inspired-by and inspired-to components of customer inspiration correlated significantly with their related constructs, with correlation coefficients ranging from .19 to .73 (“Study 4” column in Table 3). Furthermore, both customer inspiration states also correlated highly with general inspiration state (rby = .75, rto = .63, ps < .001), indicating convergent validity. For all constructs, the square roots of the AVEs were larger than the correlations with any other constructs, and combining any constructwith either inspired-by or inspired-to significantly decreased the overall model fit (Dc2( 7) > 136.97, ps < .001), supporting discriminant validity.
Effects on inspired-by. To test the proposed effect of imagery appeals and avoidance motivation as a boundary condition, we submitted the scores for inspired-by to a 2 imagery appeal: high vs. low) · 2 (motivation: approach vs. avoid) ANOVA. As expected, we found a significant interaction effect on the inspired-by component between imagery appeal and type of motivation (F( 1, 249) = 9.01, p < .01). No other effects were significant. Planned contrasts revealed a positive effect of imagery appeal on inspired-by for advertisements that featured approach goals (MHighAppeal = 4.95, MLowAppeal = 4.21; t(249) = 3.26, p < .01). In contrast, there was no significant effect for advertisements that featured avoidance goals MHighAppeal = 4.52, MLowAppeal = 4.75; t(249) = .99, p = .32), indicating the expected boundary condition.
Effects on inspired-to. As before, we tested a mediated moderation to account for the inspired-to component of inspiration. A mediation analysis using a bootstrapping procedure with 1,000 samples revealed a significant indirect effect of imaginary appeal on inspired-to via inspired-by for the approach motivation conditions (Bapproach = .64, 95% CI = [.26, 1.01], p < .001), but not for the avoidance motivation conditions Bavoid = -.19, 95% CI = [-.58, .18], p = .35). As reported in Table 4, there were no remaining direct effects, which supports a fully mediated moderation hypothesis of customer inspiration.
Predictive validity. We assessed the ability of customer inspiration to predict outcomes beyond the effects of related marketing constructs and general inspiration. A MANCOVA revealed significant effects of inspired-by (V = .07; F( 2, 243) = 8.46, p< .001) and inspired-to (V= .06; F( 2, 243) = 7.12, p< .001) on attitudes toward the advertisement and on purchase intentions. Follow-up hierarchical regressions confirmed that the inclusion of the two customer inspiration states significantly improved the predictions of customers’ attitudes and purchase intentions ps < .001; see Table 5). Inspection of the regression coefficients revealed that inspired-by significantly predicted attitudes toward the offer (Bby = .34, SE = .08, p < .001) and marginally predicted purchase intentions (Bby = .16, SE = .09, p = .08). Purchase intentions were also predicted significantly by the inspired-to component (Bto = .19, SE = .05, p < .001). This provides further indication that inspired-by may relate more to the deliberation phase, while inspired-to relates to the implementation phase (see Study 3a). Collectively, our results further support the reliability and validity of the proposed customer inspiration scale.
Firms are increasingly exploring ways to develop and market solutions and ideas rather than products or services and to provide information that customers want to receive or even seek out (Marketing Science Institute 2016). In this research, we draw attention to inspiration as an understudied construct in marketing research that holds the potential to support managers in promoting new ideas that increase demand, foster exploration behavior, and strengthen customer loyalty. We define customer inspiration as a customer’s temporary motivational state that facilitates the transition from the reception of a marketing-induced idea to the intrinsic pursuit of a consumption-related goal. As such, customer inspiration holds a unique position at the very beginning of the customer journey (Lemon and Verhoef 2016) that links the activating reception of a new idea with the intention to pursue a consumption-related goal. Due to an accelerated lifestyle and the nonstop availability of purchase options that shortens the customer journey, this moment of inspiration is becoming increasingly important from a marketing perspective.
On the basis of our theoretical definition of customer inspiration, we develop and validate a ten-item, two-factor scale to measure customer inspiration (see Table 2). Empirical results find consistently high convergent and discriminant validity of the scale and show its unique position in a nomological network of related marketing constructs. Finally, we present evidence for the experimental and predictive validity of the proposed construct—under laboratory conditions as well as in the field. We conclude that the scale satisfies all criteria for newly developed construct measures and has the potential to add a new perspective to marketing theory.
This article offers a first link between two literature streams—the psychological inspiration literature and marketing literature—by introducing a contextualized conceptualization of inspiration in marketing that is compatible with recent conceptualizations of general inspiration. In this way, we want to spark a lively exchange of ideas across these two disciplines and open a new field of study for marketing and psychology scholars alike.
Customer inspiration has the potential to change the strategies that marketing managers use to increase demand, exploration behavior and, ultimately, customer loyalty. We find substantial evidence for inspiration leading to exploration behavior (Study 3a) and greater purchase intention (Studies 3a, 3b, and 4). Inspiration, thereby, suggests a new type of strategy for creating demand and opportunity to promote high-margin products. For instance, the New York-based retailer Story embraces an inspiration-type business model by building its entire merchandise and store layout on specific themes that change every few weeks like an art gallery (Harris 2014). Unlike other—and essentially backward-looking—marketing metrics such as satisfaction, customer inspiration is thus redirecting managerial attention toward exposing customers to new and surprising ideas, offering a new perspective for marketing managers.
Furthermore, inspiration could serve as a means for increasing brand attachment and strengthening customer relationships. We find evidence that customer inspiration correlates with attitudinal consequences such as loyalty and satisfaction (Studies 2, 3a, 3b, and 4). While the moment of inspiration is a temporary, “hot” state that peaks quickly and vanishes afterward, the experience may thus result in higher repurchase intentions and positive word of mouth that increase customer lifetime value and create positive long-term impact for a company.
With regard to the drivers of customer inspiration, our studies identify two ways for firms to elicit inspiration. First, firms may inspire customers by presenting existing products in new or unexpected combinations. For instance, presenting inspirational content in the form of recipe suggestions alongside grocery products can inspire customers, even in utilitarian purchase contexts (Studies 3a and 3b). Similarly, fashion companies may present their products in combination to show customers how to create new outfits, and home furnishings retailers can display their products in fully furnished rooms rather than as separate furniture items. Moreover, we find that such manipulations have the strongest effects for customers who actively search for new ideas (i.e., have an idea shopping motive). Second, our results suggest that imagery processing may foster inspiration (Study 4), thus encouraging firms to use engaging imagery. New technologies such as virtual and augmented reality, digital signage, and online tools (e.g., Pinterest’s visual search) could support the creation of visual content to inspire customers.
Summing up the managerial insights, we note that a valid and reliable measure of customer inspiration, and the realization that such measures are important to outcomes in practice, may serve as the foundation for an evidence-based marketing of ideas.
While our study has identified several key drivers of inspiration, we acknowledge that many more sources of customer inspiration exist and deserve further exploration. Our proposed ten-item scale offers the flexibility to measure customer inspiration independent from its source, and it thus presents a universal in situ measure for tapping into this new field of research. Because it is intentionally designed as a parsimonious measure, the scale would be easy to administer within existing surveys. As a valid and reliable tool, the scale can, therefore, create a basis for future studies on customer inspiration in the customer journey.
Because a comprehensive assessment of the wider nomological network is beyond the scope of this study, a productive area for future investigation is the relationship between customer inspiration and established marketing constructs. For example, recent research has discussed the importance of customer engagement as a construct for measuring and managing a customer’s value addition to the firm (Brodie et al. 2011; Pansari and Kumar 2016). Customer inspiration may foster customer engagement by creating strong positive attitudes, motivations, and loyalty. In line with recent research on the writer–reader contagion of inspiration (Thrash et al. 2017), inspired customers may also pass their inspiration on to other customers by creating and sharing content (e.g., viral videos, social media posts).
Future research could also investigate alternative paths between constructs in the nomological network. For example, while our results suggest that customer inspiration fosters positive affect, it also correlates with impulse purchases, which can trigger negative emotions (Rook and Fisher 1995). Furthermore, because customer inspiration is related to increased purchase likelihood, as well as to satisfaction and loyalty intentions, future research could investigate whether purchase behavior mediates the effect of customer inspiration on satisfaction and loyalty. Clearly, more research is needed on the role of customer inspiration in marketing.
The intent to inspire customers should lie at the heart of marketing, yet inspiration has received little attention in prior research. By conceptualizing customer inspiration and by developing a sound measure for this new construct, our study establishes a first step toward firmly embedding inspiration in management practice. We hope that our work—like the research on satisfaction in the 1980s—stimulates academic research and offers firms new approaches to develop and market solutions that improve everyday shopping experiences for customers and that will eventually lead to longer and more valuable customer relationships.
1In the retailing contexts of groceries (n = 101), fashion (n = 120), sports (n = 204), consumer electronics (n = 226), and furniture (n = 267), trained students randomly approached shoppers and asked them to ( 1) name spontaneous associations with inspiration and ( 2) describe an inspiring customer journey.
2The resulting item scores revealed considerable diversity in experts’ evaluations (intraclass correlation [ICC]( 2, 10) = .58). Interestingly, there was general agreement among academic experts (ICC( 2, 5) = .70) but substantial disagreement among managerial experts (ICC( 2, 5) = .19). This may indicate differences in the prevalent perspectives on customer inspiration in various consumer industries. To account for these differences, we opted for a comparatively conservative elimination criterion.
3Note that the traditional .05 cutoff value for RMSEA is less preferable when applied to models derived from small (N < 250) and possibly moderate (N < 500) sample sizes, since they tend to overreject appropriate models (Hu and Bentler 1999).
4Due to practical constraints, it was not possible to counterbalance the two conditions. Therefore, we made the decision to place the high inspirational content condition second, so that any effect due to the mere novelty of the presentation itself would attenuate rather than confound the expected effect of our manipulation on customer inspiration. Furthermore, as none of the participants on the second Saturday indicated that they had visited the store on the previous Saturday, mere exposure is unlikely to account for the observed effects.
DIAGRAM
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Record: 51- Customer Satisfaction and Its Impact on the Future Costs of Selling. By: Lim, Leon Gim; Tuli, Kapil R.; Grewal, Rajdeep. Journal of Marketing. Jul2020, Vol. 84 Issue 4, p23-44. 22p. 2 Diagrams, 6 Charts. DOI: 10.1177/0022242920923307.
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Customer Satisfaction and Its Impact on the Future Costs of Selling
Although scholars have established that customer satisfaction affects different dimensions of firm financial performance, a managerially important but overlooked aspect is its effect on a firm's future cost of selling (COS), that is, expenditures associated with persuading customers and providing convenience to them. Accordingly, this study presents the first empirical and theoretical examination of the impact of customer satisfaction on future COS. The authors propose that while higher customer satisfaction can lower future COS, the degree to which a firm realizes this benefit depends on its strategy and operating environment. Analyzing almost two decades of data from 128 firms, the authors find that customer satisfaction has a statistically and economically significant negative effect on future COS. While the negative effect of customer satisfaction on future COS is weaker for firms with higher capital intensity and financial leverage, this effect is stronger for more diversified firms and for firms operating in industries with higher growth and labor intensity. The authors also find that these effects may vary across two components of COS, cost of persuasion and convenience.
Keywords: capital structure; cost of selling; customer satisfaction; diversification; leverage; marketing–finance interface; strategic flexibility; strategic intent
Popular wisdom suggests that customer satisfaction relates to the top and bottom line (i.e., gross sales and net profits of firms; e.g., [23]). Accordingly, an impressive body of empirical research has examined the effect of customer satisfaction on measures of financial performance as reflected in stock market–based measures such as stock returns and risk (e.g., [29]; [75]) and operational measures such as market share, profitability, and cash flows (e.g., [ 4]; [36]). Although a firm derives its net profits from the generation of sales after accounting for the associated costs, the relationship between customer satisfaction and costs remains largely unexamined. Investigating the impact of customer satisfaction on cost-based measures is important because aggregate profit-based metrics might not capture the true effect of customer satisfaction on costs (see [11]).
Of particular salience to marketing managers is the cost of selling (COS); that is, the expenditures incurred by a firm to persuade customers to purchase its offerings and make it convenient for customers to do so. Firms often incur substantial costs related to persuading customers to purchase, such as sales commissions and marketing and advertising expenses. Firms also incur significant costs in trying to make purchases convenient for customers by absorbing freight-out costs and providing convenient or generous payment options that can result in bad debt expenses if customers default on payments (e.g., [71]; [82]). Indeed, marketing managers are constantly under pressure to look for marketing initiatives that produce the desired product-market outcomes at lower costs (e.g., [53]). The COS is also closely tracked by investors and analysts, as a reduction in COS is generally viewed as a positive signal in financial markets (see [37]). For example, Qualcomm enjoyed a surge in its stock price after it informed investors that it had been successful at reducing its selling-related expenditures ([28]). Similarly, when Best Buy pledged to continue to further reduce its selling-related expenses in 2014, analysts provided a positive forecast for its stock price (Forbes Trefis [27]).
Prior research has suggested that customer satisfaction is a market-based asset that can potentially reduce a firm's future COS by increasing customer retention and loyalty and by generating positive word of mouth and higher willingness to pay (e.g., [65]). However, how this effect of customer satisfaction on future COS varies across firm and industry factors remains conceptually and empirically unclear. As such, we focus on the impact of customer satisfaction on future COS and the contingencies that influence this relationship to make two key contributions.
First, drawing on almost two decades of data comprising 1,207 observations from 128 firms, we present the first empirical examination of the impact of customer satisfaction on future COS. As such, we respond to recent calls by [53] to study the effect of marketing actions on the costs incurred by a firm. Combining a text-analysis tool with data from COMPUSTAT and 10-K reports, we present a novel approach to measure COS by isolating the "selling" component of the firm's selling, general, and administrative expenses (SG&A). Consistent with our expectations, we find that customer satisfaction has a statistically and economically significant negative impact on future COS. Specifically, for an average firm in our sample, a 1-point increase in customer satisfaction, as measured on a 100-point scale of the American Customer Satisfaction Index (ACSI), corresponds to a decrease of almost US$130 million in future COS.[ 6] This amount is equivalent to nearly 3% of the average COS in our sample. This finding, therefore, is of direct importance for chief executive officers (CEOs) because cost reduction is their topmost concern (PwC 2018).
Second, we develop a contingency framework that outlines the conditions that moderate the impact of customer satisfaction on future COS. Drawing on prior research (e.g., [78]), we propose that a firm's strategic focus and flexibility and its operating environment are likely to influence the extent to which customer satisfaction lowers future COS. Underscoring the importance of strategic focus, results show that the negative effect of customer satisfaction on future COS strengthens as diversification of firms increases. Consistent with our expectations about the moderating effects of strategic flexibility, we find that the negative effect of customer satisfaction on future COS weakens as capital intensity and financial leverage increase. Results also bring to fore the impact of operating environment, as we find that the negative effect of customer satisfaction on future COS strengthens as industry growth and labor intensity increase.
We complement our focal analysis with a post hoc exploration of the effect of customer satisfaction on two components of COS: cost of persuasion and cost of convenience. We find that cost of persuasion seems to drive the effect of customer satisfaction on future COS, as the effect of customer satisfaction on the future cost of convenience is substantially smaller in magnitude than its effect on the future cost of persuasion. In addition, the moderating effects of firm strategy and operating environment predominantly manifest in the case of customer satisfaction's effect on the future cost of persuasion as opposed to the future cost of convenience. Taken together, results concerning the contingency framework and post hoc exploration provide a nuanced view of the impact of customer satisfaction on future COS.
Cost of selling refers to the costs incurred by a firm in persuading customers to purchase its offerings and in making it convenient for them to do so.[ 7] Typically, to persuade a customer, a firm implements activities aimed at providing information and developing product/service perceptions (see [31]). Such activities include engaging sales personnel to serve as product/service experts to customers, implementing a marketing plan to enhance brand image, or purchasing banner advertisements to promote a new product/service. Selling also involves activities that make it easier for customers to purchase a firm's offerings (e.g., [ 9]; [13]). For example, firms offer delivery services to customers to reduce the time and effort required for them to travel to a store or offer ancillary services such as providing flexible payment terms.
We draw on prior research (e.g., [49]; [60]) and derive a firm's COS from its SG&A. Specifically, SG&A comprises 16 expense items (COMPUSTAT Online Help Manual SG&A 2017). According to the Generally Accepted Accounting Principles, SG&A can be separated into selling expenses and general and administrative expenses ([49]). Of the 16 expenses that constitute SG&A, we consider the following 5 to make up COS: commissions, marketing expense, advertising expense, freight-out expense, and bad debt expense ([60]).
Firms typically pay commissions to sales personnel and channel partners for successfully converting a prospective customer. Firms also incur marketing and advertising expenses when building brand awareness, promoting a product, or introducing a new one. These activities are aimed at influencing customers' understanding and perceptions of the firm's offerings (e.g., [99]). As such, we view commissions, marketing expenses, and advertising expenses as the costs of persuading customers and, therefore, consider them part of COS.
Costs related to freight-out and bad debt expenses reflect costs incurred to make it convenient for customers to purchase a firm's offerings. Freight-out expense is the transportation cost that the firm incurs when it delivers products/services to customers ([104]). Thus, a firm incurs freight-out expense if it provides delivery services to customers to reduce the time and effort required to purchase its offerings. Bad debt expense reflects the loss that the firm incurs when customers renege on payments for products/services they bought ([71]). Firms, therefore, run the risk of incurring bad debt expense if they offer customers the flexibility to pay later or in installments. As such, we consider both freight-out and bad debt expenses as part of COS.
It is important to note that COS is distinct from cost-related concepts such as SG&A, cost of goods sold (COGS), and operating expenses (OPEX). Rather, COS is a subset of SG&A that is an aggregated metric comprising 16 distinct expense items. While COS reflects the costs related to selling a firm's offerings, COGS comprises costs incurred by a firm in the production of its offerings (COMPUSTAT Online Help Manual COGS 2019).[ 8] Finally, COS is also a subset of OPEX, which is the sum of COGS and SG&A (COMPUSTAT Online Help Manual Operating Expense OPEX 2018). We illustrate the relationships between COS and these cost-related concepts in Figure 1 and Table A1 of Web Appendix A.
Graph: Figure 1. Comparing cost of selling with other cost-related concepts.aThe expense items listed under General and Administrative Expense comprise only the expense items that are relevant to our research context (for a more detailed discussion, see Table 1).
Drawing on the extant literature ([65]; [92]), we propose that customer satisfaction should lower future COS by reducing the potential expenses in persuading customers and offering convenience. Importantly, the extent of this effect should be a function of the firm's strategy and its operating environment (see Figure 2).
Graph: Figure 2. Impact of customer satisfaction on future COS.
A firm's strategy reflects the fundamental organizational sources of competitive advantage that address two key issues ([40]): ( 1) what firms want and ( 2) what firms have. The question of "what firms want" brings the issue of strategic focus (or intent) to the forefront ([40]). The question of "what firms have" brings organizational resources to the forefront ([103]), where we examine organizational strategic flexibility, a polymorphous construct that assesses malleable firm resources that can easily take on different forms based on organizational contexts and needs ([35]). A firm's operating environment is an indicator of the customer's perspective because it reflects the nature of the preferences and expectations of its current and potential customers ([78]). In this way, the moderating impact of a firm's operating environment demonstrates the effectiveness of the firm in benefiting from customer satisfaction to lower future COS.
Customer satisfaction refers to customers' postconsumption comparison of their expectations and perceptions of performance of a product or service ([43]). Increase in customer satisfaction should lower a firm's future COS because satisfied customers respond in several beneficial ways. First, customer satisfaction enhances customer loyalty intentions and repurchase behaviors ([38]). Satisfied customers also have lower price sensitivity ([107]) and greater willingness to pay ([47]). As such, salespeople are likely to find greater success rates in persuading satisfied customers to purchase than unsatisfied customers ([80]). Therefore, commissions required to incentivize salespeople should reduce as customer satisfaction increase. Second, increase in customer satisfaction signals improvements in the quality of a firm's offerings relative to competitors ([86]); consequently, satisfied customers promote a firm's offerings through positive word of mouth ([65]). The quality signal, coupled with the positive word of mouth, should reduce future advertising and marketing expenses needed to persuade customers to purchase ([101]).
An increase in customer satisfaction should also lower spending related to providing convenience to customers. Satisfied customers should be more tolerant of inconvenience associated with the consumption of a firm's offerings than unsatisfied customers ([ 9]). In fact, satisfied customers often inconvenience themselves to purchase from a firm they are satisfied with even if competitors' offerings are conveniently available ([92]). For example, consider the offers for free shipping often used in promotions to keep prices competitive and generate additional sales ([62]). As price sensitivity reduces with increase in customer satisfaction ([107]), the attractiveness of competitors' free shipping offers should reduce as satisfaction increases.
Similarly, firms typically offer customers the flexibility to pay later or in installments in hopes that putting off payments to incentivize customers to spend more ([74]). However, as repurchase behaviors increase with customer satisfaction ([38]), the importance of payments terms should reduce with an increase in satisfaction. As such, higher customer satisfaction should reduce a firm's bad debt expenses, as it need not depend on the provisions of flexible payment terms to generate sales. Taken together, higher customer satisfaction should lower a firm's future COS through reductions in the potential costs that relate to persuading customers and providing convenience to them. Formally,
- H1: As customer satisfaction of a firm increases, its future COS decreases.
The strategic focus and flexibility of a firm should influence its willingness and ability to benefit from customer satisfaction. First, strategic focus represents the extent to which a firm concentrates its business portfolio on few versus many business sectors ([56]). As firms expand their operations over many businesses (i.e., increase firm diversification), their strategic focus reduces ([26]). Importantly, a firm's strategic focus can serve as either a facilitator or an inhibitor when it examines organizational and operating changes that are required to pursue opportunities related to customers ([110]). For example, diversified firms may enjoy reputation spillovers across markets that can potentially reduce their need for advertising. Second, strategic flexibility can affect organizational ability to deploy resources to utilize customer satisfaction to lower future COS ([61]). Indeed, firms with resource constraints can find it difficult to, for example, exploit the positive word of mouth from satisfied customers to lower their advertising expenses because they lack the flexibility to alter promotional programs. Extant research in marketing and operations suggests that a firm's reliance on capital equipment and its debt burden are crucial indicators of strategic flexibility ([44]; [77]). Therefore, to assess strategic flexibility, we consider two key resource positions: the degree to which a firm employs capital equipment (i.e., capital intensity) and its debt burden (i.e., financial leverage).
A firm's extent of diversification refers to the breadth of its offerings ([58]). Existing research in operations and marketing suggests that firms that provide a wider range of products and services can benefit from operational and financial synergies (e.g., [58]; [100]). In particular, diversification allows firms to enjoy operational synergies through the transfer of skills and tacit knowledge across multiple businesses ([83]). Indeed, a core competency of diversified firms is to employ specific knowledge acquired in one business to formulate solutions for problems and capitalize on the favorable circumstances in other businesses ([112]). This ability to transfer a variety of knowledge across multiple businesses serves as a catalyst in transferring both simple and complex customer knowledge ([32]) across diverse products and services. The gains from sharing customer knowledge should increase as firm diversification increases; this knowledge should enable firms to utilize the benefits of customer satisfaction to reduce their future COS. Put differently, the benefits due to higher customer satisfaction in each business sector—for example, the positive word of mouth and/or the higher quality signals—are likely to spill over to other business sectors, thus allowing diversified firms to lower their future costs required to persuade and offer convenience to customers. Therefore, we expect the following:
- H2: The negative effect of customer satisfaction on future COS is stronger (weaker) for more (less) diversified firms.
In contrast, prior research also suggests that a diverse range of offerings can lead to an increase in operational complexity and therefore lower resource allocation efficiency ([66]). Greater diversification can also result in significant constraints on the attention of the top management team (TMT; [100]), which, in turn, should reduce emphasis on activities that reduce costs. It is also possible that there is little overlap in the customer segments across multiple businesses, which makes it difficult for diversified firms to benefit from customer knowledge transfers. The lack of TMT attention, as well as the inability to benefit from customer knowledge transfers, should lower the likelihood of diversified firms utilizing these benefits. Therefore, we propose an alternative hypothesis:
- H2alt: The negative effect of customer satisfaction on future COS is weaker (stronger) for more (less) diversified firms.
Capital intensity reflects the degree to which a firm relies on capital investments such as property, plant, and equipment for its operations ([20]). Higher capital intensity, therefore, implies lower strategic flexibility. Capital-intensive firms typically rely on automation but deemphasize reliance on employee specific skills ([44]). Such firms tend to gain competitive advantage from reducing costs and therefore find opportunities for cost reduction to be attractive ([72]). However, the benefits from satisfied customers should be less valuable for firms with higher capital intensity. Higher capital intensity means that a firm relies more on tangible signals of the quality of its offerings such as manufacturing plants and physical stores. Attractiveness of intangible signals, such as the positive word of mouth, reduces in the presence of such tangible signals. Thus, although the potential cost savings may provide initial incentives for capital-intensive firms to leverage customer satisfaction to lower future COS, the reduced salience of these benefits is likely to negate these motivations.
As employees enable benefits from satisfied customers (e.g., [44]; [110]), even capital-intensive firms with higher customer satisfaction should lack the capability to use customer satisfaction to lower future COS. In fact, due to reliance on automation in capital-intensive firms, their desire to benefit from satisfied customers would require substantial transition of existing routines, such as retraining their workforce (e.g., [44]; [77]). Because capital-intensive firms have high fixed costs and low variable costs (e.g., [63]), their motivation to undertake revision to routines that might raise variable costs should be low ([20]). Thus, we expect the impact of customer satisfaction on future COS to weaken as capital intensity increases because of scarce strategic flexibility to utilize customer satisfaction.
- H3: The negative effect of customer satisfaction on future COS is weaker (stronger) for firms with higher (lower) capital intensity.
Financial leverage reflects the degree of debt that a firm has relative to its total assets ([77]). As debt increases with an increase in financial leverage, financial flexibility declines because the accumulation of debt restricts the availability of uncommitted cash flows ([88]). The constraints on financial flexibility arise because leveraged firms devote a substantial amount of cash flows toward fulfillment of interest payments ([68]). Lower financial flexibility creates incentives to reduce costs and therefore should motivate leveraged firms to realize the benefits of customer satisfaction to lower future COS ([ 3]). However, lack of financial flexibility also limits the capacity to pursue customer-related opportunities ([68]). Furthermore, because customers are typically concerned about the quality of offerings ([67]), it is unlikely for leveraged firms to derive benefits from customer satisfaction to lower future COS. Indeed, prior research has suggested that an increase in financial leverage lowers the likelihood of customers making specific investments with suppliers ([51]).
In summary, we expect an increase in financial leverage to weaken the negative impact of customer satisfaction on future COS despite the predisposition of leveraged firms to engage in cost-reduction activities. This weakening arises because financial leverage should not only reduce a firm's financial flexibility to utilize the benefits of customer satisfaction but should also lower customers' perceptions of the quality of the firm's offerings. More formally,
- H4: The negative effect of customer satisfaction on future COS is weaker (stronger) for firms with higher (lower) financial leverage.
A firm's operating environment provides information about the nature of the preferences and expectations of its current and potential customers and, therefore, should affect the impact of customer satisfaction on future COS (e.g., [46]; [78]). We draw on prior work to identify three industry conditions in a firm's operating environment that reflect competitive pressures, growth prospects, and labor intensity in managing customers. First, reflecting the centrality of competitive conditions for COS, we examine the moderating effect of industry concentration ([65]; [68]). Second, consistent with the logic that COS should be a critical consideration in growth markets ([ 1]), we examine the moderating effect of industry growth. Third, consistent with prior work that emphasizes the pivotal role of labor intensity for customer satisfaction, we consider the moderating effect of industry labor intensity ([22]).
Industry concentration captures the degree of competition in an industry such that as industry concentration decreases, competitive intensity increases, and as a result, customers have more options to choose from ([25]). With the increase in options, customers are more likely to consider competing firms as substitutes and thus have lower loyalty and higher price sensitivity, which should make it difficult to retain even highly satisfied customers ([65]; [68]). As such, the efficacy of the benefits of higher customer satisfaction in lowering future COS should weaken as industry concentration decreases because the options available to customers increase. Conversely, the decrease in the number of options as industry concentration increases makes it difficult even for dissatisfied customers to discontinue their relationships with the firm ([65]). Formally,
- H5: The negative effect of customer satisfaction on future COS is weaker (stronger) for firms operating in industries with lower (higher) concentration.
The rate of growth in demand in an industry (i.e., industry growth) plays an integral role in strategic marketing models ([46]). Findings from existing research suggest that salience of the benefits of customer satisfaction (i.e., positive word of mouth and lower price sensitivity) should increase with industry growth because customers' reliance on word-of-mouth communication increases due to reduced availability of alternative options ([111]). Indeed, criticality of word-of-mouth communication should increase with industry growth due to the presence of a new and diverse base of customers in such industries (see [50])—that is, customers unfamiliar with firms' offerings.
Prior research also suggests that price elasticity increases with industry growth ([10]) as competing firms are willing to make costly investments—such as pricing below their costs—to capture greater market share ([ 1]). In this way, customers' lower price sensitivity resulting from higher customer satisfaction is also more critical in growth industries because customers who are satisfied with the firm's offerings are unlikely to be swayed by its competitors' tactics because they tend to have a higher willingness to pay ([47]). Thus, the negative impact of customer satisfaction on future COS should be stronger in growth industries because the cost-reducing benefits of higher customer satisfaction (free word of mouth, less price-induced switching) are more relevant in such industries. Formally,
- H6: The negative effect of customer satisfaction on future COS is stronger (weaker) for firms operating in industries with higher (lower) growth.
Industry labor intensity reflects the extent to which firms in an industry are reliant on employees to produce and deliver products and services ([22]). In labor-intensive industries, customers should find it difficult to compare competing offerings due to heterogeneity in offerings that reliance on labor creates ([22]). This difficulty in comparing competing offerings should make customers loyal ([84]). Furthermore, customers' uncertainty in evaluating a firm's offerings increases with an increase in labor intensity as the intangible nature of offerings should increase their perceived consumption risks ([26]). Therefore, to reduce such risks, customers increase their reliance on word-of-mouth communication to obtain information concerning the offerings ([ 8]). Because the impact of customer satisfaction on future COS can be driven by its effect on the presence of loyal customers and positive word of mouth, it is likely that this effect strengthens as labor intensity increases because customers are more loyal and rely more on word-of-mouth communication in such industries. That is, the negative influence of customer satisfaction on future COS should strengthen as labor intensity increases. Thus,
- H7: The negative effect of customer satisfaction on future COS is stronger (weaker) for firms operating in industries with higher (lower) labor intensity.
We obtain the customer satisfaction score and the accounting data on each firm from the ACSI and the Standard & Poor's COMPUSTAT databases, respectively. We define a firm's industry using its primary four-digit North American Industry Classification System (NAICS) code. In addition, we follow precedent in the finance and accounting literature to exclude firms from the utilities and the financial services industries (e.g., [21]).[ 9]
We collect the ACSI scores from the first quarter of 1994 to the fourth quarter of 2013. Given that the ACSI releases customer satisfaction scores on an annual basis but does so in different quarters for firms in different industries, we use the quarterly accounting data from COMPUSTAT and align it with the four quarters between the releases of the ACSI scores. We only include firms for which at least two consecutive years of customer satisfaction data are available, as our model requires the future values of COS. Our sampling criteria yield 1,207 pooled time series and cross-sectional observations from 128 firms.
The ACSI collects customer satisfaction data from over 50,000 customers every year through telephone interviews. The customer satisfaction scores for each firm are scaled from 0 to 100. For firms that own multiple brands covered by ACSI, we use the average customer satisfaction scores across all brands as a measure (see [68]).
We calculate the extent of firm diversification as one minus the Herfindahl index of the firm's sales across all its business segments (e.g., [58]) and the firm's capital intensity as the ratio of its net plant, property, and equipment to its total assets (e.g., [72]). Consistent with prior research (e.g., [106]), we measure the firm's financial leverage as the ratio of its total long-term debt to its total assets. Industry concentration is the four-digit NAICS Herfindahl index of firm sales (e.g., [36]), and industry growth is the difference in the natural logarithm of the total sales of all firms within the same four-digit NAICS code at the end of the current year from the end of the preceding year (e.g., [105]). We measure industry labor intensity as the average ratio of the number of employees to the total sales of the firms within the same four-digit NAICS code (e.g., [64]).
We measure a firm's COS using the selling expenses obtained from its SG&A. Specifically, out of the 16 distinct expense items in SG&A (see COMPUSTAT Online Help Manual SG&A 2017), we consider the following five expense items—commissions, marketing expense, advertising expense, freight-out expense, and bad debt expense—as selling expenses and thus part of a firm's COS ([60]). To account for the differences in size across firms, we scale selling expenses by the firm's total sales.
Following prior research that decomposes SG&A (e.g., [49]; [60]), to measure a firm's COS, we subtract the expense items that are not relevant to a firm's COS from its SG&A. Table 1 outlines the expense items in SG&A and the methodology and data sources utilized to subtract items that are not relevant to a firm's COS.
Graph
Table 1. Deriving COS from SG&A.
| SG&A Item | Description of Item in COMPUSTAT | Relation to COS | Method of Subtraction | Data Source(s) |
|---|
| Selling Expenses |
| Commissions | Commissions | Part of COS | — | — |
| Marketing expense | Marketing expense | Part of COS | — | — |
| Advertising expense | Advertising expense | Part of COS | — | — |
| Freight-out expense | Freight-out expense | Part of COS | — | — |
| Bad debt expense | Bad debt expense, provision for doubtful accounts | Part of COS | — | — |
| General and Administrative Expenses |
| Operating expense | Operating Expense, Total when a separate COGS figure is given and there is no SG&A | Not relevant to COS but subtraction of item is not required because we do not consider cases when SG&A is not reported | — | — |
| Parent company charges | Parent company charges for administrative services | Not relevant to COS but subtraction of item is not required because we do not consider firms that are subsidiaries | — | — |
| Engineering expense | Engineering expense | Not relevant to COS and needs to be subtracted | Using text-analysis tool WRDS SEC Analytics Suite | SEC filings |
| Foreign currency adjustments | Foreign currency adjustments when included by the company | Not relevant to COS and needs to be subtracted | Using text-analysis tool WRDS SEC Analytics Suite | SEC filings |
| Indirect costs | Indirect costs when a separate COGS figure is given | Not relevant to COS and needs to be subtracted | Using text-analysis tool WRDS SEC Analytics Suite | SEC filings |
| Strike expense | Strike expense | Not relevant to COS and needs to be subtracted | Using text-analysis tool WRDS SEC Analytics Suite | SEC filings |
| Extractive industries' expenses | Extractive industries' lease rentals or expense, exploration expense, R&D, and geological and geophysical expenses | Not relevant to COS and needs to be subtracted | Using text-analysis tool WRDS SEC Analytics Suite | SEC filings |
| Directors' compensation | Directors' fees and remuneration | Not relevant to COS and needs to be subtracted | Using annual data on total compensation for directors (DT: TOTAL_SEC) and verify using text-analysis tool WRDS SEC Analytics Suite | COMPUSTAT EXECUCOMP, SEC filings |
| Pension-related expenses | Pension, retirement, profit sharing, provision of bonus and stock options, employee insurance, and other employee benefit expenses, for non-manufacturing companies | Not relevant to COS and needs to be subtracted | Using annual data on pension and retirement expenses (DT: XPR) and verify using text-analysis tool WRDS SEC Analytics Suite | COMPUSTAT, SEC filings |
| R&D | R&D, unless included in COGS by the company | Not relevant to COS and needs to be subtracted | Using quarterly data on R&D expenses (DT: XRDQ) and verify using text-analysis tool WRDS SEC Analytics Suite | COMPUSTAT, SEC filings |
| Research revenue | Research revenue that is less than 50 percent of total revenues for two years | Not relevant to COS and needs to be subtracted | Using quarterly data on R&D expenses (DT: XRDQ) and verify using text-analysis tool WRDS SEC Analytics Suite | COMPUSTAT, SEC filings |
1 Notes: SEC = U.S. Securities and Exchange Commission; WRDS = Wharton Research Data Services; EXECUCOMP = Executive Compensation; DT = Data Item. We present the 16 SG&A items based on the definition provided by COMPUSTAT (see COMPUSTAT Online Help Manual SG&A 2017). The decomposition of SG&A expenses into selling expenses and general and administrative expenses is adapted from [60]. For more information on the text analyses conducted using the WRDS SEC Analytics Suite, refer to Web Appendix B.
Although there are 11 expense items that are not relevant to a firm's COS (see Table 1), not all of them require subtraction from SG&A. Given that we are only focusing on firm-year observations for which SG&A is reported (i.e., for operating expense) and we do not have subsidiary firms in our sample (i.e., for parent company charges), operating expense and parent company charges are not applicable to our research context. As such, subtraction of these expense items is not required.
However, we need to subtract the following five items—engineering expense, foreign currency adjustments, indirect costs, strike expense, and extractive industries' expenses—from SG&A. Because they are not available in COMPUSTAT as separate items, we draw on the text-analysis tool WRDS SEC Analytics Suite to search through the 10-K filings of the firms within our sample for these items and subtract them from SG&A if they are disclosed separately (for detailed information on WRDS SEC Analytics Suite and the text analyses procedure, see Web Appendix B). By doing so, we follow "SEC Regulation S-X (17 CFR Part 210) §210.402 Items not material" and the materiality principle in accounting (see, e.g., [24]). That is, if these items are not disclosed as separate items in the 10-K filings, then they are not material (i.e., the amount is not significant enough to be disclosed as a separate item). To subtract the remaining four items—directors' compensation, pension-related expenses, research-and-development expenses (R&D), and research revenue—from SG&A, we draw on data at either the annual or quarterly level depending on the data availability in COMPUSTAT (for more details on the data sources, see Table 1). Taken together, we need to subtract nine expense items from SG&A to obtain a firm's COS.
Before we subtract any items, it is important that we verify that these items are indeed included in SG&A to avoid erroneously removing an item when it is not actually included. This data step is vital because a firm's definition of SG&A may not always be consistent with COMPUSTAT's. For example, Atlantic Richfield does not consider items such as R&D, pension, and exploration expenses as part of its definition of SG&A, even though these items are listed as a part of the itemized expenses of SG&A according to COMPUSTAT's definition. Thus, we adopt the following process to subtract items from SG&A.
First, we obtain a firm's SG&A from COMPUSTAT. Second, we collect information on the nine expense items (see Table 1), which are likely to be included in SG&A but are irrelevant to COS. Third, for each firm, we first determine which of the nine items are included in its SG&A and then subtract only those items that are included. When subtracting items from SG&A, we use a ratio-based approach in which we express the item as a fraction of the total SG&A for that fiscal year before multiplying this fraction with the SG&A for a specific quarter within that same fiscal year.
We present the comparisons of other commonly used SG&A-based measures of selling-related expenses in Table 2—that is, a firm's SG&A scaled by its total sales (SG&A/TS) and the difference in its SG&A and R&D expenditures scaled by its total sales [(SG&A − R&D)/TS]. As Table 2 shows, the difference in means for these two alternative measures and our focal measure is positive and statistically significant. For some firms, this difference can mean an overestimation of almost US$3 billion (e.g., Apple) if we use the SG&A/TS measure and more than US$750 million (e.g., HP Inc) if we use the [(SG&A − R&D)/TS] measure. Importantly, our findings are consistent with recent work showing that the utilization of these commonly used SG&A-based measures of selling expenses is likely to result in an inflation of firms' COS and possibly an erroneous estimation of its effects (see [89]).
Graph
Table 2. Comparing COS with Other SG&A-Based Measures of Selling-Related Expenses.
| Level Differences |
|---|
| COS/TS |
|---|
| Difference in Means | | Comparing the Focal Measure with the Corresponding SG&A-Based Proxy When There Is... |
|---|
| Absolute Value (SE) | Dollar Value (in Millions of Dollars) | | No Difference | <1% Difference | 1%–5% Difference | 5%–10% Difference | 10%–20% Difference | >20% Difference |
|---|
| SG&A/TS | .019 (.001)*** | 432.002 | Percentage of observations | 6% | 14% | 36% | 18% | 12% | 15% |
| Dollar value of difference in means (in millions of USD) | 0.000 | 27.591 | 145.280 | 202.445 | 647.791 | 1800.957 |
| (SG&A – R&D)/TS | .007 (.000)*** | 144.168 | Percentage of observations | 10% | 20% | 48% | 14% | 5% | 4% |
| Dollar value of difference in means (in millions of USD) | 0.000 | 23.967 | 140.540 | 197.434 | 532.668 | 520.671 |
| Correlation Matrixa |
| Variable | 1 | 2 | 3 |
| 1 | COS | 1.000 | | |
| 2 | SG&A − COS | .295 | 1.000 | |
| 3 | SG&A − R&D − COS | .512 | .360 | 1.000 |
- 2 ***p <.01 (two-sided).
- 3 a To obtain meaningful correlations of COS with the other SG&A-based measures of selling-related expenses, we subtract COS from SG&A and SG&A − R&D because COS is a subset of SG&A (see Table 1 and COMPUSTAT Online Help Manual SG&A [2017] for the definitions of COS and SG&A, respectively). The difference in means, their corresponding standard errors, and the correlations appear in their original values (i.e., before applying any variable transformations. We compute the dollar values of the difference in means using values from the unscaled versions of the measures (i.e., COS, SG&A and SG&A − R&D, respectively). Correlations that are significant at p <.10 (two-sided) appear in bold. There are 1,207 observations from 128 firms.
- 4 Notes: SG&A − R&D = SG&A minus R&D; TS = total sales; COS/TS = ratio of COS to TS; SG&A/TS = ratio of SG&A to TS; (SG&A − R&D)/TS = ratio of SG&A minus R&D to TS; SG&A − COS = SG&A minus COS; SG&A − R&D − COS = SG&A minus R&D minus COS.
In addition to the six moderators, we also control for several financial characteristics that can influence a firm's spending behavior and thus its future COS. Specifically, prior research has suggested that the financial performance of a firm can drive its spending behavior such that firms that are less profitable are likely to lower their future COS ([76]). As such, we include Tobin's q (i.e., a firm's market-to-book ratio) to account for firm performance ([81]).
Furthermore, the amount of slack organizational resources that a firm possesses may also influence its ability to respond to changing demands of customers ([45]) and thus affect its spending behavior. To account for factors that can potentially drive a firm's future COS, we include three variables. First, given that changes in inventory levels can influence a firm's capacity to react to supply and demand variations ([58]), we include inventory slack to capture the spare physical inventory of a firm ([ 7]). Second, we include retained earnings to take into account the resources that a firm allocates in preparation for unanticipated circumstances and implementation strategies ([12]). Finally, to consider the firm's effectiveness in its usage of liquid assets (e.g., cash) to generate sales, we include working capital ([61]).
Prior research has suggested that when firms have limited budgets, they can potentially make trade-offs between their spending behaviors in R&D and marketing to allocate funds effectively ([16]). Thus, we include R&D intensity as a control variable. In addition, we also account for the difference in firms' spending behaviors across industries. Existing research suggests that when the unpredictability in the nature and quantity of customers' requirements increases, it becomes harder for firms to rely on customers' prior knowledge ([46]). The constant changes in customers' preferences can potentially influence a firm's willingness to adjust its future COS. Thus, we control for industry turbulence to account for the extent to which industry demand changes rapidly and unpredictably ([25]). We present all control variables, their measures and data sources, and references to prior literature supporting the use of these measures in Web Appendix C, Table C1.[10]
We use a linear model specification to test our hypotheses, in which we treat COS as a function of customer satisfaction, moderators, and control variables. Because a firm's future COS is likely to depend on its current customer satisfaction and other explanatory variables, we incorporate this temporal separation into the model with a panel data structure. In addition, our model also takes into account the following identification challenges emanating from endogeneity concerns due to four types of omitted variable biases.
First, exogenous shocks concerning boom and bust business cycles can influence a firm's COS and customer satisfaction. For example, it is well documented that marketing budgets are curtailed during bust periods ([96]). Thus, to control for exogenous shifters that might influence a firm's COS, we include year dummies (i.e., time-specific fixed effects). Second, variables such as organizational culture that are largely stable over time ([42]) can also influence both how much a firm spends on marketing (i.e., COS) and the level of customer satisfaction it achieves. For example, firms that place more emphasis on customer engagement can spend more on selling-related activities and also have higher levels of customer satisfaction. Therefore, to capture firm-specific variables that do not vary over time (e.g., organizational culture), we also include firm-specific fixed effects. Because industry idiosyncrasies can be teased out using both the time- and firm-specific fixed effects (see [52]), inclusion of these fixed effects also accounts for industry variables that do not change over time.
Third, firm-specific variables that change over time can also potentially influence a firm's COS and its customer satisfaction levels. To illustrate, the mindset of the CEO and/or chief marketing officer (CMO) can determine the emphasis on customer satisfaction or the firm's expenses on marketing-related activities (i.e., its COS) over two- to five-year periods (i.e., based on average tenures of CEOs and CMOs; e.g., [33]). Thus, we also include a rich set of covariates to proxy for any time-varying omitted variables.
Fourth, given that prior research has used SG&A as a predictor of customer satisfaction (e.g., [75]), reverse causality can also be a cause for concern. For example, one can argue that a firm's current COS can instead have an impact on its current customer satisfaction levels. We aim to mitigate such concerns in our model by lagging the explanatory variables by one year (e.g., [97]). However, given that customer satisfaction is persistent, the use of lagged variables may not completely correct for the potential reverse causality. Literature in economics suggests that reverse causality can be framed as a form of omitted variable bias, where the omitted variable varies over time (e.g., see econometric texts such as [109]]). In this way, the inclusion of a rich set of covariates also accounts for concerns relating to reverse causality if there is autocorrelation in the explanatory variables despite the temporal separation. Thus, we can specify our model as follows:
Graph
1
where is the fixed effect that captures the firm-specific heterogeneity in , is the future COS of firm in industry at time , is the customer satisfaction of firm in industry at time , is firm diversification, is capital intensity, is financial leverage, is industry concentration, is industry growth, is industry labor intensity, is the vector of firm- and industry-specific control variables, are the year dummy variables, and is the random error term.
The identifying assumption in Equation 1 is that beyond the firm-specific fixed effects, the omitted variables do not vary over time ([33]). Although we include a rich set of covariates in Equation 1 to account for the time-varying omitted variables, it is difficult to argue that we can observe all important variables that may influence both firms' customer satisfaction levels and their COS. Therefore, to account for the time-varying omitted variables, we adopt a two-step control function approach with an appropriate instrumental variable (e.g., [41]).
We perform the control function estimation by first estimating an auxiliary equation with customer satisfaction as the dependent variable using the following specification:
Graph
2
where captures firm fixed effects that account for the firm-specific heterogeneity in customer satisfaction and is the random error term (other variables, including time fixed effects, are defined as previously).
The auxiliary equation specified in Equation 2 includes the six moderators ( , , , , , and ), the rich set of covariates ( ) and the year dummy variables ( ). For identification purposes, we also include weighted peers' customer satisfaction ( ) as an instrument. We identify peer firms as firms with brands that are classified in at least one common ACSI-defined sector with that of the brand(s) owned by the focal firm (for sector definitions, see [ 2]]).[11] An ACSI-defined sector consists of several ACSI-defined industries. For example, the ACSI-defined industries Airlines and Consumer Shipping belong to the Transportation sector in ACSI (see Web Appendix D, Table D1). In this way, a firm's (e.g., Delta's) peers can either be from the same ACSI-defined industry as that of the firm (e.g., United) or from another industry that is in the same ACSI-defined sector (e.g., FedEx belongs to the Transportation sector).[12] Before delving into the operationalization of the instrument, we note that the instrument meets the standard instrument relevance and exclusion restriction criteria, as we discuss subsequently. In addition, we also aim to use an instrument with a property we refer to as "granularity," such that a granular instrument implies that the instrument varies at the level of the endogenous explanatory variable; we delve into this and other instrument validity criteria after defining the instrument.[13] Specifically, we define the instrument as follows:
Graph
3
where denotes the weight of the relationship between focal firm and peer firm in ACSI-defined sector at time , and is the customer satisfaction score of the peer firm in ACSI-defined sector at time . Consistent with our measurement of a firm's overall customer satisfaction, for firms that possess multiple brands across ACSI-defined sectors, we take the average weighted peers' customer satisfaction scores across these sectors.[14] We discuss the operationalization of the weights for the relationship between the focal firm and its peers, and present examples of the measurement of the instrument in Web Appendix D.[15]
To evaluate the conceptual quality of the proposed instrument, we follow recent recommendations and delve into discussing instrument relevance, exclusion restriction, and granularity (see [33]; [94]). Instrument relevance implies that the proposed instrument conceptually correlates with the endogenous variable. According to the long-standing conceptualization of customer satisfaction, customers determine their satisfaction levels with a firm on the basis of the comparisons that they make between their expectations and their perceptions of the performance of the firm's products/services ([43]). Because customers often distribute their purchases amongst several competing firms, their perceptions of the performance of the products/services of the firm's peers are likely to influence their expectations of the firm's offerings ([54]). Thus, customers are likely to evaluate a firm's customer satisfaction relative to its peers ([55]), and peers with similar firm characteristics are likely to have greater influence (see, e.g., [108]). Taken together, the weighted customer satisfaction scores of a firm's peers are likely to influence its customer satisfaction level, thus making it a relevant instrument for customer satisfaction.
Exclusion restriction implies that the proposed instrument does not correlate with the omitted variables that are a part of the error term but is likely to be correlated with the endogenous variable ([109]). To provide a theoretically grounded explanation for the proposed exclusion restriction, consider the example of the degree of TMT integration, a plausible omitted variable that is likely to be correlated with customer satisfaction and COS. TMT integration reflects the extent of collaboration, shared information, joint decision making, and shared vision within the TMT ([39]). These attributes, in turn, are critical for coordinating actions among TMT members and for improving the quality of strategic decisions ([15]). As such, the extent of TMT integration within a firm should be correlated with its level of customer satisfaction ([95]). However, there is little reason to believe that our proposed instrument (i.e., the weighted customer satisfaction levels of a firm's peers) would correlate with a firm's degree of TMT integration.
First, prior research has suggested that a firm's extent of TMT integration is more likely to be influenced by attributes within the firm, such as concurrent changes in the diversity of team members or CEO mindset ([95]), as opposed to outside factors that are not within its control. Second, the customer satisfaction of a firm's peers is a representation of the joint decisions made by both the customers of the firm and its peers. Because customers rarely possess knowledge of the identities of one another, it is highly implausible for them to collectively adjust their satisfaction ratings of all the firm's peers due to changes in a firm's degree of TMT integration. Coordination among peer firms to imitate the firm's level of TMT integration is also unlikely, as it is not common for peers to jointly monitor the implementation of specific strategies from a particular firm ([41]).
Importantly, the identification of a firm's peers using the ACSI-defined sector classification also mitigates the possibility of cooperative monitoring of an individual firm from all its peers. Specifically, because our definition of a firm's peers also includes peer firms that are from an adjacent (but not the same) ACSI-defined industry as that of the focal firm, it is implausible for the customer satisfaction levels of a firm's peers to correlate with its degree of TMT integration.[16] For example, consider the Transportation sector as presented in Web Appendix D, Table D1. While UPS and FedEx have little incentive to collaborate with the other airlines to monitor and react to changes in Delta's level of TMT integration, it is also improbable for Delta to change the extent of its TMT integration due to the changes in the customer satisfaction levels of UPS and FedEx. Taken together, both prior theory and the construction of our instrumental variables indicates that the weighted peers' customer satisfaction meets the exclusion restriction and is a valid instrument.
A common criticism against the use of peer-based instruments is granularity, when there is insufficient systematic variation in the group composition ([ 6]) such that the likelihood for a firm to behave in a certain way varies with the behavior of the group but does not sufficiently vary with the exogenous characteristics of the group ([69]). Consider the common operationalization of peer-based instrument—the industry average value excluding the focal firm. In this case, the only source of variance in the instrument across firms comes from the exclusion of the focal firm while calculating the value of the instrument, resulting in little change in its value across firms and over time. In addition, the simple exclusion of the focal firm's value also makes it impossible to determine whether the focal firm's behavior is being influenced by that of its peers or that the behavior of its peers is actually an aggregation of the behavior of individual firms ([70]).
The measurement of the weighted peers' customer satisfaction scores relies on the construction of groups that are partially overlapping in terms of the ACSI-defined sector classifications and the weighing of firms by their characteristics within each ACSI-defined sector.[17] In this way, the use of this measure is likely to increase the variation in group composition and thus can preclude concerns associated with granularity as "it breaks down the linear dependence between endogenous and exogenous peer variables" that varies at the individual firm level (see [94], p. 13). Reassuringly, we find that there is substantial variation in the weighted peers' customer satisfaction scores across ACSI-defined sectors (see Web Appendix E, Figure E1).
In the second step, we then estimate Equation 1 with the predicted residuals obtained from Equation 2. As such, our final equation is as follows:
Graph
4
where is the predicted residuals from estimating the auxiliary regression (Equation 2). Following the recommendation of [87], we also implement the bootstrap to obtain standard errors that consider the additional source of variation due to the use of an estimate (i.e., from Equation 2) in Equation 4. Using 500 bootstrap samples, we first obtain 500 sets of predicted residuals from the estimation of Equation 2 and then use each set of predicted residuals in the estimation of Equation 4 ([85]).
We outline the descriptive statistics and bivariate correlations between the variables and present the results from the auxiliary equation (Equation 2) in Table 3 and Table E1 of Web Appendix E, respectively. Consistent with our expectations, we find that weighted peers' customer satisfaction significantly and positively predicts firms' customer satisfaction . Results from the F-test for instrument strength also suggest that our focal instrument is not a weak instrument, as we find that the F-statistic is 98.570 ( ), well above the cutoff of 10 ([98]). We report the estimated results of both a model that includes only the main effect of customer satisfaction and the covariates (M1), and a model that also includes the moderating effects (M2) in Table 4. The condition indices of M1 and M2 (i.e., 12.170 and 14.000, respectively) are both well below the more rigorous cutoff criterion of 20 ([34]). Thus, multicollinearity does not seem to be an issue for either model. As shown in Table 4, M1 already supports H1, with a significant negative effect for customer satisfaction . Importantly, the addition of the hypothesized interaction effects resulted in a significant increase in model fit ( ). Thus, we focus our subsequent discussion on the results from M2.
Graph
Table 3. Descriptive Statistics and Correlation Matrix.
| Correlation Matrix |
|---|
| Variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 |
|---|
| 1. Future cost of sellinga | 1.000 | | | | | | | | | | | | | |
| 2. Customer satisfaction | .162 | 1.000 | | | | | | | | | | | | |
| 3. Firm diversification | .044 | .235 | 1.000 | | | | | | | | | | | |
| 4. Capital intensity | −.286 | −.217 | −.357 | 1.000 | | | | | | | | | | |
| 5. Financial leverage | −.084 | −.241 | −.074 | .147 | 1.000 | | | | | | | | | |
| 6. Tobin's q | .160 | .252 | .029 | −.183 | −.029 | 1.000 | | | | | | | | |
| 7. Inventory slack | .227 | .347 | .048 | −.094 | −.107 | −.144 | 1.000 | | | | | | | |
| 8. Working capital | .041 | .105 | .030 | −.433 | −.230 | .136 | .247 | 1.000 | | | | | | |
| 9. Retained earnings | .039 | .283 | .057 | .136 | −.403 | .057 | .244 | −.044 | 1.000 | | | | | |
| 10. R&D intensity | −.064 | .134 | .058 | −.362 | −.190 | .310 | −.108 | .384 | −.119 | 1.000 | | | | |
| 11. Industry concentration | .198 | .258 | .153 | −.240 | −.173 | .065 | .200 | .169 | −.037 | .051 | 1.000 | | | |
| 12. Industry growth | −.030 | −.061 | −.065 | −.005 | −.008 | .080 | −.118 | .007 | −.031 | .041 | −.082 | 1.000 | | |
| 13. Industry turbulence | −.033 | −.107 | −.072 | −.058 | .050 | .120 | −.177 | −.027 | −.062 | .084 | .059 | .261 | 1.000 | |
| 14. Industry labor intensity | .002 | .037 | .114 | −.114 | −.020 | .211 | −.159 | .103 | −.029 | .181 | .018 | .030 | .004 | 1.000 |
| Mean | .209 | 76.960 | .294 | .337 | .245 | 1.866 | .139 | .087 | .212 | .012 | .207 | .049 | .111 | .014 |
| SD | .098 | 6.547 | .301 | .185 | .194 | 1.467 | .117 | .159 | .506 | .027 | .164 | .089 | .064 | .019 |
| Min | .026 | 58.000 | .000 | .026 | .000 | .328 | .000 | −.241 | −2.242 | .000 | .043 | −.269 | .000 | .002 |
| Max | .537 | 87.500 | 1.000 | .748 | 1.127 | 9.102 | .527 | .596 | 1.276 | .149 | .951 | .308 | .357 | .135 |
- 5 a Future cost of selling refers to the cost of selling of a firm in the following year, where we refer to year as the aggregation of data over the four quarters corresponding to the period between the release of ACSI scores.
- 6 Notes: Correlations that are significant at p <.10 (two-sided) are in bold. The mean, standard deviation, minimum, maximum, and correlation values of the variables appear in their original values (i.e., before applying any variable transformations). There are 1,207 observations from 128 firms.
Graph
Table 4. Impact of Customer Satisfaction on Future COS.
| Hypothesis and Expected Sign | M1: Main Effects Only | M2: Full Model |
|---|
| Coeff. (SE) | Coeff. (SE) |
|---|
| H1: − | Customer satisfaction | −.004 (.002)** | −.005 (.002)** |
| H2: ? | Customer satisfaction × Firm diversification | | −.003 (.001)*** |
| H3: + | Customer satisfaction × Capital intensity | | .005 (.002)*** |
| H4: + | Customer satisfaction × Financial leverage | | .002 (.001)* |
| H5: − | Customer satisfaction × Industry concentration | | .003 (.003) |
| H6: − | Customer satisfaction × Industry growth | | −.005 (.002)*** |
| H7: − | Customer satisfaction × Industry labor intensity | | −.035 (.017)** |
| Firm-Level Controls | | |
| Firm diversification | −.013 (.008) | −.012 (.008) |
| Capital intensity | .077 (.022)*** | .074 (.022)*** |
| Financial leverage | .018 (.013) | .027 (.014)** |
| Tobin's q | .004 (.002)* | .004 (.002)* |
| | Inventory slack | .178 (.041)*** | .198 (.041)*** |
| Working capital | −.021 (.021) | −.023 (.020) |
| Retained earnings | .010 (.007) | .010 (.007) |
| R&D intensity | −.621 (.196)*** | −.691 (.191)*** |
| Industry-Level Controls | | |
| Industry concentration | −.033 (.035) | −.036 (.037) |
| Industry growth | −.031 (.014)** | −.026 (.013)* |
| | Industry turbulence | −.062 (.025)** | −.063 (.024)*** |
| Industry labor intensity | −.093 (.087) | −.006 (.107) |
| Residualsa | .003 (.002) | .004 (.002)* |
| Firm fixed effects | Included | Included |
| Year dummies | Included | Included |
| Constant | −.003 (.008) | −.005 (.008) |
| Summary Statistics | | |
| | R2 | .140 | .176 |
| | Wald (d.f.) | 91.260 (32)*** | 127.510 (38)*** |
| Condition Index | 12.170 | 14.000 |
| N (n) | 1,207 (128) | 1,207 (128) |
- 7 *p <.10.
- 8 **p <.05.
- 9 ***p <.01.
- 10 a We obtain the residuals from the estimation of the auxiliary regression in Web Appendix E, Table E1.
- 11 Notes: Two-sided tests of significance. SE = bootstrap standard errors derived from performing 500 bootstrap replications using the approach illustrated in [85]; N (n) = Total number of observations (unique firms). All continuous variables are mean-centered and Winsorized at the 1st and 99th percentile levels.
The predicted residuals term is statistically significant ( ), thus indicating the importance of accounting for the endogeneity of our focal independent variable, customer satisfaction (see [87]). Consistent with H1, we find that customer satisfaction has a significantly negative effect on future COS . Parameter estimates also provide support for H2 (as opposed to H2alt), as we find that the negative effect of customer satisfaction on future COS becomes stronger as diversification increases . We also find support for H3, as the negative effect of customer satisfaction on future COS becomes weaker as capital intensity increases . Results also indicate that the negative effect of customer satisfaction on future COS becomes weaker as financial leverage increases , thus providing marginal support for H4. We also find support for H6 and H7, as the negative effect of customer satisfaction on future COS becomes stronger as industry growth and industry labor intensity increase . Contrary to our expectations, however, there is insufficient evidence to support H5, as we find that changes in industry concentration do not significantly influence the negative effect of customer satisfaction on future COS .
We conduct several sensitivity analyses to examine the robustness of our conclusions across the use of alternative instruments, an instrument free approach, alternative model specifications, different measures of industry classification and of the focal dependent variable, and alternative sample composition. As elaborated in Web Appendix F, we continue to find broad support for the proposed hypotheses.
Given that COS reflects both cost of persuasion and convenience, a natural question concerns how the effects of customer satisfaction differ between these two components of COS. To explore this question, we first break down COS into cost of persuasion (comprising commissions and marketing and advertising expenses) and cost of convenience (comprising freight-out and bad debt expenses).[18] Next, we estimate separate models for the two components using the same specification as in our focal analysis.
Table 5 outlines the results of our model for the future cost of persuasion and convenience. Across the two dependent variables, we continue to find that customer satisfaction has a significant negative main effect. In addition, we find that the negative effect of customer satisfaction on both the future cost of persuasion and convenience is stronger in industries with higher growth. Whereas the results for the impact of customer satisfaction on the future cost of persuasion are largely in line with the proposed hypotheses, those for its impact on the future cost of convenience differ in three keys ways.
Graph
Table 5. Splitting COS: Impact of Customer Satisfaction on Future Cost of Persuasion and Cost of Convenience.
| Hypothesis and Expected Sign | M1:Cost of Persuasion | M2:Cost of Convenience |
|---|
| Coeff. (SE) | Coeff. (SE) |
|---|
| H1: − | Customer satisfaction | −.004 (.002)* | −.001 (.001)** |
| H2: ? | Customer satisfaction × Firm diversification | −.004 (.001)*** | .001 (.000)* |
| H3: + | Customer satisfaction × Capital intensity | .006 (.002)*** | .000 (.000) |
| H4: + | Customer satisfaction × Financial leverage | .002 (.001)* | .000 (.000) |
| H5: − | Customer satisfaction × Industry concentration | .007 (.003)** | −.003 (.002)* |
| H6: − | Customer satisfaction × Industry growth | −.003 (.002)* | −.001 (.001)** |
| H7: − | Customer satisfaction × Industry labor intensity | −.037 (.017)** | .005 (.004) |
| Firm-Level Controls | | |
| Firm diversification | −.014 (.009) | .002 (.003) |
| Capital intensity | .072 (.022)*** | .009 (.005)* |
| Financial leverage | .030 (.014)** | −.002 (.004) |
| Tobin's q | .003 (.002) | .001 (.001)*** |
| | Inventory slack | .179 (.044)*** | .012 (.013) |
| Working capital | −.031 (.020) | .010 (.005)** |
| Retained earnings | .007 (.007) | .003 (.002)** |
| R&D intensity | −.706 (.186)*** | .015 (.033) |
| Industry-Level Controls | | |
| Industry concentration | −.020 (.037) | −.016 (.011) |
| Industry growth | −.020 (.014) | −.003 (.004) |
| | Industry turbulence | −.068 (.025)*** | .004 (.006) |
| Industry labor intensity | −.004 (.103) | .014 (.022) |
| Residualsa | .003 (.002) | .001 (.001)* |
| Firm Fixed-Effects | Included | Included |
| Year Dummies | Included | Included |
| Constant | .001 (.009) | −.006 (.004) |
| Summary Statistics | | |
| | R2 | .192 | .128 |
| | Wald (d.f.) | 143.560 (38)*** | 66.870 (38)*** |
- 12 *p <.10.
- 13 **p <.05.
- 14 ***p <.01.
- 15 a We obtain the residuals from the estimation of the auxiliary regression in Web Appendix E, Table E1.
- 16 Notes: Two-sided tests of significance. SE = bootstrap standard errors derived from performing 500 bootstrap replications using the approach illustrated in [85]; N (n) = Total number of observations (unique firms). All continuous variables are mean-centered and Winsorized at the 1st and 99th percentile levels. There are 1,207 observations from 128 firms.
First, the magnitude of the impact of customer satisfaction on the future cost of persuasion is significantly higher than its negative effect on the future cost of convenience. Put differently, the benefits of higher customer satisfaction are more salient for persuading customers via initiatives of advertising, marketing spending, and commissions to sales personnel, as compared to spending efforts on providing convenience to customers. One plausible explanation for this observed difference concerns the higher stickiness of convenience from the perspective of customers. Indeed, prior research has suggested that convenience serves as a significant switching cost for customers ([92]), where they are likely to formulate their expectations of a firm's provision of convenience based on industry norms. As such, although satisfied customers tend to be loyal ([38]), a firm's ability to encourage actual repurchases may still be limited if it does not offer sufficient convenience ([102]). In fact, it is plausible that this higher salience of convenience for customers might explain the robustness of the negative effect of customer satisfaction on the future cost of convenience across levels of capital intensity, financial leverage, and labor intensity.
Second, consistent with H2, the negative effect of customer satisfaction on the future cost of persuasion is stronger for diversified firms (i.e., the benefits of customer satisfaction are more salient for such firms because they can leverage these benefits across multiple business segments; e.g., [112]). However, the weaker negative effect of customer satisfaction on the future cost of convenience for more diversified firms is consistent with H2alt (i.e., benefits of satisfied customers are less salient for diversified firms because the operational complexity of such firms makes it difficult for them to leverage these benefits across diverse business segments; e.g., [66]). Indeed, one can argue that provision of convenience for customers requires significant and complex investments in logistics and financial structures (e.g., [93]).
Third, the negative effect of customer satisfaction on the future cost of convenience is stronger for concentrated industries. This effect is consistent with the expectations of [92], who find that convenience is important for customers in concentrated industries. As industry concentration decreases, competitive offerings increase and customers' access to options increases as well. As such, even loyal customers may be tempted to shift their purchases to a competitor (e.g., [65]). Taken together, the post hoc analyses underscore the need for future research to theoretically and empirically examine the two components of COS (i.e., cost of persuasion and convenience).
Synthesizing the literature in economics ([13]), marketing ([68]), and operations ([58]), we present the first empirical study of the effect of customer satisfaction on the future COS of a firm. Indeed, extant research that examines the financial effects of marketing assets does not investigate their cost implications. Thus, our key theoretical contribution is in outlining the arguments for the negative effect of customer satisfaction on future COS and bringing to fore the firm and industry level contingencies. In addition, we also qualify the received view that customer satisfaction drives profits predominantly through its effects on revenue expansion by identifying it as an asset that enables a firm to achieve a "dual emphasis" of not only revenue expansion but also cost reduction ([65]; [75]).
Our findings also complement existing research that identifies circumstances under which firms are more or less likely to utilize customer satisfaction information (e.g., [78]). First, we contribute to a nascent body of literature by showing that the strategic focus of a firm not only moderates the effectiveness of its spending behavior (see [72]) but also influences the impact of marketing assets, such as customer satisfaction on future COS. In addition, our post hoc analyses identify a boundary condition as we find that the negative effect of customer satisfaction on the cost of persuasion (convenience) is stronger (weaker) as firm diversification increases (decreases).
Second, we contribute to prior work that underscores the importance of strategic flexibility in moderating the financial outcomes of marketing assets (e.g., [61]). Consistent with this stream of research, we find that the negative impact of customer satisfaction on future COS and cost of persuasion is weaker for firms with higher capital intensity and financial leverage. The negative impact of customer satisfaction on future cost of convenience, however, does not vary across levels of strategic flexibility.
Third, the results also highlight the influence of firms' operating environment on the effect of customer satisfaction on future COS. In particular, we find that the negative impact of customer satisfaction on future COS strengthens as industry growth and industry labor intensity increase. Taken together, these findings contribute to theory development by complementing existing studies that examine the heterogeneity of customer satisfaction across different operating environments (e.g., [59]).
The current study also has implications from a methodological perspective as we outline an approach to measure COS by isolating the "selling" component of a firm's SG&A. Given that firms do not publicly disclose COS as a separate item, existing research views advertising and sales force spending as indicators of the selling-related expenditures of the firm ([57]). To adopt a more comprehensive measure of selling-related expenses, studies also use COMPUSTAT's reported SG&A is also frequently utilized. Although these studies typically exclude R&D expenses in their measure (e.g., [76]), a firm's SG&A still consists of several other expense items that might not be relevant to its COS ([49]; [60]). In fact, we find that using aggregated SG&A-based measures of selling-related expenses can result in an overestimation of more than 20% when compared with our measure of COS (see Table 2). Importantly, our approach also has sufficient face validity as we find that in firms for which more detailed costs are available, our measure of COS can discriminate between firms with higher versus lower costs (see Web Appendix A, Table A2).
Furthermore, by presenting an approach to isolate the "selling" component of a firm's SG&A, we also augment recent research that examines the suitability of using SG&A to capture different marketing-related concepts ([89]). Specifically, our findings indicate the need for a more nuanced approach to the use of SG&A in future research. Whereas the utilization of our approach in decomposing a firm's SG&A is beneficial for studies that aim to investigate the antecedents or consequences of COS or its specific components (i.e., cost of persuasion and convenience), it may not be necessary for instances where COS is to be included as a covariate.
Given that cost reduction is a top priority for CEOs ([90]), marketing managers are often pressured to produce the same product-market outcomes at lower costs ([53]). Thus, our findings have direct managerial implications. In particular, the economic significance of customer satisfaction's effect on COS has direct communication implications for senior marketing managers as we find that a one-point increase in ACSI customer satisfaction score on a 100-point scale lowers future COS by almost US$130 million for an average firm in our sample. Building on the results of the post hoc analyses, we also derive the dollar values of the impact of customer satisfaction for the specific components of COS. As illustrated in Table 6, higher customer satisfaction can lead to an approximate decrease of US$100 million in the future cost of persuasion and US$30 million in the future cost of convenience. These findings are of direct importance to CMOs as they can now articulate the economic value of customer satisfaction for reducing future COS to internal and external constituents. This is especially crucial from an internal perspective as prior research suggests that customer-satisfying executives are often underappreciated ([48]). In this way, CMOs can now incorporate our findings in their communications to the CEO and the chief financial officer to underscore the economic value of customer satisfaction.
Graph
Table 6. Marginal Effects of Customer Satisfaction on Components of Future COS Across Changes in the Moderating Variables.
| Marginal Effects |
|---|
| Components of COS |
|---|
| Cost of Persuasion | Cost of Convenience |
|---|
| in % | Dollar Value(In Millions of USD) | in % | Dollar Value(In Millions of USD) |
|---|
| Main Effect | | |
| Customer satisfaction | One-point increase | −.421* | −$105.681 | −.120** | −$30.028 |
| Moderating Effect of Strategic Focus | | |
| Firm diversification | Low | −.312 | −$78.331 | −.136** | −$34.182 |
| High | −.548** | −$137.618 | −.100* | −$25.178 |
| High − low | −.236*** | −$59.287 | .036* | $9.003 |
| Moderating Effect of Strategic Flexibility | | |
| Capital intensity | Low | −.522** | −$131.163 | −.114** | −$28.563 |
| High | −.314 | −$78.869 | −.126** | −$31.568 |
| Low − high | −.208*** | −$52.294 | .012 | $3.005 |
| Financial leverage | Low | −.455** | −$114.325 | −.117** | −$29.410 |
| High | −.395* | −$99.353 | −.121** | −$30.480 |
| Low – high | −.060* | −$14.971 | .004 | $1.070 |
| Moderating Effect of Operating Environment | | |
| Industry concentration | Low | −.505** | −$126.788 | −.081 | −$20.469 |
| High | −.354 | −$88.875 | −.150** | −$37.637 |
| Low – high | −.151*** | −$37.913 | .068* | $17.167 |
| Industry growth | Low | −.403* | −$101.293 | −.112* | −$28.126 |
| High | −.439** | −$110.234 | −.127** | −$32.000 |
| High – low | −.036* | −$8.941 | −.015** | −$3.875 |
| Industry labor intensity | Low | −.386* | −$96.876 | −.124** | −$31.216 |
| High | −.422* | −$106.070 | −.119** | −$29.975 |
| High − low | −.037** | −$9.194 | .005 | $1.241 |
- 17 *p <.10.
- 18 **p <.05.
- 19 ***p <.01.
- 20 Notes: Two-sided tests of significance. High (low) = the marginal effects of customer satisfaction on future cost of persuasion (convenience) when the corresponding moderating variable is high (low); low − high = difference in the marginal effects of customer satisfaction on future cost of persuasion (convenience) when the corresponding moderating variable is low versus when it is high; high − low = difference in the marginal effects of customer satisfaction on future cost of persuasion (convenience) when the corresponding moderating variable is high versus when it is low. We identify the moderating variable to be high (low) if its value is equivalent to 80th (20th) percentile value of the distribution of the corresponding variable within our sample of 1,207 observations from 128 firms. We multiply the marginal effects by 100 for ease of interpretation and report their dollar values in millions of dollars. We derive the dollar values of the marginal effects by multiplying the marginal effects with the average firm sales (i.e., before Winsorizing and mean-centering) within our sample of 1,207 observations from 128 firms. We compute the marginal effects using the results from Table 5 and dollar values for marginal effects that are significant (p <.10, two-sided) are in boldface.
From an external perspective, CMOs can use our findings to articulate customer satisfaction as a leading indicator of lower future COS values and can disclose it to investors and financial analysts who closely watch COS and its components. As such, our results complement recent work that encourages the incorporation of a customer lifetime value approach to firm valuation ([73]). Indeed, a direct implication for consulting firms and analysts that use the customer lifetime value approach for firm valuation is that their models should account for the effects of customer satisfaction on future COS.
Results of our contingency framework also identify specific conditions under which senior managers can expect higher customer satisfaction to result in a higher or lower reduction in the future COS of the firm. In addition, these findings also have direct implications for firm valuation models, as they highlight critical contingencies.
We find that the negative impact of customer satisfaction on the future cost of persuasion is stronger for more diversified firms, as they can enjoy almost US$60 million more in cost savings (see Table 6). In contrast, the cost savings in terms of the future cost of convenience is approximately US$9 million lower for more diversified firms (see Table 6). This suggests that despite the cost savings in terms of the future cost of persuasion, more diversified firms struggle to leverage the benefits of higher customer satisfaction to lower their future cost of convenience. A direct implication for managers in these firms is to evaluate their formal and informal mechanisms for sharing customer insights relating to the provision of convenience. Such an exercise could identify specific steps that they can take to enhance the effectiveness of customer satisfaction in lowering the future cost of convenience.
We also find that the negative impact of customer satisfaction on the future cost of persuasion is weaker for firms with higher capital intensity. In fact, the difference in cost savings can be more than US$50 million (see Table 6). To overcome this relative disadvantage, managers in such firms can conduct a cost-benefit analysis to explore potential payoffs from increasing the salience of their positive word of mouth for prospective customers and from increasing customer-facing opportunities for their employees. Similarly, whereas managers in firms with higher financial leverage are less likely to utilize benefits of customer satisfaction due to urgent financial pressures, our results indicate that they should carefully assess whether their current systems and procedures are creating impediments to utilizing the benefits of customer satisfaction. It is plausible that due to managerial attention being diverted to servicing high levels of debt, resource allocation to selling activities are following suboptimal routines that do not consider higher levels of customer satisfaction.
Our findings for firms' operating environments also bring to fore the nuances of the effect of customer satisfaction on COS and its components. First, the variance in the effect of customer satisfaction on COS and its components across the three industry conditions are of direct importance for the valuation models of financial analysts and investors that seek to understand the future COS for firms in such industries. Second, given that the received view is that the effect of customer satisfaction on financial performance is expected to be weaker in less concentrated industries ([ 5]), our findings alert managers to the financial benefits of customer satisfaction especially in terms of the lower future cost of persuasion in less concentrated industries as it can amount to almost US$40 million more in cost savings (see Table 6). At the same time, the weaker negative effect of customer satisfaction on the future cost of convenience in such industries suggests that managers will also need to reevaluate their efforts in terms of how they use their understanding of customers to allocate their spending in their provision of convenience.
Finally, the variation in the effects of customer satisfaction on COS and its components across industry growth and labor intensity alert managers to reevaluate their mechanisms for allocating resources in selling efforts. For example, managers of firms with higher customer satisfaction in industries with lower growth and labor intensity should carefully consider whether they are overinvesting in selling efforts and not utilizing the benefits afforded by higher customer satisfaction.
The current study not only presents the first step in understanding the effect of customer satisfaction on future COS but also offers several avenues for further research. First, more research is required to establish the generalizability of our results beyond the ACSI database as it comprises mainly large business-to-consumer firms that are publicly listed in the United States. There might exist differences in customer satisfaction across countries and cultures resulting in differing cost advantages ([79]). Second, given that firms are aware that ACSI tracks the satisfaction of their customers, it is possible for firms in the ACSI data set to be more conscious about their customer satisfaction levels compared with those that are not. As such, future research should explore the impact of customer satisfaction across a larger sample of firms. Third, prior research has suggested that some aspects of selling-related expenses can, in turn, also have an impact on higher customer satisfaction ([75]). For example, an advertising strategy that conveys higher quality could influence customer expectations and, therefore, customer satisfaction. As such, research is needed to examine specific sales efforts that are likely to have a cyclical relationship with customer satisfaction.
Fourth, our post hoc analyses suggest that while the impact of customer satisfaction on the future cost of persuasion is largely consistent with the proposed hypotheses, those for its impact on the future cost of convenience are significantly different. As such, by decomposing COS into more components, it is possible to also identify other potential differences in the impact of customer satisfaction on the different components of COS. Fifth, given that firms often engage in initiatives to improve customer satisfaction ([91]), and that reducing costs is a critical concern for CEOs ([90]), more research is required to explore the effects of customer satisfaction on other cost related concepts. Finally, future research can also build on the current study to examine the cost implications of other marketing assets such as brand licensing.
Supplemental Material, jm.19.0073-File002 - Customer Satisfaction and Its Impact on the Future Costs of Selling
Supplemental Material, jm.19.0073-File002 for Customer Satisfaction and Its Impact on the Future Costs of Selling by Leon Gim Lim, Kapil R. Tuli and Rajdeep Grewal in Journal of Marketing
Footnotes 1 Associate EditorNeil Morgan
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 ORCID iDsLeon Gim Lim https://orcid.org/0000-0002-7060-7356 Rajdeep Grewal https://orcid.org/0000-0003-4467-2717
5 Online supplement: https://doi.org/10.1177/0022242920923307
6 1To derive the dollar value of the estimated effect, we multiply the estimated effect by the average sales of the firms in our sample (for a similar approach, see [36]]). In line with this computation, the dollar value of the estimated effect represents the approximate monetary size of the effect for an average firm (in terms of size) in our sample, as we use sales as a measure of firm size. This computation applies to all dollar values of the estimated effects that we discuss.
7 2This conceptualization of COS is consistent with [13], p. 481), in which he argues that a firm's selling costs can comprise the costs associated with both the provision of convenience and persuasion.
8 3According to COMPUSTAT, typical expenses contained in COGS include maintenance and repairs, rent and royalty, salary expenses, lease expenses, and supplies (see COMPUSTAT Online Help Manual COGS 2019). Depending on firm and/or industry, COGS is also termed "cost of revenues" (e.g., Yahoo Inc.) or "cost of sales" (e.g., Wal-Mart Stores Inc.).
9 4This exclusion is required because firms in the utilities industry operate in a monopoly environment and have different financial reporting requirements, which thus makes it difficult to compare their financial performance metrics with firms from other industries. Similarly, the financial reporting requirements and the regulatory restrictions of firms in the financial services industries (banks, insurance, and brokerage firms) differ significantly from firms in other industries.
5We mean-center all continuous variables to facilitate the interpretation of the interaction effect parameter estimates and Winsorize all continuous variables at the 1st and 99th levels to lower the impact of outliers.
6The resulting classification of firms in the ACSI-defined sectors maps closely to that of the Standard Industrial Classification (SIC) and the NAICS codes because the ACSI sectors are originally defined based on the SIC codes (see [30], p. 9) and, subsequently, the NAICS codes (see [14], p. 3). To illustrate, the firms that belong to the Transportation sector in ACSI (see Web Appendix D, Table D1) also belong to the Transportation by Air sector based on their two-digit SIC codes (i.e., 45) and the Transportation and Warehousing sector based on their two-digit NAICS codes (i.e., 48–49).
7We do not consider other brands that belong to the focal firm as peers. For example, the computation of Delta's weighted peers' customer satisfaction score does not include any information from Northwest (see Web Appendix D, Table D1).
8The motivation for granularity comes from [6], in which the goal is to ensure enough variation in the instrument across observational units for estimating peer effects (see also [94]). [33] also develop a similar idea in their instrument when they utilize a peer instrument with partially overlapping peers (which is similar to our case; in essence, we utilize a peer instrument as in [33]] but do not estimate peer effects as in [6]] and [94]]).
9For example, in 2012, ACSI classified brands from Microsoft Corporation in the E-Business sector (i.e., Bing and MSN) and the Telecommunications and Information sector (i.e., Microsoft). As such, to compute the weighted peers' customer satisfaction score for Microsoft Corporation in 2012, we take the average of its weighted peers' customer satisfaction score in both the E-Business sector and the Telecommunications and Information sector. We perform such computations for only 39 observations (i.e., approximately 3%) of our sample.
10Briefly, using four key firm characteristics (i.e., market valuation, geographical diversification, size, and age), we adopt the classical multidimensional scaling method to obtain the Euclidean distances between a focal firm and its peers and used these distances to compute the weights for the instrument (for details, see Web Appendix D).
11We thank an anonymous reviewer for this suggestion.
12Our approach is similar in spirit to existing approaches that address granularity. For example, [94] use data about different operating business segments of firms to construct peer groups that are partially overlapping.
13We utilize the same procedure as outlined in the "Measures and Data" section to derive the two components. First, we identify and subtract freight-out and bad debt expenses from our focal measure of COS to compute the cost of persuasion. We then take the sum of the freight-out and bad debt expenses to arrive at the cost of convenience. For the set of keywords used in the text analyses to search for freight-out and bad debt expenses in firms' 10-K filings, see Web Appendix B.
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Data Privacy: Effects on Customer and Firm Performance
Although marketers increasingly rely on customer data, firms have little insight into the ramifications of such data use and do not know how to prevent negative effects. Data management efforts may heighten customers’ vulnerability worries or create real vulnerability. Using a conceptual framework grounded in gossip theory, the authors link customer vulnerability to negative performance effects. Three studies show that transparency and control in firms’ data management practices can suppress the negative effects of customer data vulnerability. Experimental manipulations reveal that mere access to personal data inflates feelings of violation and reduces trust. An event study of data security breaches affecting 414 public companies also confirms negative effects, as well as spillover vulnerabilities from rival firms’ breaches, on firm performance. Severity of the breach hurts the focal firm but helps the rival firm, which provides some insight into mixed findings in prior research. Finally, a field study with actual customers of 15 companies across three industries demonstrates consistent effects across four types of customer data vulnerability and confirms that violation and trust mediate the effects of data vulnerabilities on outcomes.
Managers and academics alike contend that collecting and using customer data is an effective way to improve marketing returns (McAfee and Brynjolfsson 2012;
Schumann, Wangenheim, and Groene 2014). Consultants suggest that firms can use customer information to generate “productivity and profit gains that are 5 to 6 percent higher than those of the competition” (Biesdorf, Court, and Willmott 2013, p. 40). In turn, firms spend $36 billion annually to capture and leverage customer data (Columbus 2014). However, such efforts also increase customers’ data vulnerability, or perceptions of susceptibility to harm due to unwanted uses of their
personal data, such as those that can result from data breaches
or identity theft. Thus, data collection efforts may have a dark
side, and customers often express negative reactions to privacy practices (Marcus and Davis 2014). Yet firms have little insight into the potential ramifications of customer data management efforts and do not know how to prevent negative outcomes.
Therefore, we aim to enhance understanding of the effect of customer data vulnerabilities on customer behavior and firm performance as well as key mediating mechanisms and mitigation strategies.
We argue that customer perceptions of vulnerability to harm due to firm data practices better conceptualize data management effects than privacy concerns. Using gossip theory, we predict strong negative responses to disclosures of personal information by “gossipers”–or firms, in this case (Foster 2004; Richman and Leary 2009). Yet gossip theory also identifies two key factors that might suppress the damaging effects of data vulnerability: transparency and control. With a predicted continuum of potential harm, we evaluate the distinct effects of data access vulnerability (the firm has access to the customer’s personal data), data breach vulnerability (the firm or a close rival suffers a data breach), and data manifest vulnerability (a data breach enables customer data to be misused; e.g., identity theft) on the firm itself. For example, if customers provide their personal information to a retailer such as Home Depot, they experience data access vulnerability. If Home Depot suffers a data breach, the potential for harm becomes more salient to its own customers as well as to Lowe’s customers (spillover vulnerability), even if the latter are not directly affected.
To test this conceptual model, we conduct three complementary studies. In Study 1, we run a series of experiments to delineate the effects of data access vulnerability from a customer’s perspective. We examine how firms’ mere access to customer information creates specific negative emotional and cognitive outcomes. By manipulating data access vulnerability, transparency, and control, we also provide a strong test of mitigation strategies. An event study in Study 2 investigates the customer vulnerability created by 414 data security breaches that affected 261 public companies. We analyze stock price data for both the breached firms and their closest rivals; we also
Journal of Marketing Vol. 81 (January 2017), 36-58 consider firm policies that might mitigate this harm, such as the provision of more transparent information about data use or
granting greater control to customers over the use of their
personal information. Finally, Study 3 examines all four types
of data vulnerability (access, breach, spillover, and manifest) with a field study involving actual customers of 15 companies, whose transparency and control practices we captured from current privacy policies. This study confirms that suppression occurs across all types of vulnerability and substantiates
proposed mediating mechanisms of violation and trust on
customer outcomes.
This work contributes to extant literature in four ways. First, by assessing customers’ feelings of vulnerability, we provide a theoretical foundation for understanding how firms’ data management practices affect customer behaviors and firm performance. A customer-centric perspective is rare in descrip
tions of the effects of information management on perfor
mance. However, customer data vulnerability parsimoniously
captures multiple salient aspects, including privacy concerns,
data breaches, and identity theft, whether or not customers experience real financial harm. The negative customer effects appear mainly due to anxiety about the potential for data misuse
and feelings of violation, rather than actual data misuse (Scharf
2007). Capturing the effects of this sense of vulnerability thus is
critical. The perceptions of data vulnerability negatively affect
performance across the continuum; for example, among the
respondents in Study 3, 10% reported that they would be more
likely to fabricate their personal information, 23% would be
more likely to speak negatively, and 22% would be more likely to switch when a firm simply accesses their personal data. In Study 2, we find that an actual data breach reduces the focal firm’s stock value by -.29% and its closest rival’s by -.17%.
Second, the severity of a data breach by the focal firm determines whether spillovers to its closest rival have positive or negative effects on performance, a finding that helps resolve some mixed prior findings (Ko and Dorantes 2006; Malhotra and Malhotra 2011). The severity of a data breach aggravates its negative effect on the firm’s stock price, whereas this effect of severity switches for the rival firm. That is, as the severity of the breach at the focal firm increases, it improves the rival firm’s performance. Two mechanisms operate on rival firms during a focal firm’s data breach: a negative spillover effect due to concerns about a similar data breach at the rival firm, and an offsetting positive competitive effect that benefits the rival firm because customers of the damaged focal firm might switch to this rival. Thus, at low levels of severity (-2 SD), the net effect of a data breach by the focal firm on a rival firm is -.7%, whereas at high levels, the net effect reaches +1.7%.
Third, with our application of gossip theory, we identify and
test two managerially relevant mitigation strategies that are
effective across the range of data vulnerabilities. Making a firm’s data management policies more transparent and providing customers with control over their data can suppress the
negative effects of vulnerability on performance. These strat
egies also interact to suppress even further the negative performance effects on focal firms, spillover to rivals, and even the negative effects of identity theft. The consistent beneficial effects–shown across three studies using event study and experiment methodologies, measured at both firm and customer levels, with different operationalizations–strongly support their mitigation of the negative effects of customer data vulnerability. For example, according to model-free median split analyses, firms with low (vs. high) transparency experience a 1.5 times larger drop in stock price after a data breach. Firms that offer high control suffer no effect of breaches on their stock price, whereas firms that offer low control experience negative returns of -.3%. The high transparency and low control strategy is especially harmful, prompting consumers to express willingness to pay a 5% price premium to switch firms, compared with a low-low condition (Study 3).
Fourth, two mediating mechanisms, emotional violation and cognitive trust, effectively link all manifestations of customer data vulnerability to performance. Access to a customer’s personal or sensitive data alone increases perceptions of vulnerability, causing customers to feel violated and reduce their trust in the firm. We also show in Study 3 that emotional violation and cognitive trust mediate the effects of data vulnerability on customers’ falsifying behaviors, negative word of mouth (WOM), and switching behaviors. These mediating effects prove notably robust across industries, types of data vulnerability, and demographic characteristics.
Understanding Customer Data Vulnerability
As firms expand their efforts to collect and use customer data, customers grow more concerned about their privacy and the potential for harm. These concerns often are labeled “privacy issues,” though the construct of privacy is relatively amorphous and cannot capture the essence of customers’ psychological attitudes, such that “privacy is a concept in disarray. Nobody can articulate what it means” (Solove 2006, p. 476; for a comprehensive review of privacy literature in marketing, see also Martin and Murphy [2016]). We propose that customer data vulnerability, or a customer’s perception of his or her susceptibility to being harmed as a result of various uses of his or her personal data, instead is a critical construct for privacy literature in that it drives customers’ responses to firms’ efforts to collect and use their data. Gossip theory describes how people respond to the unsanctioned collection, use, or disclosure of their personal information (Dunbar 2004; Foster 2004), and we consider it germane for understanding how customers respond when firms collect and use their personal data.
Customer Data Vulnerability
Vulnerability implies susceptibility to injury or harm (Smith and Cooper-Martin 1997). When a firm collects, stores, and uses customers’ personal information, it increases the potential for harm and, thus, their feelings of vulnerability. Most negative customer effects resulting from data use thus stem from customers’ anxiety about the potential for damage or feelings of violation, rather than actual data misuse or financial or reputation harm (Scharf 2007). As legal perspectives have argued, customers experience harm at the moment of the breach, regardless of whether their data subsequently are misused (Fisher 2013). Therefore, it is critical to capture the effects of customers’ vulnerability, rather than focus only on damages.
We delineate customer data vulnerability along a continuum of potential harm (see Figure 1, Panel A). The most benign form exists when companies have access to a customer’s personal data–that is, data access vulnerability. This mere access means that firms have “detailed digital dossiers about people” and can engage in “widespread transfer of information between a variety of entities” (Solove 2003, p. 2). Customers limit how and with whom they share sensitive information to reduce this vulnerability, using disclosure management processes such as reactance or refusal (Acquisti, John, and Loewenstein 2012). Yet companies already possess and continue to actively seek increasing volumes of customer information, such that data access vulnerability is a widespread and growing concern for customers (Tucker 2014).
Data breach vulnerability increases customers’ perceptions of susceptibility to harm even more, because it implies that a firm that already has their private data–or one of its close rivals–has suffered an actual security lapse. The U.S. Identity Theft Resource Center estimates that nearly 130 million personal records have been subjected to risk from data breaches (www.idtheftcenter.org). Ultimately, not everyone whose records have been compromised experiences victimization, but the unknown scope and lack of control over this threat makes this type of vulnerability especially troubling to customers. The perception of vulnerability increases as a result of a data breach at a firm that possesses the customer’s data (focal firm) but also, indirectly, with breaches at close competitors (rival firms), because these events increase the salience of the belief that similar breaches are possible.
This latter spillover effect (spillover vulnerability) arises when customers perceive greater susceptibility to harm because a firm similar to one that has their data suffers a data breach. Our proposed continuum (Figure 1) shows that spillover creates less vulnerability than a data breach at a focal firm a customer actually uses. Although vulnerability is made salient to a customer when a close competitor firm suffers a breach, we expect that there is less vulnerability than when a focal firm suffers a breach. Nonetheless, to illustrate, analysts assessing the damage to Home Depot’s stock price in the wake of its 2014 data breach accurately predicted negative effects for Lowe’s too (Trefis Team 2014).
Finally, data manifest vulnerability occurs when customer data actually are misused, causing harm to the customer. Disclosures and fraudulent activities represent the most severe form of vulnerability by moving beyond susceptibility to a state of actual harm. Even when the actual damage that a customer experiences is minor, the event significantly increases perceptions of data vulnerability. Thus, the greatest effects tend to stem not from actual data misuse but from accompanying feelings of violation and the indeterminate nature of the threat (Anderson 2013; Scharf 2007; Solove 2003).
In contrast with research focusing on customer privacy perceptions, empirical studies of customer data management have primarily addressed how customers disclose personal information (Moon 2000; White 2004) and begin to trust firms as a result of their data management processes (Bart et al. 2005; Schlosser, White, and Lloyd 2006). A separate but related literature stream investigates data security breaches and their effects on a firm (e.g., Hsieh et al. 2015; Sen and Borle 2015).
We summarize selected relevant literature in Table 1, revealing that research into how data management affects both customers and the firm is relatively limited.
Gossip Theory
Customers’ psychological and behavioral responses to feelings of vulnerability can be informed by gossip theory, considering the common notion of unsanctioned transmissions of personal information about a vulnerable third party. Gossip is evaluative communication about an absent third party (Feinberg et al. 2012; Foster 2004), and gossip researchers report that approximately two-thirds of all communications in public social settings are devoted to such social topics (Dunbar 2004). Thus, most people are adept at detecting gossip, guarding against becoming a gossip target, and minimizing their vulnerability to it (Beersma and Van Kleef 2012; Mills 2010). When they learn they are the target of gossip, people typically react negatively (Baumeister, Zhang, and Vohs 2004), with a range of negative emotional and cognitive responses (Leary and Leder 2009), including heightened feelings of betrayal and violation (Richman and Leary 2009) and deteriorating levels of trust (Turner et al. 2003). Thus, applying gossip theory to a business context suggests that customer data vulnerability may lead to feelings of emotional violation and lowered cognitive evaluations of trust.
Gossip theory also identifies two factors that suppress the negative effects of unsanctioned transmissions of information: transparency and control. Transparency implies the target’s awareness of and details about which information is being shared. The gossip target knows the scope of potential harm and can develop strategies to counter negative effects. Control is the extent to which the target believes (s)he can manage the flow of information (Emler 1994). A perceived lack of control over personal information, on learning about its transmission, exacerbates negative affect surrounding a gossip event, even if the valence of the information being spread is not negative (Feinberg et al. 2012). As two forms of empowerment, control and transparency thus may help people manage the negative effects of their own vulnerability (Baker, Gentry, and Rittenburg 2005).
Effect of Data Access Vulnerability on Customer Behaviors (Study 1)
Our research progression reflects the proposed continuum of customer data vulnerability (Figure 1). In Study 1, we investigate customer response to data access vulnerability, the most benign form, which implies only the potential for harm when a firm has access to customer personal information. Accordingly, it constitutes a conservative test of the conceptual model. We use a series of experiments and manipulate data access vulnerability, transparency, and control to test the effects of these theoretically derived suppressors. In Study 2, we use an event study methodology to capture the effects of data breach and spillover vulnerabilities and to determine whether those effects can be suppressed by transparency and control. Finally, in the field study with actual customers and firm privacy policies, Study 3 manipulates each type of vulnerability to test the suppressors and mediation across multiple outcomes.
Data Vulnerability Effects and Suppressors
The negative reactions of gossip targets to learning about a gossip event can manifest as emotions and as cognitive-based judgments, often experienced simultaneously (Richman and Leary 2009). Negative emotions may take the form of hurt feelings, mental states of betrayal, or feelings of violation (Mills 2010; Williams 2007). In business, customers’ feelings of violation appear in the form of backlash, in conjunction with their more generalized feelings of anger and betrayal (Marcus and Davis 2014). Furthermore, whether negatively or positively valenced, gossip often leads to deteriorated trust (Turner et al. 2003), as do customers’ concerns about online security (Bart et al. 2005; Schlosser, White, and Lloyd 2006). Thus, we expect customer data vulnerability to affect both the emotional mechanism of violation and the cognitive mechanism of trust. Emotional violation captures a customer’s negative affect, resulting from a perception of a firm’s failure to respect her or his peace, privacy, or other rights (Gre´goire and Fisher 2008). Cognitive trust instead is the customer’s willingness to rely on a firm in which (s)he has confidence (Palmatier 2008).
Gossip theory advises that data use transparency (hereinafter “transparency”) provides customers with information about how the firm collects, shares, and protects their data. Transparency grants customers knowledge about what information they provide to the firm, how it is used, and which partner firms may access that data. In addition, customer control (hereinafter
“control”) over information use and data management decisions should help customers feel empowered in high vulnerability contexts, which may suppress their feelings of violation (Kumar, Zhang, and Luo 2014; Tucker 2014). With control, a customer can determine whether to participate in certain forms of data sharing, which reduces uncertainty and perceptions of sneakiness. When data access vulnerability already is low, these perceptions likely are weak anyway, so providing customers with transparency and control should have little effect on either violation or trust. However, it could suppress damaging effects on violation and trust when data access vulnerability is high.
Specifically, we propose that transparency and control, separately and interactively, mitigate the damaging effects of all types of customer data vulnerability on firm- and customer-level performance effects, including the positive effect on violation and the negative effect on trust (Baumeister, Zhang, and Vohs 2004). Prior research has shown that negative responses to gossip diminish with disclosures of the facts of the situation (Beersma and Van Kleef 2012), suggesting the suppressing effect of transparency. Customers also might choose to engage in some company data practices but opt out of others. Providing knowledge and granting control are positive signals of the firm’s intentions too, so they should suppress the negative link between high vulnerability and trust.
Finally, the interaction of transparency and control may suppress the damaging effects on both violation and trust when data access vulnerability is high. If firms provide customers with
TABLE:
TABLE:
| Study | Areas of Focus | Context | Key Findings |
|---|
| Data Access Vulnerability |
| Bart et al.(2005) | Online trust, privacy, security,website presentation, brand strength | Model estimation with data from 6,831 customers | Navigation and presentation, advice, and brand strength are more influential predictors of online trust than are privacy and security. Online trust mediates the relationship between website characteristics and behavioral intentions more strongly for some productcategories than others. |
| Schlosser, White, and Lloyd (2006) | Trusting beliefs (ability, benevolence,integrity),website investment, privacy/security | Website manipulation experiments | Website investment/design is the strongest factor leading to purchase intentions and trust. Privacy andn security statements increase benevolence and integrity dimensions of trust but do not increase consumers’ willingness to buy online. |
| John, Acquisti, and Loewenstein (2011) | Environmental cues, privacy concerns, willingness to divulge highly sensitive information | Online experiments | Contextual information, including both intrusiveness and the professional look of a questionnaire response format, encourages more or less customer information disclosure. Priming with a privacy statement decreases disclosure. |
| Acquisti, John, and Loewenstein (2012) | Conformity, reciprocity, injunctive and descriptive norms, herding effect | Online experiments | Customers are willing to disclose increasingly sensitive information when they believe others have done so. Respondents disclose sensitive information more freely when placed at the beginning of a questionnaire (cf. random or end placement). |
| Schumann, Wangenheim, and Groene (2014) | Social norms and reciprocity, privacy concerns, advertising effectiveness, usergenerated content | Field studies, online experiments | Customers increasingly accept targeted advertising in exchange for a website’s free services. Customers report targeted advertising as an alternative form of online currency to voluntarily repay a website for customization and other marketing benefits. |
| Tucker (2014) | Customer privacy controls, targeted ads, personalized ads, reactance theory | Facebook campaign–level clickthrough data | For a nonprofit using personalized (vs. nonpersonalized) and targeted (vs. nontargeted) ads on Facebook, people responded more favorably to personalized ads when they had the ability to control their personal privacy settings. |
| Data Breach Vulnerability |
| Schatz and Bashroush (2016) | Stock market value, data breach (single and repeated) | Event study with 25 publicly traded U.S. companies | Preliminary evidence suggests that data breaches are bad for performance. The effect worsens when a firm has experienced more than one breach. |
| Hsieh et al. (2015) | Stock market value, data loss events, firm size, data loss costs | Event study of 103 U.S. public firm data breaches | Data loss events negatively affect firm performance. Companies should invest more in data security efforts. |
| Sen and Borle (2015) | Data breach risk, firm location, industry, type of past breach | Data breaches between 2005 and 2012 | State-level data breach disclosure laws can influence breach risk in certain industries. Because greater security spending heightens breach risk, information technology dollars may be suboptimally allocated. |
| Malhotra and Malhotra (2011) | Stock market effect of firm data, breach of customer data, severity | Event study of 93 publicly traded firm data breaches | Firm market value is negatively affected by a breach in both the short and long runs, but it is more detrimental in the long run. Larger firms suffer greater market value loss than smaller firms, and larger firms suffer more from large breaches. There is no effect of severity on smaller firms. |
| Acquisti, Friedman, and Telang (2006) | Firm performance, data breach scale, scope, type (e.g., employees, customers, third party), information type, industry | Event study of 79 publicly traded firm data breaches | A data breach has a significant negative effect on stock market value the day that the breach is announced. The cumulative effect increases the day of the announcement but then decreases and becomes nonsignificant over time. |
| Ko and Dorantes (2006) | Firm performance, data breach | Matched-sample comparison methodology with 19 data breaches | The focal firm’s performance decreased relative to unaffected peer firms (examined as a control group). This study finds both short-term and long-term negative effects of a data breach on performance and identifies a fourth-quarter recovery effect. |
| Data Manifest Vulnerability |
| Romanosky, Telang, and Acquisti (2011) | Consumer identity theft, data breach disclosure laws | Victim identity theft reports from the U.S. Federal Trade Commission spanning 2002–2009 | Research asks whether data breach disclosure laws actually reduce identity theft. The authors find that, on average, statutes reduce identity theft caused by breaches by 6% in evidence of their effectiveness. |
| Milne, Rohm, and Bahl (2004) | Consumer identity theft, online shopping behavior, privacy attitudes, offline data protection practices | Consumer surveys | Findings across three surveys suggest that consumers are not sufficiently protecting themselves from identity theft. Authors advocate for greater firm and government protection, given consumers’ reported lack of understanding about adequate ways to protect themselves from harm online. |
both transparency and control, the combination should generate strong feelings of empowerment, even if their vulnerability is significant (Baker, Gentry, and Rittenburg 2005). Empowerment then can reduce expectations of perceived harm due to data access vulnerability, because customers believe they have knowledge about and control over the use of their data, which mitigates their negative emotional responses and attributions (Emler 1994).
H1: The positive effect of data access vulnerability on emotional violation is suppressed by (a) transparency, (b) control, and (c) the interaction of transparency • control.
H2: The negative effect of data access vulnerability on cognitive trust is suppressed by (a) transparency, (b) control, and (c) the interaction of transparency • control.
Experimental Data and Design
We used a series of 2 • 2, between-subjects experiments to assess customer responses to firms’ mere access to their data. In three experiments (Studies 1a-c), we manipulated high and low levels of ( 1) data access vulnerability • transparency, ( 2) data access vulnerability • control, and ( 3) transparency • control. All constructs, definitions, and operationalizations are in Table 2. We sought participants from Amazon Mechanical Turk to gauge customer insights across a range of demographic profiles and backgrounds. We recruited 200 respondents for each of the three experiments for 50 participants per cell. We created scenario descriptions (Appendix A) to convey high and low levels of each manipulated variable, presented in a randomized design. After reading the descriptions of data access vulnerability, transparency, and control, respondents evaluated the scenario company on measured scales for violation and trust (see Appendix B).
In Studies 1a and 1b, we investigated the ability of transparency and control, respectively, to mitigate potential damaging effects of data access vulnerability on violation and trust. Two separate between-subject experiments served to test our hypotheses with scenarios that placed participants in a association” effect, such that crises can harm firms that represent close rivals to an affected firm (Borah and Tellis 2016). These negative spillover effects occur because customers believe the nature or root cause of the crisis is endemic to the entire category or industry (Cleeren, Van Heerde, and Dekimpe 2013). After a data breach by a focal firm, customers of rival firms may feel more vulnerable, which creates a cascade of actions and negative spillover to the rival firm’s performance, due to anticipation in the stock market.
Yet brand scandal literature has offered an alternative perspective, in which a data breach event creates a positive competitive effect for the closest rival that mitigates or even offsets the negative spillover. If the breach creates severe negative publicity, customer backlash, and financial harm to the focal firm, that firm’s customers might switch to a rival. Customers often shift from a firm experiencing a brand crisis, and the switch ultimately may be permanent (Roehm and Tybout 2006). The rival firm gains sales and profits from new customers, which improves its financial performance. Therefore, we propose alternative hypotheses.
TABLE:
| Constructs | Definitions | Studies 1 and 3 | Study 2 |
|---|
| Data access vulnerability | Customer expectation of susceptibility to the harm that can come from the disclosure of their personal data | Experimentally manipulated extent (high/low) to which company has access to personal, sensitive, or private customer information | N.A. |
| Spillover vulnerability | Extent to which the customer feels vulnerable as a result of the data breach of a firm that is a close rival of a firm (s)he uses | Experimentally manipulated event in which customers learn a close competitor of a company they use is the victim of a data breach | Closest competitor firm data breach event (yes/no) |
| Data breach vulnerability | Extent to which the customer feels vulnerable as a result of a firm’s security lapse, making data vulnerability salient | Experimentally manipulated event in which customers learn that a company they use has been the victim of a data breach. | Corporate data breach event (yes/no) |
| Data manifest vulnerability | Extent to which the customer feels vulnerable as a result of actual misuse of personal information, making data vulnerability salient; can occur through fraudulent activity including, but not limited to, identity theft | Experimentally manipulated event in which customers learn that a company they use has been the victim of a data breach and that their information has been used fraudulently, in the form of identity theft | N.A. |
| Data breach severity | The scope, reach, and impact of a firm’s data security breach | N.A. | Log of number of customer records compromised in data breach |
| Data use transparency | Customer knowledge of a firm’s access to her or his data and understanding of how it is going to be used (Awad and Krishnan 2006) | Experimentally manipulated extent (high/low) to which a company’s data management policies are clear, straightforward, and easy to understand | Count of whether the following various elements are explained: opt-out policy, data capture, data use, data sharing with third parties, contact information available for privacy requests |
| Customer control | Customer perception of the extent to which (s)he can manage a firm’s use of her or his personal data (Tucker 2014) | Experimentally manipulated extent (high/low) to which customers have control over the firm’s use of data | Count of number of opt-out choices as detailed in the firm’s privacy policy |
| Emotional violation | Customer perception of a firm’s failure to respect peace, privacy, or other rights (Grégoire and Fisher 2008) | Extent to which customers feel violated or betrayed by firm’s use of data | N.A. |
| Cognitive trust | Willingness to rely on an exchange partner in whom one has confidence (Palmatier 2008) | Extent to which customers report trusting a firm and its behaviors | N.A. |
| Financial performance | Focal firm (rival firm) financial performance | N.A. | Firm’s abnormal stock market returns calculated by the market model |
| Falsifying behavior | Customer fabrication of personal information when transacting with a company (Lwin, Wirtz, and Williams 2007) | Customer-reported likelihood of providing inaccurate personal information to a company with which (s)he interacts | N.A. |
| Negative WOM | Customer negative communications to others about a company (De Angelis et al. 2012) | Customer-reported likelihood of spreading negative WOM about the firm to friends and family | N.A. |
| Switching behavior | Customer likelihood of discontinuing the relationship in favor of a similar alternative (Palmatier, Scheer, and Steenkamp 2007) with reduced data access vulnerability | Customer reported likelihood of switching to a comparable firm, including trying its products/services and paying a premium to switch | N.A. |
H3a: Data breach vulnerability negatively affects firm performance.
H3b: Data breach vulnerability negatively affects a rival firm’s performance (spillover effect).
H3b(alt): Data breach vulnerability positively affects a rival firm’s performance (competitive effect).
Customer Data Breach Vulnerability Suppressors
Firms might use several strategies to lessen the detrimental effects of customer data breach vulnerability on performance. Gossip theory suggests that a target’s vulnerability decreases when the target has knowledge about the gossip event (transparency) and the ability to manage the spread and impact of the information (control) (Mills 2010; Smith 2014). In a customer data breach vulnerability context, we argue that firms’ data use transparency (i.e., the extent to which it explains its data collection, use, storage, and protection) and its provision of customer control (i.e., granting customers the ability to determine what information they give to the firm, how it is used, and which partner firms may access those data) can mitigate the damaging effects of all types of customer data vulnerability on firm- and customer-level performance effects.
According to gossip theory, for a target to address a gossip event, it must know that the gossip is occurring (Eder and Enke 1991). Transparency then should be a critical suppressor, with the potential to mitigate the harm wrought by customer data breach or spillover vulnerability on performance, because customers gain the knowledge they need to evaluate the potential harm. Transparency implies that customers have knowledge of the nature and scope of data the firm possesses and how those data are used. Typically, firms provide transparency in the form of a privacy policy or information collection disclosure notification. In addition, company information collection strategies that are overt versus covert in nature influence how customers respond to firms’ personalization efforts (Tucker 2014). In a similar sense, transparency seems critical for firms to avoid the “creepiness factor” often associated with data and analytical inferences about customers (Cumbley and Church 2013).
H4: The negative effect of data breach vulnerability on firm performance is suppressed by transparency (i.e., suppressing both data breach and spillover effects).
Providing control is another key strategy. When they learn that gossip has occurred, targets often try to regain control of their information (Emler 1994). Salvaging this control also represents a key restorative element after a damaging gossip event (Williams 2007). Providing customers with control enables them to manage and adjust their personal data preferences with the firm. To bestow control on customers, firms generally rely on opt-in and opt-out decisions (Kumar, Zhang, and Luo 2014) and allow them to manage their individual settings and preferences governing the use of their data. For example, after Facebook suffered a data breach in 2010, it responded with policies and systems that promised to “keep people in control of their information” (Steel and Fowler 2010, p. A1).
H5: The negative effect of data breach vulnerability on firm performance is suppressed by control (i.e., suppressing both data breach and spillover effects).
Beyond these distinct suppressive effects, the most potent force for reducing the damaging effects of vulnerability on performance may result from their combined or interactive effect. Customer knowledge (transparency) and control represent key areas for investigation in online privacy research (Caudill and Murphy 2000), and we know of no studies that investigate them empirically as they function together. Yet the methods that gossip targets use to manage and mitigate unsanctioned transmissions of their information suggest that transparency and control can work concurrently to benefit customers. Strong transparency and control give customers more knowledge of the firm’s data management practices and the ability to manage their data portfolio through opt-out choices. Customers who achieve transparency know of the potential harm but have no way to manage it; customers who have control can manage their data but have insufficient knowledge to make informed decisions.
H6: The negative effect data breach vulnerability on firm performance is suppressed by the interaction of transparency • control (i.e., suppressing both data breach and spillover effects).
When more people receive gossip, the target becomes more vulnerable (Mills 2010; Smith 2014), so the negative reaction upon learning of the event should be greater (Turner et al. 2003). Paralleling this logic, we expect that greater data breach severity, or the scope, reach, and impact of the firm’s data breach, imposes a more negative effect on the breached firm’s performance. This enhanced negative effect might stem from the need for more resources to recover from a more severe breach, the greater number of disgruntled customers who potentially spread negative WOM, and the heightened potential for defection. Malhotra and Malhotra (2011) find no effect of breach magnitude on firm performance, but approximately half of their sample lacked information about magnitude. We also expect that the data spillover effect expands in the wake of more severe breach events because they affect more customers and strengthen the guilt-by-association mechanism. That is, with larger breaches, more customers learn of the breach and are exposed to the negative publicity surrounding it, which makes vulnerability even more salient for customers of rival firms.
Similar to our alternative logic outlined previously that a data breach by the focal firm could be beneficial to close rivals, the positive customer gains from a data breach at the rival firm might be enhanced by the severity of the focal firm’s data breach. As the data breach grows more severe, the focal firm’s customers may perceive higher levels of vulnerability, increasing their likelihood of defection. The rival firm then can gain sales from customers who defect and should find it easier to acquire new customers, relative to its breached competitor.
H7a: The negative effect of data breach vulnerability on firm performance (data breach effect) is aggravated by the severity of the focal firm’s data breach.
H7b: The negative effect of data breach vulnerability on rival firm performance (spillover effect) is aggravated by the severity of the focal firm’s data breach.
H7b(alt): The negative effect of data breach vulnerability on rival firm performance (competitive effect) is alleviated by the severity of the focal firm’s data breach.
Study 2: Methodology
In Study 2, we evaluate the effects of customer data breach vulnerability on firm performance (i.e., abnormal stock returns) as well as the influence of two managerially relevant vulnerability suppressors (transparency and control). Because of our interest in the precise effect of data breaches, we employ an event study to gauge the impact of data breaches with known timestamps on subsequent stock prices (Srinivasan and Hanssens 2009). An event study leverages the efficient market hypothesis, which states that a stock price at a particular point in time reflects all available information up to that point (Fama 1998; Sharpe 1964). Any change in the stock price that results from new information reflects the present value of all expected current and future profits from that new information. We pose our hypotheses according to customers’ responses to data breach events because customer behavior is the primary driver of firm performance. Information about firms’ data management practices is available to the overall market (e.g., privacy policies), and market actors try to anticipate the relevance of many diverse factors on future sales and profits. We expect customer-level effects to be manifest in immediate changes in stock price (i.e., efficient market theory). This approach is consistent with previous event studies in marketing (Borah and Tellis 2014; Homburg, Vollmayr, and Hahn 2014).
Data. To analyze the relationship between data breaches and stock returns, we first identify data breaches of publicly traded firms using the Capital IQ, Factiva, Lexis-Nexis, and privacyrights.org databases. We use multiple sources to ensure that the data collection is as exhaustive as possible and to remove any ambiguous breach announcements. The unit of analysis is each specific data breach. We collect stock returns for the firm that suffered the data breach and for its closest rival. Our sample includes any global, publicly listed firm, so we collect stock price and market index data from various stock exchanges (e.g., New York Stock Exchange, Paris Stock Exchange, London Stock Exchange). Using Dun and Bradstreet’s Hoover’s Database, we identify the closest (revenues nearest to the focal firm) publicly listed rival of each focal firm. The initial sample consists of 414 breached firm-day observations across 261 unique firms and 414 rival firm-day observations across 221 unique rival firms. We drop 18 breached firm- and 10 rival firm-day observations, because we could not obtain their stock price data at the time of breach.
Event studies are subject to three important assumptions: market efficiency, unanticipated events, and confounding events. The assumption of market efficiency can be difficult to reconcile with a long event window. Assuming efficient information processing of the breach announcement, the event window ought to be as short as possible (McWilliams and Siegel 1997). Because the market should incorporate data breach information quickly, we use windows ranging from -1 to +1 days around the event to calculate abnormal returns. Finally, we control for an array of confounding events around the -1 to +1 window, including dividend declarations, contract signings, earnings information, or mergers and acquisitions. We drop any observations with confounding events around the three-day breach window, excluding 103 events for focal firms and 105 events for rival firms due to confounds. Ultimately, we get 293 breached firm-day observations across 199 unique firms and 299 rival firm-day observations for 176 unique firms.
Measures. A summary of the definitions and operationalizations of the independent variables is in Table 2. We created data use transparency and customer control variables using a mix of automation and manual coding. For each focal firm and its closest rival, we obtained the privacy policy statements from the firm’s website when the breach occurred, using the Wayback Machine Internet archive. A newly developed Python code scraped each iteration of the focal firm and its closest rival’s privacy policy documents over time, enabling us to select the documents that were current and active on the breach date. After obtaining the relevant privacy policy, we employed manual coding to measure the independent variables, with a careful reading of each privacy policy. We next created scores for both transparency and control, reflecting whether or not (0, 1) firms included specific information in their privacy policy (see Table 2). Two research assistants, who did not know the study hypotheses, coded the privacy policy documents with a standardized coding schema. Their interrater agreement was greater than 85%, and all disagreements were resolved through discussion with the first author.
For the transparency independent variable, we used a count of the dummy variables across multiple elements of the privacy policy that signal openness and willingness to provide information to customers. Specifically, we coded whether the firm ( 1) explains its opt-out policy, ( 2) explains how it captures data, ( 3) explains how it uses data, ( 4) explains its data sharing internally and with third parties, and ( 5) provides contact information for privacy requests. If a firm’s privacy policy has all five characteristics, the policy earns a score of 5 for transparency. To create the control independent variable, we counted the number of opt-out choices in the firm’s privacy policy, ranging from 0 to 5. Specifically, we coded whether the consumer ( 1) can opt out of marketing communication, ( 2) can opt out of saving data usage (e.g., search history), ( 3) can opt out of storing personal information (e.g., credit card number), ( 4) can opt out of sharing data with third parties, and ( 5) can opt out of tracking. Details of the data coding and measure validation are in Web Appendix A. The measure of data breach severity reflected the natural logarithm of the number of customer records compromised in the data breach. Descriptive statistics and correlations for Study 2 variables appear in Table 3, Panel A.
Model development and estimation. We use a market model to calculate abnormal returns. Abnormal returns to a stock, due to some event, offer controls for fluctuations in price across the whole market. The market model is superior to a capital asset pricing model for cross-sectional event studies (Campbell, Cowan, and Salotti 2010; Homburg, Vollmayr, and Hahn 2014); furthermore, Fama-French and Carhart factors are not available for firms listed in non-U.S. stock exchanges. To gather firm and market stock returns, we relied on the Center for Research in Security Prices (CRSP) and Kenneth French’s website for U.S. companies and Thomson ONE and Yahoo Finance for firms listed in non-U.S. exchanges. “Returns” refer to the cumulative average abnormal returns (Web Appendix B). Because we hypothesize that the same mitigation strategies work for focal and rival firms, we pool the data for the breached firm and its nearest rival and use the variable breach firm (1 = breached firm, 0 = closest rival) to specify which data belong to each. In turn, the main effects model is as follows:
( 1) Returns = b0 + b1Breach Firm + b2Data Use Transparency + b3Customer Control + b4Data Breach Severity + b5Capital Slack + b6Size + b7Competitive Intensity + b8Number of Breaches of Breach Firm + b9Number of Breaches of Rival Firm + b10Industry Fixed Effects + b11Year Fixed Effects + b12Time Trend + ei:
To estimate this model, we use the xtreg command in Stata 13.0. Because the same firm can have multiple breaches in our sample, we use a panel regression and the vce (cluster firmid) option to account for clustering by firm. We also estimate a model that includes the interactions of transparency and control (Equation 2) and features separate coefficients for the effect of severity on focal and rival firms, such that we multiply them by breach firm and (1 - breach firm), respectively. Thus, we can test our alternative hypotheses that advance opposite predictions about the effect of severity on rival firm performance. Specifically, a5 captures the impact of severity on the focal firm’s returns; a6 captures the impact of severity on the rival’s returns.
( 2) Returns = a0 + a1Breach Firm
+ a2Data Use Transparency
+ a3Customer Control
+ a4Data Use Transparency • Customer Control
+ a5Data Breach Severity • Breach Firm
+ a6Data Breach Severity • ð1 - Breach FirmÞ
+ a7Capital Slack + a8Size
+ a9Competitive Intensity
+ a10Number of Breaches of Breach Firm
+ a11Number of Breaches of Rival Firm
+ a12Industry Fixed Effects
+ a13Year Fixed Effects + a14Time Trend + ei:
TABLE:
| Variables | Mean | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
|---|
| 1. Firm performance | .00 | .02 | 1.00 | | | | | | | | | | | |
| 2. Data breach vulnerability | .49 | .50 | -.03 | 1.00 | | | | | | | | | | |
| 3. Data use transparency | 3.71 | 1.46 | -.01 | .05 | 1.00 | | | | | | | | | |
| 4. Customer control | 1.03 | .91 | .05 | -.01 | .51 | 1.00 | | | | | | | | |
| 5. Data breach severity | 10.30 | 4.01 | -.01 | -.02 | -.05 | -.03 | 1.00 | | | | | | | |
| 6. Capital resource slack | .24 | .76 | .00 | -.05 | .11 | -.01 | .10 | 1.00 | | | | | | |
| 7. Firm size | 10.49 | 1.63 | -.01 | -.03 | .08 | .00 | -.05 | .05 | 1.00 | | | | | |
| 8. B2C vs. B2B | .53 | .50 | -.01 | .03 | .06 | .10 | -.09 | .06 | .39 | 1.00 | | | | |
| 9. Services vs. goods | .69 | .46 | .02 | .01 | -.15 | -.05 | .06 | .00 | -.22 | -.15 | 1.00 | | | |
| 10. Competitive intensity | .01 | 1.02 | .01 | -.01 | .00 | .09 | -.22 | -.08 | .18 | .02 | -.26 | 1.00 | | |
| 11. Firm prior number of breaches | .40 | 1.07 | .02 | .38 | .06 | .05 | .05 | .03 | .15 | .00 | .12 | .01 | 1.00 | |
| 12. Rival prior number of breaches | .48 | 1.31 | -.01 | -.36 | -.06 | -.06 | .07 | .08 | .24 | .10 | .13 | -.01 | -.14 | 1.00 |
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 |
|---|
| 1. Change in vulnerability | 1.00 | | | | | | | | | | | | | | | | |
| 2. Data use transparency (privacy policy) | -.01 | 1.00 | | | | | | | | | | | | | | | |
| 3. Customer control (privacy policy) | -.16 | .03 | 1.00 | | | | | | | | | | | | | | |
| 4. Data use transparency | -.15 | .53 | .11 | 1.00 | | | | | | | | | | | | | |
| 5. Customer control | -.18 | .12 | .72 | .57 | 1.00 | | | | | | | | | | | | |
| 6. DEmotional violation | .55 | -.09 | -.23 | -.01 | -.03 | 1.00 | | | | | | | | | | | |
| 7. DCognitive trust | -.62 | .01 | .12 | .07 | .03 | -.51 | 1.00 | | | | | | | | | | |
| 8. DFalsifying information | .38 | -.07 | -.05 | -.06 | -.05 | .52 | -.54 | 1.00 | | | | | | | | | |
| 9. DNegative word of mouth | .48 | -.01 | -.01 | -.07 | -.12 | .57 | -.61 | .60 | 1.00 | | | | | | | | |
| 10. DSwitching behavior (%) | .43 | -.03 | -.03 | -.08 | -.06 | .35 | -.47 | .33 | .46 | 1.00 | | | | | | | |
| 11. Value | -.15 | .19 | .12 | .50 | .58 | -.09 | .05 | -.05 | -.01 | -.06 | 1.00 | | | | | | |
| 12. Fairness | -.27 | .14 | .05 | .51 | .56 | -.14 | .10 | -.08 | -.08 | -.18 | .59 | 1.00 | | | | | |
| 13. Privacy concern | .12 | .09 | .01 | .02 | .02 | .12 | -.18 | .02 | .06 | .08 | -.02 | -.06 | 1.00 | | | | |
| 14. Data vulnerability event experience | -.15 | -.07 | -.07 | -.09 | -.06 | -.14 | .10 | -.15 | -.02 | .00 | .04 | -.09 | .16 | 1.00 | | | |
| 15. Age (categorical) | -.05 | .09 | .06 | -.20 | -.28 | -.02 | .04 | -.05 | .03 | .04 | -.31 | -.13 | .01 | .04 | 1.00 | | |
| 16. Gender (1 = male, | 0 = female) | -.06 | .01 | .03 | .00 | .00 | -.07 | .18 | -.06 | -.03 | -.01 | .05 | .06 | -.12 | .05 | -.07 | 1.00 |
| 17. Population size (categorical) | .02 | .03 | .04 | -.04 | -.01 | .02 | .01 | -.10 | -.02 | .06 | -.02 | -.06 | -.03 | -.07 | -.14 | -.16 | 1.00 |
| Mean | 1.29 | 3.80 | 2.13 | 4.62 | 3.84 | 1.01 | -1.27 | .51 | .85 | 4.28 | 4.37 | 5.02 | 4.53 | 3.38 | 3.48 | .58 | 3.01 |
| Standard deviation | 1.62 | 1.08 | 1.13 | 1.56 | 1.51 | 1.59 | 1.51 | 1.43 | 1.45 | 17.21 | 1.40 | 1.21 | 1.43 | 1.56 | .95 | .51 | 1.33 |
Univariate analysis of data breaches on returns. We first analyze market response for focal and rival firms separately. Data breaches have negative and significant effects for the focal firm at 5% in the (-1, 0) and (-1, +1) window, with average returns of -.29% and -.27%, respectively. The Wilcoxon signed rank test reveals that market responses to focal firm breaches are negative and significant at 1% in the (-1, 0) window and at 5% in the (0, 0) negative and significant for rivals, at 5% in the (0, 0) window with average returns of -.14%, and at marginal significance of 10% in the (-1, 0) window with average returns of -.17%. The Wilcoxon signed rank test confirms that market responses to breaches for rival firms still are negative and marginally significant at 10% for both the (0, 0) and (-1, 0) windows. In support of H3a and H3b, a data breach leads to significantly negative abnormal returns for focal and rival firms, implying a negative effect of data breach our approach and examine all forms of customer data vulnerability in parallel, including data access vulnerability, data breach vulnerability, spillover vulnerability, and data manifest vulnerability (Figure 1, Panel A). We consider how company-level transparency and control might suppress an increase in felt customer vulnerability following a data breach, identity theft, or similar event. In this field study, we query actual customers of five large firms across three industries, then use those companies’ current data management policies (i.e., same privacy policy coding used in Study 2) to blend key aspects of Studies 1 and 2. In addition, we link vulnerability to customer outcomes, including falsifying personal information, spreading negative WOM, and engaging in switching behaviors, to test the proposed mediating mechanisms (violation and trust) while controlling for privacy concerns and participants’ prior experience with a data breach or identity theft.
TABLE:
| Windows | Returns | t-Statistic | Sig. Level | Wilcoxon | Sig. Level |
|---|
| (0, 0) | -.14% | -2.61 | .01 | -2.77 | .01 |
| (-1, 0) | -.23% | -2.90 | .00 | -3.30 | .00 |
| (0, 1) | -.05% | -.70 | .49 | -1.63 | .10 |
| (-1, 1) | -.14% | -1.45 | .15 | -2.23 | .03 |
| Windows | Returns | t-Statistic | Sig. Level | Wilcoxon | Sig. Level |
|---|
| (0, 0) | -.15% | -1.72 | .09 | -2.02 | .04 |
| (-1, 0) | -.29% | -2.38 | .02 | -2.87 | .00 |
| (0, 1) | -.13% | -1.31 | .19 | -1.96 | .05 |
| (-1, 1) | -.27% | -2.01 | .05 | -2.48 | .01 |
| Windows | Returns | t-Statistic | Sig. Level | Wilcoxon | Sig. Level |
|---|
| (0, 0) | -.14% | -2.00 | .05 | -1.90 | .06 |
| (-1, 0) | -.17% | -1.69 | .09 | -1.76 | .08 |
| (0, 1) | .03% | .29 | .77 | -.42 | .68 |
| (-1, 1) | -.01% | -.04 | .97 | -.72 | .47 |
Falsifying information occurs when customers fabricate the personal information they provide to a company (Lwin, Wirtz, and Williams 2007). With negative WOM, customers spread unflattering information about the firm to family, friends, and acquaintances (De Angelis et al. 2012). Switching behavior (switching) implies that customers defect to a competitor to avoid the focal firm’s data management practices. All types of data vulnerability should lead to feelings of emotional violation, which then should lead to falsifying information, spreading negative WOM, and switching because feelings of violation and betrayal generate strong desires to punish the gossip source, such as by lying, telling others of these practices, and shifting business to other firms (Gre´goire and Fisher 2008; Smith 2014). However, customers aim to reward and increase their dealings with trusted partners because of norms of reciprocity and reduced perceptions of the likelihood of opportunistic behaviors (Palmatier et al. 2006). Because all types of data vulnerability likely lessen such trust, they also should increase falsifying behaviors, negative WOM, and switching through this route.
H8: The positive effects of customer data vulnerability on (a) falsifying behavior, (b) negative WOM, and (c) switching behavior are mediated by emotional violation.
H9: The positive effects of customer data vulnerability on (a) falsifying behavior, (b) negative WOM, and (c) switching behavior are mediated by cognitive trust.
TABLE:
| | | Model 1 | Model 2 |
|---|
| Variables | Hypotheses | Coefficients | Standardized Coefficients | Coefficients | Std. Coef. |
|---|
| *p < .05. |
| **p < .01. |
| aThe industries include finance, retail, online, high-tech, food/health, and services (manufacturing as reference category). |
| bThe year fixed effects are for 2004 (reference category) through 2015. |
| Main Effects |
| Breach (1 = focal; 0 = rival) | | -.0019 (.0014) | -.0830 | .0096** (.0038) | .0100** |
| Transparency | H4 | -.0009 (.0009) | -.0544 | -.0018* (.0010) | -.113* |
| Control | H5 | .0017* (.0009) | .0660* | -.0029 (.0024) | -.1126 |
| Interaction Effects |
| Transparency • Control | H6 | | | .0012* (.0006) | .225* |
| Severity of breach • Breach | H7a | | | -.0006* (.0003) | -.163* |
| Severity of breach • (1 - Breach) | H7b/7b(alt) | | | .0005* (.0002) | .124* |
| Controls |
| Data breach severity | | .0000 (.0002) | .0016 | | |
| Capital resource slack | | .0001 (.0007) | .0042 | -.0001 (.0007) | -.0043 |
| Firm size | | -.0001 (.0004) | -.0068 | -.0001 (.0004) | -.0031 |
| B2C vs. B2B | | .0004 (.0014) | .0152 | .0000 (.0014) | -.0004 |
| Services vs. goods | | .0012 (.0016) | .0522 | .0012 (.0016) | .0515 |
| Competitive intensity | | .0010 (.0050) | .0060 | -.0005 (.0052) | -.0031 |
| Prior number of breaches for focal firm | | .0008 (.0008) | .0368 | .0009 (.0008) | .0446 |
| Prior number of breaches for rival firm | | -.0001 (.0006) | -.0056 | -.0001 (.0006) | -.0040 |
| Time trend | | .0000 (.0000) | .0003 | .0000 (.0000) | .0003 |
| Industry fixed effect includeda | | Yes | | Yes | |
| Year fixed effect includedb | | Yes | | Yes | |
| | | | | -.0296* (.0143) | |
| Fit statistics | | OverallR-square: .03 | | Overall R-square: .045 | |
| Wald chi-square | | 70.93 | | 91.3 | |
| N | | 583 | | 583 | |
Experimental Data and Design
Using experiments, we test all four types of vulnerability with customers by asking them to evaluate companies they presently use. We recruited 202 people from Amazon Mechanical Turk and assigned them randomly to one of three industries: retail, financial services, or technology. A list of five companies in each industry then appeared, and participants selected the company whose products and services they use most frequently. If they did not use any of those firms, they were directed to one of the remaining two industry lists. Participants were to be excluded from the study only if they did not use any of the 15 total firms across the three industries. Questions related to the extent of company patronage confirmed that respondents were highly involved with their chosen firm (mean product/service use = 6.15 [out of 7]).
After selecting a company, participants answered questions that provided baseline measures of their relationship with that company, including perceptions of vulnerability, violation, trust, falsifying behavior, negative WOM, and switching likelihood. Respondents also indicated the extent of transparency and control provided by the company. After completing these baseline questions, participants saw one of four randomly displayed e-mail messages (50 participants per cell), reportedly from the company they selected. One e-mail explained that the firm had been the victim of a data breach (data breach vulnerability), another indicated that the main competitor of the firm had been the victim of a data breach (spillover vulnerability), and a third noted that the firm had been the subject of a data breach and the participant thus had been the victim of identity theft (data manifest vulnerability). A fourth and final condition simply alerted the customer to a change in firm privacy policy designed to serve as a control (data access vulnerability). The treatments and manipulation checks for Study 3 appear in Appendix D.
After reading their assigned e-mail, participants again completed the measures of vulnerability, violation, trust, level interaction of transparency and control, which is significant (b = -.09, p < .05). Again, contrasts (via median split) show that the high transparency • high control combination led to lowest levels of violation (p < .05) and highest levels of trust (p < .01) across all groupings.
TABLE:
| | Model 1 | Model 2 | Model 3 | Model 4 |
|---|
| Variables | b (SE) | b (SE) | b (SE) | b (SE) |
|---|
| *p < .05. |
| **p < .01. |
| ***p < .001. |
| Industry-Level (Level 3) Effects |
| Retail | .01 (.19) | .03 (.17) | .02 (.16) | .04 (.16) |
| Technology | .05 (.20) | .06 (.22) | .03 (.19) | .06 (.21) |
| Company-Level (Level 2) Effects |
| Transparency | -.01 (.04) | -.05 (.05) | -.01 (.03) | -.04 (.05) |
| Control | -.11 (.06)* | -.16 (.07)* | -.11 (.05)* | -.16 (.07)* |
| Transparency x Control | | -.09 (.07)* | | -.09 (.07)* |
| Customer-Level (Level 1) Effects |
| Data Vulnerability Event |
| Data transparency | | | -.11 (.07)* | -.14 (.07)* |
| Customer control |
| | | -.12 (.07)* | -.10 (.07)* |
| Transparency x Control | | | | -.09 (.06)* |
| Data breach event | .70 (.17)*** | .69 (.17)*** | .71 (.17)*** | .69 (.17)*** |
| Identity theft event | .52 (.17)*** | .51 (.17)*** | .53 (.16)*** | .51 (.16)*** |
| Spillover event | -.54 (.17)*** | -.54 (.17)*** | -.56 (.17)*** | -.57 (.17)*** |
| Customer-Level Controls |
| Privacy concern | .11 (.06) | .12 (.06) | .11 (.06) | .10 (.06) |
| Prior event experience | -.13 (.06)* | -.14 (.06)* | -.11 (.06)* | -.12 (.06)* |
| Age | -.09 (.06) | -.09 (.06) | -.03 (.06) | -.03 (.06) |
| Gender | -.05 (.12) | -.04 (.12) | -.06 (.12) | -.04 (.12) |
| Population size | -.17 (.13) | -.17 (.18) | -.14 (.13) | -.15 (.13) |
| Level 3 R2 | .06 | .06 | .06 | .06 |
| Level 2 R2 | .28 | .31 | .28 | .30 |
| Level 1 R2 | .57 | .58 | .60 | .61 |
Next, for the mediation analyses, we used a partial least
squares (PLS) model. To parse participant heterogeneity from
our model, we relied on change variables to incorporate the
pre- and postassessments of the measured variables in our model. In the PLS model, Dvulnerability becomes the antecedent condition, predicting change in violation (Dviolation) and change in trust (Dtrust). To examine H8 and H9, we investigated whether Dviolation and Dtrust mediate the customer outcome variables–namely, the changes in falsifying (Dfalsifying), negative WOM (Dnegative WOM), and switching likelihood (Dswitching). Partial least squares
conventions of resampling through bootstrapping with 500 iterations (Hulland 1999) produced findings in full support of both H8 and H9, which increases confidence in our conceptual model.
TABLE:
| | | Model 1 | Model 2 |
|---|
| Structural Paths | Hypotheses | b (SE) | b (SE) |
|---|
| PROCESS Test of Indirect Effects |
| △Vulnerability △Violation △Falsifying | g H8a | .17 (.04)*** (CI = [.10, .26]) | |
| △ Vulnerability △DViolation △ NegativeWO | M H8b | .17 (.04)*** (CI = [.10, .27]) | |
| △ Vulnerability △ Violation △ Switching | g H8c | .53 (.05)*** (CI = [.42, .65]) | |
| △ Vulnerability △ Trust △ Falsifying | g H9a | .21 (.06)*** (CI = [.11, .34]) | |
| △ Vulnerability △Trust △Negative WO | M H9b | .22 (.05)*** (CI = [.12, .33]) | |
| △ Vulnerability △Trust △ Switching | g H9c | 1.95 (.50)*** (CI = [1.09, 3.01]) | |
| Effects on Mediating Mechanisms |
| △ Vulnerability △ Emotional violation | | .55 (.10)*** | .56 (.11)*** |
| △ Vulnerability △ Cognitive trust | | -.63 (.08)*** | -.58 (.11)*** |
| Effects of Mediating Mechanisms on Performance |
| △ Emotional violation △ Falsifying behavior | | .34 (.12)** | .33 (.12)** |
| △ Cognitive trust △ Falsifying behavior | | -.36 (.12)** | -.36 (.12)** |
| △ Emotional violation △Negative WOM | | .38 (.10)*** | .37 (.10)*** |
| △ Cognitive trust △Negative WOM | | -.40 (.11)*** | -.41 (.12)*** |
| △ Emotional violation △ Switching behavior | | .16 (.13)* | .16 (.13)* |
| △ Cognitive trust △ Switching behavior | | -.38 (.12)*** | -.39 (.12)*** |
| Controls on Mediating Mechanisms |
| △ Vulnerability x Value △Violation | | | n .06 (.20) |
| △ Vulnerability Fairness △ Violation | | | -.08 (.19) |
| △ Vulnerability Value △Trust | | | -.44 (.08)** |
| △ Vulnerability Fairness △Trust | | | -.70 (.04)*** |
| R2 |
| △ Emotional violation | | .30 | .30 |
| △ Cognitive trust | | .38 | .41 |
| △ Falsifying | | .37 | .39 |
| △Negative WOM | | .46 | .46 |
| △ Switching behavior | | .24 | .24 |
We also employed the PROCESS model (Preacher and
Hayes 2008) to test the two mediating mechanisms of Dviolation and Dtrust on outcomes. In support of H8a, the indirect effect of Dvulnerability on Dfalsifying through Dviolation was significant, with a confidence interval (CI) that excluded 0 (b = .17, CI = [.10, .26], p < .01). The indirect effect growing. We argue that this response is the result of customers’ perceptions of vulnerability. Our examinations, at both firm and customer levels, confirm that vulnerability generates negative outcomes for firms, including negative abnormal stock returns and damaging customer behaviors (i.e., falsifying information, spreading negative WOM, and engaging in switching behaviors). Data transparency and customer control practices can suppress these detrimental effects. We provide a rigorous test of our conceptual framework by offering internally valid insights with a series of experiments using manipulated data access variables, reflecting the most benign form of vulnerability (Study 1). We provide externally valid insights into the effects of customer data breach vulnerability on firm performance, spillover vulnerability effects on rivals, and managerial tools to suppress harm (Study 2). Finally, we examine these insights collectively using a field study, to understand the effects of all types of data vulnerability from the customer’s perspective (Study 3). That is, we examine how firms’ data management efforts that create any type of customer vulnerability can lead to negative outcomes, and we identify the emotional and cognitive mechanisms through which these negative outcomes occur.
Theoretical Implications
This research offers three main theoretical contributions. First, customers perceive harm and respond negatively to firms’ collection and use of their data. The tests across all types of customer data vulnerability show significant negative effects, some of which are manifest even without any direct financial harm to the customer. This customer-centric view shows that people identify potential harm due to firms’ data management efforts. Accordingly, vulnerability offers a more precise construct to understand customer responses to firms’ use of their information than general privacy issues or financial damages. Study 3, testing multiple manifestations of data vulnerability, shows significant effects across each type. Yet, our findings show that privacy concern was not an important predictor of negative customer behavior outcomes. Legal experts already have begun thinking this way about data privacy, noting that “generalized harm already exists; we need not wait for specific abuses to occur” (Solove 2003, p. 8).
Second, we use gossip theory as a unifying lens to describe how customer vulnerability creates strong negative customer responses. Gossip theory has both theoretical and intuitive appeal for evaluating how people respond to unwanted customer information access and use, when they learn of it. In confirmation of a key premise of gossip theory, we find that people have a welldeveloped sense of how they are perceived and evaluated by others (Richman and Leary 2009), even if those others are firms. Our data breach event study and customer experiments demonstrate that when gossip becomes salient, it produces a range of negative emotional and cognitive responses from the target toward the source (Baumeister and Leary 1995; Leary and Leder 2009). In Study 2, we find significant negative stock performance and spillover effects; in Studies 1 and 3, we demonstrate customers’ heightened feelings of violation and deteriorating trust. Consider, for example, this comment on an online post in response to a security flaw by Comcast: “As a consumer, do I just sit and wait for all my stuff to get hacked [feelings of vulnerability]?… It’s very frustrating [emotional response]!” (Gordon 2014).
Third, we extend two peripheral elements of gossip theory that characterize how people manage gossip’s spread. In our customer-focused investigations in Study 1, the series of experiments confirms that transparency and control work synergistically to mitigate feelings of violation and enhance trust, which aligns with the Study 2 findings that show that transparency and control promises in data management practices reduce the damage to firm performance in the wake of a data breach. In the data breach event study, we coded the different elements of each firm’s privacy policy as a proxy for its data management practices. Similar coded elements in the customer field experiments in Study 3 suppress the increase in vulnerability after a data privacy event. These effects thus demonstrate that people are aware of how companies manage their data, and their practices matter for reducing felt vulnerability. The strong, significant, synergistic effects of transparency and control across three studies with different measures and in different contexts, speaks to their powerful ability to work in combination to suppress customer vulnerability. Likewise, these combinative effects suggest ties to informed choice theory, stemming from transparency’s emphasis on knowledge and control’s emphasis on choice (Cranage 2004); extensions along these lines represent a fruitful area for further research.
Managerial Implications
Our findings suggest that firms need a more tempered approach to data and analytics initiatives that involve the collection and use of customer information. They must consider their approaches to data management carefully to avoid negative effects. Customer data practices may help the firm identify and better understand customers and segments, but these same practices can create vulnerability throughout the customer cohort. In Study 2, we draw on firm privacy policies to understand how firms access, manage, and communicate about customer data. Our significant findings demonstrate that firms must acknowledge privacy policy dimensions as meaningful proxies for their actual data practices. In Study 3, by blending variables from company privacy policies with individual-level responses, we show that customers are aware of data practices, which affects critical behavioral outcomes. Data practices are important for all firms, considering our spillover effect findings. Even noncompromised firms can suffer substantial financial performance detriments when a close competitor has a breach.
The transparency, control, and breach severity dimensions suggest additional managerial best practices. Transparency and control combine to moderate the relationship between various types of vulnerability and performance. Across three studies and five outcome variables, we find that a potent vulnerability-suppressing combination provides customers with clear transparency and control over their personal information. High transparency and control reduces the spread of negative WOM, deters switching, and suppresses negative stock price effects. For example, Citigroup had a privacy policy (in place at the time of the breach) that was low on both transparency and control, such that when it suffered a breach, the damages were aggravated, resulting in a loss of $836 million in value in the (-1, 0) window. According to the propensity score method for counterfactual analysis (Web Appendix C), if Citigroup had high transparency and high control, it would have suffered a loss of only about $16 million in stock value. That is, Citigroup might have saved about $820 million had it simply offered its customers greater transparency and control related to use of their personal information.
The other combinations also suggest useful takeaways for managers. When provided with high transparency but low control, customers perceive more violation and lower trust across all studies. Thus, it is a dangerous practice for firms to tell customers exactly how they will be collecting data without also providing them with some say over those practices. If they lack control, customers are left to worry about the various potential uses of their data–uses that have been made salient by their transparency. Knowledge alone has mixed effects as a vulnerability suppressor. Therefore, if firms intend to reveal their data use practices to customers, they also need to provide them with some element of control over the information.
The combination of low data use transparency and high control instead creates a situation of uninformed autonomy. Customers have the ability to change their preferences, so they respond favorably, even if their opt-in and opt-out
choices are somewhat blind, without full knowledge of what and how the firm uses their information. More research is needed to reveal the full effects of this strategy, perhaps by
using choice theory. In critical work on understanding choice, Iyengar (2010, p. 285) notes, “If you believed you had choice, you benefitted from it, regardless of whether you actually exercised it.” Collectively, these contrasts suggest that providing customers some level of control is a powerful managerial tool for generating positive firm outcomes. The amount of customer control provided might not need to reach full and
total autonomy; rather, some level of perceived control may be sufficient to obtain the desired mitigating effects. By allowing customers to opt in or out of various data practices, firms could promote their increased overall willingness to provide personal
information. Finally, managers need to identify their competitors’ data
practices, the effects on their own firm’s performance, and how these effects might vary with the severity of a data breach. A positive competitive effect that can overwhelm the negative spillover effect. Consider Anthem’s data breach in February 2015, which affected as many as 80 million customers. The high severity of this breach led its rival Aetna to gain approximately $745 million (2.2% returns) on the event day, due to competitive effects. In contrast, Nvidia’s breach, which affected just 400,000 user accounts in July 2012, led its rival Advanced Micro Devices to lose approximately $48 million (-1.4% returns) on the event day, seemingly due to the spillover effect of this less severe breach.
Limitation and Further Research
Our investigation considers what happens to customers and firms in a relatively short period surrounding data access or a data security event. To investigate how firms engage with their customers to recover from these negative events, further research might address how firms make amends or restore benevolent aspects of their customer relationships following vulnerability-inducing events, which would represent an
TABLE:
| Study | Mean (High) | Mean (Low) | Sum of Squares | F-Test | p |
|---|
| Study 1a: Vulnerability 3 Transparency on Emotional Violation |
| Vulnerability | 5.50 | 3.31 | 240.18 | (1, 198) = 99.67 | .000 |
| Transparency | 5.85 | 2.50 | 135.16 | (1, 198) = 75.15 | .000 |
| Study 1b: Vulnerability 3 Control on Emotional Violation |
| Vulnerability | 5.74 | 3.74 | 198.19 | (1, 198) = 90.41 | .000 |
| Control | 4.77 | 2.77 | 201.00 | (1, 198) = 93.50 | .000 |
| Study 1c: Transparency 3 Control on Violation and Trust |
| Transparency | 5.47 | 2.64 | 399.29 | (1, 197) = 151.00 | .000 |
| Control | 4.73 | 2.72 | 200.56 | (1, 197) = 76.20 | .000 |
Experiment Scenarios
Introduction
The following scenario asks you to imagine a company you often deal with to buy products and services. You shop with this retailer an average of once a week, both in the store and online. You make a large number of purchases with them.
Vulnerability
High. This firm has access to all your personal information, including your financial information and background, your credit card numbers, and your detailed purchase history.
Low. This firm has access to only limited personal information, including basic demographics and recent purchase information. They do not store credit card numbers or other financial information, and do not keep your detailed purchase history.
Transparency
High. This company is very transparent in how they manage your personal information. For example, their data management activities are clear to you, and their policies are easy to understand.
Low. This company is very vague in how they manage your personal information. For example, their data management activities are unclear to you and their policies are difficult to understand.
Control
High. This company gives you great control in how they manage your personal information. For example, you may change at any time your personal settings that dictate how your information is used.
Low. This company does not give you any control in how they manage your personal information. For example, you do not have the ability to choose the ways in which your personal information is used.
TABLE A1
TABLE:
TABLE:
| Construct Items | Loading |
|---|
| Study Variables |
| Vulnerabilitya (CR = .97/.99; AVE = .84/.91) |
| The personal information that the company has about me makes me feel: |
| • Insecure | .94/.97 |
| • Exposed | .92/.97 |
| • Threatened | .93/.97 |
| • Vulnerable | .91/.95 |
| • Susceptible | .94/.96 |
| Data Use Transparencya (CR = .98; AVE = .91) |
| The company’s customer data management activities are: |
| • Unclear to me/Clear to me | .95 |
| • Confusing/Straightforward | .96 |
| • Difficult to understand/Easy to understand | .96 |
| • Vague/Transparent | .95 |
| Customer Control (adapted from Mothersbaugh et al. 2012; CR = .96; AVE = .87) |
| I believe I have control over what happens to my personal information. | .91 |
| It is up to me how much the company uses my information. | .94 |
| I have a say in how my information is used by the company. | .95 |
| I have a say in whether my personal information is shared with others. | .94 |
| Emotional Violation (adapted from Grégoire and Fisher 2008; CR = .98/.98; AVE = .91/.91) |
| Regarding the company’s customer data activities, I feel: |
| • Violated | .95/.95 |
| • Betrayed | .96/.95 |
| • Not respected | .96/.95 |
| • Taken advantage of | .95/.95 |
| Cognitive Trust (adapted from Palmatier 2008; CR = .96/.98; AVE = .85/.94) |
| Regarding this company’s customer data activities, I think: |
| • I trust the company. | .96/.97 |
| • The company is very trustworthy. | .96/.97 |
| • I have confidence in the company’s behaviors. | .94/.97 |
| • The company is reliable. | .94/.96 |
| Falsifying Information (adapted from Lwin, Wirtz, and Williams 2007; CR = .96/.96; AVE = .88/.89) |
| When thinking about how I provide personal information to the company: |
| • I am likely to give the company false information. | .95/.95 |
| • I purposely try to trick the company when providing my personal data. | .96/.96 |
| • I think it is fine to give misleading answers on personal questions. | .91/.92 |
| Negative WOM (adapted from Grégoire and Fisher 2006; CR = .97/.98; AVE = .92/.95) |
| I would likely: |
| • Spread negative word of mouth about the company. | .97/.98 |
| • Bad-mouth the company to my friends, relatives, or acquaintances. | .97/.98 |
| • Tell others not to choose them if asked about their products/services. | .93/.96 |
| Switching Behavior (adapted from Palmatier, Scheer, and Steenkamp 2007; CR = .95/.96; AVE = .85/.90) |
| If another company offered the same product/services but did not collect any data about your activities, how likely would you be to: |
| • Shift all of my business to this new company | .91/.94 |
| • Try this new company’s offering | .94/.96 |
| • Pay a premium to use this new company | .92/.94 |
| Control Variables |
| Value (adapted from Chellappa and Sin 2005; CR = .94; AVE = .80) |
| • I receive value from the ways this company uses my customer data. | .90 |
| • I save money (or can use free services) by providing my information. | .86 |
| • I value how my information is used to customize my experience. | .93 |
| • This company saves me time by using my personal information. | .90 |
| Fairnessa (CR 5 .97; AVE 5 .89) |
| Regarding this company’s use of your customer information: |
| • I believe their use of my customer information is fair. | .94 |
| • I believe the company accesses my information in a fair way. | .96 |
| • I believe the company’s use of my information is ethical. | .94 |
| • The company manages my information in an equitable way. | .94 |
| Privacy Concern (adapted from Malhotra, Kim, and Agarwal 2004; CR 5 .94; AVE 5 .80) |
| I am sensitive to the way companies handle my personal information. | .85 |
| It is important to keep my privacy intact from online companies. | .90 |
| Personal privacy is very important, compared to other subjects. | .92 |
| I am concerned about threats to my personal privacy. | .90 |
| Data Breach/Identity Theft Experience |
| Customer Demographics (Gender, Age, City Population) |
TABLE:
| Control Variables | Rationales | Operationalizations |
|---|
| Capital resource slack | Uncommitted resources can enable or prevent a firm from effectively managing a breach. | Ratio of a firm’s annual sales to gross property, plant, and equipment (PPE) relative to its industry at a four-digit SIC level (Modi and Mishra 2011); that is, in the same four-digit SIC |
| Firm size | Larger firms might garner more negative reactions. | Log (number of employees) |
| Industry | Effect of breach may vary by industry. | Dummy-coding for financial, retail, technology, online, or health care industries |
| Competitive intensity | Competitive rivalry may affect the market cost of the breach. | Herfindahl index: Sum of squared market share of the firm i, with the industry (I) defined at the three-digit SIC level, for the year prior to the breach. |
| Time | Reaction to breaches may strengthen/weaken over time. | Days since first breach in the sample time frame |
| Year dummies | Year dummies control for macroeconomic effects. | Date of earliest public report of breach, converted into binary variables for years 2006–2015 |
| B2B vs. B2C | Effect of breach may vary by whether the firm focuses on business or end customers. | The firm’s primary four-digit SIC code classifies it as B2B (e.g., chemicals, primary metal, business services, engineering, accounting, research, management and related services) or B2C (e.g., food and kindred products, apparel, hotels, travel agents), using the scheme by Srinivasan et al. (2011) and Borah and Tellis (2014). |
| Goods vs. services | Effect of breach may vary by whether the firm focuses on goods or services. | The firm’s primary four-digit SIC code classifies it as goods (e.g., chemicals, primary metal, food and kindred products, apparel) or services (e.g., business services, engineering, accounting, research, management and related services, hotels, travel agents), using the scheme by Srinivasan et al. (2011) and Borah and Tellis (2014). |
| Focal firm prior breaches | The negative effect of breaches for the firm might increase if the firm has had breaches in the past. | Count of the number of breaches of the focal firm |
| Rival firm prior breaches | The negative effect of breaches for the firm might decrease if the closest rival has had breaches in the past. | Count of the number of breaches of the nearest rival of the focal firm |
Appendix D: Study 3 Manipulation Checks and Experiment Scenarios
Introduction
From the following list, please select the [industry name] company whose products and services you use most often.
• Retail: Target, Walmart, Amazon, Costco, Best Buy • Technology: Apple, Microsoft, Google, Facebook, HP • Financial Services: Chase Bank, Wells Fargo, Bank of America,
Citibank, American Express
Please imagine that you receive the following e-mail message from the company.
Data Access Vulnerability
Thank you for being a valued customer of [Selected Company]. We appreciate our relationship with you. We are writing to inform you that the terms of our privacy policy have changed. You may access the full policy on our website. [Selected Company] is committed to protecting our customers against fraudulent activity. Our relationship with you and our other valued customers is our top priority.
Vulnerability mean = 3.26 out of 7.00
Data Breach Vulnerability
Thank you for being a valued customer of [Selected Company]. We appreciate our relationship with you. Unfortunately, it has come to our attention that [Selected Company] has been the victim of a data breach. Through our internal investigation, we have determined that your customer profile was one of those compromised. However, at this time, [Selected Company] investigators have not detected any fraudulent activity in your account. Although we understand this is disappointing news, please know that our relationship with you and our other valued customers remains a top priority.
Vulnerability mean = 5.57 out of 7.00
Data Spillover Vulnerability
Thank you for being a valued customer of [Selected Company]. We appreciate our relationship with you. It has come to our attention that our primary competitor has been the victim of a data breach. Although data breaches are becoming more common, at this time [Selected Company] investigators have not detected any fraudulent activity in your account. Although we understand this is disappointing news for some, please know that our relationship with you and our other valued customers remains a top priority.
Vulnerability mean = 3.94 out of 7.00
Data Manifest Vulnerability
Thank you for being a valued customer of [Selected Company]. We appreciate our relationship with you. Unfortunately, it has come to our attention that [Selected Company] has been the victim of a data breach. Through our internal investigation, we have determined that your customer profile was one of those compromised. [Selected Company] investigators also have detected fraudulent activity in your account. Thus, it appears you have been the victim of identity theft. Although we understand this is disappointing news, please know that our relationship with you and our other valued customers remains a top priority.
Vulnerability mean = 5.24 out of 7.00
DIAGRAM: Appendix D: Study 3 Manipulation Checks and Experiment Scenarios
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Record: 54- Demonstrating the Value of Marketing. By: Hanssens, Dominique M.; Pauwels, Koen H. Journal of Marketing. Nov2016, Vol. 80 Issue 6, p173-190. 18p. 2 Diagrams, 6 Charts, 1 Graph. DOI: 10.1509/jm.15.0417.
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Record: 55- Design Crowdsourcing: The Impact on New Product Performance of Sourcing Design Solutions from the "Crowd". By: Allen, B. J.; Chandrasekaran, Deepa; Basuroy, Suman. Journal of Marketing. Mar2018, Vol. 82 Issue 2, p106-123. 18p. 2 Diagrams, 5 Charts. DOI: 10.1509/jm.15.0481.
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Design Crowdsourcing: The Impact on New Product Performance of Sourcing Design Solutions from the "Crowd"
The authors examine an increasingly popular open innovation practice, "design crowdsourcing," wherein firms seek external inputs in the formof functional design solutions for new product development from the "crowd." They investigate conditions under which managers crowdsource design and determine whether such decisions subsequently boost product sales. The empirical analysis is guided by qualitative insights gathered from executive interviews. The authors use a novel data set from a pioneering crowdsourcing firm and find that three concept design characteristics—perceived usability, reliability, and technical complexity—are associated with the decision to crowdsource design. They use an instrumental variable method accounting for the endogenous nature of crowdsourcing decisions to understand when such a decision affects downstream sales. The authors find that design crowdsourcing is positively related to unit sales and that this effect is moderated by the idea quality of the initial product concept. Using a change-score analysis of consumer ratings, they find that design crowdsourcing enhances perceived reliability and usability. They discuss the strategic implications of involving the crowd, beyond ideation, in helping transform ideas into effective products.
Increasingly, firms are tapping into a wide range of external sources of knowledge to source innovations (Chesbrough 2003; Laursen and Salter 2006). One popular facet of this new trend is the leveraging of online infrastructure to tap an underexplored and richly heterogeneous pool of knowledge resident in the general population of consumers for innovative ideas (Bayus 2013), a practice termed "crowdsourcing." Extant research on the efficacy of using external sources of knowledge for innovation has centered on the opportunity identification stage (Foss, Lyngsie, and Zahra 2013). For example, research in marketing has examined the practice of involving the crowd during ideation (e.g., Bayus 2013; Poetz and Schreier 2012) and has suggested that such early involvement in new product development (NPD) empowers potential consumers while enabling firms to attract more participants overall and more diverse participants to the idea generation process (Fuchs, Prandelli, and Schreier 2010; Schreier, Fuchs, and Dahl 2012).
Recent evidence has suggested that the role of external knowledge sources may go beyond opportunity identification and extend to opportunity exploitation stages (Foss, Lyngsie, and Zahra 2013).Congruent with this idea, firms are increasingly using crowdsourcing in phases following ideation, specifically, in the solicitation of actionable design solutions (see Table 1), a practice we call "design crowdsourcing." We define design crowdsourcing as the practice of soliciting functional design[ 1] solutions from the crowd. For example, crowdsourcing platforms, such as Redclay.com, allow firms to submit new product design briefs and seek crowd input for the development and/or refinement of the design. In such situations, the client firm already has a new product idea, but may lack the resources or know-how to bring this idea into fruition, and, therefore, seeks external input from the broader user community to help create a manufacturable design.
TABLE: TABLE 1 Examples of Companies Utilizing Design Crowdsourcing and Design Crowdsourcing Platforms
TABLE 1 Examples of Companies Utilizing Design Crowdsourcing and Design Crowdsourcing Platforms
| Organization | Company Description | Use of Design Crowdsourcing |
| Quirkya | Pioneering socially developed product company founded with the vision of making invention accessible. | After selecting a new product idea, Quirky asks its community to help with the design by "[submitting] sketches, images, videos, and prototypes that illustrate industrial design directions for [the product]." |
| Crowdspringa | Platform for bringing companies and designers together | Various companies post creative briefs with the needs and requirements for new industrial and consumer products. Designers submit design concepts for the product and the firm chooses the ones to utilize. |
| Unilever | Consumer packaged goods company | Unilever operates an open-innovation website, Unilever Foundry, where it collaborates with its community, and many projects involve design crowdsourcing. As stated on its website, "Often we will have specific challenges on which we'd welcome your collaboration: a new formula, a new technique, new packaging or a fresh design solution to a product we already have in mind" (Unilever 2016, emphasis added). |
| Fiat | Global car company | Fiat crowdsourced the design of its Fiat Mio: "Fiat, sought a design for its 2009 concept vehicle, the Fiat Mio. Rather than turning inward to its core team of designers and engineers to come up with the new look, the company… let the world decide how the car would look, feel, and drive" (Markowitz 2011). |
| eYeka | Third-party website that serves as crowdsourcing platform for brands. Client list includes Procter & Gamble, Nestle, and Citroen | Firms post ideas for new products along with creative briefs and ask the community to submit design ideas. For example, one firm asked for designs for a new interactive learning and entertainment product for children, and the community submitted design ideas. |
| Local Motors | Open-innovation car company and community | Local Motors launches a car idea to its community and asks for help in creating and implementing the design. The community, consisting of designers and engineers, collaborates and submits designs for the various car parts. |
| General Electric (GE) Open Innovation | Branch of GE that generates ideas from consumers via crowdsourcing challenges | GE gives a description of the product it is looking for, along with a few sample sketches, and asks the community to submit design concepts. Submissions include text descriptions of how the product works, along with pictures/sketches. Winners receive cash rewards. |
| Hyve Crowd | A German third-party crowdsourcing site. Clients include Audi and BMW. | Firms post various requests for product-related ideas, many of which include design requests. For example, companies highlight a specific type of product they are looking for and ask the community to submit concepts and design solutions. |
| Red Clay | Platform that connects brands with a community of industrial designers. | Small businesses submit product design briefs that are worked on by a community of industrial designers. The project is matched to a small group of designers who submit designs that are then chosen by the firm in a contest format. Thereafter, the firm owns all IP. |
| Design2Gather | Third-party crowdsourcing platform designed to help firms develop ideas into actionable designs | Companies post product ideas that are then worked on by hundreds of designers to develop a manufacture ready design. Their tagline is "making your idea reality." |
aSources of the data for the empirical analysis in the study.
B.J. Allen is Assistant Professor of Marketing, Sam M. Walton College of Business, University of Arkansas (email: BAllen@walton.uark.edu). Deepa Chandrasekaran (corresponding author) is Assistant Professor of Marketing, University of Texas at San Antonio (email: deepa.chandrasekaran@utsa.edu). Suman Basuroy is Department Chair and Graham Weston Endowed Professor of Marketing, University of Texas at San Antonio (email: Suman.Basuroy@utsa.edu). The authors thank the Carolan Research Institute and Dr. Joel Saegert for helping fund this research; participants at the Marketing Science Conference and PDMA Research Forum for their helpful comments; and Dr. Ram Ranganathan, Dr. Raji Srinivasan, and Dr. Richard Gretz for detailed comments on earlier versions of the article. The authors are deeply grateful to all interviewees for their invaluable insights. Michael Haenlein served as area editor for this article.
These compelling anecdotal examples raise interesting research questions: Does design crowdsourcing lead to a better new product development process? Does design crowdsourcing lead to improved products and performance? Whether and how crowdsourcing affects critical downstream activities, such as executing ideas in the NPD process, has received little research attention. In fact, studies have highlighted the significant challenges of internalizing external input into the NPD process, including the rejection of outside input by insiders (Katz and Allen 1982), the costs of distant searches (Afuah and Tucci 2012), and the difficulty of communicating tacit information needed for problem solving (Von Hippel 1994). Furthermore, scant research has linked crowdsourcing to product performance (for an exception, see Nishikawa, Schreier, and Ogawa 2013), and current literature has focused primarily on crowdsourcing during ideation.
The goals of this research are to examine ( 1) whether and how design crowdsourcing affects the NPD process; ( 2) what design antecedents lie behind the decision to crowdsource; ( 3) whether design crowdsourcing has a positive impact on product performance—and, if so, to identify some boundary conditions for this effect; and ( 4) whether design crowdsourcing helps improve the functional design attributes of product ideas. We use the knowledge-based theory (Alavi and Leidner 2001; Chang and Taylor 2016), as well as exploratory insights from interviews (suggested by Kumar et al. [2016] to uncover new phenomena), to propose that ( 1) the crowd is a repository of design knowledge and design crowdsourcing is a mechanism that enables firms both to tap into the broader community for workable design solutions and to assimilate/exploit these solutions to aid the transformation of new product ideas into products, ( 2) such identification and the exploitation of external design solutions will improve new product performance, and ( 3) the efficacy of design crowdsourcing on performance will depend on the quality of the original product ideas.
We test our hypotheses using a novel data set of 86 new products collected from Quirky, a pioneering, community-driven NPDwebsite. The empirical analysis on this data set indicates that the probability of design crowdsourcing was influenced by a need to increase perceived usability and reliability and to decrease technical complexity. Using an instrumental variable procedure (Wooldridge 2010) for dealingwith endogenous binary variables, we find that design crowdsourcing has a positive effect on sales, as proposed, with an important boundary condition. The positive impact of design crowdsourcing on sales is contingent on the idea quality of the original product concept—design crowdsourcing is associated with increased sales when the idea quality of the product concept is low. Furthermore, design crowdsourcing enhances perceived reliability and usability from idea to final product.
Our results suggest that design crowdsourcing can help managers move a greater number of ideas through development by using the community's assistance in making (initially) lesspromising ideas marketable, thus improving the effectiveness of the NPD process. Rather than discarding such ideas, firms may use external sources of knowledge to develop them and interact with these sources extensively to ensure that the outcome is of high quality. In addition, we highlight specific design functionalities that managers can improve using design crowdsourcing, allowing for a more targeted approach when leveraging crowdsourcing. Finally, our findings suggest opportunities for crowdsourcing platforms to market themselves as solution spaces that provide tangible downstream benefits through enhanced functional attributes. The next sections present the conceptual development as well as managerial insights into design crowdsourcing leading to the hypotheses, data, modeling methodology, results, and discussion.
Grant (1996, p. 112) states that "fundamental to a knowledge-based theory of the firm is the assumption that the critical input in production and the primary source of value is knowledge." A firm's ability both to create new knowledge and to apply knowledge forms the basis of developing a competitive advantage (Alavi and Leidner 2001). One online mechanism that grants firms access to a wide, diverse knowledge pool is crowdsourcing (Schreier, Fuchs, and Dahl 2012). Extant marketing literature has treated the crowd as a resource base for new ideas and treated crowdsourcing as amechanism that enables the identification of new ideas from this resource base. However, firms may also need to engage external resources, such as the crowd, for opportunity exploitation (Foss, Lyngsie, and Zahra 2013).We propose that the crowd is also a knowledge source for design solutions, which are utilized to solve firm-specific problems in the context of new product development.
Knowledgemanagement theory suggests that the identification/ acquisition and assimilation/exploitation of externally generated knowledge improves innovation performance (Cohen and Levinthal 1990). However, in the context of design crowdsourcing, little is understood about how this process manifests itself in practice. Given the lack of research into design crowdsourcing, our research begins with qualitative interviews to investigate this question and then integrates the findings from the interviews with a literature review.
We conducted exploratory interviews with practitioners from the United States, China, Italy, Israel, and the United Kingdom who had extensive experience with crowdsourcing (see Table 2). Because the purpose of these interviews was to assist in theory development, we ensured that the interviewees were familiar either with the experiences of established firms that engaged in crowdsourcing or with start-ups whose business models involved crowdsourcing. We followed a standard format and approach for each interview.[ 2] The authors carefully read the interview transcripts and notes and documented the main concepts and themes that emerged.
TABLE: TABLE 2 Description of Managerial Interviews
TABLE 2 Description of Managerial Interviews
| No. | Title | Firm Description |
| 1 | Vice President, Marketing and Technology | Consulting firm, helps organizations with crowdsourcing & marketing |
| 2 | Co-Founder and Chief Operating Officer | Third-party design crowdsourcing platform |
| 3 | Senior Brand Manager | Large manufacturing firm known for use of crowdsourcing product design |
| 4 | Editor (researches and publishes articles on crowdsourcing) | Website for business news, research, and insights |
| 5 | Brand Manager | Large CPG firm that organizes numerous crowdsourcing campaigns |
| 6 | Consumer Trends Consultant (consults on crowdsourcing) | Marketing consulting firm that advises organizations on consumer trends |
| 7 | Founder and Chief Financial Officer | European crowdsourcing company |
| 8 | Content Manager | European crowdsourcing company |
| 9 | Founder | Consulting firm that helps firms facilitate open innovation challenges |
| 10 | Creative Director | Third-party crowdsourcing firm that works exclusively in design crowdsourcing |
| 11 | Co-Founder and Chief Operating Officer | Third-party design crowdsourcing site with emphasis on "design challenges" |
| 12 | Senior Technologist (led development of first crowd- sourced laptop) | One of the largest computer manufacturers in the world |
| 13 | Former President | One of the largest crowdsourcing firms in the world |
In this subsection, we explore the link between design crowdsourcing and new product performance by utilizing common themes from the interviews and the literature review.
Design crowdsourcing helps firms move product ideas into development. "How can I execute my innovative ideas?" is a question that represents an increasing concern for chief executive officers and business executives (eYeka 2016). For example, an executive of a design crowdsourcing firm noted this about her clients:
These people come with new ideas in innovation; they have a great innovation, but they're not really sure how to make that innovation happen. (Cofounder and chief operating officer, design crowdsourcing firm)
Design crowdsourcing helps make development a reality in situations where firms know what product or solution they want but are looking for an executable design. The difference between using the crowd to obtain design solutions and using the crowd for ideation itself seems to be twofold: First, the emphasis of ideation crowdsourcing may be on an unconstrained flow of ideas,whereas design crowdsourcing involves the crowd tackling a focused need and, thus, all submissions and iterations are working toward solving the same problem. Second, the solution space in design crowdsourcing may also be smaller (i.e., more manageable). From our investigation of design crowdsourcing websites, the number of design submissions (being in the tens or hundreds and not thousands, as, for instance, in ideation challenges) were more tractable for clients (especially small businesses). Thus, design crowdsourcing moves product ideas closer to development and, thus, to delivering value:
Different crowds [are viewed] as layers of technology that can powerfully work together.
In online sourcing from a crowd … you're connecting multiple people to get to an end solution. The ideas become more powerful when you bring these different skill sets together … to get to a solution a little more efficiently. (Cofounder and chief operating officer, third-party design crowdsourcing platform)
Design crowdsourcing helps identify new sources for and types of design solutions. A consistent theme from our interviews was that although in-house specialists may be constrained by their past experience while trying to create new design solutions, crowdsourcing brings in novel and fresh solutions to design problems. For example, when asked why managers would crowdsource product design, one expert noted:
[Managers are influenced by] a desire to bring a fresh insight to the design process. Crowdsourcing can help with the design process by bringing new ways of thinking and unique ideas. An in-house team can be impacted by things like legacy ideas, office politics, and being too close to the product. By bringing in outside help it brings a fresh approach, which aids the creative process. (Editor and author on crowdsourcing)
When asked whether there were differences in the kinds of solutions companies were looking for, an executive commented on the criticality of diversity of perspectives:
I think it really depends on the company because some of our smaller and medium sized companies don't have any design talent in-house, so they're really looking for that design. Then the companies that do have design talent in-house, they're looking to get more of a new perspective and understanding that when you pull more than two designers into a project, you're going to get a very diverse amount of perspective, which starts to really begin the true design thinking of why we design and go through the full process, which is pulling those different ideas together, iterating on them this idea that there's a community to build on them versus one person'sway of thinking. (Cofounder and chief operating officer, design crowdsourcing firm)
This point is consistent with the literature that finds that user involvement in design generates greater numbers of diverse, need-specific, and unconstrained designs (Schreier, Fuchs, and Dahl 2012) compared with in-house design. Furthermore, managers believed that the utilization of the crowd led to a greater congruency between design and user needs. As noted by a leading design expert:
Just having ideas doesn't work. The question is, really, who are you solving it for? Insights and the human side of design is the most important aspect you can add…. I think using design as a differentiator is what we're seeing in the market. (Former president of a leading crowdsourcing firm/design consultancy group)
Managers are continually looking for ways to respond to consumers' wants and needs in a way that optimizes firm resources (Fennell and Saegert 2004). Firms are thus able to use design crowdsourcing to integrate knowledge to develop a product more congruent with consumer needs, which is more likely to succeed when it enters the marketplace.
Design crowdsourcing increases available resources for NPD. Nearly every manager interviewed mentioned that design crowdsourcing serves as a resource-supplement strategy that simplifies and accelerates the flow of the NPD process:
A lot of those (client) companies are small. They need to move quick and they need to keep their prices down, so budget becomes a big concern. Innovation becomes a concern. (Cofounder and chief operating officer, design crowdsourcing firm)
I'm doing all the things that I'm doing as a typical product development cycle, but I'm actually accelerating that by getting the crowd involved…. You know your product, you know your design. You know what you're good at, but you're intentionally leveraging the crowd to get into the market fast. (Vice president, crowdsourcing consulting firm)
Popular press publications also use words like "efficiency," "simplify," and "streamline" when describing why firms crowdsource during NPD. Traditional NPD processes are constrained by resource availability, such that only a small number of the "best" ideas can be implemented. Accessing the crowd increases the knowledge resources available to a firm by both leveraging the skills and expertise of hundreds of people outside the organization and freeing up firm resources, allowing for the development of a greater number of ideas.
Design crowdsourcing may be iterative and collaborative. Design crowdsourcing is not just about obtaining new ideas but also about refining and fine-tuning ideas, and it provides the capability to engage in a high degree of collaboration with the broader community. This represents one of the key differences from traditional ideation crowdsourcing, wherein the firm may select novel ideas, but there is not much collaboration going forward (Bayus 2013). The selected designers, suppliers, and clients often (depending on the crowdsourcing platform)work together collectively, using insights they gain from design submissions to iterate toward a manufactureready product. Because members of the "crowd" are neither familiar specialists nor a part of the internal team, there is a need for closermonitoring and internal involvement to move toward a solution. Furthermore, the process of iteration often results in a better translation of tacit suggestions to workable solutions:
My crowd is going to be an extended team within my company. (Vice president, crowdsourcing consulting firm) Today we have an on demand industrial design community…. They start to look at a lot of these different crowds as layers of technology that can powerfully be able to start the work together.
In online sourcing from a crowd [you are not just] able to connect [with one person], but you're connecting multiple people to get to an end solution. (Cofounder and chief operating officer, design crowdsourcing platform)
In summary, a firm has the choice of whether to involve the crowd in the design phase or to simply refine the product design in-house. Our exploratory insights suggest that design crowdsourcing enables firms to ( 1) translate ideas into executable solutions, ( 2) provide access to new sources that can provide novel and meaningful design solutions, ( 3) help increase the resources available for NPD, and ( 4) help create a more iterative and collaborative process in integrating external solutions with inhouse guidance. Because a firm's "ability to identify, assimilate, and exploit knowledge from the environment" is related to a firm's innovative performance (Cohen and Levinthal 1990, p. 128), it follows that identifying, assimilating, and exploiting knowledge using design crowdsourcing should increase a new product's performance.
In the specific case of NPD, we expect all four of these factors to contribute to new product success, as prior literature has suggested that ( 1) the development of an increased number of new products that more accurately reflect customer preferences during the NPD process improves NPD performance (Joshi and Sharma 2004); ( 2) design newness and creative solutions that are novel and meaningful to consumer needs are key determinants of new product success (Talke et al. 2009); ( 3) slack creates resources that help better exploit existing competencies, explore new competencies, and develop innovations (Atuahene-Gima 2005); and ( 4) conscious and meaningful customer interactions in the form of engaging with the design of new products (along with or in addition to ideas) will provide a differentiating advantage to the firmin themarketplace (Ramani and Kumar 2008).
Thus, drawing on the insights derived from our interviews and past theory, we propose:
H1: Design crowdsourcing has a positive effect on new product performance.
A key premise of the prior hypothesizing on crowdsourcing's positive effect on new product performance is that crowdsourcing the design will help make the product more marketable. What if the initial raw concept (idea) was already marketable? Kornish and Ulrich (2014) establish that better ideas, as assessed by commercial value (purchase intent of the raw concepts), lead to increased sales. Their finding raises two important questions: ( 1) Is there incremental value added by involving the crowd in suggesting design solutions if the initial product idea itself is good? and ( 2) Can firms extract value from lower-quality ideas rather than from discarding them?
The managerial insights showed that design crowdsourcing likely leads to an evolutionary process of the new product idea. As one manager said of a product that she managed, "The product just kept developing and iterating." Because design crowdsourcing draws on the knowledge of the crowd to improve functional attributes and involves a process of iteration, it is likely that product ideas with a significant need for improvement will benefit most from the process. We propose that when the idea quality is low, the incremental value of design crowdsourcing will be high. When the initial quality of the raw idea is high, the firm might be better off with in-house design.
This line of thinking is consistent with the broader nature of organizational conflict in the exploration of new and exploitation of current knowledge. Andriopoulos and Lewis (2009) conduct a comparative case study approach of five leading ambidextrous firms in the product design industry. They note that whereas exploitation demands efficiency and convergent thinking to improve product offerings, exploration involves search and experimentation efforts to generate novel recombinations of knowledge, creating tension. Furthermore, there is tension between, on the one hand, the use of standardized best practices for NPD that may breed rigidity, and, on the other hand, engagement in new routines that may bring in fresh thinking and free up resources but may also be less efficient. Organizations often have best practices and routines in place to progress their most promising ideas with their in-house research and development/design teams. Thus, for the best ideas, design crowdsourcing may be less beneficial, because the challenges associated with processing and assimilating new and diverse design solutions may outweigh potential benefits. However, for less marketable ideas, design crowdsourcing may facilitate the process with better interaction to help evolve and develop ideas, leading to better performance. Furthermore, lower-quality concepts can be used as an opportunity to learn which attributes are important to customers, which in turn helps firms develop higher-quality products (e.g., Ries 2011, p. 107). Design crowdsourcing can help uncover such attributes to improve the NPD process. Thus, we propose,
H2: The positive impact of design crowdsourcing on new product performance is greatest for products with low initial idea quality.
Design crowdsourcing is not a one-size-fits-all strategy to be leveraged ubiquitously. Rather, as one executive noted, "[it needs to be] a very cautious and well-designed, well-thoughtout approach." The decision to design crowdsource is strategic and based on product, people, and cost considerations. The next question we consider is how specific design attributes of the product conceptmay guide the choice of design crowdsourcing. We searched extensively within various literature streams for product design attributes that influence product success and user acceptance (e.g., Poetz and Schreier 2012). We retained five influential design attributes judged by current literature to be relevant and useful in enhancing user response and experience, as well as two core objectives for the utilization of external inputs from users.[ 3] We then assessed whether the crowd's design knowledge and inputs may help better these attributes. Next, we describe briefly how these functional attributes influence decisions to crowdsource (see Web Appendix Table WA1 for references to these attributes from extant literature and managers).
Technical complexity. We define technical complexity as the perceived degree of complexity due to the technical nature of the design. New products in their initial phases are often complex and need to be simplified. The more complex the design, the costlier it is to build, sell, and service a product (Radjou and Prabhu 2015) and the greater the need for access to a wider range of capabilities, user involvement, and design choices (Gann and Salter 2000; Hobday 2000). Insights from theory and practice (Web Appendix TableWA1) suggest that managers may use design crowdsourcing to simplify the technical complexity of the product. Thus, we expect that the probability of design crowdsourcing will increase with increased levels of perceived technical complexity of the product idea.
Usefulness. Usefulness is defined as the product's ability to meet customer needs (Moldovan, Goldenberg, and Chattopadhyay 2011). User-designed products are perceived as better able to meet the needs of customers than professionally designed products (Poetz and Schreier 2012). Drawing on extant literature and current practice (Web Appendix Table WA1), we expect that the probability of design crowdsourcing will increase with lower levels of perceived usefulness of the product idea.
Reliability. Perceived reliability relates to how well a product is likely to perform, encompasses aspects such as durability and dependability (e.g., Grewal et al. 1998), and influences perceptions of value and purchase intentions. Managers may look to the crowd for ideas on enhancing reliability. For instance, many design proposals on Crowdspring (a design crowdsourcing website; see Table 1) use the words "durable," and "reliable" in describing what they want in a product design sketch. Even when someone is given a simple product brief, evaluations of durability can be assessed. For example, one industrial designer working on a simple paper sketch, said, "whenever I am sketching, I want to make sure … it looks durable, that's going to have to come across in the overall design" (Troy 2015). Thus, we expect that the probability of design crowdsourcing will increase with lower levels of perceived reliability of the product idea.
Usability. We define perceived usability as the expected extent of effort (physical or mental) required to use the new product. March (1994, p. 144) notes that "user-centered design … encompass[es] the cognitive aspects of using and interacting with a product, or how logical and natural a product is to use." Thus, user inputsmay be valuable in enhancing usability of the concept, which can be assessed in early stages (see additional insights in Web Appendix Table WA1). For example, in the product briefs submitted to Crowdspring, managers requested a product that "is easy to setup and use," "will be easy to install," and "is unobtrusive and easy to use." Thus, we expect that the probability of design crowdsourcing will increase with lower levels of perceived usability of the product idea.
Novelty. Novelty refers to the degree of newness or originality of the product (e.g., Moldovan, Goldenberg, and Chattopadhyay 2011; Talke et al. 2009). Poetz and Schreier (2012) demonstrate that user-designed products scored higher on novelty than professionally designed products (see practice insights in Web Appendix Table WA1). Thus, we expect that product ideas with lower perceived novelty will have a higher likelihood of being crowdsourced. In summary,
H3: The probability of design crowdsourcing increases with perceptions of (a) higher levels of technical complexity, (b) lower levels of usefulness, (c) lower levels of reliability, (d) lower levels of usability, and (e) lower levels of novelty of the original product concept.
Nonlinearity of antecedents. While the associations proposed in H3 relate to the initial directional nature of the relationships, these relationships need not be strictly monotonic. For example, when creating new products, Rust, Thompson, and Hamilton (2006) recommend offering enough functionality for the product to not be too simplistic, but not so much that consumers perceive the product as being too difficult to use, suggesting a nonlinear effect of usability on product success. Similarly, while managers noted that they are more likely to use crowdsourcing as technical complexity increases, as one of the interviewed managers noted, in some instances crowdsourcing is not possible, "because you cannot expect the general crowd to be intelligent in terms of your mechanical [engineering]." In the absence of a specific theory, we do not propose precise directions but leave it to the empirics to model these nonlinearities. We synthesize these collective insights and theories into Figures 1 (broad conceptual framework) and 2 (specific design crowdsourcing—performance link).
We collected data on new product concepts from publicly available information from Quirky, a pioneering, community-driven NPD website where members submit new product ideas and participate in development efforts. Staff sorted through idea submissions and selected idea(s) to move forward. Once an idea was selected, Quirky's management decided what help they wanted from the community. The Wall Street Journal described the process as such:
Each week, Quirky's staff whittles down the stream of new ideas into a dozen or so top picks that are scrutinized and voted on…. At that point, engineers and designers, working out [of] a vast red brick warehouse in New York and three other locations, turn sketches into marketable products, tapping the online community for suggestions about design, product names and price points. (Simon 2014)
Quirky's community members were promised a portion of the product sales in exchange for their participation. Quirky's staff chose to ask for help in designing the product and selecting the name, logo, or pricing, or any combination thereof. We captured whether Quirky asked its customers to aid in the design phase. As stated on the website, in the design phase, Quirky asked its community members to "submit sketches, images, videos, and prototypes that illustrate industrial design directions for [the product]. We'll use the top concepts as a starting point for our final design." The staff made the final decision on design selection. Quirky had no obligation to utilize any ideas from the community, and the staff selected the phases in which to involve the community. Note that this is a similar situation faced by firms that have a product idea and must decide whether to further develop the design in-house or crowdsource the design.
Our data set includes the 86 different products sold on the Quirky website during our data collection period in October 2014. Quirky was very transparent with details about the NPD process, design contributions, and sales, which makes this original data set unique and valuable for addressing our research questions. We gathered three key pieces of information from the website: First, we retrieved the raw new product idea as submitted by the original community member, including the text describing the product and, if available, pictures or sketches submitted by the original inventor. (Web Appendix Figure WA1 provides an example of the Quirky design process.) Second, we collected information on whether Quirky subsequently asked the community to help with the product design (yes/no). The third key variable we collected from the website was monthly unit sales for each product, which Quirky published on its website.
New product performance. To assess new product performance, we used data on total sales of new products (including sales from both its website and retailing partners [e.g., Amazon, Costco]). Because not all products are released at the same time, we used total units sold in the first year, starting with the first complete month that Quirky reported. Of the products in our data set, we observed sales for a full 12-month period for 66 products (77%). For the remaining 20 products, we used a simple three-month moving average approach to estimate the sales for the missing months.[ 4]
Decision to design crowdsource. We collected information on whether Quirky asked the community to help in the functional design of each of its selected ideas (yes/no). This event was clearly defined by Quirky as the "design phase." In response to such requests for help, community members submitted product drawings or sketches from which the Quirky staff selected the best one. Community members had a high degree of autonomy when it came to submitting designs. The submission could be similar or different from the original idea; all that was required was that it kept to the general essence of the product's purpose. Of the products in our data set, 22 (26%) did not go through a design phase with community help.
Functional attributes of design. We utilized consumer ratings of the functional design attributes because managerial crowdsourcing decisions will be based on consumer perceptions. As one manager noted, "This leads to your … question [about when one incorporates the end user]. I believe it is key to empathize with the end user(s) throughout the entire design process." We recruited 119 undergraduate business students at a large U.S. university to assess raw product ideas. We took a similar block design approach as in prior research and divided the 86 products into 14 different blocks, with 6–7 products in each block (Kornish and Ulrich 2014), to simplify the survey and minimize respondent fatigue. Each raw design for each final product sold on Quirky's website was randomly assigned to one of the 14 blocks.[ 5]
Each respondent viewed the product concept (picture and description) and was asked to evaluate each design on items relating to technical complexity, usefulness, reliability, usability, and novelty. Each product was evaluated seven times, on average, by independent raters. Responses to each question were averaged across the respondents who evaluated that product. The scale questions and their reliabilities appear in Table 3. To check the validity of the model, we successfully tested the construct scales using a confirmatory factor analysis. The root mean square error of approximation is .085, comparative fit index is .964, Tucker—Lewis index is .949, and average variance extracted is .800, all of which show good measurement validity. While larger samples are usually desirable when performing confirmatory factor analyses, these fit statistics provide confidence that our constructs meet validity assumptions. All questions were on a 1–7 Likert scale, anchored by "strongly disagree" and "strongly agree." We had one filtering question to filter out those respondents who were not paying attention (discussed in Table 3). All construct scales were averaged across their scale questions to create composite construct scores.
TABLE: TABLE 3 Product Concept Constructs and Reliabilities
TABLE 3 Product Concept Constructs and Reliabilities
| Construct | Construct Questionsa | Reliabilityb |
| Technical complexity | 1. The design of this product seems highly complex. |
| 2. This product appears very technical. | .869 |
| Usefulness (adapted from Moldovan, Goldenberg, and Chattopadhyay 2011) | 1. This product would be beneficial. | |
| 2. This product fulfills a need. | .803 |
| Product reliability (adapted from Grewal et al. 1998) | 1. The product appears be reliable. | |
| 2. The product appears to be of good quality. | |
| 3. The product appears be dependable. | .922 |
| Usability (adapted from Davis 1989; see also Venkatesh et al. 2003) | 1.1 would find it easy to get this product to do what I want it to do. | |
| 2. My interaction with this product would be clear and understandable. | |
| 3. It would be easy to become skillful in using this product. | .887 |
| Novelty (adapted from Dahl, Chattopadhyay, and Gorn 1999) | 1. This product is unique. | |
| 2. This product is original. | |
| 3. This product is one of a kind. | .930 |
| Initial idea quality (measured via purchase intent; adapted from Schreier, Fuchs, and Dahl 2012) | 1. I would seriously consider purchasing this product right now. | |
| 2. I would actively search for this product. | |
| 3. To me, purchasing this product in the future is highly probable. | .966 |
aOn a Likert scale measured by 1 = "strongly disagree," and 7 = "strongly agree." bCronbach's alpha or correlations if two-item scale.
Notes: To filter out respondents who were not paying attention, we included a test question worded, "If you are paying attention, click on 'somewhat agree' in the evaluation of each product." Any respondent who missed multiple test questions across the products they evaluated were removed from the analysis. This left us with 97 of the 119 respondents.
Idea quality. Along with the design constructs, after viewing the product concept, respondents answered three questions (scale items in Table 3) capturing the purchase intent construct (adapted from Schreier, Fuchs, and Dahl 2012). Similar to prior research, we use purchase intent as the measure of idea quality (e.g., Kornish and Ulrich 2014).
Control variables. Wecollected data for product category, price, and characteristics of the idea's inventor. Our products fall into five categories, as defined by Quirky: electronics (37 products), home (17 products), kitchen (23 products), and travel and health (9 products combined). Given the small number of products in the travel and health categories, we reclassified these products into one of the other three categories.
We tested our hypotheses using two different models that are integrated in a two-step process. First, we modeled the factors that influence whether firms will crowd source product designs using a binary response (probit) model. In this case, we modeled the dichotomous outcome (crowd source design: yes/no) against the various ratings of design attributes of the submitted product concepts (e.g., usability, novelty) and other variables. Second, we examined the impact of crowd sourcing product designs on market outcomes (unit sales) using an instrumental variable regression controlling for the endogenous nature of the crowd sourcing decision (Wooldridge 2010), since the firm self selects which products will be crowdsourced (i.e., the event is not purely exogenous). The two-step process allows us to observe the variables that influence the decision to crowdsource design and enables us to utilize the probit model to address the endogenous nature of the crowdsourcing variable in the model predicting sales.
Wooldridge (2010) outlines the following procedure for dealing with endogenous binary variables that utilizes the following process, where Y represents the dependent variable of interest (in our case, unit sales), G represents the binary endogenous variable (design crowdsourcing), Z represents the instruments, and X represents the vector of control variables: ( 1) estimate a binary choice model of dichotomous variable G on Z and a set of controls X, ( 2) obtain the fitted probabilities of Ĝ estimated in step 1, and ( 3) estimate a two stage least squares (2SLS) instrumental regression model, regressing Y on G and X, using Ĝ as an instrument for G. This procedure has a few notable advantages. First, it takes the binary property of the endogenous variable into account. Other procedures, such as the standard 2SLS, may produce biased estimates in finite samples. Second, note that this is different than directly inserting the fitted probabilities of the probit in place of the endogenous variable. Using the estimated probabilities in place of the dichotomous variable in a standard ordinary least squares requires very strict assumptions on the error terms and the functional form to be a valid option (Adams, Almeida, and Ferreira 2009). Third, the 2SLS procedure is robust to misspecification in the probit model and provides consistent estimates with asymptotically valid errors when using standard corrections for heteroskedasticity in the instrumental variable estimation (Adams, Almeida, and Ferreira 2009; Wooldridge 2010, p. 939, procedure 21.1). We present these two models sequentially next.
Model for predicting product performance. The model used to test the relationship between crowdsourcing the design and performance, as measured by unit sales, can be represented by the following functional form:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 1)
where Ln (UnitSales)i is the natural log of all units sold for product i in the first year. CrowdsourceDesigni is a dummy variable that takes on a value of 1 if the product design was crowdsourced and 0 otherwise. Idea Qualityi is the initial idea quality of the product and is measured via purchase intent for the raw concept as discussed previously (Table 3). We created an interaction term between CrowdsourceDesigni and Idea Qualityi to test for the moderating effect of idea quality on design crowdsourcing. We hypothesized this interaction to be negative, indicating that design crowdsourcing is less impactful for high-quality product ideas. HolidayLaunchi is a dummy variable that controls for whether the product was first introduced (its first few months on themarket) during the holiday season (November or December) to control for the positive proliferation effect that may come from launching the product during a high-volume period. Ln(Price)i is the natural log of the selling price of the product at the time of data collection. PCharacteristicsi represents the five design-related constructs that we predict will influence design crowdsourcing, along with their squared terms. We inserted these as control variables because it is possible that these constructs will also affect the unit sales of the product. In addition, because we predicted that they will influence the decision of whether to crowdsource the design, we included them as controls to assure that the crowdsourcing variable is capturing variance unique to crowdsourcing's effect. PCategoryi represents a vector of product category dummies.
Instrumenting for price. In addition, price is often considered an endogenous variable, given the simultaneous relationship between price and demand. Cost is used as an instrument for price because it is a determinant of price but remains orthogonal to the error term (e.g., Rossi 2014). Rossi (2014, p. 666) states that "the idea here is that costs do not affect demand and therefore serve to push around price (via some sort of mark-up equation) but are uncorrelated with the unobserved demand shock." The cost of raw materials does not influence demand because consumers are not aware of the cost of the items. We used the cost of raw materials as an instrument for price. Following Kornish and Ulrich (2014), for the Quirky data, we used the pictures and descriptions of the final products being sold on the website to estimate the cost of the materials used to manufacture the product. We recruited three mechanical engineering doctoral students from the same university, with an average age of 28.5 years and allwith industrywork experience. These students estimated the cost of the raw materials used to produce the final product in a separate task. The three doctoral students first researched the current market costs of raw materials (e.g., metal, plastic, cotton) and then used the product pictures and descriptions of the final product to estimate the cost of the raw materials (in dollars) used tomanufacture the product. We averaged their cost estimates to develop an instrument for price and utilized the instrument in the 2SLS procedure. The correlation between the natural log of price and the natural log of the raw material cost is .792.
Model for predicting design crowdsourcing. Following the methodology outlined previously, we specify the model predicting whether an item will be crowdsourced using a discrete choice specification. We derive a probit model for the design crowdsourcing decision of the new product concept i:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 2)
where Φ is the standard normal cdf, and
( 3) where Technicali, Usefuli, Reliabilityi, Usabilityi, and Noveltyi correspond to their respective construct ratings. These constructs were measured by the construct scores developed from the surveys described previously. In addition to each linear term, we also included a quadratic term for each of these constructs. Controlsi represents a vector of control variables. As noted by Wooldridge (2010), the probit model should contain all exogenous control variables that are inserted in Equation 1. Instrumentsi represents a vector of variables used as instruments (explained in detail subsequently).
So that the probit (Equation 2) results can be utilized in the 2SLS procedure for Equation 1, the probit model must contain additional instruments (as noted by Instrumentsi in Equation 2) that are not simultaneously listed in Equation 1. These variables should influence design crowdsourcing decisions but should remain unrelated to unit sales. Next,we describe the instruments (inventor characteristics and cost variables) and justify their use as instruments (Rossi 2014).
Social network: number of community members the inventor is following. The managers in our interviews highlighted that a primary motivation behind crowdsourcing is to secure the engagement of many people. When managers crowdsource, they must forecast the likelihood that there will be enough potential problem-solvers (Afuah and Tucci 2012) to ensure diverse and better solutions. Thus, we seek a variable that will signal to the firm that a large number of people are likely to participate in providing solutions.We propose that the inventor's social network size is a good proxy for the likelihood that a large number of people will be aware of the product and, thus, will participate in the crowdsourcing process. Indeed, social connections of the inventor are something firms take into account (Lohr 2015). Social networks can lead to a better crowdsourcing process, due to improved reciprocity, collaboration, feedback, and integration of ideas (Piller,Vossen, and Ihl 2012). Therefore, we have a strong rationale that the social network of the inventor will matter in the decision to crowdsource design.
We look for a variable that approximates the inventor's social network and meets exclusion restrictions. The Quirky community profile allowed us to capture the number of people the inventor is "following." The act of following forms a tie in the networks literature (e.g., McGee, Caverlee, and Cheng 2013), where the strength of the tie is indicated by markers such as reciprocity in following. The literature predicts that there is greater mobility of information and social cohesion through weak ties than through strong ties (Granovetter 1973), where weak ties represent links with distant acquaintances, such as, in this context, following someone on an online network. Thus, irrespective of whether the follower is followed back, the act of following is a tie and all such ties form the social network. Furthermore, businesses monitor brand-related conversations on social media platforms to gain access to valuable information, influential people, and relevant conversations (Kumar and Mirchandani 2012). Similarly, by choosing to follow other people/inventors, the inventor keeps abreast of any key developments (e.g., inventions, opinions, trends). The act of following is an act of engagement/listening (e.g., Crawford 2009) and not an entirely costless act, as the inventor may choose to follow people on a crowdsourcing platform like Quirky depending on his or her available time and cognitive resources. Thus, an inventor who is following a large number of people belongs to a larger network with the attendant reciprocal advantages, and his or her ideas are more likely to benefit from a better crowdsourcing process. We show subsequently that this measure is also statistically informative. Thus, the number of people the inventor follows is an informative and relevant instrument that meets exclusion restriction requirements because it is unlikely to directly affect sales, as this measure is not salient to the average buyer on Quirky's website.[ 6] In addition, the measure contains exogenous information driving design crowdsourcing. The number of people the inventor follows is the decision of the inventor and not a result of external forces.
Product cost. We used the cost (the same instrument used for price) of the item as a second instrument. Nearly every manager we interviewed highlighted crowdsourcing as a way to reduce manufacturing and production costs. Thus, because the cost of goods sold includes the cost of raw materials and the production cost of the individual item, if the cost of raw materials is high, managers are more likely to look for ways to reduce production costs; this will enable the firm to keep the total cost as low as possible. As noted previously, we used the doctoral students to estimate the cost of the materials used to manufacture the product. This measure of cost is a valid instrument, as it is unlikely that cost directly affects consumer demand (Rossi 2014), but it does affect the crowdsourcing decision. We further included a quadratic term for cost, because not all increases in costswill be associated with the same increases in crowdsourcing. For example, the change in probability of crowdsourcing between items that cost $10 versus $20 might be quite different than between items that cost $200 versus $210.
Finally, we also included interaction terms between the instruments—namely, cost and the number of community members that the inventor is following. The desire to lower costs will trump other external cues (such as the inventor's network) to determine whether the firm should crowdsource. Thus, the hypothesized direction for the number of people the inventor is following will hold at low levels of cost, but the effect should be nonlinear per our managerial insights, which we capture with the interaction with cost. The probit model indicates that all of the instruments are significant (see Table 4), and the pseudoR2 increases from .286 to .476 with the inclusion of these instruments. We next discuss results for Equation 2 and then Equation 1.
TABLE: TABLE 4 Probit Model: Predicting Design Crowdsource
TABLE 4 Probit Model: Predicting Design Crowdsource
| Coefficient Est. | SE | p-Value |
| Functional Design Elements | | | |
| Technical | 4.322 | 1.568 | .006 |
| Technical2 | -.685 | .206 | .001 |
| Useful | -3.698 | 12.283 | .763 |
| Useful2 | .421 | 1.155 | .715 |
| Reliability | -32.391 | 11.311 | .004 |
| Reliability2 | 3.158 | 1.133 | .005 |
| Usability | -62.245 | 19.018 | .001 |
| Usability2 | 5.418 | 1.663 | .001 |
| Novelty | 4.509 | 4.886 | .356 |
| Novelty2 | -.543 | .541 | .316 |
| Instruments (Nondesign Elements) | | | |
| Social Network: Following | .003 | .001 | .004 |
| Ln(ItemCost) | 1.808 | .957 | .059 |
| Ln(ItemCost)2 | -.530 | .239 | .027 |
| Following × Ln(ItemCost) | -.009 | .003 | .001 |
| Following × Ln(ItemCost)2 | .006 | .002 | .000 |
| Additional Controls from Equation 1 | | | |
| Idea quality | 1.534 | .507 | .002 |
| Holiday launch | 1.204 | .495 | .015 |
| Category dummies included | Yes | | |
| Observations (N) | 86 | | |
| Log pseudolikelihood | -25.638 | | |
| Pseudo R2 | .476 | | |
Notes: For brevity, product category dummies along with the constant are estimated but not displayed. Robust standard errors are presented. The z-valuesforthe instruments are as follows: Following (z = 2.91), Ln(ItemCost) (z = 1.89), Ln(ItemCost)2 (z = -2.22), Following χ Ln(ItemCost) (z = -3.39), Following χ Ln(ItemCost)2 (z = 3.62).
The summary statistics and correlation matrix are included in Table WA2 of the Web Appendix.[ 7] Table 4 displays the results for Equation 2. Heteroskedasticity-robust standard errors are used in computing the Wald tests. The results show that while the estimated coefficients for Useful and Novelty are not significantly different from zero, the linear and quadratic terms for all the other constructs are statistically significant. Specifically, Usabilityi has a negative linear term (b = -62.245, p = .001) and a positive quadratic term (b = 5.418, p = .001); this suggests that, initially, the probability of crowdsourcing design decreases with an increase in usability. After the perceived usability reaches a certain level, the probability of crowdsourcing design increases. This suggests that firms are more likely to crowdsource designs that appear overly difficult or too easy to use. The lowest probability occurs at about its mean,where the squared term dominates the linear term, around 5.74 on the 7-point Likert scale (≈ 62.245/[2 × 5.418]). The effect of perceived reliability of the raw concept on the choice to crowdsource design follows a somewhat similar pattern. The negative linear term (β = -32.391, p = .004) suggests that as the perceived reliability of the concept increases, the probability of crowdsourcing design decreases. Thus, at low levels of perceived reliability, the firm is more likely to seek the help of the community in developing the design. The positive quadratic term (β = 3.158, p = .005) suggests that after a certain level of reliability—which occurs at roughly 5.13 (≈ 32.391/[2 × 3.158]), increases in perceived reliability are not associated with a decrease in design crowdsourcing.
Technical complexity (Technicali) of the product follows a pattern opposite those of usability and reliability. The positive linear coefficient (β = 4.322, p = .006) demonstrates that the more technical a product idea is, the more likely the firm is to crowdsource design. However, with higher levels of technical complexity, marginal increases in technical complexity are associated with a decreasing probability of crowdsourcing, as indicated by its negative quadratic term (β = -.685, p = .001). The inverted U-shape of technicality shows that the highest probability of crowdsourcing is 3.155 (≈ 4.322/[2 × .685]) on the 7-point scale, with the lowest probability occurring at the ends. This supports the notion presented by some of the managerial insights that firms are less likely to crowdsource designs that are too technical, because the community will lack the needed expertise, and supports extant research that suggests that crowdsourcing is less likelywhen a firm doubts the crowd's ability/expertise to evaluate solutions (Afuah and Tucci 2012).
Overall, these results suggest a strong relationship between the probability of crowdsourcing a design and the design attributes of the raw product concept. The constructs we hypothesized, with the exception of usefulness and novelty, were related to the decision to design crowdsource, in support of H3a, H3c, andH3d.We discuss further validation of the importance of these design characteristics on design crowdsourcing decisions next.
We next demonstrate that these design constructs influence similar decisions in a different data context. The following analysis is not meant to replicate the exact same decision but to provide convergent evidence that design constructs drive design crowdsourcing decisions.
Our second data set comes from Crowdspring, an online crowdsourcing platform where firms post design challenges. Crowdspring allows clients in need of design help to post their requirements and get responses from the crowd (see Web Appendix Figure WA2). On average, a brief receives over 90 entries, and the client typically picks a winning design from these entries. We obtained data on 27 completed design projects from Crowdspring. We retained consumeroriented products and deleted fashion-related products (e.g., clothes, jewelry) because these projects deal with aesthetics more than functional design, leaving a total of 20 projects. We collected the winning design(s) and selected ten other "nonwinning" designs at random for each of the design briefs. The submitted product designs (the winning designs plus the ten chosen at random) were evaluated using the same construct scales (Table 3) by respondents from Amazon's Mechanical Turk (MTurk). Each respondent (there was an average of eight respondents per product) saw a picture of the design submission and a write-up describing the product. Their answers were averaged to form the constructs' scores; all construct reliabilities (Cronbach's alpha) or correlations (for two-item measures) were greater than .80.
Using this new data set, we tested whether the same design characteristics that affected the earlier design crowdsourcing decision also influence which design managers select as the winning design from all submissions (and presumably choose to implement). We utilized a binary regression method similar to the previous analysis because the outcome variable is dichotomous (whether the design was chosen or not), with one modification: we controlled for the fact that each design is not independent but is clustered within a specific project and that the winner is determined from the specific cluster. We did this using a conditional logistic regression model, which is fitted to such situations where the data are based on matched cases (groups), via the "clogit" command in STATA 13. This controls for the conditional nature of the outcome variable where the likelihood estimation is calculated relative to the group. We included all five design characteristics from Equation 2 (for the results, see Table WA3 of the Web Appendix).
Two of the significant constructs from our prior analysis on Quirky data are also significant in this data set (p < .10)— usability and reliability—with both linear and quadratic terms significant. The probability of a design being chosen (as a winner) increases as Usability increases (β = 10.612, p = .079); its quadratic term, Usability2 (β = -1.004, p = .069), indicates a tapering effect, suggesting that it increases at a decreasing rate. Reliability positively increases the probability of a design being chosen, (β = 18.877, p = .091), with Reliability2 indicating a tapering effect (β = -1.882, p = .081). Technical complexity was not statistically different from zero for either the linear or quadratic terms (p > .10). It could be that there may be a higher degree of variation in this construct when measuring across different products (such as with Quirky), but not across different designs for the same product, as in Crowdspring, where managers may have been more explicit about technicality. The significant results for two design constructs, usability and reliability, and their quadratic terms, across data contexts provide convergent evidence for their influence on crowdsourcing decisions.
Wenext present the results on the impact of design crowdsourcing on postlaunch new product performance using data from Quirky.
Table WA2 of the Web Appendix presents the summary statistics and correlation matrix. Equation 1 is estimated with 2SLS using the instrumental procedure, as previously noted, where the predicted result from the probit model (Equation 2) is used as an instrument. Table 5 shows the results of Equation 1 utilizing two nested models. First, we present the results of the main-effects-only model (excluding the multiplicative term b3) and then present the results of the full model with interaction effects. Both models use heteroskedasticity-robust standard errors. Table 5 also shows the first-stage F-statistics and the partial R-squares of the instruments as estimated in the first-stage regressions of the 2SLS procedure. The diagnostics for the crowdsourcing dummy, the crowdsourcing • idea quality interaction, and price instruments show that, collectively, our instruments are not weak (Stock and Watson 2003).
TABLE: TABLE 5 The Effect of Crowdsourcing the Design on Unit Sales
TABLE 5 The Effect of Crowdsourcing the Design on Unit Sales
| A: Results for Equation 1 |
| Model 1: Main-Effects-Only Model | Model 2: Full Model |
| Coefficient Est. | SE | p-Value | Coefficient Est. | SE | p-Value |
| CrowdsourceDesign | .566 | .895 | .527 | 12.824 | 4.116 | .002 |
| IdeaQuality | -.149 | .431 | .729 | 1.512 | .656 | .021 |
| CrowdsourceDesign χ IdeaQuality | | | | -3.188 | .945 | .001 |
| Controls | | | | | | |
| Ln (Price) | -.965 | .373 | .010 | -1.627 | .482 | .001 |
| Technical | -1.517 | 1.115 | .173 | -2.641 | 1.438 | .066 |
| Technical2 | .198 | .148 | .180 | .337 | .192 | .079 |
| Useful | -1.749 | 4.503 | .698 | -.627 | 5.080 | .902 |
| Useful2 | .138 | .454 | .761 | .091 | .495 | .854 |
| Reliability | -3.144 | 1.627 | .053 | .047 | 2.806 | .987 |
| Reliability2 | .407 | .173 | .019 | .192 | .264 | .467 |
| Usability | 2.320 | 3.829 | .545 | 4.826 | 4.622 | .296 |
| Usability2 | -.251 | .372 | .499 | -.530 | .456 | .245 |
| Novelty | -3.846 | 3.265 | .239 | -5.703 | 3.437 | .097 |
| Novelty2 | .458 | .357 | .200 | .702 | .376 | .062 |
| HolidayLaunch | .047 | .354 | .893 | -.118 | .440 | .788 |
| Category dummies included | Yes | | | Yes | | |
| Observations (N) | 86 | | | 86 | | |
| R-squareda | .295 | | | .208 | | |
| B: Instrument Diagnostics for Equation 1 |
| Model 1: Main-Effects-Only Model | Model 2: Full Model |
| First-Stage F-Stat. | Partial R2 | First-Stage F-Stat | Partial R2 |
| CrowdsourceDesign | 29.618 | .377 | 19.905 | .379 |
| CrowdsourceDesign χ IdeaQuality | | | 28.056 | .427 |
| Ln(Price) | 37.569 | .519 | 28.952 | .520 |
aR-squared is shown for directional purposes only. R-squaredfor2SLS models is not interpreted the same as ordinary least squares (percentage of variance explained). See Wooldridge (1999).
Notes: Robust standard errors presented. A constant and category dummies are estimated but not displayed for brevity.
The results in Table 5 demonstrate an interesting relationship between design crowdsourcing and unit sales. Model 1 (main effects only) shows that the design crowdsourcing dummy does not significantly affect sales (β = .566, p = .527). This indicates that the effect of design crowdsourcing, on average, is not statistically different from zero. However, Model 2, the full model that includes interaction, presents a more nuanced picture. The estimate for CrowdsourceDesign (β = 12.824, p = .002) and the interaction between CrowdsourceDesign and IdeaQuality (β=-3.188, p = .001) are both statistically significant.We remind the reader that the beta coefficients in Model 1 (the main-effects-only model) represent the estimation of the main effect, or the average effect across the dependent variable based on the conditional mean function E(y|x) (Baum 2013). The coefficient for CrowdsourceDesign in Model 2 (the full model with interaction) represents the estimation of the simple effect or the estimated impact of CrowdsourceDesign when IdeaQuality is at zero (for a discussion of simple effects in interactive models, see Echambadi and Hess 2007). The significance of CrowdsourceDesign in the model with interactions and lack of significance in the model without interactions suggest that crowdsourcing the design helps sales, but only when the concept has low IdeaQuality. The sign of the interaction term helps make sense of this distinction. The negative interaction term suggests that the positive effect of design crowdsourcing on sales dissipates as the idea quality of the product concept increases.
To increase the managerial relevance of our findings, we aim to show that crowdsourcing the design helps, on average, all products with low levels of IdeaQuality (not only those at zero) We measure the marginal effect at different low levels of IdeaQuality. The marginal effect of CrowdsourceDesign is positive and significant at various levels of IdeaQuality. In fact, the positive effects do not become insignificant (p > .10) until around IdeaQuality's mean. Thus, we find support for H1 when the product idea shows room for improvement, and we find support for H2. Table WA4 in the Web Appendix shows robustness of this analysis to alternative measures of sales. We also show that the results are robust, accounting for the fact that some products originated from the same raw design by using clustered standard errors (TablesWA5 andWA6 in the Web Appendix).
A logical follow-up question to the preceding analysis is whether design crowdsourcing improves the functional design attributes from idea to final product. To test this notion, we collected additional data to assess the design of the final product as presented on the Quirky website. We replicate the maineffects-only model (Model 1 from Table 5), replacing unit sales (the previous dependent variable) with the change scores of the three design characteristics found to be significant in the probit model (reliability, technical complexity, and usability) as the new dependent variables. We assess the change scores, using consumer ratings, by measuring the improvement from the initial product idea to the final product for each of the measured design characteristics. We utilized the same design, same filtering questions, and the same construct questions used for the initial product ideas to assess the design characteristics of the final product, using students from the same population, but excluding any respondents who participated in evaluating the raw concepts. After aggregating the questions into the final constructs, we calculated a change score for each of the design constructs by subtracting the initial rating from the final rating. For example, DReliabilityi, the dependent variable, would be calculated as Reliablityi, final — Reliability i, initial, where Reliability i,final relates to the score for perceived reliability of the final product and Reliability i,initial relates to the score for perceived reliability of the initial idea. This model used the same instrumental variables procedure as before and controls for the initial level of the product characteristic ratings.
Three different models were run, using ∆Reliabilityi, ∆Technicali, and ∆Usabilityi as the dependent variables, respectively, and design crowdsourcing dummy as the key independent variable. The results (Web Appendix Table WA7) show that the design crowdsourcing dummy positively influences ∆Reliabilityi (β = .548, p = .079) and ∆Usabilityi (β = .772, p = .000), but not ∆Technicali (β = -.211, p = .542). These results suggest that design crowdsourcing enhances perceived reliability and usability from product idea to final product.[ 8]
Use of student sample. Another potential concern with our data could be that we used undergraduate business students to assess design ratings and idea quality. The primary objective of crowdsourcing is to design products that are aligned with what people want. Thus, it is clear that consumer preferences around product design attributes drive the crowdsourcing decision. Our use of student surveys is representative of these considerations of obtaining consumer preferences to aid managerial decision making. Students are often used as proxies for general consumers (e.g., Aaker and Keller 1990; Larson and Billeter 2013). Furthermore, students are a specific target segment for Quirky, as evident from some of the product descriptions, such as "dorm occupants needn't schlep their shower shoes; just hook a cord around them and they're along for the ride." Therefore, students are representative of a strong consumer base for Quirky. However, to show that the student ratings are similar to the ratings from other general segments, we collected product ratings for a subsample of the same products from two different groups of respondents: one group recruited from MTurk and another group of business professionals (master of business administration [MBA] graduates, using a panel provided by Qualtrics).[ 9] We randomly selected 42 products (3 products from each of the 14 blocks) and had each group rate the products on the same construct questions shown in Table 3. We compared these ratings with the earlier ratings for the same 42 products taken from our original (undergraduate business) sample. We first performed a comparison of between-sample similarities using correlations between the samples, and then a comparison of within-sample similarities using the Jennrich test (Jennrich 1970).
The high and significant correlation between the MTurk ratings and the undergraduate ratings across the 42 products for technical complexity (.714, p < .001), usefulness (.528, p = .003), reliability (.305, p = .049), usability (.609, p < .001), and novelty (.449, p = .003) demonstrates that the construct scores are consistent across these different respondent groups. Next, we used the Jennrich test of equality of correlation matrices, which formally tests whether the correlational structure for the five constructs differs between the groups (Jennrich 1970). In other words, it tests whether the correlation matrix for the student sample is similar to the correlation matrix in the MTurk sample (e.g., Compas et al. 1989; Gande and Parsley 2005), without requiring the assumption of equal means or standard deviations (Gande and Parsley 2005). The test shows no significant difference (p = .87). In other words, we fail to reject the null hypothesis that the correlational structures are equal, indicating that the relationships among constructs are similar across samples. Similarly, if we include the design crowdsourcing dummy and the idea quality rating in the correlation matrices, the test again shows no significant difference (p = .61).
Next, we recruited 135 business professionals using a Qualtrics panel to rate the same 42 products. All constructs between the two samples (students and MBA professionals) were significantly correlated (p < .01), except for Reliability, demonstrating that the ratings on the construct scores are generally consistent across these two respondent groups. The Jennrich test showed no significant difference (p = .59) between matrix structures. Similarly, if we include the design crowdsourcing dummy and rating on idea quality, the test again shows no significant difference (p = .72). We elaborate on the Jennrich test and other robustness checks regarding our sample in the section on additional robustness checks in the Web Appendix.
Nonlinearities. One possible question is whether the use of all of the quadratic terms is warranted in the model predicting the probability of design crowdsourcing. We have noted that extant theory suggests that at least two of the constructs should be nonlinear (technical complexity and usability). Therefore, we replicate Equation 2 but include only two quadratic terms for technical complexity and usability (see Web Appendix Table WA8). The same constructs that are significant in the previous analysis (Table 4) are significant (Technical, Technical2, Reliability, Usability, and Usability2) and in the same direction. In addition, the same constructs that were previously not significant (Useful and Novelty) are still not significant.
The use of design crowdsourcing to seek external inputs during design is emerging as a significant practice. Our article is one of the first to build and test a theoretically grounded model of factors that influence the design crowdsourcing decision and the effect of design crowdsourcing on performance, providing implications for both academics and managers.
Knowledge management theory. Our exploratory insights contribute to the knowledge management literature by revealing how design crowdsourcing, as a mechanism, can help improve the NPD process and performance. Design crowdsourcing aids in knowledge identification by aggregating diverse sources of user-based design knowledge and extracting novel, workable, and meaningful design solutions. The literature on ideation crowdsourcing reveals similar insights on the use of the crowd as a resource base for ideas during the opportunity identification stage. We complement this research by illustrating how crowdsourcing can strategically tap into the crowd during the critically important design stage. Design crowdsourcing bolsters opportunity exploitation by supplementing NPD resources and creating a more collaborative process of integrating external solutions with in-house guidance, thereby contributing to product development and performance.
Crowdsourcing theory. We add to the extant crowdsourcing literature by illuminating the antecedents of design crowdsourcing and by examining design crowdsourcing's effect on new product performance. Exploratory interviews and results reveal that the inherently iterative, userdriven, and evolutionary process of design crowdsourcing can lead to a more focused search for innovative solutions while simultaneously enhancing product effectiveness. Our analysis reveals that product ideas with significant need for improvement may likely benefit the most from this iterative process that allows for crowd-driven refinement and enhancement of such ideas.
Product design theory. We contribute to the literature on product design by finding that design elements—usability, reliability, and technical complexity—matter in influencing crowdsourcing decisions. Surprisingly, usefulness and novelty do not emerge as significant drivers from our data. One post hoc explanation is that novelty and usefulness matter during idea selection, where the emphasis may be on differentiation, while the other dimensions are of consequence during design selection, where the emphasis may be on objective functionality and user experience. Based on the design literature (Noble and Kumar 2010), it seems that managers may be seeking utility from design crowdsourcing to enhance function (rational value), and user experience (kinesthetic value), rather than differentiation (emotional value). Furthermore, this research establishes that the crowd can serve as a knowledge resource for design solutions, and design crowdsourcing can improve user perceptions of design from ideas to products.
Our results provide three important managerial implications. First, whereas managers may fear losing control of the design process by opening it up, our interviews indicate that they can maintain better control over the design process, while creating slack for their research and development/ design team, through the process of engagement/iteration with users/designers by selecting appropriate design crowdsourcing platforms. Furthermore, managers are faced with pressure to generate greater numbers of innovative products while being constrained by internal resource limitations. Therefore, they often prioritize only their best product ideas and concepts, discarding many others. Our results suggest that design crowdsourcing can helpmanagers move a greater number of ideas through development by using the community's help in making (initially) less-promising ideas marketable. Thus, we address the question we posed previously: there is incremental value to be extracted from even initially lesspromising ideas. Rather than discard such ideas, firms may use external sources of knowledge to develop them, and interact with these external sources extensively to ensure that the outcome is of high quality. Newer crowdsourcing firms, such as Crowdspring, are now setting up systems for the idea generator (client firm) to provide feedback to the community as the community aids in the design process. This feedback process may be rated and monitored by the crowdsourcing platform. It is this interactive and iterative process of design and development that eventually moves ideas into production, and herein lies the true value of design crowdsourcing.
Second, our analysis suggests design crowdsourcing increases the perceived reliability and usability from ideation to final product. Managers of client firms aiming to improve specific functional attributes of design may turn to crowdsourcing as a supplementary design resource.
Third, we provide insights to the crowdsourcing platforms, as well as the client firms, on better managing the process of crowdsourcing. First, design crowdsourcing firms are increasingly facing pressure from members of design communities, who perceive a threat posed by the availability of thousands of low-cost designs provided by the crowd. (Grefe 2016). Our research suggests that design crowdsourcing can help improve specific design functionalities through a process of iteration and feedback. Design crowdsourcing firms can (re) position themselves as intermediaries that help solve genuine product needs. Second, this research emphasizes the iterative process of design. However, given the start-up nature of many of the crowdsourcing platforms, there may be difficulties in empathizing with the end user(s) throughout the entire design process. As an executive remarked when we presented our summary results, "[Empathizing] sometimes falls by the wayside due to outside constraints such as budget, timing, exhaustion, or purely wanting to keep things simple." Our results may provide insights to such platforms on the optimal timing of user engagement at different phases of NPD. Third, our qualitative interviews suggest that managing the design crowdsourcing process may not be trivial, similar to insights from ideation research that suggested that firms may be overwhelmed with ideas from the crowd, and, thus, this research suggests that the decision to crowdsource should not be taken lightly. This is one reason for the growing popularity of third-party platforms, as these platforms assist in managing much of the process and provide important guidance to managers.
We note a few limitations of this research and discuss research opportunities. First, our key results are based on the product performance (sales) of a single firm. Although our results have significant implications, they do not directly speak to the viability (or profitability) of the overall business models.[10] Future research will benefit from a large-scale study involving nonplatform firms. Second, our sample for the product concepts originated from students. While we validate our constructs using another crowdsourcing platform and ratings from other sources, future studies can make substantial contributions by using broader consumer and managerial surveys. Third, we do not consider firm capabilities, whichmay influence the decision to seek an external solution (Afuah and Tucci 2012). While this limitation is mitigated because all our products come from the same firm, caution should be used when extrapolating these results to other companies. Fourth, all of the product ideas in this data set originated from the community, and crowdsourcing design may have different results depending on whether the product idea had originated internally or from the community. Fifth, there are likely differences in efficacy or the degree of collaboration depending on whether design solutions were consumer generated or designer generated. Although the current context cannot address these issues, these are promising questions for future research. Future research could examine effective mechanisms to incentivize collaboration in crowdsourcing platforms and determine best practices for managing design crowdsourcing. It is our hope that our findings motivate further research on crowdsourcing decisions in various phases within the NPD process.
Endnotes 1 Many of the design crowdsourcing platforms focus on the manufacturing makeup of design; thus, our study focuses on functional design rather than aesthetics. This use is also consistent with extant research studying the early phases of design with a "focus on functional performance in product design, as opposed to the product's aesthetic qualities or appearance" (Dahl, Chattopadhyay, and Gorn 1999, p. 19).
2 After a brief description of the research project, each interviewee was asked about issues related to crowdsourcing and how those outside the organization help with providing design solutions. In three of the cases, the interviewee chose to reply to these questions by email, in which case further emails were sent to follow up on responses, if needed. We supplemented these insights with a search for popular press articles to gain a broader understanding of how crowdsourcing aids product development, using the LexisNexis database, as well as by examining firms' internal websites, design crowdsourcing websites, and blog posts.
3 To the best of our knowledge, this is the first article to test all five design variables simultaneously in the same study. Subsets of these variables are linked together theoretically in extant literature. For example, Bloch (1995) groups durability, technical sophistication, and ease of use as product-related beliefs created or influenced by product form and classifies novelty as affecting how consumers perceive the product relative to other products. Noble and Kumar (2010) divide design elements into three categories: rational value, kinesthetic value, and emotional (differentiating) value. Our five identified dimensions thus emphasize function (reliability and technical complexity providing rational value), user experience (ease-of-use and usefulness providing kinesthetic value), and differentiation (novelty providing emotional value).
4 We used unit sales within the first year for a few reasons: First, most products have observed sales for one year, so it limits extrapolating beyond what is known. Second, it focuses our analysis on performance soon after initial launch; this timing seems reasonable because Talke et al. (2009) show that product design affects sales most at the beginning of a product's life cycle. Our results are robust to other possible methods of completing yearly sales, such as proportional annualization (Chandrasekaran et al. 2013) or measuring sales at three or six months (see Table WA4 in the Web Appendix).
5 We note that some of the final products originated from the same raw design and design phase, but because we wanted to have a unique estimate for each final product, we allowed each final product's raw design to be rated separately. We ensured that each of these designs were inserted into different blocks. We ran robustness checks to demonstrate that this does not influence the results, which we describe subsequently.
6 To find information about who an inventor follows, a consumer would have to consciously click through the website to find the inventor's profile. Furthermore, much of Quirky's sales occurs in other retailer settings, for example, in Walmart, where such retail customers will know nothing about individual inventors.
7 There may be a possible concern about the quadratic terms having a multicollinearity problem as a result of their linear terms. As Allison (2012) notes, high correlation between variables and their product is expected and "is not something to be concerned about, because the p-value for [the product] is not affected by the multicollinearity,… so the multicollinearity has no adverse consequences." Furthermore, the variance inflation factor analysis of the base models for Equation 2 (without interactions and nonlinear terms) confirms that multicollinearity is not an issue due to high correlations between constructs, because all of the variance inflation factor values were substantially less than ten.
8 We rationalize post hoc that technical complexity may be more significant in the antecedents model than in the change-score analysis because technical complexity may be more internal and, thus, significant when firm capabilities matter in the decision of whether to crowdsource. The change-score results demonstrate that crowdsourcing improves those attributes of design that are perhaps more user-centric.
9 For the MTurk sample, we recruited 165 respondents to rate 42 products (average age = 39.8 years old, 39% male, median household income: $50,000–$99,999). Respondents in the Qualtrics sample had all graduated from an MBA program (average age = 48 years old, 64% male, medium household income: $50,000–$99,999, average work experience in business: 18 years). The same scales were used for the five design constructs and the average rating across the respondents (at least five respondents per product) was obtained. All Cronbach's alpha/correlations are above .80 for the MTurk sample and above .70 for the Qualtrics sample.
Quirky has since filed for bankruptcy and changed its website structure. We thank our anonymous reviewers for highlighting this point.
DIAGRAM: FIGURE 1 Conceptual Framework for Testing
DIAGRAM: FIGURE 2 How Design Crowdsourcing Affects New Product Performance: A Closer Look at the Link Proposed in Figure 1
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B.J. Allen is Assistant Professor of Marketing, Sam M. Walton College of Business, University of Arkansas
Deepa Chandrasekaran (corresponding author) is Assistant Professor of Marketing, University of Texas at San Antonio
Suman Basuroy is Department Chair and Graham Weston Endowed Professor of Marketing, University of Texas at San Antonio
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Record: 56- Designed to Succeed: Dimensions of Product Design and Their Impact on Market Share. By: Jindal, Rupinder P.; Sarangee, Kumar R.; Echambadi, Raj; Sangwon Lee. Journal of Marketing. Jul2016, Vol. 80 Issue 4, p72-89. 22p. 1 Diagram, 11 Charts, 1 Graph. DOI: 10.1509/jm.15.0036.
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Record: 57- Detecting, Preventing, and Mitigating Online Firestorms in Brand Communities. By: Herhausen, Dennis; Ludwig, Stephan; Grewal, Dhruv; Wulf, Jochen; Schoegel, Marcus. Journal of Marketing. May2019, Vol. 83 Issue 3, p1-21. 21p. 2 Diagrams, 5 Charts, 1 Graph. DOI: 10.1177/0022242918822300.
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Detecting, Preventing, and Mitigating Online Firestorms in Brand Communities
Online firestorms pose severe threats to online brand communities. Any negative electronic word of mouth (eWOM) has the potential to become an online firestorm, yet not every post does, so finding ways to detect and respond to negative eWOM constitutes a critical managerial priority. The authors develop a comprehensive framework that integrates different drivers of negative eWOM and the response approaches that firms use to engage in and disengage from online conversations with complaining customers. A text-mining study of negative eWOM demonstrates distinct impacts of high- and low-arousal emotions, structural tie strength, and linguistic style match (between sender and brand community) on firestorm potential. The firm's response must be tailored to the intensity of arousal in the negative eWOM to limit the virality of potential online firestorms. The impact of initiated firestorms can be mitigated by distinct firm responses over time, and the effectiveness of different disengagement approaches also varies with their timing. For managers, these insights provide guidance on how to detect and reduce the virality of online firestorms.
Keywords: message dynamics; online brand community; online firestorms; text mining; word of mouth
More than 65 million firms leverage online brand communities to connect with customers and achieve known performance benefits, such as increased online reputation, brand patronage, and customer spending ([ 4]; [43], [51]). However, online communities also engender significant risks of online firestorms—that is, negative electronic word of mouth (eWOM) that receives substantial support from other customers in a short period of time ([63]). Similar to prominent online firestorm examples, such as #deleteUber and United Airlines' passenger removal incidents, less publicized negative eWOM messages by dissatisfied customers also can go viral; a single 466-word Facebook post by a disgruntled customer in Odeon Cinemas' Facebook brand community prompted more than 94,000 likes, damaging the firm's reputation and causing it to lose thousands of customers ([23]).
Detecting, preventing, and mitigating this virality of negative eWOM in online brand communities therefore constitutes a critical managerial priority ([40]), yet 72% of firms rate their preparedness for online firestorms as "below average" ([24]). Managers seem to have a limited understanding of how to respond to negative eWOM describing dissatisfactory consumption experiences ([80]), nor do they know how to predict the evolution of negative eWOM messages or address angered mass audiences exposed to such negative eWOM. Lacking clear guidelines, firms continue to suffer damages from negative eWOM. We aim to address this gap by identifying sources of firestorms and detailing appropriate sequences for firm responses to negative viral content.
Extant marketing research has described the spreading of word of mouth (WOM) as a contagious process, whereby receivers "catch" others' emotions through social transmission ([ 8]). The relatively rare research that specifically investigates negative WOM suggests that its contagiousness primarily depends on the sender's emotions ([10]; [38]) and the relationship between senders and receivers ([14]; [58]). Yet few studies have applied these valuable insights to an online brand community context, so we identify sender and relational aspects pertinent to firestorms of negative eWOM in online brand communities.
In addition to identifying sources of the spread of negative eWOM, firms need to pinpoint how to respond ([16]). Services recovery research has proposed several viable approaches to negative customer experiences, including empathic and explanatory responses (e.g., [12]). In contrast with traditional complaint channels, however, the online brand community makes customers' complaints and the firm's recovery efforts visible. Therefore, beyond mitigating the complaining customer's unsatisfactory consumption experience, the firm needs to craft a response that can minimize any negative effects on the wider audience of online brand community members. By investigating regulation strategies that can reduce receivers' susceptibility to negative emotions ([33]), we investigate how firms should tailor their responses to limit the virality of negative eWOM. In so doing, we do not limit our assessment to a single response, because customers in online brand communities often evaluate cross-message developments ([79]). Therefore, in an extension of Batra and Keller's (2016) work, we consider how sequences of firm responses might mitigate the virality of online firestorms as they evolve.
With these empirical assessments of ways to detect, prevent, and mitigate the virality of negative eWOM in online brand communities, we offer three main contributions. First, we draw on negative WOM research to investigate how sender and relational aspects aid in the detection of potential firestorms, then specify how different levels of emotional arousal and the strength of the senders' structural ties and their similarity to the online brand community relate to the virality of negative eWOM. At an operational level, we establish a reliable, computerized technique to determine the similarity of language use within online brand communities. Second, our findings provide insights into firms' ability to prevent online firestorms by issuing responses designed to engage with or disengage from customers online. More explanatory responses are best for negative eWOM messages containing above-average negative high-arousal emotions; the effectiveness of disengaging approaches varies. Third, we identify structured sequences of different engaging responses across multiple firm messages as a novel, actionable approach to mitigate the impact of evolved online firestorms.
To achieve these aims, we first systematically delineate sender and relational aspects that trigger greater virality of negative eWOM. We then systematize extant knowledge on common firm responses and contrast their effectiveness with the arousal of negative eWOM and viable cross-message composition. We test our hypotheses on large-scale data, reflecting negative customer posts from the online brand communities of 89 S&P 500 firms, which constitute potential online firestorms. In the final section, we summarize the findings, discuss the implications, and list some limitations.
Extant marketing research primarily has focused on identifying the presence and efficacy of positive eWOM ([81]), but open customer communication also bears the risk of unprecedented, rapidly discharged, large quantities of negative eWOM ([63]). To cope with negative experiences and warn others, customers share negative consumption experiences in online brand communities. Often highly emotional, such posts may emerge, diffuse, and dissolve quickly ([37]). Similar to positive eWOM, the extent to which other customers approve of and share negative eWOM determines its virality and firestorm potential. Prestudy interviews with 16 social media managers responsible for online brand communities suggest that firms regard negative customer posts as evolved online firestorms if the firm's initial response does not suffice to prevent the negative eWOM from "catching fire" among other customers. Every like or comment that follows means that another customer may be lost. At the outset, every negative post has the potential to cause an online firestorm; not every post does so. Compared with positive eWOM, negative WOM is transmitted more often and is more influential ([40]), so firms must detect and adequately respond to negative posts in online brand communities to avoid potential public debacles, customer defections, and profit reductions ([63]).
Both product- and service-related WOM evaluations are shared through a social transmission process, like emotional contagion ([ 8]). The Web Appendix contains an overview of studies that detail drivers of and firm responses to eWOM. Various studies have indicated that eWOM contagiousness depends on the emotions conveyed, the structural ties of the sender, or the perceived similarity between the sender and receivers; however, the joint impact of these determinants on the virality of negative eWOM is unknown. Moreover, although some studies have examined the effectiveness of the presence of firm responses, they do not differentiate the circumstances in which a certain type of response is more effective. Finally, firms might need to respond to the same negative eWOM several times to resolve customers' negative experiences, and insight is lacking on how such responses should be sequenced over time. To fill these critical research gaps, we investigate ways to detect, prevent, and mitigate online firestorms arising from negative eWOM messages.
Conventional wisdom suggests that customers in online brand communities first read about the cause of negative eWOM messages and then decide whether to approve and share them. However, faced with the information overload that tends to characterize communication exchanges on social media platforms, customers might not elaborate in detail on the arguments and instead could resort to heuristic processing ([35]). Accordingly, research has suggested that the relative transmission of WOM is a result of the contagiousness of heuristics related to the sender's message and the relationship aspects between the sender and receivers ([14]; [38]; [58]).
For example, particularly expressive people seem to transmit emotions effectively ([ 5]). Although emotions are not verbal properties, the verbal use of emotional words makes them relatively accessible and contagious. With increased use of affective words in a post, it efficiently reveals and makes accessible the intent or simplest raw feelings underlying the posting customer ([19]). At a granular, word-use level, increasing the number of negative emotion words in eWOM translates directly into stronger behavioral responses by message recipients ([54]). Even if the content is unrealistic, more negative emotional messages are shared more frequently ([13]). However, general negativity is a broad concept, and the influence of negative emotional expressions might depend further on people's relative arousal levels ([70]). For example, in their study of urban legends, [38] investigate high-arousal disgust emotions rather than just general negative emotions. Similarly, online firestorms may be more likely to arise from high-arousal (e.g., "This is so frustrating") rather than low-arousal (e.g., "This is disappointing") negative eWOM. [10] show that New York Times newspaper stories that include more intensive high-arousal emotions (e.g., fear/anxiety, anger) prompt emailed shares to others more frequently than stories with more intensive low-arousal emotions (e.g., sadness). Thus, rather than simply being one-dimensional, the contagiousness of emotionally charged negative eWOM in online brand communities may depend on the level of arousal. Therefore, we posit:
- H1: The intensity of high-arousal emotion words in negative eWOM messages relates to greater virality in online brand communities compared with the intensity of low-arousal emotion words.
The decision to approve or share eWOM also depends on the relationship and relational cues between the sender and receiver ([ 9]). Emotional contagion theorists cite the importance of interpersonal relations that enable message recipients to evaluate others and devise appropriate responses ([ 5]). Marketing research on WOM suggests that tie strength and perceptions of similarity are two primary relational cues that cause receivers to regard senders as more proximate ([14]).
Tie strength is relevant in various information-sharing contexts; it refers to both the frequency of communication and the importance attached to the relations ([ 4]; [14]; [67]). Despite considerable debate about the relative advantages of weak and strong ties, researchers commonly agree that strong ties increase the likelihood that social actors will share sensitive information ([64]) and engage in collective action ([60]). Measures of tie strength rely on a range of variables ([58]; [64]), including frequency of contact ([67]). Weak ties reflect members of a community who interact less frequently with each other, whereas strong ties describe relationships of members who interact frequently ([15]). Frequent, positive encounters typically (if not always) lead to stronger structural ties ([58]) and increase opportunities to transmit opinions ([28]). This assumption is also in line with [15] suggestion that the more frequent and empathic the communication is between two users, the more likely that one user's opinion will influence the other user's opinion. Strong structural ties, as characterized by more frequent interactions, in turn increase imitative behavior within networks ([57]). Members with an exceptionally great number of ties within a community often act as opinion leaders, who influence purchase decisions and product adoptions ([49]). Certainly, the potential of well-connected customers to influence others in a brand community is likely to be stronger than the potential of less connected members in the same community ([29]). If the member who posts negative eWOM has stronger structural ties in the online brand community, the firestorm potential of the post thus should be greater:
- H2: Stronger structural ties between the sender of negative eWOM and the receiving online brand community relate to greater virality.
In addition, perceived similarity (or homophily perceptions; [14]) between the sender and customers in online brand communities may relate to the virality of negative eWOM messages. Although perceptions of similarity are not required for contagion to occur, they can act as qualifiers of information relevance ([35]). For example, [ 3] find that perceptions of similarity between customers explain more than half of the effect of behavioral contagion on new product adoption. Although interactions in online brand communities tend to be relatively anonymous, studies drawing on psycholinguistic research suggest that perceptions of similarity in computer-mediated settings are an automatic outcome of a linguistic style match (LSM). The similar use of function words—or LSM between two or more conversation partners—represents a form of psychological synchrony that elicits perceptions of similarity, approval, and trust in receivers ([47]). Just like conversation dyads, communities may develop a distinctive collective communication style ([25]). An individual customer's alignment with a common, community-level communicative style may elicit similarity perceptions and in turn influence the approval likelihood by the collective ([34]). [54] confirm that the congruence of a customer review with the typical linguistic style demonstrated by a product interest group on Amazon influences other customers' purchase behavior. Accordingly, negative eWOM that matches the typical linguistic style of an online brand community (i.e., evokes perceptions of similarity) should induce greater online firestorm potential:
- H3: Closer LSM between the sender of negative eWOM and the receiving online brand community relates to greater virality.
The growing influence of online evaluations on customer behavior has increased managerial and research interest in firm recovery strategies that can reduce the contagiousness of negative eWOM ([56]). Recovery in the context of online brand communities is unique though, in that the customer's complaint and a firm's recovery efforts are visible to thousands or even millions of other customers. Effective recovery thus must ( 1) adequately restore relationship equity to the complaining customer and ( 2) prevent the negative eWOM message from spreading to other customers in the online brand community. The viability of common recovery approaches—offering an apology, compensation, responding empathically, or providing explanations—has been investigated mainly in bilateral firm–customer communication contexts (e.g., [41]). To gain further insight into the suitability of these approaches for reducing the contagiousness of emotions in negative eWOM, we turn to theory about emotion regulation strategies ([33]) and propose that firm responses to negative eWOM might be classified as disengaging or engaging.
A disengaging approach to emotion regulation implies reacting in ways to avoid or block elaboration, rather than preparing an adaptive response ([73]). Observations and anecdotes suggest that avoidance and nonresponse is the poorest approach to regulating the virality of negative eWOM. As an alternative, firms might try to halt an ongoing public online conversation by suggesting a communication channel change (e.g., "Please contact our service center"). Such a channel change suggestion might be effective for pushing customers to the right channels ([ 2]), but it is unclear how other online brand community react to being excluded from the continued conversation. The effectiveness of offering compensation to the complaining customer also is uncertain. Some service research has suggested that halting further elaborations is an effective recovery strategy (e.g., [12]), yet [31] find that compensation is not always effective and instead may depend on other response features.
Active engagement with negative eWOM messages instead might be more appropriate ([80]). Service recovery literature has outlined two primary response approaches that represent active firm–customer conversational elaboration ([41]): empathic or explanatory. To express empathy, a spontaneous affective response ([42]), a firm might sympathize (e.g., "We understand that you are unhappy") or shift to a positive outlook (e.g., "We hope you have a better experience next time"). Highly empathic responses may enhance the complainant's and online brand community's perceptions of interactional justice and signal politeness and courtesy, which may reduce the virality of negative eWOM. An engaged firm response also might include substantiated explanations, and the number of reasons offered has more influence than the actual content of those reasons on decision outcomes ([72]). When firms provide more substantiated arguments, it may enhance perceptions of response quality and effort among brand community audiences (e.g., "We could not assist you quickly because the store was extremely busy"). By providing more explanation, firms might enhance evaluations of their recovery efforts ([12]).
However, in line with cognitive appraisal theory and an affect infusion model, [45] posit that an affective approach, such as empathy, is more effective in affect-intensive environments characterized by social interactions and spontaneous decisions, such as online brand communities. In general, then, more empathic responses might be better suited to regulating the contagiousness of negative eWOM. According to the affect infusion model, the relative impact of cognitive responses, such as explanations, increases with stronger affect and higher involvement ([27]). Research on emotion regulation strategies further indicates that some stimuli may be too emotionally intense for an empathic response to suffice, and instead, receivers may seek explanations to reappraise the situation ([32]). The more contagious the emotions in a negative eWOM message, the more attention customers will pay to the message and the stronger their expectations about what needs to be done to remedy the situation ([39]). In such situations, customers are more likely to engage in deliberate processing of negative eWOM by cognitively reappraising the situation; that is, they consider more information and perform more intricate evaluations of the explanations ([52]). Empathic responses may help shift the attention of consumers who experience low-arousal emotions, but firms might better mitigate the virality of high-arousal emotions by offering more explanations. Thus, the relative effectiveness of firm responses for preventing online firestorms may be contingent on the intensities of the high- and low-arousal levels in the negative eWOM message:
- H4: More explanation, rather than more empathy, in firm responses is better suited to contain negative eWOM with more intensive high-arousal emotions.
- H5: More empathy, rather than more explanation, in firm responses is better suited to contain negative eWOM with more intensive low-arousal emotions.
Through observational learning processes, as an online firestorm evolves, and other members support the negative eWOM, its perceived reliability should increase ([21]). Thus, customers pay even more attention to the negative eWOM and form revised expectations about what needs to be done to remedy the situation. Therefore, beyond the compositional elements of individual firm responses, when negative eWOM evolves into an online firestorm, multiple firm responses become necessary to mitigate its detrimental impacts.
As [ 6] suggest, online messages build on one another, and thus their sequence can determine their success in terms of persuading customers, building brand equity, or driving sales. [79] find that posting the same (vs. mixed) consecutive brand message decreases (vs. increases) customers' engagement. Considerations of cross-message dynamics in firm responses may advance understanding of how to mitigate evolved online firestorms too. For example, by empathically sympathizing with the customer in the first reply, then issuing a second, complementary response that provides explanations, the firm might reduce the overall virality of the negative eWOM, compared with a situation in which it repeats its offer of sympathy in the second response, which might cause frustration ([50]). We predict that such cross-message sequencing should mitigate the virality of negative eWOM to a broad customer audience ([ 6]):
- H6: Consecutive firm responses with varying rather than repeated (a) empathic intensity and (b) explanatory intensity are better suited to mitigating evolved online firestorms.
We used Facebook's Application Programming Interface (API) and processed detailed information on potential online firestorms from the official Facebook brand communities of all U.S. firms listed on the S&P 500 between October 1, 2011 (the introduction of the timeline feature), and January 31, 2016 (the introduction of emojis). We chose this setting for three reasons. First, it is common for customers to use Facebook to interact with firms in their brand communities and to complain through this channel. Second, unlike other rating and review sites that encourage only customers to share their views, firms actively participate in Facebook conversations and respond to customer posts ([71]). Third, in line with previous research on online brand communities (e.g., [53]; [65]), the count of likes and comments indicates the degree to which others approve and share a message and provides an objective measure of virality.
We selected all firms that target private customers, have an official Facebook brand community, and allow user posts in their community (see the Web Appendix). Because of our focus on negative eWOM, we analyzed text-based features to determine which posts were negative in two steps. We first applied the R Quanteda package ([ 7]) using Linguistic Inquiry and Word Count (LIWC) text-mining dictionaries to derive the intensity of positive- and negative-emotion words in each post (for more details, see [46]]). We then applied the Stanford Sentence and Grammatical Dependency Parser ([75]) to subdivide each post into sentences and identify dependencies between emotion words and negations (i.e., bigrams). The parser first identifies the presence of an emotion word and then, in cases of negation, automatically assesses whether there is a grammatical relationship (e.g., in the sentence "The service was not nice," the negation "not" is grammatically related to the adjective "nice" and therefore reverses it). Negated positive emotion words were counted toward negativity and negated negative emotions words toward positivity. If a customer post is more negative than positive overall, we count it as a potential online firestorm.
We excluded 48,480 posts that contained a video, picture, event, or external link, because we cannot control for this external content. Furthermore, we excluded 140 posts with fewer than three words, because every full thought requires at least a subject, verb, and object to be understandable for receivers. On Facebook, zero counts of likes and comments from other customers might occur for two reasons: ( 1) The post may have been viewed by other customers but prompted no reaction, or ( 2) the post may not have been displayed to other customers. Thus, we excluded 128,681 posts with no customer reactions, because we are interested in the inflation of virality. We also replicated our analysis with the sample including posts with no customer reactions, and we report the results in the Web Appendix.
With these restrictions, our final sample counted 472,995 negative customer posts in English across 89 online brand communities. The posts averaged 99 words (SD = 121.44, ranging from 3 to 8,121 words) and received 2.95 likes and comments on average (SD = 59.22, ranging from 1 to 37,760 likes and comments). Included in this final sample are both well-publicized incidents (e.g., customer post complaining that a police officer was prohibited from using a coffeehouse chain bathroom in September 2015) and less drastic cases in which complaints were supported by a only small number of other customers. Notably, for the well-publicized incidents, newspapers and news portals cited the original Facebook post (e.g., "A Facebook user reported that a police officer was prohibited from using a bathroom" [www.snopes.com]). Figure 1 displays illustrative examples of online firestorms and firm responses. In terms of timing, 78% of the online firestorms emerged and dissolved within a day, such that the last comment was posted within 24 hours of the initial negative post, and 93% of firm responses arrived within one hour. This short time frame is a unique feature of online firestorms that differentiates our study from previous research on product-harm crises (e.g., [18]).
Graph: Figure 1. Illustrative examples of online firestorms and firm responses.Notes: Posts are edited to exclude company names and customer names.
Table 1 contains the operationalizations and sources of all variables, along with our rationale for including the control variables, and Figure 2 displays hypotheses and the measurement approach. The key variable of interest is the virality of negative eWOM, measured as the total number of likes and comments a post receives from other customers.[ 5] The total number of likes and comments correlate closely (r =.81), justifying their use as a composite variable. Because of community-level differences in virality (some online brand communities feature thousands of posts every day; others just a few), we use the deviation from the community average. Then, noting the data range and extreme values of virality (see the Web Appendix), we add a constant to have only positive values and apply a logarithmic transformation. To investigate how firms prevent and mitigate online firestorms, we include only likes and comments posted after the respective firm's response ([80]), which ensures that virality has been influenced by the firm response. Importantly, the API does not allow us to capture time stamps for likes. However, comments are time-stamped and known to evolve simultaneously with likes over time ([66]). Therefore, we use the amount of comments following a firm response to approximate the number of likes.
Graph: Figure 2. Hypotheses and measurement approach.
We measure the intensity of high and low arousal for each negative post with computerized text analysis. In a top-down manner ([46]), we compared each word in a message with predefined emotion word categories. We then calculated an intensity score per emotion word category: the proportion of total words that match each dictionary. In line with the main four negative emotion types in the circumplex model ([70]), we classified the proportion of word use related to fear/anxiety, anger, and disgust as the intensity of high-arousal negative emotions and the proportion of sadness as the intensity of low-arousal negative emotion. We used existing LIWC dictionaries for fear/anxiety, anger, and sadness ([62]), but we needed to develop a new dictionary to derive disgust. We provide this dictionary and details on its development in the Web Appendix. To validate the new dictionary, we compared statistical differences in arousal levels, according to an extremity measure from the Evaluative Lexicon 2.0 (EL 2.0; [68]), between our disgust words and words classified as representing low negative arousal (i.e., sadness). We find a significant difference (F = 7.57, p <.01), with extremity mean scores of 3.24 and 2.89 for disgust and low arousal, respectively. This result confirms that our disgust dictionary matches the EL 2.0 measure for expression extremity.
Strength of structural ties has been measured using different variables, including subjective and objective measurements. Because we cannot collect perceptions of tie strength across the millions of brand community users, we followed [67] and operationalized strength of structural ties (SST) as "the frequency of communication" ([14], p. 356). Formally, SST for customer i posting negative eWOM at time t in community c is:
Graph
1
where t − 1 is the entire period prior to the post at time t, and SSTic is the sum of likesc, commentsc, and sharesc that customer i received from others in the brand community c before the post at t, as well as the sum of likesi and commentsi the customer gave to others in the brand community c prior to the post at t (all calculated based on the comment timestamp). The API does not allow us to identify customers who share a certain post, due to privacy restrictions.
We derived the degree of LSM between customer i posting negative eWOM at time t with the receiving brand community c in three steps. First, we mined the use intensity of each of the nine function word categories j separately in focal customer i's message and across all customer messages (negative and positive) in the brand community c posted in the previous three months in response to the focal negative eWOM post (moving community average). Second, the degree of similar use intensity LSM of each function word category (FWj) by customer i posting the negative eWOM into community c comes from the formula:
Graph
2
Third, by aggregating all nine LSM scores with equal weights, we obtain an LSM score bound between 0 and 1, and scores closer to 1 reflect greater degree of communication style matching between customer i and the online brand community c.
We measured the intensity of empathy, or the degree of spontaneous affective response ([42]) a firm provided, as the proportion of affect words in the response text, according to the LIWC dictionary for affect. Using the LIWC dictionary for causal expressions, we also measured the intensity of explanation in a firm's responses. We then measured variations in the response sequences as the standard deviation in empathic and explanatory intensities across all firm responses.
Following prior research, we account for multiple control variables that might influence the virality of negative eWOM (see Table 1). Firm-related aspects that influence eWOM include industry membership, brand familiarity, and brand reputation. At the online brand community level, we account for community size, member attentiveness and expressiveness, firm engagement frequency, average structural tie strength among members, and variance in linguistic style. Post-related aspects include the number of competing inputs at the time of the post, the sentiment of the previous post, post length, post complexity, and the frequency with which the customer had complained on Facebook in the past. Furthermore, firm response–related aspects include whether a firm responds or not and the firm response time. We used dummy variables to account for whether a firm offered an apology, offered compensation, or suggested a communication channel change; a firm can use more than one response approach in the same message for this measure (e.g., combining apology and compensation). Finally, as fixed effects, we account for the year and month to control for seasonality in user activity and policy changes (see the Web Appendix). We also include fixed effects for weekends and time of day ([48]). The Web Appendix reports correlations and descriptive statistics. Firms responded at least once to 331,370 out of 472,995 negative posts, yielding an average response rate of 70%. Across all firm responses, suggesting a channel change (61%) and apologizing (53%) were most commonly used, while compensation was used less often (3%). The degree to which explanations were offered (8% of the time) was slightly more than the use of empathy (6%). We found that 15,762 negative posts achieved above-average virality and got multiple firm responses, suggesting that 3% of potential online firestorms evolved during the period of observation. The Web Appendix reveals the evolution of the number of potential firestorms, average virality, and average firm response rates over time. While the number of potential online firestorms increased during the study period (and we control for this increase with time-related fixed-effect), both the average virality and the average percentage of firm responses are rather stable over time.
Graph
Table 1. Operationalization and Sources of All Variables.
| Variable and Time | Operationalization | Source | Rational for Inclusion (Controls) and Related Studies |
|---|
| Dependent Variable | | | |
| Virality | Combined sum of likes and comments the post received from other customers any time after it was posted (Viralityt_1; brand community-centered and log-transformed). To investigate how firms prevent and mitigate online firestorms, we only considered virality after the first firm responses (Viralityt_2) or after the last firm responses (Viralityt_3). | Facebook API | De Vries, Gensler, and Leeflang (2012); Lee, Hosanagar, and Nair (2018); Relling et al. (2016); Stephen, Sciandra, and Inman (2015) |
| Post Predictors | | | |
| Intensity of high arousal | LIWC dictionaries "anx" and "anger," new dictionary "disgust" for the focal post (number of matching words in the post, expressed as percentage of total words). | Text mining | Berger and Milkman (2012); Hewett et al. (2016) |
| Intensity of low arousal | LIWC dictionary "sad" for the focal post (number of matching words in the post, expressed as percentage of total words). | Text mining | Berger and Milkman (2012); Hewett et al. (2016) |
| Strength of structural ties | Frequency of communication with the online brand community before the post (see Formula 1 in the text). | Facebook API | Risselada, Verhoef, and Bijmolt (2014) |
| LSM | Degree of communication style matching with the online brand community before the post (see Formula 2 in the text). | Text mining | Ireland and Pennebaker (2010); Ludwig et al. (2013) |
| Firm Response | | | |
| Intensity of empathy | LIWC dictionary "affect" for firm responses (number of matching words in the response, expressed as percentage of total words). | Text mining | Fehr, Gelfand, and Nag (2010) |
| Intensity of explanation | LIWC dictionary "cogproc" for firm responses (number of matching words in the response, expressed as percentage of total words). | Text mining | Seibold and Meyers (2007) |
| Variation in firm responses | Variance in the proportion of empathic and explanatory words across all firm responses. | Text mining | Villarroel Ordenes et al. (2018) |
| Firm Controls | | | |
| GICS | Global Industry Classification Standard: consumer discretionary (49%) and consumer staples (21%) versus other (30%). | Standard & Poor's | The amount of negative eWOM may depend on the industry (Stephen, Sciandra, and Inman 2015). |
| Brand familiarity | Familiarity of each firm (on a scale from 0% to 100%). | Reputation Institute | Low brand familiarity may lead to less engagement with negative eWOM (Baker, Donthu, and Kumar 2016). |
| Brand reputation | Reputation perceptions of each firm (on a scale from 0% to 100%). | Reputation Institute | Support for negative eWOM may depend on the firm's reputation (Baker, Donthu, and Kumar 2016). |
| Brand Community Controls | | | |
| Brand community size | Number of page likes for the online brand community (in millions of page likes). | Facebook API | Greater brand community size gives negative eWOM a larger audience. |
| Brand community attentiveness | Average number of likes and comments per customer post in each online brand community (for both positive and negative posts). | Facebook API | More attentive members may be more susceptible to negative eWOM (Hatfield, Cacioppo, and Rapson 1994). |
| Brand community expressiveness | Average of LIWC dictionary "affect" for all posts in the online brand community (percentage of total words). | Text mining | More expressive members may be more susceptible to negative eWOM (Hatfield, Cacioppo, and Rapson 1994). |
| Average firm engagement | Average number of firm responses per customer post in each online brand community (for both positive and negative posts). | Facebook API | Firm engagement may stimulate negative eWOM (Homburg, Ehm, and Artz 2015). |
| Average tie strengths | Average tie strength of each customer who posted, commented, or liked within the online brand community. | Facebook API | Average tie strengths may increase the effectiveness of negative eWOM (Katona, Zubcsek, and Sarvary 2011). |
| Variance in linguistic style | Variance in LSM of all customer posts in each online brand community (for both positive and negative posts). | Text mining | Variance in linguistic style may decrease the effectiveness of negative eWOM (Ludwig et al. 2014). |
| Post Controls | | | |
| Competing inputs | Number of other posts in the online brand community on the day of the focal negative customer post (both customer and firm posts). | Facebook API | Exposure to competing stimuli may decrease the virality of emotions (Coenen and Broekens 2012). |
| Sentiment previous post | LIWC dictionaries "posemo" minus "negemo" for the previous customer post in the online brand community. | Text mining | A preexisting mood state may increase the virality of emotions (Coenen and Broekens 2012). |
| Post length | Average words per sentence. | Text mining | Longer posts may convey more information and thus increase virality (Berger and Milkman 2012). |
| Post complexity | Average words with more than six letters per sentence. | Text mining | Posts that are more complex suggest eloquence in writing which may increase virality (Vásquez 2014). |
| Negation in post | LIWC dictionary "negate" (in percentage of total words). | Text mining | Posts with negation may be more difficult to understand and thus decrease virality. |
| Previous complaints | Number of negative posts from the same customer prior to the focal negative post. | Facebook API | A high frequency of complaints from the same customer may decrease virality (Ma, Sun, and Kekre 2015). |
| No firm response | No firm response on the negative customer post (dummy coded). | Facebook API | Virality may increase if members believe the firm is ignoring them (Homburg, Ehm, and Artz 2015). |
| Firm response time | Time stamp of negative customer post minus time stamp of first firm response (converted to hours). | Facebook API | A faster response should be beneficial for firms and thus may decrease virality (Homburg, Ehm, and Artz 2015). |
| Firm Response Controls | | | |
| Compensation | Newly developed dictionary; see the Web Appendix (dummy coded). | Text mining | Offering compensation may satisfy the complaining customer and decrease virality (Bitner, Booms, and Tetreault 1990). |
| Apology | Newly developed dictionary; see the Web Appendix (dummy coded). | Text mining | Apologizing may please the complaining customer and decrease virality (Bitner, Booms, and Tetreault 1990). |
| Channel change | Newly developed dictionary; see the Web Appendix (dummy coded). | Text mining | Evoking a channel change may take the conversation away from the brand community and decrease virality (Ansari, Mela, and Neslin 2008). |
1 Notes: We use time dummies to control for year, month, weekend, and time of day.
The incidences of negative eWOM are nested within the online brand communities, and thus the negative posts and firm responses might be interdependent. To determine whether a multilevel approach is warranted, we first conducted a one-way analysis of variance with random effects to reveal any systematic between-group variance in the virality of negative eWOM. We find significant between-group variance (χ2(88) = 818,729, p <.01). In addition, the design effect of 36.74 suggests that a multilevel structure is possible ([59]). The maximum variance inflation factor score across all models is 3, indicating no potential threat of multicollinearity.
We specified a series of separate hierarchical models, with parameters at the post and the firm/brand community level, using full information maximum likelihood estimation and grand mean-centering. Virality, our focal outcome measure, is operationalized differently across these models along three time periods. Viralityt_1 is the total number of likes and comments from other customers any time after posting at t. Viralityt_2 is the number of likes and comments from other customers any time after the first firm response, and Viralityt_3 is the number of likes and comments from other customers any time after the last firm response. We provide more detailed explanations for each variable in Table 1. Thus, we assess the predictors of virality for all 472,995 negative posts across the 89 brand communities as follows:
Graph
3
where t_1 is the time period after the time t of customer i's post in brand community c;
- = the combined sum of likes and comments post i receives from other customers in community c any time after it was posted (brand community-centered and log-transformed);
- = community-specific controls using the Global Industry Classification Standard: GICS Consumer Discretionaryc, GICS Consumer Staplesc, Brand Familiarityc, and Brand Reputationc;
- = Brand Community Sizec, Brand Community Attentivenessc, Brand Community Expressivenessc, Firm Engagementc, Average Tie Strengthsc, and Variance in Linguistic Stylec;
- = Competing Inputsic, , Post Lengthic, Post Complexityic, Negation in Postic, and ;
- = 1 if there is no firm response at any time, and 0 otherwise;
- = Intensity of High Arousalic, Intensity of Low Arousalic, , and ;
- = dummy variables for years (baseline is 2015), month (baseline is December), weekend day (baseline is week day), and time of the day (baseline is night time, EST);
- = brand community–specific error term; and
- = post-specific error term.
Next, to determine how firms can prevent viral online firestorms, we examine 331,370 negative posts that received at least one firm response according to the following equation:
Graph
4
where t_2 is the time period after the first firm response;
- = the combined sum of likes and comments post i received from other customers in community c any time after the first firm response (brand community-centered and log-transformed);
- = time until the first firm response;
- = , , , , and ; and
- = Intensity of High Arousalic × , Intensity of High Arousalic × , Intensity of Low Arousalic × , and Intensity of Low Arousalic × .
Finally, we investigate how firms can mitigate the evolved online firestorms represented by 15,762 negative posts that achieved above-average virality and to which firms responded multiple times:
Graph
5
where t_3,..., T is the time period after the last firm response;
- = the combined sum of likes and comments post i received from other customers in community c any time after the last firm response (brand community-centered and log-transformed);
- = include all firm responses after the first firm response, , , , , and ; and
- = and (across all Firm Responsesic).
The results provide support for our hypotheses that the intensity of high-arousal emotions (vs. low-arousal emotions), SST, and LSM relate to the virality of negative eWOM (Table 2, Model 3).[ 6] Both intensities of high arousal (γ =.186, p <.01) and low arousal (γ =.026, p <.01) relate positively to virality. However, a t-test reveals that the intensity of high arousal is more strongly related to virality than the intensity of low arousal (t = 35.15, p <.01), in support of H1. In addition, SST (γ = 1.432, p <.01) and LSM (γ =.025, p <.01) relate positively to virality, in support of H2 and H3. Considering the relative influence of the drivers, we find that SST exerts the strongest impact on virality (all t ≥ 77.97, p <.01).
Graph
Table 2. Predictors of Potential Online Firestorms.
| DV = Total Virality of Negative Customer PostSample = All Posts with and Without Firm Response |
|---|
| Model 1 | Model 2 | Model 3 |
|---|
| γ | t/r | γ | t/r | γ | t/r |
|---|
| Level 2: Firm/Brand Community | | | | | | |
| Firm controls | | | | | | |
| GICS consumer discretionary | −.185 | −1.48 | −.190 | −1.56 | −.190 | −1.56 |
| GICS consumer staples | −.100 | −.62 | −.100 | −.63 | −.100 | −.64 |
| Brand familiarity | .149 | .57 | .139 | .55 | .139 | .55 |
| Brand reputation | −.640 | −.69 | −.664 | −.74 | −.665 | −.74 |
| Brand community controls | | | | | | |
| Brand community size | .002 | .18 | .002 | .20 | .002 | .20 |
| Brand community attentiveness | .009 | .15 | .011 | .20 | .011 | .20 |
| Brand community expressiveness | 5.641 | 1.33 | 5.736 | 1.38 | 5.726 | 1.38 |
| Average firm engagement | .287 | 1.60 | .290 | 1.66 | .291 | 1.66 |
| Average tie strengths | −.230† | −1.87 | −.247* | −2.06 | −.247* | −2.06 |
| Variance in linguistic style | 5.363† | 1.80 | 5.498† | 1.89 | 5.500† | 1.89 |
| Level 1: Customer Post | | | | | | |
| Post controls | | | | | | |
| Competing inputs | −.001** | .03 | −.001** | .03 | −.001** | .03 |
| Sentiment previous post | −.001 | .00 | −.001 | .00 | −.002 | .00 |
| Post length | .005** | .07 | .005** | .07 | .005** | .06 |
| Post complexity | .018** | .02 | .012** | .01 | .017** | .01 |
| Negation in post | .021** | .01 | −.022 | .01 | .004 | .00 |
| Previous complaints | .017** | .00 | −.212** | .05 | −.213** | .05 |
| No firm response | .030** | .14 | .030** | .14 | .029** | .14 |
| Post predictors | | | | | | |
| Negative emotions | | | .133** | .06 | | |
| Intensity of high arousal (H1) | | | | | .186** | .07 |
| Intensity of low arousal (H1) | | | | | .026** | .01 |
| Strength of structural ties (H2) | | | 1.435** | .13 | 1.432** | .13 |
| LSM (H3) | | | .032** | .05 | .025** | .04 |
| Log-likelihood | −1,019,495 | −1,030,248 | −1,031,025 |
| Change in log-likelihood | | 10,752** | 11,530** |
| NLevel 2 | 89 brand communities |
| NLevel 1 | 472,995 negative posts |
- 2 †p <.10.
- 3 *p <.05.
- 4 **p <.01.
- 5 Notes: Significance is based on two-tailed tests. We report t-values for Level 2 and effect size r for Level 1. Change in fit in comparison with the model with control variables only. We test GICS consumer discretionary and GICS consumer staples against GICS other. Fixed effects for year, month, weekend, and time of day are included in the model and reported in the Web Appendix. Slopes of high-arousal and low-arousal emotions are different at p <.01 and t = 35.15. An additional model that compared the effects of different high-arousal emotions (fear/anxiety, anger, disgust) with the low-arousal emotion (sadness) revealed that fear/anxiety (t = 10.75, p <.01), anger (t = 37.52, p <.01), and disgust (t = 1.74, p <.10) are all more strongly related to virality than sadness. The small difference between disgust and sadness is in line with Berger and Milkman's (2012) finding and can be attributed to the relative scarcity of disgust in the negative eWOM messages. We also find that anger relates more strongly to virality then other high-arousal emotions (all ts ≥ 21.34, p <.01).
Regarding brand community controls, we find that average tie strength relates negatively to virality (γ = −.247, p <.05), and greater variance in linguistic style relates positively to virality (γ = 5.550, p <.10). Online firestorms thus appear to occur less in brand communities in which members have stronger connections with one another and are more similar. Regarding the post controls, we find that competing inputs (γ = −.001, p <.01) and previous complaints (γ = −.213, p <.01) relate negatively to virality. Conversely, post length (γ =.005, p <.01) and post complexity (γ =.017, p <.01) relate positively to virality. Finally, a lack of firm response is significantly related to increased virality (γ =.029, p <.01), clearly indicating the importance of actively managing negative eWOM in online brand communities.
When considering the main effects of the intensities of empathy and explanation in firm responses in Model 5 (Table 3), we find that the increased use of empathy (γ = −.069, p <.01) leads to significantly lower virality than the increased use of explanation (γ = −.011, p <.01; t = 14.42, p <.01). Regarding other firm responses that reflect disengaging from the conversation, we find that responses that contain an apology (γ = −.004, p <.01) or a suggestion for a channel change (γ = −.005, p <.01) relate negatively to virality. However, immediately offering compensation fosters the virality of negative eWOM (γ =.003, p <.01).
Graph
Table 3. Preventing Potential Online Firestorms.
| DV = Virality After the First Firm ResponseSample = All Posts with at Least One Firm Response |
|---|
| Model 4 | Model 5 | Model 6 |
|---|
| γ | t/r | γ | t/r | γ | t/r |
|---|
| Level 2: Firm/Brand Community | | | | | | |
| Firm controls | | | | | | |
| GICS consumer discretionary | −.206 | −1.45 | −.206 | −1.44 | −.206 | −1.44 |
| GICS consumer staples | −.104 | −.57 | −.105 | −.57 | −.104 | −.57 |
| Brand familiarity | .119 | .40 | .118 | .40 | .117 | .40 |
| Brand reputation | −.842 | −.80 | −.833 | −.79 | −.834 | −.79 |
| Brand community controls | | | | | | |
| Brand community size | .003 | .24 | .003 | .24 | .003 | .24 |
| Brand community attentiveness | .019 | .28 | .018 | .28 | .019 | .28 |
| Brand community expressiveness | 5.971 | 1.23 | 5.968 | 1.23 | 5.960 | 1.23 |
| Average firm engagement | .323 | 1.57 | .323 | 1.58 | .323 | 1.58 |
| Average tie strengths | −.310* | −2.20 | −.309* | −2.20 | −.310* | −2.20 |
| Variance in linguistic style | 5.996† | 1.76 | 5.995† | 1.76 | 5.993† | 1.76 |
| Level 1: Customer Post | | | | | | |
| Post controls | | | | | | |
| Competing inputs | .003** | .03 | .003** | .03 | .003** | .03 |
| Sentiment previous post | −.005** | .01 | −.005** | .01 | −.005** | .01 |
| Post length | .005** | .08 | .006** | .08 | .006** | .08 |
| Post complexity | .017** | .01 | .018** | .02 | .018** | .02 |
| Negation in post | .005 | .00 | .002 | .00 | .002 | .00 |
| Previous complaints | −.171** | .03 | −.179** | .03 | −.178** | .03 |
| Firm response time | .001** | .02 | .001** | .02 | .001** | .02 |
| Post predictors | | | | | | |
| Intensity of high arousal | .220** | .08 | .217** | .08 | .252** | .09 |
| Intensity of low arousal | .025** | .01 | .025** | .01 | .027** | .02 |
| Strength of structural ties | 1.047** | .08 | 1.044** | .08 | 1.038** | .08 |
| LSM | .022** | .04 | .024** | .04 | .023** | .04 |
| First firm response | | | | | | |
| Compensation | | | .003** | .01 | .003** | .01 |
| Apology | | | −.004** | .02 | −.004** | .02 |
| Channel change | | | −.005** | .03 | −.006** | .03 |
| Intensity of empathy (EMP) | | | −.069** | .04 | −.065** | .04 |
| Intensity of explanation (EXP) | | | −.011** | .01 | −.006* | .00 |
| Intensity of high arousal × EMP (H4) | | | | | 2.678** | .05 |
| Intensity of high arousal × EXP (H4) | | | | | −1.437** | .04 |
| Intensity of low arousal × EMP (H5) | | | | | −.042 | .00 |
| Intensity of low arousal × EXP (H5) | | | | | −.206** | .01 |
| Log-likelihood | −770,911 | −771,760 | −773,267 |
| Change in log-likelihood | | 849** | 1,508** |
| NLevel 2 | 89 brand communities |
| NLevel 1 | 331,370 negative posts |
- 6 †p <.10.
- 7 *p <.05.
- 8 **p <.01.
- 9 Notes: Significance is based on two-tailed tests. We report t-values for Level 2 and effect size r for Level 1. Fixed effects for year, month, weekend, and time of day are included in the model and reported in the Web Appendix.
When considering the interaction effects in Model 6 (Table 3), we find a positive interaction between the intensity of high arousal and the increased use of empathy in the firm response (γ = 2.678, p <.01). The increased use of empathy in a response to a post with a high intensity of high arousal thus increases, rather than decreases, virality. Conversely, we find a significant negative interaction between the increased intensity of high arousal and the increased use of explanation in the firm response (γ = −1.437, p <.01). Therefore, the increased use of explanation in a response to a post with a high intensity of high arousal significantly reduces its virality, as depicted in Figure 3. A t-test further reveals significant differences between providing more empathy versus more explanation in buffering the effect of high arousal (more empathy: γ =.386, p <.01; more explanation: γ =.180, p <.01; t = 23.43, p <.01). Overall, these results support H4 and indicate that intensive high arousal is better contained with more explanation.
Graph: Figure 3. Firm response strategies moderate the effect of high-arousal emotions on virality.Notes: Viralityt_2 is measured after the first firm response.
Contrary to our expectations, we find no negative interaction between the intensity of low arousal and the increased use of empathy in the firm response at conventional significance levels (γ = −.042, p =.30) and a negative interaction between the intensity of low-arousal emotions and the increased use of explanation in the firm response (γ = −.206, p <.01). A t-test reveals no significant differences between providing more empathy versus more explanation in buffering intensive low-arousal emotions (more empathy: γ =.025, p <.01; more explanation: γ =.016, p <.01; t = 1.63, p =.10). Thus, H5 is not supported.
Variations in the intensity of empathy (γ = −.185, p <.05) and intensity of explanation (γ = −.205, p <.01) relate negatively to virality in Model 9 (Table 4), in support of H6a and H6b. These findings suggest that firms should vary their response formulation to decrease the virality of evolved online firestorms. We further find that an increased use of explanation in subsequent responses increases virality (γ =.083, p <.01). To mitigate the virality of negative eWOM, firms should vary their response, focusing on more empathy in later responses. Notably, at an evolved stage of an online firestorm, firm responses that contain an apology (γ =.031, p <.01) or a channel change (γ =.038, p <.01) positively relate to virality. These findings reveal that such response strategies not only are ineffective in mitigating evolved online firestorms but even fuel the fire. In contrast, offering compensation in a subsequent response negatively relates to virality (γ = −.045, p <.01).
Graph
Table 4. Mitigating Evolved Online Firestorms.
| DV = Virality After the Last Firm ResponseSample = All Posts with More Than One Firm Response and Above-Average Virality |
|---|
| Model 7 | Model 8 | Model 9 |
|---|
| γ | t/r | γ | t/r | γ | t/r |
|---|
| Level 2: Firm/Brand Community | | | | | | |
| Firm controls | | | | | | |
| GICS consumer discretionary | .021 | 1.34 | .022 | 1.40 | .022 | 1.40 |
| GICS consumer staples | −.012 | −.54 | −.015 | −.67 | −.015 | −.67 |
| Brand familiarity | −.006 | −.16 | −.007 | −.16 | −.007 | −.17 |
| Brand reputation | −.009 | −.08 | −.005 | −.04 | −.004 | −.04 |
| Brand community controls | | | | | | |
| Brand community size | .001 | 1.08 | .002 | 1.15 | .002 | 1.15 |
| Brand community attentiveness | −.008 | −1.24 | −.007 | −1.19 | −.007 | −1.19 |
| Brand community expressiveness | −.304 | −.50 | −.295 | −.48 | −.296 | −.49 |
| Average firm engagement | .014 | .53 | .012 | .46 | .012 | .46 |
| Average tie strengths | −.020 | −1.45 | −.020 | −1.45 | −.020 | −1.45 |
| Variance in linguistic style | .616 | 1.08 | .615 | 1.07 | .616 | 1.07 |
| Level 1: Customer Post | | | | | | |
| Post controls | | | | | | |
| Competing inputs | −.001 | .00 | −.001 | .00 | −.001 | .00 |
| Sentiment previous post | −.030 | .01 | −.029 | .01 | −.029 | .01 |
| Post length | .013** | .07 | .013** | .07 | .013** | .07 |
| Post complexity | −.017 | .01 | −.016 | .00 | −.016 | .00 |
| Negation in post | −.087 | .01 | −.082 | .01 | −.082 | .01 |
| Previous complaints | −.707** | .03 | −.710** | .03 | −.710** | .03 |
| Firm response time | .003** | .05 | .003** | .05 | .003** | .05 |
| Post predictors | | | | | | |
| Intensity of high arousal | .363** | .06 | .364** | .06 | .364** | .06 |
| Intensity of low arousal | .122** | .03 | .124** | .03 | .124** | .03 |
| Strength of structural ties | 5.796** | .11 | 5.813** | .11 | 5.812** | .11 |
| LSM | .045** | .03 | .045** | .03 | .045** | .03 |
| First firm response | | | | | | |
| Compensation | .018† | .01 | .017 | .01 | .017 | .01 |
| Apology | −.011* | .02 | −.012** | .02 | −.012** | .02 |
| Channel change | −.008† | .02 | −.010* | .02 | −.010* | .02 |
| Intensity of empathy | −.084 | .01 | −.072 | .01 | −.072 | .01 |
| Intensity of explanation | −.129** | .03 | −.127** | .03 | −.127** | .03 |
| Subsequent firm responses | | | | | | |
| Compensation | −.044** | .04 | −.045** | .04 | −.045** | .04 |
| Apology | .032** | .06 | .031** | .06 | .031** | .06 |
| Channel change | .040** | .08 | .038** | .08 | .038** | .08 |
| Intensity of empathy | −.110** | .03 | −.016 | .00 | −.021 | .00 |
| Intensity of explanation | .042 | .01 | .082** | .02 | .083** | .02 |
| Variance in firm response | | | −.198** | .04 | | |
| Variance in empathy (H6a) | | | | | −.185** | .02 |
| Variance in explanation (H6b) | | | | | −.205** | .03 |
| Log-likelihood | −1,463.20 | −1,480.45 | −1,475.86 |
| Change in log-likelihood | | 17.25** | 12.66** |
| NLevel 2 | 72 brand communities |
| NLevel 1 | 15,762 negative posts |
- 10 †p <.10.
- 11 *p <.05.
- 12 **p <.01.
- 13 Notes: Significance is based on two-tailed tests. We report t-values for Level 2 and effect size r for Level 1. Fixed effects for year, month, weekend, and time of day are included in the model and reported in the Web Appendix.
In our research design, endogeneity might arise out of reverse causality, omitted variables, or a learning effect. First, the timestamps provided allow us to avoid reverse causality by considering only customer comments that occur after the respective firm response. Second, we address omitted variables stemming from the 89 firms with firm-fixed-effects regressions to account for the unobservable heterogeneity of each brand community ([ 1]). All results are fully in line with the main analyses, as displayed in the Web Appendix. Third, we account for a learning effect in an analysis in which we include a continuous time variable that captures the 52 months of our study period (1 = October 2011 to 52 = January 2016). If endogeneity from a learning effect biases our results, this time variable would reduce the virality of negative posts and/or increase the effectiveness of the response strategies. Instead, when we include the time variable in our prevention model, we find a nonsignificant main effect on virality (p =.98). The positive interaction effects of the time variable with empathy (γ =.010, p <.01) and explanation (γ =.002, p <.08) indicate that both response strategies become less effective over time. Taken together, these additional analyses suggest it is unlikely that endogeneity biases our results.
Recent research has suggested that different drivers of virality may reinforce one another ([54]). Thus, we tested for potential interaction effects among the four drivers of online firestorms in Model 3. We find that both SST (γ = 28.840, p <.01) and LSM (γ =.743, p <.01) increase the effect of high-arousal emotions. In addition, LSM increases the effect of low-arousal emotions (γ =.107, p <.01), and SST and LSM reinforce each other (γ = 2.079, p <.01). When we tested whether SST influences the effectiveness of empathy and explanation, we find that both empathy (γ = −4.00, p <.01) and explanation (γ = −5.83, p <.01) are more effective in reducing the virality of negative eWOM from customers with strong structural ties in the brand community.
In line with previous research (e.g., [65]), we regard the combined number of likes and comments as the most appropriate measure for virality. For robustness, we tested our results with three separate, alternative measures of virality: likes, comments, and shares. Previous research has indicated mixed results, such that [53] find no major difference in using likes or comments as measures, but [20] find different results using likes versus comments to measure virality. We replicate all the models with these different outcome measures and report the results in the Web Appendix; nearly all estimates for the hypothesized effects are directionally similar and significant at conventional levels.
Extensive literature has addressed the benefits of eWOM (e.g., [ 4]), but theoretical and empirical work devoted to negative eWOM in brand communities is scarce. Drawing on research on negative WOM (e.g., [14]; [38]), we combine multiple sender and relational aspects that likely increase the virality of negative eWOM in online brand communities. We then empirically assess their role in driving virality across 472,995 potential online firestorms in 89 online brand communities of S&P 500 firms. Integrating common recovery approaches from service literature ([41]) with emotion regulation strategies ([33]), we also highlight the relative effectiveness of different firm response approaches and cross-response variations to prevent and mitigate online firestorms. Thereby, our study makes three primary contributions to extant marketing research (for a summary of the results, see Table 5).
Graph
Table 5. Overview of Results.
| Hypotheses | Effect on Virality of Negative eWOM | Support |
|---|
| H1 | High- versus low-arousal emotions | Intensity of high arousal > Intensity of low arousal | Supported (Table 2) |
| H2 | Strength of structural ties | Strong structural ties > Weak structural ties | Supported (Table 2) |
| H3 | LSM | Closer LSM > More distant LSM | Supported (Table 2) |
| H4 | Firm response to high-arousal emotions | For high arousal: More explanation > More empathy | Supported (Table 3) |
| H5 | Firm response to low-arousal emotions | For low arousal: More empathy > More explanation | Not supported (Table 3) |
| H6a | Variation in empathy in firm responses | Variation in empathy > Similar intensity of empathy | Supported (Table 4) |
| H6b | Variation in explanation in firm responses | Variation in explanation > Similar intensity of explanation | Supported (Table 4) |
First, we advance research on how to detect the online firestorm potential of negative eWOM with an empirical investigation of prototypical conceptions of different drivers of virality and their interrelations. In line with prior marketing research on negative WOM ([14]; [38], [58]), we show that the virality of negative eWOM in online brand communities varies depending on sender and relational aspects. As an extension of [54] findings about sharing newspaper articles, we find that in online brand communities, the use of more high-arousal-emotion words in negative eWOM increases its virality and makes it relatively more contagious than the use of low-arousal-emotion words. We also find that stronger structural ties between the complaining customer and the receiving online brand community relate to greater virality of negative eWOM. Thus, Brown and Reingen's (1987) conclusions, gathered from a small offline community, hold in large digital communities. Notably, owing to data limitations, we were only able to use the frequency of communication as tie strength indicator, not the importance attached to the relationship. Moreover, in this otherwise anonymous context, we use the degree of LSM as an indicator of interpersonal similarity between senders and receivers. In line with [54], we find that closer LSM between the complaining customer and the receiving online brand community relates to greater virality of negative eWOM. In contrast with previous studies, we consider a broader set of drivers of virality and test their relative importance and interrelationships. Structural ties are the strongest driver of virality. Furthermore, strong structural ties and a close LSM amplify the virality effect of high-arousal emotions in negative eWOM. To put our estimated effects into perspective with related research, we calculate effect sizes r using the formula from [69]. The identified drivers show effect sizes ranging from.04 to.13. Taken together, our study thus advances negative eWOM research with a theoretically grounded framework of sender and relational aspects, useful for detecting potential online firestorms in brand communities.
Second, as suggested in prior research (e.g., [56]), we consider the effectiveness of firm response approaches to reduce the contagiousness of negative eWOM in online brand communities. Contributing to this emergent stream of research, we reconcile research on service recovery ([41]) and emotion regulation ([33]) to delineate common, theoretically grounded firm response alternatives. We empirically confirm the common knowledge that not responding to a negative customer post is a firm's worst choice and should be avoided. In line with [44], we further find that responding fast is important. When actively engaging in elaboration with the complaining customer within the online brand community, the increased use of empathy is more effective overall. However, if a negative eWOM message contains exceptionally intense high-arousal emotions, increasing the amount of explanation is more effective for preventing and mitigating its virality. Contrary to our expectations, we find that explanation rather than empathy best contains negative eWOM containing severe low-arousal emotions (e.g., sadness). Indeed, in line with [33], customers who are experiencing severe emotions (either anger or sadness) are not able to shift attention but are always looking for explanations beyond empathy.
Initially aiming to block and disengage, rather than engage in elaborate online discussions, firms are best advised to offer an apology or suggest a channel change. At a later stage, once the negative eWOM has gathered support within the online brand community, these disengagement approaches are not only ineffective but may further increase a post's virality. Offering to take a customer's complaint offline seems to remove at least some negative conversations before they go viral. However, at a later stage, when the support of others for the negative eWOM has increased its perceived reliability ([21]), both the complaining customer and the community likely feel further encouraged by a firm's admittance of guilt (i.e., apology) and disgruntled if forcefully removed from the conversation (i.e., channel change). Moreover, the switching effect of channel change is in line with recent research from [30], who find that for customers using indirect revenge behaviors with public exposure (e.g., complaining online in a brand community), desire for revenge increases over time. Offering compensation only mitigates the virality of the negative eWOM message when used as a later response by the firm. Some controversy exists regarding the effectiveness of compensation as a means to recover from a service failure. That is, compensation may dissipate customers' frustrations ([12]), but offering it without explanation increases attributions of control and indicates an admission of guilt, evoking more negative evaluations ([11]). In line with [31], we therefore suggest that compensation is most effective if it follows an offer of an explanation or empathy. With these findings on how to prevent online firestorms, we extend debates about service recovery strategies to negative eWOM in online brand communities.
Third, online firestorms that have evolved often require multiple responses. We contribute to research on social media sharing by analyzing the implications of variations in firm response sequences ([ 6]). Several firm responses are likely to be interpreted jointly rather than in isolation ([79]). We find that varying response approaches, rather than consistently responding in the same way, can reduce the virality of evolved online firestorms in brand communities. Firms that use the same intensity of empathy or explanation to respond to negative eWOM increase, rather than mitigate, its contagiousness to other community members. Together with our findings that the effectiveness of compensation, apology, and channel change depends on when they are used, we add new insights into how best to sequence multiple firm responses in social media. Finally, as a useful resource for research, we have developed text-mining dictionaries to derive firms' response approaches automatically using a top-down text-mining approach (see the Web Appendix). We carefully followed traditional dictionary development standards ([46]), so researchers who want to examine written or transcribed firm responses in firm–customer exchanges may use these dictionaries as a starting point for their own investigations of firm response strategies.
By investigating how to detect, prevent, and mitigate the virality of negative eWOM in online brand communities, we offer several actionable implications for managers. We discuss them in the following subsections.
Brand community managers, who struggle to identify potentially threatening negative eWOM messages, should consider complainers' message formulations, beyond what is literally said, as well as their relationship with other members of the community. First, by using our dictionary-based, straightforward, automatic text-mining approach, managers can assess the high- and low-arousal levels of negative messages to predict their potential virality. The higher-arousal-emotion words a message contains, the more likely it is to go viral. Second, managers should assess the tie strength of the customer posting the negative message. Negative messages by customers who frequently interact with other community members are more likely to go viral than messages by customers who are relative strangers in the community. Third, text-mining tools can track the brand community's dominant communication style continuously and contrast it with the style of each negative customer post. Posts that closely match the dominant communication style are more likely to go viral. Taken together, the identified drivers explain 25% of virality across all examined brand communities. Finally, the different drivers amplify one another, so managers should be particularly cautious of complaining customers with strong structural ties who closely match the community's dominant communication style.
Unlike in a traditional service recovery setting, the success of managers' responses in preventing online firestorms critically depends on their ability to satisfy both the complainant and the brand community. Not responding is the worst choice, but the firm response also needs to be fast and tailored to the customer's message. In an initial response, empathy is generally most effective for containing negative eWOM. However, very negative messages that use an exceptional amount of high-arousal emotion words (e.g., "angry," "hate") demand more explanation. To disengage from the conversation and reduce the virality of negative eWOM upfront, managers should apologize or ask the unsatisfied customer to use another channel to raise the issue. Offering compensation immediately is not advised; it is effective only as a later response. We find that an appropriate response strategy can reduce virality of an intensive high-arousal post by up to 10%, which may equal hundreds of angry customers supporting and sharing negative eWOM.[ 7]
Some negative eWOM cannot be prevented from going viral or "catching fire" among other customers, and managers will need to respond multiple times. These responses are likely to be viewed collectively, rather than in isolation, so managers should consider each response as part of an overall response sequence. Rather than consistently posting the same message, managers should vary the use of empathy and explanation to mitigate the further virality of negative eWOM messages. An explanatory approach is viable as a first response to exceptionally intense high-arousal negative eWOM, and later firm responses should use increased empathy. If used at a later response stage, apologizing or suggesting a different communication channel will "feed the fire" and increase the virality of the negative eWOM. Instead, offering compensation should be the last resort for managers to prevent further elaborations and reduce the virality of negative eWOM. Using an appropriate response strategy over time can reduce subsequent virality by up to 11%.[ 8]
Our results are consistent with the proposition that firms need to manage negative eWOM in their online brand communities actively to prevent or mitigate their detrimental effects (e.g., [40]; [63]). Although we believe our findings have broad applicability, managing online firestorms is a vast and largely neglected field of research that is of critical importance to managers. Thus, it is important to recognize some limitations of our study and suggest further research. Although our large-scale study offers theoretical and empirical insights into textual aspects related to the virality of negative eWOM, using what [46] call a top-down approach, we also acknowledge that alternative bottom-up approaches might usefully derive specific service or product failures and their severity to test the suitability of the response approaches we outline. Similarly, in other communication contexts where heuristic processing is less prevalent, the implications of systematic content in firm responses should be assessed (e.g., size of the compensation, legitimacy of the argument).
Moreover, the scope of our study is limited to all posts visible in the communities. On Facebook, firms have the option to remove comments. Deletion criteria may include especially offensive (e.g., racist, derogatory) messages. This option may bias our results for high-arousal emotions, because we do not observe deleted posts. Managerial reports strongly discourage deleting negative customer posts on social media (e.g., [22]), but extreme posts missing from our data set could further increase the virality effect, or the effect may taper off or even reverse with an extreme use of high-arousal emotions. The consequences of deleting customer posts remain to be investigated. In addition, we could not assess perceptions of source credibility or how source credibility may interact with the use of empathy or other firm responses to make them more or less effective. Therefore, further research might seek novel ways to determine the importance of source credibility for message acceptance. Similarly, lacking an ability to account for the attitudinal importance that customers attribute to their ties in brand communities, continued research could extend our study by assessing the implications of weak and strong tie perceptions for sharing negative eWOM in brand communities. Potential measures that could be adapted for this purpose could be obtained from [77]. Furthermore, we were not able to obtain data on the number of friends due to privacy restrictions in Facebook's API terms and conditions. However, in line with [61], we believe that strength of structural ties (i.e., the number of encounters with other users in the brand community) influences virality regardless of the number of friends.
Interestingly, we found that customers expressing intense negative emotions, irrespective of whether they are high (e.g., anger) or low (e.g., sadness) on arousal, are looking for explanations rather than empathy. Future research should consider how the relative lack of emotionality might relate to the suitability of firm's response options. Finally, it was surprising that immediate compensation leads to more virality. Potentially, if firms offering an initial compensation that is perceived as not high enough could lead to an offense. If firms would then offer a higher compensation at a later stage this might lead to less virality. Thus, future research should investigate whether offering greater levels of compensation at later stages lead to the observed effects.
Supplemental Material, DS_10.1177_0022242918822300 - Detecting, Preventing, and Mitigating Online Firestorms in Brand Communities
Supplemental Material, DS_10.1177_0022242918822300 for Detecting, Preventing, and Mitigating Online Firestorms in Brand Communities by Dennis Herhausen, Stephan Ludwig, Dhruv Grewal, Jochen Wulf and Marcus Schoegel in Journal of Marketing
Footnotes 1 Associate EditorChristian Homburg served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The first author received a grant from the Basic Research Fund (GFF) of the University of St. Gallen for this project.
4 Online supplement: https://doi.org/10.1177/0022242918822300
5 1Likes and comments are more common indicators of virality than shares on Facebook. Of 472,995 negative eWOM incidences, 274,155 (52%) received at least one like, and 235,545 (50%) received at least one comment, but only 9,434 (2%) were shared at least once by other customers (see the Web Appendix). We also replicated the analyses with the number of likes, comments, and shares as separate measures of virality (see the Web Appendix).
6 2The fixed effects for year, month, weekend, and time of day for all models appear in the Web Appendix.
7 3For the calculation, we compared the expected virality of an intensive high-arousal post where firms respond with above-average empathy, below-average explanation, and with a compensation with the expected virality of an intensive high-arousal post where firms respond with below-average empathy, above-average explanation, and with apology and channel change (while keeping all other predictors constant).
8 4For the calculation, we compared the expected virality of a post where firms first respond with compensation, later with apology and channel change, and no variation in empathy and explanation to the expected virality of a post where firms first respond with apology and channel change, later with compensation, and with variation in empathy and explanation (all other predictors constant).
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By Dennis Herhausen; Stephan Ludwig; Dhruv Grewal; Jochen Wulf and Marcus Schoegel
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Record: 58- Device Switching in Online Purchasing: Examining the Strategic Contingencies. By: de Haan, Evert; Kannan, P.K.; Verhoef, Peter C.; Wiesel, Thorsten. Journal of Marketing. Sep2018, Vol. 82 Issue 5, p1-19. 19p. 1 Diagram, 8 Charts, 1 Graph. DOI: 10.1509/jm.17.0113.
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Device Switching in Online Purchasing: Examining the Strategic Contingencies
The increased penetration of mobile devices has a significant impact on customers’ online shopping behavior, with customers frequently switching between mobile and fixed devices on the path to purchase. By accounting for the attributes of the devices and the perceived risks related to each product category, the authors develop hypotheses regarding the relationship between device switching and conversion rates. They test the hypotheses by analyzing clickstream data from a large online retailer and apply propensity score matching to account for self-selection in device switching. They find that when customers switch from a more mobile device, such as a smartphone, to a less mobile device, such as a desktop, their conversion rate is significantly higher. This effect is larger when product category–related perceived risk is higher, when the product price is higher, and when the customer’s experience with the product category and the online retailer is lower. The findings illustrate the importance of focusing on conversions across the combination of devices used by customers on their path to purchase. Focusing on the conversions on a single device in isolation, as is usually done in practice, significantly overestimates conversions attributed to fixed devices at the expense of those attributed to mobile devices.
mobile; device switching; online conversion; cross-device; perceived risk
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By Evert de Haan; P.K. Kannan; Peter C. Verhoef and Thorsten Wiesel
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Record: 59- Do Consumers Always Spend More When Coupon Face Value is Larger? The Inverted U-Shaped Effect of Coupon Face Value on Consumer Spending Level. By: Jia, He (Michael); Yang, Sha; Lu, Xianghua; Whan Park, C. Journal of Marketing. Jul2018, Vol. 82 Issue 4, p70-85. 16p. 1 Diagram, 2 Charts, 5 Graphs. DOI: 10.1509/jm.14.0510.
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Do Consumers Always Spend More When Coupon Face Value is Larger? The Inverted U-Shaped Effect of Coupon Face Value on Consumer Spending Level
Commonly, a coupon can be applied to one of several vertically differentiated products sold at different prices within the same product line of a brand. With such a product-line coupon, consumers need to decide on the specific product to buy, resulting in different levels of consumer spending. One field data set and four lab experiments demonstrate that the relationship between coupon face value and consumer spending level may not always be intuitively positive; under certain circumstances, it could take an inverted U-shape. The authors develop a threshold-based model to explain the inverted U-shaped effect of coupon face value on consumer spending level and show that this effect occurs when the price level of products is high, when consumers have a strong saving orientation, when they experience low information load from processing a small number of products, when they are inclined to engage in thorough product comparison, or when they have a weak preexisting preference for a specific level of product benefit.
Supplement: http://dx.doi.org/10.1509/jm.14.0510
Price-based promotions have been widely used by retailers to stimulate product sales (DelVecchio, Krishnan, and Smith 2007). In marketing practice, it is quite common for sellers to offer product-line coupons to consumers. A product-line coupon is not restricted to a specific product. Instead, it can be used to buy one of several vertically differentiated products sold at different prices within the same product line of a brand.1 For instance, consumers may get a $50off coupon with which they can enjoy a price discount on any model of a Dell laptop computer series. In another scenario, consumers may receive an unrestricted $10-off coupon from a restaurant, which can be applied to any combination of dishes and drinks.
Extensive marketing research has examined the promotional effects of product-specific coupons, wherein a discount is restricted to a specific product (e.g., Alba et al. 1999; Chandran and Morwitz 2006; Chen, Monroe, and Lou 1998; Chen and Rao 2007; Lee and Tsai 2014; Mishra and Mishra 2011; Nunes and Park 2003; Raghubir 1998; Shiv, Carmon, and Ariely 2005). Yet much less attention has been paid to product-line coupons, wherein an unrestricted discount can be applied to any option within the same product line of a brand. In this scenario, the consumer must decide not only whether to redeem the coupon (e.g., Cheema and Patrick 2008) but also what specific product to buy with the coupon, which may lead to different spending amounts.
In this research, we focus on product-line coupons in an amount-off format and examine how the face value of a product-line coupon could influence consumers’ specific product choice and, consequently, their spending level. One would intuitively expect that a larger coupon face value would incentivize consumers to spend more. In contrast, by developing a threshold-based model, the present research identifies conditions in which the relationship between coupon face value and consumer spending level does not take a positive form but, instead, an inverted U-shape.
With one field data set and four lab experiments, we demonstrate that the inverted U-shaped effect of coupon face value on consumer spending level occurs when products are expensive, when consumers have a strong saving orientation, when they experience low information load from processing a small number of products, when they have a high tendency to compare options thoroughly in their decision process, or when they have a weak preexisting preference for a specific level of product benefit. These findings contribute to the price-based promotion literature and offer important implications for managing coupon face value.
A consumer’s spending decision during a transaction is highly susceptible to the influences of sales promotions (e.g., Park, Iyer, and Smith 1989; Ramanathan and Dhar 2010). As Table 1 summarizes, prior research has mainly examined how the monetary value of a price-based promotion (i.e., promotion depth) influences consumers’ purchase incidence and quantity in the focal promotion period as well as their responses in the postpromotion period, in the case of restricted product-specific promotions. The present research fills a gap in the literature by focusing on the circumstance of unrestricted product-line coupons and by investigating how coupon face value shapes consumers’ spending level under this circumstance.
To simplify the theoretical analysis, we begin with a scenario in which consumers make a binary choice between a high-priced, high-benefit option (i.e., a high spending level) and a low-priced, low-benefit option (i.e., a low spending level) and then discuss the boundaries of our analysis. The aim of this research is to examine how consumers’ choice of a high spending level (SH) over a low spending level (SL) varies as the face value of a product-line coupon in an amount-off format (C) increases from zero to the lower bound of potential spending (0 < C < SL < SH). We first discuss two forces that may drive consumers’ spending decision opposingly and then analyze how the relative strengths of these two forces may shift as coupon face value increases.
Consumers’ total amount of spending is constrained by their mental budget (Karlsson et al. 2004, 2005; Larson and Hamilton 2012; Stilley, Inman, and Wakefield 2010; Van Ittersum, Pennings, and Wansink 2010). When a coupon is present, consumers may experience an increase in their mental budget for product expenditure and thus are encouraged to spend more, which was documented by Heilman, Nakamoto, and Rao (2002) as a “psychological income effect.” Similarly, Dre`ze, Nisol, and Vilcassim (2004) found that promotions increase in-store expenditures. These findings suggest that an increase in the face value of a product-line coupon will propel consumers to spend more by choosing a more expensive product that provides greater benefits due to an increase in their mental budget (Allenby and Rossi 1991).
According to this budget-increase perspective, the relationship between the face value of a product-line coupon (C) that consumers receive and their likelihood of choosing a high level of spending (LH) over a low level of spending can be expressed as: ( 1) LH = a • C + b, where b is the baseline attractiveness of the high spending level, and a is positive and represents that consumers’ likelihood of choosing a high level of spending increases as coupon face value increases. Nevertheless, another theoretical perspective suggests that coupon face value may have an opposite effect on consumer spending level.
Research in marketing and economics has shown that when people evaluate price discounts, they often base their spending decision on the savings percentage associated with a specific discount, which is the ratio of the monetary value of the discount to the original product price (Nunes and Park 2003; Saini, Rao, and Monga 2010; Saini and Thota 2010; Tversky and Kahneman 1981). From this perspective, it is likely that consumers may focus on savings percentages associated with redeeming a product-line coupon for different products in their spending decision. Given that consumers could enjoy a greater savings percentage by applying the same coupon to a less expensive product than to a more expensive one, the less expensive product would become more attractive when consumers base their spending decision on savings percentages.
To further illustrate this perspective in a simplified binary choice scenario, consumers may compare the savings percentage associated with choosing a high spending level (C/SH) with that associated with choosing a low spending level (C/SL). Consequently, their likelihood of choosing a high spending level (LH) over a low spending level is determined by the relative savings percentage, which is denoted by C/SH – C/SL, such that: ( 2) LH = C=SH - C=SL + b = ðSL - SHÞ=ðSL • SHÞ • C + b;
where b again represents the baseline attractiveness of the high spending level, and (SL – SH)/(SL • SH) is negative because SL is smaller than SH. As Equation 2 indicates, as coupon face value increases, the relative savings percentage related to choosing the high (vs. the low) spending level decreases. Consequently, consumers’ likelihood of choosing the high spending level should also decrease. In summary, the savings-comparison perspective suggests a negative relationship between coupon face value and consumer spending, such that the relative attractiveness of choosing the high spending level is reduced when coupon face value increases.
TABLE: TABLE 1 Review of Literature on Influences of Promotion Depth on Consumer Responses
TABLE: TABLE 1 Review of Literature on Influences of Promotion Depth on Consumer Responses
| Study | Key Insights into Influences of Promotion Depth on Consumer Responses | Product-Specific Discount | Product-Line Coupon |
|---|
| In the Promotion Period | | |
|---|
| Purchase Incidence | Purchase Quantity | Postpromotion Responses | Choice of Vertically Differentiated Products |
|---|
| Gupta (1988) | Discount depth has a positive influence on purchase incidence and purchase quantity. | ✔ | ✔ | | |
| Grewal, Marmorstein, and Sharma (1996) | A moderate discount is most likely to increase consumers’ information processing. | | | | |
| Grewal et al. (1998) | The positive influence of discount depth on purchase intention is stronger for consumers with less domain-specific knowledge. | ✔ | | | |
| Alba et al. (1999) | A deep discount leads to lower retrospective estimates of a brand’s price level, particularly when price information is easy to process. | | | ✔ | |
| Anderson and Simester (2004) | In the postpromotion period, a deep discount increases repeated purchases from first-time customers but decreases repeated purchases from established customers. | ✔ | ✔ | ✔ | |
| Thomas, Blattberg, and Fox (2004) | Discount depth has a positive influence on reacquired customers’ relationship duration with a firm. | | | ✔ | |
| DelVecchio, Krishnan, and Smith (2007) | A deep discount reduces price expectation and purchase incidence in the postpromotion period. | ✔ | | ✔ | |
| Biswas et al. (2013) | An exaggerated discount decreases purchase intention by inducing an inference about the poor quality of an unknown product. | ✔ | | | |
| Andrews et al. (2014) | A moderate discount is the most effective in terms of stimulating product purchase when a charitable cause is paired with the discount. | ✔ | | | |
| Aydinli, Bertini, and Lambrecht (2014) | The positive influence of discount depth on purchase incidence is stronger for products that are rich in affect. | ✔ | | | |
| Fong, Fang, and Luo (2015) | The positive influence of the discount depth of a mobile promotion on purchase incidence is stronger for recipients shopping in an area where a business competitor is located. | ✔ | | | |
| Del Rio Olivares et al.(2018) | In a relational setting (e.g., insurance), a moderate initial discount of the first contract is the most effective in terms of retaining customers after the first contract has expired. | | | ✔ | |
| This article | Under certain circumstances, the face value of a product-line coupon has an inverted U-shaped influence on consumers’ spending levels associated with choices of vertically differentiated products. | | | | ✔ |
Ostensibly, the savings-comparison perspective leads to a prediction opposite to that based on the budget-increase perspective. We reconcile these two mechanisms by analyzing when each would be more prevalent. We argue that, consistent with one’s intuition, the budget-increase mechanism is the default mechanism driving the effect of coupon face value because it serves as a straightforward heuristic for consumers. In contrast, the savings-comparison mechanism is more cognitively complex and effortful, and thus its activation should depend on specific conditions.
Relevant to the focal issue, previous research has shown that consumers are not responsive to savings percentages of pricebased promotions unless the savings percentages exceed a certain threshold (Chen, Monroe, and Lou 1998; Gupta and Cooper 1992). From this threshold account, we predict that the relative strengths of the budget-increase mechanism and the savings-comparison mechanism may vary as coupon face value increases. This is because the magnitude of the relative savings percentage associated with choosing a high spending level over a low spending level (C/SH – C/SL) is directly determined by coupon face value (C).
Consider the following example for a numerical illustration. Suppose that the same coupon can be used for buying either Product A (price = $40) or Product B (price = $60). When coupon face value is as small as $5, the relative savings percentage associated with choosing Product A over Product B is only 4.1% (i.e., $5/$40 – $5/$60). Such a difference may be too small to exceed the threshold above which consumers start to use the relative savings percentage as an important basis for decision making (Chen, Monroe, and Lou 1998; Gupta and Cooper 1992). Thus, we expect that when the face value of a product-line coupon is relatively small, the savings-comparison mechanism will not be activated. At this stage, we expect that coupon face value will influence consumer spending level mainly through the budget-increase mechanism and thus will have a positive impact on consumer spending level.
In the same numerical example described in the last paragraph, if coupon face value increases to a large amount, such as $25, the relative savings percentage becomes 21% (i.e., $25/ $40 – $25/$60). At this stage, the relative savings percentage (i.e., 21%) may become large enough to exceed a certain threshold (Chen, Monroe, and Lou 1998; Gupta and Cooper 1992) so that it is more likely to be used as an important decision input for consumers. As a result, coupon face value may influence consumers’ spending level mainly through the savingscomparison mechanism that suppresses the budget-increase mechanism. Consequently, consumers could be more attracted by a lower spending level that is associated with a higher savings percentage, and consumer spending will decrease as coupon face value further increases. Building on these threshold-based analyses, we propose that the face value of a product-line coupon in an amount-off format might have an inverted U-shaped effect on consumers’ total amount of spending.
The previous threshold-based analyses suggest that the savingscomparison mechanism remains inactive when coupon face value is small because the relative savings percentage is too small to be used as an important decision input. In contrast, at large coupon face values, the relative savings percentage becomes large enough to exceed a certain threshold so that it could potentially serve as a meaningful decision input for consumers. However, consumers’ potential adoption of the relative savings percentage as a decision input could still be inhibited in the following circumstances: ( 1) when consumers have a weak saving orientation and thus do not eventually base their spending decision on the relative savings percentage (H1), ( 2) when consumers cannot figure out the relative savings percentage in the first place because of either a low cognitive ability constrained by information overload (H2) or a low tendency to compare savings percentages associated with different options (H3), or ( 3) when consumers have a preexisting preference for a certain product that provides a specific level of benefit and thus do not base their product choice on external factors at all, including savings percentages (H4). In these cases, the savings-comparison mechanism and, consequently, the inverted U-shaped effect of coupon face value on consumer spending will still not emerge. Figure 1 shows the conceptual framework summarizing these moderators. We develop our hypotheses in the following paragraphs.
First, we discuss a circumstance under which consumers differ in the motivation to enjoy a greater savings percentage by choosing a low-priced option. Prior research has shown that consumers can vary substantially in frugality, in terms of being “restrained in acquiring and in resourcefully using economic goods and services to achieve longer-term goals” (Lastovicka et al. 1999, p. 88). In the focal research context, we focus on the saving orientation component of being frugal in product acquisition. When consumers care about saving money and minimizing their product acquisition cost, they should be more attracted by the greater savings percentage associated with choosing a low-priced option. For these consumers, the savings-comparison mechanism should be more likely to emerge at large coupon face values. Therefore, the inverted U-shaped effect should be more likely to occur.
In contrast, consumers who have a relatively weaker saving orientation should be less motivated to base their spending decision on a sizable relative savings percentage at large coupon face values. Instead, these consumers may be either more likely to take advantage of an increase in their mental budget by simply spending more when coupon face value increases or constantly inclined to choose the high-priced option regardless of coupon face value. Thus, we propose: H1: Consumers’ saving orientation moderates the effect of coupon face value on consumer spending level, such that coupon face value has (a) an inverted U-shaped effect for consumers who have a strong saving orientation but (b) a simply positive effect or no effect for consumers who have a weak saving orientation.
Second, while upwardly adjusting one’s spending level according to an increase in his or her available budget is effortless, roughly calculating and comparing savings percentages necessarily requires a greater extent of cognitive processing (Kahneman 2011). This implies that if consumers have already spent a large amount of cognitive resources browsing and processing excessive product information, further calculating and comparing savings percentages would become a highly formidable task because of the depletion of cognitive resources (Shiv and Fedorikhin 1999). In this case, consumers would not be able to figure out the relative savings percentage, which cannot be further used as a decision input as well. Consequently, the savings-comparison mechanism would be unlikely to operate, and the budget-increase mechanism would remain the driver for a positive effect of coupon face value on consumer spending as coupon face value further increases. Thus, we expect that information overload in product presentation will diminish the inverted U-shaped effect and instead foster a positive effect of coupon face value on consumer spending. In contrast, the inverted U-shaped effect should be more likely to occur when product information is not excessive for consumers to process. Thus, we formally propose: H2: Information load moderates the effect of coupon face value on consumer spending level, such that coupon face value has (a) an inverted U-shaped effect when information load is low but (b) a simply positive effect when information load is high.
Third, given that comparing the savings percentages associated with different spending levels to calculate the relative savings percentage tends to be cognitively demanding (Kahneman 2011), a facilitating condition for the savings-comparison mechanism to dominate the budget-increase mechanism at large coupon face values is that consumers indeed engage in a deep level of comparative information processing. Following this logic, the inverted U-shaped effect should be more evident for consumers who have a high tendency to compare options thoroughly in their decision process. In contrast, if consumers have a low tendency for thorough comparison, they would be less likely to figure out the relative savings percentage. Consequently, they would not further rely on the relative savings percentage as a decision input. For these consumers, the savings-comparison mechanism may not be triggered. Instead, the budget-increase mechanism would be still prevalent and lead to an overall positive effect of coupon face value on consumer spending even when coupon face value increases to a large amount. We formally propose:
H3: Consumers’ tendency to compare options in their decision process moderates the effect of coupon face value on consumer spending level, such that coupon face value has (a) an inverted U-shaped effect for consumers who have a high tendency for comparison but (b) a simply positive effect for consumers who have a low tendency for comparison.
TABLE: TABLE 2 Summary of Effects of Coupon Face Value on Consumer Spending Level
| Coupon Face Valueb | |
|---|
| Studya | Sample Size | Stimulus | Moderator | Low | Medium | High | Dependent Measure |
|---|
| 1 | 48,787 | Restaurant spending | Price level | High | 302.8 | 484.6 | 452.4 | Spending amount() |
| Low | 171.5 | 395.3 | 489.8 |
| 2 | 300 | Mobile hard drive | Saving orientation | High | 35.5% | 59.7% | 44.1% | Choice share of the high-priced option |
| Low | 67.9% | 69.8% | 71.3% |
| 3 | 1,162 | Korean barbecue | Information load | Highc | 14.7 | 15.1 | 15.5 | Spending amount ($) |
| High | 40.4% | 42.2% | 43.8% | Choice share of the high-priced option |
| Low | 44.2% | 57.6% | 45.6% |
| 4 | 282 | Godiva chocolate | Tendency for comparison | High | 44.8% | 69.3% | 39.7% | Choice share of the high-priced option |
| Low | 37.9% | 52.0% | 67.0% |
| 5 | 202 | Mobile hard drive | Preference for a high benefit level | High | 92.0% | 89.5% | 88.1% | Choice share of the high-priced option |
| Low | .0% | 52.1% | 4.4% |
| ES1 | 403 | Mobile hard drive (high vs. midprice) | Preference for a high benefit level | High | 61.8% | 58.3% | 52.1% | Choice share of the high-priced option |
| Low | 6.7% | 23.9% | .9% |
| | | Mobile hard drive (high vs. low price) | Preference fora high benefit level | High | 66.9% | 78.2% | 88.3% | Choice share of the high-priced option |
| Low | 1.0% | 6.1% | .3% |
| ES2 | 161 | Godiva chocolate | Brand liking | High | 61.2% | 56.4% | 52.2% | Choice share of the high-priced option |
| Low | 24.5% | 58.6% | 31.6% |
| NI | 300 | Korean barbecue | Tendency for comparison | High | 33.0% | 51.5% | 22.1% | Choice share of the high-priced option |
| Low | 32.0% | 40.2% | 51.6% |
Finally, another underlying assumption for the inverted U-shaped effect is that consumers have a weak preexisting preference for a specific level of product benefit. These consumers need to make a trade-off between price and benefit for different options within the same product line of a brand. Thus, their product choice is easily susceptible to the influences of situational factors, such as coupons. However, if consumers have a strong preexisting preference for a product that provides a specific level of benefit (e.g., a preference for a certain capacity of a mobile hard drive), their product choice should be less influenced by coupon face value. As a result, for these consumers the inverted U-shaped effect will disappear. Thus, we propose:
H4: Consumers’ preexisting benefit-level preference moderates the effect of coupon face value on consumer spending level, such that coupon face value has (a) an inverted U-shaped effect for consumers who have a weak preexisting benefit-level preference but (b) no effect for consumers who have a strong preexisting benefit-level preference.
Five studies explore conditions for the proposed inverted Ushaped effect and investigate a set of moderators theoretically relevant to the savings-comparison mechanism. Study 1 uses field data to examine consumer spending at restaurants and provides correlational evidence for the inverted U-shaped relationship between coupon face value and consumer spending when the price level of a restaurant is relatively high, which may induce a strong saving orientation (H1) or a high tendency to compare (H3). Studies 2–5 provide more evidence for causality by experimentally manipulating coupon face value in a controlled lab setting and show that the inverted U-shaped effect would be more likely to occur when consumers have a strong saving orientation (H1), experience low information load (H2), have a high tendency for comparison (H3), or have a weak preexisting benefit-level preference (H4). Table 2 summarizes the results of the aforementioned studies as well as three additional studies.
In Study 1, we provide preliminary evidence for the existence of an inverted U-shaped effect of coupon face value in an amount-off format on consumer spending level by using actual consumer spending data from restaurants. When eating in restaurants, consumers can choose different combinations of dishes and drinks with different prices and quantities. Therefore, the same coupon can be linked to different levels of total spending on consumption. Because the dishes and drinks are consumed together while consumers dine in restaurants, they can be regarded as parts of an integrated product, making consumption in restaurants an ideal empirical context for our hypothesis testing.
We obtained the field data from a third-party restaurant review site in China (similar to Yelp.com). Consumers who registered for the review site were provided with a membership card and could download coupons available on the review site onto their accounts. When consumers used their membership cards at participating restaurants, the actual transaction amounts were recorded. The data set contained 48,787 observations from a major city in China on a weekly basis from May 2005 to March 2008, with 26,660 registered consumers who spent money in 106 participating restaurants that posted coupons involving amount-off discounts on the review site.2 We regressed total consumer spending per transaction on a set of variables specified as follows: ( 3) Spending = b0 + b1FaceValue + b2FaceValue2 + b3PriceLevel + b4FaceValue • PriceLevel + b5FaceValue2 • PriceLevel + e:
The dependent variable was the total amount of money (in Chinese yuan [CNY]) involved in an individual consumer’s single transaction with a specific restaurant (Spending; i.e., total amount paid plus coupon face value). To test the inverted U-shaped effect of coupon face value (FaceValue; in CNY), we included both the linear and squared terms of this variable. We also examined the moderating role of the price level (PriceLevel; in CNY) of a restaurant in the inverted U-shaped effect. Price level was operationalized as the average spending amount per person consumers reported on the review site for a specific restaurant. We expect that a higher price level will activate a stronger saving orientation (H1) or a higher tendency for thorough comparison (H3) and thus facilitate the inverted U-shaped effect. In contrast, we expect that a lower price level will not trigger a strong saving orientation or a high tendency to compare among consumers. Thus, it would be more likely to foster a simply positive effect of coupon face value on consumer spending. To test this prediction, we entered the firstand second-order interactions between coupon face value and price level in the model.3
Central to our prediction, the model specified in Equation 3 generated a negative second-order interaction between coupon face value and price level (B = -.0002, t = -7.48, p < .001), suggesting that the price level of a restaurant moderated the nonlinear effect of coupon face value on consumers’ spending amount per transaction in the focal restaurant. We further decomposed this second-order interaction using a spotlight analysis (Aiken and West 1991; Fitzsimons 2008). When price level was high (1 SD above the mean), we observed a positive linear term (B = 2.27, t = 10.78, p < .001) and a negative squared term (B = -.01, t = 6.67, p < .001) of coupon face value. Initially, consumer spending increased with coupon face value. Yet after coupon face value reached 162 CNY (approximately US$24), a further increase in coupon face value started to decrease consumer spending. In contrast, when price level was low (1 SD below the mean), the model only revealed a simply positive effect (B = 1.39, t = 11.24, p < .001) of coupon face value on consumer spending (for a graphical illustration, see Figure 2; for the detailed statistics for this and other studies, see the Web Appendix).
Study 1 demonstrates an inverted U-shaped effect of coupon face value on consumer spending when price level is high but a positive effect of coupon face value on consumer spending when price level is low. This study has two limitations. First, the findings are preliminary given that the nature of the data is correlational. We could not exclude the possibility that consumers who actively look for high-value coupons are less likely to buy high-priced items in the first place. Second, we assume that a higher price level may trigger a stronger saving orientation (H1) or a higher tendency for comparison (H3). These two proposed constructs await further testing. We address these two limitations in the follow-up lab experiments. Specifically, we examine saving orientation in Study 2 and tendency for comparison in Study 4.
In Study 2, we manipulated coupon face value to provide direct evidence for the causal relationship between coupon face value and consumer spending level. Whereas Study 1 provides preliminary evidence for the moderating role of saving orientation by assuming that it is associated with price level, in Study 2 we directly measured participants’ saving orientation. If the inverted U-shaped effect is indeed driven by the fact that the savings-comparison mechanism dominates the budget-increase mechanism at larger coupon face values, this effect should be more evident when consumers have a strong saving orientation. In contrast, if consumers have a weak saving orientation, the savings-comparison mechanism will not be triggered, and thus the inverted U-shaped effect should not be observed (H1).
In this study, we simplified consumer spending level to a binary choice between a low-priced, low-benefit product and a high-priced, high-benefit product from the same product line of a brand. We also provided participants with a third option to not redeem their coupons for the two presented products (i.e., no-purchase option). In this setup, we were able to examine how the face value of a product-line coupon influences both product category purchase incidence (i.e., whether participants would choose to redeem their coupons in the first place) and spending level (i.e., which product participants would choose when they have decided to redeem their coupons). Spending level (rather than product category purchase incidence, which has been extensively examined in prior research) is the primary focus of the present research.
Study 2 adopted a 5 (coupon face value: $5, $15, $25, $35, or $45) • 2 (saving orientation: weak vs. strong) between-subjects design, with saving orientation measured as an individual difference variable. Three hundred U.S. residents (145 women; Mage = 35.69 years, SD = 11.76) from Amazon Mechanical Turk (MTurk) participated for monetary compensation. In this study, participants were asked to imagine that they had received a coupon that could be used to buy a mobile hard drive at an online store. We chose this product category because the performances of different mobile hard drives can be unambiguously differentiated. Thus, participants had to make a clear trade-off between product benefit and product price, making their product choice highly susceptible to the influence of coupon face value. Participants imagined that the coupon they received could be used to buy one of two mobile hard drives of the same brand. The two mobile hard drives differed in capacity ( 1,000 GB vs. 2,000 GB), revolutions per minute (RPMs; 5,400 vs. 7,200), and price ($55.45 vs. $99.95; for a description of product stimuli and a coupon example, see Figure WA1 in the Web Appendix). Participants indicated which of the two mobile hard drives they would like to buy using the coupon they received. They could also choose to not redeem the received coupon, such that they had three options in total (i.e., low-priced hard drive, high-priced hard drive, or no purchase).
At the end of the survey, we measured participants’ saving orientation by borrowing items related to saving money in product acquisition from the frugality scale (Lastovicka et al. 1999), including “I am willing to wait on a purchase I want so that I can save money,” “There are things I resist buying today so I can save for tomorrow,” “I believe in being careful in how I spend my money,” and “I discipline myself to get the most from my money” (1 = “strongly disagree,” and 7 = “strongly agree”). We also more directly asked participants the extent to which they put more emphasis on “lower price” ( 1) or “better performance” ( 7) on a seven-point bipolar scale (reverse coded). These items were averaged to form a saving orientation index (a = .75).
Purchase incidence. First, we examined the effect of coupon face value on product category purchase incidence in a logistic regression (0 = “not purchasing any product,” and 1 = “choosing either the low-priced product or the high-priced product”). Although purchase incidence increased from 86.9% to 93.4% as coupon face value increased from $5 to $45, such an increase was not significant (B = .02, Wald( 1) = 1.17, p = .28).4 Furthermore, neither the quadratic effect of coupon face value nor the first- or second-order interactions between coupon face value and saving orientation were significant (ps > .78) when they were added to the logistic regression. Given that, in this study, coupon face value did not significantly affect product category purchase incidence, we dropped the “no-purchase” option from the dependent variable and further investigated the effect of coupon face value on participants’ spending level.
Spending level. To examine the moderating role of saving orientation, we entered saving orientation, coupon face value, coupon face value’s squared term, and the firstand second-order interactions between coupon face value and saving orientation into a logistic regression with product choice as the dependent variable (0 = “low-priced option,” and 1 = “high-priced option”). Central to our theorization, the second-order interaction between coupon face value and saving orientation was negative and marginally significant (B = -.002, Wald( 1) = 2.69, p = .10). This result indicates that participants’ saving orientation moderated the nonlinear effect of coupon face value on spending level.
In a further spotlight analysis (Aiken and West 1991; Fitzsimons 2008), when saving orientation was relatively stronger (1 SD above the mean), the linear term of coupon face value was positive (B = .11, Wald( 1) = 3.86, p = .05), and its squared term was negative (B = -.002, Wald( 1) = 4.25, p = .04), suggesting that the relationship between coupon face value and consumer spending was inverted U-shaped. Participants’ spending level was the highest when coupon face value was between $25 and $35. When saving orientation was relatively weaker (1 SD below the mean), coupon face value had only a directionally positive yet nonsignificant effect on spending level (B = .004, Wald( 1) = .09, p = .77; for a graphical illustration, see Figure 3). As Figure 3 shows, a weaker saving orientation encouraged a stronger preference for the high-priced option (i.e., around 70%) in the first place. Thus, room for an increase in the choice share of the highpriced option could be very limited. Taken together, these results support the moderating role of saving orientation (H1).
Study 2 establishes the causal effect of coupon face value on consumer spending level by using an experimental approach and provides support for H1’s assertion that coupon face value has an inverted U-shaped effect on spending level only when consumers’ saving orientation is relatively stronger. In the following experiments, we seek further evidence for the savingscomparison mechanism that underlies the inverted U-shaped effect by examining other theoretically relevant moderators. Given that, in Study 2, we found that coupon face value did not significantly influence product category purchase incidence, in the following studies we do not include a “no-purchase” option in the choice set, to simplify the experimental design.
According to our theorization, information overload should inhibit the inverted U-shaped effect because such overload depletes consumers’ cognitive resources that are necessary for carrying out a thorough comparison among savings percentages. Instead, under information overload, consumers should follow a less effortful path by simply adjusting their spending level in line with an increase in their mental budget as coupon face value increases (H2). In Study 3, we tested this hypothesis and, specifically, focused on the description (Townsend and Kahn 2014) and number (Scheibehenne, Greifeneder, and Todd 2010) of products as two sources of information overload. Another purpose of this study is to generalize the findings of Study 2 to another product category. Whereas the mobile hard drive stimuli used in Study 2 were primarily utilitarian, we chose a pair of product stimuli that were primarily hedonic for Study 3.
In Study 3, we examined participants’ choice of food menus and investigated the effects of two sources of information overload: ( 1) the description of products and ( 2) the number of products in a choice set. For this purpose, this study adopted a 4 (coupon face value: $2, $4, $6, or $8) • 2 (perceived information load of product descriptions: low vs. high) • 2 (number of presented products: small vs. large) between-subjects design, with the second factor measured at the end of the study. In total, 1,162 U.S. residents (515 women; Mage = 33.63 years, SD = 11.46) were recruited from MTurk and took a survey for monetary compensation.
Participants imagined that they received a coupon that could be used in a Korean barbecue buffet restaurant. Then, they were presented with Korean barbecue buffet menus, which differed in price and variety of dishes. In the small-number condition, participants could choose from only two Korean barbecue buffet menus: a five-item menu at $12.99 and a ten-item menu at $19.99. Those in the large-number condition could choose from six Korean barbecue buffet menus, which contained five to ten items each (these included the two menus presented in the small-number condition; for a description of product stimuli and a coupon example, see Figure WA2 in the Web Appendix). After viewing the coupon and menus, participants indicated their menu choice.
At the end of the survey, we measured the perceived information load of product descriptions by asking participants the extent to which they thought that “there was too much information in the menu descriptions” and “it was difficult to process all the menu information” on a seven-point scale (1 = “not at all,” and 7 = “very much”), which formed an information load index (a = .82). Compared with the product stimuli used in Study 2, which listed only three key attributes of mobile hard drives, the stimuli employed in this study listed all the dishes contained in the buffet menus and, thus, presented moderately excessive information even when the number of presented products was small. Such amounts of information would create a reasonable variation in perceived information load among participants and thus facilitate our hypothesis testing regarding the moderating role of information load. We expect that when the number of presented products is small, the effect of coupon face value on consumer spending level will be inverted U-shaped when participants’ perceived information load from processing product descriptions is low. Conversely, this effect will become positive when consumers’ perceived information load is high. In contrast, when the number of presented products is large, the effect of coupon face value on consumer spending level will be positive because the presence of multiple products plus the moderate amount of information per product already increases the perceived information load of all participants to a relatively high level (H2).
When the number of presented products was small. We conducted a logistic regression in which level of spending (0 = “low-priced option,” and 1 = “high-priced option”) was regressed on the linear and squared terms of coupon face value, perceived information load, and their first- and second-order interactions. There was a positive second-order interaction (B = .04, Wald( 1) = 5.41, p = .02) between coupon face value and perceived information load, suggesting that the nonlinear effect of coupon face value was moderated by perceived information load.
In a further spotlight analysis (Aiken and West 1991; Fitzsimons 2008), for participants who perceived low information load in product descriptions (1 SD below the mean), there was a positive, marginally significant linear effect (B = .54, Wald( 1) = 3.16, p = .08) and a negative, marginally significant quadratic effect (B = -.05, Wald( 1) = 3.24, p = .07) of coupon face value, suggesting that the effect of coupon face value was inverted U-shaped. Participants’ choice share of the high-priced menu was the highest when coupon face value was between $4 and $6. In contrast, for participants who perceived high information load (1 SD above the mean), coupon face value had a directionally positive yet nonsignificant effect on spending level (B = .02, Wald( 1) = .15, p = .70; for a graphical illustration, see Figure 4, Panel A).
When the number of presented products was large. In line with our theorization, a large number of presented products also created higher information load (Msmall = 2.41, SD = 1.40 vs. Mlarge = 3.31, SD = 1.77; F( 1, 1,160) = 91.47, p < .001). Because the dependent variable in this condition was no longer a binary choice and instead had six levels, we treated it as continuous and ran an ordinary least squares regression. We found that coupon face value increased participants’ spending level (B = .12, t = 2.53, p = .01; for a graphical illustration, see Figure 4, Panel B) when they experienced higher information load as a result of a larger number of presented products.
Taken together, the results support H2 and demonstrate that information load plays an important role in determining the effect of coupon face value on consumer spending, such that the effect takes an inverted U-shape only when consumers experience low information load from processing a small number of presented products. Yet when a larger number of presented products, with a moderate amount of information per product, imposes information overload on consumers, the effect becomes simply positive.
We propose that the occurrence of the savings-comparison mechanism results in the inverted U-shaped effect. In Study 4, we further demonstrate the savings-comparison mechanism by examining the moderating role of consumers’ tendency to compare options in their decision process. Consumers who are more inclined to compare options thoroughly should be more likely to engage in savings percentage calculation and comparison, such that the savings-comparison mechanism will emerge at large coupon face values and result in an inverted Ushaped effect. In contrast, consumers who are less inclined to compare options would not bother to engage in savings calculation and comparison. Thus, for these consumers, the budgetincrease mechanism should always be dominant, resulting in an overall positive effect of coupon face value on consumer spending level (H3). We tested this hypothesis in Study 4.
Study 4 also aims to exclude two alternative explanations. The face value of a product-line coupon could affect consumers’ inferences about product quality (Biswas et al. 2013; Chandran and Morwitz 2006; Darke and Chung 2005) and marketers’ persuasion attempts (Hardesty, Bearden, and Carlson 2007). To rule out these two alternative explanations, we directly measured these variables to control for their possible influences in Study 4.
Two hundred eighty-two undergraduate students (174 women; Mage = 20.03 years, SD = 1.42) from a large U.S. West Coast university participated in Study 4. This study adopted a 4 (coupon face value: $5, $10, $15, or $20) • 2 (tendency for thorough comparison: low vs. high) between-subjects design, with the second factor measured as an individual difference variable.
We used Godiva chocolate boxes as our focal stimuli. Participants imagined that they received a coupon that could be used to buy either a Godiva spring chocolate gift box ($28.98, 16 pieces) or a Godiva signature chocolate truffle gift box ($38.98, 18 pieces) for their own consumption. Then, they indicated which gift box they would like to buy (for a description of product stimuli and a coupon example, see Figure WA3 in the Web Appendix). These two boxes differed in price, quantity, and flavor and thus represented two different Godiva products.
After participants made their product choice, they further answered a set of ancillary questions regarding the chocolate boxes. First, we measured quality inference by asking participants to rate the two Godiva chocolate gift boxes on two bipolar scales anchored at “very poor quality” ( 1) versus “very good quality” ( 7) and “very poor condition” ( 1) versus “very good condition” ( 7), which formed a quality inference index (a = .85). Second, we assessed participants’ inference about the persuasion attempt associated with the coupon by asking them the extent to which the promotional offer seemed “to be a sales gimmick used to get consumers to buy,” “to have strings attached,” and to be “too good to be true” (1 = “not at all,” and 7 = “very much”), adapted from Hardesty, Bearden, and Carlson (2007). Given that the reliability of the persuasion knowledge scale was too low (a = .46) in our study, we conducted analyses on the three individual items separately.
At the end of the survey, to measure participants’ tendency to compare options thoroughly in their decision process, we followed Parker and Schrift (2011) by combining the items from two subdimensions of the shortened maximization scale (Nenkov et al. 2008), including “When I am in the car listening to the radio, I often check other stations to see if something better is playing, even if I am relatively satisfied with what I’m listening to”; “When I watch TV, I channel surf, often scanning through the available options even while attempting to watch one program”; “No matter how satisfied I am with my job, it’s only right for me to be on the lookout for better opportunities”; “I often find it difficult to shop for a gift for a friend”; “When shopping, I have a hard time finding clothing that I really love”; and “Renting videos is really difficult. I’m always struggling to pick the best one” (1 = “strongly disagree,” and 7 = “strongly agree”). We averaged these six items to form a tendency for comparison index (a = .67), which represents both the breadth and the depth of option comparison in people’s decision process.
Prior research has shown that a maximization mindset predicts the effort that people exert in their decision-making process across various domains (Iyengar, Wells, and Schwartz 2006; Levav, Reinholtz, and Lin 2012). In particular, Nenkov et al. (2008) show that the items used in our tendency for
comparison index strongly predicted the amount of information participants processed, the time participants took to make decisions, and the number of options participants considered in a decision task. Parker and Schrift (2011) further demonstrate that the tendency for comparison index represents a comparative thinking style. Taken together, the tendency for comparison index captures people’s chronic inclination to perform a thorough comparison among different options in their decision process.
As a supplementary measure for tendency for comparison, participants’ decision time was also recorded in this study (i.e., how many seconds they spent before their last clicks on the web page on which they made their product choices). Because a higher tendency for comparison should lead to longer decision time (Nenkov et al. 2008), we expect that decision time will moderate the effect of coupon face value in a similar way tendency for comparison does.
The role of tendency for comparison. We entered tendency for comparison, coupon face value, its squared term, and its first- and second-order interactions with tendency for comparison into a logistic regression with product choice as the dependent variable (0 = “low-priced option,” and 1 = “highpriced option”). The second-order interaction between coupon face value and tendency for comparison was negative and significant (B = -.01, Wald( 1) = 4.14, p = .04), indicating that the nonlinear effect of coupon face value was moderated by tendency for comparison.
We further conducted a spotlight analysis to understand this moderation (Aiken and West 1991; Fitzsimons 2008). When tendency for comparison was relatively high (1 SD above the mean), the linear term of coupon face value was positive (B = .49, Wald( 1) = 7.46, p = .006), and the squared term of coupon face value was negative (B = -.02, Wald( 1) = 7.80, p = .005). The highest choice probability of the high-priced option occurred when coupon face value was between $10 and $15, demonstrating an inverted U-shaped effect of coupon face value on consumer spending level. In contrast, when tendency for comparison was relatively low (1 SD below the mean), there was a simply positive effect of coupon face value on consumer spending level (B = .08, Wald( 1) = 5.77, p = .02), confirming H3 (for a graphical illustration, see Figure 5). Moreover, adding product quality or items of persuasion knowledge as a covariate in the logistic regression did not change the results reported previously regarding the effects of coupon face value and tendency for comparison, which suggests that inferences about product quality and persuasion attempt from different coupon face values could not explain our results.
The role of decision time. As predicted, participants’ decision time as a supplementary measure for tendency for comparison was indeed correlated with their tendency for comparison (r = .19, p < .001). Further moderated logistic regression analyses show that although the second-order interaction between coupon face value and decision time did not reach significance (B = -.001, Wald( 1) = 1.77, p = .18), the effects of coupon face value at the high and low levels of decision time were similar to those at the high and low levels of tendency for comparison. For participants who spent more time making their product choices (1 SD above the mean), the effect of coupon face value was inverted U-shaped (linear term, B = .44, Wald( 1) = 5.74, p = .02; quadratic term, B = -.02, Wald ( 1) = 5.23, p = .02). In contrast, for those who spent less time making their product choices (1 SD below the mean), the effect of coupon face value was directionally positive (linear term, B = .04, Wald( 1) = 1.54, p = .21). Overall, the effects of decision time were consistent with those of tendency for comparison. A comparison of the results of the two moderated regressions also suggests that the tendency for comparison scale is a more sensitive measure than the decision time measure in revealing when the inverted U-shaped effect would emerge, given that for some participants, longer decision time might result from the difficulty in calculating the savings percentage rather than from a thorough product comparison.
By demonstrating that consumers’ tendency to thoroughly compare options determines whether the effect of coupon face value on consumer spending level is inverted U-shaped or simply positive, Study 4 further supports the role of the savingscomparison mechanism in shaping the inverted U-shaped effect.
Study 5 examines consumers’ preexisting preference for a specific level of product benefit as a boundary condition for the inverted U-shaped effect (H4). To provide further evidence for the savings-comparison mechanism, we also measured participants’ consideration of savings percentages in the decisionmaking process. In our theorization, only at large coupon face values do consumers start to base their spending decision on savings percentages. At this stage, the savings-comparison mechanism occurs, resulting in the inverted U-shaped effect. According to such theorization, we expect that participants’ consideration of savings percentages mediates the negative effect (i.e., the “downward” side) of coupon face value on spending level at large coupon face values, but it does not mediate the positive effect (i.e., the “upward” side) of coupon face value on spending level at small coupon face values.
Design and Procedure
Study 5 used a 5 (coupon face value: $5, $15, $25, $35, or $45) • 2 (preexisting benefit-level preference: weak vs. strong) between-subjects design, with preexisting preference measured as an individual difference variable. Two hundred two U.S. residents (92 women; Mage = 34.94 years, SD = 10.77) from MTurk participated in exchange for monetary compensation. The procedure and stimuli of this study were the same as those of Study 2, except for the following three differences.
First, in Study 5 participants made a binary choice between two mobile hard drives, with the more expensive mobile hard drive providing greater benefits (for a description of product stimuli and a coupon example, see Figure WA1 in the Web Appendix). Second, after participants made their product choice, they indicated the extent to which they thought about the percentage of the list price that they could save by using the coupon when making their choice (1 = “not at all,” and 7 = “very much”). Participants’ rating on this scale serves as a measure for their consideration of savings percentages. Third, at the end of the survey, participants rated the extent to which they had a clear preference for the capacity of a mobile hard drive (1 = “no preference,” and 7 = “a strong preference for 2,000GB over 1,000GB”). A higher score represents a stronger preexisting preference for a higher level of product benefit.
The role of preexisting preference. We entered preexisting benefit-level preference, coupon face value, the squared term of coupon face value, and the first- and second-order interactions between coupon face value and preexisting preference into a logistic regression with product choice as the dependent variable (0 = “low-priced option,” and 1 = “highpriced option”). We obtained a positive second-order interaction between coupon face value and preexisting preference (B = .01, Wald( 1) = 11.47, p < .001), indicating that the quadratic effect of coupon face value was moderated by preexisting preference.
We further conducted a spotlight analysis (Aiken and West 1991; Fitzsimons 2008). When preexisting preference for the 2,000 GB capacity was relatively weaker (1 SD below the mean), the positive linear term (B = 1.30, Wald( 1) = 13.71, p < .001) and the negative squared term (B = -.02, Wald( 1) = 13.47, p < .001) of coupon face value confirmed the inverted U-shaped relationship between coupon face value and consumer spending. Participants’ spending level was the highest when coupon face value was between $25 and $35. When preexisting preference for the 2,000 GB capacity was relatively stronger (1 SD above the mean), the effect of coupon face value became nonsignificant because the choice share of the 2,000 GB hard drive was above 88% regardless of coupon face value (B = -.01, Wald( 1) = .20, p = .65; for a graphical illustration, see Figure 6, Panel A). These results establish preexisting preference for a specific level of product benefit as a boundary condition for the inverted U-shaped effect (H4).
The role of savings percentage consideration. In this study, we also found that coupon face value had an inverted Ushaped effect on consumer spending level in the overall sample even when the moderating role of preexisting benefit-level preference was not modeled. Thus, we further tested the mediating role of savings percentage consideration in the overall sample.
Consistent with our theorizing, a regression analysis shows that participants’ consideration of savings percentages increased as coupon face value increased, indicating that savings percentages indeed became a more important decision input for participants as the percentages increased (B = .03, t = 3.38, p = .001). Given that the effect of coupon face value on spending level was initially positive but started to turn negative when coupon face value was between $25 and $35, we further examined the differential effects of savings percentage consideration in two data ranges, from $5 to $25 and from $35 to $45, respectively.
We conducted mediation analyses using a bootstrapping approach (Hayes 2013). When coupon face value was from $5 to $25, neither the direct effect of savings percentage consideration on consumer spending (B = -.08, Wald( 1) = .80, p = .37) nor the mediating effect of savings percentage consideration between coupon face value and consumer spending (95% confidence interval = [-.011, .002]) was significant. In contrast, at larger coupon face values from $35 to $45, both the direct effect of savings percentage consideration on consumer spending (B = -.31, Wald( 1) = 4.27, p = .04) and the mediating effect of savings percentage consideration between coupon face value and consumer spending (95% confidence interval = [-.029, -.001]) were significant. These results further support the activation of the savings-comparison mechanism at large coupon face values that underlies the inverted U-shaped effect.
In summary, Study 5 identifies preexisting preference for a specific level of product benefit as a boundary condition for the inverted U-shaped effect. This study also provides convergent evidence for the proposed savings-comparison mechanism by demonstrating the mediating role of savings percentage consideration at large coupon face values but not at small coupon face values.
We further conducted two extension studies that assessed the moderating role of consumers’ preexisting benefit-level preference. We elaborate on these next.
Three-option choice set. In the first extension study (U.S. online sample, N = 403), we generalized the findings of Study 5 from a two-option choice set (i.e., low price vs. high price) to a three-option choice set (i.e., low price, midprice, or high price; for a description of product stimuli, see Figure WA4 in the Web Appendix). Replicating Study 5, a multinominal logistic regression shows that both ( 1) the relative choice share of the high-priced hard drive over the midpriced hard drive and ( 2) the relative choice share of the high-priced hard drive over the lowpriced hard drive had an inverted U-shaped relationship with coupon face value only when consumers’ preexisting preference for a larger hard drive capacity was relatively weaker (for detailed results, see the Web Appendix).
Brand liking. In the second extension study (U.S. undergraduate sample, N = 161), we used brand liking to operationalize preexisting preference for a specific level of product benefit. Consumers with a strong brand liking may also have a strong preexisting preference for the product that provides greater benefits and thus is more expensive, regardless of the face value of the product-line coupon they receive, given that a strong brand liking reduces consumers’ price sensitivity (Ailawadi, Lehmann, and Neslin 2003). As a result, for these consumers, the inverted U-shaped effect of coupon face value on consumer spending on this brand should disappear as well. The second extension study using the Godiva stimuli from Study 4 (for a description of product stimuli, see Figure WA3 in the Web Appendix) supports the moderating role of brand liking. Specifically, the inverted U-shaped effect was revealed only when consumers had a relatively weaker liking for Godiva (for a graphical illustration, see Figure 6, Panel B; for detailed results, see the Web Appendix).
The present research explores conditions in which the face value of a product-line coupon has an inverted U-shaped effect on consumer spending level. Our findings are robust for both hedonic products (e.g., food and snack) and utilitarian products (e.g., mobile hard drive) and across well-known brands (e.g., Godiva chocolate), less famous brands (e.g., Touro mobile hard drive), and fictional brands (e.g., Korean barbecue buffet). We provide evidence for the savings-comparison mechanism by showing that the inverted U-shaped effect is contingent on the price level of products (Study 1), consumers’ saving orientation (Study 2), information load (Study 3), and consumers’ tendency for thorough comparison (Study 4), and this effect is driven by consumers’ savings percentage consideration (Study 5). We also identify a preexisting preference for a specific level of product benefit as a boundary condition for the inverted U-shaped effect (Study 5).
Our research contributes to the literature on price-based sales promotions. Whereas most existing research in marketing has focused on a price discount or coupon that is restricted to a specific product with a fixed price (e.g., Alba et al. 1999; Chandran and Morwitz 2006; Chen, Monroe, and Lou 1998; Chen et al. 2012; Chen and Rao 2007; Lee and Tsai 2014; Leone and Srinivasan 1996; Mishra and Mishra 2011; Nunes and Park 2003; Raghubir 1998; Shiv, Carmon, and Ariely 2005; Thomas and Morwitz 2009), our research examines a popular marketing practice in which a coupon can be used for different products within the same product line of a brand. When a product-line coupon is redeemed, the spending level associated with a specific product choice becomes a very crucial issue—one that, to the best of our knowledge, has not been studied in the literature. Our research makes an important first attempt to examine how coupon face value influences consumer spending level in this context.
More importantly, contrary to what the conventional wisdom would predict, we show that an increase in coupon face value does not always lead to an increase in consumer spending level. Instead, for product-line coupons, the effect of face value on consumer spending level could be inverted U-shaped under some circumstances. There are two streams of research on sales promotions that lead to opposite predictions on the effects of coupon face value on consumer spending level. Although the budget-increase perspective suggests a positive effect, the savings-comparison perspective predicts the opposite. Our research reconciles these two opposing predictions by proposing a threshold-based account and by showing that the magnitude of coupon face value could determine the effect of coupon face value on consumer spending level.
It is quite common for retailers to offer deep discounts, especially during major holidays. Prior research has suggested that deep discounts can be detrimental when they dilute brand images (Dodson, Tybout, and Sternthal 1978) or lower consumers’ future price expectations (DelVecchio, Krishnan, and Smith 2007), which may further negatively affect future product sales in the long run. Our research suggests that the negative effects of deep discounts can take place more immediately. When a deep discount is offered in the format of an amount-off coupon that can be used to buy different products within the same product line of a brand, it may hurt sales because a larger coupon face value may motivate consumers to choose less expensive options under certain circumstances.
Fortunately, our findings offer a contingency approach for effectively managing the face value of a product-line coupon to avoid this negative consequence. Drawing on our findings, marketers can determine when they should offer product-line coupons with either a large or a moderate face value, depending on whether the relationship between coupon face value and consumer spending is simply positive or inverted U-shaped. Specifically, marketers can use a large coupon face value to encourage consumer spending when the large coupon face value eventually leads to a high spending level. We identify the positive influence of coupon face value on consumer spending when products are less expensive, when product-line coupons can be applied to a large number of products, or when a firm is able to deliver customized product-line coupons to a target group of consumers who are less inclined to engage in thorough product comparison (e.g., when the firm is able to identify these consumers by tracking their product browsing history). In these cases, marketers could be confident in the power of a large coupon face value to increase consumers’ spending level.
In contrast, marketers might consider utilizing a moderate coupon face value when the effect of coupon face value on consumer spending takes an inverted U-shape because a large coupon face value may backfire in terms of inducing consumers to spend less. Our findings suggest that a firm should offer product-line coupons with a moderate face value when the firm’s products are expensive or when product-line coupons can be applied to only a small set of products, because in these cases the relationship between coupon face value and consumer spending is inverted U-shaped. Moreover, we advise marketers to choose a moderate coupon face value if they are able to identify consumers who care more about saving money, who are motivated to conduct thorough product comparison, who do not have a strong preexisting preference for a specific level of product benefit, or who do not have a strong liking for the focal brand. These consumers’ spending amount can be maximized when the face value of a product-line coupon is at a moderate level.
Although the present set of studies focuses on the research context in which a coupon can be applied to different products sold at different prices within the same product line of a brand, we expect that the findings derived from this context would still hold when a retail store provides consumers with a coupon that can be used for vertically differentiated products offered by different brands within the same product category. This is because, in the latter context, consumers need to make a similar trade-off between price and benefit for different products, just as participants in our experiments did. Our findings are also applicable to scenarios in which the same coupon can be linked to different combinations of products that are consumed together, such as food and drink items during a restaurant visit. Given that these interrelated items can be regarded as components of an integrated product package, our theoretical framework can also predict the relationship between consumers’ total spending on these items and coupon face value.
The underresearched context of unrestricted product-line coupons provides several interesting avenues for further exploration. First, our studies examine a scenario in which face values of a product-line coupon are below the lower bound of consumers’ potential spending level. What would happen when coupon face values fall between the lower and upper bounds of the retail price range of products to which a coupon can be applied is a subject for future research. Second, future studies could build more sophisticated models to more accurately specify the range of the turning point of the inverted U-shaped effect. Finally, in the present research the operationalizations of some moderators may have limitations. For example, we operationalized information overload by increasing the number of presented products in Study 3. Yet a large number of products could activate other constructs, such as consumers’ expected choice regret (Iyengar and Lepper 2000). Future studies could address such limitations to provide more evidence for the savings-comparison mechanism that underlies the inverted Ushaped effect.
a“ES” indicates two extension studies for Study 5; “NI” indicates one study that is not included. Details are available on request. bMeans and choice shares are estimated from regression coefficients. “Low” and “high” represent the lower and upper bounds of coupon values; “medium” represents the coupon value at which the inverted U-shaped curve reaches its peak point. cHigh information load due to a relatively larger number of products presented.
Footnotes 1 The context in which the same coupon is applicable to several vertically differentiated products is different from the one in which the same coupon can be used for different package sizes of the same product (Krishna and Shoemaker 1992).
2 We treat other amount-related coupons (e.g., free dishes) as amount-off coupons by entering their equivalent monetary values into the independent variable because these coupons also reduce consumers’ overall acquisition cost in an amount-off format during restaurant visits.
3 The rating and volume of the online reviews for a restaurant may influence consumers’ spending level (Lu et al. 2013). When these variables are entered into the model as covariates, the effects of coupon face value remain unchanged.
4 In all of our studies, the significance levels of the regression coefficients are similar when we code the actual value of the coupon face value variable and when we code it in an increasing level.
GRAPH: FIGURE 2 Coupon Face Value, Price Level, and Spending Level (Study 1)
GRAPH: FIGURE 3 Coupon Face Value, Saving Orientation, and Spending Level (Study 2)
GRAPH: FIGURE 4 Coupon Face Value, Information Load, and Spending Level (Study 3)
GRAPH: FIGURE 5 Coupon Face Value, Tendency for Comparison, and Spending Level (Study 4)
GRAPH: FIGURE 6 Coupon Face Value, Preexisting Preference, and Spending Level (Study 5)
DIAGRAM: FIGURE 1 Conceptual Framework
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Does It Pay to Recall Your Product Early? An Empirical Investigation in the Automobile Industry
Defective products are often recalled to limit harm to consumers and damage to the firm. However, little is known about why the timing of product recalls varies after an investigation is opened. Likewise, there is little evidence on whether recall timing affects stock markets. This study tests the effect of problem severity on time to recall, the role of brand characteristics in moderating this relationship, and the stock market impact of time to recall. The authors test the hypotheses on a sample of 381 recall investigations in the automobile industry between 1999 and 2012. The results show that although problem severity increases time to recall, this relationship is weaker when the brand under investigation ( 1) has a strong reputation for reliability and ( 2) has experienced severe recalls in the recent past. However, the relationship between problem severity and time to recall is stronger when the brand is diverse. Importantly, the results reveal that stock markets punish recall delays. The study suggests that time to recall has significant implications for managers and policy makers.
Online Supplement: http://dx.doi.org/10.1509/jm.15.0074
Defective products affect the physical safety of consumers and expose manufacturers to liability claims, fines, and loss of reputation. Consequently, defective products are often recalled to limit damage to consumers and firms. In the United States, the Consumer Products Safety Commission reported a total of 390 recalls in 2014, ranging from dishwashers to toys and cribs.[ 1] In the U.S. automobile industry, the National Highway Transportation and Safety Agency (NHTSA) has overseen recalls involving hundreds of millions of vehicles (Rupp and Taylor 2002).
Typically, when a product is suspected of defects, a government agency can initiate an investigation. While many investigations end with the product being cleared of suspected defects, a significant number of investigations also culminate in a recall. The decision of whether and when to recall is not a simple one. Recalls are costly; announcing and implementing one is associated with both direct costs in repair, restitution, or liability and indirect costs such as losses in reputation and market value (Chen, Ganesan, and Liu 2009; Hora, Bapuji, and Roth 2011). Consequently, recalls could have a devastating impact on a firm's performance, sometimes even threatening its survival. Thus, a firm has reasons to avoid a quick recall and instead wait for the investigation to conclude.
However, delaying a product recall may lead to higher direct and indirect costs through fines, liability damages, and most importantly, diminished reputation (Tang 2008). In 2012, Toyota was fined $17.35 million dollars for delaying a floor mat recall (Koyitty 2012). The U.S. Department of Justice fined General Motors $900 million for willfully delaying the recall for a faulty ignition switch and, thus, defrauding customers (Isidore and Marsh 2014). Therefore, although recalls are adverse events in general, a quick response may attenuate the damage (Dani and Deep 2010) and enhance consumer welfare through improved product safety and performance. In effect, the recall behavior of a firm, especially the timing of recall, is important because it has legal and financial consequences for the firm and economic and safety-related consequences for consumers (Marucheck et al. 2011). The considerable variation in time taken by firms to recall after an investigation is initiated (Wieder 2011) raises important questions for managers and policy makers.
Why do some firms recall earlier than others after an investigation is opened? Do stock markets respond to the timing of recalls? The objective of this research is to investigate the variation in and performance consequences of time to recall. We define time to recall as the time elapsed between the opening of an external, formal defect investigation and the announcement of a recall by the firm. The article makes two contributions to marketing theory and practice. First, to date, no study to our knowledge has examined the factors that influence the timing of product recalls. The product recall literature in marketing, as summarized in Table 1, has predominantly focused on the consequences of recalls for the firm's bottom line (e.g., Borah and Tellis 2016; Chen, Ganesan, and Liu 2009; Cleeren, Van Heerde, and Dekimpe 2013; Liu and Shankar 2015; Van Heerde, Helsen, and Dekimpe 2007) and the firm's ability to learn and prevent future recalls (Haunschild and Rhee 2004; Thirumalai and Sinha 2011). However, little attention has been paid to the actual recall behavior of the firm. By examining the time to recall, our study offers insights on when firms are likely to respond swiftly to defects that are under external investigation.
TABLE: TABLE 1 Literature Overview on Product Recalls
| Study | Performance Outcomes | Recall Timing | Prerecall/Postrecall | Industry | Relevant Findings |
| Borah and Tellis (2016) | ✓ | | Postrecall | Automotive | Product recalls create negative chatter for not only the affected brand but also other brands of the same firm or in the same category (perverse halo). This chatter negatively affects firm performance. |
| Chen, Ganesan, and Liu (2009) | ✓ | | Postrecall | Consumer goods | The stock market responds negatively to recalls announced before the firm receives any reports of injuries. Firms with higher brand quality are less likely to announce a proactive recall. |
| Cleeren, Van Heerde, and Dekimpe (2013) | ✓ | | Postrecall | Fast-moving consumer goods | When firms acknowledge blame in a crisis, they are more likely to experience decreases in advertising effectiveness and increases in price sensitivity. Crisis publicity increases advertising effectiveness for the brand and category. |
| Dawar and Pillutla (2000) | | | Postrecall | Soft drinks | While strong expectations buffer the impact of a crisis on brand equity (relative to weak expectations), consumers punish those brands more for a stonewalling response than response that is ambiguous or supportive. |
| Hora, Bapuji, and Roth (2011) | | ✓ | Postrecall | Toys | The lag between product sold and recall is influenced by the type of product defect and entity announcing the recall. |
| Liu and Shankar (2015) | ✓ | | Postrecall | Automotive | Recalls negatively influence brand sales and advertising effectiveness. The reaction is more negative when the recall is severe, faces greater attention, and involves higher-quality brands. |
| Rubel, Naik, and Srinivasan (2011) | ✓ | | Postrecall | Automotive | To recover from a crisis, firms should reduce advertising spending before the crisis and increase it after the crisis. This recommendation is supported using data from the Ford Rollover crisis. |
| Van Heerde, Helsen, and Dekimpe (2007) | ✓ | | Postrecall | Food | A product-harm crisis reduces a brand's performance and the effectiveness of its marketing instruments. The brand also becomes more vulnerable to competitive effects. |
| The current study | ✓ | ✓ | Prerecall | Automotive | In an opened safety investigation, the severity of the problem enhances time to recall. However, this effect is weaker when brands have (1) higher reliability and (2) greater intensity of recalls in the past. In contrast, the effect is stronger for brands that are diverse. Finally, the time to recall is negatively related to stock market performance. |
Drawing on insights from the behavioral theory of the firm, we posit that time to recall is influenced by the firm's ability to fully investigate the defect and its motivation to wait for the outcome of the investigation. We argue that a key factor that delays recall post an investigation is the severity of the problem. Severe problems are defined as those with serious consequences. We test our hypothesis using defect investigations involving the major automakers from 1999 to 2012 in the United States. We analyze time to recall using a Weibull accelerated failure time (AFT) hazard model. Indeed, this study finds that, as we expected, problem severity increases the time to recall.
Interestingly, we find that there is variation in the magnitude and direction of the relationship between problem severity and time to recall. For a given level of problem severity, increase in brand reputation for reliability reduces the time to recall, whereas increase in brand diversification (i.e., breadth of offerings of the brand) increases the time to recall. Furthermore, our study finds that increase in past recall intensity of the brand helps lower the time to recall. This finding implies that product recalls do have a positive effect in regulating the behavior of firms and promoting consumer safety. These insights help clarify why recall timing varies across brands. Collectively, our study shows that the recall timing behavior of firms is complex and nuanced.
Second, we test the performance impact of product recall timing decisions. We assess performance using a short-term event study methodology. As noted previously, prior research has not examined whether the timing of product recalls affects stock markets. Our study finds that stock markets react more negatively when time to recall increases. This finding serves a cautionary note to firms that respond slowly to potential problems in the hopes of avoiding a recall altogether.
Products are recalled when suspected defects undermine their performance and/or safety. Recalls are offered to all consumers of a product, including those who may have not yet experienced any problem associated with the defect. Product defects are investigated and recalls are often supervised by government agencies that, among other activities, inform the public about a recall and monitor its completion. In the United States, these include agencies such as the Consumer Product Safety Commission, Food and Drug Administration, and NHTSA. To complete a recall, firms either repair the product or allow customers to return it for a refund.
Product failures occur for many reasons. They can arise from flaws in design or production (Mackelprang, Habermann, and Swink 2015; Ramdas and Randall 2008), problems with materials and suppliers (Chao, Iravani, and Savaskan 2009), and unanticipated use (and misuse) of the product by consumers (Berman 1999). Sources internal or external to the firm may be responsible for the defect (Folkes and Kotsos 1986). As a Ford spokesperson noted during an investigation into a defect causing a fire hazard involving the Ford F-150 series: "Fires happen for a variety of reasons ranging from faulty repair, improper modification to the vehicle with aftermarket parts and wiring, prior accident damage, and even arson. This is why each complaint or allegation must be reviewed on a case-by-case basis" (Thomas 2005). Thus, thorough investigations are often required before the source of the problem can be identified and a remedy provided. The investigation could show that the product is safe and need not be recalled. For these reasons, a hasty recall might lend credibility to an unsubstantiated claim (Smith, Thomas, and Quelch 1996).
Product recalls are costly propositions. Recall costs can be direct or indirect. Direct costs include all expenditures related to the recall process: expenses for repair, refund, or replacement, including costs associated with retrieving the defective product (Jayaraman, Patterson, and Rolland 2003). The magnitude of these costs depends on the nature of the problem, the size of the product population to be recalled, and consumer response to the product recall. Thus, firms could be motivated to delay recalls and push the direct, tangible costs of recall into future time periods. For example, in internal company documents, Toyota claimed that it saved $100 million by delaying a full recall (McCurry 2010). Furthermore, the potential for short-term loss of sales might also lead to a delay in recall to meet sales and profit targets even as the firm considers the effect of the recall on brand reputation.
Recalls also lead to indirect costs associated with declines in reputation and market performance. Brand reputation depends on stakeholder perceptions about the brand's safety and reliability (Keller 1993; Stahl et al. 2012). Recalls constitute negative information about a brand' s performance and could thus damage its reputation if stakeholders update their beliefs about the brand. Recalls can also lead to a downturn in the firm' s market performance (Rhee and Haunschild 2006). This decline can occur because firms may withdraw a recalled product from the market or consumers may switch to competitors' products.
These indirect recall costs, however, are often contingent on how the firm responds to a safety problem. If a recall becomes inevitable, quick responses lead to lower losses in brand reputation than a stonewalling or defensive response (Dawar and Pillutla 2000; Siomkos and Kurzbard 1994). Delayed recalls, in contrast, can damage the brand's reputation and also increase litigation risk. If indirect costs of a recall could exceed its direct costs (Rupp 2004), a quick recall might be warranted. Thus, although firms may have compelling reasons to delay recalls, a quick response may help in containing the indirect costs (Dani and Deep 2010). Overall, the recall timing decision is far from straightforward because firms face competing pressures and constraints.
The behavioral theory of the firm characterizes firms as systems of structurally distributed attention, defined as the noticing, encoding, interpreting, and focusing of time and efforts by firms' decision makers. This theory implies that firms selectively attend to market information, conduct limited search, and find satisficing solutions to problems. A satisficing solution involves striving to meet multiple goals such as market share and brand reputation while operating within a profit constraint (Cyert and March 1992). We approach recall timing decisions from the vantage point of the behavioral theory of the firm. From this perspective, the ability and motivation of firms to respond to a defect investigation in light of these multiple and often conflicting goals are key determinants of their actions.
Firms divide their managerial attention and resources among numerous events and prioritize the ones that need a quick response. Attention is likely to be focused on external events likely to affect the firm' s long-term ability to generate cash flows (Argote and Greve 2007; Cyert and March 1992). Prior research has examined firm responses to such events, including competitive actions such as new product introductions or market entry (Chen and Hambrick 1995; Gatignon, Anderson, and Helsen 1989; Jayachandran and Varadarajan 2006) or responses to technological advances (Lee and Grewal 2004). The opening of a safety investigation constitutes such an event given that the investigation could result in a recall, which studies have shown to be a value-relevant event (e.g., Chen, Ganesan, and Liu 2009). Firm responses can be categorized along several dimensions, but in the context of safety investigation, the most relevant response characteristics are the likelihood of a recall and the associated timing. Prior research has shown that these response dimensions are a function of the motivation and ability of the firm to respond to the external event (Chen and Hambrick 1995; Smith et al. 1991).
We argue that the more severe the defect, the greater the attention to the investigation because a recall involving a severe defect is costlier than a recall involving less severe problems. However, although problem severity triggers the search for a solution, it does not necessarily lead to a quick response, because it reduces the ability and motivation of firms to respond. We contend that the primary factor that drives recall timing is problem severity.
In addition, we propose that the effect of problem severity on time to recall will be contingent on the characteristics of the brand. Specifically, we argue that the impact of problem severity on time to recall will be moderated by ( 1) brand reliability, ( 2) brand diversification, and ( 3) pastrecall intensity of the brand. Drawing on insights from the behavioral theory of the firm, we explain how these brand characteristics moderate the relationship between problem severity and time to recall by altering the ability and motivation of the firm to respond to a potential defect of high severity that is under investigation.
Problem severity refers to the seriousness of the consequences of product defects from the standpoint of consumer safety. We expect problem severity to be significantly related to recall timing because of its impact on the ability and motivation of firms to respond. The ability of the firm to provide a quick response is closely linked to whether the firm can identify a potential solution to fix the defect. In this regard, severe problems will trigger "problemistic" or problem-oriented investigations in firms. However, problemistic search behavior is myopic in that the investigation will rely on traditional routines and, thus, may not quickly arrive at a solution (Argote and Greve 2007). Therefore, although all safety investigations might trigger the search for a solution, the firm's ability to provide a quick response to severe problems is especially limited.
When a product defect is suspected, apart from searching for a solution, the firm also strives to determine who is to be held accountable for the failure (Sitkin 1992). Assessing accountability becomes more consequential for severe problems, especially because of the desire to prevent such problems in the future. However, fixing internal responsibility may put employees who are entrusted with finding a solution to the problem at risk for loss of reputation, demotion, or even job loss. Thus, the desire to avoid responsibility might lower motivation to share information and pursue a solution among those likely to be held accountable (Madsen and Desai 2010). The ensuing response would be similar to that predicted by the "threat-rigidity" hypothesis (Staw, Sandelands, and Dutton 1981), in which the key concern may not be solving the problem but rather protecting the interest of the dominant coalition by controlling decisions.
Research has also shown that stakeholders are more likely to punish severe recalls than less severe recalls (Cheah, Chan, and Chieng 2007; Chen, Ganesan, and Liu 2009; Liu and Shankar 2015). Furthermore, lawsuits are more likely in cases of severe defects, and therefore, the stakes are higher. Thus, as problem severity increases, firms will also be motivated to avoid external accountability and delay the recall. The benefit of delaying is that the firm could avoid a recall altogether if the investigation finds that the problem does not constitute a safety defect that warrants a recall. Therefore, firms may be motivated to delay a response during the investigation even when there are numerous complaints. For example, the New York Times recently reported that despite 150 complaints of injuries or deaths attributed to a steering problem with the Ford Focus, Ford had not initiated a recall (Jensen 2015). Overall, when the firm is investigated for severe defects, the process may take longer and delay the recall decision. We offer the following baseline hypothesis:
H1: Problem severity increases time to recall.
How does the relationship between problem severity and time to recall vary as a function of brand reliability? Brand reliability is a brand's reputation as a provider of dependable products. Reputation—whether for a brand or for the firm itself—is a critical asset that a firm strives to protect. For instance, Warren Buffett, chief executive officer of Berkshire Hathaway, in a July 2010 letter, exhorted his managers to zealously guard Berkshire' s reputation: "We can afford to lose money—even a lot of money…. But we can't afford to lose reputation—even a shred of reputation" (Protess, Rusli, and Craig 2011). A strong brand reputation confers several advantages. It attracts and retains customers, reduces their price sensitivity, and enhances revenues (Keller 1993; Stahl et al. 2012). There are two reasons why brand reliability should moderate the relationship between problem severity and time to recall.
The damage to brand reputation from a crisis is a function of prior expectations and whether the recall strategy is consistent with those expectations (Cleeren, Dekimpe, and Helsen 2008; Dawar and Pillutla 2000). Consumers are particularly sensitive to information that violates expectations of high reliability (Heath and Chatterjee 1995). Defects of high problem severity already violate those expectations. Consequently, a quick recall would be more consistent with the expected response from brands of high-reliability reputation. A delayed recall for severe problems by higher-reliability brands would further violate expectations. That is, although the brand's reputation for reliability will be harmed by the negative information conveyed by a recall, a quick response could potentially limit the damage (Dawar and Pillutla 2000; Siomkos and Kurzbard 1994). Thus, higher-reliability brands will be motivated to recall faster than their lower-reliability counterparts as they face investigations for serious problems.
Second, whether the affected brand has a reputation for being reliable is indicative of the firm's ability to investigate a defect and determine the root cause faster. Firms that position their brands on reliability are likely to do so by building strong research and engineering expertise. This expertise could help the firm identify the type and source of the flaw (e.g., materials/sourcing, product) more quickly. Thus, reliable brands will be quicker to offer remedies/solutions for severe problems than brands that are less reliable. Overall, as brand reliability increases, the motivation and ability to hasten recalls for severe problems will increase. Thus, we expect:
H2: The higher a brand's reliability, the weaker the relationship between problem severity and time to recall.
Brand diversification refers to the number and variety of products marketed under a brand. How is the relationship between problem severity and recall timing affected when brand diversification increases? Brand diversification influences the firm' s need to investigate the problem more thoroughly, thus deepening the search process. While diverse brands market different products, firms often share components and systems across products to reduce costs. This practice introduces uncertainty around safety investigations. When a safety investigation is opened, firms need to perform checks on multiple models to accurately determine the source of the defect. Furthermore, the investigation could become complex because shared components or systems could perform differently in subbrands as a result of interactions with other systems (Ramdas and Randall 2008). Therefore, failure in a brand does not always imply that all subbrands would be necessarily at risk. This uncertainty places greater pressure on diverse brands to accurately investigate safety issues (Ramdas and Randall 2008).
To exacerbate the problem, prior research has also shown that firms that address multiple segments through diversification are less capable of integrating complex knowledge and learning from their own experience, compared with their more focused rivals (Haunschild and Sullivan 2002; Ingram and Baum 1997). Thus, diversified brands are likely to be less adept at investigating severe problems, resulting in delayed recalls. Moreover, the fear of negative spillovers to related products in the portfolio that would expand the scope of the recall may inhibit the firm' s motivation for a quick recall in the hope of avoiding one after the investigation. Overall, increase in brand diversification is likely to reduce both the ability and motivation of firms to recall quickly when faced with problems of high severity. Thus, we propose:
H3: The higher a brand's diversification, the stronger the relationship between problem severity and time to recall.
Past recall intensity refers to the extent of product recalls experienced by the firm in the recent past. The arguments for how past recall intensity could influence the relationship between defect severity and time to recall are equivocal. Prior research has shown that firms can learn from failures such as accidents or product recalls (Madsen and Desai 2010; Thirumalai and Sinha 2011). Recent recalls should help firms develop the managerial and administrative competence to deal with information about product failures more effectively, making their own investigation process more efficient. Thus, a firm that has experienced recent recalls is more likely to have relevant knowledge stock that it can tap into in case of a new investigation. Consequently, recent recalls sensitize the firm to the trajectory of investigations and expedite recalls. This learning will be especially valuable when the products under investigation are potentially facing severe problems.
However, past recalls could also limit a firm's ability to recall early. The recall process is tedious. Details about the recall must be communicated to channel members and consumers, a remedy offered, and the supply chain reconfigured for the reverse flow of goods (Jayaraman, Patterson, and Rolland 2003). The remedial process can take a long time because consumer return rates are often low. Even after several months, firms have to devote resources to motivate consumers to comply with the recall. A recent U.S. Government Accountability Office report shows that the remediation rates average approximately 70% for automobiles 18 months after the recall. For other products, with which firms have a lower ability to track consumers, these rates are lower. Thus, firms with high past recall intensity are likely to be resource-constrained and have lower ability to issue a quick recall. Given the equivocal arguments for the moderating role of past recall intensity, we do not offer a formal hypothesis but treat it as an empirical issue. Table 2 summarizes the rationale for these hypotheses.
We expect time to recall to adversely influence stock market performance. As previously discussed, recalls are costly events that not only require a resource commitment in the short and medium run but also can damage marketing assets such as brands for many years. In the case of a recall, time to recall reveals information to the firm' s stockholders about its responsiveness to safety concerns. Stock markets are likely to respond to recall timing decisions because they significantly shape consumer sentiment toward the brand. That is, time to recall has value relevance for stock markets beyond information about the recall's direct and indirect costs.[ 2] As we noted previously, firms turn inward and investigate a defect when they suspect a severe problem, slowing down the recall process. Longer time to recall signals that the firm lacks either the ability or the motivation to respond to safety problems in a timely manner. That is, it does not have the knowledge and processes in place to make a decision quickly and it does not have the motivation to recall quickly to safeguard consumer safety. In contrast, a quick recall signals to stock markets that the firm is responsive to safety concerns and can make recall decisions quickly. Thus, stock markets are likely to view time to recall as a proxy for the firm's commitment to safety concerns. Therefore,
H4: Time to recall is negatively associated with stock market performance of the firm.
TABLE: TABLE 2 Overview of Rationale for Hypotheses
| Main Effects/Moderator Effects | Ability to Recall Faster | Motivation to Recall Faster | Net Effects on Time to Recall | Rationale |
| Problem severity (H1) | –a | –a | Increaseb | • Triggers problemistic search |
| | | | • Recall is more likely to be punished |
| | | | • Need to determine internal accountability |
| Problem severity × | 1 | + | Decrease | • Late recall can damage reputation |
| Brand reliability (H2) | | | | • More likely to have systems in place to determine root cause of failure |
| Problem severity × Brand diversification (H3) | – | – | Increase | • Need to test multiple products for presence of defect |
| | | | • Lower ability to integrate complex knowledge |
| Problem severity × Past | – | + | ? | • More likely to have knowledge stock |
| brand recall intensity | | | | • Likely to have resource constraints, diverted attention |
aProblem severity will lower the firm's ability to recall faster and motivation to recall faster.
bThe net effect of problem severity is an increase in time to recall.
Notes: Plus (minus) signs represent strengthening (weakening) of the effect in question. The net effect on time to recall is given in the fourth column.
To test the hypotheses, we collected data on safety investigations in the automotive industry. We focus on the automotive industry for several reasons. First, examining recall timing in one industry eliminates confounding from extraneous industry-specific effects and enhances internal validity. In addition, the automotive industry is a salient one because of the comprehensiveness of the data available; the opening of an investigation and the announcement of a recall are well documented in this industry. For these reasons, the automotive industry has been the context of other studies in the recall area (e.g., Borah and Tellis 2016; Haunschild and Rhee 2004; Jarrell and Peltzman 1985; Kalaignanam, Kushwaha, and Eilert 2013; Liu and Shankar 2015).
Investigations in the automotive industry are initiated by the NHTSA. The NHTSA was instituted in response to the National Traffic and Motor Vehicle Safety Act of 1966. The responsibilities of NHTSA include establishing minimum performance standards for automobiles, verifying whether these standards are met, investigating noncompliance, and directing recall campaigns (Rupp and Taylor 2002). The NHTSA has overseen thousands of recalls involving hundreds of millions of vehicles since its inception in 1966. Most of the recalls are voluntary yet supervised by the NHTSA.
If the NHTSA suspects a product defect, it can open an investigation into this particular issue. Investigations can be triggered by consumer complaints, queries into ongoing or past recalls, service bulletins, product testing, or petitions. At the end of the investigation, NHTSA will either require the manufacturer to recall the product or close the investigation into the issue for the time being. The manufacturer, however, can issue a recall at any point during the investigation. The NHTSA assesses the proposed recall on the basis of whether it will remedy the safety problem.
Our data set includes all investigations on safety issues related to passenger vehicles between 1999 and 2012. The year 1999 was the first for which data were available for all investigations and thus serves as the starting year for the sample. The final data set includes 381 investigations; 201 of these eventually ended in a recall.
We assembled the data for the study from numerous sources. Investigation-related information such as opening, closing, and recall dates, as well as information about problem severity, was collected from NHTSA. Other data sources, particularly for the brand-related variables, include Consumer Reports, Ward's Automotive Yearbook, Automotive News Market Data Book, and firms' annual reports. In our empirical setting, "brand" refers to the auto make and "firm" refers to the manufacturer. For example, Acura is the brand in our context, whereas Honda is the manufacturer. Table 3 lists the specific measures and data sources.
Dependent variables. Time to recall (TIMETORECALL) refers to the time elapsed between the opening of an investigation and the time of recall announcement by the manufacturer. We collected information about investigation opening and recall announcement dates from NHTSA investigation and recall reports. We treat investigations not ending in a recall as right-censored because of the possibility that a closed investigation could be reopened if additional problems are observed (for an alternative model that relaxes this assumption, see the "Additional Analyses" subsection). The measure of time to recall represents the difference between the investigation opening date and recall announcement date (or investigation closing date for censored observations), measured in days.
Our performance measure is the short-term abnormal returns (ARs) accruing from the recall announcement to the focal firm, using the event study methodology (e.g., Hendricks and Singhal 1996). Event studies typically allow ( 1) testing for the existence of information effects (i.e., the impact of an announcement on shareholder value) and ( 2) identifying factors that explain changes in shareholder value (Kalaignanam et al. 2013, p. 754). We assess the information effects of an announcement by computing the difference between the observed return, Rit, on the event date and the expected returns, E(Rit), estimated on a benchmark model. The premise of an event study methodology is that an announcement reveals new information to stock markets, causing the markets to adjust the valuation of the firm on the basis of the expected impact of the new information. A product recall announcement reveals to stock markets at least two pieces of new information. First, the stock market learns that a safety investigation underway was serious enough to lead to a product recall. This is new information because there is uncertainty about the outcome of a safety investigation. Second, the stock market also learns about the time the firm took to investigate and issue a product recall. This constitutes new information to stock markets because the time taken in a safety investigation would be relevant only after a product recall is announced. We estimate the expected returns, E(Rit), using the Fama-French four-factor model (Carhart 1997; Fama and French 1993):
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
where Rmt is the stock returns of the benchmark market portfolio; SMBt is the difference between rate of returns of small-and large-stock firms (i.e., small — large); HMLt is the difference in returns between high and low book-to-market ratio stocks (i.e., high — low); and UMDt is the momentum factor, defined as the difference in returns between firms with high and low past stock performance (i.e., winners — losers). α, β, γ, δ, and σ are the parameter estimates obtained by regressing Rit on the four factors.
TABLE: TABLE 3 Data Sources and Operationalization
| Variable | Operationalization | Data Sources |
| Time to recall (TIMETORECALL) | Time between opening date of investigation and the date of recall (in days). | NHTSA |
| Stock market reaction (CAR) | Cumulative abnormal returns in the [-2,2] window around the recall announcement. | Center for Research in Security Prices |
| Problem severity (PROBSEV) | Principal component score of number of (1) complaints, (2) crashes/fires, (3) injuries, and (4) fatalities. Higher scores indicate more severe problems | NHTSA |
| Brand reliability (RELIABILITY) | Three-year average of problem scores of all models of a make (1 = "most problems," and 5 = "fewest problems") (Rhee and Haunschild 2006). Higher scores indicate more reliable brands. | Consumer Reports |
| Brand diversification (DIVERSE) | Principal component score of (1) number of models and (2) variation in engine sizes across models (Rhee and Haunschild 2006). Higher scores indicate more diverse brands. | Ward's Automotive Yearbook |
| | Automotive News Market Data Book |
| | Consumer Reports |
| Past recall intensity (PASTRECINT) | Count of the total number of vehicles recalled by the brand in the previous year. Higher numbers indicate greater past recall intensity. | NHTSA |
| Firm profitability (ROA) | Return on assets = Net income/Total assets | Compustat |
| Recall size (RECSIZE) | Number of vehicles investigated for the defect. | NHTSA |
| Past publicity (PASTPUBLICITY) | Count of the number of recalls in the past year that received coverage in The Wall Street Journal. | LexisNexis |
| Investigation type (INVTYPE) | SQ = 1 if investigation opened as service query, 0 otherwise; RQ = 1 if investigation opened as recall query, 0 otherwise; EA = 1 if investigation upgraded to engineering analysis, 0 otherwise. | NHTSA |
| Brand sales (BRANDSALES) | Sales of the brand involved in the recall. | Compustat |
We estimate the daily stock returns for each firm between 260 and 30 days prior to the event day using the Fama-French four-factor model. Abnormal returns are estimated as the difference between the observed returns and the expected returns:
ARit = Rit — E(Rit)
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
The abnormal returns are aggregated for a firm over an event period [-tļ, t2] and are given by
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
When information leakage (for t1 days before the event) and/or dissemination over time (for t2 days after the event) occur, the abnormal returns for a firm are aggregated over the "event period" [t1, t2] into a cumulative abnormal return (CAR).
Independent variables. We operationalize problem severity (PROBSEV) using complaints/accidents data that are available at the beginning of the investigation. The NHTSA collects information about the number of ( 1) consumer complaints, ( 2) crashes or fires, ( 3) injuries, and ( 4) fatalities to analyze the safety hazards posed by automobiles in the market. We performed principal component analysis on these four items and generated a univariate score to operationalize problem severity. Higher scores imply greater problem severity.
We obtained the data for brand reliability (RELIABILITY) using Consumer Reports' assessment of vehicle reliability. The reliability ratings by Consumer Reports influence perceptions of brand reliability (Rhee and Haunschild 2006). In line with prior research, we aggregate the information to the make (i.e., brand) level. As noted previously, the term "brand" in our setting refers to theautomakeand "firm" refers to the manufacturer. For example, Cadillac is the brand and General Motors is the firm in our setting. In the automotive industry, metrics such as brand equity (e.g., Harris EquiTrend) or customer satisfaction (e.g., American Customer Satisfaction Index) is commonly assessed at this level. Consumer Reports surveys consumers regarding problems with a particular model and aggregates the information into problem rates. From this data, brand reliability is measured using a five-point scale of problem rates, with higher scores reflecting higher reliability (and thus fewer problems; Rhee 2009). We collected problem scores for the previous three years for each of the models associated with a particular make (our level of aggregation) and averaged these model scores across the make to generate the reliability score for the make (Rhee and Haunschild 2006).
Brand diversification (DIVERSE) represents the number and variety of products marketed under a brand. Consistent with prior research (Dobrev 2000; Rhee and Haunschild 2006), we operationalize brand diversification in terms of the number of product lines and range of engine capacities of the models produced by the brand. We performed principal component analysis on these two indicants to generate a univariate score to represent brand diversification (Rhee and Haunschild 2006). Higher scores on this component reflect greater brand diversification. We collected information about the brand's past recall intensity (PASTRECINT) from recall reports published by NHTSA. We operationalize past recall intensity as the count of the total number of recalled vehicles of the brand in the previous year.
We also control for firm profitability (ROA), recall size (RECSIZE), and past publicity (PASTPUBLICITY). The relationship between firm profitability and time to recall is not altogether clear, because strong financial performance might reduce the firm's motivation to respond but enhance its ability to respond because of the availability of slack resources (Jayachandran and Varadarajan 2006). We expect recall size to enhance recall delays and past publicity to shorten the time to recall. Furthermore, we collected data on the type of the investigation (INVTYPE) at the time of closing. Investigations by NHTSA could be categorized as ( 1) preliminary evaluation (PE), ( 2) service query (SQ), ( 3) recall query (RQ), and ( 4) engineering analysis (EA). A PE is triggered by consumer complaints, petitions, or outcomes of NHTSA product testing. An SQ is initiated after NHTSA reviews technical service bulletins issued by the manufacturer. An RQ is initiated when NHTSA assesses the adequacy of the scope of prior recalls. Finally, an EA is initiated when detailed analyses is needed to evaluate the product defect.
Our principal interest is in testing factors that influence the time to recall and stock market reactions to time to recall. A potential econometric concern for assessing stock market reaction the second stage is that time to recall is endogenous. That is, firms are likely to be aware of the consequences of time of recall and would therefore (rationally) make decisions about when to recall on the basis of strategic considerations, some of which may be unobserved. Accordingly, the model needs to account for the endogeneity of time to recall to yield unbiased coefficients. We account for this endogeneity using the control function approach (Petrin and Train 2010). The logic of this approach is that if there are unobservables that drive the time-to-recall decision, including a control variable in the structural regression model (second stage) reduces the correlation between the endogenous regressor and the error term.
The model estimation proceeds in two stages. In the first stage, we model the likelihood of recall and time to recall using a hazard model. Not much theory or empirical evidence is available to predict the shape of the time to recall process. Therefore, to overcome misspecification bias, we compare several distributions. These distributions are continuous because the recall could occur at any time. In particular, we estimate AFT models incorporating exponential, Weibull, log-logistic, and gamma time-to-recall distributions. Table 4 reports the model fit for these distributions. As Table 4 shows, the Weibull distribution offers the best fit based on the Bayesian information criterion (BIC) statistic. Accordingly, we use a Weibull distribution for the time-to-recall process (t) with a scale parameter λ and shape parameter p. The probability density function for the Weibull time-to-recall process can be expressed as:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
where h(t) is the hazard function and S(t) is the survivor function. To facilitate direct interpretation in terms of time, we use an AFT metric. The survivor function can be written as:
S(t) = exp(-λtp).
Solving for t yields:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
Reparametrizing 1/λ1/p in terms of exp(Xß), the Weibull time-to-recall process in an AFT metric can be expressed as in Equation 1.
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
where TIMETORECALL = time elapsed between opening of investigation and recall for investigation i, S(t) is the survivor function, PROBSEV = severity of problem in investigation i, RELIABILITY = reliability of brand j, DIVERSE = diversity of brand j, PASTRECINT = pastrecall intensity of brand j, ROA = return on assets of firm/manufacturer k, RECSIZE = number of vehicles included in the investigation i, PASTPUBLICITY = past publicity of recalls for make j, INVTYPE = investigation type, aļ-a13 are the parameter estimates, and ei is the error term. Equation 1 could suffer from specification issues if un-observable firm characteristics that influence time to recall are ignored. To account for this possibility, we use clustered robust standard errors (Liang and Zeger 1986). We cluster the standard errors on the firm and account for within-cluster correlations or unobserved firm heterogeneity in the model estimation. Note that clustering standard errors by firms is equivalent to modeling firm-specific random effects for the intercept (Moulton 1986) and are larger than those obtained from conventional estimation, thereby making the hypothesis tests more conservative. The clustered robust standard errors also control for firm-specific heteroskedasticity (Bertrand, Duflo, and Mullainathan 2004). As a robustness check, we also estimate the model with fixed firm effects.
TABLE: TABLE 4 Model Comparisons with Different Distributions of Time to Recall
| Distributions | Log-Likelihood | BIC Statistic |
| Exponential | -323.22 | 728.53 |
| Log-normal | -317.71 | 717.51 |
| Log-logistic | -342.47 | 767.03 |
| Weibull | -312.40 | 706.89 |
In this model, past publicity is the exogenous variable that identifies the system. From a theoretical perspective, past publicity is exogenous because although it is likely to influence time to recall, it should not be related to CARs in the second stage. The exogeneity of past publicity stems from the efficient market hypothesis, which argues that the effects of past recalls that were publicized should already be incorporated in the stock prices. Thus, past publicity is not "news" per se and should not elicit any reaction from stock markets. To understand the relevance of past publicity for time to recall, we estimated an alternative specification that excludes past publicity from the model. The BIC for this alternative model is 721.37, which is greater than the model that includes past publicity (BIC = 706.89). Thus, past publicity improves the model fit and does a good job of predicting time to recall.
In the second stage, we test the effect of time to recall on stock market reactions. As noted previously, we account for the endogeneity of time to recall using a control function. The control variable we include in the second stage are the residuals from the first stage (actual time to recall — predicted time to recall). By including residuals from the first stage, ordinary least squares can be used to generate unbiased coefficients of stock market reactions to time to recall decisions. In addition, note that the second stage only features investigations that ended in a recall. The CAR is modeled as a linear combination of time to recall, recall size, problem severity, and brand sales. We include recall size and problem severity to account for the direct and indirect costs of the recall. To account for the possibility that stock markets respond more adversely to product recalls of significant brands, we include brand sales (BRANDS ALES) in the model.
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
where TIMETORECALL = time to recall, CAR = cumulative abnormal returns for the recall announcement in the event window [t1, t2], RECSIZE = number of vehicles included in the investigation i, BRANDS ALES = sales of the brand involved in the recall, RESIDUALFS = residuals from the first stage (i.e., actual time to recall — predicted time to recall), υi is random error. As before, we use clustered robust standard errors for inferences from Equation 2. We discuss the results next.
Table 5 presents the summary statistics and correlations between key variables in the study. The correlations between the independent variables are within prescribed limits (variance inflation factors < 10) and do not pose a threat to the validity of the findings. The mean time to recall is 295 days, and the standard deviation of time to recall across investigations is 248 days. Table 6 reports the average time to recall across manufacturers in our sample. As Table 6 shows, the mean time to recall for manufacturers such as General Motors and Ford is higher than other manufacturers. Toyota has the lowest mean recall time in our sample.
We also find considerable variation in the severity of problems across investigations. On average, the mean number of complaints at the time of investigation is 27 (SD = 96), mean number of crashes is 1.72 (SD = 7.5), mean number of injuries is .71 (SD = 2.24), and mean number of fatalities is .04 (SD = .32). We also find reasonable within and between variance in the brand characteristics. For instance, the between and within variances for ( 1) brand reliability are 69% and 31%, respectively; ( 2) brand diversification are 80% and 20%, respectively; and ( 3) past recall intensity are 56% and 44%, respectively. The within variance arises because the data span investigations of brands over a 13-year period between 1999 and 2012. The mean CARs to recall announcements is -.6%, which translates into a mean shareholder loss of $168 million (average market capitalization = $28 billion).
We report the results for the time-to-recall model in Column I of Table 7. The standard errors are clustered robust standard errors that control for within-firm correlation (i.e., equivalent to random effects) and for heteroskedasticity. As Table 7 shows, the shape parameter is greater than 1 (1.76, p < .01), suggesting that the hazard of recall increases over the time of the investigation. H1 states that problem severity enhances time to recall. The coefficient for the impact of problem severity on time to recall is positive and significant (.84, p < .01).
TABLE: TABLE 5 Descriptive Statistics and Correlation Matrix
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| 1. TIMETORECALL | 1 | | | | | | | | | |
| 2. PROBSEV | .47 | 1 | | | | | | | | |
| 3. RELIABILITY | -.17 | -.04 | 1 | | | | | | | |
| 4. DIVERSE | .09 | .04 | -.05 | 1 | | | | | | |
| 5. PASTRECINT | .12 | .18 | .09 | .21 | 1 | | | | | |
| 6. ROA | -.07 | -.01 | -.02 | -.11 | -.06 | 1 | | | | |
| 7. RECSIZE | .18 | .30 | -.01 | .16 | .07 | .01 | 1 | | | |
| 8. PASTPUBLICITY | -.07 | .04 | .07 | .08 | .47 | -.06 | -.01 | 1 | | |
| 9. BRANDSALES | .05 | .13 | .14 | .66 | .39 | -.09 | .24 | .25 | 1 | |
| 10. CAR[-2, 2] | -.18 | -.13 | .03 | .10 | .06 | -.34 | -.04 | .04 | .03 | 1 |
| M | 295 | .07 | 4.12 | .01 | 2.51 | .02 | 182,197 | 5.22 | 996,217 | -.6 |
| SD | 248 | 1.37 | .35 | 1.14 | 6.46 | .12 | 291,971 | 20.80 | 1,024,140 | .4 |
Substantively, this result implies that a unit increase in the severity of the problem increases time to recall by 52%.[ 3] While our model does account for unobserved firm effects (through clustered standard errors), we tested the robustness of the results by including fixed firm effects. As Column II in Table 7 shows, the main effect of problem severity is positive and significant. We also performed additional analyses to determine whether this result is robust to an alternative measure of problem severity that includes crashes, injuries, and fatalities but excludes several complaints. We report the results from this analysis in Column III of Table 7. As Table 7 illustrates, the finding remains positive and significant.
H2 hypothesizes that the relationship between problem severity and time to recall will be weaker as brand reliability increases. Consistent with H2, the coefficient for the interaction between problem severity and brand reliability is negative and significant (-.18, p < .05). Given that our model is nonlinear, we compute average marginal effects to gain a better understanding of this interaction effect. Specifically, we compute the average marginal effect of problem severity on time to recall at low and high levels of brand reliability. We set low and high levels of brand reliability at 10% and 90%, respectively. We find that a unit increase in problem severity increases time to recall by 357 days at low levels of brand reliability and by 259 days at high levels of brand reliability. Therefore, the time to recall for brands of higher reliability is shortened by 98 days (i.e., 357 days — 259 days) for a unit increase in problem severity. The direct effect of brand reliability on time to recall is also negative and significant (-.29, p < .05).
H3 states that the relationship between problem severity and time to recall will be stronger as brand diversification increases. Consistent with this prediction, the results in Table 7 show that the interaction between problem severity and brand diversification is positive and significant (.08, p < .05). Again, we compute average marginal effects to gain substantive insights into this interaction. The average marginal effect of a unit increase in problem severity on time to recall at low levels of brand diversification is 279 days and at high levels of brand diversification is 344 days. Substantively, this finding means that the time to recall for problems of higher severity increases by 65 days at high levels of brand diversification. Therefore, when brands target multiple segments of customers through their offerings, their response to product safety investigations slows down significantly. This result is similar in spirit to results from previous research on structural inertia, which finds that a firm in a "wide niche" (addressing multiple segments) would have less ability to act as a reliable and accountable collective entity (Carroll and Swaminathan 2000; Dobrev 2000) and thus would respond more slowly to crises. We do not find the direct impact of brand diversification on time to recall to be significant (p > .10).
We did not propose a hypothesis for the moderating influence of past recall intensity on the relationship between problem severity and time to recall. The results in Table 7 show that although the impact of past recall intensity on time to recall is insignificant (p > .10), the interaction of problem severity and past recall intensity is negative and significant (-.0074, p < .01). A unit increase in problem severity increases time to recall by 368 days at low levels of past recall intensity and by 172 days at high levels of past recall intensity. Thus, problem severity shortens time to recall by 196 days for brands with high levels of past recall intensity. The evidence strongly supports the moderating influence of past recall intensity on the relationship between problem severity and time to recall and is in favor of the learning argument and not the resource constraint argument.
TABLE: TABLE 6 Mean Time to Recall by Manufacturers
| Manufacturer | Time to Recall (Days) |
| Chrysler | 293.89 |
| Ford | 360.95 |
| General Motors | 375 |
| Honda | 265.5 |
| Hyundai | 249.28 |
| Nissan | 265.71 |
| Toyota | 213.91 |
| Volkswagen | 229.61 |
| Volvo | 285 |
Notes: Only manufacturers with at least ten recalls are included.
TABLE: TABLE 7 Weibull AFT Regression Results of the Predictors of Time to Recall
| Variable | Column I (Random Firm Effects) | Column II (Fixed Firm Effects) | Column III (Problem Severity Measure Excluding Complaints) |
| PROBSEV (α1) | .84*** (.32) | .90 ** (.37) | .18** (.07) |
| RELIABILITY (α2) | -.29** (.12) | -.20* (.11) | -.38** (.15) |
| DIVERSE (α3) | .06 (.04) | .10** (.04) | .05 (.04) |
| PASTRECINT (α4) | -.008 (.0072) | -.002 (.005) | -.004 (.006) |
| PROBSEV × RELIABILITY (α5) | -.18** (.08) | -.19** (.09) | -.037 (.08) |
| PROBSEV × DIVERSE (α6) | .08** (.03) | .09** (.04) | .10*** (.03) |
| PROBSEV × PASTRECINT (α7) | -.0074*** (.0011) | -.0079*** (.0021) | -.006*** (.0009) |
| ROA (α8) | -.13 (.12) | -.14 (.32) | -.09 (.12) |
| RECSIZE (α9)a | .025 (1.08) | .082 (1.25) | .06* (.03) |
| PASTPUBLICITY (α10) | -.006*** (.001) | -.006** (.003) | -.006*** (.001) |
| Investigation type fixed effects (α11-13) Unobserved firm effects | Two significant Clustered standard errors | Two significant Fixed effects, one significant | Two significant Clustered standard errors |
| Shape parameter | 1.76*** (.12) | 1.79*** (.09) | 1.70*** (.09) |
| Log-likelihood | -312.40 | -306.19 | -318.16 |
| Number of observations | 352 | 352 | 352 |
*p < .10.
**p < .05.
***p < .01.
aUnit is ten million vehicles.
Notes: Clustered robust standard errors in parentheses appear in Columns I and III, and robust standard errors appear in Column II. The dependent variable is TIMETORECALL.
With regard to control variables, we find that past publicity is negatively related to time to recall (-.006, p < .01). Therefore, in addition to past recall intensity, publicity of past recalls appears to sensitize firms to new investigations and recall earlier. We also find that the engineering analysis and recall query investigation types have higher times to recall relative to the service query investigation type. This finding suggests that certain investigations offer clearer assessments of the safety risks resulting in firms recalling earlier.
H4 states that time to recall is negatively associated with stock market performance. We examined the daily average abnormal returns for the 73 recall announcements for which stock returns were available. We do not find a statistically significant abnormal return on the announcement day -.12% (p > .10). We examined abnormal returns for a period of seven days around the event. The only time window with a significant CAR is [-2,2]: -.6% (p < .05). Although the CAR appears to be modest in size, the associated shareholder losses are not trivial given the large market capitalization of automobile firms in the United States. To test H4, we estimated Equation 2 using CAR [-2, 2] as the measure of stock market performance. We note that examining cross-sectional abnormal returns in the significant window is consistent with previous research in marketing (Geyskens, Gielens, and Dekimpe 2002; Raassens, Wuyts, and Geyskens 2014). Table 8, Column I reports the results of this analysis. The reported standard errors in Table 8 are clustered robust standard errors. Consistent with Щ, the coefficient for the impact of time to recall on CARs is negative and significant (-4.11 × 10-5, p < .05) (for results with bootstrapped standard errors, see Table 8, Column IV). Therefore, firms that delay recalls tend to be punished more by stock markets. Regarding control variables, recall size is negatively associated with CARs (-2.6 × 10-5, p < .05). We do not find problem severity to be significantly related to CARs (p > .10). We also performed cumulative abnormal tests for the impact of time to recall on an alternative event window [-1, 1] even though returns were statistically insignificant for this window. We report the results of these analyses in Column II of Table 8. We find that time to recall is again negatively related to CARs (-1.34 × 10-5, p < .05).
As noted previously, we tested the stock market reactions for product recall timing decisions after accounting for its endogeneity. We also tested the impact of time to recall on CARs without endogeneity controls. To facilitate comparisons, we used the full set of variables from the first stage. These results are reported in Column III of Table 8. The results again suggest that time to recall is negatively related to CARs (-4.8 × 10-5, p < .05). Collectively, our results suggest that even after accounting for size of the recall (i.e., proxy for direct costs for recalls) and problem severity, time to recall has a negative and significant impact on stock market reactions. The implication is that the time to recall is an important variable with value relevance for stock markets.
We performed numerous additional analyses to examine the robustness of the results to alternative explanations, models and specifications.
TABLE: TABLE 8 Results of the Impact of Time to Recall on Short-Term CARs
| Variable | Column I CAR[—2, 2] | Column II CAR[—1, 1] | Column III All Variables from First Stage (No Control for Endogeneity) | Column IV CAR[—2, 2] Bootstrapped SE |
| TIMETORECALL (η1) | -4.11 × 10-5** (1.71 × 10-5) | -1.34 × 10-5** (4.7 × 10-6) | -4.8 × 10-5** (1.81 × 10-5) | -4.11 × 10-5** (1.76 × 10-5) |
| RECSIZE (η2) | -2.6 × 10-5** (1.6 × 10-5 | -9.83 × 10-6 (1.75 × 10-5) | -.061 (.11) | -2.6 × 10-5** (1.81 × 10-5) |
| BRANDSALES (η3) | -.004 (.005) | -.004 (.004) | -.026* (.016) | -.004 (.007) |
| PROBSEV (η4) | -.001 (.001) | -.0026** (.0003) | -.0028* (.0017) | -.001 (.001) |
| RESIDUALFS (η5) | -7.28 × 10-6 (1.74 × 10-5) | -3.25 × 10-6 (5.12 × 10-6) | | -7.28 × 10-6 (1.81 × 10-5) |
| RELIABILITY (η6) | | | -.013 (.017) | |
| DIVERSE (η7) | | | .0049 (.0058) | |
| PASTRECINT (η8) | | | -.0017 (.0042) | |
| PROBSEV × RELIABILITY (η9) | | | .007 (.008) | |
| PROBSEV × DIVERSE (η10) | | | .0008 (.004) | |
| PROBSEV × PASTRECINT (η11) | | | -9.21 × 10-5 (1.54 × 10-4) | |
| ROA (η 12) | | | .097 (.10) | |
| PAST PUBLICITY (η13) | | | -.0004 (.001) | |
| Investigation type fixed effects (η14_15) | | | None significant | |
| Unobserved firm effects | Clustered standard errors | Clustered standard errors | Clustered standard errors | Clustered and bootstrapped standard errors (500 replications) |
| Number of observations | 73 | 73 | 73 | 73 |
| R-square | .09 | .09 | .28 | .09 |
*p< .10.
**p < .05.
Notes: Clustered robust standard errors in parentheses. The dependent variable is CARs over different windows.
Does the timing of product recall differ for U.S., European, and Asian nameplates? Although our empirical analyses accounted for manufacturer-or firm-specific heterogeneity by using clustered standard errors, one could wonder whether there are significant differences between U.S.-, Europe-, and Asia-based manufacturers with respect to how they respond to safety investigations. To test this possibility, we included dummy variables for the manufacturer headquarters and reestimated the recall timing model. These results appear in Column I of Table 9. Table 9 shows that the U.S. and Asian headquarters dummies are not significantly different (p > .10) compared with the base manufacturer headquarter dummy (i.e., Europe). Moreover, the pattern of results for our hypothesized variables is remarkably similar after the inclusion of these dummies. Thus, it is reasonable to conclude that there are no significant differences in the timing of product recalls based on where the manufacturer is headquartered.
Does the timing of product recall vary depending on the price tier of the brand? Recall that the unit of analysis for this study is the "make" or "brand." That is, Chevrolet and Cadillac are makes or brands for the parent firm (or manufacturer) General Motors. A potential difference between various brands from firms is that they belong to different price tiers. Does the response of firms to safety investigations differ on the basis of the price tier of the make involved in the investigation? A priori expectations would be that luxury brands are likely to recall earlier compared with other brands. We performed an additional analysis to evaluate this possibility. Drawing on the average base prices of models of the make, we created a "Luxury Brand" dummy variable, with luxury makes (or brands) coded as 1 and others as 0. The results of the recall timing model after including the Luxury Brand variable appear in Column II of Table 9. The luxury brand dummy is not statistically significant, though it is directionally consistent with expectations. Thus, we rule out the possibility of the brand' s price tier driving the time-to-recall results.
Alternative model to allow for the possibility that the right-censored investigations never experience product recalls in the future? The Weibull AFT regression model used to test the hypotheses assumes that the investigations that closed without a product recall are right-censored. That is, these investigations would also lead to a recall in the future, though we do not observe a product recall at the end of 2012. One could wonder whether all investigations would indeed culminate in a product recall. To relax this assumption, we turn to an alternative model, the split hazard model, which incorporates both incidence and the time to recall in the same framework. The logic of this model is that time to recall is conditioned on the likelihood of a recall occurring. We used a logit specification to model product recall incidence and Weibull specification to model time to recall (as before). The results from this new model appear in Columns IIIa and IIIb in Table 9. As Table 9 shows, the conclusions from this model are identical to those from the standard duration model.
Are there long-term abnormal returns for product recall announcements? In this subsection, we examine whether the market quickly and accurately incorporates the performance implications of product recall announcements in the share price. Alternatively, it may take the market a long time to figure out the performance consequences of the product recall announcement.[ 4] Support for the efficient market hypothesis can be found if the long-term abnormal returns for the announcing firm in question are not significantly different from the long-term abnormal returns for a comparison benchmark. Consistent with previous research, we use the calendar-time portfolio methodology to test the long-term abnormal returns for product recall announcements (Sorescu, Shankar, and Kushwaha 2007).
TABLE: TABLE 9 Robustness Analyses
| Variable | Column I Including Manufacturer Headquarters Dummies | Column II Including Luxury Brand Dummy | Column IIIa Split Population Duration Model Incidence of Product Recall: Logit | Column IIIb Split Population Duration Model Time to Recall: Weibull |
| PROBSEV | .82*** (.36) | .8** (.36) | .50*** (.21) | .76** (.34) |
| Asia-based manufacturer | -.015 (.11) | | | |
| U.S.-based manufacturer | .04 (.10) | | | |
| Luxury brand | | -.039 (.079) | | |
| RELIABILITY | -.29* (.14) | -.29** (.13) | -.34 (.27) | -.17 (.18) |
| DIVERSE | .06 (.04) | .06 (.05) | .25* (.13) | .09* (.05) |
| PASTRECINT | -.0022 (.0052) | -.0079 (.0059) | .017 (.032) | -.0058 (.0042) |
| PROBSEV × RELIABILITY | -.17* (.09) | -.17* (.09) | -.12** (.06) | -.070** (.025) |
| PROBSEV × DIVERSE | .08** (.04) | .09*** (.03) | -.23 (.18) | -.18*** (.05) |
| PROBSEV × PASTRECINT | -.0074*** (.001) | -.0075*** (.0017) | .09*** (.03) | -.0026*** (.0009) |
| ROA | -.09 (.12) | -.08 (.12) | .15 (.13) | .29* (.17) |
| RECSIZE | -.23 (1.12) | -.31 (1.05) | .29* (.15) | -.11 (.13) |
| PAST PUBLICITY | -.006 (.001) | -.006 (.001) | .04** (.02) | -.004 (.003) |
| Investigation type fixed effects | 2 of 3 significant | 2 of 3 significant | None significant | 2 of 3 significant |
| Unobserved firm effects | Clustered standard errors | Clustered standard errors | Clustered standard errors | Clustered standard errors |
| Intercept | 3.27*** (.64) | 3.26*** (.56) | .95*** (.33) | .76** (.31) |
| Shape parameter | 1.76*** (.12) | 1.77*** (.12) | 1.23*** | (.22) |
| Log-likelihood | -311.02 | -312.34 | -707.32 |
| Number of observations | 352 | 352 | 352 | |
*p < .10.
**p < .05.
***p < .01.
The calendar-time portfolio methodology involves creating portfolios of stocks of firms that have announced a product recall. Firms are added to the portfolio on the date of announcement and held in the portfolio for the period of time for which we wish to calculate returns (see Fama 1998; Mitchell and Stafford 2000). We use the Fama-French three-factor model to calculate the one-year and two-year abnormal returns. The intercept term of these models is the measure of the average monthly abnormal returns of the portfolio. The results (see Table 10) suggest that the intercept term is not statistically significant for either the one-year or two-year portfolio returns (p > .10). Thus, the long-term abnormal returns analyses indicate that the efficient market hypotheses cannot be rejected and that the returns to product recall timing decisions accrue in the short run.
Do the results hold across individual components of problem severity? Recall that problem severity is derived using principal component analyses on the number of complaints, number of crashes/fires, number of injuries, and number of deaths. One could wonder whether the results hold across these individual components because of the possibility that they are qualitatively different. To assess this possibility, we reestimated the results using these individual components as proxies for problem severity. The results of these analyses are reported in the Web Appendix. The pattern of results is fairly consistent across crashes/fires, injuries and deaths. The only difference is that some of the effects are not statistically significant (p > .10) for the number of complaints (although they are directionally consistent with our theory). This analysis provides additional evidence supporting our theorized effects.
Research has shown that product recalls have significant consequences for firms as well as end customers. However, little evidence is available regarding why some firms recall earlier than others do. We develop and test hypotheses that examine the impact of problem severity on time to recall. Then, we examine how brand reliability, brand diversification, and brand's past recall intensity pose boundary conditions, affecting the relationship between problem severity and time to recall. Thereafter, we demonstrate the effect of time to recall on firm performance. Next, we summarize our contributions in relation to the research questions identified at the beginning of the article.
TABLE: TABLE 10 Robustness Checks: Long-Term Abnormal Returns
| Average Month | Average Month |
| in (0, 12) | in (0, 24) |
| Intercept (abnormal returns) | -.0061 | -.0049 |
| Market factor (beta) | 1.2945*** | 1.2753*** |
| Size factor (SMB) | -.1030 | -.0880 |
| Book-to-market factor (HML) | .6384*** | .5962*** |
| Adj-R2 | 42.54% | 42.56% |
***p < .01.
Notes: Standard errors are heteroskedasticity consistent. SMB = small minus big; HML = high minus low.
Our study makes two important contributions to the product recalls and brand management literature. First, we contribute to the product recall literature by examining time to recall when firms face safety investigations by external entities. Extant research has thus far focused on how product recalls impact stock market performance (Chen, Ganesan, and Liu 2009), effectiveness of marketing instruments (Cleeren, Van Heerde, and Dekimpe 2013; Liu and Shankar 2015; Van Heerde, Helsen, and Dekimpe 2007) and on how firms learn and recover following product-harm crises (Cleeren, Dekimpe, and Helsen 2008; Gao et al. 2015; Rubel, Naik, and Srinivasan 2011). By examining the timing and incidence of product recalls, we generate valuable insights on the prerecall process—an aspect of recall decisions that has not yet been investigated in the literature. Our findings reveal that firms experience competing pressures with respect to the timing of recall decisions. The satisficing responses they pursue are contingent on the brand' s position in the marketplace.
Our main effect finding is that the time to recall for firms is higher when the problems encountered are severe in nature. When firms receive information about a potentially severe problem, they often turn inward, which adversely influences their ability to find a quick solution to the problem. In addition, following severe problems, determining accountability is an important issue. Under such conditions, firms may be less likely to share information about the failure, preferring instead to protect themselves from the political fallout surrounding failure investigation (Madsen and Desai 2010). Thus, the threat of being held accountable may cause firms to be somewhat rigid in their response to investigations (Staw, Sandelands, and Dutton 1981).
However, we find significant variation in time to recall decisions for severe problems. Brand characteristics play a critical role in mitigating or exacerbating response times. When a brand perceived as highly reliable is faced with a severe problem, we find that firms are likely to make a recall decision faster than when the brand is perceived as less reliable. This finding is consistent with previous research findings that customers penalize brands with higher reliability more for violating expectations (Liu and Shankar 2015; Rhee and Haunschild 2006). We argue that because of this increased market expectation, reliable brands tend to recall early and soften the damage to their reputational assets. Furthermore, consistent with previous research (Kalaignanam et al. 2013), this finding suggests that reliable brands should be better equipped to understand root causes for potential problems and provide remedial action in a timely manner.
Our study also finds that brand diversification can be an organizational liability in certain situations. Although diversified brands are able to cater to varying needs of the market, this characteristic impedes firm response when faced with a crisis. Specifically, the threat of demand side negative spillovers is significant for a more diverse brand. When a brand faces a safety investigation involving a severe problem, the specter of this incident spilling over to subbrands in the portfolio looms large and adversely affects the recall timing decision. This finding is consistent with research that finds negative spillovers after a brand scandal to be driven by the relatedness of the brands in the portfolio (Lei, Dawar, and Lemmink 2008). In some industries, firms are able to develop a diverse brand portfolio by using common components and systems (e.g., Fisher, Ramdas, and Ulrich 1999; Ramdas and Randall 2008). This supply-side spillover exposes the subbrands in the portfolio to greater risk when faced with a crisis and makes them more vulnerable. The pattern of results in our study should encourage researchers to further explore the growth versus vulnerability trade-offs (e.g., higher time to recall) from brand diversification and the implications thereof for performance.
Perhaps the most notable set of results involves the moderating impact of past recall intensity on the relationship between problem severity and time to recall. The results suggest that problem severity is less likely to lead to a recall delay when the firm has faced recalls in its recent past. Although it may still be the case that managing these recalls puts constraints on the firm, the significant negative interaction effect shows that firms are more able or more willing to recall earlier, giving support to the learning argument. Prior research has suggested that negative publicity from recalls might actually heighten awareness toward the brand and the category (Cleeren, Van Heerde, and Dekimpe 2013). Along similar lines, we find that increased publicity around past recalls has a significant impact in reducing the time to recall for subsequent investigations. Thus, negative publicity around previous product recalls acts as a catalyst in focusing firm attention. We note that only a small proportion of a firm' s recalls typically receive media attention. Therefore, minor product recalls that are not picked up by major media outlets may not be as effective in altering firm behavior. Collectively, the moderating influence of past recall intensity and direct influence of past publicity is encouraging from a public policy standpoint because large and publicized product recalls appear to have a positive effect in regulating firms' responses to subsequent safety investigations by increasing their ability and motivation to respond to an investigation.
The second contribution is that we offer empirical evidence on the performance consequences of product recall timing decisions. The findings reveal that firms that take longer to recall are penalized more by stock markets than firms that recall earlier. Previous research has predominantly focused on testing the stock market reactions to product recall announcements and has shown when the stock market reacts negatively to these announcements (Chen, Ganesan, and Liu 2009; Thirumalai and Sinha 2011). Our study diverges from this literature stream in showing that beyond the announcement of the recall, its timing also has value relevance for stock markets. Market analysts should care about the timing of product recalls, especially those for serious defects, because of the direct implications for cash flows and potential fines. Our study should encourage future researchers to include time to recall as a variable in models that examine the performance consequences of product recalls.
The findings from this manuscript have implications for both managers and policy makers. Whether and when firms should announce and implement a recall has been mostly examined from a product safety perspective. Clearly, it is imperative that firms respond to significant product safety concerns. Recalls are initiated to remedy serious product defects to reduce product-associated injuries and accidents. Our study shows that not all firms are willing and able to respond to information about a severe defect quickly. Yet the longer recall is delayed, the more likely it is that the product increases costs to the firm and society.
One key implication for managers is that the market reacts to the information embedded in the time that the firm takes to announce a recall during an investigation. We performed additional analyses to better understand the economic implications of the findings. We computed the direct impact of a unit change (i.e., marginal effects) in problem severity on time to recall and the indirect impact on shareholder wealth. Table 11 presents the results of this post hoc analysis. The results suggest that a unit increase in problem severity increases time to recall by 309 days (at average levels of brand reliability), 304 days (at average levels of brand diversification), and 256 days (at average levels of past recall intensity).[ 5] In terms of economic significance, Table 11 suggests that the corresponding losses in shareholder wealth (at average levels of brand characteristics) because of delayed time to recall are $112 million, $109 million, and $75 million, respectively. Thus, our finding implies that although recalls are adverse events for firms, delaying them when faced with severe problems imposes significant costs on firms in addition to the possibility of lawsuits and fines. The recent recall by General Motors in 2015 for faulty ignition switches was the outcome of a prolonged safety investigation that was initiated because of significant number of injuries and deaths in the market (Valdes-Dapena and Yellin 2015). General Motors was fined $900 million (Johnson, Bomey, and Gardner 2015) and faces numerous lawsuits for the delay (Samilton 2016).
TABLE: TABLE 11 Post Hoc Analyses: Assessing the Managerial Relevance of the Findings
| Marginal Effects of Problem Severity Under Conditions Of… | Predicted Time to Recall (Days) | Net Present Value of Shareholder Loss |
| Brand Reliability | | |
| Low | 357 | ($140 million) |
| Average | 309 | ($112 million) |
| High | 259 | ($56 million) |
| Brand Diversification | | |
| Low | 279 | ($34 million) |
| Average | 304 | ($109 million) |
| High | 344 | ($137 million) |
| Past Recall Intensity | | |
| Low | 368 | ($156 million) |
| Average | 256 | ($75 million) |
| High | 172 | ($14 million) |
Notes: Low, average, and high values are set at the 10th, 50th, and 90th percentiles. Net present value is computed as CAR × Market capitalization. The average market capitalization of firms in the sample is $28 billion. Marginal effects refers to the change in the expected value of the dependent variable for a unit change in the independent variable.
Moreover, managers need to recognize that multiple factors either impede or enable a fast recall decision. Our study finds evidence that brands with greater reputation for reliability are able to speed up recall decisions for severe problems. Therefore, brands with lower reliability face a significant challenge when responding to safety investigations. Lower reliability damages brands' competitive position by preventing a quick response to safety investigations. In economic terms, we find that the difference in shareholder losses for lower-and higher-reliability brands because of recall timing is $84 million (see Table 11). We note that this economic loss is for a single safety investigation. Thus, if a lower-reliability brand is faced with multiple safety investigations of severe problems, the economic losses because of delayed recalls would be even greater. The implication for lower-reliability brands is that they need to invest at least some resources in building routines and procedures that enable them to respond to safety investigations in a timely manner. We caution that building a reputation for reliability is a long-term process that cannot occur instantaneously. However, often investments in reliability are not viewed as priority because of the need to pursue growth through new products. In this context, the magnitude of the economic losses we document should provide a compelling rationale for brands to invest in reliability improvements as well.
Our findings also suggest that brand diversification might be a double-edged sword for firms. In general, brand diversification enables firms to target multiple customer segments and pursue rapid growth. However, in many industries (e.g., personal computers, consumer electronics, automobiles), firms pursue brand extensions by using shared components or platforms across subbrands to lower the cost of offering product variety (Ramdas and Randall 2008). Our study suggests that such sharing places firms at a disadvantage when investigating potential product recalls. That is, when such brands face an investigation of a severe problem, there is additional burden on diverse brands in assessing the scope of the problem as well as preparing the supply chain to remedy the defect. It is not surprising that when faced with an investigation for a power steering system for Chevrolet (i.e., a diverse brand in our sample), a senior spokesperson in the company noted, "We are redoubling efforts on pending product reviews to bring them forward and to resolve them quickly. We will not sacrifice accuracy for speed" (Healey and Meier 2014). Furthermore, the fear of spillover to other related products will also limit the motivation to undertake a quick recall for serious problems. The post hoc analyses reveals that the difference between low-and high-diversification brands in time to recall is 65 days, and the subsequent shareholder loss as a result of recall delays is $103 million. From a policy standpoint, the implication is that regulators may need to be proactive in nudging diverse brands to respond to safety investigations in a timely manner.
Our findings should interest policy makers because product recalls seem to have a silver lining in the long run. In line with prior research, we find that firms are able to learn from prior recalls (e.g., Haunschild and Rhee 2004; Thirumalai and Sinha 2011) and make recall decisions more quickly for severe defects. Initially, we did not advance a hypothesis regarding the moderating effect of the brand's past recall intensity as managing several recalls in a short period could also pose a constraint on a firm' s resources and attention. However, our results suggest that firms are more sensitive to severe problems and recall earlier when they have experienced recalls in recent time periods. In terms of economic significance, the difference in shareholder loss for low and high levels of past recall intensity is approximately $142 million (see Table 11). These differences are significant because firms often lament that product safety is overregulated in North America and that many defects do not pose significant threats to consumers. Consistent with previous research (Kalaignanam et al. 2013), we find that product recalls, though expensive, have a positive role in altering firm behavior. Therefore, in assessing the shareholder losses that result from product recalls, managers are well advised to adopt a broader perspective by taking into account how product recalls (involving severe problems) help them in responding early to new investigations and recouping some of the incurred losses.
While the manuscript provides interesting insights into product recalls, the fact that the study is limited to the automobile industry implies that caution is warranted in generalizing our findings to other settings. The benefit of using data from a single industry is that we are able to improve the internal validity of the findings. In addition, the recall process varies across industries. The legal and other aspects that drive product investigations have substantial industry-specific idiosyncrasy that clear empirical assessments are potentially possible using only within-industry samples. In addition, because we are focusing on the delay in recall from the opening of a government investigation, the auto industry, unlike other industries, gives us a clear measure of recall delay. It is therefore not surprising that many studies on product recalls have also focused on a single industry setting rather than multiple industry settings (e.g., Borah and Tellis 2016; Hora, Bapuji, and Roth 2011; Rhee and Haunschild 2006; Rubel, Naik, and Srinivasan 2011; Thirumalai and Sinha 2011; Van Heerde, Helsen, and Dekimpe 2007). Furthermore, it is important to note that the automotive industry is a highly relevant industry from an economic perspective, representing 3%—3.5% of the gross domestic product in the United States. Nonetheless, an avenue for further research is to investigate firms' recall timing decisions in other industries to help in generating empirical generalizations.
Second, although our empirical analysis accounts for the direct and indirect costs of product recalls, the adverse reaction of stock markets does not necessarily imply that investors construe recall delays as stonewalling by firms. It is plausible that firms acknowledge the defect but simply lack the ability to issue a recall in a timely manner. More research is needed to disentangle and distinguish willful ignorance from the inability to respond faster to safety investigations. Likewise, although our study relied on theoretical arguments related to learning and problemistic search to develop our predictions, these effects are not estimated in the study. The challenge in explicitly measuring learning and search is that there are no reasonable proxies for these constructs. Therefore, it may not be appropriate to directly attribute the study's findings to these mechanisms. We hope our study spurs more research on the timing decisions of product recalls.
Endnotes 1 See https://www.cpsc.gov/Global/About-CPSC/Budget-and-Performance/CPSCFY2014AFR.pdf.
2 Prior research has shown that the stock market reacts negatively to direct and indirect costs of a recall (Chen, Ganesan, and Liu 2009; Jarrell and Peltzman 1985; Thirumalai and Sinha 2011). Indicators of recall-related costs are, for example, recall size and problem severity.
3 Computed as 100 X [exp(a) — 1], where a = .74 × 1/1.76 (1.76 is the shape parameter coefficient from Table 6).
4 The difference between short-term abnormal returns and long-term abnormal returns is related to the issue of market efficiency and security mispricing. Evidence of significant long-term abnormal returns implies that there are biases in information processing that result in over-or underreaction to announcement information (Kalaignanam et al. 2013). In other words, short-term abnormal returns and long-term abnormal returns suggest only the time horizons over which markets respond to information. As such, they do not connote short-term and long-term firm performance.
5 The marginal effects at average levels of the moderators are different from each other because these are average marginal effects (and not marginal effects at the means). That is, the average marginal effect of a given moderator is computed over the entire range of observed values of other moderators rather than at their mean levels. For example, to compute average marginal effects at average levels of brand reliability, we set brand reliability at 4.1 (the mean) and estimate the average marginal effect over all the observed values of the other moderators (brand diversification and past recall intensity).
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~~~~~~~~
Meike Eilert is Assistant Professor, Gatton College of Business and Economics, University of Kentucky.
Satish Jayachandran (contact author) is James F. Kane Professor of Business.
Kartik Kalaignanam is Associate Professor, Darla Moore School of Business, University of South Carolina.
Tracey A. Swartz is a doctoral candidate, Darla Moore School of Business, University of South Carolina.
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Doing Well Versus Doing Good: The Differential Effect of Underdog Positioning on Moral and Competent Service Providers
This research examines how consumers make trade-offs between highly competent, less moral service providers and highly moral, less competent service providers. Counter to research on general impression formation, which shows that moral traits dominate competence traits, the authors demonstrate that when choosing between service providers, consumers systematically value competence more than morality. However, underdog positioning moderates this effect. When a moral service provider is positioned as an underdog, consumers feel empathy, thereby attenuating the dominance of competence. Notably, although underdog positioning can help a moral provider overcome a deficit in competence, it does not help a competent service provider overcome a deficit in morality or a warm provider overcome a deficit in competence. Thus, underdog positioning is particularly well suited for less competent service providers who are highly moral.
Service Providers
This research examines how consumers make trade-offs between highly competent, less moral service providers and highly moral, less competent service providers. Counter to research on general impression formation, which shows that moral traits dominate competence traits, the authors demonstrate that when choosing between service providers, consumers systematically value competence more than morality. However, underdog positioning moderates this effect. When a moral service provider is positioned as an underdog, consumers feel empathy, thereby attenuating the dominance of competence. Notably, although underdog positioning can help a moral provider overcome a deficit in competence, it does not help a competent service provider overcome a deficit in morality or a warm provider overcome a deficit in competence. Thus, underdog positioning is particularly well suited for less competent service providers who are highly moral.
Keywords: services, morality, empathy, competence, underdog mechanics, and accountants. The evening news may reveal that a highly effective politician has committed adultery; an acquaintance may disclose that an award-winning real estate agent cheated on her taxes; and online reviews may suggest that a skilled auto mechanic demeans his coworkers. Once negative information about a service provider’s moral behavior becomes public, will consumers choose a highly competent but less moral service provider, or will they choose one who is more moral but less competent?
In this article, we study consumers’ choices between service providers who are morally upstanding but less competent (i.e., Tom in the opening example) and those who are highly competent but suffer from moral shortcomings (i.e., John). We examine situations in which competence and morality dimensions are independent, meaning that the better performance of the more competent service providers (e.g., the auto mechanic’s skill in diagnosing problems) is not due to their immoral behavior (e.g., demeaning his coworkers). For ease of exposition, we refer to the more competent, less moral service provider as the “competent provider” and the less competent, more moral service provider as the “moral provider.”
Extensive research in psychology has shown that morality concerns dominate competence concerns in perceptions of other people as well as groups (Leach, Ellemers, and Barreto 2007; Wojciszke 1994; Wojciszke, Bazinska, and Jaworski 1998). Therefore, we might expect evaluation of service providers to show the same effect. However, service providers enable consumers to achieve task-related goals, such as increasing their wealth, buying a house, or getting their car repaired (Kirmani and Campbell 2004). Thus, in the domain of choosing service providers, it is unclear that morality will dominate competence.
Journal of Marketing Vol. 81 (January 2017), 103–117
In the current research, we propose that consumers systematically value competence more than morality when choosing among service providers: the goal of accomplishing the service task is more important than the goal of being ethical. We focus on cases in which the immorality of the service provider does not directly harm consumers, such as when an accountant commits adultery or a real estate agent cheats on her taxes. In these cases, we predict that consumers will be more willing to choose a competent, less moral provider over a less competent, more moral provider, qualifying prior research on impression formation.
Moreover, we examine a moderator of this effect, underdog positioning. We demonstrate that choice of the competent service provider is attenuated when the moral service provider is positioned as an underdog. We provide evidence that empathy with the moral underdog leads to this reversal. However, qualifying previous research on the positive effects of underdog positioning (Paharia et al. 2011), we show that underdog positioning does not help a competent provider overcome a deficit in morality.
Another contribution of our research is to theoretically and empirically distinguish between morality and warmth traits. Whereas prior literature (e.g., Aaker, Vohs, and Mogliner 2010; Cuddy, Fiske, and Glick 2008) has considered morality (i.e., integrity) and warmth (i.e., sociability) traits together, we separate the two. In Study 1, a content analysis of online reviews for five different services shows that consumers write about all three types of traits: competence, morality, and warmth. However, only mentions of competence and morality traits influence the perceived usefulness of the reviews. We also distinguish between morality and warmth experimentally in Study 3. We find that although underdog positioning helps a moral provider overcome a deficit in competence, it does not help a warm provider do the same, highlighting a boundary condition for the underdog effect (Paharia et al. 2011).
We use multiple methods to test our predictions. In Study 1, we content-analyze real online reviews to measure the extent to which consumers spontaneously mention competence, warmth, and morality-related attributes when evaluating different types of service providers. Studies 2–5 use an experimental paradigm in which participants choose between a highly competent but morally deficient service provider and a highly moral but less competent service provider. Study 2 shows that underdog positioning helps the moral provider but not the competent provider in a real choice with financial consequences. Study 3 extends this to investigate trade-offs between competence and warmth and shows that an underdog positioning helps moral providers, but not competent or warm ones. Study 4 shows mediation by empathy. Finally, Study 5 directly manipulates empathy to show that it, rather than other mechanisms, seems to drive the results. Thus, Studies 4 and 5 attempt to provide evidence of the mechanism underlying the differential effect of underdog positioning on choice of service providers through both mediation and moderation.
Competence, Morality, and Warmth
Competence and warmth are two fundamental dimensions in social perception (Cuddy, Fiske, and Glick 2008; Rosenberg,
104 / Journal of Marketing, January 2017
Nelson, and Vivekananthan 1968). Competence captures traits related to effective provision of service, such as knowledge, skill, and intelligence. Traditionally, warmth has been defined to include traits related to others’ intentions, including morality, kindness, and friendliness (Cuddy, Fiske, and Glick 2008). However, recent research has suggested that warmth traits should be separated from morality traits because the two have different effects on impression formation (Goodwin, Piazza, and Rozin 2014). Moral traits include being sincere, fair, principled, honest, and trustworthy, while warmth traits include being sociable, playful, happy, and funny (Goodwin, Piazza, and Rozin 2014). Morality and warmth are sometimes correlated, and some traits (e.g., humility, gratitude, kindness) are both warm and moral; however, the three dimensions of competence, morality, and warmth are conceptually and empirically distinct (Goodwin, Piazza, and Rozin 2014; Leach, Ellemers, and Barreto 2007).
It is important to distinguish among these three types of traits because their relative influence in the evaluation of others differs. A large body of research in psychology has demonstrated that morality traits tend to be more important than competence-related traits when forming general impressions of others (De Bruin and Van Lange 2000; Goodwin, Piazza, and Rozin 2014; Leach, Ellemers, and Barreto 2007; Wojciszke, Bazinska, and Jaworski 1998). Moral character determines whether others may be harmful or helpful to us, and concern for morality may have developed as a way of ensuring cooperation in social groups (Greene 2013). Research has also suggested that morality traits (described as “moral character”) dominate warmth traits in general impression formation (Goodwin, Piazza, and Rozin 2014). In other words, we rely more on others’ moral character than on their friendliness and cheerfulness in forming impressions. Following this stream of literature, we posit that competence, morality, and warmth traits will have distinct effects on consumers’ evaluations of service providers.
Does the dominance of morality over competence traits transfer from general impression formation to evaluation of service providers? Previous work has suggested that, unlike when forming a general impression, when choosing someone to negotiate a conflict, competence concerns may dominate morality concerns (Wojciszke, Bazinska, and Jaworski 1998). Morality may be more important in interpersonal relationships than in relationships with service providers because morality reflects relational concerns, whereas competence reflects achievement concerns (Ybarra, Chan, and Park 2001). When choosing a service provider, consumers are concerned with accomplishing a goal with the service provider’s help. When consumers choose a salesperson to help them buy the right car or a real estate agent to help them sell a house, they may not be able to accomplish their goal unless the service provider is sufficiently skilled (Kirmani and Campbell 2004). Consumers are more likely to get a car that fits their needs when they work with a salesperson who is highly knowledgeable about automobiles. Therefore, consumers who are choosing a service provider may focus more on the provider’s ability to help achieve task-related goals (Kirmani and Campbell 2004) than on the provider’s morality or warmth.
H1: When making trade-offs between competence, morality, and warmth traits, consumers are more likely to choose a competent service provider than a moral or warm one.
We note an important boundary condition for this prediction: the dominance of competence over morality is unlikely to occur when the immoral behavior harms the consumer. Consumers are unlikely to choose an auto mechanic who puts faulty parts in their car or an accountant who embezzles their money because these immoral actions are directly harmful to them. In this article, we focus on situations in which the immorality of the service provider does not directly harm or help the consumer (e.g., an auto mechanic may cheat on his income taxes, an accountant may commit adultery). When the immorality occurs outside the service domain, consumers may use moral decoupling or moral rationalization to justify choosing the immoral service provider (Bhattacharjee, Berman, and Reed 2013; Paharia and Deshpandé 2009). For instance, they may reason that the immoral behavior is irrelevant to their assessment of the service provider’s performance (Bhattacharjee, Berman, and Reed 2013) or interpret the immoral behavior as not so wrong after all (Paharia and Deshpandé 2009). When the service provider’s immorality does not directly harm the consumer, we predict that competence will be given more weight than morality.
Underdog Positioning
If competence is rewarded more than morality, how should less competent service providers compete in the marketplace? Previous research has suggested that underdog positioning can help small firms compete with larger ones (Paharia, Avery, and Keinan 2014). An underdog is a person, brand, or firm who is at a disadvantage in terms of resources yet has passion and determination to overcome these obstacles (Paharia et al. 2011). Underdog narratives are pervasive in sports, politics, literature, film, and consumer brands. Some consumers prefer underdog brands because they identify with the struggles of such brands and share their passion to succeed when the odds are stacked against them (Paharia et al. 2011).
Will underdog positioning be equally effective for service providers who must overcome a deficit in perceived competence, perceived morality, or perceived warmth? Although prior research has suggested that underdog positioning may help brands and firms overcome a deficit in perceived competence (Paharia, Avery, and Keinan 2014; Paharia et al. 2011), it has not distinguished between deficits in perceived competence, morality, and warmth.
We propose that underdog positioning is better suited to a moral service provider overcoming a deficit in perceived competence than to a competent provider overcoming a deficit in morality or a warm provider overcoming a deficit in competence. Positioning a moral service provider as an underdog will elicit empathy, or “sharing another’s feelings by placing oneself psychologically in that person’s circumstance” (Lazarus 1991, p. 287). Underdogs are needy because they lack resources available to their competitors (e.g., money, powerful connections, physical strength). However, need alone is not sufficient to elicit empathy. Consumers value the welfare of underdogs more when they personally connect with the underdog’s struggles and passion to succeed (Paharia et al. 2011). Research has shown that empathic concern is higher when people identify with others (De Waal 2008; Krebs 1975). Thus, highly moral but less competent underdogs seem to possess key characteristics that elicit empathy.
Why should underdog positioning be more effective for moral than for competent or warm service providers? Empathy is an automatic, emotional response (De Waal 2008; Haidt 2012) that encourages people to behave morally. Empathy is aroused when consumers perceive that someone is in need and they value the welfare of that person (Batson et al. 2007). According to the empathy-altruism hypothesis (Batson et al. 1981), people are motivated to help others in need and whose welfare they value when they feel empathy toward them. Consumers are likely to feel greater empathy for a moral than for a competent service provider for two reasons. First, in the services context, the moral provider is lacking in the most important dimension, competence; therefore, the moral provider will be perceived as being in greater need of the consumer’s support than the competent provider. The competent provider, who has a deficit in morality, does not need the consumer’s help in the service domain. Second, consumers are more likely to value the welfare of a moral service provider because people tend to like moral others more than competent others (Wojciszke 1994). Therefore, the competent provider should not elicit as much empathy as the moral provider with underdog positioning.
We also predict that underdog positioning will exert a stronger influence on choices between moral and competent service providers than on choices between warm and competent service providers because morality dominates warmth in impression formation (Goodwin, Piazza, and Rozin 2014). Thus, when neither morality nor warmth are related to the competence domain, an advantage in morality (vs. warmth) should be better able to overcome a shortcoming in competence. We propose:
TABLE:
| | Condition |
|---|
| Dependent Measure | Control (No Underdog; n = 39) | Competent Lifeline Is Underdog (n = 40) | Moral Lifeline Is Underdog (n = 41) |
|---|
| Choice of moral lifeline | 36%a | 33%a | 73%b |
| Competent lifeline’s perceived competence | 5.47a (1.15) | 5.45a (1.08) | 4.96b (1.06) |
| Moral lifeline’s perceived competence | 4.15a (.91) | 4.74b (.93) | 4.87b (1.11) |
| Competent lifeline’s perceived morality | 3.05a (1.21) | 3.85b (1.69) | 3.46ab (1.76) |
| Moral lifeline’s perceived morality | 6.17a (.78) | 5.57b (1.00) | 5.96ab (1.20) |
H2: Underdog positioning has an asymmetric effect on choice among service providers, such that (a) positioning a moral service provider as an underdog significantly increases consumers’ likelihood of choosing the moral service provider, and
(b) positioning either a competent or a warm service provider as an underdog does not increase consumers’ likelihood of choosing that provider.
H3: Empathy mediates the effect of underdog positioning on consumers’ choice of service providers.
Overview of Studies
We test these hypotheses in five studies. Because online reviews are an important source of information for consumers selecting service providers, Study 1 examines the extent to which consumers spontaneously mention competence, morality, and warmth traits in online reviews and whether mentions of these traits influence the perceived usefulness of the reviews. Studies 2–5 use an experimental paradigm in which participants make choices involving two types of trade-offs: ( 1) choosing between a highly competent but morally deficient service provider and a highly moral but less competent service provider and ( 2) choosing between a highly competent but cold service provider and a warm but less competent service provider. Study 2 involves a real choice with financial consequences and shows that consumers are generally more likely to choose a competent than a moral service provider (H1). However, underdog positioning moderates this effect, with stronger effects for moral than competent service providers (H2). Study 3 manipulates two types of trade-offs (competence vs. morality and competence vs. warmth) and shows that underdog positioning significantly shifts choice for moral providers but not for competent or warm providers (H2). Study 4 replicates the asymmetric effect of
underdog positioning for moral versus competent providers in a different service context and shows that empathy mediates the choice of moral underdog providers. Finally, Study 5 directly manipulates empathy to show that empathy toward the service provider is sufficient to drive the results.
Study 1: Competence, Morality, and Warmth in Online Reviews
A fundamental assumption of our conceptual model is that competence, morality, and warmth are relevant evaluative dimensions when consumers choose among service providers. To assess whether consumers spontaneously consider competence, morality, and warmth attributes when evaluating service providers, we examined the text of real online reviews. When consumers review service providers online, they provide open-ended comments in addition to ratings. If they spontaneously mention attributes related to competence, morality, and warmth in their open-ended comments, this suggests that these dimensions are important in relating their experiences and decision-making process to others. Moreover, online reviews are an important source of information for consumers choosing among service providers. If other consumers reading these reviews rate them as more useful when they include information about competence, morality, and warmth attributes, it will underscore the relevance of these dimensions in consumers’ evaluation processes.
TABLE:
| | Competent Trainer (John) | >Noncompetent Trainer (Tom) |
|---|
| Dimensions | Factor 1 (28.5%) | Factor 2 (24.7%) | Factor 3 (18%) | Factor 1 (42%) | Factor 2 (20.4%) | Factor 3 (10.7%) |
|---|
| Honest | .90 | -.12 | .12 | .18 | .86 | .11 |
| Not manipulative | .79 | .12 | .02 | .17 | .75 | .04 |
| Sincere | .79 | .18 | .02 | .42 | .74 | .11 |
| Trustworthy | .90 | -.02 | .07 | .22 | .79 | .23 |
| Competent | .10 | -.06 | .83 | .10 | .09 | .84 |
| Clever | .05 | .39 | .48 | .16 | .16 | .63 |
| Skilled | .01 | -.12 | .88 | -.02 | .09 | .87 |
| Knowledgeable | .03 | -.14 | .88 | -.04 | .06 | .86 |
| Friendly | -.01 | .88 | -.03 | .85 | .28 | .07 |
| Warm | .22 | .79 | -.22 | .81 | .39 | .00 |
| Social | -.23 | .83 | .07 | .88 | .02 | .14 |
| Nice | .43 | .70 | -.15 | .82 | .40 | .01 |
Method
We randomly sampled reviews from Yelp for each of five different types of service providers: doctors, hair stylists, house cleaners, mechanics, and massage therapists. These categories reflect a range of services in which competence, morality, and warmth may be valued differently. Some of these service providers focused on personal care (doctors, hair stylists, and massage therapists) while others focused on care for objects (house cleaners and mechanics); some were credence services (doctors and auto mechanics) while others were experience services (house cleaners, massage therapists, and hair stylists). We collected 29–30 reviews for each type of service provider, resulting in a total of 147 reviews across the five types of service providers. In addition to the review text, we collected reviewers’ ratings of the service provider and usefulness ratings provided by other consumers who read the review.
Two raters coded each review for mentions of competence, morality, warmth, and other attributes (1 = mentioned, 0 = not mentioned) as well as the valence of the attribute (-1 = negative, 0 = neutral, 1 = positive). Reliability, computed using Rust and Cooil’s (1994) measure for qualitative data, was .83. Competence attributes enable “people to efficiently attain their goals or obstruct the goal attainment” (Wojciszke, Bazinska, and Jaworski 1998, p. 1253) and include diligence, level of education, efficiency, knowledge and thoroughness. Morality attributes pertain to “breaking or maintaining moral rules and/or … doing good or bad things to others” (Wojciszke, Bazinska, and Jaworski 1998, p. 1253); these include ethics, honesty, and trustworthiness. Warmth attributes also relate to interactions with others but focus on the kindness and friendliness of these interactions (Goodwin, Piazza, and Rozin 2014); these include
the regressions on usefulness ratings show, when information on morality is available to consumers, it is considered a valuable input for evaluation of service providers, especially when it is negative. In the next four studies, we directly test our hypotheses by manipulating the competence, morality, and warmth traits of service providers.
Study 2: Underdog Positioning and Choice of Competent Versus Moral
Service Provider
The purpose of Study 2 was to examine trade-offs between competence and morality traits of service providers when choices between service providers were incentive compatible. Participants played an online trivia game modeled after the television show Who Wants to Be a Millionaire? in which they received compensation in line with the number of trivia questions they answered correctly. Before beginning the game, participants chose a “lifeline” they would be able to call on to help them answer a question. Two lifelines were offered to participants: a competent lifeline (an advice giver described as being highly competent but who had engaged in immoral behavior) and a moral lifeline (an advice giver described as being less competent but morally upstanding).
Method
The design was a 3 (underdog positioning: control, competent lifeline as underdog, moral lifeline as underdog) •
2 (lifeline: competent, moral) mixed design. Underdog positioning was manipulated between subjects and lifeline
was manipulated within subjects. One hundred twenty online participants (44% male; Mage = 30 years) were paid
$.20 for participating in the study and an additional $.50 for every question they answered correctly. Participants were told they would receive ten trivia questions and would have 20 seconds to answer each question.
After answering a sample question, participants chose a lifeline to help them with one question during the game. Lifeline 1 represented the competent lifeline and Lifeline 2 represented the moral lifeline. Specifically, participants were told:
Lifeline 1: He is very good at answering trivia questions, and when he helps participants with questions in this game, his success rate is 86%. He frequently boasts about being able to obtain personal information about people without their permission and has posted embarrassing details about several people he has worked with on Facebook.
Lifeline 2: He is relatively good at answering trivia questions, and when he helps participants with questions in this game, his success rate is 55%. He is very considerate and enjoys helping other people. He has never posted negative or embarrassing information about others on Facebook and would consider doing so a violation of trust.
Underdog positioning was manipulated by information about external disadvantage as well as determination, both of which are necessary to be an underdog (Paharia et al. 2011). In the underdog conditions, participants read two additional statements indicating that one of the lifelines “comes from a poor family and overcame a lot to attend and graduate from college. People describe him as having a true passion for learning.”
Participants chose one lifeline to help them during the game. Before playing the game, they rated the lifelines’ morality on items from Leach, Ellemers, and Barreto (2007; “dishonest/honest,” “insincere/sincere,” “manipulative/not manipulative,” and “not trustworthy/trustworthy”; a = .88 and .84) and competence (“incompetent/competent,” “not clever/clever,” “not knowledgeable/knowledgeable,” and “unskilled/skilled”; a = .84 and .87) on seven-point scales. During the game, when participants asked for help, the competent lifeline confidently suggested the correct answer. In contrast, the moral lifeline confidently ruled out two of the incorrect answers but did not distinguish between the remaining two answers. After the game, participants rated the helpfulness of their lifeline and their satisfaction with their lifeline. Finally, they received payment based on the number of questions they had answered correctly.
TABLE:
| | Competence Versus Morality Condition | Competence Versus Warmth Condition |
|---|
| Dependent Measure | No Underdog (n = 65) | Competent Underdog (n = 64) | Moral Underdog (n = 62) | No Underdog (n = 63) | Competent Underdog (n = 63) | Warm Underdog (n = 65) |
|---|
| Choice of noncompetent coach | 28%a | 22%a | 48%b | 30%a | 18%a | 25%a |
| Competent coach’s perceived competence | 5.75a (1.0) | 5.77a (.78) | 5.70a (.95) | 5.79a (.81) | 5.83a (.96) | 5.96a (.69) |
| Noncompetent coach’s perceived competence | 4.11a (1.28) | 3.89a (1.03) | 4.21a (.92) | 3.86a (.94) | 3.88a (.98) | 4.13a (1.06) |
| Competent coach’s perceived morality | 3.24ab (1.25) | 3.57a (1.04) | 3.01b (1.16) | 4.35c (.94) | 4.85d (.91) | 4.77d (1.10) |
| Noncompetent coach’s perceived morality | 5.85a (1.08) | 5.82a (1.06) | 5.97a (.95) | 5.16b (1.02) | 5.24b (.99) | 5.34b (1.10) |
| Competent coach’s perceived warmth | 4.10ab (1.07) | 4.25a (.84) | 3.90b (.99) | 3.17c (1.01) | 3.18c (.93) | 3.11c (1.02) |
| Noncompetent coach’s perceived warmth | 5.67a (1.07) | 5.55 (.95) | 5.69a (.92) | 6.06b (.95) | 6.27b (.76) | 6.16b (.97) |
Discussion
Using incentive-compatible choices, Study 2 demonstrates that competence dominates morality in the absence of underdog information. Because participants were paid on the basis of the number of questions they answered correctly, they earned more money when they chose the competent lifeline (M = $1.90) than when they chose the moral lifeline (M = $1.46). Consistent with H1, only 36% of participants in the control group chose the moral lifeline. When the moral lifeline was positioned as an underdog, however, choice of the moral lifeline increased to 73% (H2a). Moreover, underdog status did not increase choice of the competent lifeline.
It is noteworthy that although information about the competence of the two lifelines was provided in objective terms (success rate of 86% vs. 55%) and this manipulation clearly led to differences in competence perceptions in the control condition, the underdog manipulation mitigated the competence gap when the moral lifeline was presented as an underdog. Because morality and competence ratings were collected after participants chose their lifelines, we believe that this lack of significant differences in competence ratings in the moral underdog condition reflects participants’ motivation to evaluate the moral lifeline more positively (as a result of higher empathy). In other words, we believe that this mitigation of the competence gap in the moral underdog condition is a consequence of greater empathy toward the provider, but it is not a necessary condition for the effect to occur.
To further explore the asymmetric effect of underdog positioning, in the next study we manipulate two trade-offs consumers may encounter when evaluating service providers: a competence versus morality trade-off and a competence versus warmth trade-off. We expect that underdog positioning will be more influential when consumers are trading off competence and morality than when they are trading off competence and warmth.
Study 3: The Differential Effect of Underdog Positioning for Moral and Warm Service Providers
Study 3 compared the effects of underdog positioning on two different types of trade-offs when choosing a personal trainer (competence vs. morality and competence vs. warmth). Next, we detail the method and results.
Method
The design was a 3 (underdog positioning: control, competent provider is underdog, noncompetent provider is underdog) • 2 (type of trade-off: competence vs. morality, competence vs. warmth) • 2 (provider: competent vs. noncompetent) mixed design. Underdog positioning and type of trade-off were manipulated between subjects and service provider was manipulated within subjects. Participants were 382 undergraduate students who completed the study as part of a series of studies in a behavioral lab for course credit. Variations in degrees of freedom reflect missing observations.
Participants imagined that they had decided to start working with a personal trainer at their campus gym. They read reviews of two personal trainers, John and Tom. In all conditions, John was the competent trainer and Tom was the noncompetent trainer. The reviews contained both the trade-off and the underdog positioning manipulation. Like the real reviews we examined in Study 1, the reviews we constructed mentioned competence, morality, and warmth attributes. Specifically, in the competence versus morality trade-off condition, the review of John, the competent but less moral trainer, stated:
John Williams is very knowledgeable about workouts. He was able to create a personalized workout that was just the right level for me, working the muscles I wanted to train. Going to John, I get a terrific workout that continually changes!
Although he is very competent at being a trainer, I have to say that I am a bit disturbed by his ethics. While I was waiting for him one day, I heard him laughing with someone about using a handicapped parking sticker so he could get a parking spot close to the gym. I don’t think that’s funny when so many people come onto campus to go to the medical center and they really need to park close.
In contrast, Tom was described as a less competent trainer but more moral.
I have worked out with Tom Smith for the past couple of months. The workout is OK. I feel like he probably does the same thing with everyone. He will change things to focus on different muscle groups if I ask him to, but I have to be the one who reminds him to make a change.
One thing I really do like about Tom, though, is his honesty. He never tries to take advantage of a situation. It might be less convenient sometimes, but he’s told me plenty of times that he cares about doing the right thing.
In the competence versus warmth trade-off condition, the second paragraph of each review was changed to focus on warmth rather than morality. The review of John stated:
Although he is very competent at being a trainer, I have to say I am a bit disturbed by his unfriendly behavior towards the other trainers. He is polite with me, but I have seen him be downright rude to people because he’s too busy to talk or even say hello.
The review of Tom stated:
One thing I really do like about Tom, though, is how warm he is to everyone. He is always very friendly and it’s clear that he cares about other people.
TABLE:
| | Condition |
|---|
| Dependent Measure | Control (No Underdog; n = 27) | Competent Coach Is Underdog (n = 29) | Moral Coach Is Underdog (n = 28) |
|---|
| Choice of moral coach | 24%a | 14%a | 48%b |
| Competent coach’s perceived competence | 5.74a (.73) | 5.64a (.97) | 5.62a (1.05) |
| Moral coach’s perceived competence | 4.57a (1.25) | 4.86a (1.00) | 5.68b (.85) |
| Competent coach’s perceived morality | 3.00a (.77) | 3.91b (1.30) | 3.08a (1.37) |
| Moral coach’s perceived morality | 6.09a (.76) | 5.70b (.74) | 6.26a (.70) |
| Empathy toward competent coach | 2.64a (1.58) | 4.09b (1.49) | 2.22a (1.22) |
| Empathy toward moral coach | 3.48a (1.72) | 3.38a (1.22) | 5.30b (1.11) |
Analagous to Study 2, we manipulated underdog positioning by adding the following two sentences at the end of the review indicating that the trainer came from a disadvantaged background and was passionate about fitness:
One day I was talking with him and it sounded like he’d grown up in a tough neighborhood with few opportunities. He has gotten his personal trainer certification while working at a few different gyms, and he is clearly passionate about fitness.
The same wording was used across conditions. As a manipulation check, we asked participants to rate the extent to which they would characterize each trainer as an underdog, someone who had to overcome obstacles to succeed, and someone who has passion for what he does (seven-point scales; 1 = “not at all,” and 7 = “very much”; John a = .70, Tom a = .79).
After reading the reviews, participants chose which trainer they would like to hire. Next, they rated each trainer’s perceived superscripts indicate a significant difference between the means (p < .05).
choice. We proposed that the key driver of the effect is empathy toward the provider (H3). In the next study, we test whether empathy mediates the effect of underdog positioning on choice.
Study 4: Mediation by Empathy
Method
To test mediation, we used the same design as in Study 2, but a new service context: that of choosing a career coach. The design was a 3 (underdog positioning: control, competent coach, moral coach) • 2 (coach: competent, moral) mixed design. Underdog positioning was manipulated between subjects and coach was manipulated within subjects. Eighty-seven undergraduate students completed the study in the lab as part of a course requirement. Variations in degrees of freedom reflect missing observations.
Participants read a description of two career coaches. Mike was the more competent, less moral coach, while John was the moral, less competent coach. The description stated:
Coach Mike is a 2007 graduate of University of Maryland and was hired last fall. Last semester, 80% of the students he worked with had accepted jobs by the time they graduated. Over the summer, he was reprimanded for misusing Office of Career Services funds (e.g., claiming expenses for having lunch with his friends who were not business contacts of the University of Maryland).
Coach John is a 2006 graduate of University of Maryland. He has been with the Office of Career Services for almost two years. Over the last year, 55% of the students he worked with had accepted jobs by the time they graduated. He has a spotless record and has always been careful to claim only true business expenses on his expense report.
We manipulated underdog positioning by stating that “he comes from a poor family and attended college with fewer resources than others. People describe him as being passionate about mentoring and as not giving up easily.”
After reading the descriptions, participants responded to a series of measures about the coaches. First, they chose which coach they would prefer to work with. Perceived morality (competent coach a = .81, moral coach a = .68) and competence (competent coach a = .72, moral coach a = .84) were measured as in the previous studies. Feelings of empathy were the effect. Following Preacher and Hayes’s (2008) bootstrapping procedures (PROCESS Model 4), we found that positioning the moral coach as an underdog enhanced empathy toward the moral (vs. competent) coach, which in turn enhanced choice. The confidence interval (CI) for the indirect effect from feelings of empathy excluded zero (1.05, 95% CI = [.09, 2.35; see Figure 2).
TABLE:
| | Empathy |
|---|
| | Low Empathy | Control | High Empathy |
|---|
| Dependent Measure | No Underdog (n = 46) | Moral Coach Is Underdog (n = 47) | No Underdog (n = 52) | Moral Coach Is Underdog (n = 48) | No Underdog (n = 50) | Moral Coach Is Underdog (n = 46) |
|---|
| Choice of moral coach | 17%a | 21%a | 19%a | 42%b | 30%ab | 41%b |
| Competent coach’s perceived competence | 5.60a (.86) | 5.56a (.86) | 5.44a (.99) | 5.77a (1.00) | 5.55a (1.13) | 5.41a (.95) |
| Moral coach’s perceived competence | 4.34a (1.09) | 4.74ab (.94) | 4.37a (1.05) | 5.07b (1.08) | 4.63a (.85) | 4.97ab (1.06) |
| Competent coach’s perceived morality | 3.59a (1.02) | 3.24a (1.25) | 3.53a (1.15) | 3.56a (1.56) | 3.23a (1.28) | 3.26a (1.24) |
| Moral coach’s perceived morality | 5.52a (.90) | 5.70a (1.04) | 5.41a (1.05) | 6.24b (.77) | 5.53a (.97) | 6.34b (.70) |
Discussion
This study replicated the findings of Studies 2 and 3 using a different service context (career coaches) and a different manipulation of moral behavior. In addition, it demonstrated that differences in empathy for the two service providers mediate the effect of underdog positioning on choice. In the next study, our goal is to provide further evidence for the role of empathy in choosing moral underdog providers. Using the same career coach scenario, we directly manipulate empathy rather than measure it. If empathy drives choice of moral underdogs, we expect the effect of underdog positioning for moral providers to be attenuated when participants are asked to remain as objective as possible and to avoid empathizing with the service providers.
Study 5: Manipulation of Empathy
Method
Study 5 used a 3 (empathy: control, low, high; between subjects) • 2 (underdog positioning: no underdog vs. moral underdog; between subjects) • 2 coach (moral vs. competent; within subject) mixed design. Participants were 289 undergraduate students who completed the study as part of a course requirement in a behavioral lab. Variations in degrees of freedom reflect missing observations.
The career coach and underdog manipulations were the same as in Study 4. Empathy was manipulated between subjects using Batson et al.’s (2007) standard wording, adapted to our scenario. In the control (no empathy instruction) condition, participants received the same instructions as in Study 4. In the low empathy condition, participants were asked to take an objective perspective when evaluating the career coaches. Specifically, participants were told, “As you read about them, try to take an objective perspective toward what is described. superscripts indicate means are signi cantly different (ps < .05).
condition, suggesting that empathy is necessary for the results to occur.
Discussion
This study shows that inducing high empathy simulates the effect of positioning a service provider as an underdog. Consistent with our previous studies, participants who did not receive empathy instructions were more likely to choose the moral coach when he was described as an underdog than when he was not. In addition, participants encouraged to feel empathy were more likely to choose the moral coach than those encouraged to make decisions objectively. This finding suggests that empathy attenuates the dominance of competence over morality in the services context. Moreover, because the empathy manipulation did not influence the perceived competence of the service providers, we have more evidence that as a mechanism, empathy is sufficient to shift choice between competent and moral service providers.
General Discussion
The article examines the trade-offs consumers make between competence, morality, and warmth when evaluating service providers. We qualify previous work on impression formation, which has found that morality generally dominates competence when evaluating others, to show that consumers tend to favor competent service providers over moral ones. However, we demonstrate that choice of the moral service provider relative to the competent service provider is significantly increased when the moral service provider is positioned as an underdog. These results were robust across a variety of service providers, including trivia game lifelines, personal trainers, and career coaches. The effect sizes in our studies were large: in Studies 2, 4, and 5, choice shares were doubled when the moral service provider was positioned as an underdog, and in Study 3, the choice share increased by 71%.
Moreover, we qualify prior research on underdog positioning (Paharia et al. 2011) by showing that underdog positioning can overcome some deficits more effectively than others. Although underdog positioning can help a moral service provider overcome a deficit in perceived competence, it is less effective in helping a competent provider overcome a deficit in morality or a warm service provider overcome a deficit in competence. We discuss the three areas in which our research makes important contributions: ( 1) understanding trade-offs between competence and morality in evaluating service providers, ( 2) conceptually and empirically distinguishing between warmth and morality traits when evaluating service providers, and ( 3) demonstrating when underdog positioning influences choices between service providers.
Competence Versus Morality
Our studies demonstrate that when immoral behavior is not directly harmful to consumers, they systematically value competence more than morality when choosing among service providers. Pursuit of task-related goals makes them willing to overlook moral violations when these violations are not directly related to the service being provided. Although these results run counter to the literature on impression formation and in-group evaluations, which has shown that morality concerns trump competence concerns in interpersonal interactions (Goodwin, Piazza, and Rozin 2014; Leach, Ellemers and Barreto 2007; Wojciszke, Bazinska, and Jaworski 1998), our results are consistent with a preference for more competent but less moral others when choosing a negotiator for a labor dispute (Wojciszke, Bazinska, and Jaworski 1998). Thus, when consumers are intent on achieving their task-related goals, competence dominates morality.
In our studies, participants received information about the traits of the service provider. In Study 3, we provided information about the service provider’s competence, morality, and warmth by mentioning these attributes in the service provider reviews we constructed. In the marketplace, information about specific service providers is becoming more available to consumers not only through the text of online reviews, as on Yelp, but also through ratings of specific dimensions. For example, on RateMDs.com, consumers provide star ratings for specific attributes of doctors, including knowledge, punctuality, helpfulness, and staff. Similarly, on Angie’s List, consumers grade service providers on the dimensions of quality, price, responsiveness, punctuality, and professionalism. Information about morality attributes is provided on GoodGuide.com, which rates firms on their health, environment, and social impact. Thus, whether it is through the text of reviews or ratings of specific dimensions, information about competence, morality, and warmth attributes of service providers is increasingly available to consumers.
Apart from these third-party sources, service providers can provide morality information through advertising materials or on their own website. Our findings suggest that service providers should emphasize their moral attributes when they are favorable. For instance, on its own website, the service provider could explicitly ask customers to rate dimensions that favor the provider, such as environmental consciousness if they invest in being green, or social responsibility if they work a lot with charities. The more evidence service providers can offer about their morality (e.g., through customer testimonials or ratings), the more likely it is that customers will focus on morality in the decision process.
Our results about the trade-offs between competence and morality apply to a variety of service organizations. Organizations in many domains, including politics and athletics, seem willing to overlook employees’ moral violations when the employees are highly competent. For instance, Lionel Messi, arguably the world’s top soccer player, has been charged with tax fraud. However, this has not had a detrimental impact on his club’s decision to keep him, nor has it affected his endorsement contracts.
A worthwhile question for further research is whether both customers and firms easily overlook all types of moral violations. When the immoral behavior has the potential to harm the consumer, such as when an accountant engages in tax fraud or when a soccer player cheats during a game, consumers should be less likely to hire the accountant or buy products endorsed by the soccer player. Moreover, some immoral behaviors may be perceived as so egregious that no one would want to associate with the competent provider. For instance, celebrity chef Paula Deen lost her endorsements, her cooking show, and many fans when she admitted to using a racial epithet. Immoral behaviors like these may not be as easily rationalized or decoupled (Bhattacharjee, Berman, and Reed 2013) from evaluation of the service provider.
Morality Versus Warmth
A second contribution of this article is its demonstration that morality and warmth traits have distinct effects when consumers evaluate service providers. Whereas prior work in marketing has considered morality and warmth traits together (e.g., Aaker, Vohs, and Mogliner 2010), we show that they can be distinct. Consistent with recent literature in psychology, we empirically distinguish morality (i.e., integrity) from warmth (i.e., sociability). In our content analysis of online reviews in Study 1, we found that mentions of morality attributes, but not warmth attributes, affected the perceived usefulness of the reviews. In Study 3, when trade-offs between competence and morality were compared with trade-offs between competence and warmth, we observed that underdog positioning overcame moral providers’ deficits in competence, but underdog positioning did not overcome warm providers’ deficits in competence. Thus, we demonstrate that morality and warmth have distinct effects on evaluation of service providers.
These findings also have implications for research on branding. For instance, sincerity is one of the five key dimensions of brand personality (Aaker 1997), and it is measured using attributes that are associated with both warmth and morality. In Study 3 as well as in prior research (Leach, Ellemers, and Barreto 2007), sincerity loaded with morality rather than warmth. It may be worthwhile for future studies to investigate whether warm versus moral brand personalities have different effects. Our research would suggest that for brands positioned on sincerity, transgressions of morality would be much more damaging than would transgressions of warmth.
Underdog Positioning
Finally, we contribute to the literature on underdog positioning (e.g., Paharia et al. 2011) by showing conditional boundaries for when underdog biographies are effective. Our findings suggest that underdog positioning may work for moral brands but not for either competent or warm brands. Paharia et al. (2011) show that associating a brand with an underdog biography increases consumer preference. However, in their studies, consumers did not have to make trade-offs between
brand quality and other brand attributes. Instead, they were simply asked to choose between underdog and top dog brands. Our results suggest that the effectiveness of underdog positioning is contingent on the perceived characteristics of the target brand. In particular, we show that empathy is less likely to be elicited for underdog brands or service providers that have been linked to immoral behaviors.
Importantly, we show that although adopting an underdog positioning can provide an effective buffer against competence shortcomings, it does not effectively guard against morality lapses. Competent but “sinful” providers do not elicit as much empathy as moral but less competent providers. An important implication of this is that highly ethical service providers with lower performance ratings may increase their market share relative to more competent providers if they actively promote their underdog origins as well as their passion and determination to succeed. Further research may examine positioning that is more effective for competent or warm service providers relative to moral service providers. For example, positioning that evokes pride rather than empathy may be more effective for competent service providers.
Our findings suggest that underdog positioning is particularly effective for brands that are positioned as moral (i.e., honest, socially conscious, organic, or green). Indeed, many underdog brands in the current marketplace seem to have a moral positioning. For example, Honest Tea, which specializes in healthy, organic tea beverages, has run marketing campaigns with a morality theme, playing on its brand name. The “Our Story” section of its website emphasizes its underdog roots (the founder “brewing batches of tea in his kitchen”) as well as its focus on health, the environment, and social responsibility. Although Honest Tea is no longer a true underdog, having been acquired by Coca-Cola in 2011, our results suggest that there is a good fit between its underdog roots and the brand’s emphasis on morality. A search of underdog brands generates many other examples of brands that also play up their health, social consciousness, or environmental consciousness, such as Nantucket Nectars, Ben & Jerry’s, TOMS Shoes, Burt’s Bees, Lifeway, and Chipotle; all of these brands emphasize both their underdog roots and their morality on their websites. Our findings apply particularly well to startup companies because their competence is often in question relative to larger, more established competitors. Underdog positioning should be particularly effective if startups also emphasize their morality, whether this is related to health, environmental issues, or social responsibility.
In summary, our findings show significant and large shifts in the choice shares of moral service providers as a result of highlighting their underdog status. We think these findings are particularly useful for small companies and service providers as well as for nonprofit organizations. Consumers may perceive these organizations as more moral but less competent when compared with large or for-profit businesses. A successful positioning strategy for nonprofits may be to highlight morality-related attributes (e.g., integrity, honesty, ethical behavior) and couple these with underdog biographies as points of differentiation. In particular, if these organizations are in a market in which a dominant player has questionable ethics, they may benefit from the synergistic effect of morality attributes and underdog positioning.
GRAPH: FIGURE 1
GRAPH: B: Frequency of Positive, Neutral, and Negative Attributes in Sampled Online Reviews
DIAGRAM: Doing Well Versus Doing Good: The Differential Effect of Underdog Positioning on Moral and Competent Service Providers
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Record: 65- Driving Brand Engagement Through Online Social Influencers: An Empirical Investigation of Sponsored Blogging Campaigns. By: Hughes, Christian; Swaminathan, Vanitha; Brooks, Gillian. Journal of Marketing. Sep2019, Vol. 83 Issue 5, p78-96. 19p. 1 Diagram, 7 Charts, 2 Graphs. DOI: 10.1177/0022242919854374.
- Database:
- Business Source Complete
Driving Brand Engagement Through Online Social Influencers: An Empirical Investigation of Sponsored Blogging Campaigns
Influencer marketing is prevalent in firm strategies, yet little is known about the factors that drive success of online brand engagement at different stages of the consumer purchase funnel. The findings suggest that sponsored blogging affects online engagement (e.g., posting comments, liking a brand) differently depending on blogger characteristics and blog post content, which are further moderated by social media platform type and campaign advertising intent. When a sponsored post occurs on a blog, high blogger expertise is more effective when the advertising intent is to raise awareness versus increase trial. However, source expertise fails to drive engagement when the sponsored post occurs on Facebook. When a sponsored post occurs on Facebook, posts high in hedonic content are more effective when the advertising intent is to increase trial versus raise awareness. The effectiveness of campaign incentives depends on the platform type, such that they can increase (decrease) engagement on blogs (Facebook). The empirical evidence for these findings comes from real in-market customer response data and is supplemented with data from an experiment. Taken together, the findings highlight the critical interplay of platform type, campaign intent, source, campaign incentives, and content factors in driving engagement.
Keywords: advertising campaign intent; brand engagement; consumer decision journey; influencer marketing; social media influencers; social media platform; social network sites; sponsored bloggers
Consumers are increasingly relying on peer-to-peer communications; for this reason, influencer marketing has continued to grow in importance as a key component of firms' digital marketing strategies ([ 3]). Nearly 75% of marketers today are using influencers to spread word of mouth (WOM) about their products and brands on social media. Influencer marketing is often considered critical to strengthening online brand engagement ([67]). Consequently, 65% of multinational brands have indicated plans to increase spending on influencer marketing, with spending expected to reach $10 billion by 2020 ([ 7]; [65]). However, despite the explosion of these social influencers, their effectiveness is still low; for an influencer on Facebook, the average engagement rate per post is.37%; on Twitter, it is even lower at.05% ([77]).
A large and important category of influencer marketing is sponsored blogging, in which companies solicit bloggers to post about specific products and brands (i.e., "sponsored posts") ([57]). Bloggers can help generate WOM about a brand, product, or service directly through the content of their sponsored posts. Firms have deployed sponsored blogging both successfully (i.e., Nokia's camera phone campaign in Finland) and unsuccessfully (i.e., Dr Pepper's "Raging Cow" campaign) ([17]). However, the field needs to develop a better understanding of what drives the success of influencer marketing as a whole and sponsored blogging in particular. Given the significant marketing expenditures dedicated to this strategy and the paucity of knowledge on success drivers, this is an important research gap worth addressing.
Sponsored blogging is a hybrid approach combining aspects of paid and earned media (e.g., [15]; [59]). We distinguish this phenomenon from a purely paid media strategy because influencers engage in WOM and have control over the ultimate message of the advertisement. As companies reimburse bloggers (with either cash or free goods) to generate posts on social media, influencer marketing is distinct from organically generated WOM. Because influencer marketing blends elements of paid and earned media, we can distinguish this from prior research focusing on paid and owned media (e.g., [20]; [59]) or earned media, including online WOM (e.g., [38]). We also extend the traditional advertising literature on the impact of source credibility and message content ([32]).
We provide a comprehensive framework that examines the drivers of sponsored blogging strategies, including blogger characteristics, content characteristics, and campaign incentives and, in doing so, contribute to the literature in three ways. First, this study advances prior research by examining how social influencers (or sponsored bloggers) can influence consumers at different stages of the consumer purchase funnel by examining different campaign intents (e.g., awareness vs. trial). Second, this research sheds light on the important role of campaign intent as a moderator of the impact of blogger (i.e., expertise) and content (i.e., hedonic value) characteristics on social media engagement. Third, we suggest that the type of social media platform (blogs vs. Facebook) can moderate the impact of these factors on engagement.
Our theoretical basis for predictions derives from the literature on the elaboration likelihood model (ELM) ([75]). We focus on two moderators that indirectly affect consumers' ability and motivation to engage in effortful processing. The first involves social media platforms (blogs vs. Facebook), which vary in their level of distraction and involvement, implying differences in consumers' ability and opportunity to engage in effortful processing. The second is the stage of the consumer decision journey (CDJ) (awareness vs. trial), which may imply increasing levels of motivation closer to trial. Early in the CDJ, consumers process through the peripheral route, whereas later they process through the central route ([15]). Taken together, we argue that both the platform and the stage of CDJ act as key moderators.
Our findings show that in a blog context, blogger expertise, campaign intent, hedonic value of post, and campaign giveaways are key drivers of engagement. In addition, blogger expertise exerts a greater impact in awareness (vs. trial) campaigns. On Facebook, hedonic value exerts a positive impact, and trial campaigns benefit more from the use of hedonic content. Campaign giveaways exert a negative impact, highlighting the potential cannibalizing role of one platform on another (blog vs. Facebook). Taken together, the findings shed light on various factors that govern how influencer campaigns elicit consumer engagement across multiple platforms. Panel A of Figure 1 presents our conceptual framework on the blogging platform, and Panel B presents the same for the Facebook platform.
Graph: Figure 1. Conceptual framework of factors influencing sponsored blogging campaign effectiveness.
This study advances prior research by examining how social influencers can affect consumers at different stages of the consumer purchase funnel. This research suggests that the type of social media platform moderates the impact of social influencer and post characteristics. We develop a framework of strategies based on the social media platform in use and the firm's campaign intent to inform practitioners about the type of content and influencer to use under each condition. The findings have implications for practitioners who want to employ influencers and show that the choice of bloggers should be guided by campaign intent.
This research uses real in-market customer response data, assembles a large data set of sponsored blogging campaigns, measures various characteristics, and links these to concrete brand engagement outcomes. Thus, our field data provide a unique vantage point and draw a richer picture of not only what constitutes an effective influencer marketing campaign but also how this varies across social media platforms. We supplement the findings by collecting data in a lab study.
Research on influencer marketing examines elements of sponsored advertising, product type, source characteristics, and sponsorship disclosure. The findings include differentiating impacts of expertise, product involvement ([95]), customer involvement ([28]), sponsorship disclosure ([86]), and two-sided messages ([84]). Recent research on influencers indicates that information seekers' objectives and issue involvement drive a blog's influence ([ 5]). [58] demonstrate the importance of message content, source credibility, and homophily in influencer marketing. Table 1 provides a review of research on the key variables of influencer marketing.
Graph
Table 1. Previous Research Related to Sponsored Blogging Key Variables.
| Research | Independent Variables | Dependent Variables |
|---|
| Blogger Characteristics | Audience Characteristics | Product Type | Sponsorship Disclosure | Network Characteristics | Post Content | Brand Awareness | Campaign Intent | Social Media Platforms | Reader Review Valence | Behavioral Intention | Attitude | Effectiveness | Credibility | Trust | Engagement |
|---|
| Zhu and Tan (2007) | ✓ | | ✓ | ✓ | | | | | | | ✓ | ✓ | | | | |
| Magnini (2011) | | | | ✓ | | | | | | | | | ✓ | | ✓ | |
| Fu and Chen (2012) | | ✓ | | | | | | | | ✓ | | ✓ | | | | |
| Lu, Chang, and Chang (2014) | | | ✓ | ✓ | | | ✓ | | | | ✓ | ✓ | | | | |
| Colliander and Erlandsson (2015) | | | | ✓ | | | | | | | | ✓ | | ✓ | | |
| Ballantine and Yeung (2015) | | | | ✓ | | | | | | ✓ | ✓ | ✓ | | ✓ | | |
| Hwang and Jeong (2016) | | | | ✓ | | | | | | | | | | ✓ | | |
| Rooderkerk and Pauwels (2016) | ✓ | | | | | ✓ | | | | | | | | ✓ | | ✓ |
| Uribe, Buzeta, and Velásquez (2016) | ✓ | | | ✓ | | ✓ | | | | | ✓ | | ✓ | ✓ | | |
| Van Reijmersdal et al. (2016) | | | | ✓ | | | | | | | ✓ | ✓ | | | | |
| Balabanis and Chatzopoulou (2019) | ✓ | | | | ✓ | | | | | | | | | ✓ | | ✓ |
| Hollebeek and Macky (2019) | | | | | | ✓ | | | | | | | | | ✓ | ✓ |
| Lou and Yuan (2019) | ✓ | | | | ✓ | | | | | | | | | ✓ | ✓ | |
| This study | ✓ | | | | ✓ | ✓ | | ✓ | ✓ | | ✓ | | | | | ✓ |
Related research also examines native advertising, or the phenomenon of sponsored social media posts or news articles disguised to resemble nonsponsored content. [89] examine native ads and unveil how their effectiveness changes across serial positions by analyzing a large-scale data set. [92] examine how source credibility plays a critical role in perceptions of native advertising. Other research has also examined the phenomenon of social dollars, or the effects of online social connections on users' product purchases in an online community. [74] demonstrate that social dollars vary depending on type of product (hedonic vs. functional), user experience, and network density. Together, these findings shed light on the important role of influencers, particularly those embedded in social networks, on consumer choices and purchase behavior.
Our key dependent variable for the primary field study is social media engagement. We follow [40], p. 555) and define engagement as a "customer's cognitive, emotional, and behavioral activities." More specifically, our focus is on indirect customer engagement, which includes incentivized referrals, social media conversations about products/brands, and customer feedback to companies ([73]). These types of actions contribute to a firm's revenue, as referred customers are typically more profitable than those not referred ([72]; [85]). This impact of engagement on profitability has also received empirical verification across business-to-business ([55]) and business-to-consumer ([56]) contexts, and its benefits can derive from both cost reduction and revenue enhancement ([33]).
Consumer engagement literature highlights several potential factors that may influence consumer engagement, including emotionality, direct firm actions, and product involvement ([33]; [73]). We derive our key factors from this literature and add new factors, such as overall campaign intent, influencer characteristics (i.e., source expertise and post content), and level of involvement elicited by the social media platform. The customer engagement activity we focus on is social media interactions with sponsored influencer content, and we operationalize this as likes and comments on sponsored posts.
Firms often launch influencer marketing campaigns on multiple platforms simultaneously. The blog platform constitutes the primary environment for sponsored bloggers to exert their influence. People who choose to interact with bloggers and their postings are typically followers of the blogger. Followers have opted to obtain information posted by bloggers and therefore are likely highly involved in the environment. This high involvement translates into several facets of blog campaigns that help strengthen engagement.
While there are various differences across social media platforms, a key difference is the rationale or motivation for consumers to engage with platforms. Some consumers seek out platforms (e.g., blogs) for their content, which implies a higher level of motivation to engage in effortful processing of content. Others may primarily use platforms (e.g., Facebook) to connect with others, implying a focus on relationship maintenance ([47]). Another key difference is the level of distraction prevalent on a platform. Platforms such as Facebook are relatively less involving and more distracting for each individual post because of the large amount of information and content provided ([93]). In our research, Facebook is a relevant platform (in addition to blogs) because many bloggers post links to their blog posts on Facebook.
To provide a priori evidence of the differences in social media platforms, we pretested blogs and Facebook (the two relevant platforms in this study) using a survey of participants (N = 264, Mage = 35.2 years, 50.0% male) on Amazon Mechanical Turk. Participants were randomly assigned to one of two platform conditions: blog or Facebook. Recalling their last time on the platform, they reported how distracted they felt and the degree to which they were seeking specific content on the platform on scales from 0 to 100. Controlling for age and gender in both regression models, we found that distraction was higher on Facebook than blogs (F( 3, 260) = 7.22, p <.01; Mblog = 32.65, MFacebook = 42.83; bplatform = –10.67, p <.01), and specific content seeking was higher on blogs than Facebook (F( 3, 260) = 3.61, p <.01; Mblog = 59.19, MFacebook = 48.14; bplatform = 11.25, p <.01). These results lend support to our argument that platform distraction and content search differ between blogs and Facebook, with distraction being lower and content seeking being more common on blogs. Therefore, Facebook should result in low-involvement processing of information.
Given the low-involvement nature of the Facebook platform, consistent with the ELM ([75]), there should be a greater emphasis on peripheral cues (e.g., number of followers, hedonic content, timing and number of posts). Conversely, in line with ELM predictions, in high-involvement platforms such as blogs, argument quality should exert a greater impact on persuasion; this implies that source expertise and post content should play important roles in eliciting engagement on blog platforms. We articulate these differences in our separate predictions we develop for blogs and Facebook platforms.
Broadly, influencer marketing campaigns have two goals: ( 1) to increase awareness and ( 2) to encourage trial. From a marketer's perspective, awareness campaigns are an easier-to-achieve goal and do not require any overt action on the part of consumers. Trial campaigns, which encourage consumers to make a purchase, are typically linked to consumer actions (e.g., purchase, app download) and therefore have a more overt persuasion intent and also a higher hurdle to generate customer engagement. These advertising goals (awareness vs. trial) can also affect the activation of persuasion knowledge of consumers, depending on whether there is a more direct advertising motive, as in the case of a trial campaign, or a less direct advertising motive, as in the case of an awareness campaign.
These campaign intents align with the beginning and end of the consumer's decision journey, which typically involves multiple stages in a hierarchy of effects, such as awareness, knowledge, liking, preference, conviction, and purchase. Prior research has examined this dichotomy of awareness versus trial intent in a traditional advertising context (e.g., [66]). As noted previously, the processing route to persuasion differs depending on the stage of the CDJ, with early stages being processed through the peripheral route and later stages being processed through the central route ([15]). We propose that campaign intent is a potential moderator that can influence engagement differently depending on the stage of the CDJ. We predict that trial intent versus awareness will have a greater impact on the blogging platform.
Source expertise refers to the level of credibility a source possesses. Expertise reflects the extent to which a consumer is qualified to discuss a subject ([ 2]), such as source qualifications ([10]), competence, knowledge, education, expertise, and ability to share knowledge ([39]). This can derive from informational power, in which the expert has knowledge that others do not have ([22]). Expert power can be knowledge within a specific domain (e.g., law) ([27]). Endorsers are more likely to be considered experts if they are competent and have relevant knowledge ([42]).
Source expertise affects attitude change ([43]; [64]; [70]), level of confidence and positivity ([82]), and behavioral changes ([18]) and leads to higher levels of persuasion ([76]). Higher levels of persuasion are a result of high source expertise leading to a deeper processing of the advertising message ([42]). In an influencer marketing context, expertise increases behavioral intention toward products ([84]). In a sponsored blogging context, consumers will prefer products endorsed or referred by a blogger with expertise because they perceive the message as more persuasive and credible ([48]; [95]). Thus,
- H1: Blogger expertise has a positive impact on blog engagement. The higher the blogger's expertise, the higher is the number of blog post comments.
Both high- and low-expertise bloggers may be considered influential, under varying circumstances. Despite the expected positive impact of blogger expertise on engagement in a sponsored blogging context, source expertise can also have a neutral (or even negative) effect in some situations. Prior research suggests that in the presence of an extreme advertising claim, the positive impact of source expertise diminishes ([31]). Depending on the context, type of claim, and stage in the decision-making process, source expertise may even have a nonsignificant (or negative impact) on engagement. The nonsignificant impact of source expertise also stems from the countervailing positive impact of low expertise bloggers (novice endorsers). Novice endorsements can be as effective as those from experts ([88]). Therefore, we expect blogger expertise to vary in its impact depending on campaign intent.
Involvement affects blogger success, such that for low-involvement products, a blogger with low expertise can have greater success ([95]). Under different stages of the CDJ, we similarly anticipate blogger expertise to affect engagement differently depending on the level of involvement. The peripheral processing that occurs early in the CDJ gives more attention to peripheral cues, such as expertise. In the early stages of the CDJ, expertise becomes a more important influencer for persuasion than homophily ([90]). Regarding an awareness intent, early in the CDJ we expect high expertise to be beneficial. For trial campaigns that correspond to later in the CDJ process and lead to higher motivation for central processing, we predict the opposite effect. Regarding a behavioral versus attitudinal change, low-expertise (vs. high-expertise) sources can be more effective ([23]). In line with this reasoning, audiences may perceive a source with higher expertise as less similar to them (i.e., less homophilous). For a campaign with a trial intent, we expect low-expertise (vs. high-expertise) bloggers to be more effective. In turn, this pattern of effects will result in a differential impact of higher blogger expertise depending on campaign intent. Thus,
- H2: There is an interaction effect between campaign intent and blogger expertise. Specifically, (a) when blogger expertise is high, awareness campaigns are more effective in generating brand engagement; (b) when blogger expertise is low, trial campaigns are more effective at generating brand engagement.
The hedonic value of a post refers to the enjoyment, emotions, and entertainment a consumer experiences from reading the post. Evidence suggests that hedonic content can have an impact on attitudes and WOM ([ 9]; [49]). In a traditional advertising context, researchers have shown that hedonic value captures attention ([81]) and influences attitude toward an ad ([49]). [ 8] suggest that specific emotions (e.g., awe, anxiety) trigger arousal, which in turn results in greater virality of online content. [71] extend these findings and argue that consumers share expressive or assertive brand messages more frequently than directive brand messages. Relatedly, [37] indicate that hedonic content can be a key factor in the virality potential of online firestorms. Building on these findings, we expect a post featuring high hedonic value content to increase arousal, deepening customer engagement. Therefore, we posit a general positive impact of hedonic content on the blogging platform.
- H3: Post content, in terms of hedonic value, is positively related to engagement in blog post comments.
Campaign incentives are marketing actions designed to elicit specific responses and engagement from consumers. The purpose of a campaign incentive is "to give followers a free item (or a chance to win a free item) in exchange for them sharing, liking, following, and/or reposting a picture" ([68]). For example, Rafflecopter is a giveaway platform widely used by sponsored bloggers, and the requirements to enter each giveaway are at bloggers' discretion. For some campaign giveaways, bloggers require consumers to comment on the blog post itself, while others require a different action (e.g., become a Twitter follower, share the giveaway) to enter the giveaway.
Campaign incentives are a direct firm action to increase customer engagement ([87]). Other benefits of giveaways include an increased desire to buy more, higher-quality perceptions of the product, and increased WOM about the product ([91]). In [ 9] study, consumers who received a free product talked about it 20% more than those who did not receive the product for free. Therefore, we expect the presence of incentives to increase blog engagement because they elicit consumer comments for a chance to win the giveaway.
- H4: Campaign incentives are positively related to engagement, such that inclusion of a campaign incentive leads to more blog post comments.
As mentioned, influencer marketing campaigns co-occur across platforms. The Facebook platform is a secondary environment for sponsored bloggers to link to the posts on their respective blog pages. In contrast with the blogging platform, people who encounter the Facebook post may or may not follow the blogger's blogging page. In other words, followers could come across the Facebook posts because they follow the person or because a friend has shared or interacted with the post. The Facebook platform is one example of a low-involvement, high-distraction social media platform. This lower involvement primes a different set of important features while diminishing the importance of others.
As noted previously, we expect awareness and trial campaign intent to align with the CDJ. Engagement generated by sponsored posts may vary depending on campaign intent. In trial campaigns, we expect Facebook participants' willingness to engage in campaigns with overtly commercial intent to be low. [47] propose that the primary motivations for Facebook usage are to gain knowledge, acquire new connections, and strengthen existing relationships. An overtly commercial intent, as in the case of trial, can interfere with the intended usage of the platform and therefore be met with resentment by users. Because gaining knowledge and encountering ideas and information are reasons to use Facebook, these are more in line with the awareness intent. The awareness intent is more of a helping motive associated with WOM communications in the network. Facebook users may spread WOM about awareness campaigns because doing so generates positive feelings and strengthens social connections ([36]). Thus,
- H5: Campaign intent has a positive impact on engagement on the Facebook platform. Specifically, awareness (vs. trial) campaigns generate more Facebook engagement (i.e., likes).
Evidence shows that engagement on Facebook is positively related to hedonic content ([13]). The primary rationale for this is that the hedonic value generates an emotional response ([24]), which leads to higher arousal and a greater propensity to like and share in online settings ([ 8]; [26]). Research based on the ELM ([75]) indicates that when consumers are less involved with products, they use peripheral routes to process information ([28]). On a low-involvement platform, we expect hedonic value to be more salient to the reader. Under low involvement, [14] finds that attention-getting online advertising appeals were more effective. Consequently, we predict that hedonic content associated with blog posts is highly relevant to low-involvement platforms such as Facebook, as it helps overcome low involvement by raising the interestingness of a post. In support of this idea, the in-store shopping literature (e.g., [ 4]) shows that the hedonic value of a shopping experience plays a key role in elevating involvement and inducing purchase behavior. For a low-involvement, high-distraction platform such as Facebook, peripheral cues, such as hedonic value, should be important. Thus,
- H6: The hedonic value of blog posts has a positive impact on Facebook engagement (i.e., likes).
In addition to the preceding main effect predictions, we anticipate an interaction effect of campaign intent and hedonic value on Facebook engagement. Hedonic content leads to a greater likelihood of message forwarding ([13]) and increased private sharing of news articles ([ 8]), but these links are dependent on the context of sharing and type of outcome being studied ([83]). Ads viewed as too "outrageous" may result in lower purchase intent and persuasion, but this is not the case when the ad also leads to consumer responses such as comments ([83]).
As the Facebook platform is a low-involvement, high-distraction environment, in which people are predisposed toward information overload ([52]), opportunity and motivation to process become key components in information processing ([61]). A sufficiency threshold dictates that the amount of processing a person is willing to undertake is dependent on the perceived risk involved ([12]; [63]). Under a trial intent, in which the perceived risk would be higher than in an awareness context, we anticipate a high sufficiency threshold and, thus, central processing.
In addition, a consumer's motivation to process an ad will be higher when there are more hedonic cues ([61]). [11] posit that when people identify a Facebook post as an advertisement, they develop feelings of distrust, which may imply a threat to the relationship. In this case, a highly hedonic post can alleviate this threat. Heuristic processing is likely to occur when the person views the information as agreeing with his or her beliefs ([30]). Taken together, this would imply that for a trial intent, a post high in hedonic value could overcome the disposition to process more systematically. This suggests that when campaigns involve purchasing (e.g., trial), the hedonic value of the post will be valuable. Thus,
- H7: High (vs. low) hedonic value has a more positive impact on trial campaigns than awareness campaigns.
The data come from The Motherhood, a leading agency for sponsored blogging campaigns that focuses on "mommy" bloggers. The data consist of 1,830 sponsored posts written by 595 bloggers,[ 7] collected from September 2012 to December 2016.[ 8] These blog posts came from 57 different campaigns, including Awesome Avocados, Banner Alzheimer, Chef Boyardee Little Chef, Latte Love, and Barnes & Noble.
For each campaign, companies work with the blogging agency to coordinate campaign details, such as the intended message, target audience, and goals. Bloggers are recommended for the campaign on the basis of their demographics, age of children, and expertise, and they can choose to work on projects in line with their interests, availability, and willingness to work within the set budget. Bloggers are required to disclose that they are sponsored bloggers either at the beginning or at the end of the blog posts. Depending on the budget and requirements of a given campaign, each blogger receives compensation (in the form of either money or free products). Bloggers then write and post content on their own blog websites about the campaign; bloggers get paid more if they post something on multiple social media channels.
Every blog post had an option for blog readers to leave comments. Comments of other readers are visible to any subsequent reader of the blog, but other readers do not receive notification when a new comment has been posted. Our primary measure of engagement is the number of comments each blog post received (see Table 2 for constructs, Table 3 for descriptive statistics of the variables, and Table 4 for the correlations).
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Table 2. Constructs and Measures.
| Construct | Definition | Operationalization |
|---|
| Facebook post likes | Primary measure of Facebook engagement is the number of likes each post received. | Count; the total number of likes per blogger per campaign |
| Blog post comments | Primary measure of engagement is the number of blog post comments each blog post received. | Count; the total number of comments per blog post, blogger, and campaign |
| Facebook posts | Number of Facebook posts per blogger per campaign. | Count; control variable |
| Followers | Represents blogger's social media presence and is also an indication of blogger strength. | Quantitative; the average number of twitter and Facebook followers that a blogger has in online network |
| Awareness campaign | Increases brand awareness and spreads information to consumers; occurs at an early stage in the purchase funnel because consumers are not yet trying to evaluate whether to purchase the product. | Categorical; campaign intent is focused on raising awareness about a specific brand |
| Trial campaign | Encourages consumers to make a purchase; typically linked to actions required of consumers (e.g., purchase). | Categorical; campaign intent is focused on increasing purchase or trial behavior |
| Expertise | Is indicative of how bloggers portray themselves as a source of information as a sponsored blogger. | Quantitative; a sum of the person's educational affiliation and blogger credentials. Range: 0–2 |
| Functional | Functional value captures the believability and informativeness of a post. | Quantitative; a factor score of content that is genuine/sincere, honest, informative, pleasant, relatable, understandable, believable, and relevant, as well as usage consideration |
| Hedonic | Hedonic value of a post refers to the enjoyment, emotions, and entertainment a consumer experiences from reading a post. | Quantitative; a factor score of content that is attention getting, creative, emotional, energetic, humorous, memorable, strong, unique, and warmhearted |
| Giveaways | Marketing actions designed to generate specific responses and engagement from consumers. | Categorical; campaign-level variable, whether or not a giveaway was included as part of the campaign |
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Table 3. Descriptive Statistics.
| Variable | N | M | SD |
|---|
| Number of Facebook post likes | 1,398 | 17.53 | 100.64 |
| Number of blog post comments | 1,826 | 21.23 | 70.02 |
| Average number of followers | 1,267 | 21,246.10 | 24,812.43 |
| Weekend post | 1,830 | 14.8%a | .40 |
| Type of campaign: awareness | 1,830 | 35.1%a | .48 |
| Type of campaign: trial | 1,830 | 64.9%a | .48 |
| Expertise (sum of credentials and education) | 1,816 | .36 | .63 |
| Blogger travel/foodie | 1,816 | 0 | 1 |
| Blogger persona | 1,816 | 0 | 1 |
| Blogger lifestyle | 1,816 | 0 | 1 |
| Blogger values | 1,816 | 0 | 1 |
| Functional value of post | 1,830 | 0 | 1 |
| Hedonic value of post | 1,830 | 0 | 1 |
| Giveaway | 1,825 | 25.5%a | .44 |
| Inverse Mills ratio | 1,819 | .27 | .18 |
1 aPercentage of occurrences.
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Table 4. Variable Correlations.
| Variable | No. of Facebook Likes | No. of Blog Comments | Avg. No. of Followers | Weekend Post | No. of Facebook Posts | Awareness Campaign | Blogger Expertise | Blogger Travel/ Foodie | Blogger Persona | Blogger Lifestyle | Blogger Values | Functional Value | Hedonic Value | Giveaway |
|---|
| No. Facebook likes | 1 | | | | | | | | | | | | | |
| No. of blog comments | .0028 | 1 | | | | | | | | | | | | |
| Avg. no. of followers | .0775* | .0067 | 1 | | | | | | | | | | | |
| Weekend post | .1082* | −.0382 | .0480 | 1 | | | | | | | | | | |
| Number of Facebook posts | .0993* | −.0078 | .0010 | .0513* | 1 | | | | | | | | | |
| Awareness campaign | .1550* | −.0981* | .0283 | .0230 | .0066 | 1 | | | | | | | | |
| Blogger expertise | −.0737* | .1761* | −.0068 | −.0082 | .0824* | −.1822* | 1 | | | | | | | |
| Blogger travel/foodie | −.0072 | .0400 | .2400* | .0258 | −.0654* | .0115 | .0095 | 1 | | | | | | |
| Blogger persona | .0188 | −.0016 | .1245* | .0027 | .0007 | .0226 | .1687* | −.0006 | 1 | | | | | |
| Blogger lifestyle | −.0103 | .0223 | −.1028* | .0026 | .0290 | .0166 | .0423 | −.0034 | .0046 | 1 | | | | |
| Blogger values | −.0090 | −.0040 | −.0951* | −.0166 | .0223 | .0304 | .0001 | −.0102 | −.0047 | −.0010 | 1 | | | |
| Functional value | .0209 | −.0270 | .1124* | −.0194 | −.0057 | .0792* | −.0356 | .0443 | −.0136 | −.0078 | −.0054 | 1 | | |
| Hedonic value | −.0297 | −.0491* | −.0771* | .0114 | .0297 | −.0290 | .0337 | −.0302 | −.0201 | .0002 | −.0063 | .0000 | 1 | |
| Giveaway | −.0432 | .2873* | −.0242 | −.0166 | −.0043 | −.0531* | .0533* | .0160 | .1035* | .0343 | −.0538* | −.0608* | −.0062 | 1 |
- 2 *p <.05.
- 3 Notes: The unit of analysis is the blog post.
Bloggers frequently used their social media outlets to post about different blog campaigns. Facebook followers of a blogger can see the new post in their Facebook news feed, while others need to seek out the post directly from the blogger's Facebook profile. To measure Facebook engagement, we counted the number of Facebook post likes.
Companies typically divide campaigns into two categories: those designed to raise awareness and those designed to increase trial. In our data set, of the 57 total campaigns, 29 had an awareness intent and 28 had a trial intent. The awareness campaigns focused on increasing brand recognition. For example, an AT&T Mobile School Safety campaign encouraged people to talk to their children about using mobile phones safely. These campaigns were not directly trying to motivate people to make a purchase but instead were focused on building brand awareness. By contrast, the trial campaigns focused on increasing consumer trial for products or services. Examples of this type of campaign included Church Hill Classics' diploma frames and Veritas Genetics' at-home BRCA test.
We identified campaigns (29%) in terms of whether they included a campaign incentive (i.e., a giveaway). Giveaways typically request that readers like or share a blog post to be entered for a chance to win a prize. For example, Johnson & Johnson's Donate-a-Photo campaign had a giveaway prize of $100 worth of products.
The average number of Twitter and Facebook followers represents a blogger's social media presence and is also an indication of blogger strength. We used bloggers' Twitter and Facebook followers for two reasons: ( 1) Facebook and Twitter are two of the largest social media platforms, and ( 2) the number of followers on the blog web pages themselves are unavailable. We use the natural log of the average number of Twitter and Facebook followers in the models to account for the large spread, and we mean-centered them improve interpretation of coefficients. We also use alternative operationalizations and reestimate the main model using these measures (for the results, see the Web Appendix).
First, we pulled the public profiles of each blogger in our data set, as described on their blog pages. Second, three coders (blind to the hypotheses) examined the bloggers' public profiles and listed key themes that captured their psychographic profiles (i.e., interests, activities, and opinions; see the Web Appendix). This procedure revealed 14 main psychographic profile dimensions, dummy coded as 1 if present in the profile and 0 if not. Third, we used factor analysis with varimax rotation[ 9] to identify five overarching characteristics of bloggers.[10]
We measure blogger expertise by the presence of the person's educational affiliation and blogger credentials in his or her profile. Prior research has also used blogger profiles to manipulate source ([84]). An educational affiliation includes reference to a specific higher education degree (e.g., "Bachelor of Arts"), while blogger credentials refer specifically to status as a credible blogger (e.g., "social media consultant," "Nielsen 50 Power Mom"). Blogger expertise, which ranges from 0 to 2, is the sum of the two measures.
Weekend post is an indicator variable for whether the post occurred on a weekend, coded as 1, or a weekday, coded as 0. We used this to capture weekend versus weekday seasonality. We incorporate this as a control variable for the temporal element.
The number of Facebook posts serves as a control variable. For sponsored blogging campaigns, bloggers will post on blogs and then post on Facebook linking to the blog post. To control for the number of Facebook posts, we include this as a variable in the model.
Three coders classified the hedonic and informational value associated with a given blog post; we prequalified the coders to match the demographics of the bloggers' audiences. We based our measures on [94], who develop a 20-item emotion scale for advertisements. We used coders from Amazon Mechanical Turk to measure various aspects of sentiment on a seven-point scale (1 = "not at all," and 7 = "extremely"), including how much the blog posting was attention getting as well as how boring, creative, emotional, energetic, genuine/sincere, honest, humorous, informative, irritating, memorable, pleasant, strong, unique, warmhearted, relatable, understandable, believable, and relevant the post was. We selected coders who were as similar to the blog audience as possible (i.e., they were also mothers with a child under 18 years in the household). We solicited three coders for each blog post and asked them to code only a subset of blog posts (typically three posts each), suggesting that there are variations introduced across different blog posts from the varying identities of coders.
First, to measure the agreement between coders and calculate a more accurate alpha score, we used the methodology [79] describe and computed the Shrout–Fleiss single intraclass correlation score agreement of.998, which is considered quite high ([51]). As a second approach to assess reliability, we estimated a standardized alpha within three coders for each sentiment value for each blog post, to account for the different coders on each post. We then averaged these standardized alphas and obtained an average reliability of.51 and a median reliability of.56.
Each blog post was coded for a variety of sentiment variables, some of which may be correlated. To reduce the dimensionality of the data and increase parsimony, we conducted a factor analysis. Factor analysis with varimax rotation revealed two factors with eigenvalues greater than 1 (for factor loadings, see Table 5), which we labeled as "functional" and "hedonic." The variables that loaded most highly on perceived functional value were genuine/sincere, honest, informative, pleasant, relatable, understandable, believable, relevant, and benefits believable. The variables that loaded most highly on perceived hedonic value were attention getting, creative, emotional, energetic, humorous, memorable, strong, unique, and warmhearted.
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Table 5. Varimax Factor Pattern Rotation for Blog Post Sentiment Variables.
| Factor 1 | Factor 2 |
|---|
| Sentiment Variables | Functional | Hedonic |
|---|
| Attention getting | .44 | .66 |
| Boring | −.47 | −.51 |
| Creative | .33 | .78 |
| Emotional | .17 | .71 |
| Energetic | .35 | .70 |
| Genuine/sincere | .69 | .49 |
| Honest | .73 | .43 |
| Humorous | −.08 | .68 |
| Informative | .66 | .34 |
| Irritating | −.65 | −.17 |
| Memorable | .37 | .76 |
| Pleasant | .61 | .56 |
| Strong | .37 | .74 |
| Unique | .28 | .79 |
| Warmhearted | .53 | .64 |
| Relatable | .66 | .48 |
| Understandable | .75 | .07 |
| Post believable | .84 | .17 |
| Relevant | .67 | .27 |
| Benefits believable | .85 | .18 |
| Consider using | .68 | .37 |
Because bloggers are chosen to participate in campaigns, selection bias may occur. To address this potential problem, we implemented a [35] selection model. The first-stage model used a probit regression to predict a blogger's selection for a campaign. To achieve identification, the set of covariates in the Stage 1 probit model must contain at least one variable that can supply the exclusion restriction—that is, it must affect blogger selection for a campaign but not directly affect the engagement generated by the blogger ([29]; [35]). Industry practice is to match bloggers with common interests in and similarity to the focal campaign. In line with this method, we use the bloggers' profile descriptions and employed varimax factor rotation to create a psychographic index score using psychographic categories not directly related to the outcome of engagement: travel/foodie, persona, lifestyle, and values.
The variable providing the exclusion restriction used in the Stage 1 probit model is blogger selection of most similar other bloggers. To determine the blogger most similar to the target blogger, we created a blogger-by-campaign matrix. We multiplied this matrix by its transpose to create a blogger-by-blogger matrix, which showed which bloggers coappeared (i.e., participated in the same campaign) most frequently. We selected the blogger who appeared most often with the target blogger and used his or her selection for a campaign as an independent variable in the Stage 1 probit (for details of this procedure and a specific example, see "Details on Stage 1 Probit Model" in the Web Appendix).
Table 6 provides the results of the Stage 1 probit model. We find that the intercept (b = −2.2744, p <.01), similar blogger selection (b = 1.5550, p <.01), and the travel/foodie blogger psychographic (b =.0374, p <.01) are significant for selection in the Stage 1 probit model. The exclusion criterion, similar to the blogger selection, indicates that when a similar blogger to the target blogger is selected for a campaign, the target blogger is more likely to be selected for the campaign. We then included the inverse Mills ratio from the first stage as an independent variable in all second-stage models. The inverse Mills ratio was not significant in the blog post comments (z = 1.22, p =.223) or Facebook post likes (z = −.060, p =.950) models.
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Table 6. Stage 1 Probit Selection Model.
| Variable | Blogger Selection |
|---|
| Intercept | −2.2744** |
| (.0188) |
| Similar blogger selection | 1.5550** |
| (.0283) |
| Blogger travel/foodie | .0374** |
| (.0144) |
| Blogger persona | .0113 |
| (.0146) |
| Blogger lifestyle | −.0169 |
| (.0136) |
| Blogger values | .0100 |
| (.0152) |
| Model fit | LR χ2(5) = 3,117.20 |
| Pseudo-R2 =.255 |
- 4 *p <.05.
- 5 **p <.01.
- 6 Notes: Standard errors are in parentheses.
The dependent variables of interest, blog post comments and Facebook post likes, are count variables. Therefore, we considered using either a Poisson distribution or a negative binomial distribution for count data. A likelihood ratio test indicated that there was overdispersion in the data for the blog post comments (χ2 = 6,231, p <.001) and Facebook post likes (χ2 = 31,000, p <.001) models. Thus, we used a negative binomial model instead of a Poisson model. In addition, we find no correlation between the error terms of the two models. For each post i, we estimated the following second-stage model equations:
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Graph
Before we describe our full model results (see Table 7), several main effects results are worth noting. In the main-effects-only model (see the Web Appendix), we find that campaign intent exerts a differential main effect on each platform, with awareness intent being more effective for Facebook and trial intent being more effective for blogs. We conjecture that because of Facebook's lower commercial intent, an awareness campaign is potentially more readily shared among peers in an organic fashion. The purpose of campaign incentives (i.e., giveaways) is to encourage participation with specific tasks. The negative impact of incentives on Facebook and the positive impact on the blog platform highlight the potential cannibalizing effect of one social media platform on another.
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Table 7. Model Results Table.
| Variable | Blog Post Comments | Facebook Post Likes |
|---|
| Intercept | 2.0403** | 1.4032** |
| (.1479) | (.1968) |
| Number of followers | .3514** | .2055* |
| (.0821) | (.0941) |
| Weekend post | −.1837 | .7038** |
| (.1641) | (.2168) |
| Number of Facebook posts | N/A | .5728** |
| N/A | (.0891) |
| Awareness campaign | −.2351† | .7416** |
| (.1417) | (.1883) |
| Blogger expertise | −.1228 | .0253 |
| (.2000) | (.2591) |
| Functional value of post | .0298 | .0520 |
| (.0790) | (.0906) |
| Hedonic value of post | .2616** | .2215* |
| (.0888) | (.1007) |
| Giveaway | .4526* | −.7840** |
| (.1834) | (.2245) |
| Awareness × blogger expertise | .7283* | −.4534 |
| (.2911) | (.3393) |
| Awareness × functional value of post | −.1269 | −.1533 |
| (.1185) | (.1308) |
| Awareness × hedonic value of post | −.2167 | −.4824** |
| (.1323) | (.1356) |
| Awareness × giveaway | .4322 | .5312 |
| (.3020) | (.3364) |
| Awareness × number of followers | −.1413 | .1457 |
| (.1127) | (.1301) |
| Inverse Mills ratio | −.1255 | .1255 |
| (.3601) | (.4343) |
| Overdispersion (α) | 4.0632** | 3.7449** |
| (.1915) | (.1829) |
| AIC | 6662.87 | 5581.87 |
| BIC | 6739.68 | 5505.78 |
| −2 log-likelihood χ2 | 100.64** | 221.91** |
- 7 †p <.10.
- 8 *p <.05.
- 9 **p <.01.
- 10 Notes: Standard errors are in parentheses. AIC = Akaike information criterion; BIC = Bayesian information criterion.
Table 7 reports the results of the second-stage model with blog post comments as the dependent variable (N = 1,237). The Akaike information criterion for this model was 6,663, the Bayesian information criterion was 6,740, and the likelihood ratio test was significant (χ2(13) = 100.64, p <.01). Variance inflation factors (VIFs) were all below 1.09, with an average VIF of 1.04, indicating no issues with collinearity. We found a significant main effect of our control variable, average number of followers (b =.3514, p <.01), indicating that this factor significantly drives the number of blog post comments.
The main effect of campaign intent (awareness vs. trial) was marginally significant in the blog post comments model (b = −.2351, p <.10), while the main effect of blogger expertise was not significant in the final model incorporating interaction effects (b = −.1228, n.s.), which does not support H1. The interaction between type of campaign (awareness vs. trial) and blogger expertise was positive in the blog post comments model (b =.7283, p =.01), in support of H2. Further investigation of the interaction effect reveals that the simple slope for high blogger expertise was significant (p <.05), indicating that the impact of high blogger expertise varies by campaign intent, in support of H2a. The simple slope for low blogger expertise was marginally significant (p =.10), suggesting no differences in effectiveness of low-expertise bloggers across awareness and trial campaigns; thus, H2b is not supported (or receives weak support at p =.10). Figure 2, Panel A, summarizes the pattern of effects for the interaction between campaign type and blogger expertise. Perceived functional value of the post was not statistically significant, but the hedonic value of the post had a significant impact on the number of blog post comments (b =.2616, p <.01), in support of H3. Campaigns that included campaign incentives also significantly increased the number of blog post comments (b =.4526, p =.01), in support of H4.
Graph: Figure 2. Blog post comments and Facebook post likes (empirical study).
The results indicate that high blogger expertise is beneficial when paired with awareness campaigns but has a lesser effect in the case of trial campaigns. While we initially hypothesized that for low-expertise bloggers, more engagement would occur under trial than awareness intent, we find no evidence of this relationship. We find that hedonic content is positively associated with an increase in blog post comments. We return to this point in the "Discussion" section.
Table 7 also reports the results of the second-stage model with Facebook post likes as the dependent variable. The Akaike information criterion for this model was 5,508, the Bayesian information criterion was 5,582, and the likelihood ratio test was significant (χ2(14) =221.91, p <.01). The VIFs were all below 1.06, with an average VIF of 1.04, indicating no issues with collinearity. The number of Facebook posts was significant (b =.5728, p <.01). We found a significant main effect of the average number of followers (b =.2055, p <.05), indicating that this drives Facebook post likes. Campaigns that included giveaways also significantly decreased the number of Facebook post likes (b = −.7840, p <.01).
Blogger expertise was not significantly related to the number of Facebook post likes (b =.0253, n.s.). The main effect of campaign intent (awareness vs. trial) was significant in the Facebook post likes model (b =.7416, p <.01), in support of H5. We found a significant main effect of hedonic value (b =.2215, p =.03), in support of H6. There was a significant, negative interaction effect of campaign intent and hedonic value (b = −.4824, p <.01). In light of the positive main effects of hedonic value and awareness campaigns, the negative interaction term implies that hedonic value is positively related to Facebook post likes for trial campaigns and negatively related to Facebook post likes for awareness campaigns, providing support for H7. Panel of B of Figure 2, which plots the pattern of results, shows that posts low in hedonic value can weaken engagement, particularly for trial campaigns. Taken together, the results indicate that multiple factors can increase engagement in sponsored Facebook posts. Regarding the control variables, having more Facebook posts, posts on weekends, and a higher number of followers are all related to an increased number of Facebook post likes. Posts lower in hedonic content are particularly harmful when paired with trial campaigns on Facebook.
We find that the blog platform and Facebook platform exhibit differences in drivers of engagement. Campaign incentives negatively affect the Facebook platform but not the blog platform; we conjecture that this is due to the cannibalizing effect of the blogging platform. Timing of the posts (weekends vs. weekdays) also positively affects Facebook, but this effect is not consistent for the blog platform model.
In this study, we evaluated the effectiveness of influencer marketing campaigns using an empirical database of sponsored bloggers. The results provide support for most of our hypothesized effects (with the exception of H1 and H2b). Across both models, we find a positive impact of the number of followers. Controlling for the number of followers, we find that blogger expertise, campaign intent, hedonic value, and interactions among these variables influence engagement on blog and Facebook platforms. We also find differences in the success drivers of sponsored blogging campaigns across the platforms. High blogger expertise interacts with campaign intent on the blog platform but not the Facebook platform.
We find a significant interaction between campaign intent and hedonic value on Facebook platforms. Specifically, our findings indicate that hedonic value exerts a greater impact in trial campaigns, which supports the explanation that hedonic content may provide a reason for Facebook users to share information or like a blog post with an overtly commercial intent, confirming the compensatory role of hedonic value in mitigating the negative effect of a less desired post. In addition, we find a negative effect of campaign incentives on Facebook post likes for both awareness and trial campaigns, potentially due to cross-platform cannibalization of the Facebook platform by the blogging platform. Campaign incentives may cause participants to interact with a blog post more directly in the blogging environment, even though they may have first encountered the information on Facebook.
In addition, we estimated a series of alternative models for robustness checks, including examining when posts are cross-posted on blogs and Facebook, alternative measures of content sentiment, alternative specifications of number of followers, varying measures of post engagement, and alternative coding for the varimax factor rotations. The robustness checks also included another version of the Stage 1 probit model specification, models using a Gaussian copula, and fixed-effects negative binomial models. The results of these alternative specifications are consistent with our reported findings (for details, see the Web Appendix, as well as an overall summary of the robustness check results in the "Details on Stage 1 Probit Model" section).
Our results thus far have been based on data collected from a real-world context (actual campaigns featuring sponsored bloggers), providing high generalization and meaningful insights into the complex interplay of multiple factors that influence how these campaigns actually function in real life. However, field data limit our ability to manipulate key independent variables, creating the possibility that extraneous variables could account for the effects. To account for this possibility and improve our ability to draw meaningful conclusions from this research, we aimed to replicate our findings in a tightly controlled setting, by experimentally manipulating our key variables. We focused on finding additional support for a key interaction effect observed in the blog platform setting—namely, the interaction between campaign intent and blogger expertise in a blog setting.
The purpose of Study 2 is to replicate one of the more counterintuitive results (i.e., the blogger expertise × campaign intent interaction on the blog platform) to provide further support for H2. We posit that campaign intent will have a differential impact on purchase likelihood in the case of high-expertise bloggers but will not affect purchase likelihood in the case of low-expertise bloggers.
This pretest served to ( 1) link blogger profile characteristics with perceived expertise and ( 2) check the strength of our manipulation of blogger expertise. We kept the sample population as similar to the target audience as possible. Those sampled were women with children under 18 years of age (N = 97). The pretest was a between-subjects design with two expertise levels (high and low) manipulated using blogger profile descriptions (for details, see the Web Appendix). To create a robust measure of expertise, we used the items from [69] scale to measure celebrity endorser expertise. On a scale from 0 ("strongly disagree") to 100 ("strongly agree"), participants rated whether they believed the blogger was expert, experienced, knowledgeable, qualified, and skilled. We averaged these five items together (α =.96) to create an overall perceived expertise score.
We controlled for homophily to rule this out as an alternative explanation of the blogger expertise effects. Therefore, participants rated three items regarding blogger homophily (0 = "strongly disagree," and 100 = "strongly agree"): "I feel that the blogger is similar to me," "I feel that the blogger is a peer," and "I feel that the blogger thinks like me." We averaged these three items together to create an overall homophily score (α =.95). Controlling for age and homophily, we found that perceived expertise is higher under the high blogger expertise manipulation than the low blogger expertise manipulation (Mhigh = 62.20, Mlow = 59.37; F( 3, 93) = 20.13, p <.01). In addition, we found a difference in perceived homophily for high- versus low-expertise bloggers when controlling for age, such that homophily is higher in the case of low-expertise bloggers (Mhigh = 46.89, Mlow = 60.26; F( 1, 94) = 7.50, p <.01).
The goal of this pretest was to test the manipulation of campaign intent. This pretest was a three-condition (campaign intent: awareness, trial, control) between-subjects design (N = 164).[11] Participants read a sponsored blog post; the awareness and trial campaigns were both about an educational mobile game targeted at middle schoolers. The posts were identical, except that the trial campaign had an additional message at the bottom that read "BUY NOW!!!" In the control condition, participants read an unrelated post about finding the right job.
We measured persuasion knowledge using scale items from [ 1]. Participants rated the author of the blog post on a nine-point bipolar scale for three items: "good/bad," "not pushy/pushy," and "not aggressive/aggressive." We averaged these items to create a persuasion knowledge measure for the source (α =.85). Controlling for age, we found a significant difference in perception of the source based on the campaign intent manipulation (Mawareness = 3.38, Mtrial = 4.27, Mcontrol = 3.35; F( 3, 160) = 2.81, p <.05). Using planned pairwise contrasts, we found a significant difference in persuasion knowledge between the trial and awareness campaigns (p <.05) and between the trial and control campaigns (p <.05). This indicates that persuasion knowledge is higher in trial campaigns than in either the awareness or control campaign conditions.
This experiment was a 2 (expertise: high, low) × 2 (campaign: awareness, trial) between-subjects design. The sample came from a Qualtrics panel of mothers (N = 395). Our context for this study is an educational paid app (Water Bears) targeted at middle schoolers (and their parents), designed to improve spatial reasoning. Participants read identical sponsored blog postings about Water Bears (similar to what was used in the pretest). In the trial condition, an additional phrase at the bottom stated: "BUY NOW!!!" The expertise conditions were identical to those in the pretest. Participants rated how likely they would be to purchase the Water Bears app on a scale from 0% ("not likely at all") to 100% ("very likely").
We analyzed the data using analysis of variance containing all the main effects (i.e., blogger expertise, campaign intent, and their interaction). The overall model was significant (F( 7, 387) = 22.29, p <.01). The main effect of expertise was not significant (F( 1, 387) =.95, p =.33), but the main effect of campaign intent was significant (F( 1, 387) = 8.56, p <.01). In support of our hypothesized effect, the interaction between expertise and campaign intent was also significant (F( 1, 387) = 8.88, p <.01). We controlled for age, homophily, whether participants had children in middle school, and whether they follow sponsored bloggers online. After controlling for these variables, the test of simple slopes indicated that, consistent with H2a, the impact of high blogger expertise on purchase likelihood is stronger for awareness campaigns than for trial campaigns (Mawareness = 34.10, Mtrial = 20.66; F( 1, 387) = 13.32, p <.01). That is, when blogger expertise was high, participants expressed a higher purchase intent for the awareness campaign than the trial campaign. Next, examining purchase likelihood under low blogger expertise, we found no significant difference between awareness and trial campaigns (Mawareness = 30.24, Mtrial = 30.29; F( 1, 387) =.14, p =.7106), which, consistent with our empirical results, fails to support H2b. The impact of a low-expertise endorser on purchase likelihood does not depend on campaign intent. This pattern of findings confirms those from our empirical data set (see Figure 3).
Graph: Figure 3. Blogger expertise and campaign intent effects on purchase likelihood (Study 2).
Study 2 provides additional, supplemental evidence for the interaction between source expertise and campaign intent on a blog platform. We show that in the case of high-expertise bloggers, awareness intent yields a higher purchase likelihood than trial intent. By demonstrating this effect using purchase likelihood in an experimental setting, we provide further support for the validity of this finding. However, the results in Study 1 are driven by high expertise, while the results in Study 2 are driven by differences due to low expertise. This may be because the outcome variable (purchase intent) is closer to the trial condition (which is driven by low expertise) while Study 1's outcome variable (engagement in the form of blog comments) is much closer to awareness building.
This research sheds light on the key drivers of success of influencer marketing campaigns and offers a novel contribution by examining the interplay of social media platforms and success factors. We find that while network, blogger characteristics, and content characteristics affect multiple types of sponsored blogger engagement, the level of platform involvement and the campaign intent matter for the degree of success. We use both field data based on a large data set of influencer marketing campaigns and a controlled experiment to show convergent evidence of the majority of the hypothesized effects. By understanding this framework to increase engagement, companies can choose bloggers more effectively, matching their characteristics to campaign goals.
We expect the sponsored blogging results to differ from those for other social media and paid media for two reasons. First, the nature of influencer marketing is distinct from both WOM and traditional advertising because influencers blend elements of paid and earned media. From a motivation standpoint, while traditional advertising may have multiple goals to meet brand equity–based objectives, influencers have additional loyalty to their followers. Second, the influencer designs and implements the message, not the company. This is also distinct not only from traditional advertising and spokesperson marketing tactics, due to bloggers' creative freedom, but also from pure organic WOM, because bloggers are sponsored by the company. With these influencer nuances in mind, we expect that consumers will interpret the message and source differently depending on where and how it is presented.
Our key contributions involve understanding the interplay of post content characteristics (i.e., hedonic value of a blog post), source expertise, and campaign characteristics (i.e., campaign intent and incentives in an awareness-building or trial campaign) on campaign intent and social media platform. While campaign intent has received attention in advertising literature ([66]), our study is the first to examine the impact of influencer marketing campaign intent on engagement. We find that campaign intent is an especially pertinent moderator to many of the relationships in our study. For example, campaign intent moderates the relationship between source expertise and blog post engagement. Campaign intent also moderates the relationship between hedonic content and Facebook post engagement. These findings suggest that the relationship among source, content, and engagement should not be assessed in isolation from campaign intent.
In addition, we contribute to the literature on blogger expertise by demonstrating conditions in which expertise has ( 1) a positive impact, ( 2) a negative impact, and ( 3) no impact. Specifically, we demonstrate that in some conditions, source expertise is positive, and in others, it is nonsignificant. Expert endorsement is beneficial under an awareness intent, while a novice endorsement is beneficial under a trial intent. This effect holds under high-involvement, low-distraction social media platforms. On low-involvement, high-distraction social media platforms, however, source expertise does not affect engagement. We provide a more nuanced explanation of expertise and its role in online brand engagement. Taken together, these findings provide a richer understanding of source expertise in the case of influencer marketing.
We extend prior research on influencer marketing by highlighting the importance of consumer skepticism differences, which may cause campaign intent (awareness or trial) aimed at different stages of the CDJ to function differently. At early stages in the CDJ, consumers are open to guidance from those with high perceived expertise. However, closer to trial, consumers are open to endorsements that originate from either less expert (presumably more homophilous) or more expert sources. This difference is only true in high-involvement platform settings such as blogs. Understanding the contextual effects guiding the impact of source expertise in online influencer marketing settings is a key contribution of this research. It extends prior works on influencer marketing settings that focus on either expertise ([84]; [95]) or stage of the CDJ ([44]) but do not examine their interplay.
We also argue that the motivations driving people to use social media platforms influence how they view different types of influencer marketing campaigns. In a blog environment, in which users are motivated to process information deeply and to engage with bloggers' information and content, trial campaigns are better received. In a Facebook environment, in which users' motivations are more focused on sharing information with peers, awareness campaigns have a more positive impact. Given this general preference for awareness (vs. trial) campaigns on Facebook, hedonic value of a blog post takes on more significance in the context of trial campaigns.
Furthermore, our findings show that post content (i.e., hedonic value) is important in generating post engagement. We extend the findings of [ 8], who argue that hedonic content increases social transmission and virality of online messages. We find that hedonic value has a significant effect on both blog and Facebook platforms. We also show that on low-involvement, high-distraction social media platforms, hedonic content can be beneficial when paired with trial campaigns, perhaps because of their overtly commercial intent. In awareness-building campaigns, in which user motivations involve sharing information, hedonic value may be distracting to the primary goal. Thus, hedonic content of online communications is not always beneficial to marketing campaigns.
Our findings are revealing on the impact of campaign incentives, which research has previously shown to increase WOM ([ 9]) and enhance quality perceptions of a product ([91]). We demonstrate that incentives (a chance to win a giveaway) generate WOM benefits in the form of increased engagement (i.e., blog post comments). This finding advances the literature by showing that increased WOM and engagement can be generated without giving a free product to every person; simply offering a chance to be the recipient is enough to induce the benefits of free products. This greatly reduces the costs associated with running a campaign with free product incentives while still generating a similar response.
One potential rationale for why giveaways have a positive effect in a blog environment but a negative effect in Facebook posts is that giveaways are typically executed in a blog platform setting, and blogs cannibalize Facebook engagement. An alternative explanation, which could be the focus of further research, is that high-involvement platforms in general are more conducive to driving engagement through free goods and incentives. Prior research suggests that when consumers are more involved, they want to minimize risk through information search in their decision-making processes ([21]), and they might view free products or incentives as a way to lower the risk of a new purchase. This is worth examining under a broader understanding of customer engagement.
This article offers a unique contribution by examining the differences between social media platforms. While we empirically focused on blogs and Facebook, the findings can be extended to other social media platforms. As platforms continue to develop, the extent of involvement generated by a platform can help inform decisions on influencer marketing strategies. Moreover, the focus of this research was on online engagement, which sheds more light on customer profitability than a mere focus on customer attitudes or preferences. Furthermore, our examination of cross-platform impacts (i.e., blogs and Facebook) dovetails with other research examining how different advertising media may synergistically improve customer engagement and profitability. [50] investigate the dynamic interaction between paid search and display ads. We extend their findings by focusing on one form of social media marketing that straddles the earned and paid social media types. Therefore, our findings are of particular relevance in light of the increased blurring between these two types of social media marketing. The variations observed across social media platforms indicate that the type of platform can affect the profitability of digital marketing expenditures.
We offer novel insights to managers implementing influencer marketing campaigns. First, this article delineates best practices for sponsored bloggers based on marketing campaign intent and platform. When trying to bolster awareness campaigns on a blogging platform, managers should feature the expertise and credibility of the blogger. However, in the case of trial campaign intent, campaigns by both expert and novice sources will be equally successful.
Second, when implementing campaigns on Facebook or any other high-distraction platform, managers should vary content strategy depending on campaign intent. Trial campaigns can benefit from featuring posts with high hedonic value, particularly in high-distraction environments such as Facebook. Furthermore, when choosing bloggers to implement a strategy involving multiple high-distraction platforms, managers should focus on selecting bloggers with a large follower base to ensure the highest penetration and engagement.
Third, with regard to the impact of campaign intent on outcomes, we recommend that managers use the appropriate drivers of success for blog engagement (i.e., blogger expertise, campaign incentives) in awareness campaigns and rely on hedonic-valued content on blog platforms. We further recommend that managers avoid using campaign incentives on Facebook or other low-involvement, high-distraction platforms, such as Twitter or Instagram, and instead focus on the hedonic value of post content.
Researchers have begun examining the impact of social media expenditures on firm performance and shareholder value (e.g., [19]). By examining sponsored blogging across social media platforms, we contribute to extant literature examining the differential profitability impacts of these platforms, thereby extending research that examines across-media profitability impacts of advertising expenditures ([80]). In addition, many companies have tried to quantify the value of social media engagement. Estimates range from $.33 to $8 per Facebook like, while social media shares are estimated at $8 per retweet and $14 per Facebook post share ([34]). We conservatively estimated the dollar value of each type of engagement at $1 for a Facebook post like, $1 for a Facebook post comment, $2 for a Facebook post share, and $2 for a blog post comment. We multiplied the number of blog post comments and Facebook post likes, comments, and shares by an estimated dollar value for each type of engagement. We then summed these values and used them as the revenue per campaign, per blogger, generated by engagement. Next, we calculated return on engagement (ROE) by dividing the total revenue generated by the total cost for each campaign by blogger. We modeled ROE using campaign intent, expertise, campaign incentives, and hedonic content. We found a marginally significant, positive relationship of expertise and a significant, positive relationship of campaign incentives on ROE (for details, see the Web Appendix). This implies that both blogger characteristics and campaign intent can affect firms' bottom lines. By optimizing social influencer marketing strategies with these results, firms can increase ROE for influencer marketing campaigns.
This research is subject to certain limitations, which may present new directions for further research. We examined only a limited set of outcome metrics associated with a particular blog post and did not directly test for the impact on return on investment (ROI). However, [53] show that both social media and customer WOM increase ROI, and [54] demonstrate the relationship between engagement and ROI. Further research could increase the set of outcome measures of a given campaign by considering the direct impact of a blog post on consequential outcomes, such as sales and ROI. Further exploration of why customer engagement could affect these performance outcomes is worth examining and would extend [33] framework. Our measurement of key constructs, such as sentiment, relied on post hoc measures based on judges evaluating each blog post for factors such as creativity/uniqueness and personal relevancy. A more direct measure would involve having the audiences of a given blogger rate his or her posts for various aspects of sentiment. In addition, research could include a field experiment of blogger choice informed by this research versus the current methods for selecting bloggers for campaigns. In addition, this research uses the bloggers' network size at the time of the posts but does not formally take into account the entire past performance or longevity of the bloggers' careers. This could be an important variable to consider in future research. Finally, we acknowledge the highly evolutionary nature of social media platforms. Therefore, while we explore the effects of campaign, source, and hedonic value in terms of two social media platforms, we recommend considering these findings in the light of platform characteristics versus specific platforms.
In general, sponsored blogging and influencer marketing have been the target of ethical debates in recent years. Some critics argue that social influencers fail to reveal their sponsorship by companies, thereby creating a perception that their sponsored posts are organic WOM. This type of deceptive marketing practice has been at the heart of various Federal Trade Commission investigations of Instagram posts in recent years ([46]). The [25] has reached out to influencers directly and reiterated its requirements to disclose any endorser and advertiser connection. As noted, all sponsored posts in the current research included a declaration of sponsorship at the beginning of the blog post. Still, there is room for research on how sponsored blogging as an advertising medium is distinct from other forms of advertising that consumers view unambiguously as paid advertising.
Supplemental Material, DS_10.1177_0022242919854374 - Driving Brand Engagement Through Online Social Influencers: An Empirical Investigation of Sponsored Blogging Campaigns
Supplemental Material, DS_10.1177_0022242919854374 for Driving Brand Engagement Through Online Social Influencers: An Empirical Investigation of Sponsored Blogging Campaigns by Christian Hughes, Vanitha Swaminathan and Gillian Brooks in Journal of Marketing
Footnotes 1 Authors' NoteThis research was conducted as part of the first author's doctoral dissertation at the University of Pittsburgh.
2 Associate EditorDhruv Grewal
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors received funding from a Marketing Science Institute grant.
5 ORCID iDVanitha Swaminathan https://orcid.org/0000-0002-8752-8881
6 Online supplement: https://doi.org/10.1177/0022242919854374
7 1Our focus is on only one blogging agency, and several of these bloggers work with other blogging agencies as well, suggesting that their sponsored activities may include other campaigns outside the ones in this data set.
8 2As we subsequently describe, the data on blog posts involved coding across a variety of independent and dependent variables. In the process of coding data pertaining to the variables, we encountered some missing information for a few variables due to the nature of data collection from individual blog post websites (i.e., nonpermanent URLs). Thus, our final sample size for analysis is 1,237.
9 3Details using tetrachoric as an alternative rotation are available in the Web Appendix.
4The analysis revealed five blogger characteristics: expertise, travel/foodie, persona, lifestyle, and values (for the rotated factor patterns, see the Web Appendix). Travel/foodie consists of travel and food and wine. Persona reflects professional reference, technology and social media reference, and brand affiliation. Lifestyle comprises homeschooling, an environmental affiliation, and a health affiliation. Finally, values are based on religious and political affiliations.
5Those sampled were women with children under 18 years of age.
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Record: 66- Dynamic Governance Matching in Solution Development. By: Colm, Laura; Ordanini, Andrea; Bornemann, Torsten. Journal of Marketing. Jan2020, Vol. 84 Issue 1, p105-124. 20p. 1 Diagram, 6 Charts. DOI: 10.1177/0022242919879420.
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Dynamic Governance Matching in Solution Development
Facing competitive and commoditization threats, many companies shift to solution offerings, albeit with mixed results. With a qualitative analysis of dyadic data (suppliers and customers), this article investigates an important, often overlooked reason for such mixed outcomes: the complex, dynamic role of governance matching. This study identifies a series of tensions arising from solution-specific exchange conditions and the matched governance mechanisms actors use to address them: temporary asset colocation, network closure, knowledge-based boundary objects, rights allocation agreements, and liaison champions. It also reveals the dynamic nature of governance matching. Solutions evolve in three phases—experimentation, integration, and evolution—in which single mechanisms have different functions (safeguarding and/or coordination), provide contingent and transient benefits, and can be used in combination to address complex tensions. This study also identifies two decision points, mutual commitment and balanced power, that separate the three phases; their outcomes help explain why certain solution efforts do not take off, others stall, and still others revert to mere spot exchanges. Beyond contributing to solutions literature, these findings provide actionable insights to marketing managers.
Keywords: business-to-business relations; dynamic models; governance matching; governance mechanisms; services; servitization; solutions
Facing competitive and commoditization threats, many business-to-business companies increase the service part of their offers ([12]), such that they integrate product and service content to satisfy customer needs—an offering commonly referred to as a "solution" ([61]). Anecdotal evidence notes famous successes (e.g., IBM, Rolls Royce) along with comparably unsuccessful examples (e.g., Xerox, Siemens) of solutions ([51]). Empirical analyses also suggest that solution benefits are difficult to achieve ([17]; [52]), leading some authors to speculate that companies might struggle to exploit their solutions' potential because the development process remains poorly understood ([ 3]; [38]).
With its inherent service shift, solution development exhibits some differential features compared with other types of business-to-business exchanges. First, value in solution relationships is intensively coproduced in interactions between the supplier and the customer, who cannot simply exchange products or services but rather need to combine their key resources to cocreate an integrated offer ([68]). Coproduction makes solution exchanges more complex and equivocal and requires actors to negotiate and "normalize" their divergences through a set of specific governance choices ([57]). Second, the service shift induces radical changes in relational habits, putting the customer at the center of the supplier's value proposition and altering tasks and duties attributed to suppliers and customers in the exchange ([73]). In such circumstances, the involved actors need time and several interaction episodes to adapt and make sense of one another's perspectives ([60]). A recent survey in the health care industry highlights such an initial lack of mutual understanding as a major hurdle to solutions development ([63]). As a direct consequence of these sensemaking requirements, the service shift in solutions and its associated governance issues unfold dynamically ([27]). These features create a context of emerging exchange conditions that expose solution actors to evolving governance tensions (e.g., the potential for opportunistic behavior by one of the actors) that, if not addressed properly, reduce their willingness to engage in value-creation initiatives ([70]).
Extant solutions literature has not investigated the resulting governance issues, though some studies have indirectly suggested a key role of governance in determining the fate of solutions. For instance, to achieve the full potential of a solution, it is known that customers must share key resources with the supplier ([44]), and the supplier must take responsibility for tasks that the customer previously performed ([67]). But under such circumstances, how can actors avoid opportunism threats that emerge from responsibility sharing? Moreover, how can they prevent information leakage during the exchange of critical resources? Considering that the dyadic interaction between actors extends over time ([18]; [19]) and that actors exhibit divergent perspectives that need to be aligned ([65]), other tensions are likely to emerge and potentially affect the solution development process. Taken together, these notions reflect what analysts and practitioners identified as a key challenge to many industrial firms' servitization efforts—that "both parties need a mechanism to resolve disputes" ([22]).
Against this backdrop, our research sheds light on an overlooked issue: How can solution actors succeed in governance matching, such that they implement appropriate mechanisms to avoid or address the tensions that emerge from solution-specific features? Governance matching is a critical factor for developing business relationships, in that "managers seek to maximize performance by matching exchanges, which differ in attributes, to governance structures, which differ in their capacities to respond effectively to disturbances" ([71], p. 79).
With this study, we aim to ( 1) identify systematically the various governance tensions associated with solution development and how actors might match specific mechanisms to those tensions and ( 2) detail how the tensions vary over the course of solution development and how actors thus adjust to deal with their evolution. To achieve these objectives, we conduct a multimethod (interviews and observations), in-depth, qualitative investigation of solution development efforts by a leading supplier of measurement and automation control systems and its clients.
Figure 1 depicts the dynamic framework of solution development that we derive on the basis of this investigation; it also serves as a road map for the article. As the framework reveals, solutions, from a governance standpoint, evolve in three main phases (experimentation, integration, and evolution), separated by two decision points (mutual commitment and balanced power). For each phase, we delineate the central components of the governance matching sequence (conditions arising, tensions, and mechanisms) that explain how solutions evolve from a governance standpoint. Table 1 provides a glossary of the key terms used in this framework.
Graph: Figure 1. Notes: Two-way arrows represent the governance matching process.
Graph
Table 1. Governance in Solutions: Glossary of Key Terms.
| Term | Explanation |
|---|
| Condition arising | Solution-specific features that characterize exchanges in solution development. |
| Role shifting | Condition arising in the experimentation phase, when the supplier takes over some responsibility for tasks that the customer previously performed. |
| Indirect links to rivals | Condition arising in the experimentation phase as a result of customers sharing strategic information with a supplier common to other competitors. |
| Interdependence | Condition arising in the integration phase, according to which each solution actor's activity depends on that of the counterpart. |
| Unpredictability | Condition arising in the evolution phase, due to the high level of uncertainty about solutions' future development. |
| Governance tensions | Exchange disturbances between solution actors due to specific conditions arising in solution development. |
| Governance mechanisms | Remedies (tools, devices, and tactics) that solution actors employ to address the governance tensions that may emerge in solution development. |
| Temporary asset colocation | Governance mechanism that stimulates collaboration while reducing opportunism. It involves supplier and customer physically sharing proprietary assets through temporary agreements during the experimentation phase. |
| Network closure | Governance mechanism that reduces the risk of information leakage to competitors who share the same supplier. It involves communitarian networking initiatives for solution actors in the experimentation phase. |
| Knowledge-based boundary objects | Governance mechanisms that help solution actors to share relevant, tacit knowledge during the integration phase. It involves the joint use of artifacts embedding knowledge. |
| Rights allocation agreements | Governance mechanisms that reduce exploitation risks when solution actors integrate each other's knowledge during the integration phase. It involves specific contractual clauses that clarify ownership rights on solution outcomes. |
| Liaison champions | Governance mechanism that addresses unforeseen contingencies in the evolution phase. It involves defining a managerial role (potentially for each actor) with enough agency to balance proactivity and adaption requirements during solution development. |
| Governance matching | The use of governance mechanisms to address/avoid potential tensions, deriving from specific exchange conditions arising in solution development. |
| Decision points | Moments of choice that separate solution development phases, in which both actors decide if there are the premises to advance or not the development process. |
| Mutual commitment | It entails solution actors' bilateral willingness to engage further in the development process. It reflects the moment of choice for actors whether to advance from the experimentation to the integration phase. |
| Balanced power | It entails a situation in which solution actors become more interdependent in a symmetric fashion. It reflects the moment of choice for actors whether to advance from the integration to the evolution phase. |
In the first phase, actors engage in small-scale experimentation, so governance matching aims to safeguard them from opportunistic behaviors by their counterpart, as well as from any competitive threats that may result from shifting roles and responsibilities. Asset colocation and network closure are the two key mechanisms in this phase. If the match works, actors develop mutual commitment and engage in tacit knowledge integration practices. Governance matching in the second phase involves the coordination of goals and intangible assets, along with safeguarding from potential knowledge appropriation by the counterpart. Boundary objects and rights allocation agreements ensure governance matching in this phase. If this integration evolves in a context of balanced power, actors proceed to the third phase of solution development, in which governance matching aims to coordinate the interplay of adaptation and proactivity to deal with unforeseen contingencies. Liaison champions are the core mechanism in this phase. If any matching attempts fail, the process may stall, revert to spot market exchanges, or evolve as vertical integration. As our further analysis reveals, governance matching in solutions entails a careful blending of contractual and relational mechanisms to address the complex, evolving set of tensions that actors face over the course of solution development. Specifically, the aforementioned mechanisms delimit risk and promote a collective climate in the experimentation phase, facilitate knowledge sharing processes in the integration phase, and stimulate a forward adaptation attitude among actors in the evolution phase.
From a theoretical standpoint, this study contributes to existing solutions literature by specifying the relevant, overlooked role of governance matching, as well as how solution actors execute these challenging, dynamic matching activities. Moreover, we illustrate what happens when governance matching fails and why solution development may take unplanned directions. From a managerial standpoint, this study recommends actors to engage in governance engineering activities to succeed in solution development. They should take a learning-by-doing approach, monitoring and anticipating the various evolutionary paths that solution development may take, and engage in periodic decisions about whether and how to continue in the process. In short, our study reveals that successful solution development requires a negotiated, socially constructed process that features the complex, joint activity of governance matching.
The discriminating alignment principle in transaction cost economics asserts that certain conditions in an exchange (e.g., asset specificity) generate specific tensions between parties (e.g., opportunism), which must be addressed with proper governance mechanisms (e.g., contracts). Identifying proper governance mechanisms (i.e., governance matching) is thus crucial, offering "the means by which order is accomplished in a relation in which potential conflict threatens to undo or upset opportunities to realize mutual gains" ([72], p. 37). When successful, the actors can realize "economizing advantages," such as reduced transaction costs and increased willingness to engage in value-creation initiatives ([30]).
Different manifestations of the stylized conditions–tensions–mechanisms sequence implied by governance matching appear in prior research ([54]). For example, transaction cost theory originally prioritizes asset specificity and uncertainty as key exchange attributes that raise potential opportunism and lock-in risks, which in turn require increasing vertical integration (or hybrid forms of governance, as later acknowledged). Agency theory instead highlights information asymmetry, which can hamper monitoring and control and may require formal contracts to align actors' behaviors. In contrast, relational marketing focuses on trust and commitment as key features of a successful relationship, suggesting the use of informal social rules as proper mechanisms to reduce risk and facilitate coordination. Stewardship theory further proposes the benefits of an ex ante determination of mutual goals. Although not exhaustive, this list reveals the multiplicity of conditions that might arise in a relationship and, accordingly, the heterogeneous mechanisms available to actors to face tensions and ensure governance matching.
We define governance mechanisms as the tools, devices, and tactics available to managers to regulate the conduct of the exchange relation ([48]). They entail concrete activities and processes that managers can deploy and execute, which may reflect shared beliefs or formal rules of conduct ([33]). Governance mechanisms have two main functions: safeguarding actors from exchange hazards and coordinating their behaviors ([24]). Typical hazards include various instances of opportunism and other threats related to information sharing; coordination refers to an optimal combination of activities and outcomes ([33]). Traditionally, safeguarding and coordination have been accomplished using contractual or relational governance forms ([ 9]). Contracts regulate an exchange through formal promises and obligations to perform future actions ([45]) and can vary in their complexity, with respect to roles and responsibilities, monitoring issues, and outcomes ([56]). Relational forms instead use social processes embedded in repeated exchanges, which promote different social norms, such as flexibility, solidarity, mutuality, harmonization, and restrained uses of power ([ 8]). According to the discriminating alignment principle, because governance forms vary in their capacity to ensure safeguarding and/or coordination goals, actors must identify the proper mechanisms for dealing with specific tensions as they emerge in exchanges ([71]).
In the context of solutions, which involves high levels of coproduction and the potential for radical shifts in relational habits, we expect actors to face a series of emerging exchange conditions that can, if left unaddressed, generate a series of heterogeneous governance tensions, which also evolve dynamically over the various phases of solution development. Achieving governance matching in solutions thus seems particularly challenging and may require the use of multiple contractual and relational mechanisms to ensure both safeguarding and coordination goals as they unfold over time. Therefore, we aim to identify specific manifestations of governance tensions that arise over the course of a solution development effort, as well as to detail how actors dynamically address them with proper mechanisms. This study accordingly constitutes a middle-range theoretical extension of existing governance models (e.g., [28]; [70]), with which we aim to identify operational meanings and actionable insights for the specific context of solutions ([29]).
From a thorough review of solutions development literature (see Web Appendix W1, Table W1, for search and selection criteria), we generated two interesting insights regarding the role of governance in solutions. First, as Table 2 shows, a set of core papers provide interesting outcomes but do not take a governance view; they mostly focus on firm-level resources and capabilities. However, they reveal that actors often express divergent expectations of solution development ([65]) and face various capability integration issues in the process ([44]; [67]). Such considerations may generate actor misalignment and responsibility-sharing challenges, which need to be matched with proper governance mechanisms. Other studies also indicate that solution development is embedded in long-term relationships ([18]) that evolve along different paths ([19]). The governance issues associated with solution development thus are likely to change over time, requiring a dynamic approach to governance matching. The outcomes of these studies implicitly suggest the presence of dynamic governance issues, which warrant more attention.
Graph
Table 2. Governance in Solutions Literature: Core Studies on Solution Development.
| Study | Theoretical Framework | Core Concepts | Empirical Sample | Development Perspective | Governance Focus | Governance Tension Sources |
|---|
| Tuli, Kohli, and Bharadwaj (2007) | None | Effectiveness drivers (solution definition) | Bilateral (unmatched) | Execution steps | No | Actor misalignment |
| Ulaga and Reinartz (2011) | Resource-based view | Supplier capabilities | Unilateral (supplier) | Static | No | Responsibility sharing |
| Macdonald, Kleinaltenkamp, and Wilson (2016) | Value-in-use | Supplier and customer capabilities | Unilateral (customer) | Static | No | Resource integration |
| Friend and Malshe (2016) | Resource-based view and service ecosystem | Supplier capabilities | Unilateral (supplier) | Static | No | (Temporal) adaptation |
| Forkmann et al. (2017) | Business model | Supplier business model and customer capabilities | Bilateral (firm level) | Static | No | Relational embeddedness |
| The current study | Transaction cost economics | Governance tensions & mechanisms | Bilateral (matched, product-market level) | Dynamic (evolutionary phases) | Yes | |
Second, as Table 3 reveals, a few recent works have explored single governance elements in a fragmented fashion, issuing preliminary evidence that responsibility sharing ([73]), misalignment ([59]), and uncertainty ([40]; [66]) generate governance challenges for actors engaged in solution development. Yet a holistic view of governance matching in solution development is still lacking in these studies, though their joint consideration would better reflect the breadth and relevance of governance issues in solutions.
Graph
Table 3. Governance in Solutions Literature: Studies on Solutions with Specific Governance Issues.
| Study | Study Type and Focus | Empirical Sample | Development Perspective | Governance Tension Sources | Governance Mechanism |
|---|
| Worm et al. (2017) | Quantitative, solution effects (profitability growth) | Unilateral (supplier) | None | Responsibility sharing; uncertainty | None |
| Kreye (2017) | Qualitative, single element of solution development (impact of political aspects and culture on uncertainty) | Bilateral | Static | Uncertainty (relational) | Informal governance |
| Reim, Sjödin, and Parida (2018) | Qualitative, single element of solution development (customer adverse behavior) | Unilateral (supplier) | Static | Actors' misalignment | Agency mechanisms |
| Ulaga and Kohli (2018) | Essay, single element of solution development (selling) | None | Execution steps | Uncertainty | Solution salesperson |
| Hartmann, Wieland, and Vargo (2018) | Conceptual, single element of solution development (selling) | None | Dynamic | None | Institutional alignment |
| The current study | Qualitative, entire process of solution development | Bilateral (matched, product-market level) | Dynamic (evolutionary phases) | | |
Taken together, evidence from the aforementioned literature (see Web Appendix W1 for more details on each study) highlights the usefulness and need for a systematic, thorough analysis of heterogeneous, dynamic governance issues that emerge in solutions development, as we attempt here.
In line with our exploratory research questions, we adopt a grounded theory approach to perform a qualitative inquiry of a relevant case ([16]). Consistent with the iterative logic of qualitative research ([37]), we rely on both theory and data to integrate existing theoretical accounts with field evidence and shed new light on the solution development process. Following [64], we use theories as informed priors to frame and reconcile the results of the grounded analysis ([47]).
Because our research objectives do not include causal reasoning, we employ a narrative (vs. propositional) style of theorizing to obtain a process model of generative processes (i.e., governance tensions and mechanisms) that unfold as a general sequence of events (phases, decision points, scenarios), leading to the outcome under investigation (i.e., solution development) ([11]). Core outputs of a narrative style are a storyline populated with rich explanatory details, organized in abstract patterns, as well as a plot that depicts core processes and sequences, which serves as a guideline for the story (Figure 1).
Following clinical case technique principles ([ 5]; [50]), we seek to reconcile the key empirical findings (governance mechanisms) with theoretical knowledge on governance, to gain deeper understanding of the role of specific mechanisms and reveal how governance matching works. First, we classify the mechanisms according to two conceptual categories: governance form (contractual vs. relational) and governance function (safeguard vs. coordination). Second, drawing on existing governance theories, our reconciliation effort identifies how the specific mechanisms solve the governance tensions they are supposed to address. Overall, we aim to improve understanding of the rationale for governance matching efforts in solution development.
We use a single, relevant case ([74]), involving a leading, Italy-based, global supplier of measurement and automatic control systems that serves multinational, market-leading customer companies in diverse industries. This approach offers several advantages. First, we can perform a purposeful sampling of matched supplier–customer dyads; for each customer company, we knew the counterpart in the supplier's business unit responsible for it. Accordingly, the analysis features matched supplier–customer quotes from the same dyad for each identified governance challenge and mechanism (Web Appendix W2 includes additional representative quotes). Second, focusing on one single, committed supplier granted us continuous access to the board and members at various hierarchical levels, full support for our interactions with key customers, and several possibilities to discuss and validate the findings. Third, an individual case supports a micro-level, in-depth investigation, focused at the product-market level of analysis, which represents the dimension at which governance matching actually operates ([20]). Fourth, using the information available at the time of sampling—which we obtained from the supplier company and validated in interviews with the respective customers—we included dyads engaged in solution development at different levels of maturity (early vs. advanced). Thus, we encountered solutions in different phases of development and could generate a fine-grained, dynamic model of solution development (Figure 1).[ 5] Fifth, the sampling produces heterogeneity in relationship tenures and industries, which helped us investigate potential patterns across cases.
The focal supplier is a well-established industry leader that delivers high-tech, customized products that support customers' production processes and components. Customers represent a wide array of industries (automotive, electronics, transport, health care, food), and the supported measurements range from end-of-line quality tests in assembly processes to prototype testing and benchmarking activities in production or research and development. The company's strategic intent is to shift from being mostly an equipment or test bench supplier to becoming a solution provider or even a pure service player (e.g., running test campaigns in its own research center and delivering the results to customers). In this sense, its offering represents what [67] call process delegation service—that is, the most advanced form of a product–service hybrid offering. In the chief executive officer's words, "We have been used to selling tons of intelligent iron to our customers, but now we should also try to sell them our intelligence applied to their data." In support of the appropriateness of our sampling approach, we note that the aggregate service share of this company's turnover increased, from less than 5% to more than 20% in the previous ten years, with some variation across different products and markets. Thus, we have a unique opportunity to examine a significant service transition process, spanning both preliminary attempts and more established episodes of solution development.
We applied different data collection techniques at the different stages of our study. Specifically, we conducted 29 semistructured interviews with 12 managers of the supplier and 17 managers of customers, in line with the dyadic nature of our investigation ([23]). The interviews took place in two rounds, between November 2015 and September 2016. They lasted 69 minutes on average and were audiotaped and transcribed verbatim into 388 pages, supplemented by 150 pages of notes. To obtain further information about implementation efforts, we carried out five in situ observations, of one day each, between September 2014 and October 2018. With this approach, we visited the supplier on a regular basis to capture internal changes or advances in its solution development processes over time, while also directly observing some relationships with customers (e.g., conference at the supplier's headquarters, inauguration of a new facility). In addition, we carried out opportunistic conversations with other organization members to further explore unexpected issues that emerged during the interviews or observations.
To ensure rigor, process verification, and content validity, we employed member checks and validated insights with participants throughout the research process. [49] note the relevance of this step, not only at the end of but also during the study, when additional information can still direct the ongoing research. So, for example, we contacted the participants from the first round of interviews, asking them to confirm our classification of solution development processes with specific customers as rather early or more advanced. We also presented the intermediate research results in three dedicated workshops, with 20, 20, and 10 participants from the supplier company (the second workshop also was video recorded). With this feedback, we reviewed and reinforced the preliminary findings, included in subsequent research steps. Finally, we presented the results in a half-day workshop with seven managers from the supplier company. This step helped us gather feedback about the entire framework of our analysis, which we integrated in the conclusive version of the study. Specifically, Table 4 provides a summary of the data collection process, outlining the objectives and results of each step, and Table 5 lists the different data sources and dyadic information. Further details regarding data sources and analysis procedures are provided in Web Appendices W3–W5.
Graph
Table 4. Data Collection Process.
| Date | Data Collection Step | Main Goal | Main Results |
|---|
| Sep. 2014 | First in situ observation | Understand company and business model to evaluate appropriateness for our research | Company is exemplary setting to examine a supplier's solution implementation process with heterogeneous customers |
| Jul. 2015 | Second in situ observation | Attend inauguration of new testing laboratory at the supplier's headquarters (with customers); define sampling logic | Identification of list of dyads to investigate |
| Sep. 2015 | Third in situ observation | Attend inauguration of the German subsidiary (with customers) | Further support for the appropriateness of the case for the research |
| Nov. 2015 | First Round of Interviews | Investigate solution implementation from the supplier side | Dyad and product market as proper levels of analysis; solution implementation as a negotiated process in the dyad |
| Apr. 2016 | First workshop, intermediate results presentation | Get feedback on intermediate findings | Support and extension of early evidence and insights for the next steps |
| Apr. 2016 | Fourth in situ observation | Investigate customer behavior at industry-specific conference held at supplier's headquarters | Competitors interact and exchange information in a neutral setting |
| Jun.–Sep. 2016 | Second round of interviews | Investigate solution implementation from the customer side | Heterogeneous perceptions of solutions salience and governance aspects of the dyads |
| Dec. 2016 | Second workshop, intermediate results presentation | Get feedback on intermediate findings | Support and extension of intermediate results and insights for the next steps |
| Jul. 2017 | Member check with first-round participants | Double-check information collected at the beginning of the investigation | Broad confirmation of the sampling dimensions |
| Sep. 2017 | Third workshop, intermediate results presentation | Get feedback on intermediate findings from German subsidiary | Support and revision of results and additional information on German customers |
| May 2018 | Interjudge reliability check (with PRL method) | Check reliability of outcomes with two independent judges | Confirmation of outcomes,.79 interjudge reliability rate |
| Jun. 2018 | Fourth workshop, final results presentation | Get feedback on final results and conceptual framework | Support and revision of final outcomes |
| Oct. 2018 | Fifth in situ observation and ad hoc interviews | Collect information on specific topics | Integration of final outcomes |
| May 2019 | Ad hoc interviews | Collect information on specific topics | Integration of final outcomes |
Graph
Table 5. Data Sources.
| Dyad Code | Supplier | Customer | Preliminary Stage | Product Market | Rel. Tenure |
|---|
| Sup. Code | Position | Int. Length | Cust. Code | Position | Int. Length | Lang. |
|---|
| Dyad 1, Sup1_Cust1 | Sup_1 | R&D Manager | 103 min | Cust_1 | Industrialization Manager | 59 min | German | Early | Automotive (Tier 1) | >10 years |
| Dyad 2, Sup1_Cust2 | Cust_2 | Purchasing Director | 123 min | English | Late | Automotive (OEM) | >10 years |
| Dyad 3, Sup2_Cust3 | Sup_2 | Sales Manager | 73 min | Cust_3 | Purchasing Manager | 58 min | Italian | Late | Automotive (Tier 1) | >10 years |
| Dyad 4, Sup3_Cust4 | Sup_3 | Head of Business Development | 58 min | Cust_4 | Head for Engine Test Bed Technology | 67 min | Italian | Early | Automotive (OEM) | <5 years |
| Dyad 5, Sup3_Cust5 | Cust_5 | Designing Engineer | 58 min | German | Early | Automotive (OEM) | <5 years |
| Dyad 6, Sup4_Cust6 | Sup_4 | General Manager | 49 min | Cust_6 | Tests Methods Development Responsible | 95 min | German | Late | Automotive (OEM) | <5 years |
| Dyad 7, Sup5_Cust7 | Sup_5 | Aftersales Manager | 63 min | Cust_7 | Assistant Director | 75 min | German | Late | Automotive (Tier 1) | <5 years |
| Dyad 8, Sup6_Cust8 | Sup_6 | Sales Manager | 64 min | Cust_8 | Thermodynamics Engineer | 62 min | English | Early | Automotive (OEM) | >10 years |
| Dyad 9, Sup6_Cust9 | Cust_9 | Production Engineering Manager | 64 min | Italian | Early | Automotive (Tier 1) | >10 years |
| Dyad 10, Sup7_Cust10 | Sup_7 | Head of Business Unit | 61 min | Cust_10 | Board Member with Responsibility for Services | 58 min | English | Early | Electronics (electrohydraulic components) | <5 years |
| Dyad 11, Sup7_Cust11 | Cust_11 | Program Manager | 68 min | English | Late | Electronics (engines and power generation) | >10 years |
| Test Engine Division Manager | 54 min | English |
| Dyad 12, Sup8_Cust12 | Sup_8 | Aftersales Manager | 97 min | Cust_12 | Research and Development Director | 57 min | Italian | Early | Electronics (home appliances) | >10 years |
| Dyad 13, Sup9_Cust13 | Sup_9 | Research and Innovation Director | 48 min | Cust_13 | Mechanical Chief Engineer | 53 min | Italian | Early | Food (chocolate and confectionery) | <5 years |
| Dyad 14, Sup10_Cust14 | Sup_10 | Key Account Manager | 73 min | Cust_14 | Research and Development Developer | 64 min | German | Late | Electronics (home appliances) | >10 years |
| Dyad 15, Sup11_Cust15 | Sup_11 | Head of Business Unit | 61 min | Cust_15 | Purchasing Director | 62 min | English | Late | Health care (hospital) | <5 years |
| Dyad 16, Sup12_Cust16 | Sup_12 | Engineering Manager | 109 min | Cust_16 | Head of Technical Sourcing | 50 min | German | Late | Transportation (railway switches) | <5 years |
1 Notes: OEM = original equipment manufacturer.
The presentation of the results follows the sequence of phases foreshadowed in Figure 1. When analyzing the data (interviews and observations), we accounted for the relative maturity of the solution development processes and therefore were attentive to any governance issue specific to earlier or more advanced processes. In the subsequent cross-case comparison, we could relate statements describing recurrent governance issues from the interviews to the maturity of the respective process. Thus, the three phases discussed hereinafter are a direct reflection of the dynamic emergence of governance issues over the course of the solution development process. This is portrayed in the process model in Figure 1, which constitutes the primary outcome of our narrative style of theorizing and depicts the process and the dynamics of solution development.
At the onset of solution development, actors face a first arising exchange condition: role shifting. The supplier takes over some responsibility for tasks that the customer previously performed, which may require the customer to provide information and resources, as the following quotes reveal:
We always sold the equipment to these large clients, but sometimes they actually need some small tests or some small solutions. So, we go to them and say, "We perform these tests for you, we take your place." (Sup_4, Dyad 6)
To test our products, we have to share information with them. We do it sort of on a need-to-know basis, where we share the amount that is needed to be shared,...so that they can do this new activity for us. (Cust_11, Dyad 11)
In these new, challenging roles, the actors face an initial governance tension: They realize that solution development requires a tighter, more intense form of coproduction, but they fear sharing critical resources, because it might leave them vulnerable to opportunistic threats, so they question the extent to which they should cooperate at all ([69]). As the following quotes indicate, the key tension sparks the question: How can actors foster deeper cooperation while safeguarding themselves from opportunism threats?
I have to decide case by case: do I want that [information sharing] or not? How secret are the data and how should we deal with them?...Also, I don't need to be able to do everything by myself; however, I have to pay attention that I am always able to have a say in these collaborative models. Otherwise the counterpart can fool you in any possible way. (Cust_7, Dyad 7)
Some of our customers...are very reluctant when it comes to sharing their know-how, because it implies passing information to us, and in their eyes, this means "to the outside." (Sup_1, Dyads 1 and 2)
Faced with this governance tension, several dyads in our sample offer a common answer: They physically colocate proprietary assets (e.g., machinery, employees) using temporary agreements. With simple contractual forms, these customers transfer specific physical equipment (e.g., components) to suppliers' premises, while suppliers send some personnel (e.g., resident engineers) to customers' premises, in both cases for specific periods of time.
We have developed a test installation and had a person from our supplier [resident engineer] who was staying with us and was responsible for the whole software development. This was welcomed very positively, from the plant's responsible employees. (Cust_14, Dyad 14)
They [the supplier's managers] provided us the opportunity to send them a number of fuel injectors that they would test and then give back to us with a report. (Cust_2, Dyad 2)
It has now been a couple of weeks that these two people from [customer company] have been here with us and working in our lab. We are trying to set up a new test installation to do some trials together and see what comes out. (Sup_1, Dyads 1 and 2)
Interestingly, recent reports on successful solution cases in the business press have also mentioned the role of engineers as "swap inventory" at customers' premises ([ 7]). In a similar vein, tire manufacturer Michelin, when introducing fleet management solutions, started hosting customers to make them aware of requirements and advantages of solution development (such that an adequate driving style prevents damages to tires, which is in Michelin's interest, and reduces fuel consumption, which is in the customer's interest; [39]).
Through these colocation activities, actors engage in preliminary episodes of deep collaboration, with two important advantages from a governance standpoint. First, each actor commits to a condition of temporal asset specificity ([46]), so at least in the short run, both the supplier and the customer signal their commitment. While it is possible that the actor that transfers assets or personnel does so because it faces less risk compared with the counterpart, neither is exclusively exposed to a risk of exploitation during colocation ([70]). Second, beyond being temporary, asset colocation does not require massive investments, so actors gain additional protection from the established upper bound on the possible risk of failure—a sort of "affordable loss" in case the collaboration does not work ([58]).
[Collaborations can start with us giving the supplier] access to a field of ours without a direct order from our side, and the supplier does some initial work at its own expense. Then we share the results, thanks to the fact that we are both [willing] to bear the consequences of small initiatives. (Cust_6, Dyad 6)
Implemented through simple contractual forms, asset colocation emerges as a first mechanism that solution actors adopt during early development phases to face the governance dilemma of initiating a deeper cooperation in a novel relational context marked by potential skepticism. Through colocation, actors can engage in small-scale experiments that serve as solution trials, in a context marked by delimited, controlled risk. Their simple contractual nature is effective for managing bilateral governance issues that can be identified in advance and involve proprietary assets ([33]):
So, there is more what we would call risk sharing. Essentially, we enter into contracts to do this, and when we enter into these contracts, we are saying that, you know, this is what we are going to accomplish and this is what our company is responsible for. (Cust_15, Dyad 15)
The contracts we use at the onset of the relationship are pretty standardized. They mostly deal with privacy issues, not with money or economic aspects, without strong enforcements. Such contracts mainly set up the timing of the collaboration while delimiting its scope (e.g., dos and don'ts of a resident engineer and for how long). (Sup_6, Dyad 8)
Notably, this approach conflicts with a traditional view, which holds that formal mechanisms hinder commitment at the start of a business relationship ([36]). Instead, in a solutions context, simple contracts may help avoid unspoken assumptions and unintended behavioral consequences early in the process ([ 8]). Metaphorically, colocation as a contractual mechanism acts as a sort of "trust Trojan horse," used to gain confidence and safeguard from the counterpart by leveraging physical closeness.
A second exchange condition arising in the first phase of solution development is the presence of indirect links with rivals, reflecting the systematic links of the solution provider with various actors. These linkages likely involve concurrent relationships with any particular customer's competitors. When engaging in solution development, the customer recognizes that the common supplier might both receive and provide its rivals with some sensitive knowledge:
They have to walk the line between multiple customers that do similar things. And they can't break [these customers'] trust; however, they can give you anecdotal type of feedback with no names of anybody else. (Cust_8, Dyad 8)
Developing solutions requires a cooperative environment between the supplier and customer to achieve joint value ([44]). However, the presence of indirect links between the supplier and rivals may make customers reluctant to seize the opportunity arising from joint solution development due to the risk of indirect information leakage to those competitors ([ 2]). Even if customers recognize the potential gained from developing solutions, they worry that the supplier might share crucial information with their competitors, even if inadvertently, as the following quotes reveal:
It often happens that the client tells me he wants a system doing this and that, but doesn't tell me why. He doesn't tell me why, because he is afraid that the news about him having a certain problem will spread and become public. (Sup_3, Dyads 4 and 5)
I do not go a priori to my supplier, tell them everything, and then ask them, "How can you help me?" They are a partner of ours, that's true, but they are not only our supplier, they work with others as well...so we necessarily limit the information flow. (Cust_4, Dyad 4)
To handle this tension, the dyads in our sample set up relational mechanisms aimed at realizing network closure. The supplier aims to embed a set of selected customers (also rivals) in a collective, cooperative network ([31]). Collective initiatives in the form of industry-specific activities or communities represent networking events designed to build a harmonized, noncompetitive, cultural climate among selected customers that are also competitors in the value chain. These workshop-like initiatives provide forward-looking opportunities to solution actors that otherwise might not be seized, but they also represent "neutral" environments, where actors feel free to discuss and share new features for existing solutions, as well as broader interests:
So, we created this concept of a "market club," where almost everyone is happy to discuss with others, and this idea of exchanging with others is a completely different and pleasant experience. (Sup_4, Dyad 6)
The system that they are operating with the customer is very experience-centered: They visit each customer-hospital regularly, and then there is this annual meeting in which they discuss with their customers the innovations to implement and what specific needs are emerging among them. (Cust_15, Dyad 15)
So, we created an international community. This community takes place every two years and is only open to some sites [clients], not to every site. It is an international event which we open to the sites that helped us create the markets. (Sup_11, Dyad 15)
Consistent with the concept of clans, whose shared values and beliefs help monitor cooperation efforts implicitly ([ 4]), closed-network initiatives increase the level of social monitoring among participants and circumscribe the flow of information shared in networks ([31]). This mechanism also became evident in our in situ observations. Some respondents went even further, claiming positive spillovers from working with a supplier that is at the center of a network that includes their competitors:
[Fourth in situ observation] Two managers from competitor companies met at the event held at the supplier's facility during a coffee break. It was clear that they knew each other but probably had not seen each other for a long time. They exchanged a few comments about the workshop and the nice location, before discussing details of the presentation that had just finished (about future technological trends characterizing the industry). Each one prudently exposed his view by stating which standard he considered the most relevant for the upcoming years. Before returning to the workshop, they agreed to continue the conversation later that day.
Interviewer: How do you feel about the fact that your supplier is also working with your main competitors?
Interviewee: I actually see that as a benefit! Because I think in terms of understanding where the industry itself is going, they have a better idea of what many people in the industry are doing, that I may not have. So if they have something about my competitors or a supplier, then they are able to develop those capabilities and either have those when I need them or know how to get them. I don't [think] any proprietary information that I am concerned about will be passed to our competitors, because I think that if that would happen, in their business that would be very detrimental. (Cust_2, Dyad 2)
Closed-network communities thus emerge as a second mechanism that actors employ in the first phase of solution development to address the tension involved in the balance between the opportunities spurred by solutions and the threats from competitors that share the same supplier. Network closure constitutes a relational mechanism to address this tension, because by sharing harmonization norms, it defines a "social matrix" in which all actors agree to participate, and it aligns the scope and expected behavior of all these participants ([45]). Harmonization norms are particularly effective in the early experimentation phase because they act as socialization devices ([70]) that expose prospective partners to common goals and values that they can internalize and align in future collaborations:
We host this event [in the automotive industry] every four years. We try to invite as many [original equipment manufacturers] and tier ones as possible to each event, because this is a meeting point, a relational and networking chance with and among the various customers that come. And by doing so, they also see our context. (Sup_5, Dyad 7)
The first phase is essentially a trial period in which actors experiment with various interactions to develop solutions. Simple contractual mechanisms, in the form of asset colocation initiatives, and relational mechanisms, featuring closed-network harmonization efforts, both have important roles: they facilitate collaboration efforts to seize early opportunities while also safeguarding actors from initial exchange threats, such as opportunism and information leakage. Whether the experimental period is short or long, our data reveal two possible outcomes: the trial efforts are successful, and actors decide to engage even more seriously, or the experimentation is not productive, and relationships dedicated to solution development become more sporadic (see left side, Figure 1).
The first scenario results from the emergence of mutual commitment among actors, including their bilateral willingness to make short-term sacrifices to realize long-term benefits ([ 1]). As we describe subsequently, the sequential exchange of commitment generates interdependence across the actors' organizations ([34]), which deepens solution development efforts. Several quotes describe situations in which both actors, after some cooperation, decide to devote more resources to the relationship and accept more risk to move forward:
Your management has to put resources and accept some level of risk to compete. If you are interested in that, then I am glad to give you an opportunity. (Cust_8, Dyad 8)
With this customer, we invest a lot in innovative solutions now, especially in robotics, and all this started with an initial collaboration that allowed us to kickstart bigger projects....At this point, it is not a mere give-and-take relationship anymore, but we are getting really "in tune" with this customer. (Sup_10, Dyad 14)
Solution means really taking care of the customer,...which means making sure that the investment the customer did when buying from you is perpetuated over time....So, the customer sees this [attitude to continue] as added value. (Sup_6, Dyads 8 and 9)
The second scenario instead involves an exit option ([32]). The actors, dissatisfied by their experimental collaborative attempts, decide to remove any relational component from their solution exchanges and conceive of them as mere spot transactions, mostly governed by price mechanisms. In this case, solution development remains embryonic and follows a market-based trajectory, represented in Figure 1 by the first dotted downward line. In the following quote, after an initial effort, one customer felt "abandoned" by the supplier in the experimentation phase, preventing the relationship from developing:
Nobody from the supplier company comes proactively to us. Conversely, one time the supplier told us they wanted to understand how to develop a specific measurement solution, and we agreed they could install one in our facility in the U.S....but they also made it end there. They never came back saying, "Look, the results are interesting." They basically left us alone with it, and I think this was really a pity. (Cust_1, Dyad 1)
Such outcomes might be a result of the difficulties that the supplier experiences in its effort to develop solutions that truly match both customers' and the supplier's needs and expectations. This evidence about the exit scenario is also in line with empirical findings detecting a curvilinear impact of servitization on performance ([52]): before they can reap the lucrative payoffs of offering solutions, some actors become frustrated and forgo service investments prematurely.
If the first decision scenario applies, actors enter the second phase of solution development, which we call integration. Due to the accumulation of mutual commitment pledges from the experimentation phase, actors are now willing to engage in deep coproduction practices implied in solution development. However, such practices require actors to interact in the presence of a new exchange condition, namely, interdependence. Each actor is dependent on the other to achieve solution goals, and mutual (vs. individual) interests should guide their joint value–creating relationships ([41]). The quotes citing the emergence of interdependence in this phase of solution development acknowledge that single actors' activities are instrumental to the achievements of the wider system, and their strategic assets (e.g., tacit knowledge) must be exchanged and integrated for this purpose:
I don't think we would completely avoid [knowledge sharing], even if we could, because you don't go anywhere without the supplier. (Cust_4, Dyad 4)
It is crucial that we share with our customers as much as we know about the problem, and as much as we know about what we consider important about it, and our customers should do exactly the same with us. This way communication works, this way innovation can be achieved. (Sup_4, Dyad 6)
There are several suppliers to whom we grant deep access to our core knowledge...to the point that, in some cases, we even bring our knowledge to their premises for joint initiatives. (Cust_8, Dyad 8)
Yet the condition of interdependence also raises a governance tension between the opportunity for knowledge integration and the incentive to pursue knowledge appropriation ([53]). Integrating the knowledge bases would be in the interest of the wider system, but the involved actors may refrain from doing so. First, knowledge assets constitute sensitive information in tacit form that is difficult to exchange, and their transfer may generate opportunism and exploitation risks ([69]). Second, because tacit knowledge is a core asset, each actor also might try to retain it, to limit its dependence on its counterpart ([41]). The following quotes highlight this tension:
It is inevitable that [suppliers] get in touch with some sensitive information of ours. You can limit it, but you cannot eliminate it; otherwise, the whole thing doesn't work. (Cust_4, Dyad 4)
Yes, they could learn a lot about our product, by testing our product and developing something to test our product. I would say, that they definitely learn....So, it is a concern for us, and because of that we share only the confidential information that we feel is needed, that they need to know, and we try not to share anything more than that. (Cust_15, Dyad 15)
To address this governance tension, the actors in our sample generate boundary objects, or artifacts of various types that reflect embedded knowledge, such as codeveloped machines, shared layouts, trained employees, or metric dashboards ([10]). Boundary objects are intended to facilitate information exchanges related to knowledge assets by establishing shared meaning for them, which is crucial for solution development, because providers and customers usually interpret solution content differently ([65]). The examples cited in the following quotes reflect these efforts:
We controlled all the aspects in the workflow and we decided to work with this supplier in our process, and since that time, it has ended in a kind of shared road map, implementing and developing new systems. It's been kind of a shared vision...that has been, I guess, three-and-a-half years now. (Cust_15, Dyad 15)
We started a tight collaboration [with the customer] aimed at developing an injection measurement system for a car's engine together. While we invented the solution, we decided to develop and implement the application together with the customer within our Kite lab and work on it jointly for further improvement. (Sup_2, Dyad 2)
We jointly developed a chemotherapy mixing machine with the main local hospital, and when it was still a prototype, it was hosted in their facilities and further jointly developed from there. (Sup_11, Dyad 15)
These uses of boundary objects constitute a relational governance mechanism: the exchange of knowledge assets is difficult to codify in advance and cannot be safeguarded by contractual mechanisms, so joint responsibility is fundamental to solution coproduction ([33]). In this phase, boundary objects are effective because they leverage so-called mutuality norms, or a sense that each party's success is a function of everyone's success and that one party cannot prosper at the expense of another ([ 8]):
We almost are required to interchange with the supplier. You cannot grow without your supplier, and the growth is joint. Thus, it is obvious that I have to share information, so then in the end it leads to joint profits. (Cust_9, Dyad 9)
Furthermore, boundary objects also are important because they help align incentives and facilitate perspective taking, such that each actor imagines the other's viewpoint ([43]). By "walking a mile in others' shoes," the parties can identify different views on goals and behaviors and align them better. If realized, perspective taking provides a basis for adopting a stewardship logic, devoted to maximizing the goals of the counterpart ([13]):
What I think is also very important is to have someone who looks at the tasks from a different perspective and asks important questions, which also help [in] achieving a bigger picture of the topic. Taking this other perspective is extremely helpful for me, and we need to foster this. (Cust_6, Dyad 6)
Some people of the business units now think more like clients than like us....Now they are into their system. (Sup_9, Dyad 13)
It's a mission for us to wear the customer's shoes and understand how he consumes [energy], how it works, what its processes look like....If he spends 100,000 euros for electricity, I should ask myself, what could I save if I were him? (Sup_2, Dyad 3)
Perspective taking and the stewardship logic both support a more balanced power relationship, avoiding a situation in which one actor might feel too dependent on the other.
Knowledge integration is truly challenging in solution development, because it entails a long-term commitment to exchange core assets, which promises high benefits (gains from innovative solutions) as well as risks (exploitation of knowledge assets) and potentially unpredicted outcomes (reallocation of power among actors). To handle the governance tension associated with integrating versus appropriating knowledge, several dyads in our sample thus complement their uses of mutuality norms and boundary objects with contractual mechanisms aimed to safeguard against potential exploitation of core competences and maintain balanced power relationships:
When we and our client share core knowledge, we both negotiate in a detailed contract the conditions under which the know-how of one actor can be used by the counterpart, inside and outside the solution partnership (Sup_4, Dyad 6)
When we engage in the codevelopment of particularly innovative solutions with clients, we involve lawyers and managers to write down in a contract the ownership rules regarding the joint outcomes of the project. (Supplier's lawyer)
We have confidentiality agreements with few suppliers, and so we feel like we can trust them and so invest in them...not only monetarily, but technology-wise. (Cust_11, Dyad 11)
The use of multiple governance mechanisms (in this case, combining the use of contracts with boundary objects based on relational norms) to address the same tension (integrating vs. retaining knowledge) is consistent with the principle of plural governance forms ([ 6]), according to which single mechanisms are not always traded off but rather can be combined to handle challenging governance tensions like those that arise in the integration phase of solution development. These contracts also are more complex than those associated with colocation initiatives and used at the onset of solution development. Although complex contracts remain incomplete, their use can "deter behaviors that could compromise the performance of a buyer–supplier exchange" ([56], p. 709). In our case setting, such contracts provide guidance for determining rights allocations among the solution actors. First, they establish the ownership of the outcomes jointly developed in the solutions context, with a goal of maintaining the balanced relationship. Second, the contracts define the know-how that remains under the control of each individual actor, the use of which requires permission, so they provide additional safeguards against the exploitation of core assets. It is worth noting that, because they are specific to each dyad, the details of the rights allocation agreements are highly customized and negotiated between solution actors.
The integration phase of solution development is thus characterized by the emergence of interdependence. Our data reveal that this interdependence can evolve in two ways, leading to different solutions trajectories, as depicted in the center of Figure 1. In one case, actors' interdependence grows in a balanced fashion, so the relationship evolves symmetrically ([62]). In this case, the actors continue to coinvest in solutions, and the development trajectory enters a third phase called evolution (see the next section):
After five years, ours is an equal relationship; there is even a friendship. This relationship of reciprocal safety and transparency made new business opportunities and solution evolutions possible. (Sup_12, Dyad 16)
In another case, interdependence evolves in an unbalanced way, and one actor becomes significantly less powerful and more dependent on the other. Such dependence asymmetry negatively influences business relations, by fostering coercive uses of power and reducing the actors' willingness to compromise ([25]; [41]). The emergence of this power unbalance evokes two possible outcomes. First, the more powerful actor may decide to manage the relationship using hierarchical or bureaucratic mechanisms, even to the extreme level of a quasi- or full vertical integration:
So, we started working as an armed branch of [company X], in the sense that they got the order and we managed it. Then in 2005 we detached ourselves from them and started moving our own steps....So, what we did was to go to [company X's] clients and asked them to come with us. (Sup_8, Dyad 12)
Second, the less powerful actor may feel dissatisfied and worried about the unbalanced evolution of the relationship, to the point that it considers reducing the effort it devotes to the solution process, reverting to an exchange that resembles a market-like relation (both possibilities are represented by the second downward dotted line in Figure 1):
This was a question I asked to myself silently: "Is the supplier's aim to sell me the solution now and to take the business away from me, becoming a competitor of mine in two years? Basically, I would be nourishing a competitor of mine, then....I see a big quo vadis in this, but to some extent also dynamite. (Cust_16, Dyad 16)
If the first decision scenario from the previous section applies, actors that have grown interdependent in a balanced way can enter the third phase of solution development, which we call evolution. The codevelopment efforts of the supplier and customer have evolved through a symmetrical relationship that has enabled both of them to benefit. At this point, they mutually expect to perpetuate their joint efforts over time to exploit the full potential of solutions. However, their long-term, forward-looking perspective exposes these actors to the new arising condition of unpredictability. Future exchanges among these solution actors may be exposed to unforeseen changes in upstream and/or downstream market conditions, which "reflect the tug and pull of new contingencies and participants" ([60], p. 69). Such changes could be beneficial or could leave the joint efforts for solution development obsolete:
Technology can change our customer's priorities and then we have to adapt, too. (Sup_3, Dyads 4 and 5)
We learn something new daily, and if my supplier is very strict...it will be more difficult for me to satisfy my own customers, and the quality of the project in the end will be not so good. (Cust_10, Dyad 10)
These unpredictable conditions generate a new governance tension in this phase, in that actors must balance adaptation and proactivity attitudes to handle future contingencies. On the one side, uncertainty requires adaptive routines ([26]) to guarantee flexibility in the evolution of the solution development process. In the first of the following quotes, an informant from the supplier side explicitly refers to the importance of being responsive to customer requests, particularly when the process is already advanced, as also confirmed by the customer in the second quote:
We go to our customers and say "So, you need test equipment. Do you have a budget? Should you not have it, we can still do it for you, don't worry: we make our spaces, people, and technologies available for you to do testing activities." (Sup_1, Dyad 2)
We like to do this [being flexible] and to have that flexibility also from the supplier: we like the idea of having something that you can adapt. (Cust_11, Dyad 11)
On the other side, unpredictability requires a proactive attitude, which enables actors to anticipate future trends and act even in the presence of partial information and weak signals ([14]). Such a forward-thinking, out-of-the-box approach also gives solution actors a signal that their counterpart cares about the long-term relationship and is willing to resolve problems:
We have to be good at understanding that we cannot live only for the present and the current demand, but we have to anticipate needs and ask ourselves questions about what can be useful for the customer in the future....This also requires changing communication schemes. (Sup_4, Dyad 6)
It is always nice when your [supplier's] colleagues add some ideas that go beyond the actual commissioning. Our arms are always wide open here. Very often this leads to subsequent commissions. Ideas for discussion and suggestions are always welcome. (Cust_6, Dyad 6)
When we hired some Brazilian engineers, did [the customer] ask us to do so? No! We did more; we asked him to come with us to a Brazilian university and select the person that we were going to hire. So we had a person coming from the same culture and speaking the same language [of the customer]. (Sup_4, Dyad 6).
Yet because proactivity focuses on anticipating future conditions while adaptation relies on responding to present conditions, considering them simultaneously is difficult and generates another governance tension. From one side, proactivity provides early mover advantages but generates rigidities, because actors commit to premature market conditions, which can make later adaptation difficult ([42]). From another side, adaptation fosters flexibility and avoids rigidities but reduces strategic foresight, thus constraining actors to think and act within the existing market conditions ([14]). The following quotes reveal this tension:
We should try to be a bit more proactive sometimes, but at the same time we cannot go in a direction where the customer feels we are not responsive to its needs and will take the projects away from us. (Sup_3, Dyads 4 and 5)
It is a pity because sometimes we are very proactive in proposing some [new] solutions to the customer, but he perceives them as necessary and useful only when it is too late. (Sup_6, Dyad 8)
I think as a supplier sometimes you might get the perception that the market is not interested in that [solution innovation]—and maybe the market is really not interested in that....This might be because you have a very risk-adverse customer base that likes a lot of the [existing] competences. (Cust_15, Dyad 15)
The solution actors in our sample try to address this governance tension by establishing an executive liaison champion, an organizational role that ensures sufficient agency to take proactive decisions and enough vision to adapt to the actual relationship context. This empowered champion is different from a key account manager, which mostly handles relationship uncertainty from the seller side ([66]). Liaison managers might be employed by the supplier, the customer, or both and have enough agency to anticipate changes and make flexible decisions, thus acting as a bilateral governance mechanism:
We are trying to privilege more the relational rather than economic aspects, so there is the need to have people who are also smart in understanding the client's [needs] instead of [simply] having a technical key account. (Sup_1, Dyad 2)
What has been crucial was the creation of a team where there were two project leaders—one for the supplier, and this was me, and one for the client. These involved regularly some industry experts, but also gave continuity to the project from a relational standpoint, for example, meetings for updates, tests, and further developments, as well as continuous engineering. (Cust_12, Dyad 16)
A liaison champion thus is an additional relational mechanism that solution actors use in the evolution phase to leverage flexibility and solidarity norms. It helps actors consider agreements as starting points that can be modified as the market, exchange relationship, and fortunes of the parties evolve. Moreover, it facilitates actors' alignment even in the face of adversity and the ups and downs of marketplace competition ([ 8]):
So often I need you to be very flexible and fast-responding to my requests, because we are working on the product and on the process at the same time. (Cust_10, Dyad 10)
Over time, the interplay of adaptation and proactivity produces an ongoing process of institutional alignment, involving both innovative activities and mundane adjustments ([27]). An informant from the supplier company describes this long-term process using a marriage metaphor:
When you are engaged with somebody you focus on being liked by the other half, on reciprocal fit, and on all those things you need to do to please the other person. There is way more diplomacy, because the focus is on starting a long-term relationship. So you know you have to avoid certain behaviors; otherwise you are out. When you get married, it is a different story, because priorities change: when the relationship is established, your focus moves from liking each other to solving common problems, that is, from aesthetics to effectiveness. (Sup_3, Dyads 4 and 5)
The key takeaway of our analysis is that solution development evolves through different phases (experimentation, integration, and evolution), characterized by specific exchange conditions that may expose actors to various governance tensions. To address such evolving tensions, actors use a series of different mechanisms that change over the course of solution development.
Consistent with clinical case technique principles, we now reconcile the key empirical findings (i.e., mechanisms) with theoretical knowledge on governance, to gain a deeper understanding of the role of specific mechanisms and reveal how governance matching exemplifies the discriminating alignment principle. Table 6 summarizes the outcome of this effort. In accordance with governance literature ([24]), we classify each mechanism first according to its intended function (safeguard vs. coordination) and the form it takes (contractual vs. relational). Then, integrating various streams of literature, we discuss precisely how each mechanism addresses the specific tensions emerging in solutions development.
Graph
Table 6. Governance Mechanisms for Solutions: A Theoretical Reconciliation.
| Solution Development Phases | Governance Mechanisms | Form of the Mechanism | Function of the Mechanism | Effects of the Mechanism | Theoretical Support |
|---|
| Experimentation | Temporary asset colocation | Contract (simple) | Safeguard (opportunism) | Risk delimitationTemporal asset specificity Affordable losses
| Contractual theory Transaction cost theory Effectuation theory
|
| Network closure | Relational (harmonization) | Safeguard (competition) | Collective climateParticipation Monitoring
| Clan theory Network theory
|
| Integration | Knowledge-based boundary objects | Relational (mutuality) | Coordination (intangible assets) | Knowledge sharingGoal and action alignment Value allocation rules
| Knowledge management Perspective taking Stewardship logic
|
| Rights allocation agreements | Contract (complex) | Coordination (goals)Safeguard (opportunism) |
| Evolution | Liaison champion | Relational (flexibility) (solidarity) | Coordination (uncertainty) | Forward adaptationInnovation Flexibility
| Proactive behavior Social exchange theory
|
In the first phase (experimentation), actors use both relational and contractual governance mechanisms to safeguard from two different exchange threats, with the final effect of delimiting risk in the new exchange environment. Temporary asset colocation helps address opportunism (vertical) threats by restricting the time and investments required in the first solution trials, thus ensuring temporal asset specificity ([46]) and affordable loss conditions ([58]). Network closure instead aims to address competition (horizontal) threats, by promoting a collective, noncompetitive climate that ensures participation and social monitoring conditions in early solution exchanges ([31]). Temporary asset colocation can be implemented through simple contracts; network closure represents a relational governance form that leverages harmonization norms.
In the second phase (integration), actors still use contractual and relational governance mechanisms but, in this case, to coordinate the goals and exchanges of intangible assets, in addition to safeguarding actors from further exchange threats ([69]). The effect is to facilitate the core, challenging process of knowledge sharing, characterized by deeper cooperation efforts but with potentially misaligned incentives. The development of boundary objects helps align actors and facilitate the integration of tacit knowledge ([10]), while the use of rights allocation agreements ensures that knowledge integration occurs in a context of shared value appropriation rules ([56]). Boundary objects represent relational forms leveraging on mutuality norms. Rights allocation agreements are implemented through complex negotiated contracts.
In the third phase (evolution), actors use one specific relational governance mechanism, which helps them coordinate their behaviors in an exchange context marked by future uncertainties, with the final effect of stimulating the coexistence of adaptation and proactivity inclinations ([14]; [26]). Establishing a liaison champion role helps actors understand when to remain responsive to their counterpart's requests and when to act to anticipate the market. This relational governance form leverages flexibility and solidarity norms.
Table 6 suggests that efficient governance matching requires a contingent, dynamic approach, possibly involving several forms of governance mechanisms to achieve safeguarding and/or coordination functions whose salience changes across various solution development phases. The complexity of this contingent and dynamic approach can be appreciated in various ways. First, ensuring safeguard in the first phase requires the use of both relational and contractual mechanisms because the threats are different. Second, the integration phase requires paying attention to safeguarding and coordination, and actors need to use both contractual and relational mechanisms to achieve coordination while the contractual form also must ensure safeguard. Third, the evolution phase requires a focus on certain coordination issues that can be addressed only by using a specific relational form of governance.
To the best of our knowledge, this study is the first to investigate solution development systematically as a governance problem. In this light, our analysis provides two main contributions with various implications for marketing literature and practice.
First, we identify key components of the governance matching sequence (conditions arising, tensions, and mechanisms) that characterize solution development. Being characterized by intense coproduction efforts and changes in the relational habits, solutions introduce actors to new exchange conditions, such as role shifting, indirect links to rivals, interdependence, and unpredictability. Such conditions generate a series of tensions among actors: collaborating while avoiding opportunism, seizing opportunities while eluding information leakage, integrating versus retaining core knowledge assets, and balancing proactivity and adaptation. Our analysis reveals that success in solution development requires the actors to address the tensions with a heterogeneous mix of contractual and relational governance mechanisms—namely, temporary colocation contracts, closed-network relational initiatives, boundary objects for knowledge sharing, rights allocation agreements, and liaison champions. A smooth and successful solution development process thus requires ongoing and careful governance matching efforts.
Second, we reveal the dynamic nature of governance matching efforts in solutions, whose development evolves through different phases separated by key decision points. In the experimentation phase, governance matching aims to safeguard actors placed in a new exchange context, by delimiting their risks and promoting a collaborative climate. In the integration phase, it instead focuses on facilitating knowledge sharing practices by coordinating actors' goals and actions while safeguarding them from opportunism. In the evolution phase, the role of governance matching shifts to promote a forward adaptation attitude among actors, coordinating their activities in the presence of unforeseen contingencies. Moving from one phase to the other is not automatic. If actors do not feel mutual commitment at the end of the first phase, solutions remain embryonic, and market-like exchanges prevail. If actors sense a power imbalance at the end of the second phase, solution development may stall or revert, because either the weaker actor prefers to engage in market-like exchanges or the stronger one leans toward vertical integration.
The aforementioned contributions generate several implications for marketing literature. In particular, we complement existing literature on solution development that mainly focuses on actors' organizational resources; we show that solution development implies a careful negotiated process at the dyadic level, in which actors suffer limited freedom and cannot unilaterally execute a strategy with only their own resources and competences. Achieving progress in solution development thus requires setting governance mechanisms that align properly with new exchange conditions in the solutions context. Our findings introduce the central role of governance matching in ensuring a successful solution development process.
As a further extension of extant literature on solutions, we reveal that their development requires a complex contingent approach to governance matching. The efficacy of single mechanisms is specific to solution phases, and in each phase, mechanisms of different forms combine to address complex emerging tensions. In this sense, our study reveals how to implement proper governance matching to ensure a successful solution development process. Such findings provide a middle-range extension of literature on the plurality of governance forms ([ 9]; [70]) in the solutions context.
Due to the complexity of governance matching, solution development also cannot be taken for granted. In this domain, our findings contribute to existing solutions literature by revealing why some development efforts evolve smoothly, while others halt (lack of mutual commitment) or, after a promising start, stall or revert to deservitization (unbalanced power). A dynamic perspective on business exchanges has long been recommended ([15]) but is rarely applied to solution development, despite recent calls ([38]). Our multipath framework in Figure 1 offers a dynamic perspective and also responds to broader calls for dynamic models that outline how business relationships evolve in nonlinear fashions in specific contexts ([35]; [75]), such as those in which solutions are expected to evolve.
In summary, this study contributes to existing solutions literature by detailing
- The relevant but overlooked role of governance matching in solution development.
- What it means for actors to execute the challenging, evolving activity of governance matching.
- What happens when governance matching is not achieved.
Some of these findings also inform broader literature streams pertaining to the plurality of governance forms and dynamic models of business relationships.
Despite its qualitative nature, our study features heterogeneous markets, includes a broad sampling, and is characterized by a managerial focus, so that it can produce some practical implications for solution developers. At the managerial level, the core insight of our study is that the main goal for solution actors is to systematically resolve, with proper governance mechanisms, the evolving tensions that originate from solution exchanges' specific features. Developing firm-level capabilities is not enough to succeed, because solutions do not "naturally" evolve, and without ensuring a complex activity of governance matching, actors do not have the proper incentives to engage in the bilateral value creation activities that are at the heart of solution development.
As a first implication from this major insight, our investigation provides practical guidelines to properly execute the governance matching activity, which consists of a series of "governance engineering" tasks ([21]) that actors should include in their managerial agenda. The first of these tasks is monitoring the exchange conditions, which means systematically checking the status of the relation with the solution counterpart. Some (successful) dyads in our sample stress the importance of being always vigilant on tracking the changing conditions that may characterize the relationship with the solution counterpart and create various occasions of interaction to this purpose:
We need to continuously monitor how it is going with the client: the challenge is to getting used to examine and reexamine the state of the relationship, which may vary frequently and suddenly. (Sup_4, Dyad 6)
To understand the evolution of an important relationship we organize periodic customer-specific initiatives called "open days," in which we invite our partner, and sometimes its clients, and talk about everything may concern current and future exchanges. (Sup_4, Dyads 4 and 5)
Being vigilant is important for solution developers, but our results indicate that when tensions inevitably emerge, actors need to succeed in a second governance engineering task: designing a coherent set of mechanisms. As described previously, this goes beyond engaging in the simple choice among single mechanisms by trading off their individual characteristics, but reflects the capability to design proper combinations of contractual and relational mechanisms to handle the complex and evolving tensions that characterize solutions development. Executing design tasks requires actors to mobilize, and sometimes combine, the various types of knowledge (technical, managerial, and legal) necessary to ensure governance matching across the various phases of solution development. The following quote reflects this circumstance.
Not being able to manage conflicts and mediate positions with your solution counterpart is often a problem. Ideally, who manages the relation should possess some technical knowledge, a business/economics attitude, and legal expertise: sometimes you need a team. (Sup_1, Dyads 1 and 2)
Our results also indicate that the tasks of being vigilant and designing mechanisms should be put in a dynamic perspective: solution developers need to periodically assess the development process, evaluate its progress, and take actions accordingly. Our framework/road map depicted in Figure 1 points out two criteria, mutual commitment and balanced power, which can concretely help solution actors to decide whether to engage in the next steps of the development process because without achieving governance matching, continuing the solution development may not be in the best interest of the actors. The importance of developing a managerial tool to ensure dynamic assessment and informed decisions concerning solution development has also been recalled by our informants:
We and our clients would love to have a framework, a dashboard to systematically evaluate our behaviors in these innovative solution projects, to learn where each of us excels and especially what is going wrong...in order to learn from the past and be able to replicate. (Sup_3, Dyads 4 and 5).
In addition to the development of governance engineering tasks, a second practical implication of our study regards the role of existing ties in implementing governance matching. Solution actors face radical changes in their relational habits, because the start of the development process forces them to reframe the relational approaches they have used with business partners. Thus, contrary to the majority of cases in business relations, to succeed in solutions development, actors cannot rely on existing ties or capitalize on prior relationships; they even could act as obstacles to change. Rather, they must reestablish mechanisms to manage their relations from scratch. A quote from our third workshop with the supplier nicely depicts this need:
We have noticed that it is easier for us to sell solutions to our new customers, because there is no preexisting standard and thus we can set up the relationship from the beginning, based on that business model. With our existing customers, instead, we face more difficulties since they prefer to stick to the schemes they already know and want to continue with the "traditional" supply. (Manager of the supplier company).
Accordingly, to succeed in solution development, managers should adopt a learning-by-doing approach, characterized by trials and real-time adjustments to deal with the transient nature of the benefits associated with governance mechanisms. As described previously, learning by doing implies that actors engage in periodic structural decisions about whether to continue the solutions development, depending on the extent to which some mutual, intermediate outcomes have been reached. Thus, evolving towards solution offers is by no means a "no brainer," as some observers still speculate ([55]), and actors' agency mainly determines why some solutions never take off, others stall after a promising start, and still others revert to deservitization.
In summary, from a managerial standpoint, solution development requires actors to ( 1) include governance engineering activities (monitor exchange conditions, design sets of mechanisms, and assess the development process) in their managerial toolkit to achieve the challenging governance matching process and ( 2) modify their approach to business relations by overcoming existing ties and establishing new rules for relationships, in a learning-by-doing fashion.
As with any qualitative inquiry, this study is limited in its capability to support empirical generalizations. Our purposive sample represents a wide range of markets, yet it was dominated by representatives of the automotive industry, so our results can be generalized only theoretically ([74]). Large-scale, quantitative studies should try to test the validity of our findings regarding the role of aligned governance mechanisms. For example, studies might investigate if boundary objects actually facilitate knowledge sharing or if closed-network participation really contributes to opportunity seizing. With our purposive sampling logic, the results also provide only preliminary insights into change and transition processes. The vast time required for such processes means that investigating these aspects would demand a detailed, longitudinal case analysis, which could add new, micro-level evidence of the shifts from one phase to another.
Another limitation stems from the contingent value of our findings, which may be highly idiosyncratic to each solution case and its specific exchange features. Other relationship contexts, less characterized by high coproduction or changes in relational habits, may require different (simpler) governance matching activities. Some of the mechanisms we identify also might be redundant or even inefficient. For example, not all vertical relations expose customers to potentially harmful indirect links to rivals; network closure mechanisms likely would not be necessary if the supply network were sparse. Similarly, boundary objects are critical when knowledge assets are substantially involved, but exchanges mainly based on products or components may not need this governance mechanism and instead could be ruled by simple contracts that detail the economic terms and nondisclosure agreements. In contexts in which exchanges are relatively repetitive, a key account manager might be sufficient to manage the main issues as they evolve. To this point, it would be interesting to extend our analysis to other empirical settings, such as those characterized by different supply chain structures or distinct governance problems.
Supplemental Material, jm.18.0351-File003 - Dynamic Governance Matching in Solution Development
Supplemental Material, jm.18.0351-File003 for Dynamic Governance Matching in Solution Development by Laura Colm, Andrea Ordanini and Torsten Bornemann in Journal of Marketing
Footnotes 1 Associate EditorJan Heide
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The first author acknowledges financial support from project funds on Marketing-Procurement interaction at the University of Stuttgart.
4 Online supplement: https://doi.org/10.1177/0022242919879420
5 1When we discuss solution development challenges across the various phases, we primarily draw on data from dyads that have experienced those phases, though we also include quotes that foreshadow future challenges or recount past challenges by actors in different phases.
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Effect of Alliance Network Asymmetry on Firm Performance and Risk
Persistent high failure rates of new product alliances call for identification of factors that might improve alliance outcomes. In this research, the authors identify two attributes of alliance network asymmetry that affect alliance performance and performance uncertainty: differences in the number of prealliance direct ties, which can create asymmetry in the volume of resources of the two firms, and differences in the interconnectivity among prealliance indirect ties, which leads the firms to possess different types of resources. The authors theorize that absolute levels of such asymmetries have curvilinear effects on alliance performance and performance uncertainty, which materialize as a focal firm's abnormal returns and risk, respectively. They demonstrate that direct tie asymmetry has an inverted U-shaped effect on the focal firm's abnormal returns and a U-shaped effect on its risk. Indirect tie asymmetry also has a U-shaped effect on the focal firm's risk. However, the focal firm's innovation quality and preexisting ties with its partner flatten these curvilinear effects. The findings have implications for partner selection in new product alliances.
Keywords: abnormal returns; alliance network asymmetry; firm risk; interdependence asymmetry; prealliance ties
More than 50% of new product alliances fail to achieve their desired objectives (e.g., [20]; [38]; [46]). In consideration of such high numbers, scholars have examined factors that improve chances of alliance success (e.g., [23]; [65]). Yet the persistent high failure rates affirm the continued relevance of research that identifies specific factors that might improve the likelihood of alliance success. In this study of new product alliances, we use the theoretical lens of interdependence asymmetry ([49]) to identify prealliance network ties between a firm (hereinafter "focal firm") and its alliance partner (hereinafter "partner firm") as important to alliance performance and uncertainty, which are established metrics of alliance success ([42]; [98]).
We focus on two attributes of prealliance network ties: direct tie asymmetry, which we define as the absolute difference in the number of direct ties between two firms forming an alliance relationship, and indirect tie asymmetry, which we define as the absolute difference in the interconnectivity of indirect ties between two firms forming an alliance relationship. Our use of asymmetry as an absolute difference is much in line with the findings in prior empirical studies on interfirm interdependence asymmetry. Most studies that consider directional levels of asymmetry conclusively show that asymmetry in interfirm relationships affects the fundamental relationship and therefore triggers similar attitudes and behaviors from both firms, regardless of which firm the asymmetry favors (e.g., [36]; [48], [49]; [87]). Other studies have examined absolute levels of interdependence asymmetry and concluded that asymmetry affects the relationship, and therefore both firms react similarly to the asymmetry (e.g., [30]). Thus, in our context of alliance relationships, our definitions of direct and indirect tie asymmetry in absolute rather than directional terms are appropriate. We also provide multiple robustness tests in subsequent sections to establish face validity of absolute levels versus directional levels of asymmetry.
First, we discuss direct tie asymmetry. The number of a firm's direct ties correlates strongly with the volume of resources the firm can access from its network. These resources mainly comprise institutional knowledge resources dominant in the network ([40]) and relational resources, such as social legitimacy and attractiveness as a potential alliance partner ([ 1]). For example, in the network of alliances presented in Figure 1, Boehringer Ingelheim has more direct ties than its partner Retractable Technologies. Thus, relative to its partner, Boehringer Ingelheim not only has access to more institutional knowledge from the network, due to direct access to a greater number of alliance partners, but also has more social visibility, which makes it a more attractive potential partner to many other firms in the network.
Graph: Figure 1. Network asymmetries between Retractable Technologies and Boehringer Ingelheim.
Second, in addition to direct ties, we acknowledge the strategic importance of firms' indirect ties (i.e., partners' partners) (e.g., [ 9]; [23]). The strategic nature of a firm's indirect ties stems from their interconnectivity ([89]). If indirect ties are closely interconnected, fewer disconnected clusters are in the firm's network. Such a position in the network has benefits similar to that of a highly interconnected network position (i.e., access to institutional knowledge resources in the network); it also has costs, such as limited access to breakthrough knowledge resources ([ 9]; [28]), which are critical for breakthrough innovations. Given the strategic importance of interconnectivity among indirect ties, we examine how indirect tie asymmetry (i.e., the absolute difference in the extent of interconnectivity among indirect ties of two firms) at the time of alliance formation affects the alliance relationship. We provide an illustration of indirect tie asymmetry in a newly formed alliance between Retractable Technologies and Boehringer Ingelheim in Figure 1. While some indirect ties of Retractable Technologies are positioned in relatively disconnected clusters (e.g., Biovail Corporation, Thoratec Corporation), many indirect ties of Boehringer Ingelheim are located in highly interconnected clusters (e.g., Novo Nordisk, Schering-Plough). Thus, given their relative positions in the network, Retractable Technologies has access to breakthrough knowledge resources, while Boehringer Ingelheim has access to institutional or dominant knowledge resources.
In this study, we are theoretically interested in how direct and indirect tie asymmetry in the alliance create interdependence asymmetry in the relationship. Direct tie asymmetry creates interdependence asymmetry in the alliance because one firm possesses a higher volume of resources than the other. However, indirect tie asymmetry creates interdependence asymmetry in the alliance for different types of resources (i.e., a firm with institutional knowledge resources depends on the partner for breakthrough knowledge resources, and vice versa). Interdependence asymmetry in the relationship offers both benefits and costs to the alliance. On the one hand, interdependence asymmetry creates resource reliance between a focal firm and its partner, which in turn facilitates coordination and mutual commitment that not only increase alliance performance (e.g., [70]) but also reduce uncertainties of alliance performance (e.g., [16]). On the other hand, too much interdependence asymmetry may create power imbalance, fostering mistrust and a lack of commitment between the firms ([30]) and thereby jeopardizing alliance performance and making it unstable (e.g., [32]). As alliance performance will naturally affect the extent to which the alliance generates financial returns for a focal firm, we use the focal firm's financial performance as our first dependent variable. In addition, uncertainty in alliance performance will manifest itself in the volatility of the focal firm's financial returns from the alliance. Thus, we use the focal firm's financial performance uncertainty as our second dependent variable.
In summary, we argue that both direct and indirect tie asymmetry are sources of benefits and costs in an alliance, the former due to differences in the volume of resources and the latter due to differences in the type or nature of resources to which each firm in the alliance has access. Corroborating the presence of both benefits and costs of direct and indirect tie asymmetry, our results indicate that direct tie asymmetry has an inverted U-shaped effect on the focal firm's financial performance, and both direct and indirect tie asymmetry have U-shaped effects on the focal firm's financial performance uncertainty. We also identify important contingencies that can have an impact on the effects of direct and indirect tie asymmetry. We propose that the focal firm's motivation to maintain the alliance relationship with the partner serves as a contingency. As long as the focal firm is motivated to maintain the alliance relationship with its partner, it will have incentives to mitigate the costs of the relationship from direct and indirect tie asymmetry, and consequently the effects of tie asymmetry on alliance outcomes will improve ([86]). The focal firm's motivation to maintain the alliance relationship might stem from the importance it gives to interfirm relationships as a source of learning (as measured by its innovation quality; [12]), as well as from the extent to which the firm and its partner have interdependencies outside the focal alliance due to preexisting ties (as measured by total interdependence of the focal firm and the partner; [45]). Our results indicate that innovation quality and total interdependence significantly moderate (i.e., flatten) the curvilinear effects of direct and indirect tie asymmetry.
This research contributes to the marketing literature in three ways. First, we add to the strategic alliance literature that examines the effect of alliance attributes on the focal firm's performance (for a literature review, see Table 1). Whereas prior research has suggested that individual network ties influence a focal firm's performance (e.g., [65]; [98]), we contend that the network ties of both firms in the alliance should be considered simultaneously. At the level of the dyad, we can incorporate the notion of asymmetry in direct and indirect ties, the levels of which can significantly contribute to the success of the alliance. For example, when alliance success is measured in terms of market capitalization ($) of a focal firm, we find that the focal firm improves its market capitalization by $86.9 million by choosing an alliance partner with moderate (50th percentile) rather than low (25th percentile) asymmetry in direct ties. This improvement in market capitalization is significantly less (only $67.9 million) if the firm chooses an alliance partner with high (90th percentile) rather than low (25th percentile) direct tie asymmetry.
Graph
Table 1. Contributions to the Literature on Strategic Alliances and Interfirm Relationships.
| Study | Dependent Variable | Own/Partner Network | Dyad-Level Construct | Network as Source of: | Interdependence Asymmetry |
|---|
| Returns | Risk | Benefit | Cost | Interdependence Asymmetry | Operationalized | Absolute Measure |
|---|
| Furlotti and Soda (2018) | No | No | No/no | Yes | No | No | Yes | Yes | Yes |
| Bos, Faems, and Noseleit (2017) | Yes | No | No/no | No | No | No | No | No | No |
| Griffith et al. (2017) | No | No | No/no | No | No | No | No | No | No |
| Fang, Lee, Palmatier, and Han (2016) | Yes | No | No/no | No | Yes | No | No | No | No |
| Fang, Lee, Palmatier, and Guo (2016) | Yes | No | Yes/no | No | Yes | Yes | No | No | No |
| Fang, Lee, and Yang (2015) | Yes | No | Yes/yes | No | No | No | No | No | No |
| Mani and Luo (2015) | Yes | Yes | Yes/no | No | Yes | Yes | No | No | No |
| Scheer, Miao, and Palmatier (2015) | No | No | No/no | No | No | No | No | Yes | No |
| Thomaz and Swaminathan (2015) | No | Yes | Yes/yes | No | Yes | Yes | No | No | No |
| Yang et al. (2014) | Yes | No | Yes/no | No | Yes | No | No | No | No |
| Cui (2013) | No | Yes | No/ no | No | No | No | No | No | No |
| Lahiri and Narayanan (2013) | Yes | No | No/no | No | No | No | No | No | No |
| Lavie, Kang, and Rosenkopf (2011) | Yes | No | No/no | No | No | No | No | No | No |
| Xiong and Bharadwaj (2011) | Yes | No | Yes/no | No | No | No | No | No | No |
| Scheer, Miao, and Garrett (2010) | No | No | No/no | No | No | No | No | No | No |
| Lin, Yang, and Arya (2009) | Yes | No | Yes/yes | No | Yes | No | No | No | No |
| Sarkar, Aulakh, and Madhok (2009) | Yes | No | No/no | No | No | No | No | No | No |
| Swaminathan and Moorman (2009) | Yes | No | Yes/yes | No | Yes | No | No | No | No |
| Grewal et al. (2008) | Yes | No | No/no | No | No | No | No | No | No |
| Lavie and Miller (2008) | Yes | No | No/no | No | No | No | No | No | No |
| Gulati and Sytch (2007) | No | No | No/no | No | No | No | No | Yes | No |
| Kalaignanam, Shankar, and Varadarajan (2007) | Yes | No | No/no | No | No | No | No | No | No |
| Lin, Yang, and Demirkan (2007) | Yes | No | Yes/no | No | Yes | Yes | No | No | No |
| Luo, Rindfleisch, and Tse (2007) | Yes | No | No/no | No | No | No | No | No | No |
| Goerzen and Beamish (2005) | Yes | No | Yes/no | No | No | Yes | No | No | No |
| Wuyts, Dutta, and Stremersch (2004) | Yes | No | No/no | No | No | No | No | No | No |
| Kumar, Scheer, and Steenkamp (1998) | No | No | No/no | No | No | No | No | Yes | No |
| Geyskens et al. (1996) | No | No | No/no | No | No | No | No | Yes | Yes |
| Kumar, Scheer, and Steenkamp (1995) | No | No | No/no | No | No | No | No | Yes | No |
| This study | Yes | Yes | Yes/yes | Yes | Yes | Yes | Yes | Yes | Yes |
Second, studies suggest that alliances are characterized by differences in firm size ([33]), financial resources ([104]), and innovation resources ([83]), all of which represent typical sources of interdependence asymmetries in alliances. Our research contributes to the strategic alliance literature by bringing a network perspective to the notion of interdependence asymmetry. Such a perspective has substantive implications for a focal firm in terms of its choice of an alliance partner. For example, in context of new product alliances in the biopharmaceutical industry, our results indicate that a focal firm with high innovation quality can gain $87.3 million and a focal firm with preexisting ties with the partner can gain $99.03 million in market capitalization, when considering a potential partner with which it has a moderate difference in direct ties (i.e., 50th percentile) versus another potential partner with which it has a low difference in direct ties (i.e., 25th percentile). Furthermore, a firm can reduce the financial risk of an alliance by 68.7% by choosing an alliance partner with which it has moderate (50th percentile) rather than low (25th percentile) indirect tie asymmetry. Thus, in terms of both financial returns and risk reduction, a focal firm may be able to improve its chances of alliance success by selecting partners with specific prealliance network asymmetry.
Third, unlike many prior interfirm relationship studies that treat interdependence asymmetry as a directional construct (e.g., [87]), we show that absolute levels of interdependence asymmetry are meaningful because, regardless of which firm the direct and indirect tie asymmetries favor, it is the alliance that is affected. Although alliance performance is unobservable in our data, the focal firm's financial outcomes reflect the alliance's performance.
The number of a firm's direct ties in a network directly correlates with the volume of resources it can access from its network, such as institutional knowledge ([26]; [40]) and social legitimacy ([ 1]; [79]). When the focal firm and the partner firm possess similar number of direct ties, they are in a symmetric position of interdependence for resource volume, as both firms are equally dependent on each other. A movement in either direction (toward the focal or partner firm) from this position of equivalence will create direct tie asymmetry. As we theorize subsequently, in either direction, direct tie asymmetry has benefits and costs for the alliance and consequently affects the focal firm's performance outcomes.
As direct tie asymmetry increases moderately in either direction, interdependence asymmetry also increases moderately (i.e., one firm increasingly relies on the other for supplementing its lower supply of resources). With this resource reliance, the less resourceful firm may readily accept the other firm's expectations of resource sharing and alliance routines ([104]), which should facilitate its collaboration in the alliance to the benefit of the other firm. Such reliance should also strengthen cooperation in the alliance, because the less resourceful firm gains access to larger alliance networks with proportional resource advantages. As both firms stand to gain, increases in interdependence asymmetry due to direct tie asymmetry will benefit the alliance, thereby incentivizing both firms to commit to the alliance. Several studies have shown that if both firms are committed to an alliance, less miscommunication and more mutual cooperation occur ([61]; [72]), which should not only make the alliance stable (i.e., reduce the uncertainty in alliance performance) ([16]) but also increase alliance performance ([70]). Reduced uncertainty in alliance performance implies less volatility in returns from the alliance, which should naturally engender less financial performance uncertainty of the focal firm. Increases in alliance performance should increase the returns from the alliance for the focal firm, which should naturally yield greater financial performance for the focal firm.
However, as interdependence asymmetry becomes increasingly skewed toward either firm, the resulting power imbalance may be high enough to give rise to costs at an increasing rate. As a result, the more powerful firm (regardless of whether it is the focal or the partner firm) may unfairly dictate ongoing alliance terms, knowing that the less powerful firm is more likely to acquiesce to its demands. It may coerce the less powerful firm to accept norms extended from its own network, even though they may not complement norms embedded in the less powerful firm's network. Furthermore, high power imbalance sets the stage for moral hazard on the part of the more powerful firm (i.e., appropriating benefits without proportional efforts) ([44]). Such behavior might provoke the less powerful firm's distrust and consequent lack of commitment to the alliance, invariably jeopardizing the performance of the alliance ([32]). Power imbalance also makes the alliance unstable, due to disruptive communication between the two firms and conflicting approaches toward alliance goals ([32]). Thus, regardless of whether the power imbalance favors the focal firm or the partner, the alliance is negatively affected. In other words, at high levels of direct tie asymmetry, the costs of interdependence asymmetry can outweigh the benefits for the alliance, thereby decreasing the focal firm's financial performance and increasing its financial performance uncertainty.
A case in point is the new product alliance between Roche (a multinational pharmaceutical firm) and Trimeris (a domestic biotechnology firm) in 1999. Roche had a declining HIV (human immunodeficiency virus) pipeline, which it wanted to replenish using Trimeris's HIV-related expertise in Fuzeon technology. Although both firms were large, Roche clearly had more external resources, with approximately 150 research-and-development (R&D) collaborations. Trimeris, though a biotechnology firm with high internal R&D expertise, had fewer R&D collaborations at the time. Roche actively supported Trimeris in developing drugs in the Fuzeon portfolio until problems arose from declining profits of the portfolio in 2004. Roche unilaterally put a sudden hold on clinical developments in the Fuzeon portfolio after assessing Fuzeon's role in its own long-term performance. This decision put the alliance in jeopardy, and analysts began predicting the losses each firm might incur from a potentially failed alliance ([100]). Although collaborations were renewed almost completely on Roche's terms ([56]), this example reveals how both firms gained from interdependencies, but at the cost of power plays that adversely affected alliance performance.
In summary, as the direct tie asymmetry increases, at first the focal firm's financial performance increases and related uncertainty decreases. However, after a certain threshold level, the effects reverse; the focal firm's financial performance decreases and related uncertainty increases. Thus,
- H1: An increase in direct tie asymmetry leads to (a) an inverted U-shaped effect on the focal firm's financial performance and (b) a U-shaped effect on its financial performance uncertainty.
High interconnectedness of indirect ties of a firm indicates that the firm has access to abundant institutional knowledge resources embedded in a network (e.g., [ 9]; [28]). Institutional knowledge resources refer to overlapping technological know-how across most firms in the network ([77]) and to established norms of commercialization routines and innovation techniques in the network ([43]). By contrast, low interconnectedness of indirect ties indicates the presence of relatively disconnected firms in the network that are hubs of breakthrough knowledge ([ 9]; [28]). Moving to a dyadic perspective, when both the focal firm and the partner firm have similar interconnectedness among their indirect ties, access to institutional and breakthrough knowledge resources may also be similar. From this equivalent position, indirect tie asymmetry increases in either the direction of the focal firm or that of the partner firm.
A moderate increase in indirect tie asymmetry should give one firm access to more institutional knowledge resources and the other more breakthrough knowledge resources. In this sense, a moderate increase in indirect tie asymmetry should strengthen the interdependence of the alliance, as each firm depends on the other for unique knowledge resources. Consequently, both the focal firm and the partner firm will expend effort to commit to the alliance. Mutual commitment leads to less miscommunication and uncertainty about each other's approach toward the alliance, thereby facilitating alliance stability and increasing alliance performance ([61]; [70]). In turn, greater alliance stability should lower volatility in alliance returns and consequently lessen the focal firm's financial performance uncertainty. Increased alliance performance should increase returns from the alliance, thereby increasing the focal firm's financial performance.
The alliance between Sanofi (a multinational pharmaceutical firm) and Alnylam (a large biotechnology firm) initiated in 2014 is a case in point. Alnylam was developing breakthrough genomics innovations, and most pharmaceutical firms, including Sanofi, wanted a slice of the genomics market in 2014. We assume that the breakthrough nature of Alnylam's innovations partly resulted from its position in a network with few interfirm connections. This position is rather likely, as the alliance with Sanofi was one of Alnylam's initial R&D collaborations with a highly interconnected firm ([11]). Later in 2017, although the Food and Drug Administration suspended late-stage clinical trials, and prospects for a breakthrough hemophilia drug developed through the alliance seemed shaky, Sanofi kept its resource commitment and continued to actively back the alliance. This example shows how more interconnected, resourceful firms might be dependent on alliance partners that provide access to breakthrough knowledge resources.
However, when the knowledge resources of both alliance partners are too dissimilar, the firms are unable to leverage each other's expertise because of a lack of common ground ([50]; [76]). For example, when the types of knowledge resources diverge substantially between the firms, the R&D department of the firm with more institutional knowledge resources is unable to understand or communicate effectively with the R&D department of the firm with more breakthrough knowledge resources ([14]; [101]). The resulting lack of knowledge assimilation leads to mutual dissatisfaction, uncertainty in alliance goals, and lack of cooperation ([75]). A classic case study is the alliance between Alza Corporation, a start-up with cutting-edge advanced drug delivery systems, and Ciba-Geigy, a multinational pharmaceutical firm. The divergent knowledge resources became the core problem between these two firms. In the first two years of the alliance, Ciba-Geigy could not understand Alza's path of technology development, and as a result, it objected to expenses devoted to the development of the path-breaking technology initiated by Alza. In turn, Alza complained about Ciba-Geigy's lack of understanding of the technology. The alliance formally terminated after five years ([39]).
At high levels of indirect tie asymmetry between the focal firm and its partner, although the firm closest to highly interconnected clusters has substantial access to institutional knowledge resources, it may struggle to assimilate the breakthrough nature of knowledge resources of the partner firm. In turn, this struggle might lower its perceived value of the partner firm. Although the inability to assimilate breakthrough knowledge resources represents growth-related opportunity costs ([96]), immediate firm survival is rarely at stake because of buffers from institutional resources, such as dominant knowledge patterns and innovation protocols that facilitate product commercialization ([60]; [67]). By contrast, the firm with access to more breakthrough knowledge resources will have higher immediate opportunity costs if it loses access to institutional knowledge resources, which represent real-time lifelines for the survival of firms typically positioned in disconnected parts of the network ([47]). This scenario likely tilts interdependence asymmetry in favor of the firm with access to more institutional knowledge resources, which creates a power imbalance in the alliance. This imbalance has costs for the alliance, due to the more powerful firm losing interest and the resulting mistrust of the less powerful firm. Thus, costs from the increased likelihood of one-sided power plays and the consequent lack of mutual commitment rise at an increasing rate, leading to increased alliance instability and decreased alliance performance. Correspondingly, at high levels of indirect tie asymmetry, the alliance suffers because costs outweigh the benefits, thereby increasing the focal firm's financial performance uncertainty and decreasing its financial performance.
In summary, as indirect tie asymmetry increases, at first the focal firm's financial performance uncertainty decreases and financial performance increases. However, after a threshold level, the effects reverse. Therefore, we propose a nonlinear relationship:
- H2: An increase in indirect tie asymmetry leads to (a) an inverted U-shaped effect on the focal firm's financial performance and (b) a U-shaped effect on its financial performance uncertainty.
We summarize the theoretical mechanisms for the main effects of direct tie asymmetry (H1) and indirect tie asymmetry (H2) in Table 2. We present the conceptual framework in Figure 2.
Graph: Figure 2. Conceptual framework.
Graph
Table 2. Theoretical Mechanism for Main Effects (H1 and H2).
| Level | Mechanism | Implications | Outcome |
|---|
| Direct Tie Asymmetry (absolute difference of number of direct ties of focal and partner firm) |
| Low | Focal firm and partner have similar volume of institutional knowledge resources and relational resources that they can access from their direct ties. | No interdependence asymmetry | No effects |
| Medium or Optimum | One firm has a higher volume of institutional knowledge resources and relational resources. Firm with more resources supports partner, and in turn partner accepts terms and conditions of the former. Thus, both firms benefit. | Interdependence asymmetry provides mutual benefit | Alliance performance increases, uncertainty in alliance performance declines |
| High | Volume of institutional knowledge resources and relational resources is completely skewed in favor of one firm. | Creates power imbalance. Costs due to moral hazard by more powerful firm and mistrust on the part of the less powerful firm outweigh mutual benefits. | Alliance performance weakens, uncertainty in alliance performance increases |
| Indirect Tie Asymmetry (absolute difference of interconnectivity of focal and partner firms' indirect ties) |
| Low | Focal firm and partner have similar access to both institutional knowledge resources and breakthrough knowledge resources. | No interdependence asymmetry | No effects |
| Medium or optimum | One firm has access to more institutional knowledge resources, and other firm has access to more breakthrough knowledge resources. The unique but different types of knowledge resources are complementary, thus both firms benefit. | Equivalent interdependence provides mutual benefit | Alliance performance increases, uncertainty in alliance performance declines |
| High | The institutional knowledge resources of one firm and breakthrough knowledge resources of the other firm are too distant for the firms to assimilate. Inability to assimilate has higher immediate opportunity costs for firm with breakthrough knowledge resources than for firm with institutional knowledge resources. | Interdependence asymmetry is unbalanced as the firm with institutional knowledge resources loses interest, and the firm with breakthrough knowledge resources remains dependent. Costs from resulting power imbalance become prominent, such as moral hazard of more powerful firm and mistrust on part of less powerful firm. | Alliance performance weakens, uncertainty in alliance performance increases |
We apply a basic principle of the interfirm relationship literature that, regardless of differences, a focal firm will actively cooperate in an interfirm exchange with a partner as long as it is motivated to maintain the relationship (e.g., [87]). This perspective suggests that the focal firm will likely cooperate (i.e., try to minimize costs of asymmetries), insofar as it is motivated to maintain the alliance relationship with the partner firm. In the case of high direct tie asymmetry, costs arise because the power imbalance created by large differences in resource volume ultimately results in mistrust in the alliance. In this scenario, if a focal firm (regardless of its resource volume) is motivated to maintain the alliance relationship, it will proactively seek ways to resolve the mistrust. Similarly, in the case of high indirect tie asymmetry, costs arise from the firms' inability to assimilate each other's unique knowledge resources. In this scenario, if the focal firm (regardless of its resource type—institutional or breakthrough knowledge) is motivated to maintain the alliance relationship, it will proactively resolve barriers to assimilating its partner's knowledge resources.
We propose that two factors influence the focal firm's motivation to maintain the interfirm relationship in an alliance: innovation quality and total interdependence. First, firms that focus on innovation quality often depend on the exchange of complex and tacit know-how that is only possible in informal interfirm relationships ([13]). Firms with high (vs. low) innovation quality view interfirm relationships in alliances as critical to their innovation outcomes ([12]). Consequently, the motivation to maintain any alliance relationship should be greater in firms with high innovation quality. Second, total interdependence is the extent to which the firms contribute to each other's performance through preexisting ties beyond the specific alliance ([45]). Thus, the focal firm should be more motivated to maintain the alliance with the partner firm if it has other preexisting relationships outside this specific alliance.
Innovation quality refers to the extent to which knowledge generated by the focal firm is useful to future knowledge generation within and across domains ([51]). Firms that focus on innovation quality value external knowledge sources, especially informal interfirm relationships, as they are vital for the transfer of tacit external knowledge ([13]). The quality of a firm's innovation depends on the extent to which the firm recognizes the potential impact of external knowledge by interacting informally with alliance partners ([ 2]). Social relationships are the most critical ingredient in high-quality ideas ([ 7]). Firms with high innovation quality value informal learning from external knowledge sources more than firms with low innovation quality ([99]). By definition, informal learning from external sources requires taking time to develop informal relationships with other firms (e.g., alliance partners). As a result, a focal firm with high (vs. low) innovation quality is more likely to be motivated to maintain an alliance relationship and expend efforts to mitigate costs arising from direct and indirect tie asymmetries. Given higher cost mitigation, focal firms with high innovation quality are less likely to see the benefits from tie asymmetries taper off beyond a threshold. As a result, the nonlinear effects of direct and indirect tie asymmetries (H1 and H2) on the focal firm's performance outcomes are flattened when the firm has high (vs. low) innovation quality.
- H3: As a focal firm's innovation quality increases, (a) the nonlinear effect of direct tie asymmetry on financial performance flattens, (b) the nonlinear effect of direct tie asymmetry on financial performance uncertainty flattens, (c) the nonlinear effect of indirect tie asymmetry on financial performance flattens, and (d) the nonlinear effect of indirect tie asymmetry on financial performance uncertainty flattens.
Total interdependence refers to the extent to which the focal firm and the partner firm rely on each other, beyond the specific alliance, to achieve performance outcomes. Total interdependence reflects the overall extent to which two firms are enmeshed in each other ([45]) and can occur from multiple active alliance relationships and a high degree of mutual learning beyond the specific alliance. Total interdependence between firms serves as a relational platform that supports new exchanges between them ([64]; [87]). A breakdown of any single exchange can potentially cascade to all other exchanges between the firms. Thus, if the focal firm and the partner firm are interdependent outside the alliance, the focal firm should remain motivated to maintain this specific alliance relationship (i.e., the focal firm is more likely to mitigate costs arising from direct and indirect tie asymmetries to facilitate the alliance relationship). As total interdependence increases, due to higher cost mitigation, the focal firm is less likely to see the benefits from tie asymmetries taper off beyond a threshold. Thus, as the degree of total interdependence between the focal firm and the partner increases, the nonlinear effects of direct and indirect tie asymmetries (H1 and H2) on the focal firm's performance outcomes are flattened.
- H4: As total interdependence between the focal firm and partner increases, (a) the nonlinear effect of direct tie asymmetry on financial performance flattens, (b) the nonlinear effect of direct tie asymmetry on financial performance uncertainty flattens, (c) the nonlinear effect of indirect tie asymmetry on financial performance flattens, and (d) the nonlinear effect of indirect tie asymmetry on financial performance uncertainty flattens.
We collected alliance data from Knowledge Express, which provides alliance information in the biopharmaceutical industry. This industry has high rates of alliance failure (58%) ([46]), but the value of interfirm networks is well acknowledged ([65]; [93]). The raw data in Knowledge Express include new product alliance announcements. Although the database reports termination dates, many alliances may go inactive without issuing formal notices of termination. To account for this possibility, we rely on alliance initiation dates to arrive at our final sample. For a network matrix at year t, we consider only alliances that initiated as far back as five years, thus assuming an average alliance duration of five years (e.g., [88]). The sample consists of 2,483 alliance observations, including 1,507 firms over 17 years (1997–2013).[ 6] Of the two firms in an alliance, we define a firm as focal if either it is the only public firm in the relationship (about 21% of observations) or, when both firms are public, the firm reportedly initiated the new product alliance in the announcement (about 7% of observations).
We gather financial performance measures, financial performance uncertainty measures, and other financial information from COMPUSTAT and CRSP. We collect patent data from the U.S. Patent and Trademark Office. The final sample consists of 452 firms and 708 alliances. We provide descriptions and measures of all constructs in Table 3.
Graph
Table 3. Variable Description and Operationalization.
| Variable | Operationalization | Reference |
|---|
| Dependent Variables | | |
| Financial performance | (see Web Appendix A for details) | Sorescu, Chandy, and Prabhu (2007) |
| Financial performance uncertainty | Idiosyncratic risk: Standard deviation of estimated residuals of Fama–French three-factor model over one-year postalliance initiation. | Luo and Bhattacharya (2009); Rego, Billet, and Morgan (2009) |
| Independent Variables | | |
| Direct tie asymmetry | We calculate separately the degree centrality of focal firm i and partner firm j at t − 1 and divide each by their sales at t − 1. Then, we take the absolute difference. | Swaminathan and Moorman (2009) |
| Indirect tie asymmetry | We calculate the degree of "network constraint" of the focal firm i and partner firm j of each alliance in t − 1 , where if i and j are connected, if i and j are not connected, and is the total number of direct connections of firm i. Then, we take the difference by subtracting the degree of network constraint of firm j from that of firm i. We then use the absolute value of this difference. | Buskens and Van de Rijt (2008) |
| Moderating Variables | | |
| Innovation quality | Number of granted patents at t − 1 weighted by the extent of forward citations (to a maximum period of five years) each patent received (results also robust to seven-year period). | Lahiri (2010); Lanjouw and Schankerman, (2004) |
| Total interdependence | (Total current alliances of focal firm i with partner j/total no. of current alliances of focal firm i)t − 1 + (total current alliances of partner j with focal firm i/total current alliances of partner firm j)t − 1. The focal firm's dependence on another firm is based on the other firm's replaceability and contribution to performance (Emerson 1962). Thus, in our context, if alliances with a partner comprise a higher proportion of total current alliances, the partner should be less replaceable and the partner's contribution to the focal firm's performance should be higher. The same logic applies for the partner. | Adapted from Geyskens et al. (1996) |
| Control Variables | | |
| R&D absorptive capacity | Derived from an input/output stochastic frontier estimation with R&D know-how of focal firm as dependent variable at t − 1 (for details, see Web Appendix B) | Narasimhan, Rajiv, and Dutta (2006); Xiong and Bharadwaj (2011) |
| Common partners | If the focal firm i and its partner j are each linked via an alliance to the same third firm k at t − 1, firm k is defined as the common partner; if there is at least one common partner between firm i and its partner j, we code this variable as 1, otherwise 0. | |
| Firm size asymmetry | Absolute difference of firm sales at t − 1 of the focal and the partner firms. | |
| Financial resource asymmetry | Absolute difference of firm EBIT at t − 1 of the focal and the partner firms. | |
| Innovation output asymmetry | Absolute difference of the number of patents/sales granted at t − 1 of the focal and the partner firms. | |
| Strategic emphasis asymmetry | . | Swaminathan, Murshed, and Hulland (2008) |
| Closeness centrality asymmetry | We first compute the average shortest distance between each firm and any other firm in the network; then, we take the absolute difference (i.e., subtracting the closeness centrality of partner firm j at t − 1 from the focal firm i at t − 1). | Freeman (1978) |
| Marketing absorptive capacity | See details in Web Appendix B. Derived from an input output stochastic frontier model with firm sales as the dependent variable. | Dutta, Narasimhan, and Rajiv (1999); Narasimhan, Rajiv, and Dutta (2006) |
| Cash ratio | (Cash + Short-term investments)/Total assets. | Bates, Kahle, and Stulz (2009) |
| Leverage | Long-term debt/(Long-term debt + Total shareholders' equity). | Luo and Bhattacharya (2009) |
| Past alliance experience | Number of alliances announced by the focal firm in the past five years. | |
| Alliance management capability | Year over year difference in average abnormal returns (calculated across all alliances of the focal firm in a year) for ten periods preceding every alliance. Using an exponential smoothing model, we estimate the average over the ten-year period and use the estimate as our measure. As our data do not provide this ten-year period preceding every alliance in our sample, we used Factiva to supplement our data. | Swaminathan and Moorman (2009) |
We are interested in examining how direct and indirect asymmetry influence the financial performance and financial performance uncertainty of the focal firm after alliance formation. We measure the focal firm's financial performance and financial performance uncertainty after one year of alliance formation. We propose that direct and indirect tie asymmetries create interdependence asymmetry at the time of alliance initiation, with benefits and costs gradually materializing over the course of the alliance. Thus, a realistic period in which benefits and costs may play out to generate outcomes for the focal firm is the one-year mark after alliance initiation.
Given this one-year time frame, we use one-year buy-and-hold abnormal returns (BHAR), which is a metric of long-term equity returns from an event. We compute this metric by subtracting the performance of a benchmark portfolio of stocks with similar risk profiles to those of the focal firm from the performance of a focal firm's stock. It is commonly used to quantify long-term performance metrics of different types of marketing relevant events, such as mergers and acquisitions ([91]) and sales takeoff ([66]). The advantage of BHAR over other long-term metrics of equity returns, such as calendar time portfolio, is that with the former, we can measure firm-specific abnormal returns, which is critical to test our theoretical framework, in a cross-sectional analysis ([92]). A typical measure of a firm's financial performance uncertainty is idiosyncratic risk ([62]; [78]), which can reflect at least 80% of a firm's total equity risk ([29]). We use the Fama–French three-factor model to construct the firm's idiosyncratic risk (for estimation details, see Web Appendix A).
We test for several short-term and long-term metrics. We use cumulative abnormal returns (CARs) at 30, 60, and 180 days (after controlling for all other firm-specific announcements reported in the media in the period) and BHAR for durations longer than a year. We use firm risk over each of these durations as well. (We discuss details in the "Postestimation and Robustness Tests" subsection.)
We operationalize direct tie asymmetry as the absolute difference in number of alliance partners of the focal firm and the partner firm in the year preceding the alliance. Similarly, we operationalize indirect tie asymmetry as the absolute difference in network constraint in the year preceding the alliance. We use network constraint as the base for indirect tie asymmetry because it can capture the nature of knowledge resources potentially flowing to a firm. The network constraint measure (e.g., [ 5]; [28]) is a summary measure that captures the extent to which a firm's direct partners are connected with other firms that, in turn, are interconnected ([ 9]). Network constraint reflects knowledge redundancy of the firm due to a paucity of disconnected firms in its network.
For example, in Figure 3, Panel A, the focal firm has three direct partners—A, B, and C—that are highly interconnected. However, the indirect partners—D, E, F, and G—are nominally connected. Thus, although the direct network is interconnected, the larger network to which the focal firm belongs has several relatively disconnected firms with corresponding breakthrough knowledge flows to which the focal firm may be privy. By contrast, consider the focal firm in Panel B of Figure 3. Here, Á, B´, and C´ are direct partners of the focal firm but are not connected with each other. As such, the focal firm may be perceived as having access to fewer institutional knowledge resources and more breakthrough knowledge resources; however, the lack of connections among direct partners in Panel C loses significance because of the high interconnectedness among indirect partners D´, E´, F´, and G´. Thus, although the direct partners are not interconnected, the high interconnectedness among indirect partners hinders the flow of breakthrough knowledge resources to the focal firm. Overall, then, in Panel C the focal firm has less access to breakthrough knowledge resources than the focal firm in Panel B and thus is more constrained in the network.
Graph: Figure 3. Network constraint.
Alliance formation and implementation begin in year t. Direct and indirect tie asymmetries represent prealliance or ex ante conditions (measured at t − 1) at the time of alliance formation between two firms. Ex ante attributes typically have enduring effects on the alliance and, as such, influence alliance outcomes ([73]). They also include our moderators, such that direct and indirect tie asymmetries at t − 1 juxtapose with the focal firm's innovation quality at t − 1 and total interdependence with the specific partner firm at t − 1 (for details, see Table 3). More specifically, we measure innovation quality of the focal firm using its citation-weighted patents ([51]; [53]) at t − 1. For total interdependence, we first calculate the ratio of the number of alliances between the two firms to the total number of alliances of each firm, and then add the ratio of each firm (e.g., [30]).
We include variables that may ( 1) be empirically reflected by our independent variables but are conceptually different and/or ( 2) influence a focal firm's financial performance and related uncertainty. Using this logic for including control variables, we account first for the nature of alliances in the biopharmaceutical industry. In this industry, alliances are typically formed between ( 1) large and small firms ([42]), ( 2) firms with high and low financial resources ([71]), and ( 3) firms with high and low innovation output ([37]). Given that these prealliance differences (regardless of which firm they favor) may create interdependence asymmetry in an alliance, we need to account for them to ensure that our network-based measures of interdependence asymmetry do not reflect these typical sources of interdependence. Thus, we calculate the absolute levels of three asymmetry constructs using firm size, firm profits, and innovation output between the focal firm and the partner at t − 1. We also calculate the absolute difference in strategic emphasis at t − 1 because the firm that gives more relative importance to value creation may be more competent at fulfilling new product alliance goals. In addition, we include the absolute level of closeness centrality asymmetry as another dyadic measure that might correlate with one of our independent variables. Closeness centrality of a firm captures the average shortest distance between the focal firm and any other firm in the network. Closeness centrality and direct ties can serve as different dimensions of a focal firm's resource accessibility in the network ([81]).
After accounting for dyadic differences, we turn to the focal firm's attributes that may directly affect both its current and future financial performance and financial performance uncertainty. We account for marketing absorptive capacity of the firm at t − 1 because the efficiency of translating external knowledge into marketable products is a critical cash-flow-generating capability of any firm ([17]). We include the focal firm's cash ratio at t − 1 to account for fundamental uncertainty in the firm's business operations ([ 4]). We also include firm leverage because the extent of debt a firm incurs influences its choice of strategies as well as financial outcomes (e.g., [62]). We include the focal firm's past alliance experience, as greater alliance experience proxies for better ability to manage any specific alliance ([61]). We also explicitly account for alliance management capability along the lines of [94]. Finally, we include dummy variables as required if major events, such as acquisitions, new alliance announcements, and top management turnover, occurred in the one-year window after the alliance initiation.
We denote the focal firm as i, the partner firm as j, their alliance as k, and the year of their alliance as t. We use the following general linear specification:
Graph
1
where the dependent variable (DV) captures financial performance in one equation and financial performance uncertainty in a separate equation, DTA is the absolute level of direct tie asymmetry, ITA is the absolute level of indirect tie asymmetry, IQ is innovation quality of the focal firm, and TI is total interdependence between the focal firm and the partner firm. The first vector includes the control variables at the alliance level, the second vector includes the control variables at the focal firm level, and η represents the indicator variables for major events of the focal firm. Finally, is the generic error term.
The aggregate analyses (by pooling all firms) may mask variances in estimated effects because of the unique characteristics of each focal firm i. Many such characteristics are unobserved. Unobserved heterogeneity may lead to biased estimates. Thus, to capture the unobserved heterogeneity, we include fixed effects of focal firm We also account for time-specific fixed effects ( , given that exogenous events at a specific time may affect firms' strategic choices about an alliance initiation as well as performance.
It is possible that the asymmetry in network ties of firm i with firm j is a choice on the part of firm i as it builds its alliance portfolio and tries to adjust its financial performance and related uncertainty. The potential bias can be addressed by accounting for unobservable factors using the control function approach ([74]). More specifically, in Equation 1, we introduce a control function using ( 1) instruments that are likely to be correlated with the endogenous variables but not with the error terms and ( 2) exogenous variables from Equation 1. To obtain the control function, we first regress the direct and indirect tie asymmetries on the exogenous variables and the two instrumental variables and then regress the quadratic terms of asymmetries on the exogenous variables and the two instrumental variables and their quadratic terms, respectively. Next, we include the estimated errors from the four regressions ( , , , and ) in Equation 1.
We acknowledge that statistical tests cannot uncover suitable instrumental variables ([82]), though we should find variables that meet the relevance criterion and satisfy the exclusion restriction ([ 3]). For our two instrumental variables, first we identify a direct competitor of the focal firm by the closest similarities in two dimensions: ( 1) patent domains at t − 1 ([ 6]) and ( 2) R&D productivity at t − 1 (i.e., number of issued patents and R&D expense). Similarity in patent domains indicates that a direct competitor operates in the same marketplace as the focal firm. As new product alliances are based on R&D activities, a direct competitor should also be a firm that has similar learning outcomes, as captured by R&D productivity. Second, we measure the direct competitor's direct tie asymmetry and indirect tie asymmetry, and similar to the measures for the focal firm, both are absolute levels and averaged across the competitor's new product alliances at t − 1. Firms in the same industry are known to exhibit isomorphic tendencies in their alliance strategies ([35]). As a result, direct competitors' similar strategies might affect a focal firm's network strategies. In terms of the exclusion criterion, it is not practical to assume that a competitor's tie asymmetries will directly affect unobserved factors (e.g., the unobserved value of a potential partner firm) that influence the focal firm's performance dimensions. Rather, the competitor, through its tie asymmetries, may build its own R&D absorptive capacity, which in turn may affect the focal firm's competitive advantage and, therefore, financial performance and financial performance uncertainty. To account for this possibility, we include the direct competitor's R&D absorptive capacity (Competitor's RDCap) as an additional control variable in the second stage.
Firms that seek alliances may be systematically different from those that do not. We use Heckman's two-stage approach to account for the selection bias. In the first stage, we use the sample of all public firms in the industry, and then for each t, we create a dependent variable, which is 1 if a focal firm announced a strategic alliance at t and 0 otherwise. We then run a probit model using all exogenous variables from Equation 1. We incorporate a dummy variable that reflects whether a direct competitor (as defined previously) initiated an alliance at t − 1 to serve as an instrument. This variable affects the focal firm's decision to initiate an alliance at t but does not directly affect any unobservables that influence the focal firm's financial performance or financial performance uncertainty. Then, we calculate the inverse Mills ratio and include it in the second stage (Equation 2). The representative equation to be estimated is
Graph
2
where represents the vector of control functions (i.e., , , , and ). Finally, we bootstrapped the errors (500 iterations) in Equation 2 (e.g., [74]).
We provide the descriptive statistics and correlations among the variables of interest in Web Appendix B, Table WB1. The F-values of regressions with each of our four endogenous variables (direct tie asymmetry, squared direct tie asymmetry, indirect tie asymmetry, and squared indirect tie asymmetry) as dependent variables and only instruments as independent variables are all above 10, and all instruments are statistically significant at the 95% confidence interval. In addition, in the full first-stage regressions with instruments and exogenous variables from Equation 2, we find that most of our instruments are statistically significant at the 95% confidence interval. Thus, we do not have a weak instrument problem. We report the results of the first-stage regression and selection model in Tables WC1 and WC2 in Web Appendix C, respectively. We also provide the results of Equation 2 (second stage) by gradually introducing direct and indirect tie asymmetries to the model in Web Appendix C (Table WC3). We find that when our four focal constructs enter the model, R-square increases significantly, indicating the meaningfulness of absolute levels of direct and indirect tie asymmetries in explaining the focal firm's BHAR and risk.
We report parameter estimates of Equation 2 (second stage) for one-year BHAR (financial performance) and one-year firm risk (financial performance uncertainty) in Table 4. We find support for our main effect hypotheses (H1a and H1b). Specifically, we find that increasing direct tie asymmetry initially has a positive effect on BHAR (linear effect: b =.59, p <.05), and then at higher levels, it has a negative effect on BHAR (quadratic effect: b = –.09, p <.01), indicating a significant inverted U-shaped effect as hypothesized in H1a. Furthermore, we find that increasing direct tie asymmetry initially has a negative effect on firm risk (linear effect: b = –.42, p <.05), and then at higher levels, it has a positive effect on firm risk (quadratic effect: b =.07, p <.05), indicating a significant U-shaped effect as hypothesized in H1b. Next, we find support for H2b but not H2a. Specifically, as hypothesized in H2b, we find that increasing indirect tie asymmetry initially has a negative effect on firm risk (linear effect: b = −.12, p <.05), and then at higher levels, it has a positive effect on firm risk (quadratic effect: b =.07, p <.05), indicating a significant U-shaped effect.
Graph
Table 4. Main Estimation Results.
| | Hypotheses Supported | BHAR (One-Year) | Risk (One-Year) |
|---|
| | Coef. | SE | Coef. | SE |
|---|
| Main Effects | | | | | |
| Direct tie asymmetry | H1a and H1b | .59** | .28 | −.42** | .20 |
| Direct tie asymmetry squared | H1a and H1b | −.09*** | .03 | .07** | .03 |
| Indirect tie asymmetry | H2b | .13 | .72 | −.12** | .06 |
| Indirect tie asymmetry squared | H2b | .05 | .19 | .07** | .04 |
| Innovation quality | | −.00 | .00 | .01 | .10 |
| Total interdependence | | .29** | .13 | −.05** | .02 |
| Interactions with Innovation Quality | | | | | |
| Direct tie asymmetry × innovation quality | | .05 | .04 | −.03 | .04 |
| Direct tie asymmetry squared × innovation quality | H3a and H3b | .17*** | .02 | −.19*** | .04 |
| Indirect tie asymmetry × innovation quality | | 1.05 | 1.17 | .02* | .01 |
| Indirect tie asymmetry squared × innovation quality | H3d | −.03 | .93 | −.49** | .23 |
| Interactions with Total Interdependence | | | | | |
| Direct tie asymmetry × total interdependence | | .26 | .49 | −.04 | .05 |
| Direct tie asymmetry squared × total interdependence | H4a and H4b | 1.03*** | .20 | −.08*** | .03 |
| Indirect tie asymmetry × total interdependence | | .07 | .80 | .12 | .45 |
| Indirect tie asymmetry squared × total interdependence | H4d | .06 | .06 | −1.08*** | .03 |
| Controls | | | | | |
| R&D absorptive capacity | | .35* | .18 | −.03** | .01 |
| Common partners | | −.32 | 1.74 | −.08 | 1.37 |
| Size asymmetry | | .04** | .02 | −.05*** | .02 |
| Profit asymmetry | | .71* | .38 | −.26 | 1.26 |
| Innovation output asymmetry | | .03** | .01 | .02 | .03 |
| Strategic emphasis asymmetry | | −.06 | .12 | −.07 | .06 |
| Closeness centrality asymmetry | | .05 | .08 | −.05* | .02 |
| Marketing absorptive capacity | | 1.98** | .99 | −.18* | .09 |
| Cash ratio | | −17.96 | 23.75 | −1.06 | 1.09 |
| Firm leverage | | .00 | .01 | .04* | .02 |
| Alliance experience | | .15** | .07 | −.07** | .03 |
| Alliance capability | | 6.02* | 3.18 | −1.45* | 1.23 |
| Competitor's R&D absorptive capacity | | −.00 | .27 | .00 | 1.01 |
| Inverse Mills ratio | | −78.92* | 42.20 | 16.09* | 9.19 |
| | −6.05** | 3.00 | .23 | .19 |
| | −39.04 | 20.12 | 8.15** | 4.07 |
| | −.06*** | .01 | .29 | 1.51 |
| | −43.85 | 68.03 | 1.02* | .59 |
| Firm fixed effects | | Incl. | | Incl. | |
| Year fixed effects | | Incl. | | Incl. | |
| Intercept | | −38.93*** | 13.93 | −25.83* | 13.45 |
| Adjusted R2 | | .18 | | .22 | |
1 *p <.1.
- 2 **p <.05.
- 3 ***p <.01.
- 4 Notes: BHAR reflects financial performance, and risk is idiosyncratic risk or financial performance uncertainty. All dependent variables belong to the focal firm. Errors are bootstrapped. Estimates are rescaled. Results for financial performance (CAR and BHAR) and risk with alternative time specifications are reported in Tables WE5 and WE6, respectively, in Web Appendix E.
We propose in H3a and H3b that as the focal firm's innovation quality increases, the nonlinear effect of direct tie asymmetry on BHAR and firm risk flattens, respectively. We find support for both hypotheses. For BHAR, we find a significant interaction of innovation quality with the quadratic term of direct tie asymmetry (b =.17, p <.01; see Figure 4, Panel A). For firm risk, we find a significant interaction of innovation quality with the quadratic term of direct tie asymmetry (b = −.19, p <.01; see Figure 4, Panel B). In H3c and H3d, we propose that as innovation quality increases, the nonlinear effect of indirect tie asymmetry on BHAR and firm risk flattens, respectively. We find support only for H3d (firm risk). Specifically, we find a significant interaction of innovation quality with the quadratic term of indirect tie asymmetry (b = −.49, p <.05; see Figure 4, Panel C). We note that the main effect of innovation quality is nonsignificant in all our models, which is surprising, as this variable is a critical ingredient of biopharmaceutical firms' R&D productivity. However, we find that all our models include interactions that perhaps mask the main effects. In a separate analysis without interactions, we find that the main effect of innovation quality on BHAR is positive and significant (b =.07, p <.05) and the corresponding effect on risk is marginally significant (b = −.04, p <.10). This analysis indicates that the innovation quality of a focal firm on its own is not enough to influence alliance outcomes; instead, it needs to be aligned with other alliance- and firm-level factors.
Graph: Figure 4. Moderating effects of innovation quality.
In H4a and H4b, we propose that as total interdependence between the two firms increases, the nonlinear effect of direct tie asymmetry on BHAR and firm risk flattens, respectively. We find support for both hypotheses. For BHAR, we find a significant interaction of total interdependence with the quadratic term of direct tie asymmetry (b = 1.03, p <.05; see Figure 5, Panel A), in support of H4a. For firm risk, we find a significant interaction of total interdependence with the quadratic term of direct tie asymmetry (b = –.08, p <.01; see Figure 5, Panel B), in support of H4b. In H4c and H4d, we propose that an increase in total interdependence flattens the nonlinear effect of indirect tie asymmetry on BHAR and firm risk, respectively. We find support only for H4d (firm risk). Specifically, we find a significant interaction of total interdependence with the quadratic term of indirect tie asymmetry (b = −1.08, p <.01; see Figure 5, Panel C). We provide illustrative examples on how to interpret interaction effects and figures to support our hypotheses in Web Appendix D.
Graph: Figure 5. Moderating effects of total interdependence.
In Web Appendix E, we provide detailed discussions on the theoretical meaningfulness of absolute levels of direct and indirect tie asymmetries, based on ( 1) a literature review of interdependence asymmetry in interfirm relationships (see Table WE1) and ( 2) the results of estimating Equation 2 with directional values of direct and indirect tie asymmetries as additional control variables (see Table WE2). To empirically demonstrate our theory that absolute levels of asymmetry are more meaningful in our context than directional asymmetry, we reestimate Equation 2, replacing absolute direct and indirect tie asymmetries with directional tie asymmetries. The results show that absolute levels of direct and indirect tie asymmetries have greater model fit (one-year BHAR: Akaike information criterion [AIC] = 1613.11, one-year risk: AIC = 953.27) than directional values of direct and indirect tie asymmetries (one-year BHAR: AIC = 1,614.26, one-year risk: AIC = 994.05), validating the theoretical meaningfulness of absolute levels of tie asymmetries in our context (see Table WE3).
As an alternative endogeneity correction, we employ the two-stage least squares estimation method in which we use the instruments to predict the endogenous variables. Most of the estimated coefficients remain robust to the alternative endogeneity correction method. Table WE4 in Web Appendix E summarizes the control function and two-stage least squares results for BHAR (one-year) and risk (one-year).
We use equity-based measures to capture both financial performance and financial performance uncertainty. To test the robustness of our results with accounting-based measures of the focal firm's financial performance and financial performance uncertainty, first we substitute BHAR with the focal firm's sales, profits, and operating cash flow, because strategic actions likely affect shareholder value through top- and bottom-line performance as well as cash flow levels ([41]). Second, we substitute firm risk with cash flow volatility, as investor perceptions of financial performance uncertainty depend on cash flow volatility ([84]). We find that most of our relevant results for financial performance hold for operating cash flows (not sales and profits). Most of our results for financial performance uncertainty hold for cash flow volatility. We present these results also in Table WE4 in Web Appendix E.
We use one-year BHAR and firm risk, because partners slowly develop perceptions of relative contributions and rewards, and as such, the effects of interdependence asymmetry between the partners appear over time ([69]). In line with the logic of gradual realization of outcomes, it is possible that the effects of direct and indirect tie asymmetries appear sooner than one year after the alliance initiation. Moreover, the effects may persist beyond one year after alliance initiation. To assess such effects, we estimate our theoretical model using abnormal returns and firm risk over multiple periods, including 30 days, 60 days, 180 days, two years, three years, four years, and five years. We stop at five years because we assumed an alliance duration of five years. We use CARs based on the Fama–French three-factor model for 30, 60, and 180 days. For event windows less than a year, BHAR is not recommended ([92]). For all event windows, we control for all firm-specific announcements that suggest major events or developments, such as new alliance announcements, top executive turnover announcements, and new product launch announcements. The results for these alternative time specifications are available in Table WE5 in Web Appendix E for financial performance and Table WE6 for performance uncertainty.
We find that the effects of direct and indirect tie asymmetries on abnormal returns and risk are mostly consistent for all periods up to two years. For three years and more, all effects on abnormal returns and risk disappear. It likely takes about two years for alliance partners to resolve issues related to prealliance tie asymmetries (in the context of high-tech alliances, see [57]]).
Prealliance network ties of a focal firm yield benefits, as these ties generate multiple resources. However, when these ties are assessed relative to those of the partner firm in an alliance, the resulting asymmetry, regardless of its direction, creates interdependence asymmetry in the alliance that has both benefits and costs, which in turn affect financial performance and performance uncertainty of the focal firm.
Our sample, which represents the biopharmaceutical industry, is unique in terms of the critical importance of R&D and new product alliances to firm performance. For example, the R&D spend in 2017 was 21.4% of total revenue on average (see [18]; [97]). The only other industry with comparable R&D spending is the semiconductor industry, with average R&D spending ranging from 10% to 30% of revenue ([90]). According to industry reports, R&D spending in the computer and software industry has increased steadily, with some of the largest firms spending up to 20% of revenues on R&D.[ 7] Given the comparable R&D spending and the importance of new product alliances in the semiconductor and software industries, it is reasonable to expect similar effects in these high-tech contexts.
Our research offers several contributions. First, with regard to networks, direct and indirect ties are sources of resource volume and variety (e.g., [80]). Existing research thus far has adopted a firm-level perspective of the role of such network ties in alliance outcomes, but it has overlooked the notion that alliance performance is an outcome of dyadic relationships. This is an important distinction, as the interdependence of the two firms in an alliance is a function of each firm's network ties relative to those of its partner.
Second, the notion of interdependence asymmetry in extant literature is typically directional (i.e., what happens to the focal firm if asymmetry favors the partner firm) (see [87]). In contrast with most prior research, we focus on absolute levels of asymmetry. We do so because regardless of which firm's direction asymmetry shifts, the performance of the alliance, which is a dyadic relationship, will be affected. It is important to note that our theorization does not negate the findings of prior research. Although we assess the performance metrics of a focal firm, we conjecture that absolute levels of direct and indirect tie asymmetries affect the partner firm's performance possibly in the same direction as the focal firm ([30]), though the scale of the impact might differ.
Third, to our knowledge, this empirical study is the first to assess the impact of interdependence asymmetry in an alliance on both the focal firm's abnormal returns and risk. Given that other firm-level differences may create interdependence asymmetry, we also create an index to control for all such relevant factors and estimate its effects on the focal firm's financial performance and financial performance uncertainty using Equation 2. We find that the index has significant nonlinear effects on our dependent variables. We present details on creating this index in Web Appendix F and present our empirical results in Table WF1.
Our results on CAR show how changes in direct tie asymmetry affect the focal firm's market capitalization. We assess the average market capitalization of the top 100 firms in the biopharmaceutical industry as $27.07 billion as of 2017.[ 8] Our results with a CAR of 180 days suggest that if direct tie asymmetry increases from the 25th percentile to the 50th percentile, the corresponding increase in market capitalization is $86.8 million. The corresponding increases at high and low levels of innovation quality are $87.3 million and $85.1 million, whereas the corresponding increases at high and low levels of total interdependence are $99.03 million and $88.19 million, respectively. However, if direct tie asymmetry increases from the 25th percentile to the 90th percentile, the corresponding increase in the focal firm's market capitalization is $67.9 million. The corresponding increases at high and low levels of innovation quality are $88.9 million and $29.6 million, whereas the corresponding changes at high and low levels of total interdependence are $98.5 million and −$151.2 million, respectively. As these results indicate, to improve the chance of forming a successful alliance, managers should be cautious when or perhaps completely avoid selecting an alliance partner in the last condition, with both high direct tie asymmetry (i.e., large difference in number of direct ties) and low total interdependence (i.e., no preexisting alliances with the partner).
We are unable to show economic significance of indirect tie asymmetry because of the nonsignificant effects on CAR. However, managers should also note the importance of a potential alliance partner's indirect ties, specifically how well these ties are interconnected relative to the interconnectivity of their firm's own indirect ties. If we calculate changes in the focal firm's predicted risk from an increase in indirect tie asymmetry from the 25th percentile to the 50th percentile of our data (keeping everything else in Equation 2 constant), we find a 68.7% decrease in the focal firm's risk. The corresponding decrease in risk when indirect tie asymmetry increases from the 25th to the 90th percentile is 24%. When we calculate high and low levels of the moderators, we find that while all combinations reduce predicted risk to different degrees, one specific combination highlights a worst-case scenario. Specifically, a focal firm's predicted risk increases by 56.8% with a combination of high indirect tie asymmetry (25th–90th percentiles) and low total interdependence. Thus, to improve the odds of forming a successful alliance, a focal firm should avoid seeking an alliance partner with which it has a large difference in interconnectivity of indirect ties and no preexisting ties.
Our research has a couple of limitations. We restricted our study to alliances between public firms only. However, in the biopharmaceutical industry, alliances are sometimes between a public pharmaceutical firm and a private biotechnology start-up. Examining the extent to which a partner firm is affected and whether the type of partner firm (e.g., public vs. private) matters is beyond the scope of this study but represents a direction for further research. In addition, although interdependence asymmetry is of relevance to alliances in general, the role of prealliance network ties may not be as clear when we consider global alliances comprising multiple cultures and political environments, both of which present environmental contingencies worth investigating.
Supplemental Material, Web_Appendix_NEW - Effect of Alliance Network Asymmetry on Firm Performance and Risk
Supplemental Material, Web_Appendix_NEW for Effect of Alliance Network Asymmetry on Firm Performance and Risk by Anindita Chakravarty, Chen Zhou and Ashish Sharma in Journal of Marketing
Footnotes 1 Author Contributions All three authors contributed equally to the article.
2 Associate Editor Vanitha Swaminathan
3 Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.
5 Online supplement: https://doi.org/10.1177/0022242920943104
6 1 We create another data set with an assumption of alliance duration of seven years. In this way, we can test whether our empirical results are sensitive to assumptions of alliance duration. We find that all our main effects and most of the moderating effects are robust in the alternative sample.
7 2 See https://theatlas.com/charts/N1Gs8E4v (accessed August 6, 2020).
8 3 See https://gfmasset.com/2017/01/the-200-largest-drug-pharmaceutical-companies-by-market-cap-2017/ (accessed August 6, 2020).
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By Anindita Chakravarty; Chen Zhou and Ashish Sharma
Reported by Author; Author; Author
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Record: 69- Effects of Contract Ambiguity in Interorganizational Governance. By: Zheng, Xu (Vivian); Griffith, David A.; Ge, Ling; Benoliel, Uri. Journal of Marketing. Jul2020, Vol. 84 Issue 4, p147-167. 21p. 1 Diagram, 7 Charts, 1 Graph. DOI: 10.1177/0022242920910096.
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Effects of Contract Ambiguity in Interorganizational Governance
This work introduces the concept of contract ambiguity from the law literature into the interorganizational governance literature. Within the context of franchising, the authors present a three-study multimethod design empirically establishing the construct of contract ambiguity of franchisor obligations, providing new insights into the strategic design of contracts and their outcomes. In Study 1, the authors establish construct validity by demonstrating that contract ambiguity of franchisor obligations is distinct from contract specificity and contract completeness of franchisor obligations, with differential outcomes. In Studies 2 and 3, the authors demonstrate that contract ambiguity of franchisor obligations increases an interest-based (vs. a rights-based) conflict solving approach, implying greater cooperation and joint problem solving, and reduces franchisee-initiated litigation. The findings also indicate that while contract ambiguity of franchisor obligations decreases franchisee-initiated litigation, this effect is amplified by higher levels of franchisor training programs but mitigated by the presence of a franchisee association. The article closes with a discussion of implications for academics and practitioners.
Keywords: contracts; cooperation; franchising; governance; interorganizational; litigation
A contract, or provision of a contract, is defined as ambiguous if it is reasonably susceptible to more than one interpretation ([20]; [51]). A lack of contractual clarity pertaining to the responsibilities of each partner to a relationship can hamper interorganizational effectiveness ([35]; [72]). As such, both literature and best practice argue that contract wording should be clear and unambiguous ([51]). However, ambiguous statements, in reference to a franchisor's or franchisee's obligations, are common. For example, the Arby's Restaurant Group franchise agreement states the franchisor may "charge proposed supplier the reasonable costs" and that the franchisee will "take reasonable measures to protect the confidentiality [of the franchisor's proprietary information]" (emphasis ours). Similarly, the Buffalo Wild Wings franchise agreement states that with respect to the advertising fund, the franchisor "will make a good faith effort to expend such fees in a manner that we determine is in the general best interests of the System" (emphasis ours). But, what do "reasonable costs," "reasonable steps," or "good faith effort" mean?
The importance of interorganizational contracting has stimulated significant research, with two particularly noteworthy streams: the content of written contracts and the joint effects of contractual and noncontractual elements. First, researchers have examined the content of written contracts as formal governance mechanisms, primarily focusing on the concepts of contract specificity and contract completeness.[ 6] "Contract specificity" is defined as the extent to which the contract states elements, such as implementation procedures, technical specifications, and resolution of problems ([16]; [54]; [56]), whereas "contract completeness" is defined as the extent to which relevant clauses are codified in a contract ex ante and in subsequent ex post governance efforts (e.g., [38]). Research on these contracting aspects have increased our understanding of interorganizational effectiveness, finding, for example, that the chosen level of contract specificity is a trade-off that balances ex ante contract costs with ex post transaction problems ([54]), and that greater contract completeness minimizes opportunism ([56]).
Although interorganizational research has examined contract specificity and contract completeness, scholars have not explored the concept of contract ambiguity. This is surprising, as the law literature notes that contract ambiguity is a unique, prevalent, and substantive contracting issue (e.g., [20]; [24]; [51]). Its use in contracts, simultaneously with aspects such as contract specificity, is common. Take for instance, the standard form of the 7-Eleven franchise agreement. The franchisor's obligations in the franchise agreement are ambiguous in relation to Section 6, part (b), "Conditions to Occurrence of Effective Date," stating in part, "We agree to use our best efforts to make the store available to you within a reasonable time" (emphasis ours). However, the franchisor's obligations are specific in relation to Section 26, part (e)( 3), "Termination: Transfer and Refund Rights," stating in part, "If you elect the Refund, the Refund will be calculated by deducting twenty thousand dollars ($20,000) from the Franchise Fee you paid when you signed this Agreement; dividing the remainder thereof by one hundred eighty (180)." The use of contract ambiguity in practice, even when general convention recommends its elimination, suggests that its existence is not accidental, but likely strategic. The strategic nature of contract ambiguity is most notable when one considers that the contract in a franchise system is unilaterally designed by the franchisor, which works to protect its own prerogatives ([41]; [57]). Given this rationale, we focus our attention on contract ambiguity of franchisor obligations in franchise agreements.[ 7]
The issue of contract ambiguity dovetails with a second stream of contract investigation: the joint effects of contractual and noncontractual elements (e.g., [16]; [59]). Although written contracts serve as the foundation of a franchise system, they are but one aspect of governance (i.e., noncontractual elements also affect the administration of a relationship and therefore should be investigated jointly with written contracts; e.g., [44]; [50]; [59]). For example, [16] find that greater contract specificity enhances a retailer's relational behaviors in supporting the manufacturer's product throughout the duration of the contract. [29], p. 81) argues that socialization (i.e., the process by which new parties learn skills and internalize another party's values, goals, and rules; [30]), complements written contracts. Although research has examined some joint effects, there is much still to learn. For example, do franchisor training programs or the presence of a franchisee association help or hinder a franchisor when it drafts a franchise agreement with greater contract ambiguity of franchisor obligations?
We address these issues using a three-study multimethod design, contributing to the literature in three ways. First, we extend the interorganizational contracting literature by introducing the concept of contract ambiguity from the law literature ([12], [13]), discussing the strategic role contract ambiguity plays in governing interorganizational relationships. Our findings, derived from a survey of franchisees (Study 1), establish its nomological validity, demonstrating that contract ambiguity of franchisor obligations is empirically distinct from both contract specificity and contract completeness of franchisor obligations, because it has differential effects on franchisee outcomes (e.g., cooperative performance, perceived costs of litigation, and franchisee intention to litigate). In addition, our post hoc analysis suggests that contract ambiguity creates significant financial implications on firms, and thus its use may be strategic in nature. For example, a post hoc analysis indicates that a one-unit increase in franchisee-initiated litigation leads to a 7% (i.e., $45,285.38) decrease in franchisor net income, with more substantive changes when considering the marginal effects of noncontractual elements effects.
Second, we advance the contracting literature by demonstrating franchisee responses (i.e., interest- over rights-based conflict solving approach and franchisee-initiated litigation) to contract ambiguity of franchisor obligations. Study 2 employs a scenario-based experimental design. We find that contract ambiguity of franchisor obligations stimulates an interest-based (vs. a rights-based) conflict solving approach, indicative of cooperation and joint problem solving. In addition, using a data set drawn from multiple archival sources encompassing a ten-year window (2004–2013) across 106 franchise systems, we demonstrate in Study 3 that contract ambiguity of a franchisor's obligations decreases franchisee-initiated litigation. Echoing prior research on contract design (e.g., [ 3]), our study demonstrates that a carefully designed contract may help firms solicit greater communication and cooperation with exchange partners and avoid undesirable relational outcomes (e.g., litigation).
Third, we contribute to the literature on the joint effects of contractual and noncontractual governance in interorganizational relationships (e.g., [16]; [50]; [59]). Our findings, based on our longitudinal data set used in Study 3, demonstrate that while contract ambiguity of franchisor obligations decreases franchisee-initiated litigation, this effect is amplified by higher levels of franchisor training programs but is mitigated by the presence of a franchisee association. The results pertaining to franchisor training programs extends the theoretical work of [29], demonstrating that franchisor training programs can allow for franchisor–franchisee socialization that aids in governance when employed jointly with contract ambiguity of franchisor obligations. In addition, our findings add to an emerging literature on franchisee association effects (e.g., [18]), indicating that the presence of a franchisee association, where socialization occurs primarily among franchisees, mitigates the dampening effect of contract ambiguity of franchisor obligations on franchisee-initiated litigation, countering the franchisor's strategic use of contract ambiguity of franchisor obligations.
Written contracts facilitate interorganizational governance by mitigating coordination problems ([38]; [50]). Wording in written contracts should be clear and unambiguous ([51]). However, contrary to this guidance, both the law literature (e.g., [12], [13]) and practice suggest that contract ambiguity is prevalent (see Table 1). In our research, we contend that contract ambiguity of franchisor obligations is a strategic decision used by the franchisor for governance purposes.
Graph
Table 1. A Summary of the Law Literature Addressing Ambiguity in Contracts.
| Authors (Date) | Journal | Abstract |
|---|
| Scott and Triantis (2006) | Yale Law Journal | This article advances a theory of contract design in a world of costly litigation. It contends that although vague terms have fallen into disfavor with contract theorists, there is justification for their frequent use in commercial practice. |
| Choi and Triantis (2008) | Journal of Legal Studies | Contract theory holds that verification costs are obstacles to complete contracting. However, real-world contracts often contain provisions that seem costly to verify. This article demonstrates how verification (or litigation) costs operate as a screen on the promisee's incentive to sue and as an effective sanction against the breaching promisor. |
| Choi and Triantis (2010) | Yale Law Journal | This article proposes a framework to improve the awareness of the strategic use of vagueness in contracting. The conventional rules-standards analysis suggests that vague terms are justified when the expected larger litigation costs in enforcing standards are outweighed by the lower costs of drafting. The authors demonstrate that litigation costs, when properly harnessed, can improve contracting by operating as a screen on the seller's decision to sue. |
| Robinson and Stuart (2007) | Journal of Law and Economics | An analysis of 125 strategic alliance contracts indicates that contracts often specify provisions that are unobservable or difficult to verify. The authors contend that this suggests a role for expected litigation as an enforcement tool in contract design. |
| Drahozal and Hylton (2003) | Journal of Legal Studies | The authors examine franchise contracts and conduct an empirical analysis of the determinants of arbitration agreements among franchising parties. The results indicate that deterrence factors outweigh litigation costs in the design of dispute resolution agreements. This indicates that the probability of arbitration is significantly higher when the parties rely on implicit contract terms for governance and compliance with those terms is difficult to ensure. |
| Schwartz and Scott (2010) | Yale Law Journal | Contract interpretation remains the largest single source of contract litigation between business firms. Although much academic commentary suggests otherwise, sophisticated commercial parties incur costs to cast obligations expressly in written and unconditional forms to permit a party to stand on its rights under the written contract, to improve party incentives to invest in the deal, and to reduce litigation costs. |
| Chakravarty and MacLeod (2009) | RAND Journal of Economics | Economic models of contract generally assume that courts enforce obligations based on verifiable events (corresponding to the legal rule of specific performance). This leaves open the question of optimal contract design given the available remedies used by the courts. This article demonstrates a central feature of contracts is the inclusion of governance covenants that shape the scope of authority and regulate the ex post bargaining power of parties (including possible litigation). |
A franchise agreement is a single, standard written contract used in the franchise system. A single contract is used because a franchisor seeks standardization and coordination of franchisees to maintain brand value and enhance franchise system sales ([ 2]; [33]). These contracts are unilaterally designed by the franchisor, with a potential franchisee deciding whether to accept contract terms ([41]). [57] argue that the power advantage of the franchisor results in the franchisor developing the contract to protect its own prerogatives. This allows the franchisor to use contract ambiguity in relation to its obligations for strategic purposes (e.g., to facilitate greater cooperation and joint problem solving as well as to minimize franchisee-initiated litigation).
Although the franchise agreement is established by the franchisor, franchisees are not docile partners. Rather, franchisees strive for autonomy to maximize profit ([33]; [39]). Given a franchisor's focus on standardization and coordination and a franchisee's drive for autonomy, franchisor–franchisee relationships can experience conflicts on contractual matters ([ 2]; [ 9]; [25]), which can escalate to costly litigation ([ 2]; [34]). For example, in April 2018, a franchisee filed suit against Steak 'n Shake in an effort to be allowed to set its own menu pricing ([55]). Similarly, in June 2017, a Tim Hortons franchisee sued its franchisor for $500 million in relation to the franchisor's use of advertising fund revenue ([52]).
To begin exploring franchisees' responses to contract ambiguity of franchisor obligations, we examine two types of conflict solving approaches introduced in the contracting literature: interest-based and rights-based ([48]). The interest-based approach is cooperative, fostering joint problem solving and mutually acceptable solutions (e.g., [ 8]). Contracts using ambiguous contract terms motivate parties to engage one another when facing conflicts, so as to articulate their expectations and interests ([24]). Alternatively, the rights-based conflict solving approach is argumentative, focusing on legitimate claims (e.g., [ 8]; [73]). Contracts using unambiguous terms (i.e., those having one definite meaning) foster a rights-based approach, as the contract gives each party "facts" to which they believe they are entitled (e.g., [27]; [21]).
Furthermore, creating a contract with greater contract ambiguity of the franchisor's obligations provides the franchisor protection from claims that it has breached the contract. For example, how is one to prove that a franchisor did not negotiate in "good faith"? [12] and [61] note that litigation costs associated with vague terms are a barrier to litigation due to the difficult nature of verifying that a contractual breach occurred. [13], p. 853) contend that contract ambiguity may be used strategically as a "screen on the promisee's incentive to sue." Thus, a franchisor can minimize franchisee-initiated litigation by employing contract ambiguity of its obligations in the franchise agreement.
While contract ambiguity pertaining to the franchisor's obligations aid the franchisor in minimizing franchisee-initiated litigation, the use of ambiguous terms (e.g., "reasonable costs," "good faith effort") can cause misunderstandings. As such, franchisors need to also consider the joint effects of contractual and noncontractual elements (e.g., [16]; [59]). First, franchisors may employ training programs, including both initial and ongoing training opportunities, to facilitate socialization between the franchisor and the franchisees ([29]). For example, Potbelly Sandwich Shop offers not only a 10–12-week initial training program but also encourages franchisees to participate in regular training both at company-owned shops and at the franchisor's site (where the franchisor provides additional mentoring and guidance). These efforts work to facilitate the development of shared values and the alignment of goals between parties, providing a context for increased discussions.
Second, franchisors need to consider the effects of franchisee associations ([42]). We contend that when franchisees participate in a franchisee association (whether franchisor sponsored, franchise system specific, or independent), socialization occurs among franchisees, which may create resistance to franchisor efforts ([18]). This is particularly concerning because some franchisors are actively encouraging the development of franchisee associations (often as an alternative to franchisees joining an independent association, such as the American Association of Franchisees and Dealers). For example, in 2017 Kahala Franchising LLC encouraged its franchisees to form cooperative advertising associations to maximize the efficient use of local advertising media.
Building on our conceptualization, we argue that contract ambiguity of franchisor obligations has multiple outcome effects. First, we theorize that in response to a greater level of contract ambiguity of franchisor obligations, franchisees respond with an interest-based (vs. rights-based) conflict solving approach, implying greater cooperation and joint problem solving. Contract ambiguity creates room for interpretation, necessitating communication between franchisee and franchisor to reconcile interests ([24]). An interest-based approach to conflict solving works within a cooperative framework, whereas a rights-based approach works to determine who is right or wrong ([ 8]). With greater contract ambiguity of a franchisor's obligations, franchisees seek interpretation from the franchisor and solving problems jointly.
Second, contract ambiguity is a cost inhibitor ([12], [13]), thereby decreasing franchisee-initiated litigation. The more ambiguous the contract terms, in relation to the franchisor's obligations, the more difficult (and costly) it is to prove that a franchisor failed to meet its obligations. The cost of litigation is particularly notable given the different financial situations of each party. [47], p. 59) notes that "a franchisor has greater economic resources with which to fund litigation, giving it a distinct advantage in the process regardless of the merits of the franchisee's claims or defenses. For franchisees, however, it may be difficult to finance a protracted lawsuit." Unless he is willing to pay high litigation costs to establish his cases, the franchisee, facing a contract with a greater level of contract ambiguity, would be less likely to opt for litigation against the franchisor. Formally,
- H1 : Increased contract ambiguity of franchisor obligations increases a franchisee's interest-based (vs. rights-based) conflict solving approach.
- H2 : Increased contract ambiguity of franchisor obligations decreases franchisee-initiated litigation.
Franchisee-initiated litigation disrupts franchise system operations and poses significant financial consequences to the franchisor. Scholars suggest that elements other than those written in the contract can play an important governance role when exchange partners face contractual hazards ([16]; [44]). As such, here we focus on how franchisor training programs and franchisee associations moderate the negative influence of contract ambiguity of franchisor obligations on franchisee-initiated litigation.
Most franchisors, working to ensure standardization and coordination across the franchise system, enact initial and ongoing training programs. The presence of higher levels of franchisor training programs provides an opportunity and a context for the franchisor and franchisees to communicate on a regular basis, articulating their expectations and interests. In addition, it helps reduce misunderstandings through the creation of a common frame of reference and development of shared values ([ 6]; [30]). Working jointly with contract ambiguity of franchisor obligations, franchisor training programs can prevent latent conflicts from escalating to serious litigation through increased communication for clarification. In other words, with higher levels of franchisor training programs, it is more accessible, and thus more likely, for franchisees to opt for discussions to advance their interests as opposed to seeking relief from the court. Thus, we contend that increased franchisor training programs amplifies the negative relationship between contract ambiguity of franchisor obligations and franchisee-initiated litigation, which works in favor of the franchisor. Formally,
- H3 : Increased use of franchisor training programs amplifies the negative relationship between contract ambiguity of franchisor obligations and franchisee-initiated litigation.
Franchisee associations provide an opportunity for the development of common frames of reference and cohesion among franchisees ([18]). Because all franchisees are subject to the same contract ambiguity of franchisor obligations, franchisees in an association will tend to be supportive of another franchisee's position. Perceptions of injustice, such as those stimulated by contract ambiguity of franchisor obligations, are reinforced through cohesion ([46]), making franchisees more confident in resisting the powerful franchisor. This argumentation is consistent with the concept of countervailing power ([22]) in marketing channels (e.g., [19]), which argues that less powerful members to an exchange band together to offset the power of a more powerful partner. By banding together, franchisees are also able to share costs, countering the financial resource advantage of the franchisor. For example, in May of 2019, 20 franchisees, representing 32 of the franchisor's 53 restaurants of Deli Delicious, formed an association to protest the franchisor's lack of leadership and transparency of business practices, accusing the franchisor of price fixing, retaliation, and so on ([76]). By banding together, these franchisees countered the power asymmetry between the franchisor and the individual franchisee. Thus, we argue that the negative relationship between contract ambiguity of franchisor obligations and franchisee-initiated litigation will be mitigated by the presence of a franchisee association, disfavoring the franchisor. Formally,
- H4 : The presence of a franchisee association mitigates the negative relationship between contract ambiguity of franchisor obligations and franchisee-initiated litigation.
We employ a three-study multimethod research design to examine our underlying logic (see Figure 1). Study 1 establishes nomological validity of contract ambiguity of franchisor obligations. We use a survey of 146 franchise owners to establish discriminate validity of contract ambiguity of franchisor obligations from contract completeness and contract specificity of franchisor obligations, as well as differential effects on outcomes. Study 2 examines franchisee response to contract ambiguity of franchisor obligations, gaining insight into the process underlying our arguments. A scenario-based experiment with 92 online respondents formally tests H1 and examines process elements. Study 3 establishes external validity for our model, formally testing H2, H3, and H4 within a data set drawn from multiple archival sources encompassing a ten-year window (2004–2013) across 106 franchise systems.
Graph: Figure 1. Overview of three studies.
In Study 1 we worked to determine, through a survey of franchise owners, if contract ambiguity of franchisor obligation can be empirically distinguished from other contract constructs (i.e., contract completeness and contract specificity of franchisor obligations). To distinguish a new construct, it is important to ( 1) test the discriminant validity of the construct from similar constructs through confirmatory factor analysis and ( 2) establish a nomological network in which the construct and similar constructs may have differential effects on the same outcomes.
Using a market research company's (Dynata) national business panel, we invited business owners (qualifying if self-reporting as a franchisee) to participate in a study. One hundred fifty-two franchisee owners completed the survey. After deleting six surveys due to straightlining, we were left with 146 observations. On average, the respondents were 41.45 years old and had 21.52 years of work experience. Each of them owned their franchise for an average of 8.38 years and averaged 48.36 hours of required franchisor training in the prior year. We compared early versus late respondents in relation to key study variables (i.e., CAFR, CCFR, CSFR, COST, INTENT, and COOPER) to assess nonresponse bias. The results of a multivariate analysis of covariance (MANCOVA) indicated no differences, suggesting that nonresponse bias is not a threat to the results.
Focal study constructs were captured through multi-item scales (see Table 2). We developed the scales for contract ambiguity (CAFR), contract completeness (CCFR), and contract specificity (CSFR) of franchisor obligations to adhere to the construct conceptualizations and to the items used in the literature. We used a seven-point, three-item, bipolar semantic differential scale for each contracting construct. To ensure valence consistency, items related to the contract being ambiguous, not specific, and not complete were on the left side, and items related to the contract being unambiguous, specific, and complete were on the right (we reverse-coded contract ambiguity items so that higher numbers refer to greater contract ambiguity of franchisor obligations). Consistent with [54], we provided construct conceptualizations to respondents prior to scale items.
Graph
Table 2. Definition, Measurement, Data Sources, and Literature Support for Focal Variables.
| Variables | Definition | Measures | Data Sources | Literature | Study |
|---|
| Contract ambiguity of franchisor obligations (CAFR) | The extent to which the contract, or provision of a contract, pertaining to a franchisor's obligations is reasonably susceptible to more than one interpretation | "Terms in a franchise agreement are sometimes reasonably susceptible to more than one interpretation and other times are not. The franchisor's obligations, as indicated in the terms of the contract,...""are open to interpretation/are not open to interpretation" (r)"have several meanings/have only one meaning" (r)"are ambiguous/are not ambiguous" (r) | Primary data collections (Study 1: α =.945; Study 2: αCAFR =.940, αCAFE =.957) | Farnsworth (1967), Martorana (2014) | 1, 2 |
| (CAFROa) | | The number of vague words used in franchise contracts to describe franchisor obligations for franchisor i in year t | Franchise contracts appended in FDDs | Choi and Triantis (2010), Schwartz and Scott (2008) | 3 |
| Contract completeness of franchisor obligations (CCFR) | The extent to which relevant clauses related to franchisor obligations are codified in a contract ex ante, and subsequent ex post governance efforts | "Franchise agreements can be drafted to cover many contingencies that may occur in the operations of the relationship or only a few. The franchisor's obligations, as indicated in the terms of the contract, are...""not complete/complete""not thorough/thorough""not exhaustive/exhaustive" | Primary data collections (Study 1: α =.972; Study 2: αCCFR =.911; αCCFE =.927) | Kashyap, Antia, and Frazier 2012 | 1, 2 |
| (CCFROa) | | The number of contractual clauses in a franchise contract for franchisor i and year t | | Kashyap, Antia, and Frazier (2012) | 3 |
| Contract specificity of franchisor obligations (CSFR) | The extent to which a contract states franchisor obligations explicitly, such as implementation procedures, technical specifications, resolution of problems, and so on | "Terms in a franchise agreement are sometimes detailed and specific and other time are not. The franchisor's obligations, as indicated in the terms of the contract, are...""not specific/specific""not detailed/detailed""not expressed formally/expressed formally" | Primary data collections (Study 1: α =.964; Study 2: αCSFR =.893; αCSFE =.906) | Dean, Griffith and Calantone (2016), Mooi and Ghosh (2010), Ouchi (1980) | 1, 2 |
| (CSFROa) | | The addition of the frequency of (1) a "whereas clause," (2) a "definition clause," (3) appendices, and (4) specific numbers (excluding irrelevant ones such as those indicating sequences, etc.) in each contract for franchisor i in year t | Franchise contracts appended in FDDs | Scott (2015) | 3 |
| | The number of contractual clauses in franchise contracts for franchisor i in year t | Franchise contracts appended in FDDs | Kashyap, Antia, and Frazier (2012) | 3 |
| Perceived costs of litigation of franchisee (COST) | The franchisee's perception of the time and financial cost involved in taking legal action against the franchisor | "Think about the wording of your franchise agreement in relation to the franchisor's obligations. Please indicate your agreement/disagreement with each of the following statements from 'strongly disagree' to 'strongly agree.'"I believe that it would be costly in terms of time and money to sue my franchisor for not fulfilling their responsibilities stated in the contract.""I would have to expend considerable time and money to sue my franchisor for not fulfilling their responsibilities in the contracts.""I think taking legal action against my franchisor for not meeting their responsibilities as expressed in the contract would require a significant financial and time commitment." | Primary data collections (Study 1: α =.852; Study 2: α =.933) | Mollica and Gray (2001) | 1, 2 |
| Franchisee intention to litigate (INTENT) | The franchisee's intention to take legal action against the franchisor | "Think about the wording of your franchise agreement in relation to the franchisor's obligations. Please indicate your agreement/disagreement with each of the following statements from 'strongly disagree' to 'strongly agree.'"I believe that I could sue my franchisor if they did not fulfill the terms stated in the contract.""I could initiate legal action against my franchisor if they fail to fulfill their responsibilities under the terms of the contract.""If my franchisor does not meet the terms of the contract, I could sue them." | Primary data collections (Study 1: α =.921; Study 2: α =.944) | Mollica and Gray (2001) | 1, 2 |
| Cooperative performance (COOPER) | The firm's satisfaction with the outcomes of cooperation | "Regarding your relationship with the franchisor, please indicate your agreement/disagreement with each of the following statements.""Overall, we are satisfied with the performance of this cooperation.""The cooperation has realized the goals we set out to achieve.""The cooperation has contributed to our core competencies and competitive advantages." | Primary data collection (Study 1: α =.911) | Li et al. (2010) | 1 |
| Franchisee relative response (RELRES) | The difference between franchisee's interest-based approach (which is cooperative, fostering joint problem solving and mutually acceptable solutions) and a rights-based approach (which is argumentative, focusing on legitimate claims) used to solve conflict | "Think about the wording of the franchise agreement in relation to the franchisor's obligations. How would you work to resolve disagreements in relation to this language? Please indicate your agreement/disagreement with each of the following statements from 'strongly disagree' to 'strongly agree.' When disagreements arise, we work to...""find consensus""identify opportunities for joint problem solving""find outcomes that serve our common interests""identify solutions that are mutually beneficial""reach an agreement""establish the legitimacy or illegitimacy of behaviors""determine who was right or wrong""determine who violated the terms of the contract""identify the valid and invalid actions of each party""identify violations of the norms of the agreement" | Primary data collection (Study 2: Interest-based α =.916; Rights-based α =.912) | Brett, Goldberg, and Ury (1990) | 2 |
| Franchisee-initiated litigation (FELIT) | Litigation against the franchisor initiated by the franchisee | Number of litigation initiated by franchisees for franchisor i in year t | FDDs | Antia, Zheng, and Frazier (2013) | 3 |
| Franchisor training programs (FRTPa) | | The number of training hours provided by franchisor i in year t | Bond's Franchise Guide,Entrepreneur Magazine | Palmatier, Dant, and Grewal (2007), Kalra and Soberman (2008) | 3 |
| Franchisee association (FEASSOC) | | A dichotomous variable indicating whether there is a franchisee association established within the franchise system i in year t | Bond's Franchise Guide,Entrepreneur Magazine | El Akremi, Mignonac, and Perrigot (2011) | 3 |
1 a Natural log-transformed.
2 Notes: (r) = reverse-coded.
To establish the nomological network, we measured perceived costs of litigation of franchisee (COST), franchisee intention to litigate (INTENT), and cooperative performance (COOPER). Seven-point Likert scales (1 = "strongly disagree," and 7 = "strongly agree") were used to capture these constructs. Perceived costs of litigation of franchisee and franchisee intention to litigate were measured by three-item scales, adapted from [53]. Cooperative performance was measured by a three-item scale adapted from [45].
We controlled for the respondents' years of work experience (YFR); the number of franchised outlets owned (NUMOFR); the number of years of owning franchised outlets (YOWN); and their experiences in reviewing franchise contracts (REV), which is measured by a categorical variable (1 = "I am responsible for reviewing the contract," 2 = "My attorney is responsible for reviewing the contract," and 3 = "Someone else other than my attorney or myself is responsible"). We also controlled the focal firm's sector (SECTOR) k (k = 1,..., 11) based on the North American Industry Classification System. Descriptive statistics and correlations are provided in Table 3.
Graph
Table 3. Descriptive Statistics and Correlation Matrix (Study 1).
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|
| 1. Perceived costs of litigation | 1 | | | | | | | | | |
| 2. Intention to litigate | −.59* | 1 | | | | | | | | |
| 3. Cooperative performance | .31* | −.54* | 1 | | | | | | | |
| 4. Contract ambiguity of franchisor obligationsa | .23* | −.59* | .48* | 1 | | | | | | |
| 5. Contract completeness of franchisor obligationsa | .16* | −.25* | .13 | .23* | 1 | | | | | |
| 6. Contract specificity of franchisor obligationsa | .17* | −.22* | .24* | .27* | .02 | 1 | | | | |
| 7. Years of work experience | .01 | .04 | −.09 | −.13 | −.05 | −.02 | 1 | | | |
| 8. Number of franchised outlets owned | .15* | −.09 | .07 | .04 | −.04 | .10 | .03 | 1 | | |
| 9. Years of owning franchised outlets | .08 | −.07 | .12 | .03 | −.08 | .04 | .27* | .12 | 1 | |
| 10. Experience in reviewing contract | .06 | −.03 | −.03 | .01 | −.09 | .06 | .04 | −.09 | −.06 | 1 |
| Mean(SD) | 5.66(.91) | 2.54(1.30) | 5.39 (.98) | 4.50(1.17) | 4.25(1.27) | 4.18(1.26) | 21.52(10.03) | 2.14(.93) | 8.38(3.65) | 1.26(.50) |
- 3 *p <.05.
- 4 a The construct is measured by a bipolar semantic differential scale.
- 5 Notes: N = 146.
The fit statistics of the measurement models are good (χ2(32) =56.28, p <.005; comparative fit index =.938; incremental fit index =.938; root mean square error of approximation =.066). The composite reliabilities of the latent constructs are all greater than the.70 benchmark, indicating convergent validity. Contract ambiguity of franchisor obligations presents discriminant and convergent validity from contract specificity and contract completeness of franchisor obligations, on the basis of the following. First, an oblique (quartimax) rotation yields a clean three-factor structure, in which all loadings of the items, on their corresponding factors, range in magnitude from.87 to.98, whereas the magnitude of all cross-loadings is less than.15. Second, the covariances between contract ambiguity and the other two constructs in structural equation modeling are less than.30 (rhoCA, CS = −.23; rhoCA, CC = −.22). Third, the chi-square difference tests are significantly different from 1 for each pair of constructs, with the average variances extracted being larger than the corresponding squared interconstruct correlations.
We used regression analysis to examine the effects of contract constructs of franchisor obligations on three outcome variables. The standardized coefficients provided in Table 4 indicate that greater contract ambiguity of franchisor obligations increases the perceived costs of litigation of the franchisee (b =.17, p <.05), decreases franchisee intention to litigate (b = −.55, p <.001), and increases cooperative performance (b =.43, p <.001), respectively. In contrast, greater contract completeness of franchisor obligations was found to lessen franchisee intention to litigate (b = −.13, p <.05), but did not have an effect on the perceived costs of litigation of the franchisee (b =.13, n.s.) or cooperative performance (b =.04, n.s.). Contract specificity of franchisor obligations significantly increased cooperative performance (b =.11, p <.05) but did not affect perceived costs of litigation of the franchisee (b =.10, n.s.) or franchisee intention to litigate (b = −.06, n.s.). The results demonstrate that contract ambiguity of franchisor obligations is a distinct construct from contract completeness or contract specificity of franchisor obligations.
Graph
Table 4. The Impact of Contract-related Constructs on Outcome Variables (Study 1).
| Perceived Costs of Litigation | Intention to Litigate | Cooperative Performance |
|---|
| Contract ambiguity of franchisor obligations (CAFR)a | .17 (1.98)* | −.55 (−7.79)*** | .43 (5.61)*** |
| Contract completeness of franchisor obligations (CCFR)a | .13 (1.54) | −.13 (−1.87)* | .04 (.55) |
| Contract specificity of franchisor obligations (CSFR)a | .10 (1.27) | −.06 (−1.09) | .11 (2.02)* |
| Years of work experience | .03 (.23) | −.03 (−.51) | −.04 (−.56) |
| Number of outlets owned | .12 (1.50) | −.06 (−1.03) | .03 (.46) |
| Years of owning franchised outlets | .07 (.76) | −.05 (−.65) | .13 (1.77)* |
| Experience in reviewing contracts | .08 (.15) | −.04 (−.66) | −.03 (−.35) |
| Sector dummy 1 (retail trade) | .19 (1.86)* | −.08 (−.94) | −.00 (−.01) |
| Sector dummy 2 (transportation and warehousing) | .19 (1.71)* | −.05 (−.51) | .14 (1.34) |
| Sector dummy 3 (professional, scientific and technical services) | .14 (1.35) | −.08 (−.93) | .07 (.75) |
| Sector dummy 4 (management of companies and enterprises) | .01 (.07) | .06 (.66) | .07 (.64) |
| Sector dummy 5 (cleaning and maintenance) | −.10 (−.99) | .06 (.68) | .03 (.32) |
| Sector dummy 6 (administrative and support and waste management and remediation services) | −.05 (−.41) | .06 (.66) | .06 (.55) |
| Sector dummy 7 (educational services) | .04 (.42) | −.05 (−.59) | −.06 (−.70) |
| Sector dummy 8 (health care and social assistance) | −.02 (−.18) | .03 (.37) | .09 (.89) |
| Sector dummy 9 (arts, entertainment, and recreation) | .08 (.77) | .01 (.12) | −.03 (−.28) |
| Sector dummy 10 (accommodation and food services) | .03 (.30) | .03 (.29) | .10 (.99) |
| Adjusted R2 | .084 | .365 | .251 |
| Δadjusted R2 by inclusion of CAFR | .018 | .271 | .163 |
| Δadjusted R2 by inclusion of CCFR | .009 | .011 | .003 |
| Δadjusted R2 by inclusion of CSFR | .005 | .009 | .026 |
| The highest VIF | 1.187 | 1.187 | 1.187 |
- 6 *p <.05.
- 7 **p <.01.
- 8 ***p <.001.
- 9 a The construct is measured by a bipolar semantic differential scale.
- 10 Notes: N = 146; t-statistics are in parentheses; standardized coefficients are provided.
To examine whether the proposed mediation effect of perceived costs of litigation of franchisee holds, we employed the bootstrapping procedure developed by [28], Model 4) to test for mediation (95% confidence interval (CI); 10,000 bootstrap resamples). We tested the extent to which the effect of contract ambiguity of franchisor obligations on franchisee intention to litigate was mediated by perceived costs of litigation of franchisee. The results indicated that greater contract ambiguity increased perceived costs of litigation of the franchisee (b =.17, p <.01), which in turn decreased franchisee intention to litigate (b = −.68, p <.001). In addition, the 95% CI for the indirect path through the mediator (i.e., perceived litigation cost) did not include zero (−.12; 95% CI = [−.23, −.03]). The results support our theorization that contract ambiguity of franchisor obligations influences franchisee intention to litigate through perceived costs of litigation of franchisee. We conducted similar mediation tests for contract completeness and contract specificity of franchisor obligations but did not find any significant mediating effect of perceived costs of litigation of franchisee in relation to them.
Moreover, to compare the relative importance of contract constructs, we conducted a dominance analysis by comparing the change of adjusted R-square attributable to inclusion of each construct ([ 4]). The results showed that inclusion of contract ambiguity of franchisor obligations results in the largest change in adjusted R-square for all three outcome variables (Δadjusted R2COST, CA =.018; Δadjusted R2INTENT, CA =.271; Δadjusted R2COOPER, CA =.163), indicating its importance to contractual governance. This further supports our introduction of contract ambiguity of franchisor obligations into the literature.
In Study 2, we worked to gain insight into franchisee responses to contract ambiguity of franchisor obligations. Specifically, we explored whether a greater level of contract ambiguity affects franchisee's response approach to conflicts (interest-based vs. rights-based) as hypothesized in H1, and whether perceived costs of litigation are the underlying process through which contract ambiguity of franchisor obligations influences a franchisee's intention to litigate. We developed a scenario-based experiment to allow for a formal test of H1.
We employed a one-factor, two-condition (contract ambiguity of franchisor obligations: high, low), between-subjects design. Franchisor and franchisee managers, three scholars, and two legal experts assessed the scenario wording and response formats prior to administration. Appendix A presents the manipulation of contract ambiguity of franchisor obligations.
To identify working professionals, we used Amazon's Mechanical Turk respondent pool. We invited individuals, based in the United States and qualifying if self-reporting at least five years of work experience, to participate in a study about franchising. We obtained 92 completed responses (HighCAFR n = 52; LowCAFR n = 40). Respondents averaged 29.18 years of age and 11.18 years of work experience; 75% of respondents were male and 25% were female. Respondents represented a range of economic sectors (e.g., transportation and warehousing [14.1%]; health care and social assistance [12.0%];, arts, entertainment, and recreation [12.0%]; accommodation and food services [12.0%]).
We randomly assigned respondents to one of the two treatment conditions. Respondents in each treatment condition were asked to imagine that they owned a franchise of a Hawaiian-themed restaurant chain. A summary of the franchise was then presented. The scenario indicated that all franchisees would operate under a standard franchise agreement. Respondents were asked to carefully read a section of the franchise agreement. A series of questions followed.
The questions captured the constructs in the conceptual path that we believed respondents would engage in, with regard to the task. We began by capturing interest- and rights-based conflict solving approaches, followed by perceived costs of litigation and franchisee intention to litigate. We next captured contracting constructs in relation to both franchisor and franchisee obligations as a manipulation check and then collected demographic information.
To assess the extent to which a respondent employed an interest-based (vs. a rights-based) conflict solving approach (as hypothesized in H1), we captured the difference between two five-item Likert-type scales ([48]) assessing interest- and rights-based approaches and labeled this construct franchisee relative response (RELRES). Interest-based response items included ( 1) find consensus, ( 2) identify opportunities for joint problem solving, ( 3) find outcomes that serve our common interests, ( 4) identify solutions that are mutually beneficial, and ( 5) reach an agreement. Rights-based response items included ( 1) establish the legitimacy or illegitimacy of behaviors, ( 2) determine who was right or wrong, ( 3) determine who violated the terms of the contract, ( 4) identify the valid and invalid actions of each party, and ( 5) identify violations of the norms of the agreement. Acknowledging that understanding a firm's conflict solving approach is a matter of the extent to which firms rely on each approach, we calculated the difference by deducting the rights-based from interest-based approach score. We used measures identical to those used in Study 1 for perceived costs of litigation, franchisee intention to litigation, and contract constructs (CAFR, CCFR, and CSFR, respectively; see Table 2). To minimize spuriousness of the results, we controlled for respondents' years of work experience, age, and gender.
To assess our manipulation, we examined means across treatments. The results indicated a difference in contract ambiguity of franchisor obligations across conditions (Mhigh = 5.32, Mlow = 4.59; t = 2.50, p <.01). No differences were observed for other contract characteristics across the two conditions (contract ambiguity of franchisee obligations: Mhigh = 4.76, Mlow = 4.83; t = −.21, p >.05; franchisor contract specificity [CSFR]: Mhigh = 4.17, Mlow = 4.11; t =.28, p >.05; franchisee contract specificity [CSFE] Mhigh = 4.13, Mlow = 3.98; t =.63, p >.05; franchisor contract completeness [CCFR]: Mhigh = 3.88, Mlow = 3.79; t =.33, p >.05; franchisee contract completeness [CCFE]: Mhigh = 3.94, Mlow = 3.79; t =.63, p >.05).[ 8] The results indicated a successful manipulation of contract ambiguity of franchisor obligations.
We ran a MANCOVA with the dependent variables of franchisee relative response; franchisee intention to litigate; perceived costs of litigation; and the covariates of work experience, age, and gender. Differences across treatments were observed (Wilks' Λ =.71, F = 11.77, p ≤.001). Post hoc pairwise comparisons using the Bonferroni procedure identified mean differences, consistent with our arguments, for franchisee relative response (greater interest- than rights-based response; Mhigh = 1.02, Mlow = −.52; p <.001), which supports H1. In addition, we found significant mean differences for perceived costs of litigation of the franchisee (Mhigh = 4.63, Mlow = 3.60; p <.001) as well as for franchisee intention to litigate (Mhigh = 3.35, Mlow = 3.93; p <.05). The covariates were not significant.
Next, to examine process elements of the model, we followed the bootstrapping procedure developed by [28], Model 4) to test for mediation (95% CI; 10,000 bootstrap resamples). We tested the extent to which the effect of contract ambiguity of franchisor obligations on franchisee intention to litigate was mediated by perceived costs of litigation. The results indicated that greater contract ambiguity of franchisor obligations increased perceived costs of litigation (b =.26, p <.01), which decreased franchisee intention to litigate (b = −.82, p <.001). In addition, the 95% CI for the indirect path through the mediator (i.e., perceived litigation costs) did not include zero (−.21, 95% CI: [−.38, −.03]). In addition, the direct effect of contract ambiguity of franchisor obligations on franchisee intention to litigate became insignificant when the mediator was added (b =.10, n.s.), suggesting full mediation. The results support our theorization that contract ambiguity of franchisor obligations influences franchisee intention to litigate through perceived costs of litigation.
To explore whether franchisee relative response may be a possible mediator for the relationship between contract ambiguity of franchisor obligation and franchisee intention to litigate, we added franchisee relative response as another mediator in the model (Model 4), and the results showed that franchisee relative response had no effect on franchisee intention to litigate (b =.05, n.s.), and the indirect effect though franchisee relative response was not significant (95% CI: [−.02,.07]), allowing us to reject the mediating effect of franchisee relative response.
Given the use of a difference measure (i.e., relative response), we conducted additional analysis (MANCOVA) with separate dependent variables of franchisee interest-based response; franchisee rights-based response; franchisee intention to litigate; perceived cost of litigation of franchisee; and the covariates of work experience, age, and gender. Differences across treatments were observed (Wilks' Λ =.71, F( 4, 84) = 8.87, p <.001). The covariates were not significant. Post hoc pairwise comparisons using the Bonferroni procedure identified mean differences in relation to interest-based response (Mhigh = 4.70, Mlow = 3.91; p <.001) and rights-based response (Mhigh = 3.68, Mlow = 4.44; p <.001), perceived costs of litigation of franchisee (Mhigh = 4.63, Mlow = 3.60; p <.001) as well as franchisee intention to litigate (Mhigh = 3.35, Mlow = 3.93; p <.05). We defined a planned contrast between interest-based and rights-based response and found a significant difference (MIB = 4.36, MRB = 4.00; t = 2.19, p <.05). To further understand the data, we ran a planned contrast between interest-based and rights-based response using a split file analysis. The results indicated that in the treatment with low contract ambiguity of franchisor obligations, franchisors employ a rights-based over interest-based response (MIB = 3.91, MRB = 4.44; t = −1.95, p <.05), whereas in the high contract ambiguity of franchisor obligations treatment condition franchisors employ an interest-based over a rights-based response (MIB = 4.70, MRB = 3.68; t = 7.51, p <.01).
In Study 3, we establish external validity for our model using archival data. We first tested whether greater contract ambiguity of franchisor obligations decreased franchisee-initiated litigation (H2). Next, we examined the moderating effects of franchisor training programs (H3) and the presence/absence of a franchisee association (H4). Furthermore, we explored the effect of franchisee-initiated litigation on franchise system performance.
We constructed a database by obtaining the franchise disclosure documents (FDDs) filed in 2013 by a random sample of 106 franchisors, drawn from the electronic filings of franchisors in one state of the United States that required registration of franchisors. Then, we obtained the FDDs filed with the state's regulatory authorities for the years between 2004 and 2013 and bought the ones not registered in that state during this time window for the same set of companies from FRANdata. The FDDs contained the franchise contracts and the details of franchisor-related litigation history.
We coded contract ambiguity, completeness, and specificity of both franchisor and franchisee obligations, franchisee-initiated litigation, the number of franchised outlets, financial performance of the franchise system, and total assets of the franchise system from the text of the FDDs and the franchise contracts appended in the FDDs. We then coded franchise age, franchisor training and franchisee association, and industry classification of each franchisor in our sample from Bond's Franchise Guide and Entrepreneur Magazine's Franchise 500, with the latter used as a convergent validity check and as a supplemental source when data were missing from Bond's Franchise Guide. The data set yielded an unbalanced panel data of 703 observations across 106 franchisors over a ten-year window (2004–2013).
Our unit of analysis was the individual franchise system i (i = 1,..., 106) in year t (t = 2004,..., 2013), with outcome variables being time-varying. We measured our dependent variable franchisee-initiated litigation (FELIT) as the number of litigation events initiated by franchisees for franchisor i in year t, excluding accumulative litigation from previous years.
We measured contract ambiguity of franchisor obligations (CAFRO) by the number of vague words used in the franchise contracts pertaining to franchisor obligations. Although there were many words indicating vagueness in contract drafting, we relied on an in-depth literature review of legal studies and included the words suggested by prior literature to indicate vague standards in contracts, which yielded a list of ten major words (see Appendix B). Second, following prior research (i.e., [ 2]), we had four research assistants read the franchise contracts appended in the FDDs. We had the coders count the number of vague words used in each section of the franchise contract, indicating whether the words were in relation to franchisor or franchisee obligations. The four research assistants worked in pairs. After each research assistant finished coding, the other research assistant checked the work independently and resolved any disagreements by discussion. The level of agreement between the coders was.96. In addition, one of the authors checked a random subsample of the coding to ensure convergent validity. We then aggregated the total number of vague words used in different contractual sections for franchisor obligations, including franchisors' assistance to franchisees with regard to operations, advertisement, and other miscellaneous items, for franchisor i in year t.
We measured franchisor training programs (FRTP) by the annual number of training hours provided by the franchisor, including both initial training and training throughout the year as specified in the franchise agreement, as a larger number of training hours increases communication between the franchisor and franchisees allowing for the development of shared understanding and relational norm development ([36]; [71]). Compared with other methods, training programs are considered easier and more effective for communication ([58]). Franchisee association (FEASSOC) is a dichotomous variable based on whether a franchisee association, which hosts regular meetings and other occasions for franchisees to share information, is established within the franchise system ([18]).
In addition to the hypothesized predictors, we also controlled for other factors. First, following [38], we controlled for contract completeness of franchisor obligations (CCFRO) by capturing the number of contractual clauses in franchise agreements in relation to franchisor obligations. We first generated an exhaustive list of contractual clauses regarding franchisor obligations compiled from all franchise contracts in our data set. We then had the coders count the exact number of clauses covered in each contract for franchisor i in year t. For contract specificity of franchisor obligations (CSFRO), as there was no readily available measure in the literature, we generated its measure based on the conceptualization of this construct in the law literature. Following [66], we measured contract specificity of franchisor obligations by the addition of the frequency of ( 1) a "whereas clause," ( 2) a "definition clause," ( 3) appendices, and ( 4) specific numbers (excluding irrelevant ones such as those indicating sequences) in each contract in relation to franchisor obligations. To reduce the skewness of CCFRO and CSFRO, we generated log-transformed values for both variables. In addition, we controlled for firm-specific characteristics including royalty rate (ROYAL), franchise age (AGE), number of franchised outlets (NUMFR), and total assets of franchisors (TOTASS) as reported in the FDDs, Bond's Franchise Guide, and Entrepreneur Magazine's Franchise 500. Finally, because different sectors may have different practices in contract drafting, we controlled for the focal firm's sector (SECTOR) k (k = 1,..., 11) based on the North American Industry Classification System. Table 2 provides the summary of the measurement and supporting literature. Table 5 provides the descriptive statistics and correlation matrix for the variables included in this study.
Graph
Table 5. Descriptive Statistics and Correlation Matrix (Study 3).
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|
| 1. Franchisee-initiated litigation | 1 | | | | | | | | | |
| 2. Contract ambiguity of franchisor obligationsa | −.01 | 1 | | | | | | | | |
| 3. Franchisor training programsa | .02 | .08* | 1 | | | | | | | |
| 4. Franchisee associations | −.02* | −.01 | .08* | 1 | | | | | | |
| 5. Contract completeness of franchisor obligationsa | −.15* | .41* | .17* | −.03 | 1 | | | | | |
| 6. Contract specificity of franchisor obligationsa | .01 | .13* | .20* | −.05 | .21* | 1 | | | | |
| 7. Royalty rates | −.01 | −.10* | −.22 | −.05 | −.06 | −.22* | 1 | | | |
| 8. Franchisor age | .21* | −.07* | .27* | −.07 | −.07* | .08* | −.18* | 1 | | |
| 9. Number of franchised outletsa | .11* | .11* | .16* | .31* | −.09* | .11* | −.16* | .48* | 1 | |
| 10. Total assetsa | .02 | .05 | .31* | .24* | .01 | −.01 | −.21* | .45* | .48* | 1 |
| Mean(SD) | 5.23(3.53) | 8.34(6.66) | 4.28(1.11) | .80(.47) | 3.88(.47) | 5.37(.58) | .07(.04) | 16.29(14.85) | 5.06(1.30) | 8.76(3.09) |
- 11 *p <.05.
- 12 a Natural log-transformed.
To better understand the franchise agreement, we compared the degree to which the contract obligations are ambiguous for franchisors and franchisees. To control for the influence of the length of contracts, we divided the degree of contract ambiguity, measured by the number of vague words, by the contract completeness, measured by the number of contractual clauses, for franchisor and franchisees' obligations, respectively. The average ratio (i.e., ambiguity/completeness) for franchisors' obligations was 3.02 (min = 0, max = 19, SD = 1.08). The average ratio (i.e., ambiguity/completeness) for franchisees' obligations was 1.59 (min = 0, max = 14, SD =.88). Although contract ambiguity appears larger in relation to franchisor obligations, no significant difference was observed (t = 1.03, p >.05).
To examine the role of contract ambiguity of franchisor obligations and its joint effects with franchisor training programs and the presence/absence of a franchisee association on franchisee-initiated litigation, the following model features were considered. First, we had multiple equations, with correlated error terms. As such, we needed an estimation method that would allow the simultaneous estimation of multiple equations. Second, the dependent variables are continuous in Equations 1, 2, 4, 5, 6, and 7 but are dichotomous in Equation 3. Therefore, we needed an estimation method that would allow us to simultaneously estimate equations with mixed-type outcome variables. Finally, because the data were longitudinal and had repeated observations for each franchisor i, we needed to account for such clustering effects.
Drawing on these considerations, we employed a conditional mixed process regression model (CMP). The CMP enables a simultaneous estimation of a recursive system of equations with mixed types of outcome variables that allows for clustering analysis by using a simulated maximum likelihood estimation algorithm ([26]; [62]). This approach yields consistent and more efficient estimates of interest compared with the traditional two-stage least squares method ([ 1]). Given this advantage, CMP has been used in prior literature (e.g., [ 2]; [38]).
To account for the endogeneity of contract ambiguity of franchisor obligations, franchisor training programs, and the presence of a franchisee association, we needed valid instrumental variables that both were relevant (i.e., strongly correlated with the focal variables) and met the exclusion criterion (i.e., uncorrelated with the error term of the explanatory equation). We specified first-stage equations for each of the endogenous regressors, including the three theoretical constructs (CAFRO, FRTP, FEASSOC) and three control variables (CCFRO, CSFRO, ROYAL). We employed these variables' corresponding mean values across all franchise systems in the focal franchisor's sector, excluding the focal franchisor i, lagged one year, as their instruments ([ 1]; [23]).
We tested whether the instrumental variables met the requirements of the relevance and exclusive restrictions. First, we examined the Cragg–Donald Wald F-statistics on our instrumental variables for each of the first-stage equations, which were all above the rule-of-thumb threshold of 10 ([69]), with the lowest being 293.85. This suggests that our instruments satisfy the requirement for relevance (i.e., strongly correlated with the endogenous variables). Second, we used the Durbin–Wu–Hausman test to examine exogeneity of the instruments ([60]). We tested the exogeneity of each instrumental variable, and could not reject the null hypothesis that the focal instrument was uncorrelated with the error term in the second-stage equation. These tests suggest the validity of the instrumental variables.
The empirical model is specified as follows. The first-stage equations for the endogenous variables CAFRO, FRTP, FEASSOC, CCFRO, CSFRO, ROYAL are presented in Equations 1–6. Each is regressed on its corresponding sector mean level (one year lagged). We included the one-year-lag control variables. We then specified the individual effect of contract ambiguity of franchisor obligations and its joint effects with franchisor training programs and franchisee association on franchisee-initiated litigation in Equation 7. Following prior research (e.g., [ 1]; [23]), we examined the impact of independent variables with one-year-lag term on the dependent variables and used cluster-robust error to account for the clustering effect of repeated observations for each franchisor i.
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1
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2
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3
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4
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5
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6
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7
where the variance–covariance matrix Ω ∼ MVN(µ, σ2).
Table 6 presents the standardized coefficients of CMP regression estimates. The chi-square statistic of 3,627.80 (p <.001) suggests that the predictors in our model have significant effects on the outcome variables. Our results show that contract ambiguity of franchisor obligations negatively affects franchisee-initiated litigation (b = −.08, p <.01), supporting H2.[ 9] This effect is strengthened by franchisor training programs (b = −.05, p <.01), supporting H3, and weakened by the presence of a franchisee association (b =.12, p <.05), supporting H4.
Graph
Table 6. Conditional Mixed Process Regression Estimates (Study 3).
| Contract Ambiguity of Franchisor Obligations | Franchisor Training Program | Franchisee Association | Contract Completeness of Franchisor Obligations | Contract Specificity of Franchisor Obligations | Royalty Rate | Franchisee- Initiated Litigation |
|---|
| Intercept | −46.12 (−5.67)*** | 45.69 (91.31)*** | .10 (.38) | −5.86 (−7.92)*** | 2.09 (1.94)* | .83 (1.00) | 7.07 (3.94)*** |
| Contract ambiguity of franchisor obligationsa(i)kt − 1 | .78 (23.54)*** | −.64 (−.87) | .00 (1.44) | .01 (1.18) | .36 (1.92)* | .01 (1.35) | |
| Franchisor training programsa | −.00 (−.87) | .69 (33.66)*** | .00 (.63) | .00 (.61) | −.00 (−.40) | .00 (1.10) | |
| Franchisee association(i)kt − 1 | 1.49 (1.06) | −14.83 (−1.85)* | .96 (96.43)*** | .10 (2.91)** | −.10 (−2.40)** | .03 (.96) | |
| Contract completeness of franchisor obligationsa(i)kt − 1 | −.96 (−2.36)* | 3.28 (.35) | −.01 (−.40) | .54 (14.52)*** | .10 (2.20)** | .01 (.13) | |
| Contract specificity of franchisor obligationsa(i)kt − 1 | 3.74 (1.84)* | −9.07 (−.28) | −.02 (−.38) | 1.32 (10.10)*** | .01 (2.49)** | −.12 (−.86) | |
| Royalty rates(i)kt − 1 | −10.09 (−.43) | −24.24 (−.38) | .71 (.59) | 5.44 (2.26)* | 11.09 (3.71)*** | .60 (2.52)** | |
| Contract ambiguity of franchisor obligationsa | | | | | | H2 | −.08 (−2.88)** |
| Franchisor training programsa | | | | | | | .26 (1.93)* |
| Franchisee association | | | | | | | −.21 (−.52) |
| Contract ambiguity of franchisor obligationsa × Franchisor training programsa | | | | | | H3 | −.05 (−2.63)** |
| Contract ambiguity of franchisor obligationsa × Franchisee association | | | | | | H4 | .12 (2.12)* |
| Control Variables | | | | | | | |
| Contract completeness of franchisor obligationsa | | | | | | | .52 (1.58) |
| Contract specificity of franchisor obligationsa | | | | | | | −.10 (−.23) |
| Royalty rates | −.03 (−.49) | 2.43 (1.63) | −.01 (−.17) | −.01 (−.98) | .04 (5.63)*** | −.01 (−.99) | −.17 (−1.37) |
| Franchisor age | | | | | | | −.19 (−1.46) |
| Number of franchised outletsa | .01 (.67) | .38 (1.30) | −.00 (−.70) | .01 (2.59)** | −.01 (−.86) | .00 (2.12)* | .03 (3.07)** |
| Total assetsa | −.29 (−1.71)* | 7.92 (2.09)* | −.01 (−.83) | −.03 (−1.70)* | −.03 (−1.46) | −.05 (−2.94)** | .12 (.98) |
- 13 *p <.05.
- 14 **p <.01.
- 15 ***p <.001.
- 16 a Natural log-transformed.
- 17 Notes: n = 703; z-statistics are in parentheses.
Unlike contract ambiguity of franchisor obligations, contract completeness and contract specificity of franchisor obligations did not have significant effects on franchisee-initiated litigation (b =.52, n.s.; b = −.10, n.s., respectively), indicating that these are distinct contracting concepts with differential effects. Number of franchised outlets had a positive effect on franchise-initiated litigation (b =.03, p <.01), indicating that a larger franchise system incurs more franchisee-initiated litigation.
To better understand our results, we conducted post hoc analyses. First, we explored the moderating effects by tests of the simple slopes at low (−1 SD) and high (+1 SD) values of FRTP ([15]). Figure 2, Panel A, indicates that the inverse association between CAFRO and FELIT is stronger for franchise systems with high levels of FRTP (simple slope = −.35, p <.001) compared with low levels of FRTP (simple slope = −.24, p <.001), which indicates that a high level of FRTP strengthens the negative impact of CAFRO on FELIT. Second, with regard to the effect of FEASSOC, because it is a dichotomous variable, we calculated the slopes at FEASSOC = 0 and FEASSOC = 1. Figure 2, Panel B, indicates that the negative relationship between CAFRO and FELIT is reversed for franchise systems when FEASSOC = 1 (simple slope =.04, p <.05) compared with that when FEASSOC = 0 (simple slope = −.08, p <.01). The post hoc analysis supports our hypotheses.
Graph: Figure 2. Moderating effects (Study 3).
To evaluate the substantive importance of our model, we examined whether franchisee-initiated litigation (FELIT) has a significant impact on franchise system financial performance. We specified a random-effect generalized least squares estimation to examine the effect of franchisee-initiated litigation on revenues and costs of the franchise system, as well as on franchisor net income. The results, available in Web Appendix A, show that franchisee-initiated litigation has a significant negative effect on franchise-related revenues of the firm (b = −.11, p <.001), a significant positive effect on total costs (b =.19, p <.001), and a significant negative effect on franchisor net income (b = −.07, p <.01). These findings suggest that FELIT bears significant financial implications for franchisors by affecting both the revenue and cost aspects of the franchise operations, thus also influencing franchisor net income.
To further evaluate the marginal effects of FELIT on franchisor net income, we calculated the margins of franchisor financial performance. The results indicated that the average level of financial performance we examined in our data is $646,934. In addition, results in Web Appendix A indicated that a one-unit increase in franchisee-initiated litigation leads to a 7% decrease of franchisor net income, which translates into $45,285.38 (i.e., $646,934 ×.07) in terms of franchisor net income. Incorporating the results of the simple slope analysis, at a high/low level of franchisor training program, a one-unit change in contract ambiguity of franchisor obligations incurs a −35%/−24% change in franchisee-initiated litigation, which is equivalent to a 2.45%/1.68% increase in franchisor net income. Multiplying it by $646,934, the marginal effect of contract ambiguity of franchisor obligations on franchisor net income is $15,849.88/$10,868.49, with a high/low level of franchisor training program, respectively. In a similar fashion, the marginal effect of contract ambiguity of franchisor obligations on franchisor net income is −$1,811.41/$3,622.83, with/without the presence of a franchisee association. These analyses indicate significant financial implications of using contract ambiguity of franchisor obligations strategically in contract design.
To ensure that contract ambiguity of franchisor obligations, individually and jointly with the two noncontractual elements investigated, has distinctive effects on franchisee-initiated litigation in comparison with other contract-related constructs, we ran a random-effect generalized least squares estimation of the interaction terms of franchisor training programs and franchisee association with contract completeness and contract specificity of franchisor obligations, respectively. The results, reported in Web Appendix B, indicate that except for the interactive effect between contract specificity of franchisor obligations and franchisee associations (b = −1.99, p <.05), the other interaction terms do not have significant effects on franchisee-initiated litigation, nor is the main effect of contract completeness or contract specificity of franchisor obligations significant.
This study worked to gain a greater understanding of contract ambiguity in interorganizational relationships. Specifically, we examined contract ambiguity of a franchisor's obligations, and its joint effects with franchisor training programs and presence/absence of a franchisee association, in the management of a franchise system. Our findings offer initial insights into franchise system management, providing implications for marketing academics and marketing practitioners.
First, we extend the interorganizational contracting literature (e.g., [10]; [16]; [38]; [37]; [50]) by introducing the concept of contract ambiguity from the law literature, bringing forth the likely strategic role contract ambiguity plays in governing business transactions. This advances the literature in that although legal scholars recognize contract ambiguity's prevalence and substantive impact on relationships (e.g., [12], [13]; [17]; [67]), marketing scholars have yet to investigate this topic. This work demonstrates that contract ambiguity of franchisor obligations is distinct from other contracting constructs (e.g., contract completeness and contract specificity of franchisor obligations evidenced in Studies 1–3). Furthermore, our findings provide empirical support to the arguments of [57], who contend that the power advantage of the franchisor results in the franchisor developing the contract to protect its own prerogatives.
Second, this work extends the contracting (e.g., [16]; [38]) and franchise (e.g., [ 2]; [ 9]; [37]) literature by demonstrating franchisee responses to contract ambiguity of franchisor obligations. The results indicate that contract ambiguity of a franchisor's obligations stimulates a franchisee's use of an interest-based over a rights-based conflict solving approach (Study 2) and minimizes both intended and actual franchisee-initiated litigation via increased perceived litigation costs (Studies 2 and 3). The ability of contract ambiguity of franchisor obligations to limit franchisee-initiated litigation demonstrates the strategic importance of contract ambiguity, providing empirical support to the conceptual arguments put forth by [12] and insights as to the underlying process by which this occurs. Corroborating prior research on the importance of contract design (e.g., [ 3]), our research demonstrates that a carefully designed contract can be used to foster cooperation and avoid undesirable relational outcomes in business transactions.
Third, this study extends the interorganizational literature on the joint effects of contractual and noncontractual elements (e.g., [16]; [59]). Our findings demonstrate that franchisor training programs strengthen the negative effect of contract ambiguity of franchisor obligations on franchisee-initiated litigation. Franchisor training programs provide an opportunity and context for discussion, allowing for the development of shared values and cohesion, which helps suppress conflicts in the relationship. Thus, these findings advance the literature on the development and outcomes of shared values (e.g., [ 6]; [30]) within a franchise system.
Furthermore, we find that the presence of a franchisee association can be harmful to the franchisor by mitigating the negative relationship between contract ambiguity of franchisor obligations and franchisee-initiated litigation. These findings, consistent with the arguments of countervailing power ([22]), suggest that a franchisee association builds bonds between franchisees, who work together to offset the power differential in the franchise system (evidenced by the case of Deli Delicious noted previously). This finding extends the literature on franchisee associations (e.g., [18]), demonstrating the consequences of cohesion when connecting peer members of a franchise system. In a larger context, our work contributes to the literature on agent cooperation or agent collusion in the multi-agent context (e.g., [46]; [77]).
Our results reveal several actionable insights for interorganizational governance. First, for franchisors, contract ambiguity of franchisor obligations can be a strategic tool deployed to enhance joint problem solving and collaboration and to deter franchisee-initiated litigation, enhancing financial performance of the franchise system. As indicated by our post hoc analysis, a one-unit increase in franchisee-initiated litigation leads to a 7% (i.e., $45,285.38) decrease in franchisor net income, with more substantive changes when considering the joint effects of noncontractual elements. These results highlight the strategic importance of contract ambiguity of franchisor obligations in hedging against franchisee litigation. As contracts predominate in interorganizational governance, our findings also bear implications to certain interorganizational contexts. For example, the franchisor–franchisee context mirrors the governance context of powerful manufacturers, which act as contract drafters, with less powerful suppliers, which are contract takers.
Second, our work highlights the importance of noncontractual elements in interorganizational governance. Our findings suggest that franchisor training programs, when combined with contract ambiguity of franchisor obligations, serve as a buffer against franchisee litigation. These results highlight the importance of franchisors not only viewing training programs as vehicles for increased franchisee efficiency but also perceiving such efforts as an important mechanism that can aid in socializing franchisees, thereby facilitating the management of the franchise system. Thus, we recommend that franchisors invest in training programs that can build shared values with franchisees to enhance cohesion. Extending this to the broader interorganizational governance context, we would highlight the simultaneous practice of socialization efforts with employment of contract ambiguity. For example, a powerful retailer may use supplier training programs as an opportunity to clarify misunderstandings that may arise from the contract, facilitating joint problem solving and collaboration.
Third, our results suggest franchisors should be aware of the potential negative consequences of a franchisee association, carefully managing relations with an association for the betterment of the franchise system. Consider the case of Denny's. In 1988 Denny's formed the Denny's Franchisee Council. This council gave franchisees a voice with which to communicate with corporate. However, the Denny's Franchisee Council was abolished in 1997, becoming independent from corporate sponsorship, reforming as the Denny's Franchisee Association. This may be an example wherein the franchisor-sponsored association stimulated bonding between franchisees (instead of bonding with the franchisor), creating countervailing power to the detriment of the franchisor. These findings also extend to other network governance situations. For example, several manufacturers have crated virtual supplier portals, working to not only build relationships with suppliers but also encourage suppliers to connect with one another. Our findings, applied to this context, would caution that the increased connectivity and bonding among suppliers could work counter to the manufacturer's governance efforts.
Although this work makes several contributions to the literature, it is not without its limitations. First, while we find that franchisors use contract ambiguity of franchisor obligations as a cost inhibitor to minimize litigation, there may be other explanations as to why contract ambiguity of franchisor obligations exists. For example, scholars may want to investigate whether franchisors employ contract ambiguity of franchisor obligations with the intention of engaging in active or passive opportunism ([68]). This seems plausible, as scholars have argued that opportunism occurs within franchise systems (e.g., [18]; [74]).
Second, franchisors draft agreements with contract ambiguity of both franchisor and franchisee obligations. Scholars could investigate what stimulates heightened levels of contract ambiguity of franchisee obligations. One potential avenue of investigation could be the concept of fairness (e.g., [40]; [43]). For example, scholars could examine whether potential franchisees are more likely to enter into, or remain a part of, a franchise system in which contract ambiguity of members' obligations is roughly equivalent, resulting from perceptions of fairness.
Third, while this work brought forth new insights into the effects of a franchisee association, more insights may be gained by examining the effects of different types of associations. For instance, franchisees could participate in a franchisor-sponsored association, franchise-system-specific associations controlled by franchisees, or an independent franchisee association (e.g., American Association of Franchisees and Dealers). Although all of these associations provide a context for socialization among franchisees, they may provide different opportunities for franchisor–franchisee socialization, thereby influencing franchise system governance. Investigation of the possible discrete effects across association types is warranted.
Fourth, this work was specifically placed within the context of a franchise system. However, as we have noted throughout, there are multiple situations in which power differentials in interorganizational relationships exist, wherein one party may want to exert strategic influence through the inclusion of contract ambiguity in relation to its obligations (e.g., powerful buyers working with less powerful suppliers, large retailers working with small brand manufacturers in a store-within-store context, firms possessing highly valued assets engaging in alliances). Future research should test the boundary parameters of the phenomenon.
Supplemental Material, jm.18.0413-File003 - Effects of Contract Ambiguity in Interorganizational Governance
Supplemental Material, jm.18.0413-File003 for Effects of Contract Ambiguity in Interorganizational Governance by Xu (Vivian) Zheng, David A. Griffith, Ling Ge and Uri Benoliel in Journal of Marketing
Imagine that you own a franchise of the Hawaiian-themed restaurant chain named Taste of the Islands. The franchise, based in Honolulu, Hawaii, was established in 1991 and currently has 285 locations across the United States. The chain is centered on a menu offering of the traditional Hawaiian plate lunch. A standard plate lunch consists of two scoops of white rice, macaroni salad, and an entrée (e.g., beef teriyaki, spicy shrimp).
All franchisees operate under a standard franchise agreement covering all aspects of the business. Please read Section 9 of the franchise agreement very carefully. You will be asked a series of questions pertaining to the responsibilities of both the franchisor and franchisees as detailed in Section 9.
- The Franchise must be managed by a Person who has successfully completed, within ninety (90) days of employment, the training program(s) required by Franchisor.
- Franchisor will make a reasonable effort to [must] offer monthly training courses (in-person or online) for personnel engaged in operating or managing Taste of the Islands Restaurants.
- Franchisee must conduct review training session for all Franchisee's employees monthly for them to properly operate, administer and manage the Restaurant in accordance with the Standards.
- Franchisor will make a good faith effort to [must] provide updated training materials on a quarterly basis based on best practices identified within the franchise system.
- For all activities under Section 9, Franchisee will be responsible for paying all Travel Expenses, and the salary and other compensation for individuals while they attend training.
- Franchisor will make a good faith effort to [must] rotate training locations throughout the United States in an effort to fairly balance costs of travel to Franchisees.
Franchisor will make a good faith effort to [must] make its representatives available at Franchisor's designated offices at reasonable hours [during normal business hours] to consult with and advise (but not provide legal counsel) Franchisee regarding the design, operation, and management of the Restaurant as a Taste of the Islands restaurant.
Graph
| Words | References |
|---|
| 1. Reasonable/unreasonable, reasonably/unreasonably | "contract terms under a vague standard such as...reasonableness" (Henderson 2012, p. 129)"implied standards, such as reasonableness" (Schwartz and Scott 2008, p. 1662) |
| 2. Best efforts | "We suggested that parties may choose a vague standard (such as 'best efforts')" (Choi and Triantis 2010, p. 852)."parties to complex transactions sometimes use vague standards such as 'best efforts'" (Schwartz and Scott 2008, p. 1662) |
| 3. Good faith/bad faith | "implied standards, such as...good faith" (Schwartz and Scott 2008, p. 1662) |
| 4. Fair/unfair, fairly/unfairly | "contract terms under a vague standard such as fairness" (Henderson 2012, p. 129) |
| 5. Satisfaction/unsatisfaction, satisfactory/unsatisfactory | "vague standard, which includes the word 'satisfaction'" (Stewart 2006, p. 231) |
| 6. Adequate/inadequate | "This vague standard of "adequate representation" (Scott 2002, p. 586)"Vague standards of review, such as the 'fair, adequate, and in the public interest'" (Lund 2015, p. 67) |
| 7. Equitable/inequitable | "The vague standard of 'equitable utilization'" (Benvenisti 1996, p. 402) |
| 8. Sufficient/insufficient | "vague standards such as 'sufficient' analysis" (Bilir 2012, p. 152) |
| 9. Appropriate/inappropriate | "vague standard such as 'appropriateness'" (Henderson 1990, p. 765). |
| 10. Significant/insignificant, significantly/insignificantly | "Obviously, such a vague standard as 'significant' presents an easy run-around for states wishing to make cuts to provider payments" (Clark 2012, p. 201) |
Footnotes 1 Associate EditorStefan Wuyts
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors gratefully acknowledge the financial support of research fund from the Research Grant Council of Hong Kong SAR (11500915).
4 ORCID iDXu (Vivian) Zheng https://orcid.org/0000-0002-5562-0253
5 Online supplement: https://doi.org/10.1177/0022242920910096
6 1We note that other contract constructs are also used in the literature. For example, "detailed contract drafting" is defined as the specification within the contract of the roles and responsibilities to be performed, the outcomes to be delivered, and the adaptive processes for resolving unforeseeable outcomes (e.g., [75]). Similarly, "explicit contracting" is defined as the extent to which a contract attempts to see into the future and explicitly state today (i.e., in the present) how various situations that might occur in the future would be handled if they were to occur (e.g., [50]).
7 2Prior research has investigated contracting constructs at the dyad level (i.e., overall level of a contract's completeness). In the current article, given that the franchise agreements are unilaterally designed by the franchisor (e.g., [41]) and aim to protect the franchisor's prerogatives ([57]), we take a one-sided perspective. We believe this approach is appropriate and consistent with the literature, which has examined constructs such as trust, commitment, dependence, and transaction-specific investments, either from a dyadic or a one-sided perspective, based on the phenomenon being studied.
8 3In an effort to clearly distinguish contract elements related to the franchisor (FR), we collected perceptions related to franchisee (FE) obligations as well. We used items for our contracting constructs presented in Table 2 and adapted the lead-in to each construct. For example, we stated "The franchisee's obligations, as indicated in the terms of the contract, are..."
9 4We also examined the moderating effect of franchisor training program on the relationship between contract ambiguity of franchisor obligations and franchisees' intention to litigate using data from Study 1. Our findings were consistent with those of Study 3 (even though measures and constructs were slightly different).
5We have italicized text in this section to differentiate the different conditions; text was not italicized for participants.
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By Xu (Vivian) Zheng; David A. Griffith; Ling Ge and Uri Benoliel
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Record: 70- Effects of Liberalization on Incumbent Firms' Marketing-Mix Responses and Performance: Evidence from a Quasi-Experiment. By: Ramani, Nandini; Srinivasan, Raji. Journal of Marketing. Sep2019, Vol. 83 Issue 5, p97-114. 18p. 2 Diagrams, 6 Charts, 1 Graph. DOI: 10.1177/0022242919860085.
- Database:
- Business Source Complete
Effects of Liberalization on Incumbent Firms' Marketing-Mix Responses and Performance: Evidence from a Quasi-Experiment
Many markets are liberalizing by opening up their economies to foreign competition, with the expectation that this will increase economic growth. While foreign competitors with superior technology and management practices pose serious threats to incumbent firms, they also provide them an opportunity to gain new marketing knowledge. How do incumbent firms respond to liberalization? Can incumbent firms' marketing-mix responses affect their performance following liberalization? Addressing these questions, the authors examine incumbent firms' marketing-mix responses to liberalization and the impact of these responses on performance, using the quasi-experiment of liberalization reforms in India. Estimation results from a panel of 3,927 firms in the period 1989–2000 suggest that while all incumbent firms intensified their product and promotions in response to liberalization, only incumbent firms with greater domestic market knowledge intensified their advertising and distribution responses. Furthermore, incumbent firms' marketing-mix responses significantly affect their performance outcomes. The research's findings extend theory and provide practical guidelines on how incumbent firms can design marketing-mix responses to liberalization to improve performance.
Keywords: emerging markets; incumbent performance; liberalization; marketing-mix responses; quasi-experiment
In recent years, several governments have liberalized, opening their domestic markets to foreign investment. Some have argued that liberalization of a market stimulates economic growth ([34]). Yet, because of the superior technologies, products, and management practices of foreign firms, managers of incumbent firms worry that liberalization will hurt their firms' performance ([58]). How do incumbent firms respond to the opening up of their markets through liberalization? In this research, we examine incumbent firms' marketing-mix responses to liberalization.
From a theoretical perspective, a significant body of work has examined firms' responses to competition. Extant marketing literature on foreign competition studying the opening up of markets has examined the entry strategies of grocery retail firms into transition economies following liberalization ([30]) and the entry of multinational firms into fast-growing emerging markets ([41]). From an incumbent firm perspective, extant research in marketing has examined the effect of new product introductions, short-term marketing attacks, and new domestic firm entry on incumbent firms' marketing responses and performance ([ 4]; [32]; [53]; [59]; [63]).
However, the marketing literature has overlooked how liberalization affects the marketing-mix responses of incumbent firms. We consider this a surprising omission, as liberalization represents a dramatic transition from a protected market to a more open, consumer-oriented market ([46]), suggesting an important role for incumbent firms' marketing-mix responses. In addition, existing studies on incumbent firms' responses have focused on a single industry, such as retail or banking, studying marketing-mix responses specific to the industry in question, which raises the question: what are the common factors that drive marketing-mix responses of firms in different industries to liberalization? In Figure 1, we present an organizing framework for the extant research on competition, which we further elaborate on in Table A1 of Web Appendix W1.
Graph: Figure 1. Organizing framework for research on competition.
Scholars in economics and international business have studied liberalization, focusing on the effects of liberalization on the performance of incumbent firms. Some studies report that liberalization improves incumbent firms' performance (e.g., [37]) through access to new knowledge, whereas others report that liberalization can hurt incumbent firms' performance (e.g., [ 5]; [43]). One reason for these conflicting findings may be that incumbent firms differ in their marketing-mix responses to liberalization, which in turn may affect their performance. Yet, as we noted previously, incumbent firms' marketing-mix responses to liberalization have been overlooked in the literature.
Crucially, although prior research has examined firms' marketing-mix responses to domestic competition ([ 4]; [63]), from the perspective of incumbent firms, liberalization creates distinct competitive characteristics with no comparable analog in domestic competition. Following liberalization, there are many more new competitors originating in different countries with superior marketing and management practices ([36]; [51]) than in domestic competition. Thus, for incumbent firms, liberalization is a source of knowledge about superior marketing and management practices. At the same time, following liberalization, incumbent firms, relative to foreign entrants, have an advantage in their knowledge of domestic institutions and markets. Consequently, it is unclear whether insights on incumbent firms' marketing-mix responses to domestic competition, the primary focus of previous research, apply to liberalization.
Thus, we examine incumbent firms' marketing-mix responses to liberalization and how their marketing-mix responses, in turn, affect their performance. We consider the "4Ps" of incumbent firms' marketing-mix responses, which we have adapted as advertising, product, promotions, and distribution ([ 9]). We note that although the traditional conceptualization of the 4Ps of the marketing mix includes price, we are unable to use it because the unit of analysis for price is a product, rather than the firm, which is the unit of analysis for this research. Instead, we consider the firm's promotions—that is, its spending on discounts and rebates, which is available at the firm level and may be considered a proxy for its pricing strategies. Specifically, we examine whether incumbent firms intensify (i.e., increase) their marketing-mix responses or mute (i.e., decrease) them in response to liberalization.
We note that there are different types of liberalization, including trade liberalization, which lowers import tariffs to bring in cheaper products; stock market liberalization, which allows foreigners to purchase shares in the country's stock market; and foreign direct investment (FDI) liberalization, which removes restrictions related to foreign firm ownership, encouraging foreign firm entry in a market. Because FDI liberalization not only increases competition for incumbent firms but also creates learning opportunities for them through observation of foreign entrants, we focus on the effects of FDI liberalization on incumbent firms' marketing-mix responses.
The research's findings also have high managerial relevance. Countries liberalize in a quest for economic growth, creating challenges for managers of incumbent firms who are, naturally, concerned about the negative effects of liberalization. By studying the factors that influence incumbent firms' marketing-mix responses and the effects of these responses on performance, this research provides managers of incumbent firms insights on developing appropriate marketing-mix responses to liberalization.
Knowledge is a key asset representing a source of sustainable competitive advantage for firms ([33]; [52]). Applying this concept, we propose that incumbent firms' knowledge plays a unique role in the context of liberalization. Although incumbent firms have an advantage in their knowledge of domestic institutions and market forces, compared with foreign entrants, they are disadvantaged in their knowledge of superior management practices and how to effectively operate in liberalized open markets. Anchoring our theoretical development in the knowledge-based view of the firm ([33]), we propose that the domestic market knowledge and foreign market knowledge of incumbent firms will influence their marketing-mix responses (i.e., advertising, product, promotions, and distribution) to liberalization.
To establish empirical identification of the effects of liberalization on incumbent firms' marketing-mix responses, we seek a context where the liberalization of the market is exogenous. One such context is India in 1991, where, following a severe balance of payments crisis, the Indian government enacted FDI liberalization reforms. We use the exogenous variation in the FDI liberalization of Indian industries (some industries were liberalized while others were not) to estimate the causal effect of liberalization on incumbent firms' marketing-mix responses using a differences-in-differences approach. We measure incumbent firms' domestic market knowledge by their business group affiliation and their foreign market knowledge by their foreign exchange earnings and spending. We then examine the effects of incumbent firms' marketing-mix responses on their performance, measured by their profitability.
The results indicate that, on average, incumbent firms intensified their product and promotion responses to liberalization. Furthermore, there is heterogeneity in incumbent firms' marketing-mix responses to liberalization: whereas incumbent firms with greater domestic market knowledge intensified their marketing-mix responses to liberalization, incumbent firms with greater foreign market knowledge muted their marketing-mix responses. Additional analysis relating incumbent firms' marketing-mix responses to liberalization to their performance indicates contingent effects based on their domestic and foreign market knowledge.
The study's findings make four contributions to the extant literature. First, we extend the marketing-mix response literature, which has primarily focused on incumbent firms' responses to domestic competition, by generating insights on incumbent firms' marketing-mix responses to liberalization, an important and substantive context which has hitherto not been examined in the literature. Second, the extant marketing literature on incumbent firm responses has primarily focused on how the size and financial capacity of incumbent firms affect their marketing-mix responses and performance. By demonstrating the key role of domestic and foreign market knowledge of incumbent firms on their marketing-mix responses, we identify a novel driver of their responses. Third, we also contribute to the marketing metrics literature by considering all four marketing-mix variables (advertising, product, promotions, and distribution) driven in part by our focus on a key emerging market (India) and the use of the Prowess database of the Centre for Monitoring Indian Economy (CMIE). In this regard, we note that much of the extant research in marketing metrics has focused on advertising and research-and-development (R&D) spending. Finally, this research's findings also contribute to the economics and international business literature on liberalization, which has overlooked the marketing attributes of incumbent firms.
For managerial practice, the integration of the various findings across incumbent firms' marketing-mix responses and performance models indicates that a more intense marketing-mix response following liberalization does not necessarily ensure superior performance. The specific pattern of findings generates actionable guidelines for managers of incumbent firms facing liberalization, based on their domestic and foreign market knowledge. Our findings are also useful to ( 1) managers of foreign firms entering liberalized markets, to identify which incumbent firms will be strong competitors; ( 2) policy makers, to help incumbent firms perform better following liberalization, thus achieving economic growth without hurting domestic firms; and 3) investors, to identify incumbent firms for investment in newly liberalized markets.
We organize the rest of the article as follows. We first present our conceptual framework and theory related to the effects of liberalization on incumbent firms' marketing-mix responses. We then discuss the data, method, and results. We conclude with a discussion of the paper's theoretical contributions, implications for marketing practice, and limitations and opportunities for further research.
We first provide an overview of liberalization and discuss how liberalization differs from domestic competition, the focus of most previous research on competition in the marketing literature. Next, we discuss the related literature that informs incumbent firms' marketing-mix responses to liberalization. Following that, using the knowledge-based view of the firm ([33]) as the theoretical anchor, we propose that incumbent firms' domestic market knowledge and foreign market knowledge will influence their marketing-mix responses to liberalization.
Liberalization intensifies competition in an industry by increasing the number of firms and introducing new ways to compete in the marketplace ([16]). Typically, during liberalization, the permitted level of foreign ownership of firms is increased to encourage the entry of foreign firms. If a foreign firm is already in the market, liberalization provides the firm's foreign owner an opportunity to increase ownership and control over its operations in the market ([21]). Liberalization is undertaken by host countries both in the expectation of generating foreign exchange and jobs and, more importantly, to realize benefits to the economy through spillovers from foreign firms ([28]). Spillovers from liberalization include, for example, improvements in incumbent firms' practices through diffusion of the superior management practices of foreign firms entering the market ([73]). Following liberalization, the host country devotes considerable attention and resources to attracting foreign firm investments (Kosová [43]).
Foreign firms that enter a market following liberalization are typically multinational enterprises that take advantage of differences in knowledge and expertise around the world ([71]). Thus, through exposure to sophisticated foreign competitors, liberalization enables incumbent firms to acquire knowledge of efficient production techniques, innovative marketing strategies, and novel technologies and product designs ([12]). For all of these reasons, we suggest that liberalization offers incumbent firms opportunities for learning, which occurs when their experiences generate systematic changes in their behaviors ([49]).
Liberalization differs on multiple dimensions from domestic competition, the primary focus of most prior marketing scholarship (e.g., [25]; [53]). First, when markets are liberalized, incumbent firms that have been accustomed to operating in a protected market experience a shock, as the market transitions from one that is closed to one that is more consumer oriented ([10]). Following liberalization, incumbent firms with experience only in protected markets may be disadvantaged relative to foreign entrants with experience in liberalized, open markets ([38]). Thus, incumbent firms may be unfamiliar with the strategies and practices of foreign firms entering the market following liberalization. Compared with domestic competition, liberalization is likely to create high uncertainty for incumbent firms. Second, foreign entrants following liberalization have superior marketing and management practices and strengths in creating intangible assets such as brands and/or innovative technologies ([51]; [35]) to which incumbent firms may have had little prior exposure. Thus, liberalization may be a source of substantial knowledge for incumbent firms, which can learn more about new technologies, brand management, and business processes from their foreign competitors ([11]) than may be possible from domestic competitors. Third, the foreign firms encountered by incumbent firms following liberalization originate in different countries ([72]). Thus, following liberalization, there is greater heterogeneity in the competitors faced by incumbent firms. This is in contrast to domestic competition, in which, by definition, competitors are from the local market. Finally, a competitive advantage for incumbent firms facing liberalization is their knowledge of domestic consumers, market forces, and institutions, which is less likely to be an advantage during domestic competition, in which all firms have this common local knowledge.
Scholars in economics and international business have devoted attention to identifying the effects of liberalization on incumbent firms' performance ([ 5]; [37]). Liberalization can improve incumbent firms' performance by enabling them to acquire new knowledge from foreign firms ([61]). However, foreign firms can crowd incumbent firms out of markets for product, labor, and capital, causing them to lose market share and/or exit the industry ([ 2]). In summary, there is mixed evidence on how liberalization affects incumbent firms' performance, which appears to vary on the basis of their size, financial strength, and technological capabilities ([14]). We conjecture that one reason for the mixed evidence is that prior research has ignored the marketing responses of incumbent firms to liberalization, a crucial market disruption. Thus, we contend that a more complete picture of the effects of liberalization on incumbent firms' performance can be obtained through a study of the marketing-mix responses of incumbent firms to liberalization.
Marketing can improve firm performance by increasing brand equity, customer equity, and customer satisfaction ([ 3]; [29]), all of which can help incumbent firms compete against incoming foreign firms following liberalization. In addition, foreign firms that enter a market following liberalization have strong intangible assets and marketing practices ([36]). Thus, incumbent firms may need to intensify their marketing-mix responses to counteract foreign competitors. Furthermore, observing and imitating foreign entrants may cause incumbent firms to intensify their marketing-mix responses. Finally, liberalization alters demand patterns, highlighting the need for firms to offer and deliver new products and services in new ways valued by consumers. Thus, it may be necessary for incumbent firms to intensify their marketing-mix responses to maintain strong performance. Next, we provide a brief description of how intensifying the four marketing-mix responses may improve incumbent firms' performance following liberalization, making them better positioned to compete in the new environment.
Advertising strengthens brand image, leads to greater awareness ([56]), differentiates products, and builds brand equity ([42]). Advertising also signals product quality, creating a positive and enduring effect on sales ([ 9]) and profitability through differentiation.
Like advertising, product activity (e.g., innovations) enhances perceived quality, increases purchase likelihood, and builds brand equity ([13]). Product line length, specifically, is positively related to sales and performance in the long run ([ 8]).
Promotions allow a firm to increase the sales of its products and extract cash flows from its intangible assets of brand equity and technology ([15]).
Distribution ensures that a firm's products are readily available, increases customer demand, and is a strong driver of both its sales and profits ([ 8]; [60]).
There are many reasons why the entry of foreign firms after liberalization may affect incumbent firms' marketing-mix responses. First, following liberalization, the market transitions from being protected to consumer oriented. To counter the uncertainty of the changing environment, incumbent firms may respond by intensifying their marketing-mix responses—that is, advertising, product, promotions, and distribution ([ 9]).
Second, foreign firms that enter a market after liberalization, with their superior intangible assets and marketing practices ([36]; [51]), present learning opportunities for incumbent firms ([49]). Thus, any knowledge transfers from foreign firms to incumbent firms—through imitation, forward and backward linkages with suppliers and distributors, and employee transfers ([61])—may increase the intensity of incumbent firms' marketing-mix responses.
Third, incumbent firms' existing ties to trade partners, brand recognition, and knowledge of consumers may be a competitive advantage that foreign entrants lack. To utilize this competitive advantage, following liberalization, incumbent firms may intensify their marketing-mix responses so that they can deliver products that consumers value and better cater to consumers' preferences; this, in turn, may help incumbent firms retain consumers as well as maintain and improve their performance.
Finally, because foreign entry following liberalization typically entails local manufacturing by foreign firms, they may have made significant local financial investments, signaling their long-term commitment to the local market. Most foreign firms that enter a market after liberalization possess strong financial and managerial resources ([51]). Thus, incumbent firms may infer that these foreign entrants will continue to operate in the market in the long run even if they are not profitable in the short run ([44]). Given the serious nature of the competitive threat posed by foreign firms, incumbent firms may respond by intensifying their marketing-mix responses. Accordingly, we hypothesize:
- H1: In response to liberalization, incumbent firms intensify their marketing mix.
Firms compete not only through the creation, replication, and transfer of their own knowledge but also through their ability to absorb the knowledge of competitors ([71]). Thus, following liberalization, incumbent firms' responses and performance will be determined by their existing knowledge as well as their ability to absorb new knowledge about superior management and marketing practices from foreign entrants.
A firm's ability to recognize the value of new, external knowledge, assimilate it, and apply it to commercial ends is largely a function of its prior knowledge ([23]). Because liberalization creates knowledge diffusion of superior management and marketing practices, we propose that incumbent firms' existing knowledge will affect their ability to absorb new knowledge from foreign entrants, which, in turn, will affect their marketing-mix responses. Following this line of reasoning, we propose that incumbent firms' marketing-mix responses to liberalization will be influenced by their knowledge, which enhances their responsiveness and adaptability to institutional changes ([33]).
Specifically, we propose that, when faced with liberalization, incumbent firms with greater domestic market knowledge are better situated to learn superior management and marketing practices from foreign entrants ([23]; [50]). Likewise, we propose that incumbent firms' prior knowledge of foreign markets, firms, and consumers may help them assess the threat posed by foreign firms and learn from these firms' marketing actions ([11]). Thus, we propose that incumbent firms' domestic and foreign market knowledge will influence their marketing-mix responses to liberalization. We present the conceptual framework in Figure 2.
Graph: Figure 2. Conceptual framework relating liberalization to incumbent firms' marketing-mix responses.Notes: For ease of presentation, we do not show the main effects of the various moderators on incumbent firms' marketing-mix responses, though we do include these in our estimated model.
Incumbent firms with greater domestic market knowledge have accumulated knowledge of the tastes and preferences of domestic consumers and the practices of domestic trade partners ([65]). This domestic knowledge will be a source of competitive advantage relative to foreign entrants, which incumbent firms can leverage to better address the market's needs by intensifying their marketing-mix responses. Incumbent firms' domestic market knowledge may create a synergistic effect, enabling them to learn effectively from foreign firms. Thus, we propose that incumbent firms with higher domestic market knowledge may imitate foreign entrants that are much stronger in marketing, thereby intensifying their marketing-mix responses.
Moreover, because incumbent firms with greater domestic knowledge have superior knowledge of domestic institutions and market forces ([50]), they may have greater awareness of the competitive threats posed by foreign entrants following liberalization and respond to these threats by intensifying their marketing-mix responses. Firms perceive competitors with similar resources and capabilities as relevant threats ([20]). Consequently, following liberalization, incumbent firms with high domestic market knowledge may consider incoming foreign firms their natural competitors and, in response, intensify their marketing mix. Thus, we hypothesize:
- H2: The effect of liberalization on incumbent firms' marketing-mix responses is stronger for incumbent firms with high (vs. low) domestic market knowledge.
Incumbent firms with greater foreign market knowledge, by definition, may have prior insights into the marketing practices of foreign firms. Through their ties with various economic actors in foreign markets (e.g., importers, exporters, trade partners, buyers, competitors, and governments), firms with greater foreign market knowledge can learn about more efficient product designs and marketing strategies deployed by foreign firms ([27]). Thus, such prior foreign market knowledge may enable incumbent firms to absorb new knowledge from foreign entrants and imitate their practices. Furthermore, foreign market knowledge may increase incumbent firms' awareness of the superiority of foreign firms, causing them to perceive foreign entrants as strong threats. All of these arguments suggest that incumbent firms with foreign market knowledge may intensify their marketing-mix responses to liberalization.
At the same time, as incumbent firms with greater foreign market knowledge may have competed with foreign firms in other markets, they may have already increased their marketing mix to attract foreign buyers and may not intensify their marketing-mix responses to liberalization. Furthermore, the knowledge gap on marketing practices between incumbent firms with foreign market knowledge and foreign entrants may not be substantial, such that liberalization may not provide strong learning opportunities for these incumbent firms ([47]). Thus, incumbent firms with greater foreign market knowledge may mute their marketing-mix responses to liberalization. Given that the extant literature suggests competing predictions of how incumbent firms' foreign market knowledge may affect their marketing-mix responses, we propose the following competing hypotheses:
- H3: The effect of liberalization on incumbent firms' marketing-mix responses is (a) stronger or (b) weaker for incumbent firms with higher foreign market knowledge.
Liberalization creates uncertainty and turbulence for incumbent firms as well as opportunities to learn superior management and marketing practices from incoming foreign firms, to which incumbent firms may respond by intensifying their marketing mix. Incumbent firms' marketing-mix responses may be influenced by their extant knowledge—specifically, their domestic market knowledge and foreign market knowledge. While incumbent firms' domestic market knowledge may cause them to intensify their marketing-mix responses, the extant literature provides competing predictions on how incumbent firms' foreign market knowledge may influence their marketing-mix responses, which we resolve empirically.
A key concern for estimating the effects of liberalization on incumbent firms' marketing-mix responses is endogeneity, which can arise from two primary sources: reverse causality, and omitted variables. Reverse causality may occur if incumbent firms' marketing-mix responses erect strategic barriers, discouraging foreign firms from performing effectively in the new market. Alternatively, omitted variables can be a source of endogeneity, as foreign firms may enter markets with high growth prospects or weak incumbent firms. Because the inclusion of firm-level controls and firm fixed effects may not capture all sources of endogeneity, we seek a context with an exogenous shock of liberalization, which provides a quasi-experimental setting for hypothesis testing. One such context, which provides an institutional setting to make robust inferences, is India's FDI liberalization reform in 1991, as we describe next.
Before 1991, the Indian government had a protectionist, inward-focused economic policy. In early 1991, various macroeconomic developments including deficits, increase in oil prices, and political uncertainty led to a balance-of-payments crisis in the Indian economy. To manage this economic crisis, the Indian government sought financial assistance from the International Monetary Fund, which offered support conditional on the implementation of liberalization reforms that would integrate the Indian economy with the global economy ([66]). In response, the Indian government enacted FDI liberalization reforms in August 1991. This liberalization entailed the reduction of FDI barriers (i.e., the percentage of FDI equity allowed was increased from 40% to 51% in 46 of the 129 primary industry categories defined according to a three-digit industrial code; [55]). In the remaining industries, the limit on FDI equity remained at 40%, and foreign investors had to obtain approval from the Indian government to increase their investment above 40%. After liberalization, FDI inflows dramatically increased. The stock of FDI in India increased from less than US$155 million in 1991 to US$586 million by 1993 ([54]).
To eliminate political opposition to the FDI liberalization reform, the Indian parliament enacted it without much debate, creating an exogenous shock for incumbent firms. As Dr. Chelliah, a member of the Planning Commission (the body responsible for the reform) noted, "When we started economic reforms in 1991...we didn't have time to sit down and think exactly what kind of a development model we needed....There was no systematic attempt to see two things: one, how have the benefits of reforms distributed, and two, ultimately what kind of society we want to have, what model of development should we have?" ([68]). In support of the view that India's 1991 liberalization reform was an exogenous shock, it has been used as a quasi-experimental setting in the economics ([ 1]; [66]) and finance ([ 6]) literature streams.
Thus, we exploit the exogenous shock of FDI liberalization reforms in India in 1991 to estimate the causal effects of liberalization on incumbent firms. The exogenous FDI liberalization reform offers two advantages with respect to estimation. First, it enables us to alleviate concerns resulting from endogeneity. Because the FDI liberalization reform was sudden and unanticipated, there was no time for firms to lobby for or against it, precluding concerns of reverse causality. Furthermore, the presence of restrictive policies related to foreign competition before the reform prevents an unobserved variable (e.g., the firm's intention to compete with foreign firms) from influencing the key explanatory variables (domestic and foreign market knowledge) and dependent variables (marketing-mix responses). Second, the quasi-experimental setting of the FDI liberalization reform in India (i.e., by which some but not other industries were liberalized) enables us to account for other macroeconomic factors that may affect incumbent firms' marketing-mix responses. Thus, we are able to isolate the causal effect of liberalization on the marketing-mix responses of incumbent firms ([67]) and the contingent effects of incumbent firms' knowledge on their marketing-mix responses to liberalization.
The Industrial Policy Resolution of 1991 ([55]) provides the list of industries that were liberalized to FDI. We empirically confirm the exogeneity of FDI liberalization in India using kernel density plots of firm characteristics (i.e., assets, profitability, and sales; see Figure A1 of the Appendix) in industries that were liberalized (vs. not). The kernel density plots indicate that the distribution of firm characteristics is largely similar across the two groups before FDI liberalization. In addition, in Table A1 of the Appendix, we report t-tests comparing average values of key variables across liberalized and unliberalized industries before FDI liberalization in 1991 and find no significant differences.
We use data on Indian and foreign firms from the CMIE Prowess database to examine the effects of liberalization on incumbent firms' marketing-mix responses. Firms in this database account for 75% of all corporate taxes and more than 95% of excise duties collected by the Indian government. We collect data on both incumbent and foreign firms' advertising, promotions, and distribution spending; number of products; group membership; foreign exchange earnings; foreign exchange spending; total assets; total sales; earnings before interest, taxes, depreciation, and amortization (EBITDA); and R&D spending between 1988 and 2000. We provide details of the marketing data classification by Prowess in the Web Appendix W2. The first year for which data is available in the Prowess database is 1988. We lose one year because of lagging explanatory variables, so the final sample spans a 12-year period (1989–2000).
We exclude observations of incumbent firms ( 1) incorporated after 1991, ( 2) for which asset information is missing, ( 3) for which the reporting period is not 12 months, and ( 4) from industries with fewer than two firms, and lose one year of observations due to lagged variables, after which, we are left with a sample of 16,636 firm-year observations for estimation. We describe the constructs, measures, and data sources in Table 1.
Graph
Table 1. Constructs, Measures, and Data Sources.
| Construct | Measure | Data Source |
|---|
| Dependent Variables | | |
| Advertising | Advertising spending/Total assets | CMIE Prowess Database |
| Product | Number of products | CMIE Prowess Database |
| Promotions | Promotion spending/Total assets | CMIE Prowess Database |
| Distribution | Distribution spending/Total assets | CMIE Prowess Database |
| Independent Variables | | |
| Post | Indicator variable = 1 for observations after liberalization, 0 otherwise | CMIE Prowess Database |
| Liberalization | Indicator variable = 1 if the three-digit NIC industry is liberalized in 1991, 0 otherwise | Industrial Policy Resolution of India, 1991 |
| Domestic market knowledge | Indicator variable = 1 if firm belongs to a business group, 0 otherwise | CMIE Prowess Database |
| Foreign market knowledge | (Foreign exchange spending + Foreign exchange earnings)/Total assets | CMIE Prowess Database |
| Control Variables | | |
| Firm size | Total assets, lagged | CMIE Prowess Database |
| Firm profitability | EBITDA/Total Assets, lagged | CMIE Prowess Database |
| Firm R&D | Research and development spending | CMIE Prowess Database |
| Domestic competition | Industry sum of domestic firms' total sales/Industry sum of all firms' total sales | CMIE Prowess Database |
| Industry concentration | Herfindahl–Hirschman index | CMIE Prowess Database |
The liberalization variable is coded as 1 for firms in industries that were liberalized to FDI and 0 for firms in industries that were not liberalized to FDI (M =.467, SD =.499). As the FDI liberalization policy was implemented at the three-digit National Industrial Classification (NIC) level, we compute the variable at this level.
We code the post variable as 1 for firm-year observations after FDI liberalization in 1991 and 0 for those before FDI liberalization (M =.894, SD =.308).
Advertising represents the firm's spending on media. To exclude any scale effects of firm size, we scale advertising spending (and other spending) by the firm's total assets (M =.010, SD =.025). Product represents the length of the firm's product portfolio, which we obtain from firms' annual reports (Indian firms are required by the Companies Act of 1956 to disclose product-level information in their annual reports). We measure the firm's product by the number of products reported by the firm in a given year (M = 4.573, SD = 4.430). Promotions represents a firm's spending on rebates, discounts, and sales promotions (M =.021, SD =.038). Distribution represents a firm's spending to deliver products to various channel intermediaries, such as retailers, wholesalers, and distributors, including on consignment and loss of goods in transit (M =.026, SD =.040).
We measure a firm's domestic market knowledge using its business group affiliation. Firms affiliated with business groups have cross-shareholding and interlocking directorates, which facilitate knowledge sharing between business group firms ([11]). Typically, within business groups, there is a core administrative department ([70]) responsible for sharing information. Prowess classifies firms as belonging to business groups or not, based on multiple sources including continuous monitoring of firm shareholding, news announcements, and understanding of firms' group-related behaviors ([22]). Following prior research using this classification ([17]), we code the domestic market knowledge variable as 1 if the firm belongs to a business group and 0 otherwise (M =.498, SD =.500).
Extant research ([31]; [45]) suggests that firms can gain experience of foreign sellers by importing inputs and can gain knowledge about foreign buyers' practices and processes by exporting to them ([64]). Thus, we operationalize a firm's foreign market knowledge using a sum of two measures: ( 1) foreign exchange spending, which includes spending on import of raw materials, stores and spares, finished goods, capital goods, royalties and technical knowhow, and ( 2) foreign exchange earnings, which includes earnings from export of goods and services. To obtain the measure of foreign market knowledge, we sum these two measures and scale it by the firm's total assets (M =.146, SD =.313).[ 6] For missing values of foreign exchange spending and foreign exchange earnings, we impute values to.0001.[ 7]
We control for firm size using the lag of total assets, firm profitability using the lag of return on assets (EBITDA/Total assets); firm R&D using R&D spending[ 8]; domestic competition using the cumulative market share of all domestic firms in an industry, thus accounting for the relative participation of domestic firms in an industry; and industry concentration using the Herfindahl–Hirschman index. We provide the descriptive statistics and correlation matrix of all the key variables used in the estimation, winsorized at.25%, in Table 2.
Graph
Table 2. Descriptive Statistics and Correlation Matrix of Key Variables.
| Mean | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|
| 1. Advertising | .010 | .025 | | | | | | | |
| 2. Product | 4.573 | 4.430 | .019** | | | | | | |
| 3. Promotions | .021 | .038 | .304*** | .014** | | | | | |
| 4. Distribution | .026 | .040 | .184*** | .012* | .165*** | | | | |
| 5. Post | .894 | .308 | −.031*** | −.084*** | −.035*** | −.029*** | | | |
| 6. Liberalization | .467 | .499 | −.041*** | .050*** | −.018*** | −.006 | −.056*** | | |
| 7. Domestic market knowledge | .498 | .500 | .070*** | .228*** | .018*** | .046*** | −.110*** | .000 | |
| 8. Foreign market knowledge | .146 | .313 | −.020*** | .032*** | .049*** | .233*** | −.010** | .040*** | .006 |
1 *p <.10.
A quasi-experiment is defined as a naturally occurring contrast between a treatment and a comparison condition in which the cause cannot be manipulated ([24]). We argue that a quasi-experiment occurred during the time of FDI liberalization in India because of an exogenous event: the FDI liberalization of some Indian industries but not others. As a result of FDI liberalization, there was a natural treatment group (firms in industries liberalized to FDI) and a control group (firms in unliberalized industries), which we use to measure the causal effect of liberalization.
We examine the effects of liberalization on incumbent firms using the differences-in-differences method, which is well-suited to establishing causal claims in a quasi-experiment ([18]; [67]). The differences-in-differences method compares the effect of the event (in this case, FDI liberalization) on incumbent firms in industries that are liberalized (the treatment group) with those in industries that are not (the control group). To examine the effect of liberalization on incumbent firm's marketing-mix responses, we subtract the average of a firm's marketing-mix response before the event from the average value of its marketing-mix response after the event. However, other factors might have changed as well. Thus, we use incumbent firms in the control group to account for any other observable or unobservable factors.
We provide model-free evidence by dividing incumbent firms into two groups: liberalized firms (the treatment group) and unliberalized firms (the control group). We examine two periods, pre- (before FDI liberalization in 1991) and post- (after FDI liberalization in 1991). We compute the difference for the firms in the liberalized group pre- and postliberalization for each marketing-mix response variable by collapsing to averages for each period. Similarly, we compute the difference for the firms in the unliberalized group and then compute the difference between these two differences to obtain the differences-in-differences estimate. The model-free evidence suggests that, in response to liberalization, incumbent firms intensified their product (b =.617, p < .01) and promotions (b =.003, p <.05), but not their advertising and distribution responses.
To estimate the differences-in-differences model, we regress the incumbent firm's marketing-mix response on the main effects of liberalization and post variables, their interaction, and the control variables. We control for unobserved heterogeneity using firm fixed effects and for any effects of time using year fixed effects.
Graph
1
where represents the firm fixed effect, represents the causal effect of liberalization on incumbent firms' marketing-mix responses, and represents the year fixed effects. We estimate four such equations for each marketing-mix response of incumbent firms: advertising, product, promotions, and distribution.
We exploit the cross-sectional variation in the treatment and control groups to estimate the heterogeneous treatment effects of liberalization on incumbent firms' marketing-mix responses contingent on their domestic market knowledge and foreign market knowledge ([67]). This specification enables us to examine the effects of knowledge on incumbent firms' marketing-mix responses in liberalized industries (vs. those in unliberalized industries) following liberalization. Thus, we estimate the following model:
Graph
2
where i is the subscript for the firm and t is the subscript for the year, refers to the firm fixed effect, represents the year fixed effects, refers to the heterogeneous treatment effect of liberalization on incumbent firms with domestic market knowledge, and refers to the heterogeneous treatment effect of liberalization on incumbent firms with foreign market knowledge. Because we include firm fixed effects and year fixed effects, main effects and interaction terms completely predicted by the fixed effects drop out of the estimation.
We first estimated the differences-in-differences model without the control variables, followed by estimation with the inclusion of the control variables. We present the results of the differences-in-differences model without the control variables in Columns 1–4 of Table 3, Panel A. The results suggest that H1 is partially supported. Although liberalization did not affect incumbent firms' advertising (b = −.001, n.s.), incumbent firms intensified their product (b =.267, p <.01), promotions (b =.002, p <.05), and distribution (b =.002, p <.01) in response to liberalization.[ 9]
Graph
Table 3. Differences-in-Differences Estimates.
| (1) | (2) | (3) | (4) |
|---|
| Advertising | Product | Promotions | Distribution |
|---|
| A: Differences-in-Differences Estimates Without Control Variables |
| Post × Liberalization | −.001 (.001) | .267*** (.065) | .002** (.001) | .002*** (.001) |
| Intercept | .011*** (.001) | 3.917*** (.110) | .024*** (.001) | .028*** (.001) |
| Firm fixed effects | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes |
| F-statistic | 2.19 | 77.61 | 9.62 | 7.58 |
| Prob > F | .008 | .000 | .000 | .000 |
| Observations | 19,479 | 40,543 | 29,096 | 25,067 |
| Number of firms | 4,270 | 7,542 | 5,648 | 4,840 |
| B: Differences-in-Differences Estimates with Control Variables |
| Post × Liberalization | −.000 (.001) | .256*** (.086) | .003*** (.001) | .001 (.001) |
| Firm size (× 10−4) | .001 (.010) | 17.066*** (1.415) | −.043** (.020) | .001 (.019) |
| Firm profitability | .006*** (.001) | .139 (.113) | .012*** (.002) | .014*** (.002) |
| Firm R&D | .000 (.000) | −.185*** (.051) | .001 (.001) | −.001 (.001) |
| Domestic competition | −.002 (.003) | .515 (.336) | .011*** (.004) | .004 (.005) |
| Industry concentration | .001 (.002) | .223 (.246) | −.005 (.004) | .012*** (.004) |
| Intercept | .007** (.004) | 3.797*** (.400) | .012** (.005) | .013*** (.005) |
| Firm fixed effects | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes |
| F-statistic | 2.44 | 48.42 | 11.63 | 9.54 |
| Prob > F | .001 | .000 | .000 | .000 |
| Observations | 16,636 | 34,159 | 24,735 | 21,211 |
| Number of firms | 3,927 | 7,073 | 5,242 | 4,512 |
| C: Differences-in-Differences Estimates with Heterogeneous Treatment Effects |
| Domestic market knowledge × Post × Liberalization | .003** (.002) | .330* (.185) | .005** (.002) | .004* (.002) |
| Foreign market knowledge × Post × Liberalization | .003 (.003) | −.094 (.302) | −.007** (.003) | −.008** (.004) |
| Foreign market knowledge × Liberalization | −.004 (.003) | −.092 (.313) | .003 (.004) | .013*** (.004) |
| Domestic market knowledge × Post | −.002* (.001) | −.254* (.142) | −.001 (.002) | −.000 (.002) |
| Foreign market knowledge × Post | −.001 (.002) | .038 (.212) | .005** (.002) | .002 (.002) |
| Post × Liberalization | −.003* (.002) | .047 (.163) | .001 (.002) | −.000 (.002) |
| Foreign market knowledge | .003* (.002) | .555** (.219) | .010*** (.002) | .015*** (.002) |
| Firm size (× 10−4) | .001 (.010) | 17.086*** (1.414) | −.041** (.019) | .005 (.019) |
| Firm profitability | .006*** (.001) | .068 (.113) | .010*** (.002) | .010*** (.002) |
| Firm R&D | .000 (.000) | −.190*** (.051) | .000 (.001) | −.001 (.001) |
| Domestic competition | −.002 (.003) | .518 (.335) | .011*** (.004) | .004 (.004) |
| Industry concentration | .001 (.002) | .189 (.246) | −.006* (.004) | .009** (.004) |
| Intercept | .007* (.004) | 3.710*** (.402) | .011** (.005) | .011** (.005) |
| Firm fixed effects | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes |
| F-statistic | 2.46 | 38.84 | 19.11 | 36.01 |
| Prob > F | .000 | .000 | .000 | .000 |
| Observations | 16,636 | 34,159 | 24,735 | 21,211 |
| Number of firms | 3,927 | 7,073 | 5,242 | 4,512 |
- 4 *p <.10.
- 5 **p <.05.
- 6 ***p <.01.
We present the results of the differences-in-differences model including the control variables in Columns 1–4 of Table 3, Panel B. Again, the results suggest that H1 is partially supported. Although liberalization did not affect incumbent firms' advertising (b = −.000, n.s.) and distribution (b =.001, n.s.), incumbent firms intensified their product (b =.256, p <.01) and promotions (b =.003, p <.01) in response to liberalization.
In Columns 1–4 of Table 3, Panel C, we present the results of the heterogeneous treatment effects model, including the effects of incumbent firms' domestic market knowledge and foreign market knowledge. The results support H2, as incumbent firms with greater domestic market knowledge intensify their advertising (b =.003, p <.05), product (b =.330, p <.10), promotions (b =.005, p <.05), and distribution (b =.004, p <.10) to a greater extent in response to liberalization.
The results partially support H3b, as incumbent firms with greater foreign market knowledge intensify their promotions (b = −.007, p <.05) and distribution (b = −.008, p <.05) to a lesser extent in response to liberalization. However, liberalization has no effect on the advertising (b =.003, n.s.) and product (b = −.094, n.s.) of incumbent firms with greater foreign market knowledge.
Next, we discuss the effects of the control variables. Firm size has a positive effect on incumbent firms' product (b = 17.086, p <.01) and a negative effect on their promotions (b = −.041, p <.05). Firm profitability has a positive effect on incumbent firms' advertising (b =.006, p <.01), promotions (b =.010, p <.01), and distribution (b =.010, p <.01); firm R&D has a negative effect on incumbent firms' product (b = −.190, p <.01); domestic competition has a positive effect on incumbent firms' promotions (b =.011, p <.01); and industry concentration has a negative effect on incumbent firms' promotions (b = −.006, p <.10) and a positive effect on incumbent firms' distribution (b =.009, p <.05).
The results of the differences-in-differences model suggest that incumbent firms intensified their product and promotions in response to liberalization. The results of the heterogeneous treatment effects model suggest that although incumbent firms with greater domestic market knowledge intensified all four marketing-mix responses to liberalization, incumbent firms with greater foreign market knowledge muted two of their marketing-mix responses (promotions and distribution) to liberalization.
We conduct additional analyses to rule out alternative explanations and to verify the robustness of our results to different samples. Because multicollinearity prevents the inclusion of some control variables (i.e., industry fixed effects, tariff changes, number of foreign firms, and foreign firms' marketing) in one comprehensive model, we conducted separate analyses with these control variables, as we discuss next.
In addition to FDI liberalization, in 1991, the Indian government also liberalized trade by decreasing trade tariffs in some industries to increase the attractiveness of the market for foreign firms to sell their products (manufactured outside India). Trade liberalization can increase dumping and predatory price-based competition ([39]). To establish that the results are robust to the concurrent reduction in trade tariffs, we control for the reduction in trade tariffs across industries in the estimated model, using an indicator variable coded as 1 if the incumbent firm's industry had reduced tariffs in 1991 (0 if not). We present these results in Table C1 of Web Appendix W3, which are consistent with those in Table 3.
Incumbent firms in industries where there were greater number of foreign entrants may be more motivated to intensify their marketing-mix responses. To check if this is the case, we reestimate the model by controlling for the number of foreign firms in an industry. We present these results in Table C2 of Web Appendix W3, which are again consistent with those in Table 3.
Incumbent firms' marketing-mix responses to liberalization may be influenced by the marketing actions of foreign entrants. Thus, we reestimate the models by controlling for foreign firms' advertising in an industry by dividing the average advertising intensity of foreign firms by the average advertising intensity of all firms in the industry in Equation 2 for incumbent firms' advertising responses. Similarly, we reestimate the models by controlling for foreign firms' product, promotions, and distribution in each of the corresponding equations. We present these results in Table C3 of Web Appendix W3, which are consistent with those in Table 3.
To rule out the possibility that industry effects are driving the estimation results, we reestimate the model with the inclusion of industry fixed effects interacted with the Post variable. We present these results, which are consistent with those in Table 3, in Table C4 of Web Appendix W3.
To ensure that our results are robust across different samples and to different choices for the last year in the panel data, we reestimate the model excluding observations from the year 2000. We present these results, which are consistent with those in Table 3, in Table C5 of Web Appendix W3.
We next examine whether incumbent firms' marketing-mix responses to liberalization helped or hurt their performance. To do this, we estimate a model where the dependent variable is incumbent firms' profitability and the key independent variables are their advertising, product, promotions, and distribution responses to liberalization. Because incumbent firms' marketing in the current period may be affected by liberalization, we lag all marketing-mix response variables by one year. To simplify the empirical estimation and avoid the estimation and interpretation of four-way interactions, for the interaction term between Post and Liberalization we use a continuous measure, the extent of foreign competition in a given industry in each year. Thus, for the performance model, we measure the extent of liberalization using the cumulative market share of all foreign firms in the industry.
We estimate a model including all three-way interactions of incumbent firms' marketing-mix responses and liberalization with the two moderators from the first stage, domestic market knowledge and foreign market knowledge. We control for unobserved heterogeneity using firm fixed effects and for annual changes using year fixed effects. To control for any industry-level changes in the performance of incumbent firms after liberalization, we include the interaction term between the Post variable and industry dummies in the model. Similar to the estimation of incumbent firms' marketing-mix responses, we control for firm size, firm profitability, firm R&D, domestic competition, and industry concentration. However, because domestic competition is perfectly predicted by the extent of foreign competition in an industry, it drops out of the estimation. Next, we present the equation for the performance model, where i is the subscript for the firm, t for the year, and j for industry,
Graph
We present the performance model in Table 4. We present a comparison of incumbent firms' marketing-mix responses with the effect of their marketing-mix response on performance (using the directionality of the three-way interactions) in Table 5. Next, we discuss the convergence and/or divergence between incumbent firms' marketing-mix responses and appropriate marketing-mix responses as indicated by the parameter estimates of the three-way interaction terms in the performance model.
Graph
Table 4. Incumbent Firms' Marketing-Mix Responses and Performance during Liberalization.
| Firm Performance |
|---|
| Domestic market knowledge × Liberalization × Advertising | −.712 (.857) |
| Domestic market knowledge × Liberalization × Product | −.007 (.009) |
| Domestic market knowledge × Liberalization × Promotions | −.573 (.611) |
| Domestic market knowledge × Liberalization × Distribution | 1.610* (.866) |
| Foreign market knowledge × Liberalization × Advertising | 1.940 (1.297) |
| Foreign market knowledge × Liberalization × Product | −.010 (.009) |
| Foreign market knowledge × Liberalization × Promotions | 1.661* (.859) |
| Foreign market knowledge × Liberalization × Distribution | −2.257*** (.732) |
| Domestic market knowledge × Liberalization | .000 (.096) |
| Foreign market knowledge × Liberalization | .100 (.084) |
| Domestic market knowledge × Advertising | .353 (.271) |
| Domestic market knowledge × Product | .002 (.002) |
| Domestic market knowledge × Promotions | −.153 (.145) |
| Domestic market knowledge × Distribution | −.320** (.146) |
| Foreign market knowledge × Advertising | −.527 (.433) |
| Foreign market knowledge × Product | .002 (.002) |
| Foreign market knowledge × Promotions | −.090 (.146) |
| Foreign market knowledge × Distribution | .054 (.096) |
| Liberalization × Advertising | .184 (.695) |
| Liberalization × Product | .004 (.008) |
| Liberalization × Promotions | −.194 (.400) |
| Liberalization × Distribution | −.107 (.797) |
| Liberalization | −.078 (.072) |
| Foreign market knowledge | .057*** (.012) |
| Advertising | .005 (.229) |
| Product | −.000 (.001) |
| Promotions | .171 (.114) |
| Distribution | .259** (.123) |
| Firm size (× 10−4) | −.038 (.120) |
| Firm profitability | .210*** (.012) |
| Firm R&D | .013*** (.004) |
| Industry concentration | −.017 (.032) |
| Intercept | .091*** (.024) |
| Post × Industry fixed effects | Yes |
| Firm fixed effects | Yes |
| Year fixed effects | Yes |
| F-statistic | 11.09 |
| Prob > F | .000 |
| Observations | 9,929 |
| Number of firms | 2,433 |
- 7 *p <.10.
- 8 **p <.05.
- 9 ***p <.01.
Graph
Table 5. Comparison of Findings on Incumbent Firms' Marketing-Mix Responses and Performance.
| Effect on Marketing-Mix Response | Effect of Marketing-Mix Response on Firm Performance |
|---|
| Domestic Market Knowledge |
| Advertising | Increase | No effect |
| Product | Increase | No effect |
| Promotions | Increase | No effect |
| Distribution | Increase | Increase |
| Foreign Market Knowledge |
| Advertising | No change | No effect |
| Product | No change | No effect |
| Promotions | Decrease | Increase |
| Distribution | Decrease | Decrease |
The results of the marketing-mix response model in Table 3 indicate that incumbent firms with greater domestic market knowledge intensified their advertising, product, promotions, and distribution in response to liberalization. The results of the performance model (Tables 4 and 5) indicate that this was partially the appropriate strategy for them, as their distribution (b = 1.610, p <.10) responses improve their performance. However, the results of the performance model indicate that for incumbent firms with greater domestic market knowledge, their advertising (b = −.712, n.s.), product (b = −.007, n.s.), and promotions (b = −.573, n.s.) responses—which they intensified—did not improve their performance following liberalization, suggesting opportunities for them to improve their performance.
While the results of the marketing-mix response model (Table 3) indicate that incumbent firms with greater foreign market knowledge muted their promotions and distribution responses to liberalization, the performance model (Tables 4 and 5) indicates that this was also partially the appropriate strategy for them to follow, as both decreasing their distribution responses (b = −2.257, p <.01) and increasing their promotions responses (b = 1.661, p <.10) can improve their performance.
Several markets are liberalizing, transitioning from protected markets to open markets. These transitions dramatically change the competitive environments faced by incumbent firms in these markets, suggesting a key role for their marketing responses on their performance. Yet there are few insights on incumbent firms' marketing-mix responses to liberalization. Addressing this research gap, we theorize and find that the market knowledge of incumbent firms influences their marketing-mix responses to liberalization. We exploit a quasi-experiment, the liberalization of the Indian economy in 1991, to causally identify the effects of liberalization on incumbent firms. We conclude with a discussion of the findings' contributions to theory, implications for managerial practice, and the limitations and opportunities for further research.
To the best of our knowledge, this is the first study to examine the marketing-mix responses of incumbent firms to liberalization. In doing so, it introduces an important phenomenon, liberalization, to the marketing literature, highlighting differences between liberalization and domestic competition, and demonstrates the opportunities in studying foreign competition ([35]). Through this study, we contribute to the marketing literature on competitive response, which has thus far focused on domestic competition ([ 4]; Mukherji et al. [53]). Prior research on domestic competition has suggested that no marketing response is the most common response for incumbent firms ([ 4]; [63]). However, by studying a new type of competition, we find that incumbent firms do respond aggressively to liberalization through their marketing mix, which, in turn, affects their performance. Given that the study of competition is an integral part of marketing, our focus on how firms respond to liberalization enables us to contribute to a significant stream of research in marketing.
Second, through our study, we extend the literature on marketing-mix responses and liberalization in a novel way. Extant research has focused on incumbent firms' size and financial capacity as drivers of their response and performance during domestic competition and liberalization ([ 4]; [53]; [72]) but has overlooked incumbent firms' knowledge, a key source of competitive advantage. We show that incumbent firms' knowledge can help explain their marketing-mix responses to liberalization and their subsequent performance. We draw attention to the importance of incumbent firms' domestic market knowledge and foreign market knowledge in the context of liberalization. In doing so, we add a new angle to the literature on liberalization and competitive response, highlighting the need to account for incumbent firms' knowledge. More generally, this research's findings also contribute to the broader marketing literature on market knowledge ([49]; [50]).
Third, much of the marketing metrics literature has focused on the effect of advertising ([56]; [69]) and R&D on firm performance (e.g., [62]). By considering the effects of four different types of marketing mix—advertising, product, promotions, and distribution—on firm performance in one study, we extend this literature in a novel way. Moreover, extant studies on marketing-mix responses have primarily examined a single marketing variable, such as advertising or product ([53]), or study industry-specific marketing-mix responses, such as product assortment in the retailing industry ([ 4]). Thus, we contend that these studies may not provide a complete picture of the effects of the marketing mix and risk suffering from omitted variable bias. By considering all 4Ps of the marketing mix and including firms across multiple industries, we provide a comprehensive picture of incumbent firms' marketing-mix responses.
Finally, through this research, we contribute to the international business and economics literature, in which there is mixed evidence on the effects of liberalization on incumbent firms' performance (e.g., [ 5]; [43]). We suggest that one reason for the mixed evidence is that extant research has not accounted for important differences between incumbent firms facing liberalization, especially regarding their marketing-mix responses. By demonstrating that incumbent firms respond to liberalization through their marketing mix, which in turn, affects their performance, we help clarify the mixed evidence on the effects of liberalization on incumbent firms. Thus, we extend the literature on liberalization, demonstrating that incumbent firms' marketing-mix responses are a key determinant that must be accounted for when studying the effects of liberalization on the performance of incumbent firms.
The findings have implications for managers of incumbent firms facing liberalization, managers of foreign entrants, policy makers, and investors. We discuss these in the following subsections.
Managers of incumbent firms are naturally concerned about the effects of liberalization on their firms' performance. For example, in response to the potential liberalization of the Indian retail sector, Kishore Biyani (chief executive of the largest incumbent retailer in India), stated in opposition to the reform, "The retail sector...should not be given away to the foreign players while it is too young to compete on a level-playing field" ([19], p. 83). More recently, founders of incumbent Indian technology startups, who have been fiercely battling U.S. entrants including Amazon and Uber, have argued that foreign competitors destroy domestic industry and entreated the Indian government to introduce protectionist measures ([57]). Our findings offer a mechanism by which managers of incumbent firms can effectively compete with foreign competitors following liberalization (i.e., by adjusting their marketing-mix responses).
Our findings suggest that incumbent firms with greater domestic market knowledge should intensify their distribution in response to liberalization. Incumbent firms' knowledge of domestic distribution networks and trade partners is a strong advantage for them, which they can exploit by intensifying their distribution to achieve superior performance following liberalization. Although incumbent firms with greater domestic market knowledge intensify their advertising, product, and promotions responses to liberalization, the findings of the performance model indicate that they do not benefit from these responses. Thus, these incumbent firms could reallocate resources away from these marketing-mix elements to improve performance.
Furthermore, our findings suggest that incumbent firms with higher foreign market knowledge cut back on their promotions and distribution in response to liberalization. A potential reason for this may be that these incumbent firms may already be aware of the strength of foreign firms with respect to building intangible assets through advertising and product introductions and may consider additional spending on promotions and distribution to be superfluous in combating these entrants. However, the findings from the performance model suggest that while cutting back on their distribution is an appropriate response to liberalization for incumbent firms with higher foreign market knowledge, these firms can further improve their performance by intensifying their promotions. Finally, these incumbent firms do not respond to liberalization through their advertising and product, which is the appropriate strategy for them, because intensifying these marketing-mix elements does not improve their performance.
Managers of foreign firms entering newly liberalized markets can use this research's findings to benchmark themselves against incumbent competitors and understand their potential marketing-mix responses. For example, for foreign firms entering a market following liberalization, incumbent firms with higher domestic market knowledge and foreign market knowledge with appropriate marketing-mix responses are likely to emerge as strong competitors.
Policy makers may be tempted to heed the demands of business leaders to raise barriers to protect domestic firms from foreign competitors. For example, in response to the Indian government's move to open up the aviation sector to foreign players, Ajay Singh (Chairman of one India's largest aviation incumbent firms) stated, "We believe the ultimate objective of policy should be to strengthen indigenous aviation....We believe work needs to be done by the government to ensure that we keep strengthening indigenous aviation...making sure the growth remains profitable growth in the country" ([40]). Our study identifies incumbent firms' marketing-mix responses as a mechanism to prevent their crowding out following liberalization. Policy makers need not accede to the demands of incumbent business leaders to heighten protectionist barriers but can find ways to facilitate incumbent firms' learning from foreign entrants (e.g., by fostering alliances and trade associations).
Our findings suggest that incumbent firms' marketing-mix responses play an important role in their performance during liberalization. Thus, institutional investors should consider incumbent firms' marketing-mix responses when deciding their targets of investment. Our findings show that when markets liberalize, incumbent firms with higher domestic market knowledge and foreign market knowledge that adjust their marketing-mix responses achieve superior performance, suggesting opportunities for investors in newly liberalized sectors.
Our study has some limitations that offer opportunities for further research. First, to gain an understanding of the complex phenomenon of liberalization, we focus on the effects of FDI liberalization on incumbent firms. Further research on the marketing-mix responses and performance of incumbent firms during other forms of liberalization, including trade liberalization and stock market liberalization, would be useful.
Second, we study two factors (domestic market knowledge and foreign market knowledge) that affect incumbent firms' marketing-mix responses and performance following liberalization. Further research on other factors, including the chief executive officer's and marketing leadership's foreign education or work experience, family ownership, the motivation of foreign entrants, employee salaries, mergers and acquisitions, market share, level of technology, product portfolio and price, laggards versus leaders, and tangible and intangible government incentives would be useful. In addition, although we examine incumbent firms' product line length, because of the lack of data availability, we are unable to study the effects of liberalization on incumbent firms' product innovativeness and quality, which emerges as an area for research. Similarly, we use business group affiliation as a measure of domestic market knowledge. Future research using other measures of domestic market knowledge, including primary data through surveys, would be useful.
Third, we examine incumbent firms' marketing-mix responses to liberalization in a single market, India. While this allows for a clean test of the effects of liberalization on incumbent firms, future research could examine whether our findings generalize to other markets (e.g., the United States, Brazil, China).
In conclusion, we believe that the findings of this first study on the role of incumbent firms' marketing during liberalization provide novel insights on incumbent firms' marketing-mix responses to liberalization and their subsequent performance implications. Given the increasing pace of liberalization in markets around the world, we hope that our study stimulates additional work in this area.
Supplemental Material, DS_10.1177_0022242919860085 - Effects of Liberalization on Incumbent Firms' Marketing-Mix Responses and Performance: Evidence from a Quasi-Experiment
Supplemental Material, DS_10.1177_0022242919860085 for Effects of Liberalization on Incumbent Firms' Marketing-Mix Responses and Performance: Evidence from a Quasi-Experiment by Nandini Ramani and Raji Srinivasan in Journal of Marketing
Graph: Figure A1. Kernel density plots.Notes: Figure A1 includes the kernel density plots of key firm characteristics (i.e., firm size, sales, profitability, advertising, promotion, and distribution) across liberalized (vs. unliberalized) industries.
Graph
Table A1. t-Tests Across Liberalized and Unliberalized Industries.
| Liberalized | Unliberalized | p-Value |
|---|
| Advertising | .014 | .017 | .514 |
| Product | 6.347 | 7.034 | .532 |
| Promotions | .019 | .024 | .279 |
| Distribution | .023 | .028 | .290 |
| Domestic market knowledge | .723 | .775 | .335 |
| Foreign market knowledge | .168 | .116 | .295 |
| Firm sizea | 44.36 | 45.25 | .942 |
| Firm profitability | .134 | .120 | .346 |
| Firm R&D (× 10−1)a | .003 | .010 | .274 |
| Domestic competition | .775 | .820 | .476 |
| Industry concentration | .335 | .376 | .527 |
- 10 aMeasured in USD million.
- 11 Notes: This table contains comparisons of average values across three-digit NICs that were unliberalized versus those that were in 1988, before liberalization.
Footnotes 1 Authors' NoteThe authors thank Deepa Chandrasekaran, Rajesh Chandy, Jason Duan, Cesare Fracassi, Ty Henderson, Gary L. Lilien, Vijay Mahajan, Leigh McAlister, Raghunath Singh Rao, Garrett Sonnier, Gerry Tellis, and participants at the 2015 Marketing in Israel Conference, the 2015 Northwestern University Causal Inference Camp, the 2016 Theory + Practice in Marketing Conference in Houston, the 2016 University of Houston Doctoral Symposium, the 2016 AMA Winter Marketing Academic Conference in Las Vegas, the 2016 Yale China India Insights Conference in London, the 2018 AMA Winter Marketing Academic Conference in New Orleans, and the 2018 Theory + Practice in Marketing Conference in Los Angeles.
2 Associate EditorSatish Jayachandran
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors thank the Center for International Business Education and Research at the University of Texas at Austin for financial support.
5 Online supplement: https://doi.org/10.1177/0022242919860085
6 1We reestimate the marketing-mix responses models using only foreign exchange earnings as a measure of foreign market knowledge, and the results are not significantly different.
7 2We reestimate the marketing-mix responses models imputing values of 0 for these variables, and the results are not significantly different.
8 3Because few firms report R&D spending, we impute a value of.0001 for missing values of R&D spending.
9 4We reestimate the differences-in-differences model using a simultaneous estimation, and the results are not significantly different.
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Record: 71- Effects of Traditional Advertising and Social Messages on Brand-Building Metrics and Customer Acquisition. By: de Vries, Lisette; Gensler, Sonja; Leeflang, Peter S.H. Journal of Marketing. Sep2017, Vol. 81 Issue 5, p1-15. 15p. 7 Charts. DOI: 10.1509/jm.15.0178.
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Effects of Traditional Advertising and Social Messages on Brand-Building Metrics and Customer Acquisition
This study examines the relative effectiveness of traditional advertising, impressions generated through firm-toconsumer (F2C) messages on Facebook, and the volume and valence of consumer-to-consumer (C2C) messages on Twitter and web forums for brand-building and customer acquisition efforts. The authors apply vector autoregressive modeling to a unique data set from a European telecom firm. This modeling approach allows them to consider the interrelations among traditional advertising, F2C impressions, and volume and valence of C2C social messages. The results show that traditional advertising is most effective for both brand building and customer acquisition. Impressions generated through F2C social messages complement traditional advertising efforts. Thus, thoroughly orchestrating traditional advertising and a firm’s social media activities may improve a firm’s performance with respect to building the brand and encouraging customer acquisition. Moreover, firms can stimulate the volume and valence of C2C messages through traditional advertising that in turn influences brand building and acquisition. These findings can help managers leverage the different types of messages more adequately.
Online Supplement: http://dx.doi.org/10.1509/jm.15.0178
Every year, U.S. firms invest approximately $130 billion in traditional advertising (e.g., television, radio, print, and outdoor) to build their brands and increase sales (eMarketer 2014). Yet empirical evidence has suggested that firms are gradually shifting their traditional advertising investments to, for example, social media to pursue similar objectives (eMarketer 2016; Hudson et al. 2016; Statista 2016). Many firms have established a social media presence by operating pages on social networking sites such as Facebook. Firms post messages on these pages to interact with consumers by exploiting the network structure and to ultimately build the brand and stimulate sales (De Vries, Gensler, and Leeflang 2012). We call these posts firm-to-consumer (F2C) social messages.
To leverage these messages, managers need to know how effective F2C social messages are for building the brand and influencing consumer behavior. Previous research has shown that F2C social messages have a positive effect on existing customers’ expenditures (e.g., Goh, Heng, and Lin 2013; Kumar et al. 2016). However, we lack knowledge about the effectiveness of firms’ social media activities in comparison to their traditional advertising investments. Moreover, we know little about potential complementary effects of F2C social messages and traditional advertising (Kumar et al. 2016). Such knowledge is, however, critical for managers to leverage and orchestrate traditional advertising and F2C social messages effectively (Chen and Xie 2008; Edelman 2010). Furthermore, previous studies have focused on the impact of F2C social messages on existing customers’ behavior but have not investigated the potential impact on new customer acquisition.
In addition to a firm’s own efforts to build the brand and affect consumer behavior, it is well known that messages initiated by consumers influence other consumers (e.g., Babic´ Rosario et al. 2016; Hennig-Thurau, Wiertz, and Feldhaus 2015; You, Vadakkepatt, and Joshi 2015; Zhu and Zhang 2010). Such messages can be product reviews as well as messages posted on forums, microblogs (e.g., Twitter), brand communities, and other social media sites. We call messages that are initiated by consumers and targeted to other consumers consumer-to-consumer (C2C) social messages. Managers need a clear understanding of the effects of C2C social messages on the brand and consumer behavior relative to the impact of their own efforts. Moreover, managers need to know whether their own communication activities affect C2C social messages because this would allow them to exert some influence on what consumers say about the brand. Previous studies that compare traditional advertising and C2C social messages have indicated that C2C social messages can be more effective for increasing sales and customer acquisition (e.g., Trusov, Bucklin, and Pauwels 2009). Moreover, these studies have suggested that traditional advertising and consumer messages are complements (Fossen and Schweidel 2017; Gopinath, Thomas, and Krishnamurthi 2014). Yet few studies have considered C2C and F2C social messages jointly, and the findings of these studies with respect to the effectiveness of these messages are mixed (Goh, Heng, and Lin 2013; Kumar et al. 2013). To date, no empirical research has considered traditional advertising, F2C social messages, and C2C social messages simultaneously to compare the effectiveness of the different types of messages. Thus, we also have little knowledge about the interrelations among the different messages, though there is no doubt that such interrelations are likely to exist (Hewett et al. 2016).
The aim of this study is to close this gap in the literature by examining the relative effectiveness of traditional advertising, F2C social messages, and C2C social messages for both brand building and customer acquisition over time, and to study the interrelations among the different messages. We focus on customer acquisition because it is a critical performance measure that has just recently received more attention (Katsikeas et al. 2016). By considering customer acquisition (i.e., number of new customers), we are able to study the behavioral outcomes of traditional advertising, F2C social messages, and C2C social messages. By accounting for brand-building metrics (i.e., consumers’ brand awareness, consideration, and preference), we can examine both indirect and direct effects of the different messages on customer acquisition (Bruce, Peters, and Naik 2012).
We collected a unique data set from a European telecom firm (which maintains contractual relationships with consumers) and Nielsen that contained weekly data on traditional advertising, F2C social messages, and C2C social messages over 119 weeks. We also have weekly information about brand-building metrics and customer acquisition. The traditional advertising measure comprises the firm’s joint expenditures on television, radio, print, and outdoor advertising. The number of impressions of firm-initiated messages on Facebook based on likes, comments, and shares of the firm’s original messages represent F2C social messages. We therefore use the term F2C impressions when describing and discussing the results of the empirical study. The impressions provide information about the spreading of a firm’s message. We consider Facebook because it is the firm’s main social media platform to communicate with consumers. Consumer-toconsumer social messages include the number (C2C volume) and valence (C2C valence) of messages initiated by consumers about the firm on Twitter and the most popular forums in the country where the focal firm operates. We do not consider online reviews because the content of the reviews is mostly about phones and less about the specific services offered by the focal firm. By taking C2C social messages on Twitter and forums into account, we cover the majority of C2C social messages about the focal firm.
To elicit the effectiveness of traditional advertising, F2C impressions, and C2C social messages (C2C volume and C2C valence), we use vector autoregressive (VAR) modeling. This methodology allows us to determine the relative effectiveness of traditional advertising, F2C impressions, and C2C social messages by computing their elasticities for the brand-building metrics and customer acquisition on the basis of impulse response function (IRF) analyses (e.g., Dinner, Van Heerde, and Neslin 2014). In addition, the VAR model approach enables us to examine the interrelations among traditional advertising, F2C impressions, and C2C social messages (Hewett et al. 2016).
With our work, we contribute to the extant literature in several ways. First, we consider traditional advertising, impressions generated through F2C social messages, and C2C social messages simultaneously and compare their effectiveness. Second, we elaborate on the complementary effects of and interrelations among traditional advertising, F2C impressions, and C2C social messages. Third, we take both brand-building and behavioral metrics into account to assess the effectiveness of the different messages over time. Using brand-building and behavioral metrics allows us to address current calls to consider multiple performance metrics at different levels to derive more insightful managerial implications (Katsikeas et al. 2016). Accordingly, our study is more comprehensive than previous studies and allows for richer insights that help managers to orchestrate the different messages effectively.
The results show that the different messages are effective in building a brand and enhancing customer acquisition. With respect to building a brand, traditional advertising is most effective in creating awareness and consideration. However, C2C valence is most effective in spurring preference. Traditional advertising is again most effective in enhancing customer acquisition, followed by F2C impressions and C2C volume. The results suggest that the firm’s social media activities complement its traditional advertising efforts. In addition, traditional advertising enhances the volume and valence of C2C social messages, which in turn spur consumers’ preference and acquisition. Given the effectiveness of traditional advertising, managers should carefully trade off its effectiveness and costs (i.e., efficiency) when making marketing investment decisions.
In the next section, we elaborate on previous research related to our study and highlight the need for an empirical study that addresses the gap in research. Then, we describe our data and introduce the modeling approach. Subsequently, we present and elaborate on the empirical findings. Finally, we conclude with a discussion of the study’s implications, limitations, and research opportunities.
Previous Research on the Effectiveness of Traditional Advertising, F2C Social Messages, and C2C Social Messages
The effectiveness of traditional advertising, F2C, and C2C social messages can be assessed by examining their impact on brand-building and behavioral outcomes. Brand awareness, consideration, and preference are three commonly used metrics to evaluate the effects on brand building (e.g., Draganska, Hartmann, and Stanglein 2014; Srinivasan, Vanhuele, and Pauwels 2010). Recent studies have demonstrated the brand-building and sales capabilities of a single type of message—traditional advertising (e.g., Sethuraman, Tellis, and Briesch 2011; Srinivasan, Vanhuele, and Pauwels 2010), C2C social messages (e.g., Hennig-Thurau, Wiertz, and Feldhaus 2015), and F2C social messages (e.g., Goh, Heng, and Lin 2013; Hutter et al. 2013; Kumar et al. 2016; Rishika et al. 2013). Yet research considering more than just one type of message is scarce, as we illustrate in Table 1.
TABLE: TABLE 1 Overview on Studies Considering More Than One Type of Message
| Authors | Traditional Advertising | C2C | F2C | Brand-Building Metrics | Sales | Acquisition |
|---|
| Bruce, Foutz, and Kolsarici 2012 | + | Online reviews | | | + | |
| Fossen and Schweidel 2017 | + | Twitter | | | | |
| Gopinath, Thomas, and Krishnamurthi 2014 | + | Forum | | | + | |
| Onishi and Manchanda 2012 | + | Blog | | | + | |
| Stephen and Galak 2012 | + | Blog, community | | | + | |
| Trusov, Bucklin, and Pauwels 2009 | + | WOM referrals | | | | + |
| Villanueva, Yoo, and Hanssens 2008 | + | WOM referrals | | | | + |
| Goh, Heng, and Lin 2013 | | Facebook community | + | | | + |
| Kumar et al. 2013 | | WOM | + | | + | + |
| Kumar et al. 2016 | + | | + | | + | |
| This study | + | Microblog, forums | + | + | | + |
Some studies compare the effectiveness of traditional advertising and C2C social messages (Table 1). Note that these studies also contain other forms of C2C social messages than the ones we consider. The results of these studies indicate that C2C messages are more effective than traditional advertising at generating sales for microlending loans (Stephen and Galak 2012) and acquiring customers for a web hosting service (Villanueva, Yoo, and Hanssens 2008) or a social network (Trusov, Bucklin, and Pauwels 2009). Moreover, C2C social messages and traditional advertising work as complements for enhancing sales of cell phone introductions (Gopinath, Thomas, and Krishnamurthi 2014) and movies (Bruce, Foutz, and Kolsarici 2012; Onishi and Manchanda 2012). Overall, these studies suggest that C2C social messages may be more effective than traditional advertising in stimulating sales and acquisitions. However, we lack knowledge on the relative effectiveness of traditional advertising and C2C social messages to build a brand.
Very few studies that consider F2C social messages take other messages into account (Table 1). Comparing F2C and C2C social messages, Goh, Heng, and Lin (2013) find that C2C social messages are more effective than F2C social messages for evoking apparel purchases.1 Kumar et al. (2013) show that F2C social messages lead to substantially more C2C social messages, which, in turn, affect sales of an ice cream store. The study shows the viral capacities of F2C social messages and that different types of social messages can enhance one another. Kumar et al. (2016) find that F2C social messages have positive effects on retail sales, even when controlling for traditional advertising. Overall, the studies on F2C social messages provide scattered insights into the relative effectiveness of these messages on behavioral outcomes and do not provide any insights into the relative effects on brand building.
The discussion of previous studies shows that there are two major gaps in the literature: (1) no simultaneous assessment of the relative effectiveness of traditional advertising, F2C social messages, and C2C social messages and (2) a lack of knowledge of the effects of these messages on brand-building metrics. Yet it is important to consider traditional advertising, F2C social messages, and C2C social messages jointly because the different messages are omnipresent today and are likely to affect consumers simultaneously. Moreover, the different messages are part of the “echoverse,” that is, the communications environment of a firm (Hewett et al. 2016). Thus, we need to acknowledge that the different message types might influence one another. For example, a recent study by Fossen and Schweidel (2017) suggests that traditional advertising positively affects the volume of C2C social messages about the advertised brand. Firms generally do not have much influence on what consumers talk about online (C2C social messages), but if traditional advertising affects C2C social messages, firms actually do have a tool to influence these messages indirectly. Firms might plan their F2C social messages in accord with their traditional advertising activities or vice versa. Moreover, F2C social messages might stimulate consumers to talk about the brand on other social media sites (e.g., Kumar et al. 2013). Because previous studies have considered only a limited set of messages, we lack insights into the interrelations among the different messages. Knowledge about these interrelations, however, enables managers to exert greater influence on the echoverse and, finally, on critical performance metrics.
Table 1 also shows that current studies have considered only behavioral performance measures, thereby simply treating intervening processes as a “black box” (Srinivasan, Vanhuele, and Pauwels 2010). Accounting for brand-building metrics, however, allows for examining both indirect and direct effects of messages on customer acquisition (Bruce, Peters, and Naik 2012). Considering brand-building metrics alongside behavioral metrics helps managers better understand the full effects of the different messages.
Because previous studies have provided only scattered insights into the relative effectiveness of the different messages, it is difficult to form expectations beforehand. Thus, we refrain from formulating propositions. We rather provide empirical insights into the relative effectiveness of the different message types (i.e., traditional advertising, F2C social messages, and C2C social messages) on brand-building and behavioral outcomes and the interrelations among these messages.
Empirical Application
Data
We use contractual data on customer acquisition from a European telecom firm related to its activities in one European country. Moreover, we obtained data from the firm’s social listening tool on C2C social messages and data from its Facebook page to measure F2C impressions. We combine these data with Nielsen data on traditional advertising and complement all data with survey panel data from an external company on brand-building metrics. The data period ranges from week 30 in 2011 to week 44 in 2013, with all data reported on a weekly basis. Table 2 contains a detailed overview of all variables, their descriptions, measures, and sources.
The focal brand was one of the top five telecom providers in the market of mobile subscription plans in the specific country at the time of the study. There were four main competitors, which, together with the focal brand, had a combined market share of approximately 80%. However, the focal brand was not the largest of these five brands because of its specific target group, which comprises young adults between 16 and 30 years old—a target group that is connected through social media and uses online media actively (Statista 2014). We have information from the survey panel that approximately 80% of the target group had a Facebook account and that about 60% logged in to their account daily. At the time of the study, the firm had the largest Facebook page in the country with respect to the number of “brand fans” (on average, 100,000 consumers). The firm actively used traditional advertising, and the average number of weekly impressions was approximately 436,881.2
Traditional advertising. We use joint gross media expenditures on TV, print, radio, and out-of-home to measure traditional advertising investments (Table 2). Nielsen does not directly observe how much firms actually spend on traditional advertising through TV, print, radio, and out-of-home because firms are reluctant to provide this information. Therefore, Nielsen measures advertising expenditures indirectly, using public and national television channels, and provides data about commercials according to their gross rating point tariffs (i.e., excluding discounts and price negotiations).
F2C impressions. We use the weekly number of viral impressions of the firm’s posts on Facebook (Table 2). This number considers impressions of the focal firm’s posts on Facebook when consumers like, comment on, or share the messages with each other. We consider viral impressions because they reflect the spreading activity through the network: those impressions might be particularly effective for building a brand and generating acquisitions.
The focal brand’s original F2C messages contain promotional messages about phones, subscriptions, or service offers of the focal firm but also information unrelated to the category such as recommendations for going out and sweepstakes. In general, F2C social messages are positive, but shared F2C social messages may have different content and may differ in valence from the original message if consumers comment negatively. We do not have information about the valence of shared F2C social messages, but previous research has shown that the share of negative comments to firms’ posts is rather small (De Vries, Gensler, and Leeflang 2012). We do not consider any consumer-initiated conversations about the firm on Facebook. Such messages would be C2C social messages according to our definitions, and the focal firm has no information about C2C social messages posted on Facebook.
C2C social messages. We measure the volume (i.e., number) and valence of messages initiated by consumers on forums and Twitter, whereby Twitter accounts for the largest part. Thus, we consider specific types of C2C social messages. Yet Twitter and the most popular forums capture the majority of C2C social messages about the focal firm according to conversations with the management. The number of C2C social messages reflects the chance of consumers seeing these messages ( i.e., the more C2C social messages are posted, the greater the likelihood that consumers will see them). The valence of C2C social messages echoes the sentiment in the marketplace and is the difference in shares of positive and negative messages (Table 2). The values for the valence measure range between -1 and +1, where -1 (+1) indicates that no positive (negative) but only negative (positive) messages are posted in a certain week. If valence is equal to zero, there are as many positive as negative messages posted.
Brand-building metrics and customer acquisition. A third-party organization gathered data on different brand-building metrics related to the brand: unaided brand awareness, consideration, and preference (Table 2). The share of consumers who mention the focal brand spontaneously as a brand operating in the specific industry measures unaided brand awareness. Brand consideration is the share of consumers who would consider the focal brand for a given purchase occasion. Brand preference is the share of consumers who prefer the focal brand to competing brands. Each week, this organization interviews 130 target consumers (i.e., young adults), producing an accumulated 15,470 interviews (some consumers might be interviewed more than once) over 119 weeks. The sample is not random but targeted and weighted. Over the weeks, the demographic characteristics of the panel members remain the same. Previous studies considering brand-building metrics used similar samples (Bruce, Peters, and Naik 2012; Srinivasan, Vanhuele, and Pauwels 2010). Customer acquisition is the number of newly acquired customers per week (Table 2).
TABLE: TABLE 2 Description of Variables
| Variable Name | Description | Measurement Unit | Source |
|---|
| Endogenous Variables |
| Traditional advertising | Telecomfirm’s traditional grossmedia expenditures on TV, radio, print, and out-of-home advertising | Gross media expenditures (V) | Nielsen |
| F2C impressions | Number of impressions of the focal firm’s messages on Facebook based on likes, comments, and shares of those messages | Impressions | Facebook Insights |
| C2C volume | Total number of C2C social messages (positive, neutral, and negative) on forums and Twitter | Volume | Online tool of the telecom firm |
| C2C valence | Sentiment in the marketplace [(positive C2C messages – negative C2C messages)/(all C2C messages)] | Share | Online tool of the telecom firm |
| Unaided awareness | Respondents list all telecom providers they know | Percentage of respondents | External party via telecom firm (survey) |
| Consideration | Respondents list the telecom providers they would consider if they had to choose one | Percentage of respondents | External party via telecom firm (survey) |
| Preference | Respondentsname the telecomprovider they would prefer if they had to choose a new telecom provider | Percentage of respondents | External party via telecom firm (survey) |
| Acquisition | Number of newly acquired customers | Volume | Telecom firm’s database |
| Control Variables |
| Holidays | Public and school holidays | Dummy | Own research |
| Media events | Important news items related to the telecom sector, specific telecom providers, or new technology | Dummy | News archives online |
| Buzz events | Important interventions that created online buzz | Dummy | Social media |
| Promotions | Number of promotions by focal firm divided by the number of promotions by focal firm + four most important competing firms | Percentage | Nielsen |
| Traditional advertising competition | Traditional media expenditures on television, radio, print, and outdoor by the four most important competitors | Gross media expenditures (V) | Nielsen |
| C2C social messages competition | Number of C2C messages on forums and Twitter about the four most important competitors | Volume | Online tool of the telecom firm |
Control variables. Several other factors could also affect the brand-building metrics and customer acquisition. Namely, we consider promotions, media and buzz events, holidays, and competition. First, promotions are important stimuli to attract new customers and might also affect brand-building metrics (Pauwels, Hanssens, and Siddarth 2002). We gathered all the individual descriptions of price promotions for the focal firm and its four main competitors. The price promotions apply to annual or two-year subscription plans (e.g., 50% discount for 24 months). All telecom providers in the market use similar promotions. To control for the effect of price promotions, we consider a variable that reflects the promotion intensity of the focal firm—that is, the number of price promotions of the focal firm in a specific week divided by the total number (focal firm + competitors) of price promotions in that week (Table 2). The value of this variable ranges between 0 and 1 and equals 1 if the focal firm is the only firm in a given week that offers a price promotion.
Second, we control for media and buzz events to consider extraordinary short-term interventions. To control for media events, we searched national news archives for important news related to the telecom sector, specific telecom providers, or new telecom-related technology. These news items might describe service failures (e.g., a fire caused service disruptions), new subscription terms being introduced by telecom providers, introduction of new mobile phone models, or major quality improvements of the network. News could probably also cover major price shifts of one or more telecom provider. However, during our observation period no such interventions occurred. Moreover, we identified social media buzz events by inspecting F2C impressions and C2C volume. Buzz events are described by a large deviation from the mean value (i.e., mean +3 SD) and could be either positive or negative. We identified three buzz events, which were related to announcements of new mobile service offers of the focal brand that created large amounts of short-term online buzz.
Third, we consider national holidays. Public (e.g., Easter, Christmas) and school holidays could affect the number of acquisitions. The school holiday during the summer actually covers almost the complete months of July and August. In these months, many consumers are traveling. National holidays might also be related to investments in traditional advertising and consumers’ social media activities.
Finally, we consider competitors’ advertising activities and the volume of C2C social messages related to competitors, both of which lead to more clutter and might decrease the likelihood that consumers notice traditional advertising or C2C social messages by or about the focal firm. We cannot control for competitive F2C social messages/impressions, because this information was not available. Because the main competitors have a much smaller Facebook presence, we believe this is not problematic (Pauwels 2004; Srinivasan, Vanhuele, and Pauwels 2010).
Descriptive Statistics
Table 3 illustrates the substantial variation in traditional advertising, F2C impressions, and C2C volume and valence for the focal brand over time. The gross media expenditures for traditional advertising are, on average, 407,347 EUR. The average number of weekly F2C impressions is 121,153. According to a manager of the focal firm, the firm posted, on average, one F2C social message per day during our observation period. Thus, the weekly number of impressions is generated by about seven firm posts. However, the F2C messages differ with respect to their virality. The F2C social messages reach approximately 46,055 unique consumers every week who are not “fans” of the firm’s social media page (not reported in Table 3). The average number of active users of the page is 17,340 per week, with a maximum of active users of 126,566 in a specific week (not reported in Table 3). The average number of C2C social messages is 1,778. The average valence of C2C social messages equals -.50, which indicates that the sentiment in the market is generally negative. This observation is not surprising given that we study a commodity.
To keep the absolute acquisition numbers anonymous, we constructed an index. Table 3 shows that customer acquisition also varies over time. Moreover, we observe large variations in the brand-building metrics. For example, brand awareness equals 53% on average but ranges between 37% and 68%. This rather large range might seem surprising; however, the considered brand is relatively smaller than its four main competitors. The variation actually suggests that brand-building metrics might be affected by traditional advertising, F2C impressions, and C2C social messages.
In the Web Appendix, we provide time series graphs and highlight some interesting potential relations between the different messages and the brand-building metrics. For example, these graphs suggest a positive relation between traditional advertising, awareness, and consideration. Moreover, the time series graphs suggest a positive relation between F2C impressions and consideration. In addition, peaks in preference seem to follow peaks in C2C volume, which might indicate that C2C social messages positively affect preference. This model-free evidence suggests that the different messages might be related to variations in the brand-building metrics. Yet part of the variation in brand-building metrics might also be due to measurement error (e.g., Naik and Tsai 2000). Because we are interested in the relative effectiveness of traditional advertising, F2C impressions, and C2C social messages, a bias induced by measurement error might not be that critical. However, to test for potential biases due to measurement error, we conduct a robustness check.
Table 4 reports the bivariate correlations among the variables whereby we eliminated any trend in the variables before computing the correlations. In general, many correlations are significant, which seems promising for further analyses. Insignificant correlations might be a result of the multivariate nature of the relations. Thus, we might find significant relations when we consider the multivariate nature of the relations appropriately.
TABLE: TABLE 3 Descriptive Statistics of Relevant Variables
| | M | SD | Min | Max |
|---|
| aWe deleted one outlier whose value was three times the standard deviation below the mean. |
| bFor confidentiality reasons, we provide an index for customer acquisition. |
| Endogenous Variables |
| Traditional advertising (EUR) | 407,346.90 | 329,632.80 | 21,430.00 | 1,246,570.00 |
| F2C impressions | 121,152.80 | 305,556.50 | 1,570.00 | 2,262,655.00 |
| C2C volume | 1,778.28 | 718.67 | 568.00 | 3,430.00 |
| C2C valence | -.50 | .22 | -.99 | .21 |
| Unaided awarenessa (share) | .53 | .07 | .37 | .68 |
| Consideration (share) | .30 | .05 | .18 | .42 |
| Preference (share) | .15 | .03 | .08 | .22 |
| Acquisitionb (index) | 100.00 | 38.42 | 40.74 | 218.89 |
| Control Variables (Exogenous) |
| Promotions (share) | .30 | .23 | .00 | 1.00 |
| Traditional advertising competition (EUR) | 4,884,590.00 | 1,336,693.00 | 1,632,151.00 | 7,956,551.00 |
| C2C volume competition | 29,872.75 | 14,657.07 | 15,015.00 | 153,314.00 |
Methodology
We are interested in the effects of traditional advertising, F2C impressions, and C2C social messages on both brand building and customer acquisition over time, as well as the interrelations among them. Thus, we need to employ a method that allows for considering these complex (inter)relations. We use a VAR model with exogenous variables (VARX). We focus on the cumulative effects (i.e., short- and long-term effects) of the different messages over time and compute elasticities with impulse response functions. This way, we can compare the relative effectiveness of traditional advertising, F2C impressions, and C2C social messages.
TABLE: TABLE 4 Correlations Among Variables (Detrended)
| | ln(TA)t | ln(C2C_vol)t | ln(C2C_val)t | ln(F2C)t | ln(A)t | ln(Con)t | ln(Pref)t | ln(Acq)t |
|---|
| *p < .10. |
| **p < .05. |
| ***p < .01. |
| ln(TA)t–1 | .568*** | .149 | .309*** | -.199** | .206** | .216** | .040 | .299*** |
| ln(C2C_vol)t–1 | .166* | .868*** | .180* | -.031 | .069 | -.106 | -.149 | -.267*** |
| ln(C2C_val)t–1 | .278*** | .238*** | .319*** | -.169* | .046 | -.017 | .124 | .316*** |
| ln(F2C)t–1 | .062 | -.074 | -.075 | .282*** | -.126 | .022 | -.061 | .001 |
| ln(A)t | .066 | .017 | .029 | .045 | 1.000 | .212** | .115 | .025 |
| ln(Con)t | .143 | -.153* | .012 | -.118 | .212** | 1.000 | .422*** | .225** |
| ln(Pref)t | .004 | -.174* | .136 | -.050 | .115 | .422*** | 1.000 | .281*** |
| ln(Acq)t | .202** | -.288*** | .265*** | -.009 | .025 | .225** | .281*** | 1.000 |
| Promotionst | .062 | -.188** | .025 | -.103 | .149 | .261*** | .197** | .402*** |
| ln(TAcomp)t | .129 | .041 | .149 | .078 | .086 | -.018 | .053 | .132 |
| ln(C2Ccomp)t | .110 | .665*** | .297*** | .003 | .007 | -.128 | -.033 | -.124 |
TABLE: TABLE 5 Results of the Granger Causality Tests
| | Dependent Variables |
|---|
| Dependent Variable Granger-Caused By … | Traditional Advertising | C2C Volume | C2C Valence | F2C Impressions | Awareness | Consideration | Preference | Acquisition |
|---|
| Traditional advertising | – | .056 | .081 | .002 | .001 | .016 | .060 | .008 |
| F2C impressions | .001 | .083 | n.s. | – | .078 | .071 | .057 | n.s. |
| C2C volume | .027 | – | .025 | .000 | .086 | .094 | .041 | n.s. |
| C2C valence | n.s. | .100 | – | n.s. | n.s. | n.s. | .063 | n.s. |
| Awareness | n.s. | .078 | .077 | .031 | – | .037 | .055 | n.s. |
| Consideration | .016 | n.s. | .074 | n.s. | .087 | – | n.s. | .080 |
| Preference | .041 | .002 | n.s. | .046 | .005 | .094 | – | .085 |
| Acquisition | .003 | .052 | .030 | n.s. | .018 | .026 | .000 | – |
We first test whether traditional advertising, F2C impressions, C2C social messages (volume and valence), brand-building metrics, and acquisition are actually endogenous. To this end, we conduct Granger causality tests. We use one to four lags when conducting the Granger causality test and report the lowest p-values of this test in Table 5 (Trusov, Bucklin, and Pauwels 2009). The results in Table 5 show that 41 out of 56 effects are significant at the 10% level. Thus, most variables Granger-cause each other. We model a full dynamic system to adequately capture endogeneity and account for interrelations and feedback effects. Feedback effects include effects among brand-building metrics; effects of brand-building metrics and customer acquisition; and effects of brand-building metrics and customer acquisition on traditional advertising, F2C impres- sions, and C2C social messages. Moreover, there are no theoretical reasons to impose restrictions on the parameters, which might cause biases in the later impulse response analyses (Enders 2004, p. 292).
TABLE: TABLE 6 Unit Root Test Results (PP Test)
| | PP Test Statistic | Stationary? |
|---|
| Traditional advertising | -5.743*** | 3 |
| F2C impressions | -8.146*** | 3 |
| C2C volume | -4.693*** | 3 |
| C2C valence | -9.849*** | 3 |
| Unaided awareness | -10.542*** | 3 |
| Consideration | -11.123*** | 3 |
| Preference | -12.504*** | 3 |
| Acquisition | -3.431* | 3 |
| Traditional advertising competition | -5.621*** | 3 |
| C2C social competition | -8.342*** | 3 |
Next, we test for stationarity of the time series. Because we consider a constant term and a deterministic time trend to capture the impact of omitted, evolving variables, we use the Phillips–Perron (PP) test to assess stationarity (Pauwels 2004). The widely used augmented Dickey–Fuller test has low power in this case (e.g., Enders 2004). All metric variables are stationary because the PP test is significant for all variables (Table 6).
We specify the full dynamic system of the VARX model in Equation 1. The vector of endogenous variables—traditional advertising (TA), F2C impressions (F2C), volume of C2C social messages (C2C_vol), valence of C2C social messages (C2C_val), awareness (A), consideration (Con), preference (Pref), and customer acquisition (Acq)—is explained by its own past values, and it accounts for the dynamic relations among those variables. We include constant terms (a) and a deterministic time trend (dt) for all endogenous variables (e.g., Pauwels 2004). We control for media and buzz events (X1(2) equals 1 if an event occurs and 0 otherwise), holidays (X3 equals 1 if a holiday occurs and 0 otherwise), competitive traditional advertising (TAcomp), volume of competitive C2C social messages (C2Ccomp), and promotion intensity (P). We use an ln-ln specification. Because the C2C valence measure ranges between [-1, +1], we add the value +1 to the original values before applying the ln-transformation. where t indicates the week, j indicates the number of lags included in the model, and J is the maximum number of lags. The matrix Q contains the parameters for the exogenous dummy variables Xi. The matrix B contains the parameters for the exogenous metric control variables TAcomp, C2Ccomp, and P. The parameters Fij, i for the lagged endogenous variables reflect the direct (diagonal) and indirect (off-diagonal) effects among the endogenous variables. Finally, et are the error terms for each endogenous variable.
The final prediction error, Akaike information criterion (AIC), Schwarz information criterion (SC), and Hannan– Quinn information criterion all suggest that the number of endogenous lags in the VARX model is one (here: J = 1). The estimated model is a stationary VARX model because the absolute value of the autoregressive parameters is less than one (|Fs| < 1; see the Web Appendix; Dekimpe and Hanssens 1995). Both the Lagrange multiplier and Portmanteau autocorrelation test indicate no autocorrelation in the residuals (Web Appendix), which is an important assumption of a VARX model (e.g., Hamilton 1994).
Because the endogenous parameters of a VARX model are not interpretable (e.g., Dekimpe and Hanssens 1995), we use orthogonalized IRFs to examine the impact of traditional advertising, F2C impressions, and C2C social messages on brand-building metrics and customer acquisition. We use a Cholesky decomposition that transforms the VARX model into a system with uncorrelated error terms, such that the impulses can be interpreted orthogonally (e.g., Dekimpe and Hanssens 1995; Hamilton 1994). A possible difficulty with the Cholesky decomposition is that researchers must determine a priori a causal ordering of the variables in the system. The first variable in the system will affect all other variables, but the others cannot directly influence this first variable. For example, acquisition might have feedback effects on advertising (e.g., decline in acquisition results in more traditional advertising) but firms usually cannot change their advertising investments instantaneously (e.g., Dekimpe, Hanssens, and Silva-Risso 1999; Leeflang and Wittink 1992; Pauwels 2004). Thus, in a model with only traditional advertising and performance measures, it makes sense to put performance last (e.g., Dekimpe, Hanssens, and Silva-Risso 1999). However, in our model, acquisition could have same-period feedback effects on F2C impressions and C2C social messages. Therefore, we continuously change the ordering of the endogenous variables and compute averages over the different responses that result from one-standard-deviation shocks (e.g., Dekimpe and Hanssens 1995). To derive the standard errors of the estimates, we use Monte Carlo bootstrapping with 1,000 runs (e.g., Wiesel, Pauwels, and Arts 2011). Drawing on the IRFs, we compute the cumulative elasticities (accumulation of significant effects with t-statistics greater than 1 in absolute value, following previous studies such as Dekimpe, Hanssens, and Silva-Risso [1999], Pauwels [2004], and Trusov, Bucklin, and Pauwels [2009]). This way, we can compare the effects across traditional advertising, F2C impressions, and C2C social messages (Ataman, Van Heerde, and Mela 2010; Dinner, Van Heerde, and Neslin 2014). All effects are nonpersistent because they abate after a few weeks.
Results
Relative Effectiveness of Traditional Advertising, F2C Impressions, and C2C Social Messages
As Table 7 shows, traditional advertising messages are effective in building the brand because they create awareness (.024) and consideration (.022), meaning that a 1% increase in traditional advertising leads to a .024% increase in awareness and a .022% increase in consideration, respectively. These elasticities resemble elasticities found in previous research (Srinivasan, Vanhuele, and Pauwels 2010). Traditional advertising also positively affects acquisition; a 1% increase in traditional advertising leads to a .202% increase in newly acquired customers, which also corresponds to elasticities found in meta-analyses (Sethuraman, Tellis, and Briesch 2011). We also observe that the effect of traditional advertising lasts longer for customer acquisition than for awareness and consideration (weeks 2–9 vs. weeks 2–4 and week 2, respectively).
Firm-to-consumer impressions affect consideration and acquisition significantly. A 1% increase in F2C impressions leads to a .007% (week 3) increase in consideration and a .103% (weeks 1–6) increase in customer acquisitions. Valence of C2C messages affects brand preference; a 1% increase in the valence of C2C social messages results in a .042% increase in preference (week 2). Volume of C2C social messages, instead, affects acquisition; a 1% increase in the number of C2C social messages leads to a .056% increase in customer acquisitions (weeks 3–5).
When comparing traditional advertising, F2C impressions, and C2C social messages, we find that traditional advertising is most effective in creating awareness and consideration. A potential reason for traditional advertising’s effectiveness with respect to awareness might be that traditional advertising is broadcasted over many different channels, which contributes to its large reach (Tellis 2004). In addition to its large reach, traditional advertising seems to inform consumers about the brand and its offerings (Vakratsas and Ambler 1999). Consumers can evaluate whether the brand or product fits their needs, and in this way traditional advertising influences consumers’ consideration sets (Terui, Ban, and Allenby 2011). Furthermore, we find that F2C impressions are effective in creating consideration, but the effect is much smaller than that of traditional advertising. Consumers seem to consider the brand simply because people they know talk about it, which is in line with previous research (Schulze, Scho¨ler, and Skiera 2014). Moreover, only C2C valence affects preference significantly. The reason might be that C2C social messages target consumers who are interested in a product category and search for product information (Lu et al. 2014; Stephen and Galak 2012). Consumer-to-consumer social messages usually emphasize consumers’ product experiences, which support evaluations of different alternatives (Lu et al. 2014; Muthukrishnan and Kardes 2001; Schlosser 2011). The higher credibility and the unique type of information that is provided (compared with traditional advertising and F2C impressions) might make C2C social messages more helpful for consumers to evaluate the brand and its offerings and to influence preference (Gilly et al. 1998; Gruen, Osmonbekov, and Czaplewski 2006; Smith, Fischer, and Chen 2012).3 A potential reason for the insignificant relation between traditional advertising, F2C impressions, and preference might also be that consumers are less receptive to these messages because they primarily follow different activities, such as consuming a movie while watching TV or connecting with friends on social media. Consumers might be less likely to deeply elaborate on the messages, which might limit their impact on preferences (Adomavicius et al. 2013; Gupta and Harris 2010; Tellis 2004).
Traditional advertising is, however, most effective in generating acquisition (.202 vs. .103 and .056, respectively). Traditional advertising’s reach and the provided information seem to help consumers to make their final purchase decision (Sethuraman, Tellis, and Briesch 2011). Nevertheless, F2C impressions and C2C volume also affect customer acquisition as suggested by previous studies (Babic´ Rosario et al. 2016; Goh, Heng, and Lin 2013; Trusov, Bucklin, and Pauwels 2009; Villanueva, Yoo, and Hanssens 2008).
TABLE: TABLE 7 Cumulative Effects (Elasticities) of Traditional Advertising, F2C Impressions, and Volume and Valence of C2C Social Messages on Brand Building and Customer Acquisition and Interrelations
| | Impulses in … |
|---|
| | Traditional Advertising | F2C Impressions | C2C Volume | C2C Valence |
|---|
| Responses of … | Elasticity | Wear-In | Wear-Out | Elasticity | Wear-In | Wear-Out | Elasticity | Wear-In | Wear-Out | Elasticity | Wear-In | Wear-Out |
|---|
| Awareness | .024 | 2 | 4 | – | – | – | – | – | – | – | – | – |
| Consideration | .022 | 2 | 2 | .007 | 3 | 3 | – | – | – | – | – | – |
| Preference | – | – | – | – | – | – | – | – | – | .042 | 2 | 2 |
| Acquisition | .202 | 2 | 9 | .103 | 1 | 6 | .056 | 3 | 5 | – | – | – |
| Traditional advertising | | | | .223 | 2 | 4 | – | – | – | – | – | – |
| F2C impressions | -.345 | 2 | 4 | | | | – | – | – | -.265 | 2 | 3 |
| C2C volume | .037 | 1 | 1 | – | – | – | | | | – | – | – |
| C2C valence | .096 | 3 | 5 | – | – | – | – | – | – | | | |
Interrelationships Among the Messages
We examine how traditional advertising, F2C impressions, and C2C social messages affect one another (Table 7). We find that a 1% increase in F2C impressions increases traditional advertising by .223% in the subsequent weeks, whereas a 1% increase in traditional advertising decreases F2C impressions by .345% in the subsequent weeks. These results suggest that the time series of traditional advertising and F2C impressions move asynchronously; peaks in traditional advertising follow peaks in F2C impressions.
Peaks in F2C impressions and traditional advertising can be expressed by positive changes in F2C impressions (DF2Ct = (F2Ct/F2Ct-1) - 1) and traditional advertising (DTRADt = (TRADt/TRADt-1) - 1). We compute the correlation between DF2Ct and DTRADt+1 to align the two time series. This correlation is positive and equals .182, which offers some modelfree evidence for the finding that traditional advertising follows social media activities. Moreover, personal conversations with marketing managers of the focal firm confirmed this firm behavior. As one marketing manager mentioned in a personal conversation, the focal firm coordinates its marketing activities across the different channels (i.e., social media and traditional advertising) on the basis of its understanding of the market. Another marketing manager exemplified in another personal conversation that she believes that social media is very effective for the target group and might influence effectiveness of traditional advertising positively. Therefore, she initiates marketing campaigns on social media followed by investments in traditional advertising. The parameters in the VARX model also reflect this firm behavior. We find a positive parameter for F2C impressions in week t - 1 on traditional advertising investment in week t (V = .100, t = 2.310), whereas we find a negative parameter for traditional advertising in week t - 1 on F2C impressions in week t (V = -.314, t = -1.874). Consequently, we find a positive elasticity for F2C impressions on traditional advertising and a negative elasticity for traditional advertising on F2C impressions when conducting the IRF analyses (Table 7).
Moreover, we find that a 1% increase in traditional advertising positively affects C2C volume with .037%, confirming previous research showing that a firm’s advertising messages spur online messages among consumers (e.g., Fossen and Schweidel 2017; Hewett et al. 2016; Onishi and Manchanda 2012). Thus, the firm’s advertising stimulates consumers to talk about the firm to others. In addition, consumers who do talk tend to react favorably to traditional advertising; a 1% increase in traditional advertising increases the valence of C2C social messages by .096%. In addition, we find a negative elasticity from valence of C2C social messages to F2C impressions (-.265). There might be multiple explanations for this effect (e.g., no spillover effect between platforms, the firm does not react to favorable C2C social messages in their F2C social messages). Unfortunately, we cannot use our data to explore the specific reason.
Feedback Effects and Control Variables
We find evidence for some feedback effects and discuss the most noteworthy ones. Improvements in acquisition lead to more F2C impressions (a 1% increase in acquisition leads to .239%more impressions), which could be caused by increases in the number of consumers who like the brand and become active users of the page—at least temporarily. Moreover, awareness positively affects volume and valence of C2C social messages; a 1% increase in awareness leads to a .028% increase in C2C volume and a .129% increase in C2C valence. This result suggests that traditional advertising also indirectly affects the volume and valence of C2C social messages through awareness.
We next discuss some of the notable findings from the exogenous parameters (Web Appendix). The deterministic trend is significant and negative for traditional advertising (a = -.014), indicating that traditional advertising investments slightly diminish over time. The deterministic trend for C2C volume is instead significant and positive (a = .006), indicating a slight positive trend over time. The media events dummy affects volume of C2C social messages as well as acquisition significantly and positively (q = .072 and q = .096, respectively). The effects could be caused by the fact that this variable captures, for example, new phone introductions, which might lead consumers to talk about these introductions and to an increase in newly acquired customers. The buzz events stimulate awareness (q = .075). However, buzz events are negatively related to C2C valence and preference (q = -.592 and q = -.324, respectively). These results indicate that consumers discussed the focal firm’s new mobile service offerings that created the buzz critically.
Comparison with Alternative Models
To test whether our proposed model is appropriate and robust, we also estimated a restricted model, which is based on the idea that there exists a certain ordering among the brandbuilding metrics such that there is a path from awareness to consideration to preference (e.g., Vakratsas and Ambler 1999; see Web Appendix).4 We estimated this model by using a seemingly unrelated regression model because this is most appropriate when the right-hand side variables of the equations are not identical (Enders 2004). The results and the explanatory power of the seemingly unrelated regression model are comparable to the VARX model (Web Appendix). However, the unrestricted VARX model fits conceptually better to the suggested relationships, is generalizable, and thus seems more appropriate. It allows for adequately capturing the complex (inter)relations between the different messages, the brand-building metrics, and customer acquisition over time.
To show the robustness of our results and to examine whether the brand-building metrics might be prone to measurement error, we also estimate a VARX model without the brand-building metrics, all else being equal. Measurement error could possibly lead to inconsistency or upward biases in the parameter estimates (Bruce, Peters, and Naik 2012; Naik and Tsai 2000). Model fit of this model is slightly lower than that of the original model (ΔAIC = 2.26; ΔSC = .78). We computed the elasticities for the effects of traditional advertising, F2C impressions, and C2C social messages on acquisition. A 1% increase in traditional advertising leads to a .219% increase in acquisition (full model: .202%), a 1% increase in F2C impressions leads to a .126% increase in acquisition (full model: .103%), and a 1% increase in C2C volume leads to a .102% increase in acquisition (full model: .056%). Again, the valence of C2C messages does not affect acquisition.We observe that the effect of volume of C2C social messages on acquisition is higher in this reduced model, but the substantive findings do not change. Thus, the substantive results are robust against different model specification and do not seem to be affected by potential measurement errors in the brand-building metrics.
As a final robustness check, we estimated a VARX model with F2C reach instead of F2C impressions. This model fits the data equally well (AIC = 4.22, SC = 7.35). We again find that traditional advertising is most effective in stimulating acquisitions, followed by F2C reach and C2C volume (.205, .088, and .072, respectively). To conclude, the alternative model specifications show robustness of our results.
Discussion
Summary of Findings and Theoretical Contributions
Our study contributes to the literature on the effectiveness of traditional advertising, F2C impressions, and C2C social messages by demonstrating their impact on brand building and customer acquisition. By considering attitudinal and behavioral outcome measures, we respond to a recent call to consider multiple outcome measures in empirical studies (Katsikeas et al. 2016). Whereas previous empirical studies have questioned the relative effectiveness of traditional advertising (e.g., Trusov, Bucklin, and Pauwels 2009; Villanueva, Yoo, and Hanssens 2008), we find support that traditional advertising is still effective today.
More specifically, the results indicate that traditional advertising is the most effective way to influence consumers’ awareness, consideration, and customer acquisition. Firmto-consumer social messages and the impressions generated through these messages are also effective in stimulating consideration and acquisitions beyond traditional advertising. This finding is in line with previous studies (Kumar et al. 2016). Consumer-to-consumer social messages, instead, are effective in creating preference and acquisitions. That is, valence of C2C social messages stimulates preference while volume of C2C social messages stimulates acquisitions. However, C2C social messages are least effective in stimulating customer acquisitions. Our results differ in that regard from previous findings suggesting that C2C social messages are more effective in generating sales and acquisition than traditional advertising (Stephen and Galak 2012; Trusov, Bucklin, and Pauwels 2009; Villanueva, Yoo, and Hanssens 2008). Yet it is important to note that the previous studies considered communities and word-of-mouth (WOM) referrals, which are different from the types of C2C social messages we consider in this study. As such, the specific type of C2C social messages might influence the effectiveness of these messages.
Moreover, we find that interrelations among traditional advertising, F2C impressions, and C2C social messages exist. Our results thus support previously discussed complementary relations (e.g., Bruce, Foutz, and Kolsarici 2012; Fossen and Schweidel 2017). Our study provides additional evidence that traditional advertising spurs volume of C2C social messages (Fossen and Schweidel 2017). Furthermore, traditional advertising generates more favorable C2C social messages. We do not find a relation from F2C impressions on C2C social messages, as some previous studies did (Kumar et al. 2013). A potential reason for the insignificant relation might be that Kumar et al. (2013) specifically design a social media campaign to maximize C2C social messages through F2C social messages. Finally, we find evidence for feedback effects. These findings illustrate the complexity of the relations among the firm’s “echoverse” and outcome variables and highlight the need for methodological approaches that can capture those relations to effectively orchestrate a firm’s efforts to build a brand and improve customer acquisition (Hewett et al. 2016). We illustrate that VARX models can capture the complex (inter)relations and allow for assessing the relative effectiveness of traditional advertising, F2C impressions, and C2C social messages.
Managerial Implications
This study offers four important managerial implications. First, traditional advertising is still an effective medium to build a brand and to enhance customer acquisition. If managers consider shifting marketing investments from traditional advertising to other types of messages, they should take not only costs but also effectiveness into account. Our results further suggest that F2C social messages can complement traditional advertising efforts if they spread through the social network (Fulgoni 2015). Overall, traditional advertising and the firm’s social media page are powerful means for brand building and customer acquisition. Thoroughly orchestrating traditional advertising and F2C social messages might improve a firm’s performance. Second, investments in traditional advertising prompt more and more favorable C2C social messages. The positive impact of traditional advertising on the volume and valence of C2C social messages allows managers to exert greater influence on the echoverse and, finally, on critical performance metrics (Hewett et al. 2016). Third, the positive feedback effect of customer acquisition on F2C impressions suggests that newly acquired customers engage with the brand through social media and leverage the firm’s marketing efforts. Fourth, for managers it is useful to track the effects of traditional advertising, F2C impressions, and C2C social messages on both brand-building and behavioral metrics. Monitoring brand-building and behavioral metrics leads to insights that help managers to orchestrate and leverage different types of messages more adequately.
Limitations and Further Research
The study also has some limitations that offer fruitful areas for further research. Because we did not observe the costs of current levels of monetary investments in the different messages, we cannot offer specific advice about how to allocate marketing budgets efficiently. Further research should try to derive specific implications on budget allocation in a complex world where traditional advertising, F2C social messages, and C2C social messages are interrelated.
The data set did not comprise information about, for example, paid social media; online reviews; or display, search engine, and mobile advertising, because these types of messages are rarely used by the focal firm or not relevant. Therefore, not considering these different types of messages did not affect our substantial results. However, future studies might extend the set of messages under investigation to enhance our knowledge about the relative effectiveness of the messages and their interrelations in specific settings. Moreover, we did not observe traditional WOM, which might have resulted in an omitted variable bias. Thus, future studies should collect information on traditional WOM.
Further research might also consider competitive actions more extensively.5 In our study, the main competitors of the focal firm hardly used social media. Therefore, we think that neglecting competitive F2C did not affect our results. Further research may also want to include other social media sites than Facebook (e.g., Instagram).
In our study, the attitudinal and behavioral metrics originated from two different data sources (survey vs. sales measures). We are aware that survey data are estimates themselves and are prone to measurement error (Bruce, Peters, and Naik 2012; Srinivasan, Vanhuele, and Pauwels 2010), which could lead to inconsistency in the parameter estimates (Bruce, Peters, and Naik 2012). Bruce, Peters, and Naik (2012) suggest a factor solution to eliminate measurement error in case of multiple (>10) brand-building metrics. Alternatively, Naik and Tsai (2000) develop new estimators that use Kalman filtering and wavelet theory to eliminate biases from measurement error in dynamic advertising models. Such corrections are highly relevant if one wants to derive optimal budget allocation decisions. In our study, we are interested in the relative effectiveness of the different messages. If biases occur as a result of measurement error in the brand-building metrics, all parameters for the different messages are equally affected. The results of the VARX model without brand-building metrics further indicate that the effectiveness with respect to acquisition is not affected by potential measurement errors in the brand-building metrics.
Furthermore, we would applaud studies proposing new methods for obtaining brand-building data that can be directly linked to behavioral metrics, especially because these measures are frequently collected with the help of small panel samples, which might impede a comprehensive analysis. This was also the case in our study and might have caused some insignificant results.
The F2C measure in our study is not a “clean” measure because it considers impressions and not the original posts of the focal firm. Yet it captures the spreading capability of F2C social messages. Further research might consider the actual number of posts and the content of these messages. It is likely that some F2C social messages are more engaging than others. However, it is not possible to disentangle which messages obtained more impressions than others. Moreover, it could be that some other external sources drive F2C impressions. We encourage other researchers to reexamine the found effects and to gain knowledge on the underlying processes that explain the effectiveness F2C social messages.6 Moreover, it would be worthwhile to consider the content of the comments on firms’ posts. This would allow for more insights into what consumers talk about and how engaging the firm’s posts are. Furthermore, it would be useful to shed more light on the drivers of the lagged effect of F2C impressions because this effect contributes to the impressions’ effectiveness. In addition, consumers might propagate a firm’s messages through other platforms (e.g., YouTube) and might amplify the effectiveness of traditional advertising and F2C social messages in this way. Further research could explore this topic. Finally, it might be that firms actually do sponsor C2C social messages. Unfortunately, we are not able to observe this in our data. An interesting question is how consumers would react if they learned that C2C social messages are incentivized by the firm (Verlegh et al. 2013).6
Despite its limitations, this study is the first to compare the relative effectiveness of traditional advertising, F2C impressions, and C2C social messages and provides initial insights into the complex (inter)relations. This article thus makes a significant contribution to the existing literature and offers fruitful avenues to enhance further research.
TABLE 1
Overview on Studies Considering More Than One Type of Message
1Rishika et al. (2013) consider both firm and consumer messages in a firm-initiated social media community, but they aggregate the messages and, thus, do not distinguish between the two types of messages.
2We derived the number of impressions from the ad spending and
TABLE 2
Description of Variables
TABLE 3
Descriptive Statistics of Relevant Variables
TABLE 4
Correlations Among Variables (Detrended)
TABLE 5
Results of the Granger Causality Tests
TABLE 6
Unit Root Test Results (PP Test)
3It could be the case that C2C social messages actually do contain messages that are sponsored by firms. This occurs, for example, when firms provide free products (e.g., mobile phone) to consumers and ask them to write a review about this product. We cannot infer from our data whether this happened for our brand. We mention this issue as a limitation of our study, and we thank an anonymous reviewer for this suggestion.
TABLE 7
Cumulative Effects (Elasticities) of Traditional Advertising, F2C Impressions, and Volume and Valence of C2C Social Messages on Brand Building and Customer Acquisition and Interrelations
4We thank one of our anonymous reviewers for this suggestion. We also tested another VARX model where we exogenously control for months by adding 11 monthly dummies to the VARX model instead of the HOLIDAY dummy. However, because many additional parameters need to be estimated, model fit does not improve.
5We thank one of our anonymous reviewers for this suggestion.
6We thank one of our anonymous reviewers for this suggestion.
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Record: 72- Evaluating the Effectiveness of Retailer-Themed Super Saver Events. By: Guyt, Jonne Y.; Gijsbrechts, Els. Journal of Marketing. Mar2020, Vol. 84 Issue 2, p92-113. 22p. 9 Charts. DOI: 10.1177/0022242919896334.
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Evaluating the Effectiveness of Retailer-Themed Super Saver Events
In response to pressure to defend their stand sales against discounters, grocery retailers started engaging in retailer-themed super saver events: promotional events ( 1) specific to the retailer, in which they ( 2) mass advertise ( 3) unusually deep, immediate deals ( 4) across a broad range of categories ( 5) under a common savings theme and deal format. Given these characteristics, such events are expected to generate higher awareness and interest than typical day-to-day promotions, thereby enhancing visits and purchases during the event but also reducing them before and after. The authors evaluate these effects by analyzing 44 retailer-themed super saver events operated by the largest Dutch grocery retailers over four years. They find a substantial increase in visits and total purchases during the event, especially among nonprimary customers and hard-discount shoppers. The larger part of this lift stems from the use of an overarching event theme. Consumers buy less in anticipation of the event and visit the store more often afterward, but for smaller baskets—typically leading to a null effect in terms of profit. Finally, our results suggest that rather than the deal depth or advertising budget, the number of items and media resonance of the theme are key drivers of event success.
Keywords: discounts; grocery retailing; promotions; savings events
Pressured to defend their sales against (hard) discounters such as Aldi, Lidl, or Walmart, traditional grocery retailers have increased their promotional activities considerably in recent years ([22]; [42]). Studies suggest, however, that simply offering more or deeper discounts hardly generates incremental increases in traffic or sales. Given the high promotional clutter, consumers are often unaware that a product is on sale (e.g., [54]) and, thus, do not buy more, even though they could benefit from the discount ([23]). Even if such deals are noted, they often fail to attract consumers who would otherwise not visit the store. Research has indeed found limited evidence of direct grocery-store switching due to promotions ([25]). This has led "a growing number of industry stakeholders [to question] the long-term viability of retailers' promotional activities" ([42]).
To address these problems, several grocery retailers have ventured into retailer-themed super saver events (ReTSS). These are promotional events ( 1) specific to and designed by the retailers, in which they ( 2) mass advertise ( 3) unusually deep, immediate deals ( 4) across a broad range of categories ( 5) with a common savings theme and deal format. Examples are Kroger's "Cart Buster Savings Event" (also known as "Mega Sales"), during which the U.S. retailer claims to offer over $100 in savings on a set of products; Woolworths's "Two-Day Super-Sale" event, featuring 50% discounts on hundreds of grocery products throughout its online store; Éxito's (the leading grocery chain in Colombia) "Días de Precios Especiales," offering exceptional discounts across a range of items; and Dutch retailer Albert Heijn's "Hamsterweeks," which entice consumers to buy large quantities of groceries through a broad set of buy-one-get-one-free (BOGO) offers over consecutive weeks.
Through such events, retailers hope to improve, or at least consolidate, their market position[ 6] by revitalizing their customer base (i.e., attracting extra visitors to the store) and increasing current customers' spending at the store ([21]). By integrating the offers into a common deal format and savings theme, and combining feature ads with mass media to advertise them, these events may generate extra attention and signal unusual bargain opportunities. As such, they may attract extra visitors and/or expand current customers' purchase baskets, thereby generating incremental business.
Anecdotal evidence suggests that traffic and basket sizes do increase during the ReTSS period (e.g., [12]; [21]) and that such events are "the engine behind revenue growth" ([21], p. 2). This indicates that ReTSS may help traditional retailers defend against price fighters. However, it does not imply that ReTSS are a panacea. First, regular store customers may anticipate upcoming ReTSS and postpone purchases until the event. Moreover, the deep, storewide, and uniformly tagged promotions may simply entice those customers to stock up on larger inventories of more promoted items and to buy less after the event. Second, newly attracted visitors may stay away in postevent weeks, when the retailer's promotional activity returns to business as usual. Thus, some industry analysts express doubts about the net outcomes of these ReTSS ([37]), and any claims that ReTSS are the road to promotion success remain unsubstantiated.
A rigorous analysis of ReTSS is currently lacking; this sets the stage for our research. Our contribution is twofold. Substantively, we conceptualize how the combined characteristics of ReTSS could make their effect different from that of business-as-usual retailer promotions. We develop a conceptual framework that lays out the behavioral mechanisms, and, although we do not test these mechanisms per se, we use them to form expectations for how ReTSS affect store visits and purchases. Empirically, we document the impact of ReTSS on these metrics before, during, and after the event. In so doing, we address several questions: Do ReTSS attract extra visitors to the store during the event? Do they increase visitors' purchases at the store? Are these effects incremental—that is, do increases during the event period outweigh negative pre- and postevent dips? We address these questions by studying weekly store visits and purchases of a panel of households, across the top seven Dutch grocery chains (of which four engage in ReTSS), during a period covering over 200 weeks and 44 ReTSS (nine themes, with several occurrences). We study the impact of ReTSS as a whole and show that it is stronger than the mere discounting and advertising effect. In addition, we explore which consumers respond more favorably to these events, what makes some ReTSS more successful than others, and how they affect the retailer's bottom line.
The article is organized as follows. After a brief review of background literature, we describe the ReTSS and outline their characteristics. Next, we develop a framework for their anticipated effects on store visits and purchases before, during, and after the event. We then present the models followed by the empirical estimates, which we use as inputs for simulations to test and further explore the proposed effects. We conclude with summary insights for academics and managers.
This section offers a brief literature review on the nature of, and evidence for, promotional effects. Previous work has identified the components of promotional responses and indicated those that contribute to a net gain (excellent overviews are given in [ 2]], [ 4]], [51]], and [52]]). From a retail perspective, the total lift during the promotion period can be split into an effect due to changes in visits or purchases per visit and further decomposed into ( 1) deceleration (consumers postponing visits/purchases at the promoting store in anticipation of the promotion); ( 2) cross-store switching, either direct (consumers visiting the promoting store instead of a competitor) or indirect (consumers shifting purchases between stores they would visit anyway); ( 3) expansion (due to consumers engaging in more shopping trips and/or consuming more in the promoted categories); ( 4) acceleration (consumers visiting/buying earlier than they usually would to benefit from the promotion); and ( 5) halo effects (promotions lifting the purchases of other [nonpromoted] categories in the store).
Of these components, only expansion, halo effects, and cross-store switching contribute to the incremental promotion lift for the retailer. Deceleration and acceleration produce pre- and postpromotion dips that must be deducted from the lift during the promotion period.[ 7] Thus, to appreciate the truly incremental outcome of promotions, one needs to consider not only immediate effects (during the promotion period) but also effects before (leads) and after (lags) the promotion period.
Empirical studies on the impact of grocery retailer promotions abound. These works typically study the impact of what we refer to as "business-as-usual promotions": frequent discounts, premiums, or coupons on individual brands or stockkeeping units (SKUs) that are not part of an overarching event and that may be announced through feature ads or in the store's weekly flyer. Most of these studies focus on the effect of brand- or SKU-level offers in isolation, with brand or category sales as the outcome of interest[ 8] (for an excellent discussion, see, e.g., [ 1]] and [52]]). The results show that at the category level, the larger share of the promotional sales lift comes from purchase acceleration, followed by store switching, with only a small portion stemming from increased consumption (e.g., [29]; [51]). The store switching appears to be mostly indirect—consumers shifting category purchases among stores they visit anyway ([13]; [29])—such that, to the extent that competing supermarkets run promotions for different categories, the question remains how these effects translate to sales for the retailer as a whole.
A smaller subset of research examines the combined effect of different promotional offers at the store level. Some of these document the impact of promotional calendars—in other words, the sequence of promotional actions over time ([36]; [43]; [46]). Others analyze how the total number of products promoted by a retailer and their average discount depth affect store traffic and sales (e.g., [22]; [27]; [49]; [55]). The general finding is that more and deeper promotions can increase traffic and basket size, but that this effect is often small, with elasticities in the range of.05 to.2 ([22]; [27]). Moreover, to the extent that consumers accelerate purchases to benefit from a promotion ([ 3]), only a portion of this temporary lift in traffic or spending is incremental.
Retailer-themed super saver events (ReTSS) exhibit a unique combination of characteristics. They promote storewide benefits across a broad range of categories in the store. The offers are immediate (i.e., the consumer receives the price cut or extra quantity at the time of purchase) and unusually deep. More importantly, the event-related deals share a common format that is easy to recognize (e.g., "One Euro only," "BOGO"), and they are presented to consumers under a theme that is unique to the retailer. This theme is not just apparent in-store or featured in the store flyer; it is also supported with mass-media advertising that emphasizes the considerable potential savings. In terms of timing, retailers themselves choose when the event takes place and for how long. As an example, the Hamsterweeks event, organized several times per year by Dutch chain Albert Heijn, involves BOGO offers on items across 50 categories, rotating during three consecutive weeks. The event is advertised on national TV, in newspapers (and on the radio) using images (sounds) of a hamster carrying the chain's logo and dragging large amounts of groceries out of the store while crying out, "Hamsterééééén!"[ 9]
Table 1 compares ReTSS with other retailer promotions. As shown there, ReTSS share some characteristics with other promotional activities. However, what sets ReTSS apart is the joint occurrence of these characteristics. Unlike popular, category, and seasonal events, which typically focus on specific types of products, ReTSS span categories storewide. In contrast to popular and seasonal events, the timing of the ReTSS is retailer-specific.[10] Unlike business-as-usual promotions that use store flyers and in-store tags focused on activation, ReTSS combine these with mass-media ads designed to form attitudes. The discounts that are part of the ReTSS event are immediate (unlike in temporary loyalty programs, in which consumers save for rewards) and unusually deep (typically 30%–50%; other promotions are, on average, less than 20%).[11] Perhaps the most discriminating feature of ReTSS is the use of a common deal format under a common (retailer-specific) theme focused on monetary savings. Other promotion activities either lack an overarching theme (i.e., business-as-usual promotions) or, if they do have a theme, encompass a variety of offers, some of which are not even savings-related (e.g., popular events including premium gadgets linked to the event, category and seasonal events also pertaining to temporary additions to the assortment). In our empirical analysis, we control for the presence of such other events when assessing the impact of ReTSS on visits and purchases.
Graph
Table 1. ReTSS Events Versus Other Promotion Activities.
| Promotion Activity/Event |
|---|
| ReTSS | Business-as-Usual Promotions | Popular Events | Temporary Loyalty Programs | Category Events | Seasonal Events |
|---|
| Examples | Kroger's "Cart Buster Savings" event, Woolworths's "Two-Day Super-Sale" event | 20% off on Campbell soup at Tesco, free bowl with purchase of Mars chocolates at Carrefour | Win free tickets for Tomorrowland at Carrefour, partner of the festival, save soccer player cards at Albert Heijn during European Championship Soccer | Metro's "Fontignac Knives Collection" program, Safeway's "Spiegelau Glasses" program | Sainsbury's "Baby's Big Event," C1000's "Best Deals with the C1000 Butcher" event | Ritchies supermarkets' "Happy Easter" sale, Sainsbury's Black Friday deals |
| Characteristic | |
| Unusually deep immediate discounts | ✓ | × | × | × | × | × |
| Store-wide, covers broad range of categories | ✓ | ✓ | × | ✓ | × | × |
| Common savings theme | ✓ | × | ✓ | ✓ | ✓ | ✓ |
| Common deal format | ✓ | × | × | ✓ | × | × |
| Use of mass media to communicate the promotion (alongside store flyer and in-store flagging)a | ✓ | ×a | ✓ | ✓ | ✓ | ✓ |
| Timing determined by retailer | ✓ | ✓ | × | ✓ | ✓ | × |
1 aBusiness-as-usual promotions are typically not communicated through mass media (though the retailer often uses mass-media image advertising during business-as-usual weeks).
This section outlines a conceptual framework that examines the effects of ReTSS on consumer and store outcomes. We proceed in three steps. First, we argue how the joint characteristics of ReTSS increase promotion awareness and perceived benefits. Next, we delineate how this influences the different components of promotion response identified in previous literature. Although we do not observe these mechanisms directly, we use them in a third and final step to form expectations about consumers' visits and purchases at the store before, during, and after the ReTSS period, which we subsequently test empirically.
Why do promotions often fail to generate incremental visits and purchases? We identify two main reasons for this. First, consumers—and especially those who are not regular store customers—are often not aware of the promotion. As indicated by [ 7], p. 122), shoppers are "perhaps more than ever...seemingly in a perpetual state of partial attention." They are bombarded with promotional messages, yet experience high search costs ([23]). This makes it difficult for specific deals to stand out from the clutter and to reach potential customers. Second, the deals may not seem interesting enough to trigger a promotional purchase at the store. They are often not unique to the retailer—promotions that run concurrently with other stores yield less bang for the buck ([29])—and, many times, the benefits are too small to warrant the cost of an extra visit or even to justify the effort to look for the item in-store. In the following subsections, we argue how their combination of characteristics may allow ReTSS to overcome these hurdles.
Promotions at the store are typically communicated through store flyers for which readership may be high among current customers, but less so among potential customers (e.g., [18]; [53]). By combining store flyers with mass-media advertising, ReTSS cover a broader target audience, including noncustomers of the store. In addition, the presentation as an event with a common theme may create higher resonance ([ 7]) and trigger word of mouth, thereby further enhancing promotional reach.
Moreover, consumers who encounter ReTSS messages are more likely to encode them. The combined use of different modes (mass media and store flyers) may render the ReTSS more salient ([48]; [56]). Furthermore, the common event theme provides a hook that fosters message processing ([28]; [31]), especially so because—unlike season-sale or popular external events—the theme and timing of ReTSS is retailer-specific and does not automatically coincide with above-average competitive clutter.
In summary, consumers are more likely to be aware of ReTSS events. We expect this to hold during and after the event period but also—to the extent that these events are announced up-front or recur around the same period—in the period leading up to it.
Retailer-themed super saver events should generate higher interest by providing larger perceived benefits. They offer deeper-than-normal monetary discounts that are immediately available across a broad set of items and, therefore, appeal to many (current and potential) customers. This is reinforced by the integrated nature of the campaign: unifying the ReTSS deals through a common format and theme that is unique to the store may produce signaling value ([58]) and enhance the perceived monetary benefits.
In addition, ReTSS offer nonmonetary benefits, which further contribute to consumers' promotional response ([14]): convenience, smart-shopper, and exploration benefits. First, ReTSS hold the promise of important convenience benefits. The broad offer propagates the store as the place of choice, with a multitude of deals under one roof, making it worth a visit. The common format makes it clear to consumers what to look for and easy to spot the deals in-store, reducing the search cost of promotional shopping ([23]).
Second, the use of complementary media may instill psychological triggers to participate. While store flyers and in-store deals stimulate action, mass (TV) advertising is effective at eliciting emotions that heighten the success of direct sales incentives ([ 7]; [41]). In this vein, mass advertising that presents the ensemble of ReTSS deals as one large, not-to-miss event may create value-expression or smart-shopper benefits: the feeling of being a responsible shopper when visiting the store ([ 6]; [14]).
Finally, ReTSS can create exploration benefits. To the extent that ReTSS deals are announced in mass media to cover a broad set of categories but are not individually listed in those ads or grouped in one place inside the store, consumers may become curious to discover which specific items are covered. This may stimulate them to look for (promoted) items in-store and to enjoy traveling the aisles ([14]).
Table 2, Panel A, summarizes the links between ReTSS characteristics and consumer awareness and interest (due to perceived monetary and nonmonetary benefits). We expect these links to apply to ReTSS in general but to be particularly strong for events with more items, higher advertising budgets, deeper discounts, and more resonant themes.
Graph
Table 2. ReTSS: Consumer Mechanisms and Store Outcomes.
| A: Impact of ReTSS Characteristics on Consumer Awareness and Interest |
|---|
| Characteristic | Generates Higher... |
|---|
| Awareness | Interest |
|---|
| Monetary Benefits | Nonmonetary Benefits |
|---|
| Unusually deep immediate discounts | | ✓ | |
| Store-wide, covers broad range of categories | | ✓ | ✓ |
| Common savings theme and deal format | ✓ | ✓ | ✓ |
| Use of mass media (alongside store flyer and in-store flagging) | ✓ | | ✓ |
| Timing determined by retailer | ✓ | | |
| B: Implications for Store Visits |
| Consumer Mechanism | Resulting Promotion Component | Impact on Store Visits |
| Awareness | Interest | | Before | During | After |
| Monetary Benefits | Nonmonetary Benefits |
| ✓ | ✓ | ✓ | Trip deceleration | ↓ | ↑ | |
| ✓ | ✓ | ✓ | Direct cross-store switching | | ↑ | |
| ✓ | ✓ | ✓ | Visit expansion | | ↑ | |
| ✓ | ✓ | ✓ | Trip acceleration | | ↑ | ↓ |
| ✓ | | ✓ | Postpromotion direct switching from (or to) other stores | | | ↑ (or ↓) |
| | | Expected visit outcome | ↓ | ↑ | ↓ |
| C: Implications for Store Purchases |
| Consumer Mechanism | Resulting Promotion Component | Impact on Store Purchases (Volume) |
| Awareness | Interest | | Before | During | After |
| Monetary Benefits | Nonmonetary Benefits |
| ✓ | ✓ | ✓ | Purchase deceleration | ↓ | ↑ | |
| ✓ | ✓ | Indirect cross-store switching | | ↑ | |
| ✓ | ✓ | Purchase expansion | | ↑ | |
| ✓ | ✓ | Purchase acceleration | | ↑ | ↓ |
| ✓ | ✓ | Halo effects | | ↑ or ↓ | |
| ✓ | | ✓ | Postpromotion indirect switching from (or to) other stores | | | ↑ (or ↓) |
| | | Expected purchase outcome | ↓ | ↑ | ↓ |
2 Notes: The table summarizes how the impact of ReTSS differs from business-as-usual promotions. Panel A links event characteristics (left side) to resulting consumer mechanisms (right side). For instance, because of the unusually deep, immediate discounts, ReTSS entail larger monetary benefits than regular promotions (✓). Panels B and C indicate which of these consumer mechanisms (left side) influence which promotion component (middle), and how this component affects visits (right side of Panel B) and purchases per visit (right side of Panel C). ↑ = the promotion component in the row enhances the outcome variable in the column; ↓ = the promotion component reduces the outcome variable; empty cells indicate that it has no impact. For instance: trip acceleration enhances visits during (↑) but reduces visits after (↓) the event, postpromotion direct switching to other stores reduces visits after the event (↓), and so on.
How do increases in awareness and perceived benefits translate to consumer reactions to ReTSS, over and above business-as-usual promotions? To see this, we discuss how they influence the aforementioned promotion-response components, split into visit responses (Table 2, Panel B) and purchase responses given a visit (Table 2, Panel C).
The high awareness of ReTSS may make consumers decelerate visits in anticipation of the event ([40], [45]), and more strongly so than for regular promotions. This is bolstered by the interest generated by these events: the large expected monetary benefits and emphasis on smart shopping reinforce consumers' desire to be economical ([14]; [58]).
During the promotion period, we expect ReTSS to trigger more direct cross-store switching than other promotion activities. Their high reach and salience make even noncustomers aware of the event. Attracted by the promise of substantial monetary benefits (which may act as a commitment device; [33]) and smart-shopper benefits (which make them feel like a responsible shopper; [14]), these consumers may decide to visit the promoting store instead of a competitor. Moreover, because the perceived benefits are likely to exceed the cost of a visit ([ 9]), consumers are more prone to engage in extra trips during the event (visit-expansion effect).
The ReTSS mechanisms that attract new consumers may also trigger current customers to more strongly accelerate their shopping trips. While this further increases store traffic during the event, it leaves these consumers with an unusually high inventory that reduces their visit propensity in postevent weeks (see, e.g., [24]).
Finally, more so than other promotions, ReTSS may produce direct cross-store switches that persist for some time after the event. These switches can be in either direction. On the positive side, increased awareness may produce more sustained switches toward the promoting store. New customers may have found their way to the store and, having become more familiar with it during the event, or realizing its attractive features in-store, return even after the ReTSS ends ([ 4]; [53]). Moreover, increased awareness may produce a rewarded behavior effect of consumers feeling obliged to those who treat them well ([16]) and becoming more committed to the store at the expense of competing stores. On the negative side, the stronger promotion salience and emphasis on smart shopping may trigger a reference-price effect and reduce consumers' willingness to visit at regular prices ([30]). Even consumers who did not visit the retailer during the event may exhibit such an effect and switch to competing stores subsequently ([57]).
The promotion events may also affect consumers' purchases on a given visit, in a way that differs from their regular promotion response. Even if they maintain their visits before the ReTSS, awareness of and interest in the upcoming event may make current customers more strongly decelerate certain purchases—depleting inventories of items in their pantry to replenish them during the event.
During the event period, and once customers are in the store, the combination of benefits (rather than their reach or salience) especially sets ReTSS apart from other promotions. These benefits may provoke multiple, sometimes countervailing, purchase responses. The monetary and convenience benefits may enhance indirect cross-store switching, stimulating consumers to procure promoted items at the ReTSS store instead of other visited stores ([14]). These same benefits may also foster purchase expansion, as when people buy and consume more of the promoted products ([ 5]; [ 4]). Moreover, because they find the offer so interesting, consumers (in particular current customers; [ 4]) may more strongly accelerate their purchases, which increases purchase volume in the course of the ReTSS period but produces deeper postevent purchase dips. When it comes to halo effects, the impact of ReTSS relative to business-as-usual promotions is equivocal. On the one hand, the perceived monetary gains and smart-shopper benefit may produce a windfall or licensing effect and justify extra expenses ([32]; [50]). Especially when coupled with the exploration benefits (consumers traveling more aisles), this may result in more (impulse) buying of nonpromoted items ([ 8]; [44]). On the other hand, the monetary and smart-shopping benefits may foster cherry-picking ([23])—that is, more consumers visiting the store for promoted items alone. As such, ReTSS may also come with smaller baskets.[12]
Likewise, indirect store switching after the event can go two ways. On the positive side, ReTSS may more strongly expand the future basket at the expense of competitors because newly reached consumers have discovered the strengths of the store ([ 4]) or because the highly salient deals have elicited reciprocity and a shift in commitment to the store ([16]). On the negative side, this same promotion salience, along with the increased emphasis on smart shopping, may make customers less willing to pay the full price and cause shifts to rival stores after an event ([52]).
Adding up the responses[13] in each period (across the rows in Table 2, Panels B and C), the bottom of Table 2 summarizes the anticipated visit and purchase outcomes before, during, and after the event. Compared with business-as-usual promotions, we expect ReTSS to lower (extant) customers' visits and purchases prior to the event. In the course of the ReTSS period, we anticipate more visits, including visits from new customers. Because the increase in basket size from temporal shifts, expansion, or increased halo effects, on the one hand, is likely to exceed any negative effects of cherry-picking (reduced halo effects), on the other, we also expect purchases per visit to more strongly increase. Following the event, although newly acquired customers may continue to visit and buy at the store for some time, we expect this effect to be outweighed by below-baseline levels for extant customers, so we anticipate a larger postevent drop in visits and purchases.
How these effects net out over time is not clear a priori, and we leave it as an empirical issue. Next, we present the household-level visit and conditional-purchase models used to verify these effects.
As indicated previously, a ReTSS may influence both the decision to visit a retailer and the basket size at that retailer. Similar to [19] and [58], we model this in two layers.
The first layer captures a household's decision about whether to visit a retailer in a given week. Like [58], we focus on retailer-visit incidence rather than retailer choice for a given visit, because large-scale events may well affect households' trip organization and number of store visits (e.g., they may begin to split their grocery trips between their regular and the promoting store in a given week). We specify the probability that household h visits retailer r in week w as follows:
Graph
1
with
Graph
2
where is a latent variable reflecting the utility of visiting the store; are the parameters; is a vector of household-, retailer-, and/or week-specific utility drivers (further specified subsequently); and is an extreme-value distributed random component. A household may visit multiple retailers in a given week (i.e., may be positive for different retailers that the household visits in w), and these patronage decisions are likely to be interdependent. To capture these interdependencies and to accommodate the possibility that the random components have retailer-specific variances, we assume a multivariate heteroskedastic extreme-value distribution for and use a Farlie–Gumbel–Morgenstein copula model to specify and estimate the corresponding retailer-visit probabilities (see, e.g., [11]; [15]; Web Appendix W2).
The second layer relates to households' purchases at a retailer in a given week, equal to 0 if the retailer is not visited, and some quantity otherwise. We model purchases conditional on a retailer visit as follows:
Graph
3
where if household h visited retailer r in week w; are parameters to be estimated; is a vector of household-, retailer-, and/or week-specific regressors (specified in the next section); and is a random component. This conditional purchase model is estimated only on observations in which the household actually visited the store. To correct for this selection issue (i.e., the nonrandom occurrence of the store visits), we use the approach suggested by [17]: we include the term in Equation 3 to ensure unbiased parameter estimates.[14] Moreover, the variance of the random purchase-quantity component is likely to depend on the number of stores visited that week (i.e., on the split of the total basket), and purchases at one store are likely to be interrelated with purchases at (an)other store(s) visited in the same week. We capture this through a multivariate normal specification on the random components with heteroskedastic variances and nonzero covariances between visited retailers within a household and week (details are in Web Appendix W2).
To accommodate unobserved household heterogeneity, the parameters in the visit and purchase models follow a normal mixing distribution (with means and standard deviations to be estimated). We estimate Equations 1–3 with simulated maximum likelihood.
Our primary data source consists of GfK panel data from 2008, week 31, until the last week of 2012. The data set contains household purchase histories as well as weekly price levels and feature activities at all Dutch retailers. We consider household purchases at the top seven retailers in terms of market share. Table 3, Panel A, provides some descriptive statistics for the considered retailers, which, together, cover about 60% of the Dutch grocery market. To ensure stable estimates, we retain only households that remain in the panel for more than 26 weeks and that make at least ten visits to (any of) the top seven chains throughout our observation window. For tractability, we estimate our models on a random subsample of 1,000 households. On average, a household visits 1.19 retailers per week and spends €31.73 per visited retailer. Albert Heijn has by far the highest weekly visit rate (i.e., fraction of weeks with a chain visit, averaged across households), followed by Aldi, C1000, and Lidl. Weekly spending by store visitors is more comparable across chains, with slightly higher levels for Albert Heijn and lower levels for the hard discounters.
Graph
Table 3. Descriptives.
| A: Retailer Descriptives |
|---|
| Retailer | Albert Heijn | C1000 | Aldi | Lidl | Jumbo | Plus | SdB |
|---|
| Format | Hi–lo | Hi–lo | HD | HD | EDLP | Hi–lo | Hi–lo |
|---|
| Market share (%) | 34.3 | 17.5 | 13.6 | 11.3 | 9.9 | 7.4 | 6.0 |
| Average visit ratea | .38 | .24 | .25 | .21 | .11 | .12 | .09 |
| Average purchase amount (€/week)b | 45.58 | 39.7 | 30.01 | 25.79 | 41.65 | 41.72 | 33.45 |
| B: Descriptives of ReTSS Events |
| Event | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| Retailer | Plus | C1000 | Albert Heijn | Plus | C1000 | Plus | C1000 | Albert Heijn | SdB |
| Event Characteristics |
| Offer | Deal format | 50% off | Items for €1 | BOGO | BOGO | Multibuy | (Up to) 50% off | (Up to) 50% off | Items for €.99 | (Up to) 50% off |
| Average depth | 50% | 40% | 50% | 50% | 40% | 30% | 40% | 30% | 30% |
| # events (total) | 1 | 12 | 8 | 4 | 3 | 2 | 5 | 2 | 7 |
| # weeks/eventc | 4 | 3 | 3 | 4 | 3 | 3 | 3 | 3 | 3 |
| Scope (products per week)d | 10–20 | 100 | 50 | 50 | 50 | 50 | 30 | 99 | 50 |
| Resonancee | 1 | 57 | 73.25 | 7.5 | .5 | 5 | 3.25 | 5 | 5 |
| Overall Promotion Pressure During Event Weeks (Including Concurrent Business-as-Usual Promotions) |
| # feature promotions | Level | 1,053 | 2,039 | 2,223 | 760 | 1,641 | 831 | 1,034 | 3,554 | 787 |
| Index | 1.16 | 1.48 | 1.16 | .83 | 1.19 | .91 | .75 | 1.85 | 1.45 |
| Mass-media advertising | Level (€) | 328,245 | 881,994 | 756,147 | 589,446 | 660,743 | 30,035 | 1,106,662 | 1,087,833 | 305,510 |
| Index | .64 | 1.03 | .68 | 1.15 | .77 | .06 | 1.29 | .98 | 1.73 |
| Discount depth (%) | Level | 13.27 | 18.90 | 16.08 | 21.06 | 16.81 | 17.55 | 15.51 | 20.85 | 19.81 |
| Index | .89 | 1.22 | 1.31 | 1.40 | 1.08 | 1.19 | 1.00 | 1.69 | 1.27 |
- 3 a Fraction of weeks with household visit, averaged across households.
- 4 b Weekly spending per household, conditional on store visit (in euros), averaged across households.
- 5 c A detailed timeline for the ReTSS is given in Web Appendix W3.
- 6 d Number of promotions that fall under the event-theme heading, based on anecdotal information and industry/press reports. "Products" can refer to entire brands or SKUs.
- 7 e Average of LexisNexis mentions in event-year.
- 8 Notes: Hi–lo = adoption of a high–low pricing scheme; HD = hard discounter; EDLP = everyday low pricing. Level = the average value at the retailer during event weeks (including any concurrent business-as-usual promotions or advertising). Index = the value relative to weeks without a ReTSS event at the same retailer. During ReTSS weeks, the retailer also features other promotions that do not fall under the event-theme. Depending on the number and discount depth of such other promotions, the total number of feature promotions and discount depth during ReTSS weeks may be higher or lower than usual. Likewise, mass-media budgets can be lower during ReTSS weeks than non-ReTSS weeks because of mass-media image campaigns or communication of other events during non-ReTSS weeks.
To identify the promotion events that qualify as ReTSS, we combine several additional sources. We begin from a data set compiled by GfK that contains descriptive information, by retailer and week, on promotional actions that are somewhat broader (i.e., covering more than one specific brand/SKU in a specific category). As such, it includes a very diverse set of promotional events. For each event, it contains the name as communicated by the retailer, the event timing, and, in many instances, information on the deal format and the promotional conditions. Wherever the latter event-specific information is missing, we supplement it with data from newspapers and industry sources available online. Our second source consists of Nielsen data on weekly advertising spending, by retailer and by medium. These data allow us to gauge the advertising support received by these events through mass media (TV, print, and radio). Drawing on the event information and the characteristics in Table 1, two independent judges classified each event as one of the following types: Seasonal Events (e.g., promotion events related to Easter, Christmas), Temporary Loyalty Programs (e.g., saving stamps for collectables), Category Themes (e.g., "Best Deals with the C1000 Butcher"), Popular Events (e.g., buying merchandising products for World Cup Soccer), Business-as-Usual promotions (premiums, quantity discounts, or coupons on specific items that are not part of an overarching theme), and ReTSS. The classification by the experts was identical in 98.4% of the instances, and the few disagreements were resolved through discussion.
Table 3, Panel B, provides an overview of the ReTSS. We identify nine different ReTSS, all of which occur at traditional retailers, as hard discounters have no such savings events. On average, a ReTSS event lasts three weeks, with a maximum of four weeks, and some events recur multiple times during our observation period (Web Appendix W3 documents the calendar times of the events). Each ReTSS has a distinctive theme (e.g., "Hamsterweeks"), whose core message is that the consumer can save large amounts of money by shopping at the retailer during the event. All ReTSS deals follow a unified format, with a consistent (low) price point (e.g., all for €1; Event 2), deep percentage discount (e.g., 50% off in Events 6 and 7), or a multibuy offer (e.g., BOGO in Events 3, 4, and 5). The items promoted under the theme heading usually rotate weekly. While the number of items varies (ranging from <30 per week for Event 1 to 100 per week for Event 2), the deals span a wide range of categories. Moreover, as Table 3 shows, retailers use mass-media advertising during each ReTSS event.
Retailers may run different types of promotions concurrently; for example, during ReTSS weeks, consumers may also receive (business-as-usual) deals on specific brands and SKUs that are not part of the ReTSS offer. To isolate the impact of ReTSS per se, it is therefore imperative to assess (and account for) the overall depth and breadth of weekly promotional activity at the store level. Next to the event list and the advertising data, the GfK scanner panel provides us detailed indications—for each SKU in each week—on actual prices and promotions/feature appearances. We use these data to calculate, for each retailer-week, the total number of SKUs advertised in the store flyer (including offers that do and do not fall under the event theme), and the discount depth on promoted items (details on the operationalization are provided in the variables section). Table 3, Panel B, provides summary statistics for those variables during ReTSS, in absolute terms, as well as relative to nonevent weeks at the same retailer. It shows that the focal retailer carries more SKUs on feature and offers deeper discounts (p <.01 for all events) in ReTSS weeks than in other weeks.[15]
Even if all ReTSS events do enjoy mass-media support, advertising spending is not always higher on average in event weeks than in nonevent weeks (see Table 3). This is because retailers advertise other types of events as well (see Table 1 and Web Appendix W1) or engage in image advertising unrelated to promotions, and because ad investments are subject to seasonal and long-term changes.[16] To grasp the presence and timing of (extra) advertising support related to our savings events, we regress retailers' weekly advertising spending (stacked for the four retailers involved in these events) against retailer-specific constants, time-related variables (i.e., year and quarter dummies, a trend, and end-of-year and beginning-of-year dummies), and variables related to the occurrence of the ReTSS. Specifically, we include dummy variables for ( 1) the week before the start of an event, ( 2) the first event week, and ( 3) the remaining event weeks. The results show that there is no significant lead-week advertising effect, but that advertising is typically higher in the first week of the event and lower in remaining event weeks.
Table 4 provides model-free evidence on the impact of the different ReTSS. For each retailer and event, it reports the mean (standard deviation) of the weekly unconditional purchase amount per household (in euros), the weekly visit propensity per household, and the purchase amount conditional on a visit in that week. It does so for event weeks as well as baseline weeks (in which no ReTSS takes place at the focal retailer) and calculates the change rate.
Graph
Table 4. Model-Free Evidence.
| Event | Weekly Purchase Amount/Household (in Euros) | Weekly Visit Propensity/Household | Conditional Purchase Amount (Per Visit, in Euros) |
|---|
| Baselinea | Event Weeks | Ratio | Baseline | Event Weeks | Ratio | Baseline | Event Weeks | Ratio |
|---|
| 1 | 2.977(.360) | 2.460(.063) | .826 | .092(.007) | .086(.002) | .941 | 32.422(2.708) | 28.488(.216) | .879 |
| 2 | 7.020(.652) | 8.042(.766) | 1.146 | .216(.010) | .249(.011) | 1.153 | 32.470(2.433) | 32.257(2.516) | .993 |
| 3 | 14.082(1.065) | 14.875(1.092) | 1.056 | .382(.012) | .404(.013) | 1.056 | 36.832(2.631) | 36.816(2.237) | 1.000 |
| 4 | 2.977(.360) | 3.165(.227) | 1.063 | .092(.007) | .094(.006) | 1.026 | 32.423(2.708) | 33.646(1.721) | 1.038 |
| 5 | 7.020(.652) | 7.138(.389) | 1.017 | .216(.010) | .226(.006) | 1.044 | 32.470(2.433) | 31.632(1.510) | .974 |
| 6 | 2.977(.360) | 3.408(.308) | 1.145 | .092(.007) | .107(.011) | 1.164 | 32.422(2.708) | 31.969(1.052) | .986 |
| 7 | 7.020(.652) | 7.075(.700) | 1.008 | .216(.010) | .213(.008) | .986 | 32.470(2.433) | 33.142(2.141) | 1.021 |
| 8 | 14.082(1.065) | 14.051(.139) | .998 | .382(.012) | .423(.007) | 1.106 | 36.832(2.631) | 33.239(.531) | .902 |
| 9 | 2.362(.966) | 2.647(.680) | 1.121 | .081(.032) | .094(.022) | 1.151 | 28.949(2.129) | 28.129(1.771) | .972 |
- 9 aBaseline weeks are weeks for the focal retailer without a ReTSS event for that retailer.
- 10 bStandard deviation between brackets.
A few tentative observations can be made. First, for most events (seven of nine), spending levels are higher during event weeks. Second, this overall spending shift conceals countervailing forces: whereas retailer patronage (i.e., the number of households visiting the chain at least once) typically increases during event weeks, spending per visitor often decreases. Third, there are differences between events: some ReTSS show sizable increases in sales (e.g., Events 2 and 6, with spending levels that are almost 15% higher), others seem less successful (e.g., Event 1, during which we observe a 17% sales decline). The ReTSS effects also vary within retailers, as illustrated by Events 2 and 7, which—although organized by the same chain—show different spending increases.
However, the values in Table 4 should be treated with caution. First, they do not distinguish quantity from price effects (consumers paying less per unit during event weeks). Second, because they do not control for changes in other variables that co-occur with the events, they cannot be interpreted as causal effects. Third, they do not allow us to separate the dynamic (over-time) impact of the events. Fourth, they do not account for reaction differences among consumers. Our formal modeling approach addresses these issues.
Table 5 describes the variables and their operationalization. For each household, we set aside a 26-week initialization period and use the remaining observations for calibration.
Graph
Table 5. Variable Descriptions.
| Variable Name | Variable Description |
|---|
| Seasonal and state-dependence variables | | Dummy equal to 1 in the last (first) two week(s) of the year, zero elsewhere |
| Dummy equal to 1 in the week before and following Easter, zero elsewhere |
| Inventory of household h in week w, obtained as previous inventory () plus last-week purchases (across all stores) minus weekly household consumption rate (based on), and then standardized within household and year |
| Dummy equal to 1 if household h visited r in the previous week, zero otherwise |
| Logarithm of purchase volume of household h at retailer r in the previous week. Because households who did not visit r in week w − 1 have zero lagged purchases, a small amount (€.01) is added before taking logs. |
| Share of household h's visits to retailer r in a 26-week initialization period among the seven retailers |
| Average weekly purchase volume of household h at retailer r (conditional on a visit), in a 26-week initialization period |
| Other marketing-mix variablesb | | Regular price index for retailer r in week w − 1 relative to the market average, obtained as a weighted category average using household-category weightsa |
| Assortment size index for retailer r in week w − 1 relative to the market average, based on product availability in the preceding four-week period and obtained as a weighted category average using household-category weightsa |
| Distance in meters between household address and nearest outlet of retailer r (updated quarterly), log-transformed |
| , | Dummy equal to 1 if a loyalty, popular, seasonal or category event occurs at retailer r in week w |
| Lagged variable for other events: dummy equal to 1 if a loyalty, popular, seasonal, or category event finished at retailer r in week w − 1 |
| Event-relatedc variables: immediate | | Weekly TV, radio, and print ad expenditures (excluding store flyer) for retailer r in week w relative to the market average (in thousands of Euros), log-transformed |
| Percentage of assortment in feature promotion at retailer r in week w relative to the market average, obtained as a weighted category average using household-category weightsa |
| Average discount depth on promoted items at retailer r in week w relative to the market average, obtained as a weighted category average using household-category weightsa |
| event #Xrw | Dummy equal to 1 during ReTSS event #X at retailer r (X = 1 → NE) |
| Weighted average of ReTSS dummies for competing retailers in week w, with inverse of log of distance as weights |
| Event-relatedc variables: dynamic | | Lead (lag) dummy equal to 1 in the week preceding (following) an event at retailer r (0 otherwise) |
| Variable equal to.5,.333 and.25 in the second, third, and fourth week following the ReTSS event (0 otherwise), to capture lagged effects beyond the immediate postevent week. |
| Dummy activated only in the postevent week and equal to 1 if household h visited r during the preceding ReTSS event (0 otherwise) |
| Variable activated only in the postevent week and equal to the maximum (logged) weekly volume of household h at r during the preceding event (0 otherwise) |
| Variable equal to.5,.333, and.25 in the second, third, and fourth week following the event if household h visited r during the preceding event (0 otherwise) |
| Variable equal to.5,.333, and.25 in the second, third and fourth week following the event, multiplied with the maximum (logged) weekly volume of household h at r during the preceding event (0 otherwise) |
- 11 a See Web Appendix W4 for details.
- 12 bMarketing-mix effects unrelated to the ReTSS events, but including other types of promotion events.
- 13 c Variables related to the ReTSS events. These include advertising, feature and discount depth, which may be higher than usual during event weeks due to extra event-specific investments or deals in those weeks.
- 14 Notes: All purchase volumes are expressed in constant average prices.
The dependent variable in the retailer-visit model is a dummy equal to 1 for each retailer patronized by the household in the considered week, and 0 otherwise. In the purchase-volume model, the dependent variable is the logarithm of the volume purchased ( ) at each retailer visited by the household in a given week. Note that, like [35], we express the purchase volume, which is an aggregate across categories with different volume units (e.g., liters, grams), in constant average prices: we first multiply each category purchase quantity by the category's average unit price (across retailers in an initialization period) and then sum up over categories. By using constant prices, we ensure that variations in the purchase variable reflect only changes in quantity. The log-transform accommodates skewness in the purchase distribution and facilitates the interpretation and comparability of the parameters across households with different purchase levels.
As explanatory variables, next to retailer constants, we incorporate multiple drivers of store visits and purchases identified in the literature (see, e.g., [19]; [52]). A first set comprises seasonal and state-dependence variables. To account for seasonality, we include end-of-year, beginning-of-year, and Easter dummies ( . Households that buy more in a given week (at any retailer) may be less inclined to shop or purchase in subsequent weeks because of built-up inventory; this is captured by the variable , which we standardize within households. The visit-incidence model further includes a state-dependence variable ( indicating whether the household visited the retailer in the previous week) and a retailer-share variable ( measured as the share of household visits to the retailer in a 26-week initialization period). Likewise, the conditional purchase model includes the household's lagged (log) purchases to capture state-dependence effects, and the (log of) average weekly purchases at the retailer in the initialization period .
Second, we incorporate marketing variables unrelated to the ReTSS. These include the (log-transformed) distance to the nearest retailer outlet and a household-, retailer-, and week-specific regular price and assortment variable ( constructed using household-specific category weights (see Table 5 and Web Appendix W4) and then indexed relative to the average across retailers in that same week to accommodate competitive effects. Because households cannot observe regular prices and assortments prior to visiting the store, we use past-week values to reflect households' expectations for these variables. To account for the occurrence of other types of promotion events, we include dummy variables for popular events ( ), temporary loyalty programs ( , seasonal events ( ), and category events ( ). The variable accommodates any dips in the week following these other events.
The third set of variables captures the impact of the ReTSS events during event weeks. These include the (log of) investments in mass media , the percentage of SKUs featured in the store flyer in the considered week ( , and the average discount depth for promoted items ( )—the latter variables, again, aggregated at the store level with household-specific category weights and indexed relative to the market average. These variables capture the overall communication and promotional activity of the store in the considered week,[17] including any changes in advertising, featuring, or discounting due to the ReTSS. In addition, for each of the NE ReTSS events, we include a dummy variable equal to 1 during event weeks at the retailer, and 0 otherwise. The event-dummy coefficients capture the extra impact of the ReTSS, over and above their effect through communication and discounts. Thus, a significant positive coefficient would imply that offering and advertising the deals under a common savings theme enhances their impact. To allow for a differential event effect on retailer-visit incidence and purchases of more- versus less-customary store shoppers, we include an interaction with the initial retailer-share variable ( in both equations. Assuming a positive main effect of the ReTSS events, a negative coefficient for the interaction would mean that the event produces a smaller visit or purchase lift among customary shoppers at the store. Events at competing retailers can affect the visit propensity through , a distance-weighted variable of ReTSS dummies at rival retailers.
The fourth set of variables relates to the ReTSS dynamics. captures any anticipation effects in the week prior to the ReTSS event, whereas ( captures carryover effects in the first (four) weeks following the event (see also Table 5). Because anticipation and carryover effects may differ between more and less customary shoppers, we again include interactions with the households' initial retailer-visit share. Finally, the tendency to revisit the store or the impact of previous on current purchases may be different for shoppers who patronized the store during the event: it may be higher because of increased store salience and a positive store experience, or lower because the previous visit or purchases were promotion-induced. To accommodate this, we add extra lagged variables to the visit ( , ) and the purchase equation ), activated only for households that visited the retailer during the preceding event. These variables operate over and above the regular and variables, and capture deviations from business-as-usual carryover effects due to the ReTSS event.[18] Drawing on the Belsley–Kuh–Welsch diagnostics and the correlation matrices, we find no multicollinearity.[19]
Table 6 reports the results for the visit-incidence model. With an average probability for hits of.654, the model fits the data well, and far better than chance.[20] For simplicity of exposition, in this section we focus on the estimated population means. We briefly discuss the control variables (i.e., the seasonal, state dependence, and other marketing variables) first. Inventory reduces the propensity to visit a store. The coefficient of the household's initial retailer share is positive, pointing to explained heterogeneity, as is the lag-visit parameter, indicating that shoppers tend to revisit a retailer where they shopped before. Distance exerts a negative, and assortment a positive, impact. The regular-price coefficient is not significant, probably because most of the price variation is promotional and thus captured in the discount variable. Except for seasonal events, the coefficients of non-ReTSS events (i.e., loyalty, popular, or category events) are not significant, nor is their lag—indicating that they do not alter traffic relative to business-as-usual weeks (which serves as the reference).
Graph
Table 6. Parameter Estimates.
| | Visits | Conditional Purchase Volume |
|---|
| Variable Name | Mean | SD | Mean | SD |
|---|
| Seasonal and state-dependence variables | | | | | |
| EoYw | .039* | .013 | .059** | .024 |
| BoYw | .249** | .004 | .064** | .016 |
| Easterw | .190** | .006 | .078** | .010 |
| −.070** | .001 | −.034** | .013* |
| 1.275** | .696** | — | — |
| — | — | .011** | .020** |
| 5.125** | 2.939** | — | — |
| — | — | .948** | .020** |
| Other marketing-mix variables | | .003 | .005* | .001 | .006** |
| .242** | .255** | .037** | .017** |
| −.617** | .072** | −.018** | .006** |
| .002 | .017 | −.030* | .030* |
| −.011 | .015 | −.014* | .029** |
| .037* | .026 | .011 | .007 |
| .004 | .001 | −.013* | .031** |
| −.017 | .045** | −.011* | .0001 |
| Event-related variables: immediate | | .055** | .080** | .007* | .010** |
| .056** | .023** | −.020** | .036** |
| .028* | .020** | .012* | .014** |
| event #1rw | −.020 | .092 | −.001 | .100* |
| event #2rw | .472** | .012 | .182** | .007 |
| event #3rw | .424** | .068* | .201** | .091** |
| event #4rw | .251** | .080 | .123* | .003 |
| event #5rw | .180** | .027 | .046* | .078** |
| event #6rw | .434** | .019 | .173** | .091* |
| event #7rw | .134** | .009 | .084** | .008 |
| event #8rw | .447** | .070 | .110** | .060 |
| event #9rw | .279** | .023 | .098** | .006 |
| −.461** | .018 | −.149** | .014 |
| −.154** | .001 | .004 | .027 |
| Event-related variables: dynamic | | .024 | .003 | −.004 | .009 |
| −.411** | .066 | −.067* | .017 |
| −.332** | .012 | −.018 | .006 |
| −1.065** | .064 | −.113** | .029 |
| −.838** | .028 | −.085** | .024 |
| | −2.541** | .175 | −.196** | .003 |
| .878** | .025 | — | — |
| — | — | .007* | .0002 |
| 2.938** | .002 | — | — |
| | — | — | .013** | .001 |
| correction_factor (McFadden–Dubin) | | — | — | .010* | .028** |
| Constant | .521** | .293** | .180** | .025** |
| Retailer Dummies | yes | yes | yes | yes |
- 15 *p <.05.
- 16 **p <.01.
- 17 Notes: Two-tailed tests of significance. For brevity, means and standard deviations of the retailer dummy coefficients and parameters of the error correlation structure are estimated but not reported.
Turning to the immediate ReTSS-related effects, we find that discount depth, percentage of SKUs featured, and ad spending have the expected positive impact. In addition, all events show positive and significant dummy coefficients—except Event 1, whose impact is not significant. For the remaining events, the coefficients range from β =.134 (Event 7, p <.01) to β =.472 (Event 2, p <.01). Overall, this indicates that ReTSS do enhance store patronage during event weeks beyond the pure discount or advertising effects. The negative parameter associated with the Retailer Share × Event interaction (β = −.461, p <.01) indicates that ReTSS draw disproportionately less from regular store customers. A competing retailer event lowers the likelihood of a store visit for the focal retailer (β = −.154, p <.01). This confirms that, because of their high awareness and unusual perceived benefits, the events attract new customers through extra visits and/or direct store switching.
As for the dynamics, the coefficient of the lead-event dummy is not significant (p >.10), but its interaction with the household's prior retailer share is significantly negative (β = −.411, p <.01), indicating that regular customers postpone store visits in anticipation of the event. We obtain negative coefficients for the lagged-event variables in the week following the event (β = −.332, p <.01) and afterward (β = −1.065, p <.01), as well as for their interactions with the households' initial retailer-visit share (β = −.838, β = −2.542, p <.01). This is consistent with stronger acceleration effects among heavier (i.e., more frequent) customers. Interestingly, though, the coefficients of (β =.878, p <.01) and (β = 2.938, p <.01) are positive and larger. These effects operate over and above the regular revisit tendency captured by lag_visit. Thus, households that did not patronize the store during the event seem even less likely to do so in postevent weeks, possibly because the previous-deal salience and emphasis on smart shopping make them less willing to patronize the store at full prices. Conversely, visitors during the event, who built up store familiarity, have an even stronger tendency to return to the store.
The right-hand side of Table 6 reports the results for the purchase-volume model conditional on a visit. The pseudo-R2, compared with a null model with store intercepts only, equals.702, and the mean absolute percentage error of predicted versus actual purchase volumes is only 6.92%, pointing to high explanatory power. Turning to the parameter estimates, we first consider the control variables. Households buy more at their customary store, as shown by the positive coefficients of average initial retailer purchases and lagged purchases. However, they procure smaller baskets when their inventory is still high. Purchases are higher in nearby stores with larger assortments. The regular-price coefficient is not significant—possibly, again, due to lack of variation. Once in-store, households spend less during loyalty, popular, and category events than during business-as-usual promotion weeks.[21]
Our focus is on the ReTSS-related effects. As for the immediate effects, we find that baskets increase with deeper discounts and more mass-media advertising. Whereas feature activity enhances households' propensity to visit the store, it has a negative impact in the conditional-purchase model, suggesting that feature ads attract smaller-basket shoppers.[22] The event-dummy coefficients again reflect the impact of the event over and above the discounts and advertising investments per se. They are significantly positive for eight of nine events (and insignificant for the other): the effect is largest for Event 2 (β =.182), Event 3 (β =.201), and Event 6 (β =.173) (all ps <.01). The coefficient of the Retailer Share × Event interaction is negative but smaller in absolute value (β = −.150, p <.01). Thus, even for regular customers, baskets tend to increase during event weeks because customers stock up on promoted items (acceleration), consume more (expansion), and/or explore the aisles or feel licensed to purchase extra nonpromoted items (halo effects). In contrast, although events at nearby rival stores reduce households' propensity to visit, they do not affect purchases beyond the competitive impact already included in the relative discount variable (p >.10).
Turning to the dynamics, the main lead effect is insignificant (β = −.004, p >.10), but its interaction with retailer-visit share is negative (β = −.067, p <.05), indicating that current customers decelerate purchases if they suspect an upcoming event. Finally, we obtain an interesting pattern of postevent effects. Heavier customers show deeper quantity dips immediately following the event (β = −.085, p <.01) and in the few weeks afterward (β = −.196, p <.01), a pattern indicative of purchase acceleration. However, consumers who bought more at the store during the event show a positive effect afterward (β =.0073, p <.05; β =.013, p <.01), consistent with the notion of increased store familiarity or commitment.
Although the coefficients in Table 6 shed light on the significance of event effects, they do not give a clear picture of the effect sizes or the net outcome of the (countervailing) dynamics. We use simulations to provide such insights. Using the actual data as a backdrop, we dynamically predict the panelists' visit sequence and purchase volumes per visit for each retailer, using their posterior estimates of the visit and purchase models and based on the procedure described in [47]. Starting from the first week, we calculate the panelist's visit probability for each retailer in the subsequent week. We then simulate 100 shopping sequences, each time taking a random draw from this probability to predict whether the retailer is visited in the shopping sequence in that week. For each visited retailer, we calculate the panelist's purchase volume based on the conditional-purchase model coefficients. Using these values, we update all the dynamic variables (i.e., inventory and all lagged variables) for the next week. We then average the results across shopping sequences to obtain the panelist's visits, conditional purchases, and total purchases (not conditional on a visit; thus equal to 0 in weeks without a store visit) per week and retailer. Using the actual retailer prices during event and nonevent weeks, we also obtain the corresponding spending levels. Finally, we add a layer to the simulations in which we draw sets of values for the means and standard deviations of the mixing distributions and obtain the corresponding posterior estimates by household and the associated ReTSS effects. We use the distribution of these outcomes to assess the statistical significance of the effects.
We simulate three scenarios. In the Baseline scenario, we use the actual levels of the non-event-related explanatory variables but, in weeks where a ReTSS occurred, we set the event-related dummies to 0, and the promotion (discount depth and percentage of SKUs on feature) and advertising variables equal to their expected level in nonevent weeks.[23] In the Event + Support scenario, we fix a (three-week) event period. For each retailer, in turn, we assume that an event at that retailer took place (i.e., we activate all event-related variables for that retailer, set the promotion and advertising variables to their values during the retailer's event period, and flag the presence of a competing event for other retailers). To separate the impact of increased advertising and discounting from that of the event theme as such, we also consider a Support Only scenario, in which we keep advertising, features, and discounts at their event levels but set the event-related dummies to 0. For each retailer and week, we then compare the households' visit propensity, purchase volumes, and spending in the Event + Support and Support Only scenarios with the Baseline.
Table 7, Panel A, reports the change in weekly visit propensity, purchases (spending) conditional on a visit, and total weekly store purchases (spending) during the event period, based on comparison of the Event + Support and the Baseline scenarios. It does so for the average, worst, and best event (results for individual events are in Web Appendix W5). As we expected, for eight of nine events, ReTSS leads to significant increases in store visit propensity. Visit incidence during event weeks increases by 7.75% on average (a 1.58-percentage-point increase, p <.01), but with variation across events (lowest value: −.25% for Event 7, highest value: 20.22% for Event 6). As the "Conditional Volume" row indicates, average basket sizes during ReTSS weeks also increase, but the effect is minimal (+.34% on average) and statistically insignificant. Combining the two, we find that the "Total Volume" typically increases during event weeks, with an average lift of 8.47% (p <.01) and an increase of up to 20.74% for the most successful event (Event 6). Retailers thus enjoy a clear upswing in visits and total purchases[24] in the course of the event. On average, this lift in purchase volume translates to a 3.67% immediate spending increase, with significant positive numbers for six of nine events, ranging from a decrease of 8.94% for the worst-performing event and to an increase of 14.61% for the best-performing event.
Graph
Table 7. Overall Impact of ReTSS over Time.
| Average | Worst | Best |
|---|
| Level | % | Level | % | Level | % |
|---|
| A: During |
| Visit | 1.58** | 7.75% | −.06 | −.25% | 2.21** | 20.22% |
| Cond. volume | 11.89 | .34% | −69.33** | −1.94% | 178.82** | 5.00% |
| Total volume | 50.15** | 8.47% | −18.14* | −6.69% | 54.05** | 20.74% |
| Total spending | 27.38** | 3.63% | −30.22* | −8.94% | 117.86** | 14.61% |
| B: Before |
| Visit | −.56* | −2.70% | −.34* | −3.64% | −.23* | −2.31% |
| Cond. volume | −75.15** | −2.02% | −158.13* | −4.24% | −3.72 | −.10% |
| Total volume | −38.03** | −7.84% | −75.00** | −9.10% | −15.12** | −7.61% |
| Total spending | −37.96** | −7.84% | −78.30** | −9.10% | −16.51** | −7.61% |
| C: Aftera |
| Visit | .36* | 1.45% | −.09 | −1.08% | .31** | 3.07% |
| Cond. volume | −31.39** | −.86% | −48.77** | −1.33% | −3.33* | −.09% |
| Total volume | −2.08 | −.58% | −3.82** | −2.30% | 1.56 | .58% |
| Total spending | −2.00 | −.58% | −4.17** | −2.30% | 1.58 | .58% |
| D: Net Effect (All Periods)b |
| Visit | .59** | 2.68% | −.00 | −.02% | .74** | 6.91% |
| Cond. volume | −24.22** | −.66% | −47.71** | −1.31% | −.78 | .01% |
| Total volume | 7.98* | 1.08% | −7.07** | −2.71% | 40.49** | 4.93% |
| Total spending | −2.35* | −.10% | −17.85** | −3.74% | 21.39** | 3.23% |
- 18 *p <.05.
- 19 **p <.01.
- 20 a Impact over the eight-week period following the event.
- 21 b Impact over the 12-week period (1 pre-event week + 3 event weeks + 8 postevent weeks).
- 22 Notes: One-tailed tests of significance based on distribution across parameter draws. % = the percentage change relative to the no-event baseline. Results for all events are given in Web Appendix W5. "Worst" and "Best" correspond to the lowest and highest % figures across events for a given period and outcome variable. Conditional volume = the change in purchase volume over the considered period, per household, given a visit, and expressed in constant monetary value (Euros). Total volume = the change in total purchase volume (in Euros) over the considered period, per household, unconditional on a store visit (so: zero if the household did not visit the store in those weeks). Total spending = the revenue equivalent of total volume, based on actual prices during (or before/after) the event. The economic significance of these figures is clear from Table 9, where we report the equivalent revenue value at the market level.
The question remains ( 1) to what extent extra visits or higher purchases during the event period are due to the ReTSS as such, rather than merely to the accompanying promotion or advertising effort, and ( 2) if they are offset by negative pre- and postevent effects. Using the simulation results (i.e., comparing the Event + Support and Baseline scenarios), we calculate the changes in visits and total purchase volumes before, during, and after the event. We also consider the impact of increased mass advertising, featuring, and discount support absent an event theme (i.e., the difference between the Support Only and Baseline scenarios; Figure WA1 in Web Appendix W5 plots these results for three exemplar events). Two findings emerge. First, the larger part of the uplift during event weeks (across all events: 89.24% for visits, 80.91% for conditional volume) stems from the event as such: simply stepping up advertising or promotion activities entails a much smaller increase in visits and purchases (for details by event, see Web Appendix W5).[25] Second, the ReTSS impact extends beyond the event period and subsides in about eight weeks.
Building on these insights, Table 7 reports, for each event, the impact (Event + Support minus Baseline) in the preceding week (Panel B), the eight weeks following the event (Panel C), and the net impact (Panel D). The table confirms the presence of negative anticipation effects in visits (−2.70% on average), basket sizes (−2.02%), and total purchase volumes (−7.84%; all ps <.01). Interestingly, the pattern of postevent effects is mixed. Against expectations, visits are still higher on average in the eight-week period following the event (+1.45%, p <.05), but this effect is nullified by lower purchase volumes per visit (−.86%, p <.01). Combining the figures across periods, we find that while the events yield a net visit increase on average (+2.68%, p <.01), this is partly offset by smaller basket sizes (−.66%, p <.01), resulting in only a 1.08% (p <.05) net increase in total purchase volume. For total incremental spending, we observe a bleak overall picture: the average being close to 0 (−.10%, p >.05), albeit again with differences between events (−3.74% to +3.23%).
Drawing on our conceptual framework and estimation results, we expect the values in Table 7 to conceal reaction differences between more and less customary shoppers of the store. To further explore this, for each retailer and event, we consider the visit and purchase effects for bins of customers with lower versus higher prior visit shares at that retailer (each bin representing an incremental 10% prior visit rate; plots for exemplary events are given in Web Appendix W5). We find that visits increase especially for nonregular customers of the store; for example, for Event 4 (which is close to average), the visit propensity increases by 21% for consumers with a 5% prior-visit probability (first bin) against a status quo for those with a 45% prior-visit rate (fifth bin). These consumers also account for the largest lift in purchase volumes; for example, for Event 4, the total purchase lift amounts to 35% for consumers with a 5% prior-visit rate, but only 8.3% for those with a 45% prior-visit rate. Higher-bin customers do not generate net volume gains: these consumers do not increase their visit rates, and their extra purchases during event weeks are likely cannibalizing nonpromotion purchases during or following the event.
If ReTSS yield extra business during the event, who suffers? To address this, we check the changes in visits and purchases in rival stores produced by a ReTSS at the focal store and calculate the portion of these changes that is borne by traditional versus hard-discount chains. We find that the larger share the competitive shifts (i.e., about 67% [70%] of the competitive visit [purchase] losses) is at the expense of traditional supermarket rivals, but this may merely be because they represent a larger share of the market (72%) to begin with. To explore this further, we identify the consumers who contribute most strongly (top 10%) versus least strongly (bottom 10%) to the ReTSS's (immediate) visit and purchase lift and compare their store-type allegiance in the initialization period. Interestingly, we find that for each event and for both visits and purchases, the more responsive consumers have a significantly higher share of wallet at hard-discount chains (on average, more than twice the share: 31% vs. 14%; for more details, see Web Appendix W6). Thus, hard-discount shoppers in particular incur extra visits and increase their purchase volume in response to the event. In summary, this indicates that although both traditional competitors and discounters suffer, ReTSS events disproportionately draw business from hard-discount rivals.
The results show substantial differences in impact between ReTSS events. What drives these differences? In line with our conceptualization, the success of a ReTSS (over and above the accompanying advertising and discounting) may depend on the deal format (i.e., uniform [low] price per product, BOGO, or percentage discount), scope (number of products eligible for the ReTSS each week), discount depth, (extra) amount spent on advertising, and resonance of the event theme. To explore this further, we conduct a moderator analysis[26]: we rerun the visit and conditional-purchase models after replacing the event dummies with a function of these characteristics. Because, unlike the other characteristics, theme resonance is not directly observable, we approximate it through media attention to the ReTSS, as reflected in the number of LexisNexis mentions (offline and online articles that refer to the ReTSS) for each year in which it runs. To reduce the concern that ReTSS's success drives the media attention (rather than the other way round), we use previous-year values for the LexisNexis mentions. We also add the number of times the event has run before as a potential driver, the impact of which may be positive (higher event recognition) but also negative (wear-out).
Table 8, Panels A and B, summarize the key results. Recall that because advertising and promotion are separately accounted for in the model, these coefficients indicate what makes the event as such more successful, over and above the underlying ad budgets, features, and discounts. We find that while the ReTSS's design characteristics hardly shape the conditional-purchase effects (Table 8, Panel B), they do influence its impact on visits (Table 8, Panel A). Stronger synergetic effects are generated from ReTSS that cover more items. Deal format also matters: (deep) percentage discounts and BOGOs contribute equally strongly to ReTSS success, whereas uniform prices (the financial advantage of which is less clear) bring somewhat lower visit and purchase lifts. Discount depth is not significant, probably because it hardly varies within deal formats. And although retailers always use mass media to advertise the ReTSS, higher levels for those budgets do not differentiate more from less successful events. We do find a strong positive association with press coverage (LexisNexis mentions in the previous year), which enhances both visit propensity and basket size. Together, this suggests that it is the content of the message that matters (rather than the advertising weight) and underscores that having a unique, resounding theme is key. Finally, ReTSS events that ran more frequently in the past do worse in terms of visits and basket size, suggesting the presence of wear-out.
Graph
Table 8. Impact of ReTSS Characteristics.
| Population Mean | Population SD | Population Mean | Population SD |
|---|
| Variable Name | A: Visits | B: Conditional Purchase Volume |
|---|
| Deal format (BOGO = reference) | | | | |
| Fixed price | −.343* | .051* | −.055 | .007 |
| % off | .034 | .003 | −.026 | .038* |
| Scope | .010** | .0001 | .0008 | .0003* |
| Discount depth | −.013 | .0013* | .002 | .001* |
| Advertising budget | .022 | .034* | .032 | .058** |
| Theme resonance | .0013** | .0003 | .0011** | .0005** |
| Number of previous occurrences | −.003** | .002** | −.0035** | .000 |
- 23 *p <.05.
- 24 **p <.01.
- 25 Notes: Two-tailed tests of significance. For brevity, we report only the coefficients of the ReTSS characteristics (i.e., the moderator variables). The full set of estimation results for visits and conditional purchases can be requested from the first author.
So far, we have documented how ReTSS events affect consumer visits, purchase volumes, and spending. However, even if spending increases, an event may still be unprofitable if the revenue increase for the retailer does not outweigh the margin losses on ReTSS-promoted items. To calculate the profit implications rigorously, we would need detailed information on ( 1) the specific items sold under the ReTSS heading for each event week, ( 2) regular retailer margins on these items compared with items that consumers may have shifted away from, and ( 3) retailer pass-through for all these items in event and nonevent weeks. Because we do not have such data, we resort to back-of-the-envelope approximations of event profitability. For each store and event, we do know the average fraction of revenue sold on deal in event and nonevent weeks and the average discount depth for items sold on deal in such weeks. Based on these figures, the total (gross) profit associated with revenue Rrw for a given retailer and week can be approximated by (see Web Appendix W7):
Graph
4
where m is the average retailer unit margin absent promotions (expressed as a fraction of the selling price), gw is the fraction of the promotional discount borne by the retailer, is the advertising budget, PromSharerw is the fraction of revenue sold on promotion, and DDrw is the average discount depth. The latter three variables are obtained from the data for event weeks versus nonevent weeks. Based on anecdotal evidence and prior literature, we set m at.25. We then use our simulation outcomes for the Event + Support and Baseline scenarios to calculate the total revenue and margin difference between these scenarios and rescale this difference (obtained for our household sample) to the market level,[27] so that we can deduct the change in advertising budget associated with the event. We do so for different values of gw, which, based on informal exchanges with retailers, we set between.1 and.3.[28]
Table 9, Panel A, reports the revenue implications at the market level. The table also shows that while the share sold on promotion is higher during event weeks, nonpromotional revenue decreases for only three of nine events and actually increases for Event 6 (+6.46%), in support of a halo effect. Table 9, Panel B, displays the profit outcomes. It shows that the profit implications remain quite limited. If the retailer bears a larger part of the discount during event than during nonevent weeks (e.g., gw_event =.3 and gw_regular =.1; Case 3 in Table 9, Panel B), ReTSS entail a small loss on average (−.44%), with statistically significant losses for five of nine events. If the retailer can convince the manufacturer to contribute more strongly during the ReTSS than usual (something that, as our interviews with retailers reveal, volume-oriented manufacturers or those under threat from hard-discounters are willing to do), the picture becomes slightly different. For instance, if gw drops from.3 in nonevent weeks to.1 in event weeks (Case 4 in Table 9), the loss turns into a small profit gain (+1.47%).[29]
Graph
Table 9. Profitability Analysis.
| Event | Average |
|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|
| A: Revenue Impact of Events |
| Revenue change during eventa | −4,307* | 20,621** | 35,571** | 470 | −9,138** | 10,455** | −12,612** | 31,416** | 1,738 | 8,246** |
| 14.61% | 11.67% | .59% | −6.48% | 13.09% | −8.94% | 10.31% | 3.17% | 14.61% | 5.58% |
| Incremental revenue (all periods)b | −7,412** | 17,187** | 25,822** | −2,276 | −17,579** | 9,700** | −21,670** | 24,842** | −3,284* | 2,814 |
| −2.28% | 2.96% | 2.10% | −.70% | −3.02% | 2.99% | −3.73% | 2.02% | −1.50% | .47% |
| Share sold on promo | | | | | | | | | | |
| Nonevent weeks | .219 | .196 | .182 | .218 | .207 | .217 | .209 | .188 | .228 | .207 |
| Event weeks | .210 | .298 | .261 | .235 | .243 | .263 | .192 | .257 | .233 | .244 |
| Nonpromo revenue increase during eventc | −4.30% | .07% | .89% | −1.59% | −10.73%** | 6.45%* | −6.98%** | .93% | 2.50% | −1.30% |
| B: Event Profitabilityd |
| Case 1: Retailer contribution.3 during event weeks,.3 during nonevent weeks |
| −1,902** | 3,758** | 5,797** | −809 | −4,612** | 2,307** | −5,639** | 5,201* | −938* | 351 |
| −2.48% | 2.81% | 1.95% | −1.10% | −3.38% | 2.88% | −4.31% | 1.78% | −1.86% | .25% |
| Case 2: Retailer contribution.1 during event weeks,.1 during nonevent weeks |
| −2,053** | 4,967** | 7,602** | −439 | −4,456** | 2,713** | −5,886** | 8,107** | −789 | 1,085 |
| −2.59% | 3.60% | 2.51% | −.58% | −3.17% | 3.28% | −4.35% | 2.71% | −1.51% | .75% |
| Case 3: Retailer contribution.3 during event weeks,.1 during nonevent weeks |
| −2,540** | 2,719** | 4,195** | −1,448* | −5,754** | 1,701** | −6,793** | 3,551* | −1,449** | −646* |
| −3.21% | 1.97% | 1.38% | −1.90% | −4.09% | 2.06% | −5.02% | 1.19% | −2.77% | −.44% |
| Case 4: Retailer contribution.1 during event weeks,.3 during nonevent weeks |
| −1,416* | 6,006** | 9,205** | 199 | −3,314** | 3,320** | −4,733** | 9,757** | −277 | 2,083** |
| −1.84% | 4.49% | 3.10% | .27% | −2.43% | 4.14% | −3.61% | 3.33% | −.55% | 1.47% |
- 26 *p <.05.
- 27 **p <.01.
- 28 a In thousand Euros, at market level.
- 29 b Increase as fraction of base level during the total 12-week period (1 pre-event week, 3 event weeks, 8 postevent weeks).
- 30 c Increase as fraction of nonpromotional base revenue during event weeks.
- 31 d Incremental gross profit during the total 12-week period.
- 32 Notes: One-tailed tests based on distribution across parameter draws. The table contains revenue and (gross) profit effects of one three-week event occurrence. Extra advertising due to the event is approximated as 26% extra spending in first event week over retailers' average budget, based on regressions of (log) ad spending against event weeks.
Again, however, profitability varies across events. Linking profit figures to event characteristics, we find that larger scope (.774, p <.05) and high resonance (.638, p <.10) (which were associated with stronger lifts during the event) also positively correlate to event profitability. "Uniform price" deal formats (which did worse in terms of immediate visit and purchase response) bring higher profits (correlation:.584, p <.10), possibly because their deals are less deep and they encourage consumers less to stock up on the product.
Unlike business-as-usual retailer promotions—for which previous research revealed only weak evidence of direct store-switching—we find that ReTSS can substantially increase the number of shoppers drawn to a store. Even though basket size (conditional on a visit) typically does not change, this implies that shoppers buy more in total at the retailer during event weeks: 8.5% more on average, and up to 21% more for the more successful events.
In particular, households that rarely patronize the retailer absent the event and that spend a larger share of their grocery budget at hard discounters shop and buy more at the store during event weeks. This corroborates that, based on their characteristics, ReTSS enjoy higher awareness than business-as-usual discounts and bring higher perceived monetary and nonmonetary benefits that outweigh the hurdles of an (extra) store visit. It also underscores that ReTSS can indeed revitalize the retailer's customer base. An intriguing observation, however, is that in some instances ReTSS (like our Event 1) reduce purchase volumes during event weeks. Because we observed and tested only shoppers' behavioral responses, we can only speculate on the underlying reasons. If retailers mass advertise the event theme but the actual scope of the offer is too small, this may produce a reactance effect. Insights into consumers' mindset metrics could further verify mechanisms underlying these effects.
Only a small part of the lift in visits and purchases stems from the (increase in) discounts/features and advertising budget during the events. Instead, the event as such leads to marked performance improvement. Consistent with findings on popular event advertising ([28]; [31]; [38]), this confirms that unifying and communicating the deals under a common savings theme creates extra synergies. These synergies appear particularly strong for events with higher media resonance and that involve more items. Thus, not only do the deals or ad budget as such matter; what matters in particular is the thematic framing as a super saver event, which allows retailers to break away from the clutter and convince households that the gains are worthwhile. Future studies could pursue how retailers can craft and market savings themes for maximum buzz, as data on the virality of advertising is becoming more readily available.
From a broader perspective, our findings underscore the critical importance of promotion communication and framing. In reality, the actual number of promoted items during the ReTSS (and the potential for extra savings relative to nonevent weeks) remains limited, and much of the ReTSS success stems from the theme that makes consumers aware and generates the perception of large and frictionless savings. Yet previous studies have shown that consumers do learn from experience. If an event does not yield the hoped-for savings, consumers may not be attracted by it next time and may even develop a negative attitude toward the retailer. Thus, our finding that specific ReTSS events lose effect over time may result not only from theme wear-out but also from consumer disappointment with actual savings. Future studies could analyze how the interplay between anticipated and actual savings shapes consumers' ReTSS response.
The impact of ReTSS extends beyond the event period. The higher awareness and perceived benefits make some consumers lie in wait and decelerate visits and purchases prior to the event. This holds even though retailers do not seem to mass advertise the event beforehand and likely stems from the fact that most ReTSS recur roughly around the same time(s) each year. The question remains how retailers can circumvent these negative lead effects without jeopardizing the success during the event period. Should retailers randomize the timing of their event to prevent current customers from postponing their visits? Or, conversely, should they mass advertise an upcoming event, such that rival-chain customers hold back on their purchases at competing stores and buy more with the retailer during the event? The answers will depend not only on the size and composition of the retailer's current customer base but also on consumers' (psychological) reaction to (not) being notified up front—an issue for further study.
The weeks following an event show a higher-than-usual number of visits but smaller basket sizes. Newly attracted customers are more likely to return to the store after the event, consistent with a store-salience and familiarization effect. However, consumers who bought at the chain during event weeks buy smaller quantities subsequently, possibly because they built up inventory or because the emphasis on smart shopping has reduced their willingness to purchase at full price. For the average event, the net result across periods is still an increase (albeit small: about 1% on average) in purchase volume. As for profitability, our back-of-the-envelope calculations suggest that unless the retailer bears the brunt of the extra discount depth, the ReTSS neither helps nor hurts the bottom line. In all, our results thus clearly show the immediate and medium-term outcomes of the events in terms of traffic, sales, and profit.
Retailers may have additional, longer-term, motives to establish these events, such as improving the store's price image or fostering current customers' loyalty. Moreover, in time, more (frequent) ReTSS actions may lead to a new type of promotion trap: retailers being caught up in a race for events that stand out. On the consumer side, more exposure to events may desensitize shoppers and dilute their interest in (and response to) specific events. As longer data series including more events become available, analysis of these long-term outcomes becomes a fruitful area for study.
Our results reveal that ReTSS can be an effective way for traditional retailers to (temporarily) regain customers and increase in-store purchases. Consumers who spend a higher share of their grocery budget at hard discounters are especially likely to increase their visits and purchases at the traditional chain in response to the event. Moreover, even if such events do not increase profits, they do not really hurt the bottom line, either. Thus, although not a panacea, ReTSS events can be a valuable defense tool, strengthening the retailers' share of wallet among light customers and preventing them from permanently defecting to discount stores.
However, not all events succeed. Generating uplift in visits and purchase volumes calls for a sufficiently large event scope. Retailers should find the right balance between raising awareness and expectations and honoring promises by offering (deep) enough deals. As for format, whereas percentage-off discounts and BOGOs—which clearly emphasize the monetary advantage—appeal most strongly to consumers, ReTSS with uniform prices seem more profitable for the retailer. Although advertising matters, the key to success is not in increasing the advertising budget per se. Instead, the media resonance of the savings theme appears to be key. This is not surprising, given that most of the incremental gains come from nonregular customers who may be more responsive to sources other than the chain's communication. Thus, apart from creating a unique and easy-to-recognize theme, retailers should strive for more earned rather than owned media impressions and focus on how to make the theme go viral. As a caveat, we also find evidence of wear-out, urging retailers to craft novel themes in time. Our retailer interviews suggest that turning the event theme into a brand of its own, and/or using market influencers to promote it, may prove fruitful here.
Our findings caution retailers to be wary of consumers lowering their purchase volumes prior to and after the event. In addition, deep discounting may hamper revenue and profitability. To guard against these dangers, retailers could try to capitalize on the exploration benefits of ReTSS, by judiciously steering consumers through the aisles in search of the ReTSS offers, and on the licensing effect, by displaying impulse items in indulgence categories next to the ReTSS deals. Finally, given that ReTSS weeks attract extra consumers (in particular, hard-discount shoppers), the events may be a unique way for national-brand manufacturers to increase volume or present consumers with their (new) brand offerings. Retailers could use these arguments to increase the promotion contribution of manufacturers during events, an essential ingredient of ReTSS profitability.
The effects of ReTSS may differ in countries with different retailer landscapes or business-as-usual promotion activity. Given our framework and findings, these events would be most instrumental for traditional chains severely threatened by hard discounters, in markets with substantial promotional clutter. While our focus was on grocery retailers, similar savings events emerge in nongrocery settings, such as Inno's "Crazy Days," Asda's "Green Is the New Black" savings event, or Amazon's "Prime Days" (recently extended to Whole Foods). And although some of our ReTSS effects (e.g., visit or purchase expansion) may hold in those settings as well, others (e.g., purchase acceleration and stockpiling) may not, or may emerge in a different time frame—aspects that we leave for future study.
Supplemental Material, jm.18.0319-File003 - Evaluating the Effectiveness of Retailer-Themed Super Saver Events
Supplemental Material, jm.18.0319-File003 for Evaluating the Effectiveness of Retailer-Themed Super Saver Events by Jonne Y. Guyt and Els Gijsbrechts in Journal of Marketing
Footnotes 1 Associate EditorKusum Ailawadi
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 ORCID iDJonne Y. Guyt https://orcid.org/0000-0001-8131-7664
5 Online supplement: https://doi.org/10.1177/0022242919896334
6 1As confirmed by interviews with two retailers and two industry experts.
7 2Within-store shifts at the time of the promotion (also referred to as cannibalization) do not enhance visits or purchases but may dampen revenue and profit. We discuss these effects in the "Profitability" subsection.
8 3An interesting exception is [2], who study the impact of such brand-specific promotions on the retailer's total unit sales and profit.
9 4The event theme refers to the European hamster that hoards food in storage chambers and hibernates during winter, living off the stored food. The slogan "Hamsterééééén" is designed to incentivize consumers to stockpile to benefit from the BOGO offers.
5Even though the retailer can choose which popular event to feature, it cannot choose the timing of that event.
6Web Appendix W1 provides summary statistics on discounts and advertising during different types of events as encountered in our study.
7The deep and salient ReTSS discounts may also produce unusual shifts from nonpromoted to promoted items within the store ([22]). Although this leaves the purchase quantity unchanged, it may dampen current customers' (increase in) monetary spending. We address this issue in the "Profitability" subsection.
8For a similar approach, see, for example, [39], [26], and [34].
9This approach is similar to two-step estimation of Tobit II models (see, e.g., [20], p. 146). In that approach, the first layer (in our setting, visit incidence) is estimated using all observations. The second layer (in our setting, purchases conditional on a visit) is estimated only on the subset of observations in which the first layer is "activated" (in our case, an actual visit took place) but includes a correction term (the inverse Mills ratio) to account for the fact that only a selection of the total sample is considered. The difference between the Tobit II approach and ours is that we use a logit (instead of a probit) model in the first stage and use the McFadden–Dubin correction factor (instead of the inverse Mills ratio) to account for the selection issue.
10See the "Variables and Operationalization" subsection for measurement details. A comparison with discount depth and advertising levels during other event types is provided in Web Appendix W1.
11This explains the very low advertising index for Event 6 at Plus: the chain markedly increased all its advertising investments from the second year onward, and this event occurs only in the first observation year.
12We use same-week values for these variables. Mass media and feature ads are observable outside the store. This also holds for discounts to the extent that they appear in the flyer and/or on the store's website. If (many) consumers fail to consult these sources, this will simply dampen the discount coefficient in the store visit model.
13Because the model does not include separate lagged discount, advertising, or feature variables, the lagged variables related to the ReTSS capture the postpromotion effects of the event including its support activities (i.e., the extra discounts, advertising, and feature linked to the event).
14All correlations remain (well) below.7, except for that between lag_event_pvolume and the interaction ret_share × lag_event, which equals.715. In the Belsley–Kuh–Welsch analysis, the highest condition index (39.9 for visits, 42.7 for spending) never has two variables with variance-decomposition proportions higher than.5.
15With random assignment, the probability for hits would be.20—that is, the fraction of household-weeks in the data set in which a particular store is (vs. is not) visited, averaged across stores.
16Because the retailer's overall promotion depth and breadth in a given week are separately accounted for in the model, this suggests that if more of the promotions are linked to loyalty programs (often delayed rewards) or concentrated in specific categories/linked to specific popular events, the purchase lift is lower.
17For a similar argument, see [19]. We note that because more feature activity significantly enhances visits to the store, the total impact on (unconditional) purchases is positive.
18The "expected" ad spending is the actual ad spending minus the estimated percentage reduction in the first event week, based on a regression of retailers' weekly (log of) ad spending against dummies for the pre-event week, the first event week, and the remaining event weeks. The "expected" discount (feature) level is the actual level minus the estimated extra discounts (features) during event weeks, based on a regression of retailers' weekly discounts (features) against event dummies. In all regressions, we control for trend, seasonal, and retailer fixed effects.
19Note that the absolute impact on total purchases in Table 7 represents the increase for a "random" household. Because many households never visit a particular store, these figures may appear very low, but their economic significance is discussed in the profitability section.
20We also ran separate simulations for Advertising Support Only and Discount/Feature Support Only and find that the largest impact comes from discount/feature support. Details are available on request.
21Because we cannot claim that the link between the event characteristics and outcomes is strictly causal, the moderator analysis is exploratory and should be treated with some caution.
22We do this using the standard "translation key" of the data provider, based on the number of households in the market.
23Using the terminology of [10], this would correspond to a "pass-through rate" of between 1.11 and 1.43, respectively. These are realistic rates, according to our practitioner sources.
24Note that event profitability in Table 9 is lower in Case 3 (gw_event =.3 and gw_regular =.1) than in Case 1 (gw_event =.3 and gw_regular =.3) because, in Case 3, shifts in promotional purchases from nonevent to event weeks entail a higher promotion contribution for the retailer.
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By Jonne Y. Guyt and Els Gijsbrechts
Reported by Author; Author
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Record: 73- Exploring the Effects of "What" (Product) and "Where" (Website) Characteristics on Online Shopping Behavior. By: Mallapragada, Girish; Chandukala, Sandeep R.; Qing Liu. Journal of Marketing. Mar2016, Vol. 80 Issue 2, p21-38. 32p. 1 Diagram, 17 Charts, 2 Graphs, 1 Map. DOI: 10.1509/jm.15.0138.
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Record: 74- Featuring Mistakes: The Persuasive Impact of Purchase Mistakes in Online Reviews. By: Reich, Taly; Maglio, Sam J. Journal of Marketing. Jan2020, Vol. 84 Issue 1, p52-65. 14p. 2 Diagrams, 2 Charts. DOI: 10.1177/0022242919882428.
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Featuring Mistakes: The Persuasive Impact of Purchase Mistakes in Online Reviews
Companies often feature positive consumer reviews on their websites and in their promotional materials in an attempt to increase sales. However, little is known about which particular positive reviews companies should leverage to optimize sales. Across four lab studies involving both hypothetical and real choices as well as field data from a retailer's website (Sephora), the authors find that consumers are more likely to purchase a product if it is recommended by a reviewer who has (vs. has not) made a prior purchase mistake. The authors define a purchase mistake as a self-identified suboptimal decision whereby people purchase a product that subsequently fails to meet a threshold level of expected performance. This persuasive advantage emerges because consumers perceive reviewers who admit a purchase mistake as having more expertise than even reviewers whose purchase experience has not been marred by mistakes. As a result, in marketers' attempts to increase the persuasive influence of reviews featured in their promotional materials, they may inadvertently decrease it by omitting the very information that would lead consumers to be more likely to purchase recommended products.
Keywords: online reviews; persuasion; mistakes; expertise; word of mouth
Potential purchasers place a great deal of stock in product reviews written by previous purchasers. More and more, these product reviews are written, read, and evaluated online ([34]). Online reviews gain as much trust as personal recommendations for the majority of consumers (85%; [40]), and a glowing review motivates behavior more than discounts and other offers (in the domains of durable goods and electronics; [28]). Consumers seem to want product reviews (www.iperceptions.com), and firms seem happy to offer such reviews on their websites, in their advertising, and elsewhere ([ 3]; [16]; [22]; [46]). Indeed, firms are increasingly engaging in efforts around "review solicitation" and "online reputation management," incentivizing previous purchasers to write reviews in exchange for discounted or free products.
As firms invest in, and consumers trust, online reviews, their management has come to occupy a more prominent role in marketing practice. Likewise, consideration of the review characteristics exerting the most impact has come to occupy a more prominent role in marketing theory. Positive reviews generally boost sales while negative reviews hurt sales ([15]; see also [23]), and firms certainly opt to feature positive over negative reviews. [41] presented evidence going one step further, examining not simply whether the review was positive but rather how the review conveyed its positivity: in explicitly stating that they endorse the product, online reviews are more likely to lead to purchases than those with more implicit positivity. This more nuanced examination of the content of online reviews—going beyond the mere valence of the review—opens the door to complementary approaches that ask not whether reviewers like the product but what the reviewers say when expressing their positivity or negativity, providing insight as to how firms can optimize the persuasive influence of featuring reviews. To illustrate how we propose to add to this growing and important literature, consider the following two real reviews from Amazon:
When my first Canon battery expired, I purchased a knock-off. What a mistake. It lasted about 1/3 as long as the Canon. (Richard J. Martin, review for a Canon battery)
The Canon battery is essential to have. Knock-off brands don't last a quarter as long as the Canon branded batteries. ("tac cat," review for a Canon battery)
Both of these reviews are positive, but which would—and which should—Canon feature: Richard, who previously purchased a product that turned out to be a mistake, or "tac cat," whose purchase experience was not marred by a mistake?
The present investigation develops a theoretical model in proposing that admitting to having made a previous mistake acts as a powerful cue through which potential purchasers infer that the reviewer has gained significant expertise about a focal product domain, which in turn increases the potential purchasers' likelihood of purchasing the product that the reviewer recommends. In keeping with academic and applied norms, we refer to the communication under consideration as a "review" and the communicator as a "reviewer." However, defined formally, reviews need only describe the reviewer's experience, whereas "recommendations" advise on what to do. Because we want to better understand how the content of communication shapes its ability to persuade, the communications in our experiments will be presented as reviews yet worded as recommendations that prescribe a certain course of action to allow us to measure the degree to which our participants are persuaded (cf. [56]).
Drawing from previous conceptual work, we define a purchase mistake as a self-identified suboptimal decision whereby people purchase a product that subsequently fails to meet a threshold level of expected performance ([25]; [32]; [39]). Accordingly, we are not concerned with mistakes of execution, whereby someone purchases a product unintentionally or by accident. Rather, as an example of a mistake in the form of a suboptimal decision, a consumer might purchase a speaker system only to find that the speakers do not perform as well as anticipated. Does this purchase mistake—specifically, as admitted by the mistaken purchaser—alter the willingness of other potential purchasers to heed the advice of this mistaken purchaser?
One possibility is that admitting to having made a mistake undermines other people's inclination to follow any advice from the mistaken reviewer. After all, admitting to a mistake necessitates the making of a mistake, and mistakes may signal incompetence: The mistaken reviewer was not sufficiently thoughtful, intelligent, and/or knowledgeable to adequately assess the relevant product specifications and make a good purchase decision ([ 2]; [18]; [54]). People often judge others who make mistakes as incompetent and punish them (e.g., [14]; [20]; [30]; [42]), so awareness of a reviewer's previous purchase mistake might predict an outcome resulting from an inference of incompetence and akin to punishment: refusing to take the reviewer's advice.
Another possibility, which forms the foundation of our theoretical model, is that people do not necessarily attribute others' admission of a purchase mistake to incompetence (i.e., a stable, permanent cause; [17]; [24]; [43]). Rather, we theorize that a reviewer's admitted purchase mistake signals a temporary lack of knowledge, which is an unstable cause that can change over time ([17]; [24]; [31]; [43]). Provided that a lack of knowledge is temporary and, thus, fixable, we further propose that reviewers who admit to having made a mistake will prove especially likely to be seen as having rectified this lack of knowledge (i.e., to have gained expertise since the purchase mistake; we conceptualize expertise as a subcomponent of the broader construct of consumer knowledge in keeping with [ 1]]).
These predictions derive from two lines of reasoning. First, to echo and advance a point made previously, all mistake admissions require a mistake to have occurred, but not all mistakes that are made result in the admission of a mistake. In other words, only a subset of mistaken purchasers will ever admit to their mistakes, and even fewer mistaken purchasers will admit to their mistakes in a public forum (like online reviews). We propose that admitting to having made a mistake inherently conveys that the mistaken reviewer has gained new expertise. In the case of product purchases, the mistaken reviewer, in admitting the mistake, must now know not only that the original product has fallen short of initial expectations (forming the basis of a negative assessment of the product) but also that a different course of action (purchasing a different product) would have proven better (for other opportunities to learn from mistakes and signal that learning to others in online reviews, see the "General Discussion" section). On top of this (which applies even in admitting a mistake to oneself), we reason that the public admission of having made a mistake signals that mistaken reviewers are especially confident in their current assessment (e.g., product review), resulting from new expertise gained, insofar as they are willing to engage in the costly behavior of conceding something negative about themselves. Conversely, when reviewers acknowledge switching between brands without admitting a mistake, that reviewer's reason for switching remains more vague: admitting a mistake signals a gaining of expertise, whereas no such admission could result from any number of factors (e.g., promotions, stockouts, variety seeking; [50]; [51]; [52]).
Second, we propose that the impact on others of a reviewer admitting to a mistake also results from the experience of those others (here, potential purchasers reading online reviews) in making mistakes of their own. Everyone makes mistakes, and a common response is to exert additional effort to learn from the mistake to avoid making a similar mistake again in the future ([ 4]; [13]; [35]; [44]). Indeed, making a mistake (vs. enjoying success) prompts greater subsequent effort in the mistaken domain through the use of self-regulation ([13]): the mistake signals a discrepancy between a desired state (e.g., owning a good product) and the current state (e.g., having made a bad purchase), which triggers attempts to reduce the discrepancy and attain the desired goal ([12]; [44]). People experience this effect consciously: they are often clearly aware that making a mistake causes them to exert more effort in similar situations in the future ([12]). Just as people themselves exert greater effort following a mistake (vs. a success), they may believe that others exert greater effort after their mistakes as well. Because people assume that greater effort produces better outcomes ([29]), we formally predict the following:
- H1: Consumers infer that reviewers who admit to having made a previous purchase mistake (vs. reviewers who mention having made a successful purchase) have more expertise in the mistaken product domain.
We also predict that these inferences shape the persuasive impact of reviews. This is because the perception of a reviewer's expertise is a primary determinant of whether people purchase the products that a reviewer recommends: people are more likely to follow the recommendations of those who appear to have more expertise about the domain in which they are making a recommendation ([41]; [45]; [53]; [55]). At this downstream level, we predict the following:
- H2: Consumers are more likely to purchase a product recommended by reviewers who admit to having made a previous purchase mistake (vs. reviewers who mention having made a successful purchase).
- H3: Consumer inferences of expertise for reviewers who admit to having made a previous purchase mistake (vs. reviewers who mention having made a successful purchase) mediate the relationship between admission of a mistake and likelihood of purchasing the recommended product.
Our hypothesis development has centered on inferences of expertise, but we do not propose that any admission of having made a previous mistake always enhances perceived expertise in a way that should increase advice taking. Rather, consumers should be able to discern whether a review in which a mistake was admitted presents compelling evidence that the reviewer has learned from that mistake. As an implication of this predicted sensitivity on the part of consumers, we propose—as a boundary condition to our main effect—that consumers should be more likely to follow the purchase-related advice of reviewers who admit to having made a previous mistake only when the mistake conveys that the reviewer has gained expertise since making the mistake. Specifically, we theorize that the expertise only should be seen as strengthened in the domain in which the reviewer made the mistake. Accordingly, potential purchasers should heed the mistaken reviewer's advice for products in that domain but, provided the expertise does not transfer readily across domains, not for products in any other domain. Formally, we predict the following:
- H4: Consumers are more likely to purchase a product recommended by reviewers who admit to having made a previous purchase mistake (vs. reviewers who mention having made a successful purchase) in the same domain, whereas consumers are no more likely to purchase a product recommended by reviewers who admit to having made a previous purchase mistake (vs. reviewers who mention having made a successful purchase) in a different domain.
Study 1 tests our predictions that people infer mistaken reviewers to have more expertise in the product domain in which the mistake was made (H1), that purchase advice is more likely to be accepted from a mistaken reviewer (H2), and that the former accounts for the latter (H3). Study 1 tests these predictions using an incentive-compatible design, and Study 2 tests these same predictions (H1–H3) but by assessing inferred expertise with a different measure in the interest of providing a robustness check. Study 3 manipulates not only admission of a mistake but also the domain alignment of the mistake, introducing a key moderator to provide evidence for the boundary condition articulated in H4. Study 4 introduces several modifications to the general design of Studies 1–3 to provide evidence for a robust effect relating mention of making a mistake to acceptance of purchase advice (H2) for a real purchase decision. Finally, Study 5 examines mistaken reviewers' persuasive impact in the field by examining real reviews from a popular website (Sephora). Thus, these data suggest that the persuasive power of mistakes is sufficiently robust to emerge in the noisy real world.
The inclusion of orthogonal experimental factors in these designs indirectly speaks to several alternative explanations that we discuss in turn, and a posttest of Study 3 directly measures other inferences consumers might make of reviewers who admit mistakes, finding that they do not account for the effect observed throughout the present investigation. We identify several such possibilities for these findings. First, a positive review for a focal product that includes admission of a mistake acknowledges the existence of both well-performing and underperforming brands. This may result in a brand comparison effect, whereby any mention of poor performance for one brand makes the other, well-performing brand look better (consistent with the effectiveness of comparative advertising; [21]). A second, related explanation might hold not for perception of the two brands but for perception of the reviewer: in mentioning the two brand performances, observers might infer that the mistaken reviewer has more carefully considered both the positives and the negatives of the focal alternatives. Third, a review that includes the admission of a mistake necessarily includes both positive information (a favorable review for the focal product) and negative information (that the previous purchase was a mistake), in contrast to the unilateral positivity of the successful purchase conditions. As a result, perhaps exposure to any negative information in a review orients consumers to potential losses, looming larger than potential gains and prompting them to accept more readily the advice in a review (e.g., as the result of risk aversion: [26]). Should this be the case, then mention of any mistake might strengthen the tendency to accept the advice in a review. These alternative explanations would predict that consumers should still be persuaded by a reviewer who first makes a mistake in one product domain and then makes a successful product purchase in a different domain. Instead, we predict and test in Study 3 that admission of a mistake will change only inferred expertise in the same, focal product domain, rendering moot advice regarding other product domains (H4). Separately, reviewers who admit mistakes might be perceived differently on multiple different inferred characteristics by observers, including specific personality traits and as having global expertise that extends across different product domains. Accordingly, a posttest of Study 3 measures several such potential inferences, finding no connection between them and the admission of a mistake.
While we designed Study 3 with the goal of speaking against several alternative psychological explanations for our effect, we designed Study 4 with the goal of speaking to practitioners interested in the breadth by which our effect might be applied. For this reason, Study 4 departs from Studies 1–3 in subtle but meaningful ways. First, it presents not a single review but a set of ten reviews in which we either did or did not embed a single review in which the reviewer mentions making a previous purchase mistake. Second, rather than reviews for fictitious brands or brands with which participants ostensibly have little familiarity, Study 4 presents reviews for a known brand. Using a known brand facilitated our third change: having participants make a consequential choice. To compliment the consequential setup in Study 1 (in which one participant received a chosen outcome), all participants in Study 4 received the outcome they choose. Thus, Study 4 attests to the robustness of our effect in tandem with the large-scale data-mining approach adopted in Study 5.
In an incentive-compatible context, Study 1 tests H1–H3: consumers infer that a reviewer has more expertise about a product category if the reviewer admits to previously making a mistake in purchasing a product from that category than if the reviewer does not, and this inference of expertise accounts for the greater tendency of consumers to choose in line with this reviewer's advice.
One hundred sixty participants (mean age = 35 years; 39% male) from a large East Coast U.S. university participated in a laboratory study in exchange for course credit. This study was run as part of a session containing unrelated surveys from different researchers. All participants read that, as additional compensation for their participation in the lab session, they would be entered into a lottery for a prize. Participants further read that if they won the lottery, they would receive a pair of headphones and that they would choose which one of two sets of headphones they preferred to receive as their prize. All participants then viewed information about the two sets of headphones, which were called Orbin and Raymour (see Web Appendix A). They also read a consumer review that was (ostensibly) the most recently submitted review for these headphones. Specifically, participants saw that the most recently submitted review was written by a consumer named Sam. Participants were randomly assigned to one of two conditions: a mistaken reviewer condition or a successful reviewer condition. In both conditions, Sam's review recommended the Orbin headphones (purchased most recently) and also described a previous purchase of headphones, providing his experience with both sets of headphones in terms of general evaluation as well as performance on the same particular attribute (a sensor). In the mistaken reviewer condition, Sam noted that this previous purchase was a mistake:
A couple years ago when I was searching for the last pair of headphones that I bought, I ended up buying the Nidec VIA headphones, and that was a mistake—it turned out that the headphones had a bad type of sensor and therefore did not work well. I recently decided to upgrade my headphones to a newer model, and I considered both the Orbin and Raymour headphones. I chose the Orbin headphones. I've had them for a month, and they are good—they have great features, including a good type of sensor, and they work well. I would recommend them. (Sam K.)
Conversely, in the successful reviewer condition, Sam noted that this previous decision was successful:
A couple years ago when I was searching for the last pair of headphones that I bought, I ended up buying the Nidec VIA headphones, and that was a good choice—it turned out that the headphones had a good type of sensor and therefore worked well. I recently decided to upgrade my headphones to a newer model, and I considered both the Orbin and Raymour headphones. I chose the Orbin headphones. I've had them for a month, and they are good—they have great features, including a good type of sensor, and they work well. I would recommend them. (Sam K.)
Next, participants were asked whether they preferred to receive the Orbin or the Raymour headphones if they won the lottery. The instructions emphasized that this decision was real. Participants entered their decision by selecting a radio button that was labeled either with "Orbin" or "Raymour." They also rated how much they perceived that the reviewer had learned about how to choose good headphones (1 = "Not much at all," and 7 = "A lot"). At the end of the study, participants were debriefed (i.e., they were informed that the lottery was real but was for a pair of Sony headphones rather than for the brands that they read about in the study).
As we predicted, participants more often chose the recommended headphones when they were recommended by a mistaken reviewer (93.1%) than when they were recommended by a successful reviewer (79.5%; χ2(d.f. = 1, N = 160) = 5.87, p =.015; Cohen's d =.390). Furthermore, participants perceived that the mistaken reviewer learned more about how to choose good headphones (M = 5.96, SD = 1.01) than the successful reviewer (M = 4.97, SD = 1.39; t(158) = 5.07, p <.001, Cohen's d =.811).
We hypothesized that the mistaken (vs. successful) reviewer more strongly influenced participants' real choices because participants inferred that the mistaken reviewer gained more expertise about how to choose good headphones. Consistent with this prediction, a mediation analysis with 5,000 bootstraps revealed that perceived learning mediated the effect of condition on participants' real choices (95% confidence interval [CI] for the indirect effect = [.1614,.8766]; see Figure 1).
Graph: Figure 1. Mediation model in Study 1.Notes: The path coefficients are unstandardized betas. Values in parentheses indicate the effect of condition on the dependent variable after controlling for the mediator. 95% CI for the indirect effect = [.1614,.8766]. *p <.05. **p <.01. ***p <.001.
In summary, Study 1 documents the persuasive power of mistakes in an incentive-compatible context. Moreover, Study 1 suggests that this power arises because people infer that a purchaser who makes a mistake subsequently gains more expertise—assessed here in the form of learning—about how to identify a good product in that domain than a purchaser who originally chose successfully. Here, the mistaken reviewer identified the previous purchase as a mistake on the basis of its performance on one particular attribute (a sensor), creating potential confounds (e.g., reviewer evaluation of this highly technical attribute leading to observer inferences of preexisting expertise) that we address in our subsequent studies by having the mistaken reviewers concede only that they made previous purchase mistakes without detailing the reasons for this conclusion.
Study 1 provides, in a consequential choice setting, evidence consistent with the prediction that consumers are more persuaded by reviewers who have (vs. have not) previously made a purchase mistake through an inference that those reviewers have learned more. As we have noted, we believe that an inference of learning implies the gaining of new consumer knowledge in the form of expertise, though Study 1 assessed only the former. Thus, in Study 2, we test the same hypotheses as Study 1 (H1–H3) using a modified empirical approach. Specifically, we utilize a hypothetical scenario design to complement the incentive-compatible design of Study 1, and we measure our proposed mediator by asking whether perceivers judge mistaken reviewers to have more knowledge in the relevant domain compared with successful reviewers.
Eighty participants (mean age = 34 years; 63% male) in an online participant pool participated in a study in exchange for monetary payment. All participants imagined that they lived in Seattle and were looking for a local florist. They further imagined that they had narrowed their choices down to two local artisan floral shops that served only the local Seattle area—FlowersNow and FreshBlooms. Participants viewed information about the two floral shops (Web Appendix B) and imagined that they looked at the websites of the two florists to help them make their decision. On the FreshBlooms website, participants saw that there was a review written by a consumer named Sam. In both conditions, Sam's review recommended FreshBlooms and also described a previous choice he had made between two florists when he lived in a different state. In the mistaken reviewer condition, Sam noted that this previous decision was a mistake:
I just moved to Seattle from Boston. When I was in Boston, I decided to get flowers once from a local florist in Boston (which sells flowers only in the Boston area). That was a mistake—the florist I chose was not a good florist. Now that I've moved to Seattle, I wanted to get flowers for my housewarming party. My final choice came down to flowers from FlowersNow or FreshBlooms. After looking into both options, it was clear to me that FreshBlooms is the better florist. I decided to get flowers from FreshBlooms, and that was a great choice. I recommend FreshBlooms! (Sam K.)
Conversely, in the successful reviewer condition, Sam noted that this previous decision was successful:
I just moved to Seattle from Boston. When I was in Boston, I decided to get flowers once from a local florist in Boston (which sells flowers only in the Boston area). That was a good choice—the florist I chose was a good florist. Now that I've moved to Seattle, I wanted to get flowers for my housewarming party. My final choice came down to flowers from FlowersNow or FreshBlooms. After looking into both options, it was clear to me that FreshBlooms is the better florist. I decided to get flowers from FreshBlooms, and that was a great choice. I recommend FreshBlooms! (Sam K.)
Next, participants reported whether they would choose to buy flowers from FreshBlooms or FlowersNow. They also completed a two-item index of their perceptions of the reviewer's knowledge. Specifically, they indicated how much knowledge the reviewer now had about how to choose a good florist and how much knowledge the reviewer had about whether to buy flowers at FreshBlooms or FlowersNow. Participants responded to each question on separate seven-point scales (1 = "Not a lot," and 7 = "A lot"). We combined the items into an index of perceived knowledge (r =.53, p <.001). We predicted that the mistaken (vs. successful) reviewer would more strongly influence participants' choices because participants would perceive the mistaken reviewer as more knowledgeable.
As we predicted, participants more often chose the recommended florist when it was recommended by a mistaken reviewer (90.9%) than when it was recommended by a successful reviewer (61.7%; χ2(d.f. = 1, N = 80) = 8.54, p =.003, Cohen's d =.691). Furthermore, participants perceived that the mistaken reviewer had more knowledge (M = 4.68, SD = 1.27) than the successful reviewer (M = 3.84, SD = 1.19; t(78) = 3.03, p =.003, Cohen's d =.683). To test the mediating role of perceived knowledge in determining the effect of condition on choice, we conducted a mediation analysis with 5,000 bootstraps. As we hypothesized, perceived knowledge mediated the effect of condition on choice (95% CI for the indirect effect = [.0545,.2834]; see Figure 2).
Graph: Figure 2. Mediation model in Study 2.Notes: The path coefficients are unstandardized betas. Values in parentheses indicate the effect of condition on the dependent variable after controlling for the mediator. 95% CI for the indirect effect = [.0545,.2834]. *p <.05. **p <.01. ***p <.001.
Along with Study 1, Study 2 provides evidence for the persuasive power of mistakes. Moreover, in Study 2, we establish that this power arises because consumers infer that a reviewer who admits to a mistake has more knowledge than an equivalent reviewer who originally chose successfully. Having documented a robust effect in Study 2, in Study 3 we probe a potential moderator of the persuasive power of mistakes.
Our theoretical model proposes that the persuasive power of reviews featuring mistakes comes from an inference that the reviewer has significant expertise (H3). As a result, any information that undermines the tendency for observers to infer that the reviewer has this expertise should, in turn, undermine the extent to which the review is persuasive. Thus, to provide convergent evidence, Study 3 utilizes a moderation-of-process design ([49]) that manipulates not only the admission of a mistake but also an additional experimental factor designed to compromise the inferred expertise of the reviewer, which also addresses potential alternative accounts. Specifically, if the persuasive power of mistakes is due to the belief that reviewers' purchase mistakes signal that the reviewers have gained more expertise about the product domain, then mistaken reviewers' recommendations will be more persuasive only when the reviewers' purchase mistakes occur in the same product category as their focal review (H4). We tested this prediction in Study 3 by presenting participants with a review from either a mistaken or successful reviewer, in keeping with the designs of Studies 1 and 2, but departing from those studies in that the previous purchase was in either the same or a different product category as the review.
Two hundred ninety-nine participants (mean age = 32 years; 49% male) from Amazon's Mechanical Turk (MTurk) completed an online study in exchange for monetary payment. The participants' task was to decide which one of two in-ceiling speaker systems they would prefer to purchase: the Mikana XPI in-ceiling speaker system or the Rokana SX2 in-ceiling speaker system. Participants viewed the specifications of the two speaker systems (which were identical to the specifications of the headphones described in Study 1, as the specifications could reasonably apply to both product categories; see Web Appendix A) and read two reviews written by a consumer named Taylor. Both reviews were ostensibly on a website that featured consumer reviews for different electronics. The focal review, which was the same in all conditions, noted that Taylor purchased a Mikana XPI speaker system and recommended it. However, the content of the nonfocal review differed by condition. Specifically, participants were randomly assigned to read that Taylor had previously purchased a bookshelf speaker (i.e., a product in the same domain as the focal product) or a printer (i.e., a product in a different domain from the focal product) and that this decision was either a mistake or a success (see Web Appendix C). These manipulations produced a 2 (nonfocal product type: speakers vs. printer) × 2 (nonfocal review type: mistake vs. success) design.
After participants viewed Taylor's reviews (both reviews were explicitly noted as written by Taylor), they decided whether they would purchase the Mikana XPI or the Rokana SX2 speaker system. Participants entered their decision by selecting a radio button that was labeled either with "Mikana XPI" or "Rokana SX2."
We conducted a binary logistic regression using nonfocal review type (mistake vs. success), nonfocal product type (speakers vs. printer), and their interaction to predict participants' choices. The regression revealed an interaction on choice (b = 2.68, z = 2.67, p =.008). In a conceptual replication of the previous studies, when the reviewer had made a previous speaker purchase, participants who read that the previous purchase was a mistake chose the recommended speaker more often (97.4%) than participants who read about a successful purchase (87.0%; b = 1.72, z = 2.17, p =.030, odds ratio = 5.597). Conversely, when the reviewer had made a previous printer purchase, we found no such effect (mistake = 86.5%, success = 94.4%; b = −.96, z = −1.56, p =.119).
If a mistake's persuasive power arises because admitting a mistake signals some broadly positive character trait (e.g., the integrity to admit one's mistakes publicly), then mistaken reviewers should be more persuasive regardless of the domain of their mistake. Thus, we conducted a posttest to establish whether mentioning a mistake—either in the same domain or a different domain as the focal product—alters how consumers perceive the mistaken reviewer. The design of the posttest mirrored that of the main study, save for a switch from measuring choice to measuring several such potential perceptions. Specifically, the posttest began with participants' assessment of the extent to which a mistaken reviewer is discerning, has integrity, is likeable, and is similar to the self. The posttest also measured whether the mistake was surprising, because surprise can orient attention to a focal piece of information and increase persuasion as a result ([ 8]). Finally, in the interest of presenting evidence not only against these alternative explanations but also in support of our proposed mechanism, participants rated the perceived expertise of the mistaken reviewer to conceptually replicate the mediation results from Studies 1 and 2. We examined these issues using a posttest rather than in the main study to avoid potential demand effects from asking participants about both their purchase intentions and their perceptions of the mistaken reviewer in the same study.
We recruited a separate sample of MTurk participants (N = 350; mean age = 38 years; 50% male), who were randomly assigned to view the same information presented to participants in the main study. Posttest participants then completed measures, presented in a random order, assessing their perceptions of the extent to which Taylor was discerning and had high integrity, the extent to which they liked Taylor and were similar to Taylor, and the extent to which Taylor's reviews were surprising. In addition, participants completed a two-item index of their perceptions of Taylor's expertise (adapted from [41]). Specifically, they indicated how much of an expert they thought Taylor was about speakers and how knowledgeable they thought Taylor was about speakers. Similar to [41], we combined these items into an index of perceived expertise that correlated at a level (r =.66, p <.001) commensurate with their original work (r =.53). Participants indicated their responses on separate seven-point scales (1= "Not at all," and 7 = "Very much").
As we expected, the interaction between nonfocal product type and nonfocal review type was significant for perceived expertise (F( 1, 346) = 4.43, p =.036). When the reviewer had made a previous speaker purchase, the mistaken reviewer was perceived as more expert (M = 4.64, SD = 1.13) compared with the successful reviewer (M = 4.31, SD =.99; Fisher's least significant difference: p =.043; Cohen's d =.311). In contrast, when the reviewer had made a previous printer purchase, there were no differences in perceived expertise between the mistaken reviewer (M = 4.59, SD = 1.09) and the successful reviewer (M = 4.74, SD = 1.01; F < 1). Participants' perceptions of the extent to which Taylor was discerning, had integrity, was likeable, and was similar to themselves, as well as the extent to which their mistake was surprising did not differ as a function of product type and review type (Fs( 1, 346) < 2.52, ps >.114; see Table 1).
Graph
Table 1. Descriptive Statistics in the Posttest of Study 3.
| DV | Speaker Mistake | SpeakerSuccess | Printer Mistake | PrinterSuccess | Analysis (Interaction) |
|---|
| Discerning | 4.21 (1.49) | 4.13 (1.40) | 4.53 (1.31) | 4.18 (1.40) | F(1, 346) =.83, p =.362 |
| Integrity | 4.76 (1.18) | 4.48 (.97) | 4.78 (.90) | 4.85 (1.10) | F(1, 346) = 2.52, p =.114 |
| Liking | 4.27 (1.34) | 4.23 (1.33) | 4.52 (1.19) | 4.55 (1.28) | F(1, 346) =.06, p =.810 |
| Similar | 5.56 (1.45) | 5.76 (1.16) | 6.17 (.95) | 6.15 (1.02) | F(1, 346) =.73, p =.393 |
| Surprise | 4.66 (1.22) | 4.60 (1.27) | 4.89 (1.17) | 4.80 (1.23) | F(1, 346) =.01, p =.930 |
| Expertise Index | 4.64 (1.13) | 4.31 (.99) | 4.59 (1.09) | 4.74 (1.01) | F(1, 346) = 4.43, p =.036 |
1 Notes: Statistics in parentheses are standard deviations.
These results are inconsistent with the possibility that a mistake's persuasive power arises because they signal some broad character trait that enhances discernment, integrity, likeability, or perceived similarity to the observer. Theoretically, such character traits should have been signaled just as well by a printer-related mistake as by a speaker-related mistake, in which case we would not have found the predicted interaction. Moreover, Study 3's posttest confirmed that Taylor was perceived equivalently on each of these dimensions regardless of whether he made a prior mistake purchasing a printer or speakers. Study 3, however, does not speak to one remaining potential inference: that the mistaken reviewer had high expertise from the outset instead of gaining expertise. Although at face value this possibility might seem inconsistent with making a mistake in the first place, we conducted a supplementary study (reported in Web Appendix D) to demonstrate that the persuasive power of mistakes arises not because people assume that a mistaken reviewer had a lot of knowledge to begin with to discern their mistake (and not because of any other variable that consumers may assume plays into reviewers' ability or willingness to acknowledge a mistake, or because of some other difference in the content of mistake- vs. success-based reviews), but rather because they assume that a mistaken reviewer has acquired more expertise as a result of their mistake.
The first three studies identified the psychological foundation underlying why mentioning a mistake causes consumers to place more credence in the advice of those reviewers. In the pursuit of this objective, the first three studies prioritized internal validity over external validity; Study 4 shifts its balance to consider the impact of mistaken reviewers in more ecologically valid contexts. While considering a purchase, consumers regularly read not only a single review in isolation but, rather, multiple reviews to form an overall conclusion. To capture this reality, participants see not one but ten reviews for a focal product in Study 4; two experimental conditions vary whether one review, embedded within that set of ten, mentions a prior purchase mistake. Furthermore, would the effect of mentioning mistakes extend from the fictitious or generic brands used in our previous studies to established, known brands presumably higher in brand equity? Study 4 considers this robustness issue by using a real brand (Altoids mints) as the focal product under consideration. Finally, as this study aims to provide the clearest point of direct application, it requires all participants to make a real purchase choice. Whereas the choice in Study 1 was incentive-compatible insofar as one participant would ultimately receive the chosen outcome, incentive compatibility is strengthened in Study 4, which asks all participants to make a real purchase decision. We predicted that mention of a mistake would still prove powerful under this more naturalistic set of conditions.
Two hundred forty-nine participants (mean age = 35 years; 43% male) from a large U.S. East Coast university participated in a session of laboratory studies in exchange for monetary payment. This study was run as part of a session containing unrelated surveys from different researchers. All participants were told that they would be asked to read several reviews of spearmint mints made by a brand of which we presumed participants to have at least some knowledge (Altoids) and then asked to make a choice. Participants were randomly assigned to one of two conditions: a mistake condition or a no-mistake condition. All participants viewed a total of ten reviews that were presented in a random order. Nine of these reviews were identical between the experimental conditions and were taken from actual Altoids spearmint mints reviews posted on Amazon (including star rating, title of review, and review text; for all reviews, see Web Appendix E). We varied the content of the remaining review to either include a reference to the reviewer having made a previous mistake (mistake condition) or not (no-mistake condition). After reading the reviews, participants were told that as additional compensation for the survey session, they could choose to receive either one pack of Altoids spearmint mints or one additional dollar added to their payment. Participants entered their decision by selecting a radio button that was labeled either with "Altoids Spearmint Mints" or "One Additional Dollar," and the researcher then provided the participant with their chosen form of additional compensation.
A chi-square analysis revealed that participants reading a set of reviews that contained one review in which the reviewer mentioned making a mistake were more likely to choose the Altoids spearmint mints over additional monetary compensation (34.9%) than were participants for whom the provided set of reviews did not contain a review mentioning a mistake (22.0%; χ2(d.f. = 1, N = 249) = 5.14, p =.023; Cohen's d =.290). Thus, as we predicted, even when the review mentioning a mistake has only a minority presence (i.e., comprises one review out of ten), it still exerts an effect powerful enough to change consumer choice. Notably, the behavior under consideration here closely reflects real consumer decision making, as our participants learned about a widely known brand and subsequently made a consequential choice (between receiving the branded product or receiving additional money). These results attest to the strength and robustness of our effect, which we extend in Study 5 to a different naturalistic context.
In our final study, we test the external validity of our findings by examining whether the persuasive power of mistakes is sufficiently robust to emerge in the noisy real world. To that end, we examine consumer reviews on Sephora's online retail platform. Conveniently, the Sephora review platform has a feature whereby readers can rate whether reviews are helpful, which is indicative of whether they are persuasive (see [ 7]; [38]; see also the pilot test in the "Results and Discussion" subsection of this study). We predict that reviews referencing a purchase mistake will be linked to consumers finding the review more helpful (as measured by reader-provided ratings of helpfulness).
The Sephora website includes six product categories (makeup, skincare, hair, tools and brushes, fragrance, and bath and body). The category to be scraped (hair) was randomly chosen and, after determining 40 products as the maximum number able to be scraped within a reasonable time frame, 40 haircare products were randomly chosen. Within that subset, we scraped all reviews starting in August 2017 until the time of scraping (December 24, 2018). For the entire resulting set of 5,727 reviews, we used a series of indicator variables to tag whether each review mentioned a previous purchase mistake. Specifically, the (case-insensitive) indicators were: mistak: the string "mistak" is in the review; mistook: the string "mistook" is in the review; my_bad: the phrase "my bad" or "my error" is in the review; I_wrong: the word "I" is within 35 characters of a word starting with "wrong" (without a period, question mark, or exclamation mark in between, which are proxies for sentence divisions); my_fault: the word "my" is within 35 characters of the word "fault" (again, without a period, question mark, or exclamation mark in between); and our_fault: the word "our" is within 35 characters of the word "fault" (again, without a period, question mark, or exclamation mark in between). This tagging led to the identification of 502 reviews referencing a prior mistake. Two independent judges (interjudge reliability: r =.92) reviewed these 502 reviews, tagging 86% of them to be about mistakes in choice.[ 5] To equate sample sizes, we then randomly selected 502 reviews that did not reference a mistake from the remaining scraped data set, resulting in a data set of 1,004 reviews.
Previous research has suggested that helpfulness votes are a proxy for persuasiveness ([ 7]; [38]). We conducted our own pilot test to further verify this conclusion. That is, we tested whether consumers rate a review as helpful when it guides their purchase decision. To that end, we recruited 72 participants from MTurk who reported that they had previously rated an Amazon review as helpful or unhelpful. We then asked them to describe (in an open-response box) the factors that influence their decisions about whether to rate a review as helpful or unhelpful. On the next survey page, we showed them the description that they had written and asked them whether they wrote that they were more likely to rate an Amazon review as helpful when the review made them want to follow the reviewer's advice (a measure of persuasion; [10]; [11]; [36]). A chi-square analysis revealed that most people (72.2%) reported that they rate a review as helpful when it is persuasive (χ2 (d.f. = 1) = 14.22, p <.001). Thus, consistent with prior literature ([ 7]; [38]), reviews' helpfulness ratings serve as a proxy for their persuasive power.
We computed a measure of each review's persuasive power by subtracting the number of unhelpful votes from helpful votes associated with each review and dividing that by the total number of votes (helpful − unhelpful)/(helpful + unhelpful). This measure served as our dependent variable, and it ranged from −1 to 1 (M =.078, SD =.329; Mhelpful = 3.672, SD = 16.621; Munhelpful =.774, SD = 3.285). As a first step, we regressed this helpfulness measure on whether the review referenced a mistake (−1 = no, 1 = yes). As we predicted, the regression revealed that reviews referencing a mistake (vs. those not referencing a mistake) were deemed more helpful (b =.076, SE =.010, p <.001). Next, we regressed the helpfulness measure on whether the review referenced a mistake (−1 = no, 1 = yes) and the following control measures (all continuous control variables were mean-centered): review length (number of words), valence of review (−1 = negative, 0 = both positive and negative, 1 = positive; coded by two independent judges with high interjudge reliability: r =.93), star rating (1–5), explicit recommendation (−1 = no, 1 = yes; feature included on the Sephora website), loyalty program membership tier (with higher numbers indicating more dollars spent at Sephora in a calendar year; 1 = "Insider," 2 = "VIB," and 3 = "Rouge"), reviewer expertise (with higher numbers indicating more reviews posted to the Sephora site; 1 = "Rookie," 2 = "Rising Star," 3 = "Go Getter," and 4 = "Boss"), number of images uploaded with the review, explicit mention of another brand in the review (−1 = no, 1 = yes; coded by two independent judges with high interjudge reliability, r =.94), and date of review (calculated as number of days since December 30, 1899 on the Gregorian calendar). The regression revealed a significant effect of review length on helpfulness, such that longer reviews were found to be more helpful (b =.001, SE <.000, p <.001). None of the other control variables had a significant effect on helpfulness (ps >.304). Most relevant to our focal theorizing, the regression also revealed that reviews referencing a mistake (vs. those not referencing a mistake) were deemed more helpful in the full analysis controlling for review length, valence of review, star rating, explicit recommendation, loyalty program membership, reviewer expertise, number of images uploaded with the review, explicit mention of another brand in the review, and date of review (b =.056, SE =.014, p <.001; see Table 2).[ 6]
Graph
Table 2. Results of Regression Analysis on Helpfulness Index for Study 5.
| Predictor | b | SE | | t | p |
|---|
| (Constant) | .083 | .024 | | 3.456 | .001** |
| Mistake reference | .056 | .014 | .171 | 4.136 | .000*** |
| Number of words | .001 | .000 | .144 | 4.291 | .000*** |
| Valence of review | .018 | .017 | .044 | 1.027 | .305 |
| Star rating | −.005 | .017 | −.023 | −.305 | .761 |
| Explicit recommendation | −.029 | .028 | −.077 | −1.052 | .293 |
| Loyalty program member | .014 | .013 | .035 | 1.109 | .268 |
| Reviewer expertise | .191 | .230 | .026 | .828 | .408 |
| Number of uploaded images | .023 | .035 | .021 | .648 | .517 |
| Mention of another brand | −.011 | .020 | −.017 | −.552 | .581 |
| Date of review | −2.672E-5 | .000 | −.009 | −.236 | .813 |
- 2 Notes: The R-square of the simple model (without controls) is.053; the R-square of the full model with controls is.080.
- 3 *p <.05.
- 4 **p <.01.
- 5 ***p <.001.
In summary, Study 5 provides evidence that Sephora users rate reviews that mention a purchase mistake as more helpful. Even after including numerous controls, the relationship between mention of a mistake and review helpfulness still holds. We targeted helpfulness as a meaningful construct of consideration, as our pilot test and previous research indicate that a helpful review is a persuasive review, and marketing managers interested in increasing sales begin with the goal of persuading consumers to purchase their products. Thus, these results underscore the applied relevance of mentioning mistakes by providing evidence for a robust connection to review helpfulness using data taken from a real online retailer.
People are often skeptical about whether the reviews they encounter are authored by well-informed consumers and thus first evaluate whether a reviewer is credible before deciding whether to rely on his or her review (e.g., [45]; [53]; [55]). We find that people are more likely to conclude that a reviewer has more expertise, and are thus more likely to purchase the product that a reviewer recommends, if the reviewer admits to having made a purchase mistake in that domain. The current research thus suggests that featuring purchase mistakes in online consumer reviews offers a promising opportunity as a persuasive tactic. Our research therefore provides important, practical insight into the inputs underlying consumers' decisions about whether to purchase reviewed products while also making several theoretical contributions. First, whereas substantial research has documented the negative inferences that observers make about mistake makers, our research uncovers the conditions in which learning about others' mistakes leads people to perceive those others more positively (cf. products made by mistake; [47])—in particular, the conditions in which mistaken reviewers are perceived to be more expert and better able to identify the optimal course of action than even reviewers whose previous experience has not been marred by mistakes. We integrate the persuasion and attribution theory literature streams to illuminate a powerful factor that shapes the persuasive impact of consumer reviews on purchase decisions.
In addition to illuminating the persuasive impact of mistake makers and the mechanism underlying this phenomenon, we also examined several alternative explanations. Merely comparing two brands proved insufficient to evoke our effect in Study 3, which instead provided evidence consistent with H4, which posited domain dependence. The posttest of Study 3 provided further evidence inconsistent with the possibility that inferences regarding mistaken reviewers being discerning, having integrity, being likeable, and being similar to the message recipient contribute to the phenomenon we document, nor did potential surprise from admitting a mistake. We note that these findings do not preclude the possibility that some types of purchase mistakes could alter perceptions of reviewers (for these or other traits) in a manner that would affect their persuasive power. Nevertheless, the fact that the proposed phenomenon emerged—for both purchase decisions in the main study and inference of expertise in the posttest of Study 3—when the reviewers were perceived equivalently on these dimensions suggests that these accounts are insufficient to explain our findings.
Furthermore, the mere delivery of negative information does not appear to increase mistaken reviewers' persuasive impact (neither through loss aversion [[26]] nor through alternate routes including but not limited to a negativity bias, a blemishing effect, mistake-induced perceptions of warmth, or negativity-induced perceptions of competence [[19]; [48]]). If the mere delivery of negative information drove mistaken reviewers' persuasive impact, then the reference to a mistake in a different product domain could have increased persuasion. We did not observe this outcome. These results are thus inconsistent with the possibility that the mere delivery of negative information increases mistaken reviewers' persuasive impact.
These findings also cannot be accounted for by a pratfall effect (clumsy actions that enhance the attractiveness of superior others; [ 5]). For one thing, this alternative possibility is theoretically implausible: pratfalls only affect assessments of superior, and thus potentially threatening, others ([ 5]), and it seems unlikely that participants in the current studies perceived their fellow consumers to have a superiority needed for the pratfall effect to emerge. Moreover, Study 3 finds that the mere presence of a mistake is insufficient to increase a reviewer's persuasive impact, as would be predicted by the pratfall effect; thus, the current results are inconsistent with this potential alternative explanation. Taken together, these results thus suggest that acknowledging mistakes can play an important role in promoting persuasion and influencing purchase decisions.
The inferential process documented in these studies suggests that there are likely several boundaries to the persuasive power of mistakes. We documented one in the current research (H4): when a reviewer makes a mistake in a different domain from the focal purchase (Study 3), potential purchasers are no longer more persuaded by the reviewer. Our theoretical framework predicts several other boundaries deserving of further attention as well. First, we have considered what happens when a prior purchase mistake is corrected—presumably through the process of gaining expertise—in a subsequent purchase. However, if an initial mistake goes uncorrected (as in the case of a reviewer who mentions making not one but two successive mistakes), then a lack in ostensibly gained expertise should result in a lack of reviewer persuasiveness. This might suggest, for instance, a boundary around review valence, as a negative product review would reasonably be more likely to include mention of a (second) mistake than would a positive review. Second, mistaken reviewers may indeed be seen to have gained expertise, but they may no longer be more persuasive if people discount the utility of that expertise. In particular, not all mistakes lead to learning that is relevant for other consumers. For example, suppose a person buys a speaker system and subsequently realizes that the purchase was a mistake because the color of the speakers does not match the person's home decor. That person may well have learned something from that experience, but the expertise gained from this mistake bears no relevance to others whose homes are decorated with different color schemes. If so, admitting to making this kind of subjective, idiosyncratic mistake may not increase a reviewer's persuasive impact because others may recognize the irrelevance of the mistake (and thus the irrelevance of the mistaken reviewer's subsequent expertise) to their own decisions. This would suggest more broadly that our effect should hold more strongly for more global, general reviews of products (but see Study 1).
Our theorizing further suggests that if consumers attribute a reviewer's purchase mistake to stable incompetence that cannot be fixed, they will be less likely to infer that expertise has been gained and may thus be less influenced by the mistaken reviewer's product reviews. Accordingly, future research could profit from investigating the factors that affect people's expertise attributions. For example, observers may judge that an admitted mistake is attributable to stable incompetence if the observers perceive that the correct product choice was patently obvious at the time of the mistake. In such cases, observers may infer that the cause of the mistake is most likely to be an absence of basic common sense rather than a fixable lack of knowledge about the product category. Extending the common sense thread, should the content of the review contain not only mention of a mistake but also other content indicative of a lack of expertise (e.g., stating that a newly purchased speaker serves as a fantastic paperweight, using poor grammar), our model would predict that the reviewer's advice would have less impact. The severity of a mistaken outcome may further moderate observers' attributions. In the current studies, the consequences of the admitted mistakes were relatively minor—for example, in Study 1, the reviewer's mistake merely resulted in the purchase of suboptimal headphones. People may perceive that mistakes that cause more severe consequences signal stable incompetence: because people assume that more severe outcomes emanate from larger antecedents ([33]), they may further assume that larger antecedents are more foreseeable and attribute foreseeable mistakes to incompetence. We encourage future research to examine these possibilities.
If the incompetent occupy the low end of the expertise spectrum and reviewers who admit to their previous mistakes find themselves situated much higher along that same spectrum, what might determine where other reviewers fall on the basis of their reviews? Our research, in complementing existing work ([41]), attests to the continued importance of addressing this question, as inferred expertise increases the likelihood that consumers will heed the advice of reviews (as either featured by brands or encountered on review repositories). For future research consideration, we underscore one noteworthy domain in which consumers regularly have the opportunity to learn about products: that of purchase decisions made by friends and family. In all of our experiments, reviewers acknowledge their own mistakes on the basis of direct prior experience: they made the purchase, used the product firsthand, became aware of its shortcomings, and gained sufficient expertise to identify this purchase as a mistake and to rectify it in a subsequent purchase.
Incorporating the vantage point of mistaken purchases made by others, we identify situations in which consumers might also gain new information. First, a consumer might proceed through all of the steps in the aforementioned sequence, save for making the purchase in the first place. That is, the consumer might borrow a product purchased by a friend and then proceed to use and hold a negative evaluation of it. Would this consumer learn anything? We propose that it depends on the preexisting opinion held by (or at least acknowledged by) the consumer. If the consumer knew all along that their friend's purchase was a dud and using it only verified this opinion, then summarizing this event in a review should not signal any learning on the part of the consumer authoring the review. Such a review might be appraised in a manner similar to the reviewers in our studies who made two successful purchases without any mention of a mistake—as stable, rather than trending upward in expertise—and would observe a degree of persuasiveness akin to theirs. However, the consumer instead might have held high or neutral expectations for the product borrowed from the friend, only to have them dashed after using the product firsthand. Should this type of consumer write a review recapping their experience, we expect that it would convey that learning had taken place (i.e., a knowledge-based update to the reviewing consumer's degree of expertise possible even in the absence of making the mistaken purchase per se), which our results suggest is key in translating the admission of a mistake (be it a mistaken purchase or simply a mistaken opinion) into powerful persuasion.
Perhaps reviewers who use products purchased by others and describe how they learned from the experience would be highly persuasive but still not as persuasive as reviewers who made the purchase mistake themselves. This possibility, echoing the aforementioned possibility of a continuum or spectrum of reviewer persuasiveness, might arise as the result of our proposal that the admission of a personal purchase mistake is more costly (and more diagnostic of confident expertise) than the admission of a personal incorrect (favorable) opinion about a product. To be sure, should the friend in this situation (rather than the consumer doing the borrowing) write a review attesting to their own purchase mistake, our results suggest this review would be maximally persuasive. But what if, instead, the friend merely described their experience to the consumer and the consumer then authored a review summarizing the friend's experience—would the reviewing consumer (rather than the friend) have an impact on potential purchasers? We offer that the answer to this question might depend on the degree to which those potential purchasers see the reviewing consumer as socially close to the friend who made the mistake. If the relationship seems distant, then readers might discount the potential for the reviewer to learn from the friend's mistake. But, if the relationship seems close, then readers might believe that the reviewer gained just as much expertise as the mistaken friend ([27]), bolstering the degree to which potential purchasers place stock in the review.
The primary takeaway of our research for practitioners advises the featuring of mistakes to drive more online traffic and, ultimately, more sales. As such, outlets at which online retailers have control to structure the decision environment provide the best point of entry for this recommendation. Though, to be sure, they cannot control the content of online reviews authored by independent consumers, online retailers do have the power to flag certain reviews as "featured" or "highlighted," warranting placement ahead of an otherwise long and undifferentiated list of reviews. By identifying one or multiple reviews that mention a previous purchase mistake and bumping them up to the top, online retailers can make online shoppers more likely to see, read, and accept the advice of these reviews that our research suggests prove especially persuasive. However, it is not only online retailers that aspire to put helpful reviews in front of their audience. Review curation websites benefit from persuading customers not toward any one particular course of purchase-related action but, instead, toward feeling that the website itself offers a valuable source of information. If customers believe that sites such as Yelp, TripAdvisor, and Rotten Tomatoes offer a consistent, reliable set of reviews, they return to them more frequently ahead of various purchases, with such increased traffic in turn increasing advertising revenues for curation sites. Broadly speaking, then, any firm or brand in the business of offering helpful, positive advice should feature reviews that mention previous purchase mistakes.
As previously noted, companies frequently feature consumer reviews to market their products. Thus, in addition to providing novel insight into the inferences that people make about others' purchase mistakes, this research also has significant practical import because it suggests that marketing managers may strategically omit information that actually increases persuasive influence. In other words, featuring reviews that include purchase mistakes might be a widely underused persuasive tactic: in their attempts to increase their persuasive influence, managers may inadvertently decrease persuasive influence through the missed opportunity of failing to feature mistakes. The present investigation thus offers the clear directive to incorporate more (perhaps any) mention of mistakes when featuring reviews to promote products.
We propose that this directive may be especially well-suited to smaller firms with fewer marketing-related resources at their disposal. What the effect documented by the present investigation lacks in magnitude and everyday prevalence, it makes up for in subtlety and ease of implementation. As such, it may help level the playing field between large corporations that can pour significant resources into market research, carefully determining which reviews to feature, and smaller companies that lack such deep market research pockets. These relatively smaller companies may take comfort in (and win sales from) the insight that featuring a review referencing a prior mistake acts as a simple but beneficial tool in shaping consumer preference.
Firms are not alone in their desire to persuade, and our findings not only might warrant consideration by the marketing departments of large corporations but also might be brought to bear on any attempt to convince others to take purchase-related advice. Dovetailing with the literature on word of mouth ([ 6]; [ 9]; [37]; [41]), we offer two additional domains well-suited to apply our work. First, friends often give purchase-related advice to each other, driven either by the relatively selfless joy of facilitating a positive purchase experience for their friend (e.g., a great gym) or by more self-interested motives (e.g., a gym that promises a referral bonus). We propose that both objectives should be facilitated by making mention of a previous mistake, extending our work to closer interpersonal relationships.
Second, a growing number of individuals have taken to online forums to build personal brands around product reviews and tutorials, as evidenced by the thousands of personal blogs reviewing electronics and YouTube channels demonstrating how to apply makeup. Insofar as these influencers hope to build their personal brands in the form of likes, follows, and mentions, they need to be seen as credible experts. Our model offers the possibility that perhaps they would be more likely to attain their objectives should they incorporate mention of previous mistakes into their content. Aside from the content of the review, they might also persuade others to click on their written and recorded reviews in the first place by including mention of a mistake in the title itself (e.g., "Learn from my mistake!"). Only after navigating to their review will others read and incorporate the reviewer's purchase-related advice. We note, though, that the decision to follow an influencer on social media in perpetuity results from a confluence of many factors that may or may not overlap with the momentary evaluation of helpfulness and one-time purchase decisions around which the present investigation centered. Still, this application of the phenomenon documented and detailed herein would suggest that inferred expertise, through admission of mistakes, can not only drive sales but also build brand equity writ large.
Supplemental Material, jm.18.0205-File003 - Featuring Mistakes: The Persuasive Impact of Purchase Mistakes in Online Reviews
Supplemental Material, jm.18.0205-File003 for Featuring Mistakes: The Persuasive Impact of Purchase Mistakes in Online Reviews by Taly Reich and Sam J. Maglio in Journal of Marketing
Footnotes 1 Associate EditorDonna Hoffman
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242919882428
5 1A supplementary analysis on only this reduced set of reviews from the manual coders yielded the same pattern of results as those presented in the main text. Web Appendix F presents this regression analysis.
6 2For descriptive statistics of the variables included in Table 2, see Web Appendix G. For a regression analysis on raw number of helpfulness votes, see Web Appendix H. For a discussion and regression analysis of an extended model that predicts a Sephora-specific outcome (i.e., the number of "loves" included on the product page on the Sephora website), see Web Appendix I.
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By Taly Reich and Sam J. Maglio
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Record: 75- Format Neglect: How the Use of Numerical Versus Percentage Rank Claims Influences Consumer Judgments. By: Sevilla, Julio; Isaac, Mathew S.; Bagchi, Rajesh. Journal of Marketing. Nov2018, Vol. 82 Issue 6, p150-164. 15p. 4 Graphs. DOI: 10.1177/0022242918805455.
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Format Neglect: How the Use of Numerical Versus Percentage Rank Claims Influences Consumer Judgments
Marketers often claim to be part of an exclusive tier (e.g., "top 10") within their competitive set. Although recent behavioral research has investigated how consumers respond to rank claims, prior work has focused exclusively on claims having a numerical format. But marketers often communicate rankings using percentages (e.g., "top 20%"). The present research explores how using a numerical format claim (e.g., "top 10" out of 50 products) versus an equivalent percentage format claim (e.g., "top 20%" out of 50 products) influences consumer judgments. Across five experiments, the authors find robust evidence of a shift in evaluations whereby consumers respond more favorably to numerical rank claims when set sizes are smaller (i.e., <100) but more favorably to percentage rank claims when set sizes are larger (i.e., >100), even when the claims are mathematically equivalent. They further show that this change in evaluations occurs because consumers commit format neglect when making their evaluations by relying predominantly on the nominal value conveyed in a rank claim and insufficiently accounting for set size.
Keywords: base rate neglect; framing effects; percentages; rank claims; rankings
When making purchase decisions, consumers often need to sift through a vast amount of information on the many products or services in the marketplace. To facilitate the comparison and evaluation of these different options, numerous third-party entities (e.g., Motor Trend, U.S. News & World Report, Bon Appetit) regularly prepare and disseminate ranked lists of competitive offerings within a particular category. Because they are able to summarize complex information in an easily digestible manner, ranked lists are valued by consumers in a variety of domains. Indeed, prior research has suggested that consumers consult ranked lists for comparative information when choosing a restaurant, hospital, college, hotel, car, book, and so on (e.g., [16]; [18]; [26]; [32]).
The value of ranked lists to consumers is not lost on marketers. When a company learns that it has been included in a ranked list, it frequently communicates this information to its customers. As a result, rank claims regularly appear in advertising and other marketing communications. For example, of the new U.S. restaurants that GQ ranked as the ten best new restaurants of 2017, eight reported this honor on their websites or shared it on social media.[ 6] More generally, 78% of companies in the Fortune 100 referenced rank claims on their primary websites (often on the site's landing page or in the newsroom section [all Fortune 100 websites accessed in June 2017]).
Rank claims are typically communicated using a numerical format (e.g., "Product X is in the top 10") or a percentage format (e.g., "Product X is in the top 20%"). Both formats are commonly used in practice.[ 7] As an example, Insight Global adopted a numerical format when communicating its inclusion in Comparably's "Best Places to Work" list, noting in a recent press release that it was "a top 20 best place to work in the nation."[ 8] In contrast, LinkedIn used a percentage format in an email to members whose user profiles were viewed most in 2012, congratulating them for being in the top 1%, 5%, or 10% of the company's 200 million members ([36]). Importantly, communicators must often choose which rank claim format to use. For instance, Hampton Inn annually designates the top 5% (i.e., top 100) of its 2,000 hotel properties as Lighthouse Award winners, and honorees and tourist sites can decide whether to communicate this selection using percentages (e.g., "top 5% of 2,000 hotels") or numerical ranks (e.g., "top 100 of 2,000 hotels") in their marketing collateral.[ 9]
Despite the regular occurrence of both rank claim formats, it remains an open question as to whether consumers will judge an item more favorably if its rank is communicated using one format versus the other. Prior research on rankings has shown that merely being included in a third-party list can enhance brand evaluations, with superior ranks judged more favorably ([16]; [26]) both for goods and services (e.g., [17]; [18]; [22]; [23]; [32]). Yet, in spite of the recent surge of research on rankings, little is known about how consumers judge, interpret, and evaluate different rank claim formats. Specifically, because prior research has focused on only numerical rank claims, it is unclear how percentage rank claims are processed and evaluated relative to numerical ranks. The present research attempts to address this gap in the literature.
In addition to introducing the concept of percentage rank claims to the burgeoning literature on ranking effects, we demonstrate when and why numerical and percentage formats might produce different evaluations even when they make mathematically equivalent claims. Specifically, we find that the integration of two pieces of information that are routinely conveyed in rank claims—nominal value (e.g., the number 10 in a "top 10" claim or the number 20 in a "top 20%" claim) and set size (i.e., the number of items in the ranked set)—determines whether numerical or percentage formats will produce more favorable evaluations.
Next, we describe our specific predictions and the theoretical bases for them, followed by five experiments that were designed to test our proposed effect and the underlying process. We conclude with a more detailed discussion of the contributions and implications of this work for both academics and practitioners.
In this research, we provide evidence for the differential impact of equivalent numerical (e.g., Product X is in the top 10 out of 50 products) versus percentage (e.g., Product X is in the top 20% out of 50 products) rank claim formats on consumer judgments and decisions. According to the principle of description or frame invariance (e.g., [ 2]; [43]), the same position on a ranked list should produce the same evaluation irrespective of which format is used. If this were the case, there should be no difference in how the aforementioned claims (i.e., top 10 vs. top 20% out of 50 products) are evaluated, despite their distinct formats. However, others have shown that the format of information presentation affects judgments ([14]; [15]). For example, [15] document that communicating mathematically equivalent predictions as odds ratios versus percentage probabilities affects risk perceptions.
In the context of ranked lists, we posit that the size of the set in which an item is being judged (e.g., out of 50 products) will influence whether a numerical claim or an equivalent percentage claim engenders more favorable evaluations. Specifically, we expect equivalent rank claims that use a numerical (vs. percentage) format to elicit more positive evaluations when the set size is relatively small. However, when the set size is relatively large, we expect evaluations to shift such that percentage (vs. numerical) claims will lead to more positive evaluations. We attribute this shift in evaluations to format neglect, a novel bias in which consumers fail to fully account for claim format because they infer that nominal value is more important to their evaluations than set size (even though both components should be considered in conjunction). As we discuss subsequently, our theorizing enables us to delineate an exact set size that serves as the inflection point for the shift in evaluations between equivalent numerical claims and percentage claims. In addition to demonstrating how and why format neglect influences evaluations, we also demonstrate two ways in which this bias can be eliminated. Next, we summarize the extant literature on ranking effects and discuss why consumers might commit format neglect when making their judgments.
Prior research on rankings has documented several heuristics that affect consumer evaluations. For example, [18] find that rank improvements resulting in a ranked item entering a new round-number tier, such as the "top 10" (e.g., moving from 11 to 10), are viewed more favorably than equivalent rank improvements that do not span tiers (e.g., moving from 12 to 11 or from 10 to 9). This "top-ten effect" stems from a subjective categorization process that does not properly account for equivalent changes in rank. In a similar vein, [22] document a "ranking effect" in which consumers rely primarily on favorable numerical rankings within a particular list when making product decisions, without fully considering the prominence or status of the ranked list itself. For example, consumers in one study were willing to pay more for the highest-ranked automobile model in a lower-status category (e.g., the Volkswagen Passat V6) than the lowest-ranked model in a higher-status category (e.g., the Audi 4). Taken together, these findings suggest that when processing rank-related claims, consumers' evaluations and/or decisions can be biased because they do not equally consider or accord the same importance to each piece of information that they receive.
We extend this stream of research by documenting a novel bias—format neglect—that differentially influences how consumers evaluate rank list claims. If consumers were to fully take claim format into account when making their evaluations, they would need to integrate the nominal value expressed in the claim with set size. We propose that instead of doing this, consumers insufficiently factor in set size and overrely on nominal values when making their evaluation, thereby committing format neglect. In support of our position that consumers insufficiently account for set size, we next discuss a related bias—base rate neglect—that has produced evidence that decision makers often fail to fully utilize information that is both relevant and available to them at the time of their decision.
Extensive research suggests that when making judgments, consumers often give greater importance to focal and specific information about a case while neglecting other relevant general information (e.g., [ 6]; [19]; [24]; [28]; [44]). One such class of errors occurs when consumers neglect base rates. Base rate neglect occurs when participants rely more on a salient, individuating feature or characteristic and disregard or discount the more general piece of information about the population as a whole. In a seminal article, [28] showed that when participants were provided the description of a target person (i.e., specific case-related information) along with the proportion of people by profession in the total population (i.e., general base-rate information), judgments were not significantly influenced by the relative incidence of these professions in the population. For example, when a target person was described as "short, slim, and likes to read poetry," participants were likely to predict that the target was a professor rather than a truck driver despite the greater incidence of truck drivers (vs. professors) in the overall population.
Consumers' failure to sufficiently account for relevant information has been demonstrated in many contexts. For instance, when making probability judgments, consumers exhibit a variation of base rate neglect called ratio bias or denominator neglect ([35]), in which they pay greater attention to the number of times a target event has occurred and fail to fully consider the number of opportunities for the event to occur. As a result, when evaluating the likelihood of randomly drawing a red jelly bean from a tray, consumers believe their chances to be higher if the tray contains 10 red and 90 white jellybeans versus 1 red and 9 white jelly beans ([30]). This bias occurs because consumers rely too much on the number of red jelly beans (the numerator) and too little on the total number of jelly beans (the denominator) in the tray.
We believe that a similar mechanism may be at play when consumers encounter rank claims. Specifically, we anticipate that consumers may insufficiently account for the set size when making evaluations because they believe this information is less important than nominal value, despite the fact that both pieces of information should be considered together to appropriately account for the rank claim's format. Set size may be perceived as less diagnostic to consumers' evaluations because it is less dynamic across items (i.e., set size is the same for each item in a set, whereas nominal value is not) and over time, which makes it appear less focal or specific to the item that is being judged.
Although our proposed mechanism of format neglect resembles base rate neglect, it also differs from previous instantiations of base rate neglect in several ways. For example, whereas denominator neglect (e.g., [30]; [35]) constitutes a case of base rate neglect in which both the numerator and the denominator vary simultaneously (1/10 vs. 10/100), format neglect occurs even when the denominator (set size) remains constant and only the numerator (nominal value) and claim format are varied. Furthermore, although prior research has implied that base rate neglect often arises from attentional oversight (e.g., [21]), we propose that format neglect is an evaluation bias. In other words, it is not necessarily the case that consumers fail to notice set size or are unable to correctly recall the set size conveyed in a claim. Rather, our contention is that consumers do not fully consider the implications of set size when producing their evaluations because they think it is relatively unimportant, resulting in format neglect.
It is crucial to note that although consumers' failure to insufficiently account for set size when evaluating rank claims might be considered an instantiation of base rate neglect, this by itself is inadequate to develop our hypotheses. In other words, although base rate neglect certainly contributes to format neglect, the two are not synonymous. Take the example of "top 10 out of 50" and "top 20% out of 50," which are equivalent numerical and percentage claims. Whereas prior research on base rate neglect might predict that consumers will rely relatively less on set size, the base rate neglect literature is agnostic as to how evaluations of "top 10" versus "top 20%" might differ when set sizes are equivalent, as may be the case in our research paradigm. Unlike denominator neglect, the values of 10 and 20% are not comparable given their different formats. Because of the inadequacy of base rate neglect alone to inform our prediction that consumers will overrely on nominal values, we draw from prior research exploring how consumers interpret and use percentages.
When evaluating rank claims of different formats, one might expect consumers to rely on processing ease. Because consumers find whole numbers more intuitive than percentages ([20]; [31]), numerical claims may be more cognitively fluent and easier to process than percentage claims. Because fluency generally increases liking ([33]), numerical claims might be expected to produce more positive evaluations than equivalent percentage claims. As such, "top 10" and perhaps even "top 30" would be preferred over "top 20%."
We suspect, however, that numerical and percentage rank claims will not differ substantially from each other in terms of processing ease as consumers are likely to be experienced at processing both types of formats. Instead, we predict that consumers will overrely on the nominal value conveyed in a rank claim (e.g., 10 in "top 10 out of 50" or 20 in "top 20% out of 50"). Our prediction is consistent with several known biases involving percentages, in which consumers rely heavily on nominal values while ignoring or underweighting other diagnostic information. For example, [ 5] showed that when evaluating the accuracy of probabilistic forecasts (e.g., 70% vs. 30% chance of occurrence), consumers erroneously judge the estimate with the higher nominal value (70 vs. 30) as being more accurate. Consumers seem to rely chiefly on the nominal value of the forecast and infer that the magnitude of this value corresponds with the forecaster's confidence in his or her prediction.
Likewise, extensive research in pricing contexts has shown that consumers overrely on nominal values when encountering percentages. For example, [ 9] found that when offered multiple discounts (e.g., 10% off followed by 20% off), consumers simply add the nominal values, leading to an overestimation (30% vs. 28%). Relatedly, [ 8] demonstrated that when evaluating promotions, consumers prefer a bonus pack over an economically equivalent price discount when both are expressed as percentages. For example, they found that consumers favor a bonus pack quantity increment of 50% over an economically equivalent price discount of 33.3% because they directly compare the nominal values of 33.33 with 50. [20] showed that when subjectively judging the distance between two numbers whose difference is expressed in percentages, consumers are influenced by the nominal values conveyed in the percentages. For example, when comparing the numbers 1,500 and 1,000, consumers judged 1,500 to be much larger than 1,000 when 1,500 was presented as being 50% more than the latter, versus if 1,000 were described as being 33.3% less than the former. Although this result may be partially a function of the different frames (i.e., "more than" vs. "less than"), the authors suggested that participants' overreliance on nominal values (i.e., 50 vs. 33.3) was also a contributing factor. Collectively, this research shows that consumers tend to overrely on nominal values when making percentage-based judgments.
Whereas many previously identified biases in the processing of percentage information have been attributed to attentional oversight or calculation complexity (e.g., [ 5]; [ 8]; [10]; [20]), we posit that consumers believe that nominal value is more important to the evaluation task than other information contained in the rank claim. As a result, consumers will overrely on nominal values when making their evaluation. Because smaller nominal values typically indicate superior ranks, we expect consumers to judge an item represented by a smaller nominal value more favorably than an equivalent (or nearly equivalent) item with a larger nominal value. Thus, consumers will react more favorably to claims using smaller nominal values regardless of format. For example, "top 10" will be judged as better than an equivalent rank of "top 20%" because the number 10 is smaller than 20.
Because numerical ranks and percentage ranks have different mathematical properties, they often require different nominal values to denote an equivalent position in a set. Furthermore, while the nominal value of a numerical rank claim is an absolute measure of an item's position in a set (e.g., top 10 out of 50), it is a relative measure in a percentage claim (e.g., top 20% out of 50). As a result, the same relative position in a set can lead to numerical claims with very different nominal values as a function of set size. For example, when expressed as a percentage claim, a product ranked in the top 20% would have the same nominal value (i.e., 20) irrespective of whether there were 50 or 200 products in its set. However, this product would have a nominal value of 10 or 40 when expressed as a numerical claim, depending on if there were respectively 50 or 200 products in the set.
Given that percentages are, by definition, proportions with respect to a set of 100, we propose that a set size of 100 will act as the inflection point for our proposed effect as this is the point where numerical and percentage values converge (e.g., top 20% out of 100 is equivalent to top 20 out of 100). In accordance with format neglect, consumers who rely relatively more on nominal value but relatively less on set size should evaluate numerical and percentage claims similarly when set size is equal to 100.
However, when an item is part of a small set with fewer than 100 items, the same objective position in the ranked list—say, 10 out of 50—will have a smaller nominal value when it is expressed numerically (top 10 out of 50) than when a percentage format is used (top 20% out of 50). Given our prior theorizing, we expect a numerical format to elicit more positive evaluations relative to a percentage format when the set size is smaller than 100. Conversely, a different pattern of effects will emerge when the set size is larger than 100. In these cases, the same objective position in the ranked list—say, 10 out of 200—will have a larger nominal value when it is expressed numerically (top 10 out of 200) than when a percentage format is used (top 5% out of 200). Thus, we propose that the effect of claim format on consumer evaluations will depend on set size. Stated formally, we hypothesize the following:
- H1a: Consumers evaluate a ranked item that is part of a set of more than 100 items more favorably when it is described with a percentage (vs. numerical) rank claim format.
- H1b: Consumers evaluate a ranked item that is part of a set of less than 100 items more favorably when it is described with a numerical (vs. percentage) rank claim format.
- H1c: Consumers evaluate a ranked item that is part of a set of exactly 100 items equally favorably when it is described with a numerical or percentage rank claim format.
While our H1a–c delineate when consumer evaluations will diverge or converge, H2 explains why these effects occur. As previously discussed, we contend that the underlying mechanism for these shifts in consumer evaluations is format neglect, a novel bias that emerges when consumers encounter rank claims. In essence, format neglect is the net result of consumers relying too much on nominal value and too little on set size. Stated formally:
- H2: Consumers rely more (less) on an item's nominal value (set size) when evaluating a rank claim because they consider it more (less) important to their evaluations.
We report findings from five experiments that document an interaction between rank claim format and set size on consumer evaluations and demonstrate that this interaction arises because of format neglect. Experiment 1 provides initial support for our main thesis (H1a and H1b) in a laboratory-controlled environment. This study also provides evidence for H2, the existence of format neglect, by showing that participants rely on nominal values more than set sizes when formulating their evaluations. Experiment 2 provides further support in favor of format neglect as the underlying mechanism and corroborates our claim that the inflection point for this effect occurs when set size is 100 (H1c). In Experiments 3 and 4, we identify interventions that can be used to debias consumers. These theoretically derived interventions also provide additional support for our proposed format neglect mechanism (H2) by demonstrating that when the importance of set size on evaluations is highlighted perceptually (Experiment 3) or cognitively (Experiment 4), the effects found in our earlier experiments are attenuated. Finally, Experiment 5 is a field experiment conducted at a cheese shop over a 12-week period. It demonstrates our basic effect in the context of actual purchasing behavior, thereby enhancing the external validity of this work.
In Web Appendix A, we report findings from four additional studies that demonstrate further robustness and generalizability of our findings, while highlighting other approaches for debiasing. Experiment 6 shows that even marketing professionals who are likely to have considerable familiarity with rank claims are susceptible to format neglect. Experiments 7 and 8 show that format neglect applies to nonmarketing communications and that claim format can even influence judgments about a student's academic performance. Experiments 8 and 9 provide evidence that debiasing may be possible if participants are forced to consider both percentage and numerical format simultaneously, either by converting one format to the other (Experiment 8) or by jointly evaluating a numerical claim and a percentage claim (Experiment 9).
In Experiment 1, we vary set size but equate the relative favorability of an item in the set, irrespective of whether participants encounter a numerical or percentage rank format. If participants do in fact rely more (less) on nominal value (set size) when making their evaluations, we should find that percentage rank claims are favored when set size is large (i.e., > 100), but also that numerical rank claims are favored when set size is small (i.e., < 100). Experiment 1 also aims to provide support for our format neglect mechanism by comparing the extent to which participants consider nominal values and set size when making judgments.
Experiment 1 was a lab study conducted with 233 students at a large public university in the U.S. (50.2% female; average age = 20.69 years, SD = 1.34) who participated in exchange for course credit. The study involved a 2 (claim format: numerical rank, percentage rank) × 2 (set size: small, large) between-participants design. Participants read a brief summary of the sales performance of a particular product, GLS, relative to the other products in its category on Amazon. Depending on set size condition, participants were informed that there were 50 products in GLS's product category (small set size) or 500 products (large set size). Participants in the numerical format conditions who were also assigned to the small set size condition learned that GLS was among the top 20 of the 50 products in its category, whereas those assigned to the large set size condition learned that GLS was among the top 200 of the 500 products in its category. Participants in the percentage format conditions learned that GLS was among the top 40% of products in its category irrespective of the set size. For the wording of stimuli in Experiment 1 and all subsequent studies, see Web Appendix B.
After reviewing the description of GLS's sales performance, participants indicated how well GLS was performing on an unnumbered sliding scale (0 = "not very well," and 100 = "very well"). On a new screen, we asked participants to explain in an open-ended text box what information they used to determine how well GLS was performing. Participants advanced to another screen where they stated how much they had considered rank information (i.e., nominal value) and the number of products in GLS's category (i.e., set size) when evaluating GLS's performance (1 = "not at all," and 9 = "very much"). Participants' answers revealed the relative importance they placed on rank information versus set size when evaluating the product. Afterward, participants proceeded to another screen where they were asked to recall GLS's rank (i.e., its nominal value) and the number of products in its category (i.e., its set size) by entering these numbers in text boxes.
A 2 (claim format) × 2 (set size) between-participants analysis of variance (ANOVA) on participants' evaluation of GLS's performance revealed a main effect of set size (F( 1, 229) = 4.09, p =.04, ηp2 =.02), such that participants in the small set conditions (M = 61.24, SD = 25.59, N = 118) evaluated GLS more favorably than participants in the large set conditions (M = 54.90, SD = 26.86, N = 115). There was no main effect of claim format (F( 1, 229) = 1.02, p =.31, ηp2 =.004). More germane to our theorizing, we also observed a significant interaction between set size and claim format (F( 1, 229) = 16.76, p <.001, ηp2 =.07). Planned contrasts revealed that among participants in the small set size conditions (i.e., 50 products), those who encountered the numerical (i.e., top 20) claim evaluated GLS more favorably (M = 66.54, SD = 19.52, N = 57) than participants who encountered the mathematically equivalent percentage (i.e., top 40%) claim (M = 56.28, SD = 29.49, N = 61; F( 1, 229) = 4.82, p <.03, ηp2 =.02). Conversely, among participants in the large set size conditions (i.e., 500 products), those who encountered the numerical (i.e., top 200) claim rated GLS's performance lower (M = 46.20, SD = 29.73, N = 56) than participants who encountered the identical percentage (i.e., top 40%) claim (M = 63.17, SD = 20.91, N = 59; F( 1, 229) = 12.85, p <.001, ηp2 =.05). These results appear in Figure 1.
Graph: Figure 1. Effectiveness of numerical versus percentage rank claims depends on set size (Experiment 1). Notes: Higher numbers indicate more favorable evaluations. Participants evaluated a product's sales performance more (less) favorably when its performance was described using a numerical rank claim versus an identical percentage rank claim if the product was a member of a small (large) set of products.
A mixed ANOVA with self-reported consideration of nominal value and set size measured within-participants revealed a main effect of measure (F( 1, 229) = 83.97, p <.001, ηp2 =.27), which indicates that participants were more likely to consider nominal value (M = 6.77, SD = 1.86) than set size (M = 5.13, SD = 2.46). None of the interactions were significant (all ps >.34). As a complement to this self-reported measure, we examined the rationales provided by participants for the evaluations they had provided. Two independent coders, blind to condition, decided whether each rationale indicated that the participant had considered the product's rank (i.e., the nominal value) during the evaluation process (1 = yes, 0 = no). They also determined whether each rationale indicated that the participant had considered the number of products in GLS's category (i.e., set size) (1 = yes, 0 = no). Coder agreement was an acceptable 84% ([29]; [40]) and differences were resolved by discussion between the coders. Consistent with our hypothesis that consumers rely more on the nominal value in a rank claim when making an evaluation (H2), 57.5% of participants' rationales indicated that they had considered the product's nominal value whereas only 22.3% of rationales indicated that they had considered the product's set size (χ2( 1) = 65.61, p <.001), and this disparity was observed across conditions (all ps <.01).
Unlike these measures, recall rates of nominal value and set size were universally high (i.e., >75%) across conditions. Taken together, these results support our proposal that consumer evaluations may be driven by perceived importance and not by recall accuracy.
Although Experiment 1 provided support for H1a, H1b, and H2, it did not test whether the inflection point for the observed shift in evaluations between numerical and percentage rank claims occurs when set sizes have 100 items (H1c). Experiment 2 aims to test H1c as well.
Experiment 2 was conducted with 784 participants (43.1% female; average age = 35.54 years, SD = 12.11) recruited through Amazon Mechanical Turk (MTurk). Participants were asked to evaluate Brand TFN, a brand whose real name had purportedly been withheld for the purpose of the study. Participants were informed that TFN had been evaluated and ranked by a consulting firm that evaluates and ranks brands across a variety of industries. Subsequently, participants received information about TFN's rank (i.e., its nominal value), using either numerical or percentage format, and the number of brands in its industry (i.e., its set size).
There were ten between-participant conditions in Experiment 2. Four of the conditions emulated the design of Experiment 1, in that the actual rank of Brand TFN was equally favorable across these conditions but claim format (i.e., numerical vs. percentage) and set sizes varied (i.e., either greater or less than 100). Those in the numerical format condition learned that TFN had been ranked in the top 10 of 40 brands (small set size) or the top 100 of 400 brands (large set size), whereas those in the percentage format condition learned that TFN had been ranked in the top 25% of 40 (small set size) or 400 brands (large set size). To test our proposition that a set size of 100 serves as an inflection point where consumers are indifferent between equivalent numerical claims and percentage claims, we added two inflection point conditions that had either a numerical format (i.e., top 25 of 100 brands) or a percentage format (i.e., top 25% of 100 brands). Based on the numbers we used, Brand TFN was in the top 25% of brands in its industry across all six of these experimental conditions.
So that we could test whether the inflection point of 100 was robust irrespective of claim favorability, we included four more inflection point conditions. The first two of these extra conditions had either a numerical format (i.e., top 10 of 100 brands) or a percentage format (i.e., top 10% of 100 brands) and were superior to the six other claims. The last two conditions had either a numerical format (i.e., top 40 of 100 brands) or a percentage format (i.e., top 40% of 100 brands) and were inferior to the six other claims. In addition to establishing the robustness of 100 as an inflection point, these paired conditions enable us to compare equivalent rank claims in which nominal value and set size remain constant and only format is manipulated. This is useful as it helps us rule out the possibility that rank format may influence consumer evaluations independent of either nominal value or set size.
After reviewing rank information about TFN, participants in all conditions were asked to indicate, in their opinion, how well TFN was performing in its industry (1 = "not very well," and 10 = "very well"), the extent to which TFN was one of the best in its industry (1 = "not one of the best," and 10 = "one of the best") and if they would consider buying the brand if they were shopping in that industry (1 = "would not consider," and 10 = "would consider"). These three questions were combined to form a single evaluation measure (α =.91).
We separated our analysis into two main parts. First, we focused on the six conditions that were equally favorable (i.e., top 25%) but in which claim format and set size were varied. Thus, we estimated a 2 (claim format: numerical rank, percentage rank) × 3 (set size: small, inflection, large) between-participants ANOVA on participants' evaluation of TFN performance. There was no main effect of claim format (F( 1, 462) =.027, p >.86, ηp2 <.001). However, results revealed a main effect of set size (F( 2, 462) = 3.81, p =.023, ηp2 =.02), such that participants in the large set conditions (M = 7.36, SD = 2.08, N = 156) evaluated TFN less favorably than both participants in the small set conditions (M = 7.85, SD = 1.78, N = 164; F( 1, 462) = 6.62, p =.01) and participants in the inflection set conditions (M = 7.79, SD = 1.49, N = 148; F( 1, 462) = 4.60, p =.03). Evaluations of participants in the small set conditions and inflection set conditions did not differ significantly (F( 1, 462) =.13, p >.71).
More importantly, we also observed a significant interaction between set size and claim format (F( 2, 462) = 4.03, p =.02, ηp2 =.02). Planned contrasts revealed that among participants in the small set size conditions (i.e., 40), those who encountered the numerical (i.e., top 10) claim evaluated TFN more favorably (M = 8.15, SD = 1.76, N = 77) than participants who encountered the equivalent percentage (i.e., top 25%) claim (M = 7.58, SD = 1.76, N = 87); F( 1, 462) = 4.07, p <.05). Conversely, among participants in the large set size conditions (i.e., 400), those who encountered the numerical (i.e., top 100) claim evaluated TFN less favorably (M = 7.06, SD = 2.39, N = 74) than participants who encountered the identical percentage (i.e., top 25%) claim (M = 7.63, SD = 1.72, N = 82); F( 1, 462) = 3.94, p <.05). These results are consistent with the findings of Experiment 1.
However, in the two equivalent inflection point conditions (when set size was equal to 100), evaluations provided by participants who encountered the numerical (i.e., top 25) claim (M = 7.75, SD = 1.52, N = 70) did not differ from evaluations provided by participants who saw the identical percentage (i.e., top 25%) claim (M = 7.83, SD = 1.47, N = 78); F( 1, 462) =.07, p >.79). These results appear in Figure 2.
Graph: Figure 2. Effectiveness of numerical versus percentage rank claims depends on set size (Experiment 2). Notes: Higher numbers indicate more favorable evaluations. Participants evaluated a brand more (less) favorably if its rank was described using a numerical claim versus an identical percentage claim if the brand was a member of a small (large) set of brands. However, at the inflection set size of 100 brands, participants evaluated the brand the same irrespective of whether its rank was described using a numerical claim or an identical percentage claim.
In the second part of our analysis, we again considered the same two inflection point conditions (i.e., top 25 of 100, and top 25% of 100), as well as the four other inflection point conditions. This resulted in three pairs of conditions with set sizes equal to 100 and the same nominal value for the numerical and percentage conditions. The nominal values in these pairs of conditions were 10, 25, and 40, respectively. We estimated a 2 (claim format: numerical rank, percentage rank) × 3 (nominal value: 10, 25, or 40) between-participants ANOVA on the evaluation of TFN. Results revealed a main effect of nominal value (F( 2, 458) = 56.41, p <.001, ηp2 =.20), such that participants in the superior (i.e., 10) nominal value conditions (M = 8.52, SD = 1.68, N = 145) evaluated TFN more favorably than those in the middle (i.e., 25) nominal value conditions (M = 7.79, SD = 1.49, N = 148; F( 2, 458) = 15.28, p <.001) and the inferior (i.e., 40) nominal value conditions (M = 6.34, SD = 2.25, N = 171; F( 2, 458) = 92.39, p <.001). Moreover, participants in the middle nominal value conditions evaluated TFN more positively than those in the inferior nominal value conditions (F( 2, 458) = 44.92, p <.001). There was neither a main effect of claim format (F( 2, 458) =.10, p >.75, ηp2 <.001) nor a significant interaction between nominal value and claim format (F( 2, 458) =.07, p >.93, ηp2 <.001). Together, this suggests that claim format does not affect evaluations when set size is 100.
Experiment 2 demonstrates that the effect of rank claim format on consumer evaluations depends on set size but also shows that this effect is absent when set size is equal to 100. Thus, this experiment corroborates our hypothesis (H1c) that a set size of 100 acts as an inflection point for our effect. In the next two experiments, we discuss interventions that can be used to debias consumers even when set sizes are smaller or larger than 100.
Whereas we have examined identical numerical rank and percentage rank claims in our experiments thus far, in Experiment 3 we test whether inferior numerical rank claims are preferred over superior percentage rank claims when set sizes are small. In addition, as we have demonstrated in multiple contexts how set sizes influence the assessments of rank claims as a function of format, hereinafter we focus on either a small or large set. This enables us to provide more focused and nuanced support for our theorizing. In Experiment 3, we use a small set, and in Experiment 4 we use a large set.
Prior research has suggested that perceptual cues can change the perceived importance of information, which in turn affects consumers' reliance on this information when making judgments. For example, [27] found that in situations in which both numbers and units are presented together, highlighting numbers (units) perceptually leads to greater reliance on one versus the other in judgments. Likewise, in the context of risk perception, Stone and colleagues ([38]; [39]) showed that when graphical formats are used to convey ratio information, highlighting the numerator or the denominator increases consumers' reliance on this information. If our theorizing is correct, then making set size seem more important through its visual presentation is likely to increase consumers' reliance on set size, which should mitigate reliance on the nominal value information and thereby attenuate format neglect. Therefore, in Experiment 3, participants encounter inferior numerical rank claims and/or superior percentage claims, but we manipulate set size importance by varying whether set size information is underlined, bolded, and presented in a larger and different-colored font than rank information.
We conducted Experiment 3 with 300 U.S. participants (54.3% female; average age = 35.29 years, SD = 9.35) recruited using MTurk. The study involved a 2 (claim format: numerical rank, percentage rank) × 2 (importance of set size information: high, low) between-participants design. Participants were asked to read a brief advertisement for a (fictional) library, Midtown Library. Participants were informed that Midtown Library was ranked in either the top 10 (numerical rank claim) or the top 30% (percentage rank claim) of the 20 libraries in its greater metropolitan area by Interlibrary Magazine. Unlike our previous studies, these claims were nonequivalent; being in the top 10 (out of 20) is an inferior claim (top 50%) relative to the percentage claim of being in the top 30% (in top 6).
In conditions with low set size importance, rank information was underlined, bolded, and presented in a red font that was larger than the other text in the claim. However, in those conditions in which set size importance was high, set size information (instead of rank information) was underlined, bolded, and presented in a larger red font.
After reviewing the advertising claim for Midtown Library, participants were asked two questions that served as our key dependent variables. Specifically, they were asked to indicate, in their opinion, how well Midtown had performed (1 = "not very well," and 11 = "very well") and how well it is likely to perform in the future (1 = "not very well," and 11 = "very well"). These two questions were combined to form a single evaluation measure (r =.83).
An assumption of this study is that underlining, bolding, and presenting a portion of text in a larger, different-colored font increases its perceived importance. To confirm this, we asked participants (on a new screen toward the end of the study) to rate the extent that performing each of these actions (underlining, bolding, using a different font color, and increasing the font size) affects the perceived importance of a portion of text (1 = "decreases perceived importance," 5 = "does not affect perceived importance," and 9 = "increases perceived importance"). After combining the four items into a composite measure (α =.87), we found that the mean perceived importance rating (M = 7.32, SD = 1.35, N = 300) was significantly higher than the scale midpoint (t(299) = 29.85, p <.001). This confirms that our visual presentation manipulation increased the perceived importance of the target item.
On a new screen, participants were asked to recall Midtown Library's rank (i.e., its nominal value) and the number of libraries in its area (i.e., its set size) by entering these numbers in text boxes. Finally, participants were asked to indicate the extent to which they considered themselves a library expert on a nine-point scale (1 = "not at all," and 9 = "very much"). Inclusion of self-reported library expertise as a covariate had no impact on our results; therefore, we will not discuss it further.
A 2 (claim format) × 2 (set size importance) between-participants ANOVA on the composite measure revealed neither a main effect of claim format (F( 1, 296) = 1.85, p > 18, ηp2 <.01) nor set size importance (F( 1, 296) =.93, p >.33, ηp2 <.01). However, we observed a significant interaction between claim format and set size importance (F( 1, 296) = 4.21, p =.04, ηp2 =.01). Planned contrasts revealed that when set size importance was low, those who encountered the numerical rank (i.e., top 10) claim evaluated Midtown Library more favorably (M = 8.78, SD = 1.88, N = 67) than those who encountered the objectively superior percentage rank (i.e., top 30%) claim (M = 8.06, SD = 1.97, N = 79); (F( 1, 296) = 5.65, p <.02). However, when set size importance was high, those who encountered the numerical rank (i.e., top 10) claim evaluated Midtown Library no differently (M = 8.15, SD = 1.71, N = 75) than those who encountered the objectively superior percentage rank (i.e., top 30%) claim (M = 8.29, SD = 1.66, N = 79); F( 1, 296) =.24, p >.62. These results appear in Figure 3. As in our previous studies, recall rates of nominal value and set size were universally high (i.e., >75%) across conditions.
Graph: Figure 3. Set size importance attenuates format neglect n small sets (Experiment 3). Notes: Higher numbers indicate more favorable evaluations. When set size importance was low, participants evaluated a library more favorably if it used an inferior numerical rank claim versus a superior percentage rank claim. However, this bias was eliminated when set size importance was high. In all cases, the library was part of a small group of libraries (set size = 20).
In Experiment 4, we manipulate set size importance by asking participants to type in the set size that they had been shown before providing an evaluation. If our theorizing is correct, cognitively reinforcing set size in this way will communicate to participants the importance of set size and should attenuate any differences we observe in the evaluations of those who encounter a numerical versus percentage rank claim. Because Experiment 3 focused on a small set context, in Experiment 4 we test the effectiveness of this proposed debiasing intervention in the context of a large set size. We also directly measure the perceived importance of nominal value and set size. We predict that the perceived importance of nominal value will exceed that of set size only when set size importance is low.
We conducted Experiment 4 with 362 U.S. participants (45.6% female; average age = 35.36 years, SD = 11.17) recruited using MTurk. The study involved a 2 (claim format: numerical rank, percentage rank) × 2 (relative importance of set size information: high, low) between-participants design. Participants were informed that they were deciding whether to invest in the Bantam mutual fund and had consulted the website FundTracker.com, which ranks mutual funds. Those randomly assigned to the numerical rank condition learned that the Bantam Fund had been ranked as one of the top 150 mutual funds in its class. Participants in the percentage rank condition learned that the Bantam Fund had been ranked as one of the top 30% of mutual funds in its class. In addition, all participants were given identical information about set size ("Number of mutual funds in the Bantam Fund's class: 500").
Participants in the high-set-size-importance condition were asked to review the information they had been provided and to enter (in a text box) the number of mutual funds in Bantam Fund's class. Those in the low-set-size-importance condition were not asked to input set size information into a text box. We expected that the act of entering set size would cognitively reinforce set size and increase importance accorded to it.
Subsequently, all participants evaluated the Bantam Fund by responding to three ten-point items (1 = "not likely to be a good investment/not likely to generate a positive return/not likely to consider purchasing," and 10 = "likely to be a good investment/likely to generate a positive return/likely to consider purchasing"), which served as our key dependent variables. These three items were combined to form a single evaluation measure (α =.89).
Finally, participants were asked to recall the Bantam Fund's rank (i.e., its nominal value) and the number of mutual funds in its class (i.e., its set size) by entering these numbers in text boxes. Participants advanced to another screen where they indicated how much they had relied on rank information (i.e., nominal value) and the number of other mutual funds in Bantam's class (i.e., set size) when evaluating the Bantam Fund (1 = "not at all," and 7 = "very much"). This question enabled us to directly assess whether the relative importance of nominal value versus set size differed across conditions.
A 2 (claim format) × 2 (set size importance) between-participants ANOVA on the composite evaluation measure revealed a main effect of claim format (F( 1, 358) = 4.08, p =.044, ηp2 =.01) but no main effect of set size importance (F( 1, 358) =.04, p >.83, ηp2 <.01). More germane to our theorizing, we observed a significant interaction between claim format and set size importance (F( 1, 358) = 9.02, p <.01, ηp2 =.03).
Planned contrasts revealed that when set size importance was relatively low, those who encountered the percentage rank (i.e., top 30%) claim evaluated the Bantam Fund more favorably (M = 8.09, SD = 1.43, N = 89) than those who encountered the equivalent numerical rank (i.e., top 150) claim (M = 7.11, SD = 2.22, N = 92); (F( 1, 358) = 12.65, p <.001, ηp2 =.03). However, when set size importance was high, those who encountered the percentage rank (i.e., top 30%) claim evaluated the Bantam Fund no differently (M = 7.47, SD = 1.90, N = 86) than those who encountered the numerical rank (i.e., top 150) claim (M = 7.66, SD = 1.76, N = 95; F( 1, 358) =.48, p >.48, ηp2 <.01). These results appear in Figure 4.
Graph: Figure 4. Set size importance attenuates format neglect in large sets (Experiment 4). Notes: Higher numbers indicate more favorable evaluations. When set size importance was low, participants evaluated a mutual fund more favorably if it used a percentage rank claim versus an equivalent numerical rank claim. However, this bias was eliminated when set size importance was high. In all cases, the mutual funds were part of a large group of mutual funds (set size = 500).
We then conducted a 2 (importance measure: rank, set size) × 2 (claim format: numerical rank, percentage rank) × 2 (set size importance: low, high) mixed ANOVA, with self-reported importance of nominal value versus set size measured within-participants. We detected a main effect of measure (F( 1, 358) = 15.39, p <.001, ηp2 =.04), which indicates that participants considered nominal value (M = 5.82, SD = 1.34) to be more important than set size (M = 5.46, SD = 1.48). There was also a significant interaction between importance measure and set size importance (F( 1, 358) = 31.61, p <.001, ηp2 =.08). Participants in the low-set-size importance condition considered nominal value (M = 6.06, SD = 1.07, N = 181) to be more important than set size (M = 5.17, SD = 1.60; F( 1, 358) = 45.61, p <.001, ηp2 =.11). However, participants in the high-set-size importance condition considered nominal value (M = 5.58, SD = 1.53, N = 181) and set size (M = 5.74, SD = 1.30) to be equally important (F( 1, 358) = 1.44, p >.23, ηp2 <.01. No other main effects or interactions were significant (all ps >.10). These results give us confidence that our cognitive reinforcement manipulation affected perceived importance in the hypothesized direction. Unlike ratings of perceived importance, recall rates of nominal value and set size were again universally high (i.e., >75%).
Our first four experiments provided extensive support for our proposed effect and the underlying format neglect mechanism. Experiment 5 tests whether the use of a numerical rank claim versus an equivalent percentage rank claim affects actual purchase behavior. In this field experiment, a product that is part of a large set (i.e., >100) is described using a numerical rank claim or a percentage rank claim. We predicted that real shoppers will purchase the product more often when it is described using a percentage rank claim versus an equivalent numerical rank claim.
Experiment 5 was conducted at a cheese shop (Beecher's Handmade Cheese) in a popular tourist location (Pike Place Market) of a large U.S. city (Seattle). At the store's walk-up counter, patrons can order prepared foods (e.g., macaroni and cheese) and/or purchase wrapped cheese blocks from a large display case containing 67 different cheeses. Next to each cheese in the display case is a 4.5-inch by 2.5-inch sign that states the cheese's name and includes other descriptive information (e.g., flavor, production process). The shop's management team allowed us to conduct a 12-week field experiment in which we varied the information provided in the display case about a particular Camembert-style cheese known as Cirrus. Cirrus was chosen for this experiment because it had recently received an award for technical excellence and aesthetic quality at the 2017 American Cheese Society competition that could accommodate our desired rank format manipulation.
The standard Cirrus sign in the store's display case (the control condition in our study) contained information about how and where the cheese was produced, along with the cheese's retail price ($9.95 per block). We created two new versions of the Cirrus sign containing numerical or percentage rank claims that were equivalent and truthful. The numerical rank claim stated, "Of the 2,024 entrants in the 2017 American Cheese Society competition, only 411 were selected to receive awards for technical excellence and aesthetic quality. We're proud that Cirrus was one of the 411 cheeses to receive an award." The percentage rank claim stated, "Of the 2,024 entrants in the 2017 American Cheese Society competition, only 20% were selected to receive awards for technical excellence and aesthetic quality. We're proud that Cirrus was one of the 20% of cheeses to receive an award."
This field experiment was conducted over 12 consecutive weeks between July and October 2017. For the first two weeks and the last two weeks of the experiment, the control sign (without any American Cheese Society rank information) was displayed. For the eight weeks in between, we rotated the numerical rank signs and percentage rank signs on Sundays, prior to the store opening, on a predetermined weekly or bi-weekly basis. In total, the no rank (control) sign, numerical rank sign, and percentage rank sign were each displayed for 28 days (i.e., four weeks). Unit sales of Cirrus, as well as overall sales of all cheeses in the display stand (for use as a potential covariate), were tracked on a daily basis.
We analyzed the field data from Experiment 5 in two different ways, with and without a log transformation, and obtained similar results. Because these data were not positively skewed (skewness =.92), we use and report the untransformed measure (values of skewness between −2 and +2 are considered normal; [13]). A one-way ANOVA of experimental condition (no rank, numerical rank, percentage rank) on the daily unit sales of Cirrus cheese returned a significant result (F( 2, 81) = 9.71, p <.001, ηp2 =.19). Compared with the no-rank condition (M = 2.39 units, SD = 1.73), significantly more Cirrus cheese blocks were sold per day when either the percentage rank (i.e., top 20%) sign (F( 1, 81) = 19.41, p <.001) or the numerical rank (i.e., top 411) sign was displayed (F( 1, 81) = 4.71, p <.04). More germane to our theorizing, however, significantly more Cirrus cheese blocks were sold on the days when the percentage rank sign was displayed (M = 4.86 units, SD = 2.49), as compared with the numerical rank sign (M = 3.61 units, SD = 1.99; F( 1, 81) = 5.00, p <.03).
In addition, we tested whether an equal proportion of total Cirrus cheese blocks was sold on the percentage-rank days compared with the numerical-rank days. In contrast, we found that a total of 136 Cirrus cheese blocks were sold on the 28 days when a percentage rank sign was displayed but only 101 Cirrus cheese blocks were sold on the 28 days when a numerical rank sign was displayed. These proportions (57.4% vs. 42.6%, respectively) differ significantly from 50% (χ2( 1) = 5.17, p =.023).
Finally, to test whether our results were observed even after partitioning out daily variation in store traffic, we conducted an analysis of covariance (ANCOVA) of experimental condition on the daily dollar sales of Cirrus cheese, with the daily dollar sales of all other cheeses included as a covariate. We reasoned that daily variation in store traffic would be captured by differences in overall cheese sales per day. This ANCOVA returned a significant effect of experimental condition on Cirrus dollar sales (F( 2, 80) = 10.11, p <.001, ηp2 =.20). The covariate (i.e., dollar sales of all other cheeses) was also a significant predictor of Cirrus dollar sales (F( 1, 80) = 9.95, p <.01, ηp2 =.11). Compared with the no-rank condition, significantly more Cirrus cheese blocks were sold per day when either the percentage rank (i.e., top 20%) sign (F( 1, 80) = 20.08, p <.001) or the numerical rank (i.e., top 411) sign was displayed (F( 1, 80) = 6.42, p <.02). Furthermore, more Cirrus cheese blocks were sold on the days when the percentage rank sign was displayed as compared with the numerical rank sign (F( 1, 80) = 3.71, p <.06).
The results of Experiment 5 indicate that equivalent numerical versus percentage rank claims influence consumers' actual purchase behavior. We found that consumers were more likely to purchase a product that is part of a large set when it was described using a percentage claim. Although this experiment represents an extreme test of our proposed effect given the large difference in nominal values between conditions (i.e., 20 vs. 411), it is important to note that these numbers were factually accurate and were obtained from the 2017 American Cheese Society competition. Our results suggest that consumers utilize available rank claim information to make real purchase decisions, and that equivalent numerical versus percentage rank claims can differentially affect consumers in a manner consistent with format neglect. Thus, we believe that Experiment 5 provides a useful demonstration of the external validity of this research.
In this research, we investigate how the claim format used to convey an item's position on a ranked list influences consumer evaluations. Although recent research examining consumer response to rankings and ranked list claims has identified several psychological factors that influence consumers' evaluation of a ranked item (e.g., [17]; [18]; [22]), it is unclear whether equivalent claims using a numerical (e.g., "top 10," "top 20") versus percentage (e.g. "top 3%," "top 10%") format will be evaluated differently. Given that both claim formats are frequently observed in marketing communications, understanding how different rank claim formats influence consumer evaluations is theoretically and managerially consequential.
Across nine experiments (including those reported in Web Appendix A) that span multiple settings and contexts (including a field study), we find converging evidence of a shift in evaluations whereby consumers respond more favorably to numerical rank claims when set sizes are smaller (i.e., <100) but more favorably to percentage rank claims when set sizes are larger (i.e., >100), even when the claims are mathematically equivalent. To better assess the magnitude and robustness of this effect, we conducted a meta-analysis of claim format and set size on consumer evaluations across our experiments, using a statistical tool developed by [25]) for single-paper meta-analyses. For Experiments 1–5, the meta-analysis revealed significant contrasts when set sizes were large (estimate =.83, SE =.20; z = 4.21, p <.001) or small (estimate =.94, SE =.21; z = 4.50, p <.001), as well as a significant interaction (estimate = 1.77, SE =.29; z = 6.16, p <.001). A second meta-analysis that included the four experiments reported in the Web Appendix A revealed similarly strong effects. See Web Appendix D for additional detail on these meta-analyses.
Despite the robustness of our findings, some caveats are in order. While both numerical and percentage ranks are used frequently, ranked lists are not available for all categories of products and services, which may limit the utility of our findings. For example, ranked lists are more popular in certain contexts (e.g., restaurants, hotels, travel locations, cities to live in or visit, universities to attend, firms to work for) and for certain offerings (e.g., cars, laptops). Furthermore, although we focus exclusively on rank claims in this research, consumers are likely to use other information to assess the relative performance of products and services. For example, they may use consumer or expert reviews or ratings in conjunction with or in place of rankings to guide their decision making. These alternative sources may limit the situations in which our findings can be directly applied. In addition, although we examined many different small set sizes (i.e., 20, 40, and 50 items) and large set sizes (i.e., 200, 300, 400, 500, 2,024) in the nine studies described in this article and Web Appendix A, we did not systematically examine set sizes that were close to the inflection point of 100 (e.g., 75 vs. 125 items). Thus, we cannot state definitively how consumers will respond to such set sizes. Finally, there may be discontinuities at certain ranks where an interaction between set size and claim format does not occur. For example, it may be the case that the numerical rank of 1 is always judged as being superior to its percentage rank counterpart, irrespective of set size, because being in the first position on a list holds special significance that attenuates our effect. Future research might investigate such moderators.
At a theoretical level, the present research makes four discrete contributions. First, whereas existing research has focused exclusively on numerical rank claims, we introduce the concept of percentage rank claims to the academic literature. Second, we elucidate how different rank claim formats (i.e., numerical vs. percentage claims) influence consumer evaluations. Our experiments indicate that even when an item's objective rank is unchanged, the use of a percentage claim format versus a numerical claim format can dramatically alter consumer evaluations. Furthermore, even inferior ranks may be evaluated more favorably relative to superior ranks depending on how they are communicated. For example, in Experiment 3, we found that a library ranked among the top 10 out of 20 libraries was evaluated more positively than a library ranked in the top 30% although the latter is objectively superior (top 6). Third, we uncover a novel bias—format neglect—that explains the shift in evaluations that we observe. We show that format neglect results from two related biases that occur when consumers evaluate rank claims: ( 1) the insufficient utilization of set size information and ( 2) the overreliance on nominal values that convey an item's rank. Fourth, by bridging the literatures on base rate neglect and the processing of percentage information, we provide insights on how consumers integrate nominal value and set size information in their evaluations and document consequences of this integration process. Next, we discuss how format neglect relates to and differs from base rate neglect.
Prior research has identified conditions under which consumers underweight general, base-rate information when making predictions (e.g., [19]; [28]). We extend this research and demonstrate that a phenomenon akin to base rate neglect—the underutilization of set size—emerges in ranking contexts. Unlike previous work on base rate neglect, in which the bias occurs because consumers neglect base rates of different magnitudes and instead treat them as if they were equivalent (e.g., [ 6]; [19]; [24]; [28]), we find that consumers make inconsistent judgments related to rank claims even when base rates (i.e., set sizes) are the same. For example, we show that consumers differentially evaluate an item ranked in the "top 10" versus the "top 20%" out of 50, in spite of the base rate (set size) being identical (i.e., 50) across claims. While prior research on base rate neglect might have anticipated that consumers would rely relatively less on set size when encountering a rank claim, this literature is agnostic as to whether evaluations of the "top 10" versus "top 20%" claims in this example would differ.
The second bias, which states that consumers will overrely on nominal values when making evaluations, is also a critical component of format neglect. According to this bias, "top 10" will be evaluated more favorably than "top 20%" because "10" is a smaller number, and small numbers are typically more favorable in the contexts of rankings. However, when set sizes are larger (e.g., 500), overreliance on nominal values predicts the opposite—a percentage claim should be judged more favorably. This is because a percentage rank claim of top 20% would correspond to a numerical rank of top 100, and "20" is a smaller number. Of course, this prediction is contingent on consumers insufficiently accounting for set size when making their evaluation. Thus, both the underutilization of set size and the overreliance on nominal values are needed for format neglect to emerge.
Our research not only explains how format neglect (i.e., the overreliance on nominal values and the underutilization of set size) causes this shift in evaluations but also reveals when such a shift is less likely to occur. As we demonstrate, the inflection point for the shift in evaluations we observe is a set size of 100 items. For set sizes smaller than 100, using a numerical rank elicits more positive evaluations compared to an equivalent percentage rank; however, for set sizes larger than 100, a percentage rank elicits more positive evaluations.
Our work also enriches knowledge in the area of numerical cognition by identifying a new context in which consumers process relatively complex forms of numerical information incompletely or inaccurately (e.g., [ 1]; [ 3], [ 4]; [ 5]; [ 8]; [ 9]; [11]; [12]; [20]; [41]; [42]). Extending prior research, which has mostly been limited to the areas of probabilities (e.g., [ 1]; [ 5]; [14]), ratios (e.g. [12]; [41]) and price discounts (e.g., [ 7]; [ 8]; [ 9]; [11]; [20]; [42]), we provide the first demonstration that consumers may be biased in their evaluations of rank claims as a result of the claim format employed. Our results show that consumers' overreliance on nominal values when encountering percentage information emerges in a context unrelated to probability or pricing.
Future work might study whether other contexts are similarly susceptible to the effects that we observe in rank claims. For example, consider research on food and restrained eating (e.g., [34]; [37]). The same information (e.g., calorie intake) can be communicated with numerical values (500 calories) or percentages (25% of daily intake, assuming a recommended daily intake of 2,000 calories). It may be the case that presenting this information in one format versus another leads to different inferences. For example, if consumers overrely on nominal values when making magnitude judgments, 500 calories may be perceived as much larger. This effect might reverse for other nutrients, for which the recommended daily intake may be lower (e.g., Vitamin C has a recommended daily intake of 80 milligrams). In addition, it may be the case that prevention versus promotion orientations or gain versus loss frames are more compatible with a particular format. We leave all of this for future research to examine.
The present work has important implications in the managerial and public policy realms as well. Given the abundance of information to which we are exposed in our daily lives, we are often unable to process it fully. Ranked lists are useful because they enable consumers to efficiently compare consumption options (e.g., top televisions, best cars). This research shows that relatively subtle features of rank list claims, such as claim format and set size, may bias how we evaluate items referenced in rank claims.
Of course, firms often aim to use rankings as a vehicle to promote the positive aspects of their products and services. This research illustrates that certain rank claim formats may at times help firms depict their products in a more favorable light depending on the set size of the item being promoted. Our findings suggest that marketers may potentially boost how consumers evaluate their products and services by merely selecting the appropriate rank claim format for a particular set size. Firms should abide by the following clear and prescriptive managerial guidelines if their goal is to maximize consumer evaluations—when set size is smaller than 100, use nominal rank claims, but when set size is greater than 100, use percentage rank claims. The effects documented in this research can be implemented by marketers in any communications that reference the rank of their products or services, including their advertising, company website, or social media.
In addition, marketers may sometimes have the ability to alter the set size used in their claims (e.g., top Chinese restaurants in SoHo vs. top Chinese restaurants in Manhattan). Given the underutilization of set size information that we observed, it may be advantageous for marketers to focus on narrow (vs. broad) sets in their communications, because nominal values (irrespective of format) are typically smaller (i.e., the rank will be superior) for narrow sets. Future research might consider this possibility.
A more charitable application of our results is that firms, public policy makers, and other entities can become better equipped to objectively convey rank information to consumers. Given the widespread use of ranked lists, as well as consumers' dependence on ranked lists to make decisions, it is important to educate consumers on how to avoid potential pitfalls in evaluating information about rankings and to draw their attention to those ranking aspects that may unknowingly be underemphasized in the evaluation process. For example, if consumers become aware of how different rank formats influence their judgments, they may be able to devise strategies to avoid falling prey to format neglect. Indeed, we identify and test several debiasing interventions that consumers and/or policy makers might implement to reduce the impact of claim format.
It is worth mentioning that, in all our experiments, we provide respondents full information about the rank claims (i.e., the nominal value and set size) and still obtain robust support for format neglect. However, marketers are not required to reveal all this information in their claims—for example, they may only provide nominal value information bereft of the set size it is drawn from. Doing so may exacerbate the effects of format neglect. Indeed, because managers can strategically decide which format to use to showcase their offerings and may have an incentive to use approaches that present their product in a seemingly better light, it is important for them to be aware of the potential ethical repercussions of engaging in such practices given the robustness of this bias. Public policy makers should also be aware that format neglect emerges even when full information about nominal value and set size is presented; thus, the use of interventions to debias consumers such as those documented in the present research may be beneficial for consumers.
More broadly, numerical and percentage formats are often used interchangeably outside of ranking contexts. Thus, our findings may be utilized by public policy watchdogs to illustrate and warn consumers about the fallacy of description or frame invariance. Increasing consumers' awareness of format neglect could potentially improve their decision making not only when evaluating rank claims but also when judging other types of information (e.g., nutritional information, financial information) that are commonly represented by both numerical and percentage formats.
Supplemental Material, DS_10.1177_0022242918805455 - Format Neglect: How the Use of Numerical Versus Percentage Rank Claims Influences Consumer Judgments
Supplemental Material, DS_10.1177_0022242918805455 for Format Neglect: How the Use of Numerical Versus Percentage Rank Claims Influences Consumer Judgments by Julio Sevilla, Mathew S. Isaac, and Rajesh Bagchi in Journal of Marketing
Footnotes 1 Author ContributionsThe authors contributed equally to this work and are listed in reverse alphabetical order.
2 Area EditorWayne Hoyer served as area editor for this article.
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
5 Online supplement: https://doi.org/10.1177/0022242918805455
6 1These eight restaurants were Aska (Brooklyn), Flowers of Vietnam (Detroit), Han Oak (Portland, Oregon), Kemuri Tatsu-ya (Austin), Rooster Soup Co. (Philadelphia), Side Chick (Los Angeles), Tarsan i Jane (Seattle), Young Joni (Minneapolis) (we accessed all restaurant websites in November 2017).
7 2We conducted a study with 216 university marketing communications professionals revealing that 96.5% of universities use numerical rank claims and 74.7% of universities use percentage rank claims to communicate rank information to constituents. For details, see Experiment 6 in Web Appendix A.
8 3See https://www.insightglobal.com/insight-global-ranked-a-top-20-best-place-to-work-in-the-nation/.
9 4See, for example, http://www.fingerlakeswinecountry.com/articles/post/hampton-inn-by-hilton-elmirahorseheads-as-one-of-top-performing-properties-by-brand/.
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By Julio Sevilla; Mathew S. Isaac and Rajesh Bagchi
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Full Disclosure: How Smartphones Enhance Consumer Self-Disclosure
Results from three large-scale field studies and two controlled experiments show that consumers tend to be more self-disclosing when generating content on their smartphone versus personal computer. This tendency is found in a wide range of domains including social media posts, online restaurant reviews, open-ended survey responses, and compliance with requests for personal information in web advertisements. The authors show that this increased willingness to self-disclose on one's smartphone arises from the psychological effects of two distinguishing properties of the device: ( 1) feelings of comfort that many associate with their smartphone and ( 2) a tendency to narrowly focus attention on the disclosure task at hand due to the relative difficulty of generating content on the smaller device. The enhancing effect of smartphones on self-disclosure yields several important marketing implications, including the creation of content that is perceived as more persuasive by outside readers. The authors explore implications for how these findings can be strategically leveraged by managers, including how they may generalize to other emerging technologies.
Keywords: natural language processing; self-disclosure; technology; user-generated content
Among the many recent changes in consumer markets, two trends have been particularly transformative. The first is the emergence of online communication as a central medium through which firms and customers interact. This medium has yielded a wealth of textual data including social media posts, online reviews, and chats that can provide firms with real-time insights into customer opinions, needs, and preferences (e.g., [64]). The second trend is the emergence of the smartphone as the dominant platform on which these communications take place. Whereas online activities were once limited to at-home or in-office sessions on personal computers (PCs), with smartphones these activities can now occur at virtually any time and place. As a consequence of these two trends, when firms analyze user-generated content today, it is increasingly likely that it was created by consumers on their smartphone rather than their PC.
In this article, we explore a question that lies at the intersection of these two trends: As consumers continue to generate content on their smartphone, might this shift be altering what consumers share about themselves—and thus what firms can learn about their customers? Across three field studies (and three replication studies) examining thousands of customer-generated posts from various contexts—as well as two preregistered experiments—we provide evidence that content created by consumers on their smartphones tends to be more self-disclosing than that created on PCs. We show, for example, that social media posts and customer reviews written on smartphones tend to be composed in a more personal, intimate linguistic style, and that consumers are more willing to admit certain types of personal information when using their smartphone, such as experiences with products that are private or embarrassing. This effect is robust across different measures (human judgments, automated measures) and different forms of disclosure (e.g., open-ended survey responses, online reviews, compliance rates with calls to action [CTAs] in web ads). Importantly, this effect has significant marketing implications; for example, the more personal and intimate nature of smartphone-generated reviews results in content that is more persuasive to outside readers, in turn heightening purchase intentions.
We also investigate the mechanisms that underlie the observed differences in disclosure, demonstrating that the greater tendency to self-disclose on smartphones arises from the combination of two factors unique to the device. The first is that the highly personal nature of smartphones—resulting from both their constant accessibility and frequent use for personal or intimate activities (e.g., texting with family and friends)—fosters distinct feelings of psychological comfort on the device that facilitate users' willingness to self-disclose. Second, the difficulty of creating content on the smaller form of the device (screen and keyboard) leads consumers to narrow their attentional focus to the task at hand, which also facilitates disclosure.
The study of self-disclosure is among the oldest in the social sciences, spanning the fields of psychology (e.g., [18]; [21]), human–computer interaction (e.g., [35]; [62]), and survey research (e.g., [27]; [65]). This topic is of growing interest to marketing researchers, who have explored how consumers' willingness to provide personal information over a computer is affected by factors such as the nature of the web interface (e.g., [31]), online privacy policies ([ 2]), and the degree of reciprocity in an interaction with an agent ([43]). Here, we follow [ 1] to define "self-disclosure" as the voluntary communication of feelings, thoughts, or other information deemed to be private and that might make the discloser feel vulnerable (see also [18]; [46]). For example, disclosure might involve expressing one's candid feelings about a service experience or admitting to incriminating consumption behaviors.
A primary focus of research in this area has been to identify situational factors that drive people's willingness to self-disclose. For example, while people tend to be inherently protective of their private feelings and thoughts, they are more willing to share personal information when they feel a greater sense of privacy in their environment (e.g., [20]; [22]; [31]) or if they perceive their particular audience as more anonymous (e.g., [34]; [57]; [61]). Likewise, the degree of psychological comfort evoked by a context can drive self-disclosure. Therapists, for example, find that patients tend to be more self-disclosing in physical environments that foster feelings of security and familiarity, or when they feel more at ease with a counterpart in conversation (e.g., [13]; [25]).
Relevant to our work is research examining how different communication modalities affect people's willingness to self-disclose, particularly in computer-mediated versus face-to-face environments (e.g., [ 8]; [32]; [35]; [62]; [65]). A consistent finding in this literature is that people are often willing to disclose more about themselves when communicating over a computer (e.g., email, instant messaging) than in person (e.g., [ 8]; [53]; [62]). While these effects have been discussed both in terms of depth of disclosure—the sensitivity of what people reveal—and breadth, or the amount revealed, a recent meta-analysis found that communicating through a computer (vs. face-to-face) has a larger effect on depth of disclosure ([53]). That is, while people may not necessarily disclose more when communicating through a computer compared to face-to-face, what they do disclose tends to be more intimate.
Although a few explanations have been proposed for why people might disclose more through computers than in person (e.g., [35]), accounts generally point to the comparative anonymity of interacting through computer screens (e.g., [62]). Specifically, when people interact in person they receive a wealth of social cues that make them more concerned about how they come across to others ([55]). This concern about others' reactions then works to reduce willingness to disclose personal or intimate information in face-to-face interaction (e.g., [11]). When one expresses oneself through a computer, however, these social cues are less salient because of the physical distance or isolation of one's audience, which can increase willingness to disclose (e.g., [35]; [54]; [65]).
While there is consistent evidence that consumers often disclose information that is more intimate or sensitive when communicating over computer-mediated interfaces relative to face-to-face, it is less clear whether depth of disclosure is influenced by the computing interface itself—specifically, smartphones versus PCs. For example, communication on both types of device involves interaction through a screen, suggesting that the salience of social cues—and in turn, users' willingness to disclose—might be similar across devices. Indeed, some limited support for this invariance has been provided by [ 3], [39], and [58], who found that when sensitive questions were first posed on a mobile device and then later on a PC (and vice versa), respondents showed high test–retest reliability, suggesting that device effects, if they exist, may be small. However, there have been no prior attempts to examine whether the use of smartphones (vs. PCs) affects degrees of self-disclosure in more complex real-world settings, such as when consumers post on social media, write reviews, or respond to open-ended questions on surveys.
In this work, we hypothesize that while smartphones and PCs share some commonalities, they differ along two dimensions that, taken together, influence consumers' willingness to self-disclose: ( 1) the extent to which consumers experience psychological comfort while on the device and ( 2) the degree of attentional narrowing arising from the form factor of the device (e.g., size). These two elements, depicted in Figure 1, form the foundation of our main hypothesis:
Graph: Figure 1. Theoretical process model showing how use of one's smartphone (vs. PC) can lead to greater depth of disclosure.Notes: The model hypothesizes two parallel causal paths of mediation: one stemming from greater focus on the disclosure at hand, and the other through feelings of enhanced psychological comfort on the device.
- H1: Consumers will tend to exhibit enhanced depth of disclosure—sharing personal feelings, thoughts, and other information deemed to be more intimate and private—when creating content on their smartphone versus their PC.
Next, we discuss the process by which we hypothesize that comfort and attentional narrowing lead to greater self-disclosure.
The first factor that we argue enhances depth of disclosure on one's smartphone versus PC is the increased psychological comfort that consumers tend to derive from their phone (e.g., [17]; [59]). For example, [41] found that after an induction of stress, participants assigned to engage in a task on their smartphone reported a greater increase in psychological comfort (and thus, greater relief from stress) than those assigned to engage in the same task on their laptop, or even an otherwise similar smartphone belonging to someone else.
The enhanced feeling of comfort associated with one's smartphone (vs. other devices) is thought to arise from a unique combination of positive, personal associations with the device ([41]). For example, whereas PCs tend to be used more for work purposes, smartphones are often relied on for texting with friends and family, watching entertaining videos, or catching up on social media updates (e.g., [47]; [56]). Moreover, given their portability, smartphones are almost always within arm's reach—in one's pocket or purse during the day, by one's bedside at night—such that consumers learn that they can rely on their smartphone to engage in these personal activities whenever and wherever they want (e.g., [14]). As a result, the device becomes a general source of comfort and security for owners ([41]).
Critically, this difference in comfort bears important implications for depth of disclosure across devices. Prior work has shown that situational factors—such as mere differences in how a website is designed—can foster enhanced feelings of privacy and security and, thus, greater willingness to self-disclose in online surveys ([31]). These results are consistent with research showing that people are more likely to self-disclose in environments that evoke positive affect ([24]), such as feelings of comfort and security (e.g., [13]; [25]; [42]). We therefore propose the following:
- H2: Consumers are more willing to share sensitive information on their smartphone (vs. PC) in part because they tend to experience greater psychological comfort while on the device.
Prior work shows that differences in form influence the process of content generation across smartphones versus PCs. For example, because it is more difficult to write on its smaller keyboard and screen, users tend to generate shorter content on their smartphone (vs. PC) when completing open-ended surveys (e.g., [10]; [39]; [66]) and writing online reviews ([40]; [50]). Given these form-factor constraints, the amount of information that people disclose—or the breadth of self-disclosure—should similarly be lower when sharing from a smartphone versus PC.
Although the smaller form of smartphones (vs. PCs) may limit the amount of information that consumers share, we argue that it has the countervailing effect of enhancing depth of disclosure, or the intimacy of information disclosed. One line of evidence consistent with this prediction comes from [40], who found that when consumers write reviews on their smartphone versus PC, they tend to use a greater proportion of emotional words (e.g., "love," "amazing"). Although expressions of emotionality do not necessarily imply more self-disclosure per se, enhanced emotionality is widely considered one of several linguistic markers of greater depth of disclosure (e.g., [28]).
The rationale for our hypothesis is as follows. A large body of research shows that when tasks are more difficult, people tend to respond by focusing more intently on the most essential aspects of the task in lieu of peripheral information (e.g., [12]; [15]; [44]). As such, the relative difficulty of engaging in activities on a smartphone due to its smaller keyboard and screen may similarly narrow users' attention to the task they are engaging in on the device. Consistent with this, research shows that smartphone users often experience "inattentional blindness" when using their device, narrowing their focus to the activity onscreen while blocking out external surroundings (e.g., [29]; [37]). As an illustration of this narrowing effect, a large-scale field study found that 46% of pedestrians who were on their smartphone failed to notice a unicycling clown passing within one meter of them ([16]).
Building on this, we propose that when engaging in disclosure of personal information on one's smartphone (vs. PC), the relative difficulty of completing the task on its smaller keyboard and screen will similarly focus users on the most essential elements of the task—sharing one's personal thoughts and feelings—and less on peripheral thoughts and cues that might otherwise inhibit disclosure. This prediction is consistent with recent work demonstrating that people asked to complete an online survey while under cognitive load (i.e., remembering names)—possibly paralleling the difficulty of generating content on a smartphone (vs. PC)—were more willing to respond to sensitive survey questions ([60]). Formally, we hypothesize,
- H3: The smaller form of smartphones (vs. PCs) narrows consumers' attention down to the communication at hand, which, in turn, enhances depth of disclosure when generating content on the device.
We report the results of five empirical studies that support the proposed effect of device use on depth of disclosure in user-generated content, as well as explore the mechanisms underlying the observed differences (Figure 1). We begin by offering large-scale field evidence for the basic effect in analyses of tweets about a variety of topics (Study 1) and analyses of online restaurant reviews (Study 2). In two preregistered experiments we then test for the causal effect of device use on self-disclosure, as well as the proposed underlying mechanisms (Studies 3 and 4). We conclude by demonstrating the real-world generality of the effect by examining consumers' compliance with sensitive CTAs in web ads across devices (Study 5).
The purpose of the first field study was to test for the proposed differences in depth of disclosure on a major social media platform, Twitter. To control for potential differences across devices in both the timing of posts and topical content, we analyzed a data set of 369,161 original tweets[ 5] that each included one of 203 "trending hashtags" within a single 12-hour period in December 2015.[ 6] The hashtags covered a wide range of topical domains—including pop culture, sports, and a terrorist attack that occurred in San Bernardino, California—which allowed us to test for the generalizability of any observed differences between smartphones and PCs.
Prior to the main analysis, the data underwent four waves of preprocessing. First, a dichotomous indicator of whether a tweet was written on a smartphone or PC was created by identifying each tweet's originating platform (e.g., a tweet written on a smartphone would be evidenced by "Twitter for iPhone," and on PC by "Twitter Web Client"); tweets originating from ambiguous or unknown sources (e.g., third-party apps) were removed, leaving 296,473 original tweets. Because we were interested in analyzing tweets generated by typical users rather than commercial networks or professional bloggers, in the second stage of preprocessing we removed 1,305 tweets with known commercial usernames (e.g., CNN) or that were posted from accounts with exceptionally large followings, which we defined a priori to be the top 1% of the distribution of followers (more than 32,978 followers).[ 7] This yielded a final data set of 293,039 tweets (59.6% originating from smartphones). Next, to allow for a within-user analysis of differences in depth of disclosure, the third stage of preprocessing involved identifying the 2,121 users from the total set of qualified users who tweeted from both a PC and smartphone.
In the final wave of preprocessing, the 203 "trending hashtags" in our data were topically categorized by human judges. To achieve this, we recruited a sample of 150 Amazon Mechanical Turk (MTurk) participants and first asked them to familiarize themselves with ten randomly assigned hashtags (of the possible 203) by clicking on a hyperlink that led them to a set of tweets that had recently included the hashtag. After participants felt familiar with the content associated with an assigned hashtag, they were instructed to indicate whether it belonged to one or more of seven possible categories: news (e.g., #CaliforniaShooting), sports (e.g., #ArmyNavy), entertainment/pop-culture (e.g., #GoldenGlobes), amusement (e.g., #ImAWreckCause), moral causes (#GenocideVictimsDay), technology (e.g., #GoogleDemoDay), and economy/finance (#futureofwork). This process yielded roughly 15 judgments for each of the 203 hashtags, and each hashtag was categorized on the basis of the grouping (e.g., "news," "sports") to which it was most frequently assigned.[ 8]
We undertook two approaches to analyzing whether tweets created on smartphones showed greater depth of self-disclosure than those created on PCs: an automated analysis of linguistic markers (e.g., use of first-person pronouns; see, e.g. [19]) as well as human assessments of the content. Each of these approaches will be described in turn.
Multiple researchers have sought to identify linguistic markers indicative of greater self-disclosure in text (e.g., [ 5]; [ 9]; [19]; [48]; [63]). To illustrate these markers, in Web Appendix 1 we provide examples of texts from each of our studies that were assessed by human judges as being high or low in self-disclosure. Consistent with prior work on the linguistic markers of self-disclosure, the examples show how texts judged to be self-disclosing tend to be accompanied by more extensive use of ( 1) first-person pronouns (e.g., "I," "me"), ( 2) references to family and friends, and ( 3) words that convey emotionality—particularly negative emotions ([28]; [45]). The presence of these common linguistic markers forms the foundation of algorithms designed to automatically detect depth of disclosure in online texts ([ 4]; [ 6]; [51]; [63]).
Drawing on this extant literature, we subjected the tweets to analysis by Linguistic Inquiry and Word Count (LIWC; [49]), which contains dictionaries for each of the aforementioned linguistic markers (first-person pronouns, references to family and/or friends, and negative emotionality).[ 9] We also examined LIWC measures for "authentic" and "analytical" writing styles. According to [49], writers using a more authentic style create texts that are more personal and vulnerable, such that higher authenticity scores point to greater depth of disclosure. Moreover, texts written in a less analytical style, as indicated by more narrative language and references to personal experiences, would also be suggestive of greater depth of disclosure.
Finally, to ensure that the LIWC analysis provided valid measures of the degree of self-disclosure in tweets, we undertook a cross-validation analysis that regressed human judgments of the depth of disclosure for a sample of the tweets on each of the six LIWC measures of disclosure (see Web Appendix 2 for a description of the method and results). The findings confirmed, for example, that tweets rated as more self-disclosing by human judges tended to include a greater proportion of first-person pronouns and references to family and friends, and were written in a more authentic—but less analytic—writing style as measured by LIWC.
In Table 1 we report the results of two statistical approaches to analyzing how the linguistic content of tweets created on smartphones differs from that created on PCs. One approach involved a set of six univariate analyses that modeled each of the LIWC dimensions of interest (e.g., use of first-person pronouns, analytical and authentic writing styles) as a function of the originating device. The second was a multivariate logistic regression that predicted the likelihood of a tweet being written on a smartphone versus PC as a function of the six LIWC dimensions simultaneously. The analyses controlled for the word count of the texts—which tends to be greater for content written on PCs versus smartphones ([40])—as well as for the hashtag categories.[10] Finally, the table reports separate results for the overall corpus of tweets as well as a within-user analysis of the subset of users who tweeted from both devices, which allowed us to better control for possible issues of self-selection across devices.
Graph
Table 1. Study 1: Univariate Least-Square Mean Differences Between Smartphone- and PC-Generated Tweets Along LIWC Dimensions and Coefficients of Multivariate Logistic Regression Modeling the Likelihood That Tweets Were Written on Smartphones (vs. PCs) as a Function of These Dimensions.
| Univariate Tests of Least-Square Means |
|---|
| Entire Corpus (N = 293,039) | Users Tweeting on Both Devices (N = 41,452) |
|---|
| Trait | PC | Smartphone | F | Pr > F | PC | Smartphone | F | Pr > F |
|---|
| Analytical style | 72.98 | 69.08 | 1,059.54 | <.001 | 70.97 | 69.54 | 126.17 | <.001 |
| Authentic style | 25.49 | 28.94 | 681.54 | <.001 | 25.68 | 29.00 | 287.06 | <.001 |
| First person | 2.23 | 3.29 | 2,742.45 | <.001 | 2.52 | 3.20 | 244.55 | <.001 |
| Family | .50 | .76 | 714.18 | <.001 | .55 | .64 | 112.70 | <.001 |
| Friends | .25 | .32 | 142.79 | <.001 | .28 | .34 | 37.07 | <.001 |
| Negative emotion | 1.52 | 1.62 | 40.73 | <.001 | 1.34 | 1.55 | 98.18 | <.001 |
| Word count | 14.70 | 13.12 | 3,816.09 | <.001 | 13.50 | 12.56 | 201.08 | <.001 |
| Multivariate Logistic Models (Criterion: Smartphone-Generated Tweet) |
| Entire Corpus (N = 293,039) | Users Tweeting on Both Devices (N = 41,452) |
| Word Frequencies | Linguistic Style | Word Frequencies | Linguistic Style |
| Trait | Estimate | Pr > χ2 | Estimate | Pr > χ2 | Estimate | Pr > χ2 | Estimate | Pr > χ2 |
| Analytical style | | | −.004 | <.001 | | | −.001 | .008 |
| Authentic style | | | .002 | <.001 | | | .003 | <.001 |
| First person | .033 | <.001 | | | .03 | <.001 | | |
| Family | .023 | <.001 | | | .01 | <.001 | | |
| Friends | .022 | <.001 | | | .02 | <.001 | | |
| Negative emotion | .005 | <.001 | | | .01 | <.001 | | |
| Word count | −.041 | <.001 | −.0036 | <.001 | −.02 | <.001 | −.02 | <.001 |
| Model Fit | Model Fit | Model Fit | Model Fit |
| LR χ2 | % Concordant | LR χ2 | % Concordant | LR χ2 | % Concordant | LR χ2 | % Concordant |
| 8,202.16 | 59.4 | 5,278.53 | 64.4 | 985.13 | 58.1 | 866.58 | 57.6 |
1 Notes: Analytical and authentic writing styles are measured as scores out of 100, whereas types of words are measured as percentages. The effects of writing styles were modeled separately because the scores are composites of some of the word-use measures.
The results provide strong initial support for H1 among the full data set of tweets as well as the subset of users who tweeted from both devices. For the full data set, across hashtag categories tweets written on smartphones tended to contain greater proportions of first-person pronouns (Msmartphone = 3.29 vs. MPC = 2.23; F( 1, 293,038) = 2,742.45, p <.001), references to family (Msmartphone =.76 vs. MPC =.50; F( 1, 293,038) = 681.54, p <.001) and friends (Msmartphone =.32 vs. MPC =.25; F( 1, 293,038) = 142.79, p <.001), and negative emotional words (Msmartphone = 1.62 vs. MPC = 1.52; F( 1, 293,038) = 40.73, p <.001). These results are further supported by differences in writing style across devices: tweets written on smartphones tended to display a less analytical but more authentic writing style (analytical: Msmartphone = 69.08 vs. MPC = 72.98; F( 1, 293,038) = 1,059.54, p <.001; authentic: Msmartphone = 28.94 vs. MPC = 25.49; F( 1, 293,038) = 681.54, p <.001). As shown in Table 1, the within-user analyses of tweets written by the same users on their smartphone and PC yielded a similar pattern of results.
Finally, we examined whether the size of these effects varied by the particular hashtag category (e.g., sports, politics). The results (reported in Web Appendix 3) suggest that while many of the aggregate results hold across the hashtag categories, there was some variance in the size and, in some cases, direction of the effects. For example, while the use of first-person pronouns and references to family and friends were consistently greater on smartphones in domains that are intuitively more amenable to self-disclosure—such as news (e.g., #CaliforniaShooting), moral causes (e.g., #GenocideVictimsDay), pop culture (e.g., #CouplesTherapy), and sports (e.g., #FIFA)—these same markers were less evident in the more impersonal domains of finance (e.g., #DiscussTheDeals) and technology (#bufferchat). These latter two categories also showed less authentic and more analytical writing styles, a reversal that is perhaps unsurprising given the conversational norms that generally surround discussions of finance and technology (e.g., a tone that is more objective than subjective).
To provide further evidence for differences in disclosure, we subjected the full set of tweets from two of the hashtag categories—2,261 tweets about the San Bernardino terrorist attack (53% smartphone), and 1,009 tweets about pop culture (56% smartphone)—to assessment by human judges. These two categories were selected so that we could test for the effect across topics that differed substantively in context and valence. We recruited an independent sample of 1,925 MTurk participants to assess up to 10 randomly selected tweets, yielding an average of 7.08 judgments per tweet. Participants were blind to both the hypothesis of this study and the originating device of the tweet.
To measure depth of disclosure in the tweets, participants rated their agreement with a set of items used in prior work (e.g., [ 7]; [33]; [63]) on a seven-point scale (1 = "Not at all," and 7 = "Very much so"):
- Self-focus: "To what extent does the writer focus on him/herself in this tweet (e.g., how he/she felt, what he/she did)?"
- Internal states: "To what extent does the writer reveal his or her personal feelings, thoughts, or opinions?"
- Personal information: "To what extent does the writer disclose personal information about him/herself?"
- Vulnerable: "To what extent does the writer disclose information that might make him/her feel emotionally vulnerable?"
- Controversial: "To what extent is the writer expressing potentially controversial statements/views?"
- Offensive: "To what extent is the writer expressing views that may be offensive to others?"
- Impulsive: "To what extent does it seem like the writer was impulsive when writing his/her tweet?"
An exploratory factor analysis revealed that these items loaded onto two dimensions of disclosure: "intimate information" (self-focus, internal states, personal information, vulnerable; α =.78) and "lack of censorship" (controversial, offensive, impulsive; α =.85). Web Appendix 1 shows examples of tweets that scored high versus low on intimacy of information disclosed.
Paralleling the analyses of the automated measures, we undertook two sets of analyses of the human assessments of the tweets. The first set included two univariate analyses that separately modeled our measures of perceived depth of intimate disclosure and lack of censorship as a function of the originating device, and the second set involved a multivariate logistic analysis that predicted the likelihood that a tweet had been written on a smartphone or a PC as a function of the perceived depth of intimate disclosure and lack of censorship (simultaneously). As with the automated analysis, each model controlled for the word-count differences between devices.
The results provide convergent validity for the central hypothesis that consumers express greater depth of disclosure when writing on their smartphone versus PC. First, tweets written on smartphones (vs. PCs) were assessed by judges as conveying more intimate information—an effect that held for the tweets about both the San Bernardino attack (Msmartphone = 3.04 vs. MPC = 2.84; F( 1, 7,854) = 34.47, p <.001) and the pop-culture topics (Msmartphone = 5.01 vs. MPC = 2.76; F( 1, 5,180) = 2,210.08, p <.001). In contrast, while tweets written on smartphones were judged as more uncensored on average (see Web Appendix 4), the effect was primarily driven by the tweets about pop-culture topics (Msmartphone = 5.73 vs. MPC = 3.71; F( 1, 5,169) = 1,297.90, p <.001), as there was no perceived difference in degree of censorship among tweets about the San Bernardino attack (Msmartphone = 4.15 vs. MPC = 4.16; F < 1). Thus, tweets posted from smartphones were consistently viewed as more intimately self-disclosing than those posted from PCs, and were less consistently seen as more unfiltered or uncensored. (The results of the multivariate logistic regression, which models the likelihood of the tweets being written on a smartphone versus PC as a function of the two human-judged dimensions of disclosure, mirror these results; see Web Appendix 4.)
The results of the first field study provide initial evidence that user-generated content written on smartphones tends to convey greater depth of disclosure than content written on PCs (H1). This effect was robust across both automated measures (e.g., percentage of first-person pronouns, references to family) and human judgments of disclosure. These results were also robust across a variety of contexts that ranged from serious breaking-news events (a terrorist attack) to frivolous online amusements (e.g., "#TheWorstSecretSantaGifts").
Although we find that the pattern of results consistently held across the hashtag categories in our data set, to further test for the robustness of this effect we conducted the same analyses reported above for three additional Twitter data sets. Two were obtained from public sources: ( 1) a corpus of 67,408 tweets about the 2018 FIFA World Cup posted on Kaggle ([52]), and ( 2) a corpus of 201,258 tweets posted about the 2016 U.S. presidential election on election day ([36]). We also analyzed ( 3) an original corpus of 18,346 tweets on hashtags covering news, sports, and amusement/entertainment on a single day in January 2017. The results of these analyses, reported in Web Appendix 5, closely replicate those reported above: Whether users were tweeting about a sporting event, election, or entertaining topic, tweets written on smartphones (vs. PCs) consistently contained greater proportions of first-person pronouns, references to family and friends, and emotional words—particularly those conveying negative affect. They also tended to have a more authentic and less analytical style.
Study 1 offered initial evidence that at least one type of user-generated content—tweets about a variety of topics—tends to contain greater depth of disclosure when written on smartphones versus PCs. One important question, however, is whether the observed differences in depth of disclosure might yield meaningful downstream marketing implications. Prior work would suggest, for example, that content written in a more self-disclosing manner would lead readers to feel a greater sense of similarity to the writer, which may result in content that is more persuasive to outside readers ([30]; see also [23]). In Study 2 we therefore tested for the robustness of the effect in a domain in which self-disclosure might have material impacts on consumer behavior: online restaurant reviews on TripAdvisor.
The data set contained a corpus of 10,185 TripAdvisor restaurant reviews written on smartphones or PCs between April 2014 and July 2017. The reviews were a random sample drawn from a larger corpus utilized in previous work ([40]) and were comparably balanced between smartphones (N = 5,097) and PCs (N = 5,088). The data included the name of the restaurant, date of the visit, and text of the review. Mirroring the approach to analysis in Study 1, we undertook two analyses to measure the depth of disclosure in the reviews. The first was to subject the texts to analysis by LIWC, which as in Study 1 yielded measures of a battery of linguistic markers of self-disclosure (e.g., first-person pronouns).
The second approach subjected the same reviews to assessment by MTurk judges who rated the reviews along two dimensions. The first was the perceived depth of disclosure, measured along two items adapted from Study 1 that were relevant in the context of restaurant reviews: "To what extent did the writer focus on him/herself in this review (e.g., how he/she felt, what he/she did)?" and "To what extent did the writer reveal his or her personal feelings, thoughts, or opinions?" (α =.63). Importantly, two additional measures were now included to capture a potential downstream consequence: "How persuasive would you find this review to be if you were considering going to this restaurant?" and "How interested would you be in visiting this restaurant?" As in Study 1, all items were measured on a seven-point scale (1 = "Not at all," and 7 = "Very much so"), with participants blind to the hypothesis and originating device of the review.
As in Study 1, we undertook both univariate and multivariate logistic regression analyses of the degree to which content written on smartphones differed from that written on PCs along a battery of LIWC measures that are suggestive of self-disclosure: use of first-person pronouns, references to family/friends, negative emotionality, and authentic and analytical styles. Again, the analysis controls for differences in word count, which was significantly higher in PC-generated reviews (Msmartphone = 69.87 words vs. MPC = 96.28 words; F( 1, 10,182) = 347.76, p <.001).
Note that because reviews are, by definition, personal—typically first-person—accounts of one's consumption experience, logically the vast majority of reviews should appear self-disclosing (at least to some extent). Still, even in this comparatively self-disclosing context, the results conceptually replicate those of Study 1. Reviews written on smartphones tended to include greater proportions of first-person pronouns (Msmartphone = 2.35 vs. MPC = 2.19; F( 1, 10,182) = 9.55, p =.002), references to family (Msmartphone =.38 vs. MPC =.32; F( 1, 10,182) = 9.55, p =.002) and friends (Msmartphone =.52 vs. MPC =.43; F( 1, 10,182) = 36.34, p <.001), and negative emotional words (Msmartphone =.75 vs. MPC =.67; F( 1, 10,182) = 7.09, p =.008). Finally, as in Study 1 smartphone-generated reviews had a less analytic writing style (Msmartphone = 64.43 vs. MPC = 65.95; F( 1, 10,182) = 9.40, p =.002), though here we did not find a difference in authentic writing style (Msmartphone = 34.63 vs. MPC = 35.07; F < 1). (Binary logistic regression analyses, presented in Web Appendix 6, yield similar results.)
Next, we undertook the same analyses as in Study 1 to test for differences in human assessments of the content. Consistent with Study 1, the results show that reviews written on smartphones (vs. PCs) were rated as containing greater depth of disclosure (Msmartphone = 4.67 vs. MPC = 4.52; F(1, 9,551[11]) = 18.10, p <.001). The results also provide evidence for a key downstream implication of this effect: reviews written on smartphones were rated by judges as being more persuasive than those written on PCs (Msmartphone = 4.97 vs. MPC = 4.74; F( 1, 9,540) = 49.40, p <.001). Finally, readers were more interested in visiting restaurants reviewed by other customers on their smartphones than restaurants reviewed on PCs (least square Msmartphone = 4.68 vs. MPC = 4.61; F( 1, 9,840) = 3.90, p =.048)—an effect that was strengthened after we controlled for valence of the review (as captured by the percentage of negative emotional words; Msmartphone = 4.69 vs. MPC = 4.60; F( 1, 9,539) = 5.74, p =.016).
Next, we conducted a serial mediation analysis (SAS Proc Calis) to test whether reviews with greater depth of disclosure in smartphone-generated content led to greater persuasiveness and, thus, greater interest in the restaurant under review. The results provided an excellent fit to the data (Bentler comparative fit index =.998; root mean square residual =.008) and, critically, supported the hypothesized model. Reviews written on smartphones (vs. PCs) contained greater depth of disclosure (bdevice → disclosure =.05; t = 4.79, p <.001); reviews containing greater depth of disclosure were more persuasive (bdisclosure ��� persuasive =.28; t = 30.06, p <.001); and more persuasive reviews heightened readers' interest in visiting the restaurant (bpersuasive → interest =.62; t = 96.67, p <.001). Finally, the model supported an overall positive indirect effect of device on interest in visiting the restaurant (total indirect effect: bdevice → disclosure → persuasive → interest =.03; t = 4.71, p <.001).
Across two field studies we provide consistent evidence that customers tend to convey greater depth of disclosure when generating content on their smartphone than on their PC—as evidenced by tweets about a variety of topics (Study 1) and by restaurant reviews (Study 2). It is worth noting that the size of the effects was somewhat smaller among the restaurant reviews (e.g., Cohen's d for human-judged depth of disclosure =.09) compared with the tweets (Cohen's d =.14)—a result that is not surprising given that, by construction, customer-generated reviews are first-person accounts of personal consumption experiences, making it harder to observe differences in degree of disclosure across devices. Nevertheless, even in this context, reviews written on smartphones still exhibited greater depth of disclosure than those written on PCs.
Study 2 also indicates that the greater depth of disclosure in smartphone-generated content carries important downstream consequences. Outside readers found reviews written on smartphones (vs. PCs) to be more persuasive, which, in turn, heightened their interest in visiting the restaurant under review. These results are broadly consistent with those of [26], who found that reviews containing a mobile indicator (e.g., a "written on mobile" label) are more persuasive to outside readers than those containing a PC indicator. They argued that this occurred because readers infer that mobile-generated reviews are more credible given the relative difficulty of writing on the device. It is important to emphasize, however, that the outside readers in our study were given no information about the device on which the content was written, such that reviews written on smartphones (vs. PCs) were rated as more persuasive based solely on their content.
Finally, it is worth noting that one possible explanation for why reviews and/or tweets written on smartphones (vs. PCs) appeared more self-disclosing is that they were composed at the same time as an event or experience, when personal feelings may have been more salient. Two results, however, argue against such a timing explanation: ( 1) restaurant reviews written on smartphones included relatively more—not fewer—references to the past (M = 6.90) than those written on PCs (M = 6.33; F( 1, 10,182) = 42.39, p <.001) and ( 2) the tweets in Study 1 were posted nearly simultaneously from smartphones and PCs. Nevertheless, because the association between smartphone use and self-disclosure remains correlational, and the underlying mechanism remains uncertain, in the next two studies we attempt to increase our knowledge by investigating the effect in a more controlled setting.
The purpose of Study 3 was twofold. The first aim was to test whether the differences in depth of disclosure observed in the first two field studies replicate in a more controlled setting wherein participants are randomly assigned to generate content on their smartphone or PC. The second aim was to test whether the greater depth of disclosure in smartphone-generated content is driven by the proposed mechanisms for the effect: first, a greater sense of psychological comfort (H2), and second, greater attentional narrowing on one's smartphone versus PC (H3).
Study 3 involved two data-collection phases. In the first phase, participants were randomly assigned to use their smartphone or PC to write about an upsetting personal experience; in the second phase, participants' descriptions of their personal experience were evaluated by an independent sample of judges for depth of disclosure. In this section, we describe each of these phases in turn.
We preregistered this study on AsPredicted.org, which included the preregistration of our predicted hypotheses as well as exclusion criteria.[12] Our final data set included responses from 715 participants from a Qualtrics panel (60% female) who were randomly assigned to complete a two-part survey on either their smartphone or their personal computer (for the complete survey instrument, see Web Appendix 7). In the first part of the survey—which served to administer the disclosure task—participants were asked to use their assigned device to describe an upsetting personal experience (in four to five sentences). The specific instructions were as follows:
Think of a topic or event in your life that made you upset (e.g., an article you read that made you angry; an argument you had with a friend that upset you). In the space below please describe what made you upset, including your thoughts and feelings about the topic or event.
After participants completed the disclosure task, they were asked to use the same device to respond to a set of scales that measured the proposed drivers of depth of disclosure:
- Psychological comfort. Participants responded to five items adapted from [41] that measured the extent to which they associated feelings of psychological comfort with the use of their assigned device (1 = "Not true at all," and 7 = "Very true"): ( 1) "Using my smartphone (PC) provides a source of comfort," ( 2) "Having my smartphone (PC) with me makes me feel secure," ( 3) "When I am using my smartphone (PC) I feel I am in my safe space," ( 4) "Just holding my smartphone (PC), no matter what I do with it, makes me feel comforted," and ( 5) "Touching or holding my smartphone (PC) makes me feel calmer." Responses to these items were averaged into an index of "psychological comfort" (α =.88).
- Attentional narrowing on disclosure. Participants indicated the extent to which they agreed with each of three statements about how they felt while writing about their personal experience: ( 1) "I drowned out my environment when writing," ( 2) "I got lost in what I was writing," and ( 3) "I felt a sense of privacy when writing" (1 = "Not true at all," and 5 = "Very true"). Responses to these items were averaged to create an index of "attentional narrowing" (α =.66).
Next, although our theory does not make direct predictions about whether consumers accurately perceive the depth of disclosure of their own writing, to explore this we asked participants to rate their beliefs about the sensitivity of the information that they shared in their descriptions. This was measured in terms of their agreement with four items (on a five-point scale): "I would hesitate to share this experience with someone I just met," "I felt I was revealing something very personal about myself when describing this experience," "The experience is a very private matter," and "There were sensitive parts of that experience that I intentionally chose not to write about." Responses to these four items were combined to form a "self-judged disclosure" index (α =.77).[13]
Finally, to control for possible factors that might additionally influence depth of disclosure across devices, we asked participants to indicate ( 1) whether they had completed the study in a private or public setting, and 2) the extent to which they were generally concerned about privacy issues on their assigned device. Responses were based on their agreement with two items on a five-point scale: "There are some things that I avoid doing on my smartphone (PC) (e.g., finance-related activities)" and "I worry a lot about the privacy of the data on my smartphone (PC)" ("general privacy concern" index; α =.83).
To measure the key dependent variable—depth of disclosure in the descriptions as perceived by outside judges—we recruited an independent sample of 649 judges from MTurk to assess up to ten randomly assigned texts written by respondents in the main study (judges were blind to both originating device and hypothesis). After reading each text, participants were asked to rate it along the same four items that were used to create the "intimate disclosure" index in Studies 1 and 2 (α =.76). We obtained three assessments for each description, yielding a total of 2,129 judgments.
As in the first two studies, to test for differences in depth of disclosure across devices we undertook analyses using both automated measures and human judgments of the texts. For the automated analysis, the 715 descriptions were subject to analysis by LIWC ([49]), from which we extracted the same set of linguistic markers of self-disclosure analyzed in the previous studies. Similar to the previous results, descriptions written by participants on their smartphones made greater use of personal pronouns (Msmartphone = 14.36 vs. MPC = 13.26; F( 1, 713) = 4.97, p =.026), contained more references to family (Msmartphone = 1.71 vs. MPC = 1.16; F( 1, 713) = 7.02, p =.008), and expressed greater negative emotionality, though this effect did not reach the a priori level of significance (Msmartphone = 5.57 vs. MPC = 5.00; F( 1, 713) = 3.29, p =.07; Wald χ2 = 3.38, p =.06). In contrast, here we did not see a significant difference in references to friends (Msmartphone =.65 vs. MPC =.62; F < 1) or in writing styles (authentic: Msmartphone = 37.72 vs. MPC = 39.70; F < 1; analytical: Msmartphone = 53.33 vs. MPC = 53.89; F < 1).
External judges' assessments of the descriptions provided more direct evidence for differences in depth of disclosure. As we predicted, participants assigned to use their smartphone to write about an upsetting personal experience created content that was rated by outside judges as more disclosing (M = 4.85) compared with content created by participants assigned to use their PC (M = 4.55; F( 1, 1,911) = 22.09, p <.001). Importantly, this effect was sustained after controlling for factors that might covary with depth of disclosure, such as the length of the descriptions as well as the age and gender of the writers (depth of disclosure: least square Msmartphone = 4.84 vs. MPC = 4.56; F( 1, 1,908) = 17.08, p <.001). The results also hold after we control for the setting in which the writers completed the study—though it is worth noting that, across conditions, 93% of participants completed the study in a personal (vs. public) place.
To investigate whether the effect of device use on depth of self-disclosure could be explained by the proposed mediation model (Figure 1), we estimated a structural path model that included the hypothesized drivers of the effect. The model hypothesized that the direct effect of smartphone (vs. PC) use on human-judged depth of self-disclosure is described by two causal paths: one in which smartphone use evokes greater feelings of psychological comfort, thereby enhancing depth of disclosure (device → psychological comfort → disclosure), and another in which smartphone use leads to more narrowed attention on the communication at hand, which also enhances depth of disclosure (device → attentional narrowing → disclosure).
We obtained maximum-likelihood estimates of the path coefficients using SAS's Proc Calis, and they supported the hypothesized causal structure. Specifically, the analysis supported the parallel positive path from smartphone (vs. PC) to psychological comfort (bdevice → comfort =.05; t = 1.98, p =.024), and a positive path from comfort to depth of disclosure (bcomfort → disclosure =.35; t = 1.79, p =.037). Likewise, the analysis confirmed a significant positive effect of smartphone (vs. PC) use on degree of attentional narrowing on the disclosure task (bdevice → attentional narrowing =.07; t = 3.05, p =.001), and a significant positive path from attentional narrowing to depth of disclosure (battentional narrowing → disclosure = 1.27; t = 3.95, p <.001). The results also showed a significant total indirect effect of smartphone (vs. PC) use on depth of disclosure through the parallel paths of attentional narrowing and psychological comfort (total indirect effect: b =.10; t = 4.61, p <.001).
We undertook two additional analyses for which we did not make a priori predictions. We first examined whether the observed differences in depth of disclosure arose for participants' own perceptions of their descriptions. Notably, the results revealed that participants assigned to write on their smartphone indeed rated their description as more disclosing (M = 2.88) than did those assigned to write on their PC (M = 2.69; F( 1, 713) = 5.22, p =.023). Thus, both outside readers and the writers themselves appeared to perceive the greater depth of disclosure of smartphone-generated content.
We next examined whether participants' general privacy concerns might influence depth of disclosure across devices. First, as might be expected, participants in the smartphone condition were more likely to agree with the statement, "There are some things that I avoid doing on my [device]; e.g. finance-related activities" compared with those in the PC condition (Msmartphone = 3.47 vs. MPC = 3.11; F( 1, 713) = 11.21, p <.001). Interestingly, however, this greater general privacy concern on smartphones did not seem to influence depth of disclosure on the device. Privacy concerns were not statistically correlated with outside judges' assessments of the depth of disclosure (Pearson r = −.01; p =.729; N = 1,913) but were positively correlated with participants' own perceptions of the depth of disclosure in their accounts (Pearson r =.15; p <.001; N = 715). As a result, inclusion of privacy concerns as a covariate did not alter the effect of device on external judgments of disclosure (least square Msmartphone = 4.86 vs. MPC = 4.54; F( 1, 1,910) = 22.84, p <.001), but it did temper the effect of device on self-perceptions of disclosure (least square Msmartphone = 2.98 vs. MPC = 2.72; F( 1, 712) = 2.85, p =.051).
Study 3 provides a conceptual replication of the findings of the two field studies, showing that participants randomly assigned to write about an upsetting personal experience on their smartphone generated content that revealed greater depth of disclosure than did those assigned to use their PC. This effect was observed not only in terms of the automated measures and external human judgments analyzed in the prior studies, but in terms of the writers' own perceptions of depth of disclosure in their descriptions. The results also show that the effects observed in the prior studies generalize to another domain of user-generated content of potential interest to firms: a context wherein consumers are asked to reveal private information in an open-ended survey. Finally, and most importantly, the results of Study 3 provide initial evidence in support of the proposed mechanisms underlying the effect. As we hypothesized, the greater depth of disclosure in smartphone-generated content was driven by a greater sense of psychological comfort on the device (H2) as well as more narrowed attention on the disclosure task at hand (H3).
In a second experiment, we explored whether the findings of the first three studies generalize to a context that is often of importance to marketers: customer compliance with requests for private or sensitive information. In Study 4 we therefore asked participants to describe a private and potentially embarrassing product experience, with a focus on whether those using their smartphone (vs. PC) would be more willing to comply with the request rather than opt out of doing so.
An independent sample of 1,389 participants was recruited from a Qualtrics panel (71% female) and randomly assigned to complete the study either on their smartphone or their PC. We preregistered the study on AsPredicted.org,[14] which included the preregistration of our predicted hypotheses as well as the same exclusion criteria as in Study 3. The general procedure was similar to that used in Study 3, involving two data-collection phases. The first phase asked participants to disclose a product that they had purchased which they considered to be private and potentially embarrassing (by responding in an open-ended text box) and then to describe their experience with that product (in a second open-ended text box on the subsequent screen). The specific instructions were as follows:
This survey is part of a market research study aimed at helping companies better understand consumers' experiences with different types of products. Think of a product that you use, or have used in the past, which you consider to be private and possibly embarrassing—that is, something that you might not want others to know about. For example, perhaps you have purchased products to prevent hair loss—or perhaps you sometimes buy certain foods to binge on when you're feeling sad. In the spaces below, please first indicate what this product is (e.g., "weight loss supplement"). Then, please tell us about your experience with this product, such as what led you to buy it, and how you feel about using it.
The key dependent variables of interest were ( 1) whether participants were willing to disclose such a product or whether they opted out of doing so (e.g., by writing "N/A") and, if they complied with the request for information, ( 2) the depth of disclosure expressed in their description of the product (which we measured in phase 2). Finally, participants were asked to use their assigned device to respond to the same items as those used in Study 3 to measure the proposed mechanisms—their psychological comfort (α =.86) and attentional narrowing on the task at hand (α =.68)—as well as a series of questions measuring possible covariates and demographic information (see Web Appendix 8).
For phase 2 of the study, we first identified the subset of 975 participants who did disclose a private product (and met the preregistered inclusion criteria),[15] and we then recruited a separate sample of 374 MTurk judges to rate the descriptions on two dimensions. The first dimension was the sensitivity of the product described, which was measured based on judges' agreement with the following items (1 = "Not at all," = 7 = "Very much so"): "This product was..." ( 1) "very private," ( 2) "potentially embarrassing," ( 3) "not one that would be discussed with a stranger," ( 4) reveals something personal about the user, and ( 5) "very intimate." Responses to these items were combined into an index of "product sensitivity" (α =.92). The second dimension was the depth of disclosure in the descriptions, which was measured using the same items as in Study 3 (α =.77). Thus, while our main analysis compared across devices the rates of compliance (or participants' willingness to disclose the personal product vs. opting out), this second phase enabled us to compare the depth of disclosure conveyed in participants' product descriptions, conditional on their having disclosed one.
Across conditions, 134 (11%) of all participants refused to comply with the request to describe a private or sensitive product, as indicated by responses such as "none" (73%) or "I do not buy these types of products" (27%). More importantly, as we predicted, rates of compliance differed between conditions. Participants were significantly more likely to disclose a private or embarrassing product purchase when responding on their smartphone (93%) versus their PC (86%; likelihood-ratio χ2 = 13.35, p =.003). This effect still held after controlling for three measured factors that may have incidentally contributed to differences in compliance across devices: the gender of participants, their age, and whether the study was completed in a public place (least square Msmartphone =.94 vs. MPC =.85; likelihood-ratio χ2 = 23.14, p <.001).
We next tested whether there were differences in the depth of disclosure in the product descriptions among the subset of participants who were willing to disclose such a product. Again, the results show that smartphone-generated content was assessed by outside judges as more self-disclosing than that generated on PCs. Specifically, products disclosed by participants on their smartphone were rated by outside judges as more sensitive in nature than those disclosed on PCs (Msmartphone = 4.54 vs. MPC = 4.35; F( 1, 4,803) = 4.54, p <.001), and the accompanying descriptions of their products were also rated as conveying greater depth of self-disclosure (Msmartphone = 4.83 vs. MPC = 4.70; F( 1, 4,741) = 13.41, p <.001).[16] In contrast, unlike in Study 3, where all participants complied with the writing task, members of this self-selected group of participants were unaware that they were being more self-disclosing when writing about their habits, as here we found no significant difference in self-perceptions of depth of self-disclosure between devices (Msmartphone = 3.29 vs. MPC = 3.31; F < 1).
To test for the proposed drivers of differences in depth of disclosure, we undertook two structural equation analyses: one for the decision to comply with the disclosure task (vs. opting out) and another for the depth of disclosure in the product descriptions provided by those who did comply. In this particular study, given that one of the proposed mechanisms—degree of attentional narrowing on the disclosure task—was meaningful only for participants who actually agreed to disclose, our analysis of participants' willingness to comply examined only the mediating effect of the degree of psychological comfort associated with the device. Our analysis for differences in depth of disclosure among participants who did comply, in contrast, examined the full set of mediators (as in the prior studies).
For the analysis of differences in rates of compliance, path model estimates derived using SAS's Proc Calis supported the theorized mediating effect of psychological comfort. As we predicted, smartphones (vs. PCs) were associated with greater psychological comfort (bdevice → comfort =.12; t = 4.25, p <.001), and greater psychological comfort was associated with a higher probability of compliance (bcomfort → compliance =.08; t = 3.48, p <.001). Critically, we also observed a significant indirect effect of device on compliance through comfort (b =.01; t = 2.68, p =.007). Thus, the greater willingness to comply with a request for private information was in part driven by the enhanced feeling of psychological comfort that participants associated with their smartphone versus PC (H2).
Next, to test for the proposed drivers of depth of disclosure as in Study 3, we collected MTurk assessments of the product descriptions provided by the subset of participants who disclosed the private product. As in Study 3, we estimated the model in which depth of disclosure was driven by two parallel paths: one in which smartphones yield greater psychological comfort (device → comfort → self-disclosure) and another in which smartphones induce greater attentional narrowing on the disclosure task (device → attentional narrowing → self-disclosure). For this study, we measured depth of disclosure as a latent construct with two manifest measures: sensitivity of the product described and sensitivity of the information conveyed in the product description (r =.54; α =.70).[17]
The model provided an excellent fit to the data (Bentler comparative fit index =.998; root mean square residual =.000) and offered support for the hypothesized causal structure. Consistent with H2, we find support for positive path coefficients leading from smartphone (vs. PC) to psychological comfort (bdevice → comfort =.12; t = 8.37, p <.001), and from psychological comfort to depth of disclosure (bcomfort → disclosure =.39; t = 5.43, p <.001). Likewise, consistent with H3, we find support for positive path coefficients from smartphone (vs. PC) to attentional narrowing (bdevice → attentional narrowing =.03; t = 1.91, p =.028), and from attentional narrowing to depth of disclosure (battentional narrowing → disclosure =.10; t = 6.13, p <.001).
To test for the real-world generalizability of the results of Study 4, in the final field study we again examined whether consumers were more willing to provide sensitive information on their smartphone versus PC. To do this, we collaborated with the advertising technology company Taboola (https://www.taboola.com), which provided data on the daily performance of 19,962 CTA web ad campaigns that were run on both smartphones and PCs between November 2018 and August 2019. The data included a total of 631,013 observations representing each of the dates on which the 19,962 campaigns were run.
Call-to-action ads are of interest because they request personal information from consumers to further interact with the firm or brand. The ad campaigns spanned 22 different categories (e.g., finance, music, family) and varied widely in the sensitivity of the product/service advertised (e.g., online games, fitness products, financial services) as well as the depth of personal information that was being requested (e.g., email addresses, estimated credit scores). Web Appendix 9 provides examples of the CTAs for several ad categories. For each ad campaign run on a given date, the data included information about the platform on which the ad was targeted (smartphone, PC), the ad category to which it belonged (e.g., health, dating, lifestyle), the number of consumers who were presented with the ad (i.e., impressions[18]), and the number of consumers who complied with the information request in the ad (i.e., conversions). To test whether consumers were more responsive to CTAs eliciting personal information on their smartphone or PC (H1), we calculated the conversion rate (i.e., the number of conversions divided by the number of impressions) for each ad category on each platform, which served as our main dependent variable.
The campaigns included in the data reached extremely large audiences, achieving 75.8 billion impressions over the ten-month period of study. As is commonly the case with web ads, however, the rate at which consumers clicked on the ads and provided all the requested information—recorded as a conversion—was quite low (e.g., [38]), with 84.68% of all ad-dates reporting no conversions on either PC or smartphone. To account for this highly skewed distribution of responses, we subjected the conversion rates across devices to a series of negative binomial regressions that modeled conversion rates as a function of ( 1) the device on which the ad was administered and ( 2) fixed effects that controlled for variance in conversion rates across ad categories, as well as interactions between the device and ad category.
The results of these analyses, summarized in Web Appendix 10, robustly support H1. Consistent with the results of Study 4, consumers were more likely to comply with requests for personal information in ads when using their smartphone versus PC. Specifically, CTA ads on smartphones had an average conversion rate of.28%, whereas those on PCs had an average conversion rate of.02% (t = 12.48, p <.001; see Web Appendix 10). This effect was larger when analyzing just the subset of ad dates for which there were nonzero conversion rates. Here, the average conversion rate on smartphones was.54% relative to.03% on PCs (t = 25.89, p <.001). While the difference in the conversion rates is small in absolute terms, the.26% increase in conversions on smartphones (vs. PCs) translates to millions of customer responses when applied to the billions of impressions achieved across the campaigns.
One natural concern with this analysis is that the higher conversion rates on smartphones may have accrued to factors other than users' willingness to self-disclose per se, such as differences in the contexts in which the devices are used or how the ads are displayed/formatted. If consumers are indeed more willing to self-disclose on their phone (vs. PC), differences in conversion rates should be larger among ad categories where the information elicited is more personal or sensitive in nature, such as ads for dating sites, financial services, and health products; in contrast, conversion rates should be more comparable for categories that are less sensitive, such as music, food, and news. We therefore examined how differences in conversion rates varied by ad category.
To test the association between the sensitivity of the ads and compliance across devices, Taboola provided ad content for a random sample of 1,061 ads from each of the 23 categories that, importantly, included descriptive titles for each ad (e.g., "Calculate Your Maximum Social Security Benefit Instantly" from the "finance" category). We then recruited 686 MTurk participants to assess (on a seven-point scale) up to ten of the ad titles along three correlated items measuring the personal nature and sensitivity of the ad: "This ad is about a very sensitive/private topic," "If I responded to this ad I would expect to be asked a number of personal questions (e.g., my address, finances)," and "If I provided information requested in this ad I feel I would be disclosing something intimate or private about myself." We averaged across these items to create a "personal/sensitive nature" index for each ad title (α =.70).
We then estimated the following negative binomial regression, which modeled the observed conversion rates for each of the individual ad campaigns as a function of device, judged sensitivity of each ad category (from which ads were sampled), and the interaction between sensitivity and device:
Graph
where CRijk is the observed conversion rate for ad campaign i from ad category j on device k, Dk is a dichotomous indicator for device k (smartphone = 1, desktop = −1), Sij is the judged sensitivity of ad campaign i from category j, and b0,..., b3 are empirical parameters. The key parameter of interest is the coefficient of the interaction between device and sensitivity (Dk × Sij).
The results, presented in Web Appendix 11, confirmed that consumers were more likely to comply with calls to action in ad categories that were more personal and/or sensitive on a smartphone versus a computer. Specifically, we find a positive effect of smartphone (vs. PC) (b = 1.98; t = 4.27; p <.001), as well as a negative main effect of perceived ad sensitivity (b = −.34; t = −2.53; p =.011) on conversion rates. More importantly, we find a positive interaction between device and ad sensitivity on conversion rates (b =.40; t = 2.78; p =.005), suggesting that, as hypothesized, the tendency for conversion rates to be higher on smartphones (vs. PCs) is enhanced for ads that are more personal or sensitive in nature.
As an illustration of this interaction effect, the three categories judged to be the most sensitive on average—dating (M = 4.64), financial services (M = 3.88), and health (M = 3.56)—were associated with the largest differences in average conversion rates between smartphones and PCs (dating: Msmartphone − PC diff. =.67%, p <.001; financial: Msmartphone − PC diff. =.51%, p <.001; health: Msmartphone − PC diff. =.40%, p <.001). In contrast, categories judged to be among the least sensitive in nature—music (M = 2.13), fashion (M = 2.87), and pets (M = 2.93)—showed very limited compliance overall, and no statistically significant difference between smartphones and PCs (music: Msmartphone − PC diff. = −.0004%, p =.164; fashion: Msmartphone − PC diff. = −.002%, p =.096; pets: Msmartphone − PC diff. = −.008%, p =.514; see Web Appendix 12).
In recent years, smartphones have increasingly come to supplant personal computers as the major medium through which consumers provide and share information. In this work we offer evidence that this change has served to alter not only how consumers communicate but also what they share: across five experimental and field studies, we find that consumers tend to be more self-disclosing on their smartphones than their PCs. We find this effect robustly across ( 1) multiple domains of user-generated content (e.g., six field data sets, open-ended survey responses), ( 2) different forms of self-disclosure (e.g., self-generated posts, admissions of embarrassing information), and ( 3) different measures of disclosure (automated measures, external human judgments, the writers' own perceptions of depth of disclosure, and compliance with sensitive CTAs in ads). We also find evidence for the two proposed parallel drivers of this effect, showing that enhanced disclosure on smartphones (vs. PCs) is driven by greater feelings of psychological comfort that consumers associate with their phone and the relative difficulty of generating content on the device, which narrows attention on the disclosure task at hand (and away from peripheral cues or thoughts).
One question that remains is whether consumers are generally aware that they disclose differentially across their devices; for example, when consumers use their phone to tweet, are they aware that they may be revealing more about themselves? While in Study 3 participants reported being more disclosing after completing the disclosure task on their phone versus PC, it is not clear whether they were aware of a general effect of this device. To examine this issue more directly, we recruited an independent sample of 544 MTurk participants and asked them to indicate their beliefs about their willingness to disclose across devices.[19] We also administered similar scales to those used in Studies 3 and 4 to measure the degree to which participants believed that they experience greater psychological comfort and attentional narrowing on their smartphone versus PC. All responses were this time rendered on comparative scales, which were anchored at 1 ("Much more true of my laptop") and 5 ("Much more true of my smartphone"), with 3 ("Equally true of my laptop and smartphone") serving as the midpoint.
The results suggest that consumers seem to be generally aware of the differences in self-disclosure observed in our studies. Respondents indicated that they tend to be more self-disclosing when creating content on their smartphone compared with their PC (Mdisclosure = 3.13; t(545) = 3.42, p <.001). Furthermore, participants reported that they generally associate stronger feelings of psychological comfort (Mcomfort = 3.69; t(545) = 21.36, p <.001) and tend to feel a greater attentional narrowing in activities (Mattentional narrowing = 3.52; t(545) = 16.99, p <.001) when using their smartphone versus PC. Thus, consumers seem to be at least somewhat aware of the distinct psychological experiences they undergo on their smartphone versus PC, as well as the differences in the types of information they tend to disclose across devices.
The finding that consumers are more willing to self-disclose on their smartphone (vs. PC)—and the identified mechanisms that give rise to it—hold several actionable implications for marketers. Perhaps the most direct is that if a firm wants to obtain sensitive or personal information from consumers, it should target them on their smartphone rather than their PC. We found evidence for this in Study 4, for example, where participants who were asked to admit to purchasing a private or embarrassing product were 6% less likely to do so when asked on their PC than when asked on their smartphone. While small in absolute terms, this difference would be quite meaningful for any firm relying on consumer self-reports to gauge consumption rates. Further evidence is provided from the large-scale field data in Study 5, where consumers were more compliant when ads requesting information were targeted on their smartphone (vs. PC)—a difference that was especially large for requests that were more sensitive in nature. Again, while the absolute size of this effect may seem small (Msmartphone − PC diff. =.19%; Msmartphone =.28% vs. MPC =.09%), when applied to the billions of impressions produced by the ad campaigns, this difference in compliance rates potentially translates to millions of additional customer leads for firms.
The finding that social media posts and open-ended survey responses produced on smartphones were more self-disclosing also suggests that smartphone-generated content may offer more diagnostic or accurate insights into consumer preferences. Consistent with this, in Study 3 participants self-reported that they had disclosed information that was more private and personal on their smartphone than did participants on their PC. Building on this result, future work might explore whether the observed effects generalize to domains of disclosure with a measurable benchmark of "truth" or accuracy of information. For example, might consumer preferences disclosed on smartphones (vs. PCs) be more predictive of market outcomes?
Finally, we found that the greater depth of disclosure in smartphone-generated content has the major downstream consequence of being more persuasive to outside readers. Study 2 demonstrated that restaurant reviews written on smartphones were perceived as 5% more persuasive on average than those written on PCs and, when positive, were associated with a 2% increase in readers' interest in visiting the restaurant. The effect of device use was even larger on the high extremes of the perceived persuasiveness and interest scales. Reviews written on smartphones were 33% more likely to receive a "7" on a seven-point scale of persuasiveness (raw difference +4.5%) and 28% more likely to receive a "7" on a seven-point scale of interest in visiting the restaurant (raw difference +3.9%). Thus, firms could identify which reviews will be more persuasive to outside customers by simply identifying their originating device.
In our research we showed that content produced on smartphones (vs. PCs) tends to be more self-disclosing because of two drivers: ( 1) the tendency for smartphones to be associated with heightened psychological comfort and ( 2) the narrowing of attention that often arises while completing a task on the device. We conceptualize these drivers as two independent factors with a relative influence that likely varies across consumers as well as contexts. For example, consumers vary in the degree to which they derive psychological comfort from their phone as a function of whether they use the device more for work versus hedonic purposes ([41]). Nevertheless, according to our theory, consumers who derive little psychological comfort from their phone might still be more self-disclosing when generating content on the device because of the narrowing of attention that tends to arise when writing on its smaller keyboard and screen. Similarly, one might conjecture that attentional narrowing would not arise when performing a simple task on one's phone, such as clicking a multiple-choice button when responding to survey questions; in such cases, however, consumers might still show enhanced depth of disclosure due to the feelings of comfort that often arise on the device.
These two paths suggest actionable levers by which firms might influence consumers' willingness to self-disclose. For example, if firms wish to encourage consumers to be more self-disclosing in survey responses, our findings suggest that they should design surveys in a way that enhances respondents' feelings of psychological comfort—such as by exposing them throughout the survey to images or even sounds that are comforting or relaxing. From a consumer-welfare perspective, the mechanisms also have implications for consumers who want to avoid being too self-disclosing in certain contexts; for instance, they might consider eschewing their phone for their laptop when writing a work email or when responding to long, open-ended survey questions.
One suggested area for future research is exploring whether the observed effects extend to newer technologies. Here, we argued that the small size of smartphone screens has an attention-narrowing effect that heightens consumers' willingness to self-disclose when generating content. Given that some new technologies—such as smart watches—have screens so small that they render typing extremely difficult, we predict that attempting to write on such small devices may prevent consumers from disclosing altogether. Another emerging technology to consider is voice-enabled assistants such as iPhone's Siri or Amazon's Alexa. Would the observed effects on smartphones versus PCs still hold if consumers used voice commands instead of written responses to disclose personal information? One might predict, for example, that sharing personal information verbally—rather than writing it—might evoke the psychological experience of face-to-face interaction, which (as noted previously) has been shown to reduce disclosure relative to written communication through computers (e.g., [32]). We believe that these are important and intriguing questions that merit future investigation.
Future research could also further explore the role that the psychological comfort associated with one's smartphone plays in enhancing depth of disclosure on the device. For example, incidental experiences that precede disclosure—such as the extent of comfort derived from browsing certain online content on the device—may influence users' subsequent willingness to disclose in the short term, regardless of the device on which they are responding. In this case, firms may want to expose consumers to certain types of comforting or relaxing content prior to eliciting sensitive information from them. Moreover, to the extent that this psychological comfort arises from one's established associations with the device ([41]), consumers might be more willing to respond to personal or sensitive questions on their own smartphone than on an otherwise similar phone belonging to someone else. This bears important implications for medical professionals, for example, who have begun to increasingly administer surveys to patients using in-office tablets. Our findings suggest that medical professionals might consider sending sensitive survey questions to their patients so that they can respond instead on their personal smartphone.
Future research could also test for boundaries of the types of information that consumers are willing to reveal on their smartphone versus PC. While we found evidence for the effect among some highly sensitive disclosures (e.g., providing one's bankruptcy history or issues with substance abuse in Study 5), many of the disclosures examined in our studies did not involve highly sensitive information—for example, descriptions of restaurant experiences (Study 1). While we do find that the effect extends to admissions of embarrassing or private purchases (Study 4), it is possible that the observed differences across devices may disappear when it comes to disclosures that could be more personally harmful, such as sharing one's social security number or financial information.
Supplemental Material, jm.19.0560-File003 - Full Disclosure: How Smartphones Enhance Consumer Self-Disclosure
Supplemental Material, jm.19.0560-File003 for Full Disclosure: How Smartphones Enhance Consumer Self-Disclosure by Shiri Melumad and Robert Meyer in Journal of Marketing
Footnotes 1 Associate EditorMarkus Giesler
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 Online supplement: https://doi.org/10.1177/0022242920912732
4 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
5 1These data come from a master corpus of 9 million tweets that were scraped in early December 2015. This same master corpus was the basis for a separate subset of data used in [40] to test a different theory (using different measures).
6 2A preliminary analysis confirmed that there were no significant differences in the average time of posting between devices across the hashtag categories.
7 3Given their small representation in the data (1%), the statistical results are robust to the exclusion of commercial accounts.
8 4For example, if a hashtag was judged by 62% of MTurk judges as focusing on news and 38% as focusing on finance, it was categorized as a "news" hashtag for the purpose of analysis.
9 5Exploratory analyses of other LIWC categories indicated that tweets written on smartphones (vs. PCs) also tended to use relatively more informal language (i.e., netspeak, nonfluencies, swear words). While a more informal writing style might also point to greater self-disclosure on smartphones, we focus on the four linguistic markers that have been validated in prior work (e.g., [51]).
6Models estimated controlling for neither the hashtag category nor word count yielded similar results.
7Differences in degrees of freedom across these analyses arose because of missing responses to some scale items for certain reviews.
8The data set was originally composed of 1,040 descriptions that were subjected to two preregistered exclusion criteria: descriptions were excluded if they (1) contained content unrelated to the task (e.g., nonsense words, text copied from unrelated sources) and/or (2) were either too brief (<15 words) or too poorly written to be assessed by human judges. Of all the open ended-responses, 229 (22%) were excluded on this basis. An additional 77 responses were deleted for failing two embedded attention checks. Preregistration is available at http://aspredicted.org/blind.php?x=d7vx69.
9The original survey also included the item "The experience I wrote about reveals something about who I am as a person." An exploratory factor analysis, however, indicated that this item loaded onto a second dimension that was unrelated to the other items, and we therefore excluded it from the index.
10Preregistration is available at http://aspredicted.org/blind.php?x=kr7j3g.
11Of the 1,197 respondents who disclosed a private product, 222 provided narratives that failed the preregistered criteria (e.g., too short for textual analysis), leaving 975 descriptions for analysis.
12The effect of device on depth of disclosure is strengthened when we controlled for the length of the description, which tended to be longer on PCs (Msmartphone = 4.85 vs. MPC = 4.69; F(1, 4,740) = 21.52, p <.001).
13We obtain similar path-model results when the two measures of disclosure are modeled separately.
14We counted an impression in this data set only if an ad was successfully served to viewers (e.g., an ad blocked by an ad blocker would not register as an impression).
15To measure participants' self-reported disclosure behavior, we constructed a new index based on four items: "When I use this device to post content on social media, chat with friends, etc. I tend to be..." (1) "less censored," (2) "less inhibited," (3) "more honest in what I write," and (4) "more disclosing of what I really think or feel" (α =.87).
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Record: 78- Gamified Information Presentation and Consumer Adoption of Product Innovations. By: Müller-Stewens, Jessica; Schlager, Tobias; Häubl, Gerald; Herrmann, Andreas. Journal of Marketing. Mar2017, Vol. 81 Issue 2, p8-24. 17p. 5 Diagrams, 1 Graph. DOI: 10.1509/jm.15.0396.
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Gamified Information Presentation and Consumer Adoption of Product Innovations
The format that firms use to convey information about product innovations is paramount to the market success of these innovations (Noseworthy and Trudel 2011;
Reinders, Frambach, and Schoormans 2010; Townsend and
Kahn 2014; Wood and Moreau 2006). However, traditional
information presentation formats often fail to stimulate consumers’ curiosity or to effectively communicate the advantages of these innovations (Wood and Lynch 2002).
To overcome these limitations, firms have started to use games to present information about product innovations. For
instance, Nike used a basketball video game called Nike Shox to convey information about its new “Shox” line of athletic shoes (Bogost 2007). In this application of gamified information presentation, consumers had the opportunity to configure a pair of shoes for themselves and to then experience the product’s characteristics in the video game.
We propose that presenting information about a product innovation in the form of a game, which we refer to as “gamified information presentation,” ignites two parallel psychological processes by which it promotes consumer innovation adoption. The first process operates through an increase in consumer curiosity about the innovation because consumers become more playful as they participate in the game. The second process operates through a greater perceived advantage of the innovation relative to competing products, which is driven by the enhanced perceived vividness of presentation associated with conveying product information through the game.
We present evidence from seven studies (including two field experiments) that provides support for this theorizing by showing that gamified information presentation systematically promotes innovation adoption and that it does so through the two proposed psychological processes. We demonstrate this effect across multiple product domains and with different implementations of gamified information presentation, indicating the robustness of the effect and the generalizability of the findings.
This work primarily contributes to the emerging literature on the effects of incorporating games into the shopping process (Lounis, Neratzouli, and Pramatari 2013; Schlager et al. 2014). Prior research in this area has considered games as tasks that prompt consumers to take specific actions, mainly by creating enjoyable product and brand experiences (e.g., Kuo and Rice 2015; Van Reijmersdal, Rozendaal, and Buijzen 2012). By contrast, the present research shows that using games as vehicles for conveying information about product innovations promotes consumer adoption of the latter and reveals the psychological forces that drive this effect.
The remainder of the article is organized as follows. First, we conceptualize gamified information presentation. Subsequently, we develop our hypotheses about how gamifying the presentation of information about a product innovation affects consumer adoption of that innovation. We then present evidence from seven studies that supports this theorizing across multiple product domains. The article concludes with a discussion of the theoretical and practical implications of the findings.
Theory and Hypotheses
Conceptualization of Gamified Information Presentation
On the basis of prior work on games (Malone 1981; Ritterfeld et al. 2009) and computer-mediated communication (Daugherty, Li, and Biocca 2008; Hoffman and Novak 1996), we conceptualize gamified information presentation as the use of games as vehicle for conveying information (in our case, about a product innovation). The key characteristic of gamified information presentation is information–game integration, which refers to the game allowing consumers to experience novel product features as they actively participate in the information presentation. Conversely, games that lack information integration do not convey information about the product innovation.
Consider the following example of gamified information presentation. The German automobile manufacturer Audi presented an innovative four-wheel drive technology through a racing game (“Audi A4 quattro Experience”; Haynes 2012). The gamified presentation of information about this product feature presented the information in an integrated manner: consumers experienced information about the innovative feature as they played the racing game. Notably, by steering miniature cars, consumers had an active role in this information presentation.
Effect of Gamified Information Presentation on Consumer Adoption of a Product Innovation
Building on this conceptualization of the gamified information presentation, we propose that this presentation format ignites two parallel psychological processes by which it increases consumers’ inclination to adopt a product innovation. First, this format enhances consumers’ playfulness, which stimulates their curiosity. Second, consumers perceive the gamified information presentation as more vivid, which heightens their perception of the innovation’s advantages compared to other products.
Prior work has shown that individuals who engage in gamerelated activity become more playful (Csikszentmihalyi 1975, 1990). Accordingly, conveying product information through a game should render consumers more playful than other information presentation formats. Moreover, we argue that this state of increased playfulness affects how consumers respond to the presentation of a product innovation because playfulness stimulates flexibility and spontaneity (Aroean 2012; Barnett 2007) and increases individuals’ interest in exploring new things (e.g., novel technologies; Hoffman and Novak 1996, 2009; Webster, Trevino, and Ryan 1993). For instance, playful individuals should be more inclined to try the functions of a novel technology (Ghani and Deshpande 1994) because they do not perceive doing so as particularly challenging (Venkatesh 2000). In general, individuals who are more playful display higher levels of curiosity (Trevino and Webster 1992).
Curiosity is defined as the desire to approach novel things (Kashdan, Rose, and Fincham 2004; Kashdan and Silvia 2009; Spielberger and Starr 1994). Consumer curiosity affects consumer product choice by promoting the selection of unfamiliar products (McAlister and Pessemier 1982; Van Trijp, Hoyer, and Inman 1996) and increases consumers’ willingness to try new consumption experiences (e.g., watching novel sports on television; Park et al. 2015). Hence, consumers who are more curious about a product innovation should be more inclined to choose it over familiar, established alternatives. In summary, we propose that the gamified presentation of information about a product innovation renders consumers more playful, which stimulates their curiosity about the innovation, ultimately increasing their inclination to adopt it.
In addition to this first process, we propose that consumers perceive product information that is conveyed in the form of a game as particularly vivid. In contrast to other presentation formats (e.g., text-, image-, or video-based presentations), gamified information presentation requires consumers to actively participate in the presentation as they play the game. We propose that the active role that consumers have in gamified information presentation has important consequences for how they perceive the information presentation. Games create vivid experiences (Choi, Kim, and Kim 1999; Gerrig and Prentice 1996; Steuer 1992). For instance, consumers receive direct sensory feedback about how the product innovation behaves in specific usage situations, which increases the perceived vividness of the presentation (Schlosser 2003). A more vivid presentation not only tends to attract consumers’ attention to the product (Shiv and Huber 2000) but also facilitates their product comprehension, as consumers can more vividly imagine using the product innovation (Feiereisen, Wong, and Broderick 2008). Thus, when a game conveys information about a product innovation, consumers should be better able to imagine using that innovation, which is critical in enabling them to grasp its advantages relative to competing products, ultimately increasing their inclination to adopt the innovation (Rogers 1995). In summary, we propose that consumers perceive the gamified presentation of information about a product innovation to be more vivid, which increases the perceived advantages of the innovation relative to competing products, thereby promoting consumers’ inclination to adopt it.
Overall, we hypothesize that gamified information presentation ignites two parallel psychological processes through which it promotes the adoption of the presented product innovation: ( 1) by increasing consumer playfulness, which stimulates curiosity about the innovation, and ( 2) by enhancing the perceived vividness of the information presentation, which boosts the perceived advantage of the innovation relative to (less innovative) competing products. These hypotheses are stated formally as follows, and they are also depicted in the conceptual model shown in Figure 1.
H1: The gamified presentation of information about a product innovation increases consumer adoption of that innovation.
H2: The effect of gamified information presentation on innovation adoption is serially mediated by (a) increased consumer playfulness and (b) heightened consumer curiosity about the innovation.
H3: The effect of gamified information presentation on innovation adoption is serially mediated by (a) greater perceived vividness of the information presentation and (b) a greater perceived advantage of the innovation relative to competing products.
In the following sections, we present evidence from seven studies that provides support for this theorizing. The first three studies demonstrate the positive effect of gamified information presentation on consumer innovation adoption. Two of these— Studies 1 and 3—are field experiments in different domains, and Study 2 is a more tightly controlled experiment that conceptually replicates Study 1. The results of Studies 4a and 4b provide initial insights into the two psychological processes that drive the effect of gamified information presentation on innovation adoption. Study 5 examines these processes in greater depth. Finally, Study 6 shows that both informationgame integration and active consumer participation are necessary for gamified information presentation to promote innovation adoption.
Study 1
The objective of Study 1 was to provide an initial test of the overall effect of gamified information presentation on consumer adoption of a product innovation (H1). We collaborated with a large European automobile manufacturer that gave us the opportunity to conduct a field experiment on its live website by incorporating a game into its product configurator.
Method
The field experiment was conducted during a four-week period. The sample consisted of 1,494 prospective customers who used the automobile configuration tool on the manufacturer’s website. The outcome of interest was whether customers chose a particular product innovation—a multifeature assistance system package that automates tasks that drivers would otherwise have to perform themselves (e.g., automatic distance regulation with the vehicle in front and automatic assistance in maintaining the lane)—when configuring their car. A singlefactor (game vs. control) between-subjects design was used for this experiment. Customers who set out to use the car configuration tool were randomly assigned to one of two conditions (without them knowing). In the control condition, customers configured their car by sequentially selecting features in various categories (i.e., engine, trims and packages, exterior design, interior design, and equipment). Throughout this process, customers were presented with descriptions of the various features from which they were choosing. In the game condition, customers followed the same procedure, with one exception: just before they were presented with the description of the focal innovation, they were invited to play a quiz game (i.e., attempt to correctly answer three questions, e.g., “Which of the features that the assistance package offers is currently unique in the car industry?” with the possible answers (a) “lane assistant,” (b) “parking assist system,” and (c) “predictive efficiency assistant”). Customers could advance in the game only if they answered a given quiz question correctly. Visual feedback elements (e.g., progress bar, colorful icons indicating the correctness of an answer, feedback messages, grayed-out buttons) guided customers through the game. The objective was to answer the three questions correctly in as few attempts as possible. The game was successfully completed once a customer had answered all questions correctly, which was indicated by a feedback message. Ultimately, customers in both conditions decided whether to adopt the focal product innovation by including it in their car configuration (coded as 0 = “no innovation adoption,” 1 = “innovation adoption”).
Results
Of the 1,494 customers who participated in this field experiment, 655 were randomly assigned to the control condition and 839 to the game condition. Among the latter group, 254 played the game and 585 did not (instead proceeding directly to the description of the multifeature assistance system package). To
Study 2
Study 2 was designed to complement the field evidence from Study 1 by examining the overall effect of the gamified presentation of information about an innovation on consumer adoption of that innovation (H1) in a more tightly controlled experimental setting.
Method
A total of 136 consumers (MAge = 35.63 years, SDAge = 11.66; 44.12% female; NGame = 78, NControl = 58) recruited from an online panel completed the study in exchange for monetary compensation. The focal product innovation was a car assistance system that helps drivers stay in their lane and on the road even under slippery conditions (e.g., icy roads). In this experiment, participants were randomly assigned to one of the two conditions (control vs. game) of a single-factor betweensubjects design. The procedure of the experiment was as follows. All participants received the same descriptive information about the car assistance system “Lane Assist.” After reading the description of this feature, participants in the game condition played a short driving game, whereas those in the control condition instead completed a control task that required them to click on a button 50 times, which had been pretested to require the same amount of time and effort as completing the driving game. In the game condition (see Appendix A), participants assumed the role of a driver steering a car down a course that represented a road (using the arrow keys on their keyboard). The goal of the game was to drive one lap of the course in the shortest amount of time possible. Over the course of the game, participants encountered several other cars that they had to navigate around. The game was completed successfully once a participant reached the final destination (represented by a finish line). The road on which participants steered the virtual car was programmed to look icy (i.e., it was ice blue in color), and without the assistance system, it was difficult to keep the car on the road. Participants played the game twice (on the same course). First, they did so without the assistance system. Whenever the car left the road, it came to a complete halt before the driver was able to continue. Participants then played the game once more, but this time with the activated assistance system. While playing the game, participants experienced that it was easier to drive the car (i.e., they had a more stable driving experience) on the icy road when the assistance system was activated. This had been pretested to be true. Participants of Study 1 indeed completed the driving task in significantly less time with the assistance system (MAssist = 24.37, MNoAssist = 40.65; t(77) = 6.49, p .001). feature, but the study was conducted in a more tightly controlled experimental setting.
Study 3
The first two studies demonstrated the positive effect of gamifying the presentation of product information on consumer innovation adoption in the domain of automobiles. The key objective of Study 3 was to examine in a field setting whether gamified information presentation is also effective for consumers who browse the Internet without a clear goal in mind and not solely for those who visit a website with a clear purpose (as with the prospective car buyers in Study 1). Accordingly, we designed Study 3 as a field experiment in which we recruited consumers who were not aware that they were participating in a study. We also corroborate the preceding findings in a different product domain (i.e., bicycle accessory).
Method
The product innovation in this field experiment was a novel bicycle accessory: a valve cap that glows whenever the bicycle is in motion. This safety feature makes a bicycle and its riders significantly more visible to others in dark conditions, and it can be added to any standard bicycle. A pretest revealed that this feature was unfamiliar to, and considered innovative by, the majority of average consumers at the time of the study. For the purpose of this study, we used the fictitious brand name “Glow Valve” for this innovative product. We highlighted the innovative features of the Glow Valve and described it as a bike accessory innovation in various parts of the product presentation. During a two-week period in early 2016, we placed paid advertisements for Glow Valve on several online platforms (e.g., Facebook and Instagram; see Appendix B). A total of 2,551 consumers clicked on one of these advertisements. Of these consumers, 1,028 (NGame = 443; NControl = 585) arrived at a website (constructed and controlled by us) that presented information about Glow Valve, and unbeknownst to them, they became participants in the field experiment. On arrival, participants were randomly assigned to one of the two conditions (game vs. control) of a single-factor between-subjects design. In the game condition, a video game was used to convey the key product information about Glow Valve (see Appendix B). Specifically, participants played a game in which they rode a bicycle at night, steering it along a road using the arrow keys on their keyboard. The goal of the game was to ride as far as possible in a given amount of time. Upon acceleration, the Glow Valve was illuminated (i.e., it “glowed”), the bike became more visible to participants, and it lit up the road such that participants could detect obstacles more easily. Thus, participants experienced the unique feature of Glow Valve while playing the game, which closely aligns with our conceptualization of gamified information presentation in that the game served as the vehicle for conveying information about the product innovation (i.e., the information is integrated into the game). By contrast, individuals in the control condition received the same information about Glow Valve and its key feature, but it was presented only in a text-based format. Subsequently, participants in both conditions received additional information about Glow Valve (e.g., what type of batteries it requires) as well as a picture of the
Studies 4a and 4b
Studies 4a and 4b are companion experiments that were designed to provide initial insights into the psychological forces that drive the positive effect of gamifying the presentation of information about a product innovation on consumer adoption of that innovation. In particular, Studies 4a and 4b were designed to test whether consumer curiosity about the innovation and a greater perceived relative advantage (i.e., the two distal mediators) mediate the effect of the gamified information presentation on product innovation adoption (H2b and H3b). Study 4a does so in connection with innovative bicycle tires (presented through a bike game), and Study 4b uses a novel type of tennis ball (presented through a tennis game) to further extend the scope of our empirical evidence to additional product domains and different implementations of gamified information presentation.
Study 4a
A total of 94 consumers (MAge = 35.25 years, SDAge = 11.39; 54.26% female; NGame = 43, NControl = 51) recruited from an online panel completed this experiment in exchange for monetary compensation. The focal product innovation was a novel type of bicycle tires.
Method. Participants were randomly assigned to one of the two conditions of a single-factor between-subjects design (game vs. control). Participants in both conditions received text-based information about an innovative kind of bicycle tires named “Marathon” whose unique product feature is that they significantly improve traction and control relative to standard bicycle tires and thus can reduce the risk of a crash. Participants in the game condition played a modified version of the bicycle game used in Study 3 (see Appendix A). In this version, participants rode a bike, first with standard bicycle tires and then with Marathon tires, on a muddy road. In both games (i.e., with standard tires and with Marathon tires), participants had 30 seconds to ride as far as they could. The centrifugal force of the bike game had been modified such that players could sense that the Marathon tires had excellent traction (i.e., they did not veer off the road in the curves). In contrast, without the Marathon tires, participants slid away in the curves because the centrifugal force accelerated them in the curves to the extent that it was very difficult to stay on the road. Whenever participants went off the road, the bike decelerated heavily, and they needed to return to the road in order to perform well in the game. Thus, participants in the game condition experienced that the innovative Marathon tires provided them with greater control over their bike than standard tires, as their bike did not veer off the road in the curves.
Those in the control condition received the same product information as those in the game condition. However, instead of playing the bicycle game, the control group completed the same control task used in Study 2.
We used the same measures for product innovation adoption as in Study 2 (r = .92). Consumer curiosity about the product innovation was measured using a three-item scale (“How curious do you feel about Marathon tires?” “How interested would you be in reading more about Marathon tires?” and “How interested would you be in trying out Marathon tires at a bicycle shop?” where 1 = “not at all,” and 7 = “very”; Menon and Soman 2002; a = .95). We measured the perceived relative advantage of the product innovation using a three-item scale (“I believe that Marathon tires, in general, are the best way to improve my riding experience,” “Overall, I believe using the Marathon tires is advantageous,” “Using Marathon tires improves my riding experience,” where 1 = “strongly disagree,” and 7 = “strongly agree”; Meuter et al. 2005; a = .93). The convergent and discriminant validity of these three constructs was verified using confirmatory factor analysis. The measures of model fit exceed the commonly recommended thresholds (comparative fit index [CFI] = .990; Tucker–Lewis index [TLI] =
A parallel mediation analysis using the bootstrapping procedure (Model 4, Hayes 2013) showed that the effect of gamified information presentation on adoption of the product innovation was mediated indirect-only (Zhao, Lynch, and Chen 2010) by consumer curiosity and perceived relative advantage, with the direct effect becoming nonsignificant once these two mediators were included (BIndirect_Curiosity = .28, 95% confidence interval [CI95%] = [.03, .71]; BIndirect_RelativeAdvantage = .40, CI95% = [.10, .91]; BDirect = .26, CI95% = [-.21, .72]; BTotal = .94, CI95% = [.27, 1.61]; see Figure 2, Panel A). This result provides initial support for our theorizing about the psychological forces that drive the positive effect of gamifying the presentation of information about a product innovation on consumer adoption of that innovation.
Study 4b
A total of 94 participants (MAge = 34.27 years, SDAge = 11.84; 42.55% female; NGame = 38, NControl = 56) recruited from an online panel completed this experiment in exchange for monetary compensation. Study 4b was designed as a conceptual replication of Study 4a, with two key differences: the focal product innovation was a novel type of tennis ball, and the implementation of gamified information presentation was adapted to this product domain.
Method. Participants were randomly assigned to one of the two conditions of a single-factor between-subjects design (game vs. control). Participants in both conditions received the same text-based information about standard tennis balls, followed by some initial information about an innovative type of tennis ball named “U.S. Open 2016” (the experiment was conducted shortly after completion of the 2015 U.S. Open tournament) that has the unique features of allowing players to hit the ball more accurately and with greater force. Participants in the control condition received all information about the innovative features of the U.S. Open 2016 ball in a text-based form, whereas in the game condition, this information was conveyed through a tennis game (see Appendix A). The latter was a video game in which participants repeatedly hit a tennis ball against a wall of bricks with the objective of dismantling the entire wall, one brick at a time. As soon as the ball hit a brick, there was a visual effect, and the brick disappeared. When all bricks had disappeared,
the game was completed. Participants in the game con
dition played the game twice, once with the standard ball
and once with the U.S. Open 2016 (which was programmed
such that participants could hit more precisely and play
faster). Thus, participants experienced the innovative fea
tures of the U.S. Open 2016 tennis ball as they played
the game. We used the same measures as in Study 4a for all constructs (aCuriosity = .94; aRelativeAdvantage = .93; rInnovationAdoption = .77).
The fit measures of a confirmatory factor analysis (CFI = .984; TLI = .974; RMSEA = .087) and the comparison of the values for average variance extracted (AVECuriosity = .84; AVERelativeAdvantage = .83; AVEInnovationAdoption = .79) with their highest squared correlations (r2Curiosity_RelativeAdvantage = .48; r2Curiosity_InnovationAdoption = .59; r2RelativeAdvantage_InnovationAdoption =
.71) revealed adequate convergent and discriminant validity.
relative advantage to be greater (MGame = 4.96 vs. MControl = 4.18; t(92) = 2.59, p < .05).
A parallel mediation analysis using the bootstrapping procedure (Model 4, Hayes 2013) revealed that the effect of gamified information presentation on consumer adoption of the product innovation was mediated indirect-only by curiosity and perceived relative advantage, with the direct effect becoming nonsignificant once these two mediators were included (BIndirect_Curiosity = .35, CI95% = [.12, .70]; BIndirect_RelativeAdvantage = .52, CI95% = [.16, .99]; BDirect = .20, CI95% = [-.19, .58]; BTotal = 1.08, CI95% = [.36, 1.79]; see Figure 2, Panel B). This pattern of results closely resembled that of Study 4a despite being obtained in a different product domain and in connection with a different implementation of gamified information presentation.
Discussion
Studies 4a and 4b provide support for our theorizing that gamified information presentation promotes innovation adoption through two parallel psychological processes: by stimulating consumer curiosity about the innovation (H2b) and by increasing the perceived relative advantage of the product innovation (H3b). However, the proposed roles of consumer playfulness (as a driver of increased curiosity) and of the perceived vividness of information presentation (as a driver of greater perceived relative advantage) have yet to be tested. We do so in Study 5.
Study 5
The evidence presented thus far has shown that gamified information presentation promotes consumer adoption of a product innovation, and it has identified curiosity about the innovation and the perceived relative advantage of the innovation as mediators of this effect. In Study 5, we more closely examine the psychological processes underlying the effect of gamified information presentation on product innovation adoption. In particular, we examine whether the gamification of information presentation stimulates consumer curiosity in a product innovation through increased playfulness (H2a) and whether it boosts the perception of relative advantage through greater perceived vividness of the information presentation (H3a).
Method
A total of 182 consumers (MAge = 32.65 years, SDAge = 10.42; 42.31% female; NGame = 91, NControl = 91) recruited from an online panel completed Study 5 in exchange for monetary compensation. We again used the U.S. Open 2016 tennis ball as the product innovation. Participants were randomly assigned to one of the two conditions (game vs. control) of a single-factor between-subjects design. After completing either a tennis game in which they experienced the advanced features of the innovative tennis ball or a control task (both the game and the task were the same as in the preceding study), participants chose between (a four-ball can of) the innovative U.S. Open 2016 tennis balls at a price of $15 and (a four-ball can of) standard tennis balls at $10. The key dependent variable was whether participants adopted the innovative U.S. Open 2016 tennis balls
(coded as 1) or instead chose the standard tennis balls (coded
as 0). We measured consumer curiosity (a = .94) and the per
ceived relative advantage of the product innovation (a = .91) as in the prior studies. In addition, we measured consumers’ state
of playfulness using a four-item scale adapted from Moon and Kim (2001; “The presentation was fun,” “The presentation was enjoyable,” “I was happy,” and “I was explorative,” where 1 = “strongly disagree,” and 7 = “strongly agree”; a = .92) and the
perceived vividness of the presentation using a four-item scale (“How vivid did you find the presentation of U.S. Open 2016?” “How much did the presentation of U.S. Open 2016 bring to mind concrete images or mental pictures?” “How much did the
presentation of U.S. Open 2016 provide features to help you imagine using it?” and “How much did the presentation of U.S.
Open 2016 include features that helped you visualize a product trial?” where 1 = “not at all” and 7 = “very much”; Schlosser 2003; a = .94). their curiosity) and that consumers perceive the information presentation to be more vivid (increasing the perceived relative advantage of the innovation). Both of these parallel psychological processes are supported in this study. Thus, the results of Study 5 provide additional insights into the psychological forces that govern the effect of gamifying the presentation of information on innovation adoption and further support our theorizing about the nature of the specific processes that drive this effect.
Study 6
In conceptualizing gamified information presentation, we have argued that it is critical that the information that is to be conveyed to the consumer be integrated into the game. Study 6 provides a test of this assertion. Specifically, it examines the consequences of a lack of information–game integration—that is, when consumers play a game that does not convey product information and product information is presented separately. In addition, this study seeks to demonstrate the role of consumers’ active participation in a game. Inherently, gamified information presentation requires consumers to actively engage in game play. Thus, showing that active participation is critical for gamified information presentation to promote innovation adoption would establish a clear boundary from other (passive) formats of information presentation (e.g., Singh, Balasubramanian, and Chakraborty 2000).
Method
A total of 252 consumers (MAge = 34.70 years, SDAge = 11.30; 40.80% female; NIntegration_ActiveParticipation = 64, NIntegration_ NoActiveParticipation = 64, NNoIntegration_ActiveParticipation = 64,
NNoIntegration_NoActiveParticipation = 60) recruited from an online panel completed Study 6 in exchange for monetary compensation. Participants were randomly assigned to one of the four conditions in a 2 (information–game integration: yes vs. no) • 2 (active participation: yes vs. no) between-subjects design. We
again used the U.S. Open 2016 tennis ball as the product
innovation (as in Study 5). At the beginning of the experiment,
participants in all conditions received the same (text-based)
information about the innovative tennis ball. Participants then continued as follows. Those in the “active participation” conditions played the tennis game used in Study 5, in which they
actively experienced the innovative features of the U.S. Open 2016 ball. By contrast, participants in the “no active participation” conditions passively watched a video of the same tennis game. The second factor, information–game integration, was manipulated so that the game (or the video) was framed
either as conveying information about the innovative tennis ball or as ostensibly unrelated to the product. Specifically, in the “integration” conditions, participants were informed that they could experience (watch) the described features of the U.S.
Open 2016 in the game (video), whereas no such information was provided in the “no integration” conditions. Thus, participants in all conditions received the same product
information and identical audiovisual stimuli, with the only
differences being whether they were able to actively control
the direction and speed of the tennis ball (active partic
ipation) and whether they were told that the stimuli conveyed product information (information–game integration). In line with our conceptualization and the approaches taken in the preceding studies, the full gamified information presentation (“game”) condition included both active participation and information–game integration.
To examine whether these manipulations were effective, we measured active participation using a four-item scale (“I felt active during the presentation of the U.S. Open 2016,” “I rather passively observed the presentation of the U.S. Open 2016,” “My role during the presentation of the U.S. Open 2016 was very active,” and “I felt passive during the presentation of the U.S. Open 2016,” where 1 = “strongly disagree,” and 7 = “strongly agree”; a = .91) and information–game integration using a three-item, seven-point semantic differential that cap
tured how closely the information was integrated with the game (“The game was not integrated at all with the information” to “The game was very well integrated with the information”; “The game did not demonstrate the U.S. Open 2016” to “The game demonstrated the U.S. Open 2016 very well”; “The game did not convey information about the U.S. Open 2016” to “The game conveyed information about the U.S. Open 2016”; a = .92).
We measured consumer curiosity about the innovation (a = .94), the perceived advantage of the innovation relative to competing products (a = .92), consumer playfulness (a = .92), and the perceived vividness of the presentation (a = .94) using the same scales as in Study 5. Whether a participant adopted the
innovative product (coded as 1) or chose the standard tennis
balls instead (coded as 0) served as the key dependent variable
in this study. The measures of model fit of a confirmatory factor
analysis (CFI = .969; TLI = .961; RMSEA = .075) and the comparison of the values of average variance extracted
(AVECuriosity = .79; AVERelativeAdvantage = .80; AVEVividness = .80; AVEPlayfulness = .76) with their highest squared correlations (r2Curiosity_RelativeAdvantage = .42; r2Curiosity_Vividness = .39; r2Curiosity_Playfulness = .40; r2RelativeAdvantage_Vividness = .42; r2RelativeAdvantage_Playfuness = .29; r2Vividness_Playfuness = .44) support the constructs’ convergent and discriminant validity.
Discussion
The findings of Study 6 confirm our assertion that information– game integration is essential for the positive effect of gamifying the presentation of information about a product innovation on consumer adoption of that innovation. In addition, they show that consumers’ active participation in a game is a critical aspect of gamified information presentation, which distinguishes this format from passive (i.e., video-based) indirect product experiences. Finally, the results of Study 6 help rule out a potential alternative explanation of the effect of gamified information presentation in our experiments whereby participants might have felt bored in the control conditions; thus, the results corroborate that gamified information presentation stimulates curiosity. Participants in the active participation/no integration condition played the same game as those in the gamified information presentation condition (and did not have to complete a cumbersome task as in the previous studies), which isolates information–game integration as a key aspect that drives the positive effect of gamified information presentation on consumer adoption of innovations.
General Discussion
Theoretical Implications and Future Research
This research advances our understanding of the effects of incorporating games into the shopping process (Lounis, Neratzouli, and Pramatari 2013; Schlager et al. 2014). Prior research in this area has considered games as tasks that prompt consumers to take specific actions, primarily because they create enjoyable product and brand experiences (e.g., Kuo and Rice 2015; Van Reijmersdal, Rozendaal, and Buijzen 2012). By contrast, the present article examines the use of a game as vehicle to convey information about a product innovation to consumers, which we refer to as gamified information presentation.
Evidence from seven experiments in various product domains shows that gamified information presentation ignites two parallel psychological processes by which it affects consumer innovation adoption. The first process operates through increasing consumer curiosity about the innovation, which is stimulated by a heightened sense of playfulness. The second process operates through increasing the perceived advantage of the innovation relative to competing products, which is caused by the enhanced perceived vividness of the innovation presentation. The two processes in concert render consumers more inclined to adopt a product innovation when information about it is conveyed to them in the form of a game.
At a broader level, the present research contributes to the literature on information presentation formats and how they affect the market success of innovations (Chandy et al. 2001; Noseworthy and Trudel 2011; Wood and Moreau 2006). Gamified information presentation is a novel format of presenting information that is distinct because it creates a unique combination of vivid and playful experiences. Specifically, while formats that use direct product experiences (i.e., granting consumers the opportunity to actually try a product) may also create vivid product experiences (Smith and Swinyard 1983; Wright and Lynch 1995), gamified information presentation uses games as a medium to present product information, which stimulates consumer playfulness. Moreover, other information presentation formats that use a medium (e.g., video, text) to convey information also lack a game and do not require consumers to actively participate and thus fail to stimulate playfulness. As demonstrated, the unique combination of stimulating consumer playfulness and creating the perception of greater vividness is particularly beneficial for presenting product innovations. Playfulness leads to greater curiosity, which is particularly important when consumers learn about an innovation. Meanwhile, greater perceived vividness enables effective communication of the advantages of innovative product features that might be difficult for consumers to understand. For instance, even experience attributes, which typically require direct product experience (Wright and Lynch 1995), can be conveyed effectively through games, as demonstrated (for tennis balls) in Studies 4b, 5, and 6.
The present research also contributes to the domain of experiential marketing (Babin, Darden, and Griffin 1994; Hoffman and Novak 1996, 2009; Schmitt 1999) in that it advances our understanding of how playful experiences govern individual buying behavior. For instance, Mathwick and Rigdon (2004) find that playfulness positively influences consumers’ attitudes toward a website and the presented brand. We add to this research by demonstrating that games that are intertwined with information presentation and not only (website) design elements can render consumers more playful in a shopping context, which in turn significantly affects their purchase behavior (by promoting innovation adoption). We also demonstrate that these playful experiences stimulate consumers to respond more favorably to the conveyed information as they become more curious about a product innovation.
Future research might examine moderators of the effect of gamified information presentation on innovation adoption. Consumer characteristics (e.g., autotelic personality; Csikszentmihalyi 1975, 1990) might influence consumers’ openness to such gamified information presentation. For example, if consumers are more serious minded and goal focused, they might be reluctant to engage in gamified information presentation.
Practical Implications
Successful product innovations are a key driver of firm performance (Banbury and Mitchell 1995; Henard and Dacin 2010), and one important determinant of their success is how firms choose to convey information about such innovations to consumers (Reinders, Frambach, and Schoormans 2010; Townsend and Kahn 2014). This work shows that using games to present product innovations can be a highly effective means of boosting consumer adoption of product innovations.
Choice of gamified products. The findings of this research show that gamified information presentation promotes consumer adoption of a wide range of products (e.g., from hedonic products such as tennis balls to utilitarian products such as a car assistance system). While our focus has been on product innovations, we conjecture that gamified information presentation may also promote the adoption of some noninnovations. In particular, products with advantages or features that are difficult to understand (Zhao, Hoeffler, and Dahl 2009) or that fail to stimulate curiosity in and of themselves might benefit from being presented through a game, independent of whether they are classified as product innovations. Consider, for instance, the example of a pencil that is constructed of a novel material that is difficult to describe but that allows for smoother writing than conventional pencils. Gamified information presentation may be better able to convey the advantages of the pencil than traditional (e.g., text- or image-based) information presentation formats by illustrating the pencil’s haptic properties through a game. Moreover, presenting product information in the form of a game might stimulate curiosity about the pencil as consumers become more playful as a result of engaging in the game.
The present research has focused on products that are superior to established products (and, thus, on the promotion of product adoption). The net impact of using gamified information presentation in connection with products that are inferior to established products is not obvious. While the vividness path should hinder innovation adoption since gamified information presentation would more clearly expose the product’s disadvantages relative to competing alternatives, the playfulness path should still stimulate consumer curiosity. A rigorous examination of the interplay between these two forces would be a promising avenue for future research. In summary, we propose that firms should also consider using gamified information presentation for products other than innovations, and even for products that do not have a distinct advantage relative to their competitors’ offerings.
Selection of games. This research reveals two key properties of gamified information presentation that firms should consider when contemplating the use of games to convey product information: information–game integration and consumers’ active participation. A low-level implementation would be for firms to merely display information about products (e.g., on billboards) within a game. Yet, prior work has found no evidence of this actually having a significant positive effect on consumers’ purchase intentions (Chaney, Lin, and Chaney 2004). More importantly, the evidence presented here shows that it is critical that the product information is integrated into the game and that consumers actively engage in the game play. However, selecting a game that integrates information and stimulates consumers’ active participation is not trivial. On the one hand, games are complex and highly diverse—ranging from quiz games (see Porsche 2015) to action games (e.g., Audi A4 quattro Experience; Haynes 2012), for instance. On the other hand, the game must match the product innovation to effectively present information about the innovation (Gross 2010). Games also vary in the level of consumer activity that they entail. While some games do not require much (e.g., Volkswagen’s BlueMotion Roulette, in which consumers guessed the distance that one could drive a car on a tank of fuel; Behance 2011), others demand a high level of activity (e.g., BMW presented the innovative features of its new BMW M3 in a virtual racing game experience; Sessa 2013). According to our results (Study 6), the latter type should be more effective because it stimulates consumers’ playfulness and, consequently, their curiosity about the product innovation. Thus, firms should select a game that ( 1) is capable of conveying the advantages of a product innovation and ( 2) motivates consumers to actively participate in the information presentation.
Consumers’ end devices. Firms should consider that not all end devices (e.g., mobile phones, tablets, computers) are equally suitable for displaying information (Yadav and Pavlou 2014). Computers have the graphic capabilities (e.g., graphic cards, game engines) to render images in high speed and can therefore display sequences of images that could best portray the design and visible features of a product innovation. By contrast, mobile devices have the haptic capabilities (e.g., through vibration machines) to present experiential information
(Lurie and Mason 2007). Moreover, the various devices also differ in their screen size, which affects consumers’ experience during game play (Hou et al. 2012) and thus should also have consequences for the extent to which an innovation can be displayed. Therefore, the effectiveness of gamified information presentation depends critically on firms’ ability to anticipate what end devices consumers will use, as well as on the crossdevice adaptability of the implementation.
Role of web analytics. Gamified information presentation is most naturally implemented in web-based customer interfaces, which provide firms with the opportunity to use web analytics to monitor and analyze its key success factors. We propose that due to the complexity of games and the idiosyncratic capabilities of different devices, firms should be mostly concerned with defining variables that assess a game’s performance (i.e., whether it runs smoothly). For instance, the consumer’s hardware and software (e.g., operating system, installed software, graphics cards) may limit the performance of a game. Any error or disruption may come at the expense of the presentation’s playfulness or vividness, ultimately lowering innovation adoption rates. Because consumers are free to quit a game anytime, we propose that two key variables are whether consumers start and the time by which they abandon the game. The latter, especially, could serve as a proxy for the playfulness that the game stimulates (Hoffman and Novak 2009) and thus come close to evaluating consumers’ curiosity. As firms’ tracking capabilities become more sophisticated, firms may also track variables in the game (e.g., number of cursor moves), which might be closer to assessing consumers’ playfulness or whether they actively participate in the information presentation. In summary, we propose that smart web analytics are key to developing successful gamified information presentations.
Collaboration with professional game developers. Given the complexity and variety of games and the idiosyncratic capabilities of different end devices, a significant amount of technical capabilities are required to fully harness the potential of gamified information presentation. Therefore, firms may benefit from closely collaborating with professional game developers, who have the expertise required to create games that convey information and that motivate consumers to take an active role (Hagen 2011), which ultimately stimulate the psychological processes that we have shown to be critical to the success of gamified information presentation.
In conclusion, this research demonstrates that games can be a highly effective vehicle for conveying product information to consumers and that firms can harness gamified information presentation to promote the adoption of innovations by attracting new customers and motivating existing customers to upgrade to new generations of products.
DIAGRAM: FIGURE 1 Conceptual Model
DIAGRAM: FIGURE 2 Gamified Information Presentation Promotes Innovation Adoption Through Increased Consumer Curiosity and Perceived Relative Advantage (Studies 4a and 4b)
DIAGRAM: FIGURE 4 Both Active Participation and Information–Game Integration Are Essential for the Positive Effect of Gamified Information Presentation on Innovation Adoption (Study 6)
DIAGRAM: APPENDIX A Video Games Used in Studies 2, 4a, and 4b–6 (Annotated Sample Screenshots)
DIAGRAM: APPENDIX B Advertisements, Stimuli, and Innovation Adoption Measure Used in Study 3
DIAGRAM: FIGURE 3 Gamified Information Presentation Promotes Innovation Adoption Through ( 1) Consumer Playfulness and Curiosity and ( 2) Vividness of Presentation and Perceived Relative Advantage (Study 5)
DIAGRAM: Gamified Information Presentation and Consumer Adoption of Product Innovations
DIAGRAM: Gamified Information Presentation and Consumer Adoption of Product Innovations
DIAGRAM: Gamified Information Presentation and Consumer Adoption of Product Innovations
DIAGRAM: Gamified Information Presentation and Consumer Adoption of Product Innovations
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Record: 79- Gear Manufacturers as Contestants in Sports Competitions: Breeding and Branding Returns. By: van Everdingen, Yvonne; Hariharan, Vijay Ganesh; Stremersch, Stefan. Journal of Marketing. May2019, Vol. 83 Issue 3, p126-144. 19p. 1 Diagram, 5 Charts, 2 Graphs. DOI: 10.1177/0022242919831996.
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Gear Manufacturers as Contestants in Sports Competitions: Breeding and Branding Returns
Several manufacturers make substantial investments to compete in sports contests, using the gear they develop and market. However, no systematic analysis of the breeding (i.e., innovation) and branding (i.e., marketing) returns from such investments exists. In this study, the authors conceptualize and empirically estimate the breeding and branding returns that such manufacturers obtain. The authors gather data for 30 car brands of 16 manufacturers over the period 2000–2015 regarding their participation, spending, and performance in Formula One championships, annual patent citations, and research-and-development (R&D) budgets as well as monthly vehicle registrations, advertising expenditures, and Formula One TV viewership. The authors find that only gear manufacturers with relatively high levels of R&D spending obtain a positive and significant breeding return from competing in sports contests. While most brands obtain positive branding returns, the lower the level of advertising spending for the brand, the greater the branding returns they obtain from competing in these contests. Thus, research-intense (compared with advertising-intense) gear manufacturers have more to gain from competing in sports contests. These findings can help guide manufacturers in budget allocation decisions on sports competitions, R&D, and advertising.
Keywords: advertising spending; innovation performance; R&D spending; sales performance; sports competitions
Many firms are sports sponsors in one way or another. Marketing researchers have posed a historical interest in examining the branding returns from such sponsorships and have shown that professional sports sponsoring increases the brand's exposure, recall, recognition, affect, trust, loyalty, and sales (e.g., [13]; [30]; [33]; [34]; [39]; [50]; [59]). However, in quite a few cases, firms' interest seems to go beyond such branding returns to achieve what we call breeding (i.e., innovation) returns from athletes using their gear in sports competitions.
Such breeding returns, beyond branding returns, from involvement in sports can be most easily envisioned in cases where gear manufacturers choose to go beyond mere sponsoring and actively compete in a sports contest. A gear manufacturer that competes in a sports contest participates with its own team that uses the manufacturer's gear and goes head-to-head against other participating contestants. Gear manufacturers may use the extreme conditions under which athletes in the team use their gear and closely cooperate in developing and testing new technologies that may improve their team's performance, entailing breeding returns. Moreover, branding returns from competing in a sports contest in this way may be different from branding returns from mere sponsoring. For example, one may envision that the performance of the firm as a contestant may affect its branding return.
The breeding and branding returns gear manufacturers may obtain from entering sports competitions are relevant to many firms. Race bike manufacturer Trek invests $14 million annually to compete with its Trek-Segafredo team in the Union Cycliste Internationale (UCI) World Tour ([52]), car manufacturer Daimler invests around $200 million annually to compete with its Mercedes-AMG Petronas Motorsport team in the Formula One (F1) Championship ([54]), and ski manufacturer Atomic invests approximately $9 million annually to compete with its own team in the International Ski Federation (FIS) Alpine Ski World Cup ([48]). Competing firms may invest varying amounts in such sports competitions, with varying success, both of which may affect their breeding and branding returns. The allocation of resources to competing in sports contests is likely not independent of the firm's investment in other areas, of which research and development (R&D) and advertising seem most relevant as one considers the breeding and branding returns of competing in sports contests. This, in turn, raises the question of whether breeding and branding returns depend on the firm's R&D and advertising spending. Our key research question is therefore the following: To what extent do gear manufacturers that compete in sports contests gain positive outcomes in terms of breeding, branding, or both, and are these outcomes contingent on the gear manufacturer's R&D and advertising spending?
To the best of our knowledge, so far no study has conceptualized or systematically analyzed both branding and breeding returns that gear manufacturers obtain from competing in sports contests, nor whether such returns depend on the manufacturer's R&D spending (for breeding returns) and advertising spending (for branding returns). This is what the current study aims to offer. Analyses of breeding and branding returns are of great interest to marketing managers, analysts, and academics because they relate to accountability of board-level strategic investments (e.g., [43]).
Empirically, we constructed a novel data set on car manufacturers' participation, spending, and performance in the F1 World Championship. Our sample consisted of 30 automobile brands sold by 16 car manufacturers, among which 10 brands from 9 car manufacturers competed in F1 at some point during our sample period of 2000–2015. To examine the breeding effect, we supplemented F1 data with information on these 16 manufacturers' R&D spending and on their innovation performance (measured in terms of patent citations). To investigate the branding effect, we obtained the brands' advertising spending and sales performance, in terms of number of vehicle registrations in five countries (France, Germany, Italy, Spain, and the United Kingdom).
Our study provides the following new insights. First, competing in sports contests and R&D spending are complements-competing in sports contests generates a significantly positive breeding return only for gear manufacturers with relatively high levels of R&D spending (more than €3.8 billion annually in our F1 context). Second, we find that competing in F1 and advertising spending are substitutes. Brands with low advertising budgets obtain greater branding returns from competing in sports contests than those with high advertising budgets. While all brands in our sample obtain positive branding returns from participating and increasing their spending in F1, only brands with less than €10.6 million in monthly advertising benefit from improving their performance in F1. In summary, research-intense gear manufacturers (i.e., firms that spend heavily on R&D but limitedly on advertising) have more to gain from competing in sports contests, as compared with advertising-intense gear manufacturers (i.e., firms that spend little on R&D but heavily on advertising).
This article contributes to the existing literature in several ways. First, it shows that firms may obtain breeding and/or branding returns from their involvement in sports competitions, whereas prior literature has examined only branding returns and, thus, offers a partial view, at best. Second, it conceptualizes competing by a firm in sports contests as inherently different from mere sponsoring. It also provides an analytical framework for estimating the returns for firms that compete in sports contests and provides the first estimates of such returns ever reported in the literature. Third, we show that returns from competing in sports contests cannot be assessed without accounting for other related decisions of the respective firms, such as R&D and advertising spending. Fourth, for brand exposure, we are the first to empirically demonstrate that saturation effects occur even across greatly dissimilar exposure vehicles (in our case, car advertising and competing in F1 by car manufacturers). This complements prior literature that has demonstrated such saturation effects only among fairly similar exposure vehicles (e.g., [56]). It may also contradict managerial practice to leverage sports investments with greater advertising spending.
The findings in this research are relevant not only to managers and analysts in the automotive industry specifically (including tier 1 suppliers) but also to other sports gear manufacturers, for which competing in sports contests is a relevant consideration (e.g., motorsports, cycling, skiing). They can use these findings to assess the potential economic outcomes of competing in sports contests. Moreover, these findings may guide gear manufacturers in a trade-off of budget allocation between contending in sports competitions on the one hand and R&D and advertising on the other hand.
Manufacturers' investments in sports competitions can be classified in terms of the following two dimensions: the type of involvement in the sports contest (sponsor vs. contestant) and the type of deployed resources that the manufacturer uses in the sports contest (gear vs. nongear).
The manufacturer is involved as a contestant if it competes in the sports contest with a team. In contrast, a manufacturer that is involved as a sponsor in sport contests provides financial and/or in-kind assistance (e.g., a company's products) to an individual athlete, a team, or a competition in return for access to the commercial potential of the sponsored object ([28]; [35]).
For manufacturers, competing in sports contests is different from being a sponsor in three ways. First, the manufacturer owns all or part of the team and, therefore, has greater responsibility for and more control over the team than a sponsoring manufacturer. For example, when Red Bull became the owner of the Jaguar F1 team instead of being a sponsor, it incorporated the company name in the team name and gained control over the design of the car's paint scheme, which helped the firm gain higher visibility ([25]).
Second, manufacturers that compete in sports face off against other contestants from similar industries in the sports competition. For example, Mercedes competes against other car manufacturers in F1, and Trek competes against other race bike manufacturers in the UCI World Tour. This is different in case of sponsors. For example, Wilson is the racket sponsor of various tennis players (e.g., Roger Federer), but these players do not form a Wilson team that competes in tennis championships against, for example, a Babolat team; rather, the individual tennis players compete against each other (e.g., Roger Federer competes against Rafael Nadal).
Third, the brand name of the manufacturer that competes in sports is strongly linked to the performance of the manufacturer's team in the competition. Contestants are ranked on the basis of their relative performance vis-à-vis competing brands in the sports contest (see, e.g., https://data.fis-ski.com/alpine-skiing/brand-ranking.html and http://www.skysports.com/f1/standings). In contrast, because sponsors do not compete themselves in the sports, they are not ranked on the basis of the performance of the athletes or teams they sponsor. For example, the Association of Tennis Professionals (ATP) rankings show the official singles rankings of the ATP World Tour, featuring the world's top-ranked players in men's professional tennis, but do not show the names of manufacturers whose rackets the players used.
We define "gear" as clothing, goods, and equipment made by the manufacturer to use in the sport. Gear manufacturers provide the set of tools that will enable the individual athlete or team to compete, whereas nongear manufacturers do not. For example, Nike sponsored Tiger Woods by providing him with Nike equipment, apparel, and shoes and is thus a gear sponsor. Trek, providing its own team with Trek race bikes to compete in the UCI World Tour, is a gear contestant.
From the manufacturer's point of view, two important (related) differences exist between deploying gear and nongear resources to a sports competition. First, for the manufacturer, there is a strong fit between the gear deployed in the sports competition and the gear sold in the commercial market, thereby bridging these two markets. For example, Nike sold golf balls in the main market similar to those used by Tiger Woods in golf tournaments. Second, in case of gear sponsors and contestants, resources and competencies deployed for the competition may spill over to the main market and vice versa, which may lead to technology transfers. As an example, Wilson and Roger Federer cocreated a tennis racket, the Wilson Pro Staff RF97 Autograph, first to be used by Roger Federer in his matches, but later on, a commercial version of the racket was sold to the main market ([ 5]). There have also been many technology transfers from F1 race cars to cars for the general public (e.g., antilock brakes, electronic throttles, traction control).
The distinctions we make help clarify the positioning of the present study in the existing literature on sports sponsoring. So far, most studies have focused on nongear sponsors and have shown that sports sponsoring by means of providing nongear support entails branding effects. [39], for example, show that extensive logo exposure from sponsoring a sports league increases brand recognition and likability equally as much as a 30-second TV ad. [59] show that brand recall and recognition for Heineken increased over the brand's years of sponsoring the Union of European Football Association (UEFA) Champions League, with the largest increase in the second year. And, [34] have shown that sponsoring a large sports event, such as the Summer Olympics, can increase brand trust and loyalty. The strength of such branding effects for nongear sponsors appears to vary depending on the fit between the sponsor and the brand and the successes of the sponsored objects ([30]; [34]; [39]; [50]; [59]).
A few studies have focused on the effects of being a gear sponsor. [13] is the first study to investigate the relation between sponsoring and the sales performance of the sponsoring brand. It shows a positive effect of being a gear sponsor—that is, it examines the effects of Nike's gear sponsoring (golf balls, among other equipment) of Tiger Woods on the brand's sales performance of golf balls, and how this effect depends on Tiger Woods's performance in the competition. In a subsequent study, [18] shows that this endorsement effect is stronger for novice golfers than for experts.
In contrast, the potential outcomes of manufacturers' investment in becoming a gear contestant have been completely ignored in the literature so far. This is an interesting context, because multiple gear contestants from the same industry typically participate in a particular sports competition (e.g., many large ski manufacturers participate with their own ski teams in the FIS Alpine Ski World Cup), with varying level of investments (e.g., ski manufacturer Head invests twice as much as ski manufacturer Atomic) and varying levels of success (e.g., both Head and Rossignol outperformed Atomic in the overall World Cup brand rankings in 2018). This provides a unique opportunity to investigate branding effects, as brands are shown to the audience in direct comparison to competitors' brands, which may lead to more pronounced branding effects. In addition, this context offers the opportunity to investigate breeding returns of firms' involvement in sports competitions. Because gear contestants own participating teams, the teams' performance is directly related to the gear contestant's brand, and therefore, these gear manufacturers are most likely continuously searching for new product technologies that may help improve the teams' performance. In line with these considerations, this study contributes to the literature by examining the breeding and branding effects that result from manufacturers' participation, investments, and successes in sports competitions as gear contestants.
Figure 1 graphically summarizes our conceptual framework and shows the relation between a gear manufacturer competing in a sports contest and its innovation and sales performance (i.e., the breeding and branding effects, respectively). We operationalize competing in three ways: participation, spending, and performance in the sports competition. Participation denotes that the manufacturer is one of the contestants. Contingent on participation, we investigate the influence of ( 1) the amount manufacturers spend on competing (spending) and ( 2) the level of success in competing in the sports contest (performance), as different levels of spending and performance may affect the breeding and branding returns.
Graph: Figure 1. Conceptual model.
The theoretical base of our conceptual framework relies on the resource-based view (RBV) of the firm, which states that a firm's resources and capabilities (i.e., a firm's capacity to deploy these resources) help give it a sustained competitive advantage ([60]). We posit that the manufacturer's team is a resource, and competing in a sports contest can be viewed as a capability to leverage the manufacturer's asset of having its own team. The manufacturer's team is a resource that, either singly or with other manufacturer resources (e.g., R&D and advertising spending), can be the basis for a sustained competitive advantage in terms of an increase in the manufacturer's innovation performance and the brand's sales performance. Specifically, we hypothesize that R&D spending moderates the relationship between a gear manufacturer competing in a sports contest and its innovation performance because R&D is the most fundamental resource available to firms to produce technological know-how and generate innovations ([21]; [61]). In a similar vein, we hypothesize that advertising spending moderates the relation between a gear manufacturer competing in a sports contest and the sales performance of its brand(s) because advertising is generally an important source to increase a brand's sales performance (e.g., [55]).
We define the breeding effect as the effect of a gear manufacturer competing in a sports contest on its innovation performance (i.e., the manufacturer's innovation outputs; e.g., patents; [ 4]). Atomic, for example, developed its "Doubledeck" ski technology first for use by professional athletes on the Atomic ski team that competes in the FIS Alpine Ski World Cup; after the technology was proven successful, the brand transferred this technology to its commercial skis.
Competing in sports contests is valuable to a gear manufacturer because it offers the manufacturer the opportunity to develop and test new technologies under the most demanding circumstances. Competing generates valuable resources and competencies for converting new product ideas into innovations, increasing a manufacturer's innovation performance ([12]). The breeding effect of a gear manufacturer competing in a sports contest on its innovation performance can be explained as follows. First, a gear manufacturer competing in sports contests creates a parallel path of R&D activities in addition to its regular R&D processes. Because sports competitions are characterized as highly demanding in terms of both speed and accuracy, gear manufacturers that compete in such competitions develop and test new technologies specific to the demands of these sports competitions. This parallel path of R&D activities, along the technical frontier, can improve a firm's overall innovation performance ([ 3]; [16]). Second, the immediate performance feedback from competing in a sports contest stimulates learning through trial-and-error experiences. When a gear manufacturer is experimenting with new technologies, the performance feedback provides insights into these technologies' usefulness and quality. Such feedback facilitates the development of tacit knowledge and the discovery of otherwise unnoticed opportunities, which may increase the gear manufacturer's innovation performance ([ 8]; [ 9]).
In summary, we expect that a gear manufacturer competing in a sports contest improves its innovation performance, leading to the following hypothesis:
- H1: Competing in a sports contest by a gear manufacturer is positively related to the gear manufacturer's innovation performance.
We expect a direct relation between R&D spending and innovation performance as well as a positive moderating effect of R&D spending on the positive relation between a gear manufacturer competing in a sports contest and its innovation performance. Firms use R&D expenditures to create internal knowledge and to evaluate the potential outcomes of the created knowledge ([42]). Prior literature ([ 6]; [49]) has shown that a higher level of R&D spending entails a higher likelihood of patents being granted and/or the granted patents being intellectually valuable (in terms of citations), suggesting a direct effect of R&D spending on a firm's innovation performance.
In addition to this direct effect, there are two reasons to expect a complementary effect between gear manufacturers competing in sports contests and these gear manufacturers' R&D spending. First, RBV theory emphasizes the role of firm-specific capabilities and competencies that stretch the firm's resources and help give it a sustained competitive advantage ([60]). Previous literature has shown that R&D spending is positively related to three important capabilities that may harness the innovation opportunities that result from having a team in a sports competition (i.e., absorptive capacity, product development capabilities, and patenting skills). By actively engaging in R&D in a particular field—in this case, innovation development in their focal industry—manufacturers increase their absorptive capacity (i.e., the capacity to acquire, assimilate, and exploit information they generate in another context, such as competing in a sports contest; [14]). They may also increase their product development capabilities (i.e., the capacity to turn this information and knowledge into breakthroughs; [14]; [58]). Finally, the higher the R&D spending, the greater a firm's patenting skills, which may help in patenting the breakthrough innovations, resulting from technology testing by the manufacturer teams in the sports competitions ([49]).
Second, higher R&D spending is an important resource for the generation of creative innovation ideas ([ 9]). The new ideas from the regular R&D process may find their way into the equipment used by the manufacturers' teams, and competing by these teams in sporting contests may then provide valuable testing ground.
In line with these arguments, we expect that R&D spending strengthens the positive effect of a gear manufacturer competing in a sports contest on its innovation performance, leading to the following hypothesis:
- H2: The relationship between competing in a sports contest by a gear manufacturer and that gear manufacturer's innovation performance is positively moderated by the gear manufacturer's R&D spending.
We define the branding effect as the effect of a gear manufacturer competing in a sports contest on the sales performance of its brands. For example, Renault's F1 title in 2006 entailed a direct increase in its car sales ([23]).
Competing in a sports contest may positively influence a gear manufacturer's most important intangible resources (i.e., the brand's awareness, image, and reputation), thereby creating a sustainable competitive advantage and, eventually, higher sales ([ 2]; [15]; [31]). A gear manufacturer competing in a sports contest may generate branding returns for two main reasons. First, by entering sports competitions, gear manufacturers gain increased brand exposure because sports competitions have large viewership (e.g., 352.3 million people viewed the F1 championship globally in 2017; [53]). The brand's exposure increases with the brand's performance in the competition because the better-performing brands will receive more media attention than those at the back of the pack ([30]). Literature on the mere-exposure effect suggests that repeated exposure to a brand's stimuli, such as words, pictures, logos, and brands, will entail an affective response toward these stimuli, leading to higher brand preferences and higher brand equity, which subsequently leads to higher sales performance ([ 1]; [29]; [39]; [62]).
Second, in the context of gear manufacturers competing in sports contests, signaling is an important additional logic beyond mere exposure for explaining the effect of competing on the sales performance of the gear manufacturer's brand(s). Signaling refers to the action a seller takes to convey information about the unobservable product quality to the buyer ([41]). Previous studies on signaling have focused on the transmission of quality signals in different forms, including brands ([20]), brand alliances ([41]), prices ([46]), advertising expenditures ([19]), and warranties ([10]). We argue that competing in sports contests, under the extreme conditions these contests entail and directly in comparison with competitors' products, enables the respective firm to demonstrate the performance and quality of its products and brands. A new technology that a competing firm introduces in such contests may yield strong reputational and quality returns to the main market. Thus, competing in a sports contest acts as a positive signal on the quality of the manufacturer's brand(s), which may result in higher sales, as perceived quality has been shown to be one of the most important universal brand benefits influencing a consumer's brand purchase intention and brand choice ([19]; [57]).
In line with these arguments, we develop the following hypothesis:
- H3: Competing in a sports contest by a gear manufacturer is positively related to the gear manufacturer's sales performance.
We postulate that advertising spending will moderate the relationship between a gear manufacturer competing in a sports contest and the sales performance of its brand(s) for two main reasons. One argument for a negative interaction effect of a gear manufacturer competing in sports contests and its advertising spending is the saturation effect. Because gear manufacturers repeatedly show their brands during sports competitions, simultaneously increasing advertising spending will lead to saturation resulting from an increased number of brand exposures ([11]; [47]; [56]). This effect will be even more pronounced for brands with higher spending in sports competitions because higher spending leads to a more prominent display of the brand names, and logos. Similarly, the saturation effect will be greater for brands that perform well in the competition because better-performing brands receive more media attention and, thus, more brand exposures compared with brands that do not perform well ([13]).
Second, according to [32], there is a negative interaction effect between two market signals that are similar in nature, owing to the reduced effectiveness of one signal in the presence of another signal of similar type. Because both competing in sports contests and advertising involve up-front expenditures, they are similar in nature. That is, both advertising spending and competing in sports contests can be viewed as (substitute) signals of high product quality, compared with rivals.[ 6] Therefore, we expect a negative interaction effect between them.
In line with these arguments, we develop the following hypothesis:
- H4: The relationship between a gear manufacturer competing in a sports contest and the sales performance of its brand(s) is negatively moderated by its advertising spending.
The empirical context of our study is the F1 championship, which is the leading sports championship in single-seat auto racing, established by the Fédération Internationale de l'Automobile (FIA) in 1945. The F1 season runs from March to November and consists of a series of 19 Grand Prix races across different countries worldwide. Yet F1 has a strong heritage in Europe, where approximately 50% of the races still take place. Recent F1 race seasons have had an average of 11 teams participating with two cars, and every team enrolled in an F1 season competes in all the races of the year. At the end of the season, a world championship is awarded to one driver and one team with the highest total points earned during the races.
The F1 context constitutes a perfect environment for testing our breeding and branding effects hypotheses. In terms of breeding potential, F1 teams, in which the car manufacturer's R&D personnel closely collaborate with the drivers and technical engineers, generate hundreds of ideas a year to improve automobile performance (e.g., aerodynamics, suspension setup, weight distribution, fuel efficiency). Because races are typically every two weeks, there is a rapid cycle of developing new ideas, testing them, and analyzing whether the modifications improve race performance. We believe that F1 is also an interesting area in which to investigate branding effects, because participating car brands gain a lot of brand exposure primarily due to the TV viewership of F1 races ([30]).
We collected data at the manufacturer-global-year level for the breeding analysis and at the brand-country-month level for the branding analysis. Patent data, which we use to measure innovation performance, is only unambiguously available at the manufacturer-global-year level, while data on car registrations, our measure of sales performance, is available at the brand-country-month level. Moreover, data for variables in the breeding part of the conceptual framework, such as R&D spending, are available only at the manufacturer-global-year level, whereas data for the variables in the branding part of the conceptual framework, such as advertising spending, are available at the brand-country-month level.
We decided to focus on Europe because F1 has its heritage in Europe and is still strongly European oriented, with many F1 drivers being of European origin, and approximately half of F1 races every year take place in Europe. Within Europe, we selected five countries—France, Germany, Italy, Spain, and the United Kingdom—on the basis of data availability on brands' monthly sales performance and the highest percentage of F1 TV viewership. Specifically, the percentage of the population within a country that has watched 15 or more minutes of at least one race during the 2010 F1 season was 52% for France, 51% for Germany, 60% for Italy, 71% for Spain, and 56% for the United Kingdom (Formula One Global Broadcast Report 2010). Moreover, many drivers participating in F1 between 2000 and 2015 are from one of these countries (11 drivers of German origin, 11 drivers of U.K. origin, 8 drivers of French origin, 6 drivers of Italian origin, and 6 drivers of Spanish origin), and these countries produce highly successful drivers (e.g., Michael Schumacher, Sebastian Vettel, Nico Rosberg, Giancarlo Fisichella, Fernando Alonso, David Coulthard, Jenson Button, Lewis Hamilton). We are aware that our sample of countries enhances the likelihood of finding a branding effect. The branding effect we identify may thus be lower in countries with less heritage in F1, with smaller F1 viewership, or in which no races take place.
Table 1 lists the brands chosen in our sample countries for the branding analyses, the corresponding car manufacturers used in the breeding analyses, and the manufacturers' car brands that participated in F1 during 2000–2015, including the years in which they participated. We selected the 30 car brands (see Table 1, second column) using the following three criteria. First, we selected the top 20 brands in terms of vehicle registrations in our five sample countries. Second, we identified the brands that competed in F1 during our sample period. Among the top 20 brands, 7 brands competed in F1 during 2000–2015. We added Ferrari, Jaguar, and Lotus, which were not in the top 20 brands in terms of registrations but also competed in F1 between 2000 and 2015.[ 7] Third, we added seven niche brands that did not compete in F1 but are comparable to Jaguar, Ferrari, and Lotus in terms of the segments in which they operate: Aston Martin, Bentley, Lexus, Lamborghini, Maserati, Porsche, and Volvo. Our sample of 30 car brands accounts for more than 90% of passenger vehicle registrations in the five selected European countries. These 30 brands mapped into 16 car manufacturers (see Table 1, first column), among which 10 brands from 9 manufacturers competed in F1 at some point during our sample period (see Table 1, third column).[ 8]
Graph
Table 1. Selected Manufacturers and Brands.
| Manufacturer | Selected Brand(s) | Brand(s) and Years in Which They Competed |
|---|
| BMW AG | BMW | BMW (2000–2010)a |
| Daimler AG | Mercedes-Benz | Mercedes-Benz (2000–2015) |
| Fiat Automobiles S.p.A. | Alfa Romeo, Fiat, Ferrari, Lancia,b Maserati | Ferrari (2000–2015) |
| Ford Motor Company | Aston Martin (until 2007), Ford, Jaguar (until 2008), Volvo (until 2010) | Ford (2003, 2004)Jaguar (2000–2004)a |
| General Motors Company | Opel (Vauxhall in the United Kingdom) | Did not participate |
| Groupe PSA | Citroen, Peugeot | Peugeot (2000) |
| Groupe Renault | Renault | Renault (2001–2015) |
| Honda Motor Co., Ltd | Honda | Honda (2000–2008, 2015) |
| Hyundai Motor Company | Hyundai | Did not participate |
| Kia Motor Corporation | Kia | Did not participate |
| Mazda Motor Corporation | Mazda | Did not participate |
| Nissan Motor Company Ltd | Nissan | Did not participate |
| Porsche AGc | Porsche | Did not participate |
| Proton Holdings Berhad | Lotus | Lotus (2010–2015)a |
| Toyota Motor Corporation | Lexus, Toyota | Toyota (2002–2009) |
| Volkswagen Group | Audi, Bentley, Lamborghini, Seat, Skoda, Volkswagen | Did not participate |
1 aOutsourced development of the race team: BMW (2010), Jaguar (2000–2004), and Lotus (2010–2015). Our key findings are robust to the exclusion of these data points from our sample. See "Robustness Checks" subsection.
- 2 bLancia brand is not sold in the United Kingdom.
- 3 cDespite the connection between Porsche AG and the Volkswagen Group, we treat them as two separate entities. Until 2012, Porsche AG and Volkswagen had strong ties, including equity stakes in both directions. In 2012, the two companies actually merged, but they reported separately until the end of our data period.
The dependent variable in the breeding model is a manufacturer's innovation performance, which we measured as the total number of citations obtained by the manufacturer's patents that were granted during a given year. Prior studies have used this method to measure innovation performance (e.g., [27]; [61]). There were, however, two issues in measuring the number of citations. First, for each patent, we observed only a portion of the period over which it could be cited. Specifically, it takes several years to realize patents' full citation potential. Second, the length of this observed citation period varied depending on when the manufacturer applied for the patent. For example, 13 years of citation data were available for patents applied for in 2001 (i.e., from 2001 to 2013),[ 9] whereas only 3 years of citation data were available for patents applied for in 2011 (i.e., from 2011 to 2013). We addressed this problem as follows. We first estimated the shape of the citation-lag distribution for each manufacturer using data on patents granted during 1986–1999. This distribution provides the fraction of citations that a manufacturer's patent obtains every year after the patent is granted. We used this distribution to calculate the total number of citations for patents granted in a particular year as follows. We first observed the total number of citations between the year in which the patent was granted until 2013 (e.g., three years for patents filed in 2011), and then we divided this value by the fraction of the citation-lag distribution that lies in this time interval ([27]).
We measured the moderator variable (i.e., manufacturer's R&D spending) as the ratio of yearly spending in R&D in euros to the number of employees within the organization to control for different sizes of manufacturers. Prior studies (e.g., [45]) have found R&D spending per employee to be less sensitive to the spurious effects of business cycles, accounting manipulations, and asset sales than R&D spending as a proportion of sales.
We operationalize the independent variable (i.e., competing as a gear manufacturer) in three ways: F1 participation, F1 spending, and F1 performance. F1 participation is a dummy variable that takes a value of 1 during the years in which a manufacturer participated in F1 as a gear contestant, and 0 otherwise. We operationalize F1 spending as the yearly spending of the gear manufacturer on its F1 team in euros. Such spending may cover out-of-pocket expenses, such as materials, contracts with first-tier suppliers on components, or employees on the manufacturer's payroll dedicated to the participating team. We measure F1 performance as the number of points the manufacturer's team accumulated during the entire F1 season. We distinguish between F1 participation and F1 spending because some manufacturers participate in F1 without any spending. For instance, the manufacturer Proton Holdings Berhad participated between 2010 and 2015 using the Lotus brand name. However, the manufacturer neither spent any money on the team nor managed the team. Similarly, Ferrari did not make any direct financial contribution to its team's budget between 2013 and 2015. This is because the payments that Ferrari received from the Formula One Group have increased in recent years; therefore, it was no longer necessary for the manufacturer to offer financial backing to the team. In these two cases, we treat the manufacturers (and the corresponding brands) as participants but treat their spending as zero.
One of the dependent variables in the branding analyses is the brand's sales performance, which we measured as the total vehicle registrations of the brand across all its models in a given country during a particular month. We measured advertising spending, which is both a dependent and a moderating variable in the branding analyses, as the total advertising spending of a brand in a country in euros across all media types in a given month.
A brand as gear contestant in F1 is the independent variable in the branding model, which we also expressed in three levels: F1 participation, F1 spending, and F1 performance. F1 participation is a dummy variable that takes a value of 1 during months in which a brand participated in at least one F1 Grand Prix race and 0 otherwise. We measured the monthly F1 spending by multiplying the brand's annual F1 spending by the proportion of races in a year that took place in the particular month. All manufacturers except Ford participated with a single brand during our sample period. Therefore, we considered the manufacturer's F1 spending to be equal to the brand's F1 spending. Ford participated with the Jaguar brand name in 2000, 2001, and 2002, and with both the Ford and Jaguar brand names in 2003 and 2004. We allocated Ford's (manufacturer) F1 spending to its individual brands as follows. We allocated the entire manufacturer's F1 spending to the Jaguar brand for 2000–2002 and split the manufacturer's F1 spending between the Ford and Jaguar brands during 2003 and 2004. We assume that the manufacturer spent the same amount of money on the Ford brand in 2003 and 2004 as it did prior to 2000 when participating with only the Ford brand, and the rest of the manufacturer's spending was allocated to the Jaguar brand. Such allocation is also in line with publicly available information (e.g., [26]). We note, however, that our findings of the branding analyses are not sensitive to alternative allocations of F1 spending to the Ford and Jaguar brands (see the "Robustness Checks" subsection). We measured a brand's monthly F1 performance as the number of points accumulated by that brand during a particular month. Note that for F1 participation, F1 spending, and F1 performance, we assigned the same value for all countries.
We included four control variables in the branding model. First, because different countries may have varying levels of exposure to the brands during different racing months, we controlled for country-specific monthly exposure of participating brands by including the monthly number of viewers that watched the live F1 races on TV in the particular country. Second, we included the number of new product introductions of each brand in a particular month as a control variable because we expected it to influence sales performance. We defined this variable as the number of new products, denoted as a unique combination of brand, segment, model, body group, and generation year, introduced by the brand during that month. Third, we included the effects of lagged advertising spending and lagged sales performance on current advertising spending to capture carryover effects and state dependence ([17]). Finally, we included competitors' sales performance and competitors' advertising spending and measured them as the total number of new vehicle registrations and total advertising spending of all other brands in that country during the particular month, respectively.
For the breeding analysis, we collected data on innovation performance, R&D spending, and F1 competing, in terms of participation, spending, and performance, for the 16 manufacturers. We obtained data on innovation performance in terms of yearly patent applications and their corresponding citations from the Worldwide Patent Statistical Database (PATSTAT) of the European Patent Office from 2000 to 2013, a period of 14 years. We obtained data on yearly R&D spending (2000–2013) from the car manufacturers' annual reports.[10] We converted all R&D spending figures reported in other currencies to euros using historical exchange rates. For most firms, the fiscal year matched the calendar year (January to December). We adjusted the R&D spending of other firms with a different fiscal year so that it matches with the calendar year. Finally, we gathered information on car manufacturers' yearly participation and performance in F1 from ESPN (www.espn.co.uk/f1/), and we obtained yearly spending in U.S. dollars for all car manufacturers that participated in F1 between 2000 and 2015 from Formula Money. We used historical exchange rates to convert U.S. dollars to euros.
For the branding analysis, we collected monthly data on sales performance and advertising spending of the selected 30 brands across the five countries between January 2000 and December 2015 (192 months).[11] We obtained data on sales performance in terms of monthly new passenger vehicle registration for each car brand-model, in every segment, body group, and generation year across the five European countries from R.L. Polk & Co. We obtained data on country-specific brand level monthly advertising spending for all car brands from Nielsen Company. Advertising spending figures in France, Germany, Italy, and Spain are expressed in euros and those in the United Kingdom are expressed in British pounds. We converted pounds to euros using historical exchange rates. We use the same sources mentioned previously to obtain information on the brands' F1 participation, spending, and performance. Finally, we obtained information on the monthly number of television viewers who watched the live F1 races in every country of our sample from Eurodata TV Worldwide.[12]
Tables 2 and 3 provide means, standard deviations, and correlations among different variables we used in the breeding and branding analyses, respectively. Patent citations (innovation performance) are positively correlated with a manufacturer's F1 participation (r =.252), F1 spending (r =.479), and R&D spending (r =.094) but negatively correlated with a manufacturer's F1 performance (r = −.111). A brand's new vehicle registrations (sales performance) are positively correlated with a brand's F1 participation (r =.079), F1 spending (r =.113), F1 performance (r =.048), and advertising spending (r =.495).
Graph
Table 2. Descriptive Statistics for the Variables Used in the Breeding Analysis.
| Variable | Mean (SD) | Innovation Performance | F1 Participation | F1Spending | F1Performance | R&D Spending |
|---|
| Innovation performance (patent citations in '000s) | 6.943 (9.476) | 1.000 | | | | |
| F1 participation (no/yes) | .341 (.475) | .252 | 1.000 | | | |
| F1 spending (billion €s) | .044 (.071) | .479 | .851 | 1.000 | | |
| F1 performance (in hundreds of points) | .643 (1.595) | −.111 | .562 | .321 | 1.000 | |
| R&D spending (million €s per employee) | .018 (.012) | .094 | −.110 | −.020 | −.154 | 1.000 |
4 Notes: All variables in the breeding analysis are measured at the manufacturer-global-year level.
Graph
Table 3. Descriptive Statistics for the Variables Used in the Branding Analysis.
| Variable | Mean (SD) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|
| 1. Sales performance—vehicle registrations (in thousands) | 5.699 (9.113) | 1.000 | | | | | | | | |
| 2. F1 participation (no/yes) | .132 (.338) | .079 | 1.000 | | | | | | | |
| 3. F1 spending (billion €s) | 1.784 (5.801) | .113 | .782 | 1.000 | | | | | | |
| 4. F1 performance (in hundreds of points) | .033 (.150) | .048 | .569 | .421 | 1.000 | | | | | |
| 5. Advertising spending (million €s) | 5.496 (8.533) | .495 | .063 | .095 | .067 | 1.000 | | | | |
| 6. New product introductions | .085 (.346) | .111 | .100 | .113 | .088 | .069 | 1.000 | | | |
| 7. F1 TV viewers (in millions) | .675 (2.023) | .070 | .856 | .701 | .467 | .089 | .087 | 1.000 | | |
| 8. Competitors' sales performance—vehicle registrations (in millions) | .178 (.080) | .176 | .054 | .069 | −.010 | −.092 | .018 | .076 | 1.000 | |
| 9. Competitors' advertising spending (in billion €s) | .184 (.142) | −.056 | .003 | −.012 | .028 | .431 | −.008 | .083 | −.065 | 1.000 |
5 Notes: All variables in the branding analysis, except F1 participation, F1 spending, and F1 performance, are measured at the brand-country-month level. F1 participation is measured at the brand-global-year level and F1 spending and performance are measured at the brand-global-month level.
In line with prior studies (e.g., [ 6]; [22]), we used a one-year lag between R&D spending and patent citations. Because we consider a gear manufacturer that competes in a sports contest as a resource that creates a parallel path of R&D, we consider a one-year lag also for the effect of a gear manufacturer competing in F1 on patent citations. Specifically, we modeled innovation performance, measured as the number of patent citations (InnPerfmy), of manufacturer m in year y as follows:
Graph
1
where μm denotes a manufacturer-specific fixed effect, F1my−1 denotes manufacturer m as a gear contestant in F1 in year y − 1, expressed in terms of F1 participation (F1partmy−1), log of F1 spending (ln[F1spendmy–1]) or log of F1 performance (ln[F1perfmy–1]), R&Dmy−1 denotes the manufacturer m's R&D spending in year y − 1. We log-transform F1 spending, F1 performance, and R&D spending to allow for their decreasing marginal returns on the manufacturer's innovation performance. As we operationalized being an F1 contestant in three different ways, we estimated Equation 1 three times, each time with another operationalization of the F1 variable (participation, spending, or performance). Because we mean-centered ln(R&Dmy–1), θ1 captures the effect of F1 participation, spending, or performance for a manufacturer with mean level of R&D spending; θ2 captures the simple effect of R&D spending; θ3 captures the interaction between F1 participation, spending, or performance and R&D spending; ∊my∼N(0,σ2∊) and is the error term. We estimate the model in Equation 1 using ordinary least square regression.
Table 4 reports the parameter estimates of the breeding model. Columns 3, 5, and 7 contain the parameter estimates of the model that includes only the main effects of competing in F1 and R&D spending, but not the interaction between the two, when the F1 variable is measured as F1 participation, F1 spending, and F1 performance, respectively. It shows that the main effect of competing in F1 (i.e., the effect of competing independent of the R&D level) on innovation performance is significant when competing in F1 is operationalized as F1 participation or F1 spending, but not when it is operationalized as F1 performance. Therefore, we can confirm H1, except when we operationalize competing by performance.
Graph
Table 4. The Effects of Manufacturers Competing in F1 on Innovation Performance (Breeding Analysis).
| Variable | Parameter | Estimates for the Model in Which Competing in F1 Is Measured As... |
|---|
| F1 Participation | Log of F1 Spending | Log of F1 Performance |
|---|
| Main Effects Only | Full Model | Main Effects Only | Full Model | Main Effects Only | Full Model |
|---|
| F1 participation (H1)a | θ1 | .470*** | .151 | N.A. | N.A. | N.A. | N.A. |
| F1 spending (H1)a | N.A. | N.A. | .027*** | .010 | N.A. | N.A. |
| F1 performance (H1)a | N.A. | N.A. | N.A. | N.A. | .038 | −.055 |
| R&D spending per employee | θ2 | .419*** | .334** | .424*** | .342** | .407*** | .295** |
| F1 participation × R&D spending per employee (H2) | θ3 | N.A. | 1.145*** | N.A. | N.A. | N.A. | N.A. |
| F1 spending × R&D spending per employee (H2) | N.A. | N.A. | N.A. | .062*** | N.A. | N.A. |
| F1 performance × R&D spending per employee (H2) | N.A. | N.A. | N.A. | N.A. | N.A. | .346*** |
| R2 | .860 | .865 | .860 | .866 | .854 | .864 |
| Number of observations | 205 |
- 6 *p <.10.
- 7 **p <.05.
- 8 ***p <.01.
- 9 aBecause R&D spending per employee is mean-centered, the simple effect of F1 participation, spending, or performance denotes the effect for a manufacturer with mean level of R&D spending per employee.
- 10 Notes: N.A. = not applicable.
Columns 4, 6, and 8 of Table 4 contain the parameter estimates of the full model that includes the simple effects of competing in F1 (θ1) and R&D spending (θ2) as well as the interaction effect between them (θ3) when the F1 variable is measured as F1 participation, F1 spending, and F1 performance, respectively. Note that one cannot interpret these simple effects as main or marginal effects of F1 involvement, in the presence of the interaction effects between F1 and R&D spending (i.e., they cannot be used to test H1). We find that the interaction effect (θ3) is positive and significant at the.01 level in all three models.
To interpret these findings, we plotted the effects of a car manufacturer competing in F1 on its innovation performance for the 5th percentile (€5,567) to the 95th percentile (€35,989) of annual R&D spending per employee in our data set (mean R&D spending per employee = €18,177). Figure 2, Panel A, shows the effect of manufacturers' participation on innovation performance across different levels of R&D spending. We obtained the mean effects (see the solid line) and the 95% confidence intervals (see the dotted lines). Similarly, Figure 2, Panels B and C, respectively plot the effect of 1% increase in F1 spending and the effect of 1% increase in F1 points on innovation performance across different levels of R&D spending. The breeding effect is significant at the 5% level when both the dotted lines indicating the 95% confidence interval are above or below the x-axis. All three figures show that there is a synergistic effect of car manufacturers competing in F1 and R&D spending. Combining the simple effect of F1 and the interaction effect between F1 and R&D spending, we find a significant, positive effect of F1 participation, spending, and performance on innovation performance for manufacturers with high (above-mean) levels of R&D spending (specifically, greater than €18,200, €17,800, and €23,500 annual R&D spending per employee or €3 billion, €2.9 billion, and €3.8 billion annual R&D spending for F1 participation, F1 spending, and F1 performance, respectively). This finding supports H2.
Graph: Figure 2. Interactions between competing in F1 and R&D spending per employee on innovation performance.Notes: The dotted lines represent 95% confidence intervals. Innovation performance is measured as patent citations.
We modeled sales performance, measured as the number of new passenger vehicle registrations, (SalesPerfijt) for brand i in country j at month t, as follows:
Graph
2
where αij denotes the brand-country fixed effect, which captures time-invariant brand-specific and country-specific effects.[13] Incorporating such fixed effects alleviates the risk of endogeneity arising from idiosyncratic variations in brands (e.g., mainstream vs. niche brands) and countries ([40], p. 602). F1it denotes the brand's manufacturer being a gear contestant in F1, which we operationalize as either F1 participation (F1partit), log of F1 spending (ln[F1spendit]) or log of F1 performance (ln[F1perfit]) of brand i in a particular month t, and AdGWijt is the advertising goodwill of brand i in country j in month t. We employ the standard [37] exponential decay goodwill model for each country-brand combination. Specifically, we model the goodwill as , where Adijt is the advertising spending of brand i in country j at month t, and ρ is the carryover parameter, which we find using a grid search ([36]).[14] The squared-root term accounts for the decreasing marginal returns from advertising spending. Viewersjt denotes the number of people watching the live F1 races in country j in month t. We interact Viewersjt with F1it because we expect the effect of F1 TV viewership on sales performance to depend on the extent to which the brand competes in F1 (e.g., if it participates, spends a certain amount, and has won a certain number of points). NPIijt denotes the number of new products introduced by brand i in country j during the month. We model the effect on sales performance of new products introduced by the brand during the last 12 months (i.e., ). CompSalesPerfijt denotes the number of registrations of all other brands in the country.
β1 captures the main/simple effect of the brand competing in F1 on sales performance, β2 denotes the main/simple effect of advertising goodwill on sales performance, and β3 denotes the interaction effect between the brand competing in F1 and advertising goodwill. Because we mean-center ln(Viewersjt), β1 and β3 capture the simple effect of the brand competing in F1 and interaction effect between the brand competing in F1 and advertising goodwill respectively for an average level of F1 TV viewership. Moreover, β4 captures the effect of the number of viewers for gear contestants depending on the level of the brand competing in F1, β5 denotes the effect of new product introductions during the past 12 months, β6 denotes the effect of competitors' sales performance, and ∊ijt is the error term.
In Equation 2, advertising spending may be endogenous to sales performance because the unobservable monthly shocks in car registrations may be correlated with those affecting advertising spending (e.g., an important sales exhibition may occur in a given month in a given country). We model such endogeneity by including an instrumental variable and by modeling the error terms of the sales performance (Equation 2) and advertising spending equations (see Equation 3) to be correlated with each other. We use the brand's total monthly advertising spending in the other four countries as the instrumental variable. The brand's advertising spending in other countries is likely to be related to the brand's advertising spending in the focal country because manufacturers allocate advertising budgets across countries ([ 7]; [24]). However, we expect the advertising spending in other countries to be exogenous to sales performance in the focal country. The advertisements in each of the five countries in our analysis were most likely in different languages (i.e., French in France, German in Germany, Italian in Italy, Spanish in Spain, and English in the United Kingdom). Moreover, manufacturers customize advertising content to local markets (e.g., [44]). To check whether the advertising spending in other countries is a reasonable instrument for advertising spending, we carried out an auxiliary regression in which we used the log of advertising spending as the dependent variable and the log of the total advertising spending in the other four countries as the independent variable. The R2 of this regression is.590, indicating that using advertising spending in other countries to account for advertising endogeneity is a reasonable instrument ([51]).
In addition to accounting for the endogeneity of advertising spending, we are interested in examining the effect of how the brand competes in F1 on advertising spending (see Figure 1). In line with this, we modeled the advertising spending for brand i in country j in month t (Adijt) (in logarithmic units) as follows:
Graph
3
where γij denotes a country-brand specific fixed effect to account for time-invariant brand-specific and a country-specific advertising level, Adijt−1 denotes lagged advertising spending, SalesPerfijt–1 denotes lagged sales performance, CompAdijt denotes advertising spending of all other brands in the country during a particular month to account for competitive pressure in advertising spending or a common trend in advertising patterns, and denotes the total advertising spending for brand i in all four countries other than the focal country j in month t, which is the instrumental variable for advertising. We model the error terms of Equations 2 and 3 to be jointly distributed as . We estimate these equations using seemingly unrelated regression technique as suggested by [40], p. 591). Similar to the breeding analysis, we estimated the branding model three times, each time with another operationalization of the F1 variable (F1 participation, F1 spending, or F1 performance).
Table 5 reports the parameter estimates of the branding model. Columns 4, 6, and 8 contain the estimates of the model excluding the interaction effect between competing in F1 and advertising goodwill. Examining the parameter estimates of the sales performance equation (see the upper part of Table 5), we find for the main-effects-only model that the main effect of competing in F1 (i.e., the effect of competing independent of the level of advertising goodwill) is significant if we operationalize competing as participation and spending, but not in the case of performance. Therefore, H3 is confirmed, except when we operationalize competing as performance.
Graph
Table 5. The Effects of Brands Competing in F1 on Sales Performance and Advertising Spending (Branding Analysis).
| Dependent Variable | Independent Variable | Parameter | Estimates for the Model in Which Competing in F1 Is Measured As... |
|---|
| F1 Participation | F1 Spending | F1 Performance |
|---|
| Main Effects Only | Full Model | Main Effects Only | Full Model | Main Effects Only | Full Model |
|---|
| Sales performance | F1 participation (H3) | β1 | .076*** | .128*** | N.A. | N.A. | N.A. | N.A. |
| F1 spending (H3) | N.A. | N.A. | .006*** | .012*** | N.A. | N.A. |
| F1 performance (H3) | N.A. | N.A. | N.A. | N.A. | −.002 | .016*** |
| Advertising goodwill | β2 | .032*** | .033*** | .032*** | .033*** | .033*** | .033*** |
| F1 participation × Advertising goodwill (H4) | β3 | N.A. | −.004*** | N.A. | N.A. | N.A. | N.A. |
| F1 spending × Advertising goodwill (H4) | N.A. | N.A. | N.A. | −4.3 × 10−4*** | N.A. | N.A. |
| F1 performance × Advertising goodwill (H4) | N.A. | N.A. | N.A. | N.A. | N.A. | −.001*** |
| F1 participation × Number of F1 TV viewers | β4 | .021*** | .023*** | N.A. | N.A. | N.A. | N.A. |
| F1 spending × Number of F1 TV viewers | N.A. | N.A. | .003*** | .004*** | N.A. | N.A. |
| F1 performance × Number of F1 TV viewers | N.A. | N.A. | N.A. | N.A. | .016*** | .017*** |
| New products introduced in last 12 months | β5 | .018*** | .018*** | .018*** | .018*** | .018*** | .018*** |
| Competitors' sales performance | β6 | .959*** | .960*** | .958*** | .958*** | .966*** | .966*** |
| Ad spending | F1 participation | δ1 | .150*** | .150*** | N.A. | N.A. | N.A. | N.A. |
| F1 spending | N.A. | N.A. | .004 | .004 | N.A. | N.A. |
| F1 performance | N.A. | N.A. | N.A. | N.A. | .033* | .033* |
| F1 participation × Number of F1 TV viewers | δ2 | −.036 | −.036 | N.A. | N.A. | N.A. | N.A. |
| F1 spending × Number of F1 TV viewers | N.A. | N.A. | .003 | .003 | N.A. | N.A. |
| F1 performance × Number of F1 TV viewers | N.A. | N.A. | N.A. | N.A. | .019 | .019 |
| New product introductions | δ3 | .018 | .018 | .019 | .019 | .019 | .019 |
| Lagged advertising spending | δ4 | .447*** | .447*** | .447*** | .447*** | .447*** | .447*** |
| Lagged sales performance | δ5 | .030 | .030 | .031 | .031 | .033 | .033 |
| Competitor's advertising spending | δ6 | .922*** | .922*** | .923*** | .923*** | .921*** | .921*** |
| Advertising spending in other four countries (IV) | δ7 | .112*** | .112*** | .113*** | .113*** | .112*** | .112*** |
| R2 | | .956 | .956 | .956 | .957 | .956 | .956 |
| Number of observations | | 24,810 |
- 11 *p <.10.
- 12 **p <.05.
- 13 ***p <.01.
- 14 Notes: N.A. = not applicable; IV = instrumental variable.
In the full model (see columns 5, 7, and 9 of Table 5), the simple effects of being a gear contestant (β1) and advertising goodwill (β2) are positive and significant at the.01 level in all three models. The size of the simple effects is calculated taking into account the level of the other independent variable. Because the models include an interaction effect between competing in F1 and advertising goodwill, we cannot separately interpret the simple effects (i.e., the simple effects do not offer an appropriate test of H3). The interaction effect between competing in F1 and advertising goodwill (β3) is negative and significant at the.01 level in all three models.
To interpret these findings, we plotted the effect of competing in F1 on sales performance for the 5th percentile (€0)[15] to the 95th percentile (€21.8 million) of monthly advertising spending in our data set (assuming equal values of past advertising goodwill). In Figure 3, Panel A, we show the effect of a brand's participation in F1 on sales performance across different levels of monthly advertising spending. Similarly, in Figure 3, Panels B and C, we plot the effect of a 1% increase in F1 spending and the effect of a 1% increase in F1 points on sales performance, respectively, across different levels of monthly advertising spending. We obtained the mean effects (solid lines) and the 95% confidence intervals (dotted lines). The branding effect is significant when both the dotted lines indicating the 95% confidence interval are above or below the x-axis. These findings indicate that higher advertising spending lowers the effect of the brand competing in F1, thus supporting H4. This suggests a substituting effect between competing in F1 and advertising spending. Nevertheless, all brands have a positive branding effect from F1 participation and F1 spending and brands that spend less than €10.6 million on monthly advertising also have a positive branding effect from F1 performance.
Graph: Figure 3. Interactions between competing in F1 and advertising spending on sales performance.Notes: The dotted lines represent 95% confidence intervals. Sales performance is measured as vehicle registrations.
In addition to the effects of F1 and advertising, we find that β4 is positive and significant at the.01 level, indicating that an increase in the number of F1 TV viewers strengthens the positive effect of the brand competing in F1 on sales performance. β5 is positive and significant at the.01 level in all three models. This indicates that sales performance has a positive relationship with the number of new products introduced during the last 12 months. Competitors' sales performance (β6) is positively related to the sales performance of the focal brand and is significant at the.01 level for all three models. This could capture the trend in automobile registrations for all brands in the country.
The lower part of Table 5 presents the parameter estimates of the advertising equation (Equation 3). We find that δ1 is positive and significant at the.01 level for the model in which competing in F1 is measured as F1 participation, positive and significant at the.10 level for the model in which competing in F1 is measured as F1 performance, and insignificant for the model in which competing in F1 is measured as F1 spending. This indicates that brands spend more on advertising when they participate in F1 or perform well in F1.
Furthermore, regarding the control variables in the advertising equation, δ2, δ3, and δ5 are not significant. Lagged advertising spending (δ4) has a positive and significant (p <.01) effect on current advertising spending, for all three models. δ6 is positive and significant for all three models at the.01 level. This denotes that an increase in advertising spending of other brands in the country leads to an increase in the focal brand's advertising. This may capture either a competitive response or a trend in advertising spending among all brands within a country. Finally, we find that the instrumental variable (δ7) is positive and significant for all three models (p <.01), denoting that advertising spending in other countries has a significant effect on the brand's advertising spending in the focal country.
We checked the robustness of our results in four ways. First, we excluded data points from the breeding analysis in which the manufacturer outsourced the gear (engine) used during a racing season (see Table A1 in the Web Appendix). Specifically, although BMW withdrew from racing in 2010, the BMW Sauber F1 team competed using Ferrari engines ([38]). Similarly, Jaguar used Cosworth engines and Lotus used Cosworth, Renault, and Mercedes engines during the years they competed in F1. Second, we allocated an equal amount of F1 spending to Ford and Jaguar during 2003 and 2004, when both the manufacturer's brands participated in F1, and reestimated the branding model with F1 spending (see Table A2 in the Web Appendix). Third, we excluded the niche brands that competed in F1 during our sample period (Ferrari, Jaguar, and Lotus) from our branding analyses to examine whether we observe the negative moderation effect between a manufacturer being a gear contestant and its advertising spending due to the differences between large and small brands in our sample (see Table A3 in the Web Appendix). Finally, although we treat breeding and branding analyses differently, we checked for the robustness of the inclusion of innovation performance and R&D spending in the branding analysis. Following [ 6], we employed a three-year lag for the effect of R&D spending and a two-year lag for the effect of innovation performance (see Table A4 in the Web Appendix). We note that our findings are robust to all the aforementioned changes. The results of these analyses reaffirm our main findings that the manufacturer's R&D spending and competing in F1 are complementary of each other whereas the brand's advertising spending and competing in F1 are substitutes.
We provide useful insights for managers and analysts, specifically those in the automotive industry, including tier 1 suppliers in that industry, and more generally to those in sports gear industries, for which being involved as a gear contestant in sports competitions is a relevant consideration. First, we show that manufacturers with higher R&D spending stand to gain more from the breeding consequences of investments in sports competitions. For example, we show in Figure 2, Panel A, that car manufacturers that spend at least €3.8 billion annually on R&D (e.g., BMW, Honda) benefit from competing in F1, while manufacturers that spend less than that (e.g., Fiat, Renault) do not. Thus, if manufacturers decide to invest in F1 to enhance their patent base, they have to complement it with a high R&D budget to fully exploit the innovation potential that F1 offers.
Second, we show that a gear manufacturer's brand competing in sports contests and the gear manufacturer's advertising spending for that brand are substitutes in inducing an increase in sales performance of that manufacturer's brand(s). The branding returns are the largest among brands that have the lowest advertising (e.g., Ferrari, Jaguar, Lotus). Competing in a sports contest clearly helps the gear manufacturer build its brand by showing its products and brand(s) in a relevant context. Therefore, manufacturers do not have to complement competing in sports contests with a large advertising budget.
In summary, our findings may guide manufacturers in budget allocation decisions on sports competitions, R&D, and advertising. Our two main findings imply that firms that already spend a lot on advertising and relatively little on R&D have much less to gain from being a gear contestant in sports competitions as compared with firms that spend little on advertising and spend a lot on R&D. Thus, research-intense firms have more to gain from investing heavily in sports competitions as a gear contestant, as compared with advertising-intense firms. This study provides primary evidence from the automotive industry but is generalizable in logic to other industries. Both skiing and cycling, for example, have prime competitions of similar status as F1 in automotive to which our conceptual framework would generalize.
Our study adds to the literature on investments in sports competitions as follows. First, it shows that firms may obtain breeding and/or branding returns from competing in sports contests, whereas previous literature examined branding returns from only sponsoring and, thus, offers a partial view, at best. Second, our study conceptualizes how competing in a sports contest is inherently different from merely sports sponsoring. It also provides an analytical framework for estimating the returns for firms that compete in sports contests and provides the first estimates of such returns ever reported in the scholarly literature. Third, our findings also add to the RBV theory, as we show a new type of resource (i.e., owning a manufacturer team) as well as new type of capability (i.e., competing in sports contests), that together may lead to a competitive advantage (i.e., breeding and branding effects). Fourth, our evidence of significant interaction effects between different manufacturer resources suggests that the returns to a manufacturer's resource should not be studied in isolation but in combination with other resources that could be exploited to achieve the same outcome, which is in line with the RBV. Specifically, we report that a gear manufacturer's R&D spending strengthens the effect of the relation between competing in sports contests and its innovation performance, while a gear manufacturer's advertising spending for a competing brand weakens the effect of the relation between competing and the brand's sales performance. Competing in F1 and advertising heavily at the same time is less effective. We are the first to empirically demonstrate that saturation effects occur even across greatly dissimilar exposure vehicles (in our case, car advertising and competing in F1). This complements prior literature that has demonstrated such saturation effects only among fairly similar exposure vehicles (e.g., [56]). It may also contradict managerial practice to leverage sports investments with greater advertising spending.
As with any first exploration of a new phenomenon, several interesting future research directions remain, specifically for studies focusing on competing in as well as sponsoring sports contests. First, one could test the conceptual framework used in this article in another context, such as the Tour the France, or in other markets (e.g., emerging countries) to show the generalizability of the breeding and branding effects of competing by gear manufacturers in sports contests.
Second, a useful extension of the current study would be to examine and compare branding effects (e.g., brand sales performance) between competing in and sponsoring of a sports contest. So far, studies have investigated the branding effects for gear and nongear sponsors separately, while our study focuses on the branding effects of gear contestants only. A comparison of the branding effects and the underlying theories that drive potential differences in consumer responses to sponsoring and competing might provide valuable new insights.
Another interesting research topic related to the branding effects might be the extent to which a specific link in manufacturers' advertisements to the investments in the sports competitions (e.g., "We sell on Monday what we race on Sunday," or in relation to success, "If we can do it there, we can do it everywhere") would positively elevate the branding effects. A related issue would be to investigate the mediating effect of advertising spending on the relationship between competing in sports contests and sales performance, especially when the relationships between the variables are nonlinear in nature.
A fourth avenue for further research is to investigate the extent to which being a gear sponsor, rather than a gear contestant, would also lead to breeding effects in terms of a better innovation performance, and if so, if these breeding effects are comparable to, or stronger than, or weaker than the breeding effects of gear contestants. So far, studies on gear sponsors have only provided evidence for a positive branding effect (e.g., [13]), while breeding effects of being a gear sponsor have been totally neglected. However, because gear sponsors do collaborate with athletes to develop new products (e.g., Wilson collaborated with Roger Federer to develop new tennis rackets [[ 5]]), it is relevant to investigate whether and to what extent these collaborations between the gear sponsor and the sponsored athletes entails breeding effects (e.g., patents or patent citations) for the sponsoring manufacturer. And, relatedly, it would be worth investigating what role the strength of the linkage between the manufacturer's R&D and the sponsored or competing team's R&D plays in developing impactful corporate patents.
To conclude, as many manufacturers have increasingly been involved in sports competitions over the past few centuries, either as a contestant or a sponsor, the return on these investments has become an important management priority. Although academic research has covered several relevant branding issues related to manufacturers being a sponsor, the aforementioned research areas suggest that the outcomes of manufacturers' investments in sports competitions would still be an important research area for years to come.
Supplemental Material, DS_10.1177_0022242919831996 - Gear Manufacturers as Contestants in Sports Competitions: Breeding and Branding Returns
Supplemental Material, DS_10.1177_0022242919831996 for Gear Manufacturers as Contestants in Sports Competitions: Breeding and Branding Returns by Yvonne van Everdingen, Vijay Ganesh Hariharan and Stefan Stremersch in Journal of Marketing
Footnotes 1 Author ContributionsThe authors contributed equally to the article.
2 Associate EditorAric Rindfleisch served as associate editor for this article.
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors thank Erasmus Committee for Promoting Applied Research (CSTO), Erasmus Research Institute of Management (ERIM), Erasmus Center for Marketing and Innovation, and IESE Business School for their financial support.
5 Online supplement: https://doi.org/10.1177/0022242919831996
6 1Note that this expectation is solely based on the magnitude of advertising spending as a signal (i.e., the more a brand spends on advertising, the more it signals high quality) and not on advertising content that may be either aligned with or not aligned with competing in sports contests. We regard advertising content as outside the scope of the article and formulate this hypothesis ceteris paribus (thus, including independent of variation in advertising content). We return to this issue in the "Discussion" section.
7 2The key findings of our study are not sensitive to the exclusion of these three brands from our sample (see the "Robustness Checks" subsection).
8 3In some cases (BMW in 2010, Jaguar during 2003–2004, and Lotus during 2010–2015), although the brand name appeared in the team's name, the manufacturer did not supply the engine used in the races. Therefore, we do not expect breeding effects to be present for these cases. However, because the brand name appeared on the team's name, branding effects may still be present. Nevertheless, we show that the results of our breeding analyses are robust when we exclude these observations from our data set (see the "Robustness Checks" subsection).
9 4Patent information is based on the priority year and is made available after the date of publication of the application. There is a time lag of up to 30 months between the application of the patent and the availability of the information in the PATSTAT database (https://ec.europa.eu/eurostat/cache/metadata/EN/pat%5fesms.htm). Therefore, we use information on patents until December 2013.
5Porsche's annual reports were not available prior to 2004. For these early years we assumed Porsche's R&D spending to be 8% of its owner Volkswagen Group's R&D spending, which was the case during the fiscal year 2005–2006.
6Registration data are available in Germany until September 2015 and in France until August 2013.
7For Spain, reliable viewership data is available only from 2004. Therefore, we drop the observations prior to 2004 for Spain from our branding analysis.
8We regret that we do not have information on monthly prices of passenger vehicles. Because there is very little variation in prices at the brand level (most price variation occurs at the model level or because of customizations), we believe that the effect of the price level on sales performance is captured by the fixed effect in Equation 2.
9We estimated the model with different values of the carryover parameter and chose the model with the highest R2. We obtained the highest model fit when the carryover parameter was set to 86%.
10This indicates that 5% of our observations have zero advertising spending. This is not uncommon given that we measure advertising spending at the monthly level.
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By Yvonne van Everdingen; Vijay Ganesh Hariharan and Stefan Stremersch
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Record: 80- Gift Purchases as Catalysts for Strengthening Customer–Brand Relationships. By: Eggert, Andreas; Steinhoff, Lena; Witte, Carina. Journal of Marketing. Sep2019, Vol. 83 Issue 5, p115-132. 18p. 1 Diagram, 6 Charts, 2 Graphs. DOI: 10.1177/0022242919860802.
- Database:
- Business Source Complete
Gift Purchases as Catalysts for Strengthening Customer–Brand Relationships
Gift giving is an effective means to strengthen interpersonal relationships; it also may initiate and enhance customer–brand relationships. Through a field study conducted with an international monobrand retailer of beauty products, a combination of propensity score matching with difference-in-differences estimations, and two experimental scenario studies, this research demonstrates that gift buyers spend 63% more in the year following a gift purchase than a matched sample of customers who purchase for their personal use. Specifically, gift buyers increase their purchase frequency (25%), spend more per shopping trip (41%), and engage in more cross-buying (49%). The sales lift is particularly pronounced among new customers. Identity theory suggests customer gratitude and public commitment as mediating mechanisms. Gift purchase design characteristics (i.e., assistance during gift purchase and branded gift wrapping) influence the strength of the mediating mechanisms.
Keywords: gift giving; gratitude; propensity score matching; public commitment; relationship marketing
Gift purchasing is an intriguing consumer behavior with great economic relevance; in the United States alone, consumers spend an average of $1,851 annually buying gifts for their family and friends ([71]). A gift purchase, or buying a product to give to another person through some form of ritual presentation ([ 3]), constitutes a special buying occasion and a relational investment ([13]; [39]). By giving and receiving gifts, people create and sustain their mutual interdependence ([61]), in that each gift creates social indebtedness that persists until the next instance of gift giving, when the recipient and donor can reverse roles. By evoking gratitude-based reciprocity ([51]), gifts act as relational catalysts that nurture and consolidate interpersonal relationships long after the actual gift exchange ([14]).
Gifts not only create and reinforce social ties but also bond the donor to the gifted brand. Gift purchases often have symbolic meaning and evoke situational involvement, complex decision-making processes, and increased willingness to pay ([22]). They may engage customers deeply with the gifted brand ([37]) and promote their future brand loyalty ([72]). Yet empirical research has not addressed the consequences of gift purchases for building and sustaining customer–brand relationships. [ 7] links customers' past gift spending to future personal and gift spending, but otherwise, we know of no research that investigates the effects of gift purchases on customers' brand relationships.
To advance understanding of this relevant and largely overlooked effect, we explore within a monobrand retailing context the link between gift purchases and customers' attitudes and future purchase behaviors toward the gifted brand. In so doing, we develop theoretical insights that can help managers leverage gift purchases as brand relationship catalysts. In contrast with most relationship marketing instruments (e.g., loyalty programs), encouraging customers' gifting behavior does not trigger any considerable costs and instead generates instant returns.
We extend a relationship marketing perspective to the intriguing consumer behavior of gift purchasing with a mixed-method approach that combines a field study and two experimental studies employing monobrand retailing contexts. In a field study, we test whether gift purchases strengthen customers' brand relationships, using transactional customer data to quantify the performance ramifications of gift purchases. We further disentangle the effects on purchase frequency, spending per shopping trip, and cross-buying to evaluate how a gift purchase affects future buying behavior. Then, in a first experimental scenario study, we test our conceptual model to explain why the catalytic effect occurs. Gift purchasers, compared with consumers who purchase items for their personal use, appear to form stronger attitudes toward the brand, which enhance their future purchase behavior. In turn, we identify enhanced customer gratitude and public commitment as psychological mechanisms to explain the gift purchase–future purchase behavior link in a controlled setting. Moreover, we specify when gift purchases have more pronounced effects on future purchase behavior by considering prior customer relationships as a contingency variable. In a second scenario experiment, we examine how different gift purchase design characteristics (i.e., assistance during gift purchase and branded gift wrapping) differentially influence the attitude-strengthening mechanisms.
With this unique and novel perspective, we contribute to marketing research and practice in three main ways. First, we theoretically propose and empirically demonstrate a catalytic effect of gift purchases for customer–brand relationships. With objective sales data from an international monobrand retailer of beauty products, we show that the mere act of purchasing a gift (cf. nongift purchase) leads to subsequent sales increases of 63%. In a post hoc analysis, we also determine that gift purchasers engage in more shopping trips (25% lift), more spending per shopping trip (41%), and more cross-buying (49%). Second, building on identity theory, we establish customer gratitude and public commitment as mediating mechanisms that explain the impact of gift purchases on customers' attitude strength and future purchase behavior. Third, we show that gift purchases are especially effective among less experienced buyers who engage in few or no shopping trips prior to their gift purchase. This insight is valuable for marketing practice because infrequent customers often represent a substantial proportion of firms' customer bases and are difficult to attract with traditional relationship marketing tools. Finally, we demonstrate that different gift purchase characteristics evoke unique psychological responses: when purchasers receive assistance during their gift selection process, they experience greater gratitude. A gift packaged in the provider's branded wrapping paper brings the customer's public commitment to the fore. By clarifying the catalytic effects of gift purchases for customer–brand relationships, this research thus offers novel insights for marketing research and practice.
To shed light on the catalytic effect of gift purchases on customer–brand relationships, we contrast gift purchases with personal use purchases (Table 1) and delineate the implications of their differences for a purchaser from an identity perspective, which then serves as the theoretical foundation for our conceptual model development. Gift purchases involve buying a product (good or service) to give to another person as a present (i.e., no expectation of monetary compensation), using some sort of ritual presentation ([ 3], [ 4]; [ 6]). The primary objective is to please the gift recipient, for the purpose of "establishing, defining, and maintaining [an] interpersonal [relationship]" with him or her ([ 4], p. 100; [48]). In turn, a personal use purchase involves buying a product for personal consumption, with the underlying motive of satisfying one's own individual needs or wants.
Graph
Table 1. Differences Between Gift Purchases and Personal Use Purchases.
| Distinguishing Attributes | Gift Purchase | Personal Use Purchase |
|---|
| Purchase Characteristics | | |
| Definition | Divergence of buyer and consumer: The purchased gift is given to another person as a present (with no expectation of monetary compensation) through some sort of ritual presentation (Belk 1976, 1979; Belk and Coon 1993). | Convergence of buyer and consumer: The product is purchased for one's own consumption. |
| Primary goal | Pleasing the recipient: The primary motive is to please the gift recipient, thereby "establishing, defining, and maintaining [an] interpersonal [relationship]" with him or her (Belk 1979, p. 100; Otnes, Lowrey, and Kim 1993). | Fulfilling personal needs or wants: The purpose is to satisfy a personal, utilitarian, or hedonic consumption motive. |
| Interpersonal risk | High: Gift givers often experience substantial social risk or anxiety (Wooten 2000), due to the high visibility. An effective gift can be highly bonding; an unsuccessful gift can severely hurt the interpersonal relationship between the giver and recipient (Sherry 1983). | Low: Even if visibly consumed, purchases have limited potential to threaten social or interpersonal relationships. |
| Components of Interpersonal Risk Involved in Purchase | |
| Choice accuracy risk | Enhanced: Product selection is challenging because the buyer is not the ultimate user, and gift buyers have limited knowledge of the recipients' preferences, but selecting the wrong gift risks damaging their social relationship (Wooten 2000). To fulfill the primary motive to please the gift recipient, gift buyers may select products that conflict with their own taste (Otnes, Lowrey, and Kim 1993; Ward and Broniarczyk 2011). Given this increased complexity, gift purchasers are more activated and invest more cognitive effort in the purchase decision, spending more time, visiting more stores, studying more information, and engaging in more advice seeking (Clarke and Belk 1979; Gronhaug 1972). | Limited: The search effort still might be high, but product selection overall is easier because the buyer knows or develops individual preferences in the product selection process rather than guessing at somebody else's preferences. Choosing the wrong product does not incur significant damage and instead just prompts a different product choice in the next purchase occasion. |
| Signaling risk | Superordinate: Gift givers aim to signal empathy, thoughtfulness, closeness, and intimate knowledge of the gift recipient (Ward and Broniarczyk 2016), both directly in the one-to-one relationship and indirectly to a wider audience (e.g., when publicly presenting a gift at a birthday party). In addition, gift buyers tend to display a higher willingness to pay (Fuchs, Schreier, and Van Osselaer 2015; Moreau, Bonney, and Herd 2011) and use gift prices to indicate the importance of their relationship with the recipient (Wang and Van der Lans 2018). | Subordinate: Signaling generally plays a subordinate role (cf. conspicuous consumption) and occurs only indirectly (i.e., to the general public rather than to a specific person). Personal use buyers typically exhibit a limited willingness to pay, such that they seek good value for money and carefully assess whether a product offers a net benefit. |
| Implications from an Identity Perspective | |
| Purchase-related identity | Gift giver: Buyers act in their social role or identity of a gift giver who buys for another person. The identity directly involves both the self and the other. | Personal consumer: Buyers act in their social role or identity as personal consumers of products. The identity focuses on the self and may only indirectly involve others. |
| Identity salience | Superior: Gift buyers not only expose but also explicitly transmit their identity to others through gift giving, such that products "become containers for the being of the donor, who gives a portion of that being to the recipient" (Sherry 1983, p. 159). As social, relational beings, people try to show that they are empathic, thoughtful, and caring. An identity as a gift purchaser or donor thus is highly salient. Giving to others offers a salient self-enhancement tactic and opportunity to strengthen or damage social bonds. | Relatively lower: Consumers make purchase decisions and use products to fulfill their needs or wants, as well as to communicate a desirable identity to others (Fournier 1998). Yet identity-relevant products remain with the buyer, so no transmission of identity information takes place, as in gift giving. Personal use purchases are relevant identity signals but are less salient than gift purchases. |
Gift purchases and personal use purchases are fundamentally distinct because of their differential level of interpersonal risk involved. Gift purchases constitute a relational investment and an effective way to nurture the interpersonal, social tie with the gift recipient ([13]). Thus, donors sense substantial interpersonal risk and may even feel anxiety when purchasing gifts ([79]). Gift purchases can affect the recipient's perceptions of the donor, such that a successful gift can evoke strong bonding, whereas an inappropriate gift can disrupt the interpersonal relationship between the donor and recipient ([62]). Products bought for personal use, even if they are visible to others, have only limited potential to threaten social, interpersonal relationships. The interpersonal risk that gift purchasers sense stems from two important challenges, such that their gift needs to ( 1) meet the recipient's preferences (choice accuracy risk) and ( 2) signal appropriate relational meaning to the recipient (signaling risk) ([39]).
First, gift purchasers experience considerable choice accuracy risk, because identifying and selecting the right product is more complex and challenging in a gift purchase than in a personal use purchase situation. For gift purchases, the buyer and consumer diverge; the buyer is not the ultimate user. Gift buyers may have limited knowledge of recipients' preferences or might even feel forced to choose a product that is incongruent with their own taste in an effort to please the gift recipient ([48]; [77]). Given this enhanced complexity, gift purchasers are more activated and devote more cognitive effort to their purchase decision, such that they invest more time, visit more stores, study more information, and engage in more advice seeking ([16]; [28]). In contrast, even if the search effort is high when buying for personal use, the product selection overall is easier, because the buyer knows or develops individual preferences in the product selection process, rather than guessing at somebody else's preferences.
Second, gift purchases involve notable signaling risk. Through gifts, donors try to signal their empathy, thoughtfulness, and closeness to and intimate knowledge of the gift recipient ([78]). Moreover, gift purchasers want to convey generosity and thus tend to display a higher willingness to pay ([22]; [44]), using gift prices to signal to the recipient the importance that the donor attaches to the relationship ([75]). Signaling in gift purchasing is both direct, in the one-to-one relationship between donor and recipient, and indirect, relative to a wider audience (e.g., when publicly presenting a gift at a birthday party, encompassing people the donor may know personally; [79]). In personal use buying, signaling plays a subordinate role. Even when this function exists, such as for conspicuous consumption instances, it occurs more indirectly (i.e., toward the general public rather than a specific, known person) than in gift giving.
Identity literature proposes that the self consists of multiple identities ([68]). Being a purchaser of products constitutes an important component of overall identities; what we buy and consume defines us to both ourselves and others ([21]; [67]). In this research, we contrast two relevant, common consumer identities ([ 5]), gift purchaser (donor) and personal use purchaser, in terms of their impact on customer–brand relationships. A central, deeply ingrained human raison d'être is self-enhancement, such that people strive to maximize positive perceptions of the self ([38]; [70]). Identity theory ([12]; [69]) suggests that some identities are more salient for self-enhancement than others. Specifically, because "humans are fundamentally a social species" ([31], p. 96), social interactions and relationships are crucial to the construction, maintenance, and enhancement of the self ([19]; [42]). To make sense of ourselves, we rely on feedback and seek appraisals from relationship partners. Their real, perceived, or imagined reactions exert powerful influences on the self ([70]). Notably, because gift giving is a sensitive, self-relevant act, such that "objects become containers for the being of the donor, who gives a portion of that being to the recipient" ([62], p. 159), a consumer's identity as a donor—which entails the immediate involvement of a relevant other (i.e., gift recipient) and, thus, interpersonal risk—is potentially more self-enhancing or "self-gratifying" ([ 5], p. 158) than buying for personal use.
A successful gift purchase reinforces and elevates the customer's salient identity as a donor and provides self-enhancement, so it also could have an effect on the strength of the customer–brand relationship.[ 5] We employ an identity perspective ([69]; [70]) to derive the conceptual model in Figure 1.
Graph: Figure 1. Gift purchases enhance customers' future buying behavior.Notes: H2a and H2b represent serial mediation hypotheses.
An effective gift purchase augments an important social identity and evokes a more self-enhancing experience than an effective personal use purchase does ([ 5]; [62]), so a gift purchase may spur stronger reactions toward the brand. Specifically, donors, compared with personal user purchasers, should exhibit enhanced purchase behavior following a gift purchase because acting as a donor constitutes an important identity function, in which the purchaser tries to gain positive feedback and social approval and thereby enhance the self ([19]; [60]). To clarify the underlying psychological processes, we propose that gift purchases trigger donors' ( 1) gratitude and ( 2) public commitment toward the gifted brand, which in turn foster attitude strength and purchase behaviors.
First, a gift purchase triggers customer gratitude, defined as "emotional appreciation for benefits received" ([51], p. 1). If a customer perceives a greater need for a benefit, his or her gratitude increases ([51]). Donors sense a pressing need to find an appropriate gift. The relevance of gifts to reinforce favorable donor identities and build and sustain social relationships further suggests that finding the right gift entails considerable choice accuracy risk and thus might be arduous ([ 2]), such that finding the right gift represents a solution to a salient problem and a valuable benefit. Therefore, gift purchasers, compared with customers purchasing for personal use, may experience more gratitude toward the brand that provides the gift.
Second, gift purchases lead to greater public commitment than purchases for personal use. Public commitment involves publicly taking a positive position toward a brand ([34]; [47]), and a gift purchase is a publicly visible decision, at least to the gift recipient, involving signaling risk. By gifting a branded item, donors indicate that they deem the focal brand appropriate and commit themselves, through their donor identity, to the brand. The brand provides a public means for the donor to convey an identity as a capable gift giver ([62]), which makes the brand a part of the customer's self and bonds them. Personal use purchases instead involve less visibility, so these purchasers likely experience less public commitment and brand connections ([33]).
Through increases in customer gratitude and public commitment, gift purchasers should form stronger attitudes toward the brand. We define attitude strength "as the positivity or negativity (valence) of an attitude weighted by the [...] certainty with which it is held" ([53], p. 1). Thus, attitude strength consists of two elements: attitude valence and attitude certainty. People's judgments are affected by their feelings ([51]), so if gift purchasers feel a positive emotion like gratitude toward the brand, their attitude valence should increase ([ 1]). Likewise, what people publicly commit to by passing it on to somebody else as a gift still becomes part of themselves and enhances their liking for the object ([ 5]). In addition, feeling gratitude and making public commitments increase people's confidence in their attitudes ([18]; [27]), thereby enhancing attitude certainty as well. Attitudes then guide behavior ([53]). In the context of gift buying, a gifted brand may not always be suitable for personal use (e.g., when a nonsporty person buys a sports gear brand as a gift), impeding personal use purchases. Still, a strong attitude may lead the gift giver to repurchase the brand in future gifting occasions. Thus, strong attitudes likely enhance future purchase intentions and behaviors toward the brand for additional gift and/or personal use purchases ([55]; [56]).
- H1: Customers purchasing a gift (vs. product for personal use) display higher future purchase behaviors toward the selected brand.
- H2: Customers purchasing a gift (vs. product for personal use) display higher future purchase behaviors toward the selected brand, serially mediated by (a) gratitude and then attitude strength and (b) public commitment and then attitude strength.
The effect of a gift purchase might vary according to the prior relationship between the customer and the focal brand, which reflects customers' previous purchase experience, or the number of shopping trips in which they engage before their first gift purchase. Customers with many past purchase experiences likely have more knowledge of and stronger attitudes toward the brand ([24]; [35]), whereas customers with less experience with the brand may have weaker attitudes. The catalytic effect of purchasing a self-enhancing gift then may be more influential for the latter group, whose weak attitudes at the time of their first gift purchase allow for a more notable increase in attitude strength and future purchase behavior.
For gift purchasers seeking self-enhancement by reinforcing their donor identity, buying a brand that they have had little experience with is a particularly risky choice. If the gift succeeds (i.e., pleases the recipient), the risky purchase constitutes a positive, transformational relationship event for the donor ([29]). Depending on the customer relationship stage, different events exert varying impacts; initially, relational confidence is lower and risks are higher, so a transformational relationship event is more likely, which can spur strong customer reactions and alter the relationship trajectory. Thus, for less experienced customers early in the brand relationship, compared with more experienced ones, the gift purchase can create a positive transformational relationship event, which more likely induces favorable behavioral changes.
- H3: The positive effect of purchasing a gift (vs. product for personal use) on purchase behavior is weaker among customers with more purchase experience.
Because gift purchases foster customers' gratitude and public commitment toward the brand, we propose two design characteristics whose presence likely enhances gift purchasers' corresponding attributions to the brand. First, the focal brand could deliberately reduce the complexity of the gift purchase situation, such as by offering helpful assistance, thereby stimulating increased gratitude among gift purchasers. Supportive behavior by frontline employees increases customer gratitude in general ([ 9]). For gift purchasers in particular, dedicated assistance helps them solve the pressing problem of finding an appropriate gift ([51]). We anticipate a higher level of customer gratitude when the donor receives helpful consultations or assistance during the gift selection process.
- H4: Gift purchasers who receive assistance (vs. no assistance) during their gift purchase process exhibit more gratitude toward the brand.
Second, the brand can leverage gift purchasers' public commitment by offering branded gift wrapping. Gift wrapping represents an important aspect of ritual presentation ([62]; [63]). That is, high quality gift wrapping services that feature branded packaging (e.g., paper, bags, boxes) help the donor augment the gift presentation. They also make the joint effort by the donor and the brand to please the recipient more salient, to the donor, the recipient, and any potential audience. The donor visibly embraces the brand as a partner that enhances a donor identity, which emphasizes gift purchasers' public commitment to the brand ([27]; [34]).
- H5: Gift purchasers who present their gift in branded gift wrapping (vs. in nonbranded gift wrapping) exhibit greater public commitment toward the brand.
With Study 1, we investigate the strengthening effect of gift purchases on customer–brand relationships in a field setting, using transactional, real-life data gathered from an international monobrand retailer of beauty products (H1). We also consider whether customers' prior relationship with the monobrand retailer influences the catalytic effect of gift purchases (H3). With a field study, we establish the external validity of our conceptual model and quantify the effect of gift purchases on customer–brand relationships, using actual firm data.
The field data come from an international monobrand retailer that produces and sells high-end beauty products through both physical and online stores. The beauty industry is an appropriate context, because gifts account for a high percentage of companies' overall sales ([46]). We analyze customer transactions with a monobrand retailer, such that the manufacturer, retailer, and salespeople all represent the same brand. This setting is well suited for our fundamental research questions, because we avoid commingling the relational effects of gift purchases across distinct manufacturer and retailer brands. The focal company provided information about which products consumers purchased and when, over an observation window from January 2011 to December 2013. A customer is identified in the database if (s)he has provided a valid email address to the monobrand retailer. Each customer included in the database receives a personal ID number. Transactions are matched to customers according to the email address or ID number that they indicate when checking out or the credit card used in previous transactions. The data set contains 84,112 customers, randomly sampled from six markets (United States, United Kingdom, France, Germany, Spain, and Italy). With these transactional customer data, we determine the extent to which gift purchases (vs. personal use purchases) affect donors' future purchase behaviors.
Observational field data provide insights with high external validity, but to use them to assess causal claims, we also must address endogeneity ([25]). In line with recent studies ([10]; [11]; [32]), we apply a quasi-experimental approach that combines propensity score matching (PSM) with a difference-in-differences estimation to test the causality of a gift purchase on future purchase behavior. With PSM, we address self-selection effects ([36]) before we analyze the matched sample using a difference-in-differences estimation that controls for cross-sectional and time-series effects ([45]). By combining PSM and difference-in-differences modeling, we can control for observed and unobserved confounds of the effect of purchasing a gift ([32]).
The purchase of a gift constitutes the experimental treatment. We aim to establish whether this treatment causes a certain outcome (i.e., enhanced future purchase behavior) by identifying differences between customers in treatment versus control conditions. That is, we compare customers who purchased a gift (treatment group) with customers who never purchased a gift from the monobrand retailer (control group). Web Appendix A details the construction of treatment and control group from the original sample. We include only customers who made their first documented purchase in the data set during our observation window verified by their create date in the database; for customers in both groups, no prior purchases were gifts. We identify 547 customers who made their first gift purchase during a treatment period aligned with a holiday season (October–December 2012), which accounts for a large percentage of all gifts given each year ([14]; [40]). We coded a transaction as a gift purchase if the shopping basket contained at least one of the following items: a gift set, gift card, or gift wrapping. In the control group, 5,770 customers never purchased a gift but engaged in a transaction during the treatment period. Thus, customers in both the treatment and control groups engaged in purchases during the same period, but their intended uses (gift or personal) varied. Web Appendix B illustrates the windows for the sample selection, PSM, and difference-in-differences modeling.
Because participants were not randomly assigned to receive the treatment but self-selected into the treatment group, gift purchasers could differ systematically from purchasers in the control group. To account for this potential self-selection bias ([24]; [29]; [36]), we employ PSM and create an artificial control group. First, in a binary logistic regression, we calculate each customer's propensity to buy a gift (see Table 2, Panel A). Second, the matching procedure links each customer in the treatment condition with a statistical twin from the control group, who did not buy a gift but has statistically the same propensity to do so. With a caliper matching procedure, we match each treatment case to its nearest neighbor only if the two propensity scores fall within a prespecified tolerance zone ([17]; [73]). Limiting the propensity scores to differ by a maximum of.001—well below the recommended tolerance zone of.008, according to the [64] rule—we match 541 customers from the treatment condition with customers who never purchased a gift (Table 2, Panel B). Third, we evaluate matching quality and compute percentage reductions in bias (PRB) for the matches. The average PRB for all predictors is 96%, indicating a strong reduction of self-selection biases. Fourth, following [11], we compute standardized differences in means before and after the matching (Table 2, Panel B). After matching, the maximum standardized differences in means are.03, far below the recommend value of.25 ([11]).
Graph
Table 2. Study 1: Propensity Score Matching Results.
| A: Determinants of Gift Purchase Propensity |
|---|
| Exogenous Variable | Coefficient | Wald | | | | | | |
|---|
| Constant | −2.142*** | 1,173.86 | | | | | | |
| Sales volume per shopping trip | .006** | 7.75 | | | | | | |
| Number of shopping trips | −.168** | 6.06 | | | | | | |
| Number of different segments | .349*** | 24.85 | | | | | | |
| Number of discounts per shopping trip | −.516** | 6.53 | | | | | | |
| Customer status (existing/new) | −1.316*** | 24.48 | | | | | | |
| Relationship duration (days) | −.002** | 7.27 | | | | | | |
| B: Means Before and After Matching |
| Means Before Matching | Standardized Mean Difference | Exogenous Variable:Customer Behavior Before Treatment(Jan. 2011–Sep. 2012) | Means After Matching | Standardized Mean Difference | PRBa |
| Control Group(n = 5,770) | Treatment Group (n = 547) | Mean Comparison p-Value | Control Group(n = 541) | Treatment Group(n = 541) | Mean Comparisonp-Value |
| 17.40 | 12.10 | .00 | −.17 | Sales volume per shopping trip | 10.04 | 10.19 | .93 | .01 | 97% |
| 1.10 | .44 | .00 | −.49 | Number of shopping trips | .47 | .43 | .69 | −.03 | 95% |
| 1.25 | .71 | .00 | −.32 | Number of different segments | .63 | .66 | .81 | .01 | 96% |
| .21 | .09 | .00 | −.37 | Number of discounts per shopping trip | .08 | .09 | .64 | .03 | 92% |
| .41 | .19 | .00 | −.55 | Customer status (existing/new) | .17 | .18 | .81 | .01 | 97% |
| 79.49 | −1.99 | .00 | −.60 | Relationship duration (days)b | −5.62 | −3.84 | .83 | .01 | 98% |
| | | | | | | | Average PRB: | 96% |
1 **p <.05.
- 2 ***p <.01.
- 3 aIn line with [24], we calculated the PRB using a formula from [58].
- 4 bFor a fine-grained assessment of customers' relationship duration prior to their treatment period purchase, we counted the days between the first documented purchase and the first day of our treatment period (October 1, 2012). Thus, for a customer making his or her first overall purchase 30 days into the treatment period (i.e., around November 1, 2012), a negative relationship duration results (−30).
- 5 Notes: Two-tailed tests of significance.
Next, we analyze the matched sample of customers using a difference-in-differences approach, such that we compare sales differences (i.e., posttreatment sales − pretreatment sales) for customers in the treatment versus the control group. This approach controls for both customer characteristics that are time invariant and time trend effects that could confound the causal effects of a gift purchase ([ 8]; [26]; [32]). We use the following specification to test H1:
Graph
where Salesigt is customer i's sales from group g at time t; Treatmentg is a dummy variable that equals 1 for customers in the treatment group and 0 for customers in the control group; Time periodt is a dummy variable that takes a value of 1 in the posttreatment period and 0 in the pretreatment period; and β3, the interaction coefficient for Treatmentg and Time periodt, represents the primary coefficient of interest, capturing the causal effect of purchasing a product as a gift or for personal use on sales differences ([32]). Furthermore, as individual level covariates, we include five country dummy variables (i.e., Germany, Spain, France, United Kingdom, and Italy), where the United States serves as reference category. We thus capture sales levels for the treatment and control groups between October 2011 and September 2012 (i.e., pretreatment period) and then from January to December 2013 (posttreatment period) (Figure 2, Panel A). With these measures, one year before and one year after the treatment, we avoid seasonal effects.
Graph: Figure 2. Study 1: Field study supports gift purchases' impact on future sales.
Table 3 contains the estimation results for the difference-in-differences model. We find that β3 is positive and significant; purchasing a gift versus purchasing a product for personal use leads to a sales increase of $27.43 in the year after the gift purchase, in support of H1. Figure 2, Panel A, depicts the levels and changes in average sales for the treatment and control groups. After matching, in the pretreatment period, we find a nonsignificant difference of $1.01 in average sales; the two groups do not differ in sales levels before the treatment, substantiating the effectiveness of our PSM. After the treatment period, however, gift purchasers exhibit significantly higher average sales levels than nongift purchasers: With sales of $70.67, they spent $27.43 more on average than the counterfactual trend level of $43.24 (i.e., sales of the treatment group if they did not receive the treatment), representing a sales increase of 63%.
Graph
Table 3. Study 1: The Effect of Purchasing a Gift on Average Sales.
| Variables | Coefficient |
|---|
| Difference-in-Differences Estimation (n = 1,082) | |
| Constant | 99.17*** |
| Treatment | 30.59*** |
| Time period | 49.08*** |
| Treatment × time period | 27.43*** |
| Country = Germany | −20.67*** |
| Country = Spain | −18.34** |
| Country = France | −36.09*** |
| Country = United Kingdom | −43.98*** |
| Country = Italy | −25.31*** |
| Robustness Check: Test of Parallel Trend Assumption (n = 1,082) |
| Constant | 24.81*** |
| Treatment | 2.12 |
| Fake time period | 6.80*** |
| Treatment × fake time period | 2.56 |
| Country = Germany | −7.90** |
| Country = Spain | −9.75*** |
| Country = France | −12.54*** |
| Country = United Kingdom | −16.18*** |
| Country = Italy | −11.01*** |
| Robustness Check: Validity of Treatment Group Construction (n = 541) |
| Constant | 69.08*** |
| Fake treatment | 6.78 |
| Time period | 20.05*** |
| Fake treatment × time period | 3.20 |
| Country = Germany | −30.32*** |
| Country = Spain | −8.94 |
| Country = France | −32.72*** |
| Country = United Kingdom | −33.83*** |
| Country = Italy | −30.92*** |
- 6 **p <.05.
- 7 ***p <.01.
- 8 Notes: One-tailed tests of significance.
We also consider how a prior customer–brand relationship might influence the effect of gift purchases on future sales. Using a floodlight analysis, we determine levels of prior purchase experience at which the sales effect is stronger or weaker ([65]). We find that the interaction between the treatment (i.e., gift vs. personal use purchase) and purchase experience exerts a negative, significant effect on sales differences (b = −13.30, p <.05), in line with H3. As Figure 2, Panel B, shows, the sales effect of gift purchases significantly diminishes with more purchase experience, and a gift purchase exerts a stronger effect among less experienced customers. The floodlight analysis identifies a Johnson–Neyman point at a pretreatment purchase frequency of 1.18; the sales effect of a gift purchase is significant for customers who engaged in no more than one shopping trip before the treatment.
In line with [32], we conduct robustness checks to confirm the success of our quasi-experimental strategies, including a placebo regression that tests whether the parallel trend assumption holds before the treatment and a second placebo regression that uses a fake treatment group to validate the construction of our treatment group. First, the identifying assumption behind the difference-in-differences approach is that without the intervention, the two groups would behave the same way. To confirm whether the two groups display similar trends in their purchase behavior before the treatment, in a placebo regression, we use sales data from the first half of the pretreatment period (October 2011–March 2012) as "fake" pretreatment data and then consider the second half (April–September 2012) as "fake" posttreatment data. The β3 coefficient of the interaction between treatment and "fake" time period should not be significant if the pretreatment parallel trend assumption holds. As Table 3 shows, the difference-in-differences estimator for the placebo estimation is not significant (2.56, p >.05), affirming the parallel trend assumption for our study.
Second, we validated the construction of our treatment group with another placebo estimation that uses a "fake" treatment group ([32], p. 101). From among the control group, we randomly chose 50% as fake treatment customers and 50% that remain as fake control customers. A significant difference-in-differences estimator would render the construction of the original treatment group questionable. However, as Table 3 shows, the difference-in-differences estimate of this second placebo estimation is not significant (3.20, p >.05), which helps validate the original construction of the treatment and control groups.
We also conducted post hoc analyses of our field data to confirm the robustness of our findings and delineate how a gift purchase affects different facets of future customer behavior. We link gift purchases to other sales-related variables of interest; with additional difference-in-differences estimations, we assess how purchasing a gift affects the number of shopping trips, spending per shopping trip and cross-buying in the year after the treatment period. Overall, purchasing a gift (vs. a product for personal use) translates into enhanced future buying behavior. Gift purchasers return.21 times more often than nongift purchasers, on average. Relative to the counterfactual trend level of.81 shopping trips, it represents a 25% increase in the number of shopping trips. They spend $8.41 more per transaction than nongift buyers, for a 41% increase in spending per shopping trip. Finally, purchasing a gift positively influences cross-buying behavior. The relative gain in the number of product segments among gift purchasers is.42, for a cross-buying increase of 49%. These findings affirm the robustness of our results and also demonstrate that the mere act of purchasing a gift, instead of a product for personal use, has positive effects on various sales-related variables in the year after the gift purchase.
Having established gift purchases' positive effect on customers' future purchase behavior, we conduct an experimental scenario study with three goals. First, we replicate and isolate the behavioral effect of gift purchases on the customer–brand relationship in a controlled setting with high internal validity. Second, we demonstrate how the behavioral effect identified in Study 1 unfolds, including the mediating mechanisms that explain why gift purchases affect customers' future purchase behavior (H2). Third, we specify gift purchases' effect in a consumer electronics setting—that is, a different, more utilitarian industry context.
We employ a two-group design, such that the two groups vary in the type of purchase: gift or personal use. For the online data collection, we recruited 146 adult respondents from Germany, according to age and gender quotas. We distributed links to the online questionnaire through email and social media. Participation was voluntary, and as an incentive, all participants who completed the questionnaire entered a lottery to win one of two Amazon gift cards (value equivalent to $30). Their mean age was 33 years, and 56% were women. The data confirm that gift giving is a common, relevant consumer behavior; on average, these respondents purchased 18 gifts per year and spent $37.10 per gift.
Each participant, assigned randomly to the experimental scenarios, received a short scenario and questionnaire. The scenario described the participant's relationship with a fictitious brand, "FunTech," which produces and sells consumer electronics. All participants were told upfront that they had always been satisfied with the company, so their prior experiences were constant. After the description of their prior relationship with the brand, we measured participants' attitude strength before the manipulation; the two groups did not differ in their attitudes toward the brand (Mgift = 30.66, Mpersonal = 29.30; F =.48, p >.05). Next, both groups had to imagine that they purchased headphones, but for the manipulation, we varied the intended use of the product. Participants in the treatment group were told that they were searching for a product to give as a gift to their cousin, who is a close friend and to whose birthday party they had been invited. In the control group, participants read that they were searching for headphones for their personal use. The price of the headphones was held constant, at $59. A detailed description of the scenarios is in Web Appendix C.
The manipulation checks support the effectiveness of our manipulation. On a seven-point Likert-type scale, ranging from 1 = "strongly disagree" to 7 = "strongly agree," participants indicated accurately whether they purchased the headphones as a gift (Mgift = 6.63, SD = 1.08; Mpersonal = 1.67, SD = 1.44; t = 23.36, p <.01). The realism check also suggests that respondents easily imagined the described situation (M = 5.75, SD = 1.50).
We adapted established multi-item scales to measure customer gratitude ([51]), public commitment ([34]; [47]), attitude valence, attitude certainty ([53]), and purchase intentions ([74]) (see Table 4). In line with [53], we multiplied attitude valence by attitude certainty to capture attitude strength. We assessed the convergent and discriminant validity of our scales using Amos 24.0. As shown in Table 5, all the convergent validity measures exceed their common thresholds. Fornell and Larcker's (1981) criterion confirms discriminant validity.
Graph
Table 4. Studies 2 and 3: Construct Measures and Item Loadings.
| Item Loading |
|---|
| Construct Items (Scale Source) | Study 2 | Study 3 |
|---|
| Customer Gratitude (adapted from Palmatier et al.2009) | | |
| I appreciate what [brand] does for me. | .96 | .88 |
| I cherish what [brand] does for me. | .96 | .86 |
| I feel grateful for what [brand] does for me. | .97 | .97 |
| I feel thankful to [brand]. | .84 | .95 |
| Public Commitment (adapted from Kiesler1971; Nyer and Dellande2010) | | |
| I visibly show that I am a customer of [brand]. | .81 | .88 |
| By means of this purchase, I overtly demonstrate to others that I am a customer of [brand]. | .92 | .94 |
| This purchase visibly reveals me as a customer of [brand]. | .86 | .93 |
| Attitude Valence (adapted from Park et al.2010) | | |
| I enjoy being a customer of [brand]. | .92 | .91 |
| I evaluate [brand] positively. | .96 | .97 |
| I like [brand]. | .93 | .95 |
| I have a positive opinion about [brand]. | .93 | .95 |
| Attitude Certainty (adapted from Park et al.2010) | | |
| I can evaluate [brand] reliably. | .82 | .87 |
| I am sure that my evaluation of [brand] is accurate. | .93 | .92 |
| I am convinced of the opinion I formed about [brand]. | .97 | .95 |
| I have trust in my attitude toward [brand]. | .95 | .95 |
| Attitude Strength (adapted from Park et al.2010) | | |
| Attitude valence × attitude certainty | N.A. | N.A. |
| Purchase Intention (adapted from Wagner, Hennig-Thurau, and Rudolph2009) | | |
| I would continue buying products from [brand]. | .78 | .96 |
| I would consider [brand] my first choice supplier for consumer electronics products/healthy food products. | .74 | .88 |
| I would continuously purchase products from [brand] in the future. | .96 | .93 |
| In the future, I would purchase products from [brand]. | .96 | .96 |
9 Notes: N.A. = not applicable. All items were measured on a seven-point Likert-type scale (1 = "strongly disagree," and 7 = "strongly agree").
Graph
Table 5. Studies 2 and 3: Descriptive Statistics and Correlations.
| Construct | M | SD | AVE | 1 | 2 | 3 | 4 |
|---|
| 1. Customer gratitude | 4.22/5.18 | 1.51/1.36 | .87/.84 | .96/.95 | | | |
| 2. Public commitment | 5.27/5.32 | 1.31/1.52 | .75/.84 | .58/.53 | .90/.94 | | |
| 3. Attitude strength | 33.44/35.61 | 1.73/10.95 | N.A. | .57/.75 | .53/.55 | N.A. | |
| 4. Purchase intention | 6.02/5.93 | .96/1.22 | .74/.87 | .46/.74 | .56/.58 | .76/.87 | .92/.96 |
10 Notes: N.A. = not applicable; AVE = average variance extracted. Study 2 (Study 3) values are reported before (after) the slash symbol (/). Cronbach's alphas are reported on the diagonal (Study 2/Study 3).
Compared with those in the control condition, participants in the gift purchase condition reported higher levels of customer gratitude (Mgift = 4.45, Mpersonal = 3.96; F = 3.91, p <.05) and public commitment (Mgift = 5.51, Mpersonal = 5.01; F = 5.32, p <.05). To test whether these shifts also prompt stronger attitudes and purchase intentions, we use bootstrapping procedures ([30]). We conduct the mediation analysis with the PROCESS macro (Model 80; 5,000 bootstrapped samples) and simultaneously test the parallel and serial mediation properties of the conceptual model. The path coefficients are in Table 6. As recommended by [15], we use one-tailed testing for the directional hypotheses and rely on 90% confidence intervals (CI90%) to test the indirect effects, such that the resulting upper and lower bounds specify the 95% one-sided CI ([23]).
Graph
Table 6. Study 2: Path Coefficients.
| Independent Variable | | Dependent Variable | Mediating Variable | Hypothesis | 90% CIa | Coefficient | SE | t-Value |
|---|
| Direct Effects | | | | | | | |
| Gift purchase | → | Customer gratitude | | | | .49** | .25 | 1.98 |
| Gift purchase | → | Public commitment | | | | .49** | .21 | 2.31 |
| Gift purchase | → | Attitude strength | | | | −.77 | 1.55 | −.49 |
| Customer gratitude | → | Attitude strength | | | | 3.45*** | .60 | 5.72 |
| Public commitment | → | Attitude strength | | | | 2.49*** | .70 | 3.58 |
| Gift purchase | → | Purchase intention | | | | −.03 | .11 | −.27 |
| Attitude strength | → | Purchase intention | | | | .06*** | .00 | 13.69 |
| Indirect Effects | | | | | | | |
| Gift purchase | → | Purchase intention | Attitude strength | | [−.20,.12] | −.05 | .10 | |
| Gift purchase | → | Purchase intention | Customer gratitude and attitude strength | H2a | [.02,.20] | .10 | .06 | |
| Gift purchase | → | Purchase intention | Public commitment and attitude strength | H2b | [.01,.16] | .08 | .04 | |
- 11 **p <.05.
- 12 ***p <.01.
- 13 aWe rely on the bootstrapped 90% confidence intervals (CI), such that the resulting upper and lower bounds specify the 95% one-sided confidence interval ([23]).
- 14 Notes: R2 Customer gratitude = 3%; R2 Public commitment = 4%; R2 Attitude strength = 40%; R2 Purchase intention = 57%. One-tailed tests of significance.
Gift purchasers exhibit greater purchase intentions, mediated by customer gratitude and then attitude strength (bindirect_CusGra =.10, CI90% = [.02,.20]), in support of H2a. In line with H2b, gift purchasers also display greater purchase intentions mediated by public commitment and then attitude strength (bindirect_PubCom =.08, CI90% = [.01,.16]). When we control for the mediators, the remaining direct effects of gift purchase on attitude strength and purchase intentions are not significant, indicating that the effect is entirely mediated by customer gratitude and public commitment. The sum of the hypothesized indirect effects is significant (b =.18, CI90% = [.05,.31]), such that the two mediators significantly explain the positive effect of purchasing a gift, rather than a product for personal use, on future purchase intentions. The indirect effect through customer gratitude and then attitude strength explains 58%; that through public commitment and then attitude strength accounts for 42%. Thus, the affective response of customer gratitude and its subsequent influence on attitude strength is slightly more influential for explaining the indirect effect of purchasing a gift on future purchase intentions.
Study 3 provides a second scenario-based experiment to extend our findings in three ways. First, we investigate when the proposed mediating mechanisms driving the behavioral effects of gift purchases are more or less pronounced (H4 and H5). We test two managerially relevant gift purchase design characteristics that monobrand retailers can proactively deploy to affect customer gratitude or public commitment. Second, we substantiate the chain of effects of our mediating mechanisms on attitude strength and then future purchase behavior. Third, we study gift purchases in another relevant retail context (i.e., specialty foods), to enhance the generalizability of our findings.
To investigate how different gift purchase design characteristics influence the proposed mediating mechanisms, we focus on gift purchases and consider ( 1) the assistance provided by the focal firm during the gift purchase and ( 2) whether the gift purchaser presents his or her gift to the recipient in branded gift wrapping. The 2 (assistance during gift purchase: yes vs. no) × 2 (branded gift wrapping: yes vs. no) full-factorial between-subjects design includes four experimental groups. For the data collection, we recruited respondents from Prolific (https://prolific.ac/; [54]). Participants who completed the questionnaire received $1.10. Among the 159 respondents, the mean age was 36 years, and 53% were women. Only U.S. respondents were allowed to participate; they indicated they purchased 13 gifts per year and spent $37.31 per gift on average.
As in Study 2, each participant received a short scenario and questionnaire. The scenario described the participant's relationship with a fictitious brand, "NaturalYum," which produces and sells healthy and organic foods, snacks, and drinks. The scenario indicated they had always been satisfied with the brand. Next, all participants were told that they were searching for a product to give as a gift to their cousin, who is a close friend and to whose birthday party they had been invited. All groups had to imagine that they purchased a box of different food items for cooking and snacking, personalized to match their cousin's taste and priced at $50, which they presented to their cousin on the day of the birthday party.
For the manipulations, we varied the gift purchasing experience. To manipulate assistance during gift purchase, we varied the level of support provided, such that participants in the (no-) assistance condition imagined that a NaturalYum employee (no employee) approached them to provide helpful assistance. To manipulate branded gift wrapping, we asked participants to imagine that their gift was wrapped in branded paper such that NaturalYum's brand logo was printed across the paper and thus visible to others. Participants in the no branded gift wrapping condition read that their gift was wrapped in plain paper that did not feature NaturalYum's brand logo. We report the full manipulations in Web Appendix C.
The manipulation checks support the effectiveness of our manipulations. On a seven-point Likert-type scale, ranging from 1 = "strongly disagree" to 7 = "strongly agree," participants indicated accurately whether they received assistance from shop personnel during their gift purchase (Massistance = 6.56, SD = 1.25; Mno assistance = 1.90, SD = 1.93; t = 18.09, p <.01) and if they presented the gift in branded gift wrapping (Mbranded wrapping = 6.87, SD =.43; Mno branded wrapping = 1.61, SD = 1.63; t = 27.94, p <.01). The realism check suggests that respondents were easily able to imagine the described situations (M = 6.42, SD =.90).
We used the measures for customer gratitude and public commitment from Study 2. For control purposes, we also used the measures from Study 2 for attitude strength and purchase intentions. We assessed the convergent and discriminant validity of our scales in Amos 24.0. As we show in Table 5, all the convergent validity measures exceed their common thresholds. [20] criterion confirms discriminant validity.
We employ planned contrasts to test the hypothesized effects of assistance (branded gift wrapping) on customer gratitude (public commitment), while holding the other design characteristic constant, respectively. To establish the effect of assistance on customer gratitude, we compare our assistance and no branded gift wrapping condition with our full control condition (no assistance and no branded gift wrapping). Gift purchasers who receive assistance (vs. no assistance) express more customer gratitude (Massistance = 5.72, Mno assistance =4.39; F = 18.75, p <.01), in support of H4. For the effect of branded gift wrapping on donors' public commitment, we compare our branded gift wrapping and no assistance condition with our full control condition. The type of gift wrapping matters, such that donors who present their gift in branded (vs. not branded) gift wrapping exhibit greater public commitment (Mbranded wrapping = 5.77, Mno branded wrapping = 4.66; F = 11.38, p <.01), as we predict in H5. Figure 3 depicts the mean differences.
Graph: Figure 3. Study 3: Gift purchase characteristics differentially affect gift givers' psychological responses.Notes: One-tailed hypothesis testing.
As controls, we test whether increases in customer gratitude and public commitment translate into higher attitude strength and ultimately higher purchase intentions. In a mediation analysis in the PROCESS macro (Model 6; 5,000 bootstrapped samples), we find a significant, indirect effect of assistance during gift purchase on purchase intentions, mediated by customer gratitude and then attitude strength (bindirect_CusGra =.85, CI90% = [.47, 1.26]). The indirect effect of branded gift wrapping on purchase intentions, mediated by public commitment and then attitude strength, is also significant (bIndirect_PubCom =.46, CI90% = [.19,.77]).
Gifts are effective relationship-building catalysts. Extant research explores them almost exclusively from an interpersonal perspective, examining their impact on donor–recipient relationships. In contrast, we consider the catalytic effect of gift purchases from a novel relational perspective, with the prediction that the purchase of a gift can initiate and strengthen customer–brand relationships. We study this effect in a monobrand retailing context, because it avoids confounding the relational effects of gift purchases between a manufacturer and a retailer brand. Monobrand retailing is widespread in modern retail environments ([76]), and brands such as The Body Shop, Lush, and Zara rely on monobrand stores, both physical and online. However, this purposeful study design also represents a limitation, such that further studies are needed to generalize the results to retail settings in which the retailer and gift manufacturer are separate brands, as we discuss subsequently.
Yet with this approach, we find support for our conceptual model across three different industries (beauty, consumer electronics, and specialty foods), encompassing both hedonic and utilitarian contexts. The empirical studies consistently indicate that purchasing a product as a gift strengthens donors' attitudes and boosts their future purchase behavior toward the gifted brand. Compared with buying a product for personal use, gift purchases enhance customers' gratitude and public commitment, which foster their attitude strength and future purchase intentions and ultimately manifest in higher-cost, more frequent, and more diverse purchases from the gifted brand. In addition to mediating effects, we establish prior purchase experience as an important contingency. That is, the sales effect of gift purchases is more pronounced for customers with less experience with the brand. Furthermore, assistance during the gift purchase process and branded gift wrapping represent two gift purchase design characteristics that augment customer gratitude and public commitment, respectively.
By establishing gift purchases as catalysts for building and strengthening customer–brand relationships, this study links the gift-giving and relationship marketing literature streams. In addition to the interpersonal relationship between the donor and the recipient, gifts can strengthen the relationship between the donor and the brand. This customer relationship perspective provides a fresh view on gift-giving behavior, which in turn could initiate a new stream of marketing research.
In particular, our insights advance literature that refers to gifting as a form of customer engagement ([ 7]; [37]). Laden with symbolic meaning, gift purchases create buying situations with special importance for customers' identity and are critical touchpoints during customer journeys. With so much at stake, gift purchases can deepen the customer relationship with the brand, with positive impacts on key customer metrics such as attitude strength and future purchase behaviors. Thus, our research suggests gift purchases as an opportunity for retailers to engage customers with their brand, especially former light buyers with little purchase experience.
We also identify mechanisms that explain why gift purchases strengthen customers' brand relationships. Drawing from identity theory ([12]; [69]), we derive two potential mediators that map onto the choice accuracy and signaling risks involved in gift purchases. Compared with customers who buy products for themselves, gift givers have stronger attitudes toward the brand due to their greater customer gratitude and public commitment. These findings enrich understanding of how customer–brand relationships grow. In line with prior literature ([49]; [66]), we find that both mechanisms simultaneously explain the impact of gift purchases on customers' brand relationships. That is, customer gratitude helps build strong brand relationships during the special purchase situation. For many customers, finding an appropriate gift is challenging and may even trigger gift anxiety ([44]; [79]). If the retail brand can help customers solve this problem and alleviate choice accuracy risk, it earns their emotional appreciation. Gift purchasers, compared with personal use purchasers, experience increased gratitude, which enhances their attitude valence and certainty (i.e., attitude strength) toward the brand, which then promotes future purchases ([51]). Public commitment instead arises in ritual presentation. By presenting gifts to recipients, donors face signaling risks and take a public stand for the purchased brand; the brand becomes a public means to convey the donor's identity as a capable gift giver ([62]). In response to this enhanced public commitment, gift purchasers develop stronger attitudes toward the brand, relative to personal use purchasers, due to increased liking of and certainty about the brand. The stimulation of public commitment and attitude strength then leads to augmented purchase behavior.
Finally, we investigate when gift purchases strengthen customers' brand relationships. Gift purchases are distinct; we take different gift purchase design characteristics into account by investigating their impacts on the proposed mediating mechanisms. The assistance received and availability of branded gift wrapping significantly influence donors' psychological responses; their gratitude is higher if they receive assistance, and their public commitment is higher if the retailer packages the gift in branded gift wrapping. We thus advance extant literature by demonstrating that different psychological mechanisms come to the fore, depending on the characteristics of the gift purchase.
Marketing managers can leverage gift purchases as effective relationship marketing instruments in monobrand retail settings. We propose five means to do so. First, managers should identify products to position as gifts and promote them as such. Many gift promotions are driven just by holiday seasons, and thus brands fail to exploit their full potential for building strong customer–brand relationships. Marketing managers should systematically highlight selected products in marketing communications and offer promotional incentives for purchasers looking for a gift. The returns on such initiatives can be substantial. In our field study in the beauty industry, gift buyers spent 63% more in the year following their gift purchase than a matched sample of customers purchasing for personal use. Our results also clarify how this incremental spending effect emerges: compared with people purchasing for their personal use, gift buyers undertake more shopping trips (25%), spend more per trip (41%), and cross-buy more (49%). Thus, gift purchases have profound, multifaceted impacts on future buying behavior—a link that does not appear in any prior marketing studies to the best of our knowledge. In contrast with other relationship marketing instruments (e.g., loyalty programs) that require extensive immediate investments and costs in the hope of future returns ([43]), promoting gift purchases demands little additional cost, and it produces a dual effect: instant returns from the focal gift purchase, and then additional sales from gift buyers' expanded future purchase behavior.
Second, managers should target new rather than experienced customers with gift purchase promotions, in recognition of the negative interaction effect of purchase experience on the link between gift purchases and future purchase behavior. In our field study, the sales lift is significant for customers who made no more than one shopping trip before their initial gift purchase. Relationship marketing literature already has identified varying effects at different customer relationship stages ([29]; [50]; [80]). In early stages, customers possess weaker brand attitudes and are more receptive to marketing actions ([24]; [35]), so the catalytic effect of gift purchases is stronger among new customers. Gift purchases also might help activate infrequent customers, who often represent a large proportion of firms' customer bases ([57]) but are not addressed by relationship marketing instruments that target more experienced, frequent customers (e.g., loyalty programs).
Third, managers should take concerted actions to facilitate customers' gift selection process. Helping customers find the right gift spurs their gratitude, which helps explain the positive impact of gift purchases on the customer–brand relationship. Short-term feelings of gratitude can drive long-lasting performance outcomes in customer relationships ([51]), so retail store managers should train and encourage frontline employees to assist customers proactively in their gift selection process. Firms might also develop advanced online filters to help their customers identify an appropriate product for specific gift-giving occasions. For example, the European online shop Radbag offers a sophisticated "gift finder" on its website, enabling customers looking for a gift to filter products according to the recipient and his or her personality (e.g., party animal, globetrotter, workaholic, hipster), their own willingness to pay, and the gift's intended meaning (e.g., romantic, funny, exclusive, innovative).
Fourth, as a low-hanging fruit, monobrand retailers should make their brand more prominent on gift packaging. Gift buyers' public commitment emerges as a second mediator that explains the positive impact of gift purchases on brand relationships. By providing high-quality, branded gift packaging, retailers can strengthen their customers' public commitment to the brand and stimulate long-lasting attitudinal and behavioral performance outcomes. Leading firms in various industries such as Hugo Boss, Rosenthal, and Normann Copenhagen rely on branded gift wrapping, which effectively enhances the brands' salience in gift presentation.
Finally, retailers should encourage existing customers to recommend the brand to their peers as a good choice for upcoming gift-giving occasions. For example, retailers could provide digital or more traditional wish-list tools that facilitate voicing of customers' gift preferences. In addition to the primary sales effect of the gift, such an initiative could also bring new customers with strong attitudes toward the gifted brand.
We find support for our overall conceptual model across two research formats and three industry contexts, such that we substantiate our prediction that gift purchases are effective catalysts of customer relationships in a monobrand retailing context. The study limitations also suggest some promising research avenues. First, we shed light on the favorable consequences of gift purchases for customer relationships, by treating the gift purchase as a given and investigating its effects on customers' subsequent attitudes and behaviors. A vital next step is to learn how to stimulate gift purchases among customers in the first place. Extant literature on gift selection provides initial insights into which store attributes become more important when choosing a gift or how, for example, the level of customer anxiety might be reduced (e.g., [41]; [44]).
Second, our hypothesis development assumes the gift recipient responds positively to the gift. We consider this assumption justifiable; even if a recipient does not like the gift, social norms require gift recipients to react with gratitude ([59]; [78]). However, gift recipients could express dislike of a gift, such as when the gift giver and gift recipient have a close and trusting relationship in which honesty norms prevail. Studies of how gift recipients' negative responses affect the customer–brand relationship could investigate whether the catalyzing effect on the gift giver's brand relationship is impeded in this case. Depending on the giver–recipient relationship, the intensity of the prior customer–brand relationship, or the quality of the gift purchase experience provided by the brand, the gift giver might disregard the recipient's reaction and identify further with the brand, potentially leading to enhanced bonds.
Third, in constructing our treatment and control groups, we included only customers who made their first documented purchase (i.e., became customers) from the monobrand retailer during the overall observation window, to ensure that the treatment and control group customers had made no prior gift purchases and to establish an undiluted effect of the gift purchase. Consequently, customers in our sample are relatively new (i.e., maximum tenure of one year and nine months at the start of the treatment period if a customer made his or her first purchase in January 2011). This necessary prerequisite affirms the unambiguous causal inference of our proposed effect, but it also might limit the generalizability of our findings beyond customers in early relationship stages ([29]). Research that builds on our findings could examine whether the positive effect of a gift purchase holds for customers who have patronized a focal firm for a couple of years before they make their first gift purchase, such that a gift purchase at a later relational stage might take a solidified brand relationship to the next level. Along this line, further research could also examine whether the sequence of purchasing a brand for personal use or as a gift plays an important role.
Fourth, in our monobrand retailing context, the producer and retailer of the purchased gifts coincide. When gift products are manufactured by one company and marketed by another, an intriguing research question pertains to which company will reap the benefits of selling gifts to customers ([52]). Will customers change their attitudes and behaviors in favor of the company that produces the gift, the retailer that supports their gift selection process, or both? How can the brand manufacturer and retailer effectively team up to share the responsibility of providing the customer with an effective gift purchase experience (e.g., assistance, gift wrapping) and jointly reap the relational benefits of selling gifts? Further research should investigate gift purchases in which the gift producer and retailer differ to determine their simultaneous effects on the different types of customer relationships.
Fifth, further research might explore the impact of gifts on recipients' brand relationships. Similar to purchasing and giving a gift, receiving a gift may initiate and foster a brand relationship, possibly mediated by recipients' public commitment to the brand. Insights into this effect would complement our relational view of gift giving with a third perspective (giver–recipient, giver–brand, recipient–brand).
Supplemental Material, DS_10.1177_0022242919860802 - Gift Purchases as Catalysts for Strengthening Customer–Brand Relationships
Supplemental Material, DS_10.1177_0022242919860802 for Gift Purchases as Catalysts for Strengthening Customer–Brand Relationships by Andreas Eggert, Lena Steinhoff and Carina Witte in Journal of Marketing
Footnotes 1 Associate EditorWerner Reinartz
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242919860802
5 1An implicit assumption underlying our theoretical argument is that the gift is successful, such that the recipient reacts positively (whether as a genuine response or in accordance with social norms). We discuss the potential implications of a negatively valenced reaction by the gift recipient subsequently.
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Record: 81- Group Marketing: Theory, Mechanisms, and Dynamics. By: Harmeling, Colleen M.; Palmatier, Robert W.; Fang, Eric; Wang, Dainwen. Journal of Marketing. Jul2017, Vol. 81 Issue 4, p1-24. 24p. 3 Diagrams, 5 Charts, 1 Graph. DOI: 10.1509/jm.15.0495.
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Group Marketing: Theory, Mechanisms, and Dynamics
Group marketing uses the psychological mechanisms underlying group influence to drive customer behaviors that are beneficial to the firm. It is predicated on the firm’s ability to guide two necessary and sufficient conditions: (1) a customer’s awareness of an affiliation with the focal group and (2) exposure to group norms. By examining what it means to be affiliated with a group; determining how group norms are inferred, applied, and maintained; and testing a wide variety of ways in which these conditions become manifest, this research demonstrates the theoretical foundations of group marketing. Groups influence purchase behaviors by altering information and identity appraisals during decision making. Time in a purchase domain emerges as a critical determinant of the strength of group influence. Although previous research has suggested that social influence diminishes over time, a longitudinal field study and an experiment reveal that this prediction holds only when information appraisal dominates; an opposite effect arises when identity appraisal dominates. Group efficacy strengthens, but product price weakens, the effects of groups on purchase behaviors.
Online Supplement : http://dx.doi.org/10.1509/jm.15.0495
Humans are among the most “groupish” animals on the planet … [and] have an innate group psychology that regulates their group interactions.
—Van Vugt and Kameda (2012, p. 298)
Group marketing—the use of the psychological mechanisms underlying group influence to drive behaviors that benefit the firm—appeals to marketers because of the strong and pervasive effects of groups on the decision making and behaviors of their members (Festinger, Schachter, and Back 1974; Van Vugt and Kameda 2012) and because groups are ubiquitous in modern life. Exemplifying this point, more than 1 billion people use the social media platform Facebook to organize in groups each month, providing firms more visibility and access to consumers (Guynn 2016). Discussed in marketing as consumer tribes (Cova, Kozinets, and Shankar 2012), brand communities (Muñiz and O’Guinn 2001), consumption communities (Thomas, Price, and Schau 2013), reference groups (Bearden and Etzel 1982), fan clubs (Schau, Muñiz, and Arnould 2009), or other aliases, groups alter how people decide which products to purchase (Kozinets 1999). In response, 53% of marketing executives report allocating some portion of their budgets to group marketing (CMO Council 2015). In the past five years, for example, Nike has shifted more than one-third of its multibillion dollar marketing budget to support group-based initiatives. The Nike+ system, which helps customers build groups, generated $840 million in sales in one year (Cendrowski 2012). Therefore, this research aims to provide a theoretical foundation for group marketing, identify the factors that determine its effectiveness, and show how its effectiveness evolves over time.
At the core of group marketing is the “group” and the processes by which this group alters the customer’s decision making. Thus, an initial step in building a theory of group marketing is to explore the notion of the group across its diverse conceptualizations. Research has suggested that a minimal condition for triggering group-based behaviors (e.g., conforming) is a psychological affiliation with a social category or a shared collective perception among members of their own social unity (Goldstein, Cialdini, and Griskevicius 2008; Turner 1982). Beyond this psychological affiliation, arguably the most critical characteristic for predicting group influence on product purchases is the strength of group norms, or the informal understanding of what group members consider typical in relation to a product (Cialdini and Goldstein 2004). Thus, the necessary and sufficient conditions for a group to influence a person’s purchase behavior are that (s)he is (1) psychologically affiliated with the group and (2) exposed to group norms.
Once these necessary conditions are met, group influence occurs through an alteration of two fundamental decisionmaking processes: (1) information appraisal, which captures the importance and perceived value of information to a focal decision (Cruz, Henningsen, and Williams 2000), and (2) identity appraisal, or the degree to which the focal decision is selfreinforcing (Bolton and Reed 2004). Group influences on these mechanisms can drive conforming behaviors in which the customer matches her or his attitudes and behaviors with the group’s (Morgan and Laland 2012). However, this influence may change as time in a domain increases. By isolating information and identity appraisals, we aim to explain the divergence between research that suggests social influence diminishes over time (Risselada, Verhoef, and Bijmolt 2014) and evidence that indicates group marketing programs gain effectiveness over time (Cendrowski 2012). Overall, we propose that group marketing effectiveness depends on the dynamic interplay of the group’s influence on information and identity appraisals.
Using a three-study, multimethod design, we examine the theoretical foundations of group marketing. With an experiment, Study 1 tests the necessary conditions for group marketing and demonstrates the mediating role of information and identity appraisal, through which a customer’s affiliation with a group influences purchase behavior. In Studies 2a and 2b, we investigate the differential dynamic effects of these two mechanisms, using conditions that make one mechanism more salient than the other. With more than 4 billion longitudinal data points collected from approximately 11,000 participants in a massive multiplayer online role-playing game, in Study 2a we focus on functional versus social product purchases to isolate the dynamic effects of information and identity appraisal, respectively. In this research context, we can observe the effect of groups on members from the moment they enter a new purchase domain and capture a dynamic, nonlinear effect of group norms on purchases. The experiment in Study 2b isolates the decision type from other potential product confounds and examines the dynamic effects of groups on customers in a familiar purchase domain.
This research therefore makes three main contributions. First, we demonstrate that a customer must be psychologically affiliated with the focal group and also exposed to group norms for that group to influence behavior predictably. Without these minimal conditions, the customer has neither the motivation nor the means to conform with the group, and group influence is limited (Morgan and Laland 2012). In Study 1, assigning participants to an arbitrary group and exposing them to group norms (i.e., recommendations) activates implicit assessments that a recommendation from another member of the group is more diagnostic (information appraisal) and more accordant with the participant’s self-identity (identity appraisal) than when this same recommendation comes from a non–group member. In turn, the participants are 1.4 times more likely to choose a product that conforms to group norms and willing to pay significantly more for the conforming product than an alternative (objectively superior) product.
Second, by disentangling the effects of the group on information and identity appraisals and identifying time in the purchase domain as a critical determinant of the strength of a group’s influence, we demonstrate that the predictions of diminishing group influence over time are true only if information appraisals dominate. The opposite holds when identity appraisals are dominant. For people new to a purchase domain, the effect of the group norm on purchase behaviors follows an inverted U-shape when information appraisals dominate but a
U-shape when identity appraisals dominate, as we show in Study 2a. For social decisions, the effect of group norms weakens initially, as the decision maker aims to protect a unique personal identity, but then grows as the group becomes a more significant contributor to the person’s sense of social identity. The effects are confirmed in Study 2b on purchasers in a familiar domain. For social decisions, people who have spent an extended time in the domain (18 years) are willing to pay three times more for a product that conforms to the group than people who have spent a relatively short time there (3 years). The reverse is true for functional decisions.
Third, from these theoretical foundations, we present key process steps in executing group marketing. A company must first identify desirable customers and then establish a salient group through the use of either a firm-managed group or an external, independent group. Then, regardless of the type of group established, the firm must develop the necessary conditions for group marketing. Adapting group marketing strategies according to the customer’s time in a specific domain also is essential to their effectiveness.
Theoretical Underpinnings of Group Marketing
Group marketing entails the use of the psychological mechanisms that underlie group influence to drive behaviors that benefit the firm; its theoretical foundation requires an adequate conceptualization of what constitutes a “group.” Thus, as a first step, we examine the scholarly history surrounding groups, along with how they are understood today, to identify the necessary and sufficient conditions for a person to feel part of a group and what it means once (s)he obtains this sense. Group marketing success in turn depends on the conditions that allow a group to drive predictable behaviors. Group norms are essential to this process and provide a standard that an individual member tries to match. We therefore examine group norms; how they are inferred, applied, and maintained; and how this influences decision making and behavior. Finally, we theorize that group marketing effectiveness depends on the degree to which the group alters a person’s information and identity appraisals during decision making, and we consider how the strength of group influence changes over time.
Psychology of Groups
The “group” is a useful abstraction that encompasses a wide variety of constructs studied in marketing, such as brand communities (Muñiz and O’Guinn 2001), consumer tribes (Cova, Kozinets, and Shankar 2012), consumption communities (Thomas, Price, and Schau 2013), and reference groups (Bearden and Etzel 1982). In the deep scholarly history surrounding groups, there is disagreement about what constitutes a group, what is required for a person to feel (s)he is part of a group, and what triggers the psychological mechanisms associated with groups (Turner 1982). Early definitions suggest that a group is “two or more persons who are interacting with one another in such a manner that each person influences and is influenced by each other person” (Shaw 1976, p. 11). Other definitions incorporate interdependent goal pursuit (Festinger, Schachter, and Back 1974) and interpersonal attraction or liking between members (Lott and Lott 1965).
Interaction, shared goals, and affection, however, may be unnecessary for people to feel like part of a group. Turner (1982, p. 94) suggests that “members of a social group seem often to share no more than a collective perception of their own social unity and yet this seems to be sufficient for them to act as a group.” People tend to organize the world into categories (Fiske 1992); if a person internalizes a category into a conception of the self, (s)he will feel like a member of that group and act in accordance with this group membership. In a variety of experiments in which participants were merely made aware (explicitly or implicitly) of their affiliation with a temporary and arbitrary group, this psychological group affiliation triggered such group behaviors as intergroup discrimination, intragroup altruism, and perceived in-group superiority (Brewer 1999; Crano 2000). It was associated with perceived intragroup similarity and intergroup dissimilarity (behavioral and attitudinal; Hogg and Turner 1987).
Marketing theories on groups are consistent with this psychological perspective and explicitly theorize about the importance of “consciousness of kind, … [a] connection that members feel toward one another, and the collective sense of difference from others not in the community” in building and maintaining brand-based groups (Muñiz and O’Guinn 2001, p. 413; Schau, Muñiz, and Arnould 2009). A common empirical design is to heighten a participant’s awareness of membership within a group independent of the firm (e.g., ethnicity, gender), which uncovers the same pattern of effects across groupbased behaviors (Goldstein, Cialdini, and Griskevicius 2008; Naylor, Lamberton, and West 2012). Even further, this conceptualization of groups provides clarity compared with the more general marketing notion of customer segments, which assumes no psychological connections between the customer and the category (i.e., segment) and does not require the customer’s knowledge of his or her marketer-selected categorization. Thus, the necessary and sufficient condition for people to feel they are, and act as, a group is an acknowledgment of membership within a common social category.
Group Norms and Theories of Group Influence
Once a person is affiliated with a group, it can influence behavior, such that (s)he “comes to think feel, behave, and define [him- or herself] in terms of group norms rather than unique properties of the self” (Terry and Hogg 1996, p. 780); (s)he conforms to the group. Fundamental to this process is the existence and enforcement of group norms, or shared informal understandings of what is typical and acceptable, as defined by the group (Cialdini and Trost 1998). Group norms represent the “group prototype that describes and prescribes beliefs, attitudes, feelings, and behaviors that optimally minimize in-group differences and maximizes intergroup differences” (Terry and Hogg 1996, p. 780). Theory has suggested that group norms form because they provide the group and its members with an expedient way to meet their needs or are consistently accompanied by rewards (e.g., praise). Dissenting from strong group norms can prompt negative emotional (e.g., anxiety), physical (increased heart rate), and social (sanctioning) responses (Morgan and Laland 2012). In marketing, group norms become important when they take on product or brand relevance and serve as “manuals of ‘how to consume’” (Schau, Muñiz, and Arnould 2009, p. 39). We use the term “group product norms” to capture those relevant to a particular product. Thus, norms define how to conform and provide a means of predicting a group’s influence on a person’s purchase behaviors.
Although norms are often transferred through overt interactions among group members (e.g., storytelling, demonstrations, rituals), they can be inferred with relatively little or even no direct contact. Once a person is psychologically affiliated with a group, (s)he will “construct a context-specific group norm from available, and usually shared, social comparative information” (Terry and Hogg 1996, p. 780). A person can “infer the common characteristics of [the] category from individual exemplars and then assign them to all members,” including the self (Turner 1982, p. 30). Even when a person is arbitrarily assigned to a group, (s)he tends to infer group norms from any available information, which in some cases is only the knowledge of the self as an exemplary group member (Naylor, Lamberton, and Norton 2011). Finally, group norms can be communicated through a non–group member, suggesting that no interaction with other group members is needed (Goldstein, Cialdini, and Griskevicius 2008). Without knowledge of the group norm, however, predicting conforming behavior becomes impossible because there is no known standard against which customers will gauge their own behavior.
This reasoning suggests that the effectiveness of group marketing is predicated on a firm’s ability to guide two necessary and sufficient conditions that dictate whether a group will influence an individual member’s behavior. First, the person must be psychologically affiliated with the focal group. This affiliation can be manifest in several ways. It might be arbitrarily assigned, assumed through self-selected membership, or primed even if the customer acknowledges no prior affiliation (e.g., “You are part of the running community”). Second, the person must be exposed to group norms. Just as there is more than one way for a person to recognize affiliation with a group, there is more than one way a person can be exposed to and infer group norms. We propose and test three. Group norms can be (1) communicated by a single suspected or known group member; (2) inferred from the observation of group members’ behaviors; or (3) presented through third-party communication of a norm described as affiliated with the focal group.
Mechanisms Underlying Group Influence
When a person affiliates with a group, his or her cognitive processing changes fundamentally, “as if the [group’s] resources, perspectives, and identity along with [his or her] own, are accessed and are affected by the outcomes of any action [he or she] might take” (Aron and McLaughlin-Volpe 2001, p. 89). This shift affects two fundamental decision-making processes: information and identity appraisal (Deutsch and Gerard 1955).
Information appraisals. Group affiliation can affect decision making by altering information appraisals, such that information from the group seems more accurate or diagnostic than information from other sources. Because group affiliation creates feelings of similarity and superiority among group members, it provides a heuristic for filtering the vast amounts of potentially relevant information. Group norms provide guidelines for how to act in a given situation, without requiring the investment of time or cognitive effort but while still offering an outcome with a high probability of effectiveness (Cialdini and Trost 1998). Thus, to maximize effectiveness, a member often interprets group norms more favorably relative to other information sources and pursues group-consistent behaviors (Kaplan and Miller 1987).
Identity appraisals. Group affiliation can also affect decision making by altering identity appraisals, such that the salience of group identity increases for self-relevant decisions (Bolton and Reed 2004; Escalas and Bettman 2005). People have an ingrained need to belong but also to be distinct, and those two needs must be balanced to maintain a positive selfconcept (Brewer 1991). Developing and maintaining a social identity can be key to this objective. A social identity is a core aspect of the self-concept, achieved through a self-awareness of membership in a social category and the evaluative and emotional implications of this membership (Tajfel and Turner 1985). When a person affiliates with a group, the group becomes an “extension of the self beyond the level of the individual,” represented by the shift in which “I becomes we” (Brewer 1991, p. 476). Group norms can define what is appropriate for enacting the self (i.e., presenting the self to others). Thus, to maintain a positive self-concept, a person often uses group norms as a reference for self-relevant behaviors and pursues purchase behaviors that conform to the group’s.
Dynamic effect of group mechanisms on behavior. Evidence is mixed about how a group’s influence on members might vary dynamically (Cendrowski 2012; Risselada, Verhoef, and Bijmolt 2014). Although time in the group affects these relationships, it should uniformly strengthen the group’s influence on both information and identity appraisals rather than present a condition in which opposing effects may occur (Algesheimer, Dholakia, and Herrmann 2005). However, we theorize that time in the relevant purchase domain, the specific sphere of activity or knowledge relevant to the focal purchase decision, will create conditions in which group norms have differential effects on information and identity appraisals, which may be key to resolving this conflict. When people enter a new purchase domain (e.g., new hobby, first child), they face numerous, unfamiliar product decisions, and their previous knowledge and behaviors may not be relevant for making effective or appropriate product choices. By definition, this new domain falls outside the decision maker’s current understanding of self, such that (s)he may feel like an outsider. In these new domains, membership in a domain-specific group may be particularly important, because it provides access to valuable, domain-relevant information and helps alleviate the discomfort of an outsider position as the person increasingly integrates new roles into her or his self-concept.
However, as time progresses, the person becomes more familiar with the purchase domain and is no longer a lowknowledge buyer but rather starts to repeat behaviors learned in the domain, which affects the relative value of group-provided information (i.e., information appraisal). As the person’s commitment to the domain deepens, (s)he stops feeling like an outsider and instead accepts the domain as part of his or her self-identity, which in turn affects the relevance of group norms for identity management (identity appraisal). Thus, we propose that the net effect of the group on behavior depends on the sum of its dynamically varying influence on information and identity appraisals as the person’s time in a purchase domain increases.
Testing the Necessary Conditions of Group Marketing (Study 1)
Building on extant research, we first demonstrate the necessary conditions of group marketing (Study 1)—namely, that a customer must be aware of his or her affiliation with the focal group and must be exposed to the desired group norm. In addition, we test whether these effects occur through the group’s impact on information and identity appraisals. In Studies 2a and 2b, we investigate the dynamic aspects of these effects. Table 1 lists the objectives, approaches, theoretical tests, and key takeaways from all three studies.
Study 1: Conceptual Model and Hypotheses
The effectiveness of group marketing lies in its ability to leverage a person’s affiliation to a group to drive behaviors that ultimately benefit the firm. A common group marketing strategy uses advocates within a group to provide recommendations to other possible customers (Kozinets et al. 2010). Because this advocate serves as a group exemplar, the recommendation provides a means to infer group norms. Group members are perceived as more similar and superior, so the information they provide seems more trustworthy and accurate than information from non–group members (Meyerson, Weick, and Kramer 1995). Thus, affiliation and exposure to group norms through a recommendation can alter information appraisals. People also use recommendations to identify appropriate behavior for their identity management efforts. However, what is “appropriate” depends on a person’s definition of self; membership in a group marks an expansion of the self to incorporate that group’s identity. This membership also alters identity appraisals, such that recommendations from other group members appear more relevant to identity management than recommendations from nonmembers (Brewer 1991). Thus, decisions based on groupprovided recommendations seem more accurate and in line with the self than decisions based on the same recommendation from a non–group member, which should increase purchase behaviors that reflect group norms.
H1: (a) Product choice and (b) willingness to pay for the focal product is greatest when a customer is affiliated with a group and exposed to group product norms (i.e., test of necessary conditions).
H2: When affiliated with a group (vs. no group affiliation), the effect of a customer’s exposure to group product norms on purchase behavior is mediated by (a) information and (b) identity appraisals (i.e., test of mediating mechanisms).
Study 1: Design and Sample
To test the necessary conditions of group marketing, we use a 2 (group membership vs. no membership) · 2 (recommendation vs. no recommendation) between-subjects experimental design in which we manipulate group membership and exposure to a recommendation, then measure both information and identity appraisals. With this design, we can compare a focal condition, in which the customer is associated with a group and exposed to its norms (group · recommendation), against each of the three other conditions for theory testing. First, the no group · no recommendation condition captures unbiased individual choice and serves as our control, so we can calculate the full effect of the group on purchase behavior. Second, the group · no recommendation condition offers a comparison with a mere social presence condition. Third, we test the effect of a group recommendation against general word of mouth (no group · recommendation condition), so the same recommendation comes from a non–group member.
TABLE 1
Testing the Theoretical Foundations of Group Marketing
TABLE:
| Study: Research Design | Objective | Condition 1: Customer Awareness of Affiliation with Group | Condition 2: Customer Exposure to Group Norms | Strength of Group Norms | Key Takeaways |
|---|
| Study 1: Experiment | • Confirms two necessary conditions for group marketing to influence behavior: (1) customer awareness of affiliation with focal group and (2) exposure to group norms. • Demonstrates how the group alters information and identity appraisals. | Group (vs. no group) affiliation is primed using arbitrarily assigned groups. | Group norms are exposed to participants through a group member’s electronic recommendations. | Group norm strength held constant across experimental conditions. | • Groups influence purchase behavior even when group affiliation is irrelevant and arbitrary and exposure to group norms is brief. • Group influence operates by altering information and identity appraisals in decision making. |
| Study 2a: Longitudinal field study | • Resolves conflicting findings on dynamic group influence in a new decision domain. • Confirms the differential impact of the group on information and identity appraisals on purchase behavior over time. | Group affiliation through self-selected membership in the focal group. | Group norms are inferred from direct interactions with and observations of other group members. | Group norm strength is measured on the basis of the consistency of observable product purchase behaviors within the group. | • Group norms have differential effects on purchase decisions as time in the domain increases. • For people new to a purchase domain, the effect of group norms on purchase behavior follows an inverted U-shape for functional decisions if the information appraisal dominates. • For people new to a purchase domain, the effect of group norms on purchase behavior follows a Ushape if the identity appraisal dominates. |
| Study 2b: Experiment | • Isolates the decision context (functional or social) from other potential product confounds as a key moderator of group influence over time. • Provides causal evidence of the impact of group product norms on purchase behavior. | Existing group association made salient through external communication. | Participants are exposed to group norms using a feedback mechanism (poll) that conveys aggregate group behaviors. | Group norm strength is manipulated using false feedback, reflecting group-based agreement on product choice as split between products, either 50/50 (low) or 25/75 (high). | • For people familiar with a purchase domain, group product norms have a diminishing effect on purchase decisions if the information appraisal dominates. • For people familiar with a purchase domain, group product norms have an increasing effect on purchase decisions as time in the domain increases if the identity appraisal dominates. |
Participants, recruited through Amazon’s Mechanical Turk, consisted of 222 adults (59% women) with a median age of 35–44 years, ranging from 18 years to older than 65 years. In pretests, we selected an appropriate product: athletic shoes, which can be evaluated using functional or social perspectives, are gender neutral, and have attributes that can be evaluated positively or negatively by a group. As a stringent test of group influence, we used a product choice exercise that required participants to choose between Products A and B, such that Product A was objectively superior to Product B from both functional (i.e., higher attribute values) and social (i.e., more appealing colors) perspectives (see the Appendix). In a pretest, Product A was consistently chosen over Product B (72% vs. 28%) and earned higher quality, visual appeal, and overall product ratings (p < .01). In addition, all product attributes were fictitious (e.g., Cuprotex, Flexion), reducing the potential that preexisting notions might influence the product choice.
Study 1: Procedure
After they provided demographic information, half of the participants were put into an arbitrary group and informed that the remainder of the tasks would be conducted with their group; the other half proceeded to the product choice task without any group assignment and were informed that they would complete the remaining tasks individually. For the group manipulation, group membership was based on a trivial criterion—whether, in a series of choice tasks, they chose sunset or sunrise. By using minimal conditions to prime affiliation to a group, we ensure that the groups in our experiment have no significant real-world meaning, in terms of prior beliefs or other implicit affiliations, which minimized potential confounds. The group manipulation worked as expected, in that participants in the group condition reported feeling more like they were part of a group (Mnongroup = 3.20, Mgroup = 4.22; F(1, 220) = 16.20, p < .01).1
Next, participants were randomly selected to receive (vs. not receive) a recommendation during their product choice task.
Participants in the no-recommendation condition saw a photo of the two products and a description of their attributes. Participants in the recommendation condition received the same product photos and descriptions, along with “live comments” that consisted of two comments in favor of Product A and two comments in favor of Product B from other participants. If the participant was also assigned to the group procedure, the comments in favor of Product B came from a member of the participant’s arbitrarily assigned group (e.g., Sunset Group Member 002). Comments in favor of Product A came from non–group members (e.g., Sunrise Group Member 009). Although participants in the nongroup condition read the same information and labels, they were not assigned to a shared group. The manipulations worked as expected, in that participants in the recommendation condition acknowledged receiving a recommendation more than those in the control condition (Mcontrol = 2.05, Mrec = 3.65; F(1, 220) = 39.05, p < .01). We did not find any significant interaction of group membership and recommendation manipulations on perceptions of the recommendation (p = .24). The recommendation was perceived similarly in the group and nongroup conditions, thereby enabling us to isolate the effects of the same recommendation from a group versus a non–group member. Finally, participants made their product selection and stated how much they were willing to pay for each product. We transformed this result into a relative measure (willingness to pay for Product B - willingness to pay for Product A). Because we predict that group affiliation alters the way people process information about a product decision, information appraisals should be manifest in evaluative assessments of a product, so we measure its impact using assessments of product quality (Cruz, Henningsen, and Williams 2000). For identity appraisals, we use self–brand connection, which captures the degree to which the focal product appears self-relevant (Escalas and Bettman 2005). We also captured individual color preferences and age as controls. Table 2, Panel A, contains the descriptive statistics; the Appendix offers more details on the procedures and measures.
Study 1: Results and Discussion
A chi-square test of the number of participants who chose Product B within each of the four conditions was marginally significant (c2(3) = 6.48, p = .09), and the group · recommendation condition exhibited significantly more conforming behavior. Specifically, 45% of participants chose Product B, compared with 24%–32% in all other conditions providing support of H1a. The 2 (group vs. no group) · 2 (recommendation vs. no recommendation) factorial analysis of covariance revealed a marginally significant interaction effect on willingness to pay (F(1, 222) = 3.34, p = .07). To test H1b, we decomposed this finding using three orthogonal contrasts to compare the group · recommendation (i.e., both necessary conditions) condition with all other conditions (i.e., the contrast coding compared group · recommendation vs. [1] no group · no recommendation, [2] no group · recommendation, and [3] group · no recommendation). Consistent with our hypothesis, all contrasts were significant, such that participants in the group · recommendation condition were willing to pay a significantly higher amount for the conforming product (all ps < .05) than were those in the other three conditions.2 Compared with the unbiased individual choice condition (no group · no recommendation), only the group recommendation condition was significantly different. Thus, there is no influence on willingness to pay when only one of the necessary conditions is met. Figure 1 illustrates these effects.
TABLE 2
Descriptive Statistics and Correlations
TABLE:
| Correlations |
|---|
| | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
|---|
| 1. Group affiliation | 3.72 | 1.96 | | | | | | | | | | | |
| 2. Group product norms | 2.85 | 2.07 | 0.14 | | | | | | | | | | |
| 3. Information appraisal | 5.11 | 1.21 | 0.08 | 0.11 | | | | | | | | | |
| 4. Identity appraisal | 3.33 | 1.67 | 0.2 | 0.27 | 0.51 | | | | | | | | |
| 5. Willingness to pay | -7.07 | 19.51 | 0 | 0.22 | 0.4 | 0.56 | | | | | | | |
| 6. Color preference | 5.59 | 1.44 | 0.04 | 0.03 | -.01 | -.16 | -.18 | | | | | | |
| 7. Age | 2.57 | 1.17 | 0.03 | -.08 | 0.01 | 0.02 | -.02 | -.14 | | | | | |
Notes: We report correlations for functional/social decisions. Similar to Iyengar, Van den Bulte, and Valente (2011), we deleted cases that do not contribute to the likelihood function of the hazard models and report the descriptive statistics for the remaining sample. Correlations that are greater than or equal to .14 (Study 1), .01 (Study 2a), and .18 (Study 2b) in absolute value are significant at p < .05.
Following the bootstrapping procedures described by Preacher and Hayes (2008), we conducted a moderated mediation analysis (PROCESS Model 7; 5,000 bootstrapped samples) to test the underlying processes. We entered both information and identity appraisals as mediators of the effect of exposure to the group product norm (0 = no recommendation, 1 = recommendation) on willingness to pay, with group membership as the moderator (0 = nongroup, 1 = group). Consistent with our predictions in H2, the highest-order index of moderated mediation was significant for both the information (index = 1.82, SE = 1.10; 95% confidence interval [CI] = [.25, 4.80]) and identity (index = 7.01, SE = 2.52; 95% CI = [2.66, 12.81]) appraisals. Decomposing this finding further, the information (effect = 1.56, SE = .77; 95% CI = [.38, 3.55]) and identity (effect = 5.82, SE = 1.87; 95% CI = [2.48, 9.84]) mechanisms mediated the effect of the recommendation on willingness to pay in the group condition, whereas these effects were attenuated in the nongroup condition (information: effect = -.26, SE = .65; 95% CI = [-1.97, .76]; identity: effect = -1.19, SE = 1.53; 95% CI = [-4.44, 1.62]).
Study 1 thus provides a foundation for investigating group marketing by first demonstrating the two necessary conditions for group influence. When a customer is knowingly associated with the focal group and exposed to group norms, (s)he is willing to pay significantly more for a product that conforms to the group than when none (unbiased individual) or only one (mere social presence, general social influence) of the conditions is present. The same recommendation becomes nearly three times more influential when coming from a group member than a non–group member. This result is particularly powerful considering the experimental design, because it shows that even very brief, arbitrary group membership can dramatically influence individual behavior. We also demonstrate that groups influence decision making by altering information and identity appraisals. Although the recommendations were equally favorable toward both products, only when the product was recommended by a group member (vs. non–group member or no recommendation) was it appraised more favorably.
Testing the Dynamic Effects of Group Marketing (Studies 2a and 2b)
The effects of groups on information and identity appraisals are independent and can vary over time. We predict that the effect of groups on purchase behavior through information appraisals likely follows an inverted U-shape, such that the effect strengthens at first and then weakens as people become more familiar with the purchase domain. The effect of groups through identity appraisals instead should follow a U-shape as time in the domain increases, weakening at first but then becoming stronger (see Figure 2). We capture the dynamic effects by observing group members over time as they enter a new purchase domain in Study 2a or make a more familiar purchase in Study 2b.
Study 2: Conceptual Model and Hypotheses
Although group influences on information and identity appraisals can drive conforming behavior, in certain conditions, one mechanism might predominate over the other. Tasks that enable people to discover a correct answer, rather than express an opinion, increase information appraisals (Kaplan and Miller 1987). Identity appraisals instead are stronger when the decision involves high levels of conspicuousness or emphasizes social and emotional relations over evaluations of factual information (Bearden and Etzel 1982). Accordingly, to isolate the different effects of groups on information and identity appraisals, we consider decision processes associated with the purchase of functional and social products. Functional products provide utility; when faced with the need to purchase them, people tend to rely on data and available information (Crano 2000), gauging the accuracy of this information to predict the product’s functional benefits. Social products instead act as “symbols and sentiments used to build individual and social identities and communicate meanings to others,” so these decision processes rely more on building and maintaining the self-concept (Schau, Muñiz, and Arnould 2009, p. 1011).
Regardless of whether purchases are functional or social, the strength of a group norm can affect the degree of influence a group has on a product decision. When the information circulating within the group about a product becomes more consistent, the group norm strengthens and is more clearly defined. When more people respond to the same situation (e.g., purchase decision) in the same way, they increasingly perceive that behavior as correct, triggering a “consensus implies correctness” appraisal (Cialdini and Trost 1998, p. 163). For functional decisions, it increases assessments of the group’s credibility and enhances the member’s confidence and trust in the information (Meyerson, Weick, and Kramer 1995), which then appears more diagnostic for the decision and influences information appraisals (Figure 2).
Group norms also should affect the identity appraisals that occur during social product decisions. If the group norm is weak, members cannot easily identify group-specific cultural markers or socially appropriate behaviors. As it strengthens, more consistent norms clarify the collective group identity and more clearly define “symbols that mark the identity and practices that distinguish members from non-members” (Komito 1998, p. 99). In addition, with strong group norms, inconsistent purchase behavior raises a more dramatic contrast. The resulting cognitive dissonance prompts discomfort for the dissenting group member and challenges the group’s identity, which can provoke sanctions from other group members who work to maintain its integrity (Fehr and Fischbacher 2004). Thus, during social decisions, strong group norms alter identity appraisals in favor of the group, which motivates members to make product purchases that conform with the purchases of other group members.
Dynamic effect of groups on information appraisals. Functional decisions require information appraisals; a person uses information to determine the uncertainty and risks associated with a decision. People attempt to alleviate these risks by appraising both external information (i.e., behavior of others) and internal information (personal experiences). With more time in a domain, a tension arises between these sources of information. The interplay between knowledge acquired from the group and private knowledge determines the net dynamic effect of groups on conforming behavior. When a person has spent only brief time in a domain, (s)he lacks fundamental domain knowledge, so publicly observable group behavior is more diagnostic than private knowledge. Yet this lack of domain knowledge limits the person’s capacity to interpret and absorb information in the domain (Zahra and George 2002). As domain knowledge grows, the capacity to identify relevant information from observations of the group increases, so group influence strengthens as the new member builds domain knowledge and absorbs information from group behavior more readily.
Over time, private knowledge continues to build and eventually outweighs the knowledge gained from observations of group behavior. Group information spreads quickly and becomes redundant (Risselada, Verhoef, and Bijmolt 2014). With more time in the domain, frequent interactions with external ties provide diverse perspectives and new knowledge that is not available within the group (Perry-Smith and Shalley 2003), leading to greater domain expertise and, thus, more confidence in the person’s autonomous decision-making abilities. That is, as private knowledge increases over time, the value of group information relative to other information lessens, the person attends less to group information, and the impact of group norms on purchase behavior weakens. Figure 3 summarizes the overall conceptual model.
H3a: For functional decisions, the strength of the effect of group product norms on purchase behavior follows an inverted U-shape as a member’s time in the domain increases.
Dynamic effect of groups on identity appraisals. Social decisions require a high degree of self-evaluative processing, in which the person determines the value of the decision for managing his or her identity. Thus, a person’s self-concept and how it is defined play essential roles. The self is relatively stable and resistant to change, but with more time in a domain, the selfconcept expands to incorporate new identities relevant to that domain and key social structures within it (Brewer 1991). This process involves a shifting balance between two significant components of identity: personal identity and social identity. As time in a domain increases, a tension arises between preserving the unique, personal identity versus managing the depersonalized social identity. In this process, the group provides a basis for self-evaluation.
When a person enters a new purchase domain, his or her unique, personal identity predominates, and (s)he is more characteristic of an outsider. This position can create a sense of individuality and extreme distinctiveness, leaving the person at risk of isolation and potential negative emotions (Brewer 1991). The group can provide a means to alleviate this vulnerability and reduce the outward contrast by supplying directions for how to enact an identity that is appropriate for the new domain. To gain acceptance, the person attempts to create outward perceptions of similarity with the group by pursuing as many conforming behaviors as necessary to alleviate the discomfort. However, this process of diluting the unique, personal self can threaten the identity, which often prompts people to pursue self-protective action to reduce the amount of self-dilution required to fit in to the new domain. As time in the domain increases, it becomes easier to identify core norms (e.g., language, roles) versus those that can be violated without issue and thereby manage the inconsistencies between outward actions and inward self-conceptions (Sedikides and Green 2000). The customer likely can comply with the minimum required group norms and avoid violating vital group norms while still protecting a unique self-concept. Therefore, group influences on conforming behavior should be strong at first but then weaken as the member learns how to preserve a unique identity.
Yet the self is essentially social in nature. As a person assimilates to the domain over time, the self expands, and the outsider becomes an insider. This transformation requires the person to abandon some unique properties and redefine the self as a prototypical member of that domain. To manage this selfdilution, which can threaten a sense of distinctiveness, group norms that once defined how to fit in emerge as guides for how to stand out through intergroup comparisons (Brewer 1991).
The group identity becomes a strong contributor to the person’s sense of distinctiveness, and the group norms that previously threatened the self transform to become self-reinforcing. The member thus conforms to group norms not only to be accepted but also to create favorable impressions relative to others in the domain (Schau, Muñiz, and Arnould 2009). In summary, we propose that the influence of groups on conforming behavior weakens initially as the person rejects group norms to preserve a personal identity, then becomes stronger as the group contributes more to identity, and the person uses group norms to maintain her or his self-concept.
H3b: For social decisions, the strength of the effect of group product norms on purchase behavior follows a U-shape as a group member’s time in the domain increases.
Factors moderating group influence. People use a wide range of heuristics to determine when to conform with others and whom to copy, according to situational factors (Morgan and Laland 2012). Some factors might strengthen or weaken the effect of groups. For example, group efficacy, or the ability of the group to perform effectively in the domain, might change a member’s interpretations of group information and the value of the group to the self. When a group is objectively more effective in a domain, providing visible signs of success, the information it provides likely is perceived as more credible and legitimate, and people are more likely to imitate group behaviors (Lascu and Zinkhan 1999). Therefore, the perceived value of group information for making functional decisions increases, which should strengthen the effect of the group’s norms on purchase behaviors (Crano 2000). Group efficacy also might increase the appeal of the group as a contributor to the person’s identity and arise as a significant source of self-confidence, pride, self-worth, and positive distinctiveness (Grier and Deshpande´ 2001). For social decisions, group efficacy should strengthen the effects of the group norm on conforming behavior by increasing the motivation to conform for identity enhancement.
H4: For (a) functional and (b) social decisions, the positive effect of group product norms on purchase behavior is enhanced by group efficacy.
Finally, product characteristics, such as product price, affect the influence of groups on conformity behavior. For functional products, a higher price increases the person’s desire to make a correct choice. If a correct answer exists, group members likely are motivated to exchange information more comprehensively and examine it carefully. The more extensive information search that results may extend beyond the group’s boundaries and weaken the effect of group norms. For social products, as the price increases, the desire to make an appropriate decision again increases, because more expensive social products offer greater signaling power for a personal identity, which may weaken the effect of group norms on the self.
H5: For (a) functional and (b) social decisions, the positive effect of group product norms on purchase behavior is suppressed by product price.
Study 2a: Dynamic Effect of Groups in New Purchase Domains
We test our conceptual model using observations of both functional and social product purchases to isolate the distinct, dynamic effects of groups on conforming behavior through information and identity appraisals. This study captures early dynamic effects to understand the role of groups when members make purchases in a new domain.
Study 2a: sample. Study 2a took place within the context of a massive multiplayer online role-playing game. The game featured a three-dimensional, immersive virtual world, similar to Second Life. In these computer-mediated environments, participants’ avatars inhabit, socialize, and perform economic and social activities. Participants can choose careers and engage in different tasks and activities, including organizing into groups to perform various tasks (e.g., hunting treasure, growing agricultural products, starting virtual families). Adding to the realism, participants can purchase virtual goods by exchanging real money for virtual currency, then use the virtual currency to buy products from virtual stores. From the firm, we obtained a detailed log file that contained all participants’ activities, such as the time each participant logged in or out of the game, their detailed interactions with one another, the content of their interactions, and the products they purchased. The data began with the initial launch date of the game and spanned 64 subsequent days. Thus, we could unobtrusively observe the groups and capture the dynamic, nonlinear effect of group product norms on participants’ purchases from the very moment they
Study 2a: measures. All correlations and descriptive statistics are in Table 2, Panel B; the construct definitions, operationalizations, and equations are in Table 3. One of the basic descriptors of a norm is that it represents typical behavior within the group. Thus, to quantify the strength of each potential group norm relative to a given product, which is a shared group-level variable that is relevant to participant i0 for product j in group g at time t (GNi0jgt), we calculated the lagged percentage of participants who purchased the focal product j in group g at time t. This measurement allows the strength of norms to vary by product and allows each product norm to be independent of one another, as in real life. Take, for example, a group of teenage girls. Strong norms are easily observed in their common clothing choices. However, a choice of T-shirt style should not affect the normative choice of cell phones, so their product norms are independent. To capture the dynamic effects of groups over time, we measured the time each participant spent in the domain (Ti0t) at time t. The data set provides detailed login and logout times, which we used to calculate the total minutes each participant spent in the domain before buying a product. For group efficacy (GEgt), we calculated the average number of tasks successfully completed by all participants in group g at time t. To assess the role of product price (Pjt), we used data about the price a participant paid for product j purchased at time t.
Two experts with in-depth knowledge of the context coded the products as functional, social, or hybrid, according to the firm-provided descriptions of each product. For example, functional products included tools that helped the participants increase farming output or herbal supplements to increase their avatars’ physical health. Social products included accessories and souvenirs that mainly enhanced the participant’s image within the domain. Finally, we dropped products that were equally functional and social (e.g., vehicles). We also excluded products with average purchase frequencies of less than 25% of all products. Because approximately twice as many functional as social products emerged from these procedures, we randomly selected 24 functional and 12 social products to match the overall sample. Of the more than 200 groups in the domain, we randomly selected 40 groups; 1 contained too many missing values. Therefore, our data consist of more than 4.7 billion data points (52,833 observations · 39 groups · 36 products · 64 days). Although participants could belong to multiple groups, our model is based on the participants’ dominant group, identified by where they spent the most time.3 Following prior research on hazard models (Iyengar, Van den Bulte, and Valente 2011), we coded the initial purchase of a product as 1 if participants purchased during the observation period, before we truncated the data, and 0 otherwise.
Most marketing research has examined conforming behavior at the network level (Van den Bulte and Lilien 2001), but we aim to isolate the effects of group norms on conforming behavior while also controlling for network effects. Therefore, we calculated network contagion (NCi0jt) as a participant’s exposure through various social interactions (communication, joint tasks, and transactions) to other participants who had previously purchased the product. We also controlled for the installed user base (IUBjt), gender (Gi0), customer average wallet (AWi0t), average customer performance (ACPi0t), group size (Sgt), friendship centrality (number of friends that participant i0 made at time t, FCi0t; participants can “friend” each other in the domain), and business relationship centrality (number of business partners buying from or selling to participant i0 at time t, BCi0t), all of which might affect individual product purchases.
TABLE 1
Testing the Theoretical Foundations of Group Marketing
TABLE 2
Descriptive Statistics and Correlations
Notes: We report correlations for functional/social decisions. Similar to Iyengar, Van den Bulte, and Valente (2011), we deleted cases that do not contribute to the likelihood function of the hazard models and report the descriptive statistics for the remaining sample. Correlations that are greater than or equal to .14 (Study 1), .01 (Study 2a), and .18 (Study 2b) in absolute value are significant at p < .05.
TABLE 3
Key Constructs, Definitions, and Operationalizations
TABLE:
| Constructs (Label) | Definitions | Operationalizations |
|---|
| Group product norm (GNijgt) | Informal understanding of what is typical among group members, relevant to a specific product. | WhereN represents users in the domain, yi0jt captures whether participant i0 purchased product j at time t, zijt is set to 1 if participant I purchased product j at time t and 0 if not, wi0gt equals 1 if participant i0 joined group g at time t and 0 otherwise, and gii0t equals 1 if user I and i0 and 0 are in the same group at time t, 0 if not. |
| Time in domain (Tit) | The cumulative time a person spends in a given domain. | The total number of minutes each participant spent in the group g (based on login and logout times) until (s)he bought the product (at time t); if a participant did not purchase the product, we used the point at which we truncated the data. |
| Group efficacy (GEgt) | The ability of the group to perform effectively in a given domain. | Wi0gt-1, where Taskit represents how many tasks participant I finished at time t, and wigt-1 equals 1 if participant I joined group g at time t and 0 otherwise. |
| Product price (Pjt) | Cost of a good. | How much a participant paid for product j purchased at time t. |
| Network contagion (NCijt) | A person’s exposure to prior product purchasers through various social interactions. | Where yi0jt captures whether user i0 purchased product j at time t, zijt is set to 1 if user I purchased product j at time t and to 0 if not, and gii0t equals 1 if a connection formed at time t between user I and i0 and 0 otherwise. |
| Installed user base (IUBjt) | Number of members who purchased product j throughout the entire domain. | The number of members in the entire domain who purchased the product before participant i0 buys product j at time t. |
| Gender (Gi) | Customer gender. | Men = 1, women = 0. |
| Customer average wallet (AWit) | Average amount of financial resources an individual has available. | 1Ii0t=t, where Ii0t is the income that participant i0 earned in the game at time t. |
| Customer average performance (ACPt) | Individual measure of ability to complete tasks successfully within the domain. | t 1taski0t=t, where taski0t is the task that participant i0 finished at time t. |
| Group size (Sgt) | Number of people in a particular group. | n i0 =1wi0gt, where wi0gt equals 1 if person i0 joins group g at time t and 0 otherwise. |
| Friendship centrality (FCit) | Number of friends before time t. | Number of friends that participant I made at time t. |
| Business relationship centrality (BCit) | Number of business partners before time t. | Number of business partners that participant I made at time t. |
TABLE 4 Dynamic Effects of Group Product Norms on Purchase Behaviors
| | Hypothesis | Functional Decisions: Product Purchase | Social Decisions: Product Purchase |
|---|
| Model 1 | Model 2 | Model 3 | Model 4 |
|---|
| Group product norms | | .20 (.01)** | .23 (.01)** | .28 (.02)** | .30 (.04)** |
| Moderator: Dynamic Factor |
| Group product norms × Time in domain | | | .12 (.03)** | | -.16 (.01)** |
| Group product norms × Time in domain2 | H3 | | -.17 (.02)** | | .27 (.11)** |
| Moderator: Group Factor |
| Group product norms × Group efficacy | H4 | | .04 (.00)** | | .05 (.03)* |
| Moderator: Decision Factor |
| Group product norms × Product price | H5 | | -.03 (.01)** | | -.05 (.02)** |
| Controls |
| Time in domain | | .13 (.03)** | .17 (.04)** | .21 (.03)** | .18 (.04)** |
| Time in domain2 | | -.08 (.01)** | -.09 (.01)** | -.25 (.02)** | -.16 (.03)** |
| Group efficacy | | .18 (.03)** | .13 (.02)** | .14 (.14) | .18 (.07)** |
| Product price | | -.09 (.10) | -.06 (.10) | -.02 (.00)* | -.02 (.00)** |
| Network contagion | | .06 (.02)** | .08 (.03)** | .15 (.04)** | .11 (.02)** |
| Installed user base | | .17 (.02)** | .13 (.01)** | .11 (.02)** | .09 (.03)** |
| Customer gender | | .30 (.03)** | .24 (.05)* | .26 (.03)** | .20 (.06)** |
| Customer average wallet | | .25 (.07)** | .37 (.02)** | .20 (.05)** | .27 (.01)** |
| Average customer performance | | .24 (.05)** | .21 (.03)** | .16 (.03)** | .11 (.02)** |
| Group size | | .21 (.03)** | .32 (.01)** | .15 (.02)** | .24 (.06)** |
| Friendship centrality | | .17 (.04)** | .11 (.01)** | .18 (.08)* | .10 (.01)** |
| Business relationship centrality | | .11 (.01)* | .16 (.04)* | .18 (.06)** | .20 (.03)** |
| Inverse Mills ratio | | .63 (.42) | 1.02 (.36)* | .51 (.41) | .93 (.30)** |
| Log pseudo-likelihood | | -173,018.73 | -170,933.06 | -48,977.38 | -48,772.31 |
| Akaike information criterion | | 173046.73 | 170969.06 | 49005.38 | 48808.31 |
TABLE:
| | Hypothesis | Functional Decisions: Product Purchase | Social Decisions: Product Purchase |
|---|
| Model 1 | Model 2 | Model 3 | Model 4 |
|---|
| Group product norms | | 14.20 (5.74)* | 13.15 (6.73) | 17.56 (5.90)** | 17.56 (5.90)** |
| Moderator: Dynamic Factor |
| Group product norms × Time in domain | H3 | -17 (.07)* | -20 (.12) | .17 (.06)** | .26 (.10)** |
| Group product norms × Time in domain2 | | | | | .00 (.00) |
| Controls |
| Time in domain | | -.02 (.04) | -.02 (.04) | -.03 (.03) | -.02 (.03) |
| Typical purchase price | | .15 (.09) | .15 (.09) | .14 (.12) | .13 (.12) |
| Color preference | | .90 (2.22) | .84 (2.24) | .37 (2.30) | .18 (2.30) |
| Intercept | | -27.90 (15.03) | -27.41 (15.18) | -17.06 (18.82) | -17.57 (18.78) |
| R2 | | 0.09 | 0.08 | 0.14 | 0.14 |
*p < .05. **p < .01. Notes: The cells contain regression coefficients. Standard errors are in parentheses.
Study 2a: analysis. Because the data are right censored, standard approaches are not suitable for analyzing purchases. Hazard models can analyze the effects of time-varying and timeconstant covariates on a participant’s purchase probability while accounting for right censoring (Cameron and Trivedi 2005). Similar to prior work (Iyengar, Van den Bulte, and Valente 2011), we used the lagged measure before users’ adoption time t for all independent variables in our estimation. Therefore, we formulated the hazard model hðtjXi0gjt-1Þ for participant i0’s adoption of product j in group g at time t as follows:
where t is the time that participant i0 in group g purchased product j, a is the baseline of the hazard model that represents the purchase of product j at time t in group g, Xi0jgt-1 is a row vector of covariates that indicates that participant i0 purchased product j in group g at time t - 1, and b is a column vector of parameters to be estimated for product j in group g. Consistent with prior literature (Cameron and Trivedi 2005), we calculated participant i0’s purchase hazard rate hðtjXi0jgt-1Þ for product j in
where FðtÞi0jg is a cumulative distribution function of product j for participant i0’s purchase in group g at time t. To estimate the results, we used a partial likelihood estimation employing the proc phreg procedure in SAS 9.4. For the models of the predicted effects of the group product norm on purchase behaviors for functional (inverted U-shape) and social (U-shape) decisions over time, as moderated by group efficacy and product price, we test the following equation:
where GNi0jgt-1 captures the group norm of product j in group g at time t-1, Ti0t represents the amount of time participant i0 has spent in the domain at time t-1, GEgt-1 is the efficacy of group g at time t-1, Pjt is the price of product j at time t-1, NCi0jt-1 is network contagion relevant to participant i0 about product j at time t-1, IUBjt-1 indicates the installed user base of product j at time t-1, Gi0 is the gender of participant i0, ACPi0t-1 reveals average participant performance at time t-1, AWi0t-1 stands for the participants’ average wallet at time t-1, Sgt-1 indicates group size at time t-1, FCi0t-1 is friendship centrality, and BCi0t-1 is business relationship centrality at time t-1.
Because we expect opposing effects of groups on information and identity appraisals, which would be lost if we aggregated the data, we ran two parallel models (functional and social) to isolate the different hypothesized curvilinear effects. A Schoenfeld test confirmed the assumptions of proportional hazard for both the functional (.68, p > .20) and social (.62, p > .20) models. To control for unobserved heterogeneity, we used a Cox proportional hazard model. Because participants could voluntarily join groups, our estimation may suffer from self-selection effects, so we adopted a two-stage self-selection model. For each group g, we conducted a probit selection model to determine whether customer i0 participates. As independent variables, we included group efficacy (GEgt), group size (Sgt), gender (Gi0), customer average wallet (AWi0t), average customer performance (ACPi0t), friendship centrality (FCi0t), and business relationship centrality (BCi0t). We calculated the inverse Mills ratio and added it to our model (Equation 3) as a control.
Study 2a: results and discussion. To account for scaling differences, we standardized all variables before entering them into the models. The base model included the group norm (GNi0jgt), time in the domain (Ti0t), group efficacy (GEgt), product price (Pjt), and the control variables. The final model also tested for the moderating effects of time in the domain (T2i0t), group efficacy (GEgt), and product price (Pjt). The differences in the –2 log-likelihood between the base and final models (functional Dc2(4) = –2,085.67, p < .01; social Dc2(4) = 205.07, p < .01) provided support for including the interaction terms in the model. As Table 4, Panel A, illustrates, for functional decisions, time in the domain has a significant positive moderating effect (b6 = .12, p < .01), but the quadratic term of time in the domain has a significant negative moderating effect (b7 = -.17, p < .01), so the influence of the group norm on purchase behavior strengthens at first and then weakens over time, in support of H3a. In support of H3b, time in the domain has a significant negative moderating effect (b6 = –.16, p < .01), and the quadratic term of time in the domain has a significant positive moderating effect (b7 = .27, p < .01) on purchase behaviors, suggesting a U-shaped relationship for social decisions. Group efficacy strengthens the effect of the group product norm on conforming purchase behavior for functional decisions (b8 = .04, p .10), so we cannot confirm H4b. Finally, price weakens the effect of the group product norm on purchase behavior for both functional (b9 = -.03, p
To test the effect of group norms relative to network contagion, we ran a chi-square difference test that compared the –2 log-likelihood from our original model with an alternative model in which the coefficients for the group product norm and network contagion were set to be equal. For both functional (Dc2(4) = 163.47, p < .01; b1 = .20 > b12 = .06) and social (Dc2(4) = 72.61, p < .01; b1 = .28 > b12 = .15) models, the original model fit significantly better than the alternative model, and group norms had a stronger effect than the social network.
To test the robustness of our findings, we conducted three additional analyses (see Web Appendix A). First, we ran a series of sensitivity analyses with different distribution assumptions: Weibull, exponential, and lognormal. The exponential duration distribution uses a constant hazard rate that does not vary with time, the Weibull distribution allows for a hazard rate with a monotonically increasing or decreasing rate (scale), and the lognormal distribution can structure an accelerated failure model. The results are largely consistent, though the Akaike information criteria for the Cox proportional hazard model are lower, indicating a better fit. Second, to ensure that our effects are due to the differences between social and functional decision contexts, not sampling differences, we ran a series of analyses of variance to test for mean differences between the two contexts across all covariates in the model. None of the tests was significant (p > .05). Third, we conducted the same test between sampled and nonsampled groups. Again, none of the tests was significant (see Web Appendix B).
Study 2b: Dynamic Effect of Groups in Familiar Purchase Domains
In Study 2a, we examined the dynamic effects of group norms on customer decisions when the customers are new to a purchase domain. In Study 2b, we examine the effects of groups when people are relatively more familiar with the domain. In addition, with Study 2a we used different products to identify decisions for which information and identity mechanisms should be most prominent, which helped us study the effect of groups over time using real customer purchases. Yet we could not isolate functional or social effects from other product characteristics. So, in Study 2b, we keep the product constant to address this potential confound.
Study 2b: design and sample. Study 2b uses two parallel experiments for functional and social products, each with one manipulated (group norm) and one measured (time in domain) variable. We captured the real time spent in the focal domain over the course of several years, and we used fictitious running groups. In this context, variations in time spent in the domain can be captured easily, group membership can be simulated through an experimental design, and people purchase both functional and social products. We used the same product choice situation from Study 1 and recruited 247 participants from Amazon’s Mechanical Turk.
Study 2b: procedure. Because running was the focal domain, we measured time in the domain as the self-reported amount of time participants had been running over their lifetime, calculated using the average time per week and number of years running. To simulate the feeling of being in a group, we used their state of residence as a demarcation, informing participants, “Congratulations! You’ve been selected to be part of our special [State Name] Runners Test Group! You will now join a selective group of people from [State Name] as [State Name] Runner 023.” To confirm the effectiveness of this manipulation, we asked participants if they felt they were part of a group, using a seven-point Likert type scale (functional = 5.85, social = 6.00).
The description of the task—selecting the product that either performed better (functional) or was more self-expressive (social)—served as the manipulation for the decision context. Previous studies have suggested that a manipulation that indicates the decision has one correct answer (functional) or involves making a judgment (social) makes information and identity appraisals more salient, respectively (Kaplan and Miller 1987). Participants in the functional condition indicated that their decision was based on product performance more than did those in the social condition (Mfunct = 5.79, Msoc = 4.86; F(1, 246) = 35.08, p < .01); those in the social condition noted that their decision was based on the self-expressiveness of the product more than did those in the functional condition (Mfunct = 4.32, Msoc = 5.75; F(1, 246) = 59.07, p < .01). The participants then reviewed the two products and fictional feedback (i.e., visual representation of group members’ product choices) from others who were reported to be in the same [State Name] Runners Test Group. The feedback served as the manipulation of the strength of the group norm and is similar to manipulations used in previous studies on group norms (Goldstein, Cialdini, and Griskevicius 2008). The manipulation performed as expected (functional Mlow = 3.61, Mhigh = 5.28; F(1, 120) = 43.33, p < .01; social Mlow = 3.74, Mhigh = 4.96; F(1, 125) = 35.15, p < .01). Finally, participants made their product selection and stated how much more they were willing to pay for the chosen product than for the other option. The correlations and descriptive statistics are in Table 2, Panel C (for the experimental stimuli and measures, see the Appendix).
Study 2b: results and discussion. Table 4, Panel B, presents the results of the regression analysis. We examined the effect of the interaction between group norms and the time spent in the domain on willingness to pay, controlling for the typical price paid for products in the focal product category and color preferences. As a replication of Study 2, we tested the quadratic coefficient for time, but in both models, including this term worsened overall model fit4 (Models 2 and 4; Table 4, Panel B). This effect likely arose because our sample only captures the right-hand side of the curvilinear effects. That is, participants were new to the domain in Study 2a (two months), whereas the average participant in Study 2b had been in the domain for 11 years. Thus, we tested the hypotheses using the results from the linear Models 1 and 3 in Table 4.
In line with our arguments, for functional decisions, time in the domain should negatively moderate the effect of the group product norm on willingness to pay for the conforming product (i.e., second half of the curvilinear effect); it accordingly weakened the effect of the group norm on willingness to pay (b = –.17, SE = .07, p < .05), consistent with H3a. For social decisions, the effect of the group norm on willingness to pay (b = .17, SE = .06, p < .05) for the conforming product strengthens as time in the domain increases, consistent with H3b. Thus, with more time in the domain, group norms exert less influence on functional decisions and more influence on social decisions, in line with Study 2a.
General Discussion
In this research, we attempt to provide a theoretical foundation for group marketing by exploring the notion of the “group” and how membership within a group can drive behaviors that conform to the group norm. Across three studies incorporating multiple methods, we demonstrate that when a customer is aware of her or his affiliation with the group and exposed to a group norm, it can alter information and identity appraisals during decision making, such that the customer tends to match her or his purchase behaviors with those of the group. Thus, this research contributes to marketing research on groups.
It also contributes to the discourse about the dynamic effects of groups on behavior and provides some resolution to contrasting findings that suggest increasing or decreasing social influence over time (Cendrowski 2012; Risselada, Verhoef, and Bijmolt 2014). We show that this conflict stems from a failure to account for two key factors. First, we distinguish information from identity appraisals. Second, we identify the time a person has spent in the purchase domain as a critical determinant of the group’s dynamic effect. The group influence on purchase behavior through information appraisals diminishes over time; the reverse is true when an identity appraisal is most salient. If a person is new to a domain, (s)he is similar to an outsider and works to protect a unique personal identity while also reducing the discomfort of standing out from the domain. Thus, group influence is weak and limited to reducing outward contrasts. Over time, group norms become key for enacting a social identity, and the person conforms with the group to manage the self-concept. Finally, group efficacy strengthens and product price weakens a group’s influence on members’ behavior. To provide guidance to managers, we use these theoretical foundations to articulate the key process steps in executing group marketing and present these steps in Figure 4.
Managerial Insights: Process for Executing Group Marketing
In line with the theory we have presented, effective group marketing becomes a matter of strategically guiding the conditions that create group influence and dynamically customizing this as the customer’s time in a domain increases. The first step in the group marketing process, as in many other marketing strategies, is to identify desirable customer targets using indicators such as projected customer lifetime value (Venkatesan and Kumar 2004) or customer engagement value (Kumar et al. 2010). Second, marketers must establish a salient group affiliation, which requires deciding between building a firmmanaged group or identifying a customer’s existing group affiliation, independent of the firm. Providing a firm-managed group (e.g., Barnes & Noble book clubs, Nike running clubs) has many benefits; it facilitates the firm’s access to group members, and allows the firm to suggest group norms and manage norm exposure. However, it also accrues higher costs, such as those for platform design and management (Dholakia, Bagozzi, and Pearo 2004) or offline meeting spaces. Leveraging customers’ affiliation with an independent group instead requires fewer resource investments but also cannot provide the access and control benefits of firm-managed groups. People maintain psychological affiliations to groups at various levels of abstraction, from concrete groups (e.g., single working mothers) to broader social categories (e.g., parents), each associated with different group norms (Goldstein, Cialdini, and Griskevicius 2008). Thus, for both providing and leveraging groups, it is key to determine which group affiliation is most effective (e.g., Harley Davidson Owners vs. New Bikers; Schouten and McAlexander 1995), according to the firm’s ability to identify and access members of the group and assessment group norms.
Third, the firm must develop the necessary group marketing conditions to activate group-based psychological processes. It needs to build the customer’s awareness of her or his affiliation with the group and then expose her or him to the desired group norms. As our research demonstrates, firms can strategically control group affiliation at the decision moment. Study 1 shows that disclosing information about a customer providing a product review (e.g., group membership) alters the effectiveness of that review. Reinforcing these findings, Naylor, Lamberton, and Norton (2011) find that reviews from similar or ambiguous reviewers are more persuasive than from dissimilar reviewers. In Study 2b, we show that firm communication can prime group affiliation. Similarly, Goldstein, Cialdini, and Griskevicius (2008) demonstrate that merely changing the wording (e.g., “other hotel guests,” “other guests in this room”) in marketing communication from more abstract to more concrete groups enhanced persuasiveness. Naylor, Lamberton, and West (2012) show that limiting disclosure of group member attributes to only their common brand usage (vs. also providing demographic information) can enhance the likelihood of new members joining the group because demographic attributes may put the group at risk of being perceived as an out-group. Thus, strategically disclosing or limiting group relevant information in customer-to-customer communication, marketing communication, or even group member recruitment can guide a customer’s affiliation to the firm’s desired group.
Firms also have many options for exposing the customer to group norms. If the firm provides groups, it can guide the development of beneficial norms through techniques such as storytelling, documenting, and creating rituals that perpetuate those desired norms. Group providers then can enhance these beneficial norms by influencing the status associated with compliance. For example, “Jeep Jamborees” are off-road challenges; when attendees complete the challenges, they earn status benefits within the Jeep brand community. If firms instead leverage existing groups, their focus should be on exposing customers to existing norms through marketing communication. As Study 1 suggests, seeding strategies—such that the firm incentivizes a group member to advocate certain behaviors—can be particularly effective for transmitting norms to customers in an independent group. Alternatively, the firm could communicate the norm directly using aggregate group information in its marketing, as in Study 2b.
The final step is adapting group marketing to the amount of time a customer has spent in the domain. People just entering a new domain typically represent an appealing target for acquisition. They exhibit both high demand for new products and a potentially long lifetime in the domain. Our findings suggest that group norms are more influential for information appraisals in this period. For group providers, communicating elements of the group that facilitate information exchanges (e.g., firm-sponsored training programs) could be effective for influencing the purchase behavior of people new to the domain. However, as a person becomes more familiar with the domain, the group influence on identity appraisals begins to dominate, so group providers should start investing in marketing that facilitates socialization among members, such as interactive forums or brand-fests. Marketers leveraging groups can customize their group marketing more effectively using product positioning. In Study 2b, we reframed a single product using a functional versus a social decision context. When customers have just entered a specific domain, the firm should focus on the functional benefits of the products, to increase their conforming behavior. Positioning the same product with social benefits instead might trigger self-protective responses and decrease purchase behavior. Later, though, repositioning the product according to its social benefits can enhance group marketing effectiveness.
Limitations and Further Research
With our mixed research design, we can take advantage of both longitudinal data, with objective customer purchases and behavioral observations over time, and experimental designs that feature controlled manipulations. However, further research could examine which factors change over time (e.g., personal expertise). We replicate our findings across distinct purchase contexts, but other settings may provide different insights. Groups can form for many reasons, so a systematic investigation of group type is warranted. We examined two key constructs that likely leverage or hinder group influence, but others might be considered too—for example, group-level factors (e.g., stability, permeability) might be particularly informative. Group marketing effectiveness may also depend on marketers’ ability to gather information from the group, which implies a useful research extension. Our study focuses exclusively on group influences on purchase behavior, but the same mechanisms and their dynamic influence over time may hold for other customer decisions, such as the choice to contribute content or recruit other group members.
Most research, including ours, has conceptualized group affiliation as a person’s membership, but membership is not required. A person might associate with a group to which (s)he aspires to belong. Alternative forms of affiliation, especially negative ones, warrant investigation. Research on reactance has suggested that when group norms are particularly strong or limiting, they may promote nonconformity rather than conformity (Brehm and Brehm 2013). We did not observe this effect in our research context; it represents a potential dark side of group marketing that should be investigated further. Subtle cues such as choice categories (Wittenbrink and Henly 1996; Mogilner, Rudnick, and Iyengar 2008) could provide a means of inferring group norms and influencing behavior, which warrants further investigation. Finally, people may conform through inaction; conformity by omission requires further consideration (Cialdini and Trost 1998).
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1To verify that participants in the group condition felt that they were a part of a group, we ran a pretest in which we tested the sunrise/sunset group manipulation. After completing the group manipulation, participants (n = 50) rated their perceptions of both sunrise and sunset group members on a series of attributes, including friendliness, intelligence, helpfulness, likeability, and cooperativeness. In a series of paired sample t-tests across all attributes, participants rated members of their arbitrarily assigned group more favorably (all ps < .05).
2The effects we observe theoretically could reflect a degradation of the nonconforming product (Product A) rather than an enhancement of the value of the conforming product (Product B). A nonsignificant analysis of variance comparing the mean willingness to pay for just Product A (nonconforming product) rules out this alternative explanation (F(3, 218) = 1.22, p = .30).
3As a robustness check, we tested the model on a subsample of participants who were members of only one group for the duration of the study. The results remained consistent, as we show in Web Appendix A.
4As a robustness check, we used a combined sample and tested the moderating effect of the decision context. As we expected, we found a significant (b = 3.01, SE = 1.02, p < .01) three-way interaction (decision context · group norm · time).
*Group marketing condition is significantly different from each of the three other conditions (all ps < .05). Notes: To aid in interpretability, willingness to pay was transformed by a constant, such that all means are positive.
FIGURE 2 Dynamic Effects of Group Product Norms on Purchase Behavior
Notes: Constructs in italics were tested in Study 1. Constructs in roman font were tested in Studies 2a and 2b.
FIGURE 4 Process for Executing Group Marketing
APPENDIX
Experimental Designs, Objectives, Procedures, and Manipulations
TABLE:
| | Manipulations and Experimental Stimuli |
|---|
| Procedures and Measures | No Group Manipulation | Group Membership Manipulation |
|---|
| Recommendation manipulation, in which half of the participants were randomly exposed to four live comments from other participants, such that half recommended Product A and half recommended Product B. | No Recommendation No additional information. | Recommendation Order of comments randomized. Content of comments counterbalanced. |
TABLE:
| | Manipulations and Experimental Stimuli |
|---|
| Procedures and Measures | No Group Manipulation | Group Membership Manipulation |
|---|
| • Establish all participants’ membership within a group. All participants were told they qualified to be part of a group, based on their responses to the previous demographic and filler questions. | Group membership control | |
| • Group membership manipulation check: “I was part of a special group for this study.” | “Congratulations! You’ve been selected to be part of our special [State Name] Runners Test Group! You will now join a selective group of people from [State Name] as [State Name]Runner023.” |
| • Present participants with the decision context manipulation. Participants complete the same product choice task as in Study 1. | Functional [Social] Decision Manipulation |
| • Functional decision manipulation check: “When considering the two products, I carefully considered product performance.” | One of the most important features of a product is its ability to perform a set of uses for which it was designed [how much it expresses who you are]. Imagine you’re in the market for a new pair of running shoes. Your last shoes performed poorly. As a result, now you are ONLY concerned with finding a pair of shoes that will perform well [really express who you are]. |
| • Social decision manipulation check: “When considering the two products, I carefully considered product style.” | |
| Present the group product norm manipulation and capture conforming purchase behavior. Half of participants were randomly selected to receive either the weak or strong group norms manipulation. Participants chose between products and rated how much they were willing to pay. | Group Product Norms Manipulation We’ve provided you with a poll from the other [State Name] Runners Test Group members to help you choose between the products. |
TABLE:
| | Manipulations and Experimental Stimuli |
|---|
| Procedures and Measures | No Group Manipulation | Group Membership Manipulation |
|---|
| • Group product norms manipulation check: “All of the group members’ opinions reinforced each other”; “There was a very clear group opinion”; “The group was very cohesive.” | Weak Group Product Norms Manipulation | Strong Group Product Norms Manipulation |
| • Willingness to pay measure: “How much more are you willing to pay for [chosen product] than [other product]?” (Willingness to pay for Product B - Willingness to pay for Product A). | | |
Prins Constantijn over belang EU
DIAGRAM: FIGURE 1 Study 1: Empirical Test of Necessary Conditions for Group Marketing
DIAGRAM: A: In Functional Decisions, Information Appraisal Predominates
DIAGRAM: B: In Social Decisions, Identity Appraisal Predominates
DIAGRAM: FIGURE 3 Effect of Group Product Norms on Purchase Behavior
DIAGRAM
PHOTO (BLACK & WHITE)
PHOTO (BLACK & WHITE)
PHOTO (BLACK & WHITE)
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Record: 82- Hands Off My Brand! The Financial Consequences of Protecting Brands Through Trademark Infringement Lawsuits. By: Ertekin, Larisa; Sorescu, Alina; Houston, Mark B. Journal of Marketing. Sep2018, Vol. 82 Issue 5, p45-65. 21p. 1 Diagram, 7 Charts. DOI: 10.1509/jm.17.0328.
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Hands Off My Brand! The Financial Consequences of Protecting Brands Through Trademark Infringement Lawsuits
Well-known brands are frequently imitated, misused, or tampered with. Firms facing these threats routinely turn to the legal system and file trademark infringement lawsuits in an attempt to prevent revenue losses and brand equity dilution. In this article, the authors address the largely unexplored issue of brand protection. First, they categorize all major types of trademark infringement. Second, using signaling and prospect theories, they present a conceptual model that outlines the financial consequences of defending a brand in court. The authors test the predictions of this framework using a large sample of trademark infringement lawsuits and find that although investors react negatively in the short term to firms’ filing and even to firms’ winning such cases, the long-term performance of firms that successfully leverage the legal system to protect their brands is positive.
brand protection; brand threats; trademark infringement litigations; event study; stock market reaction to lawsuits
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By Larisa Ertekin; Alina Sorescu and Mark B. Houston
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Record: 83- Harvesting Brand Information from Social Tags. By: Nam, Hyoryung; Joshi, Yogesh V.; Kannan, P. K. Journal of Marketing. Jul2017, Vol. 81 Issue 4, p88-108. 21p. 1 Diagram, 8 Charts, 3 Graphs. DOI: 10.1509/jm.16.0044.
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Harvesting Brand Information from Social Tags
Social tags are user-defined keywords associated with online content that reflect consumers’ perceptions of various objects, including products and brands. This research presents a new approach for harvesting rich, qualitative information on brands from user-generated social tags. The authors first compare their proposed approach with conventional techniques such as brand concept maps and text mining. They highlight the added value of their approach that results from the unconstrained, open-ended, and synoptic nature of consumer-generated content contained within social tags. The authors then apply existing text-mining and data-reduction methods to analyze disaggregate-level social tagging data for marketing research and demonstrate how marketers can utilize the information in social tags by extracting key representative topics, monitoring common dynamic trends, and understanding heterogeneous perceptions of a brand.
Online Supplement: http://dx.doi.org/10.1509/jm.16.0044
The advent of user-generated content has revolutionized the art and science of marketing research by making available a significant amount of online data that reflect consumers’ opinions, attitudes, and preferences for products, services, and brands. Marketing scholars have proposed many methods to obtain insights on brand perceptions and competitive market structure by mining search data (e.g., Ringel and Skiera 2016), microblog data (e.g., Culotta and Cutler 2016), online reviews (e.g., Lee and Bradlow 2011; Tirunillai and Tellis 2014), and posts on an online discussion forum (Netzer et al. 2012). At the same time, marketing practitioners have obtained brand perceptions and brand associative networks by mining online discussions and posts about brands (e.g., Nielson’s Brand Associative Map, McKinsey’s Brand Navigator).
In this article, we focus on user-generated social tags associated with brands and propose an approach to analyze the large set of brand associations obtained from social tags for marketing research. Users largely employ social tags in social media platforms to organize and discover content in line with its topical relevance. Many forms of online content can be tagged: for instance, web links (links to a video, photo, blog post, or news article) are tagged in Delicious; images and photos are tagged and shared on Pinterest, Instagram, and Facebook; videos are tagged on YouTube; and tweets are tagged (via hashtags) on Twitter. Mining social tags provides marketing researchers with unique opportunities to understand brand associations that are directly and explicitly mentioned by individual users/consumers.
Despite the popularity of the use of social tags, there is comparatively little research on what marketing researchers can learn from social tags. A recent study by Nam and Kannan (2014) shows that social tags contain significant informational value in understanding brand associative network and competitive market structure and predicting firm performance. Although Nam and Kannan demonstrate the value of social tagging data, they leave several key issues unexplored. First, it is not clear whether social tags produce new, different insights relative to existing approaches in eliciting brand associations. Such a comparative analysis would highlight the relative advantages and limitations of using social tags for marketing research. Second, Nam and Kannan’s study focuses on aggregate-level information contained in a large set of social tags. They aggregated and summarized more than 7,000 tags obtained from the millions of individual tagging activities into brand-level metrics such as valence and competitive overlap of brand associations. Although those metrics provide a useful summary of high-level brand information, they do not provide insights into disaggregate-level information in brand associations and individual users. The richness inherent in the disaggregate-level tagging data can enable marketing managers to exploit (1) user-level disaggregate brand association information (e.g., How can managers identify and describe heterogeneous perceptions on the brand? What representative topics describe the brand perceptions of the distinct user segments, and what insights can these topics provide on improving overall brand perceptions?) and (2) temporal disaggregate brand information (e.g., Which brand associations are dynamically correlated, and how do they contribute to the evolving brand image?). We focus on these hitherto unexplored issues to highlight the value of social tags for marketing research.
Our goal in this article is to present an approach to collect and analyze social tagging data for marketing research and show how marketing managers can derive useful insights from social tags. We present an application of existing text-mining and datareduction methods to understand consumers’ brand perceptions reflected in social tagging data. Our research employs data reduction models such as latent Dirichlet allocation (LDA) topic modeling (e.g., Blei 2012; Blei, Ng, and Jordan 2003; Griffiths and Steyvers 2004; Puranam, Narayan, and Kadiyali 2017; Tirunillai and Tellis 2014), dynamic factor analysis (DFA; e.g., Du and Kamakura 2012; Zuur et al. 2003), and clustering analysis to help managers efficiently process a large volume of qualitative information. We show how social tags can be used to infer the major latent topics underlying consumers’ categorization of tags associated with a brand and to understand heterogeneous perceptions of a brand with the emergence of new content related to the brand; in addition, we identify tags’ latent factors on the basis of their correlations over time and show how the factors evolve dynamically.
Table 1 summarizes the unique positioning of our research relative to recent approaches developed for processing brand information obtained from large amounts of data generated by consumers. As Table 1 shows, this article proposes a method for obtaining brand associative network structure by employing the information contained in tagging data, which has been underexplored in marketing compared with search data (e.g., Ringel and Skiera 2016), microblog data (e.g., Culotta and Cutler 2016), and data from online discussion forums (e.g., Netzer et al. 2012). Our approach provides unique and distinctive insights first and foremost because our approach uses brand associations that are directly and explicitly stated by users/consumers to determine brand associative structure and brand position. In contrast, in extant research, brand associative strengths are indirectly inferred from the similarity between brand followers and exemplar account followers (Culotta and Cutler 2016), product positions are indirectly inferred from search and comparison patterns (Ringel and Skiera 2016), and brand associations are discovered by an automatic keyword extraction algorithm (Netzer et al. 2012). Second, this article proposes a methodology to analyze and visualize a large set of brand perceptions/attributes. In contrast, Ringel and Skiera (2016) focus on visualizing positioning of a large set of products, and Culotta and Cutler (2016) focus on deriving social perceptions of three brand attributes for a large set of products. Our study shows how to analyze a large set of qualitative brand perceptions/attributes (>1,000 tags) and acquire managerial insights for marketing strategies.
Given that our main objective is to illustrate how marketing managers derive new and distinctive insights from the information contained in social tags, this article is structured as follows. We first lay out the conceptual foundation that establishes the appropriateness of using social tags to capture brand associations. We then describe the procedures to obtain brand associations from social tags and discuss the similarities and distinctiveness of our proposed social tag–based approach compared with existing approaches to generate brand maps, including text mining and customer interviews. Subsequently, we illustrate how existing text-mining methods and datareduction methods can help marketers process and monitor the tag information using perceptual brand maps by deriving representative topic distributions of distinct consumer segments and extracting common dynamic trends. We conclude with a discussion of the applications of social tags in specific settings and the associated managerial implications and outline areas for further research.
Background for Social Tags
Many social media platforms employ social tags (e.g., hashtags in Twitter, pins in Pinterest, geotags in diverse social media platforms; for examples, see Web Appendix A). In these social tagging platforms, users interpret, categorize, and summarize a large volume of content, including textual data (e.g., reviews, blogs, microblogs, news articles) and nontextual data (e.g., images, videos, songs). Tags may include descriptive words (e.g., “news,” “style,” “tips”), identifiers for the brand and subbrand (e.g., “mac,” “iphone,” “ipod,” “iwatch”), identifiers for the category (e.g., “computers,” “smartwatch”), and descriptors of how consumers think and feel about the focal brand (e.g., “cool,” “inspiration”).
Social media platforms employ social tagging systems primarily because (1) tags enhance the customer experience by promoting user convenience in content discovery and content categorization and (2) tags enable the platform to track and manage user content through user-generated topical categories. Interpreting the meaning of social tags is contingent on the type of objects being tagged (textual posts, images, photos, etc.) and users’ motivations for social tagging. Table 2 shows the taxonomy of existing social tagging systems in (1) content management platforms and (2) microblogs and social network platforms. The differences in tagging systems mainly arise from their design (Huang, Thornton, and Efthimiadis 2010).
In content management platforms, social tags primarily serve as a tool to manage a collection of content. Representative examples include social bookmarking platforms (e.g., Delicious, Tumblr, reddit) and content curation platforms (e.g., Pinterest, Last.fm). In these platforms, users build and manage a collection of content and search, discover, and share content using social tags (e.g., Ames and Naaman 2007; Gilbert et al. 2013; Strohmaier, Ko¨rner, and Kern 2010). Thus, the tags associated with brands/products in these platforms provide insights into (1) how online users interpret and perceive content associated with brands/products, (2) how a brand is grouped together with competing brands, and (3) how potential customers construct the consideration set.
Microblogs and social network platforms (e.g., Facebook, Twitter, Instagram) employ tagging systems to help users categorize, search, monitor, and participate in discussions based on user-defined tags. Because posts in microblogs and social network platforms have a short life, tags in these platforms compared with tags in content curation platforms are more often about temporally relevant information and emergent topics, which may appear and disappear quickly (Huang, Thornton, and Efthimiadis 2010; Teevan, Ramage, and Morris 2011). Thus, investigating the trends of tags in these platforms enables marketing managers to monitor emergent topics, track engagement in each topic, and efficiently capture customer views through keywords associated with brands. In addition, geotags that reveal geolocation information of posts on social media platforms provide geographical details of user experiences related to a brand.
From a user perspective, the key motivations for social tagging fall into two categories: content classification and content description (see also Strohmaier, Ko¨rner, and Kern 2010). Researchers have found that people create different types of keywords depending on their motivations for tagging. For instance, when people intend to categorize content, they are more likely to use high-level attributes as tags; yet when people intend to describe content, they are more likely to use contextual attributes as tags (Strohmaier, Ko¨rner, and Kern 2010). In addition, each type of motivation could be driven by self-oriented needs (e.g., organization of content for one’s own reference), social communication (e.g., information sharing and opinion expression regarding the content with other users), or a combination of both (see also Ames and Naaman 2007). Table 3 summarizes the key motivations for social tagging.
Whatever the user’s motivation, the process of social tagging involves interpreting the gist of the content by relating the content to concepts organized in the user’s memory and subsequently describing, categorizing, or communicating that content. Therefore, tags reflect not only the content that is tagged but also a succinct representation of the user’s knowledge structure—that is, his or her mental representation of related concepts. Thus, one can view social tags as the outcome of categorization, description, or communication of content filtered through the lens of a person’s knowledge structure—that is, an individual-specific, thoughtful interpretation of content.
Figure 1 presents an illustrative example of social tagging process for two users, using one of the most popular web links tagged with Apple on Delicious: Guy Kawasaki’s blog post on what he learned from Steve Jobs. User A’s social tags, based on her previous tagging activities, indicate that she has been interested in corporate strategy (e.g., recommended_reading, guide, strategy, casestudy, businessmodel, marketing, innovation) related to various information technology firms (e.g., Yahoo, Microsoft, Google, Apple). This previous interest might lead her to engage with Kawasaki’s blog post and tag it with “innovation,” “guide,” “recommended_reading,” and “Apple.” In contrast, User B’s previous tagging history indicates that she has been primarily into technology, management, and development; thus, she describes the same blog post with tags such as “product,” “management,” and “stevejobs.” As such, each user associates different keywords with an object/content depending on her interest, motivation, and knowledge structure, which may be reflected in previous tagging activities.
From the perspective of a brand, an individual user’s tags associated with a collection of documents and online content related to the brand can provide useful information regarding the user’s knowledge structure associated with this brand. Each document or piece of content can be viewed as a brand-related stimulus, to which the user responds with tags that reveal partial information of his or her knowledge structure about the brand. In addition, as users encounter new content online over time, this may also interact with, change, or shape their knowledge structure and category schema, through either categorization or analogy (Buchanan, Simmons, and Bickart 1999; Griffiths, Steyvers, and Tenenbaum 2007). Tags created to categorize or describe this content can potentially reveal such dynamics. Similarly, differences in tags that different people use for the same content can provide insights regarding the extent of heterogeneity in users’ knowledge structures about a focal brand. This renders tags ideal for exploring how users believe, think, feel, and reason about brands.
In summary, social tags can provide a rich and heterogeneous interpretation of a brand across and within individuals over time as their knowledge structure is shaped by their encounters with stimuli (content, interactions, and experiences; Nam 2012; Nam and Kannan 2014). As such, these tags provide a rich associative structure reflective of how consumers relate to brands. Thus, social tags can serve as effective input for
An Overview of Our Method
Figure 2 outlines our method to collect and analyze brand information from social tagging data. It consists of four stages: (1) data collection, (2) association elicitation, (3) aggregation and visualization, and (4) analysis of disaggregate-level data. For the sake of illustration, we choose Apple as our focal brand.
Step 1: Data Collection
Data collection starts with specifying the set of content in a social tagging platform in a specified time frame. For instance, we consider social tags from Delicious, a top 500 global website in terms of traffic rank (Alexa.com 2011), with a three-month global Alexa traffic rank of 252 (March 13, 2011, through June 12, 2011). Thus, this social bookmarking website is widely accessed and fairly representative in terms of gathering a broader of opinions from online users. Step 1 in Figure 2 shows examples of bookmarks tagged with “Apple” and other keywords by different users.
To collect bookmarks relevant to Apple, we first specify the start list of social tags and then collect the set of content tagged with those social tags. The start list of social tags can be constructed by drawing on a combination of (1) brand name and subbrands, (2) tags frequently linked to a brand, (3) tags frequently linked to competitors, and (4) predefined keywords identified from previous surveys and/or consumer interviews. Researchers can specify the set of competitors using (1) a predefined set of competitors by marketing managers; (2) an external source such as Standard Industrial Classification (SIC) code, Hoover’s database, or Google Finance; and (3) co-occurrence patterns of social tags across brands in the tagging networks (i.e., brands that have many of the same tags associated with them as the focal brand). In the data used for our analysis, we identify the set of Apple’s competitors using multiple data sources: other firms with the same four-digit SIC code, Hoover’s classification of competitors, Google Finance, viewing history provided by Yahoo Finance, and the tagging cooccurrence structure on Delicious. We include as competitors companies that appear at least three times in these five sets.1 These include Microsoft, Google, Blackberry, Nokia, Dell, and HP. This set can be easily redressed or expanded as desired by the focal brand manager. Once the tagging data associated with the start list can be collected, the data can be aggregated on any given time frame (hourly, daily, weekly, monthly, quarterly, or yearly basis). Depending on managerial objectives, an appropriate time window can be specified.
Step 2: Association Elicitation
The second step is to identify a set of relevant core social tags for the focal brand. Step 2 of Figure 2 illustrates the association elicitation process. This goal is achieved by evaluating all tags associated with a brand using predefined metrics and identifying tags that score the highest on these metrics. To do so, we propose the use of the following metrics.
Associative strength metrics. Core associations for the focal brand can be identified on the basis of the associative strength between a brand and a tag. A key metric for capturing associative strength between a brand and a tag is “co-occurrence volume,” measured as the number of times a brand is linked to a tag through a bookmark. Thus, the co-occurrence volume of tag j with brand I for a given time window t, NtðBi, TjÞ is defined as the volume of bookmarks linked to both brand I and tag j during the time window t. In other words, this metric captures how many brand i–tag j pairs are created during time window t.
An alternative way for capturing associative strength is to scale the co-occurrence volume, NtðBi, TjÞ, by the bookmark volume linked to the brand and the bookmark volume linked to the tag, as specified in Equation 1. This metric, “scaled cooccurrence volume,” measures the cosine distance between each brand and each tag; prior research has found this metric to be useful in capturing the similarity between two tags in Delicious (Robu, Halpin, and Shepherd 2009). where is the volume of bookmarks linked to brand I during time window t, and NtðTjÞ is the volume of bookmarks linked to tag j during time window t. Note that there are other alternatives for capturing the associative strength between a brand and a tag based on assigning different weights to each social tag. For instance, one could use the weight based on the number of other tags mentioned in each bookmark or the weight based on the order of social tag mentioned in each bookmark. In the data we analyze, we find alternative weighted metrics to be highly correlated with the co-occurrence metric. (For additional discussion on alternative associative strength metrics, see Web Appendix B.)
The required number of tags. The set of relevant brand associations can be obtained by specifying the level of explanatory power desired. Depending on the desired amount of information, a marketing manager can flexibly choose different cutoffs. For instance, Table 4, Panel A, shows the number of tags needed to explain 95% and 90% of co-occurrence volume of all tags linked to each brand based on bookmark data generated in 2009. For instance, 95% of co-occurrence volume of tags linked to Apple can be explained with 2,254 tags (37% of all tags linked to Apple) and the minimum co-occurrence volume of these tags is 31. The choice of the set of relevant associations can be further complemented by specifying a prerequisite level of co-occurrence for each tag (e.g., co-occurrence volume greater than 5 and 10). Table 4, Panel B, presents the percentage of co-occurrence volume explained by multiple decision rules. For instance, once a researcher selects associations whose co-occurrence volume is greater than 10, (s)he can explain 98.6% of co-occurrence volume of Apple with 3,703 tags (61% of all tags) and 91% co-occurrence volume of Blackberry with 1,064 tags (26% of all tags).
Step 3: Aggregation and Visualization
In Step 3, the goal is to combine the associations discovered in the elicitation stage into a holistic description of the brand. Visualization of brand associative networks helps meet different managerial objectives. For instance, a brand-centric map can present the key associations for the focal brand, or for each competing brand. Distinct associative maps can be created using either all keywords, only valenced keywords (that capture both positive and negative sentiment), or only descriptive, neutral keywords. Step 3a in Figure 2 shows the various social tags associated with Apple during 2009. The size of a node represents the volume of keywords generated, and the width of the edge represents the associative strength between two nodes, proportional to the co-occurrence volume of the two keywords. Although Step 3a does not consider the intertag relationships, one can build brand associative network with intertag relationships. Such associations can also be represented in a multibrand map to highlight interconnected associations across brands. Step 3b of Figure 2 presents the associative networks for multiple brands (i.e., the focal brand and its top competitors: Apple, Blackberry, Dell, Google, HP, Microsoft, and Nokia). From this figure, a manager can gain insight regarding relative positions of each brand on a network of keywords of interest. To further derive a spatial representation of the competitors in the market, existing methods to construct a perceptual map—such as multidimensional scaling (e.g., DeSarbo et al. 1996; Shugan 1987), Bayesian models of graph formation (e.g., Hui, Huang, and George 2008), or correspondence analysis (e.g., Carroll, Green, and Schaffer 1986)—can be employed.
The aggregated brand information can be further explored with the brand metrics potentially related to the diagnostic value of brand assets (Nam and Kannan 2014). These metrics can capture dynamics within social attention generated by a brand; the richness, valence, and dispersion of brand associations; and the competitiveness of a brand (for more discussions about brand metrics based on social tags, see Web Appendix C). Although the information obtained from these aggregated social tags provides insights on brand health and brand equity, such aggregation does not fully harvest rich, qualitative information contained in social tags. For example, the aggregated metrics (1) do not provide insights on which keywords are more frequently associated together, (2) do not identify which keywords are dynamically more correlated and thus move together in the social tagging platform, and (3) ignore the possibility that there could be distinct clusters of consumers who interpret the content differently. Thus, to harvest a large volume of qualitative information, it is critical to overcome these challenges by employing tools that analyze disaggregate-level data.
Step 4: Analysis of Disaggregate-Level Data
Step 4’s goal is to understand and interpret disaggregatelevel information contained in social tags by applying existing text mining techniques and data reduction methods. We focus on two levels of disaggregate information in social tagging data: user-level disaggregate brand association information and temporal disaggregate brand association information. The first challenge is how to obtain a qualitative summary of the associative relationship of a large volume of brand associations created by individual users. We show how existing LDA models can help identify the representative underlying topics that capture the brand perceptions of distinct user segments through the associative relationships between more than 1,000 user-generated keywords associated with a brand. These insights are key to improving the over-all brand perceptions by homing in on specific segments with perceptions that are not aligned with the core brand perceptions and examining how these perceptions could be improved through appropriate communication campaigns or marketing plans directed at these segments. The subsequent challenge is to investigate temporal disaggregate brand association information and discover which associations are more frequently paired with a brand over time. We apply DFA and show how it provides insights into dynamic relationships between brand associations. By doing so, we shed light on how the brand image is evolving over time and correlate it with specific events. Thus, analyzing disaggregate-level information in social tagging data provides a richer view of brand associative structure compared with aggregate-level social tag metrics.
Comparing the Social Tag–Based Approach with Existing Approaches
In this section, we evaluate the suitability of the use of social tags for discovering brand associations by comparing the brand associations elicited from our approach with those elicited from existing approaches. We discuss the key differences of the proposed social tag–based approach with the two most common existing approaches: primary data–based approach and text mining. Then, we discuss whether the proposed social tag– based approach elicits new, distinct brand associations relative to existing approaches. The analysis suggests that the social tag–based approach can serve as a complementary method for eliciting brand associations. We also discuss the scope and limitation of the proposed social tag–based approach.
Existing Approaches to Elicit Brand Associations
Existing approaches vary in terms of the nature and richness of information contained in the data, as well as in the resources and expertise required for successful implementation. The differences mainly arise from the elicitation process of core brand associations and the nature of the collected data. Next, we discuss the similarities, complementarities, and differences of the following select established approaches: Zaltman’s metaphor elicitation technique (ZMET; e.g., Zaltman 1997; Zaltman and Coulter 1995), brand concept maps (BCMs; e.g., John et al. 2006; Joiner 1998), categorization and sorting (e.g., Blanchard, Aloise, and DeSarbo 2016; Blanchard and DeSarbo 2013; Hamilton et al. 2014; Ratneshwar and Shocker 1991), and the more recent text-mining approaches (e.g., Lee and Bradlow 2011; Netzer et al. 2012; Tirunillai and Tellis 2014).
Primary data–based approach. Many prior studies have employed primary data (consumer surveys and interviews) to elicit brand associations. We primarily review the three existing popular methods: ZMET, BCMs, and categorization and sorting. The primary assumption of the ZMET approach (Zaltman 1997; Zaltman and Coulter 1995) is that a significant portion of consumers’ thoughts and knowledge is stored in a nonverbal form and cannot be fully elicited with verbal communication. Thus, ZMET employs in-depth personal interviews using qualitative techniques such as Kelly’s repertoire grid, laddering exercises, and verbal/nonverbal cues (e.g., images during the elicitation stage) to understand the core associations linked to a topic. Although ZMET can help identify deep, unconscious thoughts and feelings related to a brand by using multiple qualitative approaches as well as both verbal and nonverbal aspects of a consumer’s behavior, this process is quite challenging to implement and often involves close interactions with only a few consumers. The elicitation stage is highly time and labor intensive (e.g., seven to ten days for subjects to collect visual images and two-hour indepth, one-on-one interviews to obtain an individual brand map). Accessibility is another issue for ZMET because it requires interviewers with expertise in qualitative elicitation techniques, thus raising administrative costs.
The BCM method (e.g., John et al. 2006; Joiner 1998; Novak and Gowin 1984), in contrast, employs more structured procedures to elicit core associations, map the associations, and synthesize individual maps into consensus maps. To elicit core associations, BCM utilizes prior consumer research as well as input from the brand management team. Then, through one-on-one interviews, the researchers create individual concept maps primarily drawing on identified core associations. The final consensus map is developed on the basis of the aggregated frequency of the individual maps, revealing a hierarchical associative structure with differential associative strengths. Compared with ZMET, the BCM method is somewhat easier to administer and analyze. In addition, it flexibly accommodates inputs from managers. However, it may not be adequate for eliciting unconscious feelings and brand associations that need additional in-depth probing.
Researchers have also employed the categorization and sorting method to elicit brand perceptions and category perceptions (e.g., Blanchard, Aloise, and DeSarbo 2016; Blanchard and DeSarbo 2013; Hamilton et al. 2014; Ratneshwar and Shocker 1991). In this approach, respondents are typically asked to sort a set of objects (brands) into categories according to their perceived similarity. Compared with ZMET and BCMs, sorting is a relatively easy task for the respondents and can be completed faster, with less respondent fatigue (Bijmolt and Wedel 1995). However, in this approach, the brand attributes for the basis of the sorting task are predetermined by researchers, and multiple rounds of data collection are required to learn dynamics in brand associations.
Thus, the methods based on primary data face the following challenges: (1) they are labor intensive because they employ qualitative analysis and one-on-one personal interviews, (2) they often require specialized expertise, (3) they are often implemented at specific time periods and are a static representation of brand perception rather than a dynamic brand map over time, (4) they involve small sample sizes and tend to be very expensive if the focal brand tries to obtain a brand map from a larger sample, and (5) they are based on stated brand associations and thus bound by the elicitation techniques.
Text-mining approach. Recent work using text mining has offered promise for addressing some of the problems identified with ZMET and BCM. Text mining is a tool that helps discover patterns in raw text and extract relevant information from textual data. Recent marketing studies employing text-mining tools have created brand-associative networks by automatically identifying keywords from user-generated content such as posts on online user forums or online user reviews (e.g., Lee and Bradlow 2011; Netzer et al. 2012; Tirunillai and Tellis 2014). Here, the elicitation stage consists of multiple steps: cleaning and preparing the text and extracting appropriate information from the text (Netzer et al. 2012). Researchers use a rule-based approach, a machine-learning approach, or a hybrid of these two approaches to extract the information in the text (Netzer et al. 2012). In the aggregation and visualization stage, researchers identify the associative relationships on the basis of the co-occurrence pattern of the identified keywords.
Compared with a primary data–based approach, the brand association elicitation process in text-mining approaches enables researchers to obtain brand associations from a large volume of data with a low level of human labor, given the automation offered during the elicitation process. Thus, brand associations elicited from automatic keyword extraction in a text-mining approach (1) can be automatically acquired, thus cutting down on labor, time, and expertise requirements; (2) can be automatically updated on a real-time basis, thus providing a dynamic, rather than a static, map; (3) are constructed from a larger customer base, with minimal additional costs; and (4) can track an extensive set of associations, including competitors’ brand associations. Nevertheless, the keyword extraction process in text mining tends to be computation intensive, as it employs multiple stages of model estimation and data training for the keyword extraction process.
Evaluation of the Social Tag–Based Approach
The proposed social tag–based approach has several advantages over the existing methods. Table 5 summarizes the comparison of the social tag–based approach to existing methods. The major contrast is at the association elicitation stage. Primary data–based approaches employ the consumers’ direct input from surveys, interviews, or sorting tasks typically designed to collect brand images/attributes and thus tend to generate more subjective keywords, be more sensitive to sample size, and often depend on researchers’ interpretation. Text mining employs automatic keyword extraction algorithms that can be sensitive to the assumptions of a researcher or a marketing manager. In contrast, the social tag–based approach utilizes brand associations directly generated by consumers in association with their interactions with brands. This, however, makes them sensitive to (yet capable of capturing) consumers’ biases and potential social influences. In this subsection, we discuss similarities, differences, and complementarities of our proposed social tag–based approach with existing approaches based on two illustrative empirical studies.
The social tag–based approach versus the primary data–based approach. We investigated whether the social tag–based approach elicits new, distinct brand associations compared with one of the primary data–based approaches, the BCM approach. To obtain a BCM for Apple, we conducted one-on-one interviews with 23 subjects. Following John et al.’s (2006) methodology of obtaining consensus BCM, we (1) ask subjects “what comes to mind when [they] think about Apple,” (2) give them detailed instructions as to how to draw a BCM (using the example presented in John et al. [2006, Figure 2, p. 553]), (3) ask them to draw their own concept map, and (4) draw Apple’s consensus BCM using responses from all subjects. Forty-seven associations, mentioned by more than 25% of the respondents, are present in the consensus map. For comparison, a social tag–based brand map for Apple is constructed by drawing on the co-occurrence relationships between each association in the consensus BCM and the brand during a corresponding time window (six-months of social tagging data).
We investigated the correlation between the two metrics from the consensus BCM—the frequency and weighted frequency2 of each association—and the co-occurrence volume of the corresponding social tag with the brand. Overall, the frequency of each association in BCM and the co-occurrence volume of the corresponding social tag is significantly correlated (r = .56, p < .01), as is the weighted frequency metric of each association in BCM and the corresponding co-occurrence volume metric of social tags (r = .54, p < .01). The correlation between BCM and our social tag–based approach is reasonably high given that we compared the social tags of more than 20,000 responses with BCMs obtained from 23 respondents.
Although our analysis suggests that brand associations elicited from a social tag–based approach are significantly consistent with brand associations elicited from BCM, several differences exist. We found that evaluative associations (e.g., “cool,” “innovative”) more frequently appeared in BCM, whereas descriptive associations (e.g., “iPod,” “computer”) more frequently appeared in social tags. This is because (1) the question in the survey induces respondents to generate more attitudinal and evaluative associations and (2) respondents tend to think about the brand in a more holistic way when they are given the brand name; in tagging online, users are given specific context and thus tend to think about more details. Despite such differences, we found that the frequency of each association in the consensus BCM and the tag co-occurrence volume for evaluative 30 keywords is highly correlated (r = .70, p < .01) and that for descriptive 17 keywords is also highly correlated (r = .74, p < .01). We obtained similar results for the weighted frequency of associations in the consensus BCM.3 We conducted similar analyses for the other two brands (Microsoft and Google) and found a similar pattern. In summary, although primary data–based approaches are more likely to reveal subjective, evaluative keywords than the social tag–based approach, there is a significant similarity in the brand attributes obtained through the primary data–approach and a social tag–based approach.
The social tag–based approach versus the keyword extraction process in text mining. We investigated whether the social tag–based approach elicits different brand associations from the keyword extraction process in a text-mining approach. We compared the social tags created on a blog post written by Guy Kawasaki about what he learned from Steve Jobs (see the example in Figure 1) with a text-mining analysis on the same blog post. The blog post contains 83 sentences, 1,143 words, and 6,529 characters. Following the text-mining procedure illustrated in Netzer et al. (2012), we identified 331 distinct keywords after removing the stop words, and these keywords were mentioned 597 times in the blog. Drawing on 70 users’ social tagging activities, we identified 57 distinct keywords, which were mentioned 217 times in social tags. Table 6 illustrates the list of the top keywords identified by a social tag–based approach and text-mining approach.
As we expected, the information entropy4 for the keywords identified by the social tag–based approach is lower (3.15) than that identified by text mining (5.51). The distribution of social tags is more concentrated on several representative keywords (the top four most frequently mentioned tags represent 50% of tag usages) than the distribution of keywords identified by text mining on the blog post (the top five most frequently mentioned keywords represent 10% of keyword usages). Social tags are not an accurate reflection of all the keywords in the original blog content, which can lead to a potential omission bias; nevertheless, they can serve as an efficient filter of the original content. The correlation between the term frequency of social tags and the term frequency of all the keywords (597 distinct keywords) identified by text mining was .574 (p
Consumers’ tagging activities are not a mere reflection of the original blog content. Twenty-four percent of social tags (14 out of 57 social tags) were terms used in the original blog text. Seventy-six percent of social tags were never mentioned in original text and represent consumers’ own interpretation of the content (e.g., “inspiration,” “entrepreneurship,” “innovation”), the author information (e.g., “Guy Kawasaki”), and categorization cues (e.g., “business,” “strategy,” “idea”). That is, the social tag–based approach provides an additional layer of insights on the original text because it incorporates consumers’ input. The keywords identified by text mining on a blog post are fixed and objective (only varying by algorithm choice in textmining approaches and assumptions by researchers and managers), whereas the keywords identified by social tagging on blog posts vary as more consumers create tags. The brand associations discovered in the social tag based–approach are affected by each customer’s own point of view and potential social influence. Thus, the social tag–based approach can effectively capture people’s perceptions on a subject that is filtered through their own experiences and memories. When such social, individual-level bias is not desirable to interpret the raw text (e.g., when deriving an objective interpretation of the content) or when the raw text includes all the individual-level bias, eliciting brand associations from text mining can be a better solution. However, if a researcher wants to obtain customers’ subjective interpretation of the content, wherein customers’ biases are incorporated, the social tag–based approach is preferable.
More importantly, we believe that the social tag–based approach can be a complementary tool for association elicitation when training the data for the text mining process. The keyword extraction process in text mining often requires human labor for better adaptation of algorithms to the domain of study (e.g., identifying the list of stop words, identifying important keywords such as brand names and product attributes). To this effect, Netzer et al. (2012) highlight the need to replace the initial training of data in text mining with tagging work using crowdsourced marketplaces. We believe the use of social tags can guide this training process by providing the set of keywords retrieved by consumers/readers/users and complement the data-training process in keyword extraction text mining. For instance, in the previous example of text mining, researchers can employ the start list of keywords identified in social tags to mine the text. Some of the keywords that were not included in the top ten keywords using text mining’s keyword extraction process can be discovered when researchers incorporate the input from social tags.
In summary, social tags are an inexpensive source of brand associations derived from large-scale content preprocessed by customers and online users. Thus, we recommend the use of social tags as a complementary tool to the keyword extraction of a text mining approach when a marketing manager needs to obtain a succinct summary directly generated by engaged online users on large-scale data.
Analysis of Disaggregate Information in Social Tags
In this section, we show the value of disaggregate level data in social tags. In addition, we illustrate how the challenges of understanding disaggregate-level information in social tags can be resolved by employing existing language processing and data-reduction techniques.
Value of User-Level Disaggregate Information in Social Tags
A reasonable assumption for tagging behavior is that each individual user interprets a single content/object differently. Such a difference mainly arises from (1) heterogeneous knowledge structure characterizing individual mental schema and (2) heterogeneous motivations for tagging, which may be time and context dependent within each person.5 As Figure 2 illustrates, each user associates different keywords with an object/content depending on his or her interest, motivation, and knowledge structure, which may be reflected in previous tagging activities. Thus, understanding the disaggregatelevel associative structure of keywords is critical for harnessing rich information contained within social tags.
To further investigate heterogeneous perceptions of the same content, we collected 57 users’ social tags on Kawasaki’s blog post and conducted a clustering analysis on the similarity of tagging patterns across users. The hierarchical clustering using a parameterized Gaussian finite mixture model (Fraley and Raftery 2007) finds four segments as optimal (log-likelihood = 760.25; n = 54; d.f. = 247; Bayesian information criterion = 535.22). The model allocates 21 users in segment 1, 24 users in segment 2, 4 users in segment 3, and 5 users in segment 4. Users in segments 1 and 2 associated with fewer social tags (M = 4.19 and M = 4.45, respectively) than users in segments 3 and 4 (M = 8.25 and M = 7.4, respectively). Figure 3, Panel A, presents the aggregate perceptual map based on all social tags associated with this blog, and Figure 3, Panel B, presents the perceptual map for each segment. Segments 1 and 2’s perceptions are similar (e.g., Apple, Stevejobs, Kawasaki, inspiration, lesson), yet segment 2’s focus is more on Steve Jobs than on Apple. Segments 3 and 4 are using relatively fewer tags: segment 3’s focus is similar to that of segments 1 and 2; yet this group tends to be more specific about other reference sites (e.g., Lifehacker, Twitter). Segment 4’s primary focus is on Steve Jobs and does not link the article to Apple.
Thus, by employing heterogeneous representations of brand maps using disaggregate-level tagging data, marketers can understand and visualize heterogeneity in brand perceptions and gain insights for segmentation and targeting. For example, marketers can analyze popular online content such as a news article on a brand or a product recall using tags to understand how consumers code it—what are the variations in how consumers react to and tag the content? What are the relative volumes of tags? What are relative sizes of the segments? This analysis can provide useful insights into the impact of such online content on brand perceptions as well as how the firm should react, if need be.
Discovering Representative Topics for Customer Segments
The collection of disaggregate social tagging information provides a large volume of semantic information because many consumers associate content with their own keywords. It is common to observe more than 1,000 bookmarks created for a brand each day on social tagging platforms. The volume of the social tags associated with these bookmarks is also large (over several thousand) and the distribution of keywords is sparse and has a long tail. Thus, to interpret such sparse, high-dimensional, qualitative information generated by heterogeneous customers, dimensionality reduction is critical. We illustrate how marketing managers can extract useful insights from such a large volume of semantic information in disaggregate social tags by identifying latent topics with LDA topic models (e.g., Blei 2012; Blei, Ng, and Jordan 2003; Griffiths and Steyvers 2004; Tirunillai and Tellis 2014).
Model. The goal of topic models is to explain the collection of documents (corpora) with a mixture distribution of probabilistic topics. Topic models assume that a word in a document is generated by sampling a topic from the topic distribution and then sampling a word from the word distribution given the selected topic. As such, the probability of a user associating a tag with content can be modeled with a similar rationale: a user first selects a topic that best describes the content to be tagged and then selects the tags on the basis of the word distribution given the latent topic. More specifically, the topic model is specified as follows:
where PðtagbiÞ is the probability of observing tag I in bookmark b; b represents a bookmark where b = 1, …, B; I represents a tag where I = 1, …, N; N is the number of distinct tags in all the bookmarks; k represents a latent topic where k = 1, …, K; P(zib = k) is the probability that the kth topic was sampled for the ith tag in bookmark b; and Pðtagbijzbi = kÞ is the probability of tag I in bookmark b given topic k. We estimated the model using LDA (e.g., Blei, Ng, and Jordan 2003; Griffiths and Steyvers 2004; Tirunillai and Tellis 2014) employing Markov chain Monte Carlo Gibbs sampling with a conjugate Dirichlet prior distribution (for the likelihood functions and sampling procedures, see Appendix D).
Data and results. We employed an LDA topic model to uncover the representative topics in social tags in the sample of 2,000 bookmarks associated with Apple. The 2,000 bookmarks contain 11,851 social tags (1,982 distinct tags) after we (1) deleted non-English words and numbers, (2) removed stop words, and (3) stemmed the tags in line with Porter (1997)’s methods. For the analysis, we removed bookmarks that only had the “Apple” tag as well as tags that appeared only once in the collection of bookmarks. As a result, we had 1,869 bookmarks associated with 8,610 tags (763 distinct tags). The distribution of 8,610 keywords is sparse and has a long tail. Figure 4 shows the distribution of the most frequently mentioned tags, defined as tags with more than .5% of the entire volume of tags. Apple’s products (iPhone, Mac, and iPad) are the three most frequently mentioned tags.
We started the estimation of LDA topic model with two latent topics and gradually increased the number of latent topics to find the best model. We determined the optimal number of latent topics using the posterior log-marginal likelihood following the method used in prior studies (e.g., Griffiths and Steyvers 2004). We selected the model with 15 latent topics (marginal log-likelihood = -35,468.52) as the best model.
Table 7 summarizes the most important tags in each latent topic based on the highest distribution probability in each latent topic. We interpreted these topics as “iPhone video photography,” “Mac software products,” “iPhone computer software,” “Mobile technology rivalry,” “Mac operations system and music interface,” “Safari and JavaScript for Apple products,” “iPod and technology review blog,” “Web design and graphic,” “Comic and humor,” “Free tools and software,” “Hardware repair for Apple products,” “Apple developer,” “New products and Steve Jobs,” “Apps and download interface,” and “Tutorials and tips.” All the latent topics are strongly related to Apple’s products (e.g., “iPhone,” “mac,” “osx,” “iPod,” “iPad,” “safari,” “iTunes”). These latent topics, which represent the entire set of social tags, reflect characteristics of the original content (e.g., “Apple developer” and “iPod and technology review blog”), users’ interpretations of the content associated with Apple (e.g., “Mobile technology rivalry” and “New products and Steve Jobs”), and users’ motivations to tag the content (“Tutorials and tips” and “Free tools and software”). That is, these tags provide a summary of users’ interpretation of the brand-related content through the lens of the users’ own knowledge, experience, and schema. For instance, even though an article about Google’s new mobile phone does not discuss Apple or Apple’s products, some consumers may interpret the article related to Apple and tag the article with Apple and or Apple’s products. Thus, understanding these latent topics in the entire set of social tags helps marketers grasp consumers’ deeper interpretations of brand-related content.
Our data contain different associative keywords for Apple generated by different users. Thus, the latent topics listed in Table 7 can also be interpreted as the topics representing overlapping user segments6 with different perceptions and interests related to the brand. To understand which topic is the most prominent in our data, we calculated the posterior probability of each topic for each of 1,896 bookmarks and classified the bookmarks into the topic with the highest posterior probability. The most prominent user segments were users engaging on the topics of “iPhone video photography” (12.4% of bookmarks), “Mac software products” (11.6% of bookmarks), “iPhone computer software” (9.7% of bookmarks), and “Mobile technology rivalry between Google and Apple’s iPhone” (8.6% of bookmarks). Such information helps marketing managers identify distinct interests and brand perceptions within customer segments and gain insights for segmentation and targeting.
Furthermore, the identified important keywords in each latent topic in Table 7 help us understand the associative structure between brand associations by revealing the underlying interrelationship between tags based on user-level tagging activity. For instance, the topics strongly associated with iPhone are “video and photography,” “computer products,” “mobile market rivalry,” and “Safari JavaScript,” whereas the topics strongly associated with iPad are “Safari JavaScript,” “comic and humor,” and “Steve Jobs and new products.” Such interconnectedness between keywords provides a better understanding of the associative structure of brand associations across subbrands and product attributes.
In summary, social tag–based topic models help managers understand the representative topics capturing distinct user segments’ brand perceptions and interests. The results provide insights on what the most prominent topics associated with a brand and its products are, and how users interpret and categorize content related to the brand and its products. This creates an incisive snapshot of how the brand is perceived through online user-generated content. Such topics can be useful for a brand to understand the impact of its short-term tactics (e.g., reaction to a new product launch or a public relations campaign, a new advertisement campaign), gauge users’ perceptions through the online buzz surrounding these events (e.g., Hewett et al. 2016), and refine its tactics accordingly. Furthermore, marketing managers can employ distinct topics for better segmentation and targeting strategies and understand the characteristics of distinct interest segments (e.g., size of the segments and types of brand associations). If the brand perceptions of a segment are not quite aligned with the core brand perceptions the firm desires, the firm can design and target communication campaigns and/or other marketing campaigns at the appropriate segment to set the perceptions right. Developing topics by applying LDA on social tags generated on either side of a major event such as a brand recall (before and after the event) can also provide how brand perceptions, their importance, and associated segment sizes change as a result of the event.
Understanding the Evolution of Top-of-Mind Brand Associations
Analyzing trends and the temporal dynamics of information at the disaggregate level in social tags helps marketers identify managerially interesting changes within top-of-mind brand associations. Such information enables marketers to take steps to proactively manage their brand equity by detecting trending keywords (for see further discussion, see Appendix C). Tracking trends in social tags requires an understanding of how keywords associated with the brand evolve over time and which keywords move together. Shedding light on the common trends hidden behind trending social tags provides deeper insights into the dynamics of brand associations.
Model. To understand how consumers’ mental associations connected with a brand evolve over time, we employed a DFA (e.g., Du and Kamakura 2012; Zuur et al. 2003), an analytical tool that can uncover common trends in multivariate time series. Here, our objective was to find latent trends that explain the dynamics in social tags associated with a brand. We specified the DFA model as follows:
where yt (N · 1 vector) is a vector of standardized cooccurrence volume of N keywords associated with a brand, and ft (m · 1 vector) is a vector of latent dynamic factors, with m < N. Equation 3 specifies how the comovements of the N-dimensional vector yt can be explained by the m-dimensional latent dynamic factors ft. The factor-loading matrix Z (N · m matrix) determines the correlation between observations yt and latent factors ft. The dynamics of latent factors ft are assumed to be governed by the state equation specified in Equation 4. Following Du and Kamakura (2012) and Zuur et al. (2003), we calibrated the model with the expectation–maximization algorithm.
Data and results. We employed the DFA model to analyze the 36-month time series of co-occurrence volume of 25 keywords with the brand name Apple in the Delicious platform from January 2007 to December 2009. (These keywords were a subset of the most frequently used tags in our LDA analysis.) The DFA results suggest that this 36-month time series of cooccurrence volume of 25 keywords can be represented by 9 dynamic latent factors (for the factor loadings for 25 associations and the prediction performance, see Appendix E). Drawing on the factor loadings following a varimax rotation, we interpreted each latent factor as follows:
• Factor 1: “Mac and Howto,” with high factor loading scores on “tips,” “howto,” “osx,” “mac,” and “tools,” indicating that user interest on content about tips and tools moves with the interest on content about Macs and OS X.
• Factor 2: “Mobile and Technology,” with high factor loading scores on “technology,” “mobile,” “iPhone,” “iPod,” and “design.”
• Factor 3: “(Hardware and Computer),” with high negative factor loading scores on “hardware,” “technology,” and “computer.”
• Factor 4: “Windows,” with highest factor loading score on “windows.”
• Factor 5: “iTunes and Music,” with high factor loading scores on “iTunes,” “music,” and “iPod.”
• Factor 6: “Design, Fun, and Cool,” with high factor loading scores on “design,” “fun,” and “cool,” indicating that positive perceptions about Apple (“cool” and “fun”) move with consumer interest on content about Apple’s design.
• Factor 7: “Growth Trend,” representing the growth of the usage of 25 keywords with Apple over three years and indicating that keywords a with relatively lower factor loading score (e.g., “Internet”) show relatively slower growth patterns and keywords with a relatively higher factor loading score (e.g., “iPad”) show relatively higher growth compared with the dynamics of this growth trend factor.
• Factor 8: “(Design and iPod),” with high negative factor loading scores on “design” and “iPod.”
• Factor 9: “(Internet and Inspiration),” with high negative factor loading scores on “Internet,” “design,” and “inspiration.”
Figure 5 shows the trends of these nine latent factors plotted on the basis of the factor scores. We find that consumer interest in the content about “Mac and Howto” peaked at October 2007, when a new version of Mac OS X was introduced, and declined gradually. Consumer interest in “Mobile and Technology” associated with Apple increased gradually and peaked at August and September in 2008 and 2009, when Apple launched a new iPhone, while consumer interest in “Hardware and Computer” and “Windows” in relation to Apple gradually declined over the three years. Such trends indicate the change in positioning for Apple over the time frame: consumers became less likely to associate content in the computer/hardware category and the competing operating system (Windows) with Apple but more likely to associate content related to mobile and technology with Apple. Consumer interest in “iTunes and Music” in relation to Apple was relatively constant over the three years, with peaks during the summer season. Consumer interest in “Design, Fun, and Cool” in relation to Apple peaked in June 2007, when the first generation of the iPhone was launched, and declined gradually. Consumer interest in “Design and iPod” peaked in September 2007, when the first generation of iPod touch and other new iPod products were launched, and declined gradually. Consumer interest in “Internet and Inspiration” associated with Apple increased gradually with a peak in March 2009.
Thus, we demonstrate that rich dynamic information contained in social tags associated with a brand can be summarized with latent dynamic factors without significant loss of information. Monitoring such latent trends provides marketing managers with a better understanding of the trends in top-ofmind associations for a brand and the evolving brand image over time. Although LDA is useful in getting instantaneous feedback on how users perceive the brand, latent dynamic factors are useful to track the specific combination of highly correlated tags over time and for monitoring brand perceptions over time. By comparing such trends with those for a competing brand, a firm can also monitor the relative positioning of its brand relative to the competition in users’ minds through the way people interpret online content.
Discussion and Conclusions
Managerial Implications
The power of social tags resides in the interpretations that users generate for any type of content they observe, in the form of unconstrained and open-ended keywords. Social tags, compared with keywords identified from text mining, provide powerful insights into how users view the content or items filtered through their own knowledge structures and social influences, using their own words and phrases. Thus, social tags complement the current automatic keyword identification in text mining by providing the set of start list of keywords to mine. Our comparison analysis suggests that keywords underrepresented or not discovered by automatic keyword identification in text mining were frequently used as social tags—taggers deemed these keywords important for describing and categorizing the content. By balancing the start list of keywords (social tags) and the list of frequently mentioned keywords discovered by automatic keyword identification in text mining, marketing researchers can obtain better insights on consumer-generated textual data.
Social tags generated by individual consumers provide marketing researchers with a unique opportunity to observe consumers’ heterogeneous interpretations of content about brands and products. As online content such as newspaper articles, blogs, commentaries, and reviews emerges for a brand in response to an event (e.g., Chipotle’s food safety incidents, an expose´ on Amazon’s corporate culture), an analysis of the associated tags using clustering techniques can reveal how consumers interpret such content conditional on their mental schema. An LDA analysis on disaggregate social tagging data can show topics representing distinct consumer groups with different perceptions and interpretations of content about the brand. Such use of social tags helps marketing researchers discover answers to the following questions: Are there heterogeneous groups of users who think differently about the event—some who view it much more seriously than others, and vice versa? How different are the perceptions before and after an event of consequence (e.g.,
Puranam, Narayan, and Kadiyali 2017)? Are some users willing to forgive the brand, whereas others vow never to patronize it again? The relative sizes of these segments can also indicate how widespread the change in consumers’ perceptions about the brand is. An instantaneous snapshot of the higherlevel topics under which consumers cluster helps marketing managers discover distinct interests and perceptions in customer segments and gain insights for segmentation and targeting activities related to their brand using different marketing campaigns.
An investigation into the dynamic relationships between user-generated social tags can reveal how—as more content emerges on the event, its aftermath, and firm actions—consumers’ perceptions of the brand change on major factors comprising the tags they use. As we observed in the example of Apple, the factor “Design, Fun, and Cool” in relation to Apple peaked in June 2007, when the first generation of iPhone was launched, and declined gradually. Such information can be immensely useful for the firm to understand how customer perceptions are changing as a function of its actions and competitor actions over the longer term. While similar information can be obtained using other methods such as text mining and primary research methods, it is the ubiquitous nature and type of tagging data that leads to its several advantages over the other methods. It is an inexpensive way to obtain brand associations derived from large-scale content preprocessed by customers and online users.
Given these advantages of analyzing individual-level social tags, they form an inexpensive and continuous data input to implement a brand monitoring dashboard (1) to understand how brand associations vary across segments, and to estimate the size of such segments; (2) to monitor the changes in the topics associated with a brand over time, and specifically in response to important events related to the brand; and (3) to understand changing perceptions of the brand over time. While some of these capture short-term changes, others (using DFA) reflect longer-term changes in brand perceptions. Such a dashboard could be very useful to managers because it would enable them to identify different perception-driven segments for targeting, positioning, and other marketing efforts.
Usage Situations, Limitations, and Further Research
The information contained in social tags is distinct from that in other forms of user-generated content. A unique characteristic of tagging data is that it reflects the associative structure that forms the basis for developing rich semantic networks between keywords and brands. Social tagging data could be perceived as similar to online search data because both enable researchers to obtain the trend of co-occurrence between two or multiple keywords. However, social tagging activity is distinct in that it is more reflective of user perceptions or interpretations about an event, content, or news related to a brand; in contrast, online search is more of a goal-oriented behavior. Thus, tagging data are perhaps more appropriate when marketers are interested in obtaining consumers’ perceptions on a brand. Such situations are common, and there is significant interest on the part of brand managers and industry advisors in providing meaningful solutions that can help capture and represent such perceptions. For instance, recent buzz-tracking solutions including YouGov BrandIndex7 and McKinsey’s Brand Navigator8 are aimed at brand managers, helping them find answers to questions such as how their brand performs with respect to competitors, how the brand performance varies across markets and segments, and how brand managers can improve brand positioning. Social tags can address these questions by providing brand managers with data that represent consumer perceptions regarding both their focal brand and competing brands in a dynamic, real-time setting. In addition, while most of the current solutions focus on the valence and volume of buzz, social tags reveal how individual consumers categorize and describe brands, thus helping brand managers understand associative relationships between brand attributes and identify heterogeneous perceptions regarding a brand.
While the use of social tags is less vulnerable to potential errors involved in the elicitation stage (e.g., algorithm choice in text-mining approaches and/or assumptions by researchers and managers), its interpretation is bounded by each consumer’s own point of view and potential social influences (s)he faces. Thus, when such social, individual-level bias is not desired (e.g., when the goal is to obtain an objective interpretation of the content), other approaches such as text mining could perhaps be more effective. However, when a researcher wants to obtain customers’ subjective interpretation of content, in which customers’ biases are incorporated, a social tag–based approach may be better. Thus, we recommend the use of social tags over a text-mining approach when marketing managers need to obtain a succinct subjective summary directly generated from online users on large-scale data.
We must acknowledge several caveats in using the proposed social tag–based approach. First, like other types of usergenerated data, one must question how representative the selected sample of data is. Social tagging data highly rely on users’ input, and thus users’ self-selection plays a role in their participation decision on tagging platforms and their choice of content to tag. For instance, those who tag a brand name or subbrand name might be more knowledgeable about the brand and more engaged with the news and content related to the brand. Furthermore, the characteristics of taggers can depend on the characteristics of a particular social tagging platform. For instance, on Delicious, users tend to be more tech savvy; on Pinterest, the majority of users are female. To tackle this selfselection bias and obtain more representative data, it is critical for marketing managers to understand whether characteristics of their aggregate customer base are in line with the characteristics of taggers.
Second, as indicated in comparisons with the primary data–based approach and the text-mining approach, tags only indicate the gist of information generated by consumers. Given that most users associate five to ten keywords with a brand name in our data, it is possible that social tags may not be exhaustive in terms of the associations individual users generate for a brand. For instance, in the event that a user associates “cool” and “innovation” with the brand, it clearly tells us that these two keywords represent the strongest top-of-the-mind brand associations for this user. However, if a user did not associate “inspirational” with the brand, it does not necessarily mean that (s)he does not think the brand is inspirational. It is just that the other terms could have been more salient under the norms or other situational constraints. In addition, it is possible that the user may rate the brand as being very inspirational either on a survey questionnaire or during in-depth discussions. Thus, it would be beneficial for marketing managers occasionally to complement the findings from a social tag–based approach with a primary data–based approach such as sorting, personal interview, and surveys, in which marketing managers directly ask participants to recall, think about, and evaluate all the important aspects of brand associations.
Third, the interpretation of social tags can depend on the characteristics of the social tagging platform. For instance, social tags generated on content management platforms provide insights on perceptions and categorizations related to the brand, whereas social tags generated on microblogs and social network platforms provide insights on user engagement and the context of brand usage experiences. Thus, researchers need to take into account the platform’s characteristics when interpreting the information contained within social tags.
Fourth, selecting the appropriate semantic unit for analysis (i.e., tokenization) can be a challenging process in the social tag–based approach. The social tag–based approach relies on users’ input and thus allows for different levels of tokenization. Compared with automatic keyword extraction in text mining (e.g., 1-gram, 2-gram tokenization), the keywords elicited in a social tag–based approach are more flexible for incorporating users’ interpretations. However, it is questionable if a 1-gram tokenization in social tags is more appropriate than the natural language susceptible to individual biases and habits. Thus, we recommend that the findings from social tags be complemented with the findings from primary data such as surveys, sorting, and interviews. In addition, social tags may not always be available for all brands. When a brand is unable to engage a sufficient number of users in tagging activities, there may not be enough data to extract brand-related information and insights.
Finally, there are many possible avenues for further research in this area. First, although we did not take the semantic distance between keywords into account while in our analysis (i.e., all synonyms are treated as distinct keywords), a potential future direction could be to consider a lexical database of words such as WordNet (e.g., Miller 1995) and incorporate this information into the brand association elicitation process. Second, additional metrics that rely even more on network characteristics, such as centrality metrics and network density, can be used to provide marketers with more integrative information about their brands’ associative networks. Third, tags can be especially useful to understand perceptions about nontextual content (e.g., pictures, music, video). Future studies can model the categorization process of nontextual content related to a brand and show how such tags can be used for brand equity management. Finally, further research can investigate a better representation of the growth in the dynamic network, such that metrics calculated at different points in time are more comparable. We hope that this work serves as a modest start toward these future directions.9
1Here, we employ both external criteria such as SIC code and Hoover’s and consumer-driven criteria such as viewing history from Yahoo Finance and tagging co-occurrence structure on Delicious. Researchers can further consider obtaining a snowball sample of competitors based on tagging structure.
2Similar to John et al. (2006), we give level 1 (direct) associations a weight of 3, level 2 (indirect) associations a weight of 2, and level 3 or lower (indirect) associations a weight of 1. The weighted frequency is calculated as the sum of the multiplication of this weight given to each association and the associative strength provided by each respondent.
3The weighted frequency of associations in the consensus BCM and the tag co-occurrence volume for evaluative keywords is also highly correlated (r = .74, p (r = .73, p < .01).
4We defined entropy in line with existing literature (Godes and Mayzlin 2004) as
5Consumers’ perceptual maps can vary across situation and time within each consumer (DeSarbo et al. 2008). The heterogeneity we discuss in this section captures both individual-specific heterogeneity and context-dependent heterogeneity but does not differentiate between the two because of the nature of the data, such that only a single tagging behavior on a single piece of content is observed for each person.
6Because our LDA model is at a document level, the user segments allow for multiple memberships for each user.
7https://today.yougov.com/find-solutions/brandindex.
8http://solutions.mckinsey.com/brandnavigator.
9We provide all materials, including the data, data manipulations, and codes, in one package at the following link for researchers interested in more details on our methodology and to encourage future work in this area: https://www.dropbox.com/sh/wg1c3z4mbgys3hz/AABWWo1h9VNGaGpLc81lmHVwa?dl=0.
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TABLE 1 Comparison with Previous Studies to Analyze Brand Information from a Large Amount of Data
TABLE:
| | Netzer et al. (2012) | Nam and Kannan (2014) | Ringel and Skiera (2016) | Culotta and Cutler (2016) | This Study |
|---|
| Objective | To visualize competitive market structure using text mining on big data | To investigate the informational value of the customer-based brand equity derived from consumer tagging data in firm valuation | To understand asymmetric competition in the product categories | To propose a new methodology to infer attribute-specific brand ratings based on the similarity between exemplar accounts and brand follower accounts | To propose a new approach to analyze a large set of brand attribute information obtained from usergenerated tagging data for marketing research |
| Data | 169 car models and 1,200 attributes | 60 brands with 7,000+ attributes | 1,000+ products with no attributes | 200 brands with 3 attributes | 7 brands with 6,000+ attributes |
| Sources | Online discussion forum | Social tags | Search data from a product comparison website | Twitter | Social tags |
| Brand association elicitation | Text mining algorithms | User-generated social tags | N.A. | Predefined by researchers | User-generated social tags |
| Output | Visualization of market structure of brands | Customer-based brand equity metrics | Visualization of market structure of products | Social perception score on an attribute of brands | Aggregate and disaggregate brand perception map |
| Segmentation | K-means clustering | No | Multilevel Louvain | No | LDA parameterized Gaussian finite mixture model |
| Dynamics | No | Yes | No | No | Yes |
| Dynamic dimension reduction | No | No | No (dimension reduction) | No | Yes (DFA) |
| External validation | • Purchase data • Survey | • BCM • Search data • Blog data | Survey | Survey data | BCM (survey) |
Notes: N.A. = not applicable. BCM = brand concept map.
TABLE 2 Overview of Social Tagging Systems
TABLE:
| | Content Management Platforms | Microblogs and Social Network Platforms |
|---|
| Examples | • Social bookmarking platforms (Delicious, Tumbler, reddit) • Content curation platforms (Pinterest, last.fm) | • Facebook • Twitter • Instagram |
| Why do users use tags? | • To build, manage, and share a collection of content based on topical relevance • To search and discover content using others’ tags. | • To describe and share the gist of social communication based on topical relevance (geolocation, members, type of events, emotions, and thoughts) • To search, monitor, and generate the trending topics |
| Insights for marketing managers? | • To understand users’ interpretations and perceptions about content • To capture customers’ consideration set • To understand competitive market structure | • To monitor engagement and participation in emergent topics • To capture the voice of customers • To understand the context of brand usage experience |
TABLE 3 Motivations for Creating Social Tags
TABLE:
| | Description | Characteristics of Tags |
|---|
| Content Classification/Categorization |
| Self-oriented motivation | Building own system of classification of content | High-level attributes |
| Social communication | Helping others find content in the category (discover content) and have a better categorization system (discover tags) | High-level attributes |
| Content Description |
| Self-oriented motivation | Presenting the gist of the content (especially useful when the content is not textual; e.g., images, music, products) | Contextual attributes |
| Social communication | Creating trending topics and participating in discussions on trending topics | Contextual attributes |
FIGURE 1 Illustration of the Social Tag Creation Process
FIGURE 2 Illustrative Description of Social Tag–Based Marketing Research
We created the map using Delicious bookmark data generated in 2009. The size of the circle is proportional to the volume of bookmarks linked to each keyword, and width of the link is proportional to the co-occurrence volume of two keywords with Apple, which is stated in the number on each link. bWe created social tag maps using Delicious bookmark data generated in 2009 using the Fruchterman–Reingold graph algorithm. The size of the node is proportional to the volume of bookmarks linked to each keyword, and the opacity of the link is proportional to the co-occurrence volume of a keyword with each brand.
TABLE 4 Decision Rules to Select the Set of Relevant Brand Associations
| % Co-Occurrence Volume Explained | Apple | Blackberry | Dell | Google | HP | Microsoft | Nokia |
|---|
| 95% |
| Number of tags | 2,254 | 1,647 | 1,972 | 2,578 | 2,105 | 2,012 | 1,727 |
| % of tags | 37% | 40% | 48% | 39% | 43% | 36% | 41% |
| Minimum co-occurrence volume | 31 | 5 | 4 | 79 | 6 | 28 | 6 |
| 90% |
| Number of tags | 1,430 | 969 | 1,258 | 1,685 | 1,300 | 1,298 | 1,057 |
| % of tags | 24% | 23% | 30% | 26% | 27% | 23% | 25% |
| Minimum co-occurrence volume | 65 | 12 | 7 | 161 | 12 | 59 | 13 |
TABLE:
| % Co-Occurrence Volume Explained | Apple | Blackberry | Dell | Google | HP | Microsoft | Nokia |
|---|
| N(B, T) > 5 |
| Number of tags | 4,502 | 1,632 | 1,517 | 5,737 | 2,219 | 3,840 | 1,843 |
| % of tags | 74% | 39% | 37% | 87% | 45% | 69% | 44% |
| % explained | 99.4% | 94.9% | 92.3% | 99.8% | 95.5% | 99.2% | 95.6% |
| N(B, T) > 10 |
| Number of tags | 3,703 | 1,064 | 953 | 5,210 | 1,420 | 3,139 | 1,235 |
| % of tags | 61% | 26% | 23% | 79% | 29% | 57% | 29% |
| % explained | 98.6% | 91.0% | 86.4% | 99.6% | 91.0% | 98.3% | 91.7% |
Notes: Tables are based on social tags created for each brand in 2009. Similar tables can be created using the scaled-volume and weighted-volume metrics.
TABLE 5 Comparison of Methodologies to Obtain Brand Associations
TABLE:
| | Primary Data (ZMET, BCM, Sorting) | Text Mining (Lee and Bradlow 2011; Netzer et al. 2012) | Social Tag–Based Approach |
|---|
| Association elicitation | • In-depth personal interviews • Both verbal and nonverbal cues (e.g., photos, images) • Prior consumer research • Manager’s opinions/insights •Consumer interview • Sorting and categorization task | • Elicited by a text-mining tool • Rule-based • Machine learning • Hybrid approach | • Directly stated by consumers/online users • Available as secondary data |
| Aggregation and visualization | Participants create a map or visual montage (ZMET) and develop their brand maps in personal interview (BCM) Maps derived based on the perceived similarity between brands (objects) (sorting) | Maps based on elicited product attributes/brand associations from text-mining model | Maps based on tags stated by consumers/users in the absence of a researcher |
| Richness of information | • Deep understanding of a brand • Unconscious aspects can be revealed • Hierarchical associative structure • Perceived similarity between brands | • Large-scale data • Dynamics of associations • Competitive intelligence | • Large-scale data • Dynamics of associations • Competitive intelligence • Undirected • Ability to capture the associations on nontextual data (images, music, etc.) |
| Limitations | • Data from few subjects • Difficult to track dynamics • Difficult to collect large-scale data (data on indirect competitors or large number of brand associations) • Difficult to quantify associative strengths | • Constrained by algorithmic interpretation • Requires human labor for data training | • The number of social tags elicited by consumers is limited • Sensitive to customers’ biases and potential social influences |
| Costs | High to moderate (qualitative analysis expert required; primary data collection required) | Moderate (multiple stages of textmining processes) | Low (publicly available and readily accessible) |
TABLE 6 Keyword Comparison Between Social Tag–Based Approach and Text-Mining Approach
TABLE:
| Keywords | Frequency | % |
|---|
| Social Tag–Based Approach |
| stevejobs | 43 | 20.50% |
| apple | 22 | 10.50% |
| inspiration | 22 | 10.50% |
| guykawasaki | 18 | 8.60% |
| startup | 13 | 6.20% |
| entrepreneurship | 11 | 5.20% |
| lesson | 9 | 4.30% |
| business | 7 | 3.30% |
| design | 7 | 3.30% |
| twitter | 3 | 1.40% |
| wisdom | 3 | 1.40% |
| Text-Mining Approach |
| stevejobs | 20 | 3.40% |
| people | 14 | 2.30% |
| big | 9 | 1.50% |
| player | 9 | 1.50% |
| apple | 8 | 1.30% |
| product | 8 | 1.30% |
| app | 6 | 1.00% |
| challenge | 6 | 1.00% |
| hire | 6 | 1.00% |
| tell | 6 | 1.00% |
| unique | 6 | 1.00% |
FIGURE 3 Social Tag Maps of Article A
FIGURE 4 Distribution of Social Tags
Notes: The percentage of tag volume represents the percentage of each tag in the entire set of tags associated in our sample of bookmarks (1,869 bookmarks associated with 8,610 tags; 763 distinct tags).
TABLE 7 Characteristics of Representative Topics
TABLE:
| Topics | % of Bookmarks | Top Five Keywords |
|---|
| Pihone video photography | 12.40% | iphone, video, photography, refer, tech |
| Mac software products | 11.60% | mac, software, web, product, audio |
| iPhone computer software | 9.70% | iphone, computer, software, accessory, business |
| Mobile technology rivalry | 8.60% | mobile, google, html, technology, iphone |
| Mac operation system and music interface | 6.70% | mac, osx, macintosh, music, itunes |
| Safari and JavaScript for Apple products | 6.50% | iphone, safari, javascript, macosx, ipad |
| iPod and technology review blog | 6.20% | blog, ipod, review, technology, game |
| Web design and graphic | 6.30% | design, icon, refer, webdesign, graphic |
| Comic and humor | 5.50% | ipad, comic, funny, flash, humor |
| Free tools and software | 5.70% | tool, free, util, software, osx |
| Hardware repair for Apple products | 4.00% | mac, hardware, macbook, ipod, repair |
| Apple developer | 5.60% | develop, program, video, cocoa, wwdc |
| New products and Steve Jobs | 3.40% | mac, stevejobs, wallpaper, new, ipad |
| Apps and download interface | 4.00% | app, application, itunes, interface, download |
| Tutorials and tips | 3.70% | howto, tutorial, macosx, tip, hack |
FIGURE 5 Trends of the Latent Dynamic Factors
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Record: 84- Help Me Help You! Employing the Marketing Mix to Alleviate Experiences of Donor Sacrifice. By: Bradford, Tonya Williams; Boyd, Naja Williams. Journal of Marketing. May2020, Vol. 84 Issue 3, p68-85. 18p. 3 Charts. DOI: 10.1177/0022242920912272.
- Database:
- Business Source Complete
Help Me Help You! Employing the Marketing Mix to Alleviate Experiences of Donor Sacrifice
Nonprofit organizations often rely on individuals to execute their mission of addressing unmet societal needs. Indeed, one of the most significant challenges facing such organizations is that of enlisting individuals to provide support through the volunteering of time or donation of money. To address this challenge, prior studies have examined how promotional messages can be leveraged to motivate individuals to support the missions of nonprofit organizations. Yet promotional messages are only one aspect of the marketing mix that may be employed. The present study examines how donor-based nonprofit organizations can employ the marketing mix—product, price, promotion, place, process, and people—to influence the experiences of sacrifice associated with donation. The authors do so through an ethnographic study of individuals participating in living organ donation. First, they identify the manifestation of sacrifice in donation. Next, they define three complementary and interactive types of sacrifice: psychic, pecuniary, and physical. Then, they articulate how the marketing mix can be employed to mitigate experiences of sacrifice that emerge through the donation process. The authors conclude by discussing implications for marketing practice and identifying additional research opportunities for sacrifice in the realm of donation.
Keywords: charitable giving; marketing mix; organ donors; place; price; product; promotion; sacrifice
Nonprofit organizations contribute $985.4 billion to the U.S. economy ([43]) and serve the public interest by providing a wide array of crucial services, goods, and resources—from food and shelter to body parts. Organizations tend to employ the promotion element of the marketing mix to persuade individuals to donate; however, there may be opportunities to use additional elements. The greatest challenge such organizations have in executing their missions is that of securing sufficient donations from individuals ([ 9]; [67]). All types of donations from individuals entail sacrifice, yet those who provide anatomical parts in support of health care treatments make undisputed sacrifice. Because not all donations are born of the same degree or type of sacrifice, it is necessary to understand sacrifice in relation to donation so that organizations can better overcome this obstacle when recruiting donors. Thus, the question guiding this research is, How can organizations use marketing-mix variables to reduce experiences of sacrifice in donation?
Studies on consumer shopping behavior have focused primarily on the monetary sacrifice made to obtain value imparted by organizations through the marketing mix ([30]; [33]; [34]); in contrast, the charitable giving literature has focused on promotion to increase the number of donors and size of donations ([21]; [38]; [49]; [66], [67]). Although there is recognition that the elements of the marketing mix influence shopping behaviors ([34]), there is little insight into how marketing-mix elements—product, promotion, price, place, process, or people—may be employed to support charitable giving. While promotion to attract donors is certainly important, it is likely insufficient to convey the full complement of donations needed. Consider, for example, the variance in degree of sacrifice sought. For some organizations, little effort is required (e.g., church usher, PTA member, Meals on Wheels driver); for others, the sacrifice is more extensive (e.g., Make-A-Wish granter, foster parent, organ donor). The present study examines living organ donation, a process in which one undergoes elective surgery to remove an organ for transplantation into another person. Given that any kind of organ donation represents an extreme form of sacrifice, the transplantation phenomenon serves as an excellent focal point for examining the sacrificial burdens involved in donation and opportunities to overcome them through the marketing mix.
This research suggests that different elements of the marketing mix may be used to address sacrifice related to donating behavior. Our findings suggest that a combination of marketing-mix elements may reduce experiences of sacrifice and thereby increase donation behaviors. This research contributes to literature recognizing that consumer reluctance to donate must be overcome ([21]; [38]; [49]; [67]). This reluctance has been addressed by prior research, which has emphasized that promotional messages may be used to procure necessary donations. This study extends scholarship on donation by leading our inquiry beyond that of promotion. Specifically, we describe how sacrifice manifests in the donation process and identify roles for the marketing mix to overcome potential reluctance to make such sacrifices. Relevant to an examination of marketing mix are such variables as product, price, place, process, people, and promotion.
In addition, this research contributes an understanding of anatomical parts as a particular type of possession separate from money, time, or other objects. While the donation of anatomical parts has been explored in the social sciences ([53]; [56]; [60]), it is not a focus of marketing literature, though the market for such parts is significant and growing. This research also contributes an understanding of how nonprofits may attract organ donors by more intentionally and systemically overcoming concerns of potential donors. Where prior research has considered donations of money, which can be replenished ([37]; [38]); possessions for which individuals have sentimental attachments ([67]); or time, for which all individuals have the same irreplaceable amount each day ([49]), this research investigates the growing market of anatomical parts for transplantation.
In addition, this study offers practical applications by suggesting how marketing-mix elements can be employed to overcome the barriers that may hinder individuals from donating. By better understanding how individuals may experience sacrifice through donation, we provide insights and tools for nonprofit managers focusing on how to use the marketing mix to encourage donation and thereby increase supply to meet demand.
To contextualize this study, we begin with a succinct review of the marketing and social science research on donation and sacrifice. We then present our methodology, including an overview of living organ donation within the U.S.-based transplantation market. We close with our findings, followed by a discussion of implications for practice and theory.
Nonprofit organizations deliver services to their clients made possible through donations from individuals ([ 9]; [67]). These donations are depicted as gifts of "life" or "hope" that support others in need ([54]). Such donations are most often provided by individuals who intentionally offer their support without receiving tangible rewards ([26]; [47]; [60]; [65]; [69]). These donations can be categorized as gifts to society that encompass the sacrifice of forgone opportunities ([36]; [41]; [56]). It is worth noting that these contributions are substantively distinct from contributions made to obtain some benefit for the self, such as with "pay what you want" pricing approaches ([17]). More specifically, contributions to nonprofit organizations are most often provided to deliver a benefit to others. Next, we provide a brief review of the marketing literature on donation and sacrifice.
Marketing and consumer researchers have primarily examined how the promotion element of the marketing mix can be employed to attract donors and increase donations. Studies provide insight into how messages may influence potential donors, turning a lens on the relative importance of the help sought ([23]), the role of individual identity ([14]; [48]), the motives for participating ([65]; [66]), or the impact on the donor ([23]; [66]). The focus of those studies has been to identify and understand conditions by which appeals may arouse sufficient interest for individuals to donate to an organization. Although promotion has a role in transforming individuals into donors, prior research does not illuminate how coupling other marketing-mix elements together with promotion may influence donation.
Awareness of opportunities is an important factor in securing donations, particularly in the case of organ donations ([31]; [64]), and leads many organizations to focus on promotion. Knowledge acquisition is certainly a contributing factor for those who choose to donate anatomical parts, yet additional requirements are necessary to transform them into donors. For example, even after passing the first hurdle of developing a desire to donate, potential donors must still qualify to participate ([12]; [60]). Thus, it is necessary to investigate the donation experience to better understand the marketing mix's role in attracting and securing donors.
Donations to organizations have been viewed as gifts to society ([11]; [56]; [60]). Like other types of gifts, these are born of sacrifice ([41]; [54]). Importantly, not all donations involve the same degree of sacrifice, as individuals possess several resources they may gift as donations. There are monetary gifts, which are viewed as replenishable and fungible. There are gifts of time, something qualitatively different from money in that time may not be stored or replaced ([23]; [32]). Other possessions that may be donated have value in the degree and source of individuals' attachment to them ([ 7]; [67]). Lacking in this conversation is an understanding of how the marketing mix can address the types or degrees of sacrifice that may be associated with the donation of possessions.
In the marketing literature, the concept of sacrifice is focused primarily on price—what consumers give up to obtain value ([18]; [25]; [70]). Beyond money, research identifies consumer sacrifice as the expending of energy, effort, or time ([ 4]; [10]; [15]; [30]; [42]). The degree of sacrifice, conveyed by price, may serve as information to consumers ([19]; [25]), inform perceptions of alternative offerings, or provide indicators of offering quality ([58]; [63]). In addition to the sacrifice one may make to obtain an offering, there is the sacrifice that manifests as a consequence of forgoing other options ([36]; [63]). While individuals may feel minimally burdened by the particular form of sacrifice made, some sacrifices may be deemed too great, thereby reducing a consumer's willingness to purchase an offering ([ 8]; [19]). While price is often equated with sacrifice in the market, there also is recognition within the literature that consumers make sacrifices beyond price to attain desired outcomes.
Extra-economic sacrifices are found in investments of time, effort, or energy ([ 1]; [ 8]; [22]; [30]; [70]). Time is a limited and perishable resource. It is most often viewed as that which may be monetized and is perceived as a cost ([ 4]; [27]; [70]), considered in relationship to search and intended patronage ([ 4]; [29]), or viewed as a precursor to attaining desired offerings ([13]; [33]). As a type of sacrifice, time is often described in conjunction with effort. Sacrifices of effort are depicted as labor or inconveniences necessary to attain benefits ([42]; [46]). Effort is evident in the cocreation of market-derived experiences where consumers are active participants ([16]; [20]; [51]; [61]). Sacrifices of effort may include that of choice when individuals opt to provide gifts in response to specific recipient requests ([15]; [39]; [68]).
Sacrifices of energy are described as psychic or emotional expenditures encompassing the contemplation associated with a consumption opportunity ([ 1]; [ 3]; [ 4]; [ 6]). While a primary focus in the literature is on monetary sacrifice for value that is conveyed through the marketing mix, it is necessary to examine how the marketing mix can be used to address sacrifice experienced by donors. Although time and effort may emanate from the embodied self, sacrifice of the physical self is less often contemplated. Nonetheless, [22] examine the employment of physical and mental energy to transform a previously used object; [40] considers the physical nature of effort involved in providing relocation assistance; and [35] recognize the physical peril individuals accepted when they secretly shared additional food with other inmates in Nazi concentration camps. Together, those findings illustrate that monetary sacrifice alone may be insufficient for some forms of consumption and that promotions are likely insufficient to overcome sacrifices beyond those of awareness.
The purpose of this study is to understand the nature of sacrifice in donation so as to guide organizations in overcoming obstacles to obtain donations. Because living organ donation indisputably involves great sacrifice, it provides a clear context in which to understand sacrifice in relation to donation. Furthermore, an organ must be donated voluntarily and may only be offered as a gift in the United States ([44]; [62][ 4]). Next, we provide an overview of the phenomenon followed by a discussion of data collection and analysis.
Living organ donation is orchestrated by medical personnel and associated transplant centers within the transplantation market. Whereas early transplants relied on organs from deceased individuals, living organ donation is increasing as health care innovations provide opportunities for transplanting organs from living, genetically unrelated individuals ([11]; [50]). Nonetheless, with demand for organs outpacing supply, living organ donors are increasingly sought. Without a transplant, individuals experiencing organ failure may undergo various treatments that sustain life, though often at diminished quality. All clinical costs associated with donation are funded by participating organizations (e.g., organ procurement organizations, insurers, transplant centers within hospitals) and are coordinated by a transplant team ([64]).
Individuals may donate one kidney, a portion of their liver, a lung, or part of their intestine. We study the experiences of living kidney donation, as they are the most frequent type. The organ donation process is complex, requiring physiological and psychological clearances of donors. Living donors may be directed, meaning they donate to a known other (e.g., loved one, colleague), or nondirected, thereby donating to an unknown other. Nondirected donors provide an organ to the next individual on the transplant list with whom they are a match, or to support a donor chain. No matter the recipient, the donation process is the same.
This process begins with education and culminates with surgery. The organ donation and transplantation process includes informing potential donors about the steps to qualify and the consequences of participation. Once they choose to participate in the process, individuals are assessed for their overall fitness. Qualification begins with procuring an extensive medical history, which provides for an assessment of overall health as well as evidence of current and potential (physical or mental) disease. Next is tissue and blood testing to assess the viability of a match to a recipient. When an individual is identified as a clinical match to a recipient, and it is determined that removing the organ is not likely to be detrimental to the donor, surgery is scheduled.
Kidney transplants occur across two surgeries. First is the nephrectomy, removal of the kidney from the donor, a surgery that typically lasts four hours. Next is the transplantation, the insertion of the donated kidney into a recipient, which lasts approximately three hours. Surgery leaves a donor with an immediate and significant degradation of bodily functionality, coupled with the physical trauma of the procedure. Donors are hospitalized on average between two and four days after the procedure, followed by a recovery period at home of two to six weeks. Recipients often emerge from surgery feeling well due to the immediate functionality provided by the transplanted kidney. Both parties are required to participate in follow-up tests to monitor their respective kidney function, though the requirements differ.
Because the present study focuses on the experience of living donation, we deemed ethnography to be the most appropriate research method. Given that the process to become a living organ donor is quite extensive, a larger number of people begin the screening process than actually donate. This is due to any number of reasons, including a prospective donor's current or projected health, willingness to proceed through various clinical tests, or decision to terminate the process. To better understand sacrifice within living donation, this study thus examines only those individuals who completed the living kidney donation process.
Prior to beginning this study, the authors themselves participated in organ transplantation. The second author made her need known, as advised by her physician. The first author volunteered to be tested and ultimately became the second author's donor. The process, from the precipitating event through recovery, transpired over a period of nine months. Each author recovered without incident. Throughout this process, field notes were captured.
Study participants were solicited through clinicians, online living organ donor support forums, and snowball sampling, with varying outcomes. They included individuals from different regions in the United States who participated in both directed and nondirected donations. Each author has a relationship with a nephrologist (kidney physician) with whom they shared the intention of this study. Those physicians were asked to share study information with their patients as they saw fit. The physician provided those patients who expressed an interest in participating in this study with the authors' contact information. Within online donor forums, the first author posted notices inviting willing participants to initiate contact through a social media platform, direct message, or email. In both recruiting approaches, more individuals expressed interest in participating in the study than actually followed through to participate in interviews. No compensation was provided to individuals who participated in this study.
Our sample includes 20 individuals representing diversity in race, age, sex, sexual orientation, elapsed time since donation, donor and recipient outcomes, type of donation (i.e., directed, nondirected, or donor chain), and location (see Table 1). The participants include eight nondirected donors and six individuals who had complications or became aware of their recipients' complications. Although statistics indicate that the majority of donors continue to be in good health postdonation, some suffer donation-related complications. Our participants' clinical outcomes range from expected recoveries to varying degrees of acute or chronic physical and emotional disease. Most, but not all, recipients had resumed a healthy lifestyle free of dialysis.
Graph
Table 1. Overview of Study Participants.
| Pseudonym | Sex | Age (Years) | Type of Donor | Donor Outcomes | Recipient | Recipient Outcomes |
|---|
| Alison | Female | 40s | Nondirected | As expected | Stranger; different race | As expected |
| Derrick | Male | 50s | Directed | As expected | Wife | As expected |
| Erica | Female | 30s | Nondirected (paired kidney program) | As expected | Mother | As expected |
| Franklin | Male | 70s | Nondirected | As expected | Stranger; met after 15 months | As expected |
| Gregory | Male | 60s | Directed | Chronic pain | Colleague's daughter | As expected |
| Hannah | Female | 50s | Directed | As expected | Neighbor; developed relationship with extended family | Kidney died; recipient went on dialysis |
| Isaac | Male | 40s | Directed | As expected | Professor; reconnected via Facebook | As expected |
| Jacob | Male | 50s | Directed | Surgical complications, financial complications | Coworker; different ethnicity | As expected |
| Kenneth | Male | 40s | Nondirected (donor chain) | As expected | Stranger | As expected |
| Lizbeth | Female | 40s | Directed | As expected | Brother | As expected |
| Meredith | Female | 50s | Directed | As expected | Son | As expected |
| Nancy | Female | 50s | Directed | Depression, kidney disease | Friend | Continues to have health challenges due to chronic disease |
| Octavia | Female | 40s | Directed | As expected | Mother | As expected |
| Penelope | Female | 40s | Directed | Initial complications due to previous surgery; recovery as expected | Brother | As expected |
| Quintessa | Female | 30s | Directed | As expected | Husband | As expected |
| Reginald | Male | 50s | Directed | As expected | Brother | As expected |
| Sadie | Female | 60s | Directed | As expected | Husband | Less than half normal activity resumed |
| Tabitha | Female | 20s | Directed | As expected | Cousin | As expected |
| Victoria | Female | 50s | Directed | As expected | Niece | As expected |
| Wilma | Female | 50s | Directed | As expected | Brother | As expected |
We collected ethnographic data through semidirected phenomenological interviews, participant observation in living donation, and online donor forums. We downloaded the posts of individuals identified in online forums, which often provided an archived timeline of their experience, and these served as projective tasks within interviews. Given our own experiences, we quickly established rapport with study participants.
We began interviews by asking individuals to describe how they became a living organ donor. Accounts shared in response to the initial question were probed using emic terms to facilitate interview continuity. In addition to learning of each unique circumstance, we asked individuals to describe how they learned of the need, made the choice to donate, and experienced testing, surgery, and recovery. They were also asked to describe the process, who was involved in the process, the emotional and physiological outcomes for themselves and the recipient (when known), and the timing of the transplant. Interviews ranged in duration from one to four hours, with an average of 90 minutes and some follow-up exchanges on social media and email. Data were collected by phone and through face-to-face meetings at the convenience of participants. Interviews were audiotaped and transcribed.
Interview transcripts and field notes provide the basis for our analysis and interpretation. Data analysis began with a review of the donation process as described by donors. This review revealed that the donation process was the same for all participants regardless of center type, testing protocol, or surgical method, thus allowing for comparison across phases in the process. Next, codes were generated from readings of the anthropology, theology, market, and consumer research on donation (e.g., time, money, effort). Those initial codes were supplemented with emic terms (e.g., wait, goal, endure) from the initial analysis of the transcripts and field notes.
Analysis continued with each transcript being coded. Next, transcripts were analyzed across each phase of the donation process: learning about the opportunity, making the choice to participate, qualifying (i.e., determining the degree of match), and fulfilling the commitment to volunteer (i.e., surgery, recovery, and donor outcomes). In addition, we analyzed transcripts across outcomes in terms of meeting expectations (e.g., successful outcome), exceeding expectations (e.g., easier, faster), or falling below expectations (e.g., poor outcomes for the self or the recipient). Thus, two types of analyses—diachronic (i.e., across the process) and synchronic (i.e., within similar phases or outcomes of the process)—were performed ([ 2]; [57]; [59]).
We identified emergent themes through an iterative process comprising analysis of the transcripts, the coded data, and the literature ([57]). Data collection and analysis continued until saturation was attained. We conducted member checking in follow-up discussions and emails with four participants.
We codify the living organ donation process in three key phases: deliberate, decide, and donate. Through our participants' experiences, we find that the marketing mix is the primary means by which organizations may support the donation process and, in particular, mitigate donor sacrifice that emerges as individuals become donors who offer their possessions for the benefit of others. We identify roles for six marketing-mix elements that aim to manage sacrifice experiences: product, promotion, place, price, process, and people. Furthermore, we identify three complementary and interactive types of sacrifice: psychic, which reflects the employment of mental or emotional energies; physical, which encompasses investments of components and functioning of the bodily self as well as modifications to behaviors; and pecuniary, which comprises investments of possessions, time, or money. We find the each of the three types of sacrifice may emerge during any of the phases within donation (see Table 2).
Graph
Table 2. Definitions of Sacrifice as Experienced Across the Three Phases of the Donation Process.
| Psychic | Pecuniary | Physical |
|---|
| Deliberate | Mental effort to consider the option | Expenditures associated with exploring the opportunity | Behaviors or actions exerted to assess opportunity |
| Decide | Mental energy to weigh benefits and concerns of selection | Expenses related to choosing to pursue an opportunity | Behaviors or actions employed to choose an opportunity |
| Donate | Recognition that a choice removes other possible choices | Costs incurred with making the contribution | Being present to provide contribution |
In line with this categorization, we find that there may be opportunities for organizations to address the types of sacrifice that may evince across any one of the three phases of the donation process. While both individuals and organizations participate in each of the phases in the process, the degree of relative influence varies, such that the deliberation phase is more heavily influenced by the individual and the donation phase by the organization. Next, we depict participant experiences through data excerpts to illuminate relationships between sacrifice and the marketing mix within each phase of the process. Although the phases are presented as discrete units, the experience is more of a continuum in that data may encompass aspects of more than one phase.
The first phase in the process is one of deliberation, in which organizations prominently employ promotion to raise awareness of the donation opportunity. For many donor-reliant organizations, the product and process are entwined in delivering the intended outcomes and associated benefits. Here, too, we find that organizations may benefit when they more fully depict the product as comprising both the donation and the transplantation. Through our informant experiences, we identify roles for the product and the process that, together, provide donors with opportunities to contemplate the benefits and risks of participating (for themselves and for the recipient). The participants in our study come to learn of this particular volunteer opportunity in a variety of ways, from observing a loved one's decline in health to encountering promotional (and public relations) messages. Regardless of the means through which individuals learn of the donation opportunity, they necessarily employ psychic sacrifice to better understand the requirements and implications of participation in the process.
One informant, Gregory, initially learned of living organ donation through a story on National Public Radio's This American Life program. He describes how that story prompted him to consider participating as a living organ donor, though he was not moved to act until he received a request for help. While promotion stirred his interest in the product, it was insufficient to motivate action to participate. He learned, through a group email, that his colleague's daughter was diagnosed with end-stage renal disease and was a candidate for transplantation. Though he did not know the daughter, he describes feeling compelled to offer to become her donor:
I received an email from [a colleague] on a Sunday morning that his daughter had just gone onto the transplant list....It was a request [saying] that she needed a kidney—he was letting other people know. And [the email] stated her blood type, and it was mine. I spent about an hour wrestling with it, looking for a justifiable reason not to volunteer. And finding none, I decided that I would volunteer to be tested. (Gregory)
The information from his colleague, coupled with knowledge garnered from a donor story in the media, compelled Gregory to donate. He describes learning of the opportunity to act along with the awareness of the product and process as integral to awakening his calling. Gregory's acknowledgment of his calling encompasses psychic sacrifices with respect to relinquishing a sense of control over the choice to participate. His sacrifice of choice was not due to any external forces but, rather, an alignment of his choice with his calling.
Within the deliberation phase, individuals acquire additional knowledge about the process by which the donation will be used to deliver the product and associated benefits for the nonprofit's client. For most of our informants, the initial information requests are related to the specifics of donation in terms of what they contribute to the product and the process. That often begins with a desire to understand the requirements necessary to participate:
I called [a transplant center in my city] just to see if I was even a candidate....I was going to be 61 in February, and I thought quite possibly I would be too old. They said that because of my age I would be considered a marginal donor in their system. I called [another transplant center] where the surgery was to be performed and they said, according to their system, I was fine. So, I began the long evaluation process. (Gregory)
Across transplant centers, the product—retrieving a donated organ and transplanting it into one in need—is the same. Gregory pursued the donation opportunity in the face of mobility challenges, legal blindness, and the concern that he may be too old to participate. In fact, when he presented himself to a local center as a donor, he was rejected due to age. While it is uncommon for individuals to comparison shop for a transplant center, there are several instances in our data in which individuals found aspects of a center's process or people to more readily mitigate sacrifices posed by the donation. Thus, individuals might find one center to be more attractive than another, which may influence where or how they choose to participate.
When individuals learn about donation opportunities through intimate relationships, as is the case with a spouse or siblings, they may experience a strong desire to donate even before fully understanding the product, process, or its impact on them. That desire also has the potential to stir psychic sacrifice as individuals pursue a known product with little information about the process around it. Wilma's brother was in need of a kidney, yet she had little understanding of what would be required of her. The transplant center personnel began educating Wilma from their first conversation when she requested information on how to become her brother's donor:
I just called [the center], and [the transplant coordinator] sent me out my package and we went from there.... I think [my brother and sister-in-law] wanted to control [the process]. I think they just found out that [the transplant center] wasn't going to let them control it anyway. Their blind selection of a donor was to protect both ends, both the recipient and the donor. I felt very...taken care of, very considered. They were always looking out for me. They said, "You can stop this process any time you want. Even if you're a perfect donor and you get the heebie-jeebies, it's okay, you can stop it."...I knew at any time I could say no and so, therefore, I didn't feel like I wanted to say no....They were very kind, they were very helpful, very professional.... We do feel like we've been on a ride and I think it's not just me, 'cause I'm the donor. But it's the whole family—my dad, my brother Bill—just all of us feel like this has been a long process. (Wilma)
As Wilma's knowledge increased, so did her comfort with donation. From the initial stages of the process, the people responsible for facilitating the process to deliver the product conveyed the ways in which they would help Wilma navigate and support her through the process. The people and their focus on Wilma's well-being helped mitigate experiences of psychic sacrifices even before they emerged.
Promotion focuses predominantly on why one should donate, not on how messaging can help attenuate the psychic sacrifices individuals may make as they navigate relationships affected by donation. For example, an individual's decision whether to donate an organ can have major relational impacts within their network of family, friends, supporters, and naysayers due to the potential health risks and uncertain recovery period involved. Consider the experience of Gregory, who terminated his relationship with his longtime partner when she questioned his desire to donate. In addition to supporting potential donors, it is crucial for the process and promotion to attend to the support network of those donors. An example is found in Wilma's experience, in which she describes how the people in the center focused on communicating the process and her role within it to deliver the product as support for her as well as to alleviate her family's trepidation. Then there is Nancy, who incurred travel costs because she felt the need to communicate to her family in person regarding her intention to donate. The people in Nancy's center were less helpful in supporting her desire to understand the process in detail, which resulted in her incurring financial costs. Perhaps if the people and process were more supportive, Nancy would have been able to avoid pecuniary sacrifices in support of her donation.
The process tends to focus on the potential donor, with some inquiries about their support system. This approach in organ donation is derived from laws that prohibit the sharing of medical information with people other than the patient. While legally compliant, such an approach often leaves potential donors lacking in assistance as they attempt to encourage their support system to come on board. Consider another informant, Kenneth, who described his wife's dismay as he aimed to initiate a kidney transplant donor chain. A donor chain is possible when donor–recipient pairs who are not clinical matches participate as part of a group of donors and recipients, where each donor contributes to another recipient such that at least two transplants result ([11]). Kenneth knew he had an opportunity to positively affect many lives through participation in the chain, as his donation would make subsequent transplants possible. He explains that he put his marriage at risk as a result of his decision to donate:
I was part of the biggest chain that has been so far....I knew that I was starting it....I'm married and my wife told me she was going to leave me if I did it. I said, okay, and she didn't [leave me]. But I wasn't going to let that stop me because she's worried or whatever. I wasn't going to let that stop the benefit that it was going to be to other people, I didn't think that was right.... She never came around.... I think it still kind of bugs her that I went against what she wanted. Almost in a way, it's like I had an affair or something. (Kenneth)
Potential donors often invest mental energy when contemplating becoming an organ donor and speaking to their close circle about it. The possibility that their health could be negatively affected may well produce personal stresses and, as was true for Kenneth, stress within their close relationships. Kenneth was driven to contribute what he perceived as the immeasurable good that would emanate from his cumulative psychic and physical sacrifices, and therefore he excluded his wife from a life-altering decision. He draws parallels between his kidney donation and an affair, a state of emotional and/or physical perfidy. Reconciling this requires him to sacrifice his wife's opinion and support, which are of great value in a peaceful marital union. Yet Kenneth, akin to many of our study participants, describes positive aspects that emerge through donation. The codification of those experiences would serve organizations in the development of promotions and process components to support potential donors and their support systems, as well as infuse opportunities within the process and the people supporting it to celebrate such experiences.
The experiences of the previous informants underline how promotion, designed as it is to disseminate knowledge to potential donors about the opportunity to donate, is insufficient in addressing the various types of psychic sacrifice that emerge through the donation process. The process contemplates the clinical needs of an individual, yet organizational managers should consider and prepare for the types of psychic sacrifices donors make, from contemplating the opportunity, informing loved ones of their decision, and navigating support throughout transplantation, including the postdonation phase. There are a variety of products for which marketers commonly address potential fears (e.g., "safe when used as intended"). Because messaging around organ donation does not typically address the various sacrifices that manifest, there is a large window of opportunity for tailoring the marketing mix to address this deficiency.
The integration of promotion, product, and process also provides opportunities for organizations to support potential donors as they contemplate engaging in donation. Another participant, Sadie, learned of her husband's need for a transplant when accompanying him to a doctor's visit. During that discussion, she learned about and was motivated to consider becoming her husband's living organ donor:
When you live with someone and all of a sudden you see them losing weight, you see them walking around like a zombie having no energy....He was doing the peritoneal dialysis, and he had to hook himself to the machine every night by eight o'clock.... The reason [the medical team] did this for him was because he liked to play golf. They were trying to make it so that he could maintain his lifestyle.... He was on dialysis for six months, but it was an awful six months.... When I went with him [for a checkup], the nephrologist informed me that a lot of wives are giving their husbands kidneys.... I thought, "Well, I have one foot in the grave and one on a banana peel. I can do this!" (Sadie)
The same medical team that proffered in-home dialysis to address her husband's renal failure also offered organ donation as an alternative. The physician shared the benefits of living organ donation and also began to introduce information to enable Sadie to ponder such an option. Even though it was a more complex offering than dialysis, she welcomed an opportunity to take a more active role in improving her husband's health. When discussing it as a family, their son offered to donate instead of Sadie. She declined his offer as he was recently married, had a newborn, and had just started a new career. Thus, she enacted psychic sacrifice in her assessment of the opportunity, the relative risk to the possible donors (i.e., herself vs. her son), the potential impact to her own health, and the hope to enjoy a more spontaneous and active life than that which dialysis accommodated. These sacrifices are not accounted for within the process, leaving donors to manage them on their own when organizations can anticipate such experiences and should proactively address them.
The deliberation phase is likely inspired, in some part, by the promotional element of the marketing mix. However, it is insufficient to address the multifaceted experiences of psychic sacrifice individuals bring to the deliberation phase. Prior research has found that psychic sacrifice may be enacted in response to promotional messaging. For example, a recent University of Pittsburgh Medical Center (UPMC) television commercial depicts a line of individuals slowly making their way through an ominous tunnel with the voiceover: "At UPMC, living donor transplants put you first so you won't die waiting." Similarly, the National Kidney Foundation initiated the promotion "#BigAskBigGive," which provides individuals with guidance on how to talk with others about becoming a living donor. Promotional materials serve to inform, persuade, and invite action by individuals to consider participating in a process to deliver a specific product. Similar to for-profit organizations, which must align promotions with other aspects of the marketing mix, it is necessary for donor-reliant nonprofits to consider how other aspects of the marketing mix can be employed to address psychic sacrifices that may emerge in the deliberation phase of donation. Potential donors experience psychic sacrifice in contemplating what it means to undergo an elective surgery where the result is to remove functionality from their physical self and provide that functionality to another. Psychic sacrifices also serve as precursors for other types of sacrifices to manifest. Importantly, the mitigation of psychic sacrifices through a clearly and compassionately positioned and communicated product and process may provide encouragement to individuals to proceed to the decision and donation phases of the process—phases that likely require additional forms of sacrifice for which donors will seek support.
Individuals undergo the decision phase of the process as they review the donation opportunity and determine their plans. While organ donation for transplantation, as a product, consists of a similar set of criteria and testing protocols across transplant centers and a consistent set of surgical procedures, there are some differences. These differences reflect each organization's approach to organ donation—specifically, the approaches of those with distinct roles associated with the entirety of the transplantation process. As individuals decide whether to donate, they assess not only the opportunity but also the organization. Thus, the decision to donate may be influenced by aspects of the product, the process to deliver it, the people who enable its delivery, and the place where the donation will occur. Where psychic sacrifice allows individuals to move forward with sincere contemplation, the decision phase finds individuals facing psychic, pecuniary, and physical sacrifices. Organizations have opportunities to mitigate these sacrifices, thereby likely contributing to the experience of donors and perhaps increasing the likelihood that individuals will choose to become donors.
The opportunities for donor-reliant organizations to employ product, process, and people aspects of the marketing mix become more impactful as individuals assess the opportunity to make their decision. One informant, Penelope, donated to her brother after he survived a failed transplant from a deceased donor. She and her family were angst-ridden by his tenuous health and recount being summoned to the hospital because his physicians were uncertain if he would live. She aimed to better understand the impact of donation as part of the product, as well as the implications of participating on her lived experience:
[The transplant center] had a reception for donors and recipients, a little cookie and cake thing where people who had [volunteered to donate] talk to those of us who are going to do it. [They] talk about their experience. That was great because I got to see people who had done it.... I was getting nervous. I was excited because I was going to help my brother but I was still nervous. That was my first surgery ever. (Penelope)
In Penelope's case, the organization provided individuals considering where to donate an opportunity to learn about the experience from former donors. Sponsoring this event also provides an opportunity for the organization to help donors manage experiences of psychic sacrifice as they weigh saving another's life while risking their own. By expanding the process to include additional people, the organization has opportunities to provide additional support to potential donors and perhaps improve their decision process.
The contributions of an organization's people in the decision phase are crucial to the process and to the donor's perceptions of it. Individuals who choose to donate, as well as their friends and family, may question the extent to which organizations recognize the depth of sacrifices required to do so. Wilma and her husband wanted to learn how people in the organization, and in particular the surgeon, viewed the process:
My husband asked a question, "What does that feel like once you take that kidney out of there and you take it over to the other person? How do you feel about it?...Do you kind of feel like God? Like you're saving this person's life?" [The surgeon] said, "Well, I am the physician who takes out the kidney. My patient is the donor. And, the donor comes in healthy....I am very particular about my job, because in the whole hospital, I'm the only person with patients who come in healthy and go out impaired." And I thought, "Wow! He understands."...I felt relieved or assured by him saying that. I knew that he understood the gravity of the donation. (Wilma)
Individuals considering donation recognize that transplantation provides significant benefits for both the recipient and the organization. However, potential donors are acutely aware that those benefits emerge through their sacrifices. As Wilma shared, individuals may encounter compassion in those who play roles in the provisioning of transplantation or in their initial contact with the organization. Such experiences facilitated by organizations through the people and processes that support the product allow individuals to receive validation of their sacrifices and enable their willingness to contribute the sacrifices required to fulfill the donation.
With a decision to donate made, individuals begin the qualification phase. Potential donors are provided a detailed description of the process, including an overview of the criteria required for participation, the testing sequence, and the possible consequences of participation. The choice to donate is fraught with uncertainty, as it does not mean that an individual will be accepted as a donor. As such, individuals have different approaches to sharing their intentions with others. The first author described angst when contemplating with whom to share:
I want to tell [my friend] about my plans [to donate]. She might think I'm crazy. I can't hear anything negative about [donating]. It's enough that [the transplant coordinator] said I could die! But what if [the transplant center] rejects me? How will I explain that? (Field notes)
It is commonplace for individuals to share important happenings in their lives with others. The desire for acceptance of one's decision and support for it is common among the participants in our study, and many seek out such support in online donor forums.
The experience of qualification feels more extensive than how it is presented to potential donors. Participants generally express astonishment at the degree of testing required:
I thought it was just a blood test. I learned I had more tests to take.... I thought, "Oh Lord, this is going to be impossible!"...Everything was going along and [the transplant coordinator] came back and said we are an identical match!...I think one of the difficult things is we don't know how to ask a sibling to donate. It's a sacrifice. (Reginald)
The transplant coordinator orchestrates progression through the qualification phase based on clinical results from a series of escalating tests (e.g., blood tests to CT scan). These tests may be the first opportunity for individuals to experience the place where their donation will occur and, as such, leaves an indelible impression. One of our informants, Nancy, was deciding whether to conduct her tests at the local transplant center or the one where she was a potential match to a recipient. Ultimately, she felt it necessary to meet the people who would orchestrate and conduct her donation. She organized a visit incurring travel, accommodation, and vacation time costs to travel from one state to another in the Western part of the United States:
I did online research.... The transplant center sent me a [video] and I read the literature that they gave me.... I decided to go to [the next state over] where the transplant happened—I wanted to do the blood matching there....I wanted to meet the people.... I read the possible adverse effects like pneumonia, blood clots, and death. I felt comfortable, but I still wanted to know more.... When I came to the appointment with the transplant surgeon, who's actually a cardio surgeon, he's not even a nephrologist!...I had lots and lots of questions. I wanted to know what was going to happen during the surgery and he just kind of waved me off and said, "Oh you don't need to know that; let's not worry about that. We'll take the kidney out of your old caesarean scar. You won't have any new scars and the rest of it we won't worry about." He just wouldn't give any more information and I even asked, "Are there any other living donors? Is there somebody I could talk to?"...They said, "Oh no, we don't do that." (Nancy)
As individuals proceed through the process, their awareness of the impending surgery and its associated risks becomes more of a reality. Where Penelope describes an opportunity to interact with former donors, Nancy was not allowed to do so. Thus, process contributed to Penelope's reduction in experiences of sacrifice by enabling her to see former donors, yet it accumulated additional sacrifices for Nancy. Furthermore, where Wilma's experience of sacrifice was attenuated by the health care staff, for Nancy it was not. Although these donors continued through the process, there are opportunities for organizations to mitigate experiences of sacrifice through marketing-mix elements that may also enhance the overall donation experience.
An increasing awareness of the associated risks provides insights into the various sacrifices these individuals undergo. There are two that seem to be most angst producing: ( 1) the possibility of death and ( 2) the possibility of being rejected. In Nancy's visit, it becomes evident that although the product is similar, there were opportunities to employ alternatives for the communication of the process as well as interactions with transplantation staff to address her experiences of sacrifice related to her well-being and, ultimately, her life. Other participants spoke of the angst experienced as they pondered whether they would meet the criteria to donate. Participants stated that they have sufficient information about the process from the promotional and product materials as it relates to reasons why they may not be accepted as donors, or the rare but possible outcome of death. Yet their confidence in the process is influenced and experience of sacrifice altered when they are exposed to the people within it.
Throughout the process, individuals often seek some affirmation that everything will work out satisfactorily. That is often evident in how individuals pursue the qualification process. For Nancy, it entailed travel to the transplant center to gather first-hand knowledge of the overall process and people within it. For others, like the first author, there are sacrifices made to ensure success with each step throughout qualification with the hopes of increasing the probability of acceptance. For example, the first author was required to complete the 24-hour urine volume test four times, as the results were different than expected by clinicians:
Seriously? I drink a lot of water—the two jugs [of urine] are all mine! Off to get the new jugs and another [urine collection] hat.... I don't like the jugs at the [local clinic] so I will get them from the [local] transplant hospital—it's a drive, but anything is better than redoing this test! (Field notes)
Testing often requires that individuals rearrange their lives to accommodate travel, clinical appointments, and testing procedures. As with the surgery costs, tests are covered by the recipient's insurance. However, some of these activities necessitate the expenditure of money (e.g., copay, gas, parking). In addition, the tests themselves typically require that individuals provide access to their body and bodily products to assess fitness for organ donation. Thus, testing to qualify may lead to psychic, pecuniary, and physical sacrifices. These sacrifices emerge during a fragile time in the process when individuals are anxiously awaiting to hear whether they can progress to the next phase of testing until they are accepted as donors.
The nexus between place, people, and process in the decision phase represents an ideal point at which marketers can influence the donation experience. At this juncture, there is an escalation of commitment evident as individuals proceed from consideration to making a decision to actively pursuing the final phase of donation. Alison, a nondirected donor, wanted to donate in response to a story she heard on NPR. She is a busy mom with a career who wanted to donate on her terms. She identified a convenient location for her donation and prepared a schedule that negotiated necessary donation-related time commitments with the demands of her life. Alison describes her experiences of sacrifice and how the organization employed people throughout the process to attenuate anticipated anxiety as she passed from one level of clearance to the next round of testing:
You kind of felt like you were on the show Survivor. Every time [the transplant coordinator] would email or call me, I would be like, "Was our blood a match?" Every time you had to have that blood draw, you were praying that you still were on the island! That you weren't going to get the call, "Sorry, you've been rejected. You can't donate."...Every time I knew I passed the next test, I was like, "Yes! Okay! One step closer!" (Alison)
The presentation of the self for extractions of fluids and tissues serves to prepare individuals for ever-increasing physical sacrifices culminating with the nephrectomy. The relationship the transplant coordinator builds with the potential donor is key. The commitment to the process is commonly expressed because individuals anticipate progressing through to the donation stage. Ideally, the transplant coordinator supports this anticipation with commendations as potential donors undergo sequential tests and celebrations when they advance through stages in the process.
The decision phase encompasses the full complement of sacrifices, but psychic sacrifices in particular usher in opportunities for additional experiences of sacrifice as individuals move through different phases. Key to the donation is the integration of process and people (surgeons, counselors, etc.) within place to deliver on the transplantation product. Furthermore, while incurred costs are pecuniary sacrifices, organizations provide a variety of alternatives to help individuals assuage or avoid incremental costs. Doing so likely requires additional donor confidence in the team communicating and managing the process. Within for-profit offerings, sacrifices associated with price may signal desirable attributes ([70]). However, incurred costs within donation tend to reflect a need for organizations to communicate more with potential donors such in order to mitigate such costs. Within the decision phase, donor sacrifices may be managed through a combination of marketing-mix elements to support donors as they make a crucial decision.
The donation phase is reached when an organization's efforts to secure donors materializes. This phase culminates with the creation of a product (a donated organ for transplant) that provides valued benefits to clients. For those attracted to donate, organizations orchestrate the delivery of the product benefits through place, which houses the requisite people and processes. This phase of living organ donation then concludes with the emergence of the most critical sacrifice: the nephrectomy. While organizations cannot eliminate the totality of sacrifices associated with this phase, they can—through the careful specification of the product including roles for donation and thoughtful facilitation of product delivery through place, people, and processes—attenuate experiences of sacrifice.
As the people within organizations prepare donors for surgery to complete the donation, there is an opportunity to contribute to donor confidence and comfort in order to reduce experiences of sacrifice. Increasing comfort with the part of the process that encompasses the details of surgery is a crucial component of the experience. One informant, Victoria, donated to her niece. Once credentialed as a donor, she recounts how she aimed to gather as much information as possible to better understand how her kidney would be removed:
I'm one of those people that goes and does as much research as possible. As soon as they told me I was a match, I'm like, "Okay, what's the surgery going to be like?" I actually found on YouTube a video of the actual surgery so I sat and watched that.... The surgeon actually has to slide their hand [into the abdomen] to retrieve the kidney.... It wasn't long after that I was meeting the surgeons. I met the one gentleman who came in and the first thing I looked at—his hands were huge! I was just like, "Oh my gosh, are you my surgeon?" He says, "No actually, I'm going to do the transplant [into the recipient]." I'm like, 'Oh good!' He kind of looked at me funny, and I said, "Your hands are huge!" Shortly after that, I met my surgeon. It was a woman and she has these beautiful, little, tiny hands! (Victoria)
In the deliberation phase, donors are most often concerned with factors related to transplant center successes. After deciding to pursue donation and being accepted to donate, individuals often turn their focus to the surgical process that results in donation. Like Victoria, donors express concern with recovery and factors that may influence it, including the size of the incision or degree to which organs are displaced. Though transplant centers do not assign surgeons based on hand size (or personality, or specialty area), it is crucial to understand the importance of people within the process. Organizations should have an awareness of what factors may increase perceived donor sacrifice and how they can proactively manage them.
Once in the donation phase, sentiments about completing the process become more salient. Hannah, a hospice nurse who describes herself as one who avoids "medical stuff," describes how interaction with her surgeon reduced her concerns with donation:
I just love [my surgeon], there was something about him. And surgeons are usually so detached and so task-based. He was just a lovely man. We talked about different ways he could do the surgery. I said, "Well, I'm going to be asleep so I want you to be comfortable with how you're doing this."...He did end up doing the open [nephrectomy]. I have a six-inch scar.... And then he asked me, "This is a nice thing you're doing. Is there something we can do for you?" I said, "Well this is going to sound a little strange, but I would love to have a picture of my kidney going to him..." He just looked at me and said, "Bring a camera!" So we got a disposable camera and I have pictures of my kidney in the metal bowl with him working on it and [the other] surgeon coming to get [the kidney]. (Hannah)
The organ donation and transplantation process involves people at every stage who take on crucial roles. For example, donors most frequently describe the transplant coordinator as an orienting figure in the process. Another central figure is the surgeon, whom people assume to be competent, albeit stereotypically impersonal. As surgeons show compassion toward donors and the sacrifices they experience through surgery and recovery, the donors feel cared for within the process. Conversely, recall Nancy's encounter where she felt the surgeon was dismissive toward her inquiries. Hannah's and Nancy's experiences underscore that just as health professionals can enable the progression of the process in a manner where donor sacrifices are managed, they can also amplify experiences of sacrifice.
The nephrectomy, the most obvious physical sacrifice by donors, occurs during a surgical procedure with donors fully anesthetized. Physical sacrifice is thus experienced primarily through the recovery process. Penelope describes a postsurgical recovery experience that is common among living organ donors:
When I came out of surgery, I felt like I had been run over by ten trucks. One after the other! They just kept running me over. One after the other. I was a mess, just a mess. But deep down, I was happy because I could hear them telling me that my brother was fine.... You can't look at the moment of surgery. You have to look at the end result. (Penelope)
The transplant team provides an overview of all aspects of the process, including recovery. Recovery, both immediately after surgery and extending weeks afterward toward the goal of regaining full strength, is particularly challenging for donors given their high levels of health prior to donation. Recovery often requires that individuals refrain from several activities, including work, for anywhere from two to six weeks. The totality of sacrifices necessary by individuals to contribute to transplantation is most often deemed worthy, as exemplified by Penelope. The recovery portion of the donation process focuses primarily on clinical outcomes. While important, there are opportunities for organizations to support donors in the experiences of both physical sacrifice and psychic sacrifice as they strive to fully recover, in addition to pecuniary sacrifice through lost income and incurred costs.
For most donors, the process ends once they obtain medical clearance to resume their regular activities. For the organization, the process comes to a close approximately six weeks after surgery with the postsurgical lab work. Although the likelihood of negative outcomes is low for kidney donors, when they do occur, a timely and appropriately compassionate response by the organization is important. Recall Nancy, who traveled to another state to donate her organ. During postsurgery recovery, she experienced unexpected outcomes that were not explained or anticipated by the clinicians or found in her research:
The transplant was successful. They had told me in advance that I'd probably stay in the hospital six days because I had so far to travel to go home.... Before I was discharged, I noticed that I had lost feeling in my one leg, in my one upper thigh of my left leg. I mentioned it to the doctor and they said, "It will disappear in six months." So, I literally marked on my calendar for the six months. And, the pain did not go away—it was intensifying. I wrote [the transplant center] and insisted that they examine me again. And, they confirmed that I had neurological damage in that leg. (Nancy)
Throughout the process, individuals are made aware of possible complications. While some complications from organ donation are resolved within the first year of surgery through additional clinical intervention (e.g., hernia repair) or lifestyle adjustments (e.g., fluid intake to address abnormal lab metrics), other complications may extend much longer (field notes). As with Nancy, Gregory experienced complications:
It was in my exit interview, six days after surgery when I was released to come home, the same surgeon who operated on me said that it would probably be six to eight weeks before I would be out of the woods entirely.... At 8 weeks when I asked for a refill of pain medication, actually 8 weeks and 1 day, they said they had trouble with providing any pain medication after 8 weeks, and at 12 weeks when...the surgeon called me, I was surprised that he did, but a Saturday night he called me, and he said he had never had a patient who 12 weeks out was still in pain.... They never offered anything in terms of solution.... It felt to me like my internal organs were out of their normal position.... I asked if I could receive water therapy and [the surgeon] approved that. I asked if he would approve myofascial release work which I had learned about and I thought could help with what I was told was the scars were forming and the nerve tissue was probably entangled in the scars and myofascial release might work, and he denied that, he said no he wouldn't approve that. [He was] quite dismissive; as if I was saying, you know there's a witch doctor down the street. (Gregory)
These experiences are similar to those of some donors who continue to be challenged as a result of surgical complications that may require accommodation for an extended period of time. Thus, it is important that health care providers equip themselves to manage donor experiences that encompass a range of outcomes, including those of prolonged and unexpected sacrifice.
Although the process includes tests to assess mental and physical fitness and risks for donation, there are negative outcomes. As with Nancy and Gregory, medical complications that yield physical sacrifices are most often treated as exceptions to the process and may result in encounters with people who are not equipped to manage them within the context of the donation experience. Beyond physical complications, individuals may experience additional psychic sacrifices after donation. Consider Lizbeth, who donated to her brother with whom she had a standoffish relationship. Throughout the process, she describes feeling angst and frustration that she would have to donate to keep peace with her parents and brother. As a reluctant donor, she describes her experience:
Donors, even donors who wanted to do this, feel like after, that "I was just a kidney walking in there with arms and legs attached." You'll find a lot of donors feel neglected and abandoned....I called the doctor and now they don't want to talk to me. Now that I gave up the organ, now I'm not important to them. [It's] kind of like a girl who goes out with a guy and he said, "I love you! I love you!" and she sleeps with him. Then afterwards, he doesn't call her. Like that feeling of, "I gave something that was precious to me and now you don't even appreciate it." (Lizbeth)
Even years after the transplant and with great health, Lizbeth harbors resentment that neither the process nor the people within it did much to care for her emotionally and physically. While transplant organizations are in need of donors' organs, it is critical those donors are fully cared for in a manner that does not leave them feeling abandoned or exploited. It is thus imperative that organizations develop a supportive process staffed with compassionate people to mitigate sacrifices by individuals with less than ideal emotional or physical outcomes, a process that may well also enhance the product by heightening appeal to potential donors.
Nonprofits typically focus on messaging that promotes their product, which in the case of organ donation organizations translates as engendering a desire among potential donors to sacrifice an organ for a person in need. The experiences of donors participating in the present study reflect the kinds of sacrifices that are common within the organ donor community and emphasize that such sacrifices need to be addressed by organ donor organizations. By pursuing mitigation strategies in the form of various marketing-mix elements, organizations can convey a cohesive value proposition in their quest to procure donors, one that speaks directly to the sacrifices that often accompany the donation of an organ.
This study of living organ donation contributes to the literature by describing how elements of the marketing mix may be employed to attenuate donor experiences of sacrifice. Prior research has focused on how promotional messages may be employed to make individuals aware of donation opportunities and to overcome reluctance on the part of potential donors. While the aims of these promotions are crucial, we suggest how the marketing mix can be employed to mitigate concerns about the sacrifices often experienced by individuals as they advance through the donation process feeling valued as integral participants. As part of that strategy, we identify roles for the marketing mix—product, price, place, process, people, and promotion—that extend consideration beyond that of promotion. Thus, this research contributes an understanding of how organizations can more intentionally and systemically overcome potential donors' concerns and thereby increase the population of donors.
Nonprofits contribute significant value to society together with support from the individuals who contribute to them. Securing donations is a primary challenge and focus for the delivery of these organizations' missions ([ 9]; [67]). These findings are of particular interest to managers of nonprofit organizations who rely on individuals to offer contributions born of sacrifice that enable those organizations to deliver on their missions. Although these findings emerged from a particular type of donation, they are relevant to organizations that depend on contributions born of sacrifice, such as those seeking families to host foreign exchange students, those striving to facilitate the adoption of children who are difficult to place, those providing hospice support to individuals and their families during end-of-life transitions, or those offering compassionate care to individuals in crisis (e.g., sexual assault, domestic abuse, suicidal tendencies). These findings provide insight into how organizations can secure contributions, a necessary component of supply, to meet demand.
Prior research primarily has focused on how nonprofit organizations may employ promotional messaging to inspire contributions from individuals. We agree that promotion is certainly necessary, yet the present findings provide evidence suggesting that managers may be better served in meeting their missions by considering how to effectively employ the entirety of the marketing mix to attract individuals for available donation opportunities. We suggest that managers consider the composite of sacrifices required from individuals as they proceed through each phase of donation, and that managers employ the marketing mix to proactively and compassionately address the various types of sacrifice that emerge.
We identify actions for managers to employ the marketing mix—product, place, price, promotion, people, and process—in addressing each of the three types of sacrifice identified in the donation process (see Table 3). In addition to those specific actions identified, there are some general considerations for organizations. Product is reflected most clearly in a nonprofit organization's mission statement and manifests in the offering to which the donation supports. Place focuses on how disparate entities are integrated to support an individual's escalation of commitment from interested to committed as well as the delivery of the offering. Price is the component that conveys the costs incurred by donors to provide the contributions. Promotion is most often found in messages educating and persuading potential donors by conveying their importance to delivery of the offering. An organization's people are an important factor in delivering the entirety of the process and serve as a guide for donors throughout the process.
Graph
Table 3. Marketing and Organizational Considerations and Actions to Alleviate Sacrifices and Attract Organ Donors.
| Marketing-Mix Element | Type of Sacrifice |
|---|
| Psychic | Pecuniary | Physical |
|---|
| A: Marketing Considerations and Messaging to Alleviate Sacrifices and Attract Organ Donors |
| ProductOffering created from donation | Delineate donor attributes (e.g., blood type, health metrics) required for the transplantation offering. | Identify potential costs associated with securing supply and ensure expedient reimbursement to donors. | Specify donor consequences of donation beyond transplant outcome and include adequate follow-up to assess progress toward intended outcomes. |
| PromotionEducation about offering and persuasion to donate | Share impact of donation for recipients and community. | Communicate that participating as a donor is cash neutral, and proactively include reimbursement process. | Employ donor testimonials on the range of bodily impacts throughout the process and describe relevant support. |
| PlaceEnvironment for product sourcing, creation, and delivery | Provide a virtual tour of the process and the locations for each phase. | Facilitate access to direct billing for testing or immediate reimbursement for out of pocket expenses. | Assess organizational readiness prior to donor arrival to ensure designated space and adequate environment preparation. |
| PriceIncurred costs to participate | Explain how transplant costs are covered by recipient insurance; explain how donor contribution enables the process. | Eliminate costs incurred for donation proactively; ancillary costs related to donation should be promptly reimbursed. | Provide materials to ensure donor comfort throughout donation and recovery free of charge. |
| ProcessSteps to source inputs for, create and deliver offering | Deliver training for donor communication to ensure the steps proceed in a respectful and compassionate manner before, during, and after the transplant. | Identify steps most likely to create costs (e.g., lab tests, transport to hospital, hotel stays while testing) and offer support (e.g., direct bill lab orders; prepaid hotel or transport) to donors. | Recognize the most likely physical and behavioral post-surgical challenges for donors; provide support to prepare and follow up with donors sufficiently. |
| PeopleIndividuals tasked with steps within the process | Provide necessary information and decision authority to process managers as they support donors within the process. | Ensure that participants who are facilitating the process have the capability to approve costs and facilitate reimbursements. | Equip participants to support donor physical and behavioral challenges with actionable, compassionate plans. |
| B: Organizational Considerations and Actions to Alleviate Experiences of Sacrifice |
| ProductOffering created from donation | Delineate impact of donation to organization mission and society. | Reduce cash outlays and reimburse quickly. | Connect challenge to impact of donation. |
| PromotionEducation about offering and persuasion to donate | Communicate individual and cumulative benefits of donation. | Identify possible out of pocket expenses and note how they will be compensated. | Identify physical and behavioral requirements to participate and engage former donors to communicate "do-ability." |
| PlaceEnvironment for product sourcing, creation, and delivery | Create virtual tours and provide maps to facilitate navigation. | Identify and mitigate potential costs incurred by donors. | Provide comfortable workspace for donor contribution. |
| PriceIncurred costs to participate | Explain how costs related to service creation and delivery are covered; connect donor contribution to delivery of client benefits. | Eliminate costs to participate as a donor; document the process for what may be reimbursed and how. | Provide all materials necessary to support donation. |
| ProcessSteps to source inputs for, create and deliver offering | Provide training to successfully support donors with respect and compassion. | Review steps to identify where costs may emerge and proactively manage them. | Recognize steps that may provide donors with physical difficulties and simplify. |
| PeopleIndividuals tasked with steps within the process | Ensure that participants have adequate compassion in their roles supporting donors to meet the organizational mission. | Provide decision-making authority to participants who support donors incurring costs and reimbursement processes. | Equip participants with tools and resources to provide donors with necessary support to donate successfully. |
The process component reflects the steps required for individuals to transform from potential to actual donors, and it is the manifestation of the donation. The process we define is composed of three phases. In the deliberation phase of the process, individuals considering the opportunity are more involved in moving the process forward with some input from the organization. Within the decision phase, there is a balance of influence between individuals and organizations. As individuals move through to the donation phase, the balance of influence shifts toward the organization. Thus, an awareness of the process and perceptions of the organization to which individuals are contributing is also important. To an extent, donors are invited "backstage" ([28]) as they contribute to the creation of offerings for others. As such, it is imperative that organizations understand what they are asking of donors and how donors may experience sacrifice. Furthermore, it is important for donors to experience a degree of success, particularly when they are not able to readily observe the outcomes of their donations. Therefore, it is important that the processes to which donors contribute provide them with satisfaction that may be in some ways commensurate with the sacrifices they make to participate.
Importantly, process and people influence each phase of the donation experience and should be audited regularly to ensure that the interfaces between them and each phase, as well as the other marketing-mix components, are integrated. Furthermore, it may be helpful for managers to examine the milestones within a donation experience by assessing the extent to which those milestones are critical transition points for an individual to continue with the process of becoming a donor. Prior research has suggested that recognition may not be impactful to those who already contribute to nonprofit organizations ([66]). However, it may be that when the process to become a donor is more involved, it may be useful for organizations to provide motivation that inspires individuals to continue through the process.
The integration of each of the six marketing-mix elements is more likely to result in an environment in which individuals feel their donations are valued and respected. Each marketing-mix element should be aligned to engender the desired response to the organization: that of converting an individual into a volunteer. A great deal of marketing research focuses on the types of messages or individual characteristics that are more likely to yield larger contributions for nonprofits. In the present research, we instead focus on how the marketing mix can be engaged to prepare individuals to engage in a donation opportunity. We find that marketing-mix elements mitigate sacrifice, which serves to engage individuals in the donation task and thereby increases the likelihood that they will continue. For organizations where donation may continue, the enactment of such sacrifices is likely to engender loyalty and continuity.
The implications of these findings are obviously important for organizations in need of tissue or organs to deliver on their mission. However, these findings are also relevant to organizations in need of donations generally. Consider the Center for the Homeless, a nonprofit serving those individuals without secure housing that is reliant on grants, fundraising, and donations. In particular, individuals donate clothing and food supplies, organize various life skills workshops for adults, and staff and equip a classroom for children. While there are various donors who contribute resources to support operations, the creation and maintenance of a Montessori classroom at this shelter is partially reliant on donor support. These donors contribute a significant amount of time, talent, and money annually to maintain a fully functioning classroom (e.g., books, computers, supplies) in addition to supporting training and funding for a full-time Montessori teacher.
The Center for the Homeless generates much promotion to increase awareness that there are homeless children in need of support, yet these findings suggest that it may be more effective for the center to leverage the composite of the marketing mix to attract donations to the Montessori classroom. Promotions may be helpful to clearly articulate the intention of providing quality education for homeless children at the center in such a manner that manages the psychic sacrifice individuals may experience as they contemplate the opportunity. However, more is needed to explain the role of this particular product, as it is nontraditional in the realm of a homeless shelter as well as a school. The product is education that serves as a bridge, aiding students in catching up until they are once again enrolled in a school. Thus, the product may involve specialized processes and require additional people beyond the teacher to provide adequate education. The place—that is, the Montessori classroom within the center, is organized to aid children to be treated like students who are able to learn. Thus, place includes features of a traditional classroom (e.g., textbooks, reading pods) while accommodating the necessarily transient and multiple-grade-level nature of its students. As with other types of donation opportunities, there may be incurred costs for donors (e.g., background check to work with minors, art project supplies). The marketing mix could be employed by the Montessori classroom to attract not only donations but also volunteers. More specifically, the center could more fully employ the marketing mix to attenuate psychic sacrifice (as individuals recognize they have limited capacity to assist homeless children), pecuniary sacrifices to participate, or the physical sacrifice that stems from being in an environment (e.g., smells, security, equipment) different from what they typically imagine encountering.
Organizations are not static, as evident in alterations to their operations, offerings, and positioning. As for-profit organizations alter their offerings, they often try to retain existing consumers and attract new ones, recognizing that each will invest differing psychic energies to consume the offering ([45]). Similarly, nonprofit organizations could adjust their offerings to remain relevant to those they serve, thereby maintaining or growing their client base. For example, Habit for Humanity could upgrade its offerings by adapting the marketing mix through product attributes (e.g., new houses, disaster recovery, retail outlet), distribution (e.g., local and global builds), market messaging (e.g., model home challenges, Women Build Week), processes (e.g., one-time vs. long-term), or people (e.g., retail staff, policy advocates, board members). As they do so, it is important that they assess how those changes affect the degree of sacrifice required for existing and potential donors and operationalize the marketing mix to address those sacrifices. These examples underscore the importance of understanding how the marketing mix can be employed to mitigate sacrifice that emerges in the donation process as well as to enhance the overall donation experience. The deft employment of the marketing mix to extend the tenure of donors may also accrue other benefits to organizations such as confidence in operational projections, service stability, or reduction in expenditures to delivery services.
The extension of donor engagement may be viewed as a form of loyalty. Similar to brand loyalty, which has a positive impact on a firm's bottom line ([ 5]), it is likely that donor loyalty evident in their continued engagement with an organization also has a positive impact on an organization's performance. Consider blood donation, a relatively noninvasive procedure to obtain human tissue. Blood is donated to organizations that bank it for clinical usage. Some individuals consistently donate every eight weeks, often at the same facility. When individuals continue to donate to an organization, it is likely that they experience less psychic (e.g., contemplation), physical (e.g., blood draw, testing), and pecuniary (e.g., transport, time) sacrifice compared with their consideration and choice of new donation opportunities (e.g., child advocacy). Such continuity may also reduce the number of donors who switch their support to another organization or, worse yet, depart the donor marketplace. When organizations successfully communicate the value that donors help deliver to the marketplace and stimulate desire for individuals to donate and minimize sacrifices through the marketing mix, individuals are likely to engage as donors.
The present study investigates living organ donors. While there are a growing number of living organ donors annually, the majority of transplants occur with organs offered by deceased donor families. Those families employ a different calculus when considering donation of their loved one's organs ([24]; [31]; [52]; [55]). Where the present research focuses on those who make a choice to donate, further research is warranted to assess how the marketing mix can be employed to mitigate sacrifices for deceased donor families.
We focus on individual donors and their sacrifices. These individuals are embedded in social networks where those relationships likely influence the donation experience. The gift-giving literature provides insights into how the nature of social networks may shape the process and experience ([ 8]; [15]; [54]). An examination of donors' social networks and their influences may provide additional insights. Recall Lizbeth and Wilma. Lizbeth found her social network to be lacking in compassion and support, whereas Wilma found hers to be filled with care and consideration. Each recounted distinct experiences of the support they sought in the marketplace, which may be related to what was provided by their social network. Thus, there is an opportunity to understand how and to what extent social networks influence the donation experience providing donor-reliant organizations opportunities to understand and adequately prepare for donor support.
The current research theorizes sacrifice at the individual consumer level. Scholars in anthropology and theology theorize sacrifice at a community level in relation to social cohesion. Similar to the indigenous Chukchee people, who worked together to attain benefits for the collective ([41]), it may be that members of contemporary societies can be inspired to work together in support of beneficial societal outcomes. For example, several movements requiring individuals to employ sacrifices to attain societal benefits have gained momentum in recent years (e.g., #BlackLivesMatter, #MeToo, Get Out The Vote, #NeverAgain). Participation in those movements most likely involves psychic (e.g., contemplation of consequences of action and inaction), pecuniary (e.g., donations), and physical (e.g., protests) sacrifices. Therefore, it may be that sacrifices related to consumer movements may be viewed as enhancing participant commitment. Thus, it is important to explore how entities pursuing societal benefits (e.g., movements, nonprofits, civic organizations) can employ the marketing mix to attract and retain participants. It may be that for some movements, experiences of sacrifice are part of the personal benefit in addition to the societal benefits donors seek, and thus, organizations will have to understand to what extent and under what conditions they are to attenuate experiences of sacrifice.
Scholars have also suggested that organ donation may be viewed as a form of gift-giving to society ([11]; [56]; [60]). Such gift-giving contributes much to the public good. Even as the marketing mix may be employed to generate even greater degrees of gift-giving, it must also be recognized that the same tools also may result in less-than-ideal outcomes. For example, the Susan G. Komen Foundation raises funds for breast cancer research and makes grants for breast cancer screening to organizations. The foundation effectively employed the marketing mix to inspire donations as individuals paid to participate in three-day races and secured additional contributions from others. In recent years, the organization has reduced grant-making capacity due to the decreased number of individuals willing to make donations. While monies were employed for the organization's mission, a significant amount was used for what donors perceived to be excessive non-mission-critical expenditures. Thus, it is imperative that scholars also consider factors that influence the relationship between marketing-mix elements, donor sacrifice, and perceived organizational effectiveness.
Consumer sacrifice allows donor-reliant organizations to attain their missions. We expand prior theories of sacrifice with an explanation of its three types and how they may be managed through the marketing mix. This explanation provides opportunities for managers to better understand how to more fully leverage the marketing mix to inspire individuals to partner with them by reducing experiences of sacrifice. Thus, those seeking more effective ways to procure donations for their organizations will benefit from understanding the nature of the relationship between sacrifice and the employment of the marketing mix to position their offerings.
Footnotes 1 Associate EditorCraig Thompson
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 1See https://www.uniformlaws.org/committees/community-home?CommunityKey=015e18ad-4806-4dff-b011-8e1ebc0d1d0f (accessed March 5, 2020).
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Record: 85- Highlighting Effort Versus Talent in Service Employee Performance: Customer Attributions and Responses. By: Leung, Fine F.; Kim, Sara; Tse, Caleb H. Journal of Marketing. May2020, Vol. 84 Issue 3, p106-121. 16p. 3 Diagrams. DOI: 10.1177/0022242920902722.
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Highlighting Effort Versus Talent in Service Employee Performance: Customer Attributions and Responses
Firms often attribute their service employees' competent performance to either dedicated effort or natural talent. However, it is unclear how such practices affect customer evaluations of service employees and customer outcomes. Moreover, prior work has primarily examined attributions of one's own performance, providing little insight on the impact of attributions of others' performance. Drawing on research regarding the warmth–competence framework and performance attributions, the current research proposes and finds that consumers expect a more communal-oriented and less exchange-oriented relationship when a service employee's competent performance is attributed to dedicated effort rather than natural talent, as effort (vs. talent) attribution leads consumers to perceive the employee as warmer. The authors further propose customer helping behaviors as downstream consequences of relationship expectations, finding that effort (vs. talent) attribution is more likely to induce customers' word-of-mouth and idea provision behaviors. The findings enrich existing literature by identifying performance attributions as a managerially meaningful antecedent of relationship expectations and offer practical guidance on how marketers can influence consumers' relationship expectations and helping behaviors.
Keywords: attribution theory; communal and exchange relationships; competence and warmth; customer helping behaviors; service employee performance
When firms communicate information about their service employees' competent performance, they often attribute it to either dedicated effort or natural talent. For example, on their websites, financial services firms such as Citigroup state that "Citi works tirelessly.... We strive to create the best outcomes," and Partners Group Holding asserts that "we work hard and deliver outstanding results." In contrast, Manulife Financial highlights that the "talent of our employees is what makes Manulife Financial a successful organization," and BlackRock states, "Our best solutions come from the contributions of a group of talented and smart people" (for more examples, see Web Appendix W1). We systematically examined the company websites of the top service firms on the 2018 Forbes Global 2000 list and found that many top financial and health care services firms mention these two types of performance attribution on their websites (see Figure 1). Despite the real-world prevalence of references to these two types of performance attribution, it is unclear how firms' promotions of performance attributions affect customer evaluations of service employees and customer outcomes.
Academic research suggests two types of performance attribution: one to dedicated effort and the other to natural talent ([11]; [16]; [56]). Psychology and marketing literature has mainly examined a fixed or malleable view of people's own performance and its impact on how they judge themselves (e.g., judgments of one's own intelligence or personality; [16]; [17]), their own performance (e.g., academic performance; [31]; [56]), or brands/products ([40]; [42]), providing little insight on relationship judgments and behaviors toward others. However, in service relationships, beliefs about others' performance can influence relationships with those others (e.g., how a customer views a service employee's performance can influence the customer's relationship with the employee; [ 9]). We recognize this characteristic in service relationships, as well as a lack of research on the attributions of others' performance and their impact on relational aspects; thus, in this article we examine how attributions of service employees' performance influence consumers' relationship expectations with and behaviors toward service employees.
We propose that attributions of service employees' competent performance can change the extent to which customers expect a more communal-oriented (or less exchange-oriented) relationship. Prior work has conceptualized consumers' relationship expectations with service employees along the communal–exchange continuum ([ 3]; [13]). In a communal relationship, consumers expect a service employee to take genuine care of them and understand their needs as a friend or family member would. In an exchange relationship, consumers consider a service employee strictly as a business partner and expect the employee to provide services that will be worth their money. We propose that consumers will expect a more communal and less exchange-oriented relationship when a service employee's competent performance is attributed to dedicated effort rather than natural talent, because effort (vs. talent) attribution leads consumers to perceive the employee as warmer. We further examine customer helping behaviors toward firms (i.e., voluntary and discretionary behaviors that aid firms beyond those required in the purchase of products and services; [ 8]; [10]) as downstream behavioral consequences of relationship expectations. In particular, we propose that highlighting service employees' effort (vs. talent) can increase customer helping behaviors such as word of mouth (WOM) and idea provision.
Our investigation of service employees' performance attributions makes several theoretical and managerial contributions. First, the current research broadens our understanding of social judgments in commercial relationships. Although a considerable body of research has investigated the relation between judgments of competence and warmth ([32]; [58]), this prior work has mainly examined how a certain level of competence is related to warmth perception. The current research examines the attributions of competence as a new dimension that influences warmth perception, holding the objective level of competence constant. Second, the marketing literature has focused on the downstream consequences of a communal versus exchange relationship with consumers ([ 3]; [ 5]; [54]), but very few studies have proposed firm tactics that could induce a certain type of relationship expectation (communal or exchange). For instance, [41] showed that a company's communal obligations (e.g., providing medical care on the basis of need instead of ability to pay) can influence consumers' relationships with the company. The current research enriches the existing literature by examining performance attributions as an antecedent of relationship expectations. Third, as we have mentioned, whereas most prior work in the marketing literature has focused on attributions of one's own performance and their effect on product evaluation and choice ([40]; [42]), our work examines the attributions of others' performance.
Our findings also provide important marketing insights. Figure 1 indicates that firms often attribute their employees' performance to effort or talent. Our research proposes that firms can strategically implement such performance attributions to evoke a type of relationship expectation that they want to promote (e.g., highlighting employees' effort when a firm wants to promote a communal-oriented relationship with customers). Thus, performance attribution is a managerially meaningful antecedent of relationship expectations, because it can be embedded in communication messages without requiring customers to have direct interactions with service employees. Our research also provides implications on customer attention to communication messages. We suggest that customers' relationship expectations can be manifested in their attention to service employee information. If firms want to attract customers' attention to person-related information (e.g., personal background information about service employees), they can highlight the effort of their employees, whereas if they want customers to focus on job-related information (e.g., what service employees do), they can highlight the talent of their employees. Finally, our research suggests that promoting different types of performance attribution can shape customer behavior. Specifically, by highlighting employees' effort (vs. talent), firms can increase customer helping behaviors such as sharing the firms' information on social networks or providing new product ideas.
Graph: Figure 1. Prevalence of service employee performance attributions among top service firms.Notes: The figures represent the percentages of top service firms on the 2018 Forbes Global 2000 list that explicitly communicate either dedicated effort or natural talent (or both) or that do not provide performance attribution information on the company websites. Two independent coders were instructed to code performance attributions on the web pages in which firms deliver communication messages toward their customers. Agreement between the coders was high (83%), and disagreements were resolved by discussion.
Research in social psychology as well as marketing has supported the notion that when people form impressions about others, they tend to make judgments along two fundamental dimensions: competence (e.g., capability, skillfulness, efficacy) and warmth (e.g., friendliness, helpfulness, trustworthiness) ([ 2]; [19]). For example, judgments of competence and warmth shape consumers' relationships with commercial partners, such as nonprofit and for-profit firms ([ 1]), salespeople ([48]), and brands ([33]). This line of research has investigated the relations between judgments of competence and warmth, mainly by examining how a certain level of competence is related to warmth perception. Some studies have reported that a higher level of competence results in greater warmth perception ([46]; [51]), whereas others have shown that a lower level of competence leads to greater warmth perception ([32]; [59]).
Extending the existing literature, the current research examines the attributions of competence as a new dimension of competence influencing warmth perception, holding the objective level of competence constant. Research in social psychology has corroborated dedicated effort and natural talent as two internal sources of people's performance ([11]; [53]). In the case of dedicated effort, competent performance is believed to be the result of commitment, perseverance, and hard work. In the case of natural talent, competent performance is believed to be the result of innate aptitude. This typology is also in line with implicit theories suggesting that people's performance can be attributed to malleable traits such as effort or to fixed traits such as natural talent ([16]; [42]). In addition, prior work on attribution theory has made it clear that competence can be attained through either dedicated effort or natural talent. [57] suggests that "when associated with aptitude [natural talent], the concept of competence is conceived as mainly uncontrollable, whereas when associated with effort expended, the attainment of competence is conceived as controllable" (p. 79). Thus, conceptually, effort and talent are two different attributions of competence. Bridging these two streams of research on the warmth–competence framework and performance attributions, we examine how information on different attributions of competent performance changes warmth judgments and, in turn, relationship expectations.
We posit that attributing a service employee's competent performance primarily to dedicated effort (vs. natural talent) makes consumers perceive the employee as warmer. Extant research indirectly supports this proposition. Prior work has found that when a person's performance is attributed to effort, that person is more likely to be seen as "one of us" ([30]), because most people generally believe that they also need to exert high effort to succeed ([35]). Indeed, when students learn about successful scientists' hard work in their scientific discoveries, they are more likely to see the scientists as ordinary people ([38]). Prior work also has indicated that those who are socially close are perceived to be warmer than those who are socially distant ([37]). For instance, compared with out-group members, in-group members are rated as having a greater capacity to experience emotions and being higher in warmth ([27]). Therefore, we propose that compared with talent attribution, effort attribution will lead the customer to perceive the service employee as warmer.
In contrast, because most people tend to believe that only a few individuals possess natural talent ([18]), talent attribution can increase perceived social distance. Geniuses and exceptionally talented individuals are typically perceived to "have" something that most people do not have and, thus, are seen as different ([22]). In addition, [38] note that viewing scientists as individuals with a special aptitude for science discourages students from feeling connected with the scientists. When one feels disconnected from another individual, one is less likely to attribute the ability to feel to that person ([37]). For instance, compared with in-group members, out-group members are rated as lacking emotional capacity and as being more self-centered ([27]). Furthermore, people tend to see naturally talented others as disconnected from human experiences and emotionally inert ([35]), and gifted intellectuals are considered to be more antisocial than others ([43]). Teachers often view gifted and talented students as emotionless, antisocial, and insensitive to the feelings of others ([ 7]; [24]). Therefore, compared with effort attribution, talent attribution that can increase perceived social distance between a customer and an employee will make customers perceive a service employee as less warm.
Our research further posits that the perceived warmth of service employees is the basis for consumers' relationship expectations with those employees. Consumers in a communal relationship expect a service employee to take care of them and consider their needs ([ 3]; [13]). In contrast, in an exchange relationship, parties understand that the benefits received should correspond to the benefits given, focusing on self-interest ([13]; [36]). Although commercial relationships always involve elements of exchange relationships, such as monetary exchange, consumers' relationship expectations can vary on the communal–exchange continuum, because consumers can expect different degrees of communality in commercial relationships depending on the situation ([ 4]; [ 5]).
When consumers perceive a service employee to be warm, they will likely expect that the employee will be cooperative, have other-profitable (rather than self-profitable) intentions, and show genuine concern for consumers' needs ([ 5]; [33]). Such expectations are consistent with the norms of communal relationships. In contrast, people tend to expect a cold person to show less empathy for others and care more about him- or herself than about others ([33]). In addition, when people see others as being low in warmth and lacking emotional responsiveness, they can more readily perceive those others as instruments for their own goals ([28]). For example, viewing others as emotionless helps managers make decisions in difficult situations (e.g., layoff decisions) by seeing those individuals as objects or instruments to achieve their goals ([29]). Perceiving others' self-centered intentions and focusing on the instrumentality of others are behaviors in line with the characteristics of exchange relationships. Thus, when customers perceive an employee as warmer (less warm), they will expect a more (less) communal-oriented relationship with him or her along the communal–exchange continuum. Thus,
- H1: Consumers expect a more communal-oriented (i.e., less exchange-oriented) relationship when a service employee's competent performance is attributed to dedicated effort rather than to natural talent.
- H2: The effect of service employees' performance attributions on consumers' relationship expectations is mediated by warmth judgments regarding the service employees.
Figure 2 depicts our conceptual framework and the flow of the studies. We first present five studies providing empirical evidence for the link between performance attributions and relationship expectations. Study 1a shows that when a service employee's competent performance is attributed to dedicated effort rather than to natural talent, consumers expect a more communal- and less exchange-oriented relationship with the employee. In Study 1b, we examine simultaneous attribution to both effort and talent. We then test whether perceived warmth underlies the effect of performance attributions on relationship expectations by directly measuring the variable (Study 2a) and by manipulating the perceived warmth of the service employee (Study 2b). Study 3 uses eye-tracking technology to show that effort attribution leads consumers to pay more attention to person- than to job-related information about the service employee. We then develop our hypothesis for customer helping behaviors as downstream consequences of relationship expectations and present two studies, one using a real firm context (Study 4) and the other in a field experiment (Study 5), to support the hypothesis.
Graph: Figure 2. A conceptual framework of the current research.
In Study 1a, we attribute a service employee's competent performance either to dedicated effort or to natural talent and test whether effort attribution leads participants to expect a more communal-oriented relationship with the employee. We also examine a control condition in which no information about performance attribution is provided.
Two hundred seventy participants (106 women; mean age = 37.59 years) were recruited online from Amazon Mechanical Turk in exchange for monetary compensation.
Participants were told that the medical society in a U.S. city periodically featured the city's top physicians and were asked to provide feedback on an article. All participants read identical information on performance, such that a physician had received a peer review rating in the top 10% of general physicians in the city. Then, in the effort attribution condition, participants read statements attributing the physician's performance to effort (e.g., "[He/she] puts a lot of effort into the work"), whereas in the talent attribution condition, they read statements attributing the physician's performance to talent (e.g., "[He/she] is naturally skillful at the work"; see Web Appendix W2). The control condition article only stated the physician's performance without any information on performance attribution. As a manipulation check, participants indicated the extent to which they thought the physician had achieved his or her level of performance because of effort or talent with three items (e.g., "Put a lot of effort into his or her work/Was naturally talented at his or her work"; α =.96).
Next, participants rated the degree to which they would expect their relationship with the physician to be communal- or exchange-oriented using eight items adapted from [ 3]. Five items tapped into communal relationship expectation (e.g., "a person with whom I would want to interact outside of business") and three tapped into exchange relationship expectation (e.g., "a person with whom I would interact only for business purposes"; 1 = "not at all," and 7 = "very much"). In all studies, we followed prior work ([ 3]; [48]) and combined the reverse-coded items on exchange relationship expectation with the items on communal relationship expectation (α =.89). Web Appendix W3 lists measurement items for all studies.
Participants responded to questions related to the design ("I like the design of the article"), credibility ("I think the content is credible"), and understandability ("I think the content is easy to understand") of the article (1 = "strongly disagree," and 7 = "strongly agree"), as well as their knowledge of health care services ("How much do you know about health care services in general?"; 1 = "not at all," and 7 = "very much"), attention to the study (1 = "paid little attention," and 7 = "paid a lot of attention"), and mood (1 = "feel bad," and 7 = "feel good") as control variables. The control variables did not differ across the conditions (ps >.10).
A one-way analysis of variance (ANOVA) revealed a significant effect of performance attributions among the conditions (F( 2, 267) = 42.85, p <.001, =.24). Participants in the effort attribution condition (M = 2.35, SD = 1.71) were more likely to attribute the physician's performance to dedicated effort than those in the talent attribution condition (M = 4.83, SD = 2.05; t(267) = −8.70, p <.001, d = −1.31). Moreover, performance attribution in the control condition (M = 2.94, SD = 1.89) scored in the middle and was significantly different from that in the effort attribution condition (M = 2.35, SD = 1.71; t(267) = 2.03, p <.05, d =.33) and that in the talent attribution condition (M = 4.83, SD = 2.05; t(267) = −6.85, p <.001, d = −.96). Therefore, neither effort nor talent attribution seems to be a default attribution in the absence of attribution information.
A one-way ANOVA revealed that performance attributions had a significant effect on relationship expectations (F( 2, 267) = 8.50, p <.001, =.06). Planned contrasts revealed that participants expected their relationship with the physician to be more communal when the physician's performance was attributed to effort (M = 3.62, SD = 1.15) than when it was attributed to talent (M = 2.88, SD = 1.18; t(267) = 4.12, p <.001, d =.64), in support of H1. In addition, participants' relationship expectations in the control condition (M = 3.24, SD = 1.25) were significantly lower than those in the effort attribution condition (M = 3.62, SD = 1.15; t(267) = −2.08, p <.05, d = −.32) and higher than those in the talent attribution condition (M = 2.88, SD = 1.18; t(267) = 2.07, p <.05, d =.30).
Study 1a offers preliminary evidence for our primary proposition that individuals expect a more communal-oriented relationship with a service employee whose performance is attributed to effort rather than to talent. The findings also show that either effort or talent attribution changes relationship expectations, compared with when there is no attribution, which indicates that neither of the performance attributions may be the default attribution in consumers' minds. Rather, firms can strategically create communication messages to highlight effort or talent, which can move customers' relationship expectations with their service employees along the communal–exchange continuum. Some might argue that there may be other more direct ways to develop communal relationships, such as by treating customers well and satisfying them. However, these tactics require actual interactions with customers. The current research suggests that communication messages that do not involve interactions with customers can still create a certain type of relationship expectation. In the next study, we additionally examine a situation in which the performance is simultaneously attributed to both effort and talent.
Although researchers have agreed that effort and talent attributions are on opposite ends of a continuum ([31]), and our research focuses on the relative emphasis on effort or talent, firms might communicate both effort and talent, as Figure 1 illustrates. Thus, in Study 1b, we examine simultaneous attribution to both effort and talent. Prior work on attribution theory has shown that people tend to perceive that naturally talented people's achievements come without effort ([52]; [53]). Therefore, providing information about a service employee's natural talent without any information about his or her effort can increase social distance ([38]) and lower warmth perception. However, prior work has also shown that learning that even talented people (e.g., great scientists like Einstein) had to exert high effort to succeed can increase people's sense of relatedness with those talented people ([30]; [38]). Thus, we argue that, compared with talent attribution only, simultaneous attribution to dedicated effort and natural talent can help consumers understand that even a talented employee is someone like them—that is, someone who needs to put in a lot of effort to achieve good performance—which will enhance warmth judgments of and a communal relationship expectation toward the employee.
One hundred twenty-five undergraduate students (81 women; mean age = 20.38 years) from a large university in Hong Kong participated in this laboratory experiment in exchange for monetary compensation. Effort and talent attribution was similar to that in Study 1a. Participants read an article about an accountant whose competent performance (e.g., "has ranked Jesse in the top 15% among CPAs in Hong Kong") was attributed to either effort (e.g., "Jesse puts a lot of effort into the work") or talent (e.g., "Jesse is naturally skillful at the work"). In the effort-and-talent attribution condition, participants read statements attributing the accountant's performance to both effort and talent (e.g., "Jesse puts a lot of effort and is naturally skillful at the work"; see Web Appendix W4). After reading the article, participants indicated their relationship expectations with the accountant as in Study 1a.
A one-way ANOVA revealed that performance attributions had a significant effect on relationship expectations (F( 2, 122) = 3.08, p <.05, =.05). In a replication of the previous findings, effort attribution (M = 3.47, SD = 1.00) induced a more communal relationship expectation than talent attribution (M = 2.99, SD =.96; t(122) = 2.22, p <.05, d =.49), further supporting H1. In addition, the effort-and-talent attribution (M = 3.43, SD =.92) induced a more communal relationship expectation than the talent attribution (M = 2.99, SD =.96; t(122) = 2.11, p <.05, d =.47), but it was not different from the effort attribution (M = 3.47, SD = 1.00; t(122) = −.18, p =.86, d = −.04).
Study 1b reveals that attributing a service employee's performance to both dedicated effort and natural talent yields an effect similar to that of effort attribution only. As long as effort is made salient, consumers perceive a more communal (or less exchange-oriented) relationship with the service employee compared with a situation in which effort information is not salient. Note, however, that our findings do not imply that highlighting both effort and talent is always preferable to highlighting only one or the other. For example, compared with talent attribution only, attribution to both effort and talent can create expectations of a more communal relationship, and such expectations may not align with the service propositions of a firm that tends to engage in exchange-oriented relationships. In the next study, we test the mechanism for the effect of performance attributions on relationship expectations by directly measuring the perceived warmth of a service employee.
In Study 2a, we investigate the mechanism underlying the effect of performance attributions on consumers' relationship expectations. We predict that attributing a service employee's performance to effort (vs. talent) leads participants to perceive the employee as warmer and therefore to expect a more communal-oriented relationship with that employee.
Two hundred thirty-five undergraduate students (150 women; mean age = 19.98 years) from a large university in Hong Kong participated in this laboratory experiment in exchange for monetary compensation. Participants were told that a bank on campus was promoting an investment program for university students. They then read an advertisement featuring an investment manager whose competent performance (e.g., "winner of best employee of the year award and ranked in the top 1% in performance") was attributed to either effort (e.g., "I work very hard to pick my investments") or talent (e.g., "I am talented at picking my investments"; see Web Appendix W5). In this study, we used "top 1%" to reduce the range of the performance level in participants' mind to control competence perceptions.
Participants indicated the extent to which they thought the investment manager had achieved his or her level of performance because of effort or talent, using a semantic differential scale with three items (e.g., "Put a lot of effort into his or her work/Was naturally talented at his or her work"; α =.97).
Participants then indicated their relationship expectations with the investment manager as in Studies 1a and 1b (α =.89). We also measured the extent to which participants perceived the investment manager to be warm with six items (e.g., "friendly," "warm"; 1 = "not at all," and 7 = "very much"; [25]; α =.89).
To ensure that the performance attribution manipulation did not induce different competence perceptions, we measured perceived competence of the investment manager with six items (e.g., "competent," "capable"; 1 = "not at all," and 7 = "very much"; [25]; α =.89). We also measured perceived attractiveness of the investment manager to check whether the performance attribution manipulation affects attractiveness perceptions. Neither of the variables differed across conditions (all ps >.20).
Participants in the effort attribution condition (M = 2.10, SD = 1.22) were more likely to attribute the investment manager's performance to effort than those in the talent attribution condition (M = 5.53, SD = 1.53; t(233) = −18.99, p <.001, d = −2.48).
Again, in support of H1, participants expected a more communal relationship when the investment manager's performance was attributed to effort (M = 3.21, SD = 1.21) rather than to talent (M = 2.75, SD = 1.14; t(233) = 2.98, p <.01, d =.39).
Participants perceived the investment manager as warmer when his performance was attributed to effort (M = 4.47, SD = 1.05) rather than to talent (M = 3.87, SD = 1.17; t(233) = 4.15, p <.001, d =.54), in support of H2. To establish discriminant validity between perceived warmth and relationship expectations, we performed a confirmatory factor analysis. For each construct, the average variance extracted exceeded.50 (perceived warmth = .51, relationship expectations = .57). [20] test also revealed that both average variances extracted were higher than the shared variance of.15, confirming that they represent distinct constructs.
We tested perceived warmth as a possible mediator with a bootstrapping analysis using PROCESS Model 4 ([45]; see Figure 3). Results revealed that the indirect effect of performance attributions on relationship expectations through perceived warmth was significant (indirect effect =.20, SE =.07, 95% confidence interval = [.09,.36]).
Graph: Figure 3. Mediation analysis (Study 2a).
Study 2a shows that consumers expect a more communal relationship with a service employee when the employee's performance is attributed to effort rather than to talent, because they perceive such an employee to be warmer. This study also established discriminant validity between perceived warmth and relationship expectations. Warmth judgment and communal relationship expectation, though correlated, are conceptually distinct constructs. [25] conceptually and empirically separated warmth perceptions (perceptions of a trait) and inferred communal intent (perceptions of a motive behind a trait or action). Perceived warmth of a service employee is a perceived trait of that employee that is not specific to a given service context, whereas a communal relationship expectation involves the predicted norms in the relationship with a service employee in a specific service context. This study also shows that performance attributions do not necessarily change perceived competence of the service employee, which is in line with prior work suggesting that effort and talent are two different types of attribution of competence ([56], [57]). In the next study, we test our proposed mechanism by directly manipulating the warmth of the employee.
Study 2b uses a moderation-of-process strategy ([50]) to manipulate the warmth of a service employee to provide further evidence for warmth as a mediator for the effect of performance attributions on relationship expectations. If effort (vs. talent) attribution leads consumers to expect a more communal (or less exchange-oriented) relationship because the employee is perceived as warmer, information signaling that the employee is warm should attenuate the proposed effect. We employ a 2 (performance attribution: effort vs. talent) × 2 (warmth: yes vs. no) between-subjects design.
Three hundred seventy-one undergraduate students (233 women; mean age = 20.30 years) from a large university in Hong Kong participated in this laboratory experiment. Participants read website information about a physician whose competent performance (e.g., "Dr. Lee received a peer review rating in the top 5% among general practitioners in Hong Kong") was attributed to either his effort (e.g., "Dr. Lee spends a lot of time [and] works really hard to develop personalized health improvement programs") or talent (e.g., "Dr. Lee has a sharp instinct [and is] naturally skillful at developing personalized health improvement programs"; see Web Appendix W6).
To manipulate the warmth of the physician, we provided additional information that can increase warmth perceptions but is not directly related to the employee's behavior toward his or her customers. Warmth is particularly relevant to the prosocial domain, because people rely on warmth judgments to predict whether a person is well-intentioned toward other people ([19]). Thus, we manipulated the warmth of a service employee by informing participants that the employee donates a part of his earnings to various charity organizations. No such information was mentioned in the control condition.
To test the effectiveness of the warmth manipulation, we conducted an independent pretest (n = 170; 106 women; mean age = 20.88 years). After reading the website information (excluding information on performance attributions), participants indicated the extent to which they perceived the physician to be warm and competent, as in Study 2a. A t-test revealed that participants perceived the physician as warmer in the warmth condition (M = 5.11, SD =.77) than in the no-warmth condition (M = 4.44, SD = 1.13; t(168) = 4.45, p <.001, d =.69). However, perceived competence did not differ across the two conditions (p =.22).
We measured relationship expectations with the physician as in the previous studies. We also measured participants' expectations about the employee's service process quality with four items (e.g., "unfavorable/favorable," "bad/good"; α =.93) and service outcome quality with four items (e.g., "unfavorable/favorable," "bad/good"; α =.92). Performance attribution manipulation did not change these expectations (all ps >.30).
We ran a 2 (performance attributions: effort vs. talent) × 2 (warmth: yes vs. no) ANOVA on relationship expectations. The results revealed a significant main effect of performance attributions (F( 1, 367) = 8.07, p <.01, =.02), no significant effect of warmth (F( 1, 367) =.49, p =.48, =.001), and a significant interaction (F( 1, 367) = 4.07, p <.05, =.01). Planned contrasts revealed that our previous findings were replicated in the no-warmth condition; specifically, effort attribution (M = 4.03, SD = 1.15) induced more of a communal relationship expectation than did talent attribution (M = 3.47, SD = 1.05; t(367) = 3.41, p =.001, d =.51), in support of H1. In contrast, this effect was attenuated in the warmth condition (Meffort = 3.88, SD = 1.09 vs. Mtalent = 3.79, SD = 1.14; t(367) =.59, p =.56, d =.08), in support of H2.
In this study, we directly manipulated the mediating variable (i.e., warmth of a service employee). The results support our mechanism that effort (vs. talent) attribution leads consumers to perceive a service employee to be warmer by showing that information signaling that the employee is warm attenuates the effect of performance attributions on relationship expectations. This study also shows that performance attributions do not change participants' expectations about the employee's service process quality and service outcome quality, thus ruling these out as possible alternative explanations for our proposed effects. In the next study, we examine the effect of performance attributions on customer attention.
To enhance the validity of our findings, Study 3 provides further evidence for the effect of performance attributions by using an alternative, more objective measure of relationship expectations: consumers' attention to service employee information. We argue that consumers' relationship expectations can be manifested in their attention while reading advertisements. Prior work has shown that under an exchange relationship, individuals focus on their counterparts' instrumental function to ensure that the benefits they are to receive fulfill their own goals ([ 2]; [ 3]). Furthermore, [47] argue that when individuals focus on others' instrumental function, they tend to overlook the facts relating to the personal lives and experiences of those others. Therefore, we predict that if talent attribution leads to greater expectation of an exchange relationship, consumers will pay more attention to information pertaining to the service employee's instrumental function (e.g., what the service employee can do for them) than to personal information about the employee (e.g., personal background information). We use an eye-tracking technique to capture participants' attention toward service employee information, which allows us to measure a subconscious or preconscious reflection of relationship expectations ([44]).
One hundred forty-seven undergraduate students (110 women; mean age = 20.82 years) from a large university in Hong Kong participated in this laboratory experiment. We used an eye-tracking device, The Eye Tribe, powered by the software GazeLab (30 Hz), which collects raw eye movement data points every 33.3 milliseconds. This eye tracker was integrated into a 15.4-inch monitor at a resolution of 1,680 × 1,050 pixels. As participants viewed the stimuli shown on the screen, a discreet infrared camera located below the screen unobtrusively recorded participants' attention.
Participants were told that their university's medical society was editing a newsletter, and they were asked to read an article featuring an interview with a physician from the university's health clinic. On the first page of the article, we manipulated performance attributions as in Study 2b. When we defined the performance attribution information as an area of interest (i.e., a selected region of the stimulus of which eye-movement metrics are extracted), participants in the two conditions did not differ in terms of the attention they paid to the manipulation stimuli (Meffort = 4.58 seconds, SD = 3.70; Mtalent = 4.23 seconds, SD = 3.02; t(145) =.61, p =.54, d =.10). We excluded any participants who did not fix their attention on the performance attribution information because they were neither exposed to the effort nor the talent attribution manipulation.
To test the effectiveness of our manipulation, we conducted an independent pretest (n = 92; 67 women; mean age = 20.60 years). After reading an article about the physician, participants indicated the extent to which they thought the physician had achieved his level of performance because of effort or talent, using a semantic differential scale as in previous studies. Participants in the effort attribution condition (M = 2.41, SD = 1.37) were more likely to attribute the physician's performance to effort than those in the talent attribution condition (M = 4.60, SD = 1.53; t(90) = −7.26, p <.001, d = −1.51). Participants also indicated the extent to which they perceived the physician to be warm and competent, as in Study 2a. We also measured participants' expectations about the overall quality of the physician (1 = "very bad," and 7 = "very good"). A t-test revealed that participants perceived the physician as warmer when his performance was attributed to effort (M = 5.42, SD =.82) rather than to talent (M = 5.07, SD =.83; t(90) = 2.04, p <.05, d =.42). However, perceived competence and expected overall quality did not differ across the two conditions (ps >.30).
Participants were then presented with two columns of additional information about the physician (see Web Appendix W7). One column presented person-related information about the physician, such as the physician's background (e.g., "Dr. Lam is 32 years old and was born and raised in Hong Kong"). The other presented job-related information, such as information about what the physician could do for the participants (e.g., "Dr. Lam investigates [students'] current health states and conducts physical examinations to establish risk factor levels"). We counterbalanced the presentation of each column. Each of the two columns of service employee information was defined as a separate area of interest. For each participant, we calculated the ratio of time spent fixating on person-related information to the time spent fixating on job-related information. Because this ratio was positively skewed (skewness = 8.55, SE =.20; Shapiro–Wilk's W =.32, p <.001), we used the log-transformed ratio as the dependent measure. Moreover, we measured participants' knowledge of health care services, mood, and arousal as control variables and found that these variables did not differ across conditions (all ps >.40).
A 2 (performance attributions: effort vs. talent) × 2 (presentation order: person-related information on the left vs. right) ANOVA revealed that the log-transformed ratio of fixation time was higher when the physician's performance was attributed to effort (M =.26, SD = 1.12) rather than to talent (M = −.05, SD = 1.24; F( 1, 143) = 4.00, p <.05, =.03), in support of H1. Thus, when the performance was attributed to effort (vs. talent), participants spent a relatively greater proportion of time attending to the physician's person-related information than to the physician's job-related information. The main effect of the presentation order was significant; the log-transformed ratio of fixation time was higher when person-related information was presented on the left (M =.63, SD = 1.23) than on the right (M = −.44, SD =.85; F( 1, 143) = 38.53, p <.001, =.21), consistent with the tendency to read English text from left to right ([49]). However, the interaction between performance attributions and presentation order was not significant (F( 1, 143) =.02, p =.90, <.001).
This study validates the theoretical and managerial importance of relationship expectations by showing that it can be reflected in consumers' attention to advertisements, not just in self-reported relationship expectation measures. Specifically, effort attribution leads consumers to spend a greater proportion of time attending to person-related information compared with job-related information about the service employee, consistent with the norms of communal relationships. This study provides practical insights on how to utilize performance attributions in communication messages. For instance, firms often communicate their service employees' personal background information to enhance consumers' connection with the employees ([55]). Our findings suggest that in such a situation, firms can attribute their employees' performance to effort rather than to talent. We also showed that the observed effects cannot be attributed to changes in competence or quality perceptions. In the next section, we develop a hypothesis regarding downstream consequences of relationship expectations and present two studies to provide empirical evidence supporting the hypothesis.
To demonstrate the managerial and practical importance of service employee performance attributions, we examine downstream consumer behaviors resulting from relationship expectations. Specifically, we examine customer helping behaviors for firms as a result of relationship expectations. Drawing on prior work, we define customer helping behaviors as voluntary and discretionary behaviors toward firms that aid the firms beyond those required in the purchase of products and services ([ 8]; [10]), which can include spreading WOM (e.g., sharing product/service information on one's social networks), providing suggestions for product and service improvements, participating in firm activities, and helping other customers ([ 8]; [23]; [26]). Although the link between relationship expectations and customer helping behaviors has not been directly tested, prior research has suggested that customers are more likely to engage in helping behaviors when they believe a service employee places the welfare of the customers above the employee's own immediate self-interest ([10]), which is consistent with characteristics in communal relationships ([ 3]). Therefore, we predict that when an employee's performance is attributed to effort, thus inducing more of a communal relationship expectation, consumers will have a higher likelihood of engaging in helpful behaviors. Formally,
- H3: Consumers are more likely to engage in customer helping behaviors toward a firm when its service employees' competent performance is attributed to dedicated effort rather than to natural talent.
Prior research has identified both WOM and idea provision as important customer helping behaviors that can promote firm interests. Scholars have found that WOM can influence the way consumers make purchase decisions and, thus, affect sales ([ 6]), and that customers' participation in idea provision can enhance new product financial performance ([12]). In the next two studies, we test the effect of performance attributions on these two customer helping behaviors. In Study 4, we used a real firm context and measured individuals' WOM behaviors. We show that customers are more likely to help a firm share information on social networks when the employees' performance is attributed to effort than to talent. In Study 5, we conducted a field experiment to examine customers' provision of new product ideas. The findings indicate that effort attribution makes customers more likely to provide new product ideas.
In Study 4, we explore WOM behaviors as a downstream consequence of relationship expectations. We predict that when a firm highlights its service employees' dedicated effort (vs. natural talent), thus inducing a more communal-oriented relationship expectation, customers will be more likely to share the firm's information on social networks.
One hundred fifty-five undergraduate students (98 women; mean age = 20.21 years) from a large university in Hong Kong participated in this laboratory study for monetary compensation. To increase realism of the experimental context, we used a real fitness center in Hong Kong, which operates in multiple locations and offers two types of classes with trainers: one combining yoga and fitness training, and the other combining Thai boxing and fitness training.
Participants were given website information about this fitness center and its trainers. They were told that the fitness classes were instructed by a team of highly qualified fitness trainers who have won awards and championships in Hong Kong and overseas. We attributed these performances to either effort (e.g., "A group of hardworking trainers...will dedicate their efforts") or talent (e.g., "A group of talented trainers...have good natural skills"; see Web Appendix W8).
We also conducted an independent pretest (n = 80; 55 women; mean age = 20.69 years). Participants in the effort attribution condition (M = 2.75, SD = 1.37) were more likely to attribute the fitness trainers' performance to effort than were those in the talent attribution condition (M = 4.89, SD = 1.36; t(78) = −6.97, p <.001, d = −1.57), indicating that our manipulation was successful. Participants also indicated the extent to which they perceived the fitness trainers to be warm and competent, as in Studies 2a and 3, and how experienced the trainers seemed to be (1 = "not at all," and 7 = "very much"). A t-test revealed that participants perceived the fitness trainers as warmer when their performance was attributed to effort (M = 4.85, SD =.92) than to talent (M = 4.26, SD = 1.25; t(78) = 2.44, p <.05, d =.54). However, perceived competence and experience did not differ across the two conditions (ps >.10).
Participants then read a message from the fitness trainers asking participants for their help to share the fitness center's website on social networks. Following [14] measure of WOM behaviors, participants were led to believe that by clicking a share button, they would share the website on a social network of their choice. After choosing their favored social network(s), participants were informed that they would not actually share the website. As an incentive, customers who chose to share the website could enter a lucky draw for a chance to win a free trial class at the fitness center (worth HK$200 or US$25).
We measured relationship expectations with the fitness trainers as in the previous studies. We also measured participants' general tendency to share information on social media (1 = "never," and 7 = "very frequently"), which did not differ across conditions (p >.50).
A cross-tabulation analysis revealed that participants in the effort attribution condition (58.97%) were more likely to share the fitness center's website on social networks than those in the talent attribution condition (42.86%; χ2( 1) = 4.03, p <.05), in support of H3.
A t-test analysis revealed that participants expected a more communal relationship when the fitness trainers' performance was attributed to effort (M = 3.93, SD = 1.30) than to talent (M = 3.31, SD = 1.30; t(153) = 2.95, p <.01, d =.48), in support of H1.
We tested relationship expectations as a mediator for the effect of performance attributions on sharing behavior with a bootstrapping analysis using PROCESS Model 4 ([45]). Results revealed that the indirect effect of performance attributions on sharing behavior through relationship expectations was significant (indirect effect = −.41, SE =.19, 95% confidence interval = [−.85, −.13]).
Using a real firm context, Study 4 offers important marketing implications by examining WOM behaviors as a customer outcome of relationship expectations. The findings support our prediction that when a firm highlights its service employees' dedicated effort as opposed to their natural talent, thus inducing a more communal relationship expectation, customers are more likely to engage in helpful behaviors by sharing the firm's information on social networks. Instead of providing an exact performance level as in previous studies, we offered a description of the fitness trainers' achievements (i.e., a team of highly qualified fitness trainers who have won awards and championships) to generalize our findings. In this study, we also showed that the trainers described as hardworking were perceived to be warmer, but not more competent or more experienced, than those described as talented. In the next study, we examine the effect of performance attributions on another type of customer helping behaviors.
In Study 5, we conducted a field experiment at the coffee shops of an international coffee chain to test the effect of performance attributions on customers' provision of new product ideas. This coffee chain employs user-design philosophies to generate new product ideas through its website and has implemented many crowdsourced ideas. We predict that customers will be more likely to provide new product ideas when firms highlight their service employees' dedicated effort (vs. natural talent), thus inducing more of a communal relationship expectation.
Over a two-week period, we launched a "Share Your Ideas" campaign (hereinafter, "campaign") at two locations of the coffee chain. In the shops, we prominently displayed marketing materials (e.g., posters on walls, poster stands, table stickers) highlighting the baristas' dedicated effort for one week, and those highlighting their natural talent for another week. To control any confounding effects associated with particular dates, we simultaneously ran the campaign at two coffee shops, each located in a different large university in Hong Kong. We counterbalanced the performance attribution conditions between the two shops (i.e., talent attribution condition in Shop A and effort attribution condition in Shop B in the first week, and vice versa in the second week).
We contracted a professional graphic designer to create the campaign's marketing materials (for sample materials, see Web Appendix W9). The marketing materials in the effort attribution condition highlighted the baristas' effort (e.g., "We are a group of hardworking baristas! Please share your beverage ideas with us. We put a lot of effort into creating perfectly composed drinks"), whereas those in the talent attribution condition highlighted the baristas' talent (e.g., "We are a group of talented baristas! Please share your beverage ideas with us. We are naturally skillful in creating perfectly composed drinks"). We displayed the marketing materials throughout the shops (see Web Appendix W10).
We placed feedback forms throughout the shops that customers could voluntarily pick up, fill out with their ideas and suggestions, and submit to a collection box. The feedback forms included a performance attribution manipulation (for samples of the feedback forms, see Web Appendix W11). We measured participants' general liking of the coffee chain (1 = "not at all," and 7 = "very much") and frequency of visits (1 = "never," and 7 = "very frequent") as control variables. We also measured perception of the baristas' beverage-making skill level (1 = "not good at all," and 7 = "very good") to ensure that the performance attribution manipulation did not lead to differences in perceived competence of the baristas. As an incentive for their participation, customers who submitted a feedback form could enter a lucky draw for a chance to win a HK$300 (US$38) coffee chain coupon.
To test the effect of performance attributions on customers' likelihood of submitting a feedback form, we examined the number of submitted feedback forms as a percentage of the total number of sales transactions. We obtained the numbers of weekly sales transactions of the two coffee shops from their managers and found that the number of total transactions was not significantly different across the two shops.
We conducted three types of analyses on customers' likelihood of submitting a feedback form. First, a cross-tabulation analysis indicated that customers were more likely to submit a feedback form when they were exposed to effort attribution information than to talent attribution information (5.24% vs. 3.36%; χ2( 1) = 40.21, p <.001), in support of H3. In addition, two separate analyses showed that the finding was consistent for both Shop A (4.38% vs. 2.90%; χ2( 1) = 16.99, p <.001) and Shop B (6.48% vs. 3.99%; χ2( 1) = 24.49, p <.001).
Second, we ran a binary logistic regression of the submission of feedback forms (1 = submitted, 0 = not submitted) on performance attributions (dedicated effort vs. natural talent), shop dummy (Shop A vs. Shop B), and their interaction. There was a significant main effect of performance attributions (b = −.51, SE =.10, Wald( 1) = 24.04, p <.001, Exp(B) =.60). Thus, customers in the effort attribution condition were more likely to submit a feedback form than were those in the talent attribution condition, in support of H3. There was a main effect of the shop dummy (b = −.41, SE =.09, Wald( 1) = 20.05, p <.001, Exp(B) =.66) and a nonsignificant interaction (b =.08, SE =.15, Wald( 1) =.31, p =.58). Two separate logistic regression analyses (one for each shop) indicated that customers in the effort attribution condition were more likely to submit a feedback form than were those in the talent attribution condition for both Shop A (b = −.43, SE =.11, Wald( 1) = 16.76, p <.001, Exp(B) =.65) and Shop B (b = −.51, SE =.10, Wald( 1) = 24.04, p <.001, Exp(B) =.60).
To further enhance the robustness of our findings, we adopted the rare events logistic regression method (ReLogit; [34]). Given that our binary event of interest (i.e., submission of feedback forms) was relatively rare (4.31% of the sample), ReLogit corrects for rare event biases and standard error inconsistency, thus providing more accurate estimates than traditional logistic regression models. The ReLogit results were consistent with those from the logistic regression models.
We further tested the effect of performance attributions on the number of suggestions provided. Two research assistants blind to the research hypotheses independently counted the number of suggestions provided on the submitted feedback forms (Cohen's kappa =.71, p <.001), and disagreements were resolved through discussion. They were instructed to count only the related suggestions and exclude suggestions unrelated to the given question on the coffee chain's beverage offerings (e.g., "I love you [the name of the coffee chain]").
We used a Poisson regression, because the dependent variable was count data ([15]). We regressed the number of suggestions on performance attributions, shop dummy, and their interaction. The results revealed a significant main effect of performance attributions (b =.28, SE =.10, z = 2.91, p <.01), in support of H3. There was also a significant main effect of shop dummy (b = −.23, SE =.11, z = −2.07, p <.05) and a significant interaction (b = −.41, SE =.15, z = −2.73, p <.01). Split-group Poisson regressions showed that participants at Shop A provided a greater number of suggestions in the effort attribution condition (M = 1.62, SD = 1.33) than in the talent attribution condition (M = 1.23, SD = 1.21; b =.28, SE =.10, z = 2.91, p <.01). However, the effect was not significant at Shop B (Meffort =.86, SD = 1.14; Mtalent =.97, SD =.95; b = −.13, SE =.11, z = −1.13, p =.26).
These effects persisted after we controlled for participants' liking of the coffee chain and frequency of visits. Therefore, the effects could not be attributed to individual differences in these factors. Moreover, performance attributions did not change the extent to which participants perceived the baristas to be skillful (p =.44).
In a natural field setting, Study 5 shows that when a firm highlights its service employees' dedicated effort (vs. natural talent), customers ( 1) are more likely to submit feedback forms and ( 2) provide a greater number of suggestions, though the latter effect was significant at only one shop. Moreover, in this study we did not provide the exact performance level so as to generalize our findings, although we believe that customers consider the coffee chain's baristas to be a competent group among coffee shop employees in general (especially among our participants, who actually visited the coffee chain). In addition, our performance attribution manipulation did not change perceptions of the baristas' beverage-making skill level. Thus, our effect cannot be attributed to participants' perception that baristas depicted as naturally talented (vs. hardworking) were more skillful and competent and, thus, were less likely to need suggestions from customers. We replicated the findings in a laboratory experiment, in which we also measured relationship expectations (for details, see Web Appendix W12).
The current research demonstrates that message cues that attribute a service employee's competent performance to dedicated effort (vs. natural talent) lead consumers to expect a more communal and less exchange-oriented relationship due to an increase in the perceived warmth of the employee. Study 1a showed that participants expected a more communal relationship with a service employee whose competent performance was attributed to effort rather than to talent, whereas Study 1b revealed that simultaneous attribution to both effort and talent yields an effect similar to that of effort attribution only. In directly measuring the perceived warmth of an employee, Study 2a showed that the effect of performance attributions on relationship expectations is mediated by this construct. We manipulated the perceived warmth of an employee in Study 2b and showed further support for the mediating role of warmth. In Study 3, we used eye-tracking technology and found that effort attribution led participants to pay more attention to person- than job-related information about the service employee, reflecting expectation of a more communal-oriented relationship.
Studies 4 and 5 explored customer helping behaviors as downstream consumer outcomes of relationship expectations. In Study 4, we used a real firm context (i.e., fitness center) and showed that participants were more likely to spread WOM for a firm when its service employees' performance was attributed to effort than to talent. Finally, in Study 5, we conducted a field experiment and showed that effort attribution, which induced a more communal relationship expectation, made participants more likely to provide new product ideas.
The marketing literature has focused on attributions of one's own performance and demonstrated their impact on brand or product evaluations ([40]; [42]). The current research highlights the importance of studying attributions of others' performance, because even for the same level of performance, people's beliefs about performance attribution can change judgments of those others ([11]; [52]). For instance, people expect hardworking others to perform better on novel tasks ([11]). We suggest that it is also important to understand the role of attributions of others' performance in consumer outcomes in service relationships, because how a customer views a service employee's performance can determine the customer's relationship with the employee ([ 9]). Therefore, this study fills the gap in prior work by examining how attributions of service employees' performance influence consumers' relationship expectations with and behaviors toward the service employees.
The current research also augments existing knowledge on the two fundamental dimensions of social judgment—competence and warmth—by linking the warmth–competence framework ([19]) with the literature on performance attribution ([16]; [57]). Prior work has investigated relationships between judgments of competence and warmth ([32]; [58]), mainly by examining how a certain level of competence is related to warmth perception. Extending the existing literature, the current research examines the attributions of competence as a new dimension of competence influencing warmth perception, holding the objective level of competence constant.
The current research also enriches the existing literature by identifying performance attributions as an antecedent of relationship expectations. The marketing literature has focused mainly on the downstream consequences of a communal versus exchange relationship with consumers—for example, whether consumers' perceptions of a communal versus exchange relationship influences their evaluation of brands ([ 3]; [ 4]), loss aversion tendency ([ 5]), and responses to service failures (Wan, Hui, and Wyer 2011). However, given the lack of research on the antecedents of relationship expectations, marketers may have little practical guidance on how they can shape expectations about a particular type of relationship in the minds of consumers. Addressing this gap, we find that the attributions of service employees' competence can alter consumers' expectations about their relationships with the employees along the communal–exchange continuum. In addition, the current research suggests that relationship expectations can be reflected in consumers' attention, not just in self-reported relationship expectation measures. Our use of eye-tracking technology allowed us to measure the subconscious or preconscious reflection of relationship expectations.
In addition, the current research contributes to the literature on customer helping behaviors by identifying performance attributions as a new antecedent of such behaviors ([ 8]; [23]; [26]). As customer helping behaviors (e.g., spreading WOM, providing new product ideas) are becoming notable marketing goals for brands and firms, factors that encourage such behaviors are both theoretically and managerially important. We have shown that effort attribution, as opposed to talent attribution, increases the likelihood of customer helping behaviors.
Our findings offer practical implications, because firms can highlight either effort or talent as the primary source of service employees' competent performance to induce a relationship expectation that corresponds to their service propositions. For instance, firms that emphasize communality in their services (e.g., Disneyland, Starbucks) can attribute their employees' performance to effort, leading consumers to expect a more communal relationship with their employees. In contrast, if these firms attribute employee performance to talent, thus inducing a more exchange relationship expectation, the discrepancy between consumers' relationship expectations and their actual service experience may hurt service satisfaction.
Our findings also demonstrate that, depending on whether a firm attributes its service employees' performance to effort or talent, consumers will pay attention to different types of service employee information, reflecting their expected relationships with the employees. This helps guide firms in designing their marketing materials. For example, when firms want their consumers to pay attention to a service employee's personal (job-related) information, they might want to attribute the employee's performance to effort (talent).
Moreover, this research shows that the effect of performance attributions on relationship expectations has consequences for customer helping behaviors that offer managerial insights. Specifically, we gathered empirical evidence suggesting that marketers can implement effort or talent attributions in their communication messages to influence customers' actual WOM and idea provision behaviors. Marketers regard WOM—electronic WOM in particular—as "one of the most significant developments in contemporary consumer behavior" due to its ability to influence the way consumers make purchase decisions and affect sales ([ 6], p. 297). Marketers are also increasingly involving customers in idea generation for new products, because such a tactic can enhance new product financial performance ([12]). As firms strive to achieve these marketing goals, our research findings offer insights into how firms can motivate these customer helping behaviors using their communications messages. According to our findings, firms are advised to attribute their employees' performance to effort, rather than talent, when they want to encourage customers to share firm information on social networks or to suggest new products or services. We believe our proposed effect of performance attributions on relationship expectations can also influence other types of customer helping behaviors, such as participating in firm activities and helping other customers.
What factors shape consumers' expectations about their relationship with a service employee is an important practical question, because it can have significant consequences on consumer outcomes ([ 3]; [ 4]; [54]). Although it is true that firms can develop communal relationships through other methods—for example, by generally treating customers well and satisfying them—these tactics require actual interactions with customers. The current research suggests that communication messages that do not involve interactions with customers also can move customers' relationship expectations along the communal–exchange continuum, in turn influencing consumer behaviors. Marketing practitioners can utilize this knowledge about highlighting effort and/or talent to design their website communications, print advertisements, and social media strategy going forward, or to reevaluate the effectiveness of their current communication strategies.
This article offers several fruitful directions for future research. First, future studies can examine whether the performance attribution effects can be extended to other contexts. For instance, our proposed effects may not be limited to person perception. Because people tend to view a relationship with a brand, product, or firm similarly to a relationship with a person ([21]; [39]), the attributions of brands' or firms' competent performance might influence consumers' perceived relationships with those brands or firms. Future studies could also explore service failure contexts. For example, researchers can investigate whether attributing poor service performance or negative service outcomes to an employee's lack of effort (or natural talent) can lead to differences in a consumer's willingness to forgive. In addition, future studies could explore how consumers might interpret information on performance attributions of firms whose performance is uncertain (e.g., startups).
Even though our last two studies show that effort (vs. talent) attribution is more likely to increase customer helping behaviors, we do not argue that effort attribution is always more beneficial to firms than talent attribution. In a supplementary study (Web Appendix W13), we measured membership sign-up behavior as a different downstream behavior in the same fitness training context. The findings show that because customers who generally do not want to proactively interact with service employees during a service process (e.g., by offering their own opinions about the training program) prefer a more exchange-oriented (i.e., less communal-oriented) relationship with a service employee, firms are more likely to acquire them if the service employee's performance is attributed to talent rather than effort. Future studies could explore other consequences of service employees' performance attributions for consumer behaviors, such as loyalty to the same service employee and reactions to service recovery, as well as other individual and situational factors that influence customers' relationship preferences.
In addition, future research could explore how relationship expectations may interact with actual service experience to affect customer satisfaction. For instance, customers who experienced an exchange-oriented relationship with a service employee in digital interactions may be less satisfied with the same experience when they are exposed to effort attribution (vs. talent attribution) that induces a more communal relationship expectation. Future research could also explore consumer heterogeneity in terms of attributions of service employee performance. Because the focal point of this research was to delineate the effects of service firms' performance attributions, we did not directly explore consumers' heterogeneity in their attributions, which might depend on the industry or context. This heterogeneity may be presumed to interact with service firms' endogenous attribution decisions.
Supplemental Material, jm.18.0374-File003 - Highlighting Effort Versus Talent in Service Employee Performance: Customer Attributions and Responses
Supplemental Material, jm.18.0374-File003 for Highlighting Effort Versus Talent in Service Employee Performance: Customer Attributions and Responses by Fine F. Leung, Sara Kim and Caleb H. Tse in Journal of Marketing
Footnotes 1 Associate EditorWayne Hoyer
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is supported by a grant from the Hong Kong SAR Research Grants Council awarded to the second author (HKU17500715).
4 ORCID iDsFine F. Leung https://orcid.org/0000-0002-7988-2944 Sara Kim https://orcid.org/0000-0003-0105-0184 Caleb H. Tse https://orcid.org/0000-0003-0572-8829
5 Online supplement: https://doi.org/10.1177/0022242920902722
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Record: 87- How Consumers' Political Ideology and Status-Maintenance Goals Interact to Shape Their Desire for Luxury Goods. By: Kim, Jeehye Christine; Park, Brian; Dubois, David. Journal of Marketing. Nov2018, Vol. 82 Issue 6, p132-149. 18p. 2 Diagrams, 4 Charts, 3 Graphs. DOI: 10.1177/0022242918799699.
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How Consumers' Political Ideology and Status-Maintenance Goals Interact to Shape Their Desire for Luxury Goods
This research distinguishes between the goal of maintaining status and advancing status and investigates how consumers' political ideology triggers sensitivity to a status-maintenance (vs. status-advancement) goal, subsequently altering luxury consumption. Because conservative political ideology increases the preference for social stability, the authors propose that conservatives (vs. liberals) are more sensitive to status maintenance (but not status advancement) and thus exhibit a greater desire for luxury goods when the status-maintenance goal is activated. Six studies assessing status maintenance using sociodemographic characteristics (Studies 1, 2, and 3a) and controlled manipulations, including ad framing (Study 3b) and semantic priming (Studies 4 and 5), provide support for this proposition. The studies show that the effect is specific to status maintenance and does not occur ( 1) in the absence of a status goal or ( 2) when the status-advancement goal (a focus on increasing status) is activated. Overall, the findings reveal that conservatives' desire for luxury goods stems from the goal of maintaining status and offer insights into how luxury brands can effectively tailor their communications to audiences with a conservative ideology.
Keywords: social status; status maintenance; status advancement; luxury goods; political ideology
A key function of luxury goods—a €262 billion market in 2017 ([19])—is to signal consumer status ([26]; [29]; [67]). Status, broadly defined as the respect and admiration received from others ([46]), is a fundamental human goal ([ 4]) that drives consumers' desire for luxury goods ([10]; [25]). This research formally distinguishes between two goals related to status, one reflecting the desire to maintain one's status (hereinafter the "status-maintenance goal") and one reflecting the desire to advance one's status (hereinafter the "status-advancement goal"). In practice, the notion of status maintenance frequently permeates luxury brand communications. Consider, for example, a watchmaker's famous slogan, "You never actually own a Patek Philippe. You merely look after it for the next generation." Similarly, DAMAC Properties, a Dubai-based luxury real-estate company, informs prospective buyers that its properties will "complement [their] stature," and Rolex reminds potential buyers that "Class is forever" (Web Appendix A). When will consumers be more sensitive to messages emphasizing such status maintenance and subsequently desire luxury goods more?
One answer to this question begins with the idea that people's views on status often stem from their broader social beliefs (e.g., [13]; [32]), the core of which is political ideology ([ 7]). Building on the finding that conservative (vs. liberal) ideology emphasizes the need to sustain the current social order ([36]), we posit that conservative political ideology (hereinafter "political conservatism") increases the importance of status maintenance but not status advancement or a general sensitivity to status (i.e., the value put on status in general). Because consumers tend to act on goals that are both important (i.e., valuable and prioritized; [24]) and activated (i.e., cognitively salient and accessible; [44]), we predict that political conservatism increases the desire for luxury goods when the status-maintenance goal is activated. We also propose that activating the status-maintenance goal among conservatives heightens their preference for social stability, increasing their desire for goods viewed as helping to maintain the social order, such as luxury goods. We probe whether this effect ( 1) stems from consumers' preference for social stability and ( 2) occurs when the status-advancement goal is activated or in the absence of a status goal.
This research contributes to the literature in two ways. First, we contribute to the status literature by distinguishing between status-maintenance and status-advancement goals. Whereas the bulk of the work tends to treat status as a single construct and focuses on how the presence or absence of a status goal affects luxury consumption (e.g., [58]; [62]), our research reveals how status goals with different foci (status maintenance vs. status advancement) differentially affect luxury consumption. Notably, our work empirically demonstrates the importance of this conceptual distinction by showing that political conservatism increases sensitivity to the status-maintenance goal but not the status-advancement goal, subsequently inducing a greater desire for luxury goods.
Second, we contribute to the nascent literature on political ideology and luxury consumption ([54]) by going beyond previous efforts tied to sociopolitical views and marketing, particularly the areas of political consumerism ([76]), political campaigns ([33]), and prosocial or environmental behaviors ([40]; [75]). [54] find that conservatives differentiate themselves through products that signal that they are better than others (vertical signaling) while liberals differentiate themselves through products that signal their uniqueness (horizontal signaling). In contrast with their study, which explores the different types of signaling strategies people employ through luxury goods, our approach examines how different status goals may lead consumers to engage in vertical signaling (i.e., desire for luxury products) or not (i.e., desire for nonluxury products). In doing so, we complement previous work by shedding light on the motivations of conservatives when purchasing luxury goods. By demonstrating that conservatives desire luxury goods more than liberals under the status-maintenance goal, we reveal that their aspiration for vertical signaling stems from their desire to keep their current status as it is.
Finally, our findings also hold several practical managerial implications by offering a more sophisticated approach to luxury market segmentation. Because luxury products appeal to specific audiences, knowing how to segment and target them has long been central to the management of luxury brands ([20]). In practice, however, luxury brands often use vertical segmentation (e.g., by income), resulting in increasingly complex submarkets (e.g., "true luxury, masstige, premium, ultrapremium, opuluxe, hyperluxe, affordable luxury"; [38]). Our study pinpoints an accessible, easy-to-measure variable—namely, political ideology—and identifies how and when it predicts consumers' appetite for luxury goods. Indeed, political ideology is regularly assessed through opinion polls (e.g., Pew Research Center, Gallup), is easily identifiable along a geographic map ([12]) and media outlets ([34]), and offers more granular consumer insights (e.g., town level; for examples of available data sources, see Web Appendix B).
Overall, our findings imply that rather than targeting a segment on the basis of mere wealth or even status, a luxury brand that emphasizes status maintenance (e.g., Patek Philippe) may be more successful when targeting a wealthy conservative segment. This goal may be achieved by running targeted marketing and communications campaigns ( 1) on media platforms patronized by conservatives (e.g., Fox), ( 2) in conservative geographic areas (e.g., Texas), or ( 3) online, particularly on social media, by leveraging digital footprints indicative of conservatism (e.g., [15]) (e.g., of how brands match the media used to their consumers' political orientations, see Figure 1; for managerial guidelines, see Figure 2).
Graph: Figure 1. Examples of media sponsorship by political leaning. Notes: Information collected from SponsorFeedback.com. Left-leaning platforms include MSNBC, NBC, CNN, CBS, and ABC. Right-leaning platform includes Fox. For each brand, we computed the percentage reflecting media sponsoring on the basis of the number of shows sponsored by each brand across left- and right-leaning media platforms. For example, AT&T sponsors three shows (The Rachel Maddow Show, Andrea Mitchell Reports, and Morning Joe) exclusively on MSNBC. Therefore, 100% of AT&T's sponsorship goes to left-leaning media.
Graph: Figure 2. Decision guidelines for luxury brand managers.
Goals are internal representations of desired states ([ 6]), be they physical needs (e.g., the need to eat) or self-actualization ([73]). Considered a fundamental human goal ([ 4]; [25]), status confers many psychological and social benefits to consumers ([23]; [50]). As such, status is a key factor in marketing because of its important role in shaping consumers' desire for luxury goods ([10]; [25]; [67]). Simply making status salient through reminders of successful similar people ([47]) or in the form of power threats ([22]) can increase the desire for luxury options, traditionally defined as high-quality, exclusive, and (often) conspicuous ([20]; [38]).
Departing from the view that a single status goal (i.e., status maximizing) underlies consumers' desire for luxury, the current research proposes that the status goal has a dual nature, reflecting a desire to maintain or advance one's social standing, depending on the extent to which the desired status state is currently being or has yet to be experienced. When consumers believe they are currently experiencing the desired social status they have in mind (e.g., they hold a high position in their community because of their advanced degrees), they may focus on maintaining their social status (i.e., status-maintenance goal). By contrast, when consumers believe they have yet to experience the desired social status they have in mind, they may focus on advancing social status (i.e., status-advancement goal). Although most research has largely ignored this distinction, managers in the luxury industry recognize and appeal to both status goals. That is, while luxury brands' taglines often emphasize status maintenance, luxury marketers also appeal to consumers' desire to "climb the ladder." For example, Audi calls on consumers to "update [their] status," Aston Martin announces that its car will "add value" to consumers' lives, and India-based tailor High Status Fabrics asserts that its tailor-made suits will "elevate" consumers (Web Appendix A).
Building on this distinction, we investigated how political ideology, a key variable shaping consumers' view of the social strata, may uniquely influence the importance of retaining status and ultimately guide the desire for luxury goods. We begin with the idea that luxury goods represent status signals ([29]) that help "conserve" the social hierarchy by reducing uncertainty about consumers' roles and prerogatives. In other words, consumers "read" others' statuses through their consumption, which reinforces the social hierarchy over time ([ 3]) and helps maintain consumers' status in relation to others' ([10]; [14]; [67]). In seventeenth- and eighteenth-century Europe, for example, the so-called sumptuary laws governed the ownership and display of fashion (e.g., gold embroidery) for the purpose of sustaining the existing social order ([18]; [67]). Because consumers' views of status often rest on their broader social beliefs (e.g., [13]; [32]), we examine how political ideology, which lies at the core of broader social beliefs, systematically predicts the importance consumers put on status maintenance (vs. status advancement). Next, we turn to prior work on political ideology to build our hypotheses.
The term "political ideology" refers to beliefs and principles that reflect a person's views on how society should be governed ([ 7]). Political ideology is typically measured on a spectrum ranging from liberal to conservative, a classification that is judged as the most parsimonious ([51]) and also predictive of consumers' behavior ([35]). Conservatism emphasizes the importance of keeping things as they are ([17]), and as such, conservatives often engage in the same daily routines ([37]). For example, conservatives tend to prefer familiar to unfamiliar music ([27]) and favor established brands over nameless or new brands ([39]). Conservatism also triggers a greater sensitivity to the existing social structure. For example, conservatives tend to judge others on their position in the social strata rather than question the fairness of the social system. Accordingly, they tend to evaluate those with high status more favorably than those with low status, regardless of their own status, resulting in in-group favoritism among high-status conservatives and out-group favoritism among low-status conservatives ([45]).
Building on these findings, we propose that political conservatism increases the importance of pursuing and satisfying the status-maintenance goal. The more conservative the person is, the more he or she will view the pursuit of status maintenance as important. Given the pervasiveness of people's tendency to "look upward" ([21]; [53]), however, there is no obvious reason to expect that political ideology would affect the status-advancement goal. In other words, we would expect status maintenance to be increasingly important to consumers as their political conservatism increases but status advancement to be invariant in relation to political conservatism. Confirming these predictions, a pilot study (for details, see Web Appendix C) showed that conservatives (M = 4.83, SD = 1.40) viewed status maintenance as more important than liberals (M = 3.67, SD = 1.79; F( 1, 76) = 7.61, p =.007) but conservatives (M = 3.70, SD = 1.77) and liberals (M = 3.60, SD = 1.94) did not differ in how important they viewed status advancement (F( 1, 76) =.04, p =.84).
As consumers typically pursue multiple goals simultaneously ([44]), goal importance alone does not guarantee that a goal will be pursued. Instead, the extent to which a goal guides actual behavior will depend on its motivational (goal importance) and cognitive (goal activation) properties ([24]; [44]). Thus, a goal is most likely to shape consumer behavior when it is both important and activated ([24]; [44]). For example, although impulsive people put more importance than nonimpulsive people on satisfying the pursuit of pleasure, they may not automatically exhibit greater preferences for all pleasurable items (e.g., sweet food) unless that specific goal is activated ([55]). Similarly, although conservatives put greater importance on status maintenance than liberals, they may not automatically have a greater desire for goods that help maintain the social hierarchy (i.e., luxury goods) if there are other goals (e.g., relationship goals, health goals) that are more salient and cognitively accessible at any given moment. Therefore, we reason that the status-maintenance goal will motivate luxury consumption most when it is both important and activated; thus, we predict the following:
- H1 : Political conservatism increases the desire for luxury goods when the status-maintenance goal is activated, but it does not affect the desire for luxury goods when the status-advancement goal is activated or when there is no status goal.
We further propose that the effect of political ideology on the desire for luxury goods when the status-maintenance goal is important and activated stems from consumers' increased motivation to keep things as they are—that is, a preference for stability. Prior research suggests that the motivation to keep things as they are permeates both personal (i.e., the desire to keep one's life regular and predictable, namely, preference for personal stability) and social domains (i.e., the desire to keep the social structure as is, namely, preference for social stability; [17]; [37]). If conservatives' desire for luxury stems from their aim to keep the social structure as it is by visibly communicating their own status to others, we predict that their preference for social stability (vs. personal stability) will drive the effect. Although it is unclear how purchasing luxury goods may satisfy the desire to stick with the same daily routines (behaviors tied to personal stability), consumers may be keen to turn to visual status symbols that help them keep the social structure as it is. Therefore, we predict that the preference for social stability will underlie conservatives' (vs. liberals') greater desire for luxury goods when the status-maintenance goal is important and activated (Figure 3):
- H2 : When the status-maintenance goal is activated, increased preference for social stability mediates the effect of political conservatism on the desire for luxury goods.
Graph: Figure 3. Conceptual map.
Six studies test the hypotheses by employing different measures of political conservatism and desire for luxury (Table 1). Studies 1–3a measure the role of status-maintenance activation through status position, while Studies 3b–5 use external manipulations. Goal activation may depend on internal chronic factors (i.e., typically stable features such as character traits) or external manipulations ([44]). For example, although an indulgence goal (i.e., focusing on immediate enjoyment, such as spending on luxury goods, over long-term considerations) is typically more strongly activated among people low than high on hyperopia, momentarily activating an indulgence goal through a manipulation (e.g., a writing task asking participants to focus on the role of enjoyment in their lives when deciding how to spend money) raises the desire to indulge for any person regardless of their hyperopia score ([30]).
Graph
Table 1. Summary of Study Design and Results.
| IV Measure | DV Measure | Moderatora | Key Test | Desire for Luxury |
|---|
| Lowa | Higha |
|---|
| Study 1 (N = 21,999 purchase data set) | Categorical | Republican Democrat | Car purchase (choice) | Measured (continuous) | SES | Spotlight analysis | β = –.03z = –.18p >.8 | β =.35z = 9.65p <.001 |
| Study 2 (N = 194, MTurk) | Conservative Liberal | Desire for real luxury brands | Mc-Arthur scale | β = –.52t = –2.17p =.032 | β =.45t = 1.93p =.055 |
| Study 3a (N = 174, MTurk) | Republican Democrat | Preference for social stabilityb | β =.58t = 1.26p =.209 | β = 1.73t = 4.34p <.001 |
| Study 3b (N = 403, MTurk) | Continuous | Two 7-point scales | WTP for a product | Manipulated (categorical) | Ad framing | Simple effect of political conservatism | β = –1.65t = –.70,p =.487 | β = 5.45t = 2.33p =.02 |
| Study 4 (N = 264, MTurk) | Mehrabian (1996) Conservatism scale | Desire for real luxury brands | Writing task 1 | β = –.14t = –1.76p =.079 | β =.21t = 2.27p =.024 |
| Study 5 (N = 303, college students) | Same as Study 3b | WTP for a luxury-framed product | Writing task 2 | β = –11.20t = –1.40p =.163 | β = 22.33t = 2.93p =.004 |
| NR1 (N = 274, MTurk) | One 7-point scale | Desire for real luxury brands | Writing task 1 | β = –.03t = –.59p =.556 | β =.16t = 2.51p =.013 |
| NR2 (N = 297 French lab) | Same as Study 4 | WTP for a luxury-framed product | Writing task 2 | β = 9.86t = 1.02p =.311 | β = –5.75t = –.60p =.549 |
- 10022242918799700 a Status-maintenance goal activation.
- 20022242918799700 b Study 3a does not measure desire for luxury, but the process (i.e., preference for social stability); therefore, its results reflect the preference for social stability.
- 30022242918799700 Notes: IV = independent variable; DV = dependent variable; NR1 and NR2 indicate two studies not reported herein but included in the Web Appendix. NR1 is a replication of Study 4 with an American sample (Web Appendix W), and NR2 is a replication of Study 5 with a French sample (Web Appendix V). Spotlight analysis examines the difference between conservative and liberal at one standard deviation above the status position mean (i.e., high) and one standard deviation below the status position mean (i.e., low). Simple effect of political conservatism examines the slope of political conservatism in the status-maintenance goal condition (i.e., high) and in the status-advancement goal condition (i.e., low).
Studies 1 and 2 establish that the desire for luxury goods increases with political conservatism when the degree of status-maintenance activation is high but remains unchanged when it is low. Specifically, Studies 1 and 2 use consumers' current status position in the social strata to infer the degree of status-maintenance activation. Given that consumers tend to protect and maintain favorable conditions ([64]), a high-status position should trigger greater activation of status maintenance than a low-status position. Indeed, high-status consumers express greater concerns about maintaining their status than low-status consumers ([60]). Furthermore, a pretest reveals that the degree of status-maintenance activation increases along with status position (Web Appendix D). Study 1 examines 21,999 car purchase decisions and finds that Republicans tend to purchase more luxury cars than Democrats when the status-maintenance goal is positionally activated (i.e., high status position). Study 2 replicates the effect using a different measure of status position.
Subsequent studies aim to rule out a potential alternative explanation for the findings in Studies 1 and 2. That is, Studies 1 and 2 compare a condition when the degree of status-maintenance activation is high with one when it is low, and thus it could be argued that the effect comes from the difference in the level of status activation (i.e., how much consumers focus on and think about status in general) rather than in the level of status-maintenance activation (but see Web Appendix D for the pretest result showing that the level of status activation does not differ across status positions). Studies 3b to 5 further address this account by directly manipulating the status-maintenance goal. In addition, they use a condition in which a status goal is activated, but without an emphasis on maintenance (i.e., status-advancement activation condition) as another control condition to strengthen the argument that maintaining status (not just any status goal) drives the effect. To this end, we manipulate both status-maintenance and status-advancement goals and show that political conservatism triggers a greater desire for luxury goods when the status-maintenance goal is activated; however, this effect does not occur when the status-advancement goal is activated or in the absence of a status goal.
Studies 3a and 3b provide evidence for the underlying role of preference for social stability. Specifically, these studies show that when the degree of status-maintenance goal activation is high (i.e., high-status position), political conservatism increases the preference for social stability (Study 3a) and that this shift in preference mediates the effect of political conservatism on the desire for luxury goods (Study 3b). Finally, Studies 4 and 5 activate status goals using a manipulation independent of the consumption task and demonstrate how they can spill over to luxury consumption. Study 5 also directly varies the product framing.
Across the studies, we interpret political ideology as a generalized personality orientation along the liberal–conservative spectrum and capture the construct both categorically as a party affiliation (Republican vs. Democrat) and continuously as a degree of political conservatism ([37]; [74]). This approach follows the use in prior research of continuous measures of political ideology ranging from liberal to conservative (e.g., [40]; [54]). Thus, we use the terms "conservatives" and "Republicans" (when measured categorically) or "political conservatism" (when measured continuously) interchangeably. Note that though we use the term "political conservatism" for the simplicity of language, we view liberalism and conservatism as the two ends of a single spectrum capturing variation in preference for stability, the mechanism at work in our focal effect. Therefore, although we interpret the results by focusing on political conservatism, the opposite interpretation focusing on political liberalism is also possible. Across all studies, we systematically apply the same sample filtering criteria and include the same set of covariates (i.e., age, gender, and income) in our analyses (Web Appendix E).
This study tests the main hypothesis (H1) by leveraging a unique secondary data set measuring political ideology, status position, and actual car purchases. We assess the degree of status-maintenance activation from a consumer's current status position. We predicted that political conservatism would increase the likelihood of purchasing a luxury car among consumers with high status but not low status.
We analyzed car purchase data between October 2011 and September 2012 from a survey conducted by a U.S.-based consulting company (Strategic Vision). Specifically, the survey was sent to car buyers across 50 states and the District of Columbia 3 to 4 months after the date of purchase. Consumers voluntarily filled out the survey at their homes or offices. Of the 416,571 consumers in total, 38,939 disclosed their political affiliation. After we accounted for the control variables, the final sample size was 21,999 consumers (35.36% female; Mage = 53.64 years). The survey also included other questions not tied to our hypotheses.
Car buyers revealed their political affiliation categorically: Republican, Democrat, Independent, Libertarian, Green, Tea Party, and other. Of those who disclosed their political affiliation, 12,881 (33%) identified themselves as Republicans and 12,000 as Democrats (31%). We excluded 36% of consumers who did not identify themselves as Republican or Democrat (a percentage similar to prior work; e.g., 32% in [49]) from the main analyses. After we included the control variables, the final sample consisted of 11,324 Republicans and 10,675 Democrats.
We classified the cars in the data set as nonluxury or luxury (for the full list, see Web Appendix F) using the classification published by Luxury Society, a leading Switzerland-based analyst and news publisher for the luxury industry. This classification relies on the volume of luxury-related online queries performed on search engines such as Google in 2011 in the United States. Among all brands, 22% (78%) are classified as luxury (nonluxury).
Education and income are two key foundational facets of status ([14]; [70]), respectively reflecting focal intangible and tangible positional assets tied to people's rank in the social hierarchy ([ 4]). Following this perspective, we assessed consumers' status position through their socioeconomic status (SES), or the sum of their standardized education and income ([ 5]; [42]). This approach is similar to that of prior studies in the status literature that combine education and income to measure status position ([ 1]; [ 5]; [43]). In our data, education fell into five categories: ( 1) did not finish high school, ( 2) high school graduate, ( 3) did not finish college, ( 4) college graduate, and ( 5) postgraduate degree; income included 25 intervals (i.e., less than $10,000 = 1, over $500,000 = 25).
We performed a logistic regression on political conservatism (Republican = 1, Democrat = 0), SES, and their interaction to predict luxury car purchases, with age and gender as covariates. As expected, political conservatism (β =.38, z = 10.74, p <.001) and SES (β =.54, z = 37.94, p <.001) increased luxury car purchases. There was also a significant political conservatism × SES interaction (β =.09, z = 2.98, p <.01; Table 2, Model 2). To probe the interaction, we conducted a spotlight analysis to examine the effect of political conservatism on luxury car purchases at both high and low levels of SES. As this study measures actual purchases, we used objective indicators of SES based on [65], [66]) to determine high and low levels of SES in our sample (Web Appendix G). As hypothesized, political conservatism significantly increased luxury car purchases among high-SES consumers (β =.35, z = 9.65, p <.001), while it had no significant impact on luxury car purchases among low-SES consumers (β = –.03, z = –.18, p >.8).
Graph
Table 2. Study 1: Luxury Car Purchase Likelihood.
| Effect | Model 1 | Model 2 |
|---|
| Age | .00*** (.00) | .00**** (.00) |
| Gender | –.17**** (.04) | –.17**** (.04) |
| SES | .54**** (.01) | .49**** (.02) |
| Political conservatism | .38**** (.04) | .31**** (.04) |
| Political conservatism × SES | | .09*** (.03) |
| Constant | –1.78**** (.10) | –1.74**** (.10) |
| Observations | 21,999 | 21,999 |
- 40022242918799700 ***p <.01. ****p <.001.
- 50022242918799700 Notes: Standard errors are in parentheses. Political conservatism is coded as 1 if Republican and 0 if Democrat. Gender is coded as 1 if male and 0 if female.
To probe the robustness of the findings, we conducted additional analyses using education and income separately as single measures of status position. The focal finding that political conservatism increases luxury car purchase among consumers having high-status positions holds regardless of whether the status position is assessed through education (β =.35, z = 7.67, p <.001) or income (β =.20, z = 5.44, p <.001) alone (for details, see Web Appendix H, Robustness Analysis 3). Further tests of robustness entailed ( 1) employing a different classification of luxury cars (Web Appendix H, Robustness Analysis 1) and ( 2) widening the political ideology categorization to the Green and Tea parties (Web Appendix H, Robustness Analysis 2). The results from these tests systematically replicate the focal results. In addition, we empirically address the possibility that the effect stems from a greater desire for conventional brands, rather than luxury brands per se. Indeed, conservatives exhibit greater sensitivity to conventions and traditions ([37]), and luxury perceptions often rest on tradition and history ([38]). Ruling out this possibility, additional analyses (Web Appendix I) show that the effect holds for both luxury brands perceived as conventional and nonconventional. Overall, Study 1 provides robust evidence for H1 that political conservatism increases the desire to purchase a luxury car among consumers with high status but not among those with low status, presumably because high status activates the status-maintenance goal.
The results of Study 1 show that high-SES Republicans were 9.8% more likely to purchase a luxury car than high-SES Democrats. To obtain a more specific estimate of the effect, we collected the average price for each car model in the data set. On average, high-SES Democrats spent $29,022 and high-SES Republicans $33,216 to purchase a new car. Practically, a luxury car seller may expect to gain a 14.45% increase in sales from high-SES Republicans than from high-SES Democrats.
Of note, we found an unexpected main effect of political conservatism (i.e., the effect of political conservatism at the mean of SES). Given that we did not find a similar main effect in any of the other studies, we conjecture that this effect may stem from the average SES of the sample used in this study being higher than that of ( 1) the samples used in the other studies and ( 2) the U.S. population. Specifically, the average education level was college graduate, while less than 40% of Americans between ages 25 and 64 years had at least a 2-year college degree when the study was conducted. The mean income level was $72,500 (approximately within the 70th percentile of the U.S. population). Therefore, it is likely that the spectrum of SES in Study 1 mostly captured medium- to high-SES consumers. This interpretation is consistent with the survey participants being actual buyers of new cars. Study 2 aims to address this concern by using a different sample and measure of status position. While Study 1 shows real-life evidence of the effect, its nonexperimental nature makes assessing causality difficult. In addition, given that Study 1 contains only buyers of new cars willingly volunteering to take the survey, there is a potential for sample selection bias (i.e., a focus on wealthy customers). We address these issues in the following studies by replicating the effect in a more controlled setting (Studies 2 to 5) and directly manipulating status goals (Studies 3b to 5).
One hundred ninety-four participants (56% female; Mage = 38 years) recruited from Amazon Mechanical Turk (MTurk) completed the survey for a small monetary compensation. They responded to measures of political conservatism, desire for luxury brands, status position, and demographics (i.e., age, gender and income), in that order.
Participants chose between one of three options: "conservative," "liberal," or "neither" ([28]). In line with [49], our analyses focused on 136 participants (57% female; Mage = 38 years) who reported being either Republican (N = 51) or Democrat (N = 85).
Participants indicated their desire for seven luxury and seven nonluxury U.S. fashion and car brands, which we pretested to vary the extent of perceived luxury and high status (Web Appendix J). We included only U.S. brands, as political conservatism can affect perceptions of foreign brands ([ 8]). We presented the brands sequentially, in random order, and participants indicated how much they liked each brand on a seven-point scale (1 = "not at all," and 7 = "very much").
We used the McArthur scale (Web Appendix D; [ 1]).
Following prior research ([69]; [72]), we used a difference score between evaluations of luxury and nonluxury brands as our dependent variable (DV). We first averaged participants' evaluations of luxury (El, α =.82) and nonluxury (Enl, α =.75) brands before computing a difference score (El – Enl) for each participant. Higher values indicated a greater desire for luxury than nonluxury brands.
We regressed participants' desire for luxury brands on political conservatism (conservative = 1, liberal = 0), status position, their interaction, and the three covariates. The results revealed a significant political conservatism × status position interaction (β =.30, t(129) = 2.72, p =.007; Table 3). As predicted, a spotlight analysis conducted at one standard deviation above the mean of status position revealed a significantly greater desire for luxury brands among conservatives than liberals (β =.45, t(129) = 1.93, p =.055). A spotlight analysis at one standard deviation below the mean of status position revealed that conservatives had a lower desire for luxury brands than liberals (β = –.52, t(129) = –2.17, p =.032). Simple effect analyses revealed that status position significantly predicted the desire for luxury brands among conservatives (β =.32, t(129) = 3.27, p =.001) but not among liberals (β =.02, t(129) =.29, p =.77).
Graph
Table 3. Study 2: Preference for Luxury Brands Relative to Nonluxury Brands.
| Effect | Model 1 | Model 2 |
|---|
| Status position | .010 (.053) | .016 (.054) |
| Political conservatism | −.051 (.153) | −.036 (.154) |
| Political conservatism × status position | .329*** (.110) | .302*** (.111) |
| Age | | .007 (.005) |
| Gender | | −.142 (.154) |
| Income | | −.026 (.043) |
- 60022242918799700 ***p <.01
- 70022242918799700 Notes: Standard errors are in parentheses. Status position varies from 1 (bottom) to 10 (top) and are mean centered. Political conservatism is coded as 1 if conservative and 0 if liberal. Gender is coded as 1 if male and 0 if female.
Replicating Study 1, Study 2 showed that political conservatism increased the desire for luxury brands among consumers with high but not low status, providing further support for our hypothesis (H1). In contrast with Study 1, there was no main effect of political conservatism at the mean status position. In Studies 3a and 3b, we test the underlying role of preference for social stability.
Study 3a aims to show that when the status-maintenance goal is important (i.e., when holding conservative political ideology) and activated (i.e., when having a high-status position), preference for social stability increases. That is, we expected conservatives to exhibit greater preference for social stability than liberals at high- but not low-status positions.
One hundred seventy-four participants (50% female; Mage = 34 years) recruited on MTurk completed the survey for a small monetary compensation. They completed measures of political conservatism, preference for social stability, status position (McArthur scale), and demographics (i.e., age, gender and income), in that order.
Participants indicated the political party they identify with by choosing between one of three options: "Republican," "Democrat," or "neither" ([28]). Again in line with [49], our analyses focused on 123 participants (51% female; Mage = 34 years) who reported being either Republican (N = 41) or Democrat (N = 82).
A series of seven-point scales (1 = "not at all me," and 7 = "very much me") assessed preference for social stability. Items are "I don't like when the social order changes too rapidly around me," "Seeing too many changes in society tends to make me worry," and "Too many changes and reforms to the current social structure makes me feel uneasy" (α =.91).
We regressed participants' preference for social stability on political conservatism (Republican = 1, Democrat = 0), status position, their interaction, and the three covariates. The results revealed a significant effect of political conservatism (β = 1.15, t(116) = 3.74, p <.001) and a marginally significant political conservatism × status position interaction (β =.32, t(116) = 1.90, p =.059). A spotlight analysis conducted at one standard deviation above the mean status position revealed a significantly greater preference for social stability among conservatives than liberals (β = 1.73, t(116) = 4.34, p <.001). At the mean status position, conservatives exhibited a greater preference for social stability than liberals (β = 1.16, t(116) = 3.74, p <.001). However, a spotlight analysis at one standard deviation below the mean status position revealed no difference between conservatives and liberals (β =.58, t(116) = 1.26, p =.209). Simple effect analyses showed that status position significantly predicted the preference for social stability among conservatives (β =.45, t(116) = 2.94, p =.004) but not liberals (β =.13, t(116) = 1.19, p =.235).
Study 3a provides initial evidence that preference for social stability may underlie the effect by showing that conservatives exhibit greater preference for social stability than liberals at high- but not low-status positions. Study 3b provides additional evidence for the role of social stability through moderation and mediation.
The objectives of Study 3b were threefold. First, we used two standards of comparison: a condition of status-advancement activation and a condition that does not activate any status goal. Consistent with prior research showing that activating a status goal increases the desire for luxury compared with a no-status goal condition (e.g., [47]), we expected that activating either the status-maintenance goal or the status-advancement goal would increase the desire for luxury compared with a condition without any status goal activation. In addition, we expected that political conservatism would only affect the desire for luxury when the status-maintenance goal was activated, not when the status-advancement goal was activated or in the absence of a status goal. Second, to provide stronger support for the proposed causal relationship, we directly manipulated status goals, rather than inferring goal activation from status position as in Studies 1, 2, and 3a. Third, we provide support for our hypothesis (H2) by examining the mediating role of consumers' preference for stability. We measured preference for stability in both personal and social domains and expected that the desire to maintain the existing social structure (i.e., preference for social stability) would underlie the effect.
We randomly assigned 403 participants (52% female; Mage = 36 years) recruited on MTurk to one of three conditions (status goal: status-maintenance vs. status-advancement vs. no-status). After indicating their political conservatism as part of an initial survey, participants took a second survey framed as a print ad evaluation task during which they viewed one of three versions of the same eyewear product (our status goal manipulation). Next, they indicated their willingness to pay (WTP) for the eyewear product in U.S. dollars (the main DV) before reporting their age, gender, income, and preference for personal and social stability.
Participants reported their political ideology (1 = "extremely liberal," and 7 = "extremely conservative") and political affiliation (1 = "strong Democrat," and 7 = "strong Republican") on seven-point scales. We averaged these items to form a measure of political conservatism (α =.91; M = 3.54, SD = 1.56; [39]).
Participants viewed one of three different versions of a print ad for an eyewear product (Web Appendix K). The no-status condition featured the eyewear as an economical line that was functional and affordable, while the status-maintenance and status-advancement conditions both featured the eyewear as a luxury line that was state-of-the-art and in limited supply. All three print ads featured the same image and layout, but the tagline varied according to the status goal condition: "Eyewear for everyone" (no-status), "Update your status with status" (status-advancement), and "Keep your status with status" (status-maintenance). A pretest confirmed that each of the ads successfully activated the target status goal, without varying the extent of emphasis on social status (Web Appendix L).
A series of seven-point scales (1 = "not at all me," and 7 = "very much me") assessed preference for personal stability ("I prefer life to be regular and predictable," "I just want to stick to the same regular routine in my life," and "I do not like changes in life"; α =.91). The same three-item measures as in Study 3a assessed preference for social stability (α =.94).
A mixed general linear model performed on WTP, with status goal as a categorical variable, political conservatism as a continuous variable, and the covariates, revealed a main effect of status goal (F( 2, 394) = 16.98, p <.001) and a status goal × political conservatism interaction (F( 2, 394) = 2.50, p =.083). Consistent with the perspective that having a status goal increases consumers' WTP compared with the absence of a status goal ([47]), participants in both the status-maintenance (M = 64.30, SD = 58.30; F( 1, 394) = 33.97, p <.001) and status-advancement (M = 50.00, SD = 34.54; F( 1, 394) = 7.61, p =.006) conditions were willing to pay significantly more than participants in the no-status condition (M = 37.54, SD = 19.52) for the eyewear.
To probe the interaction, we examined the slopes of political conservatism in each status goal condition. As expected, political conservatism predicted WTP for the eyewear emphasizing status maintenance (β = 5.45, t(396) = 2.33, p =.02). By contrast, there was no effect of political conservatism on WTP for the eyewear emphasizing status advancement (β = –1.65, t < 1, p =.487) or the eyewear without a status emphasis (β =.67, t < 1, p =.754; Figure 4). Furthermore, spotlight analyses revealed that WTP for the eyewear emphasizing status maintenance was significantly higher than WTP for the eyewear emphasizing status advancement among conservatives (one standard deviation above the mean of political conservatism; β = –25.21, t(256) = –3.00, p =.003) than among liberals (one standard deviation below the mean of political conservatism; t < 1, p =.627). Conversely, WTP for the eyewear emphasizing status maintenance was significantly higher than WTP for the eyewear without a status emphasis at all levels of political conservatism (all ps <.01).
Graph: Figure 4. Study 3b: WTP (USD) for an eyewear product.
We next examined the mediating role of preference for social stability (N = 401 for this analysis after removing two participants who did not complete these measures). Because status goals moderate the effect of political conservatism on WTP, as reflected in the significant effect of political conservatism in the status-maintenance condition, we expected social stability to mediate the effect only in this condition. Therefore, we coded the status-maintenance condition as 1 and the status-advancement and no-status conditions as –1 and conducted a moderated mediation analysis by using a bootstrapping procedure ([31], Model 15) with a generated sample size of 5,000, including the same covariates. This model estimated the effect of political conservatism on WTP directly as well as indirectly through social stability, with both direct and indirect effects moderated by status goals (Table 4). The first part of the model regressed preference for social stability on political conservatism and showed a significant main effect of political conservatism (β =.50, t(396) = 10.46, p <.001). The second part regressed WTP on political conservatism, status goals, preference for social stability, the political conservatism × status goals interaction, and the preference for social stability × status goals interaction. The results revealed a significant social stability × status goals interaction (β = 4.44, t(392) = 3.25, p =.001), while the political conservatism × status goals interaction was no longer significant (β =.83, t < 1, p =.59). Importantly, the bootstrapping analysis showed that the conditional indirect effect of political conservatism on WTP was significantly mediated by preference for social stability in the status-maintenance condition (β = 4.11, SE = 2.28; 95% confidence interval [CI] = [.45, 9.53]) but not in the other two conditions (β = –.35, SE =.52; 95% CI = [–1.38,.65]).
Graph
Table 4. Study 3: Test of Moderated Mediation by Social and Personal Stability.
| Social Stability as a Mediator |
|---|
| Consequent |
|---|
| Social Stability | Desire for Luxury (WTP) |
|---|
| Antecedent | Coeff. | SE | t | p | Coeff. | SE | t | p |
|---|
| Political conservatism (X) | .502 | .048 | 10.456 | <.0001 | .719 | 1.543 | .466 | .642 |
| Social stability (M) | — | — | — | — | 3.750 | 1.369 | 2.738 | .007 |
| Status goals (V) | — | — | — | — | 10.935 | 2.137 | 5.118 | <.0001 |
| M × V | — | — | — | — | 4.442 | 1.367 | 3.250 | .001 |
| X × V | — | — | — | — | .832 | 1.528 | .545 | .586 |
| Age | .008 | .007 | 1.271 | .204 | –.327 | .175 | –1.870 | .062 |
| Income | –.090 | .040 | –2.030 | .043 | 2.831 | 1.174 | 2.412 | .016 |
| Gender | –.022 | .153 | –.142 | .887 | 5.017 | 4.066 | 1.234 | .218 |
| Model summary | R2 =.223 F(4, 396) = 28.334, p <.0001 | R2 =.114F(8, 392) = 6.336, p <.0001 |
| Personal Stability as a Mediator | | | | | | | | |
| Consequent |
| Social Stability | Desire for Luxury (WTP) |
| Antecedent | Coeff. | SE | t | p | Coeff. | SE | t | p |
| Political conservatism (X) | .222 | .050 | 4.518 | <.0001 | 2.191 | 1.417 | 1.547 | .123 |
| Personal stability (M) | — | — | — | — | 3.211 | 1.397 | 2.299 | .022 |
| Status goals (V) | — | — | — | — | –4.127 | 6.171 | –.669 | .504 |
| M × V | — | — | — | — | 3.573 | 1.395 | 2.562 | .011 |
| X × V | — | — | — | — | 2.525 | 1.404 | 1.798 | .073 |
| Age | .006 | .007 | .945 | .345 | –.299 | .176 | –1.699 | .090 |
| Income | –.056 | .045 | –1.249 | .212 | 2.554 | 1.176 | 2.172 | .031 |
| Gender | –.047 | .157 | –.297 | .767 | 4.139 | 4.079 | 1.015 | .311 |
| Model summary | R2 =.053F(4, 396) = 5.570, p =.0002 | R2 =.102F(8, 392) = 5.566, p <.0001 |
80022242918799700 Notes: Political conservatism is mean centered. Status goal is coded as 1 if status-maintenance and –1 if status-advancement or no-status. Gender is coded as 1 if male and 0 if female.
As an additional robustness check, we conducted the same analysis by comparing ( 1) status maintenance with status advancement and ( 2) status maintenance with no status. All results held at a standard level of significance (Web Appendix M). However, the same bootstrapping analysis using preference for personal stability as a mediator showed that preference for personal stability did not mediate the effect of political conservatism on WTP in the status-maintenance condition (β = 1.50, SE = 1.05; 95% CI = [–.05, 4.27]) or in the other two conditions (β = –.08, SE =.25; 95% CI = [–.64,.39]; for details, see Table 3). Overall, the indirect effect of political conservatism on WTP for luxury goods was mediated by preference for social stability and moderated by status goals, providing support for H1 and H2.
To test the generalizability of our effect, in Study 4 we employed a status goal manipulation independent of the evaluation task—namely, a writing task that induced participants to focus on status maintenance or status advancement. In addition, we used another well-established measure of political conservatism consisting of multiple items ([48]).
We randomly assigned 264 participants (48% female; Mage = 35 years) recruited on MTurk to one of two status goals conditions (status-maintenance vs. status-advancement). After indicating their political conservatism as part of an initial survey, participants took part in a pretest for a future study on written language (our status goal manipulation). Next, as part of a consumer survey, they indicated their desire for six car brands. Finally, they answered the same demographic questions as in the previous studies.
We assessed political conservatism using a scale ([48]) successfully employed in prior research (e.g., [75]). Sample items include "I am politically more liberal than conservative" and "I cannot see myself ever voting to elect conservative candidates" (1 = "strongly disagree," and 7 = "strongly agree"; α =.88). We averaged these scores to form an overall political conservatism score (M = 3.90, SD = 1.57).
Participants engaged in a short writing task. In the status-maintenance (status-advancement) condition, participants read:
A recent analysis of global socioeconomic insight revealed that Americans are expected to experience a decline [improvement] in their status in the next 5 years. This means that relative to citizens of other developed countries, Americans are expected to face more challenges and difficulties in maintaining [more chances and opportunities to improve] their social standing. Now please think about 2–3 ways you may be able to maintain [improve] your social standing in the next few years and list them in the space below.
A pretest confirmed that the status goal manipulation successfully activates different goal foci, while keeping the emphasis on status constant across conditions (Web Appendix N).
The format was the same as in Study 2, except the DV comprised only car brands (for pretest details, see Web Appendix O). Participants indicated the extent to which they wanted a product from six car brands (1 = "not at all," and 7 = "very much").
As in Study 2, our DV was the difference score between participants' desire for luxury (M = 3.91, SD = 1.52; α =.63) and nonluxury (M = 3.31, SD = 1.63; α =.82) brands. We regressed the desire for luxury brands on political conservatism, status goal (status-maintenance = 0, status-advancement = 1), and their interaction as well as the three covariates. There was a significant political conservatism × status goal interaction (β = –.35, t(257) = –2.91, p =.004; Web Appendix P). To probe the interaction, we examined the slopes of political conservatism in each condition. In the status-maintenance condition, political conservatism predicted the desire for luxury brands (β =.21, t(257) = 2.27, p =.024). Unexpectedly, we observed a marginally significant effect in the status-advancement condition, such that political conservatism negatively predicted the desire for luxury brands (β = –.14, t(257) = –1.76, p =.079). A spotlight analysis further revealed that the desire for luxury brands among conservatives (one standard deviation above the mean of political conservatism) was higher in the status-maintenance condition than in the status-advancement condition (β =.58, t(260) = 2.21, p =.03). Among liberals (one standard deviation below the mean of political conservatism), the desire for luxury brands was higher in the status-advancement condition than in the status-maintenance condition (β = –.66, t(260) = –2.52, p =.012).
Overall, Study 4 further demonstrates that political conservatism increases consumers' desire for luxury when the status-maintenance (but not status-advancement) goal is activated (H1) in a context in which the goal is activated independent of the consumption task. One limitation of Study 4 is that the status-maintenance goal condition might have triggered a feeling of loss by prompting participants to think that they may experience a decline in status (unlike in the status-advancement goal manipulation). Although findings on whether losses are more motivating than gains are mixed ([57]), in Study 5 we address this concern by employing a nonloss framed manipulation for the status-maintenance goal.
The objectives of Study 5 were twofold. First, we employed a nonloss-inducing status goal manipulation. Second, we varied the framing of a single product as luxury or nonluxury to demonstrate the luxury-specific nature of the effect. Indeed, if a preference for social stability drives the effect, we should observe the effect only for goods that act as stabilizers of the social hierarchy (i.e., luxury goods), not for goods that do not typically have this association (i.e., nonluxury goods). Practically, the study offers a vivid example of how luxury managers can leverage the findings by changing their product framing.
Three hundred three students (45% female) from a large U.S. college on the West Coast were approached on campus by experimenters and voluntarily participated in exchange for a free snack. Participants were randomly assigned to one of four conditions of a 2 (product framing: luxury vs. nonluxury) × 2 (status goal: status-maintenance vs. status-advancement) between-subjects design with political conservatism as a continuous variable. After assessing political conservatism with the same two-item measure as in Study 3b (α =.72; M = 3.55, SD = 1.06), we manipulated status goal. Finally, participants indicated their WTP for a set of headphones framed as a luxury or a nonluxury product, which served as our DV. We chose headphones because this product is relevant, status signaling, and accessible to our population ([11]).
We randomly assigned participants to one of two writing tasks. In the status-maintenance (status-advancement) condition, they read:
Recent research on college well-being has revealed that one key to a satisfying and successful college life is to maintain stable [improve] social status. By "social status," we mean a relative social standing or the level of respect and admiration received by others in a society. Now, please think about all the respect and admiration you receive [ways you lack respect and admiration] from people around you in various domains of your life. When you're done reflecting, please think about 2–3 ways you can maintain [enhance] your current social status for next few years and list them in the space below.
A pretest confirmed that our status goal manipulation successfully activates different goal foci, while keeping the emphasis on status constant across conditions (Web Appendix Q).
We randomly assigned participants to read and evaluate the new headphones framed as luxury or nonluxury. Common to both conditions was the image of the product (Web Appendix R). However, in the luxury condition the tagline read "Top of the Top, the L-Pro line," and the description used luxury-related words such as "luxurious" and "prestigious." In the nonluxury condition, the tagline read "Made for comfort, the for-all headphones," and the description used words such as "convenience" and "handiness." A pretest confirmed that our product framing manipulation was successful (Web Appendix S).
A three-way analysis of variance on WTP, with status goal (status-maintenance vs. status-advancement) and product framing (luxury vs. nonluxury) as categorical variables and political conservatism as a continuous variable, revealed a significant three-way interaction (F( 1, 295) = 5.66, p =.018; Figure 5). There was a main effect of product framing, such that participants were willing to pay more in the luxury condition (M = 77.58, SD = 74.80) than in the nonluxury condition (M = 32.56, SD = 29.68; F( 1, 295) = 54.52, p <.001), but no main effect of status goal (Mmaintenance = 58.29, SD = 62.03; Madvancement = 50.83, SD = 59.26; F( 1, 295) = 1.92, p =.167).
Graph: Figure 5. Study 5: WTP (USD) for headphones framed as luxury.
In addition, the status goal × political conservatism interaction was significant only in the luxury condition (F( 1, 144) = 9.25, p =.003; nonluxury condition: p =.297). To explore this interaction further, we examined the slopes of political conservatism in each status goal condition. When the status-maintenance goal was activated, political conservatism positively predicted WTP (β = 22.33, t(144) = 2.93, p =.004), but when the status-advancement goal was activated, political conservatism did not predict WTP (β = –.11.20, t(144) = –1.40, p =.163). A floodlight analysis revealed that the effect of the status-maintenance (vs. status-advancement) goal on WTP turned significant at.3 standard deviation above the mean of political conservatism (β = 26.45, t(144) = 2.13, p =.035).
Overall, Study 5 demonstrated that political conservatism increases consumers' WTP for products framed as luxury when their status-maintenance goal is activated, providing further support for our hypothesis (H1). By contrast, political conservatism did not predict WTP for the product framed as nonluxury (β = 5.14, t(151) = 1.51, p =.133), even when the status-maintenance goal was activated. In addition, WTP for the product framed as nonluxury did not differ across status goal conditions among conservatives (at one standard deviation above the mean of political conservatism; t < 1, p =.356). This finding confirms the proposition that the effect is specific to luxury goods because consumers view these goods as reinforcing the social hierarchy ([21]). By varying the framing of a single product, this study provides vivid evidence of when such framing may increase consumers' responses to advertising depending on the dominant political ideology in the target market.
Six studies reveal that political conservatism increases the desire for luxury goods when a status-maintenance goal is activated but not when a status-advancement goal is activated or in the absence of a status goal. To increase the generalizability of our findings, we used four different product categories (cars, fashion clothes, eyewear, and headphones). Furthermore, to provide convergence on our effects, we employed multiple measures of political conservatism (Table 1). Finally, a meta-analysis ([68]) revealed that political conservatism significantly increased the desire for luxury goods across status-maintenance goal conditions (dmaintenance =.24, z = 3.65, p <.001) but not across status-advancement conditions (dadvancement = –.12, z = –1.82, p =.07; for details, see Web Appendix T). A p-curve analysis ([61]) showed that the studies contain evidential value (significantly right skewed; p <.001) and are sufficiently powered, such that the evidential value is not inadequate (not flatter than 33% power, p >.999; Web Appendix U).
Our research makes two important theoretical contributions. First, we contribute to the literature on social status by providing empirical evidence for when status-maintenance versus status-advancement goals may matter in consumption contexts. Despite the influence of social status on consumption, research has mostly treated status as a single construct (e.g., [ 4]; [46]). By showing that consumers engage in status-driven consumption in response to different status goals, our work provides first empirical support for the idea that consumers' need for status might be multidimensional.
Second, we contribute to the nascent literature ([54]) that links consumers' views on social hierarchy with their purchases facilitating their expression of status (i.e., luxury goods; [21]). Going beyond the question whether conservatives may desire luxury products more than liberals, we investigate when this pattern is likely to occur. In doing so, we shed light on the motivational underpinning behind conservatives' desire for luxury goods—that is, their desire to maintain, rather than advance, their status.
Investigation of political ideology carries important implications for managers because it provides a powerful segmentation tool (e.g., [71]). Indeed, people of varying political ideologies differ in the brands they favor and media outlets they follow. As a result, brands tend to match the media used to their consumers' political orientations (Figure 1). For example, Jeep, the most desired car brand among Democrats, only sponsors left-leaning media (e.g., http://sponsorfeedback.com), and Apple, another patron of left-leaning media, actually stopped advertising on the Fox Network during the 2012 season because the company judged the broadcasted content as conflicting with its core philosophy ([ 2]). Apart from one-time surveys mapping political party to brands, however, managers lack both resources giving them a systematic understanding of how political ideology may influence brand choices and guidelines on how to leverage political ideology as a segmentation tool. As luxury products appeal to specific audiences, refining the segmenting and targeting of consumers is central to the management of luxury brands ([20]).
To this end, we offer a more sophisticated but practical approach to luxury market segmentation. Our findings support an approach that takes into account two factors that together predict consumers' appetite for luxury goods: political ideology and status-maintenance activation. Importantly, data on both factors are accessible and identifiable at granular levels, making it easy for managers to leverage our findings when designing their segmentation and targeting strategies. First, managers can easily identify political ideology along geographic segmentation ([12]) or through agencies (e.g., Pew Research Center, Gallup, ICPSR) in great detail (e.g., town level; see Web Appendix B for examples of available data sources). They can also assess political ideology through consumers' preferences for media outlets ([34]) or recognizable digital footprints on online platforms such as social media (e.g., "likes" for a political issue, following of a political figure; [15]; [41]; [56]). Second, they can easily assess status-maintenance activation by ( 1) using positional metrics such as education, income, and SES, which are often available in survey data (e.g., Consumer Expenditure Survey, U.S. Census); ( 2) identifying socioeconomic contexts that activate status-maintenance motives (e.g., economic downturns, when consumers may attempt to reassure their standing through luxury consumption; [52]); or ( 3) momentarily heightening activation by altering the framing used in brand communications.
We also provide detailed guidelines on how managers of luxury brands can motivate luxury consumption (Figure 2). First, managers should consider whether their brands currently leverage status maintenance. If so, our findings suggest that simply identifying and targeting a conservative segment can increase their effectiveness. To do so, the brand could run targeted marketing campaigns ( 1) on media platforms patronized by conservatives (see Figure 1; [34]), ( 2) in conservative geographic areas (see Web Appendix B; [12]), or ( 3) online by targeting individual consumers associated with digital footprints indicative of conservatism ([15]). If the brand does not currently emphasize status maintenance, managers could consider leveraging status-maintenance messages in their communications or targeting individual or situational contexts with high status-maintenance activation (e.g., consumers having high-status positions). In summary, our findings provide insights into how managers can drive luxury brands using political ideology or differential status goals rather than targeting a segment on the basis of wealth or status.
Our research is not without limitations, which offer potential avenues for further research. First, our efforts center only on the political spectrum from conservative to liberal; however, a growing number of people are finding it difficult to identify with either mainstream political ideologies or new ones (e.g., Independents). In Study 2, for example, 36% of the consumers who disclosed their political ideology identified themselves as neither Republican nor Democrat. Little is known about how political orientations other than Republican or Democrat might influence the way people construe status and engage in luxury consumption. Consistent with prior research (e.g., [54]), we treated liberalism and a low degree of conservatism synonymously, given our focus on conservatism. Furthermore, although research has shown that the single spectrum ranging from liberalism to conservatism can effectively capture variations in personal and sociopolitical perspectives, including a preference for social stability ([51]), liberalism and conservatism may differ on other dimensions for which a continuous scale measurement may be ill-suited. Additional research is necessary to further unpack the relationship between liberalism and conservatism and investigate their interplay with consumption practices.
Second, we limited our research to the U.S. context. Given previous findings that political conservatism carries different meanings across different countries ([ 9]; [16]), the extent to which our results may replicate in a non-U.S. context is unclear. As an initial exploration, we conducted a study using the same design as in Study 5 in France. Although the study did not reveal a significant political conservatism × status goal interaction (see Web Appendix V), a crucial difference was that political conservatism was significantly lower in the French sample (M = 3.53, SD =.88) than the U.S. sample (M = 3.90, SD=1.57, t(296) = –7.24, p <.001). While average political conservatism did not differ from the scale midpoint in the U.S. sample (t(263) = –1.07, p =.287), this difference was significant in the French sample (t(296) = –9.19, p <.001). In addition, the reliability of the Mehrabian scale was much lower in France (α =.52) than in the United States (α =.88), indicating that assessing political conservatism in France may involve different processes. Thus, an important boundary condition for the effect may lie in the characteristics of the distribution of political conservatism among the population of interest.
Third, future studies could investigate the conditions that may lead liberals to desire luxury goods more than conservatives. Across two of our studies, liberals showed a greater desire for luxury than conservatives when their current status position was low (Study 2) and when the status-advancement goal was activated (Study 4). These mixed results suggest that there are conditions in which political liberalism triggers luxury consumption. Of note, this effect was significant only in the studies employing real brands as the DV (Studies 2 and 4). Although we pretested brands on key dimensions tied to our investigation (e.g., luxurious, liking; Web Appendix J), the luxury brands may also differ from nonluxury brands on other dimensions not captured by the pretest. For example, consumers may perceive these brands as more horizontally differentiating (e.g., unique, innovative, creative) than nonluxury brands, leading to the observed effect ([54]).
A fourth potential avenue, equally appealing to managers and researchers, would be investigating contexts that activate status maintenance. As an initial attempt, we used the high-status position to activate status maintenance when testing our hypotheses in Studies 1 and 2. Beyond consumers' current status—a broad construct that may influence factors other than the degree of status-maintenance activation—researchers could try to identify individual or group variables that naturally induce changes in status-maintenance activation. As status-maintenance goal activation stems from a person's current status state overlapping with his or her desired status state, situations that increase the overlap between states may foster status-maintenance activation. For example, a person who just obtained a promotion and was given new positional assets such as a company car may be more likely to focus on maintaining his or her current position than a person who was not promoted. In addition, among those holding a desired status position, increases in the instability of their current status position may activate status maintenance ([63]). Thus, making salient the prospect of sliding back from a current status position (e.g., an elected official unsure about being reelected, an executive manager approaching the end of a contract term) may help activate the status-maintenance goal.
Finally, our work reflects a resurgence of interest in political ideology in the social sciences ([37]) and is a response to the emerging interest in political ideology in marketing ([35]; [59]). As [59], p. 500) notes with regard to political ideology, "understanding the psychology of liberals and conservatives can inform a range of managerial decisions." In this spirit, we hope our initial steps will pave the way for new efforts that increase understanding of the role of political ideology in marketing.
Supplemental Material, DS_10.1177_0022242918799699 - How Consumers' Political Ideology and Status-Maintenance Goals Interact to Shape Their Desire for Luxury Goods
Supplemental Material, DS_10.1177_0022242918799699 for How Consumers' Political Ideology and Status-Maintenance Goals Interact to Shape Their Desire for Luxury Goods by Jeehye Christine Kim, Brian Park, and David Dubois in Journal of Marketing
Footnotes 1 Associate EditorVikas Mittal served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThis research was supported by an INSEAD grant, the INSEAD Alumni Fund, the HKUST initiation grant, and the HKUST start-up grant.
4 Online supplement: https://doi.org/10.1177/0022242918799699
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By Jeehye Christine Kim; Brian Park and David Dubois
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Record: 88- How Do Specialized Personal Incentives Enhance Sales Performance? The Benefits of Steady Sales Growth. By: Patil, Ashutosh; Syam, Niladri. Journal of Marketing. Jan2018, Vol. 82 Issue 1, p57-73. 17p. 8 Charts. DOI: 10.1509/jm.15.0523.
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How Do Specialized Personal Incentives Enhance Sales Performance? The Benefits of Steady Sales Growth
The authors study specialized personal incentives (SPIs), which are cash rewards granted to salespeople for meeting interim performance goals within the regular sales quota period (monthly, quarterly, etc.). Because firms often institute multiple SPIs, the authors are able to investigate whether different sales achievement trajectories have differential impacts on salespeople’s period-end sales performance. The authors find that a steadily growing sales trajectory in a sales period is more strongly associated with period-end success than a sales trajectory that is relatively flat early but has a sharp spike later in the period. Furthermore, although salespeople who had high performance in the prior month (i.e., high-performance state) may be able to draw on superior selling strategies (compared with other salespeople), they too experience a boost in sales performance in the current month by earning SPIs. Notably, the authors also find that although earning SPIs benefits all salespeople, there is a U-shaped relationship between a salesperson’s performance state and his or her month-end sales performance. For any specific number of SPIs earned, the probability of meeting and exceeding month-end quotas is boosted more for salespeople with low- and high-performance states than for salespeople with a medium-performance state.
One of the important issues we investigate in this research is whether different types of sales production trajectories of salespeople in a sales period are associated with different period-end sales performance in the context of the quota in that period. Notably, we compare the period-end performance of a salesperson whose sales trajectory is steadily growing throughout the sales period with that of a salesperson who had relatively low sales production early in the period followed by very rapid sales production success later in the period. Consider two salespeople, A and B—with exclusive territories, equal abilities, and equal quotas—at a firm where the sales period is one month. Assume that both A and B achieved 50% of their current month’s quota by the 20th of the month. Assume as well that A achieved 20% of the month’s quota by the 10th of the month and another 30% between the 10th and the 20th. In contrast, B achieved only 5% of the month’s quota by the 10th of the month and another 45% between the 10th and the 20th. So, A had a relatively steady growth in her sales production from the 1st through 20th of the month, whereas B had lower sales until the 10th but experienced a steep spike from the 10th through the 20th. Thus, A’s sales trajectory was steadily growing through the first two-thirds of the month, whereas B’s sales trajectory was barely rising in the first third of the month but grew very rapidly in the second third of the month.
Suppose that, on the 20th of the month, their sales manager wants to know who between A and B will have better sales performance in the context of exceeding their sales quotas at the end of that month. Because both A and B had earned 50% of their quota by the 20th of the month, one perspective might suggest that A and B will have equal performance at the end of the month. A second perspective suggests that because B has experienced recent momentum as of the 20th of the month, she will have better month-end sales performance than A. Unlike A, B has recency and momentum of sales production; having made more sales between the 11th and the 20th of the month, most of B’s sales are recent. Both recency and momentum have been found to be important predictors of future outcomes in many different contexts (e.g., momentum investing, “hot hand” phenomenon). Thus, one could infer that B might have superior month-end performance to A. A third perspective is based on the fact that A has been more consistent than B, as A had a steadily rising sales production until the 20th of the month. In line with findings such as the “spacing effect” (Janiszewski, Noel, and Sawyer 2003) as well as subgoals literature (Fishbach, Zhang, and Dhar 2006), A will have better month-end sales performance than B. Indeed, given the theoretical rationales presented, both the second and third perspectives are relevant and reasonable outcomes, even if one of the two salespeople (either A or B) had covered a higher proportion of her equal month-end quota by the 20th of the month than the other.
In this research, we rely on specialized personal incentives (SPIs) to test the association between sales trajectory types and period-end performance. An SPI is an interim (within a sales period) incentive that some firms incorporate for their sales force. Such “special incentives” can take the form of “spiffs” (defined by Zoltners, Sinha, and Lorimer [2006] as “special performance incentive for the field force”). The specific type of SPIs we focus on are intermediate, extra incentives that salespeople can earn in addition to their period-end bonus for exceeding interim quotas (which we refer to as “SPI quotas”). These SPI quotas require salespeople to achieve a specific percentage of the period-end quota by a specific time in that sales period. In a context similar to SPIs, Chung, Steenburgh, and Sudhir’s (2014) pioneering study shows that quarterly bonuses help mainly the weak performers by serving as pacers to keep them on track in achieving their regular annual bonuses. In contrast, we investigate whether different types of sales-production trajectories differentially affect salespeople’s period-end performance. We also investigate whether salespeople’s performance state (performance at the end of the prior period) interacts with different types of sales-production trajectories in the next period to determine their performance at the end of the next period.
Conceptually, SPI quotas are best understood in the context of the relationship of subgoals with superordinate goals (Fishbach, Zhang, and Dhar 2006). Subgoals are lesser goals that form a part of the superordinate goal. Specialized performance initiative quotas have the same relationship to regular period-end quotas that subgoals have to superordinate goals. Fishbach, Zhang, and Dhar state, “Setting goals and monitoring progress toward goal achievement is fundamental to theories of self-regulation” (p. 232). If an interim milestone increases commitment for the superordinate goal, then that interim milestone is beneficial in reaching the superordinate goal (Austin and Vancouver 1996; Devezer et al. 2014; Fishbach and Dhar 2005; Fishbach, Shah, and Kruglanski 2004). In line with this, the SPIs at our data-provider firm, being explicitly tied to the period-end quota and distinctively expressed as increasing cumulative percentages of the period-end quota, should enhance commitment to meeting the period-end quota. The sales quota period at our data-provider firm is one month. This firm has instituted two explicit SPI quotas, namely ( 1) “reaching 20% of the monthly quota by the 10th of the month for the early SPI,” and ( 2) “50% of the monthly quota by the 20th of the month for the later SPI” (see Figure 1).
Regarding the scenario presented previously, salesperson A has earned both SPIs (i.e., the early SPI as well as the later SPI) that month, whereas B earned only the later SPI. In the context of our data-provider firm, without a systematic empirical analysis, it is not obvious a priori whether earning both SPIs (steadily growing sales trajectory) has a higher association with superior period-end performance than does earning just the later SPI (a recent spike in the sales trajectory conveying recent momentum). Alternatively, does earning both SPIs have a lower comparative association with period-end performance? The unique aspects of our data enable us to test the association between the nature of the salesperson’s sales trajectory and his or her period-end sales performance without confounding it with absolute dollar sales achieved. In contrast, if the SPIs at a firm are not cumulative (e.g., if they are expressed as achieving $x in sales by the 10th of the month, and $y in sales between the 10th and 20th), as is the case with quarterly quotas, then earning more SPIs necessarily leads to higher sales than does earning fewer SPIs.
Our research contributes by examining the following research questions: ( 1) Is a steadily growing sales trajectory (in which both SPIs are earned) associated with higher end-ofperiod performance compared with a sales trajectory with a recent spike (in which only the later SPI is earned), or vice versa? ( 2) Does salespeople’s performance state (i.e., their performance level at the end of the prior month) determine the extent to which they will benefit from earning SPIs in the subsequent month? ( 3) Is earning SPIs in one period associated with earning SPIs in the next period?
A preview of our results: First, earning both SPIs in a period is more strongly associated with superior period-end performance than is earning only the later SPI in that period (even though, a priori, the two may be indistinguishable on the 20th of the month). So, a steadily growing sales trajectory enhances the chances of success relative to a trajectory with a sharp spike. Second, we find that earning SPIs benefits all salespeople. Yet, notably, there is a U-shaped relationship between a salesperson’s performance state and his or her sales performance at the end of the next month. For any specific number of SPIs earned, the probability of meeting and exceeding month-end quotas is boosted more for salespeople with low- or high-performance states than for salespeople with a medium-performance state. This result highlights the importance of earning SPIs even for salespeople with a high-performance state. This finding also indicates that although salespeople with a high-performance state likely develop better sales strategies than do other salespeople, these superior strategies alone do not provide the upper bound of possible performance for the salespeople with a high-performance state. The salespeople with a high-performance state nevertheless benefit by earning SPIs (i.e., being self-regulated). Third, we find that earning SPIs in one sales period is associated with earning SPIs in the next period.
This article is organized as follows. First, we review relevant literature and present our hypotheses. We then present the empirical setting. Next, we respectively describe and present the results of our first empirical analysis, which offers support for the effects of earning SPIs on the period-end sales achievement. Then, we respectively describe and present the results of our second empirical analysis, which tests whether earning SPIs in one period is associated with earning them in the next period. Finally, we present the general discussion and managerial implications.
The literature on the psychology of goal achievement suggests that people find it difficult to start a task when they face a challenging goal (Gollwitzer and Brandstaetter 1997; Heath, Larrick, and Wu 1999). This literature notes that one way to overcome the “starting problem” is to use subgoals. Heath, Larrick, and Wu (1999, p. 93) state that “proximal subgoals are more likely to produce eventual success.” In extending this to the sales force context, the within-period interim SPI quotas are subgoals and the period-end quotas are superordinate goals.
However, it is not always optimal to instill subgoals, and the literature presents mixed evidence on the motivational abilities of subgoals. Experiencing early success in reaching a subgoal can create complacency and thereby can lead to a reduction in effort (Schunk 1983, 1984; Vancouver, Thompson, and Williams 2001). Extant research has shown that achieving early subgoals leads to weaker performance on the superordinate goals. In a celebrated example, cab drivers in New York were found to drastically reduce effort after some “earning” goals were met early in the day (Camerer et al. 1997).
Given the conflicting evidence about the efficacy of sub-goals, how should a sales manager perceive SPIs? The key lies in how salespeople infer subgoal achievement, and how managers can influence it. Fishbach, Zhang, and Dhar (2006) and Fishbach and Dhar (2008) argue that the achievement of the superordinate goal can be diminished or enhanced on the basis of whether the subgoal’s success is respectively framed and inferred as progress or as commitment. If achieving the subgoal is viewed as progress already made, people decrease effort due to a sense of complacency. Yet if subgoal achievement is viewed as increasing commitment toward the superordinate goal, people put in greater effort, and subgoal success positively affects the superordinate goal. We argue that the SPIs at our data-provider firm are designed to instill commitment to the superordinate goal (we provide more details in the “Empirical Setting” section), and therefore, for the remainder of this article and the subsequent hypotheses, we focus on such SPIs. We thereby hypothesize,
H1: Earning any SPI in a month (i.e., only the early SPI, only the later SPI, or both SPIs), versus not earning any SPI in the month, is associated with higher month-end sales performance.
The next question that arises is whether earning only the later SPI (vs. only the early SPI) in a sales period is more effective at positively influencing period-end performance, or vice versa. This issue relates to the distance of the subgoal from the period-end superordinate goal. By the time the salesperson has achieved the later SPI, (s)he is closer to the superordinate goal than (s)he would be after achieving the early SPI. In this context, Bonezzi, Brendl, and De Angelis (2011) provide helpful guidance. They argue that if a person uses the initial state as the reference point for monitoring advancement (i.e., “to-date frame”), the perceived marginal value of incremental advances made decreases. For example, reading one more page of a lengthy book is perceived as yielding less advancement after having read 200 pages than after having read 50 pages. In addition, getting closer to the end-goal can galvanize the person to expend greater effort. In such a context, Bonezzi, Brendl, and De Angelis argue that if a person uses the desired end state as the reference point (i.e., “to-go frame”), the perceived marginal value of advancement increases. Reading one more page is viewed as yielding more advancement when 50 pages remain than when 200 pages remain.
The issue, therefore, is whether salespeople use the “to-date frame” versus “to-go frame” when reviewing their advancement toward their period-end sales quota. It is reasonable to argue that SPIs focused on the period-end superordinate goal (as is the case at our data-provider firm, because the SPIs are presented as percentage of month-end quota) should trigger the “to-go frame” in salespeople. Compared with when a salesperson earns only the early SPI in a sales period, the (s)he is closer to the period-end sales quota reference point when (s)he earns only the later SPI in a sales period. Following the argument presented by Bonezzi, Brendl, and De Angelis, any marginal advancements in sales production made after earning only the later SPI in a period (being closer to the end of the period) should be viewed as being more motivating and effort inducing than the marginal advancements in sales production after earning only the early SPI in that period. Thus,
H2: Compared with earning only the early SPI in a month, earning only the later SPI in the month is associated with higher month-end sales performance.
Next, we turn to the issue of earning two SPIs versus only one SPI in a period, in terms of their association with meeting and exceeding end-of-period quotas. It is important to determine whether earning both SPIs (i.e., steadily growing trajectory) has a stronger association than earning only the later SPI (i.e., slow growth in sales early in the month with a very rapid rise later) with superior period-end performance, or vice versa. Note that if earning more SPIs were to automatically imply more sales, then this would be a foregone conclusion. However, in our case, it is not clear a priori whether this supposition holds. Recall again the two salespeople A and B, both of whom have achieved exactly 50% of the current month’s quota by the 20th of the month. Salesperson A (salesperson B) has achieved 20% (5%) of this month’s quota by the 10th of the month and another 30% (45%) between the 10th and the 20th. Although their sales production is at 50% of the month-end quota on the 20th of the month, salesperson A has earned both SPIs in that month. In contrast, salesperson B has earned only the later SPI in that month. Essentially, looking back from the 20th of the month, for the same amount of sales, the sales of salesperson B are more “bunched” toward the end compared with those of salesperson A, whose sales are more “spaced out” and therefore steadier.
Literature on learning has found evidence of a “spacing effect,” which suggests that, for the same lump sum, spacing events apart results in better outcomes than amassing them together at the end (Janiszewski, Noel, and Sawyer 2003; Kornell 2009). Dempster (1988) states, “The spacing effect—which refers to the finding that for a given amount of study time, spaced presentations yield substantially better learning than do massed presentations—is one of the most remarkable phenomena to emerge from laboratory research on learning” (p. 627, emphasis added). To apply the spacing argument to our example of salespeople A and B, judged from the 20th of the month, salesperson A has better learning and therefore will likely have better performance with respect to month-end quota. Why does the spacing effect work? Many studies on the spacing effect are in the context of learning and are based on the spreading of the “repetition effect,” whereby more opportunities to learn are valuable, implying that last-minute “cramming” is often counterproductive (Dempster 1987; Schmidt 1983). There is an extensive literature on the “learning orientation” of salespeople, and selling effectively to achieve quotas triggers salespeople’s learning orientations (Ahearne et al. 2010; Sujan, Weitz, and Kumar 1994). Thus, trying to achieve the multiple quotas associated with earning multiple SPIs gives the salespeople more learning opportunities, thus enhancing their performance. So,
H3: Compared with earning only the later SPI in a month, earning both SPIs in a month is associated with higher month-end sales performance.
Note that, as we discussed in the introduction, on the 20th of the month, salespeople who earn only the later SPI (salesperson
B) have recency in sales production momentum (during the second third of the month). Extant literature has also presented evidence suggesting that coming in later with a burst of recent momentum is more beneficial than having early success. The most telling among these likely is the “hot hand” phenomenon. Evidence from literature streams such as behavioral decision theory and others has found that both recency and momentum are important predictors of future outcomes in many contexts. For example, recency of purchase is one of the prime factors in the recency, frequency, and monetary model of customer lifetime value. The literature on innovation has also shown that late entrants (which have momentum on their side) can “leapfrog” over early pioneering firms (Golder and Tellis 1993). In addition, momentum investing in stocks is a strategy aimed at capitalizing on the belief that large recent increases in the price of a stock will be followed by additional increases (Investopedia 2017). Furthermore, recent findings in the literature have shown evidence for the “hot hand” phenomenon (i.e., a person who has recently experienced success may have a greater chance of further success in additional attempts; e.g., Andrews 2014; Gelman 2015; Green and Zwiebel 2013; Miller and Sanjurjo 2015). These arguments provide evidence for outcomes that contradict H3. This evidence suggests that salesperson B, due to her recency and momentum around the 20th of the month, should have better month-end performance than salesperson A and thus that earning only the later SPI in the month will be associated with higher month-end sales performance than earning both SPIs. However, to avoid confusion, we refrain from formally presenting an alternate hypothesis that contradicts H3. Instead, we simply rely on the results of our empirical analysis to test whether H3 is supported.
It is also noteworthy that expectancy theory (Oliver 1974; Vroom 1964) could have been used to arrive at some of our hypotheses, such as H1. Expectancy theory addresses motivation in terms of value and likelihood associated with outcomes. Expectancy theory posits that as the probability of an event increases, people will be more motivated to put in effort. However, it is difficult to present H2 or H3 by relying only on expecting theory. For example, we are not aware of anything in expectancy theory that would have enabled us to make the predictions that we have presented in H3. Expectancy theory does not provide clear guidance on whether the subjective probabilities of exceeding the period-end quota are enhanced to the same extent (or to different extents) if the salesperson earns only the later SPI (vs. both SPIs) in a period. Thus, in deriving the logic for our hypotheses, we preferred to rely on self-regulation theory and concepts such as subgoals.
Next, we investigate whether salespeople with different performance states from the prior month (i.e., low-performance vs. medium-performance vs. high-performance states) benefit differentially from earning both SPIs in the next month in the context of exceeding their performance target at the end of the next month. In education research, it is well-established that weaker students benefit from frequent testing. Crooks (1988, p. 469) argues, “Weaker students may benefit from identification of more attainable intermediate goals, thus making possible the pattern of repeated successes that leads to improved self-efficacy.” Likewise, Chung, Steenburgh, and Sudhir (2014) find that instead of holding only annual quotas for salespeople, instituting quarterly quotas in addition to the annual quotas is associated with the highest improvement in annual performance among weak salespeople. In comparison, the improvement among medium- and high-performing salespeople was modest. Extending these findings to our context, we propose that earning both SPIs in the next month will be associated with the highest boost in the next month’s performance in salespeople with a low-performance state (i.e., salespeople with low performance in the immediately preceding sales period) compared with the boost experienced by salespeople with high-performance or medium-performance states. Thus,
H4: Compared with salespeople with high- or medium-performance states, salespeople with a low-performance state experience a higher boost in month-end performance by earning both SPIs (vs. no SPIs) in that month.
Finally, we focus on understanding whether earning SPIs in the prior period is likely to enhance the likelihood of doing so again in the next period. Lal and Srinivasan (1993) argue that the salesperson’s decision to put in effort has an intratemporal component, such that efforts in one period could affect efforts in the next. In the psychology literature on goals, a large body of work has investigated the persistence of goal-directed behaviors (Austin and Vancouver 1996; Locke and Latham 1990a, b). In our context, with the month-end quota being the superordinate goal, the effort to earn SPIs is a goal-directed behavior, which literature suggests should have some persistence. The literature on routinization also mentions the automatic mental associations among superordinate goals and goal-directed actions (earning SPIs), which can be instrumental in attaining these superordinate goals (Aarts and Dijksterhuis 2000; Ohly, Sonnentag, and Pluntke 2006). Extant research has suggested that once the goal is activated, the accessibility of such goal-directed actions depends on the frequency or recency with which they are applied in the future (Bentler and Speckart 1979; Wyer and Srull 1989), thus suggesting that earning one or more SPIs (i.e., incorporating goal-directed action) in a month is likely to increase chances of earning SPIs in the subsequent month. Indeed, Aarts and Dijksterhuis (2000, p. 54) conceptualize that “frequent and consistent performance of a goal-directed action in a specific situation facilitates the ease of activating the mental representation of this behavior (and hence the resulting action itself) by the situation.” In the sales context, the persistence of earning SPIs in subsequent periods is relevant, as sales managers would like to know whether instituting SPIs could induce persistence effects of the action of earning SPIs. Note that compared with earning only the early SPI or later SPI, earning both SPIs (repetition of earning SPIs in a month) has higher recency and frequency. Thus,
H5: Compared with not earning any SPI in the prior period, earning only the early SPI in the prior period is more strongly associated with earning both SPIs in the current period.
H6: Compared with earning only the early SPI in the prior period, earning only the later SPI in the prior period is more strongly associated with earning both SPIs in the current period.
H7: Compared with earning only the later SPI in the previous period, earning both SPIs in the prior period is more strongly associated with earning both SPIs in the current period.
Empirical Setting
The data for this study are from a medium-sized nationally branded personal care CPG manufacturer selling products in an emerging economy. The firm has national-level brand recognition and a respectable market share in a few of its product categories. It has a team of more than 400 salespeople selling to retailers in geographically exclusive territories all over the country. More than 90% of retailers in this emerging economy are independent and unorganized—mostly small family-owned businesses with little to no bargaining power. Such retailer customers do not have specific temporal patterns to their buying cycles and purchase their merchandise only when their stock is depleted. They do not have the available cash required to time their purchases to enable the salespeople to play timing games. The salespeople visit each store in their territory twice each month (roughly 15 days apart), and the transactions occur during their visit to the store.
The data-provider firm has a “monthly” sales horizon period for the regular quota-bonus incentives for its sales force. Figure 1 provides a visual depiction of the bonus plan for the sales force at this firm. The data set contains details on the monthly sales quotas (i.e., targets) imposed on each salesperson, as well as the monthly sales achievement (i.e., production) of that member. As indicated previously, this firm also has two SPIs in place every month. The early SPI is for meeting 20% of the regular monthly sales quota by the 10th of the month, and the salesperson is granted a cash award (300 units in the local currency) for doing so. The later SPI is for meeting 50% of the regular quota by the 20th of the month, and the salesperson is granted a cash award (800 units in the local currency) for doing so. Each salesperson can earn neither, either, or both SPIs each month. On average, salespeople at this firm had earned SPI cash bonuses of 501 each month (SD = 503) in the local currency.
The explicit nature of the SPIs at this firm (stated as percentages of the month-end quota) makes it very clear to the salespeople that the month-end quotas are the main target of interest, and the SPIs are the means to that end. Therefore, the design of the SPIs is such that the superordinate goal is always salient in the salespeople’s minds: after achieving the early SPI, for example, they are likely to think, “I just have accomplished 20% of the monthly quota.” Such a design draws their attention to the month-end quota as the superordinate goal and strengthens their commitment to it (Fishbach and Dhar 2008; Fishbach, Zhang, and Dhar 2006). The psychological forces described here should hold after earning the early SPI, the later SPI, or both SPIs.
In addition, at the end of each month, all salespeople are evaluated on whether they have met or exceeded their regular monthly quotas, and it is on this basis that they receive month-end bonuses. At the end of every month, this firm categorizes all of its salespeople into one of the following four performance categories on the basis of their performance relative to quota: ( 1) the “fold” category, if they fail to meet their sales quota that month (no cash bonus); ( 2) the “minimum” category, if their sales achievement is between 100% and 110% of their quota that month (cash bonus of 2,250 units in local currency); ( 3) the “stretch” category, if their sales achievement is between 110%–120% of their quota that month (cash bonus of 3,200 units); and ( 4) the “outstanding” category, if their sales achievement is greater than 120% of their quota that month (cash bonus of 4,200 units). The dependent variable in the first of our two empirical analyses is the ratio of each salesperson’s sales production to his or her sales quota each month (referred to as sales-to-quota ratio [SQR]). On average, salespeople at this firm had sales achievements of 324,035 (SD = 224,488) each month in the local currency of the country. Furthermore, on average, the salespeople at this firm earned end-of-month cash bonuses of 2,619 each month (SD = 1,563) in the local currency.
This CPG manufacturing firm provided us a data set that held details for 33 months, starting from April 2009. This data set holds 9,384 observations for 813 salespeople, in the form of a panel. We executed two empirical analyses on these data (referred to herein as the First Analysis and the Second Analysis), as we explain in the following sections. Table 1 presents an explanation of several variables, including the covariates
(i.e., independent variables), used in the two analyses as well as descriptive statistics for continuous covariates.
The First Analysis is focused on testing support for H1–H4. For the remainder of this article, we refer to salespeople with “outstanding” performance in the prior month state as having a high-performance state, those with “stretch” performance in the prior month state as having a high-mediumperformance state, those with “minimum” performance in the prior month state as having a low-medium-performance state, and those with “fold” performance in the prior month state as having a low-performance state. Furthermore, the high-medium-performance state and low-medium-performance state are together referred to as the medium-performance state. These states are parallel to those used by Steenburgh (2008), who categorizes salespeople in line with their state from the perspective of performance against quota. Steenburgh studies how salespeople’s states in the context of performance to date (i.e., before and after quarter-end dates) affect revenue production in a given period.
TABLE: TABLE 1 Covariates Used in the Analyses
TABLE:
| Covariate | Definition |
|---|
| EarlySPIpt | An SPI Type indicator variable that takes a value of 1 if salesperson p earned the cash award for earning only the early SPI (and not the later SPI) in month t. This variable identifies the main effect of salesperson p earning only the early SPI in month t. |
| LaterSPIpt | An SPI Type indicator variable that takes a value of 1 if salesperson p earned the cash award for earning only the later SPI (and not the early SPI) in the month t. This variable identifies the main effect of salesperson p earning only the later SPI in month t. |
| BothSPIpt | An SPI Type indicator variable that takes a value of 1 if salesperson p earned the cash award for earning both SPIs in month t. This variable identifies the main effect of salesperson p earning both SPIs in month t. When all three indicators EarlySPIpt, LaterSPIpt, and BothSPIpt are 0, it implies that salesperson p did not earn any SPI during month t. |
| lag_Minpt | A Performance State variable assuming a value of 1 if salesperson p achieved the “minimum” sales performance categorization at the end of the month just prior to month t. This variable identifies the main effect of the state that salesperson p had achieved “minimum” sales at the end of the prior month. |
| lag_Stretchpt | A Performance State indicator variable assuming a value of 1 if salesperson p achieved the “stretch” sales performance categorization at the end of the month just prior to month t. This variable identifies the main effect of the state that salesperson p had achieved “stretch” sales at the end of the prior month. |
| lag_Outspt | A Performance State indicator variable assuming a value of 1 if salesperson p achieved the “outstanding” sales performance categorization at the end of the month just prior to month t. This variable identifies the main effect of the state that salesperson p had achieved “outstanding” sales at the end of the prior month. When all three indicator variables lag_Minpt, lag_Stretchpt, and lag_Outspt are 0, it implies that salesperson p had achieved “fold” sales category at the end of the month just prior to month t. |
| SQRpt | The ratio of the sales achievement of salesperson p to his or her sales quota at the end of month t. This continuous variable is the dependent measure in the First Analysis. |
| SPIpt | The type of SPI (i.e., none, early, later, or both) that salesperson p earned in month t. This is a four-category nominal variable, also referred to as “SPI Type.” |
| Quotapt | The sales quota (in the currency of the country in which the firm is located) for salesperson p in month t. This variable has been incorporated in the model to control for the effects of the sales quota imposed on salesperson p in month t on the sales performance categorization that salesperson p achieves at the end of month t. The average monthly quota is 295,958 (SD = 186,532) in the local currency. |
| Zonei pt, i = 1–3 Southpt Northpt Eastpt | Control indicator variables indicating the geographical zone in the country of the city in which salesperson p operates. These variables have been incorporated to control for the effect of salesperson p’s location. When all these indicators are 0, it implies that salesperson p operates in a city within the country’s “West” zone. |
| Trendt | A control variable taking values 1 through 33 for the particular month and is incorporated to control for time trend. In other words, this variable takes whatever value the t subscript has. |
| Qtrj pt, j = 1–3 Qtr1pt, Qtr2pt, Qtr3pt | Control indicator variables indicating the quarter to which month t belongs. These variables have been incorporated to control for seasonality effects. When all three indicators are 0, it implies that the quarter under consideration is the fourth quarter in the year. |
| logBaseSalarypt | The natural log of the base salary of salesperson p in month t. It is a control variable. The average base salary in the data set is 15,020 (SD = 3,491) in the local currency. logAgep The natural log age (in years) of salesperson p at the beginning of the first month in the data. It is a control variable. The average age in this data set is 32.50 years (SD = 8.51). |
| logSeniorityp | The natural log of the number of months that salesperson p has been with the firm at the beginning of the firstmonth in the data. It is a control variable. The average seniority in this data set is 42 months (SD = 11.11) |
| Genderp | The gender of salesperson p. It is a control variable. Eighty-one percent of the salespeople employed by this firm were male. |
| lag_EarlySPIpt | An indicator variable that takes a value of 1 if salesperson p earned the cash award for earning only the early SPI in the month just prior to month t. |
| lag_LaterSPIpt | An indicator variable that takes a value of 1 if salesperson p earned the cash award for earning only the later SPI in the month just prior to month t. |
| lag_BothSPIpt | An indicator variable that takes a value of 1 if salesperson p earned the cash award for earning both SPIs in the month just prior to month t. |
Notes: The subscript p denotes a salesperson; we have data for 813 salespeople. The subscript t denotes the month; we have data for 33 months, so t has a value of 1–33.
We investigate the association among the interaction of the type of SPI earned by each salesperson in the current month and the salesperson’s performance state in the prior month on the dependent measure, SQR in the current month. Recall that SQR (M = 1.10, SD = .315) is each salesperson’s current month’s sales production measured against the quota for the month. The first set of substantive covariates is the “type of SPI earned in the current month” (i.e., no SPI vs. early SPI vs. later SPI vs. both SPIs) by each salesperson (“SPI Type” hereinafter). The second set of substantive covariates is “sales performance state in the prior month” (i.e., fold vs. minimum vs. stretch vs. outstanding; “Performance State” hereinafter). The last set of substantive covariates is the interaction between SPI Type and Performance State.
To operationalize the SPI Type covariate, we rely on indicator variables EarlySPI, LaterSPI, and BothSPI, which respectively assume a value of 1 if the salesperson earned only the early SPI, only the later SPI, or both SPIs in the month. When all indicators (EarlySPI, LaterSPI, and BothSPI) have a value of 0, it implies that the salesperson did not earn any SPI in that month. Furthermore, to operationalize the Performance State covariate, we rely on indicator variables lag_Min, lag_Stretch, and lag_Outs, which respectively assume a value of 1 if the salesperson had achieved “minimum,” “stretch,” and “outstanding” sales performance category at the end of the prior month. When all three indicators are 0, it implies that the salesperson had achieved “fold” category at the end of the prior month. Consequently, the first observation in each salesperson’s panel had to be discarded because it did not have data on sales performance categorization achieved by the salesperson in the lagged period. Table 2, Panel A, presents frequency cross-tabulation of “performance category in current month” and of “type of SPI earned in current month” (i.e., SPI Type). Similarly, Table 2, Panel B, presents a frequency cross-tabulation of “performance category in current month” and of “performance category in prior month” (i.e., Performance State). The Web Appendix presents further details on the data.
Note that the prior month’s Performance State indicator variables (lag_Min, lag_Stretch, and lag_Outs) enable us to check whether this sales force is playing timing games. As Steenburgh (2008) has highlighted, timing games imply that salespeople move orders from one period to the next, or vice versa. If this has occurred, per Steenburgh’s logic, lower levels in the prior month’s sales Performance State (e.g., lag_Min) should be more strongly associated with SQR in the current month than higher levels in prior month’s sales Performance States (e.g., lag_Stretch, lag_Outs). Alternatively, if salespeople have moved orders from the upcoming period to the earlier period, then higher levels of Performance State (e.g., lag_Stretch, lag_Outs) should have a lower association with SQR at the end of the upcoming month compared with that observed for lower levels of Performance State (e.g., “fold,” “minimum”). We empirically checked for timing games, and we did not find any evidence to support it. We provide additional details on this in the next section.
On this issue, one might wonder whether salespeople might be inclined to move orders to the next month, thereby enhancing chances of earning the early SPI in the next month, once they cross their earnings ceiling at 120% of the quota at the end of a month. However, given that this firm is operating in an emerging economy, the customers of this firm mostly are small family-owned enterprises that are usually cash-strapped. Such buyers do not have the reserve funds and therefore have little ability to strategically time their purchases to acquiesce to such requests from the salespeople to manipulate order timing.
Furthermore, even if a salesperson is categorized as belonging to “outstanding” or “stretch” performance categories in four or more months over the previous six months, the sales managers at the firm do not necessarily increase that salesperson’s quotas in future periods. With such ongoing monitoring, salespeople at this firm are, in general, unlikely to time their orders to hit the early SPI in the next month. In addition, the bonus amount of 300 currency units of the early SPI is too small (specifically, less than one-fifteenth of the highest month-end bonus of 4,200 currency units) to risk playing timing games, in the face of monitoring by sales managers. Another issue is whether salespeople might play timing games with the SPI quotas and delay in registering sales production. However, salespeople are unlikely to delay registering sales because there are no benefits (financial or otherwise) of doing so.
We also consider whether there exist any Performance State–related momentum (or persistence) effects implying that success (failure) in exceeding the quota in the prior month is associated with success (failure) in the current month as well. If this is supported empirically, it likely suggests that, compared with salespeople with medium- and low-performance states, salespeople with high-performance states may be able to (at least temporarily) develop superior strategies about making a sale in the next period. These superior strategies may enable a high-performance-state salesperson to have superior performance in the next month as well. If this is the case, the magnitudes of the regression coefficients of lag_Stretch and lag_Outs should be positive and higher than the magnitude of the coefficient of lag_Min. In such a case, the magnitude of the regression coefficients of lag_Outs should also be higher than the magnitude of the coefficient of lag_Stretch. Managerially speaking, if salespeople’s Performance State has an association with their performance in the next period, it can have important implications for managers in developing new and effective types of compensation plans.
If Performance State–related momentum effects exist, then another substantive issue is to understand whether the SPI Type earned in the current month is more effective than the prior month’s sales Performance State in determining the current month’s SQR. If the SPI Type earned in the current month is less important than Performance State, then being self-regulated in the next month and earning SPIs may not even matter for the salespeople with a high-performance state. If salespeople with a high-performance state are adept at selling and have superior strategies than others, they may be able to continue being high performers in the future, without the need for self-regulation or learning through spacing effects. In this perspective, high performers can produce results anytime and in relatively little time.
Alternatively, there is the perspective that all salespeople—even those with a high-performance state—should benefit by being self-regulated. If high-performance-state salespeople need to inculcate self-regulation and earn SPIs in the next month to experience high performance in the next month, it will further underscore the importance of SPIs. Understanding this issue is important because extant literature has not investigated whether high-performance-state salespeople benefit from earning SPIs and being self-regulated.
Finally, we also want to study the impact of the interaction between SPI Type and Performance State on the current month’s SQR. Thus, we also incorporated nine (i.e., 3 · 3) interaction effect terms, given that the SPI Type covariate has three indicator variables (EarlySPI, LaterSPI, and BothSPI) and the Performance State covariate has three indicator variables (lag_Min, lag_Stretch, and lag_Outs). In addition, we incorporated several control variables (e.g., geographic location, quarter) into the model. For a list of all variables used in our two analyses, see Table 1.
We also tested for evidence of ratcheting of sales quotas. To that end, we undertook a series of three regressions presented in Chung, Steenburgh, and Sudhir (2014) and did not find any significant effects, suggesting that it is unlikely that this firm ratchets its salespeople’s quotas. Managers at the firm also shared that they did not believe in this practice, as it can demotivate high performers. Thus, following Chung, Steenburgh, and Sudhir, we treat each salesperson’s quotas as exogenous. The random intercepts linear regression model used in the First Analysis is
It is noteworthy that some of the covariates in the analysis in Equation 1 may be potentially endogenous. We provide details on the strategy that we incorporated to address this endogeneity concern in the context of the regression model in Equation 1 in the Web Appendix.
TABLE: TABLE 2 Cross Tabulations
| | | Performance Category in Current Month |
|---|
| | | Fold | Minimum | Stretch | Outstanding |
|---|
| A: SPI Type and Performance Category in Current Month |
| SPI Type earned in the Current Month | No SPI | 1,824 | 1,228 | 497 | 582 |
| Early SPI | 23 | 176 | 82 | 105 |
| Later SPI | 45 | 364 | 258 | 583 |
| Both SPIs | 24 | 575 | 483 | 1,722 |
| Performance Category in Prior Month | Lag_Fold | 901 | 532 | 209 | 283 |
| Lag_Min | 571 | 965 | 344 | 450 |
| Lag_Stretch | 176 | 435 | 401 | 384 |
| Lag_Outs | 268 | 411 | 366 | 1,875 |
We executed the sampler for a total of 90,000 draws. The first 35,000 draws were discarded as burn-in. Subsequently, 55,000 of the Markov chain Monte Carlo (MCMC) draws were obtained for every parameter. Table 3 summarizes these posteriors for the relevant covariates in the random intercepts linear regression model in Equation 1.
Main Effect of SPI Type on SQR
Next, we focus on the main effects of earning early SPI, later SPI, or both SPIs in a month (vs. not earning any SPI) on SQR. Compared with not earning any SPI, earning the early SPI in a month (i.e., EarlySPI covariate; b = .242, p < .05) is associated with increasing the SQR earned by the salesperson in that month. Thus, H1 is supported. Furthermore, compared with not earning any SPI in a month, earning the later SPI in a month (i.e., LaterSPI covariate; b = .282, p < .05) is associated with a higher increase in SQR earned by the salesperson in that month, thereby suggesting additional support for H1. However, the magnitude of the coefficient of LaterSPI covariate (b = .282) is higher than that of the EarlySPI covariate (b = .242). This indicates support for H2,1 thereby indicating that earning the later SPI in a month is better than earning just the early SPI at enhancing SQR at the end of that month. From the standpoint of achieving overall success toward a superordinate goal, the finding that late success is more successful at achieving a subgoal than early success is an interesting outcome from a behavioral perspective.
Compared with not earning any SPI in a month, earning both SPIs in a month (i.e., BothSPI covariate; b = .339, p < .05) is also associated with an increase in the SQR earned by the salesperson in that month. Because the coefficient of the BothSPI covariate (b = .339) is higher than that of the LaterSPI covariate (b = .282), earning both SPIs in a month is better than earning just the later SPI at enhancing SQR at the end of that month. Thus, H3 is supported. In addition, recall that, at this firm, a salesperson who earns only the later SPI and a salesperson who earns both SPIs need to cross the same sales threshold (50% of quota) by the 20th of the month.
Main Effect of Performance State on SQR
Table 3 suggests that the main effect of the “outstanding” performance state in the prior month (lag_Outs) is larger than that of the “stretch” performance state (lag_Stretch), which, in turn, is larger than that of the “minimum” performance state (lag_Min). This is because the magnitude of the coefficient of lag_Outs (b = .187, p < .05) is higher than that of lag_Stretch (b = .149, p < .05), which, in turn, is larger than that of lag_Min (b = .067, p < .05). Therefore, we cannot infer that this sales force is playing any timing games, a result that is consistent with Steenburgh (2008). The result also suggests that there are performance state–related momentum effects. In line with our previous discussion, we infer that compared with salespeople with medium-performance and low-performance states, salespeople with high-performance states are likely able to incorporate superior selling strategies, enabling them to continue having high performance in the next period.
Furthermore, our results suggest that both increasing levels of SPI Type (from EarlySPI to BothSPIs) and increasing levels of Performance State (from lag_Min to lag_Outs) are associated with increasing SQR in the current month. However, our results also indicate that SPI Type in the current month is more effective than Performance State at enhancing SQR in the current month. This is because the regression coefficient of even the lowest level of SPI (i.e., EarlySPI coefficient; b = .242) is larger than the coefficient of the highest level of performance state (i.e., lag_Outs; b = .187). Essentially, from the perspective of enhancing salespeople’s performance in the current sales period, having a high-performance state in the prior period is less beneficial than earning SPIs from early on in the current period. However, to get an overall idea of the complete effects, we also need to consider the interaction effects.
Interaction Between SPI Type and Performance State on SQR
Because the model in Equation 1 is a linear model, the regression coefficients themselves also depict the marginal effects. Note that we will need to rely on the estimate of the main effects as well as that of the interaction effects to understand the complete effects. As an example, for a salesperson who had an “outstanding” performance state in the prior month and who has earned both SPIs in the current month, the prediction for SQR in the current month is the sum of the following estimates presented in Table 3:
• .815 (adjusted estimate for the intercept);
• .187 (the main effect of the “outstanding” performance state in the prior month; i.e., the coefficient of the lag_Outs covariate);
• .339 (the main effect of earning both SPIs in the current month; i.e., the coefficient of the BothSPI covariate); and
• -.039 (the coefficient of BothSPI · lag_Outs interaction term covariate).
TABLE: TABLE 3 Estimates for SQR Linear Regression in the First Analysis
| Covariate | Estimates |
|---|
| EarlySPI | .242 (.032) |
| LaterSPI | .282 (.023) |
| BothSPI | .339 (.017) |
| lag_Min | .067 (.011) |
| lag_Stretch | .149 (.014) |
| lag_Outs | .187 (.013) |
| EarlySPI · lag_Min | 2.092 (.042) |
| LaterSPI · lag_Min | 2.075 (.029) |
| BothSPI · lag_Min | 2.089 (.022) |
| EarlySPI · lag_Stretch | 2.134 (.046) |
| LaterSPI · lag_Stretch | 2.146 (.032) |
| BothSPI · lag_Stretch | 2.127 (.025) |
| EarlySPI · lag_Outs | 2.118 (.042) |
| LaterSPI · lag_Outs | 2.079 (.028) |
| BothSPI · lag_Outs | 2.039 (.021) |
| Trend | .002 (.000) |
| Zone-South | .000 (.011) |
| Zone-North | .016 (.014) |
| Zone-East | -.013 (.014) |
| Qtr1 | -.013 (.009) |
| Qtr2 | -.001 (.008) |
| Qtr3 | -.005 (.008) |
| logAge | -.008 (.016) |
| logBaseSalary | 2.059 (.011) |
| logSeniority | .011 (.014) |
| Gender | -.015 (.015) |
| Mean of random intercept (i.e., b0p) | 1.380 (.044) |
| SD of random intercept (i.e., b0p) | .112 (.004) |
Notes: The numbers in parentheses are standard deviations of the estimates. A boldfaced cell indicates a statistically significant estimate (i.e., p <.05) for the coefficient.
The resulting value is an SQR prediction of 1.302. We interpret this result as follows: a salesperson with an “outstanding” sales performance state in the prior month who has earned both SPIs in the current month will have an SQR of 1.302. Likewise, a salesperson with an “outstanding” performance state in the prior month but who did not earn any SPI in the current month will have an SQR prediction of 1.002 (i.e., the sum of .815 [estimate of the intercept] and .187 [the coefficient of lag_Outs]). The predicted SQR values for these two salespeople (1.302 vs. 1.002, respectively) highlight that salespeople with a high-performance state (“outstanding” state in the prior month) benefit by earning both SPIs relative to not earning any SPI. Essentially, a salesperson who has an “outstanding” performance state in the prior month has a boost in his or her SQR of .299 (i.e., 1.302 – 1.002) in that month if (s)he were to earn both SPIs (vs. no SPIs) in that month. Thus, we infer that although a high-performance state may enable the salesperson to have confidence and superior strategies in the next period, these superior strategies alone do not guarantee that high-performance-state salespeople will achieve the upper bound of their performance. By regulating themselves through earning SPIs, these salespeople were able to enhance their sales performance and reach their full potential.
Table 4 presents an overview of the model-based predictions for SQR under the different conditions that we have discussed. All the predictions are based on the regression coefficients of the substantive covariates that are estimated from the model in Equation 1 and were presented in Table 3. For example, Table 4 suggests that the predicted SQR in the current month is 1.100 (sum of .815 [intercept], .149 [coefficient of lag_Stretch], .282 [coefficient of LaterSPI], and -.146 [coefficient of LaterSPI · lag_Stretch interaction term]) for a salesperson who had a “stretch” performance state and who earned only the later SPI in the current month.
Furthermore, for all the performance states (i.e., “fold,” “minimum,” “stretch,” and “outstanding”), the last row in Table 4 presents the boost in SQR if the salesperson had earned any specific SPI (vs. no SPIs) in the current month. For example, a salesperson who has a “minimum” performance state will experience a boost in SQR of .150 (i.e., 1.032 – .882) in that month if (s)he earns the early SPI (vs. no SPIs) in that month.
Table 4 suggests that, in general, the amount of boost in SQR that salespeople receive in earning (vs. not earning) SPIs is U-shaped going from high-performance state to low-performance state. For example, compared with salespeople who had “stretch” or “minimum” performance states in the prior month, salespeople who had either “outstanding” or “fold” performance states in the prior month experience a higher boost in their SQR when they earn both SPIs versus when they do not earn any SPI. Specifically, the boost in SQR by earning both SPIs in a month is higher for salespeople with an “outstanding” performance state (i.e., .299) or a “fold” performance state (i.e., .339) compared with that for salespeople with “minimum” (i.e., .250) or “stretch” (i.e., .212) performance states. Incidentally, the U-shaped relationship is more pronounced in the case of earning both SPIs compared with earning either the early SPI or later SPI.
Recall that H4 was stated specifically in the context of earning both SPIs in a month.
Furthermore, this general U-shaped relationship in the boost to SQR is observed even in the context of earning higher SPIs (e.g., only the later SPI) versus earning lower SPIs (e.g., only the early SPI); however, for brevity, we do not show the calculations for this context. In general, the lowest levels of boost in SQR were experienced by salespeople with the “stretch” (i.e., medium) performance state in the prior month.
Overall, though we find some support for H4, our results also run partially counter to H4. Our finding that, like low-performance-state salespeople, even high-performance state salespeople benefit highly by earning an SPI is a novel one. Such U-shaped or inverted U-shaped nonlinear relationships have been observed in the literature. For example, Malhotra (1983) finds that low-knowledge and high-knowledge people are less likely to rely on extrinsic information in their decision making than are medium-knowledge people.
As we have stated, we did not anticipate this U-shaped relationship between performance state and the boost in month-end performance. Nonetheless, given the novelty and potential importance of this finding, we provide a theoretical (albeit post hoc) explanation for our finding. In so doing, we rely on two well-established constructs in social psychology, ( 1) self-esteem (i.e., one’s perceived sense of self-worth; Heatherton and Polivy 1991; Schunk 1991) and ( 2) self-efficacy (i.e., self-confidence in one’s abilities; Bandura and Cervone 1983; Locke and Latham 1990a, b). Note that prior research in psychology has shown that these constructs interact with positive and negative external feedback (Bandura and Cervone 1983; Silverman 1964), which is akin to salespeople receiving feedback on the basis of achieving different SPIs in a sales period (i.e., no SPI vs. early SPI only vs. later SPI only vs. both SPIs) as a means of affecting their future performance. These conceptual parallels facilitate our efforts to provide a plausible explanation for our findings.
Schunk (1991) proposes that high levels of achievement enable people to maintain high self-esteem. Accordingly, we suggest that high-performance-state salespeople should a priori have the highest expectation for doing well in the next month, and earning the SPI(s) provides validation of their high expectations. That is, earning SPI(s) in the next month acts as a positive signal and a reinforcement that enhances the self-esteem of such salespeople. Consequently, they will maximize their efforts to further boost their performance going forward (Baumeister and Tice 1985).
Conversely, we speculate that the low-performancestate salespeople will a priori have the lowest expectations of doing well in the next month. In such a scenario, earning SPI(s) in the next month leads to a violation of their priors in a positive direction such that their perceptions of self-efficacy will increase. Education research has shown that students with enhanced self-efficacy expend more effort and persist with a task longer than those with low self-efficacy (Schunk 1984). Thus, we argue that low-performance-state salespeople who earn SPI(s) in a month will be motivated to work very hard to boost their performance at the end of the month.
Finally, we argue that the medium-performance-state salespeople suffer merely because they are stuck in the middle. Medium-performance-state salespeople may not experience as much of an ego boost from earning SPIs as their high-performance-state counterparts, because the former have less a priori grounds to construe the achievement of SPIs as self-esteem-oriented credible signals of their competence and self-worth. Moreover, medium-performancestate salespeople may not experience as much of an efficacy-boost from earning SPIs as their low-performancestate counterparts, because self-efficacy issues may not have been salient to medium-performance-state salespeople in the first place. As a result, medium-performance-state salespeople attain a relatively smaller boost in performance from earning SPIs.
The Second Analysis investigates support for H5–H7. Specifically, it tests whether earning SPIs in the prior month is associated with earning both SPIs in the current month. Our dependent variable for the Second Analysis is SPI Type in the current month, a disordinal nominal variable with four categories (no SPI, early SPI, later SPI, or both SPIs). Thus, we executed an MCMC-based random-effects multinomial probit regression, wherein not earning an SPI in the current month is the base category. We use the type of SPI earned in the prior month (identified by indicator variables lag_EarlySPI, lag_LaterSPI, or lag_BothSPI), Performance State (identified by indicator variables lag_Min, lag_Stretch, or lag_Outs), and the nine interaction terms among them as substantive covariates. The control variables we used in this Second Analysis were almost the same as the ones used as in the First Analysis (see Table 1).
We executed the following random-effects multinomial probit regression: where SPI is a four-category nominal dependent variable representing the SPI Type earned by the salesperson in the current month. We executed a random-effects multinomial probit regression with “no SPI” as the base category. Some of the covariates in the analysis in Equation 2 are potentially endogenous as well. We addressed the endogeneity of such covariates in this model as we did in the First Analysis.
TABLE: TABLE 4 Predictions for SQR and Performance Boost Under Different Conditions
| | Performance State |
|---|
| | “Outstanding” | “Stretch” | “Minimum” | “Fold” |
|---|
| | Both SPIs | Later SPI | Early SPI | No SPI | Both SPIs | Later SPI | Early SPI | No SPI | Both SPIs | Later SPI | Early SPI | No SPI | Both SPIs | Later SPI | Early SPI | No SPI |
|---|
| aEffect of salary has been adjusted in the magnitude of intercept. |
| Intercepta | 0.815 | 0.815 | 0.815 | 0.815 | 0.815 | 0.815 | 0.815 | 0.815 | 0.815 | 0.815 | 0.815 | 0.815 | 0.815 | 0.815 | 0.815 | 0.815 |
| Main effect of Performance State | 0.187 | 0.187 | 0.187 | 0.187 | 0.149 | 0.149 | 0.149 | 0.149 | 0.067 | 0.067 | 0.067 | 0.067 | 0 | 0 | 0 | 0 |
| Main effect of SPI Type | 0.339 | 0.282 | 0.242 | 0 | 0.339 | 0.282 | 0.242 | 0 | 0.339 | 0.282 | 0.242 | 0 | 0.339 | 0.282 | 0.242 | 0 |
| Performance State · SPI Type | -0.039 | -0.079 | -0.118 | 0 | -0.127 | -0.146 | -0.134 | 0 | -0.089 | -0.075 | -0.092 | 0 | 0 | 0 | 0 | 0 |
| Sum (i.e., prediction for SQR in current period) | 1.302 | 1.205 | 1.126 | 1.002 | 1.176 | 1.1 | 1.072 | 0.964 | 1.132 | 1.089 | 1.032 | 0.882 | 1.154 | 1.097 | 1.057 | 0.815 |
SQR Boost A = Both SPIs - no SPI B = Later SPI - no SPI C = Early SPI - no SPI | A = .299 | B = .203 | C = .124 | N.A. | A = .212 | B = .136 | C = .108 | N.A. | A = .250 | B = .207 | C = .150 | N.A. | A = .339 | B = .282 | C = .242 | N.A. |
aEffect of salary has been adjusted in the magnitude of intercept. Notes: N.A. = not applicable.
We executed the sampler for a total of 75,000 draws for the model in Equation 2. The first 35,000 draws were discarded as burn-in. Subsequently, 40,000 draws of the MCMC chain were obtained for every parameter. Table 5 summarizes the posteriors for the regression coefficients of relevant covariates in the model in Equation 2. Table 6, Panel A, presents the marginal main effect of SPI Type earned in the prior month (lag_EarlySPI, lag_LaterSPI, and lag_BothSPI) on each of the four categories of the SPI Type earned in the current month (no SPI, early SPI, later SPI, and both SPIs). Table 6, Panel B, presents the marginal main effect of Performance State achieved in the prior month (lag_Min, lag_Stretch, and lag_Outs) on the four categories of the SPI Type earned in the current month. Recall that we had 9 (i.e., 3 · 3) interaction effect terms; thus, their marginal effects on the four performance categories will lead to 36 (i.e., 9 · 4) marginal effects.
Panels A–C of Table 7 present these marginal interactions for the four categories of the dependent variable (type of SPI earned in the current month), respectively, for when lag_EarlySPI, lag_LaterSPI, and lag_BothSPI had values of 1.
As we did in the First Analysis, here, too, we need to use the main effects (in Table 6, Panels A and B) as well as the interaction effects (in Table 7, Panels A–C) to understand the complete effects. For example, for a salesperson who earned both SPIs and had an “outstanding” Performance State in the prior month, the probability of earning both SPIs in current month is the sum of the following:
• 0 (main effect of lag_BothSPI on earning both SPIs from Table 6, Panel A)—although the estimate has a mean value of -.151, we use 0 because this estimate is nonsignificant;
• -.073 (main effect of lag_Min on earning both SPIs in the current month, from Table 6, Panel B); and
• .470 (interaction effect of earning both SPIs in the prior month; i.e., lag_BothSPI and earning “outstanding” performance in the prior month [lag_Outs] from Table 7, Panel C).
The resulting value is .397 (i.e., 39.7%). That is, a salesperson who earned both SPIs and had an “outstanding” Performance State in the prior month has a 39.7% higher probability of earning both SPIs in the current month compared with one who did not earn any SPI and had a “fold” Performance State in the prior month.
The marginal probabilities presented in Table 6, Panels A and B, and Table 7, Panels A–C, suggest that for all Performance States except “fold” in the prior month, a salesperson’s probability of earning both SPIs in the current month increases, provided that the salesperson earned the later SPI or both SPIs in the prior month. We make this inference because, in general, the marginal effects in the rightmost column in Table 7, Panels B and C, are positive and significant. Note that all the marginal main effects in Table 6, Panel A, are nonsignificant. Combined, these results suggest that earning the early SPI (compared with not earning any SPIs) in the prior month does not increase the probabilities of earning an SPI in the current month. However, earning the later SPI or both SPIs (rightmost column in Table 7, Panels B and C, respectively) in the prior month positively enhances the probability of earning both SPIs in the current month. Note also that this period-to-period SPI persistence is relatively stronger for salespeople with high-performance states (such as “Outstanding” or “Stretch”) in the context of earning both SPIs in a period. Furthermore, not earning any SPI or even earning only the early SPI does not enhance the probability of earning both SPIs in the next month. Overall, we find support for H6 and H7, but not for H5.
In this research, we investigate whether different types of sales production trajectories of salespeople during a period are associated with different levels of sales performance at the end of that period. Most notably, we compared the period-end sales performance of a salesperson whose sales production trajectory steadily grew until a certain point within the sales period with that of a salesperson who had relatively low production early in the period followed by very rapid sales production success later in the period. This is an empirical issue because extant literature has presented conflicting evidence on which of the two types of trajectories should have a stronger association with enhanced period-end sales production performance. To that end, we empirically investigate the effect of earning SPIs in a sales period on salespeople’s period-end performance.
The SPIs have certain similarities with quarterly bonuses in the context of the annual quota-bonus plan. Both situations present salespeople with intermediary goals in the context of the superordinate period-end quota. Thus, because there is a sub-goal, the setting is similar in the sense that working toward an intermediary goal has an effect on the achievement of the overall goal. Yet a key aspect of our data is that the SPIs at our data-provider firm are cumulative and specified as a percentage of the period-end quota that must be achieved by a certain time within the period, whereas quarterly quotas are specified only as dollar-amount targets. As such, at firms with only quarterly quotas in an annual quota-bonus plan, salespeople will achieve more sales by frequently (vs. less frequently) meeting quarterly quotas. In contrast, the cumulative nature of the SPIs at our data-provider firm enabled us to investigate some unique issues that have not yet been identified in the extant literature. This characteristic of the SPIs at our data-provider firm allowed us to make two important contributions.
First, we were able to investigate whether there are benefits to earning two SPIs (vs. only the later SPI) in the context of period-end performance, even when, at some point in the sales period, earning two SPIs may have the same level of absolute sales as earning only the later SPI. Essentially, we investigated the role of incurring a steadily growing sales trajectory versus one with recent spikes on period-end sales performance. We find that salespeople will have better performance when they earn both SPIs rather than only the later SPI. Managerially speaking, for any level of sales achievement, a steadily growing sales trajectory is better than making few sales initially and then making a lot of sales to catch up, even though the latter approach involves the benefits of recency and momentum through a rapid spike in sales production. In addition, we find that earning only the later SPI is superior to earning only the early SPI for enhanced period-end sales performance. Managerially speaking, the firm could emphasize the later SPI, either by increasing the cash reward for earning the later SPI or by coaching salespeople to make it more salient. Alternatively, this also means that firms that are in a position to incorporate only a single SPI should have the SPI be triggered later, rather than earlier, in the sales period (thus mimicking the later SPI).
TABLE: TABLE 5 Estimates for Multinomial Probit Regression in Second Analysis
| | Category Estimates |
|---|
| Covariate | Early SPI | Later SPI | Both SPIs |
|---|
| lag_EarlySPI | -6.047 (12.357) | .290 (.378) | .181 (.342) |
| lag_LaterSPI | .142 (.316) | -.345 (.339) | .234 (.211) |
| lag_BothSPI | -4.41 (4.239) | .146 (.347) | -.397 (.352) |
| lag_Min | -.009 (.089) | .159 (.069) | 2.411 (.066) |
| lag_Stretch | -.168 (.135) | .186 (.092) | 2.387 (.092) |
| lag_Outs | -.127 (.124) | .291 (.085) | 2.192 (.084) |
| lag_EarlySPI · lag_Min | 6.998 (12.359) | -.560 (.409) | .157 (.367) |
| lag_EarlySPI · lag_Stretch | 6.941 (12.361) | .010 (.423) | .416 (.387) |
| lag_EarlySPI · lag_Outs | 6.379 (12.356) | -.018 (.414) | .200 (.378) |
| lag_LaterSPI · lag_Min | .150 (.350) | .746 (.354) | .598 (.233) |
| lag_LaterSPI · lag_Stretch | .382 (.368) | .837 (.363) | .757 (.245) |
| lag_LaterSPI · lag_Outs | .022 (.347) | .688 (.349) | .356 (.231) |
| lag_BothSPI · lag_Min | .475 (.423) | .085 (.362) | 1.637 (.360) |
| lag_BothSPI · lag_Stretch | .514 (.424) | .142 (.367) | 1.715 (.367) |
| lag_BothSPI · lag_Outs | .477 (.424) | .261 (.358) | 1.808 (.361) |
| Quota | .000 (.019) | -.020 (.014) | .014 (.012) |
| Zone-South | -.073 (.092) | .305 (.069) | 2.313 (.060) |
| Zone-North | -.183 (.109) | 2.201 (.086) | 2.668 (.078) |
| Zone-East | .040 (.109) | .197 (.084) | 2.158 (.073) |
| Trend | .009 (.004) | .009 (.003) | -.002 (.002) |
| Qtr1 | .094 (.077) | -.010 (.057) | -.091 (.050) |
| Qtr2 | .049 (.074) | .063 (.053) | .136 (.046) |
| Qtr3 | -.085 (.074) | .009 (.052) | .057 (.045) |
| logSalary | 2.148 (.074) | 2.536 (.061) | -.004 (.052) |
| logAge | 2.280 (.135) | -.047 (.096) | -.113 (.090) |
| logSeniority | -.089 (.120) | -.083 (.087) | -.134 (.080) |
| Gender | -.070 (.129) | -.002 (.091) | 2.219 (.088) |
| Mean of random intercept | 1.002 (.027) | 4.150 (.056) | .419 (.094) |
| SD of random intercept | .473 (.002) | .486 (.003) | .383 (.008) |
Notes: The numbers in parentheses are standard deviations of the estimates. A boldfaced cell indicates a statistically significant estimate (p < .05) for the coefficient
Second, we highlight that salespeople’s Performance State (i.e., the performance that salespeople experienced in the prior period) interacts with the shape of the sales production trajectory in the next period to differentially affect sales performance at the end of the next period. Essentially, earning both SPIs has a stronger benefit for salespeople with a high-performance state or a low-performance state, as compared with the benefits accruing to the salespeople with medium-performance states. The benefit of SPIs for the weak performers has been established in the education psychology and sales literature; however, our finding that SPIs benefit high-performance-state salespeople is novel. The observation that salespeople’s Performance State interacts with their sales production trajectory in the upcoming period to affect their performance in that period has implications for managers in the context of developing more effective incentive schemes. A potentially helpful intervention that managers could undertake is to implement a higher cash reward for the SPI in the next period for salespeople who have high performance (vs. medium performance) in a period. The higher cash reward for the SPIs in the next period should lead high-performance-state salespeople to put more effort into earning both SPIs in the next period (i.e., incur a steadily growing sales production trajectory in the next period), thus incurring the enhanced boost in period-end sales performance, thereby also benefiting the firm.
The design of the SPIs at our data-provider firm (stated explicitly as percentages of the month-end quota) makes it very clear to salespeople that the month-end quotas are the main target of interest, and the SPIs are the means to that end. Our empirical analysis suggests that the SPIs at our data-provider firm indeed increase commitment toward the period-end quota. However, drawing on arguments made by Fishbach and Dhar (2008), it is likely that a different pattern of results could potentially emerge if SPIs end up being perceived as highlighting progress made rather than enhancing commitment to the period-end quota.
The implication is that managers should design SPIs such that salespeople continually focus on the end-of-period quota even when they achieve the SPI quotas. This could be done in various ways, but we find that a simple framing in which the interim quotas are explicitly expressed in percentage terms of the end-of-period quota is very effective in maintaining salespeople’s focus on the end-of-period quota. In contrast, we sense that specifying targets in dollar amounts, as is done in the quarterly quotas of the traditional annual quota-bonus plan, is likely less effective. The implications are wide if future research can test whether SPIs that explicitly incorporate period-end quota (such as those implemented at our data-provider firm) garner higher commitment for period-end quotas than the implicit quarterly pacers in the popular quarter-annual quota-bonus scheme. If so, then this finding would suggest that managers may be better off redesigning the popular quarter-annual quota-bonus scheme to ensure that the quarterly quotas are specified as an explicit percentage of the annual quota rather than in dollar terms, as is the current practice.
A limitation of this research is that we have not been able to clarify why high-performance-state salespeople benefited by earning SPIs in our study, whereas high-performing salespeople did not benefit much when quarterly quotas were put in place in
Chung, Steenburgh, and Sudhir’s (2014) study. There might be multiple reasons for this. First, we rely on performance states, whereas Chung, Steenburgh, and Sudhir identified performance categories by undertaking latent-class analysis in their counterfactual analysis. Second, in contrast to the SPIs at Chung, Steenburgh, and Sudhir’s data-provider firm, the unique design of the SPIs at our data-provider firm might be instrumental in increasing the strength of this phenomenon. Third, there are industry, cultural (both firm and social), and country differences between our studies. Our data-provider firm is a medium-sized CPG firm (with a respectable market share in a few of its product categories) in an emerging economy experiencing rapid growth, whereas Chung, Steenburgh, and Sudhir’s data-provider firm is a large office-products vendor based in the West.
TABLE: TABLE 6 Marginal Main Effects
| Covariate | No SPI | Early SPI | Later SPI | Both SPIs |
|---|
| A: SPI Type Earned in Prior Month |
| lag_EarlySPI | .424 (.513) | -.571 (.879) | .090 (.122) | .057 (.117) |
| lag_LaterSPI | -.004 (.187) | .056 (.111) | -.134 (.129) | .082 (.073) |
| lag_BothSPI | .639 (.551) | -.529 (.811) | .051 (.126) | -.151 (.132) |
| lag_Min | .102 (.048) | -.002 (.031) | .058 (.024) | 2.158 (.026) |
| B: Performance State |
| lag_Stretch | .137 (.066) | -.054 (.042) | .067 (.032) | 2.150 (.037) |
| lag_Outs | .009 (.062) | -.042 (.041) | .107 (.030) | 2.073 (.032) |
Notes: The numbers in parentheses are standard deviations of the estimates. A boldfaced cell indicates a statistically significant estimate (p < .05) for the coefficient.
Future research should test our conjectures, because several factors might be moderating the phenomenon described in this article. At our data-provider firm, salespeople were clearly categorized as belonging to one of the four categories at the end of each month. As such, their Performance State was salient to salespeople in the subsequent month. In addition, during each month, each salesperson received feedback through SPIs and thus was cognizant of the SPIs (s)he had earned in each month. Therefore, as a countercheck, future research could test relevant outcomes when salespeople’s Performance State and the SPI Type earned were not made explicit and salient to them. Finally, future research could also confirm whether our post hoc explanation for the U-shaped relationship indeed holds.
TABLE: TABLE 7 Marginal Interaction Effects
| Covariate | No SPI | Early SPI | Later SPI | Both SPIs |
|---|
| A: Earning Early SPI in Prior Month · Performance State |
| lag_Min | -.426 (.502) | .593 (.849) | -.214 (.150) | .047 (.124) |
| lag_Stretch | -.711 (.531) | .591 (.889) | -.007 (.148) | .127 (.115) |
| lag_Outs | -.642 (.499) | .586 (.887) | -.004 (.145) | .061 (.125) |
| B: Earning Later SPI in Prior Month · Performance State |
| lag_Min | 2.470 (.166) | .060 (.125) | .219 (.084) | .191 (.064) |
| lag_Stretch | 2.610 (.170) | .145 (.136) | .237 (.077) | .228 (.058) |
| lag_Outs | 2.345 (.170) | .016 (.119) | .208 (.089) | .121 (.074) |
| C: Earning Both SPIs in Prior Month · Performance State |
| lag_Min | 2.566 (.297) | .169 (.160) | .023 (.126) | .373 (.039) |
| lag_Stretch | 2.601 (.301) | .183 (.192) | .043 (.125) | .375 (.035) |
| lag_Outs | 2.729 (.282) | .170 (.193) | .088 (.122) | .470 (.057) |
Notes: The numbers in parentheses are standard deviations of the estimates. A boldfaced cell indicates a statistically significant estimate (p < .05) for the coefficient.
At our data-provider firm, the costs associated with instituting SPIs are modest: the payout for the early SPI (late SPI) is less than one-tenth (approximately one-fourth) of the average payout for achieving the regular monthly quota. Thus, relative to their costs, SPIs seem to have a larger “psychic value” in instilling self-regulation to our data-provider firm.
Specialized personal incentives are managerially powerful interventions because they have some state persistence effects: salespeople who earn SPIs in a period are more likely to earn SPIs in the next period than those who do not. Thus, earning SPIs in any sales period has a double benefit: it has a direct positive association with enhanced sales performance in the current period, and it is positively associated with earning SPIs in the next period, thereby indirectly enhancing sales performance in the next period. Consequently, salespeople may be able to increase their chances of earning future SPIs and enhance their future period-end performance if they are able to meet SPIs in the current period.
For that to happen, one potential intervention that sales managers could undertake is simply to increase the cash incentive associated with earning SPIs. By doing so, a greater proportion of the firm’s sales force is likely to start taking SPIs seriously, which, in turn, should enhance their period-end performance. Another intervention that sales managers could implement is to shift an increasing proportion of cash incentives from the end of the period to the point when the SPI is triggered within the period. Specifically, future research could undertake a field experiment in which cash incentives are split in different proportions across within-period SPIs versus at the end of the period bonus. This might enable managers to arrive at an optimal allocation scheme of splitting cash across within-period SPIs and the period-end bonus. In addition, because the salespeople with high- and low-performance states experience the greatest performance boost by earning both SPIs, another intervention could be for managers to put in place higher incentives to earn SPIs, as compared with incentives provided for salespeople who have a medium performance state.
There are several other limits to the extent to which explicit SPIs can increase salespeople’s sales achievement. Other aspects of SPIs that future research could investigate include the optimal number of SPIs a firm should institute, whether that number depends on the length of the quota period, and whether SPIs can be nonmonetary (e.g., coupons, vouchers, gifts, recognition/commendations from the sales manager). In addition, the spacing between SPIs within the sales period is another important factor that we leave for further investigation. Future research could also study salesperson-level profitability to determine whether it has a similar pattern of effects as that observed in this research for sales performance. Finally, future research could investigate whether SPIs induce forward-looking behavior and timing games in situations wherein the magnitude of the SPIs offered to the sales force is high or comparable to the period-end bonus.
Notes: The subscript p denotes a salesperson; we have data for 813 salespeople. The subscript t denotes the month; we have data for 33 months, so t has a value of 1–33.
Notes: The numbers in parentheses are standard deviations of the estimates. A boldfaced cell indicates a statistically significant estimate (i.e., p <.05) for the coefficient.
aEffect of salary has been adjusted in the magnitude of intercept. Notes: N.A. = not applicable.
Notes: The numbers in parentheses are standard deviations of the estimates. A boldfaced cell indicates a statistically significant estimate (p < .05) for the coefficient.
Notes: The numbers in parentheses are standard deviations of the estimates. A boldfaced cell indicates a statistically significant estimate (p < .05) for the coefficient.
Notes: The numbers in parentheses are standard deviations of the estimates. A boldfaced cell indicates a statistically significant estimate (p < .05) for the coefficient.
Footnotes 1 It is noteworthy that we do not check for significance of the differences in these effects.
DIAGRAM: FIGURE 1 Incentive Plan Scheme
PHOTO (BLACK & WHITE)
PHOTO (BLACK & WHITE)
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Record: 89- How Does Consumers' Local or Global Identity Influence Price–Perceived Quality Associations? The Role of Perceived Quality Variance. By: Yang, Zhiyong; Sun, Sijie; Lalwani, Ashok K.; Janakiraman, Narayan. Journal of Marketing. May2019, Vol. 83 Issue 3, p145-162. 18p. 1 Diagram, 2 Charts, 4 Graphs. DOI: 10.1177/0022242918825269.
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How Does Consumers' Local or Global Identity Influence Price–Perceived Quality Associations? The Role of Perceived Quality Variance
Globalization has substantially influenced the world economy. However, managers have a limited understanding of how local–global identity influences consumers' price perceptions and behavior. In this research, the authors propose that consumers' local (vs. global) identity leads to a greater tendency to make price–perceived quality (PPQ) associations. Perceived quality variance among comparison brands is a key mechanism underlying these effects. Two field studies (Studies 1 and 7), seven experiments (Studies 2–6, 9, and 10), and a systematic review of secondary data (Study 8) provide converging and robust evidence for the effect of local–global identity on PPQ. Consistent with the perceived quality variance account, when quality differences among the brands are made salient, PPQ associations of consumers high in global (but not local) identity significantly increase, compared with baseline conditions. However, when perceived quality similarities are made salient, PPQ associations of consumers high in local (but not global) identity significantly decrease. Product type and distribution of customer ratings represent natural boundaries for the relationship between local–global identity and PPQ. The authors conclude with the implications for managers' targeting endeavors. We also provide specific tools that marketers can use in ads and point-of-purchase materials to encourage or discourage consumers in making PPQ associations.
Keywords: local–global identity; perceived quality variance; price–perceived quality
Most marketers strive to find ways to charge high prices for their products. However, it is often difficult to do so without improving objective product performance or adding more attributes. For example, Netflix recently faced a huge uproar when it tried to raise prices without increasing perceptions of value. Its management team could have avoided this reaction by segmenting its market and starting the price increase in consumer segments that equate higher prices with higher quality. In the current research, we propose that if marketers focus on consumers with a local (vs. global) identity, their odds of success can drastically increase, as these consumers tend to view higher prices as signals of superior quality.
Nevertheless, researchers are only starting to understand the role of local and global identities in consumer behavior (e.g., [ 7]). For example, it is unclear whether these identities differentially influence one of the most important relationships found in the pricing literature—namely, consumers' tendency to use product price to judge quality—that is, make price–perceived quality (PPQ) associations ([13]). Given its importance, there is renewed interest among researchers in examining the phenomenon ([44]).
Indeed, managers currently seem to be puzzled about the potential role that consumers' local–global identity may play in their tendency to use price to judge quality. Our recent in-depth interviews with 15 senior level managers from Fortune 500 corporations revealed that managers across industries considered local or global communities in their pricing decisions, but none knew when such strategies might be effective and why (for key quotes, see Appendix A). This notion is illustrated by the following quote from a director of a firm's pricing division:
When we try to introduce local flavors...it makes people think of their local communities....Here, we are careful to make sure that our product is seen as premium. You know...having a twist on the local ingredient is important. Similarly, it is important to have a reasonably higher price since it communicates premium-ness, and then reinforce it with advertising and packaging. But we don't know for sure why such consumers prefer premium brands. That is largely a mystery.
So, the question is, how and why may consumers' local–global identity influence their PPQ associations? Extant findings seem to suggest that consumers with a global (vs. local) identity tend to have an abstract (vs. concrete) construal (as implied by [32]]), which in turn positively affects PPQ ([44]). In contrast, we propose that consumers with a local (vs. global) identity are more likely to make PPQ associations. Although there can be several reasons for this relationship, we focus on one—namely, greater perceived quality variance. We propose that a salient local (vs. global) identity is associated with a general dissimilarity-focus mindset. The enhanced salience of quality variance, in turn, leads people to focus more on price—one of the most direct and obvious cues used to compare brands—to infer product quality ([14]; [15]). We further demonstrate that contextual and product-related factors that influence perceived quality variance (e.g., services vs. goods, hedonic vs. utilitarian products, and convergent vs. divergent reviews) moderate the influence of local–global identity on PPQ.
The issues we address have significant implications for the cross-cultural and pricing literature streams. First, by examining the role of local–global identity, we bring a fresh perspective to the cross-cultural literature, which is dominated by the individualism–collectivism dimension ([15], [17]; [18]; [19]; [37]). Second, we contribute to the pricing literature by examining how an important but underexplored factor, local–global identity, influences PPQ associations. Third, we are the first to uncover perceived quality variance as a new consequence of local and global identity. Fourth, we show that the strength of the association between local–global identity and PPQ associations varies by factors that influence perceived variance in brand quality.
Managerially, our findings suggest that marketers of relatively high-priced products should situationally activate consumers' local identity, which facilitates PPQ. Furthermore, in line with the perceived quality variance account, for products that charge a premium price over competing products, marketers can use situational cues to increase perceived quality variance and facilitate consumers' PPQ. In contrast, for products that adopt a low-price strategy, marketers can use situational cues to reduce perceived quality variance. Our findings also suggest the importance of adapting marketing strategies to different regions: in rural areas where local identity is likely to be salient, consumers likely have high levels of PPQ, whereas in metropolitan areas where global identity is more salient, marketing campaigns are needed to enhance consumers' PPQ so that consumers perceive higher prices to be signals of superior quality. Similar strategies can be applied to countries around the world that are high in local or global identity. These insights also help address a current debate on whether companies should be more locally oriented, and how this may affect consumers. Next, we discuss the link between local–global identity and PPQ, followed by hypothesis development and empirical testing using both field and lab studies.
Recent research delineates two distinct consumer identities (i.e., local identity and global identity), reflecting how strongly people associate with the local and the global community, respectively ([34]). Individuals whose local identity is salient ("locals") are faithful and respectful of local traditions, interested in local events, and identify with people in their local community, whereas those with a salient global identity ("globals") favor globalization, view the world as a "global village," and blur the lines of distinction between local and nonlocal people and events ([ 1]; [45]). Furthermore, consumers high (vs. low) in local identity prefer local products and brands, whereas those high (vs. low) in global identity prefer global products and brands ([45]).
Individuals from more globalized countries, such as the United States and Canada, tend to have a stronger global identity because they are more likely to meet different types of people, encounter different cultures, and access stories and news from other countries. In contrast, those from more localized countries (e.g., China, India) tend to have a stronger local identity because of their restricted access to other cultures ([ 1]; [ 6]). Research has further suggested that global and local identities can also be fruitfully activated through priming procedures (e.g., [40]; [45]).
At the national level, there is evidence that people in countries with different levels of local–global identity differ in their tendency to use price to infer product quality. For example, Chinese and Indian consumers (who are high in local identity) make stronger PPQ associations than do U.S. and Canadian consumers (who are high in global identity) ([42]). Similarly, Polish (high in local identity) make higher PPQ associations than Germans (high in global identity) ([46]). However, these findings are inconsistent with those of another study, which shows that there is no difference in PPQ across different countries ([ 4]). Yet because these studies do not focus on cultural differences, we do not know whether local–global identity was responsible for these results. Some previous research has attributed these national differences to cultural dimensions other than local–global identity ([14]; [17]). More importantly, no previous research has offered theoretical explanations for the possible effect of local–global identity on PPQ. A clearer theorization of the mechanism through which local–global identity affects PPQ will advance our understanding of how consumers differ in their propensity to make price–quality inferences, and why. We propose that perceived quality variance is a key mechanism through which local–global identity affects PPQ, as discussed next.
The ability to make comparative judgments is a fundamental human characteristic ([31]). People tend to follow one of two comparison processes—namely, dissimilarity focus and similarity focus—to make judgments ([30], [31]). We propose that locals (vs. globals) are more likely to focus on dissimilarities than similarities, because locals (vs. globals) tend to discern greater differences between local and nonlocal communities, which motivate them to associate more values with local traditions and local events. In contrast, because globals view the world as a "global village" and blur the lines of distinction between local and nonlocal people and events, they are more likely to focus on similarities. For example, Koreans (who are high in local identity) draw clear distinctions between in-group and out-group members, whereas Americans (who are high in global identity) do not ([35]). In addition, prior studies have also pointed to an association between high (vs. low) degrees of local identity and perceived dissimilarity from out-group members. In particular, activating one's own traditions and values can enhance intergroup aggression, especially when the in-group and out-group are in conflict ([39]). Conversely, research has suggested a link between openness to diversity (a characteristic of globals but not locals) and a similarity-focus mindset. For example, openness to diversity reduces perceived difference from other group members ([11]).
The dissimilarity focus among locals (vs. globals) also extends to nonsocial domains. For example, when asked to answer partially redundant questions (e.g., to rate both academic satisfaction and general life satisfaction), Chinese (high in local identity) spontaneously recognize the redundancy problem (e.g., academic satisfaction is part of general life satisfaction) and adjust their responses accordingly; however, Germans (high in global identity) do not detect the redundancy ([36]). Similarly, [22] showed that, when evaluating two videos, individuals with overseas experiences (high in global identity) are able to identify more similarities than those without overseas experiences (high in local identity).
In the context of product evaluations, when a local identity is salient, we propose that individuals will have a dissimilarity-focus mindset and perceive greater variance among brands in the marketplace. The perception that brands are dissimilar should motivate locals to look for cues to make sense of the distinctions. However, when a global identity is salient, we propose that individuals will have a similarity-focus mindset and view things as homogeneous, leading to lower perceived quality differences among brands. The perception that brands are similar discourages consumers from expending effort to differentiate them (see [31]) and to look for cues that enable such distinction. Next, we discuss how these differences may influence the tendency to use price as an indicator of product quality.
Our focal hypothesis that perceived quality variance mediates the relation between local–global identity and PPQ associations (see Figure 1) relies on the proposed link between perceived variance among comparative brands and PPQ. We expect this association for several reasons.
Graph: Figure 1. The impact of local–global identity on PPQ associations.
Consumers who perceive greater variance among comparative brands may be more motivated to look for cues to mentally differentiate the brands, as doing so may enable them to satisfy the fundamental human need to make sense of the world ([14]). In situations where nonprice cues are not diagnostic, such as when performance-related attributes are not alignable, perceived dissimilarity among comparative brands drives consumers to rely on alignable cues (e.g., price) that readily enable comparison between brands to infer quality. Indeed, price is intuitively one of the most important alignable product attributes ([14]; [27])—a dominant and salient attribute that enables consumers to directly and quickly compare brands ([15]; [27]; [33]). Thus, people who want to make sense of dissimilar objects (i.e., locals) are more likely to use price as a cue. When they need to determine brand quality, these consumers may be more likely to make PPQ associations.
In contrast, those who perceive low variation in quality tend to view high- and low-priced brands as not differing much in quality and therefore are less motivated to look for and use cues that distinguish quality. Such individuals may be less likely to use price as a cue for inferring product quality. Accordingly, when consumers perceive the difference between two brands to be obvious, they selectively access information that supports the dissimilarity ([43]). However, when perceived difference across brands is low, consumers are likely to view the quality of high- and low-priced brands to be similar and are thus less likely to use any cues (e.g., price) to differentiate the brands.
- H1 : When evaluating brand quality, locals have a greater tendency than globals to make PPQ associations.
- H2 : The effect of local (vs. global) identity on PPQ associations is mediated by perceived variance among comparative brands in the marketplace.
To advance our understanding of the underlying role of perceived quality variance, we also examine potential boundary conditions for the effect of local–global identity on PPQ associations. We have argued that locals (vs. globals) perceive greater variance in the quality of brands, which increases their tendency to use price to judge a product's quality. Thus, when quality differences among brands are made salient through a contextual cue (compared with a control condition wherein they are unchanged), globals—who, by nature, perceive less quality variance and have greater potential for increase—should be more likely to notice the differences among the brands and thus use price as an indicator of brand quality. However, such a contextual cue is less likely to increase the PPQ associations of locals, whose tendency to see variation (and thus, to make PPQ associations) is already high ("ceiling effect").
Similarly, when quality similarities among brands are made salient, locals—whose baseline tendency to discriminate among brands is high and has a greater potential for decrease—should be less likely to perceive brands as different and, therefore, have a lower tendency to make PPQ associations, compared with a control condition in which quality variance is unchanged. However, globals' baseline tendency to discriminate among brands is low and is difficult to decrease further ("floor effect"). Thus, their tendency to make PPQ associations should be unchanged when quality variance is reduced, relative to a control condition. We hypothesize the following:
- H3a: When the quality difference among brands is made salient (compared with a control condition in which quality variance is unchanged), globals' tendency to make PPQ associations is elevated, whereas locals' tendency to use PPQ associations is unaffected.
- H3b: When the quality similarity among brands is made salient (i.e., quality variance is reduced, compared with a control condition in which quality variance is unchanged), locals' tendency to make PPQ associations is decreased, whereas globals' tendency to use PPQ associations is unaffected.
In real-life situations, consumers make choices not just about physical goods but also about services. Given that services are intangible and heterogeneous, their perceived quality difference is inherently greater than that of goods ([24]). Greater variation in the quality of services (vs. goods) should increase globals' tendency to make PPQ associations because their baseline tendency to differentiate brands is low and has greater potential for increase. However, because locals' tendency to make PPQ associations is already high, there is little room to increase it further (the same "ceiling effect" argument outlined previously). As a result, they should exhibit little change in PPQ when evaluating services (vs. goods).
- H4: When evaluating services (vs. goods), globals' tendency to make PPQ associations is significantly higher, whereas locals' tendency to make PPQ associations does not differ.
Beyond product type, another context that naturally changes consumers' perceived quality difference is when they see divergent or convergent customer ratings on products that interest them. Online reviews increasingly influence consumer purchase decisions ([38]). However, these reviews do not necessarily agree with one another. Convergent customer ratings in a product category (i.e., when most people leave similar ratings for products in that category) are likely to give customers an impression that various products in this category are of similar quality (i.e., low quality variance). In contrast, divergent customer ratings (i.e., people's opinions are all over the place and there is no dominant view) are likely to give customers an impression that the quality of products in this category differs greatly. Drawing on H3, we predict the following:
- H5a: When the distribution of customer product reviews is divergent (compared with a control condition), globals' tendency to make PPQ associations is significantly increased, whereas locals' tendency to make PPQ associations does not differ.
- H5b: When the distribution of customer product reviews is convergent (compared with a control condition), locals' tendency to make PPQ associations is significantly reduced, whereas globals' tendency to make PPQ associations does not differ.
We tested our hypotheses in eight studies. Study 1 provided initial evidence on the link between local–global identity and PPQ associations in a shopping mall with real consumers (H1). Study 2 replicated Study 1's findings in a different context and demonstrated perceived quality variance as a key mechanism underlying these effects (H2). The next three studies examined several contextual moderators, including salience of quality variance/similarity (Study 3), product type (services vs. goods; Study 4), and distribution of customer ratings (convergent or divergent; Study 5). Study 6 primed both local–global identity and construal level to examine their differential effects on reliance of price as an indicator of quality and reconciled the seemingly contradictory predictions between our theory and those of construal level theory. Study 7 brought our theory to the field to examine how situationally activated local/global identity affects consumers' monetary expenditures. Finally, Study 8 provides the results of a meta-analysis of previous studies on PPQ associations conducted across different countries. Notably, consistent with prior research (e.g., [ 6]), our empirical work addresses the relative effects of local (vs. global) identity.
We designed Study 1 to test the effect of local (vs. global) identity on PPQ with real consumers in a shopping mall and to assess whether local–global identity can be situationally activated in a real consumption setting. Respondents were 164 shoppers at a shopping mall in the city of Hohhot, China, who were intercepted by the researchers and shown a brochure that described either a "Think Local Movement" or a "Think Global Movement" to manipulate local and global identity, respectively ([ 6]; for stimuli, see Web Appendix 1). Thereafter, participants were told that a well-known apparel company was considering releasing some shoes and caps to be sold at the mall and had hired us to conduct a test on consumers' quality perceptions of their products. The researchers then showed them three pairs of running shoes and three caps, with price tags attached (Shoe A: ¥299; Shoe B: ¥599; Shoe C: ¥799; Cap A: ¥39; Cap B: ¥69; Cap C: ¥99). Following [17], participants rated all six products on quality, reliability, and dependability (1 = "Very Low," and 7 = "Very High"), which were averaged to form a quality evaluation for both shoes (αs =.89 to.90) and caps (αs =.88 to.89).
Following [45], we assessed the validity of the identity manipulation using a three-item scale, anchored by 1 = "Global Citizen," and 7 = "Local Citizen" (e.g., "For the time being, I mainly identify myself as a..."; α =.86; for the full scale and other measures used in this article, see Web Appendix 2). Results indicated that participants assigned to the local (vs. global) identity condition perceived themselves more as local citizens (for the local–global identity manipulation check results in this study and other studies, see Web Appendix 3). Participants also reported their age, gender, and household income.
A 2 (identity) × 2 (product category; dummy coded 1 = shoes and 0 = caps) repeated-measure analysis of variance (ANOVA) on the correlation between retail prices and subjective quality evaluations (i.e., PPQ associations) revealed a significant main effect of identity (F( 1, 162) = 8.36, p <.01) but nonsignificant effects of product category and its interaction with identity (ps >.15), suggesting that PPQ associations did not vary by product category. Thus, the data were pooled across the product categories. For both product categories, participants in the local (vs. global) identity condition made significantly higher PPQ associations, as predicted in H1 (shoes: Mlocal =.68 vs. Mglobal =.40; t(162) = 2.98, p <.01; caps: Mlocal =.71 vs. Mglobal =.50; t(162) = 2.15, p <.05). Rerunning the analyses with age, gender, and household income as covariates did not change the pattern of results, and none of these demographic variables were significant (all ps >.40).
We designed a follow-up study to replicate Study 1's finding in the United States, using 69 consumers (49 men; Mage = 31–40 years) shopping at an apparel store in an upscale mall. Respondents were guided to a table where they saw four caps marked with different prices (Cap A: $10; Cap B: $20; Cap C: $30; Cap D: $40). They were asked to rate the quality of each cap on a 0 to 100 scale. For each participant, the correlation between retail prices and quality ratings served as our dependent variable. Local–global identity was manipulated by the T-shirt the employee was wearing. The local-identity T-shirt contained the logo "Think Local" and the phrase "supporting the link to local community," whereas the global-identity T-shirt contained the logo "Think Global" and the phrase "supporting the link to the whole world" (for a picture of these T-shirts, see Web Appendix 4). After completing quality ratings for each cap, participants rated the three-item local–global identity manipulation check questions (α =.91) as in Study 1. Results showed that participants in the local (vs. global) identity condition made significantly higher PPQ associations (Mlocal =.50 vs. Mglobal =.02; t(67) = 3.19, p <.01).
In a real-life setting, Study 1 supported H1's prediction that locals (vs. globals) have a greater tendency to make PPQ associations. We conducted another study (Study 9 in Web Appendix 5) to test the generalizability of our findings over single-quality-cue and multiple-quality-cue formats. Results of this study replicated the findings of Study 1 and demonstrated that the effect of local–global identity on PPQ held in both multiple- and single-quality-cue conditions. In the next study, we aimed to test the mechanism underlying the link between local–global identity and PPQ.
One hundred ninety-six Amazon Mechanical Turk (MTurk) workers (89 men; Mage = 37.25 years, SD = 12.32) from the United States participated in Study 2, which entailed a 2 (identity: local vs. global) × 2 (price level: high vs. low) between-subjects design. Following [32], we manipulated local–global identity using a sentence-unscrambling task with ten sentences (the first ten items in Web Appendix 6). Those assigned to the local (global) identity condition were instructed to construct ten grammatically correct sentences using such sentences as "Events know I local (global)." The manipulation check questions (α =.94) were as in Study 1 (for results, see Web Appendix 3).
Then, participants answered three questions on dissimilarity focus (e.g., "At this time, I feel that I could easily identify differences in a set of comparative objects"; α =.60), and seven questions on perceived quality variance using a scale adapted from [ 2]; e.g., "The quality of alarm clocks in the marketplace varies a lot"; α =.90). Both scales were anchored by 1 = "Strongly Disagree," and 7 = "Strongly Agree."
Next, following [14], participants viewed information about three brands of alarm clocks—the target brand and two comparison brands—which provided baseline price information. Participants were randomly assigned to either the high- or low-price condition, using identical product descriptions. The target brand was priced the highest (lowest) in the high (low) price condition, with equal relative price range (from 43% [15/30] to 75% [15/20], see Web Appendix 7). In addition, we used fictitious brand names to minimize the potential confounds. Afterward, participants rated the target brand on the same three-item quality measure as in Study 1 (α =.84).
A 2 (identity) × 2 (price) ANOVA on the quality index revealed no effect of local–global identity or price (ps >.11) but, more importantly, showed a significant identity × price two-way interaction (F( 1, 192) = 4.55, p <.05). Consistent with H1, locals rated the target brand as having significantly higher quality in the high-price condition (M = 5.54) than in the low-price condition (M = 5.03, t(102) = 2.63, p <.01). In contrast, the quality ratings for globals did not vary across the two price conditions (Mlow price = 4.98 vs. Mhigh price = 4.92; t(90) =.29, p =.77).
A bootstrapping procedure with 10,000 iterations using Model 15 of [ 9] PROCESS showed that the indirect effect of local (vs. global) identity on PPQ associations through perceived quality variance was positive (.11) and significant (95% confidence interval [CI] = [.02,.29], excluding zero), in support of H2.[ 5]
Study 2 demonstrated that the effect of local (vs. global) identity on PPQ associations is mediated by perceived quality variance, in support of H2. Relative to globals, locals perceived higher levels of quality difference among comparative brands in the marketplace, which in turn led to greater PPQ associations. As we show in Study 10 (Web Appendix 8), price sensitivity and risk aversion cannot be alternative explanations of our findings.
Our theorization suggests that local (vs. global) identity induces a general dissimilarity-focus mindset, which in turn enhances perceived quality variance, leading to higher PPQ. To assess the proposed serial mediation, we followed [28] to test two mediation models. We first tested whether dissimilarity focus mediates the effect of local–global identity on perceived quality variance. We then tested whether perceived quality variance mediates the effect of dissimilarity focus on PPQ (mediated-moderation model). As expected, for the first model, a bootstrapping with 10,000 iterations using Model 4 showed that the indirect effect of local–global identity on perceived quality variance through dissimilarity focus was positive (.18) and significant (95% CI = [.04,.36], excluding zero). Furthermore, the second mediated-moderation model (Model 15) showed that the indirect effect of dissimilarity focus on PPQ through perceived quality variance was also positive (.12) and significant (95% CI = [.01,.28], excluding zero).[ 6] These results provide support for our conceptualization. Next, we provide further evidence of the mechanism by manipulating the mediator "perceived quality variance."
Three hundred eighty-seven MTurk workers (134 men; Mage = 39.84 years, SD = 12.82) from the United States participated in exchange for a small monetary incentive. The experiment consisted of a 2 (identity: local vs. global) × 2 (price level: high vs. low) × 3 (quality variance: enhanced, reduced, unchanged) between-subjects design.
We manipulated local and global identity as in Study 2. Participants were then randomly assigned to one of the three quality variance conditions, which used a news report from a reputable magazine. In the quality variance–enhanced (reduced) condition, participants read a report from an interview with an expert regarding the quality of products in the marketplace, which included an excerpt stating the expert's opinion that "durable appliances offered by different manufacturers in fact do (do not) have significant differences in product quality." In the quality variance unchanged (control) condition, no such news was presented. Afterward, participants were shown the same three brands of alarm clocks as in Study 2. We added microwaves (for the product stimuli, see Web Appendix 8) as an additional product to enhance the generalizability of our findings. Participants were asked to rate the target brands on the same three-item quality index as in Study 1 (αalarm clock =.90 and αmicrowave =.93).
Finally, as a manipulation check for quality variance prime, participants were asked to recall the news and indicate the expert's opinion about product quality (1 = "has significant differences across products," 2 = "does not have much difference across products," and 3 = "I don't know about this information"). Results showed that most participants in the variance-enhanced condition selected 1 (93.8%), whereas most participants in the variance-reduced condition selected 2 (89.5%), and most participants in the variance-unchanged (i.e., control) condition selected 3 (73.6%; χ2 ( 4) = 504.48, p <.01). Thus, quality variance was successfully primed.
We conducted a 2 (identity) × 2 (price) × 3 (quality variance) × 2 (product category; dummy coded 1 = alarm clock, and 0 = microwave) repeated-measure ANOVA on the quality index. Results revealed only a significant main effect of product category (F( 1, 385) = 16.93, p <.01); no other effects were significant (ps ranged from.11 to.51), suggesting that PPQ associations did not vary by product category. Thus, the data were pooled across the product categories. Results of the pooled data revealed no main effect of identity (F( 1, 385) = 1.96, p =.16), a significant main effect of price (F( 1, 385) = 20.79, p <.01) and variance (F( 2, 385) = 3.00, p =.05), no effect of identity × variance two-way interaction (F( 2, 385) =.82, p =.44), and significant two-way interactions between identity and price (F( 1, 385) = 6.40, p <.05) and between price and variance (F( 1, 385) = 7.77, p <.01). More important and consistent with H3a and H3b, there was a significant three-way interaction among identity, price, and quality variance (F( 2, 385) = 3.17, p <.05).
In the control (i.e., variance-unchanged) condition, a 2 (identity) × 2 (price) ANOVA revealed no effect of identity or price (ps >.18), and a significant identity × price two-way interaction (F( 1, 385) = 9.44, p <.01). Locals rated the target brands as superior in quality in the high (vs. low) price condition (Mhigh price = 4.88 vs. Mlow price = 4.21, t(68) = 3.93, p <.01). However, globals did not rate the brands as significantly different across the price conditions (Mlow price = 4.53 vs. Mhigh price = 4.26, t(53) = 1.26, p =.21), in support of H1.
Next, we compared the PPQ associations in the variance-enhanced (vs. unchanged) conditions among locals and globals separately. For globals in the variance-enhanced and unchanged conditions, a 2 (variance) × 2 (price) ANOVA revealed no effect of salience (F( 1, 385) =.18, p =.68) or price (F( 1, 385) = 1.33, p =.25), and a significant quality-variance × price two-way interaction (F( 1, 385) = 8.40, p <.01), suggesting that enhancing the salience of quality variance significantly influenced globals' tendency to make PPQ associations. Contrasts suggested that globals made PPQ associations in the variance-enhanced condition (Mlow price = 4.02 vs. Mhigh price = 4.65; t(63) = −3.75, p <.01), but not in the variance-unchanged condition (Mlow price = 4.53 vs. Mhigh price = 4.26; t(53) = 1.26, p =.21; Figure 2).
Graph: Figure 2. The moderating effect of salience of quality variance on the relationship between local–global identity and PPQ associations (Study 3).
For locals in the variance-enhanced and unchanged conditions, a 2 (variance) × 2 (price) ANOVA revealed no effect of salience (F( 1, 385) =.01, p =.91) and a significant effect of price F( 1, 385) = 36.61, p <.01). Consistent with our hypothesis, there was no effect of variance × price two-way interaction (F( 1, 385) = 2.03, p =.16), suggesting that enhancing the salience of quality variance did not change locals' tendency to make PPQ associations. As shown in Figure 2, locals in both variance-enhanced (Mlow price = 4.02 vs. Mhigh price = 5.11; t(62) = −5.39, p <.01) and variance-unchanged (Mlow price = 4.21 vs. Mhigh price = 4.88; t(68) = −3.93, p <.01) conditions made PPQ associations. Taken together, these results supported H3a.
Furthermore, we compared the PPQ associations in the variance-reduced (vs. unchanged) conditions among locals and globals separately. For globals in the variance-enhanced and unchanged conditions, a 2 (variance) × 2 (price) ANOVA revealed no effect of variance, price, or the variance × price two-way interaction (all ps >.05), suggesting that reducing the salience of quality variance did not change globals' tendency to make PPQ associations. Contrasts showed that globals did not make PPQ associations in the variance-reduced (Mlow price = 4.61 vs. Mhigh price = 4.77; t(77) = −.68, p =.50) or variance-unchanged (Mlow price = 4.53 vs. Mhigh price = 4.26; t(53) = 1.26, p =.21) conditions (see Figure 2).
For locals in the variance-reduced and unchanged conditions, a 2 (variance) × 2 (price) ANOVA revealed no effect of salience (F( 1, 385) =.64, p =.42) but a significant effect of price (F( 1, 385) = 6.25, p <.05) and a significant variance × price two-way interaction (F( 1, 385) = 4.47, p <.05), suggesting that reducing the salience of quality variance significantly influenced locals' tendency to make PPQ associations. As Figure 2 illustrates, contrasts showed that locals did not make PPQ associations in the variance-reduced condition (Mlow price = 4.63 vs. Mhigh price = 4.69; t(62) = −.25, p =.80), but did so in the variance-unchanged condition (Mlow price = 4.21 vs. Mhigh price = 4.88; t(68) = −3.93, p <.01). These results support H3b.
Our framework suggests that locals (vs. globals) perceive greater quality variance among comparative brands, which in turn leads them to rely on price to infer the quality of these brands. Accordingly, situationally enhancing the salience of quality variance increased globals' but not locals' tendency to make PPQ associations, compared with a control condition in which quality variance was not changed. Similarly, situationally increasing the salience of quality similarity (compared with a control condition in which quality variance was unchanged) reduced locals' tendency to use price to indicate quality but did not affect globals' tendency to make PPQ associations, because globals already perceived low variance in quality to begin with.
We designed the following two studies to extend Study 3 by using natural moderators, including product type (Study 4) and the distribution of customer ratings (Study 5). If our proposed mechanism holds, when the evaluation objects are services (vs. goods) or when the ratings from other customers are divergent (vs. control), we should replicate the findings in the variance-enhanced condition, as stated in H4 and H5a. However, when the ratings are convergent (vs. control), we should replicate the findings in the variance-reduced condition (H5b).
Two hundred seventy-eight MTurk workers (101 men; Mage = 39.89 years, SD = 12.22) from the United States participated in a study comprising a 2 (identity: local vs. global) × 2 (price: high vs. low) × 2 (product type: services vs. goods) between-subjects design. The procedure, manipulation of local–global identity, and measures were the same as in Study 3, except for three important differences: ( 1) we included three services (carpet cleaning, landscaping, and airline services; for stimuli, see Web Appendix 9); ( 2) in addition to the two products used before (i.e., alarm clock and microwave), we added sewing machines to ensure equivalence with the number of services; and ( 3) instead of keeping relative price range constant, we kept the same prices for the two baseline brands (e.g., $20 and $30). After examining descriptions of the three brands (i.e., the target brand and two other brands) for each product, participants rated the target brands on the same three-item quality index as in Study 1 (αs ranged from.82 to.93).[ 7]
For goods, we analyzed the data using a 2 (identity) × 2 (price) × 3 (category of goods; dummy-coded as 2 = sewing machine, 1 = alarm clocks, and 0 = microwave) repeated-measure ANOVA with quality index as the dependent variable. The analysis revealed that none of the effects related to category of goods were significant (ps >.26). For services, we analyzed the data using a 2 (identity) × 2 (price) × 3 (service type) repeated-measure ANOVA with quality index as the dependent variable. The analysis revealed a significant main effect of service category (F( 1, 131) = 3.83, p =.05), but none of its interactions with other factors were significant (ps >.50). Thus, we pooled the data separately for goods and services.
Using the pooled data, we conducted a 2 (identity) × 2 (price) × 2 (product type) ANOVA on the quality index. Results revealed no effect of identity (F( 1, 270) =.35, p =.58) but did show significant effects of price (F( 1, 270) = 13.20, p <.01), product type (F( 1, 270) = 21.06, p <.01), product type × price two-way interaction (F( 1, 270) = 4.83, p <.05), and price × identity two-way interaction (F( 1, 270) = 5.23, p <.05); however, there was no effect of product type × identity two-way interaction (F( 1, 270) =.01, p =.94). Consistent with H4, there was a significant three-way interaction among identity, price, and product type (F( 1, 270) = 4.05, p <.05).
For goods, a 2 (identity) × 2 (price) ANOVA revealed no effect of identity or price (ps >.33), but we did find a significant identity × price two-way interaction (F( 1, 270) = 9.13, p <.01). Locals rated the target brands as superior in the high- (vs. low-) price condition (Mlow price = 4.45 vs. Mhigh price = 4.94, t(71) = −2.93, p <.01), whereas globals rated the target brands as equivalent in quality across price conditions (Mlow price = 4.78 vs. Mhigh price = 4.52, t(66) = 1.51, p =.14). These findings replicated those of Studies 1 and 2.
Next, we compared PPQ associations for services (vs. goods) among globals and locals separately. For globals, a 2 (product type) × 2 (price) ANOVA revealed no effect of price (F( 1, 270) =.99, p =.32) but a significant main effect of product type (F( 1, 270) = 10.82, p <.01) and a significant product type × price interaction (F( 1,270) = 8.87, p <.01). Globals made PPQ associations when evaluating services (Mlow price = 4.80 vs. Mhigh price = 5.31; t(61) = −2.66, p =.01) but not goods (Mlow price = 4.52 vs. Mhigh price = 4.78; t(66) = −1.51, p =.14; Figure 3). For locals, a 2 (product type) × 2 (price) ANOVA revealed significant effects of product type (F( 1, 270 = 11.34, _I_p_i_ <.01) and price (F( 1, 270) = 17.74, p <.01). More important and consistent with H4, there was no effect of two-way product type × price interaction (F( 1, 270) =.04, p =.85). Locals made PPQ associations when evaluating both services (Mlow price = 4.84 vs. Mhigh price = 5.39; t(70) = −2.98, p <.01) and goods (Mlow price = 4.45 vs. Mhigh price = 4.94; t(71) = −2.93, p <.01; Figure 3). Thus, these results supported H4.
Graph: Figure 3. The moderating role of services versus goods on the relationship between local–global identity and PPQ associations (Study 4).
Participants were 785 MTurk workers (278 men; Mage = 39.33 years, SD = 13.13) from the United States who were randomly assigned to a 2 (identity: local vs. global) × 2 (price: high vs. low) × 3 (customer rating distribution: convergent, divergent, control) between-subjects design. The procedure, manipulation of local–global identity, product stimuli, and measures were as in Study 2 except for two differences: ( 1) we used microwaves in this study, and ( 2) before making judgments on the target brand, participants saw a summary table of customer ratings, which we used to manipulate the distribution of customer ratings. In the divergent-rating condition, the customer reviews were almost equally distributed across the "poor," "good," and "excellent" categories, whereas in the convergent-rating condition, customer reviews concentrated on the "good" category (for stimuli, see Web Appendix 10). Although the distribution of customer ratings differed, the average rating was the same across convergent and divergent conditions. In the control condition, there was no information about customer reviews.
Thereafter, participants viewed information about three brands (i.e., the target brand and two other brands) of microwaves and evaluated the target brand on the three-item quality measure as in Study 1 (α =.90). Participants were then asked to rate perceived differences between microwaves in the marketplace using the perceived quality variance measure as in Study 4 (α =.81). Participants in the divergent-rating condition (M = 5.22) perceived more quality variance than those in the control condition (M = 4.97; t(526) = 2.22, p <.05), whereas those in the convergent-rating condition (M = 4.67) perceived less quality variance than those in the control condition (M = 4.97; t(519) = −2.41, p <.05), suggesting that our manipulation was successful.
A 2 (identity) × 2 (price) × 3 (rating distribution) ANOVA on the quality index revealed no effect of identity or rating distribution (ps >.10), a significant effect of price (F( 1, 773) = 51.55, p <.01), no significant two-way interactions (ps >.21), and, importantly, a significant three-way interaction among identity, price, and rating distribution (F( 1, 773) = 5.32, p <.01).
In the control condition, we expected to replicate the findings of Study 2. A 2 (identity) × 2 (price) ANOVA revealed no effect of identity (F( 1, 773) =.12, p =.73), a significant effect of price (F( 1, 773) = 16.75, p <.01), and a significant identity × price two-way interaction (F( 1, 773) = 10.90, p <.01). Participants primed with local identity rated the target brand as having higher quality in the high- (vs. low-) price condition (Mlow price = 3.71 vs. Mhigh price = 4.56; t(138) = −5.50, p <.01). However, those primed with global identity rated the target brand equivalently in the two price conditions (Mlow price = 4.10 vs. Mhigh price = 4.19; t(122) = −.56, p =.58).
Next, we compared PPQ in the divergent (vs. control) conditions among locals and globals separately. For globals in the divergent and control conditions, a 2 (rating distribution) × 2 (price) ANOVA revealed no effect of rating distribution (p >.11), a significant effect of price (F( 1, 773) = 16.31, p <.01), and a significant ratings distribution × price two-way interaction (F( 1, 773) = 10.70, p <.01). Contrasts showed that globals made PPQ associations in the divergent condition (Mlow price = 3.53 vs. Mhigh price = 4.39; t(122) = −5.44, p <.01), but not in the control condition (Mlow price = 4.10 vs. Mhigh price = 4.19; t(122) = −.56, p =.58; Figure 4). For locals in the divergent and control conditions, a 2 (rating distribution) × 2 (price) ANOVA revealed no effect of rating distribution (p >.15), a significant effect of price (F( 1, 773) = 59.68, p <.01), and no effect of rating distribution × price two-way interaction (F( 1, 773) =.01, p =.92). Contrasts showed that locals made PPQ associations in both the divergent (Mlow price = 3.85 vs. Mhigh price = 4.72; t(138) = −5.49, p <.01) and control (Mlow price = 3.71 vs. Mhigh price = 4.56; t(138) = −5.50, p <.01; Figure 4) conditions, in support of H5a.
Graph: Figure 4. The moderating role of convergent versus divergent ratings on the relationship between local–global identity and PPQ associations (Study 5).
Furthermore, we compared PPQ in the convergent (vs. control) conditions among locals and globals separately. For globals in the convergent and control conditions, a 2 (rating distribution) × 2 (price) ANOVA revealed no effect of rating distribution, price, or the rating distribution × price two-way interaction (ps >.19), suggesting that providing convergent customer reviews did not change globals' tendency to make PPQ associations. Contrasts showed that globals did not make PPQ associations in the convergent (Mlow price = 3.90 vs. Mhigh price = 4.12; t(126) = −1.25, p =.21) and control (Mlow price = 4.10 vs. Mhigh price = 4.19; t(122) = −.56, p =.58; Figure 4) conditions. For locals in the convergent and control conditions, a 2 (rating distribution) × 2 (price) ANOVA revealed no effect of rating distribution (p >.13), a significant effect of price (F( 1, 773) = 14.84, p <.01), and a significant rating distribution × price two-way interaction (F( 1, 773) = 13.10, p <.01), suggesting that providing convergent customer reviews influenced locals' tendency to make PPQ associations. Contrasts showed that locals did not make PPQ associations in the convergent condition (Mlow price = 4.29 vs. Mhigh price = 4.31; t(127) = −.15, p =.88), but did so in the control condition (Mlow price = 3.71 vs. Mhigh price = 4.56; t(138) = −5.50, p <.01; Figure 4). Taken together, these results supported H5b.
Using product type (Study 4) and distribution of customer ratings (Study 5) as natural boundary conditions, these studies provided additional evidence for the "perceived quality variance" account. We also conducted a study (Study 11 in Web Appendix 11) to examine hedonic (vs. utilitarian) product type as another natural moderator. Hedonic (vs. utilitarian) products by nature have greater perceived quality variance because different consumers tend to evaluate hedonic products using divergent criteria, whereas the evaluation of utilitarian products is mainly based on well-defined criteria ([12]). Our framework suggests that when evaluating hedonic (vs. utilitarian) products, globals' tendency to use PPQ associations will be elevated, whereas locals' tendency to use PPQ associations will be unaffected. Our results supported this prediction. These studies enhanced the external validity of our findings and showed direct evidence of the managerial implications of this research.
In the next study, we aim to reconcile the seemingly contradictory findings predicted by our theory and those of [44]. These authors found that an abstract (vs. concrete) construal enhances PPQ associations. If globals (vs. locals) have a greater abstract (instead of concrete) construal (as implied by [32]]), this account predicts that they would be more likely to make PPQ associations, which is opposite to our prediction.
We believe that the seemingly contradictory predictions are due to the conceptual distinction between local–global identity and construal level. Our theorization predicts that a local (vs. global) identity induces a dissimilarity-focus mindset, which in turn motivates the search for, and use of, diagnostic cues to make sense of the quality differences between brands. In contrast, construal-level theory suggests that abstract (vs. concrete) information such as price tends to exert greater impact on representations and judgments when construal level is high (vs. low; [44]). Thus, although a local identity and low-level construal both may lead to greater perceived differences among comparative objects ([21]), locals are driven by their innate dissimilarity-focus mindset, which motivates them to look for and use diagnostic cues such as price to justify brand differences. However, a low- (vs. high-) level construal reduces the tendency to use abstract cues such as price to judge product quality.
We tested the distinction between local–global identity and construal level in the context of product choices. Specifically, we manipulated the diagnosticity of product attributes through trade-offs among product features. As an example, take three features of a digital camera: megapixels, optical zoom, and price. When attributes do not contain trade-offs (e.g., "low in price but high in both megapixels and optical zoom" vs. "high in price but low in both megapixels and optical zoom"), the decision scenario is quite similar to the stimuli of [44], Experiment 2), in which the comparison was between a low-price, high-quality option and a high-price, low-quality option. In such a situation, perceived quality variance among comparative brands is made salient by the diagnosticity of product attributes. When construal level is experimentally made high, we expect to replicate Yan and Sengupta's findings (i.e., price has more impact in the high- than in the low-construal condition). However, the prediction of local–global identity can have two possible directions, depending on whether the construal-level account or our proposed quality variance account holds. The construal-level account predicts that price, being an abstract cue, will be used as a quality cue more by globals (vs. locals) because they are abstract (vs. concrete) thinkers. However, the quality-variance account suggests that the impact of price will not differ across locals and globals (as in H3a).
Given that trade-offs significantly lower the diagnosticity of product features ([ 5]; [10]; [25]), when attributes contain trade-offs (e.g., low price, high in megapixel, low in optical zoom, representing a low-price, mixed-quality option), perceived quality variance among the comparative brands is not made salient (similar to the control condition in Study 3). In such a situation, if the quality-variance account holds, price should affect locals (vs. globals) more, as specified in H1. If the construal-level account holds, we predict price, being an abstract cue, to have more of an impact on globals (vs. locals), who are abstract (vs. concrete) thinkers. In addition, according to [44], quality attributes are concrete product cues (i.e., low-level construal), whereas price is an abstract cue (i.e., high-level construal). Because the manipulation of diagnosticity is only on quality (and not on price) cues, we expect diagnosticity to moderate the effect of construal level on PPQ in the low-construal-level condition, but not in the high-construal-level condition. The next study tests these predictions and rules out decision-making effort as another alternative explanation.
We randomly assigned 470 college students (239 men; Mage = 26.60 years, SD = 10.88) to one of the conditions in a 4 (local identity, global identity, high-level construal, low-level construal) × 2 (diagnosticity of quality cues: high vs. low) between-subjects design. Local and global identities were manipulated as in Study 2; the manipulation check items were the same as in Study 2 (α =.88). Following [ 6], we primed construal level by asking participants to think and write about why they should improve their academic performance (high construal) or how to improve their academic performance (low construal). To check the manipulation, we used the Behavior Identification Form (BIF; [41]; see Web Appendix 2).
Participants were then given a description of two cameras and asked to determine which was of higher quality. The two cameras differed in price and two other nonprice cues (megapixels and optical zoom). The diagnosticity of nonprice cues was manipulated through consistency in megapixels and optical zoom (see Web Appendix 12). In the high-diagnosticity condition, the two nonprice cues were in the same direction: the high-price ($240) camera was low in both megapixels (15 MP) and optical zoom (10×), and the low-price ($200) camera was high in both megapixels (18 MP) and optical zoom (12×). This design is consistent with [44]; Experiment 2). Because one option had a higher price but was of lower quality than the other option, the quality variance between these two options was salient, as shown by [ 5]; [10]; [25]). We used a pilot study (N = 78) to validate the manipulation of diagnosticity. Participants were randomly assigned to either the high- or low-diagnosticity condition and rated perceived quality variance using two items (α =.85): ( 1) "The quality of cameras in the marketplace varies a lot," and ( 2) "There are huge differences among cameras." Results showed that participants in the high- (vs. low-) diagnosticity condition perceived more variance in quality of cameras (Mhigh diagnosticity = 5.61 vs. Mlow diagnosticity = 4.99; t(76) = 2.31, p <.05).
Participants also completed a two-item measure of task involvement (α =.85): ( 1) "How involved were you when judging the two cameras?" (1 = "Not at all," and 7 = "Very much so") and ( 2) "How much thought did you put into the task of evaluating the two cameras?" (1 = "Not at all," and 7 = "A lot"). We also recorded the actual time that participants spent making the choice as another measure of effort.
As we expected, participants in the local (vs. global) identity condition were more likely to perceive themselves as local citizens (Mlocal = 4.64 vs. Mglobal = 4.12; t(231) = 2.25, p <.05). However, participants in the high- and low-construal level conditions did not differ in this aspect (Mhigh construal = 4.30 vs. Mlow construal = 4.40; t(235) = −.41, p =.68). Those in the high-construal condition (M = 18.14) scored higher on the BIF than those in the low-construal condition (M = 15.57; t(235) = 3.66, p <.01), indicating that construal level was primed successfully. Interestingly, consistent with [32], participants in the global identity condition (M = 16.26) scored higher on the BIF than those in the local identity condition (M = 14.03; t(231) = 3.09, p <.01), suggesting that local–global identity prime indeed affects construal level.
In the low-diagnosticity condition, consistent with our prediction that price would have more impact in the local (vs. global) identity condition, the proportion of participants who selected the high-price camera as superior was higher in the local (31.67%) versus the global (10.91%) identity condition (χ2( 1) = 7.27, p <.01). However, the proportion of participants who selected the high-price camera as superior did not differ across the high-level (28.33 %) and low-level (18.33%) construal conditions (χ2( 1) = 1.68, p =.20; Figure 5).
Graph: Figure 5. The effect of local–global identity and construal level on PPQ associations (Study 6).Notes: The y-axis indicates choice of the high-price option as having better quality.
In the high-diagnosticity condition, the proportion of participants who selected the high-price camera as having better quality was higher (χ2( 1) = 7.44, p <.01) in the high-level construal condition (22.41%) than in the low-level construal condition (5.08%); this is consistent with [44] finding that price has more of an impact in the high-level construal condition than in the low-level construal condition. However, the proportion of participants who selected the high-price camera as superior did not differ between the local identity condition (29.63%) and the global identity condition (26.56%; χ2( 1) =.14, p =.84).
To test our prediction that when diagnosticity is high (vs. low), globals will perceive the high-price item to be of better quality (i.e., elevated PPQ), whereas locals' quality perceptions will be unaffected (H3a), we compared the choice of the high-price option in the high- (vs. low-) diagnosticity condition among locals and globals separately. The proportion of globals who selected the high-price camera as being of better quality was higher (χ2( 1) = 4.65, p <.05) in the high-diagnosticity condition (26.56%) than in the low-diagnosticity condition (10.91%). However, the proportion of locals who selected the high-price camera as being of better quality did not differ (χ2( 1) =.06, p =.84) between the high- (29.63%) and low- (31.67%) diagnosticity conditions (see Figure 5).
To test our expectation that diagnosticity (high vs. low) will moderate the effect of construal level on PPQ in the low-construal level condition but not in the high-construal level condition, we conducted additional analysis across construal levels. Consistent with our expectations, in the high-level construal condition, the proportion of participants who selected the high-price cameras as having better quality did not differ across the low- (28.33%) and high- (22.40%) diagnosticity conditions (χ2( 1) =.55, p =.46), indicating that they were not affected by diagnosticity; however, in the low-level construal condition, the proportion of participants was higher in the low- (18.33%) than in the high- (5.08%) diagnosticity condition (χ2( 1) = 5.03, p =.03), suggesting that they were significantly influenced by diagnosticity of nonprice cues.
We used two measures to assess the effort participants invested in the decision task: ( 1) a self-reported task involvement measure and ( 2) processing time (in seconds). Results showed that neither task involvement (Mlocal = 5.29 vs. Mglobal = 5.49; t(231) = −1.02, p =.31) nor processing time (Mlocal = 40.62 vs. Mglobal = 36.12; t(231) =.39, p =.70) differed across the identity conditions. Therefore, decision-making effort cannot explain our findings.
This study provided direct evidence on the difference between local–global identity and construal level and reconciled the seemingly contradictory findings. Moreover, it ruled out effort in decision task as another alternative explanation for our findings. Next, we report a field experiment with real behavioral measures to test the external validity of the findings.
The purpose of this study was to investigate a behavioral consequence of local–global identity and PPQ associations in a real choice task involving monetary expenditures. [ 3] found that consumers who make stronger PPQ associations spend more money on purchases to acquire higher-quality products. In the context of choosing a water bottle from four options at different prices, we expect that locals (vs. globals) are more likely to purchase expensive water bottles and that this effect is mediated by PPQ associations.
Eighty-one U.S. consumers (33 men; Mage = 23.65 years, SD = 6.76) shopping at a local bookstore were recruited with an offer of $20 in total compensation, which could include a water bottle of their choice with the remaining amount in cash. As in Study 1, participants were given a brochure that described either a "Think Local Movement" or a "Think Global Movement," which was used to manipulate local and global identity, respectively (Web Appendix 13).
Next, participants were instructed that the study would involve consumers' evaluation of water bottles and were reminded of the compensation scheme. They were also told that if they so chose, they could receive $20 in cash and no water bottle (two consumers chose this option, one from the local identity condition and one from the global identity condition).[ 8] Thereafter, we asked participants to evaluate four different water bottles actually sold in the bookstore (priced at $4.99, $9.99, $14.99, and $19.99) and administered the four-item PPQ associations scale from [23]; adapted to assess state, rather than chronic, PPQ associations for water bottles; sample item: "At this moment, I believe that the higher the price of a water bottle, the higher the quality"; α =.89). Participants were then asked to choose one of the four water bottles and were paid the remaining amount of $20 in cash. Finally, participants rated the three-item local–global identity manipulation check questions (α =.92) as in Study 1 (for results, see Web Appendix 3).
As we predicted, participants assigned to the local (vs. global) identity condition spent more on the water bottle (Mlocal = $14.52 vs. Mglobal = $9.43; t(77) = 4.44, p <.001) and had significantly higher PPQ associations (Mlocal = 5.12 vs. Mglobal = 4.34; t(77) = 2.28, p <.05), indicating that participants primed with local (vs. global) movements perceived a much stronger relation between the price of a water bottle and its quality; this, in turn, influenced their choice and spending behavior. Indeed, participants with a situationally activated local (vs. global) identity spent 53.98% more. Although PPQ is not a theorized mediator (which is perceived quality variance), we ran a mediation test to provide evidence that the amount spent is driven by PPQ, and not by other variables. A bootstrapping procedure with 10,000 iterations using Model 4 of PROCESS showed that the indirect effect of local–global identity on amount of money spent through PPQ associations was positive (.79) and significant (95% CI = [.12, 1.99], excluding zero), suggesting that individuals with an accessible local (vs. global) identity were willing to spend more money on purchases because of higher PPQ associations.
To enhance the generalizability of our findings, we performed a systematic review on PPQ associations documented in previous studies (for database development, coding procedures, and detailed results, see Web Appendix 14). Given that these studies were conducted in different countries, we used country-level local–global identity as an explanatory factor for PPQ. Following [ 6], we used the KOF Index of Globalization (http://globalization.kof.ethz.ch/) to capture country-level local–global identity, with a higher score reflecting a greater degree of global identity (and a lower degree of local identity).
The mean standardized r across the studies in our database was.208 (95% CIBS = [.199,.218], p <.001), suggesting that, in general, consumers use price to infer brand quality. However, there was substantial heterogeneity in PPQ associations (χ2 = 2,681.54, p <.001). Thus, we conducted moderation analysis through a meta-regression using the Comprehensive Meta-Analysis 3.0 software, with standardized r as the common effect size metric, country-level Globalization Index as the independent variable, and other country-level variables (i.e., gross domestic product per capita, competitive environment, and Hofstede's five cultural dimensions [individualism–collectivism, power distance, uncertainty avoidance, masculinity, and long-term orientation]) and study-level factors (price range, product durability, study type, and publication type) as covariates.
Consistent with our theorizing, results showed a negative relationship between the Globalization Index and PPQ (β = −.02, Z = −3.07, p <.01). Among the country-level variables, competitive environment was positively related to PPQ associations (β =.08, Z = 10.41, p <.001), whereas gross domestic product per capita had a negative effect (β = −.05, Z = −4.99, p <.001). Of the five cultural dimensions, only uncertainty avoidance (β = −.04, Z = −5.73, p <.001) was significantly associated with PPQ associations. Of the study-level factors, there were significant effects of product durability (β = −.09, Z = −7.11, p <.001), study type (β = −.10, Z = −7.02, p <.001), and publication type (β =.06, Z = 4.71, p <.001) but no significant effect of price range (p =.14).
As we show in Appendix B, all studies provide converging evidence for the effect of local–global identity on PPQ, using a variety of measures and manipulations of the key variables. In a shopping mall with real consumers, Study 1 showed that locals (vs. globals) have a greater tendency to make PPQ associations. Study 2 shed light on the mediating role of perceived quality variance. Study 3 revealed that when the quality difference among brands is made salient, globals' (but not locals') tendency to make PPQ associations is elevated, whereas when the quality difference among brands is reduced, locals' tendency to make PPQ associations is lowered, whereas globals' tendency to use PPQ is unaffected. The next two studies examined the moderating roles of product type (services vs. goods; Study 4) and online reviews (convergent vs. divergent; Study 5). Study 6 reconciled the seemingly contradictory predictions between our theory and those of construal-level theory. Study 7 reported a field experiment with real behavioral measures to prove the external validity of our findings. Study 8 presents secondary evidence, further showing how local–global identity may affect PPQ at the national level, lending additional support for external validity. Study 9 (Web Appendix 5) showed that the effect of local–global identity on PPQ is held in both multiple- and single-quality-cue conditions. Study 11 (Web Appendix 11) revealed that hedonic (vs. utilitarian) product type represents another natural moderator of the relation between local–global identity and PPQ associations.
Our findings offer contributions to the price–quality judgments and local–global identity literature streams. Previous cross-cultural research has mainly focused on the dimensions of individualism–collectivism ([16], [17]; [20]; [37]) and power distance ([ 8]; [14]). Although the world has been moving toward globalization in recent years, we know little about how this trend may affect consumers' use of price as a signal of quality. From the limited evidence in cross-country studies ([ 4]; [42]; [46]), it is unclear whether the effect of local–global identity on price–quality judgments even exists. Our research is the first to demonstrate the existence of this effect.
Furthermore, our research contributes to the local–global identity literature by identifying perceived variance among comparative objects as a new qualitative difference between these two identities. This important discovery can advance our understanding about why locals are faithful to local traditions: local identity heightens perceived differences, driving locals to focus on the uniqueness of their traditions and overlook the common elements between their traditions and those of other communities. This discovery likely has implications beyond PPQ associations, such as on categorization and brand extensions. Finally, our research also contributes to the price–quality judgments literature by identifying a novel mechanism that drives consumers to use price to judge quality—that of perceived quality variance. Because of this mechanism, situational factors that make quality variance salient or reduced—such as product type, expert opinions, or distribution of customer ratings—can change consumers' tendency to make PPQ.
As presented in Appendix A, managers actively consider the likelihood that consumers would use PPQ in their product evaluations and use such information in their marketing strategies. They are also aware of the role that local or global communities play in pricing decisions. However, none of our informant managers had a clear idea of when such strategies might be effective and why. This research helps address some of these questions. Our findings indicate that when promoting high-price products, marketers can situationally activate consumers' local identity, because consumers tend to use price to judge a product's quality when their local identity is salient. Communication appeals or contextual cues, such as "Think Local" movement (Studies 1 and 7) or T-shirt (the follow-up study to Study 1), can be used to achieve this goal. Ads or messages that feature local cultural symbols may enhance the accessibility of the local identity. TV channels that feature local traditions can be effective as well. Conversely, when promoting low-price products, marketers can activate consumers' global identity to reduce PPQ. Contextual cues (e.g., ads that feature multicultural symbols and globalization) may enhance the accessibility of global identity.
Another approach to increase consumers' PPQ associations is to alter consumers' perception of dissimilarity among brands to match with a pricing strategy. For products that charge a premium price over competing products, marketers can use situational cues (e.g., expert opinion, as in Study 3; distribution of customer ratings, as in Study 5) to increase perceived quality variance and facilitate consumers' associations between price and product quality. In contrast, for products that take a low-price strategy, marketers can use these situational cues to reduce, rather than increase, perceived quality variance.
Our findings on how product type (service vs. goods, hedonic vs. utilitarian products) affects customers' perceived quality variance provide insight into marketing strategies associated with services, hedonic products, and new products. Marketers of these products can capitalize on our findings by wisely allocating their ads budget: there is no need to build up price–quality associations in the minds of target consumers, because these products naturally induce perceived quality variance, which in turn leads to enhanced PPQ. Previous research has argued that consumers have more diversified views on innovations than on existing products, especially the radically new innovation with first-of-its-kind, groundbreaking technologies ([26]). Our theory suggests that consumers are prone to make PPQ associations when adopting these products.
Our research is the first to show the important role that distribution of customer ratings plays in influencing consumers' PPQ. When people post similar ratings for products in a category, potential buyers may have an impression that products in that category are of similar quality. In contrast, when people's opinions are all over the place and there is lack of a dominant view, potential buyers tend to perceive high quality variance among the products in that category. Armed with this information, marketers using skimming pricing should welcome, rather than suppress, different opinions from previous users, as divergent online reviews can actually enhance consumers' PPQ. However, firms with penetration pricing may need to strive for consumers' convergent opinions, as similar customer ratings can reduce consumers' tendency to view the product's low price as an indicator of its low quality.
Our findings also provide useful guidelines for firms to adapt their strategies to different regions and address the question about whether companies should be more locally or globally oriented. For products to be marketed to the places where people tend to have a salient local identity (e.g., rural areas), local flavors and ingredients can be used in the products. In addition, because these consumers are more likely to make PPQ associations, marketers may not need to allocate much ad budget to convince consumers about price–quality associations. However, when marketers enter places where people are high in global identity (e.g., metropolitan areas), they should know that consumers in these places do not have an established mental connection between price and quality. Thus, additional effort is needed to increase perceived dissimilarity among brands in the marketplace to enhance price–quality associations. Similar strategies can be used for international marketing strategies. Previous research ([ 1]; [ 6]) has shown that individuals in globalized countries are more likely to have a stronger global identity, whereas those from more localized countries tend to have a stronger local identity.
First, although treating the country-level Globalization Index as a proxy of local–global identity in Study 8 is in line with previous research ([ 6]), it may violate the conceptualization that these two identities are orthogonal. Second, this study may suffer from alternative explanations, such as product life cycle. Although this concern is alleviated by the variety of product stimuli used in our studies, we need to be cautious of Study 8's conclusions. Third, while a sacrifice mindset ([ 6]) cannot explain our moderation studies, future research should examine whether sacrifice mindset can account for the relationship between local–global identity and PPQ in domains not examined in the current manuscript. Finally, in this research we focused only on price–perceived quality. Given that price–quality judgments can also be quality–perceived price, it may be fruitful for future researchers to apply our theory to examine how quality levels affect consumers' price expectations.
Supplemental Material, DS_10.1177_0022242918825269 - How Does Consumers' Local or Global Identity Influence Price–Perceived Quality Associations? The Role of Perceived Quality Variance
Supplemental Material, DS_10.1177_0022242918825269 for How Does Consumers' Local or Global Identity Influence Price–Perceived Quality Associations? The Role of Perceived Quality Variance by Zhiyong Yang, Sijie Sun, Ashok K. Lalwani and Narayan Janakiraman in Journal of Marketing
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| Executives | Quotes |
|---|
| 1. Quotes Related to PPQ Associations |
| Innovation and Marketing DirectorAge: 44 years oldPseudonym: "Mark" | "In the sneaker market, usually higher price (e.g., $200 as compared to $100) means a more premium technology or a better feature is offered....Customers sometimes cannot differentiate between technologies from different companies and so price is often a signal of how much more premium the technology is." |
| Senior Product ManagerAge: 34 years oldPseudonym: "Eric" | "The consumer electronics we sell are much more affordable than those from other leading brands. We are, however, aware that the low price can suggest lower performance, so we are careful to make sure that consumers can compare between our products and our competitors on essential features to show that they are indeed somewhat comparable and price is our competitive advantage." |
| Associate Director—Shopper InsightsAge: 53 years oldPseudonym: "Anne" | "For baby products and beauty products consumers are often willing to pay high prices. And I do believe that how consumers view unknown baby brands or beauty products does depend on price." |
| Shopper Marketing ManagerAge: 30 years oldPseudonym: "Holly" | "For most people that drink wine occasionally, price is a very important factor that indicates how good the wine is as much as a wine rating.... So a $11 bottle of wine is definitely viewed as higher quality than a $4 bottle." |
| Senior Director—InsightsAge: 46 years oldPseudonym: "Pat" | "At our wholesale club for unknown brands if the price is too low...customers might perceive them as bad products." |
| Communication and Promotions ManagerAge: 46 years oldPseudonym: "Sam" | "Price is used to judge quality...for sure....In dog sweaters, it is difficult to judge quality, so I'm sure that my pet parents use price, in addition to other factors, to choose between options." |
| Senior Manager—Business PlanningAge: 41 years oldPseudonym: "Marco" | "If you see the smartphones we sell, the X series [name changed] is much cheaper than the Y series [name changed], by about $400 on average. However, they are about 90% the same in terms of product features. We do realize that the higher price is one of the reasons why individuals see higher quality in the Y series products." |
| 2. Quotes Related to Consideration of Local or Global Identities in Pricing Decisions |
| Director—PricingAge: 43 years oldPseudonym: "Evan" | "The tortilla chip market is pretty unique. When we try to introduce local flavors...it makes people think of their local communities....Here, we are careful to make sure that our product is seen as premium. You know...having a twist on the local ingredient is important. Similarly, it is important to have a reasonably higher price since it communicates premium-ness, and then reinforce it with advertising and packaging. Otherwise what will differentiate us from all the local chips by smaller players? But we don't know for sure why such consumers prefer premium brands. That is a mystery." |
| Manager—Pricing and RevenueAge: 39 years oldPseudonym: "Eric" | [Brand name] is a very uniquely flavored soft drink. Most of our customers in the southern states of the U.S., are very tuned to their local communities and think of our brand as a traditional brand. In these markets we resist offering too many discounts to not seem cheap, as compared to the Northeast, where I believe, most of the soft drinks are global brands." |
| Senior Director—InsightsAge: 46 years oldPseudonym: "Pat" | "For deep value cards that we offer in our wholesale club i.e., where we give $40 value gift cards for $25, we are careful to consider the type of restaurant the card is for (local BBQ restaurant vs., a national restaurant chain) because consumer perceptions of value or whether it is a premium restaurant depend on price. In this we find differences between patrons at our Mexico stores as compared to our U.S. stores." |
| Global Director—PricingAge: 48 years oldPseudonym: "Sal" | "I am sure that the annual books we produce for schools, which are often premium priced, are evaluated differently by different markets vis-à-vis the cheaper Shutterfly. Would be good to know where consumers appreciate our higher quality and why?" |
| Category Development ManagerAge: 51 years oldPseudonym: "Larry" | "Craft beer marketers often orient their brands to the specific local market and make people think of who the consumer is and how the brand relates to the consumer. I remember a craft beer trying to price very low. That strategy didn't work as well as they imagined it would. Craft beer drinkers often are willing to pay a higher price for the better taste, you see....A cheaper craft beer would be pretty suspect, I guess." |
| Senior Director, Global MerchandisingAge: 47 years oldPseudonym: "Jesper" | "If you consider our PCs, we are one of the largest software and hardware manufacturers in the world and I manage all the retail stores across the world for our devices. What I have seen is that the global shopper (well-travelled and exposed to all brands and products) is very different from the nonglobal shopper. The global shopper I believe is less likely to use price as the determinant of product purchase, they want us to back it with product features." |
Graph
| Studye | Sample Size | Condition | Dependent Measure | PPQ Associations |
|---|
| Local Identity | Global Identity |
|---|
| 1 | 164 | Physical goods (shoes and cap) | Correlation between price and quality evaluation | .70 | .45c |
| Follow-up | 69 | Physical goods (cap) | Correlation between price and quality rating | .50 | .02c |
| 2 | 196 | Physical goods (alarm clock) | Quality index | High Price | Low Price | High Price | Low Price |
| 5.54 | 5.03a | 4.92 | 4.98bc |
| 3 | 387 | Quality variance unchanged (alarm clock and microwave) | Quality index | High Price | Low Price | High Price | Low Price |
| 4.88 | 4.21a | 4.26 | 4.53bc |
| Quality variance enhanced (alarm clock and microwave) | Quality index | High Price | Low Price | High Price | Low Price |
| 5.11 | 4.02a | 4.65 | 4.02ad |
| Quality variance reduced (alarm clock and microwave) | Quality index | High Price | Low Price | High Price | Low Price |
| 4.69 | 4.63b | 4.77 | 4.61bd |
| 4 | 278 | Physical goods (alarm clock, microwave, and sewing machine) | Quality index | High Price | Low Price | High Price | Low Price |
| 4.94 | 4.45a | 4.52 | 4.78bc |
| Services (carpet cleaning, airline, landscape) | Quality index | High Price | Low Price | High Price | Low Price |
| 5.39 | 4.84a | 5.31 | 4.80ad |
| 5 | 785 | Control (microwave) | Quality index | High Price | Low Price | High Price | Low Price |
| 4.56 | 3.71a | 4.19 | 4.10bc |
| Divergent customer reviews (microwave) | Quality index | High Price | Low Price | High Price | Low Price |
| 4.72 | 3.85a | 4.39 | 3.53ad |
| Convergent customer reviews (microwave) | Quality index | High Price | Low Price | High Price | Low Price |
| 4.31 | 4.29b | 4.12 | 3.90bd |
| 6 | 470 | Low diagnosticity (camera) | Choice of the higher-quality product | 31.67% | 10.91%c |
| High diagnosticity (camera) | Choice of the higher quality product | 29.63% | 26.56%d |
| 7 | 81 | Perception for specific product (water bottle) | PPQ associations scale | 5.12 | 4.34c |
| NR1 | 549 | Utilitarian product (alarm clock) | Quality index | High Price | Low Price | High Price | Low Price |
| 5.13 | 4.36a | 4.87 | 4.66bc |
| Hedonic product (wine) | Quality index | High Price | Low Price | High Price | Low Price |
| 5.17 | 4.57a | 5.39 | 4.56ad |
| NR2 | 197 | Physical goods (alarm clock) | Quality index | High Price | Low Price | High Price | Low Price |
| 4.95 | 4.26a | 4.78 | 4.67bc |
| NR3 | 118 | Overall perception | PPQ associations scale | .30 | .07c |
1 a The difference between high- and low-price conditions was significant (p <.05), indicating that participants made PPQ associations.
- 2 b The difference between high- and low-price conditions was not significant (p >.05), suggesting that participants did not make PPQ associations.
- 3 c The difference between local and global identity conditions was significant (p <.05), showing that locals (vs. globals) had a greater tendency to make PPQ associations.
- 4 d The difference between local and global identity conditions was not significant (p >.05), indicating that locals and globals had a similar level of tendency to make PPQ associations.
- 5 e NR1–NR3 were not reported in the current version of the article.
Footnotes 1 Associate EditorTimothy Heath served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242918825269
5 1We further analyzed the mediated moderation model using [29] approach. We tested this mediated moderation by first regressing the quality index onto local–global identity, price, and their interaction term. This analysis revealed an identity × price interaction (β =.26, t = 2.13, p <.05). Second, we used the same model with perceived quality variance (i.e., our mediator) as a dependent variable. This analysis revealed a significant effect of local–global identity (β =.20, t = 2.02, p <.05) but a nonsignificant effect of the identity × price interaction (β =.02, t =.16, p =.87). Third, we regressed quality index onto the same model plus perceived quality variance and its interaction with price. As expected, we found a significant perceived quality variance × price interaction (β =.66, t = 2.20, p <.05). This last model revealed that the identity × price interaction was no longer significant (β =.20, t = 1.71, p =.09), suggesting a complete mediated moderation.
6 2The mediated-moderation model using [29] approach indicated a complete mediated moderation.
7 3A pilot study with 40 MTurk workers (16 men; Mage = 30.43 years, SD = 9.55) from the United States supported our assumption that services are perceived to vary more in quality than goods. For each of the six products noted previously (three goods and three services), participants rated the first two items of the perceived quality variance measure from Study 2 (αs ranged from.61 to.78; for the stimuli of alarm clock and microwave, see Web Appendix 8 (Study 10); for the stimuli of sewing machine and three services, see Web Appendix 9). Results suggested that participants perceived services (M = 5.09) to have greater variance in quality, compared with goods (M = 4.50; t(39) = 4.11, p <.01).
8 4To be consistent with [3], we excluded these two consumers from analysis and only reported the results with a sample of 79. However, including these two consumers in the analysis did not change the pattern of results or their significance level.
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By Zhiyong Yang; Sijie Sun; Ashok K. Lalwani and Narayan Janakiraman
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Record: 90- How Does Local-Global Identity Affect Price Sensitivity? By: Gao, Huachao; Zhang, Yinlong; Mittal, Vikas. Journal of Marketing. May2017, Vol. 81 Issue 3, p62-79. 18p. 1 Diagram, 7 Charts, 3 Graphs. DOI: 10.1509/jm.15.0206.
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How Does Local-Global Identity Affect Price Sensitivity?
The authors propose that when consumers' local identity is accessible, they are less likely to be price sensitive because of a sacrifice mindset. Six studies using divergent measures of the independent and dependent variables as well as diverse samples (students and nonstudents, U.S. and Chinese residents, primary and secondary data) produce consistent results. Furthermore, the authors demonstrate themediating role of a sacrifice mindset by bothmeasuring and manipulating this construct; they also identify boundary conditions of the association between a consumer's local identity and price sensitivity. Previous research has shown that consumers with a local identity display lower price sensitivity to brands with a local origin. In contrast, the results from this research show that consumers with a local identity display lower price sensitivity even to products with an ambiguous origin. Firms using a globalization strategy can try to activate consumers' local identity tomake themless price sensitive to their brands, without having to position the brands as local.
Factors such as increased labor costs, inflation, and higher raw-material costs can lead companies to raise prices (Ailawadi and Farris 2013). From 2000 to 2015, the U.S. consumer price index (CPI) increased 37%, from 173.6 to 237.8 (Bureau of Labor Statistics 2016), yet companies frequently struggle with price increases. For example, Whole Foods, a premium grocery chain, had difficulty increasing prices to maintain its premium branding, eventually deciding to lower prices across many categories to maintain its customer base (Gasparro 2015).
Scholars have called for research on factors that make consumers less price sensitive (Campbell 2007), and previous research has shown that customers who are relatively more satisfied have lower price sensitivity (Homburg, Hoyer, and Koschate 2005; Kumar et al. 2014). Extending this research, the current work examines whether and how consumers' local versus global identity (Arnett 2002; Zhang and Khare 2009) may affect their price sensitivity. Companies sometimes position their products as having local roots (Loureiro and Hine 2002) as a way to reduce consumers' price sensitivity. Our work indicates that this may not be necessary; indeed, we show that activating consumers' local identity decreases their price sensitivity to all products, not just local products. Guided by our results, firms should be able to optimize customer value without having to change their positioning as a "local" company. More specifically, firms can use communication strategies to activate consumers' local identity to decrease their price sensitivity. Firms can also use valid and reliable scales (Tu, Khare, and Zhang 2012) to identify customers who should be relatively less price sensitive, all else being equal. Typically, global companies can execute both these strategies without resorting to the more costly approach of local production or positioning the company as having local roots.
Extant literature has suggested that price can serve as an indicator of both quality and sacrifice (Dodds, Monroe, and Grewal 1991; Zeithaml 1988). Focusing on the sacrifice aspect, our core argument is that a local identity among consumers may evokeasacrifice mindset (Xu and Wyer 2007,2012). In turn, we argue that with such a mindset, consumers should be more likely to consider a price increase acceptable. This argument is consistent with recent developments in behavioral-mindset theory, which indicate that goal-related constructs made salient in one task can affect subsequent tasks unrelated to the former task (Xu and Wyer 2007). Relevant to the current research, the sacrifice mindset—activated by a local identity—can provide the mediating mechanism between a consumer' s local identity and price sensitivity.
The empirical literature has shown that factors such as product involvement, consumer innovativeness, brand parity, and brand loyalty can affect consumer price sensitivity (Bijmolt, Van Heerde, and Pieters 2005; Tellis 1988). The current research contributes to the pricing literature by showing that price sensitivity can be influenced by consumer identity, which may be evoked by seemingly mundane factors such as name- letters, birthday (Coulter and Grewal 2014), and gender (Puccinelli et al. 2013). Specifically, we show that a sacrifice mindset activated by a local identity can decrease consumers' price sensitivity to all, not just local, products.
The empirical evidence comprises secondary data, lab experiments, randomized field studies, and a customer survey to balance internal validity and generalizability (Chen et al. 2012). Study 1 uses a macro-level secondary data set to estimate the association between an indirect measure of local-global identity and price sensitivity to food products. Study 2 directly measures local-global identity at the individual level. Study 3 is a randomized field experiment that directly manipulates local- global identity to causally link local identity with actual changes in consumers' purchase quantity. Studies 4 and 5 examine the mediating role of a sacrifice mindset using two methods: measurement of mediation (Study 4) and moderation of process (Study 5). The results from both Studies 4 and 5 confirm the mediating role of a sacrifice mindset. Study 6 identifies a boundary condition for the effect of local identity on price sensitivity. Specifically, the effect is mitigated if the reason for the price increase is to support a global (vs. local) culture. We summarize these studies in Table 1, along with additional studies not reported herein.
Price sensitivity refers to the relative change in consumers' purchase quantity, purchase likelihood, or willingness to pay after a price increase (Wakefield and Inman 2003). In Campbell's (2007) study, consumers with lower (vs. higher) price sensitivity reported that their purchase quantity or purchase likelihood would decrease less after a price increase. A price increase can play an important role in consumers' purchase decisions (Dodds, Monroe, and Grewal 1991). On the one hand, a higher price suggests higher quality (Rao and Monroe 1989). This quality-related logic suggests a positive association between increased price and purchase likelihood. On the other hand, a higher price suggests the sacrifice consumers may need to make to obtain the benefits of the purchased product (Zeithaml 1988). A sacrifice—in the form of higher prices—for the product currently purchased may constrain consumer liquidity in the future, thus suggesting a negative relationship between price and purchase likelihood. The current research focuses on the sacrifice role of pricing. In the absence of any indication that product quality has been degraded or upgraded (e.g., product attributes are unchanged after the price increase), consumers may focus on price as an indicator of sacrifice, not just quality.
Arnett (2002) proposes that consumers in globalized economies are likely to hold both a local and a global identity. A local identity refers to consumers' mental associations pertaining to their respect for local traditions and culture while also identifying with people in their local community. A global identity refers to consumers' interest in global culture and identification with people around the world. More recently, these ideas have been empirically verified and extended. Zhang and Khare (2009) use identity-accessibility theory (see Hong et al. 2000) to demonstrate that consumers' local identity can be activated by making it more accessible through tasks that prime thoughts, ideas, and concepts consistent with a local identity. These tasks include reading a carefully worded advertisement or completing sentences pertaining to a local identity. When activated, the local identity evokes a sacrifice mindset, which then acts as a link between the activated local identity and the subsequent task (Wyer and Xu 2010; Xu and Wyer 2012). According to behavioral-mindset theory, an identity can activate a mindset that may increase the salience of key concepts, subsequently affecting unrelated tasks, such as processing of pricing information. For example, Xu and Wyer (2007) activated a comparison mindset by asking consumers to compare different animals or candidates in an election, after which they engaged in an unrelated task on product choice. Xu and Wyer show that the comparison mindset increased consumers' purchase likelihood for all products encountered in the subsequent, unrelated tasks. Similarly, a sacrifice mindset activated by a local identity should mediate its effect on price sensitivity.
A sacrifice-mindset explanation differs from an identity- consistency explanation used in previous studies. Previous studies have shown that consumers with a stronger local identity are willing to pay more for locally produced products (Loureiro and Hine 2002), display higher attachment to their local community (Cowell and Green 1994), and are willing to sacrifice their personal well-being for local traditions (Arnett 2002). Thus, sacrifice is associated with something that is local in origin. In contrast with these arguments based on identity consistency, a sacrifice-mindset explanation suggests that a local identity evokes the general concept of sacrifice. This salience of the sacrifice concept influences subsequent consumer decision making—in this case, decision making related to pricing and purchase. Specifically, a sacrifice mindset reduces consumers' price sensitivity to all products, not just local products. Why does this occur?
In the absence of any changes to product attributes and product quality, a price increase likely focuses consumers on sacrifice (Dodds, Monroe, and Grewal 1991). Consumers with a sacrifice mindset should be more willing to make a sacrifice and thus be more accepting of a price increase. In turn, this should manifest as lower price sensitivity. Consistent with this notion, Chaudhuri and Holbrook (2001) find that consumers with a greater willingness to sacrifice are more likely to pay a premium price for products. Accordingly, we propose that consumers with a local (vs. global) identity are more likely to accept price increases or show lower price sensitivity because of the evoked sacrifice mindset. Specifically, activating a local (vs. global) identity tends to make the general activity of making sacrifices more accessible and salient in consumers' minds. Then, when they confront a decision involving increased prices, the thought of making a sacrifice to obtain the specific product may or may not be salient. However, among consumers whose local (vs. global) identity has been activated, a sacrifice mindset should also likely be activated. When faced with a decision about pricing, consumers with an activated local identity will be more likely to make sacrifices, including monetary sacrifices. This should manifest as lower price sensitivity—that is, a willingness to pay higher prices or maintain purchase intentions in the face of a price increase.
To reiterate, the identity-consistency argument focuses on consumers' sacrifice of a product of local origin to reinforce their local identity. In contrast, the sacrifice-mindset argument—drawing from the posited independence of the pricing decision and local identity—argues that the observed effect should not be restricted to products of local origin. Rather, and surprisingly, sacrifice-mindset theorizing argues that the evoked sacrifice mindset triggers lower price sensitivity among those with a local identity, regardless of the origin of the products under consideration. To this point, our empirical testing focuses on products whose origin is ambiguous and indeterminate, at best. Consistent with these arguments, we posit the following:
H1 Consumers with a local identity are less price sensitive than those with a global identity.
H2: A sacrifice mindset mediates the effect of local identity on lower price sensitivity.
TABLE: TABLE 1 Summary of Effects of Local-Global Identity on Price Sensitivity
| | | | Price Sensitivity |
| Study | Sample Size | Moderator | DV Measure | Local Identity | Global Identity |
| 1 | 122 (59 after controlling cultural values) | | Price sensitivity index for food itemsa | -.34 (.03) | -.36 (.06) |
| 2 | 186 | • Original price | Purchase quantity of organic | 20.09 (12.48) | 20.20 | (13.04) |
| | • Increasing 5% | eggs after different levels of | 19.53 (16.96) | 18.03 | (11.93) |
| | • Increasing 10% | price increasea | 19.08 (13.97) | 16.63 | (8.96) |
| | • Increasing 15% | | 17.18 (11.63) | 13.39 | (10.51) |
| 3 | 1,292 | • Before price t | Actual purchase quantity for | 9.40 (3.03) | 9.53 | (3.82) |
| | (organic egg) | organic egg, milk, and rice | | | |
| | • After price t | before and after price | 8.86 (2.51) | 8.01 | (2.72) |
| | (organic egg) | increasesa | | | |
| 754 | • Before price | | 2.34 (1.98) | 2.39 | (2.13) |
| | ↑ (milk) | | | | |
| | • After price | | 2.20 (1.91) | 1.89 | (1.27) |
| | ↑ (milk) | | | | |
| 277 | • Before price | | 10.83 (6.24) | 11.78 | (6.88) |
| | ↑ (rice) | | | | |
| | • After price | | 10.53 (5.24) | 7.55 | (4.01) |
| | ↑ (rice) | | | | |
| 4 | 244 | | Likelihood to purchase after | 3.27 (1.75) | 2.74 | (1.31) |
| | | price increasea | | | |
| 5 | 404 | • Sacrifice | Likelihood to purchase after | 3.40 (1.78) | 3.59 | (1.60) |
| | • Control | price increasea | 3.28 (1.55) | 2.82 | (1.30) |
| 6 | 379 | • Global reason | Acceptance of maximum price | 5.40 (1.03) | 5.66 | (1.39) |
| | • Local reason | increasea | 7.63 (1.06) | 5.29 | (1.28) |
| NR1 | 156 | | Price sensitivity scaleb | 4.93 (1.26) | 5.53 | (.84) |
| NR2 | 199 | | Preference for higher-priced | 4.53 (1.58) | 4.09 | (1.80) |
| NR3 | 144 | | Preference for higher-priced smartphone in Chinaa | 5.79 (1.15) | 5.33(1.40) |
| NR4 | 204 | | Preference for higher-priced smartphonea | 4.87 (1.30) | 4.38(1.75) |
| NR5 | 97 | | Likelihood to reduce purchase quantity after price increaseb | 4.01 (1.78) | 4.94(1.67) |
| NR6 | 182 | • Self-focus | Willingness to paya | $470.44 (82.98) | $475.79 (111.93) |
| | • Control | | $513.22 (59.17) | $469.54(70.69) |
| NR7 | 196 | • Deal-proneness | Preference for higher-priced | 4.84 (1.75) | 4.98 (1.58) |
| | • Control | smartphonea | 5.63 (1.21) | 4.79(1.63) |
| NR8 | 221 | • Sacrifice | Likelihood to reduce purchase | 4.16 (1.49) | 4.03(1.27) |
| | • Control | quantity after price increaseb | 4.06 (1.54) | 4.65(1.26) |
| 14 studies | 5,057 participants | | Different operationalizations of price sensitivity | Consistent support that local- (vs global-) identity co price sensitive |
aHigher values indicate lower price sensitivity.
bHigher values indicate higher price sensitivity.
Notes: DV = dependent variable. Values for price sensitivity are means, with standard deviations in parentheses. Studies 1 and 2 cell means are calculated on the basis of ±1 SD of the Globalization Index and local-global identity measure, respectively. NR1-NR8 indicate the eight studies that we did not report in the current version of the article. For details of these studies, please contact the first author.
These hypotheses extend Zhang and Khare's (2009) work in three important ways. First, whereas Zhang and Khare focus on local products, our theorizing goes beyond local products to show that consumers with a local identity can be less price sensitive, even when the product origin is not clearly stated as being local. Second, our proposed process is based on a sacrifice mindset, rather than on identity consistency. Third, our focus is on price sensitivity, which is a qualitatively different construct than product preference, the focus of Zhang and Khare's research. Erdem, Swait, and Louviere (2002) find that brand credibility differentially influences consumers' product preferences and price sensitivity. Monroe (1973) argues that strategies to manage product preference and price sensitivity vary substantially among firms.
We obtained consumers' price sensitivity to food products, which served as the dependent variable, from 142 countries in 2005 from the U.S. Department of Agriculture's (USDA's) International Food Consumption Patterns Data Set.[ 1] Price sensitivity is measured at the country level and ranges from -.38 to -.23, indicating that consumers react negatively to price increases for food products. We obtained data for the primary predictor variable, country-level local-global identity, in 2005 from the KOF Index of Globalization,[ 2] which measures each country's level of global integration on economic, social, and political dimensions (Dreher 2006). According to Arnett (2002), consumers from a more globalized country have more opportunity to access people, cultures, and news from other countries through television and the Internet. In contrast, consumers from less globalized counties, such as North Korea or Cuba, are more likely to have a stronger local identity given their restricted access to globalized cultures. As such, we use the Globalization Index as a proxy measure of country-level local-global identity, with a higher score indicating stronger global (weaker local) identity.
To address the correlational nature of this data set, we included covariates that might be related to price sensitivity. First, we included each country's per capita income. Previous research has suggested that price sensitivity to food products decreases as consumers' income increases (Tellis 1988). The per capita income data came from the USDA's International Food Consumption Patterns Data Set. Second, we included a measure of each country' s competitive environment as a covariate because increased competition likely increases consumers' price sensitivity. The World Economic Forum' s Global Competitiveness Report[ 3] provides detailed information about 144 countries' competitive environment. Inclusion of these four variables—price sensitivity index, globalization index, per capita income, and competitiveness index—resulted in a final sample size of 122 countries. Third, differences in cultural values among countries may be related to price sensitivity (Petersen, Kushwaha, and Kumar 2015). We included Hofstede's (2001) five cultural dimensions as control variables: individualism, power distance, uncertainty avoidance, masculinity, and long-term orientation. Missing values on these cultural dimensions decrease our sample size from 122 to 59. We estimated several models as reported in Table 2.
The dependent variable, price sensitivity, has negative values, such that lower values indicate higher price sensitivity. For example, -.38 indicates a higher price sensitivity than -.23. Models 1 and 2 test the association between price sensitivity and globalization without the cultural dimensions. Specifically, Model 1 (adjusted R2 = .1835) only includes two covariates: per capita income and competitive environment. Consistent with Tellis (1988), this model shows a positive association between per capita income and price sensitivity (β = .0804, t(119) = 5.36, p < .001). We find a negative association between competitive environment and price sensitivity (β = -.0174, t(119) = -3.42, p < .001), such that increased competitiveness increases consumers' price sensitivity. Model 2 adds the country-level Globalization Index. Model 2 shows a statistically significant improvement in model fit over Model 1 (adjusted R2 = .3651; Aadj-R2 = .1816; p < .05). In Model 2, the association between the Globalization Index and price sensitivity is negative and statistically significant (β = -.0016, t(118) = -5.92, p < .001). Thus, price sensitivity increases as a country's globalization score increases.
Models 3 and 4 show results after we control for the five cultural dimensions. The sample size is reduced to 59 because the cultural dimension scores were only available for 59 of the 122 countries. With the relatively small sample size, we ran the analysis in two phases. In Model 3, we aimed to ascertain the statistically significant cultural dimensions that should be covariates. Then, we ran Model 4, including only the statistically significant covariates along with the key predictor, Globalization Index. As Table 2 shows, the statistically significant covariates in Model 3 (adjusted R2 = .7725) are per capita income and uncertainty avoidance. We included these two variables as covariates in Model 4. The last column of Table 2 summarizes the results from Model 4 (adjusted R2 = .8332; Δadj-R2 = .0607). Consistent with the previous models, per capita income was positive and significant (β = .1431, t(55) = 13.36, p < .001). Uncertainty avoidance was negative and statistically significant (β = -.0003, t(55) = -4.09, p < .001). Importantly, Globalization Index was significantly and negatively associated with price sensitivity (β = -.0009, t(55) = -4.40, p < .001). These results are consistent with H1.
TABLE: TABLE 2 Regression Results of Globalization Index on Price Sensitivity (Study 1)
| Model 1 | Model 2 | Model 3 | Model 4 |
| N = 122 | N = 122 | N = 59 | N = 59 |
| Intercept | -.30*** (.02) | -.26*** (.02) | -.38*** (.02) | -.32*** (.013) |
| Per capita income | .08*** (.02) | .14*** (.02) | .12*** (.01) | .14*** (.01) |
| [.64] | [1.11] | [.91] | [1.11] |
| Competitive environment | -.02*** (.01) [-.41] | -.01n s. (.00) [-.16] | -.00n.s. (.00) [-.05] | |
| Uncertainty avoidance | | | -.0004*** (.0001) [-.25] | -.0003*** (.0001) [-.22] |
| Masculinity | | | .0002n s. (.0001) [.10] | |
| Power distance | | | .0001n s. (.0001) [.07] | |
| Individualism | | | -.0000n.s. (.0001) [-.03] | |
| Long-term orientation | | | .0000n s. (.0001) [.01] | |
| Globalization Index | | -.0016*** (.0003) [-.80] | | -.0009*** (.0002) [-.36] |
| R-square | .20 | .38 | .80 | .84 |
| Adjusted R-square | .18 | .37 | .77 | .83 |
*p < .05.
**p < .01.
***p < .001. n s p > .10.
Notes: Lower values of price sensitivity indicate higher price sensitivity. Values in parentheses indicate standard errors corresponding to the unstandardized coefficients reported. Values in square brackets indicate the corresponding standardized coefficients.
Study 1 provides evidence supporting H1. The use of country- level globalization as a proxy of consumers' local-global identity is fully consistent with Arnett' s (2002) original theorizing that consumers from more globalized countries have more access to global cultures, whereas those from less globalized countries tend to have access only to their local cultures. Thus, although consumers from more globalized countries might develop a local identity together with their global identity, their global identity is likely to be stronger than that of consumers from less globalized countries.
Despite high external validity, these correlational results preclude any causal inferences. Furthermore, they are not based on individual-level data. They may also be subject to alternative explanations owing to omitted variables in Table 2. We address these concerns in the subsequent studies.
Study 2 was a paper-and-pencil, mall-intercept survey conducted with consumers in China. Participants were 186 consumers shopping at a large, upscale shopping mall in the city of Hefei, China, who were given a small cash incentive (5 RMB). Participants' age ranged from21 to 55 years (Mage = 32.88 years, SDage = 7.44 years), 50% were women, and their annual income ranged from 10,000-200,000 RMB (Mincome = 56,578.95 RMB, SDincome = 30,163.59 RMB).
Procedure. Members of the research team approached participants at the mall. Those who agreed to participate were guided to a quiet room to finish a short survey. The survey included a price sensitivity measure, some filler tasks, the local- global identity scale, and demographics.
Price sensitivity. Participants indicated the quantity of organic eggs they would purchase after different levels of price increases. Specifically, participants were asked to indicate the quantity of organic eggs they would purchase per shopping visit at the current market price (2 RMB/egg), recorded as E0. Then, they were told that reliable sources indicated that the price for organic eggs was about to increase in the next few weeks. Participants indicated their purchase quantity of organic eggs per shopping visit if the price were to increase by 5%, 10%, and 15% (presented in this exact order), recorded as E1, E2, and E3 respectively. This price sensitivity measure examines changes in purchase quantity at different levels of price increases.
Local-identity index. We used the eight-item scale from Tu, Khare, and Zhang (2012) to measure consumers' local-global identity (see Appendix A). The scale includes four items measuring global identity (e.g., "I identify that I am a global citizen") and four items measuring local identity (e.g., "I identify that I am a local citizen"). Both the global identity (α = .89) and local identity (α = .81) subscales showed adequate reliability. Therefore, we separately averaged the four items for each scale to form a global-identity index and a local-identity index.
According to H1, consumers with a stronger local identity should display lower price sensitivity. In our data, purchase quantity changes with the level of price increase. To capture the trend in changes for one variable (purchase quantity) based on the changes in another variable (price; Muthen and Muthen 2012), we conducted a growth modeling analysis with MPlus7. We defined participants' purchase quantity at each price level as the outcome variables (E0@0, E1@1, E2@2, E3@3). From this, we estimated the intercept (defined as i) and the slope (defined as s), which serves as a measure of price sensitivity. More specifically, the slope in the growth model reflects the trend in quantity change at different levels of price increases. We allowed both the intercept and the slope to be predicted by local and global identity; local and global identity were free to be correlated. We included consumers' age, gender, and income (log transformed) as control variables to predict the intercept and slope.
The model fits the data well (X2 = 104.33, p = .021; comparative fit index = .982; root mean square error of approximation = .044; standardized root mean square residual = .048). The intercept of the estimated slope is -.917 (p < .001), indicating that as the price for organic eggs increased, consumers' demand (i.e., intended purchase quantity) decreased. The variance of the slope is 45.63 (p = .008), indicating that the slope tends to vary across individual consumers. Among the control variables (age, gender, and income), only the effect of income on the price sensitivity measure was positive and significant (β = 2.286, SE = 1.001, p = .022). This indicates that as consumers' income increased, their price sensitivity decreased.
The price sensitivity measure, the estimated slope (s), was significantly affected by both local and global identity. More specifically, the effect of local identity on s was positive and statistically significant (β = .386, SE = .159, p = .015); a one-unit increase in consumers' local identity decreased their price sensitivity for organic eggs by .386. This is consistent with H1. In contrast, the effect of global identity on s was negative and statistically significant (β = -.642, SE = .184, p < .001); a one- unit increase in global identity increased price sensitivity by .642.
By measuring both local-global identity and price sensitivity among consumers at a shopping mall, we found evidence supporting H1. This study presented three price increases in an increasing order (5%, 10%, and 15%) instead of randomizing them. It would be worthwhile to determine whether randomizing the order would change the results. Although it has a high level of generalizability, the evidence from Studies 1 and 2 is correlational. Furthermore, the price sensitivity measure in Study 2 is hypothetical in nature. In Study 3, a field experiment, we manipulated local-global identity by priming it through an advertisement. Importantly, we measure price sensitivity using actual changes in quantity purchased by real customers in response to actual price increases.
We conducted Study 3, a field study, in a grocery store located in Hefei, China. We contacted the management of the grocery store, which informed us that the prices of organic eggs, rice, and milk were going to increase in the next month. We report the analysis for organic eggs in detail. Table 3 and Figure 1 show the analysis and results for fresh milk and rice.
Data collection. We collected consumers' purchase quantity of organic eggs before (2 RMB/egg) and after (2.3 RMB/egg) the price increase. The price of organic eggs increased on September 12, 2015. We collected data over a 35-day period (August 27-September 30, 2015), which included a 16-day period before the price increase and a 19-day period after the price increase. During this period, there were 1,341 transactions (597 before and 744 after the price increase). Our research focus is on individual consumers' purchase decisions. Therefore, we excluded 49 transactions made by commercial customers, such as restaurant owners (purchase quantity range: 100-2,800, M = 1,280.82, SD = 934.36). This left 1,292 data points for the final analysis (purchase quantity range: 4-32, M = 8.90, SD = 3.07).
Priming procedure. We intercepted customers as they entered the store. Each consumer was randomly assigned to receive a brochure, which manipulated either local or global identity (Appendix A). Specifically, the local-identity brochure described a "Think Local Movement" supporting the local community and local businesses, focusing on local news, and preserving the local traditions. The global-identity brochure described a "Think Global Movement" supporting global brands and global businesses, focusing on global news, and highlighting cultures from other parts of the world. To reinforce the priming, participants signed the bottom of the brochure to show their support for the specific movement.
Participants were told that by returning the signed brochures to the cashier at checkout, they would have the chance to win a reward based on draw (first prize: 500 RMB cash [1 consumer[ 4]], second prize: 200 RMB cash [2 consumers], third prize: 150 RMB cash [5 consumers], fourth prize: 20 RMB cash [30 consumers]). At the checkout station, the cashier collected the signed brochures and recorded consumers' purchase quantity of organic eggs. At the same time, consumers received a randomly generated number and left their contact information for the draw to be held on October 1, 2015.
From the brochures and the recorded purchase quantities, we determined the identity condition (local [N = 651] vs. global [N = 641]) to which a particular participant belonged, the date of the purchase (indicating before or after the price increase), and the purchase quantity of the organic eggs. In this field study, we can draw causal inferences about actual purchases because of the random assignment of the participants to the two levels of the priming manipulation.
TABLE: TABLE 3 Local-Global Identity and Price Change on Purchase Quantity (Study 3)
| Variables | Organic Egg | Fresh Milk | Rice |
| Intercept | 15.32* | (7.29) | 122 22*** | (32.51) | -544.72* (281.23) |
| Weekend | -.00(.00) | .41† (.23) | .49 (.75) |
| Holiday | .03(.02) | -.12(.33) | -.46(1.31) |
| CPI | -.14†(.07) | -.85(1.12) | -5.29(3.76) |
| Log(revenue) | .15†(.08) | -.48(.99) | 8.81(6.08) |
| Local-global identity | .00(.02) | -.15(.29) | -1.26(1.04) |
| Price increase | -.23*** (.04) | -.87*(.45) | -6.81***(1.58) |
| Local-global identity x Price increase | .11**(.03) | .72*(.36) | 4.07**(1.39) |
| AR(1) | .08**(.03) | -.45***(.03) | -.51***(.05) |
| R-square | .07 | .21 | | .32 | |
| Sample size | 1,292 | 754 | 277 | |
†p < .10.
*p < .05.
**p < .01.
***p < .001.
Notes: AR(1) = first-order autoregressive. The main values indicate regression coefficients, and the values in parentheses indicate the standard errors of the corresponding coefficients. The prices for fresh milk and rice were increased on September 9 (from 2.8 RMB to 3.0 RMB/bottle, 13-day data before price increase and 22-day data after price increase for fresh milk) and September 11 (from 5.2 RMB to 5.6 RMB/kg, 15-day data before price increase and 20-day data after price increase for rice), respectively. During this period, there were 783 transactions (266 before price increase, 517 after price increase) with fresh milk and 306 transactions (131 before price increase, 175 after price increase) with rice. We excluded 29 transactions with fresh milk (purchase quantity range: 20-120, M = 61.38, SD = 36.13) and 29 transactions with rice (purchase quantity range: 100-400, M = 156.90, SD = 63.70) made by business consumers. This resulted in 754 data points for fresh milk (Nl0cal = 385 and Nglobal = 369; purchase quantity range: 1-8, M = 2.16, SD = 1.80) and 277 data points for rice (Nlocal = 138 and Nglobal = 139; purchase quantity range: 5-25, M = 10.04, SD = 5.88) used in the final analyses.
H1 predicts that consumers with a local identity (local prime: "Think Local Movement") are less price sensitive than those with a global identity (global prime: "Think Global Movement"). To test H1, we conducted an autoregression analysis (Akaike information criterion = 490.33, R2 = .0681) with the purchase quantity of organic eggs as the dependent variable. Identity (0 = global identity, 1 = local identity), price condition (0 = before price increase, 1 = after price increase), and their interaction were independent variables. The four control variables were weekday (0) versus weekend ( 1), holiday ( 1) versus not (0), CPI, and log- transformed overall revenue of the store on the day of the purchase. Table 3 reports the results for eggs, milk, and rice. Again, for brevity, we discuss the results for eggs only.
We found no statistically significant impact of whether the eggs were purchased on a weekend (β = -.003, t( 1,282) = -.61, p = .54) or on a holiday (β = .028, t( 1,282) = 1.23, p = .21). There was a marginally significant impact of CPI (β = -.143, t( 1,282) = -1.95, p = .052) and store revenue/day (β = .145, t( 1,282) = 1.89, p = .058). As CPI decreased or revenue increased, purchase quantity for organic eggs increased. The main effect of identity was statistically nonsignificant (β = .002, t( 1,282) = .09, p = .92), and the main effect of the price increase was negative and statistically significant (β = -.230, t( 1,282) = -5.93, p < .001), indicating that the quantity of organic eggs purchased decreased after the price increase.
In testing H1, we find that the interaction effect of identity and price increase was statistically significant (β = .107, t( 1,282) = 3.30, p = .001), indicating that the decrease in purchase quantity after the price increase differs between the two identity conditions. Panel A of Figure 1 depicts the results (see Panel B for milk and Panel C for rice). A follow-up analysis indicated that consumers with an accessible local or global identity showed similar purchase quantity of organic eggs before the price increase (Mlocal = 9.40 vs. Mglobal = 9.53; t( 1,282) = -.49, p = .62). However, their purchase quantity showed a statistically significant difference after the price increase (Mlocal = 8.86 vs. Mglobal = 8.01; t( 1,282) = 3.78, p < .001; Cohen's d = .21). Specifically, consumers with both local (Mtefore = 9.40 vs. Mafter = 8.86; t( 1,282) = 2.28, p = .023; Cohen's d = .13) and global (Mbefore = 9.53 vs. Mafter = 8.01; t( 1,282) = 6.33,p < .001;Cohen'sd= .35) identity reduced their purchase quantity. However, the reduction in quantity was significantly less for local-identity than global-identity consumers (localbefore — after = .54 vs. globalbefore — after = 1.52; t( 1,282) = -5.96, p < .001; Cohen's d = .33). In response to the price increase, those primed with a local identity showed lower price sensitivity. This pattern of results, as depicted in Figure 1, provides support for H1. (The results in Panels B and C of Figure 1 also confirm H1.)
Using a randomized field experiment with actual purchase behavior, Study 3 offers support to H1. We conclude that consumers primed with a local identity tend to be less price sensitive than their global identity counterparts. This study has high external and internal validity. Managerially, the local- global identity prime used in this study can be easily adapted to develop communication materials. Although the effect sizes for the comparisons are small to moderate compared with traditional criteria, they are in response to a subtle brochure handed to customers in a field design. The effect size would likely be substantially larger if it were part of a sustained communication campaign.
In Study 4, we test the theorized mediating role of a sacrifice mindset (H2). The study measures both sacrifice mindset and monetary sacrifice. According to our theorizing, the sacrifice mindset activates a general tendency to sacrifice. If this is true, we expect both the sacrifice mindset and the monetary sacrifice to mediate the association between local identity and price sensitivity. In contrast, if our sacrifice mindset explanation is false, only one or none of the constructs should mediate this association.
Study 4 uses a between-subjects design, in which we manipulated identity (local vs. global). Participants were 224 undergraduate students (Mage = 23.19 years; 50% female), who participated for partial course credit.
Procedure. Participants completed either the local or global identity prime based on random assignment. Next, they read a purchase scenario in which the price of the product had recently increased. Then, they completed measures of price sensitivity, sacrifice mindset, and monetary sacrifice. We randomized the order of these three measures within each participant to ensure that order effects would not explain the observed results. Finally, we measured the manipulation check and demographics.
Identity prime. The identity priming task used an advertisement similar to the one used in Study 3, but in English (Appendix A). Three items from Zhang and Khare (2009) comprised the manipulation check: "For the time being, I am mainly thinking that…"; "At this moment, I feel that…"; and "On top of my mind right now are thoughts in agreement with saying." (1 = "I am a global citizen," and 7 = "I am a local citizen"). We averaged the three items to form a composite score (a = .96). A higher composite score indicates a stronger local identity.
Purchase scenario. Participants saw a picture of a fictitious brand of coffeemaker with its key features and specifications (see Appendix B; Kwak, Puzakova, and Rocereto 2015). They then read the following:
Imagine you are considering purchasing a coffeemaker. After searching the options available on the market, you decided to purchase the model Ultra Pro from Coffee-Smart. Then, you went to Coffee-Smart's website to check the features and found out the price of this model was $54.99. However, one week later, when you finally decided to make the purchase, you found the price has changed from $54.99 to $65.99.
Price sensitivity. We measured purchase intention for the coffeemaker after the price increase with a five-item scale adapted from Grewal, Monroe, and Krishnan (1998) (see Appendix C). We averaged the five items to form a purchase intention index (a = .95). Higher values on this index indicate lower price sensitivity.
Sacrifice mindset. We adapted the items for the sacrifice mindset from the general-sacrifice scale that Swann et al. (2014) and Davis, Le, and Coy (2011) recommend (see Appendix C). We averaged seven items to form the sacrifice-mindset index (a = .89). Higher values on this index indicate a stronger sacrifice mindset.
Monetary sacrifice. Following Dodds, Monroe, and Grewal (1991), we define monetary sacrifice as the perceived amount of money consumers give up to gain the product. We measured this construct using items from Grewal, Monroe, and Krishnan (1998) (Appendix C). The average of the items formed a monetary-sacrifice index (six items; a = .96). Higher values indicate stronger monetary sacrifice tendency.
Manipulation check. The results from a one-way analysis of variance (ANOVA) confirmed that the identity prime was successful (Mlocal = 4.79 vs. Mglobal = 4.05; F( 1, 222) = 7.77, p = .006).
Discriminant validity. We ran an exploratory factor analysis with Varimax rotation on all items pertaining to the three key constructs: price sensitivity, sacrifice mindset, and monetary sacrifice. The items loaded on their respective factors (Appendix C). We also ran a confirmatory factor analysis in which each item loaded significantly on the focal latent construct. A three-factor solution fit the data better than a one-factor solution.
Price sensitivity. H1 states that consumers primed with a local identity should display lower price sensitivity than those primed with a global identity. That is, purchase intentions after a price increase should be higher among those with a local than a global identity. According to a one-way ANOVA, those primed with a local (vs. global) identity showed higher purchase intentions (Mlocal = 3.27 vs. Mglobal = 2.74; F( 1, 222) = 6.61, p = .011), in support of H1.
Mediation analysis through sacrifice mindset and monetary sacrifice. H2 postulates a sacrifice mindset as the process underlying the effect of local identity on price sensitivity. According to the mindset explanation, both a general sacrifice mindset and a monetary sacrifice should mediate the observed effect of local identity on price sensitivity.
We conducted a mediation analysis with both sacrifice mindset and monetary sacrifice as mediators. We used the syntax provided by Hayes (2013; PROCESS Model 4). The results indicated that the indirect effect of identity on price sensitivity is mediated through ( 1) a sacrifice mindset (indirect effect = .0779, SE = .0385, 95% confidence interval [CI] = (.0240, .1784); z = 1.9702, p = .0488) and ( 2) a monetary sacrifice (indirect effect = .3357, SE = .1412,95% CI = (.0467, .5937); z = 2.2686, p = .0233). These results, summarized in Figure 2, confirm H2.
Consumers with a local identity showed a stronger sacrifice mindset and a higher monetary sacrifice. Specifically, an ANOVA showed a higher sacrifice mindset among those with a local identity (Mlocal = 5.03 vs. Mglobal = 4.67; F( 1,222) = 5.67, p = .019). Similarly, monetary sacrifice was higher among those with a local identity (Mlocal = 3.68 vs. Mglobal = 3.22; F( 1,222) = 5.28, p = .023). These results also confirm H2.
The results provide support for H1 and H2 using a different product category (appliances: coffeemaker) than Study 3. The mediation analysis is consistent with the theorizing that a consumer' s activated local identity triggers a sacrifice mindset at both the monetary and nonmonetary levels. As a key contribution, this study shows that the sacrifice mindset, when activated, operates at both the monetary and non- monetary levels.
In Study 5, we replicate these findings and show mediation using a moderation-of-process approach. Some researchers have criticized the traditional approach used to test mediation in this study because of its correlational nature. To address this, Spencer, Zanna, and Fong (2005) advocate a stronger test of mediation using a moderated analysis in which the mediator is directly manipulated. In this context, the mediator—sacrifice mindset—should be manipulated. The following pattern of results would support mediation: willingness to sacrifice should be high among all consumers who receive a sacrifice-mindset prime; as such, we should not observe any difference in price sensitivity between local- and global-identity consumers who receive a sacrifice-mindset prime. This should occur because both local- and global-identity consumers who receive a sacrifice-mindset prime should have similarly high levels of willingness to sacrifice and, thus, similarly low levels of price sensitivity (Spencer, Zanna, and Fong 2005). In contrast, willingness to sacrifice should vary among all consumers who do not receive a sacrifice-mindset prime (i.e., control condition). Among these consumers, we expect to replicate the original results: Local-identity consumers should exhibit lower price sensitivity. Study 5 tests this mediation logic for H2 by directly manipulating the sacrifice mindset.
Study 5 was a 2 (identity: local vs. global) X 2(sacrifice mindset: primed vs. control [no sacrifice]) between-subjects design. Participants were 404 U.S. consumers recruited from Amazon Mechanical Turk (MTurk). The participants were 18-73 years of age (Mage = 35.71 years), 50.25% were women, and 41% had an annual income above $50,000.
Procedure. Each participant was randomly assigned to one of the four experimental cells. Participants first completed the local-global identity priming task and the sacrifice-mindset manipulation task. The order of these two tasks was counterbalanced. Reassuringly, task order had no significant effect on the manipulation check of identity (F( 1, 396) = .96, p = .32), the manipulation check of mindset (F( 1,396) = 2.59, p = .11), or the price sensitivity measure (F( 1, 396) = .62, p = .43). Thus, we conclude that the presentation order is not significant and excluded it from further analysis. Next, participants saw a purchase scenario with price increases. They indicated their purchase intention after the price increase—our measure of price sensitivity. Finally, they finished the manipulation checks and demographics.
Identity prime. The identity priming task was the same as in Study 4. The three items from Study 4 served as the manipulation check (a = .96).
Sacrifice mindset. To manipulate the sacrifice mindset, we asked the participants to write a short essay. In the sacrifice- mindset condition, participants read the following text:
The word "sacrifice" means people give up something valuable for something else more important or worthy. For instance, parents sacrifice their personal comfort for their children, soldiers sacrifice their well-being to defend their nation, and so on.
Next, participants wrote down all their current thoughts and feelings about the importance of sacrifice in approximately 30 words. Participants in the control condition were asked to review their daily routine and write their thoughts and feelings at the moment also in approximately 30 words.
Sacrifice-mindset manipulation check. Participants rated the following three items: "I feel the urge to make the necessary sacrifice," "I believe sacrifice is a great virtue," and "I am willing to forgo desired activities for something more important" (1 = "strongly disagree," and 7 = "strongly agree"). Their average formed the sacrifice-mindset manipulation check (a = .82), with higher values indicating a higher sacrifice mindset.
Price sensitivity. Participants first encountered a purchase scenario adapted from Campbell (2007; Study 1): "Imagine that you are decorating your apartment and have been looking for a new rug. Last week, you saw one that you liked at one of the stores in the downtown area and have decided to take a friend to go look at it again. Today, while you and your friend are looking at the rug, you noticed on the price tag that the current price for the rug is 25% higher than the price you observed on your visit last week." Then, they indicated their purchase intention for the rug at the current price. The same five-item scale used in Study 4 (see Appendix C) (Grewal, Monroe, and Krishnan 1998) formed the purchase intention index (a = .96), with higher values indicating stronger purchase intentions after the price increase, or lower price sensitivity.
Manipulation check (local-global identity). We conducted a full-factorial ANOVA on the identity-check composite with the identity prime, mindset manipulation, and their interaction as the independent variables. Reassuringly, only the main effect of the identity prime was statistically significant (F( 1,400) = 72.20, p < .001). Participants primed with a local identity reported a stronger local identity (Mlocal = 4.73) than those primed with a global identity (Mglobal = 3.22). Neither the main effect of the mindset manipulation (F( 1, 400) = 1.96, p = .16) nor the interaction effect was significant (F < 1). Thus, the local-global identity prime was successful, and it was not confounded by the manipulation of the mediator.
Manipulation check (sacrifice mindset). To assess the success of the manipulation of the sacrifice mindset, we conducted a full-factorial ANOVA on the sacrifice-mindset manipulation check composite. The main effect of the manipulation was successful (F( 1,400) = 12.08,p < .001) and in the expected direction (Msacriflce = 5.25 vs. Mcontrol = 4.89). The main effect of identity prime was statistically significant (F( 1, 400) = 3.72, p = .05), such that participants primed with a local identity showed a stronger sacrifice mindset (Mlocal = 5.17 vs. Mglobal = 4.98). The interaction was statistically nonsignificant (F( 1, 400) = 1.76, p = .18).
A follow-up analysis indicated a successful manipulation of the sacrifice-mindset prime. When primed with a sacrifice mindset, both local- and global-identity consumers had a similarly strong sacrifice mindset (Mlocal = 5.28 vs. Mglobal = 5.22; t(400) = .43, p = .66). In the control condition (i.e., no-sacrifice prime), the local-identity consumers showed a stronger sacrifice mindset than the global-identity consumers (Mlocal = 5.06 vs. Mglobal = 4.72; t(400) = 2.26, p = .024).
Hypothesis testing. To test H2 using the moderation-of- process approach, we need to replicate the local-identity effect on price sensitivity under the control condition but observe no difference between the local-global identity conditions when manipulating the sacrifice mindset. A full-factorial ANOVA with price sensitivity as the dependent variable showed that the main effect of identity was not statistically significant (F < 1), the main effect of mindset manipulation was statistically significant (F( 1, 400) = 7.85, p = .005), and their two-way interaction effect also was statistically significant (F( 1, 400) = 4.11, p = .043).
A follow-up contrast indicated that under the control condition (i.e., no sacrifice-mindset condition), local-identity consumers showed lower price sensitivity than global-identity consumers by indicating higher purchase intentions for the rug after a price increase (Mlocal = 3.28 vs. Mglobal = 2.82; t(400) = 1.99, p = .047). In contrast, when both groups had an activated sacrifice mindset, this effect was attenuated, with both local- and global-identity consumers showing a similarly lower level of price sensitivity (Mlocal = 3.40 vs. Mglobal = 3.59; t(400) = -.85, p = .39). Thus, local- and global-identity consumers were similarly less price sensitive when they were primed with a sacrifice mindset. This result confirms the sacrifice mindset as an underlying mediator for the local- identity effect (see Figure 3).
Using the moderation-of-process approach, we show that price sensitivity is attenuated when the sacrifice mindset is made salient for both local- and global-identity consumers. Managerially, however, companies may sometimes provide specific reasons for price increases. To provide managerial guidance, we assess whether reasons provided for price increases can weaken or strengthen the intervening role of a sacrifice mindset. By identifying these situational variables, we also hope to guide managers on effective ways to mitigate the effect of local-global identity on price sensitivity. Empirically identifying situations when the association between local identity and price sensitivity can be weakened provides a stronger test of our theorizing.
According to Briley, Morris, and Simonson (2000), consumers may be motivated to behave in an identity-consistent way when making the decision can help reinforce an activated identity. One way to do this is to provide justification for the behavior that is consistent with the activated identity, such as giving a specific reason for making the decision. If the reason is consistent with the activated identity, it may enhance the effect of the evoked mindset; in contrast, a reason that is inconsistent with the activated identity may mitigate the effect of the evoked mindset. For example, consumers generally have a compromise mindset, in that they tend to choose the compromise option rather than an extreme option in a choice set. Consistent with behavioral-mindset theory, those with an activated Eastern cultural identity are even more likely to choose compromise options than those with an activated Western cultural identity. However, this effect can be further enhanced or mitigated if consumers are given reasons for making their choice. If the reason uses a compromise-based logic, the mindset effect among consumers with an Eastern cultural identity can be enhanced.
Following a similar logic, we argue that the lower price sensitivity that results from a sacrifice mindset among local- identity consumers may be mitigated or enhanced depending on the reasons provided for making the sacrifice. If the reason is consistent with consumers' local identity, the effect of the sacrifice mindset should be enhanced, and vice versa. Specific to our research context, reasons for making a sacrifice that are related to the local community should enhance the observed effect of a sacrifice mindset among those with a local identity. This should occur because making a sacrifice for the local community should reinforce a consumer' s local identity. In contrast, when the reason for sacrifice is global in nature—inconsistent with the consumer's local identity—the effect of the sacrifice mindset should be attenuated. Formally:
H3: The reason provided for a price increase (i.e., sacrifice reason) moderates the association between local identity and price sensitivity. If the sacrifice reason is locally focused, the association between a local identity and price sensitivity will be further enhanced such that local-identity consumers will show even lower price sensitivity. In contrast, if the sacrifice reason is globally focused, this association will be attenuated such that both local- and global-identity consumers will display similarly high levels of price sensitivity.
U.S. consumers recruited through MTurk participated for a small cash incentive (N = 379). We used a 2 (identity: local vs. global) X 2 (reason for sacrifice: locally focused vs. globally focused) between-subjects design. Participants' ages ranged from 19 to 71 years (Mage = 34.79 years, SDage = 10.85 years), 43% were women, and 45% had an annual income above $50,000.
Procedure. Each participant was randomly assigned to one of the four experimental cells. After completing the local- or global-identity prime, participants read a scenario about buying a webcam. The scenario stated that the price of the webcam was about to increase. Embedded in the purchase scenario was a message about the reason for the price increase. Then, we measured price sensitivity followed by the manipulation check and demographic information.
Identity prime. The local-global identity prime was similar to the one used in Study 4. It was administered in an advertising format (see Appendix A).
Webcam scenario. Participants were asked to imagine that they were buying a webcam for their personal computer and had decided to buy the newest webcam from HighTech (a fictitious brand). Then, they read a product description of the "HighTech HD Pro Webcam Z720," which included a picture of the webcam and its key attributes (adapted from Kwak, Puzakova, and Rocereto 2015; see Appendix D). Finally, they were told that HighTech was about to increase its webcam prices, including the Z720 model in which they were interested.
Reason-for-sacrifice manipulation. To manipulate the reason for making a sacrifice, participants read a short explanation from the chief marketing officer (CMO) of HighTech regarding the reason for making the price change. Specifically, under the locally focused reason-for-sacrifice condition, the CMO explained that HighTech was making an effort to improve the local community by hiring more people from the consumers' local community, donating more to local schools and institutions, and preserving local traditions. Under the globally focused reason-for-sacrifice condition, the CMO explained that HighTech was making an effort to increase globalization by hiring more people from other parts of the world, donating more to poor countries, and preserving the global culture.
Price sensitivity. Participants indicated the maximum price increase they would consider acceptable to continue buying the HighTech Z720 Webcam on a 15-point scale (1 = "price increase by 1%," and 15 = "price increase by 15%"). Higher values indicate that participants have lower price sensitivity (i.e., are more tolerant of a price increase).
We conducted a pretest with a new set of 67 MTurk participants, to assess the realism of the advertising methodology and reasons for the price increase. Participants ranged in age from 19 to 58 years (Mage = 33.43; 32% female).
Pretest procedure. Participants completed the identity priming and a three-item realism check scale adapted from Darley and Lim (1993): "I find the advertisement for this movement to be realistic," "I could imagine an actual movement as described here," and "I believe the movement could happen in real life." We averaged the three items, with a higher value indicating that the assessment was more realistic (a = .83).
Next, participants viewed the webcam advertisement in which the CMO explained the reason for the price increase (i.e., reason for sacrifice manipulation). Then, they completed a three-item realism check scale: "I find the webcam advertisement to be realistic," "I could imagine an actual webcam company doing the things described in the advertisement," and "I believe the webcam advertisement could be in press in real life." We averaged the three items, with higher scores indicating that the assessment was more realistic (a = .90).
Finally, participants completed a manipulation check for identity priming (three-item scale used in Study 4; a = .96). They completed items assessing the strength of local reasons and global reasons. The two items for the local reasons were "I believe by purchasing the webcam, I could help my local community" and "I believe by purchasing the webcam, I could make my local community better" (r = .91, p < .001). The two items for the global reasons were "I believe by purchasing the webcam, I could help globalization" and "I believe by purchasing the webcam, I could help people around the world" (r = .86, p < .001).
Pretest results. First, we verified that the manipulations were successful. A full-factorial ANOVA on the identity-check composite indicated that only the main effect of identity priming was statistically significant (Mlocal = 4.83 vs. Mglobal = 2.33; F( 1,63) = 40.69, p < .001). The identity priming was successful. A full-factorial ANOVA on the local-reason composite showed only a main effect of the local-reason manipulation on the local- reason composite (Mlocal reason = 5.10 vs. Mglobal reason = 3.34; F( 1,63) = 26.12, p < .001). Similarly, only the main effect of the global-sacrifice reason affected the global-reason composite (Mlocal reason = 3.27 vs. Mglobal reason = 5.56; F( 1, 63) = 47.18,p < .001). Thus, the sacrifice-reason manipulation was successful.
Second, we assessed the realism of the identity prime. An independent sample t-test compared the average for the local- and global-identity participants with the scale's midpoint ( 4). Both the local-identity (5.39 vs. 4; t(31) = 7.79, p < .001) and global-identity (5.66 vs. 4; t(34) = 8.72, p < .001) groups rated significantly higher than the midpoint. Furthermore, there was no statistically significant difference on the realism scale between the local- and global-identity participants (Mlocal = 5.39 vs. Mglobal = 5.66; F( 1, 65) = 1.08, p = .30). Thus, both groups viewed the prime as similarly realistic.
Third, we assessed the realism of the webcam advertisement. Independent sample t-tests indicated that both the local-reason (5.22 vs. 4; t(36) = 5.54, p < .001) and global- reason (4.95 vs. 4; t(30) = 3.93, p < .001) advertisement was more realistic than the scale's midpoint ( 4). A one-way ANOVA indicated that the two versions of the advertisement were similar on perceived realism (Mlocal reason = 5.22 vs. Mglobal reason = 4.95; F( 1, 63) = .72,p = .40). In summary, participants perceived the stimuli as realistic, and the manipulations worked as intended.
H3 postulates an effect of local-global identity on price sensitivity when the sacrifice reason is locally focused. In contrast, when the sacrifice reason is globally focused, the effect should be mitigated, such that both local- and global-identity consumers should have high price sensitivity. To test this prediction, we ran a full-factorial ANOVA on the price sensitivity index (higher scores indicate lower price sensitivity) with identity prime, sacrifice reason, and their interaction as the independent variables.
The main effect of identity prime (F( 1, 375) = 10.71, p < .01) and the main effect of sacrifice reason (F( 1,375) = 8.70, p < .01) were statistically significant. Their interaction was also statistically significant (F( 1, 375) = 46.66, p < .001). Figure 4 shows the pattern of results. Specifically, when the sacrifice reason was locally focused, local-identity (vs. global-identity) consumers showed lower price sensitivity through their acceptance of a higher price increase (Mlocal = 7.63 vs. Mglobal = 5.29; t(375) = 5.07, p < .001). In contrast, this effect was statistically nonsignificant when the sacrifice reason was globally focused. Both local- and global-identity consumers showed similarly high price sensitivity (Mlocal = 5.40 vs. Mglobal = 5.66; |t| < 1). Thus, H3 is supported; local identity was associated with lower price sensitivity when the sacrifice reason given by the CMO was locally but not globally focused.
This study shows that a local identity lowers price sensitivity when the sacrifice reason is locally focused. Theoretically, this result also clarifies and supports the underlying mechanism we propose by showing the separate effects of sacrifice mindset and identity consistency. This insight is also useful for managers. By focusing consumers on sacrificing for a locally focused reason, firms can further decrease consumers' general price sensitivity emanating from a local identity. Importantly, firms can easily use the reason for sacrifice as part of their communication strategies.
This research makes three key contributions. First, it robustly shows that consumers with a local identity (vs. a global identity) have lower price sensitivity and are more tolerant of price increases. In addition to the six studies reported herein, we replicated this effect in eight additional studies numbered NR1-NR8 in Table 1. These studies document the association for a variety of categories (food in general, eggs, rice, milk, coffeemaker, rug, and webcam); with U.S. and Chinese consumers; and using primary data, secondary data, and a randomized field study. Thus, we conclude that the phenomenon reported is robust and generalizable.
Second, this research articulates and tests the underlying process: a local identity tends to activate a sacrifice mindset, which mediates the effect of local identity on lower price sensitivity. We show this mediation beyond that of monetary sacrifice. In addition, we conducted studies showing that the effect of local identity does not operate through mere ethno- centrism.[ 5] Our results also tease out the sacrifice-mindset explanation from a rival explanation of identity-consistent behavior. Further research could explore other potential mediators for this observed link by systematically testing sacrifice mindset as a mediator.
Third, we show how managers can enhance or attenuate the association between local identity and price sensitivity through appropriate messaging about the reason for a price increase. By providing locally focused reasons, managers can strengthen the effect of a sacrifice mindset along with identity consistency. Theoretically, these results show the robustness of our theorizing and extend the nomological network of focal constructs in different directions.
Our results have important theoretical implications for the literature on price sensitivity and identity-based marketing. Price sensitivity affects consumer welfare and company profitability (Tellis 1988). Further research is necessary to better understand how consumers can improve their welfare by engaging in activities that lead to intangible benefits. It may be that consumer welfare is improved when those with a local identity reap intrinsic rewards as a result of their decreased price sensitivity. In the same vein, consumer identity should be incorporated as a key antecedent of price sensitivity. The current literature on price sensitivity has largely taken a utilitarian approach, but a social identity- based approach can enhance understanding of price sensitivity in different ways.
The results show that consumers' local identity can affect their price sensitivity, and they may be willing to pay higher prices for all products, not just local products. Higher prices can enhance consumers' postpurchase consumption experience and enjoyment (Shiv, Carmon, and Ariely 2005) and, eventually, increase customer loyalty. Thus, it may be that consumers' local identity may also affect—directly and indirectly—their postpurchase consumption experience and brand loyalty. In addition to verifying these consequences of local identity, research could explore whether these effects vary depending on customer characteristics, such as gender, and product characteristics, such as utilitarian or hedonic attributes.
According to Zhang and Khare (2009) and Loureiro and Hine (2002), consumers with an activated local identity prefer local products because of identity consistency, which reinforces their local identity. Our results suggest a broader approach rooted in behavioral-mindset theory (Xu and Wyer 2007). As such, price sensitivity decreases for all products—regardless of local origin—as a result of a sacrifice mindset. Though counterintuitive, Study 6 shows that identity consistency and a sacrifice mindset may be complementary approaches to reducing price sensitivity among consumers with a local identity. Additional research is required to understand conditions that may moderate the joint effect of identity consistency and sacrifice mindset.
This research shows that one mediator of the observed association between local identity and price sensitivity among consumers is a sacrifice mindset. However, other constructs may also mediate this association. Examining these additional mediators could further clarify the difference between a consumer's local identity and ethnocentric tendencies. Consumer behaviors other than price sensitivity may also be worth examining. These include consumption enjoyment, repurchase behavior, and behaviors such as product boycotts and activism. To what extent do local identity and ethnocentrism jointly and separately affect these additional behaviors? Are there additional mediators of these behaviors? These are issues for further research.
This research should be interpreted in light of its limitations. First, we take a narrow view of price sensitivity, though it is a broad construct that includes response to price increase, willingness to pay, and preference for the high-price/high-quality option, to name a few (Wakefield and Inman 2003). Future studies could help clarify the scope of price sensitivity beyond the studies we report herein.
Second, each individual study we report could be methodologically improved. For example, we examined a price increase by 5%, 10%, and 15% in Study 2; by 20% in Study 4, and by 25% in Study 5. Study 6 examines a continuum of price increase from 1% to 15%. Although the price increase range of 5%-25% used in our studies reflects managerial practices in the real world (Campbell 2007; Kwak, Puzakova, and Rocereto 2015), caution should be taken when drawing conclusions about the results of a price increase beyond this range.
Third, the mindset construct is sufficiently abstract that it can be difficult to operationalize. Accordingly, existing research in the mindset literature has tended to avoid measuring it directly. In contrast, we measure it directly to show mediation through a general sacrifice mindset and monetary sacrifice. Yet consumers may make other types of sacrifices such as those based on family ties, sacrificing time and effort, and so forth. Research could investigate these types of sacrifice mindsets.
This research suggests several practical and effective ways CMOs can manage price increases by lowering consumers' price sensitivity. First, CMOs need not worry about the constraint of local production, local positioning, and so forth, to gain the benefit of lowered price sensitivity among consumers. We show a decrease in price sensitivity among local-identity consumers for products with an unspecified country origin. This finding is particularly important for firms with a global sourcing strategy to lower product costs. More importantly, we show that the two processes are not in conflict—rather (as we show in Study 6), they can be used symbiotically to lower price sensitivity. Studies 3 and 6 also show how firms can easily implement these ideas through their communication strategies.
Second, global firms can use short but well-established scales (e.g., Tu, Khare, and Zhang 2012) to target customers with a chronic local identity. Because these consumers are more likely to tolerate price increases, they may be better suited for introductions of higher-priced and, correspondingly, higher- quality products. Further research could assess whether they are also more tolerant of newly developed products.
Third, to maximize the benefit of mindset theory and identity-consistency theory, firms should clarify the reasons behind the price sacrifice. For example, Wal-Mart announced price increases in conjunction with its support for local causes. According to Jacobs, Graham-Squire, and Luce (2011), by claiming that the price increase would help it hire more local workers, Wal-Mart significantly increased its revenue by 14.5%, even though it cost consumers $12.49 more per year on average. Furthermore, Wal-Mart achieved this without having to source its products locally. In Table 1, our analysis shows that consumers with a local identity showed a 17.38% decline in price sensitivity, on average, than consumers with a global identity. Similarly, in the grocery store field study, we found that its annual revenue could increase by 13%, if all consumers behaved like those with a local identity.
Our findings are also informative for the proliferating "buy local" movements (http://www.thinkshopbuylocal.com). The Local Farms, Food and Jobs Act, sponsored by Senator Sherrod Brown of Ohio and Representative Chellie Pingree of Maine, invests approximately $200 million to help local farmers. While such efforts may fulfill many objectives, they are perhaps misguided in affecting customers' price sensitivity. In addition to extolling the virtues of buying local, communication efforts should try to activate consumers' local identity. This will enable local producers to compete without having to lower their price.
Finally, we recognize that there is also growth in antilocal movements claiming that buying local may not be in the best economic interests of consumers (Sexton 2011) because local products may not be the lowest-priced products. However, our research shows that consumers obtain identity-related benefits by supporting local brands. By better measuring and incorporating these psychological benefits into consumer welfare, managers can make more nuanced decisions. A good start would be to segment customers according to their local identity and examine the value proposition that best appeals to them.
Notes: Forfresh milk(Panel B), consumers with accessible local orglobal identity showed similar purchase quantity before the price increase (Mlocal = 2.34 vs. Mglobal = 2.39; t(749) = -.02, p = .82); purchase quantity was statistically significantly different after the price increase (Mlocal = 2.20 vs. Mglobal = 1.89; t(749) = 1.94, p = .05; Cohen's d = .21). Specifically, consumers with both local (Mbefore = 2.34 vs. Mafter = 2.20; t(749) = .71, p = .48; Cohen's d = .07) and global (Mbefore = 2.39 vs. Mafter = 1.89; t(749) = 2.54, p = .011; Cohen's d = .27) identities reduced their purchase quantity. However, the reduction in quantity was significantly less for local- identity than global-identity consumers (localbefore — after = .14 vs. globalbefore — after = .50; t(749) = -1.98, p = .047; Cohen's d = .22). For rice (Panel C), consumers with accessible local or global identity showed similar purchase quantity before the price increase (Mlocal = 10.83 vs. Mglobal = 11.78; t(273) = -.91, p = .36); their purchase quantity showed a statistically significant difference after the price increase (Mlocal = 10.53 vs. Mglobal = 7.55; t(273) = 3.30, p = .001 ; Cohen's d = .57). Specifically, consumers with both local (Mbefore = 10.83 vs. Mafter = 10.53; t(273) = .31, p> .75; Cohen'sd = .05) and global (Mbefore = 11.78 vs. Mafter = 7.55; t(273) = 4.36, p < .001; Cohen's d = .75) identities reduced their purchase quantity. However, the reduction in quantity was significantly less for local- identity than global-identity consumers (localbefore — after = .30 vs. globalbefor — after = 4.23; t(273) = -2.84, p = .005; Cohen's d = .49).
Notes: 1 = "less likely to purchase after price increase," and 7 = "very likely to purchase after price increase." The higher value indicates less price sensitivity.
Notes: 1 = "willing to buy the webcam after 1 % of price increase," and 15 = "willing to buy the webcam after 15% of price increase." The higher value indicates lower price sensitivity.
Endnotes 1 For the USDA International Food Consumption Patterns Data Set, see http://www.ers.usda.gov/data-products/international-food- consumption-patterns.aspx#26207.
2 Forthe KOF Index of Globalization, see http://globalization.kof.ethz.ch.
3 Forthe Global Competitive Report, see http://reports.weforum.org/global-competitiveness-report-2014-2015/previous-gci-reports.
4 Numbers in square brackets indicate the number of consumers who won each award.
5 In two separate studies, we measured and controlled for eth- nocentrism. Ethnocentrism did not mediate the observed effect, and the association between local identity and price sensitivity persisted even after we controlled for ethnocentrism. The results are available on request.
GRAPH: FIGURE 1 The Effect of Local-Global Identity on Purchase Quantities Before and After Price Change (Study 3)
GRAPH: FIGURE 3 The Moderating Role of Manipulated Sacrifice Mindset (Study 5)
GRAPH: FIGURE 4 The Moderating Role of Sacrifice Reason on the Effect of Local Identity on Price Sensitivity (Study 6)
DIAGRAM: FIGURE A1 Advertisement Manipulation
DIAGRAM: FIGURE 2 The Mediating Role of Sacrifice Mindset and Monetary Sacrifice (Study 4)
PHOTO (COLOR): FIGURE D1 Webcam Ad
PHOTO (COLOR)
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Zhang, Yinlong, and Adwait Khare (2009), "The Impact of Accessible Identities on the Evaluation of Global Versus Local Products," Journal of Consumer Research, 36 (3), 524-37.
Local- Global Identity Scale (Study 2; Tu, Khare, and Zhang 2012)
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| Sacrifice Mindset | | |
| (Davis, Le, and Coy 2011; Swann et al., 2014; 1 = "strongly disagree," and 7 = "strongly agree") | | |
| I believe sacrifice is a great virtue. | .58 | .89 |
| I am willing to give up my personal benefits for a bigger cause. | .82 | |
| Sacrificing is important and seems easy at the moment. | .86 | |
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| I feel the urge to make the necessary sacrifice. | .87 | |
| Sacrifices are necessary to achieve long-term goals for oneself and for society. | .81 | |
| I am willing to forgo desired activities for something more important. | .55 | |
| Monetary Sacrifice | | |
| (Grewal, Monroe, and Krishnan 1998; 1 = "strongly disagree," and 7 = "strongly agree") | | |
| I am willing to pay the increased price of $65.99. | .71 | .96 |
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| After evaluating the coffeemaker features, I am confident that I am getting quality features for $65.99. | .87 | |
| If I acquired this coffee maker, I think I would be getting good value for the money I spend. | .87 | |
| Purchase Intention After Price Increase | | |
| (Grewal, Monroe, and Krishnan 1998; 1 = "very low," and 7 = "very high") | |
| If I were going to buying a coffeemaker, the probability of buying this coffeemaker at the current price is … | .76 | .95 |
| The likelihood that I would purchase this coffeemaker at the current price is … | .68 | |
| The probability that I would consider buying this coffeemaker at the current price is … | .76 | |
| At the current price, the likelihood that I would seriously consider buying this coffeemaker is … | .88 | |
| The probability that I am willing to buy this coffeemaker at the current price is … | .84 | |
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Huachao Gao is Assistant Professor of Marketing, Peter B. Gustavson School of Business, University of Victoria.
Yinlong Zhang is Professor of Marketing, University of Texas at San Antonio.
Vikas Mittal is J. Hugh Liedtke Professor of Marketing, Rice University.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 91- How to Separate the Wheat from the Chaff: Improved Variable Selection for New Customer Acquisition. By: Tillmanns, Sebastian; Ter Hofstede, Frenkel; Krafft, Manfred; Goetz, Oliver. Journal of Marketing. Mar2017, Vol. 81 Issue 2, p99-113. 15p. 6 Charts, 2 Graphs. DOI: 10.1509/jm.15.0398.
- Database:
- Business Source Complete
How to Separate the Wheat from the Chaff: Improved Variable Selection for New Customer Acquisition
Online Supplement : http://dx.doi.org/10.1509/jm.15.0398
To maximize customer equity, firms can aim to optimize new customer acquisition, customer retention, and the profitability of all customer relationships. Customer management research primarily has focused on customer retention and profitability, while the acquisition of prospects has largely been ignored (Thomas 2001). Neglecting new customer acquisition efforts can lead to an aging or shrinking customer portfolio. Thus, companies must continuously identify and acquire prospects to grow and rejuvenate their customer base; however, this is both costly and risky because acquisition rates often fall far below 1%, and acquisition costs easily exceed $100 per customer (Reinartz, Thomas, and Kumar 2005). Improving acquisition campaigns can help lower these costs, but several questions arise: Should firms risk targeting too many prospects to achieve a large number of new customers or should they be selective, thus trading off high response rates against a very limited number of responses from very promising prospects? Do the answers to these questions differ depending on whether we consider response rates, cross-selling opportunities, or expected economic value of new customers per campaign?
When selecting addresses of prospects from list vendors, firms often use demographics or panel-based variables such as lifestyles. However, managers question whether additional data acquired from list vendors are worth the expenditure (Blattberg, Kim, and Neslin 2008). Should address selections be based on a large number of variables or a limited number? How can the best-performing variables be identified? Moreover, the variables selected can have either a simple linear effect or a complex nonlinear effect on the expected acquisition response. Thus, address selection should identify the functional form of their effects on prospects’ responses.
Several studies have shown that advanced methodological approaches can improve firm performance in areas related to customer acquisition, such as predicting future purchases or churn (e.g., Kumar, Venkatesan, and Reinartz 2006). The managerial relevance of such approaches becomes apparent when the incremental profit contributions of advanced and traditional approaches are compared. For example, Neslin et al. (2006), Lemmens and Croux (2006), and Kumar, Venkatesan, and Reinartz (2006) report improvements in revenues and profitability of up to several hundred million U.S. dollars following the application of various estimation and prediction techniques such as Bayesian approaches.
Whereas most studies on customer selection models have focused on current customers, very little empirical research has been devoted to new customer acquisition (see Table 1). Exceptions include Steenburgh, Ainslie, and Engebretson (2003), who investigate how modeling a hierarchical data structure can improve predictive performance. Even though the authors stress that the issue of variable selection is outside the scope of their study, they are aware that using a large number of predictor variables generally diminishes the out-of-sample predictive performance. Thus, they select 4 out of 200 variables available for address selection that produce the best prediction results for a benchmark model, in which the hierarchical structure of their data is ignored. Cao and Gruca (2005) show that incorporating a defection risk in prospect selection can positively influence profitability, and they use a principal component analysis (PCA) to reduce data dimensionality. Notably, although several studies on current customers focus on comparing different prediction methods for customer churn (e.g., Lemmens and Croux 2006; Neslin et al. 2006), in the context of new customer acquisition, only the study by Kim et al. (2005) provides such a systematic comparison. Regarding dimension reduction, the authors find that a logit shows a better predictive performance if it uses a subset of variables (i.e., less than 10% of the ones available) selected by a genetic algorithm. If a neural network instead of a logit is used for prediction, the predictive performance can be improved even more. However, because the authors do not consider campaign costs and prospect profitability, they are not able to determine optimal campaign sizes but rather limit their selection of prospects to the top 20%. While Cao and Gruca (2005) estimate concrete profitability measures, they restrict the analyses to the top 30% of prospects, and Steenburgh, Ainslie, and Engebretson (2003) use a conceptual approach for considering profitability measures. The latter researchers claim that direct marketers often assume complex, nonlinear relationships with response as the core dependent variable, and they therefore call for further research to examine nonparametric approaches. While Cao and Gruca (2005) also apply a parametric approach, Kim et al. (2005) make use of parametric and nonparametric prediction models, though they do not explicitly focus on differences between the two techniques and the associated nonlinear relationships.
In this article, we introduce a Bayesian variable selection model that includes both parametric and nonparametric specifications and compare it with various nested parametric and nonparametric approaches for selecting prospects. As benchmarks, we apply approaches commonly used in practice (i.e., a PCA followed by a logistic regression), and machine learning (i.e., bagging). Similar to the literature on prospect selection, we compare different approaches on the basis of their out-ofsample predictive performance. However, because campaign costs and the value of a response also drive campaign size and profitability, we use information provided by our cooperation partner to derive the optimal profits for each of the examined approaches. We investigate how the number of considered variables and variations in the expected values of responses affect response rates, cross-selling rates, and campaign profitability. Finally, we contribute to an understanding of how these approaches generate insights from big data (i.e., a great variety of demographic, socioeconomic, psychographic, and behavioral variables), which the Marketing Science Institute (2016) has recently identified as a top research priority. In the context of life insurance policies, our results demonstrate the following:
- Bayesian variable selection that includes both parametric and nonparametric specifications outperforms its nonparametric and parametric counterparts as well as approaches including managerial judgments, methods commonly used in practice, and machine learning. Similarly, panel-based metrics show surprisingly high predictive power. In our application, we show that acquisition campaigns are profitable only when these metrics are included.
- In a series of post hoc analyses, we find that a higher number of variables leads to an increase in mailing volumes and campaign profits, albeit at decreasing rates. We also identify the number of variables that maximize campaign profit.
- We observe a notable asymmetric effect: in new customer relationships with economic values 20% higher than our baseline case, the optimal campaign size is about twice the size of the baseline campaign. In contrast, in new relationships with very low economic values, firms should send out a minimum number of mailings. There is also a compensatory effect: as the economic value of responses increases, crossselling rates increase while the response rates decrease.
The remainder of this article is organized as follows: First, we discuss the challenges of predicting customer response behavior and the usefulness of various sources of information in new customer acquisition. Second, we discuss Bayesian variable selection and benchmark methodologies. Third, we describe our data, model estimation results, and implications for campaign performance, which we examine more closely in a series of post hoc analyses. We conclude with a discussion of the results and present future research opportunities.
The Challenge of Predicting Prospects’ Responses
Several factors hinder new customer acquisition research. Companies have limited internal information on prospects, which makes it difficult to predict their responses to acquisition activities (Thomas 2001). Firms can overcome this issue by purchasing additional data at the address level to facilitate new customer acquisition (Reinartz and Kumar 2003). For example, some list vendors offer several hundred variables for millions of households (Blattberg, Kim, and Neslin 2008). When faced with such vast numbers, companies must deal with potentially correlated predictors. Mining such data without selecting the most predictive variables could lead to overfitting and poor predictions of prospects’ responses. Blattberg, Kim, and Neslin (2008) and Varian (2014) emphasize that new methods for selecting small sets of the most predictive variables are promising avenues for future research on database marketing.
Because knowledge is limited regarding the relevance of specific sources of information when selecting prospects’ addresses (Blattberg, Kim, and Neslin 2008; Reinartz and Kumar 2003), we conducted interviews with industry experts to generate insights into the managerial challenges of using such information in business practice. All nine experts were executives in charge of analytical customer relationship management.
TABLE 1 Representative Empirical Research for Prospect Selection
TABLE:
| Study | Study Context | Sample Size | Considered Variables | Variable Selection/Dimension Reduction Approach | Prediction Approach | Nonparametric (NP)/Parametric (P) Specification | Prospect Value Considered? |
|---|
| Notes: ELSA = evolutionary local selection algorithm; NP + P = nonparametric and parametric specifications combined. |
| Steenburgh, Ainslie, and Engebretson (2003) | Education | 71,730 | 6 individual characteristics; 4 out of over 200 sociodemographics (ZIP code level) | Variables that produce the best prediction results in the benchmark | Hierarchical Bayes | P | Conceptually |
| Cao and Gruca (2005) | Financial services | 11,710 | 24 measures on credit history and experience; 10 sociodemographics | PCA | Binary probit, bivariate probit with sample selection | P | Yes |
| Kim et al. (2005) | Financial services (Study 1) | 9,822 (Study 1) | 51 sociodemographics and 42 variables on insurance ownership (Study 1) | PCA, ELSA | Logit, neural network | P, NP | No |
| | Donations (Study 2) | 191,779 (Study 2) | 406 sociodemographics and donation-related variables (Study 2) | | | | |
| This study | Financial services | 86,741 | 100 sociodemographics and behavioral variables | PCA, spike-and-slab prior, managerial judgments, tree | Logit, bagging, Bayesian | P, NP, NP + P | Yes |
In general, these experts agreed that sociodemographic information is of limited use in predicting customer response and that such information gathered from outside vendors is particularly problematic because its quality is often unknown and the data are highly aggregated. This assessment is in line with research on customer retention (e.g., Gupta et al. 2006). However, Verhoef and Donkers (2001) find that sociodemographics are important predictors of the ownership of different types of insurance (our empirical context), and in accordance with previous research (e.g., Reinartz and Kumar 2003), several experts emphasized the usefulness of sociodemographic information for profiling profitable customers or customer segments.
Another relevant source of information identified by our experts is panel based and includes variables such as lifestyles, psychometrics, or semiometrics. These “soft” variables are drawn from household surveys that extrapolate results into larger databases using readily observable data, such as demographics. For example, the semiometrics we use in our study are determined from a panel maintained by TNS and extrapolated by means of individual household sociodemographic information, with the assumption that customers’ values and attitudes can be measured by how they judge certain words (Lebart et al. 2014). From reactions to 210 different words, 14 dimensions are identified, including the extent to which customers are socially minded, culturally minded, critically minded, and religiously minded.
When firms try to gather useful information to target new prospects, using methods that accurately predict prospects’ response behaviors on the basis of the limited information available becomes imperative because this is directly related to campaign performance. More specifically, firms need to select a subset of predictors to identify the most promising prospects. Including hundreds of predictors may lead to overfitting, which reduces predictive accuracy and in turn hurts the profitability of a campaign. Predictive accuracy also depends on the specified functional relationship between each predictor and response probability, which poses a major challenge for large numbers of predictors. In this study, we investigate the relevance of selecting the appropriate variables and functional relationships, as Table 2 illustrates.
A basic approach would ignore the variable selection problem and add all the predictors to a nonparametric specification (Model 1) or a parametric specification (Model 2). Such approaches are likely to overfit the data, as these methods use a large number of predictors. Therefore, firms might rely on the expertise of analysts, managers, or industry experts to select the variables (nonparametric Model 3 and parametric Model 4). Another approach that might improve prediction relies on Bayesian variable selection (George and McCulloch 1993; O’Hara and Sillanpa¨a¨ 2009) with a nonparametric (Model 5) or parametric (Model 6) form. However, Bayesian selection can be applied to both predictors and the functional forms of their effects; full Model 7 thus includes both parametric and nonparametric specifications and allows for the selection of appropriate functional forms. Models 1–6 are specific (i.e., nested) cases of the full Model 7. Finally, we contrast Models 1–7 with two benchmarks. First, we followed a common practice in industry to reduce data dimensions and conducted a PCA of all predictors followed by a logistic regression analysis of responses (Benchmark B1). Second, we use bootstrap aggregation (bagging) as a more sophisticated and nonparametric benchmark (Benchmark B2).
TABLE 2 Specification of Alternative Approaches to Direct Mail
TABLE:
| Model | Variable Selection/Dimension Reduction | Nonparametric (NP) or Parametric (P) | Restrictions for Nested Models |
|---|
| Benchmark Models |
| B1 | PCA | P | |
| B2 | Tree | NP | |
| Nested Models |
| 1 | None | NP | wj = 1, for all j; β(j) = 0, for all j |
| 2 | None | P | wj = 1, for all j; gj(xij, θm(j) = 0, for all j |
| 3 | Managerial | NP | wj = { 1 : if variable j selected by expert judgment 0 : otherwise β(j) = 0, for all j |
| 4 | Managerial | P | wj = { 1 : if variable j selected by expert judgment 0 : otherwise gj(xij, θm(j), gj(xij, θm(j) = 0, for all j |
| 5 | Bayesian | NP | wj freely estimated; β(j) = 0, for all j |
| 6 | Bayesian | P | wj freely estimated; gj(xij, θm(j) = 0, for all j |
| 7 | Bayesian | NP + P | No restrictions |
Our study examines data on responses to a direct mail campaign obtained from a leading German insurance company seeking to acquire new life insurance customers. The data set incorporates responses to direct mails sent to approximately 200,000 addresses and involves about 100 variables from a list vendor. We compare the performance of the proposed full Bayesian selection Model 7 with the two benchmarks and the six alternative approaches discussed. The assessment is based on the number of leads generated in a holdout sample and the profit implications of applying any of these approaches to a vast pool of seven million prospects.
Methodology and Estimation Method
Research Context
We aim to select the most promising prospects from a list vendor’s address list. Such lists contain contact information as well as socioeconomic, demographic, and panel-based variables. Dealing with the massive amounts of predictors available for each prospect poses a challenge: the data often consist of variables from various sources, and some of these variables could be highly correlated, which reinforces the need to address the aforementioned multicollinearity problem. For example, age is likely to correlate with a first child’s age. Furthermore, much of the data that list vendors acquire from various sources contain related content or information at different aggregation levels and close to perfect collinearity might arise if some values are extrapolated on the basis of other variables in the database. For example, demographics are typically linked to psychographic survey data obtained from a smaller survey sample. One predictor might reduce the effectiveness of another highly correlated variable. In addition, the quality of some variables might vary from case to case—for example, when a prospect’s age is estimated on the basis of her or his first name. Thus, marketers’ conceptual considerations quickly become distorted because the data quality of some variables is unknown.
Bayesian Model Selection and Alternative Approaches in New Customer Acquisition
The issues discussed in the previous subsection pose two major challenges. First, the number of available predictors is large, so including all variables in a model might lead to overfitting of the data, reducing the efficiency of predicted response probabilities and producing suboptimal acquisition strategies. Second, selecting a model that includes the most informative variables for predicting responses is not straightforward. With K predictors, the total number of models to be considered is 2K. Because K is large, the number of alternative models is extremely large, which prevents scholars and practitioners from testing the performance of all possible models. Many predictors might also be constructed on the basis of other predictors, so the added value of including any particular variable in the model depends on which other variables are included.
A Bayesian variable selection approach using a spike-andslab prior offers a potential solution (George and McCulloch 1993; Scheipl, Fahrmeir, and Kneib 2012; Varian 2014). The spike-and-slab prior allows each variable to be selected into a model through a weakly informative prior with large variance (the slab) or deselected through a highly informative prior with zero mean and variance close to zero (the spike). In addition, although previous approaches (e.g., Bult and Wansbeek 1995) have assumed linear effects, the functional form of the effect of these predictors is unknown. The proposed model accommodates nonparametric effects as well through the use of a generalized additive model structure (Hastie and Tibshirani 1990). Combining Bayesian model selection with nonparametric effects allows for the inclusion of both linear and nonparametric terms so that the model decides whether a linear effect or a nonparametric effect of a particular variable, or both, should be included. For details on the model formulation, see Web Appendix A.
As an alternative to Bayesian variable selection, best subset and forward-or backward-stepwise selection methods could be used to reduce the number of predictors. They are straightforward in that they rely on the complete enumeration of all possible combinations of variables (best subset). However, these approaches are not feasible for large numbers of predictors; they use heuristics that might overlook optimal combinations of variables. Alternative approaches impose a penalty on the inclusion of additional coefficients, as in the Lasso method (Tibshirani 1996). This method traverses the model space efficiently and is computationally efficient, which provides a strong advantage over Bayesian methods when marketers need real-time analyses and predictions. However, such advantages are less pertinent in new customer acquisition contexts that rely on direct mail or outbound call centers. In addition, the irrepresentable condition for the Lasso method is easily violated by correlated predictors (Meinshausen and Yu 2006). Because predictors for new customer acquisition tend to be highly, or even perfectly, correlated, the Lasso method is inappropriate for our study context.
As a benchmark and industry standard in direct marketing, we apply a PCA to transform variables into components that serve as predictors in a logistic regression (Benchmark B1). We apply the method of alternating least squares, which optimizes the properties of the transformed variables’ covariance matrix to perform a PCA of qualitative and quantitative data (Young 1981). A similar benchmark was used by Kim et al. (2005) for evaluating the predictive performance of neural networks guided by genetic algorithms and showed that this benchmark performs at a competitively high level. It is important to note that PCA reduces the dimensionality of the predictors, which reduces overfitting and thus serves as an appropriate benchmark for the proposed Bayesian selection approach as well.
Another important aspect of our proposed model is that it captures nonlinear effects. We therefore introduce a benchmark (Benchmark B2) that relies on classification trees, which cut the predictors into regimes to predict response in a nonparametric fashion. More specifically, we compare our model with bootstrap aggregation (or bagging; Breiman 1996) of such classification trees, a machine learning technique that is likely to perform well in data-rich environments (Kim et al. 2005; Varian 2014). Lemmens and Croux (2006) introduced bagging to the marketing literature and show its superior performance in predicting customer churn. Our objective is to understand the relative performance of two factors: ( 1) different variable selection approaches (no variable selection, managerial selection based on expert opinions, or Bayesian variable selection) and ( 2) different functional forms of the effects of the predictor values (linear, nonparametric, and their combination). Combining both linear and nonparametric effects in a model is possible only for the Bayesian variable selection approach, so we have seven (3 • 2 + 1) models to evaluate. All these models are nested in the full model presented previously and are contrasted with the two Benchmarks B1 and B2.
We expect our Bayesian variable selection model to perform well because it overcomes the limitations of the other approaches described previously. The model stochastically traverses through the model space (unlike alternative selection methods), prevents overfitting because of regularization of the spike-and-slab prior, is likely to overcome multicollinearity because it is unlikely to select correlated variables simultaneously, and allows for the selection of the functional form of the relationship between predictor variables and probability of response. In addition, using Bayesian estimation enables us to compute expected response probabilities, integrating out over the posterior distribution of the model space.
Table 2 summarizes the restrictions imposed on the full Bayesian variable selection model to obtain the different estimation approaches as well as the two benchmarks. We derived the base case of including all predictors in the model by setting all effects of the predictors to the slab so they could be freely estimated. We obtained the managerial selection by allocating the predictors that industry experts and managers find important to the slab and all remaining variables to the spike. For Bayesian variable selection, a beta hyperprior distribution is used. We obtained the submodels that use only linear or only nonparametric effects of the metric variables by setting the basis functions or the linear effects, respectively, to zero for all predictors. No restrictions were imposed on the full Bayesian selection model, which included both linear and nonparametric effects. To estimate the model, we used Markov chain Monte Carlo, which recursively drew the model parameters from the full conditional distributions and used a standard third-degree spline with a second-order difference penalty (for details, see Web Appendix A).
Data
Response data gathered from a direct marketing campaign by a major German insurance company formed the basis of our empirical analysis. The campaign aimed to acquire new customers using personally addressed direct mails containing a response option. A leading list vendor provided us with approximately 100 variables, reflecting information at different aggregation levels (e.g., personal, household, microcells of 20 households, and other higher levels of aggregation) for 86,741 of the targeted addresses. These variables offered information on demographics, household and neighborhood characteristics, personality, and mail order preferences (for an overview, see Web Appendix B). We tracked the targeted customers’ responses and obtained 254 responses, equivalent to a response rate of .29%. Our industry partners know that the vast majority of prospects respond within 28 days after the campaign. To decrease a potential bias resulting from right-censored data, we doubled this time frame to observe responses up to 56 days. To calibrate our model and investigate its predictive power, we randomly split the data into two parts. We chose a calibration sample that contained 20,000 prospects and used the remaining 66,741 prospects as a holdout sample.
Endogeneity may pose a concern in new customer acquisition. It can occur because of the correlations of one or more independent variables with the error term, which would bias the estimates. For example, a company might react with higher prices in a direct mailing campaign when response rates increase in certain geographic regions (i.e., dynamic pricing). Considering price as an independent variable for predicting response behavior likely results in endogeneity problems. In our empirical context, however, no marketing decision variables strategically set by the firm entered as a predictor. In addition, all prospects received the same offering. Another source of endogeneity might stem from the firm strategically selecting addresses in the estimation sample. The addresses in our sample were, by definition, not selected at random, which might have introduced selection and endogeneity concerns. However, because we use the same set of variables and the same pool of addresses for every methodological approach in our model comparison, we controlled for potential bias.
To select variables for models informed by expert judgements, we elicited opinions of 20 industry experts on how suitable different types of predictors are when acquiring new life insurance customers. Our experts were senior managers responsible for direct marketing campaigns in major companies from various industries. We obtained assessments from each expert for selecting valuable addresses, on a scale from 0 (“not suitable at all”) to 100 (“very suitable”), which we normalized on a range from 0 to 1. Because of the large number of predictors, we used a single item to measure the suitability of variables that came from the same original source, had a similar measurement approach, and contained related content. In general, the industry experts evaluated the majority of variables as suitable for selecting valuable addresses (see Web Appendix B). We selected variables according to whether their related general item was significantly more suitable for selection than items registering an indifferent evaluation (i.e., evaluations above .50 on a 0–1 scale; .05 significance level). Thus, we identified 43% of our variables as suitable and used them in Models 3 and 4. Estimation of the other models relied on all available predictors.
TABLE 3 In-Sample Performance (Predicted Response Rates)
TABLE:
| | | | Predictive Performance in % |
|---|
| Model | Variable Selection/Dimension Reduction | Nonparametric (NP) or Parametric (P) | Top 5% | Top 10% | Top 15% | Top 20% |
|---|
| B1 | PCA | P | .900 | .800 | .733 | .675 |
| B2 | Tree | NP | 6.900 | 3.450 | 2.300 | 1.730 |
| 1 | None | NP | 2.600 | 1.950 | 1.500 | 1.225 |
| 2 | None | P | 2.900 | 1.850 | 1.533 | 1.250 |
| 3 | Managerial | NP | 1.500 | 1.200 | 1.000 | .875 |
| 4 | Managerial | P | 1.100 | 1.000 | .867 | .800 |
| 5 | Bayesian | NP | 1.200 | .950 | .667 | .625 |
| 6 | Bayesian | P | 1.200 | .850 | .833 | .775 |
| 7 | Bayesian | NP + P | 1.200 | .900 | .800 | .750 |
TABLE 4 Out-of-Sample Performance (Predicted Response Rates)
TABLE:
| | | | Predictive Performance in % |
|---|
| Model | Variable Selection/Dimension Reduction | Nonparametric (NP) or Parametric (P) | Top 5% | Top 10% | Top 15% | Top 20% |
|---|
| B1 | PCA | P | .450 | .420 | .400 | .382 |
| B2 | Tree | NP | .539 | .479 | .360 | .330 |
| 1 | None | NP | .300 | .315 | .330 | .337 |
| 2 | None | P | .360 | .390 | .380 | .382 |
| 3 | Managerial | NP | .360 | .345 | .340 | .337 |
| 4 | Managerial | P | .360 | .330 | .340 | .337 |
| Mean across | Models | 1–4 | .338 | .349 | .345 | .347 |
| 5 | Bayesian | NP | .539 | .494 | .440 | .442 |
| 6 | Bayesian | P | .569 | .509 | .450 | .442 |
| 7 | Bayesian | NP + P | .569 | .524 | .489 | .464 |
| Mean across Models 5–7 | | | .559 | .509 | .460 | .449 |
| Improvement (Bayesian vs. traditional models) | | | 66% | 46% | 33% | 30% |
| Mean across Models 1, 3, and 5 (NP) | | | .400 | .385 | .367 | .370 |
| Mean across Models 2, 4, and 6 (P) | | | .420 | .415 | .390 | .387 |
| Improvement (P vs. NP models) | | | 5% | 8% | 6% | 5% |
Results
Predictive Performance
In Table 3, we report the in-sample predictive performance of Benchmark B1 (PCA), Benchmark B2 (bagging), and Models 1–7 regarding their ability to predict the true responses of the 20,000 addresses in our calibration sample. New customer acquisition campaigns are typically based on scoring methods that rank the prospective addresses according to their predicted response propensities, and then the firm targets the top x% of customers (see, e.g., Kim et al. 2005). Because we took a Bayesian approach, we selected addresses using a scoring approach based on the (posterior) predictive probability of response to a direct mail sent to a particular address. The interest typically is in identifying the top percentile of candidates, so we used each model’s ability to accurately predict the responses for the top 5%, 10%, 15%, and 20% of addresses as measures of predictive accuracy. In each sweep of the Markov chain Monte Carlo, we generated the predicted probabilities for each address, then computed the proportion of true responses in the data for the top x% and averaged across sweeps. As we expected, the response rates decreased across models as the percentages increased because it was more difficult to recover responses when less attractive addresses (in terms of predicted response probability) were added to the targeted pool. The biggest drop resulted when moving from the top 5% to the top 10%. Benchmark B2 (bagging) and traditional approaches (Models 1 and 2) generated much higher response rates than approaches in which the variable selections were based on managerial judgments or on Bayesian estimation. However, Models 1 and 2 likely suffered from overfitting because they included all variables in the estimation. The more parsimonious Models 3–7 and benchmark B1 (PCA) predicted worse in-sample. A high insample predictive performance does not imply that the same set of variables, or their respective coefficients, is useful for predicting the response rates of different addresses. To quantify the true predictive accuracy of our models, we estimated their out- of-sample predictive performance by predicting the response rates of our holdout sample of 66,741 prospects (see Table 4). Models 1 and 2, without any variable selection, performed very poorly, despite using all the information available. The managerial judgment Models 3 and 4 also failed to generate reasonable response rates. On average, these four traditional approaches produced mean predicted response rates of .34%–.35%, while the PCA benchmark led to substantially higher predicted response rates of .38%–.45%. Benchmark B2 (bagging) revealed relatively high response rates of .54% and .48% for the top 5% and 10% prospects, respectively. Notably, the predictive performances for the top 15% and 20% prospects were low (.36%–.33%), indicating that the approach might not be suitable to differentiate between prospects with a low response probability. For the Bayesian variable selection Models 5, 6, and 7, we arrived at average response rates of .45% (top 20% prospects) to .56% (top 5% prospects). These models did not suffer from overfitting and thus generated response rates 30%–66% higher than the traditional approaches (Models 1–4). From a managerial viewpoint, the key finding here is that the very best prospects (top 5% of potential new accounts) showed the most substantial improvement in response rates among the nested models.
In summary, all three Bayesian variable selection specifications outperformed the selection by experts, nonselection (i.e., including all variables in the model), and the two benchmarks. The spike-and-slab selection procedure thus can be deemed an effective method to identify variables and to maximize predictive accuracy in new customer acquisition campaigns.
To investigate whether allowing for linear and/or nonlinear functional forms makes a difference in predicting response rates, we also compared the predictive performance of the spikeand-slab procedure by limiting the effects to linear (Model 6) and nonparametric (Model 5) forms. Linear functional forms in the Bayesian estimation performed equally well, or even better, than nonparametric forms. Because estimating linear effects is faster than calibrating nonlinear forms, managers and scientists might consider linear functional forms an efficient choice for big data variable selection problems. The Bayesian selection approach also offers the option of letting the procedure find the best functional form for any explanatory variable, represented by Model 7. As we expected, Model 7 outperformed Model 6 in three of four subsamples, though Models 6 and 7 performed equally well among the top 5% prospects. As Table 4 shows, a comparison of our parametric Models 2, 4, and 6 with the nonparametric effects in Models 1, 3, and 5 revealed a rather minor improvement of 5%–8% in parametric estimation. However, the full Model 7 included both parametric and nonparametric estimation and exhibited an improvement of more than 40% compared with a purely nonparametric estimation. Thus, the Bayesian selection procedure in its most flexible form generated the highest predicted response rates. To further validate our findings, we calculated the predictive performance for the bottom x% for Model 7 as well and generated response rates that were, on average, 8.8 times higher for the top x% than for the bottom x%. These results imply that Model 7 is truly effective in separating the “wheat” (respondents) from the “chaff” (nonresponding prospects).
Profit Implications
Although the preceding results demonstrate superior predictive performance of Bayesian variable selection in generating outof-sample responses, especially with the inclusion of both linear and nonlinear effects, the effect of implementing the different approaches on a direct marketer’s profits remain unclear. We therefore conducted a simulation study to assess, out-ofsample, the profits (or losses) from implementing the different approaches (Benchmarks B1 and B1 as well as Models 1–7).
We used the same holdout sample of responses as in the previous section and computed the expected campaign profits for each method. Conditional on the model parameters in the likelihood Q = ðb, f, dÞ, we simulated for each sweep
(q = 1, :::, Q) the response probabilities in the holdout set pfriðoqÞm=thpeðypio=st1erQioðrqÞpÞr.edWicetivtheednisstroirbtuetdiotnhoesfeeapcrhoabdadbrielistsieis: within a sweep and count the number of actual responses in the
set of addresses that lands in the top x% (ranging on a grid from .05%–15%, with step lengths of .05%). By letting ^yðqÞ denote
the number of actual responses from the top x% in sweep q, we
can compute the expected (posterior) profits of the campaign,
conditional on the costs and value of a response, by averaging
the computed profits per customer across sweeps. The financial
service provider keeps the fixed costs per campaign low
(V10,000) by outsourcing only a few activities. The variable
costs include the cost of using an address obtained from the
address list broker (V.10 per mailing), production costs (V.10
per mailing), and postage costs (V.2800 per mailing for up
to 100,000 mailings, V.2688 per mailing for 100,001–500,000
mailings, or V.2260 per mailing for 500,001 mailings and
more). The value of each response is V100. The expected profits
of a campaign, unconditional on the true parameter values,
can
be
calculated
as
10; 000, where vmcðmÞ represents the variable mailing costs
of a mailing of size m. We scale up the results to represent a
typical campaign, so that the address list contains approx
imately seven million usable addresses. For each of the seven
approaches, we calculated the expected profits E[p] for all
values on the grid, then selected the optimal x for which the
expected profits of the top x% mailings were largest. We
constrained the mailing size to a minimum of the top .5% of
the usable addresses (35,000 addresses). Because firms must
designate or book internal and external resources in advance
(e.g., from agencies or letter shops), not conducting a pro
posed direct mail campaign is not an option, even when the
campaign is expected to be unprofitable.
Table 5 lists the optimal mailing levels (both top x% and
absolute number) and the corresponding profits of each of the
seven approaches and the two benchmark models. With losses
ranging from a little below V12,000 to more than V18,600, we
can see that the campaign performance of the approaches that do
not use variable selection or select variables by relying on
managerial insights is insufficient; the direct marketer would be
better off canceling all future direct marketing efforts. Mean
while, Benchmark B1 reveals a loss close to zero, while the
Benchmark B2 results in profits of approximately V26,000. The
models using Bayesian variable selection consistently reveal
substantial profits: approximately V40,000, V50,000, and
V60,000 for nonparametric (Model 5), linear (Model 6), and
both specifications (Model 7), respectively. Our proposed full
Model 7 delivers the highest profit levels, affirming the value
of using Bayesian joint selection for linear and nonparametric
effects. This approach increases profits by more than V60,000
over the losses incurred in using Benchmark B1.
TABLE 5 Profit Levels for Alternative Approaches to Direct Mail
TABLE:
| | Optimal Campaigns |
|---|
| Model | Variable Selection/Dimension Reduction | Nonparametric (NP) or Parametric (P) | Top x% | Mailings | Profits/Losses |
|---|
| B1 | PCA | P | 1.5% | 105,000 | -134.54 |
| B2 | Tree | NP | 9% | 630,000 | 25,921.80 |
| 1 | None | NP | .5% | 35,000 | -18,607.75 |
| 2 | None | P | .5% | 35,000 | -17,034.51 |
| 3 | Managerial | NP | .5% | 35,000 | -17,427.81 |
| 4 | Managerial | P | .5% | 35,000 | -11,894.87 |
| 5 | Bayesian | NP | 12.0% | 840,000 | 39,541.88 |
| 6 | Bayesian | P | 9.5% | 665,000 | 52,776.18 |
| 7 | Bayesian | NP + P | 12.5% | 875,000 | 60,508.37 |
Post Hoc Analyses
In a series of follow-up analyses, we investigated how our predicted response rates and the profits of the optimal selection Model 7 varied with the number of selected variables and the value of a response. We systematically varied the number of variables (two, four, six, eight, and ten) on the basis of the top posterior inclusion probabilities (i.e., the top two through ten variables that are most likely to predict response). Because we are interested in obtaining substantive insights, we examined the percentage of respondents identified (“mailings to top x%”) of the seven million usable addresses for customer acquisition campaigns and predicted out-of-sample response rates, cross-selling rates, and optimal profits from the top x% respondents. Crossselling has been linked to customer profitability and lifetime value (Reinartz, Thomas, and Kumar 2005), and selling additional policies to new customers is a major source of customers’ long-term value for the insurance industry (Verhoef and Donkers 2001). To determine cross-selling rates, our collaboration partner provided a random sample of transaction data pertaining to 15,101 regular customers. The data contained information about customers’ birth dates, gender, and whether additional insurance policies were purchased (in our case, occupational disability insurance). Drawing on information from our collaboration partner, we estimated the contract value of an additional policy as half a life insurance policy’s value. To determine the likelihood of a prospect purchasing an additional insurance policy, we constructed a cross-table based on gender and age quartiles and calculated the proportion of customers who purchased an additional disability policy for each cell (range: .04–.26). We subsequently matched these probabilities with the (holdout) acquisition data to obtain an estimate of the probability of purchasing an additional policy. We calculated optimal campaign profits as described in the profit implications section, taking individual cross-selling effects into account. We also varied the economic value of each response to the mailing campaigns and assumed values of V80, V90, V100, V110, and V120. According to our collaboration partner, this range represents reasonable deviations of 10% and 20% from the baseline of V100, leading to 25 combinations of numbers of variables and expected response values (see Table 6). We explore the outcomes of the analyses in the “Discussion” section.
Discussion
Insights Obtained from the Model Comparison In the context of our study (i.e., new customer acquisition involving big data and a large number of predictors), we included a PCA-based approach as a baseline (which we denoted as B1) because it helps reduce the dimensionality problem. Our study indicates that PCA/logit is capable of reducing overfitting by reducing the dimensionality of the predictor space and shows competitive predictive performance. However, this two-stage tandem approach ignores the measurement error in the factor scores, makes interpretation more difficult, and does not take into account the potential nonlinear relationship between predictors and the response variable. If practitioners are aware of potential nonlinear relationships in their data and still want to reduce the risk of overfitting the data, they might choose a nonparametric approach such as bootstrap aggregation of classification trees (bagging Benchmark B2), which has been developed in the machine-learning literature. Our study shows empirically that pronounced nonlinear relationships exist, and bagging predicts well for the top 10% prospects. Nevertheless, although bagging is suitable to improve upon the overfitting problems of trees, it loses its simple and interpretable structure (Breiman 1996). Furthermore, in our application, bagging performs worse for the top 15%–20% of prospects and underperforms in terms of campaign profits. Thus, if firms plan to conduct large campaigns, they should focus on alternative approaches that are better able to differentiate response behavior in lower ranges. Notably, trees do not tend to work well if the underlying relationship really is linear (Varian 2014), which might be critical if the available data include many variables that take many different functional forms. Similarly, our parametric Bayesian approach reveals a better predictive performance compared with its nonparametric counterpart.
The quality of predictor variables from list vendors and their functional relationships with response behavior are often unclear. Therefore, the proposed full Bayesian selection model, which is capable of identifying the appropriate functional form and to select the variables that matter, is likely to perform better. Furthermore, the full Bayesian selection model is easy to interpret and reduces overfitting because of the regularization of predictors through the spike and slab prior. In our study, we show that if companies follow these recommendations, their new customer acquisition campaigns are likely to improve their profits and can even avoid losses.
TABLE 6 Post Hoc Analyses
TABLE:
| | Economic Value of a Response | |
|---|
| Key Performance Indicators | V80 | V90 | V100 | V110 | V120 | Averages Across Values |
|---|
| Two Variables |
| Mailings (top x%) | 35,000 (.50%) | 35,000 (.50%) | 35,000 (.50%) | 525,000 (7.50%) | 525,000 (7.50%) | 231,000 (3.30%) |
| Response value (rate) | 12,618 (.45%) | 14,197 (.45%) | 15,777 (.45%) | 225,720 (.39%) | 246,241 (.39%) | 102,911 (.43%) |
| Cross-selling value (rate) | 795 (12.87%) | 894 (12.87%) | 994 (12.87%) | 14,511 (12.98%) | 15,830 (12.98%) | 6,605 (12.92%) |
| Optimal profits (costs) | -13,387 (26,800) | -11,708 (26,800) | -10,030 (26,800) | 6,581 (233,650) | 28,422 (233,650) | 225 (109,540) |
| Four Variables |
| Mailings (top x%) | 35,000 (.50%) | 525,000 (7.50%) | 525,000 (7.50%) | 595,000 (8.50%) | 1,400,000 (20.00%) | 616,000 (8.80%) |
| Response value (rate) | 19,939 (.71%) | 248,894 (.53%) | 276,551 (.53%) | 335,211 (.51%) | 730,097 (.43%) | 322,138 (.54%) |
| Cross-selling value (rate) | 1,314 (13.43%) | 17,089 (13.80%) | 18,988 (13.80%) | 23,072 (13.82%) | 53,023 (14.50%) | 22,697 (13.87%) |
| Optimal profits (costs) | -5,547 (26,800) | 32,334 (233,650) | 61,889 (233,650) | 94,813 (263,470) | 176,721 (606,400) | 72,042 (272,794) |
| Six Variables |
| Mailings (top x%) | 35,000 (.50%) | 525,000 (7.50%) | 1,190,000 (17.00%) | 1,470,000 (21.00%) | 1,680,000 (24.00%) | 980,000 (14.00%) |
| Response value (rate) | 17,076 (.61%) | 246,168 (.52%) | 562,395 (.47%) | 743,227 (.46%) | 904,082 (.45%) | 494,590 (.50%) |
| Cross-selling value (rate) | 1,111 (13.26%) | 16,468 (13.43%) | 38,284 (13.63%) | 50,849 (13.70%) | 62,060 (13.74%) | 33,754 (13.55%) |
| Optimal profits (costs) | -8,614 (26,800) | 28,986 (233,650) | 83,739 (516,940) | 157,855 (636,220) | 240,462 (725,680) | 100,486 (427,858) |
| Eight Variables |
| Mailings (top x%) | 35,000 (.50%) | 525,000 (7.50%) | 1,365,000 (19.50%) | 1,785,000 (25.50%) | 2,100,000 (30.00%) | 1,162,000 (16.60%) |
| Response value (rate) | 15,440 (.55%) | 246,812 (.52%) | 638,323 (.47%) | 892,326 (.45%) | 1,113,419 (.44%) | 581,264 (.49%) |
| Cross-selling value (rate) | 1,032 (13.52%) | 16,923 (13.78%) | 44,566 (13.99%) | 62,338 (14.00%) | 77,896 (14.02%) | 40,551 (13.86%) |
| Optimal profits (costs) | -10,328 (26,800) | 30,085 (233,651) | 91,396 (591,492) | 184,251 (770,413) | 286,711 (904,604) | 116,423 (505,392) |
| Ten Variables |
| Mailings (top x%) | 35,000 (.50%) | 560,000 (8.00%) | 770,000 (11.00%) | 945,000 (13.50%) | 2,065,000 (29.50%) | 875,000 (12.50%) |
| Response value (rate) | 10,143 (.36%) | 248,101 (.49%) | 369,168 (.48%) | 485,595 (.47%) | 1,042,961 (.42%) | 431,194 (.44%) |
| Cross-selling value (rate) | 694 (13.79%) | 16,906 (13.69%) | 25,245 (13.71%) | 33,255 (13.71%) | 71,504 (13.72%) | 29,521 (13.72%) |
| Optimal profits (costs) | -15,963 (26,800) | 16,446 (248,561) | 56,392 (338,022) | 106,277 (412,573) | 224,771 (889,694) | 77,584 (383,130) |
| Averages Across Numbers of Variables |
| Mailings (top x%) | 35,000 (.50%) | 434,000 (6.20%) | 777,000 (11.10%) | 1,064,000 (15.20%) | 1,554,000 (22.20%) | 772,800 (11.04%) |
| Response value (rate) | 15,043 (.54%) | 200,835 (.50%) | 372,443 (.48%) | 536,416 (.46%) | 807,360 (.43%) | 386,419 (.48%) |
| Cross-selling value (rate) | 989 (13.37%) | 13,656 (13.52%) | 25,616 (13.60%) | 36,805 (13.64%) | 56,063 (13.79%) | 26,626 (13.58%) |
| Optimal profits (costs) | 210,768 (26,800) | 19,228 (195,262) | 56,677 (341,381) | 109,955 (463,265) | 191,417 (672,006) | 73,302 (339,743) |
Insights Obtained from the Variable Selection
The full Bayesian selection Model 7 is a promising approach, in light of its predictive accuracy and profit implications. In this section, we use Model 7 as our reference point to contrast key findings obtained from the various approaches. Web Appendix C provides an overview of the relative importance of the predictors in Models 1–7. For the Bayesian selection Models 5–7, we calculated the posterior inclusion probabilities by averaging across sweeps; we also calculated the posterior coefficient of variation for Models 1–4 to represent their effect sizes.
The results of the full Bayesian selection model reveal that eight variables substantially explain prospects’ response behaviors, as indicated by the posterior inclusion probabilities of .10 or higher: household composition; last mail order recorded; mail order preference for management topics; main focus on owning a house (financial typology); and the extent to which prospects are socially, religiously, culturally, and critically minded (semiometric variables). This finding implies that firms can save time and money by acquiring a rather limited number of variables about prospects. In addition, our Model 7 improves the interpretability of data by identifying a parsimonious set of input variables that drive the response.
Many of the variables that our experts identified as important for targeting potential new customers did not help differentiate between respondents and nonrespondents. Explanations for this result are the unknown quality and the aggregation level of data acquired by list vendors���a problem that often occurs in big data environments. Thus, it is necessary to take sophisticated selection approaches that implicitly consider the quality of the available predictors. For example, most experts did not find the semiometric variables to be relevant, even though all remaining models identify them as significant indicators. Surprisingly, four of the eight variables selected in full Model 7 were semiometric measures. Direct marketers should take them into consideration when targeting new customers for life insurance products. Because no linear patterns are apparent for any semiometric variable, prospect addresses must be treated very differently, even though they might fall into similar categories. Similarly, the Bayesian selection Models 5 and 7 indicate a substantial nonlinear effect for the last mail order recorded variable as shown in Figure 1. The inverted U-shaped effect shows that prospects are less likely to respond if the last mail order was either recent or a long time ago. Contrary to the Bayesian selection models, traditional approaches (Models 1–4) show a significant linear relationship, implying that prospects would be more likely to respond if their last mail order happened some time ago.
The list vendor identified various consumption foci and investigated whether individual mail order behavior was related to these foci. The only significant finding in full Model 7 is a nonlinear effect for mail order preference for management topics; the traditional approaches (Models 1–4) identify almost all variables on the basis of mail order preferences as drivers of response. In other words, although direct marketers of life insurance products tend to target prospects that have pronounced mail order preferences on any topic, full Model 7 suggests they should target prospects with limited preferences for management topics only and refrain from mailing to prospects who score high on this variable.
We also detected consistent results for both household composition and financial typologies across our seven models. Household composition comprises household size, surname similarity, and gender distribution in a household (see Figure 2). In general, this variable shows a high predictive power in all examined models. Moreover, both Bayesian and traditional approaches select financial typology variables. Similar to semiometrics, financial typology variables are based on survey data collected from a sample of people, extrapolated to the address list on the basis of demographics. If prospects’ financial typology indicates “main focus is owning a house,” they showed a higher likelihood of responding in both Bayesian and traditional models, which makes sense because having life insurance substantially helps finance real estate transactions.
For our PCA benchmark (B1), we identified 26 principal components with eigenvalues higher than 1 that explained 67% of the predictors’ original variance. Components 2, 4, 14, and 23 revealed a substantial effect according to their posterior coefficients of variation in the subsequent logistic regression. For their interpretation, we examined the components’ pattern of eigenvectors (for details, see Web Appendices D and E). The magnitude of the coefficients for the second component’s eigenvector is highest for semiometric and financial typology variables as well as the gender variable. Component 4 is characterized by variables, indicating that children are living in a household, and Component 14 asserts key importance for two variables: professional title holders and small business in household. Finally, Component 23 is characterized by the variables of vans in neighborhood, county, and several mail order preference variables. Thus, similar to the full Bayesian model, semiometrics and financial typology variables emerge as important predictors. With regard to household characteristics, household composition is an important predictor for the full model, while predictors related to the number of children and professional life are of major importance for the Benchmark B1.
To calculate the relative importance of the bagging benchmark (B2), we follow Lemmens and Croux (2006) by aggregating the splitting improvements over all nodes, for which a predictor was selected as a splitting variable, and aggregating across all bootstrapping samples (for the results, see Web Appendix F). The results show that household composition, county, life stage, and different mail order preferences are among the particularly relevant predictors. Notably, the most important panel-based metric (financial typology; i.e., main focus is on owning a house) is only the 11th most important predictor, closely followed by several semiometric variables.
In contrast, five of the eight variables selected by the full Bayesian variable selection approach are panel based (i.e., main focus is on owning a house [financial typology] and the extent to which prospects are socially, religiously, culturally, and critically minded [semiometrics]). Because it takes additional effort to derive these measures, we investigated their incremental contribution by excluding all panel-based metrics from the analysis and applying the full Bayesian variable selection model to the reduced data set. Compared with the original model, the predictive performance dropped up to 17% for the estimation sample and up to 26% for the holdout sample. The initial profit of V60,508 for the top 12.5% turned into a loss of V14,433 for the top .5% of prospects. Thus, investing in panel-based metrics provided an incremental profit contribution of approximately V75,000 for a single campaign.
Insights Obtained from the Post Hoc Analyses
Because additional variables come at a cost, direct marketers would benefit from understanding the incremental benefits of adding predictors to the model. We made an attempt to address this issue and, at the same time, investigate the sensitivity of the results regarding the economic value of a response. In Table 6, we calculated the optimal mailing strategies for the full Model 7 when including two, four, six, eight, or ten variables in the model and economic values ranging from V80 to V120, resulting in 25 scenarios. On average, response rates and crossselling rates are highest for the inclusion of four variables; the optimal mailing volume and profits are highest for eight variables. Similarly, we observe that additional variables’ marginal information value increases at decreasing rates, and with regard to medium and high response values, eight variables are profit maximizing. Thus, adding variables does not necessarily improve the quality of the predicted responses in profit optimal campaigns. When only these eight variables are included in the model and V100 is the expected response value, adding two more variables improves the response rate by .01% points. However, this improvement cannot compensate for the profits lost because of lower mailing volume, and thus, profits decrease by approximately one-third. Although response rates are frequently regarded as a core metric in direct marketing, our findings imply that direct marketers must trade off larger campaigns accompanied by waste against smaller campaigns characterized by relatively high response rates with a smaller absolute number of respondents.
The number of addresses selected for new customer acquisition campaigns is strongly associated with the responses’ economic value. Table 6 shows that prospects with lower response rates are more likely to be profitable, and thus optimal mailing volumes increase, while response rates decrease. With higher response values, the mailing volume increase at substantially higher rates than the response rates decrease. Compared with our baseline expected economic value of V100 of a response, the optimal average mailing volumes doubled when the economic values increase by 20%. However, when this economic value is 20% lower than our baseline, the optimal mailing volume is reduced by more than 95%. Thus, marketers should carefully determine the economic value of a response, because small deviations might have a major impact on optimal mailing volume.
Our analyses also reveal an interesting compensatory effect between cross-selling and response rates: crossselling rates generally increase with increasing response values, but response rates decrease. Campaign outcomes are determined by increasing shares of value contribution through cross-selling when response values increase. In other words, the higher the prospects’ expected value, the more difficult it is to convert them successfully. However, once converted, they become even more valuable. This finding is in line with the observation that for the most valuable group of customers, acquisition costs are above average (Reinartz, Thomas, and Kumar 2005). Li, Sun, and Wilcox (2005) offer a possible explanation for this effect.
They argue that customers with certain demographics (such as a high income) have less time to spend searching for the best products. Consequently, they are more likely to crossbuy from their vendors after becoming customers. Households that are difficult to acquire might be more prone to buying additional products after they become customers.
Managerial Implications
Managing complex data situations. Big data offers firms tremendous value creation opportunities. Analytical techniques can provide valuable insights in data-rich environments that guide subsequent theory development and practice. Practitioners and academics have identified a need for approaches that allow for variable selection and for modeling complex relationships (e.g., Blattberg, Kim, and Neslin 2008; Neslin et al. 2006; Varian 2014).
In our application, the Bayesian variable selection approach recommends sending more mailings to prospects than traditional methods suggest; it shows that selecting approximately 800,000 addresses is appropriate for responses with an economic value of V100, whereas other methods imply that not sending any mailings is the best way to avoid losses. Even if these prospective addresses do not lead to immediate individual responses, applying Bayesian variable selection methods creates more awareness of a firm’s offerings in a greater number of prospects.
The functional form also has a powerful effect on predicting new customer responses, as Figure 1 indicates. In a follow-up questionnaire sent to the respondents of our management survey, we asked 11 managers to evaluate the functional relationships related to the metric variables in our study. The majority assumed a nonlinear effect regarding the last mail order recorded (82%), but opinions were divided in terms of mail order preferences (64%), age (64%), and household characteristics (55%). The responses reflect a high degree of uncertainty, but understanding of such effects is critical because assuming linear relationships when the true effects are nonlinear implies waste; that is, many poor addresses receive a mailing. We suggest that companies should apply a Bayesian approach to select variables and their functional form. They also could conduct a sensitivity analysis of both the expected value of a response and the number of variables included in the prediction model.
The use of sociodemographic information. Sociodemographic data frequently are of little use, because of their negligible relationship with relevant customer behavior (Gupta et al. 2006). With the flexibility of the Bayesian variable selection approach, we can reveal relationships between sociodemographic information and the behavior of interest (in our case, response to direct mail). This capability is especially valuable for sociodemographics gathered from list vendors, which often are aggregated and of unknown quality. Notably, we find substantive effects for several variables that are based on panels from market research agencies and extrapolated by means of individual household sociodemographic information (e.g., semiometrics on being religiously minded). We suggest that panel-based metrics exhibit their predictive power through different combinations of sociodemographic variables. List vendors also directly combine sociodemographic variables to create meaningful variables. One such variable is household composition, which reveals high predictive power. Companies might use customer surveys to augment new variables, based on combinations of sociodemographics, then include them in the variable selection procedure.
Managing the costs of data acquisition. Blattberg, Kim, and Neslin (2008) question whether customer characteristics acquired from list vendors are worth the added expense. Our research reveals that information on customer characteristics pays off when firms apply appropriate modeling techniques. From an information value perspective, our new model is surprisingly powerful in predicting new customer responses, even though we select only 8 of 100 variables. Including all the variables clearly does not facilitate the task; the negative effects of overfitting appear to dominate the informational value of adding variables. If only a few variables are needed to “separate the wheat from the chaff” in address selection, firms can reduce their budgets for renting addresses while still identifying the most attractive prospects.
Further applications. The application of the proposed Bayesian variable selection approach can be easily adapted to other relevant management topics. For example, predicting customer churn generally involves a large number of variables, often referred to as the “curse of dimensionality” (Gupta et al. 2006, p. 148). Managers can incorporate highly predictive customer relationship characteristics as additional variables in their models (e.g., recency, frequency, monetary value). Neslin et al. (2006) call for sophisticated statistical approaches to predict customer churn; they regard the relative contribution of variable selection in customer churn model approaches as an important avenue for further research.
In the context of customer win-back, Kumar, Bhagwat, and Zhang (2015) state that firms should leverage data from customers’ first lifetimes to reacquire them. Pick et al. (2016) identify several survey measures that predict the return of customers. Nevertheless, because such measures are generally difficult to obtain from lost customers, the authors recommend that managers use proxy variables from their customer database. A Bayesian variable selection approach can help managers identify variables that are predictive of customer win-back.
Limitations and Research Opportunities
Our field application is limited to one leading insurance company with vast data and an extensive list of variables at its disposal. Further research could replicate our study using other firms and industries or different types of customer contact. Although right-censoring is less likely in our study, it might be problematic in other customer acquisition contexts. For example, responses to acquisition mailings with regard to contractual relationships might take a long time if prospects first have to cancel existing contracts. Research could extend the proposed Bayesian variable selection model to a discrete hazard model, in which the event of a response at a particular point in time is a nonparametric function of the duration since the mailing was sent.
In our model comparison, we used survey data to measure the performance of models with variables selected by experts. We used a discrete cutoff, based on t-tests, to decide whether managers would incorporate particular variables in the model. However, instead of using such discrete cutoffs (included vs. not included), the full Bayesian variable selection model could be extended by incorporating experts’ judgment scores through a beta prior distribution imposed on the selection probabilities of the prior. Such an extension could incorporate the best of both worlds, resulting in improved variable selection and improved campaign performance. Further research could examine methods of eliciting such beta priors through survey measures.
According to Thomas, Reinartz, and Kumar (2004), customer acquisition rates in some businesses are accurate proxies of marketing performance (e.g., subscriber-based magazines). In the specific context of life insurance products, actuaries generate life insurance quotes based on risk, having already controlled for the margin potential. Researchers might integrate the prospect response with the economic value and risk evaluations. For other industries (e.g., retailing), it would be appealing to gain insight into which types of variables indicate a prospect’s likelihood of response and to identify customers with higher margin potential.
GRAPH: FIGURE 1 Last Mail Order Recorded: Comparison of Nonparametric and Linear Functional Form
GRAPH: FIGURE 2 Categorical Effects of Household Composition
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Record: 92- How to SHIFT Consumer Behaviors to be More Sustainable: A Literature Review and Guiding Framework. By: White, Katherine; Habib, Rishad; Hardisty, David J. Journal of Marketing. May2019, Vol. 83 Issue 3, p22-49. 28p. DOI: 10.1177/0022242919825649.
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How to SHIFT Consumer Behaviors to be More Sustainable: A Literature Review and Guiding Framework
Highlighting the important role of marketing in encouraging sustainable consumption, the current research presents a review of the academic literature from marketing and behavioral science that examines the most effective ways to shift consumer behaviors to be more sustainable. In the process of the review, the authors develop a comprehensive framework for conceptualizing and encouraging sustainable consumer behavior change. The framework is represented by the acronym SHIFT, and it proposes that consumers are more inclined to engage in pro-environmental behaviors when the message or context leverages the following psychological factors: Social influence, Habit formation, Individual self, Feelings and cognition, and Tangibility. The authors also identify five broad challenges to encouraging sustainable behaviors and use these to develop novel theoretical propositions and directions for future research. Finally, the authors outline how practitioners aiming to encourage sustainable consumer behaviors can use this framework.
Keywords: corporate social responsibility; ecological behavior; environmentally friendly behavior; sustainable consumer behavior
"We are jeopardizing our future by not reining in our intense but geographically and demographically uneven material consumption...By failing to adequately limit population growth, reassess the role of an economy rooted in growth, reduce greenhouse gases, incentivize renewable energy, protect habitat, restore ecosystems, curb pollution, halt defaunation, and constrain invasive alien species, humanity is not taking the urgent steps needed to safeguard our imperilled biosphere.....
—World Scientists' Warning to Humanity: A Second Notice ([266])
I always make the business case for sustainability. It's so compelling. Our costs are down, not up. Our products are the best they have ever been. Our people are motivated by a shared higher purpose—esprit de corps to die for. And the goodwill in the marketplace—it's just been astonishing.
—Ray [90], Founder and CEO of Interface Carpet
Our behaviors as individual consumers are having unprecedented impacts on our natural environment ([305]). Partly as a result of our consumption patterns, society and business are confronted with a confluence of factors—including environmental degradation, pollution, and climate change; increasing social inequity and poverty; and the growing need for renewable sources of energy—that point to a new way of doing business ([213]). In response, many companies are recognizing the need for a sustainable way of doing business, and across industries we see firms such as Interface Carpet, Unilever, Nike, and Starbucks embedding sustainability into the DNA of their brands ([138]). The current research provides a review of the literature regarding sustainable consumer behavior change and outlines a comprehensive psychological framework to guide researchers and practitioners in fostering sustainable behavior.
There are many reasons why understanding facilitators of sustainable consumer behavior should be of interest to marketers. One reason is reflected in the [266] quote: marketers should be cognizant that the consumption mindset that conventional marketing encourages is a key driver of negative environmental impacts ([73]; [248]). Second, as the Ray Anderson quote suggests, businesses able to adapt to the demands of our changing world, including the urgent demand for sustainability, will be more likely to thrive in the long term and enjoy strategic benefits ([30]). A sustainable business focus has advantages such as identifying new products and markets, leveraging emerging technologies, spurring innovation, driving organizational efficiency, and motivating and retaining employees ([147]). Moreover, research suggests that socially and environmentally responsible practices have the potential to garner more positive consumer perceptions of the firm, as well as increases in profitability ([50]; [198]; [231]; [285]).
Firms that are able not only to operate more sustainably but also to consider new models of business that offer and encourage sustainable consumption can potentially earn greater long-term profits ([174]). In one example, the growth of the "sharing economy" demonstrates the substantial environmental and economic gains possible through shifting consumers sustainably—in this case, from owning products to accessing existing products and services. Although the question of how marketing relates to sustainable consumption has historically received attention in the form of identifying the "green consumer" segment ([ 8]; [170]), scholars now call for work on the predictors of sustainable consumption ([173]; [213]; [214]). Rather than merely targeting the green consumer segment, marketers can expand their market for the long-term mutual benefit of the firm and the planet. Thus, as firms operate and offer products and services in a more sustainable manner, they might simultaneously wish for consumers to recognize, embrace, and reward their sustainable values and actions in ways that spur sustainable consumption and maximize the firm's sustainability and strategic business benefits.
The current work is motivated by the need for a comprehensive review and framework related to the key drivers of sustainable consumer behavior change. We build on existing work that has aptly outlined the steps marketers can take to identify, foster, and evaluate sustainable behavior ([211]; [248]). Although this existing work details the social marketing concept and spotlights examples, it does not provide a comprehensive psychological framework for influencing consumer behavior change. Extant work often concentrates on a more focused set of factors that motivate sustainable behavior ([116]; [247]; [303]).[ 6] The first intended contribution of the present work, then, is to outline a comprehensive framework to help both practitioners and researchers encourage sustainable consumer behavior. On the practitioner side, access to a broader framework (including all the major factors from the literature) will allow practitioners to develop the most effective interventions. Second, the unique, process-driven focus of our framework (as opposed to the intervention focus of previous work) ensures that as technologies and societies change, practitioners can easily apply our framework to new situations. Thus, a key contribution is that we offer a comprehensive set of tools firms can use as they pursue their sustainability and strategic business goals. Third, undertaking a more complete review allowed us to delineate a broader set of challenges to sustainable consumer behavior change that can inform both practitioners and researchers. We discuss these challenges—the self–other trade-off, the long time horizon, the requirement of collective action, the problem of abstractness, and the need to replace automatic processes with controlled processes—in the theoretical contribution section. Finally, we use these challenges to sustainable consumer behavior change to introduce a set of novel theoretical propositions to guide further conceptual development and future research.
At first glance, it might appear that the goals and assumptions of marketing are incompatible with the goals and assumptions of sustainability. Traditional marketing encourages growth, promotes an endless quest for satisfying needs and wants, and seems to view resources as ever abundant ([73]; [314]). In contrast, a sustainability focus suggests that utilized resources can be renewed by mimicking the circular flows of resources in nature, and it respects the fact that capacity of both resources and the environment are limited ([209]; [217]). We argue that, because of this apparent contradiction, marketing and sustainability are inextricably intertwined. Furthermore, we take the optimistic view that marketing and behavioral science have much to say about how we might influence consumption to be more sustainable. We review the literature and highlight ways in which consumers can be encouraged to behave more sustainably. Our review of the literature has led to the emergence of the acronym SHIFT, which reflects the importance of considering how Social influence, Habit formation, Individual self, Feelings and cognition, and Tangibility can be harnessed to encourage more sustainable consumer behaviors.
The SHIFT framework can help address the "attitude–behavior gap" that is commonly observed in sustainability contexts. Although consumers report favorable attitudes toward pro-environmental behaviors ([323]), they often do not subsequently display sustainable actions ([18]; [111]; [172]; [360]). This discrepancy between what consumers say and do is arguably the biggest challenge for marketers, companies, public policy makers, and nonprofit organizations aiming to promote sustainable consumption ([155]; [257]).
Thus, although consumer demand for sustainable options is certainly on the rise ([113])—for example, 66% of consumers (73% of millennials) worldwide report being willing to pay extra for sustainable offerings ([225])—there is room to further encourage and support sustainable consumer behaviors. We define sustainable consumer behavior as actions that result in decreases in adverse environmental impacts as well as decreased utilization of natural resources across the lifecycle of the product, behavior, or service. Although we focus on environmental sustainability, we note that, consistent with a holistic approach to sustainability ([228]), improving environmental sustainability can result in both social and economic advances ([63]; [270]). We examine the process of consumption including information search, decision making, product or behavior adoption, product usage, and disposal in ways that allow for more sustainable outcomes. Thus, sustainable consumer behaviors could include voluntarily reducing or simplifying one's consumption in the first place ([184]; [208]); choosing products with sustainable sourcing, production, and features ([193]; [253]); conserving energy, water, and products during use ([189]; [354]); and utilizing more sustainable modes of product disposal ([353]).
Unlike typical consumer decision making, which classically focuses on maximizing immediate benefits for the self, sustainable choices involve longer-term benefits to other people and the natural world. Although broader marketing strategies can be useful in this domain, marketers also need a unique set of tools to promote sustainability. We endeavor to outline the key drivers of sustainable consumption with one comprehensive framework. Our review of existing literature on sustainable consumption began with an initial selection of top marketing journals: Journal of Marketing, Journal of Marketing Research, Journal of Consumer Psychology, and Journal of Consumer Research. These behavioral marketing and consumer behavior journals are the most highly regarded in the field, having high impact factors (above 3.0), and they are all featured on the Financial Times Top 50 list. Using this set of journals, we conducted a literature search using specific keywords on Web of Science. The keywords included: sustainab* or ecolog* or green or environment* or eco-friendly and consum* or behavi* or choice or usage or adopt* or disposal.
This set of papers was then read and grouped into themes, which formed the five factors in the SHIFT framework. We used these five categories because they emerged in our initial review as being the most frequently occurring concepts, and because they allowed us to summarize the literature on sustainable behavior change in an inclusive manner. To extend our review, we then searched the literature more broadly by using our first set of search terms and replacing the third search word with more specific labels that were relevant to our five themes. We refined our search to include behavioral sciences, business, psychology multidisciplinary, economics, and management journals. For example, for the first section on social influence, we searched "social influence" and "norms." Our results allowed us to identify additional articles in peer-reviewed academic journals in marketing, psychology, and economics. We then read and reviewed these articles in terms of quality and relevance, which were determined through consensus among the authors before inclusion in our analysis. Our review identifies a set of 320 articles, some of which are used to frame the introduction (n = 40) and the rest represent the SHIFT factors (n = 280). Next, we discuss the five identified routes to sustainable consumer behavior change (refer to Web Appendix G for a summary of articles representing our SHIFT factors).
The first route to influencing sustainable consumer behaviors is social influence. Consumers are often impacted by the presence, behaviors, and expectations of others. Social factors are one of the most influential factors in terms of effecting sustainable consumer behavior change ([ 1]). We examine how three different facets of social influence—social norms, social identities, and social desirability—can shift consumers to be more sustainable.
Social norms, or beliefs about what is socially appropriate and approved of in a given context, can have a powerful influence on sustainable consumer behaviors ([68]; [247]). Social norms predict behaviors such as avoiding littering ([69]), composting and recycling ([238]; [353]), conserving energy ([89]; [120]; [152]; [277]), choosing sustainably sourced food ([86]), selecting eco-friendly transportation ([141]), choosing green hotels ([317]), and opting for solar panels ([44]). The Theory of Planned Behavior suggests that, along with subjective norms, attitudes and perceived behavioral control shape intentions, which predict behavior. This framework has been applied to sustainable behaviors ([136]; [143]).
Cialdini and his colleagues use the term "descriptive norm" to refer to information about what other people are doing or commonly do ([69]; [263]). Descriptive norms can be stronger predictors of sustainable consumer behaviors than other factors such as self-interest, and people tend to underestimate how influential such norms can be ([227]). Descriptive norms are most effective when combined with reference to similar contexts ([104]). In one example, descriptive norms communicating that others were taking part in a hotel energy conservation program were more effective than a traditional environmental message, especially when the descriptive norms referred to the same hotel room as the guest's ([120]). Although descriptive norms are often very influential, if the majority of people are not engaging in the desired sustainable behavior, highlighting a descriptive norm might unintentionally lead to decreases in the desired action ([66]; [277]). One field study sheds light on an exception to this: when community organizers themselves installed (vs. did not install) solar panels on their homes (a behavior that reflects low norms), they were able to recruit 62.8% more residents to do the same ([175]).
In contrast, "injunctive norms" convey what behaviors other people approve and disprove of. Such norms can thereby influence sustainable behaviors ([152]; [263]; [277]), but they should be used carefully ([177]). Injunctive norms are most effective when combined with thoughts about the ingroup and when they do not threaten feelings of autonomy, which can lead to "reactance" responses ([353]). Thus, both descriptive and injunctive norms can affect sustainable behaviors, but they should be used with care.
The impact of social influence depends on people's "social identities" or sense of identity stemming from group memberships ([315]). For example, consumers are more likely to engage in sustainable actions if ingroup members are doing so ([120]; [136]; [347]). Moreover, viewing the self as a member of a pro-environmental ingroup is a key determinant of pro-environmental choices and actions ([102]; [133]; [329]). Seeing the self as similar to a "typical recycler" predicts recycling intentions, over and above other factors such as attitudes, subjective norms, and perceived behavioral control ([204]).
One additional implication of social identities is that individuals desire to view their ingroups positively ([258]) and do not wish to see their ingroup outperformed by other groups ([100]). This is particularly true of outgroups that the consumer does not wish to be associated with, known as "dissociative groups." In one example, researchers examined intentions to undertake sustainable actions such as water conservation, composting organics, and recycling ([354]). When people learned that a dissociative reference group had performed better on a positive, sustainable behavior (thus casting the ingroup in a negative light), the focal group members increased their own positive behaviors. These effects were augmented in public settings, because this is a condition under which the collective self is most relevant. One practical implication of this work is that friendly challenges could be encouraged between competing groups ([339]), such as cities, neighborhoods, organizations, or business units.
Another finding stemming from the social identity literature is that social identity effects are heightened for those high in "ingroup identification." Identifying with being "an organic consumer" or "a green consumer," for example, predicts organic purchases ([32]; [33]). Moreover, majority group members, as well as minority group members who are high in ingroup attachment, receive messages encouraging sustainable consumption more positively ([124]). Highlighting a shared, superordinate ingroup identity can increase acceptance of information related to sustainable actions, especially for those who are high in ingroup identification ([279]).
Another means by which social influence can impact sustainable behaviors is through "social desirability." Consumers tend to select sustainable options to make a positive impression on others ([123]), and they endorse high-involvement sustainable options (e.g., hybrid vehicles) to convey social status to others ([126]). However, observers sometimes view sustainable behaviors negatively, leading some consumers to avoid pro-environmental actions ([49]; [216]; [232]; [269]; [287]). In one instance, males avoided appearing "eco-friendly" because it was associated with feminine traits ([49]). One implication, then, is to make sustainable products or behaviors socially desirable and to buffer against potential negative perceptions linked to sustainable consumption.
Moreover, consumers are more likely to act in a socially desirable manner in public contexts in which other people can observe and evaluate their actions ([123]; [128]; [249]). In addition, encouraging public commitments to engage in sustainable consumer behavior can increase such actions ([52]; [121]). For example, those who committed to participate in a hotel energy conservation program and wore a pin as a public symbol of this commitment were the most likely to engage in the program ([20]).
Whereas some sustainable behaviors (e.g., installing an efficient showerhead) require only a one-time action, many other sustainable behaviors (e.g., taking shorter showers) involve repeated actions that require new habit formation. Habits refer to behaviors that persist because they have become relatively automatic over time as a result of regularly encountered contextual cues ([178]). Because many common habits are unsustainable, habit change is a critical component of sustainable behavior change ([335]). Many behaviors with sustainability implications—such as food consumption, choice of transportation, energy and resource use, shopping, and disposal of products—are strongly habitual ([84]; [337]). Interventions that break repetition, such as discontinuity and penalties, can disrupt bad habits. Actions that encourage repetition, such as making sustainable actions easy and utilizing prompts, incentives, and feedback, can strengthen positive habits.
The habit discontinuity hypothesis suggests that if the context in which habits arise changes in some way, it becomes difficult to carry out the usual habits that would occur. In other words, a disruption in the stable context in which automatic behaviors arise can create ideal conditions for habit change. Life changes (e.g., a recent move) make people more likely to alter their eco-friendly behaviors ([24]; [338]; [341]). Thus, combining context changes with habit formation techniques can be one way to encourage sustainable behaviors.
Penalties are essentially types of punishment that decrease the tendency to engage in an undesirable behavior. A penalty might take the form of a tax, a fine, or a tariff on an unsustainable behavior. Fines can encourage behavior change in domains that can be monitored, such as the disposal of waste ([107]), whereas taxes and tariffs can be effective in domains that involve strong habits (e.g., driving gasoline-powered vehicles; [176]). Although penalties can certainly deter unsustainable behaviors in some instances, they can trigger backfire effects if the penalty seems unreasonable ([107]) and can lead to negative affect and defensive responses ([42]; [112]; [303]). Moreover, penalties can be difficult to enforce and monitor ([42]). Thus, it is often desirable to turn to positive behavior change strategies instead, which we discuss next.
One means of transitioning people from an old habit to a new one is to have them consider implementation intentions, or thoughts about what steps they will take to engage in the action ([178]). Such intentions can positively influence recycling ([146]) and sustainable food-purchasing habits ([99]). Then the new behavior can be encouraged through repetition and by positive habit formation techniques such as making it easy, prompts, feedback, and incentives.
Many sustainable actions are viewed as effortful, time-consuming, or difficult to carry out, which can be a barrier to sustainable actions ([210]). Thus, one strategy to encourage sustainable habit formation is to make the action easier to do (Van [331]). Contextual changes that improve the ease of engaging in sustainable behaviors, such as placing recycling bins nearby, requiring less complex sorting of recyclables, and offering showerheads with "low-flow" settings, encourage such behaviors ([48]; [108]; [197]). One means of making sustainable actions easier is to make them the default ([106]; [318]). In one example, when sustainable electricity was set as the default option, individuals were more likely to stick with it ([252]). Because consumers are often low on cognitive resources, simplifying the decision-making process can allow them to more automatically form sustainable habits ([303])
Another means of encouraging sustainable habit formation is the use of prompts: messages that are given before the behavior occurs to remind the consumer what the desired sustainable behavior is ([182]). Prompts can positively affect many sustainable behaviors including waste disposal, energy usage, and recycling ([236]). Prompts to engage in sustainable behaviors work best when they are large, clear, easy to follow, and placed in proximity to where the behavior will be performed ([19]; [348]). Because prompts are easy to employ and cost-effective, they can be a good initial behavior change strategy ([278]), but they are best utilized in combination with other strategies ([76]).
Rewards, discounts, gifts, and other extrinsic incentives can increase desired behaviors and positive habit formation. Monetary incentives such as rebates, tiered pricing, and cash can encourage people to adopt and maintain sustainable behaviors ([80]; [291]; [357]). Incentives have been shown to influence sustainable behaviors such as waste disposal and cleanup ([23]), energy usage ([ 2]), and transportation choices ([97]). Although incentives can encourage the adoption and maintenance of sustainable behaviors, they do have potential drawbacks ([43]). Smaller monetary rewards are often less motivating than other types of incentives such as a free gift, a lottery entry, or social praise ([137]; [150]). Second, incentives to engage in sustainable behaviors can lead to actions that are short-lived ([168]). Consumers initially respond positively to rewards, but the sustainable behavior often disappears once the incentive is removed ([53]). Thus, one-time sustainable actions are easier to encourage with incentives than are longer-term changes ([112]). Furthermore, incentives can have the unintended consequence of decreasing the desired behavior because the intrinsic motive to engage in the action is reduced ([46]).
Another means of encouraging sustainable habit formation is to use feedback. This involves providing consumers with specific information about their own performance on a task or behavior. Feedback can be given for actions like water and energy usage, and it can be provided with reference to the consumer's own past behaviors or in comparison to the performance of other individuals ([ 3]; [103]; [320]). Research suggests that feedback is more effective when it is presented over an extended period of time, in real-time, and in a clear manner ([65]; [103]; [166]). Sharing group feedback with households and in work settings can also be an effective behavior change strategy ([75]; [275]; [277]; [290]).
Factors linked to the individual self can have a powerful influence on consumption behaviors. The concepts discussed in this section include positivity of the self-concept, self-interest, self-consistency, self-efficacy, and individual differences.
Individuals desire to maintain positive self-views and can reaffirm the positivity of the self-concept through consumption ([88]). As a result of the desire to view the self positively, people often exhibit self-defensive reactions to learning that their own behaviors have negative environmental impacts ([82]; [101]) and derogate others displaying more sustainable actions ([216]; [361]). Moreover, people display motivated biases including the tendency to seek out and reinforce information that confirms preexisting views ([346]). Furthermore, people avoid some forms of sustainable behavior change (e.g., travel behaviors) because changing can threaten the self ([221]). In one example, threats to Republican self-identity led to backfire effects such that Republicans decreased support for climate change mitigation policies in response to climate change communications ([142]) or were less likely to choose an eco-friendly option ([129]). Thus, positively associating sustainable behaviors with the self-concept and buffering against self-threatening information can be critical for sustainable behavior change. For example, self-affirmation, or the endorsement of important self-values, mitigates self-protective responses and leads to greater endorsement of sustainable actions ([49]; [256]; [297]).
The self-concept also relates to sustainable behaviors in that the possessions people own can become extensions of their identity ([36]). One way this sense of extended self manifests is that people can be unwilling to part with possessions that are linked to the self because of a sense of identity loss ([358]). Winterich and her colleagues showed that this identity loss was mitigated by having the consumer take a picture of a sentimental product before considering donating, which led to increased possession donation. Giving possessions to others not only has positive sustainability implications but it can also lead to greater well-being for the giver ([85]). Finally, consumers take better care of and are less likely to trash (vs. recycle) identity-linked products ([322]).
In addition to wanting to see the self in a positive light, people want to see the self as being consistent. Self-consistency research shows that a consumer reaffirming a component of the self-concept (e.g., being environmentally concerned) or engaging in a sustainable behavior at one time point often leads to consistent sustainable behaviors in the future ([330]). Similarly, initial personal commitments to act sustainably can increase the likelihood of subsequently behaving in a sustainable manner ([41]; [168]), especially when they are made in writing ([190]). Along with individual consistency, a firm adhering to green values can lead to increased consumer conservation behaviors ([343]). Furthermore, evidence suggests that people who engage in a sustainable action in one domain are often more likely to perform sustainably in other domains as well (i.e., positive spillover; [158]; [179]; [190]; [230]; [324]). Self-assessments of the consumer's behavior can also affect consistency. For example, those who felt that the end sustainability goal was unimportant were less motivated to pursue the end goal when they were unable to achieve subgoals (e.g., failing to recycle a newspaper; [77]). Moreover, cuing people that a given behavior has positive sustainability outcomes leads them to see themselves as being more environmentally concerned and to be more likely to choose eco-friendly products ([71]). Finally, simply reminding consumers of a time when their behavior was inconsistent with a personally held value related to sustainability can subsequently lead the consumer to behave in a manner consistent with those sustainable values ([81]; [249]).
Although there are many examples of self-consistency effects, inconsistency effects can also arise. Licensing effects may occur wherein individuals who have engaged in a sustainable action at one time point will later be less likely to engage in another sustainable or positive behavior ([251]; [268]; [321]). For example, researchers found that people who took part in a "green" (vs. conventional) virtual shopping task that asked them to select from sustainable products were subsequently more likely to behave in an antisocial manner ([207]). The availability of pro-environmental technologies and resources also can lead to negative spillover effects ([292]; [296]). For example, [58] found that consumers used more resources when they knew that a recycling option was available.
Moreover, both inconsistency and consistency can emerge in the same context. People who brought a reusable shopping bag to the market subsequently spent more money on both sustainable and indulgent food options ([167]). Furthermore, making a sustainable choice decreases subsequent sustainable behaviors for those low in environmental consciousness but increases these behaviors for those highly conscious of environmental issues ([110]). Consistency rather than inconsistency effects may be more likely to occur when connected to transcendent rather than self-interested values ([96]).
Economic and evolutionary theories both suggest that appeals to self-interest can be leveraged to influence pro-environmental behaviors ([125]; [240]). One strategy is to highlight the self-benefits associated with a given sustainable product, service, or behavior ([123]; [227]). Research shows that sustainable attributes have a greater influence on consumers if self-relevant motives are fulfilled (vs. not fulfilled; [273]). Another means of appealing to consumer self-interest is to highlight self-benefits that can counteract the barriers to sustainable action ([119]; [179]). Such barriers include the belief that sustainable attributes can have negative implications for aesthetics ([194]), functional performance ([196]; [224]; [324]), effort ([155]), or affordability ([60]; [119]; [149]). Messages that appeal to self-interest are most effective in private ([123]) and when the individual self is primed in some way ([353]). Research suggests that a focus on self-interest is not always effective alone ([210]). Moreover, self-interests can crowd out pro-environmental motivations ([280]), especially when appeals include self-focused and environmentally focused reasons for acting sustainably ([91]).
According to [27], self-efficacy involves beliefs that the individual can engage in the required action and that carrying out the behavior will have the intended impact. Consumers' feelings of self-efficacy predict their sustainable attitudes as well as their tendencies to continue to enact sustainable behaviors over time ([13]; [70]; [93]; [171]; [351]). According to [245], [246]), consumers are most likely to choose sustainable options when consumer compromise is low and when there is high confidence that a particular behavior will make a difference (i.e., self-efficacy is high).
An important individual difference is "personal norms" or beliefs regarding a sense of personal obligation that are linked to one's self-standards ([25]; [153]; [282]; [307]). Individual differences in personal norms around sustainability predict sustainable behaviors, including recycling ([132]), selecting sustainable food ([356]), and being willing to pay more for sustainable options ([131]; [308]). Other research has focused on differences in environmental concern ([ 6]; [244]; [283]). Marketers can find success targeting those with strong personal norms and values around sustainability or by strengthening existing personal norms through priming ([249]; [301]; [302]; [336]). In addition, individual differences in mindfulness ([21]; [31]; [241]; [288]) as well as perceptions of feeling connected to nature ([226]) have been shown to predict environmental concern and sustainable behaviors. Furthermore, traits such as extraversion, agreeableness, conscientiousness, and environmental concern predict green buying behaviors ([105]; [199]).
Finally, demographics have been shown to relate to sustainable consumption behaviors ([79]; [117]; [220]). Gender differences in which women exhibit more sustainable consumer behaviors are sometimes noted. This may occur partly because women tend to be higher in traits such as agreeableness, interdependence, and openness to experience ([83]; [90]; [195]). Other work finds that those who are younger, more liberal, and highly educated are likely to engage in pro-environmental behaviors ([118]; [122]; [267]; [284]). It makes sense to target responsive segments with sustainability appeals ([ 8]; [171]; [180]), and interventions should be tailored to reflect the specific needs and motivations, barriers, and benefits of the target consumer ([ 3]; [22]; [74]).
We introduce the concepts of feelings and cognition together because, generally speaking, consumers take one of two different routes to action: one that is driven by affect or one that is more driven by cognition ([289]). This proposition is consistent with theories suggesting that either an intuitive, affective route or a more deliberative, cognitive route can dominate in decision making ([94]; [160], [161]). We note that this distinction is likely to be highly relevant in the domain of reacting to information about ecological issues ([206]). We first outline how negative and positive emotions can impact pro-environmental behaviors. Then we discuss the role of cognition in determining sustainable actions by considering information and learning, eco-labeling, and framing.
Consumers often consider the negative emotional consequences of either engaging or not engaging in sustainable behaviors (Rees, Klug, and Bamberg 2015). Generally speaking, it is important to avoid creating negative emotional states that are too intense ([172]). Instead, more subtle activation of negative emotions can be effective ([212]; [249]). We next address the impact of three specific negative emotions: fear, guilt, and sadness.
Communications regarding sustainable behavior often use "fear appeals" that highlight the negative consequences of a given action or inaction ([29]). On the one hand, communications that leave the individual feeling as though the consequences are uncertain and temporally distant can make the situation seem less dangerous and can lead to inaction ([192]). On the other hand, using strong fear appeals can lead to a sense of being unable to overcome the threat and can result in denial ([233]). Because of this, it is best to use moderate fear appeals and to combine these with information about efficacy and what actions to take ([187]; [237]).
Guilt can influence sustainable intentions and behaviors ([57]; [154]; [195]; [201]; [218]; [234]). This is largely due to the consumer assuming individual responsibility for the unsustainable outcomes ([185]), leading people to feel morally responsible for the environment ([163]). Research shows that "anticipated guilt" can also influence people to act in a pro-environmental manner ([127]; [162]; [200]; [299]). Anticipated guilt is more effective at encouraging sustainable behavior when consumers are subtly asked to consider their own self-standards of behavior rather than when they are exposed to explicit guilt appeals, which can backfire ([249]). "Collective guilt" can also be a motivator of pro-environmental action ([100]). Information conveying that one's country has a significant carbon footprint leads to a sense of collective guilt, and such feelings predict willingness to support sustainable causes and actions ([100]; [201]).
In addition to fear and guilt, researchers have examined sadness as a driver of sustainable attitudes and behaviors ([286]). Sadness was shown to lead to more pro-environmental behaviors such as using an energy footprint calculator and allocating higher donation amounts to a sustainable cause ([281]). However, once the emotion dissipated, differences in sustainable actions were eliminated between those who had received the sadness message versus a non-affective message. Thus, emotions such as sadness are more influential while consumers are experiencing them.
Consumers are more inclined to engage in pro-environmental actions when they derive some hedonic pleasure or positive affect from the behavior ([72]). Sustainable behaviors can both decrease negative and increase positive emotions ([234]; [264]; [312]). On the one hand, engaging in sustainable actions has been shown to result in "warm glow" feelings that can spill over and lead to more favorable evaluations of the overall service experience ([114]). Positive emotions such as joy and pride have been shown to influence consumer intentions to decrease plastic water bottle usage, and optimism can motivate the maintenance of sustainable behaviors over time ([250]). On the other hand, research suggests that positive emotions can work to negatively impact sustainable consumer behaviors. For example, unsustainable actions such as driving gas-powered automobiles are linked to positive affective benefits ([300]).
Meanwhile, feelings of "affinity towards nature" predict sustainable attitudes and intentions ([165]). Studies demonstrated positive sustainable actions in response to "cute" appeals (e.g., communications featuring cute animals), particularly when the consumer exhibits "approach" motivational tendencies ([342]). This is driven by increased feelings of tenderness in response to such appeals.
The role of specific positive emotions such as pride in determining sustainable consumer behaviors is also relevant ([40]). Pride is a self-conscious and moral emotion stemming from a sense of responsibility for a positive outcome ([185]). Those who feel a sense of pride have been shown to be more likely to subsequently engage in sustainable behaviors, in part because pride enhances feelings of effectiveness ([ 9]). Finally, positive environmental actions can lead to feelings of hope, which can increase climate activism and sustainable behaviors ([98]; [293]). Feelings of hope can be augmented by framing climate change as a health issue as opposed to an environmental issue ([222]).
One basic means of persuading consumers to engage in eco-friendly actions is to present information that conveys information regarding desired (and undesired) behaviors and their consequences ([210]). Some have lamented that people's dearth of understanding and knowledge—due to lack of exposure to information ([115]), information overload ([148]; [223]), and confusion ([62])—can contribute to low uptake of sustainable behaviors. Moreover, intelligence ([16]), education ([117]), and knowledge ([186]) are linked to greater responsiveness to environmental appeals and engagement in eco-friendly behaviors. In many ways, knowledge is relevant across all our SHIFT factors. The consumer must have knowledge of the social norm, must be aware of and understand the prompt or feedback, and must comprehend information related to self-values, self-benefits, self-efficacy, etc.
Providing information through appeals that highlight why the desired behavior or product is sustainable can be effective in giving consumers the initial knowledge they need regarding actions and consequences ([248]; [313]). Indeed, one is unlikely to engage in more deliberate forms of sustainable behavior change if one is not informed about the problem, potential positive actions, and possible consequences ([117]). Meta-analytic reviews suggest that information has a significant albeit modest influence on pro-environmental actions ([76]; [236]). However, research also reveals that interventions providing information only are often not enough to spur long-term sustainable changes ([ 2]; [236]). Because of this, combining information with other tactics can be more effective ([159]; [211]; [248]; [304]). Some work even suggests that detailed knowledge can backfire. Those with the highest levels of science literacy displayed more ideology-reinforcing bias than their counterparts, which was attributed to their science knowledge making them better able to support their own pre-existing viewpoints ([159]).
Eco-labeling is one means of conveying information about the sustainable attributes of a product ([242]). Labels that are attention-grabbing, easily understandable, and consistent across categories can enable consumers to make better informed eco-friendly decisions ([45]; [316]; [319]). It has been suggested that eco-labels would be more effective if they were contrasted against negative labels that highlight products with environmentally harmful attributes ([45]). Eco-labeling can seem more transparent and unbiased if it is certified by a third party that validates the sustainability claims ([203]). However, it is important to note that some work suggests eco-labels do not play a strong role in predicting consumer food selections ([130]).
Marketers can strategically choose message framing to encourage sustainable choices ([325]). Because consumers care more about future losses than about future gains ([140]), labels on energy-efficient appliances should compare energy costs rather than savings ([51]; [215]). Furthermore, marketers can aggregate information to make a bigger impact, using lifetime (vs. annual) energy costs for appliances ([164]) and cost per 100,000 miles labeling to promote sales of efficient cars ([54]). Loss-framed information is especially effective when combined with concrete information on how to engage in the behavior. For example, loss-framed messages were most effective in improving the quantity and accuracy of residential recycling behaviors when they were combined with detailed information about how to recycle (vs. more general reasons regarding why we should recycle) ([351]). Also, framing can have differential effects on different segments of consumers. In the United States, framing a carbon price as a carbon offset (vs. a tax) has a strong effect on Republicans but has little impact on Democrats and a moderate impact on Independents ([139]). In another example, framing an appeal in terms of "binding moral values" (e.g., duty, authority, consistency with ingroup norms) leads to more positive recycling intentions and behaviors among Republicans, whereas appealing to "individualizing moral values" (e.g., fairness, empathy, individuality) leads to more positive reactions among Democrats ([169]). Notably, such matching effects in message framing are often driven by perceptions of fluency or the ease of processing and comprehending the meaning of stimuli ([169]; [351]).
One unique facet of sustainable consumption is that eco-friendly actions and outcomes can seem abstract, vague, and distant from the self ([259]). Most sustainable consumer behaviors involve putting aside more immediate and proximal individual interests to prioritize behaviors with ill-defined consequences that are focused on others and are only realized in the future ([ 7]; [298]). Moreover, consumers are not likely to act on issues that are impalpable in nature ([125]). Pro-environmental outcomes are difficult to track and measure because changes emerge slowly over time and uncertainty surrounds problems and their solutions ([56]; [115]; [345]). Uncertainty can also emerge due to firm actions such as greenwashing ([62]). Next, we outline some solutions to the tangibility problem.
Whereas sustainability is naturally future-focused, consumers are often present-focused. Moreover, when consumers judge a future environmental payoff to be distant, it becomes less desirable in the present ([140]; [339]). One solution to this mismatch is to encourage the consumer to think more abstractly and/or to focus on future benefits of the sustainable action ([259]). Those who have a greater focus on the future engage in more pro-environmental behaviors ([14]; [157]). Asking individuals to focus on future generations can reduce present-focused biases ([340]), and prompting the consideration of legacy increases sustainable choices ([362]).
Communications that relate the more immediate consequences of pro-environmental behaviors for a given city, region, or neighborhood can make environmental actions and outcomes seem more tangible and relevant ([183]; [271]). Drawing on people's attachments to a specific place ([78]; [116]), emphasizing personal experiences with climate change impacts ([345]), and using current issues such as extreme weather events can lead to more sustainability-oriented beliefs and actions ([188]).
Another way to tackle intangibility is to make sustainability issues more relevant and concrete for the self ([ 4]; [14]; [188]; [259]; [298]). This can be done by communicating the immediate impacts of environmental problems such as climate change ([243]) and outlining clear steps to make a difference ([351]). Communications can make the consequences of inaction (or action) clear by using techniques such as vivid imagery, analogies, and narratives ([206]).
A challenge for sustainable behaviors is that consumers often have a desire to own material goods. One means of moving toward more sustainable consumption is to promote dematerialization ([73]) in which consumers decrease emphasis on the possession of tangible goods. This could include consumption of experiences ([326]), digital products ([17]; [37]), or services ([191]). This is consistent with the notion that marketing is evolving to be more focused on the provision of services, intangible resources, and the cocreation of value ([332]). Trends such as the "sharing economy," with its ideal of collaborative consumption of idle resources ([85]), and "voluntary simplicity" in which consumers simplify their lifestyles rather than focus on possessions ([64]) indicate that consumers can fulfill their needs without the possession of tangible products being a focal goal.
In our literature review, we identified five routes to sustainable behavior change while delineating specific behavior change strategies within each route. The focus of the review portion of this article has been to identify what the main drivers of sustainable consumer behavior are according to existing research. In the next section, we will go further to highlight a set of theoretical propositions regarding when and why each of the routes to sustainable behavior change (i.e., the SHIFT factors) will be most relevant. We do so by outlining a set of key challenges that make sustainable consumption distinct from typical consumer behaviors: the self–other trade-off, the long time horizon, the requirement of collective action, the problem of abstractness, and the need to replace automatic with controlled processes. We examine each of these challenges to sustainable consumer behavior change through the lens of our SHIFT framework and outline key theoretical propositions and directions for future research.
Our first challenge to sustainable consumer behavior is that consumers often perceive such actions as having some cost to the self, such as increased effort, increased cost, inferior quality, or inferior aesthetics ([194]). At the same time, sustainable consumer behaviors lead to positive environmental and social impacts that are external to the self ([55]). Thus, although the traditional view of consumer behavior holds that consumers will choose and use products and services in ways that satisfy their own wants and needs ([295]), views of sustainable consumer behaviors often imply putting aside wants that are relevant to the self and prioritizing and valuing entities that are outside of the self (e.g., other people, the environment, future generations, etc.).
The self–other trade-off has implications for how social influence might operate in the context of encouraging sustainable consumer behaviors. Although sustainable consumption often comes at some cost to the self, we suggest that identity signaling can be a self-relevant positive repercussion that can outweigh the costs of sustainable action. This assertion is supported by work showing that consumers are more likely to select sustainable options when the setting is public or status motives are activated ([123]; [126]). A novel proposition building on this work is that product symbolism might have more impact on consumer attitudes and choices when a product is positioned on sustainable versus traditional attributes. By the term "symbolic," we refer to the notion that some products are better able to convey important information about the self to others ([38]; [349]). The marketer could highlight either symbolic benefits (i.e., convey relevant information about the self to others) or functional aspects (i.e., information about satisfying practical needs) linked to a product ([39]). Because there may be fewer direct self-benefits related to a sustainable action, linking a sustainable option with symbolic benefits could be a fruitful strategy.
- P1: When a given behavior or product is positioned on the basis of its symbolic attributes (vs. functional attributes), consumers may exhibit more positive attitudes and behaviors if the option is framed in terms of being sustainable versus a traditional product.
Another way of overcoming the self–other trade-off is to consider the individual self ([109]). In particular, how the individual views his or her own self-concept might predict sustainable consumer behaviors. Whereas some individuals tend to have a more independent view of the self (i.e., the self is separate and distinct from others), some have a more interdependent self-construal (i.e., the self is connected with others; [205]). One possibility is that those who think of the self in terms of an interdependent self-construal (both as a measured individual difference and as a primed mindset; [350]) might be more inclined to engage in sustainable behaviors ([15]), particularly when such actions assist ingroup members ([87]). Moreover, research could examine how to activate even broader, more transcendent construals of the self that encompass not only the self and close others but also other species and the biosphere. Encouraging such transcendent self-views might effectively increase eco-friendly actions.
- P2: Encouraging the self-concept to be seen as broader than the self (either interdependent or transcendent) will lead to increases in sustainable behaviors.
At the same time, a specific focus on the individual self might be linked to sustainable actions in a way that overcomes uncertainty and is motivating. Giving people a sense of agency (i.e., allowing individuals to perceive themselves as the causal agents of behavioral outcomes) offers them a perception of empowerment and the ability to actually effect change. This might be done through priming of agency to motivate individuals to achieve a given sustainable goal ([328]). Because outcomes of sustainable actions are often abstract and uncertain, agency priming might be a relevant motivational tool in the domain of sustainable behavior change. Thus:
- P3: Agency primes will lead to an increased tendency to engage in sustainable behaviors.
Research on the individual self in prosocial contexts also highlights the potential importance of moral identity in overcoming the self–other trade-off. Moral identity refers to a cognitive schema around moral traits, goals, and values ([12]). The strength of moral identity can vary as an individual difference (e.g., moral identity centrality), and it can be activated by situational priming ([10]). Moral identity predicts altruistic and ethical behaviors ([12]), and those higher in moral identity appear to have an expansive "circle of moral regard" that includes entities further from the self such as outgroup members ([260]). Because of this, individuals who are high in moral identity or who have moral identity primed in some way might be more likely to endure some costs to the self to contribute to a greater good. Although research has looked at moral identity in the domain of prosocial behaviors ([261]), to our knowledge no prior work has examined whether individuals view sustainable behaviors as moral obligations that are predicted by moral identity.
- P4: Both individual differences in moral identity and moral identity primes will increase sustainable consumer behaviors.
The self–other trade-off is also linked to how consumers perceive the costs and benefits of sustainable consumption. The literature lacks sufficient work examining the positive consumer associations with sustainability. Although there are a number of studies on the negative associations of sustainable consumption, there are very few that explicitly examine the positive associations. For example, sustainability might be linked to positive feelings about design when it is in the context of innovative, out-of-the-box thinking. Tesla, for example, capitalizes on such associations. Furthermore, it seems likely that sustainability has positive associations with health, local and fresh food, and the outdoors and nature. Sustainable options that connect to growing trends such as healthy and vibrant living, being a "foodie," and being an outdoor enthusiast might do well. Although some research shows that the concept of "organic" is linked to positive associations around health and even being lower in calories ([274]), more work could certainly examine implicit positive associations of sustainability in other domains as well.
- P5: Sustainable options and behaviors might have unique positive associations when compared to traditional options, including being healthier, more innovative, and being linked to the outdoors and nature.
The self–other trade-off highlights a heavier research emphasis on the role of "negative self-related" emotions such as guilt and fear. Future work might look further at the role of "positive feeling states that are related to entities outside of the self" in influencing sustainable consumption. For example, researchers have examined the impact of awe—a sense of wonder we feel in the presence of something vast that transcends the individual self—on prosocial behaviors more generally ([254]). However, to our knowledge no work looks at how awe impacts sustainable consumer behaviors. Extant work does show that empathy might be linked to prosocial behaviors ([334]). Although empathy is defined in different ways, it is often conceptualized as an affective state "that stems from the apprehension of another's emotional state or condition, and that is congruent with it" ([92], p. 91). Moreover, outwardly focused emotions such as moral elevation might also predict sustainable actions. Moral elevation refers to feelings of warmth and expansion that are linked to admiration and affection in response to seeing exemplary behavior on the part of another individual ([11]; [135]). Examining emotions like awe, empathy, and moral elevation are all directions for future research.
- P6: Outwardly focused positive emotions such as awe, empathy, and moral elevation will predict positive sustainable consumer behaviors.
Another possibility, linked to focusing on the self versus others, is to examine the role of aspirational social influence in sustainable consumer behavior change. Is it possible to make the sustainable option or behavior socially desirable to the self by connecting it to aspirational role models such as celebrities and athletes? Although research covers the motivational roles of both ingroup members ([120]) and dissociative outgroup others ([353]), there is a paucity of research on the impact of aspirational others on influencing sustainable consumer behaviors. One possibility is that aspirational branding could be harnessed to create positive, socially approved associations around the notion of sustainable lifestyles. Marketers could accomplish this by linking sustainable actions to aspirational others in a way that fosters a sense of desirability, luxury, and value linked to sustainable products and behaviors.
- P7: Connecting sustainable products and behaviors to aspirational role models in a way that cultivates a sense of inspiration and luxury might increase sustainable behaviors.
Our second challenge to sustainability involves the reality that sustainable behaviors require a long time horizon for outcomes to be realized. Invariably, asking individuals to engage in a pro-environmental behavior means that some of the consequences will be achieved only at a future point in time ([ 7]). As we have seen, consumers view payoffs to be less desirable the further off the payoffs are in the future ([140]). Relative to sustainable behaviors, most traditional consumer behaviors have consequences that are more immediate. Many payoffs linked to sustainability are so far off in the future that they will not even be observed in the consumer's own lifetime. We call this challenge the "long time horizon."
The notion of the long time horizon is related to the individual self in that it is linked to self-control. Indeed, self-regulation research demonstrates that people have a difficult time regulating the self to forgo benefits in the present for longer-term payoffs in the future ([35]; [219]). Sustainable behaviors present a unique self-regulation dilemma. Whereas most self-regulatory acts involve holding off on some positive reward now in order to receive a later payoff that reflects a self-relevant goal (e.g., not eating ice cream in the present so one can fit into a favorite dress on an upcoming vacation), sustainable behaviors involve putting off something positive now for a future positive outcome that is not only temporally distant but broader than the self (e.g., not purchasing a sporty car to reduce carbon emissions, the effects of which will only be realized in the future and will benefit the environment and other people). Although one would think that the self-control literature has much to say about sustainable behavior change, little work has explicitly looked at the role of self-regulation in determining sustainable actions. Existing work shows that those who have their regulatory resources depleted are more susceptible to temptations and impulse buying ([34]). Given that many sustainable behaviors require an effortful cost to the self in the short term for an uncertain future payoff, examining the dynamics of self-control in this domain could be productive. It is possible that sustainable behaviors require even more self-control than other self-control behaviors. For example, the same action (e.g., being vegan) could be positioned in terms of sustainability versus health goals, and it may be that self-regulation is more likely to fail for sustainability reasons given that such behaviors have fewer clear future implications for the self. Research might examine this and consider how to enhance self-regulation in the sustainability domain. One idea involves interventions to make the natural world part of the extended self, thereby transforming future environmental benefits into self-benefits, which could improve self-regulation.
- P8: Those whose regulatory resources are somehow limited will be more likely to lapse in terms of engaging in sustainable behaviors (vs. other types self-control behaviors).
The long time horizon associated with sustainable behavior is related to feelings in that people often have to undergo hedonic costs to the self in the present to maximize some positive sustainable outcome in the future. Needless to say, this is often difficult, as people are usually hesitant to give up their own affective benefits. However, acting in a manner that helps others has been shown to provide positive affect, which is sometimes termed the "warm glow" effect ([114]). Focusing on how sustainable behaviors can create positive affect in the present might increase sustainable behaviors. We propose that:
- P9: Sustainable behaviors that provide greater immediate (vs. long-term) warm glow feelings or positive affect will lead to decreased perceptions of the long time horizon and increase the likelihood of sustainable actions.
The long time horizon is linked to tangibility as well. Although people generally care less about future outcomes, the degree to which they care varies across individuals. People with higher "discount rates" care less about future outcomes ([140]). Likewise, people with lower consideration of future consequences ([309]) express weaker pro-environmental intentions ([156]). Therefore, tangibility interventions (such as communicating local and proximal impacts) may be especially effective for these individuals. In contrast, those with low discount rates and high consideration of future consequences are already attuned to future outcomes and may be less influenced by tangibility interventions. Thus:
- P10: Individuals with higher discount rates and low consideration of future consequences might be more sensitive to heightening the tangibility of environmental outcomes.
In addition, the long time horizon and self–other trade-off are both linked to how tangibility could play a role in determining sustainable consumer behaviors. Environmental impacts are not likely to be observed until the future, most likely by future generations. As such, interventions that increase the tangibility of the effects of acting (or not acting) sustainably on future generations might encourage more sustainable actions. One possibility involves perspective-taking interventions ([202]) that encourage the consumer to adopt the viewpoint of future generations. Thus, we propose that:
- P11: Individuals will be more motivated to engage in sustainable consumer behaviors when they either dispositionally or situationally take the perspective of future generations.
A final implication of the long time horizon is linked to all of the SHIFT factors. One striking facet of the current review is that most of the existing research involves surveys or experiments that take place at a single point in time ([151]). Future research could profitably examine the longitudinal effects of different interventions on sustainable behaviors. Moreover, a dichotomy that our framework highlights is the short-term versus long-term focus of the different behavior change strategies. Although some of the constructs are driven by the immediate context and lead to short-term behavior change, other constructs lead to more enduring behavior change over the long term. For example, although tools related to feelings and cognition and habit-formation tools that focus on in-the-moment behavior shaping can be effective in the current context, sustainable actions can disappear once they are removed. It may be optimal to ensure a balance of in-the-moment behavior-shaping tools (e.g., incentives, penalties, making it easy) with ways of making these behaviors last over time (e.g., relating the actions to the consumer's morals, values, self-concept, self-consistency). Future research could test this possibility.
- P12: Sustainable consumer behaviors may be best promoted over the long term by using a combination of in-the-moment tools and lasting-change tools.
Sustainable behaviors often require collective as opposed to individual action ([26]). A large group of people must undertake sustainable behaviors for the benefits to be fully realized. This differs from traditional consumer behaviors in which the outcome is realized if the individual engages in the action alone. This is also distinct from other behaviors with a long time horizon like health promotion behaviors (e.g., exercising and eating healthy) because these can be enacted at the individual level with observable results.
The "challenge of collective action" is relevant to how social influence might operate when considering sustainable (vs. conventional) actions. When people observe others engaging in an action, this may increase perceptions of collective efficacy or "a group's shared belief in its conjoint capabilities to organize and execute the courses of action required to produce given levels of attainments" ([28], p. 477). Although collective efficacy has received little attention in the sustainability domain, researchers have examined it in the contexts of organizational leadership ([61]) and political action ([333]). Drawing on this work, we suggest that collective efficacy can be a compelling motivator of sustainable consumer behavior. In fact, because sustainable outcomes require that actions be undertaken on a very large scale, it may be that collective action is more motivational in the domain of sustainability than other positive behavior domains. This is an open question for future research to examine. Thus:
- P13: Messages communicating both the behaviors of others (collective action) and collective efficacy will increase the tendency to engage in sustainable actions.
The consideration of feelings has potential implications for how to overcome the challenge of collective action. Although some research has looked at the role of collective emotions (i.e., feelings that group members widely share as group-level goals are pursued or thwarted; [311]), the types of emotions studied in this domain have been limited to past group actions resulting in guilt or pride ([ 9]; [40]). Meanwhile, sustainable actions might be better fostered using other types of collective emotions. For example, collective feelings of anger and hope have been shown to predict collective action ([359]). Thus, we propose:
- P14: Collective, future-oriented emotions such as anger and hope might foster sustainable consumer behaviors.
In a similar vein, cognitions about collective actions might also facilitate sustainable behaviors. Because sustainable behaviors have the unique property of requiring collective action, one possibility is that communicating collective-level outcomes such as climate justice could be influential in encouraging such behaviors. Although thoughts about perceived ability to restore justice have been shown to lead to actions such as selecting fair-trade products ([352]), it might be the case that conveying collective notions of justice (e.g., communicating information about collective impacts and consequences of unjust, unsustainable actions) would be impactful in the domain of encouraging sustainable consumer behaviors. In particular, communication about inequitable distributions of negative environmental threats and how these are felt by communities that are the most vulnerable might be a compelling message ([181]).
- P15: Communicating information about climate justice might motivate sustainable consumer behavior change.
Collective action is also linked to tangibility. Anecdotally, a popular technique for motivating green behavior is to advertise the collective impact. For example, "If everyone in the United States washed their clothes with cold water instead of hot, we would save around 30 million tons of CO2 per year" ([294]). Despite the popularity of this type of messaging to promote green behavior in an applied context, to the best of our knowledge it has not been tested in the academic literature. We predict that this type of messaging has differential impacts for tangible versus intangible outcomes due to two opposing forces. On the one hand, collective impact framing highlights the collective action problem (e.g., "There's no way everyone in the U.S. would do this!"), which might decrease sustainable action. On the other hand, it scales up the perceived size of the impact, which could increase sustainable behavior ([54]). Because people are often insensitive to large numeric changes in environmental outcomes ([272]), such that "3 million" tons of CO2 would be treated the same as "300 million," it may be more effective to use tangible representations featuring visual images and analogies (e.g., "a garbage heap the size of the Empire State Building").
- P16: Tangible (vs. intangible) collective impact framing increases pro-environmental behavior.
We note that many unsustainable behaviors have become learned in ways that make them automatic rather than controlled in nature. Engaging in sustainable consumption thus often means (at least initially) replacing relatively automatic behavioral responses with more effortful new responses (e.g., carrying one's own shopping bag). This challenge can be related to habit formation. Recall that one means of influencing habitual change is by leveraging discontinuity, or the notion that major life change events can allow for other forms of habit change to occur. It is also possible that a certain mindset (beyond rare major life changes) can lead to habit change ([255]). Individuals who have a "fresh start" mindset exhibit more positive attitudes toward products that allow for a fresh start, and they hold more positive intentions to donate to charities focused on giving recipients a new beginning ([255]). The authors define a fresh start mindset as "a belief that people can make a new start, get a new beginning, and chart a new course in life, regardless of their past or present circumstances" (p. 22), and they show that it can be both measured and manipulated. A fresh start mindset might be applicable in terms of habit formation. Taking a "fresh start" view of a new behavior might serve as a form of discontinuity that makes habit change more likely.
- P17: Those who have a fresh start mindset (measured or manipulated) will be more inclined to change to sustainable consumer behavior habits.
Although the adoption of sustainable behavior often requires overriding an automatic habit with a controlled one, this process may be facilitated by tangibility. Because tangible outcomes are more vivid and immediate, they may provoke more experiential (rather than analytic) processing ([59]), leading people to base their decisions more on emotions and heuristics. Therefore, tangibility may increase the effectiveness of heuristic-based interventions (such as defaults or framing) and decrease the effectiveness of calculation-based interventions (such as attribute scaling; [54]). For example, when buying a car online, representing the fuel efficiency as cost per 100,000 miles may be more effective, whereas when buying a car in person, a personal anecdote from the salesman about rarely needing to fill up the tank might be more effective. Thus, we propose:
- P18: Tangibility interventions shift people from analytic to experiential processing and will therefore moderate the effectiveness of other interventions.
Our last challenge to encouraging sustainable consumer behaviors is that such actions are often characterized as being abstract, uncertain, and difficult for the consumer to grasp ([259]). Furthermore, the consequences of sustainable actions can involve uncertain and fuzzy outcomes ([345]). Although distant future outcomes are often abstract, immediate and local environmental outcomes are also frequently abstract (e.g., energy efficiency, air quality, biodiversity). Although traditional consumer behaviors can carry different elements of risk and uncertainty, the outcomes of choices in traditional consumer contexts are usually more clear and certain than they are in sustainable consumer contexts.
The problem of abstractness can be addressed by considering social influence. One reason why people are influenced by social factors is because we often look to the expectations and behaviors of others when the situation is uncertain ([67]). There is evidence, for example, that unfamiliar behaviors are more likely to be influenced by norms than are more familiar behaviors ([349]). Thus, when the sustainable consumer behavior is in some way ambiguous (e.g., "Exactly what is the most sustainable option for baby diapers?") or uncertain (e.g., "Will engaging in this behavior really have the desired impact?"), people may be more influenced by social factors. Those who are high in the individual differences of uncertainty avoidance ([145]) might be more influenced by social factors when abstractness is high. Thus:
- P19: When the sustainable action or the outcome is ambiguous, uncertain, or new in some way (vs. being clear, certain, and well-established), social factors such as the presence of, behaviors of, and/or expectations of others will be more influential in determining behavior. This might be pronounced among those high in uncertainty avoidance.
Habit formation can also be relevant in tackling the problem of abstractness. Climate change and other issues are serious, nebulous, and can have large-scale consequences, making the acts carried out by individuals seem small and inconsequential. This can lead to green fatigue, or demotivation that is the result of information overload and lack of hope for meaningful change ([310]), and such hopelessness can be demotivating to consumers ([134]). One solution may be to celebrate small and concrete wins that can positively reinforce further sustainable actions and keep consumers engaged.
- P20: Rewarding small milestones will encourage consumers to continue engaging in environmentally friendly behaviors and help avoid green fatigue.
The problem of abstractness also relates to the individual self. In fact, one way to combat the problem of abstract and uncertain outcomes might be to directly consider how they could impact the individual self. As we have seen, making sustainable impacts and outcomes seem local and relevant to the self can encourage sustainable consumer behaviors. However, future research might consider other means of connecting sustainable outcomes more clearly to the self. For example, [144] manipulated a focus on the future self by showing people a digital image of what their future self might look like. These researchers found that increasing connectedness to the future self increases willingness to invest in retirement savings ([144]). It is possible that manipulations that create a connection between the current and future self will lead to increases in sustainable consumer behaviors.
- P21: Those consumers who are encouraged to focus on the future self will be more likely to engage in sustainable consumer behaviors.
Sustainable behaviors can also be made to feel less abstract by making the current emotional benefits and costs more concrete. Future work might examine which different communication modes are most appropriate for making individuals feel emotions linked to sustainable behaviors. Images are known to activate emotions more readily in contexts such as communicating about intergroup conflicts ([47]). Visual information may best communicate how environmental issues will affect others in order to elicit concrete emotions, and these communications may potentially have an enhanced effect on those who are visualizers ([265]).
- P22: Visual communications (vs. text) will be effective at eliciting other-focused emotions such as love and empathy and lead to greater participation in sustainable actions. This effect will be enhanced for individuals who are visualizers.
The problem of abstractness can be related to feelings. Allowing consumers to understand the impact of their actions might help facilitate relevant emotions and reduce perceived abstractness. In the domain of charitable giving, highlighting the impact has been shown to lead to greater emotional rewards attached to the behavior ([ 5]). Previous work, however, has not looked at the specific emotions tied to impact in sustainable consumer behaviors. For example, making the potential impact clear and concrete may be more likely to lead to anticipatory pride (vs. other anticipatory states) linked to the sustainable action.
- P23: Making the positive impact of sustainable behavior more certain in the present will result in greater pride and lead to greater likelihood of carrying out such behaviors in the future.
Feelings might also be linked to the problem of abstractness in another way. The ubiquity of social media and sharing exposes consumers to others who might communicate their actions linked to sustainability. For instance, people may share pictures of their commute by bike or by carpool, along with how they are feeling during the journey. Experiencing positive emotions leads to greater feelings of closeness (Van [327]; [344]), and we tend to feel greater empathy for and thus more strongly experience the emotions of close others ([95]). Thus, close others sharing their emotions involved in carrying out sustainable behaviors should be more effective at reducing abstractness by increasing the strength of the emotions we expect to feel when we engage in the behavior.
- P24: Social distance will lead to emotional contagion when emotional responses to sustainable behaviors are shared with others, such that close (vs. distant) others sharing how they experience positive emotions when carrying out sustainable behavior will make the benefits of the behavior seem more concrete.
Finally, the problem of abstractness is linked to tangibility. One possible way to increase tangibility of actions and outcomes (and to make information less abstract) is to employ analogies. Because sustainability is an abstract and intangible concept, comparing a sustainable action or outcome to a familiar experience or example unrelated to sustainability might facilitate greater connection between the consumer and the concept of sustainability. Thus, future work might examine the following:
- P25: When the action or behavior is sustainable (vs. traditional), analogies will be more likely to encourage consumer behavior change.
Our SHIFT framework shows different tactics that can be used to influence sustainable consumer behaviors (see Web Appendix A). We note that no single route to behavior change identified by the framework works "best." Rather, we suggest that practitioners should understand the specific behavior, the context in which the behavior will occur, the intended target of the intervention, and the barriers (and benefits) associated with the behavior (see Web Appendix B; for more detailed information on how to think about the relevant factors to encourage behavior change, see [210]; [246]; [276]; [279]). We note that there are often multiple barriers to sustainable behavior change, and therefore combining strategies can be impactful ([239]; [306]).
Although our framework highlights the different drivers of sustainable behavior change, it can also be used to think about potential barriers to sustainable action. In particular, one way to use the framework is to consider the primary and secondary barriers to engaging in the desired behavior and then select relevant tactics to overcome them. A primary barrier is the barrier that elicits the strongest avoidance response in the target consumer, and a secondary barrier is the factor that elicits the next strongest avoidance response. Thinking about barriers in terms of the SHIFT factors (e.g., a barrier can be linked to social influence [the sustainable action is seen as socially undesirable] and habit [the existing unsustainable action is highly habitual]) can help the practitioner draw connections to the tools within the framework that might facilitate change. We provide examples of possible focal consumer behaviors in Web Appendix C, and Web Appendix D shows potential strategies that can be drawn from our framework based on the primary and secondary barriers to action.
In one example of identifying primary and secondary barriers that explicitly relied on the SHIFT framework, [353] gathered data on the motives of residents who were hesitant to engage in grasscycling (i.e., composting grass clippings by allowing them to decompose naturally). The researchers discovered that residents' hesitance was due to barriers related to social norms (primary barrier: the norm was that nobody was engaging in the behavior and that it did not seem approved of) and individual factors (secondary barrier: the behavior was perceived to be costly to the self). The authors developed and tested two different solutions that addressed the key barriers by using strategies related to social norms and the individual self. These researchers created messages that were delivered to residents on door hangers, and they tracked residential grasscycling practices over time (both before and after the intervention). First, when the individual was prompted to think of the collective self ("Think about how we as a community can make a difference"), descriptive norms ("Your neighbors are grasscycling—you can too") and injunctive norms ("Your neighbors want you to grasscycle") were most effective. Second, when the person was prompted to think about the individual self ("Think about how you as an individual can make a difference"), highlighting relevant self-benefits worked best ("Grasscycling improves your lawn quality"). By tackling the key barriers linked to social influence and the individual self, the authors increased sustainable behaviors in a large-scale field study.
Another example involves Our Horizon, which is a nonprofit with a mandate to discourage gasoline consumption caused by driving automobiles. Two focal barriers to decreasing gasoline usage are social factors (it is both socially normative and socially desirable to drive) and tangibility (consumers report uncertainty about the impacts of driving less). Our Horizon has responded by developing a strategy to target both social norms and tangibility. Our Horizon encourages local governments to implement warning labels on gas pumps similarly to the way many nations now place warning labels on tobacco packaging. The labels that the organization plans to implement serve to both ( 1) help communicate what is normatively approved of and ( 2) describe concrete and personally relevant local impacts (see Web Appendix F). Although we offer examples to illustrate the SHIFT principles in practice, it is important to recognize that different behaviors and segments will have unique barriers and benefits to behavior change. We include more examples of using barriers to identify tactics based on our Framework in Web Appendix E.
As we have seen, thinking about the primary and secondary barriers to pro-environmental behavior change is one means by which marketers, policy makers, and nonprofits can use the SHIFT framework. However, there is one important nuance: the practitioner should make sure that the tools employed are complementary rather than oppositional to each another. For example, in the grasscycling study described previously, messaging that reflected the individual self along with social norms was less effective than communicating about the individual self and self-benefits (or the collective self and social norms), because consistent messaging leads to goal-compatible outcomes ([353]). In another example, highlighting the extrinsic benefits of engaging in a sustainable action along with intrinsic benefits can be less impactful than communicating intrinsic benefits alone, because extrinsic motives are not compatible with intrinsic motives ([42]; [91]).
A question with practical and theoretical significance is whether our framework can be applied to other behaviors, such as prosocial actions or health behaviors, or if the factors are unique to sustainable behaviors. We conjecture that many of the facets of our framework may apply to the other positive behaviors as well. However, we note that there are some elements that may be unique to sustainable consumption. For example, health behaviors are not subject to the challenge of collective versus individual action to the same degree that sustainable behaviors are. Although health behavior changes can collectively have positive economic and societal benefits ([355]), health behavior change also undeniably primarily has individual benefits ([229]). Although health and prosocial behaviors (e.g., charitable giving) both carry problems of tangibility, sustainable behaviors and outcomes are likely perceived as being even less tangible than health and prosocial behaviors. This is an open question for future research to explore, and applying the framework in other domains certainly has theoretical and practical potential.
In summary, we have reviewed and categorized the behavioral science literature, uncovering five broad psychological routes to encouraging sustainable consumer behavior change: Social influence, Habit formation, the Individual self, Feelings and cognition, and Tangibility. We anticipate that this SHIFT framework will be helpful in guiding practitioners interested in fostering sustainable consumer behavior. Moreover, we expect that this framework will assist researchers in conceptualizing different means of influencing sustainable consumer behavior and will spur further research in this essential domain. At the end of the day, we hope that our framework will help stimulate sustainable consumer behavior change and allow firms wishing to operate in a sustainable manner to do so in ways that can maximize both their sustainability and strategic business goals.
Supplemental Material, DS_10.1177_0022242919825649 - How to SHIFT Consumer Behaviors to be More Sustainable: A Literature Review and Guiding Framework
Supplemental Material, DS_10.1177_0022242919825649 for How to SHIFT Consumer Behaviors to be More Sustainable: A Literature Review and Guiding Framework by Katherine White, Rishad Habib and David J. Hardisty in Journal of Marketing
Footnotes 1 Associate EditorWayne Hoyer served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors thank Sitra, the Finnish Innovation Fund, for financial support for the preliminary stages of this research. In addition, Professors White and Hardisty gratefully acknowledge grants from the Social Sciences and Humanties Research Council (SSHRC) of Canada.
4 Online supplement: https://doi.org/10.1177/0022242919825649
5 ORCID iDKatherine White https://orcid.org/0000-0002-3794-8247
6 1[303] focus on three key motivators of sustainable behavior change: weighing costs and benefits, moral and normative factors, and affective factors. [247] provides a comprehensive review but does not give a detailed analysis of habit formation, emotional factors, or tangibility. [116] gives a broad review of theories and techniques but does not delve as deeply into issues linked to habit, the self-concept, cognition, or tangibility.
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Record: 93- How Well Does Consumer-Based Brand Equity Align with Sales-Based Brand Equity and Marketing-Mix Response? By: Datta, Hannes; Ailawadi, Kusum L.; van Heerde, Harald J. Journal of Marketing. May2017, Vol. 81 Issue 3, p1-20. 20p. 1 Diagram, 7 Charts, 4 Graphs. DOI: 10.1509/jm.15.0340.
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How Well Does Consumer-Based Brand Equity Align with Sales-Based Brand Equity and Marketing-Mix Response?
Brand equity is the differential preference and response to marketing effort that a product obtains because of its brand identification. Brand equity can be measured using either consumer perceptions or sales. Consumer-based brand equity (CBBE) measures what consumers think and feel about the brand, whereas sales-based brand equity (SBBE) is the brand intercept in a choice or market share model. This article studies the extent to which CBBE manifests itself in SBBE and marketing-mix response using ten years of IRI scanner and Brand Asset Valuator data for 290 brands spanning 25 packaged good categories. The authors uncover a fairly strong positive association of SBBE with three dimensions of CBBE—relevance, esteem, and knowledge—but a slight negative correspondence with the fourth dimension, energized differentiation. They also reveal new insights on the category characteristics that moderate the CBBE-SBBE relationship and document a more nuanced association of the CBBE dimensions with response to the major marketing-mix variables than heretofore assumed. The authors discuss implications for academic researchers who predict and test the impact of brand equity, for market researchers who measure it, and for marketers who want to translate their brand equity into marketplace success.
Online Supplement: http://dx.doi.org/10.1509/jm.15.0340
Brand equity is a central construct in marketing theory and practice. Firms invest considerable effort over many years to build the equity of their brands. They reap the benefits of that investment in product market and financial market outcomes and leverage their brand equity to introduce brand extensions. The academic literature has studied each of these phenomena: building brands and their equity (Keller 1993); the association of marketing spending with brand equity (Sriram, Balachander, and Kalwani 2007; Stahl et al. 2012); the product market outcomes of brand equity such as market share, price premium, revenue premium, and profit premium (Ailawadi, Lehmann, and Neslin 2003; Goldfarb, Lu, and Moorthy 2009; Srinivasan, Park, and Chang 2005); the financial market outcomes of brand equity such as stock market returns, risk, and market value (Aaker and Jacobson 1994; Mizik and Jacobson 2008; Rego, Billett, and Morgan 2009); and the factors that enhance or limit a brand's ability to leverage its equity into brand extensions (Aaker and Keller 1990; Batra, Lehmann, and Singh 1993; Bottomley and Holden 2001). Thus, there is a rich literature on the antecedents and consequences of brand equity.
However, what is brand equity, and how is it measured? Perhaps the most widely accepted definition of brand equity is Keller's (1998) conceptualization: the different preference and response to marketing effort that a product obtains because of its brand identification compared with the preference and response that same product would obtain if it did not have the brand identification. Although there are almost as many measures of brand equity as researchers and consultants working in this area, there are two broad measurement approaches: one based on what consumers think and feel about the brand (consumerbased brand equity [CBBE]) and one based on choice or share in the marketplace (sales-based brand equity [SBBE]).
The rationale for perceptual measures is that brand equity resides in the hearts and minds of consumers. Academics have proposed systems of constructs to measure CBBE. The most notable among them are Aaker's Brand Equity Ten (Aaker 1996) and Keller's (1993) CBBE system, which later evolved into the CBBE pyramid (Keller 2001). Over the years, several market research and consulting companies have developed their own CBBE constructs and measures. Some examples are Young & Rubicam's Brand Asset Valuator (BAV), YouGov's Brand Index, the "beliefs" part of Millward Brown's Brand Dynamics, Harris Interactive' s EquiTrend, the Attitudinal Equity component of IPSOS' s Brand Value Creator, and the Equity Engine model of Research International (now part of TNS). These systems use large-scale consumer surveys to measure perceptions of brands along several dimensions. Although each CBBE system has its own measures, they tap into many of the same or related dimensions, as pointed out by Keller (2001).
Sales-based measures of brand equity are marketplace manifestations of these consumer perceptions. In line with Keller's (1998) conceptualization, SBBE is the part of a brand's utility that comes in addition to the contribution of its objectively measured attributes and marketing mix. It is generally measured by the brand intercept in a choice or market share model and is also referred to as the "residual" approach to measuring brand equity. It has been estimated from self-reported choices in conjoint and survey data (Park and Srinivasan 1994; Srinivasan, Park, and Chang 2005) and from actual brand choices and sales recorded in scanner data (Kamakura and Russell 1993; Sriram, Balachander, and Kalwani 2007). Importantly, Keller (1998) has pointed out that the extant measures do not include an important aspect of brand equity—enhanced consumer response to the brand's marketing mix.
Despite the importance of brand equity in marketing theory and practice and despite the fact that firms spend considerable sums of money to track CBBE and SBBE, no empirical study to date has systematically investigated the link of CBBE with SBBE or marketing-mix response. The goal in this article is to fill that gap by addressing the following research questions:
- What is the overall association between the major dimensions of CBBE and SBBE across product categories?
- How do category characteristics moderate this association?
- What is the association between the major dimensions of CBBE and consumer response to marketing-mix variables of a brand?
We address these research questions with widely used measures of CBBE and SBBE for a large set of consumer packaged goods (CPG) brands over time. Specifically, we combine ten years of annual CBBE data from BAV with ten years of weekly SymphonyIRI scanner data from which we estimate the intercept measure of SBBE as well as marketing-mix elasticities. We conduct the analysis for a total of 290 brands across 25 CPG categories for which both SBBE and CBBE measures are available.
We document several findings that are new to the literature and important for marketing practice. We find that three of the CBBE dimensions—relevance, esteem, and knowledge, which are highly correlated with one another—have a positive association with SBBE, whereas the fourth dimension—energized differentiation—has a small negative association with SBBE. The association is moderated by category characteristics. The effect of relevance, esteem, and knowledge on SBBE is stronger in categories with more social value and more choice difficulty reflected in lower concentration. In contrast, energized differentiation leads to higher SBBE in more hedonic categories and in more concentrated categories. The pattern of association between CBBE and marketing-mix response varies by CBBE dimension and by marketing variable. We find that relevance, esteem, and knowledge are associated with stronger advertising, price promotion elasticities, and feature/display elasticities but with lower distribution elasticities. Energized differentiation is linked with stronger advertising elasticities but with weaker price promotion elasticities.
This analysis is important for both researchers and practitioners. Academic researchers use any of a variety of CBBE or SBBE measures that they happen to have access to, and, unless we have a good understanding of whether and how the different measures align, we have little idea whether the findings reported with one type of measure will hold up with another. In addition, positive consumer perceptions are only useful to managers insofar as they translate into equity in the marketplace. As we discuss subsequently, both underand overachievement on SBBE compared with a brand's CBBE should be treated as red flags for further diagnosis and action. This analysis also provides guidance on which dimensions of CBBE managers should prioritize depending on the nature of the category. Finally, although conventional wisdom says that CBBE results in stronger marketing-mix elasticities, prior research has not put that wisdom to a comprehensive empirical test (Keller and Lehmann 2006). Our findings regarding how the different dimensions of CBBE affect each of the major marketing-mix elasticities are new to the academic literature and help managers in adjusting their marketing mix to leverage their CBBE.
Figure 1 presents the guiding conceptual framework for our research. We discuss this framework in detail in the following subsections.
The CBBE measures that are compiled by industry sources cover a broad set of brands and categories and are based on large-scale consumer surveys. A few have been used in academic studies. For instance, the EquiTrend measures have been related to stock performance (e.g., higher returns; Aaker and Jacobson 1994), lower idiosyncratic firm risk and cost of capital (Rego, Billett, and Morgan 2009), and better stock performance during the 2008 economic downturn (Johansson, Dimofte, and Mazvancheryl 2012). The YouGov measures have also been related to stock returns and idiosyncratic risk (Luo, Raithel, and Wiles 2013). The BAV measures, which we employ in this article, have recently been used in a more wide-ranging set of studies. Mizik and Jacobson (2008) show that BAV measures are associated with unanticipated changes in stock returns after controlling for changes in accounting rates of return. Stahl et al. (2012) examine the effect of these CBBE measures on customer acquisition, retention, and profit margin in the automobile industry. Peres, and Shachar (2013) show how they drive offline and online word of mouth. Thus, prior research has established the relevance of BAV's CBBE measures. It is the first and perhaps most widely used CBBE system, compiling the perceptions of tens of thousands of consumers each year on thousands of brands.
Although BAV measures consumer perceptions on a large number of brand attributes, BAV Consulting (bavconsulting. com) has identified four pillars—energized differentiation, relevance, esteem, and knowledge—as the key dimensions to track a brand's equity in addition to an overall Brand Asset score.[ 1] Variants of these dimensions exist in most other CBBE systems as well. The specific measures used by BAV appear in Web Appendix A. We examine how these dimensions are associated with SBBE and with marketingmix response.
Energized differentiation primarily measures a brand' s uniqueness and ability to stand out from competition as well as its ability to meet future consumer needs. Differentiation is something that marketers invariably strive for (e.g., Kotler and Keller 2015; Moon 2010). As Stahl et al. (2012, p. 47) note, it is the "mantra of marketing."
Relevance measures how appropriate a brand is for consumers and how much it fits into their lives. It is viewed by BAV as the source of a brand' s staying power (Mizik and Jacobson 2008). Keller (2001) equates it to consumer consideration in his CBBE pyramid and Aaker (2012) writes that becoming indispensably relevant in a category with "must-have" characteristics and simultaneously making competitors irrelevant is a brand's route to growth.
Esteem measures how much people like the brand and hold it in high regard. Keller (2001) views it as positive quality and credibility perceptions. Similarly, quality and leadership are an important part of Aaker's (1996) Brand Equity Ten measures. BAV encompasses both quality and popularity within esteem and views it as third in the progression of a brand's development, after energized differentiation and relevance.
Knowledge measures consumers' awareness and understanding of what the brand stands for. Importantly, it is not just awareness of the brand but of its identity, which is built from the brand' s communications as well as from personal experience with the brand. Brand Asset Valuator views knowledge as the culmination of brand-building efforts, and in line with that view, Keller (2001) associates it with brand resonance at the pinnacle of the CBBE pyramid.
There is a long and well-established tradition in the literature of measuring SBBE as the brand intercept in a choice or market share model (e.g., Kamakura and Russell 1993; Srinivasan 1979). Some models provide individual-level SBBE estimates (Park and Srinivasan 1994; Rangaswamy, Burke, and Oliva 1993), but they are often based on conjoint or other surveybased data. Others use scanner panel choice data to provide segment-level estimates (Kamakura and Russell 1993), or store or market sales data to provide aggregate estimates (Goldfarb, Lu, and Moorthy 2009; Sriram, Balachander, and Kalwani 2007).
Because the goal of this research is to assess the association between the most widely used CBBE and SBBE measures in a generalizable and externally valid way, we use national data for a large number of categories and estimate SBBE as brandspecific intercepts in a market share attraction model. The model, which we describe in detail subsequently, specifies a brand's attraction as a function of its physical attributes, marketing mix, and other control variables.
We next present our expectations about the association between CBBE and SBBE, the category factors that moderate this association, and the link between CBBE and marketing-mix elasticities. Table 1 summarizes these expectations in the form of numbered propositions to which we refer throughout the discussion.
Brands with high CBBE are more likely to receive selective attention from consumers, be included in their consideration sets, be evaluated positively, and be chosen at the point of purchase (Hoeffler and Keller 2003). Therefore, we expect a positive association between CBBE and SBBE overall, but not all the dimensions of CBBE may be equally associated with SBBE. Brands that rate high on relevance, esteem, and knowledge have succeeded in developing a broad and deep appeal among consumers. These are the brands that many consumers believe are personally appropriate to them, think highly of, and understand well. Therefore, we expect that these three CBBE dimensions should be associated positively with SBBE (P1 in Table 1). Among the three, relevance is closely associated with brand penetration, and knowledge represents the pinnacle of CBBE. Therefore, we expect these two dimensions to be most strongly associated with SBBE.
The argument is different for energized differentiation. This CBBE dimension captures uniqueness and distinctiveness from other brands. Yet, as Stahl et al. (2012) note, this uniqueness may appeal strongly to some consumers but not to others. Indeed, Stahl et al. find a negative effect of this dimension on customer acquisition and retention in the automobile industry. Moreover, the discrepancy hypothesis in psychology suggests that consumers like new things that are sufficiently different from familiar ones, but not if they are too different (Haber 1958; for an example, see Miller, McIntyre, and Mantrala 1993). Thus, although energized differentiation may generate word of mouth, especially online (Lovett, Peres, and Shachar2013), and garner higher prices and margins (Stahl et al. 2012), we expect that it is associated with lower levels of SBBE (P2).
Consumers use strong brands as diagnostic cues to reduce risk and uncertainty and to obtain social and emotional benefits from their choices. However, because these risks and benefits are not equally important across product categories, the brand is not equally relevant to consumers' decision process in different categories (Fischer, Volckner, and Sattler2010). Weexpectthat CBBE should be more strongly associated with SBBE in categories in which the brand is more relevant. In particular, the association should be stronger in categories with ( 1) more serious negative functional consequences of making the wrong choice; ( 2) higher information cost of making a choice and, therefore, higher need to simplify choice; ( 3) higher symbolic or social value of the choice; and ( 4) higher experiential benefit from consumption (Fischer, Volckner, and Sattler 2010; Laurent and Kapferer 1985; Steenkamp and Geyskens 2014). In line with these different roles that brands fulfill, we examine four category characteristics that may moderate the link between CBBE and SBBE.
Functional risk. Functional risk is the consumer's subjective assessment of the risk that the product will not do its job if (s)he makes the wrong choice in a category (Steenkamp and Geyskens 2014). The risk may be higher in some categories because the consequences of the wrong choice are perceived to be more serious (e.g., diapers, deodorant) or because there are stronger quality differences among products in the category (e.g., coffee). Other categories have less functional risk because differences in quality are not that consequential. For categories with higher functional risk, there is more at stake and consumers' choices are more influenced by the brand's promise (Erdem, Swait, and Louviere 2002; Fischer, Volckner, and Sattler 2010). Thus, we expect that CBBE will translate into SBBE easily for such categories. We expect that relevance, esteem, and knowledge will translate into SBBE more positively for high functional risk categories because these dimensions make the brand a familiar and appropriate choice (Pi.i). However, differentiated brands can be perceived as risky. If a brand is extremely strong and differentiated on one aspect, the implication for consumers can sometimes be that it is not as good on other aspects (e.g., Keller, Sternthal, and Tybout 2002; Raghunathan, Naylor, and Hoyer 2006). Therefore, we expect that energized differentiation is less likely to translate into SBBE for high functional risk categories (P2.1).
Category concentration. Brands serve as a way to simplify choice and reduce the information costs associated with choosing among a broad array of alternatives (Erdem and Swait 1998; Keller and Lehmann 2006). The concentration of a category reflects the array of alternatives from which most consumers choose. When concentration is low, consumers are faced with many smaller brands; they need cues to facilitate decision making in such crowded categories. Brands with high relevance, esteem, and knowledge provide these cues and can stand out in a crowded field. Thus, we expect these three dimensions to be more positively associated with SBBE in less concentrated categories (P1.2). Conversely, consumers face a more manageable choice set in highly concentrated categories. They can compare alternatives more deliberately and extensively, which makes a brand's uniqueness a more decisive factor in choice. Therefore, we expect that greater category concentration will enhance the impact of energized differentiation on SBBE (P2.2).
TABLE: TABLE 1 Association of CBBE with SBBE and Marketing-Mix Elasticities: Propositions
| Expected Association With… |
| CBBE Dimension | SBBE | Regular Price Elasticity | Promotional Price Index Elasticity | Feature/Display Elasticity | Distribution Elasticity | Advertising Elasticity |
| Relevance, esteem, and knowledge (REK) | P-j! REK has a positive association with SBBE. Pi .1: The association between REK and SBBE is stronger for higher-functional risk categories. P12: The association between REK and SBBE is stronger for less concentrated categories. P13: The association between REK and SBBE is stronger (more positive) for higher-social value categories. P14: The association between REK and SBBE is stronger for more hedonic categories. | P3: Higher REK is associated with a weaker (less negative) regular price elasticity. | P5: Higher REK is associated with a stronger (more negative) promotional price elasticity. | P7: Higher REK is associated with a stronger feature/ display elasticity. | Pg: There are arguments for both a positive and a negative association of REK with distribution elasticity. | P-11: Higher REK is associated with a stronger advertising elasticity. |
| Energized differentiation (ED) | P2: ED has a negative association with SBBE. P2.i: The association between ED and SBBE is weaker for higher-functional risk categories. P22: The association between ED and SBBE is stronger for more concentrated categories. P23: The association between ED and SBBE is stronger (more positive) for higher-social value categories. P24: The association between ED and SBBE is stronger for more hedonic categories. | P4: Higher ED is associated with a weaker (less negative) regular price elasticity. | P6: Higher ED is associated with a weaker (less negative) promotional price elasticity. | P8: Higher ED is associated with a weaker feature/ display elasticity. | Pi0: Higher ED is associated with a weaker distribution elasticity. | P12: Higher ED is associated with a stronger advertising elasticity. |
Social value. One reason that consumers choose strong brands is because of their symbolic or social value (Fischer, Volckner, and Sattler 2010; Laurent and Kapferer 1985; Steenkamp and Geyskens 2014). Social value may be higher in categories that are more visible to others (e.g., cigarettes) or are more often shared with others (e.g., beer). Consumers are more likely to value strong brands in categories that are high in social value, so higher levels of CBBE should more readily translate into SBBE in such categories (P13 and P2.3). We expect this positive moderating effect to hold especially for brands high on esteem, relevance, and knowledge because these brands are more likely to be recognized and respected by others.
Hedonic categories. Consumers also derive emotional value and enjoyment from brands. This is more important in hedonic categories, which are evaluated, chosen, and consumed primarily on the basis of their sensory attributes and overall image rather than on individual, physical attributes (Holbrook and Hirschman 1982; Voss, Spangenberg, and Grohmann 2003). Consumers process hedonic categories more holistically and therefore may rely on cues such as the brand (Melnyk, Klein, and Volckner 2012). Accordingly, we expect the association of CBBE with SBBE to be stronger in hedonic categories (P1.4 and P2.4). Among the CBBE dimensions, we expect that the impact of energized differentiation on SBBE will be particularly enhanced in hedonic categories because differentiation enables brands to capitalize on the unique and personal multisensory sensations they offer.
As we noted previously, brand equity refers not only to consumer preferences and choice but also to more favorable marketing response. Hoeffler and Keller (2003) synthesize the theoretical and conceptual mechanisms by which strong brands can get differential response to their marketing activities. Some researchers have empirically examined whether brands with higher revenue premiums receive a better response to coupons and distribution (Slotegraaf, Moorman, and Inman 2003), price cuts (Ailawadi, Lehmann, and Neslin 2003), or have greater long-term promotion effectiveness (Slotegraaf and Pauwels 2008). Other work has studied how attitudinal metrics such as awareness and consideration mediate the effect of marketing actions on sales (Hanssens et al. 2014). However, none of them have studied the impact of CBBE dimensions on response to the major marketing-mix variables at a brand's disposal—regular price, promotional price discount, feature/display activity, advertising, and distribution.
Price elasticity. On the one hand, higher brand equity is expected to be associated with weaker price elasticity (e.g., Erdem, Swait, and Louviere 2002; Sivakumar and Raj 1997). On the other hand, high-share or high-quality brands tend to receive a stronger response to price discounts (e.g., Blattberg and Wisniewski 1989; Sethuraman 1996). Previous studies have highlighted the importance of distinguishing between response to regular price changes and promotional price discounts. High-CBBE brands are expected to be less sensitive to regular price changes over time and thus have lower (less negative) regular price elasticities. We expect that this holds for high relevance, esteem, and knowledge (P3) and for high energized differentiation (P4).
However, high-CBBE brands have a larger pool of potential customers that can be attracted with their promotional price discounts. This especially applies for brands high on knowledge, relevance, and esteem, reflecting their strong and broad appeal. Those brands are expected to be associated with stronger (more negative) promotion price elasticities (P5). However, as we noted previously, brands with high energized differentiation appeal only to specific segments. Other segments may not be persuaded to buy even when the brand is on price promotion (P6).
Feature/display elasticity. Following the same logic, brands high on knowledge, relevance, and esteem have a larger potential pool of customers to attract through features and displays, leading to a stronger elasticity for these activities (P7). Conversely, features and displays will be less of a draw for highly differentiated brands (P8).
Distribution elasticity. For distribution, the prediction is less clear cut. Certainly, strong brands have high distribution, but what are the returns to that distribution? Additional distribution points allow consumers to act on their preference to buy, and more consumers prefer high-equity brands. This suggests a stronger distribution elasticity for high-equity brands. However, a hallmark of strong brands is consumers' willingness to search for them. If consumers search for these brands and buy them wherever they are available or switch to whichever flavors, sizes, and so on of the brand that a retailer stocks (rather than buying a less preferred brand), then returns to additional distribution will be lower (Farris, Olver, and De Kluyver 1989). Thus, we do not predict a priori whether brands with high relevance, esteem, and knowledge have a stronger or weaker distribution elasticity (P9).
Brands with high energized differentiation appeal to certain but not all consumers. These consumers already search for and buy these brands. Other segments may not be persuaded to buy these brands even with greater availability, reflected in a lower distribution elasticity (P10).
Advertising elasticity. Brand equity is expected to make a brand's advertising efforts more effective because consumers pay more attention to, react more positively to, and retain more information from the brand' s marketing (Hoeffler and Keller 2003). This means that a brand's advertising efforts are more salient and impactful, and thus CBBE should be associated with higher advertising elasticities. We expect this to be the case for brands with high relevance, esteem, and knowledge (P11).
Although a smaller pool of consumers for brands with high energized differentiation suggests weaker advertising response, differentiated brands have unique selling propositions that can be effectively communicated through advertising. They may also be less prone to the interference that has been shown to hurt consumer memory of brands with a large number of associations (Meyers-Levy 1989), and thus we expect that these brands have stronger advertising elasticities (P12).
We analyze a large set of CPG brands across 25 product categories in the United States. Annual data on the four CBBE dimensions are provided by BAV Consulting. We obtained weekly store-level scanner sales data to estimate SBBE and elasticities from the IRI Marketing Science data set (Bronnenberg, Kruger, and Mela 2008). We obtained monthly advertising (traditional media and online) from Kantar Media. Finally, we conducted a consumer survey on Amazon Mechanical Turk to obtain the three perceptual category characteristics (functional risk, social value, and hedonic nature).
The IRI data span the period from 2001 to 2011 and the BAV data span the period from 2002 to 2012, so the empirical analysis covers the ten-year overlap period from 2002 to 2011. The sample selection is as follows. We start with all categories in the IRI data set except for toothbrushes and photo film.[ 2] We select the subcategories that comprise substitutable products and that are covered throughout the ten-year period. We separate ketchup and mustard, two condiment types, as two categories. We merge razors with blades and frozen dinners with frozen pizza because many of the same brands are in both categories.
Next, we define and select brands in each category. Although the Universal Product Code description file in the IRI data set has a field for the brand name, that field is rather narrow. For example, it has separate values for Folgers, Folgers Cafe Latte, Folgers Coffee House, Folgers Select, and several other variants of Folgers coffee, whereas BAV tracks Folgers Coffee as a whole. Therefore, we first code all the variants of each brand in each category into their parent brand.[ 3] In most cases, this coding is consistent with BAV's brand definition. In the instances in which BAV's definition is more disaggregate, we follow BAV (e.g., in separating Coke from Diet Coke and Budweiser from Bud Light). We rank brands according to their market share and include those that jointly account for at least 90% of category sales. Furthermore, we delete brands with less than two years of consecutive data and categories with fewer than three brands. This results in 441 brands across 25 categories. Brand Asset Valuator data are available for 290 of these brands (see Table 2).
We note that some brands exist in multiple CPG categories, having expanded from their primary category (e.g., Kraft cheese) into additional ones (e.g., Kraft mayonnaise). Similarly, some brands have expanded into CPG categories from outside the grocery channel (e.g., Starbucks coffee). Consequently, the CBBE measures for these brands reflect equity built in their primary markets, while the SBBE measures reflect equity built in secondary markets into which they have extended. We flag all such cases with a secondary market indicator variable to control for this in the empirical analysis.
Market concentration is operationalized as the total share of the top four brands in the category and is computed from the IRI data (Tirole 1988). For the remaining three moderators, which are perceptual constructs, we conducted an online survey of 752 U.S. respondents on Amazon Mechanical Turk. Respondents first indicated how often they made a purchase in each of the 25 categories and then rated all categories they had purchased at least once in the past two years on two of the three constructs. To avoid overburdening respondents, we used two items per construct (details are in Web Appendix B). Table 2 includes means of the category characteristics.
We obtain SBBE and marketing-mix elasticities for each brand in each category using a market share model estimated with IRI data. Then, we examine the association of the four dimensions of CBBE with SBBE, test for the moderating effect of the four category characteristics, and study the link between CBBE and marketing-mix elasticities.
We use a multinomial logit attraction model for market share (Cooper and Nakanishi 1988; Fok, Franses, and Paap 2002). The model is estimated for each of the 25 categories, using data aggregated up to the national brand-week level. The attraction model has several benefits. It is easily linearized and estimated, it is logically consistent with market shares between 0 and 1 and adding up to 1, and it captures cross effects between brands. It is an aggregate analog of the individual brand choice model from which SBBE can be estimated as the time-varying brandspecific intercept.
We expand this model in several ways to obtain valid estimates of SBBE and marketing-mix elasticities. We include both the physical search attributes of a brand and its marketing-mix variables as explanatory variables (Goldfarb, Lu, and Moorthy 2009; Kamakura and Russell 1993; Sriram, Balachander, and Kalwani 2007). Thus, the brand-year-specific intercept reflects the attraction attributable to the brand name after controlling for these observables (i.e., SBBE).
We use the differential-effects version of the multinomial logit model, allowing not only the intercept but also the marketing-mix coefficients to be brand specific. In addition, brands may strategically set their marketing mix in response to unobserved demand shocks. In particular, they may respond to anticipated season-induced changes in demand. Although seasonal effects are more likely to occur in sales data than in market share data, we use quarterly dummies with brandspecific coefficients to mitigate this source of endogeneity.
We also control for potential endogeneity resulting from other unobserved shocks using Gaussian copulas that directly model the joint distribution of the potentially endogenous regressors and the error term through control function terms (Park and Gupta 2012). The copula method does not require instrumental variables and therefore is particularly useful when valid instruments are difficult to find (Rossi 2014). That is the case in our setting, in which we have five potentially endogenous marketing-mix variables measured at the national level for more than 400 brands from 25 categories. With a normally distributed error term, an identification requirement for the Gaussian copula method is that the endogenous regressors are not normally distributed. In our application, ShapiroWilk tests at p < .10 confirm this for 99% of the cases.
TABLE: TABLE 2 Sample Description
| | | Mean No. of | Mean of | Mean of | Mean of | |
| No. of | No. of BAV | Years | Social | Hedonic | Functional | Mean of Cat. |
| Category | Brands | Brands | per Brand | Valuea | Naturea | Riska | Concentrationa |
| Beer | 59 | 37 | 9.9 | 3.39 | 5.96 | 3.44 | .47 |
| Carbonated Soft Drinks | 27 | 21 | 9.8 | 2.72 | 5.32 | 3.01 | .56 |
| Cigarettes | 25 | 21 | 10.0 | 3.11 | 4.26 | 3.10 | .65 |
| Coffee | 30 | 23 | 9.4 | 3.07 | 5.52 | 3.64 | .74 |
| Cold (RTE) Cereal | 23 | 20 | 10.0 | 2.72 | 4.75 | 3.16 | .48 |
| Deodorants | 19 | 17 | 9.7 | 2.44 | 2.83 | 3.53 | .51 |
| Disposable Diapers | 6 | 4 | 9.2 | 2.45 | 2.10 | 3.72 | .99 |
| Household Cleaners | 15 | 9 | 9.7 | 2.59 | 2.18 | 3.41 | .59 |
| Ketchup | 5 | 3 | 10.0 | 2.05 | 3.45 | 2.58 | 1.00 |
| Laundry Detergents | 20 | 17 | 9.9 | 2.56 | 2.40 | 3.53 | .60 |
| Margarine & Spreads | 13 | 6 | 10.0 | 2.17 | 3.09 | 2.72 | .65 |
| Mayonnaise | 7 | 3 | 10.0 | 2.15 | 3.07 | 2.83 | .93 |
| Milk | 19 | 4 | 9.9 | 2.34 | 3.39 | 2.81 | .90 |
| Mustard | 12 | 5 | 10.0 | 2.12 | 3.30 | 2.46 | .90 |
| Peanut Butter | 11 | 5 | 9.4 | 2.30 | 4.37 | 2.98 | .92 |
| Frozen Pizza & Dinners | 26 | 15 | 9.4 | 2.47 | 3.61 | 3.22 | .47 |
| Razors & Blades | 5 | 3 | 10.0 | 2.52 | 2.61 | 3.72 | .99 |
| Salty Snacks | 17 | 7 | 9.6 | 2.56 | 5.16 | 2.97 | .74 |
| Shampoo | 28 | 17 | 9.1 | 2.84 | 3.09 | 3.56 | .57 |
| Soup | 8 | 6 | 9.0 | 2.16 | 3.40 | 2.94 | .98 |
| Pasta Sauce | 15 | 14 | 9.6 | 2.30 | 3.89 | 3.05 | .70 |
| Sugar Substitutes | 10 | 5 | 8.1 | 2.52 | 2.82 | 2.76 | .89 |
| Toilet Tissue | 10 | 5 | 10.0 | 2.45 | 2.40 | 3.60 | .70 |
| Toothpaste | 15 | 13 | 9.9 | 2.53 | 2.79 | 3.43 | .87 |
| Yogurt | 16 | 10 | 8.9 | 2.43 | 4.03 | 3.01 | .79 |
aCategory concentration is the total market share of the top four brands in a category. Both social value and functional risk of a category are measured on two-item, five-point Likert scales (1-5), with higher values representing higher scores. Hedonic nature is measured on a two-item, sevenpoint sematic differential scale (1-7), with higher values representing more hedonic categories. For measurement details, see Web Appendix B.
We estimate the smoothing constant for advertising stock, which we define next along with all the other model variables. Finally, we account for serial correlation by applying the Prais-Winsten correction (Greene 2012). Thus, the complete model for the M brands (where M can vary over time to accommodate brand entry or exit) in each category is as follows:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
where we drop the category index c to simplify exposition, and
MSbt = Unit market share of brand b in week t;
Abt = Attraction of brand b in week t;
αby = Brandand year-specific intercept for brand b in
year y;
DumYear ty = Indicator variable, equal to 1 if week t is part of
year y, and 0 otherwise;
RegPricebt = Regular price of brand b in week t, deflated by a
category-specific Consumer Price Index to account
for category-wide price changes;
PriceIndexbt = Actual price of brand b in week t divided by its regular
price to measure its promotional price discount;
FDbt = Intensity of feature and/or display support for brand b
in week t;
Distrbr = Total distribution of the stockkeeping units (SKUs) of
brand b in week t;
AdStockbt: = Smoothed advertising spending or advertising
stock of brand b in week t, where AdStockbt =
lAdStockbt-1 + (1 λ)Advertisingbt;
Attrbal = Fraction of the SKUs of brand b that have attribute
level l for attribute a;
Quarterqt = Quarterly dummy for quarter (q = 1 if week t is in
quarter q, and 0 otherwise), mean-centered at the
brand level;
Copulakbt = Gaussian copula (control function term) for marketing-
mix variable k of brand b in week t to control for
potential endogeneity of the variable; and
ebt = Normally distributed error term for brand b in week t.
The market shares, attributes, and marketing-mix variables in the model are aggregated up to the national brand-week level from store-level SKU data. The aggregation procedure and the variable operationalization are in Web Appendix C. We note that the model distinguishes between regular and promotional price through two separate variables, RegPrice and PriceIndex, respectively. FD captures the additional effect of feature/display support beyond the impact of a promotional price discount. The Distr variable measures the percentage of SKUs on the shelf that belong to the brand, thus incorporating both distribution breadth and the depth of the product line in distribution. The number of attribute variables differs across categories as the number of attributes and the levels of each attribute vary (for details, see Web Appendix D). The Gaussian copula for each marketing variable Xbt for brand b in week t is Copulabt = Φ -1 [Hb(Xbt)], where Φ -1 is the inverse distribution function of the standard normal, and Hb($) is the empirical cumulative distribution function of Xb. Finally, the brand-year intercepts measure SBBE and are estimated for all years Yb for which data on brand b are available.
The attraction model for a category can be written as a system of M equations. Because shares sum to 1, the dependency across equations reduces the rank of the system to M 1. For estimation, the system can be normalized by geometric meancentering (Cooper and Nakanishi 1988) or with respect to a base brand (Bronnenberg, Mahajan, and Vanhonacker 2000). Both approaches are mathematically equivalent, and we use the latter for computational ease (Fok, Franses, and Paap 2002).
To linearize Model 1, we take its logarithm for each of the M brands. Next, we subtract a base brand B from both sides of each of the other M 1 equations. The base brand is selected as the brand (or one of the brands) with the most observations. We estimate this system of M -1 seemingly unrelated equations for each category using feasible generalized least squares:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
The yearly intercepts for the base brand (α By) are normalized to zero for identification. To calculate SBBE for the base brand, we use the assumption of the attraction model that the total attraction across brands is constant over time, leading to brand b's SBBE in year y:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
We compute the corresponding standard errors using the delta method.
To select the advertising smoothing constant l for the AdStock variable, we use a grid search on the interval [0, .9] in increments of .1 that yields the best likelihood. As Equation 3 shows, all other parameters in the system of equations are directly estimated, including the brand-specific marketing-mix response coefficients. From these coefficients, we compute each brand's marketing-mix elasticities as follows (Cooper and Nakanishi 1988, p. 33):
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
where MSb and Xb are brand b's average market share and marketing instrument X, respectively.
We estimate the market share model across all brands to ensure good coverage of each category and valid estimates of SBBE and marketing elasticities. After estimating this model, we run a second-stage analysis to test the link between the SBBE and elasticity estimates on the one side and CBBE on the other side. We use the estimates from Equations 4 and 5 as dependent variables and regress them on CBBE and other relevant covariates (we provide more details in the following section). In the second-stage analysis, we use weighted least squares (WLS) to account for the uncertainty in the SBBE and elasticity estimates from the first stage. We also use clustered standard errors to account for the fact that each brand contributes multiple observations to the SBBE model. This two-stage approach is in line with an established tradition in the marketing literature (e.g., Nijs et al. 2001; Srinivasan et al. 2004; Steenkamp et al. 2005).[ 4]
Table 3 summarizes the marketing-mix elasticities obtained across the 441 brands in 25 product categories. The weighted averages of the elasticities have the expected signs, and their meta-analytic Z-statistics (Rosenthal 1991) are significant. The relative magnitudes of the mean regular price elasticity (-.79) and the promotional price elasticity (-2.59) are in line with meta-analytic results (Bijmolt, Van Heerde, and Pieters 2005). The mean feature/display elasticity is significant, though it appears small (.02). Note, however, that this effect is over and above the effect of promotional price cuts which are captured by the price index variable. The mean advertising elasticity equals only .001, consistent with prior research (Sethuraman, Tellis, and Briesch 2011; Sriram, Balachander, and Kalwani 2007; Van Heerde et al. 2013). In Web Appendix E, we summarize elasticity estimates and advertising smoothing constants l by category.
TABLE: TABLE 3 Summary of Market Share Elasticity Estimates
| Elasticity Estimatea | |
| Marketing-Mix Variable | M | SD | 90% Interval of EstimatedElasticities |
| Regular price | -.79*** | 1.18 | (-2.74, .53) |
| Promotional price index | -2.59*** | 1.97 | (-5.64, -.45) |
| Feature/display | 02*** | .05 | (-.04, .19) |
| Distribution | .40*** | .47 | (-.10, 1.03) |
| Advertising stock | .001** | .02 | (-.02, .04) |
**p < .05.
***p < .01.
aWeighted means and standard deviations across 441 brands in 25 categories; weights are equal to the inverse of the estimated standard errors. Significance tests are based on meta-analytic Z-values.
Previous research (Ataman, Van Heerde, and Mela 2010) has reported higher elasticities for distribution breadth than ours (.40), but we note that their measure of distribution is percentage of product category volume (%PCV), whereas we use a brand's total distribution (Web Appendix C). Total distribution elasticity is expected to be lower than the elasticity for brand PCV because an increase in a brand' s total distribution often adds SKUs to an existing assortment in stores, some of the sales of which are cannibalized from existing SKUs of the brand. However, an increase in %PCV adds stores that previously did not stock any SKUs of the brand.
Overall, therefore, the elasticities have face validity and are consistent with prior research. We note that the copula correction terms are statistically significant in 70% of the cases (1,427 out of 2,046 at p < .10), underscoring the importance of dealing with endogeneity.
Figure 2 shows the association between CBBE and SBBE in the most recent year of the data (2011) for two categories with a large number of brands—beer and laundry detergent. Beer is more hedonic and high on social value, while detergent is less hedonic and high on functional risk. To provide a general overview, Figure 2 uses BAV's composite Brand Asset score for CBBE; we examine its dimensions in detail next. In this and subsequent analyses, we standardize measures across brands in each category to allow for comparability. To underscore the difference between a brand's SBBE and its market share, Figure 2 also plots the Brand Asset score against market share.
Figure 2 illustrates the coverage of the data, the overall positive association between CBBE and SBBE, and the face validity of various brand positions. Several well-known brands achieve high scores on both CBBE and SBBE (e.g., Budweiser and Bud Light for beer, Tide and Arm & Hammer for laundry detergents). Others, such as Bass Ale and Surf detergent, score low on both CBBE and SBBE. We also note that the highest-market share brands are not necessarily the ones with the highest SBBE, a point to which we return subsequently.
Correlations. Table 4 shows correlations between the SBBE and CBBE measures across the 2,423 brand-year observations in the sample. As we expected, the pattern of association of CBBE dimensions with SBBE is bifurcated, with three dimensions—relevance, esteem, and knowledge—showing a similar pattern, and energized differentiation showing a very different pattern. In line with proposition Pi, we find moderate positive correlations (ranging between .35 and .53) of SBBE with the first three CBBE dimensions. Energized differentiation has a small negative correlation with SBBE (-.14), in line with P2.
The CBBE dimensions have more positive correlations with market share than they do with SBBE. For instance, relevance and esteem have respective correlations of .56 and .55 with market share. This finding makes sense. Sales-based brand equity is the "residual" attraction of a brand after controlling for its physical attributes, its marketing mix, and its marketing-mix response, whereas market share is the joint result of all these elements. To the extent that high-CBBE brands are of higher quality, have a more attractive marketing mix, and have stronger response to the marketing mix, CBBE should be more positively associated with market share than with SBBE.
Principal component analysis. Before we estimate the second-stage models, we need to account for the high correlations between some of the CBBE dimensions that could cause multicollinearity. Therefore, we conduct a principal component analysis to reduce them to a smaller number of orthogonal components. We extract the two principal components with eigenvalues greater than 1, capturing 89% of the variance in the four dimensions. As the correlation pattern in Table 4 suggests, the first component has very high loadings of relevance (.93), esteem (.95), and knowledge (.88) and a low loading of energized differentiation (.02). In line with Mizik and Jacobson (2009), we name this component "relevant stature" (RelStat). The second CBBE component has a very high loading of energized differentiation (.99) and low loadings of relevance (.14), esteem (.07), and knowledge (-.11), and we label it "EnDif." We use these principal component scores in the rest of the analysis.[ 5]
To test the link between the CBBE dimensions and SBBE and the moderating influence of the category characteristics, we regress SBBE on the two CBBE principal components, the four category characteristics, and their interactions with the CBBE components. In addition, we use the secondary market indicator variable to account for brands that have extended into new domains from the ones where their CBBE is built, as discussed previously. A benefit of such extensions is that firms can leverage their brand equity instead of building it from scratch in a new market. However, the SBBE for brands operating in—from their perspective—secondary categories is likely to be lower than would be expected on the basis of the CBBE in their primary categories. Therefore, we expect this variable to have a negative coefficient.
TABLE: TABLE 4 Correlations of SBBE with CBBE
| Correlation With… |
| CBBE Dimension | Esteem | Knowledge | Energized Differentiation | SBBE | Market Share |
| Relevance | .85*** | .64*** | .02 | .39*** | .56*** |
| Esteem | | 70*** | .04** | .35*** | 55*** |
| Knowledge | | | 20*** | .53*** | .52*** |
| Energized differentiation | | | | -.14***. | -.08*** |
**p < .05.
***p < .01.
Notes: Correlations computed on 2,423 brand-year observations for 290 brands in 25 categories for which CBBE measures are available. Data are standardized within category before computing correlations.
The regression model for SBBE of brand b in year y is:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
where RelStat and EnDif are the two CBBE principal components, C4 is category concentration, Hed is the perception of how hedonic the category is, FuncRisk is the perceived functional risk of the category, Social is the perceived social value of the category, and SecMkt is the dummy variable for whether the brand is in a secondary domain.
We mean-center the category characteristics so that the coefficients of the CBBE principal components can be interpreted as their effects at average values of category characteristics. Because SBBEby is an estimated parameter, we use WLS to estimate Equation 6. The weight is the inverse of the standard error of SBBEby divided by its standard deviation to account for the standardization applied by category. We use robust clustered standard errors because there are multiple observations per brand.
Table 5 shows the model results. The CBBE components and category moderators explain 47% of the variance in SBBE, and most of the effects are consistent with the propositions in Table 1. Relevant stature has a positive effect on SBBE (δ = .52, p < .01; Pi supported). This is enhanced by a category's social value (δ = .29, p < .10; P^ supported) but reduced for more concentrated categories (δ = -.50, p < .10; P12 supported). Thus, the greater the social signaling value of a category and the more fragmented it is, the more readily the status of a brand translates into SBBE. We also find that the more hedonic the category, the smaller the effect of relevant stature on SBBE (δ = -.09, p < .05; P1.4 not supported). We do not find a significant role for the category' s perceived functional risk (P1.1 not supported).
TABLE: TABLE 5 Regression of SBBE on CBBE Principal Components and Category Moderators
| Independent Variable | Expectation (Proposition) | Estimate | SE |
| Principal component for relevant stature (RelStat) | + (P1) | .52*** | .04 |
| x Category functional risk | + (P1.1) | -.01 | .17 |
| x Category concentration | (P1.2) | -.50* | .30 |
| x Category social value | + (P1.3) | .29* | .17 |
| x Category hedonic nature | + (P1.4) | -.09** | .04 |
| Principal component for energized differentiation (EnDif) | (P2) | -.08** | .04 |
| x Category functional risk | (P2.1) | -.08 | .19 |
| x Category concentration | + (P2.2) | .72*** | .26 |
| x Category social value | + (P2.3) | -.05 | .21 |
| x Category hedonic nature | + (P2.4) | .13*** | .05 |
| Secondary market | - | -.59*** | .13 |
| Category social value | | -.21 | .24 |
| Category hedonic nature | | .03 | .06 |
| Category functional risk | | -.08 | .21 |
| Category concentration | | .26 | .35 |
| Constant | | .64 | .67 |
| R2 | | .47 | |
| Number of brands | | 290 | |
| Number of observations | | 2,423 | |
*p < .10.
**p < .05.
***p < .01.
Notes: The dependent variable is a brand's SBBE, and the model is estimated using WLS, with weights equal to the estimated SBBE's inverse standard error. Data are standardized within category before model estimation. Robust clustered standard errors are reported.
Energized differentiation has a small, significant negative main effect (δ = -.08,p < .05; P2 supported). However, there are two category characteristics with positive moderating effects. Energized differentiation pays off more in terms of SBBE in more concentrated categories (δ = .72, p < .01; P21 supported), in line with the argument that if a category has a few big brands, consumers can better ascertain and appraise a brand's unique aspects. Energized differentiation also has a more positive effect on SBBE for more hedonic categories (δ = .13, p < .01; P24 supported), consistent with the notion that for these categories, consumers are better able to appreciate—and, thus, choose—unique brands. We do not find evidence for the moderating roles of functional risk and social value (P2.1 and P2.3 not supported).
Table 5 shows that brands that have extended into secondary domains have lower SBBE than what would be expected on the basis of their primary market CBBE (δ = -.59, p < .01). The main effects of the category characteristics are not significant, which is to be expected because the dependent variable is standardized by category.
Table 6 shows the estimates from the WLS regression models for the five marketing-mix elasticities. The explanatory variables are the two CBBE principal components: RelStat and EnDif. As before, all variables are standardized by category.
As we expected (see Table 1), higher scores on relevant stature are associated with more positive advertising (δ = .07, p < .10; P11 supported), more positive feature/display (δ = .16, p < .01; P7 supported), and more negative promotional price elasticities (δ = -.14, p < .01; P5 supported). These findings confirm the notion that brands strong on relevant stature have a large pool of (latent) customers interested in buying the brand. Promoting the brand through advertising, price promotions, and feature/display activities pays off for these brands. However, brands that are higher on relevant stature have lower distribution elasticities (δ = -.19, p < .01). Of course, such brands have the most distribution, but consumers are willing to go the extra mile to buy them, making gains in distribution less important, in line with Farris, Olver, and De Kluyver (1989).
Brands high on energized differentiation are in a very different position: their promotional price elasticity is weaker (δ = .09, p < .10; P6 supported). This result is in line with the idea that energized differentiation is associated with niche brands whose buyers are less price sensitive. These brands do have a stronger advertising elasticity (δ = .08, p < .10; P12 supported), in line with having a clear value proposition to communicate.[ 6] We do not find significant effects of the CBBE components on regular price elasticity (P3 and P4 not supported) or a significant effect of energized differentiation on the feature/ display or distribution elasticity (P8 and P10 not supported).
Drawing on the national performance of 290 CPG brands in 25 categories across ten years, we have examined the empirical association between CBBE and SBBE. Using widely accepted measures in the literature and in practice, we link the underlying dimensions of CBBE not only to brand intercepts but also to the effectiveness of five major marketing-mix variables. Next, we discuss the main insights organized along key themes. Within each theme, we offer managerial implications and, if applicable, opportunities for further research. Table 7 provides an overview of the main findings.
The link of SBBE with three of the four CBBE dimensions is positive and fairly strong. Thus, investments into CBBE pay off if they build consumers' awareness and understanding of what the brand stands for (knowledge), make the brand appropriate to the consumer (relevance), and enhance consumer regard for the brand (esteem). Examples of the brands in this study that do very well on these three CBBE dimensions and on SBBE are Budweiser, Coke, Marlboro, Folgers, Secret, Lysol, Tide, and Doritos. These brands have found a way to communicate what they stand for, be relevant across different segments of the market, and be held in high esteem. Overall, knowledge is the dimension that is most strongly correlated with SBBE. This provides generalizable empirical support for the conceptual proposition that building an understanding of what the brand stands for is the ultimate accomplishment for equity in the marketplace.
At the same time, we have also documented a small negative association between SBBE and the fourth CBBE dimension—energized differentiation—which reflects a brand's uniqueness compared with competitors and its agility to meet changing consumer demands. Thus, a strongly differentiated brand does not necessarily appeal to the masses. Specifically, the sample includes several niche-type brands that are low on knowledge and high on energized differentiation, with relatively low SBBE, many of which are fairly new, such as Fat Tire and Blue Moon beer, Bear Naked cereal, Axe deodorant, and Seventh Generation and Method household cleaners. These products entered the market during the period of analysis or in the decade before it. They needed to be different to find a place in the market, and several have not (yet) expanded beyond the niche in which they entered. As a result, their SBBE is low.[ 7]
TABLE: TABLE 6 Regression of Marketing-Mix Elasticities on CBBE Principal Components
| Effect on Elasticity Of… |
| Regular Price | Promotional Price | Feature/Display | Distribution | Advertising |
| CBBE Principal Component | Expectation (Proposition) | Estimate | Expectation (Proposition) | Estimate | Expectation (Proposition) | Estimate | Expectation (Proposition) | Estimate | Expectation (Proposition) | Estimate |
Relevant stature (RelStat) | + (Pa) | -.02 (.07) | (Ps) | -.14*** (.05) | + (P7) | .16*** (.05) | + or (Pg) | -.19*** (.05) | + (Pn) | .07* (.04) |
Energized differentiation (EnDif) | + (P4) | .08 (.06) | + (Pe) | .09* (.05) | "(Pa) | .08 (.06) | (P10) | .03 (.05) | + (P12) | .08* (.05) |
| Constant | | .03 (.06) | | .07 (.05) | | -.15** (.06) | | -.05 (.05) | | -.06 (.05) |
| R2 | | .01 | | .06 | | .04 | | .06 | | .02 |
| N | | 290 | | 290 | | 276 | | 290 | | 226 |
*p< .10.
**p < .05.
***p < .01.
Notes: Standard errors appear in parentheses. The model is estimated using WLS, with weights equal to the elasticities' inverse standard errors. Data are standardized within category before estimation. Thus, the variation being explained is across brands within a category, not across categories. N is smaller for feature/display and advertising because some brands lack variation in these variables.
TABLE: TABLE 7 Association of CBBE with SBBE and Marketing-Mix Elasticities: Summary of the Findings
| Association With… |
| CBBE Dimension | SBBE | Marketing-Mix Elasticities |
| Relevance, esteem, knowledge, combined in relevant stature | • There is positive and significant correlation between SBBE and relevance (.39), esteem (.35), and knowledge (.53). • The effect of relevant stature on SBBE is significantly positive (P1 supported). • The effect of relevant stature on SBBE is stronger for ◦ Less concentrated categories (P1.2 supported). ◦ High—social value categories (P1.3 supported). ◦ Less hedonic categories (P1.4 not supported). • The effect of relevant stature on SBBE is not significantly moderated by functional risk (P1.1 not supported). | • Higher relevant stature is associated with ◦ No significant difference in regular price elasticity (P3 not supported). ◦ A stronger (more negative) promotional price elasticity (P5 supported). ◦ A stronger feature/display elasticity (P7 supported). ◦ A weaker distribution elasticity (P9 no prediction). ◦ A stronger advertising elasticity (P11 supported). |
| Energized differentiation | • There is negative and significant correlation betweenSBBEand energized differentiation (-.14). • The effect of its principal component on SBBE is significantly negative (P2 supported). • The effect of energized differentiation on SBBE is stronger for ◦ More concentrated categories (P2.2 supported). ◦ More hedonic categories (P2.4 supported). • The effect of energized differentation on SBBE is not significantly moderated by ◦ Functional risk (P2.1 not supported). ◦ Social value (P2.3 not supported). | • Higher energized differentiation is associated with ◦ No significant difference in regular price elasticity (P4 not supported). ◦ A weaker (less negative) promotional price elasticity (P6 supported). ◦ No significant difference in feature/display elasticity (P8 not supported). ◦ No significant difference in distribution elasticity (P10 not supported). ◦ A stronger advertising elasticity (P12 supported). |
However, high energized differentiation does not mean that a brand has to be a niche player. Several mature brands in the sample (e.g., Dr Pepper, Coke, Special K, Lysol, Doritos, Tide) do reasonably well on energized differentiation as well as on the other CBBE dimensions and, thus, on SBBE. Presumably, the combination of CBBE dimensions gives these brands more staying power in the long run, though it also takes several years of consistent brand development to build up the combination.[ 8]
We have focused on the contemporaneous association between CBBE and SBBE. Further research could examine the dynamics of how current CBBE dimensions might drive future SBBE. We conducted some preliminary analysis and did not find any difference between contemporaneous and oneor two-year lagged effects. However, at least for new brands, energized differentiation in the early years may have a positive effect on SBBE in later years. There may also be dynamic effects among CBBE dimensions. For example, energized differentiation in the present may enhance esteem in later years. Note, though, that brand equity is built over years, not weeks or months, so a long time unit of analysis and a much longer data period would be needed to assess the dynamics in its evolution.
Variation in the association between CBBE and SBBE across categories is explained by the extent to which brands serve as cues for simplifying choice and provide social value and personal enjoyment. As before, patterns differ for energized differentiation versus the other three CBBE dimensions, which we combine into relevant stature.
The spotlight analysis in Figure 3 illustrates the effect sizes. Using the estimates (Table 5), we compute the effect of CBBE on SBBE for categories in the 10th versus the 90th percentile of the distribution of category characteristics. The coefficients represent changes in SBBE measured in standard deviations resulting from a one-standard-deviation increase in CBBE. The effect of relevant stature on SBBE is substantially stronger for the 90th versus 10th percentile on social value (.63 vs .37); it is also considerably stronger for high versus low hedonic nature (.67 vs .41) and for low versus high concentration (.61 vs .35).
The spotlight analysis also demonstrates that energized differentiation can enhance SBBE in some circumstances. For highly hedonic categories, the effect is positive (.09). This is also the case for highly concentrated categories (.17).
These results offer guidance to brand managers on which CBBE dimensions to prioritize contingent on the category. For categories that have high social value (e.g., beer, cigarettes), are fragmented (e.g., frozen pizza and dinners), and/or are less hedonic (e.g., disposable diapers), it especially pays off to focus on relevant stature instead of highlighting differences. The brand' s positioning and communication should explain what the brand stands for (enhancing brand knowledge), make it relevant for many consumers, and enhance its esteem. While relevant stature cannot be ignored, brands that are in hedonic (e.g., coffee) or concentrated categories (e.g., ketchup), or those with lower social value (e.g., mayonnaise, mustard), should highlight or enhance energized differentiation. Because differences between brands are more appraisable in these categories, marketers must communicate both the brand' s unique selling points and its efforts to meet consumers' needs.
The results on the association of CBBE with marketing-mix elasticities caution against a broad-brush assumption that brand equity enhances all marketing-mix response. Reality is more nuanced—both along the dimensions of CBBE and across marketing-mix elements. We find that relevant, well-known brands held in high esteem benefit more from price discounts and display/feature support. A spotlight analysis (see Figure 4) based on the model estimates in Table 6 illustrates that this impact is sizable. For example, brands at the 90th versus 10th percentile on relevant stature average a price promotion elasticity of -3.32 versus -2.64, a 26% increase in magnitude; they also benefit from more positive advertising elasticities (.005 vs .001), though the magnitudes are small overall. Importantly, distribution elasticities are smaller for brands in the 90th versus the 10th percentile on relevant stature (.33 vs .59). Brands high in relevant stature get broad distribution, but their marginal return on distribution is lower because consumers are willing to search for them. This result is not simply because such brands have reached a saturation point in distribution. We do not measure all commodity volume or PCV-weighted brand distribution, which is indeed close to 100% for most big brands. Instead, we measure the weighted share of SKUs on the shelf, which is much lower even for the strongest brands. The implication is that high-relevant stature brands should prioritize better promotional pass-through and feature/display support over additional SKUs on the shelf.
In contrast, brands that excel in energized differentiation benefit relatively less from price promotions (-2.73 vs. -3.13 for the 90th vs. 10th percentile). They are better supported through their relatively effective advertising investments (.005 vs .001) and their marginally higher return on distribution (.49 vs .46). It is important for such brands to balance the pull and push sides of their marketing mix so that neither gets too far ahead of the other, especially because many of them are new and may have limited marketing budgets.
This research shows that ( 1) the dimensions of CBBE are not well-aligned, ( 2) CBBE does not always align well with SBBE, and ( 3) CBBE aligns better with market share than with SBBE. The nature of these "misalignments" has important ramifications for academic research, for firms tracking brand equity, and for brand managers using these measures as diagnostic tools.
Dimensions of CBBE. As we noted previously, academic researchers use measures of brand equity in a variety of contexts such as new product extensions, marketing mix, financial outcomes, and strategic brand alliances. Understandably, researchers are constrained by the availability of CBBE data. However, because different dimensions of CBBE and SBBE are likely to have very different effects on the phenomena of interest, our work implies that researchers should make and test more specific predictions related to the particular measures they use rather than rely on broad-based predictions related to brand equity. This research also cautions against combining very different measures into a composite brand equity score, because this may mask varying or even opposing effects of the underlying measures. Our analysis suggests that it is particularly important to track energized differentiation separately from the other dimensions.
CBBE versus SBBE. The finding that the alignment of CBBE with SBBE is strong but not perfect offers a diagnostic opportunity. New and important insights can emerge from outliers, not just from observations that are in line with the overall association between CBBE and SBBE. Figure 5 plots SBBE against CBBE for the beer category in 2011, using a regression line and its 95% confidence interval for the mean. Brands above the confidence interval can be termed "overachievers" because they garner significantly more SBBE than expected based on their CBBE. Conversely, brands below the confidence interval can be viewed as "underachievers."
A notable overachiever is Corona, the Mexican beer brand that succeeds in the marketplace despite relatively poor taste ratings (Stock 2014). Its success has been attributed to a consistently advertised "sand, sun, and lime wedge" image. The challenge for an overachiever such as Corona is to find out through marketing research why its relatively strong SBBE is not mirrored in a strong position in the hearts and minds of consumers (CBBE). Otherwise, the brand may not sustain its marketplace strength.
A notable underachiever is Fat Tire, a brand that is highly differentiated and that began national distribution around 2002. Its position is in line with the pattern that newer brands tend to be underachievers because it takes time for the positive attitudes they build to percolate into marketplace choices. New brands should monitor the development of their SBBE over time and ensure that they migrate upward on the CBBE-SBBE plot. Tracking market share is not enough, because that can be propped up with price cuts and other temporary tactics. Miller is also an underachiever, but unlike Fat Tire, its position is not attributable to newness or to differentiation, making it a larger cause for concern. Such an underachiever must also research why its relatively favorable CBBE position does not manifest itself in SBBE—what is stopping consumers from acting in line with how they think and feel about the brand?
Our purpose is not to explain why specific brands are under-or overachievers but to illustrate the value of the analysis as a diagnostic tool. Irrespective of whether a marketer concludes that its place on the plot is a cause for concern, it is useful to compare each CBBE dimension with SBBE and, if a brand is significantly "off the line," diagnose the cause for it.
CBBE versus SBBE versus market share. Sales-based brand equity removes from a brand's market share the effects of its objective attributes and marketing mix, so that a brand' s features and (possibly temporary) tactics do not confound its intrinsic equity. Obviously, when consumers choose brands, they take into account the whole package (SBBE + attributes + marketing mix), not just SBBE. Therefore, it is not surprising that CBBE aligns more strongly with market share than with SBBE. Other researchers have argued that brand equity is also reflected in consumers' subjective perceptions of a product' s experience attributes (Goldfarb, Lu, and Moorthy 2009; Park and Srinivasan 1994; Srinivasan, Park, and Chang 2005). Further research could separate the effects of "experience" from "search" attributes and examine how CBBE affects perceptions of these different attribute types.
We tested several propositions on how CBBE links to SBBE, on how this link is moderated by category characteristics, and on how CBBE links to marketing elasticities. We find support for many of our propositions, but the ones for which we do not find support deserve examination. There is only one case in which we find a significant effect in the opposite direction than anticipated: the effect of relevant stature on SBBE is smaller for more hedonic categories. An explanation for this finding is that the more personally enjoyable a category is (a characteristic that is more inward-looking), the less important are broad appeal and status (characteristics that are more outwardlooking) for SBBE.
For some other propositions, we do not find significant effects. One intriguing null result is that functional risk does not strengthen the effect of relevant stature on SBBE, though Fischer, Volckner, and Sattler (2010) identified functional risk as a driver of brand relevance. An explanation is that their research examined vastly different categories ranging from CPG to electronics, retail stores, and automobiles, whereas we study CPG categories with less variation in perceived functional risk.
Another notable result is the lack of effect of CBBE on regular price elasticity. We anticipated that high-CBBE brands would have lower regular price elasticities. However, highCBBE brands may face a relatively small loss in demand when their regular price increases, but a relatively strong gain in demand when their regular price decreases (Ailawadi, Lehmann, and Neslin 2003). Our model assumes symmetric elasticities, but further research could allow for asymmetric effects. A final result worth investigating is the insignificant effect of energized differentiation on feature/display and distribution elasticities. Our expectations were based on the notion that differentiated brands mostly appeal to specific consumer segments, reducing the overall draw of feature/display and additional distribution. However, distribution and merchandising may, like advertising, make more consumers in those segments aware of the differentiated (and often new) brands.
No research is perfect, and ours is no exception. Further research can attempt refinements to our study to deepen the insights. For instance, we use aggregate scanner data to measure SBBE because this matches the national level of the CBBE data. Future studies could estimate less aggregate storeor market-level models and study geographical variation. We have examined one type of CBBE and one type of SBBE measure. Although the measures we chose are arguably the most widely used in the literature, there is certainly value in examining others. In addition, further research could try to estimate an integrated model where the intercepts and response parameters of the market share model are specified as a function of (time-varying) CBBE measures while allowing for parameter heterogeneity and endogeneity. A transfer function dynamic HLM could be suitable (Peers, Van Heerde, and Dekimpe 2016).
Despite its limitations, this article offers new insights into the strength and nature of the relationship between CBBE and SBBE measures. The finding that these measures align quite well but not perfectly, and that there are important differences at the level of component parts, shows that there is room for important follow-up questions for both brand managers and academic researchers in this domain.
Notes: Data are shown for the most recent year (2011). All measures except market share are standardized across brands for comparability. Regression lines show the association between the measures.
A. Effect of Relevant Stature on SBBE
Notes: C4 = category concentration. Effects are computed at the 10th and 90th percentiles of the category characteristics using the regression results in Table 5.
Notes: Effects are computed in two steps: First, we calculate the impact of relevant stature and energized differentiation at their 10th and 90th percentiles on standardized elasticities, using the regression results in Table 6. Then, we convert the effect to unstandardized elasticities by multiplying them with the (weighted) standard deviation of estimated elasticities and adding their (weighted) mean.
Notes: Data are shown forthe most recent year (2011) and are standardized for all measures across brands to facilitate comparability. Regression lines with 95% confidence bounds are shown.
and Extramural Fellow, CentER at Tilburg University (e-mail: heerde@massey.ac.nz). The authors contributed equally to this research. The authors thank BAV Consulting, especially Michele Jee, and SymphonyIRI Inc. for providing the data used in this article as well as Rong Guo of the Tuck School at Dartmouth for her invaluable assistance with data preparation. They also thank Matthew Paronto for his help and seminar participants at University of Groningen, Universidad Carlos III de Madrid, Wageningen University, the AiMark Summit, and the 2016 Marketing Science Conference, as well as Marnik Dekimpe, Paul Farris, Dennis Fok, Kevin Keller, Scott Neslin, Florian Stahl, and Franziska Volckner for helpful comments. All analyses in this article are by the authors and not by SymphonyIRI Group or BAV Consulting. Rajkumar Venkatesan served as area editor for this article.
Endnotes 1 The original four pillars identified by BAV Consulting were differentiation, relevance, esteem, and knowledge. Mizik and Jacobson (2008) then identified energy as a fifth pillar, which has since been combined with differentiation as energized differentiation.
2 We dropped toothbrushes and photo film because their sales volume is provided in counts without information on the number of toothbrushes in a package or the number of exposures in a roll of film.
3 A file listing the IRI subcategories and IRI brands included in our analysis, along with our coding of their respective parent brands, is available for download under "Supplemental Material" at http:// dx.doi.org/10.1509/jm.15.0340.
4 A hierarchical linear modeling (HLM) framework that models market shares in a first layer and explains the intercepts and response parameters in a second layer is theoretically more efficient. However, we have ten years of weekly data to estimate the brand-specific parameters with precision, so the potential efficiency advantage of HLM is likely to be small (Gelman 2005). Conversely, an HLM would have to deal with missing data in the second layer for the 151 out of 441 brands without BAV data. Replacing the missing data with zeros or averages, or using a missing data dummy, would introduce biases because the missing data may not be random (Schafer and Graham 2002). Adopting a Bayesian data imputation approach would add substantial complexity. For these reasons, and because it accounts for uncertainty in the model estimates and for error dependencies, we believe the two-stage regression is preferable to HLM here.
5 We also converted the principal component-level results back to the level of the individual four CBBE dimensions (e.g., Rust, Lemon, and Zeithaml 2004). Those results are summarized in Web Appendix F.
6 The category characteristics may moderate the effects of the CBBE components on elasticities (e.g., Erdem, Swait, and Louviere 2002). Although they are third-order effects for which we do not have strong expectations, we did test them. Complete results are available in Web Appendix G.
7 Indeed, if we exclude these newer brands from the analysis, the association between energized differentiation and SBBE becomes insignificant.
8 To test for any concurrent synergies, we included an interaction between energized differentiation and relevant stature in the regression of SBBE on CBBE components but found that it was not statistically significant.
GRAPH: FIGURE 2 The Association of CBBE with SBBE and Market Share
GRAPH: FIGURE 3 Effect of Relevant Stature and Energized Differentiation on SBBE for Different Levels of Moderators
GRAPH: FIGURE 4 Marketing Elasticities for Different Levels of CBBE
GRAPH: FIGURE 5 Association Between SBBE and CBBE: Beer Category
DIAGRAM: FIGURE 1 Guiding Framework
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~~~~~~~~
Hannes Datta is Assistant Professor of Marketing, Department of Marketing, Tilburg University.
Kusum L. Ailawadi is Charles Jordan 1911 TU'12 Professor of Marketing, Tuck School of Business, Dartmouth College.
Harald J. van Heerde is Research Professor of Marketing & MSA Charitable Trust Chair in Marketing, School of Communication, Journalism & Marketing, Massey Business School, Massey University.; Extramural Fellow, CentER at Tilburg University.
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Record: 94- Immediate Responses of Online Brand Search and Price Search to TV Ads. By: Du, Rex Yuxing; Xu, Linli; Wilbur, Kenneth C. Journal of Marketing. Jul2019, Vol. 83 Issue 4, p81-100. 20p. 9 Charts, 3 Graphs. DOI: 10.1177/0022242919847192.
- Database:
- Business Source Complete
Immediate Responses of Online Brand Search and Price Search to TV Ads
This study aims to deepen the understanding of evaluating TV ad spots by their immediate effects on important online activities. The authors merged minute-by-minute brand search and price search data with spot-level TV advertisement data for the three leading pickup truck brands in the United States over an 11-month period. They presented a generalizable modeling framework and used it to estimate the size and variation of immediate online responses to TV ads. The average elasticity of brand search to a brand's own national ads is.09, and the average elasticity of price search to a brand's own national ads is.03. Given ad audience size, immediate search responses vary with ad creative characteristics, audience category interest, slot of the break, program genre, and time factors. Overall, the results show that ordinary TV ads lead to a variety of immediate online responses and that advertisers can use these signals to enrich their media planning and campaign evaluations.
Keywords: TV advertising; attribution; brand search; price search; programmatic
Although TV ad spend in the United States was surpassed by digital in 2016, TV remains an important medium, accounting for approximately 37% of total ad spend ([ 8]). In 2020, advertisers in the United States are projected to spend $70 billion on TV advertising ([10]). Digital advertising has overtaken TV and other offline media for many reasons; for example, the perceived ease with which digital advertisers can quantify the relative effectiveness of different ad insertions on the basis of behavioral responses such as click-throughs and conversions. Such a capability allows digital advertisers to have greater confidence in their selections of ad creative and media placements.
By contrast, with the exception of informercials and other direct-response-oriented ads, most traditional TV advertisers have not relied on behavioral response measures to determine the relative effectiveness of different ad copy or media placements. Instead, in evaluating ad creative, TV advertisers have relied on either "gut feel" or attitudinal measures collected through focus groups or sample surveys. In planning media placements, TV advertisers have long relied on program ratings and basic audience demographics such as age and gender.
Meanwhile, consumers' self-reported television usage has not fallen: it was reported at 2.77 hours per weekday per person in both 2013 and 2017 ([ 1], [ 2]). So-called "second screening" behaviors, particularly during commercial breaks, have rapidly become pervasive, with 178 million Americans regularly using a second-screen device while watching TV ([ 9]). Ready access to a second screen empowers TV viewers to take immediate actions after seeing an ad, such as searching for product reviews, attributes, or prices; expressing opinions on social media; or placing an order on the advertiser's website. Given rampant ad blocking, ad fraud, and nontransparency in digital advertising markets, advertisers that aim to influence online actions may wish to continue advertising in offline media such as television. They may further wish to use detailed online response data to help refine media plans and campaign evaluations.
Both practitioners (e.g., [29]; [43], [44]) and researchers (e.g., [17]; [30]) have recognized that TV ads can cause immediate—within minutes—post-ad spikes in various online activities (e.g., searches or app downloads for the advertised brand, visits to the advertiser's website). The prevalence and immediacy of such ad-driven online responses raise a tantalizing question: Can TV advertisers use post-ad spikes in online activities to assess the relative effectiveness of different ad spots? If the answer is affirmative, it would have the potential to dramatically improve how TV ad copy and media placements are chosen, which would ultimately lead to enhanced cost-effectiveness of TV as an advertising medium.
Indeed, recognizing such potential, many attribution vendors have introduced services that promise to link spikes in online activities to the individual TV ads that caused them, helping advertisers select ad copy and media placements to maximize immediate online response. We are aware of more than a dozen vendors that offer such services. For example, Google Analytics 360 TV Attribution pairs minute-level ad airing data with search and website traffic data and uses a machine learning algorithm to "accurately attribute digital activity to your TV ad spots...to help you make smart choices about your advertising investment."[ 5] Similarly, Neustar MarketShare's TV attribution application, in collaboration with comScore Rentrak, measures the impact of TV ad spots on website visits and inbound calls, which "shows why your ads work. Or don't. (Was it the network? Creative? Timing? A combination of those?)" TVSquared ADvantage claims to have helped more than 700 brands, agencies, and networks improve TV campaign effectiveness by tracking "how TV drives response via phone, app, mobile, web and SMS," thereby allowing advertisers to "understand spot-level and campaign-wide performance by day, daypart, network, genre, program, creative and audience."[ 6] In 2018, Adobe Advertising Cloud TV, a leading platform for programmatic TV ("automated, data-driven planning and buying of television advertising"; [39]), launched a partnership with TVSquared ADvantage to allow advertisers to optimize national, cable, and local TV buys on the basis of spot-level online response.[ 7]
In addition to advertising attribution vendors, many start-ups and other digital-first marketers have developed similar capabilities in-house. Private conversations with practitioners indicate that many are using these practices to mechanically refine their TV ad creative and media schedules without a deeper understanding of the TV-to-online spillovers. It is against this backdrop that we conducted the current study, with the following intended contributions.
Methodologically, all the TV attribution vendors have kept the core algorithm behind their proprietary solution a trade secret, providing little detail to the public for an independent and impartial evaluation. Getting attribution right is notoriously difficult, even more so for mass media such as television. It is one matter to show that TV ads can cause statistically significant immediate post-ad spikes in online activities; it is another to measure spot-level responses with such precision that one can quantify the relative performances of different ad creative and media placements. We see many challenges that need to be adequately addressed:
- Establishing a proper baseline at the minute level. The baseline must be flexible enough to account for complex trend and seasonality (e.g., minute-of-hour, hour-of-day, day-of-week patterns). In addition, many other factors can influence the baseline and thus cause potential misattribution. For example, a portion of the post-ad spike in online activities could have been caused by the absence of programming content during a commercial break, as opposed to the presence of a particular ad spot. Alternatively, many correlated observables and unobservables can influence both the focal advertiser's ads and online activities. For example, a competitor's media schedule could be correlated with the focal advertiser's, and competitive ad spots could have an immediate spillover on the focal advertiser's online metrics.
- Separating signal from noise. Many online activities are inherently very noisy, and many TV ad insertions may produce subtle signals, especially when the number of impressions generated by a TV spot is small or the response rate per impression is low.
- Assigning attribution across overlapping spots. It is not uncommon that multiple spots of the same TV advertiser may be aired on different networks at approximately the same time—in the same minute or with overlapping durations (e.g., ad insertions by a heavy prime-time advertiser or during a blitz campaign). One therefore must be able to assign attribution across overlapping spots in a logically coherent manner.
- Accounting for a multitude of moderating factors. The amount of immediate online response to an ad spot is determined by the number of ad impressions and the response rate per impression. The former requires a reliable measure of ad audience size (as opposed to just the program rating or ad spend). The latter can be a function of ad creative characteristics, media placement, and audience composition. Because these moderating factors can be correlated with one another, one needs to account for them simultaneously to minimize omitted variable biases.
We aim to develop a rigorous, yet practical, approach to addressing these challenges. We propose a modeling framework that links ad insertions to minute-by-minute online metrics and illustrate it with "real-world" size data compiled from multiple sources. We intend to provide practitioners and researchers alike a transparent and replicable tool for TV attribution based on immediate online response. Advertisers, agencies, and networks can use our method as a benchmark in evaluating the proprietary solutions offered by attribution vendors.
In addition to making a methodological contribution, we intend to make a substantive contribution by answering the following empirical questions, which can potentially serve as reference points for future research in this area:
- What is the typical rate of immediate online response to a regular TV ad spot? How does the elasticity compare with those reported in prior studies?
- How long does the immediate response last? How does the response rate vary by minute after an ad insertion? Does it peak in the minute the ad is shown and then decay exponentially, or does it peak in the minute after the ad is aired and then fade away gradually?
- How is the response rate similar or different for brands from the same product category?
- Is there immediate online response to competitors' TV ads? Are competitive spillovers positive or negative? How do spillovers between brands compare with own-brand effects? Are spillovers asymmetric between leader and follower brands?
- How is the immediate online response affected by ad creative quality? All else being equal, is there more immediate online response to ads that are deemed as more informative? What about ads that are rated as more likable, or ads that make the advertised product more desirable?
- How do media placement factors affect the rate of immediate online response to an ad spot? All else being equal, how much higher is the response rate for the first slot in a commercial break? What about prime time versus non–prime time, broadcast versus cable, live sports versus other programs, weekend versus weekdays?
- How does audience category interest affect the rate of immediate online response?
- How may answers to the previous questions vary depending on the nature of the online activity in question (e.g., brand search vs. price search)?
As the empirical context for our study, we focus on three top pickup truck brands in the U.S.—Ford F-Series, Chevy Silverado, and Ram Trucks—for four key reasons. First, car shoppers engage in various online activities before making a purchase, with a purchase funnel that can last for weeks or months ([19]). Car shoppers are exposed to numerous ads and promotions from a myriad of online and offline sources. These exposures make it nearly impossible to quantify the impact of any regular TV ad spot on sales or brand attitudes ([ 7]). This, in turn, makes the automotive industry highly relevant for testing the potential of refining TV media planning and campaign evaluation by using immediate online responses to TV ads.
Second, these three pickup truck brands represent a set of well-defined direct competitors, allowing us to compare and contrast effect estimates to identify similarities and differences as well as to quantify the direction and degree of competitive spillovers. According to Motor Intelligence, Ford F-Series had a market share of about 31% in 2016, followed by Chevy Silverado at 22% and Ram Trucks at 18%, continuing a 33-year trend of stable market share rankings in a $40 billion category ([41]).
Third, these three brands offer a fertile ground for investigating how various ad creative–, media placement–, and audience-related factors may moderate the rate of immediate online response to TV ads. During the period under study (a span of 493,920 minutes), the three brands ran 27,562 ad spots on national TV, deploying 169 distinct pieces of creative and spanning a wide range of dayparts, pod positions, broadcast and cable networks, and program genres. This allows us to quantify immediate online response to TV ad insertions under a wide variety of conditions. Furthermore, we have access to ad audience data and a measure of audience interest in the pickup truck category for each national spot. This allows us to separate, for the first time in research in this area, the effects of ad creative and media placements from those of audience characteristics.
Fourth, these three brands present a conservative test of our modeling framework for quantifying the immediate online response attributable to individual ad spots. Ford F-Series, Chevy Silverado, and Ram Trucks are all mature brands that are well known to U.S. consumers (in contrast to newer or lesser known brands, for which ad viewers may exhibit a stronger tendency to respond immediately by searching online). Furthermore, many prior studies in this area have examined ad spots in "must-see" TV programs that had tens of millions of viewers (e.g., the Super Bowl, the Olympic Games). The average and median audience per spot for the "ordinary" national TV ads included in our study are.5 million and.2 million, respectively. In addition, no prior study has examined local TV ads, which tend to have a much smaller audience per spot, presumably causing a much smaller and thus harder-to-detect post-ad spike in online activities. Unlike national spots, we do not directly observe the audience size for each local spot. As a result, we use the spend estimate of each local spot as a proxy. In our study, the three pickup truck brands had in total 750,672 local ad insertions, with an average (median) spend of $348 ($159) per spot. The upshot is that the empirical context of our study enables us to test whether our modeling framework is sensitive and reliable enough to quantify immediate online response to ordinary national and local TV ads, thus making our findings more generalizable to everyday circumstances encountered by the majority of TV advertisers.
Before proceeding, it is important to acknowledge that while it is useful and insightful to model how and when TV ad spots can drive immediate brand and price searches, such midfunnel performance metrics are only part of a bigger picture because advertisers are ultimately interested in driving bottom-line performance metrics such as sales. Although it is beyond the scope of the current study, more research is needed in linking the former with the latter.
The rest of the article consists of the following. The next section discusses how our study relates to and extends the existing research. We then present the proposed modeling framework and the data used to illustrate it. We report the empirical findings and results from what-if analyses. We conclude with a discussion of the managerial implications and directions for future research.
As the second-screen phenomenon during television advertising has become more prevalent, a growing body of research has documented some of its effects on various online metrics. Table 1 provides an overview of this stream of research and identifies the key dimensions that distinguish the current study from prior work.
Graph
Table 1. Literature on Online Response to Offline TV Ads.
| Research | Response Variables | Time Window/Unit of Analysis | Moderating Effects | Competitive Spillovers | Ad Content Data | Ad Audience Data |
|---|
| Zigmond and Stipp (2010)a | Online search (Google) | | No | No | No | No |
| Hu, Du, and Damangir (2014) | Online search (Google) and sales | Monthly | No | No | No | No |
| Laroche et al. (2013) | Online search | Weekly | No | No | No | No |
| Tirunillai and Tellis (2017) | Online chatter | Daily | No | No | No | No |
| Chandrasekaran, Srinivasan, and Sihi (2018) | Online search (Google) | Three-day window | Yes | No | Yes | No |
| Joo et al. (2014) | Online search (Google) | Hourly | No | No | No | No |
| Joo, Wilbur, and Zhu (2016) | Online search (AOL) | Hourly | Yes | No | Yes | No |
| Guitart and Hervet (2017) | Customer conversions | Hourly | No | No | No | Yes |
| Lewis and Reiley (2013) | Online search (Yahoo!) | Minute | No | No | No | No |
| Liaukonyte et al. (2015) | Brand website traffic on desktop and laptop (direct and search engine referrals), and online purchases on desktop and laptop | Two-minute window | Yes | No | Yes | No |
| Fossen and Schweidel (2017) | Online WOM | Two-minute window | Yes | No | Yes | No |
| Kitts et al. (2014) | Web traffic | Five-minute window | No | No | No | No |
| Hill et al. (2016) | Online search (Bing) | Minute | No | No | No | No |
| He and Klein (2018) | Online sales | Minute | No | No | No | Yes |
| Current research | Online brand search (Google) and online price search (car shopping websites) | Minute | Yes | Yes | Yes | Yes |
1 a[43] published several case studies with only data visualizations and no formal econometric analysis.
[43], [44]) published the first case studies that documented large post-ad spikes in Google search for the advertised brands following TV ads during the opening ceremonies of the 2008 and 2010 Olympic Games. Since then, several studies have found a similar positive effect of TV ads on online search ([ 5]; [17]; [20]; [22]; [26]; [29]). Further research has shown that TV ads lead to other online responses as well, including brand website traffic ([24]; [30]), online word of mouth (WOM; [13]; [25]; [38]), and online conversions ([14]; [16]). These results stem from various types of analyses using monthly, weekly, daily, hourly, or minute-level data. Given that over 90% of TV ad spots are shorter than one minute, the most granular analysis at the minute level is more desirable because a smaller data interval could better eliminate potential aggregation bias ([37]). We therefore focus on comparing the current research with previous studies that have also conducted analyses at the minute level (see Table 1).
Even though there seems to be a broad consensus that television advertising leads to a variety of behavioral responses online, only a handful of previous studies have investigated how factors related to ad creative and media placements moderate those effects. [30] found that the effects of TV ads on brand website traffic and subsequent online purchases vary depending on whether the ads have an action, information, emotion, or imagery focus. [13] showed that featuring a hashtag or the web address in the call to action increases subsequent online brand WOM for ads that air in the first slot of a commercial break, but featuring a phone number reduces subsequent online chatter.
Both articles used innovative ad content measures—[30] employed research assistants to code content, and [13] brought in data produced by a firm called iSpot by analyzing advertisement videos. We expand this small number of studies by examining how consumer attitudinal responses to ad creative (collected by a firm called Ace Metrix from large panels of survey respondents) moderate immediate online brand and price search response, in addition to other media- and audience-related moderators. Therefore, the moderating effects of ad content enter the analysis as ad creative quality ratings, rather than specific content elements within individual ad creatives. Given the large and diverse nature of stimuli encoded within TV ads, it is possible that these summary evaluations are both more parsimonious and more complete measures of content than prior studies were able to access.
More broadly, the current study contributes to the literature in five notable ways. First, it is the first article to study the effect of television advertising on price search (i.e., requesting price quotes at car shopping websites), and it further allows for direct comparison of those effects to the effects of TV ads on brand search from Google. By differentiating between brand search and price search, it helps improve our understanding of how TV-to-online spillovers vary across different stages of the purchase funnel. The answers can influence brands' media planning and buying practices to reach targeted audience at the "right" moment of the shopping journey.
Second, most of the existing studies measured advertising exposure using either ad expenditures ([17]; [18]; [20]; [22]; [26]) or ad gross rating points ([14]; [16]), which are typically measured at the telecast or quarter-hour level. To the best of our knowledge, we are the first to use spot-level ad audience size data in quantifying the rate of immediate online response to regular TV ads. This is important because consumer ad avoidance varies throughout commercial breaks and sharpening the resolution of the number of viewers exposed to each ad spot facilitates greater statistical power and estimation precision.
Third, we were able to gain access to an important measure at the spot level—namely, the proportion of viewers who were contemporaneously in the market for a new pickup truck. It seems likely that TV-to-online spillovers will be strongly influenced by the proportion of viewers who are category shoppers, but this has never been quantified in any similar context. The possibility was previously tested in the TV/YouTube context by [ 6], who showed that accurate evaluation of ad effects depends critically on viewers' preexisting brand knowledge. As far as we know, we are the first to quantify how audience interest in the advertised category affects viewers' immediate online search response after seeing a TV ad.
Fourth, the current study contributes to the literature on advertising competitive spillovers. Perhaps the most similar paper from this literature is [33], who found that advertisements for restaurants increased phone referrals to competing restaurants. Our results complement a larger literature showing positive/negative/no competitive spillovers, including [ 3] in catalog retailing, [21] in cruises, [36] in prescription drugs, [28] in online display advertising, [11] and [12] in targeted promotion, and [34] in brick-and-mortar store feature advertising. The direction and degree of competitive spillovers have not been reported in the context of TV ads and online responses. The current research aims to uncover how TV ads spill over to brand and price search for competitors' products and further to quantify possibly asymmetric effects between competitors.
Finally, we offer a generalizable modeling framework that should prove useful to brands that want to quantify the immediate online behavioral consequences of their TV ad creative, the programs during which the ads run, and the people who view the ads.
In this section we present a framework for modeling online activities at the minute level, which is decomposed into the sum of a baseline, an immediate response caused by TV ads, and an error term. The baseline is allowed to have, among other things, an hourly fixed effect and a within-hour trend that can vary by hour of the week. The immediate response to an ad spot is modeled to have a duration and a flexible decay pattern that are determined empirically. The immediate impact of each ad on online activity is modeled as the product of the ad audience size (or cost, when audience data are not available) and a response rate, which in turn depends on the characteristics of the ad creative, media placement, and audience. The error term is serially correlated, with the pattern determined empirically. Although parts of the model are tailored to the automotive industry (e.g., we separate ad spots into own national, competitor national, own local, and dealers associations), the framework is readily adaptable to other empirical contexts.
Let us assume online activity for brand b in minute t, , consists of the following components:
Graph
1
where
- denotes the baseline of activity l for brand b in minute t (i.e., what would have been the volume of brand or price search if there had been no TV ads), which we specify as a function of fixed hour effects, within-hour trends that can vary by hour of the week, and the volume of search for a control keyword ("SUV") in minute t, as described in more detail subsequently;
- and denote the total audience (in millions) exposed to national TV ads in minute for, respectively, brand b and each of its competitors ;
- and denote the rates at which ad audiences and , respectively, respond to an ad exposure at minute with online activity l for brand b in minute t;
- and denote the spend (in $10,000s) on local TV ads by, respectively, brand b and its dealers associations in minute [ 8];
- and denote the rates at which ad spend and , respectively, generate, after an i-minute delay, online activity l for brand b in minute t; and
- denotes the error term, which is given a moving-average representation, with , to allow for a flexible pattern of serial correlation.
Of key interest is —that is, for every one million exposures to a national TV ad of brand b in minute , the number of online responses of type in minute t, which we specify as
Graph
2
where denotes a baseline rate of i-minute delayed response, captures a long-term trend in viewers' tendency to respond immediately to brand b's national TV ads, and captures the moderating effect of the jth "lift factor," , which characterizes brand b's TV ads in minute . There are three broad types of factors that can moderate the rate of immediate online response to a TV ad spot: those related to the ad creative, the media placement, and the audience.[ 9] For minutes with overlapping ads (i.e., spots aired in the same minute but on different networks), is calculated as the audience size-weighted average. Note that the exponential formulation implies that the jth lift factor has a multiplier effect—all else being equal, for one unit increase in , the response rate would be scaled by a multiple of .[10]
Compared with the rate of immediate response to a brand's own national TV ads ( ), we adopt a simpler specification for the rate of immediate response to competitors' national ads ( ) to obtain a more parsimonious investigation of the competitive spillover of TV advertising on immediate online response. Due to the lack of data on all three types of moderating factors, we model the rate of immediate response to own local ads ( ) and own dealers association ads ( ) in a similar fashion:
Graph
3
It is critical to specify the baseline flexibly to avoid conflating advertising effects with correlated unobservables. To minimize such concerns, we formulate as follows:
Graph
4
where
- denotes a fixed effect for the specific hour containing minute t, accounting for the average baseline activity in each given hour of the sample period.
- denotes a fixed effect that accommodates a distinct local trend in baseline activity for each hour of the week (i.e., Monday 12 a.m., Monday 1 a.m.,..., Sunday 11 p.m.). It is included to control for unobservables that have within-hour trends and may correlate with within-hour TV ad insertion patterns.[11]
- denotes the number of searches containing the keyword "SUV" in minute t, which serves as a control for consumers' general tendency to search for large automobiles in any given minute of the sample period.
In summary, we see three main ways in which endogeneity could bias the estimates of immediate online response to TV ad insertions. In Web Appendix A, we discuss these main threats and explain how we alleviate those concerns through a combination of model specification and data richness.
To calibrate the model described in Equations 1–4, we first take the difference between pairs of consecutive minutes within each hour, canceling out the hour-of-sample fixed effects . This relieves us of the need to estimate these fixed effects, which are numerous but not of primary interest. Formally, by applying the first-difference operator (i.e., ), we can transform the original model into the following mathematically equivalent representation:
Graph
5
We estimate Equation 5 using nonlinear least squares with serially correlated residuals. For all three brands, brand search response becomes statistically undetectable nine minutes after the start of own national TV ads (i.e., M = 9), and five minutes after the start of competitor national ads, own local ads, and dealers association ads (i.e., N = 5). Price search response becomes indistinguishable from zero after six minutes for own national ads (i.e., M = 6) and four for competitor national, own local ads, and dealers association ads (i.e., N = 4).
For each of the three pickup truck brands, we compiled a rich set of data from multiple sources, from February 15, 2015, through January 23, 2016, a span of 493,920 minutes, avoiding Super Bowl outliers to focus on regular TV spots. The rest of the section describes data from each source and how we merged them for our empirical analyses.
We obtained minute-by-minute brand search volume data by combining extracts from Google Trends and AdWords Keyword Planner.[12] Google Trends provides brand search indices by week-within-sample period, hour-within-each-week, and minute-within-each-hour. Google AdWords Keyword Planner provides monthly total brand search volume estimates. We apportioned Keyword Planner's monthly total brand search volume estimates according to Google Trends' brand search indices to obtain, sequentially, brand search volume estimates for each week, each hour within each week, and finally, each minute within each hour.
We obtained minute-by-minute price search volume data from Autometrics (www.autometrics.com), which has agreements with major car shopping websites in the United States to process records of car shoppers requesting online price quotes from local dealerships. Each record consists of the time stamp of an online price quote request, the car shopping website through which the request was made, the brand and the model of the vehicle requested, and the zip code entered by the car shopper who made the request. We are able to access these records aggregated by brand and minute, thus forming the price search data used in this study. Private conversations with Autometrics and automotive executives indicate that the amount of online price quote requests is a common key performance indicator in the industry, often used as a proxy for the number of car shoppers who are close to the end of the purchase funnel.
Using the same method we used to obtain minute-by-minute brand search volume data for the three pickup truck brands, we obtained the number of Google search queries containing the word "SUV" in each minute. The purpose of collecting this data was to improve baseline search volume estimates for each focal truck brand by using SUV search volume as a control for factors that vary by the minute and may influence online searches for large automobiles such as SUVs and pickup trucks (e.g., the presence of a TV commercial break).
During the sample period, the three focal truck brands ran a total of 27,562 ad spots on national TV at a cost of $210 million. For each of these national spots, we obtained audience size data from comScore's "TV Essentials" database. ComScore collects TV viewing data passively from 52 million digital set-top boxes in 22 million households. ComScore has nearly a thousand-fold advantage over Nielsen's sample size of 26,000 households,[13] enabling it to provide reliable audience size estimates for "long-tail" television networks and programs. By having a reliable estimate of the actual audience size of each ad spot (as opposed to using program ratings or cost estimates as proxies), we are in a position to quantify, for the first time in the literature, the amount of immediate online response to TV ad spots on a per impression basis (analogous to click-through rates of display ads).
We obtained ad creative scores from Ace Metrix, a provider of competitive intelligence on advertising content. Ace Metrix identifies new national TV ad creative and, within 24 hours of its first airing, exposes each creative to 500 online panelists and records their attitudinal responses through a standardized survey. The panelists were asked to indicate their level of agreement with a battery of statements about the ad creative in question on a scale of 0–100 (0 = "Not at all," and 100 = "Very much"). We were able to obtain survey ratings from Ace Metrix for ad creatives that accounted for 92% of the national TV impressions in our sample. Three scores are of particular interest: AdInfo (how informative an ad creative is), AdLike (how likable an ad creative is), and AdDes (how much an ad creative has made the advertised brand desirable), which map roughly into the three broad stages in the hierarchy of effects—cognitive, affective, and conative ([27]). The survey statements used to generate these three scores are, respectively, "I learned something," "I like this ad," and "I want that! (whatever you think the commercial is about)."
For each national TV ad insertion in our sample, we obtained media placement data from Kantar Media's "Stradegy" database, a comprehensive source for competitive advertising intelligence that covers all ad spots run on major national networks and local broadcast stations. For each national spot, we observe the date, start time, duration (30 seconds for 99% of ads in the sample), advertised brand, ad creative identifier, pod position, TV network, program genre, and a cost estimate.
Polk Automotive Intelligence (Polk, hereinafter) collects data on all new automobile registrations in the United States. In partnership with comScore, for each national TV ad spot, Polk uses proprietary algorithms to estimate what fraction of the ad's audience was contemporaneously a potential purchaser or lessee of a new pickup truck.[14] We obtained these spot-level estimates and refer to them as AudienceCategoryInterest, the sample averages of which are 17.2% for Ford's ad audience, 16.7% for Chevy's, and 17.7% for Ram's.
During the sample period, the three focal truck brands ran a total of 750,672 ad spots (318,238 from manufacturers and 432,434 from dealers associations) on local TV stations at a cost of $261 million, with $106 million spent by manufacturers and $155 million spent by dealers associations. For each of these local spots, we obtained from Kantar Media's "Stradegy" database the date, start time, duration, advertised brand, and a cost estimate. Unfortunately, comScore's "TV Essentials" database does not cover local spots. As a result, we used spot-level cost estimates as a proxy for spot-level audience sizes. In addition, neither Ace Metrix nor Polk covers local TV ad spots, preventing us from having ad creative scores or ad audience category interest estimates for these local spots.
In our proposed modeling framework, national ad audience sizes, local ad spend, and national ad lift factors are minute-level measures. To convert spot-level data to minute-level measures and merge them across sources, we do the following.
For national spots that straddled consecutive minutes, we assume a constant number of viewers at each second of the spot's duration. For example, for a 30-second spot that started at 19:50:45 and had an audience size of 1 million, we assume it generated 15 million impression-seconds from 19:50:45 to 19:50:59 and 15 million impression-seconds from 19:51:00 to 19:51:14. For each minute, we aggregate impression-seconds from all the national spots that had exposures during any second of that minute. We then divide the total impression-seconds in each minute by 60 to arrive at our minute-level measure of average national ad audience size (i.e., and in Equation 5).
For local spots that straddled consecutive minutes, we split the cost estimate of each spot into each minute, proportional to the number of seconds run in each minute. We then aggregate the costs by minute to arrive at our minute-level measure of local ad spend (i.e., and in Equation 5).
Finally, for minutes with exposures from multiple national ad spots, we calculate our minute-level lift factors (i.e., in Equation 5) by taking the weighted averages across all the ad spots that had any exposures in each minute, with the weight being the impression-seconds each spot had in each minute.
Table 2 presents descriptive statistics of the minute-by-minute brand and price search data. Overall, the variation in brand and price searches across brands conforms to the three brands' relative position in market share. During the sample period, Ford F-Series was searched 36 million times on Google (or 72 times per minute) and 8 million times on major car shopping websites (or 16 times per minute); Chevy Silverado was searched 21 million times on Google (or 43 times per minute) and 7 million times on major car shopping websites (or 13 times per minute); Ram Trucks was searched 19 million times on Google (or 39 times per minute) and 3 million times on major car shopping websites (or 6 times per minute).
Graph
Table 2. Descriptive Statistics of Minute by Minute Brand and Price Search Data.
| Ford | Chevy | Ram |
|---|
| Total number of minutes | 493,920 | 493,920 | 493,920 |
| Total number of brand searches (million) | 35.7 | 21.2 | 19.3 |
| Number of Brand Searches Per Minute | | | |
| Mean | 72 | 43 | 39 |
| SD | 40 | 26 | 12 |
| Minimum | 1 | 1 | 3 |
| 25th percentile | 38 | 20 | 32 |
| Median | 71 | 41 | 39 |
| 75th percentile | 102 | 62 | 46 |
| Maximum | 722 | 504 | 490 |
| Total number of price searches (million) | 8.1 | 6.6 | 2.7 |
| Number of Price Searches Per Minute | | | |
| Mean | 16 | 13 | 6 |
| SD | 10 | 9 | 5 |
| Minimum | 0 | 0 | 0 |
| 25th percentile | 9 | 7 | 2 |
| Median | 15 | 12 | 4 |
| 75th percentile | 22 | 18 | 8 |
| Maximum | 153 | 613 | 330 |
Table 3 presents descriptive statistics of the spot-level advertising data. During the sample period, Ford F-Series spent $191 million on television advertising, 42% of which on 1,777 national manufacturer spots, 8% on 39,229 local manufacturer spots, and 50% on 264,488 dealers association spots. Chevy Silverado spent $135 million on television advertising, 47% of which on 12,653 national manufacturer spots, 26% on 112,209 local manufacturer spots, and 27% on 125,885 dealers association spots. Ram Trucks spent $145 million on television advertising, 45% of which on 13,132 national manufacturer spots, 40% on 166,800 local manufacturer spots, and 15% on 42,061 dealers association spots. During the sample period, Chevy Silverado aired 72 unique pieces of national ad creative, followed by Ram Trucks with 67 and Ford F-Series with 30.
Graph
Table 3. Descriptive Statistics of Spot-Level Advertising Data.
| Ford | Chevy | Ram |
|---|
| Total spend on television advertising (million) | $191.0 | $135.1 | $145.0 |
| National Manufacturer Ads | | | |
| Total spend (million) | $80.9 | $63.8 | $65.4 |
| Total number of spots | 1,777 | 12,653 | 13,132 |
| Total number of spots straddling consecutive minutes | 866 | 6,327 | 6,531 |
| Total number of impression-minutes (million) | 1,379 | 2,421 | 2,585 |
| Total number of unique ad creative | 30 | 72 | 67 |
| Avg. number of seconds per spot | 30.0 | 30.4 | 30.0 |
| Avg. number of impression-minutes per spot (million) | .8 | .2 | .2 |
| Avg. spend per spot | $45,540 | $5,042 | $4,982 |
| Avg. spend per 1,000 impression-minutes | $58.7 | $26.4 | $25.3 |
| Local Manufacturer Ads | | | |
| Total spend (million) | $14.5 | $34.5 | $57.3 |
| Total number of spots | 39,229 | 112,209 | 166,800 |
| Total number of spots straddling consecutive minutes | 18,724 | 55,778 | 81,512 |
| Avg. number of seconds per spot | 28.7 | 30.0 | 29.6 |
| Avg. spend per spot | $371 | $307 | $343 |
| Local Dealers Association Ads | | | |
| Total spend (million) | $95.5 | $36.9 | $22.3 |
| Total number of spots | 264,488 | 125,885 | 42,061 |
| Total number of spots straddling consecutive minutes | 128,401 | 62,299 | 20,782 |
| Avg. number of seconds per spot | 29.4 | 29.8 | 30.0 |
| Avg. spend per spot | $361 | $293 | $530 |
Table 4 presents descriptive statistics of the minute-level advertising data that was merged across sources and used in model estimation.[15] Ford F-Series had far fewer minutes with national ads ( 2,562) than Chevy Silverado and Ram Trucks (each with more than 18,000) but much larger audiences per ad minute (540,000 vs. 13,000–14,000, on average). This is because Ford's national spots were far more concentrated in broadcast networks, especially during professional football games, which also tend to be more expensive on a per impression basis, leading to a much higher average spend per national spot for Ford (about $45,000) than Chevy (about $5,000) and Ram (about $5,000). Ace Metrix data indicate that Ford ads were rated as the most informative and likable on average and Chevy ads induced the most desire to purchase. Median audiences per ad minute were far smaller than the averages, with the medians ranging from about 60,000 for Chevy and Ram to 150,000 for Ford, underscoring the "ordinary" TV ad spots that predominate the sample.
Graph
Table 4. Descriptive Statistics of Minute-Level Advertising Data.
| Ford | Chevy | Ram |
|---|
| Number of minutes with national manufacturer ads | 2,562 | 18,036 | 18,651 |
| Number of minutes with exposures from multiple national manufacturer ads | 239 | 1,035 | 1,071 |
| Number of minutes with local manufacturer ads | 43,130 | 100,078 | 134,052 |
| Number of minutes with exposures from multiple local manufacturer ads | 26,840 | 57,398 | 64,947 |
| Number of minutes with local dealers association ads | 176,214 | 105,490 | 51,218 |
| Number of minutes with exposures from multiple local dealers association ads | 83,081 | 70,824 | 39,113 |
| Audience Size Per National Manufacturer Ad Minute (Million) | | | |
| Mean | .54 | .13 | .14 |
| SD | 1.21 | .34 | .38 |
| 25th percentile | .04 | .02 | .02 |
| Median | .15 | .06 | .06 |
| 75th percentile | .44 | .15 | .13 |
| Maximum | 14.38 | 18.40 | 14.44 |
| Avg. spend per local manufacturer ad minute | $337 | $344 | $427 |
| Avg. spend per local dealers association ad minute | $542 | $349 | $435 |
| Avg. ad informativeness score (AdInfo)a | .74 | .32 | −.73 |
| Avg. ad likability score (AdLike)a | .42 | .28 | −.29 |
| Avg. ad desirability score (AdDes)a | .31 | .52 | −.42 |
| % of ad audience interested in pickup truck category | 17.2 | 16.7 | 17.7 |
| % of Ad Audience Exposed to Ads That Are Placed In... | | | |
| First slot | 44.5 | 32.9 | 33.5 |
| Prime time | 40.5 | 39.8 | 39.6 |
| Broadcast networks | 57.3 | 13.8 | 19.3 |
| Pro football | 25.7 | 2.9 | 3.7 |
| Weekend | 68.0 | 49.5 | 53.5 |
2 aThe three Ace Metrix scores are standardized across ad creative.
Figure 1 visualizes the patterns of minute-by-minute brand searches for three one-hour periods—for each focal brand, we zoomed in on the hour containing ad insertions that had the highest spend in the sample period, which all occurred during nationally televised professional football games. Each gray bar in Figure 1 depicts a commercial break during the telecast, and each dash vertical line indicates an ad insertion by a focal brand.
Graph: Figure 1. National TV ads and post-ad brand search spikes.Notes: The panels present three one-hour windows that contain national TV ad insertions with the largest audience size for each of the three brands. In all three panels, gray bars indicate the time windows for commercial breaks. Dashed vertical lines mark the starting time of the ad insertions for a focal brand.
In Figure 1, Panel A, we see two ad insertions for Ford F-Series. The first began at 9:16:20 PM, lasted for 30 seconds, and had a middle pod position and an average audience of 21.9 million. In the minute before the ad insertion, there were 152 own-brand searches; in the minute after the ad insertion, there were 664 own-brand searches, a 4.4-fold spike. A back-of-the-envelope calculation suggests that the immediate own-brand search response rate, one minute after the ad insertion, could be approximately 23 per million [= (664 − 152)/21.9].
The second Ford ad insertion, with different creative, began at 9:22:04 PM, lasted for 30 seconds, had a first pod position and an average audience of 22.3 million. The volume of own-brand searches had a five-fold spike, from 144 in the minute before the ad insertion to 722 in the minute after, suggesting an immediate own-brand search response rate of roughly 26 per million [= (722 − 144)/22.3]. From Figure 1, Panels B and C, we see spikes of similar magnitudes in minute-by-minute brand searches for Chevy and Ram, after their respective ad insertions.
Besides the immediate post-ad spikes in searches for the advertised brands, there are several other patterns in Figure 1 that are remarkable. First, all the focal ad insertions (especially the ones by Chevy and Ram) seem to have preceded spikes in brand searches for their direct competitors (Ford in particular), suggesting positive competitive spillover in immediate online response to TV advertising. Second, we see no noticeable spikes in searches for the three focal brands during commercial breaks that did not have any of their ad insertions. This suggests that the brand search spikes are caused mainly by the presence of the focal brands' TV ads, rather than by the absence of the game. Third, brand search volume reverted to its pre-ad baseline within five minutes or less. Finally, no noticeable dips appear below the pre-ad baseline following the post-ad spikes, which might imply that the ads produced truly incremental search rather than accelerating search that would otherwise have occurred a few minutes later.
The striking visualization presented in Figure 1 offers clear but anecdotal evidence of immediate online response to TV ads. The patterns we observe in Figure 1 could prove to be the exception rather than the rule, because the vast majority of ad spots have audiences that are two orders of magnitude smaller. Can one reliably quantify the immediate online response to regular TV ads and how the response rate may be moderated by various lift factors? The next section presents our empirical findings by applying our proposed modeling framework to the comprehensive data we have managed to stitch together from multiple sources.
This section presents the parameter estimates for the main effects ( , , , and ) and the moderating effects ( ) based on Equation 5, the estimating equation. It concludes with what-if analyses based on the calibrated model. Web Appendix C presents the parameter estimates related to the baseline ( , , and ), which are not of primary interest but are important from the standpoint of model calibration.
Table 5 reports the main effect estimates of own national TV ads ( ), averaged across spots by minute following an ad insertion. In terms of own-brand search response, from the minute the ad was aired to the ninth minute afterward, one million ad impression-minutes (i.e., an average ad audience of one million over a span of 60 seconds) would generate, on average, 40.2 immediate brand searches for Ford F-Series, 33.8 for Chevy Silverado, and 17.8 for Ram Trucks, following the order of the brands in total brand search volume and market share. These effect estimates indicate that the rate of immediate own-brand search response per viewer is, respectively,.0040% for Ford,.0034% for Chevy, and.0018% for Ram, which are smaller than the typical click-through rates for online display ads (.05%) ([ 4]). That said, given the large number of total national ad impression-minutes (1,379 million for Ford, 2,421 million for Chevy, and 2,585 million for Ram), the total number of immediate own-brand searches attributable to national TV ad spots are still substantial (about 55,000 for Ford, about 82,000 for Chevy, and about 46,000 for Ram).
Graph
Table 5. Main Effects of Own National Spots.
| Minute After Ad Insertion | Brand Search ResponsePer One Million Impression-Minutes | Price Search ResponsePer One Million Impression-Minutes |
|---|
| Ford | Chevy | Ram | Ford | Chevy | Ram |
|---|
| 0 | 4.46* | 4.78* | 1.99* | −.15 | −.08 | −.20 |
| 1 | 23.70* | 18.83* | 10.47* | 1.11* | .34 | −.06 |
| 2 | 6.72* | 5.49* | 3.30* | 1.41* | .39* | .20 |
| 3 | 2.39* | 2.53* | 1.46* | 1.26* | .06 | .32* |
| 4 | .89* | 1.47* | .96* | 1.11* | .59* | .22* |
| 5 | .20 | .45 | .10 | .86* | .46* | .26* |
| 6 | .72* | .49* | −.05 | .64* | −.10 | −.19 |
| 7 | .88* | .23 | −.16 | | | |
| 8 | .12 | .02 | .02 | | | |
| 9 | .15 | −.53* | −.29 | | | |
| Total incremental search | 40.24* | 33.76* | 17.80* | 6.24* | 1.65* | .55* |
| Average elasticity | .22 | .10 | .06 | .20 | .02 | .02 |
| Median elasticity | .07 | .05 | .02 | .05 | .01 | .01 |
3 *p <.01.
In terms of price search response, from the minute the ad was aired to the sixth minute afterward, one million ad impression-minutes would generate, on average, 6.2 immediate price searches for Ford, 1.7 for Chevy, and.6 for Ram, following the order of the brands in total price search volume and market share. These effect estimates indicate that the rate of immediate price search response per viewer is much lower than the rate of brand search response:.0006% for Ford,.0002% for Chevy, and.0001% for Ram. This is not surprising, in that there tend to be more shoppers at the upper funnel, who are more likely to conduct brand searches, than shoppers at the lower funnel, who are more likely to conduct price searches. Nevertheless, because the total number of ad impression-minutes is large, the total number of immediate price searches attributable to national TV ads is nontrivial: about 8,600 for Ford, about 4,000 for Chevy, and about 1,400 for Ram. It is also a testament to the power of the data and modeling framework in detecting weak signals.
How do these effect estimates compare with what has been reported in the literature? To facilitate comparison, we report at the bottom of Table 5, summarized across all the ad minutes and by brand, the average and median elasticities of minute-level brand and price searches to national TV ads. We see heterogeneity across the brands and between the types of search response. Following the order in market share, the average elasticities of brand search are, respectively,.22 for Ford,.10 for Chevy, and.06 for Ram. The average elasticities of price search are, respectively,.20 for Ford,.02 for Chevy, and.02 for Ram.
Across all the ad minutes and brands, the average elasticities of brand search and price search are, respectively,.09 and.03, which are comparable to the average elasticity of sales to advertising (.12) reported by [35] and those that have been reported in the literature of online response to offline TV ads. For example, [18] find that the average elasticity of brand search to advertising (across 21 vehicles) is.04; [20] report an average elasticity of.17; [22] report an average elasticity of.07 for less established brands; [14] find that the elasticities of conversion to advertising range from.05 to.11 in car insurance, health insurance, and banking industries; and [17] report elasticities of mobile search between.13 and.17.
Figure 2 plots the percentages of total immediate search response realized by minute following an ad insertion. For own-brand search, on average about 12% of the cumulative effect is realized in the minute the ad is aired, followed by approximately 58% in the following minute and 17%, 7%, and 4% in the second, third, and fourth post-ad minutes, respectively. For price search, the vast majority of response occurs between the first and the fifth post-ad minutes, with each of the five minutes accounting for about 20% of the cumulative effect. These temporal patterns suggest that ( 1) for both brand search and price search, nearly all of the immediate response takes place within five minutes of a TV ad insertion, and ( 2) brand search response arises and dissipates more quickly than price search response, which is intuitive because, on average, it takes more time to conduct a price search through a car shopping website than a brand search through Google.
Graph: Figure 2. Percentage of cumulative search response to own national ads by minute after ad insertion (averaged across brands).
In addition to quantifying the average effects of ad spots, TV advertisers are equally interested, if not more so, in quantifying how contextual factors may moderate immediate online response, which can help them assess the relative effectiveness of different ad creative, media placements, and audience targeting criteria. We allow the contextual factors (i.e., in Equation 5) to moderate the response rate multiplicatively. Thus, all else being equal, for one unit increase in , the response rate and the elasticity are expected to be lifted by times. In other words, one can interpret s as the multiplier effects of the contextual factors, whose estimates we report in Table 6. A multiplier significantly different from 100% indicates that the corresponding factor has a significant impact on immediate brand or price search response.
Graph
Table 6. Moderating Effects of Lift Factors.
| Brand Search Response/Elasticity Multipliera | Price SearchResponse/Elasticity Multipliera |
|---|
| Ad creative–related factors | Informativenessb("I learned something") | 119%* | 143% |
| Likabilityb("I like this ad") | 108%* | 80% |
| Desirabilityb("I want that!") | 110%* | 69% |
| Media placement–related factors | First slot (vs. other pod positions) | 122%* | 154%* |
| Prime time (vs. other dayparts) | 123%* | 111% |
| Pro football (vs. other programs) | 155%* | 127% |
| Broadcast (vs. cable networks) | 88%* | 154%* |
| Weekend (vs. weekday) | 91%* | 164%* |
| Audience-related factors | Audience category interestc | 102%* | 108% |
- 4 *p <.01.
- 5 aThe multipliers are calculated as .
- 6 bThe scores are standardized to have a standard deviation of one.
- 7 cAudienceCategoryInterest is measured in percentage points.
In terms of brand search response, the multipliers associated with the three ad creative scores (standardized to have a standard deviation of one) are all significantly greater than 100%, suggesting that, all else being equal, ad creative deemed by viewers as more informative ("I learned something from the ad"), likable ("I like this ad"), or desirable ("I want that!") generates more immediate brand searches. This is reassuring in the sense that advertisers selecting ad creative on the basis of either traditional survey-based copy testing scores or immediate brand search response would make similar choices. The estimated multipliers (119% for informativeness, 108% for likability, and 110% for desirability) indicate that, on average, one standard deviation of improvement in an ad creative's attitudinal response could lead to approximately 10% to 20% improvement in brand search response.
In terms of price search response, the multipliers associated with the three ad creative scores are further away from 100%, but none are significant at the 99% confidence level. We see two potential explanations. It could simply mean that the signal-to-noise ratio is not high enough to reliably quantify the moderating effects of ad creative scores on immediate post-ad price search. An alternative explanation could be that price search is more likely a lower-funnel behavior, whereas national TV ads are more often used to further upper-funnel goals, which makes the creative scores of national TV ads a less reliable predictor of immediate price search response.
The contrast between the results for brand search and price search suggests that advertisers should be cautious in relying on any single online response measure in assessing the relative effectiveness of ad creative. Although it appears that ads with more favorable attitudinal response are associated with more immediate post-ad brand searches, they do not seem to generate more immediate price searches. Thus, it is important to ascertain ( 1) whether the signal-to-noise ratio is high enough to reliably quantify the moderating effects of ad creative–related factors and ( 2) how critical favorable attitudinal response is in generating the behavioral response the marketer seeks.
For both brand and price search, all else being equal, spots run in the first slot of a commercial break generate significantly higher rates of immediate online response (+22% and +54%, respectively). Note that we obtain these strong effects after controlling for the audience size of each ad spot. In other words, these effects are not due to the fact that more viewers may have watched the first ad in a commercial break before they changed channels. We speculate that these positive first-slot effects resulted because ad viewers are more attentive during the first ad in a commercial break, before their cognitive capacity is depleted by subsequent ads in the break. It could also be the case that viewers have more time to conduct online searches after watching the first ad in a commercial break, having to worry less about missing the TV programming after the break. In short, our results are consistent between brand and price search and suggest that the first slot in a commercial break could be worth a double-digit premium due to a more attentive/responsive audience.
Similar to the first-slot effect on brand search response, we observe that ad spots run during prime time or a professional football game generate significantly more immediate brand searches (+23% and +55%, respectively), after having controlled for ad audience size. The positive lift of prime time could be due to the fact that viewers are more attentive to the commercials and TV programming during the daypart that is typically associated with TV viewing. Another intuitive explanation is that the second-screening phenomenon is the strongest during prime time because more TV viewers have ready access to their mobile devices, enabling them to conduct immediate post-ad search online. It could also be that prime time coincides with when most car shoppers conduct online research for cars and are thus more likely to respond to car ads. The strong positive lift of professional football games is also intuitive. We suspect that viewers are more attentive to the commercials during live sports programming.
The effects of prime time and professional football on price search response are also positive (+11% and +27%, respectively), but not significant at the 99% confidence level. The lack of statistical significance is another sign that the signal-to-noise ratio in the price search data may not be high enough to reliably quantify the moderating effects of some lift factors.
Unlike the effects of first slot, prime time, and professional football, which are directionally consistent between brand search and price search, the effects of broadcast and weekend diverge between the two types of online response. Ad spots run on broadcast networks generate significantly fewer immediate brand searches per viewer (−12%) and significantly more immediate price searches per viewer (+54%). We speculate that these divergent effects occur because broadcast viewers are, on average, less affluent than cable viewers and are therefore more price sensitive, which makes broadcast viewers (relative to cable viewers) more likely to conduct price searches and less likely to conduct brand searches.
Ad spots run on weekends generate significantly fewer immediate brand searches per viewer (−9%) and significantly more immediate price searches per viewer (+64%). We speculate that these divergent effects occur because car shoppers are more likely to visit dealerships and make purchases on weekends than on weekdays. As a result, relative to weekdays, car shoppers are, on average, more likely to conduct price searches (operationalized as requesting price quotes from local dealerships in our study) and less likely to conduct brand searches on weekends.
The divergent broadcast and weekend effects on brand versus price search show that media placements that can generate more of one type of online response may generate less of other types of digital activity. This cautions TV advertisers against relying on any single immediate online response metric in selecting media placements, as there is unlikely a media plan that can optimize all types of online response. That said, if the advertiser does have one type of online response that it intends to focus on for a particular campaign, large lifts in performance and cost effectiveness can accrue from quantifying the multiplier effects of various media placement factors and then making media buys accordingly.
All else being equal, for every one-percentage-point increase in AudienceCategoryInterest, the number of immediate brand searches per ad viewer increases by 2%, which is significant at the 99% confidence level. The amount of immediate price searches per ad viewer also increases but the increase is not significant. To put the effect size of AudienceCategoryInterest on brand search response into perspective, consider an ad spot with AudienceCategoryInterest at, say, 27%, which is ten percentage points above the average of 17%. Our effect estimate indicates that, all else being equal, one would expect to see a brand search response rate that is 17% higher than the average. This finding suggests that spot-level audience characteristics data furnished by third-party vendors (e.g., Polk, comScore, Acxiom, Datalogix, Experian, Nielsen) can be validated through their correlation with immediate post-ad online response. In our empirical context, the spot-level audience category interest estimates have demonstrated strong face validity, which is reassuring for TV advertisers that increasingly rely on rich audience data for targeted media buys.
Table 7 reports the effect estimates of competitor national TV ads ( ) on focal brand search and price search, averaged across spots by minute following an ad insertion. In terms of total brand search response (cumulative from the minute the ad was aired to the fifth minute afterward), we see positive and significant spillover across all six directional dyads. These significant and consistent effect estimates suggest that TV ads can trigger not only immediate searches for the advertised brand but also its competitors. We speculate that this occurs because TV ads can remind viewers of alternatives to the advertised brand, which in turn could spur them to search the competitor brand for comparison. It also might be that TV ads remind consumers of category needs, thereby leading consumers interested in competing brands to search those brands directly, without a comparison.
Graph
Table 7. Main Effects of Competitor National Spots.
| Minute After Ad Insertion | Brand Search Response Per One Million Impression-Minutes | Price Search Response Per One Million Impression-Minutes |
|---|
| Chevy | Ram | Ford | Ram | Ford | Chevy | Chevy | Ram | Ford | Ram | Ford | Chevy |
|---|
| ↓ | ↓ | ↓ | ↓ | ↓ | ↓ | ↓ | ↓ | ↓ | ↓ | ↓ | ↓ |
|---|
| Ford | Ford | Chevy | Chevy | Ram | Ram | Ford | Ford | Chevy | Chevy | Ram | Ram |
|---|
| 0 | .40 | .81* | .60* | .07 | .64* | .53 | −.15 | .19 | .05 | .42* | .21* | −.21 |
| 1 | 4.85* | 2.31* | 2.59* | .33 | 2.15* | 1.54* | .01 | .02 | .14 | .18 | .06 | −.04 |
| 2 | 2.18* | .54* | .69* | .10 | .94* | .63* | −.41* | .15 | −.05 | .60* | .22* | −.07 |
| 3 | .42 | .87* | −.01 | .09 | .50* | .15 | .21 | −.18 | −.13 | −.12 | .20* | −.23* |
| 4 | .83* | −.32 | −.04 | .37 | −.01 | .70* | −.56 | −.15 | .21 | −.53* | .28* | .55* |
| 5 | −.48 | .13 | .42* | .13 | −.04 | .29 | | | | | | |
| Total | 8.20* | 4.35* | 4.25* | 1.09* | 4.18* | 3.84* | −.91 | .03 | .23 | .55* | .98* | −.01 |
| Avg. elasticity | .01 | .01 | .04 | .003 | .05 | .01 | −.01 | .0003 | .01 | .01 | .12 | −.0004 |
8 *p <.01.
In terms of magnitude, the estimated main effects on own brands are much larger than competitive spillovers. For one million impression-minutes, an average Ford spot generates 40.2 Ford searches versus 8.4 Chevy/Ram searches, an average Chevy spot generates 33.8 Chevy searches versus 12.0 Ford/Ram searches, and an average Ram spot generates 17.8 Ram searches versus 5.4 Ford/Chevy searches. It is remarkable that the data and modeling framework reliably quantified the sizes of competitive spillovers, even though the competitor brand search response rate is extremely low:.0008% for Ford,.0012% for Chevy, and.0005% for Ram. The implied average elasticities of brand search to competitor national TV ads range from.003 to.05.
Ford receives the most competitive spillovers (8.2 from Chevy and 4.4 from Ram). This suggests, unsurprisingly, that the category leader is probably the default or the reference option in most shoppers' consideration set. As a result, it receives the most comparison searches.[16]
Finally, in terms of competitive spillovers in price search response, we find mostly insignificant effect estimates. This could be another sign that the signal-to-noise ratio in the price search data may not be high enough for our model to reliably quantify immediate post-ad competitor price search. It could also be that, as car shoppers approach the end of the purchase funnel, they are less likely to comparison shop between brands and more likely to comparison shop between local dealerships of the same brand for the best price.
Table 8 reports the effect estimates of local manufacturer ads and dealers association ads , averaged across spots by minute following an ad insertion. In terms of brand search response, from the minute the ad was aired to the fifth minute afterward, local manufacturer/dealers association ads costing about $10,000 would generate, on average, 8.1/7.0 immediate brand searches for Ford, 6.4/8.7 for Chevy, and 6.4/��1.2 (insignificant at the 99% confidence level) for Ram. Averaged across the three brands, the implied elasticity of brand search to local manufacturer ads and dealers association ads are, respectively,.002 and.001. In terms of price search response, the effects are, respectively, 6.3/2.0 for Ford, −2.2/2.8 for Chevy, and.6/1.0 for Ram. Averaged across the three brands, the implied elasticities of price search to local manufacturer ads and dealers association ads are, respectively,.0002 and.002.
Graph
Table 8. Main Effects of Local Spots.
| Minute After Ad Insertion | Brand Search Response Per One Million Impression-Minutes | Price Search Response Per One Million Impression-Minutes |
|---|
| Local Manufacturer Ads | Local Dealers Association Ads | Local Manufacturer Ads | Local Dealers Association Ads |
|---|
| Ford | Chevy | Ram | Ford | Chevy | Ram | Ford | Chevy | Ram | Ford | Chevy | Ram |
|---|
| 0 | 1.85* | 2.96* | .82* | 2.62* | 2.00* | −.26 | 1.86* | −.18 | −.16 | .53* | 1.57* | .13 |
| 1 | 3.90* | 1.99* | 3.00* | 3.10* | 3.80* | .68 | 1.80* | −1.05* | .26* | −.04 | −.40 | .37 |
| 2 | 1.16 | 1.04* | 1.97* | .78* | 1.75* | .19 | .85 | −.45 | .38* | .88* | .30 | −.01 |
| 3 | .59 | .92* | −.08 | .47 | .14 | −.24 | .69 | −.78* | −.27* | .39* | .19 | .53* |
| 4 | −.17 | .08 | .36 | −.33 | −.12 | −.55 | 1.06* | .24 | .35* | .22 | 1.18* | .02 |
| 5 | .76 | −.56 | .33 | .33 | 1.15* | −1.02* | | | | | | |
| Total | 8.09* | 6.43* | 6.41* | 6.96* | 8.71* | −1.20 | 6.26* | −2.21* | .56* | 1.99* | 2.84* | 1.04* |
| Avg. elasticity | .0004 | .001 | .003 | .003 | .002 | −.0002 | .002 | −.002 | .002 | .005 | .003 | .002 |
- 9 *p <.01.
- 10 Notes: It is a bit counterintuitive that the immediate price search response rate for Chevy local manufacturer ads is negative and significant (−2.21). It could simply be a type I error. Or it could be that Chevy local manufacturer ads have already provided sufficient information that it makes price search unnecessary.
Several aspects of the results are worth noting. First, because the total spend on local ads is large ($110 million for Ford, $71 million for Chevy, and $80 million for Ram), the total number of immediate searches attributable to local TV ads is substantial: about 78,000 for Ford, about 54,000 for Chevy, and about 37,000 for Ram in brand searches; and about 28,000 for Ford, about 10,000 for Chevy, and about 6,000 for Ram in price searches. Summed across the three brands, there was a total spend of $261 million on local ads, which generated about 169,000 immediate post-ad brand searches and about 44,000 price searches.
It is also instructive to compare the immediate post-ad searches attributable to local spots with those attributable to national spots, which are presented in Table 9. Relatively speaking, in terms of generating immediate brand search response, national spots are the most cost effective (on average 8.7 per $10,000 spend), followed by local manufacturer spots (6.6 per $10,000 spend) and local dealers association spots (6.4 per $10,000 spend). The opposite is true when it comes to generating immediate price search response: local dealers association spots are the most cost effective (on average 2.1 per $10,000 spend), followed by local manufacturer spots (1.2 per $10,000 spend) and national spots (.7 per $10,000 spend).
Graph
Table 9. National Versus Local Spots in Immediate Post-Ad Search Response (Averaged Across All Insertions).
| Brand Search Response | Price Search Response | Sum of Brand and Price Search Response | Ratio between Price and Brand Search Response |
|---|
| National Spots | | | | |
| Per $10,000 spend | 8.7 | .7 | 9.4 | 1 vs. 13.1 |
| Per 1 million impression-minutes | 28.7 | 2.2 | 30.9 | 1 vs. 13.1 |
| Local Manufacturer Spots | | | | |
| Per $10,000 spend | 6.6 | 1.2 | 7.8 | 1 vs. 5.7 |
| Local Dealers Association Spots | | | | |
| Per $10,000 spend | 6.4 | 2.1 | 8.4 | 1 vs. 3.1 |
This reversal of relative cost effectiveness in generating brand versus price search has an intuitive explanation in that the content of TV ads for these three truck brands typically varies systematically between national and local spots. National spots are purchased exclusively by the manufacturers and, according to a content analysis by [40], typically carry brand-oriented messages with relatively few price-oriented messages. Local TV spots are purchased by both manufacturers and local dealers associations, with both parties designing ads that extensively communicate current market-specific pricing and promotion terms. As a result, TV viewers respond accordingly: the ratio between price and brand search response is the highest for price-focused local dealers association spots (1:3) and the lowest for brand-focused national spots (1:13). We view this intuitive finding as another testament to the face validity of our effect estimates and, in turn, the power of the data and modeling framework.
Finally, it is worth remembering that, unlike national spots, we do not have access to reliable audience measures for local spots, which requires us to rely on spot-level cost estimates provided by Kantar Media as a correlate of local ad audience size. As a result, we can only quantify immediate search response rate on a per impression-minute basis for national spots. To the extent that the same amount of spend can purchase more impressions on local TV than on national TV, the amount of immediate search response per viewer is likely lower on local TV than on national TV. That said, because there is likely greater measurement error in local ad exposure than in national ad exposure, our local spot effect estimates are likely to have more downward error-in-variable bias than their national counterparts.
How can TV advertisers leverage our modeling framework and the resulting effect estimates to assess the relative effectiveness of different ad spots and thereby refine their selection of ad creative and media placements? This subsection presents several what-if analyses to demonstrate the potential usefulness of our approach in practice.
Given the calibrated model, we can simulate the amount of incremental brand and price searches if the TV advertiser were to have a different allocation of ad spend across media placements, target audiences, and ad creative. Because media placement factors and target audiences tend to be correlated with one another, for simplicity, we focus our what-if analyses on ad creative selection. We simulate what could have happened to immediate search response if Ford had reallocated its national TV impression-minutes across ad creative while maintaining the allocation across media placements and target audiences.
For the ten pieces of Ford ad copy with creative scores, we simulate the immediate search response under the scenario in which 100% of the national TV ad impression-minutes that accrued to the ten pieces of ad copy had been allocated instead to only one piece of ad copy. Figure 3 presents, for each of the ten pieces of ad copy, the percentage differences (relative to the average across the ad copy) in generating immediate brand and price searches. The first ad copy from the left, which has the highest score in informativeness and below-average scores in likability and desirability, could have generated 15.6% more brand searches and 31.8% more price searches. However, none of the other nine pieces of ad copy could have generated more of one type of search without generating less of the other. This exercise again highlights a key takeaway: TV advertisers should be cautious if they rely on only one particular type of online response in evaluating and selecting ad creative, because it can be difficult for any single piece of ad creative to excel in driving all types of online response. Rather, TV advertisers should monitor a variety of online activities and align the performance metric with the specific objective of each campaign (e.g., brand building vs. price promotion).
Graph: Figure 3. Percentage difference in search response if Ford had used only one ad copy for national TV.Notes: Each bar represents the percentage difference (relative to the average across the ten pieces of Ford ad copy for which we observe ad creative scores) in generating brand/price searches if 100% of the national TV ad impression minutes that accrued to the ten pieces of ad copy had been allocated to just one of them.
To make the previous simulation more realistic, we consider an alternative scenario: What would have happened if Ford had allocated 20% of the national TV ad impression-minutes to each of the five top-performing pieces of ad creative (out of the ten)? When we use immediate brand search response as the selection criterion, the top five pieces of ad copy could have generated 9.4% more brand searches while producing only 1.5% fewer price searches. When we use immediate price search response as the selection criterion, the top five pieces of ad copy could have generated 12.5% more price searches while producing only 3.5% fewer brand searches. These simulations demonstrate that substantial gains could be made by applying our proposed modeling framework in ad creative selection. Equipped with additional information in real world applications, TV advertisers could conduct similar what-if analyses in refining their plans of media placement and audience targeting.
Compared with digital media, most TV advertisers have traditionally been unable to access behavioral response measures at the spot level, frustrating efforts to select ad creative or media placements on the basis of their relative effectiveness in achieving particular behavioral objectives. Thanks to the increasing prevalence of the second-screening phenomenon, a new class of attribution vendors has emerged, promising that TV advertisers can measure immediate post-ad spikes in online activities and use those measures to assess the relative effectiveness of ad spots.
It is against this backdrop that we conducted our study. We focused on three top pickup truck brands, for which we compiled a rich data set by stitching together information from multiple sources, covering a span of nearly half a million minutes. We focused on two types of online activities: brand search and price search. We observed 27,562 ad spots on national TV and 750,672 spots on local TV. By merging the spot-level ad data with the minute-level search data, we built a comprehensive testing ground to demonstrate the worth and insights available from estimating the linkage between TV ad spots and immediate online response.
Our research offers several key takeaways. First, for both brand search and price search, there is a detectable spike immediately after a regular ad insertion, be it on national or local TV. The rate of response follows the order of the brands in total search volume and market share. We believe our focal brands offer a conservative setting because they are decades old and many, if not most, category consumers are intimately familiar with them. We suspect that brands that are newer or lesser known, or transact primarily online, would likely see even greater responses.[17]
Second, nearly all of the immediate response occurs within five minutes of an ad insertion, with brand search response peaking in the minute after the ad is aired and then dissipating quickly, while price search response is spread out more evenly over the five post-ad minutes.
Third, in addition to generating immediate own-brand searches, national TV ad insertions also lead to significant competitor-brand searches. The category leader receives larger positive competitive spillovers than its rivals. For price search, however, we detected little competitive spillover, probably because as car shoppers approach the end of the purchase funnel, they are less likely to comparison shop between brands and more likely to comparison shop between local dealerships of the same brand for the best price.
Fourth, relatively speaking, national spots appear to be more cost effective in generating immediate brand search response, whereas local spots appear to be more cost effective in generating immediate price search response. Although this reversal of relative cost effectiveness is a novel finding, it is intuitive in the sense that the three focal brands' national spots are typically more brand-oriented, whereas their local spots are mostly focused on price promotions.
Fifth, ad creative with more favorable attitudinal response seems to be associated with more immediate post-ad brand searches. On average, a one-standard-deviation improvement in ad creative quality (as measured by survey-based ratings of ad informativeness, likability, and desirability) could result in a 10% to 20% improvement in post-ad brand search response. However, the moderating effects of ad creative characteristics are muted when it comes to generating immediate price searches. This suggests that TV advertisers should be cautious in replacing survey-based creative ratings with any single online response measure, especially when the indicator pertains to a lower-funnel activity such as online price quote requests.
Sixth, media placement factors and audience category interest can also moderate the rate of immediate search response. TV ads ( 1) placed in the first slot of a commercial break, ( 2) aired during prime time, and ( 3) aired during professional football games cause more immediate brand and price searches. Ad spots run on broadcast networks or weekends generate significantly fewer immediate brand searches but significantly more immediate price searches. A one-percentage-point increase in audience category interest leads to a 2% increase in immediate brand search, providing support for the practice of TV advertisers relying on increasingly rich audience characteristics data for targeted media buys.
Managerially, our findings about positive lifts of certain media placements (e.g., first slot, prime time, live sporting event) and audience category interest suggest that when TV advertisers intend to focus on maximizing one particular type of online response, large gains in effectiveness could accrue from quantifying and balancing the multiplier effects of various media and audience factors against their cost differentials. That said, the findings about divergent effects of broadcast/cable, weekend/weekday, national/local, and ad creative characteristics on brand versus price search caution advertisers against relying on any single immediate online response metric in assessing media placements and ad copy, as there is unlikely to be a media plan or ad creative that would be optimal for all types of online response.
Practically, unlike the proprietary methods used by advertising attribution vendors, our proposed framework for modeling behavioral response at the minute level is transparent and readily replicable. The brand search data used to estimate the model are accessible to any brand, both for itself and for its competitors. The price search data represent a type of online response that has not been studied in the prior literature. Admittedly, because our sources for search data (Google and Autometrics) are unlikely to capture all the relevant search responses, our estimates of response rates are likely downward biased. The estimates of elasticities and moderating effects should be more robust to the fact that our data are unlikely a census of brand and price searches.
TV advertisers could further extend our modeling framework to include website traffic, online transactions, social media activities (as in [13]]) or other important behavioral indicators that vary at the minute level. We suspect that the reliability of spot-level attribution will depend on the signal-to-noise ratio. The strength of the signal will depend on the size of the ad audience and the tendency of ad viewers to respond immediately, which can be weaker, for example, for brands that compete in low-involvement categories. The level of noise shall depend on variability, relative to the mean, of minute-by-minute online activity. One way to overcome a low signal-to-noise ratio is to include a large number of ad spots over an extended period of time, as we demonstrated in the current study.
A deeper understanding of immediate online response to TV ad spots opens up multiple areas for further research. While advertisers may ultimately care about the impact of advertising on sales, it remains a challenge for many TV advertisers, such as the ones in the current study, to quantify the impact of any regular TV spot on sales because consumers can be exposed to a myriad of ads and promotions from both online and offline sources over weeks or even months. To close the attribution loop, following immediate online responses through to purchases or other types of transactions is a critically important step forward. Future research needs to address the question of whether the rate of immediate online response is positively correlated with the amount of online and offline response accrued over time and, ultimately, with incremental sales attributable to a single spot. If such positive correlation could be established, TV advertisers could be more confident in the validity of using the relative sizes of immediate online response to assess the relative effectiveness of different ad spots.
Besides driving sales in the near term, TV campaigns often have long-term brand-building goals. Although our study has examined the correlation between three survey-based attitudinal measures and immediate search response, it remains a fertile ground to systematically investigate the relationship between an ad's efficacy in changing various mindset metrics (e.g., awareness, value and quality perception) and its efficacy in generating different online activities.
Methodologically, the current study relies solely on a time-based identification strategy to detect the immediate effects of TV ads on online search. A powerful direction for future research would be to combine time-based identification with a spatial identification strategy, as exemplified by [15]. This seems applicable to national advertisements, as exogenous variation across time zones may allow for even more accurate predictions of counterfactual online response. Similarly, dividing online response by geographic origin and merging local response with local ad exposure may greatly enhance the signal-to-noise ratio.
The dependable and sizable influence of TV ads on online brand and price search cautions marketers against the use of simplistic "last-touch" attribution strategies, as they may overestimate the effect of search engine marketing and underestimate the generative influence of TV advertising. Traditional and digital advertising budgets are still commonly divided between siloed agencies with little or no coordination between them. The TV-to-online spillover observed in this study renews the call for holistic integration and evaluation of ad campaigns and cross-media synergies (e.g., [23]; [31]).
To conclude, our study contributes to a larger effort to understand how measurable funnel actions correspond to the reach, placement, and content of TV advertising. It could be fruitful to extend the current literature on TV-to-online spillovers to other broadcast media, such as radio, where the same fundamental challenge of spot-level ad performance assessment and attribution exists. We are confident that the drive for marketing accountability will continue and that multitaskers' immediate online response to traditional advertising will be prominently featured as marketers refine their understanding of how advertising affects the customer journey.
Supplemental Material, DS_10.1177_0022242919847192 - Immediate Responses of Online Brand Search and Price Search to TV Ads
Supplemental Material, DS_10.1177_0022242919847192 for Immediate Responses of Online Brand Search and Price Search to TV Ads by Rex Yuxing Du, Linli Xu and Kenneth C. Wilbur in Journal of Marketing
Footnotes 1 Associate EditorRobert Leone served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242919847192
5 1https://www.youtube.com/watch?v=bmnPtVXx43k (accessed October 2018).
6 2https://tvsquared.com/products/advantage/ (accessed October 2018).
7 3https://www.adobe.com/experience-cloud/topics/programmatic-tv.html (accessed October 2018).
8 4The average cost of one local TV ad exposure is approximately $.01, so $10,000 approximates one million ad exposures ([32]).
9 5One could also posit interactions among these factors, but such interactions would require substantially more data for robust identification.
6An alternative to the exponential formulation would be linear, which leads to qualitatively the same results but slightly inferior goodness-of-fit in our empirical analyses.
7Instead of hour of the week, we have also tried 30-minute and two-hour fixed effects to account for alternative local trends in baseline search. The empirical results are essentially the same.
8Search volume data were collected for all queries containing "f150," "f 150," "f-150," "f250," "f 250," "f-250," "f 350," "f 350," "f-350," "silverado," "dodge ram," "ram trucks," ram 1500," "ram 2500," "ram 3500," "ram truck." To construct minute-by-minute search indices, we set the time window for each Google Trends inquiry to one hour. We then obtained hour-by-hour search indices by setting the time window for each inquiry to one week. Finally, we obtained week-by-week search indices by setting the time window to the whole sample period. The Google Trends server limited the number of queries served daily, so several months were required to collect the sample analyzed in this paper. The time cost of data collection is the primary reason that nonbranded keywords are not analyzed, as the number of relevant nonbranded keywords likely exceeds the number of relevant branded keywords, and nonbranded search volume has previously been found to be less likely to respond to branded TV ads ([20]). According to Google Correlate, the branded keywords correlate highly across competing brands, but few generic keywords correlate highly with the branded keywords.
9http://www.thevab.com/national-tv-measurement/ (accessed September 2017).
10Admittedly we do not have detailed information on how this fraction is calculated. However, our empirical results suggest a way to validate the usefulness of such proprietary data.
11Web Appendix B presents visualizations of the minute-level data used in model calibration.
12[42] investigated the differentiating effects of a focal brand's advertising on its rival brand's sales between the "market leader" and "market challenger" and found similar patterns of results.
13In Web Appendix D, similar to Figure 1, we visualize the patterns of minute-by-minute brand searches for two newly introduced daily fantasy sports brands—Draft Kings and Fan Duel—during a one-hour telecast of a professional football game, wherein each brand ran two spots. All four spots were followed by an immediate multifold spike in both own- and competitor-brand searches (no noticeable spikes during commercial breaks without the daily fantasy sports ads). A back-of-the-envelope calculation suggests that the average one-minute post-ad own-brand search response rate could be around 420 per million ad viewers, which would be an order of magnitude greater than what we observed for the three pickup truck brands.
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By Rex Yuxing Du; Linli Xu and Kenneth C. Wilbur
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Record: 95- Improving Cancer Outreach Effectiveness Through Targeting and Economic Assessments: Insights from a Randomized Field Experiment. By: Chen, Yixing; Lee, Ju-Yeon; Sridhar, Shrihari (Hari); Mittal, Vikas; McCallister, Katharine; Singal, Amit G. Journal of Marketing. May2020, Vol. 84 Issue 3, p1-27. 27p. 1 Diagram, 7 Charts, 2 Graphs. DOI: 10.1177/0022242920913025.
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Improving Cancer Outreach Effectiveness Through Targeting and Economic Assessments: Insights from a Randomized Field Experiment
Patients at risk for hepatocellular carcinoma or liver cancer should undergo semiannual screening tests to facilitate early detection, effective treatment options at lower cost, better recovery prognosis, and higher life expectancy. Health care institutions invest in direct-to-patient outreach marketing to encourage regular screening. They ask the following questions: ( 1) Does the effectiveness of outreach vary among patients and over time?; ( 2) What is the return on outreach?; and ( 3) Can patient-level targeted outreach increase the return? The authors use a multiperiod, randomized field experiment involving 1,800 patients. Overall, relative to the usual-care condition, outreach alone (outreach with patient navigation) increases screening completion rates by 10–20 (13–24) percentage points. Causal forests demonstrate that patient-level treatment effects vary substantially across periods and by patients' demographics, health status, visit history, health system accessibility, and neighborhood socioeconomic status, thereby facilitating the implementation of the targeted outreach program. A simulation shows that the targeted outreach program improves the return on the randomized outreach program by 74%–96% or $1.6 million to $2 million. Thus, outreach marketing provides a substantial positive payoff to the health care system.
Keywords: causal forests; machine learning; personalized health care marketing; cancer screening; randomized field experiment
In 2018, over 1.7 million new cases of cancer were diagnosed in the United States, and the cost of cancer care surpassed $147 billion ([49]). Following the guidelines of the [50], health care institutions encourage at-risk patients to undergo regular screening, as this opens the door for early detection, more cost-effective treatment options, and better recovery prognosis. Regular screening reduces mortality rates for lung (20% drop; [33]), breast (20%–40% drop; [52]), and liver (37% drop; [71]) cancers. Moreover, cancer screening can reduce annual treatment costs by nearly $5,000 ([10]).
Health care institutions invest heavily in direct-to-patient outreach interventions to increase screening completion among at-risk patients. For example, Johns Hopkins Hospital's cancer center uses emails, letters, seminars, and community events to encourage screening completion among patients ([34]). With 1.7 million outreach interventions launched in 2015, and $123 million spent on prevention and education efforts,[ 5] only 8% of U.S. adults over 35 years old utilize preventive services ([13]). This percentage is too low. Health care institutions face three challenges in improving outreach effectiveness.
First, most studies examine only the main effects of medical interventions (e.g., [57]; [58]), neglecting variation due to patient demographics, health status, visit history, health system accessibility, and neighborhood socioeconomic status ([26]). By incorporating this heterogeneity in patient response to outreach interventions, health care institutions can implement "personalized health care marketing" and boost outreach effectiveness. Second, medical scholars typically compare the relative efficacy of outreach interventions using a single-period research design (e.g., [ 8]). Given the importance of regular screening compliance over multiple periods ([19]), it is critical to evaluate screening compliance over multiple periods. Third, quantifying the return on outreach interventions to incorporate the health benefits and financial cost of interventions will help health care institutions communicate the tangible value they bring to the community and enable funding agencies to sustain these interventions ([ 2]). As the director of cancer education for the Stanford Cancer Center notes, "durable, long-term solutions will require a substantial investment in academic/community partnerships to improve cancer education" ([21]).
To addresses these three challenges, we use a multiperiod randomized field experiment conducted at a large hospital system with at-risk patients for hepatocellular carcinoma (HCC), the most common type of primary liver cancer ([58]). Patients were randomly assigned (1:1:1) to three conditions: usual care, outreach alone, or outreach with patient navigation. Usual care is the baseline condition in which physicians offer preventive care recommendations at their discretion during a patient's usual care visits. As we describe subsequently, outreach alone and outreach with patient navigation provide two different levels of marketing using outreach mails, outreach calls, and customized motivational education by trained patient navigators. The focal outcome is the patient's screening completion status within 6 months (Period 1), 6–12 months (Period 2), and 12–18 months (Period 3) of the initial randomization. This enables an investigation of the impact of outreach interventions on regular screening compliance. We evaluated screening completion status every 6 months, as this interval has been demonstrated to increase early detection and survival compared with longer screening intervals ([56]). To incorporate patient heterogeneity, we iteratively construct the focal covariates based on the extant medical literature and pragmatic considerations that the study design affords, including patients' demographics, health status, visit history, health system accessibility, neighborhood socioeconomic status, and prior screening compliance.
Relative to the baseline condition, outreach alone (outreach with patient navigation) increases screening completion rates by 10–20 (13–24) percentage points, but the effectiveness of the two outreach interventions does not significantly differ. Central to this article, the similarity in these main effects masks considerable heterogeneity in outreach effectiveness due to patient-level differences ([11]). We uncover patient-level treatment effects of these two interventions using causal forests, a state-of-the-art development in the machine learning and economics literature ([66]). Results show the following: ( 1) compared with outreach alone, outreach with patient navigation induces a higher proportion of patients with significant positive heterogeneous treatment effects in Periods 2 (9%) and 3 (23%); ( 2) the increased screening completion from outreach alone or outreach with patient navigation is higher for patients who are female, are part of a racial/ethnic minority, have a better health status, have a more frequent visit history, are covered by medical-assistance insurance, reside in closer proximity to clinics, and reside in a more populated neighborhood; ( 3) the increase in screening completion due to outreach alone is higher for patients who are younger, commute faster, and reside in a neighborhood with more public insurance coverage; in contrast, the increase in screening completion as a result of outreach with patient navigation is higher for patients who are older and reside in a higher-income neighborhood.
Incorporating patient-level differences in their responsiveness to outreach interventions, and a well-established scheme of cost–benefit calculation that quantifies health benefits and financial costs associated with outreach interventions (e.g., [28]), we assign patients to the baseline, outreach-alone, or outreach-with-patient-navigation condition in each period on the basis of their predicted treatment effect and predicted net return. As a result, the commensurate return on the patient-level targeted outreach program is $3,704,270–$4,167,419 when extrapolated to 3,217 eligible patients in the hospital's database. The targeted outreach program improves the return on the randomized outreach program ($2,130,921) by 74%–96%.
We make several contributions to marketing theory and health care practice. First, the literature on marketing interventions in health care (Table 1, Panel A) typically relies on experimentally manipulated moderators, such as test accuracy ([41]) or consumer goals ([68]); while theoretically interesting, they are impractical for health care institutions to implement. Health care institutions can readily utilize observable patient characteristics—such as ethnicity, visit history, and insurance coverage—that are of theoretical relevance. The bulk of the marketing literature has focused on attitudinal consequences using self-reports of behavioral intentions (e.g., [12]), risk perceptions (e.g., [47]), and attitudes (e.g., [ 9]) in a lab setting. While insightful, they are of little practical relevance to addressing actual health care behaviors such as screening completion among real patients.
Graph
Table 1. Literature Review on Cancer Care and Preventive Care.
| Reference | Context (Sample) | Dependent Variable | Independent Variable | Approaches to Understand Heterogeneity of Treatment Effect | Identification Method | Evaluation of Return on Intervention |
|---|
| Objective Behavior (vs. Self-Reported) | Degree of Intervention | Use of Patient Characteristics as Sources of Heterogeneity | Evaluation of Patient-Level Treatment Effectiveness |
|---|
| Our study | Liver cancer (patients with suspected or documented risks of cirrhosis) | ✓Actual behavior: screening completion | Three types of outreach(no outreach, outreach alone, outreach with patient navigation) | ✓Demographics, health status, visit history, health system accessibility, neighborhood socioeconomic status | ✓ | Randomized field experiment | ✓ |
| A: Marketing Literature | | | | | | |
| Block and Keller (1995) | Sexually transmitted disease and skin cancer (college students) | XSelf-reported perceptions:in-depth processing, attitude, intentions | Two types of efficacy (high vs. low) | X | X | Lab experiment | X |
| Keller and Block (1996) | Smoking (college students) | XSelf-reported perceptions: persuasiveness | Two types of messages (low vs. high fear evoking) | X | X | Lab experiment | X |
| Jayanti and Burns (1998) | Primary care (patients) | XSelf-reported intentions: preventive health care behavioral intention | No intervention (health knowledge/motivation, health consciousness, self-efficacy/response efficacy, health value) | X | X | Survey | X |
| Luce and Kahn (1999) | Breast cancer (women in mammography waiting rooms) | XSelf-reported perceptions: perceptions of vulnerability and test inaccuracy | Two types of results (normal vs. false alarm) | X | X | Lab experiment | X |
| Cox and Cox (2001) | Breast cancer (women from social volunteer organizations) | XSelf-reported perceptions: attitudes and beliefs toward the target behavior | Two types of messages (statistical vs. anecdotal) | X | X | Lab experiment and in-depth interviews | X |
| Keller, Lipkus, and Rimer (2002) | Breast cancer (older women and college students) | XSelf-reported perceptions: risk perception | Two types of mood (depressives vs. nondepressives) | X | X | Lab experiment | X |
| Menon, Block, and Ramanathan (2002) | Hepatitis C (college students) | XSelf-reported perceptions: intention, concern, self-positivity bias, self-risk estimates, message effectiveness | Two types of message cues (frequent vs. infrequent) | X | X | Lab experiment | X |
| Kahn and Luce (2003) | Breast cancer (women in mammography waiting rooms) | XSelf-reported perceptions: stress, disutilities of harm, retest intention | Two types of results (normal vs. false alarm) | X | X | Lab experiment | X |
| Bowman, Heilman, and Seetharaman (2004) | Prescription drugs for chronic ailments (patients) | ✓Approximated behavior: approximated drug consumption | No intervention (advertising cues, salience/mindfulness, benefits/costs of compliance, threats) | X | X | Secondary data | X |
| Bolton, Cohen, and Bloom (2006) | Smoking cessation aids (college students) | XSelf-reported perceptions: risk perception, smoking-cessation intention | Two types of message (remedy vs. none) | X | X | Lab experiment and field study | X |
| Bolton et al. (2008) | High cholesterol (older men) and weight management (staff and students) | XSelf-reported perceptions: healthy lifestyle intentions, risk/health perceptions | Two types of health remedy (drug vs. supplements) | X | X | Mailed surveys and lab experiments | X |
| Du, Sen, and Bhattacharya (2008) | Oral health (disadvantaged Hispanic families) | XSelf-reported perceptions: belief, behaviors | Two types of health education (participation vs. no participation) | ✓Acculturation (self-reported) | X | Focus group interviews and quasiexperiment | X |
| Cox, Cox, and Mantel (2010) | Prescription drug for the early detection of skin cancer (young adults) | XSelf-reported perceptions: behavioral intention, attitude, perception | Two (high vs. low frequency) × two (high vs. low severity) types of side effects | X | X | Lab experiment | X |
| Wang, Keh, and Bolton (2010) | Western versus Eastern medicine (college students) | XSelf-reported perceptions: preference for health remedies, healthy lifestyle intentions | Two (high vs. low) diagnosis uncertainty × two (alleviating symptoms vs. curing illness) goals | X | X | Lab experiment and field study | X |
| Samper and Schwartz (2012) | Skin cancer and flu shots (adults) | XSelf-reported perceptions: risk perception, intentions to consume care | Two levels of medication price (high vs. low) | X | X | Lab experiment | X |
| Lisjak and Lee (2014) | Unprotected sex, kidney diseases and chlamydia tests (college students) | XSelf-reported perceptions: intention, perceived vulnerability | Two levels of depletion (depletion, nondepletion) | X | X | Lab experiment | X |
| B: Medical and Health Care Literature | | | | | |
| Schut et al. (1997) | Grief counseling (widows and widowers) | XSelf-reported perception: psychological distress | Three types of intervention (control, emotion-focused, problem-focused grief counseling) | ✓Gender | X | Randomized field experiment | X |
| Murray et al. (2004) | Drug use (elderly patients with chronic heart failure) | ✓Objective behavior: medication adherence | Two types of intervention (usual care, pharmacist support) | X | X | Randomized field experiment | X |
| Unger et al. (2004) | Smoking prevention (middle school students) | XSelf-reported behavior: lifetime smoking | Two smoking prevention curricula (standard, multicultural curriculum) | ✓Ethnicity, gender | X | Randomized field experiment | X |
| Sullivan et al. (2005) | Asthma care (children with asthma) | XSelf-reported estimated behavior: symptom-free days | Three types of intervention (usual care, peer leader-based physician behavior change intervention, planned asthma care intervention) | X | X | Randomized field experiment | ✓ |
| Basch et al. (2006) | Colorectal cancer (African American patients) | ✓Objective behavior: screening completion | Two types of intervention (tailored telephone outreach, mail outreach) | X | X | Randomized field experiment | X |
| Ziegelmann, Lippke, and Schwarzer (2006) | Orthopedic rehabilitation (rehabilitation center patients) | XSelf-reported intentions: coping/action plans, physical activities | Two types of intervention (standard care, interviewer-assisted planning) | ✓Age | X | Randomized field experiment | X |
| Wang et al. (2007) | Depression (employees) | XSelf-reported perception: depression severity | Two types of outreach (control, telephonic outreach) | ✓Health conditions | X | Randomized field experiment | X |
| Andersson et al. (2008) | Liver disease (patients with compensated cirrhosis) | ✓Simulated behavior: screening completion | No intervention (intervention is not the main focus) | X | X | Simulation | ✓ |
| Ferrante, Chen, and Kim (2008)a | Breast cancer (women with suspicious mammogram results) | ✓Objective behavior: diagnostic interval | Two types of outreach (no outreach, outreach with patient navigation) | ✓Ethnicity, health conditions | X | Randomized field experiment | X |
| Gabram et al. (2008) | Breast cancer (African American women) | ✓Objective behavior: screening completion | One condition (community education/outreach initiatives) | X | X | Cross-sectional study | X |
| Muller et al. (2009) | Colorectal cancer (adults aged 50 to 80 years) | ✓Objective behavior: screening completion | Three types of outreach (no outreach, letter reminder, email reminder) | X | X | Randomized field experiment | X |
| Humiston et al. (2011) | Influenza immunization (older patients) | ✓Objective behavior: immunization completion | Two types of outreach (standard of care, outreach intervention) | ✓Ethnicity | X | Randomized field experiment | X |
| Gupta et al. (2013) | Colorectal cancer (uninsured patients) | ✓Objective behavior: screening completion | Three types of outreach (no outreach, mailed invitation to return a test, mailed invitation to schedule colonoscopy) | X | X | Randomized field experiment | X |
| Wigg et al. (2013) | Liver disease (patients with cirrhosis and complications from chronic liver failure) | ✓Objective behavior: outpatient care attendance rates | Two types of intervention (absence or presence of chronic disease management programs) | X | X | Randomized field experiment | X |
| Wetherell et al. (2013) | Anxiety disorders (patients diagnosed with anxiety disorder) | XSelf-reported perception: anxiety level | Two types of intervention (usual care, computer-assisted collaborative care) | ✓Age | X | Randomized field experiment | X |
| Baker et al. (2014) | Colorectal cancer (mostly Hispanic female patients) | ✓Objective behavior: screening completion | Two types of outreach(no outreach, outreach with patient navigation) | X | X | Randomized field experiment | X |
| Beste et al. (2015) | Liver cancer (patients with cirrhosis who have visited Veterans Affairs facilities) | ✓Objective behavior: screening completion | Two types of intervention (no reminder, point-of-care reminder) | X | X | Quasiexperiment | X |
| Goldman et al. (2015) | Colorectal cancer (patients with no documentation of screening) | ✓Objective behavior: screening completion | Two types of outreach (usual care, multifaceted intervention) | ✓Visit history, time | X | Randomized field experiment | X |
| Singal et al. (2017) | Liver cancer (patients with suspected or documented risks of cirrhosis) | ✓Objective behavior: screening completion | Three types of outreach(no outreach, outreach alone, outreach with patient navigation) | X | X | Randomized field experiment | X |
| Coronado et al. (2018) | Colorectal cancer (adults aged 50 to 74 years) | ✓Objective behavior: screening completion | Two types of outreach (no outreach, mailed outreach) | X | X | Randomized field experiment | X |
| McCarthy et al. (2018) | Contraception (young people in Tajikistan) | ✓Objective behavior: use of contraception | Three types of intervention(control, mobile app messaging intervention) | ✓Gender | X | Randomized field experiment | X |
| Singal et al. (2019) | Liver cancer (patients with suspected or documented risks of cirrhosis) | ✓Objective behavior: screening completion | Three types of outreach(no outreach, outreach alone, outreach with patient navigation) | ✓Race, visit history, health status | X | Randomized field experiment | X |
1 a The authors note that they were unable to find the heterogeneity of treatment effect due to the small sample size in each subgroup.
2 Notes: References are provided in Web Appendix H.
Second, we extend the medical literature on cancer outreach effectiveness, which has focused primarily on the main effects of cancer outreach interventions from randomized field studies (Table 1, Panel B). The causal forests approach provides a practical way to improve the efficacy and external validity of field experiments by systematically exploring the treatment-effect heterogeneity across intervention types, across patient subgroups, and over time without prespecifying the sources of heterogeneity ([23]; [42]).
Third, we provide insights into what patient subgroup benefits more (less) from outreach interventions, offer ways to customize the interventions, and help practitioners allocate limited financial resources to those with the largest potential gains. For example, while outreach programs typically target diverse, socioeconomically difficult-to-reach disadvantaged patient populations to improve their health outcomes ([58]), patients more responsive to outreach interventions tend to be female, be part of a minority, be in good health status, have more frequent visit history, be covered by medical-assistance insurance, reside in closer proximity to clinics, and reside in more populated neighborhoods. Thus, simply targeting one or two patient characteristics may not maximize the gains from the outreach interventions.
Fourth, our approach provides a roadmap for implementing personalized health care marketing by customizing outreach interventions and quantifying the return on such interventions. Using patient-level treatment effect estimates with valid confidence intervals, we not only provide a tool that can recommend the most suitable intervention for each patient given their profile but also provide an individual-level cost–benefit analysis to measure the return on personalized health care marketing investments.
Our field experiment is based on the cancer outreach efforts of a large hospital system to increase regular screening completion for early detection of HCC, the most common form of liver cancer, among patients with higher risk of HCC. Most patients with liver cancer do not display symptoms until it reaches an advanced stage; they often miss the time window during which treatment options, such as transplant and surgical resection, are effective. The five-year survival rate for early-stage liver cancer patients who undergo surgery is 60%–70%, while the five-year relative survival rate for liver cancer is 18% ([ 1]). Yet, the utilization rate of HCC screening is below 20% in the general cirrhotic population, and even lower among low-socioeconomic-status and non-Caucasian patients ([59]).
The outreach program was designed to promote regular screening (i.e., obtain a screening every six months). The six-month screening interval is in line with the evidence-based recommendations issued by the American Association for the Study of Liver Diseases and National Comprehensive Cancer Network (e.g., [44]; [65]). It was initially based on tumor doubling times and ( 1) is better for early detection than longer intervals (e.g., 12 months; [56]) but worse than shorter intervals (e.g., 3 months; [63]) and ( 2) minimizes patient and provider burden ([15]).
The hospital system conducted a randomized trial between December 2014 and March 2017. The study was approved by the University of Texas Southwestern Medical Center Institutional Review Board. The trial protocol is available on clinicaltrials.gov (NCT02312817), where the study is registered. The random assignment (1:1:1) consisted of one baseline condition (no outreach) and two conditions with outreach interventions (outreach alone and outreach with patient navigation), with the outcome being HCC screening completion status.[ 6]
The eligibility criteria for patient inclusion using established norms have been developed in the medical field (for details, see Web Appendix A1). From the 3,217 eligible patients in the hospital's database, 1,800 patients were randomly selected for the study.[ 7]
As we summarize in Figure 1, each patient was randomly assigned to one of three conditions in a 1:1:1 ratio:
- No outreach or usual care (baseline condition) : Patients received visit-based HCC screening as recommended by primary or specialty care providers and were not contacted by the outreach marketing team. For patients who scheduled ultrasounds, the hospital system placed automated reminder telephone calls two days before the ultrasound appointments.
- Outreach-alone intervention : As in the baseline condition, patients were eligible for usual care, as offered through their usual outpatient encounters. Patients were also mailed a one-page letter, which contained information on the risk of HCC in patients with cirrhosis and the benefits and risks of HCC screening, a brief summary of the screening procedure, and a recommendation to the patient to make an appointment for an ultrasound (for details, see Web Appendix A2). To increase participation, the staff then made outreach calls to nonresponders (i.e., patients with returned mail and those who did not respond to mailed invitations within two to four weeks). During telephone calls, trained research staff followed standardized scripts. Mails and telephone calls were in English or Spanish, depending on patients' preferences. In addition, the hospital system placed automated reminder telephone calls two days before appointments for patients who scheduled ultrasounds.
- Outreach-with-patient-navigation intervention: Like patients in the baseline condition and those in the outreach-alone condition, patients in this condition were eligible for care as offered through their usual outpatient encounters. Patients in this condition had an experience identical to those in the outreach-alone condition, with two additions: ( 1) a telephone script used during outreach telephone calls and ( 2) an additional reminder call from the research staff. During telephone calls, if patients in this condition declined to make an appointment for screening, the research staff used a standardized telephone script to identify potential barriers and then provided customized motivational messages to encourage screening participation. Examples of barriers include preparation involved, pain during the test, and so on (for details, see Web Appendix A3). For instance, if a patient is concerned about the preparation required for the screening, the research staff alleviates this concern by stating, "A liver ultrasound is a quick procedure. The ultrasound usually takes less than 30 minutes and the appointment should take around one hour from start to finish." For scheduled ultrasounds, the hospital system's research staff called the patients five to seven days before the appointments to provide a reminder, address any concerns, and reschedule the appointment if needed. For these patients, the hospital system also placed automated reminder telephone calls two days before the ultrasound appointments. Overall, as shown in Figure 1, this condition is the most intense and comprehensive intervention in the study.
Graph: Figure 1. Study design: Cancer outreach interventions.aThe following patients were excluded: 185,539 patients who did not meet cirrhosis criterion, 9,921 patients with comorbid conditions or with Child C cirrhosis, 405 patients with the history of HCC or suspicious mass on imaging, 78 patients whose language is not English or Spanish, and 42 patients with no contact information.bPatients in the outreach-alone and outreach-with-patient-navigation conditions could receive usual care.
To encourage regular screening completion, the study repeated the outreach-alone and outreach-with-patient-navigation interventions during each of the three periods. We define Period 1 as the time within 6 months of the first randomization, Period 2 as the time between month 6 and month 12 since the first randomization, and Period 3 as the time between month 12 and month 18 since the first randomization. In summary, the hospital system undertook the outreach interventions in all three periods, each period being six months apart, and each patient belonging to the same condition across the three periods. The goal of this design is to encourage screening in each period.
Once a patient has completed the screening in the first period, the patient does not exit the pool and is contacted in the second and third six-month periods. There are two exceptions to the repeated interventions: ( 1) if the patient completes the screening and is diagnosed with HCC during the experiment, the patient exits the pool as the providers must refer the patient for HCC treatment instead of routine screening; ( 2) if the patient completes the screening and dies during the course of the experiment, the patient cannot complete the screening in later periods. As a result, the sample size is 1,800 for Period 1, 1,772 for Period 2, and 1,743 for Period 3.[ 8] The sample sizes in the baseline, outreach-alone, and outreach-with-patient-navigation conditions are (600, 600, 600) for Period 1, (591, 592, 589) for Period 2, and (577, 584, 582) for Period 3.
Screening completion status is measured as a patient getting an abdominal imaging screening test ( 1) or not (0).[ 9] We observe the dependent variable for each patient in Periods 1, 2, and 3.
Taking theoretical and pragmatic considerations into account, we followed a four-step iterative approach to determine the focal covariates that inform patient heterogeneity in response to outreach interventions. This process of including covariates starts from original yet tentative variables available to researchers and is informed by a multifaceted understanding of theory models, prior studies, research questions, and practice. The approach resembles a theory-in-use process (e.g., [70]) that iteratively intertwines exploratory and confirmatory research to incorporate the interplay of heterogeneity with treatment.
- Step 1: Utilize original variables. We begin with the variables that are available in the electronic medical record system (EMR) and are relevant to practitioners and well-documented in medical research (and thus relevant to academic scholars).[10] These systems store and track key patients' information such as patient demographics (e.g., [69]), health status (e.g., [25]), and visit history record ([60]). As Table 1 shows, previous studies in health care have analyzed these "ready-for-use" variables in the EMR (e.g., [32]; [45]).
- Step 2: Construct theoretically relevant variables. We use the information available in the EMR to construct new variables that are not captured by the raw unrefined data but draw on theories such as health belief model and protection motivation theory (e.g., [40]; [48]). Thus, a patient's health insurance and location information proxy their insurance coverage (financial access to care) and proximity to clinics (geographical access to care). This is consistent with the research showing that health system accessibility and "improving health system accessibility across the socio-economic spectrum" ([ 7], p. 19) is a strategic priority for policy makers ([53]).
- Step 3: Explore external secondary data sources. To supplement the previous steps, we also gather additional data from external secondary sources. Socioeconomic factors can help marketing researchers develop a better understanding of understudied and underserved consumers ([43]). We collect data on each patient's neighborhood socioeconomic status—including educational attainment, income, commute time, private/public health insurance coverage, employment status, and population—by collecting zip-code-level data from American Community Survey.
- Step 4: Incorporate contextually relevant variables. Along with variables that are static in nature, we include each patient's screening compliance in prior periods. Incorporating cancer screening compliance across different periods ([19]) captures the temporal variation in screening completion. It also informs us how outreach effectiveness might vary due to patients' prior behavioral pattern.
In summary, we include six sets of patient characteristics: ( 1) demographics including age, gender (coded as 1 if a patient is female, 0 otherwise), ethnicity (non-Hispanic Caucasian, Hispanic, Non-Hispanic African American, or other/unknown), and primary language (English, Spanish, or other); ( 2) health status, which includes Child-Pugh B (coded as 1 if Child-Pugh score is higher than 6, 0 otherwise), Charlson Comorbidity Index, presence of documented cirrhosis (coded as 1 if yes), etiology of liver disease (hepatitis C, hepatitis B, alcohol, nonalcoholic steatohepatitis, or other); ( 3) visit history, which includes the number of primary care visits in the year prior to cohort entry and receipt of hepatology care (coded as 1 if the patient received the hepatology care prior to cohort entry, 0 otherwise); ( 4) health system accessibility, which includes insurance coverage (commercial, Medicaid, medical assistance/charity, Medicare, self-pay, or unknown) and proximity to clinics (coded as 1 if there are more than three clinics in the zip code that matches the first three digits of the zip code[11] where the patient resides, 0 otherwise); ( 5) neighborhood socioeconomic status, which includes educational attainment (percentage with a bachelor's degree or higher), income (per capita income), average commute time, insurance coverage (percentage with a private or public health insurance plan), unemployment rate, and population measured at the three-digit zip code level[12]; and ( 6) screening completion status in the prior period(s) (coded as 1 if a patient completes the screening test in Periods 1 or 2, 0 otherwise). Table 2 describes each variable, its operationalization, and descriptive statistics. Web Appendix B compares the means of all variables across three conditions. Differences are statistically nonsignificant, showing that random assignment was successful.
Graph
Table 2. Summary Statistics.
| Variable | Definition | Mean | SD | Min | Max |
|---|
| Dependent Variable | | | | |
| Completion in Period 1 | Whether a patient underwent an abdominal imaging screening test, which includes ultrasound, MRI, and CT, in Period 1 (0–6 months after cohort entry). Coded as 1 if the patient completed, and 0 otherwise. | 39.3% | — | 0 | 1 |
| Completion in Period 2 | Whether a patient underwent an abdominal imaging screening test, which includes ultrasound, MRI, and CT, in Period 2 (6 months and 1 day–12 months after cohort entry). Coded as 1 if the patient completed, and 0 otherwise. | 38.4% | — | 0 | 1 |
| Completion in Period 3 | Whether a patient underwent an abdominal imaging screening test, which includes ultrasound, MRI, and CT, in Period 3 (12 months and 1 day–18 months after cohort entry). Coded as 1 if the patient completed, and 0 otherwise. | 34.9% | — | 0 | 1 |
| Independent Variable | | | | |
| Outreach intervention | A baseline condition and two outreach intervention types: | | | | |
| 1. No outreach (usual care) | 33.3% | — | 0 | 1 |
| 2. Moderate outreach (outreach alone) | 33.3% | — | 0 | 1 |
| 3. Intensive outreach (outreach with patient navigation) | 33.3% | — | 0 | 1 |
| Demographics | | | | | |
| Age (years) | Age of the patient at cohort entry | 55.3 | 10.5 | 21 | 90 |
| Gender | Gender of the patient (0 = male, 1 = female) | 40.6% | — | 0 | 1 |
| Ethnicity | | | | | |
| Non-Hispanic Caucasian | Non-Hispanic Caucasian = 1, otherwise = 0 | 28.3% | — | 0 | 1 |
| Hispanic | Hispanic = 1, otherwise = 0 | 37.8% | — | 0 | 1 |
| Non-Hispanic African American | Non-Hispanic African American = 1, otherwise = 0 | 32.1% | — | 0 | 1 |
| Other/unknown | Other/unknown = 1, otherwise = 0 | 1.7% | — | 0 | 1 |
| Language | | | | | |
| English | English = 1, otherwise = 0 | 76.9% | — | 0 | 1 |
| Spanish | Spanish = 1, otherwise = 0 | 22.7% | — | 0 | 1 |
| Other | Other = 1, otherwise = 0 | .3% | — | 0 | 1 |
| Health Status | | | | | |
| Child Pugh B | Whether a patient is Child Pugh B, coded as 1 if Child Pugh Score > 6, 0 otherwise. | 28.3% | — | 0 | 1 |
| Charlson Comorbidity Index | Charlson Comorbidity Index Score | 2.9 | 2.4 | 0 | 12 |
| Documented cirrhosis | Cirrhosis diagnosis (0 = suspected cirrhosis, 1 = known cirrhosis) | 79.6% | — | 0 | 1 |
| Etiology of liver disease | | | | | |
| Hepatitis C | Hepatitis C = 1, otherwise = 0 | 51.0% | — | 0 | 1 |
| Hepatitis B | Hepatitis B = 1, otherwise = 0 | 3.4% | — | 0 | 1 |
| Alcohol | Alcohol = 1, otherwise = 0 | 17.6% | — | 0 | 1 |
| Nonalcoholic steatohepatitis | Nonalcoholic steatohepatitis = 1, otherwise = 0 | 16.6% | — | 0 | 1 |
| Other | Other = 1, otherwise = 0 | 11.3% | — | 0 | 1 |
| Visit History | | | | | |
| Number of prior primary care visits | Number of primary care visits in the year prior to cohort entry | 5.2 | 4.8 | 0 | 38 |
| Receipt of hepatology care | History of hepatology care in the year prior to cohort entry, coded as 1 if the patient received the care, 0 otherwise. | 25.7% | — | 0 | 1 |
| Health System Accessibility | | | |
| Insurance coverage | | | | | |
| Commercial | Yes = 1, otherwise = 0 | 3.0% | — | 0 | 1 |
| Medicaid | Yes = 1, otherwise = 0 | 20.6% | — | 0 | 1 |
| Medical assistance/charity | Yes = 1, otherwise = 0 | 41.0% | — | 0 | 1 |
| Medicare | Yes = 1, otherwise = 0 | 24.7% | — | 0 | 1 |
| Self-pay | Yes = 1, otherwise = 0 | 2.0% | — | 0 | 1 |
| Unknown | Yes = 1, otherwise = 0 | 8.7% | — | 0 | 1 |
| Proximity to clinics | Whether a patient has a close geographical proximity to clinics. Coded as 1 if there are more than three clinics in the zip code that match the first three digits of the zip code where the patient resides, 0 otherwise. | 66.7% | — | 0 | 1 |
| Neighborhood Socioeconomic Statusa | | | |
| Educational attainment (%) | Percentage of people who are 18 years and over and received a bachelor's, master's, professional, or doctorate degree | 33.6 | 6.6 | 13.8 | 52.9 |
| Income ($) | Per capita income: mean income computed for every man, woman, and child in the same zip code | 35,223.8 | 4,117.7 | 15,839.6 | 42,925.3 |
| Average commute time (minutes) | Mean travel time to work from home during the reference week | 27.1 | 2.1 | 19.1 | 33.6 |
| Private health insurance coverage (%) | Percentage of civilian noninstitutionalized population with the insurance coverage provided through an employer or union, a plan purchased by an individual from a private company, or military health care. | 58.6 | 7.2 | 35.3 | 74.8 |
| Public health insurance coverage (%) | Percentage of civilian noninstitutionalized population with the insurance coverage provided through the federal programs Medicare, Medicaid, and Veterans Affairs Health Care, as well as the Children's Health Insurance Program and individual state health plans. | 28.6 | 4.2 | 19.9 | 39.7 |
| Unemployment rate (%) | Percentage of civilians 16 years old and over classified as unemployed | 4.0 | .4 | 2.5 | 5.7 |
| Population | Number of people 16 years and over | 1,148,050 | 371,086 | 10,824 | 1,791,015 |
- 3 a We used zip code to identify the neighborhoods. All zip-code-level covariates are aggregated to the level of the first three digits by calculating the sum (i.e., population) or mean (bachelor's degree or higher, mean travel time to work, and per capita income) across all five-digit zip codes that share the same first three digits.
- 4 Notes: After we exclude patients who were diagnosed with HCC or deceased, the screening completion rate is 38.5% in Period 2 and 35.4% in Period 3.
Figure 2, Panel A, shows the number of patients who completed screening in different periods. Whereas 435 patients (24%) completed the screening only once during the three periods, 660 patients (37%) did so more than once. For all three periods, more patients completed the screening with outreach alone (102) and outreach with patient navigation (134) than the usual care (36) condition. The evidence suggests that both interventions increase HCC screening.
Graph: Figure 2. Model-free evidence.
Figure 2, Panel B, shows screening completion rates in each condition in each period after excluding the patients who were deceased or diagnosed with HCC in the previous period(s). In Period 1, 25% in the no-outreach condition, 45% in the outreach-alone condition, and 48% in the outreach-with-patient-navigation condition underwent screening. The screening completion rate in the outreach-alone condition (difference =.198, p <.01) and the outreach-with-patient-navigation condition (difference =.232, p <.01) is significantly higher than the no-outreach condition. Results in Periods 2 and 3 show a similar pattern. Comparing the screening completion rate in the outreach-alone condition and the outreach-with-patient-navigation condition, there is no statistically significant difference in Period 1 (difference =.033, n.s.) or Period 2 (difference =.033, n.s.), and Period 3 (difference =.051, p <.10). The model-free evidence suggests that both outreach conditions outperform the baseline condition but do not differ in effectiveness relative to each other.[13]
To draw inferences about the causal effect of different interventions, researchers typically estimate and compare the average treatment effects (i.e., main effects) of randomized interventions. Such a comparison may not consider that treatment effects vary across subgroups within and across treatment conditions. Moreover, to avoid searching for particularly responsive subgroups, medical researchers must register preanalysis protocols for clinical trials to specify which subgroups will be analyzed. Such protocols may fail to identify strong but unexpected treatment-effect heterogeneity, especially in emergent fields in which moderators are ex ante ambiguous. We use causal forests to address these two challenges ([66]). Causal forests enable nonparametric estimation of patient-level treatment effects with valid asymptotic confidence intervals, without restrictions on the number of covariates or the need for a larger number of experimental conditions or repeated measures. Causal forests also alleviate concerns regarding spurious treatment-effect heterogeneity due to searching for particularly responsive subgroups (Web Appendix D1 and D2 compare causal forests with several established approaches). Next, we outline the potential outcome framework, followed by an overview of causal forests.
For illustration purposes, we consider the case of one period and the outreach-alone intervention (treatment condition) compared with no outreach/usual care (control condition). For a set of independent and identically distributed patients i = 1,..., n, we observe the outcome of interest Yi (screening completion), treatment assignment Wi (i.e., whether the patient is assigned to the outreach-alone or no-outreach condition), and vector of patient characteristics Xi (e.g., patient demographics, health status). Following the potential outcome framework ([55]), for each patient i, there are two potential outcomes: if a patient is assigned to the treatment condition, we observe the outcome Yi = Yi1, and if the patient is assigned to the control condition, we observe Yi = Yi0. We define the conditional average treatment effect (CATE) (i.e., treatment effect at x) to assess whether the treatment effect is heterogeneous among subgroups:
Graph
1
The fundamental challenge to identifying the CATE is that we only observe one of the two potential outcomes: Yi1 and Yi0. Thus, we must invoke the assumption of unconfoundedness to estimate the CATE ([54]). As patients are randomly assigned to one of the experimental conditions, the treatment assignment Wi is independent of the potential outcomes conditional on Xi (i.e., ). This assumption implies that the treatment is as good as random within each subpopulation indexed by Xi = x. Thus, given the data (Xi, Yi, Wi), we can revise Equation 1 to the following:
Graph
2
Common approaches to estimate the function include nearest neighbor matching and kernel methods, but these methods do not perform well in the presence of many covariates or complex interactions among covariates ([66]).
Causal forests combine causal inference in economics with random forests in machine learning. Random forests ([14]) deploy supervised machine learning algorithms to achieve high out-of-sample prediction accuracy with very little tuning, particularly with high dimensional data with underlying nonlinear relationships ([31]). Random forests ( 1) build a large collection of individual decision trees such that each tree predicts the outcome variable given the vector of covariates and ( 2) average the predictions from those trees. First, each tree is trained on a bootstrap training sample (not on the original sample) with a randomly chosen subset of covariates (not with all the covariates), and it is built by recursively partitioning the chosen covariate space into splits, determining each split by minimizing the mean squared error of the prediction of outcomes in the case of regression trees. Given the tree split, each tree clusters the most similar observations into a terminal node known as a leaf. To predict the outcome of an observation outside of the estimation sample, each tree makes a prediction using the mean of outcomes in the leaf where this new observation belongs. Finally, a random forest averages the prediction from those trees.
Researchers have recently adapted random forests to draw inferences. The technique known as causal forests utilizes an algorithm for flexible modeling of interactions in high dimensions by building many causal trees and averaging their predictions to estimate the treatment effect function τ(x). Causal forests provide valid asymptotic confidence intervals for the treatment effects ([66]).
Given a profile of patient characteristics x, tree-based models help identify the most similar patients locally in the patient characteristics space with an adaptive neighborhood metric (i.e., similar patients are in the same leaf). [66] adapt the regression tree to estimate the within-leaf treatment effects by taking the difference between the mean outcomes of treated and control units in the same leaf:
Graph
3
To ensure consistency and asymptotic normality, [66] prove a bias-reducing condition called honesty: a tree achieves honesty if each bootstrap training sample only uses the outcome of interest Yi to estimate the within-leaf treatment effect based on Equation 3 or to determine where to split the covariate space, but not both. In other words, the bootstrap training sample is further split into two subsamples: one used to build the tree (i.e., understand where the treatment heterogeneity is given the vector of covariates),[14] and the other used to estimate the treatment effects given the tree structure.
Using this process, causal forests produce an ensemble of B such trees ([14]; [66]), each of which outputs an estimate and averages the predictions from those trees to compute an estimated CATE: .
This aggregation scheme also helps reduce variance and smooths sharp decision boundaries ([16]). The variance estimate of causal forests is defined as follows ([24]; [66]; [67]):
Graph
4
where is the treatment effect estimate from the bth tree. Nib ∈ {0, 1} indicates whether the bootstrap training sample i is used for the tree b, n(n − 1)/(n − s)2 is a finite-sample correction for forests grown by subsampling without replacement, and the covariance is taken with respect to all B trees in the forest. Equations 3 and 4 produce a treatment effect estimate and a confidence interval for each patient.
In marketing, causal forests have been applied in the context of customer retention ([ 5]), information disclosure and physician payments ([30]), and adoption of voice-activated shopping devices and consumers' purchase quantity, spending amount, and search activities ([61]). To our knowledge, this is the first study to use causal forests in the context of randomized health care field experiments.
Following [58], we have two different treatment conditions (outreach alone and outreach with patient navigation) and three different periods. We use the following procedure to perform six causal forest estimations. Additional aspects of the estimation are summarized in Web Appendix D3.
- Step 1. Using patient characteristics as covariates,[15] we applied causal forests to obtain each patient's treatment effect estimate in the sample that includes patients in the baseline (condition 1, sample size = 600) and those in the outreach-alone condition (condition 2, sample size = 600) in Period 1. For each patient i in condition 1 in Period 1, the patient-level treatment effect estimate is (i.e., the difference between the outcome we observe for the patient i in condition 1 and the outcome that would be realized if this patient were in condition 2); for each patient in condition 2 in Period 1, the patient-level treatment effect estimate is (i.e., the difference between the outcome we observe for the patient i in condition 2 and the outcome that would be realized if this patient were in condition 1). We term this first causal forest estimation , where P1 refers to Period 1, and the superscript 12 refers to the comparison of the baseline condition ( 1) and the outreach-alone condition ( 2).
- Step 2. After excluding the patients who were deceased or diagnosed with HCC in the previous period(s), we repeated Step 1 to obtain and (condition 1, sample size = 591; condition 2, sample size = 592) in Period 2 and and (condition 1, sample size = 577; condition 2, sample size = 584) in Period 3. As discussed, we included one (two) additional covariate(s) indicating whether a patient has completed the screening test in the prior period(s) in the causal forest estimation of Period 2 ( 3). We term these second and third causal forests and , where P2 and P3 refer to Period 2 and Period 3, respectively, and the superscript 12 refers to the comparison of the baseline condition ( 1) and the outreach-alone condition ( 2).
- Step 3. We repeated Step 1 to obtain each patient's treatment effect estimate in the sample that includes patients in the baseline (condition 1, sample size = 600) and those in the outreach-with-patient-navigation condition (condition 3, sample size = 600) in Period 1. For each patient in condition 1, the patient-level treatment effect estimate is (i.e., the difference between the outcome we observe for the patient i in condition 1 and the outcome that would be realized if this patient were in condition 3); for each patient in condition 3, the patient-level treatment effect estimate is (i.e., the difference between the outcome we observe for the patient i in condition 3 and the outcome that would be realized if this patient were in condition 1). We term this fourth causal forest , where P1 refers to Period 1, and the superscript 13 refers to the comparison of the baseline condition ( 1) and outreach-with-patient-navigation condition ( 3).
- Step 4. We repeated Step 2 to obtain and in Period 2 (condition 1, sample size = 591; condition 3, sample size = 589) and and (condition 1, sample size = 577; condition 3, sample size = 582) in Period 3. We term these fifth and sixth causal forests and , where P2 and P3 refer to Period 2 and Period 3, respectively, and the superscript 13 refers to the comparison of the baseline condition ( 1) and the outreach-with-patient-navigation condition ( 3).
Table 3 and Figure 3 show the distribution of the patient-level treatment effect estimates based on the causal forest estimation. Relative to the baseline condition, outreach alone (outreach with patient navigation) increases screening completion rate by between 10 and 20 (13 and 24) percentage points (Table 3, Panel A). Causal forests enable us to construct confidence intervals for patient-level treatment effect estimates. As we report in Table 3, Panel B, outreach-alone intervention induces positive and statistically significant treatment effects among 100%, 74%, and 66% of the patients in Periods 1, 2, and 3, respectively (p <.05), while the outreach-with-patient-navigation intervention does so among 100%, 83%, and 89% of the patients in Periods 1, 2, and 3, respectively (p <.05).
Graph
Table 3. Summary of Average Treatment Effects (ATEs) and Patient-Level Conditional Average Treatment Effects (CATEs) by Outreach Type.
| A: ATEs by Outreach Type |
|---|
| Outreach Alone | Outreach with Patient Navigation |
|---|
| N | ATE | SE | 95% CI | N | ATE | SE | 95% CI |
|---|
| Period 1 ATE | 1,200 | .200 | .026 | .148 | .251 | 1,200 | .235 | .026 | .184 | .287 |
| Period 2 ATE | 1,183 | .109 | .027 | .057 | .162 | 1,180 | .128 | .028 | .074 | .182 |
| Period 3 ATE | 1,161 | .101 | .025 | .052 | .150 | 1,159 | .128 | .026 | .077 | .178 |
| B: Patient-Level CATEs by Outreach Type | |
| Outreach Alone | Outreach with Patient Navigation |
| N | Mean | SD | Min | Max | N | Mean | SD | Min | Max |
| Period 1 |
| Patient-level CATEs | 1,200 | .199 | .036 | .112 | .311 | 1,200 | .236 | .046 | .119 | .366 |
| Significant patient-level CATEsa | 1,200 | .199 | .036 | .112 | .311 | 1,200 | .236 | .046 | .119 | .366 |
| Proportion of significant patient-level CATEs | 100% | 100% |
| Period 2 |
| Patient-level CATEs | 1,183 | .108 | .038 | −.006 | .192 | 1,180 | .127 | .042 | .031 | .230 |
| Significant patient-level CATEsa | 875 | .125 | .024 | .057 | .192 | 975 | .139 | .036 | .052 | .230 |
| Proportion of significant patient-level CATEs | 74% | 83% |
| Period 3 |
| Patient-level CATEs | 1,161 | .099 | .036 | .014 | .192 | 1,159 | .126 | .030 | .047 | .207 |
| Significant patient-level CATEsa | 767 | .118 | .028 | .047 | .192 | 1,030 | .131 | .027 | .062 | .207 |
| Proportion of significant patient-level CATEs | 66% | 89% |
- 5 a Statistical significance is at the 95% level.
- 6 Notes: CI = confidence interval.
Graph: Figure 3. Distribution of patient-level CATEs.Notes: ATE refers to the average treatment effect; SE refers to the standard error; CATEs refer to conditional average treatment effects.
Germane to the focus of this article, there is substantial heterogeneity in those significant patient-level treatment effects: ( 1) compared with outreach alone, outreach-with-patient-navigation intervention induces a higher proportion of patients with significant positive treatment effect estimates in Periods 2 (83% − 74% = 9%) and 3 (89% − 66% = 23%), and ( 2) patient-level treatment effect estimates of outreach-alone (outreach-with-patient-navigation) intervention range from 5–31 (5–37) percentage points. Next, we investigate the sources of heterogeneity.
We examine the treatment effect heterogeneity by correlating treatment effect estimates with patient characteristics. Accordingly, we estimated the following equations:
Graph
5a
Graph
5b
where and refer to patient-level treatment effect estimates of outreach alone and those of outreach with patient navigation, j denotes the three-digit zip code, and t denotes the period. We pooled the estimates across periods and included period-fixed effects (ηt) to capture common time-varying observables that may affect them and clustered standard errors at the patient level to allow for heteroskedasticity and correlated errors within patients over time.
Columns 1 and 2 of Table 4 report the results showing the sources of heterogeneity in patient-level treatment effects. Next, we discuss the patient characteristics associated with the treatment effect heterogeneity.
Graph
Table 4. Sources of Heterogeneity in Patient-Level CATEs.
| (1)Outreach Alone | (2)Outreach with Patient Navigation | (3)Outreach Alone | (4)Outreach with Patient Navigation |
|---|
| Est | SE | Est | SE | Est | SE | Est | SE |
|---|
| Demographics | | | | | | | | |
| Age | −.003*** | (.000) | .006*** | (.000) | −.003*** | (.000) | .005*** | (.000) |
| Gender (female = 1) | .014*** | (.001) | .007*** | (.001) | .013*** | (.001) | .007*** | (.001) |
| Hispanic | .011*** | (.001) | .004*** | (.001) | .012*** | (.001) | .005*** | (.001) |
| Non-Hispanic African American | .010*** | (.001) | .011*** | (.001) | .010*** | (.001) | .011*** | (.001) |
| Other/unknown | .009*** | (.002) | .007* | (.003) | .008*** | (.002) | .007* | (.003) |
| Spanish | .014*** | (.001) | .015*** | (.001) | .013*** | (.001) | .014*** | (.001) |
| Other | −.002 | (.002) | .003 | (.005) | −.001 | (.004) | .004 | (.005) |
| Health Status | | | | | | | | |
| Child-Pugh B | −.012*** | (.001) | −.003*** | (.001) | −.011*** | (.001) | −.003*** | (.001) |
| Charlson Comorbidity Index | −.003*** | (.000) | −.002*** | (.000) | −.004*** | (.000) | −.003*** | (.000) |
| Documented cirrhosis | −.002* | (.001) | −.002* | (.001) | −.002** | (.001) | −.003** | (.001) |
| Hepatitis B | −.002* | (.001) | .001 | (.001) | −.001 | (.001) | .002 | (.001) |
| Alcohol-induced | −.004* | (.002) | −.001 | (.003) | −.005** | (.001) | .000 | (.002) |
| Nonalcoholic steatohepatitis | −.003*** | (.001) | −.000 | (.001) | −.003*** | (.001) | .000 | (.001) |
| Other | −.000 | (.001) | .005*** | (.001) | −.001 | (.001) | .004*** | (.001) |
| Visit History | | | | | | | | |
| Number of prior primary care visits | .010*** | (.001) | .011*** | (.001) | .020*** | (.001) | .022*** | (.002) |
| Receipt of hepatology care | .003*** | (.001) | .003* | (.001) | .002* | (.001) | .001 | (.001) |
| Health System Accessibility | | | | | | | | |
| Commercial | −.009*** | (.002) | −.005* | (.002) | −.009*** | (.001) | −.002 | (.002) |
| Medicaid | −.010*** | (.001) | −.001 | (.001) | −.009*** | (.001) | .001 | (.001) |
| Medicare | −.016*** | (.001) | −.002* | (.001) | −.015*** | (.001) | −.002* | (.001) |
| Self-pay | −.012*** | (.002) | −.003 | (.003) | −.012*** | (.002) | −.002 | (.002) |
| Unknown | −.013*** | (.001) | −.008*** | (.001) | −.011*** | (.001) | −.005** | (.001) |
| Proximity to clinics | .008*** | (.001) | .005** | (.002) | .008*** | (.001) | .007*** | (.001) |
| Neighborhood Socioeconomic Status | | | | | | | | |
| Educational attainment (%) | .002 | (.004) | −.011 | (.006) | .003 | (.004) | −.011 | (.006) |
| Income ($) | −.000 | (.003) | .012** | (.004) | .000 | (.003) | .011** | (.004) |
| Average commute time (minutes) | −.004*** | (.001) | −.000 | (.002) | −.003** | (.001) | −.002 | (.001) |
| Private health insurance (%) | −.000 | (.004) | .002 | (.005) | −.002 | (.003) | .008 | (.005) |
| Public coverage (%) | .011** | (.004) | .006 | (.005) | .009** | (.003) | .009* | (.004) |
| Unemployment rate (%) | .002 | (.001) | .003 | (.002) | .002 | (.001) | .005 | (.002) |
| Population | .005* | (.002) | .005* | (.002) | .003 | (.002) | .004* | (.002) |
| Period Fixed Effects | | | | | | | | |
| Period 2 dummy | −.091*** | (.001) | −.109*** | (.001) | −.091*** | (.001) | −.109*** | (.001) |
| Period 3 dummy | −.100*** | (.001) | −.110*** | (.002) | −.100*** | (.001) | –.110*** | (.002) |
| Exploratory Interactions | | | | | | | | |
| Primary care visit2 | | | | | −.003*** | (.000) | −.004*** | (.000) |
| Primary care visit × Age | | | | | .000 | (.000) | .000 | (.001) |
| Primary care visit × Gender | | | | | .000 | (.001) | −.003** | (.001) |
| Primary care visit × Hispanic | | | | | −.003** | (.001) | −.002 | (.002) |
| Primary care visit × African American | | | | | .001 | (.001) | −.001 | (.001) |
| Primary care visit × Spanish | | | | | −.005*** | (.001) | −.005*** | (.002) |
| Primary care visit × Child-Pugh B | | | | | −.003*** | (.001) | −.003** | (.001) |
| Primary care visit × Charlson Index | | | | | −.000 | (.000) | −.001 | (.000) |
| Primary care visit × Medicaid | | | | | −.000 | (.001) | .003 | (.002) |
| Primary care visit × Medical assistance | | | | | −.002* | (.001) | −.001 | (.002) |
| Primary care visit × Medicare | | | | | −.001 | (.001) | −.001 | (.002) |
| Primary care visit × Proximity to clinics | | | | | −.000 | (.001) | .002 | (.001) |
| Intercept | .190*** | (.001) | .225*** | (.002) | .193*** | (.001) | .227*** | (.002) |
| Clustered standard error | Yes | | Yes | | Yes | | Yes | |
| R2 | .770 | | .727 | | .783 | | .741 | |
| N | 3,544 | | 3,539 | | 3,544 | | 3,539 | |
- 7 *p <.05.
- 8 **p <.01.
- 9 ***p <.001.
- 10 Notes: The baseline categories of main effects are male, non-Hispanic Caucasian, Hepatitis C, English, medical assistance/charity, and Period 1. We scaled continuous variables to zero mean and unit variance. As we pooled the estimates of three periods, sample sizes are 3,544 (1,200 + 1,183 + 1,161) and 3,539 (1,200 + 1,180 + 1,159) (see sample size columns of Table 3, Panel B).
Older patients are less responsive to the outreach-alone intervention than younger patients ( = −.003, p <.001) but they are more responsive to outreach-with-patient-navigation intervention than younger patients ( =.006, p <.001). A possible explanation is that older adults prefer to use information that is customized to their needs rather than generic information that can be overwhelming ([20]), which makes them less responsive to direct mails than younger adults ([35]). The interactive and personalized nature of the navigation over the telephone provides targeted and useful information to older patients, making it more effective ([37]).
Female patients are more responsive to both outreach interventions than male patients (outreach alone: =.014, p <.001; outreach with patient navigation: =.007, p <.001). This is likely due to the higher prevention and loss-minimization focus among women ([64]). According to agency-communion theory ([18]), men focus on maximizing gains while women focus on minimizing the downside potential of their decision. Outreach messages for cancer screening, by design, approach health care from a prevention and loss-minimization focus.
Hispanic patients are more responsive to both outreach interventions than Caucasian patients (outreach alone: H =.011, p <.001; outreach with patient navigation: H =.004, p <.001). Likewise, non-Hispanic African American patients are more responsive to both outreach interventions than Caucasian patients (outreach alone: AA =.010, p <.001; outreach with patient navigation: AA =.011, p <.001). Similarly, patients whose primary language is Spanish are more responsive to both outreach interventions than those whose primary language is English (outreach alone: =.014, p <.001; outreach with patient navigation: =.015, p <.001). Due to language and access barriers, such patients may have relatively fewer opportunities to learn about the health screening information than ethnic majority groups ([17]; [62]). Given the lower baseline access, outreach interventions that provide information on screening opportunities should be more effective among such groups than their counterparts ([39]).
Patients in a poorer health status (those with Child-Pugh B) are less responsive to both outreach interventions than patients with a better health status (outreach alone: = −.012, p <.001; outreach with patient navigation: = −.003, p <.001). The pattern is consistent when Charlson Comorbidity Index and the presence of documented cirrhosis are used as indicators of health status. A possible explanation is that outreach interventions might make patients fearful of finding out they have cancer ([ 4]) and experience death anxiety ([29]). Those with poor health will experience higher death anxiety due to lower optimism about their health ([ 3]), which reduces adaptive coping and thus decreases the utilization of health care services ([48]). We also find that compared with patients with Hepatitis C, those with Hepatitis B are less responsive to outreach alone (coefficients ranging from −.004 to −.002), but this is not the case for outreach with patient navigation.
Patients with a higher number of prior primary care visits are more responsive to both outreach interventions than those with fewer prior primary care visits (outreach alone: =.010, p <.001; outreach with patient navigation: =.011 p <.001). Similarly, patients who previously received hepatology care are more responsive to both outreach interventions than patients with no prior hepatology care (outreach alone: =.003, p <.001; outreach with patient navigation: =.003, p <.05). At its core, a patient's prior visit history signifies the extent to which a patient has a favorable attitude toward utilizing health care services to pursue their health goals ([38]) and has familiarity with the utilization process ([27]). This should motivate patients to get screened.
Patients with insurance coverage through medical assistance/charity are generally more responsive to both outreach interventions than patients with other types of insurance (outreach alone: = ranging from −.016 to −.009, p <.001; outreach with patient navigation: = ranging from −.008 to −.001, p <.001 through n.s.). Patients who receive health care at a low cost due to medical assistance/charity, with access to the corresponding insurance, are more likely to respond to outreach interventions because of their ability to overcome financial hardships to utilize screening services. A patient's ease of accessing health care services is based on not only their ability to pay for the service but also their proximity to health care providers. We find that patients with closer proximity to care are more responsive to both outreach interventions than patients with further proximity to care (outreach alone: =.008, p <.001; outreach with patient navigation: =.005, p <.01).
Patients who live in more educated neighborhoods are not necessarily more or less responsive to interventions (outreach alone: =.002, n.s.; outreach with patient navigation: = −.011, n.s.). Yet patients who reside in a higher-income neighborhoods are more responsive to outreach with patient navigation (outreach alone: = −.000, n.s.; outreach with patient navigation: =.012, p <.01), which implies that those in low-income neighborhoods are less responsive to this intervention. Patients in low-income neighborhoods face unique challenges such as higher rates of obesity, chronic disease, environmental pollutants, and incarceration ([36]). The prevalent health and environmental challenges in these communities might cause anxiety among community members and lead them to be pessimistic about their health ([22]), thus making them less responsive to outreach intervention. Patients in neighborhoods with longer average commute times are less responsive to outreach alone (outreach alone: = −.004, p <.001), but this is no longer the case for outreach with patient navigation (outreach with patient navigation: = −.000, n.s.). Patient navigation alleviates perceived costs associated with a screening by providing the information on the estimated duration for the appointment, so patients who live in a highly trafficked community will no longer show resistance to a screening.
While patient-level insurance coverage should capture the impact of health system accessibility, the neighborhood-level health insurance coverage can also offer additional insights. Patients' responsiveness to the outreach interventions does not vary by the degree of private health insurance coverage in their neighborhood (outreach alone: = −.000, n.s.; outreach with patient navigation: =.002, n.s.). However, patients in a neighborhood with a greater public health insurance coverage are more responsive to the outreach-alone intervention but not to the outreach-with-patient-navigation intervention (outreach alone: =.011, p <.01; outreach with patient navigation: =.006, n.s.).
Neighborhood unemployment rate does not significantly affect patients' responsiveness to the interventions (outreach alone: =.002, n.s.; outreach with patient navigation: =.003, n.s.). Yet patients from neighborhoods with more dense populations are more responsive to both interventions (outreach alone: =.005, p <.05; outreach with patient navigation: =.005, p <.05), implying that patients in rural areas are less responsive to interventions. These results bear a notable caveat: a potential aggregation bias due to the measurement of neighborhood variables at the three-digit-zip-code level may have led to the null effect.
As described in Columns 3 and 4 of Table 4, we explore possible combinations of patient characteristics with the interactions between prior primary care visits and other patient characteristics. This is akin to examining higher-order interactions in an analysis of variance. This analysis offers several insights.
The marginal benefit of additional primary care visits diminishes such that a patient's primary care visit has a nonlinear effect on outreach intervention effectiveness. Referring to Columns 3 and 4 of Table 4, there is a positive linear coefficient (outreach alone: b =.020, p <.001; outreach with patient navigation: b =.022, p <.001) and a negative quadratic coefficient (outreach alone: b2 = −.003, p <.001; outreach with patient navigation: b2 = −.004, p <.001) for the effect. Jointly, the coefficients capture diminishing returns such that a patient's first few primary care visits yield large marginal returns. Given the importance of the initial visits, health care professionals can enhance outreach effectiveness by targeting patients who have made fewer than numerous visits in the past.
The interactions between prior primary care visits and patient characteristics can help practitioners further identify the responsive subgroups. For example, Spanish-speaking patients' responsiveness to outreach interventions is attenuated as primary care visits increase (coefficient = −.005 p <.001 for both outreach alone and outreach with patient navigation). It could be that Spanish-speaking patients perceive that outreach interventions are less informative than primary care visits. Practitioners may target Spanish-speaking patients who have no prior primary care visits in the past. Patients with Child-Pugh B are even less responsive to outreach interventions as primary care visits increase (outreach alone: coefficient = −.003 p <.001; outreach with patient navigation: coefficient = −.003, p <.01), suggesting that increased primary care visits compound the perception of a fear and death anxiety of having cancer triggered by outreach interventions and thus decrease the use of screening. Overall, our post hoc analysis highlights the need to understand how outreach effectiveness varies by the combination of patient characteristics.
The dynamics in the proportion of treatment effects that are statistically significant across each condition display an interesting pattern. As Figure E1 in Web Appendix E shows, there is very little heterogeneity in Period 1 in terms of the proportion of treatment effects that are statistically significant. Specifically, 100% (100%) of the treatment effects due to outreach alone (outreach with patient navigation) are statistically significant in Period 1. However, compared with outreach alone, outreach with patient navigation induces a higher proportion of patients with significant positive treatment effect estimates in Period 2 (83% vs. 74%) and Period 3 (89% vs. 66%). The effectiveness of the outreach with patient navigation relative to outreach alone improves over time. Medical and health care professionals believe that exposure to outreach nurtures the motivation for screening compliance, which is the crucial first step. Once patients are compliant to screening, potential barriers to getting screening may be addressed in subsequent periods. Thus, it is possible that the heterogeneity in the proportion of significant treatment effects could be related to the repetition of the treatment over different periods.
As a thought experiment, we conducted an analysis investigating the extent of heterogeneity in the proportion of treatment effects that are statistically significant when we "turn off" the repeated nature of the treatment. In this analysis, we define the dependent variable as whether a patient undergoes a screening at least once in the short (0–6 months), medium (0–12 months), and long (0–18 months) runs. The goal of this approach is to study whether interventions can bring at-risk patients in for a screening at least once during the three periods investigated. We also wanted to understand if heterogeneity, in terms of proportion of treatment effects that are statistically significant, manifests when we "turn off" the repeated nature of the treatment. Web Appendix Figure E2 reports the screening completion rates and plots the patient-level treatment effects, and Table E2 reports the summary of the treatment effects across each condition. The main takeaways are as follows:
- There is little heterogeneity in the proportion of treatment effects that are statistically significant. Web Appendix Table E2 reports that outreach-alone intervention induces positive and statistically significant treatment effects among 100%, 100%, and 99.9% of the patients in Periods 1, 2, and 3, respectively (p <.05), while the outreach-with-patient-navigation intervention does so among 100%, 99.7%, and 98.5% of the patients in Periods 1, 2, and 3, respectively (p <.05).
- There remains substantial heterogeneity in the magnitude of treatment effects. Web Appendix Table E2 reports that patient-level treatment effect estimates of outreach-alone (outreach-with-patient-navigation) intervention range from 10–32 (11–37) percentage points.
Web Appendix Figures E1, E2, and Table E2 jointly show that the heterogeneity in the proportion of treatment effects that are statistically significant is related to the dynamics induced by the repeated nature of the treatment. Future research should investigate the sources of these dynamics.
We evaluate the return on outreach interventions among patients and across three periods:
Graph
6
where refers to the probability that patient i assigned to outreach type k completes the screening in period t.
- If a patient completes the screening test:
- ○ The health care institution generates Benefitikt for patient i receiving intervention type k in period t, captured by the quality-adjusted life years of a patient attributable to the screening (typically expressed in the financial value in the medical literature).
- ○ The health care institution incurs Screening Costikt for patient i receiving intervention type k in period t, which includes the costs of an ultrasound/MRI/CT test or a combination of these tests (i.e., each patient can complete multiple tests).
- ○ Conditional on being detected with an early tumor, the health care provider incurs Treatment Costikt for patient i intervention type k in period t, which includes the costs of tumor resection, liver transplantation, and local ablative therapies.
- If a patient does not complete the screening test:
- ○ The health care institution incurs Opportunity Costikt if patient i receiving intervention type k in period t develops advanced HCC, which creates costs.
- Irrespective of whether the patient completes the screening test:
- ○ Outreach Costikt is incurred if the health care institution employs an outreach program. The outreach costs are higher for the outreach-with-patient-navigation than the outreach-alone condition and are zero for the baseline condition.
Research has documented that HCC screening completion with biannual ultrasound extends patients' quality-adjusted life expectancy by 1.3 months, and HCC screening utilization with MRI does so by 2 months ([28]). Patients may complete an ultrasound, an MRI, a CT scan, or a mix of these tests. We assume the average quality-adjusted life expectancy to be 1.65 months for each patient who completes the screening. The medical literature posits that the financial value per quality-adjusted life year is $50,000 ([ 2]; [28]). Thus, total benefits can be obtained by multiplying the number of quality-adjusted life years by the financial value per quality-adjusted life year ($50,000) (i.e., multiply total number of patients who complete the screening by average quality-adjusted life expectancy).
Table 5 presents the results of the benefit–cost calculation using Equation 6 for each condition in each period. We use the observed values in the data (e.g., actual number of patients who visit) in conjunction with parameters (e.g., early detection rate) from the medical literature to calculate the return in each condition in each period. Web Appendix F documents details on parameters from the medical literature.
Graph
Table 5. Return on Nontargeted Outreach Interventions.
| Cost and Benefit by Intervention Type and Across Period | Usual Care | Outreach Alone | Outreach with Patient Navigation |
|---|
| Period 1 | Period 2 | Period 3 | Total | Period 1 | Period 2 | Period 3 | Total | Period 1 | Period 2 | Period 3 | Total |
|---|
| Sample Size | 600 | 591 | 577 | | 600 | 592 | 584 | | 600 | 589 | 582 | |
| Costs | | | | | | | | | | | | |
| Outreach costs | $0 | $0 | $0 | $0 | $52,157 | $39,099 | $32,030 | $123,285 | $60,594 | $51,338 | $45,617 | $157,548 |
| Screening costs | $88,541 | $90,667 | $76,339 | $255,547 | $111,335 | $118,351 | $94,233 | $323,919 | $127,598 | $105,015 | $100,258 | $332,871 |
| Treatment costs | $557,975 | $580,293 | $468,699 | $1,606,967 | $1,000,634 | $944,837 | $859,281 | $2,804,752 | $1,075,031 | $1,015,514 | $967,156 | $3,057,700 |
| Opportunity costs | $539,226 | $521,252 | $540,424 | $1,600,902 | $396,631 | $405,019 | $422,993 | $1,224,642 | $372,665 | $378,656 | $385,846 | $1,137,168 |
| Total costs | $1,185,742 | $1,192,212 | $1,085,462 | $3,463,416 | $1,560,756 | $1,507,305 | $1,408,536 | $4,476,598 | $1,635,888 | $1,550,523 | $1,498,876 | $4,685,287 |
| Benefits | | | | | | | | | | | | |
| Health benefits | $1,031,250 | $1,072,500 | $866,250 | $2,970,000 | $1,849,375 | $1,746,250 | $1,588,125 | $5,183,750 | $1,986,875 | $1,876,875 | $1,787,500 | $5,651,250 |
| Net benefits (health benefits − total costs) | −$154,492 | −$119,712 | −$219,212 | −$493,416 | $288,619 | $238,945 | $179,589 | $707,152 | $350,987 | $326,352 | $288,624 | $965,963 |
| Net benefits per patient | −$257 | −$203 | −$380 | −$840 | $481 | $404 | $308 | $1,192 | $585 | $554 | $496 | $1,635 |
| Total net benefits among population (N = 3,217) | −$276,111 | −$217,211 | −$407,397 | −$900,718 | $515,825 | $432,818 | $329,759 | $1,278,402 | $627,292 | $594,157 | $531,788 | $1,753,237 |
| Return on Nontargeted Outreach Interventions | | | | | | | | | | | | |
| Total net benefits among population (N = 3,217) | $867,007 | $809,764 | $454,150 | $2,130,921 | | | | | | | | |
11 Notes: We extrapolated the calculation to the 3,217 patients eligible for randomization. Outreach costs = Number of call hours × Cost per hour. Screening costs = Number of ultrasounds completed × Cost per ultrasound + Number of CTs/MRIs completed × Average unit cost (CT/MRI). Treatment costs = Early detection probability × Average cost of treatment (if detected early). Opportunity costs = Annual HCC probability × Annual cost of advanced HCC. Health benefits = Quality-adjusted life gain × Financial value per quality-adjusted life year.
We observe that 150/600, 156/591, and 126/577 patients in no-outreach condition completed the screening in Periods 1, 2, and 3, respectively. The total benefits of the no-outreach condition across the three periods are estimated to be $2,970,000.
The screening costs are the total costs of ultrasound, CT, and MRI tests completed. The number of ultrasound (CT and MRI) tests completed is 127 (69), 149 (68), and 113 (59) in Periods 1, 2, and 3. The cost per ultrasound is $143, while the average cost of CT and MRI is $1,020. Thus, the total screening costs are estimated to be $255,547. Focusing on treatment costs, 5% of the total number of screening tests typically result in early tumor detection. The average treatment cost per patient for early tumor detection is $74,397 ([28]). Given that 150/600, 156/591, and 126/577 patients in the no-outreach condition completed the screening in Periods 1, 2, and 3, 5% of them would undergo treatment costs, giving a total treatment cost of $1,606,967. Opportunity cost is incurred if patients who have not completed the screening develop advanced HCC. The annual cost of advanced HCC is $41,320, and the annual HCC probability is 2.9% ([28]). Multiplying the number of patients who have not completed the screening in the no-outreach condition by the probability of HCC (2.9%) gives us a total opportunity cost of $1,600,902. Finally, outreach costs are zero in the no-outreach condition. Subtracting the total cost from the total benefit, the total return in the no-outreach condition is −$493,416, which translates to a loss of $840 per patient to the health care system.
Compared with the no-outreach condition, there are higher benefits in the outreach-alone case ($5,183,750 vs. $2,970,000), as there are 269/600 patients, 254/592 patients, and 231/584 patients who have completed the screening in Periods 1, 2, and 3, respectively. Using the same approach as the one used for no-outreach condition to calculate the costs, the screening costs, treatment costs, and opportunity costs, respectively, are $323,919, $2,804,752, and $1,224,642 in the outreach-alone condition. In addition, the total number of hours devoted to outreach calls is 3,477, 2,607, and 2,135. Assuming a $15 hourly wage, the total outreach cost in the outreach-alone condition is $123,285. Thus, the total return in the outreach-alone condition is $707,152, which translates to a gain of $1,192 per patient to the health care system.
Compared with the no-outreach condition, there are higher benefits in the outreach-with-patient-navigation condition ($5,651,250 vs. $2,970,000), as there are 289/600 patients, 273/589 patients, and 260/582 patients who would complete the screening. Following the same calculation approach, the screening costs, treatment costs, opportunity costs, and outreach costs are $332,871, $3,057,700, $1,137,168, and $157,548, respectively, in the outreach-with-patient-navigation condition. Thus, the total return in the outreach-with-patient-navigation condition is $965,963, which is substantially greater than that in the no-outreach condition and translates to a gain of $1,635 per patient to the health care system.
No outreach results in a net loss of $840 per patient to the medical hospital, whereas outreach alone (outreach with patient navigation) generates a monetary gain of $1,192 ($1,635) per patient. When extrapolated to the 3,217 patients eligible for randomization from the hospital's patient database, the cancer intervention results in a loss of $900,718 from no outreach or usual care, a gain of $1,278,402 from outreach alone, and a gain of $1,753,237 from outreach with patient navigation. In this scenario, the total gain is $2,130,921.
Thus far, the calculation of return on cancer interventions has been based on the random assignment of patients and on patients remaining in the same condition over three periods. However, ( 1) there is heterogeneity in patient-level treatment effects of outreach-alone intervention and in those of outreach-with-patient-navigation intervention, ( 2) not all patient-level treatment effect estimates are statistically greater than 0, ( 3) treatment effect heterogeneity varies across periods, and ( 4) the net return on outreach interventions varies across intervention types and over time. As such, given each patient's characteristics in a particular period, outreach with patient navigation is unlikely to be uniformly more effective than outreach alone. This poses two questions: ( 1) Given each patient's observed characteristics, which intervention type is most suitable for each patient? and ( 2) For the same patient, does the most suitable intervention vary across periods? Accordingly, we conduct a simulation that assigns each patient to the most suitable condition in each period based on two types of allocation schemes: ( 1) predicted treatment effect and ( 2) predicted net return (see detailed procedure in Web Appendix G).
Conceptually, in each period, given each patient's profile, we compare each patient's treatment effect estimate in their corresponding condition (e.g., condition 2) with their simulated treatment effect estimate in the counterfactual condition (e.g., condition 3). Then we assign this patient to the best-suited intervention that generates a significantly higher treatment effect estimate (e.g., condition 3, p <.05). If none of these estimates is significantly larger than 0, we assign this patient to condition 1.
As we show in Table 6, Panel A, the recommended allocation for each period is as follows:
- Period 1: 0%, 99.9%, and.1% of patients are assigned to the no-outreach, outreach-alone, and outreach-with-patient-navigation conditions, respectively.
- Period 2: 9.0%, 74.0%, and 16.9% of patients in each condition, respectively.
- Period 3: 8.4%, 66.9%, and 24.7% of patients in each condition, respectively.
Graph
Table 6. Simulation Results Based on Patient-Level Treatment Effect Estimates.
| A: Recommended Allocation |
|---|
| Condition | Usual Care | Outreach Alone | Outreach with Patient Navigation | Sample Size |
|---|
| Period 1 | 0% | 99.9% | .1% | 1,800 |
| Period 2 | 9.0% | 74.0% | 16.9% | 1,772 |
| Period 3 | 8.4% | 66.9% | 24.7% | 1,743 |
| B: Return on Patient-Level Targeted Outreach Interventions |
| Usual Care | Outreach Alone | Outreach with Patient Navigation |
| Period 1 | Period 2 | Period 3 | Period 1 | Period 2 | Period 3 | Period 1 | Period 2 | Period 3 |
| Cost and Benefit by Intervention Type and Across Period | | | | | |
| Sample size after reallocation | 0 | 160 | 147 | 1,799 | 1,312 | 1,166 | 1 | 300 | 430 |
| Net benefits per patient | −$257 | −$203 | −$380 | $481 | $404 | $308 | $585 | $554 | $496 |
| Total net benefits among population (N = 3,217) | $0 | −$58,838 | −$103,076 | $1,546,617 | $961,383 | $661,788 | $1,045 | $301,773 | $393,578 |
| Return on Patient-Level Targeted Outreach Interventions | | | | | |
| Period 1 | Period 2 | Period 3 | Total(Improvement) |
| Total net benefits among population (N = 3,217) | $1,547,662 | $1,204,318 | $952,290 | $3,704,270 (74%) |
There are four noteworthy takeaways from this recommendation. First, the recommended split deviates from the original allocation based on the randomized controlled trial (1:1:1), suggesting that targeting induces asymmetric allocation of patients to different conditions. Second, there is a fraction of patients who stay in the baseline condition in Periods 2 and 3. For these patients, neither of the interventions is more effective than the baseline. Third, we reallocate most patients to the outreach-alone condition, suggesting that health care institutions can achieve the same level of effectiveness by aligning only moderate outreach efforts with these patients. Fourth, over time, the outreach-with-patient-navigation condition seems to be more effective, given the higher allocation to this condition (.1% in Period 1, 16.9% in Period 2, and 24.7% in Period 3).
Table 6, Panel B, shows the return on patient-level targeted outreach interventions. When extrapolated to the 3,217 patients eligible for randomization from the hospital's patient database, patient-level targeted outreach program across conditions generates a gain of $1,547,662, $1,204,318, and $952,290 in Periods 1, 2, and 3, respectively. The total net return on patient-level targeted outreach program is $3,704,270, or 74% higher than that on nontargeted outreach program based on the random assignment ($3,704,270–$2,130,921 = $1,573,349).
What if the health care system aims to maximize the overall return derived from assigning each patient to the most suitable intervention? As such, we can assign each patient to the intervention that gives the highest predicted net return based on Equation 6 rather than only the highest predicted treatment effect. Specifically, in each period, we compare each patient's predicted net return in the corresponding condition (e.g., condition 2) with their simulated patient's estimated net return in the counterfactual condition (e.g., condition 3). Then we assign the patient to the best-suited intervention that generates a significantly higher net return (e.g., condition 3, p <.05). If none of these estimates is significantly larger than 0, we assign this patient to condition 2 because the net return in the baseline condition is negative across all three periods (recall Table 5).
As we show in Table 7, Panel A, the recommended allocation for each period is:
- Period 1: 0%, 87.2%, and 12.8% of patients are assigned to the no-outreach, outreach-alone, and outreach-with-patient-navigation conditions respectively.
- Period 2: 0%, 79.3%, and 20.7% of patients in each condition, respectively.
- Period 3: 0%, 68.7%, and 31.3% of patients in each condition, respectively.
Graph
Table 7. Simulation Results Based on Patient-Level Estimated Return.
| A: Recommended Allocation |
|---|
| Condition | Usual Care | Outreach Alone | Outreach with Patient Navigation | Sample Size |
|---|
| Period 1 | 0% | 87.2% | 12.8% | 1,800 |
| Period 2 | 0% | 79.3% | 20.7% | 1,772 |
| Period 3 | 0% | 68.7% | 31.3% | 1,743 |
| B: Return on Patient-Level Targeted Outreach Interventions |
| Usual Care | Outreach Alone | Outreach with Patient Navigation |
| Period 1 | Period 2 | Period 3 | Period 1 | Period 2 | Period 3 | Period 1 | Period 2 | Period 3 |
| Cost and Benefit by Intervention Type and Across Period |
| Sample size after reallocation | 0 | 0 | 0 | 1,570 | 1,406 | 1,198 | 230 | 366 | 545 |
| Net benefits per patient | −$257 | −$203 | −$380 | $481 | $404 | $308 | $585 | $554 | $496 |
| Total net benefits among population (N = 3,217) | $0 | $0 | $0 | $1,349,743 | $1,030,263 | $679,950 | $240,462 | $368,163 | $498,837 |
| Return on Patient-Level Targeted Outreach Interventions | | | | | |
| Period 1 | Period 2 | Period 3 | Total(Improvement) |
| Total net benefits among population (N = 3,217) | $1,590,205 | $1,398,426 | $1,178,788 | $4,167,419 (96%) |
There are two takeaways from this recommendation. First, no patient stays in the baseline condition under this allocation scheme, reflecting the goal of maximizing overall return. Second, while we still reallocate most patients to the outreach-alone condition, the outreach-with-patient-navigation intervention seems to be even more effective over time given the higher allocation to this condition than the previous allocation (e.g., 31.3% vs. 24.7% in Period 3).
Table 7, Panel B, shows the return on patient-level targeted outreach interventions. When extrapolated to the 3,217 patients eligible for randomization from the hospital's patient database, the total net return on the patient-level targeted outreach program is $4,167,419, or 96% higher than that on the nontargeted outreach program based on the random assignment ($4,167,419 − $2,130,921 = $2,036,498). In summary, patient-level targeted outreach interventions improve the payoffs to the health care system by 74%−96%, or $1.6 million to $2 million.
The difference in allocation based on predicted treatment effect versus predicted net return shows the versatility of our approach in providing practical guidance to medical professionals and policy makers. The nature and magnitude of benefits can shift based on the goals that a health care institution sets for itself. Using this approach, an organization can set its strategic goals to maximize the benefits from personalized outreach. These results also confirm that the cumulated benefits from repeated and upgraded health education through outreach with patient navigation can be enhanced using individually tailored outreach over time.
Relying only on the main-effects analysis, scholars might conclude that the outreach with patient navigation and outreach alone are equally effective. However, our application of causal forests uncovers patient heterogeneity in outreach effectiveness and leads to different conclusions and important practical implications. Specifically, patients with different characteristics respond very differently to each intervention. For example, patients who are more responsive to outreach alone or outreach with patient navigation tend to be female, be part of minority populations, be in better health status, be covered by medical assistance, have closer proximity to clinics, and reside in a populated neighborhood. Patients who are more responsive to outreach alone tend to be younger, have faster commutes, and reside in neighborhoods with more public insurance coverage. Patients responsive to outreach with patient navigation tend to be older and reside in a higher-income neighborhood. Over time, the outreach-with-patient-navigation intervention becomes more effective for an increased proportion of patients. As such, we illustrate time-varying heterogeneity in the outreach effectiveness.
A cost–benefit analysis shows that the baseline condition results in a net loss of $840 per patient, whereas outreach alone (outreach with patient navigation) generates a gain of $1,192 ($1,635) per patient. When extrapolated to the 3,217 eligible patients, the total net gain of the nontargeted cancer outreach program across conditions is $2,130,921, which implies that outreach marketing provides a substantial positive payoff to the health care system. Our simulation shows that targeted outreach interventions can enhance this return by 74%−96%.
For the marketing discipline, this article provides a framework for better understanding and analyzing sufficiently powered field experiments that are based on random assignment of heterogeneous customers to different treatments. Instead of focusing only on the main effects of the treatment or a subset of individual-level covariates, causal forests flexibly predict personalized treatment effects based on high-dimensional, nonlinear functions of those covariates. Such an approach also obviates the need for choosing several one-way interactions a priori to test for heterogeneity or searching over many interactions for particularly responsive subgroups. Accordingly, this article provides a methodological solution to the field's concern of external validity. Many empirical findings are typically much less generalizable than we imagine, because researchers lack a process and corresponding insights to identify moderators (i.e., the interaction of treatment and unmodeled/unmanipulated background factors) ([23]; [42]).
For the emerging discipline of personalized health care, we show that causal forests can identify particularly responsive subgroups without the need for a larger number of experimental conditions. While modern health care has implemented personalized medicine using genetic information, most health care outreach and educational programs still rely on untailored communications. Practitioners who manage these programs should recognize that the use of a large number of patient characteristics can substantially improve the outreach responsiveness through a tailored approach.
Our research also responds to a recent call for boundary-breaking marketing-relevant research ([43]) in several ways. First, our covariates are motivated by "real-world phenomena, rather than the constructs and theories in the marketing" (p. 11). Our findings that treatment effects vary across covariates not only engage "academics in other disciplines" (p. 1) but also offer important implications to the extant literature and theory going forward. Second, our findings have "life and death implications" (p. 9)—they help detect liver cancer at early stages. Third, our covariates, such as ethnicity, language, insurance coverage, and neighborhood socioeconomic status, elucidate how outreach effectiveness may vary among "understudied consumers such as minorities, privileged or impoverished classes, and marginalized consumers (e.g., special needs populations)" (p. 5).
We urge hospitals and medical centers with outreach programs to leverage patient information to improve the effectiveness of outreach investments. Hospitals and health care practitioners should realize that a "one-size-fits-all" outreach program is neither effective nor economic. The use of machine learning can power data-driven patient-centric outreach programs that are also dynamically adaptive. Practitioners should consider both cross-sectional and temporal adaptation of outreach programs to maximize the benefit of health care interventions.
We urge policy makers in the federal, state, and local health departments, American Hospital Association, American Cancer Society, and American Liver Foundation to financially support personalized outreach programs. More hospitals should reach out to the underrepresented populations as they are more responsive to outreach messages; however, this requires additional resources and training. Incentivizing hospitals to reach out to patients with varying personal, clinical, structural, and socioeconomic backgrounds can also be effective. They should also engage a multidisciplinary group from health care, marketing, computer science, and other disciplines to fund an accumulation of comprehensive databases to facilitate even better targeting of patients to improve outcomes.
First, because patients have different barriers to screening, future research should test the effectiveness of different barrier-reduction strategies by analyzing the nature of communication between patients and the staff with the use of call recordings. Second, our study focuses on the endpoint outcome: screening completion. Future research could apply the notion of customer journey to disentangle which parts of the intervention (e.g., barrier discussion during an outreach call vs. reminder calls) are more effective at not only increasing completion but also reducing no-show rates or time to response, further enhancing the return. Third, although we track individual patients, outreach designed to serve an individual may have influenced other members of the household. Future research could study possible spillover effects of outreach interventions.
Supplemental Material, WEB_APPENDIX_3_17_2020 - Improving Cancer Outreach Effectiveness Through Targeting and Economic Assessments: Insights from a Randomized Field Experiment
Supplemental Material, WEB_APPENDIX_3_17_2020 for Improving Cancer Outreach Effectiveness Through Targeting and Economic Assessments: Insights from a Randomized Field Experiment by Yixing Chen, Ju-Yeon Lee, Shrihari (Hari) Sridhar, Vikas Mittal, Katharine McCallister and Amit G. Singal in Journal of Marketing
Footnotes 1 Associate EditorOded Netzer
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by National Cancer Institute R01 CA222900 and AHRQ R24HS022418. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or AHRQ.
4 Online supplement: https://doi.org/10.1177/0022242920913025
5 1See https://www.cancer.org/content/dam/cancer-org/online-documents/en/pdf/infographics/where-does-your-money-go-infographic-print.pdf (accessed February 25, 2020).
6 2The hospital system is the sole safety-net provider for Dallas County, which minimizes omitted variable bias that could emanate from competitive efforts by other organizations in the area.
7 3The hospital system obtained a waiver of informed consent to minimize volunteer bias.
8 4Out of 28 patients excluded in Period 2, 12 were excluded because they were diagnosed with HCC in Period 1, and 16 were deceased. Out of 57 patients excluded in Period 3, 23 were excluded due to being diagnosed with HCC in Period 2, and 38 were deceased (4 of them were both diagnosed with HCC and deceased).
9 5The abdominal imaging screening includes an ultrasound, magnetic resonance imaging (MRI), or computed tomography (CT).
6In 2017, 85.9% of office-based physicians in the United States used an EMR system (https://www.cdc.gov/nchs/fastats/electronic-medical-records.htm).
7According to Health Insurance Portability and Accountability Act, we are not allowed to obtain patients' identifiable location information such as address and zip code. Thus, we obtain the deidentified version (i.e., the first three digits of the zip code).
8Because we observe only the first three digits of patients' zip code, all zip-code-level covariates are aggregated to the three-digit level by calculating the sum (i.e., population) or mean (percentage with a bachelor's degree or higher, mean travel time to work, and per capita income) across all five-digit zip codes that share the same first three digits.
9Web Appendix C suggests that, on average, the significant difference in "no-show rates" in Period 3 seems to drive the main effects of outreach interventions on screening completion, while, statistically, there is no difference in scheduling rates across two outreach interventions across all three periods. This finding also seems to suggest that reminder calls made by the research staff in the outreach-with-patient-navigation condition may guide patients toward screening completion by lowering the probability that they would not show up. However, pinning down the exact mechanisms is beyond the scope of this research.
10Unlike regression trees, where the splits are determined by minimizing mean squared error of the prediction of outcomes, causal trees are built by minimizing the expected mean squared error of predicted treatment effects, which is equivalent to maximizing the variance of treatment effects across leaves minus a penalty for within-leaf variance ([6]).
11We scale continuous variables to zero mean and unit variance and expand categorical variables via one-hot encoding.
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By Yixing Chen; Ju-Yeon Lee; Shrihari (Hari) Sridhar; Vikas Mittal; Katharine McCallister and Amit G. Singal
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Improvised Marketing Interventions in Social Media
Online virality has attracted the attention of academics and marketers who want to identify the characteristics of online content that promote sharing. This article adds to this body of research by examining the phenomenon of improvised marketing interventions (IMIs)—social media actions that are composed and executed in real time proximal to an external event. Using the concept of quick wit, and theorizing that the effect of IMIs is furthered by humor and timeliness or unanticipation, the authors find evidence of these effects on both virality and firm value across five multimethod studies, including quasiexperiments, experiments, and archival data analysis. These findings point to the potential of IMIs in social media and to the features that firms should proactively focus on managing in order to reap the observed online sharing and firm value benefits.
Keywords: firm value; humor; improvisation; improvised marketing interventions; social media; virality
Digital communications have emerged as one of the most important means for firms to engage with customers ([14]; [32]; [36]; [74]). Anecdotal evidence suggests, however, that a growing number of consumers have become disenchanted and have grown suspicious—if not tired—of digital communications such as online advertisements ([75]). To help overcome this consumer fatigue, we explore the potential of improvised marketing interventions (IMIs)—the composition and execution of a real-time marketing communication proximal to an external event—to improve the effectiveness of digital communication.
Consider Oreo's famous tweet in response to the power outage during Super Bowl XLVII in 2013. Within moments of the power outage, Oreo tweeted, "Power out? No problem," along with a starkly lit image of a solitary Oreo cookie. A caption within the photo read, "You can still dunk in the dark." This exemplar of IMI received 15,000 retweets within the next eight hours, creating significant publicity for Oreo at minimal expense. By contrast, a Super Bowl ad costs an average of $4.5 million ([75]). This example demonstrates that an IMI can provide a strong boost to a brand's positive electronic word of mouth (WOM).
Prior research has highlighted the potential for improvisation ([44]; [45], [46]) and explored the benefits of firms' active presence on various digital platforms, including consumers' willingness to make positive comments about the firm online (see [14]; [25]; [28]; [37]; [43]; [65]). Yet critical questions remain. The [41], in fact, points to the limited research in this area and, in setting out its research priorities for 2016–2018, stresses the need for "getting marketing 'right' in real time."
Inspired by the potential of IMIs, in this research we consider the following questions: First, is IMI's underlying promise real? That is, to what extent does an IMI (vs. a non-IMI) result in greater virality? Second, what particular type of IMI message is most likely to achieve virality? And, third, how—if at all—do IMIs contribute to firm value? Drawing on research related to quick wit ([11]; [23]), we propose that IMIs are effective because they occur in real time, offer humor, and are either unanticipated or timely. We theorize this combination of traits to predict message virality and firm value. We test our theory using five studies. Study 1 uses a quasiexperiment during the Super Bowl on highly granular data to test whether an IMI increases virality more than a non-IMI. Study 2 uses an experiment to manipulate the key factors driving IMIs and test their effects on virality. Study 3 is based on a unique data set of 462 IMIs across 139 brands, spanning 58 industries over a six-year period to assess the relationship between IMI messages' content with virality and firm value. And Study 4 uses IMI messages and non-IMI messages from the airline industry to examine the relationship between IMI messages' content and virality as well as firm value. Finally, in Study 5 we generalize our findings using a random sample of S&P 500 firms to enhance the realism of the effects of IMI on virality and firm value. Table 1 provides an overview and lists the unique advantages of each study.
Graph
Table 1. Overview of Studies.
| Differences Across Studies | Study 1 | Study 2 | Studies 3a and 3b | Study 4a and 4b | Study 5a and 5b |
|---|
| Method (sample) | Within-firm analysis; quasiexperiment and synthetic control | Online experiment | Event study and regression | Panel data regression | Panel data regression |
| Observations | Oreo's tweets during the tent-pole event (N = 79,860); each retweet that mentioned names (Oreo and 15 rivals) on Twitter | Participants from a crowdsourcing platform (N = 771) | Hand-collected data set of IMI tweets across 139 brands over a six-year period (N = 462); private firms and observations with confounding events are dropped (N = 123) | Ten airlines' tweets within a two-month time frame from a third-party data provider (N = 232); we use the same procedure in Study 3b for the return model (N = 126) | Random sample of 25 firms from S&P 500 companies within a one-month time frame from a third-party data provider (N = 470); excluding observations with confounding events (N = 226) |
| Design/independent variables | Data at one-second level to examine Oreo's IMI led to an increase in virality | 2 (humor: high vs. low) × 2 (unanticipation: high vs. low) × 2 (timeliness: high vs. low) between-subject design | Tweet coding for humor and unanticipation. We estimated timeliness using minutes between a tweet created by the brand's account and when the corresponding event occurred. | Identify IMI and non-IMI tweets; same tweet coding procedure as Study 3 | Identify IMI and non-IMI tweets; same tweet coding procedure as Study 3 |
| Dependent variables | Volume of retweets and other social media metrics including volume of tweets, favorites, and difference of positive and negative tweets | Intention to retweet | Volume of retweets received by the specific IMI at the end of year; firm's abnormal stock market returns | Volume of retweets; firm's abnormal stock market returns | Volume of retweets; firm's abnormal stock market returns |
| Control variables | Time during Super Bowl, outage event, day of Super Bowl, event fixed effect | Brand familiarity, clip familiarity, clip liking, Twitter activeness, gender, age, creativity | Brand reputation, followers, B2C, positivity, negativity, word count, authenticity, tone, readability, informal words, social power, market size, turbulence, competition | Followers, friends, Klout score, positivity, negativity, word count, authenticity, tone, readability, informal words, social power, holiday or not, video or photo | B2C, positivity, negativity, word count, authenticity, tone, readability, informal words, social power |
| Findings | IMI generated stronger virality relative to non-IMI | Humorous IMI coupled with timeliness drives virality; humorous IMI tinged with unanticipation leads to virality | Replication of Study 2; humorous IMI was more likely to lead to higher firm value when humor was coupled with (1) high timeliness or (2) high unanticipation | Replication of Study 3; IMI generated both greater virality and greater firm value relative to non-IMI | Replication of Study 4 |
| Unique study advantages | Quasiexperiment that uses a single firm's IMI and non-IMI; additional synthetic control method for counterfactual analysis alleviates endogeneity concerns | Experiment that provides stronger evidence of causality and allows a clean test of hypotheses; alternative coding for humor, timeliness, and unanticipation | Large cross-section of industries and longer time frame; hand-collected unique data across 58 SIC industries over a six-year period | Panel data of ten firms that allows examination of the unique effect of IMI versus non-IMI; alternative coding of unanticipation (seven-point scale) | Panel data of 25 S&P firms permits a generalizable effect of IMI versus non-IMI on an objective firm performance metric (abnormal returns), accounting for selection bias |
By studying IMI, we aim to make the following novel contributions to the extant marketing literature and practice. First, we contribute to the work on improvisation and electronic WOM in social media by developing new knowledge using an array of studies that capture the phenomenon of IMI. [49], p. 639) urge, "Managers need to know...which specific marketing communication actions...stimulate electronic WOM conversations." We respond to their call and extend current knowledge by theorizing and systematically examining the type of IMI messages that have the greatest potential for achieving virality. Previous research that has examined firm-generated content and virality has investigated neither IMI nor the interplay between IMI and virality (see Table 2). While it is likely that there are important implications of this research beyond social media, we note that we are studying IMI in a social media context because of the ease of changing marketing actions in this context and the opportunity it presents for virality. In so doing, we shed light on the understudied phenomenon of IMI that can play a significant role in generating virality and firm value.
Graph
Table 2. Review of Relevant Literature on Firm Generated Content and Virality.
| Studies IMIs? | Focus | Focal Independent Variables Considered (Drivers of Virality) | Focal Dependent Variables Considered | |
|---|
| Humor | Unanticipation | Timeliness | Virality | Firm Value | Multimethod |
|---|
| Porter and Golan (2006) | No | Compare virality with TV advertising | Yes | No | No | Yes | No | No; cross-sectional data |
| Bampo et al. (2008) | No | Viral marketing via digital links | No | No | No | Yes | No | Yes; cross-sectional and simulation data |
| Brown, Bhadury, and Pope (2010) | No | Viral advertising | Yes | No | No | Yes | No | No; experimental panel data |
| Berger and Milkman (2012) | No | Emotional content and virality | Yes (Amusement) | Yes (Surprise) | No | Yes | No | Yes; one study with panel data, and two with experimental design |
| Tucker (2015) | No | Persuasiveness of viral ads | No | No | No | Yes | No | No; archival panel data |
| Kumar et al. (2016) | No | Firm-generated content on customer behavior and profitability | No | No | No | No | Yes | No; archival panel data |
| Seiler, Yao, and Wang (2017) | No | Online WOM effects on demand | No | No | No | Yes | No | No; cross-sectional data using difference-in-differences |
| Gong et al. (2017) | No | Effect of tweeting on product demand | No | No | No | Yes | No | No; cross-sectional data using difference-in-differences |
| Colicev et al (2018) | No | Different roles of owned and earned media on shareholder value | No | No | No | No | Yes | No; archival panel data |
| Lee, Hosanagar, and Nair (2018) | No | Advertising content | Yes | No | No | No | No | No; archival panel data |
| Miere et al. (2019) | No | Marketer-generated content | No | No | No | No | No | Yes; two studies with panel data, and one with experimental design |
| Tellis et al. (2019) | No | Online video ads advertisers upload on YouTube | Yes (Amusement) | No | No | Yes | No | No; cross-sectional secondary data |
| This study | Yes | Improvised tweets in response to external event | Yes | Yes | Yes | Yes | Yes | Yes; one cross-sectional study, two studies using panel data, one study using time-series data, and one study with experimental design |
Second, we use the concept of quick wit for studying the virality of IMI messages and their role in influencing firm value. We define quick wit as situational humor that trades on timeliness and unanticipation ([11]; [23]). Firms today face significant challenges of breaking through the clutter of competing messages in the marketplace and reaching out to an increasingly wary audience ([51]; [75]). In this study, we advance the novel idea that IMI—through quick wit and, in particular, the interaction between humor paired with timeliness and humor paired with unanticipation—enables firms to drive both virality and firm value.
Third, we extend prior research and contribute to the literature on the marketing–finance interface about the role of marketing in driving firm value by studying how IMI captures financial value for a firm ([14]; [62]; [67]). In contrast to prior work, which links the valence of user-generated messages (positive or negative) to firm value ([66]), we theorize and empirically examine the content of IMI messages, and we study their impact on firms' abnormal stock market returns.
While improvisation has been studied in marketing and organizational research ([44]; [45], [46]), we advance the novel idea that firms should put people and processes in place to facilitate the improvised composition and execution of real-time marketing communications in response to external events. By "improvised," we follow the spirit of the definition proposed by [46], who define improvisation as the degree to which composition and execution converge in time. Therefore, in our setting, the closer the creation and execution of a tweet in time, the more improvisational the tweet. Because marketing professionals do not have advance knowledge of some of these events, they need to be empowered to react spontaneously to such unanticipated occurrences. Such events often are not easily predicted (e.g., a blackout during a Super Bowl), receive heightened attention from the potential audience, and require marketing professionals to leverage this heightened attention with effective IMIs that trade on quick wit. We study IMIs in a social media context because of the ease of changing marketing actions and the opportunities for virality in this context. We note implications beyond social media in the "Future Research" subsection.
Attracting an audience's attention using firm-generated content such as advertisements remains a key challenge for most—if not all—firms ([10]). Doing so in a positive and engaging way that avoids the creation of consumer pushback and resentment is harder still. We use the theory of quick wit to argue for the special role played by IMI that contains humor tinged with timeliness or unanticipation in facilitating virality and enhancing firm value. Quick wit relies on situational humor that trades on a degree of timeliness and unanticipation ([11]; [23]). In accord with [73], we define humor as a psychological response characterized by laughter, happiness, or joy arising from pun, play on words, events, or images. Timeliness is defined as the time taken to respond to an external event, and unanticipation is defined as the unexpected way in which a communication responds to an external event. Wit or appreciation of humor has a major influence on the quality of an interaction and can shape the impression a person forms of another ([72]). It can, for instance, decrease tension in a heated conversation or enliven a boring one ([70]), reduce dysfunctional stress and anxiety ([27]; [77]), and create positive feelings among conversation partners and facilitate bonding ([40]; [70]). Furthermore, wit is specific to a particular event or social context ([ 4]; [40]) and is most effective when elicited in a timely or unexpected, spontaneous way ([76]). One proposed strategy to break through the clutter and noise in the marketplace, therefore, is to engage social media users in a conversation about "what is happening now" ([26], p. 96) in a witty way.
Research shows that people in general and internet users in particular have a desire to engage with events as they happen in a spontaneous manner ([35]; [70]). Social media users increase their own social capital by sharing a message that signals to others that they are "in the know" ([ 2]; [15]; [69]). People also share information with others to participate in online communities, show concern for others, and be helpful ([65]). Improvised marketing interventions in response to current events help social media users contribute to their communities in more valuable and meaningful ways than they could with outdated and uninteresting news. With this information sharing, these users help firms grab the attention of other users within and potentially beyond the firms' social networks. Heightened interest by social media users has been shown to kick-start new online discussion or invigorate existing talk about a firm among customers ([67], [68]). Responding to current events with an IMI thus helps firms grab social media users' attention. Drawing on these arguments, we hypothesize,
- H1: IMI messages lead to greater virality than non-IMI messages.
Humor has been argued to influence the nature of human relationships and communication in significant ways ([11]; [17]; [72]). Here, we advance the novel argument that IMIs are only likely to become viral when they contain humor and timeliness (or unanticipation). We argue that humor, timeliness, and unanticipation individually would not have a significant main effect on virality for IMI because of the unique nature of the IMI phenomenon, which demands that a humorous message has to be paired with timeliness or with unanticipation to generate virality. We theorize why these pairwise interactions will drive virality next.
Theory on quick wit has highlighted that humor's effectiveness is closely associated with timing and unanticipation ([40]; [76]). Researchers have argued, for example, that "timing is everything" in the delivery of humor and in its opportunity to engage an audience in a positive manner ([ 5]). We expect IMI's humor to interact with timeliness in driving virality for at least two reasons. First, research on quick wit and conversational style suggests that, in addition to humor, speed of response attracts an audience's attention, which consequently initiates further conversation ([27]; [70]). Oreo's message, for example, was tweeted within a few minutes of the lights going out during the Super Bowl. The message was, therefore, very timely. If the same message were sent out a few weeks or months after the game had ended, the message would have been relatively less timely, and its witty elements would have been less impactful. Second, theory of quick wit suggests that timeliness injects new fuel into a marketing communication's humor, providing more impetus for people's desire to bond with others through swift sharing ([ 8]). It is important to note that humor is often situationally dependent. A witty message might attract an audience's attention in one instance but may seem only mildly funny or completely irrelevant and irritating when outdated ([ 5]; [40]; [76]). Thus, we hypothesize,
- H2: The interaction between IMI humor and timeliness positively affects virality.
Prior work has found that unanticipation also plays an important role in the delivery of humor by creating incongruous relationships, such as unexpected events, objects, or observable deviations from an implied standard ([ 5]; [16]; [17]). From a quick-wit perspective and an image-related perspective ([23]; [69]), sharing humorous and unexpected or surprising content makes social media users look good to other users. As these perceptions, in general, are important to social media users, they inspire this higher level of interest ([ 2]). While people may feel uncomfortable and thus are less willing to share an unanticipated message in certain circumstances, such as when the content of the message is sad, IMIs that contain humor and unanticipation help social media users surprise and delight others and to engage them in a light-hearted, positive way ([76]; [77]). Thus, improvised marketing communication that is characterized by humor and unanticipation is likely to attract the attention of social media users and encourage people to share such content with others. Drawing on these arguments, we hypothesize,
- H3: The interaction between IMI humor and unanticipation positively affects virality.
Taken together, we propose that IMI that contains quick wit—humor with timeliness or unanticipation—is likely to attract users' attention in social media and drive virality.
We study firm value by using abnormal stock market returns, which represent changes in the market capitalization of firms. Stock prices capture firm value as per the efficient market hypothesis, which states that at a particular point in time stock prices fully reflect all currently available information about a firm ([58]). Thus, any change in the price of a stock due to the arrival of new information reflects the present value of all expected current and future profits from that new information ([58]).
We theorize that IMIs can increase stock price for at least two reasons: ( 1) the investors' belief that the IMI's virality itself will increase brand attitudes (e.g., awareness, purchase intent, advocacy) and ( 2) the IMI's signal that the brand is confident enough about its own reputation and its employees' judgment to empower them for IMI. Building on our previous arguments regarding humor, timeliness, and unanticipation, we expect IMIs with quick wit to have an important impact on firm value. Our rationale is that the interactions between humor and timeliness and between humor and unanticipation in IMI attract the attention of investors who see that the firm is proactively co-opting current events with heightened attention for the brand's purpose. Heightened attention and potential for virality may affect revenues and earnings in the future. Succinctly put, as social media users are more likely to be attracted to IMI, investors are more likely to infer from such marketing communications that more consumers will be aware of the firm, talk about it positively to other consumers, and be interested in its product offerings in the future, thus influencing future firm financials.
Second, we argue that IMIs tinged with humor and timeliness and humor and unanticipation signal that the brand is confident enough about its own reputation and its employees' judgment to empower them for IMI. Numerous signaling mechanisms can influence investor behavior. When a firm increases its advertising spending, this can draw investors' attention to the firm. Some investors perceive advertising as a signal of a firm's well-being ([30]). [29] find that prelaunch advertising for a film generates positive stock returns even before the film makes any box-office returns. We argue that IMI may attract investors' attention, as they infer that the brand is in a good place because it trusts and believes in its own marketing teams to carry out IMIs with the necessary pairings of humor and timeliness or unanticipation that can succeed in driving virality. Thus, IMI acts as an alternative source of information for investors to judge a firm's marketing capability, which has been shown to have direct and significant effects on firm value (for a recent review, see [ 3]). Drawing on these arguments, we hypothesize,
- H4: The interaction between IMI humor and timeliness positively affects firm value.
- H5: The interaction between IMI humor and unanticipation positively affects firm value.
We conduct Study 1 to determine whether IMIs drive virality and, if so, to what extent they generate greater virality than non-IMIs (H1) using a quasiexperiment related to the Super Bowl. Study 2 is an experiment that provides evidence of key causal effects underlying the phenomenon. We further examine the extent to which the interactions between IMI humor and timeliness (H2) as well as humor and unanticipation (H3) generate stronger virality in Studies 3a, 4a, and 5a using observational data. Finally, we test the extent to which IMIs that contain humor and unanticipation (H4) and humor and timeliness (H5) are associated with greater firm value in Studies 3b, 4b, and 5b using observational data.
To test H1, we use a context that enables us to determine whether IMI messages lead to an increase in virality compared with non-IMI messages. Specifically, we use Oreo's Super Bowl XLVII Tweet, "You Can Still Dunk in the Dark" (see Figure WA1 in Web Appendix A), as our context for testing the impact of IMI on virality. Oreo sent this tweet on February 3, 2013, at 9:58 Eastern Standard Time (EST) during Super Bowl XLVII. In the third quarter of the game, a partial power outage in New Orleans's Mercedes-Benz Superdome suspended play for 34 minutes, earning the game the nickname, "the Blackout Bowl." We compare Oreo's "Dunk in the Dark" IMI tweet (hereinafter, OreoDunkIMI) with other Oreo tweets that are non-IMI. In this design, the firm is a control for itself. We use the number of shares (i.e., retweets) as our measure of virality. Though not focal to our hypothesis, we also test whether IMI leads to an increase in social media metrics that are important to managers: volume of tweets, likes (favorites), and sentiment of chatter (the difference between positive and negative tweets, using the Linguistic Inquiry and Word Count [LIWC] dictionary). We wrote a script that downloads from Twitter the volume of retweets, tweets, and favorites mentioning @oreo from 8:00 p.m. EST on February 1, 2013, to 11:00 p.m. EST on February 5, 2013, allowing us to obtain 99 hours of data and thereby ensuring that our data collection is as comprehensive as possible.
We analyze the data around the two-hour window of the OreoDunkIMI tweet and other Oreo non-IMI messages at the one-second level to determine whether Oreo's IMI message led to a greater increase in virality than its non-IMI messages. Note that our chosen time window (60 minutes before and after a tweet) covers the 34 minutes of the power outage. Because our interest is in cleanly testing whether the Oreo IMI led to virality, we drop tweets by Oreo posted after OreoDunkIMI, as virality for other Oreo tweets might be confounded with virality for OreoDunkIMI. We find that Oreo posted ten tweets before the OreoDunkIMI during our sample time frame. The first tweet that Oreo sent within our sample time frame was on February 2, 2013 at 2:10 pm EST. Therefore, our analysis includes the OreoDunkIMI and 10 other Oreo tweets.
For our model-free analysis, we first compare average virality per second before and after OreoDunkIMI. That is, we take the difference between the post-OreoDunkIMI average virality per second (in the 60 minutes after the tweet) and the pre-OreoDunkIMI average virality per second (in the 60 minutes before the tweet). We then compare average virality per second before and after each of the other ten Oreo tweets. In our model-free analysis (see Figure 1), we find that on average there were 12 and 18 Oreo retweets per second in the 60 minutes before and after Oreo's other tweets, respectively. For OreoDunkIMI, we find that there were approximately 115 Oreo retweets per second in the 60 minutes after the "Dunk in the Dark" tweet. By contrast, Oreo had on average 7.5 retweets per second in the 60 minutes prior to the "Dunk in the Dark" tweet. Thus, the graphical analysis shows that the OreoDunkIMI had a substantial impact on Oreo's virality compared with its other tweets.
Graph: Figure 1. Study 1: Virality between oreo IMI versus Non-IMI tweets using within-firm analysis.*** p <.001.Notes: All errors bars represent standard errors (95% confidence intervals).
We test whether the model-free result of the substantial impact of OreoDunkIMI on Oreo's virality holds using a regression specification. Formally, we run a difference-in-differences regression with the following specification:
Graph
1
Here, t stands for one second. Viralityit is the number of Oreo retweets, OreoDunkIMIt is an indicator variable that takes the value of 1 if the Oreo tweet is the "Dunk in the Dark" tweet and 0 if it is one of the other ten tweets posted by Oreo, Postt is an indicator variable that takes the value of 1 for each Oreo tweet during the 61 minutes[ 5] (including the event minute and 60 minutes after the tweet) after any of the 11 Oreo tweets in the analysis (including the "Dunk in the Dark" tweet) and 0 for each Oreo tweet during the 60 minutes before any of the 11 Oreo tweets in the analysis.
We include a set of control variables (Controls) to ensure that the results are robust. First, we include an indicator variable that takes the value of 1 if the time period in our analysis overlaps with the Super Bowl game. It is indeed possible that users could have a higher propensity to tweet when the Super Bowl is on due to the excitement that the game and its advertisements generate ([22]). Second, we include an indicator variable (OutageEvent) that takes the value of 1 if the time period in our analysis overlaps with the time of the Super Bowl Blackout. It is conceivable that the outage event itself created an increase in social media usage.[ 6] Third, as the data are in panel format, we include individual tweet–level fixed effects to control for unobserved features and heterogeneity at the tweet level. eit is the unobservable random error term.
The parameter of interest is β that captures the impact of Oreo's "Dunk in the Dark" tweet. The standard errors are robust standard errors clustered for each of our 11 Oreo tweets. Overall, our data set is at the second level and covers 121 minutes (60 minutes before the tweet is posted, the event minute of the tweet, and 60 minutes after the event minute of the tweet) and 11 Oreo tweets, resulting in 79,860 (121 × 60 × 11) rows of data.
We find β in Equation 1 to be positive and highly significant (β = 47.79, p <.001), which indicates the positive and significant effect of OreoDunkIMI for virality (see Table 3, column 1) in support of H1, and for the other social media metrics such as volume of tweets, likes (favorites), and sentiment of chatter (see Table 3, columns 2, 3, and 4, respectively). For robustness, we also utilize a 61-minute window around an Oreo tweet, analyzing 30 minutes of pre- and 30 minutes of posttweet virality plus the event minute. We find results similar to our main specification (see Table WA1 in Web Appendix A). In addition, we examine the unit-specific quantitative and time-varying estimate of the treatment effect of the OreoDunkIMI on Oreo's virality using the synthetic control method ([ 1]; [68]). Figure 2 depicts the trajectory of Oreo's virality (solid line) against the synthetic control's virality (dotted line) during the sample time horizon, which includes the preintervention period (before OreoDunkIMI) and the postintervention period (after OreoDunkIMI). For our synthetic control method details, see Web Appendix B. We find that immediately after OreoDunkIMI, there is a rise in virality for Oreo compared with the counterfactual of Oreo not putting up the OreoDunkIMI. Specifically, the effect peaks at the fifth hour after the Oreo tweet, with a difference of 12,383 retweets between Oreo with the "Dunk in the Dark" IMI and the counterfactual Oreo that did not post the "Dunk in the Dark" tweet. We find that the effect lasts for about ten hours, and the effect then reaches its asymptote. Thus, in terms of the dynamics at the hourly level ([48]), we find that the wear-in time (lag before the peak impact on virality is reached) is five hours, and the wear-out time (time after the peak impact before virality effects die out) is also five hours.
Graph
Table 3. Effect of IMI on Social Media Metrics One Hour Before and After the Oreo Tweet.
| (1) | (2) | (3) | (4) |
|---|
| Variables | Volume of Retweets | Volume of Tweets | Volume of Favorites | Sentiment of Chatter |
|---|
| IMI tweet (1 = IMI, 0 = non-IMI) | 7.52 | 2.90 | .78 | 1.85 |
| (.63) | (1.34) | (1.36) | (1.33) |
| Time after Oreo tweet (1 = after the tweet, 0 = before the tweet) | 9.00 | 3.19 | .96 | 2.01 |
| (.63) | (1.15) | (1.26) | (1.14) |
| IMI tweet × Time after Oreo tweet | 47.79*** | 8.28*** | 2.07*** | 5.31*** |
| (5.93) | (5.62) | (5.36) | (5.63) |
| Time during Super Bowl | −6.26 | −1.69 | −.42 | −1.01 |
| (.78) | (1.17) | (1.12) | (1.20) |
| Outage event | −6.48 | .35 | .10 | .18 |
| (.87) | (.25) | (.27) | (.20) |
| Intercept | 3.81 | −.10 | −.07 | −.29 |
| (.40) | (.06) | (.14) | (.25) |
| R-square | 1.10% | 12.27% | 9.31% | 11.86% |
| Overall test of significance (F-tests) | 11.47 | 151.90 | 111.82 | 146.22 |
| Wald test of significance | .000 | .000 | .000 | .000 |
| Time trend included | Yes |
| Event fixed effects | Yes |
| Day dummy included | Yes |
| N | 79,860 |
1 ***p <.001.
2 Notes: t-statistics in parentheses.
Graph: Figure 2. Virality of oreo with the "Dunk in the Dark" IMI versus synthetic oreo without the 'Dunk in the Dark' IMI using synthetic control method.
Across the two methods employed in Study 1, we find strong evidence that IMI messages generate greater virality than non-IMI messages (H1). Though this study utilizes a within-firm analysis and a synthetic control method to test the relation between IMI and virality, it does not unpack and test the key characteristics of IMI that can drive virality (H2 and H3). To afford greater confidence in the causal connection between IMI and virality and examine the effects of humor paired with timeliness or unanticipation, we turn to an experimental design in Study 2.
Study 2 manipulates the humor, timeliness, and unanticipation in IMI messages from a fictitious company in response to a fictitious event. This study enables us to demonstrate that humor is distinct from unanticipation and from timeliness while also controlling for consumers' heterogeneity, including activeness on social media, general liking of the event, brand familiarity, and demographics. Study 2 also enables us to test the extent to which our findings are unique to IMI and whether unanticipated humor produces a similar virality.
Eight hundred participants recruited from Amazon Mechanical Turk took part in this study for a prorated equivalent of $8 per hour. Participants who passed our initial screening question (whether they had a Twitter account) were randomly assigned to one of eight conditions in a 2 (humor: high vs. low) × 2 (timeliness: high vs. low) × 2 (unanticipation: high vs. low) between-subjects experiment. Twenty-nine participants failed the attention check ("I'm a living person"; 1 = "strongly disagree," and 7 = "strongly agree") by disagreeing with the attention check statement. Thus, our analyses are based on 771 observations (Nfemale = 380 [49.3%]; Mage = 37 years, SD = 11.22). Participants' average activity on social media (Twitter) was 4.82 (SD = 1.49) on a seven-point scale ("I'm very active on Twitter"; 1 = "strongly disagree," and 7 = "strongly agree"). As part of this study, participants completed two tasks followed by a survey. The first task asked all participants to watch a short video clip that was about two minutes long (https://www.youtube.com/watch?v=Z7PlUGbsXlQ), which served as the event that would inspire brands to tweet. After watching the clip, participants read a tweet that was pretested (N = 216) as high (or low) in humor and unanticipation (for detailed stimuli information, see Web Appendix C). For the high-timeliness condition, right after watching the clip, participants were told that the assigned tweet was posted by a brand called Wild Foods when the clip was aired on TV. In the low-timeliness condition, after a one-minute break, participants were told that the assigned tweet was posted by Wild Foods quite a while after this clip aired on TV and after many other brands had already tweeted about it.[ 7]
Next, participants rated their willingness to retweet ("I would like to retweet this message"; 1 = "strongly disagree," and 7 = "strongly agree"). Participants then completed manipulation check measures and rated the IMI's humor ("The tweet content is humorous," "The tweet content is funny," and "The tweet content is hilarious"; α =.96), unanticipation ("The tweet content is very unexpected," "The tweet content is very surprising," and "The tweet content is very unanticipated"; α =.94), and timeliness ("The tweet was very timely in response to the video clip," "The tweet was very speedy in response to the video clip," and "The tweet was very quick in response to the video clip"; α =.96). We also controlled for participants' familiarity with the fictitious brand ("I'm familiar with the Wild Foods brand"), familiarity with the video clip ("I'm familiar with the video clip just watched"), liking of the video clip ("I like the clip just watched very much"), and level of activity on social media ("I'm very active on Twitter") (all anchored by 1 = "strongly disagree," and 7 = "strongly agree"), as well as gender and age as potential confounds.
A 2 (humor) × 2 (timeliness) × 2 (unanticipation) analysis of variance (ANOVA) on humor supports the manipulation of humor. As we expected, participants in the high-humor condition rate the IMI's content as more humorous (Mhigh = 4.59, SE =.08) than in the low-humor condition (Mlow = 3.40, SE =.08; F( 1, 763) = 106.57, p <.001, partial η2 =.13). Furthermore, a 2 × 2 × 2 ANOVA on timeliness yields a main effect of timeliness; participants in the high-timeliness condition rate the IMI tweet to be more timely (Mhigh = 5.06, SE =.08) than participants in the low-timeliness condition (Mlow = 3.86, SE =.08; F( 1, 763) = 112.17, p <.001, partial η2 =.13). Finally, participants in the high-unanticipation condition rate the IMI's unanticipation as higher (Mhigh = 4.55, SE =.08) than those in the low-unanticipation condition (Mlow = 3.93, SE =.08; F( 1, 763) = 27.50, p <.001, partial η2 =.04). No other significant main or interaction effect emerges (ps >.07).
A 2 (humor) × 2 (timeliness) × 2 (unanticipation) ANOVA on intention to retweet as the dependent variable shows a main effect of humor (Mhigh = 3.97 vs. Mlow = 3.38; F( 1, 763) = 17.39, p <.001, partial η2 =.02), timeliness (Mhigh = 3.90 vs. Mlow = 3.45; F( 1, 763) = 9.55, p =.002, partial η2 =.01), and unanticipation (Mhigh = 3.84 vs. Mlow = 3.51; F( 1, 763) = 5.12, p =.024, partial η2 =.01). Critically, and in line with our theorizing, we find the two pairwise interactions between humor × timeliness (F( 1, 763) = 5.17, p <.05, partial η2 =.01) and humor × unanticipation (F( 1, 763) = 5.98, p <.05, partial η2 =.01) to be significant, in strong support of H2 and H3. Neither two-way unanticipation × timeliness (p >.96) nor three-way interaction of humor × timeliness × unanticipation is significant (p >.68). To interpret our findings, we plot the line diagrams depicted in Figure 3, Panels A and B. Furthermore, to test whether our findings are unique to IMI or whether unanticipated humor produces similar virality, we compare the number of retweets in the high humor/high unanticipation/high timeliness condition with the number of retweets in the high humor/high unanticipation/low timeliness condition. The results demonstrate that timeliness boosted virality by a significant level. Specifically, participants note a greater willingness to retweet for unanticipated humor that is high in timeliness (M = 4.66, SD = 2.13) than low in timeliness (M = 3.96, SD = 1.92; t(193) = 2.41, p =.017).
Graph: Figure 3. Study 2 experiment results.Notes: All errors bars represent standard errors (95% confidence intervals).
In support of H2 and H3, the two pairwise interactions between IMI's humor × timeliness and humor × unanticipation affected virality. Furthermore, Study 2 underscores that our findings are unique to IMI and that unanticipated humor does not lead to a similar opportunity for virality. To provide further evidence of H2 and H3 using actual retweet activity of IMI messages in the field, we conducted Study 3a.
For the purpose of Study 3a we compile a data set of tweets that is comprehensive enough to include brands from several industries and cover a substantial time period. Following [35], we focus on IMI messages that are ( 1) related to tent-pole events, which occur at regular intervals (e.g., the Super Bowl, Oscars, Grammys, Winter Olympic Games), ( 2) related to specific events on established dates for which some details remain uncertain (e.g., messages speculating about which character might get killed in the final episode of the popular TV series Breaking Bad), ( 3) related to specific events on uncertain dates (e.g., messages related to the birth of a royal baby or the enactment of the marriage equality law in the United States), or ( 4) related to trending topics addressed by popular Twitter hashtags (e.g., #thedress, #bendgate, and #ruinaraptrack). Using these four criteria for the brands listed in the published Interbrand 100 ranking and most engaged in IMI activities as noted by [35], we identified 462 IMI messages from 139 brands across 58 different industries[ 8] over the six-year period between 2010 and 2015. We compiled an archive of this set of IMI messages by taking a screenshot of each message in our data set and capturing the following information for each tweet: the full text of the tweet, the brand that controlled the Twitter handle, the number of followers of the Twitter handle, the total number of tweets posted from the Twitter handle, the date and time the tweet was posted, and the number of retweets received. We measure our dependent variable (Viralityirt) as the total volume of retweets for each specific IMI from day 1 of year t when the IMI was posted to the end of year t.[ 9] See Table 4 for a summary of variable definitions and operationalizations, which we detail next.
Graph
Table 4. Constructs, Definitions, and Operationalization in Studies.
| Constructs | Definition | Study | Source |
|---|
| Virality | Number of shares of a marketing message (Tellis et al. 2019)—that is, volume of retweets of a tweet. | 1, 3, 4, 5 | Twitter, third-party |
| Return | The firm's abnormal stock market returns is calculated using the Fama–French five-factor model following Kenneth French's website. | 3, 4, 5 | Center for Research in Security Prices |
| Humor | Tweets are characterized by laughter, happiness, or joy arising from puns, plays on words, events, or images (Warren and McGraw 2016). | 2, 3, 4, 5 | Experiment, manual coding |
| Unanticipation | The unexpected way in which a tweet responds to an external event. | 2, 3, 4, 5 | Experiment, manual coding |
| Timeliness | Time taken to respond to an external event (in minutes). | 2, 3, 4, 5 | Experiment, Twitter, Google News |
| Brand reputation | Brand has been on Interbrand 100 ranking for years 2010 to 2015. | 3 | Interbrand |
| Brand followers | Number of followers of the brand on the day that brand made IMI. | 3, 4 | Twitter |
| Brand friends | Friends are different from followers, as friends mutually follow each other. | 4 | Third-party |
| Brand Klout | Brands' online social media influence scores. | 4 | Third-party |
| Holiday | Tweets are likely to be shared in holiday season (Tellis et al. 2019). | 4 | Calendar |
| Video | Video content has the tendency to go viral (Tellis et al. 2019). | 4 | Manual coding |
| Readability | Comprehension of a tweet can affect sharing. Automated readability index is calculated as 4.71 (characters/words) +.5(words/sentences) − 21.43. | 3, 4, 5 | Third-party |
| Positivity and negativity | Valence of tweet content (Berger and Milkman 2012). | 3, 4, 5 | LIWC |
| Word count | Short tweets are more prone to virality (Berger and Milkman 2012). | 3, 4, 5 | LIWC |
| Authenticity | Content is personal, humble, and honest (Pennebaker et al. 2015). | 3, 4, 5 | LIWC |
| Tone | Affect-ladenness of tweet content (Berger and Milkman 2012). | 3, 4, 5 | LIWC |
| Informal words | Tweet content is likely to be informal (Pennebaker et al. 2015). | 3, 4, 5 | LIWC |
| Social power | Authoritative, powerful, and confident language style (Pennebaker et al. 2015) | 3, 4, 5 | LIWC |
| B2C | Firm's focus on B2C (vs. B2B) according to firm's four-digit SIC code (Bahadir, Bharadwaj, and Srivastava 2008). | 3, 5 | Compustat |
| Market size | Total sales volume within firm's four-digit SIC code (Karuna 2007) | 3 | Compustat |
| Turbulence | Industry differences may affect firm value. We calculate industry turbulence by first calculating the standard deviation of sales in firm's core product industry (at four-digit SIC level) across the prior four years and then dividing it by the mean value of industry sales for those years (Fang, Palmatier, and Steenkamp 2008). | 3 | Compustat |
| Competition | Competitive rivalry may affect firm value. Herfindahl index is used to measure competition at the four-digit SIC level (Fang, Palmatier, and Steenkamp 2008). | 3 | Compustat |
3 Notes: Third-party is SimplyMeasured, which now is a part of Sprout Social.
We assess IMI messages' level of humor and unanticipation following well-established procedures for textual coding ([ 9]; [47]). We rely on human coders to classify the extent to which the content exhibited specific characteristics (i.e., humor and unanticipation) because automated coding systems are not available for these variables. The coders were blind to the study's hypotheses. We recruited one industry practitioner and one researcher who independently rated the 462 IMI messages' humor and unanticipation. They received the text and creation time of each tweet, a web link to the tweet's full text, and coding instructions (for details, see Web Appendix D). An IMI message's level of humor is measured with a seven-point scale, ranging from 1= "serious" to 7= "humorous." Tweets with content that is earnest or formal or has gravity are coded as serious, whereas tweets with content that is funny, jocular, or light-hearted are coded as humorous ([71]). The IMI messages' level of unanticipation is measured with a three-point scale, ranging from 1 = "low" to 3 = "high."
First, we selected a random set of tweets unrelated to the selected sample for the coders to practice (N = 80). We explained the coding scales and engaged in extensive coder training using the 80 tweets unrelated to the selected sample. Coders discussed the results of the test cases. We reviewed discrepancies and clarified the definitions to minimize future discrepancies in the coding of the actual IMIs used in the study. We then gave coders copies of each of the IMIs that composed our sample. Overall, intercoder agreement for both the coding of humor and unanticipation was high (rs ≥.70). Disagreements between the two coders were resolved through discussion. The computed intercoder agreement was based on the correlation between the ratings of the two coders ([38]). We capture timeliness as the time passed (in minutes) between the occurrence of the event and the IMI tweet. We determine the exact event time by first using the creation date of the IMI message as an anchor. We then search on Google News, customizing our search date range to two days before and two days after the creation date of the IMI message. Figure WA5 in Web Appendix D displays the histogram of timeliness for this study. We reverse-code the timeliness measure for our empirical tests so that a higher level means more timely for ease of interpretation.
We incorporate several key control variables that can affect virality. First, consumers might be more prone to share messages from well-reputed brands ([65]). Thus, we control for brand reputation by including an indicator variable for brands listed in the Interbrand 100 ranking for the year of the IMI tweet. Second, because a large base of brand followers will be more likely to share messages than a smaller base of followers (for "brand fan following," see [14]), we capture the number of the brand's Twitter followers. We also control for the notion that business-to-consumer (B2C) firms might be more adept than business-to-business (B2B) firms in using social media, following [61] classification of firms into B2C and B2B categories. In addition, we control for the various types of content within the tweet. First, comprehension of a tweet can affect users' sharing. Thus, we use an index that measures readability. Specifically, we use the automated readability index; the formula for the measure is: 4.71(characters/words) +.5(words/sentences) – 21.43. Second, we use LIWC to count the percentage of positive, negative, and informal words ([28]; [50]), as sentiment and informality of the tweet could influence sharing. Third, because the level of authenticity and tone of the language used in a tweet might influence virality, we account for these characteristics of content in the tweet using the LIWC dictionary. Fourth, we control for tweet length by the number of words used in the tweet, as short responses may be more prone to virality. Fifth, we also take the square of the tweet length to account for the idea that very short or long tweets may lead to less virality.
We use the following specification for the model:
Graph
2
where Viralityirt is the number of retweets for IMI r posted by brand i at time t; Humorirt indicates the humorousness of IMI r posted by brand i at time t; Timelyirt is the timeliness of IMI r posted by brand i at time t, which is calculated as minutes between tweet post time and event time; Unanticipateirt represents the unanticipation of IMI r posted by brand i at time t; Controlirt is an array of variables for IMI r by brand i at time t; and the error term ∊irt captures unexplained variation in Viralityirt. We also control for brand-level heterogeneity to account for brand-level unobservables, and we control for month and year effects because the level of tweeting and virality may differ depending on the year and month the tweets were posted.
Descriptive statistics and correlations for the variables that appear in Equation 2 are in Web Appendix E. Multicollinearity is not a concern, and the variance inflation factor for the model is under 5. Multicollinearity is not a concern for every other regression specification that we estimate, as the variance inflation factor is under 10 for Studies 3b–5b. Table 5, Panel A, shows the results after estimating Equation 2. The dependent variable is the number of retweets for each IMI. We find a significant and positive interaction effect between humor and timeliness on virality (4.52, p <.05) as well as a positive and strong significant interaction between humor and unanticipation (12,991.59, p <.05) in support of H2 and H3, respectively.
Graph
Table 5. The Effect of IMIs on Virality and Returns (Study 3).
| Variables | Viralitya | Returnb |
|---|
| IMI humor | −13,197.53 | −4.00e-3 |
| (.99) | (.71) |
| IMI timeliness | −28.42* | −5.00e-5* |
| (1.97) | (2.21) |
| IMI unanticipation | −55,143.81 | −.03* |
| (1.76) | (2.14) |
| IMI humor × Timeliness | 4.52* | 1.40e-5* |
| (2.24) | (2.28) |
| IMI humor × Unanticipation | 12,991.59* | .01* |
| (2.13) | (2.59) |
| IMI timeliness × Unanticipation | 6.14 | −1.97e-6 |
| (1.44) | (.69) |
| B2C | 3,671.02 | .02 |
| (.30) | (.77) |
| Positive content | −713.37 | 8.30e-4** |
| (.79) | (2.74) |
| Negative content | 1,712.32 | 4.40e-4 |
| (.79) | (.50) |
| Authenticity in content | −301.51 | 1.16e-5 |
| (1.73) | (.19) |
| Tone in content | 370.56 | 1.17e-4 |
| (1.63) | (1.52) |
| Readability index | −608.55 | −2.60e-4 |
| (.80) | (.53) |
| Informal words | −121.66 | 5.11e-5 |
| (.10) | (.05) |
| Social power | −229.60 | −9.10e-5 |
| (1.04) | (1.25) |
| Word count | −2,891.69 | 3.50e-4 |
| (.88) | (1.00) |
| Word count2 | 44.91 | N.A. |
| (.44) | N.A. |
| Brand reputation | −12,108.25 | N.A. |
| (.97) | N.A. |
| Brand followers | .03*** | N.A. |
| (20.63) | N.A. |
| Turbulence | | .02 |
| | (.34) |
| Competition | | −.01 |
| | (.10) |
| Market size | | 8.29e-8 |
| | (.31) |
| Intercept | 94,601.13 | −3.14 |
| (.68) | (.45) |
| Adj. R-square | 50.45% | 19.28% |
| Overall test of significance | 504.44 (Wald) | 1.97 (F-test) |
| Wald test of significance | .000 | .039 |
- 4 *p <.05.
- 5 **p <.01.
- 6 ***p <.001.
- 7 a N = 462; brand, year, and month fixed effects.
- 8 b N = 123 (3 observations had missing data for some of the independent variables in the model); brand and year fixed effects.
- 9 Notes: t-statistics in parentheses; N.A. = not applicable.
Study 3a offers descriptive evidence of the significant effects of IMI's humor × timeliness and humor × unanticipation on virality of IMI messages from 139 brands across 58 different industries over a six-year period. We next test whether these effects carry over to an objective measure of firm performance (i.e., firm value captured by a firm's stock market abnormal returns). Study 3b thus tests H4 and H5.
We use the event study method ([59]) to test H4 and H5. The event study approach builds on the efficient market hypothesis that states that any change in the stock price due to the arrival of new information reflects the present value of all expected current and future profits from that new information ([20]; [57]). We collect stock returns data for the firms owning the brands that tweeted the IMI messages in Study 3a between 2010 and 2015 from the Center for Research in Security Prices. The initial sample is 462 IMIs from 139 unique brands. As we can only run an event study on publicly listed firms, we drop 17 brands (and 38 IMIs) that are owned by private firms. Our sample thus consists of 424 IMIs across 122 unique brands.
Assuming efficient information processing of the IMI message, "an event window should be as short as possible" ([42], p. 636). Because the market should incorporate IMI message information quickly, we use the window ranging from four days before and after the event to calculate the abnormal returns. In addition, we control for an array of confounding events around the nine-day window, including declarations of dividends, contract signings, earnings information, or mergers and acquisitions. We use a window of nine days because measurement windows of up to ten days have been used in prior research ([31]; [60]; [64]) and also to ensure that an announcement not related to IMI announced four days before the event does not spill over to the returns on the event day and beyond. We drop any observations with confounding events within the nine-day IMI window, which we identify from the Capital IQ, Factiva, and LexisNexis databases and various online sources. We thus exclude 298 IMI tweets due to potential confounds. In the end, we retain 126 IMI tweets from 67 unique brands that posted IMI messages. For a summary of the definitions and operationalization of the independent and control variables for this study, see Table 4. Almost all of the control variables in Study 3a are used in this study, too, but we drop the square of the length of the tweet as the variable does not add to the model's explanatory power (i.e., adjusted R2 is lower than the model without the square of the tweet length because its t-statistic is below 1). Moreover, we control for competitive effects by including the turbulence and competition in the industry in which the brand operates using the measure used by [19]. We also control for the size of the company by the market size ([33]). We use the SIC code for the three aforementioned variables. Finally, we control for the year of the tweet. The descriptive statistics and correlations appear in Web Appendix E.
We calculate the abnormal stock returns using the Fama–French five-factor model ([21]; for details, see Web Appendix F). We use the term "returns" to refer to cumulative average abnormal returns (CAAR). Next, we determine an appropriate event window (t1, t2) that is long enough to ensure the dissemination of information regarding the IMI message ([63]). Therefore, we calculate returns for alternative event periods, each ranging from t1 to t2 to CAARi (−t1, t2). Our model is as follows:
Graph
3
where subscripts i, r, and t have the same interpretations as in the model formulation in Study 3a.
We begin by analyzing market responses for the focal IMIs (see Table 6, Panel A). We obtain positive returns for the (−1, 0), (0, 0), and (−2, +2) windows; however, these returns are not significant. The event window with the highest t-value and absolute value is the event day (0, 0) window. Thus, consistent with previous research, we use this window for all analyses (i.e., CAAR [0, 0]; [53]).
Graph
Table 6. Univariate Results of IMI Dimensions on Returns.
| A: Study 3 IMI Study |
|---|
| Windows | Abnormal Returns | t-Value |
|---|
| (0, 0) | .09% | .75 |
| (−1, 0) | .02% | .26 |
| (0, +1) | −.02% | −.26 |
| (−1, +1) | −.04% | .65 |
| (−2, +2) | .00% | .07 |
| (−3, +3) | −.02% | .49 |
| (−4, +4) | −.03% | .45 |
| B: Study 4 Airline Study |
| Windows | Abnormal Returns | t-Value |
| (0, 0) | .08% | .46 |
| (−1, 0) | −.05% | .38 |
| (0, +1) | .10% | .88 |
| (−1, +1) | .01% | .11 |
| (−2, +2) | −.03% | .40 |
| (−3, +3) | −.03% | .55 |
| (−4, +4) | −.04% | .69 |
| Categories | Average Abnormal Returns | p-Value(one-tailed) | t-Value |
| Non-IMI | −.04% | .618 | .30 |
| IMI | .36% | .041 | 1.78 |
| Difference | .40% | | |
| p-Value (one-tailed) | .04 | | |
| t-Value | 1.67 | | |
| C: Study 5 S&P Firms Study |
| Windows | Abnormal Returns | t-Value |
| (0, 0) | .04% | .69 |
| (−1, 0) | .07% | 1.42 |
| (0, +1) | .01% | .29 |
| (−1, +1) | .04% | .96 |
| (−2, +2) | −.01% | .18 |
| (−3, +3) | −.02% | .72 |
| (−4, +4) | .01% | .34 |
| Categories | Average Abnormal Returns | p-Value(one-tailed) | t-Value |
| Non-IMI | .04% | .241 | .70 |
| IMI | .29% | .003 | 2.96 |
| Difference | .40% | | |
| p-Value (one-tailed) | .02 | | |
| t-Value | 2.24 | | |
The effect of the IMI message for focal firms is positive but not significant for the (0, 0) window (.09%, p >.05). However, our main emphasis is to understand if the interactions of humor and timeliness or unanticipation can lead to a significant increase in returns.
Our first focal interaction is the coefficient of tweet humor × tweet timeliness, which we find to be positive and significant (.000014, p <.05) (Table 5, Panel B), in support of H4. Furthermore, we find the coefficient humor × unanticipation to be positive and significant (.01, p <.05). This result supports H5.
Employing the event study approach, Study 3b offers descriptive evidence for the significant influence of IMI's humor × timeliness and humor × unanticipation on firm value. However, both Studies 3a and 3b did not include non-IMI tweets, and our results may be biased by this selection and analysis of only IMI tweets. Thus, we conduct a new set of studies (Studies 4a–5b) in which we analyze both IMI and non-IMI tweets to test H2, H3, H4, and H5.
We obtain a corpus of every tweet sent by ten airlines operating in the United States (Alaska Airlines, American Airlines, Delta, Frontier Airlines, Hawaiian Airlines, JetBlue Airways, Southwest Airlines, United Airlines, US Airways, and Virgin America) over a two-month period (December 1, 2013, to January 31, 2014) from a third-party data provider called SimplyMeasured, which is now a part of Sprout Social.[10] For the two-month period, we focus on tweets with text and photos or videos for two reasons. First, we want to capture multimedia IMI tweets, which are more conducive to virality ([ 2]; [65]; [71]). Second, we want to make the coding of the IMI characteristics manageable because the coding is done by human raters. It is a nontrivial task to code three constructs for more than 10,000 tweets from these ten airlines over our sample time period. This sampling strategy led to a sample of 692 tweets that had text with either a photo or a video. From this sample, we dropped 460 tweets as they were either retweets or replies. We thus had a final sample of 232 tweets, out of which 68 were IMI and 154 were non-IMI.[11] Following the same coding procedure that we used in Study 3, we captured each tweet's humor, timeliness, and unanticipation, as well as a set of control variables that could affect virality for our empirical analysis. Intercoder reliability was again high on all dimensions (all rs ≥.70). Web Appendix E lists correlations and descriptive statistics for the variables in this study.
We use a panel regression and the vce (cluster brand id) option to account for clustering by brand. We estimate the model that includes the main effects of IMIs, humor, timeliness, and unanticipation and the interactions of humor and timeliness, humor and unanticipation, and timeliness and unanticipation for IMI tweets, following the specification used in prior studies ([54]). However, we also include the humor construct for non-IMIs. We do not use the constructs of unanticipation and timeliness for non-IMIs because, by definition, these constructs are specific to IMIs. Thus, we multiply the main and two-way interactions of humor, timeliness, and unanticipation by IMIs. We specify the following model for testing our hypotheses using virality generated for brand i as the dependent variable on the focal independent variables along with brand-specific control variables:
Graph
4
where Viralitycit is the number of retweets (at end of 24 hours from time t) for tweet c posted by brand i at time t; IMIcit indicates that tweet c is an IMI posted by brand i at time t; Humorcit indicates the humorousness of tweet c posted by brand i at time t; Timelycit indicates the timeliness of tweet c posted by brand i at time t; Unanticipatecit indicates the unanticipation of tweet c posted by brand i at time t; and θcit indicates the error term. δ5 and δ6 are the focal coefficients that test H2 and H3, respectively. ControlVarcit is an array of control variables to ensure that our point estimates are unaffected by any omitted variable bias. Along with the same set of control variables that are related to the content of the tweet in Study 3a, we control for tweet type (photo, video), the brand's number of followers, friends, and Klout score (an often-used score for measuring the influence of a social media entity); tweet seasonality using an indicator variable with the value of 1 for the dates from December 22 to January 4 because the time period of the study overlaps with the holiday season, and 0 otherwise; a year dummy to account for macro trends; hour-of-the-day dummies to control for variation in virality by hour; and day-of-the-week dummies to control for differences during work days and weekends. Our results are the same if we omit these time-related variables.
Table 7, Column A displays the results in three models. In Model 1, we find that the interaction of humor and timeliness is positive and significant for IMI tweets (.01, p <.01), in support of H2, and the interaction of humor and unanticipation is positive and significant for IMI tweets (12.27, p <.05), in accord with H3. We correct for self-selection in the choice to send out IMI tweets by choosing the predictors for the selection equation carefully and ensuring that we fulfil exclusion restrictions. We fulfil the exclusion restriction by having at least one variable (i.e., IMI intensity by nonfocal firm) in the selection equation (Table WA10 in Web Appendix G) that does not appear in the substantive Equation 4. Doing so facilitates model identification while correcting for sample selection. Thus, our results are robust to selection bias. Details of the selection model are in Web Appendix G, and coefficient estimates are in Table 7, Column A, Model 2, again supporting H2 and H3. The inverse Mills ratio is not significant (60.70, n.s.).
Graph
Table 7. The Effect of IMI and Non-IMI Tweets on Virality and Returns (Study 4).
| Variables | A: Virality | B: Returns |
|---|
| (1) | (2) | (3) | (1) | (2) |
|---|
| IMI (IMI =1, non-IMI = 0) | 130.00 | 133.60 | 172.40 | .04* | .03 |
| (1.71) | (1.72) | (1.41) | (2.35) | (1.61) |
| IMI humor | −32.39 | −33.18 | −31.90 | −3.20e-3 | −3.13e-3 |
| (−1.74) | (−1.71) | (−1.19) | (−.63) | (−.62) |
| IMI timeliness | −.01 | −.01 | −.01 | −4.03e-6 | −3.25e-6 |
| (1.55) | (1.47) | (1.45) | (1.93) | (1.60) |
| IMI unanticipation | −51.32 | −51.34 | −52.32* | −.01* | −8.41e-3* |
| (−1.66) | (−1.67) | (−2.06) | (−2.41) | (−2.46) |
| IMI humor × Timeliness | .01** | .01** | .01* | 7.71e-6*** | 6.83e-6** |
| (2.85) | (2.74) | (2.13) | (3.74) | (3.26) |
| IMI humor × Unanticipation | 12.27* | 12.26* | 12.33* | 1.27e-3* | 1.17e-3* |
| (2.02) | (2.01) | (2.44) | (2.43) | (2.05) |
| IMI timeliness × Unanticipation | −.01*** | −.01** | −.01 | −6.02e-6*** | −5.38e-6*** |
| (3.30) | (3.06) | (1.91) | (4.09) | (3.56) |
| Non-IMI humor | −1.69 | −1.90 | 7.20 | 7.89e-4 | −1.22e-3 |
| (−.57) | (−.64) | (.54) | (1.62) | (−.68) |
| Brand followers | −2.57e-6 | 3.77e-5 | −4.03e-6 | 2.22e-8* | 2.44e-8** |
| (−.16) | (.76) | (−.24) | (2.41) | (2.86) |
| Brand friends | 9.29e-6 | −2.66e-4 | 3.42e-5 | −1.41e-7* | −1.52** |
| (.04) | (−.60) | (.13) | (−2.16) | (−2.67) |
| Brand Klout score | 3.92*** | 3.18** | 3.86** | −4.03e-4* | −4.06* |
| (5.28) | (3.00) | (2.90) | (−2.09) | (−2.18) |
| Positive content | 6.21 | 5.38 | 6.21 | −2.04e-3*** | −2.03e-3*** |
| (1.68) | (1.51) | (1.54) | (−5.22) | (−6.44) |
| Negative content | −3.68 | 6.90 | −3.53 | .01*** | .01*** |
| (−.72) | (.47) | (−.37) | (4.23) | (4.67) |
| Word count | 2.24 | 2.60 | 2.01 | −5.53e-5 | −6.04e-5 |
| (1.20) | (1.38) | (.67) | (−.32) | (−.46) |
| Word count2 | −.02 | −.02 | −.01 | N.A. | N.A. |
| (−1.02) | (−1.07) | (−.34) | N.A. | N.A. |
| Authenticity in content | −.20 | −.67 | −.19 | −2.51e-4 | −2.85e-4* |
| (−.61) | (−1.01) | (−.50) | (−1.69) | (−2.15) |
| Tone in content | −.64 | −.27 | −.61 | 3.02e-4** | 3.14e-4*** |
| (−1.25) | (−.43) | (−1.55) | (3.29) | (3.99) |
| Readability index | −1.44 | −2.64 | −1.56 | −8.28e-4 | −8.19e-4 |
| (−1.18) | (−1.14) | (−1.04) | (−1.23) | (−1.25) |
| Informal words | .25 | .26 | .37 | 5.60e-4*** | 6.11e-4*** |
| (.21) | (.22) | (.27) | (4.81) | (4.56) |
| Social power | −.27 | −.27 | −.35 | 2.15e-5 | 3.98e-5 |
| (−.97) | (−.96) | (−.73) | (.34) | (.56) |
| Video (video = 1, photo = 0) | −2.74 | −42.24 | −2.35 | −.02 | −.02 |
| (−.32) | (−.78) | (−.12) | (−1.28) | (−1.54) |
| Holiday dummy | 2.86 | 29.39 | 1.13 | .02** | .02** |
| (.09) | (.58) | (.02) | (2.77) | (3.23) |
| Inverse Mills ratio | | 60.70 | | .04* | .04** |
| | (.78) | | (2.36) | (2.80) |
| Non-IMI timeliness | | | −1.70e-3 | | −7.15e-6* |
| | | (.08) | | (2.17) |
| Non-IMI unanticipation | | | 12.50 | | −2.39e-3 |
| | | (1.21) | | (−.92) |
| Non-IMI humor × Timeliness | | | −1.49e-3 | | 6.95e-7 |
| | | (.31) | | (−.99) |
| Non-IMI humor × Unanticipation | | | −2.49 | | −7.38e-7 |
| | | (−1.05) | | (−1.13) |
| Non-IMI timeliness × Unanticipation | | | 2.12e-3 | | −6.55e-4 |
| | | (−.45) | | (1.39) |
| Intercept | −274.60*** | −338.50** | −310.40 | −.06** | −.06** |
| (−3.83) | (−2.92) | (−1.68) | (−2.62) | (−3.25) |
| Adj. R-square | 25.98% | 25.68% | 24.32.% | 34.34% | 34.61% |
| Overall test of significance (Wald) | 157.07 | 156.83 | 155.24 | 94.37 | 100.16 |
| Wald test of significance | .000 | .000 | .000 | .000 | .000 |
- 10 *p <.05.
- 11 **p <.01.
- 12 ***p <.001.
- 13 a N = 232; day, hour, and year fixed effects.
- 14 b N = 126; day and year fixed effects.
- 15 Notes: t-statistic in parentheses; N.A. = not applicable.
To empirically address any potential shortcoming of the noncomparison between IMI and non-IMI, we run a regression including the interactions of humor and timeliness and humor and unanticipation for both IMI and non-IMI. Thus, we use the following specification:
Graph
5
We measure unanticipation for non-IMI following the same coding structure as for IMI in Study 3. For non-IMI's timeliness, we use the average timeliness for each airline. Table 7, Column A, Model 3 displays the effects. We find that the interaction of humor and timeliness is positive and significant for IMI tweets (.01, p <.05), in support of H2, and the interaction of humor and unanticipation is positive and significant for IMI tweets (12.33, p <.05), in accord with H3. Thus, our results are robust even when we include non-IMI constructs.
Study 4a shows that the significant interaction of humor and timeliness and the interaction of humor and unanticipation on virality persist even after we include non-IMI tweets. In Study 4b, we next explore whether our two interactions of interest significantly affect firm value even if non-IMI tweets are included in tests of H4 and H5.
We use the event study method utilized in Study 3b to test H4 and H5 and use the same data employed in Study 4a. The initial sample is 232 tweets from ten unique airlines. As we can only run an event study on publicly listed firms, we drop two private firms. Our sample thus consists of 188 IMIs across eight unique firms. Using the same procedure as in Study 3b for confounding events, we exclude 62 tweets due to potential confounds. In the end, we retain 126 tweets from eight unique firms. We use the same control variables that are utilized in Study 4a but drop the square of the length of the tweet as reasoned previously. The descriptive statistics and correlations appear in Web Appendix E.
We calculate the abnormal stock returns using the [21] five-factor model and use the term "returns" to refer to cumulative average abnormal returns.
We begin by analyzing market responses for the eight focal firms, (see Table 6, Panel B). We obtain positive returns for the (0, 0), (0, +1), and (−1, +1) windows; however, these returns are not significant. The event window with the highest t-value (.88) and absolute value (.10%) is the event day (0, +1) window. Thus, we use this window for the subsequent analyses (i.e., CAAR [0, +1]).
On the one hand, the effect of the IMI message on returns for focal firms is positive and significant for the (0, +1) window (.36%, p <.05, one-tailed test). On the other hand, the effect of the non-IMI message on returns for the (0, +1) window is negative, albeit not significant (−.04%, p >.62, one-tailed test). We find a significant difference between IMI and non-IMI tweets such that IMI tweets generate.40% higher returns than non-IMI tweets (t(126) = 1.67, p <.05, one-tailed test).
The model formulation is similar to Equation 4, with the dependent variable now being "returns" rather than virality and including the inverse Mills ratio calculated for Study 4a. As we show in Table 7, Column B, Model 1, our first focal interaction is the coefficient of humor × timeliness for IMI tweets. We find this interaction to be positive and significant (.00000771, p <.001), which supports H4. Furthermore, we find the coefficient humor × unanticipation (.00127, p <.05) to support H5.
We also estimate Equation 5 replacing virality with returns and including the interactions of humor and timeliness and humor and unanticipation for non-IMI tweets. Our results hold after inclusion of these interactions (see Table 7, Column B, Model 2).
Study 4b offers additional evidence for the significant influence of IMI's humor × timeliness and humor × unanticipation on firm value when including non-IMI tweets. However, one wonders whether the results generalize to other industries, using newer data, and examining a broader set of tweets that include text, links, videos, and images. We thus conduct Studies 5a and 5b.
We randomly select a sample of 5% of the firms listed in the S&P 500 to test H2 and H3. The detailed list of firms is in Web Appendix H. These firms span industries ranging from energy to information technology. We collect every tweet sent out by these firms for the month of April 2019. Note that, again, we did not extend the time frame and sample because it is a nontrivial task to code the characteristics of IMI for more than 1,000 tweets. This sampling strategy led to a total of 470 tweets sent (out of which, 100 were IMI and 370 were non-IMI). Following the same coding procedure that we used in Study 3, we captured the tweet's humor, timeliness, and unanticipation, as well as a set of control variables that could affect virality for our empirical analysis. Intercoder reliability was again high on all dimensions (all rs ≥.70). Web Appendix E lists correlations and descriptive statistics for the variables in this study.
We run the same model utilized in Study 4a to estimate the effects. We include the same content-based control variables in Study 4a but also include the industry type (i.e., B2C vs. B2B), as B2C firms may tweet differently than B2B firms. We also include the inverse Mills ratio in this specification (for the details of the calculation, see Web Appendix I).
Table 8, Column A, Model 1, displays the results. We find that the interaction of humor and timeliness (.02, p <.05) supports H2, and the interaction of humor and unanticipation (391.90, p <.05) supports H3.
Graph
Table 8. The Effect of IMI and Non-IMI Tweets on Virality and Returns (Study 5).
| A: Virality | B: Returns |
|---|
| Variables | (1) | (2) | (1) | (2) |
|---|
| IMI (IMI = 1, non-IMI = 0) | 2,056.60* | 2,109.80* | 1.07 | 1.52 |
| (2.08) | (2.14) | (1.44) | (1.44) |
| IMI humor | −1,073.70 | −1,075.90 | −.48 | −.50 |
| (−1.59) | (−1.57) | (−1.39) | (−1.44) |
| IMI timeliness | −.03 | −.03 | −1.18e-4 | 1.24e-4 |
| (.63) | (.62) | (1.10) | (1.15) |
| IMI unanticipation | −790.40** | −788.20** | −.57** | −.58** |
| (−2.75) | (−2.75) | (−3.17) | (−3.29) |
| IMI humor × Timeliness | .02* | .02* | 2.39e-5** | 2.41e-5*** |
| (−1.98) | (−1.96) | (−2.97) | (−3.34) |
| IMI humor × Unanticipation | 391.90* | 391.50* | .24** | .24** |
| (2.03) | (2.02) | (2.96) | (3.07) |
| IMI timeliness × Unanticipation | 1.57e-4 | 2.67e-4 | 1.53e-5 | −1.66e-5 |
| (−.01) | (−.01) | (−.54) | (−.58) |
| Non-IMI humor | 6.96 | 196.40 | −.04 | .16 |
| (.43) | (1.05) | (−.87) | (.75) |
| Word count | 2.33 | .35 | −.01** | −.01** |
| (.16) | (.03) | (−3.20) | (−2.91) |
| Word count2 | −.10 | −.07 | N.A. | N.A. |
| (−.35) | (−.28) | N.A. | N.A. |
| Positive content | 6.97 | 6.32 | −.03* | −.04** |
| (.63) | (.62) | (−2.53) | (−2.59) |
| Negative content | −46.60 | −40.66 | .12*** | .12*** |
| (−1.04) | (−1.01) | (3.37) | (3.34) |
| Readability index | 9.15 | 9.50 | −.04 | −.04* |
| (1.00) | (1.03) | (−1.92) | (−2.09) |
| Authenticity in content | 1.76 | 2.01 | −2.41e-3 | −2.22e-3 |
| (1.59) | (1.56) | (−1.21) | (−1.06) |
| Tone in content | 1.82 | 1.87 | −3.50e-3 | −3.38e-3 |
| (.89) | (.89) | (−.85) | (−.82) |
| Informal words | −37.30 | −34.65 | −.02 | −.03 |
| (−1.38) | (−1.42) | (−.83) | (−.84) |
| Social power | 1.75** | 1.79** | 1.99e-3 | 2.13e-3 |
| (3.14) | (3.05) | (.78) | (.79) |
| B2C | 6.41 | 21.21 | .07 | .10 |
| (.15) | (.38) | (.67) | (.86) |
| Inverse Mills ratio | 645.80 | 618.90 | −1.71** | −1.74*** |
| (1.10) | (1.10) | (−3.24) | (−3.39) |
| Non-IMI timeliness | | 3.24e-3 | | 2.31e-6 |
| | (−.27) | | (.06) |
| Non-IMI unanticipation | | −63.84 | | .12 |
| | (−.81) | | (.65) |
| Non-IMI humor × Timeliness | | .02 | | −1.60e-5 |
| | (−1.01) | | (−.90) |
| Non-IMI humor × Unanticipation | | −17.20 | | −.04 |
| | (−1.21) | | (−.84) |
| Non-IMI timeliness × Unanticipation | | −.01 | | 5.82e-6 |
| | (.96) | | (.29) |
| Intercept | −1,177.90 | −1,159.90 | 2.71* | 2.35* |
| (−1.17) | (−1.23) | (2.39) | (2.32) |
| Adj. R-square | 33.98% | 34.07% | 22.9% | 22.64% |
| Overall test of significance (Wald) | 287.44 | 293.39 | 91.83 | 95.83 |
| Wald test of significance | .000 | .000 | .000 | .000 |
- 16 *p <.05.
- 17 **p <.01.
- 18 ***p <.001;
- 19 a N = 470; day and hour fixed effects.
- 20 b N = 226; day fixed effects.
- 21 Notes: t-statistic in parentheses; N.A. = not applicable.
Following Study 4a, we also include the interactions of humor and timeliness and humor and unanticipation for IMI and non-IMI (see Equation 5). The focal results remain consistent after inclusion of these interactions (Table 8, Column A, Model 2).
Study 5a includes non-IMI tweets and shows that the significant interaction of humor and timeliness and the interaction of humor and unanticipation on virality persist even after inclusion of non-IMI tweets for a random sample of S&P firms across different industries, for every type of tweet, and for relatively newer data. As in Study 4b, we next explore whether our two primary interactions of interest significantly affect firm value. Thus, Study 5b tests H4 and H5.
The initial sample is 470 tweets. We exclude 244 tweets due to potential confounds across the nine-day window of a tweet. We hence use 226 tweets for the analysis.
Similar to the former studies, we use the abnormal stock returns using the [21] five-factor model and use the term "returns" to refer to cumulative average abnormal returns. Note that we do not include firm- or competition-based measures such as size and turbulence, respectively, because these measures do not vary within a month and are captured by the firm fixed effect that we include in the model.
We begin by analyzing returns (see Table 6, Panel C). We obtain positive returns for the (0, 0), (−1, 0), (0, +1), (−1, +1), and (−4, +4) windows; however, these returns are not significant. The event window with the highest t-value (1.42) and absolute value (.07%) is the event day (−1, 0) window. Thus, we use this window for the subsequent analyses (i.e., CAAR [−1, 0]). Next, the effect of the IMI message on returns for focal firms is positive and significant for the (−1, 0) window (.29%, p <.01, one-tailed test). On the other hand, the effect of the non-IMI message on returns for the (−1,0) window is positive albeit not significant (.04%, p >.24, one-tailed). We find that IMI tweets generate.40% higher returns than non-IMI tweets (t(126) = 2.24, p <.05, one-tailed test).
We use the same control variables that are utilized in Study 5a but drop the square of the length of the tweet as reasoned previously. The descriptive statistics and correlations are shown in Web Appendix E. Our first focal interaction is the coefficient of humor × timeliness for IMI tweets (.0000239, p <.001) (Table 8, Panel B, Model 1), in support of H4. Furthermore, we find the coefficient humor × unanticipation for IMI tweets to be positive and significant (.24, p <.01), in support of H5.
Similar to Study 4b, we also estimate Equation 5, replacing virality with returns and including the interactions of humor and timeliness and humor and unanticipation for non-IMI tweets. Our results hold after inclusion of these interactions (see Table 8, Column B, Model 2).
Study 5b offers evidence of the significant influence of IMI's humor × timeliness and humor × unanticipation interactions on firm value after including non-IMI tweets across a random sample of S&P 500 firms from an array of industries using newer data and examining a broader set of tweets that include text, links, videos, and images.
Overall, across the five studies that span different methods utilizing archival and experimental data, we find evidence that an IMI generates virality and leads to a significant boost in virality compared with a non-IMI, and that IMIs characterized by humor and timeliness or unanticipation can enhance virality and firm value.
Digital advertising has grown considerably and is projected to account for more than 50% of total advertising spending in industrial economies by 2020 ([18]). Yet consumers often say that social media ads are overwhelming, repetitive, and irrelevant ([24]). Against the backdrop of consumer advertising fatigue, the current research about IMI highlights a set of novel and important findings that advance marketing theory and practice. We believe IMI's potential for virality and greater firm value are relevant for any firm wishing to achieve greater exposure and increase visibility to consumers and positively influencing the stock market.
This article makes several important contributions. Despite calls to study marketing events that happen in real time, no prior research has rigorously defined such marketing interventions. We introduce a formal definition of fast, mass-market (not customer-specific) responses to external events. To date, the role of improvised composition and execution of a real-time marketing intervention proximal to an external event in generating virality and adding to firm value has remained unexplored. Our study of IMI theorizes and articulates its essential characteristics and analyzes the opportunity for virality and enhanced firm value.
Indeed, there have been calls to study phenomena such as IMI from a wide array of sources—scholars and editors of scholarly journals ([ 2]; [36]; [49]) as well as business publications such as The Economist and Financial Times. Thus far, however, such studies have been fairly rare in the marketing literature. In addition to defining IMI, we theorize and begin to examine its potential influence of IMI and its key characteristics on virality and firm value. We use an array of unique and varied data sets, designs, and methods to estimate this influence. This article's provocative findings serve as the basis for further research on the dynamic and important yet poorly understood IMI marketing phenomenon, and on its influence on virality and firm value—thus addressing an important real-world marketing problem.
We use quick wit to advance the idea that IMI may help businesses reach out to and connect with an audience that is increasingly tired and wary of advertising messages. Thus far, the impact of humorous IMIs timed early in relation to an external event or tinged with unanticipation has not been well understood. The current research thus develops new theory about the critical role of a dose of humor, which only generates virality and firm value, if it is paired with timeliness or unanticipation. Specifically, while existing theory helps us understand the relevance of humor in day-to-day human interactions, we extend the theory on communication, consumer online engagement, and firm value by theorizing the relevance of quick wit in the context of ongoing events. Thus, our work adds to current theory by advancing that quick wit enables businesses to stay relevant, be part of, and—more critically—be a proactive driver of the ongoing discourse and of individuals' thought processes.
Our finding about IMI's impact on firms' abnormal returns also encourages future researchers to examine other novel marketing activities in the digital and mobile realm and attempt to show the financial impact of these marketing activities.
In addition to advancing theory, our work on IMIs has critical implications for managers. Many managers believe that a firm's marketing message is best preplanned well ahead, organized, and 100% under the control of the firm. The potential advantages of such an approach are well understood. However, this strategy can also lead to a brand being seen as out of touch, distant from its target audience, and failing to capture the zeitgeist, or trends, feelings, and ideas that are typical at the time.
Our results encourage marketing managers to carefully consider not what they say but, importantly, how and when they say it using social media. Often, in-house marketing teams lack the responsiveness and latitude to trade on the opportunity presented by a current event and tie their brand message to the event for maximum impact. We highlight this hidden opportunity for managers to spot trends and utilize those trends to seed advertising campaigns that can become viral. Numerous brands have yet to discover the potential of this marketing method. We encourage firms to empower marketing teams with the latitude to keep a close eye on trends and spontaneous chatter, and to quickly formulate witty messages in response to these events. Because people employed in marketing departments do not have advance knowledge of some of these events, they need to be empowered to react spontaneously. We acknowledge that this flexibility may necessitate relinquishing some level of control over the message at times, and it is possible that a marketing team may hit the "send" button too quickly. It is important that firms identify the right employees to execute IMI (e.g., their sense of humor and timing should be on point and not offensive). Nightmarish examples abound of consumer backlash against a brand's own social media posts when the brand reputation is fragile ([13]). However, given the right employees who are empowered to act, not only do IMI messages around current events create potential for enhanced brand awareness and greater financial returns, but also they may cost a fraction of the advertising expenses for sponsoring events like the Summer Olympics or World Cup. Furthermore, we determine the extent to which the characteristics of IMI affect not only perceptual metrics such as virality but also the objective metrics of a firm's stock returns—a metric that is of immense interest to a firm's managers and shareholders. Today, social media platforms such as Twitter constitute an additional, significant source of novel information for investors.
Managers often believe that the only time they can influence stock market investors is when the firm releases its quarterly or monthly sales reports. Our findings show that IMIs can provide investors with instantaneous, critical information about the firm's marketing performance in social media and its marketing capability. Drawing on Study 3b data, we find that an IMI with high humor and high unanticipation can generate $5.1 million, on average, in market capitalization while high humor and high timeliness can generate $3.1 million, on average, in market capitalization. These figures are comparable to prior analysis on the dollar impact of online reviews ([66]). Firms are encouraged, then, to make use of IMI messages by using humor paired with timeliness and unanticipation, as these messages can influence investor behavior and subsequently the stock market. Ultimately, managers need to consider IMI proactively to be part of and shape the current zeitgeist—rather than be driven by it—and to achieve greater virality and generate stronger stock market returns.
This work has several limitations, which reflect opportunities for further research. First, our theorizing on quick wit is general in scope and applies to a variety of marketing communications and social media. Our empirical context, however, is limited to Twitter, a single social media platform. Although it helps alleviate concerns regarding platform-level heterogeneity and thus enhances the internal validity of our study, a promising avenue for future research is to study IMI's role in driving virality and firm value across other social media platforms and modes of communication and investigate whether one channel can have spillover effects on the other. Second, we use only one measure of virality: retweets. Future research might create a more complete empirical picture by testing other, more explicit and fine-grained measures of virality (e.g., number of shares by early propagators). Third, while our findings may inform "social media war rooms" (e.g., the 2019–2020 U.S. Democratic primary debates), one wonders about IMI's effect when it relates to events that have a very negative valence (e.g., earthquakes, wildfires) or has very low fit with the parent brand's image. Fourth, across all the models, we did not find a significant and positive main effect of humor on either virality or firm value (see Web Appendix J). Thus, our findings indicate that stand-alone humor cannot drive virality or firm value for IMI but must be paired with timeliness or unanticipation. This is a thought-provoking finding, and we invite future research to examine reasons for why humor alone does not have a significant influence on virality or firm value for IMIs. In addition, it would be interesting to examine whether aspects other than humor paired with timeliness and unanticipation could achieve virality for other types of firm-generated content. Fifth, we speculate that our effects on firm value might be driven by investors reacting to the possibility of IMI to generate virality, as we control for selection in Studies 4 and 5. However, it might be an interesting avenue for future scholars to examine the contingent effect of brand confidence and employee empowerment on the relationship between IMI and firm value. Finally, although the context of our study is social media, our findings may generalize to traditional media contexts (e.g., radio, digital billboards, electronic signs, personal selling) as well. Additional research on IMIs and the conditions in which they are most effective will shed greater light on this important phenomenon.
Supplemental Material, jm.18.0503-File003 - Improvised Marketing Interventions in Social Media
Supplemental Material, jm.18.0503-File003 for Improvised Marketing Interventions in Social Media by Abhishek Borah, Sourindra Banerjee, Yuting Lin, Apurv Jain and Andreas B. Eisingerich in Journal of Marketing
Footnotes 1 Associate EditorKoen Pauwels
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242919899383
5 1We use the event minute to ensure that we do not miss out on any virality activity in the event minute.
6 2We thank an anonymous reviewer for this comment.
7 3To rule out confounds including (1) the type of "competitive" timeliness (earlier timeliness manipulation referencing other brands) and (2) the creativity of the tweet potentially influencing sharing, we conducted a post hoc test using Amazon Mechanical Turk participants (N = 202) for two conditions: high humor, high unanticipation, and high timeliness (HHH) and high humor, high unanticipation, and low timeliness (HHL). Specifically, we measured low timeliness as the "tweet was posted by Wild Foods quite a while after this clip aired on TV" and with no mention of competitors and captured creativity by asking the extent to which the tweet content is very creative, innovative and ingenious (α =.93). The manipulation of timeliness worked as expected (Mlow = 4.43, SD = 1.73 vs. Mhigh = 5.64, SD = 1.10; F(1, 200) = 34.84, p <.001). Critically, intention to share the tweet was higher in condition HHH (M = 4.92, SD = 1.95) than HHL (M = 4.03, SD = 1.98; t(200) = 3.21, p <.01). In addition to tweet creativity we included other controls, such as clip liking, gender, age, brand familiarity, clip familiarity, and users' activeness on Twitter. While main effects of creativity (F(1, 193) = 18.44, p <.001) and clip liking (F(1,193) = 10.63, p <.01) were observed, the significant difference between the HHH and HHL conditions in terms of willingness to share remained unchanged (F(1, 193) = 4.72, p <.05).
8 4Based on Standard Industry Classification (SIC) codes.
9 5Prior research has shown that most retweets happen the same day that the message is posted ([55]). We believe, therefore, that there will be a minimal difference between the number of retweets in a year and those in a day.
6We use two months because of data availability issues. The third-party data provider (SimplyMeasured, which now is a part of Sprout Social) could allow us access to detailed individual-level tweet data only for two months.
7In Studies 4 and 5, IMI messages are identified using the same criteria used in Study 3.
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Record: 98- Inferring Corporate Motives: How Deal Characteristics Shape Sponsorship Perceptions. By: Woisetschläger, David M.; Backhaus, Christof; Bettina Cornwell, T. Journal of Marketing. Sep2017, Vol. 81 Issue 5, p121-141. 21p. 2 Diagrams, 5 Charts.
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Inferring Corporate Motives: How Deal Characteristics Shape Sponsorship Perceptions
Sponsoring joins brands with sports, the arts, and events in mutually beneficial partnerships. In the context of sports, the authors examine how sponsorship deal characteristics affect consumer inferences, attitudes, and behavioral intentions toward a sponsor and a sport property in a partnership. The authors develop a conceptual framework that links a holistic set of sponsorship deal characteristics (i.e., contract length, regional proximity of the sponsor, sponsorship fee, and sponsorship type) to individual consumer perceptions. Study 1 tests the framework in a field study of 2,787 consumers across 44 sponsorships. Study 2 largely confirms the findings of the field study in an experimental study. Overall, the results show that regionally proximate and long-term partnerships benefit as consumers make positive inferences about partnership fit and sponsor motives. In contrast, consumers associate high sponsorship fees, international sponsors, and naming-rights relationships with calculative motives and perceive these factors negatively. For managers, finding that sponsorship deal characteristics matter is important not only for sponsor–property relationships but also for relationships between the sponsoring brands and consumers.
Online Supplement: http://dx.doi.org/10.1509/jm.16.0082
In 2012, Chevrolet began a seven-year sponsorship of Manchester United. The deal caused a stir because of the price ($600 million) and because the Chevy brand, as American as baseball and hot dogs (Baxter 2014), was sponsoring a soccer team from the English Premier League. With the $85 million per year “quid pro quo” connotations of the international deal, it is not surprising that some viewed the partnership as a fiasco even after the deal-signing dust settled (Rechtin 2014).
The overarching nature of a sponsorship deal negotiated by management includes characteristics such as duration, contract fee, and relationship type. Although these dealmaking characteristics set the stage for the sponsorship relationship, they tend to be overlooked as factors that contribute to consumer perceptions. Against this background, our research objective is to understand the role of deal-level partnership characteristics in shaping consumer inferences, attitudes, and behaviors toward both a sponsor and the sponsored club.
Sponsorship is a cash or in-kind fee paid to a property (typically in sports, arts, entertainment, or causes) in return for access to the exploitable commercial potential of that property (IEG 2017). It has become a mainstay of strategic marketing communications, as evidenced by expenditures that reached US$60 billion worldwide in 2016 (IEG 2017). Brands are eager to connect with the passion of sports, their media coverage, and their audiences through sponsorship. Consequently, sports dominate the growing global sponsorship market: In North America, where 37% of worldwide sponsorship spending occurs, 70% of all sponsorship expenditures pertain to sports (IEG 2017). Sponsorship has expanded through growth of both sporting events and sponsored properties. For example, most major sporting venues around the world now have a corporate sponsor name—a significant increase compared with two decades ago (Cornwell 2014). According to IEG (2016), sponsorship right fees account for a substantial share of the total worldwide marketing spending (between 16% and 25% over the past 15 years). Despite this extensive use of sponsorship in marketing, managers still rely on “gut feeling” when entering sponsorship agreements (DeGaris, Dodds, and Reese 2016) rather than on marketing research.
Drawing on arguments from attribution and identity theories (Heider 1958; Stryker and Burke 2000), we propose that deal characteristics initially set by the sponsor and sponsee determine consumer inferences about sponsorship fit and sponsor motives, which in turn shape attitudes toward the sponsor and sponsee. We examine the proposed effects of sponsorship deal-level characteristics (i.e., contract length, regional proximity, sponsorship fee, and sponsorship type) on consumer-level outcomes as well as the mediating effects in a field study and an experimental field study. The field study (Study 1) focuses on outcomes for sponsors of 44 sponsorships of the German Football League, using representative field data from 2,787 consumers. Here, we consider the perceptions of people familiar with particular partnerships and use their broadbased knowledge and exposure to the partnership as a foundation for attributions and inference making. Soccer is a high-profile sport that comprises 71% of the sports sponsorship market in Germany. The context of Study 2 is handball, a comparatively low-profile professional sport with a sponsorship share smaller than 2% (Nielsen Sports 2015). Here, holding sponsorship type constant, we manipulate the remaining partnership characteristics and examine effects on the sponsored property. Study 2 largely confirms the results obtained in Study 1 using a betweensubjects experimental study design.
This research contributes to the literature in three ways. First, we conceptually link the level of sponsorship decision making with the consumer view. Although research has made substantial progress in advancing understanding of consumerlevel determinants of sponsorship outcomes (e.g., Cornwell 2008) and managerial decision making in sponsorship (e.g., Farrelly and Quester 2005), these two streams have developed independently. By linking sponsorship deal characteristics to consumer inference making, the current work sheds light on the interplay between deal characteristics and consumer-level outcomes. Second, we show empirically that perceptions of sponsorship fit and attributions of sponsor motives are associated with sponsor partnership characteristics. Third, illustrating that consumer responses to sponsorship deal characteristics are not limited to the sponsor, this work is the first to consider the influential link between deal characteristics and sponsored properties by finding that decisions in the sponsorship deal affect consumer loyalty toward the sponsored property. Furthermore, the research design responds to calls for studies conducted outside laboratories or venues and based on broad experience (Cornwell and Humphreys 2013), and it extends existing field study approaches by allowing for a comparative evaluation of managerial-level aspects with individual-level response.
Theory Development
Conditions and Outcomes of Inference Making: The Multiple Inference Model
Inference making refers to any construction of meaning beyond information that is readily available (Dick, Chakravarti, and Biehal 1990; Harris 1981). In attribution theory, inference making is a common strategy used to make sense of observed behaviors (Heider 1958). The multiple inference model (MIM) of attribution (Reeder et al. 2004) explains how perceivers integrate several inference traits and motives. In particular, the MIM considers three content conditions that determine attribution outcomes: free choice, no choice, and ulterior motives. The MIM suggests that a generally positive behavior results in positive trait evaluations under both free-choice and no-choice conditions, whereas the ulterior motive condition leads to a negative trait attribution (Reeder et al. 2004). The mechanisms through which the three conditions affect trait attributions are explained by three motive-relevant traits: unselfishness in the free-choice condition, obedience in the no-choice condition, and selfishness in the ulterior motive condition (Reeder et al. 2004). Because sponsorship is an intentional behavior and thus subject to attributions of intent (Maselli and Altrocchi 1969), consumers are likely to infer sponsor motives from available information, whereby the three motivational conditions of the MIM correspond to three possible sponsor motives.
First, people might infer that a sponsorship forms from “free choice” or good intentions directed at the sponsored property. For example, in sponsoring the development league of the National Basketball Association (NBA), Gatorade, a company already with high-profile NBA sponsorships, may communicate its passion for the game (Barca 2017), thereby expressing affective motives.
Second, people might assume that by engaging as sponsors, firms are doing their civic duty in supporting properties. In particular, people might expect local companies with high economic relevance for a city or region to act as sponsors of local events or teams, such as Michigan-based Little Caesars’ sponsorship of the Detroit Red Wings’ arena. Similarly, Travelers Insurance sponsored a Professional Golfers’ Association tournament in New England when there was discussion of losing the event if a sponsor was not found. Here, the sponsorship can be interpreted as a response to a normative call. In keeping with the MIM, sponsorship is a volitional act. Therefore, no choice better reflects “limited choice,” in which stakeholder pressure or expectations may be a motivation to engage in a sponsorship.
Third, firms engage in sponsorship to reach markets and sell products and services. Here, people might view a sponsorship relationship (e.g., between Chevrolet and Manchester United) as largely commercial or calculative, intended to work like advertising in reaching worldwide markets. Given the nature of sponsorship as a marketing communication instrument, a certain calculative motivation seems natural. In summary, this study proposes that people infer a mix of motives, with the types of motive attributions (affective, normative, and calculative) mapping well with attribution theory discussions.
Antecedents to Inference Making: Deal Characteristics as Identity Signals
Although the MIM helps explain how different motives influence subsequent trait attributions, little is known about why perceivers might assume that an observed action is the result of free choice, no choice (or limited choice), or ulterior motives. Here, a key notion of attribution theory is that various types of information act as antecedents to the attribution-making process (Kelley and Michela 1980). Inferences may stem from facts, such as the annual amount of the sponsorship support a team receives, or may be determined by subtle cues, such as local situational referencing to communicate geographic proximity. For example, consumers are regularly exposed explicitly to sponsorship information (e.g., a news announcement of a new shirt sponsorship deal) or implicitly when attending or watching a match on television. Notably, people need not know all the details about individual aspects or characteristics of an object to make inferences (Dick, Chakravarti, and Biehal 1990).
Identity theory suggests that a person can adopt multiple identities or self-concepts, which become salient depending on the particular situation (Arnett, German, and Hunt 2003; Stryker and Burke 2000). Likewise, organizations can be considered social actors, assuming multiple roles associated with particular role expectations and behaviors (Whetten and Mackey 2002). Sponsoring firms have two specific role identities, one as a business entity and one as a sponsor, implying collaboration with and support of the sports partner. In turn, the sports partner holds the role as a sports club and has a businessrelated role identity. Against this background, the concept of role-identity salience (Callero 1985) suggests that the extent to which a sponsor’s or a club’s role identities are expressed can vary between them. Importantly, increased commitment of the organization to a role identity makes that role more salient both for itself in its actions and for others in their observations (Callero 1985; Stryker and Burke 2000). Specifically, identity theory suggests that the nature of the sponsorship deal provides information about the roles each partner is committed to and expressing. This communication is framed by the perceptions of other sponsor–club relationships because they help form the basis of role-based expectations (Stryker and Burke 2000).
Herein, we conceptualize deal characteristics as the cues antecedent to consumer-level attributions, though research on these characteristics as signals is rather limited (Table 1). Focusing on contract duration, Walraven et al. (2016) investigate 72 sport sponsorships, revealing a small positive effect of contract duration on relative efficiency, assessed from a consumer-based perspective. Gwinner (1997) also recognizes the importance of sponsorship specifics (e.g., domain, size, history) in the transfer of event image to a brand and, thus, as relevant to consumers. Gwinner does not, however, explicate the importance of sponsorship type in terms of the nature of the deal made (e.g., stadium naming rights, team sponsorship). The role of geographic location as an antecedent to consumer responses to sponsorship appears mainly in studies on social sponsorship (e.g., Grau and Folse 2007; Russell and Russell 2010). Here, whereas some studies have shown positive effects of locally focused campaigns, others have suggested that geographically splitting donations or even exclusively allocating donations to foreign beneficiaries is more effective than supporting causes at home (Schons, Cadogan, and Tsakona 2015). Finally, although theory suggests that the contract fee is a signal relevant to consumers, studies in the adjacent social sponsorship domain provide mixed evidence of the effect of donation amount on consumer perceptions (e.g., Koschate-Fischer, Stefan, and Hoyer 2012). Considering these various characteristics, an expected sponsor role may be more readily perceived for a company engaged in a longterm relationship with a geographically nearby team, as cues of longevity and proximity help promote salience of the firm’s sponsor role identity. Alternatively, a geographically distant sponsor paying a great deal, such as Chevrolet in the Manchester United example, may raise suspicions.
Conceptualization and Research Model
Figure 1 depicts our conceptual model. Referring to the top box, and following from identity theory, the sponsor partnership characteristics signal the commitment of the sponsorship partners to their role identities as sponsor and club. The top box also depicts control variables such as sponsor firm size and team sport success, allowing isolation of the effects of deal characteristics from other sponsor and property variance. The “consumer inference making” box shows deal characteristics and characteristics of both the sponsor and sponsored as input to the inference-making process. Our model treats sponsorship fit as particularly relevant to inference making because the perception of a sponsor fitting well with a property influences downstream processes such as attitudes and behavioral intentions (e.g., Pappu and Cornwell 2014; Simmons and Becker-Olsen 2006; Speed and Thompson 2000).
Because audiences are at arm’s-length from partnering organizations, they must “reason … back from an effect to its underlying cause” (Pizarro, Tannenbaum, and Uhlmann 2012, p. 186) in their judgment of sponsor motives. Consumers observe the characteristics of the relationship, consider the fit between the partners, and attribute affective, normative, and calculative motives to the sponsor in the inference-making process. This process, in turn, influences outcomes for sponsors and sponsored properties (see middle-right box of Figure 1). The inferred motives are input to attitudes toward the sponsor (sponsor attitude, hereinafter) and attitudes toward the club (club attitude), which are antecedents to loyalty to the sponsor (sponsor loyalty) and loyalty to the club (club loyalty), respectively. The model also acknowledges that the motive, attitude, and loyalty dimensions are not independent of one another.
TABLE: TABLE 1 Selective Research Considering Managerial-Level Antecedents and Consumer-Level Outcomes
| Research | Context | Study Design | Management-Level Antecedents | Main Consumer-Level Variables | Integration of More Than One Managerial Level Aspect? | Multilevel Mediating Effects Assessed? | Outcomes for Property Measured? | External Validity |
|---|
| Becker-Olsen, Cudmore, and Hill (2006) | CrM | Experimental | € Created fit € Message source | € Clarity of positioning € Attitude toward the sponsorship € Firm equity | Yes | No | No | Moderate |
| Cornwell et al. (2006) | Cultural event sponsorship | Experimental | € Congruity € Articulation of reason | € Sponsor recall | Yes | No | No | Low |
| Koschate-Fischer, Stefan, and Hoyer (2012) | CrM | Experimental | € Donation amount | € Willingness to pay € Company€“cause fit € Attitude toward helping others € Warm glow motive € Cause involvement € Cause organization affinity € Attributed motives | No | No | No | Low |
| Olson and Thj’m’e (2011) | Sports sponsorship | Experimental | € Audience similarity € Geographic similarity € Attitude similarity € Time | € Motivation € Product use € Overall fit € Effect on sponsor | Yes | No | No | Moderate |
| Pappu and Cornwell (2014) | Sports sponsorship | Experimental | € Sponsorship relationship fit € Sponsor€“nonprofit similarity | € Attitude toward sponsorship, sponsor, and nonprofit € Clarity of positioning € Sponsor€“nonprofit similarity | Yes | No | Yes | Moderate |
| Rifon et al. (2004) | Health sponsorship | Experimental | € Congruence € Brand- versus corporatelevel sponsorship | € Altruism attribution € Sponsor credibility € Sponsor attitudes | Yes | No | No | Moderate |
| Schons, Cadogan, and Tsakona (2015) | CrM | Experimental | € Geographic allocation of donation budget € Size of donation budget € Company€€s reach of operations € Purchase intention | € Perceived morality of favoring in-group € Justice restoration potential | Yes | No | No | Moderate |
| The current research | Sports sponsorship | Comparativefield study of 44 different sponsorships Experimental | € Contract length € Regional proximity € Sponsorship fee € Sponsorship type € Contract length € Regional proximity € Sponsorship fee € Sports success € Sponsorship fit | € Sponsor attitude € Sponsor loyalty € Club attitude € Club loyalty € Affective motives € Calculative motives € Normative motives € Sponsorship fit | Yes | Yes (Study 1) | No (Study 1) Yes (Study 2) | High (Study 1) Moderate (Study 2) |
Construct Relationships and Hypothesis Development
Consumer-Level Effects of Fit and Motive Inferences on Sponsorship Outcomes
A central objective of the research is to determine whether consumer-inferred motives of the sponsor affect sponsorship outcomes. Consumers who perceive a sponsor as engaging in a sponsorship out of an emotional attachment to the property evaluate the sponsoring brand more favorably (Deitz, Myers, and Stafford 2012; Rifon et al. 2004). Theoretically, this is consistent with the MIM (Reeder et al. 2004), which states that a positive behavior perceived as occurring under free choice, rather than acting for ulterior motives, results in positive trait attitudes toward the subject. This link between a behavior perceived as resulting from free choice and attribution of a positive character trait is mediated by unselfishness. In the sponsorship context, we expect the attribution that a sponsor is affectively motivated to lead to positive attitudes toward the sponsor and greater loyalty. The attribution of affective motives is likely to strengthen the perceived relationship between the partners, resulting in an improved evaluation of the property with regard to brand attitude and loyalty. In line with prior research (e.g., Simmons and Becker-Olsen 2006), we differentiate between attitudinal (i.e., sponsor attitude and club attitude) and behavioral (i.e., sponsor loyalty and club loyalty) sponsorship outcomes. Thus,
H1: Inference of affective motives is positively related to (a) sponsor attitude, (b) club attitude, (c) sponsor loyalty, and (d) club loyalty.
When firms engage in sponsorship, external expectations may result in normative motives that are easily visible to audiences. Consumers who are aware of a team in a community usually view a sponsor of local sports as a good corporate citizen. In support of this, the MIM proposes that positive behaviors occurring in response to external conditions (no choice) result in the attribution of positive traits by perceivers. Normative commitment is also communicated through the supply of financial support. When an individual athlete, team, or event struggles to find financial footing, companies that step forward are perceived as responding to need. Dispositional obedience helps explain this seemingly contradictory positive outcome of the no-choice motivational condition: sponsors that engage out of normative motives obediently fulfill stakeholders’ expectations. In this case, the motive-related trait of obedience is generally associated with positive outcomes (Reeder et al. 2004). Thus,
H2: Inference of normative motives is positively related to (a) sponsor attitude, (b) club attitude, (c) sponsor loyalty, and (d) club loyalty.
When attributing calculative motives, consumers are suspicious of the partnership intent, and this response to the relationship may negatively influence brand perceptions (Pappu and Cornwell 2014; Yoon, Gu¨rhan-Canli, and Schwarz 2006). Investigations into cause-related sponsorship and corporate social responsibility have extensively discussed inferences of calculative motives (e.g., Ellen, Webb, and Mohr 2006), in which the disconnect between firm- and publiccentered reasons for engagement is readily apparent in highprofile cases. Theoretically, the inference of an ulterior motive, of which selfishness is the mediating motive-related trait, is likely to result in negative outcomes (Reeder et al. 2004). In such cases, the reasons for engagement come into question, as in the case with Chevrolet and Manchester United, and thus we expect inference of calculative motives to influence downstream attitudes and behaviors:
H3: Inference of calculative motives is negatively related to (a) sponsor attitude, (b) club attitude, (c) sponsor loyalty, and (d) club loyalty.
Sponsorship fit refers to the perceived congruence between sponsor and property on key dimensions, such as product category (Simmons and Becker-Olsen 2006) and image (Gwinner and Eaton 1999), and is often critical to outcomes such as brand image, brand attitude, behavioral intentions, and brand meaning clarity (e.g., Mazodier and Merunka 2012; Pappu and Cornwell 2014; Speed and Thompson 2000). Theory suggests that information regarding fit is available to consumers as a result of deal making and triggers sense making. Sense making following a trigger event, in which people in an organization must consider managerial decisions (e.g., Weick 1995), is well established in organizational literature, but it also applies to the individual assessing organizations at a distance. In sense making, a consumer may wonder why certain partners have come together. The basis of fit for sponsorship success is commonly explained by people’s need for congruence (Heider 1958); if consumers perceive a sponsorship as incongruent, they will seek alignment, as incongruent sponsorships cause psychological tension and affect sponsorship outcomes negatively (e.g., Simmons and Becker-Olsen 2006). In contrast, congruence fosters a positive attitude toward the sponsor.
While the sponsorship and corporate social responsibility literature generally agrees on the importance of perceived fit in explaining relevant outcomes (for a review, see Peloza and Shang 2011), the interrelationship between perceived fit and inferred motives is less clear. Several studies have conceptualized perceived fit as an antecedent to perceived motives (e.g., Rifon et al. 2004; Yoon, Gu¨rhan-Canli, and Schwarz 2006). Alternatively, motives may act as a moderator on the fit–outcome link (e.g., Barone, Norman, and Miyazaki 2007). Here, in line with attribution research and research in social psychology (Molden 2009), the nature of both the sponsor and the property (e.g., a running shoe brand and running event vs. a bank and running event) and their engagement in a partnership (i.e., traits and social circumstances) provide the social context for judging motives and developing attitudes. Under high-fit conditions, consumers will perceive a partnership in terms of affective motives. Conversely, unless reasons for the relationship are clearly articulated, fans of the team or customers of the brand may attribute ulterior motives to the partnership. This argumentation is also in line with Kelley’s (1973) discounting principle, according to which consumers discount an explanation if an alternative explanation exists (Rifon et al. 2004).
H4: Sponsorship fit is positively related to (a) sponsor attitude, (b) affective motives, (c) normative motives, (d) club attitude, (e) sponsor loyalty, and (f) club loyalty and negatively related to (g) calculative motives.
Effects of Sponsor Partnership Characteristics on Sponsorship Perceptions
Because a broad range of sponsorship characteristics may exert an influence on individual consumer perceptions, we sought empirical confirmation for our candidate variables. To obtain information on sponsor partnership characteristics, we analyzed a sample of 92 newspaper and trade journal articles published between 2002 and 2012 that dealt with sponsorship partnerships. The results showed that the most frequently communicated characteristics are past or future contract length (appearing in 83% and 100% of the analyzed cases, respectively), regional proximity (78%), sponsorship fees (90%), and sponsorship type (100%).
Contract length. Long-term sponsorships require a high level of mutual commitment and trust between the involved parties (Farrelly and Quester 2005). The longer partners have been together, the more they seem to go together, because they develop an overlapping set of brand associations over time. These shared associations support the perception of fit. According to identity theory, firms deciding for a long-term relationship evince genuine commitment (Stryker and Burke 2000). In sports, a long-term sponsor has likely been with a team through winning and losing seasons, when recruitment of players has gone well and not gone well. Thus, consumers are less likely to infer that the firm’s motivation for sponsoring is predominantly commercial, as attributions of “real” commitment increase with duration (Ellen, Webb, and Mohr 2006). Walraven, Bijmolt, and Konig (2014) provide empirical evidence of such long-term sponsorship effects in their five-year study of 25,000 consumers, in which awareness was most notably elevated in the second year. Thus:
H5: Contract length is positively related to (a) sponsorship fit, (b) affective motives, and (c) normative motives and negatively related to (d) calculative motives.
Regional proximity. The role of place is fundamental in identity formation (Stedman 2002). On this basis, identity theory suggests that being in a club’s region raises the extent to which a firm’s sponsor role identity becomes salient. Owing to the natural connection from being in the same region, geographically proximate sponsorships are particularly effective in signaling high levels of commitment to the sponsor role, which in turn should contribute positively to attributions of high sponsorship fit and affective motives. With regard to normative motives, Close et al. (2006) show that a sponsor’s close connection not only with the sponsee but also with the community is crucial for an effective sponsorship. Ceteris paribus, consumers should therefore view geographically close sponsors as acting in line with expectations of community involvement. Similarly, sponsor stakeholders such as employees and partners are likely to perceive sponsoring sports close to home particularly favorably (Yang and Goldfarb 2015).
In contrast, sponsorship of a team with no regional linkages to the sponsor might arouse consumer suspicion, leading to a shift in attributional reasoning (Yoon, Gu¨rhan-Canli, and Schwarz 2006). Inferences about the sponsor’s motives are likely to rest on more complex reasoning, increasing the likelihood of attributing calculative motives. Consequently, consumers will evaluate sponsors not regionally connected with sponsored properties less positively in terms of sponsorship fit and affective and normative motives and more negatively in terms of calculative motives than sponsors with a strong connection with the region.
H6: Regional proximity is positively related to (a) sponsorship fit, (b) affective motives, and (c) normative motives and negatively related to (d) calculative motives.
Sponsorship fee. High sponsorship fees are often a highly visible form of firm spending. For example, during the economic crisis of 2009, Bank of America, having received U.S. government bailout funds, was sharply criticized for sponsoring the NFL Experience, an event surrounding the Super Bowl (Chuchmach et al. 2009). High sponsorship fees are typically associated with high media exposure and large audience attendance (Wishart, Lee, and Cornwell 2012). Firms’ decision for a large- rather than small-scale sponsorship communicates the commercial nature of the sponsorship in terms of brand exposure. From an identity theory perspective, and because sponsorship is generally perceived as less commercial than advertising (Olson 2010), high sponsorship fees may reflect less commitment to a company’s role as a sponsor. That is, such fees may communicate that the sponsor is simply buying media coverage. While high sponsorship fees can lead to feelings of gratitude in grassroots contexts, costly contracts in high-profile sports may conflict with expectations of a sponsor role. Signaling intentions to market through sponsorship, high fees thus may be perceived as less congruent with the sponsored properties’ identity and subsequently elevate attributions of predominantly calculative motives. Alternatively, low sponsorship fees may communicate support without expectations of high marketing value. Thus, low fees should make it easier for a firm to meet role expectations as a sponsor.
H7: Sponsorship fee is negatively related to (a) sponsorship fit, (b) affective motives, and (c) normative motives and positively related to (d) calculative motives.
Sponsorship type. Gwinner (1997) argues that the type of sponsorship can affect outcomes for sponsors and properties. In sports, the nature of sponsor engagement with a property is typically venue naming, apparel or “shirt sponsorships” (in Europe), and in-venue or perimeter logo presentation. Analogous to the argument for sponsorship fee, identity theory suggests that a higher level of prominence in sponsorship type will increase the likelihood that consumers will perceive a firm as less committed to its sponsor role identity. Grohs and Reisinger (2014) find a negative relationship between sponsorship exposure and perceptions of the sponsoring brand. This implies that especially prominent sponsorship types (e.g., naming rights, shirt sponsorships) may be perceived negatively because they highlight the business role identity of sponsors. Thus,
H8: High-prominence sponsorship types are negatively related to (a) sponsorship fit, (b) affective motives, and (c) normative motives and positively related to (d) calculative motives.
Control Variables
In addition to the proposed effects of the conceptual framework, other variables might offer alternative explanations for any effects observed. Differences in the sponsored property, sponsor characteristics, and consumer characteristics may all play an important role. For example, the prominence of the property (e.g., measurable by the number of fans), differences in sport success, and the club image may be directly related to inferred sponsor motives, sponsorship fit, and attitude toward the sponsor. In examining the role of sponsorship partnership characteristics in Study 1, we control for variance in the sponsored properties’ characteristics. We also include firm size to rule out the potential explanation that inferred motives are more negative for larger firms, whose motives may be discounted because of their relatively higher power (Kelley 1973). At the consumer level, we include a control for existing customer relationships with the sponsor. Customers might infer more favorable motives as a result of higher commitment to the firm and better product knowledge (Lacey, Close, and Finney 2010), which provides the basis for assumptions of similarity (Kelley 1973) between the sponsor and the property. This aspect is particularly important because sponsorship research often does not control for consumer experience with a brand (Cornwell 2008). Finally, we control for fan status and sociodemographic variables of age and gender. Prior research has found that these variables are related to sponsorship or cause-related marketing perceptions (e.g., Roy and Cornwell 2004; Schons, Cadogan, and Tsakona 2015).
Study 1
Data Collection and Measures
Study 1 data comprise a survey-based representative consumer field study and descriptive information from a professional sponsorship database that includes contract length, the sponsor headquarters, and sponsorship fees. Surveys were collected by Respondi, a leading online panel provider in Germany. Criteria for representativeness of the German population were age (18–65 years), gender, and region of residence. People were invited to participate on a continuous basis within a time frame of two weeks. Because of the rolling enrollment to obtain a quota sample, calculating nonresponse bias was not possible. In total, 2,787 respondents filled out the survey and received a monetary incentive of V.70 for participation. Average response time was seven minutes and 36 seconds. The average age of the respondents was 43.3 years (SD = 13.9), and 51.7% were women. After answering introductory questions about involvement and identification (i.e., fan status), respondents indicated their familiarity with each of the 25 sport properties (i.e., teams such as FC Bayern Munich, Borussia Dortmund, and 1. FC Ko¨ln) using dichotomous recognition measures. Because clubs were preselected on the basis of size and familiarity in Germany, small second-division clubs with small regionally limited audiences were not considered. Thus, our sample reflects the characteristics of highly visible professional sports clubs and their sponsors and represents the lion’s share of the sponsorship market of soccer in Germany. Next, respondents were randomly assigned to one of the clubs they had marked as familiar to them. Random assignment helped avoid bias from social identification with a particular club. For each of the 25 clubs, two sponsors were preselected on the basis of their relevance to respondents in their consumer role. Therefore, business-to-business sponsors were not included in the survey.
Respondents were asked to sequentially evaluate up to two sponsors of the particular assigned club, based on the following approach: First, respondents were provided with an industry cue and asked to recall a sponsor of the club from the particular industry. Brand attitudes of respondents who did not recall any sponsor were significantly lower (M = 3.00, SD = 1.02) than those of respondents who recalled a sponsorship relationship (M = 3.49, SD = 1.02; p = .000). Second, the respondents evaluated brand attitude and sponsorship fit (items taken from Simmons and Becker-Olsen [2006]). Third, respondents indicated their familiarity with the sponsorship using sponsorship recognition and a single item on familiarity. Fourth, we measured perceived motives with scales adapted from Allen and Meyer (1990), Becker-Olsen, Cudmore, and Hill (2006), and Ellen, Webb, and Mohr (2006). Because motive inferences without awareness of the sponsorship are likely to depend on other brand- or club-related associations, familiarity serves as a prerequisite for sponsorship-induced motive inferences. Therefore, respondents were only asked about sponsor motives if they were at least somewhat familiar with the sponsorship (i.e., a value of two on the five-point scale). Respondents not at least somewhat familiar with the sponsor brand were routed to a new loop to evaluate the second sponsor of the club. The procedure was repeated for the second preselected sponsor for the particular club. Finally, sociodemographics (age and gender) and place of residence (postal code) were collected. Figure 2 outlines the overall consumer-level data collection process and the enhancement of the data set with the managerial-level data. We retained only sponsor partnerships with at least 20 observations in the analysis, which resulted in 2,997 evaluations of 44 sponsors.
For the sponsorship-level analysis, we drew objective data characterizing the 44 sponsorships from a professional sponsorship database (Sponsors.de) and other secondary data sources (e.g., press releases) covering 2002–2012. Searches for announcements and renewal notices produced specific numbers that we then cross-checked with publicly available data to ensure accuracy and consumer-level visibility of the sponsorship characteristics. Sponsors represented a variety of industry sectors: automotive (four), banking (five), beverage (six), consumer goods (seven), energy (three), fashion (two), gambling/lottery (two), insurance (five), pharmaceuticals (two), retailing (three), telecommunications (one), tourism (one), and transport (three).
Sponsorship partnership characteristics. We calculated contract length as the number of years a sponsor had been committed to the club (M = 7.45, SD = 9.99) and measured sponsorship fee on a yearly basis (M = V3.43 million, SD = 4.74). Because the distribution of the data was skewed, we conducted a logarithmic transformation to test whether contract length affected inference making and fit at a diminishing rate. We coded sponsorship type with two dummy variables (for 19 shirt sponsorships such as Emirates Airlines, shirt sponsor of the Hamburger SV, and nine naming-rights sponsorships such as brewery Veltins at Schalke 04), with perimeter advertising, such as outdoor fashion brand Jack Wolfskin at Mainz 05 (16 cases), as the reference category. We coded regional proximity of the sponsor with two dummy variables differentiating international (headquarters outside Germany) and local (same city or less than 30 km in distance) sponsors, with national sponsors as the reference category.
Sponsor characteristics. We measured size of the sponsoring companies by the number of employees (M = 43,192, SD = 98,704). Again, we used a logarithmic transformation because of data skewness. We did not use differences in sales or firm value because appropriately weighting the values of sponsors from different industries is difficult. Characteristics of the sponsored property. The model controls for the club’s prominence, likability, differences in success, and other differences at the level of the sponsored property by adding dummy variables. These variables control for variance attributable to the clubs.
Analysis Overview
This study uses multilevel structural equation modeling to test the relationships of the two levels of data in a single analysis, accounting for the variability associated with each level of hierarchy. The two “nested” data files represent consumer-level (n = 2,997) and sponsorship-level (n = 44) data, resulting in an average cluster size of 68. Notably, the group-level sample size (44 sponsorships) is higher than the minimum sample size of 20 typically suggested in the literature (e.g., Preacher, Zhang, and Zyphur 2011). Even so, this is still a small sample size; therefore, we interpret significant findings at the .1 level for group-effects only. A further basic premise for multilevel modeling is a sufficient variation between the groups of observations. Intraclass correlations can serve as indicators because they measure the degree of similarity within the same cluster and are recommended to be greater than .05 (Preacher, Zyphur, and Zhang 2010). The correlations calculated for the dependent variables at the consumer level are substantial for the majority of the examined variables: .03 (calculative motives), .07 (sponsorship fit), .07 (normative motives), .08 (affective motives), and .15 (brand attitude). These are sufficient to justify use of multilevel modeling. We examine the measurement reliability of the reflective constructs at the consumer level through multilevel confirmatory factor analysis using Mplus 7.4 (Muthe´n and Muthe´n 2015). Table 2 shows the results.
Composite reliabilities for the reflective constructs exceed .6, the recommended threshold (Bagozzi and Yi 1988). Moreover, findings show discriminant validity between the constructs, as none of the squared correlation coefficients between any of the constructs exceed the average variance extracted for a construct (Fornell and Larcker 1981; see Web Appendix A). We tested for common method bias following Podsakoff et al.’s (2003) recommended procedure and modeled an unmeasured latent method factor to estimate attenuated scores for composite reliability and average variance extracted (Table 2). The attenuated scores are above the required levels, leading us to conclude that common method bias is not a significant issue in the study. On the sponsorship level, correlations of the indicators are low to moderate (Web Appendix B).
Results
Consumer-level effects. The results (presented in Table 3) show that affective motives are significantly and positively related to sponsor attitude (b = .310, p = .000). Findings show no relationship between normative motives and sponsor attitude (b = –.013, p = .568). As the positive correlation indicates (Web Appendix A), the hypothesized positive effect of normative motives on brand attitude is displaced by affective motives. Attributions of calculative motives are significantly and negatively related to sponsor attitude (b = –.049, p = .018). Sponsorship fit is positively related to sponsor attitude (b = .188, p = .000). These results provide support for H1a, H3a, and H4a but not for H2a. In line with H4b, H4c, and H4g, sponsorship fit is positively related to the attribution of affective (b = .609, p = .000) and normative (b = .303, p = .000) motives but negatively related to calculative motives (b = –.091, p = .004). Furthermore, the effects of sponsorship fit on sponsor attitude are partially mediated by affective (b = .189, p = .000) and calculative (b = .004, p = .018) motives (Web Appendix C).
Cross-level effects. H5a–H8d examine the effects of variables characterizing the sponsor partnership on consumer perceptions of sponsor motives and sponsorship fit. A central finding is that contract length is significant and positively related to the intercepts (level differences) of affective motives (b = .440, p = .000), normative motives (b = .683, p = .000), and sponsorship fit (b = .625, p = .012), while attributions of calculative motives (b = –.199, p = .371) are not directly affected. The model controls for a potentially distorting effect caused by the relationship between contract length and brand attitude (i.e., that long-term sponsors differ systematically from short-term sponsors in brand attitude), but this effect is nonsignificant (b = .120, p = .552). Assessment of multilevel mediation reveals that the effect of contract length on brand attitude is fully mediated by affective motives (b = .136, p = .000) and sponsorship fit (b = .118, p = .014). In addition, the analysis finds partial mediation for contract length through sponsorship fit on affective (b = .381, p = .006), normative (b = .189, p = .008), and calculative (b = –.057, p = .043) motives. The results confirm H5a–H5c and provide evidence of an indirect effect of contract length through sponsorship fit on the perception of calculative motives (H5d).
Regarding regional proximity, the full model suggests that respondents perceive international sponsors as less fitting than national sponsors, but this finding is not significant at the .05 level (H6a, b = –.276, p = .067), and findings show no effect for local sponsors (H6a, b = –.158, p = .673). In support of H6b, respondents perceive international sponsors as having fewer affective motives (b = –.388, p = .000) than national sponsors, while they attribute more affective motives to local sponsors (b = .254, p = .018) than national sponsors. Multilevel mediation reveals that affective motives fully mediate the negative effect of international sponsor origin on brand attitude (b = –.120, p = .000). In a similar vein, affective motives fully mediate the positive effect of local origin on brand attitude (b = .079, p = .012). The results indicate no significant differences for normative motives of local (b = –.116, p = .289) or international (b = .062, p = .349) sponsors. The indirect negative effect of international sponsor origin through sponsorship fit on normative motives trends in the expected direction (H6c, b = –.084, p = .069) but is only significant at the .1 level. Related to H6d, respondents tend to perceive international sponsors as more (b = .402, p = .054) and local sponsors as less (b = –.399, p = .048) calculative than national sponsors.
TABLE: TABLE 2 Study 1: Measurement of Latent Constructs and Results of Confirmatory Factor Analysis
| Construct | Factor Loading | Composite Reliability (CMF-Attenuated Results) | Average Variance Extracted (CMF-Attenuated Results) |
|---|
| Sponsor Attitude (Simmons and Becker-Olsen 2006) | | .947 (.927) | .857 (.810) |
Please evaluate [Brand] on the basis of the following attributes: € [Brand] is very likable. € [Brand] is a very good brand. € [Brand] is a very attractive brand. | .918 .927 .932 | | |
| Sponsorship Fit (Simmons and Becker-Olsen 2006) | | .928 (.880) | .812 (.710) |
Please evaluate the connection between [brand] and [club]: € Dissimilar € similar € Not complementary € complementary € Low fit € high fit | .885 .921 .897 | | |
| Affective Motives (adapted from Allen and Meyer 1990) | | .954 (.827) | .873 (.616) |
Please evaluate the following statements about the relationship between [brand] and [club]: € [Brand] feels emotionally attached to this club. € This club has a great deal of meaning for [brand]. € [Brand] feels a strong sense of belonging to this club. | .904 .943 .955 | | |
| Normative Motives (adapted from Ellen, Webb, and Mohr 2006) | | .861 (.858) | .674 (.669) |
Please evaluate the following statements about the relationship between [brand] and [club]: € A reason for [brand] to get involved as a sponsor is that they feel a moral obligation of their environment. € [Brand] is principally engaged in the sponsorship, because they feel that it is expected from a company this size. € [Brand] is a loyal sponsor, primarily because customers, employees or other important target groups expect it. | .809 .805 .849 | | |
| Calculative Motives (adapted from Allen and Meyer 1990; Becker-Olsen, Cudmore, and Hill 2006; Ellen, Webb, and Mohr 2006) | | .928 (.925) | .866 (.860) |
Please evaluate the following statements about the relationship between [brand] and [club]: € The major motive of [brand] €€ s sponsorship is self-interest. € [Brand] sponsors [club] mainly to take advantage of it. € A reason for [brand] to sponsor [club] is that it would be too costly to terminate this partnership.a | .970 .889 | | |
| Fan status (coded 1 = fan) | € | € | € |
| Customer (coded 1 = customer) | € | € | € |
| Age | € | € | € |
| Gender (coded 1 = female) | € | € | € |
For sponsorship fee, the results indicate a positive effect on calculative motives (b = .356, p = .009), lending support to H7d. Calculative motives fully mediate the effect of sponsorship fee on brand attitude (b = –.017, p = .047). Findings show that sponsorship fees tend to affect normative motives negatively (b = –.235, p = .060) but provide only limited support for H7c. Similarly, we do not observe significant effects of sponsorship fee on sponsorship fit (b = –.354, p = .166) and affective motives (b = .009, p = .915), leading to the rejection of H7a and H7b. All other mediating links are nonsignificant as well.
TABLE: TABLE 3 Study 1: Relationship Between Sponsorship Deal Characteristics and Consumer Perceptions
| | A: Individual-Level Effects | |
|---|
| | Full Model | Mediated-Effects Model |
|---|
| | Standardized Coefficient | R2 | Standardized Coefficient | R2 |
|---|
| Sponsor Attitude | | 33.0% | | 32.8% |
| Sponsorship fit ( + ) | .188*** | | .188*** | |
| Affective motives ( + ) | .310*** | | .302*** | |
| Calculative motives ( €“ ) | -.049** | | -.054*** | |
| Normative motives ( + ) | -.013n.s. | | | |
| Fan statusa | .023n.s. | | .023n.s. | |
| Customera | | .277*** | | .277*** |
| Agea | .034** | | .033** | |
| Gendera | | .014n.s. | | .013n.s. |
| Sponsorship Fit | | 1.8% | | 1.8% |
| Fan statusa | .064*** | | .065*** | |
| Customera | .108*** | | .106*** | |
| Agea | -.042** | | -.042** | |
| Gendera | -.001n.s. | | -.002n.s. | |
| Affective Motives | | 40.3% | | 40.2% |
| Sponsorship fit ( + ) | .609*** | | .609*** | |
| Fan statusa | .024* | | .025* | |
| Customera | .096*** | | .094*** | |
| Agea | .077*** | | .077*** | |
| Gendera | .066*** | | .066*** | |
| Normative Motives | | 10.6% | | 10.6% |
| Sponsorship fit ( + ) | .303*** | | .302*** | |
| Fan statusa | .035** | | .035** | |
| Customera | .058*** | | .058*** | |
| Agea | .067*** | | .068*** | |
| Gendera | .042** | | .043** | |
| Calculative Motives | | 2.7% | | 2.7% |
| Sponsorship fit ( €“ ) | -.091*** | | -.092*** | |
| Fan statusa | .014n.s. | | .014n.s. | |
| Customera | .019n.s. | | .018n.s. | |
| Agea | .073*** | | .075*** | |
| Gendera | -.106*** | | -.105*** | |
TABLE: TABLE 3 Continued
| B: Sponsor-Partnership Level Effec |
|---|
| | Full Model | Mediated-Effects Model |
|---|
| | Standardized Coefficient | Standardized Coefficient |
|---|
| Affective Motives b |
| Sponsorship fee | .009n.s. | |
| International sponsor | -.388*** | -.360*** |
| Local sponsor | .254** | .164n.s. |
| Contract length | .440*** | .416*** |
| Sponsorship type (naming rights) | .151n.s. | |
| Sponsorship type (shirt) | .002n.s. | |
| Firm size | -.220*** | -.209** |
| Normative Motivesb |
| Sponsorship fee | -.235* | -.344*** |
| International sponsor | .062n.s. | |
| Local sponsor | -.116n.s. | |
| Contract length | .683*** | .805*** |
| Sponsorship type (naming rights) | -.034n.s. | |
| Sponsorship type (shirt) | -.102n.s. | |
| Firm size | -.282*** | -.342*** |
| Calculative Motives b |
| Sponsorship fee | .356*** | .476*** |
| International sponsor | .402* | .499*** |
| Local sponsor | -.399** | -.370* |
| Contract length | -.199n.s. | |
| Sponsorship type (naming rights) | .196* | .223** |
| Sponsorship type (shirt) | .171n.s. | |
| Firm size | .255n.s. | |
| Global fit indices | CFI = .990; TLI = .986; RMSEA = .015; SRMR (within) = .023; SRMR (between) = .043 | CFI = .989; TLI = .984 RMSEA = .016; SRMR (within) = .022; SRMR (between) = .045 |
Finally, the analysis shows that sponsorship type (naming right) tends to increase the attribution of calculative motives (b = .196, p = .053) but provides only limited support for H8d. All other effects of sponsorship type on the perceptions of sponsor motives and sponsorship fit and all other mediation relationships are not significant. Therefore, H8a–H8c are rejected.
Other Effects
Consumer-level effects. Fan status is positively related to the evaluations of sponsorship fit (b = .064, p = .003) and normative motives (b = .035, p = .012), while its effect on affective motives (b = .024, p = .053) and calculative motives (b = .014, p = .517) is nonsignificant. These findings suggest that consumers perceive greater congruence between sponsors and teams when they are fans of that team. In addition, respondents who were customers of the sponsor at the time of the survey show significantly higher evaluations of sponsorship fit (b = .108, p = .000) and the sponsor’s affective (b = .096, p = .000) and normative (b = .058, p = .005) motives, while findings indicate no significant differences for calculative motives (b = .019, p = .322). These findings imply that existing relationships with firms result in more favorable evaluations of firm intent and obligation in sponsorship. Female respondents evaluate affective (b = .066, p = .000) and normative (b = .042, p = .042) motives more positively. Conversely, male respondents show high values for calculative motives (b = –.106, p = .000). The results show no significant gender difference in terms of sponsorship fit (b = –.001, p = .954). These results suggest that men are more critical than women in their evaluation of sponsor motives. Findings also show significant age effects on all dependent constructs. Sponsorship fit shows lower values with increasing age (b = –.042, p = .032), and agreement on all sponsor motives is higher with increasing age (affective: b = .077, p = .000; normative: b = .067, p = .001; calculative: b = .073, p = .000).
Sponsor characteristics. Size of the sponsor is negatively related to the attribution of affective (b = –.220, p = .000) and normative (b = –.282, p = .002) motives and significantly affects brand attitude (b = .416, p = .071) at the .1 level. Relationships to sponsorship fit (b = –.013, p = .937) and calculative motives (b = .255, p = .126) are not significant. These findings suggest that consumers consider motives of large firms less positively because they view these firms as less affectively and normatively motivated than smaller firms.
Discussion
Study 1 shows that consumers differentially assess the motives for corporate sponsorship and that important outcomes are largely determined by their assessment of those motives. Finding a positive relationship between affective motives and sponsor brand attitude reflects the notion that consumers receive sponsorship “in a halo of goodwill” (Meenaghan 2001, p. 101). In contrast, calculative motives are negatively related to sponsor brand attitude. On the individual level, while the effect of calculative motives is weaker than the effect of affective motives, consumers do perceive variance with regard to a commercial or selfish intent of a sponsorship. In the data, inference of normative motives does not beget a positive attitude toward the brand. This finding may be explained by research on the relationship between the NBA and its child-supporting beneficiary sponsorship “NBA cares.” Research has found that consumers (i.e., ticket purchasers) expect a professional sports team to engage in community social responsibility. Thus, a firm doing things an audience already thinks it should be doing may not yield positive affect (Lacey, Kennett-Hensel, and Manolis 2015).
At the sponsor partnership level, the results shed light on why motives are inferred. In particular, the objective characteristics of sponsorship deals are reflected in terms of significantly different evaluations of affective, normative, and calculative motives and sponsorship fit. Importantly, consumers value sponsors that commit to long-term relationships. Shortterm sponsorships trigger an inference of calculative motives through sponsorship fit. The effects for regional proximity suggest that consumers appreciate regionally related brands but view national and international sponsors as less affectively motivated. Sponsorship fees are also negatively related to the attribution of a sponsor’s normative motives and positively related to calculative motives. Apparently, consumers question the motives of firms associated with high sponsorship spending. This finding is important because the analysis controls for the alternative explanation that large firms may be automatically associated with negative motives. The analysis provides compelling evidence that people perceive large firms as being less affectively and normatively committed. Sponsorship type does not play a major role in the attribution of sponsor motives, with one exception—respondents perceive naming-rights sponsors as more calculative.
Study 1 was a field study in which motives were inferred from respondents’ memory about the partners in the sponsorship.
The strength of the field study is that all information available to a person when assessing the partners serves as input to the motive inference. As with any field study, however, alternative explanations stemming from unmeasured variables (e.g., preexisting attitudes toward sponsors) or other sponsorship-related aspects might account for the effects observed. Therefore, we aim to replicate the results in an experimental field study with a fictitious sponsor in Study 2. This study also uses a different sport and considers the sport property’s characteristics as well as sponsor and club loyalty.
Study 2
Empirical Approach
Study 2 is a between-subjects experimental study, in which we manipulated key partnership characteristics (i.e., contract length, regional proximity, and sponsorship fee) and examined effects on the sponsored property. We held sponsorship type constant because we observed no strong differences in Study 1. To account for potential industry sector differences and sport success of the sponsored property, we also manipulated sponsorship fit and sport success. To control for noise related to sponsor brand equity, we employed a fictitious brand and chose handball as the context of the study for three reasons. First, sponsorship is a relevant revenue stream for handball clubs, which are professional clubs that pay their players. Second, handball is less popular than soccer, and therefore knowledge about sponsorship deals is weaker, allowing us to credibly manipulate sponsor partnership characteristics. Third, this lower-profile sport enables us to examine whether the findings of Study 1 hold in a different environment and also whether important differences can be identified. We collected survey-based data through the online panel provider Respondi with separate samples for each of the two pretests and the main study. Sampling requirements and incentives were comparable to Study 1. All items were measured on seven-point scales (Likert-type or semantic differentials). Two pretests identified two handball clubs that differed in sport success, two industry sectors with high and low sponsorship fit, distance perceptions of different regions, and a fictitious brand; the pretests also served to test manipulations for the main study. Web Appendix D reports the results.
Scenarios. Study 2 is a 2 · 2 · 3 · 2 · 2 between-subjects factorial experimental study, with manipulated levels of contract length, regional proximity, and sponsorship fee, as well as sponsorship fit and sport success of the club. We designed this study primarily as a main-effects study, with the central goal being to replicate the field study findings under controlled conditions. From the fictitious press release used in the second pretest, the manipulations resulted in 48 different press releases (for a full description of the design, see Web Appendix E).
Procedure and respondents. The main study comprised 576 respondents (average age 43.19 years, SD = 13.91; 44.6% female). They were first asked to indicate their involvement with handball and then randomly assigned to one of the 48 scenarios. After exposure to a scenario, respondents evaluated the dependent variables of attitude and behavioral intentions toward the handball club and sponsor. Next, they assessed the affective, normative, and calculative motives of the sponsor and sponsorship fit. In both sections, we randomized construct order to avoid any order effects and conducted manipulation checks. Finally, we collected control variables (e.g., fan status), demographics, and postal code. The questionnaire concluded with the disclosure of the hypothetical nature of the scenarios.
Manipulation checks were successful, given the significance of the mean value differences of contract length (Mlong - short = 2.72; p = .000), regional proximity (Mreg - nat = 2.15, Mreg - int = 2.42, Mnat - int = .26; p = .000), sponsorship fee (Mhigh - low = 3.04; p = .000), sponsorship fit (Mhigh - low = .58; p = .000), and success in sports (Mmore - less = .63; p = .000). We used the same constructs as in Study 1 but measured them on seven-point Likert-type scales. In addition, we included measures for loyalty intention related to the sponsor and the sponsored property. We measured sponsor loyalty with two items (“It is very likely that I will buy products of [sponsor] in the future” and “It is very likely that I will recommend [sponsor] to my friends and colleagues in the future”) in accordance with Vogel, Evanschitzky, and Ramaseshan’s (2008) scale. We adapted the measure from Biscaia et al. (2013) and extended it to our context to measure club loyalty. Respondents were asked to answer the following questions: “It is very likely that I will visit a match of club X in the future,” “It is very likely that I will recommend club X to my friends and colleagues,” “It is very likely that I will purchase tickets of club X in the future,” “It is very likely that I will purchase merchandise (e.g., a scarf, a jersey) of club X in the future,” “It is very likely that I will watch games of club X on the television in the next season,” and “It is very likely that I will follow club X on its social media channels (e.g., Facebook, Twitter).” The scales are reliable for sponsorship fit (Cronbach’s a = .81); affective (a = .96), normative (a = .87), and calculative (a = .84) motives; club (a = .95) and brand (a = .94) attitude; loyalty toward the club (.93); and loyalty toward the brand (.93). Descriptive statistics and correlations appear in Web Appendix F.
Results
Multivariate analysis of variance (MANOVA) results show significant multivariate effects for the interaction between length and regional proximity (Wilks’ l = .944, F = 1.896, p = .018). No other interactions are significant. The results show significant main effects for contract length (Wilks’ l = .906, F = 6.737, p = .000), regional proximity (Wilks’ l = .898, F = 3.593, p = .000), sponsorship fee (Wilks’ l = .951, F = 3.338, p = .001), sponsorship fit (Wilks’ l = .943, F = 3.902, p = .000), and sport success (Wilks’ l = .930, F = 4.894, p = .000).
Follow-up analyses of variance revealed that the interaction between length and regional proximity is significant for affective motives (p = .036), sponsor attitude (p = .007), club attitude (p = .013), sponsor loyalty (p = .012), club loyalty (p = .031), and sponsorship fit (p = .001). Moreover, main effects of sponsorship fee on calculative motives (p = .024) and sponsorship fit (p = .003) are significant. Contract length has significant main effects on affective motives (p = .000), calculative motives (p = .000), sponsor attitude (p = .007), and sponsorship fit (p = .011). Regional proximity is significantly related to affective motives (p = .000), calculative motives (p = .026), and sponsorship fit (p = .000). Sport success shows significant effects on club attitude (p = .000) and club loyalty (p = .015), while sponsorship fit is significantly related to normative motives (p = .043) and is successfully manipulated by its relationship to the perception of sponsorship fit (p = .000). Web Appendix G gives descriptive statistics.
For regional (p = .045) and international (p = .023) sponsors, sponsorship fit is higher when contract length is high, lending support to H5a. Contract length is unrelated to sponsorship fit for the national sponsor (p = .245). Contract length also leads to attribution of affective motives—this effect is stable for the regional (p = .032) and national (p = .019) sponsors and is especially pronounced for the international sponsor (p = .000). These findings provide support for H5b. The results show no effect of contract length on normative motives (H5c). However, calculative motives are inferred for short sponsorships (p = .000), lending support to H5d. In addition, findings establish positive effects of contract length on sponsor attitude (p = .007).
For regional proximity, the results indicate four interaction effects of contract length on sponsorship fit, affective motives, sponsor attitude, and club attitude. Respondents perceive short-term partnerships of regional sponsors as congruent as short-term partnerships of national sponsors and long-term partnerships of international sponsors. Sponsorship fit is highest for regional and long-term partnerships, in support of H6a. Affective motives and fit of national sponsors are better for national than international sponsors when contract length is short. This effect is reversed for long-term sponsorships. Apart from contract length, affective motives of regional sponsors are higher than those of national and international sponsors.
These results provide mixed support for H6a and H6b. The results show no significant effects of regional proximity on normative motives (H6c). The effect on calculative motives indicates that the more distant sponsors are from the sponsored property, the more they are perceived as calculative (H6d, p = .026). Sponsor attitude generally increases with contract length but remains the same for national sponsors. Although the interaction between contract length and regional proximity of regional and international sponsors does not directly influence club attitude, we find a negative effect for national sponsors.
In line with our theoretical reasoning, consumers perceive higher sponsor spending as more calculative (H7d). Contrary to H7a and Study 1’s results, findings show a positive effect of sponsorship fee on sponsorship fit for the professional, yet lower-tier, sport of handball. The results show no direct significant effects of sponsor spending on affective (H7b) and normative (H7c) motives. Sport success shows a positive effect on club attitude, meaning that loyalty toward a club is indirectly influenced by the success of a team.
As in Study 1, we estimated a structural equation model in which we modeled all significant main effects reported in the MANOVA on the conceptual model (Table 4). The results show that sponsor loyalty is positively affected by affective motives (H1c), sponsorship fit (H4e), and contract length, and the effects are mediated by sponsor attitude. In contrast with Study 1, normative (H2a) and calculative (H3a) motives are not significantly related to sponsor attitude, which is influenced by sponsorship fit (H4a), affective motives (H1a), and contract length. Findings show a direct and significant negative effect of calculative motives on club loyalty (H3d). Club loyalty is also indirectly influenced by affective motives (H1d), sponsorship fit (H4f), and sport success through club attitude (Web Appendix H). As the MANOVA shows, the manipulations affect sponsorship fit and the three motive dimensions.
TABLE: TABLE 4 Study 2: Relationship Between Sponsorship Deal Characteristics and Consumer Perceptions
| | Full Model | Mediated-Effects Model |
|---|
| | Standardized Coefficient | R2 | Standardized Coefficient | R2 |
|---|
| Sponsor Loyalty | | 31.7% | | 29.6% |
| Sponsor attitude ( + ) | .482*** | | .544*** | |
| Sponsorship fit ( + ) | .077n.s. | | | |
| Affective motives ( + ) | .040n.s. | | | |
| Calculative motives ( €“ ) | -.039n.s. | | | |
| Normative motives ( + ) | .054n.s. | | | |
| Club Loyalty | | 18.5% | | 16.4% |
| Club attitude ( + ) | .334*** | | .352*** | |
| Sponsorship fit ( + ) | .049n.s. | | | |
| Affective motives ( + ) | .096* | | .095** | |
| Calculative motives ( €“ ) | -.089** | | -.061*** | |
| Normative motives ( + ) | .052n.s. | | | |
| Sport success ( + ) | .002n.s. | | | |
| Sponsor Attitude | 28.0% | | 27.6% | |
| Sponsorship fit ( + ) | | .341*** | | .357*** |
| Affective motives ( + ) | .224*** | | .208*** | |
| Calculative motives ( €“ ) | .048n.s. | | | |
| Normative motives ( + ) | .065n.s. | | | |
| Contract length ( + ) | .085** | | .081** | |
| Local sponsor ( + ) | -.030n.s | | | |
| International sponsor ( €“ ) | -.028n.s | | | |
| Club Attitude | | 19.3% | | 18.9% |
| Sponsorship fit ( + ) | .251*** | | .262*** | |
| Affective motives ( + ) | .143** | | .128** | |
| Calculative motives ( €“ ) | .043n.s. | | | |
| Normative motives ( + ) | .042n.s. | | | |
| Sponsorship fit (manipulated) ( + ) | .008n.s. | | | |
| Sport success ( + ) | .226*** | | .226*** | |
| Sponsorship Fit | | 11.0% | | 11.2% |
| Contract length ( + ) | .086** | | .088** | |
| Local sponsor ( + ) | .146*** | | .147*** | |
| International sponsor ( €“ ) | -.074n.s. | | -.077n.s. | |
| Sponsorship fee ( €“ ) | .126*** | | .128*** | |
| Sponsorship fit (manipulated) ( + ) | .201*** | | .202*** | |
| Sport success ( + ) | .079* | | .079* | |
| Affective Motives | | 39.2% | | 39.0% |
| Sponsorship fit ( + ) | .542*** | | .552*** | |
| Contract length ( + ) | .153*** | | .151*** | |
| Local sponsor ( + ) | .159*** | | .149*** | |
| International sponsor ( €“ ) | -.015n.s. | | -.001n.s. | |
| Normative Motives | 3.5% | | 3.4% | |
| Sponsorship fit ( + ) | .165*** | | .183*** | |
| Sponsorship fit (manipulated) ( + ) | .061n.s. | | | |
| Calculative Motives | | 6.2% | | 5.7% |
| Sponsorship fit ( €“ ) | -.141*** | | -.151*** | |
| Contract length ( €“ ) | -.152*** | | -.149*** | |
| Local sponsor ( €“ ) | -.033n.s. | | | |
| International sponsor ( + ) | .044n.s. | | | |
| Sponsorship fee ( + ) | .102*** | | .105*** | |
| Global fit indices | CFI = .933; TLI = .918; RMSEA = .067; SRMR = .059 | | CFI = .932; TLI = .922; RMSEA = .066; SRMR = .066 | |
General Discussion
This research establishes a link between sponsor partnership characteristics and consumer evaluations of sponsorships and sheds light on the mediating roles of perceived fit and motive attributions. Table 5 summarizes findings of the two studies. In general, the results of the experimental study provide support for the effects observed in the field study.
The first important finding is that consumer inference making about sponsor motives affects sponsorship outcomes both directly and indirectly. Both studies show that sponsorship fit and the attribution of affective motives result in positive attitudes toward the sponsor. We find that affective motives matter more for sponsorship outcomes in highprofile sports such as soccer than in less prominent sports such as handball. Affective motives are potentially more appreciated in a high-profile sport in which commercialization is ever present. In contrasting findings, sponsorship fit plays a major role in handball but matters less in soccer. This is likely due to the narrow draw of handball. Though popular in Germany, it is not a universal sport and has limited universal sponsor appeal. These findings may also be due to the use of a fictitious sponsor for which inference making relies heavily on the product category when brand information is unfamiliar. In Study 1, calculative motive attributions show negative effects on attitude, thus confirming the importance of calculative motives in high-profile sport contexts. In Study 2, the finding that calculative motives negatively affect loyalty toward the sponsored property may relate to consumers blaming clubs for “selling out” to the highest bidder. Study 2 provides further evidence that affective motive attributions affect sponsored properties. In both contexts, normative perceptions seem to be displaced by affective motives in their role as antecedents of sponsorship outcomes. As a theoretical explanation, the MIM suggests that the no-choice condition of normative motives leads to positive trait attributions as well.
Second, this research shows that deal-making characteristics significantly influence consumer inference making about sponsor partnerships. Both studies find that managerial dealmaking decisions contribute to sponsorship fit perceptions and motive attributions. With regard to duration, both studies show positive effects of contract length on sponsorship fit. In addition, sponsors are more affectively motivated when sponsorship contracts are longer and the consumer perceives the sponsor as a better-fitting relationship partner. Study 1 finds that consumers infer normative motives when sponsors commit themselves over a longer period. Notably, the results imply that long-term partnerships are not per se perceived as more favorable. Rather, the findings suggest that sponsorship fit and the inferred affective motives act as mediators in the improvement of sponsorship outcomes. Furthermore, both studies show that longer contract length helps reduce the attribution of calculative motives.
Both studies find evidence that regional proximity contributes to higher sponsorship fit and attribution of affective motives and dampens inference of calculative motives. Study 1 shows that sponsorship fit perceptions are higher if the sponsor is not international, and Study 2 finds that local sponsors fit better. Consumers appear to view regional and national brands as acting responsibly in their role as sponsor and perceive international sponsors as more calculative. Following an indirect path, sponsorship fit and brand attitude (Study 1) are more negative for international sponsors, whereas local and national sponsors (Study 1) are more favorably perceived in terms of affective motives, which in turn positively influence brand attitude and sponsorship fit (Study 2). These findings are in line with prior work reporting significant, positive effects of perceived geographic similarity on sponsorship fit (Olson and Thjømøe 2011). Study 2 offers a more differentiated view of the role of regional proximity in relation to contract length, in that international and regional sponsors profit from a long-term commitment differently than national sponsors. A potential explanation is that by committing for a longer time, an international sponsor raises perceptions, bringing them closer to those typical for a national sponsor. The results suggest that contract length can effectively counterbalance adverse origin perceptions. Nevertheless, both national and international sponsors may face a ceiling on perceptions that can be bettered by regional sponsors.
Studies 1 and 2 also show that higher sponsorship fees are associated with the attribution of calculative motives. Expensive engagements are clearly more prominent and visible; therefore, consumers might perceive more costly sponsorships as being linked to higher sponsor expectations of return on investment. Subtle persuasion attempts common with smaller fees are less likely to generate resistance to communication (Carrillat and d’Astous 2012), particularly with lower-profile professional sports. Important differences emerge for the effects of sponsorship fee on other variables. Study 1 finds a negative effect of high sponsorship fees on normative motives. In contrast, Study 2 finds positive effects of higher sponsorship fee on affective and normative motives mediated by sponsorship fit. Both effects are readily interpreted through a contextual lens. Sponsorship fees in the soccer context are notorious for their excess, whereas consumers view handball fees as keeping the sport alive.
The finding in Study 1 of an effect of naming-rights sponsorships on calculative motives is in line with research reporting negative fan reactions to stadium renaming, which can be perceived as commercially oriented and threatening to fan identity (Woisetschla¨ger, Haselhoff, and Backhaus 2014). This finding suggests that differences in sponsorship types can be more relevant when leveraging strategies are deployed, in that spending more to secure the sponsorship may amplify possible negative perceptions. Despite important differences observed, the conceptual model works well in high-level and less prominent sports.
Managerial Implications
The key implication for sponsorship management is that managers of sponsors and sponsored properties should think about deal characteristics from a broad-based communications perspective. By establishing linkages between the fundamentals of the relationship and consumer perceived fit and inferred motives, this research shows that the importance of sponsorship deal characteristics stretches beyond the relationship of the sponsor and the sponsored property to affect consumers. Managers who regard sponsorship relationship announcements in popular press and trade publications as simply communicating facts should instead think of them as communicating about sponsor motives. This might lead to differently crafted communications.
Because sponsorships are typically renewed intermittently and sponsorship relationships change over time, communication of long-term sponsorships should emphasize the ongoing nature and commitment of the relationship. Furthermore, managers might emphasize the objective of sponsorship longevity in both sponsorship selection and decisions regarding possible terminations. For partnerships in both studies, shortterm strategies significantly and negatively affected brand attitudes, behavioral intentions, and the perception of sponsorship fit. This finding does not mean that short-term sponsorship contracts are negative per se. Rather, managers should trade off not only between short-term flexibility and long-term stability (and perhaps annual savings) but also between other losses of short-term relationships and other advantages of longterm relationships, such as avoidance of potentially negative motive inferences. As such, more could be done to actively mitigate any negative impressions. For example, attitudes are negatively influenced when consumers perceive sponsors as engaging in a partnership out of self-serving motives, as suggested by short-term commitments.
The results also reveal that sponsorship characteristics affect the sponsored property. Overall, these findings suggest that club managers should not treat sponsorships as purely revenuegenerating activities. Instead, clubs need to be aware that sponsorship decision making conveys messages to fans and other stakeholders. Similar to sponsors, sponsored properties should prefer long-term commitments to short-term sponsorship deals and weight the value of regional partnerships differently than (inter)national sponsors. Short-term contracts can harm the brand of the property (Campbell 2010). Additional benefits of long-term sponsorships include overall lower search, setup, and learning costs for new partners, as well as better working relationships with existing partners. Therefore, both sides of the partnership should seek long-term relationships.
This research suggests that sponsors of high-profile sports properties should strategically address any negative effects of high sponsorship fees. Sponsorship fees will differ depending on the size and prominence of sponsored teams and their media coverage. Sponsorship fees may also allow properties to invest in players, coaches, facilities, training, or injury prevention. Sponsorship partners could clearly explain and emphasize to their audiences the benefits from sponsorship spending. Storytelling around the sponsorship spend may provide additional information for inference making. Fees could also be mentioned in the context of expenditures for other marketing investments to relativize their absolute level. The implications from the low-profile sports handball are twofold—while sponsors can benefit from a positive perception of high sponsorship fees, loyalty toward the club is affected negatively. Club managers should therefore actively try to avoid perceptions of sponsor investments being overly dominant.
TABLE: TABLE 5 Overview of Hypotheses and Findings
| | Dependent Variables |
|---|
| | Affective Motives | Normative Motives | Calculative Motives | Sponsorship Fit | Sponsor Attitude | Sponsor Loyaltyb | Club Attitudeb | Club Loyaltyb |
|---|
| Hypothesized Relationship | S1 | S2 | S1 | S2 | S1 | S2 | S1 | S2 | S1 | S2 | S2 | S2 | S2 |
|---|
| a Measured in Study 1 only. |
| b Measured in Study 2 only. |
| Individual-Level Effects |
| H1a€“H1d: Affective motives | | | | | | | | | + | + | + | + | + |
| H2a€“H2d: Normative motives | | | | | | | | | X | X | X | X | X |
| H3a€“H3d: Calculative motives | | | | | | | | | €“ | X | X | X | €“ |
| H4a€“H4g: Sponsorship fit | + | + | + | + | | | €“ | €“ | + | + | + | + | + |
| Sponsor-Partnership Level Effects |
| H5a€“H5d: Contract length | + | + | + | X | €“ | €“ | + | + | + | + | + | + | + |
| H6a€“H6d: Reg. proximity: local | + | + | X | X | €“ | X | X | + | + | + | + | + | + |
| H6a€“H6d: Reg. proximity: international | €“ | X | €“ | X | + | X | €“ | X | €“ | X | X | X | X |
| H7a€“H7d: Sponsorship fee | X | + | €“ | + | + | +/€“ | X | + | €“ | + | + | + | +/€“ |
| H8a€“H8d: Sponsorship type: shirta | X | | X | | X | | X | | X | | | | |
| H8a€“H8d: Sponsorship type: naming rightsa | X | | X | | + | | X | | X | | | | |
Another important implication for sponsorship management is that consumers perceive sponsor motives more positively when the sponsor is near a sponsored property. Local sponsors also benefit from consumer beliefs that regionally active sponsors are less selfish. Our data show that being an international sponsor indirectly harms the perceptions of sponsorship fit and, in turn, brand attitude, which is a key performance indicator of sponsorship success. One strategy for an international firm might be to emphasize local or regional operations and employees or even employees originally from the region. Furthermore, our results suggest that national and international sponsors should be able to mitigate origin-induced challenges by seeking long-term partners and designing agreements in a financially sensible way. Finally, sponsorship type exerts only limited influence on the attribution of sponsor motives and sponsorship outcomes. Venue namingrights partners need to be careful about their presentation, as this sponsorship type tends to be associated more with calculative motives than conventional perimeter advertising. Given that inference of affective motives seems to matter particularly in high-profile sports, sponsorship management should pay special attention to deal-making decisions, with contract length as the most effective lever of sponsor outcomes.
Limitations and Further Research
As with all empirical studies, the research has some limitations that offer avenues for further research. The results are crosssectional, and biases due to pooling of data are possible. Thus, further research could analyze the effects of sponsorship deal characteristics on sponsorship outcomes over time to better explain the dynamic interplay of strategic actions and perceptions. In addition, the study focuses on sport sponsorships in one country and two sports.
While there are advantages in the different designs used in the two studies, limitations should also be considered. Because the respondents in Study 1 reacted to actual sponsorship partnerships with all the concomitant communications surrounding sponsorship, they may have been influenced by a negativity bias (Rozin and Royzman 2001), in which negative information weighs more heavily than positive information in mental assessments. Study 2 addresses this concern in part by using a fictitious sponsor.
Further work in contexts such as cultural or cause-related sponsorships would be helpful in judging the generalizability and boundary conditions of the current findings. Our model is limited to fundamental variables that describe the partnership deal. We suggest including other managerial aspects of sponsor partnerships, such as sponsorship leveraging and activation and potential interdependencies between a firm’s multiple sponsoring activities. Further research might also consider the extent of any international firm’s role and success in a market as relevant to perceptions.
This research focuses on motives attributed to sponsors, but it is also possible that motives attributed to the sport property could influence overall perceptions, perhaps negatively, if, for example, the sport team or club owner is judged as having calculative motives. Furthermore, aspects such as sports enthusiasm, perceived sports attractiveness, the perception of a sponsor’s community involvement, and attitude toward media and advertising (Burnett, Menon, and Smart 1993; Close et al. 2006; Cornwell and Relyea 2000) warrant further investigation as constructs that may influence motive attributions. With regard to the mechanisms through which sponsorship characteristics shape inferred motives, an inclusion of trust and commitment as key characteristics of the quality of the relationship between sponsor and club (Farrelly and Quester 2005) could provide additional insights. In particular, research could test whether a strong commitment by the partners to their respective roles also results in consumer perceptions of a high-quality relationship. We also suggest adding dependent variables such as word of mouth and purchase behavior, because deal-level characteristics could affect these outcomes as well.
In addition, for reasons of model complexity, this study rules out differences in the level of the sponsored property by including dummy variables. A substantial amount of variance in sponsor motives and sponsorship fit can be attributed to the characteristics of the sponsored property. Numerous factors, such as differences in sport property identities, could contribute to the observed effects. Thus, research could go beyond the factors examined herein to consider how individual factors, such as differences in prominence, likability, coverage in the press, and the presence of charismatic players, coaches, owners, and representatives of teams, might influence inferred motives. Greater understanding of the role of the sponsored property would allow managers to draw conclusions about the selection and management of sponsor partnerships. Sponsoring and the aspects of a sponsorship relationship examined in this research are the defining characteristics, but they are only part of a firm’s sponsorship-linked communications platform. Perceptions could be shifted by other marketing communications. Thus, research could consider how collateral communications beyond sponsorship characteristics and fit could influence motive inferences.
Overall, the findings suggest the need for research to address the mitigation or emphasis of deal-level inferences that influence individual-level outcomes of sponsorship. The results of both the field study and the experimental field study clearly indicate that consumers infer motives from sponsor-partnership characteristics. Sponsorship management should therefore take seriously any decisions about contract duration, sponsorship fees, and the regional focus of their sponsorships, as well as the communication about these characteristics.
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TABLE 1 Selective Research Considering Managerial-Level Antecedents and Consumer-Level Outcomes
Notes: CrM = cause-related marketing.
Notes: Control variables are modeled to affect all endogenous constructs at the individual-level model (gray boxes).
FIGURE 2 Study 1: Data Collection Procedure
The minimum level of familiarity required was defined as a value of two on a five-point Likert scale.
TABLE 2 Study 1: Measurement of Latent Constructs and Results of Confirmatory Factor Analysis
This item was eliminated as a result of low factor loading. Notes: CMF = common method factor. N = 2,997. Goodness-of-fit statistics: comparative fit index (CFI) = .987; Tucker–Lewis index (TLI) = .982; root mean square error of approximation (RMSEA) = .032; square root mean residual (SRMR) (within) = .027; SRMR (between) = .082.
TABLE 3 Study 1: Relationship Between Sponsorship Deal Characteristics and Consumer Perceptions
TABLE 3 Continued
Notes: N = 2,997 (consumer level); N = 44 (sponsorship level). Two-tailed tests of significance.
TABLE 4 Study 2: Relationship Between Sponsorship Deal Characteristics and Consumer Perceptions
Notes: N = 576. Two-tailed tests of significance.
TABLE 5 Overview of Hypotheses and Findings
Notes: S1 = Study 1; S2 = Study 2; + = significant positive (mediated) effect; - = significant negative (mediated) effect; X = no relationship.
GRAPH: FIGURE 1 Conceptual Model: Sponsorship Deal Characteristics and Consumer Perceptions
DIAGRAM
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Record: 99- In-Store Mobile Phone Use and Customer Shopping Behavior: Evidence from the Field. By: Grewal, Dhruv; Ahlbom, Carl-Philip; Beitelspacher, Lauren; Noble, Stephanie M.; Nordfält, Jens. Journal of Marketing. Jul2018, Vol. 82 Issue 4, p102-126. 25p. 1 Black and White Photograph, 1 Diagram, 8 Charts, 2 Graphs. DOI: 10.1509/jm.17.0277.
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In-Store Mobile Phone Use and Customer Shopping Behavior: Evidence from the Field
This research examines consumers’ general in-store mobile phone use and shopping behavior. Anecdotal evidence has suggested that mobile phone use decreases point-of-purchase sales, but the results of the current study indicate instead that it can increase purchases overall. Using eye-tracking technology in both a field study and a field experiment, matched with sales receipts and survey responses, the authors show that mobile phone use (vs. nonuse) and actual mobile phone use patterns both lead to increased purchases, because consumers divert from their conventional shopping loop, spend more time in the store, and spend more time examining products and prices on shelves. Building on attention capacity theories, this study proposes and demonstrates that the underlying mechanism for these effects is distraction. This article also provides some insights into boundary conditions of the mobile phone use effect.
Online Supplement: http://dx.doi.org/10.1509/jm.17.0277
According to the Pew Research Center (2018), 95% of U.S. consumers own a mobile phone, and nearly 77% own a smartphone; the rates are even higher among young consumers. Penetration rates have reached approximately 65% worldwide and 84% in Europe (GSMA 2017). For many consumers, their mobile devices are tools “they couldn’t live without” (Horrigan and Duggan 2015); they rely on these devices for texts, voice or video calls, and access to the Internet, email, social networks, and games. Adults spend nearly six hours daily consuming digital media, and almost half of that consumption comes from mobile devices (eMarketer 2017). In turn, retailers and brands use mobile channels to communicate with consumers.
Consumers depend so much on their mobile devices for information and engagement though that they may become distracted from reality. According to the National Safety Council (2015), mobile phone use causes three million automobile crashes annually, prompting the need for digital highway signs that remind drivers, “No texting and driving.” This form of distraction stems from the human brain’s inability to focus on multiple tasks simultaneously; it also implies some negative impacts for retailers, especially those that rely on impulse purchases. That is, rather than browsing impulse offerings (e.g., candy, magazines, beverages) while waiting in line at checkout counters, modern consumers often use the downtime to scan information on their mobile devices (i.e., mobile blinders), without ever looking up to notice the point-of-purchase displays.
Other negative effects of in-store mobile phone use have been reported as well, including reduced consumer recall of instore marketing stimuli (Bellini and Aiolfi 2017), a failure to accomplish shopping goals (Atalay, Bodur, and Bressoud 2017), and loss of trust in brick-and-mortar stores if consumers find lower prices through their phones (Broeckelmann and Groeppel-Klein 2008). In contrast, in-store mobile phone use might evoke positive effects, such as expanded information search capabilities, wider evaluations of alternatives (Burke 2002), and greater redemption of coupons sent to mobile devices (Hui, Inman, et al. 2013; Klabjan and Pei 2011). However, we know of no studies that investigate the influence of consumers’ general in-store mobile phone use on sales, such that the pertinent effects throughout the store (not just on impulse purchases near checkout) remain uncertain.
Consumers often multitask by reviewing information on their mobile phones while they shop. Some retailers might try to discourage this behavior, fearing the same type of negative effects that arise in impulse categories. But we propose that mobile phone use could increase retailer sales, owing to consumer distraction. That is, because consumers perform multiple tasks (shopping and using mobile devices), their processing abilities diminish, such that these distracted consumers spend more time in stores, spend more time in front of product and information displays on shelves, and wander away from a set path more often. For retailers, these behaviors can translate into additional sales, especially to consumers who have diminished abilities to multitask because of their limited attentional capacity.
In examining these generalized effects of mobile phone use in greater detail, we also establish some boundary conditions. For example, as we noted in the opening paragraph, the adoption of mobile devices in daily life is vast and spans all demographic groups. Approximately 85% of U.S. customers older than 65 years of age own a mobile phone, and nearly half of them use smartphones (Pew Research Center 2018). But demographic characteristics strongly influence consumer behaviors (Mittal and Kamakura 2001), so we consider the influence of age. We also examine how the purpose of the mobile phone use (i.e., related to the shopping task or not) and the location in the store where consumers use their mobile phones (e.g., different food departments) affect shopping. Finally, we assess whether the distractions provided by phones decrease shopping satisfaction (because consumers sense that they have spent or wasted more time in the store) or increase satisfaction (because consumers can multitask and engage in enjoyable diversions while shopping).
In this article, we address the following research questions: Does mobile phone use in stores influence purchases? What mechanisms are responsible for this effect? What are the boundary conditions for the mobile phone effect? Does distraction due to mobile phone use decrease or increase customer satisfaction with the shopping experience? To explore these questions, we use eye-tracking technology and conduct two studies in six retail stores. By combining a field study with a field experiment, we address the potential limitations of each type of study. These data pertain to 411 complete shopping trips, recorded by more than 110 hours of eye-tracking videos that provide complete information about customers’ visual fields (i.e., what they look at) and their movements throughout the store, from the moment they enter until they exit. We match these data with sales receipts and survey responses.
In turn, we make several theoretical and managerial contributions. First, from a theoretical perspective, we apply attention capacity theories to demonstrate that distractions, such as in-store mobile phone use, increase consumers’ purchases. Second, we identify the behavioral mechanisms that lead to increased purchases. Distraction leads to increased purchases because consumers divert from their conventional shopping loop, spend more time in the store, and spend more time examining products/prices on shelves. Third, we reveal some boundary conditions. Accordingly, this study extends prior literature by illustrating how and when in-store mobile phone use results in greater purchases.
From a managerial perspective, our results suggest that retailers can increase purchases by encouraging customers to engage with their mobile phones while shopping, such as by adding quick-response codes that give consumers access to useful information through their mobile phones or making wi-fi readily available. As a critical takeaway for managers, we show that the effects of in-store mobile phone use on consumers’ behaviors do not harm their satisfaction with the shopping experience; indeed, these levels are no different than those reported by consumers who do not use their phones. Encouraging customers to use their phones (whether related to the shopping task or not) thus can increase store purchases without detracting from the shopping experience.
TABLE: TABLE 1 Literature on In-Store Mobile Phone Use
TABLE: TABLE 1 Literature on In-Store Mobile Phone Use
TABLE: TABLE 1 Literature on In-Store Mobile Phone Use
TABLE: TABLE 1 Literature on In-Store Mobile Phone Use
| Source | Setting | Area | Scope | General Use | Behavioral Mechanisms Measured | Age Effects on Purchases | Overall Purchases as DV | Mobile Effect (1 or —) | Findings |
|---|
| Burke (2002) | Survey | Role of handheld devices | Use of handheld devices | Y | N | N | N | + | Younger adults are significantly more interested in using handheld devices to assist them in information searches and evaluations of alternatives. |
| Broeckelmann and Groeppel-Klein (2008) | Electronics stores | Price comparisons | Retailer evaluation | N | N | N | N | – | Participants’ skills in using mobile phones determine how likely they are to use their phone for comparing prices. The greater the online price advantage, the lower the trust in the offline retailer. |
| Kowatsch and Maass (2010) | Fictive store selling mobile navigation units | Role of handheld devices | Sales intentions, return intentions | N | N | N | N | + | The perceived ease of using the portable device leads to higher perceived usefulness, which then increases purchase and patronage intentions. |
| Klabjan and Pei (2011) | Supermarket | Ads/coupons | Redemption rates | N | N | N | N | + | Customers use different strategies to take the most optimal route throughout the store. A perceived optimal route to get to the advertised product makes the customer more tolerant of the time needed to retrieve it, so redemption rates increase. |
| Hui, Inman, et al. (2013) | Supermarket | Ads/coupons | Redemption rates, unplanned spending | N | N | Y | N | + | Targeted mobile promotions that appear inside the store increase willingness to walk further into the store, where the customer otherwise would not have visited. An in- store experiment with physical coupons indicates increased total unplanned spending by 16.1%. |
| Danaher et al. (2015) | Mall | Ads/coupons | Redemption rates | N | N | N | N | + | The time when coupons are delivered and distance to the store are crucial determinants of redemption rates. Face value and product type (especially snacks) are the most important factors for redemption rates. |
| Fong, Fang, and Luo (2015) | Movie theaters | Ads/ coupons | Redemption rates | N | N | N | N | + | When mobile (text) coupons are sent in the proximity of the movie theater, redemption rates go up, versus when not in this proximity. When coupon values are high, competitors also benefit from geotargeted coupons that are redeemable only at the focal movie theater. |
| Sciandra and Inman (2016) | Mass merchandiser (POPAI) | Customer decision making | Unplanned spending, omitted planned spending | Y | N | N | N | Mixed | Customers using mobile devices for task-related activates (e.g., shopping list) buy fewer unplanned items. Customers using mobile devices for non-task-related activities (e.g., text messages) conversely increase their unplanned purchases but forget more of their planned purchases. Mobile device use for non-task-related activities may act as a source of distraction and make customers more dependent on external cues as heuristics. |
| Atalay, Bodur, and Bressoud (2017) | Supermarket | Role of handheld devices | Calories purchased, stress | N | Y | N | N | – | When people are in a mindset to consider the purpose of buying a product, multitasking on cell phones negatively affects their ability to accomplish their shopping goal. |
| Bellini and Aiolfi (2017) | Supermarket | In-store marketing effectiveness | Unplanned spending, recall of marketing | Y | N | N | N | – | In a survey of 84 customers, mobile phone users recalled less in-store marketing stimuli after purchase. There was no effect on the amount of unplanned purchases. |
| Bues et al. (2017) | Fictitious supermarket | Ads/coupons | Redemption rates | N | Y | N | N | + | The location of the customer in the store when the mobile ad is received is the strongest value driver. Personalized ads close to the product have little impact on purchase intentions. |
| Present study | Supermarket | General use | Retailer sales | Y | Y | Y | Y | + | Mobile phone use leads to increased sales. The effect is mediated by increased time spent in the store, product fixations, and customer movement patterns. The effect increases with age. |
Notes: Y 5 yes; N 5 no. Some studies report on several settings, but for this table, we focus solely on the physical retail store settings they investigate.
Recent calls for research on mobile shopping have focused on the need to understand how these devices influence the shopping process (Shankar et al. 2016). Research on mobile devices has tended to address mobile promotions or advertising (e.g., Bart, Stephen, and Sarvary 2014; for a review, see Grewal et al. [2016]) or factors that influence mobile coupon redemption, such as delivery strategies for coupons (Bues et al. 2017; Danaher et al. 2015; Klabjan and Pei 2011) or physical crowding (Andrews et al. 2016). Other research streams have explored predictors of mobile phone use (Broeckelmann and GroeppelKlein 2008; Burke 2002) or the perceived ease of use of mobile phone interfaces (Kowatsch and Maass 2010). An overview of studies of in-store mobile phone uses appears in Table 1.
As we show in Table 1, most studies have explored mobile promotion and redemption issues (e.g., Danaher et al. 2015; Fong, Fang, and Luo 2015; Hui, Inman, et al. 2013; Klabjan and Pei 2011) or how different types of handheld devices affect information searches and purchase intentions (Burke 2002; Kowatsch and Maass 2010). For example, Hui, Inman, et al. (2013) demonstrate that in-store mobile phone promotions encourage consumers to walk more circuitous routes; the authors specify that targeted mobile promotions for consumers in a store increase the distance they travel, the amount of time they spend, and their unplanned spending in the store. By offering mobile advertising concurrently with consumers’ shopping experiences, retailers seemingly can engage consumers with the brand and drive purchases.
In contrast, little research has explored general mobile phone use when shopping—such as talking, texting, or answering emails—or how these general uses determine overall purchases in the store. This latter question is critical to store managers, even more so than purchase intentions or customer preferences simulated through online experiments. We know of only two studies that consider general mobile phone use, though neither of them addresses the effect of mobile phone use on total purchases, nor do they directly measure the mechanisms responsible for any impact of mobile phone use on consumers’ behaviors. Rather, Sciandra and Inman (2016) identify the activities for which mass merchandise shoppers use their phones and test the impacts of phone use on unplanned purchases and omissions of planned purchases. When customers use their mobile phones for shopping task–related activities (e.g., shopping lists, calculations), they report shopping less for unplanned items, whereas customers using their mobile devices for unrelated tasks increase their unplanned spending. Thus, mobile devices seemingly can increase or decrease shoppers’ cognitive resources and thus the quality of their decision making. In a survey-based study in a supermarket, Bellini and Aiolfi (2017) instead find no differences in unplanned purchases according to the type of cell phone use. These results highlight the need to explicate and test underlying mechanisms that might explain the effects of mobile phone use on overall shopping expenditures.
Conceivably, when consumers focus more on their phones, they pay less attention to products on the shelves, and these mobile blinders might lead to reduced purchases overall. Alternatively, when they are distracted by tasks on their phones, consumers might pay less attention to their shopping goals or the time they have spent in the store and therefore buy more, in line with evidence that shows that when consumers deviate from their shopping goals, they purchase more unplanned items (Inman, Winer, and Ferraro 2009; Sciandra and Inman 2016; Thomas and Garland 1993). The behavioral mechanisms responsible for any such impact on purchases are highly relevant from theoretical and managerial perspectives. That is, if general mobile phone use facilitates deviations, retailers might benefit from increased purchases. These types of deviations also might reflect age effects, especially if they are a function of consumers’ attention. Attention to a given task relates to working memory, which is susceptible to aging processes (Hertzog et al. 2003; Park et al. 2002) (as we discuss in detail subsequently). Therefore, increased purchases resulting from general mobile phone use might vary as a function of consumers’ age. As Table 1 indicates, though, age effects rarely have been explored. We aim to provide an expanded test of whether in-store mobile phone use prompts consumers to take less direct routes through stores and increase their purchases (Hui, Inman, et al. 2013) by investigating the impact of general phone use and its related mechanisms on retail purchases while highlighting some boundary conditions of these effects.
Limited attentional capacity theories apply to research contexts ranging from the role of placement of products in video games (Lee and Faber 2007) to retrieval differences in auditory versus visual distractions (Choi, Lee, and Li 2013) to less deliberate processing in distracting circumstances (Chaiken 1980; Petty, Cacioppo, and Schumann 1983). These studies consistently point to the same basic premise: distraction diverts people’s attention from a focal task, so their processing of that focal task slows to some degree. We adopt this basic premise in our study and predict that, owing to limited attentional abilities, shoppers are unable to process multiple streams of information concurrently (Repovs and Baddeley 2006).
There are many reasons people experience limited attentional capacity (e.g., involvement in a focal task limits the resources available to process another task; Lee and Faber 2007), but distraction is the focus of this study. Most research on distraction and consumer behaviors (see Table 2) relies on artificial laboratory settings, pertains to areas unrelated to mobile phone use, and does not include purchases or consumer spending as outcome variables. Moreover, although working memory and distraction effects are very susceptible to aging processes (Hertzog et al. 2003; Park et al. 2002), none of the articles in Table 2 explore age effects. Instead, they focus on consumers’ evaluations of products (Biswas, Biswas, and Chatterjee 2009; Janiszewski, Kuo, and Tavassoli 2013; Lerouge 2009; Posavac et al. 2004) or food preferences (Nowlis and Shiv 2005; Shiv and Nowlis 2004) when those consumers are distracted.
Table 2 shows that studies offer mixed results regarding the effects of consumers’ distraction. The outcomes appear to depend on whether distraction limits the rehearsal and retrieval of necessary information to make an informed decision (i.e., negative effect; Biswas, Biswas, and Chatterjee 2009), heightens an affective component of the consumer experience (positive effect; Shiv and Nowlis 2004), or does not invoke intended counterarguments that might have exerted an effect (no effect; Nelson, Duncan, and Frontczak 1985). As these examples illustrate, understanding the mechanisms underlying the distraction effect is critical, and this represents one of our study’s contributions.
Various theories aim to describe shoppers’ limited attention and the boundaries of their cognitive abilities in stores and elsewhere. For example, bottleneck theories (Broadbent 1958; Fagot and Pashler 1992) describe serial processing of one piece of information at a time. When people try to process multiple pieces of information simultaneously, their information processing slows down because of the restricted bottleneck of available attention. In other words, people can try to process multiple tasks simultaneously (Navon and Gopher 1980; Norman and Bobrow 1975), but at some point, their attentional capacity restricts this processing.
Theories about working memory also are informative (Unsworth and Robison 2016). Working memory is a consumer’s cognitive ability to store, process, and manipulate information, generally described as “the set of mechanisms capable of retrieving a small amount of information in an active state for use in ongoing cognitive tasks” (Cowan et al. 2005, p. 43). It influences critical features such as reading comprehension, overall intelligence, and general reasoning; it forms people’s ability to reason, make decisions, and engage in appropriate behaviors. In the model proposed by Repovs and Baddeley (2006), working memory functions across information modalities (e.g., visual, verbal). Working memory might process language (phonological loop), process visual and spatial issues (visio-spatial sketchpad), and solve problems (central executive) simultaneously, through its different parts. However, if several tasks take up the same component of working memory, they cannot be executed successfully.
When people try to perform two tasks simultaneously, learning of the primary task diminishes because working memory enables people to stay focused on a task while blocking out distractions. In a retail setting, for example, it would not be possible to spatially navigate in the store while looking at photos on Instagram or to undertake careful evaluations of products while talking with someone on the phone. However, a strong working memory capacity implies that a person can avoid distractions and achieve task goals (Engle 2002), likely because (s)he streamlines cognitive functions to focus on taskrelevant behaviors while avoiding task-irrelevant distractors (Conway, Cowan, and Bunting 2001).
TABLE: TABLE 2 Literature Pertaining to Distraction and Consumer Behavior
TABLE: TABLE 2 Literature Pertaining to Distraction and Consumer Behavior
TABLE: TABLE 2 Literature Pertaining to Distraction and Consumer Behavior
| Paper | Settinga | Area | Scope | Mobile Use Leads to Distraction | Age Effects | Overall Purchases as DV | Effect of Distraction (1 or —) | Findings |
|---|
| Gardner (1970) | Lab experiment | Movies | Desirability ratings and recall | N | N | N | No effect | The results do not support the idea that being distracted while hearing a persuasive marketing communication influences consumers’ desire for a promoted movie. |
| Nelson, Duncan, and Frontczak (1985) | Lab experiment | Radio commercial | Message acceptance | N | N | N | No effect | The results do not support the hypothesis that distraction interferes with counterarguments, such that a receiver would accept a message discrepant with his or her beliefs. |
| Posavac et al. (2004) | Lab and mall intercept experiments | Product evaluations | Purchase intention and choice | N | N | N | – | More positive evaluations of products occur when a brand is evaluated in isolation; such brand positivity effects diminish when consumers are distracted, because processing resources for brand information diminish under distraction conditions. |
| Shiv and Nowlis (2004) | Lab experiments | Taste testing | Product preference | N | N | N | + | Higher levels of distraction lead to a preference for sampled foods, because distraction increases the affective component of somatosensory experiences, rather than the informational component. |
| Nowlis and Shiv (2005) | Lab experiments | Taste testing | Product preference | N | N | N | + | Tasting food while distracted increases the intensity of the pleasure experience and thus preference for the food sampled. |
| Mandel and Smeesters (2008) | Lab experiments | Mortality salience | Food and drink consumption | N | N | N | + | Consumption of food and drinks distracts consumers from mortality self-awareness, especially among low self- esteem consumers. |
| Biswas, Biswas, and Chatterjee (2009) | Lab experiments | Product evaluations | Product quality | N | N | N | – | Distraction negatively affects short-term memory rehearsal and retrieval, such that strong product cues presented first with distraction lead to lower product quality judgments than strong product cues presented more recently. Without distraction, the opposite is true: Strong product cues presented first are better and more diagnostic. |
| Lerouge (2009) | Lab experiment | Product evaluation | Attribute ratings and recall | N | N | N | + | Distraction after exposure to product information positively influences product differentiation for consumers with a configural mindset but not those with a featural mindset. |
| Kim and Rucker (2012) | Lab experiments | Proactive compensatory consumption | Use of products | N | N | N | + | Reactive, rather than proactive, compensatory consumption of products is more likely as a means to distract from an experienced self-threat. |
| Choi, Lee, and Li (2013) | Lab experiment | Video games | Implicit brand memory | N | N | N | – | When consumers are involved in highly immersive environments (e.g., video games), audio distractions in the game inhibit implicit brand memory, whereas visual distractions have no effect. This result only holds for familiar brands. |
| Janiszewski, Kuo, and Tavassoli (2013) | Lab experiments | Selective attention of products | Product preference | N | N | N | + | Selective attention to products increases preference for them later, because people allocate attention to the product; visual distraction heightens this effect, because neural responses to selectively attended to products increase. |
| Spielmann (2014) | Lab experiment | Print media | Attitudes toward ad and brand | N | N | N | + | Humorous ads about arousal-safety issues are effective at distracting consumers, which leads to heightened attitudes toward the brand and the ad. |
| Present study | Supermarket | General mobile use effects | Retailer sales | Y | Y | Y | + | Mobile phone use leads to increased sales. The effect is mediated by increased time spent in the store, product fixations, and customer movement patterns. The effect increases with age. |
Notes: Y 5 yes; N 5 no.
In line with this reasoning, Garaus, Wagner, and Ba¨ck (2017) show that simultaneous exposures to mobile ads and other marketing materials reduce shoppers’ attention to a target stimulus. In a retailing context, Stilley, Inman, and Wakefield (2010) claim that shoppers’ inability to process all existing information in a store is an outcome of their limited processing capacity. For grocery retailers, the challenge is to capture shoppers’ attention and develop tactics to influence their habitual in-store behavior (Mehta, Hoegg, and Chakravarti 2011). In addition, Baddeley (2010) highlights how working memory can be easily overloaded by sensory input. In a shopping context, a shopper’s working memory seemingly could be hindered by sensory inputs, such as displays on a mobile phone.
Mobile phone use. Drawing on information processing and distraction theories, we predict that when consumers allocate information processing capacity to their mobile phones, the attention that they allocate to other focal tasks (e.g., shopping) diminishes, which hinders their performance on that task. If their focal task is shopping, consumers might assign less attention to their shopping goals or lists, for example, and deviate from them more frequently than they would if they were not using their phones. Shopping goals and lists keep consumers on track, in terms of both budgets and time spent in the store (Block and Morwitz 1999; Inman, Winer, and Ferraro 2009; Thomas and Garland 1993). The more attention consumers devote to the shopping task, the less likely they are to deviate from their planned purchases. According to attention capacity theories, however, if another task captures consumers’ attention (i.e., mobile phone use), they have less information capacity remaining to allocate to the shopping task, which likely hinders the efficiency of the trip. Because consumers spend more time in the store, their purchases likely increase. Therefore, we hypothesize:
H1: Mobile phone use in stores increases consumers’ (a) total time spent in the store and, thus, (b) purchases.
When consumers use—and, thus, devote more informationprocessing resources to—their mobile devices, they also assign fewer resources to the action of moving through the store at a brisk pace; they might stop momentarily or slow down to focus on their phones. The slower pace gives consumers more time to examine products and information on shelves in their immediate proximity. Imagine a person stopping in the middle of a grocery store to talk to a client on the phone. This shopper might be stationary for 30 seconds longer than normal; while talking, (s)he likely glances around and examines information in the visual field, such as product and pricing information. In turn, the likelihood that this consumer sees products (s)he might want or need increases.
This effect might occur even when consumers look at their phones more intensely to complete a task (e.g., typing an email or text). Humans can fully analyze items within two degrees of the epicenter of their eye fixation (Anstis 1998; Pieters and Wedel 2012). Thus, even when closely engaged with their phones, consumers must look up occasionally (or stop walking) to avoid bumping into fixtures and other people. Even if just for a moment, this action forces them to fixate their eyes elsewhere, such as on products and pricing information on nearby shelves. Therefore, mobile phone use may increase the attention that people devote to shelves and displays, increasing the likelihood that the displayed products may appeal to shoppers. Thus,
H2: Mobile phone use in stores increases (a) shelf attention and, thus, (b) purchases.
The perimeter of the supermarket is prime real estate, in that it encourages purchases of products located there (Hofbauer 2016; Strom 2012); popular media also suggest that the perimeter features healthier items and encourages consumers to stick to this outer loop (Escobar 2016). To minimize their cognitive effort, many consumers follow scripts (Bower, Black, and Turner 1979; Schank and Abelson 1977), including spatial scripts in a grocery shopping context, to define how they move throughout the store. The more well-defined shoppers’ scripts are for how to proceed during a specific type of shopping trip, the more they rely on these schemas, which get stored in longterm memory (Bettman, Luce, and Payne 1998; Block and Morwitz 1999) and influence where shoppers go in the store and which products they consider. The conventional consumer loop around grocery stores represents a natural path, from which consumers are unlikely to deviate unless something distracts them. For example, distracted consumers might mindlessly walk by needed items without placing them in their basket. Once they refocus on their shopping task, they may realize what they missed and turn around to obtain it. When customers backtrack or deviate from their spatial script, they may see products that otherwise would have gone unnoticed. Therefore, distractions caused by mobile phone use may increase customer purchases by diverting shoppers from their loop. Formally:
H3: Mobile phone use in stores increases customers’ (a) loop diversion and, thus, (b) purchases.
Finally, it is not mobile phone use itself that causes increased purchases but, rather, its effects—namely, the reduced information-processing capacity that diminishes shoppers’ ability to adhere to their shopping goals, spatial scripts, and the task at hand. In turn, the previously hypothesized outcomes of mobile phone use—total time spent in the store, shelf attention, and customer loop diversion—should constitute independent mechanisms that explain why mobile phone use increases consumers’ purchases. Formally:
H4: (a) Total time spent in the store, (b) shelf attention, and (c) customer loop diversion mediate the relationship between mobile phone use and increased purchases.
Boundary conditions on the effect of mobile phone use. We also examine potential boundary conditions related to in-store mobile phone use. One key variable is customer age. Attention to a given task relates to working memory; working memory is very susceptible to aging processes (Hertzog et al. 2003; Park et al. 2002), such that consumers’ processing capabilities and choices shift with age. For example, older consumers have greater information processing difficulty than younger people (Cole and Houston 1987; Roedder John and Cole 1986). When assigned a specific search task (e.g., select products using pertinent nutrition information), older shoppers are less accurate, in terms of finding the right products, than younger shoppers (Cole and Gaeth 1990), even if they think they have devoted equal effort to the task (Cole and Balasubramanian 1993).
A supermarket setting, with its tens of thousands of unique stockkeeping units competing for shoppers’ attention, is likely to prompt age-related effects among shoppers. Such limitations imply that older consumers may become more distracted from focal tasks when they use mobile phones, whereas younger consumers can multitask more easily, owing to their greater attention capacity. The postulated mechanisms for the current study (total time spent in store, shelf attention, and customer loop diversion) then may be more pronounced for older consumers. Specifically, relative to younger consumers, older consumers distracted by their mobile phones may ( 1) be less inclined to keep their shopping goals in mind, thereby increasing the time they spend in a store; ( 2) more likely to look up or stop walking, thereby increasing the likelihood that they fixate their attention on shelf information; and ( 3) more likely to skip needed items, thereby increasing the likelihood that they turn around to retrieve them and deviate more in their shopping path.
We also examine in an exploratory fashion several other factors, such as mobile phone use (whether related to the shopping task or not) and the location in the store (i.e., grocery department) where consumers engage in their mobile phone use. Finally, we examine how mobile phone use might influence purchases differently in specific departments.
Grocery retailers often display thousands of different stockkeeping units, such that the effects on consumers’ attention vary, so we consider it essential to conduct this study with reallife data and real consumers. Testing the effects of mobile phones for just a few products in a laboratory experiment might enhance reliability, but it lacks sufficient ecological validity to test the hypotheses. Therefore, we obtained a data set of consumers of four grocery stores located in suburban areas of Stockholm, Sweden, which feature similar offerings. The stores in Studies 1 and 2 are large-scale retailers for Sweden but not supercenters; their average area was 36,140 square feet.1
Previous research has used different approaches to examine how customers behave inside stores, such as tracking them with radio-frequency identification chips on shopping carts (Hui, Huang, et al. 2013; Hui, Inman, et al. 2013) or providing them with portable video recorders (Hui, Huang, et al. 2013; Zhang et al. 2014). To obtain information about what customers explicitly fixate on, we asked them to use eye-tracking devices as they completed their shopping trips. Specifically, research associates of a marketing research company randomly contacted consumers across the four stores and asked them to participate in an eye-tracking research study on shopping behaviors. The 393 recruited participants were asked to shop as they usually do. Some minor issues with poor video quality, dead batteries in the eye trackers, or eye trackers that mistakenly turned off led to 359 full customer store visits for the analysis. An additional 65 participants did not complete the questionnaire required in the study, leaving a total of 294 participants (for the demographic profiles of Study 1 participants, see Appendix A), who ranged in age from 18 to 73 years (M = 41.51 years), and 39.46% of whom were women. The demographic data gathered from the questionnaires revealed no significant differences across the four stores (or use or not of mobile phone) in customers’ age, gender, or number of children living at home.
Tobii Pro portable eye-tracking glasses recorded the eye movements of the participants and their visual field. Eye tracking accurately captures what consumers do in the store and is well suited to examining the role of elements that might distract consumers from finishing their shopping trips as efficiently as possible (Wedel and Pieters 2008). The test administrators sat at the entrance of each store on different days of the week (Mondays–Sundays) during daytime hours (9:00 A.M.–5:00 P.M.). All consumers passing by the entrance were asked if they would be willing to participate in a research study and offered a coupon as compensation. No specific information about the purpose of the study was provided. Participants also had to respond to a short questionnaire with items related to their demographic information. After they had shopped, the participants’ glasses were collected by the test administrators, who also made copies of their purchase receipts. At this point, participants stated their satisfaction with the store visit.
TABLE: TABLE 3 Mean Differences of Using Versus Not Using Mobile Phone (Study 1)
| | Using Mobile Phone (I) | Not Using Mobile Phone (J) | Mean Difference (I – J) | t | p |
|---|
| Participants | n = 71 | n = 223 | |
| Purchases (SEK) | 414.40 | (332.56) | 293.83 | (272.78) | 120.57 | (43.49) | 2.77 | .007 |
| Items purchased (#) | 20.61 | (14.51) | 14.24 | (12.70) | 6.36 | (1.92) | 3.31 | .001 |
| Time spent in store (min) | 17.39 | (10.92) | 12.80 | (8.71) | 4.59 | (1.42) | 3.23 | .002 |
| Shelf attention | 73.13 | (55.99) | 55.71 | (47.43) | 17.42 | (7.37) | 2.37 | .020 |
| Customer loop diversion | 1.62 | (1.78) | .66 | (1.17) | .97 | (.22) | 4.29 | .000 |
| Trip satisfaction | 6.25 | (.91) | 6.32 | (1.00) | -.07 | (.13) | .49 | .626 |
Notes: For the phone use columns, the brackets contain standard deviations. For the difference column, the brackets contain standard errors. We used Welch’s t-test to correct for inequality between group variances, except for trip satisfaction. SEK = Swedish Krona.
The eye-tracking software displayed both consumers’ visual fields and where they fixated their eyes for the entire time they spent in the store (see Figure 1). The raw videos, consisting of more than 90 hours of video, were manually coded by the test administrators, using an extensive coding matrix that measures what the customer looks at and for how long. Coding quality checks were conducted by an additional researcher, using logic checks in the coded data and visual inspection as necessary. The quality checks that were conducted about mobile phone use revealed no discrepancies with the coding. In turn, we could code and convert the measures into our key variables.
First, we measured how long the customer spent in the store, starting from the time (s)he entered the store and ending when (s)he reached the checkout line (in minutes and seconds). This variable is labeled “total time in the store.” Second, the visual attention measures included the number of analytical fixations a customer made on unique products on shelves and items directly attached to shelves, such as price tags. This approach is consistent with previous in-store research that relies on eye tracking (Chandon et al. 2009). The design of the portable eyetracking glasses enables us to use the total number of fixations to operationalize attention (Hong, Misra, and Vilcassim 2016; Meißner, Musalem, and Huber 2016). A fixation was deemed analytical if the data coder assessed the length of the fixation and the scan path leading up to it as evidence of a conscious evaluation of the focal product or price tag. Every fixation on a product and price tag was recorded once; if a customer shifted his or her attention repeatedly between two products, those two products were recorded as one fixation each, for example. This variable was labeled “shelf attention.”
Third, with the eye-tracking software, customer movements in the store could be assessed. The videos were coded according to whether the customer diverted from the main customer loop, mapped as the natural path customers usually take through the different departments in the store. If a participant decided to turn around in the natural path, it was coded as a “customer loop diversion.”
Fourth, we coded each participant according to whether (s)he used a mobile phone ( 1) or not (0) during the shopping trip; we also calculated the total time participants used their mobile phones during the trip. Participants using their mobile phones used them for an average of .93 minutes (SD = .89), or 5.34% of their total time in the store. The exit survey gathered demographic variables, as well as overall satisfaction using a seven-point scale for the question item, “How satisfied are you with your store visit today?” We found no significant differences in satisfaction between customers who used their phones or not.
Fifth, we assessed customer spending from their actual receipts; we checked the number of items they purchased to affirm the robustness of the findings from our mediation and moderated mediation models for both studies (see Web Appendix A1). In unreported results, we find no significant indirect or direct effects if we use the average item price as the dependent variable (i.e., purchases/number of items). That is, mobile phone use appears to drive incremental purchases by leading customers to buy more items rather than more expensive items.
Mobile phone use: direct and mediation effects. The direct effects of mobile phone use in Table 3 reveal positive and significant main effects on purchase amounts, number of items purchased, time spent in store, shelf attention, and customer loop diversions. We tested three distinct mediation models with a bias-corrected bootstrap procedure (Model 4; Hayes 2013; Zhao, Lynch, and Chen 2010). In these models, mobile phone use (used/did not use) is the independent variable; total time spent in the store, shelf attention, and customer loop diversion are mediators; and total purchases is the dependent variable (see Figure 2).
In support of H1, mobile phone use significantly influences total time spent in the store (b = 4.59, p < .001), and total time spent in the store influences total purchases (b = 21.76, p < .001). Furthermore, mobile phone use increases shelf attention (H2a: b = 17.42, p = .01) and customer loop diversion (H3a: b = .97, p < .001), both of which enhance total purchases (H2b: battention = 3.97, p < .001; H3b: bdiversion = 69.41, p < .001). In support of H4, (a) total time spent in the store, (b) shelf attention, and (c) customer loop diversion each mediate the relationship of mobile phone use and increased purchases. All bootstrapping analyses include 100,000 iterations. We report 95% confidence intervals (CIs) throughout all tests (unless noted otherwise).
The CIs for these indirect paths do not include 0, suggesting significant indirect effects (H4a: mobile phone use → total time spent in store → increased purchases, indirect effect = 99.92, CI = [41.43, 163.33]; H4b: mobile phone use → shelf attention → increased purchases, indirect effect = 69.14, CI = [13.35, 128.75]; H4c: mobile phone use → customer loop diversion → increased purchases, indirect effect = 66.99, CI = [26.61, 118.62]).2 The direct effect coefficients are insignificant for time spent in store (p = .47) and customer loop diversion (p = .17), but they are marginally significant for shelf attention (p = .08).
If we adopt actual time spent on the mobile phone (in minutes) as the independent variable (as opposed to a di
chotomous mobile use variable), the indirect effects again are significant (H4a: indirect effect = 80.98, CI = [39.12, 126.27]; H4b: indirect effect = 59.77, CI = [18.01, 102.52]; H4c: indirect effect = 33.63, CI = [8.74, 79.67]). The direct effect coefficients again are insignificant for time spent in store ( p = .81) and shelf attention ( p = .22), but they are marginally significant for customer loop diversions ( p = .06).
Boundary condition: age moderation. Using PROCESS Model 1, we test the interaction effects between mobile phone use and standardized age on time spent in the store, shelf attention, and customer loop diversion. We find significant interaction effects for time spent in store (t(290) = 2.16, p < .05) and customer loop diversion (t(290) = 3.63, p < .001) as well as marginal significance for shelf attention (t(290) = 1.84, p < .07). Because the age measure is continuous, we also could determine the ages at which mobile use significantly affects customer outcomes. To find the absolute value of the age at which the effects become significant (p = .05), we use the Johnson– Neyman technique (Hayes 2013) and present the results in Figure 3. Specifically, 76.19% of the sample exhibited significant effects on time spent in the store, 63.61% on shelf attention, and 79.93% on customer loop diversions (Web Appendix A2).
Using eye-tracking field data, matched with survey and actual purchasing data, we determine that customers who use their phones in stores spend more. These results confirm a positive effect for retailers when shoppers use mobile devices. The mechanisms responsible include more time in the store, more shelf attention, and greater customer loop diversion, in line with attention capacity theories (Broadbent 1958; Fagot and Pashler 1992; Navon and Gopher 1980; Norman and Bobrow 1975). It is likely that mobile phone use influences increased purchases through a distraction-based mechanism.
Furthermore, our finding of moderation by age supports attention capacity theories that acknowledge the susceptibility of working memory to the aging process (Hertzog et al. 2003; Park et al. 2002). Older consumers are more susceptible to the effects of in-store phone use, such that they spend more time in the store, divert from their path more often, and devote more attention to examining shelves than do younger consumers. Despite these influences, consumers who use their mobile devices in stores report no differences in their satisfaction levels, suggesting that retailers can safely encourage in-store mobile phone use without risking a decline in customer satisfaction.
Despite the real-world nature of these data, Study 1 has two key weaknesses. First, respondents self-selected into either the mobile phone use group or the nonuse group, implying a potential for self-selection biases. That is, our result might be due to some common unobserved factor that causes respondents to self-select into one group or the other (e.g., low self-control, high level of variety seeking in experiences). Second, we infer, rather than directly measure, customers’ distraction on the basis of their other in-store behaviors. With Study 2, we intend to replicate the results from Study 1 while addressing these concerns.
First, we aim to replicate the Study 1 results, including those related to the three mediating elements that explain an increase in purchases when customers use their mobile phones. Second, to deal with the potential issue of self-selection bias, we adopted an experimental design for Study 2 in which every participant was randomly assigned to a mobile phone use or nonuse group, such that they were encouraged to use or discouraged from using their mobile phone during the shopping trip, regardless of what they usually did while shopping. Third, with a four-item scale to measure consumers’ distraction levels across conditions, we undertake a more direct test of our theoretical framework pertaining to limited attention capacity. When consumers in the mobile phone use condition use their phones, it diverts their attention from the focal task (i.e., shopping) and slows processing of that task, so consumers should acknowledge feeling more distracted in this situation, because their attention capacity is spread across different tasks. Therefore, with Study 2 we test a serial mediation model that explicitly captures consumers’ distraction levels and how those levels influence behavioral responses: increased time spent in the store, shelf attention, and customer loop diversion, which ultimately affect purchases.
Study 2 took place in two different grocery stores. We recruited 121 participants and asked them to shop as they usually do. Four participants were omitted because of technical issues with the eye-tracking videos, resulting in 117 participants and approximately 24 hours of eye-tracking video footage. Participants ranged in age from 19 to 80 years (M = 42.94 years), and 52.14% were women. Appendix B contains the demographic profiles of the Study 2 participants. The field data collection was conducted by field associates of the same marketing research company that provided the Study 1 data.
The approach was similar to Study 1, with a few minor differences. The test administrator randomly approached every fifth customer who walked past a predefined point and asked him or her to participate in a study on consumer behavior, with a scratch-off lottery ticket offered as compensation. Customers who agreed were asked if they had a mobile phone with them; only four did not, and they were disqualified from participating further. Next, each participant was assigned randomly to either the mobile phone use or nonuse group and received instructions relevant for this experimental group.
The instructions to the mobile phone group were, “We are interested in your shopping behavior when you are using your smartphone. This includes sending emails, sending or reading text messages, searching online, playing games, or any other use of the phone in any place of the store. Please use your smartphone during this shopping trip when you want, based on your own needs.” The instructions to the no-mobile phone group instead were, “We are interested in your shopping behavior when you are NOT using your smartphone. Could we ask you to please put it away for this shopping trip? This means that we would want you to avoid sending emails, sending or reading text messages, searching online, playing games, or any other use of the phone. Please do not use your phone at all, if possible.” After receiving these instructions, each customer was asked to shop as usual. Then, after the customer finished shopping, the test administrator collected the eye-tracking glasses, made a copy of the customer’s receipt, and asked the customer to complete the demographic and satisfaction survey items, as in Study 1. Furthermore, participants completed the new distraction measure at this point.
Measures and data analysis. The measures were the same as in Study 1,3 except that we added a measure of customers’ distraction levels, with four items (Cronbach’s a = .90): “I felt distracted during my shopping trip today,” “I felt I was multitasking during my shopping trip today,” “I was preoccupied with other tasks during this shopping trip,” and “I kept getting sidetracked with other issues during this trip.”
Two satisfaction items were measured using the following items: “How satisfied were you with today’s shopping trip?” and “How satisfied were you with the service in the store today?” The end points were: 1 = “very dissatisfied” and 5 = “completely satisfied.” There were no differences across the phone use groups in service satisfaction but were marginally significant for shopping trip satisfaction (p < .09). Nor did we find differences across customers using mobile phones in terms of age, gender, or household size.
Manipulation checks. In the postpurchase questionnaire, participants indicated whether they used their mobile phones. Of the 64 participants in the nonuse group, 96.9% followed the instructions and did not use their phones. Two used their phone and noted that they did so because they received a call that they “had to take.” In the use group, of 53 participants, 96.2% used their phones. Participants in the mobile use condition used their phones for an average of .74 minutes (SD = 1.36), or 4.82% of their total time in the store. These high compliance rates indicate that the experimental design worked well. Thus, all cases were included to represent these experimental groups.4
TABLE: TABLE 4 Mean Differences Due to Using Versus Not Using Mobile Phone (Study 2)
| | Using Mobile Phone (I) | Not Using Mobile Phone (J) | Mean Difference (I – J) | t | p |
|---|
| Sample size | n = 53 | n = 64 | |
| Purchases (in SEK) | 444.28 | (436.31) | 314.37 | (416.67) | 129.91 | (79.06) | 1.64 | .103 |
| Items purchased (#) | 20.85 | (19.47) | 13.22 | (13.95) | 7.63 | (3.19) | 2.39 | .019 |
| Distraction | 2.40 | (1.52) | 1.55 | (.65) | .85 | (.22) | 3.79 | .000 |
| Time spent in store (min) | 15.37 | (12.44) | 10.92 | (9.56) | 4.45 | (2.03) | 2.19 | .031 |
| Shelf attention | 63.23 | (50.39) | 37.39 | (34.84) | 25.84 | (8.18) | 3.16 | .002 |
| Customer loop diversion | .63 | (.98) | .35 | (.65) | .28 | (.16) | 1.78 | .078 |
| Overall trip satisfaction | 4.09 | (.90) | 4.36 | (.76) | -.27 | (.15) | 1.72 | .088 |
| Service satisfaction | 4.19 | (.81) | 4.23 | (.68) | -.05 | (.14) | .33 | .741 |
Notes: For the phone use columns, the brackets contain standard deviations. For the difference column, the brackets contain standard errors. Welch’s t-test was used for purchases, distraction, shelf attention, and customer loop diversions as dependent variables, due to inequalities in the variances between groups.
Mobile phone use: direct and mediation effects. With regard to the direct effects of mobile phone use (for the t-test results, see Table 4), we find significant, positive effects on distraction, number of items, time spent in store, and shelf attention, as well as marginally significant effects on customer loop diversions and purchase amounts.5
We next assess the three mediation models with a bias
corrected bootstrap procedure (Model 4; Hayes 2013). In support of H1, mobile phone use again significantly influences total time spent in the store (b = 4.45, p < .05), and total time spent in the store influences total purchases (b = 31.45, p < .001). Mobile phone use increases shelf attention (H2a: b = 25.84, p = .001) and customer loop diversion (H3a: b = .28, p < .07), which both increase total purchases (H2b: b = 7.13, p < .001; H3b: b = 238.39, p < .001). Finally, total time spent in the store, shelf attention, and customer loop diversion
all mediate the relationship between mobile phone use and
increased purchases, in support of H4. The results from the bootstrapped CIs for the indirect
effects are similar to those from Study 1, suggesting significant indirect effects for H4a and H4b and marginal indirect effects for H4c (mobile phone use → total time spent in store → increased purchases, indirect effect = 139.85, CI = [14.58, 273.61]; mobile phone use → shelf attention → increased purchases, indirect effect = 184.27, CI = [69.36, 312.31]; mobile phone use → customer loop diversion → increased purchases, indirect effect = 66.87, 90% CI = [5.80, 139.05]). For the remaining direct effects, the coefficients are insignificant for time spent in store ( p = .83), shelf attention ( p = .35), and customer loop diversions ( p = .38).
Similarly, when using actual time spent on the mobile phone (in minutes) as the independent variable, the indirect effects are significant (H4a: indirect effect = 84.76, CI = [42.74, 207.11]; H4b: indirect effect = 78.20, CI = [37.94, 187.41]; H4c: indirect effect = 79.76, CI = [14.14, 151.25]). The direct effect coefficients are insignificant for all models: time spent in store ( p = .89), shelf attention ( p = .91), and customer loop diversions ( p = .97). These results are consistent with the findings from Study 1.
Distraction as the underlying theoretical mechanism. Using Model 6 in PROCESS to examine serial mediation paths, we test distraction as the underlying construct to explain the behavioral effects obtained in the mediation models of Studies 1 and 2. The result for the phone use → distraction → time spent in store → purchases path does not include 0, in support of both mediating mechanisms (indirect effect = 116.63, CI = [29.89, 231.30]). Similarly, the paths of phone use → distraction → shelf attention → purchases (indirect effect = 79.94, CI = [12.84, 172.71]) and phone use → distraction → customer loop diversion → purchases (indirect effect = 42.50, CI = [6.60, 93.45]) do not include 0. Other possible indirect effects in the three models instead contain 0 in their CIs (i.e., phone use → distraction → purchases; phone use → time in store/ shelf attention/customer loop diversion → purchases). The direct effect (phone use → purchases) is insignificant (Table 5). Therefore, the behavioral mediators in Studies 1 and 2 can be explained further by increased distraction caused by mobile phone use, providing support for the distractionbased mechanism.
We also examine the mediating effects through distraction when using actual time spent using the phone as the independent variable. In these models, the serial mediation models again indicate that time spent using the mobile phone produces higher levels of distraction, which lead to the predicted behavioral effects (time spent in store, shelf attention, and customer loop diversions) and then to higher purchases. In this case though, the customer loop diversion model is only marginally significant (time using mobile phone → distraction → time spent in store → purchases, indirect effect = 66.98, CI = [18.63, 140.76]; time using mobile phone → distraction → shelf attention → purchases. indirect effect = 48.60, CI = [7.09, 100.18]; time using mobile phone → distraction → customer loop diversion → purchases, indirect effect = 18.16, 90% CI = [1.68, 43.41]).
TABLE: TABLE 5 Direct and Indirect Effects of Serial Mediation Models with Distraction as the First Mediator
| | Time Spent in Store (M2) | Shelf Attention (M2) | Customer Loop Diversion (M2) |
|---|
| Effect | 95% CI | Effect | 95% CI | Effect | 95% CI |
|---|
| Mobile phone use → Distraction → Purchases | -30.22 | -93.28, | 21.04 | 6.46 | -55.18, | 74.14 | 43.90 | -32.42, | 141.60 |
| Mobile phone use → Distraction → M2 → Purchases | 116.63 | 29.89, | 231.30 | 79.94 | 12.84, | 172.71 | 42.50 | 6.60, | 93.45 |
| Mobile phone use → M2→ Purchases | 30.21 | -93.26, | 161.99 | 102.49 | -8.80, | 172.71 | 17.91 | -43.86, | 96.92 |
| Mobile phone use → Purchases | 13.30 | -84.91, | 111.51 | -58.99 | –179.05, | 61.08 | 25.59 | –123.27, | 174.46 |
Notes: Confidence intervals were obtained using 100,000 bootstrapping iterations for all indirect effects. Conventional ordinary least squares regression procedures provide the CIs for the direct effects.
Boundary condition: age moderation effects. Using PROCESS Model 1, we test for interaction effects between mobile phone use and standardized age. We uncover significant interaction effects of age and mobile phone use on time spent in store (t(113) = 2.46, p < .05), shelf attention (t(113) = 1.99, p < .05), and customer loop diversion (t(113) = 3.00, p < .01). Another significant interaction emerges between age and mobile phone use on distraction (t(113) = 2.25, p < .05). Again, with our continuous measure of age, we check the point at which mobile phone use starts to have direct significant impacts on the customer measures, using the Johnson–Neyman technique (Hayes 2013). As we detail in Figure 4, 74.36% of the sample reveal effects on their distraction levels, 50.43% on time spent in the store, 66.67% on shelf attention, and 45.30% on customer loop diversions.
Location and type of use effects. Without explicit manipulations, we conduct exploratory analyses of other potential boundary conditions for the influence of mobile phone use on distraction, such as where participants were when they used their phone and what they used it to achieve. First, for the regression to test the effect of different locations in the store, we again consider the different store departments: fruits and vegetables, fresh foods, staple items, frozen foods, nonfood, and checkout. We coded these locations according to use ( 1) or no use (0) in that department. The results indicate that participants who used their phone in the fruits and vegetables (b = 1.06, p < .01) and fresh foods departments (b = .77, p < .01) were significantly more distracted (see Table 6).6
Second, we examined the activities for which shoppers used their phones, to determine how they affected distraction levels, using a similar regression analysis (1 = using the phone for a certain task, 0 = not using the phone for that task). We organized different activities into either store-related (e.g., shopping lists, retailer app use, handling transactions, searching for product information on the web) or non-store-related (all other) uses. Both types reveal positive coefficients on distraction levels, but only non-store-related activities significantly affect them (bnonrelated = .78, p < .001; brelated = .46, p = .22; see Table 6).
With a separate field study, we considered the possibility that mobile phones function more like blinders in the checkout line, where customers are relatively immobile. For this field study, conducted in two grocery stores, observers were positioned behind the checkout areas to watch how customers interacted with merchandise placed alongside the queues. Of the 972 customers observed, 132 were using their mobile devices. A chi-square test between mobile phone use and purchases from the shelf near the checkout area reveals a significant association (c2( 1) = 6.69, p < .01). On average, mobile phone use decreases purchases in the checkout area, from 13.2% (among customers not using phones) to 5.3% (among customers using their mobile phones). This effect highlights another potential boundary condition for the positive purchase effect of using a mobile phone while shopping.
Study 2 serves several purposes. First, using an experimental design, we replicate the results from Study 1, which increases the internal validity of our findings. Second, the random assignment of participants to mobile phone use/nonuse conditions negates any self-selection bias issues and thus provides more support for our finding that mobile phone use increases purchases through several behavioral mediators. Third, we provide more direct support for our theoretical framework by showing that it is not phones per se that cause increased purchases; rather, phone use causes consumers to become distracted from a focal task, and this distraction leads to other behavioral responses (i.e., more time in the store, shelf attention, customer loop diversion), which then lead to increased purchases. Fourth, we offer initial, exploratory insights into several boundary conditions on these mobile phone use effects.
The manipulation we imposed, regarding whether shoppers could use their mobile phones or not while shopping, could evoke potential demand effects. For example, consumers could feel more rushed in their purchase decisions. However, the consistency of the results with Study 1 mitigates this concern about demand effects to some extent. That is, the combination of our two studies overcomes each study’s potential biases.
This study was motivated by four research questions, which structure this discussion. We answer these in the following subsections.
Prior research has indicated both positive and negative effects of in-store mobile phone use, such that anecdotal evidence implies the detrimental effects of mobile phone use on impulse purchases, due to the influence of mobile blinders. Across two studies, using extensive eye-tracking field data matched with customer receipts and surveys, we show that customers who use their phones in stores spend more, with positive overall effects for retailers—even if gum and candy purchases might decrease. In our studies, mobile phone use translated into greater purchases in both studies.
TABLE: TABLE 6 Boundary Conditions, Study 2
| Department Used | b | p | na |
|---|
| Constant | 1.59 | .000 | . |
| Fruits and vegetables | 1.06 | .001 | 20 |
| Fresh foods | .77 | .010 | 25 |
| Staple items | -.21 | .524 | 20 |
| Frozen foods | -.02 | .970 | 5 |
| Nonfood | -.41 | .520 | 4 |
| Checkout | .51 | .163 | 11 |
| | r2 = .21; F(6, 110) = 4.91; p < .001 |
| Type of Phone Use | b | p | na |
|---|
| Constant | 1.57 | .000 | . |
| Shopping-related activities | .46 | .224 | 10 |
| Non-shopping-related activities | .78 | .000 | 49 |
| | r2 = .12; F(2, 114) = 8.04; p = .001 |
a The mobile phone use sample size is greater than 53 (number of people using their mobile device), because respondents could use their phones in different parts of the store or for both shopping-related and -unrelated activities. Notes: These analyses are possible only for Study 2, because Study 1 did not include the relevant distraction measures.
A simple explanation, consistent with attention capacity theories, is that mobile phone use causes consumers to become distracted from their shopping task. Once distracted, they spend more time in the store, attend to shelf information more, and divert from their normal path more often, which ultimately increases the amount they purchase. These results are consistent with findings from prior attention capacity research that indicate declines in task performance (e.g., recall, less deliberate processing) when consumers are distracted and divide their attention across tasks (Chaiken 1980; Craik et al. 1996; Park et al. 1989; Petty, Cacioppo, and Schumann 1983). We extend these findings by showing that mobile phone use not only distracts consumers but also leads to increased store purchases as a result. The additional intervening processes, such as the use of mobile phones, likely prompt less deliberative processing, an effect that deserves further research attention.
One question that might arise is whether other forms of distraction could have similar influences. We investigate this issue post hoc by exploring consumers who shop with others versus alone. Several studies imply that the presence of others acts as a distractor from the task at hand (Baron, Moore, and Sanders 1978; Groff, Baron, and Moore 1983; Sanders 1980; Sanders, Baron, and Moore 1978). For example, Baron, Moore, and Sanders (1978) find that the presence of others is a distraction because it causes attentional conflict. In our study context, shopping with others might distract a consumer from the shopping task, just as mobile phone use does, so we test this element and thereby provide a generalization of our predicted distraction mechanism to another in-store shopping factor.
Across both studies, we find that shopping with others leads to more purchases than shopping alone (Study 1: Malone = 283.64 Swedish Krona [SEK], Mwithothers = 473.05 SEK, p < .001; Study 2: Malone = 319.77 SEK, Mwithothers = 591.67 SEK, p < .01). The behavioral mechanisms responsible for these effects are distraction, increased time in store, shelf attention, and customer loop diversions—consistent with the mobile phone use results (see Web Appendix A3).
Consistent with research that shows that working memory is susceptible to the aging process (Hertzog et al. 2003; Park et al. 2002), we find that consumers older than 32 years become more distracted as a result of in-store mobile phone use, which ultimately increases their purchases. Again, it is not the use or distraction itself that directly increases purchases among older consumers but, rather, the effect of this mobile phone distraction on them: It leads them to deviate from their shopping tasks (e.g., goals, lists), such that they ignore time efficiency goals, try to multitask, slow their pace through the store to focus on their phones, and move outside their conventional paths through the store. All these behavioral responses help explain why older consumers are more susceptible to the distractions that result from in-store mobile phone use.
We also highlight other boundary conditions in Study 2, related to where in the store consumers use their mobile phones and for what uses. Specifically, we highlight which departments benefit from people’s use of mobile phones while in those areas (e.g., fresh fruit and vegetables) and which do not (the checkout area). We also highlight how in-store uses of mobile phones for non-shopping-related activities enhance these mobile use effects.
Our final research question reflects the interests of the managers we worked with in the stores, who were intrigued by our findings but concerned about potential pitfalls associated with encouraging in-store mobile phone use. In Study 1, we find no differences in satisfaction levels, indicating that mobile phone use does not increase or decrease customers’ satisfaction with their shopping experiences. In Study 2, use of the mobile phone marginally lowers overall satisfaction but does not influence service satisfaction. Mobile phone use increases the amount of time and backtracking consumers do in stores, so the benefits that consumers get from their mobile phone use may make up for any inefficiencies caused by their multitasking.
Theoretically, this study extends limited attention capacity theory by applying it to the unique context of in-store mobile phone use. Consumers use their mobile phones for more than just voice calls or texts, so it is important to understand how these uses affect consumers’ daily lives and alter their abilities to perform day-to-day tasks. Substantial research has suggested ways to use mobile technology to communicate with customers (e.g., Andrews et al. 2016; Danaher et al. 2015; Grewal et al. 2016), but little investigation to date has explained how general mobile phone use might interfere with customers’ performance of traditional activities, such as shopping. In addressing this gap, our results identify distraction as a key mechanism responsible for increased customer purchases, such that it leads to increased time in the store, shelf attention, and customer loop diversion—consistent with attention capacity theories. Finally, we identify boundary conditions of these effects, such that in-store mobile phone use causes older consumers to become distracted and increases the amount they purchase. We also provide preliminary evidence for the boundary roles of what consumers use their mobile phones to do and where they use them in the store.
From a managerial perspective, we demonstrate the practical benefits when customers use their mobile phones while shopping. The use of mobile phones increases their time in the store, alters their perceptions of the merchandise, and changes their shopping path. These in-store behaviors in turn result in a significant increase in purchases. Furthermore, this study shows that mobile blinders exist only in certain parts of the store (e.g., checkout aisle); they do not limit overall spending. Retailers thus might encourage instore mobile phone use, such as through direct interactions that offer coupons or targeted advertising (Hui, Inman, et al. 2013) or by rewarding customers for their participation in a mobile game or app while in stores.
Another option might be to offer phone charging devices on customer carts, which could encourage use but also prompt customers to stay longer in the store, while they wait for their batteries to get boosted. Even providing free wi-fi service and encouraging customers to use it through signage could increase purchases. Coffee shops and restaurants offer free wi-fi services to prompt customers to linger and perhaps buy more. Other types of retailers should take notice; getting customers to use their mobile devices seems to work for not only coffee shops but also grocery stores and likely other retail outlets as well. Ultimately, the goal must be to create a shopping experience that benefits both the customer and the retailer; our results show that retailers can gain increased shares and drive new purchases simply by granting customers the freedom and means to remain connected during their shopping trips.
Eye-tracking technology enables researchers to analyze behavior effectively and minimize self-reported bias and inflated survey responses. However, we lack access to measures of previous shopping behaviors, which could be of use for comparing behaviors. Additional research might seek a more comprehensive picture of not only the shopping situation but also the shopper by gaining access to loyalty card information or net promoter score measures. Research also might focus on visual scanpaths, which can also be collected by eye-tracking glasses, to detail the apparent differences in the cognitive processing of products that customers perform when they use mobile phones during their shopping trips or not. As we have argued, customers may be less analytical when distracted, and this effect could be explored with even deeper eye-tracking analyses.
Another key limitation of our novel use of portable eyetracking glasses involves coding capacity: if the entire shopping trip is the subject of interest, the videos cannot be coded using automated scripts. Most studies that rely on eye tracking in a retail setting designate a single shelf or area of interest, which can be coded automatically by computer software. Our coding had to be conducted manually, which inherently creates the potential for coding errors.
The present research focuses on the general effects of mobile phone use in physical stores. But our exploratory analyses suggest that mobile phone use has distinct influences in different parts of the store; for example, in fresh food areas (fruits, vegetables, meats, seafood, dairy, baked breads), this use leads to more distraction. One reason might be the actual location of produce departments (i.e., front of stores) in our retail settings. Consumers might not feel rushed when they start their grocery shopping trip, which allows them to be distracted more easily here than in other departments. The atmospherics of the fresh food areas also might be influential. They tend to offer more space, so consumers can more easily stop and use their mobile phones, without fear of blocking the aisles. Moreover, not all types of mobile phone use provide similar benefits (e.g., store- versus non-store-related tasks), such that non-store-related activities exert stronger effects on distraction levels. Non-store-related tasks include using mobile phones to listen to music or chat with friends. However, because of the scarcity of these activities among our sample respondents, our power to make meaningful comparisons across types of activities is limited. Additional research is needed to explore these ideas in more depth.
Further research also could extend our efforts to determine precisely what happens, for example, when customers are not moving (e.g., checkout line, deli counter) or when they are interacting with digital displays, in-store demonstrations, and service employees. Eye-tracking methodology can continue to provide greater insights into customer experience management. In line with Sciandra and Inman (2016), we anticipate a potential moderating effect of store-related uses (e.g., shopping lists, price comparisons), relative to non-store-related uses (e.g., social networking), of mobile phones. Additional research might prime customers with different mobile phone use activities to assess their effects on in-store shopping behaviors.
Finally, continued research should include different types of retailers (e.g., department stores). In the grocery store setting, in which our studies took place, price comparisons might be somewhat less important than in stores with higherpriced merchandise, such as department stores or electronic retailers. For example, the eye-tracking data in our Study 2 (mobile phone use condition) indicate vast differences, such that only 18.9% of shoppers use their phones for shoppingrelated tasks, but 92.5% use them for unrelated tasks (percentages can total over 100% as respondents can use their phones for both shopping related and unrelated tasks). The distribution of mobile uses for mass merchandisers appears more evenly split (Sciandra and Inman 2016). Furthermore, if some stores function like showrooms, mobile phones might enable consumers to purchase merchandise from the web while in the store (Rapp et al. 2015).
In conclusion, mobile phone use can lead to increased purchases for retailers, without detracting from customer satisfaction levels. We hope these results stimulate additional research on in-store mobile phone use, the role of age for customer interactions with in-store technologies, and how retailers can encourage customers’ in-store mobile phone uses.
TABLE: APPENDIX A Study 1 Information
| A: Demographics of Study Participants by Store |
|---|
| | Chain | n | Age (Years) | Gender (F%/M%) | Number of Children |
|---|
| Supermarket A | 1 | 69 | 42.57 | 36.23/63.77 | 1.01 |
| Supermarket B | 1 | 70 | 40.71 | 38.57/61.43 | 1.31 |
| Supermarket C | 1 | 83 | 40.47 | 44.58/55.42 | 1.35 |
| Supermarket D | 2 | 72 | 42.46 | 37.50/62.50 | 1.35 |
| B: Demographics of Study Participants by Condition |
|---|
| | n | Age (Years) | Gender (F%/M%) | Number of Children |
|---|
| Using mobile phone | 71 | 40.39 | 38.03/61.97 | 1.35 |
| Not using mobile phone | 223 | 41.86 | 39.91/60.09 | 1.23 |
Notes: The demographic data gathered from the questionnaires revealed no significant differences across stores in customers’ age (F( 3, 290) = .63, p = .59), gender (c2( 3) = 1.35, p = .72), or number of children living at home (F( 3, 290) = 1.48, p = .22). Similarly, there were no differences between customers using (or not using) mobile phones with regard to their age (t(292) = .90, p = .37), gender (c2( 1) = .08, p = .78), or number of children living at home (t(292) = .78, p = .44).
TABLE: APPENDIX B Study 2 Information
| A: Demographics of Study Participants by Store |
|---|
| | Chain | n | Age (Years) | Gender (F%/M%) | Household Size |
|---|
| Supermarket E | 1 | 69 | 43.59 | 43.48/56.52 | 2.48 |
| Supermarket F | 1 | 48 | 42.01 | 64.58/35.42 | 2.52 |
| B: Demographics of Study Participants by Condition |
|---|
| | n | Age (Years) | Gender (F%/M%) | Household Size |
|---|
| Using mobile phone | 53 | 42.38 | 47.17/52.83 | 2.60 |
| Not using mobile phone | 64 | 43.41 | 56.25/43.75 | 2.41 |
Notes: There were no differences between customers using (or not using) mobile phones or not in terms of age (t(115) = .39, p = .70), gender (x2( 1) = .96, p = .33), or household size, (t(115) = .91, p = .36).
Note: Each mediation pathway was run as a separate mediation model (Hayes’ [2013] model 4).
1 In comparison, the average size of a U.S. grocery store is 45,000 square feet, though chains like Aldi and Trader Joe’s are typically less than 20,000 square feet (Tuttle 2014). Thus, the grocery stores we study are about 20% smaller than typical U.S. grocery stores.
- 2 Undoubtedly, the mediators could correlate. To assess whether each mediator can still explain the relationship when taking the others into account, we reexamined the mediation models with PROCESS Model 4 when all three mediators appeared simultaneously. The total indirect effect for all three mediators included simultaneously is significant both here and in Study 2. Specifically, the individual pathways, while controlling for the other two paths, reveal that mobile phone use → time in store → sales is still significant in Studies 1 and 2, and mobile phone use → shelf attention → sales is significant in Study 1 and directional in Study 2. However, the customer loop diversion pathway becomes insignificant in both studies.
- 3 To assess the quality of the coding, a random sample of 24% of the eye-tracking videos from Study 2 were coded on the variables “time in store” and “time spent on mobile phone” by an independent coder. Both variables had high correlation with the original coding (r = .95 and r = .93, respectively).
- 4 We also ran the models without the noncompliant users. The results remained generally the same, except that we needed to apply a 90% CI for the moderating effect of age when mediated through shelf attention.
- 5 To assess whether the additional purchases came from certain departments, we considered five departments that reflect the stores’ structures: fresh fruits and vegetables, fresh foods, staple foods, frozen foods, and nonfood. Using regression analyses, we checked whether respondents in the mobile phone use condition shopped more in certain departments than did those in the nonuse condition. The results show that increased time spent on the phone exerts a positive impact on purchases in the fruits and vegetables department (b = 31.88, p < .001) and a marginal positive impact on staple food purchases (b = 17.07, p < .09). The effects in the other departments are not significant.
- 6 When we analyze just the mobile phone condition using independentsample t-tests, the effects of the fruit and vegetables, and fresh food departments continue to be significant at p < .10 one-tail significant levels.
GRAPH: FIGURE 3 Johnson–Neyman Significance Regions (Study 1)
GRAPH: FIGURE 4 Johnson–Neyman Significance Regions (Study 2)
DIAGRAM: FIGURE 2 Models Tested in Study 1
PHOTO (BLACK & WHITE): FIGURE 1 Sample Screenshots of the Visual Field and Fixations, Displayed by the Eye-Tracking Software
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Record: 100- Integrating Marketing Communications: New Findings, New Lessons, and New Ideas. By: Batra, Rajeev; Keller, Kevin Lane. Journal of Marketing. Nov2016, Vol. 80 Issue 6, p122-145. 24p. 3 Diagrams, 3 Charts. DOI: 10.1509/jm.15.0419.
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Record: 101- Integrating Theory and Practice in Marketing. By: Kumar, V. Journal of Marketing. Mar2017, Vol. 81 Issue 2, p1-7. 7p. 1 Diagram. DOI: 10.1509/jm.80.2.1.
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Section: Special Issue: Theory and Practice in MarketingIntegrating Theory and Practice in Marketing
Background of the Special Issue
Academic research in marketing, as with other scholarly domains, is largely driven by three forces: concepts, theories, and previous findings. These forces continue to add value to the knowledge base and spur new research. Marketing practice, however, is more nuanced in that managers’ decisions are likely to involve subtleties and variations in interpretation. Furthermore, the immediacy of action required can range anywhere to immediately to some point in the future. As businesses evolve, marketing academia’s understanding of them evolves as well. A key factor of this maturing understanding has been the impact of ongoing, meaningful research. This understanding has materialized through strategy development based on theoretical knowledge and subsequent implementation. In other words, the significant evidence-based knowledge created through academic research in marketing and related fields has provided organizations valuable insights for managing their businesses. Research has demonstrated that managing through legacy approaches, intuition, or organizational snapshots imposes costs and substantial risks on the organization. As a result, evidence-based knowledge creation, the forte of academic scholars at universities worldwide, continues to be essential. In such an environment, academic scholars and practitioners stand to gain because they enjoy ample scope for collaboration and knowledge development. This is where conferences such as the Theory + Practice in Marketing (TPM) play a critical role.
The TPM Conference was created in 2011 with the aim to encourage relevant research that addresses substantive problems. The marketing scholars attending that one-day symposium hosted by Columbia University voiced the need for more managerially relevant research whose focus was not restricted to merely advancing sophisticated research methods. Furthermore, participants observed a lack of sufficient substantive focus in the literature that not only rendered the marketing field irrelevant but also diluted the quality of education and research in marketing at business schools. In the following years, TPM conferences conducted at Harvard University (2012), London Business School (2013), and Northwestern University (2014) emphasized these issues and received great support from the academic and practitioner communities.
This special issue of Journal of Marketing (JM) is the outcome of the 2015 TPM Conference. This annual conference encourages and showcases research in marketing that integrates the issue of relevance, rigor, and impact on practice. The J. Mack Robinson College of Business at Georgia State University organized the 2015 TPM Conference in Atlanta on June 10–12. The conference was cochaired by myself (Georgia State University), Sunil Gupta (Harvard University), and Donald Lehmann and Bernd Schmitt (Columbia University).
In 2015, TPM entered its fifth year, in which the conference attracted many leading scholars and practitioners. Keeping with the theme of TPM, the 2015 conference was also designed to showcase research that focuses on substantive business problems and is supported by a sound methodology. The conference consisted of a series of thought-provoking presentations from academic researchers as well as senior marketing practitioners. Specifically, speakers from the marketing practitioner community included, among others, Alan Beychok (President and Chief Executive Officer, Benchmark Brands; keynote address), Marty Hinson (Vice President, Cox Communications), Greg Holzwarth (Senior Vice President, SunTrust Bank), Monica Lopez (Vice President, Georgia Pacific), Maureen Schumacher (Vice President, Intercontinental Exchange), and Scott Waid (Senior Vice President, Equifax). The topics that were covered included customer relationship management, advertising effectiveness, marketing channels, salesperson management, pricing, new media, brand management, and product development, among others.
In this endeavor, JM is proud to be associated with TPM in publishing a special issue on the papers presented at this conference. The conference received an enthusiastic response from researchers, totaling more than 120 submissions. The criteria for selection were in line with the TPM principles, and thus the first screening weeded out studies that were not yet ready for the marketplace. Seventy-two studies were selected to be presented at the conference, and all were eligible for submission to the special issue. The conference provided these authors with sound feedback (which acted as the de facto second-round review), and JM subsequently received more than 40 submissions. The journal’s acceptance criteria were stringent such that each paper had to contribute to both theory and practice. All the submitted studies went through JM’s rigorous review process and received critical feedback. Finally, only seven were accepted for final publication, an acceptance rate typical for JM.
The Need for Integrating Theory and Practice in Marketing
In the previous section, I described the “what” of the TPM conference. In this section, I discuss the “why” of the conference. In other words, why do we need such a conference, and how can it explain the ongoing academic research climate? Scholars have identified the need for theory to drive practice and for practice to spur theory development. In this way, both theory and practice play an important role in furthering knowledge generation in marketing (Jaworski 2011; Lilien 2011). I concur with this view. Indeed, a closer look at this relationship reveals not just an influence of one on another, but a tightly knit cyclical loop. I refer to this as the perpetuity of theory-practice-theory cycle of furthering science and practice in marketing (see Figure 1).
While this cyclical relationship is fairly intuitive, a few intermediate steps between the theory development and the practice of marketing are necessary for this relationship to come to fruition. First, theoretical principles form the foundation necessary to explain real-world phenomena. With the right theoretical support, it is possible to ( 1) undertake precise and rigorous empirical analyses; ( 2) establish the validity of findings; ( 3) bring in an interdisciplinary focus to research problems; and ( 4) explain, contradict, or even refute a finding. On the topic of refuting a previous finding, it is worth noting that JM continues to attract studies that have questioned conventional wisdom by providing counterintuitive findings. For example,
Plouffe et al. (2016) show that the performance of “strategic” frontline employees is more affected by the employees’ influence on both the internal business team and external business partners than by their influence on customers. Fornell, Morgeson, and Hult (2016) show that customer satisfaction produces abnormal returns and has a direct and tangible financial benefit for firms, though firms face significant costs in satisfying their customers. In addition to differing from theoretical and managerial expectations, the studies of Plouffe et al. and Fornell, Morgeson, and Hult are of great interest to marketing academics and practitioners. These studies would not have been possible if it were not for the theoretical foundation that already existed. Studies such as these are also proof that a sound understanding of existing theoretical principles opens new research avenues and broadens our knowledge base.
In breaking theoretical ground, recent academic research has tackled both traditional topics (e.g., pricing, distribution) and emerging topics (e.g., new media, big data) simultaneously. While it is refreshing to see studies that address both traditional and emerging topics (often, even within a single publication issue of JM), the focus on many occasions is more on theoretical rigor than on managerial relevance. As I mentioned in my January 2016 editorial, I believe that the marketing community will be better served if we adopt a rigor and relevance approach, as opposed to a rigor versus relevance approach (Kumar 2016). Kumar, Bhagwat, and Zhang (2015) is an example of a study that reflects the balancing of rigor and relevance in academic research. The authors address the issue of customer win-back faced by businesses. Reacquiring customers who left the firm may help firms not only regain lost profits but also take profits from competitors. In this regard, identifying the value of the reacquired customers is important to justify business action. This study empirically demonstrates how ( 1) the lost customers’ first-lifetime experiences and behaviors, ( 2) the reason for defection, and ( 3) the nature of the win-back offer made to lost customers are all related to the likelihood of their reacquisition, their second-lifetime duration, and their second-lifetime profitability per month. Specifically, Kumar, Bhagwat, and Zhang find that reacquired customers generally stay longer, and customers who defected because of price stay the longest of all after being reacquired. When a firm implemented the key learnings from this study, the second-time customers had an average lifetime value of $1,410 (vs. just $1,262 during their first relationship), thereby highlighting an important upside of win-back strategies (see also Kumar, Bhagwat, and Zhang 2016). This study effectively blends empirical rigor with managerial relevance to generate insights that not only provide much-needed relief to practitioners but also spur new research avenues for scholars.
Furthermore, firms are aiming to understand emerging topics in marketing, yet marketing academia has offered little recourse in helping firms make sense of the changing landscape. For instance, whereas big data as a topic is continually changing the way firms function, marketing academia has not provided comprehensive directions for navigating big data issues. This is due to the lack of theoretical studies exploring substantive issues in emerging topics. A few ways in which researchers can push the boundaries of theoretical advancements include ( 1) reviewing published articles in scholarly journals such as JM, Journal of Marketing Research, Journal of Consumer Research, and Marketing Science; ( 2) introducing knowledge from other disciplines such as economics, statistics, and psychology; and (c) interacting with peers in academia.
Using theoretical principles, firms can develop strategies/tactics to help them function. While the development of strategies/tactics can be in the form of a company “playbook” that provides a broad approach to the marketing function, academic research has shown that it can also arise out of theoretical and empirical research that recommends precise marketing actions. For example, Zhang et al. (2016) propose that as customers migrate through different relationship states over time, not all relationship marketing strategies are equally effective. Using a business-to-business (B2B) relationship data set, the authors identify four latent buyer–seller relationship states based on each customer’s level of commitment, trust, dependence, and relational norms. The authors also compare the relative importance of different migration strategies at various relationship stages and focus on the differential effectiveness of relationship marketing strategies across relationship states, thereby generating valuable managerial insights. Pansari and Kumar (2016) develop a theory of customer engagement (CE), wherein they posit that the quality of the relationship between the firm and the customer depends on the level of satisfaction derived from the relationship and the level of emotional connectedness of the customer toward this relationship. In other words, when a firm achieves trust, commitment, and a satisfied and emotional relationship with the customer, we can say that a relationship centered on engagement has been forged between the firm and the customer. Drawing on theoretical support, this study proposes a framework that elaborates on the components of CE as well as the antecedents (satisfaction and emotion) and consequences (tangible and intangible outcomes) of CE. Kumar and Pansari (2016) implemented the strategy of engaging customers and employees in 120 firms and showed the power of engagement as a strategy through gains in firms’ sales and profit. Studies such as these are evidence that theoretical principles can and do lead to profitable strategy development and implementation.
However, the development of strategies/tactics depends on the firms’ ability to acquire and apply knowledge. Businesses are constantly learning and adapting to marketplace pressures in the form of competition, collaborations, cross-border expansions, technology upheavals, changing consumer tastes and preferences, and regulatory restrictions, among other factors. In such a complex environment, the precise transfer of know-how is critical to ensure that the theoretical knowledge is accurately translated to strategies/tactics.
Next, the strategies/tactics have to be implemented to reap their full benefits. The implementation of strategies/tactics involves three key aspects: ( 1) how well they accommodate the current business conditions and challenges the firm faces, ( 2) how synchronous they are with the other organizational objectives, and ( 3) whether they expose the firm to any significant risks and vulnerabilities that could impede growth. The marketing discipline has produced several impactful studies that have documented the benefits of implementation. In this regard, JM and Marketing Science Institute (MSI) have collaborated several times to foster and disseminate new mar
keting knowledge. In 2004, JM published a special section on “Linking Marketing to Financial Performance and Firm Value” that was sponsored by the MSI. Again in 2009, MSI sponsored a JM special section on “Marketing Strategy Meets Wall Street” that contained articles focused on practitioner needs. JM’s annual MSI/H. Paul Root Award is also a step in this direction in that it recognizes studies for their significant contribution to the advancement of the practice of marketing. Recent awardees include You, Vadakkepatt, and Joshi (2015), Nam and Kannan (2014), Hui et al. (2013), Stahl et al. (2012), and Schmitt, Skiera,
and Van den Bulte (2011). The ideas presented in these award-winning articles continue to affect the business community and profoundly influence managers worldwide.
However, the implementation of strategies and tactics are contingent on the availability of firm resources. The resources can be broadly categorized along business and people dimensions. The business dimension requires availability of technological, marketing, and financial capabilities in addition to the ability to define and articulate the business case for the proposed strategies/tactics and the desired outcomes of change. Quantification of the projected return on investment is required not just within specific business units but also across multiple key stakeholders and business units. Cross-organization collaboration is essential to identify and assess key stakeholders, degree of risk, technological readiness, and cost involved in the implementation. On the people dimension, managers and professionals who have traditionally been responsible for all aspects of marketing now have to be brought “on board” with the proposed implementation. This includes hiring and training people across all business units with the ability and responsibility to carry out the identified strategies/tactics. For example, Kumar and Shah (2011) demonstrate the need to train the relevant stakeholders of Prudential Financial Services with the right tools to implement the suggested strategies and tactics, which resulted in an incremental revenue of approximately $500 million in the first year.
When the established theories are translated to strategies/tactics and implemented at organizations, the practitioner community stands to gain. However, not all strategy implementation directly leads to the success of the firm; some do fail. The key lies in identifying a business challenge that is of managerial interest. That is, the practical relevance of a study is established only when the impact of the research reaches the functional or line managers of the firm, and not just the academic marketing community. To achieve this, researchers must start by interacting with practitioners and decision makers. Field experiments (e.g., Petersen and Kumar 2015) and pilot studies (e.g., Rust and Zahorik 1993) are often a good way to showcase the research’s managerial potential and enthuse managers. Implementation in an organization goes even further in demonstrating the validity and applicability of the proposed solution. Toward this effect, the academic–practitioner conferences such as TPM and Marketing Strategy Meets Wall Street bring specific business challenges to the forefront and create a forum for identifying solutions for practitioner-focused problems. In addition, the Gary L. Lilien ISMS-MSI Practice Prize, which acts as an incubator for studies demonstrating significant impact on the performance of organizations, and the MSI Research Priorities, which serve as an impetus for scholarly research based on the challenges in the marketplace, continue to shape the scholarly academic–practitioner discourse. This special issue is envisioned to bring together such thoughts and discussions that can germinate future studies.
When theory successfully helps the practitioner community, the insights used and gained through the process accrue to the overall knowledge base. As more studies achieve this success across various market settings, the empirical generalization materializes. In the past, scholars have called for a close interaction between theory and empirical generalizations (Barwise 1995; Bass 1995; Bass and Wind 1995; Leone and Schultz 1980). That is, the learning to become a part of accepted wisdom and popular practice. A direct outcome of such generalization can be observed in marketing education. Currently, marketing education in business schools worldwide does little to help students understand and uncover the real issues that affect the industry. By bringing managerially relevant research into business education, future managers can be groomed to be more receptive toward academic research and even actively participate in research initiatives. When research articles comprehensively answer the “What’s in it for me?” question (as viewed from the practitioners’ perspective), they also address the relevance issue and secure the attention of the practitioner audience.
Whereas empirical generalizations help the academic and practitioner community, they can and do get questioned from time to time. Investigations that question such established wisdom can (re)shape the theoretical principles that continue to influence marketing practice. This perpetual cycle is powered by forums that facilitate the coming together of the two communities, such as TPM. In this regard, this special issue of
JM is an outreach in keeping the perpetual cycle in motion by delivering the exciting interchange of ideas to the scholarly marketing community.
Key Learnings from This Special Issue
Toeing the line of TPM, this special issue contains studies that score very highly on the relevance, rigor, and impact of marketing. These studies reflect the commitment of TPM and JM in fostering meaningful research. Next, I summarize the key learnings from the seven studies that appear in this special issue. An important element to note is that these articles span behavioral, substantive, and methodological domains in creating relevant, rigorous, and impactful research.
Gamified Information Presentation and Consumer Adoption of Product Innovations
In this article, Jessica Mu¨ller-Stewens, Tobias Schlager, Gerald Ha¨ubl, and Andreas Herrmann (2017) build on the literature on incorporating games into the shopping process to investigate the methods by which firms can use games to communicate product innovations to customers. The authors propose that “presenting information about a product innovation in the form of a game … ignites two parallel psychological process by which it promotes consumer innovation adoption” (p. 8). These processes refer to the increase of playfulness/curiosity and the increase of perceived vividness. The authors develop these propositions into a robust theoretical framework and propose three hypotheses—one hypothesis that predicts a link between the gamified presentation of information and increased consumer adoption of that innovation, and one mediating hypothesis for each of the two psychological processes.
Methodologically, the authors conduct two field studies and five additional experiments across a wide range of product domains. Study 1 tests the general effect of gamified information presentation within the context of a large European automobile manufacturer, while Study 2 runs a similar test in a more tightly controlled experimental setting. Study 3 is a field experiment wherein the participants were not aware that they were participating in a study. Studies 4a and 4b are designed to illuminate the underlying psychological forces behind the observed effect, while Study 5 tests the mediating hypotheses. Finally, Study 6 tests for the importance of integration between innovation information and actual gameplay.
The authors identify a fundamental difference between prior research on the incorporation of games in the shopping process and their current study. The former has largely understood games as pleasurable tasks that create a positive brand experience, whereas the “gamified information presentation” views games as vehicles to convey product information. Gamified information presentation also extends the broader literature streams of information presentation formats and experiential marketing. Given the managerial importance of successfully communicating new product innovations, the practical insights of this research cannot be overstated.
Sales Representative Departures and Customer Reassignment Strategies in Business-to-Business Markets
Sales representative (rep) turnover in B2B firms is responsible for a significant stalling in profits. Given that firms spend an average of 7% of profits on their sales forces, and that it is the salesperson’s job to maintain a close relationship with his or her customers, it is imperative for firms to follow the best procedure for replacing sales reps who depart the company. Otherwise, the firm runs the risk of severing the links that the salesperson made with his or her customers. In this article, Huanhuan Shi, Shrihari Sridhar, Rajdeep Grewal, and Gary Lilien (2017) aim to determine “the magnitude of the causal effect of sales rep departures on customer-level revenue” (p. 25) while parsing the advantages and disadvantages of various sales rep replacement strategies.
The authors shed light on the comparative benefits of replacing a departing sales rep with either an existing sales rep or a new hire; they also examine how the transition to an existing rep is moderated by the similarities between that rep’s past experience and customer base and those of the departing rep. This is organized according to a series of research questions that aim to determine the most effective course of action. The authors collect data from a leading distributor of electrical component products and use a difference-in-differences model specification to approximate an ideal experiment wherein reps randomly depart and are randomly replaced.
This extensive examination of the heterogeneous effects of reassignment strategies is of clear managerial relevance and should be a great boon to B2B firm decision making. This research also constitutes a step forward for the literature on interorganizational trust and sales rep effectiveness. It is my hope that more work will be conducted that demonstrates these effects across other B2B distributors and accounts for team selling and cross-selling contexts.
Optimizing a Menu of Multiformat Subscription Plans for Ad-Supported Media Platforms
Media consumption and distribution are rapidly changing, with traditional formats in decline and new ad-supported digital platforms on the rise. These platforms are primarily audiencebuilding platforms that aim to capture both consumers and advertisers and offer subscription bundling options to suit the needs of both groups. This paradigm shift necessitates further advances in the marketing research field concerning, among other things, how media firms can optimize their bundling options. In this article, Vamsi K. Kanuri, Murali Mantrala, and Esther Thorson (2017) point out that little research exists on this issue. As such, they undertake the challenge of leveraging theory and empirics to develop guidelines for designing “menus” of content subscription bundles, with the aim of maximizing profits from both consumers and advertisers.
The authors draw from literature streams on pricing in twosided markets, bundling/versioning, and product line design and develop a theoretical framework specific to contemporary media platforms. Their multistep methodology involves assessing the willingness to pay among customers for different plans, developing a two-sided market-level model of consumer/advertiser demands by format, and, finally, arriving at a profit-maximizing menu. As the authors explain in their conclusion, “the study builds a novel mixed integer nonlinear programming algorithm that can effectively determine the optimal menu of subscription plans” (Kanuri, Mantrala, and Thorson 2017, p. 61).
Although this research is primarily aimed toward media firms, the findings are relevant to the wider literature on twosided markets and product line design. These contributions are matched by the possibilities for further research suggested by the author-identified limitations. The proposed framework can be further refined to fit other media platforms beyond newspapers, while the rapid changes in advertising trends ensure the need for additional updates down the line. Finally, there is the possibility of a simultaneous optimization of menus for both consumers and advertisers.
When 1 1 1 > 2: How Investors React to New Product Releases Announced Concurrently with Other Corporate News
Nearly 7% of press releases are made concurrently with another corporate announcement made by the same firm on the same day. In line with the efficient market hypothesis, it is generally believed that there is nothing remarkable about such concurrently made announcements and that firms benefit no more or less from them than they would from the same announcements made on separate days. In this article, Nooshin L. Warren and Alina Sorescu question this line of thinking and propose that concurrent announcements adhere to what Barber and Odean (2008) define as “attention grabbing,” resulting in increased visibility and a greater number of prospective investors. Furthermore, concurrent announcements are widely understudied because they are usually eliminated from event studies owing to their confounding effects on stock returns. Thus, the authors have identified a substantial research gap with the potential for strong managerial and theoretical contributions.
Theoretically, the authors propose an alternative paradigm to the efficient market hypothesis, whereby the content of the announcements is less important than the buzz or attention generated by the timing and/or style of the messaging. The authors draw on Merton’s (1987) model of capital market equilibrium to account for disparities in the benefits of investor recognition across various firms and devote the bulk of their hypotheses to identifying the firm-specific antecedents of concurrent announcements. Finally, the authors predict that under these conditions, stock market reaction to concurrent product announcements is greater than stock market reaction to separate product announcements.
Methodologically, the authors use a comprehensive sample from RavenPack News Analytics that disregards announcements whose timing could not be controlled, as well as those without corresponding financial data. The authors use two logistical regressions and propensity score matching to test the hypotheses, which are generally supported. The managerial takeaway from this research is substantial, as the authors have identified firm-specific conditions that are conducive to concurrent corporate announcements.
The Sting of Social: How Emphasizing Social Consequences in Warning Messages Influences Perceptions of Risk
Many popular consumer behaviors incur severe health risks associated with national health epidemics, and it has fallen to government agencies to determine the most effective ways to curb these behaviors or at least to influence risk perceptions among target consumers. Conventional wisdom has long dictated that emphasizing the health consequences of these behaviors is the most compelling way of delivering warning messages to consumers. However, the literature on risk perception has revealed an underlying complexity to this approach. For warning messages to be truly effective, consumers must be convinced that they are vulnerable to the negative health consequences. The most severe health consequence communicated in the most graphic terms may make little impact on a consumer if (s)he is convinced that the risk is a distant one.
Through this article, Mitchel Murdock and Priyali Rajagopal (2017) contribute to this literature by proposing joint warning messages that couple severe, long-term health consequences with more immediate social consequences. They test the efficacy of this approach across five studies. In Studies 1 and 2, the authors demonstrate that communicating negative social consequences along with negative health consequences leads consumers to perceive themselves as more susceptible to the latter. Study 3 demonstrates the importance of sequencing, such that negative health outcomes lead directly to negative social consequences. Studies 4 and 5 yield supplementary insights, comparing the method of adding social consequences with temporal framing and revealing that the demonstrated effects encompass both intentions and product experience perceptions.
The authors have undertaken an ambitious research goal that contributes to multiple literature streams (risk perception, temporal perception, and consumer experience) while also yielding readily apparent managerial insights that public policy makers can use to create more effective warning messages. They have helpfully explained the current limitations of their research so as to stimulate further advancements in this domain.
How to Separate the Wheat from the Chaff: Improved Variable Selection for New Customer Acquisition
In the introductory paragraphs of this article, Sebastian Tillmanns, Frenkel Ter Hofstede, Manfred Krafft, and Oliver Goetz (2017) provide an overview of the serious difficulties facing firms with respect to customer acquisition. It is telling that customer acquisition is underresearched relative to customer retention and profitability. The authors focus mainly on the problem of variable selection. The common use of variables derived from list vendors has been repeatedly called into question, especially in light of the unpredictability of customer response. Their proposed solution lies in the form of a Bayesian variable selection model that includes both parametric and nonparametric specifications, which they compare with various other approaches.
The authors decide on the Bayesian variable selection approach using a spike-and-slab prior to account for the overwhelming number of potential variables available from lists, and they discount other approaches that might overlook optimal variable combinations. They obtained data from a direct marketing campaign by a major German insurance company and conducted a series of post hoc analyses that shed light on the optimal number of variables. The results affirm the superior predictive performance of the Bayesian model.
The managerial implications of this research are particularly strong in light of MSI’s identification of big data management as a top research concern. Additional contributions include the improved navigation and use of sociodemographic information and carryover insights into both customer churn and customer win-back. Hopefully, researchers in the future will try to replicate these findings in the context of other industries.
The Effects of Advertised Quality Emphasis and Objective Quality on Sales
In this article, Praveen K. Kopalle, Robert J. Fisher, Bharat L. Sud, and Kersi D. Antia (2017) draw on Federal Trade Commission statistics and the inconclusive results of various field studies to find that the commonly held belief that quality emphasis is always advantageous in advertising is misleading. For low-quality brands, quality emphasis may actually be disadvantageous. This research runs contrary to commonly held perception and intuition, resulting in a dynamic manuscript that advances the literature on information disclosure in advertising. Managerially, there is a case that the divergent effects between high- and low-quality brands studied in the automobile industry are generalizable across multiple lowinvolvement purchases, yielding wide-ranging implications that, again, run counter to current marketing norms.
Given that field studies and lab experiments have yielded contradictory results in previous research, the authors employ a multimethod approach to corroborate their findings, including ( 1) a field study within the minivan product line of the automobile industry, ( 2) related analytical evidence, and ( 3) a lab experiment that controls for differences between informed and uninformed customers. The authors propose a separating equilibrium based on key divergent effects between the advertising of low- and high-quality products. The empirical portion of the manuscript is replete with robustness checks and endogeneity corrections for the field study, manipulation checks for the lab experiment, and detailed measures for both objective quality and advertised quality emphasis.
The authors ultimately conclude that quality emphasis is ineffective if there is little to no credible, third-party support for the claim. This in turn bolsters what the authors consider the principle of marketing consistency, “which specifies that the elements of a marketing strategy should be internally consistent and therefore mutually reinforcing to be effective” (Kopalle et al. 2017, p. 124). Questions for further research abound. For instance, how do consumers parse and prioritize different sources of third-party product quality expertise? More work is expected to augment and improve on these findings in the future.
Conclusion
This special issue provides studies that have a nice blend of practical relevance and theoretical advancements. When viewed as a whole, these studies offer a rich and nuanced perspective that can spur the marketing discipline into newer areas. I would be remiss if I failed to highlight the potential of these studies in filling an important need in marketing education worldwide, especially in doctoral programs. Through this special issue, both TPM and JM are delivering on their promises to bridge the gap between substantive contributions and practical relevance in marketing research. In fostering alignment between academics and practitioners, it is JM’s privilege to direct this special issue in service of that goal.
DIAGRAM: FIGURE 1 The Perpetuity of Theory-Practice-Theory Cycle
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Record: 102- Introduction to the Special Issue--Mapping the Boundaries of Marketing: What Needs to Be Known. By: Kumar, V.; Lane Keller, Kevin; Lemon, Katherine N. Journal of Marketing. Nov2016, Vol. 80 Issue 6, p1-5. 5p. 1 Illustration. DOI: 10.1509/jm.80.6.1.
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Record: 103- Introduction: Is Customer Satisfaction (Ir)relevant as a Metric? By: Kumar, V. Journal of Marketing. Sep2016, Vol. 80 Issue 5, p108-109. 2p. DOI: 10.1509/jm.80.5.1.
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Record: 104- Investigating the Influence of Characteristics of the New Product Introduction Process on Firm Value: The Case of the Pharmaceutical Industry. By: Sharma, Amalesh; Saboo, Alok R.; Kumar, V. Journal of Marketing. Sep2018, Vol. 82 Issue 5, p66-85. 20p. 1 Diagram, 3 Charts, 2 Graphs. DOI: 10.1509/jm.17.0276.
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Investigating the Influence of Characteristics of the New Product Introduction Process on Firm Value: The Case of the Pharmaceutical Industry
Scholars identify several benefits of new product introductions (NPI), yet prior literature largely overlooks how the process of NPI generates marketplace insights and influences subsequent products. Building on the concept of absorptive capacity, the authors argue that the influence of products on firm value depends on process characteristics, namely, the pace, irregularity, and scope of NPI. Using data collected from multiple sources for products introduced by pharmaceutical firms between 1991 and 2015 and robust econometric methods that account for endogeneity and unobserved heterogeneity, this study reveals that pace and scope have an inverted U-shaped effect on firm value, whereas irregularity negatively influences firm value. Moreover, strategic emphasis and product complexity negatively moderate the relationship of the irregularity and scope of NPI with firm value. This research documents the importance of adopting a portfolio approach to the sequential introduction of new products and incorporating insights gained from previous product introductions; it cautions managers against evaluating products in isolation. The authors discuss the economic significance of these results and provide actionable guidance for managers.
new product introduction; innovation; process research; event study; firm value
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By Amalesh Sharma; Alok R. Saboo and V. Kumar
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Record: 105- JM as a Marketplace of Ideas. By: Moorman, Christine; van Heerde, Harald J.; Moreau, C. Page; Palmatier, Robert W. Journal of Marketing. Jan2019, Vol. 83 Issue 1, p1-7. 7p. 1 Chart. DOI: 10.1177/0022242918818404.
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Record: 106- Keeping the Memory but Not the Possession: Memory Preservation Mitigates Identity Loss from Product Disposition. By: Page Winterich, Karen; Walker Reczek, Rebecca; Irwin, Julie R. Journal of Marketing. Sep2017, Vol. 81 Issue 5, p104-120. 17p. 1 Diagram, 1 Chart, 4 Graphs. DOI: 10.1509/jm.16.0311.
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Keeping the Memory but Not the Possession: Memory Preservation Mitigates Identity Loss from Product Disposition
Nonprofit firms’ reliance on donations to build inventory distinguishes them from traditional retailers. This reliance on consumer donations means that these organizations face an inherently more volatile supply chain than retailers that source inventory from manufacturers. The authors propose that consumer reluctance to part with possessions with sentimental value causes a bottleneck in the donation process. The goal of this research is therefore to provide nonprofits with tools to increase donations of used goods and provide a theoretical link between the literature streams on prosocial behavior, disposition, memory, and identity. As such, the authors explore the effectiveness of memory preservation strategies (e.g., taking a photo of a good before donating it) in increasing donations to nonprofits. A field study using a donation drive demonstrates that encouraging consumers to take photos of sentimental possessions before donating them increases donations, and five laboratory experiments explicate this result by mapping the proposed psychological process behind the success of memory preservation techniques. Specifically, these techniques operate by ameliorating consumers’ perceived identity loss when considering donation of sentimental goods.
Online Supplement: http://dx.doi.org/10.1509/jm.16.0311
Consumer product disposition determines the supply chain for organizations in the secondhand goods marketplace, including nonprofits that rely on the donation of used goods. For example, Goodwill Industries, the largest retail resale nonprofit, generated $5.37 billion in retail sales in 2014 from its more than 3,000 stores and online auction site (Barrett 2015) and used these sales to fund numerous social welfare programs. Other nonprofits (e.g., Dress for Success, American Red Cross) gather donations of clothing and household goods and, instead of selling them, directly distribute them to people in need such as victims of natural disaster and political refugees. In this research, we explore a factor that distinguishes nonprofit resale stores from traditional retail marketing—specifically, the reliance on donations to build inventory. Whereas some studies have examined relationships between retailers and manufacturers for procurement (e.g., Basuroy, Mantrala, and Walters 2001; Mantrala and Raman 1999), little (if any) research has considered the unique challenge of sourcing from consumers. Because these enterprises rely on donations from consumers, they face an inherently more volatile supply chain than traditional retailers that source inventory from manufacturers. In this research, we address how organizations can strategically increase donations of used but still usable physical possessions. We do so by addressing a specific bottleneck in the disposition process, a reluctance to part with possessions with sentimental value.
Many used goods sit in storage, in homes, or in paid storage facilities rather than entering the secondary goods market where they can resume their useful life. For example, a survey conducted by eBay and Nielsen revealed that Americans have an average of 50 unused items in their homes, with some of the most common items including clothing, accessories, electronics, sporting goods, and toys (Business Wire 2007). Some of these unused but still usable goods are associated with treasured memories (e.g., toys children no longer play with, clothing worn on a special occasion that no longer fits). These product-specific memories may be a key reason why consumers are reluctant even to consider disposing of possessions they no longer use.
In this research, we therefore propose that actions designed to preserve consumers’ product-specific memories should increase their willingness to donate possessions, particularly those with sentimental value. We further propose and find that memory preservation increases donation likelihood through the link between product-specific memories and the unique personal and social identities these product-specific memories help the consumer express (Oyserman 2009). This research makes several theoretical contributions. The work adds new elements to the growing knowledge base on product disposition (Haws et al. 2012; Jacoby, Berning, and Dietvorst 1977; Okada 2001; Trudel and Argo 2013; Trudel, Argo, and Meng 2016). Much prior disposition research has focused on disposition when recipient identity is known (Brough and Isaac 2012; Lastovicka and Fernandez 2005; Price, Arnould, and Curasi 2000). A contribution of the current research is identifying how to increase product disposition when the recipient is unknown, which is the case for most donation contexts. This research also contributes to the literature on donation behavior by focusing on factors that uniquely influence donation of goods rather than time (Lee, Piliavin, and Call 1999; Liu and Aaker 2008) or money (Anik, Norton, and Ariely 2014; Savary, Goldsmith, and Dhar 2015; Small and Verrochi 2009; Smith, Faro, and Burson 2013), which have been explored more extensively. An additional contribution of this research is the examination of the unique challenges for retailers that source from consumers. Although literature has given due attention to factors in sourcing goods from manufacturers (Basuroy, Mantrala, and Walters 2001; Mantrala and Raman 1999), the current research considers personal factors that come into play when sourcing from consumers. As the reuse and sharing economy continues to grow (Brosius, Fernandez, and Cherrier 2013; Lamberton and Rose 2012), understanding consumer sourcing will be of increasing importance.
This research also has practical implications for managers of nonprofits. By demonstrating the effectiveness of specific memory preservation techniques in increasing donation of used goods, we offer a relatively low-cost, easily implementable intervention that organizations can use to increase consumer donations, thereby increasing their supply of secondhand goods. Overcoming people’s reluctance to part with sentimental goods is critical for charities to spur the donation process.
Theoretical Background
Product-Specific Memories Research on consumers’ memories has suggested that consumers treat some particularly positive memories (e.g., an anniversary dinner, a vacation) as assets to be protected (Zauberman, Ratner, and Kim 2009). To protect these memories, consumers avoid situations they think will threaten the ability to retrieve these unique memories and engage in acquisitive market behavior, such as buying physical possessions that will serve as memory markers and retrieval cues (e.g., a souvenir; Zauberman, Ratner, and Kim 2009). Recognizing that market goods may store memories for consumers, we consider how product-related memories might explain the difficulty consumers face in relinquishing goods.
We focus on goods with sentimental value, which we define as goods that represent an emotionally significant memory for the consumer. Sentimental value is emotional value that goes beyond the functional or material value of the good because of the private meaning the good has for the consumer (Holbrook 1994; Loewenstein and Issacharoff 1994; Young 1991). The sentimental value attached to a good may be the result of characteristics of the product itself (i.e., some product categories are inherently more attached to memories than others; e.g., trophies) or the result of experiences the owner has had with the product during ownership (e.g., Strahilevitz and Loewenstein 1998) or during product creation, because goods that consumers have made themselves can also come to have value beyond their functional or material value (Franke, Schreier, and Kaiser 2010; Norton, Mochon, and Ariely 2012). Goods acquire sentimental value when (1) they are from a class of goods considered inherently sentimental because they serve as memory markers connected to emotionally significant events or people in one’s life (e.g., trophies, souvenirs, family heirlooms) and/or (2) consumers have become emotionally attached to them during the course of product acquisition or consumption. Thus, all goods with sentimental value are goods that are connected to an emotionally significant memory for the consumer, although the degree of significance will vary across sentimental goods. This connection to an emotionally significant memory is what distinguishes a good with sentimental value from one that is valued more monetarily simply because one owns it (i.e., it is part of one’s endowment; Kahneman, Knetsch, and Thaler 1991).
We propose that the memories associated with sentimental goods make it difficult for consumers to dispose of possessions; consumers do not want to relinquish these memories. We further propose that preserving productspecific memories for goods with sentimental value can overcome barriers to donating, thereby increasing donations. We define memory preservation in the context of physical goods as any action designed to retain productspecific memories, including writing a note or journal entry describing the possession and its associated memories or taking a photograph of the good, such that the photograph can cue memories associated with the product even after the product is gone. We propose that memory preservation is effective at increasing the donation of goods with sentimental value because it reduces the fear of identity loss that consumers would otherwise feel when considering relinquishing possessions that are connected to the self (Belk 1988). We next turn to the link between product-specific memories and consumer identity.
Product-Specific Memories and Consumer Identity
Marketers frequently position and advertise their products as relevant to consumer memories (Sujan, Bettman, and Baumgartner 1993) and identity (Chernev, Hamilton, and Gal 2011; Forehand and Deshpande´ 2001). Such marketing can be effective because “possessions are a convenient means of storing the memories and feelings that attach our sense of past” (Belk 1988, p. 148). In other words, memories and identity are integrally linked: if a person remembers nothing of her past, she has no identity (Kihlstrom, Beer, and Klein 2002). Indeed, the most common explanations for valuing “treasured” possessions concern the memories of other people, occasions, and relationships (Csikszentmihalyi and Rochberg-Halton 1981)—that is, the owner’s various personal and social identities (e.g., an athlete, an alumnus of a particular university, a parent; Oyserman 2009). Therefore, consumers try to protect their memories in part to help retain the identity the memories represent. Because possessions with sentimental value are associated with identities, loss of these possessions can lead to strong negative reactions (Burris and Rempel 2004; Ferraro, Escalas, and Bettman 2011) and a diminished sense of self (Ahuvia 2005). Accordingly, possessions serve as an anchor for identities such that the loss of self-linked possessions results in identity loss, making consumers reluctant to donate possessions with sentimental value.
Much of the previous work exploring how consumers give up possessions with sentimental value has focused on the owner’s interest in what happens to the good after disposition. For example, Brough and Isaac (2012) show that sellers are sensitive to the way the product will be used following a transaction, accepting lower selling prices for used goods if they deem the buyer’s usage intentions for the product appropriate (e.g., intending to play a piano vs. using it as furniture). Similarly, consumers giving up possessions with sentimental value prefer recipients who know and appreciate a good’s meaning (Price, Arnould, and Curasi 2000). Unknown recipients are considered acceptable only after a shared identity is uncovered that allows the meaning of the possession to transfer along with the good (Lastovicka and Fernandez 2005). Although consumers may sometimes have control over the specific person to whom their possessions go when considering selling or giving to a friend or family member, this kind of perceived control over the future fate of a possession is lacking in most donation contexts. Large nonprofits, such as Goodwill, receive donations from millions of people (e.g., 87 million people donated to Goodwill in 2013 alone; Goodwill 2014). For most donors, the recipient of their good is someone with whom they have no relationship or, typically, even contact. Thus, a key contribution of our work beyond that of Brough and Isaac (2012) and Lastovicka and Fernandez (2005) is that we explore disposition when the sentimental value of the good cannot transfer to the recipient because consumers have no knowledge of or control over the new owner. Because our proposed mechanism to mitigate identity loss is focused on the memories represented by the good, our work differs from previous research in both our proposed method (i.e., memory preservation) and our psychological explanation (i.e., the preservation of memories to prevent identity loss). In doing so, this research offers insights for aiding consumer disposition without relying on transfer of sentimental value to the recipient.
Moderating the Effectiveness of Memory Preservation
The essence of our theoretical argument is that memory preservation strategies are effective at increasing donation of goods because preserving the memories preserves consumers’ sense of self (i.e., their identities). When concerns about identity loss are mitigated, consumers are able to let go of physical possessions. This process implies that memory preservation should be less likely to increase donation of goods lacking sentimental value. Such goods are less likely to have treasured memories associated with them and instead are retained for their future usefulness or value (Haws et al. 2012; Okada 2001). We therefore predict that preserving memories for goods will only increase donation likelihood for sentimental goods. That is, the sentimental value of the good moderates the effect of memory preservation on donation likelihood.
Our contention is that the effectiveness of memory preservation techniques is driven by mitigating the identity loss consumers would otherwise feel when donating possessions with sentimental value. Thus, one way to affirm this process is to show that if the identity most relevant to the good in question is first reinforced in some other way, consumers are more willing to donate the good because they are not as concerned about identity loss. Any activity that strengthens the association of an identity with the self-concept could serve as identity reinforcement (Reed and Forehand 2016). For example, recalling experiences in which one had confidence can reinforce the self (Gao, Wheeler, and Shiv 2009), as can recognition of an identity-relevant behavior (Winterich, Mittal, and Aquino 2013). We therefore predict that the effect of memory preservation on donation of a sentimental good should be mitigated when the relevant identity is first reinforced in an alternate manner (i.e., by focusing on all of the other non-product-specific ways in which the consumer expresses that particular identity).
The effectiveness of memory preservation also hinges on the premise that consumers want to preserve the identity reflected by the good with sentimental value. However, under some circumstances, consumers may not have a strong desire to preserve their current identity. In other words, their psychological connectedness to their future self (i.e., the perceived continuity between their present and future selves; Parfit 1984) should determine the influence of memory preservation on disposal. Although psychological connectedness has predominantly been examined as a factor affecting intertemporal choice (Bartels and Urminsky 2011; Hershfield 2011), we propose that if the future self is believed to differ from the current self, consumers should be able to more easily dispose of identity-relevant possessions regardless of memory preservation. Indeed, some research has found that when a good with sentimental value no longer represents one’s current identity, it is moved to a less central location in one’s home and only accessed when the identity is sought (Epp and Price 2010). Likewise, the effectiveness of memory preservation should be mitigated when psychological connectedness to the future self is low, because consumers should not fear identity loss from parting with goods when they do not expect their current identity to be relevant in the future.
Finally, this research focuses on donation, which differs from another common disposal method, selling, in the domains of memory and identity. We expect consumers to be reluctant to dispose of goods with sentimental value through either donation or selling when no memory preservation is present because both donation and selling typically result in the good going to a stranger who cannot know and understand the good’s value. Although memory preservation should increase willingness to donate such possessions, we propose that it will not increase willingness to sell sentimental goods. We base this prediction on a key distinction between selling and donating: the former is an economic transaction that offers personal gain and follows exchange norms, whereas the latter a noneconomic transaction with societal benefit that follows communal norms (Aggarwal and Zhang 2006; Clark, Mills, and Powell 1986). The economic focus of selling is problematic when trades are “sacred” and should not involve money (McGraw and Tetlock 2005). Indeed, there is a sizable amount of literature on buying versus selling prices and sentimentality (Boyce et al. 1992; Chatterjee, Irmak, and Rose 2013; Loewenstein and Issacharoff 1994). The primary finding of this work is that owners of a good typically demand extremely high prices that buyers are not willing to pay and/or they refuse to sell at all. This refusal to entertain the idea of selling as a method of disposal because of an item’s sacredness is likely to apply to goods with sentimental value. Therefore, we predict that even memory preservation techniques cannot increase likelihood of selling a good with sentimental value because the economic exchange associated with selling taints the sentimental value of the good regardless of whether the memory is preserved. Thus, we expect a moderating effect of disposition type on the effectiveness of memory preservation such that, when considering selling, consumers will be reluctant to dispose of the good regardless of memory preservation. In contrast, when considering donation, consumers will be more likely to dispose of the good after memory preservation.
Overview of Studies
We test our predictions in six studies. Study 1 demonstrates a meaningful increase in actual donations in the field resulting from a promotional campaign encouraging consumers to use a memory preservation strategy (i.e., taking a photo) before donating goods with sentimental value. Studies 2–6 use laboratory and Amazon Mechanical Turk (MTurk) experiments to establish our hypothesized mechanism for this effect. Study 2 supports the effectiveness of the memory preservation tactic documented in the field study by showing that consumers are reluctant to donate possessions with sentimental value because of the memories and identities linked to them. Study 3 then demonstrates that the success of memory preservation occurs by mitigating identity loss. Studies 4 and 5 use moderators to affirm the effect of memory preservation on identity loss, demonstrating that the effectiveness of memory preservation techniques is mitigated both when the productrelevant identity is first reinforced in an alternate manner before memory preservation (Study 4) and when consumers do not expect a strong connection between their current and future identities (Study 5). Finally, Study 6 demonstrates that, as theorized, memory preservation does not extend to the selling of goods.
Study 1
Before using laboratory experiments to test the psychological process we propose, we conducted a field study to demonstrate the effectiveness of memory preservation techniques in increasing overall donations. We implemented a promotional campaign encouraging memory preservation (vs. a control campaign) in a way that can be easily mimicked by managers at nonprofits. To do so, we partnered with the housing office at Penn State University to conduct a study of student donations.
Participants and Procedure
Participants were residents of one of six all-female sorority residence halls on a large university campus. The six residence halls selected were located in the same area of campus and housed upperclassmen. Each hall was similar in size (i.e., same number of beds, ranging from 126 to 157 residents per building, with a total 409 students across three buildings in the memory preservation condition and 388 students across three buildings in the control condition, for a total of 797 residents). Because we conducted this study at the end of the fall semester, residents were not required to move out. Given that moving out is likely to increase donations, we also ensured that there was an approximately equal number of residents moving out at the end of the fall semester versus returning in the spring across the conditions. There was a total of 134 of 388 (35%) residents moving out in the three control condition residence halls and 147 of 409 (36%) in the three memory preservation condition residence halls. Thus, neither the total number of residents nor the number of residents moving out differed across the conditions.
To elicit donations, we advertised a holiday donation drive, with all donations benefiting the local Goodwill. In the memory preservation condition, the campaign stated “Don’t Pack Up Your Sentimental Clutter… Just Keep a Photo of It, Then Donate,” whereas the control condition campaign stated “Don’t Pack Up Your Sentimental Clutter… Just Collect the Items, Then Donate” (see Figure 1). The messages for both conditions asked students to think about all the items that carry good memories but are no longer used. They were then prompted to either take a photo of the items and donate them or gather the items and donate them in the lobby. The promotional flyers were hung in each resident bathroom as a “stall story,” which were likely to receive focal attention given the location and likely to be seen only by dorm residents. The flyers were placed in the restrooms at the beginning of finals week because nongraduating students tend to leave for the holiday break as soon as they finish their finals. On the Monday after finals week, four undergraduate research assistants who were unaware of the hypotheses or conditions went to each residence hall. Together, they emptied the donation items out of the large plastic donation bins and counted each item, which they recorded on a tally sheet. A total of 1,146 items were counted across the six residence halls. Then, the research assistants packed up the donation items to be picked up by the local Goodwill.
Results and Discussion
We conducted a logit model on the raw count data to examine whether number of donations by residence halls in the memory preservation condition differed from that in the control condition. Of the total 1,146 items donated, 533 items were donated in the three halls in the control condition and 613 items were donated in the three halls in the memory preservation condition. This difference was significant, as we expected (Wald c2(1) = 7.81, p < .005). Put in percentage terms, the 80 additional items donated in the memory preservation condition amounted to a 15% (i.e., 80/533) increase in donations owing to the manipulation. Because there was slight variation in the number of residents between conditions, we also tested the weighted counts of 544 and 601, with greater donations in the memory preservation condition, and the effect was also significant in this adjusted logit test (Wald c2(1) = 4.64, p < .03).
The results of this field study indicate that a promotional campaign encouraging consumers to engage in memory preservation for possessions with sentimental value increases total donations. One limitation of this study is that because students could drop off donations at any time, 24 hours a day, we were unable to staff research assistants at the donation drop-offs and so were unable to survey participants directly to assess whether the items they donated were indeed possessions with sentimental value. Furthermore, because sentimental value is based on memories with emotional significance for a good’s owner, research assistants could not identify it with a visual inspection. We therefore turned to laboratory experiments to provide psychological insights into what is driving the increase in donation rate observed in Study 1. We note that we replicated the results of Study 1 in a larger field study conducted at the end of the school year at Penn State University. In this study, reported in the Web Appendix, we again observe an increase in total donations in the dorms where the promotional campaign encouraged consumers to engage in memory preservation.
Study 2
Study 2 begins the experimental exploration of the effectiveness of memory preservation by investigating why consumers do not dispose of their possessions. The findings illuminate that worries about memory and identity loss underlie consumers’ reluctance to dispose of sentimental goods, providing insight into the success of the promotional campaign used in Study 1.
Participants and Procedure
A total of 81 U.S. adults from MTurk completed the study for a small payment (51% female, 1 unspecified; Mage = 35.48 years, SD = 12.43). Participants were randomly assigned to one of two conditions (sentimental value: yes or no). All participants were asked to think of a product they currently own but no longer use that could be useful to someone else. Depending on condition, we specified that this product should either have special meaning to them or have no special meaning (see the Web Appendix). Goods with sentimental value that participants listed included clothing, shoes, stuffed animals, and golf clubs, whereas goods without sentimental value included a computer monitor, lamp, and dishes. Respondents first described the product, including acquisition and use, and then provided reasons, in an open-ended format, why they had not disposed of the product. Two coders blind to the hypotheses of the study coded these reasons (r = .84; all disagreements were resolved through discussion) into the following categories: (1) remaining value (e.g., still works, could be used again), (2) inconvenient disposal (e.g., don’t know where or how to dispose, haven’t gotten around to it), (3) product-specific memories (i.e., it brings back memories), and (4) identityrelevant (e.g., relates to type of person they are or were). Sample thoughts for each category appear in Table 1.
Next, participants reported the approximate resale value of the item, “not what the item is worth to you, but what someone else would pay for the item.” Product values ranged from $0 to $7,000 and had a log-normal distribution with a geometric mean of $20.55 (SE = $4.65). Participants then responded to two items as a manipulation check for the sentimental value of the product they imagined (“The item I thought about is special to me” and “I am emotionally attached to this item”; a = .97; 1 = “not at all,” and 9 = “extremely”). Because sentimental value may influence the difficulty of identifying an item, we also asked participants to respond to two items regarding the difficulty of identifying a product (“It was difficult to recall such a product” and “I could easily identify such a product”; a = .82; 1 = “not at all,” and 7 = “very much so”). We measured gender and age in all studies, but they did not affect results here or in subsequent studies; thus, we do not discuss them further.
Results and Discussion
Manipulation checks. An analysis of variance (ANOVA) with possession type (sentimental value: yes vs. no) predicting sentimental value (averaged across two items) revealed a main effect of possession type (F(1, 79) = 73.96, p < .01; Msentimental value = 7.21, Mno sentimental value = 2.99), as we expected. We also conducted an ANOVA on difficulty of possession selection as the dependent variable. The mean difficulties did not differ by possession type and were uniformly low (F(1, 79) = .001, p = .98; Msentimental value = 2.07, Mno sentimental value = 2.08), suggesting that participants could easily identify goods with and without sentimental value they no longer used and could dispose of. Finally, the resale value did not differ for possessions with and without sentimental value for either the logged (p > .70) or original (p > .30) values.1
Analysis of thought listings. The primary analyses were tests of the prevalence of each thought listing across the two possession types. We report all results in Table 1. Participants had more thoughts regarding the remaining value of the good and the inconvenience of disposal for goods without sentimental value than for goods with sentimental value. In contrast, those considering a sentimental good had more thoughts about product-specific memories and relevant identities than did those considering goods without sentimental value. This pattern suggests that memory and identity relevance were more important for sentimental goods than for goods that were not sentimental. A contrast code tested and affirmed this pattern (F(1, 79) = 42.84, p < .0001).
Discussion. This study supports our contention that consumers are reluctant to donate possessions with sentimental value because of the memories and identities associated with the goods rather than because of the goods’ remaining product utility or the inconvenience of donating, which were more likely to be the barriers to donation for goods without sentimental value. The unprompted association of memories and identities with goods with sentimental value provides insight into the effectiveness of the memory preservation campaign in the field study; taken together, the studies suggest that addressing these potential losses through memory preservation likely spurred consumers to engage in the donation process, increasing the total number of items donated. To provide further evidence that suggesting memory preservation of sentimental goods in a donation appeal may be critical for charities to overcome consumer reluctance to consider donation, we showed female participants (U.S. MTurk workers, n = 81; Mage = 35.54 years, SD = 11.27) one of two donation appeals (memory preservation present or absent, similar to those used in Study 1; for stimuli, measures, and detailed results, see the Web Appendix) and asked them to indicate how likely they were to look for household items to donate using three items as well as how recently they cleaned out unused goods in their home. When controlling for how recently consumers have cleaned out their homes, consumers who viewed the donation appeal suggesting memory preservation for sentimental goods reported greater motivation to look for items in their home to donate (Mpresent = 4.98 vs. Mabsent = 4.28; F(1, 78) = 4.13, p = .04). These results suggest that donation appeals suggesting memory preservation for sentimental goods may aid in overcoming consumer reluctance to begin the donation process, which may increase the number of sentimental goods donated as well as total donations simply by encouraging consumers to initiate the process of identifying items to donate.
TABLE: TABLE 1 Reasons for Not Disposing of a Possession by Thought Type (Study 2)
| Thought Type | Sample Thoughts | Sentimental Value | No Sentimental Value | t-Value |
|---|
| Remaining value (e.g., still works, could be used again) | • “Because it still works perfectly so there is no reason to get rid of it. Some younger nephews or cousins might enjoy playing it if they are ever over, so I see no reason to dispose of it.” • “The shoes are pretty uncomfortable, but I did not dispose them because I paid money for them and might use them once again.” | .33 (.48) | .67 (.48) | -3.14* |
| Inconvenient disposal (e.g., don’t know where/how to dispose, haven’t gotten around to it) | • “I still have the product because out of convenience. I haven’t put any time or energy into selling or donating it.” • “I still have it because I keep forgetting I have it. When I do remember it is in the house, I cannot find a place that will take it.” | .05 (.22) | .26 (.45) | -2.72* |
| Product memories (e.g., it brings back memories) | • “I keep it because I am holding onto the memories. I don’t want to throw it away because I would be tossing my memories along with it.” • “It is sentimental and providesmemories, but I no longer use it. I can’t bear the thought of getting rid of it because it was meaningful to me.” | .67 (.48) | .10 (.30) | 6.41* |
| Identity relevance (e.g., type of person they are or were) | • “I have not disposed of it because it provides me insight as to why I do what I do as a career as a chef. It brings me back to a time of wonderment, which I still try to do today, constantly learning and bettering myself in my culinary trade.” • “I did decide to stop using it this year. It is very heavy and cumbersome to carry even though I love it and it has sentimental value for me. I never wanted to part with it because it was a very special gift given to me from my mom and I cherish it. It is put away neatly in a bag in my closet and I have never considered it parting with it since it is so sentimental to me.” (references identity as a daughter) | .49 (.51) | .14 (.35) | 3.52* |
| Other | • “It’s been buried in the cupboard, I actually haven’t really thought about it in quite some time.” | .26 (.44) | .26 (.45) | .06 |
| Total | | 3.10 (1.48) | 3.02 (1.22) | .26 |
Study 3
The goal of Study 3 is to directly demonstrate that the effectiveness of memory preservation on donation likelihood arises specifically from the interplay between memory and identity for goods with sentimental value. This study therefore examines the effect of memory preservation for a specific good by asking participants to consider donating a good (either sentimental or not) and giving some of the participants the opportunity to engage in a memory preservation technique of their choice (e.g., writing about the object, taking a photo). We expected memory preservation to increase donation likelihood for goods with sentimental value only and to do so through a decrease in perceived identity loss.
Participants and Procedure
A total of 160 U.S. participants were recruited from MTurk for small payment. Of these, 4 participants were excluded due to incomplete responses and an additional 5 participants were excluded for listing items with resale value greater than $1,000 when instructions specified not to use items of high monetary value.2 Thus, we analyze 151 responses (40% female; Mage = 32.02 years, SD = 9.80; range = 18–61 years). The study had a 2 (sentimental value: yes vs. no) · 2 (memory preservation: present vs. absent) between-subjects design. All participants were first asked to think of a product they currently own but no longer use that could be useful to someone else. Depending on condition, we specified that this product should either have or not have sentimental value. Participants then described the product they were thinking of in one or two words, for use later in the study. Next, all participants imagined that they were cleaning out their home and came across this item. In the memory-preservation-absent condition, participants received no further information. In the memory-preservation-present condition, they were asked to imagine first preserving their memory of the item by either writing about it or taking a picture (see the Web Appendix). Two coders blind to the hypotheses of the study coded each description (r = .86; all disagreements resolved through discussion) for whether it involved taking a photo, writing a note or journal entry, or taking some other action to preserve their product-specific memories. For example, one participant wrote,
I would definitely take a few photos and tuck them away into a memories file. I would also send the picture to my parents so they could remember my baby crib that I spent time in and that my son spent time in.
Another wrote,
I would definitely take pictures of me with this item. I spent many enjoyable nights playing [my electric guitar] with my nondescript band at local clubs…. Now it sits and I only really use my acoustic.
Consistent with these examples, taking a photo was the most popular memory preservation method. Participants were most likely to indicate that they would take a photo (62%) versus write a journal entry or note (13%) or employ some other tactic (22%).
Next, we assessed donation likelihood with four items on a seven-point scale (1 = “very unlikely,” and 7 = “very likely”). The items were “Donate it to the local Goodwill,” “Donate it to a local charity for children and families in need,” “Donate it to a national nonprofit organization,” and “Donate it to an international charity collecting items for those in poverty in other countries” (a = .88). Then, we assessed perceived identity loss from disposition with two seven-point scaled items (1 = “strongly disagree,” and 7 = “strongly agree”; a = .96): “Thinking about getting rid of this item, please indicate your agreement with each statement: ‘I will feel like I lost a piece of myself,’ and ‘I will feel like a part of me is gone.’” Finally, participants responded to the same items asking them to rate sentimental value (a = .95), difficulty (a = .75), and resale value and supply the demographics used in Study 2. Product values ranged from $0 to $800, with a geometric mean of $32.99 (SD = $4.79).
Results and Discussion
Donation likelihood. We conducted an ANOVA with possession type (sentimental value: yes vs. no), memory preservation (present vs. absent), and their interaction as the independent variables and donation likelihood as the dependent variable. Possessions with sentimental value were significantly less likely to be donated (F(1, 147) = 11.87, p > .01), and memory preservation moderately increased donation likelihood overall (F(1, 147) = 3.25, p = .07). Most importantly, the focal interaction was significant (F(1, 147) = 5.74, p = .02). For possessions with sentimental value, participants had greater donation likelihood when memory preservation was present than when it was absent (Mpresent = 3.90, SD = 1.74 vs. Mabsent = 2.74, SD = 1.59; t(147) = 2.86, p > .01). For possessions without sentimental value, memory preservation did not increase donation likelihood (Mpresent = 4.19, SD = 1.85 vs. Mabsent = 4.35, SD = 1.58; t(147) = -.44, p = .66; see Figure 2).
Identity loss. The same ANOVA with identity loss as the dependent variable revealed a significant main effect of possession type (F(1, 147) = 66.07, p < .0001): participants anticipated greater identity loss when donating a possession with versus without sentimental value. There was not a significant main effect of memory preservation condition (p > .30). The focal interaction was significant (F(1, 147) = 5.19, p = .02): for products with sentimental value, participants reported less identity loss in the memory preservation present than in the memory preservation absent condition (Mpresent = 3.60, SD = 1.52 vs. Mabsent = 4.44, SD = 1.98; t(147) = 2.86, p > .01). For products without sentimental value, memory preservation did not affect identity loss (Mpresent = 2.08, SD = 1.44 vs. Mabsent = 1.73, SD = 1.22; t(147) = -.44, p = .66; see Figure 2).
Mediating role of identity loss on donation likelihood. We tested for mediated moderation using model 8 in Hayes’ (2012) PROCESS macro. Mediated moderation was supported because the indirect effect of the two-way interaction on donation likelihood through identity loss was significant (indirect effect = .08, SE = .04; 95% confidence interval [CI] = [.0122, .1927]). When the item had sentimental value, the conditional indirect effect of memory preservation was significant (indirect effect = .11, SE = .07; 95% CI = [.0054, .2958]). However, when the item lacked sentimental value, the conditional indirect effect of memory preservation was not significant (indirect effect = -.05, SE = .04; 95% CI = [-.1496, .0207]). Thus, the avoidance of identity loss seems to drive the effectiveness of memory preservation for goods with sentimental value.
Discussion. Study 3 demonstrates that when considering donation of a specific item, memory preservation increases donation likelihood for possessions with sentimental value (and not for possessions that lack sentimental value) because actions designed to preserve memories of the good before donating prevent the loss of product-relevant identities. Having revealed the psychological process in this study with hypothetical donations, we conducted a field study with St. Vincent de Paul’s State College thrift store to examine whether memory preservation indeed reduces identity loss among actual donors. We give the primary result here; details are available in the Web Appendix. In this study, 64 participants who dropped off a donation to the nonprofit in person were randomly assigned to either a memory-preservationpresent condition or a control (memory-preservation-absent) condition. They were asked to think of one specific item they were donating that was meaningful to them; we used “meaningful” because we thought it would be easier for participants to assess than “sentimental.” For participants in the memory preservation present condition, the researcher took a picture of this item with an instant camera. The photo was given to participants to keep, and the researchers did not keep copies of the photos. Photos were not mentioned in the memory-preservation-absent condition. All participants then answered a series of ratings questions about their item on a seven-point scale (1 = “strongly disagree,” and 7 = “strongly agree”) with two items for identity loss (from Study 3), one item for memory loss, and four product attachment items (Mproduct attachment = 4.24, SD = 1.60; for item wording, see the Web Appendix).
Because our theorizing pertains to the loss of identity, we limit our primary analysis to those who reported ownership of their donated item, leaving 39 participants for analysis. Participants in the memory preservation condition reported less identity loss and memory loss than those in the control condition (identity loss: 1.95 vs. 3.21; F(1, 36) = 4.49, p = .04; memory loss: 1.24 vs. 2.27; F(1, 36) = 7.89, p < .01). A mediation analysis showed that the indirect effect of memory preservation through memory loss was significant (indirect effect = -.40, SE = .26; 95% CI = [-1.00, -.01]). Thus, even among those who are already willing to donate their possessions, memory preservation at the time of donation decreased the identity loss from donating.
Study 4
Although Study 3 provided evidence of our proposed causal mechanism through mediation, the goal of Study 4 was to do so using moderation (for a discussion of establishing this type of causal evidence, see Spencer, Zanna, and Fong [2005]) using a manipulation designed to address the identity loss that underlies these effects. We therefore asked half of the participants, before they indicated the donation likelihood for a possession with sentimental value, to engage in identity reinforcement (i.e., by thinking about the various actions they take that express the identity to which their possession with sentimental value is linked). We expected a significant interaction: the effectiveness of memory preservation should be mitigated when identity reinforcement is also present because there is no longer as much of a need to preserve identity.
Participants and Procedure
A total of 237 undergraduate students who were native English speakers at Penn State University completed the study for extra course credit (39% female; Mage = 18.86 years, SD = .69). The study employed a 2 (memory preservation: present vs. absent) · 2 (identity reinforcement: present vs. absent) between-subjects design. All participants were asked to think of a possession with sentimental value, following the same instructions used in Study 3. The only difference was that we restricted the value of the item to $100 or less because we had high variation in product value in Study 3. In addition, we asked all participants to indicate which identity they most associated with this specific possession. As part of the instructions, they were given a definition of identity and several examples (for details, see the Web Appendix).
Participants who were in the memory-preservation-present condition then completed a similar memory preservation task to that used in Study 3. Again, two independent coders (r = .84; all disagreements resolved through discussion) found that taking a photo was the most popular memory preservation method (68%). In the identity-reinforcement-present condition, after being reminded of the identity that they had previously indicated the product represented, participants described the ways in which they express this identity that are not related to a physical possession (for complete stimuli, see the Web Appendix).
Immediately after completing the memory preservation and/or identity reinforcement writing (or indicating the product and relevant identity, for control participants), participants responded to the same four donation likelihood items used in Study 3 (1 = “very unlikely,” and 7 = “very likely”; a = .91). Finally, participants responded to the two sentimental value items (a = .88), two difficulty items (a = .74), measure of resale value, and demographic items used in prior studies. Product value averaged $25.19 (SD = $29.54).
Results and Discussion
Donation likelihood. An ANOVA with memory preservation (present vs. absent), identity reinforcement (present vs. absent), and their interaction as the independent variables and donation likelihood as the dependent variable revealed that memory preservation increased overall donation likelihood (F(1, 233) = 6.48, p = .01). The main effect of identity reinforcement was not significant (F(1, 233) = .68, p = .41).
Most importantly, the focal interaction was significant and in the expected direction (F(1, 233) = 4.48, p = .05). When there was no identity reinforcement, participants had greater donation likelihood when memory preservation was present than when it was absent (Mmemory preservation present = 4.35, SD = 1.80 vs. Mmemory preservation absent = 3.20, SD = 1.88; t(233) = 3.26, p > .01). This effect mimics Study 3, in which memory preservation reduces concerns about identity loss. However, when participants engaged in identity reinforcement first, memory preservation did not increase donation likelihood because the identity is not in danger of being lost (Mmemory preservation present = 4.03, SD = 1.90 vs. Mmemory preservation absent = 3.93, SD = 1.98; t(233) = .31, p = .76; see Figure 3). In addition, when memory preservation is absent, identity reinforcement increases donation likelihood (Midentity reinforcement present = 3.93, SD = 1.98 vs. Midentity reinforcement absent = 3.20, SD = 1.88; t(233) = 2.09, p = .04), consistent with our theorizing regarding the role of identity loss.
Discussion. Study 4 provides additional evidence that memory preservation is (1) effective at increasing donation likelihood for possessions with sentimental value and (2) driven by a decrease in concerns about identity loss. When identity loss is directly prevented with an identity reinforcement manipulation, the effect of memory preservation is attenuated.
Study 5
In Study 5, we test the moderating role of psychological connectedness to the future self on the effect of memory preservation on donation likelihood. The effectiveness of memory preservation should be mitigated when psychological connectedness to the future self is low.
Participants and Procedure
A total of 204 undergraduate students at Penn State University completed the study for extra course credit (50% female; Mage = 19.16 years, SD = .73). The study employed a 2 (memory preservation: present vs. absent) · 2 (connectedness to future self: high vs. low) between-subjects design. First, all participants read a short passage purportedly describing recent research on the self arguing that college students’ current identities are either highly connected to their future identities or not at all connected (manipulation adapted from Bartels and Urminsky [2011]).
Then, all participants were asked to think of a shirt they currently own but no longer use that “has special meaning from an experience or event in your life when wearing the shirt.” Participants then indicated the shirt they were thinking of in one or two words. Afterward, they were asked to imagine cleaning out their closet, coming across this shirt, and realizing they have not worn it in some time. In the memorypreservation-present conditions, they were told to imagine that they laid their shirt on their bed and took a picture of it. They then briefly described what they would think about when they looked at this picture of the shirt. In the memorypreservation-absent condition, participants described what they would think about when they saw the shirt itself (for complete stimuli, see the Web Appendix).
Participants indicated “How likely would you be to donate this shirt to a local nonprofit organization?” on a seven-point scale (1 = “very unlikely,” and 7 = “very likely”). They then responded to the two sentimental value items (a = .87), two difficulty items (a = .69), and measure of resale value used in prior studies. Product value averaged $27.41 (SD = $81.93). As a manipulation check, participants indicated their perceived level of connectedness to their future self from a set of six images of overlapping circles from Bartels and Urminsky (2011).
Results and Discussion
Manipulation check. We conducted an ANOVA with memory preservation, connectedness-to-future-self condition (high vs. low), and their interaction as the independent variables and the measure of perceived connectedness to one’s future self as the dependent variable. Participants felt more connected to their future self in the connectedness condition (F(1, 190) = 20.76, p < .0001; Mhigh connectedness = 4.22, Mlow connectedness = 3.53). Neither memory preservation nor the interaction were significant predictors of perceived connectedness to one’s future self (ps > .20).
Donation likelihood. An ANOVA with memory preservation (present vs. absent), connectedness-to-future-self condition (high vs. low), and their interaction as the independent variables and donation likelihood as the dependent variable revealed no main effects (ps > .30). The analysis of interest, however, was the interaction of memory preservation · connectedness to future self, which was significant and in the predicted direction (F(1, 200) = 4.93, p = .03). When connectedness to the future self was high, participants had greater donation likelihood when memory preservation was present than when it was absent (Mmemory preservation present = 2.85, SD = 2.02 vs. Mmemory preservation absent = 2.10, SD = 1.34; t(200) = 2.04, p = .05). In contrast, when connectedness to the future self was low, memory preservation did not increase donation likelihood (Mmemory preservation present = 2.55, SD = 1.84 vs. Mmemory preservation absent = 2.94, SD = 1.79; t(200) = -1.09, p = .28). Likewise, when memory preservation was absent, participants in the high-connectedness-to-future-self condition reported lower donation likelihood than those in the lowconnectedness-to-future-self condition (Mhigh connectedness = 2.10, Mlow connectedness = 2.94; t(200) = 2.31, p = .02). When memory preservation was present, there was no significant difference in donation likelihood between the high- and lowconnectedness-to-future-self conditions (Mhigh connectedness = 2.85 vs. Mlow connectedness = 2.55; t(200) = .83, p = .41; see Figure 4).
Discussion. Study 5 underscores the role of memories and identity as the locus of the memory preservation effect. When consumers perceive that their future self will be different from their current self, they are equally likely to donate regardless of memory preservation. This pattern provides further support for our theorizing that memory preservation operates by reinforcing the product-relevant identities one might otherwise lose when donating a possession with sentimental value; low connectedness to the future self indicates the product-relevant identity will not be as meaningful in the future and therefore does not require protecting prior to disposition.
Study 6
This final study tests the moderating role of disposition type (selling vs. donating) on the effect of memory preservation on disposition likelihood. We establish that memory preservation does not extend to selling because an economic transaction taints a sentimental good.
Participants and Procedure
A total of 110 U.S. adults from MTurk completed the study for a small payment (47% female, 7 unspecified; Mage = 37.12 years, SD = 13.45). Participants were randomly assigned to one of four conditions in a 2 (memory preservation: present vs. absent) · 2 (disposition type: donate vs. sell) between-subjects design. First, all participants were asked to think of an item in their home that is meaningful to them and that they no longer use but could be of use to someone else. They briefly indicated the item by completing the statement, “The special possession I am thinking of is…” Then, participants in the memory preservation condition were instructed to do the following: “Please take a moment to go get this item in your home and take a picture of it. You can use your phone, a camera, or the web cam on your computer to take the picture—just use whatever is most convenient. We will ask you about the picture after you take it.” Those in the memory-preservation-absent condition were instructed: “Please take a moment to go get this item in your home.” This memory preservation manipulation is conservative because it asks participants to take a picture but does not mention that the purpose of the picture is to retain memories. We also note that we were concerned that the procedure had the potential to produce noisier data than the typical study. We therefore conducted an outlier analysis in which we identified two outliers who were removed from the data set; all subsequent analysis is reported with the 108 remaining participants.3
Consistent with Study 3, all participants then imagined cleaning out their home and coming across this item and then were asked, “How likely would you be to donate (sell) this item?” depending on disposition type condition (1 = “very unlikely,” and 7 = “very likely”). They then responded to the two sentimental value items (a = .90), two difficulty items (a = .62), and measure of resale value used in prior studies. Product value averaged $52.17 (SD = $99.58).
Results and Discussion
Disposition likelihood. Neither memory preservation nor disposition type predicted disposition likelihood (ps > .30). The analysis of interest, the interaction of memory preservation · disposition type, was significant and in the predicted direction (F(1, 104) = 4.00, p = .05). When considering donation, participants had greater disposition likelihood when memory preservation was present than when it was absent (Mpresent = 2.93, SD = 1.85 vs. Mabsent = 2.04, SD = 1.36; t(104) = 2.11, p = .04), mirroring our previous studies. In contrast, when considering selling, memory preservation did not increase donation likelihood (Mpresent = 2.07, SD = 1.28 vs. Mabsent = 2.35, SD = 1.44; t(104) = -.69, p = .49; see Figure 5).
Discussion. Study 6 affirms that, as we expected, memory preservation is only effective at increasing disposition of possessions with sentimental value when considering donating them, not selling them. We discuss selling versus donating in more detail in the following section.
General Discussion
Secondhand goods play a large role in the marketplace for a variety of organizations, including nonprofits. A primary problem with the business model of these organizations is the volatility of the supply chain, because they are dependent on consumers’ disposition of their possessions. Aiding consumer disposition not only benefits organizations dependent on secondhand goods but also benefits consumers who face financial and/or psychological costs from retaining unused goods as well as society at large. In our work, we provide insight on consumer disposition, which should help managers dependent on secondhand goods better understand their supply chain and, more specifically, use memory preservation to increase donation rates. For example, we demonstrate that implementing a simple memory preservation strategy in the field substantially increased the number of donations by 15%. When product disposition rates are likely to be contingent on multiple factors such as effort/convenience, monetary value of the item, and potential for reuse (Bayus 1991; Haws et al. 2012; Jacoby, Berning, and Dietvorst 1977; Lee et al. 2015; Okada 2001), the ability to increase donations to this extent is notable. In addition to providing an effective strategy nonprofit marketers can use to increase donations, we also explicate why it works, so that future researchers can expand on it and perhaps use the psychological drivers of the effect to develop other effective promotional tactics.
Theoretical Contributions
Though relevant to several market enterprises, including recycling, storage, resale, and the product life cycle, product disposition has not been as widely researched as acquisition and consumption. Some disposition literature has examined the transfer of meaning to the new recipient (Brough and Isaac 2012; Lastovicka and Fernandez 2005; Price, Arnould, and Curasi 2000), but requiring knowledge of the recipient for disposition is not feasible in most donation contexts. The current research contributes to the disposition literature by focusing on overcoming the reluctance to part with sentimental possessions without relying on transferring the value to a known recipient during or after disposition. Our research therefore contributes to the literature on product disposition by exploring the important context of donation of used goods. In addition, this article is the first that we know of that investigates ameliorating identity loss that stems from donating a good with sentimental value through simple memory preservation techniques aimed at the donor. By focusing on preserving the memories rather than transferring the sentimental value, consumers can use this disposition aid more generally rather than in limited contexts when the recipient is known.
Our focus is on memory preservation to aid donation, though we also consider the extent to which this applies to selling. In doing so, we contribute to disposition research exploring disparities in willingness to accept and willingness to pay prices for sellers considering goods with sentimental value and potential buyers. This literature finds that consumers typically become upset at the thought of selling and demand exorbitant prices for their possessions with sentimental value that buyers may be unwilling to pay, potentially resulting in the owner not giving up the good at all (Boyce et al. 1992; Chatterjee, Irmak, and Rose 2013). The reason for this refusal appears to be a combination of loss aversion (Kahneman, Knetsch, and Thaler 1991), which applies to all possessions, along with an aversion to thinking of sentimental goods in monetary terms because of the taboo of applying money to certain sentiments (McGraw and Tetlock 2005). Selling seems to be especially susceptible to emotional taboo responses due to monetary valuation (Boyce et al. 1992; Chapman and Johnson 1995; Irwin 1994). Thus, memory preservation may not aid disposition through selling, because the mitigated identity loss that allows for donation does not overcome the taboo association of the good and memory with the money that arises from selling. Indeed, Study 6 affirms the difference between selling and donating for goods with sentimental value. Even when memories represented by a good are preserved and identity loss is mitigated such that the sentimental good can be disposed of, the economic exchange associated with selling the sentimental good limits disposition likelihood. While the current work provides evidence of how barriers to donation due to sentimental value can be overcome, further research can explore specific strategies that may encourage the selling of sentimental goods to unknown others.
Our work also contributes to the literature on consumer memory. Only recently have consumer psychologists begun to empirically examine the link between memory and identity in marketing contexts. Mercurio and Forehand (2011) find that consumer identity can influence learning through memory by showing that, when ad content is at least moderately related to a specific identity (i.e., gender) and that identity is activated at the time of content retrieval, there is a significant increase in subsequent recognition of ad content. Dalton and Huang (2014) show that viewing identity-relevant promotions and subsequently experiencing a threat to the relevant identity results in the viewer strategically forgetting information regarding that identity (i.e., lower recognition of previously viewed identity-relevant information) as a defense mechanism to cope with the identity threat. Note that although this previous research has documented a general link between memory and identity in consumption contexts, the memories explored were about marketing information (i.e., advertisements and promotions). This work did not examine autobiographical memories that were specific to an individual. The memories in these studies were therefore not characterized by a sense of “mineness” (Klein and Nichols 2012), unlike memories that give possessions sentimental value. Along with Zauberman, Ratner, and Kim’s (2009) work on memory, ours is among the first research to document strategic protection of consumer-specific consumption memories.
Relatedly, these findings contribute to the literature on consumer identity. Identity research typically involves temporarily activating the salience of a particular identity. In demonstrating a method to prevent identity loss without memory preservation (Study 5), we do not alter the salience of the identity; instead, we have consumers consider other ways that they “live” this identity, thereby reinforcing the identity. Identity reinforcement may be an important factor to consider in understanding consumer evaluations of identity-linked goods and other identity-relevant consumer decisions (Reed 2004; Reed et al. 2012). In addition, we consider the role of a static versus dynamic identity by exploring connectedness between one’s current and future self (Bartels and Urminsky 2011; Hershfield 2011). By demonstrating that people’s perceptions of the relatedness of their current and future selves can influence disposition of identity-relevant goods, we provide insight into how connectedness to one’s future self affects not only intertemporal preferences (which is how it typically has been studied in the marketing literature) but also identity-relevant behavior and disposition.
Finally, this research contributes more generally to the retailing literature. Although scholars have explored factors in sourcing goods from manufacturers (Basuroy, Mantrala, and Walters 2001; Mantrala and Raman 1999), the unique challenge that nonprofits face when sourcing goods from consumers could benefit from more attention in the literature. The current research takes a step in this direction by recognizing that a potential barrier to consumer donations is their perceived identity loss when parting with possessions. When consumers realize that the emotionally significant memories tied to sentimental goods can be preserved, they are less reluctant to begin the donation process. Understanding sourcing of used goods from consumers is not only important to nonprofits but also likely to become increasingly important for commercial social ventures and other sustainable business models dependent on used goods for reuse and repurposing.
Practical Implications
The current research has a managerial aim: to help nonprofits and other organizations understand consumer reluctance to dispose of goods with sentimental value and, in doing so, identify tactics managers can use to increase consumer donations. In Study 2, we found that the two main reasons consumers do not dispose of goods with sentimental value are due to the goods’ memories and identities rather than their financial value or likelihood of reuse. Thus, if managers can implement strategies to help consumers overcome the perceived loss of memories and identities associated with donating their possessions, they are likely to increase donations.
Consistent with these reasons for not donating, we have shown that simple memory preservation tactics significantly increase donations. Note that simply promoting a memory preservation technique by suggesting that consumers take pictures of sentimental goods before donating them effectively increased donations in Study 1. Thus, this tactic is an easy mechanism for managers to use in promotional campaigns designed to elicit donations. The field study reported in the discussion of Study 3 demonstrates that memory preservation strategies (i.e., taking a photo of sentimental items) can also be used at the time of donation to reduce consumers’ felt identity loss when donating, potentially increasing their likelihood of making future donations to the same nonprofit. Given that charity retailers are competing for merchandise (Hibbert, Horne, and Tagg 2005; Paden and Stell 2005), incorporating memory preservation into both donation appeals and/or the actual donation process should substantially increase donations.
We therefore recommend at least two actions to nonprofits seeking to increase donations: (1) hold a donation drive in which all promotional material specifically recommends that consumers take photos of possessions prior to donation so they can “keep the memories but lose the clutter” (similar to that used in Study 1) and (2) offer photo taking of donations at key drop-off centers during business hours to decrease felt identity loss at time of donations. These two actions should serve to increase donations during the campaign and train donors to engage in their own memory preservation activities so that donating possessions with sentimental value becomes easier over time.
In addition, the links that this research provides should help managers develop other mechanisms for increasing donation of goods with sentimental value in specific domains. For example, the results of Study 4 suggest that reinforcing consumers’ identities might make them more immune to worrying about losing the memories held by goods with sentimental value. Our theoretical framework suggests that other memory preservation techniques than the ones we tested would work as well, including industry- or category-specific techniques. Graduating college seniors might donate sentimental college merchandise, for example, if their name were added to a permanent wall on campus or if they were invited to participate in a special event reinforcing their identities as soonto-be alumni. In addition, the results of Study 5 support the notion that emphasizing a disconnect between current and future selves should reduce fear of identity loss and increase donation likelihood. For instance, advertising could show empty nesters enjoying travel and the simplicity of a downsized home as they consider donating items used for their children.
This research also has practical implications for consumer welfare. By showing how to decrease the identity loss associated with donating possessions with sentimental value, these findings can aid consumers who are overcome with clutter, an increasing consumer problem (Teitell 2012), and/or those who may be forced to dispose of some possessions during life transitions (e.g., downsizing to a smaller home, divorce, retirement; McAlexander 1991; Mehta and Belk 1991; Young 1991). When disposition is necessary, consumer welfare can be enhanced by engaging in memory preservation. Consumers who are struggling to part with sentimental goods but want to minimize the clutter in their home or storage areas and give new life to their possessions should take a photo, write a note, or otherwise document the memories associated with the good so they can part with the good without losing their identity. The proliferation of popular books (e.g., Marie Kondo’s 2014 bestseller, The LifeChanging Magic of Tidying Up: The Japanese Art of Decluttering and Organizing) aimed at helping consumers declutter suggests that these recommendations could significantly enhance consumer welfare. In addition, the companies tasked with helping people dispose of goods, such as estate sale companies that sell an estate for charity, might profitably use these techniques to help their consumers.
Limitations and Directions for Future Research
One limitation of the current research is that our laboratory studies involve hypothetical donation. We do report the results of two field studies (one in the context of a donation drive suggesting photos for memory preservation and the other exploring the effect of taking a photo at the time of donation). However, we were not able to code the donations we received in these field studies as having or lacking sentimental value. Another limitation of the present studies is that the consumers either were asked to engage in the memory preservation themselves (e.g., take a photo) or were able to keep the memory preservation aid (e.g., they were given a photo taken by the charity). Thus, these findings cannot speak to how important it is that the consumer retains the memory preservation aid. In other words, we do not yet know whether consumers need to be able to look at the photo again after donation for it to be effective at increasing donations/donation intentions. In addition, the current research does not verify whether the consumer must engage in a physical act of memory preservation such as taking a photo or if it is enough just to be cued to engage in memory preservation. Perhaps cuing memory preservation helps the consumer remember that the good is a physical representation of an emotionally significant memory even if no physical act of memory preservation occurs. Although Study 4 does not directly address this question, it shows that reinforcing the identity related to a product with sentimental value in an alternate way can also increase donation likelihood. Thus, thinking about taking a picture to capture the memories the product represents may be enough to reinforce the identity by encouraging consumers to actively rehearse their productrelevant memories. This possibility is especially attractive for marketers because it implies that advertising memory preservation techniques may prove effective regardless of whether a consumer physically engages in the techniques.
Another concern, particularly for nonprofits aiming to resell donated goods, may be whether the donated goods are of high resale value. In Study 2, which compared both sentimental and nonsentimental goods, we did not find a difference in self-reported market value. However, although high resale value and sentimental value are not always correlated, many unused but undonated possessions may be both, as sentimental items may be kept purposefully in good condition. Moreover, even if donated goods are not of high resale value, increasing consumers’ likelihood of returning them to the secondhand marketplace is still valuable because many goods such as textiles and electronics can be recycled, which increases the organization’s revenues (Ewoldt 2015; Steeves 2016). Furthermore, a reluctance to part with possessions with sentimental value, even though they are not of particularly high resale value, may well be the barrier that prevents consumers from starting the process of identifying items to donate (including those with high resale value). As such, charities that might not benefit directly from the donation of goods with high sentimental value but low resale value may still benefit from holding photo donation drives, as this type of donation drive may increase overall donations, including nonsentimental donations with high resale value. Further research could explore this avenue more directly and investigate whether the donation of other types of goods can be increased using memory preservation strategies (e.g., goods that are perceived as being “one of a kind” or part of a collection).
In terms of additional research, it would also be worthwhile to explore whether consumers experience more commitment to and an increased desire to donate to nonprofits that explicitly engage in or encourage memory preservation compared with those that do not. It is possible that assuaging worries about identity loss endears the nonprofit to consumers, resulting in brand loyalty and more repeat donations. Managers of nonprofits with a specific mission might also wish to know whether an identity match between the consumer and the mission of the charity might help mitigate identity loss in donating goods with sentimental value. For example, if a particular good is linked with memories and identities associated with being a successful businessperson, and the charity’s mission is specifically to help people develop business acumen (e.g., Dress for Success), might this connection between identities (even though it is not tied to a specific recipient) be enough to mitigate identity loss in a similar manner to the operation of memory preservation, thereby increasing donations?
Finally, we speculate that memory preservation could be effective in increasing disposal through methods other than giving to nonprofits, as long as that method does not involve a monetary exchange. For example, it would be valuable to know whether memory preservation increases social recycling (i.e., disposing of used goods by allowing other consumers to acquire them at no cost, such as the Freecycle Network [https://www.freecycle.org]; Donnelly et al. 2017). We leave this to future research to explore.
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FIGURE 5 Effectiveness of Memory Preservation by
Disposition Type (Study 6)
3We conducted outlier analysis using Cook’s D. Using the conservative cutoff value of 4/n (Bollen and Jackman 1985), we identified and removed two outliers. Retaining these participants reduces the interaction to nonsignificance (p = .19), but the pattern remains. We conducted the same outlier analysis for other studies, but all other studies did not have outliers or the results were not substantially altered by the removal of outliers. Thus, no outliers are removed in the reported results for the other studies.
FIGURE 4 Connectedness to Future Self and Memory Preservation on Donation Likelihood (Study 5)
FIGURE 3 Identity Reinforcement and Memory Preservation on Donation Likelihood (Study 4)
FIGURE 2 Possession Type and Memory Preservation on Donation Likelihood and Identity Loss (Study 3)
2When the five responses with extreme item values are included in analysis and value is used as a control, the pattern remains the same, with the interaction remaining significant for donation intentions (F(1, 151) = 4.39, p = .04). The interaction for identity loss becomes marginal (F(1, 151) = 3.39, p = .07), with the effect of memory preservation for sentimental goods on identity loss also reduced to marginal significance (Mpresent = 3.84, SD = 1.78 vs. Mabsent = 4.46, SD = 1.98; t(151) = 1.66, p = .09).
TABLE 1 Reasons for Not Disposing of a Possession by Thought Type (Study 2)
1We collected the same sentimental value, difficulty, and resale value measures in subsequent studies. Rated sentimental value was always greater than the scale midpoint in the sentimental value condition. Difficulty and value did not differ significantly between conditions. In addition, controlling for value did not alter the significance of the focal effect in any study other than as noted for Study 3 in footnote 3. For details on these analyses, see Web Appendix Tables W1 and W2.
FIGURE 1 Study 1 Promotional Campaign Message
A: Memory-Preservation-Absent Condition (Control)
B: Memory-Preservation-Present Condition
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Record: 107- Knowing What It Makes: How Product Transformation Salience Increases Recycling. By: Winterich, Karen Page; Nenkov, Gergana Y.; Gonzales, Gabriel E. Journal of Marketing. Jul2019, Vol. 83 Issue 4, p21-37. 17p. 6 Diagrams, 1 Chart. DOI: 10.1177/0022242919842167.
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Knowing What It Makes: How Product Transformation Salience Increases Recycling
Recycling campaigns abound, but do consumers think about what becomes of those recyclables? This research proposes that product transformation salience (thinking about recyclables turning into new products) increases recycling. The authors theorize that consumers are inspired by the transformation of recyclables into new products and that this inspiration motivates them to recycle. The authors demonstrate the effect of product transformation messages on recycling behavior using a recycling campaign (Study 1) and advertisements for products made from recycled plastic (Study 2). Study 3 demonstrates the mediating role of inspiration. Then, three field studies provide robust support for the transformation salience effect through click-through rates for recycling advertisements (Study 4), recycling rates during pre–football game tailgating (Study 5), and a reduction in the amount of recyclable materials incorrectly placed in the landfill bin by students in a university residence hall (Study 6). The authors discuss implications for the design of recycling campaigns and positioning of recycled products in the marketplace as well as theoretical contributions regarding the roles of transformation salience and inspiration in encouraging recycling and other sustainable behaviors.
Keywords: inspiration; product transformation salience; recycling; sustainability; waste audit
As the world continues to grapple with an unsustainable rate of consumption, companies and consumers alike are recognizing the need to shift to a "reduce, reuse, recycle" model of production and consumption. Consistent with this movement, more and more companies, such as Nike, Timberland, and John Lewis & Partners, are incorporating post-consumer-recycled material into their production ([17]; [36]; [57]). PepsiCo has committed to having its plastic packaging produced from 100% recycled materials in 2020 ([63]), and Evian has pledged to make all its plastic bottles entirely from recycled plastic by 2025, up from 30% today ([15]).
Despite industry progress toward using recycled material in production, consumers' recycling habits have not kept pace. More plastics were produced in 2015 than prior years, but plastics recycling fell slightly from 9.5% in 2014 to 9.1% in 2015 ([18]), which is rather stagnant given that only 9% of the 8.3 billion metric tons of plastic produced since it was invented in 1907 is estimated to have been recycled ([24]). Recycling rates are greater for other materials (e.g., aluminum cans have a 54.9% recycling rate), but overall only 25.8% of waste was recycled in the United States in 2015, with only 13% of municipal solid waste recycled globally ([17]; [18]). Such low recycling rates are not simply due to a lack of recycling initiatives. In 1995, Keep America Beautiful first partnered with the EPA for a recycling program ([32]), and America Recycles Day was launched in 1997 by the National Recycling Coalition ([21]). Academic research to understand and increase consumer recycling behavior also exists ([59]; [66]). Research has examined effects of consumer characteristics, such as gender, political beliefs, and moral values, on response to recycling messages ([10]; [33]) and tested effective message framing ([71]). As such, recycling initiatives and recommendations abound, but do consumers actually consider that recyclables can be transformed into new products?
Much of the existing recycling research and messaging overlooks a key aspect of recycling: a recyclable is a product that has future use ([65]). More specifically, "recycling is the process of collecting and processing materials that would otherwise be thrown away as trash and turning them into new products" ([19]). The current research proposes that when consumers consider how recycled materials can be given a new life by turning them into new products, which we refer to as "product transformation salience," recycling intentions and behaviors increase.
Surprisingly, there is little information and virtually no marketing research focusing on the future uses of recyclables or their transformation into new products. To be sure, there are a few examples of recycling campaigns that have provided such information, namely the "I Want to Be..." campaign by Keep America Beautiful, which shows the transformation of a recyclable into a new product (e.g., a recycled shampoo bottle becoming a hairbrush). However, recycling campaigns with such messages are few and far between. Specifically, out of 56 recycling campaigns identified by two research assistants, only 6 featured any reference to product transformation. Thus, the majority of recycling communications do not highlight the transformation of recyclables into new products. Nonetheless, as we discussed previously, more companies are using recycled materials in their products. For example, Nike produced World Cup jerseys using 100% recycled polyester, which contained approximately eight plastic bottles ([47]) and Levi's Waste<Less jeans also contain about eight bottles ([31]). Thread International recently launched a canvas backpack made from 25 plastic bottles ([ 5]) and retailer John Lewis & Partners produces towels made from 10 recycled plastic bottles along with reclaimed textiles ([57]). Yet consumers may not be aware of these company practices and thus do not consider how recyclables are transformed into new products with future use (for a pilot study confirming consumers' low product transformation salience, see the Web Appendix).
We predict that product transformation messaging, which draws attention to the transformation of recycled materials into new products, will have a positive effect on recycling. We theorize the increase in recycling will occur because of the inspiration consumers experience when considering the transformation involved in recycling. Our findings contribute to research on the factors that affect consumers' decisions to recycle versus trash objects (for recent reviews, see [66]] and [64]]) by designing a new messaging approach that increases product transformation salience. In doing so, we join an emerging trend of positive sustainability initiatives ([49]; [69]), which show promise of increasing sustainable behaviors ([70]). In documenting that transformation-based recycling messages can elicit inspiration, we contribute a novel elicitor of inspiration to the very limited research on inspiration in marketing and consumer behavior ([ 9]; [37]) while also offering insights into the difficult task of eliciting authentic experiences of inspiration ([62]). The experience of inspiration is closely linked to the elicitation of positive emotions ([62]); thus, studying the role of inspiration in recycling behavior answers a call for more research to consider the role of positive emotions in sustainable consumer behavior ([70]).
In addition to these theoretical contributions, the current research offers important managerial insights. First, the findings indicate that product transformation messaging such as that in Keep America Beautiful's "I Want to Be..." campaign should become much more prevalent, as it has the potential to boost recycling rates in a meaningful way. However, these findings are relevant to audiences beyond nonprofit and government organizations. As more and more companies use post-consumer-recycled material in their new products (e.g., Adidas's Parley shoes use approximately 11 plastic bottles; [ 2]), such companies should highlight the transformation of the new product from recycled content, as our research shows that this too can increase product transformation salience, leading to inspiration and subsequently greater recycling. Thus, both product descriptions and recycling campaigns have the potential to raise product transformation salience and increase recycling rates.
Given the importance of increasing consumers' environmentally responsible behaviors, such as recycling, researchers from many disciplines have studied factors influencing recycling (for an extensive literature review, see [70]]). For example, researchers have examined the effects of environmental awareness and concern ([54]; [68]), social norms ([25]; [72]), and financial incentives ([51]), to name a few. [28] discuss various antecedents that motivate recycling and note that the role of information as a motivator of recycling is not well studied. In particular, information that increases consumers' knowledge about recycling has received little attention in the literature, despite its potential to have a long-lasting impact on recycling attitudes and behaviors ([28]).
To be sure, some research has studied persuasive appeals for recycling, which provide consumers with information about recycling. In an early study on the psychology of recycling, [38] found that a message emphasizing the negative outcomes that can be avoided by recycling (e.g., saving trees, using less landfill space, conserving energy) is more effective than one emphasizing the negative outcomes that would be incurred by not recycling (e.g., exceeding the capacity of landfills, damaging the beauty of our surroundings and the health of our families). Other research has also examined the effects of loss- versus gain-framed appeals on environmental attitudes and behaviors ([ 6]; [58]; [70]). Although gain versus loss framing can be an effective approach in motivating some behaviors (e.g., health promotion; [ 8]; [41]), in the context of recycling, both of these approaches emphasize negative environmental outcomes that can be either avoided or incurred. Such messaging may be perceived as coercive, prompting defiance and resentment, and thereby reducing its effectiveness ([26]; [44]).
In response to this concern, recent research has started to provide support for positive, noncoercive sustainability initiatives such as providing positive aesthetic cues ([69]) or using hope appeals ([14]). Research on influencing sustainable behaviors has also begun to examine the role of positive affect. Engaging in sustainable behaviors simultaneously decreases negative affect and increases positive affect ([59]) and may elicit hope ([20]; [56]). Other research has shown that evoking the positive social emotion of pride can increase the likelihood of engaging in sustainable behavior ([49]), at least relative to guilt-inducing messages ([ 3]; [ 7]; [48]). Although these findings suggest that positive affect can play a role in sustainable behavior, there has been limited research on the use of affect as a way to increase sustainable behavior. Additional research is needed to determine what type of message information may elicit positive affect and, more importantly, motivate recycling behavior ([70]).
We propose a novel, positive approach to increasing recycling that entails providing information about the transformation of recyclables into new products, which we term "product transformation salience." Information that increases product transformation salience emphasizes the process of transforming materials that would otherwise be thrown away as trash into useful new products. We argue that this transformation information reveals new and better possibilities for one's trash, which evokes inspiration among consumers and motivates them to dispose of their waste in an environmentally responsible manner, increasing recycling intentions and behavior.
Inspiration is an evoked experience that involves transcendence of ordinary preoccupations and motivates goal pursuit or behavior ([60], [61]). [60], p. 871) conceptualize it as "a breathing in or infusion of some idea, purpose, etc. into the mind; the suggestion, awakening, or creation of some feeling or impulse, especially of an exalted kind." More recently, Böttger and colleagues conceptualized inspiration in the marketing context and defined customer inspiration as a "customer's temporary motivational state that facilitates the transition from the reception of a marketing-induced idea to the intrinsic pursuit of a consumption-related goal" ([ 9], p. 117). Thus, inspiration is evoked by an external source and is connected to the realization of new ideas ([ 9]).
Inspiration is a combination of two component processes that are both necessary to create an episode of inspiration: ( 1) an activation component, being inspired by something, in which one gains awareness of new or better possibilities that one would not have recognized on one's own, and ( 2) an intention component, being inspired to, in which one is motivated to act on newly gained awareness ([ 9]; [60], [61]). The inspired-by activation state relates to the reception of a new idea and the shift in awareness toward new possibilities, which is often described as an "Aha!" moment of realization and insight ([ 9]). Specifically, the process of being inspired by resembles the elicitation of a discrete self-transcendent emotion (e.g., awe, elevation; [52]; [62]). Recent research has suggested that inspiration may result from sources in the consumption environment such as print ads, novel product assortments, and in-store presentations, which provide a new idea, broaden consumers' mental horizons, or stimulate their imagination ([ 9]). In other words, marketing communications have the potential to increase consumer discovery of new possibilities, which can elicit an "Aha!" moment and corresponding feelings of wonderment and inspiration.
Key to our theorizing is that inspiration is triggered by an event, object, message, or another stimulus in which "new or better possibilities are revealed" ([61], p. 959). As discussed previously, consumers do not typically consider how recyclables are transformed into new products with future use and have difficulty understanding the impact of their recycling. Thus, information in an advertisement that prompts consumers to consider how recyclables are transformed into new products provides a new idea and stimulates consumers' imaginations regarding the impact of their actions. That is, providing product transformation information reveals new and better possibilities for consumers' waste that they would not otherwise consider. As such, the first component of inspiration should be met as consumers are likely to be inspired by product transformation information.
Importantly, inspiration involves a transition from the state of being inspired by an external factor that raises awareness of new or better possibilities to a state of being inspired to actualize a new idea. [ 9] demonstrated that the two components are causally linked such that when consumers are inspired by marketing stimuli, they are subsequently inspired to act (see also [55]]). In the case of product transformation information having elicited better waste disposal possibilities, consumers should be inspired to act on this information. That is, after receiving information regarding product transformation, consumers will have greater motivation to recycle relative to other types of recycling-related appeals that do not elicit inspiration.
In summary, we expect that product transformation information provided in marketing materials will serve as an inspiration-evoking stimulus because it "awakens one to better possibilities" ([61], p. 958) regarding waste disposal, which is expected to motivate consumers to dispose of their waste in an environmentally responsible manner. Thus, we predict that increased inspiration will mediate the effect of product transformation salience on recycling.
Although we theorize that inspiration will underlie the proposed effect of transformation salience, it is important to consider some related concepts that could also underlie the proposed effect. First, the experience of inspiration involves emotional elicitation, though inspiration is more complex than many of the constructs widely accepted as emotions ([62]). Given the resemblance of inspiration to an emotional state, we examine the role of positive affect in our findings.
We also consider the role of novelty, which is known to increase product evaluations ([11]; [45]), because the transformation information, which we argue will elicit inspiration, is also likely to be perceived as novel. Novelty is inherent if product transformation is not currently salient to consumers. Furthermore, inspiration (by definition) arises from new, or novel, information. Thus, we anticipate product transformation messages to be perceived as more novel than general recycling messages; however, novelty should not account for the effect of product transformation messaging on recycling. Instead, it is the inspiration stemming from transformation information that motivates consumers to recycle. Novel information without the elicitation of inspiration to act on this information should not affect recycling, given that novelty alone does not invoke a motivation to act as inspiration does. Consistent with this theorizing, we control for novelty in all studies and find that the transformation salience effect occurs regardless of the perceived novelty of the transformation information.
Moreover, in our theorizing regarding inspiration versus novelty, we propose that it is not the outcome that is inspiring but, rather, the transformation. If this is indeed the case, then the novelty of the specific product outcome into which a recyclable is transformed (e.g., whether recycled plastic bottles are transformed into another plastic bottle or into a jacket) should not influence the effect of transformation salience (as shown in Study 1), and the effect should occur even when transformation is made salient by emphasizing that recyclables can have a new life, but no specific product outcome is provided (as shown in Study 3). In addition, because construal level may influence sustainable behavior ([53]; [70]), we demonstrate that product transformation messaging does not alter construal level, temporal distance, or tangibility ([27]; [42]; [67]).
Furthermore, we consider several other explanations, including ease of visualization of the recycling process ([74]), warm glow from recycling ([73]), and perceived progress toward the goal of protecting the environment. The Web Appendix provides details on the related concepts examined in each study.
We conduct six studies to test the proposed theorizing. In Study 1, we test how recycling advertisements that show product transformation of recycled material being transformed into new products can increase recycling behavior. Study 2 demonstrates that advertisements for new products made from recycled plastic can also elicit transformation salience and increase recycling behavior. Study 3 shows that transformation salience increases recycling even when no specific product output is identified from the transformation. In addition, Study 3 tests the underlying process, showing that inspiration mediates the effect of transformation salience on recycling intentions. Then, Studies 4–6 provide robust evidence for the effect in the field. Study 4 shows, through a Google Ads campaign based on an actual recycling program by the Madewell clothing company, that people are more likely to click on a recycling advertisement to get more information about recycling when product transformation is salient in the advertisement. Study 5 was conducted during pre–football game tailgating at a large U.S. university; this field experiment revealed that incorporating product transformation salience in recycling messaging increased recycling rates. Finally, Study 6 provides convergent support for our predictions through a waste audit, whereby product transformation signage in a university residence hall decreased the amount of recyclable material found in the landfill bin compared with signage without transformation information. Across these studies (see Table 1), we employ control conditions that make recycling salient without providing transformation information, offering conservative tests of the effect of transformation salience on recycling.
Graph
Table 1. Summary of Results: Cell Means by Condition (Studies 1–5).
| Study | N | Dependent Variable | Recycling ControlMean (SD) | Product TransformationMean (SD) | Additional Transformation ConditionMean (SD) |
|---|
| 1 | 78 | Disposal behavior | 50.9%a | 80.5%b | 79.1%b |
| 111 | Recycling intentions | 5.32a (1.21) | 5.85b (.77) | 5.81b (1.08) |
| 2 | 187 | Disposal behavior | 71.7%a | 87.7%b | — |
| 3 | 150 | Recycling intentions | 5.49a (1.51) | 6.02b (.99) | 6.36b (.87) |
| 4 | 563 clicks from 280,479 impressions | Click-through rates | .18%a | .26%b | — |
| 5 | 20 | Recycling rate | 19.0%a | 58.1%b | — |
| 6 | 18 | Recoverable fraction of landfill waste | 62.9%a | 51.5%b | — |
1 Notes: Different superscripts indicate significance at p <.05. In Study 1, the product transformation column represents the same transformation condition and the additional transformation column represents the different transformation condition. In Study 3, the additional transformation column is the general transformation information condition in which no product outcome is specified. In Study 6, the mean reflects the recoverable fraction of landfill waste such that lower percentages indicate that fewer recyclables are incorrectly placed in the landfill bin.
Study 1 provides an initial test of our theorizing that product transformation salience increases recycling. We test this effect with advertisements to encourage recycling and vary whether the recycling message makes product transformation salient. Because our theorizing proposes that the effect arises from the transformation information rather than a specific product outcome from recycling, we use two different product transformation conditions: one in which the transformed product is the same as the recycled product (i.e., a plastic bottle is transformed into a new bottle; same product transformation), and one in which the transformed product is different than the recycled product (i.e., a plastic bottle is transformed into a new jacket; different product transformation). We expect both product transformation messages to increase actual recycling relative to a control recycling message that does not make transformation salient. As mentioned previously, in this and all subsequent studies, we control for novelty as an alternative explanation. We also examine several potential explanations for our findings in this study (e.g., construal level, visualization ease, warm glow).
A total of 111 undergraduate business students at Boston College participated in the study in a behavioral lab for course credit (53% female; Mage = 20.53 years, SD =.74) with 78 participants retained for analysis based on paper disposal (see Study 1 section of Web Appendix). The study was a one-factor between-subjects design with three levels: a recycling control condition and two product transformation conditions (which we did not expect to differ from each other). Participants began the study session by taking one minute to do some doodling and drawing with the paper and crayons at their station to ostensibly clear their minds before starting the survey.
After this "mind clearing task," participants were told that they would participate in an advertising study in which they would view an advertisement about recycling and respond to some questions regarding the advertisement. Participants were randomly assigned to view one of three recycling advertisements, which were pretested in this and all subsequent studies for transformation salience (for details, see the Web Appendix). In the control condition, the ad showed recyclables going into recycling bins (see Figure 1, Panel A). In the product transformation conditions, the ads showed either the same three products going into recycling bins and new products of the same type coming out (same product transformation condition; Figure 1, Panel B) or showed the three products being transformed into new product categories (different product transformation condition; Figure 1, Panel C). Participants then indicated evaluations of the advertisement including a measure of novelty ("This advertisement is novel"; 1 = "strongly disagree," and 7 = "strongly agree"). Participants then reported their recycling intentions ("How likely are you to recycle?," "How willing are you to recycle?," and "How motivated are you to recycle?"; 1 = "not at all," and 7 = "very much"; α =.84). Finally, participants responded to several related constructs presented in random order (for measures and results, see the Web Appendix) before providing demographic information.
Graph: Figure 1. Study 1 stimuli.
At the end of the research session, about 45 minutes after completing the survey, the lab assistant asked participants to clean their station for the next person by returning the crayons that were provided and disposing of the paper on their way out of the lab. Disposal of participants' "doodling" paper from the start of the research session served as the focal dependent variable to capture actual recycling behavior.
We ran a logistic regression on participants' disposal decisions (1 = placed the paper in the recycling bin, 0 = placed it in the trash can) with two dummy variables representing the two experimental conditions as independent variables and novelty as a control variable. In line with our predictions, both dummy variables had significant effects on recycling (bsame = 1.415, χ2same = 4.887, p =.027; bdifferent = 1.297, χ2different = 3.899, p =.048) such that participants were significantly more likely to place the paper in the recycling bin in the same product transformation (80.45%) and different product transformation (79.06%) conditions, as compared with the control condition (50.88%). There is no difference between the two product transformation conditions (b =.118, χ2 =.027, p =.867). Novelty was not a significant predictor (b = −.137, χ2 =.664, p =.415) and the transformation salience effect remains when novelty is not included as a control variable (for supplemental analysis, see the Web Appendix).
In this study, we show that product transformation information, regardless of whether the recycling transformation produces the same or different products than the recycled material, increases recycling compared with a control recycling message that does not make transformation salient. Importantly, the transformation messaging affects actual recycling of paper: participants were significantly more likely to dispose of their paper waste in a recycling (vs. trash) bin after being exposed to product transformation information, as compared with a control condition. This study also demonstrates that the effect of transformation salience does not depend on the specific product outcome or perceived novelty of the transformation message. One limitation is that disposal occurred after responding to recycling intentions and the randomly ordered measures of related constructs. However, all participants should have been more likely to recycle after responding to these questions, so the observed effect of the transformation manipulation on disposal when recycling is salient should provide a conservative test. Nonetheless, we address this limitation in the next study and examine whether the effect occurs when transformation salience is elicited in recycled product advertisements.
In Study 2, instead of raising transformation salience through advertisements designed to promote recycling, we incorporate the transformation manipulation into advertisements for new products made from recycled plastic. Specifically, we predict viewing advertisements for products made from recycled plastic will increase recycling behavior more than viewing advertisements for products made by companies that engage in recycling practices. With this manipulation, recycling is made salient in both conditions, but transformation of recycled material into new products is salient only in the experimental condition. In addition, because we theorize inspiration as our mechanism and prior research has shown that inspiration is distinct from, but related to, positive affect ([60]), we examine whether positive affect underlies the proposed effect.
A total of 187 undergraduate business students at Boston College participated in the study for course credit (57% female; Mage = 20.30 years, SD =.59) with 152 participants retained for analysis based on paper disposal (see Study 2 section of the Web Appendix). The study was a one-factor between-subjects design with each participant randomly assigned to one of two levels (product transformation information vs. control).
We followed a similar procedure as in Study 1, with participants doodling on a piece of paper with crayons prior to the start of the survey. Participants were then told they would participate in an advertising study in which they would be asked to view and evaluate two product advertisements. We reinforced the manipulation by having participants view and evaluate two advertisements, one for a smartphone case and one for a toy (see Figure 2, Panels A and B). The two product transformation advertisements emphasized that the advertised products are made from recycled plastic. In the control condition, the advertisements instead emphasized that the advertised products are made by companies that recycle unused plastic in their production process. The order in which the two product advertisements were presented was randomized and did not affect recycling decisions or change our obtained results.
Graph: Figure 2. Study 2 stimuli.
After viewing the advertisements, participants evaluated them to support the cover story, including measures of novelty ("The information provided in the advertisements was novel"; 1 = "strongly disagree," and 7 = "strongly agree") and positive affect (for details, see the Web Appendix). Then, participants disposed of their doodling paper in small recycling and trash bins placed below the desks of each cubicle. This procedure ensured that disposal behavior was not influenced by other session participants disposing of their paper in the same bin at the study conclusion, and it also overcame the limitation in Study 1 in which participants disposed of their doodling paper after responding to a series of recycling measures.
We ran a logistic regression on participants' disposal decisions (1 = placed the paper in the recycling bin, 0 = placed it in the trash can) with product description as the independent variable and novelty of the ad as a control. In line with our predictions, results indicated a main effect of condition (b = 1.040, χ2 = 5.615, p =.017) such that participants were significantly more likely to place the paper in the recycling bin in the product transformation condition (87.69%) as compared with the control condition (71.71%). We note that recycling rates may be higher in this study because of the greater convenience of situating the bins below participants desks, rather than near the lab exit. Regardless, we obtain the predicted effect of transformation salience. Again, novelty was not a significant predictor of recycling (b =.197, χ2 = 1.736, p =.187).
These results provide further support for our theorizing regarding the role of transformation salience in increasing recycling behavior. Notably, despite transformation only being made salient through advertisements for products made from recycled plastic, transformation salience influenced recycling of a piece of paper used in an unrelated task. This effect occurred even as recycling was made salient in the control condition (by emphasizing that the advertised products are made from companies that engage in recycling). These results provide strong support for the effect of product transformation salience.
In Study 3, we present people with advertisements that promote recycling and provide evidence of our proposed underlying process by measuring the extent to which recycling advertisements that increase transformation salience also increase inspiration. In our theorizing, we argue that the positive effect of product transformation salience on recycling occurs due to the inspiration that arises from the awareness that recyclables are transformed into new products, rather than from simply considering a specific product outcome of recycling. It is not the outcome itself that is inspiring but, rather, the transformation process. If this is indeed the case, then the effect of transformation salience should occur even when no specific product outcome is provided. To test this, we examine whether general transformation salience, emphasizing giving recycled products a new life without including a product outcome, increases recycling intentions to the same extent as product transformation salience. We also expect that inspiration will mediate the effect of both product transformation and general transformation on recycling intentions.
Participants were 150 adults from the survey website Prolific who received a small payment for participation (48.67% female; Mage = 32.66 years, SD = 11.38). Participants were randomly assigned to view and evaluate one of three recycling advertisements: product transformation, general transformation, and control (no transformation information) as part of an advertising study. The product transformation advertisement indicated that recycling makes new plastic bottles, the general transformation advertisement indicated that recycling gives recyclables a new life, and the control advertisement indicated that recycling conserves natural resources (see Figure 3, Panels A–C).
Graph: Figure 3. Study 3 stimuli.
After evaluating the advertisement (including the same novelty measure as Study 1), participants moved on to an ostensibly unrelated study in which participants indicated recycling intentions, inspiration, and measures to assess related constructs (e.g., importance of recycling, tangibility and temporal distance of the benefits of recycling, goal progress, construal level; for construct measures and results, see the Study 3 section of the Web Appendix). Finally, participants indicated demographics.
After viewing and evaluating the advertisement, participants indicated their recycling intentions with three items ("How likely are you to recycle?," "How willing are you to recycle?," and "How motivated are you to recycle?"; 1 = "not at all," and 7 = "very much"; α =.89, M = 5.96, SD = 1.20).
We used three items to assess the extent to which participants felt inspired by the advertisement ("When viewing the advertisements, to what extent did you feel: inspired/ astonished/wonder?"; 1 = "not at all," and 7 = "very much"; α =.79, M = 2.94, SD = 1.37).
We predicted that both product and general transformation advertisements would increase recycling intentions more than the control recycling advertisement. We conducted an analysis of covariance (ANCOVA) on recycling intentions with experimental condition as the predictor and novelty of the ad as a control. Experimental condition had a significant effect (F( 2, 146) = 7.15, p =.001). Novelty was not a significant predictor (F( 1, 146) =.22, p =.63). We find that both the product transformation condition (M = 6.02, t = 2.29, p =.02) and the general transformation condition (M = 6.36, t = 3.74, p =.0003) increased recycling intentions more than the control condition (M = 5.49). There was no difference in recycling intentions between the two transformation conditions (t = 1.43, p =.15).
An ANCOVA on inspiration with experimental condition as the independent variable and novelty as a control variable revealed that condition affected participants' reported inspiration (F( 2, 146) = 6.03, p =.003). Novelty was also a significant predictor (F( 1, 146) = 51.23, p <.0001). We find both the product transformation condition (M = 2.55, t = 2.07, p =.04) and the general transformation condition (M = 2.87, t = 3.44, p <.001) increased inspiration more than the control condition (M = 2.06). There was no difference in inspiration between the two transformation conditions (t = 1.36, p =.17).
To determine whether inspiration mediated the effect of transformation condition on recycling intentions, we conducted mediation analysis (Hayes PROCESS Model 4 with two dummy variables representing the two experimental conditions as independent variables and novelty as control). As compared with the control condition, we found a significant indirect effect on recycling intentions through inspiration for both the general transformation condition (bindirect =.1473, 95% confidence interval [CI] = [.0253,.3046]) and the product transformation condition (bindirect =.0887, 95% CI = [.0016,.2157]). When inspiration is included in the model with transformation condition to predict recycling intentions, inspiration is significant (B =.18, t = 2.25, p =.025), whereas the effect of product transformation condition (B =.44, t = 1.91, p =.057) is marginal and the effect of general transformation is still significant (B =.72, t = 3.03, p =.002), indicating partial mediation.
Study 3 provides further evidence for the effect of transformation salience on recycling and serves to demonstrate the role of inspiration in the effect of transformation salience on recycling. Importantly, transformation salience increases recycling intentions even when no product outcome is specified, suggesting that the effect occurs due to inspiration from thinking about the possibility of transformation, rather than by considering a specific product as an outcome of recycling. This process is further supported as several other potential explanations (e.g., construal level) are not affected by transformation salience (for results, see the Web Appendix). Having provided some evidence for the role of inspiration as well as considering the role of related constructs, we next turn our focus to demonstrating the robustness of this effect in three field studies (Studies 4–6).
Study 4's field study involves an actual advertising campaign by the clothing brand Madewell. At the time the study was conducted, Madewell was running a jeans recycling campaign in cooperation with the Blue Jeans Go Green nonprofit organization, encouraging customers to recycle their jeans, which would be transformed into housing insulation after collection. Using this campaign, we published paid advertisements on the Google Ads platform to examine whether participants would be more likely to click on a paid recycling advertisement if it featured product transformation information versus not. This study examines the effectiveness of providing product transformation information in consumers' willingness to take steps toward recycling their unwanted jeans. We anticipate that consumers will be more likely to click on a product transformation advertisement than a control recycling advertisement.
We published sponsored search ads on Google Ads. Sponsored search ads in Google appear each time a user types a prespecified search keyword. For example, when a user types the keyword "womens blue jeans," (s)he might see near or above the results of his or her search a sponsored advertisement by a company marketing blue jeans that specified this word as a keyword (see [35]).
We ran our campaign for five business days between October 29 and November 2, 2018. We specified a daily budget of $100, such that on a certain day our ads would stop appearing to consumers searching our prespecified keywords after the daily budget has been reached. The keywords we selected, based on Google's suggestions, were "blue denim," "cotton jeans," "madewell denim skirt," "jeans womens," "jeans mens," "jeans store," "where to buy jeans," "mens blue jeans," "womens blue jeans," "good jeans," "best jeans brand," "denim jeans for women," "black jeans," and "denim jeans for men." We chose a "maximizing clicks" goal for the campaign, and Google's algorithm determined the specifics of the campaign automatically. We constructed two search advertisements: a control ad, with the headline "Give jeans to be recycled | Recycle jeans you don't use," and a product transformation ad, with the headline: "Transform jeans to insulation | Recycle jeans you don't use" (see Figure 4, Panels A and B). Clicking on either search advertisement took participants to the same landing page: Madewell's webpage for its jeans recycling campaign (https://www.madewell.com/inspo-do-well-denim-recycling-landing.html).
Graph: Figure 4. Study 4 stimuli.
During the five days that our campaign ran, the ad generated 280,479 impressions and 563 clicks (.20% click-through rate). The frequency of appearance of the different messages was not random, because the Google Ads algorithm determines which ad to show depending on the goal of the campaign (maximizing clicks in our case). Therefore, we could not use the actual number of clicks per advertisement. Instead, we follow [35] and use the average percentage of clicks per appearance as our main dependent variable. We conducted a chi-square analysis of the difference between average percentage of click-through on the control versus the product transformation search ad. The analysis revealed that, as we predicted, the click-through rate was higher for the product transformation advertisement (.26%), as compared with the control advertisement (.18%; χ2 = 19.54, p <.0001). These results show that even in a short search campaign with very limited optimization, the likelihood of clicking on the product transformation advertisement was significantly higher as compared with the control recycling advertisement.
These results provide evidence that not only does transformation salience increase recycling in disposal decisions, but it also increases the likelihood that consumers will be inspired to get more information to recycle old jeans. This finding that transformation salience increases consumers' recycling information seeking provides evidence of the potential long-lasting impact that providing product transformation information can have on recycling attitudes and behaviors ([28]). While this study documents the effect on advertisement click-through rates in the field (e.g., Google search), we next aim to demonstrate the effect on actual recycling behavior in the field.
To assess the effect of product transformation messaging on recycling behavior in the field, we conducted a field study during pre–football game tailgating at Penn State University. The university already had a recycling initiative in place for football tailgating, which entails student liaisons canvassing tailgating locations to welcome visitors to campus and inform them how to properly dispose of their waste. We partnered with this existing program, manipulating the content of the recycling message that student liaisons conveyed to college football fans to either include transformation messaging or not. Recycling and landfill bags at participating tailgate locations were weighed after the game to assess recycling rates.
Student liaisons shared one of two different messages (a control recycling message or a transformation recycling message) with tailgaters during a home Penn State football game during the Fall 2018 football season. In both conditions, liaisons shared information about the proper disposal of waste at tailgates: plastic bottles and aluminum cans are recyclable and belong in blue recycling bags, with everything else (paper, other metals, other plastics, food waste, and all other waste) collected in clear landfill bags. For the control condition, no other information was shared with fans. For the transformation condition, liaisons also informed tailgaters about the transformation of each type of recyclable into a new product when informing tailgaters what to put in blue recycling bags. Student liaisons were provided with laminated visual aids to assist in communicating their assigned message (see Figure 5, Panels A and B).
MAP: Figure 5. Study 5 stimuli.Notes: The control condition (orange) and the product transformation condition (blue) correspond to locations on map in Web Appendix (Figure W1).
The focal recycling measure was the amount of recycling relative to the total amount of waste (i.e., recycling weight/[recycling weight + landfill weight]). After tailgaters had left the parking lot the evening after the game, research assistants weighed waste that was remaining at all areas in the parking lot where student liaisons had communicated with tailgaters. A total of 892.92 pounds of waste, nearly half a ton, was weighed, with 26.5% (236.34 pounds) in recycling bags. The number of recycling and landfill bags weighed was very similar by condition (47 and 47 landfill bags and 22 and 25 recycling bags weighed for control and transformation condition, respectively; for additional study details, see the Study 5 section of the Web Appendix).
To determine whether the recycling rates differed by recycling message, we calculated the recycling rate (recycling weight/[recycling weight + landfill weight]) for each of the 20 tailgating spaces. We conducted an ANCOVA with recycling rate as the dependent variable, recycling message as the independent variable, and total weight of waste at each tailgating space as a control variable. We control for total weight as an indicator of the size of the tailgate, which is not captured by the dependent variable assessing recycling rate with a ratio but may affect recycling rate (e.g., larger tailgates may have a different proportion of recyclable material or be busier with less time to sort through recycling).
The effect of recycling message condition was significant (F( 1, 17) = 17.29, p <.001), with an average recycling rate of 58.1% in the transformation salience condition and 19.0% in the control recycling condition. Total weight at each tailgating space was a significant covariate (F( 1, 17) = 10.86, p <.01). Importantly, the effect of recycling message condition holds when we do not include the total weight per space as a covariate in the regression (49.2% in the transformation condition vs. 23.8% in control condition; F( 1, 18) = 5.84, p =.03).
Given the low number of observations (N = 20 tailgating spaces), we conducted additional analysis to determine the robustness of the effect. First, we conducted a nonparametric Wilcoxon ranked-sum test for robustness. The recycling rate for the transformation salience condition was marginally higher than the recycling rate in the control condition (Z = 1.86, p =.06). In addition, due to the potential impact of outliers in such a small sample, we conducted the same ANCOVA when tailgate spaces with no recycling (n = 1) or no landfill waste (n = 1) were excluded. The effect of transformation salience remains significant (F( 1, 15) = 8.75, p <.01). Total weight at each tailgating space was still a significant covariate (F( 1, 15) = 6.39, p =.02).
These results provide evidence that making product transformation salient to consumers in a natural consumption environment increases recycling. Although there are limitations of this study given the number of variables that could not be controlled in such a field setting, we believe that the large number of factors that influence recycling during football tailgating makes this a conservative test. Nonetheless, given the limitations of potential confounds in field studies, we conducted another field experiment that assesses recycling behavior (with a different outcome measure) to test the robustness of the proposed effect.
In Study 6, we manipulated product transformation salience on posters at the waste collection stations on two university residence hall floors. Then, we conducted a waste audit of the collected landfill waste to determine the extent to which students were incorrectly placing recyclables in the landfill bin rather than the appropriate recycling bin. By using this waste audit measure, which is a common approach in waste and recycling studies when seeking to determine the extent to which participants actually recycle recyclable items ([12]; [46]; [50]), we offer converging support for our predictions.
The field experiment was conducted over a period of three weeks in the summer on two floors of the same residence hall on Penn State University's campus. A total of 82 students lived on the two coeducational residence hall floors, with each floor including male and female students (47% female).
The two floors were randomly assigned to either the product transformation salience condition or the control condition. In both conditions, we created a 4′ × 5′ large-format poster to be displayed above the floor's waste collection station for the duration of the study (see Figure 6, Panels A and B). For the control condition, the poster included information on "What goes where?" and examples of items disposed of in each of the station's seven separate bins. It also contained a "Save trash for last" prompt to check if items to be thrown away belong in a different bin than the landfill ("Can it go in another bin?"). For the product transformation salience condition, the poster included the same images as the control poster, with additional images of products made from materials disposed of in each specific bin. We included examples of material being transformed into the same product (e.g., aluminum can → new aluminum can) as well as examples of material being transformed into different products (e.g., plastic bottle → fleece jacket) as results from Study 1 showed that both approaches are effective in boosting recycling. For the product transformation poster, the "Save trash for last" prompt was changed to get individuals to consider whether the item could be made into something new ("Can it be made into something else?").
Graph: Figure 6. Study 6 stimuli.Notes: Each poster was 4′ tall by 5′ wide, placed above the floor's waste disposal station.
Unlike previous studies, the recoverable fraction of landfill waste was used as the dependent measure of interest. This measure of recycling is different from our other studies, in which higher numbers indicated more recycling. In this study, lower numbers indicate less recyclable material being placed in the landfill, which suggests consumers are engaging in more recycling. Specifically, the recoverable fraction of landfill waste is the percentage of recyclable material that was placed in the landfill waste bin instead of the appropriate recycling bin (weight of recyclables in landfill waste bin/total weight of landfill waste bin). Recyclables in landfill waste bin refers to the weight of recyclable material recovered from the landfill waste bin during the audit, while total weight of landfill waste bin refers to the weight of the landfill waste bag collected in the residence hall prior to the audit (i.e., when it still contained incorrectly sorted recyclables). This variable was obtained from a waste audit conducted by university interns on three consecutive days for each of the three weeks (for a total of nine measurements for each floor). During the waste audit, interns sorted any recyclable content into the appropriate recycling bin and placed any landfill items into a landfill bin. These postaudit bins, which only contained items from each audited landfill bag, were weighed to determine the amount of recyclable materials in the landfill waste bin for the dependent variable (for additional details, see the Study 6 section of the Web Appendix).
We predicted that increasing product transformation salience would prompt individuals to be more selective of where they placed their waste such that there would be a lower recoverable fraction of landfill waste for that condition (i.e., smaller numbers indicate fewer recyclables incorrectly placed in the landfill waste bin). The contents of each floor's landfill waste bins over the three weeks suggest this to be the case. Specifically, on the product transformation salience floor, 51.5% of the material headed to the landfill was recyclable, whereas 62.9% of the material in the control floor's landfill bin could have been recycled (Satterthwaite t(10.0) = 2.33, p =.04). In other words, on the product transformation salience floor, 48.5% of the landfill bin's contents was true landfill waste, but for the control floor, only 37.1% of the material in the landfill bin actually belonged there. Given the low number of observations (nine observations per floor), we also conducted a nonparametric Wilcoxon ranked-sum test for robustness. Results were similar for this test: the product transformation salience floor had a marginally lower ratio of recyclable material in the landfill bin than the control floor (Z = 1.68, p =.09). Thus, over the three weeks, an audit of the two floors' landfill waste provides evidence that increasing product transformation salience via recycling signage led individuals to place less recyclable material in the landfill bin. These results further support our theorizing by showing that product transformation messaging can have a significant impact on students' disposal behaviors; specifically, residents exposed to product transformation messages sent less recyclable material to the landfill.
Given the current state of disposable consumption, there is a great need to understand how to best motivate sustainable behaviors such as recycling ([29]; [34]; [70]). Over half of the material taken to landfills could have been recycled instead, and recent recycling rates are not increasing even as more companies use recycled and/or recyclable materials in their products ([15]; [43]). The current research aimed to explore a novel approach to increase consumer recycling rates, demonstrating the value of using product transformation messaging to increase recycling. We propose and subsequently show that making consumers aware of the transformation of recyclables into new products (e.g., showing that recycled aluminum can be transformed into a bicycle) can increase socially beneficial disposal behaviors. A series of studies conducted in the field and the lab provide compelling evidence (for a summary of means across studies, see Table 1) that making transformation salient increases recycling due to the inspiration consumers experience from transformation information.
The current research has important implications for effective recycling messaging as current recycling rates have plateaued ([18]). One simple way to increase recycling is to increase the extent to which consumers think about the transformation of recyclables into new products, as doing so will inspire consumers to recycle. A pilot study with consumers (reported in the Web Appendix) as well as a survey of sustainability managers (in which only 17 of 109 had previously used product transformation messaging) confirmed that providing information on the transformation of recyclables into new products is rare. As mentioned previously, one notable exception is the "I Want to Be..." campaign by the nonprofit organization Keep America Beautiful. When the organization was developing this campaign, it pitched the idea of "the potential of trash" to focus group participants, whose responses indicated their lack of awareness of product transformation from recycling (e.g., "I never thought about what things could become. Always just thought of it all just as trash"; "Touched me and made me realize there is more life to my trash"). These responses led to the key insight around which the campaign was developed: "Give your garbage another life" ([16]). Our research provides compelling evidence that when consumers consider that recyclables are transformed, they recycle more, and providing messages with product transformation information inspires consumers to participate in recycling programs. Therefore, increasing transformation salience among consumers should be a priority for organizations and public policy officials seeking to encourage the greater public to recycle at higher collective rates.
Beyond the implications of these results for public service announcements and point-of-disposal displays aimed at increasing recycling, our results also provide insights into how companies can use product transformation messages to increase recycling. We show that when advertisements for products made from recycled material make transformation salient, recycling increases. Not only does increased recycling offer societal and environmental benefits, but it is particularly useful to companies such as Unilever, PepsiCo, and many others that are using recycled materials in production, as these companies need to increase consumers' recycling rates to effectively develop a circular economy ([ 4]; [15]; [63]). Our findings demonstrate that efforts to increase recycling are more successful if the messages communicate that collected recyclables will make new products or packaging, but it is also important to consider why companies may not want to promote transformation salience. For example, consumers have mixed feelings toward products made from recycled materials like ocean plastic ([40]) and may experience some degree of disgust, depending on the product category ([ 1]). In addition, consumers may be skeptical of companies' motives or product quality when recyclable material is used in products (e.g., [23]; [39]). These potential downsides of emphasizing transformation salience should be considered in future research.
The current research also makes several important contributions to theory. First, we contribute to research studying the various factors that affect consumers' decisions to recycle versus trash objects ([59]; [70]) by demonstrating that getting consumers to consider the transformation of recyclables into new products increases recycling. As such, we join an emerging trend of designing positive sustainability initiatives ([49]; [69]) and address calls for research to examine how positive outward-focused emotions enhance sustainable behavior ([70]). Our finding that transformation salience increases consumers' recycling information seeking provides evidence of the potential long-lasting impact that providing product transformation information can have on recycling attitudes and behaviors ([28]). The proposed product transformation approach, which makes an explicit connection between the decision to recycle and its positive outcomes, has the potential to be more effective in promoting sustainable behaviors than traditional approaches focusing on the negative outcomes that might be incurred or avoided, which often trigger guilt and defiance ([22]; [44]).
Second, we contribute to the limited literature on inspiration in marketing ([52]). Only recently has inspiration been documented as being elicited from marketing communications ([ 9]). We extend this work by proposing transformation salience as a novel elicitor of inspiration and showing that public service announcements for recycling can elicit inspiration, which leads consumers to be inspired to engage in the desired behavior. Thus, we add to the current understanding of the elicitation of inspiration in sustainability contexts and in response to marketing stimuli more generally.
While recycling does reduce the amount of raw, virgin material used in the production of new products, it does not eliminate two main causes of waste proliferation: finite product life cycles and consumers' demand for new, up-to-date products. As such, the current research does not explore an overall reduction in consumption, instead focusing on what should be the final option when reduction and reuse are not possible. Our findings indicate that consumers are more likely to attempt to dispose of useful materials in a responsible way if they become aware of how such material can be transformed. That said, messages encouraging recycling through transformation salience have a responsibility to acknowledge that products made from recycled material are not environmentally costless. In fact, previous research has revealed that consumers may actually consume more if products' recyclability is made salient ([13]), given the positive affect from recycling that offsets the negative affect from waste ([59]); future research should study whether educating consumers about the transformation process as well as costs of recycling can lead to more responsible consumption ([34]).
Another avenue for future research is the extent to which transformation salience in advertisements for products made from recycled materials can not only increase recycling but also provide direct benefits to firms. Well-known brands such as Nike, Adidas, Timberland, PepsiCo, and Patagonia all offer products or use packaging made from recycled water bottles or other post-consumer-recycled materials ([36]). Additional examples include recycled plastic bottles used to produce Rothy's shoes and John Lewis bath towels, recycled paper to make EcoHelmets, and recycled milk jugs to create Green Toys. While some literature suggests that there may be potential downsides for companies using recycled materials in their products (e.g., [23]; [39]), other work suggests that consumers value the stories told by products made from recycled material ([30]). Although we have demonstrated that including such transformation information in the product description increases recycling, does transformation messaging also increase purchase intentions or provide indirect benefits such as goodwill toward brands using such materials in their products? Future research should investigate this possibility.
In summary, the current research has the potential to make a substantial impact on current recycling rates simply by incorporating information regarding the transformation of a recyclable into a new product in recycling messaging and recycled product descriptions. We hope this research not only benefits society by increasing recycling rates but also spurs additional research to better understand the importance of transformation salience and inspiration for improving sustainable behaviors. By bringing consumers' attention to the transformation of recyclables into new products, the conversation shifts from "Where does this go?" to "What can this make?"
Supplemental Material, DS_10.1177_0022242919842167 - Knowing What It Makes: How Product Transformation Salience Increases Recycling
Supplemental Material, DS_10.1177_0022242919842167 for Knowing What It Makes: How Product Transformation Salience Increases Recycling by Karen Page Winterich, Gergana Y. Nenkov and Gabriel E. Gonzales in Journal of Marketing
Footnotes 1 Associate EditorConnie Pechmann served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by a Sustainability Grant from the Smeal College of Business, Pennsylvania State University and by a research grant from Boston College.
4 ORCID iDsKaren Page Winterich https://orcid.org/0000-0002-4190-4036 Gabriel E. Gonzales https://orcid.org/0000-0003-0323-039X
5 Online supplement: https://doi.org/10.1177/0022242919842167
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By Karen Page Winterich; Gergana Y. Nenkov and Gabriel E. Gonzales
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Leaving Something for the Imagination: The Effect of Visual Concealment on Preferences
When advertising products to consumers, firms sometimes conceal key aspects in an effort to arouse consumer curiosity. This research investigates when and how visual concealment tactics may benefit or hurt aesthetic product evaluations. The authors propose that when consumers are only able to view a portion of an aesthetic product, assessments of its appeal will be influenced by two interrelated mechanisms: curiosity to see the item completed and inferences about the item's fully disclosed appearance. The authors show that heightened curiosity triggers feelings of positive affect that are transferred to the product itself, a process that may inflate preferences and choice likelihoods for products beyond what would occur if the full image were known. This transference effect, however, has an important boundary: it works only when initial consumer inferences about the appeal of the product are positive or emotionally congruent with the positive affect triggered by curiosity. The key implication is that, ironically, the products likely to benefit most from concealment tactics are those that have the least to hide. The authors provide evidence for these effects and the underlying mechanism using six experiments that manipulate concealment in a variety of task settings.
Keywords: curiosity; affect; aesthetics; online retailing; advertising
One of the oldest tactics marketers use to arouse consumer interest in innovations is the "peekaboo" ploy: deliberately withholding from view some of the aspects or attributes of a product in the hope of generating curiosity. In its 2010 ad campaign for the new Audi A3, for example, DDB Milan developed print ads that showed only the rear third of the vehicle, enticing the viewer to imagine the whole.[ 4] In 2016, Honda used a similar ad campaign for its all-new Ridgeline, in which the firm circulated a print ad showing the silhouette of the truck in a dark room.[ 5] Similar tactics are routinely used by other car manufacturers every time a new generation of a model is introduced. Likewise, [32] observe that web marketers often lure consumers into learning about new products by using "teaser" web page designs that withhold key attribute information—pages created to pique curiosity that can be resolved by clicking through additional pages. Finally, online retailers increasingly make implicit concealment decisions when determining how much of a product to depict on their websites, which can range from a few static images to an interactive, 360-degree view.
When might marketers benefit from concealing a product's image? Despite the pervasiveness of such tactics in practice, the answer is far from clear. On the one hand, if the marketer is confident that consumers will intuitively find a product attractive, there would seem to be little benefit in keeping its looks under wraps; while concealment may trigger curiosity and, in turn, interest, whatever positive gain comes from this may fall far short of that which could be realized by full disclosure. Likewise, the tactic could backfire if consumers suspect that it is a deceptive ploy being used to draw their attention to a product that, in all likelihood, will be nothing special when finally revealed. On the other hand, if curiosity piqued by the ad serves as a source of positive affect that is generalized to the product itself, even attractive products could benefit from some concealment, gaining a "curiosity bonus" that boosts product attraction beyond what it would have been if the full appearance had been revealed. Unfortunately, there is little prior theoretical or empirical work that might help marketers know when visual concealment tactics should be applied—and, if so, how they should optimally be implemented.
The purpose of this work is to take a step toward this goal by reporting the findings of a theoretical and empirical investigation into the effects of concealment tactics on product evaluations. Our work centers on a hypothesis that visual concealment triggers two interrelated psychological processes that, when combined, drive consumer evaluations about the attractiveness of a product. The first is a curiosity effect that acts as an inverted U-shaped function of the percentage of the product that has been revealed: the more that is revealed, the greater the drive to see it completed; however, beyond a certain point—when curiosity has been satisfied—the drive wanes. The second is an inference effect, whereby consumers infer how appealing the product will be from the fragments that are revealed: the more promising the fragments, the more positive the inference. We posit that curiosity serves as an independent source of positive affect that interacts with this inference, such that the more positive the inference about the likely appeal of the whole, the more the affect piqued by curiosity will be transferred to the product itself. This interaction yields a prediction about when concealment tactics are likely to be most successful: they will work best when applied to products for which consumers hold the most positive expectations about the appeal of the fully revealed design, or, ironically, those that have the least to hide. Conversely, product designs that foster lower expectations—those that have the most to hide—would find little gain from such tactics.
The present work makes important theoretical and practical contributions to our knowledge of how strategically making information available to consumers may influence preference (e.g., [11]; [10]; [32]; [37]). For example, work by [10] shows that delaying the presentation of some favorable information about a product until after consumers have prescreened all of the available options can increase preference for this product. Relatedly, [42] find that first creating and then resolving uncertainty can generate a net positive experience. Furthermore, [11] show that when consumers are nudged toward discovering an option through search rather than finding it readily available at the beginning of the decision-making process, they may derive higher preference from the newly discovered option. Thus, these prior findings show that the timing in which an option or information about an option is discovered may influence preference. The current research moves the needle further and shows that not only delaying but concealing altogether the presentation of certain amounts of favorable visual information about a product may lead to a boosting effect on preference. We propose that this occurs as a result of curiosity and positive affect associated with what the item will look like when completely revealed.
Notably, this research also enhances our understanding of how curiosity may help shape product preference in the domain of visual aesthetics, as previous work on the interplay between curiosity and product preference has mostly focused on verbal information. Furthermore, while previous work has shown that curiosity may lead to higher quality and quantity of search, which may increase subsequent preference for a product ([32]), we show that the positive role that curiosity exerts on product preference goes beyond mere search, as positivity may directly boost preference. In addition, the present work extends findings in the advertising domain by [37], who show that cropping peripheral (but not central) parts of a familiar ad (e.g., a woman in a beer commercial or a truck in a jeans commercial, rather than the products themselves) may lead to higher preference for the advertised product due to positive affect experienced as a result of resolving incongruity. Our work examines the effect of concealment in the visual aesthetics context and shows that, in some cases, concealing important features of a product may indeed lead to higher aesthetic preference for the item as a result of curiosity and positivity, even if curiosity is left unresolved. This notion is consistent with recent work showing that unresolved curiosity may influence preference and behavior, such as leading consumers to make more indulgent choices across other domains (e.g., [49]; [51]). In this case, we show that the positive affect generated by unresolved curiosity not only leads to indulgent choices across other domains but also may improve preference toward the item that is the object of curiosity.
Finally, this article makes an important contribution to the literature on visual aesthetics, as its findings run counter to prevalent findings in this area of work. Specifically, prior research has shown that aesthetic appreciation is a form of sensory gratification ([ 6]); thus, any factors that improve processing ability, such as making an item perceptually fluent ([41]) or easier to delineate from the visual field ([30]; [40]), should lead to enhanced aesthetic perceptions. In this research, we document a case in which, instead of facilitating visual processing, partially impairing it may lead to improved aesthetic evaluations due to a boosting effect of curiosity and positive affect.
We organize the article in three sections. In the next section, we develop our theoretical hypotheses based on prior research on curiosity and the transmission of positive affect. We then describe the results of one field study and five laboratory studies that test the theoretical predictions and hypothesized mechanism in a variety of task settings. We find support for our hypotheses as well as evidence that there may be a universal "ideal" proportion of exposure that maximizes consumer preference toward visually aesthetic products: across a variety of stimulus domains, we find that curiosity and positivity are maximized when approximately one-half to two-thirds of the object has been revealed. We conclude with a discussion of the implications of the work for both consumer behavior research and its applicability to marketing.
A fundamental feature of human cognition is curiosity—the drive or desire to gather new knowledge about our environment and seek order in sensory perceptions. Curiosity is defined as an intrinsic motivation to obtain and process new information ([19]; [26]; [29]; [36]; [38]). While many factors have been hypothesized to drive feelings of curiosity, perhaps the most well-known is that curiosity is piqued when there is a "closeable" knowledge gap—a modest difference between the amount of product information directly observed by the consumer and the amount of information that a consumer would ideally like to have or need ([29]; [32]).
What is the ideal size of this "knowledge gap"? While there is presumably no precise answer to this question, [29] hypothesizes that the intensity of curiosity acts as a continuous nonlinear function of the size of the gap, becoming most intense when a person sees a desired knowledge level as being just "a hair away." Consistent with this account, people are most interested in knowing the words that they feel are at the tip of their tongue, and contestants in trivia challenges are more eager to find out the capitals of more U.S. states if they already know most of the state capitals than if they know only a few ([29]). As such, feelings of curiosity might be seen as obeying a "goal-gradient" rule similar to that observed in other behavioral drive contexts, such as the increasing tendency for individuals to exert effort into completing the requirements of frequent-user programs as the target approaches (e.g., [18]; [25]; [35]).
But while the "tip of the tongue" rule may be appropriate for verbal stimuli, whether it applies to visual stimuli has not been largely investigated. One reason that it might not apply is that our visual systems have evolved to instinctively "fill in the blanks" of partially concealed objects so that we see them as complete; if a quarter of a circle is covered, our mind's eye sees it as a complete circle (e.g., [46]). Because of this, one might conjecture that, for visual stimuli, curiosity may be piqued only when comparatively small portions of the object have been revealed—enough to create a desire to see the whole, but not so much that our visual systems see it as a whole. We aim to better understand the dynamics between visual concealment and curiosity as well as the downward consequences this relationship may involve.
For the marketer, of course, an effort to conceal visual aspects of a product with the intention to trigger consumer curiosity is of limited success if it helps draw attention to the stimulus but fails to boost attitudes toward it. Implicit to such tactics, therefore, is the hope that the curiosity piqued by a partially revealed product will generate some form of positive affect, which will, in turn, then be transferred onto the featured product.
How likely is this to occur? On one hand, there is good reason to suspect that at least the first part of this process—curiosity boosting positive affect toward the product—may hold. For example, in a study of how consumers assess advertisements in which familiar, secondary objects of the ad (e.g., jeans, a truck) have been cropped, [37] found that when participants were highly motivated to process ads, the more ambiguous cropped ads were the more favorably evaluated. They proposed that the enhanced affect did not come from the ambiguous nature of the ads, per se, but rather from the positive affect that emerged from resolving that ambiguity—akin to the positive boost one expects to feel when suddenly solving a puzzle or seeing an ambiguous image come into focus. We posit that in some cases, concealment may also generate positive affect even if consumers are unfamiliar with concealed, central items and are unable to fully resolve ambiguity. We argue in favor of a process different from the resolution of ambiguity. Specifically, we propose that when subjects are exposed to a moderately concealed, visually attractive item that plays a central role as the object of evaluation, curiosity may emerge. We propose that this curiosity will then generate positive affect, which will be subsequently transmitted to the evaluation of the item.
Previous work has related a moderate level of incongruity to curiosity and positive affect, the latter surging from the anticipation of resolving such incongruity. Specifically, [ 2] and [14], [15]) proposed that people experience pleasantness when there is moderate incongruity in their environments. Similarly, [19], [20]) posited that curiosity reflected a search for a moderate level of cognitive incongruity that, in turn, was motivated by a desire for positive affect. [50] also proposed that curiosity is motivated by a desire for positive affect. Thus, all this work suggests that consumers derive positive affect from a moderate level of incongruity and curiosity. In this work, we take that proposition further and argue that the moderate incongruity and curiosity generated by a moderately concealed, visually attractive item may also generate positive affect, which may be transmitted to the object of evaluation.
The notion that curiosity as a result of uncertainty can be related to positive affect has been further supported by more recent work. For example, [52] showed that uncertainty following a positive event prolongs the pleasure it generates. In addition, recent findings have proposed that the information that must be obtained to satisfy curiosity can be seen as a reward for which consumers are willing to expend considerable economic or time resources (e.g., [22]; [31]). This reward-approaching behavior is ignited by the feelings of interest and pleasure associated with the anticipation of obtaining the missing information ([28]). Indeed, higher levels of curiosity have been linked to higher responsiveness to reward ([ 5]; [23]; [24]). In fact, the link between curiosity and the positive affect associated with the desire for satisfying it may lead consumers to make more indulgent choices across unrelated domains even when curiosity is left unsatisfied (e.g., [49]; [51]). Similarly, the present research also suggests that the positive affect generated by curiosity can have downstream consequences on behavior even when curiosity is left unsatisfied. Specifically, rather than showing that unsatisfied curiosity and its related positive affect may lead to indulgent choices across other domains, we demonstrate that curiosity and its related positive affect may influence how consumers evaluate the item that is the source of such curiosity and positive affect.
While the link between curiosity and positive affect has been well documented, whether consumers will transfer the feelings of positive affect that come with piqued curiosity to the product itself is less clear. For example, most consumers are likely well aware that concealment tactics are often used by marketers as a "gimmick" to lure consumer interest. Given this, while concealment might be quite successful in drawing consumer attention to a product and piquing curiosity, it also could activate persuasion knowledge that would deteriorate consumer attitudes toward the product ([ 9]). Alternatively, theories of feelings as information (e.g., [44], [45]) suggest that consumers might, in fact, have difficulty decoupling the curiosity-related positive affect they experience from their feelings toward the object of the curiosity—a transference that would strictly boost attitudes.
One way that these divergent predictions could be reconciled is by hypothesizing that the transference of the positive affect to the product depends on the congruence of the consumer's curiosity with their initial expectations about the likely overall appeal of the completed product image. Research on mood-congruent judgment (e.g., [ 4]; [ 8]) finds that when people are in a given mood state (e.g., experiencing the positive affect triggered by an ambiguous image), they are more likely to attend (and assimilate) stimuli cues that are most congruent with that state. By extension, the more positive a consumer's initial inferences about the likely appeal of a given product based on fragments, the more likely they may be to assimilate these positive expectations with the positive feelings they are experiencing from curiosity to see it completed. In contrast, if the initial expectations are discordant, there would be no assimilation, and whatever positive emotions that might be triggered by curiosity would not be transferred to the product itself.
In this research, we focus on testing two theoretically novel aspects: the influence of visual concealment on curiosity and positive affect and the influence that these mechanisms have on aesthetic preference. This yields two central empirical predictions about how partial revelation of a product will affect consumer judgments. The first directly follows from assumptions made from prior work on curiosity not related to visual stimuli (e.g., [29]): there should be an inverted U-shaped relationship between the percentage of a visual image that has been revealed and consumer curiosity about seeing it completed. What is empirically uncertain, however, is whether there might exist an "ideal" percentage of revelation that holds across contexts. Previously we noted that for verbal stimuli (e.g., remembering lists), curiosity is hypothesized to be piqued when only a small unknown fragment remains ([29]); however, research on visual occlusion (e.g., [46]) suggests that for visual stimuli, curiosity may be highest when a smaller proportion is revealed.
The second prediction is more nuanced and holds greater implications for marketers. We have hypothesized that consumers' initial beliefs about the likely attractiveness of the product when fully revealed would determine whether the positive affect piqued by curiosity would be transferred to the product itself—the greater the emotional congruence, the greater the transference. This, in turn, yields the possibility that the most attractive products—the ones that have the least to hide—could be the ones that would benefit the most from holding something back. Specifically, when something has been held back, the positive expectations that consumers have for the likely appeal of the completed product image would receive an extra "boost" from transference of the positive affect triggered by curiosity. As such, in the same way that we would predict an inverted U-shaped relationship between the percentage of a product that has been revealed and curiosity, there would be a similar inverted U for judgments of product attractiveness—but only for product images that show promise of being attractive.
We test these predictions in a series of six laboratory-controlled studies that employed multiple versions of concept cars, computer-generated human faces, and sneakers as stimuli and that relied on within-subject (Study 1) and between-subjects (Studies 2 to 6) designs; choice (Studies 1 and 2) and evaluations (Studies 3 to 6) dependent measures; and undergraduate (Studies 1 and 2), Amazon's Mechanical Turk (MTurk; Studies 3 to 5), and real-life (Study 6) subjects. Study 1 used a concept-cars evaluation context to test the hypothesized inverted U-shaped effect between visual concealment and curiosity, while Studies 2 to 6 aimed to provide evidence for the proposed theoretical mechanism and to shed light on potential boundary conditions for the effect. Studies 2 and 3 also replicated the effect using computer-generated human faces as stimuli. The use of these stimuli allowed us to rule out the alternative explanation that the effect may be due to different levels of attractiveness between the concealed and revealed parts of the stimuli. Study 3 tested our prediction associated with the product attractiveness boundary condition. Studies 4 and 5 delved more deeply into the role that curiosity and positive affect have on aesthetic preference. Both studies achieve this using different mediation techniques and across different product categories. Studies 5 and 6 also provided support for the generalizability and marketing applicability of the findings by introducing a new type of visual concealment manipulation and product stimulus that is relevant to electronic commerce. Furthermore, Study 6 was a field study performed with real-life social media users that further provided support for the ecological validity of our findings.
The goal of Study 1 was to provide an initial test of the prediction that there would be an inverted U-shaped relationship between visual concealment and curiosity. To test this, we included a measure of interest to assess curiosity (e.g., [29]; [32]). We also included a choice measure as an initial assessment of preference. Our prediction was tested in a marketing-related setting using futuristic car images.
One hundred ninety undergraduate students participated in this computer-based experiment. Participants were told that the objective of the study was to explore the relationship between vision and the use of computers. We used images of futuristic cars as experimental stimuli in the study for two reasons. They allowed us to test the effect in a familiar marketing setting because car manufacturers often use teaser ads that unveil just a fragment of a vehicle as a tactic to generate interest in their new designs. Furthermore, we used futuristic car models as these are unknown to the general population, so our participants had no prior exposure to them that could have biased their evaluations. Three car models were used: an Audi Rosemeyer, a concept Chrysler Imperial, and an unidentified, futuristic pickup truck (for the complete image of each car plus the sample fragment sequence for Audi Rosemeyer, see Figure 1, Panel A).
Graph: Figure 1. Stimuli used in Study 1.
The study used a 3 × 5 mixed design (car models: Audi Rosemeyer, Audi futuristic pickup truck, and concept Chrysler Imperial; fragment size: 1/6, 2/6, 3/6, 4/6, 5/6) with the concept car as the between-subjects variable and the fragment size as the within-subject factor. There were five levels for the fragment size manipulation, each presented individually in the sequence and followed by measures of curiosity and choice. Fragments were created by splitting each picture into six horizontal pieces. In all of our studies, we always kept the most important stimuli features to be presented in the latter fragments, as this manipulation allowed each incremental fragment to gradually contain a higher amount and quality of information. This helped rule out the potential alternative explanation that the inverted U-shaped effect was observed as a consequence of showing the most important features of the item at the peak point of the sequence. The first fragment would show one-sixth of the picture. The second fragment would add another sixth to the first one to complete one-third of the image. The third fragment would add another sixth to complete one-half of the picture. Fragments four and five followed this incremental pattern so that they would respectively show two-thirds and five-sixths of the complete image. We used the same presentation sequence for all car models.
Participants were allowed to observe each fragment for up to ten seconds. After each image fragment was presented, participants answered two questions: ( 1) "On a 1 to 10 scale, how interested are you to see the completely revealed image?" (1 = "not interested at all," and 10 = "very interested") and ( 2) "What would you rather get, the complete image revealed or a candy bar?" The first question aimed to measure consumer curiosity while the second measured preference for the revealed stimulus. Participants completed a practice run with a different image before the focal task to ensure familiarization with the procedure.
We performed a mixed analysis of variance (ANOVA) with the car model as the between-subjects factor and the size of the fragment as the within-subject variable based on stated interest in having the complete image revealed. Because we predicted an inverted U-shaped relationship between curiosity for the image fragment presented and the size of the fragment received, we expected that the results of the curiosity measure would be in line with this pattern. As we expected, fragment size was statistically significant (F( 4, 748) = 84.52, p <.0001). There was no effect of car model (F( 2, 187) < 1, n.s.). Table 1 provides all means by fragment size. We conducted post hoc comparisons to confirm the hypothesized inverted U effect. The most curiosity was found with the 4/6 fragments (M = 4.92), which was significantly higher than the means for the 1/6, 2/6, 3/6, and 5/6 fragments (M = 1.58, 4.18, 4.45, and 4.09, respectively; ps <.0001).
Graph
Table 1. Cell Means for Study 1.
| Fragment Size |
|---|
| 1/6 | 2/6 | 3/6 | 4/6 | 5/6 | N |
|---|
| DV = Interest in Full Image | | | | | | |
| Futuristic pickup | 1.57 | 4.36 | 4.46 | 4.90 | 3.95 | 92 |
| Audi Rosemeyer | 1.59 | 4.34 | 4.93 | 4.93 | 3.98 | 41 |
| Chrysler Imperial | 1.60 | 3.79 | 4.11 | 4.70 | 4.42 | 57 |
| Total | 1.58 | 4.18 | 4.45 | 4.85* | 4.09 | 190 |
| DV = Choosing to See Full Image over Candy Bar | | | | | | |
| Futuristic pickup | 43.5% | 53.3% | 52.2% | 57.6% | 44.6% | 92 |
| Audi Rosemeyer | 41.5% | 41.5% | 48.8% | 43.9% | 39.0% | 41 |
| Chrysler Imperial | 40.4% | 42.1% | 43.9% | 45.6% | 38.6% | 57 |
| Total | 42.1% | 47.4% | 49.0% | 51.1%* | 41.6% | 190 |
1 *Differences between the 4/6 fragment and other fragment sizes are significant at the p <.05 level.
We conducted a multinomial logistic regression on choice between candy bar and seeing the whole image with the fragment size and car model as the independent variables. There was a significant effect of fragment size (χ2 = 14.64, p <.01) and not for car model (χ2 = 1.65, p >.40). Post hoc comparisons were conducted to test the hypothesized inverted U-shaped relationship between fragment size and choice. The effect peaked at the 4/6 fragment condition, where 51.05% of subjects preferred to see the complete image. This was significantly higher than the extremes at the 1/6 (χ2 = 3.96, p <.05) and 5/6 conditions (χ2 = 9.78, p <.002). The choice share for the 1/6, 2/6, 3/6, and 5/6 fragments were 42.11%, 47.37%, 48.95%, and 41.58%, respectively. Thus, participants' choice for the complete product continuously increased from seeing the 1/6 fragments to the 4/6 fragments but decreased afterward, consistent with our prediction.
Study 1 provided initial support in favor of the effect of visual concealment on curiosity and preference. We did this by using a relevant choice paradigm and marketing relevant stimuli with concept car images. We conducted a subsequent experiment in which we replicated the effect using a different product category and a between-subjects paradigm. Furthermore, this study allows us to rule out the alternative explanation that the observed effect is due to differences in attractiveness between the concealed and the unconcealed stimuli. In addition, in this new study, we included the complete image as one of our conditions, which allowed us to demonstrate that the effect is so robust that it may lead consumers to prefer an aesthetically pleasing item when it is partially, as opposed to fully, revealed.
Study 1 provided support for the effect using a within-subject design. We observed that curiosity and choice for the whole product initially increased as larger fragments were presented but eventually waned as fragment size increased beyond a certain level, which we attributed to the evaporation of the preference boost sparked by curiosity. While the data are encouraging, one natural concern arises. It is possible that the inverted U-shaped response curve was driven—at least in part—by the use of a within-subjects design, where the decline in curiosity given larger proportions could have reflected task boredom (or satiation) or demand effects rather than a decline in curiosity, per se.
In Study 2, we aimed to address this concern with a between-subjects design. In this study, we also tried to clearly demonstrate the effect of visual concealment on aesthetic preference using a different product category with computer-generated human faces from the University of Regensburg and the University of Rostock in Germany. These faces were created on the basis of evaluations of thousands of real faces from opposite-sex participants. As a result of this, these institutions were able to generate what could be considered ideally attractive faces. The rigorous scientific procedure with which the stimuli were developed allowed us to be certain that each aspect of the images employed, either revealed or concealed, was reliably attractive. This further helped us rule out the potential alternative explanation that the effect may have been due to different levels of attractiveness between the concealed and unconcealed faces' fragments. To be consistent with the process used to develop the images, we followed the procedure to have opposite-sex subjects rate male or female faces depending on their gender. This procedure also allowed us to avoid potential noise created by the fact that some subjects may not have been as reliable or involved when judging the attractiveness of faces of their same gender as compared with faces of their opposite gender.
Furthermore, unlike Study 1, Study 2 included a condition in which participants were exposed to the complete image, which allowed us to demonstrate that the documented effect is so robust that it leads a partially concealed, aesthetically pleasing item to be perceived as more attractive than its fully revealed counterpart.
Two hundred seventy-seven students participated in the study for partial course credit. The study used a 2 × 6 between-subjects design (gender: male or female; fragment size: 1/6, 2/6, 3/6, 4/6, 5/6, and 6/6 [complete images]). Each participant was shown a set of two facial images from the opposite sex—one reference image that was shown in its entirety and a target image that was shown in different fragment levels. Faces were used as stimuli to provide a stimulus context that was likely to be of high interest to the subject population. These computer-generated facial images are shown in Figure 2.
Graph: Figure 2. Target stimuli used in Study 2.
As Figure 2 illustrates, the first image was shown in its entirety and was used as a reference component in the choice task. The second, which corresponded to a different person, was presented in one of six possible fragment levels that were varied between subjects. As in Study 1, fragments were created by splitting each target image into six horizontal panels. The one-sixth fragment revealed the top panel, the one-third fragment added the bottom panel, the one-half fragment added the second lowest, the two-third fragment included the third lowest, while the five-sixth fragment added the second highest (for sample illustrations, see Figure 3). This setup can be considered conservative, as in any of the incomplete images, participants were not shown the panel containing the eyes, which are the most important features of a face. The highest fragment level corresponded to the whole image, where participants viewed both facial images in their entirety.
Graph: Figure 3. Choice stimuli used in Study 2.
After seeing both images, participants made three binary choices: "Which of these people would you prefer to meet?," "Which of these people would you prefer to go on a date with?," and "Which of these people is more attractive?" The first two questions measured preference in general while the last question more specifically tapped into aesthetic evaluation. We expected that participants would prefer the fragments that were of moderate size as compared with those in the extremes of fragmentation. Furthermore, we expected that moderately sized fragments would be preferred over the target face when fully presented.
We analyzed responses on the three dependent measures—meeting, dating, and perceived attractiveness—through separate logistic regressions with fragment size and gender as the independent variables. We found gender to be nonsignificant in all cases (ps >.40) and do not discuss it further. We plot cell means for the three responses in Figure 4.
Graph: Figure 4. Effect of fragment size on evaluations of target faces in Study 2.Notes: The dependent measures are the proportion of participants who chose the target (fragmented) face.
There was a significant effect of fragment size on desire in meeting (χ2 = 32.85, p <.0001). The means were consistent with our prediction (M1/6 = 9.09%, M2/6 = 10.87%, M3/6 = 24.53%, M4/6 = 69.39%, M5/6 = 35.0%, and M6/6 = 40.0%). Pairwise comparisons showed that the peak was found in the 4/6 fragment level, which was significantly higher than all the other fragment levels (ps <.001). Note that when both facial images were fully shown (i.e., in the 6/6 fragment condition), participants showed a directional preference in all measures for the reference image, which suggests that the target image was not intrinsically more appealing than the reference image and cannot explain the higher preference when it was partially revealed. Furthermore, this demonstrates that we showed support for the effect in a conservative setting.
Again, there was a significant effect of fragment size on preference for dating (χ2 = 30.07, p <.0001). The means were consistent with our prediction (M1/6 = 6.82%, M2/6 = 8.70%, M3/6 = 24.53%, M4/6 = 67.35%, M5/6 = 35%, and M6/6 = 35.56%). Pairwise comparisons showed that the peak was found in the fourth presentation level, which was significantly higher than all other fragment levels (ps <.001).
The logistic regression revealed a significant effect of fragment size on perceptions of attractiveness (χ2 = 34.58, p <.0001). The means were consistent with our prediction (M1/6 = 11.36%, M2/6 = 13.04%, M3/6 = 22.64%, M4/6 = 63.27%, M5/6 = 32.5%, and M6/6 = 28.89%). Consistent with the meeting and dating results, pairwise comparisons demonstrated that the peak was found in the 4/6 fragment level, which was significantly higher than all other fragment levels (ps <.001).
Study 2 provides further support in favor of the effect of visual concealment on product preference. It does this through a between-subjects design that allows us to rule out alternative explanations such as task boredom (or satiation) or demand effects. In addition, this study provided support for the robustness of the effect, as it showed that participants preferred the target image when presented in a moderately sized fragment than in full. Furthermore, the design employed in this study was a conservative one, as empirical findings demonstrated that participants in fact directionally preferred the reference image as compared with the target image, and still the effect of visual concealment on preference persisted. So far, we have shown support for most of our predictions. We conducted a subsequent experiment in which we provided support for our remaining prediction that the effect of visual concealment on preference will not hold for stimuli that are not visually appealing due to the lack of curiosity these evoke.
The objective for Study 3 was to provide a test of a boundary condition for the effect associated with stimulus attractiveness: the inverted U-shaped relationship between fragment size and preference observed in Studies 1 and 2 is predicted not to be present in the case of fragments with negative valence that do not evoke curiosity and positive inferences. We tested this prediction using computer-generated faces from the same institutions as in Study 2, with the addition that in this study we also included stimuli classified as unattractive.
One hundred ninety-eight students participated in this within-subject design in exchange for course credit. The stimuli used were the target attractive faces from Study 2 and two additional computer-generated unattractive faces (one male and one female) from the same source (for the stimuli, see Figure 5).
Graph: Figure 5. Stimuli for Study 3.
The design was a 2 × 2 × 5 mixed factorial (gender: male or female; attractiveness: low or high; fragment size: 1/6, 2/6, 3/6, 4/6, and 5/6) with face attractiveness and gender as the between-subjects factors and fragment size as the within-subject factor. Consistent with Study 2 and with the procedure these institutions used to develop the stimuli, all participants were exposed to faces of the opposite sex. After the presentation of each fragment, participants were administered the preference question, which consisted of choosing between receiving a candy bar and seeing the complete image revealed.
We propose that the inverted U-shaped relationship between fragment size and preference for the whole image only exists for attractive stimuli but not for unattractive stimuli. As a preliminary test for this prediction, we first submitted all choice data to a repeated measures analysis using PROC CATMOD in SAS with attractiveness and gender as between-subjects factors and fragment size as a within-subject factor. As predicted, the analysis revealed a significant attractiveness × fragment size interaction (χ2 = 23.05, p =.0001) as well as a significant main effect of attractiveness (χ2 = 15.22, p <.0001). Fragment size was not significant (χ2 = 4.74, p >.30), nor was gender (χ2 < 1, p >.90). Figure 6 illustrates the mean preference for the fragment image as a function of fragment size and attractiveness of the target face.
Graph: Figure 6. Effect of fragment size and target attractiveness on preference for target face in Study 3.Notes: The dependent measure refers to the proportion of participants who chose to see the full face rather than receive a candy bar.
Next, we analyzed the data using the same procedure for the attractive and unattractive face conditions separately. In the attractive face condition, we expected to replicate the inverted U-shaped relationship between fragment size and preference, whereas in the unattractive face condition, such effect was not predicted. For the attractive face condition (N = 87), the data revealed a significant effect of fragment size (χ2 = 14.49, p <.01). Post hoc comparisons indicated that the 4/6 fragment size condition produced the nominally highest preference for the target image (49.4%), which was significantly higher than the 1/6 or 2/6 conditions (M = 28.7% and 31.0% respectively; ps <.001) but not statistically different from the 3/6 or 5/6 conditions (M = 44.8% and 46.0% respectively; ps >.16). Thus, the effect found in the attractive condition was directionally consistent with the first two studies. In the unattractive condition (N = 111), the analysis revealed a significant main effect of fragment size (χ2 = 11.36, p <.05). But here, preference for the target image was the highest for the 1/6 fragment (24.3%) and declined as the fragment size increased. As a result, preference for the target image was significantly lower when fragment size was 3/6 or larger (M3/6 = 16.2%, M4/6 = 15.3%, M5/6 = 18.0%; ps <.05). This pattern of results for attractive and unattractive fragments is consistent with our predictions.
Study 3 provided support in favor of our prediction, that is, that the effect of visual concealment on preference does not hold for unattractive stimuli, as these are less likely to evoke curiosity and positive inferences. Through the first three studies, we have shown consistent support for the hypothesized inverted U-shaped effect between visual concealment and preference and part of its underlying mechanism using within- and between-subjects designs and a wide array of choice and rating scales, dependent measures, and multiple versions of concept cars and computer-generated human faces. The next three studies aim to delve more deeply into our proposed underlying mechanism and additional boundary conditions. Specifically, while we have provided some hint that the effect is associated with curiosity, going forward, we delve more deeply into the mechanism and directly test if visual concealment indeed generates curiosity toward a partially revealed stimulus, which then leads to positive affect related to how aesthetically pleasing the item would be if completely revealed.
Study 4 aimed to provide mediation support in favor of the proposed theoretical mechanism. Because we predicted that the effect of visual concealment on preference is influenced by a curiosity drive that generates positive affect toward the stimulus and leads consumers to make positive evaluations of it, in this design, we estimated a mediation analysis that tested the serial process involving curiosity and positive affect associated with the stimulus.
One hundred fifty-two MTurk workers participated in this experiment in exchange for monetary compensation. The design employed was between subjects and, as in Study 1, at the beginning of this experiment, participants were asked to view an image of the concept car Audi Rosemeyer and answer some questions associated with this experience. Half of participants saw an image in which half of the vehicle was visually concealed while the other half saw an image in which the car was fully revealed. After participants viewed the image, they were again asked to respond to rating measures on a nine-point scale. Among others, these questions probed them about how much they liked the vehicle and how aesthetically pleasing the concept car was. These two questions intended to measure aesthetic preference for the concept car images. We also included measures on how positive consumers felt overall, how positive they felt that the car would look as attractive in person as they thought, and how curious they were about seeing the car in person. The first two questions aimed to shed light onto what type of positive affect was observed while the third question pertained to curiosity. We also included a question asking participants how likely they would be to buy the concept car they saw. This measure intended to show that this effect also influences purchase-related behavioral intentions.
We performed a t-test with visual concealment as the independent factor and curiosity as the dependent variable. As we expected, participants in the visual concealment condition were more curious about seeing the concept car in person than those in the no-visual-concealment group (Mconceal = 5.69 vs. Mno conceal = 4.53; t(150) = 2.66, p <.01).
We performed a t-test with visual concealment as the independent factor and the positivity manipulation check as the dependent variable. We expected that there would not be an effect of visual concealment on overall positive affect, as this manipulation was intended to specifically influence positivity associated with what the target stimulus would look like in person. We confirmed that this was the case, as visual concealment did not influence overall positive affect (Mconceal = 5.46 vs. Mno conceal = 5.34; t(150) < 1, n.s.).
We performed a t-test with visual concealment as the independent factor and the positive affect measure as the dependent variable. As we expected, participants in the visual concealment condition experienced more positive affect toward the stimulus than those in the no-visual-concealment group (Mconceal = 5.82 vs. Mno conceal = 4.55; t(150) = 3.21, p =.002).
We combined the liking and aesthetics dependent variables to create a preference index (r =.92). We performed a t-test with visual concealment as the independent factor and the aesthetic preference composite score as the dependent variable. As expected, participants in the visual concealment condition found the concept car model to be more aesthetically appealing than those in the no-visual-concealment group (Mconceal = 5.65 vs. Mno conceal = 4.00; t(150) = 3.75, p <.0001).
We performed a t-test with visual concealment as the independent factor and the likelihood of purchase measure as the dependent variable. As expected, subjects in the visual concealment condition expressed higher likelihood to buy the car than those in the no-visual-concealment group (Mconceal = 4.38 vs. Mno conceal = 3.24; t(150) = 2.70, p <.01).
We predicted that the effect of visual concealment on aesthetic preference would be driven by a two-stage process involving curiosity and stimulus positive affect. To confirm this, we tested for serial mediation using Model 6 ([12]) and found that the visual concealment manipulation had a significant indirect effect on aesthetic preference through a serial process involving curiosity and stimulus positive affect (β = −.38, 95% confidence interval [CI]: [−.13, −.73]). Furthermore, we performed a regression analysis including visual concealment, curiosity, and stimulus positive affect as independent variables and the aesthetic preference composite as the dependent factor and showed that both mediators, curiosity (β =.42, 95% CI: [.30,.54]) and stimulus positive affect (β =.55, 95% CI: [.41,.68]), were significant while visual concealment was not (β = −.47, 95% CI: [−.97,.04]). In addition, we found that the correlation between the curiosity and positive affect mediators (r =.57), and these with relation to the dependent variable (r =.65 and r =.68, respectively), was moderate, which provides further reliability to our results. This series of findings provides support in favor of our proposed underlying mechanism.
Study 4 provided direct support in favor of our proposed theoretical mechanism by providing a serial mediation analysis that supports our hypothesis. Study 4 also sheds light on the potential managerial relevance of the effect by showing that it may positively influence purchase likelihood. While this study provides initial mediation evidence in favor of our mechanism, it has the limitation that it only included two fragment conditions, one in which half of the stimulus was revealed and another one in which it was revealed in its entirety. To provide a more comprehensive test of our mechanism, we ran a subsequent study in which we included six fragment conditions.
Furthermore, while Study 4 provided support in favor of the managerial relevance of the effect by showing it may influence purchase likelihood, in a new study, we introduced an additional product category with sneakers, a product type that is relevant to digital commerce. Furthermore, in this new study, we manipulated visual concealment in a different way. While the first four studies focused on visual concealment tactics, in the following study we instead used a product presentation manipulation that is more relevant to electronic commerce.
Study 5 had several objectives. First, it built on the initial mediation evidence provided in Study 4 to provide more robust support for the theoretical account through a more comprehensive design and mediation analysis. Specifically, in this design we exposed participants to six visual concealment levels and showed that higher levels of curiosity and positive affect led to higher aesthetic preference as a consequence of the manipulation. To perform this comprehensive analysis, we relied on nonlinear mediation techniques ([13]). In addition to its theoretical importance, this study introduced a different type of visual concealment manipulation that provided evidence for the generalizability of the effect and marketing relevance. While in the first four studies we employed visual concealment tactics consistent with teaser ads, in this study we introduced a visual concealment approach that is more relevant to digital commerce. In this case, instead of cropping parts of images, we showed participants a different number of snapshots or angles of a product like the images shown by internet stores such as Amazon, Zappos, and so on. Thus, in this case, we did not conceal visual information about the product by blocking or cropping parts of the image but instead concealed certain visual aspects of the stimuli by presenting different snapshots of the product that, depending on the condition, revealed different amounts of visual information. The fact that we were able to replicate the effect in this new setting demonstrates that the present research also has important implications for electronic commerce, as online marketers often face the decision of how many images of their items they should display on their websites or social media pages to generate more favorable evaluations.
In addition, this study provided evidence for the effect using a new product category. Studies 1 and 4 relied on novel cars as stimuli, a product category that typically relies on visually concealed teasers. In contrast, Studies 2 and 3 employed computer-generated human faces with the objective to rule out the alternative explanation associated with different levels of attractiveness between the revealed and concealed fragments. In the present study, we instead used sneakers as stimuli, a popular product category in digital commerce. This product category, along with the visual concealment manipulation, was used to provide more realism to the design.
Finally, while this design is relevant to digital commerce, it may also raise some concerns. Specifically, it is possible that merely showing snapshots of a product may not make it as apparent that some pieces of information are being withheld from view, as was the case with the visual concealment manipulation. Given this, we featured the corresponding snapshots of the product in each condition within a matrix consisting of six boxes. Which of the six boxes were populated depended on the number of images of the product that were supposed to be featured according to the condition as well as random assignment. Thus, the use of the matrix was intended to help participants realize that one snapshot of the product was not being presented and that they needed to make their evaluations with incomplete information.
Five hundred seventy-three MTurk workers living in the United States participated in this study in exchange for monetary compensation. The design contained six between-subjects conditions that varied based on the number of product images that participants were exposed to (number of images: one, two, three, four, five, and six). Unlike prior studies where each visual concealment condition exposed participants to a set image that featured a particular amount of the product, in this case each visual concealment condition presented a particular number of images which were randomly selected. Thus, the visual concealment manipulation consisted of providing one to six snapshots of different angles of the sneakers. This ruled out the alternative explanation that the effect was due to the specific images being presented and not to the amount of visual concealment per se. The product used for this study was a Nike shoe. Figure 7 illustrates the different snapshots of the product consumers viewed.
Graph: Figure 7. Stimuli for Study 5.
Participants were told that in this study they would be asked about their opinions associated with running shoes. After seeing the images based on the conditions they were randomly assigned to, participants were asked questions such as "How attractive is the product?" and "How aesthetically pleasing is the product?" on a nine-point scale. These two questions were intended to measure aesthetic preference for the product. We also included measures on how curious participants were about seeing the product in person and how positive participants felt that the shoes would look as attractive in person as they thought. These measures corresponded to our proposed mediators.
We performed a one-way ANOVA with visual concealment as the independent factor and the curiosity measure as the dependent variable. As expected, there was a significant effect of visual concealment (F( 5, 567) = 4.09, p =.001). Because our theory proposes that a moderate amount of visual concealment will lead to more curiosity, we compared the peak of the trend, which occurred at four images of revelation, with the two extremes of the sequence. As expected, we found that curiosity was higher when four images of the item were revealed (M4 = 5.02) as compared with only one (M1 = 3.56; F( 5, 567) = 7.98, p =.005) or when the product was completely revealed (M6 = 4.05; F( 5, 567) = 7.09, p <.01). Figure 8 illustrates the means for all conditions across the different factors investigated in Study 5.
Graph: Figure 8. Effect of visual concealment on curiosity, stimulus positive affect, and preference in Study 5.
We performed a one-way ANOVA with visual concealment as the independent factor and the stimulus positivity measure as the dependent variable. As we expected, there was a significant effect of visual concealment (F( 5, 567) = 5.49, p <.0001). Because our theory proposes that a moderate amount of visual concealment will lead to more curiosity and subsequent positive affect about what the stimulus will look like if completely revealed, we compared the peak of the trend, which again took place at four images of revelation, with the two extremes of the sequence. As expected, we found that positivity was higher when four images of the item were revealed (M4 = 5.97) as compared with only one (M1 = 4.31; F( 5, 567) = 10.17, p <.005) or when it was completely revealed (M6 = 4.91; F( 5, 567) = 22.99, p <.0001).
We combined the attractiveness and aesthetics dependent variables to create an aesthetic preference index (r =.86). We performed a one-way ANOVA with visual concealment as the independent factor and the aesthetic preference index as the dependent variable. As expected, there was a significant effect of visual concealment (F( 5, 567) = 5.60, p <.0001). Because our theory proposes that a moderate amount of visual concealment will maximize aesthetic preference more than if the item was completely revealed, we compared the peak of the trend, which again took place at four images of revelation, with the two extremes of the sequence. As expected, we found that aesthetic evaluation was higher when four images of the item were revealed (M4 = 5.83) as compared with only one (M1 = 4.24; F( 5, 567) = 24.77, p <.0001) or when it was completely revealed (M6 = 4.97; F( 5, 567_SB_)_sb_ = 9.91, p <.005).
Our prediction proposed that the nonlinear effect of visual concealment on aesthetic preference will be driven by a two-stage serial process involving curiosity and stimulus positivity.
As a test of the nonlinear indirect effect, we extended [13] nonlinear indirect model with a single mediator to a nonlinear indirect model with two serial mediators. In a nonlinear indirect model wherein the indirect effect of variable X on Y through a single mediator M is nonlinear, the parameter of interest is the instantaneous indirect effect of X on Y through M. This parameter, denoted as θ, quantifies the change in the endogenous variable Y through the mediator M as the exogenous variable X is changing and can be estimated as the product of the first partial derivative of the function of M with respect to X and the first partial derivative of the function of Y with respect to M ([13]). Based on the same logic, we derived the instantaneous indirect effect of visual concealment on preference through two serial mediators, curiosity and positive affect, as the product of three terms, namely ( 1) the first partial derivative of the function of curiosity with respect to visual concealment, ( 2) the first partial derivative of the function of positive affect with respect to curiosity, and ( 3) the first partial derivative of the function of preference with respect to positive affect. Specifically,
Graph
1
where θ = the instantaneous indirect effect of visual concealment (X) on preference (Y) through curiosity (M1) and positivity (M2).
We estimated θ at three levels of visual concealment (1 SD below the optimal point, the optimal point, and 1 SD above the optimal point). We identified the optimal point of visual concealment by setting the first derivative of the equation related to preference with respect to visual concealment to zero (optimal visual concealment = 4.06, slightly above the mean of 3.61). We found that θlow =.236 (95% CI = [.106,.374]), θoptimal =.006 (95% CI = [−.063,.072], n.s.), and θhigh = −.176, (95% CI = [−.360, −.006]). These estimates suggest that increasing visual concealment from a low level to a moderate level would slightly increase preference through the effect of the increase in curiosity and, subsequently, positive affect. At the optimal concealment, preference reaches the maximum level. However, an increase of visual concealment beyond the optimal point of visual concealment would actually lead to a deterioration of preference through its effect on curiosity and then positive affect. Finally, we found that the correlation between the curiosity and positivity mediators (r =.60) and these with relation to the dependent variable (r =.64 and r =.66, respectively) was moderate, which provides further reliability to our results.
Study 5 provided further support in favor of our proposed theoretical mechanism involving curiosity and positive affect associated with the target stimulus. While Study 4 provided initial mediation support for this theorizing, its design had the limitation of only including two visual concealment conditions. In this case, we used nonlinear mediation techniques and provided support for the effect across six levels of visual concealment.
Furthermore, in this study visual concealment was manipulated in a different way, which did not involve the cropping of images but instead consisted of showing a different number of images showcasing different angles of the product. Each visual concealment condition showed a different number of randomly selected images, which also helped alleviate the alternative explanations related to the specific image shown in each visual concealment condition in past studies. In addition, the use of the matrix to cue participants that some snapshots of the product may be missing helped address the concern that this new manipulation may not have made it apparent to participants that some angles of the product were being concealed.
In addition, given the visual concealment manipulation used, this study is different from the first four designs as it transcended the advertising teaser tactic environment through the use of a visual concealment manipulation that is more in line with digital commerce environments. This provides support for the effect being relevant to this growing area of commerce. Relatedly, this study introduced the use of a new product category, sneakers, a product type consumers often buy from online retailers.
After establishing additional mediation support for the effect and its underlying mechanism and establishing its validity for marketing relevant contexts such as teaser advertising tactics and online commerce operations, we ran a final study in which we more convincingly showed the ecological validity of the effect. This study was run on a real-life advertising environment, Facebook, and we showed that the manipulation of visual concealment can affect preference in this social media setting involving real consumers.
Study 6 aimed to further demonstrate the managerial relevance of the effect. While the previous studies have used managerially relevant stimuli such as teaser images of products (like those found in advertising settings) or different snapshots of items (like those prevalent in e-commerce) as well as substantially important dependent variables such as choice and likelihood of purchase, all these studies were conducted in laboratory-controlled environments. This final study departs from that approach, as it was conducted in a real-life social media environment with real users, specifically on Facebook. The product advertised was also a real-life shoe model that had not been launched to the market yet, which allowed us to test the effectiveness of the manipulation with a product that was unfamiliar to consumers. The fact that we replicated the basic paradigm of the phenomenon in a relevant real-life environment provides support for the importance of this effect while complementing several rigorously conducted laboratory studies that establish the nature, boundary conditions, and underlying mechanism behind the effect.
The study was conducted during a 48-hour period in a five-mile radius within the center of the city where the university is located. The item used was the recently released shoe model Adidas ZX 4000 4D, which became available in May 2019, and the study was conducted in April 2019. This means that consumers were unfamiliar with the item at the moment of the experiment. We were able to obtain official images from an Adidas press release ahead of the market launch of the product. Given the limitations posed by using rare official images of a yet to be released shoe model, the image used for the low-concealment condition did not involve the display of white socks along with the shoes, while those used for the high- and moderate-concealment conditions did. However, the pattern of results obtained was consistent with prior studies, which suggests that this detail was not the driver of the results.
Because the product was a shoe model mostly purchased by younger consumers, the age range of users exposed to the item on Facebook was from 18 to 29 years old. The product was unisex, so the study included both men and women. The budget for the study was $300, which determined the reach that the advertising would have to consumers in the area.
This design had three between-subjects conditions (visual concealment: high, moderate, and low). In this case, the visual concealment manipulation consisted of presenting an image that differed in how much of the product was concealed. All participants were exposed to one image of the product, but how much of it was revealed depended on the condition in which they were randomly assigned. We used this new manipulation because of space and number-of-image limitations, as the study consisted of a Facebook ad, which involved certain restrictions. However, this gave us the opportunity to provide further support for the effect in a somewhat different real-life paradigm. We pretested the images with a similar population (N = 455) to ensure that the visual concealment manipulation worked as intended. Specifically, we first asked participants to estimate on an 11-point scale how much of the featured shoe was revealed in the image. We then asked them to estimate what percentage of the featured shoe was revealed in the image. See Figure 9 for the images.
Graph: Figure 9. Stimuli for Study 6.
The study consisted of featuring an ad of the product to Facebook users. The ad included an image of the product and the text "Click if you like this innovative shoe model!" We assessed preference for the item by counting the number of Facebook users who chose to click on the item across conditions. We predicted that the advertisement featuring moderate concealment would be selected more often, as consumers would find it more aesthetically attractive. It is worth mentioning that this study represented a conservative test for our effect, as real-life consumers often click less than 1% of the ads they are exposed to online. For example, Facebook's average click-through rate is.9% ([21]).
As we predicted, participants in the medium-visual-concealment condition (Mmed = 5.66) thought that the image revealed more of the product than those in the high-visual-concealment condition (Mhigh = 4.52; t(452) = 5.90, p <.0001) and less than those in the low-visual-concealment condition (Mlow = 7.48; t(452) = 9.43, p <.0001). Consistent with that, participants in the medium-visual-concealment condition (Mmed = 54.11%) thought that the image revealed a higher percentage of the product than those in the high-visual-concealment group (Mhigh = 43.74%; t(452) = 4.75, p <.0001) and less than those in the low-visual-concealment condition (Mlow = 71.53%; t(452) = 7.94, p <.0001).
The Facebook ad reached a total of 12,804 screens randomly divided across conditions. Consistent with previous findings on low click-through rates online, the ad was chosen only 83 times (.65%). Of these clicks, 26.5% were in the high-concealment condition, 44.6% were in the medium-concealment condition, and 28.9% were in the low-concealment condition. Despite the low click-through rate and the limited budget for the study, we found support for our hypothesis. We performed z-tests that included the number of clicks and the number of screens reached per condition. As expected, we found that the click-through rate was higher in the medium-visual-concealment condition (Mmed =.90%) than in the high-visual-concealment condition (Mhigh =.51%; z = 2.12, p =.03) and the low-visual-concealment condition (Mlow =.55%; z = 1.93, p =.05). Furthermore, there was no difference in click-through rates between the low- and high-visual-concealment conditions (z < 1; n.s.). Because our hypothesis is that the medium-visual-concealment condition would generate higher preference than the other two conditions, we collapsed the latter and compared them with the medium-visual-concealment condition (z = 2.43, p =.01).
This last study further demonstrates the managerial relevance for the effect of visual concealment on preference. While Studies 1 through 5 included real-life products, relevant forms of visual concealment (teaser ads, e-commerce images), and meaningful dependent variables (choice, likelihood of purchase), they had been constrained to laboratory-controlled environments. Study 6 establishes the managerial relevance of the effect as well as its ecological validity, as it consisted of a real-life advertisement presented to active users of the social media platform Facebook. What is perhaps more surprising is that we found evidence for the effect despite the fact that click-through rates of online advertisements are notably low and that the budget for the reach of the ad was limited to $300. This, along with the fact that the effect has been replicated in a number of studies using a varied set of stimuli and experimental paradigms, provides robust evidence for the effect.
The present research examines how the partial visual concealment of product images influences preference associated with a novel aesthetic product. Drawing on prior research on curiosity ([19]; [26]; [29]; [36]; [38]), we hypothesized that visual concealment associated with a visually attractive item would enhance aesthetic evaluation as a function of curiosity and positive affect about what the stimulus would look like if completely revealed. This led to the prediction that moderately concealing visually attractive items would generate higher aesthetic evaluations than if these were completely revealed. This would occur as a result of a boosting effect of curiosity and accompanying positive affect toward the stimulus. This hypothesis was supported using data from five laboratory studies and one field study where participants were shown partial or full images of a target stimulus (concept cars, human faces, and sneakers) and indicated their aesthetic preference associated with it. A surprising regularity in the data is that curiosity and preference were consistently maximized when approximately one-half to two-thirds of an attractive product was revealed to study participants, a level that reveals enough of the whole to support favorable inferences yet still leaves enough room for a boosting effect of curiosity and positive affect toward the stimulus.
The present findings enhance our knowledge of the stream of work investigating how marketers should administer product information to generate more positive consumer reactions. While previous work has shown that tactics such as temporarily withholding attractive information about a product in the prechoice stage and revealing it at a later point in time ([10]) or even withholding the existence of an option of interest altogether and allowing it to be discovered in later stages of the decision-making process ([11]) may lead to higher preference associated with these options. In this case, we show that preference not only can be positively influenced by strategically revealing information but also can be improved as a result of enhancing the effect of curiosity and positive affect by concealing some of the information associated with a product altogether. Thus, our findings hold several implications for how marketing managers might better use concealment tactics to enhance consumer preference for products and services. For example, perhaps the most salient finding is the idea that concealment might work best in settings where it would seem least needed: when selling highly aesthetic goods. Here, we find repeated evidence that the most attractive products benefit by holding something back.
The research also makes an important contribution to the area of visual aesthetics. Previous work in this area has traditionally demonstrated that aesthetic reaction is more favorable when consumers are able to appropriately perceive and appreciate the visual properties of an item ([16]). In fact, the first response consumers experience when exposed to an object is that of aesthetic appreciation ([ 3]), as aesthetic reaction takes place in a fleeting way ([ 1]; [ 3]; [27]; [48]). Yet we make the counterintuitive finding that partially concealing a visually attractive item may lead to disproportionately favorable aesthetic perceptions if ( 1) consumers are able to appreciate enough of the item to infer what the whole will look like and ( 2) enough of the item is concealed to elicit curiosity and positive affect.
The present work also enhances our understanding of one of the five facets of visual perception ([43]), specifically, shape. While prior work has demonstrated the role of shape completeness on preference for homogeneous items for which every single fragment is constant ([47]), the present research adds to our understanding of how completeness influences preference in cases where each piece of the stimulus is heterogeneous.
Finally, while our work was initially motivated by an applied problem in advertising design and online retailing, we suggest that this research also contributes to a deeper understanding of the effect that curiosity has on consumer and human behavior as a whole. While previous work has shown that curiosity sparks consumer interest and leads to a desire to acquire and process novel information (e.g., [26]; [29]), which may subsequently lead to higher preference ([32]), the present research goes a step further and shows that curiosity may directly result in higher aesthetic preference even in cases when the possibility of obtaining additional information is not available. Our results demonstrate that this boosting effect of curiosity on aesthetic preference is influenced by positive affect about what the object would look like if viewed in full. This finding is in line with literature on consumer behavior showing that, in some contexts, people are prone to making optimistic product evaluations in the absence of complete product information. Some of these contexts have included unexpected color or flavor names ([34]), unfamiliar brands ([39]), and new services ([ 7]). In the present work, we show a similar effect in the domain of aesthetically relevant products. However, the present findings are not as straightforward and seem at odds with extant literature on consumer inference. Specifically, early work on the effect of missing attribute information (e.g., [17]; [33]) showed that consumers tend to discount their product assessments when information about sensitive attributes such as price or quality is withheld. In this case, we are able to show that, in the context of visual concealment of aesthetically relevant items, withholding product information may generate favorable aesthetic responses if it triggers enough curiosity and positive affect among consumers.
An important avenue of future research is to explore the degree to which this same idea extends to nonaesthetic goods such as software, where what is being held back is not visual access to the whole but access to functionality. Just as we find that there is an "ideal" percentage of a visual image to reveal (about two-thirds, in our case), there may be similar ideal points in the optimal design of trial packages for software or other functional goods—points at which assessments of the fraction exceed that of the whole. Relatedly, the present findings suggest that the highest level of curiosity may not always take place when consumers are just "one hair away" from reaching full information ([29]), and that this may vary across different marketing domains.
Similarly, another direction for future work would be to investigate how the present findings might generalize to homogeneous product stimuli where a small fragment fully reveals the value of the whole. A good example of such a context is food samples, in which what is being concealed is not the product itself (e.g., a one-ounce Hershey's sample provides the same objective information about the product as a three-ounce sample) but rather the hedonic experience of multiple periods of consumption (how one will feel eating another one-ounce bar after a three-ounce taste). In future work, it would be interesting to explore whether the pattern of curiosity and positive affect observed here generalizes to such stimulus domains. For example, it would be fruitful to inquire whether the documented effect would apply to food sampling. Retailers often invite consumers to taste samples of their products (e.g., Costco, Kroger). If the present effect applies to homogeneous stimuli, it is possible that finding the ideal sample size in this domain may also lead to relatively more favorable taste perceptions and likelihood of adoption. We leave the study of the multidimensionality of the effect for such homogeneous stimuli to future research, as the effect would likely be explained by different mechanisms such as learning and satiation and not curiosity.
Footnotes 1 Associate EditorGerald Häubl
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 1See http://adsoftheworld.com/media/print/audi%5fnew?size=%5foriginal (accessed February 4, 2020).
5 2See http://www.tfltruck.com/wp-content/uploads/2013/12/Next%5fGen%5fHonda%5fTruck%5fTeaser.jpg (accessed February 4, 2020).
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Record: 110- Let Your Banner Wave? Antecedents and Performance Implications of Retailers' Private-Label Branding Strategies. By: Keller, Kristopher O.; Dekimpe, Marnik G.; Geyskens, Inge. Journal of Marketing. Jul2016, Vol. 80 Issue 4, p1-19. 23p. 1 Color Photograph, 5 Charts, 3 Graphs. DOI: 10.1509/jm.15.0154.
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Record: 111- Let's Make a "Deal": How Deal Collectives Coproduce Unintended Value from Sales Promotions. By: Campbell, Colin; Schau, Hope Jensen. Journal of Marketing. Nov2019, Vol. 83 Issue 6, p43-60. 18p. 1 Diagram, 3 Charts. DOI: 10.1177/0022242919874049.
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Let's Make a "Deal": How Deal Collectives Coproduce Unintended Value from Sales Promotions
Users of deal collectives coproduce "deals" that yield value beyond what a marketer intends when offering promotions. The authors develop an understanding of how this unintended value is coproduced through the combined actions of users in deal collectives. Users are drawn to deal collectives by a web of motivations that include subversive shopper feelings, which reflect a desire to outsmart firms and temporarily upend the market power structure. Uncovered transvaluation processes show that deal forums—due to their collective knowledge, creativity, and trust—are more effective than individual consumers at identifying, developing, and vetting opportunities to capture unintended value. The authors further reveal that unintended value can stem from untargeted promotions, pricing and promotion errors, and combinations or stacking of promotions. Strategies for monitoring deal collectives and either discouraging or supporting their activities are offered.
Keywords: deals; micro-resistance network; promotions; subversive shopper feelings; unintended value
I feel like getting a deal on stuff is just the tip of the iceberg....I'm not walking into the store and dropping my pants...and saying, "I'll pay anything," which most people do....Getting a better deal makes me feel less helpless. There's more justice in the world because I'm the small guy and the big guy's not getting away with it all the time....Rather than feeling so helpless all the time, [deals] makes you feel a bit better....I see behind the veil. It's like the Wizard of Oz. I see the guy behind the curtain. (Martin)
BigDeals (pseudonym) is one of two consumer-to-consumer U.S. platforms we examine that is devoted to creating and exploiting price promotions. Publicly, the platform describes itself as the leading and most trusted deal-sharing forum. Attracting over 10 million monthly users, the engine of BigDeals lies in its forums in which millions of users collaborate to identify, improve, and vet deals on all types of goods and services. Users of BigDeals work together to construct what they term "deals," or opportunities to purchase products or services below their listed retail price. The most desired deals are based on pricing mistakes or loopholes in promotions that are unintended by firms. We find that this unintended value, or value exceeding what the marketer anticipated ([49]), is coproduced through what we characterize as organized persistent, yet fleeting, micro-resistance actions. Deal collectives often reveal intense antagonism toward market-dominant firms but do not try to permanently alter the market.
As in Martin's quote, this resistance mission is explicit and shared among the most prevalent posters of the two deal forums. Users are frustrated by market dynamics that overwhelmingly favor large global brands, and users seek fleeting power by extracting every possible source of savings or benefit from a transaction—especially value unintended by firms. Numerous sites with the same aims exist abroad (e.g., RedFlagDeals.com in Canada, OzBargain.com.au in Australia, hotUKdeals.com in the United Kingdom), as well as for more specialized domains: travel (Flyertalk.com), technology (Anandtech.com), board games (BoardGameGeek.com), computer gaming (cheapassgamer.com), and clothing (forum.purseblog.com). The collectives we examine represent a new phenomenon of users extracting unintended value from sales promotions, raising questions unexplained by existing literature. While consumers are known to be deal prone ([30]) and inclined to participate in deal collectives, existing research only recognizes use of promotions specifically targeted for other consumers as a way of creating unintended value ([49]; [56]). Research does not show how collective processes operate to coproduce unintended value. We examine two popular deal forums to answer the following questions: ( 1) Why are consumers motivated to participate in deal collectives? and ( 2) How do consumers collectively coproduce and leverage unintended value through sales promotions?
Our data show that unintended value can arise from any firm mistake or promotional loophole that enables consumers to gain value unplanned by the marketer, not just from mistargeted promotions. This value is cocreated within deal collectives, which, because of their crowdsourced nature, makes them especially adept at identifying, refining, and vetting deals through pooled knowledge and shared trust. We identify a new form of consumer collective operating in these deal collectives, a micro-resistance network, with a core user base centered on antagonistic coproduction of promotions that provide short-term power in the marketplace. Users derive subversive shopper feelings uniquely from gaining value at the expense of firms. Our findings suggest a new dark side to promotions, coproduction, and consumer collaboration while offering advice on when deal collectives may be beneficial to firm interests and when firms should act defensively.
A market collective is any group of consumers related in some way to goods or services that are traded. Given their group nature, all market collectives create coproduced value ([44]), although the form of value they create varies and can be considered in four broad ways. Collectives can create value before purchase by designing or helping create a product or service (e.g., [28]; [43]). Likewise, collectives can coproduce value after purchase by altering or shaping consumption (e.g., [50]; [60]). While the examples we provide only sample from the rich set of research available on collective value creation before and after purchase, the present study focuses on different forms of collective value creation.
In addition to coproducing before or after purchase, collectives can coproduce value at the moment of purchase ([11]; [19]). Research on this form of value creation remains limited and focuses on the positive role of collectives. For instance, consumers are identified as using collectives to educate and acculturate foreigners on American retail practices ([45]). Coproduction at the moment of exchange is also present in the practice of pay-what-you-want pricing. Pay-what-you-want pricing occurs when a firm invites a consumer to set any price—including zero—that a seller then cannot refuse ([25]). While on the surface this practice may seem risky, research finds that norms of fairness, impression management, and potential social disapproval cause consumers to pay fair prices and protect firms ([ 2]; [10]; [17]; [31]). Research has yet to explore collectives that coproduce value at the moment of purchase in a way that is antagonistic to firms.
Finally, collectives can coproduce value by acting to alter the broader market in which goods or services are traded. This can occur when a collective organizes to reshape (e.g., [ 5]; [15]) or even protest and close (e.g., [40]; [18]) a market. To date, research in this area focuses on what are termed "movements." Consumer movements refer to collectives that are organized in pursuit of clear, macro-level ideological or institutional change in markets ([18]; [27]; [59]), such as PETA's effort to stop animal testing in cosmetics. Movements create value by pressuring institutions to make specific changes through large-scale collective action. Absent in the literature is research on collectives that protest the broad market rather than specific market actions or institutions and do so through micro- rather than macro-level actions.
We examined two deal forums, engaging in naturalistic and participant observation, in-depth interviews with forum participants and forum administrators, and discourse analysis of forum content. The observational data allow us to access and catalog ongoing behaviors and the practices they scaffold. Interviews provide users' rationales for their behaviors, which helped us understand the meanings behind the behaviors as well as the norms and order the practices enable. Our observation, interviews, and archived content form a robust corpus of data ([26]).
Our investigation focuses on online deal collectives. We examine two of the largest online sites devoted to deal hunting in the United States: BigDeals and JumboSaver (both pseudonyms). Both feature deal forums in which consumers actively collaborate to identify, vet, and combine promotions, forming deals across a wide array of products and services. We primarily address the "Hot Deals" subforum, though both sites feature dedicated subforums on more niche products and services. The forums are public; however, to post on the sites a person must register as a user. Message threads can be rated either positively or negatively; self-reported purchases are tallied; and user statistics, such as number of postings and tenure, are visible to the membership.
Forum observation, naturalistic and participant, allows us to witness what is happening on the forums. Each type of observation provides unique insights. The first author is emic to the phenomenon, participating in the deal forums for 11 years and visiting between 1 and 20 times a day. In 2015 he identified himself as a researcher examining the forum as market behavior. He maintains field notes of his experiences and interactions with users. The second author performed naturalistic observation; although she did not engage in deal construction, discussion, or vetting, she logged 156 hours on the sites and kept a set of field notes.
From the forums, we identified and downloaded 250 coproduced and vetted deals that reached a threshold attention level of 3,000 views. Each deal has its own associated thread with between 3 and 456 comments constituting the discussion of the deal. We treated each deal thread as a unique observation (N = 250).
We recruited 37 users who participated to different degrees on the sites. This ranged from users who had posted initial deals and coproduced deals through users who had never posted or even rated a deal. We interviewed three forum administrators employed by the sites. Interviews access motivational and behavioral rationales: why and how users engage in deal forums. Utilizing an interview protocol, we asked users and administrators the underlying inspirations for participating, rules of engagement, and resulting consequences. Interviews (see Table 1) were between 39 and 122 minutes long and were audio recorded and transcribed.
Graph
Table 1. Interview Participants.
| # | Pseudonym | Age (Years) | Gender | Occupation | Region of United States |
|---|
| 1 | Margaret | 59 | Female | Retail salesperson | California |
| 2 | Mike | 54 | Male | Sports instructor | Northeast |
| 3 | Paul | 31 | Male | Finance | South |
| 4 | Henry | 65 | Male | Retired | Southeast |
| 5 | Gloria | 44 | Female | Administrator | Southwest |
| 6 | Helen | 23 | Female | Retail salesperson | Midwest |
| 7 | Jeff | 34 | Male | Medical technician | Northeast |
| 8 | Riley | 23 | Male | Student | Southeast |
| 9 | Daniel | 27 | Male | Researcher | Hawaii |
| 10 | Tim | 52 | Male | Withheld | California |
| 11 | Justin | 19 | Male | Student | West |
| 12 | Alex | 39 | Male | City planner | California |
| 13 | Lex | 32 | Male | Student | Southeast |
| 14 | Max | 29 | Male | Retail salesperson | Midwest |
| 15 | Christina | 22 | Female | Nutritionist | Midwest |
| 16 | Kirk | 55 | Male | Medical doctor | Midwest |
| 17 | Chris | 33 | Male | Engineer | California |
| 18 | Paul | 24 | Male | Marketing manager | California |
| 19 | Fred | 37 | Male | Software designer | California |
| 20 | Miguel | 59 | Male | Programmer (retired) | Northeast |
| 21 | Desiree | 30 | Female | Bookkeeper | California |
| 22 | Harvey | 54 | Male | Pharmaceutical sales | Northeast |
| 23 | Claudia | 70 | Female | Legal assistant | Southeast |
| 24 | Doug | 43 | Male | Retail executive | Midwest |
| 25 | Alan | 48 | Male | Technical writer | Northeast |
| 26 | Matt | 45 | Male | Software developer | South |
| 27 | Linda | 36 | Female | Scientist | Northeast |
| 28 | Dylan | 60 | Male | Metal fabricator | Midwest |
| 30 | Keith | 45 | Male | IT consultant | Southeast |
| 31 | Devon | 55 | Male | Attorney | California |
| 32 | Shane | 56 | Male | Painter | Midwest |
| 33 | Thomas | 42 | Male | Software tester | Midwest |
| 34 | Martin | 60 | Male | Salesperson (retired) | California |
| 35 | Jorge | 45 | Male | Chemist | Southeast |
| 36 | Tyler | 47 | Male | IT manager | Northeast |
| 37 | Sebastian | 21 | Male | Retail salesperson | Midwest |
| 38 | Peter | 38 | Male | Field site administrator | Midwest |
| 39 | John | 41 | Male | Field site administrator | Midwest |
| 40 | Mick | 44 | Male | Field site administrator | California |
1 Notes: IT = information technology.
Netnographic data were captured and analyzed iteratively ([48]), triangulating methods (observation, interviews, and thread analysis). Data analysis was guided by grounded theory, as advocated in [16] and elaborated by [53]. We used the constant comparative method of analysis ([52]), coding the data and distilling thematic patterns. Initial data were analyzed separately and then reinterpreted comparatively, while subsequent data were analyzed in light of previous data, or consistent with a hermeneutic circle of understanding ([48]).
We unfold our findings across four subsections. The first two subsections reveal why users seek deals and who participates in deal collectives. The third section details different sources of promotion value in deal collectives. Finally, we describe how user collectives coproduce unintended value through a public transvaluation process.
Users seek deals for a variety of different, and often simultaneous, reasons. These include a new driver identified in this study, subversive shopper feelings that stem from a desire to co-opt market power, as well as secondary motivations identified in existing literature. Next, we discuss the motivations that fuel user participation in the collective.
Our data reveal that in deal collectives market power imbalance is a primary motivator for seeking deals that provide unintended value. Unintended value is present when promotional mistakes and loopholes in a deal unlock more value than a firm anticipated. The notion of unintended value is similar to the concept of surplus in economics, because consumers pay less than the full value received from an item, but differs in that they actively appropriate the value rather than merely receive it. Our data reveal that capturing unintended value through the adroit use of sales promotions enables users to co-opt market power, resulting in subversive shopper feelings. A specific variant of smart shopper feelings ([46], [47]), "subversive shopper feelings" refer to the specific ego-related affect emanating from a consumer shopping in a manner inconsistent with established norms or rules. Because subversive shopper feelings are dependent on a user recognizing that unintended value is present, such feelings are likely more prevalent among more engaged users.
Our informants report that under existing power dynamics, carefully constructed terms and conditions rules typically tilt promotions in firms' favor. Systemic asymmetry of information regarding the transparency of prices (costs and profit margins) bolsters firms' ability to act in their own favor. Jeff describes how he commonly finds "salespeople pushing products on customers who are not well informed: obviously the car salesman is going to sell you a car in which he derives the most profit on or he has the most commission on. So it's a very, very dangerous situation." Jeff, like many of our informants, highlights how site users are acutely aware of the power imbalance in the market.
Our interviews highlight that one of the primary aims of deal collectives is asserting power in the marketplace. Users of deal collectives accomplish this by capturing value they believe the marketer did not intend for them. Martin explains,
I have a different set of values when it comes to multi-billion-dollar corporations versus the small guy or individuals....I feel basically, f— them. If I can get deals, if I can get something off at 90% or whatever and they didn't plan it, screw them. They've been screwing me and there's nothing you can do these days. A lot of this s— is just like fighting city hall. There's no justice anymore. You take your small victories where you can....I think most people are fish.
Martin's felt vulnerability in the market is expressed by his statement that "they've been screwing me." He refers to consumers who have less information and are unaware that it is being used against them as "fish." Martin and others in the micro-resistance network are aware of their disadvantage and address it through crowdsourced information. While deals necessarily involve lower prices, our data reveal it is not price savings alone that drives consumers to deal collectives. For Martin, a deal "they didn't plan" provides an opportunity for him to temporarily invert market dynamics and restore his agency in the market. Thomas elaborates,
It is very interesting to see how, for a change, you could have the system leaned in your favor, instead of in the big stores' favor....Everybody always feels like they're getting squeezed....I feel like I'm getting screwed....When you walk in the door at these places they've got all this psychological stuff working against you to sort of guide you around to here and there and everywhere, and put the expensive stuff where you're more likely to impulse buy it. Something like [a deal] makes you feel like you've got the hand up on them for a change....It's like you're in control. Even though you're still giving them your money, you feel much more like, "I chose to do this because it benefited me more in the end than had I not gone out and found this deal."... You're not just a passive actor in it.
Thomas's quote illustrates what he sees as the hostile market dynamics present in retail. The "psychological stuff" Thomas refers to includes elements such as store layouts and atmospherics, as well tactics like loss leaders. All of these efforts make him feel like he's vulnerable to firms. Being able to construct and enact deals providing unintended value flips this power imbalance. Thomas's statements that "I chose to do this" and "you're not just a passive actor" reflects the agency he receives from deals.
Providing additional evidence of the micro-resistance network's focus on market power imbalances is their reluctance to create deals against small or altruistic firms. Jorge describes,
If it's a mom-and-pop type place...then I have to see that they're getting something out of [a deal]. If they're not, then I'm not going to buy their product, especially if it's not someone I typically do business with or I don't have any intention of doing business with again....I'll leave the deal and move on....Me hitting Walmart for a little $20 phone in the grand scheme of things, that's nothing. For Josephine Blow's place down the street, maybe that's the only thing she sells that whole day.
Jorge's quote illustrates the distinction the collective makes between large powerful companies versus smaller, more vulnerable ones. His example of not wanting to hurt a "mom-and-pop type place" illustrates a sentiment of care toward smaller firms common among users. Tyler echoes the same sentiment, explaining that "A lot of times if it's a smaller shop, I may actually contact the vendor....I break the unwritten BigDeals rule of [not] contacting the vendor. I don't want to see folks get sent sideways." Tyler's willingness to go against norms of keeping firms in the dark about mistakes illustrates the importance placed on not harming small firms. His action underscores how deals with major retailers are deliberate, antagonistic acts to gain unintended value.
Enactment of deals can provide subversive shopper feelings stemming from the mildly illicit nature of deals involving mistakes and loopholes. Desiree puts it this way:
I like things where you're taking advantage of a big company like Amazon and making them send you something for ridiculously cheap....[In such cases] I'm sure Amazon didn't intend for that to happen, but...it feels like I've gotten one over on them, it feels better.
Desiree recognizes the market dynamic of large companies having power over her, and she explicitly seeks unintended value to reduce their power. She finds value not only in the price savings deals offer but also in the knowledge that her deals are antagonistic to Amazon because it is likely losing money on them. Getting "one over on them" is a chance for Desiree to feel in control. Her use of the phrase "gotten one over on them" characterizes her gains as forbidden because it implies that a retailer would not have condoned her deal had it been fully aware of the deal. Desiree derives affective value from such knowledge. Paul echoes her sentiment, stating, "If it's already the best price and there's also that mistake, then it feels more attractive." Paul illustrates how this affective benefit is not driven by price savings because he specifically calls out the value the presence of a mistake provides him. Alan describes further, "If there's a price mistake, then people feel like [the deal is] better because they feel like they're getting something you're not supposed to be getting." Alan shows how the presence of unintended value stemming from a "mistake" in a deal drives subversive shopper feelings.
While smart shopper feelings typically motivate purchase for personal use, subversive shopper feelings can motivate purchase without any clear need or purpose, underscoring how the feelings tend to increase the more unintended value a retailer loses. We term this phenomenon "purchase for sport" because it is driven by a desire to experience subversive shopper feelings. Devon describes this behavior:
One of the more common things with [users] is they say, "I got no idea what this is or why I need one, but I got one." That is really common. You get that feeling. You get that, "Wow. I got a great deal." It just feels good. Seriously, there are plenty of guys who say, "I ordered two. Can anyone tell me what this is?"
Devon's statement that "it just feels good" refers to the subversive shopper feelings that stem from executing deals including unintended value. His reference to users buying items before understanding what they actually do occurs in our thread data. His quote illustrates users purchasing items simply for the feeling. This behavior is consistent with the sense of consumer–retailer competition described by [49] as driving pursuit of unintended value. As Fred explains, "It's kind of an 'us against them' attitude, like the retailer is the enemy, and the consumer has to win at all costs. I do see that sometimes." Fred's use of the terms "enemy" and "win at all costs" draw allusions to war, underscoring the antagonistic nature of subversive shopper feelings.
Consistent with existing literature ([46], [47]), users gain a broader range of ego-related benefits, referred to as smart shopper feelings, from the price savings present in deals they actually purchase. These ego-related benefits can include feeling smarter than other consumers, the thrill of finding a deal, or even feelings of pride or satisfaction. Unlike subversive shopper feelings, the more general nature of smart shopper feelings makes them more likely to be enjoyed by all forum users:
I don't know if I see it as beat the system; in my eyes, I do get excited, "Oh, I saved on something and somebody else probably didn't realize how to connect these things together to save."... I don't know if I would say "beat the system," but I know that I do get excited if I do save money because then I can use it on something else or think, "Oh, hey, I did a good job, somebody else wouldn't be able to do that because they wouldn't have thought about it that way." (Max)
Max derives benefit from the savings deals provide him, evidenced by him being "excited" and feeling he did a "good job" relative to less-informed consumers. Max's reference to not thinking he "beat the system" is indication that he is not aware unintended value may be present. The same may be true of Doug who stockpiled his wife's favorite tea thanks to a stacked coupon. He "was able to pick up a whole bunch of tea at ten cents a box" describing the experience by saying "It was kind of fun. We have lots of tea. My wife's British, so she loves tea." The "fun" mentioned in Doug's quote illustrates that he enjoyed smart shopper feelings in addition to the value derived from his wife actually drinking the tea. Thomas explains how smart shopper feelings can stem from rarity in deals discovered by the collective:
If they advertised it, everybody knows about that....Whereas, if you feel like, "Hey, I found this, and man, I'm really smart because I'm getting something that other people aren't able to," there probably is a psychological effect there.
Similar to thrift shopping ([ 4]; [51]), Thomas feels "really smart" getting a price other consumers are not aware of, or could even execute.
In other cases, users view the pursuit of deals as a game. Margaret describes: "It's a game, you know. I was able to get this $20 dress for $1.95....It's a game that I basically play with myself." For Margaret, deals are like a strategy game in which price is simply used to keep score. While Margaret states she plays the game with herself, in reality it is against the retailer. Alex explains, "I look at it as a game. It's an intellectual challenge to try to outsmart [retailers]....Whereas the normal person's just going to [buy] it because they think [finding a deal] is too hard, I'll go the extra mile because I think it's fun." Alex's statement that deals are "an intellectual challenge" highlights the "fun" he gets from making them competitive. His comparison to "normal" consumers paying regular price illustrates how deals enable him to also feel smart. Some users, such as Lex, see deals as a "a bit like gambling. Sometimes it works, sometimes it doesn't....I just stack [coupons] and I see if it works." Lex's quote draws similarities between the random positive reinforcement characteristic to gambling and deals. The random nature of positive outcomes can lead to strong affective responses when deals work. Paul explains, "It's a rush when you get a good deal, when you get a deal that you think is better than like anybody else thought is possible, it's a rush." For Paul, obtaining a particularly lucrative deal is akin to thrill of winning a jackpot.
Even absent purchase, mere sharing of information on deals can provide users with social benefits. Consumers with deep market knowledge (market offerings available, market offering attribute comparisons, distribution outlets, outlet selection comparisons, and market prices) and a propensity to share this market information are market mavens ([12]). Market mavens are influencers who are often early to adopt and are driven by a desire to help other consumers, not necessarily to harm corporations. Our data show many deal forum users are likely market mavens because they are driven to help fellow users find and improve deals. Mike illustrates this:
If anybody finds out about a good deal and they know they got a good price, there's a psychological payoff in telling someone else about it....It's just human nature. And on a site like JumboSaver, there's a lot of people putting up deals...only for the reason that it makes them feel good, it makes them feel smart, it makes them feel confident, it makes them feel knowledgeable. Knowing information that other people don't know is one way that human beings feel good about themselves.
Mike's quote illustrates how sharing ("putting up") deals within the collective, as an expert among experts, or a maven within a maven collective, where users of JumboSaver appreciate the nuances of a great deal, makes him feel "smart," "confident," and "knowledgeable." Some users are driven to help others for more benevolent reasons. For instance, Linda says, "I appreciate other people's posts, so I want to contribute." Her statement illustrates understanding that reciprocity is necessary for the collective to function. Other users see sharing deals as a means to help address more general inequity. Jorge states that sharing deals makes him "a humanist or maybe even a socialist in that you're trying to help fellow people that might be a little light in the pocketbook be able to have items they'd really like to have that typically they could not afford." Jorge's use of the terms "humanist" and "socialist" illustrate the strong effect he believes sharing deals can have on fellow users' lives.
Many users share deals with consumers outside of the collective to help those with less market knowledge. Paul explains, "[You want to] share with...your friends and family when you find a good deal so they can also take advantage of your deal....People are happy to hear [about good deals]." The fact that others "are happy" to learn of the deals he shares provides Paul with affective benefits. Further illustrating similarities with market mavens, users describe becoming known among friends for being their resident deal expert. Dylan describes how he has "a lot of friends that will call me—they know the value—and say 'Hey, can you find me a deal on this or that?' and I do." Dylan's statement that "they know the value" highlights the financial benefit his friends receive from the deals he finds. Dylan receives satisfaction from helping less sophisticated deal seekers as well as gaining relatively unique status within his social network as a deal expert.
While deal sites are incarnations of collective activism addressing market power dynamics, users vary in their awareness of and commitment to the goal of redressing market power imbalance. Core users, who represent the predominant amount of engagement with the site, are universally invested in the pursuit of deals containing unintended value as an act of consumer agency. Core users are described by forum administrators as "heavy participants" (Peter) or the "power users who drive the engine" (Mick). Because subversive shopper feelings are dependent on a user recognizing that unintended value is present, such feelings are likely more prevalent among engaged or core users. Engaged users appreciate the thrill provided by unintended value in good deals but are less driven by a desire to invert market dynamics. These users participate in vetting, improving, and commenting but are less likely to initially post deals. Reflecting both core and engaged users, John estimates that "10%–15% of our users create 90%–95% of the content in the forums."
Peripheral users, or "lurkers," as our administrators refer to them, may be satisfied merely by the price savings that necessarily accompany unintended value. Put simply, marginal participants care primarily about saving money and the associated smart shopper feelings, irrespective of whether the savings are intended or unintended by a firm. Although peripheral users do not formally contribute, John states that because "we measure [thread] views, in some ways [viewing is] a participation." As we discuss subsequently, view counts are an indicator to the collective of a deal's quality and ephemerality. In this article, we focus on core users because they fuel much of the forum activity and their activity is more theoretically novel.
Our data reveal that any promotional mistake or loophole can produce unintended value. We first discuss these building blocks through which unintended value arises before turning to examine how deal collectives enable and amplify the capture of unintended value. Three broad types of these mistakes or loopholes exist: errors in targeting, errors in pricing or promotional terms, and loopholes that allow combining or "stacking" multiple promotional offers together. More complex deals typically offer more unintended value.
In support of existing literature ([49]; [56]), consumers are aware that firms will sometimes send out coupons or promotions to certain targeted consumers. This was evident in analysis of numerous threads where users speculated as to why some users or areas received promotions. Paul explains that "unintended could mean a deal not intended for everyone. To give you an example, there could be a deal that was sent out to everyone on a merchant's newsletter subscription and was intended for that channel only. Then it gets posted onto the BigDeals forum." Distributing a single coupon code rather than creating unique codes for each consumer is a firm-made mistake. Devon explains,
Newegg does it all the time where they target email subscribers, but it's the same code sent to anybody that's on the list. The general public who may not be on their email list would never know about that code. The community at JumboSaver helps you consolidate or aggregate all of those different deals. You might have a guy who says, "Oh, don't forget those of us who are on the email list got a newsletter with this code."
That these offers are not promoted by firms and that users have access to them only through the forums makes the offers more valuable. Daniel describes how he "wants to capture whatever additional surplus I can by getting whatever targeted offers that may not necessarily be for me. If I can get a hold of those, I would love to, because then it's just extra benefit for me." Due to the attractiveness of such untargeted offers, companies that make the mistake of not using unique codes should expect a potential increase in sales. Alan states that users "used to say we're 'taking advantage of companies' mistakes' since if [a firm] wrote a promotion bad, the JumboSaver effect would teach them a lesson real quick." The "JumboSaver effect" Alan references is a sudden surge in orders in response to a lucrative deal shared within the collective.
Consistent with research on reference prices ([23]; [32]), forum users view prices that appear too lucrative to be retailer mistakes. Paul explains that while some "people have developed tools that scrape certain websites for price mistakes," he "wouldn't be surprised if people just kind of stumble across [price mistakes], 'Hey, this TV is normally $4,000 and its $400. It's probably missing a zero. Someone entered it wrong and it's now a good deal.'" Users openly speculate in the threads that some deals are price mistakes. Such was the case when the first author exploited what was speculated to be a promotional mistake by purchasing a $400 iPad for $40. Lex describes how such errors can arise from promotions that are not fully thought through: "Let's say you have a big travel company offer a discount on some hotels, let's say $100 discount on some hotels, but it doesn't put any minimum hotel price on it." Enterprising consumers would be keen to find hotels as close to, or even below, the $100 discount to score free or nearly free hotel rooms. Our conversations with forum administrators reveal that price mistakes often are genuine mistakes by firms, as is evidenced by retailers having asked them to remove posts about leaks of promotion codes and mistakes. Both forums developed formal policies in response and decline to remove posts except in cases where a post is illegal.
More complex deals are more likely to be unintended because their existence is usually assumed to be due to a mistake or loophole and thus unplanned by the marketer. Complexity increases when a deal involves more promotional assets, more sources of promotion assets, or both. A promotional asset refers to a single firm-created promotion such as a coupon, a sale, rebate, offer, gift with purchases/premiums, and so on. An asset source is the provider of a certain promotion and may include the retailer, manufacturer, or third-party payer. Tyler explains that deals that combine assets from different sources are more likely to be a mistake because the different sources are deemed less likely to be in sync: "Sometimes the codes that you're stacking, one is the manufacturer code, and then one is the actual vendor code. Discounts are coming from two different places." He argues that a combination of promotions from multiple sources makes a deal seem unplanned and, therefore, yielding unintended value.
Independent of multiple sources, "stacked" deals combining promotions in a single deal are less planned and more lucrative. Desiree explains bundling impacts in the presence of a loophole:
The more complicated [deal] would be perhaps more unanticipated, yes. [The firm] would expect people to maybe find one code or another, but not necessarily figure out how to use them both; whereas if Kohl's was offering a [single] 20% off coupon code, everybody's going to use it because it's plastered all over the place.
In Desiree's example, a simple 20% off is intended, and everyone can access the reduction. The more complex the deal, the less likely it was intended. Linda describes the experience of building a complex deal: "Honestly, when you're doing a hot deal, what makes it a hot deal is that you're beating the system....It's that it's not easy or spelled out for you." Here, Linda indicates how finding loopholes most consumers will never discover is a route to capturing unintended value. Deals can arise from unexpected side effects of retailer policies. Max explains,
A lot of the deals are done on Sunday or Monday because [Staples's] coupons have a two-day grace period for the expiration, facilitating use with sales from a different promotional cycle. While they have that loophole available, I don't necessarily think that those are intended deals.
Shane notes the effect a deal's complexity has on the presence of a mistake or loophole:
It's like going hunting and shooting a squirrel as opposed to shooting a grizzly bear that's coming at you. Those [deals] that are real difficult and it's so technical you have to do so many parts, you don't know if it's going to work....When you get done, you go, "Wow. I can't believe I did that."
The fact that Shane is unsure if a highly complex deal will work reflects his belief the deal was a mistake unlikely to have been intended by the retailer. Tyler echoes Shane's sentiment:
You probably feel like you're getting a little bit more value if you've done some digging and you found two codes and stack them together just because it's the art of the hunt....Sometimes it's the thrill of the hunt, and not necessarily just the price.
By "stacking," Tyler refers to using multiple promotions at once. His use of the phrase "thrill of the hunt" highlights how a deal based on a loophole provides value beyond price savings.
The complexity of promotion stacking requires knowledge of sequencing. In some cases, deals operate only if promotions are entered in a certain order. Thomas describes a deal in which Staples had $100 dollars off all demo laptops and an additional coupon for $100 dollars off any Dell computer $350 or more. The trick, according to Thomas, was to "find [a laptop] that's $351 dollars, use the Dell $100 dollars off, and then use the Staples $100 off. But if you used the Staples [coupon] first, then it's $250 and you don't get the Dell discount." Entering promotions in an incorrect order would not enable this deal to be as attractive.
While users can create unintended value individually, the nature of the collective makes it particularly adroit at generating and amplifying such deals. The heart of the collective's ability to generate unintended value lies in the discussion forums, which summon the wisdom of market crowds ([28]). There, as overviewed in Figure 1, users collaboratively identify, improve, and vet deals by making public posts to deal threads, engaging in what is termed a transvaluation process ([13]). Transvaluation refers to the process of additional value being created as a result of information being affixed to an artifact—in this case, a deal thread. Collective coproduction generates increased unintended value by enabling better deals to be constructed as well as changing the attributions users make about those deals.
Graph: Figure 1. How deal collectives generate and respond to unintended value.
Three forces work to support the collective process we outline. First, while users primarily collaborate to coproduce deals that yield unintended value, the variety of secondary benefits deals provide sustains interest in, and stability of, the collective even if unintended value is not present or on a user's radar. Second, throughout the coproduction process trust is an important collective norm that plays an important role in facilitating information sharing and enabling user action or inaction on posted deals. Trust likely stems from the belief that fellow consumers are unmotivated by financial interest ([ 3]). Finally, deal collective administrators play a role by structuring and governing the deal collaboration platform. Although administrators are very reluctant to allow us to attribute specific quotes to them, each articulated that all promotional manipulation within legal requirements is permitted, meaning activities such as hacking promotional codes and prices is prohibited. Administrators note that they rarely remove a user post, meaning that users understand the parameters of unintended value (e.g., the legal manipulation of promotions to co-opt value beyond corporate expectations).
Deals begin with a single user posting a deal that they believe has potential unintended value. The collective tends to react more positively to deals identified by trusted users. Users are trusted based their username and metrics summarizing their prior participation. Claudia explains that "you recognize some of the people. You learn to trust some of the people that are on there regularly." The trust in fellow users that Claudia describes is an important precursor to further collective response.
Initial posting is a value-creating action because it is designed to inspire other participants to strategize improvements, offer assessment, and enact the deals. Riley describes how he "posts to help other people with the deals and also to get feedback from people about what you could do better....Then you save more money." Riley's quote indicates that while he is motivated to help other people by sharing deals, he knows the collective's feedback will help improve it. Devon further explains:
What makes BigDeals work, what makes JumboSaver work, is the community. It's like in Dr. Strangelove's lab, when they said, "What's the purpose of having a nuclear device if you don't tell the other side you got a nuclear device?" What's the purpose of having a deal community if you don't tell them you've got deals? It's the community that creates the deal by this consolidation and aggregation.
Devon's reference to Dr. Strangelove underscores that the whole point of the collective is to share deals with other users. While a single user starts a deal thread through an initial posting, Devon's statement that "it's the community that creates the deal" highlights the role the collective plays in bringing a deal to life. Posting a deal is a key first step but is only the beginning of a larger process. Mike further explains the value of sharing with the collective:
You've got these networking sites like BigDeals where if one person figures it out, everybody gets to know about it. So you don't have to be a genius who understands all this stuff. You just need to keep your eyes open and get some familiarity with how it works and how to leverage a site like BigDeals....Suddenly you're leveraging the smartest guys who've been doing this for years and years and years.
Mike illustrates the value users place on the collective's knowledge base, which is evidenced in users thanking one another in threads. He acknowledges it only takes one user's comment to significantly improve a deal. Posting enables any user to access users that are diverse in knowledge, experiences, and geography. This makes the collective necessarily better than any one individual at coproducing deals and is a powerful motivator for public posting
The collective enhances the value of deals by adding additional information to each deal thread. This is consistent with what [13] describe as a transvaluation process. Participants add their promotional expertise (e.g., knowledge of the existence of available promotional offers, knowledge of how promotions combine) to a deal thread.
Deals are considered and enhanced in terms of their two underlying components: price savings and the presence of mistakes or loopholes (unintended value). While the collective focuses on the pursuit of unintended value, they are open to incidental discoveries of price savings. The heart of this improvement process lies in the forums where users interact. Fred explains the role of collaborative coproduction of deal opportunities:
You'll often see someone post a deal and then someone post a reply that either improves the deal, or puts a different spin on it, saying, "You can get the same deal on this other product," or retailer, for the same price. It is really a community effort, in terms of someone starting off with a deal and then others building on it.
Fred shows the power of the collective to enhance the deal efforts of individual users, creating something that is more than the sum of its parts. In saying "improves the deal," Fred is referring to the discovery of mistakes or loopholes that offer unintended value. The "different spin" mentioned refers to using price matching policies to arbitrage deals between retailers. Users will often report that they are able to get a retailer in a different state to price match a deal, thus avoiding sales tax. Each deal thread serves as a compendium of the collective's efforts to drive price savings and mine unintended value. Claudia describes,
Someone, they post that "X" is on sale at a bargain price at such and such a store. They'll go through the details of click[ing] on this link, etc., here it is. Someone else would chime in and say, "Hey. I've got a code here for free shipping. Use this code and you'll get free shipping." A few posts later on, someone may post, "Here is a 10% off coupon. You can combine it with the free shipping code." Again, it's a large community sharing ways of saving money, of stretching what we already have to either buy things we wouldn't have bought otherwise but which would be nice to have or buy the essentials that we do need.
Claudia's quote illustrates that the forums provide value by enabling knowledge and experience with a single deal to be amalgamated. This was repeatedly evidenced in deal threads.
The sheer number of knowledgeable users in the collective makes the group more creative and better able to leverage mistakes and loopholes, which are the source of unintended value. Devon explains that the complexity inherent in more lucrative deals often makes them suited to joint endeavors, with users building on each other's posted efforts to coproduce "stacked" deals involving simultaneous use of multiple promotions:
The way most combination deals work is you have one guy say, "Okay. Here's a coupon code for this thing at Staples," and then another guy will come up and say, "Yeah. By the way, Staples has a 10% off coupon here that will stack." Stacking is your big desire. You want to find things that will stack. Then the third guy on down, or a couple of hours later, will come in and say, "Oh. If you use this credit card, you can stack those two coupons and get 5% back on this particular credit card this month, or whatever." It's definitely a community effort. Everybody throws in. They may know one part of the deal, and then by combining it all on the forum thread, now you've got basically, by the time you get to it, an hour or two, or three hours into it, you basically got a recipe for how to combine multiple deals into one. That happens all the time....Those are the good deals.
Devon shows how the collective collaborates, building on one another's posts to maximize value by discovering complex combinations and sequencing of promotions that work together. In saying that "stacking is your big desire," Devon highlights how the unintended value stemming from the coincidental or chance nature of complex deals makes them seem even more attractive.
Interaction among users enables their complementary knowledge bases to be leveraged, forming a stronger and more comprehensive collective knowledge base. This knowledge includes information on coupons, offers, pricing tactics, and operating practices as specific to a product category, retailer, manufacturer, or market context. It includes knowledge of both available and upcoming offers, as well as if and how they can be bundled. This transvaluation process leads to development of detailed collective information on promotions instantiated in the archived and searchable forums. All of this results in the collective being able to develop much better deals than any one individual, as Thomas states:
It's not all coming from one person. It's coming from dozens or hundreds or thousands of different people. You don't have to be knowledgeable about everything to find and post a deal. The people who post the deals probably aren't knowledgeable about the things that you post. Everyone has their own little niche.
For Thomas, the diversity of the collective strengthens its ability to create deals because each individual doesn't have to be an expert on all market offerings—rather, the collective aggregates expertise. Again, trust in users plays an important role in this process because users rely on others to honestly report their experiences attempting to redeem the posted deals and experimenting with the addition of further promotions. Matt explains, "Trusting the community really is what it's about. It's almost like a social network really where you are relying on people to be honest in rating deals and things like that." The trust that Matt describes undergirds the efforts of users constantly searching and probing for new information, enabling deals providing unintended value to be collectively developed. Devon explains further,
Some of these people find things, and I'm like, "How in God's name do they ever come across that?" Sometimes it'll be mistakes or things that....The website, a fluke in the website or something where if you hit a certain combination of key strokes, or you go in and you put it in your cart, and then you log out, and then you come back in, and then you can put another coupon on it. If you didn't go and do all of those extra steps....How those guys find those things, those type of deals that require a specific recipe of different strokes, I have no idea how they find those. It's just incredible.
Devon expresses awe at the collective's ability to unearth deal opportunities based on particular sequencing and procedures. While the forum's prowess stems from its nearly constant focus on deals, it is the trust among the members that sustains coproduction.
Transvaluation operates to create value through assessment of posted deals. Collective vetting adds value to the mistakes or loopholes and raw price savings present in a deal by attaching additional meaning in terms of a deal's quality and ephemerality. As evidenced in our data, these byproducts of vetting become attached to a deal thread in multiple ways including comments, numeric ratings, purchase counts, and thread view counts.
Deal quality refers to the extent that a deal contains ( 1) price savings relative to the market and ( 2) unintended value. The public vetting process results in these quality assessments becoming attached to each deal thread, which adds value by alerting users to deals they might have incorrectly evaluated otherwise. This process often occurs at the same time a deal is being improved by the collective. Whenever a user rates a deal, reports that they purchased an item or, because view counts are public, merely views a thread, their behavior is diagnostic to others who might be considering purchasing a posted deal. Claudia unfolds the effect of transvaluation:
If I see a post for a supposedly hot deal and only one or two people have posted that they've taken advantage of it [bought the deal], or no one has posted they've taken advantage of it, it's probably not as hot a deal as the poster thought it was.
Claudia trusts the assessments of the other users, relying on the collective's response as a gauge of a deal's quality and thus presence of unintended value. Claudia's behavior reflects most users, who treat all deals as "supposedly hot" until the collective's assessment of price savings and unintended value is known. The collective vetting process is key in assessing a deal's quality. Mike explains that the scale of the collective drives this effect:
We're smarter than [retailers] because there's more of us and we're like the hackers out there who are constantly probing at the system. And if they try to fool us by saying this is a great price, it's on sale, it's 50% off but they raise the price 50% yesterday to make it look better. We're going to know that....All these people...are probing at the system.
Mike's use of the phrase "probing at the system" refers to the collective's ability to discover mistakes and loopholes in promotional systems. His choice of the word "hackers" underscores that the value such probing unlocks is unintended by firms. The second half of his quote speaks to the broad knowledge the collective possesses of reference prices, which enable it to quickly vet the quality of a deal's price savings. The fact that users such as Mike are aware of the collective's knowledge base makes the vetting process all the more powerful in creating quality assessments.
The ratings and reactions users attach to each deal thread are integral to coproduction, which places trust in other users to construct and vet deals. Matt states,
Trusting the community really is what it is about. It's almost like a social network really where you are relying on people to be honest and rating deals and things like that, and you just rely on that....95% of the time I think pretty much the ratings are spot on, in terms of whether it's a good deal [i.e., exploits unintended value] or not.
Matt indicates that trust is critical to the success of the deal collective. Users trust one another to be honest and accurate, assuming that other consumers will act in good faith and without the bias typical of advertising. The collective is particularly vigilant about ensuring that it is composed of consumers, and any suspect behavior is called out. In contrast to posts by users, posts from a firm are characterized as "shills" and are often ignored or receive negative reactions. As described by Doug, shills are not condoned, and the collective polices that: "Everybody jumps in and starts ridiculing whoever posted." Conversely, posts from established users of the collective may receive increased attention, as Claudia describes and our thread analysis revealed:
You recognize some of the people. You learn to trust some of the people that are on there regularly. You know if you see a deal, sometimes it's legitimate, and you also look for the community to add to the bare bones of the deal.
By "legitimate," Claudia is referring both to a deal containing price savings relative to the best reference prices and—ironically—to a deal being unintended. Deals posted by firms are viewed as illegitimate because they are, by definition, intended. Claudia's statement that "you also look for the community to add to bare bones of a deal" means that deals constructed by multiple users working together are viewed as more unintended than if an entire deal is posted by a single user.
Another by-product of public vetting of deals is anxiety that a deal is transitory and may end at any point in time. Such hype adds value because users are triggered to purchase a deal before the opportunity expires. We witness this meaning becoming attached to a deal thread through two mechanisms. First, public agreement that a deal is likely due to a mistake can cause consumers to publicly speculate that a retailer will shut down a deal if it is discovered. Postings within the collective spread this risk among users, creating urgency to execute a deal before it ends. The first author understands this all too well, having missed out on several deals by contemplating them for too long. He—as well as other users, as evidenced by their posts of dismay—has learned the risk of a retailer shutting down a deal increases dramatically if the collective is rapidly purchasing it. Fred describes how this inspires immediate action:
These days, price mistakes, it's more likely that the retailers will just not ship the item, and they'll just say, "Oh, it was a mistake. We're just canceling the order." There's a lot more urgency around that, to get your order in to see if it will ship out, versus a regular coupon, where it's just a coupon that the retailer issued, and everyone knows it's valid.
Fred's statement that "everyone knows" refers to the collective, which learns from previous experiences and openly shares this information in forum threads. Users will often add such comments, stating that they are acting immediately for fear of a deal ending. Harvey echoes Fred, explaining how mistakes resulting in extreme unintended value are expected to be caught:
Sometimes the retailers...have a misprint in an offer they run. Let's just say the coupon was supposed to be for $5 off a large sized container of laundry detergent that sells for $10.99, but they accidentally leave off the size restrictions and the small size bottle sells for $4.99, so it's free laundry detergent with the $5 off coupon. Sometimes the retailers catch it and they'll put up signs...."This is a mistake. It's only valid on the 120 oz size."
Harvey's example illustrates how a retailer pulling a deal due to a "mistake" serves to legitimize that unintended value was present in a deal. Stories such as Harvey's are shared to warn others that a deal is "dead" but also act to create a culture of immediate action. Witnessing deals end in such a manner sensitizes users to act quickly in the future, a by-product of collective vetting.
Public vetting of deals presents the very real risk of a user inadvertently alerting a firm to a mistake. This highlights that the collective's interests are in direct opposition to those of the firm. The risk of such a tip-off occurring is so real that norms develop to mitigate it and maintain trust within the collective. Henry states, "You can see the community—some people are very reactive to price mistakes and very gritty about them....The number-one rule on BigDeals is never call the retailer or contact the retailer to ask any questions." Fred echoes this, stating the "unwritten rule not to call the retailer if there's deals that might be price mistakes. It alerts them that there might be an issue....One of the norms of the community is don't make a fuss over this, don't call the retailer, and don't let them know." He describes how if someone does post "saying, 'I called the retailer about this, and they said such-and-such,' then those people usually just get a huge negative rating [from other users]." Both the norm and its enforcement signal shared efforts within the collective to maintain collective trust to maximize the time during which a deal is "live." However, the more popular a deal, the greater the risk of such a tip-off occurring, and the greater the urgency to execute a deal.
Second, absent belief that a deal will end due to firm action, vetting can lead to ephemerality from the belief that other consumers will purchase so quickly that stock will deplete. The fact that users compete against each other to capture deals is a notable shift from the collaboration evident during deal coproduction. This effect is similar to auction fever, in which the frenzy and excitement of auctions can cause consumers to overbid for items ([21]), but it differs because more than one item is generally available for sale. Ironically, the norm in the collective of thanking deal creators, evident through likes, comments, deal ratings, and tallied purchases, are all indicators of interest in a deal. Because view counts of individual forum threads are public, they are an important barometer of a deal's popularity. Tim explains,
A deal is at the top [of the forum] because people are talking about it and it stays on that first page and people are holding it up or down—that sort of makes it exciting. It pulls you in for some unknown variable, like, "Is this a price mistake?" Then that sort of prompts you to act in haste rather than waiting for things to settle down or things to clear itself out. There's definitely that factor where the unknown gets people excited about a deal.
Tim's example illustrates how he uses collective interest in particular deals as a gauge of a deal's attractiveness. The fact that a deal stays on the first page due to user interest is value-creating for Tim because it alerts him to a good deal, and he trusts the ratings of others in the collective. An additional by-product of collective vetting is fear that a deal is ephemeral. As discussed previously, this can result in users purchasing items without a clear understanding of what is being sold. (The first author is guilty of such behavior.)
Our findings demonstrate ways of generating unintended value that go beyond the use of targeting. This widens our theoretical understanding of how unintended value can arise. While existing research finds that consumers enjoy "winning" against marketers, we show that consumers are actually driven to pursue seemingly unplanned gains. These subversive shopper feelings are novel because they are driven by consumer antagonism, something not identified in the literature, and result in unique outcomes such as overpurchase.
Furthermore, in contrast to existing research, we demonstrate that unintended value can be collectively coproduced. The collective acts not only catalyze the deal generation process but, more importantly, stoke unintended value by changing the attributions users make about created deals. Echoing the bandwagon effect ([57]), users of deal collectives take cues from others' reactions, which can add to unintended value.
Research has generally focused on coproduction of value that occurs either before or after the purchase of a product or service ([11]; [19]). Furthermore, the limited research that does exist on value coproduction at purchase, such as group buying ([24]) and pay-what-you-want pricing ([25]; [39]), demonstrates effects that are positive in nature for firms. Deal collectives represent a novel form of coproduction that creates value at the time of purchase.
While promotions are generally regarded as having positive outcomes ([ 9]; [36]), our findings contribute by showing that promotions can be used to exact damage on firms. Deal collectives may magnify "cherry picking"—that is, only buying deals that generate negative profits for firms ([54]). Users may be less loyal to retailers or brands in general, unless they enable easy opportunities to capture unintended value. This may prompt reconsideration of how concepts such as relationship marketing and loyalty are conceptualized and measured, particularly from a behavioral perspective. Firms that serve all consumers with a long-term, relationship-building orientation may be opening themselves up to exploitation by some consumers. Furthermore, our findings suggest antagonism as a new mechanism through which promotions create value for consumers: subversive shopper feelings. While deeper discounts are generally used to drive increased sales ([ 7]; [32]), consumers may be equally excited by deals with lower discounts but that also contain unintended value. Subversive shopper feelings suggest an adversarial perspective to existing understanding of what makes promotion effective.
Our examination of deal collectives contributes to existing research on market collectives in two ways. First, deal collectives represent a novel form of antagonistic market collective that we more generally term micro-resistance networks. Like consumer movements ([59]), deal collectives are aggrieved with the market. However, deal collectives seek fleeting small moments of victory rather than the lasting institutional change sought by movements ([ 8]), and they actually act in ways to preserve current conditions to ensure sustainable access to the booty found in deals. We contribute by identifying this new form of collective and revealing the varied mechanisms that enable its operation and survival.
Second, we contribute by showing how less engaged participants contribute to the coproduction of value in a collective. While this finding echoes some emerging research ([42]; [55]), existing theory on collectives focuses on core users and often implicitly assumes that peripheral users aspire to be or act like them. Our data show that this may not be true in all cases and that peripheral users may enjoy their marginal status, never desiring core-level commitment or engagement or even gaining the same value as core stakeholders. Our data show how core and peripheral users can exist in a symbiotic relationship. At a theoretical level, these findings suggest potential value in reexamining the role of peripheral users in other collectives.
The micro-resistance networks we introduce represent a paradigm shift in how marketers view consumer response to promotions. For core users, deals represent a means of temporarily co-opting market power against powerful firms through deals that purposefully hurt firm profits and yield subversive shopper feelings. From a managerial perspective, such behavior is clearly concerning. For the much larger group of peripheral users of deal collectives, deals represent a way to save extra money and feel smarter than fellow less informed consumers. This mixed nature of deal collectives makes them a fragile entity, sometimes beneficial to firms and in other cases damaging. To provide advice to managers, we first explore the dual nature of deal collectives (see Table 2) before discussing how firms might react to them (see Figure 2).
Graph
Table 2. The Double-Edged Nature of Deal Collectives.
| Deal Collectives Can Benefit Firms If: | Deal Collectives Can Damage Firms If: |
|---|
| Strategy-related effects | Excess stock needs to be moved Stimulating trial is beneficial Purchases are part of a two-part pricing model (e.g., discounting printers with the expectation of making money from later ink sales) Increasing market share is valuable (e.g., industries with high fixed costs) Deals help in winning a standards war Deals are for services or products that cannot be stored, reducing risk of surplus purchase Deals are for high-margin items Deals are for products with minimal variable costs of production (e.g., digital goods)
| Orders are fulfilled very quickly, reducing chance of errors being detected before fulfillment Firms are legally required to fulfill orders (e.g., some error fares) Products are infrequently purchased, reducing ability to recoup any losses from a deal Products can be stored, increasing risk of surplus purchase Deals are for low-margin items, possible resulting in prices below cost Deals are for physical goods: Making fulfillment more difficult, magnifying stockouts, restricting purchase by other consumers Which often have higher variable costs of production compared with digital
|
| Consumer-related effects | Price discrimination through deals is advantageous Word of mouth is generated Deals are difficult to execute, minimizing usage
| Deals are likely to make consumers more deal prone and averse to paying full price Deals cause consumers to purchase for reasons beyond use Magnifying stockouts and costs due to loss leaders Restricting purchase by other consumers
Deals amplify antifirm sentiment
|
Graph: Figure 2. Firm stances for responding to deal collectives.
Deal collectives represent millions of consumers who are motivated to swiftly purchase deals and share them with others. The scale of deal collectives makes them a potentially powerful force that can be both beneficial and detrimental to firm interests. In Figure 2, we consider the positive and negative potential effects of deal collectives in terms of strategy and consumers. Through a strategic lens, harnessing this group of consumers can be valuable for firms in certain circumstances, such as when excess stock needs to be liquidated, trial needs to be stimulated, purchases are the first step in a two-part pricing model, or purchases help win a standards battle. Deals are valuable for services or products that cannot be stored, because the risk of surplus purchase and storage is reduced. Likewise, deals may have more potential benefit in high–fixed cost (e.g., software) or high-margin industries because risk of immediate losses is lower. At the consumer level, while price discrimination often involves effort, such as with clipping coupons ([37]), deals represent a new form of price discrimination that also relies on consumer ingenuity and creativity. Similar to gambled discounts ([ 1]), firms might use the serendipitous nature of deals to engage in price discrimination without the drop in reference price characteristic of more openly communicated discounts ([ 7]). Deals that are more difficult to execute are more beneficial to firms because they are likely to garner word of mouth but less likely to be widely used. Firms might even consider offering follow-up deals to those that are particularly popular. This would allow firms to leverage the interest generated through viral sharing of a deal as well as any resulting quasiownership effects ([21]) generated by deal coproduction.
In terms of strategy, deal collectives can be a potential liability for firms, especially when deal collectives regularly execute deals that result in pricing below cost. Industries with low margins and/or rapid order fulfillment are especially vulnerable because they present limited room for error and time to detect such postings. Deals on goods that are infrequently purchased (e.g., appliances) limit the value of the goodwill a deal might provide. The presence of unintended value in deals can drive purchase quantities that vastly outpace a users' own needs, particularly when goods can easily be stored. This can cause stockouts and logistical problems at retailers, restrict purchase of the product by other consumers, and thus invert the intended effect of promotions. At the consumer level, deal collectives may sensitize consumers to deals, potentially training them to not purchase at regular prices and taking time to undo ([34]). Finally, deals may act as a means of expressing and fueling antifirm sentiment and antagonistic consumer actions.
The potential for benefit and damage outlined in Table 2 can be used to construct a matrix of possible firm stances toward deal collectives, as shown in Figure 2.
Monitoring is in the center of the matrix because it is valuable to measure the success of deal enablement programs and is an important tool to prevent and minimize firm risk. Monitoring starts with simple listening strategies, either of deal collectives or a firm's own incoming sales data. Amazon is already known to do this, monitoring demand for products and automatically adjusting prices upward in response ([29]). Firms might create a database of all available promotions, including those from manufacturers and other partners, to proactively check for and eliminate unintentionally lucrative combinations (however, we note that this requires effort that is likely only efficient when there is a strong risk). Finally, firms might implement systems that monitor for such abuse by checking data on new accounts against existing ones and preventing duplicates. Firms should also track the extent to which individuals excessively use promotions, perhaps by calculating a customer's average margin. This information could be used to identify unprofitable unintended value–driven consumers, similar to how retailers such as Best Buy track consumer returns ([41]). Next, we describe each of the four stances firms can take toward deal collectives.
In cases in which deal collectives present both low benefit and risk, firms may act to deter their efforts. One preventative action is to not offer concurrent promotions. While somewhat challenging to coordinate with manufacturers and payment providers, such action makes the creation of "stacked" deals involving overlapping promotions impossible. Firms can dissuade deal collectives by distributing one-time-use coupons that limit any damage to a specified number of codes. An additional precaution is to tie codes to specific user accounts, minimizing the likelihood of a promotion being distributed beyond intended recipients. In situations where one-time-use codes are not feasible, language should be included that limits codes to one per person. Finally, while not the focus of this article, our data reveal that firms that are smaller or better corporate citizens are less likely to be the target of action by deal collectives.
In situations in which deal collectives present higher risks but offer relatively low benefit, firms can employ the aforementioned deterrence strategies as well as work to more actively monitor and limit deal collectives. First, any errant deals identified through monitoring can be ended. In other cases, similar to loss leaders, firms may want to create decoy deals that involve multiple combinable promotions for certain high-margin items to absorb attention. Finally, firms might leverage monitoring data to either stop sending promotions to unprofitable customers or "fire" them in a manner similar to how firms such as Best Buy and Amazon fire consumers who return excessively ([41]).
When deal collectives simultaneously present high benefits and risks, firms could employ a strategy of both aggressively monitoring and passively encouraging deal collectives' efforts. Firms could encourage deal collectives by purposely using nonunique, non-customer-specific promo codes to facilitate their sharing and exploitation multiple times. Similar to how Staples enables coupons to be used a day or two after they expire, firms could code their systems to allow expiry dates and other promotional rules to be broken. This encourages experimentation and the presence of unintended value. Finally, firms might distribute their existing promotions in a way that makes it more likely for consumers to consider and attempt to combine them. Target often accomplishes this by publishing companywide coupons (e.g., "$10 off when you spend $50") that combine with product specific deals (e.g., "Buy 3 and get a $5 gift card") and even manufacturer coupons located in the aisle.
In situations in which deal collectives present minimal risk but offer strong potential benefits, firms can not only employ the passive strategies outlined above but also actively aid their efforts. First, firms could break larger coupons into several smaller coupons that could all be used simultaneously. This provides consumers with agency and fosters exclusivity because not all consumers will do so (similar to [ 6]]). To enhance unintended value, a firm might release the coupons from different sources (e.g., retailer, credit card, manufacturer) at different times and have the coupons overlap only during a brief window of time. Firms might further consider adding restrictions to promotions that are then not enforced. While firms should advocate against lying to consumers, maintaining secrets is a common business practice ([20]; [35]), and creating an air of mystery and playfulness may stoke unintended value. Some retailers, such as Kohl's, routinely do this by allowing expired coupons to be used or nonstackable coupons to be used jointly. While consumer expectations are likely to adjust to anticipate this practice, it at least initially has the effect of making a consumer feel that they gained unintended value as well. Firms might even deliberately create opportunities for highly lucrative deals to create buzz. Ensuing media attention could then be used to reinforce mystery, celebrate successful consumers, and encourage further play and engagement.
We examined deal forums composed of engaged users trying to temporarily upend market power dynamics by coproducing promotional bundles resulting in unintended value. We may have oversampled core users and content providers (deal engineers and administrators), who highlight coordinated antagonistic coproduction. The vast majority of users who visit the site are unaware of the unintended value goal, seeking general price savings or bargains. We suspect that, as in [55], the forums we studied demonstrate resource interdependence such that the casual users and lurkers who visit the site each month support the highly engaged users' quest for unintended value. While the millions of more peripheral users are less motivated to hurt firms and less skilled at deal coproduction, the networked nature of deal collectives ensures that knowledge is rapidly disseminated among all users. This intensifies the damage caused by deals created by more experienced users, reflecting the megaphone effect ([33]). Future research should investigate the role and impact of different user profiles on the deals coproduced. We show that the deals with more user vetting and even simply more views increased the prominence of a given deal, but we did not explore this specific value nexus.
Furthermore, participants in the Hot Deals forums may be acutely interested in deals and in deal coproduction; they may be more willing to collaborate to achieve lower prices. Examination of more passive threads on these sites or others may reveal less active practices. We encourage exploration of these (perhaps more moderate) frugal consumer collectives. Deal forums permit a wide range of participation levels from entire deal construction to coproduction with other users to mere enacting of deals produced by others. While our investigation did not find evidence of differences in users' perceptions of deals directly related to collaborative deal coproduction, we recognize that existing research shows that consumers place additional value on coproduced items ([14]; [38]). Our findings suggest that the value created through coproduction is potentially transferable to other consumers, a phenomenon likely warranting further investigation.
Our findings raise several questions for future research to explore. First, the centrality of unintended value to our findings suggests a need for further research on the construct, the process through which it is generated, and its outcomes. This might include building knowledge on what might slow or limit consumers from enacting deals featuring unintended value. Research might investigate additional ways deals can be considered a mistake and, thus, unintended. More broadly, despite important work examining use value ([19]; [22]; [58]), our findings call for renewed consideration of exchange value as a driver of marketplace behavior and opportunity for coproduction.
Deal collectives are collaborative while also being competitive. Users of deal forums work together to achieve common goals, namely harnessing the wisdom of the crowd to coproduce lucrative deal opportunities. However, users can be at odds with other users. This is most apparent when the collective coproduces a deal that is limited in stock. One user's purchase may come at the expense of another's. Likewise, deals that are believed to exist because a firm is unaware of a mistake rest on the collective not informing the firm. If only a single user accidentally—or knowingly—informs the company, the entire deal is likely in jeopardy. Future research could examine the tension that these differing forces create, as well as the strategies created to attempt to overcome them.
Finally, deal collectives suggest that, in some cases, firms might enjoy better response to promotions by doing less straightforward advertising because a deal is likely to be viewed as more unintended if consumers, rather than a firm, are the ones who unearth and build it. This suggests a delicate symbiotic relationship between deal collectives and firms aiming to seed deal opportunities. Actively harnessing deal collectives is likely to be challenging and require managers who are strategic and able to harness the power of mystery. This runs counter to recent shifts toward transparency and may be difficult for some firms to operationalize. Ironically, the most authentic promotion may be one that a consumer feels should not actually exist.
Our findings reveal insights that develop and extend existing understanding of unintended value, market collectives, consumer coproduction, and promotions. We find that deal forums act as a micro-resistance network of deal-prone consumers engaged in collaborative actions with the aim of antagonizing firms and temporarily upending market power as they coproduce unintended value using firm promotions. Deal forums coproduce this value by identifying, improving, and vetting a range of promotional deals involving untargeted promotions as well as pricing and promotion errors, and by combining or stacking deals. We offer firms strategies for when and how to monitor, encourage, and discourage deal communities.
Footnotes 1 Associate EditorMarkus Giesler
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a Dean's Summer Research Funding Award, College of Business Administration, Kent State University.
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Record: 112- Lost in a Universe of Markets: Toward a Theory of Market Scoping for Early-Stage Technologies. By: Molner, Sven; Prabhu, Jaideep C.; Yadav, Manjit S. Journal of Marketing. Mar2019, Vol. 83 Issue 2, p37-61. 25p. 1 Diagram, 5 Charts, 1 Graph. DOI: 10.1177/0022242918813308.
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Lost in a Universe of Markets: Toward a Theory of Market Scoping for Early-Stage Technologies
This article examines market scoping for early-stage technologies, a fundamental yet underexplored marketing activity. Market scoping refers to managerial activities directed at the identification of market spaces for early-stage technologies. This discovery-oriented research aimed at theory development draws on an extensive, multiyear database of email trails and archival records detailing market-scoping efforts for early-stage technologies emerging from a global research university. From this longitudinal database, the authors provide an in-depth examination of managers' market space decisions and advance an initial theory of market scoping. They isolate managers' market-scoping mindset—which manifests as market ambiguity avoidance or acceptance—as a key explanatory construct shaping market space decisions and outcomes. Market ambiguity avoidance results in managers' downstream orientation toward end users; this mindset, counterintuitively, may lead to technology commercialization failure. In contrast, market ambiguity acceptance results in managers' upstream orientation; this mindset directs attention away from end users but helps uncover indirect paths to viable market spaces. This article lays the groundwork for advancing marketing research in the context of early-stage technology commercialization.
Keywords: early-stage technology; market ambiguity; technology commercialization; entrepreneurship; innovation
The ability to commercialize early-stage technologies that emerge from research labs at universities, public research organizations, and high-tech firms is a critical feature of technological progress and economic growth ([10]; [14]; [27]). Early-stage technologies face a unique, underexplored marketing challenge: they are associated with ambiguous market spaces. For example, Harvard University scientists have recently developed nanostructure meta-lenses that can focus the entire visible spectrum of light with extremely high resolution ([34]). Meta-lenses could revolutionize the design of many devices and can be linked to multiple market space options, such as smartphones, cameras, microscopes, and virtual reality applications. Although a patent application has been filed, the relevant market space for meta-lenses has yet to be identified. Jonathan Page, former director at Imperial Innovations, an influential technology investment firm, portrays the ambiguous market reality of early-stage technologies as follows:
Many [early-stage] innovations are of a "platform" nature. That is to say, they are not developed, linear fashion, into a specific product. Rather, they are innovations with a number of applications. It often takes a long dialogue with industry and with market researchers to reach the right application ([ 4], p.18, emphasis added).
The identification of market spaces (i.e., technology-to-market linkages that present new product development opportunities) for early-stage technologies is perhaps the most fundamental yet most elusive marketing competence for managers and firms in technology industries ([14]; [28]). Potential market spaces for early-stage technologies can span multiple industries, offering varying prospects characterized by much uncertainty. Moreover, adverse market space decisions often result in significant resource misallocations and commercialization failure ([27]; [27]). Indeed, technology managers and industry collaborators face a dilemma: they must focus on a market space to move technology projects forward ([49]) but retain flexibility by considering multiple market space options ([44]).
The purpose of this article is to unpack the "long dialogue" between managers and industry entities to shed light on market space identification processes for early-stage technologies. We refer to these processes as "market scoping." In line with similar research efforts in marketing focused on underexplored phenomena (e.g., [ 9]; [13]; [60]), we employ a discovery-oriented, theory-building approach to lay the conceptual groundwork for an important yet neglected substantive area in marketing ([62]). A key insight is that managers' downstream focus on end users, while well-intentioned, may result in an illusory sense of direction in situations of market ambiguity. Such a focus may create a false sense of progress and may increase the risk of failure. In contrast, we find that managers' attention to upstream industry entities—away from end users—can reveal new, indirect paths to markets and may improve the chances of success.
To put these insights in context, it is worth noting that the marketing literature places a strong emphasis on downstream, end-user phenomena. Although marketing has embraced technology and innovation management as important areas of study (e.g., [ 7]; [10]; [63]), the field is curiously silent about the early stage of technology evolution. Few studies focus on phenomena associated with ambiguous market spaces where potential end users may be unknown or difficult to identify. Scholars have directed attention to market learning processes in new product innovation (e.g., [16]), but these occur after relevant technology-to-market combinations have been established. As a result of the field's continued downstream focus, extant theory is conceptually at odds with the market reality of early-stage technologies.
The innovation and entrepreneurship literature streams have recognized the challenges that early-stage technologies pose, but market-scoping processes are not well-theorized. Prior research largely views market spaces as exogenously given entities. As a result, prior research neglects the role of managerial processes and instead focuses on technological ([42]), industry ([23]), and firm characteristics ([21]). Pertinent work on boundary-spanning search ([51]), networks of learning ([47]), and market opportunity formation ([27]) assumes relevant search spaces, learning networks, and market knowledge to be identifiable, accessible, or available ex ante. Yet it is not well understood what managers actually do when search landscapes are vast and undefined, industry networks are weak or absent, and market knowledge is limited. Furthermore, studying managerial processes that shape market spaces for early-stage technologies is challenging because "researchers lack access to information about inventions...prior to the initiation of the commercialization process" ([42], p. 1156). In other words, it is difficult to obtain rich, real-time process data that reveal market space decisions that are made before technology commercialization attempts are initiated, prior to market-specific technology development.
We aim to address these gaps in prior research by ( 1) developing an initial theoretical understanding of market scoping for early-stage technologies and ( 2) identifying the characteristics of effective and ineffective market-scoping approaches. Our theory development effort is informed by insights obtained in the context of university technology transfer. This setting is a hotbed for early-stage technologies that are partially codified, postlab, and lack commercial validation. Thus, market ambiguity is endemic, and effective market scoping is crucial for successful outcomes. Our overarching objective is to develop a theoretical framework that can lead to theory-testing endeavors and identifies new research opportunities in marketing. We develop our discovery-oriented research by drawing on an in-depth study of early-stage technologies managed by the technology transfer office of a global research university. We assemble a unique longitudinal database of email trails and archival records, spanning several years, to develop granular insights into the focal phenomenon. Email trails offer an unprecedented window into the nature of market scoping, narrated by major stakeholders involved in technology commercialization.
The conceptual framework that emerges from our discovery-oriented approach (see Figure 1) has the potential to open new avenues for marketing research in the context of early-stage technology commercialization and, more broadly, in the field of marketing under uncertainty (e.g., [13]; [41]; [48]). Specifically, we make the following contributions to the literature. First, we establish the domain of the market-scoping construct and contextualize it in the broader literature. Second, we introduce the concept of the market-scoping mindset, which refers to managers' preferences regarding the resolution of market ambiguity. This mindset manifests as market ambiguity avoidance (i.e., preference for an early resolution of market ambiguity) or market ambiguity acceptance (i.e., preference for a gradual resolution of market ambiguity). Third, we show how market ambiguity avoidance and acceptance shape market space decisions and outcomes. Overall, our evidence and theorizing suggest that market ambiguity avoidance—though focused on end users—is more likely to result in failure in the context of early-stage technologies. In contrast, market ambiguity acceptance—even though it initially draws attention away from end users—may ultimately help managers establish more viable market spaces. Our insights advance theory development in several areas, such as effectuation theory ([54]), boundary-spanning search ([51]), networks of learning ([47]), and market opportunity formation ([27]).
Graph: Figure 1. A conceptual model of market scoping for early-stage technologies.Notes: The dashed boxes and arrows present constructs and relationships that are not explicitly theorized in this article (they are outside the scope of the proposed conceptualization). We depict them for nomological comprehensiveness and to illustrate links with extant theoretical work in this area.
This article is structured as follows. We begin by describing the conceptual context of market scoping and our discovery-oriented theory development approach. Next, we develop our theory of market scoping along with a set of research propositions and discuss implications for research and practice. We end with an overview of key concepts and their operationalization and offer an agenda for future research on market scoping.
In this section, we elaborate on an initial set of constructs to establish a theoretical backdrop for our subsequent discussions. Subsequently, we provide an overview of all definitions employed in our theory development effort.
Early-stage technologies represent nascent scientific and/or technical knowledge that is postlab and partially codified. Such technologies are generally based on the identification of novel physical, mechanical, chemical, or biological principles that have been demonstrated in a laboratory setting ([ 3]). However, there is a lack of clarity about technology behaviors and attributes pertaining to potential commercial application settings (this corresponds with technology readiness level 4 as defined by NASA; see [37]).
This definition of early-stage technologies has several implications. First, early-stage technologies are typically prepatent (i.e., a patent has not been filed or granted). Second, reflecting their scientific origins in labs, they usually present "technology-push" rather than "market-pull" innovations ([40]). Third, early-stage technologies are fungible and transcend the boundaries of markets and industries ([27]; [46]; [55]). They can be altered and integrated with other technological knowledge to be transformed into market-specific technologies embodied in new products ([14]; [22]).
Given early-stage technologies' fungibility, managers have to identify relevant market spaces for them. We define market space as a set of technology-to-market linkages that present new product development opportunities for a focal early-stage technology. A technology-to-market linkage refers to the combination of technological knowledge with information about market demand ([27]). Successful technology-to-market linkages thus enable market-specific technology and product development. Market ambiguity refers to the lack of clarity about the nature, number, and commercial viability of potential technology-to-market linkages. For example, scientists believe that nanostructure meta-lenses (mentioned previously) may significantly affect the design of multiple devices. However, it is unclear how exactly meta-lenses could be applied to such devices, if there are other potential technology-to-market linkages, or if products developed for specific technology-to-market linkages will be profitable. Thus, market ambiguity can be distinguished from market uncertainty, which refers to the difficulty of predicting specific outcomes (e.g., consumer demand) for a given technology-to-market combination ([53]).
Market scoping refers to the set of managerial activities directed at the identification of the market space for a focal early-stage technology. Market scoping can thus be viewed as part of the lengthy process that is involved in converting emerging technologies into commercialized new products ([10]). Table 1 shows the different stages of this conversion process, including relevant variables and managerial activities discussed in the literature.
Graph
Table 1. Overview of the Literature on Converting Technologies into Innovations: Research and Marketing Competence Gap.
| Technology Emergence | Market Scoping | Market-Specific Product Development | Innovation Diffusion |
|---|
| Technological characteristics | "Technology-push" discovery or "market-pull" development
| Technology fungibility Technology maturity Technology codification
| Technology originality Technology generality Technology complexity Technology value
| Technology radicalness Technology disruptiveness Innovativeness New product design
|
| Market and industry characteristics | R&D intensity Networks of learning
| Technology markets
| Patent effectiveness Patent scope Market heterogeneity Opportunity riskiness Technology frames and dominant designs
| Market uncertainty Market dynamism Technical dynamism Market novelty
|
| Individual and firm characteristics | Entrepreneurial culture Absorptive capacity
| Prior knowledge Alertness
| Firm size Complementary assets
| User adoption criteria
|
| Managerial activities | Exploratory search Fuzzy front-end management
| Research gap and marketing competence gap | Market experimentation Market learning Market visioning New product development process organization
| Market-driving behaviors Market priming Market pioneering User cocreation Market creation Boundary construction
|
| Exemplary studies | Etzkowitz (1998), March (1991), Rosenkopf and Nerkar (2001), Cohen and Levinthal (1990), Powell, Koput, and Smith-Doerr (1996) | Danneels (2007), Shane (2000), Gruber, MacMillan, and Thompson (2008), Kirzner (1997) | Dencker and Gruber (2015), De Luca and Atuahene-Gima (2007), Lynn, Morone, and Paulson (1996), Nerkar and Shane (2007), Shane (2001) | Humphreys (2010), Jaworski, Kohli, and Sahay (2000), O'Connor (1998), Rogers (1976), Santos and Eisenhardt (2009) |
Prior research has examined factors that may explain the success of managers' technology commercialization efforts. This research suggests that the breadth and diversity of managers' technology and market knowledge raises their awareness of relevant technology commercialization opportunities ([27]; [55]). However, scholars have also argued that overreliance on existing knowledge endowments may result in "familiarity traps" because managers may overlook opportunities outside their knowledge corridors ([ 1]). This ties in with the research on boundary-spanning search (e.g., [51]). Such search patterns can push organizations beyond familiar knowledge domains and improve innovation outcomes. Another research stream focuses on networks of learning in technologically dynamic environments (e.g., [11]; [47]). This literature suggests that access to interorganizational networks that offer complementary technology assets facilitates the commercial exploitation of emerging technologies.
Despite these research efforts, a significant gap in the literature is the lack of attention to managerial agency in the identification of commercialization opportunities (see Table 1). Managerial agency, in this context, refers to managerial dispositions and preferences about how to engage with the external environment. Different manifestations of managerial agency orient managerial decision making very differently ([ 8]). The importance of managerial agency is acknowledged by the emerging realist perspective on commercial opportunities ([ 2]). This perspective suggests that market spaces are not exogenously given but cocreated, shaped by the subjective preferences and resource inputs of the actors involved ([48]; [54]). However, research that provides satisfactory, fine-grained details about managerial agency is scant. As a result, the current understanding of managerial preferences and decisions related to the identification of market spaces for early-stage technologies is limited. We now describe how this article seeks to address this critically important research gap.
This research is based on a discovery-oriented, multi–case study approach aimed at theory building ([19]). Multiple cases permit a replication logic, with each case serving to confirm or disconfirm inferences drawn from others ([64]). Our research follows theory development efforts in marketing that have focused on understudied phenomena (for recent examples, see [ 9]], [13]], and [60]]). The primary focus of such efforts is to develop a conceptualization that can spur subsequent theory-testing efforts. Web Appendix A provides a detailed overview of our methodological approach and decisions.
We chose university technology transfer as the context for our theory-building efforts. University labs are an important source of early-stage technologies. The technologies that emerge from these labs are typically characterized by significant market ambiguity ([12]). Nevertheless, many universities and scientists are eager to commercialize their technological discoveries ([17]). We study early-stage technologies emerging from one of the world's leading research universities (hereinafter, University). The University is a recognized leader in the systematic commercialization of university technology. Since 1995, spinoff companies have attracted private investments worth more than $1.7 billion.
This substantive context, combined with our research protocol, minimizes interproject variation in terms of ( 1) differences in team quality and ( 2) differences in technology quality and maturity (see Web Appendices B and C). First, all teams are staffed with a junior and a senior technology transfer manager, with expertise and experience in a pertinent technology field, while a member of the executive management team oversees project progress. This ensures a homogeneous level of commercialization expertise across projects. Second, the focal technologies are at a precommercial and prepatent stage when accepted by the technology transfer office and undergo a similar, rigorous selection process. The uniform application of this selection process increases the homogeneity of focal technologies' quality and maturity.
We used theoretical sampling to identify the focal technologies ([26]). Case selection aimed to uncover the relevant spectrum of market-scoping preferences, market space decisions, partnerships and external inputs, and market-scoping outcomes. Exploratory interviews with technology transfer managers helped us identify 29 technology projects. By applying a set of selection criteria (see Web Appendix A), we arrived at our extended case sample of 12 technology projects. These projects were at a comparable (early) stage of development, associated with an ambiguous market space, lacked formal industry ties, and received a similar level of marketing support (see Web Appendix C). At the same time, we ensured heterogeneity regarding certain project characteristics, such as the technology field, the route to market, and market-scoping success. This variation enables a firmer grounding of the emerging theory ([29]). From this extended case sample, we first performed exploratory analyses to develop an initial understanding of the phenomenon. This was followed by an in-depth examination of the focal case sample: six technology projects that allowed us to reach theoretical saturation. Our approach adheres to the recommended sample size of four to ten cases for theory building ([19]; for exemplars in marketing, see [13]] and [24]]).
The lead author collected data on-site over a period of 20 months. He was provided with office space, and University staff members were aware of his role as a researcher. He had complete access to project-level archival records and email data that revealed substantial details of managerial work at the University's technology transfer office. Formal and informal interviews helped triangulate our insights. Table 2 provides details of the various types of data for the six technology projects.
Graph
Table 2. Case Data.
| Case # | Nature of Technologya | Project Outcome | Email Trails | Archival Documents | In-Depth Interviews | Informal Interviews | Group Meetings | Observation Time Frame |
|---|
| Case 1 | Synthetic material | Abandoned | 3,834 | 488 | 2 | 45 | 4 | June 2009–September 2013(52 months) |
| Case 2 | Sensor technology | Codevelopment partnership | 972 | 92 | 1 | December 2009–September 2013(46 months) |
| Case 3 | Optical filter technology | Licensed | 2,756 | 406 | 1 | November 2003–November 2009(73 months) |
| Case 4 | Surface preparation technique | Spinoff company | 2,633 | 314 | 1 | May 2010–September 2013(41 months) |
| Case 5 | Chemical substance | Abandoned | 1,560 | 210 | 1 | April 2005–September 2010(78 months) |
| Case 6 | Medical device | Abandoned | 847 | 356 | 2 | July 2009–January 2013(43 months) |
| Total | 12,602 | 1,866 | 8 | 45 | 4 | 333 months |
- 10022242918813308 a The nature of the focal technologies has been disguised to prevent technology identification.
- 20022242918813308 Notes: This is an overview of the data for the focal case sample, which we used to reach theoretical saturation. We identified the focal case sample from the extended case sample of 12 technology projects. For additional details, see Web Appendices A and D.
We obtained access to complete archival records for the six focal technologies. In total, we collected 1,866 project documents (for details about the types of project documents, see Web Appendix A). In addition, the University provided us with the complete email correspondence for the six technology projects involving all project stakeholders, including technology transfer managers, University inventors, consultants, investors, industry representatives, and so on. In total, we used 12,602 email trails.
Using email records has several benefits. First, emails (with date and time stamps) provide a rich, real-time contextualization of archival data. Technology transfer managers, inventors, and external stakeholders discuss market-scoping preferences, decisions, and outcomes over email in real time on a regular basis. In addition, to keep team members informed, managers summarize important talking points and project decisions made in meetings and phone calls over email. Thus, emails provide a unique window into the focal phenomenon and help us overcome the data limitations of prior research. Second, emails minimize interviewer and interviewee biases. They validate the information from archival documents and rule out potential biases resulting from, for instance, retrospective biases, memory lapses, and impression management.
After studying the archival and email data, we conducted eight formal in-depth interviews with the six senior technology transfer managers responsible for the focal technologies. In addition, the lead author also conducted 45 informal interviews with a variety of staff members (for details, see Table 2).
Fine-grained case chronologies for the focal case sample formed the basis of data coding (for details, see Web Appendix A). The unit of analysis throughout is the project team. We used the following coding procedures to develop our theoretical model ([59]): ( 1) open coding to identify basic themes in market-scoping preferences and decisions; ( 2) axial coding to integrate these basic themes into higher-order, theoretically distinct market-scoping preference and decision categories; and ( 3) selective coding to identify the core explanatory construct: the market-scoping mindset, which manifests as market ambiguity avoidance or acceptance. Table 3, Panels A and B, illustrate our coding structure, including illustrative data excerpts for each code; these tables show how we derived distinct theoretical concepts from our data. Overall, our analytical approach is similar to that used by [53], who longitudinally tracked managerial decision making through extensive archival data.
Graph
Table 3. Coding Structure
| A: Market Ambiguity Avoidance and Subsequent Decisions | |
|---|
| First-Order Coding Categories and Representative Quotes | Second-Order Coding Categories |
|---|
Preference for a rapid resolution of market ambiguity:"We discussed the benefits of identifying a well targeted, focused application that is of the right scale and value to be accessible to this technology fairly rapidly. I think that is the right approach for you." "I wonder if we could put together a tire inflation project where we try and develop a pressure sensor application for the [technology]...we had talked about this previously. It might be a good step to have a quick win." Prioritizing market-specific inputs:"I think we should go full steam ahead on the study idea. It would be great to have some [industry] data on accuracy of drug dosage, reliability (failure rates), time to administer the drug, etc." "There are a couple of areas we need to think about—The objective/scope and plan of the project...access market insights to determine most relevant applications (size/value, attractiveness of [the technology])." Preference for partnerships based on immediate market interests:"[The company] works with the major car manufacturers and as a starting point, I would like to gauge the interest that one of these potential customers might have in [the technology] as a precursor to developing a deeper dialogue." "[The company] appears in every way to be an ideal company to develop [the technology].... They have become interested in the vaccination market...and they already have an internal development project...and made the comment that our [technology] was simpler and better!"
| Market ambiguity avoidance |
Identifying downstream end-use situations:"[The consultant] identified a few opportunities which are worth further investigation. Perhaps the most promising one is the use of [the technology] to reduce lime-scale buildup in heating systems." "I mentioned the idea of using the [technology] to indicate correct inflation pressure of car tires....I also shared the idea of using the [technology] to indicate uneven wear of the tire surface."
| Downstream market space anchoring |
Understanding relevant user benefits:"I wonder if there is another way we should continue exploring how [the technology] might benefit [the company's] business. Beyond the use of [the technology] for anti-corrosion we can see real value in using [the technology] for material reinforcement in the construction sector." "The first half is aimed at...gaining clarity of the benefits and status of the technology....We will be focusing very much on its functional benefits rather than the underlying technology." Increasing the specificity of identified use situations:"In parallel we would like to put together a straw model of the potential market—how many groups, with what characteristics, in what hospitals." "I conducted some top level market research around a proposition in jewelry...primarily for two reasons: possibility for early revenue generation and ability to charge a high-price."
|
Understanding user requirements and expectations:"They have asked if it would be possible to treat a 40 cm length of ceramic membrane with an internal diameter of 1.5 mm and a pore size of 0.2 micron." "Please find below inputs from [the company]: 1) Overcost: 0.5 Euros max is the target. 2) Washing: 40°C (washing machine), no chlorine or softener. No tumble dryer. Final product will be certified for 30 washings."
| Confirmatory market space substantiation |
Assessing the feasibility of use situations:"The trial run demonstrated two minor issues. 1) The label looks like it can bend up but it probably isn't worth the risk of breaking it. 2) The pump doesn't fit air tight with the leur as an artifact of the vacuum molding." "The good news is the [materials] retain their properties after a 30 deg C standard wash cycle (see second attached pic). The not so good news is they fell off the leggings, I think a little swelling occurs." Narrowing down the relevant market space:"You mentioned the test you performed on [the technology] produced interesting results....We had a long list of applications and have now narrowed it down, so we ended up with this short list." "My view is that [external discussions] should focus on medical textiles and construction applications. These opportunities represent more direct opportunities than automotive and e-textile applications."
|
Formalizing end-use-specific fields of use:"The only way that I can see moving forward is for us to be given an exclusive option 'in the field,' for an anticipated exclusive license 'in the field'...so that it is clear that we have been given unpolluted rights in the hematology diagnosis area, for the particular commercial areas stated." "If this approach is acceptable to [the potential licensee], would a Field restricted to 'thrombosis prophylaxis' be sufficient for your needs?"
| Closed-ended market space claiming |
Establishing direct access to end-user markets:"My hypothesis was that a technology such as yours could form the basis of a high margin, but initially low volume business selling high priced, visually stunning goods directly to the consumers." "I believe [the company] would be a useful partner both for manufacturing, and to get access to their customers in the automotive sector." Establishing clearly defined, definite market spaces:"It is not in our interests to have them having effective control in a too large field....[The company] has a position in fiber-optics and this is the field that we should restrict them to." "We now have several companies that are interested in [the technology], and, if appropriate, we would prefer to license this technology exclusively to each company in their particular field of interest."
| |
| B: Market Ambiguity Acceptance and Subsequent Decisions |
| First-Order Coding Categories and Representative Quotes | Second-Order Coding Categories |
Preference for a gradual resolution of market ambiguity:"We believe the development time necessary to bring [the technology] to market may be somewhat extended....However, we remain interested in exploring...appropriate areas for commercial exploitation." "We discussed possible future developments. At the moment I am viewing this [technology] quite positively....However, it has to be said that although there are some exciting possibilities for the future, at the moment there is no guaranteed market." Prioritizing technology-specific inputs:"I have a number of issues that I would like to explore with you: What do you consider to be the main limitations of the technology...? Are there any specific manufacturing issues associated with the various functional attributes of the [technology]? What do you see as the main competing technologies?" "Please keep the presentation short and focused on [the technology]. The idea was that they get a better feeling for the nature and capabilities of the technology and at the same time share more specific technical requirements." Preference for partnerships based on technological learning:"We have identified the possibility for an excellent joint project, which is actually more on the theory side: modeling how light is extracted from an emitting device using nanoimprint lithography....I feel that such a [project] would be 50% producing real results for [the company], and 50% pushing in new directions." "In view of the many possible applications..., we are very interested in taking discussions on this forward. [The inventor] mentioned that he could devise a research program...that could form the basis for commercial exploitation of the technology. We would be very interested in supporting such a program."
| Market ambiguity acceptance |
Identifying upstream technology regimes:"[Similar technologies] have a recognized application in a broad range of technology fields. Specific examples where [the technology] can meet a technology need are in...inorganic electrodes and metallized electrodes." "You will also meet with [a university professor] on Thursday....What it would be interesting to know from him would be how your invention will apply in semiconductors."
| Upstream market space anchoring |
Identifying relevant technology benchmarks:"As discussed a key first step is to characterize [the technology's properties] and benchmark them against other existing and established technologies." "I am happy to let you know that our team is really enthusiastic about your invention....They would like to have a feeling of how your invention compares with the current technological standards." Clarifying technological contribution to existing technological knowledge:"We showed [the corporate researcher] our images and he was astonished since he did not know of polymer based multilayers as we produce. This ties into your initial assessment of the unique layered quality." "There has been little technology centered on changing the plant to be easier to for the enzymes to break down and this is exactly where [the inventor's] technology fits.
|
Leveraging external technology assets:"[The company] clearly has manufacturing skills and equipment relevant to us...extruding, wet chemistry, film thinning, laminating, rolling and surface treatment." "This [project] aims to bring together both key IP within the field, and the know-how of the partners, providing a strong base for exploitation of the technology."
| Exploratory market space substantiation |
Exploring technology capabilities:"We are quite busy in the lab looking to demonstrate some properties of the [technology]; typically these go along the lines of replacement of the structures we create with others such as ceramics." "[The engineer] described how he had selected several substrate materials for their first trials and we talked in detail about their [tool] design. Yesterday's discussions were totally focused on technical aspects." Opening up the relevant market space:"Filling the scaffold [with a structural metal] creates a high surface area electrode. That high surface area could be used in dye-sensitized solar cells or in organic photovoltaics or even conventional solar cells." "If pilot tests prove to be efficient then [the technology] can be suitable for use in water pre-treatment applications."
|
Formalizing technology-specific fields of use:"I wonder if there is scope to claim a very wide patent in the region of 'polyelectrolyte multilayers'? Or at least on 'layer-by-layer deposition'?" "In particular could you think carefully about the Field....Presumably the field of interest to [the potential codeveloper] is 'electrochemical sensors'."
| Open-ended market space claiming |
Establishing technology platforms:"We also would like to make every effort to move this forward because [the company] have indicated that success in the LED lighting field could lead to further development and licenses for other lighting fields. We feel that this is a platform license that could lead to other commercial opportunities." "Attached an outline summary for the business plan: It is estimated that the annual market for water filtration could be worth $4 billion by 2020 with numerous potential applications in various industries." Establishing openly defined, extendable market spaces:"I think that we should say we are looking to license ideally with a broad field to a single partner....I think an option structure may be worth exploring to expand the scope to all therapeutic areas....This avoids us being tied in from day 1 and should provide [the licensee] with enough security." "[The company] is interested in this as an opportunity and clearly imagine potential markets for [the technology]. I do not think there is any doubt that [the technology] ... can be adapted to/configures for and sold into a great number of market segments."
|
We checked the reliability and validity of our findings in several ways. First, we discussed our emerging findings, including labels and definitions, with the University's technology transfer managers in four one-hour meetings. We used their feedback to refine our emerging framework. Second, three independent judges verified the themes we identified in the email data (see Web Appendix A). The interjudge reliability, calculated by the proportional reduction in loss method, was.85, well above the.7 threshold for exploratory research ([52]).
Finally, we conducted an exploratory computer-aided text analysis of email data based on our extended case sample (12 technology projects). This analysis helps triangulate and guide the overarching theme emerging from our in-depth examination of the focal case sample (6 technology projects): that managers' market-scoping mindset shapes performance outcomes. Figure 2 depicts relative frequencies of the five most frequently used market-specific keywords. Out of the 12 projects, cases 1 and 5 (failures) display the strongest emphasis on target markets, which suggests that the teams avoided market ambiguity. Cases 2 and 4 (successes) display the weakest emphasis on target markets, which suggests that the teams were willing to accept market ambiguity. Cases 3 and 6 lie in between these two extremes (with mixed outcomes). These exploratory trends provide face validity regarding the potentially significant role played by the market-scoping mindset.
Graph: Figure 2. Relative frequency of target-market keywords in email communications and project outcomes.Notes: For each case, we identified the most frequently used keywords in email interactions that reflect case-specific target markets (e.g., "packaging," "biofuels," and "confection" matched with the target markets we identified in our qualitative analysis). We then calculated weighted percentages of the frequencies of these keywords (including words with the same stem). We focused our analysis on the five most frequently used keywords per case; the sixth keyword's weighted frequency was negligible (weighted percentage of.05% or lower). These keyword frequencies indicate the attentional emphasis on target markets during market scoping. For additional details, see Web Appendices A and D. The boldfaced columns represent the six technology projects of our focal case sample that we used for theory building.
Market scoping represents a set of managerial activities directed at the identification of the market space for a focal early-stage technology. The identification of market spaces involves a variety of managerial preferences and decisions related to the search, assessment, and selection of potential market opportunities. Market scoping specifies the domain of the market space by identifying distinct technology-to-market linkages. External partners (e.g., consultants, investors, corporate managers and researchers) provide critical inputs during this process: end-user ideas, performance standards, technology benchmarks, testing facilities, technical know-how, funding, and so on. Market scoping is thus an inherently collaborative activity, performed by project teams (technology transfer managers and inventors) together with external stakeholders. As such, market scoping is cocreational and effectual in nature ([ 2]; [54]).
Prior research has typically focused on technology characteristics such as value, disruptiveness, and patent scope (e.g., [23]; [42]; [56]) to explain commercialization success (see also Table 1). However, an early insight of our discovery-oriented research was that such characteristics may have limited explanatory power because they are only partially delineated in the context of early-stage technologies. Indeed, our data suggest that market-scoping efforts often facilitate a more complete understanding of an early-stage technology and its potential. The following statement, made by a member of the executive management team of the technology transfer office, illustrates the important role of market space decisions[ 5]:
What the value of a technology is cannot be answered until where it might be deployed is determined. Even so, often what looks like a great leap forward rapidly dwindles to incremental or worse upon a little development.
In subsequent sections, we present the insights from our discovery-oriented research. Figure 1 provides an overview of the derived theoretical model and identifies the key concepts: market-scoping mindsets, market space decisions, and market-scoping outcomes.
A key insight emerging from our discovery-oriented research is that managerial agency manifests as different market-scoping preferences, which subsequently shape market space decisions very differently. We refer to managers' preferences pertaining to the identification of market spaces for an early-stage technology as the "market-scoping mindset." This mindset reflects managers' priorities and attentional emphases when engaging with the external environment. A project team's mindset can thus be viewed as comprising the collective preferences of its constituent members. In all subsequent discussion, we use the project team as our unit of analysis.
As we started exploring the extensive email data, it became evident that there was considerable variance in terms of teams' preferences regarding, for example, the nature and breadth of the search landscape, the immediacy of the market space, and the desired form of value the technology may provide. Underlying this heterogeneity is a distinct set of market-scoping preferences regarding the resolution of market ambiguity. Specifically, this mindset toward market ambiguity is based on three interrelated preferences regarding ( 1) the speed of market ambiguity resolution, ( 2) the market (vs. technology) specificity of external inputs, and ( 3) the type of partnerships. The mindset ranges between two extreme anchors shaped by the preferred speed of market ambiguity resolution: rapid or gradual resolution of market ambiguity. These anchors are closely associated with specific external input and partnership preferences reflecting teams' attentional emphases. For expositional purposes, we label these anchors as "market ambiguity avoidance" and "market ambiguity acceptance," respectively. Next, we discuss the distinctive managerial preferences associated with these anchors.
Under market ambiguity avoidance, teams prefer a priori market space representations that guide all market-scoping activities. Specifically, this mindset is based on the following manifestations of market-scoping preferences. First, teams prefer a rapid resolution of market ambiguity; that is, they prefer early market space clarity. The following statement, made by a project manager at the beginning of a technology project, demonstrates this preference for early market space clarity:
Project manager: The initial markets include automotive applications (interior fittings), coatings (for material longevity), medical applications (compression stockings), and construction (strain indicators). Early contacts from >25 companies indicate a strong appetite for engagement in these markets and market scoping is now essential.
Second, to achieve early market space clarity, teams prefer market-specific inputs from the environment, such as end-use ideas, end-user benefits, and other market characteristics such as size and growth. The following email demonstrates a project manager's effort to gather market-specific information:
Project manager: From this marketing study we are looking to define the following: ( 1) Establish a wide range of potential market applications. ( 2) Segment and prioritize the applications according to the total addressable market size, industry segmentation, barriers to entry/adoption and readiness of the technology for the application. ( 3) Establish realistic costs and market prices for the final product variants.
Third, teams prefer partnerships based on immediate market space interests to ensure early market space clarity. Such partnerships are typically driven by a "logic of consequences" ([30]; [39]): partners pursue a strictly utilitarian payoff orientation. This logic is reflected in the following statement by a corporate manager:
Corporate manager: I met [the inventor] at a conference today in Pittsburgh and was very impressed by the [technology] which he discussed in his presentation. We have a substantial business in the vaccine market and there is certainly some kind of fit here with the products which we already make and sell. We certainly have a lot of experience in making vaccine delivery systems which are cheap and effective, and which can be made reliably in quantities of hundreds of millions.
Under market ambiguity acceptance, teams prefer to discover the market space over time during the market-scoping process. They are willing to delay the identification of specific market spaces and make an effort to avoid market space preconceptions. Specifically, this mindset is based on the following manifestations of market-scoping preferences. First, teams prefer a slow and gradual resolution of market ambiguity. They leave market ambiguity unresolved and accept that the market space may have elements of indeterminacy. The following negative response by a project manager to a consultant's desire for early market space clarity illustrates this preference for a more gradual resolution of market ambiguity:
Consultant to project manager: I would really like to get out there and explore the water filtration markets. I appreciate that you had not anticipated starting any market investigation, however, I am anxious that we should not miss the opportunity.
Project manager to inventor: His enthusiasm is a good indicator—but I am a bit shy of proceeding as he describes....I'd prefer to hold off for a couple of months. I will write a suitable reply to slow him down a bit and keep all options open.
Second, teams prefer technology-specific (rather than market-specific) inputs to further explore the nature of the focal technology. For instance, in the following quote, a manager emphasizes the need for further technological inputs:
Project manager: Do we know what are the processing properties of [the technology], e.g. processing temperatures, thermal and chemical stability, possible substrates, scaling-up problems, etc.? You need to know this before you can even start thinking about possible markets....You need to understand really well what your [technology] "can do" (rather than "may do").
Third, teams prefer partners who are interested in collaboration for the sake of technological learning. Such relationships are typically based on a "logic of appropriateness" ([30]; [39]): partners follow norms matched to the needs of a specific situation (e.g., market ambiguity in the case of early-stage technologies). This logic is illustrated by the following email statement:
Corporate manager: In fact I'm more interested in collaborating for interest in the journey and what we might learn together, rather than any particular end product. With respect to your [technology], an end product in itself is not particularly relevant to us since we don't make that kind of product; this would be for other companies.
A key insight that undergirds our proposed conceptualization relates to the market-scoping mindset. Specifically, our data suggest that this mindset ranges between two extremes: market ambiguity avoidance (preferring early market space clarity) and acceptance (remaining open to a gradual resolution of market space clarity). The market-scoping mindset appears to play a central role in shaping all subsequent activities aimed at securing external investment to enable technology commercialization. The causal explanatory role of the market-scoping mindset is thus featured prominently in our proposed conceptualization, as shown in Figure 1. Accordingly, we advance the following proposition:
P1: The market-scoping mindset for early-stage technologies manifests as market ambiguity avoidance or market ambiguity acceptance. This mindset drives market space decisions which, in turn, influence market-scoping performance.
The focal market-scoping mindset is reminiscent of the concepts of "ambiguity intolerance," defined as individuals' perception of ambiguous situations as a threat ([ 6]), and "need for closure," defined as individuals' desire for a firm answer to a question ([61]). Both concepts represent personality traits. The market-scoping mindset, in contrast, refers to a set of specific managerial preferences pertaining to the phenomenon of market ambiguity. These preferences are likely shaped by prior professional experience, but personality traits may play a role too. Hence, ambiguity intolerance and need for closure may be potential antecedents of the mindset—an issue that we revisit in the "Discussion" section.
In the next sections, we use our discovery-oriented analysis to examine how teams' market-scoping mindset shapes market space decisions. We focus on three major types of market space decisions that we found across all technology commercialization projects:
- First, to develop an early sense of orientation in an ambiguous market space, project teams perform market space anchoring activities. These aim to identify an initial set of market space options suggested by potential use situations or similar technologies, through search activities and by initiating industry contacts.
- Second, to assess the validity of the potential market space, project teams carry out market space substantiation activities. These aim to demonstrate technical feasibility, through tests and experiments, to develop technically robust market space options.
- Third, to formally establish the desired market space, project teams engage in market space claiming activities. These aim to formalize the nature and scope of the developed market space options. Specifically, they aim to formally secure the developed market space options for commercial exploitation by specifying "fields of use" in commercial agreements. Fields of use identify the areas of commercial exploitation assigned to external commercialization partners.
Next, we begin by showing how market ambiguity avoidance and acceptance influence market space anchoring, substantiation, and claiming, as well as performance outcomes. Table 4 summarizes these differences and the conceptual insights from our analysis. Table 3, Panels A and B, report illustrative data excerpts for the underlying constructs. Our analysis is also guided broadly by the trend depicted in Figure 2, which suggests that market ambiguity avoidance and acceptance may work very differently during technology commercialization efforts. We rely on data excerpts from cases at the two ends of the mindset continuum (cases 1, 2, 4, and 5), where the consequences of the mindset and its relationship to outcomes are more clearly revealed. We also use data from the two cases that lie in the middle (cases 3 and 6), at the threshold of project success and failure (see Figure 2).
Graph
Table 4. Conceptual Properties of a Theory of Market Scoping for Early-Stage Technologies.
| Market Ambiguity Avoidance | Market Ambiguity Acceptance |
|---|
| Market-scoping mindset | Preference for a rapid resolution of market ambiguity Preference for market-specific external inputs Preference for partnerships based on immediate market interests
| Preference for a gradual resolution of market ambiguity Preference for technology-specific external inputs Preference for partnerships based on technological learning
|
| Market space anchoring | Downstream anchoring:Identifying downstream end-use situations and scenarios Understanding relevant end-user needs and benefits Increasing the specificity of identified use situations
| Upstream anchoring:Identifying upstream technology regimes underlying various downstream markets Identifying relevant technology benchmarks Clarifying contributions to existing technological knowledge
|
| Market space substantiation | Confirmatory substantiation:Understanding user requirements and performance expectations Assessing the feasibility of identified end-use situations Narrowing down the relevant market space
| Exploratory substantiation:Leveraging external technology assets and capabilities Exploring the focal technology's capabilities Opening up the relevant market space
|
| Market space claiming | Closed-ended claiming:Formalizing narrow, end-use-specific fields of use Establishing direct access to end-user markets Establishing clearly defined, definite market spaces
| Open-ended claiming:Formalizing broad technology-specific fields of use that transcend end-use scenarios Establishing technology platforms that may serve multiple markets Establishing openly defined, extendable market spaces
|
| Market-scoping performance outcomes | Lower value appropriability Fewer technology commercialization opportunities Delayed initial investment Less efficient resource allocation
| Higher value appropriability More technology commercialization opportunities Faster initial investment More efficient resource allocation
|
Market ambiguity avoidance is associated with the downstream anchoring of market spaces. This manifests in the following ways. Project teams aim to identify and specify distinct downstream end-use situations and scenarios. For instance, they employ application brainstorming workshops, online research, consultation of industry mentors, and other search activities to develop an initial list of potential end-use scenarios:
Project manager: Regarding the list of industrial applications I rather haphazardly suggest the following:...equipment used in the handling of gases, liquids, solid particulates or mixtures, such as in the food, chemicals and pharmaceuticals industries;...glass, such as in windows, laboratory equipment, visors and screens; ...cooking utensils;...water-borne machinery such as boats, surf-boards, submarines and oil platforms.
Teams aim to establish partnerships with downstream industry entities, such as end users or manufacturers of end-user products. Such downstream partnerships offer market-specific inputs that help achieve the preferred early market space clarity. Specifically, they help clarify market needs and user benefits relative to distinct end-use scenarios:
Consultant: We will use this market feedback to consolidate best ideas into the top 3 to 5 idea types. We will then identify organizations that represent good commercial examples of "users" in these areas and begin the process of early stage enquiries. These discussions will not be under NDA [nondisclosure agreement] (at this stage) and will be limited to "here is our expected performance benefit, how attractive to you is that prospect?" We will not discuss technology.
Furthermore, partnerships with downstream industry entities help add further details to the potential end-use scenarios whereby teams increase the specificity of market space options:
Hospital physician: I think [the device] would be particularly good for drugs provided in a sterile tray. You could have one for saline too, which is normally added to the tray separately in a way that I think is rather prone to introducing infection. As there are two different solutions on the tray (standard 10 ml and loss of resistance), it would be possible to have custom made [devices].
Market ambiguity avoidance is associated with the confirmatory substantiation of market spaces. This manifests in the following ways. Project teams, together with downstream partners, aim to confirm (or disconfirm) the feasibility of the identified end-use scenarios. To this end, they develop samples and prototypes for technology tests and evaluations:
Project manager: [We] awarded a Proof of Concept grant....Much of this money was spent on commissioning a new coating unit. This equipment...would allow [the inventors] to produce samples in appropriate materials for particular applications and to work with interested companies to assess the potential of [the technology].
Downstream partners articulate clear directions for technology tests and evaluations, in the form of user requirements and performance expectations. These are tailored to the identified end-use scenarios, guided by the needs of downstream partners:
Corporate manager: The following are some thoughts on what prototypes would be useful to us:
- Ideal size = ∼10″ × 10″
- Ideal thinness = 20 um to 100 um
- Feel = less rubbery and more like polyethylene
- Background (base film) = translucent, white, & black
The results of such technology tests and evaluations help teams confirm or disconfirm the feasibility of end-use scenarios and thus narrow down the relevant market space:
Project manager: We have learned quite a lot about what [the technology] can and can't be used for in the last few years since we filed the patent, so we should revise the applications listed in the marketing sheet. We found that it does not work where there is constant immersion in water. We don't think it is robust enough for kitchen appliances. We don't know about outdoor weathering, which would need a lot of engineering effort.
Market ambiguity avoidance is associated with the closed-ended claiming of market spaces. This manifests in the following ways. First, project teams, together with downstream partners, try to establish clear-cut market boundaries by formalizing well-defined, end-use-specific fields of use in commercial agreements:
Project manager: What areas are [company 1] interested in? If [company 2] focus on compression stockings, do we really want [company 1] covering medical textile applications as well?
Inventor: I do not know precisely the areas of application [company 1] are interested in. They are global, so probably all medical textile applications.
Downstream partners provide direct access to specific end-user markets and aim to incorporate the focal technology in end-user products. They may even aim to incorporate the technology in products that are already currently in the market:
Project manager: [The company] have much experience in this field. I think that is why they have such specific ideas about how to move forward with the [technology]. [The company] currently have an existing product that is on the market that would utilize the [technology]. I feel that this company offers a solid opportunity for getting this technology into the marketplace for this field of use.
Teams try to assign end-use-specific fields of use to several downstream industry entities to establish a clearly defined, definite market space based on downstream markets:
Project manager: I am very pleased to hear that you are interested in licensing [the technology].... I think the University would prefer to license the [technology] to several companies in their particular field of interest, as this is the best way of ensuring that the technology is developed to its full potential.
In summary, market ambiguity avoidance strongly orients teams' decision making toward downstream industry contexts where market-specific inputs and partnerships help resolve market ambiguity faster. Path dependencies then lock teams into a specific decision trajectory: They begin the market-scoping process by identifying end-use-specific market space options. Teams then test the technology against user requirements and expectations, articulated by downstream industry entities, to confirm or disconfirm the feasibility of these market space options. Finally, teams seek direct access to clearly defined end-user markets. Therefore, we offer the following proposition to guide empirical work:
P2: When the market-scoping mindset is characterized by market ambiguity avoidance, this leads to (a) downstream market space anchoring, (b) confirmatory market space substantiation, and (c) closed-ended market space claiming.
Market ambiguity avoidance is associated with unfavorable market-scoping outcomes. When teams started by focusing on end-use scenarios and identifying potential downstream applications, market-scoping efforts were not successful. Downstream industry entities, typically with an interest in a finished product that is ready for the market, apply highly stringent technology requirements, performance expectations, and intellectual property (IP) standards—which the focal early-stage technologies generally struggled to meet:
Corporate researcher: The [material] will never be competitive compared to the major plastics materials used....It is a unique material which needs unique processing. It can neither be processed with the standard equipment at the standard speed nor can it ever be synthesized as cheap as polyethylene....I strongly advise that we keep our hands off. Customers from the construction industry are extremely inflexible and under extreme cost pressure.
Despite numerous attempts, teams struggled to establish specific end-user applications. Securing external technology investment was therefore time consuming and associated with resource misallocations to unsuited market space options. In response to such failures, teams redirected their attention to alternative end-use scenarios, wasting further time and resources:
Project manager: We're currently talking to potential customers about some applications we'd thought of originally, but I've been disappointed by our inability to find an application where the value of [the technology] could be sold directly to end-users....We're currently putting together a new proposal for some funded work by a company that manufactures compression stockings. They want some unique stretch material incorporated into their product, as [the inventor] has shown the [technology] may meet those requirements.
On those rare occasions that downstream firms decided to use the technology, the uncertainty that still surrounded the technology's value led to poor value appropriation on part of the inventors and the university, as the firms imposed unfavorable commercialization terms:
Project manager: We may still get reimbursement for some of the patent expenses for the [technology] but that depends on [the company's] due diligence on the patents and if they think they are valuable enough to pay anything for.
Deputy director, technology transfer office: Sorry to see that dinosaurs are still walking the earth—keep principled and explain to the academics why selling your firstborn is not a good way of building a family.
In most events, market ambiguity avoidance was associated with the misidentification of suitable market space options and inefficient resource deployment:
Project manager: Our reasons for abandoning [the technology] are: The patent does not properly cover the method we now use, so can be worked around. The added value for the applications where it will work is not great enough to generate high interest among potential users. We've learned a lot over the last few years about applications where this technology will not work, so the number of possible applications is now fairly small. It's a shame that this conclusion has been reached after we got the...patent granted.
In summary, market ambiguity avoidance led to unfavorable commercial terms and teams failed to establish technology commercialization opportunities, despite investing significant time and resources into a variety of potential downstream market space options (see Table 4). Therefore, we advance the following proposition for empirical testing:
P3: When market spaces for early-stage technologies are determined by market ambiguity avoidance, this results in (a) lower value appropriability, (b) fewer commercialization opportunities, (c) delayed initial investment, and (d) less efficient market-scoping resource allocation.
Market ambiguity acceptance is associated with the upstream, rather than downstream, anchoring of market spaces. This manifests itself in several ways. Project teams aim to identify broad upstream technology regimes that may underlie specific downstream markets:
Project manager: Semiconductors underlie very different industries and are the most promising application area of our technology. In a sheet-like structure, the substrate has already attracted interest from leading energy companies. [The technology] could be a key component of semiconductor devices...with potential applications in solar cells and LED displays.
Teams therefore seek partnerships with upstream industry entities, such as developers of similar or competing technological systems, characterized by high levels of technological sophistication regarding a focal technology regime. Such upstream partnerships offer technology-specific inputs that help explore the nature of the focal technology. Specifically, they possess relevant scientific and technological knowledge and provide technology benchmarks that help identify the most relevant technology features and capabilities:
Corporate researcher: [The inventor] has developed a quite unique technology for membrane production. One of the nicest pieces of technology I have seen in a long time. It has wide-ranging applications resulting from the very large surface area–volume ratio, controlled nano/micro pore size, the ability to make [surfaces] out of the polymer and then to fill the holes, wash the polymer out and get the "negative" version using a more stable material, etc., etc. All of this makes the resultant [surfaces] very interesting.
Such partnerships thus help clarify the focal technology's contribution to the existing technological knowledge in an upstream context. Upstream industry entities are able to point out gaps in the technological knowledge an early-stage technology can fill:
Corporate manager: We are very interested in extending our IP around [carbonite substances] which are capable of storing and releasing large volumes of hydrogen, propane, etc. and show stable properties when cycled a number of times. This field would also include material structures for gas sequestration, particularly CO2, which could be withdrawn from gas mixtures or the atmosphere.
Market ambiguity acceptance is associated with exploratory, rather than confirmatory, substantiation of market spaces. Project teams, together with upstream partners, explore and improve the capabilities of the focal technology. Once they understand these better, they combine this knowledge with market information to derive market space options:
Project manager to investor: Moving forward, as we get more information about the market segmentation,...and simultaneously get more information about the [technology's] technical performance...there's clearly going to be a significant piece of analytical work understanding whether the two overlap sufficiently to yield a realistic investable opportunity. But I guess that will be something for you and me to worry about.
Upstream partners provide complementary technological knowledge, technology benchmarks, and research-and-development (R&D) capabilities that enable exploratory technological learning. Teams leverage these assets to explore the technology's potential capabilities:
Project manager: The discussions we will have relates to the application of our technology to the expertise of [the company] in order that trials could be carried out using [the company's] equipment to produce optical fiber based [on the technology] with a view to exploring the potential for fiber-optic applications....In the longer term we hope that [the company] will license the technology to produce optical fiber for third party customers.
Upstream partners are typically experienced in identifying downstream applications for early-stage technologies. Project teams leverage this competence to interpret the technical results from exploratory technology experiments, tests, and evaluations to derive potential end-use ideas for a focal technology:
Project manager: Given the lab results then some of the potential applications we foresee include:
- Identification of water contaminates and bacterial presence
- Antifoams, defoamers, and foam control agents
- Water disinfectants and biocides
Market ambiguity acceptance is associated with the open-ended, rather than closed-ended, claiming of market spaces. Project teams, together with upstream partners, define fields of use in line with technology features and capabilities that transcend specific end-use scenarios:
Project manager to corporate manager: What sort of field definition did you have in mind? Two options occurred to me: (a) a field defined by the type of material (b) a field defined by the use of the [technology]? My preference would probably be for version (a), a field defined by material [...].
Project manager to inventor: The idea is to use [the company] as the central point for ALL commercial activity related to your [technology]. I could imagine granting [the company] exclusive rights to make [products] out of any material. You could collaborate with [the company] to seek further applications of your technology.
Because of their technology assets, broad market access, and strategic intent (exploring novel application areas for their technological competences), upstream partners are instrumental in creating technology platforms that may serve multiple downstream markets over time:
Corporate manager: With ever increasing awareness of water quality, it is reasonable to believe that there are organizations that wish to show or prove that they are not causing water pollution. Examples: companies in sectors such as energy, agriculture, mining, etc. There will also be other markets—once the platform has been developed the applications are limited only to the availability of sensors. The potential market place is by no means limited to [a specific country] and offers true business development opportunity across the globe.
In general, teams together with upstream partners and investors aim to formally establish an openly defined, extendable market space for a focal early-stage technology:
Investor: We have to believe that the technology can be sufficiently good that it cannot only provide a better solution for existing markets but also unlock additional markets or market value. Having a two-stage go-to-market strategy of generating revenues through semiconductors first while optimizing the technology to access the wider market value through (perhaps) simplification of the process system would be absolutely great.
In summary, market ambiguity acceptance orients teams' decision making toward upstream industry contexts where technology-specific inputs and partnerships help resolve market ambiguity gradually over time. Path dependencies then lock teams into a specific decision trajectory: They initiate the market-scoping process by identifying suitable technology regimes. Teams then leverage the technology assets of upstream partners to explore the technology's capabilities. Finally, they aim to establish an openly defined market space that transcends specific end-use scenarios. Therefore, we advance the following proposition for empirical testing:
P4: When the market-scoping mindset is characterized by market ambiguity acceptance, this leads to (a) upstream market space anchoring, (b) exploratory market space substantiation, and (c) open-ended market space claiming.
Market ambiguity acceptance is associated with favorable outcomes. When teams did not initially focus on identifying end-use situations, this enabled successful market-scoping outcomes. Investors appreciated the teams' focus on upstream technology regimes rather than spreading attention across numerous downstream applications:
Investor: I think investors have been stung many times by start-ups running out of money while they pursue too many things with the money available. The approach I would think might work best is to have the IP licensed in for semiconductors only (with sublicense rights) and contract the team for that work not precluding their ability to continue some research at the University on multiple applications.
Because the technology's value, in terms of its advancement of knowledge in a specific technology regime, has been clarified, teams were able to attract external investment for establishing technology platforms faster:
Investor: The way to make the highest profits overall is to sit in the value chain as a specialist...intellectual property and materials supplier. As there is an industry out there producing the materials it seems counter-intuitive to replicate the established facilities and better to be a high margin specialist....What you end up doing is building a prototype and demonstration facility to convince licensees to sign up.
Teams were able to ensure strong value appropriability by negotiating favorable terms in commercial agreements which allow for the exploitation of additional markets:
Project manager: I have talked to [the chief executive officer] about our discussion. He has suggested that we go for the arrangement that [the company] has 'first refusal' on any variation of [the technology] that [the inventor] believes may be worth developing. [The company] is to respond in a timely and appropriate manner. If [the company] does not wish to develop such a variant then [the University] can go ahead and talk to another company.
Upstream commercialization partners agreed to pursue multiple technology commercialization opportunities. In addition, they aimed to create an innovation network of downstream market actors that would facilitate future technology adoption and diffusion:
Corporate R&D manager: Whilst this development work is being conducted the [technology] would be utilized in 2-3 niche applications to raise its profile and prove its ability....The "case study" applications must be genuine problem cases with real opportunity in using as our flagship projects. In support of these high profile projects we would also actively develop a second tier of contacts. We believe that these would consist of a variety of contacts formed in the majority from specific industrial site applications who may still have an appetite...despite the current economic climate.
In summary, teams adopting market ambiguity acceptance managed to negotiate favorable commercial terms that allowed for strong value appropriability. They also attracted external investment for multiple technology commercialization opportunities based on relatively less time and resource investment (see Table 4). Therefore, we advance the following proposition for empirical testing:
P5: When market spaces for early-stage technologies are determined by market ambiguity acceptance, this results in (a) higher value appropriability, (b) more commercialization opportunities, (c) faster initial investment, and (d) more efficient market-scoping resource allocation.
We found significant variation in how teams process and respond to unfavorable feedback regarding market-scoping outcomes. Therefore, we explored whether (and why) any shift occurs in teams' market-scoping mindsets in response to negative market information. Our data suggest that team dynamics (i.e., interaction patterns among team members including external third parties; e.g., [15]) play an important role. Our insights are based on an exploratory examination of four teams that encountered unfavorable market information: cases 1 and 5 (no mindset shifts) and cases 3 and 6 (mindset shifts).
Teams managing these cases started with market ambiguity avoidance. Early in the project, they established formalized boundary-spanning ties that connected the teams with external experts. Specifically, teams forged close, long-term relationships with industry consultants who had market-specific expertise. These consultants became an important part of the teams' market-scoping process:
Project manager to inventor: Yesterday [we] met [the consultants]—[they] wanted to talk to us about possible commercial mechanisms for taking the technology to market. They are particularly interested in some specific sectors (as you are probably aware). Their idea is that they can work jointly with us in the development of a specific application of their interest. They also think they have some blue-chip clients which are very likely to be interested in [the technology] and will be prepared to put some money in it.
The teams, along with the consultants, were eager to find attractive downstream market space options, yet their efforts typically resulted in disappointing outcomes. Despite unfavorable market feedback, the teams did not shift their market-scoping mindset. Email communications suggest that they made external, market-related attributions for the unfavorable outcomes.
Project manager to inventor: Spoke to [the corporate manager] yesterday. It is clear that...there is no clear application in their minds that would merit any kind of investment on their part at this point in time. I don't hold out much hope at the moment for any real engagement from them. They are completely preoccupied with final stages of new product development for the CES [Consumer Electronics Show] next year, so are unlikely to have any interest in anything else until late 2011.
In cases 1 and 5, in which external attributions were made, the consultants involved strongly influenced the direction of the projects. Given their market-specific interests, the consultants reinforced the teams' initial market-scoping preferences, especially when they had to confront discouraging market information:
Project manager: It is certainly interesting how small each opportunity is...based on this market assessment. It may still be worthwhile setting up a company to license the technology...so that companies can incorporate [the technology] into their existing production processes.
Consultant: I believe our thinking may well be closely aligned!...I think you are right—setting up the company, even as a shell, will provide a clean vehicle through which to pursue any specific licensing or development activities, and provide a foundation on which to build industry profile.
Furthermore, the consultants were instrumental in maintaining a continued attentional emphasis on downstream market space options, despite unfavorable market feedback:
Consultant to project manager: A very good example of why it is important to qualify the real value of an opportunity: 200,000 [product units] per year equates to only 40 sq.m of the material....I would still suggest that a meeting with [the company] might be worthwhile since [the company] may also have higher volume applications.
Project manager to inventor: I spoke to [the consultant], and I would like him to become involved in furthering commercial discussions [with the company].
Overall, the continued presence and inputs of the consultants involved in cases 1 and 5 fostered conformity and agreement among team members:
Inventor: It's very clear that a food application of the [technology] would generate a huge amount of interest. And maybe some high profile food samples would be a key step in attracting the attention of brand owners in companies like Unilever.
Consultant: I agree completely that it is the brand owners that need to get excited about this....I would certainly favor the approach of spending a little longer to make the samples better and have a higher level of certainty over our ability to deliver.
Project manager: I entirely agree. I think success...relies on the samples impressing [the companies].
This pattern of interactions shows that conformity within teams also shaped underlying market-scoping preferences, which remained relatively homogeneous. This resulted in mindset entrenchment: a continued reinforcement of the existing market-scoping mindset.
Teams managing cases 3 and 6 also started with market ambiguity avoidance. Unlike cases 1 and 5, however, these teams established flexible boundary-spanning ties with several external experts: they cycled between different consultants and involved them on an ad hoc basis, depending on the specific issue at hand. The consultants provided teams with a broader spectrum of perspectives, and their inputs often conflicted with teams' assumptions and expectations for specific market space options:
Project manager to consultant: As I mentioned I'm interested in an investigation of the disposal of [drug delivery devices]....
Consultant to project manager: Please find the attached report. [The technology] could be disposed of in the clinical waste stream if rigid plastic containers were used (i.e. not plastic bags). These are obviously significantly more expensive than plastic bags.
Project manager to inventor: Some disappointing news on the clinical disposal.
In stark contrast with cases 1 and 5, the consultants—perhaps due to their less formalized roles and involvement in the projects—did not reinforce the teams' prevailing mindset. Rather, they presented teams with a broader set of new, alternative commercial perspectives:
Consultant to project manager: Attached is a copy of the Interim Report....Our initial review of this [technology] has confirmed that we need an understanding of the supply chain since it is difficult to identify any definable group of end users. You will see that after a detailed analysis of [the technology] we have agreed [...] that the testing should be focused on optical technologies. We anticipate that we will be able to find end users once we start working with organizations in the supply chain.
As teams realized their downstream efforts were largely ineffective, they began reflecting on their market-scoping preferences and made internal attributions. They were open to the possibility that their initial preferences may have resulted in unfavorable outcomes:
Project manager: I'm starting to think we are missing the mark with drug companies....I'm going to start digging more on who does the actual drug packaging. Although manufacturers do some of this, some recent conversations suggest that it may not be as much as we thought and they may not be the most receptive audience.
Overall, cases 3 and 6 were characterized by growing conflict and disagreement within teams. Team conflict was particularly pronounced in case 3, between managers and the inventor:
Inventor to senior manager: I have serious concerns [regarding] the way [the lead project manager] is handling the [technology] project....We have opened up an exciting opportunity for [the technology] with an external company. We have ongoing dialogue with a number of other companies....[The project managers] are not prepared to accept the commercial views of myself. Every step in persuading [them] to make any move is an uphill battle.
Disagreements also focused on the underlying market-scoping preferences. In case 3, the inventor strongly favored market ambiguity avoidance. The project manager, however, became more inclined toward market ambiguity acceptance as the project progressed. Opposing the preferences of the inventor, the project manager began preferring collaborations with potential upstream partners with access to multiple downstream markets:
Inventor: [Optical companies] could bypass us completely by simply going to [the company] to buy [the technology]. We have no control over [the company] as to whom they supply and we therefore have no control over [optical applications]. I have difficulty seeing how [you] can license the technology to others if they can merely go to [the company] to purchase the stuff.
Project manager: As I've said to you before, my priority is to give [the company] all the rights they need to make [the technology] a success....[My] feeling is that our highest priority over the next month is to nail down the level of interest from [the company], particularly as they are possible manufacturers...for other customers.
Similarly, in case 6, a consultant connected the team with an upstream technology supplier. This shifted the team's attention away from the initially preferred downstream opportunities, spurring heated debates. One of the project managers was keen to collaborate with the upstream company, while the second project manager was more skeptical:
Project manager 1: [The company] viewed [the technology] as an opportunity precisely because it could be offered widely to the pharma sector. I do not have the numbers but focusing on generics first and expanding to other drugs later may expand the market for [the technology] considerably.
Project manager 2: Will everyone work with [the company]? You've suggested that they already work with all the major pharma so this may not be a problem but I'd prefer to hear a few examples....Having one partner roll out [the technology] rather than a half dozen is a lot easier from our perspective but only if the terms are right, they're capable and they're a good partner to work with....Otherwise we could be limiting the adoption of the technology.
Overall, the observed team dynamics in cases 3 and 6 were associated with increasing heterogeneity of market-scoping preferences among team members. In both cases, senior managers overseeing the projects intervened and helped resolve team conflict:
Senior manager: I did try and reinforce the message that working with [the company] to develop leads for applications is a wonderful thing. That since [the company] has rights to a more expensive material, even if [the company] ends up developing customers in a variety of fields, there are fields that may be interested in the lower tech/cost versions of the material so this is wonderful.
Eventually, in both cases, teams' mindsets shifted to market ambiguity acceptance. The mindset shift was successful for case 3, leading to a licensing deal. However, for case 6, the shift appears to have come too late. Despite the identification of new upstream opportunities, the technology transfer office decided to kill the project.
Our data suggest that negative market feedback may not by itself result in a mindset shift. For mindset shifts to occur, team dynamics play critically important functions. We found that formalized, long-term boundary-spanning ties with a small number of external experts foster team conformity. Such conformity increases the likelihood of external attributions of unfavorable market feedback and reinforces homogeneous market-scoping preferences. The end result is mindset entrenchment. In contrast, flexible, informal boundary-spanning ties with a more diverse set of external experts facilitate debate and disagreements within teams. Such interactions increase the likelihood of internal attributions of unfavorable market information and growing heterogeneity of market-scoping preferences. This facilitates mindset shifts. Collectively, these exploratory insights echo research regarding the influence of third-party linkages on agreement among group members (e. g., [15]), as well as research on team conflict and diversity (e.g., [33]). Thus, we offer the following proposition to guide future research on mindset shifts:
P6: A market-scoping mindset shift is more likely to occur when team dynamics (a) facilitate internal attributions regarding unfavorable market feedback, (b) facilitate greater heterogeneity in terms of team members' market-scoping preferences, and (c) facilitate the enactment of diverging market-scoping preferences. A shift from market ambiguity avoidance to acceptance increases the chances of market-scoping success.
Figure 1 integrates insights that emerged during our discovery-oriented research process. Table 4 provides an overview of the conceptual properties of our key concepts. A key insight at the heart of this theory development effort is the significant explanatory role played by the market-scoping mindset construct. Specifically, we find that this mindset ranges between two extremes: market ambiguity avoidance (preference for a rapid resolution of market ambiguity) or acceptance (preference for a gradual resolution of market ambiguity). The market-scoping mindset plays a central role in shaping all subsequent decisions and outcomes. Its causal explanatory role is thus featured prominently in our conceptualization.
The market-scoping mindset shapes three major market space decisions: anchoring, substantiation, and claiming. Market ambiguity avoidance orients decision making toward downstream industry contexts that can help achieve rapid market space clarity. Partnerships with downstream entities offer the preferred market-specific inputs that guide further decision making. These partnerships, in turn, create path-dependent decision trajectories focused on end-use scenarios, directed at the enactment of a priori market space representations. In contrast, market ambiguity acceptance orients decision making toward upstream industry contexts. Here, partnerships with upstream entities offer the preferred technology-specific inputs that guide further decision making, even though the immediate relevance of these inputs for potential market spaces may not be readily evident. These partnerships lead to path-dependent decision trajectories focused on technology capabilities, facilitating the gradual discovery of market spaces. In the context of early-stage technologies, it is the latter approach that eventually reveals promising paths to viable market spaces.
By directing close attention to managerial agency in technology commercialization, the proposed conceptualization advances the current state of marketing theory. We believe our work fills important conceptual gaps in the broader literature in innovation and technology commercialization and urges practitioners to rethink their go-to-market heuristics.
The proposed conceptualization advances current theory development in the areas of innovation, technology management, and entrepreneurship. By focusing on managerial agency, we present specific details of managerial work involved in early-stage technology commercialization. Prior literature has been largely silent about such details. In this regard, we would like to highlight three conceptual contributions. First, our findings enhance theory development in the context of boundary-spanning search (e.g., [51]) and networks of learning ([11]; [47]). These literature streams, while instructive, offer only limited insights when search spaces are expansive and undefined, and industry networks are yet to be established. Our findings show how managers' mindsets orient their search activities toward specific search anchors (i.e., downstream vs. upstream market space anchors). These search anchors are associated with very different types of partnerships and industry networks. Thus, our findings explain why managers perform certain boundary-spanning search activities rather than others and why they focus their attention on some networks of learning over others.
Second, we contribute to the further development of effectuation theory. Effectuation theory suggests that, in situations of uncertainty, new partnerships and contingencies are desirable because they extend the potential scope of decision making ([48]; [54]). Our research offers a more nuanced understanding of the value of partnerships and contingencies in the context of early-stage technologies. Specifically, we find that partners seeking exploratory learning experiences are more likely to benefit the commercialization of early-stage technologies; partners with market-specific interests may have limited value (and can even hinder commercialization efforts). We also find that certain events during the commercialization process—such as the identification of unexpected downstream use situations—can lead managers astray. Thus, our findings draw attention to a broader set of contingencies that operate in such contexts: when unexpected events may be beneficial, and when they may have a detrimental impact.
Third, we add an important caveat to the prevailing view that the successful formation of market opportunities depends primarily on innovators' market knowledge endowments ([27]; [55]). Direct market knowledge indeed has advantages; however, it may also result in market space biases. As we show, the identification of viable market spaces can also occur indirectly through upstream interactions. Although collaborations with upstream technology developers initially deflect attention away from potential end users, they offer technology-specific inputs that may reveal indirect paths to end users. This expanded view of knowledge endowments can serve as a useful guide for theory development regarding the formation of market opportunities for early-stage technologies.
Finally, from a broader theoretical perspective, the proposed conceptualization adds a new perspective on how managers respond to situations of ambiguity in the wider context of innovation management and entrepreneurship. The prevailing view in prior research is rather narrow in that it assumes ambiguity to be undesirable, with managers generally trying to reduce it (e.g., [36]; [58]). The notion of market ambiguity acceptance challenges and extends this perspective. It also connects this line of research with seminal work in social psychology related to individuals' ambiguity intolerance ([ 6]) and need for closure ([61]). To develop a more comprehensive understanding of how managers deal with ambiguity, researchers should consider these personality traits as important antecedents that shape distinct managerial preferences and, therefore, indirectly influence managerial decision making and outcomes.
Managers are often drawn toward commercially attractive end users and thus spend considerable time and effort looking downstream in technology commercialization contexts. Training programs designed to help practitioners frequently urge them to match technologies with downstream markets early on in the process. For example, the Association of University Technology Managers espouses a technology transfer roadmap that puts market needs at the forefront of decision making ([ 5]). Potential upstream actors are, therefore, often overlooked as a result. Yet, as our work shows, developers of similar or competing technologies can offer a more realistic assessment of a technology's prospects and may identify important deficits in a focal technology's capabilities. This could, in turn, help steer further technology development in a more meaningful direction. The technology benchmarks these developers offer may provide a more fruitful avenue for making market space decisions.
Well-articulated technology requirements and performance expectations, derived from market needs, offer clear directions for technology development. However, they can also create an illusory sense of certainty. We suggest that managers focus more attention on understanding the evolution of a focal technology's capabilities and less on the current benefits these capabilities may offer to potential end users. This would also require greater managerial attention to the external resource environment: managers need to leverage external assets to benefit the focal technology. They should try to understand what the environment has to offer and identify avenues for potential collaboration with upstream industry entities.
This research suggests that a proactive focus on identifying market spaces early, while seemingly well-intentioned, can often lead to an inflexible stance about potential market opportunities. As a result, significant market-scoping efforts continue to be directed at unsuitable market space options. This is often exacerbated by counterproductive relationships with industry consultants who reinforce such market-scoping preferences, resulting in mindset entrenchment. Therefore, we suggest that organizations implement protocols, such as rotating partnerships with industry consultants, to facilitate diversity of commercial perspectives within teams.
Table 5 provides a detailed agenda for future research, including an overview of all definitions established in this article, suggested construct measurements, and potential future research opportunities. Overall, our conceptualization can serve as a useful foundation for a productive research program on market scoping and early-stage technologies.
Graph
Table 5. Definitions of Key Concepts, Suggestions for Their Measurement, and Future Research Opportunities.
| Concept | Definition | Potential Operationalization | Potential Research Questions |
|---|
| Early-stage technology | Nascent scientific and/or technical knowledge that is postlab and partially codified | Objective measurement:Technology originated in a research lab Technology is not fully codified in a patent or paper Proof of concept is not established
| What is the role of technological, organizational, and industry factors in driving the development of early-stage technologies? |
| Market ambiguity | Lack of clarity about the number, nature, and commercial viability of technology-to-market linkages | Subjective measurement:Perceived plurality of technology-to-market linkages Perceived uncertainty regarding the nature and viability of technology-to-market linkages
| What factors explain managers' perceived market ambiguity? |
| Market space | Set of technology-to-market linkages that present new product development opportunities | Objective measurement:Field-of-use definitions in commercial agreements Field-of-use definitions in patent claims National/regional/divisional patent applications
| What are effective configurations of market space options? What are the relationships between identified technology-to-market linkages? |
| Market scoping | Set of managerial activities directed at the identification of the market space for a focal early-stage technology | Objective/subjective measurement:Monetary/time investments into the search, assessment, and selection of technology-to-market linkages
| What is the prevalence of market scoping in other organizational settings, such as corporate R&D and high-tech entrepreneurship? |
| Market-scoping mindset | Preferences pertaining to the identification of potential market spaces (specifically, preferences regarding the resolution of market ambiguity) | See market ambiguity avoidance and acceptance, which represent endpoints of the market-scoping mindset continuum | What factors lead to market-scoping mindset entrenchment in teams? How do market-scoping preferences vary across organizational settings? |
| Market ambiguity avoidance | Preference for a priori market space representations that guide all market-scoping activities | Subjective measurement:Preference for a rapid resolution of market ambiguity Preference for market-specific inputs Preference for partners with immediate market interests
| What factors explain why managers adopt a certain market-scoping mindset? What factors drive shifts in a prevailing market-scoping mindset? |
| Market ambiguity acceptance | Preference for a gradual discovery of the market space over time | Subjective measurement:Preference for a gradual resolution of market ambiguity Preference for technology-specific inputs Preference for partners with an interest in technological learning
|
| Anchoring of market spaces | Managerial efforts to identify an initial set of market space options for further consideration | Subjective measurement:Focus on downstream end-use situations versus upstream technology regimes Focus on technology end users versus technology developers
| What factors influence the value of upstream opportunities? How do prior knowledge and social/organizational networks influence the identification of initial market space options? |
| Substantiation of market spaces | Managerial efforts to demonstrate the technical feasibility of a set of market space options | Subjective measurement:Focus on user requirements and expectations versus focus on accessible external technology assets Focus on confirmatory technology testing versus exploratory technological learning
| How can firms enhance exploratory technological learning to derive meaningful market space options? |
| Claiming of market spaces | Managerial efforts to formalize the nature and scope of a set of market space options | Subjective measurement:Focus on end-use-specific versus technology-specific market space definitions Focus on closely defined market spaces versus openly defined market spaces
| How can firms establish openly defined market space options such that firms' rights are protected and access to multiple end-user markets is maximized? |
| Value appropriability | Extent to which future profits from technology commercialization can be captured by technology owners | Objective measurement:Route to market (high: spinoff, medium: codevelopment, low: licensing) Agreed profit distributions (including royalties) Degree of exclusivity of commercial agreements
| How are (precommercialization) market-scoping performance measures related to final market performance measures? What steps can firms take to increase the diagnostic value of market-scoping performance measures? |
| Technology commercialization opportunities | Number of occasions when it becomes possible to commercially pursue a specific technology-to-market linkage through product development | Objective measurement:Number of licensing (or equivalent commercialization) agreements established Number of technology-to-market linkages receiving external investment for technology development
|
| Time to first investment | Time required to achieve first external investment for technology development after initiating market scoping | Objective measurement:Elapsed time from invention disclosure by inventor to first positive investment decision
|
| Market-scoping resource efficiency | Extent to which resources are used productively during the market-scoping process | Objective measurement:Ratio between number of active/renewed agreements and number of expired/discontinued agreements Ratio between pursued and abandoned fields of use
|
This article has some limitations that present opportunities for future research. First, because our research represents a first in-depth effort to understand market scoping, we adopted a discovery-oriented, theory-building approach. While theory testing was beyond the scope of this article, Table 5 provides detailed guidance, in the form of suggested construct operationalization, for future research directed at theory testing. Such efforts could focus on the analysis of both qualitative and quantitative sources of data (e.g., company records, performance outcomes, surveys). Although email data provide an unprecedented window into the focal phenomenon, researchers should also consider using other types of qualitative data that capture preferences and decisions in informal, face-to-face interactions.
Second, we examined the commercialization of early-stage technologies at a large university's technology transfer office. This empirical setting, though substantively important, differs from more commercial settings in which market scoping may also occur. Future research should therefore examine market scoping in such corporate settings. Researchers may wish to explore other aspects of the market-scoping mindset (e.g., managers' preferences relating to the breadth of the search landscape) or discover additional market-scoping preferences pertinent to corporate settings.
Third, the linkages between market-scoping performance outcomes and eventual commercialization success were beyond the scope of this article (see Figure 1). Future research should therefore examine the market-scoping mindset's ultimate impact on traditional measures of marketing performance. Such research efforts will necessitate access to longitudinal data that provides team-level details regarding market-scoping activities for specific technology projects and subsequent market-level performance data.
The literature on managerial agency can continue to serve as a useful guide for further theory development. Specifically, prereflective aspects of managerial agency ([ 8]) and the notion of "scripts" ([25]) merit future research attention. Prereflective aspects of managerial agency refer to internalized, deep-rooted managerial dispositions resulting from embeddedness in social structures ([ 8]). Studying prereflective aspects of market scoping would help shed light on antecedent factors of the market-scoping mindset (e.g. managers' prior professional experience and educational background). An exploration of these factors could also be conducted in conjunction with managers' personality traits such as ambiguity tolerance ([ 6]) and need for closure ([61]). A related stream of research could focus on scripts—implicit schematic knowledge structures that specify behavior sequences for specific situations ([25]). Managers may enact market-scoping scripts at different stages during early-stage technology commercialization. Understanding how market-scoping scripts are formed and enacted could further illuminate the formation of market space decision trajectories.
Future research endeavors could focus on a broad array of internal and external factors that may influence market-scoping activities. We highlight several opportunities here and provide additional guidance in Table 5. First, to deepen our understanding of team dynamics and mindset shifts, future research should investigate the role of external stakeholders (e.g., consultants, investors, codevelopers) in the market-scoping process. Specifically, the institutional logics they bring to bear—related to, for example, secrecy, control, and time horizons—may influence the market-scoping preferences of team members ([45]). Second, future research should consider potential "halo effects" ([57]) that may operate given the difficulty of assessing an early-stage technology's prospects. For example, the prestige of individuals and organizations involved in the market-scoping process may affect value perceptions and expectations of external parties, such as investors. Third, future research should study organizational factors that may moderate the proposed relationship between the market-scoping mindset and market-scoping success. For example, an organization's resource endowments may play an important role. In resource-rich organizational settings, the potential downsides of market ambiguity avoidance may be less severe because managers can invest more resources in technology development to alleviate the risks of technology adoption perceived by downstream industry entities.
By implementing our research agenda, researchers can advance our understanding of the unique marketing challenges that early-stage technologies pose. The resulting research program would enable inventors, entrepreneurs, and managers to navigate ambiguous market spaces more effectively. Indeed, it would offer crucial guidance to them on how to commercialize early-stage technologies successfully, despite being lost in a potentially vast universe of markets.
Supplemental Material, DS_10.1177_0022242918813308 - Lost in a Universe of Markets: Toward a Theory of Market Scoping for Early-Stage Technologies
Supplemental Material, DS_10.1177_0022242918813308 for Lost in a Universe of Markets: Toward a Theory of Market Scoping for Early-Stage Technologies by Sven Molner, Jaideep C. Prabhu, and Manjit S. Yadav in Journal of Marketing
Footnotes 1 Associate EditorChristine Moorman served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was supported by a grant from the Swiss National Science Foundation.
4 Online supplement: https://doi.org/10.1177/0022242918813308
5 1.Given the global scope of our email database, a few words in quotes contained alternative (e.g., British) spellings. For consistency, we use U.S. spelling in the reported quotes.
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By Sven Molner; Jaideep C. Prabhu and Manjit S. Yadav
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Record: 113- Making Recommendations More Effective Through Framings: Impacts of User- Versus Item-Based Framings on Recommendation Click-Throughs. By: Gai, Phyliss Jia; Klesse, Anne-Kathrin. Journal of Marketing. Nov2019, Vol. 83 Issue 6, p61-75. 15p. 1 Diagram, 3 Charts, 2 Graphs, 1 Map. DOI: 10.1177/0022242919873901.
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Making Recommendations More Effective Through Framings: Impacts of User- Versus Item-Based Framings on Recommendation Click-Throughs
Companies frequently offer product recommendations to customers, according to various algorithms. This research explores how companies should frame the methods they use to derive their recommendations, in an attempt to maximize click-through rates. Two common framings—user-based and item-based—might describe the same recommendation. User-based framing emphasizes the similarity between customers (e.g., "People who like this also like..."); item-based framing instead emphasizes similarities between products (e.g., "Similar to this item"). Six experiments, including two field experiments within a mobile app, show that framing the same recommendation as user-based (vs. item-based) can increase recommendation click-through rates. The findings suggest that user-based (vs. item-based) framing informs customers that the recommendation is based on not just product matching but also taste matching with other customers. Three theoretically derived and practically relevant boundary conditions related to the recommendation recipient, the products, and other users also offer practical guidance for managers regarding how to leverage recommendation framings to increase recommendation click-throughs.
Keywords: advice taking; algorithms; explanations; framing; recommender systems
Many companies provide customers with product recommendations that have been generated by algorithmic recommender systems: Spotify and Netflix recommend songs or movies for their subscribers, and TripAdvisor and Yelp provide recommendations for hotels or restaurants. Amazon suggests which products consumers might want to buy, and the New York Times recommends different news articles. These personalized recommendations help customers find offerings they likely are interested in and also increase their loyalty ([21]; [31]). According to a survey by Spotify, 65% of customers find a new favorite song in the personalized playlists they receive ([30]), and Netflix asserts that its recommender system effectively reduces customer churn and saves the company more than $1 billion annually ([19]).
To improve the accuracy of these algorithmic recommendations, recommender systems frequently adopt a hybrid approach that accounts for both common preferences across customers and common attributes across products ([ 3]). Each recommendation thus is based on both user and product input; it is not straightforward to explain the basis of the recommendation descriptively. In our interviews with members of a major European e-commerce company, the data scientists expressed different opinions about whether user or product input best described the basis for their recommender system, which actually uses various inputs. In turn, this company, and others alike, could choose which component to emphasize when explaining how it derives recommendations for customers. Some companies already highlight that their recommendations are user-based by focusing on overlaps in customer preferences, such as "Customers who viewed this item also viewed..." by Amazon and "Customers also watched..." by Netflix. In contrast, other companies emphasize that recommendations are item-based, such as "Similar to [what you have listened to]" by Spotify and "More in Health" by the New York Times.
The question that then arises is which framing, user-based or item-based, is more effective in triggering clicks on a recommendation. We aim to provide an answer to this question by framing the same recommendation as user-based or item-based and comparing customers' decisions to click on it. Such clicks can increase conversion rates by stimulating customers to explore other product offerings ([50]). Prior research on customers' responses to recommendations has focused primarily on the underlying recommendation algorithms ([ 8]; [22]; [52]) or characteristics of recommended products ([15]; [43]), with limited attention to the framing provided to describe the recommendations. This gap is surprising for two main reasons. First, many recommendations rely on input from both users and items, so companies can choose to highlight different elements. Second, altering recommendation framing is a nearly zero-cost effort. To address this managerially relevant gap, we manipulate recommendation framing (user-based vs. item-based) but keep the underlying algorithms and recommended products constant.
Our central proposition is that, compared with item-based framing, user-based framing informs customers that the recommendation is generated through product matching (i.e., the recommended product is similar to the focal product) but also indicates taste matching between users (i.e., the focal product liked by oneself is also liked by other users). Customers extract information from similar others' tastes to predict their own liking of unfamiliar products ([51]), so this information should provide an additional guarantee to customers that the product will match their tastes. Consequently, we predict that recommendations framed as user-based (vs. item-based) attract more click-throughs. This prediction rests on the assumption that consumers perceive that a recommendation accurately matches their taste. We thus consider three important boundary conditions (related to the product, the customer, and other users) that might lead to perceptions of unsuccessful taste matching, such that the advantage of user-based framing over item-based framing shrinks or even reverses.
Our investigation spans a variety of data sources and consumption domains. Two field experiments involve article recommendations within WeChat, the top mobile app in China (Studies 1a and 1b). Study 2 is a behavioral experiment with painting recommendations. Then two scenario experiments focus on book recommendations (Studies 3 and 4), followed by a behavioral experiment in the same domain (Study 5). Across these various methods and product contexts, we consistently find that user-based framing attracts more click-throughs on recommendations than item-based framing when customers perceive that others' preferences match their own. We further propose three boundary conditions that potentially cause the recommendation recipient to perceive the taste matching as unsuccessful, so the advantage of user-based framing over item-based framing decreases. These boundary conditions in turn offer substantive guidance for companies on how to adapt the framing of their recommendations to maximize recommendation click-throughs.
Recommendation systems are an automated, data-driven tool that companies frequently adopt to fulfill their customers' personalization needs ([24]; [44]). Depending on what customers have viewed, liked, or purchased, these systems predict what other products they could be interested in and deliver instant suggestions. Research in marketing and information systems highlights such recommender systems as important determinants of sales ([12]; [18]; [43]). Two typical methods inform these recommendations. First, collaborative filtering identifies customers who are similar in their product rating history and recommends items that one customer likes to similar other customers. The product ratings might be explicitly provided by customers or inferred from their online behavior. Second, content-based filtering identifies the product attributes that a customer likes and recommends products with similar attributes ([ 4]). Because each method has shortcomings, companies often combine them to improve the performance of their hybrid recommender systems. Examples include Amazon's "item-to-item collaborative filtering" ([34]), and the New York Times' collaborative topic modeling ([45]). Extensive research suggests ways to improve the prediction accuracy of recommendation algorithms using hybrid frameworks ([53]).
The computationally complex algorithms pose challenges for explaining recommendations to customers. A clear, concise, accurate explanation is crucial, because it promotes customers' trust in the recommender systems ([48]) and acceptance of recommendations ([16]; [32]). To the best of our knowledge, no research in marketing has suggested the optimal methods for explaining recommendations. In information systems literature, [46] identify five recommendation explanation types. Two explanations are particularly relevant to our research: collaborative-based and content-based. As their names imply, collaborative-based explanations such as "Customers who bought this item also bought..." rely on recommender systems that adopt collaborative filtering, whereas content-based explanations, such as "Recommended because you said you owned...," involve recommender systems that use content-based filtering. The other explanation types either overlap with the content-based explanation (e.g., cased-based that specifies the items compared by the underlying algorithm) or assume unique inputs (e.g., demographic-based; [46]).
Rather than explanation styles yoked to distinct, specific recommendation algorithms ([46]), we define recommendation framing according to the various explanations that might be provided, even with the same recommender system. Because most recommender systems take a hybrid approach that combines the input from users (i.e., interuser similarity in preferences) and the input from items (i.e., interitem similarity in attributes), we compare framings that highlight one input over the other, user-based framing versus item-based framing. Accordingly, the goal of the current research is to establish the causal impact of alternating between the user-based and item-based framings on click-throughs of recommendations, rather than to provide an exhaustive categorization of explanation styles ([46]).
As we detail in Figure 1, with user-based framing, the provided explanations draw attention to the shared tastes of consumers of a focal item. This framing describes how the target user (u) is similar to other users (u′), due to their shared interest in the focal item (i), and it indicates that the focal item (i) and recommended item (i′) are related because they attract the same users (u′). Item-based framing instead highlights the match between the focal and the recommended products (i and i′), either with or without specifying their shared properties. For example, "More in Health" suggests that recommended articles will be similar to the focal item, because they fall in the same news category; "Similar to this item" also emphasizes the relationship between the items but does not cite specific product attributes.
Graph: Figure 1. Illustration of the definitions of user-based and item-based framing.Notes: In user-based framing, customers u and u′ match in their liking of product i, and products i and i′ match in their consumer u′. Item-based framing suggests that products i and i′ are related.
As these definitions make clear, both user-based and item-based framings suggest product matching between items i and i′ as the basis for the recommendation. User-based framing matches products by consumers; item-based framing suggests that products are matched on their attributes. Notably, user-based framing also suggests taste matching (users' shared taste in the focal product) as the basis for recommendation, such that it offers informational value beyond that provided by item-based framing. According to advice-taking research, consumers extract information from others' tastes to predict their own satisfaction with unfamiliar products ([36]; [51]) and tend to adopt others' preferences if they believe those others' tastes match their own ([23]; [38]). Therefore, we reason that user-based framing offers additional information (i.e., about others' tastes) that can reduce customers' uncertainty about whether they will like or dislike the recommended item. By offering additional information about taste matching beyond product matching, user-based framing can serve as a sort of double-guarantee that customers will enjoy the recommended item and thus should be more effective in triggering click-throughs. Formally,
- H1 : User-based framing increases recommendation click-throughs relative to item-based framing.
This predicted advantage of user-based framing is premised on customers' perception that the taste matching is successful. Taste matching provides valid information for customers to infer their liking of the recommended item only if they believe others' preferences reflect their personal tastes. With automated recommendations, many factors could influence the extent to which customers perceive taste matching as successful and potentially reduce or even reverse the framing effect, such that user-based framing actually becomes disadvantageous compared with item-based framing. We consider three such factors that might provide important boundary conditions to the framing effect. We purposefully select a range of factors related to the customer segment (i.e., more or less consumption experience), the products (i.e., more or less attractive focal products), and other users (i.e., more or less similar to the recommendation recipient).
User-based framing differs from item-based framing in the implication that the recommender system attempts to match users on the basis of their tastes in the focal product. Customers who have accumulated more experience in a consumption domain may be less likely to perceive this taste matching as successful, for two reasons. First, customers develop more refined and sophisticated tastes as they acquire more experience within a consumption category ([11]). With more experience, customers are better able to differentiate products and develop a more complex understanding of the category ([ 1]). Second, more experienced customers have accrued more observations of individual differences in tastes and therefore likely regard their own taste as idiosyncratic ([41]). Accordingly, they might deem a shared interest in a single or a limited set of products (i.e., focal products) as insufficient for taste matching, leaving them reluctant to converge with or rely on other users' preferences. In contrast, inexperienced customers whose tastes are still coarse ([25]) may be less skeptical of a match between their own and others' tastes ([ 9]), leading to the advantage of user-based over item-based framing. We predict,
- H2 : The advantage of user-based framing relative to item-based framing decreases for customers with more consumption experience in the focal domain.
Customers' perceptions of taste-matching success also likely depend on the products themselves. We propose that taste matching may appear less accurate if the focal product is less attractive, because customers constantly learn about their own preferences through their reactions to different products ([ 7]; [49]). More attractive focal products would serve as salient and diagnostic signals of personal preferences ([55]), which in turn should promote perceived success in taste matching with other users who presumably also like the attractive focal product. In contrast, people tend to view less attractive products as less indicative of their taste or even a negative signal of preferences, lowering the perceived accuracy of taste matching and resulting in a smaller advantage or even a disadvantage of user-based framing relative to item-based framing. Specifically,
- H3 : The advantage of user-based framing over item-based framing diminishes for unattractive focal products.
Finally, in ambiguous situations, in which the identities of other customers are not revealed, people tend to assume self–other similarity ([39]). However, some companies provide information about the users who are the basis for the recommendation, explicitly (e.g., location of other users on booking.com) or implicitly (e.g., books of "teens' choice" on Amazon). When this information points to a dissimilarity between users, it may undermine the value of taste matching. As existing research shows, dissimilarity on certain dimensions (e.g., gender) activates thoughts of self–other dissimilarity in other domains (e.g., product attitudes; [47]). Customers thus might categorize a recommendation as reflecting "nonself" tastes if it is associated with dissimilar others and deem taste-matching efforts unsuccessful. In this case, we no longer expect an advantage of user-based framing over item-based framing but rather predict that it becomes disadvantageous, because consumers tend to avoid dissimilar others' tastes ([10]). Formally:
- H4 : User-based framing decreases recommendation click-throughs relative to item-based framing in the presence of cues suggesting self–other dissimilarity.
To summarize, we posit that, compared with item-based framing, user-based framing provides additional information about the preferences of other users that customers can use to reduce their uncertainty about the recommendation; as a result, it encourages them to click on it. The informational value of user-based framing and whether it benefits or harms recommendation click-throughs depends on the perceived success of taste matching. We conducted six studies to test these predictions and our conceptual framework (see the Web Appendix for the full results of all the studies). Studies 1a and 1b test H1 (main effect) in field experiments with article recommendations. The results affirm the advantage of user-based framing over item-based framing in a managerially relevant setting. Study 2 tests H2 that consumption experience functions as a moderator, in a setting that provides painting recommendations. For Studies 3 and 4, we created book-shopping scenarios to test H3. We find consistent support for our hypotheses, whether the attractiveness of the focal product is rated by a separate batch of customers (Study 3; analogous to data gathered by companies from prior customers) or by the same customers (Study 4). In Study 4, we also leverage information about the ages of other customers to establish a dissimilarity cue that leaves user-based framing disadvantageous relative to item-based framing, as predicted in H4. Study 5 strengthens the support for H4 by using gender as a different cue of dissimilarity. In all these studies, the recommendations involve products with which customers are unfamiliar, a design element that establishes insights into how to market novel products. Because customers are unlikely to hold prior beliefs about these products, managerial strategies likely make a big difference ([15]). Our findings thus add unique theoretical insights and suggest managerial strategies for companies.
We conducted Studies 1a and 1b in collaboration with a media company that regularly pushes articles to its subscribers on WeChat, the top mobile app in China ([40]). These two field studies differ primarily in the item-based framing, which we varied to ensure that the user-based framing is responsible for the increased click-throughs. This company also offers an ideal context to test the predicted main effect (H1) for two reasons. First, it primarily publishes articles about social science research, and its subscribers represent a highly homogeneous community. In this context, readers likely view taste matching as successful in general. Second, this company had not used recommendations before we ran Study 1a, so we could observe the unique effects of framing, unaffected by prior practice.
Study 1b, conducted 14 months after Study 1a, then offers a conceptual replication with completely new stimuli. During the 14-month interval, the company did not adopt any other article recommendation and witnessed a 52% increase in the number of subscribers (from 70,488 to 107,338) on WeChat. These changes should minimize carry-over effects from Study 1a to Study 1b. Because we had no access to individual users' data, we conducted both experiments at the article level (for a similar design, see [20]).
Before the experiment started, we carefully selected 71 original articles that had been previously pushed to all subscribers, according to four criteria. First, the number of times people had read each article could not exceed 400, which is low compared with the overall average 3,071 (as of August 2017, immediately before Study 1a). Second, the article had been pushed to subscribers at least three months ago, to ensure that it was likely to be unfamiliar to most readers. Third, it reported on research on human beings, which is the main content the company disseminates, to avoid the risk that the article topic would seem odd to readers. Fourth, the article could not contain time- or event-specific content (e.g., "Top research of 2016"), because timeliness might interfere with the framing effects.
We assigned these preselected articles to three conditions: no recommendation (N = 9), user-based framing (N = 31), or item-based framing (N = 31). The assignment used stratified randomization; each condition includes approximately the same percentage of articles published in different years (12% published in 2014, 20% in 2015, 48% in 2016, and 20% in 2017). This approach helped exclude bias due to publication timing. With the control condition (no recommendation), we test whether a recommendation per se is effective, regardless of its specific framing. We limit the sample size for this control condition, because it is not our focal interest and to maximize the statistical power of the contrast between user-based and item-based framings. The recommended articles, with user-based or item-based framing, attracted more reads than nonrecommended articles (p <.001; see Figure W1 in the Web Appendix). We do not discuss the nonrecommended articles further.
We randomly paired one article in the user-based framing with another in the item-based framing. The 31 pairs of recommendations then were distributed randomly across 31 days. Every weekday, the company pushed one set of articles to all subscribers. Each set had a headline article that was most salient to readers, which served as the focal article. Each pair of recommended articles was inserted toward the end of the focal article. Therefore, the readers would only see the recommendations if they were really interested in the focal article and finished reading it. The recommendation consisted of the recommendation framing and the title of the recommended article (a hyperlink to click on and read). The user-based framing read, "People who like this article also like...," and the item-based framing specified the category that both the focal and the recommended articles fell in "More analyses of scientific research" (all focal articles were in this category). The order in which the two framings appeared (one preceded the other) was counterbalanced across days.
To measure click-throughs on the recommendations, we calculated the click-through rate (CTR) for each recommended article:
Graph
InitialRead is the number of reads of the recommended article before the experiment started. It does not differ by the framing condition (p =.543). CurrentRead is the number of reads after the experiment started, recorded at four time points of 24 hours, 48 hours, 72 hours, and two weeks after the recommendation, which enables us to determine whether the framing effect varies over time. The number of reads of the focal article (FocalRead) also was recorded at these four time points. Table 1 presents the descriptive statistics.
Graph
Table 1. Means of Number of Article Reads in Study 1a.
| Focal Articles | User- Based Framing | Item- Based Framing | Non-recommended |
|---|
| Before experiment | 0 (0) | 316 (93) | 294 (93) | 274 (103) |
| 24 hours | 2,595 (2,280) | 343 (125) | 306 (96) | — |
| 48 hours | 2,610 (2,404) | 344 (126) | 307 (97) | — |
| 72 hours | 2,793 (2,564) | 345 (126) | 308 (97) | — |
| 2 weeks | 3,071 (2,693) | 349 (132) | 310 (98) | 275 (103) |
1 Notes: Standard deviations are in parentheses.
There were 17 missing cases because we could not observe the reads of some articles at some time points. Furthermore, we excluded one outlier article in the user-based condition from the analysis, because its CTR (M = 19.25% across the time points) was disproportionately higher than the average of all the other articles (.61%). The final data set contains 228 observations: 112 in the user-based condition and 116 in the item-based condition. Due to the nested structure of the data (articles nested within days), we constructed a multilevel model with CTR as the outcome variable and random intercepts at the day level. The recommendation faming served as the predictor (0 = item-based, 1 = user-based). Because time did not moderate the framing effect (p =.919), we focus on the overall effect. Table W1 in the Web Appendix summarizes the regression results. Consistent with H1, CTR is significantly higher in the user-based condition than in the item-based condition (M =.72% vs. M =.51%; b =.22, SE =.06, t(196) = 3.79, p <.001). Including the outlier article added to the error of estimation but also magnified the framing effect (M = 1.26% vs. M =.56%; b =.70, SE =.22, t(199) = 3.11, p =.002).
These results provide initial evidence that user-based framing outperforms item-based framing. Recall that we propose this effect arises because, unlike item-based framing, user-based framing offers additional informational value by suggesting taste matching as part of the recommendation strategy. To determine whether readers interpret the two framings in this way, we distributed a follow-up survey to the subscribers (N = 780; 67% female; Mage = 24.4 years, SDage = 5.7). Note that we do not know whether the survey participants also participated in our experiment, because the experiment was conducted on the article level. The survey participants were randomly assigned to read the user-based framing (N = 409) or item-based framing (N = 371) that we used in the field experiment; then, they rated the extent to which they agreed with eight statements (1 = "strongly disagree," and 6 = "strongly agree"). Half of the statements referred to product matching as the basis for the recommendation (e.g., "The recommendation is based on articles that are similar to what I have read," "The recommendation is based on the categorization of articles"; Cronbach's α =.68), whereas the other half referred to taste matching (e.g., "The recommendation is based on readers who have similar preferences with me," "The recommendation is based on the categorization of readers"; Cronbach's α =.70).
To test whether both user-based and item-based framings imply product matching to customers but only user-based framing suggests taste matching as a recommendation strategy, we submitted the perceived product-matching and perceived taste-matching scores to a 2 (two dependent measurements) × 2 (recommendations framing: user-based vs. item-based) mixed analysis of variance. A main effect of the measurement arises; participants more readily recognize product matching than taste matching as the basis for recommendations (F( 1, 778) = 226.04, p <.001), suggesting that product matching is the default perceived recommendation strategy. In addition, we find a significant interaction between measurement and framing (F( 1, 778) = 9.10, p =.003). In support of our reasoning, participants recognize product matching as the basis for the recommendation equally in both user-based and item-based conditions (Muser = 4.83, Mitem = 4.82; t(778) = −.08, p =.941). However, participants in the user-based framing condition agree that taste matching is a basis for the recommendation to a greater extent than participants in the item-based framing condition (Muser = 4.38, Mitem = 4.18, t(778) = 3.44, p =.001). That is, user-based framing (vs. item-based framing) offers information about taste matching, in addition to product-matching information.
Consistent with H1, Study 1a demonstrates that framing recommendations as user-based rather than item-based attracts more click-throughs in a field setting. It also provides support for the notion that perceived taste matching differentiates user- from item-based framing. It remains unclear, however, whether the framing effect really is due to the additional informational value of user-based framing or if readers instead avoid reading more articles in the same category, a response potentially evoked by the item-based framing that read "More analyses of scientific research." In Study 1b, we thus use a different item-based framing operationalization but keep the user-based framing constant. If the framing effect in Study 1a is due to the informational value of user-based framing, it should emerge regardless of whether the item-based framing specifies the article category.
Study 1b contains a new set of articles and a more generic item-based framing ("Similar to this article"). We selected the recommended articles using criteria similar to those we applied in Study 1a, except we also required that they had not been recommended in Study 1a. We increased the constraint on the number of reads before recommendation, from 400 to 480 reads, to ensure a decent sample size and account for the substantial increase in the number of subscribers to the company. With these criteria, we identified 66 articles, half randomly assigned to the user-based and the other half to the item-based framing condition. The procedure is the same as in Study 1a, and the experiment lasted for 33 days.
Similar to Study 1a, we excluded an outlier article in the item-based condition that had a peculiarly high CTR (M = 24.35%) relative to the average of all the other articles (M =.95%). Thus we retain 258 observations in the final data set. Unlike Study 1a, we did not balance the year of publication across conditions; more articles published in 2018 were assigned to the user-based condition than to the item-based condition (49% vs. 37%; p =.073). Therefore, we control for publication bias (0 = published before 2018, 1 = published in 2018) in the analysis. Using the same multilevel modeling approach as in Study 1a (time did not moderate the framing effect; p =.945), we find a lower CTR for articles published in 2018 than for those published before 2018 (M =.58% vs. M = 1.06%; b = −.49, SE =.13; t(223) = −3.80, p <.001). More importantly, controlling for the publication year, CTR is higher in the user-based condition than in the item-based condition (M = 1.25% vs. M = 1.06%; b =.19, SE =.08; t(223) = 2.44, p =.015). Table W1 in the Web Appendix summarizes the regression results. The advantage of user-based framing persists but shrinks in magnitude without the covariate (M = 1.01% vs. M =.88%; b =.14, SE =.08; t(224) = 1.79, p =.075). Including the outlier article made the framing effect insignificant (b = −.32, SE =.28; t(227) = −1.12, p =.262).
Study 1b strengthens the support for H1 by showing that the advantage of user-based framing over item-based framing persists when the item-based framing does not specify the category of the articles. Taken together, the framing effects observed in Studies 1a and 1b cannot be accounted for by avoidance of same-category items. We next seek to provide evidence for our conceptualization by testing boundary conditions on the framing effect.
With Study 2 we examine our prediction that user-based framing outperforms item-based framing in terms of recommendation CTR for inexperienced customers but not for experienced customers within a consumption domain (H2). We displayed the article recommendations in Studies 1a and 1b at the end of the focal article, which guaranteed that readers liked the focal article, because they would only see the recommendation if they read it to the end. In line with this element, in Study 2 we provide product recommendations only to participants who indicated that they liked the focal product.
We recruited 403 participants located in the United States from Amazon Mechanical Turk (MTurk; 186 female participants; Mage = 37.3 years, SDage = 11.6). Data from MTurk offer reliability comparable to those gathered from offline laboratories ([26]; [42]). After giving their informed consent, participants entered an "Online Museum" study and viewed 50 paintings created between the seventeenth and twentieth centuries. Each painting was paired with a hidden recommended painting with the same theme (e.g., seascape). The recommendations feature either a user-based (N = 195) or item-based (N = 208) framing. All paintings were obtained from Google Art Project. We operationalized our proposed moderator, consumption experience, as the frequency of visiting art museums, measured on a continuous scale.
Participants viewed the 50 focal paintings in a random sequence. Next to each focal painting, there was a "like" button in the shape of a heart. We told participants to mark their favorite paintings by pressing the button. To ensure that they provided their honest opinions, we told them that they would enter into a lottery for postcards of their favorite paintings. After participants clicked on a "like" button, another button appeared, indicating either "People who like this painting also like..." (user-based framing) or "Similar painting to this" (item-based framing), depending on the randomly assigned condition. They could click on this button to view the recommended painting in a pop-up window, as well as exit the pop-up window any time to continue viewing the focal paintings. The CTR for each recommendation was calculated as
Graph
The denominator is the number of participants who liked the focal painting, and the numerator indicates how many of them chose to view the recommended painting. The CTR varied between 0% and 100%. After participants finished viewing all the focal paintings, they saw both the user-based and item-based framing and indicated which one they encountered in the "Online Museum" (94% answered correctly). To measure participants' consumption experience, we asked them to indicate how often they visited art museums in their life (1 = "never," 2 = "seldom," 3 = "sometimes," 4 = "often," and 5 = "very often"). We deliberately chose this single-item measurement to maximize the number of observations per level of consumption experience and thus to obtain reliable CTRs to estimate the effects of framing. We ended the study with a few demographic questions.
Similar to Study 1, we conducted the analyses at the level of each recommended painting. We calculated the CTR for each recommendation per framing and per level of consumption experience, resulting in a data set with 499 observations. One observation was missing because one focal painting in the item-based condition was not liked by any participants who never went to art museums. We regressed CTR on framing (0 = item-based, 1 = user-based), consumption experience (continuous from 1 to 5), and their interaction. The results, as plotted in Figure 2, indicate a significant interaction between framing and consumption experience (b = −2.23, SE =.97; t(495) = −2.30, p =.022). In support of H2, the CTR is higher in the user-based condition than in the item-based condition among people who never (b = 7.83, SE = 2.38; t(495) = 3.29, p =.001), seldom (b = 5.60, SE = 1.68; t(495) = 3.33, p <.001), or sometimes (b = 3.36, SE = 1.37; t(495) = 2.45, p =.015) visited art museums. We find no significant difference across framings for those who often (b = 1.13, SE = 1.68; t(495) =.67, p =.501) or very often (b = −1.10, SE = 2.37; t(495) = −.47, p =.642) visited art museums. On average, the CTR is slightly but significantly higher in the user-based condition than in the item-based condition (M = 46.63% vs. 43.23%; F( 1, 495) = 6.15, p =.014), probably because most participants have rather limited experience with arts (median = 2 of 5). Moreover, the CTR decreases with more consumption experience (b = −6.70, SE =.68; t(495) = −9.78, p <.001) in the user-based condition, but this trend is attenuated in the item-based condition (b = −4.46, SE =.69; t(495) = −6.49, p <.001). Table W2 in the Web Appendix summarizes the regression results.
Graph: Figure 2. Regression results of Study 2.
Study 2 provides support for H2; the advantage of user-based framing over item-based framing diminishes for people with more consumption experience. Also consistent with our theorizing that more experienced people are less likely to perceive taste matching as accurate, we find that greater consumption experience induces a greater decrease in the recommendation CTR when it is framed as user-based as opposed to item-based.
The paradigms we use in Studies 1a, 1b, and 2 guarantee that participants like the focal product; they see the recommendation only if they finish reading the article or like the focal painting. In Study 3, we relax this criterion so that all participants receive a product recommendation regardless of whether they expressed interest in the focal product; this allows us to test for a moderating role of the attractiveness of the focal product (H3).
According to our theorizing, liking the focal product is a necessary prerequisite for taste matching to be perceived as successful and thus for the advantage of user-based framing over item-based framing to arise. To test this assumption explicitly, we vary the attractiveness of the focal products and inquire into people's intentions to click on the recommendation. We expect the advantage of user-based framing to diminish or even reverse for less attractive focal products (H3). Moreover, if focal attractiveness affects perceived success in taste matching, it should relate more positively to CTR when the recommendations are framed as user-based as opposed to item-based.
Fifty participants located in the United States were recruited from MTurk to participate in a study about shopping for novels on Amazon (18 female participants; Mage = 37.57 years, SDage = 12.32). The majority (56%) had never purchased a novel on Amazon. We manipulated the framing within participants. Unlike the previous studies, the user-based framing emphasized users' actions ("Customers who viewed this book also viewed...") rather than likes. This variation is purposeful; the decision to view a book's webpage might be driven merely by the appearance of the cover, but liking a book requires understanding it. The item-based framing followed Study 2 ("Similar to this item"). This setup might be less engaging than previous studies. However, it taps into an important situation in which customers are merely browsing products without concrete goals.
We took a convenience sample of 50 novels from the "Literature and Fiction" category on Amazon that had garnered fewer than 200 reviews before the experiment started, such that they were presumably unfamiliar to most of our participants. A pretest with a separate batch of 50 participants from MTurk (23 female participants; Mage = 33.6 years, SDage = 8.4) confirms that these books are unfamiliar to MTurk workers (maximum mean familiarity is 2.14 of 10, where higher values indicate more familiarity). From the 50 books, we randomly selected 25 candidates as focal books and the other 25 candidates as recommended books. Then we randomly paired a candidate from the focal set with another from the recommended set. We aimed for an equal number of focal–recommended pairs per framing condition for the study.
The pretest demonstrates that the distribution of mean attractiveness scores across the 25 focal books centered around the scale midpoint (M = 5.21 of 10, SD =.64). We selected six focal books (three per framing condition) that represent this distribution for extrapolation (M = 5.04, SD =.64). The attractiveness scores of the selected books do not differ by framing condition (p =.941).
In the main study, participants viewed the preselected focal books in random sequences, each accompanied by a preassigned recommended book. For each recommendation, participants indicated whether they would click on the recommended book, on a ten-point scale (1 = "Definitely not," and 10 = "Definitely yes"). After they finished viewing all the recommendations, they selected the reasons that they had seen for recommendation: ( 1) user-based framing, ( 2) item-based framing, ( 3) both, or ( 4) neither. The study ended with demographic questions.
We excluded eight participants who recalled neither the user-based nor the item-based framing. This exclusion is important, because it rules out the possibility that the framing effect shrinks due to participants' lack of attention to the recommendations associated with less attractive focal books. The final data set includes 252 observations (6 books nested within 42 participants). We regressed participants' intention to click on the recommended book on three predictors: recommendation framing, the score of focal attractiveness as obtained from the pretest, and their interaction. The regression model allowed for a random intercept for each participant. Table W2 in the web appendix summarizes the regression results.
We find a significant interaction effect between framing and focal attractiveness (b = 1.10, SE =.53; t(207) = 2.08, p =.039). Consistent with H3, the advantage of user-based framing decreases for less attractive focal books. To illustrate, when focal attractiveness is one standard deviation above the mean, user-based framing increases people's intention to click on the recommendation relative to item-based framing (b =.87, SE =.42; t(207) = 2.07, p =.039). No framing effect emerges at the mean level of focal book attractiveness (b =.22, SE =.29; t(207) =.77, p =.441) or at one standard deviation below the mean (b = −.43, SE =.43; t(207) = −1.00, p =.317). For very unattractive books (1 out of 10), the model even predicts that user-based framing lowers click-through intentions compared with item-based framing (b = −4.22, SE = 2.16; t(207) = −1.96, p =.052). Furthermore, in support of our theorizing, focal book attractiveness predicts intentions to click for the user-based framing (b = 1.09, SE =.44; t(207) = 2.48, p =.014), but this trend is absent for item-based framing (b = −.01, SE =.29; t(207) = −.02, p =.981).
When all cases are included, the moderation by focal attractiveness is in the same direction and marginally significant (t(247) = 1.76, p =.080). For the similar patterns with and without data exclusion, see Figure W2 in the Web Appendix.
In support of H3, Study 3 establishes focal product attractiveness as a boundary condition for the advantage of user-based framing over item-based framing. It renders insights into the framing effect in a setting where customers are merely browsing products without explicit signals of their interest in the focal product. In Study 4, we aim to replicate this finding using a different procedure; we also test whether presenting a salient cue of self–other dissimilarity makes user-based framing disadvantageous relative to item-based framing (H4).
The majority of MTurk workers are at least 25 years of age ([28]), so we use the age group "18–24 years" as a dissimilarity cue. For this study, a bar graph indicates other customers' ages, under the title "Age of interested customers," with three bars: "18–24," "25–55," and "above 55." We highlighted the "18–24" bar and informed participants that it represented the age of customers who also viewed the recommended book. A pretest (N = 101; 62 female participants; Mage = 36.7 years, SDage = 12.4) confirmed that most MTurk workers (89%) are older than 24 years and perceive themselves as more similar to other customers in their age group than to people in the 18–24 year group (p <.001).
We recruited 360 participants from MTurk, who are at least 25 years old (169 female participants; Mage = 37.6 years, SDage = 1.19), and randomly assigned them to three conditions: user-based framing ("Customers who viewed this item also viewed..."), item-based framing ("Similar to this item"), and user-based framing with the age group dissimilarity cue.
The procedure is similar to that in Study 3, with two differences. First, instead of presenting participants with preselected books, we allowed them to self-select three focal books to view from nine books, thereby simulating browsing behavior in online stores. Second, the attractiveness of focal books was rated by the participants rather than based on the score from the pretest, which captures the heterogeneity of ratings across individuals. At the end of the study, participants evaluated how attractive they found each focal book using a ten-point scale (1 = "Not at all," and 10 = "Very attractive"; M = 6.94, SD = 1.96). The attractiveness was not influenced by the assigned conditions (p =.353; overall M = 6.94, SD = 1.96).
As in Study 3, we excluded participants (N = 133) who could not recall the framing they saw, leaving a data set with 680 observations (3 books nested within 227 participants). We took the same analysis approach as in Study 3, with two dummy predictors: user-based condition and dissimilarity cue condition, each of which could interact with the rating of the focal book's attractiveness. Because the dissimilarity (age group) cue did not interact with focal attractiveness (p =.845) and including this interaction term did not increase model fit (p =.364), we dropped it from the analysis to focus on the main effect of dissimilarity. Figure 3 plots the results.
Graph: Figure 3. Regression results of Study 4.
In line with Study 3 results, we find a significant interaction of focal book attractiveness and recommendation framing when the dissimilarity cue is absent (b =.24, SE =.10; t(451) = 2.32, p =.021). Specifically, user-based framing (vs. item-based framing) increased participants' intention to click on the recommended book when focal attractiveness scored one standard deviation above the mean (b =.70, SE =.36; t(224) = 1.93, p =.055) but not when it scored at the mean (b =.24, SE =.30; t(224) =.78, p =.434) or one standard deviation below the mean (b = −.23, SE =.36; t(224) = −.64, p =.526). For very unattractive books (1 out of 10), user-based framing even lowered click-through intentions relative to item-based framing (b = −1.18, SE =.68; t(224) = −1.74, p =.084). In addition, focal attractiveness relates more positively to click-through intentions in the user-based condition (b =.50, SE =.08; t(451) = 6.22, p <.001) than in the item-based condition (b =.26, SE =.06; t(451) = 4.01, p <.001). However, when the dissimilar cue is present, user-based framing (vs. item-based framing) decreases intentions to click on recommended books (b = −.89, SE =.32; t(224) = −2.84, p =.005).
When all cases are included, we replicate the reversal of the framing effect (t(357) = −3.11, p =.002). The moderation by focal attractiveness is in the same direction but not significant (t(717) = 1.58, p =.113). For the similar patterns with and without data exclusion, see Figure W3 in the Web Appendix.
Study 4 replicates the findings of Study 3 with a different procedure, strengthening the support for H2. Furthermore, consistent with H3, we find that the presence of a cue suggesting dissimilarity with other users makes user-based framing disadvantageous compared with item-based framing, regardless of the attractiveness of the focal books. To provide additional support for H4 and in line with prior research ([39]), in Study 5 we used gender composition as a different cue of self–other dissimilarity. Moreover, we include both dissimilar (most other users are a different gender) and similar (most other users are the same gender) cue. In line with our theorizing and prior research (Naylor, Lamberton, and West 2010), we anticipate that cueing customers with their similarity to other users will have an effect similar to user-based framing that lacks information about the identity of other users.
Study 5 follows the design of Study 2 (painting) but in the domain of books. We selected 57 books of various genres (e.g., comics, thrillers, philosophy) that were not available on the market when the study was conducted (i.e., "coming soon" category), so participants were unlikely to be familiar with them. We selected another 57 coming-soon books as recommendations and paired them with the focal books. Participants viewed the book covers, titles, author names, and genres and then marked books they would like to read by clicking on a heart button. Next, the recommendation button popped up, indicating either "Customers who like this also like..." in the user-based condition or "Similar book to this" in the item-based condition. In both conditions, we told participants that the recommendation came from readers on Amazon. Moreover, participants had the chance to win a book that they marked as "would like to read."
In the user-based condition, next to the recommendation button, participants also saw the gender composition of people who liked the focal book. Of the 57 focal books, 21 were predominantly liked by male participants (95%–100%), and 21 were mainly liked by female participants (95%–100%), so 42 books offered a cue of self–other similarity, and 42 provided a cue of self–other dissimilarity (see Table 2). In addition, 15 neutral books were liked about equally by participants of both genders (45%–55% male). These neutral books serve two purposes. First, their presence creates a more realistic book-shopping scenario, in which customers encounter books that attract either gender and those that appeal to both genders. Second, the neutral books, combined with similar-cue and dissimilar-cue books, increase the power of the contrasts relative to the item-based condition (i.e., same 57 books compared across conditions). For the similar-cue books, we expect to replicate the moderating role of consumption experience from Study 2. For the dissimilar-cue books, in line with Study 4, we anticipate that user-based framing will decrease CTR.
Graph
Table 2. Design of Study 5, User-Based Framing.
| Similar Cue | Dissimilar Cue |
|---|
| Male participants | 21 books liked by 95% to 100% men | 21 books liked by 95% to 100% women |
| Female participants | 21 books liked by 95% to 100% women | 21 books liked by 95% to 100% men |
| Total | 57 books (42 + 15 neutral books) | 57 books (42 + 15 neutral books) |
Three hundred sixteen MTurk workers participated in the study (159 male participants; Mage = 35.51 years, SDage = 1.61). After viewing the focal books, participants indicated their experience with book shopping on the item, "How often do you visit bookstores (online or offline) in general?" with the same scale from Study 2. Compared with participants in Study 2 (paintings), participants in Study 5 had more experience with books (median = 3 vs. 2; significantly higher mean, p <.001). The study ended with a few demographic questions.
We calculated separate CTRs for similar-cue books, dissimilar-cue books, and the item-based frame books. We then regressed the CTRs on two dummy predictors: similar-cue books and dissimilar-cue books, each of which could interact with consumption experience. Similar to Study 4, the interaction between the dissimilar cue and consumption experience is insignificant (p =.975), and including the interaction term does not increase model fit (p =.975). We thus drop the interaction (for the full regression results, see Table W3 in the Web Appendix). The results, as plotted in Figure 4, show that for similar-cue books, as in Study 2, there is a significant interaction between framing and experience (b = −4.88, SE = 1.63; t(623) = −3.00, p =.003). Specifically, user-based framing is more advantageous for participants who never visit bookstores (b = 8.91, SE = 4.35; t(623) = 2.05, p =.041), but this advantage decreases and even reverses as they gain more experience (seldom b = 4.03, SE = 2.98; t(623) = 1.35, p =.176; sometimes b = −.85, SE = 2.03; t(623) = −.42, p =.676; often b = −5.73, SE = 2.15; t(623) = −2.66, p =.008; very often b = −1.61, SE = 3.24; t(623) = −3.28, p =.001). In support of H4, user-based framing becomes disadvantageous, relative to item-based framing, for dissimilar-cue books (b = −6.55, SE = 1.94; t(623) = −3.38, p <.001).
Graph: Figure 4. Regression results of Study 5.
Using the paradigm from Study 2, Study 5 conceptually strengthens support for H4. Cueing customers to recognize self–other dissimilarity leads to a disadvantage of user-based framing relative to item-based framing. This study also generalizes the role of consumption experience to the domain of books.
Customers frequently receive product recommendations from recommender systems, and companies often frame them as user-based (e.g., "People who like this also like...") or item-based (e.g., "Similar to this item"). We compare these two framings while keeping the actual recommendation constant (or randomized, as in the field studies) and thereby demonstrate the advantage of user-based framing over item-based framing in terms of recommendation CTR. In two field experiments with the mobile app WeChat (Study 1a and 1b), we establish that recommending articles with user-based (vs. item-based) framing increases recommendation CTR. Study 2 identifies consumption experience as an important boundary condition for the framing effect; Studies 3 and 4 show that the effect shrinks and even reverses for unattractive focal products. Finally, Studies 4 and 5 reveal that cueing customers to their dissimilarity with other users makes user-based framing less effective than item-based framing. Table 3 summarizes the studies and the hypotheses they support. We took care to test our predictions using various product categories (articles, books, and paintings) and different paradigms mimicking real recommendation practices to establish the generalizability and robustness of the effects. The results in turn offer several contributions to literature, practical suggestions for companies that use product recommendations in their marketing strategy, and directions for further research.
Graph
Table 3. Overview of Studies.
| Study | Data Source | Outcome Variable | Supported Hypotheses |
|---|
| Studies 1a and 1b | Field experiment | CTR of recommended books (0%–100%) | H1 |
| Study 2 | Behavioral experiment | CTR of recommended paintings (0%–100%) | H2 |
| Study 3 | Scenario experiment | Intention to click on recommended books (1–10) | H3 |
| Study 4 | Scenario experiment | Intention to click on recommended books (1–10) | H3 and H4 |
| Study 5 | Behavioral experiment | CTR of recommended books (0%–100%) | H2 and H4 |
Prior investigations of recommender systems have primarily focused on technical designs (e.g., [ 4]; [ 8]; [22]) or the consequences of their use (e.g., [12]; [18]; [43]). Little research has explored the ideal ways for companies to communicate the basis of recommendations to their customers. Our research represents an initial attempt to fill this gap by comparing the effects of user-based and item-based framings on recommendation CTR. Simply changing the framing of recommendations can have an impact on this metric. We thus emphasize the importance of studying the effect of framing in addition to the technical aspects of the underlying algorithms.
Our findings also advance understanding of customers' interpretations of recommendations. As our follow-up survey in Study 1a shows, customers recognize product matching more readily than taste matching, regardless of the recommendation framing. In two pilot studies (see the Web Appendix), we also find that product matching is perceived as a more dominant recommendation strategy than taste matching. This primacy of product matching might result from the visual salience of products, relative to the latency of customers: on a typical product webpage, customers see products, not other customers, and can directly compare the products but not themselves with others. The results of our survey show that the difference between the two framings is due to taste matching. By signaling that taste matching is part of the recommendation strategy, beyond product matching, user-based framing offers additional informational value for customers that, presumably, mitigates their uncertainty about their satisfaction with the recommendation.
More broadly, our work contributes to advice-taking research ([ 5]; [29]; [37]). Prior studies have focused on how customers take advice from other users; we investigate customers' tendency to follow recommendations generated by algorithms. Consistent with findings that indicate that customers adopt others' choices ([36]) and opinions (e.g., online reviews; [13]; [54]), we demonstrate that mentioning others' preferences can encourage customers to click on recommended products. However, a fundamental difference between following recommendations and adopting others' preferences is that the former depends on customers' understanding of the "black box" of recommender systems, whereas the latter pertains to how customers navigate the social world. Recommendations framed as user-based (vs. item-based) might exert more influence on customers by adding a social component to the recommender system.
Companies heavily invest in recommender systems; global spending is estimated at $5.9 billion in 2019 ([27]). Our research suggests that it is not only the technical aspects of recommender systems that matter; the framing of recommendations exerts a notable influence as well. Companies might fail to maximize recommendation click-throughs if they rely only on item-based framing. Managers must not only develop effective recommender systems but also devote attention to how to frame the recommendations for customers. Adapting the framing, while keeping the underlying algorithm and the recommended product constant, comes with nearly zero cost, unlike developing and improving technical aspects of recommender systems.
Our field studies suggest a general advantage of user-based framing over item-based framing in a setting where customers' tastes are homogeneous and they show deep interest in the focal item (e.g., they read the entire article). Studies 2 through 5 document situations in which this advantage can diminish or even reverse. These boundary conditions are particularly important for companies to consider when deciding on the framing that they want to utilize. First, customers with less consumption experience are particularly susceptible to the impact of recommendation framing. Managers can identify these customers by analyzing their past behavior and infer the degree to which they possess consumption experience in a specific domain. Customers who seldom listen to classical music probably know little about this genre, for example, so they likely follow the lead of other classical music fans and exhibit high responsiveness to user-based framing.
Second, in situations in which customers are merely browsing on a website and do not necessarily express interest in focal products (as was the case in the paradigms of Studies 3 and 4), utilizing a user-based framing is unlikely to be advantageous compared with an item-based framing. Conversely, a user-based framing is more advantageous than item-based framing for attractive products; it can trigger customers to click the recommendation when they already have expressed some interest in the focal product, such as by reading an article or watching a video to the end. Managers can infer the attractiveness of focal products by tracking customers' real-time behavior and thereby decide whether to prioritize user-based framing. Moreover, considering that user-based framing appears particularly beneficial for products that receive high ratings from prior customers, if managers cannot easily infer a particular target customer's attitude toward the focal product, they still can decide whether to prioritize user-based framing, depending on prior customers' reactions to it.
Third, user-based framing is less effective than item-based framing when it is coupled with a cue suggesting that others (on whom the recommendation is based) are dissimilar to the recommendation recipient. This insight is critical for companies that present prior customers' information to target customers (e.g., "teens' choices"). If these selected others differ from the target customer in salient ways, the target customer might avoid a recommendation framed as user-based. To maximize the value of user-based framing, managers either should not display any cues suggestive of differences or else should selectively emphasize other customers who are similar to the target customer in some important aspect. If these displays of information cannot be adjusted, managers might compare the backgrounds of the target customer and others, then choose a user-based framing only if a match exists and item-based framing if not.
Fourth, customers more readily recognize product matching than taste matching (as shown in the follow-up survey for Study 1a). However, the advantage of user-based framing stem from customers' awareness of the taste-matching effort and their recognition of successful taste matching. Therefore, it is important for companies to make user-based framings salient, such as by increasing the font size or underscoring the framing, if they intend to leverage its value to the fullest.
We purposefully compare generic user-based and item-based framings, which are common in the marketplace, to generate externally valid and practically relevant insights. However, both framings can vary in their specificity. For example, user-based framing can refer to a specific group of users, such as friends (e.g., Spotify's "what friends are listening to"), which may alter how likely customers are to perceive taste matching as successful. A generic user-based framing is unlikely to prompt customers to question their similarity with ambiguous other users, but referring to specific friends could more easily trigger perceptions of dissimilarity. Typically, customers know their friends' tastes and therefore recognize fine-grained differences in them. In that sense, referring to friends' preferences might backfire for user-based framing, making it less effective than item-based framing. We encourage continued research into this practically relevant issue.
Similarly, companies might specify standards for item categorization. Instead of merely mentioning that the recommended item is similar to a focal item or that the two fall in a rather broad category (e.g., romantic novels), companies might emphasize books by the same author or movies by the same director. Noting the primacy of product matching as the perceived recommendation strategy, we speculate that the width of the category exerts little influence on the difference between user-based and item-based framing. However, it is possible that item categorization variations could affect certain customers; for example, those with greater consumption experience within a product category might find item-based framing more attractive if the item categorization is narrower, because they are motivated to deepen their knowledge of specific categories ([14]).
Alternatively, recommendation framing might be analyzed along dimensions other than an emphasis on different inputs (i.e., users or items), such as whether it refers to the target customer's own past behavior as a basis for recommendation. Spotify uses "Because you have listened to X" in parallel with a more generic "Similar to X" to explain its recommendations. Does explicitly referring to customers' own tastes make a difference? On the one hand, personalized explanations ("you" and "your" behavior) might cause customers to perceive greater effort by the recommender system and the recommendation as more self-relevant. On the other hand, personalization could raise customers' awareness that their private information has been collected and prompt reactance to the recommendations. Additional research could compare different recommendation framings along multiple dimensions to achieve a fuller understanding of their roles.
Although we explore three theoretically derived, practically relevant moderators, a variety of factors could shift the perceived success of taste matching and thus moderate the framing effect. According to social influence literature, for example, customers tend to perceive more self–other dissimilarity as their distance grows ([35]). Their perceptions of taste-matching success thus might depend on their geographical distance. Another pertinent factor is customers' perception of the size of the group of other users ([ 6]), as defined by the type of product. Customers interested in a niche product may infer a small group of interested other users; those considering a mainstream product likely presume a large group. Larger groups can be more influential but also appear more heterogeneous in their tastes ([33]). Studies of such influences could deepen understanding of framing effects across communities.
We suggest that user-based framing is advantageous compared with item-based framing because it signals taste matching. In our work, we provide support for this theorizing in product domains in which taste is an important decision criterion (articles, paintings, and books). We speculate that for products primarily differentiated by quality (e.g., utilitarian products such as laptops), customers' reaction to recommendations could be less sensitive to their perception of taste matching; in such instances, the informational value of taste matching is likely to diminish. Future research could examine if the advantage of user-based framing relative to item-based framing depends on whether taste or quality is the more salient decision criterion for a particular product.
Importantly, the more customers are familiar with the digital world, the more experienced they are with recommender systems and might develop their own understanding of how these systems work. For instance, ethnographic work on recommendations shows that experienced customers tend to game with the recommender system to generate desired recommendations ([17]). This suggests that experienced customers might interact with recommender systems more rationally and deliberately choose to click or not to click on recommendations with the purpose of improving the quality of future recommendations. For instance, customers might resist a recommendation related to the opposite gender's taste not only because they perceive a mismatch with their own taste, but also to avoid misidentification by the recommender system and to prevent any future recommendations associated with the other gender. The implication is that customers who are more experienced with recommender systems could be more likely to scrutinize taste matching efforts. We see this as a fruitful avenue for future research.
As a concluding remark, in a blog post, Netflix has acknowledged that it provides explanations for why it has recommended a movie or show to gain customers' trust ([ 2]). Our research advances this notion by revealing that when companies explain a recommendation to their customers, the decision of which framing to use, user-based or item-based, is crucial in terms of its impact on recommendation click-throughs.
Supplemental Material, DS_10.1177_0022242919873901 - Making Recommendations More Effective Through Framings: Impacts of User- Versus Item-Based Framings on Recommendation Click-Throughs
Supplemental Material, DS_10.1177_0022242919873901 for Making Recommendations More Effective Through Framings: Impacts of User- Versus Item-Based Framings on Recommendation Click-Throughs by Phyliss Jia Gai and Anne-Kathrin Klesse in Journal of Marketing
Footnotes 1 Associate EditorPraveen Kopalle
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed the following financial support for the research, authorship, and/or publication of this article: Financial support from the Netherlands Organization for Scientific Research (VENI grant 451-15-023 awarded to Anne-Kathrin Klesse) is gratefully acknowledged.
4 Online supplement: https://doi.org/10.1177/0022242919873901
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Record: 114- Managing Status: How Luxury Brands Shape Class Subjectivities in the Service Encounter. By: Dion, Delphine; Borraz, Stéphane. Journal of Marketing. Sep2017, Vol. 81 Issue 5, p67-85. 19p. 1 Diagram, 3 Charts. DOI: 10.1509/jm.15.0291.
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Managing Status: How Luxury Brands Shape Class Subjectivities in the Service Encounter
Although a large body of research has investigated how consumers use goods to signal their status, little is known about how brands manage status. The very few studies that have examined this topic are grounded in the traditional conception of status and focus on the possession and display of status signals. The authors offer an alternative understanding of status management by investigating the role of interactions in the service encounter. Drawing from extensive ethnographic work in luxury stores, they investigate how brands (re)configure the status games that surface in the service encounter. They show that through the material and social cues of the servicescape, brands shape consumers’ class subjectivities—that is, they make consumers behave as class subjects who have a specific understanding of their position in the social hierarchy. Thus, managing status requires the active creation and management of consumers as class subjects. There is a shift from managing branded goods that signal status to managing customer experiences that make consumers enact status positions. This research helps identify new ways to manage status brands, especially luxury brands.
The luxury business has changed dramatically over the past two decades. Luxury firms began as niche businesses, limited to the happy few who could afford their products. Today, luxury is very different; it is an actively growing sector, targeting an expanding clientele (Kapferer 2015). Scholars have made significant efforts to explain the repeated two-digit growth in the luxury industry and its sustained success (Fuchs et al. 2013; Han, Nunes, and Dre‘ze 2010).
Luxury brands are different from other types of brands because they follow logic rooted in their sociological characteristics that is fundamentally different from that of mass-market brands. It is not the objects or the brands themselves that define luxury but the social relationships that develop around them (Dion and Arnould 2011). Luxury draws on social stratification and is considered the exclusive privilege of the elite, who use it as a sign of distinction and an affirmation of their status (Han, Nunes, and Dre‘ze 2010; Kastanakis and Balabanis 2012; Ordabayeva and Chandon 2011). While new forms of hedonic and experiential motivations emerge (Berger and Ward 2010; Dion and Arnould 2011), status display remains an important driver of luxury consumption, especially for newly wealthy and powerless consumers (Han, Nunes, and Dre‘ze 2010; Ruvio and Dubois 2012). A large body of research has investigated how consumers use luxury goods to signal their status—that is, to signal their relative position in social hierarchies (for a review, see Dubois and Ordabayeva 2015). Whereas some statuses are communicated explicitly through visible codes, such as brand logos, others are communicated implicitly through subtle signals, such as iconic design or specific materials (Berger and Ward 2010; Eckhardt, Belk, and Wilson 2015).
However, very few academic studies have examined how luxury firms manage status. This is particularly important as luxury brands have expanded to reach broader and growing audiences (Kapferer 2015). Because access to luxury brands was once considered the privilege of a minority, this market expansion is challenging the status game (Bellezza, Gino, and Keinan 2014). Few studies have emphasized the importance of finding the right balance between expanding to less affluent market segments while preserving a luxury brand image in the eyes of the seriously rich, their core clientele (see Dubois and Ordabayeva 2015). Preserving this balance is crucial when expanding the customer base (Amaldoss and Jain 2005; Berger and Heath 2007, 2008) and when extending the product portfolio (Hagtvedt and Patrick 2009; Kapferer and Laurent 2016). This body of research focuses on brand and product consumption, with the assumption that elites consume a distinctive constellation of objects that express their status position and that public signaling of these status markers demonstrates their social position (Holt 1998). It focuses on how to maintain the brand as a status marker that consumers can manipulate to signal their position in the social game.
Downplaying possession and public display of status symbols, another body of research offers an alternative understanding of status by investigating the role of daily practices and interactions. It suggests that status is expressed through implicit evaluations in everyday social interactions because all interactions involve micropolitical acts of status claiming in which people constantly negotiate their positions (Goffman 1967). Thus, the focus is no longer on the possession of goods that signal status but on the status game that surfaces in social interactions. Research in sociology and, more recently, consumer research has shown that the service encounter plays a significant role in the status game (Be´langer and Edwards 2013; Leidner 1993). This research offers a useful perspective on the understanding of social dynamics and the negotiation of class differences in the service encounter (Be´langer and Edwards 2013; Hanser 2006, 2007; Jeantet 2003; Sherman 2005, 2007, 2011; Williams 2006; Wright 2005). U¨ stu¨ner and Thompson’s (2012) study of class disparities in hair salons exemplifies this perspective. The authors identify a network of structural relations that reconfigures the asymmetrical distribution of class-based resources between different class factions. They show that rather than being cooperative endeavors conducive to the formation of commercial friendships, these class-stratified market interactions produce interdependent status games and subtly manifested power struggles.
From this perspective, we examine the symbolic power of brands in social dynamics. Our aim is to understand how the brand (re)configures the social game that surfaces in the service encounter. We believe that a brand is not only a means to express one’s status but also an active stakeholder in the class struggle that produces enactment of status. We suggest that brands shape the social game in the service encounter and make consumers enact a specific status. Analyzing social games dynamics enables us to go further than the traditional Veblenian approach to possessions and the display of status symbols (Veblen 1899 [2004]). We maintain that the brand is not only a status marker, as previous research has stated, but it also makes consumers enact a position in the social hierarchy. This addresses Holt’s (1995) call for research that offers a deeper understanding of social distinction through social interactions and not just through brand or product choices.
Drawing on extensive ethnographic fieldwork in luxury stores, we investigate how the brand (re)configures the social game that surfaces in the service encounter. We contribute with insights on both branding and the servicescape. First, we show that brands manage status by shaping consumers’ subjectivities—that is, they make consumers behave as class subjects who have a specific understanding of their position in the social hierarchy. Managing status requires the active creation and management of consumers as class subjects. Thus, there is a shift from managing branded goods that signal status to managing customer experiences that make consumers enact status positions.
Second, retail spaces play a critical role in shaping class subjectivities because they provide a locus for class “learning by doing.” The physical and social cues of the servicescape form a sociomaterial assemblage that enables the brand to shape class subjectivities. The brand acts as a gatekeeper, model, and broker of class. It signifies the types of consumers that are welcome, provides a class-model that consumers must perform to feel socially accepted, and configures social hierarchies. Through that process, the brand forces consumers to enact class positions and shape class-consumer subjectivities, performing an enactment of status. Retail spaces shape status by providing a locus for the learning by doing of social position. These theoretical contributions on the role of the brand and the service-scape in the social game enable researchers to identify new ways to manage luxury brands and, more broadly, status brands.
Service Work and Class Disparities
We base our analysis on the sociology of service work, which investigates inequalities in the service encounter. Early studies aimed to highlight the defining characteristics of service work (as opposed to manufacturing work). Hochschild (1983) introduced the concept of “emotional labor” and described emotions as a central component of service work, because service workers try to produce positive emotional states in customers through their demeanor (e.g., cheerfulness, welcomeness, deference) in ways consistent with firms’ guidelines. From that perspective, several studies have analyzed the classed nature of service work. For instance, Williams and Connell (2010) describe how employers in high-end U.S. retail settings try to attract class-privileged workers who embody particular styles that correspond to the lifestyle of the brand. Otis (2008, 2011) introduces the concept of market-embedded labor, which places even greater emphasis on the class dimension of service work. She demonstrates that employers in Chinese luxury hotels expect their workers to adapt their manners and styles to match the aesthetics, taste, and expectations demanded by specific sets of consumers in these high-class settings.
A large body of service work has focused on the class inequalities between customers and service workers and investigated how these inequalities are acted out and negotiated through service interactions. For instance, studying luxury hotels, Sherman (2005, 2007) shows that inequality takes the form of workers’ interactive and situational self-subordination to consumers through expressions of deference, anticipation of guests’ needs, and customized service interactions. By shaping the entitlements and dispositions of workers and guests, interactive service work both performs and legitimizes inequalities. It reproduces class dispositions, legitimating inequality of entitlement to care. In such places, it seems normal that some (the wealthy) can make enormous demands on others. Similarly, Hanser’s (2006, 2007) studies of Chinese department stores highlight the enactment of class-coded claims to status recognition and entitlement through service interactions. These studies demonstrate the differences between a traditional socialist department store and a modern luxury department store. New expectations about service interactions, associated with high-end retail settings serving wealthy customers, involve new forms of subordination and control over workers. These ethnographies in Chinese stores show that service interactions are central to the construction of an elite culture and entitlements as well as the emergence of class cultures. Similarly, Williams’s (2006) ethnography of toy stores shows how customers enact a class-based sense of entitlement and how service interactions provide contexts in which sociological inequalities take on concrete meanings. Williams compares two toy stores that sell similar merchandise but are inscribed in very different socioeconomic patterns (upscale vs. popular) and demonstrates that retailers tailor service interactions according to their clientele and in ways that reflect classed interactional expectations.
Other studies on interactive work have focused on understanding how service workers cope with management’s demands for deference and people’s needs to assert their sense of self. For instance, U¨ stu¨ner and Thompson (2012) study rural migrant service workers in metropolitan Turkish hair salons that serve middle- or upper-middle-class women. They identify a network of structural relations that reconfigures the asymmetrical distribution of class-based resources between class factions. They show how rural migrants and lower-class urban workers accept managers’ and consumers’ governance as self-improvement rules that enable them to accomplish their own identity project as modern middle-class consumers. Sherman (2005, 2007) shows how service workers in luxury hotels respond to their subordinate position in relation to guests by establishing themselves as superior to other workers. They invoke multiple hierarchies of worth and advantage based on competence, status, need, intelligence, morality, and cultural capital to establish themselves as superior to other workers and to their clients. Highlighting skills and advantages that others lack enables service workers to recast themselves as powerful. Other ethnographic studies conducted in different contexts reveal similar workers’ strategies to reinterpret, or invert, the guest–worker hierarchy that produces worker consent (Jeantet 2003; Otis 2007).
Studies of service work offer three key insights into the understanding of class and service interactions. The first is the way in which they conceptualize class. They do not treat social class as an individual-level variable but consider it as a practice, arguing that social interactions are key situations in which class operates (Bettie 2003; Lamont and Fournier 1992). In other words, social class differences are not inherent or voluntary individual traits; they are socially constituted through daily interactions (Gray and Kish-Gephart 2013). This situational definition of class is in line with Bourdieu’s analysis of the importance of everyday interactions in understanding the dynamics of power and social domination (Hanser 2012). Downplaying public displays of class and status symbols, Bourdieu (2000) highlights that class is continually reproduced as an unintended consequence of social interactions. Social interactions generate acts of class assertion in which people constantly negotiate their social positions (for a review, see Holt 1998; Vikas, Varman, and Belk 2015). Following Bourdieu, the perspective of researchers on service work shifts from class as revenue inequality to class as “inequality-in-action” (Hanser 2012, p. 293). It is a situated accomplishment rather than a given (West and Fenstermaker 1995).
A second key insight offered by studies of service work is the way they consider the service encounter. They place the interactional dynamics of class at the center of the analysis of service interactions and reconceptualize the service encounter as an important locus for the class struggle, in which class-based entitlements are created and social hierarchies are expressed, reproduced, and reconfigured (Be´langer and Edwards 2013; Leidner 1993; U¨ stu¨ner and Thompson 2012).
A third key insight is the way they consider the brand. As Otis (2008) notes, service workers embody the values of a brand to achieve the aesthetic and interactional styles demanded by the social context. The process of internalization means that working in a luxury store conveys the status of a social elite. As representatives of a luxury brand, service employees embody a certain style, taste, and class.
In this article, we attempt to close the loop on the interrelated findings in service research by examining the symbolic power of brands in the class dynamics that surface in the service encounter. We believe that a brand is not only a means to express one’s status but also an active stakeholder in the class struggle. Our aim is to understand how the brand (re)configures the social game that surfaces in the service encounter. This addresses Holt’s (1995) call for research that offers a deeper understanding of class distinction through daily practices and not just through brand or product choices.
Ethnography in Luxury Stores
We adopted an inductive approach to our analysis of luxury service encounters. Our analysis is built on continual comparisons between the data collected through field observations and interviews with experts and consumers, inductive data analysis, and the scrutiny of data through a variety of conceptual lenses. We collected four data sets to establish a comprehensive overview of interactions in the service encounters.
In the first data collection, we focused on the customer perspective. We interviewed 30 consumers of luxury products. The interviews were nondirective; we let informants talk spontaneously about their experiences in luxury shops. We selected the respondents on the basis of our theoretical concerns (Yin 1990). Our goal was to obtain different perspectives on luxury stores by interviewing regular and occasional customers of luxury brands. We also introduced sociodemographic diversity in terms of age and nationality (see Table 1). The diversity of informants allowed us to challenge the validity and scope of emerging interpretations by triangulating the data across informants and through the search for limiting exceptions (Schouten and McAlexander 1995).
To gain a marketer’s perspective on service interactions, we interviewed 39 experts (i.e., general managers, marketing managers, consultants, store managers and sales staff; see Table 2). The interviews were nondirective; we let informants talk spontaneously about luxury stores, beginning with “grand tour” questions about participants’ professional backgrounds (McCracken 1988). To avoid prompting informants, we did not mention issues of class or status in the interviews. We discussed the characteristics of retail in the luxury industry, visitors’ instore behavior, and the management of customers in the store. Interviews lasted from 60 to 150 minutes. As with our customer sample, we selected respondents on the basis of our theoretical concerns and recruited employees with varied experience of working with luxury goods (i.e., marketing, retail, sales and store design). We emphasized sales expertise and interviewed sales trainers, store managers, and employees working in heritage, flagship, department, and regular company-owned stores (see Table 2). In addition, we attended two lectures given by store managers to our master’s of business administration students at ESSEC Business School. These two three-hour lectures focused on store management, including the selling ceremony, managing sales associates, and training. To preserve the anonymity of these high-profile informants, we withhold more detailed information. However, they work or have worked in the following companies: Balenciaga, Boucheron, Barbara Bui, Bulgari, Cartier, Ce´line, Chanel, Chaumet, Dior, Dolce & Gabbana, Givenchy, Lancel, Ralph Lauren, Christian Louboutin, Maison Kitsune, Mont-Blanc, Porsche, Pucci, Saint Laurent Paris, Elsa Schiaparelli, Tesla, Tiffany, Van Cleef & Arpels, and Louis Vuitton.
| Name | Status | Country of Origin | Age (Years) | Gender | Duration (Min.) |
|---|
| Abdullah | Regular customer | Lebanon | 24 | M | 65 |
| Alexis | Regular customer | England | 54 | M | 55 |
| Alicia | Regular customer | France | 32 | F | 55 |
| Anne | Regular customer | France | 67 | F | 45 |
| Arnaud | Occasional customer | France | 44 | M | 35 |
| Aur elie | Regular customer | France | 49 | F | 50 |
| Boris | Regular customer | France | 50 | M | 75 |
| Cecilia | Occasional customer | France | 47 | F | 40 |
| Christine | Occasional customer | England | 34 | F | 40 |
| Claire | Regular customer | France | 43 | F | 45 |
| Elisa | Occasional customer | France | 32 | F | 65 |
| Elodie | Occasional customer | France | 53 | F | 45 |
| Herve | Regular customer | Belgium | 50 | M | 50 |
| Hyewon | Regular customer | South Korea | 32 | F | 51 |
| Jacques | Regular customer | France | 62 | M | 40 |
| Jane | Regular customer | France | 35 | F | 40 |
| Jason | Occasional customer | United States | 30 | F | 45 |
| Josh | Regular customer | United States | 35 | M | 42 |
| Kate | Occasional customer | United States | 30 | F | 50 |
| Leila | Occasional customer | Morocco | 30 | F | 48 |
| Lila | Regular customer | Hong Kong | 34 | F | 49 |
| Lilou | Regular customer | France | 22 | F | 35 |
| Lisa | Occasional customer | France | 45 | F | 30 |
| Margot | Regular customer | France | 41 | F | 45 |
| Meng | Regular customer | China | 28 | F | 45 |
| Mireille | Occasional customer | France | 51 | F | 50 |
| Nico | Occasional customer | Switzerland | 51 | M | 40 |
| Raiju | Occasional customer | India | 27 | M | 60 |
| Sabrina | Regular customer | Canada | 33 | F | 47 |
| Sophie | Occasional customer | France | 27 | F | 35 |
| Yuriko | Regular customer | Japan | 36 | F | 45 |
TABLE: TABLE 1 Face-to-Face Interviews with Clients
We conducted 28 observations in luxury stores in Paris. Our aim was to collect instances of power dynamics in the service encounter and triangulate interview data. Claiming to be looking for a wedding anniversary gift, we visited a wide variety of stores in terms of brand and store characteristics. In eight cases, we visited multiple sites for the same brand to highlight convergences and divergences between outlets within the same brand. Observations lasted between 25 and 45 minutes, and we used a semi-open observational grid, giving us the opportunity to add unexpected items. The grid was organized around three dimensions: the substantive staging of the point of sale, interaction with sales staff, and personal impressions and emotions during the service encounter (Baker et al. 2002; Dion and Arnould 2011).
Simultaneously, we monitored e-stores, websites, forums, and blogs where customers and brand representatives recounted their experiences in luxury stores (Borghini et al. 2009). We hoped that this wide collection of websites would increase our chances of collecting a diversity of experiences from luxury insiders (managers and regular clients) and outsiders (occasional and nonclients). We collected 1,736 posts. Table 3 describes each forum, the questions debated in them, and the number of posts collected for each one.
We coded the 1,736 online posts and 59 narrative accounts using open coding and analyzed them using a hermeneutic approach (Thompson 1997). We continuously revised our provisional coding through an iterative process of analyzing transcripts of the verbatim interviews and relating them to our emerging theoretical understanding of our interviewees’ observations (emic meanings) and our own (etic categories). We continuously studied the literature about service encounters, luxury, service work, and status. By triangulating our qualitative data, we enabled significant insights to emerge (Thompson 1997).
Shaping a Class-Consumer Subject
Our results investigate how the brand (re)configures the social game that surfaces in the service encounter. By exploring these dynamics, we highlight the role of the material and social cues of the servicescape that give brands symbolic power in the social game and enable them to shape consumer-class subjectivity.
| Name | Expertise | Work Location | Age (Years) | Gender | Duration (Min.) |
|---|
| Alex | Advertising and branding | Multiple European countries | 60 | M | 60 |
| Anais | Sales trainer | France | 34 | F | 55 |
| Anette | Waitress | France | 32 | F | 55 |
| Anthony | Branding | France | 48 | M | 45 |
| Antoine | Store design | Multiple European countries | 55 | M | 70 |
| Antonin | Sales associate, pop-up stores | France | 24 | M | 40 |
| Bertrand | Advertising | Multiple European countries | 65 | M | 120 |
| Cecila | Communications Director | Multiple European countries | 54 | F | 45 |
| Christian | Sales associate, luxury store | England | 29 | M | 45 |
| Claire | Store manager, retailing | France | 43 | F | 45 |
| Cynthia | Sales associate, luxury store | France | 27 | F | 50 |
| David | Marketing | Switzerland | 30 | M | 40 |
| Eliane | General management | France | 47 | F | 40 |
| Elisa | Sales associate, luxury store | France | 26 | F | 45 |
| Emile | Sales associate, flagship store | Belgium | 28 | M | 60 |
| Enca | Store manager, department store | France | 36 | F | 180 (lecture) |
| Erika | Sales associate, flagship store | Hong Kong | 42 | F | 45 |
| Gerard | Store manager, department store | France | 49 | M | 50 |
| Gisele | Store manager, heritage store | France | 57 | F | 40 |
| Helmut | Marketing | Multiple European countries | 39 | M | 45 |
| Jeanne | Sales associate, heritage store | Switzerland | 28 | F | 45 |
| Juana | Sales associate, luxury store | France | 31 | F | 82 |
| Laura | Sales associate, luxury store | France | 27 | F | 90 |
| Laurie | Designer | Monaco | 47 | F | 110 |
| Li-Ann | Sales associate, department store | China | 26 | F | 45 |
| Luciano | Sales associate, luxury store | Italy | 36 | M | 75 |
| Maria | Store manager, flagship store | France | 42 | F | 180 (lecture) |
| Marianne | Marketing | France | 66 | F | 55 |
| Marie | Sales associate, department store | France | 31 | M | 45 |
| Maurice | Branding | France | 58 | M | 70 |
| Michel | Advertising | Multiple European countries | 70 | M | 35 |
| Michele | Mystery shopper | France | 27 | F | 60 |
| Nikaia | Sales associate, luxury store | Czech Republic | 29 | F | 74 |
| Shan | Sales associate, department store | Hong Kong | 30 | F | 45 |
| Shio | Sales associate, department store | China | 28 | F | 50 |
| Sophie | Retail | France | 31 | F | 50 |
| Thifaine | Sales trainer | France | 59 | F | 45 |
| Wang | Sales trainer | China | 37 | F | 57 |
| Valery | Marketing | Multiple European countries | 62 | M | 80 |
TABLE: TABLE 2 Face-to-Face Interviews with Brand Representatives and Experts
Social Legitimacy
Entering a luxury store raises questions related to social legitimacy. As Nico explains here, the spectacular and luxurious architecture and atmosphere of luxury stores create social inferiority complexes in people who are not familiar with such environments:
You feel like it’s a superior universe to yours. The shop is intimidating: you have to dare yourself to go in…. You’re curious, you’re going to go in, but stepping through the door is difficult. It gives you an inferiority complex. You’re aware of your lack of taste, you feel like a slob…. This shop sends out all the signals of luxury: beautiful materials, a subdued atmosphere, the black/gold colors…. There is something elitist in the store design. You’re entering a high-end universe, where the products, the materials are superb…. It’s like stepping into an alien environment. You’re not at your ease, the people are all more sophisticated, more classy. It’s like moving in a social environment that is superior to your own. (Nico, occasional customer)
Facing the opulence and the magnificence of stores, consumers question their own status and evaluate their social fit. They feel more or less legitimate depending on their economic capital (wealth) and/or their cultural capital (tastes and practices developed through their education and experiences). As Nico explains, this perception of social inferiority creates social intimidation and exclusion. In luxury stores, social intimidation is key to maintaining the exclusivity and desirability of the brand. When there are few clients in the store, these clients experience the feeling of exclusivity: exclusive clients who are buying an exclusive product (Joy et al. 2014). Exclusion also reinforces the desirability of the brand. As Ward and Dahl (2014) demonstrate, for aspirational products such as luxury brands, rejection drives people to attempt to affiliate by improving their attitudes toward the brand and their willingness to pay for the brand’s products.
| Forum Titles | No. of Posts | Blog Forum Link | Accessed |
|---|
| “What does luxury mean to you?” | 101 | http://clubshiseido.fr/forum/qu%E2%80%99%C3%A9voque-le-luxe-pour-vous | 02/28/15 |
| “Clerks who look down at you” | 287 | http://forum.doctissimo.fr/famille/argent-budget-famille/vendeuses-boutiques-prennent-sujet138951.htm | 10/18/13 |
| “What can we expect from sales representatives?” | 119 | http://blogs.lexpress.fr/styles/mode-personnel/2012/02/13/que-peut-on-attendre-des-vendeuses/ | 10/18/13 |
| “Internet and luxury: it’s complex” | 33 | http://blogs.lexpress.fr/styles/cafemode/2009/10/13/internetetleluxeitscompli/ | 12/20/13 |
| “Louboutin: just like dogs!” | 135 | http://blogs.lexpress.fr/styles/paris-by-light/2012/03/17/louboutincomme-des-chiens | 12/20/13 |
| “Anthony, luxury vendor: another experience of sale” | 41 | http://blogs.rue89.nouvelobs.com/lundismatin/2012/09/16/anthony-27-ans-vendeur-chez-hermes-une-autreexperience-de-la-vente-228409 | 02/28/15 |
| “Free admittance?” | 21 | http://hpyl.blogspot.fr/2013/03/entree-libre.html | 11/10/14 |
| “I have never been to Herm` es: luxury intimidates me too much” | 12 | http://www.typepad.com/services/trackback/6a00d8341c676f53ef01538e37b934970b | 12/23/14 |
| “Sales representatives in luxury” | 5 | http://www.radical-chic.com/post/2005/06/27/148-vendeuses-de-luxe | 09/15/12 |
| “What does luxury mean to you?” | 12 | https://fr.toluna.com/opinions/1028949/qu-%C3%A9voque-pour-vous-le-luxe | 09/15/12 |
| “What does luxury mean to you?” | 27 | http://fr.answers.yahoo.com/question/index?qid=20070518175118AAzLygg | 11/13/13 |
| “Oprah’s a liar: sales assistant in Swiss racist handbag row denies telling TV host that she could not view item because she couldn’t afford it” | 943 | http://www.dailymail.co.uk/news/article-2389798/Oprah-Winfrey-branded-liar-Swiss-sales-assistantracist-handbag-row.html | 07/20/15 |
TABLE: TABLE 3 Blogs and Forums
The merchandising and the atmosphere—silence or sophisticated music, large empty spaces, only a few products displayed, no price tags, and so on—intimidate consumers who are not familiar with such environments. Some objects are particularly intimidating. For instance, an informant explained that when a jewelry brand decided to show masterpieces in the windows of its store on the Champs Elyse´es to demonstrate the heritage and savoir-faire of the brand, window shopping increased while in-store traffic decreased. The high-value pieces reinforced the intimidation of low-status customers.
However, it is not only the materiality of the servicescape that is intimidating but also the fictional representations of the clients who shop in luxury stores and consume luxury products. Nico emphasizes that people in luxury stores are socially superior to him. Another informant explains that she cannot enter a luxury jewelry store on place Vendome in Paris because “it’s restricted to millionaires or business tycoons.” Interestingly, whereas consumers generally consider shopping as a female activity, informants associate luxury shopping with the figure of a powerful male. Similar to gendered spaces (Fischer, Gainer, and Bristor 1998), the intimidation of consumers who feel socially illegitimate is based on both the environment itself and the representations related to aspects of the environment and product category. The materiality of the servicescape generates a form of symbolic violence (i.e., tacit and legitimate violence that operates symbolically; Bourdieu and Wacquant 1992) on consumers. This symbolic violence intimidates consumers who feel that they are not socially legitimate in the store.
The representations associated with the servicescape also exclude consumers who, for ideological and moral reasons, do not want to assimilate the values they associate with luxury (e.g., showing off, superficiality, vulgarity, lack of morality) and do not want to immerse themselves in that world. This echoes the moral and ideological debate around luxury and the threeway opposition between moral condemnation of luxury as unnecessary and perpetuating social inequality, the epicurean vision of luxury as a source of pleasure, and the capitalist vision of luxury as a source of power and status (Godart 2011).
Sales personnel can reinforce or neutralize customers’ social intimidation and perception of their lack of social legitimacy. Previous research on sales staff in luxury stores has noted their arrogance and analyzed their impact on clients’ perceptions and behavior (Sherman 2011; Wang, Chow, and Luk 2013; Ward and Dahl 2014). Similarly, our informants related many stories in which the behavior of service employees reinforced intimidation, feelings of exclusion, and lack of legitimization. For example, the discussion “Clerks who look down at you” on the forum doctissimmo.fr generated 287 posts describing the tactics sales assistants use to force undesirable clients to leave the store. This is Delphine’s experience:
I also had a very unpleasant experience at C—, where the sales staff are not known for their friendliness… I went there on the spur of the moment, and what I was wearing that day made me look like a penniless student (a V10 fabric bag and denim shorts). It was a pure “Pretty Woman” moment: the clerk assured me there were no handbags left (no handbags?? at C—? lol), and she refused to show me any styles…. Mindblowing. (posted on blogs.lexpress.fr)
Sales staff may adopt a dismissive attitude toward customers to reinforce their lack of social legitimacy. Among our informants, both customers and sales personnel described several practices used to make customers feel uncomfortable: haughty facial expressions, unintelligible language, inappropriate answers to customers’ inquiries, not offering to help, pretending that products are out of stock, and so on. Conversely, when sales personnel judge consumers as socially legitimate because of their economic and/or cultural capital, they welcome them warmly and provide them with outstanding service. In this way, store assistants act as the gatekeepers of the social class. They ensure that only the “right type” of customer engages in consumption in the store (Hanser 2007; Johnston and Sandberg 2008). Through the way these gatekeepers welcome and treat customers, they either confirm customers’ legitimacy or exclude them. In doing so, they protect themselves from acknowledging their own subordination (Jeantet 2003; Sherman 2011) and simultaneously reinforce the brand’s symbolic power in the social game.
Frontline employees’ attitudes and behavior are formed not only by training or management but also by preexisting beliefs related to the product they are selling (Wright 2005). In selling luxury branded products, sales associates assimilate themselves with representations of luxury. Marie, a store manager, explained this in a lecture she gave to our students:
Some sales associates take themselves for divas. They have a strong personality. They are good with the clients, but they are divas. They need to express themselves…. Divas are like this:
[She stands up and starts mimicking their behavior. She puts her hand in her hair and creates an effect like in a L’Ore´al commercial and crosses the stage walking like a model. She stops walking, puts her hand back in her hair and looks at us with an arrogant attitude.]
You know… they are like that.
[She sits back.]
We have a couple of divas like that in our store. (Maria, store manager)
As Marie explains, some service workers embody the values of a brand to achieve the aesthetic and interactional styles demanded by the upper class. They internalize an idealized aesthetic and the values of the brand (Du Gay 1996; Hancock and Tyler 2000; Pettinger 2004; Otis 2011; Wright 2005) and affiliate themselves with upper-class consumers. This perception enhances their perceived power in the service interaction. They do not consider themselves as service workers in a subordinate position. Because they affiliate with upper-class consumers, they consider themselves in a more dominant situation.
Materiality plays a significant role in enhancing these beliefs. As Maria explained in the talk she gave, when sales associates wear sales uniforms with luxury brand logos on them, they have a greater tendency to behave like divas. We observed strong differences in staff attitudes for the same brand depending on the type of store. This is what Marie said about her colleagues in the heritage store of the brand for which she works:
Marie: In the shop-in-the-shop at Galeries Lafayette [luxury department store], it is a bit more friendly. I always welcome customers with open arms. I am friendly, smiling—and lots of my customers tell me how nice that is. They feel under less pressure when they shop here. But at Montaigne [the location of the heritage store], it’s different. My friend, who was a sales assistant there, had to look impeccable. She was on her feet all day long. It’s a totally different atmosphere. Even the way you represent the brand is different. At Galeries Lafayette, we have to look good, but we have a little bit more freedom, whereas at avenue Montaigne, you have to be absolutely perfect.
Interviewer: Did you have the same training?
Marie: Oh, yes, sure, we were trained in the same way, but you know, it’s really the idea of “Montaigne.” It is very cold and serious, I don’t know why. (Marie, store manager)
As Marie highlights, her colleagues working in the heritage store are more aloof simply because of their environment. They reproduce the representations of luxury that are strongly embedded in the architecture and history of the heritage store. In the most sanctified locations, such as heritage stores (Dion and Borraz 2015), store assistants appropriate the sacredness of the place by being more aloof and more distant with customers.
The power of sales associates is also embedded in the routines of the selling ceremony. Visiting luxury stores, we noted the technical vocabulary sales associates use to describe products, techniques, and the tools used to fabricate them. By mastering the language of luxury-related terms and behaviors, sales associates develop a specific register some of their clients do not possess. The sales associates who control this specific symbolic capital can control interactions through the performance of expertise. They impress consumers with the suitability and likeability of their expertise. This reinforces their power in the service interaction and the social anxiety of consumers unfamiliar with luxury. This is consistent with Cayla and Bhatnagar’s (2017) insights on service workers in India, and the prestige and recognition that gym trainers build from expert knowledge and language use, which enables them to control the service interaction.
Stores’ intimidation level depends on the physical and social cues of the servicescape—location, opulence and size of the store, merchandising, merchandise on display, the profile and number of shoppers, the profile of frontline workers, and so on. Most occasional customers told us that they prefer to shop in department stores where they feel much more comfortable. As Elisa explains, there is a retail hierarchy:
As I am used to going to luxury stores, I usually feel quite comfortable going in. I don’t even see the doorman or guards. In fact, that depends partly on the place…. For instance, jewelry stores…. Oh no!… I can’t go in there…. It’s so magical! I feel like it’s restricted to millionaires or business tycoons…. I have no problem going into luxury stores inside department stores, it’s easier, you don’t feel people are staring at you, you can have a look around, you don’t feel obliged to buy, perhaps it’s less luxurious, there is a hierarchy…. Place Vendome, that’s the image that I have of inaccessible luxury. (Elisa, occasional customer)
Elisa describes a retail hierarchy based on stores’ inaccessibility. She believes there are stores restricted to “millionaires and business tycoons” that she does not feel legitimate to enter, but also more open-access stores, such as shops within department stores, that she feels more comfortable visiting. Less intimidating department stores are viewed as gateways to luxury shops. By shopping online or in department stores, customers are initiated into the world of luxury and gain the self-confidence to shop in more exclusive places. Like the management of a product line, entry-level stores provide a first experience of the brand. Through these entry-level experiences, consumers with low social legitimacy have the opportunity to improve their brand literacy and to become more familiar with the “codes of luxury”—a set of behaviors, skills, manners, vocabulary, and understanding of the social games. As consumers do so, they enhance their cultural capital, which drives social acceptance and avoids disapproval, ridicule, and exclusion.
In summary, the physical cues of the servicescape and the representations related to the environment and the product category perpetrate a symbolic violence on consumers who believe they do not fit with these collective beliefs and thus exclude themselves. This symbolic violence is reinforced by the floor staff, who embody the values and representations associated with the servicescape cues and develop a specific classregister vocabulary and behavior that some of their clients do not have. As a result, a natural segregation is created between consumers who feel legitimate and those who do not. Notably, we observe a reversed power relation, in which the service worker is in a dominant position over clients who are at the bottom of the social hierarchy. As we show in the next subsection, the social game is also based on how consumers assess others’ social class to position themselves in a range of social hierarchies.
Social Hierarchies
Customers position themselves in the social hierarchy by asserting themselves and comparing themselves to other customers. While “new money” customers use economic capital (wealth) to assume a position superior to others, other customers invoke a multiplicity of criteria such as morality, celebrity, sophistication, expertise, good manners, and brand literacy to reinforce their domination and legitimacy. This is the case with Josh, American “old money,” on visiting a store:
The clients looked like the same Chinese tourists I’ve seen shopping in luxury stores around the world—exhibiting little personal style or sophistication, and wearing as many logos at once as possible. A young Chinese woman tried on quilted shoulder bags. She contemplated one in bright red leather, which, except for the color, looked exactly like the black one she already owned that was slung across her body. I wondered who she was, where her money came from, and for a moment felt a jolt of disgust for the Chinese nouveaux riches, which I assumed her to be. (Josh, regular customer)
Josh’s reaction echoes interclass stereotypes and tensions between new and old money. He mocks the lack of cultural sophistication, vulgarity, and ostentation of the nouveaux riches shopping at C—. He also questions the origin of their money. His disgust suggests that he assumes their wealth is illegal or sullied in some way. By criticizing “new money,” he reinforces his own social class and old money value system—that is, cultural sophistication, good taste, good manners, and wealth acquired in an acceptable way. Many of our informants, both clients and staff, strongly criticized new money and highlighted inappropriate behavior in stores, such as sleeping on the sofas, talking loudly, touching the products, being rude to sales staff, and sitting on counters. Through these critiques, they assign themselves a superior position in a social hierarchy.
This hierarchy is based on multiple criteria. For instance, Margot links customers’ attitudes and behavior in service encounters to geopolitical issues:
The Chinese and Russians have this kind of arrogance, a bit like getting their own back, because they’re aware of the reputation they have around the world, especially in France and the West, which are inclined to take the attitude, “We’ll show you how it’s done.” The Chinese and Russians both weigh in with their money…. The Chinese have a kind of inferiority complex when it comes to their history and their image, so they get in first and are rude to us before we have a chance to be rude to them. They come along with the attitude, “It’s thanks to us that you’re in this job,” and “We are the new masters of the world,” so they don’t treat sales assistants with kid gloves. (Margot, regular customer)
These social hierarchies do not necessarily map onto the traditional social boundaries drawn around personal resources (i.e., economic, cultural, and social capital), but they do map onto larger issues related to morality, beauty, or geopolitical factors. For instance, customers evoke the economic power of their country to assert their domination. By challenging the Western countries’ focus on cultural capital, non-Western customers find a way to resist the kind of cultural domination that Western brands impose (Cayla and Elson 2012; Dong and Tian 2009) and are able to assert their own domination. At stake here are the criteria determining people’s positions in the social order. This echoes the multiple hierarchies of worth and advantage that interactive service workers mobilize to establish themselves as superior to other workers and to some customers (Sherman 2005).
Sales associates play a key role in this social competition because they can emphasize clients’ status by overplaying the ceremonial for elite clients’ exclusivity, spending more time with them, and displaying attention and deference. These ceremonial rules acknowledge consumers’ roles and status (Goffman 1967). To reinforce consumers’ positions in the social hierarchy, sales associates manipulate the materiality of the servicescape. The way they deal with customers in the space materializes and objectifies the differences in clients’ positions in the social hierarchy. Annette, a waitress in a chic cafe´, told us that she was asked to seat customers according to their status:
We welcomed a couple of seniors and I sat them at a table where we usually seat clients who are dressed with brands. You know, we had specific instructions: good-looking people wearing expensive items in the front row, and others in the back. I know it sounds horrible…. Anyway, there was this retired couple who also embodied another kind of luxury, a more traditional luxury. As I had seated them in the front row, my manager immediately reprimanded me: “What are you doing? You put an old couple right here! What do they look like? They are going to scare customers. Get them out of here!” I had to make them move and sat them next to the restroom. (Annette, waitress)
Materiality crystalizes inequalities between clients. As Annette explains, in the chic cafe´, trendy clients are seated in the best places and old-fashioned seniors at the back, near the restroom. This echoes the situation in many service settings, such as cruise lines or airplanes, where elite guests are spatially isolated from the other passengers and have private facilities. These practices make status positions salient and emphasize them in a dramatic way.
The objects involved in the service are important signifiers of social hierarchies. When recounting their store visits, our informants describe many artifacts that made them feel legitimate and positioned them in the social hierarchy:
We went back to C—for a fragrance refill, which is a basic product and a minimal experience. So we went into the shop, and had not taken two steps before we were welcomed with “Hello, Mr. —, Hello, Mrs. —,” and immediately they served us champagne and all that stuff. They had recognized Je´rome. They really welcomed us in an amazing way. I kept thinking “Wow! They are so great.” I was really surprised, especially because we were only coming for a perfume refill. (Alicia, regular customer)
As Goffman (1981) notes, objects assist in defining social situations or “frames,” and indicate, among other things, social status and power. Champagne is the material artifact that best signifies social position in luxury. Given the representations associated with champagne, offering a glass is a way of declaring and acknowledging consumers’ elite status. Customers also compare the way they are served with the way others are served. Informants relate stories in which they felt degraded because they were offered juice or coffee whereas other customers were drinking champagne.
Consumers also manipulate the material cues of the servicescape to objectify their social position. The following story is a telling example of this:
At H—there are huge drapers’ tables, and on the tables are all the scarves, perfectly folded. And in front of the table, there is an assistant, dressed in a suit and standing ramrod straight. A customer wearing a fur coat comes along from the other side, thrusts her hand into the middle of the pile of scarves, lifts the whole lot up, and lets them fall any old how [laughs], so everything’s a mess. The assistant looks at her impassively. I thought to myself, “That bloody woman!” and as for the sales assistant, good for her, keeping her cool in front of such a hateful customer. (Boris, regular customer)
This customer seems interested not in the product, but in demonstrating the domination she can establish through the way she handles the products. She overplays the position of domination that the service encounter gives her through the asymmetry of obligations between customers and service workers (Goffman 1956). Objects become a locus of status struggle. Consider how this consumer uses the scarves to express her position in the social hierarchy. Interestingly, she does not seem to differentiate between the products and the sales staff, in that she treats both with contempt. The social struggle takes a material form here. This customer acts out her dominant position in the social hierarchy using the material cues in the servicescape, of which a staff member is one among many.
The status competition is not only played out in public demonstrations (Goffman 1956, 1967); it can also include private and unseen interactions with sales staff. In the way Juana describes how she manages very important clients (VICs), we see the importance of behind-the-scenes actions in acknowledging clients’ status:
We [sales associates] have always a lot of contacts, telephone contacts with VICs. They are the first to get to know the collections. Even if the collection is not displayed in store, as soon as we have a product in store, we show them a bit secretly, bringing it to the changing room. So they are the first to see and to purchase the collection…. They know I call them as soon as the collection comes, and I directly put aside specific products for them so they are secured for them. I also offer them gifts. For example, we had some clients that I knew were coming to Paris, so I booked to have lunch together. I also offer my VICs special gifts that are not related to the brand. I mean, we won’t give them a wallet, but if I know that this customer likes opera, and that she is coming to Paris, then I will book opera tickets for her. (Juana, sales associate)
Juana acknowledges elite-client status by engaging in a unique and personal relationship with them. She does not do this conspicuously in front of other clients; she keeps it very private and even secret. To receive such privileged treatment, elite clients usually book their visit in advance, and “their” sales associate accommodates them in a specific part of the store and provides them with customized service: a private lounge, preselection of products, preferred refreshments, and so on. Some stores give VICs access through a back door and exclusive access to specific areas such as a private lounge, brand museum, or workshop. These actions strengthen clients’ status by acknowledging the way customers position themselves in the social hierarchy. However, it is important to note that in these circumstances there is no direct comparison with other customers on the floor and no showing off. By having a private relationship with a brand (through brand ambassadors), customers position themselves at the top of the social hierarchy. For them, it matters less to demonstrate their status to others through overt or subtle signals than to have an exclusive relationship with the brand.
Thus, luxury service encounters imply particular patterns of social roles and relations that question consumers’ position in the social hierarchy. This social hierarchy is crystalized by the materiality of the servicescape and the selling ceremony, which assign consumers to a specific class and enable them to perform their social position simultaneously. Having discussed how symbolic hierarchies develop, in the next subsection we investigate the normative frameworks of the field that provides a class model.
Normative Behavioral Framework of Class
The service encounter provides a class model with which most customers try to conform. They manage their appearance and behavior to match their representations of a luxury client. Notably, this practice is not limited to newcomers who might be worried about their lack of social legitimacy and try to hide their social stigma (Goffman 1967); it is also used by regular customers who want to show their mastery of the codes:
From years of experiences in luxury shopping, I know how to dress up to be looked at differently in store and how to approach the sales assistants to get them to [find] the products on my shopping list faster and more efficiently than others. When I have planned a visit in advance, I wear clothes from the brand. The sales assistants tend to be more respectful and it feels like I’ve been accepted in the world of the brand. (Hyewon, regular customer)
Several of our informants described the preparations they make before shopping; others related how they feel uncomfortable if they cannot prepare appropriately. Customers try to control their body language, emotions, and appearance to conform to the normative codes. They adapt their comportment as they think appropriate to a luxury store—that is, they walk slowly, speak softly, change their way of speaking, and so on. As Goffman (1951) explains, behaviors that involve etiquette, dress, deportment, gestures, intonation, dialect, vocabulary, and bodily movements are important symbols of membership of a given class that are displayed during informal interactions. Most consumers respect the etiquette established by the brand. They want to speak the language of the servicescape, conform to the symbolic rules, and interact with employees and other consumers in the correct way. People follow unwritten rules and obey without even posing the question of obedience (Bourdieu 1998). They acquiesce to power relationships and modes of authority. This form of submission is a way for consumers to reinforce their social legitimacy and establish a higher position in the social hierarchy. It is an attempt to negotiate their class condition, get an “upgrade,” and look like elite consumers.
However, some customers who hold a strong position in the social hierarchy refuse to conform to the normative codes of behavior and submit to the symbolic violence of the brand. They subvert the rules and assert their position among lowerpositioned actors, including other customers and frontline employees. This attitude is emblematic of Meng, a regular “new money” client:
I waited in the store for about four minutes because all the sales assistants were busy with customers. The store manager (I guess, because he wasn’t wearing a uniform) greeted me when I tried on a pair of shoes by myself. He asked if I needed any help and introduced me [to] an assistant store manager who found a pair in my size. The assistant store manager asked me if it was comfortable and then reminded me about the detail on the heels and complimented me. I decided to buy them right away. After I decided to buy the shoes, he asked if I would like to see something else…. I’d noticed that other customers had been having coffee earlier, so I asked if I could have a drink. He apologized immediately, and then asked me and my friend to choose from several drinks. (Meng, regular customer)
Meng’s high economic capital gives her a dominant position in the social hierarchy but, unlike some of our other informants, she does not respect the informal rules of the store: she tries on shoes without asking and without being introduced to the product through the proper sales ritual, and she asks for a drink. Bourdieu (1984) explains that this kind of behavior is emblematic of people who have high symbolic capital (social, economic, and/or cultural). Because of their dominant position, customers with high symbolic capital can go their own way.
Other clients perform their dominant position by playing with the rules of the place. Because they own enough cultural capital, they master the rules of behaving and talking and know how to be perceived as legitimate (Jeantet 2003). Rather than directly transgressing the social rules, as Meng did, these customers prefer to demonstrate their dominant position in subtler ways that display their cultural capital. They have sufficient cultural capital to know how they can transgress the rules and legitimatize the transgression at the same time. This is a way to free themselves from the symbolic violence of the brand. For this goal to be successful, people must master certain fundamental codes of behavior that support their cultural capital (tone of voice, physical bearing, vocabulary, etc.). For instance, Anne, a regular customer, first checks that sales assistants have recognized her dominant position and know that she is familiar with the rules, before she tries to get around them:
This week I had to go to B—to get my watch repaired. I went straight to the person behind the counter and said: “I want to get my watch repaired, but I don’t want to wait for six months and pay V500.” They smiled and took me up to a small private room on the first floor…. They did the work in just three minutes and I didn’t pay anything!… They knew who they were dealing with … that I wasn’t fooled into playing their game! I knew I hadn’t respected the protocol! I had a good laugh about it. They were very polite, they looked at me, they smiled. (Anne, regular customer)
Anne transgresses the rules with the complicity of the floor staff. Because they recognize her dominant position, they agree to play her game. Thus, the normative framework is fuzzy, and its strength depends on the position of consumers in the social hierarchy.
To avoid deviant behaviors, most firms use material artifacts that constrain behavior and prevent behaviors not aligned with the behavioral model promoted by the brand: armchairs rather than sofas to avoid clients lying down; high counters to discourage clients from sitting on them; an absence of music, which forces clients to speak softly; and so on. As this retail manager explains, the materiality of the store forces people behave in very specific ways:
There are some stores without any musical background. And the silence makes the atmosphere a bit stuffy. And there are also the materials they use: dark wood…. It’s a heavy, a very heavy atmosphere. You can hear people walking, there are few people…. I remember a very small store: few products visible, you could see them through a glass wall, far away, behind the counters. What with the counter and the glass wall, you couldn’t look closely. It’s like someone was saying: “Don’t touch!” (Sophie, retail manager)
These material “affordances” (Gibson 1977) constraint clients’ behaviors and prevent them acting in a way that does not fit with the normative model of the place. In addition, most stores have several private lounges or multiple rooms that sales associates can use to create “spatial deference” toward some consumers (Goffman 1967) or, conversely, to isolate deviant consumers from other customers.
In summary, the service encounter is embedded in a normative framework that coordinates social interactions and shapes customers’ behaviors. The material and social cues of the servicescape force consumers into the roles of luxury consumers. Most customers respect the behavioral etiquette established by the brand and fit into the role of the luxury consumer that the brand provides. This form of submission is an attempt to negotiate their class condition and to perform the role of an elite consumer. However, some customers who have high symbolic capital (economic and/or cultural) may want to reject the normative model of the brand to show their dominant position. As elite-class consumers, they can reject the model of class that the brand tries to impose. As we show in the next subsection, the service encounter also plays a role in the fabrication of the class-consumer by formatting consumers’ cultural capital.
Upper-Class Cultural Capital
The service encounter shapes consumers’ cultural capital—that is, the social assets of a person (e.g., taste, hobbies, style of speech and dress) that promote social mobility in a stratified society (Bourdieu 1984). Sales assistants educate customers about the brand through the brand’s storytelling—and, more broadly, about the tastes and practices of the upper class—to offer them opportunities to acquire the cultural capital of the dominant class. As Alex explains, luxury firms help customers shift to more sophisticated and refined consumption and taste:
Consumers keep thinking, “Under what circumstances will I use all this? How will I choose?” The brand says, “Come on, I’m going to help you. Come with me, I’ll explain everything to you.” So of course, clients become more discerning because they are more educated. So, they also become more critical about things—for instance, whether goods are sophisticated and so on. And the experience with the brand becomes even more important. For instance, C—and contemporary art. With their museum of contemporary art, they show the iconography of the future, good taste, talent, sophistication. The brand takes you by the hand and offers you all this. And this is not just philanthropy; it’s not just the logo at the foot of the poster. It’s more “I’m going to explain this to you, I’m going to give you more knowledge, and then you will be able to talk about important things.” And there comes a point when clients forget their doubts because by then they become part of the network, they can talk about so-and-so’s latest exhibition. (Alex, brand manager)
The sales staff can shape customers’ taste and enhance their cultural capital and thus also participate in the formation of class behavior. They educate customers, helping them develop “good” taste—that is, the taste of the elite class. They acquire new knowledge and more sophisticated practices, which produce new subjective orientations toward their position in the social hierarchy. As consumers gain more cultural capital, they enhance their social legitimacy and their position in the social hierarchy:
The other day I invited Lin [a Chinese VIC] to the restaurant. First, she loved the place and she was perfectly dressed…. I could have taken her anywhere! She was super classy: she had a Chanel hat, she was dressed all in black, entirely Chanel, she had an Herme‘s Birkin bag, she had the watch, the one that costs V900,000…. With Lin, you really can go anywhere, she is really all right! She has evolved dramatically over the past few years. She is no longer the type of Chinese customer who says, “I’ll show you the photo of the bag I want on my iPad” with her children drinking Coke and sitting on the counter. Nowadays, she doesn’t queue. She has understood many things and she’s gotten super hard in business. She recently wanted to buy a C—watch for V500,000. C—had to pay her business class ticket to Paris, and C—also paid for the Park Hyatt, and we invited her to the restaurant for Sunday lunch. Really, she knows the tricks of the trade now! (Ge´rard, store manager)
By educating consumers, firms enable them to move up in the social hierarchy and simultaneously promote the codes of the elite class. This form of education reproduces and legitimates cultural categories that define class inequalities and enhance the brand’s symbolic power simultaneously. This is a critical process, because most customers want to move to higher positions in the social hierarchy, gain social acceptance, and avoid disapproval, ridicule, and social exclusion. This is paramount among ultra-high-net-worth individuals; approximately 65% of those with more than $30 million in assets are self-made multimillionaires, many of whom are new to luxury products (Mellery-Pratt 2015). They know that being viewed as “new money” makes them vulnerable to people at the top of the hierarchy who regard them as illegitimate. They feel a need to demonstrate that they are not just crude, money-grubbing upstarts but have some cultural sophistication. In an article by Mellery-Pratt (2015), Tina, the owner and founder of a private, invitation-only shopping service based in Shanghai, explains:
I’m not only educating clients—they’re also looking [to be educated]. It’s a step forward for them to understand and appreciate things that are beautiful and enjoy a beautiful lifestyle.
This perspective helps explain why many finishing schools have opened in China. Ultra-high-income individuals pay up to $20,000 for ten days of finishing school, where they learn the rules of upper-class etiquette and the codes of luxury brands: pronunciation, history, characteristics, how to use them, and so on. As Bourdieu (2000) notes, people devote great time and effort to reconfiguring their habitus (i.e., the practices and behaviors of their own familial and social environment) because manners, language, behavior, and tastes are embodied and naturalized in identity.
The education process is key not only for consumers who want to enhance their cultural capital, such as new money customers, but also for those who already have the appropriate cultural capital. The way Nikaia manages VICs exemplifies this:
With our VICs, we become more like a styling advisor or a fashion friend. It’s not really friendship because it’s still a completely different relationship, but I think that some of the clients really think that we are like their fashion advisor. And at V—the position is called “client advisor.” I think they really like perceiving us as advisors, as someone who gives them advice on what is fashionable now, what is going on in the fashion world, what is going on not only in fashion but also in like design scenes or in Prague in terms of culture, and restaurants, and everything…. We have morning briefings, which are mostly dedicated to things like new launches and the things that are going on in V—, but we also talk about what’s going on in fashion, what’s going on in culture. So somehow yes V—helps us in that, but I think mostly I did it myself, because I’m interested in it also. (Nikaia, sales associate)
Sales associates explain that they advise customers on many different topics that have little to do with their “selling” work. They provide advice related to the cultural capital of the elite class such as opera, design, fine restaurants, or art exhibitions. This simultaneously inscribes both client and brand in the upper-class world and gives customers access to more refined tastes and practices, what Bourdieu (1984) terms the legitimate culture of the dominant class in opposition to popular culture. Interestingly, sales associates enjoy this dimension of their work because it is also for them a way to enrich their role. Sometimes it gives them the opportunity to enter the world of the elite by accompanying clients to places and events.
As consumers’ cultural capital increases, they grow more confident and, as we noted previously, they become more powerful and may free themselves from the normative behavioral framework of the retail space. However, firms accept this risk because, through education, they position themselves at the heart of elite-class codes and the process of distinction. Consider the way Ge´rard describes Lin, his Chinese client (see the “UpperClass Cultural Capital” subsection). He emphasizes the extent to which she fits perfectly with the codes of the elite class promoted by luxury Western brands, in terms of taste and practices.
Thus, through the shaping of consumers’ tastes, luxury firms make customers perform a consumer class-model. Sales associates act as class brokers who shape not only clients’ consumption decisions but also their class performance and dispositions. They produce consumption choices appropriate to particular class positions and enhance clients’ capacity to occupy these positions. Sales associates reinforce the association between the brand and upper-class cultural capital and reinforce class cultural capital at the same time. This process reproduces social inequalities and legitimates social distinctions.
Discussion
Status Brands: From Signaling Status to Making Consumers Enact Social Position
Although a large body of consumer research has investigated how consumers use goods to signal their status (Berger and Ward 2010; Dubois, Rucker, and Galinsky 2012; Ordabayeva and Chandon 2011), very few academic studies have examined how firms manage status. They have emphasized the importance of finding the right balance between expanding to new and less affluent segments while preserving the image of a luxury brand in the eyes of their core clientele (Amaldoss and Jain 2005; Berger and Heath 2007, 2008; Hagtvedt and Patrick 2009). Whereas this body of research focuses on how to maintain the brand as a status marker that consumers can manipulate to signal their position in the social game, we show that firms also manage status by making consumers enact a position in the social hierarchy.
Downplaying possession and the display of status signals, we offer an alternative understanding of status management by investigating how brands (re)configure status games that surface in the service encounter. We show that through the material and social cues of the servicescape, brands shape consumers’ class subjectivities—that is, they make consumers behave as class subjects who have a specific understanding of their position in the social hierarchy. Therefore, the question is not only one of maintaining the brand as a status marker, as previous research has examined, but also one of making consumers enact a position in the social hierarchy. In addition to managing branded goods that signal status, firms should also craft customer experiences that make consumers enact status positions.
This echoes the emergent stream of research on the construction of the “consumer subject” (Borgerson 2005). For instance, Karababa and Ger (2011) discuss how, during the Ottoman Empire, Turks moved from being the sultan’s subjects to becoming active consumer subjects. Likewise, Giesler and Veresiu (2014) investigate the formation of the responsible consumer subject. They show that responsible consumption requires the creation of consumers as moral subjects and demonstrate that the responsible consumer subject is not a natural state of the capitalist market but a functional construction of its development and stability. Our research contributes to this emergent stream of research on the fabrication of the consumer subject by analyzing the role brands play in shaping the classconsumer subject. Managing luxury brands requires the active creation and management of consumers as class subjects.
Research has investigated the role of brands and media in shaping class representations. Studies have also analyzed the way advertising reflects the range of class audiences or suggests models of the affluent as a “better class” by showcasing its status symbols (Marchand 1985), thus reinforcing and reproducing class inequalities (Cayla and Elson 2012). Our research shows that retail spaces also play a critical role in shaping class subjectivities. While advertising and media shape and diffuse representations of upper-class consumers, retail spaces force consumers to enact class roles. Thus, retail spaces provide a locus of class learning-by-doing. This notion is in line with a recent development in sociology that does not consider social class as an individual-level variable but as a practice, arguing that social interactions are key situations in which class operates. Social class differences are not inherent or voluntary individual traits; they are socially constituted through daily interactions (Gray and Kish-Gephart 2013).
In shaping class-consumers, firms reproduce social inequalities and reinforce the brand at the same time. They reinforce the association between the brand and elite-class cultural capital. Even if the service encounter fosters upward class mobility across the social hierarchy by educating consumers about the tastes and practices of the upper class, the service encounter remains a locus of social reproduction because it legitimizes distinctions between the same objects of consumption and promotes the same ideas of good taste and good manners. Consider how luxury brands shape Asian consumers to Western codes. This education process reproduces cultural categories that enhance the brand’s symbolic power and the ideological hegemony. This is paramount in a period when widespread cultural communication has increased the circulation of symbols, the power of curator groups, and the ranges of behavior that are accepted as vehicles for status symbols (Goffman 1951). Such communication enables the brand to remain an elite-class status symbol and avoid a downward move. Thus, in shaping status, the brand also reinforces its role as a status marker.
The applicability of the insights we have generated is not limited to the luxury sector. They can be applied to other retail and service contexts. Class inequalities are present in every type of service encounter—from airport lounges and supermarkets to car dealerships and restaurants—because all these encounters bring together in one physical space people who occupy distant positions in social space. These marketplaces attract customers from different social backgrounds and with widely diverse economic capital (wealth) and cultural capital (tastes and practices developed through their education and experiences). However, the legitimacy of inequalities varies across business sectors and brands. Some firms with democratic brand values, such as sustainable and fair-trade brands, may find inequalities illegitimate and try to minimize them. In other firms, such as luxury brands, inequalities are highly legitimate, and firms try to enhance them. The legitimacy of inequalities also varies according the cultural context; some countries are marked by a high level of social stratification that sets poor and rich firmly apart, while others have a more egalitarian vision of society. In countries with strong social stratification, class inequalities are more legitimate, and firms play a key role in reproducing them.
Furthermore, our insights are not limited to class inequalities. Similar situations occur with brands that draw on other forms of symbolic inequalities and hierarchies. Consider brands embedded in communities regulated by specific behavioral norms and cultural capital (see Schouten and McAlexander 1995). For example, entering a Harley Davidson store or a surf shop could be threatening for outsiders who do not feel they fit in with the environment and have no idea how to behave and interact with a vendor. Similar to the luxury context, customers position themselves in a social hierarchy by asserting themselves and comparing themselves with other customers. These social hierarchies do not necessarily map onto the traditional social boundaries drawn around class, but they do map onto larger issues related to many embedded factors, such as role in the brand community, brand expertise, and brand literacy.
In summary, it is important to analyze not only the individual meaning of the brand but also its relationship with classifying social systems (Bevan and Wengrow 2010; Holt 1995). In this article, we shift from asking how consumers build and signal their identities to investigating how brands act as market institutions by articulating social inequalities. We show how firms shape consumers’ subjectivities—that is, how they make consumers behave as class subjects who have a specific understanding of their position in the social hierarchy. Managing status requires the active creation and management of consumers as class subjects. There is a shift from producing signals of status to producing enactment of status. These insights have important implications for the literature on servicescape.
The Classed Nature of the Servicescape
Following Bitner’s (1990, 1992) seminal articles, research on the servicescape has provided numerous insights into the role and implications of physical surroundings for the service encounter. Researchers have analyzed the way the material and the symbolic are intertwined in the servicescape to shape customers’ perceptions and behaviors (Borghini et al. 2009; Diamond et al. 2009; Dion and Arnould 2011; Hightower, Brady, and Baker 2002; Joy et al. 2014; Maclaran and Brown 2005; McGrath, Sherry, and Diamond 2013; Sherry et al. 2004). Consumer researchers have investigated the ways in which male and female gender norms and ideals are symbolically and materially encoded in the design of servicescapes and the patterns of social interaction they promote (Borghini et al. 2009;
Fischer, Gainer, and Bristor 1998; Sherry et al. 2004; Walters and Moore 2002). They show that many settings are both gendered and gendering—that is, attractive to a specific gender and reinforcing a system of existing gender roles and relations (Fischer, Gainer, and Bristor 1998; Pettinger 2004). Sherry et al. (2004) note that servicescapes provide a set of social structures that force consumers into gendered roles. For example, male consumers can be provided with a cultural lens to enact the role of athlete or tele-athlete. Our contributions refine and extend these observations.
Our research shows that the servicescape, like gendered space, is both class-based and class-shaping. In other words, the servicescape is attractive to specific class-consumers and (re)configures a system of existing class roles and relations. We show that the physical and social cues of the servicescape form a sociomaterial assemblage that enables the brand to shape class subjectivities. The cues of the servicescape shape class subjectivities in three ways (see Figure 1): they signify the types of class-consumers that are welcome (class gatekeeper), provide a model that consumers must perform to feel socially accepted (class model), and configure social hierarchies (class broker).
First, the servicescape acts as a class gatekeeper, signifying the types of class-consumers who are welcome and deterring others. The physical cues of the servicescape (architecture, interior design, atmospherics, product offerings, and merchandising) elicit representations related to the class of consumers expected in the store. Consumers who believe they do not fit with these collective beliefs feel socially illegitimate and exclude themselves from the place, which creates a natural segregation between consumers. The representations that the materiality of the servicescape elicits also impact employees. Consider the contrast between D—floor staff working in the Galeries Lafayette department store and floor staff in the brand heritage store, the former more welcoming and cheerful than the latter. Employees reproduce the social and cultural representations elicited by the servicescape. By internalizing the representations embedded in the place, employees embody the values of luxury and the status of the social elite. They manage to protect the elite status they embody by ensuring that only the “right type” of customer engages in consumption in the store. Through the way they welcome and treat customers, they reinforce the social intimidation of consumers who feel socially illegitimate and, conversely, reinforce the social fit of other consumers.
Second, the servicescape provides a class model that that shows the right way to behave in an upper-class setting (Figure 1). The representations associated with the place elicit a specific etiquette signaling social acceptability to which most customers try to conform. In addition, the physical setting and material artifacts create affordances (Gibson 1977) that constrain clients’ behavior so that they avoid acting in a way that does not fit with the etiquette of the place. This is reinforced by service workers, who provide customers with a class-model with which they can align. As service workers embody the values of the brand, they achieve the aesthetic and interactional styles demanded by the social context (Otis 2008). In luxury stores, service staff embody the style and behaviors of the eliteclass consumer that consumers can imitate. Thus, the socio-materiality of the servicescape forces consumers to perform specific class models. Consumers enact the role of the elite-class client that the retail sociomaterial environment provides and that they must perform to feel socially accepted.
Third, the servicescape acts as class broker—that is, it objectifies consumers’ position in the status hierarchy and reconfigures class membership (Figure 1). Some status acknowledgments are communicated explicitly through material artifacts such as champagne or spatial placement, whereas others are communicated implicitly through subtle signals or behaviors related to the class habitus, such as gestures, vocabulary, knowledge, and practices that acknowledge clients’ status. Consider how service workers overplay service to acknowledge clients’ status. Some are demonstrated publicly and others privately, limited to the interactions between a customer and a sales associate. Consider the intimate relationship that Juana develops with her best clients and how she tries to emphasize this relationship by providing exclusive services in a confidential way. These markers of status assign customers’ positions in the social hierarchy and force them to enact particular class positions. This is reinforced by the way customers appropriate status markers to reinforce or negotiate their status position. Through that process, luxury firms shape a brand-centric form of social hierarchy that segregates consumers and forces them to occupy and enact particular subject positions as class-consumers. In addition, sales workers reconfigure social hierarchies by formatting consumers’ taste and aligning them with the cultural capital of the upper class. They produce certain consumption choices as appropriate to particular class positions and confer clients’ capacity to occupy these positions. As consumers align with the brand’s taste, they move up in the social hierarchy. In this way, the physical and social cues of the servicescape generate a form of symbolic violence that forces clients into adopting a specific class model. The concept of symbolic violence was first introduced by Bourdieu (1998) to account for tacit, almost unconscious modes of domination occurring within everyday social interactions. Symbolic violence is so naturalized and legitimized that consumers do not experience it as violence. People follow unwritten rules and “obey without even posing the question of obedience” (Bourdieu 1998, p. 103). This form of submission is a way for consumers to reinforce their legitimacy or to establish a higher position in the social hierarchy. For those who do not actually belong to the elite, submitting to the symbolic violence of these places is an attempt to negotiate their class condition—they try to look like upper-class consumers.
An interesting complication arises when consumers and/or service workers free themselves from the symbolic violence of the brand. Some consumers who have a dominant position because of their high symbolic capital (social, economic, and/or cultural) are not willing to submit to this symbolic violence; they do not need to follow the normative framework imposed by the brand to express their social status. They have sufficient symbolic capital to know how to transgress the rules and simultaneously legitimize the transgression. In this way, they free themselves from the symbolic violence of the brand. Similarly, employees who overembody the representations of the elite class may generate organizational dysfunctions and service failures. Consider the experience of the prominent U.S. media figure Oprah Winfrey on a trip to Zurich, where a sales assistant, having failed to recognize her, replied “No, it’s too expensive,” when she asked to see a bag.
In summary, we show that the physical and social cues of the servicescape form a sociomaterial assemblage that gives the brand significant power in the status game. With this power, the brand shapes class-consumer subjectivities and produces enactment of status. This echoes the concept of “class curator,” coined by Goffman (1951, p. 303) to designate a person whose task it is to “build and service the machinery of status,” such as domestic servants, fashion experts and models, or interior decorators. Those who fill these jobs are typically recruited from classes with lower status than the class to which such services are sold. For these workers, daily class work requires them to become proficient in manipulating status symbols that signify a status position higher than the one they occupy themselves. We show that the concept of class curator should be extended to the entire servicescape, including material cues. Both the physical and social cues of the servicescape shape the machinery of status and class. Together, they act as a class gatekeeper, class model, and class broker.
Implications for Managers
Our research indicates that part of the success of luxury brands comes from the way they shape status in the service encounter—that is, how they make consumers behave as class subjects who have a specific understanding of their position in the social hierarchy. This suggests that marketing and retail managers can use the understanding of the roles of gatekeeper, class model, and class broker in luxury and apply them to the marketing of other products and services.
First, luxury firms have to protect exclusivity by managing exclusion from retail spaces and making sure that only the “right people” enter the store. Retail managers can manage exclusion by playing on the social anxiety that people at the bottom of the social hierarchy experience in upper-class locations. They can enhance social anxiety through architecture and design (e.g., closed doors, precious materials, arty shop windows, large and empty spaces), atmospherics (e.g., soft lighting, absence of music, low density), merchandising (e.g., masterpieces in shop windows with price tags, absence of price tags in store, minimal display of goods, showcasing artwork), and social cues (e.g., the presence of door staff and security guards, the dress code of floor staff). These material and social cues of the servicescape elicit representations of the typical consumers who shop in these places. Consumers with low status associate the servicescape with people who are socially distant and superior to them, thus making them feel socially illegitimate and deterring them from entering the store.
Store exclusion should be managed across a network of stores, mixing primary locations that cater to top-of-the-pyramid consumers and secondary locations, such as shop-in-shops in department stores or pop-up stores, that cater to low-status consumers and give them the opportunity to gain the selfconfidence to shop in primary places. The retail strategy of most luxury brands in large cities combines shop-in-shops in department stores, a flagship store, and several company-owned stores. These different stores cater to different clienteles and sell different products. In secondary locations, luxury stores address middle-class consumers and sell mostly small items and goods with large logos. In primary locations, they address upper-class consumers and sell more exclusive collections and ready-towear articles. As consumers move up in the social hierarchy, they obtain access to more exclusive retail stores and a more exclusive retail experience. Interestingly, this segmentation also develops within retail spaces, with some areas being restricted to elite clients. We find similar situations in hotels, planes, or cruise ships where premium clients have access to specific facilities.
To manage exclusion across a network of stores, retail managers should play on the cues of the servicescape, increasing social anxiety in primary locations and reducing it in secondary locations. Firms define the type of location (primary vs. secondary) and, consequently, the level of symbolic violence of the store, taking into consideration brand, market, competitors, client profiles, the retail portfolio (locally and internationally) and retail objectives (e.g., image, revenues).
In managing a portfolio of retail spaces based on social intimidation, firms can maintain brand desirability and perceived exclusivity. Whatever the position of the consumer in the social hierarchy, (s)he will always find a more upscale and exclusive location where (s)he feels (s)he is not accepted. Consumers who shop in secondary locations feel intimidated and rejected in primary locations. Similarly, consumers shopping in primary locations feel rejected because they are not invited into the private lounge, and those who have access to the private lounge feel rejected because they are not invited to visit the workshop or welcomed by the store manager, and so on. This is a way of reinforcing the desirability of the brand, whatever the position of the consumer in the social hierarchy.
In managing consumers’ exclusion, firms should consider the impact of the material cues in the servicescape on the floor staff who embody the values transmitted through its materiality. Service workers internalize representations of the place, which has a strong impact on their relationship with customers. In primary stores, sales staff tend to adopt a distant and superior attitude that amplifies the symbolic violence of the place. Consider the sales associate at C—who refused to sell bags to a consumer who did not correspond to the standard of the brand, claiming that the store was out of stock. Firms should find ways to align staff attitudes and behavior with the type of store and the level of exclusion desired. If firms want to downplay distant attitude, they can shift from individual to collective incentives based on several key performance indicators, including the Net Promoter Score and recommendations on websites such as TripAdvisor or Yelp. Incentivizing floor staff with such collective metrics creates a shared responsibility that drives the floor staff to welcome all customers in an appropriate way, even those who do not fit the stereotypical representations of ideal luxury brand customers. Retail managers can also use material artifacts to manage the way service workers embody the power of the brand. Consider C—sales associates, who wear branded jackets and carry branded bags and thus embody the brand in an extreme way and affiliate themselves with luxury clients.
Second, as a class model, brands format behaviors in the store and align them with the class behavior that the brand promotes. The representations associated with the materiality of the place elicit a specific socially acceptable etiquette to which most customers try to conform. They change their behavior; they walk slowly and talk softly as if they were in a museum or a church. This is reinforced by the behavior of the service workers, who are trained to adapt their manners and styles to match the aesthetics and kinesics of the upper class and thus provide customers with a class model they can imitate.
Firms should consider how to manage deviant customers who do not follow the normative rules of the service encounter. From this perspective, firms should use material affordances to avoid behaviors unaligned with the behavioral model promoted by the brand. For instance, one way to manage deviant consumers is to break the space into different areas, creating the means to isolate them.
Third, firms have to manage social hierarchies in retail spaces (e.g., positioning clients in the symbolic hierarchy, signaling their position in it, enabling them to move up in it). They do this with a wide diversity of status markers: for example, they use physical artifacts to materialize consumer’s positions, such as refreshments and beverages (e.g., champagne vs. juice vs. nothing) and spatial localization (e.g., seating customers in premium locations with premium furniture, brand masterpieces, and major art pieces vs. seating customers in a secondary location without any fancy decoration). Another way to emphasize positions in the symbolic hierarchy is to overplay the ceremonial for clients in upper positions (e.g., calling them by their name, giving them special attention and deference). For instance, on Air France planes, when all the passengers are seated, the senior cabin crew member walks through the plane and gives each platinum client a loud personal welcome, calling him or her by name. Another way to position clients in the symbolic hierarchy is to provide them with exclusive services. For instance, premium clients are encouraged to book their venue; sales associates accommodate them in specific areas and provide them customized service (including product preselection; preferred refreshments; a welcome from the store manager, creative designer, or even the chief executive officer, depending on the client’s status and the firm size). Many stores have private access (e.g., back door, private elevator) and/or specific areas inaccessible to other customers (e.g., private lounge, brand museum, workshop). Consider the Boucheron store on place Vendome, where premium clients have a separate door and are welcomed by artists, craftsmen, and designers in a private firstfloor lounge where the fine jewelry masterpieces are displayed. Thus, sales associates can use both material and behavioral status markers and manipulate them either publicly (to signal status to other clients) or privately (to signal status to the client him- or herself).
Firms can reconfigure status hierarchies by educating consumers. By enhancing their cultural capital, consumers can cross class boundaries. Firms should educate customers about products to alert them of the practices of upper class (e.g., how to use/wear products, how to appreciate products, how to talk about products). For instance, Herme‘s trains consumers on how to wear an Herme‘s scarf. Similarly, wine companies educate consumers about what constitutes a good wine, how to drink wine, how to appreciate wine, and wine culture more generally (e.g., history, wine-making techniques). This education process can take place in store, at production sites, and on the Internet. Brand advertising, websites, mobile apps, and social media provide many opportunities to educate consumers on the “good taste and good manners” of the field. Consider the Moe¨t Hennessy website and mobile app, which emphasize the “art of champagne tasting.” They also enable consumers to be upwardly socially mobile by appropriating the cultural capital promoted in higher social classes. Educating consumers about products enables them to perform their class position in the social hierarchy.
However, consumers’ education goes further than developing brand literacy; it has a larger scope and embraces the cultural and social capital promoted by the elite. Sales associates become lifestyle advisors for cultural events (e.g., opera, fine-dining restaurants, golf tournaments). In line with this, firms should train sales associates on local cultural events and organize social events (e.g., wine tastings, sailing outings, golf tournaments, polo games) where clients can enjoy upper-class leisure and meet people from the same social class—as Porsche clients do at the Porsche rally in Tuscany, where groups of clients enjoy a two-day trip on the Chianti wine trail.
Avenues for Further Research
This study suggests new avenues for research on the service encounter. One obvious extension is to investigate the fabrication of the class subject in other service/retail environments and contexts. It would be worthwhile to study a lowerclass context, such as discount stores or low-cost airlines, to understand how social legitimacy develops, how social hierarchies are structured, how the symbolic power of the brand emerges, and so on. It would also be helpful to understand the importance of the class question in contexts that promote values of equality and unity.
We have focused our analysis on understanding class inequalities. However, these inequalities interact, generating effects that are supplemental to their independent effects (U¨ stu¨ner and Thompson (2012). Further research on inequality must pay attention to the intersections of various inequalities related to class, gender, ethnicity, literacy, or disability.
U¨ stu¨ner and Holt (2010) demonstrate the importance of Western cultural references in animating class hierarchies in the Turkish context. They show how, in less industrialized contexts, social distinctions must be understood in a global context, where the West still controls access to wealth, social mobility, and transnational networks of labor and commodities. Given the emergence of many Asian luxury brands, future studies could investigate the role of non-Western references in the construction of the consumer subject.
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Record: 115- Market Intelligence Dissemination Practices. By: Gebhardt, Gary F.; Farrelly, Francis J.; Conduit, Jodie. Journal of Marketing. May2019, Vol. 83 Issue 3, p72-90. 19p. 1 Diagram, 2 Charts. DOI: 10.1177/0022242919830958.
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Market Intelligence Dissemination Practices
Market intelligence is a cornerstone of the marketing concept and essential to market-focused strategic planning and implementation. Although the importance of market intelligence is widely accepted, how managers can ensure the organization-wide generation, dissemination, and responsiveness to market intelligence remains a persistent challenge. In this article, the authors investigate market intelligence dissemination practices and their resulting managerial responses. Using qualitative methods, the authors identify five market intelligence dissemination practices that either update and reinforce organization members' existing schemas (mental models) of the market or create new, shared schemas of the market. Specifically, they find that the creation, existence, or absence of organizationally shared market schemas is crucial in explaining the effectiveness of different market intelligence dissemination practices. Thus, in addition to being experts on market intelligence, intelligence directors must be authorities on organizational learning and ways to create shared meaning structures that enable disseminated intelligence to be understood and used within their organizations. The authors conclude with suggestions for practitioners on how to manage intelligence dissemination across their organizations more effectively and efficiently.
Keywords: market intelligence; market orientation; market research; market schemas; organizational learning
Market intelligence is the cornerstone of the marketing concept. Ideally, every organizational decision is based on an intimate understanding of how that organization's target markets are likely to react to various value propositions and marketing mix configurations (e.g., [17]; [21]; [29]; [30]). Organizations that are more market-centric and better able to collect, disseminate, and respond to market intelligence consistently realize significantly higher performance levels—such as product success, customer satisfaction, and financial returns—than their less market-focused peers ([27]; [31]).
Although the importance of market intelligence is widely accepted, exactly how managers can ensure that this intelligence is generated, disseminated, and responded to across the organization is less clear. Previous studies have identified the most important attributes predicting intelligence use as organizational culture; perceived reliability, usefulness, or innovativeness of the insights; personal predispositions and task goals; trust in the source and the data; and interpersonal communications among intelligence providers and users ([14]; [37]; [38]; [41]; [43]). However, managers continue to wrestle with how to effectively disseminate and encourage the use of market intelligence in decision making.
In 2016, the American Marketing Association cited "generating and using insight to shape marketing practice" as one of seven big problems facing marketing managers today ([25], p. 34). Specifically, an "argument could be made that while our data and knowledge are rapidly growing, our actual insight is not. What does it mean to have a customer insight that can be leveraged in the marketplace?...How do organizations collect, share, store, transmit and 'use' this insight?" ([25], p. 34). Similarly, the 2016 CMO Survey reported that the "development and use of consumer insights" among chief marketing officers decreased from 2011 to 2016 and asked, "If customer insights are so important, why aren't these numbers increasing?" ([40], p. 24).
Theory and research in sociology and management suggest that the practices of knowledge sharing—rather than the attributes of the knowledge, people, or organizations—might more completely explain how and why market intelligence is (or is not) effectively disseminated and responded to. The practice literature moves from the metaphor of knowledge as an object that can be transferred or passed along ([33]) to a metaphor of practice and learning, in which knowledge is shared through organizational and community practices ([34]). Management researchers have taken a practice perspective to better understand organizational learning, strategy making, and strategy implementation ([ 6]; [24]; [55]; [56]), as have consumer researchers to better understand how brand communities are created and maintained ([50]).
This article investigates market intelligence dissemination practices (MIDPs) and subsequent responses to that intelligence. Specifically, to better understand and explain why market intelligence is used more effectively in some cases and some organizations than in others, we focus on the intelligence dissemination practices that intelligence directors (i.e., market research directors) employ. This research differs from prior research in that it investigates the specific practices of how market intelligence is disseminated, rather than more generic process or organizational characteristics, such as the degree of formality, the importance of trust, or organizational culture (e.g., [15]; [37]; [42]; [43]).
Drawing on case studies, depth interviews with intelligence directors (IDs), and practitioner presentations on best practices, we used grounded-theory development ([22]) to induct and identify five intelligence dissemination practices IDs use that fall within two broad categories: practices that update organization members' existing schemas (mental models) of the market and practices that create new, shared schemas of the market. A survey of 60 IDs supports the face validity of our five dissemination practices. Moreover, we found that IDs use a mix of the five practices within their respective organizations to ensure that they respond to market intelligence on an ongoing basis.
Our research offers three significant contributions to the literature. First, on the basis of our fieldwork and subsequent theorizing, we offer new guidance regarding optimal intelligence dissemination practices within large organizations. Specifically, we suggest an updated role for IDs as both experts on market intelligence and facilitators of organization learning relative to the market. We also provide recommendations for how IDs should use a mix of the two types of practices to ensure the ongoing dissemination and use of market intelligence within their organizations. Second, we identify two MIDPs—empathic and experiential learning practices—that replace existing market schemas (mental models) with new, shared market schemas throughout the organization. Empathic and experiential learning practices can disseminate new, discordant market intelligence throughout large organizations by changing organization members' market schemas, ensuring that end users can understand and use the intelligence, including market intelligence subsequently disseminated by the three other identified MIDPs. Finally, we identify three dissemination practices—distribution, resource centralization, and consultative selling practices—that update and reinforce organization members' existing market schemas but do not replace them. The characteristics and requirements of these practices explain why market intelligence is often not used as IDs had expected or intended.
Our article proceeds as follows. First, we describe the methods we used for our research, followed by a background note on the importance of organizationally shared market schemas to situate our findings. We then present our findings. Subsequently, we discuss our three major contributions and reconcile those contributions with the existing literature on intelligence dissemination. We then suggest future research opportunities and end with our conclusions.
To investigate MIDPs within large organizations, we used an inductive grounded theory approach ([19]; [21]; [22]). Specifically, we used a combination of case studies, depth interviews, participant observation, conference presentations, and company and industry reports on market intelligence to identify dissemination practices and how they explain effective intelligence use beyond the literature. For example, while prior research has found that interpersonal communication is important for market intelligence use (e.g., [43]), we sought to understand the forms or patterns of communication and action involved in the dissemination of market intelligence and what characterizes the logic and procedures that underpin related activities. The method of analyzing practices to better understand organizational processes originated in social theory (e.g., [34]; [49]) and its use has been growing in strategy research (e.g., [24]; [56]).
Our data collection comprised five main components involving ( 1) two extended case studies, ( 2) 35 depth interviews, ( 3) engagement at practitioner conferences and workshops, ( 4) extensive artifacts including industry and company reports on intelligence use and intranets containing commissioned research and research-focused case studies, and ( 5) a follow-up survey. Collectively, our qualitative data provided extensive information on the dissemination efforts of 27 organizations.
We began with two exploratory interviews conducted primarily to understand the nature of the ID's role and the relevance of our line of inquiry to these priorities. We then conducted the first case study with a government-owned insurance and road safety agency (RSA) that is funded by vehicle licensing fees. We chose the RSA because it was aiming to improve the quality and use of market research, which the director of marketing identified as a primary challenge because of differing opinions throughout the organization about the value of market intelligence. Over three months, one of the authors spent considerable time with the RSA conducting multiple interviews with the director of marketing, attending consumer focus groups and internal market research meetings, and analyzing numerous research reports and agency documents that were disseminated across the firm. This first case study was vital to gaining an appreciation of the challenges of improving research use and shaping our ideas about how to investigate intelligence dissemination as a practice, how dissemination was influenced by schemas, and the role of users.
We carried out the majority of our depth interviews before our second major case study. The IDs we interviewed were employed in major multinational firms (e.g., Fortune Global 500) as well as very large government service organizations (see Table 1). To gain a more complete perspective of intelligence practices, we included both product and service firms and a wide range of industry types, including banking, fast-moving consumer goods (FMCGs), telecommunications, electronics, entertainment, and pharmaceuticals. Our sample included firms known to have experienced difficulties in their approach to intelligence use, firms that had instituted particular strategies and programs to advance intelligence use, and firms that were considered leaders in the area. Interviews lasted between one and two hours and took place in Australia, Singapore, England, Switzerland, and the United States. A few key informants were interviewed more than once and by more than one of the authors. Some IDs agreed to participate only if they or their organizations were not attached to particular passages or ideas, so we anonymized all informants and their organizations to ensure everyone's confidentiality.
Graph
Table 1. Depth Interview Informants.
| ID Title | Industry | Informant Location |
|---|
| Corporate Affairs/Marketing Directora | FMCGs (PFC Case Study) | Australia |
| Global Director, Consumer Insights & Strategya | FMCGs (PFC Case Study) | Singapore |
| Strategy Director, Consumer Insights & Strategy | FMCGs (PFC Case Study) | Australia |
| Consumer Insights Manager | FMCGs (PFC Case Study) | Switzerland |
| Director of Marketinga | Government Road Safety Agency (RSA Case Study) | Australasia |
| Manager, Market Intelligence Evaluation and Research | Transportation | Australia |
| Senior Research Consultanta | Banking/Financial Services | Australia |
| Senior Vice President of Brands | Media/Entertainment | United States |
| Customer Experience Insights Manager | Postal/Courier | Australia |
| Insights Manager | FMCGs | Australia |
| Marketing Analysis Manager | Pharmaceutical | Australia |
| Consumer Insights Manager | Health, Beauty, Cosmetics | Australia |
| Head of Consumer Insights | Banking/Financial Services | Australia |
| Consumer Insights & Research Manager | Hardware/Building Products | Australia |
| Insights Director | FMCGs | United Kingdom |
| Head of Consumer Insights | Financial Services | Australia |
| Insights Director | Pharmaceuticals | Australia |
| Head of Market Insightsa | State Trustees | Australia |
| Director of Researcha | Financial Services/Global Consultancy | Australia |
| Brand Manager | Health, Beauty, Cosmetics | Australia |
| Group Manager, Consumer Insights | Telecommunications | Australia |
| Head of Consumer Insights | Energy | Australia |
| Director, Consumer Insights | Toys and Games | United Kingdom |
| Partner | Design Consultancy | United Kingdom |
| Principal Engineera | Electronics | United States |
| Insights Director | Research and Innovation Consultancy | United States |
1 aMultiple interviews were conducted with this informant.
2 Notes: The table includes only informants with whom we conducted and recorded depth interviews. It does not include everyone whom we spoke with in the course of our research, such as those associated with our case studies, at conferences, or whom we consulted more informally.
Our first case study, early interviews, and literature on management practices highlighted the value of adopting a context- and activity-focused approach to the interviews to better understand how IDs perform their roles and the practical realities of intelligence dissemination across large organizations. We began most interviews by asking IDs to describe a typical day, an approach that proved valuable for surfacing and situating the IDs' primary aims, activities, and challenges. A common challenge IDs faced was that of convincing a large and diverse employee base to use market intelligence. Thus, an objective of our interviews was to gain a rich, multilayered sense of the nature of this challenge and what IDs considered critical to dealing with it. We also asked our respondents to describe the firm's history with market intelligence, which enabled us to consider the IDs' operating assumptions, especially as firms tend to have an embedded ethos regarding the value of market intelligence ([38]).
Our initial codes directed attention to how IDs tailored interactions with users to improve intelligence use. Preliminary codes included broad themes such as "profiling" (users) and "contextualizing" (intelligence), but when we realized that various practices might exist, we refocused our efforts on isolating and comparing a broad range of IDs' activities. The insight gradually emerged that IDs adopted different practices to encourage intelligence use across the firm and to deal with persistent challenges, and that the differences in their actions depended on how they approached their role, the role of users, the type of intelligence, and resource constraints. While some IDs saw themselves as intelligence experts efficiently providing users with more or "better" data and insights, other IDs saw themselves as facilitators or educators and acted to create or reinforce a shared understanding of the market so users across the firm could interpret intelligence and perform their roles more effectively. As this understanding evolved, we began to more closely examine the ID–user relationship, the logic underlying dissemination routines and initiatives, and the ways IDs determined the effectiveness of their actions.
The second case study involved a major packaged food company (PFC). In contrast to the RSA, the PFC had recently instituted a major program to enhance intelligence use across the firm and had exceeded industry-level innovation benchmarks in the years before our case study. We carried out seven depth interviews with PFC IDs and intelligence users. We also consulted numerous intelligence reports, documents, and videos the firm provided. The videos captured key elements of a major market immersion program that we discuss in our findings.
Throughout the study, we also gathered data by attending industry conferences where market intelligence was a primary discussion topic. Examples include Marketing Science Institute (MSI) conferences on market research, product development, and so on, as well as several Ethnographic Praxis in Industry Conferences (EPIC). We obtained and transcribed audio recordings of some presentations—such as those from Procter & Gamble (P&G), Miller Brewing, Philips Electronics, and Kodak—and included them in our analysis. During these conferences, we had many informal discussions and brief interviews with IDs and, in some instances, followed up these discussions with emails to ensure an accurate understanding of the issues. Finally, we analyzed a large number of company and consulting reports, frameworks, and tools concerning intelligence use, and special interest forums (e.g., "what is an insight") and discussion papers regarding market intelligence dissemination and use. We also examined the intelligence intranets of some of our informant companies. These intranets contained a range of resources, from past research commissioned by the firm to case studies and tools for improving intelligence generation.
To aid our analysis, we imported all our transcribed interviews, presentations, and field notes into QSR's NVivo software for qualitative data analysis. Following the norms of grounded theory development ([22]), we initially coded our interviews and field notes to identify different types of intelligence dissemination practices and their relative success at ensuring that organizational members outside of the intelligence community recognized and used disseminated intelligence. To maximize the possibility of finding new insights, we also coded IDs' more general observations, concerns, and philosophies relative to intelligence dissemination.
Following the initial coding, we analyzed the commonalities and differences among informants and organizations regarding these practices to develop a common general categorization scheme that we could then use to recode all the interviews. We coded the data iteratively while simultaneously comparing our findings with literature streams in marketing, management, sociology, adult learning, and other research areas as suggested by our findings. Rather than creating a grounded theory of market intelligence dissemination unique to our research ([22]), we explicitly considered prior literature and investigated theories new to us, allowing us to create a more robust model of MIDPs that we could situate within the existing literature ([19]).
Consistent with grounded theory research, we constantly compared practices to articulate what uniquely defined them ([53]). We went through several iterations before developing a practice- and schema-based theory that best explains the data we collected, our interpretation, and our induction. We rejected or redefined several titles and various properties of the dissemination practices before settling on the final model we present here. The iterative processes of data collection, memo development, and dynamic coding allowed us to identify the five MIDPs and determine their effectiveness. During this process a few key informants agreed to be interviewed again, to further discuss their dissemination practices. We also sent explanations of our practices and copies of the manuscript to interviewees to verify the accuracy of our interpretation and representation of market intelligence practices and for additional insights ([ 4]).
Finally, to assess the face validity of our model, we undertook a descriptive survey with 60 IDs to ascertain whether our model resonated with IDs, how much IDs used the five practices, and IDs' impressions regarding each practice's efficiency and effectiveness relative to the others. We present selected results of that survey throughout our article to highlight IDs' reactions to our model. We describe the detailed methods and results in the Web Appendix.
Although we used a grounded theory approach to develop our model of intelligence dissemination practices, our final model relies on schema theory related to individuals and organizations. Thus, this section provides an overview of schema theory and organizationally shared market schemas to ensure that readers understand how we use schema-related terms in our findings.
A mental schema refers to a "cognitive structure that represents knowledge about a concept or type of stimulus, including its attributes and the relationships among those attributes" ([20], p. 98). Schema theory explains how people categorize, anticipate, and respond to various objects, people, and situations on the basis of expectations built up over time and across experiences. As a particular schema is used over time, it becomes more deeply embedded and resists change owing to ( 1) belief perseverance, whereby people ignore disconfirming data or interpret data in a way that confirms the existing schema; ( 2) mere thought, whereby contemplating the concept tends to strengthen the schema; and ( 3) secondhand judgments, whereby people continue to use a schema without reexamining the original data that it was built on ([20], pp. 149–52). Although schemas become more deeply embedded over time, use, and experience, they do change when a person realizes that a schema is inaccurate and results in negative consequences.
Schemas give events meaning and guide behaviors and thus enable individuals to understand and operate within organizations ([ 3]). Organizational schemas arise and persist as a result of experiences in organizations, particularly through organizational myths, stories, and dominant metaphors ([ 3]). Schemas become more similar—or shared—as organization members have mutual exposure to and experiences with organizational social situations ([23]).
The majority of research on organizational schemas involves values, norms, and other attributes related to managing organizations and implementing strategies (e.g., [32]; [46]) and organizational change efforts in management and organizational development ([23]). Marketing strategy research has shown the importance of shared market schemas, which form the basis for a common understanding of the market in market-oriented firms ([21]) and managerial representations of competitive advantage ([13]). We use market schemas to refer to the understanding of markets (consumers, customers, channels, partners, etc.), which can be idiosyncratic to one person; shared market schemas for schemas shared among multiple people, and organizationally shared market schemas (OSMSs) for schemas shared among all members of an organization. Much like personal schemas, schemas related to organizational life are very difficult to change ([ 3]). In this research, we adopt the terminology schema update to refer to updates of existing schemas that create a "tacit reinforcement of present understandings" and schema change to refer to "the conscious modification of [an existing schema] in a particular direction" ([ 3], p. 486).[ 5]
Market schemas for individuals within an organization can be compared to the software running on individual computers within a network of computers. Market schemas define how individuals notice, make sense of, and respond to market intelligence, much like the software on a computer allows the computer to identify, process, and respond to data. Data can be noticed and processed only if the schema/software has defined the variables to be processed a priori. Schema update practices are the equivalent of updating data within the software—the values of the variables can be altered, but not the definition and format of the variables themselves. Schema change practices are the equivalent of reprogramming the software to accommodate different input variables. Changing the format of the data sent without changing the format for the receiving software leads to loss of the mismatched data. Creating an OSMS is the equivalent of ensuring that all of the computers in the network have the same version of software (schemas) so they can all can process and respond to the new data formats (intelligence) as well as communicate with each other within the network.
Our analysis identified two overarching types of MIDPs: those that update and reinforce existing market schemas and those that change schemas. Schema update practices update the values or levels of attributes within a schema, whereas schema change practices define or change the attributes and their interrelationships within a schema ([ 3]). For example, a restaurant may use Michelin Guide's five assessment criteria to define what is "restaurant quality," as shown in Figure 1, Panel A. Changes in the levels of satisfaction for each of the five attributes—as well as any change in the overall evaluation of the restaurant—would require schema update practices so that employees would be able to react to those changes. However, if the restaurant changed its definition of restaurant quality to incorporate the SERVQUAL service quality attributes ([44]), the desired new OSMS for "restaurant quality" might look like Figure 1, Panel B. To ensure that employees understood this schema of "restaurant quality" and embed it as an OSMS would require schema change practices because the attributes comprising the schema and their interrelationships changed.
Graph: Figure 1. Stylized restaurant quality schemas.aThe Michelin Guide inspection process is detailed here: https://guide.michelin.com/sg/the-inspection-process-sg.bThis panel reflects five SERVQUAL dimensions that make up service quality, as defined by Parasuraman, Berry and Zeithaml (1991).
We identified five MIDPs, categorized by whether they update or change market schemas. Within schema update, we identified three MIDP types: ( 1) distribution, ( 2) resource centralization, and ( 3) consultative selling practices. Within schema change, we identified two MIDP types: ( 1) empathic learning and ( 2) experiential learning practices. As Table 2 shows, we characterize each of these MIDPs across four dimensions: ( 1) the intelligence dissemination metaphors used by informants, ( 2) the role of the ID, ( 3) the role of intelligence users, and ( 4) the relation of the practice with an OSMS. In the last column of Table 2, we provide examples of each type of practice.
Graph
Table 2. Summary of MIDPs.
| MIDP | Metaphors | Role of ID | Role of Intelligence Users | Relationship with OSMS | Examples |
|---|
| Distribution | Distribute intelligence | Distribution manager | Recipients | Required a priori for MIDP to be effective | Reports, presentations, emails, newsletters |
| Resource centralization | Centralize intelligence resources | Aggregator and expert | Requestors | Required a priori for MIDP to be effective | Centralized intelligence database |
| Consultative selling | Consultative selling | Salesperson | Clients | Market schemas not organizationally shared; intelligence tailored to audience | Customized reports and presentations for each user group |
| Empathic learning | Social learning through empathy | Educator | Vicarious, empathic learners | Created through empathic experiences | Ethnographic stories, videos, personas |
| Experiential learning | Transformational experiential learning | Facilitator | Adult learners as problem solvers | Created, verified and elaborated during field experiences | Consumer immersion; individual market contact |
The five identified practices were not mutually exclusive, as all of our informants mentioned using more than one practice. Everyone interviewed for our fieldwork used distribution, resource centralization, and/or consultative selling practices, and over half discussed using empathic or experiential learning practices. In our survey of 60 IDs, 58 (97%) responded that they used at least one of the three schema update practices and 46 (77%) stated that they used at least one of the two schema change practices.
We present our findings relative to our final model and in the order shown in Table 2, beginning with the three practices that update and reinforce market schemas, followed by the two practices that change market schemas.
We found that distribution, resource centralization, and consultative selling practices were effective at updating market schemas but not changing them. This distinction was most apparent when IDs explicitly noted the inability of these practices to effectively disseminate new, unexpected, or surprising market intelligence. Some of our informants then compared these schema update practices with a schema change practice that they subsequently undertook to create an OSMS. Once an OSMS was created, they were able to successfully use schema update practices to disseminate market intelligence that matched the OSMS widely and effectively across the organization.
Overall, participants made the most references to distribution practices, which are characterized by ( 1) IDs' use of distribution metaphors, with ( 2) the ID in the role of distribution manager, ( 3) intelligence users in the role of market intelligence recipients, and ( 4) the presence (or lack) of an OSMS explaining the use (or nonuse) of market intelligence. These practices included reports distributed and presentations made to internal stakeholders, dashboard and key performance indicators, and standard operating procedures for requesting and delivering market research.
Distribution practices were explained using distribution metaphors for disseminating intelligence throughout the firm, with the ID serving as a distribution or process manager ensuring the reliable and efficient delivery of market research. Distribution practices are premised on a conduit metaphor: "Ideas (or meanings) are objects. Linguistic expressions are containers. Communication is sending" ([33], p. 10). To deal with large and complex organizations, IDs established efficient ways to collect, coordinate, and distribute market intelligence across the firm, as evidenced by quotes from a market analysis manager from a pharmaceutical company and an insights director from an FMCG firm.
I'm accountable for ensuring robust procedures around the governance of market research within the organization....A lot of the research that's in place is longitudinal research that gets refreshed every three to six months, so it's really quite transactional around not having to recreate the wheel every time.
It is about intelligence to more staff across the business...and there is emphasis on "more" even if there can be [information] overload...because the system rolls on with that whether through the tracking, or syndicated [research] for example, and one-off project research for a whole host of projects [and] departments.
Intelligence directors gave particular attention to process, governance, intelligence regularity and uniformity (such as syndicated research and standardized reports), and management of a transactional relationship with external agencies and users. While distribution practices could be highly effective, they also yielded unique problems. A focus on "more intelligence to more users"—with highly specified and embedded systems and procedures—meant that intelligence could be distributed across the firm, sometimes for years, without being used or anyone questioning whether it was still useful. A group manager of consumer insights at a telecommunications company explained:
I think what's happened is we've been good at project managing—managing the system, managing the time tables, and delivering our reports to people on time...but, unfortunately, there are some examples where things haven't quite been used the way they should have been, or things have moved on.
Another informant, representing a large national bank, spoke of blindly adhering to a distribution practice and continuing to deliver intelligence across the firm that was no longer relevant owing to changes in the market or to marketing strategy:
I know with our brand tracking research, when I took that on, we'd been doing it the same way for about ten years and, to be honest, even our brand managers weren't using the research....It was not telling them anything new, nothing insightful, and so it was a case of either scrap the program which was costing, you know—close to a million dollars a year to do right across the bank—or look at how you can do it a lot better.
Most informants explained that their distribution practices were initially well-specified processes for disseminating intelligence but had become less useful over time. Processes were stopped or modified only when a new ID arrived or financial pressures exposed related problems.
Intelligence directors also shared successful instances of distribution practices. Highlighting an OSMS pertaining to customer experience and satisfaction, an ID spoke of her previous experience at a major electronics retailer regarding a distribution practice she implemented that was highly successful owing to its efficiency, timeliness, and user engagement with intelligence:
The assessment instrument was something that I put together in conjunction with [our] training college—and so the idea of mystery shopping was really...geared towards customer experience. Okay, in a loose sort of concept, does the customer walk away feeling satisfied?...So for us it was actually about, "Is the training that we have conducted being followed through?" So there is actually a sales process that they need to follow—is that being followed through? And also just basic hygiene factors, such as is the store clean? Is the person well presented with a name tag, et cetera, et cetera?...So again all this information would go back to training, and the training college would obviously be looking at the bottom performers....For those people who had [failed three times], that store would actually receive further training...so they would receive a visit from the trainers. So, there was, again...this sort of feedback loop into training.
As part of an ongoing quality assurance process, this distribution practice had a built-in feedback loop initiating corrective actions (retraining) when the mystery shopping intelligence indicated that retail locations fell below a predefined desired customer satisfaction level. This example, and others like it, highlighted that distribution practices were successful when intelligence recipients understood what the intelligence meant and how to respond within a shared understanding of the market and the organization's marketing strategy.
Across distribution practices, the presence (or lack) of OSMSs explained why market intelligence was acted on (or not). To respond to market intelligence across an organization, people need a shared understanding of what the intelligence means relative to the market, the firm, and the firm's strategy. Distribution practices worked well when the disseminated intelligence fit within an OSMS. However, even with efficient distribution of market intelligence, users who receive intelligence but do not understand what it means will be unable to respond to it.
Among our informants, many held an implicit assumption that others in the organization had the same understanding of market intelligence. For example, the marketing analysis manager at the pharmaceutical company said,
I think everyone really knows what market research is....They know what it is, they know what they need to do.
While a shared understanding of the market may or may not exist at the pharmaceutical company, other IDs noted that people in the organization often had surprisingly different views of consumers, retailers, and suppliers and how to serve them. For example, our telecommunications informant confided,
If you ask some of my team here how do we make money—"Sell more phones." I say, "Yeah okay, but it's not the phones, remember? It's the actual plan that [generates] the $2,600 that people pay every year"...so what I'm getting to is that we need to be more commercially aware of what the business is about.
Other informants noted similar challenges with assumed or conflicting market schemas. The customer experience insights manager for a postal/courier organization noted that in her organization, different schemas of "customers," "satisfaction," and the like were the result of people with different backgrounds joining the organization:
So, there's...really a fragmented kind of language in the business around customers, because for the last 12 months we've sort of had all these new people arrive. Everyone...bringing their views. [Telecom] bringing their views. Utilities bringing their views. You name a bank, we've got them here...no one brings an original idea, they always just bring what they know, so we're kind of having to work a lot with all of that as well, so that's an interesting challenge at the moment.
Resource centralization practices are characterized by ( 1) IDs' use of centralizing and resource metaphors, with ( 2) the ID as an aggregator of—and expert on—market intelligence, ( 3) intelligence users as requestors of market intelligence, and ( 4) the presence (or lack) of an OSMS explaining the use (or nonuse) of market intelligence. Artifacts indicative of these practices included centralized hard copy and electronic reports, centralized electronic databases, and market intelligence dashboards.
A metaphor of centralizing market intelligence as an organizational resource underlies resource centralization practices. For these practices, market intelligence is centralized within the organization and managed by the ID and the intelligence team, as evidenced by quotes from the director of research for a global consultancy and the head of consumer insights for a large multinational bank:
We've developed a research portal...that has key studies conducted since inception....I set up a log of studies...that just shows you exactly what the research study was, who the audience was, to key findings or something. So...anybody can have access to that and see.
What we have is a system that anybody can subscribe to, or can have access to, and they can set up a series of preferences and say, "I'm interested in mortgages...," or, "I'm interested in brand and customer satisfaction work".... They can also choose to be on distribution lists for various tracking research that we do....So, in theory, all of the research goes out to everybody that's on that system.
Within resource centralization practices, IDs described their role as aggregators of market intelligence across the organization and experts regarding all market intelligence held by the organization. For example, the group manager of consumer insights at a telecommunications company explained how a planned aggregating project would allow his team members to be almost omniscient relative to market intelligence, so that:
You would be able to come to discussions or meetings or presentations or whatever, and be able to answer questions instantly, ideally, but also from a wide knowledge base....[We could go from an] "I don't know" kind of conversation—to "Let me look at the facts, and actually I can tell you that's the exact figure you're looking for."
In addition to considering market intelligence to be a centralized resource, informants viewed themselves as an available resource with expertise in market intelligence. The postal/courier ID and the pharmaceutical company marketing analysis manager explained,
[People] like having...a research guru or someone they could kind of go to and say, "I need some stuff about the customer."
I'm on deck to consult where that's required, and I guess through the types of questions that I get, I get an idea of where there might be certain competencies lacking or a little bit of misunderstanding or a little bit of ambiguity.
Within resource centralization practices, users request market intelligence from the ID or search existing insights stored in the centralized repository. An ID from a financial services firm observed,
[Users] come to us and we'll work with them to see if it's something that's already known within the organization or if we need to put together some sort of project or piece of work that will address it.
The greatest challenge for resource centralization practices is that if no OSMS exists, or knowledge of where and how to get relevant market intelligence is lacking, organizational members may not be able to access that intelligence. This problem was described by one of our pharmaceutical informants, who, when asked if everyone understood and had access to what had been captured electronically, responded,
Everyone has access, but it's more or less a theory of "I don't know what I don't know." So, if I don't know that you've run a market research piece looking at the same customer segment that I service, and you haven't thought to inform me, then there's no way of knowing.
To address this challenge, several informants explained their efforts to create greater awareness of existing market intelligence and how firm members could access it. Essentially, they were seeking to create a new OSMS so that firm members would be aware of intelligence and be able to make sense of it. For example, the senior vice president of brands for a media/entertainment company explained,
We created a website which was called the [Company] Brand Brief. The website is accessible for everybody in the company, and on the website it's the entire presentation with all the brand values. Then we follow that up with town hall presentations and Q&A sessions with the senior team leaders in each of the organizations. That's how we secure engagement in the organizations as well.
Similarly, the postal/courier organization's customer experience insights manager described creating a new OSMS to ensure use of a forthcoming dashboard for her organization:
From a customer experience perspective, we are completely redoing our tracking program. We're going out to the market at the moment and [we're looking for] frameworks and languages that we can use as the core of anything when we talk about brand or customer experience....Particularly the consumer end of the market so...we can say "This is the baseline...if you are interested in loyalty...this is where you go....Customer [satisfaction], whatever....This is how it all fits. These are the drivers." All of that...I'm building the language basically around that. My intention is through communication and through working with the business we will make that the new language.
Consultative selling practices are characterized by ( 1) IDs' use of consultative selling metaphors, in which ( 2) the ID acts as a consultative salesperson, ( 3) intelligence users are clients, and ( 4) intelligence is tailored to the existing market schemas of each end-user group. Evidence of these types of practices included ( 1) the upfront work conducted by IDs to gain access to end-user groups, ( 2) efforts to understand and verify each group's existing market schemas, and ( 3) the tailoring of market research to each user group based on their preexisting schemas and expectations. Standing out in comparison to the other types of practices are the ID's efforts to "sell" market research services to groups within the organization and the ID's explicit acknowledgement of incongruent schemas between user groups and tailoring of the intelligence to more closely match each group's existing schemas.
Consultative selling practices manifested in the use of consultative selling metaphors, as well as how IDs referred to themselves, how they "sold" work, and their "clients." These practices were primarily found in situations where IDs relied on user groups to commission market research projects. For example, the ID for a transportation agency referred to his peers as "my internal clients" and explained how he identified and qualified market research opportunities for his team and why that was so important:
It's amazing what information gets exchanged in the kitchen or in the lift or whatever. Just because you're saying "hi" to someone...."What are you working on? Oh, you're working on a $300 million project that I didn't know about? Excellent! Can we chat? You might want to allocate some of that to research and evaluation." So having those relationships and getting known...is really important....My style isn't to be knocking on people's doors every five minutes, but it's when you do have an opportunity to talk to them, that you're delivering really high-quality stuff, so they want you back. That was my philosophy in client relationship building as a consultant....If ever you get an opportunity to deliver something, deliver it in a tailored way, a high-quality way, and an interesting and engaging way, and then they're much more likely to pick up the phone....I don't have a budget. At this time I'm a zero-budget cost center, so I've got a budget to run [my group] and that's it....So the first question I ask is, "Have you got any budget?"
Similarly, the insights manager for the postal/courier organization described how she solicited work that paralleled an outside consultant responsible for selling and managing client projects:
I suppose the trick for us, or the challenge, is...getting the design right in the first instance and then making sure that it sort of hits the mark...understanding their challenges and objectives in a pure business sense....Then once I do get the information...translating it for them into something that's really useful and powerful and demonstrating the value of it. Once I can demonstrate the value of it, then I can create that—I was going to say create that dependence...because that's what I want that to be.
The informant went on to explain how she identifies high-potential internal clients and individuals involved in the decision process—with strong parallels to business-to-business selling concepts of prospecting and identifying individuals in a buying center (e.g., [ 1]).
Intelligence directors using consultative selling practices explicitly acknowledged that different groups within their organizations had different conceptions of the market. To address this challenge, IDs tailored market intelligence for each user group on the basis of their preexisting market schemas. The primary reasons for tailoring intelligence were a concern that users would not otherwise use the market intelligence and the fact that users might not commission more intelligence (a key performance indicator for some IDs) if prior intelligence was not used or was considered to be unacceptable. A consumer insights manager for a building products manufacturer explained,
I had to reposition how I did things, from talking about things as new, to positioning it all as, "Well, you would already know this, but let's think about it differently." So trying to say, "Yes, I know that you know more than I do," because otherwise I was going to alienate the people that I needed to work with. That just wasn't going to work.
The research director for an international consultancy noted the difficulty of delivering insights that are new and incongruent with users' existing market schemas:
If the truth is what they want to hear, that's terrific. But quite often it's not. It's contrary to what they believe or anecdotal evidence or whatever it might be....I hate to [admit it,] but one of our biggest problems [is] we go through the process carefully and accurately. Get the nice results. But it is too often not used....In the end, they have to want to take it further....I think in the end you can't assume that your view of the market is their view—as in even basic stuff.
To address the considerable differences in market schemas across an organization, consultative selling practices tailor intelligence to each user community so that it is similar enough to the community's current thinking to be acceptable, but new enough to cause the community to take some action and commission more research in the future. One ID explained her surprise at the need to customize research when she moved from being an external supplier to being an employee:
Some of the language needs to change and it will depend on who the audience is. You might make five different versions of that pack.
The insights manager for the postal/courier organization described a similar tailoring process:
We might craft specific presentations for particular business areas....You're not changing the findings ever, but you might actually be saying, "Oh, well, this person is working on...driving foot traffic through their stores. So let's pull out all the stuff that's relevant to that. Can we build a narrative around that?"
Essentially, IDs modify intelligence to match the existing schemas of sponsoring groups in an effort to both update and change market schemas, consistent with the incremental bookkeeping method of schema change ([20], p. 152). The explanation of tailoring offered by the European ID for the PFC matched the concept of bookkeeping change almost verbatim:
[My goal is that users are] in complete agreement with the research and it's helping them to see things—like whilst it's not contradictory to their thinking, it's helping to kind of build on their current thinking.
While the underlying logic of the tailoring process is consistent with the bookkeeping method of schema change, almost no empirical evidence has shown that the bookkeeping method actually changes schemas—whether they be market schemas (in our research), organizational schemas in the management literature ([23]; [32]; [46]), or schemas generally in social psychology research ([20], p. 152). Instead, by tailoring the research to the existing schemas of research sponsors, consultative selling practices only update those schemas, as the new information that fits their existing schemas is understood and updated, but information that does not fit those schemas is ignored.
Common to schema update practices is the ability to disseminate market intelligence that conforms with users' existing market schemas. Our informants spent a significant amount of time discussing distribution practices, although most informants discussed them relative to their inability to effectively disseminate intelligence. Analyzing those negative stories, we found that they typically occurred when the intelligence was significantly different from the recipients' current market schemas, which would explain why users were unable to appreciate the significance of the intelligence. Positive examples of distribution practices included some type of schema change practice creating an OSMS undertaken beforehand to ensure that everyone in the firm was able to understand intelligence subsequently disseminated.
Our survey of IDs was consistent with our findings. Among 60 IDs, 95% (57/60) used distribution practices, and among them, 91% (52) used them regularly or frequently. Also consistent with our qualitative work, IDs participating in the survey rated distribution practices as the worst at "ensuring the market intelligence is used by the relevant employees." We attribute this low rating as consistent with the notion that distribution practices are ineffective without a preexisting OSMS—and distribution practices cannot create an OSMS.
Distribution practices epitomize existing conceptions of market intelligence dissemination in the marketing literature (e.g., [14], [15]; [38]; [43]), as well as how dissemination is described in numerous marketing management and marketing research textbooks (e.g., [ 8]; [30]; [36]). However, the metaphor of market intelligence as an object that can be disseminated across the organization and interpreted consistently by staff is the very archetype of knowledge transfer criticized in the learning and practice literature for being underspecified and highly limited ([16]; [18]; [34]; [49]). The practice literature has argued that for explicit knowledge to have meaning for a recipient, the recipient must understand the context of that knowledge relative to some shared, previously established understanding of how to interpret it ([18]).
This view is consistent with our findings: distribution practices are effective at generating a consistent response across the organization when recipients have an OSMS to make sense of the disseminated intelligence and the practices encompass a feedback mechanism to ensure the ongoing relevance of the intelligence to firm members. Conversely, we found that distribution practices are not effective at ensuring firm-wide responsiveness when no OSMS is present to interpret the intelligence or the market has changed and the intelligence is no longer useful, requiring an organization-wide schema change.
Resource centralization practices were the second-most-discussed practices by our informants. Within resource centralization practices, the greatest challenges to effective market intelligence dissemination and use were that users had to search for or request intelligence, often without knowing what was available, and that there was a lack of shared, contextualized understanding of what that intelligence meant, particularly because it was often generated for another objective or group. Some IDs tried to address these challenges through "road shows," standardizing the language surrounding the market and intelligence and creating a market intelligence dashboard. These challenges and efforts highlight the need for an OSMS for users to both search for existing intelligence and make sense of that intelligence. Like distribution practices, resource centralization practices were effective at updating schemas, but not changing them.
Most striking across informants was that the IDs in the process of creating centralized intelligence resources were extremely confident that resource centralization practices were a solution to the lack of intelligence use within their organizations, whereas IDs who had centralized a large portion of their market intelligence were less enthusiastic about the benefits. This finding was largely consistent with our subsequent survey of IDs. Among 60 IDs, 78% (47/60) used resource centralization practices and, among them, 83% (39) used them regularly or frequently, making resource centralization practices the third-most-popular practice. Their effectiveness ratings were also consistent with our initial analysis: IDs using resource centralization practices rated them as the least effective at creating a shared understanding of the market and the second-least-effective method (after distribution practices) at ensuring that relevant employees used the intelligence.
Within consultative selling practices, IDs referred to having to sell intelligence to their colleagues, whom they treated as clients. The selling process mimicked that described in the business-to-business and sales literature, including the prospecting funnel (leads, inquiries, prospects, new orders, and established accounts), SPIN selling (situation, problem, implication, need-payoff), and an overall consultative selling approach ([ 1]; [48]; [54]). To manage these clients, IDs made extensive efforts to understand their existing market schemas, which IDs then used to tailor research proposals and the presentation of results. The IDs believed that by tailoring the intelligence disseminated to each group's existing market schemas, they could balance providing new information that was relevant, interesting, and trustworthy but not so surprising or incongruent with existing schemas (or goals) as to be rejected—objectives that are all largely consistent with the literature on the perceived usefulness of market research (e.g., [14], [15]; [43]).
However, the avoidance of sharing intelligence that was discordant with user beliefs and objectives appeared to render the practices ineffective at creating schema changes, even when such changes were warranted. Thus, consultative selling practices were effective at updating existing market schemas but at not changing them. Furthermore, because the IDs were tailoring the intelligence to locally held market schemas, these practices increased the strength of heterogeneous market schemas within the organization. In theory, these effects would lead to lower levels of market orientation in terms of the organization members' ability to collaborate across groups and respond consistently to market intelligence organizationally ([29]).
Despite our concerns regarding the limitations of consultative selling practices, IDs spoke very positively, and even proudly, about using them. This finding was largely consistent with our survey: of the 60 IDs who responded, 52 (87%) stated that they used consultative selling practices either regularly (58%) or frequently (29%), making them the second-most-used practice, with frequency levels almost as high as for distribution practices (the most frequently used practice). Intelligence directors who used consultative selling practices rated them as the most effective at "ensuring the market intelligence is used" and the second-most effective at "ensuring employees have a similar understanding of the market" among the five practices. However, some of the respondent comments were consistent with our theorizing. For example, a retailing ID rated consultative selling practices as a 6 (out of 7) for both ensuring that intelligence is used and ensuring that employees have a similar understanding of the market, but (s)he noted in the comments:
There is not always a return in this practice and too much customization means that people have different cuts of the same information (so there's no one source of truth).
Moreover, the majority of participants in our initial work and IDs responding to our survey did not explicitly differentiate between updating schemas and changing them. Thus, given the amount of effort IDs invest in tailoring intelligence for specific groups, they clearly perceive that this is a good use of time because "it works." In the sense that IDs get immediate feedback on the intelligence disseminated, they do have a good idea of whether their "clients" are happy. An in-depth look at the interviews and a reading of survey responses make clear that consultative selling practices are effective at disseminating intelligence consistent with user groups' existing market schemas and, thus, at updating and reinforcing existing schemas. However, we found no instances in which consultative selling practices were successful at creating an OSMS or changing locally held schemas.
We found that two types of MIDPs used by IDs—empathic learning and experiential learning practices—were effective at changing market schemas. We label both practices as "learning" to reflect their abilities to change (or create) users' market schemas, paralleling the educational research on learning ([16]; [34]; [39]). Regarding the previously discussed schema update practices, IDs often were perplexed as to why end users did not use the disseminated intelligence. With respect to schema change practices that created an OSMS, IDs were much more certain that the practices would result in end users understanding and using the disseminated intelligence.
Empathy is the ability to imagine oneself in the place of another person with respect to the person's context, perceptions, history, and feelings ([28]). Intelligence directors described empathic learning practices as "transporting" organization members into the world of customers, channel members, and others as a way of "bringing the outside in" to the organization. Empathic learning practices use secondhand stories and artifacts to engender empathy to achieve schema changes and create an OSMS. Empathic learning practices are characterized by ( 1) IDs' use of empathic learning metaphors, in which ( 2) the ID creates empathic learning experiences, ( 3) intelligence users are empathic learners, and ( 4) OSMSs are created that permit users to understand current and future market intelligence. These types of practices included ethnographic storytelling, video-ethnography, photos, personas, and simulated customer environments aimed at creating a common understanding of the market within the organization.
The common metaphor across empathic learning practices was the transporting of firm members into the contextualized life of customers, enabling them to empathize with customers and have a better sense of their needs and desires. Intelligence directors emphasized the importance of sharing stories and artifacts from fieldwork and focus groups to help intelligence users understand the life of the customer, consistent with previous research on the value of such practices in communicating market intelligence to organization members ([10]; [21]). The head of consumer insights for an energy company explained the practice by noting,
You don't actually give people a piece of research, you tell them a story that makes them see how that works in their world and therefore they want you.
A design consultancy partner elaborated on the importance of weaving the "threads" of field experiences, artifacts, and other information together to craft a story that can transport people into the field:
So just like all the good parts of a story, we're trying to pull these threads together and that allows somebody who maybe hasn't been in the field, hasn't been able to spend time with the consumer...[to] begin to have a personal relationship with what that experience is for the consumer.
His colleague explained how the intelligence team, working with staff across functions and management levels as part of a series of in-house workshops, used personas and potential product-use scenarios (supported by market intelligence) to articulate why the firm's understanding of older consumers needed to change, and the value in what turned out to be a successful new line of hearing aids with a sleek design and remote control functionality.
Within empathic learning practices, the ID disseminates intelligence through stories embedded with empathic experiences to create schema change. The head of market insights at a state-owned trust company explained,
So, contact center staff, I wouldn't ever give them huge amounts of graphs and charts, but we always capture the verbatim comments. So, I can give them...a taste of the stats, but actually really using more that qualitative insight, that's the stuff that actually will always resonate....It just resonates a bit more with the audience. Similarly, if I have a focus group, I'll always include...the video recording as well. Again, it doesn't matter how many stats—stats are powerful. You have to have the stats to make sure everyone realizes it's robust, but it doesn't give you that flavor and so I'll always, always merge qual and quant data.
A European ID for the PFC explained that she chose an external research firm because it promised to furnish materials for effective storytelling:
At the moment I'm managing a project where one of the things I actually chose a supplier for was because they're giving us a multimedia output. They're not giving us just the standard debrief, but they're also going to put together a video, which is filled with collages of consumer verbatims and...things from their journals that they did, and just putting together a bit of a multimedia output...[which] we can then use that with our stakeholders in engaging them and try to bring some of these findings to life.
In addition to using ethnographic stories and artifacts that "bring the market into the organization" or "make the market come alive," IDs often use personas to help staff understand and empathize with customers and their contexts ([ 2]). Personas are "fictitious, specific, concrete representations of target users [that] put a face on the user—a memorable, engaging and actionable image....They convey information about users...in ways that other artifacts cannot" ([47], p. 11). Moreover, personas "make assumptions and knowledge about users explicit, creating a common language with which to talk about users meaningfully," as well as engendering "interest and empathy toward users" by organization members ([47], p. 14).
During the 2006 MSI conference "Business Insights from Consumer Culture," Lisa Phelan (Senior Research Manager, Home Entertainment) and Alejandra Arreaga (Research Manager, Connected Planet & Digital New Products), both from Royal Philips Electronics, presented "Making Ethnographic Research Actionable Within a Technical Community," providing an in-depth look at how the company used personas to disseminate market intelligence:
We've defined personas as concrete representations of our core consumers....[We will] say to the development manager or the product planner or the product manager, "Are we still thinking about Louisa? I completely lose her in this picture." Or, challenge and say, "Okay, let's imagine that she's in the room right now, what would she be thinking when we come to her with this as the idea? Are we describing it in a way that she can actually understand what we want to bring to the table?" [It's] a vehicle for bringing real consumers closer to the product's innovation, design, development and marketing process...[including] marketing campaigns, design direction, product road maps, product optimization, target consumer, the value proposition themselves and differentiation from the market....They are identifying consumer needs that are not currently being satisfied...[and] contextualize technology within the consumer's life...not thinking of it specifically from a technology perspective, but the entire world [of the consumer]. ([45])
Across the various types of empathic learning practices, IDs had three key objectives: ( 1) to bring an understanding of the market into the organization, ( 2) to make that representation "come alive" and engage organization members through a process of empathy, and ( 3) to create a consistent, shared understanding of the market across their organizations that could be used to develop solutions, address market needs, and understand future market intelligence. The design consultancy partner summarized the importance of a common understanding:
Especially if you have a group of people involved from different functional areas, as that is a must to get ideas flowing and in a way that is less threatening, yet focused. They know this is not a pretty description, but solid data as we have stuff we draw on...personas for example...to make that situation real. So then one will see potential value by being able to examine a value proposition this way...and another way..."Does the product currently do what it is supposed to," or "Is this new product or branding we are considering...really something better?" There are all these various things about the consumer different people can attach to.
Such shared market schemas are the natural result of cross-functional product development teams, whereby team members are sent into the field to understand markets and, through the process of sharing individual insights, create a shared understanding of the market within the team (e.g., [ 7]; [11]; [26]). However, the challenge for IDs within large companies is that not everyone can be sent into the field to collectively create this shared understanding of the market. Thus, IDs use empathic learning practices—with specific tools such as ethnographic stories, videos, and personas—to bring the market into the organization and create OSMSs. Prior work has highlighted the effectiveness of such practices in creating OSMSs ([ 2]; [10]; [21]).
The final MIDP we identified is experiential learning, which is characterized by ( 1) the IDs' use of transformational, experiential learning metaphors, in which ( 2) the ID acts as a facilitator, providing learning opportunities for organizational members, ( 3) users of intelligence verify and elaborate on ID-provided schemas through in-person market interactions, and ( 4) OSMSs are created that permit users to understand current and future market intelligence. Whereas empathic learning practices create OSMSs using ethnographic stories and other methods, experiential learning practices encourage users to verify and add to these schemas by individually going into the market and meeting with partners, channels, and customers.
During our fieldwork with the PFC we witnessed an example of this practice. "The Amazing Consumer Journey" was the PFC's effort to create a transformational experience for employees, in which they learned about their markets through immersive cross-functional sessions in which the IDs explained the research and thinking behind personas (e.g., "Amazing Budi"), followed by opportunities to meet consumers in person and then go into the market to verify this new understanding. According to the Australian consumer insights and strategy director, the Amazing Consumer Journey is
a kind of a consumer immersion program on steroids....It's run by my function, but it involves cross-functional groups....Essentially, its overarching aim is to try and place a face and name, a personification of a consumer, and bring them into our business. The journey's called Amazing Michelle here, it's been called Amazing Budi in Indonesia—everywhere the consumer's been given a face and a name. People are encouraged to go out and meet with consumers on their own turf, to meet with experts that can provide a commentary about the consumer, and to also actually be the consumer and kind of walk in their shoes and try and spend what they spend on groceries and all the rest of it....How do we create mechanisms to understand the millions of Australians that we're trying to serve with the same level of intimacy that we try to understand our friends and family? It's hard, but the Amazing Consumer Journey is one such thing, looking at data that helps describe their behavior en masse but remembering that it's data that's describing consumer behavior.
The PFC's global director of consumer insights and strategy, who had overall responsibility for the program, explained,
It's about being inclusive and bringing people on the journey and giving them the experience...then it's not just marketing and insight [who are excited] about the consumer. All of a sudden, the whole company is excited about the consumer....I've got people stopping me in the bathroom, in the corridor saying, "This is so great. I now know why I'm doing my job." That's just somebody in audit. "My job's not about audit. It's about Grace. It's about the consumer. Now I feel much closer as well at [PFC] and what we do."
During a follow-up interview a year later, the global director expanded on the program's progress and success:
A key logic is about how you articulate the intelligence, how you speak to what they relate to....Getting people really into it and focused on consumers and doing that across the firm, well this requires a whole new strategic view of how you approach the task so people bond and run with it.
The PFC's Amazing Consumer Journey is a model for experiential learning practices in that the practice was structured to be a transformational experience guided by senior management and market researchers. It also required intelligence end users to verify the proposed OSMSs on the basis of their own first-hand market experiences. In the two years following implementation of the Amazing Consumer Journey, the PFC's net revenues from new products grew from 7.6% to 11%, which the PFC attributed to better intelligence use within the firm.
During the MSI conference "Business Insights from Consumer Culture," Carol Berning (Research Fellow, Victor Mills Society) and Andrew Manning (Senior Scientist, Consumer Ethnographer), both from P&G, explained how the company had adopted experiential learning practices similar to the PFC. Specifically, they encouraged employees to have more one-on-one, in-context interactions with consumers, rather than attending focus groups or watching video recordings of focus groups. Berning and Manning explained that P&G employees conducted field visits and observational studies of more than 100,000 consumers annually:
That research is done in [consumers'] homes, where a researcher or team of researchers is talking to people in the context of their lives about a particular task or product or problem or something that they're doing. Many of these will happen in store, where we're doing shopping with consumers, where we can actually see the decision-making process and the stimuli that goes into that. Some of it is also done...in context locations that we have created in many of our facilities, where people come in and we're in a living room or we're in a kitchen or in a bathroom in one of our technical centers or our business centers around the world as opposed to a sterile table at a market research company where there is no stimuli and nothing to enrich the conversation.... So this interaction is done...by people that work in the company. We have over 2,500 people for whom their job description includes understanding the consumer....Some of those people, in fact a lot of them, are in our R&D function....Our scientists and engineers are responsible for understanding the consumer and then using those insights as they work on projects....[Finally,] we believe in internal capability. There's nothing like a person who works in the business who thinks about it day in, day out, talking to a consumer about that task. ([ 5])
Consistent with the experiential learning practices at the PFC, P&G emphasized the importance of sending out people from across the organization. The explicit goal was not to "do research" in the sense of finding out something new, but to make sure that employees understood the research that the company did have.
Development of internal capability is key. We can't continually hire people and do specific projects, and put those projects away, if this is going to become a way of life. It's a way of life. It's something we have to do every day, every week. Lots of people need to do it. It's part of the corporate memory: all of these homes that people have been in—they understand them....We have hundreds of consumers in our technical center every week, and it's good for the company to do that, because people who wouldn't ordinarily get involved in this research can...drop down and listen to some of this talk and make [their] work more meaningful. It's really very helpful. So our people get lots and lots of experience interacting with consumers in an in-context manner. ([ 5])
Experiential learning practices most closely approximate the cross-functional product development team process of creating a shared schema through shared market experiences (e.g., [ 7]; [11]; [26]). However, within experiential practices, the ID and a small group first create a foundational market schema to be shared. Once the schema is established, the ID and team create experiential learning experiences combining an explanation of the market schema along with opportunities for organization members to personally experience and verify the schemas themselves to create schema change.
Although this practice of having employees from large firms engage with customers on their own has been employed in companies such as Harley-Davidson (e.g., [21]), the theoretical importance of these encounters related to changing schemas has not been addressed in the marketing literature. Conversely, within the education and learning literature, direct personal experience is viewed as the most effective method for adult learning ([16]). Firsthand experiences, rich in context, allow learners to verify the knowledge explicitly communicated and to challenge their own and others' assumptions. Our fieldwork indicates that the efficacy of first-hand experiences in learning applies equally to organization members exposed to incongruent market intelligence: "We resist learning anything that does not comfortably fit our meaning structures, but we have a strong, urgent need to understand the meaning of our experience....We strive toward viewpoints which are more functional: more inclusive, discriminating and integrative of our experience" ([39], p. 223).
The commonality of the schema change practices is their ability to create new OSMSs among organization members. In contrast to their experience with schema update practices, IDs provided no examples of when schema change practices failed to achieve the dissemination of market intelligence. During our interviews and case studies, IDs described schema change practices as taking place on a firm-wide basis, which led to the formation of OSMSs. Undertaking these practices on a firm-wide basis to create an OSMS is an important requirement for their effectiveness. The survey of 60 IDs shows that they rated schema change practices—empathic learning (5.49) and experiential learning (5.77)—as more effective than distribution (4.56) or resource centralization (5.09) practices but less effective than consultative selling (6.13) practices at "ensuring the market intelligence is used by the relevant employees." In addition, while they rated empathic learning (5.41) practices as the most effective at "ensuring that all employees have a similar understanding of the market," they rated experiential learning (4.54) as less effective at ensuring shared market schemas than consultative selling (5.25).
Even though not every ID in the survey rated every practice, the differences in perceived effectiveness were surprising—particularly those related to creating an OSMS. However, the IDs' comments regarding the practices revealed that at least some of the IDs were using schema change practices with smaller groups rather than organization-wide. The concern with local deployment was that individuals and groups would have idiosyncratic interpretations and, thus, strong idiosyncratic and heterogeneous market schemas. Typical of these concerns regarding local and individual schema change are comments from two survey respondents regarding the challenges of experiential learning practices:
Can be cumbersome, messy and often lead to different and very subjective [takeaways].
Hard to share the "experience" with people that did not participate.
While these IDs' responses to the survey were somewhat surprising, they did expose a caveat to our initial model: empathic learning and experiential learning practices can create an OSMS when they are deployed organization-wide. However, if the same practices are employed locally—for example, with only the marketing and sales groups—local groups will experience schema change, but these new market schemas will exist only within those groups. To ensure the creation of an OSMS, IDs must employ the practices organization-wide.
Research has yet to focus on the logic and actions of those responsible for managing market intelligence dissemination across large firms. Intelligence directors are tasked with effective and efficient intelligence dissemination that generates use across the organization on an ongoing basis while also accounting for users with different disciplinary backgrounds, capability sets, and motivations for using that intelligence. Our research identifies five market intelligence dissemination practices IDs employ within large organizations. The MIDPs highlight the importance of and difference between how organization members learn in a way that significantly alters their current and future understanding of the market—by creating OSMSs—and the subsequent dissemination of market intelligence that can be interpreted and understood within a shared frame of reference. Therefore, we find that the dissemination of market intelligence requires two activities: ( 1) creation of an OSMS (schema change) and ( 2) dissemination of intelligence that can be interpreted by the OSMS (schema updates).
Our research suggests an updated role for the ID: that of both expert on market intelligence and facilitator of organizational learning relative to the market. In their role as facilitators of organizational learning, IDs must ensure that firm members are able to understand and respond to market intelligence within the firm. Although researchers have theorized the importance of organizational learning for a greater market orientation (e.g., [51]; [52]), our research allows us to propose specific dissemination practices to ensure such learning.
To make certain that everyone in the firm has the same understanding of the market, the firm must create an OSMS using one of the two schema change practices: empathic learning or experiential learning. The OSMS will ensure that everyone in the organization has the same understanding of the market but will also allow everyone to understand and respond to subsequent intelligence disseminated using a schema update practice: either distribution or resource centralization. Because an OSMS is in place, engaging in consultative selling practices is no longer necessary or useful. Rather, intelligence that fits the OSMS can be distributed to everyone in the same format as the OSMS. Conversely, if subsequent research varies significantly from the existing OSMS (i.e., it is surprising and inconsistent with people's understanding of the market), assuming that the research is valid, the ID will need to update the OSMS using one of the schema change practices.
This holistic picture of dissemination practices and what these practices indicate about managing dissemination over time also contributes to our limited understanding of how to maintain a market orientation. [21] broadly describe two key tasks for maintaining a market orientation but do not examine how to implement them. The first involves retaining employee commitment to market intelligence, which includes bottom-up buy-in through learning, and the second involves ensuring the salience of the OSMS through its refreshing or re-creation. Our research highlights that a mix of practices, if used strategically, can effectively and efficiently activate intelligence dissemination to (re-)create OSMSs and facilitate schema updates across the firm on an ongoing basis.
We identified two MIDPs—empathic and experiential learning practices—that IDs found to be extremely effective at disseminating intelligence that was incompatible with users' existing market schemas. Specifically, these two practices created new market schemas that changed or replaced prior understandings of the market by engaging users through empathy and first-hand market experiences. Using these practices across the organization, IDs are able to create new OSMSs that ensure all users have a common understanding of the market and are able to interpret subsequently distributed intelligence consistently across the organization. Having an OSMS is essential for market-oriented firms ([21]).
Our model builds on research highlighting the importance of organizational culture and interpersonal communications among intelligence providers and users ([14]; [37]; [38]; [41]; [43]). For example, because one measure of cultural strength is the consistency of various schemas among organization members ([23]), deploying these practices and creating OSMSs should generate more collaborative and trusting cultures, which would increase intelligence use ([37]; [41]). For instance, clan cultures emphasize "the development of shared organizational understanding and commitment through participative...communication processes" ([41], page 322)—essentially encompassing schema change practices that create an OSMS—and clans were the only culture within the competing values framework ([ 9]) to have a significant positive relationship with intelligence use and new product development success. In addition, joint customer visits improve dissemination frequency and perceived intelligence quality ([37]), an effect consistent with the ability of experiential learning practices to create OSMSs, which then increases the effectiveness of subsequent intelligence dissemination using schema update practices.
The effectiveness of these practices also parallels findings in the organizational literature. A study on schema changes and organizational transformation showed efforts to change cultural and procedural schemas through memos and meetings to be largely ineffective, whereas highly emotional and personal disciplinary and manipulation efforts were very effective at creating schema changes ([46]). Similarly, findings of a study of organizational empowerment were the most consistent with the conversion model of schema change ([32]), in which overwhelming disconfirmatory information contrary to existing schemas created new, replacement schemas ([20]). Empathic and experiential learning practices created significant engagement, which led employees to try to reconcile their preexisting schemas with the new information or reality indicated by these practices. The inability to reconcile these differences led to creation of new, replacement market schemas.
We also offer new insights regarding previously theorized relationships with market intelligence use that have found limited or no empirical support. [29] theorized the importance of informal versus formal intelligence dissemination practices to ensure interdepartmental connectedness and market orientation (i.e., the organization-wide responsiveness to market intelligence). However, their description of "informal dissemination" is our empathic learning practices, including the use of storytelling and personas ([29], pp. 5–6). The difference is that in our model, we suggest that to create an OSMS, empathic and experiential learning practices should be formalized as an organization-wide activity so that everyone in the firm can understand subsequently distributed market intelligence. [29] also note that intelligence dissemination can occur between departments, which we did not explicitly investigate. However, according to our model, to communicate effectively, departments sharing market intelligence must have a shared market schema (ideally an OSMS). Without a shared schema, effective market intelligence dissemination—horizontally or vertically—is unlikely.
Prior literature has proposed theoretical differences between conceptual and instrumental market intelligence use. Conceptual intelligence use is "the indirect use of information in strategy-related actions" ([41], p. 320), or intelligence that helps in "developing the managerial knowledge base" ([38], p. 56), whereas instrumental intelligence use refers to "the extent to which an organization directly applies market information to influence marketing strategy-related actions" ([41], p. 320) or the "direct application of research findings and conclusions to solve a policy problem" ([38], p. 54). Prior empirical work has found that both conceptual and instrumental use were important for new product performance and new product timeliness, whereas only conceptual use had a statistically significant relationship with new product creativity ([41]). Our model can further illuminate these results: to use intelligence instrumentally within an organization, a shared market schema must first be created—which [41] would describe as conceptual use. The need for schema change practices to establish an OSMS followed by schema update practices would explain why Moorman found that instrumental use is always accompanied by conceptual use: a user must understand the market before (s)he can notice and employ subsequent intelligence instrumentally. This finding would also explain why conceptual use alone could have a significant impact on new product creativity without explicit instrumental intelligence use.
In our research, we found that three MIDPs—distribution, resource centralization, and consultative selling practices—were largely successful at disseminating market intelligence when the intelligence was consistent with end users' existing market schemas but unsuccessful at disseminating market intelligence when it deviated significantly from those market schemas. According to schema theory, these results indicate that dissemination, resource centralization, and consultative selling practices can update the values of the attributes contained within existing mental models but are not able to change attributes or their relationships ([ 3]). This finding allows us to better explain why end users often do not use market intelligence as IDs had expected or intended.
Although our proposed model is grounded in our inductive research, it is capable of explaining several prior findings related to the difficulty of distributing knowledge within organizations generally (e.g., [12]; [23]; [32]; [46]) and market intelligence in particular ([15]; [35]; [37]; [38]; [41]; [42]; [43]). Our model is consistent with the practice literature on the importance of social interactions in creating shared schemas for organizations, as well as the necessity of shared schemas for organization members to be able to make sense of ongoing communications ([34]). Our model is also consistent with schema theory, particularly the importance of existing schemas in interpreting (or ignoring) stimuli within our world ([20]), as well as the efforts required to change schemas within organizations ([ 3]).
Our model is also able to explain both supported and unsupported findings in the literature. Research has found empirical support for the most important attributes predicting intelligence use being: organizational culture; perceived reliability, usefulness, or innovativeness of the insights; personal predispositions and task goals; trust in the source and the data; and interpersonal communications among intelligence providers and users ([14]; [37]; [38]; [41]; [43]). Our model is consistent with these findings. Regarding trust, defined as "a willingness to rely on an exchange partner in whom one has confidence," ([43], p. 315), it follows that when organization members have an OSMS, they are able to make sense of subsequently disseminated intelligence and are likely to have greater levels of trust in the source of the intelligence and the intelligence itself. Having an OSMS that frames subsequent intelligence dissemination would also increase the perceived reliability of the intelligence and its usefulness; in addition, this would improve people's ability to communicate regarding the intelligence using the OSMS. The need for a preexisting OSMS that helps users understand subsequently distributed research is also consistent with prior findings that managers are more receptive and responsive to research that is consistent with their prior beliefs ([14]; [35]). Likewise, the more an organization is in structural flux—defined as changes in personnel, structure, and procedures—the lower the perceived intelligence quality ([37]), which our model would attribute to the lack of shared schemas to make sense of intelligence and also to the inability to communicate with others about how to respond to that intelligence without a common language.
In addition to shedding new light on the impact of specific practices in disseminating market intelligence, our research raises several new questions that should be the focus of future work. First, how can IDs proactively identify when an OSMS needs to be changed? Although we identify schema change MIDPs, many IDs used these only after they had failed to effectively disseminate intelligence using a schema update MIDP that did not match an OSMS. Presumably, IDs can monitor the market and include a systematic way of gaining feedback from internal stakeholders who are close to the market. It would also seemingly require synchronizing external and internal market feedback to ensure a dynamic picture of the market and multiple lines of feedback, which would help create the case for implementing a new OSMS through schema change MIDPs when necessary.
Second, what advice can researchers provide to practitioners to help them in leveraging the five identified MIDPs? One of our findings is that IDs need to use a mix of schema update and schema change MIDPs to manage intelligence dissemination across the firm over time in an effective and efficient way. Future research could consider how firms arrive at optimal dissemination management given that using schema change MIDPs of empathic learning and experiential learning are much more expensive to implement across the organization than distribution and resource centralization practices, and the latter are typically all that are required for the efficient dissemination of intelligence that falls within a firm's OSMS. Moreover, in all of the instances when IDs discussed the use of experiential or empathic learning, those practices resulted in significant changes to the market schemas in their respective organizations—creating OSMSs. However, if experiential and empathic learning practices were used to communicate small changes within the market—such as a decrease in customer satisfaction that could be understood using existing market schemas and reacted to using existing theories of action—it seems likely that the effectiveness of these practices would decrease significantly.
Third, we explicitly focused on the dissemination of market intelligence within organizations to keep our research tractable, but the market intelligence industry is huge, and many organizations rely on outside firms—including consulting firms, advertising firms, and syndicated market intelligence firms—to provide market intelligence to them. How do these market intelligence providers effectively deliver and ensure dissemination of their findings, given that they are external to the client firms? What are the optimal ways for them to ensure usage? These are important questions for both IDs contracting for intelligence generation, as well as the firms providing market intelligence services.
Using case studies, depth interviews with IDs, and practitioner presentations on best practices, this study inducts market intelligence dissemination practices and examines related managerial logics and implications. Five distinct market intelligence dissemination practices emerge, which we categorize as practices that either update and reinforce organization members' existing schemas (mental models) of the market or create new, shared schemas of the market. We find that the creation, existence, or absence of organizationally shared market schemas is crucial to the effectiveness of these practices. Our results indicate that in addition to being experts on market intelligence, IDs must be experts on organizational learning and ways to create shared meaning structures that enable subsequently disseminated intelligence to be understood and used within organizations. They must also engage in a strategic mix of dissemination practices to ensure ongoing intelligence use.
Supplemental Material, DS_10.1177_0022242919830958 - Market Intelligence Dissemination Practices
Supplemental Material, DS_10.1177_0022242919830958 for Market Intelligence Dissemination Practices by Gary F. Gebhardt, Francis J. Farrelly and Jodie Conduit in Journal of Marketing
Footnotes 1 Associate EditorJohn Hulland served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242919830958
5 1We adopt different, but more descriptive, terms than those used in [3].
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By Gary F. Gebhardt; Francis J. Farrelly and Jodie Conduit
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Record: 116- Marketing Analytics for Data-Rich Environments. By: Wedel, Michel; Kannan, P. K. Journal of Marketing. Nov2016, Vol. 80 Issue 6, p97-121. 28p. 3 Illustrations, 3 Diagrams, 1 Chart. DOI: 10.1509/jm.15.0413.
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Record: 117- Marketing Channel Management by Multinational Corporations in Foreign Markets. By: Grewal, Rajdeep; Saini, Amit; Kumar, Alok; Robert Dwyer, F.; Dahlstrom, Robert. Journal of Marketing. Jul2018, Vol. 82 Issue 4, p49-69. 21p. 6 Charts. DOI: 10.1509/jm.16.0335.
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Marketing Channel Management by Multinational Corporations in Foreign Markets
Multinational corporations (MNCs) are adopting increasingly diverse and complex marketing channels to sell their products worldwide. They strive to manage channels that confront diverse demands from headquarters, foreign subsidiaries, and local partners as well as complex market environments. Because extant research on MNCs’ marketing channels is sparse, the authors propose an organizing framework to spur and guide research on MNC channel management. As a meta-theory that integrates economic and social elements of MNC channel management, the political economy perspective is used to propose two testable frameworks pertaining to determinants of ( 1) MNC marketing channel structures and processes and ( 2) MNC marketing channel outcomes. Building on these frameworks, the authors advance a research agenda to test substantive relationships, elaborate new constructs, and illustrate new contexts pertaining to MNC marketing channels. A set of propositions illustrates the applicability of these conceptual frameworks.
Online Supplement: http://dx.doi.org/10.1509/jm.16.0335
Multinational corporations (MNCs) hold assets and conduct operations in more than one country. According to the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development 2009), the more than 82,053 MNCs and their 807,363 affiliates currently in operation account for over 25% of world gross domestic product. Ranging from publicly traded global brands (e.g., British Petroleum, Citibank) to small private firms in emerging economies, MNCs are diverse in both their operations and their marketing. Their growing footprint accordingly has increased the diversity of MNC marketing channels (Cannon et al. 2010; Katsikeas 2006; Scheer, Kumar, and Steenkamp 2003). In addition, MNC marketing channels are complex because they involve interactions among multiple geographically and temporally separated entities, including MNC headquarters (HQ), subsidiaries, and their channel partners. Although some marketing researchers have begun to address channel management from an MNC perspective (Grewal et al. 2013; Homburg, Fu¨rst, and Kuehnl 2012), this research remains sparse and limited in the topics studied (Hult 2012). We aim to address this void by developing an organizing framework to help guide research on the ongoing management of MNC marketing channels.
Prior research on foreign marketing channels mainly pertains to so-called nonequity arrangements (e.g., exporting), whereby the focal firm does not directly invest in foreign markets but relies purely on independent intermediaries for market access (e.g., Bello and Gilliland 1997; Morgan, Kaleka, and Katsikeas 2004). In contrast, MNC channels represent direct investments in foreign subsidiaries, which then manage foreign channel partners. Such MNC marketing channels are highly resource intensive and common in foreign markets, yet they remain largely ignored by marketing scholars. From a substantive standpoint, existing research is limited as well (see Hoppner and Griffith 2015), in that it ( 1) offers only sporadic insights into MNC distribution strategies (e.g., Homburg, Fu¨rst, and Kuehnl 2012), ( 2) is often limited to cross-country replications of particular channel facets (e.g., channel monitoring; Grewal et al. 2013), and ( 3) examines channel management mainly as ex ante market entry decisions (e.g., Erramilli and Rao 1993). However, such ex ante efforts do not obviate or even substantially mitigate the need for relationship management ex post (Kirmani and Rao 2000; Williamson 1991), which is our current focus.1 Yet unlike the copious investigations of domestic channels (Coughlan et al. 2006; Frazier 1999), the ongoing management of MNC channel relationships has received virtually no attention (Palmatier et al. 2016).
Such research voids would be less of an issue if domestic channels literature could inform MNC contexts. However, several marketing channel features are unique to MNCs:
• An MNC is composed of two distinct units, HQ and subsidiaries. Both have inputs into foreign channel partner management (Grewal et al. 2013), but their perspectives tend to differ or even starkly conflict. The channel management process is thus fraught with intrafirm complexities (Nohria and Ghoshal 1994).
• As local country environments embed MNC marketing channels, different cultural and legal milieus affect them directly. In foreign markets, MNCs are also often regarded as outsiders (Makino, Isobe, and Chan 2004), so the institutional environment takes on greater primacy for acquiring legitimacy and influencing channel partners, compared with domestic markets (e.g., Feinberg and Gupta 2009).
• Often, MNCs have a number (sometimes exceeding 100) of subsidiaries (Ghoshal and Bartlett 1990), which form a network that influences the information, knowledge, and mindset that the MNC uses to manage its marketing channel operations (Lin 2001).
In light of these channel features, our literature review reveals several research gaps unique to the MNC context. First, the way considerations internal to the MNC (i.e., HQ– subsidiary relationships) constrain or enable a subsidiary’s management of the external (foreign) channel partner remains largely unexamined. Furthermore, the articulation of the impact on channel relationships of the diverse and complex environments in which subsidiaries and HQ operate is, at best, ambiguous. Finally, the role of the MNC subsidiary network in relation to marketing channels remains blurry. Other substantive and contextual limitations (e.g., paucity of studies on emerging-market MNCs) are also apparent.
Building on these research gaps, which are unique to MNC channels, we develop an inclusive framework to guide further research on ex post management of MNC marketing channels. Noting the structural and environmental complexities of MNC channels, we adopt a meta-theoretical framework to assess theories from multiple domains, summarize extant literature, identify research gaps, and define guidelines for further research (Ritzer 2001). As the locus of MNC channel management entails interactions both external (with channel partners) and internal (between HQ and subsidiary) to the MNC, our meta-theoretic approach addresses both elements. Specifically, we adopt the political economy approach (Stern and Reve 1980) and integrate ideas from social exchange, behavioral, and economic theories of the firm to enable simultaneous examination of economic and social exchange in MNC marketing channels.
Next, to obtain testable frameworks, we decompose our meta-theoretic assessment into two conceptual models. First, we identify strategic, environmental, and firm-specific determinants of MNC marketing channel structures and processes in the host country. In this model, we categorize channel structures and processes according to the political economy framework, reflecting economic and sociopolitical elements. In the second model, we address the impact of these structures and processes on MNC marketing channel performance. Across both models, we identify new research directions and offer illustrative propositions for continued research.
We intend to contribute on several fronts. Marketers have investigated a rich array of exchange relationships in domestic settings (Frazier 1983; Ghosh and John 2005; Wuyts and Geyskens 2005), but, lamentably, MNCs—which represent the most enduring and substantial commitment to foreign markets—remain largely unstudied. The few studies that examine MNC marketing channels (e.g., Anderson and Coughlan 1987) attend to ex ante modes of market entry. Against this backdrop, our first contribution is to offer an inclusive and grounded conceptual framework for examining the ongoing or ex post management of MNC marketing channels in foreign markets. Our framework, which incorporates multiple theories, is pertinent not only to multinational enterprises but also to the broader interfirm literature in marketing.
Our second contribution is a novel research agenda that we advance to guide future scholarship in the MNC channels area. This agenda considers three topics for future research, addressing:
- How attributes internal to the MNC (i.e., HQ–subsidiary relationship) affect MNC channels management;
- Facets of the home- and host-country environments and their impact on MNC channels; and
- How the larger network of the focal MNC’s subsidiaries impinge on MNC channels management.
Within each domain, we offer three directions researchers might pursue:
- Examining new theoretical relationships between preexisting constructs;
- Developing new constructs; and
- Assessing extant ideas in new geographic, product, or market contexts in which MNCs operate.
We believe that this agenda can serve as an initial blueprint to spur research on MNCs within the marketing discipline.
Our final contribution involves illustrative propositions, which concretize our research agenda and highlight the complexities unique to MNC organizations. These untested propositions combine elements of the MNC organization, channel relationships, and the environment. The insights that should follow from testing, expanding, and modifying our propositional inventory should go beyond those available from domestic relationships, which lack the distinctiveness of the MNC form.
In the next sections, we begin by reviewing literature on channel management in international markets from a political economy perspective, which reveals notable gaps in extant literature. To address these gaps, we illustrate the MNC universe that depicts MNC channels as systems of socioeconomic relationships among HQ, subsidiaries, and foreign channel partners. We unpack this framework into two conceptual models: the determinants of MNC channel structures and processes and the determinants of MNC channel outcomes. Furthermore, we identify several new directions for research in the form of new substantive relationships, new constructs, and new contexts in MNC settings—a foundational agenda for future research. After advancing sample propositions to illustrate the applicability of our framework, we conclude with the theoretical and managerial implications of our study.
Channel decisions by MNCs involve a two-step process: the MNC first selects an entry (i.e., ownership) mode to access the host country, and then decides on the management of its foreign channel partners. Extant literature describes both equity and nonequity entry modes. The former include fully and partially owned subsidiaries (i.e., joint ventures), whereas the latter include exporting, licensing, franchising, and marketing contracts, which we refer to collectively as international channels (see Web Appendix W1).
In this study, we focus on equity-based arrangements and, specifically, the management of independent foreign channel partners by wholly owned subsidiaries of MNC HQ, for several reasons. First, nonequity channel arrangements already have been examined at length (Geyskens et al. 1996; Katsikeas, Skarmeas, and Bello 2009), but little work exists on channel management through wholly owned subsidiaries.2 Second, nonequity arrangements (e.g., exporting) are finite-term relationships with limited investments from the partner firms; thus, channel management is relatively less consequential than it is for wholly owned subsidiaries, which constitute a substantial investment risk to MNCs but also afford maximal control over the channel (Hill, Schilling, and Jones 2016). Third, nonequity forms lack the substantive complexity and richness available in equity contexts. For instance, in the former, the firm is typically viewed as a cohesive unit actor, without any distinction between the roles of its different entities (HQ, subunits)— similar to domestic channel relationships. In contrast, investigations of subsidiary modes must distinguish HQ from the subsidiary because the viewpoints of HQ and subsidiaries often diverge. Fourth, focusing on the subsidiary mode keeps the conceptual scope of our research tractable.
In conventional MNC distribution channels, HQ in the home country articulates certain marketing goals for subsidiaries located in host countries, and these subsidiaries in turn manage foreign channel partners to achieve the goals. Both the HQ– subsidiary and subsidiary–foreign channel partner relationships, embedded in their respective institutional environments of the home and host countries, thus are implicated in MNC channel management.
For this literature review, we adopt the political economy framework (Achrol, Reve, and Stern 1983; Stern and Reve 1980), which integrates perspectives from behavioral and economic theories of the firm and underscores the interplay of economic and social interactions in exchanges (Arndt 1983). This approach befits an MNC context because, unlike domestic firms that operate as single and unified entities, MNCs function as combinations of distinct corporate units (HQ, subsidiaries, channel partners) involved in complex economic and social interactions. Furthermore, this framework can reveal the distribution of economic and social power among these MNC units, as well as their goals and behaviors, which need not be mutually aligned.
Per the political economy perspective (Stern and Reve 1980), we consider both economic and sociopolitical structures and processes implicated in MNC marketing channel management. Economic structures are transactional forms between firms, such as vertical integration, formal contracts, or monitoring mechanisms. Economic processes are decision-making mechanisms in exchange, such as formalization, centralization, and participation. Sociopolitical structures refer to patterns of power and dependence among channel members. Sociopolitical processes capture the dominant channel sentiments of cooperation, conflict, reactance, and legitimacy. Web Appendix W2 lists some common themes of inquiry in the political economy mold, and Web Appendix W3 provides a summary of current research on equity-based MNC channels.
Key themes. Existing research has predominantly dealt with entry mode choices such as wholly owned subsidiaries versus joint ventures. As such, there is more work on economic structures (e.g., level of integration in distribution), which are chosen ex ante, than on economic processes (e.g., channel partner participation in decision making), which are manifested ex post (e.g., Anderson and Coughlan 1987; Gatignon and Anderson 1988). Antecedents of economic structures also have drawn greater attention than their consequences, such as economic performance, returns on investment, or survival risk (e.g., Barkema et al. 1997). Investigations of the sociopolitical aspects of MNC relationships also are generally more limited than research on economic aspects. Little research in marketing thus addresses the ex post management of MNC marketing channels (Grewal et al. 2013).
Theories employed. Transaction cost theory (e.g., Erramilli and Rao 1993) is perhaps the most frequently utilized, though agency theory (e.g., Kostova, Nell, and Hoenen 2016), power-dependence theory (e.g., Hewett and Bearden 2001), and Dunning’s (1993) eclectic paradigms are also common.3 Transaction cost and agency theories feature prominently in studies of economic structures, but power dependence and social exchange theories are more common for investigating sociopolitical aspects. The application of social network theory (Burt 2000) to MNC channels is virtually nonexistent, even though MNCs are a “network” form of organization (Ghoshal and Bartlett 1990). With some exceptions (e.g., Tihanyi, Griffith, and Russell 2005), research that offers competing predictions or integrates insights from distinct theoretical foundations is scarce (Drogendijk and Holm 2010).
The corpus of work dealing with sociopolitical constructs is notably smaller than that dealing with economic factors, though the natural diversity of sociopolitical constructs likely is greater, in terms of both dependent (e.g., channel sentiments) and independent (e.g., subsidiary importance) variables. However, this trend might be shifting, considering the growing inventory of novel sociopolitical constructs such as MNC legitimacy (Kostova and Zaheer 1999) and HQ attention (Ambos, Andersson, and Birkinshaw 2010). The theoretical pathways by which sociopolitical processes (e.g., legitimacy) emerge and evolve remain unaddressed, however (Kim and Mauborgne 1998). Overall, the theories and constructs examined are similar to those studied in domestic channels, with limited infusion of MNC-specific constructs.
The environment. The quality of legal and cultural institutions is routinely mentioned in studies of HQ–subsidiary relationships (e.g., Anderson and Coughlan 1987; Contractor and Kundu 1998). Yet unlike the literature on nonequity arrangements such as export relationships (e.g., Morgan, Kaleka, and Katsikeas 2004), few articles in the MNC domain explicitly hypothesize or test the effects of institutions on MNC marketing channels. Moreover, the ways that MNC channels function under contradictory or multiple institutional pressures have “not been studied systematically” (Xu and Shenkar 2002, p. 614). It is also unclear how MNCs respond to pressure to harmonize their economic or sociopolitical structures with a host country’s cultural norms (Kostova and Zaheer 1999) and whether the pressure to harmonize leads to isomorphism in channel practices across firms.
Empirical context. The United States, Europe, and Japan are the most common data origin points (home country), and a variety of manufacturing, services, wholesale, and retail industries have been represented. However, empirical studies on MNCs headquartered in emerging markets are scarce. Most research has relied on field data, though economic structures (e.g., channel integration) often are coded with secondary measures. Given the focus on entry modes, several studies have used dichotomous dependent variables (e.g., fully owned subsidiary vs. joint venture) in a logistic regression (Dikova and Brouthers 2016). The corresponding predictors include country(e.g., country culture) and MNC- (e.g., international experience) level attributes but often ignore channel-level factors.
The previous review suggests several critical lacunae in MNC channels research. We list these in the following subsections and highlight them in Table 1.
Ongoing management of foreign channel partners. Most research in this area has discussed why MNCs choose a subsidiary mode of entry and how HQ manages subsidiaries but does not address the subsidiary–foreign channel partner relationship (e.g., Anderson and Coughlan 1987; Gatignon and Anderson 1988). Most studied correlates, including the antecedents (e.g., international experience) and consequences (e.g., HQ–subsidiary conflict) of entry mode choice, pertain to the MNC itself rather than to the channel relationship. Consequently, we know little about how subsidiaries manage foreign channel partners after foreign market entry, or how country-level institutions might impinge on channel management processes.
Interplay of MNC entities. As a subsidiary connects HQ with foreign channel partners, its channel decisions are shaped by both these entities. Yet virtually no research has centered on how a subsidiary’s dealings with HQ affect its relationships with its foreign channel partners (see Grewal et al. 2013; Hada, Grewal, and Chandrashekaran 2013).
Subsidiary network. Individual subsidiaries are part of the broader “federated network” of the MNC’s subsidiaries (Ghoshal and Bartlett 1990), yet no empirical research has described how the global network of subsidiaries or the networks of local partners in which individual subsidiaries are also embedded affect governance in MNC channels (Contractor and Kundu 1998).
Substantive limitations. The constructs and theories largely duplicate those employed in studies of domestic channels, with little infusion of new constructs tailored to the MNC context. Most studies have sidestepped questions about whether the economic and sociopolitical constructs have interactive or substitutive roles in MNC channels, whether alternative pathways exist through which these constructs influence MNC channels, or whether new theories might illuminate the roles of these constructs (see Aulakh and Kotabe 1997; Hewett and Bearden 2001). Finally, few studies go beyond MNCs headquartered in developed economies to include MNCs from developing markets (e.g., Asia, Africa).
In this section, we outline a conceptual framework both to address these research gaps and to offer suggestions for further research. As our overarching view of the MNC universe, Figure 1 depicts the elements implicated in MNC channel management: MNC organization, environment, and socioeconomic relationships among the MNC organization and its foreign channel partners. Figure 1 thus highlights the complexities inherent to MNC channel management, spanning from intra- to interorganizational and from local to global domains.
TABLE: TABLE 1 Gaps in Multinational Channels Research
| Authors | Theoretical Focus | Subsidiary—Channel Relationship Hypothesized? | HQ—Subsidiary Relationship Hypothesized? | Interplays Across Relationships Hypothesized? | Impact of Institutions on Management of Channel Partner Post Market Entry Hypothesized? | Effect of Subsidiary Network on Channel Relationships Hypothesized? |
|---|
| Anderson and Coughlan (1987) | Antecedents of economic structures | No | No | No | No | No |
| Gatignon and Anderson (1988) | Antecedents of economic structures | No | Yes | No | No | No |
| Aulakh and Kotabe (1997) | Antecedents and consequences of economic structures (i.e., channel integration) | No | No | No | No | No |
| Contractor and Kundu (1998) | Antecedents of economic structures | No | No | No | No | No |
| Hewett and Bearden (2001) | Antecedents and consequences of sociopolitical processes | No | Yes | No | No | No |
| Grewal et al. (2013) Consequences of economic processes (i.e., control mechanisms) | Yes | Yes | Yes | Yes | No |
| Hada, Grewal, and Chandrashekaran (2013) | Antecedents and consequences of sociopolitical structures and processes | Yes | Yes | Yes | No | No |
| Current study | A framework to examine MNC distribution channels (antecedents and consequences, economic and sociopolitical structures and processes, and boundary conditions) | Yes | Yes | Yes | Yes | Yes |
MNC organization. Three key corporate entities (HQ, subsidiary, and foreign channel partner) appear in Figure 1, though legally, there are just two entities: the MNC organization (box C), which includes HQ located in the home country and the subsidiary located in the host country, and the foreign channel partner in the host country. Conceptualizing the MNC (box C, Figure 1) as a single entity, similar to a domestic firm, is inadequate because the MNC HQ and subsidiary are located in different countries, so they confront different institutional regimes (Zaheer 1995). Their relationship typically is also not tightly coupled or hierarchal but rather is a nuanced, two-way affair (Birkinshaw and Hood 1998). As such, while HQ relies on the subsidiary to implement its business goals with the channel partner in the host country, the subsidiary’s own interests do not always align with HQ’s. Consequently, we conceptualize MNC channel management as involving two interrelated steps: HQ must first align the goals of the subsidiary, which then manages foreign channel partners. For unity of exposition throughout the article, we adopt HQ’s perspective to elaborate our two-step conceptualization of channel management in MNCs.
MNC environment. An MNC functions in three environments: the global industry, which HQ must address in its worldwide operations (ellipse A in Figure 1); the foreign (hostcountry) market in which the focal subsidiary functions (ellipse B in Figure 1); and the home country in which HQ is embedded (shaded region in box C in Figure 1).
Socioeconomic systems. Figure 1 illustrates three socioeconomic systems: HQ–subsidiary (i.e., internal or intrafirm) relationship (arrow D), the subsidiary–channel partner (i.e., external or interfirm) relationship (arrow E), and their interplay (arrow F). Both HQ–subsidiary and subsidiary–channel partner relationships are social systems, featuring shared tasks, power differentials, and relational norms. The lack of research into these relationships, their interplay, and the country environments in which they are embedded means that the challenges associated with ongoing or ex post management of the MNC channel relationships (E and F) have not been detailed.
We build on this overarching view of the MNC universe (Figure 1) to develop a conceptual framework for studying MNC channel management. We start with the strategic motive of the MNC that necessitates channel management in the first place (Khanna, Palepu, and Sinha 2005); we detail these motives following the multinational enterprise strategy literature (Prahalad and Doz 1987). The outcomes of interest are the MNC channel structures and processes, which we delineate on the basis of the political economy framework, and which have a long history in marketing channels research (Frazier 1983; Palmatier, Dant, and Grewal 2007).
The political economy framework asserts that the attributes of the firm and its surrounding environment influence channel relationships (Arndt 1983). This idea is also articulated as a contingency view (Lawrence and Lorsch 1967) or the strategy–environment alignment principle (Aldrich 1979), wherein the “fit” between an organization’s strategy and its context—which includes the firm’s attributes and the environment—promotes performance (Gupta and Govindarajan 1984). We employ this notion of fit to describe how MNC (firm) internal attributes and the external environment shape the impact of HQ’s strategy on channel structures and processes. To delimit the environment, we draw on the new institutional economics (North 1990) and international business (Xu and Shenkar 2002) literature streams. For MNC internal attributes, we consider the HQ–subsidiary relationship and the network of subsidiaries, both of which might affect a subsidiary’s standing with HQ. To detail these attributes, we draw on theories of interfirm relationships (e.g., agency and relational contracting theories) and the network literature (Burt 2000). Figures 2 and 3 highlight the antecedents and consequences, respectively, of the economic and social structures and processes that are implicated in MNC channel management (see also Web Appendix W4).
The dashed box (C) in Figures 2 and 3 refers to the MNC organization, comprising HQ and its subsidiary. We conceptualize the HQ’s strategy as strategic motives (box 1), which capture the corporation’s rationale for competing globally. As MNC attributes, we include the ( 1) intrafirm HQ–subsidiary linkage, as depicted by subsidiary controls (box 2), and ( 2) two elements of the subsidiary organization: intrafirm subsidiary network (box 3), which includes the focal subsidiary’s ties to the HQ’s other subsidiaries, and interfirm subsidiary network (box 3), which encompasses the subsidiary’s ties to alliance partners in the host country.
MNC strategic motives (box 1). Various perspectives exist on MNCs’ strategic motives. Levitt (1983) advocates marketingmix standardization, whereas Prahalad and Doz (1987) stress responsiveness to market changes. Others focus on the development of knowledge (Gupta and Govindarajan 1991, 2000) or the flow of capabilities (Rugman and Verbeke 2001). A review suggests three main dimensions of strategic motives (Bartlett and Ghoshal 1995): global efficiency (the MNC’s intent to extract cost efficiencies while managing revenues by pooling assets and investments across countries); worldwide learning (the MNC’s intent to assimilate knowledge from diverse experiences in different markets), and multinational flexibility (the MNC’s intent to capitalize on local resources and information to adjust rapidly to environmental changes). Thus, Nordea, the financial giant, prioritizes a flexibility strategy, whereas Unilever follows a global efficiency strategy that relies on shared services and sourcing (Martinez, De Souza, and Liu 2003).
Subsidiary controls (box 2). Although HQ relies on subsidiaries to manage foreign channel partners, HQ–subsidiary relationships are mixed-motive dyads (Birkinshaw et al. 2000) because the goals of the actors are not consistently aligned. Subsidiaries might resist HQ mandates that ignore local idiosyncrasies, whereas HQ may view a subsidiary’s desire for autonomy as an attempt to advance its narrow goals over global MNC objectives (Roth and Morrison 1990). As such, HQ must deploy governance strategies to align the subsidiary’s goals.
A formal control mechanism that HQ can use in this governance effort is monitoring (Ambos, Andersson, and Birkinshaw 2010), which can take the form of output monitoring (evaluation of subsidiary performance relative to explicit goals, such as market share and sales targets) or behavior monitoring (assessment of marketing, distribution, and selling procedures) (Kashyap, Antia, and Frazier 2012). A recent Deloitte survey indicated that more than 80% of MNCs monitor subsidiary costs and business outcomes (Gupte, Sen, and Paranjape 2013). Economic incentives are another formal governance mechanism; they operate by linking consequences to noncooperation (Jensen and Meckling 1976). Incentives can substitute for monitoring if physical distance makes monitoring infeasible (Roth and Morrison 1990). In addition, HQ can rely on informal controls such as socialization (Ouchi 1980), which infuses subsidiary managers with HQ’s values; such efforts can foster shared expectations between HQ and the subsidiary. For effective socialization, some MNCs use an expatriation strategy (i.e., recruit presocialized managers from the home country). Carlsberg, the Danish brewery, relies heavily on expatriates to manage its Asian operations (Nguyen, Nguyen, and Tran 2004).
Subsidiary network (box 3). An intrafirm network comprises the MNC’s subsidiaries, which differ in their competencies, experiences, and markets (Hoenen and Kostova 2015). As a result, individual subsidiaries have varying salience for HQ, relative to one another (Rugman, Verbeke, and Yuan 2011). The quality of ties that a subsidiary maintains to this network, as measured by tie density (contact with a high proportion of network members) and tie diversity (linkages to members offering unique resources), might grant it some leverage relative to HQ (Kadushin 2012). A network structure also implies the potential for intersubsidiary knowledge transfers (Ambos, Andersson, and Birkinshaw 2010), which could again hold relevance for channel management. Nguyen, Nguyen, and Tran (2004) describe how interactions among the global facilities of the technology giant ABB promote the crossflow of marketing ideas and knowledge to subsidiaries.
Individual subsidiaries are also enmeshed in local networks of alliance partners (e.g., suppliers, customers) who shape the subsidiary’s abilities and motivations (Van den Bulte and Wuyts 2007) and, thus, their performance (Chang, Gong, and Peng 2012). Motivation refers to the psychological processes that underlie a subsidiary’s choice of the intensity and persistence of its efforts, while ability refers to the competencies and skill sets that a subsidiary possesses to perform its business tasks (Chang, Gong, and Peng 2012). Motivation often involves legitimacy concerns (Homburg, Fu¨rst, and Kuehnl 2012) (i.e., the subsidiary’s quest to be perceived as desirable by external stakeholders; Kostova and Roth 2002), though legitimacy concerns also can be internal, such as when subsidiaries lobby HQ to affirm or validate their practices.
With respect to ability, early research adopted a top-down view, according to which HQ funnels resources to the subsidiary to help it overcome liabilities of foreignness in local markets (Dunning 1993). However, if the value-creating properties of resources are context specific (Barney 1991), resources from HQ will not be uniformly valuable in each subsidiary’s market; instead, each subsidiary must foster its own capabilities (Hennart 2010). Thus, we adopt a bilateral view, in which the HQ offers resources and competencies to the subsidiary while also assimilating subsidiary-specific knowledge (Gupta and Govindarajan 1991).
Environment (box 4). In Figure 1, ellipses A and B refer to the global industry and local host-country environments, respectively, and the shaded region in box C represents the homecountry context in which HQ is situated. We conceive them as aspects of the macroenvironment (box 4, Figures 2 and 3). The host-country environment includes informal and formal institutions as well as the local task environment. Institutions represent “humanly-devised constraints [that] structure” (North 1990, p. 97) exchange between firms. Informal institutions are the national culture, or the collectively shared beliefs and values within a country (North 1990). Formal institutions are the country’s legal environment, such as property rights and contractual laws, that circumscribe interactions among firms (Dixit 2004). The local task environment subsumes an array of market forces (e.g., competitors, infrastructure, labor) in the host country. Finally, HQ’s (home-country) context (box 4, Figure 2)—which encompasses the legal environment in the home country, the geographic context (e.g., origin in emerging vs. developed markets), the industry-product context, and so on—is also relevant (Kumar and Puranam 2011). Certain HQ attributes (e.g., cultural sensitivity, strategic aggressiveness) appear in this context too, because the home country setting shapes them significantly.
Notably, MNCs deal with two contradictory environmental pressures: HQ must control and streamline subsidiary operations across countries, but it also must be receptive to the idiosyncratic demands of host-country environments and product markets. These pressures for global integration versus local responsiveness make MNC channel management a complex task (Prahalad and Doz 1987; Roth and Morrison 1990). For example, 7-Eleven typically sells beer in its stores but had to adapt this strategy in Indonesia after a gradual emergence of a nationwide sentiment against alcohol sales in minimarkets (Danubrata and Silviana 2017).
Channel structures and processes in the host country (box 5). The outcome variables in our model include both structural (e.g., monitoring) and process (e.g., participation) aspects of a channel partner’s economic and sociopolitical outcomes. As we have noted, MNC channel management spans two interlinked relationships: HQ must align the goals of the subsidiary (arrow a, Figure 2), which then manages the channel partner, as an extension of HQ in the foreign market (arrow b, Figure 2). In line with the strategy–environment fit principle (Aldrich 1979), we regard the control mechanisms in the HQ–subsidiary relationship as determined by HQ’s strategic motives but also contingent on the properties of the environment and subsidiary networks (arrows a, c, e, i, and j, Figure 2). The channel structures and processes in the subsidiary–foreign channel partner relationship accordingly are ( 1) determined by direct effects of HQ strategic motives, subsidiary controls, the environment, and MNC-specific attributes (arrows b, g, and h, Figure 2), and ( 2) dependent on interactions among the MNC’s strategic motives, subsidiary variables, and the environment (arrows d and f, Figure 2). To avoid clutter in Figure 2, we do not show a direct link between HQ strategic motives (box 1) and channel structures and sociopolitical processes (box 5), but we acknowledge that such direct links may exist.
MNC marketing channel outcomes (box 6). The model in Figure 3 shows the economic performance of the foreign channel partner and the MNC subsidiary as our dependent variables. Economic performance refers to outcomes in terms of sales, market share, and profits (Ramani and Kumar 2008); it is also both a key outcome in the political economy paradigm and a cornerstone of economic (Amit and Schoemaker 1993) and behavioral (Hambrick and Fredrickson 2001) research on firm strategy. Channel partner performance is partner-specific, whereas subsidiary performance derives from the aggregate performance of all channel partners in the host country.
In Figures 2 and 3, we indicate important categories of variables in the political economy of MNC channel management as well as the various direct and interaction effects among them. In the next section, we leverage this framework to identify new research opportunities within the MNC channels milieu and to develop illustrative propositions about the influence of MNC variables on MNC channel outcomes (boxes 1–4, Figures 2 and 3). For brevity, most of our discussions refer to Figure 2, though we also provide some illustrative propositions with respect to the channel outcomes in Figure 3.
As Van de Ven (1989, p. 488) asserts, existing research gaps or inconsistencies “provide important opportunities to develop better and more encompassing theories.” Our proposed research agenda thus is anchored explicitly in the gaps identified in our literature review and covers three broad domains or topics corresponding to those gaps: ( 1) interplays of MNC relationships (arrow F, Figure 1; interplay of arrows a and b in Figure 2), ( 2) the institutional environment (box 4, Figure 2), and ( 3) the subsidiary network structure (box 3, Figure 2).
We also outline some potential avenues for detailing these topics, guided by Colquitt and Zapata-Phelan’s (2007, p. 1283) suggestion that introduction of new “relationships” and “constructs” is foundational to extending our understanding of organizational phenomena. Going beyond single shot studies to articulate extant ideas in “new settings[s]” (Barkema et al. 2015, p. 475) is another avenue for advancing theory. Following these recommendations, we outline three theoretical directions to develop the aforementioned research topics: ( 1) investigating new relationships among existing constructs, ( 2) elaborating new constructs, and ( 3) examining MNC channels in new contexts. Our final agenda results from crossing the three research topics with the three theoretical directions each, as reflected in the nine cells in Table 2.
TABLE: TABLE 2 An Agenda for Future MNC Channels Research: New Relationships Among Constructs, New Constructs, and New Contexts
| | New Relationships | New Constructs | New Context |
|---|
| Interplays across MNC relationships (arrow F, Figure 1) | Cell 1 1. Empirically document interplays between HQ—subsidiary and subsidiary—channel partner relationships. 2. Clarify the processes underlying the interplays between HQ—subsidiary and subsidiary—channel partner relationships. Illustrative propositions: P1, P2 | Cell 4 1. Subsidiary role cycle evolution of subsidiary roles over time within the MNC). 2. Attention discrepancy difference between HQ and subsidiary attention to the other party). Illustrative proposition: P5 | Cell 7 1. Detail challenges faced by emerging market MNCs in establishing channels in developed markets. 2. Role of unique operational routines in emerging market MNCs (e.g., jugaad in Indian MNCs). |
| Institutional environment (ellipse B, Figure 1) | Cell 2 1. Incorporate institution types and attributes (e.g., institutional complexity) in models of subsidiary—channel partner relationships. 2. Examine combinative effects of formal (contractual) and informal (cultural) institutions. Illustrative proposition: P3 | Cell 5 1. Formal institutional integration (concurrence between strength of national laws and quality of regional enforcement). 2. Institutional pliability (degree to which local governments and courts are receptive to the influence activities of firms). Illustrative proposition: P6 | Cell 8 1. Examine contextual nuances with respect to national institutions (e.g., rising protectionism in certain countries). 2. Internal governance in emerging market MNCs and its implications for channel management in developed markets, with their strict institutional requirements. |
| Subsidiary network structure (dashed box C, Figure 1) | Cell 3 1. Link subsidiary networks, ability, and motivation to foreign channel partner management. 2. Invoke new theoretical perspectives to examine subsidiary networks (e.g., social networks theory). Illustrative proposition: P4 | Cell 6 1. Established constructs from network theory (e.g., subsidiary network tie density). 2. Subsidiary network contagion (propagation of business practices and ideas across a subsidiary network). | Cell 9 1. Catalog context-sensitive variations in subsidiary networks. 2. Articulate different theoretical routes through which contextual variations in the subsidiary network affect the channel. Illustrative proposition: P7 |
Effects of interplays across MNC relationships (cell 1, Table 2). The management of (external) subsidiary–channel partner relationships depends crucially on (internal) HQ– subsidiary relationships, but, with rare exceptions, marketers have yet to examine such interplays (arrow F, Figure 1). A variety of interplays are plausible: Grewal et al. (2013) document how the effectiveness of monitoring in subsidiary–channel relationships is constrained by economic processes (e.g., participative decision making) in the HQ–subsidiary relationship. Some interplays constitute spillovers from external to internal MNC relationships. Thus, subsidiaries that have fostered commitment from their foreign channel partners should enjoy some leverage over HQ and, perhaps, gravitas within the MNC.
New insights also could be generated by establishing the processes that underlie the interplays of HQ–subsidiary and subsidiary–channel partner relationships. Grewal et al. (2013) find that participative decision making in the HQ–subsidiary relationship promotes effective monitoring in the subsidiary– channel link. Perhaps, as the authors suggest, participative decision making augments the efficiency of monitoring by enhancing the subsidiary’s clarity regarding HQ’s monitoring goals. Alternatively, participation might enhance the subsidiary’s self-determination (Foss, Foss, and Nell 2012), which motivates it to implement monitoring goals with foreign channel partners. Goal clarity and self-determination thus represent competing but untested explanations for the observed effects, which future research should resolve.
Effects of the institutional environment (cell 2, Table 2). Extant channel management studies rarely incorporate formal institutions explicitly (Dixit 2004); if they do, they focus almost exclusively on the strength of contractual enforcement. However, formal institutions have richer manifestations. Thus, Grewal, Chandrashekaran, and Dwyer (2008) highlight institutional complexity (number of interacting institutions) and dependence (extent to which institutions influence MNC outcomes). Prescriptions relating to national culture also appear mixed, as MNCs have been advised to rely on informal controls in foreign cultures (Hewett and Bearden 2001), but also cautioned against doing so (Bae and Salomon 2010). Moreover, it is unclear whether absolute cultural values in the host country or cultural differences between home and host countries matter in subsidiary–channel relationships (Bae and Salomon 2010). Beyond clarifying such confounds, researchers could disaggregate the effects of national culture to the level of individual dimensions such as individualism– collectivism (Samaha, Beck, and Palmatier 2014). Augmenting models of subsidiary–channel relationships with different institution types and their characteristics is a viable research avenue.
Another question pertains to the combinative effects of different institutions. In theory, only formal (contractual) institutions serve contract enforcement purposes (North 1990), so the enforcement properties of formal institutions should be independent of informal (cultural) institutions. Yet managerial interpretations of contracts are shaped by local culture, implying complementarities among the effects of formal and informal institutions. As an example, foreign channel partners in lowcontext cultures (which prefer explicit communication) might follow the letter of the contract, whereas firms in high-context cultures (which communicate in implicit ways) might follow the spirit of the contract (Hall 1976).
Effects of subsidiary networks (cell 3, Table 2). Extant research has not addressed the role of subsidiary networks in channel management. Considering the differences in subsidiaries’ abilities and motivations, HQ must implement governance strategies selectively across its subsidiary network. For example, formal contracts might be efficient for subsidiaries that possess standardized skills but not those that deal in creative competencies (Foss, Foss, and Nell 2012). However, dealing differently with otherwise comparable subsidiaries could inflame unfairness perceptions in the subsidiary network, thereby reducing subsidiaries’ motivation to implement HQ’s goals with channel partners.
To elaborate on the role of subsidiary networks, new theoretical perspectives should be useful. Consider tie density from social network theory (Burt 2000). Tie density is the ratio of actual to total possible connections in a network (Kadushin 2012); it captures interaction frequency. Dense ties in a subsidiary network facilitate shared ideas and competencies, which may offer subsidiaries a power advantage over foreign channel partners. Yet subsidiaries also are anchored in interfirm networks of local alliance partners in the host country. It is an open question whether or how these intra- and interorganizational subsidiary networks jointly influence MNC channel performance.
Barkema et al. (2015) contend that new constructs can be derived from the application of two powerful criteria—relevance and augmentation. Relevance implies that a preexisting construct is pertinent to MNC channels but has not previously been applied to MNC channels issues. Augmentation responds to an incompleteness in extant theory and entails either establishing new MNC channel constructs or introducing new dimensions, attributes, or nuances that extend preexisting constructs (see Table 2). Relevance thus involves the adoption of existing constructs as is, while augmentation involves alternations to or creation of constructs to improve extant theory.
Constructs reflecting interplays across MNC relationships (cell 4, Table 2). As an example of construct relevance, consider subsidiary role cycle (i.e., how subsidiary roles within the MNC evolve over time). Subsidiaries usually commence with limited roles (Buckley 2010), but as their local resource profile develops, there is “unsticking” of HQ versus subsidiary competencies, and the subsidiary’s role set expands such that it takes on new initiatives and demands greater role autonomy. Yet subsidiary roles have garnered little explicit attention (Birkinshaw and Hood 1998), despite their likely impact on channel relationships. For example, as a subsidiary becomes more autonomous, it can exhibit greater responsiveness to local partners.
Recently, Bouquet and Birkinshaw (2008) have elaborated the construct of HQ attention to the subsidiary, which is the extent to which HQ recognizes and offers credit and resources to a subsidiary for its contributions to the MNC. Ambos and Birkinshaw (2010) document a positive effect of HQ attention on subsidiary performance. We expect that, conversely, subsidiaries might vary in the level of attention they pay to HQ within the contested terrain of the HQ–subsidiary relationships. Combining notions of attention for both parties, we propose an augmented construct, attention discrepancy, to reflect the divergence between HQ attention and subsidiary attention toward the other. Empirically validating the effects of such constructs on channel outcomes would be fruitful.
Constructs pertaining to institutions (cell 5, Table 2). Regional variations often exist between de jure contractual institutions at the national level and the de facto laws enforced regionally. Enforcement is stricter in coastal areas of China than in inland areas, for example (Van Rooij and Lo 2010). Yet scholars investigate institutions almost exclusively at the national level. We propose a construct, formal institutional integration, which we define as the extent to which national contractual institutions and regional enforcement levels coincide. Institutional integration could influence a subsidiary’s choice of channel partners, based on the partner’s location in the host country.
Some MNCs lobby local governments for institutional change, such as in China, where foreign firms have helped usher in revised arbitration laws (Wilson 2008). We define a new construct, institutional pliability, as the degree to which local institutional actors (governments, courts) are open to influence from firms, which determines whether their lobbying efforts are likely to translate into actual institutional change. When institutional pliability results from the efforts of foreign actors such as MNC subsidiaries, we term it “external pliability”; if it results from the efforts of host-country actors, such as local channel partners, we call it “internal pliability.” Operationalizing and situating these constructs in a nomological network is a viable avenue for future scholarship.
Constructs pertaining to subsidiary networks (cell 6, Table 2). Here, we propose the construct of subsidiary network contagion, which we define in line with McFarland, Bloodgood, and Payan (2008) as the propagation of business practices, technologies, and marketing ideas across a subsidiary network. Consistent with our notion of contagion, prior research has described how technologies and marketing practices diffuse across subsidiaries (Almeida and Phene 2004). Contagion can spread in an uncontested fashion, such that subsidiaries adopt a practice as is and isomorphic channel practices emerge across subsidiaries, or in a contested fashion, such that subsidiaries modify their practices to suit their focal context (Centola and Macy 2007). The implications of such contagion effects remain uncharted for subsidiary channel relationships.
Extant research has mainly investigated developed-world MNCs. This leaves substantial scope to assess whether findings extend to other geographic, industry, and product contexts.
Interplays of MNC relationships in new contexts (cell 7, Table 2). Many MNCs from emerging markets in Asia, Africa, Latin America, and the Middle East seek entry into developed markets. A few, such as Johnson Electric (China), America Movil (Mexico), and Tata Motors (India), have become somewhat established abroad (Azevedo et al. 2016). However, lacking a blueprint for developed markets, emerging-economy MNCs (e.g., Mexico’s Group Habita Hotels) often find it challenging to establish distribution channels abroad, and many have become heavily dependent on local channel partners. Such lopsided dependence is especially adverse for lesser-known MNCs from the developing world because they often lack resources, international experience, and global brand names, which could be leveraged as bargaining chips with foreign channel partners. Thus, resource constraints internal to the MNC affect external subsidiary channel relationships, but such interplays for emerging market MNCs have yet to be examined.
In response to resource constraints, some emerging-market MNCs (e.g., South Africa’s digital marketer, Journey) have fostered unique operational routines. They routinely engage in bootstrapping to amalgamate product features, service values, and price points to remain competitive even with generic resources (Barkema et al. 2015). In India, some firms rely on improvisation, known locally as jugaad, to solve complex business problems with a bricolage of ad hoc resources (Saraf 2009). However, as emerging-market MNCs move to developed markets, local partners might resist such practices, which could trigger channel conflict.
Institutional environments in new contexts (cell 8, Table 2). The contextual nuances of the environments in which MNCs are embedded remain unexamined. First, the regulatory environment for each industry varies across countries; it is unclear, for example, whether findings from the U.S. retail sector extend to India, where the retail industry regulations are more stringent. Second, emerging global sociopolitical trends have implications for MNC channel operations: in the West, there are sporadic calls for protectionism (e.g., Britain’s exit from the European Union), while in the East, a nationalism-infused form of capitalism is taking root in places (e.g., India’s Patanjali has spearheaded a trend of buying swadeshi or locally made goods and eschewing foreign brands). These trends could influence the legitimacy of MNCs and their ability to find local partners.
Emerging-market MNCs also might be family owned or characterized by weak auditing practices, a lack of professional management, and poor internal controls (Bennedsen et al. 2007). From clan and ethnic affiliations in the Middle East and Africa to intersecting caste and religious links among business houses in Asia, emerging-market firms are enmeshed locally in a variety of nonmarket ties. As they enter developed markets, their compliance with the stricter institutional requirements could be stymied by their internal governance codes, which, in turn, might cast a shadow over subsidiary–channel partner relationships.
Subsidiary networks in new contexts (cell 9, Table 2). Marketers have yet to catalog contextual (e.g., geographic, product-specific) variations in subsidiary networks. Thus, subsidiaries of MNCs from Latin America (e.g., Brazil’s Braskem) typically are limited in national scope and offerings portfolio relative to US MNCs (e.g., General Electric, which is present in 130 countries). This low diversity across Latin American subsidiary networks might facilitate their ability to share information (relative to U.S. MNCs’ subsidiary networks) and perhaps reduce subsidiary dependence on channel partners for access to new ideas. There is hardly any empirical work on such topics.
The different (theoretical) routes through which contextspecific variations in subsidiary networks affect MNC channels also merit scrutiny. For instance, we have pointed to both a smaller country scope and a limited offerings portfolio as potential factors behind greater information sharing in a subsidiary network; however, determining which factor dominates in practice for (say) Latin American versus U.S. MNCs is an intriguing research question in its own right.
To aid future research, this section advances illustrative propositions that follow from our research agenda in Table 2. We embed the propositions in our conceptual framework for MNC organizations (Figures 2 and 3). Per the political economy perspective, we highlight the influence of different elements of the framework (boxes 1–4) on structures and processes in subsidiary–channel partner relationships (box 5). For brevity, we anchor most of our discussion in Figure 2. The propositions are illustrative, not exhaustive; we summarize them in Table 3 (for additional details, see Web Appendix W5).
New relationships involving interplays across MNC dyads (cell 1, Table 2). With output monitoring, HQ specifies tangible goals (e.g., sales, profit targets) for subsidiaries (Celly and Frazier 1996). Subsidiaries accomplish these goals through foreign channel partners, so output monitoring in HQ–subsidiary relationships (box 2, Figure 2) should have implications for economic processes such as procedural formalization (i.e., the extent to which clear rules and policies for exchange exist) in subsidiary–channel partner relationships (box 5, Figure 2). Formalized rules offer clarity on how to achieve the output goals, so channel partners should feel more certain about the feasibility of these goals, which, in turn, is likely to motivate goal pursuit (Jost et al. 2008). Thus, output monitoring in HQ–subsidiary relationships should promote reliance on formalization in subsidiary–channel relationships.
TABLE: TABLE 3 Summary of Illustrative Propositions
TABLE: TABLE 3 Summary of Illustrative Propositions
| Propositions | Variables and Level of Analysis | Location in Research Agenda (Table 2) | Location in MNC Framework (Figures 2 and 3) | Dependent Variables in Political Economy Terminology | Theories Implicated |
|---|
| P1 | • DV: Procedural formalization in subsidiary—channel partner relationship • IV: Output monitoring in HQ—subsidiary relationship • Moderator: Multinational flexibility strategy in HQ—subsidiary relationship | Cell 1: New relationships involving interplays across MNC dyads. | Figure 2: Boxes 1, 2, and 5; arrows a and b | Economic process (formalization) | • Interfirm relationship literature (agency theory, transaction cost theory)a • Theory of multinational enterprise strategy |
| P2 | • DV: Channel partner economic performance • IV: Procedural formalization in subsidiary—channel partner relationship • Moderator: Multinational flexibility strategy in HQ—subsidiary relationship | Cell 1: New relationships involving interplays across MNC dyads. | Figure 3: Boxes 5, 1, and 6; arrow g | N.A. (channel performance is not a structure or process) | • Interfirm relationship literature. • Theory of multinational enterprise strategy |
| P3 | • DV: Formal contracts in subsidiary—channel partner relationships • IV: Cultural distance in subsidiary—channel partner relationship • Moderator: Multiplicity of hostcountry formal institutions | Cell 2: New relationships involving institutional environment. | Figure 2: Boxes 4, and 5; arrow g (denoting both main and interaction effects inP3) | Economic structure (formal contracts) | • Interfirm relationship literature, agency theory, transaction cost theory • New institutional economics |
| P4 | • DV: Subsidiary dependence on channel partners • IV: Tie density in intrafirm subsidiary network • Moderator: Tie density in interfirm subsidiary network | Cell 3: New relationships involving (intrafirm) subsidiary network. | Figure 3: Boxes 3 and 5; arrow h (denoting both main and interaction effects inP4) | Sociopolitical structure (dependence) | • Power dependence theory • Social networks theory |
| P5 | • DV: Channel partner reactance • IV: Behavior monitoring in subsidiary channel relationships • Moderator: Attention discrepancy in HQ—subsidiary relationship | Cell 4: New constructs pertaining to interplays across MNC dyads. | Figure 2: Boxes 2 and 5, arrows a and b. | Sociopolitical process (reactance) | • Interfirm relationship literature, agency theory • Self-determination (reactance) theory • Power dependence theory |
| P6 | • DV: Subsidiary legitimacy with channel partner • IV: External pliability of hostcountry formal institutions (legal environment) • Moderator: Strength of hostcountry formal institutions | Cell 5: New constructs pertaining to institutional environment. | Figure 2: Boxes 4 and 5; arrow g (denoting both main and interaction effects in P6) | Socio-political process (legitimacy) | • Institutional theory • New institutional economics |
| P7 | • DV: Normative (informal) control in subsidiary—channel partner relationship; subsidiary (intrafirm) network diversity • IV: Industry (food vs. mining) type, b subsidiary (intrafirm) network diversity | Cell 9: New context pertaining to subsidiary (intrafirm) networks. | Figure 2: Boxes 3, 4, and 5; arrows j and h | Economic structure (informal/normative control) | • Relational contracting theory • Social networks theory • Interfirm relationships literature |
The aforementioned effect in the subsidiary–channel partner relationship (arrow b, Figure 2) should be contingent on features of the HQ–subsidiary relationship (arrow a, Figure 2), including strategic motives such as multinational flexibility, whereby the MNC capitalizes on local resources and information to react rapidly to changes in the environment (Bartlett and Ghoshal 2000). A flexibility strategy emphasizes freedom for local managers so that they can adapt quickly to changing conditions (Buckley and Casson 1998). It also implies that the subsidiary values the opinions of foreign channel partners who are attuned to conditions on the ground. Adherence to formalized procedures could be a constraint on such a strategy, limiting the channel’s ability to adjust (Johnson et al. 2003). We accordingly expect that output monitoring in the HQ–subsidiary relationship enhances reliance on formalization in the subsidiary–channel partner dyad (arrow b, Figure 2), but this effect will be diminished as the emphasis on multinational flexibility strategy in the HQ–subsidiary dyad increases (arrow a, Figure 2):
P1: Increasing output monitoring in HQ–subsidiary relationships enhances procedural formalization in subsidiary–channel partner relationships, but this effect weakens as emphasis on multinational flexibility increases.
The greater the emphasis on a multinational flexibility strategy, the more adverse the consequences of procedural formalization will be in the subsidiary–channel partner dyad in terms of channel partner outcomes. Channel partners that are constrained by formalized procedures cannot effect speedy adaptations, as demanded by the flexibility strategy, so their economic performance will deteriorate. We propose a joint interplay of multinational flexibility and procedural formalization on channel partners’ economic performance (arrow g, Figure 3):
P2: Increasing procedural formalization in subsidiary–channel partner relationships and increasing emphasis on multinational flexibility together undermine channel partner performance.
New relationships involving the institutional environment (cell 2, Table 2). Consider national culture as an institution: relationships between subsidiaries and their foreign channel partners are often characterized by cultural distance (i.e., by differences in socially engrained expectations, communication styles, or negotiation modalities between the partners; Xu and Shenkar 2002). We expect increasing cultural distance in subsidiary–channel partner relationships (box 4, Figure 2) to enhance their reliance on economic structures such as formal contracts (box 5, Figure 2), because by explicating each party’s responsibilities, formal contracts limit misunderstandings that can arise between culturally distant firms (see Williamson 1996).
This positive association between cultural distance and reliance on formal contracts in the subsidiary–channel partner dyad (arrow g, Figure 2) should be moderated positively by the multiplicity of formal institutions in the host country (box 4, Figure 2). Multiplicity refers to “environmental complexity stemming from a large number of [formal] institutional constituents” (Grewal, Chandrashekaran, and Dwyer 2008, p. 891). Multiplicity inflicts time and cost burdens on subsidiaries, because distinct institutional actors (e.g., courts, state agencies) demand conformance. In this scenario, it is in the subsidiary’s and the channel partner’s interests to employ formal contracts, because explicitly specified and documented terms should make it easier to demonstrate compliance.4
P3: Increasing cultural distance in subsidiary–channel partner relationships increases reliance on formal contracts, and this effect strengthens as multiplicity of host-country formal institutions increases.
New relationships involving subsidiary networks (cell 3, Table 2). A subsidiary’s connections to the (intrafirm) network of other subsidiaries serve as conduits for the transfer of competencies, resources, and ideas. As tie density (interaction frequency) in the subsidiary network increases, each subsidiary enjoys greater access to resources and skills, which it can leverage with channel partners. Thus, increasing tie density in the subsidiary network (box 3, Figure 2) should lower a subsidiary’s dependence on foreign channel partners (arrow h, Figure 2).
Beyond a focal foreign channel partner, subsidiaries are locally embedded in the interfirm network of other alliance partners (e.g., local suppliers), which feeds subsidiary abilities (box 3, Figure 2). The local interfirm network, if characterized by dense ties, can foster subsidiary competencies that are specialized to the subsidiary and its local context (Kadushin 2012). Tie density in the subsidiary’s interfirm network therefore should also reduce subsidiary dependence on channel partners. Because tie densities in the intra- and interorganizational subsidiary networks both influence subsidiary dependence in the same fashion, we propose a substitutive effect (Gupta et al. 2017): that is, subsidiary intrafirm network tie density is negatively associated with subsidiary dependence on foreign channel partners, but this effect is moderated negatively by tie density in the interfirm network.5
P4: Increasing tie density in the subsidiary (intrafirm) network lessens the subsidiary’s dependence on channel partners, but this effect weakens as tie density in the subsidiary’s (interfirm) network of local alliance partners increases.
New constructs pertaining to interplays of MNC dyads (cell 4, Table 2). With behavioral monitoring, the subsidiary outlines specific behaviors (e.g., sales practices, promotion strategies) for channel partners, adherence to which is expected to produce the outcomes of interest (Anderson and Oliver 1987). Such behaviors often are mandated by HQ and imposed on channel partners by subsidiaries (Grewal et al. 2013). Such monitoring, especially when mandated by HQ, which is removed from the host country, can be viewed as intrusive, because it constrains channel partners’ operational freedom (Heide, Wathne, and Rokkan 2007). As a result, channel partners may experience a loss of autonomy, which can engender reactance (i.e., psychological resistance; Deci and Ryan 2012). Thus, we expect behavior monitoring by the subsidiary (box 2, Figure 2) to trigger reactance among foreign channel partners (box 5, arrow b, Figure 2).
This baseline expectation should be moderated by our newly proposed construct of attention discrepancy (cell 4, Table 2) in HQ–subsidiary relationships (arrow a, Figure 2). Greater attention discrepancy implies that HQ offers greater recognition and investments to the subsidiary than the subsidiary does to the HQ; in effect, the subsidiary is more salient for HQ than HQ is for the subsidiary. As this discrepancy increases, the subsidiary enjoys more leeway to tailor its monitoring to the idiosyncrasies of the local market, rather than unquestioningly implementing HQ-mandated behaviors. Thus, a subsidiary might tweak HQmandated sales practices to suit local market norms and local partners’ marketing capabilities. To the degree that attention discrepancy facilities context-sensitive monitoring by the subsidiary, reactance among foreign channel partners due to behavioral monitoring should diminish.
P5: Increasing behavioral monitoring in subsidiary–channel partner relationships evokes greater reactance in channel partners, but this effect weakens as attention discrepancy in HQ– subsidiary relationships increases.
New constructs pertaining to institutional environments (cell 5, Table 2). Consider our new construct, external institutional pliability, which refers to the receptivity of host-country formal institutions to change efforts initiated by foreign firms. For example, Monsanto has pushed African governments to alter the property rights laws for seeds for cash crops (Holland and Sourice 2016). In countries in which institutional actors (government agencies, courts) are amenable to the influence of foreign subsidiaries, the latter are in a better position to comply with the resulting legal requirements. Compliance with local laws bestows legitimacy on subsidiaries, so they likely are regarded with approval by local channel partners (Kostova and Zaheer 1999). Ceteris paribus, the external pliability of host-country institutions should enhance subsidiary legitimacy among foreign channel partners (arrow g, Figure 2).
We propose that this effect will be moderated by the strength of host-country formal institutions (box 4, Figure 2). If local laws are viewed as weak, inefficient, or corrupt, their pliability to foreign subsidiaries’ influence attempts might be framed by local partners as a “hijacking” of the regulatory apparatus by an outsider MNC, diminishing the subsidiary’s legitimacy.6 Thus, given Africa’s infirm enforcement regime, foreign firms sometimes are viewed as aggressors doing business illegitimately in the country (The Economist 2011). However, as the strength of local institutions increases, pliability is less likely to be framed as an illicit manipulation of local institutions, because strong institutions resist such exploitation efforts. Instead, pliability will likely be viewed as genuine attempts by the MNC subsidiary to mend residual gaps in the institutional fabric, which should enhance that subsidiary’s legitimacy.
P6: External pliability of host-country formal institutions enhances subsidiary legitimacy, and this effect strengthens as the institutions become stronger.
Some industries demonstrate significant cross-national variations in products. In the food industry, for example, offerings usually are adapted extensively to local preferences. McDonald’s thus sells taro (sago) pie only in Taiwan and paneer (cheese) burgers exclusively in India. The subsidiary networks in these industries accordingly reflect this differentiation in local tastes, such that the operations and marketing strategies of individual subsidiaries in the subsidiary network also differ across countries. Other industries (e.g., mining) instead sell largely commoditized products across countries, which should be reflected in correspondingly lower diversity in their subsidiary networks. Thus, we posit that subsidiary network diversity varies by industry context, higher for some products (e.g., food) than others (e.g., mining).
When they are more diverse, the subsidiary networks impose more complex coordination demands on HQ (Goerzen and Beamish 2005), and imposing formal controls (e.g., monitoring) on these diverse operations is costly and time consuming. Some research has suggested that firms should deal with diversity and complexity through informal (Hedlund 1981) and bilateral (Caves 2007) decision making, so we further posit that increasing diversity in subsidiary networks is associated with informal controls (e.g., normative, based on shared expectations) in HQ–subsidiary and subsidiary–channel partner relationships (arrows j and h, Figure 2).
P7: Subsidiary network diversity increases as offerings become more differentiated across countries. Increasing subsidiary network diversity enhances reliance on informal control in HQ–subsidiary and subsidiary–channel partner relationships.
Marketing scholars (e.g., Griffith, Cavusgil, and Xu 2008; Hewett and Bearden 2001) have examined aspects of MNCs, but a specific focus on MNC channel management has been lacking. To bridge that gap, we have offered a framework for advancing MNC channels research.
Within marketing, a vigorous literature on ongoing management of domestic channels has emerged over the years. Surprisingly, such research efforts have not extended to MNC distribution channels, where the preponderant focus has been limited to (ex ante) market entry strategies. This lacuna persists even when certain core paradigms on which interfirm research rests—agency (Bergen, Dutta, and Walker 1992) and transaction cost (Williamson 1991) theories, among others—suggest the importance of aligning channels partners’ interests on an ongoing basis. We set out to address this lacuna by advancing an inclusive framework for MNC distribution channel management.
Because MNC channels span firm boundaries, countries, and institutional regimes, traditional channel management theories cannot be applied directly, nor do individual theories suffice. Instead, we incorporated diverse literature streams to identify gaps in MNC channels literature and develop a framework for future research (Figures 1–3). Anchored in a political economy framework, we highlighted the roles of MNC strategic motives, socioeconomic systems, subsidiary variables, and the environment in determining MNC channel structures, processes, and outcomes. Our framework suggests several directions for future research related to new theoretical relationships, constructs, and contexts, in three compelling categories: ( 1) interplays across MNC relationships, ( 2) institutional environments, and ( 3) subsidiary network structures.
By deconstructing the MNC firm into separate entities, we investigated nuanced interplays between intrafirm (HQ– subsidiary dyad) and interfirm (subsidiary–channel partner dyad) elements of the firm. Such interplays have not been examined to any significant degree in the domestic channels, partly because traditional theories of interfirm relationships rest on a view of firms as “unitary” actors. For example, in agency theory (Ouchi 1980) the “principal” represents the entire firm, and in the transaction cost view (Williamson 1991), goal alignment between different parts of a firm is seldom problematic. However, viewing complex organizations only as a unified whole represents a somewhat reductionist view that misses the rich interactivity between the firm’s discrete parts. Instead, our work suggests that treating organizations as “decomposable systems” (Bechtel and Richardson 2010, p. 199) can help uncover novel interdependencies within and between firms, which might not be apparent otherwise.
To demonstrate the utility of our conceptual framework, we predict some interactions among governance mechanisms in MNC relationships, institutional variables, and subsidiary networks. These predictions connect our work to the rich literature on marketing channels (Frazier 1999; Geyskens et al. 1996; Heide 1994). For example, by proposing a joint effect of cultural distance and the multiplicity of institutions on explicit contracts, P3 expands our understanding of standard governance mechanisms and how their efficacy might be delimited by specific institutional configurations. We also add some texture to network theory by bringing both intra- and interorganizational subsidiary networks to the fore. Some studies note the existence of intrafirm subsidiary networks (e.g., Bartlett and Ghoshal 2002), but the question of their joint interface with interfirm networks should be of interest to scholars in both the marketing channels domain and the broader social networks space.
Several potential research opportunities exist beyond our framework. First, we take the limited perspective of the MNC HQ, but similar channel issues, when described from the perspective of foreign channel partners, could add a complementary layer to our framework. Second, in keeping with the eclectic paradigm (Dunning 1993), a richer assortment of HQ-level variables (e.g., cultural sensitivity, global experience) merit deeper investigation—as do their counterparts in foreign channel partners. Third, a more extensive examination of national cultures could include considerations of cultural ambiguity and its effects on contract design. Finally, we focused on owned subsidiaries; researchers could study non-equity-based distribution arrangements as well.
Space constraints prevented us from discussing methodological issues, but our framework and propositions offer several possibilities for empirical testing (cells 1–3, Table 2). An intriguing question surrounds the indirect effect of certain channel factors on subsidiary performance: channel economic structures (e.g., specialized investments by the channel partner; box 5, Figure 3) might indirectly augment the subsidiary’s performance by supporting subsidiary abilities (e.g., development of marketing capabilities; box 3, Figure 3). Detailing the theoretical processes underlying the interplays across MNC relationships (cell 1, Table 2) likely requires longitudinal data, which would also extend MNC literature that traditionally examines events spatially (across countries) rather than temporally (across time) (Blazejewski and Becker-Ritterspach 2011). Tests of network theory–based propositions for subsidiaries (cell 3, Table 2) could rely on matched data from different actors, which our literature review suggests is scarce in MNC literature.
Opportunities also exist for operationalizing and situating the augmented and new constructs we have proposed (e.g., subsidiary role cycle) in a nomological network (cells 4–6, Table 2). Comparative case studies of MNC channel strategies could also be undertaken, particularly in new contexts (cells 7–9, Table 2). For example, a comparison of the distribution channels of Caterpillar (U.S. headquarters) and Komatsu (Japan headquarters)—a duopoly whose members have experienced different formative influences and path dependencies—might be illuminating. Some countries (e.g., China) are transitioning from fast to a moderate growth rate; others have only recently permitted foreign direct investment (e.g., retail in India). These transitions offer opportunities to examine channel evolution using case studies and field experiments. Finally, most emerging-market MNCs face hurdles expanding abroad, but some (e.g., India’s Tata Group) have successfully established a global footprint (Azevedo et al. 2016). A comparative study of their strategies, strengths, and constraints could be instructive.
Our work offers several managerial insights, which we detail with respect to our conceptual framework (Figures 2 and 3). It indicates the importance of interplays (i.e., the effect of the larger MNC organization itself [boxes 1–3] on channel partner management [box 5]). A case in point is Mattel, which responded to deteriorating global sales by retooling its internal organization. Given its strategic flexibility motive, Mattel’s HQ opted for informal decision making with subsidiaries to boost channel performance (Beer and Eisenstat 2004). Nestle´ resolved a similar issue differently, by shuffling employees across the subsidiary network to promote company-wide goals (Verbeke 2013). Thus, for effective channel management (box 5), HQ can align the subsidiary’s goals (box 2) in light of the HQ’s strategic motives (box 1) and also leverage the subsidiary network (box 3).
Weak legal institutions abroad represent hurdles for MNCs from the developed world. Industry majors such as Cadbury’s, Shell, and Vodafone received an unpleasant jolt recently when authorities in India altered tax laws retroactively (Chatterjee 2013). Negotiating such scenarios might require subsidiary managers to build linkages with third-party mediators, such as local industry associations and appellate tribunals in the host country (box 4, Figures 2 and 3). An inadequate appreciation of local culture (box 4) is another stumbling block, even for premier brands such as Home Depot, which had to shut down several stores in China where the easy availability of maintenance people makes a do-it-yourself ethos rare (Carlson 2013). To avoid such blunders, subsidiary managers might create dedicated roles and hire local staff to foster cultural sensitivity (box 1).
Emerging-market MNCs face distinct challenges too. Many, such as SEACOM, the submarine cable operator from Mauritius, compete on a low-cost basis (Azevedo et al. 2016); when they seek growth, they traditionally have looked internally (i.e., HQ–subsidiary link) to leverage an efficiency strategy for reining in manufacturing costs (boxes 1 and 2, Figures 2 and 3). Our framework points to another venue for action, beyond manufacturing costs: the subsidiary–channel partner link (box 5). For emerging-market MNCs, it is critical to commit marketing resources to foreign channel partners to build a strong presence in the downstream market. For example, the Chinese phone maker Huawei has a pronounced manufacturing orientation but has underinvested in branding in key developed markets. Without strong resonance with local channel partners and consumers, the brand has remained vulnerable in the United States and Europe (Shu 2013).
Many emerging-market firms, such as Zimbabwe’s agribusiness company AICO Africa Limited, are virtually compelled to expand abroad due to the political instability and market inefficiencies at home (IGD and Dalberg Report 2011). Yet with foreign brand names, offshore manufacturing, and weak home currencies, these emerging-market MNCs also face hurdles in acquiring legitimacy from U.S. channel partners (box 5, Figures 2 and 3). This legitimacy gap could be bridged with certifications from local agencies or alliances with reputable brands. Strategic acquisitions of local channel partners (box 5) could also plug the legitimacy gap, as Sun Pharmaceuticals (India) and Cemex (Mexico [building materials]) have found (Azevedo et. al. 2016).
Finally, emerging-market MNCs sometimes exhibit traditional—even anachronistic—business approaches and a reluctance to adopt practices that are standard in the developed world (Dietz, Orr, and Xing 2008). Some firms struggle to reconcile a communal logic prevalent at home with the individualistic logic of developed markets. For example, Chinese firms that have benefited from the state capitalism model, which removes most resource constraints, must learn how to develop subsidiary abilities as they expand abroad, without government support (Azevedo et al. 2016).
Because MNCs represent a common but complex organizational form, understanding how they manage their marketing channels in different product and geographic markets is key for both academics and practitioners. As a step forward, we review the MNC literature to highlight research gaps and to organize various aspects of MNC channel management practices. Two integrative frameworks, which emerge from a meta-theory approach, outline the individual and combinative effects of MNC motives, environmental variables, and socioeconomic attributes of HQ–subsidiary–channel partner linkages on MNC channel structures and processes and on MNC channel outcomes. These frameworks emphasize how MNC channel management is influenced by interplays across MNC entities, the institutional environment, and the network structure of MNC subsidiaries; these unexamined aspects represent significant gaps in our understanding of MNC channels. To bridge these gaps, we offer an agenda for future research that details opportunities for examining novel theoretical relationships, new constructs, and new contexts in each gap area. Finally, a set of illustrative propositions highlights the relevance of our framework. We hope our research will encourage scholars in both MNC and business-to-business domains to pursue programmatic research related to MNC marketing channel management.
1 In agency theory parlance (Bergen, Dutta, and Walker 1992), ex ante efforts do not resolve the moral hazard problems that arise ex post. The transaction cost paradigm (Williamson 1991) suggests that governance mechanisms are required ex post to align channel partners’ motivations in an ongoing manner.
- 2 We use the term “subsidiaries” hereinafter to refer to wholly owned MNC subsidiaries. Partially owned subsidiaries (e.g., joint ventures) mix market and hierarchical features and involve additional complexities beyond wholly owned subsidiaries. After accounting for these complexities, though, our framework is applicable to the former as well.
- 3 Transaction cost theory (Williamson 1996) holds that channel decisions that involve substantial specialized investments should be internalized within subsidiaries. Knowledge-based views argue that when the competencies being transferred to foreign markets are tacit, market exchanges are prone to failure, so firms will enter through subsidiaries (Brouthers and Hennart 2007). Dunning’s eclectic paradigm combines these views to suggest ownership, location, and internalization advantages as key determinants of entry mode choice (Rugman 2010).
- 4 Both the main and interaction effects of the two institutional variables in P3 are denoted by arrow g in Figure 2. To avoid clutter, Figure 2 does not explicitly show interaction paths between two variables (e.g., two institutional variables) in the same box.
- 5 Resources gained from the local network are specialized to the subsidiary’s local context and thus may be more valuable than those obtained from the intrafirm subsidiary network, which come from a different country and might not transfer to the focal subsidiary’s specific context. From a power dependence perspective, the significance of tie density in the intrafirm subsidiary network diminishes with increasing density in the interfirm network. However, depending on other contextual factors, the competencies acquired from inter- and intrafirm networks could serve mutually complementary roles. These distinct theoretical possibilities underline the sparseness of current research in the MNC channels domain and the need for empirical studies that can settle such questions.
- 6 Pliability is distinct from strength. Pliable institutions are open to modification, though they need not be strong, either originally or as a result of the modification. In India, formal institutions are modestly strong but not very pliable to outsiders; in Angola, institutions are weak but receptive to change (The Economist 2011).
Notes: Figure 1 illustrates the complexity of the issues facing an MNC that aims to manage distribution in foreign markets. We delineate the elements of this system in three sets: MNC corporate entities (solid and dashed boxes), the environment (ellipses A and B as well as the HQ’s home-country context or environment, in the shaded region in box C), and the socioeconomic systems among the MNC HQ, its subsidiary, and the subsidiary’s channel partner (relationships D, E, and F). (A) = global industry environment that affects the multinational corporate entity in which the MNC HQ, MNC subsidiary, and MNC subsidiary channel partner are situated; (B) = local-country environment that affects the MNC subsidiary and its channel partners, such that the shaded region in box C reflects HQ embedded in its home-country context; (C) = the MNC legal corporate entity, comprising its HQ and subsidiary; (D) = the HQ–subsidiary relationship; (E) = the relationship between the MNC subsidiary and the foreign channel partner; and (F) = interplay between the HQ–subsidiary and subsidiary–foreign channel partner relationships.
a Formal and informal controls depict both controls deployed by the HQ over its subsidiary and controls deployed by the subsidiary over its channel partners.
Notes: (a) = HQ–subsidiary relationship; (b) = MNC subsidiary–foreign channel partner relationship. The dashed line joining strategic motives (box 1) and channel partner structures and processes (box 5) is intended to suggest a direct effect of strategic motives on the foreign channel partner, beyond the effect purely mediated through subsidiary controls. Space constraints preclude the illustration of interplays between the subsidiary–channel partner (arrow b) and HQ–subsidiary (arrow a) relationships.
TABLE 1 Gaps in Multinational Channels Research
Notes: The “Relationship Hypothesized?” columns refer to whether aspects of these relationships were explicitly predicted in the study. Most listed studies consider the MNC as a single corporate entity and do not distinguish between HQ and its subsidiaries. For the institutional variables, most listed studies propose a main effect of national culture (and a few examine legal institutions) on entry mode choice, not on the ongoing management of the channel partner after market entry. Hada, Grewal, and Chandrashekaran (2013) test interactions involving environmental uncertainty, but the dynamism construct is not unique to MNC contexts. Grewal et al. (2013) consider task coordination across subsidiaries but do not invoke any network theory–based constructs per se.
TABLE 2 An Agenda for Future MNC Channels Research: New Relationships Among Constructs, New Constructs, and New Contexts
TABLE 3 Summary of Illustrative Propositions
TABLE 3 Continued
a Interfirm relationship literature is an amalgam of theories, including agency theory, transaction cost theory, and relational contracting approaches, among others (Heide 1994).
bThe industry-product context is subsumed in box 4 of Figure 2, under HQ context. Notes: DV 5 dependent variable. IV 5 independent variable.
PHOTO (COLOR): FIGURE 1 Overarching Framework
PHOTO (COLOR): FIGURE 2 Determinants of MNC Marketing Channel Structure and Processes
PHOTO (COLOR): FIGURE 3 Determinants of MNC Marketing Channel Outcomes
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Record: 118- Marketing Excellence: Nature, Measurement, and Investor Valuations. By: Homburg, Christian; Theel, Marcus; Hohenberg, Sebastian. Journal of Marketing. Jul2020, Vol. 84 Issue 4, p1-22. 22p. 3 Diagrams, 3 Charts, 1 Graph. DOI: 10.1177/0022242920925517.
- Database:
- Business Source Complete
Marketing Excellence: Nature, Measurement, and Investor Valuations
Marketing excellence is a foundational principle for the discipline that is gaining increasing attention among managers and investors. Despite this, the nature of marketing excellence and its effectiveness remain unclear. This research offers insight by addressing two questions: ( 1) How do managers understand and exercise marketing excellence? and ( 2) How do investors evaluate marketing excellence? Study 1 merges insights from 39 in-depth interviews with senior executives and secondary data from 150 firm strategies to find that marketing excellence is a strategy type focused on achieving organic growth by executing priorities related to the marketing ecosystem, end user, and marketing agility. Study 2 quantifies the impact of marketing excellence on firm value by using a machine learning algorithm and text analysis through an original dictionary to classify the text from 8,317 letters to shareholders in 1,727 U.S. firm annual reports. Calendar-time portfolio models reveal abnormal one-year returns of up to 8.58% for marketing excellence—returns that outpace those associated with market orientation and marketing capabilities. Findings offer guidance to managers, educators, and investors regarding how marketing excellence manifests—paving the way for the allocation of firm resources to ensure that marketing drives organic growth.
Keywords: agility; ecosystem; end user; grounded theory; marketing excellence; marketing–finance interface; organic growth; text analysis
Interest in marketing excellence is growing rapidly, with mentions in the international business press rising 68% between 2006 and 2016. Furthermore, investor filings show an increase in references to marketing excellence by over 100% during that time period.[ 6]
Despite this interest, effectively exercising marketing excellence and showing its value to investors present strong managerial challenges ([ 2]; [68]). For example, The CMO Survey recently revealed that companies' marketing excellence levels have stayed flat at a medium level since 2013 ([58], [59]). Moreover, as the chief marketing officer report by [36] finds, 67% of the surveyed top managers feel unable to clearly show to financial markets how their marketing activities, such as those related to marketing excellence, create growth in shareholder value.
These needs notwithstanding, the literature on marketing excellence consists of only a few studies ([ 9]; [52]; [60]), which provide two important insights. First, previous work has indicated that marketing excellence is a strategy-like activity or process that concerns the "firm's superior ability to perform essential customer-facing activities" and that this execution "occur[s] during the marketing strategy process" ([60], p. 6). Second, previous research has conceptualized the links between strategy goals and process activities necessary for marketing excellence, as exemplified by a literature-based framework encompassing organizational elements and seven strategy levers useful to achieving these goals ([60]). In this regard, marketing excellence research builds on other important work identifying market orientation activities ([42]) and marketing capabilities ([14]; [62]) that also stresses key process activities driving marketing performance.
Despite this progress, research has not specified the content areas of strategy that managers should focus on, leaving a sizable gap in knowledge as to where managers should apply the elements of marketing organization, process, and resources for marketing excellence. Furthermore, lack of evidence regarding the value of such investments limits managers' confidence in this direction. We leverage these opportunities and investigate two research questions: ( 1) How do managers understand and exercise marketing excellence? and ( 2) How do investors evaluate marketing excellence? We address these research questions in two studies using a multimethod approach.
In Study 1, we conducted 39 in-depth interviews with senior managers of global companies and augmented the data with secondary data on 150 firm strategies, applying the theories-in-use methodology ([102]; [103]). Findings reveal that marketing excellence is a strategy type focused on achieving organic growth by executing three priorities: ( 1) marketing ecosystem priority, ( 2) end-user priority, and ( 3) marketing agility priority. By delineating the components of marketing excellence, Study 1 advances the field's understanding of what marketing excellence is. These advancements are of substantive value because they provide guidance regarding where managers can invest in marketing excellence and how to communicate marketing excellence to internal and external stakeholders.
Study 2 quantifies the impact of marketing excellence on firm value, which refers to investors' expectations of future cash flows and then compares these effects with the effects of current marketing strategy concepts. We choose firm value as the central outcome variable because it is a comprehensive and accurate measure for evaluating the impact of marketing strategy concepts ([40]). Using 8,317 letters to shareholders from 1,727 annual reports from 1998 to 2016, we measured marketing excellence through a machine learning algorithm and an original dictionary and assessed its impact on firm value. Results are similar across the two marketing excellence measures: the machine learning algorithm (4.80%) and the original dictionary (8.58%) had one-year abnormal returns significantly higher than benchmark portfolios. These performance metrics are even higher in the period 2014–2017, when the marketing excellence portfolio achieved average annual returns of 16.82%, significantly outstripping market orientation and marketing capabilities portfolios (1.95% and 8.53% respectively). These results show that investors value marketing excellence more highly than they value strategies based on market orientation and marketing capabilities.
Overall, the approach and findings of this investigation advance the field's understanding of marketing excellence's substance (Study 1) and effectiveness (Study 2). The remainder of the article is structured according to the two empirical studies. In concluding, we discuss the studies' implications for future research and nonacademic audiences.
In line with prior work pursuing similar research objectives ([63]; [95]; [103]) and seminal work on theory development ([35]), we relied on the exploratory theories-in-use approach to identify managers' understanding of the nature of marketing excellence and activities central to it.
To do so, we used two types of data. First, we conducted 39 in-depth interviews with senior executives in marketing, sales, and general management within 39 global companies (see Web Appendix W1). Interviews took 45 minutes on average, yielding 460 pages of single-spaced transcripts, and addressed three main areas: ( 1) characteristics of the respondent and the firm, ( 2) managers' comprehension of marketing excellence, and ( 3) managers' activities with respect to marketing excellence (Web Appendix W2 shows the interview guide). During the interviews, we phrased the questions carefully to avoid "active listening" ([51], p. 21). To identify the range of possible manifestations of marketing excellence, we structured the third part of our interviews according to what we call the "decision fields of the marketing organization,"[ 7] such as firm structure and culture, which are consistent with the 11 strategic choices identified by [60] (Web Appendix W3).
Second, in line with prior work ([26]; [71]), we relied on firm strategy data to augment and triangulate the interview data. This enrichment enabled us to determine whether the interview data were "saturated" and whether all aspects had been included in the marketing excellence conceptualization. Specifically, we analyzed current firm strategies of 150 U.S., European, and Chinese firms listed in the S&P 500 (n = 50), Euro Stoxx 50 (n = 50), and CSI 300 (n = 50) indices (Web Appendix W1). We gathered information on firm strategies from publicly available company presentations, annual reports, news filings, and firms' home pages. Overall, we collected 320 single-spaced pages of firm strategy information.
We relied on systematized qualitative data analysis involving open, axial, and selective coding ([92]). The goal of our data analysis was to uncover managers' understanding of marketing excellence and the activities performed in relation to it. For the latter, we first identified all marketing excellence activities (i.e., level 0 categories) within each decision field from the interview and firm strategy data. We then compared marketing excellence activities across decision fields—distilling marketing excellence priorities (i.e., level 2 categories) and marketing excellence subcomponents (i.e., level 1 categories) (see Web Appendix W4). Figure 1 illustrates our data analysis procedure and Web Appendix W1 provides further details.
Graph: Figure 1. Steps in the data analysis procedure.Notes: F = functional decision field, S = structural decision field, C = cultural decision field, R = relational decision field, Ch = change decision field. Drawing on an extensive literature review and insights from the field study, we conceptualized the marketing organization in terms of these five decision fields. A decision field is a set of related strategic choices pertaining to designing firm's activities. For how this conceptualization builds on previous work on the marketing organization (e.g., [60]), see Web Appendix W3.
Previous research offered a first definition of marketing excellence as "a superior ability to perform essential customer-facing activities that improve customer, financial, stock market, and societal outcomes" ([60], p. 6). In line with recommendations in the methodological literature ([47]; [92]; [102]), we built on this initial definition and melded it with managers' understanding of marketing excellence and prior work on other conceptions of excellence such as operational excellence (e.g., [72]) and mergers-and-acquisitions excellence (e.g., [41]). These triangulation efforts resulted in three insights.
First, managers understand marketing excellence as aspirational and difficult to achieve. This finding relates to the "excellence" part of marketing excellence. Three illustrative quotes from our interviews point to this quality:
Marketing excellence is difficult and not just a one-time-only thing within the organization. What we have done today, how we have built our structures for instance, has to be reevaluated constantly to keep up [with] the pace of the external dynamics we are in today. (Marketing executive of an electronics and home appliances supplier)
We want marketing excellence, but it requires deep changes in the processes of our firm. I must tackle marketing excellence step-by-step, given our global organizational reach with varying processes and different cultures. (Sales director in the do-it-yourself tools sector)
Marketing excellence is more difficult than previous growth strategies. Previously, I could be successful by focusing on my customers and outperforming my competitors. For marketing excellence however, I need to think in entire ecosystems, which is way more complex. (Senior manager of a chemical company)
This understanding of marketing excellence as aspirational and difficult to achieve also corresponds to excellence conceptions of previous work ([48]; [75]).
Second, results show that managers understand marketing excellence as a means of achieving organic growth. This finding relates to the "marketing" part of marketing excellence. Specifically, managers noted that superior firm outcomes can be achieved through two general paths: revenue increases or cost reductions. This notion is also reflected in the literature on excellence ([75]; [78]). Moreover, our findings revealed that marketing excellence is focused on organic growth through sustainable revenue increases. As a senior manager of an international business-to-consumer (B2C) supplier pointed out,
When showcasing marketing excellence across the organization, senior managers first had to understand that marketing excellence is about how to create growth in revenues and not just about shifts in advertising budgets.... Finding new sources of sustainable revenue is at the core of marketing excellence!
Similarly, the chief marketing officer of an industrial supplier described how organic growth has become more connected with marketing:
We assigned more and more topics to marketing within our firm, one of them is "how to achieve growth." This is mainly driven by the changing markets we are currently in. So to say: Marketing has gained a seat at the table, but I have to constantly show how to configure the organization for growth.
The focus of marketing excellence therefore differs from that of operational excellence (e.g., [72]), which derives from cost reductions and reflects a firm's strategic priority of achieving superior outcomes through efficiency gains (such as by means of lean management, supply chain optimization, or total quality management). In addition, firms can achieve superior outcomes through inorganic approaches such as mergers and acquisitions, which require superiority in commercial due diligence or postmerger integration ([41]).
Third, results reveal that marketing excellence comprises activities that essentially fall into three content-related categories: the marketing ecosystem priority, the end-user priority, and the marketing agility priority. This finding relates to marketing excellence's nature as a strategy type. We label these activity categories marketing excellence priorities to reflect firms' strategic means of achieving organic growth. Table 1 provides an overview of all marketing excellence priorities, their subcomponents, definitions and illustrative quotes. We discuss the development of each of the three marketing excellence priorities in detail in the next section and elaborate how each priority can be embedded.
Graph
Table 1. Marketing Excellence Components, Illustrative Quotes, and Relevant Literature.
| Level 2 Categorya | Level 1 Categoryb | Level 0 Categoryc | Illustrative Quotes | Relevant Literature |
|---|
| Marketing ecosystem priority:A firm's strategic means of growing the business by developing mutually beneficial systems of networks | Building ecosystems in proximal and distal networks:Activities related to forming systems of relationships within the supply chain, outside the supply chain, and beyond the firm's horizon | Expand activities and contacts beyond the firm's own industry | In partnership with SAP, our vision is to create...a single sign-on capability. We can then match this information with our Connected Fitness data.... This is a transcendent moment for the Brand—going beyond the expectations of what a sports brand was thought to be. (Under Armour [2016]; listed in the S&P 500)e
| Desai (2018); Dyer and Singh (1998); Reuer and Devarakonda (2016); Swaminathan and Moorman (2009); Thomaz and Swaminathan (2015) |
| Incorporate multiple partners in value creation | We're also very excited about a strategic alliance we announced earlier this year with Lyft, the fastest-growing ridesharing company in the U.S., and the pending acquisition of Cruise Automation, a leader in autonomous technology. We believe the convergence of connectivity, ridesharing and autonomous vehicles will shape the future of personal mobility, and we're working across multiple fronts to create an integrated network of on-demand autonomous vehicles in the U.S. (General Motors [2016]; listed in the S&P 500)e We collaborate with innovative technology providers and start-ups in attractive joint projects. For automotive paints, we use real time data from our customers' painting line to optimally adjust the color based on customer needs. This allows us to ensure that the vehicle is painted in exactly the right color. (BASF [2017]; listed in the Eurostoxx 50)e
|
| Enable partners throughout the network | This is one big way in which we are evolving to become One Signet—one singularly driven company in total collaboration without silos or barriers....For instance, Signet established a jewelry design center in New York which evaluates global design trends, innovates, and helps all our merchant teams develop new jewelry collections that resonate with customers to drive results. (Signet Jewelers [2016]; listed in the S&P 500)e
|
| Share knowledge within and beyond the firm's own industry | We regularly invite experts from all kinds of industries and aim to get new impulses. For example, last week we invited a senior manager from the airline industry. The aim of this practice is to infuse our organization with a different mindset, to gather knowledge by looking beyond our own horizon. In my opinion, we even have to start hiring managers from completely different industries to gather new knowledge we need to succeed today. (Senior marketing executive of a pharmaceuticals company)d
|
| Build and shape platforms with multiple stakeholders, including competitors | Autonomous driving requires us to share safety-relevant information within an ecosystem. For example, the driving system of a competitor's car signals road damages, so that other autonomous driving systems can react instantly or change the optimal route....In the future, we need to have the mindsets to include [any external partner in our network] in our ecosystem! (Senior executive of an automotive manufacturer)d
|
| Fostering integrated ecosystems:Activities related to connecting, guiding, and steering the firm's systems of relationships | Implement connecting hubs | In our organization, we created centralized functions that offer tools for approaching a certain problem. For instance, if we have to analyze a large amount of consumer data, we consult with the "data experts" and get the necessary tools to solve this problem. (Marketing director of a global IT supplier)d
| Batra and Keller (2016); Jaworski and Kohli (1993); Lee et al. (2015); Luxton, Reid, and Mavondo (2015); Vorhies and Morgan (2003) |
| Harmonize and align work streams | One thing has changed tremendously: Today, it is about how to best combine knowledge to ultimately create value for the customer. Often, this knowledge does not reside inside our organization, our supply chain, or even our industry. Thus, we enforce an open exchange across companies, especially across industries, and want our employees to build a network in which they can share knowledge and gather outside opinions about their ideas and work. (Marketing director of a food company)d
|
| Develop integrated offerings | We more and more think our business in ecosystems today. One of the new challenges that we are concerned with in this regard is how to integrate these ecosystems with our infrastructure. In my opinion, we need this integration to leverage the ecosystems for growing the business. (Senior marketing manager of an industrial goods company)d
|
| Use modular team structure | Managing ecosystems requires more flexible structures. One thing we found useful was moving toward team-based working structures, composed of several roles and tasks required for growing business, such as value proposition, operating model, and profit model. (Head of department; chemical company)d
|
| End-user priority:A firm's strategic means for engaging with the final customer, who applies or consumes the offering, and leveraging the final customer insights for growing the business | Engaging with the end user:Activities related to building interfaces to final customers and interacting with final customers | Foster end-user orientation | In our diverse and globalized world, it is becoming more and more important to gain a better understanding of the requirements of our customers and end-consumers. (Specialty chemicals supplier Evonik Industries [2016])e Thanks to digital technology, the Group has the opportunity to get closer to its industrial and construction sector customers, and construction professionals primarily, but also, from now on, those who provide solutions, such as architects, and individuals who are sensitive to the comfort and energy efficiency of their homes. To establish a relationship with these consumers, Saint-Gobain has rolled out a brand awareness campaign aimed at the general public. (Saint-Gobain [2016]; listed in the Eurostoxx 50)e
| Deshpandé, Farley, and Webster (1993); Jaworski and Kohli (1993); Kohli and Jaworski (1990)Narver and Slater (1990); Slater and Narver (1999) |
| Enforce dialogue with end user | The way we have to do business has changed drastically: As a B2B supplier, we need to gain access to the end user! This does not mean that we will be a B2C company; however, access includes being able to gather data about the behaviors and derive specific needs. Then, we can monetize these insights by incorporating them into our products and services! (Senior marketing executive of an industrial supplier)d
|
| Detect new end user problems | For innovating the business model, we must start discovering the issues of our final customers. Only if we are able to solve the problems that our end users face tomorrow, we will remain successful in the future. (Senior marketing manager of an industrial goods company)d
|
| Using end-user knowledge for business model creation:Activities related to reshaping the business using insights regarding final customers | Generate end user–based business models | As a B2B supplier, we have traditionally started our innovation process by analyzing the needs of our direct customers. This approach is outdated! To stay ahead of the curve...we must start with the users and analyze their needs, behaviors, and trends that affect them. (Senior executive of an industrial supplier)d In general, we have to take into account two aspects: What value is our company willing to offer and what value is our company able to offer. For instance, it is essential for us [in marketing] to be able to develop a compelling value proposition to target specific market segments and address customer needs. However, in addition, we have to ask ourselves, which core competences and capabilities we need to achieve marketing excellence. (Marketing director of an electronics supplier)d
| Caridi-Zahavi, Carmeli, and Arazy (2016); Sahni, Wheeler, and Chintagunta (2018); Zott, Amit, and Massa (2011) |
| Implement visionary thinking | In my view, [marketing] is more than just defining the product itself, but more like "am I selling a car or mobility" or "do I earn money by selling a compressor or...selling compressed air." Marketing has to take over a more holistic view regarding the business model! (Senior executive from an automotive supplier)d Marketing has to look beyond existing paradigms within the industry and think...visionary. In particular, marketing has to be disobedient to create new products or think new business models from the end of the chain and bring out an offering the customer has never even thought of. (Senior executive of a strategic investor)d
|
| Reinvent business models from end users' perspectives | I have problems separating marketing activities according to the classic 4Ps. Today, marketing is more than the 4Ps. In fact, marketing's role in our firm has changed toward developing novel and expanding existing business models for our organization. (Marketing director of a mechanical engineering company)d
|
| Personalize the offering | After we started to systematically analyze data we collect in our CRM systems anyway, we were able to offer our B2B customers much better bundles and also individual price points. (Senior executive from a wholesale company)d
|
| Marketing agility priority: A firm's strategic means of executing growth activities by the marketing organization and its members through simplified structures and processes, fast decision-making, and trial and error learning | Enhancing agility of the marketing organization:Activities related to simplifying marketing structures and processes | Manage learning cycles | Today...we have to develop shorter learning cycles with more and improved feedback to offer value for the customer. In order to do so, we need a trial-and-error culture in our organization that enables us to gather feedback on our offerings more quickly. (Managing director of an e-commerce retailer)d
| Aaker (2009); Griffin and Hauser (1996); Day and Wensley (1988); Fulgoni (2018); Gruca and Sudharshan 1995); Mena and Chabowski (2015); Sinkula (1994) |
| Cut through complexity | We realize that a departmental division...causes too many coordination issues. [We need] an elimination of departmental barriers. The aim must be that we no longer talk about a marketing, sales, or technology department, but which task I have to work on and for which topic I am responsible. (Senior manager of an IT company)d The fourth and last strategic priority involves streamlining and simplifying our organization teams, and further empowering our staff. By establishing five global business units, we will improve collaboration between R&D and business operations, thereby fast-tracking the time to market for our innovations. (Sanofi-Aventis [2016]; listed in the Eurostoxx 50]e
|
| Allocate resources flexibly | We recently decided to cut the traditional boundaries between departments and manage our team "task-based."...[That is,] if a customer problem comes up, e.g. when our installation technicians or customer service documented such problems, we immediately react by staffing [diverse] team members. (Marketing director of a consumer durables supplier)d
|
| Provide efficient internal data access | We are working throughout the Group on simplifying products and processes and on common technologies and platforms that will fully digitalize the business step by step. The resulting productivity gains will be invested in future business model improvements.... In order to reach this goal, we will have to consistently align business models, products, and processes across country and company borders and minimize paper at each element of the value chain. (Allianz SE [2016]; listed in the Eurostoxx 50)e
|
| Facilitating agile marketing behaviors:Activities related to fostering fast and efficient decisions of members of the marketing organization | Engage in efficient decision making | Typically, sales was screening the market, then sales briefed marketing, which consequently created communication materials, which then had to be discussed and revised again and so on. By the time we were done with this step-by-step...approach, external conditions had changed and we lost business! (General manager of a consumer electronics company)d
| Day (2011); Grant (2019); Lee et al. (2015); Menon, Bharadwaj, and Howell (1996) |
| Remove internal barriers to change | Marketing excellence is about cutting the hierarchies: Previously, we focused on delegating operational or tactical issues. To grow today, we need to flexibly coordinate tasks no matter on the strategic or operational nature of the topic. (Head of department, chemical company)d Today, it is much easier to build a certain degree of know-how and become a serious threat for established players. Just take a look at the automotive industry and see what has happened there in terms of changed rules of the game. I think the marketing function is the right institution within the organization to sense such changes, anticipate new competitors, and systematize this information for the organization. (Senior executive of a strategic investor)d
|
| Practice trial and error | Today's dynamics require us to act flexibly and react immediately. For this, we have to be able to develop shorter learning cycles with more and improved feedback to offer value for the customer. In order to do so, we need a trial-and-error culture in our organization that enables us to gather feedback on our offerings more quickly. (Managing director of an e-commerce retailer)d
|
1 a Marketing excellence priority (i.e., a firm's strategic means that include activities managers perform for organic growth).
- 2 b Marketing excellence activity group (i.e., activities within a cluster that managers perform for organic growth).
- 3 c Marketing excellence activities (i.e., processes and actions managers perform for organic growth).
- 4 d Source = interviews.
- 5 e Source = firm strategies.
- 6 Notes: CRM = customer relationship management; R&D = research and development.
With these insights, we define "marketing excellence" as a type of firm strategy focused on achieving organic growth by executing the marketing ecosystem priority, the end-user priority, and the marketing agility priority. This revised marketing excellence definition is tailored to the three key insights derived previously: ( 1) achieving excellence is difficult, ( 2) marketing is mainly about organic revenue growth, and ( 3) marketing excellence is a strategy comprising three priorities. This revised marketing excellence definition builds on the definition by [60] but is more nuanced in two ways. First, it is more comprehensive, as it specifies the content of marketing excellence. The revised definition is therefore more in line with recommendations in the literature to include goals and content in a strategy's definition ([31]; [66]). Second, the revised definition is more concise, in that marketing excellence ultimately refers to the activities important to a firm's goal of organic growth. This update represents a useful clarification, as it enhances the dialogue between marketing researchers and practitioners and thereby addresses concerns in prior work regarding a widening gap between theory and practice ([79]).
Results revealed that managers emphasized the importance of configuring the organization for marketing excellence to focus on systems of networks for organic growth. We thus define the marketing ecosystem priority as a firm's strategic means of growing the business by developing mutually beneficial systems of networks. The marketing ecosystem priority has its theoretical roots in concepts of alliances and partnerships but extends these ideas in terms of their conceptual scope and level of complexity (Table 1). Specifically, the marketing ecosystem priority manifests in activities that fall within two main categories: building ecosystems in proximal and distal networks and fostering integrated ecosystems.
Building ecosystems in proximal and distal networks includes all activities managers perform related to forming systems of relationships within the supply chain (i.e., vertical relationships), outside the supply chain (i.e., horizontal relationships), and beyond the firm's horizon to create novel value opportunities unrelated to the current value chain (Web Appendix W5). For example, in its annual report, the sports apparel company [96], p. 5) notes that it forms collaborations in its distal network to create new offerings:
In partnership with SAP, our vision is to create...a single sign-on capability. We can then match this information with our Connected Fitness data.... This is a transcendent moment for the Brand – going beyond the expectations of what a sports brand was thought to be.
Similarly, as a senior executive of an automotive manufacturer noted, building ecosystems is essential for the firm's future autonomous driving offering:
Autonomous driving requires us to share safety-relevant information within an ecosystem. For example, the driving system of a competitor's car signals road damages, so that other autonomous driving systems can react instantly or change the optimal route....In the future, we need to have the mindsets to include [any external partner in our network] in our ecosystem!
As we have mentioned, previous research has stressed the importance of building relationships for superior performance ([20]), and has scrutinized various types of partnerships and alliances within the proximal firm environment—that is, close to the current value chain ([15]; [67]; [93]). The results of our field study extend this focus to include network systems containing actors from proximal and distal firm environments.
Fostering integrated ecosystems encompasses all activities that are related to connecting, guiding, and steering the firm's systems of relationships. For example, managers in our field study reported that an important piece of their marketing excellence initiatives was the creation of organizational governance units, such as connecting hubs or centers of excellence ([24]; [104]). These hubs coordinate, implement, and facilitate the internal and external steering mechanisms for ecosystems. Our findings revealed that such hubs can take on three important roles: internal guideline generator, knowledge keeper, and service provider. As guideline generators, the hubs provide directions for operating the ecosystem. For instance, as the marketing director of an international B2C company pointed out,
For our marketing excellence project, we created a central unit. This unit first had to scope and define marketing excellence. Then, the unit had to travel through the organization to explain directions and implementation stages.
As knowledge keepers, hubs combine and distribute knowledge throughout the ecosystem through activities such as pinpointing best practices, providing tools, or offering training to members of the organization on marketing-relevant skills. A marketing director of a global information technology (IT) supplier described how his company benefits from a knowledge-bundling hub:
In our organization, we created centralized functions that offer tools for approaching a certain problem. For instance, if we have to analyze a large amount of consumer data, we consult with the "data experts" and get the necessary tools to solve this problem.
As service providers, hubs bundle and offer operative marketing expertise to enable an efficient value-creation process throughout the ecosystem. That is, a hub bundles support activities to help streamline value creation. An internal consultant of a construction supplier described how his organization bundled supporting activities:
We bundled topics like market intelligence, CRM [customer relationship management], or communication support for our exhibitions and events into a support hub. We carved out these functions from the market teams so that everybody in the organization has access to them and thus made our value creation processes more efficient.
A chemical supplier further described how the marketing ecosystem priority becomes embedded. In exercising the marketing ecosystem priority (Table 1, level 0 category "implement connecting hubs"), the supplier created a new organizational unit, "adaptive business networks." The main task of this new unit was to define, develop, and nurture the firm's ecosystems. To do so, this unit worked with the firm's business units and the business units' external partners (Table 1, level 0 category "incorporate multiple partners in value creation"). Our respondent emphasized that in this process, the adaptive business network unit systematically integrated new contacts that were far beyond the traditional industry boundaries. For example, the chemical supplier started one collaboration with a data analytics start-up (Table 1, level 0 category "share knowledge within and beyond own industry") that resulted in new services in the coating industry. Collecting data on the protective coating wear-out not only enabled the supplier to optimize its maintenance business but also helped it offer new predictive maintenance services within and beyond the chemical industry.
Study 1 results also revealed that firms can exercise the marketing ecosystem priority to build different ecosystem types (i.e., knowledge transfer–, offering-, open source platform–, and owned platform–based marketing ecosystems). As Figure 2 shows, these types differ with respect to the firm's participation role (i.e., active vs. passive) and its positioning within the ecosystem (i.e., dominant vs. equal).
Graph: Figure 2. Types of marketing ecosystems.
In a knowledge transfer–based marketing ecosystem, the firm can build a symbiotic network of actors in which it exchanges knowledge and cooperates with other actors in the network system. As one interview respondent noted, many of the novel autonomous driving solutions are examples of knowledge transfer–based marketing ecosystems as they require sharing and accessing safety-relevant information among all ecosystem members, including competitors and nonautomotive companies.
For offering-based marketing ecosystems, the focal firm builds an interacting network of actors, in which the firm dominantly positions itself in the ecosystem by owning the integrated offering it provides. For example, with its integrated product range of hardware (i.e., iPhone and Apple TV), software (i.e., iOS and Siri), and connecting services (i.e., iCloud), Apple was able to create an offering-based ecosystem. Apple actively participates in this ecosystem as the main provider of products and services and dominantly positions itself by deciding who participates in this ecosystem.
In a platform-based marketing ecosystem, the focal firm builds a marketplace in which other actors can engage and interact. We identified two platform-based ecosystem types that are structured along the ownership role dimension (Figure 2). In owned platform–based ecosystems, the focal firm passively participates and dominantly positions itself in the ecosystem by owning the platform. For example, Amazon provides the largest marketplace in the Western world, dominantly dictating the terms of operation on its platform. Notably, Amazon now more actively participates on its own platform, establishing a hybrid approach that will be interesting to observe in the future from a regulatory and business perspective. In an open source platform–based ecosystem, the firm passively participates and equally positions itself in the ecosystem by owning the platform but not taking a dominant position in it. One example is OpenBazaar, an open source platform representing a fully decentralized e-commerce marketplace.
We define the end-user priority as a firm's strategic emphasis on engaging with the final customer, who applies or consumes the offering, and leveraging the final customer insights for growing the business. The end-user priority has its theoretical roots in the customer orientation concept but it extends the idea by assuming a more far-reaching role of the end user (Table 1). While customer orientation suggests that firms achieve superior outcomes by building and nurturing superior customer relationships, the end-user priority describes the firm's configuration of all value-creating processes from the end of the supply chain—the end user's perspective. Thus, the end-user priority entails various activities that fall in two main categories (Table 1).
The first category, engaging with the end user, includes activities related to building interfaces with final customers and interacting with final customers. Managers emphasized that activities fostering exchanges with end users are an important foundation for creating valuable offerings. Moreover, the findings revealed the presence of diverse end-user interfaces (i.e., contact points), such as need-specific channels (e.g., personalized touchpoints, seamless journeys), dialogue-enforcing instruments (e.g., online forums, chats, personalized offerings), or measures tracking the customer journey (e.g., through tracking sensors by means of "personas" or unique needs profiles). Our findings indicated that business-to-business (B2B) companies are very likely to undertake activities related to building such end-user interfaces. A senior marketing executive of an industrial supplier noted,
The way we have to do business has changed drastically: As a B2B supplier, we need to gain access to the end user! This does not mean that we will be a B2C company; however, access includes being able to gather data about the behaviors and derive specific needs. Then, we can monetize these insights by incorporating them into our products and services!
Similarly, a senior marketing executive of a global B2B supplier emphasized the need to build new competencies:
We have to be able to find completely new solutions or business models that exceed our current competencies.... We could no longer rely on our traditional engineering competencies but had to develop new market-based competencies in this digital environment.
The second category, using end-user knowledge for business model creation, comprises activities related to reshaping the business using insights regarding the final customers. For example, managers emphasized that firms need to foster their ideation processes for detecting new profit pools at the end of the chain to grow their business. Such opportunities exist because end-user needs change rapidly today. Thus, finding a value proposition and operating model derived from end-user trends is likely to result in more future-oriented, sustainable, and personalized business models. A senior executive in an industrial supplier operating in multiple segments explained how his firm concentrates on aligning all tasks with the end user:
As a B2B supplier, we have traditionally started our innovation process by analyzing the needs of our direct customers. This approach is outdated! To stay ahead of the curve...we must start with the users and analyze their needs, behaviors, and trends that affect them.
Similarly, a senior executive noted that today, marketing must think from a business model perspective, starting with the end user, and seize opportunities accordingly:
In my view, [marketing] is more than just defining the product itself, but more like "am I selling a car or mobility" or "do I earn money by selling a compressor or...selling compressed air." Marketing has to take over a more holistic view regarding the business model!
In addition, a senior executive of a strategic investor emphasized the "end of the chain" mindset necessary for marketing excellence:
Marketing has to look beyond existing paradigms within the industry and think...visionary. In particular, marketing has to be disobedient in order to create new products or think new business models from the end of the chain and bring out an offering the customer has never even thought of.
Importantly, this "end of the chain" perspective differs from previous marketing concepts, as it provides a more holistic and integrative view of marketing's role in the firm. For example, the results of our field study revealed that for marketing excellence managers were reluctant to divide marketing activities into the traditional marketing-mix of the "4Ps." The marketing director of a mechanical engineering company noted:
I have problems separating marketing activities according to the classic 4Ps. Today, marketing is more than the 4Ps. In fact, marketing's role in our firm has changed toward developing novel and expanding existing business models for our organization.
One respondent's firm illustrates how the end-user priority becomes embedded. An automotive supplier created a process that employs a novel contact management system that integrates all existing customer touchpoints (e.g., hotline, complaint management, sales force) with new end-user interfaces, such as social media, user cocreation contests, and the Internet of Things (Table 1, level 0 category "enforce dialogue with end user"). Drawing on rich user information derived from the contact management system, the development process contained a series of business model workshops, each starting with end users' perspectives (Table 1, level 0 category "generate end user–based business models"). The results of this process were entirely new business model opportunities that went far beyond the common automotive supplier business models of selling products for a price per unit. Examples of such new business model opportunities were new platforms for automated parking and car-sharing and specific senior-citizen mobility concepts (Table 1, level 0 category "reinvent business models from end users' perspectives").
We define the marketing agility priority as a firm's strategic means for executing growth activities by the marketing organization and its members through simplified structures and processes, fast decision making, and trial and error learning. The marketing agility priority is theoretically rooted in cycle-time concepts, such as new product development programs (e.g., [29]) or competitive reaction programs ([16]; [30]). However, compared with these roots, we found that the marketing agility priority has more facets and a broader scope. As Table 1 shows, the marketing agility priority entails activities within two categories.
First, enhancing agility of the marketing organization encompasses activities related to simplifying marketing structures and processes. For example, results revealed that learning processes are important to enhance marketing agility. For example, the managing director of an e-commerce retailer stressed the importance of systematic learning cycles for the success of the firm's marketing excellence initiatives:
Today...we have to develop shorter learning cycles with more and improved feedback to offer value for the customer. In order to do so, we need a trial-and-error culture in our organization that enables us to gather feedback on our offerings more quickly.
Moreover, as depicted in Table 1, the marketing agility priority promotes modularizing tasks and processes that enable organizations to act more flexibly and faster than within classical structures, such as departmental divisions. A senior manager of an IT company commented on the challenges of departmental divisions:
We realize that a departmental division...causes too many coordination issues. [We need] an elimination of departmental barriers. The aim must be that we no longer talk about a marketing, sales, or technology department, but which task I have to work on and for which topic I am responsible.
Second, facilitating agile marketing behaviors encompasses activities related to fostering fast and efficient decisions of members of the marketing organization. The general manager of a consumer electronics company described the issue of being slow and unresponsive owing to sequential and segregated processing of tasks:
Typically, sales was screening the market, then sales briefed marketing, which consequently created communication materials, which then had to be discussed and revised again and so on. By the time we were done with this step-by-step...approach, external conditions had changed and we lost business!
The results indicated that to react responsively instead, firms need to shift resources flexibly and on demand within the organization, depending on the tasks to be performed within the value-creation process. Speed and permeability require that organizational members and resources be able to move freely between the teams in keeping with the customer problem that requires resolution. As the marketing director of a consumer durables supplier emphasized:
We recently decided to cut the traditional boundaries between departments and manage our team "task-based."...[That is,] if a customer problem comes up, e.g. when our installation technicians or customer service documented such problems, we immediately react by staffing [diverse] team members.
An insurance company further exemplifies how the marketing agility priority becomes embedded. The insurance company established a new service unit tasked with simplifying existing processes and increasing market responsiveness of all business units.
To enhance responsiveness for new car insurance products, the unit reduced complexity through worldwide standardization and digitalization of the entire customer journey, from application to payment polices (Table 1, level 0 category "cut through complexity"). The simplification gave the insurance company efficient access to market data, resulting in new insights into needs (Table 1, level 0 category "efficient internal data access"). Using these data, internal start-ups created new products quickly, fostered by the company's new approach of launching beta versions (Table 1, level 0 category "practice trial and error").
Marketing excellence is connected to various strategy concepts, and our field study results allow us to specify these relationships. To do so, we follow previous marketing strategy research and relate marketing excellence to existing strategy concepts in terms of their source of competitive advantage and the scope of firm activities across the organization, market, and environment ([49]; [100]).
As Figure 3 shows, marketing excellence and previous marketing strategy concepts are similar in terms of "source of competitive advantage." Marketing excellence is focused on growth through new revenue streams and thus draws its source of competitive advantage from differentiation rather than cost leadership (Figure 3). This focus is similar to traditional marketing strategy concepts, such as the differentiation strategy ([77]), the prospector strategy ([56]), market orientation ([81]), marketing capabilities ([19]; [98]), and marketing doctrine ([12]). However, this focus is distinct from efficiency-focused strategies, such as the low-cost strategy ([77]), the defender strategy ([56]), or operational excellence ([72]).
Graph: Figure 3. Marketing excellence and related strategy concepts.
Marketing excellence and previous marketing strategy concepts are distinct in the dimension "scope of firm activities." Our results show that marketing excellence includes firm activities for shaping the organization, market, and environment. This wide conceptual scope is similar to recent developments in operational excellence ([73]), but different from traditional marketing strategy concepts, which are more focused on adapting the firm for superior performance ([26]; [77]; [98]) or shaping the firm and the market ([38]; [70]). Specifically, marketing excellence includes the entire proximal and distal environment (Web Appendix W5) as well as all internal networks in this process. In the "Discussion" section, we elaborate on this novel aspect in more detail.
Study 1 used in-depth interviews and secondary data to delineate the nature of marketing excellence. Data offered insights into the activities involved in marketing excellence as well as detailed categories of strategic actions that can be allocated to three priorities. Study 2 extends this analysis by quantifying the impact of marketing excellence on firm value using a machine learning algorithm and text analysis tools.
Study 2 is rooted in the shareholder value concept from the marketing–finance interface ([57]; [89]; [90]). This concept provides a useful theoretical background for this study because it focuses on investors' evaluations of strategic marketing concepts ([91]). We build on these insights and conceptualize marketing excellence as the focal independent variable and firm value (i.e., investors' expectations of the firm's future cash flows) as the dependent variable. We focus on firm value as the dependent variable because it reflects proximate, immediate, and comprehensive evaluations of future cash flows ([40]).
The shareholder value concept at the marketing–finance interface suggests four mechanisms by which strategic marketing concepts such as marketing excellence can drive firm value: enhancing cash flows, accelerating cash flows, enhancing the residual value of cash flows, and reducing the risk associated with the firm's strategic priorities ([91]). On the basis of these four mechanisms, we expect a positive relationship between each marketing excellence priority and firm value (Web Appendix W6 offers a detailed depiction of the rationales).
In brief, we expect that the marketing ecosystem priority positively relates to firm value, as it is likely to increase the residual value of cash flows. This expectation ensues because firms exercising this priority could receive access to new knowledge and stakeholders could unlock long-term benefits from this access. In addition, we anticipate that the end-user priority is positively associated with firm value because it is likely to increase levels of cash flows. For instance, firms exercising the end-user priority could identify untapped profit pools by developing broader and more future-oriented business models. Finally, we expect a positive relationship of the marketing agility priority and firm value mainly owing to the accelerated cash flow mechanism: by exercising this priority, firms likely enhance the responsiveness of the marketing organization and its members, increasing the speed with which cash flows are received from transactions.
Using a web crawler, we compiled a sample of 8,317 letters to shareholders from 1,727 U.S.-listed firms' annual reports from 1998 to 2016. We chose letters to shareholders ("letters") as the main source of information regarding a firm's strategic priorities because they serve as a key communication tool between the firm's management and investors ([71]; [76]), act as a benchmarking tool for investors ([ 4]), and provide a picture of top management's mental models ([ 8]). The sample from AnnualReports.com (a financial markets' information source) does not differ significantly from the population of listed firms on U.S. stock markets (n = 5,824 firms on the NYSE, NASDAQ, and AMEX) (Web Appendix W7).
We used two methods to measure marketing excellence. First, we used the supervised support vector machine (SVM) algorithm, which through a training data set automatically classified letters as to whether they refer to marketing excellence. The text classification procedure by means of the SVM algorithm comprised five steps: ( 1) preprocessing of data, ( 2) creation of a training data set, ( 3) choice of classifier, ( 4) classification of text, and ( 5) validation of the classified texts. In line with previous work using the SVM algorithm ([13]; [34]), we followed the standard procedures for each text classification step. We depict the details of the training and classification procedure in Web Appendix W8, including quotes from the letters that we used to train the SVM algorithm.
Second, we developed a new dictionary for measuring marketing excellence and its priorities. This approach is useful because dictionary methods have three advantages over algorithm approaches: they are easy to implement, allow for intuitive operationalization, and use a relatively straightforward validation process that yields findings that are transparent to readers ([34]). We report the results from both methods side-by-side to demonstrate the consistency of the findings across methods.
To develop the marketing excellence dictionary, we closely followed the best practice procedures of dictionary development (e.g., [ 7]; [34]; [45]). In line with the "empirically guided" approach ([34], p. 1288), we created an initial word list directly utilizing the managers' language from the in-depth interviews of Study 1. This initial step was followed by various validations with regard to the internal validity (i.e., concurrent, convergent, and causal validity) and external validity (i.e., generalizability, predictive validity, and robustness) that helped extend, refine, and finalize the marketing excellence dictionary. We summarize the validations in Web Appendix 8. The final marketing excellence dictionary is depicted in Table 2 and includes 218 words, which we used to measure marketing excellence in the letters to shareholders. Specifically, we created a word frequency matrix to measure the number of word occurrences in the documents. For the depiction of the dictionaries that we used to measure market orientation and marketing capabilities, see Web Appendix W9.
Graph
Table 2. Marketing Excellence Dictionary Employed for Text Classification.
| Marketing Excellence | Dictionary Word List |
|---|
| Level 2 Category | Level 1 Category | Level 0Category |
|---|
| Marketing ecosystem priority (71 words) | Building ecosystems in proximal and distal networks | Expand activities and contacts beyond the firm's own industry | Distal, scope, reach, beyond, breadth, span, industry boundaries |
| Incorporate multiple partners in value creation | Partner, alliance, joint venture, agreement, cooperation, network, partnership, interfirm, startup, partnering opportunities, prospective partners |
| Enable partners throughout the network | Enable, mutual, collective, jointly, support, access, coaching, complementary, win-win, symbiosis |
| Share knowledge within and beyond the firm's own industry | Knowledge, exchange, expert, disseminate, infuse |
| Build and shape platforms with multiple stakeholders | Platform, ecosystem, society, stakeholder, shape |
| Fostering integrated ecosystems | Implement connecting hubs | Hub, centralize, center, marketing services, central services, aggregate, combine, amalgamate |
| Harmonize and align work streams | Align, harmonize, interdepartmental, intertwine, multifunctional, interface, silo, boundary, consistency, coordinate, permeable |
| Develop integrated offerings | Integrated, offering, journey, seamless, holistic, connected, omni-channel, digital |
| Use modular team structure | Modular, glocal, autonomous, self-determined, decentral |
| End-user priority (89 words) | Engaging with the end user | Foster end-user orientation | User, end market, end-to-end, consumer, patient, client, end customer, buyer, shopper, subscriber, user needs, client needs, buyer needs, shopper needs, patient needs, user requirement, user expectation, user request, consumer need, consumer requirement, consumer expectation |
| Enforce dialogue with end user | Dialogue, interface, interaction, community, engage, customer engagement, touchpoint |
| Detect new end user problems | Intelligence, sensing, identification, identify, spotting, anticipate, observation, alert, analytics, insights, market knowledge, trend, listening, scan, user problem, client problem, shopper problem, patient problem, user analysis, smart data, data science |
| Using end-user knowledge for business model creation | Generate end user–based business models | Business model, value proposition, usp, differentiate, solution-oriented, solving, profit model, profit potential, profit pool, ideation, business case, incubator, solution, pay-per-use, freemium, subscription |
| Implement visionary thinking | Visionary, radical, out-of-the-box, fresh, obsessive, courage, audacity, risk taking, entrepreneurial, entrepreneur, start-up mentality |
| Reinvent business models from end users' perspective | Reinvent, transform, redefine, reorganize, reshape, rethink, groundbreaking |
| Personalize the offering | Personalize, design, tailored, tailor made, individualize |
| Marketing agility priority (58 words) | Enhancing agility of the marketing organization | Manage learning cycles | Learning, best practice, customer feedback, market feedback, customer suggestion, field experience, diffusion, open, transparent, |
| Cut through complexity | Complexity, simplify, simple, simplicity, streamline |
| Allocate resources flexibly | Flexible, allocation, nimble, adaptability, responsive, react, agile, flat, marketing resources |
| Provide efficient internal data access | Data provision, data availability, data access, real-time, big data, customer data, user data, IT infrastructure, market data, data gathering |
| Facilitating agile marketing behaviors | Engage in efficient decision making | Decision making, execution, implementing, fast, accelerate, speed, quick, slowness, speedily, efficient structure, efficient organization, keep pace |
| Remove internal barriers to change | Change barriers, change management, change |
| Practice trial and error | Experiment, trial and error, explore, failure, beta, test, venture, pragmatism, prototyping, curious |
In line with prior research ([44]; [61]; [88]), we used the calendar-time portfolio approach to analyze firm value implications. The calendar-time portfolio approach casts the researcher in the role of an investor with a clear investment strategy (e.g., marketing excellence).
To analyze marketing excellence's impact on firm value, we followed standard calendar-time portfolio procedures (Web Appendix W10 gives details on portfolio construction and model specification). In brief, we first constructed a "marketing excellence portfolio" that includes all stocks of firms announcing marketing excellence in their letters to shareholders. Inclusion of stocks in the marketing excellence portfolio is guided by the classifications of the aforementioned SVM algorithm or the dictionary method. Moreover, stocks are included in the marketing excellence portfolio only if the firm announced marketing excellence for the first time, thus representing "new news" to investors ([39]). Then, we estimated abnormal returns of this portfolio using a four-factor model as a benchmark ([10]).
In accordance with prior studies (e.g., [23]), we first present the cumulative, model-free returns, expressed as the value of $100 invested from April 2000 through June 2018 (i.e., the entire sampling period). More precisely, we mimicked an investor investing $100 in a marketing excellence portfolio. To gain insights into the relative value of marketing excellence versus alternative strategic marketing concepts, we consider market orientation and marketing capabilities as benchmarks. As Figure 4 illustrates, the investment in the marketing excellence portfolio grew to $1,313, which represents a compounded annual growth rate (CAGR) of 15.38%. By contrast, the market orientation portfolio grew to $744 (CAGR = 11.79%) and the marketing capabilities portfolio to $709 (CAGR = 11.50%).
Graph: Figure 4. Cumulative returns on $100 invested in marketing excellence, market orientation, marketing capabilities, and market portfolios.
In the calendar-time portfolio model, the intercept is the variable of interest. This variable reflects the average abnormal returns of the marketing excellence portfolio. Specifically, when the intercept is significantly different from zero, we can conclude that the marketing excellence portfolio earned abnormal returns relative to its expected returns, as predicted by the four-factor model ([61]).
The results indicate that investors positively adjust the firm value when reading about marketing excellence (Table 3, Panels A–C). Specifically, findings show that firms announcing marketing excellence have significant and positive one-year abnormal returns relative to market, size, book-to-market, and momentum factors (Models 1–2). After controlling for market orientation and marketing capabilities, results remained highly similar (αp = 8.89%, p <.01).
Graph
Table 3. Results from the Calendar-Time Portfolio Analysis: Firm Value Effects of Marketing Excellence.
| A: Main Analyses | B: Combinations of Priorities | C: Intention Versus Realization |
|---|
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
|---|
| SVM approach | Dictionary approach | Dictionary approach | Dictionary approach |
|---|
| Marketing excellencea | Marketing excellencea,b | Marketing excellencea,c | Marketing ecosystem and end-user priority | Marketing ecosystem and marketing agility priority | End-user and marketing agility priority | Intended marketing excellence portfolioa | Realized marketing excellence portfolioa |
|---|
| Intercept () | 4.80%**(2.19%) | 6.30%***(2.33%) | 8.58%***(2.95%) | 4.80%*(2.63%) | 5.20%**(2.58%) | 5.72%***(2.11%) | 4.83%(3.92%) | 8.25%**(3.35%) |
| () | 1.02***(.01) | .94***(.05) | .94***(.06) | .95***(.06) | .93***(.06) | .92***(.05) | .98***(.08) | .96***(.07) |
| () | .51***(.01) | .58***(.08) | .43***(.08) | .62***(.09) | .63***(.08) | .58***(.07) | .52***(.13) | .45***(.11) |
| () | .17***(.01) | .16**(.06) | .19**(.08) | .13*(.07) | .25***(.07) | .16***(.06) | .37***(.11) | .41***(.09) |
| () | .07***(.02) | −.26***(.04) | −.31***(.05) | −.29***(.04) | −.21***(.04) | −.22***(.04) | −.20***(.07) | −.17***(.06) |
| R2 | .91 | .80 | .69 | .77 | .76 | .82 | .59 | .64 |
- 7 *p <.10.
- 8 **p <.05.
- 9 ***p <.01.
- 10 a Results encompass all three components (i.e., marketing ecosystem priority, end-user priority, and marketing agility priority).
- 11 b After controlling for market orientation and marketing capabilities, results remained highly similar.
- 12 c Results encompass the first and the last year of data in the observation period.
- 13 Notes: Standard errors appear in parentheses. To facilitate interpretation, we display the intercept as annualized returns. Boldface represents significant alphas.
For the previous results, the first and last years of the data were excluded to account for influential points; thus, we next reran the calendar-time portfolio model with the entire data set to examine the trajectory of marketing excellence. Results with the entire data set reveal even higher significant abnormal returns (αp = 8.58%, p <.01; Model 3), indicating the recency and timeliness of marketing excellence.
To offer more insight, we examined portfolios of combinations of priorities using the dictionary-based marketing excellence measure. Results reveal the highest abnormal returns for the combination of all three marketing excellence priorities (Models 2–3). Moreover, the results with respect to combinations of two priorities reveal abnormal returns of the marketing agility priority in combination with both the marketing ecosystem priority (αp = 5.20%, p <.05; Model 5) and the end-user priority (αp = 5.72%, p <.01; Model 6) and a marginally significant result for the combination of the marketing ecosystem and end-user priority (αp = 4.80%, p <.10, Model 4).
We attribute these findings to the possibility that investors consider the marketing agility priority important for dealing with complexity when pursuing marketing excellence. For example, in Study 1, we find that "cutting through complexity" (i.e., promoting simple and lean activities for growth) is an essential subcomponent of the marketing agility priority. In line with these findings, we argue that investors may especially appreciate such a focus in combination with plans to broaden the firm's relationships with the external environment (as fostered by the marketing ecosystem priority) or the end user (as fostered by the end-user priority). Likewise, as the marketing ecosystem priority and the end-user priority both foster the generation of new interfaces (e.g., with new members of the distal network and with end users), investors could be concerned that exercising the two priorities simultaneously could result in accelerated additional costs or complexity, which may level out the positive expectations of each priority when exercised separately. Finally, we analyzed the impact of each marketing excellence priority separately. In line with our predictions, findings revealed abnormal returns of the marketing ecosystem priority (αp = 6.38%, p <.05), the end-user priority (αp = 5.39%, p <.05), and the marketing agility priority (αp = 3.97%, p =.05).
To assess other nuances of marketing excellence, we developed novel text analysis–based measures: "realized marketing excellence activities" and "intended marketing excellence activities" (Table 3, Panel C). We operationalized these measures by creating an interaction term. The first component of the interaction term is the marketing excellence measure used in the dictionary approach. The second component is the future- and past-tense sentences containing marketing excellence activities. Specifically, we count marketing excellence activity mentions in the letters to shareholders and calculate the percentage of future- and past-tense sentences in which marketing excellence activities are mentioned.
In the next step, we build an "intended marketing excellence portfolio" (Model 7) and a "realized marketing excellence portfolio" (Model 8) by forming subgroups based on a median split of this interaction. This procedure is in line with prior approaches that investigate moderating effects by means of the calendar-time portfolio approach ([44]; [88]). We find that the realized portfolio achieves significant abnormal returns of 8.25% (p <.05) versus nonsignificant abnormal returns of 4.83% (p >.20) for the intended portfolio. We explain this finding by agency theoretic considerations, which are closely related to this study's theoretical roots: investors may need to see tangible proof (i.e., a signal) that firms actually perform the marketing excellence activities.
We next compare the average annual returns of the marketing excellence, market orientation, and marketing capabilities portfolios (Table 4). Analogous to our approach for measuring marketing excellence, we used dictionaries to assess market orientation and marketing capabilities (Web Appendix W9 shows these dictionaries).[ 8] Findings reveal that only the marketing excellence portfolio and its subportfolios consistently outperform the market—that is, have a significant intercept αp (Table 4, Panel A).
Graph
Table 4. Relative Performance of Competing Marketing Excellence, Market Orientation, and Marketing Capabilities Portfolios.
| A: Calendar-Time Portfolio Results |
|---|
| Calendar-Time Portfolio | αp | Abnormal Returns over Four-Factor Model? | p-Value |
|---|
| Market Orientation (Kohli and Jaworski 1990) | 5.37% (2.54%)** | ✓ | .04 |
| Intelligence generation | 6.41% (2.68%)** | ✓ | .02 |
| Intelligence dissemination | .99% (2.57%) | n.s. | .70 |
| Responsiveness | 6.52% (3.09%)** | ✓ | .04 |
| Market Orientation (Narver and Slater 1990) | 3.31% (2.14%) | n.s. | .12 |
| Customer orientation | 5.47% (2.64%)** | ✓ | .04 |
| Competitor orientation | 2.56% (2.16%) | n.s. | .24 |
| Interfunctional coordination | 4.88% (2.70%)* | (✓) | .07 |
| Market Orientation (Saboo and Grewal 2012) | 3.74% (2.56%) | n.s. | .14 |
| Customer orientation | 4.57% (2.57%)* | (✓) | .08 |
| Competitor orientation | 2.65% (4.94%) | n.s. | .59 |
| Marketing Capabilities (Morgan, Vorhies, and Mason 2009;Vorhies and Morgan 2005;Vorhies, Morgan, and Autry 2009) | 5.97% (2.69%)** | ✓ | .03 |
| Market insight capabilities | 4.19% (2.93%) | n.s. | .16 |
| Marketing strategy capabilities | 6.15% (2.63%)** | ✓ | .02 |
| Marketing execution capabilities | 8.04% (3.12%)** | ✓ | .01 |
| B: Direct Comparison of Average Annual Returns According to Time |
| Average Annual Returns |
| Apr. 2001–Feb. 2009 | Mar. 2009–Dec. 2013 | Jan. 2014–Jun. 2017 |
| Marketing Excellence Portfolio Versus Market Orientation Portfolio |
| Marketing excellence | −6.48% (5.09%) | 22.33% (1.58%) | 16.82% (1.80%) |
| Market orientation | 7.50% (2.51%) | 23.53% (2.09%) | 1.95% (2.12%) |
| Δ Portfolio returns | −13.98%**t = −2.27 (p <.05) | −1.20%t = −0.45 (p =.67) | 14.87%***t = 5.00 (p <.01) |
| Marketing Excellence Portfolio Versus Marketing Capabilities Portfolio |
| Marketing excellence | −6.48% (5.09%) | 22.33% (1.58%) | 16.82% (1.80%) |
| Marketing capabilities | 1.52% (1.18%) | 23.55% (1.08%) | 8.53% (1.00%) |
| Δ Portfolio returns | −7.99%***t = −2.27 (p <.05) | −1.21%t = −.51 (p =.70) | 8.29%***t = 3.57 (p <.01) |
- 14 *p <.10.
- 15 **p <.05.
- 16 ***p <.01.
- 17 Notes: n.s. = not significant. As a robustness check, to avoid bias due to deliberately picking a split date, we checked alternative periods by altering the split date by one year, which did not affect the results. Measures in Panel B reflect the sources in Panel A. For market orientation, we chose the market orientation portfolio based on [42] as the key benchmark because this portfolio showed the strongest performance in Panel A.
Furthermore, as indicated in Table 4, Panel B, we find notable results over time. For the period from 2000 to 2009 (i.e., until the financial crisis, which also represents the lowest cumulative returns in our portfolios), we find that the market orientation and marketing capabilities portfolios significantly outperform the marketing excellence portfolio. We explain this finding by the varying relevance that investors may ascribe to the different concepts over time. For example, while in the early 2000s investors may have greatly appreciated firms' pursuit of customer orientation (which is a component of market orientation), in the absence of today's digital possibilities they may have had some concerns about the idea of organizing by marketing ecosystems (which is a component of marketing excellence). From 2009 to the end of 2013, a period of significant stock return growth in the aftermath of the financial crisis, we observe high annual returns for the marketing excellence portfolio (22.33%) and for the portfolios for market orientation (23.53%) and marketing capabilities (23.55%). However, differences among these groups are nonsignificant. From 2014 to the end of our sampling period, the marketing excellence portfolio significantly outperformed the market orientation (Δ portfolio returns = 14.87%, p <.01) and marketing capabilities portfolios (Δ portfolio returns = 8.29%, p <.01), highlighting the value relevance of marketing excellence relative to other concepts in today's digitalized world.
To examine the role of digital transformation in marketing excellence, we eliminated from the marketing excellence dictionary words potentially related to the digital transformation and reran our analyses with two reduced dictionaries (Web Appendix W11). Results yield two insights. First, marketing excellence has several important components that may be related to the digital transformation and its challenges. Specifically, the results of the adjusted dictionaries show slightly lower abnormal returns, even with portfolios excluding digital transformation words in only the narrow sense (αp = 7.26%; p <.01). Second, they reveal that marketing excellence has other important components unrelated to the digital transformation. That is, the results still show significant abnormal returns, even for the dictionary excluding digital transformation words in the broad sense (excluding terms only somewhat related to the digital transformation) (αp = 7.20%; p <.01).
Finally, we examined the relationship between marketing excellence and other dependent variables. These analyses represent an important robustness check because, from our analysis of investor reactions, we do not know whether the previously found updated investor evaluations are due to delivered benefits in terms of higher cash flows or increased sales or due to risk adjustments (i.e., investors may gain more clarity from the news of realized activities and may be more certain in their firm value evaluations).
To ensure that the analyzed firms can expect effects from their marketing excellence activities (e.g., from already implemented activities related to the three priorities), we used marketing excellence implementation as the independent variable in this robustness check. Consistent with the assessment of realized versus intended marketing excellence activities, we operationalized marketing excellence implementation as the interaction between marketing excellence and realized marketing excellence. In line with our theory, the results revealed a positive relationship between marketing excellence implementation and both cash flow and sales performance (Web Appendix W12).
This investigation, comprising two studies, offers the first empirical examination of marketing excellence. Results of the qualitative Study 1 revealed that marketing excellence is a type of strategy focused on achieving organic growth by executing three priorities: ( 1) marketing ecosystem priority, ( 2) end-user priority, and ( 3) marketing agility priority. Thus, use of a qualitative empirical approach enabled us to revise the definition of marketing excellence and develop its critical activities for organic growth. Moreover, results of the quantitative Study 2 advance knowledge of marketing excellence by examining its effects. Specifically, Study 2 results reveal that marketing excellence is value-relevant for investors: marketing excellence portfolios generate significant abnormal returns of up to 8.58% over benchmark portfolios (Table 3). In addition, results reveal that in recent years marketing excellence has been more value-relevant than market orientation and marketing capabilities (Table 4).
This investigation yields results that advance prior work on marketing excellence in several ways. First, this examination contributes to previous research by developing knowledge about marketing excellence's nature as a strategy type. This enhanced understanding allows marketing excellence insights to be dissected into four focal issues: ( 1) how to define it, ( 2) how to achieve it, ( 3) how to evaluate it, and ( 4) how to implement it. These focal issues help illustrate how this investigation, previous research, and future work on marketing excellence are related.
While previous research has focused on the first two issues with a literature-based approach, this investigation updates these ideas using empirical insights. In addition, this investigation provides initial evidence regarding the third issue. Future work could build on these insights and examine the fourth focal marketing excellence issue of how to implement it. For example, future research could identify the project management tools and steps as well as the cultural transformation beneficial for marketing excellence. What does a successful marketing excellence project look like? Which factors differentiate successful from less successful marketing excellence projects? Which tools can help facilitate the success of marketing excellence implementation? What is the timeline? How does marketing excellence implementation differ from the implementation of other marketing concepts, such as market orientation? Given the rising interest in marketing excellence, answers to these questions would be of high academic interest.
Second, Study 1's new conceptualization advances research by expanding the scope of firm activities considered relevant for marketing. In the past, marketing and strategy theory has centered on the definition of various boundaries, such as those of the firm, the business unit, or the market ([33]). This investigation advances this theoretical perspective by extending the scope of these traditional boundaries. For instance, the marketing ecosystem priority suggests two boundary changes: from market to environment and from the firm's own profit to ecosystem profit. Regarding the former, findings emphasize the importance of moving away from a sole market focus toward shaping the entire environment. Regarding the latter, this priority makes relevant maximizing the ecosystem's profit in addition to optimizing the firm's own profits. Importantly, maximization does not mean giving away firm profits but rather finding the firm's place in the ecosystem.
Owing to these changes and similar boundary shifts suggested by the end-user and marketing agility priorities, we encourage research in marketing to position itself more distinctly on an interdisciplinary basis. We make this recommendation because the findings of this investigation indicate that many traditional boundaries of the field are growing fuzzy, and overlaps between disciplines are increasing. For example, further research at the marketing–finance interface, which is currently being disrupted by the idea of customer-based firm valuation ([50]), could build on the findings of this investigation and explore the implications of the end-user and marketing ecosystem priorities for firm valuation. Future work at the marketing–accounting interface might also build on these findings and examine how policy makers can motivate and audit firms adopting marketing ecosystems or end-user approaches, such as through tax policies.
Third, Study 2 is the first to quantify the impact of marketing excellence and compare it to other marketing strategy constructs. Findings show that marketing excellence is value-relevant to investors with returns that outstripped returns associated with market orientation and marketing capabilities. Future work could build on the findings of this investigation and examine in more detail how marketing excellence affects firms' balance sheet items. For example, how and when does marketing excellence increase revenues? Is the relationship linear or accelerated? What about costs: does an initial downturn in profitability occur to reflect learning? When and under which conditions can firms expect the breakeven and turning points in the profitability of marketing excellence? Such investigations would help sort out the specific effects managers can expect when exercising marketing excellence. In addition, for relative assessments, this investigation used text-based measures to assess marketing capabilities, an approach consistent with how we measured the other benchmarks (i.e., market orientation and marketing excellence), facilitating comparison. However, this measure may not fully capture a firm's marketing capabilities, because it does not assess how well the capabilities are performed relative to rivals. Thus, we encourage the use of measurement approaches that more directly capture capabilities relative to competitors.
Managers can benefit from our findings in various ways. First, the three marketing excellence priorities can help managers develop a checklist for effectively exercising marketing excellence. For instance, managers could break down the subcomponents of the marketing excellence priorities (i.e., the level 0 categories) into specific goals and policies, using the categories for the checklist. As an example, the subcomponent "generate end user–based business models" requires managers to ( 1) define a process that specifies how business model generation should be done; ( 2) quantify the ideation (e.g., how many new ideas by which time); and ( 3) develop ways to systematically identify, assess, and measure needs and preferences of end users. To steer their organizations' marketing excellence efforts, managers could assess the implementation level of these activities through scorecards and check off the categories after sufficient progress on the activity is made.
This assessment could also help firms that have limited resources available for exercising all three marketing excellence priorities at once. Here, we recommend a sequential approach, considering the interplay between the components. To start marketing excellence implementation, such firms could choose between the marketing ecosystem and the end-user priority (as a primary means for growth) and pair that choice with the marketing agility priority (as an execution enabler), as Study 2 results revealed that these combinations also result in abnormal returns.
Second, the findings of this investigation have implications for employee recruitment, training, and development by firms exercising marketing excellence. For example, as the Study 1 results reveal, such firms focus on building capabilities in various novel aspects such as environment-shaping, end-user orientation, or flexible resources, processes, and structures. In these organizations, recruitment of people with different skill sets may be necessary, along with updates in training and development processes and content. This study's results can aid in defining competency development profiles for hiring and training procedures. The marketing excellence subcomponent "build and shape platforms with multiple stakeholders, including competitors" offers an example: managers could adjust their recruiting toward enhancing diversity in work experience and deemphasize industry experience as the central hiring criterion. For training and development, managers could educate employees on how to reach out to multiple stakeholders (e.g., selling the benefits of collaboration) and integrate key performance indicators (KPIs) in performance management systems that focus on platforms with multiple stakeholders.
Third, marketing excellence can help firms decrease agency problems by defining meaningful (behavioral) KPIs for members of the organization. As recent empirical evidence indicates ([21]; [55]), stock market indicators are used effectively in top management incentive schemes because they align top managers' behaviors with long-term firm interests. However, using stock market indicators for lower-level management is usually insufficient or impractical owing to, for example, a lack of controllability. This insufficiency might lead to significant agency problems within the firm, because KPIs that do not relate to stock market indicators (e.g., accounting-based indicators) can lead to behavior of agents (i.e., lower-level marketing executives and employees) that is misaligned with principals' interests (i.e., the CEO's or board members' interests in increasing firm value). Marketing excellence can help address this agency problem. For example, top management teams could formulate high-level strategic priorities (i.e., the components or level 2 categories) and operationalize these choices through marketing excellence subcomponents (i.e., level 0 categories). These subcomponents could therefore serve as KPIs in incentive schemes of various organizational members, as they are more controllable for these managers and employees but are also useful in aligning agents' behaviors with principals' interest in effectively exercising marketing excellence for organic growth.
Educators can draw on this study's findings in three ways. First, this investigation reveals that marketing is essentially about finding ways to foster organic growth. We recommend that educators embrace this growth theme and pitch marketing as the discipline driving such organic growth to students. Educating future managers about this essential strategic role of marketing could offer a clear view of marketing contributions to sales and stock market outcomes.
Second, while marketing education relies heavily on the traditional marketing-mix instruments, the study's findings indicate that achieving coordination in a broad set of decision fields is critical. However, evidence indicates that universities teach marketing topics in isolation, offering specialized courses for advertising, sales, or pricing, whereas courses on how to achieve coordination of marketing activities among the five decision fields are missing ([32]). Thus, we recommend adding new courses to marketing education, teaching coordination of decision fields for organic growth. For instance, such courses could convey the nature and types of marketing ecosystems and incorporate sections on how to build and manage them across decision fields. Regarding the end-user priority, such courses could explain the growing importance of end-user behavior and touchpoints for growth, particularly for B2B companies, as well as focus on questions related to end-user engagement and end-user oriented business model generation. For the marketing agility priority, such courses could educate nascent marketers in methods and tools for analyzing and establishing efficient and expeditious marketing structures and behaviors.
Third, this investigation shows that marketing excellence is an environment-shaping concept. This feature distinguishes marketing excellence from related concepts such as market orientation and marketing capabilities, which have become integral parts of courses and textbooks in the marketing strategy field (e.g., [74]). In light of this study's results, we recommend that these courses and textbooks expand to include firms' decisions and processes related to marketing excellence.
Supplemental Material, jm.18.0388-File003 - Marketing Excellence: Nature, Measurement, and Investor Valuations
Supplemental Material, jm.18.0388-File003 for Marketing Excellence: Nature, Measurement, and Investor Valuations by Christian Homburg, Marcus Theel and Sebastian Hohenberg in Journal of Marketing
Footnotes 1 Author ContributionsAll authors contributed equally to the article. Authors are listed in random order.
2 Associate EditorNeil Morgan
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
5 Online supplement: https://doi.org/10.1177/0022242920925517
6 1Sources are the Factiva database (N = 7,955 press articles from 1996–2016) and the EDGAR database (N = 1,138 Securities and Exchange Commission filings from 1996–2016, retrieved from Nexis).
7 2"Decision field" refers to a set of related strategic choices pertaining to the design of a firm's activities.
8 3For market orientation, we relied on two existing dictionaries ([81]; [101]), both of which are based on the work of [69], and on one self-developed dictionary based on the work of [42]. We chose the market orientation portfolio based on [42] as the key benchmark because this portfolio showed the strongest performance (Table 4). For marketing capabilities, we used a self-developed dictionary based on the work of [98], [64], [65], and [99].
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By Christian Homburg; Marcus Theel and Sebastian Hohenberg
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Record: 119- Marketing in the Sharing Economy. By: Eckhardt, Giana M.; Houston, Mark B.; Jiang, Baojun; Lamberton, Cait; Rindfleisch, Aric; Zervas, Georgios. Journal of Marketing. Sep2019, Vol. 83 Issue 5, p5-27. 23p. 2 Charts. DOI: 10.1177/0022242919861929.
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Marketing in the Sharing Economy
The last decade has seen the emergence of the sharing economy as well as the rise of a diverse array of research on this topic both inside and outside the marketing discipline. However, the sharing economy's implications for marketing thought and practice remain unclear. This article defines the sharing economy as a technologically enabled socioeconomic system with five key characteristics (i.e., temporary access, transfer of economic value, platform mediation, expanded consumer role, and crowdsourced supply). It also examines the sharing economy's impact on marketing's traditional beliefs and practices in terms of how it challenges three key foundations of marketing: institutions (e.g., consumers, firms and channels, regulators), processes (e.g., innovation, branding, customer experience, value appropriation), and value creation (e.g., value for consumers, value for firms, value for society) and offers future research directions designed to push the boundaries of marketing thought. The article concludes with a set of forward-looking guideposts that highlight the implications of the sharing economy's paradoxes, maturation, and technological development for marketing research. Collectively, this article aims to help marketing scholars not only keep pace with the sharing economy but also shape its future direction.
Keywords: access-based consumption; competition; consumer behavior; digital platform; marketing and society; marketing strategy; prosumer; regulation, sharing economy
At its core, marketing enables exchange between buyers and sellers ([ 8]). Traditionally, these exchanges have involved the permanent transfer of ownership. However, the digital revolution has enabled buyers and sellers to exchange offerings that increasingly render temporary access rather than permanent ownership ([88]). This revolution has proliferated across a wide range of products and services, including transportation (e.g., Lyft), lodging (e.g., onefinestay), clothing (e.g., Rent the Runway), financial services (e.g., Transferwise), food services (e.g., Deliveroo), and office space (e.g., WeWork). Given its impressive growth, it is not surprising that the sharing economy has been heralded as a global transformation ([151]) and has gained considerable interest from scholars both within (e.g., [10]; [158]) and beyond (e.g., [ 6]; [130]; [140]) the marketing domain.
Since [121] seminal work on technology-based sharing platforms, academic literature on the sharing economy has blossomed (for a review, see [112]]). However, prior research appears to downplay the sharing economy's transformative potential and instead largely views this growing trend from the lens of our traditional market economy. For example, [90] use classic marketing concepts (e.g., perceived risk, familiarity, utility) to predict whether consumers select a shared offering. Likewise, [88] provide a set of marketing prescriptions for sharing economy entities using an adapted version of a customer acquisition and retention model developed for traditional ownership-oriented firms. Furthermore, most studies in this domain try to explain the activities or impact of a particular sharing economy firm, such as Uber ([36]), Airbnb ([158]), or Zipcar ([10]). In summary, although the literature on the sharing economy provides important insights, it is often narrow and conventional in its focus.
Our goal is to enrich and extend prior research in this domain by examining the sharing economy's disruptive potential for marketing's traditional beliefs and practices. We begin by offering a broad and inclusive definition of the sharing economy and identify its key characteristics. We then explore the degree to which these characteristics, such as access instead of ownership, challenge the foundations of existing marketing thought, which are deeply rooted in the concept of resource ownership. Specifically, we first examine how the sharing economy questions important marketing institutions such as consumers, firms and channels, and regulators. We then explore how it affects the optimization of key marketing processes such as innovation, brand management, the customer experience, and value appropriation. We follow with an examination of how the sharing economy alters our traditional views of value creation for customers, firms, and society. Our article concludes with a set of forward-looking guideposts that aim to help marketing scholars predict where the sharing economy is headed and better understand its implications. Our hope is that our definition of the sharing economy, examination of its impact on our traditional view of marketing, and future-oriented guideposts will encourage scholars to move beyond current assumptions and frameworks to offer significant discoveries that have the potential to shape the future of marketing thought and practice on the sharing economy.
To understand the impact of the sharing economy, we must first define it for a marketing context and explain the ways in which it may differ from the traditional market economy. In developing our definition, we first examined prior research's efforts to define this domain (see Table 1). As this table shows, many of these definitions revolve around a common set of characteristics.
Graph
Table 1. Sharing Economy Definitions.
| Source | Definition |
|---|
| Lessig (2008, p. 143) | "Collaborative consumption made by the activities of sharing, exchanging, and rental of resources without owning the goods." |
| Bardhi and Eckhardt (2012, p. 881) | "Transactions that may be market mediated in which no transfer of ownership takes place." |
| Lamberton and Rose (2012, p. 109) | "Marketer-managed systems that provide customers with the opportunity to enjoy product benefits without ownership. Importantly, these systems are characterized by between-consumer rivalry for a limited supply of the shared product." |
| Botsman (2013) | "An economic model based on sharing underutilized asserts from spaces to skills to stuff for monetary or non-monetary benefits." |
| Heinrichs (2013, p. 229) | "Economic and social systems that enable shared access to goods, services, data and talent. These systems take a variety of firms but all leverage information technology to empower individuals, corporations, nonprofits and government with information that enables distribution, sharing and reuse of excess capacity in goods and services." |
| Stephany (2015, p. 205) | "The value in taking underutilised assets and making them accessible online to a community, leading to a reduced need for ownership." |
| Kathan, Matzler, and Veider (2016, p. 663) | "This so-called sharing economy phenomenon is characterized by non-ownership, temporary access, and redistribution of material goods or less tangible assets such as money, space, or time." |
| Sundararajan (2016a, p. 23) | "The sharing economy is an economic system with the following five characteristics: largely market based, high impact capital, crowd based networks, blurring lines between the personal and professional, and blurring lines between fully employed and casual labor." |
| Puschmann and Rainer (2016, p. 95) | "The use of an object (a physical good or service) whose consumption is split-up into single parts. These parts are collaborative consumed in C2C networks coordinated through community-based online services or through intermediaries in B2C models." |
| Habibi, Kim, and Laroche (2016, p. 277) | "An economic system in which assets or services are shared between private individuals, either for free or for a fee, typically by means of the Internet." |
| Hamari, Sjoklint, and Ukkonen (2016, p. 2049) | "The peer-to-peer-based activity of obtaining, giving, or sharing the access to goods and services, coordinated through community-based online services." |
| Frenken and Schor (2017, pp. 4–5) | "Consumers granting each other temporary access to under-utilized physical assets ('idle capacity'), possibly for money." |
| Narasimhan et al. (2018, p. 93) | "The recent phenomenon in which ordinary consumers have begun to act as sellers providing services that were once the exclusive province of ordinary sellers." |
| Arvidsson (2018, p. 289) | "A new arena of economic action that builds...on common resources that are in themselves not directly susceptible to market exchange." |
| Perren and Kozinets (2018, p. 21) | "A market that is formed through an intermediating technology platform that facilitates exchange activities among a network of equivalently positioned economic actors." |
First, prior definitions widely recognize that the sharing economy offers temporary access as an alternative to permanent ownership (e.g., [10]; [77]; [93]). In accord with prior research, our definition also acknowledges that sharing platforms provide access to both tangible and intangible resources, including physical products such as automobiles and homes, as well as less-tangible assets, such as money, space, or time ([77], p. 663); services, data, and talent ([63], p. 229); and ideas and knowledge ([23]). Many of these definitions also acknowledge that access is gained through either economic transactions or quid pro quo exchanges (e.g., [ 6]; [19]; [58]). Thus, the sharing economy entails economically motived access ([44]) rather than socially motivated sharing ([11]).
Sharing economy transactions are also typically mediated by technology platforms that allow sharing activity to be scaled by efficiently matching (or connecting) providers and users (e.g., [112]; [116]; [138]). In addition, extant definitions often conceptualize the sharing economy as a "system" (e.g., [63]; [90]) in which customers take on enhanced roles as both providers and users of resources (e.g., [59]; [104]). Sharing economy scholars often refer to these customers as "prosumers" and suggest that this system may allow excess capacity to be more fully utilized (e.g., [19]; [53]; [63]). Finally, prior research suggests that the resources (both tangible and intangible) accessed through sharing platforms may be crowdsourced (e.g., [104]; [141]). Thus, the sharing economy blurs the lines between personal versus professional and between a fully employed workforce versus casual labor ([141]).
Although prior definitions have identified some of the vital components of the sharing economy, no single definition has articulated the entire set of characteristics needed to fully capture the nuances of this emerging domain. Thus, we synthesize these characteristics and define the sharing economy as "a scalable socioeconomic system that employs technology-enabled platforms to provide users with temporary access to tangible and intangible resources that may be crowdsourced." Using this definition as our foundation, we develop a set of seven key characteristics for classifying a wide range of sharing economy entities along a continuum that reflects the degree to which an entity is part of the sharing economy. We propose that five of these characteristics (outlined previously) are defining of the sharing economy (i.e., temporary access, transfer of economic value, platform mediation, expanded consumer role, and crowdsourced supply). We also identify two additional characteristics that are typical of many sharing economy firms but may also be found in some traditional market economy entities (i.e., reputation systems and peer-to-peer exchanges).
This continuum is displayed in Table 2; Panel A applies this continuum to the automobile sector, while Panel B applies it to the financial sector. As this table shows, although some entities (e.g., BlaBlaCar) display all of characteristics of the sharing economy, others display only a few (e.g., Zipcar). Thus, our continuum recognizes the diversity in this domain and acknowledges that some firms are more archetypal of the sharing economy than others ([112]). This continuum also highlights the extent to which a particular example of the sharing economy challenges our traditional view of marketing. For sharing entities near the right-hand side of Table 2, traditional beliefs and practices should be quite applicable. For these entities, existing frameworks can likely be augmented to incorporate their new strategies and tactics. However, as firms take on more characteristics of the sharing economy (moving toward the left-hand side of Table 2), new conceptual frameworks or major revisions of existing theories may be required.
Graph
Table 2. Sharing Economy Continua
| A: Automobile Sector |
|---|
| Archetypal Sharing Economy | → | → | → | Nonsharing Economy |
|---|
| BlaBlaCar | Uber with Consumer's Car | Uber with Uber-Owned Car | Subscription Car Access (e.g., Zipcar) | Rental Car | Loaning Car to Friends or Family |
|---|
| Defining Characteristics |
| Access oriented | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Economically substantive | ✓ | ✓ | ✓ | ✓ | ✓ | |
| Technology-based matching platform | ✓ | ✓ | ✓ | ✓ | | |
| Enhanced customer role | ✓ | ✓ | ✓ | ✓ | | |
| Crowdsourced supply | ✓ | ✓ | | | | |
| Typical Characteristics |
| Reliance on reputation system | ✓ | ✓ | ✓ | | | |
| Customer and resource owner are peers | ✓ | ✓ | | | | ✓ |
| B: Financial Sector |
| Archetypal Sharing Economy | → | Nonsharing Economy |
| Peer-to-Peer Lending (e.g., LendingClub) | Crowdsourced, Bank-Mediated Lending (e.g., bnktothefuture.com) | Traditional Bank Lending (e.g., Wells Fargo) |
| Defining Characteristics |
| Access oriented | ✓ | ✓ | ✓ |
| Economically substantive | ✓ | ✓ | ✓ |
| Technology-based matching platform | ✓ | | |
| Enhanced customer role | ✓ | | |
| Crowdsourced supply | ✓ | ✓ | |
| Typical Characteristics |
| Reliance on reputation system | ✓ | ✓ | |
| Customer and resource owner are peers | ✓ | | |
We begin with the five definitional characteristics of sharing economy entities. First of all, in the sharing economy offerings are temporarily accessed rather than permanently owned (e.g., [10]). For example, as outlined in Table 2, BlaBlaCar allows consumers to gain the benefits of riding in another consumer's car for a fixed period of time without transfer of ownership. Second, this access involves economic transactions or quid-pro-quo exchanges that transfer value from one entity to another ([88]). This act of value transfer distinguishes sharing economy transactions from activities that involve more informal sharing activities that lack exchange value, such as giving a friend a ride with no expectation of payment ([11]). Third, the sharing economy is defined by reliance on a platform (often internet based) that identifies appropriate matches between providers and users of resources and facilitates their exchange ([112]). Thus, renting a car from Avis is not part of the sharing economy because of Avis's direct engagement without platform mediation. Fourth, the sharing economy expands the role of consumers, typically seeing them take on roles from both the "demand side" and the "supply side" of the economic equation ([74]). For example, Uber reframes consumers as taxi drivers, and Zipcar requires members to clean and prepare cars for the next user. Thus, in the sharing economy, consumers are often categorized as prosumers ([125]). Fifth, among archetypical sharing economy entities (e.g., BlaBlaCar, Uber), supply is crowdsourced from many individual consumers. For example, Uber drivers pool their time and resources to constitute an aggregate supply.
In addition to these five defining characteristics, some sharing economy entities possess two additional (i.e., typical) traits: reliance on a reputation system and a peer-to-peer relationship among resource providers and customers. Although these two characteristics may be typical, they are not distinct or exclusive to sharing economy entities. For instance, although Uber originally depended on peer-based resources, today a ride through Uber may be provided in an automobile that is owned by Uber itself. Moreover, some entities that rely heavily on both reputation systems and peer-to-peer transfer are not part of the sharing economy. For example, seller and buyer reputation and peer-to-peer transfer are critical features of eBay; however, eBay does not meet the five main criteria for inclusion in the sharing economy. We next explain the impact of these key sharing economy characteristics on our traditional definition of marketing.
According to the [ 3]), marketing is defined as "the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large." Our investigation into the impact of the sharing economy focuses on the three core components of this definition (i.e., institutions, processes, and value creation). Across these three components, we focus on ten specific topics (marketing institutions: consumers, firms and channels, and regulators; marketing processes: innovation, brands, customer experience, and value appropriation; and value creation outcomes: value for consumers, firms, and society). For each topic, we offer a summary of key changes that occur as a result of the sharing economy. This is followed by a set of future research directions, which are summarized in the Appendix.
As noted in the AMA definition, traditional exchange takes place among a set of marketplace institutions. According to [149], marketing has traditionally viewed institutions as entities that "made goods available and arranged for production" (p. 1). As detailed by [57], the phrase "institutions and processes" implies that institutions such as manufacturers, wholesalers, retailers, and marketing research firms are an important part of the marketing domain. In this section, we focus on three types of institutions and examine how our understanding of them may be altered by the sharing economy: ( 1) consumers (i.e., entities that consume offerings), ( 2) firms (i.e., entities that create offerings) and channels (i.e., entities that facilitate access to offerings), and ( 3) regulators (i.e., entities that govern the exchange of offerings).
At first glance, it may seem somewhat odd to consider consumers a marketing "institution." Indeed, [57] do not include consumers in their list of marketing institutions. However, as noted previously, in the sharing economy, consumers take on expanded roles, many of which were previously assigned to institutions. For example, ride-sharing providers "consume" their car and also "produce" a service for those who ride along. In essence, this conversion of consumers into institutional actors can be viewed as a transformation from "choosers and users" to "prosumers" ([125]). Moreover, in the sharing economy, prosumers may be both a producer and a consumer (e.g., the same person may be a Lyft driver on Sunday and a rider on Monday). These prosumers take on a variety of traditional firm roles such as communication, promotion, and quality control. For example, a Lyft rider may coordinate with the driver before pickup ([46]), enhance the platform's profile by providing a rating ([73]), and expand the value of the experience by sharing with others on social media ([29]). Likewise, peer-to-peer lending platforms such as LendingClub enable consumers to provide funds to one another and even use them to screen loan applications ([147]). Thus, in the sharing economy, consumers may take on institutional roles that are typically conducted by firms in the traditional economy.
The impact of the sharing economy on consumer roles presents an opportunity to reassess many traditional consumer research topics. First, consider the topic of consumer decision making. To date, this literature has focused on decision-making strategies and biases that drive the consumption of goods that are owned (e.g., [69]; [110]; [133]). Thus, an important question is, What types of judgments, heuristics and biases affect the consumption of shared (as opposed to owned) resources? The sharing economy's unique characteristics are likely to introduce a new set of heuristics and biases that may affect consumer decision making. To point to a few opportunities, traditional influences such as need ("If I'm going less than ten miles, I won't drive my own car"), brand ("buy generic but only access name brands"), product type ("buy hedonic, access utilitarian"), or lay theories ("people only share things they don't care about") may take on new meaning when resources are accessed rather than owned.
Likewise, decision-making scholars who study self-regulation have found that consumers' ability to forgo short-term pleasure in the interest of a long-term goal (i.e., intertemporal discounting) is dependent on a variety of factors including individual differences, the length of delay, and the amount at stake ([135]). However, it is unclear how intertemporal discounting operates when an offering is accessed rather than owned, or when a consumer becomes a prosumer. For example, should the substitution of an accessed indulgence (i.e., driving a luxury car) be considered a failure of self-control or a success? Is the decision to enter one's goods into a shared system an act of self-regulatory triumph, or might it represent the acceptance of unwise risk?
A second important question is, What drives consumer satisfaction in the sharing economy? Consumer satisfaction and loyalty have been examined from a variety of perspectives, ranging from [107] expectancy-disconfirmation model, to [18] dynamic model that focuses on the relationship between customers and service providers, to hidden Markov models that classify customers over the course of their experience with a firm ([105]). The degree to which established findings from these models apply to consumers engaged in the sharing economy is largely unknown. Thus, the sharing economy presents an opportunity for marketing scholars to reexamine customer satisfaction from a new vantage point that accounts for its unique aspects, such as the enhanced role of the customer and its crowdsourced supply.
For example, traditional models of consumer satisfaction focus on the consumer's role as a user of products or services. However, in the sharing economy, consumers may also be product and service providers (i.e., prosumers) and often evaluate their users. These evaluations may have an important impact on a user's future access to (and costs of) shared products and services. Thus, in the sharing economy, consumers not only are evaluating satisfaction but also are being evaluated. The degree to which this inversion affects consumer attitudes and behavior opens up a wealth of research opportunities. For example, it would be interesting to assess the correlation between the satisfaction ratings that consumers provide and receive within a sharing platform. As a starting point for this reexamination, we suggest that consumer behavior scholars consult [51] classic work in this domain, as it offers a framework for understanding consumer satisfaction as a result of consumer–product interactions that are holistic in nature and embedded in sociocultural settings.
A third research question is, How does consumer identity affect the sharing economy experience? While the prosumer role may be natural for some consumers, others may prefer more traditional roles. A consumer's degree of comfort with new roles is likely to depend strongly on identity complexity ([60]). For example, consumers who can easily identify as a financial expert may be more comfortable engaging in the risk inherent in a peer-to-peer lending platform. Alternatively, other consumers may enact traditional consumption norms even when interacting with sharing economy entities, such as taking (rather than making) a loan on LendingClub. Furthermore, some consumers may lack the training and skills to assume these types of institutional roles. A lack of consumer comfort with the prosumer role may taint their sharing economy experience and create a sense of conflict ([95]). Thus, research that identifies the characteristics of prosumers and their degree of comfort with various levels and types of prosumer responsibility would enrich knowledge in this area. What strategies can sharing economy firms employ to foster prosumer identities, and how should these roles be managed? For example, should the prosumer role be carefully scripted or allowed to emerge more gradually?
In addition to altering our view of consumers, the sharing economy may also change our understanding of firms and channel providers ([14]). Typically, a firm deploys human, physical, and financial resources to create and market a set of offerings, which then flow through a channel of marketing intermediaries to reach end users. The transactions are typically governed by financial concerns and often influenced by the relative power of the transacting parties ([27]). This system of transactions takes on a different flavor in the sharing economy. For example, due to their reliance on crowdsourcing and/or prosumers, most sharing platforms have fewer employees and more limited assets compared with traditional firms. Thus, they are more likely to leverage external providers rather than internal resources to create offerings and use these providers (rather than intermediary firms) to distribute them ([88]). As a result, the sharing economy creates unique challenges unlikely to be faced by traditional firms. Because platforms do not typically produce offerings, they cannot control quality or guarantee consistency. As evident from their peer reviews, some drivers on ride-sharing platforms and property owners on room-rental platforms provide services that fall short of customer expectations. Moreover, platforms may also struggle to retain quality service providers who use the platform opportunistically. For instance, as noted by [159], highly skilled in-home nurses may use a sharing platform to identify potential clients and then continue to transact with them outside of the platform.
Thus, in the sharing economy, individual providers have high levels of agency but are not employees or franchisees of the platform. Hence, they are not subject to legitimate power or authority ([62]). As a result, tight ex ante contracts cannot fully govern provider behavior and ex post influence attempts by the platform may also be ineffective ([27]). Moreover, sharing platforms typically do not own or control the quality of servicescapes ([17]) in which resources are delivered. These distinctions between sharing platforms versus traditional firms and channels provide several intriguing research opportunities.
Given the lack of control that sharing platforms have over service provision, an important institutional future research question is, How can sharing platforms ensure quality? The complexity of the task of ensuring quality is magnified by a sharing platform's reliance on a large number of decentralized providers (who may enter and leave the sharing system at will), and the many and varied servicescape settings in which users are provided access to a resource (vs. centralized and standardized firm-owned stores). For example, scooter sharing platforms such as Lime must try to provide a quality experience under conditions in which a prior user has left one of its scooters in a dark alley in a bad part of town. At one level, future research could provide insights into the scope and pervasiveness of these challenges. For example, do "turnover" rates of providers on sharing platforms differ greatly from the turnover of traditional firm retail employees or retail partners? How pervasive is platform exploitation by which providers and users utilize the platform solely to find an initial match, and does this phenomenon cut across product or service type? What characteristics of sharing servicescapes (e.g., location, timing, degree of privacy) most affect user satisfaction?
Future research could also offer platforms new ways to conceive of and manage the process of ensuring quality. Because providers and users are not traditional employees, new theory is likely needed to answer these questions. Although research in the franchising domain may offer some helpful insights (e.g., [ 5]), the sheer number of providers on most sharing platforms significantly alters the scale of monitoring and control that goes far beyond a typical franchise setting. Because sharing platforms often try to inculcate a sense of community among providers and users, it is tempting to imagine that cultural norms could provide adequate governance and ensure quality. However, because the desire for community appears to be lacking in most sharing platforms, it is unlikely that norms have the strength necessary to control quality ([12]). Perhaps the notion of control itself must be reconceptualized in the sharing economy to account for its particular characteristics ([72]). Alternatively, can scholars design new systems for an ex ante selection of providers to deliver quality and remain loyal? This topical domain is ripe for field experiments that test theory-driven strategies for managing quality. For example, the issue of reducing defection and opportunism by providers (and users) could be examined through experiments that compare the efficacy of platforms using incentive-based approaches (e.g., a downward-sliding scale for repeat transactions) versus value-based approaches (e.g., enhanced support such as offering training or equipment to loyal participants).
An issue for future research that spans consumers, firms, and channels is, How does the sharing economy alter our understanding of marketplace institutions at a collective level? These three institutions are components of an interrelated market system with roles that are traditionally clear-cut: firms produce output that consumers desire and channels funnel that output between firms and consumers. However, in the sharing economy these roles become blurred. Thus, the way in which sharing economy actors respond to these roles may differ compared with the traditional economy. For example, consumers may be more likely to forgive service failures in the sharing economy because they realize that the prosumers who deliver these services are real people like themselves (rather than anonymous firms).
The sharing economy may also create unique challenges, as consumers, firms, and channels must adopt new roles and take on new responsibilities. Thus, theoretical lenses, data sources, and analytical methods that account for this role complexity among both the individual components as well as the systems in which they are embedded would be especially valuable ([66]). This approach is likely to force researchers out of the lab, inspire partnerships across disciplines and methods, and prompt the acquisition of new skills. For example, complexity science ([67]), which focuses on systems of interacting components that produce emergent outcomes, may offer a useful theoretical frame to assess the interrelated roles of sharing economy participants. This approach has been broadly applied in biology, sociology, and economics but has been underutilized in marketing ([155]). Likewise, given the embeddedness of the sharing economy actors, network analysis could be employed to assess the roles, relationships, and information flows within sharing platforms as well as the degree of influence and tie strength among its actors (Van den Bulte and Wuyts [148]). Both approaches would likely require access to longitudinal and geolocated data. While challenging, these types of investigations would significantly enhance understanding of the roles and influence of customers, firms, and channel members in the sharing economy.
"Regulatory entities" refers to laws and policies used to influence actions that affect consumer, firm, and competitive outcomes. As with prior economic disruptions, the sharing economy poses fundamental challenges to existing legal frameworks ([ 4]). Issues regarding whether sharing economy firms and transactions can be effectively regulated commingle with policy decisions regarding whether they should be regulated. In response, regulators at every level of government are debating the impact of regulating sharing markets such as lodging and transportation. Open questions remain regarding the extent to which these marketplaces should be treated as traditional firms when it comes to matters of regulation, ranging from labor, to consumer health and safety, to discrimination ([45]). For example, local zoning laws that regulate traditional hotels may need to be revised to govern Airbnb. Likewise, a lack of regulation allows Lyft to treat its drivers as independent contractors rather than employees.
Because regulatory institutions are part of the marketing system, these new sharing platforms present important challenges for marketing scholars and offer an opportunity to explore a new type of institutional entity. For example, incumbent firms have argued that Uber and Airbnb are no different from traditional taxi and hotel companies and should be regulated as such to maintain a level playing field ([81]). In contrast, these platforms cast themselves as intermediaries that facilitate peer-to-peer transactions rather than traditional providers that sell to consumers ([158]). Moreover, they propose that their online reputation systems help ensure quality standards and protect consumers and that added regulation would stifle innovation and reduce consumer welfare ([106]). This debate seems highly relevant to researchers interested in how public policy intersects with both market structure and competition.
The regulatory challenges posed by the sharing economy reveal several important questions. A good starting point is, What is the role of existing regulations and policies in governing sharing economy activities? For example, little is known about the effectiveness of the review systems currently employed by most sharing economy platforms in terms of self-regulation relative to government regulation. Furthermore, research is needed to determine if and how these reputation systems should be regulated ([160]). Perhaps there are specific contexts in which self-regulation works and others where government intervention is required ([49])? For example, self-regulation may be more effective for sharing platforms that have a large number of users and providers as well as those that face stiff market competition. Moreover, while ratings systems are helpful, they are far from perfect signals of trustworthiness and present a host of issues such as bias, forced intimacy, and inflated ratings ([50]). Thus, research is needed to document the relative effectiveness of alternative governance mechanisms, such as direct enforcement by platforms (e.g., financial penalties, restricted access), financial investments by users (e.g., deposits, mutual ownership), and network effects of reputation markets ([ 1]).
Traditionally, market exchanges have been governed not only by regulatory entities but also by trust (i.e., belief in the reliability and intentions of a partner). Indeed, external regulation is mainly required under conditions in which a low degree of trust fosters opportunism among exchange partners ([33]). Trust becomes even more important in the sharing economy due to the digital anonymity underlying most ratings systems ([21]). However, little is known about the nature of trust and its role as a regulatory institution in the sharing economy, as the bulk of marketing research in this domain was conducted prior to the rise of sharing platforms (e.g., [101]). Thus, an important question is, What is the nature of trust in the sharing economy, and to what degree can it regulate sharing economy transactions? From a consumer perspective, is the trust engendered by reputation systems as strong as consumers' trust in formal regulators? From a regulator perspective, to what degree can regulators have confidence that public interest is protected by the reputations of platform brands and of individual providers within platforms? A conceptualization of the role of trust in the sharing economy would provide a valuable foundation for creating new systems for trust building that go beyond consumer reviews and that might reduce the need for government regulation.
In addition to understanding the role of regulatory mechanisms in the sharing economy, marketing scholars should also ask, How should policy entities balance the costs and benefits of implementing sharing economy regulation? Specifically, what is the right amount of regulation, and which external entities should do the regulating? On the one hand, regulators should consider issues such as protecting consumers and creating a level playing field for both new and incumbent competitors ([54]). These concerns could likely lead to increased regulation of the sharing economy. On the other hand, regulators must balance these concerns against the benefits that sharing platforms deliver. For example, Airbnb's entry has resulted in lower hotel prices and increased choice options for consumers ([48]; [158]). Likewise, Uber provides the benefits of flexible work arrangements ([32]) and improved resource usage efficiency ([36]) and may increase consumer welfare ([35]). Moreover, car-sharing services such as Turo appear to both increase product quality and enhance consumer welfare ([74]; [144]). Collectively, these benefits may dissuade regulators from placing added restrictions on sharing platforms. Although prior research has provided evidence for both positive and negative outcomes from regulation, these studies tend to examine these outcomes in isolation. Thus, future research is needed to determine the net impact of those regulations by assessing not only the benefits created by a regulation but also its costs. This type of research could be approached through an array of methodologies, including analytical modeling, survey-based, and archival research techniques.
In addition to how sharing platforms should be regulated, another interesting question is, Who should regulate the sharing economy? Many issues and concerns surrounding the sharing economy (e.g., fair labor laws, appropriate taxation) appear to demand attention beyond the local government level. To complicate matters further, regulatory institutions at all levels of government appear to be unsure of the pros and cons of regulatory policies aimed at sharing economy firms ([24]). Thus, scholarly research that documents, explains, and predicts the outcomes of various policy choices by regulators at different levels of government would be of considerable value.
According to the AMA's definition, marketing processes involve "creating, communicating, delivering, and exchanging offerings." These processes are critical for firm success ([57]). Thus, firms place considerable importance on managing each of them. In this section, we examine the impact of the sharing economy on the effectiveness of managing four types of marketing processes: ( 1) innovation, ( 2) branding, ( 3) customer experiences, and ( 4) value appropriation.
As noted by [43], firms have two essential functions, marketing and innovation. Indeed, innovation is a central theme for both marketing thought and practice and can be broadly defined as creating offerings that are different and valuable in the marketplace. Our traditional view of innovation is tightly connected to the market economy and views firms (sometimes with the aid of users) as the primary developers of innovative new offerings and the center of business models ([30]). However, based on its unique characteristics and nature, the sharing economy will make it necessary for marketing scholars to rethink innovation. Specifically, the sharing economy's unique characteristics challenge marketing's tendency to focus on product innovation and to favor breakthrough innovation over incremental innovation.
An important first question is, What is the role of product innovation in the sharing economy? The pursuit of differentiation through product innovation is widely regarded as a basis for success in the traditional market economy. In contrast, the sharing economy has heavily relied on business model innovation (i.e., various ways in which platforms extract value by enabling transactions between providers and users) rather than on product innovation ([88]). This lack of product differentiation is evidenced by the fact that some sharing economy platforms employ products that are largely identical. For example, the Chinese scooter manufacturer Ninebot supplies products to both Lime and Bird. Likewise, many cars used for Uber are also registered on Lyft. Research is needed to determine whether conditions exist under which product innovation by platforms could create value (for consumers and/or the platforms). In short, is there a role for product innovation by platforms in the sharing economy?
Instead of relying on product differentiation, innovation in the sharing economy appears to center on improving the underlying platforms on which these products are offered. Specifically, sharing platforms aim to enhance their ability to match the differentiated goods and services offered by their providers with the unique needs of their users to better provide enhanced benefits, lower price, and/or greater convenience ([40]). Future research should continue to search for ways to improve the effectiveness and/or efficiency of platform matching mechanisms. Scholars should also try to isolate the relative efficacy of different technology-enabled models that platforms can employ to identify, attract, retain, and grow desirable providers and users ([88]). In summary, it appears that the sharing economy is shifting the locus of innovation away from products and toward platforms and their business models.
The changing role of product innovation raises a related question: What is the relative role of radical versus incremental innovation in the sharing economy? Historically, scholars have focused on radical innovation, given its important role in terms of creating firm value and disrupting markets (e.g., [136]). The sharing economy challenges the very distinction between these two types of innovation. Scholars have traditionally assumed that innovation type is a strategic decision undertaken by the firm based on its internal capabilities ([136]). However, in the sharing economy, consumers have instant digital access to a portfolio of offerings that they often cocreate. For example, 3D printing technology allows consumers to use a sharing platform (e.g., Thingiverse) to download a product design (often made by another consumer) and digitally remix this design to an incremental or radical degree before converting it into physical form ([124]). As a result, in the sharing economy, incremental and radical innovation may both become routine activities performed by consumers (rather than just firms) and may not be as distinct from one another as commonly thought. As a result, the sharing economy presents an opportunity for innovation scholars to consider new innovation typologies ([93]).
This shift in perspective away from firms engaged in radical product innovation and toward platforms that leverage existing products in new ways raises a fundamental question: What are the drivers of innovation in the sharing economy? At present, innovation scholarship has largely attributed innovation activity to successfully leveraging a firm's set of internal resources, capabilities, or processes ([150]). For example, resource-capability theory suggests that innovative firms possess a set of valuable endowments and skills that enable them to create innovative new offerings that are difficult to replicate by their competitors ([70]). This characterization seems considerably less applicable to sharing economy firms, which typically possess few unique resources. As shown by [156], young technology start-ups appear to be particularly reliant on leveraging resources from larger and more established forms through alliances and relationships. Indeed, most sharing platforms have emerged from small start-ups in which the creators possessed far fewer resources and capabilities than the incumbent firms in the industries they aim to disrupt. Instead, what these start-ups seem to possess is the willingness to exploit new opportunities and the ability to look at an established industry from a fresh perspective. These capabilities enable successful start-ups to effectively leverage the resources they acquire from established partners. Thus, compared with traditional firms, successful innovation in the sharing economy may depend more on external resource exploration than internal resource exploitation. Research capable of providing a comparative assessment of the relative value of these two different resource strategies across both traditional versus sharing economy firms would be especially valuable.
Finally, intriguing questions remain regarding innovation by traditional firms in the sharing economy. Does the relative importance of key product attributes (e.g., status vs. durability) differ between products that a consumer buys for personal consumption versus a product that a prosumer plans to (also) share with other users? For example, consumers who plan rent out their cars on Turo may place more emphasis on durability. The answer to this question has important implications for how traditional firms approach innovation in the wake of the sharing economy. Likewise, should traditional firms also consider engaging in business model innovation by participating in the sharing economy? For example, some car manufacturers (e.g., GM, Volvo) have partnered with car-sharing platforms such as Turo to make it easier for owners to rent out their automobiles or have created their own sharing platforms to offer short-term rentals ([75]).
Collectively, the branding literature views brands as valuable assets to be protected and managed by a firm and clearly communicated to prospective customers ([79]). However, brands appear to play a substantially different role, and thus may be more difficult to manage, in the sharing economy. For example, there is a notable difference between platform brands (e.g., the Rent the Runway brand) compared with the brands that can be accessed through those platforms (e.g., Prada, Gucci, Louis Vuitton). Prior research has shown that sharing economy brands play a lesser role in forming one's identity, and create lower levels of brand attachment, compared with brands that are owned ([10], [ 9]). This weakened role of traditional brands seems to be compensated for in part by the growing strength of platform brands. For example, [10] suggest that platform brands exude a savvy and environmentally friendly aura. Thus, the sharing economy appears to be disrupting traditional notions about the nature and value of brands. In addition, those tasked with delivering the brand experience are rarely employees of the company (e.g., Airbnb hosts), which raises questions about how to ensure consistent, high-quality delivery ([139], [141]).
Traditional brand management revolves around engaging consumers with brands to obtain favorable outcomes such as increased loyalty, positive word of mouth, and enhanced revenues ([52]). One important way that firms enhance engagement is by cultivating a strong brand community ([102]). However, the effectiveness of such tactics in the sharing economy may be limited, as much of our knowledge about brand communities is based on an assumption of brand ownership. Indeed, research by [10] reveals that consumers are reluctant to form communities around brands that they access rather than own. Building on this insight, we suggest that although brands may lose some power as consumers access whatever is easily available through a sharing platform, the brand of the platform might actually gain power as consumers rely more heavily on the platform itself.
Thus, an interesting question is, Do communities form around sharing platform brands? If these communities do not form, what other tools can brand managers use to create engagement with platform brands? There is anecdotal evidence that Uber and Lyft riders show low levels of brand loyalty, as consumers frequently switch between the two platforms to get lower prices. In response, both platforms have recently introduced programs to incentivize consumer loyalty ([118]). Future research could examine whether these types of programs are effective for sharing platforms and how they may best be designed for sharing economy experiences. For example, given the sharing economy's inherent social nature, loyalty programs that emphasize prosocial opportunities (e.g., donations to local nonprofits) may be a particularly effective tactic. Alternatively, because most shared resources are accessed through a technology-enabled platform, loyalty programs that involve time-sensitive, experiential, and technologically delivered perks (e.g., Amazon's flash deals) may also be quite appealing. A framework that may be especially fruitful for reexamining ideas surrounding brand community in the sharing economy is the notion of brand publics ([ 7]). According to this framework, in an online environment, brands take on a more ubiquitous nature and are less likely to cultivate the type of social formations typically found in traditional brand communities.
Luxury branding, in which brands are distinguished by price and exclusivity ([80]), is another future research opportunity. Because the sharing economy lowers price and increases access, sharing platforms seem incongruent with luxury brands. This raises the following question: What are the prospects of luxury branding for the sharing economy? Although shared brands may be difficult to brand as luxuries, consumers' desire for distinction through brands may be revealed in different ways. For example, as luxury goods become more widely accessible through sharing platforms, personalized experiences may represent a more unique way of distinguishing oneself and crafting a sense of identity. Thus, in the sharing economy, brands that represent exclusive experiences may be better able to deliver status benefits compared with brands that follow the traditional dictum of exclusive (and high-priced) offerings. If luxury brands become more about experiences than objects, it seems likely that sharing platforms may begin to position and price themselves as facilitators of luxury experiences. For example, onefinestay positions itself as the luxury alternative to Airbnb by virtue of the concierge service it offers to supplement its property inventory. Airbnb plans to fight back by launching a new service rumored to be called "Airbnb Luxe" that will also focus on providing enhanced experiences that can benefit from the authenticity that comes with local partnerships ([137]). This type of positioning is a radical departure from current sharing platform branding, which tends to position on price, convenience, or sustainability. Future research that examines the paths that sharing platforms brands take as they migrate into luxury would be especially valuable.
Finally, given its potential disruptive effect on traditional branding strategies, another topic ripe for future research is brand value: What types of value do sharing platform brands provide their users? For example, WeWork, one of the fastest-growing brands in the sharing economy space, provides shared workspaces around the world that can be accessed through membership. Although start-ups and freelance workers may seem like this brand's obvious target, many traditional firms that have their own workspaces are buying WeWork memberships for their employees and trying to "WeWork-ify" their own offices ([71]). These memberships allow their employees to accrue network capital ([145]) by working in a shared space, learn new ideas from interacting with individuals from other organizations, and take advantage of the social programs that WeWork provides. As this example shows, a sharing economy brand's actual value may differ from its intended value. In addition, its brand's use value (i.e., the value derived from its tangible features; [ 9]) and network value (i.e., its "capacity to engender and sustain social relations with those people who are not necessarily proximate and which generates emotional, financial and practical benefit" [Elliott and Urry, 2010, p. 58]) appear to be more important than its symbolic value. Thus, the way marketing scholars think about and measure brand value should encompass all three of these types of value ([79]).
In addition to managing brands, traditional firms also manage customer experiences across all touch points along the journey through which their customers choose, acquire, and consume their products or services. To ensure that these experiences are high in quality, firms try to influence their service providers' behaviors (among both employees and channel members) through careful selection and training and by exerting power and influence to incentivize desirable behavior and punish bad behavior ([92]). However, these traditional tools and strategies may be less effective in the sharing economy, in which user experiences often entail accessing an offering that is owned by another consumer who is renting out its excess capacity. Thus, as noted previously, sharing platforms have only limited control over the quality of the user's experience. Furthermore, the actions of prior users may alter the condition or performance of a shared resource (e.g., a Lime scooter left lying in a dark alley). Whereas a traditional product-rental firm would clean and repair a product between renters, platforms typically depend on users to perform these tasks. Furthermore, the products and services typically accessed on platforms often display more heterogeneity than the offerings of a traditional firm. For example, unlike the uniform nature of rooms in a Sheraton hotel, Airbnb rentals display a considerable degree of variance. Collectively, these unique aspects present a considerable challenge for sharing economy firms trying to optimize the customer experience.
Considering these challenges, an important research question is, What is the nature of the customer experience journey in the sharing economy? At present, little attention has been paid to the nature of a user's experience in interacting with a sharing platform. However, research regarding consumer interactions with self-service technologies may provide a useful foundation (e.g., [37]; [98]). This body of research suggests that other users within a platform have a major impact on a focal user's experience. For example, [62] identified four barriers to customer usage of a sharing platform. Three of these barriers center on other users within the system (i.e., reliability of other users, contamination of the shared offering, and liability due to the behavior of other users). Likewise, [129] provide evidence that user misbehavior (e.g., leaving trash and spills in a shared automobile) harms the experience of subsequent users. Thus, future research should document the impact of others on future user behaviors and how these behaviors affect customer experience as well as customer lifetime value. New theory is needed to identify the conditions under which the impact of other users may be negative (e.g., contamination, misbehavior) or positive (e.g., advice, social proof) in a sharing economy setting.
In addition to altering the nature of the customer journey, customer relationships also take on a slightly different meaning in the sharing economy. In a traditional market context, a customer may develop a strong personal relationship with a specific service provider such as a waiter, barber, or dentist. However, the matching algorithms and the sheer number of participants on both sides of a sharing platform make it unlikely that a user would have enough repeated interactions with one provider to establish a close interpersonal relationship. Thus, an interesting research question is, How do user interactions with a specific resource provider affect customer experience with a sharing platform? To date, sharing economy research has focused more on relationships among users of a platform ([44]; [58]) than on user relationships with a platform (e.g., [157]). Hence, we know little about the relationships that users form with resource providers. [157] suggests that users may form relationships with individual providers. However, this type of relationship is likely the exception rather than the norm. Thus, we suspect that when evaluating their customer experience, consumers are more likely to reflect on experiences from repeated transactions across multiple platform providers, combined with reputation-market information, to form a generalized evaluation of a platform as a whole ([112]).
As a result, the impact of an encounter with a particular provider may play a weaker role for sharing platforms compared with traditional firms. If this is indeed the case, users may not view resource providers as "employees" of the platform and may be less likely to hold the platform accountable for encounters (good or bad) with these providers. Thus, exploratory research is needed to develop theory about how users view the nature and roles of resource providers relative to the platform on which they are sourced. Some insights can be drawn from extant research that recognizes that differing relational levels often coexist (e.g., [109]). This prior research can serve as a launching pad to explore the existence and relative strengths of a given user's relationships with a platform and with a particular provider. Furthermore, future research could offer insights into how users, along their consumption journey, integrate appraisals of interactions with both the platform and with individual providers (e.g., [86]). Considering the rich feedback systems employed by many platforms, researchers may find dynamic tools (such as textual and visual analysis) to be particularly useful in uncovering the attributes or cues that consumers use to evaluate providers and platforms. For example, beyond providing insights into user decision making, machine learning and artificial intelligence techniques may also be useful in helping sharing platforms identify problems in their matching mechanisms to optimize customer experiences ([68]).
A critical task for any firm is to "appropriate value...from the marketplace" ([100], p. 63). In the traditional economy, this value appropriation process involves competing with other firms for customer time, energy, and money ([39]). In the sharing economy, the appropriation of value is even more challenging, as most sharing platforms must compete not only against other sharing platforms but also with traditional firms. Marketing has long recognized that competition for customers among traditional firms is not restricted to direct competitors but also involves category-level alternatives, cross-category substitutes, and nonconsumption ([84]). However, before the emergence of sharing platforms, marketing scholarship largely centered on direct firm-to-firm competition ([150]). Moreover, marketing has traditionally viewed competition through the lens of warfare, in which firms battle for consumers ([122]). However, within the sharing economy, consumers (through their prosumer role) may become a firm's opponent and appropriate value by allowing other consumers to access their resources. As a result, sharing platforms also face the possibility of competing against their prosumer providers. Thus, the task of value appropriation appears to be particularly challenging in the sharing economy and presents several intriguing future research opportunities.
As noted previously, sharing platforms must compete against both traditional firms as well as rival platforms. Thus, an important research question is, How can sharing platforms best appropriate value? In contrast to traditional firms, sharing platforms appear to exhibit a stronger degree of network effects, as the value of a platform rises with its number of offerings and/or users ([16]). In addition, the offerings across various sharing platforms often exhibit little differentiation. For example, the scooter-sharing platforms Bird and Lime employ offerings that are nearly identical in look, function, and location. As a result, many sharing markets exhibit a winner-take-all dynamic in which a small number of providers appropriates much of the value ([151]). Thus, our current assumptions about competitive dynamics and value appropriation may be less applicable to the sharing economy. For example, in the sharing economy, competitive success may have more to do with market-level factors such as establishing a first-mover advantage ([82]) and less to do with firm-level factors such as learning how to satisfy customer needs ([42]). Consequently, the sharing economy provides an opportunity for marketing scholars to test the role of these alternative views of drivers of value appropriation.
In assessing how sharing platforms appropriate value, marketing scholars should pay particular attention to the role of prosumers, who are the main source of value delivery. Unlike the top-down decision making that characterizes traditional firms, sharing platforms rely heavily on this group to make decisions about how best to market their offerings. Clearly, some prosumers are better marketers than others. For example, while some prosumers can appropriate value through a rich social network, are fluent users of technology, and possess the emotional, cognitive, or financial resources to develop relationships with their "customers," others may be more isolated, may be less capable of maximizing the potential of sharing platforms, or need to prioritize the use of their resources in nonprosumption aspects of their lives. Thus, the degree to which prosumers learn best practices for the efficient use of resources and technology from one another over time may have an important effect on a sharing platform's ability to appropriate value. Empirical modelers could employ the large and growing amount of data available on most sharing platforms to both assess the amount of prosumer-to-prosumer learning and determine its effects on value appropriation. For example, the Timbro Sharing Economy Index ([143]), compiled by combining traffic volume data and scraped data from sharing economy websites, emphasizes both the microtransactional nature of the sharing economy and its ability as a matchmaker. By analyzing the manner in which microtransactions and matchmaking work together and spread across interpersonal networks, scholars may be able to learn more about the way that prosumer-to-prosumer interactions shape the value that accrues to the firm and to prosumers in the particular sharing system.
Another intriguing question is, How does the sharing economy affect the value appropriation of traditional firms? An increasing array of traditional firms are facing competitive threats from sharing platforms ([158]). Moreover, a traditional firm's customers may also be potential competitors because they can rent out a firm's offerings through sharing platforms during periods of nonuse ([74]). In product and service categories in which sharing alternatives exist, evidence suggests that platforms increase the role of price in customer choice and that traditional firms whose offerings are most similar to platform offerings may suffer significant losses ([158]). As a result, the rise of the sharing economy appears to present traditional firms with a set of new (and different) competitors. Thus, our standard models of competition may need to be revisited to incorporate this expanded competitive landscape. For example, [39] classic framework for assessing competitive advantage recommends that firms compare the configuration and cost of their value chains against target competitors. Clearly, this task is considerably easier in a traditional economy, in which competitors typically hail from the same industry and have similar value chains. In the sharing economy, this type of comparison not only is more difficult but also may be potentially meaningless, as competition in the sharing economy comes in many different forms, including rival sharing platforms, traditional firms, and prosumers. Thus, research is needed to develop new techniques for assessing competitive advantage in the sharing economy.
A related question is, How should traditional firms respond to the rise of sharing platforms? As noted by [36], "Uber and Lyft...[are] providing unprecedented competition in the taxi industry" (p. 177). Likewise, [158] show that Airbnb reduces hotel revenues by lowering market prices, especially among low-priced hotels. While these studies suggest that sharing platforms represent a considerable threat to traditional firms, further research is needed to more fully assess the impact of sharing platform entry and analyze the relative efficacy of different competitive responses by traditional firms. It seems likely that both the impact of sharing platforms and the response by traditional firms may vary across different types of product or service categories as well as by a firm's standing in an industry. Thus, a contingency perspective that accounts for the nature of the offering and for the competitive postures of the provider, platform, and traditional firms would help provide nuanced insights into this question.
In response to this new threat, some incumbents try to stifle sharing platforms through regulation and litigation, while others seek to enter the fray by developing or acquiring their own sharing services. For example, BMW, General Motors, and Mercedes have all recently invested in shared automobile services (ReachNow, Turo, and car2go, respectively). This approach has received support from a recent study by Boston Consulting Group, which reveals that most consumers "would prefer to engage in sharing with professional or established companies" ([151], p. 4).
Moreover, some established firms in industries where sharing is still new are trying to proactively establish a first-mover advantage. For example, Mahindra has introduced sharing to the Indian farm-equipment market by creating a platform (i.e., Trringo) that allows famers to rent equipment (made by its firm) from other farmers. Established firms could also leverage the growth of the sharing economy by developing products that can be easily shared, because "buyers are often willing to pay a premium for items that can generate revenue by being shared" ([152], p. 1). Future research is needed to assess the effectiveness of these various competitive approaches. As a starting point, qualitative approaches such as case studies or ethnographic investigations may be a good way to provide some early insights into the effectiveness of these various response strategies.
As noted previously, the AMA's definition of marketing views marketplace exchange as an activity that creates value for various sets of stakeholders. In recent years, both marketing scholars and practitioners have placed increased emphasis on value creation across the breadth of a firm's stakeholders ([87]). Thus, we examine the impact (both positive and negative) of the sharing economy on value creation across a diverse array of key stakeholders, including consumers, firms, and society.
One of the defining features of sharing economy firms lies in their capacity to offer temporary access. Prior research has suggested that temporary access may both enhance and detract consumer value. On the one hand, access-based consumption enables an offering to be available to segments of consumers who cannot afford ownership. In addition, access provides consumers who own a shared offering with the opportunity to earn value by monetizing its excess capacity ([112]). On the other hand, the sharing economy may increase consumer risk, as users compete with one another for the use of shared resources ([90]). In addition, if sharing platforms increase the absolute amount of time a product is used, owners of shared offerings may face additional costs, including increased costs for maintenance, repairs, and earlier replacement due to wear-out. A simple net calculation of such costs and benefits can yield a model that predicts the value of sharing, similar to the utility model proposed by [65] and augmented by [90]. While traditional utility models may provide a good starting point, a fuller appreciation of value creation (and erosion) in the sharing economy may require either the revision of current models or the development of new ones.
Prior research has identified the drivers of sharing utility, including utility from substitution, storage, and anticorporate sentiment ([65]). However, as the sharing economy evolves, the relative importance of these different factors is likely to change and new drivers are likely to emerge. Thus, an intriguing research question is, What new forms of consumer utility does the sharing economy offer, and how do they relate to traditional drivers of value? Due to their accessible nature, sharing economy transactions represent a form of "liquid consumption" that is "ephemeral, access based, and dematerialized" ([ 9], p. 582). Ephemerality refers to the notion that the nature of consumers' relationships to objects, services, and experiences, as well as the value derived from them, is temporal in nature and particular to a specific context ([ 9], p. 585). Although ephemerality is highly sought after in the sharing economy, future research is needed to determine the actual value of ephemerality as well as how this feature is differently valued across various consumers and contexts. In addition, little is known about the degree to which ephemerality affects consumer value by raising or lowering the value of repeated or extended consumption experiences. For example, typical drivers of consumer value (such as identity value) may be less relevant when temporary value is sought. To answer these questions, we recommend that scholars use the concept of ephemerality to delineate how and why temporary value manifests itself in the sharing economy. Ephemeral value in the sharing economy can be compared with ephemeral experiences that have been identified in prior literature (e.g., [85]) as well as to more enduring sources of value (e.g., [117]).
Going beyond considerations of basic utility, another important question is, What kinds of goods or services create the most value in the sharing economy? To answer this question, scholars should first examine the types of goods or services that can best be shared. [13] theory of social production suggests that resources that have a high degree of "modularity" (i.e., offerings can be independently sourced from geographically dispersed providers and integrated into a single platform) are most effectively shared. This may explain why car rides are commonly shared, whereas car manufacturing is not. Though persuasively argued and clearly connected to the marketing domain, [13] contention about the role of modularity has yet to be empirically examined in our field. This presents an opportunity for future efforts to build on his theoretical framework. For example, marketing scholars could determine how consumers evaluate modularity and what type of value it provides.
A final and especially intriguing question is, What types of value do prosumers seek in the sharing economy? Traditional marketing thought suggests that consumers are utility maximizers who aim to minimize costs and maximize financial rewards ([15]). However, in the sharing economy, these financial incentives may also be supplemented by social concerns and be ephemeral, as described previously. Although prior research suggests that economic motivations tend to dominate ([10]; [90]), a recent study by [34] finds that prosumers highly value the opportunity to engage in the act of sharing and value connecting with others. Moreover, these social motivations appear to also provide value to firms, as they lead to higher levels of engagement and lower levels of churn ([34]). Although these recent findings are intriguing, future research is needed to assess the role of social versus economic motivations among sharing economy participants in particular in light of the ephemeral value that consumers are seeking in this domain ([44]). This type of inquiry seems to be quite amenable to both lab-based experiments and field studies.
As noted previously, the sharing economy creates value for consumers who otherwise would not be able to access products or services sold by traditional firms, as well as for consumers who own underutilized resources. However, the degree to which the sharing economy creates value for firms is more of an open question. Clearly, sharing platforms benefit from the rise of the sharing economy because they play a central role in matching or connecting a large number of providers and users who engage in mutually beneficial exchange. Indeed, these platforms often enjoy margins that allow them to reap a good portion of the value created in these exchanges. Despite these high margins, most sharing platforms struggle to generate profits. Thus, many questions remain about how they can best create and capture value in the sharing economy to achieve long-term financial sustainability. For example, Uber's weak initial public offering suggests that investors may be skeptical about the financial sustainability of sharing platforms ([31]). Likewise, traditional firms that provide resources must also adapt to the sharing economy. On the one hand, sharing a resource across consumers implies that fewer resources may be needed to meet aggregate demand, which may intensify competition among traditional manufacturers. On the other hand, increased utilization of a shared resource can enhance the value of product ownership by encouraging more consumers to acquire these resources ([74]). Thus, the multifaceted effects of the sharing economy can both pose threats and offer opportunities to traditional firms.
One important question for scholars interested in understanding how the sharing economy creates value for firms is, How should sharing platforms best connect consumers with providers (including prosumers) in terms of matching and pricing mechanisms? This question is especially important for sharing platforms that offer services (e.g., ride sharing on Lyft, peer helping on TaskRabbit). In contrast to services offered by traditional firms, shared services often exhibit large variations in quality and consistency. Thus, these services will likely need to employ a dynamic pricing approach that reflects these differences. Ride-sharing platforms, for example, can become overburdened during heavy traffic hours and send drivers on a "wild goose chase" to pick up far-away customers, increasing drivers' costs and customers' waiting time. This potentially market-crippling problem may be alleviated by the adoption of surge pricing, which raises prices in the face of short supply (e.g., [28]). As recently shown by [56], surge pricing is useful in areas in which supply exceeds demand to manage driver availability across different market locations. However, many questions remain regarding the basis of surge pricing (e.g., locations, priority queues, ratings).
Research is also needed to understand how surge pricing strategies are affected by competition from other sharing platforms and/or traditional service providers. For example, would the adoption of surge pricing by one ride-sharing platform increase or decrease a competing platform's incentive for adoption of this pricing strategy, and under what circumstances? Moreover, the design and impact of sharing platform price structures need to be examined both conceptually and empirically. Specifically, the relative efficacy of centralized pricing of peer-to-peer offerings (i.e., the platform sets prices) versus decentralized pricing (i.e., individual providers set prices) remains an open question. These two pricing structures may have a different impact on sales and profits. Fortunately, sharing platforms are replete with a rich array of digitized transactional data that marketing scholars could potentially use to assess the performance of different pricing strategies across various market conditions and customer segments. Alternatively, scholars interested in these issues could try to enlist the cooperation of sharing platforms to conduct field experiments to help identify optimal pricing strategies.
A second research question is, How does the sharing economy impact a traditional firm's product line and pricing strategies? As recently shown by [74], the sharing economy can have both a market-expansion effect (by inducing more consumers to purchase a sharable product) and a cannibalization effect (some customers will seek shared access instead of purchase). Recent analytical models have shown that the interaction of these effects can significantly influence a traditional firm's optimal product design and pricing decisions in a sharing economy setting ([74]). However, little is known about how an industry's competitive dynamics may alter these results. Recent research suggests that some industry characteristics may have an important effect. For example, [154] and [158] suggest that sharing platforms have a larger impact on traditional firms that market lower-priced offerings as opposed to those that market higher-priced offerings. However, additional work is needed to explain the impact of a wider range of factors. For example, how might the sharing economy's impact on product line and pricing decisions vary across different industries (e.g., real estate, cars, tools, apparel, accessories) or various market conditions (e.g., more or less competition, better or worse reputation systems, more or fewer regulations)? Furthermore, does the fact that prosumers purchase products for both personal use and to rent out to others have implications for a firm's product line or pricing decisions? These type of questions could be addressed conceptually, analytically, or empirically.
In theory, the sharing economy democratizes marketplaces, expands opportunities for small businesses and individuals, and enables access to resources. For example, food-sharing co-ops can reduce food insecurity and provide culinary training for individuals attempting to enter the workforce ([76]). In addition, sharing economy rhetoric often implies that engaging in access rather than ownership enhances ecological well-being by reducing overall consumption because underutilized resources are more fully employed. If fewer products are needed, then fewer natural resources are required for production and distribution ([115]). Fewer products sold results in fewer products ending up in landfills ([22]). Despite these hopeful contentions, the question of the value of the sharing economy to society is far from closed. Thus, marketing scholars have an opportunity to assess the veracity of these proposed societal benefits and whether the sharing economy may help address other societal ills.
The prospect of the sharing economy's value for society presents several research questions. Perhaps the most important question is, Does the sharing economy enhance societal well-being? The emergence of the sharing economy has fostered considerable optimism ([ 2]). However, as the sharing economy has grown, well-being has not. In fact, according to the 2019 World Happiness Report, U.S. citizens appear to have hit a happiness nadir. Thus, the relationship between sharing economy participation and happiness is an intriguing issue. Furthermore, recent research suggests that materialism may increase the likelihood of participating in sharing systems ([38]). This finding stands in apparent contrast to prior materialism scholarship, which suggests that materialistic individuals have a strong desire to own goods (e.g., [119]; [123]). Because materialism has been widely linked to lower levels of psychological well-being as well as reduced concern for the well-being of others ([119]), the connection between materialism and preference for access versus ownership has important implications for societal welfare. Fortunately, this question can be readily assessed through both survey and experimental research techniques. For example, it would be interesting to assess the degree to which prior findings that employ the [119] material values scale replicate when applied to a sharing economy context.
In addition to well-being, another important measure of societal welfare is equality. Thus, scholars interested in this topic should ask, Can sharing economy transactions reduce inequality? Users of sharing economy services are typically highly educated affluent young people living in urban areas ([114]). On the one hand, sharing may provide value to society by facilitating wealth transfer between individuals of high socioeconomic status (i.e., users) and individuals of lower socioeconomic status (i.e., providers) in need of financial resources. However, the individuals who provide sharing services are often not classified as employees and generally lack traditional employee benefits ([132]). Furthermore, as the sharing economy grows, providers experience greater price and volume competition between platforms, which threatens to reduce wages ([26]). These trade-offs raise important questions about the positive and negative effects of sharing economy participation across socioeconomic strata and how sharing systems can reduce, rather than reinforce, income inequality. Longitudinal archival data that examines income levels across time in relation to the volume of shared products and services within a metro area could lend valuable insights into these questions.
Another increasingly important metric of societal health is the condition of our natural environment: Does the sharing economy enhance environmental sustainability? In contrast to the widely held assumption that the sharing economy reduces net consumption of scare resources. [53] recently assert that the "alleged sustainability benefits of the sharing economy are...much more complex than initially assumed" (p. 6). Likewise, [130], argues that comprehensive studies of the sustainability impact of the sharing economy are "long overdue" (p. 14). As noted by [64], it seems likely that consumers who participate in the sharing economy already engage in a variety of sustainable consumer practices. Thus, if access-based consumption merely replaces consumption that is already sustainable (e.g., if car-sharing users simply replace their usage of public transportation, as reported by [134]]), the net incremental environmental benefit of sharing may be quite limited. Moreover, as recently noted by [112], some sharing systems (e.g., "matchmakers" such as TaskRabbit) may reduce our carbon footprint, while others (e.g., "hubs," such as Zipcar or Grubhub) may increase it. Similarly, [130] reports that Airbnb may ultimately result in an increased carbon footprint because this platform enables travelers to take more trips.
Despite these various assertions, the empirical evidence gathered thus far reveals a set of mixed findings. For example, [97] find that car sharing has both a positive and negative environmental impact. Likewise, [94] show that round-trip car sharing complements public transportation usage, but that point-to-point car sharing (which is far more common) is a substitute for public transportation. It is also unclear whether most consumers care about the societal benefits of sharing. Indeed, prior research suggests that even among prosustainability consumers, the ecological benefits of sharing are mostly seen as "an added bonus," and take a back seat to price and convenience ([113], p. 1324).
To shed further light on the connection between the sharing economy and sustainability marketing strategy scholars could create a new metric that calculates a platform's return on sharing. We envision return on sharing as a metric that identifies the sustainable outcomes of sharing economy systems such as the carbon emissions that result from sharing transactions. For example, clothes sharing platforms such as Rent the Runway reduce carbon emissions from clothing manufacturing but increase emissions by shipping individual clothing items to multiple users over time. Moreover, international marketing scholars could also contribute to this debate by examining whether there are specific types of societies in which the sustainability benefits of sharing outweighs its negative impact. [ 6], p. 293) suggests that shared resources only avoid "economic tragedy" when they are embedded in a tightly woven community of "collective stewardship," a condition that most access-based "communities" rarely possess ([10]). Thus, researchers could help identify the characteristics of access-based communities and the conditions under which they exhibit this type of collective stewardship.
The sharing economy has exploded and is altering the way we travel, where we stay, and what we wear ([96]). As we have illustrated, this new development is an emerging phenomenon with important implications for marketing thought. In brief, we propose that the sharing economy challenges traditional views regarding the nature and role of marketing institutions, processes, and value creation and presents several important research questions for marketing scholars. In this final section, we aim to provide a broader view of this emerging economy by closing with a set of three forward-looking guideposts for future marketing scholarship in this domain. We hope that these guideposts help marketing scholars not only keep pace with the sharing economy but also shape its future direction.
- 1. Investigate the paradoxes and dark side of the sharing economy.
Thus far, our depiction of the sharing economy has been largely positive, as we view this emerging form of exchange as having substantial promise for enhancing the welfare of consumers, firms, and society. However, as noted by [12], the sharing economy is truly a paradox. The word "sharing" suggests a prosocial activity ([11]), in which people and organizations engage in convivial action to enhance community and conserve resources ([22]). However, in reality most platforms largely provide a form of access (rather than sharing) that takes place within an impersonal community of distant and anonymous others. Thus, some commentators consider the sharing economy "neoliberalism on steroids" and accuse sharing economy systems of amplifying "the worst excesses of the dominant economic model" ([103], p. 66). Indeed, [96] suggests that "venture capitalists have subsidized the creation of platforms for low-paying work that deliver on-demand servant services to rich people, while subjecting all parties to increased surveillance." For example, Uber condones filming passengers and provides no information about how the footage will be used ([128]). Although some sharing economy participants may see the benefits of this type of surveillance ([10]), others may be quite concerned about its potential dark side. In addition to these privacy-related concerns, some sharing economy platforms such as Grubhub offer meager financial benefits to their deliverers, most of whom make less than minimum wage. Thus, it is not surprising that Uber drivers recently mounted a strike to demand "livable incomes" ([78]). As a result of these concerns, new sharing platforms that are employee owned and pay a higher wage (e.g., Up & Go) are emerging ([142]). We encourage marketing scholars to embrace this paradox and develop frameworks that account for not only the possible benefits of the sharing economy but also its potential drawbacks. This dark side of the sharing economy is somewhat akin to the economic concept of externalities. Thus, we encourage scholars who are intrigued by this issue to review [25] essay on this topic, which he approaches from both a sociological and economic perspective.
The dark side of the sharing economy has already gained considerable attention from a small collection of economists and sociologists. Thus, marketing scholars interested in this issue have a foundation on which they can build. [20]) contends that one positive aspect of the sharing economy is the emergence of a decentralized form of trust that flows through sharing networks in the form of ratings systems. In contrast, [54] suggests that platform ratings may not be the best way to engender trust in a decentralized sharing economy. In addition, Schor and colleagues argue that as sharing economy platforms scale, they often lose their unique identity and experience a decline in prosocial characteristics as well as a rise in inequalities ([53]; [131]). For example, Couchsurfing began as a means of fostering interpersonal connections among global citizens ([127]) but lost much of this community-building focus when it transitioned to a for-profit platform in 2013 ([99]). Marketing scholars can contribute to this debate about the promise and perils of the sharing economy by adding unique perspectives of both the firms and consumers that participate in this emerging system. Given the widespread importance of this issue, we encourage our colleagues to share those insights not just in articles in marketing journals but also in books and in top journals in other fields (e.g., [55]
- 2. Examine the maturation of the sharing economy.
There appear to be two broad (and divergent) perspectives on the sharing economy's future. Detractors suggest that the sharing economy is dead ([83]; [91]), whereas proponents argue that it has just begun ([41]; [77]). We believe that the latter view is more likely to be accurate. As a point of reference, the smartphone, which fueled the rise of the sharing economy, was introduced in 2007, Airbnb was founded in 2008, and Uber launched in 2009. According to a recent Pew Research survey (May 2016), only 15% of Americans have used Uber and 11% have tried Airbnb. Thus, as noted by [77], the sharing economy "is still in its infancy" (p. 664). Beyond its youth, the sharing economy is dominated by start-up enterprises located in high-tech hotbeds around the globe, where the founders do not necessarily have experience in the industry that they are trying to disrupt. Finally, with a few notable exceptions (e.g., Airbnb), most sharing platforms are sustained by an infusion of venture capital and have yet to turn a profit. The economics for scooter-sharing firms Bird and Lime are quite tenuous ([120]), and Uber lost $4.5 billion in 2017 ([108]). Thus, the sharing economy still appears to be in its infancy.
The start of the sharing economy's second decade provides researchers with an opportunity to study its maturation process. If the maturation of the sharing economy follows the pattern seen in the traditional economy, a large portion of its early start-ups are likely to fail as it enters a shake-out stage. For example, most tool-sharing platforms have failed, and the survivors (e.g., NeighborGoods) have small numbers of active users ([83]). In contrast, several platforms for sharing yachts have recently emerged (e.g., Boatbound, Boatsetter). Likewise, peer-to-peer lending platforms (e.g., Prosper, LendingClub) are gaining traction, and sharing platforms are expanding in business-to-business contexts in industries such as workspaces (e.g., Vrumi, WeWork) and machinery (e.g., Trringo, Yard Club). Thus, marketing scholars could make a valuable contribution by developing descriptive and predictive frameworks for mapping the types of goods and services that are optimally sharable as this economy matures. It would be particularly helpful to identify patterns and creating typologies of the types of resources that have been successfully shared, failed at sharing, and have still yet to be shared.
As the sharing economy matures, many platforms appear to be outgrowing their providers. As a result, offerings are increasingly likely to be owned by the platform itself. For example, Uber has invested in a fleet of cars that it leases to drivers, and Airbnb is building a series of homes specifically designed for sharing ([41]). Likewise, banks, rather than individuals, now broker most loans arranged through peer-to-peer lending platforms ([91]). Thus, as sharing platforms mature, they appear to be becoming more like traditional firms. Future research is needed to track this evolution and identify if and when sharing platforms will evolve into more traditional enterprises or new hybrid entities.
- 3. Be on the lookout for new technologies.
As noted by [153]), "The sharing economy is still relatively young and undeveloped...and the technological possibilities...are still maturing" (p. 4, emphasis added). Thus, scholars should keep a close eye on technology developments to understand their potential impact. For example, research can contribute to the debate over whether Blockchain technologies will boost sharing platforms by providing an efficient mechanism for recording and verifying peer-to-peer transactions ([111]) or render them obsolete because Blockchain operates without the need for a central authority ([61]).
As another example, ride-sharing services such as Lyft will likely be challenged by the advent of autonomous vehicles in the near future. For example, Tesla recently announced that it has tallied over one billion miles of autonomous operation and that it is working on a "Tesla Network" in which Tesla owners will be able to share their vehicles as part of a "self-driving ride hailing service" ([89]). According to chief executive officer Elon Musk, "We absolutely see the future as a kind of shared electric autonomy....Any customer will be able to share their car at will, just as you share your house on Airbnb" ([89]). As noted by [47], shared autonomous vehicles "represent an emerging transportation mode" (p. 143) that will likely provide faster service at a lower cost than existing ride-sharing platforms. In essence, the future of the sharing economy may look very different as new technologies alter the competitive landscape. Thus, it is critical that scholars keep a sharp focus on new technological developments and their effect on the sharing economy.
If the development of the sharing economy is similar to the path of the information economy, the impact of technology is likely to be heavily influenced (in ways that are both positive and negative) by government intervention. For example, in the United States, the federal government played an important role in fostering the development of the internet by funding early-stage research in computer-to-computer communication ([126]). In terms of the sharing economy, the rise of promising new technologies such as autonomous driving are likely to be closely regulated even before they are launched ([146]). Relatedly, the Chinese government recently implemented a social credit score, in which an individual's rating across multiple platforms contributes to an overall score of trustworthiness, which affects one's ability to travel, gain access to credit, and even get a date ([20]). This new technology-based form of governmental monitoring will also likely affect the degree to which Chinese consumers can participate in the sharing economy as either providers or users. This intersection of technology and government will likely increase in the years ahead and presents intriguing new interdisciplinary research opportunities for scholars across both marketing strategy and public policy.
As our definition, explication, and examples show, the sharing economy presents an opportunity to ask new questions and develop new frameworks. To address these challenges, marketing scholars will likely need to embrace fresh perspectives, employ new data sources and methods, and look beyond their insular silos. This opportunity is particularly intriguing because the sharing economy is relevant to all facets of the marketing domain, including consumer behavior (e.g., [90]), consumer culture (e.g., [44]), analytic modeling (e.g., [74]), empirical modeling ([158]), and strategy (e.g., [88]). Thus, the important breakthroughs in this domain are likely to emerge from an intersection of scholars with different sets of skills, different types of data, and expertise in different theoretical domains. Indeed, this is the case for our author team, which, despite our diversity in terms of perspectives and methods, is united in a common belief in the revolutionary potential of the sharing economy. We hope that our thoughts about marketing in the sharing economy shed new light on this emerging system and stimulate a broad range of scholars to reexamine traditional beliefs and reinvigorate marketing thought.
- What types of judgements, heuristics, and biases affect the consumption of shared (as opposed to owned) resources?
- What drives customer satisfaction in the sharing economy?
- How does consumer identity affect the sharing economy experience?
- How can sharing platforms ensure quality?
- How does the sharing economy alter our understanding of marketplace institutions at a collective level?
- What is the role of existing regulations and policies in governing sharing economy activities?
- What is the role of trust in the sharing economy and to what degree can it regulate sharing economy transactions?
- How should regulatory entities balance the costs and benefits of implementing sharing economy regulation?
- Who should regulate the sharing economy?
- What is the role of product innovation in the sharing economy?
- What is the relative role of radical versus incremental innovation in the sharing economy?
- What are the drivers of innovation in the sharing economy?
- Do communities form around sharing platform brands?
- What are the prospects of luxury branding in the sharing economy?
- What types of value do sharing platform brands provide to users?
- What is the nature of the customer experience journey in the sharing economy?
- How do user interactions with a specific resource provider affect customer experience with a sharing platform?
- How can sharing platforms best appropriate value?
- How does the sharing economy affect the value appropriation of traditional firms?
- How should traditional firms respond to the rise of sharing platforms?
- What new forms of utility does the sharing economy offer, and how do they relate to prior drivers of value?
- What kinds of goods or services create the most value in the sharing economy?
- What types of value do prosumers seek in the sharing economy?
- How should sharing platforms best connect consumers with providers (including prosumers) in terms of matching and pricing mechanisms?
- How does the sharing economy influence a traditional firm's product line and pricing decisions?
- Does sharing economy enhance societal well-being?
- Can sharing economy transactions reduce inequality?
- Does the sharing economy enhance environmental sustainability?
Footnotes 1 EditorsChristine Moorman and Harald van Heerde
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
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Record: 120- Marketing-Mix Response Across Retail Formats: The Role of Shopping Trip Types. By: Jindal, Pranav; Zhu, Ting; Chintagunta, Pradeep; Dhar, Sanjay. Journal of Marketing. Mar2020, Vol. 84 Issue 2, p114-132. 19p. 9 Charts, 1 Graph. DOI: 10.1177/0022242919896337.
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Marketing-Mix Response Across Retail Formats: The Role of Shopping Trip Types
The authors study differences in the effects of prices, nonprice promotions, and brand line length on brand shares at different retail formats. Their conceptual framework rests on the presence of trip-level fixed and category-level variable utility components and shows how the trade-off between these components results in ( 1) different formats visited on different types of shopping trips and ( 2) differential marginal sensitivities of brand shares to changes in marketing-mix variables across trip types. Together, these provide predictions on how marketing-mix variables differentially affect brand shares at various retail formats. The authors use Nielsen Homescan and store-level data from 2011–2014 and analyze the top ten spending product categories across four retail formats—convenience stores, drugstores, supermarkets, and mass merchandisers—in over 200 Nielsen markets. Implications for brand manufacturers managing the marketing mix across different formats are offered. JEL codes: M310, L11, D4
Keywords: marketing mix; retail formats; shopping costs; shopping trips; fill-in trips; major trips; unplanned trips
How does the impact of marketing-mix variables vary across retail formats? This is a question that has continued to interest researchers as the U.S. grocery retailing and food services industry goes through a significant shift in consumer preferences for the brands consumers buy and the retail formats where they shop. Beyond supermarkets, grocery purchases can be made at multiple retail formats such as mass merchandisers; drugstores; and, to some extent, convenience stores, which has blurred the traditional associations between product categories and channels ([47]). From 2004 through 2010, mass merchandisers have increased their share of dry grocery expenditures from 19% to 25%, and the share of traditional grocery stores has declined from 63% to 57% ([23]). At the same time, there has been a significant increase in grocery sales at convenience stores as compared with supermarkets and mass merchandisers (see, e.g., [27]). Understanding differences in sensitivity to marketing-mix variables across formats is important as manufacturers vie for additional distribution coverage in nontraditional formats such as drugstores, convenience stores, and mass merchandisers, which differ not only in their wholesale margin ([ 1]) but also in the type of shopping trips made to these formats ([42]).[ 6]
In answering this question, we develop a conceptual framework that links retail formats to different types of shopping trips (major vs. minor, planned vs. unplanned) and also relates how sensitivity to marketing-mix variables varies across shopping trip types. Our conceptual framework builds on research by [41] and [44], which defines major and minor trips on the basis of dollar spending, number of items, and number of product categories purchased. In addition, we posit that while major trips are always planned, minor trips can be either planned or unplanned. "Fill-in trips" (or planned minor trips), as the name suggests, are made in between major trips. These minor trips are planned and supplement regular major trips.[ 7] By contrast, unplanned minor trips arise mostly due to unforeseen circumstances, such as running out of a specific item or ingredient that is needed urgently and requires running to the store to purchase the item. Unplanned minor trips are different from unplanned purchases that are made impulsively on a shopping trip ([44]). An unplanned minor trip is a store visit that was not planned ahead of time; purchase of the particular item, in contrast, was planned conditional on making the unplanned trip. Each trip is associated with certain fixed (independent of basket size) and variable (based on basket size and category-level marketing mix) components of utility ([ 5]; [11]), and, to the extent that consumers trade off between these components for different types of shopping trips, they might favor certain retail formats more than others depending on the type of shopping trip ([51]). Table 1 provides prima facie evidence of this. It reports the average (standard deviations in parentheses) weekly trip statistics (excluding spending on beauty products, alcohol, and general merchandise) by format across all households and Nielsen scantrack markets between 2011 and 2014. Consumers visit supermarkets and mass merchandisers much more frequently than convenience stores and drugstores, and trips to supermarkets and mass merchandisers are associated with not only higher spending but also purchase of more items in a greater number of product categories. By contrast, convenience stores have the least number of product categories and items purchased per trip. Thus, consistent with [18], it appears that consumers shop at these formats with different objectives and that these formats serve different shopping trips. In our analysis, we use revealed preference (transaction) data to cluster trips into different types and show how these trip types are correlated with retail formats.
Graph
Table 1. Spending and Trip Summary by Retail Format.
| Format | # Trips Per Week | $ Spend Per Trip | # Categories Per Trip | # Items Per Trip |
|---|
| Convenience | .07 (.19) | 13.23 (23.82) | 3.14 (4.62) | 1.73 (1.84) |
| Drug | .12 (.22) | 11.68 (14.41) | 4.64 (5.11) | 2.18 (1.94) |
| Supermarket | .74 (.72) | 32.84 (37.37) | 13.62 (15.23) | 7.84 (7.75) |
| Mass merchandiser | .38 (.43) | 37.73 (43.05) | 11.07 (13.29) | 7.04 (7.38) |
1 Notes: The table reports the average trip statistics by retail format. The average for the number of trips per week is calculated across households, whereas the averages in other columns are computed across trips. Standard deviations are reported in parentheses. These numbers are computed based on Nielsen Homescan data and include spending across all product categories. Note that mass merchandisers include dollar stores, warehouse clubs, and discount stores.
In addition to the trade-off between fixed and variable components of shopping utility, [60] show that the interplay between them introduces cross-category complementarity that lowers the consumer's sensitivity to price changes in any individual category on trips with higher disutility from the fixed component. As we show, because major trips are likely to be made to shopping formats that result in higher disutility from the fixed component, consumers are likely to be less sensitive to changes in category-specific marketing-mix variables on such trips (compared with minor trips). The linkage between trip types and marketing-mix sensitivities gives us our first set of propositions. Finally, putting together the links between ( 1) formats and trip types and ( 2) trip types and sensitivities yields our second set of propositions relating retail formats to sensitivities, the primary focus of this article.[ 8] We believe that understanding how brand shares respond to changes in marketing-mix instruments is important for manufacturers such as Procter & Gamble (P&G), for example, which recently increased prices on all of its products by between 5% and 10%. The analysis, thus, is relevant to manufacturers because it allows them to understand the cross-format implications of changes in marketing-mix instruments.
For our empirical analysis we consider the following categories: orange juice, dry dog food, ready-to-eat (RTE) cereal, ground coffee, frozen pizza, refrigerated yogurt, refrigerated milk, heavy duty liquid detergents, toilet tissue, and paper towels. These product categories: ( 1) cover a broad range of items that households shop for; ( 2) include both perishable and storable items; ( 3) differ in their average prices; and ( 4) have differential impact on timing and choice of store visit. Our empirical analysis studies the effects of three marketing-mix instruments, prices, nonprice promotions, and line lengths: ( 1) across different trip types—major, planned minor (fill-in), and unplanned minor; ( 2) in four different retail formats—grocery stores, convenience stores, drugstores, and mass merchandisers; and ( 3) for the top ten spending product categories across four product departments—dry grocery, nonfood grocery, dairy, and frozen. Our focus on offline retail formats only is driven by the fact that online spending in grocery is still around 6% and was only 2%–3% during the data period. We use four years of Nielsen Homescan (household) and Retail Management Services (RMS; store) data from 2011–2014 for these ten product categories spanning 206 designated market areas (DMAs) as defined in the Nielsen data. Note that the store-level data do not provide any trip-level information but can help explain the responsiveness of brand shares to changes in marketing activities across different formats. Household-level data have information on trips but may not fully reflect how brand shares vary across markets. Our study is, therefore, unique in linking customer shopping behavior observed in the Homescan data to brand shares in store-level data.
Consistent with previous research, we find that trips to convenience stores are more likely to be unplanned trips, trips to drugstores (and to a lesser extent mass merchandisers) are more likely to be fill-in trips, and trips to supermarkets and mass merchandisers are likely to be major trips. Utilizing trip-level information, we estimate that brand shares are most (least) sensitive to changes in prices and nonprice promotions on fill-in (unplanned) trips. At the trip level, we find partial support for our predictions for line length changes, which, as we discuss subsequently, can be attributed to the difficulty in assessing the role of line length changes at the trip level. Linking trip types to retail format, we show (using the store-level data) that brand shares are least sensitive to changes in prices and nonprice promotions at convenience stores. By contrast, brand shares are most sensitive to changes in line length at convenience stores and least sensitive to changes in line length at supermarkets and mass merchandisers. We explore the potential implications of our findings in the wake of the recent price increase in P&G products and find not only that the impact on profits of such a price increase varies by retail format but also that any potential line length changes affect profits by at least as much as price changes. Together, these findings highlight the importance of accounting for format-level differences in how consumers respond to changes in marketing-mix instruments.
Our article makes contributions that are managerially relevant while staying grounded in a theoretical framework based on building blocks that are well established in the marketing and economics literature. We show how the effects of marketing activities on brand shares vary by retail format; this provides useful information to a manager interested in evaluating how changes in prices and line lengths might translate to outcomes at the point of sale. A unique aspect of our analysis is that it includes all major brands and spans multiple store formats across several categories and for a majority of geographic markets. Focusing on such a broad set of categories and accounting for over 200 Nielsen DMAs enables us to generalize our findings beyond a few product categories, markets, and brands. Furthermore, our analysis provides insights into the building blocks of the theoretical framework directly. In particular, we ( 1) establish an empirical link, using revealed preference scanner data, between shopping trip types and retail formats and show that trips of certain types are more likely to be made at certain formats; and ( 2) study the effects of marketing-mix variables on brand shares across different shopping trip types. Importantly, we use two different sources of data (Nielsen Homescan and Nielsen RMS data) that are widely used in marketing to test the marginal effects of marketing-mix instruments at different levels of aggregation (trip type vs. retail format) and find results consistent with our propositions. To underscore the managerial usefulness of our analysis, we use the recent price changes made by P&G as a case study to quantify the changes in manufacturer profits from increasing prices and compare the profit implications across formats. A similar analysis for line length changes enables us to assess the relative importance of different marketing-mix variables in influencing brand shares. The results from this article are therefore of direct interest to researchers and manufacturers not only to gain a better understanding of how marketing-mix variables differentially affect brands in different formats but also to realize the potential implications of this for price and line length changes and profitability.
This article draws from and builds on multiple different streams of literature, the first of which pertains to shopping trip types. Previous literature has defined shopping trip types on the basis of dollar spending and number of items purchased ([29]; [41]; [44]), and studied how these shopping trips relate to propensity to purchase on price specials ([65]) and features ([42]), respectively. [43] use survey data to show that consumer store choice varies by the urgency of a trip and the quantity purchased, and [53] report that consumers derive higher utility from making major trips to discounters and hypermarkets and fill-in trips to smaller supermarkets. More recently, [37] show that the importance of factors such as convenience, price, and so on driving consumer satisfaction varies by shopping trip type, and [ 7] explore factors important in format adoption specifically for on-the-go trips. In contrast to these works, we use transaction data to empirically show how shopping trips of different types are more likely to be made to certain formats and study how the effect of marketing-mix variables on brand shares varies by shopping trip type, which has not been explored previously. Furthermore, our analysis is based on all available Nielsen markets as opposed to a few markets, thereby making the results more generalizable.
This research also builds on the literature on retail format patronage. A large portion of this literature has exclusively studied supermarkets and focused on only a subset of national brands ([ 4]; [14]; [38]), only on store brands ([22]), or on factors that affect national and store brand shares ([ 3]; [26]). [49] and [ 9] study the role of transportation costs and membership fees in consumers' store format choice, but they do so primarily through analytical methods. Finally, [28] use consumer panel data from one market and six stores to study the impact of marketing-mix variables on consumer spending at different formats. Our article differs from and builds on previous research in this area in that we ( 1) account for four different retail formats, ( 2) include the majority of the brands and all the Nielsen markets in our analysis, and ( 3) study how trips to retail formats differ on the basis of the needs they serve.
The third stream of research this article builds on relates to work on multiformat shopping, which has studied customer search and purchase incidence across channels ([45]; [55]; [63]) and channel-category associations ([39]), the role of promotions in channel choice for beauty products ([62]), the association between shopping trip types and supermarkets and convenience stores ([51]), format adoption based on basket size ([ 6]), and the trade-off between fixed and variable shopping utilities and costs that leads consumers to adopt one particular format ([ 5]; [11]; [56]) or multiple formats on different trips ([60]), respectively. These works, however, neither empirically link shopping trip types to retail formats nor study how sensitivity to marketing-mix variables varies systematically with the trip retail format, which is the focus of this article.
Finally, we extend previous research exploring the varying sensitivity of brands and purchases to marketing-mix variables across stores and/or channels. Several researchers (see, e.g., [ 8]; [21]; [31]) study how store names could vary perceptions of quality across formats, which results in differences in how consumers respond to price changes at these formats. Researchers have also explored how price sensitivity varies at the store level ([33]; [36]; [67]) and across online and offline channels ([19]). This article builds on this stream of research by providing and testing an explanation grounded in fixed and variable shopping utility and shopping trip types and extends the literature to include nonprice promotions and line length. [13] show that retailer assortment, which includes product line length, is one of the top three factors that influence retail patronage. Thus, by accounting for line length, we also complement previous research studying the link between assortment changes and sales ([10]; [12]; [24]; [50]).
Next, we discuss our conceptual framework and the direction of proposed effects for the marketing-mix variables. Our conceptual framework builds on previous work by [ 6], [ 5], and [11] and emphasizes the trade-off and the interplay between the fixed and variable components of utility a consumer gets from shopping. First, we outline the key differences across retail formats and shopping trip types and differentiate the fixed and variable components of shopping utility that form the backbone of our analysis. Together, this highlights why certain formats may be more frequently visited for certain types of trips. Next, we discuss how the effect of marketing-mix variables might differ across trip types through their impact on shopping utility. Finally, we use the similarity between shopping trip types and retail formats to show how brand shares differentially respond to changes in marketing mix across formats.
In this study, we consider four retail formats—convenience stores, drugstores, supermarkets, and mass merchandisers—which cover almost all sales of dry groceries, nonfood grocery, frozen products, and dairy products. Convenience stores, consistent with their names, tend to be scattered throughout a geographic market and are closer to residential units. By contrast, drugstores, which have in recent times expanded their line length (assortment) to sell products in different departments, are slightly larger in size and located farther away from residential areas. As compared with these formats, supermarkets and mass merchandisers tend to be located even farther from the households. This is consistent with the data collected through the National Household Food Acquisition and Purchase Survey (FoodAPS), in which each household reports the distance to the nearest and most frequented stores by format. On average, convenience stores are nearest to the households and mass merchandisers are farthest from the households. "Combination stores" in the FoodAPS data, which include drugstores, dollar stores, and general stores, are the second-closest to the households. Relatedly, we find that for 45% of the households, the closest store tends to be a convenience store; for 20% households, it is a combination store; supermarket for 9%; and mass merchandisers for 8% of the households, respectively.
In addition to distance to the household, these formats also differ in their line length, prices, and nonprice promotions. For example, given the proximity of convenience stores to households, convenience stores tend to be smaller in size, with limited shelf space devoted to the product categories that the consumer may be interested in shopping (and the categories we analyze). Furthermore, higher rental prices in these neighborhoods are likely to result in higher price levels. By contrast, supermarkets and mass merchandisers are substantially larger in size and place greater emphasis on groceries, leading to more shelf space (and line length). Drugstores, in contrast, are in between convenience stores and supermarkets both in terms of distance and in line length and prices, and they extensively engage in nonprice promotions. As we show in Table 1, shopping trips to different formats differ substantially in their overall spending, number of items purchased, and frequency of visit. While trips to supermarkets and mass merchandisers are associated with higher expenditure, mass merchandisers also exhibit more variation in expenditure, number of items, and number of categories purchased (as measured by coefficient of variation) per trip as compared with supermarkets, a finding consistent with [28], who report that trips to mass merchandisers do not substitute for trips to other formats.
Consumers' shopping objectives and needs are manifested in the types of shopping trips they undertake. Consistent with [44], [29], and [48], we broadly classify shopping trips as either major trips or minor trips. Compared with major trips, which are planned and constitute purchases in a large number of categories, minor trips are smaller trips made to the same or different stores (formats) in between major trips. We further categorize minor trips into planned (fill-in) or unplanned trips. Fill-in trips are planned trips that account for things such as depleting inventory, specific promotions, and so on ([41]; [65]). Like major trips, these trips often constitute purchases in multiple categories, but they may not be at the same scale (in terms of dollar spending and number of categories purchased) as major trips. Unplanned minor trips, however, arise due to unforeseen circumstances, such as running out of a specific item or ingredient that is needed urgently and requires running to the store to purchase the item. These trips (and, to some extent, planned minor trips) tend to be associated with an urgency in need and a time pressure to purchase ([52]). For example, a consumer may make a quick trip to purchase soda upon realizing that she is out of soda for a party that evening. This trip would be classified as an unplanned trip since the need to make the trip was not foreseen ahead of time. In contrast to major trips, unplanned trips (and fill-in trips) tend to be smaller in size and are associated with purchases in fewer product categories.
Building on [ 5], [11] formulate the utility a consumer gets on any shopping trip as a function of a fixed component and a variable component. The fixed component of utility is independent of the shopping basket and is determined by factors such as how far the consumer lives from the store, their store loyalty, and so on. The variable component, in contrast, is determined by the shopping basket and is a sum of the category-level variable utilities associated with each category in the shopping basket. The category-level variable utilities depend on the baseline need for the category and the price, line length, and nonprice promotion of the chosen brand in the category ([11]). On any given trip, a consumer aims to maximize the total utility (sum of fixed and variable utility) of shopping. The trade-off between the fixed and variable components thus influences which format is frequented for any given trip type.
In addition to the trade-off between the fixed and variable components, the interplay between these and the urgency associated with a category (trip) also determines how brand shares respond to changes in marketing-mix variables across trip types. In the absence of the fixed component of utility (i.e., no [dis]utility from distance or loyalty), consumers will purchase each category at the store (format) that maximizes the variable component of utility associated with that particular category. Consequently, they are likely to be sensitive to category-specific marketing-mix variables that determine the variable utility component. [60] show that if consumers get disutility from traveling to the store, they are less likely to visit multiple stores and purchase each category in their shopping basket at the store that gives them highest utility. Consequently, the fixed component of utility introduces a form of cross-category complementarity that lowers the consumer's sensitivity to within-category, brand-specific marketing-mix variables (prices, nonprice promotions, and line length) that influence the variable utility in any individual category. Finally, we note that the degree of cross-category complementarity induced is likely to be higher for shopping trips in which the fixed utility component plays a larger role (i.e., trips involving longer commutes by the consumer).
The urgency associated with a category or shopping trip influences the variable utility of that category by increasing the baseline need (i.e., all else being equal) that a consumer would get higher utility from purchasing the same category when it is urgently needed (e.g., for immediate consumption) than when it can be purchased later and is for future consumption. This increase in category-specific variable utility due to urgency offsets any potential disutility the consumer may get either from purchasing the brand at a higher price and without a nonprice promotion or from purchasing a different (less preferred) brand due to lower line length or unavailability ([25]; [64], Chapter 3). The latter implies that if a brand has lower line length during such a trip, then a consumer will more readily switch brands. Importantly, because the increase in baseline utility is in a small number of categories, this increase cannot overcome the disutility associated with the fixed component from making a trip to a format that is farther away. Together, this implies that the urgency associated with a category (shopping trip) lowers consumers' sensitivity to brand-specific prices and nonprice promotions but increases their sensitivity to line length changes of the brand.
Because major trips entail purchases in a larger number of categories, an increase in the variable component of utility through prices, nonprice promotions, and line length could offset the disutility from the fixed component due to longer distances or higher travel costs ([ 5]; [11]). By contrast, minor planned and unplanned trips constitute purchases in a few categories but differ in that the urgency associated with unplanned trips increases the utility from the baseline need. Because the consumer purchases only a few categories on these trips, any increase in variable utility in the small number of categories is unlikely to justify the disutility the consumer would incur from longer distance (fixed component) if such a trip was made to supermarkets or mass merchandisers. Thus, minor trips are more likely to be made to formats such as convenience stores and drugstores, which are located closer to the consumer, and major trips are more likely to be made to supermarkets and mass merchandisers located farther away. This is consistent not only with the findings in Table 1, which shows substantially higher spending and purchases in more categories at supermarkets and mass merchandisers, but also with [53], who find that convenience-related attributes affect utility more during fill-in trips.
The increase in utility from a higher baseline need on unplanned minor trips could be used to offset disutility either from the marketing-mix variables (prices, nonprice promotions, and line length) or from the fixed component due to traveling. As we have mentioned, given the small number of categories, it is unlikely that the increase in variable utility due to urgency will offset the disutility due to longer distance (fixed component) from a visit to a format located farther away. Thus, compared with planned minor trips, unplanned minor trips are likely to be made to a format closest to the consumer. Given the average distances of different formats to the consumer from the FoodAPS data, we would expect unplanned minor trips to be made to convenience stores and planned minor trips to drugstores, which act as a balance between smaller distance and limited line length (convenience stores), and longer distance and longer line length (supermarkets and mass merchandisers). This is consistent with [42], who find that fill-in trips are affected more by nonprice promotions, which, as we report in Table 3, Panels A–D, are more frequent at drugstores. Finally, Table 1 also shows that trips to mass merchandisers have more variation (as measured by coefficient of variation) in spending, number of categories, and number of products purchased. Thus, it appears that mass merchandisers are frequented for more than just major trips. We discuss the differences across shopping trip types and the correlation between these and retail formats in the "Data Description" subsection.
Graph
Table 2. Predicted Effects Summary.
| Proposition | Construct | Marketing-Mix Variable | Prediction | Supported? |
|---|
| 1 | Trip type | Line length | | No |
| 2 | Trip type | Price | | Yes* |
| 3 | Trip type | Nonprice promotion | | Yes* |
| 4 | Retail format | Line length | | Yes* |
| 5 | Retail format | Price | | Partial*, |
| 6 | Retail format | Nonprice promotion | | Yes* |
- 2 * Indicates that the estimated coefficients are significantly different from each other in the direction hypothesized at the 99% confidence level based on the t-tests.
- 3 Notes: The table provides a summary of our predictions along with whether the data support the prediction (last column).
Graph
Table 3. Brand Shares and Marketing-Mix Variables Across Product Categories.
| Product Category | Channel Format |
|---|
| Convenience | Drug | Supermarket | Mass Merchandiser |
|---|
| A: Share | | | | |
| RTE Cereal | 0% | 3% | 85% | 13% |
| Ground/WB Coffee | 0% | 3% | 81% | 15% |
| Frozen Pizza | 0% | 2% | 90% | 9% |
| Ref. Yogurt | 0% | 0% | 92% | 8% |
| Orange Juice | 0% | 2% | 92% | 6% |
| Dry Dog Food | 0% | 1% | 62% | 37% |
| Ref. Milk | 1% | 5% | 86% | 8% |
| Liq. HD Detergents | 0 | 8% | 48% | 44% |
| Toilet Tissue | | 10% | 55% | 35% |
| Paper Towels | 0% | 9% | 60% | 31% |
| B: Price | | | | |
| RTE Cereal | .62 | .30 | .24 | .22 |
| Ground/WB Coffee | .40 | .44 | .53 | .50 |
| Frozen Pizza | .29 | .22 | .28 | .23 |
| Ref. Yogurt | .24 | .17 | .15 | .16 |
| Orange Juice | .12 | .08 | .06 | .05 |
| Dry Dog Food | 2.07 | 1.66 | 1.07 | .98 |
| Ref. Milk | .05 | .03 | .05 | .04 |
| Liq. HD Detergents | .57 | .12 | .11 | .09 |
| Toilet Tissue | | .62 | .66 | .58 |
| Paper Towels | 1.35 | 1.05 | 1.46 | 1.34 |
| C: Feature | | | | |
| RTE Cereal | 1% | 27% | 15% | 10% |
| Ground/WB Coffee | 0% | 16% | 5% | 5% |
| Frozen Pizza | 0% | 10% | 10% | 4% |
| Ref. Yogurt | 5% | 0% | 9% | 4% |
| Orange Juice | 0% | 5% | 10% | 5% |
| Dry Dog Food | 1% | 5% | 4% | 6% |
| Ref. Milk | 0% | 0% | 4% | 2% |
| Liq. HD Detergents | 0% | 15% | 9% | 11% |
| Toilet Tissue | | 13% | 8% | 8% |
| Paper Towels | 0% | 17% | 8% | 8% |
| D: Line Length | | | |
| RTE Cereal | 1.41 | 1.56 | 2.56 | 2.16 |
| Ground/WB Coffee | 1.58 | 7.11 | 9.74 | 7.78 |
| Frozen Pizza | 3.99 | 4.60 | 10.85 | 7.63 |
| Ref. Yogurt | 4.91 | 6.08 | 19.76 | 12.30 |
| Orange Juice | 2.11 | 2.55 | 9.33 | 6.11 |
| Dry Dog Food | 1.44 | 2.73 | 6.77 | 6.28 |
| Ref. Milk | 7.58 | 9.18 | 10.81 | 8.97 |
| Liq. HD Detergents | 1.08 | 6.08 | 6.76 | 6.94 |
| Toilet Tissue | | 4.98 | 6.12 | 6.58 |
| Paper Towels | 1.08 | 3.88 | 5.48 | 6.69 |
4 Notes: WB = whole bean; HD = heavy duty. Panel A reports the brand share at each retail format for each category. Panels B–D report the average price, percentage purchases associated with a feature, and the line length for a brand within a product category, respectively. The numbers for the marketing-mix variables are averaged across brands at a retail chain level.
Considering the previous discussion, we offer the following conclusions. First, on any shopping trip, a consumer maximizes the sum of trip-level fixed and category-level variable components of utility and the trade-off between their magnitudes influences the format visited on that particular shopping trip. Second, the presence of the fixed component of utility introduces cross-category complementarity, which lowers consumers' sensitivity to marketing-mix instruments in any individual category. The magnitude of complementarity (change in sensitivity) is likely to be higher on trips involving a higher fixed component of utility. Third, the urgency associated with a category increases the variable component of the utility by increasing the baseline need for that category. This increase in baseline need makes consumers less sensitive to changes in prices and nonprice promotions and more sensitive to line length changes of the brand under consideration. The relative importance of these factors determines how the sensitivity to changes in marketing-mix variables varies across shopping trip types. We next discuss this separately for each marketing-mix variable.
As discussed previously, major trips are typically made to supermarkets and mass merchandisers, which tend to be located farther away from consumers. The higher disutility associated with the commute thus makes consumers less sensitive to line length changes on major trips. By contrast, minor trips (planned and unplanned) are made to formats closer to the consumer, which limits the cross-category complementarity and the reduction in sensitivity to line length changes. Furthermore, urgency associated with unplanned minor trips (and planned minor trips, to some extent) increases the utility from the baseline need of the category, which makes it easier to switch brands due to lower line length or unavailability (i.e., increases sensitivity to line length changes). In addition, unavailability of a brand on an unplanned trip is less likely to induce consumers to make an additional trip given the urgency ([17]; [20]), which makes brand switching more likely than store or format switching. Together, this leads us to the following proposition:
- P1 : Brand shares are most sensitive to line length changes on unplanned trips, followed by fill-in trips, and then major trips (where brand shares are least sensitive to line length changes).
Cross-category complementarity would imply that consumers will be less (more) sensitive to price and nonprice promotion changes on planned major (unplanned minor) trips, and the urgency associated with a category (trip) would imply the opposite (i.e., consumers will be less [more] sensitive to price and nonprice promotion changes on unplanned minor [planned major] trips). Convenience stores, which are typically frequented for unplanned trips, are open for longer hours than supermarkets and mass merchandisers. In addition, consumers spend a fraction of time in these stores as compared with supermarkets and purchase, at most, three to four items with an emphasis on immediate consumption ([57]). Smaller-format (convenience and drug) stores outgrew larger-format (supermarket and mass merchandiser) stores by almost 400% in 2015. Together, this implies that the time sensitivity associated with unplanned trips is likely to outweigh the effect of cross-category complementarity, such that consumers will be least sensitive to changes in prices and nonprice promotions on unplanned minor trips.
Fill-in trips may also share some sense of urgency, but to a lesser extent than unplanned minor trips. That said, fill-in trips have relatively small basket size compared with major trips, which would make consumers more sensitive to price and nonprice promotion changes on fill-in trips. Given this trade-off, the ordering of fill-in trips and major trips is an empirical question, but we expect the cross-category complementarity argument to dominate given the basket size, which would imply that consumers will be more sensitive to price and nonprice promotion changes on fill-in trips than on planned major trips. This is consistent with [42], who find that consumers are more sensitive to changes in nonprice promotions on fill-in trips (vs. major trips). This leads us to the following propositions:
- P2 : Brand shares are most sensitive to price changes on fill-in trips, followed by major trips, and then unplanned trips (where brand shares are least sensitive to price changes).
- P3 : Brand shares are most sensitive to changes in nonprice promotions on fill-in trips, followed by major trips, and then unplanned trips (where brand shares are least sensitive to changes in nonprice promotions).
Table 2 provides a summary of our predictions and whether the data support them. We note that while our discussion has focused on how an individual consumer responds to changes in a brand's marketing-mix variables, we formulate the propositions at the market level in terms of brand shares. This is driven primarily by the level of analysis, in which we aggregate brand choice across all consumers to compute brand shares, which we correlate with marketing-mix variables. An alternate approach could be to estimate a structural model of store and format choice with individual data; however, this would require substantively more assumptions about consumer behavior. We defer this approach to future research.
As we have discussed, unplanned minor trips are most likely to be made to convenience stores, planned minor (fill-in) trips to drugstores, and major trips to supermarkets and mass merchandisers, respectively. Given the different types of shopping trips these retail formats cater to, we would expect brand shares at different formats to differ in their sensitivity to changes in marketing-mix instruments. This is of consequence to manufacturers because consumer responses to changes in marketing-mix variables for the same brand might differ across retail formats. Building on P1–P3, in P4 we propose that the brand shares differ in how they respond to changes in prices, nonprice promotions, and line length across formats. Ex ante, we do not have predictions for how brand shares differentially respond to changes in marketing-mix instruments at supermarkets versus mass merchandisers; we believe this to be an empirical question and address it subsequently.
- P4 : Brand shares are most sensitive to line length changes at convenience stores, followed by drugstores, and then supermarkets and mass merchandisers (where brand shares are least sensitive to line length changes).
- P5 : Brand shares are most sensitive to price changes at drugstores, followed by supermarkets and mass merchandisers, and then convenience stores (where brand shares are least sensitive to price changes).
- P6 : Brand shares are most sensitive to changes in nonprice promotions at drugstores, followed by supermarkets and mass merchandisers, and then convenience stores (where brand shares are least sensitive to changes in nonprice promotions).
For this study, we use two different sources of data: the Nielsen Homescan scanner panel data and the Nielsen RMS data, both from 2011 to 2014. Although the Nielsen data before 2011 exist as well, we restrict ourselves to this period to circumvent possible confounds due to the recession in 2008. The Nielsen Homescan data include purchases made by panelists in a wide array of categories across multiple store formats. For each category, the panelists record the store at which a purchase was made, the product purchased, the price paid, and whether the product had any associated promotion. In addition, we have store characteristics information, including each store's retail format. The Nielsen RMS data include weekly pricing and volume information from over 35,000 grocery, drug, mass merchandiser, and other stores across 206 Nielsen DMAs. In addition, the data include information on the retail chain and the format of each store. We include ten categories in the our analysis: orange juice, dry dog food, RTE cereal, ground coffee, frozen pizza, refrigerated yogurt, refrigerated milk, heavy duty liquid detergents, toilet tissue, and paper towels. These categories collectively account for 15% of all household dry grocery and nonfood grocery spending, cover a broad range of items that households shop for, span multiple departments (dry grocery, nonfood grocery, frozen foods, and dairy), exhibit substantial variation in prices and share of basket, and include both perishable and storable items.
Given our primary focus on understanding how brand shares vary in their sensitivity to changes in marketing-mix variables across formats, we focus our analysis and the managerial implications section on the RMS data. To this end, as we discuss next, we base our analysis on brands and retailers selected from the RMS data and then use the same selection rule to short-list brands and retailers in the Homescan data to analyze how brand shares respond to changes in marketing-mix variables across trip types. Using the same selection rule ensures that both analyses are based on a representative set of brands and retailers, thereby allowing us to compare results across trip types and retail formats. In this sense, the trip-level analysis based on Homescan data is primarily intended to establish a link between how the trade-off between fixed and variable shopping costs leads to differences in how brand shares respond to changes in marketing-mix variables across retail formats.
Table 3, Panel A, reports the volume share by retail format. Across formats, supermarkets are the predominant format of choice for dry grocery products such as orange juice, cereal, coffee, frozen pizza, yogurt, and milk. By contrast, mass merchandisers and drugstores play an important role in nonedible categories such as dry dog food, detergents, toilet tissue, and paper towels. In our empirical analysis, we aggregate weekly data across all stores belonging to a particular retail chain in a market to the retail chain level within that market. In doing so, while on the one hand, we lose across-store (within retail chain) variation in shares and marketing-mix variables, on the other, we alleviate concerns pertaining to measurement error in marketing-mix variables because these variables are inferred only on the basis of stockkeeping units (SKUs) sold at the store level. Previous research has analyzed data at the store level but only for a subset of brands (typically top two) or stores within a format (see, e.g., [38]), whereas we study all major brands across multiple retail formats. A simple analysis of variance indicated that format level differences are as important as market-level differences in explaining variation in brand shares, and that format-level differences explain twice as much variation in brand shares as within-format, cross-retailer differences do, which justifies aggregating across stores to the retail chain level.
Table 4 provides details on the number of brands (and retail chains) retained in our final analysis. Across categories, we retain between 60% and 80% of the retail chains that have within-market shares of at least 1%. In addition, to ensure that our results are not driven by measurement error in computation of marketing-mix variables, we exclude all brands with less than 1% market share across retail chains in all DMAs and all stores that have fewer than 200 weeks of data and do not sell all the retained brands. This allows us to account for over 95% of spending in each product category while focusing on only a quarter of the total number of brands. Similarly, in the Homescan data, we retain all brands and retailers with at least 1% market share. This results in approximately 35% brands being retained, which collectively account for over 98% of the total spending.
Graph
Table 4. Summary of Brand Classification.
| Product Category | # Brands | % Retailers Retained | # Brands Retained | % Sales Retained |
|---|
| RTE Cereal | 973 (396) | 62% (65%) | 207 (98) | 90% (91%) |
| Ground and Whole Bean Coffee | 784 (143) | 66% (70%) | 147 (41) | 94% (95%) |
| Frozen Pizza | 424 (74) | 63% (66%) | 131 (30) | 96% (97%) |
| Refrigerated Yogurt | 393 (77) | 43% (43%) | 131 (31) | 97% (97%) |
| Orange Juice | 323 (30) | 57% (60%) | 101 (11) | 96% (96%) |
| Dry Dog Food | 290 (68) | 69% (71%) | 129 (39) | 97% (97%) |
| Refrigerated Milk | 512 (38) | 64% (67%) | 155 (11) | 95% (90%) |
| Liquid Heavy Duty Detergents | 276 (78) | 74% (77%) | 109 (39) | 97% (98%) |
| Toilet Tissue | 233 (40) | 74% (76%) | 109 (18) | 99% (99%) |
| Paper Towels | 221 (36) | 74% (77%) | 110 (19) | 98% (99%) |
5 Notes: The table reports, for each product category, the total number (across markets) and the average number per market (in parentheses) for the total number of brands, the percentage of retailers retained, the number of brands retained, and the percentage dollar sales of the retained retailers and brands. Store brands belonging to different retail chains are counted as different brands.
We explore the relationship between trip types and retail formats by classifying trips in the Homescan data into major, fill-in, and unplanned trips on the basis of trip characteristics independent of the retail format to which these trips are made. Specifically, we focus on the entire trip (as opposed to only the ten categories of interest) and, consistent with [41] and [44], categorize trips with higher-than-median values on total dollar spending on the trip, number of items purchased, or number of product categories as major trips and the rest as minor trips. Next, we use K-means clustering to further classify minor trips into planned minor (fill-in) and unplanned minor trips on the basis of the following trip characteristics: average price per item, number of items and categories purchased, proportion of items purchased on a nonprice promotion (deal), proportion of spending and volume coming from the four departments included in our analysis (dry groceries, frozen food, dairy, and nonfood grocery), and proportion of top ten household-specific product categories purchased in each trip.[ 9] The last variable accounts for the fact that certain more-frequently-purchased products (categories) might be more likely to be purchased on certain trip types ([40]). In addition, for each household, we compute the number of days since the last trip to account for intershopping times, which are important determinants of trip type ([41]; [48]). We standardize each of these variables at the household level to ensure that trips are classified into different clusters on the basis of within-household (as opposed to across-household) differences.
Table 5 reports, by trip type, the average and standard deviation across trips for variables that significantly differentiate across different clusters. Trips in "cluster 3" are major trips, which we obtained through the aforementioned median split. This group has, on average, substantially higher spending, more product categories and items purchased, and a higher number of household-specific top category purchases. While trips in clusters 1 and 2 are similar in terms of dollar spending, number of items, and number of categories purchased, trips in cluster 1 have a higher proportion of spending in the departments we consider and have a substantially higher proportion of items purchased on a deal. By contrast, trips in cluster 2 are ( 1) associated with a higher price, ( 2) show more variation in the price paid, and ( 3) have lower variation in the proportion of items purchased on a deal. Thus, it appears that trips in cluster 2 are unplanned trips in which consumers made fewer purchases in the four departments to which our focal categories belong and paid a higher price. Trips in cluster 1 are more likely to be fill-in trips given the higher number of items purchased in departments of interest, lower price paid, and more purchases associated with deals.
Graph
Table 5. Average Statistics by Trip Type and Retail Format.
| Cluster 1 (Fill-In) | Cluster 2 (Unplanned Minor) | Cluster 3 (Major) | Convenience | Drug | Supermarket | Mass Merchandiser |
|---|
| Averages | | | | | | | |
| # observations (millions) | 4.283 | 6.409 | 10.130 | .373 | 1.738 | 13.439 | 5.272 |
| Spend ($) | 10.45 | 10.12 | 42.45 | 12.87 | 10.22 | 27.31 | 28.46 |
| # items | 4.57 | 3.95 | 18.33 | 3.01 | 4.58 | 12.08 | 11.22 |
| # categories | 2.74 | 2.70 | 10.06 | 1.63 | 2.15 | 6.79 | 6.70 |
| Price | .85 | 1.12 | 1.00 | 1.19 | 1.01 | 1.01 | .99 |
| % dollars spend | .78 | .65 | .79 | .84 | .68 | .77 | .70 |
| % cat. spend | .82 | .70 | .82 | .88 | .74 | .80 | .75 |
| % deals | .64 | .07 | .33 | .16 | .60 | .35 | .15 |
| Standard Deviations | | | | | | | |
| Spend | 10.14 | 9.91 | 36.49 | 23.8 | 13.25 | 31.48 | 32.50 |
| # items | 4.07 | 3.77 | 15.54 | 4.36 | 4.99 | 13.80 | 13.27 |
| # categories | 2.27 | 2.24 | 7.67 | 1.66 | 1.81 | 6.78 | 7.04 |
| Price | .32 | .75 | .43 | .79 | .76 | .46 | .61 |
| % dollars spend | .30 | .32 | .19 | .30 | .36 | .24 | .27 |
| % cat. spend | .23 | .25 | .15 | .21 | .27 | .19 | .22 |
| % deals | .41 | .20 | .37 | .34 | .45 | .39 | .29 |
6 Notes: The table reports the average and the standard deviations of variables which were used to cluster trips into different types. The left side of the table reports the numbers by trip type independent of the retail format, and the right side of the table reports the same numbers by retail format independent of the trip type.
Table 5 also reports the same statistics but for trips made to different retail formats. Trips to mass merchandisers and supermarkets tend to be associated with higher spending and purchasing more products in more categories. By contrast, trips to convenience stores tend to have lower spending (but more variation in amount spent), and a higher price paid on average. Finally, trips to drugstores stand out in the percentage of items that were purchased on a deal. To assess whether trips made to different formats depend on the shopping trip types, in Table 6, Panel A, we report a cross-tabulation of trips between shopping trip types and retail formats. A simple chi-square test rejects the null hypothesis that shopping trip types are independent of the retail formats to which they are made.
Graph
Table 6. Trip Frequency and Indices by Trip Type and Retail Format.
| Unplanned Minor | Fill-In | Major |
|---|
| A: Frequency |
| Convenience store | 217,955 | 90,142 | 64,818 |
| Drugstore | 542,039 | 827,688 | 368,034 |
| Mass merchandiser | 1,966,057 | 729,685 | 2,576,170 |
| Supermarket | 3,683,072 | 2,635,438 | 7,120,630 |
| B: Indices |
| Convenience store | 1.90 | 1.18 | .36 |
| Drugstore | 1.01 | 2.32 | .44 |
| Mass merchandiser | 1.21 | .67 | 1.00 |
| Supermarket | .89 | .95 | 1.09 |
7 Notes: The table reports the frequency (cross-tabulation) of trips (Panel A) and the corresponding indices (Panel B) computed drawing on the cross-tabulation based on trip type and the retail format to which the trip was made. The reported indices are computed as the proportion of trips within the cluster that belong to the particular format divided by the proportion of total trips made to that format. An index value of 1 implies that the proportion of trips to the particular format from the cluster are not any different from the proportion of total trips to that format in general. Thus, values greater than 1 show that the format is favored for trips of the particular type.
As Panel A shows, a large number of trips are made to supermarkets, which makes this format dominate all trip types. Thus, to understand whether certain trip types are more likely to be made to certain formats, we compute indices on the basis of the cross-tabulation and report them in Table 6, Panel B. The indices are computed as the proportion of trips within a cluster that belong to a certain format, divided by the proportion of trips made to that format. Thus, a value of 1.18 in the top-left cell is based on the proportion of trips in cluster 1 that are made to convenience stores (= 90,142/4,282,953) divided by the proportion of total trips made to convenience stores (= 372,915/20,821,728). A value of 1 implies that the proportion of trips to the particular format from a cluster are not any different from the proportion of trips to the format in general. Thus, values greater than 1 point to a positive correlation between trip types and retail formats, in terms of visit frequency. As is evident, convenience stores are more likely to be frequented on unplanned trips (cluster 2), drugstores on fill-in trips (cluster 1), and supermarkets and mass merchandisers to a lesser extent, on major trips (cluster 3). Together, the similarity between trip types and retail formats, the statistical test of independence, and the frequency indices of trips of different types made to retail formats establish that there is a correlation between different types of shopping trips and the retail formats to which they are made.
We include in our analysis three key marketing-mix variables: price, line length, and nonprice promotion. The Nielsen Homescan and RMS data do not provide information on displays and promotion intensity for all stores, which is a limitation of our data. In the RMS data, we compute the marketing-mix variables for each brand at the retail chain–market–week level. In addition to price and line length, we include features as the nonprice promotion variable. To the extent that the price variable accounts for any price-related promotions, the feature variable accounts for only the non-price-related effect of promotions. Web Appendix A provides details on how these variables and those based on the Homescan panel data are computed.
Table 3, Panel B, reports the average brand prices at a retail chain level, split by retail formats. Convenience stores tend to have the highest prices, followed by drugstores for most product categories. Conforming to conventional wisdom, and consistent with [46], mass merchandisers have, on average, 11% lower prices than supermarkets across these product categories. Drugstores tend to feature products more than supermarkets and mass merchandisers, with convenience stores being least associated with features. The fact that convenience stores rarely engage in feature promotions is consistent with the nature of shopping trips which are made to this format.
In our analysis, we account for line length using the total number of unique SKUs of a brand. Previous research has included other measures of brand-level assortment based on shelf space and availability of the favorite brand ([12]), dissimilarity in product pairs ([35]) and attributes ([61]), and brand-size combinations ([10]). Given our focus in understanding more broadly how the effect of marketing-mix variables on brand shares varies across retail formats, we focus only on the total number of SKUs and defer a more detailed exploration into the effect of other assortment measures to future research. Supermarkets carry more SKUs than any other format, except for detergents, toilet tissue, and paper towels, where mass merchandisers carry comparable or longer line length than supermarkets (Table 3, Panel D). Convenience stores carry the smallest assortments among all store formats, followed by drugstores.
The first two columns of Table 7 report the averages and the standard deviations of the variation explained by different factors across product categories drawing on a simple analysis of variance run by stacking the shares of all brands across all retail chains, markets, and weeks.[10] After controlling for market and time differences, marketing-mix variables explain, on average, 39% of the within-market variation in brand shares, but there are differences across product categories, as evidenced by the standard deviation of 13%. We found that 21% of the variation in brand shares is explained by prices and features, which is consistent with the findings in [15]. On average, line length explains 19% variation in brand shares, but for six categories, line length explains more variation in brand shares (notable exceptions being refrigerated milk; ground and whole bean coffee; and, to a lesser extent, frozen pizza and orange juice; Figure 1). The right side of Table 7 reports the relative contribution of different factors across formats. Prices explain most variation in brand shares at convenience stores, features at drugstores, and line length at supermarkets and mass merchandisers. Furthermore, while supermarkets and mass merchandisers are similar in the importance of line length, prices are more important in explaining brand shares at supermarkets relative to mass merchandisers.
Graph: Figure 1. Importance of prices and line length by product category.Notes: The figure shows the scatter plot between the variance explained by prices and line length separately for each product category. The black diagonal represents the 45-degree line.
Graph
Table 7. Relative Importance of Marketing-Mix Variables by Retail Format.
| Aggregate | Store Format |
|---|
| Mean | SD | Convenience | Drug | Supermarket | Mass Merchandiser |
|---|
| R-square | 48% | 13% | 92% | 53% | 61% | 50% |
| Market | 2% | 2% | 36% | 9% | 2% | 2% |
| Time | 0% | 0% | 6% | 0% | 0% | 0% |
| Marketing Mix | 39% | 13% | 49% | 43% | 58% | 44% |
| Price | 18% | 13% | 38% | 25% | 24% | 15% |
| Feature | 3% | 1% | 0% | 4% | 3% | 2% |
| Line length | 19% | 12% | 11% | 14% | 30% | 28% |
| Competition | 6% | 4% | 0% | 1% | 2% | 3% |
8 Notes: The table reports the contribution of each of the marketing-mix variables in the explained variation in brand shares. For formats, the reported numbers are the average across all categories weighted by the brand share of each category at the particular format.
We use the Homescan data to show that the sensitivity of brand shares to changes in marketing-mix variables varies by trip type, something that cannot be addressed with RMS data given the lack of information on trips. Unlike [28], who study how total spending responds to changes in marketing mix across retail formats, we are primarily interested in understanding how the share of a brand on a particular trip type responds to changes in marketing mix. To this end, we compute the dependent variable as the share of a brand on trips of a particular type by market in a month. Notably, as we discuss in Web Appendix A, while we aggregate over different retail chains and formats, the final level of analysis is at the trip-type level as opposed to retail chain or format level. Compared with RMS data, where the analysis is done at a weekly level, the unit of analysis with Homescan data is one month, to ensure that the computed measures do not suffer from measurement error. We acknowledge that this is a limitation of our analysis but also note that aggregating to the monthly level makes it more difficult to find any effects of marketing activities.
Next, we explore how the effect of marketing-mix variables on brand shares varies by trip type and retail format. Trip types are typically not observed in the data, and thus any analysis based on trip types does not directly provide any managerially relevant implications. To this end, the primary objective of studying the varying effect of marketing mix by trip type is to motivate the underlying mechanism behind why brand shares vary in their sensitivity to changes in marketing-mix variables across retail formats. The latter has several implications for both manufacturers and retailers, some of which we discuss in the following section on managerial relevance.
To understand whether brand share sensitivity to changes in marketing-mix variables varies by trip type, we regress the brand shares on marketing-mix variables while controlling for unobserved factors through fixed effects. To facilitate direct comparison of the relative importance of different coefficients, we include in our analysis natural logarithm of price and line length. This enables us to interpret the coefficients corresponding to these variables as the percentage change in brand share for a 1% change in the variable. Specifically, we run the following regression:
Graph
1
where is the brand share of brand j in product category i on trip type k in market m in month t. We compute prices, line length, and nonprice promotions (deals) at the same level of aggregation as the dependent variable by aggregating over retail chain–specific measures as discussed in the previous section. The coefficients on the marketing-mix variables are estimated by trip type, as evidenced by the k subscript.
A shortcoming of using the Homescan data is that the marketing-mix measures are computed by aggregating over products purchased by the panelists at a more aggregate level (e.g., month). To the extent that consumers are systematically choosing what products to purchase and the prices to pay, this can result in the aggregate-level marketing-mix instruments to be correlated with the aggregate-market-level unobserved demand shocks. One way to address this issue could be to use the store-level RMS data to compute the marketing-mix variables, but the store-level data neither are at the same geographic market level (because not all stores are represented) nor include within-week variation in marketing-mix variables. Thus, to address concerns pertaining to endogeneity, we include in our analysis brand–(geographic) market–trip type fixed effects and brand-month fixed effects.
The first set of fixed effects account for any brand- and market-level unobserved differences that might vary by trip type. Although marketing-mix variables are not set by trip type, the correlation between the trip types and retail formats to which they are made might induce correlation between the marketing-mix variables and demand shocks that affect brand shares at the trip-type level. The second set of fixed effects account for any time-varying brand-specific shocks that affect brand shares (e.g., national advertising). While we believe that these fixed effects account for unobserved factors that might affect the marketing-mix variables, we reiterate that the primary objective of the trip-type analysis is to motivate the analysis at the retail format level. We acknowledge the limitation of the trip-type-level analysis and caution the reader against interpreting these results with a causal mindset.
Table 8, Panel A, reports the estimated coefficients for each marketing-mix variable by trip type. The reported coefficients across trip types (except for line length) are significantly different from each other at the 99% confidence level based on the pairwise t-tests. While the sensitivity of brand shares to changes in line length on unplanned trips is not significantly different from that on fill-in trips (.371 vs..375), we find that brand shares are most sensitive to changes in line length on major trips based on the coefficient of.638. Thus, while we do find significant differences across trip types, the direction of effects on major trips is not as proposed in P1. As we discussed in the previous section, this can in part be attributed to the fact that the meaning and operationalization of line length on a trip type is not straightforward, because line length is usually associated with a store or retail format as opposed to trip type.
Graph
Table 8. Regression Coefficients (Elasticities) by Trip Type and Retail Format.
| Price | Nonprice Promotion | Line Length |
|---|
| A: Trip Type |
| Unplanned | −.236 (.028) | −.002a (.009) | .371 (.012) |
| Fill-in | −1.041 (.029) | .092 (.009) | .375 (.013) |
| Major | −.771 (.025) | .040 (.007) | .638 (.010) |
| B: Retail Format |
| Convenience store | −.538 (.012) | .275b (.082) | 1.511 (.0129) |
| Drugstore | −1.750 (.002) | .518 (.001) | .820 (.001) |
| Supermarkets | −2.111 (.001) | .411 (.002) | .348 (.0002) |
| Mass merchandiser | −1.548 (.001) | .326b (.001) | .576 (.0003) |
- 9 a Not significantly different from 0 at the 99% confidence level.
- 10 b Not significantly different from the other estimtes at the 99% confidence level.
- 11 Notes: The table reports the coefficients (elasticities) from the regressions run by pooling data across all product categories. Panel A reports results from the regression run using Homescan data at the trip-type level, and the Panel B reports results from the regression run using RMS data at the format level. Standard errors are reported in parentheses. All estimates except for those with an a or b superscript are significantly different from 0 at the 99% confidence level and significantly different from each other at the 99% confidence level.
Consistent with P2, we find that brand shares are most sensitive to changes in prices on fill-in trips (price elasticity of −1.041) and least sensitive on unplanned trips (price elasticity of −.236). Similarly, consistent with P3, we find that brand shares are most sensitive to changes in nonprice promotions (deals) on fill-in trips (coefficient of.092) and least sensitive on unplanned trips (coefficient of −.002), which is consistent with the notion that given the increased baseline utility due to urgent need, households are willing to undergo the disutility due to lack of nonprice promotions. The coefficient on unplanned trips, however, is not significantly different from zero. In summary, our analysis at the trip-type level provides support for how brand shares respond to changes in prices and nonprice promotions across trip types, which motivates the analysis at the retail format level.
Next, we quantify how brand shares respond to changes in marketing-mix variables across retail formats. As before, we do so by pooling data across product categories to estimate format-specific marginal effects of marketing-mix variables on brand shares. Within a product category, we compute the share of all retained brands at a retail chain in a week.
As with the trip-type analysis, we include natural logarithm of price and line length to interpret their coefficients as elasticities. Specifically, we run a regression of the following form:
Graph
2
where is the brand share of brand j in product category i at retail chain c of format f in market m at time t. All marketing-mix variables are computed at the same level of aggregation as the dependent variable. A specification such as this typically suffers from endogeneity concerns arising from the correlation of marketing-mix variables with unobserved factors that are accounted for by the error term. For example, a retail chain may set marketing mix in a market on the basis of the distribution of consumer preferences faced by that chain in the particular market. Furthermore, the marketing mix for a brand may be correlated with market-specific time-varying factors such as brand advertising. To account for these issues, we include in our analysis ( 1) a retailer-format-market-specific fixed effect ( ) to account for all within-market retailer-specific time-invariant factors and ( 2) a brand-market-time fixed effect ( ) to account for factors that are specific to brands and markets and vary over time (e.g., advertising). In addition, to account for retailer-specific within-market factors that vary over time, we include in our analysis the effect of competition. We include three measures for competition—percentage of format stores belonging to the focal retail chain, ; percentage of stores in the DMA belonging to the focal retail chain format, ; and number of stores in the DMA, —and estimate product category–specific effects of competition. In addition to capturing competition, these measures account for across- and within-market shocks that vary over time. Finally, we include in our analysis product category–specific fixed effects, , to capture across category differences in average brand shares. We believe that these controls alleviate concerns pertaining to correlated unobservables. Our approach, thus, follows [54], who shows that with panel data, a combination of brand, market, and time fixed effects alleviates the need for instrumental variables, the results from which crucially hinge on the validity of the instruments and the finite sample distribution of the instrumental variable estimator.
Having said this, we also test whether the fixed effects included in the aforementioned model control for the potential endogeneity of marketing-mix variables. We do so by estimating a two-stage least squares model by including instruments for each marketing-mix variable (results reported in the Web Appendix). Specifically, we instrument for each marketing-mix variable by computing the across-market average of that particular variable for the same brand, retail chain, format, and week. These instruments were first proposed by [34] and are based on the observation that while the marketing-mix variables (e.g., price) set by a retail chain in one market would be correlated with the prices set by the retail chains in other markets, the prices in other markets would not be correlated with the focal market–specific demand shocks. If the fixed effects in Equation 2 do not control for the correlated unobservables, then the estimates based on the model that also includes instrumental variables should be qualitatively different. As we report in the Web Appendix, the estimates based on the instrumental variables are very similar, both qualitatively and quantitatively, to the estimates based only on the fixed effects model from Equation 2. Thus, we discuss the estimates without the instrumental variables here and report those based on the instrumental variables in the Web Appendix.
The coefficients on the marketing-mix variables are estimated at the retail format level, as evidenced by the f subscript. Table 8, Panel B, reports the average coefficient (elasticities) for each marketing-mix variable by retail format. For all marketing variables, the reported coefficients across formats are significantly different from each other at the 99% confidence level based on the pairwise t-tests. Consistent with P4, we find that brand shares are most sensitive to line length changes at convenience stores and least sensitive to line length changes at mass merchandisers and supermarkets, as measured by elasticities of 1.511,.576, and.348, respectively. In fact, convenience stores are almost two times as sensitive to line length changes as drugstores (elasticity of.820) and three to four times as sensitive as mass merchandisers and supermarkets, respectively. We find that brand shares are more sensitive to line length changes at mass merchandisers as compared with supermarkets, which is consistent with our finding that there is much more variation in spending at mass merchandisers, trips to which do not substitute for trips to other formats.
While we find that brand shares are much less sensitive to price changes at convenience stores (price elasticity of −.538), their sensitivity to price changes is highest at supermarkets (−2.111), followed by drugstores (−1.750), and then mass merchandisers (−1.548). The lower price sensitivity at drugstores can in part be explained by the higher relative importance of urgency (as compared to cross-category complementarity) in explaining sensitivity to marketing-mix variables at drugstores. Thus, we find partial support for P5, with brand shares being more price sensitive at supermarkets than at drugstores. In addition, brand shares are more sensitive to price changes at supermarkets than at mass merchandisers, which a finding that is consistent with the notion that cross-category complementarity is stronger at mass merchandisers because consumers travel greater distances to visit these formats. Finally, consistent with P6, we find that brand shares are most sensitive to changes in nonprice promotions (features) at drugstores and least sensitive at convenience stores, with nonprice promotion elasticities of.518 and.275 at these formats, respectively. While the direction of effects is as predicted, the differences between convenience stores and supermarkets (mass merchandisers) are only marginally (not) significant given the large standard error of.082 estimated on the nonprice promotion coefficient at convenience stores. Moreover, we estimate higher sensitivity to nonprice promotions at supermarkets (.411) than at mass merchandisers (.326), which, just like prices, could be attributed to stronger cross-category complementarity at mass merchandisers given the greater distances consumers travel to visit these formats. Overall, consistent with our theoretical predictions, we find evidence that brand shares vary in their response to changes in marketing-mix variables across retail formats.
An alternative explanation for differences in how marketing-mix activities affect brand shares across formats stems from differences in the types of households these formats attract. We do not believe this to be an issue for two reasons. First, based on the Homescan data, we find that households, on average, shop in 2.5 out of the 4 formats for the ten categories we include in our analysis. Second, if different types of households visit different formats, then, given that shopping trips are defined within a household (such that all households make all types of shopping trips), we would not expect the type of shopping trips to depend on the retail formats to which they are made. However, the fact that we can rule out independence of shopping trip type from retail format (Table 6) alleviates concerns pertaining to these differences being driven solely by differences in the type of households different retail formats attract.
Next, we use some of the recent changes made by manufacturers to understand the possible implications based on our analysis. While quantifying the exact profit changes and making a policy recommendation requires a formal analysis of consumer demand and a model of store (format) choice, we use the model estimates to provide preliminary evidence that differences in sensitivity across formats translate to potentially meaningful differences in profit changes across formats.
To highlight the marketing relevance of our findings for brand manufacturers, we consider a recent change to prices that garnered a lot of media attention. After a decade-long norm of cutting prices, P&G increased the prices on most of its products on a rolling basis starting in 2018, with the price increases varying between 4% and 10% (see, e.g., [58]). P&G rationalized these price increases based on inflation and increases in input costs. This was followed by similar price increases by other major manufacturers such as Nestle and Unilever ([16]). To understand how the impact of such a price change might vary across retail formats, we calculate the average change in profits from a 5% increase in prices, which is in line with the price increase implemented by P&G. We focus only on brands belonging to P&G (namely, Iams, Cheer, Dreft, Era, Gain, Tide, Charmin, and Bounty) in the focal product categories and assume that the price increase is accompanied by a 4% increase in input costs (i.e., the manufacturer's margin goes up by only 1%).[11] To ensure that the results are not confounded by across market differences in response to changes in marketing mix, we estimate separate coefficients for each format within each market, and separately for national and store brands. Given the relatively small share of sales at convenience stores, we focus our analysis on only drugstores, supermarkets, and mass merchandisers. We recognize, however, that given the descriptive nature of our analysis, the reader must exercise caution while interpreting our results with a causal mindset.
We assume that the percentage contribution margin for national brands is 22%, as is commonly cited in popular press, and use the numbers reported in [ 2] (Table 8) to calculate the percentage contribution margin for store brands.[12] We then combine these contribution margins with prices and shares reported in Table 3, total sales at the retail chain, and elasticity estimates from our analysis to calculate profit changes assuming that the percentage contribution margin is the same at different formats. Our analysis is descriptive in nature and does not account for the negotiations between manufacturers and retailers, which, as we discuss in the next section, is an interesting direction for future research.
Table 9 reports the average change in annual profits per brand (absolute and percentage) by retail chain separately for each product category in which P&G sells products. On average, the price increase results in a roughly 3%–4% increase in the manufacturer's profits across retail formats (almost 2%–3% for paper towels). The popular press reports a 10% increase in profits due to the price increase but attributes majority of this increase to tax breaks, thus making our results in line with the numbers reported in popular press (see, e.g., [30]; [58]). Crucially, we estimate substantial variation in profit changes across formats. In three out of four categories, the average increase in profits (in absolute terms) is lowest at drugstores, though it is highest at drugstores in percentage terms. Except for paper towels, the absolute profit changes are highest at supermarkets. These differences across formats are statistically and economically significant and point to the importance of allowing for changes in prices to have a differential impact on profitability depending on the retail format. In the Web Appendix, we report estimates by bootstrapping over first-stage standard errors and do not find any qualitative differences.
Graph
Table 9. Annual Profit Changes from a Change in Marketing-Mix Variables.
| Absolute Change | Percentage Change |
|---|
| Drug | Supermarket | Mass Merchandiser | Drug | Supermarket | Mass Merchandiser |
|---|
| Price |
| Dry Dog Food | 146 (28) | 1,453 (177) | 621 (80) | 4.24% (.02%) | 4.05% (.03%) | 3.62% (.03%) |
| Liq. HD Detergent | 553 (103) | 1,530 (150) | 974 (121) | 3.89% (.01%) | 3.84% (.01%) | 4.02% (.01%) |
| Toilet Tissue | 882 (153) | 3,151 (322) | 1,510 (201) | 3.55% (.04%) | 3.48% (.04%) | 3.33% (.04%) |
| Paper Towel | 674 (117) | 56 (240) | 174 (61) | 2.75% (.06%) | 1.75% (.08%) | 1.94% (.03%) |
| Nonprice Promotion |
| Dry Dog Food | 1 (0) | 20 (4) | 13 (2) | .02% (.00%) | .02% (.00%) | .02% (.00%) |
| Liq. HD Detergent | 26 (8) | 117 (17) | 16 (3) | .05% (.00%) | .05% (.00%) | .01% (.00%) |
| Toilet Tissue | 21 (4) | 300 (44) | 125 (22) | .09% (.00%) | .11% (.01%) | .07% (.01%) |
| Paper Towel | 87 (41) | 606 (147) | 132 (32) | .17% (.01%) | .16% (.01%) | .10% (.01%) |
| Line Length |
| Dry Dog Food | 439 (90) | 4,802 (953) | 1,936 (199) | 11.76% (.43%) | 6.32% (1.09%) | 4.41% (.15%) |
| Liq. HD Detergent | 3,535 (962) | 9,795 (1092) | 3,800 (539) | 7.17% (.13%) | 6.77% (.20%) | 5.09% (.08%) |
| Toilet Tissue | 2,179 (492) | 30,112 (3,380) | 13,416 (1,977) | 7.66% (.42%) | 14.33% (.44%) | 11.46% (.30%) |
| Paper Towel | 11,985 (5217) | 65,063 (8,687) | 19,305 (3,790) | 23.25% (.81%) | 27.12% (.79%) | 18.61% (.26%) |
12 Notes: HD = heavy duty. The table reports the changes in profit for P&G corresponding to changes in prices, nonprice promotion, and line length. The left (right) side of the table reports the absolute (percentage) change in profits. For prices, the profit changes are calculated based on a 5% increase in price and a 4% increase in input costs. For nonprice promotion and line length, profit changes are calculated based on a 1% increase in either variable, respectively. The reported numbers are based on market specific coefficients separately for each product category. We calculate the average change in profits for each brand at the chain level in each market and then average profits across all chains and markets. Standard errors are reported in parentheses. All estimates are statistically significant at the 99% confidence level.
To compare the effect of changing different marketing-mix variables, we also calculate the change in profits from increasing either nonprice promotions or line length by 1%.[13] Focusing on nonprice promotions first, we note that the impact of changing nonprice promotions on profit is substantially lower than that of changing prices. Having said this, and in contrast to the effect of price changes, changing nonprice promotions tends to have the greatest impact at supermarkets and, to some extent, mass merchandisers as compared with drugstores. This is driven by the currently higher frequency of nonprice promotions at drugstores versus supermarkets and mass merchandisers such that, on the margin, increasing nonprice promotions at supermarkets and mass merchandisers gives a higher return on investment.
Finally, focusing on line length, we estimate a statistically significant and economically meaningful effect of line length changes on manufacturer's profits. The increase in profits from increasing line length by 1% is greater than the corresponding increase in prices from a 1% increase in margin. Consistently across categories, the impact of changing line length on profits (in absolute terms) is highest at supermarkets and lowest at drugstores. However, if we focus on percentages, then line length has the greatest impact on profits at drugstores in dry dog food and liquid detergent categories, which is consistent with the higher line length elasticities we estimate for drugstores as compared with mass merchandisers and supermarkets. We do see some interesting differences across categories as well, wherein supermarkets benefit much more from line length increases in toilet tissue and paper towels categories.
While this analysis rests on the assumptions we make about profit margins, it emphasizes several key points. First, the analysis shows how price changes implemented by a manufacturer such as P&G affects profit differentially across various retail formats. The fact that the relative changes in profits are in line with the numbers reported in popular press lend face validity to the analysis. Second, the analysis highlights the importance of line length measures relative to prices in influencing profits. Importantly, and consistent with price changes, we find that the impact of line length changes varies with retail formats, and that across product categories, retail formats differentially affect profits. This not only highlights the importance of accounting for differences in retail formats when making changes to prices and line length but also calls for a more granular category-by-category analysis when assessing such trade-offs. Finally, this analysis provides a stark reminder to manufacturers that success of particular brands at a retail format could stem not simply from prices at that retailer but also from the line lengths carried. A similar implication is also valid for policy makers and members of the popular press, who have often focused on prices but not on other dimensions of the retailer' s marketing mix.
In this article, we study how changes in marketing-mix instruments such as prices, nonprice promotions, and line length differentially affect brand shares at different retail formats. Using data from the top ten product categories (based on household spending) that account for 15% of all spending on dry groceries, we find that brand shares differ in their sensitivity to marketing-mix changes across convenience stores, drugstores supermarkets and mass merchandisers, which can be explained by differences in the types of shopping trips made to these formats. Consistent with previous research, we find that ( 1) market and retailer level differences explain substantial variation in brand shares, ( 2) marketing-mix variables explain a large amount of variation in brand shares, and ( 3) among marketing-mix variables, prices and line length are the most important in determining brand shares.
Unlike previous research, we find that major shopping trips are similar to and more likely to be made to supermarkets and mass-merchandisers, fill-in trips are more likely to be made to drugstores, and unplanned trips are more likely to be made to convenience stores, respectively. We focus on the interplay between the fixed and variable components of utility (and the consequently induced cross-category complementarity) across different types of shopping trips, and form predictions on how marketing-mix variables differentially affect brand shares on these shopping trips. Given that trips of certain types are more likely to be made to certain formats, as a consequence, we derive predictions for how brand share response to changes in marketing-mix variables varies by retail format. Consistent with our predictions, brand shares on unplanned trips (fill-in trips) are least sensitive (most sensitive) to changes in prices and nonprice promotions. Focusing on retail formats, we find that brand shares at convenience stores are least sensitive to price and nonprice promotion changes. Furthermore, brand shares at convenience stores (supermarkets) are most (least) sensitive to changes in line length. Finally, we use the recent price changes by P&G as a case study and combine this with our model estimates to explore how such a change in marketing mix affects manufacturer profits differentially across different retail formats.
Our findings on the effect of line length changes on brand shares at the trip-type level (P1) are inconsistent with the proposed ordering of effects and with the estimated effects at the retail format level (P4). This stems from the construction of line length, which is particularly challenging for minor trips using Homescan data. First, we require more data to infer the line length for different trip types. Unlike brand prices and nonprice promotions, which can be observed as long as one purchase of the brand is observed in the data, for line length, we have to pool choices across consumers within a retail chain to infer the total number of chain-specific SKUs during that time period. We then compute the weighted average across retail chains where the weight is defined in line with the share of each chain that is associated with each trip type. This procedure induces several possible sampling errors due to the possibility that none of the consumers purchase one or more SKUs from a retail chain in any given period, and from the estimation of the association between retail chains and trip types. This is an issue especially for minor trips, which are associated with only 3–4 items per trip, as compared with major trips, which typically entail purchasing 18 items per trip (Table 5). Finally, given that the majority of the trips are made to supermarkets, the weight assigned to supermarkets is greater than that assigned to convenience and drugstores for all trip types. This, again, is especially critical for minor trips, which are more likely to be made to convenience and drugstores. The discrepancy between our empirical results for P1 and P4 reflects these issues.
This article contributes to the existing literature on shopping trips and format patronage in several ways. First, we combine Nielsen Homescan and store-level RMS data to study how the effect of changes in marketing-mix instruments on brand shares varies across both trip types and retail formats. In doing so, to the best of our knowledge, this is the first article that ties the shopping trip types to different retail formats. The recent literature on shopping trips has predominantly focused on survey data to classify trips into different types. By contrast, we use data on actual transactions to classify trips into different types and correlate trip characteristics across trip types and retail formats. Thus, through shopping trip types, we establish a link between the response of brand shares to changes in marketing-mix instruments and retail formats. We believe these are novel contributions to the existing literature and further our understanding as to why the same brand may respond differently to changes in marketing-mix instruments across retail formats.
Our analysis is primarily descriptive in nature and suffers from several limitations that provide directions for future research. First, our analysis using the Homescan data is done at the aggregate level but makes inferences at the trip level. While this approach allows us to correlate trip-level brand shares with marketing-mix variables in the absence of stringent assumptions, analysis at the trip level (using a structural model) might be favorable to study more "what if"–type of questions, albeit at the expense of making several assumptions about consumer behavior. Second, while we establish an empirical link between trip types and retail formats, we acknowledge that not all trips to a format are of the same type. In fact, consumers may make different types of trips to the same format for different types of categories, which could be driven in part by category-specific store loyalty ([66]). Understanding how sensitivity to marketing-mix variables varies by trip type within a format is an important topic of research which requires analysis at the individual trip level, which we defer to future research.
Third, the profitability analysis we undertake is descriptive but shows that varying sensitivity of marketing-mix variables across shopping trips and retail formats has important consequences. We believe a more detailed study of manufacturer's profitability incorporating a more formal model of format choice and the competitive reactions from changes in marketing-mix instruments will make a valuable contribution to the literature. Fourth, given our focus on cross format differences, we include only one measure of brand-level assortment based on total number of SKU in our analysis. Previous research, however, has taken a more nuanced view on assortment, studying in great detail the role of attribute similarity and other assortment measures such as sizes, shelf space allocation, availability of favorite brand, and so on. This is especially relevant given that different formats have different objectives (traffic building vs. sales for immediate consumption) with regard to the assortment they carry. Future work will benefit from exploring whether consumers' sensitivities to different assortment measures varies by retail format.
Fifth, while our analysis provides some guidance regarding how changes in line length affect manufacturer profits, we believe the analysis can be extended to understand how line length reallocation affects manufacturer profits across different retail formats. This will help researchers understand how changes in brand line length affect consumer store choice, which, in turn, determines firm profitability. Our analysis can be viewed as a first step in this endeavor. Finally, while the categories we use account for 15% of household basket expenditures, covering more categories would certainly add to the picture afforded us by the categories we have considered. This would also provide more generality to our results.
Supplemental Material, jm.17.0292-File003 - Marketing-Mix Response Across Retail Formats: The Role of Shopping Trip Types
Supplemental Material, jm.17.0292-File003 for Marketing-Mix Response Across Retail Formats: The Role of Shopping Trip Types by Pranav Jindal, Ting Zhu, Pradeep Chintagunta and Sanjay Dhar in Journal of Marketing
Footnotes 1 Associate EditorWerner Reinartz
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The first author acknowledges the support of The M.W. "Dyke" Peebles, Jr. Faculty Development Fund; Chintagunta and Dhar thank the Kilts Center for financial support.
4 ORCID iDPranav Jindal https://orcid.org/0000-0002-8305-0657
5 Online supplement: https://doi.org/10.1177/0022242919896337
6 1Kroger's recent decision to sell its convenience stores in an effort to focus on grocery retail ([32]) and Target's decision to enter in the grocery market ([59]) point to differences in factors that affect brand performance at different retail formats.
7 2Hereinafter, any reference to fill-in trips implies planned minor trips, and unplanned trips refer to unplanned minor trips.
8 3An alternative explanation for varying sensitivities across retail formats could stem from the fact that these retail formats attract very different types of consumers. We exclude this possibility in our article because households, on average, shop in 2.5 out of the 4 formats for the ten categories we study.
9 4We compute the average price index at the trip level on the basis of the volume-weighted price index of each purchased item, where the item-level price index is calculated by normalizing the price paid by the average price of the particular brand in the market. The construction of price index is, thus, similar to [28] but differs in that we use trip-level consumption weights as opposed to long-term average consumption weights, which is consistent with our focus on capturing trip-level variation in prices.
5For each variable X, we report the proportion of variation in brand shares that is explained by within-market variation in X, after controlling for across market differences. Consistent with [15], this proportion can be expressed as , where is the coefficient on X from the corresponding regression model and bs is the brand share.
6Iams was sold by P&G to Mars in 2014 in a deal announced in April 2014 and closed in August 2014. Our analysis implicitly assumes that Iams was a P&G brand until the end of 2014.
7See, for example, http://csimarket.com/Industry/industry%5fProfitability%5fRatios.php?ind=1305 (accessed December 18, 2019).
8Given that the profit calculations for price changes are done under the assumption that the margin of the manufacturer increases by 1%, we calculate profit changes from a 1% increase in either nonprice promotions or line length.
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By Pranav Jindal; Ting Zhu; Pradeep Chintagunta and Sanjay Dhar
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Negative Reviews, Positive Impact: Consumer Empathetic Responding to Unfair Word of Mouth
This research documents how negative reviews, when perceived as unfair, can activate feelings of empathy toward firms that have been wronged. Six studies and four supplemental experiments provide converging evidence that this experienced empathy for the firm motivates supportive consumer responses such as paying higher purchase prices and reporting increased patronage intentions. Importantly, this research highlights factors that can increase or decrease empathy toward a firm. For instance, adopting the reviewer's perspective when evaluating an unfair negative review can reduce positive consumer responses to a firm, whereas conditions that enhance the ability to experience empathy—such as when reviews are highly unfair, when the identity of the employee is made salient, or when the firm responds in an empathetic manner—can result in positive consumer responses toward the firm. Overall, this work extends the understanding of consumers' responses to word of mouth in the marketplace by highlighting the role of perceived (un)fairness. The authors discuss the theoretical and practical implications of the findings for better management of consumer reviews.
Keywords: word of mouth; fairness; negative reviews; customer complaints; empathy; emotions
1 star: "More like 'Mediocre Canyon.'" (Grand Canyon National Park review, Yelp)
1 star: "Just a brown lump of metal. Nothing special." (Eiffel Tower review, TripAdvisor)
1 star: "Modern films are generally much better." (Citizen Kane review, Amazon)
1 star: "The ice cream was too cold." (McDonald's restaurant review, Yelp)
Managing negative reviews is an essential task for companies because word-of-mouth (WOM) communication has a compelling influence on consumer preferences ([48]; [79]; [113]). Compared with firm-generated communications, WOM from other consumers is considered more authentic, relevant, and unbiased ([52]; [53]). Indeed, about 50% of consumers report referring to online reviews before making purchases ([87]), and 78% report trusting online reviews as much as personal recommendations ([26]). Within the WOM literature, the consensus is that consumer reviews most often lead to firm evaluations that are consistent with review valence. That is, negative reviews lead to less favorable firm evaluations, and positive reviews lead to more favorable firm evaluations (e.g., [31]; [48]). Both types of reviews, negative and positive, greatly influence consumer decision making ([81]; [54]; [105]), with negative reviews often being the most impactful ([30]; [83]).
Importantly, the increasing influx of online reviews is a challenge for managers who must deal with public expressions of customer disgruntlement that may or may not accurately reflect the objective quality of the firm's offerings ([85]). As the previous one-star reviews demonstrate, there are certainly times when the company's actual product or service experience quality does not warrant the degree of negativity that is conveyed via WOM. To demonstrate the prevalence of unfair WOM, we conducted a preliminary study examining a random selection of scraped one- and two-star hotel reviews from TripAdvisor, a major hotel-review site, for the ten top-ranked hotels in Chicago, Hong Kong, London, Los Angeles, Paris, and Singapore. Two independent coders who were blind to the hypotheses rated 1,000 reviews from these 60 hotels for their perceived fairness as well as the rationale for the ratings. The results revealed that more than one-quarter of negative reviews contained elements of unfairness (26.3%, rated as unfair or somewhat unfair; for detailed results, see Web Appendix A). Thus, it appears that unfair negative consumer reviews are somewhat prevalent.
Recently, the popular press has discussed the many ways firms respond to unfair negative reviews, from suing the reviewer ([86]; [108]) to changing how consumers can provide reviews ([20]), to publicly shaming the reviewers (Web Appendix B). Given the prevalence of unfair negative reviews, companies must understand how consumers react to such reviews, as well as how best to respond to unfair negative WOM. In this research, we find that unfair negative reviews are not necessarily bad for the firm and that the impact of negative reviews on consumers' subsequent responses to the focal firm varies as a function of the perceived fairness of the review.
We define fairness as a judgment regarding whether the outcome an individual receives is deserved and justified based on the focal individual's previous actions ([28]; [63]). This work examines reactions of third-party observers to negative reviews of a focal firm that are perceived to be unfair based on the firm's performance. We draw on work examining empathetic responding in person-to-person contexts ([19]; [39]; [99]) to argue that when consumers perceive a negative review to be unfair, they experience empathetic concerns for the firm. In turn, these feelings of empathy trigger favorable responses to the focal firm, such as increased patronage intentions and purchases.
The current research extends findings in both the WOM and service literature streams to make several contributions. First, while the majority of WOM research finds that negative reviews lead to negative consumer responses, we highlight a novel boundary condition for this effect—the degree to which the review is seen to be unfair. In doing so, we build on an emerging body of work documenting that the cues within the review content can influence consumer responses ([30]; [59]; [89]). We find that, in addition to elements that enhance the credibility of the review (e.g., temporal cues, dispreferred markers, deviatory reviews, emotionality), perceived unfairness in reviews can enhance empathy, motivating subsequent helping and positive intentions toward the firm.
Second, we explore the role of empathy as a mechanism that enhances consumer support for firms, expanding the conceptualization of firms as being treated similarly to humanlike entities in the marketplace ([ 1]; [ 4]; [49]). The existing work on emotions motivating responses to brand/service failures has primarily focused on how attributions influence negative consumer emotions such as anger ([25]; [45]; [102]). By focusing on empathy, we build on work that has begun to explore the underrepresented role of positive emotions in driving responses to negative reviews ([82]). Moreover, our focus on the role of empathy extends the fairness literature more generally given that scholars have noted that its influence is underappreciated and often "lost" within the cognitive landscape of fairness ([12]). Similarly, work exploring justice motivations in the marketing literature has not considered the role of emotion (e.g., [112]).
Finally, from a managerial perspective, we show that the mere existence of an unfair negative review can enhance empathy for the reviewed firm, leading to subsequent support behaviors. Drawing on our conceptualization of empathy as the underlying process, we then provide clear managerial interventions (i.e., firm empathetic responses and employee spotlights) that can elicit greater empathy toward firms when dealing with consumer reviews.
Consumers frequently consult reviews before making purchase decisions because WOM is perceived to be credible ([29]; [53]; [62]). As such, consumer reviews can dramatically affect firm outcomes, including willingness to pay ([67]) and product sales ([31]; [79]). The overwhelming majority of research in this domain finds that positive reviews lead to higher firm sales by enhancing positive attitudes and expectations, whereas negative reviews lower firm sales, evaluations, and customer intentions (e.g., [31]; [79]; [97]; [100]; [113]). Importantly, positive and negative reviews appear to have differing degrees of impact on consumer responses. While positive reviews are more prevalent ([50]), negative reviews are better predictors of evaluations ([62]; [83]) and sales ([81]; [31]) due to the perception of negative information as diagnostic. For example, [83] finds that negative reviews are more likely to be credited to the performance of the product itself, whereas positive reviews are often credited to social norm dynamics.
However, research has begun to cast doubt on the notion that negative reviews unconditionally lead to negative firm outcomes (see Table 1). For instance, [21] proposed that unknown firms can benefit from negative WOM because negative reviews can raise product awareness. [ 5] found that when consumers are highly committed to a brand, they are more likely to counterargue and thus discount negative brand information. [82] observed that experiencing amusement in reaction to humorous negative reviews influences responses both positively and negatively, depending on the review intention. Research by [59] found that the use of dispreferred markers as a means of softening the negative information featured in reviews ("I'll be honest...") led to higher willingness to pay for products because this makes reviewers appear more credible and likable. Finally, work by [89] finds that effusive positive emotionality can increase evaluations for hedonic products but lower evaluations for utilitarian products. Previous research has also identified attributional elements in reviews that can positively or negatively affect the interpretation of the review by consumers, such as changing the focus from dispositional drivers to external causes of the reviewers' behavior ([30]; [72]; [83]). The current research proposes a novel factor that affects consumer interpretations of negative reviews—the degree to which the review is perceived to be unfair.
Graph
Table 1. Contribution Table: Review Characteristics and their Influence on Consumers.
| Source | Focus (Review Valence) | Process | Takeaway |
|---|
| Mizerski (1982) | Diagnostic weight given to negative information in reviews. (Negative, positive) | Cognitive: Negative reviews tend to be more diagnostic. Thus, attribute the negative reviews to the product and positive reviews to social norms. | Negative reviews are more diagnostic than positive reviews. |
| Herr, Kardes, and Kim (1991) | Effects of WOM (vs. printed information) on product judgments. (Negative, positive) | Cognitive: Accessible negative information can override the effect of WOM on product evaluations. | Positive WOM results in more positive product judgments if other negative diagnostic cues are unavailable. If negative diagnostic cues are available, then this information becomes more accessible and overrides positive WOM. |
| Ahluwalia, Burnkrant, and Unnava (2000) | Effects of brand commitment on the diagnosticity of negative information. (Negative, positive) | Cognitive: High- (vs. low-) commitment consumers will counterargue and discount negative brand information. | Low-commitment consumers show greater attitude change after exposure to negative information. High-commitment consumers show less attitude change due to discounting the diagnosticity of the negative information. |
| Laczniak, DeCarlo, and Ramaswami (2001) | Attributions of negative information to the reviewer vs. the product itself. (Negative) | Cognitive: Review configurations (consensus, distinctiveness, and consistency) can shift attributions from product experience to reviewer. | Low-consensus (e.g., not agreed on by other reviewers) and low-distinctiveness (e.g., the brand review is similar for all other brands in categories) messages are more likely to be attributed to the reviewer, mitigating their negative effect. |
| Berger, Sorensen, and Rasmussen (2010) | Effects of negative reviews for established vs. unknown brands. (Negative) | Cognitive: Negative reviews can increase awareness of unknown brands. | Negative reviews hurt established brands but can help unknown brands over time. This is because negative reviews increase awareness of the relatively unknown brand, which increases short-term sales. |
| Chen and Lurie (2013) | Effect of linguistic temporal cues in reviews. (Negative, positive) | Cognitive: Cues about a recent product experience change attribution from reviewer characteristics to product experience. | Temporal cues increase the value of positive reviews on product judgments because the review is more closely linked to the actual use of the product. |
| Hamilton, Vohs, and McGill (2014) | Effect of linguistic content aimed at softening negative information in reviews. (Negative, positive, and balanced) | Cognitive: Dispreferred markers moderate the effect of negative information by changing perceptions of the reviewer. | Dispreferred markers included in balanced reviews (both positive and negative information) lead to higher WTP. This is because the reviewer is seen as more credible and likable. |
| McGraw, Warren, and Kan (2015) | Effect of humor in negative reviews. (Complaints, praises) | Affective: Amusement changes the negative-review seriousness perception, making the review seem less negative while undermining redress or sympathy goals. | Humor can help when the complaint aims to create entertainment, warning, or impression management, but can be detrimental for redress or sympathy goals. |
| Kupor and Tormala (2018) | Effect of moderately positive reviews on persuasion. (Positive) | Cognitive: Deviation from the perceived default rating increased the perceived thoughtfulness and accuracy of the reviewer. | Positive reviews that deviate from the default rating result in increased persuasiveness of the review. |
| Reich and Maglio (2019) | Effect of recommendation including an admitted mistake on product choice. (Positive) | Cognitive: Mistakes shift perception of the reviewer as having more knowledge and expertise. | The presence of an admitted mistake from a previous purchase positively increases product choice. |
| Rocklage and Fazio (2020) | Effect of emotionality on positive review persuasion. (Positive) | Cognitive: Positive emotionality toward utilitarian products seems unhelpful and lowers choice. | Positive emotionality in reviews is persuasive when the review is for hedonic products, but not persuasive for utilitarian products. |
| Our research | Effect of unfairness in negative reviews. (Unfair negative, fair negative, and positive) | Affective: Unfair negative reviews elicit empathetic concern for the reviewed firm. | Unfair negative reviews lead to higher firm support intentions (e.g., purchase intentions, WTP) due to heightened empathy for the firm. |
1 Notes: WTP = willingness to pay. A detailed version of this contribution table appears in Web Appendix C.
Most marketing research examining fairness has conceptualized the construct according to equity theory, which considers whether the outcome and input ratios of exchange partners are equivalent ([ 3]; [34]). Such work tends to examine perceptions of equity from the first-person perspective (i.e., the perspective of the individual undergoing the exchange experience; [ 9]) and has explored social comparisons in price fairness ([35]; [64]; [69], [70]) and fair treatment in service recovery (e.g., [22]; [32]; [44]; [77]; [78]; [98]; [103]). This work mostly examines reactions to both the service failure itself and the service recovery ([23]), showing how providing consumers with the compensation they feel they deserve can help retain them postfailure ([95]; [101]). Some work has found that attributions of failure will change the need for compensation. For example, if there is no perceived inequity due to attributions of external causes for the failure (low firm control over the failure or low stability of the failure), compensation may not be necessary ([57]).
In the service marketing literature, justice has primarily been conceptualized as the imbalance in fairness felt throughout a service failure and the subsequent recovery process. This literature identifies a three-dimensional model of justice as an explanation for when complaining and satisfaction might occur during service failure and recovery ([55]; [96]; [101]). Work in this tradition highlights how fairness, in terms of allocations of outcomes and resources (distributive justice; [65]; [96]), processes and procedures (procedural justice; [77]; [81]), and customer treatment (interactional justice; [32]; [98]), can all influence consumer reactions to service failure. While this prior work has examined how elements of justice and attributions can lead to firsthand responses to service failures, we focus on the reactions of consumers who learn, secondhand, of negative information about a company's performance via reviews. There is a dearth of work exploring how third-party observers respond to the learning of another entity being wronged in consumption contexts.
The current research takes an approach to conceptualizing fairness that is highly relevant in contexts in which a third-party observer sees another entity being wronged in some way ([92]). Our definition of fairness focuses on whether the outcome an individual receives is judged to be deserved and justified based on the focal actor's previous actions ([63]; [76]). While some researchers use the terms "justice" ([76]) or "deservingness" ([43]), we use the term "fairness" because this is how laypeople interpret these constructs.[ 7] This view of fairness is rooted in just-world theory, which proposes that a "justice motive" drives people to restore a sense of justice ([41]; [74]; [76]). In a just world, people get what they deserve and deserve what they get. The theory further proposes that people are motivated to defend their just-world beliefs ([90]). As a result, when people observe another entity being treated in a manner that is unfair (i.e., expectations of fairness are violated), people often seek ways to restore fairness. Work by [106] demonstrates that people will try to restore justice even when they have no relationship to the wronged party and when they stand to gain nothing. This work suggests that people are driven to restore justice not through self-interest, but due to a predisposition of sensitivity to unfairness ([36]). Expounding on this, we propose that when a review is perceived to be unfair, this leads to positive consumer actions toward the firm in ways that allow for the restoration of fairness.
Although research has not directly tested these predictions, some work stemming from attribution theory aligns with our theorizing. Attribution theories suggest that people's causal attribution of events leads to both emotional responses and behavioral outcomes ([110]). In the marketing context, for example, attributions to the firm itself (vs. external factors) for service failures lead to increased complaining behaviors ([46]). Moreover, work has shown that attributing an outcome (i.e., falling) on the dimension of controllability (i.e., being ill vs. being drunk) can lead to feelings such as pity or anger, which subsequently motivate either supportive or nonsupportive responses ([84]; [111]). The majority of work on attributions in service failure contexts supports the notion of a pathway through anger ([25]; [47]) leading to retaliatory behavior ([ 7]; [61]). To our knowledge, existing work has not examined how attributions may influence positive reactions to firms, such as increased empathy or compassion.
Thus, one could argue that, in our research, unfair negative reviews are seen as uncontrollable by the firm, and it is these perceptions of low controllability that lead to supportive responses to the firm. However, in our inquiry, we focus not on the dimensions of attributions made, but on how perceptions of unfairness (regardless of controllability) can elicit positive emotional consequences of empathy. Importantly, we show that our effects emerge even under conditions where the outcome is under the firm's control (Study 2). We also show that unfair negative reviews naturally evoke empathy, which motivates supportive actions toward the firm (Study 3).
Inherent in our conceptualization is the notion that consumers recognize unfair reviews and, in response, show a desire to restore fairness. As a preliminary test of this underlying assumption, we ran an exploratory study with an online panel (n = 73; Web Appendix D). Notably, 43% of participants spontaneously reported that an unfair negative review led to an improved view of the firm, and 35% felt motivated to support the firm. One insight derived from this study is that perceived unfairness can activate a desire to support the focal firm, which is consistent with the notion that people are often motivated to defend others after witnessing unfairness and feeling compassion ([76]). When expectations of fairness are violated, people seek ways to restore a sense of balance—for instance, by helping the person who has been wronged ([16]), compensating the victim ([75]), or choosing ethical product options that support those who have been mistreated ([112]). These effects primarily occur when people can empathize or identify with the victim in some way ([ 6]). We propose that perceptions of the unfairness of a firm's treatment by other customers will motivate reparative actions such as more favorable behaviors and patronage intentions as a means to restore a sense of fairness. Formally,
- H1: Unfair negative reviews elicit greater firm support relative to comparison reviews (i.e., fair negative reviews).[ 8]
We define empathy as a vicarious emotional response to observing another person's situation that is marked by the ability to feel warmth, compassion, and concern for others ([17]; [68]). Empathetic responding involves viewing another's situation from that person's perspective and understanding the other person's cognitive-emotional experience as if it were affecting the observer directly ([56]). Empathy has been linked to altruism and various prosocial behaviors (e.g., [10]; [33]; [107]). Specifically, empathy has been shown to activate moral concern for others ([16]), especially when the target has been wronged in some way ([66]). Furthermore, such moral concern can motivate reparative actions on the part of the observer ([15]; [91]). This sensitivity to outcomes for others can be a powerful motivator to restore a sense of fairness by compensating the victim (e.g., [16]; [27]; [38]).
We build on this previous work demonstrating that empathy leads to positive and helpful responses to other people who were wronged by proposing that the same might be true for responses to firms (see also [71]). We draw on previous work showing that empathetic responses are heightened under conditions where unfair outcomes become salient (e.g., [16]; [58]) and propose that when the consumer perceives a negative review as unfair, this naturally activates empathy toward the firm. We further predict that this increased empathy will trigger a desire to restore fairness by responding in ways that support the firm. Formally,
- H2: Feelings of empathy mediate the tendency to exhibit increased support for a firm that received an unfair negative review (vs. a fair negative review).
To provide evidence for the role of empathy as the underlying process, we employ statistical mediation and moderation approaches. Specifically, our conceptual framework predicts that unfair negative reviews naturally elicit empathy, which then motivates positive responses toward the firm (Study 2). Following from this, we also propose that conditions that reduce reviewers' ability to experience an empathetic response toward the firm (e.g., focusing on the reviewer's experience instead of the firm's) should reduce positive responses to unfair negative reviews (Study 3), but that such an effect would not occur under conditions in which empathy is not the mechanism underlying the response (i.e., fair negative and positive reviews). In addition, a key argument within our conceptualization is that perceived unfairness in negative reviews enables empathy from consumers. Thus, firm interventions that provide consumers with review elements that increase empathy for the firm should lead to more favorable consumer responses to reviews that do not naturally evoke empathy (i.e., fair negative and positive reviews; Studies 4 and 5). For our conceptual model, see to Figure 1.
Graph: Figure 1. Theoretical framework.Notes: We control for reviewer rudeness and review length.
Six studies test our theoretical framework suggesting that perceptions of unfairness in negative reviews can lead to positive consumer responses due to feelings of empathy for the reviewed firm (for a summary of results, see Table 2). First, using behavioral measures, we show that consumer support for a firm following an unfair negative review is higher than that following a fair negative review and can become akin to the support generated by a positive review (Studies 1a and 1b). We further test our conceptual model by showing that empathy mediates the effects of unfair negative reviews on responses to the firm. We also provide a unique contribution to existing research by identifying conditions under which unfair negative reviews can activate higher degrees of empathy and, subsequently, higher patronage intentions. Specifically, we show that highly unfair negative reviews (vs. moderately unfair negative reviews; Study 2) lead to more favorable purchase intentions than do positive reviews. We then show that unfair negative reviews naturally evoke empathy similar to what is observed when participants are asked to take the perspective of the firm and more positive than when they are asked to take the perspective of the reviewer (Study 3). In the final two studies, we demonstrate how managers may be able to harness empathy and increase positive responses to the firm resulting from reviews that are not naturally empathy-evoking. Study 4 illustrates that responding to reviews in an empathy-evoking manner (i.e., highly empathetic by using first-person language and employee profile pictures vs. neutral firm response) leads to greater empathy and increased positive responses to the firm for fair negative and positive reviews. Finally, in Study 5, we show that taking the perspective of an employee through an employee spotlight increases empathy for the firm following fair negative and positive reviews, also leading to more favorable purchase intentions.[ 9]
Graph
Table 2. Summary of Results by Study Condition.
| Study 1: Baseline Effect |
| Study 1a: 4ocean; N = 88, 58% female, Mage = 20.8 years, undergraduate students | Study 1b: Bottle; N = 223, 57% female, Mage = 20.7 years, undergraduate students |
| Fair Negative(n = 31) | UnfairNegative(n = 28) | Positive(n = 29) | | FairNegative(n = 74) | UnfairNegative(n = 74) | Positive(n = 75) |
| Fairness | 5.70 (.91) | 2.55 (1.30) | 5.34 (1.02) | Purchase intentions | 3.66 (1.30) | 4.78 (1.23) | 4.61 (1.41) |
| Donation ($) | 6.58 (7.36) | 13.57 (6.05) | 12.76 (6.35) | Empathy | 3.12 (1.27) | 4.95 (1.26) | 3.82 (1.42) |
| Rudeness | 2.58 (1.36) | 5.54 (1.00) | 2.72 (1.62) | Bottle choice | 1.14 (1.19) | 1.55 (1.30) | 1.12 (1.29) |
| Main finding: Unfairness in negative reviews leads to positive consumer responses such that an unfair negative review can lead to responses that are as positive as the ones following positive reviews. |
| Study 2: Mediation by Empathy for the Firm |
| Sushi scenario; N = 312, 51% female, Mage = 20.9 years, undergraduate students |
| Fair Negative(n = 78) | Moderately Unfair Negative(n = 79) | Highly Unfair Negative(n = 77) | Positive(n = 78) |
| Empathy | 3.44 (1.69) | 5.43 (1.48) | 5.85 (1.39) | 4.46 (1.57) |
| WTP | $38.78 (16.69) | $45.65 (18.66) | $52.94 (20.56) | $45.55 (26.08) |
| Rudeness | 3.17 (1.40) | 4.70 (1.28) | 4.83 (1.25) | 4.28 (1.24) |
| Main finding: While unfair negative reviews can lead to consumer responses that are as positive as those observed following positive reviews, highly unfair negative reviews can lead to responses that are even more positive than those following positive responses. Feelings of empathy for the firm drive this effect. |
| Study 3: Moderation by Empathy Manipulation |
| Panini scenario; N = 615, 46% female, Mage = 40.4 years, MTurk |
| Fair Negative | Unfair Negative | Positive |
| EmpathyManipulation | Employee Perspective(n = 67) | Reviewer Perspective(n = 71) | Control (n = 71) | Employee Perspective(n = 68) | Reviewer Perspective(n = 66) | Control (n = 64) | Employee Perspective(n = 67) | Reviewer Perspective(n = 70) | Control (n = 71) |
| Evaluation | 3.62 (1.76) | 3.24 (1.78) | 3.57 (1.68) | 4.66 (1.75) | 3.75 (1.66) | 4.63 (1.73) | 6.26 (.93) | 6.37 (.72) | 6.11 (1.20) |
| Rudeness | 4.12 (1.80) | 4.04 (1.83) | 4.27 (2.05) | 5.40 (1.47) | 4.77 (1.58) | 5.19 (1.63) | 1.85 (1.68) | 2.30 (1.98) | 2.45 (2.02) |
| Main finding: Suppressing participants' ability to experience empathy leads to less positive consumer responses following exposure to an unfair negative review. |
| Study 4: Moderated Mediation Between Review Type and Firm Response Type Through Empathy |
| Garden tools scenario; N = 599, 44% female, Mage = 38.41 years, MTurk |
| Fair Negative | Unfair Negative | Positive |
| Firm ResponseType | Neutral(n = 101) | Empathetic(n = 101) | Neutral(n = 101) | Empathetic(n = 98) | Neutral(n = 99) | Empathetic(n = 99) |
| Empathy | 4.13 (2.39) | 5.47 (2.16) | 6.73 (1.89) | 7.09 (1.63) | 4.17 (2.51) | 5.87 (2.28) |
| Purchase intentions | 4.13 (1.71) | 5.10 (1.54) | 5.90 (1.14) | 6.04 (1.00) | 5.48 (1.05) | 6.15 (.81) |
| Rudeness | 3.45 (1.69) | 3.47 (1.79) | 5.20 (1.71) | 5.17 (1.53) | 2.65 (1.89) | 2.84 (1.97) |
| Main finding: Responding to the review in a highly empathetic way can evoke empathy in the consumer that would not naturally have an increase in empathy (i.e., fair negative and positive reviews) to the same extent as the exposure to a review that naturally evokes higher empathy (i.e., unfair negative reviews). |
| Study 5: Moderated Mediation Between Review Type and Employee Spotlight Through Empathy |
| Barista scenario; N = 642, 46% female, Mage = 38.2 years, MTurk |
| Fair Negative | Unfair Negative | Positive |
| EmployeeSpotlight | Absent(n = 106) | Present(n = 108) | Absent(n = 106) | Present(n = 106) | Absent(n = 107) | Present(n = 109) |
| Empathy | 4.36 (2.47) | 5.40 (2.12) | 6.86 (1.49) | 6.76 (1.79) | 4.80 (2.10) | 5.55 (2.20) |
| Voucher value | $4.07 (1.98) | $5.03 (1.55) | $5.83 (1.17) | $6.03 (1.01) | $5.42 (1.23) | $6.23 (.83) |
| Main finding: Similar to Study 4, employee spotlights can evoke empathy in the consumer that would not naturally see an increase in empathy and positive consumer response for these review types. |
2 Notes: WTP = willingness to pay.
Study 1a and Study 1b examine consumer responses to the organization (a nonprofit [Study 1a] and a for-profit [Study 1b]) following exposure to an unfair negative review compared with both a fair negative review and a positive review using consequential measures. We anticipate that consumers responding to an unfair negative review will exhibit more positive responses compared with consumers responding to a fair negative review. In addition to a fair-negative-review condition, we include a positive review as an exploratory comparison condition. This study also casts doubt on rudeness as an alternative explanation for the observed effects by showing that the effects occur even while statistically controlling for perceptions of rudeness.
Eighty-eight undergraduate students took part in this experiment in exchange for course credit (58% female; Mage = 20.8 years). The experiment used a one-factor, three-level (review type: fair negative vs. unfair negative vs. positive) between-participants design. The dependent variable was the donation amount to the organization.
Participants were assigned to one of three conditions that all described a nonprofit organization dedicated to removing trash from waterways with funds raised through selling upcycled bracelets (for all review manipulations across studies, see the Appendix). In the fair negative review, the reviewer complained about not receiving the bracelets and experiencing difficulty reaching customer service. In the unfair negative review, the customer complained about having to wait three days for delivery, despite receiving an apology, a refund, and overnight shipping. In both negative-review conditions, the review ended with "I purchased for the cause—the whole experience sucked! Don't buy from them." In the positive-review condition, the review ended with "I purchased for the cause" (for the full stimuli, see Web Appendix E).
As a manipulation check, participants rated the extent to which they perceived the review to be fair using four seven-point scales: "fair," "deserved," "justified," and "reasonable" (1 = "strongly disagree," and 7 = "strongly agree"; α =.98).[10] Participants were then informed that three participants would be randomly selected to execute a real purchase. They learned that, if selected, they would be given $20 and could give a portion to the organization if they wished. Our dependent measure was the amount allocated to the nonprofit. Finally, participants rated how rude the reviewer was by indicating their agreement with the statement, "The review was rude" (1 = "strongly disagree," and 7 = "strongly agree").
There was a main effect of review type on perceived fairness (F( 2, 85) = 72.72, p <.001, =.63). Specifically, the unfair negative review was perceived as less fair (M = 2.55, SD = 1.30) than the fair negative review (M = 5.70, SD =.91; t(85) = 11.14, p <.001) and the positive review (M = 5.34, SD = 1.02; t(85) = 9.72, p <.001). The latter two conditions were not significantly different from each other (t(85) = 1.27, p >.20). These results support the validity of our fairness manipulation.
A generalized linear model (GLM) analysis controlling for review rudeness revealed a main effect of review type on purchase amount (F( 2, 84) = 6.47, p <.01, =.13). As we anticipated, donation amounts were lower following the fair negative review (M = $6.58, SD = $7.36) versus the unfair negative review (M = $13.57, SD = $6.05; t(85) = 4.04, p <.001) and the positive review (M = $12.76, SD = $6.35; t(85) = 3.60, p =.001). The latter two conditions did not significantly differ (t < 1).
Study 1a uses a behavioral measure to demonstrate that unfair negative reviews can lead to more positive consumer responses than fair negative reviews. In this study, unfair negative reviews lead to similarly positive responses as positive reviews. Importantly, we show that the perceived rudeness of the review does not appear to drive the observed effect, given that the results emerge even when statistically controlling for rudeness. Studies 1b replicates these findings in a for-profit context.
Two hundred twenty-three undergraduate students were recruited from marketing classes to take part in exchange for a product raffle (57% female; Mage = 20.7 years). As in Study 1a, Study 1b utilized a one-factor, three-level (review type: fair negative vs. unfair negative vs. positive) between-participants design.
Participants were randomly assigned to read one of three versions of a review that described a previous purchase from a reusable water bottle company. In the fair negative review, the reviewer complained about not receiving an answer for two weeks after contacting customer service. In the unfair negative review, the customer complained about not being able to reach customer service on Christmas Eve. In the positive-review condition, the review mentioned receiving an answer within 24 hours of reaching the company. The positive review also ended with "If you have a deadline during the week, this company works. Good experience." This study kept review length constant and asked the dependent variable immediately after the review (for the full stimuli, see Figure 2). Participants rated their purchase intentions (1 = "unlikely/improbable," and 7 = "very likely/very probable"; r =.91) and, afterward, their level of empathy for the company using three scales: "empathy," "sympathy," and "compassion" (1 = "not at all," and 9 = "very much"; α =.88; adapted from [51]]). Participants were then told that, as a token of gratitude, they would be entered into a raffle to receive either a bottle worth $25 from the reviewed company or a $15 gift card. They would receive three raffle tickets and could allot their tickets between the two raffles.
Graph: Figure 2. Study 1b: Review type manipulation.Notes: The review was rated whether it was "fair," "deserved," "justified," and "reasonable" (1 = "strongly disagree," and 7 = "strongly agree")
An analysis of variance revealed a main effect of review type on purchase intentions (F( 2, 220) = 15.82, p <.001, =.13). Purchase intentions were lower following the fair negative review (M = 3.66, SD = 1.30) compared with the unfair negative review (M = 4.78, SD = 1.23; t(220) = 5.21, p <.001) and the positive review (M = 4.61, SD = 1.41; t(220) = 4.44, p <.001). The latter two conditions were not significantly different from each other (t < 1).
Results revealed a main effect of review type on empathy (F( 2, 220) = 36.25, p <.001, =.25). Empathy was higher for unfair negative reviews (M = 4.95, SD = 1.26) compared with the fair negative reviews (M = 3.12, SD = 1.27; t(220) = 8.44, p <.001) and positive reviews (M = 3.82, SD = 1.42; t(220) = 5.22, p <.001). The latter two conditions were also significantly different from each other (t(220) = 3.24, p =.001).
Results revealed a main effect of review type on the choice of raffle tickets for the bottle (F( 2, 220) = 2.82, p =.06, =.03). Choice of the reviewed bottle tickets was higher after the unfair negative review (M = 1.55, SD = 1.30) compared with the fair negative review (M = 1.14, SD = 1.19; t(220) = 2.02, p <.05) or the positive review (M = 1.12, SD = 1.29; t(220) = 2.10, p <.05). The latter two conditions did not differ in terms of product choice (t < 1).
Replicating Study 1a, this study found that the unfair negative review led to more positive purchase intentions and greater choice of the reviewed product as compared with the fair-negative-review condition. Interestingly, in Study 1a, we found that unfair negative reviews resulted in firm support on par with positive reviews, while Study 1b shows that unfair negative reviews resulted in greater firm support when we look at the behavioral choice measure. As discussed in footnote 2, we do believe that this is likely due to the calibration of the positive reviews. However, this does not take away from our focal prediction and finding that unfair negative reviews can increase supportive consumer behaviors compared with fair negative reviews.
Studies 1a and 1b found that unfair negative reviews can lead to responses that are similar to or more positive in favorability to those arising from positive reviews. However, one interesting question is: What are the conditions under which responses to unfair negative reviews can be more favorable than positive reviews? According to just-world theory, unfairness creates an imbalance that people feel motivated to resolve ([27]; [37]). One possibility is that the greater the degree of unfairness, the greater the restorative response. This reasoning suggests that reading a review that activates perceptions of high unfairness (vs. moderate unfairness) could increase the favorability of consumer responses—to a point where those responses might even become more favorable than those arising from positive reviews. In this study, we examine the role of the degree of unfairness evoked by the review in determining consumer responses. We also cast doubt on the alternative explanation that the effect is driven by perceptions of controllability (i.e., the unfair conditions are less controllable by the firm; [110]) by demonstrating that our effects emerge even when unfair negative reviews are given in situations under the firm's control. Finally, we measure empathy and show that it mediates the focal effect.
Three hundred twelve undergraduate students participated in exchange for credit (51% female; Mage = 20.9 years). The experiment was a one-factor, four-level (review type: fair negative vs. moderately unfair negative vs. highly unfair negative vs. positive), between-participants design. The dependent variable was the willingness to pay.
Participants read an online review for a new local (fictitious) sushi restaurant. In all conditions, the reviewer described the relative enjoyment of their meal. In the positive-review condition, the customer described leaving the restaurant feeling full and recommended the restaurant. In the fair-negative-review condition, the reviewer described leaving the restaurant feeling hungry and finished the review by "NOT" recommending the restaurant. In both unfair-negative-review conditions, the reviewer did "NOT" recommend the restaurant. In addition, the reviewer expressed disappointment over the unavailability of the toro sashimi listed on the menu and, consequently, showed disregard for the restaurant. In the moderately-unfair-negative-review condition, the item was unavailable because it was sold out at the pier. In the highly-unfair-negative-review condition, the item was unavailable in the restaurant (despite being available at the pier) because the chef had heard that it made consumers at other restaurants sick and consequently took it off the menu (for the full stimuli, see Web Appendix F). These conditions were pretested to show that the highly unfair negative condition was perceived to be significantly more unfair than the moderately unfair negative condition (p <.05; for detailed pretest results, see Web Appendix F).
After reading the review, participants rated their feelings of empathy toward the restaurant using the same nine-point scale as in Study 1b (α =.91). Participants then reported their willingness to pay for a dinner for two people (excluding drinks) at the restaurant using a sliding scale anchored at $0 and $200. Finally, participants rated the reviewer rudeness (same measure as Study 1a).
A GLM analysis controlling for rudeness revealed a significant effect of review type on willingness to pay (F( 3, 307) = 5.55, p <.001, =.05; Figure 3). Participants reported a lower willingness to pay after viewing the fair negative review (M = $38.78, SD = $16.69) compared with the moderately unfair negative review (M = $45.65, SD = $18.66; t(308) = 2.07, p <.05), the highly unfair negative review (M = $52.94, SD = $20.56; t(308) = 4.24, p <.001), and the positive review (M = $45.55, SD = $26.08; t(308) = 2.03, p <.05). The highly unfair negative review led to greater willingness to pay than did the moderately unfair negative review (t(308) = 2.19, p <.05). The moderately unfair negative review led to similar willingness to pay as the positive review (t < 1). Finally, participants were willing to pay significantly more in the highly-unfair-negative condition versus the positive-review condition (t(308) = 2.21, p <.05).
Graph: Figure 3. Study 2: Willingness to pay as a function of review type.*p <.05.**p <.01.***p <.001.Notes: Error bars = ±1 SEs.
A GLM analysis controlling for rudeness revealed a significant effect of review type on empathy for the restaurant (F( 3, 307) = 23.39, p <.001, =.19) and a significant effect of the covariate F( 1, 307) = 9.94, p <.01). The highly unfair negative review (M = 5.85, SD = 1.39) led to marginally more empathy than the moderately unfair negative review (M = 5.43, SD = 1.48; t(308) = 1.72, p =.09). In addition, the highly unfair negative review led to significantly more empathy than the positive review (M = 4.46, SD = 1.57; t(308) = 5.64, p <.001) and the fair negative review (M = 3.44, SD = 1.69; t(308) = 9.76, p <.001). The moderately-unfair-negative-review condition also led to more empathy than the fair negative review (t(308) = 8.10, p <.001) and the positive review (t(308) = 3.95, p <.001). These two latter conditions were significantly different from each other (t(308) = 4.14, p <.001).
As a test for our proposed process explanation, we ran the following test of indirect effect: Review Type → Empathy → Willingness to Pay, using dummy-coded variables as predictors (using the fair negative condition as the reference category: D1 = moderately unfair negative, D2 = highly unfair negative, D3 = positive) and controlling for rudeness (PROCESS Model 4). The results revealed three significant indirect effects for the dummy-coded variables representing the highly-unfair-negative (b = 5.10, SE = 1.93; 95% confidence interval [CI95] = [1.63, 9.07]), moderately-unfair-negative (b = 4.12, SE = 1.65; CI95 = [1.30, 7.74]), and positive (b = 1.94, SE =.97; CI95 = [.27, 4.00]) review conditions. Simply put, these results are consistent with an explanation in which, over and above the effects of reviewer rudeness, higher willingness to pay in the unfair negative review condition compared with the fair negative review can be attributed to consumers' heightened feelings of empathy for the firm (for detailed results, see Web Appendix F).
Study 2 provides insight into when unfair negative reviews might result in firm outcomes that are more favorable than (vs. similar to) those in response to positive reviews. We demonstrate that the degree of perceived unfairness amplifies the positivity of responses to the firm. In particular, highly unfair negative reviews resulted in more favorable consumer responses compared with fair negative, moderately unfair negative, and positive reviews. This is theoretically important because it also rules out an alternative explanation for the results of Study 1a—that the perception of unfairness occurred because of a discounting of the negative information completely, leading to a null effect ([73]). We instead observed responses that suggest that consumers are attending to the unfair negative information, which can lead to even more favorable responses than positive reviews.
Furthermore, Study 2 tested for evidence of empathy as our underlying process. The results revealed that empathy does indeed mediate the observed effects. Importantly, our results remain significant even while we control for perceived rudeness, suggesting that rudeness does not adequately account for the observed results. In Study 3, we further examine the role of empathy by manipulating participants' ability to experience empathy toward the firm.
Study 3 provides additional evidence for empathy as the underlying mechanism by demonstrating that positive consumer responses to unfair negative reviews are moderated by participants' ability to experience empathy. Previous work has found that state empathy can be manipulated through a perspective-taking task ([19]; [99]; [104]). In the context of consumer reviews, readers can adopt either the perspective of the firm's employees or the perspective of the reviewer. Arguably, taking the perspective of the firm's employees should induce empathy toward the firm, while taking the view of the reviewer should not. If our prediction that unfair negative reviews naturally evoke greater empathy toward the firm is correct, we would expect that adopting the perspective of the employees (i.e., empathizing with the firm) when evaluating unfair negative reviews should result in similarly favorable responses response as a control condition, where no perspective is prompted. However, instructions to take the reviewer's perspective should reduce the ability to empathize with the firm and lead to less favorable consumer responses to unfair negative reviews.
Importantly, in this context, the perspective-taking prompts should not be effective in changing consumer responses when the review is fair—negative or positive. This is because fair negative reviews do not have content that would elicit empathy toward the firm, and whether the participant takes the reviewer's or the employee's perspective does not remove the instances of firm failure. Similarly, there is no room for movement on empathy for the positive condition because neither the reviewer nor the firm has done anything warranting an other-than-positive response. Thus, we would expect the effects of perspective-taking manipulations to be muted in those conditions. This study also kept the review length (i.e., word count) consistent across conditions to increase experimental control and presented the focal review along with another review to increase realism. Again, we measured and controlled for reviewer rudeness.
Six hundred fifteen participants recruited via Amazon's Mechanical Turk (MTurk) took part in this study (46% female; Mage = 40.4 years), which had a 3 (review type: fair negative vs. unfair negative vs. positive) × 3 (empathy manipulation: employee perspective vs. reviewer perspective vs. control) between-participants design. The dependent variable was restaurant evaluations.
Adapting a procedure from Batson and colleagues ([15]; [18]), we gave participants in the employee's-perspective condition the following instructions: "Try to imagine how the restaurant employees feel about what is described. Try to imagine how they were affected and feel as a result." In the reviewer's-perspective condition, participants were instructed, "Try to imagine how the reviewer feels about what is described. Try to imagine how he/she was affected and feels as a result." The control condition prompted participants to "Try to imagine what is described in as much detail as possible. Try to imagine what is going to happen as a result." It is important to note that if unfair negative reviews naturally evoke empathy, the manipulation is worded in such a way that the employee-perspective and control conditions should yield similar results.
Next, participants read the focal restaurant review. Across the two negative-review conditions, the customer recalled not being able to get his panini warmed because the kitchen had started cleaning either 55 minutes before (fair condition) or 5 minutes before (unfair condition) closing time. In the positive-review condition, the customer recalled being able to get the panini warmed even though the kitchen was about to start cleaning (for stimuli and pretests, see Web Appendix G). The review length was kept constant. Participants evaluated the restaurant on three seven-point scales, "negative–positive," "bad–good," and "dislike–like" (α =.98). Then, on a different page, participants rated the reviewer rudeness (same measure as in previous studies).
Using dummy-coded multicategorical predictors while controlling for reviewer rudeness, we regressed the restaurant evaluation on review type (with the unfair negative review condition as the reference category; X1 = fair negative, X2 = positive) and empathy manipulation (using the employee-perspective condition as the reference category; W1 = control, W2 = reviewer-perspective condition; PROCESS Model 1). Results revealed a significant interaction between the positive-review variable and the reviewer-perspective dummy variable (β =.13, b =.77, SE =.36; t(605) = 2.16, p <.05), as well as two main effects of the review types (fair negative: β = −.18, b = −.75, SE =.25; t(605) = 2.93, p <.01; positive: β =.59, b = 2.41, SE =.28; t(605) = 8.68, p <.001). We also observed main effects of the reviewer-perspective dummy variable (β = −.19, b = −.76, SE =.25; t(605) = 3.02, p <.01) and of the rudeness covariate (β =.26, b =.23, SE =.03; t(605) = 6.85, p <.001; Figure 4).
Graph: Figure 4. Study 3: Patronage intentions as a function of negative-review fairness and perspective manipulation.*p <.05.**p <.01.***p <.001.Notes: Error bars = ±1 SEs.
A simple effects analysis controlling for rudeness and using a least significant difference test shows that, overall, the positive review led to more positive evaluations (M = 6.25, SD =.97) than the unfair negative review (M = 4.35, SD = 1.75; p <.001) and the fair negative review (M = 3.48, SD = 1.74; p <.001). The latter two conditions were significantly different from each other (p <.001). More importantly, a simple main effect of the empathy manipulation emerged only for those in the unfair-negative-review condition (F( 2,605) = 6.09, p <.01). As we anticipated, for those exposed to the unfair negative review, adopting the reviewer perspective resulted in lower restaurant evaluations (M = 3.75, SD = 1.66) than did adopting the employee perspective (M = 4.66, SD = 1.75; p <.01) or being in the control condition (M = 4.63, SD = 1.72; p <.01). The latter two conditions did not significantly differ (p >.95). This crucial result suggests that unfair negative reviews naturally evoke responses akin to taking an empathetic perspective toward the firm.
By manipulating participants' ability to empathize with either the reviewer or the firm's employees, Study 3 provides support for empathy as the process underlying our effects using a moderation approach. Importantly, one main postulation is that unfair negative reviews naturally elicit an empathetic response toward the firm. Thus, in finding no differences between the employee's-perspective and control conditions, this study offers support for the proposition that unfair negative reviews naturally elicit empathetic responses,[11] which motivate greater firm support. However, focusing on the reviewer's (vs. the firm) perspective diminished this natural tendency to empathize with the service provider among those in the negative-review condition. Thus, when the ability to empathize with the firm is thwarted in some way, unfair negative reviews no longer lead to positive responses. In previous studies, it could be argued that elements of the design, such as measuring the dependent variable right after the review, having potential differences in review length, or perceived rudeness of the review could play a role in driving the observed effects. This study casts doubt on these alternative explanations by measuring the dependent variable right after the review, keeping the review length constant across conditions, and controlling for reviewer rudeness.
One might wonder why the employee's-perspective manipulation did not lead to an increase in responses to the firm for either the fair-negative or the positive-review condition. Given that no content within the reviews would result in greater empathy toward the firm, we would not expect this perspective manipulation to increase empathy or firm evaluations. For example, in the fair-negative-review condition, the employees are still clearly at fault for the negative review—they had decided to start closing early, so taking the employee's perspective should not have made the review seem any less fair. Thus, it is not surprising that the empathy manipulation did not influence responses to the fair negative or positive review. In the subsequent studies, we examine managerially relevant manipulations to increase empathy in the context of fair negative and positive reviews (i.e., reviews that do not naturally evoke empathy). If empathy can result in more positive responses to the firm, finding a way to increase empathetic responses toward reviews that do not evoke them naturally should be beneficial.
Study 4 tests a managerial intervention that can enhance empathetic responses to reviews: firm responses. Social media strategists often suggest that using a person to represent a firm is more engaging to consumers than just a firm avatar. The reasoning behind this managerial wisdom is that providing person-like cues such as the first-person language in firm communication or a profile picture of the corresponding individual may enhance perceptions of similarity and, thus, empathetic concern ([60]). We expect that firms facing negative reviews can activate more consumer empathy by replying in a way that activates consumer empathy (i.e., by speaking as a firm employee instead of as the firm itself) as opposed to communication that does not activate consumer empathy. Using a moderation approach, we show that a managerially relevant intervention can increase empathy for those reviews that are not naturally empathy-evoking. If increased empathy can have a positive effect on firm responses, then finding ways to increase empathetic response should allow for positive firm responses regardless of review type. Specifically, we suggest that increased empathy will lead those exposed to fair negative and positive reviews to report higher levels of both empathy and positive firm support intentions. However, because the baseline effect of unfair negative reviews is increased empathy, an additional empathy boost is unlikely to increase empathy perceptions for consumers exposed to the unfair-negative-review condition.
Five hundred ninety-nine participants recruited through MTurk participated in this experiment in exchange for monetary compensation (44% female; Mage = 38.4 years). The experiment used a 3 (review type: fair negative vs. unfair negative vs. positive) × 2 (firm response type: neutral vs. empathetic) between-participants design. The dependent variable was purchase intentions toward the firm described in the review.
We asked participants to read an online review from a consumer who had recently ordered a set of garden tools from a hardware store. Constant across the conditions, the consumer described how he received the tools in good condition but noted that the selection was limited. In the positive review, the customer gave a four-star review and praised the tools. In the fair-negative-review condition, the customer gave a one-star review and added a complaint about the low quality after using the tools. In the unfair-negative-review condition, the customer gave a one-star review and complained about not being able to return the tools to the store in the fall after using them to do garden work. Review length was held constant across conditions. Each review was presented with a firm reply prompting a follow-up with the customer. In the neutral-response condition, the response was generic (i.e., featuring a generic logo image and no details regarding the review) and directed the customer to a generic customer-service email for follow-up. In the empathetic response condition, the response was instead more apologetic and personable (i.e., featuring a person image and providing details regarding the review). The manipulation was presented in a manner that resembled a real review page (see Web Appendix H).
Participants once again rated their feelings of empathy toward the firm (α =.97); the extent to which they would consider supporting the focal firm if they were looking to purchase garden tools, using two seven-point bipolar scales ("unlikely–likely" and "improbable–probable"; α =.93); and the extent to which they perceived the review to be rude (as in previous studies). The results presented next control for rudeness.
We regressed purchase intentions on review type (dummy-coded multicategorical predictor using the unfair-negative-review condition as the reference category; X1 = fair negative, X2 = positive) and firm response type (coded as 0 = neutral, 1 = empathetic; PROCESS model 1). Results revealed two significant interactions between the response type and the two review-type dummy variables (fair negative review × response type: β =.13, b =.50, SE =.25; t(592) = 2.02, p <.05; positive review × response type: β =.22, b =.83, SE =.25; t(592) = 3.38, p <.001). We also observed main effects of the positive-review dummy variable (β = −.50, b = −1.54, SE =.18; t(592) = 8.53, p <.001) and of the rudeness covariate (β =.19, b =.13, SE =.03; t(592) = 5.60, p <.001; for details, see Figure 5).
Graph: Figure 5. Study 4: Purchase intentions and empathy for the firm as functions of review type and firm response.*p <.05.**p <.01.***p <.001.Notes: Error bars= ±1 SEs.
We further probed these results with a series of contrasts. In the neutral-response condition, we replicated our typical pattern of results where, compared with the fair negative review (M = 4.13, SD = 1.71), purchase intentions were higher in the unfair-negative-review (M = 5.90, SD = 1.14; t(593) = 10.03, p <.001) and positive-review (M = 5.48, SD = 1.05; t(593) = 7.66, p <.001) conditions. These latter two conditions were significantly different from one another (t(593) = 2.32, p <.05). In the empathetic response condition, the fair negative review condition led to lower purchase intentions (M = 5.10, SD = 1.54), than the unfair negative review (M = 6.04, SD = 1.00; t(593) = 5.25, p <.001) and positive review (M = 6.15, SD =.81; t(593) = 5.91, p <.001). These latter two conditions were not significantly different from one another (t < 1). Stated differently, as anticipated, we observed significantly higher purchase intentions between the empathetic and neutral firm responses for the fair negative (t(593) = 5.53, p <.001) and positive reviews (t(593) = 3.75, p <.001), but not for the unfair negative review (t < 1).
We regressed empathy ratings on review type and firm response type (PROCESS Model 1; same coding as previously). Results revealed two significant interactions between the response type and the two review-type dummy variables (fair negative review × response type: β =.19, b = 1.22, SE =.39; t(592) = 3.13, p <.01; positive review × response type: β =.15, b =.96, SE =.39; t(592) = 2.47, p =.01). We also observed main effects of the fair negative review dummy (β = −.23, b = −1.17, SE =.30; t(592) = 3.94, p <.001), positive review dummy (β = −.32, b = −1.65, SE =.28; t(592) = 5.81, p <.001), and the rudeness covariate (β =.45, b =.54, SE =.05; t(652) = 12.01, p <.001). We probed these results with a series of contrasts. In the neutral-response condition, we replicated our typical pattern of results where the unfair negative review (M = 6.73, SD = 1.89) led to higher feelings of empathy than the fair negative review (M = 4.13, SD = 2.39; t(593) = 8.52, p <.001) and the positive review (M = 4.17, SD = 2.51; t(593) = 8.35, p <.001). These latter two conditions did not significantly differ (t < 1). The pattern of results was similar in the empathetic response, where the unfair negative review (M = 7.09, SD = 1.63) led to higher feelings of empathy than the fair negative review (M = 5.47, SD = 2.16; t(593) = 5.27, p <.001) and the positive review (M = 5.87, SD = 2.28; t(593) = 3.94, p <.001). These latter two conditions were not significantly different from one another (t(593) = 1.31, p =.19). In other words, we observed a significant increase in feelings of empathy in the empathy-enhancing-response versus the neutral-response condition for the fair negative review (t(593) = 4.39, p <.001) and positive review (t(593) = 5.52, p <.001), but not for the unfair negative review (t(593) = 1.17, p >.20). This result is consistent with our prediction that firm responses can evoke empathy within conditions that do not naturally elicit empathy.
We ran a moderated-mediation analysis to test whether our observed pattern of results between the firm-response manipulation and review type on the purchase intentions could be explained by variations in empathy for the firm (PROCESS Model 7). Results revealed two significant indexes of moderated mediation at each review-type level (X1 [fair negative review]: b =.31, SE =.11; CI95 = [.11,.54]; X2 [positive review]: b =.24, SE =.10; CI95 = [.06,.46]), suggesting that the observed increase in purchase intentions in the empathetic response (vs. neutral) condition is mediated by empathy in those two review conditions.
Study 4 explored whether a managerial intervention can enhance empathetic feelings and supportive responses toward the firm. We find that when a firm responds to a review in a highly empathetic way, it can evoke both consumer empathy and more favorable purchase intentions in response to reviews that are not naturally empathy-evoking. In other words, by recognizing that unfair negative reviews naturally evoke consumer empathy, we show that managers can harness empathy to improve consumer responses to fair negative and positive consumer reviews. Our results also demonstrate that interventions aimed at increasing empathy as a motivation for increased patronage are most effective for reviews that are not already high in empathy (i.e., the fair negative and positive reviews; for detailed results, see Web Appendix H). In these cases, unfair negative reviews are akin to having a ceiling effect on empathy.
Study 5 uses another managerially relevant intervention to examine the moderating role of increased empathy for the firm. In particular, we investigate how firms can boost customer empathy by using a narrative to increase perspective taking. Recently, an emerging and managerially actionable marketing practice is to provide information about the employee or the particular manufacturer of a product in the form of employee "spotlights." For example, Lush Cosmetics, a company that makes all of its products by hand, includes a label informing consumers about the employee who made their product (for other examples, see Web Appendix J). Prior work by [14] shows that a "spotlight" on victims increases perspective taking and empathy (see also [11]; [24]). These manipulations make the beneficiary's point of view more tangible ([93]), which increases empathetic concern and subsequent helping ([94]). In a retail context, an employee spotlight (with a description and picture of the sales employee) was shown to increase consumer perspective taking and empathetic response ([80]). We predict that a spotlight manipulation will increase empathy in response to reviews that are not naturally empathy-evoking (positive and fair negative), which should lead to more positive responses. We do not expect the spotlight manipulation to increase further empathy generated by an unfair negative review.
Six hundred forty-two participants recruited through MTurk took part (46% female; Mage = 38.2 years) in a 3 (review type: fair negative vs. unfair negative vs. positive) × 2 (employee-spotlight manipulation: absent vs. present) between-participants study design. The dependent variable was the value of coffee-shop vouchers selected.
Before reading a review for a coffee shop, half of the participants were assigned to receive an employee spotlight manipulation. Specifically, participants read a short "Meet Your Barista" article introducing Alicia, a (fictitious) barista at a coffee shop. In the article, Alicia describes various aspects of her job, such as her favorite coffee drink and one thing she wishes she could do better in her job. Participants in the employee-spotlight-absent condition proceeded directly to the review evaluation.
Next, participants read the focal review. All conditions included a customer's description of trying this new coffee shop for the first time. Participants learned that the customer mentioned the odd taste of the hazelnut syrup in their drink and that the barista replied that it was a different brand than usual. In the fair-negative-review condition, the customer complained about the barista not addressing the issue and about the price of the drink at $7.50. In the unfair negative review, the barista made the $3.50 drink complimentary. There was an additional complaint in the unfair negative review that the barista did not provide an apology. In the positive review, the barista offered to make the drink complimentary, but the customers "told her it wasn't a big deal and not to worry." Review length was kept constant, and the review was presented along with another review to increase realism (for the stimuli, see Figure 6). We measured empathy toward the firm (α =.95). Then, as our main incentive-compatible dependent variable, we told participants that the coffee shop described in the scenario would be celebrating the opening of its new online store with up to 75% off coffee beans, apparel, and gear. Participants learned that one person would be randomly selected to receive a bonus of $40, but that part of that bonus could be exchanged for vouchers at the coffee shop at half the cost (e.g., $1 = $2 vouchers). We reminded participants that this was a real choice and asked them to provide us with the bonus amount they would want to exchange for vouchers (for stimuli and manipulation checks, see Web Appendix I).
Graph: Figure 6. Study 5: Review type manipulation.Notes: The review was Fair, Deserved, Justified, and Reasonable (1 = strongly disagree; 7 = strongly agree).
We regressed the voucher value on review type (dummy-coded multicategorical predictor using the unfair negative review as the reference category; X1 = fair negative, X2 = positive) and employee spotlight (coded as 0 = absent, 1 = present; PROCESS Model 1). Results revealed two significant interactions between the employee spotlight and the two review-type dummy variables (fair negative × spotlight: β =.19, b =.76, SE =.26; t(636) = 2.92, p <.01; positive × spotlight: β =.15, b =.61, SE =.26; t(636) = 2.34, p <.05). We also observed main effects of the fair negative (β = −.55, b = −1.76, SE =.19; t(636) = 9.53, p <.001) and the positive review (β = −.13, b = −.41, SE =.18; t(636) = 2.22, p <.05; Figure 7). Contrasts revealed that in the spotlight-absent condition, we replicated the basic effect: voucher value was lower in the fair-negative-review (M = $4.07, SD = $1.98) versus the unfair-negative review (M = $5.83, SD = $1.17; t(636) = 9.53, p <.001) and the positive-review (M = $5.42, SD = $1.23; t(636) = 7.34, p <.001) conditions. The unfair-negative and the positive conditions did significantly differ from each other (t(636) = 2.22, p <.05). In the spotlight-present condition, the voucher value was lower for the fair-negative-review condition (M = $5.03, SD = $1.55) compared with the unfair negative (M = $6.03, SD = $1.01; t(636) = 5.43, p <.001) and the positive (M = $6.23, SD = $.83; t(636) = 6.57, p <.001) review conditions. The latter two conditions did not differ significantly (t(636) = 1.09, p >.25). Stated differently, we observed a significant increase in voucher value in the employee-spotlight present compared with absent condition for the fair negative review (t(636) = 5.22, p <.001) and positive review (t(636) = 4.41, p <.001) conditions, but not for the unfair negative review condition (t(636) = 1.07, p >.25). Thus, the employee spotlight increased spending in conditions that do not naturally elicit a high level of empathy.
Graph: Figure 7. Study 5: Store voucher value selected and empathy for the firm as functions of review type and employee-spotlight manipulation.*p <.05.**p <.01.***p <.001.Notes: Error bars= ±1 SEs.
Using the same coding as previously, we regressed empathy on review type and employee spotlight. Results revealed two significant interactions between the employee spotlight and the two review-type dummy variables (fair negative × spotlight: β =.19, b = 1.15, SE =.40; t(636) = 2.89, p <.01; positive × spotlight: β =.14, b =.84, SE =.40; t(636) = 2.12, p <.05). We also observed main effects of the fair negative (β = −.53, b = −2.51, SE =.28; t(636) = 8.89, p <.001) and the positive review (β = −.43, b = −2.06, SE =.28; t(636) = 7.32, p <.001). We explore this pattern of results for in a series of contrasts. We replicated our general pattern of results in the spotlight-absent condition, such that the empathy was lower for the fair negative review (M = 4.36, SD = 2.47) compared with the unfair negative review (M = 6.86, SD = 1.49; t(636) = 8.89, p <.001), but it did not differ from the positive-review condition (M = 4.80, SD = 2.10; t(636) = 1.59, p >.10). The unfair-negative and the positive conditions were also significantly different from each other (t(636) = 7.32, p <.001). This supports our proposition that unfair negative reviews naturally evoke higher empathy. In the spotlight-present condition, the empathy was lower in the fair-negative-review condition (M = 5.40, SD = 2.12) compared with the unfair-negative-review condition (M = 6.76, SD = 1.79; t(636) = 4.84, p <.001), but it did not differ from the positive-review condition (M = 5.55, SD = 2.20; t < 1). The latter two conditions were significantly different from each other (t(636) = 4.34, p <.001). As we predicted, there was a significant increase in empathy between the spotlight-present and spotlight-absent conditions for the fair-negative-review (t(636) = 3.73, p <.001) and positive-review (t(636) = 2.66, p =.01) conditions, but not for the unfair-negative-review condition (t < 1). This suggests that the spotlight manipulation increased empathy for the fair negative and positive reviews.
We used moderated-mediation analysis to test whether our observed pattern of results was mediated by empathy (PROCESS model 7). Results revealed two significant indexes of moderated mediation at each review-type level (X1 [fair negative review]: b =.31, SE =.11; CI95 = [.10,.55]; X2 [positive review]: b =.23, SE =.11; CI95 = [.03,.45]), suggesting that the observed differences in voucher value as a function of the employee spotlight are driven by an increase in empathy for those two review conditions.
In Study 5, we find that an employee spotlight can effectively increase empathetic responding to reviews that are not naturally empathy-evoking (i.e., fair negative and positive reviews). This increased empathy led to higher firm support. Taken together, the results provide evidence for the notion that firms can increase empathetic consumer responses to reviews by helping consumers identify with employees. In this case, we introduced the employee to the consumer via an employee-spotlight manipulation (i.e., meet your barista; [80]). Consistent with our previous results, we observed that even without such manipulation, an unfair negative review yields positive consumer responses, presumably because of its associated heightened feeling of empathy. However, we also find that using employee spotlights enables firms to further leverage these empathetic responses, mostly for those conditions that would not naturally see an increase in empathy (i.e., positive and fair negative reviews).
Across two pilot studies, six focal studies, and four foundational studies (see Web Appendices L, M, and N), we provide converging evidence that unfair negative reviews can lead to positive responses to the focal firm due to heightened feelings of empathy. We discuss the implications of these findings for theory and practice and identify avenues for future research.
By suggesting that negative reviews can sometimes lead to positive consumer reactions toward the firm being reviewed, our results build on and extend previous WOM research ([30]; [59]; [72]; [82]). Despite a large body of research indicating that negative reviews lead to negative consumer responses ([31]; [48]), the current research reveals that when a negative review is considered unfair, this injustice can facilitate positive consumer reactions to the firm. Most importantly, we show that the mere presence of unfairness in a review is enough to increase empathy and subsequent supportive firm responses. In demonstrating this nuanced role of empathy, we add to the emerging body of work that explores emotionally motivated reactions to negative reviews (e.g., [82]). We show that perceived unfairness can evoke empathy for the firm's negative experience, which, in turn, motivates consumers to support the firm. By exploring this empathy-driven account, the current work extends our knowledge of how emotional mechanisms can influence and guide consumer responses to negative reviews. The current work also extends our understanding of attribution-emotion accounts of helping ([111]). Specifically, we show an additional way in which a cognitive-emotional (i.e., perceived unfairness → empathy) sequence can be applied to WOM and result in helping behavior toward firms.
Second, by highlighting the role of empathy in consumer responses to WOM, we build on a nascent body of work that has begun to show that consumers can experience empathy for firms, similar to the way they can experience empathy for other people or animals ([71]). In our context, reading an unfair negative review heightens empathetic concern for the firm, which motivates reparative actions. We also contribute to knowledge on empathetic reactions in consumption more generally (e.g., [ 2]; [ 8]; [42]), contending that the discrete emotion of empathy can drive supportive consumer responses to unfair negative reviews. An alternative explanation for our effects is that the results are due to sympathy rather than empathy. Although these two terms often are used interchangeably in everyday discourse and have been found to be related to each other ([42]), they differ subtly in their meaning. Empathy involves taking the perspective of and vicariously experiencing the other person's emotions, whereas sympathy involves the comprehension of another's emotional state that is accompanied by feelings of concern or sorrow ([40]). Therefore, these two emotions are quite similar and can often arise in the same context. While we believe that sympathy may also emerge in our context, we see the results as primarily driven by empathy. Converging evidence across the studies—indicating that the effects are moderated by manipulations that directly discourage (or allow for) empathetic responding (Study 3) and that activate perspective taking (Studies 4 and 5)—suggests that empathy is the most parsimonious explanation for the results.
Third, we build on work examining consumer attributions in WOM outcomes by observing how perceptions of unfairness in reviews can have emotional consequences for consumers. Extant work examines whether a review is attributed to internal consumer disposition or factors external to the consumer. For instance, attributing negative WOM to a reviewer's internal disposition is associated with fewer negative consequences for brand perception, resulting in a null effect because consumers do not incorporate the negative information cues in their judgments ([73]). This tendency is due to the locus of control, which refers to attributions regarding whether or not outcomes were under the actor's control ([109], [110]). For example, if a consumer finds that failure is out of the firm's control, dissatisfaction decreases ([23]). However, when failure is attributed to the company (vs. the consumer or extenuating circumstances), complaints increase ([46]). While controllability might predict less negative responses to negative reviews, we do not believe that perceptions of controllability are the only driver of our effects. Future research could explore the role of attributions in consumer responses to unfair negative reviews.
While most firms would prefer to avoid negative reviews altogether, a key implication of this research is that firms may be able to use unfair negative reviews to cultivate stronger consumer relationships. At a minimum, allowing unfair negative reviews to exist motivates consumer empathy and can generate supportive responses. By showing that an increase in empathy drives this effect, our research also explores ways for managers to increase empathetic responses regardless of the type of review. For example, creating employee-focused narratives as a form of firm communication (via employee spotlights) or even responding to reviews in a way that is more personal and human-like enhances empathetic responding on the part of consumers and protects the firm against the potentially adverse impacts of negative reviews.
Interestingly, our results suggest that, in addressing inherently unfair negative reviews, firms may want to emphasize the unfairness of these reviews as a way to increase empathy and subsequent patronage among third-party consumers. Given that unfair negative reviews cause consumers to experience empathy for the firm, firms may want to highlight unfair negative reviews and strategically leverage their positive downstream consequences. For example, the Drake Hotel in Toronto emphasizes unfair negative reviews from TripAdvisor as part of its marketing communications, for instance, by turning complaints about its decor into unintended praise for its hip styling (Web Appendix K). Similarly, Snowbird ski resort and the Vienna Tourism Board have both used a one-star review as the core of an ad campaign (Web Appendix K). Thus, firms can use unfair negative reviews to highlight the positive components of the brand experience.
We focused on perceptions of fairness regarding negative reviews. Perceptions of fairness also apply to positive outcomes (e.g., [28]). Future research would do well to investigate consumer responses following the perceptions of unfairness associated with unjustifiably positive reviews. In addition, the present research is limited to contexts wherein customers' reactions are measured after being exposed to a single unfair negative review; more work is needed to expand our understanding of consumer inferences—and reactions—to multiple unfair negative reviews. For instance, exposure to multiple negative feedback may lead consumers to reappraise the situation and conclude that the negative evaluations are the firm's fault.
Overall, this work highlights that empathy for firms, a relatively new and underexplored facet of consumer–firm interaction, can have compelling implications for consumers' intentions and behaviors in support of a firm that has been treated unfairly. Firms and researchers alike can benefit from a more detailed investigation of consumers' emotional engagement with firms, especially using an empathy approach.
Supplemental Material, JM.17.0430.R5_Web_Appendix_-_UPDATED - Negative Reviews, Positive Impact: Consumer Empathetic Responding to Unfair Word of Mouth
Supplemental Material, JM.17.0430.R5_Web_Appendix_-_UPDATED for Negative Reviews, Positive Impact: Consumer Empathetic Responding to Unfair Word of Mouth by Thomas Allard, Lea H. Dunn and Katherine White in Journal of Marketing
Footnotes 1 Author ContributionsThe first two authors share equal authorship.
2 Associate EditorDhruv Grewal
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article. The authors gratefully acknowledge the financial support from a Nanyang Technological University Start-Up grant awarded to Allard and a Social Sciences and Humanities Research Council of Canada grant awarded to White.
5 ORCID iDsThomas Allard https://orcid.org/0000-0001-9507-0121 Lea H. Dunn https://orcid.org/0000-0002-2457-0193 Katherine White https://orcid.org/0000-0002-3794-8247
6 Online supplement: https://doi.org/10.1177/0022242920924389
7 1We note that the average correlation between the "fair" and "deserved" item ratings across studies in the current research is r =.85.
8 2Note that we explore how unfair negative reviews can change consumer responses to firms compared with other types of reviews—both fair negative and positive reviews. We make our key hypotheses against fair negative reviews. However, we also compare unfair negative reviews with positive reviews in the studies themselves. Drawing on prior work on WOM, we anticipate that positive reviews should generally elicit positive responses from consumers. We further suggest that unfair negative reviews should also lead to positive consumer responses. However, we remain agnostic regarding whether unfair negative reviews will be similarly favorable or more favorable than positive reviews. This is because the content of the positive reviews themselves is relevant and depends on the calibration of this content. As such, we focus our formal hypotheses on comparing unfair and fair negative reviews, but in the studies themselves we do compare unfair negative reviews with both fair negative reviews and positive reviews.
9 3We note that we present four supplemental foundational studies in our Web Appendix, all of which support our conceptual framework. We first present a field experiment (n = 75) showing increased purchase following unfair negative WOM and a scenario replication controlling for perceived rudeness (n = 90; Web Appendix L). We then report a study focusing on justice-restoration motives arising from unfair negative reviews (n = 234; Web Appendix M). We also report an additional behavioral study that tests the robustness of our framework using an alternative dependent variable (n = 337; Web Appendix N).
4The fairness measure acts as a manipulation check for Study 1a. Stimuli in all other studies were pretested separately, and the results of these appear in the Web Appendix.
5A pretest of review stimuli provides additional support for this concept by showing that unfair negative reviews led to higher empathy than did fair negative or positive reviews. See Web Appendix G.
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Offerings as Digitalized Interactive Platforms: A Conceptual Framework and Implications
In an age of digitalized interactions, offerings are no longer “finished” in the traditional sense; creation of value continues by engaging actors (often consumers and their associated social networks) interacting with organizing actors (often firms and their associated organizational ecosystem) in a joint space of interactive system-environments. One can think of the Apple Watch NikePlus (AWNP) offering in which the consumer co-creates valuable experienced outcomes with a mix of applications, touchpoints, and uses, while AWNP and its organizing actors co-create environments with consumers. Actors increasingly find themselves in such a joint enactment of interactional value creation, through offerings as evolving digitalized networked arrangements of artifacts, persons, processes, and interfaces, which the authors refer to as a Digitalized Interactive Platform (DIP). This implies a broader view of value creation—one in which value is created through interactions, versus one where value is simply the exchange of a fixed offering between a firm and its customers. Offerings as DIPs have significant implications for the theory and practice of marketing.
Online Supplement: http://dx.doi.org/10.1509/jm.15.0365
According to the American Marketing Association (2013), “Marketing is the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large.” This definition centers on “offerings that have value.” Over the past decade, the technological revolution has transformed offerings into a broader combination of artifacts, persons, processes, and interfaces, which, while “having” value in the traditional sense, constitute more of a “means” for creating value through interactions. Consider for example, offerings that have both consumers and developers as users, such as the Apple iPhone; software-based platform businesses (Parker, Van Alstyne, and Jiang 2017) such as Uber and Airbnb; “smart, connected products” (Porter and Heppelmann 2014) such as a cardiac pacemaker in the Medtronic CareLink Network (Prahalad and Ramaswamy 2003); goods turned into brand experiences through digital interfaces (Ramaswamy and Ozcan 2016), such as the Starbucks app (where the good per se is not digitalized but the experience of choosing, buying, and consuming it is); and digital tools mediating actor-networked interactions (Yadav and Pavlou 2014) and emergent experiences (Hoffman and Novak 2015). In all these instances, offerings entail digitalized “platformization” of interactions that are ongoing.1 In other words, the offering is no longer “finished” in the traditional sense, and the creation of value continues in a joint space of interactional value creation, between engaging actors (often consumers and their social networks) interacting with organizing actors (often the firm and its associated organizational ecosystem). The traditional notion of offerings as goods and services to be optimized in terms of a fixed set of features and attributes is inadequate in connecting with the new opportunities for creating value in an age of digitalized interactions.
This article, therefore, seeks to develop a novel conceptualization of an offering as an evolving digitalized networked arrangement of artifacts, persons, processes, and interfaces, which we refer to as a digitalized interactive platform (DIP). Our conceptualization of offerings as DIPs implies a broader view of value creation—one where value is created through interactions, versus one where value is simply the exchange of a fixed offering between a firm and its customers. This leads to an expanded framework of interactional value creation, with additional sources of value stemming from the interactions of actors and DIP offerings. Following the MacInnis (2011) typology of conceptual contributions, our study can be seen as one of envisioning, that is, providing a new perspective. Envisioning encompasses contributions that add to the process of discovery by identifying something new. It “makes us aware of what we have been missing and why it is important,” and it can “reveal what new questions can be addressed” (MacInnis 2011, p. 138). It requires a beginner’s mind stemming from “immersion in the phenomenon of interest,” and it “encourages those with strong conceptual thinking skills to identify what others have not yet discovered” (MacInnis 2011, p. 152). Our envisioning of offerings as DIPs stems from our own reflections in practices encompassing several case situations of organizations spanning a wide range of sectors.2
Beyond the discovery process of immersion in the phenomenon of interactional value creation, our envisioning also encompassed the process of justification by using new observations to revise an existing idea (MacInnis 2011): interaction in creation of value. While the importance of interactions has been recognized in the relationship and service marketing literatures in both business and consumer markets (Gro¨nroos 2012; Gummesson 2008; Hakansson and Ford 2002; Payne and Frow 2005; Sheth and Parvatiyar 1995), our discovery process revealed that creation of value was being enhanced through interactions in offerings as DIPs that were not mere intermediaries between different types of customers, as in demandside economic markets, for instance (Rochet and Tirole 2006), or modularizations of products, as in supply-side product development, for instance (Gawer 2014). Rather, creation of value entailed mediated interactions (Callon 2016) wherein traditional exchange-based views in marketing (Vargo and Lusch 2004) were insufficient in recognizing the new dynamism of creation through interactions. As Callon (2016) has argued, such organizing practices go beyond the conventional view of markets as economic exchanges of goods and services, where autonomous and separate “blocs” of supply and demand bring into existence traditional markets of transactions, which has long been the dominant view in “marketing as exchange” (Bagozzi 1975). Although Vargo and Lusch (2004, p. 1) note that “marketing inherited a model of exchange from economics,” their service-dominant logic (SDL) has remained tethered to an exchange paradigm wherein service is exchanged for service. Furthermore, in their critique of SDL, Orlikowski and Scott (2015, p. 204) argue, “From a practice perspective, goods and services both require the coordination of activities, bodies, and artifacts to be produced and consumed.… Rather than seeing these as orthogonal, we believe there is analytical value in seeing both as constituted in practice.” In contrast, offerings as DIPs are not only constituted in practice but, as we discuss herein, also direct our attention to the joint enactment of interactional creation of value by engaging and organizing actors (Ramaswamy and Ozcan 2014, 2018).
The rest of the article is organized as follows. We first develop a conceptualization of an offering as a DIP. We start with the Apple Watch NikePlus (AWNP) offering as an illustration to motivate our conceptualization. We then explicate each of the components of a DIP and how they interact with each other. Subsequently, we illustrate how value is interactionally created through DIP offerings, from smart, connected products to brand environments and organizational ecosystems. Value is no longer just a function of product features and service attributes (i.e., offerings as “having” value). Rather, it extends to become a function of interactions among components of DIP offerings and the actualization of events therein. We identify and briefly discuss key implications of our framework that relate to the co-evolutionary nature of DIP offerings, interactional perspectives in marketing and enterprises, and the study of markets as interactional creation of value.
Consider AWNP, a smart watch described by Nike as “your perfect running partner—on your wrist”3 and developed by Apple in collaboration with Nike. The Apple Watch has a builtin GPS and heart rate sensor, shows alerts, takes calls, supports applications for tracking activity and workouts, and integrates with other health applications through an iPhone. The NikePlus edition provides an exclusive interface design, including customizable watch faces and a special band, along with the NikePlus Run Club app, featuring tracking, scheduling, and social running functionalities. In this sense, AWNP can be seen as a DIP orchestrated by organizing actors (i.e., Apple and Nike, along with partners, and even customers as collaborators; Prahalad and Ramaswamy 2000) to interactionally create value together with engaging actors. At the core of the AWNP offering is an artifact (the smart watch, with its slew of digital and sensoractuator technologies and embedded software) that enables interactional creation of value by the agency of an engaging actor (e.g., a runner) who constructs outcomes of value in different contexts (e.g., varying contexts of running such as casual jogging, training for a marathon, running for health), giving rise to experiences that are subjective to each person (Holbrook 1994). Value is formed, in the case of AWNP, in a joint space of interaction (Gro¨nroos and Voima 2013; Helkkula, Kelleher, and Pihlstrom 2012) of which every engaging actor is a part, including other persons (e.g., running partners) who may join the DIP, and which may include complementary offerings other than those from Apple (e.g., Nike shoes). The NikePlus Run Club app is itself another DIP anchored around software artifacts with interfaces and interactional processes.
The AWNP is an illustrative example of a DIP offering. We conceptualize it as an evolving digitalized networked arrangement of related artifacts (physical and digitalized, including data in the form of numbers, text, pictures, audio, and video), persons (including customers, employees, partners, and stakeholders), processes (increasingly software-enabled, such as algorithms), and interfaces (physical and digitalized), which we abbreviate as “APPI” components. This network of components affords many interactive system-environments through which interactional creation of value is enacted.4
The APPI components of a focal DIP (i.e., the Apple Watch itself) interact with other supporting DIPs such as the Run Club app, itself a part of a broader composition of running, fitness, and health assemblages (Thomas, Price, and Schau 2013). The app can also be plugged into other assemblages (e.g., as a stand-alone app on the iPhone or on the NikePlus Run Club website). For instance, in the case of a runner engaging with the Run Club app on AWNP, heterogeneous interactions with the app as a software artifact are activated via interfaces and processes as well as other, physical artifacts (e.g., Apple iPhone as a hardware artifact). Further, such runner-based assemblages can become part of other assemblages, as in a Nike-sponsored marathon in which runners interact with smart billboards, or social media interfaces that Nike employs to augment value-enhancing personal and social experiences. Interactional value creation through the Apple Watch as DIP is extended through a digital service assemblage (e.g., Apple Music), which in turn connects with other DIP components, such as playlists of running music and coaching workouts. Further, nonperson components of a DIP can autonomously interact (e.g., the sensor in the Apple Watch and iPhone software) to keep track of running performance.
In addition to a focal DIP offering, “supporting” DIP offerings thus extend the scope of interactional creation through arrangements that connect customers with other stake-holding entities (e.g., trainers) and/or with enterprises (e.g., customer service support in an Apple retail store). For instance, through a supporting DIP, a trainer can provide supporting interactions with another runner from a runner’s focal DIP (e.g., interfaces by which the trainer interactively coaches a group of runners of which the focal DIP is a part, drawing on other resources elsewhere in other supporting DIPs of which the trainer is a part) or a customer service support interaction from an Apple employee through the Apple Store iPhone app in diagnosing a performance issue with the Apple Watch.
As software becomes embedded in the hardware objects of our daily lives, digitalized platformization of interactions in business practice has forged ahead, with increasing crossfertilization of working teams across disciplinary functions in enterprises, notwithstanding the “silos” of academic disciplines. In this context, a unified perspective in conceptualizing offerings as DIPs can advance marketing as a reference discipline in a new age of interactional value creation.
We next discuss each of the APPI components of a DIP offering and how they can potentially interact with other components. These interactions could provide organizing actors with multiple ways of configuring DIP offerings, such that engaging actors could create new forms of value through their interactions with DIP offerings.5
Artifacts and interactional relations. An artifact is created and identified with its role in an interactive system-environment, determined by actor intentions, causal contribution to the system-environment, and evolutionary history. Artifacts provide visible and action-oriented interaction, entering a large variety of relations with other artifacts, persons, processes, and interfaces. Digitally coded artifacts rely on software to perform various actions generated through their ability to be editable, open, and distributable. They can be developed and used by many distributed stakeholders. Intentionality, action, and past experiences of individuals activate resources in a DIP offering through the mediation of artifacts, which, with embedded digital intelligence, enables new forms of mediated experiences of outcomes in everyday activities.
The utility of digital artifacts is contingent on a changing network of functional relations with other artifacts. The identity and function of an artifact depends on its sociotechnical position, which occurs as part of a process in a socioeconomic network. Artifacts contribute to composite and distributed intentionality in directing actions, decisions thereof, and experiences of persons. Digital artifacts have the capacity to affect or be affected in certain ways despite variations in context, by virtue of interfaces, and in combination with persons and processes constituting DIP offerings.
Persons and interactional relations. A person is seen as an embodied, experiencing being, with the capacity to engage in many different relations with other persons, artifacts, processes, and interfaces. This view of engagement recognizes persons as “experiencers” of creation through interactions (Prahalad and Ramaswamy 2004a; Ramaswamy and Ozcan 2014) in engaging with an interactive system-environment in particular contexts of space and time (for a discussion of engagement in the marketing and business research literature, see Brodie et al. 2011; Storbacka et al. 2016). Bound up with contexts of equipment in practical situations, persons as subjects of experience can interact with artifacts skillfully as extensions of their selves (Belk 2013). Personality, as a stable network of relationships among beliefs, goals, competencies, and their affects, impacts engagement under a constitutive relationship with other persons, artifacts, processes, and interfaces.
A person can interact through one or more modalities of interfaces in task flows and provide and receive feedback in pursuit of related goals. From the perspective of enterprises providing DIP offerings, employees, as persons equipped with artificial intelligence and machine learning capabilities, can change the nature of interaction with customers. From the perspective of persons as consumers, as they interact with environments provided by DIP offerings in their own contexts, engagement occurs through one or more modalities of interfaces entailing task flows and feedback in pursuit of related goals. Consumers constitute and become aware of themselves in interaction with others through social action and shared activity.
TABLE: TABLE 1 Conceptualization of a Digitalized Interactive Platform (DIP) Offering
TABLE: TABLE 1 Conceptualization of a Digitalized Interactive Platform (DIP) Offering
| Component and Its Contribution to a DIP Offering | Interactional Relations with Other Components | Interactional Creation Implications |
|---|
| Artifact : An artifact is created and identified with its role in an interactive system-environment. It is determined by actor intentions, causal contribution to it, and evolutionary history. Artifacts provide visible and action-oriented interaction, entering a large variety of relations with other artifacts, persons, processes, and interfaces. | Artifact—artifact relations: Due to the generative nature of digital artifacts, value is contingent on the changing network of functional relations with other artifacts. Artifact—person relations: Artifacts contribute to composite and distributed intentionality in directing actions, decisions thereof, and experiences of persons. Artifact—process relations: The identity and function of an artifact depends on its sociotechnical position, which occurs as part of a process in a socioeconomic network. Artifact—interface relations: Digital artifacts have the capacity to affect or be affected in certain ways despite variations in context, by virtue of interfaces. | Digitally coded artifacts rely on software to perform various actions generated through their ability to be editable, open, and distributable. Experience, intentionality, and action extend from and into the interactive system-environment through sociotechnical mediation of artifacts. |
| Person: A person is seen as an embodied experiencing being, with the capacity to engage in many different relations with other persons, artifacts, processes, and interfaces. Persons are “experiencers” of creation through interactions, in engaging with a DIP offering in particular contexts of space and time. Personality, as a stable network of relationships among beliefs, goals, competencies, and their affects, impacts engagement under a constitutive relationship with other persons, artifacts, processes, and interfaces. | Person—artifact relations: Bound up with contexts of equipment in practical situations, persons as subjects of experience, can interact with artifacts skillfully as extensions of their selves. Person—person relations: Persons constitute and become aware of themselves in interaction with others through social action and shared activity. Person—process relations: Persons disclose themselves and other entities, between past accomplishments and future projects, seen in shared practices through rule-following and language. Person—interface relations: While engaging with an interactive system-environment in particular contexts of space and time, persons can interact through one or more modalities of interfaces in task flows and provide and receive feedback in pursuit of related goals. | Persons as subjects of experience have a firstperson “agencial” perspective on disclosure of events and intentional acts, self-consciousness, and self-identity. In a group, each person has a self-concept reconfigured in terms of group properties, cognitions affected by and affecting interactions with others, and behaviors regulated by group norms. |
| Process: A process is a course of change in the properties of some enduring thing or situation, with particular direction and movement, where one stage leads to the next in a structured sequence of connected events that are coordinated causally or functionally. In DIP offerings, processes entail sequences of activities carried out with and by engaging actors through technological or interpersonal interfaces. | • Process—artifact relations: While artifacts serve as physical evidence of quality in service processes, digitally coded processes are increasingly embedded in artifacts. • Process—person relations: The “self” of a person is a structured system of actual and potential processes of experience and action. Social processes involve persons following rules and conventions along with natural and artificial processes. • Process—process relations: As service processes with contact employees depend on support processes with others in an organization, coded processes similarly interact with internal states of other coded processes. • Process—interface relations: Service processes interact with customers in face-to-face encounters, and digitally coded processes interact with persons through interfaces. | Actual processes of a DIP offering might differ from how persons perceive and experience those processes, depending on their own interactions and the representation of the DIP offering. The “organizing” of interactions is also a process. Digitally coded processes refer to transactions and flows of digital data and processed information across digital infrastructures. In digitalized offerings, digitally coded processes are embedded in artifacts; they communicate with internal states of other coded processes and interact with persons through various interfaces. |
| Interface: An interface is a point of connection between hardware, software, data, and individuals whose representations and manipulations in relation to each other produce the possibility of interaction, providing multiple modes and means of communication and translation between the external and the internal. Interfaces provide media and surfaces for representing environmental situations meaningfully to actors’ awareness and interpretation in support of decision making and problem solving in a domain of work. Interfaces enable modular architecture that minimize unnecessary interdependencies. | • Interface—artifact relations: Software works as a linguistic interface to hardware by introducing tactical constraints to a universal machine to turn it into a specialized machine as a subset of all possible uses of the hardware. • Interface—person relations: In conceptualizing the interaction at the interface, an engaging actor can be seen as being in a dialogue with the world, which implies designing the interface with a process of personalization to create the conditions for unifying agency and individualizing experiences. • Interface—process relations: Interfaces filter and produce programmed events from data that stream toward the environments of engaging actors. • Interface—interface relations: Interfaces, when distributed, are coordinated remotely and integrated centrally through standards and protocols (also interfaces themselves) that facilitate communication. | The miniaturization of technology and a handheld culture signal a shift from visual/virtual interfaces to physical/gestural interfaces that create new communication and action possibilities in combination with digital devices and material events. A technical interface does not just provide transparency to the data but has a code of its own that carries strong cultural messages. |
Processes and interactional relations. A process is a course of change in the properties of some enduring thing or situation, with particular direction and movement, where one stage leads to the next in a structured sequence of connected events that are coordinated causally or functionally. In DIP offerings, processes entail sequences of activities carried out with and by engaging actors through technological or interpersonal interfaces. Processes can occur in many relations with other processes, artifacts, persons, and interfaces. In typical service contexts, service processes render physical evidence of quality through artifacts, depend on support processes with others in a company, and interface with customers in face-toface encounters through contact employees (Bitner, Ostrom, and Morgan 2008).
Processes also intersect with persons, as the “self” of a person is affected through a structured system of actual and potential processes of experience and action. Social processes involve persons following rules and conventions along with natural and artificial processes.
The “organizing” of interactions is also a process. Digitally coded processes refer to transactions and flows of digital data and processed information across digital infrastructures. In digitalized offerings, digitally coded processes are embedded in artifacts, communicate with internal states of other coded processes, and interact with persons through various interfaces.
Interfaces and interactional relations. An interface is a point of connection between hardware, software, data, and individuals, whose representations and manipulations in relation to each other produce the possibility of interaction, providing multiple modes and means of communication and translation between the external and the internal. On the one hand, interfaces provide media and surfaces for representing environmental situations meaningfully to actors’ awareness and interpretation in support of decision making and problem solving in a domain of work. On the other hand, interfaces enable modular architecture that minimize unnecessary interdependencies.
The miniaturization of technology and a handheld culture signals a shift from visual/virtual interfaces to physical/gestural interfaces that create new communication and action possibilities in combination with digital devices and material events. Interfaces have a code of their own that carries strong cultural messages, filtering and producing programmed events from data that stream toward the environments of engaging actors. Interfaces mediate a diverse set of cultural, cognitive, and sociomaterial relations among other interfaces, artifacts, persons, and processes. When distributed, interfaces are coordinated remotely and integrated centrally through standards and protocols (themselves also interfaces) that facilitate communication. In conceptualizing the interaction at the interface, an engaging actor can be seen as being in a dialogue with the world, which implies designing the interface with a process of personalization to create the conditions for unifying agency and individualizing experiences. Table 1 provides a summary of the conceptualization of a DIP offering, emphasizing the relations of each APPI component to other components, as well as interactional creation implications.
We now discuss how the APPI components of a DIP offering can give rise to valuable outcomes through interactional creation. Various APPI components are activated when individuals engage with a DIP offering in their particular contexts of interactions. Outcomes of value are actualized through these interactions.
As an example, consider a runner training for a half marathon who has set a personal goal of finishing in less than an hour and a half—a highly ambitious time for her, given where she is today. It is an opportunity to prove her competitiveness to herself and others. We can identify various interactions the runner can engage in through the APPI components of AWNP. First, through the run-tracking interface, the runner can now access digital processes that automatically plot distance, time, pace, and calories burned. She can interact with a friendly, colorful histogram, that is, a digital artifact, of any set of data over time, assessing whether she is making progress. She can also issue running challenges as she trains, for example, about the total number of miles that her team can run over a set period of time, thereby fostering her motivation. She can also map her run, as another digital artifact made visible through an interface. She can also share her runs with other persons, annotating them with detailed data such as the nature of the road terrain and the lighting on the course. She can create her own running courses, potentially building on other digital artifacts others have devised. Depending on the specific issues she faces, she can decide whether to engage with professional runners or training specialists, who can deliver personal, one-on-one advice by looking at the data and drawing on motivational tips and messaging that have been effective with other athletes. She can, in turn, share her own wisdom and running tips, according to what works for her. She can decide to seek encouragement and guidance in training for the half marathon and activate a supporting DIP offering that includes coaching plans with benchmarking and adaptive workout regimens powered by artificial intelligence (AI) and machine learning. Thus, for the runner as an engaging actor, value becomes an emergent function of how she chooses to contextually engage with AWNP that offers a DIP with multiple pathways. Value is created partly by her—how she decides to engage—and partly by AWNP, which provides the DIP offering that reaches out to her on her own terms and invites her to connect not only with Nike but also with a vast community of runners. As a result, the running experience is cocreated between herself and AWNP.
Thus, just as value-in-use is “created by the user (individually or socially), during usage of resources and processes (and their outcomes)” (Gro¨nroos and Voima 2013, p. 144), individuals create value through their interactive engagements with DIP offerings. In the AWNP example, value in interactional creation is derived from how individual runners conceive of worth and of ends, in order to give sense and direction to their enactments (Weick 1995), and also from their interests in evaluative mechanisms to establish value (Appadurai 1988). This value also derives from dispositional roles of different stake-holding entities (Hillebrand, Driessen, and Koll 2015), such as marathon organizers and fitness equipment companies, but also runners themselves in new roles (e.g., reporter for a marathon).
Experiences of engaging actors emerge from a constellation of interactions through a DIP, where involvements in contexts of events underlie interactions, and derivation of meaningful outcomes is the basis of actor engagements. By delving deeper into the interactive agencies of actors through interactions with APPI components of DIP offerings, we can reveal hidden and untapped sources of value, enlightening organizing actors (besides engaging actors) in more effective creation of “win more” outcomes for all actors, especially in more profitable ways (Kumar and Reinartz 2016). Creation of value extends beyond the focal DIP offering through supporting environments of other DIPs. This shift from “value-in-exchange/use” to more generative “interactional creation of value” expands the scope of value creation. We next discuss how value is co-created through interactions with and among focal and supporting DIPs, from smart, connected products to brand environments to organizational ecosystems.
Smart, connected products. Consider the case of a patient with heart arrhythmia (irregular heartbeat) who is implanted with a pacemaker, a DIP offering provided by the firm Medtronic. The “patient + pacemaker” can be seen an assemblage in the focal DIP offering, coming into relation with other supporting DIPs in the Medtronic CareLink network involving doctors, hospitals, diagnostic clinics, and Medtronic service providers (Prahalad and Ramaswamy 2004a). For instance, consider the scenario of a crisis that occurs while the patient is out of town. The patient may activate and engage with an emergency call center, medical support, and hospital emergency services. The patient may need directions to the best nearby hospital sent to her smartphone, while the attending physician may need access to the patient’s medical history. The two doctors—the primary care provider back home and the physician on call at the out-of-town hospital—must coordinate their diagnosis and treatment. Doctors as engaging actors can review patient data, adjust the patient’s pacemaker remotely, and take corrective action together with the patient. Outcomes of value thus emerge from interactional creation through the contextualized location-based dynamic interactions of APPI components activated by a particular patient. This value cannot be achieved without the firm’s ecosystem that provides the environments that enable the patient to have a unique engagement. The Medtronic CareLink network multiplies the value of the pacemaker to the patient and her family and doctors. More important, the ensuing interactions cannot be controlled and staged by the firm. The patient, by co-creating with the network, is an active stakeholder in defining the interaction, the context of the events that underlie it, and what is meaningful to her.
In the AWNP example of a runner preparing for a marathon, the same person can trigger different interactional processes depending on the context of use, such as tracking the runner’s progress via activity rings or sharing her latest run with friends. In addition, the smart artifact can interact with the person to autonomously capture vital body conditions (e.g., heart rate) to feed into the Health app on her iPhone, all the while recording precise distance, speed, and pace information, through built-in GPS in sync with her iPhone via cloud services, to map out her statistics interactively once she has finished her run.
Furthermore, in a digitalized world, mixed-reality interfaces (3-D virtual reality and augmented reality) are increasingly enabling human actors to engage more intensively in Internetenabled system-environments by eliminating the distance between people and experiences and combining the simulated and the real world with immersive experiences. These interfaces are transforming not only consumers’ living experiences but also employees’ work experiences. In the case of employees as engaging actors, firms can lower real-world risks through simulated interactive system-environments. For instance, Komatsu, a leading global heavy equipment manufacturer, offers its mining customers not only smart, connected machines as a DIP but also a supporting DIP that lets training personnel in the customer organization enable firsthand immersive experiences of potentially dangerous situations for operators of heavy equipment. A key point here is that by observing how operators interact with the various APPI components of the simulated environments that mimic the real world, and using embedded intelligence to guide operator learning, the skills of employees can be enhanced without incurring real-world risk and costs.
From a firm’s perspective, the involvement of nonhuman actors such as autonomous software agents and AI-based processes also helps firms augment the value proposition of DIP offerings. For instance, consider a smart automobile, such as a Tesla, that can be seen as a DIP offering with increasingly networked APPI components involving sophisticated software, embedded intelligence, and remote interfaces. When in need of repair, a Tesla vehicle can autonomously call for a corrective software download interacting with another assemblage that does not require a human. Or, if necessary, it can send a notification to the customer with an invitation for a valet to pick up the car and deliver it to a Tesla facility, which, in turn, triggers a human interaction if the customer acts on it. Auto accessories, such as a device called Automatic (www.automatic.com), can also communicate with a conventional car’s onboard computer and use a smartphone’s GPS and data service to upgrade the car’s capabilities, providing feedback on mileage, driving patterns (rough braking, speeding, rapid acceleration), and ways to conserve gas over time. In the case of the Chevrolet Volt electric vehicle, drivers can manage the charging of the vehicle, including the ability to charge during off-peak hours, through the OnStar RemoteLink mobile app. The same app can also start a vehicle and its charging remotely, identify where the vehicle is parked, and even pay for the electricity at participating charge stations. Furthermore, by linking the vehicle with smart power utility grids, a customer can direct the power utility to control when it charges (depending on rates at various times of day) and/or when the power generated comes from renewable energy sources.
More generally, the Internet of Things is allowing any artifact to become a smarter device for sensing and communications, as well as storage, computation, and display, and to interact with other entities in assemblages, as applications on mobile phones link smart objects with resources on the Internet relevant to individuals in the context of location and time. Whether smarter lighting, temperature control, and security in homes and hotels or smarter artwork in museums with tagged text, images, and audiovisual streams, smarter artifacts can not only initiate interactions but also affect components of other assemblages and be affected by them. A smarter DIP such as a Medtronic pacemaker or a Tesla vehicle entails digitalized interactions with processes, persons, and interfaces. It can also affect components of other DIPs and be affected by them. Thus, new potential sources of value through interactional creation can be identified from the perspective of organizing actors across multiple focal and supporting DIPs purposefully configured in enterprise value–creating systems.
Brand environments. The traditional brand value creation process has mirrored the traditional view of offerings. Enterprises and stake-holding individuals are seen as having distinct roles in the process of brand value creation. Stakeholders have a stake in brand value creation, but enterprises view stakeholders as being largely passive and docile recipients of brand value creation. In contrast, in brand value co-creation, stakeholders have a more active role, contributing through their differences in views of brand value expressed through their joint interactions in creating brand value together. Brand experiences in retail and social environments are now increasingly co-created in a digitalized world (Ramaswamy and Ozcan 2016).
Consider the Apple retail store, where consumers can experience the Apple Watch before purchasing it. The consumer gains in terms of learning about emergent experiences of potentially valued outcomes through AWNP and testing the value of these experiences before becoming the firm’s customer. In the store, employees are equipped, of course, with Apple devices, but more important, they can also access an internal app that allows them to capture insights from the consumers’ experience in interactional creation. This includes employees offering customer support for AWNP through the store’s Genius Bar after a purchase. Apple makes these insights accessible to product developers internal to Apple and Nike. Furthermore, resourced capabilities are translated by actor networks into the physical Apple retail store as a learning environment, beyond a classic sales environment. The store as a platformed offering is configured to help ordinary people combat “featuritis,” the common overemphasis by technology companies on the features of products, by focusing instead on inviting people to play with Apple products as they would experience them. Apple, through its employees, rapidly gains insights into how consumers experience its products, the kind of questions they have, and how to help them in the context of their value generation through Apple’s products, sharing their learning from one customer to the next, and learning from each customer’s past experiences and future intentions.
The task for managers and employees, as brand co-creators from within the firm, is to not only gain a deeper understanding of the involvements of people in DIP offerings, but also their contexts of engagements, the events that give rise to their brand co-creation experiences, and what is meaningful to them. Consequently, firms need to incorporate a broader view of brand value creation into their operations that encompass different types of stakeholder in the organizational ecosystem (e.g., from runners, trainers, and coaches to developer-customers and enterprise partners, in the case of AWNP)—designing interactive system-environments from the perspective of interactions that generate meaningful brand experiences, focusing on what stakeholders value in brand engagements, and better managing stakeholder–brand relationships by tapping into the knowledge and skills of all individuals, both personally and as communities.
Organizational ecosystems. Consider the case of Apple’s multistakeholder ecosystem, with end-user customers on one side and developer customers and enterprise partners on the other. Apple’s online App Store facilitates interactions between end-user customers of Apple’s mobile devices, who have made over 130 billion downloads of over 2.2 million apps by over 13 million registered developer-customers as of January 2017. Apple provides tools for end-user customers to give reviews and ratings on the App Store and also to provide feedback to developer-customers, who, in turn, can interact with end-user customers as they update their apps. Apple splits the revenue with developers on a 30%–70% basis initially (where 30% goes to Apple and 70% to the developer) and a 15%–85% basis if any given user has stayed with the app for longer than a year.
Now consider the case of interactions in the partnership between Apple and Nike. Apple’s application development framework, based on the experience of the end-user customer, brings consistency to the quality of both application development and application use environments for AWNP. It simultaneously gives enormous flexibility to developer-customers in innovating a wide variety of offerings and end-user experiences, together with Apple and Nike, whose product development teams work closely with budding developer-customers. These developer-customers also share their development experiences with Apple and Nike and with the community of other developers. Moreover, by giving developer-customers the ability to visually compare the performance of different parts of the rendered application and sharing this information with Apple and Nike’s internal teams, Apple and Nike software engineers and developers also rapidly learn about the performance of customer-facing applications through its product devices early on, before market launch.
Most significantly, the AWNP extended organizational ecosystem is a multiway learning engine, facilitating dialogue within and among stakeholder communities (Ramaswamy and Ozcan 2014). Apple and Nike can continuously identify and act upon new growth opportunities. They can enable the combining of consented individual private data, social community data, open public data, and other data sources. For instance, imagine an urban AWNP user with atmospheric sensitivities (e.g., pollen allergies) being able to share fitness and sensor data with others, combining that information with open environmental data from their city, and through real-time analytics being able to cocreate a running course that avoids areas with high pollen count. Offerings as DIPs can enable firms to leverage stakeholder capabilities and accelerate their own co-development, while opening new avenues for experience innovation and value co-creation in the organizational ecosystem to attract new adherents to the DIP offering and enhance the loyalty of engaging actors in deeper and more meaningful ways. Table 2 provides a summary of interactional creation of value through DIP offerings (with AWNP as an illustrative example).
TABLE: TABLE 2 Interactional Value Creation Through Digitalized Interactive Platform (DIP) Offerings
TABLE: TABLE 2 Interactional Value Creation Through Digitalized Interactive Platform (DIP) Offerings
| Aspect | Explanation | Illustrative Implications |
|---|
| Scope of value creation | • DIP offerings afford new ways in which value can be generated through creational interactions. | • Apple Watch NikePlus (AWNP) as a DIP offering implies interactive system-environments that expand the scope of interactions among various APPI components related to running (and, more broadly, fitness and health) with embedded toolkits as mechanisms for interactional creation that enhance the space of potentially valuable actor experiences. |
| | • Value is created not only in exchange or usage of resources and processes in activities but also through interactions. | • Value is formed in a joint space of interaction of which every engaging actor is a part, including other persons (e.g., running partners) who may join an assemblage; this space may include complementary offerings from firms other than Apple (e.g., Nike shoes). |
| Experiences of outcomes of interactional creation | • Experiences emerge from a constellation of interactions of engaging actors through a DIP, where involvements in contexts of events underlie creation of outcomes through interactions. | • Engaging actors construct outcomes of value in varying contexts of running, such as casual jogging, training for a marathon, health and wellness, and so forth, giving rise to experiences that are subjective to each person. |
| | • As APPI components come together in a particular focal DIP, its interactional capacities afford a multiplicity of environments for engaging actors upon its combination of heterogeneous components, interrelated in a way that brings about evolving patterns of interactional creation. | • The same person (e.g., a runner interacting with the watch as an interface) can trigger different interactional processes depending upon the context of use, such as tracking progress via activity rings or sharing a run with friends. |
| | • Nonhuman actors also become increasingly implicated in interactional value creation. | • With a heart rate monitor on the watch, in addition to run-related sensing and machine-learning algorithms, runners gain more deeper insights into their own running experience. |
| | • Engaging and organizing actors are involved in a wider pattern of network relations that goes beyond dyadic relationships to the broader context in which their interactions occur, enabling networked entities to function as a system. | • Engaging actors gain value through new integrative experiences (e.g., specific run workouts that can be set through the NikePlus Run Club app on the iPhone before a run in which just the watch is used). In partnership with Nike, Apple’s application development frameworks, based on the end-user customer experience, bring consistency to the quality of both application development and application use environments for AWNP, while simultaneously giving enormous flexibility to developercustomers in innovating a wide variety of offerings and engagement experiences. |
| Formation of value in interactional creation | • …from dispositional roles of different stake-holding entities. | • NikePlus Run Club app on the Apple Watch entails software developed by Nike and Apple in conjunction with application software developers, some of whom happen to be runners as well. |
| | • …from interests in evaluative mechanisms to establish value | • In the AWNP example, Apple and Nike as organizing actors, along with partners and even customers as collaborators, orchestrate a DIP offering to interactionally create value together with engaging actors. |
| | • …from individuals’ conceptions of worth and of ends, to give sense and direction to their enactments. | • The Apple retail store as a platformed offering is configured for ordinary people to combat “featuritis.” Apple, through its employees, rapidly gains insights into how people actually experience its products, what kind of questions they have, and how to help customers in the context of their value generation through Apple’s products. |
Offerings as DIPs suggest a theory of value creation that expands beyond the conventional economics of supply and demand of goods and services. Instead of offerings merely “having” value, they are a “means” for interactional creation of value by customers (and other actors). In this creation of value through interactions, actors leverage resources, in anticipation of emergent experiences of valuable outcomes. As we have seen, creating value through interactions goes beyond the exchange of a fixed offering between a firm and its customers. We identify and briefly discuss key implications that relate to the co-evolutionary nature of DIP offerings, interactional perspectives in marketing and enterprises, and the study of markets as interactional creation of value.
While offerings as DIPs open new possibilities for interactional value creation, they also imply new types of interactional capabilities on the part of both organizing and engaging actors. For instance, in the case of AWNP, the WatchOS Health Kit provided by Apple enables the digitization of physical variables that facilitate new value-creational linkages of health, fitness, and wellness in the broader ecosystem in which the AWNP DIP offering is embedded. By recombining, remixing, and repurposing interactional data in novel ways, augmenting its capabilities and afforded environments, this offering multiplies the ways in which value can be generated. A particular runner can include, for instance, her trainer in personalizing her app environment and can invoke other interactive functionalities from the trainer’s environment (with the trainer’s approval) that may be relevant to the runner in evolving her interactional creation with the trainer. This connection can generate mutually beneficial outcomes; for example, a trainer can benefit from comparing performance across multiple runners, which can be of value in team coaching contexts. However, this also implies that runner and trainer are mutually disposed toward interactive engagements, with the requisite skills in navigating the interfaces, and that co-creation experiences (Prahalad and Ramaswamy 2004b) are meaningful and compelling enough to lead to continued engagements. Hence, organizing actors must continuously connect with the actual co-creation experiences of engaging actors, often through supporting DIPs, to continuously enhance the attractiveness of the value proposition of focal DIP offerings (Ramaswamy and Ozcan 2014).
Thus, once a focal DIP offering is in place, future modifications must be made based on actual experiences. Unlike traditional goods and services, DIPs are never “finished” but continuously co-evolve in a dynamic of mutual articulation, that is, how an organizing actor (e.g., marketer) managerially connects with an engaging actor’s (e.g., consumer’s) actual experiences of outcomes to create value effectively with consumers, given the consumer’s own attempt to create value with the marketer. Following Ramaswamy and Ozcan (2018, p. 200), the definition of co-creation can be enhanced as “enactment of interactional creation across a multiplicity of interactive systemenvironments, afforded by DIPs, through events entailing the interplay of agencing engagements and structuring organizations.” What this implies is that unlike with traditional goods and services, marketers must make the continuous configuration of interactional relations of a DIP offering an exercise in iterative experimentation (Blank 2013). As interactions increasingly become a source of new competitive advantage, firms must adopt an interaction orientation (Ramani and Kumar 2008) and develop new strategies for joint value creation with stakeholders (Ramaswamy and Ozcan 2013), as discussed next.
Interactional creation by actors through DIP platform offerings both enables and constrains actors, who are not viewed as “atomistic” in the traditional sense: their interactive agencies both affect, and are affected by, the relational effects of interactions and outcomes, and their connections can evolve beyond an immediate set of interacting entities over spacetime. This points to the role of DIPs in networked systemenvironments, as individuals access resources and create value through interactions. For instance, interactions in business networks entail interdependencies between actors, their activities, and their resources (Hakansson et al. 2009). At a fundamental level, just as the processes behind relationships should be seen as interaction, interacting APPI components in DIP offerings are the locus of value creation through interactions among human and nonhuman actors. In keeping with the rapid evolution of a digitalized world, the importance of the concept of interaction in digitalized environments is gaining traction in the marketing and business literature (Yadav and Pavlou 2014).
Such DIP offerings call attention to the interplay between the interactive agencies of actors and the networked structure of environments in interactional creation of value, which has significant implications for the marketing orientation of enterprises (Kohli and Jaworski 1990). In particular, it calls for more research on interaction orientation, as discussed by Ramani and Kumar (2008). As they note (p. 27), “Advances in technology have resulted in increasing opportunities for interactions between firms and customers, between customers, and between firms. An interaction orientation reflects a firm’s ability to interact with its individual customers and to take advantage of information obtained from them through successive interactions to achieve profitable customer relationships.” Customers may be valued incorrectly when customer engagement is not taken into account. They provide a comprehensive framework entailing four components of customer engagement value: customer lifetime value (CLV), customer referral value (CRV), customer influencer value (CIV), and customer knowledge value (CKV).
According to our conceptual framework, CKV can be enhanced through DIP offerings by organizing actors, enabling new outcomes of value to customers through interactions and connections with their actual experienced outcomes over time. As Kumar et al. (2010, p. 307) further note, “Using customer knowledge and feedback for new product development, for instance, may initially be costly but could greatly enhance the effectiveness of the new product development process and increase success in the marketplace.” Creating offerings together with customers has been shown to generate higher sales revenues and gross margins, with increases over time, with higher survival rates (Nishikawa, Schreier, and Ogawa 2013). This can potentially boost CRV as well, as successful interactional creation typically leads to higher “net promoter” scores (Ramaswamy and Gouillart 2010). Likewise, enterprises can also adapt customer management strategies through DIP offerings in customer-tocustomer collectives to enhance CIV. Using interactive performance management systems, enterprises could utilize customer interaction equity metrics to focus marketing efforts on appropriate strategic initiatives (Rust, Lemon, and Zeithaml 2004). This could be linked to different motives of managers in the practice of interactional creation of value. For instance, in regaining “lost” customers (Kumar, Bhagwat, and Xi 2015), enterprises can use supporting DIPs to have a dialogue with selective customers and co-design “win-back” offers and future customer relationships in mutually valuable ways.
Our conceptual framework also has implications for the study of markets and the marketing discipline more broadly. Sheth and Uslay (2007, p. 305), in their discussion of the limits of exchange (Bagozzi 1975), call for a “shift away from the sacred cow of exchange,” emphasizing that “the need for and desire of actors to cocreate value preempts and supersedes the need for exchange” (italics ours). The SDL frame (Lusch and Vargo 2014), which argues for evolving to a new dominant logic for marketing, has also remained tethered to exchange in positing that service is exchanged for service. In an age of digitalized interactional creation, DIP offerings engender markets as sites of creation of value through interactions, beyond “value-inexchange/use” of goods and services, which is still relevant but only as a subset of the larger joint space of actors’ interactional value creation. Moreover, DIPs can be imagined and configured anywhere in the value creational system, regardless of whether it concerns conventional activities of “producing” or “consuming” goods and services.
Furthermore, DIP offerings are active “mediators” in which entities are themselves entangled (Latour 2005). They are “not passive mediators or neutral channels” (Orlikowski and Scott 2015, p. 204); rather, they engage actors in enactment of interactional creation of value. The characteristics of DIP offerings are akin to the “process goods” described by Callon (2016), which are co-determined with the profiles of demanders and suppliers in a network of evolving relations, articulating supply and demand as a result of a collective, collaborative, and dynamic “series of transformations and adaptations between design, production, circulation, and consumption.” This opens new avenues in market studies (Araujo, Kjellberg, and Spencer 2008) through the lens of market practices as a co-creation.
The conceptual framework of interactional creation of value through DIP offerings, as we have discussed, provides a means to explore the complexity of a networked “interacted” business landscape, which is increasingly becoming the “new normal” in a digitalized world of interactions. We hope it stimulates further research into the shift in locus of control of marketers from orchestrating goods and services to the configuration of DIP offerings, and thereby a more expanded view of markets and marketing.
1 We use the term “digitalized” rather than “digitized” because the latter refers to digitization of offerings in terms of encoded bits (as in, e.g., streamed music). Digitalization, however, is a broader term referring not only to digitization of offerings (including virtual reality) but also direct embedding of intelligence and augmentation of physical objects (as in augmented reality), which enables further creation of value through interactions (see Yoo 2013).
- 2 These case situations have been documented elsewhere (see Prahalad and Ramaswamy 2004a; Ramaswamy and Gouillart 2010; Ramaswamy and Ozcan 2014). They range from automotive products; to consumer durables; to capital-intensive equipment and industrial goods and services; to fast-moving consumer goods; to retail, entertainment, media, and travel services; to business technology and professional services.
- 3 See, for example, a tweet by @Nike: https://twitter.com/nike/ status/773725618525442049.
- 4 More formally, these compositions of APPI components are called “assemblages” (see DeLanda 2006; Deleuze and Guattari 1987): arrangements endowed with the capacity of interacting in different ways depending on their combination of heterogeneous components, interrelated in a way that brings about evolving patterns of interactions (Callon 2007).
- 5 For additional references associated with the rest of this section, see the Web Appendix.
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Record: 127- On the Competitive and Collaborative Implications of Category Captainship. By: Alan, Yasin; Dotson, Jeffrey P.; Kurtuluş, Mümin. Journal of Marketing. Jul2017, Vol. 81 Issue 4, p127-143. 17p. 1 Diagram, 6 Charts, 3 Graphs. DOI: 10.1509/jm.15.0196.
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On the Competitive and Collaborative Implications of Category Captainship
Category captainship (CC) is a retailing practice wherein a retailer collaborates with one of the manufacturers in a product category (referred to as the captain) to develop and implement a category management strategy. Although CC has been studied using both theoretical models and surveys, empirical evidence on the benefits and drawbacks of CC is scarce. The authors use a unique data set collected during a CC implementation to empirically examine the impact of CC on the retailer, the captain, and the other manufacturers in the category. The authors find that both the retailer’s private label and the captain benefit from CC because of pricing and assortment changes. They also find that some competing manufacturers benefit from CC while others suffer. Specifically, the manufacturers that closely compete with the captain benefit, whereas the manufacturers that are in close competition with the private label suffer because the retailer protects its private label. The authors show that category sales would have been higher if the retailer had not protected its private label. This study sheds light on how joint consideration of assortment and pricing, the presence of a private label, and product characteristics may influence the outcomes of CC implementations.
Online Supplement: http://dx.doi.org/10.1509/jm.15.0196
Category management is a commonly used retailing practice in which a retailer treats a product category (i.e., a set of similar products) as a strategic business unit. A product category (e.g., canned vegetables, salty snacks, carbonated beverages) consists of products offered by national brands and may also include private label products offered by the retailer. Category management enables retailers to focus on maximizing category performance, typically measured by the sales or profitability of the entire category, instead of making decisions on a product-by-product basis (Zenor 1994). Prior research in marketing has shown that category management can be beneficial for retailers because it enables them to simplify, coordinate, and thereby improve the process of making assortment, pricing, and other merchandising decisions (ACNielsen 2005; Basuroy, Mantrala, and Walters 2001; Dhar, Hoch, and Kumar 2001).
Effective category management requires a retailer to align its product offerings with evolving consumer needs. Because retailers manage many categories, constant monitoring and interpretation of consumer trends is a costly and laborintensive task for them. Manufacturers typically have a better understanding of consumer needs because their expertise is focused on a much smaller set of products and categories (Blattberg and Fox 1995). The combination of retailers’ lack of resources and manufacturers’ superior category knowledge creates supply chain collaboration opportunities. Accordingly, many retailers manage some of their categories in collaboration with one of their leading manufacturers. These leading manufacturers are often referred to as category captains, and the practice itself is referred to as category captainship (CC; Desrochers, Gundlach, and Foer 2003; Federal Trade Commission 2001, 2003).
Category captainship has become a preferred way of executing category management. General Mills, for example, assisted one retailer in the dry packaged dinners category by replacing slow-moving stockkeeping units with faster-turning products (Progressive Grocer 2011). Abbott Nutrition helped a retailer in the baby food and consumables category by recommending a new planogram with some new products in the assortment and changing prices to reflect the new product mix (Progressive Grocer 2010). In addition, J.M. Smucker Co. helped several retailers in the canned and packaged beverages category by developing new shelf concepts and endcap displays based on consumer insights (Progressive Grocer 2015). In summary, the captain’s recommendations vary across retailers and categories and may affect assortment, pricing, and/or merchandising decisions.
The trade literature has suggested that both retailers and manufacturers can benefit from CC (e.g., Progressive Grocer 2010, 2011, 2015). However, controversies regarding CC have arisen because the captain provides recommendations to the retailer regarding not only its own products but also those of its competitors. Consequently, the captain may have a positive bias toward its own products to the detriment of the competitors’ products. The term “competitive exclusion” has often been used to refer to situations in which the captain uses its position to put its competitors at a disadvantage (Carameli 2004; Federal Trade Commission 2003).
The existing research on CC is based on legal theory (e.g., Wright 2009); surveys (Gooner, Morgan, and Perreault 2011; Morgan, Kaleka, and Gooner 2007); game theoretic models of retailer–manufacturer interactions under CC (e.g., Kurtulus¸ and Nakkas 2011; Kurtulus¸, Nakkas, and U¨ lku¨ 2014; Subramanian et al. 2010); and structural estimation, which enables counterfactual analyses regarding how a hypothetical CC implementation would have affected category decisions and performance (Nijs, Misra, and Hansen 2014). However, empirical evidence on the collaborative and competitive implications of CC is scarce, as retailers are reluctant to share CC data because of antitrust concerns (Nijs, Misra, and Hansen 2014). From a collaborative standpoint, the existing literature on CC does not provide a formal analysis of an actual CC implementation to assess whether and how CC benefits the retailer and the captain. From a competitive standpoint, there is no empirical evidence regarding whether CC benefits or hurts the competing manufacturers. Accordingly, our goal in this article is to empirically study the implications of CC for the retailer, captain, and competing manufacturers using a unique data set collected during a CC implementation.
Our data set contains 52 weeks of product-level measures for all products in a shelf-stable food category with significant private label presence. During this time period, the retailer conducted a full category review in collaboration with one of the largest manufacturers in the category. Because the retailer treated this category as a revenue generator, the category review mainly focused on potential ways to increase category sales revenue. Recommendations generated during this review led to a new assortment and pricing strategy, which was implemented in week 21. By comparing sales revenue during the preand post-CC implementation periods (i.e., the first 20 weeks and the last 32 weeks in the data), we investigate the following research questions:
- Does the retailer benefit from CC? If it does, are the benefits driven by pricing or assortment changes? Are there any other drivers beyond assortment and pricing?
- What is the impact of CC on different manufacturers in the category, including the private label and captain? Is it possible for the competing manufacturers (i.e., all manufacturers except the private label and captain) to benefit from CC?
- What determines whether a competing manufacturer benefits or suffers from CC?
Previous literature has raised similar questions (e.g., Ailawadi et al. 2010), but data-driven answers are not readily available. Addressing these questions through actual CC implementation data has several advantages over prior research on CC. First, the literature on CC considers pricing and assortment in isolation. For instance, Kurtulus¸ and Nakkas (2011) and Kurtulus¸ et al. (2014) focus on how CC influences a retailer’s assortment, whereas Kurtulus¸ and Toktay (2011) and Nijs, Misra, and Hansen (2014) study how CC affects prices for a given assortment. While the existing models cannot fully capture the CC phenomenon because of their focus on only one possible lever, our study is unique in jointly considering pricing and assortment.
Second, many retailers view the private label as a key component of a successful category strategy (Kumar and Steenkamp 2007). Accordingly, retailers often use private label performance as one of the metrics to evaluate category performance (ACNielsen 2005, Chapter 6). Despite its practical relevance and importance, the impact of CC on private label products has not been considered in the existing CC literature. Our study shows how private label presence may affect category decisions and performance in the CC context.
Third, the assortment literature in marketing has shown that products with similar attributes (e.g., same size) are more likely to compete for demand (e.g., Rooderkerk, Van Heerde, and Bijmolt 2011, 2013). Following this literature, we specify an attribute-based demand model, whereas the existing CC literature has used more stylized models (e.g., linear demand model). Our study sheds light on how a product’s similarity to the captain’s products and private label products may affect whether such a product benefits or suffers from CC.
In summary, while our empirical findings are based on one CC implementation, our contribution stems from examining several factors that have not been considered in the CC literature. In particular, our study informs practitioners and researchers by demonstrating how joint consideration of assortment and pricing, the presence of a private label, and product characteristics may influence the outcomes of CC implementations.
The rest of this article is organized as follows. First, we summarize the relevant research and build a conceptual framework. We then describe our data set and empirical model. Next, we present our findings regarding the impact of CC. Finally, we conclude with a summary of our results and their implications as well as a discussion of the limitations of our study.
Theoretical Background and Conceptual Framework
In this section, we first provide a theoretical background for our study by reviewing previous research on pricing, assortment, and merchandising in the context of category management. Because these literature streams are vast, we limit our attention to studies that are most relevant to our setting. We then use this theoretical background to develop our conceptual framework.
Theoretical Background
Retail pricing under category management. Shifting from brand-centric management of retail prices to jointly setting prices in an entire category can lead to a significant improvement in category performance (e.g., Basuroy, Mantrala, and Walters 2001; Zenor 1994). Nonetheless, several factors make pricing a challenging task for retailers in the CC context. First, retailers should pay attention to cross-price effects because sales of a product depend on not only its own price but also the prices of substitute products in the category (e.g., Besanko, Dube´, and Gupta 2005; Kadiyali, Chintagunta, and Vilcassim 2000). Second, pricing decisions are influenced by the strategic role of the category (e.g., sales or profit maximization) for the retailer (ACNielsen 2005, p. 115). For instance, Zenor (1994) and Basuroy, Mantrala, and Walters (2001) show that implementing category management to maximize category profits can lead to higher retail prices compared with a brand-centric management of retail prices. In contrast, Dhar, Hoch, and Kumar (2001) show that best-performing retailers in terms of category sales are the ones with the lowest retail prices.
Third, the presence of a private label program is a key driver of a retailer’s pricing decisions (Chintagunta, Bonfrer, and Song 2002). Many retailers give preferential treatment to private label products (Kumar and Steenkamp 2007) because they typically have higher percentage margins (Ailawadi and Harlam 2004), increase a retailer’s bargaining power relative to national brands (Chintagunta, Bonfrer, and Song 2002; Pauwels and Srinivasan 2004), and can improve store loyalty in the case of high-quality private label products (Corstjens and Lal 2000). Such a preferential treatment affects retail prices. For instance, Chintagunta (2002) shows that the retailer’s desire to increase private label market share drives the retail prices of private label products below the levels obtained under a category profit-maximization objective. In addition, private label introduction may decrease the retail prices of the national brands in the category (e.g., Chintagunta, Bonfrer, and Song 2002; Du, Lee, and Staelin 2005).
Despite increasing a retailer’s bargaining power, increased private label presence may not necessarily increase store traffic or revenues (Pauwels and Srinivasan 2004) and may lead to lower dollar margins per unit (Ailawadi and Harlam 2004). Moreover, pushing private labels too far may hurt store loyalty as a result of an inverted U-shaped relationship between a household’s private label share and store loyalty (Ailawadi, Pauwels, and Steenkamp 2008). In summary, private label presence exacerbates the difficulty of a retailer’s pricing decisions because it requires the retailer to retain a balance between private label and national brands.
Finally, transitioning from traditional category management, in which the retailer makes decisions on its own, to CC can influence prices. The research studying this transition has modeled CC as an alliance between the retailer and the captain in settings in which the retailer tries to maximize category profits (Kurtulus¸ and Toktay 2011; Nijs, Misra, and Hansen 2014). These studies predict steep price decreases for the captain’s products because the formation of an alliance between the retailer and the captain mitigates double marginalization, which enables the retailer to offer the captain’s products at significantly lower prices. The competing manufacturers, in contrast, continue to suffer from double marginalization and make relatively minor price reductions as a response to the downward price pressure exerted by the captain (Kurtulus¸ and Toktay 2011; Nijs, Misra, and Hansen 2014). That is, the captainship literature has suggested that CC leads to a decline in retail prices, with the steepest price declines for the captain’s products.
Assortment planning under category management. Finding the right assortment is a challenging task because most categories have tens or even hundreds of products, which makes it difficult for retailers to determine the right assortment size and composition. There is mixed evidence regarding the relationship between assortment size and category performance. On the one hand, a broader assortment is associated with higher sales (e.g., Borle et al. 2005; Dhar, Hoch, and Kumar 2001) because a large assortment makes it more likely for consumers to find a product that matches their needs (Boatwright and Nunes 2001). On the other hand, reducing assortment size by removing lowselling products has no or positive impact on category sales in some settings (e.g., Broniarczyk, Hoyer, and McAlister 1998; Dre‘ze, Hoch, and Purk 1994). This is because consumer choice is affected by consumers’ perception of variety, which is determined not only by the assortment size but also by other factors, such as the availability of consumers’ favorite products (Broniarczyk, Hoyer, and McAlister 1998) and the number of brands in the category (Briesch, Chintagunta, and Fox 2009). Consequently, retailers struggle in aligning their assortments with consumer needs.
Even for a fixed assortment size, finding the right assortment composition is nontrivial because products within a category can cannibalize one another’s sales. The complexity of assortment decisions has been tackled by attribute-based models that parsimoniously capture the interactions among many products. Fader and Hardie (1996) have developed a consumer choice model that characterizes each product by its attributes (e.g., brand, size, color). Subsequent research has shown that products with similar attributes (e.g., same size) are more likely to compete for demand within a category (Rooderkerk, Van Heerde, and Bijmolt 2011). Consequently, attribute-based models have been used to make assortment decisions (e.g., Rooderkerk, Van Heerde, and Bijmolt 2013).
Although the assortment research in the context of category management has focused on the retailer’s assortment decisions, several articles have studied assortment decisions in the context of CC. For instance, Kurtulus¸ and Nakkas (2011) show that CC leads to higher category sales because the captain’s consumer insights enable the retailer to offer an assortment that is better aligned with consumer needs. Furthermore, the captain can increase its own sales through excluding some competing brands from the category so that consumers switch to the captain’s products (Kurtulus¸ and Nakkas, 2011; Kurtulus¸ et al. 2014). While CC benefits the retailer and the captain, it may hurt or benefit competing manufacturers. On the one hand, CC can lead to a decline in the shelf space allocated to competing manufacturers (Kurtulus¸ and Toktay 2011) or the captain may recommend removing a competitor’s product from the assortment (Kurtulus¸ et al. 2014). On the other hand, CC is beneficial for competing manufacturers’ products introduced to the assortment on a CC implementation (Kurtulus¸ and Nakkas 2011; Kurtulus¸ et al. 2014). Overall, the CC literature suggests that both the captain and the retailer benefit, whereas the competing manufacturers may benefit or suffer after CC is implemented.
Merchandising under category management. Although our data set does not have any variables associated with merchandising, we briefly discuss merchandising efforts in the context of CC to provide a more comprehensive theoretical background for our study. In practice, the captain’s merchandising efforts focus mainly on providing the retailer with demand-enhancing services, such as shelf design, shelf-space allocation, and design of endcap displays (Subramanian et al. 2010, p. 1741). The literature has provided support for the benefits of such demand-enhancing services. For instance, a product’s sales can improve by increasing the number of shelf facings allocated to that product (e.g., Chandon et al. 2009) and changing its shelf placement (e.g., Atalay, Bodur, and Rasolofoarison 2012; Dre‘ze, Hoch, and Purk 1994). Moreover, taking into the account the decision sequence consumers follow to narrow down options (e.g., first flavor, then size) in organizing shelf displays (Nowlis, Dhar, and Simonson 2010) and designing shelves and aisles to increase the proximity of complementary products can improve category performance (e.g., Bezawada et al. 2009; Dre‘ze, Hoch, and Purk 1994).
The CC literature has also considered the benefits of demand-enhancing efforts. In particular, Subramanian et al. (2010) focus on the impact of the captain’s demand-enhancing services on category stakeholders. They suggest that such services benefit the retailer by improving category performance but may benefit or hurt the competing manufacturers depending on whether they increase overall category demand or shift demand from one brand to another. Similarly, Kurtulus¸, Nakkas, and U¨ lku¨ (2014) consider CC in a context in which the total category demand is a function of the effort that the retailer or the captain exerts into demand-enhancing services. In their setting, the competing manufacturers benefit (suffer) from the captain’s merchandising efforts if the captain keeps them in (removes them from) the assortment. In summary, the CC literature has documented that CC can improve category performance through the captain’s merchandising efforts, and such efforts might benefit or hurt competing manufacturers.
Conceptual Framework
Our theoretical discussion reveals that the key drivers of category decisions in our context are (1) category objective, (2) private label presence, (3) collaboration with a captain, (4) cross-price and cross-assortment effects, and (5) attributebased substitution among products. Drawing on these drivers, we have developed a conceptual framework (see Figure 1).
In light of the main objective of the CC implementation we study (i.e., improving category sales), our framework links CC to sales. This is aligned with practice, in which assessing category performance through sales (rather than profitability) is common in the CC context.1 Although profit maximization may be a better goal for the retailer in some categories, asking the captain to improve profitability would require the retailer to share the competing manufacturers’ proprietary information (e.g., wholesale prices) with the captain. However, legal authorities discourage retailers from sharing one manufacturer’s proprietary information with another (Desrochers, Gundlach, and Foer 2003; Federal Trade Commission 2001). As a result, many retailers define category objectives in terms of sales in CC implementations (Kurtulus¸, Nakkas, and U¨ lku¨ 2014; Kurtulus¸ et al. 2014).
Our framework posits that the captain might influence sales by making pricing, assortment, and/or merchandising recommendations. Moreover, manufacturer type (i.e., private label, captain, and competing manufacturers) and product attributes might moderate these recommendations and their impact on sales. In particular, manufacturer type determines the extent to which a manufacturer has control over category decisions, and product attributes determine substitution patterns within a category.
Impact of CC on retail prices. Our theoretical discussion suggests that implementing CC creates an alliance between the retailer and the captain, which in turn enables the retailer to lower retail prices for the captain’s products. It also reveals that the retailer may choose to lower retail prices for private label products to boost private label sales. Combining these observations with the notion that the CC implementation we study attempts to increase category sales, we expect that CC will reduce retail prices for the captain’s and private label products.
Two opposing forces affect retail prices for the competing manufacturer’s products. On the one hand, the competing manufacturers may respond to the captain’s and private label’s price reductions by reducing their wholesale prices, which may allow the retailer to lower those products’ retail prices. On the other hand, product attributes and cross-price effects may influence pricing decisions. In particular, the competing manufacturers’ products that are in direct competition with the captain’s products and/or private label products may be priced high to ensure that the captain’s products and private label products are better positioned against their close competitors. Although we do not have data on wholesale prices, these two forces suggest that the retail prices for the competing manufacturers’ may increase or decrease after the CC implementation.
Impact of CC on assortment. Our theoretical discussion suggests that the captain’s products may receive a preferential treatment after CC. Furthermore, the captain’s attempt to increase private label sales may create a similar preferential treatment for the private label. Drawing on these observations, we expect that the CC implementation will increase the assortment presence of the captain’s products and private label products. Our theoretical discussion also suggests that the competing manufacturers’ products may experience an increase or a decline in their assortment presence. From the findings related to the captain’s opportunistic behavior, we anticipate that the competing manufacturers’ products that are close substitutes to the captain’s products may experience a decline in their assortment presence. In contrast, the competing manufacturers’ products that are not in direct competition with the captain’s products may experience an increase in their assortment presence so that category sales are increased. Because the retailer may protect its private label, a product’s similarity to the private label may also negatively influence its assortment presence.
Impact of CC on sales. Our conceptual framework depicted in Figure 1 suggests that CC may influence a product’s sales through three mechanisms: pricing, assortment, and merchandising. First, the impact of pricing on a product’s sales will be determined by a combination of own- and cross-price effects. We expect the own-price elasticity to be negative and the cross-price elasticity to be positive. Second, the impact of assortment changes on a product’s sales will be determined by own- and cross-assortment effects. We expect the own-assortment elasticity to be positive and the cross-assortment elasticity to be negative. Third, the impact of CC might go beyond pricing and assortment changes because the captain may also make merchandising recommendations. In line with our theoretical discussion, we expect that the captain’s recommendations on pricing, assortment, and merchandising (if provided) will each have a positive impact on the sales of a private label product or a product that belongs to the captain. However, the impact of those recommendations on a competing manufacturer’s product might be positive or negative, depending on whether that product is in close competition with the captain’s products or private label products, as determined by the products’ similarity.
Research Setting
Implementation Summary
The retailer we study is a grocery chain that serves multiple states within one of the major geographical regions in the United States. It is one of the largest grocery retailers in its region in terms of market share and sales volume. The retailer has a reputation for offering high-quality products. The category we study is a mature, shelf-stable food category (i.e., a center-store grocery product). As per the retailer’s request, the soon-to-be captain manufacturer provided the retailer with a strengths, weaknesses, opportunities, and threats (SWOT) analysis of the category.
The main findings of the captain’s SWOT analysis, which we obtained from a presentation given by the captain to the retailer prior to the CC implementation, were as follows. The main strengths were the retailer’s strong reputation as the provider of high-quality grocery products, its local market familiarity, and its loyal customer base. The main weakness was the retailer’s lack of category knowledge, leading to poor assortment and pricing decisions. In particular, the captain had identified that despite offering a similar assortment with respect to its competitors, the retailer’s average unit price of $.72 in this category was $.07 higher than the rest of market. Moreover, the average unit price for the retailer’s private label was $.66, which was $.13 higher than its competitors’ private labels. The biggest opportunities were to leverage the retailer’s reputation and the captain’s category knowledge to improve private label performance and increase category revenues. The main threat for the retailer was to lose further market share to its competitors. In light of the captain’s SWOT analysis, the retailer decided to receive additional help through a formal CC implementation. Under the captain’s guidance, assortment and pricing recommendations were generated and implemented.
Data Description
Our data set spans 52 weeks and consists of weekly Universal Product Code (UPC)–level measures for all products in the category. In week 21, the retailer modified the category using the services of a captain, which is the second-largest manufacturer in the category after the private label. Our data are aggregated to the firm level and include the following metrics for product I in week t:
• Sales revenue, rit: The cumulative sales revenue generated from all stores.
• Sales quantity, qit: The cumulative number of units (packages) sold across all stores.
• Product distribution, dit: The percentage of stores that carry a particular product, weighted by the relative size of those stores. Formally, this measure of distribution is referred to as the all commodity volume–weighted distribution, which refers to the total annual sales revenue of a given store.
• Product size, zi: Volume of product I in ounces.
The aggregated structure of our data set prevents us from observing store- and product-level promotions and discounts. However, having access to rit, qit, dit, and zi enables us to compute the average per ounce price consumers paid for product I in week t. Specifically, when product I is carried in the assortment in week t (i.e., when dit > 0), we define its average volumetric price (i.e., price per ounce) as
(1) pit = rit. qitzi
Moreover, our data set includes a base price for each product. When a product is not carried in the assortment in a particular week (i.e., when dit = 0, qit = 0, and rit = 0), we use this base price (after converting it to a volumetric price) as pit.
Figure 2, Panel A, shows that weekly sales are higher during the postimplementation period. It also shows that there are three spikes in the weekly category sales in weeks 28, 32, and 47, which correspond to Thanksgiving, Christmas, and Easter, respectively. Thus, at least some of the increase in sales might be driven by seasonality and increased consumption in these three weeks. Presumably, when the consumption of a food item increases, consumers are more likely to search the web for recipes with the item of interest as an ingredient. Thus, we would expect the search frequency to capture a portion of variation in weekly sales. Figure 2, Panel B, shows the relative search frequency of the search phrase “category name recipe” in log scale. We obtain search frequency data from Google Trends, which is a publicly available web page providing time series data regarding the relative search frequency of a keyword or search phrase. We observe a strong positive correlation of .83 between weekly sales and web search patterns. Accordingly, we use gt, which denotes the web search frequency for the category in week t, to control for seasonality. We also control for holiday effects by including a dummy variable, ht, that assumes a value of 1 if a national holiday occurs in week t.
There are ten manufacturers in our data set. Each manufacturer owns one brand in the category. Accordingly, we use manufacturer and brand interchangeably herein. Table 1 provides an overview of these manufacturers offering a total of 110 UPCs. The private label has the largest share both in terms of the number of UPCs and sales revenue. It offers low-price products with an average volumetric price of $.07 during the preimplementation period. The category captain is the second largest manufacturer. It offers premium products with an average volumetric price of $.11 during the preimplementation period. The remaining eight manufacturers offer a total of 44 UPCs. Some competing manufacturers offer low-price products, whereas others offer premium products. For instance, the average volumetric prices for manufacturers 3 and 4 during the preimplementation period are $.08 and $.13, respectively.
The weekly average category sales are 37.24% higher after CC. The increase in sales after the implementation is disproportionately higher for the captain’s products compared with the competing manufacturers’ products. For example, Table 1 shows that the total average weekly sales of the captain increased from $18,587 to $40,076, whereas the third manufacturer’s total average weekly sales decreased from $10,739 to $6,947.
The key decision unit in a category management initiative is the subcategory or switching level, rather than the category itself. In this context, a subcategory is defined as a collection of products presumed to have similar product characteristics. For example, in the sugar and sweeteners category, subcategories would include brown sugar, granulated sugar, powdered sugar, artificial sweeteners, and so on. Our data set consists of nine subcategories. Larger subcategories typically have more manufacturers. The private label and the captain have a presence in all subcategories.
Because a subcategory is more homogeneous than a category in terms of product characteristics, it is usually assumed that the collection of products in a subcategory are perfect or near-perfect substitutes (Ko¨k and Fisher 2007). Figure 3 illustrates that such a substitution pattern is unlikely to hold in our data set. For instance, Figure 3, Panel A, shows that most products in subcategory 2 have a volume of ~15 oz., but ~8 oz. and ~11 oz. products are also available in the assortment. Similarly, Figure 3, Panel B, illustrates that some ~15 oz. products in subcategory 4 are relatively cheap, with volumetric prices around $.09 per ounce, whereas another ~15 oz. product in the same subcategory has a volumetric price of $.14 per ounce. These examples indicate that substitution among similar products (in terms of volumetric price and size) can be higher than substitution among dissimilar ones. Such a substitution pattern is likely to affect assortment changes made during the CC initiative. Accordingly, we control for product similarity in our empirical analysis. We measure the similarity between products I and j in the same subcategory s using the following metric:
In Equation 2, zi is the package size, and pi ” ð1=20Þåt2=01pit is the average volumetric price prior to the CC implementation for product i. In addition, psmax and pms in are the average volumetric prices of the most expensive and the cheapest products in subcategory s, respectively. Similarly, zsmax and zsmin are the sizes of the largest and the smallest products by volume in subcategory s, respectively. psmax - psmin and zms ax - zsmin allow us to normalize the impact of volumetric price and size, respectively, so that dij takes values in the unit interval ½0, 1?. As such, two products with the exact same size and average volumetric price would have a similarity score of 1.2
Figure 4, Panel A, demonstrates the change in the average distribution of every UPC in our data set after CC. We observe that a vast majority of products offered by the private label (manufacturer 1), the category captain (manufacturer 2), and manufacturer 4 experience an increase in their distributions. Conversely, most of manufacturer 3’s products experience a decline in their distributions. Recall from Table 1 that the average weekly sales of manufacturers 1, 2, and 4 increase, whereas manufacturer 3 experiences a decline in sales after CC. These observations are consistent with our conceptual framework suggesting that the assortment changes made during the implementation will benefit the private label and the captain’s products and that such changes may explain changes in sales.
Figure 4, Panel B, shows the change in the average volumetric price of every UPC in our data set. The average volumetric price declines for most products after CC. This observation is aligned with the theoretical predictions in the literature regarding the impact of CC on prices (Kurtulus¸ and Toktay 2011; Nijs, Misra, and Hansen 2014). Consistent with our conceptual framework, declining prices could be another reason for the increase in sales after CC.
Model
We study the impact of CC on sales by building an empirical model based on our conceptional framework illustrated in Figure 1. Specifically, we estimate a simultaneous system of equations in which we allow CC to exert both a direct and indirect (through price and assortment) influence on sales. We specify each equation in log-log form, which enables us to interpret a model coefficient as the expected proportional change in the dependent variable per proportional change in the independent variable (Gelman and Hill 2006). Furthermore, log-log model specification leads to mathematically tractable expressions regarding the impact of the CC implementation on prices, assortment, and sales. Logarithmic demand models have been used extensively in both marketing and economic applications (Hanssens, Parsons, and Schultz 2003; Lilien, Kotler, and Moorthy 1992). Because our data follow a panel structure, we fit a hierarchical model that allows for heterogeneity in parameters for each individual UPC. We begin by specifying the direct impact of CC and other demand-generating factors on volumetric sales using the following specification:
(3) logðvitÞ = b0i + b1i logðpitÞ + b2ipit + b3i logðditÞ + b4idit + b5i logðgtÞ + b6iht + b7it + b8iIðt > tÞ + esit, where
• vit are volumetric sales (i.e., qit · zi) for UPC I in week t,
• pit is an index of competitive prices for UPC I in week t. We construct this index by computing a weighted average logarithmic volumetric price of all other products in UPC i’s subcategory. A sðiÞ denotes Formally, the set of subcategory, is the weight of product j, which captures the notion that products with similar attributes (i.e., high wij values) are closer competitors.
• dit is an index of competitive distribution for UPC I in week t. We measure the competitive distribution index as a weighted average logarithmic distribution of all other products in UPC i’s subcategory. Formally, dit = åj2A wsðiÞnfig ij logðdjtÞ.3
• t is a linear indicator of time that enables us to model trend in the time series.
• is an indicator variable that is equal to 0 in the weeks
preceding CC and is equal to 1 in the weeks following. The variable t denotes the week of implementation.
Finally, as defined in the previous section, gt and ht denote the search frequency and holiday dummies, respectively. Equation 3 allows us to understand the direct impact of CC on sales performance through b8i. Because we define merchandising as all demand-enhancing actions excluding pricing and assortment, we interpret b8i as the impact of merchandising on product i’s volumetric sales.4 We capture the indirect impact of CC on sales through pricing and assortment by building a system of equations as follows: In Equations 4 and 5, coefficients control for seasonality and holiday effects, whereas coefficients a3i and g3i capture the impact of CC on UPC i’s price and distribution, respectively.
We allow for correlation in the cross-equation error terms by assuming a multivariate normal structure for the joint distribution of fesi, epi, eidg ~ Nð0, SiÞ. By doing so, we control for the potential of unobserved demand shocks that could affect our ability to infer the true impact of CC on sales performance. Si is a 3 · 3 covariance matrix that captures the degree to which unobserved shocks to our demand system jointly influence price, distribution, and sales.
Because our model is estimated using hierarchical Bayesian methods, we complete the hierarchy by specifying a distribution of heterogeneity over the complete vector of regression coefficients (Rossi, Allenby, and McCulloch 2005):
(6) where wi are UPC-specific characteristics that can moderate the relationship between CC and sales, price, and distribution. D is an estimated matrix of coefficients that characterize this relationship, and W is a full covariance matrix that captures crossUPC correlation in the estimated coefficients. Included in wi are the following variables:
• Iði2CCÞ is an indicator variable that is equal to 1 if UPC I belongs to the category captain. The reference level for this variable is the private label, so the resulting coefficient should be interpreted as one deviation away from the average private label effect.
• Iði2CSÞ is an indicator variable that is equal to 1 if UPC I belongs to a manufacturer that is in the competitive set, CS. The competitive set includes all manufacturers in the category, except the private label manufacturer and the captain. This variable’s effect should also be interpreted with respect to the private label.
• SimPL I is a continuous variable that measures the similarity between UPC I and the private label products in UPC i’s subcategory. Let A PL s, A CC s, and A CS s denote the subset of products offered by the private label, captain, and competing manufacturers in subcategory s, respectively. For UPC i, we calculate SimPL I as
In words, SimPL I is the maximum similarity between UPC I and the private label products in its subcategory. A high SimPL I value indicates that UPC I has a close substitute offered by the private label manufacturer. By definition, SimPL I = 1 for private label products. As we discussed in the “Theoretical Background” section, similar attribute-based substitution models are used in the choice modeling and assortment literature streams (e.g., Hoch, Bradlow, and Wansink 1999; Rooderkerk, Van Heerde, and Bijmolt 2011, 2013).
• SimCC I is a continuous variable that measures the similarity between UPC I and the category captain’s products in UPC i’s subcategory. We compute this variable as SimCC I = maxj2A CC s dij. By definition, SimCC I = 1 for the captain’s products.5
We include Iði2CCÞ, Iði2CSÞ, SimPL I, and SimCC I in our upper-level model as covariates because the impact of the CC implementation may differ by manufacturer type as well as whether a product is in direct competition with a product offered by the private label manufacturer or the captain.We estimate the parameters of the hierarchical model specified by Equations 3, 4, 5, and 6 using standard Bayesian methods (Rossi, Allenby, and McCulloch 2005). Specifically, we use a hybrid sampler where the collection of regression parameters for each UPC, fbi, ai, gig, are drawn using the Metropolis–Hastings algorithm. Conditional on a realization of the regression parameters for all UPCs, we use a Gibbs draw for both Si and the parameters in the upper level, D and W.We ran the sampler for 100,000 iterations and used the final 50,000 draws for inference.
After estimating model parameters, we quantify the effect of the CC implementation by measuring its impact on category sales revenues. Using volumetric sales and volumetric prices, the sales revenue for UPC I in week t can be expressed as rit = vitpit. In the log form, we have logðritÞ = logðvitÞ + logðpitÞ. Using the expression for logðvitÞ specified in Equation 3, we obtain
Accordingly, we measure the impact of the CC implementation on UPC i’s sales revenues by taking a partial derivative of Equation 8 with respect to I, the CC indicator variable. That is,
Because CC influences prices and distribution, the right-hand side of this equation has partial derivatives of the price, crossprice, distribution, and cross-distribution variables with respect to I. Differentiating the price and the distribution equations of our empirical model, 4 and 5, with respect to I gives and, respectively. Therefore, we can rewrite Equation 9 as
In Equation 10, captures the total impact of the changes in UPC i’s price and the competitors’ prices, captures the impact of the changes in UPC i’s and its competitors’ distribution, and b8i captures the impact of merchandising efforts on UPC i’s sales. In the next section, we use Equation 10 to measure and decompose the impact of CC on the entire category as well as the individual manufacturers.
Results
Table 2 shows the coefficient estimates for the sales, price, and distribution equations specified in Equations 3, 4, and 5, respectively. We report the posterior mean and 95% interval for each coefficient. The CC indicator coefficient in the sales equation indicates that merchandising efforts increase category sales by 8.6%, which is significant at p < .05. (Hereinafter, we report statistical significance at p < .05.) We also find that the search frequency variable is positively correlated with sales. The holiday indicator variable is insignificant, which implies that the search frequency variable captures not only seasonality but also the sales spikes observed during holidays. Consistent with the well-known impact of pricing, a product’s sales decrease in its own price and increase in its competitors’ prices. Sales increase in own-distribution, but we do not find a direct relationship between competitive distribution and sales.6 Finally, there is no time trend in sales.
The CC indicator coefficient in the price equation indicates that CC leads to a 9.2% decline in volumetric prices, which is statistically significant. Both the holiday indicator and search frequency variables are negative and significant, indicating that the sales increases during the holiday weeks are in part due to the steep price reductions in those weeks. The CC indicator coefficient in the distribution equation is insignificant. That is, we do not observe a systematic increase or decline in the overall distribution offered by the retailer in this category, on average.
After estimating the model coefficients, we use Equation 10 to quantify the impact of CC on different stakeholders. Specifically, we first use the posterior distributions of the coefficients that appear in Equation 10 to obtain the impact of merchandising, assortment, and pricing changes on UPC i’s sales revenues. Then, we calculate the impact of CC on a product subset (e.g., the captain’s products) by taking a weighted average of the impacts on each UPC in that subset, where the weight for each UPC equals its preimplementation market share within that subset. Table 3 shows the impact of CC on the entire category as well as the manufacturers.
Impact of the CC Implementation on Sales
Table 3 shows that the increase in category sales that can be associated with CC is 19.1%, which is statistically significant. The pricing changes boost category sales by 7.6%. The assortment changes increase sales by 2.9%. Finally, merchandising efforts lead to a 8.6% increase in category sales. The impacts of pricing, assortment, and merchandising on category sales are all statistically significant. Table 3 also shows that CC leads to a 13.9% increase in private label sales and a 42.4% increase in the captain’s sales. The impacts of merchandising, pricing, and assortment are all significant for both the private label and the captain’s products.
Our analysis shows that some manufacturers benefit from CC, whereas others suffer as manifested by declining sales. For instance, Table 3 shows that CC decreases manufacturer 39s sales by 18.2%. This decrease is driven by the decline in the presence of manufacturer 3’s products in the assortment. Manufacturer 6 experiences a sales decline as well. The decrease in manufacturer 6’s sales due to CC is 33.4%. A large portion of this decrease (i.e., 15.6%) is driven by the decline of manufacturer 6’s presence in the assortment. Contrary to manufacturers 3 and 6, manufacturer 4 benefits because CC increases its sales by 37.1%. Most of this increase (i.e., 19:3%) is due to assortment changes. The impact of CC on manufacturer 5’s sales is statistically insignificant. The remaining four manufacturers have relatively small sales, which makes it difficult to provide reliable estimates at a manufacturer level. However, the total impact of CC on these four manufacturers is positive, as manifested by a 13.1% increase in their total sales that can be attributed to CC.
There are two main observations that emerge from analyzing the changes in sales by manufacturer. First, both the private label and the category captain’s products benefit from CC. Second, some competing manufacturers also benefit from CC, whereas others suffer as manifested by their declining presence in the assortment and lower sales. While the first observation is aligned with the theoretical predictions in the literature, the second observation raises a follow-up question:
Why do some competing manufacturers benefit from CC, whereas others experience adverse consequences? We address this question next.
Competitive Implications of Category Captainship
In this subsection, we focus on the products offered by the competing manufacturers and establish a link between product attributes and assortment changes. We begin our analysis with a motivating example. Table 4 provides a summary of product attributes in subcategory 9, which is the smallest subcategory in our data set. Comparing the pre- and postimplementation average distributions shows that product 2 offered by manufacturer 3 is removed from the category after CC.7 A closer examination of product attributes reveals that product 1, which is a private label product, and product 2 have the same size and average volumetric price. Thus, one potential explanation for the removal of product 2 from the assortment is an attempt to increase private label sales by excluding the private label’s close competitors.
In light of the aforementioned example from subcategory 9, we conjecture that a competing manufacturers’ product is more likely to experience a decline in its distribution if there is a close substitute to that product offered by the private label manufacturer. Similarly, a product’s similarity with the category captain’s products may also have a negative impact on its assortment presence after CC. The hierarchical structure of our empirical model enables us to test our conjectures regarding the potential negative impact of a product’s similarity to the private label and/or the captain’s products. Table 5 reports the upperlevel model coefficients for the CC indicator variables in the sales, price, and distribution equations (i.e., Equations 3–5). Because manufacturers 3 and 6 suffer and manufacturer 4 benefits from the assortment changes, we focus on the CC indicator coefficient of the distribution equation, g3i. For a particular UPC I offered by a competing manufacturer, our hierarchical model specification implies that the estimated value of g3i can be written as
where the second line follows because Iði2CCÞ = 0 and Iði2CSÞ = 1 for a product offered by a competing manufacturer. Equation 12 captures the estimated change in the assortment presence of a product offered by a competing manufacturer as a function of its similarity to the private label and the captain’s products. The intercept, -.067, is statistically insignificant. The coefficient of SimiPL, which measures the similarity between UPC I and the private label products in the same subcategory, is negative and significant. That is, a product is more likely to experience a decline in its assortment presence if there is a similar private label product in the assortment. Conversely, the coefficient of SimiCC, which measures the similarity between UPC I and the captain’s products in the same subcategory, is positive and significant. This finding implies that the competing manufacturers’ products that are similar to the captain’s products are more likely to increase their presence in the assortment during the postimplementation period.
These findings are consistent with the objectives of the CC implementation we study. In particular, decreasing the assortment presence of manufacturers 3 and 6, which are in direct competition with private label because of their low prices, leaves the private label as the most affordable option in each subcategory. Consequently, private label performance improves. Furthermore, increasing the assortment presence of manufacturer 4, which offers premium products, enables the retailer to enrich its assortment and thereby increase overall category sales. While the protection of private label is in line with the existing literature (e.g., Chintagunta 2002), the increased presence of manufacturer 4 contrasts the literature’s predictions on the negative impact of CC on the competing manufacturers.
What would have happened if the retailer had not protected its private label?8 We address this question by revisiting Equation 11. This equation suggests that the retailer may be protecting its private label by reducing the assortment presence of the competing manufacturers’ products that are in close competition with private label. Thus, we conjecture that such products would have had a greater assortment presence in the absence of private label protection. Increasing the assortment presence of those products would have influenced category sales through own- and cross-distribution effects.
We operationalize this conjecture by considering a scenario in which the coefficient for SimPi L equals zero instead of its estimated value, -.249. Setting this coefficient to zero implies that a product’s similarity to private label does not lead to a decline in its assortment presence after CC. For instance, revisiting the example presented in Table 4, product 2 in subcategory 9 has SimiPL = 1 because product 1, which is a private label product, has the same size and average volumetric price as product 2. Equation 11 suggests that this product’s similarity to private label is associated with a -:249 · SimPi L = - :249 decline in its assortment presence. Thus, in the absence of private label protection, we conjecture that this product’s postimplementation distribution would have been higher by .249. This change would have also increased the cross-distribution values for the other products in the same subcategory. Applying the same logic to all products in the category enables us to calculate the expected own- and crossdistribution levels for each product in an alternative setting in which the private label is not protected. After calculating the new own- and cross-distribution levels for each product, we plug them into Equation 3 to calculate the impact of CC on each product’s sales in this alternative setting.
Figure 5 reports how switching from the original CC implementation setting to an alternative setting in which the private label is not protected changes the impact of CC on the entire category as well as each manufacturer. In the alternative setting, the overall category sales would have been 4.1 percentage points higher than the increase we estimate under the original setting. That is, the impact of CC on category sales is 19.1% in the original setting as reported in Table 3, whereas it is 23.2% in the alternative setting. Private label sales decline by 3.2 percentage points when private label is not protected. However, its main competitor, manufacturer 3, experiences a 13.8-percentage-point increase in its sales. The sales changes for the remaining manufacturers in the category are mixed because changing SimPi L affects not only a product’s own distribution but also the distribution of the remaining products in the category. It is important to note that although total category sales are expected to increase under this alternative regime, this finding does not necessarily imply that the retailer made a mistake in protecting the private label. Because we do not observe product margins, it is possible that the increase in sales could be associated with a decrease in profitability.
Robustness Tests
We examined alternative model specifications to assess the robustness of our findings. First, it is plausible that fitting a single coefficient for all competing manufacturers in the upperlevel model may not fully capture the differences between manufacturers. Thus, we expanded the upper-level model variables (i.e., wi) to include manufacturer-specific dummy variables. Expanding the set of upper-level model variables did not change our results. Second, we moved the product similarity variables SimiPL and SimCi C from the upper-level model to the lower model (i.e., Equations 3–5) to determine whether this change affects our findings. This alternative model specification also revealed that the competing manufacturers that offer close substitutes to the private label suffer from CC, whereas the competing manufacturers that closely compete with the captain benefit from CC. Our remaining findings (i.e., the impact of CC on various stakeholders) also remained unchanged. Finally, it is plausible that it took the retailer more than one week to fully implement CC. To ensure that implementation delays do not affect parameter estimates, we reran our analysis after dropping weeks 21–24, which gives the retailer four weeks for the CC implementation. Our results remained qualitatively similar in this alternative model specification, in which weeks 25–52 denote the postimplementation period.
Conclusion
Although CC has been implemented by many retailers and has been examined from a theoretical perspective, to the best of our knowledge, our study is a first attempt to empirically examine the outcomes of an actual CC implementation. Our research demonstrates how joint consideration of assortment and pricing, the presence of a private label, and product characteristics may influence the outcomes of CC implementations. Nevertheless, further research is needed to test whether our findings generalize beyond the CC implementation we study. The rest of this section summarizes our main findings and then discusses their implications for practitioners and researchers. We conclude the article by discussing the limitations of our study.
Three key findings emerge from the CC implementation we study that should be of interest to both practitioners and researchers. First, we find that CC improves category sales in our setting. Despite ample anecdotal evidence on the benefits of CC, our research is the first to empirically document the impact of CC on category performance. We also decompose this impact into the effects of different levers used by category captains in practice (i.e., pricing, assortment, and merchandising). Second, we examine the impact of CC on category stakeholders and find that CC benefits the private label as well as the captain. We expected an increase in private label sales in our setting because one of the opportunities identified in the captain’s SWOT analysis was the potential to improve private label performance. We also expected the increase in the captain’s sales because there should be some remuneration for the effort expended to develop and implement a category management strategy. We further find that some competing manufacturers benefit from CC, whereas others suffer. This finding is consistent with the CC literature’s mixed predictions regarding the impact of CC on the competing manufacturers.
Third, and most importantly, we examine how product attributes and the retailer’s desire to protect the private label affect category performance. Despite the existing literature’s predictions regarding the negative impact of the captain’s opportunistic behavior, we find that the competing manufacturers that are in direct competition with the captain benefit from CC. However, the competing manufacturers that are in direct competition with the private label suffer from CC because of a decline in their assortment presence. Indeed, we find that the retailer’s desire to protect its private label prevents the retailer from maximizing category sales. In particular, private label sales would have decreased but the overall category sales would have increased if the retailer had not protected its private label. We discuss the managerial implications of these findings next.
Managerial Implications
Our study leads to four main managerial insights. First, our analysis reveals that a significant portion of the increase in category sales is due to lower prices. From the retailer’s perspective, this finding is important as it illustrates potential perils of using price as a lever. When initiating a CC relationship with a manufacturer, the retailer establishes a collection of performance targets that will be used to assess CC success. Our conversations with category managers indicate that these targets are typically defined in terms of sales rather than profit because retailers are reluctant to release margin information to the captain. Given that sales targets can be achieved by lowering prices in price-sensitive categories, retailers should provide greater specificity in terms of how these targets are to be achieved. Otherwise, the captain may rely heavily on pricing, which is relatively easy to implement but may be costly in the long run because price reductions lower product margins and may heighten consumer sensitivity to price. Improving the assortment, however, is likely more difficult and carries high initial fixed costs, but it should be relatively costless in the long run.
Second, by comparing the pre- and postimplementation sales shown in Table 1, one might conclude that the captain used its authority to adversely influence its competitors. A closer examination of the category dynamics, however, reveals a different story. We find that the manufacturers that closely compete with the captain experience an increase in their assortment presence and sales. The positive impact of CC on the captain’s close competitors is in part driven by the retailer’s desire to increase category sales by offering a more attractive assortment. Recall that the retailer has a reputation for offering high-quality products. Thus, the captain, which offers premium products, would have had difficulty in justifying the removal of other premium brands (i.e., its close competitors) from the assortment. Moreover, given the retailer’s reputation, increasing category sales would have been difficult in the absence of premium brands. These observations suggest that one way for retailers to minimize the risk of opportunistic behavior by the captain is to identify the captain’s close competitors and carefully formulate performance objectives so that it is difficult for the captain to justify the removal of those competitors from the assortment.
Third, our analysis sheds light on how private label presence may influence category decisions and performance in the CC context. Consistent with the existing literature (e.g., Ailawadi and Harlam 2004; Ailawadi, Pauwels, and Steenkamp 2008), our findings suggest that there is a delicate balance between pushing the private label and maximizing category performance. On the one hand, solely focusing on category performance may lead to poor private label performance. On the other hand, putting too much emphasis on private label may hurt national brand sales, which in turn negatively affects overall category performance. Thus, retailers should carefully assess the advantages and drawbacks of private label protection prior to working with a captain.
Finally, private label presence has implications for competing manufacturers. The existing literature suggests that private label introduction, which provides affordable product options for consumers, can be beneficial for premium brands but can harm second-tier (i.e., low-price) brands in a category (e.g., Pauwels and Srinivasan 2004). Similarly, we find that the increased presence of the private label after CC hurts the competing manufacturers that are in direct competition with private label. Thus, our findings suggest that offering products that are differentiated from private label in terms of price and product attributes (e.g., package size) can help the competing manufacturers avoid being excluded from the category.
Implications for Researchers
We find that lower prices, a better assortment, and merchandising efforts all contribute to the increase in category sales in our setting. Given the joint importance of all three levers, conclusions drawn from theoretical models, which typically focus on a single lever, should be treated with caution because such models might underestimate the value of CC. Moreover, our findings regarding the negative impact of private label on the competing manufacturers indicate that theoretical studies should not overlook the private label. In particular, the objective functions used in modeling studies, such as revenue and profit maximization, may not fully reflect the retailer’s desire to protect its private label. Thus, similar to Chintagunta (2002), defining an objective function that balances private label and category performance may be more appropriate to analyze the advantages and drawbacks of CC initiatives. Our approach of modeling demand substitution on the basis of product attributes also differentiates our study from the existing CC literature. Although similar approaches have been frequently used in the assortment literature (e.g., Fader and Hardie 1996; Rooderkerk, Van Heerde, and Bijmolt 2013), the CC literature uses more stylized models (e.g., linear demand models) to capture demand substitution. Our findings suggest that an attribute-based demand substitution model may lead to more precise inferences on the competitive implications of CC.
Limitations and Future Research
Our article constitutes an important contribution to the literature on CC in large part because the uniqueness of our data set enables us to observe phenomena that have not been previously considered in the CC context. That said, our data set is also limited in a variety of ways, thus creating opportunities for further research.
First, our data set is limited to a single category and retailer. As such, our findings are idiosyncratic to the category, retailer, and the goal of the CC implementation. Further research is needed to test the generalizability of our findings, especially to settings where a retailer seeks to improve category profitability. Moreover, controlling for the retailer’s decision to initiate CC through a more comprehensive data set including multiple CC implementations and/or control categories may be useful. This is because our study may overstate the benefits of CC as the CC implementation decision in our context was in part driven by the retailer’s relatively poor category performance. It is possible that switching to a CC regime could have a smaller impact in categories that are already performing well.
It is also possible that the relative importance of pricing to assortment as a driver of category sales could differ for mature categories (such as ours) versus categories characterized by product innovation. We expect pricing to play a more important role in settings where the retailer attempts to maximize the sales revenues of a mature category. In contrast, assortment might play a more important role in categories with frequent new product introductions because it might be relatively easy to boost category performance by introducing new products in such categories. In line with these predictions, it would be useful to replicate the models developed herein across many categories and retailers. Such work could yield insights into the boundary conditions of the findings from this article and help researchers and practitioners understand when and under what conditions CC is likely to be most effective.
Second, our data set does not contain information about the products’ marginal costs. A richer data set would enable us to study a variety of interesting phenomena, including retailer profitability and consumer welfare implications of CC. Finally, because our data set is limited to a single retailer, it does not enable us to study cross-retailer effects. Prior research has suggested that at least a portion of the sale increase experienced by a manufacturer during a promotion is the result of crossretailer cannibalization (Van Heerde, Leeflang, and Wittink 2004). Given the right data, it would be worthwhile to consider cross-retailer dynamics that result from CC implementations. This type of research would illuminate the long-term implications of CC, including an understanding of the resulting competitive equilibrium. We hope that our study will pave the way for greater collaboration between retailers and researchers, facilitating further empirical research on CC.
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TABLE 5
TABLE:
| Equation | Variable | Mean | LB | UB |
|---|
| Sales, b8i | Intercept | 0.086 | 0.027 | 0.14 |
| Category captain | 0.181 | 0.022 | 0.327 |
| Competitive set (excluding private label) | 0 | -0.231 | 0.218 |
| Similarity to private label | 0.004 | -0.573 | 0.506 |
| Similarity to category captain | 0 | -0.404 | 0.395 |
| Price, a3i | Intercept | -0.138 | -0.193 | -0.083 |
| Category captain | -0.017 | -0.145 | 0.117 |
| Competitive set (excluding private label) | 0.065 | -0.146 | 0.29 |
| Similarity to private label | -0.001 | -0.473 | 0.492 |
| Similarity to category captain | -0.204 | -0.537 | 0.13 |
| Distribution, g3i | Intercept | 0.02 | -0.03 | 0.074 |
| Category captain | 0.002 | -0.151 | 0.154 |
| Competitive set (excluding private label) | -0.087 | -0.248 | 0.098 |
| Similarity to private label | -0.249 | -0.723 | -0.049 |
| Similarity to category captain | 0.255 | 0.05 | 0.599 |
Upper-Level Model Estimates for the CC Indicator Variables of the Sales, Price, and Distribution Equations
Notes: We report the upper-level coefficients for b8i, a3i, and g3i, which correspond to the coefficients of the CC indicator variables in the sales, price, and distribution equations (i.e., Equations 3–5), respectively. LB and UB are the 2.5% and 97.5% quantiles of the estimated parameter’s posterior distribution, respectively. Statistically significant values at the 95% level are highlighted in bold.
FIGURE 5
Change in the Impact of CC on Sales When There Is a Switch from the Current Regime, Which Protects the Private Label, to an Alternative Regime in Which There Is No Private Label Protection
8We thank the area editor for suggesting that we explore this question.
TABLE 4
Summary of Product Attributes in Subcategory 9
TABLE:
| | Preimplementation Averages | Postimplementation Averages |
|---|
| Product | Manufacturer | Size | Vol. Price | Dist. | Mkt. Share | Vol. Price | Dist. | Mkt. Share |
|---|
| 1 | Private label | 14.5 oz. | $.06 | 0.73 | 51.00% | $.05 | 0.71 | 64.00% |
| 2 | Manufacturer 3 | 14.5 oz. | $.06 | 0.28 | 20.00% | $.09 | 0.02 | 1.00% |
| 3 | Category captain | 14.5 oz. | $.12 | 0.21 | 20.00% | $.11 | 0.19 | 24.00% |
| 4 | Private label | 8 oz. | $.11 | 0.25 | 9.00% | $.08 | 0.29 | 11.00% |
Notes: We report the average values of volumetric price, distribution, and market share during the pre- and postimplementation periods.
TABLE 3
The Decomposition of the Impact of Category Captainship by Manufacturer
TABLE:
| | Impact of Merchandising | Impact of Price | Impact of Assortment | Total Impact |
|---|
| Manufacturer | Mean | LB | UB | Mean | LB | UB | Mean | LB | UB | Mean | LB | UB |
|---|
| 1 (PL) | 0.045 | 0.035 | 0.052 | 0.026 | 0.022 | 0.03 | 0.068 | 0.052 | 0.09 | 0.139 | 0.127 | 0.155 |
| 2 (CC) | 0.214 | 0.197 | 0.232 | 0.092 | 0.085 | 0.097 | 0.118 | 0.092 | 0.142 | 0.424 | 0.396 | 0.452 |
| 3 | -0.004 | -0.026 | 0.029 | -0.013 | -0.029 | 0.003 | 2.165 | -0.187 | -0.136 | 2.182 | -0.206 | -0.155 |
| 4 | 0.113 | 0.089 | 0.143 | 0.065 | 0.046 | 0.08 | 0.193 | 0.124 | 0.235 | 0.371 | 0.311 | 0.415 |
| 5 | -0.014 | -0.055 | 0.059 | 0.037 | 0.025 | 0.053 | 0.023 | -0.022 | 0.085 | 0.045 | -0.013 | 0.171 |
| 6 | 2.187 | -0.22 | -0.155 | 0.008 | -0.02 | 0.029 | 2.156 | -0.224 | -0.08 | 2.334 | -0.413 | -0.258 |
| All other | 0.109 | 0.087 | 0.145 | 0.009 | -0.003 | 0.029 | 0.013 | -0.026 | 0.065 | 0.131 | 0.101 | 0.185 |
| Entire category | 0.086 | 0.001 | 0.166 | 0.076 | 0.066 | 0.088 | 0.029 | 0.003 | 0.043 | 0.191 | 0.177 | 0.219 |
Notes. PL = private label. LB and UB are the 2.5% and 97.5% quantiles of the estimated impact, respectively. Statistically significant values at the 95% level are highlighted in bold.
7The average distribution for product 2 during the postimplementation period is slightly above zero because it took the retailer a few weeks to completely remove this product from the assortment.
6Our UPC-level analysis (not reported herein because of space limitations) revealed that the insignificance of the cross-distribution variable at the aggregate level is driven by two factors. First, a small number of high-demand UPCs are insensitive to the presence of substitute products. Second, own- and cross-distribution variables move in the opposite directions for some UPCs. For instance, if a product’s distribution score declines, its close competitors’ distribution scores increase, creating a colinear relationship between own- and cross-distribution variables. For these two reasons, the cross-distribution variable is insignificant at the aggregate level. Nevertheless, we show that demand substitution plays a crucial role in shaping the postimplementation assortment.
TABLE 2
Coefficient Estimates of the Sales, Price, and Distribution Equations
TABLE:
| | Sales Equation | Price Equation | Distribution Equation |
|---|
| Mean | LB | UB | Mean | LB | UB | Mean | LB | UB |
|---|
| Intercept | 6.603 | 6.51 | 6.7 | -2.405 | -2.504 | -2.304 | 0.61 | 0.512 | 0.702 |
| Own price | -1.393 | -1.500 | -1.300 | | | | | | |
| Cross-price | 0.145 | 0.053 | 0.23 | | | | | | |
| Own distribution | 1.548 | 1.461 | 1.642 | | | | | | |
| Cross-distribution | 0.024 | -.065 | 0.111 | | | | | | |
| Search frequency | 0.193 | 0.105 | 0.277 | -.138 | -.216 | -.216 | -.061 | 0.021 | -.092 |
| Holiday | 0.088 | -.008 | 0.177 | -.122 | -.199 | -.199 | -.040 | 0.017 | -.061 |
| Linear trend | -.005 | -.081 | 0.072 | | | | | | |
| CC indicator | 0.086 | 0.001 | 0.166 | -.092 | -.169 | -.004 | 0.02 | -.062 | 0.099 |
| # of UPCs | | 110 | | | 110 | | | 110 | |
| Sample size | | 5720 | | | 5720 | | | 5720 | |
| R2 | | 0.905 | | | 0.317 | | | 0.357 | |
Notes: We report coefficient estimates for Equations 3, 4, and 5. LB and UB are the 2.5% and 97.5% quantiles of the estimated parameter’s posterior
5An alternative way to measure a product’s similarity to private label or CC is to calculate an average similarity score (e.g., Our findings remain qualitatively unchanged when we use the average similarity (instead of the maximum similarity) scores as explanatory variables. We opt in for maximum similarity scores because they better capture close competition between two UPCs with similar attributes, whereas the average similarity scores are less informative about close competition between similar products.
4While our data set does not have any variables that enable us to quantify the impact of specific merchandising efforts, it is plausible that the CC implementation may have led to some merchandising changes (e.g., new shelf displays, reallocation of shelf space among products; Subramanian et al. 2010). b8i captures the aggregate impact of such merchandising efforts.
FIGURE 4
Change in Average Weekly Distribution and Volumetric Price for Each Product
Notes: PL = private label. Each bar represents a UPC. The vertical dashed lines separate different manufacturers. The manufacturers are displayed in a decreasing order from left to right in line with the preimplementation average weekly sales revenues. The change in the average weekly distribution for UPC I is computed as Ddit = ð1=32Þå5t=221dit - ð1=20Þåt2=01dit. The change in the average weekly volumetric price is computed similarly.
3We calculate cross-price and cross-distribution at the subcategory level because a subcategory is more homogeneous than a category in terms of product characteristics. Alternatively, one can calculate cross-price and cross-distribution using weights (i.e., wij values) derived from similarity scores calculated at the category level (e.g., the formula presented in footnote 2). Calculating crossprice and cross-distribution at the category level does not change our findings. The Web Appendix provides estimation results for an alternative model specification with category-level cross-price and cross-distribution variables.
FIGURE 3
Product Positions with Respect to the Average Volumetric Price and Product Size in Subcategories 2 and 4
2An alternative way to define product similarity is to incorporate where to the IiMj is an indicator variable same manufacturer, and that takes a value of 0 1 otherwise, and IiSj if I is and j belong an indicator variable that takes a value of 0 if I and j are in the same subcategory, and 1 otherwise. Incorporating brand and subcategory effects into our similarity metric does not change our findings. Thus, we use Equation 2 in our analysis because of its parsimony. The Web Appendix provides estimation results for alternative model specifications that incorporate subcategory and brand into our similarity metric.
TABLE 1
An Overview of Each Manufacturer’s Weekly Sales Before and After CC Implementation
TABLE:
| | Weekly Sales Pre-CC ($) | Weekly Sales Post-CC ($) |
|---|
| Firm | # of UPCs | Mean | Median | SD | Mean | Median | SD | % Change in Avg. Sales |
|---|
| 1 (PL) | 38 | 40625 | 37134 | 11127 | 50894 | 45920 | 12360 | 25.28% |
| 2 (CC) | 28 | 18587 | 18072 | 2092 | 40076 | 24529 | 39896 | 115.61% |
| 3 | 16 | 10739 | 10684 | 1354 | 6947 | 6419 | 2329 | -35.31% |
| 4 | 13 | 8668 | 7341 | 5218 | 12317 | 8347 | 9492 | 42.10% |
| 5 | 3 | 4975 | 4632 | 677 | 5333 | 5132 | 918 | 7.21% |
| 6 | 4 | 2112 | 2088 | 414 | 1684 | 1419 | 735 | -20.28% |
| 7 | 4 | 296 | 298 | 31 | 286 | 263 | 77 | -3.61% |
| 8 | 1 | 229 | 230 | 40 | 252 | 243 | 43 | 10.03% |
| 9 | 1 | 54 | 53 | 11 | 81 | 73 | 47 | 50.52% |
| 10 | 2 | 0 | 0 | 0 | 552 | 538 | 290 | – |
| All firms | 110 | 86285 | 82385 | 12438 | 118421 | 107082 | 42864 | 37.24% |
FIGURE 2
The Weekly Category Sales and Search Frequency for the Category in the Same Period
FIGURE 1
Conceptual Framework and Directional Expectations for the Impact of the CC on Sales for a Particular Product
Notes: Pricing and assortment boxes illustrate that the CC implementation affects pricing and assortment, which in turn affect sales through own and crosselasticities. Because our data set does not include any variables on merchandising, we model merchandising as a direct link from CC to sales. Manufacturer type and product attributes moderate the impact of CC on pricing, assortment, and merchandising changes. We discuss the empirical operationalization of these three mechanisms in the “Model” section.
1For instance, 4 of the 11 performance criteria Progressive Grocer used to identify successful category captains in its 2016 category captains of the year article emphasize sales, whereas none of the criteria emphasize profitability (Progressive Grocer 2016, p. 42). Indeed, more than half of the 62 CC examples provided in the article report implementation results based on sales, whereas only 4 examples mention profitability.
Yasin Alan is Assistant Professor of Operations Management, Owen Graduate School of Management, Vanderbilt University (e-mail: yasin. alan@owen.vanderbilt.edu). Jeffrey P. Dotson is Associate Professor of Marketing and Global Supply Chain, Marriott School of Management, Brigham Young University (e-mail: jeff.dotson@byu.edu). Mu¨min Kurtulus is Associate Professor of Operations Management, Owen Graduate School of Management, Vanderbilt University (e-mail: mumin.kurtulus@owen. vanderbilt.edu). The authors thank the JM review team for valuable feedback that greatly helped improve the quality of this article. Kusum Ailawadi served as area editor for this article.
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Record: 128- Online Relationship Formation. By: Kozlenkova, Irina V.; Palmatier, Robert W.; Fang, Eric (Er); Xiao, Bangming; Huang, Minxue. Journal of Marketing. May2017, Vol. 81 Issue 3, p21-40. 20p. 2 Diagrams, 7 Charts. DOI: 10.1509/jm.15.0430.
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Online Relationship Formation
As online shopping evolves from being primarily transactional to being more relational, sellers aim to form online relationships. This article investigates online relationship formation, identifies the performance payoffs that result from forming different types of online relationships (unilateral vs. reciprocal), and tests the most effective relationshipbuilding strategies. Study 1, based on a longitudinal buyer-level analysis of an online shopping community, reveals that buyers use community-, seller-, and buyer-generated signals to identify suitable relationship partners and reduce online shopping risk. These signals generally diminish in importance as buyers gain experience but become more important when buyers are forming reciprocal relationships. Study 2 evaluates the dynamic payoffs of online relationship formation (seller-level analysis) on sales; the effect on sales of reciprocal relationships is three times greater and lasts seven times longer than that of seller-initiated, unilateral relationships. Study 3 is a field experiment testing managerially actionable strategies for leveraging relationships to grow online sales. Tenets arising from differences between online and offline relationships, together with the results from the three studies, inform an emerging theory of online relationships.
Online Supplement: http://dx.doi.org/10.1509/jm.15.0430
Shopping on e-commerce marketplaces such as eBay and Alibaba continues to increase (Reich 2013). In the United States, more than 60% of buyers make e-commerce purchases through online marketplaces, and online retail sales are expected to exceed $330 billion in 2015 (Forrester Report 2015). In China, online marketplaces account for more than 90% of all e-commerce (Nowlin 2014). As online sales grow and customers gain e-commerce experience, online shopping also is evolving from primarily a transactional exchange to a more relational-based exchange, similar to traditional retail inter actions. To facilitate this transition, online shopping com munities offer means to reinsert the "shopping experience" and "personal interaction" into the modern retail purchasing process; when missing, such elements often represent the greatest concern consumers express with regard to online (compared with tra ditional) retailing (Dholakia and Vianello 2009; Reich 2013; Yin 2010). Sellers thus work to form online relationships with customers in online shopping communities, with the belief that doing so will increase their performance. However, little research has evaluated the actual effectiveness of online relationship building strategies (Verma, Sharma, and Sheth 2016). Thus, we aim to increase understanding of online relationship formation, as well as the performance payoffs that result from different types of online relationships (buyer/seller unilateral vs. reciprocal^and the most effective relationship-building strategies.
In many ways, online and offline relationships are similar; psychological systems and the human need for relationships are at play in both settings (Zhu et al. 2012). However, there are important differences between offline and online channels that prevent simple applications of offline strategies to online channels (e.g., anonymity). These differences affect online relationship formation by increasing the relative importance of other cues or signals to reduce uncertainty about the suitability or benevolence of potential online partners, the speed of relationship formation, and the salience of reciprocation as a signal of relational intentions.
To increase understanding of online relationship formation, its performance implications, and effective strategies, we con duct three related studies. In Study l, we attempt to identify which signals drive relationship formation, in the context of an online shopping community on the largest e-commerce platform in China. Using a hazard model, we evaluate factors that cause new buyers to form 1,074 unilateral or reciprocal relationships with sellers over a five-month data collection period, contingent on moderating factors (buyer-level analysis). In Study 2, we use the same longitudinal data collection approach but analyze the impact of unilateral and reciprocal buyer-seller relationships on the sales performance of 336 sellers (seller-level analysis). To account for the dynamic nature and potential endogeneity among variables, we use a vector autoregressive (VARX) approach. Thus, our focus is on the dynamic payoffs sellers experience from online relationship formation. Finally, Study 3 combines key insights from Studies 1 and 2 to test the effec tiveness of managerially actionable strategies for leveraging relationships to increase online sales, using a field experiment in a different online context with nearly 800 potential customers.
This article thus contributes to extant literature in five main ways. First, we provide insights into online relationship for mation dynamics from a signaling theory perspective. Buyers use signals to identify suitable relationship partners, including bilateral communication, or the direct exchange of information between a buyer and a seller; the seller's reputation, which is a signal of the seller' s quality, as perceived by the buyer; and relational observation, which refers to buyers observing their community neighbors' relational choices with sellers. By using these signals, buyers reduce their online shopping risk. How ever, we find that signals diminish in importance (with the exception of seller's reputation) as buyers gain experience because they develop and use their own expanding knowledge to make decisions. In addition, these signals are more important when buyers form more committed, reciprocal relationships in response to seller-initiated relationship efforts compared with when they form unilateral relationships. Relational observation also enhances the effects of communication and seller's rep utation, such that these factors seem more credible when they come from a source that is closely linked to the buyer.
Second, this article is the first to reveal the dynamic effects of buyer and seller-unilateral and reciprocal relationships on online sales, enabling us to investigate and detail differential payoffs across all three types of online relationships. As Study 2 shows, building a portfolio of reciprocal relationships is very important for growing online sales. The effect of reciprocal relationships on the seller's sales is three times greater and lasts far longer than does the effect of seller-initiated unilateral relationships; it also is approximately 60% greater than that of buyer-initiated unilateral relationships. To influence buyer relationship formation indi rectly, sellers could signal their value as a partner (e.g., by en hancing reputation) or directly initiate relationships with potential buyers (e.g., by following a buyer). Yet our results show that sellers' outreach efforts have limited effectiveness for increasing sales unless they can get buyers to reciprocate (e.g., follow back) because reciprocation generates substantial multiplier effects for both sales and dynamic reach. These findings lead to a mana gerially important question: How can sellers get buyers to reciprocate seller-initiated unilateral relationships?
Third, to address this question, we use a field experiment to identify and test managerially actionable strategies for using relationships to grow online sales. Specifically, we combine the Study 1 finding that relational observation has the largest effect in terms of driving buyers' reciprocal relationship formation and the Study 2 finding that the highest payoffs come from recip rocal relationships. In Study 3, we find that the rate of buyer reciprocation of seller-initiated relationships is 70% higher when buyers follow a community member (intermediary) who is already following the seller (i.e., relational observation). An intermediary's choice to follow a specific seller sends a signal to the buyer that the seller is reliable, credible, and a good "fit." Relational observation also is more effective when the repu tation of the intermediary is better than that of the buyer. These effects can be understood from a signaling perspective; signals have more weight when they come from a source that is more credible than the receiver is.
Fourth, we describe the unique characteristics of online relationships, outline supporting evidence, and discuss impli cations for building and executing online relationship market ing strategies. From these insights and findings, we offer three tenets that inform an emerging theory of online relationships.
Fifth, by integrating the results from all three studies and applying post hoc analysis, we provide managerial insights and takeaways related to the effects of various online marketing strategies at different levels of buyers' experience and for various relationship types (unilateral vs. reciprocal). For example, for new buyers (-1 SD in experience) forming reciprocal rela tionships, relational observation is the most effective means to increase relationship formation (twice as effective as commu nication and three times more effective than seller's reputation) and, ultimately, seller sales, because reciprocal relationships offer the highest payoff. However, for experienced buyers, the pattern of results reverses. For experienced buyers (+1 SD) forming reciprocal relationships, seller reputation is the most effective means to increase relationship formation (approximately three times as effective as communication and relational observation). Thus, relational observation is most effective for new buyers and least effective for experienced buyers, and the seller's reputation has opposite effects.
In 2014, global e-commerce reached $1.3 trillion in sales, and China was the leading e-commerce market, followed by the United States (eMarketer 2014). Many e-commerce purchases occur in online marketplaces, which are platforms that unite buyers and sellers. As customers purchase more products and services online, online shopping also is evolving from its roots as a transactional exchange to a more relational exchange. Customers still want an engaging community experience that is typically associated with offline shopping because they "par tially substitute shopping for recreation and use these activities to develop social activities and bonds with others" (Anderson, Swaminathan, and Mehta 2013, p. 14). As a result, within these large online marketplaces, smaller shopping communities, or subgroups that facilitate interactions among buyers and sellers around some particular interest, are emerging. The communities provide more "interpersonal" interactions and shopping expe riences (Dholakia and Vianello 2009). For example, eBay describes them as "a great place to connect with other com munity members who share similar interests, … give support, share information, and connect with fellow members" (eBay 2015).
Social media platforms such as Facebook, Twitter, and Weibo also have substantial roles in e-commerce, increasing brand and product awareness, providing information, and linking customers to online marketplaces and shopping communities. Social media can "promote deep relationships, allow fast organization, improve the creation and synthesis of knowledge, and permit better filtering of information" (Kane et al. 2009, p. 46). For example, Instagram provides links to online shopping communities (e.g., LIKEtoKNOW.it) by providing direct links to the products in the pictures of various fashion influencers. Because they enhance custom ers' shopping experience, provide socially relevant product and seller information, and reduce purchase uncertainty, online relationships are key to growing online sales. How ever, researchers argue that "online retailers find it more difficult to build a relationship with consumers as compared to brick and mortar retailers," and sellers often lack insights into how to adapt face-to-face relational strategies to an online context (Verma, Sharma, and Sheth 2016, p. 207).
Online buyers connect to sellers and other buyers to learn as well as to improve their shopping experience (Manchanda, Packard, and Pattabhiramaiah 2015). For example, "in eBay's online community, customers' discussions regarding trading issues are interspersed with personal conversations, humor, social support, and helping behaviors" (Zhu et al. 2012, p. 396). Thus, the needs that drive online relationships are similar in many ways to the needs that are satisfied by offline relationships. Regardless of the channel, the psychological underpinnings and human desire for relationships transcend the environment, so "all communi ties, whether online or offline, are subject to psychological pro cesses of identification, appreciation of members' contribution, camaraderie, and perceptions of social support" (Zhu et al. 2012, p. 404). Relationships that users develop on the Internet can be as strong and as deep as the ones in offline settings; more than 80% of respondents in one study identify their online relationships as equally important and close as their offline relationships (McKenna, Green, and Gleason 2002).
Even though the underlying psychological roots are similar, differences in offline and online shopping channels can have profound effects on online relationship formation. As Stephen and Toubia (2010, p. 217) note, "though similar to offline shop ping centers at a basic level, social commerce marketplaces are not merely online equivalents of shopping centers." There are several differences between offline and online channels. First, offline relational partners are often located in geographic proximity, particularly during the relationship formation stage, which supports richer face-to-face communication, whereas online relational partners can be anywhere in the world and might never meet face-to-face, leading to leaner communication with limited verbal and nonverbal cues (Benedicktus et al. 2010). Second, offline relational partners typically know the identity of potential partners, whereas online relational partners may have little knowledge of the true identity of potential partners. Third, many online relationships have a stable unilateral structure, whereby a relationship partner never reciprocates but remains in the unilateral relationship as a follower (Trier and Richter 2015), which is not as common in offline relationships because of the social pressure to reciprocate. Fourth, the level of social inter connectedness differs, in that offline relational partners typically have many more common friends than do online relational partners (Chan and Cheng 2004). Most of these differences increase the risk that an online partner might behave oppor tunistically, thus enhancing the importance of risk-reducing and trust-building signals during the relationship-formation process.
People form (offline and online) business relationships to reduce uncertainty and buy from trusted partners in an exchange governed by relational norms (Adjei, Noble, and Noble 2010; Palmatier, Dant, and Grewal 2007). Buyer uncertainty arises as a result of information asymmetries between sellers and buyers, and these issues are magnified in the less observable online context "because the spatial and temporal separation of the online environment creates additional information asymmetries that benefit the seller" (Pai and Tsai 2011, p. 604). Information asymmetry also makes it difficult for buyers to identify good partners in the relationship-formation process, so they increase their focus on observable signals (Kirmani and Rao 2000).
After one party identifies a potential online relational partner, the next step is to initiate a relationship by following the other party, which constitutes a unilateral relationship. Arguably, the most important step is the subsequent reciprocation by the other party, which indicates mutual interest in the bilateral relational bond, or a reciprocal relationship. To determine the suitability of a potential partner and whether to reciprocate a seller' s relationship request, buyers evaluate signals similar to the ones they would consider if they were initiating the relationship themselves. Reciprocation is the critical step in relationship formation because it "forms the basis on which the entire social and ethical life of … civilizations presumably rests" (Gouldner 1960, p. 161). By indicating greater commitment, it also en courages persistent interactions (Chan and Li 2010). Stronger, more committed bonds then yield many benefits, including relationship growth, loyalty, and the desire to reward partners directly, with more sales, and indirectly, through word of mouth (Lund, Kozlenkova, and Palmatier 2016; Palmatier et al. 2009).
Perceived risk inhibits various types of consumer transactions online (Andrews and Boyle 2008). Feelings of uncertainty and perceived risk are exacerbated in online shopping communities because people can be more anonymous online (Rotman 2010); in addition, there is an overwhelming number of sellers and the perception that "almost anyone can set up a retail presence on the Internet at a very low cost" (Biswas and Biswas 2004, p. 30). To manage this risk, online buyers aim to build relationships and look for marketplace signals to identify the best relationship partners before purchasing. As signaling theory argues, visible signals can indicate unobservable attributes and help resolve information asymmetry (Kirmani and Rao 2000). In online shopping communities, three main categories of observable signals can help buyers identify suitable partners: ( 1) signals coming directly from the seller, such as bilateral communica tion; ( 2) signals about the seller from the overall online com munity, such as the seller's reputation; and ( 3) signals from observing relationship choices of those community members with whom the buyer is closely connected, or the buyer's rela tional observation. In Study 1, we investigate the effects of these risk-reducing signals on the likelihood that a buyer forms a relationship with a seller, as well as factors that may moderate these effects (Figure 1, Panel A). In an online context, buyer relationship formation refers to a buyer following a seller and can be either unilateral (seller is not following the buyer back) or reciprocal (seller is following the buyer too). Understanding this relationship formation is critical because it is a key precursor to a buyer's ultimate purchase decisions (Ha 2004).
Bilateral communication. We define bilateral commu nication as the direct exchange of information between a buyer and a seller. In online shopping communities, communication can be initiated by either party and may include a reply or not. Communication builds trust (Palmatier et al. 2006) and en courages long-term relationships between buyers and sellers (Reinartz, Thomas, and Kumar 2005). In online communities, communication may be even more critical because even minimal or superficial communication on unimportant issues among online strangers signals trustworthiness (Nass and Yen 2010). For example, by communicating with a potential buyer, a seller can seem less anonymous and send a signal to reassure the buyer of the seller's expertise, reliability, and responsiveness, which lowers perceived risk. The seller also signals transparency and trustworthiness to the potential buyer, which should increase the buyer's desire to form a relationship (Porter and Donthu 2008; Verma, Sharma, and Sheth 2016).
Communication is especially important early in the relationship, to help "not only build initial trust but also help develop processes and norms that support lasting improve ments in relationship interactions" (Palmatier 2008, p. 61). However, over time, communication often yields diminishing returns (Palmatier et al. 2013). The longer buyers are present in an online community, the more knowledgeable, experienced, and comfortable they become, and the less risk they feel (Zhu et al. 2012). As buyers gain experience, they have fewer infor mational needs, and their perception of informational asymmetry lessens, so communication becomes less valuable and less likely to trigger the need for relationship formation as a means to manage risk.
H1 (a) Bilateral communication increases buyer relationship formation, and (b) these effects diminish with the buyer's experience.
Seller's reputation. Reputation is a signal of the seller's quality, as perceived by the buyer (Baker, Faulkner, and Fisher 1998). The seller's reputation can serve as another source of information, because a strong reputation alleviates consumers' perceived risk and potential concerns about the seller (Pavlou, Liang, and Xue 2007). In online shopping communities, a signal of the seller's reputation provides "a viable mechanism for fostering cooperation among strangers … by ensuring that the behavior of a trader toward any other trader becomes publicly known and may, therefore, affect the behavior of the entire community toward that trader in the future" (Dellarocas 2003, p. 1407). This information typically is easily accessible and highly visible in online communities (e.g., stars to rate the seller). Accordingly, 84% of online U.S. shoppers are influenced by others' perceptions of seller quality, which signal credibility and thereby reduce perceived risk (Anderson, Swaminathan, and Mehta 2013). Thus, we expect that buyers seek out and form relationships with sellers that have strong reputations.
However, the longer buyers are active in an online shopping community, the more experience, knowledge, and familiarity they gain, which makes them perceive less risk in dealing with sellers (Yoon 2002). For example, after making a few successful product returns to sellers in the community, a buyer likely will be less hesitant about dealing with other sellers, even if they do not have strong reputations. Thus, as the buyer becomes more experienced, the value of the seller's reputation as a risk reducing signal diminishes.
H2: (a) A seller's reputation increases buyer relationship formation, and (b) these effects diminish with the buyer's experience.
Buyer's relational observation. Buyers in an online community also observe the behaviors of those to whom they are closest in the online shopping community. Signals from community neighbors are especially powerful because pro spective buyers want to know not only which sellers and products are considered good in general but also which are "good for folks like us" (Van den Bulte and Wuyts 2007, p. 41). A buyer's relational observation in an online shopping context refers to buyers observing their community neighbors' relational choices with sellers. For example, if a buyer follows a fellow community member who is following a seller, the buyer receives valuable information about that specific seller's value as a potential partner. Researchers describe similar observational processes as imitation, exposure, contagion, or observational learning, depending on the context and theoretical paradigm (Nitzan and Libai 2011; Van den Bulte and Wuyts 2007). Previous research has shown that when feeling uncertain, people look to others to decide how to act (Chen 2008).
Relational observation helps buyers assess sellers' credi bility by providing a source of information or signal that they consider personally relevant (Chen, Wang, and Xie 2011). To find trustworthy sellers that fit their needs, buyers look to see where the people they follow do their shopping. For example, on Polyvore, an online marketplace for fashion products, buyers can follow other buyers whose tastes they like. When buyers observe that their relational partners have a relationship with a specific seller, they are more likely to form a relationship with that seller too. Similar to communication and reputation, the influence of relational observation should diminish as the buyer gains experience (Nitzan and Libai 2011).
Finally, we expect that relational observation works syn ergistically with both bilateral communication and the seller's reputation in increasing the likelihood that a buyer forms a relationship with a seller. Relational observation can validate the two other signals by adding credence or weight to the com munication and reputation information, because it provides an indication of "fit" that is unique to that buyer (Adjei, Noble, and Noble 2010). For example, the seller's reputation signals that the seller is generally reliable and trustworthy but gives little insight into whether the seller's offering matches the buyer's personal preferences (e.g., taste, price). Thus, the seller's rep utation and bilateral communication should have stronger impacts on the buyer's likelihood to form a relationship as relational observation of the seller increases.
H3: (a) The buyer' s relational observation increases buyer rela tionship formation, and (b) these effects diminish with the buyer' s experience.
H4: The positive effect of (a) bilateral communication and (b) the seller's reputation on buyer relationship formation is greater as the buyer's relational observation increases.
Buyer's reciprocal (vs. unilateral) relationships. In online shopping communities, buyers can initiate a relationship with a seller or reciprocate a seller-initiated relationship. Recip rocating a relationship indicates a higher psychological level of commitment on the part of the buyer than does an initial step of unilateral relationship formation, which may be only an information-gathering step, whereas reciprocation is an active relationship-building step. Thus, we expect that when the buyer is in a reciprocating (vs. an initiating) position, the three informational signals about the seller (i.e., bilateral communi cation, seller's reputation, and relational observation) become more impactful and valuable to the buyer. In offline contexts, people generally recognize that reciprocal relationships evoke exchange norms, which bind them to specific actions (Dahl, Honea, and Manchanda 2005). Research has shown that "reciprocity implicates a responsibility" (Nass and Yen 2010, p. 181), regardless of the relationship stage; experiments reveal that even complete strangers interacting online for a mere five minutes about inconsequential issues felt a sense of responsi bility to reciprocate, implying higher feelings of commitment. Nass and Yen (2010, p. 190) conclude that "experiments on reciprocity highlight a key point about social behavior: the more fundamental and basic a social rule, the less you need to do to get others to follow it," which implies that reciprocity can occur at any stage and in any type of relationship. Furthermore, feelings of reciprocity are so fundamental that they translate across cultures and even can be felt toward inanimate objects, such as computers. Buyers may want to avoid this sense of future obligation altogether; if a seller already follows them, buyers may choose to ignore it and not reciprocate, unless they are reassured by other signals about this seller. Therefore, bilateral communication, seller's reputation, and relational observation should be more important and valuable when a buyer is deciding to form a reciprocal versus a unilateral relationship.
H5: The positive effect of (a) bilateral communication, (b) the seller's reputation, and (c) the buyer's relational observation on buyer relationship formation is greater when establishing reciprocal versus unilateral relationships.
Our conceptual model aims to explicate the signals that drive relationship formation for individual buyers in an online shopping community. Several characteristics of our context make it appropriate for testing our model. First, we focus on a single category in the Taobao.com online shopping community (clothing) to reduce product heterogeneity. Second, sellers are visually distinct from buyers on this platform because they include hyperlinks to their online stores in all interactions. Every time a seller posts, replies, or follows another member, the hyperlink to the seller's online store appears. The online shopping community also enables members to communicate and share information, which can be observed. Third, any potential buyer can join the community and form unilateral relationships (follow) with other members as well as reciprocate (follow back). Thus, members can build multiple relationships, gain information about others, and observe other members' behaviors. Finally, Taobao.com is the largest e-commerce platform in China, which makes it an important online retail context.
Sample and measurement. In building the longitudinal sample, we aimed to minimize preexisting relationships by restricting the sample to new members who joined the com munity after the start of our data collection on April 1,2014. The data collection lasted 134 days, consistent with our interest in studying online relationship formation. We programmed a web crawler to search and store data from the online shopping community daily. We obtained data about 146 buyers who formed 1,074 relationships with 336 sellers.
We used existing measures whenever possible. Buyer re lationship formation is a binary variable equal to 1 if a buyer forms a relationship with (i.e., follows) a seller at time t, and 0 otherwise. Bilateral communication (COMi,j,t) indicates the number of times communication occurred between a buyer and seller before time t. Buyers and sellers generally interact in community forums by replying to each other' s postings. We thus identify text associated with "@+member ID" in the community at time t, using simple text mining techniques, and record the communication between buyer i and seller j before time t. To measure the seller's reputation (REPj,t), we code seller j' s reputation at time t as the average scores of reviews by buyers (1 = lowest, and 5 = highest) pertaining to transactions that occurred before time t. Buyers provide scores on several dimensions (e.g., product description, customer service), so each seller's average score reflects all previous buyers' ratings. The buyer's relational observation (OBSi,j,t)captures the number of other members this buyer follows who also follow the seller, prior to relationship formation between the focal buyer and seller (Van den Bulte and Wuyts 2007). This uni directional, intermediate linkage between the buyer and the seller is an important distinction between the construct of relational observation and other constructs, such as degree centrality, which captures the number of ties a buyer has without specifying the direction or position relative to the seller. The buyer's experience (EXPi t) reflects the time elapsed, in weeks, since the buyer joined the online shopping community before time t. The buyer's reciprocal relationships (RECi,t) is a dummy variable, where relationships reciprocated by the buyer equal 1, and relationships initiated by the buyer (unilateral) equal 0. As control variables, we include the seller's duration, common events, number of followers of the buyer and the seller, and the seller' s product breadth. Table 1 contains a detailed summary of all construct definitions and operation alizations; Table 2 reports the descriptive statistics and correlations of all variables.
Estimation and results. To estimate our model, we use a Cox (1972) proportional hazard regression model. Relationship formation is a time-based binary event, and the probability of relationship formation over time is a function of time-varying independent variables. Time-based phenomena can be modeled effectively with a hazard function, which can identify cross sectional and longitudinal effects as well as handle sample selection biases such as censoring. We therefore estimate a hazard model using a semiparametric partial likelihood method (Mitra and Golder 2002; Thompson and Sinha 2008). We set the hazard rate h(t) to reflect the probability of relationship formation between a buyer and a seller; it represents the instantaneous probability of an event (relationship formation), given that it has not occurred yet at time t (Kleinbaum and Klein 2005). Equation 1 represents the main-effects only model, and Equation 2 is the full model with the hypothesized interactions we use for hypothesis testing:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
TABLE: TABLE 1 Constructs, Definitions, and Operationalizations
| Constructs | Definitions | Operationalizations |
| Study 1 | | |
| Buyer relationship formation | Buyer forming a relationship with a seller (following/friending) in an online shopping community | Dummy variable, where 1 = relationship formation and 0 = otherwise |
| Bilateral communication | Direct exchange of information between a buyer and a seller prior to relationship formation, which may be initiated by either party | COMi,j,t = INTi,j,t-1, where communication (COM) between i and j at time t = number of interactions (INT) before time t (t 1) |
| Seller's reputation | Signal of seller's quality in reviews left by previous buyers | REPj,t = REVj,t-1, where reputation (REP) of seller j at time t = average score from reviews of previous transactions (1 = lowest, and 5 = highest) |
| Buyer's relational observation | Observing the behavior of others whom the buyer is following, who also follow the seller (i.e., intermediary) | OBSi,j,t = number of other members in the community the buyer follows, who also follow the seller, prior to the relationship formation between focal buyer and seller before time t (t 1) |
| Buyer's experience | Time since the buyer joined the online shopping community | EXPi,t = TIME i,t-1, where i's experience (EXP) at time t = number of weeks since i joined the community |
| Buyer's reciprocal relationships | In online shopping communities, a user following the party who initiated the relationship, making the relationship bidirectional (Van den Bulte and Wuyts 2007) | RECi,t is a dummy variable, where relationships reciprocated by the buyer = 1, and relationships initiated by the buyer (unilateral) = 0 |
| Study 2 | | |
| Seller performance | Seller's daily revenue from all buyers on the shopping platform | Revenuei,t, where seller i's performance at time t = revenue in time t |
| Seller-unilateral relationships | Relationships initiated by the seller but not reciprocated by buyer | For seller i, i 2 m, and buyer j, j 2 n, ai,j 2 A SUR(seller-unilateral relationship) = ^ j=1 ai,j = 1 aj,i " 1; A is the relationship matrix; m is the total number of sellers; n is the total number of buyers |
| Buyer-unilateral relationships | Relationships initiated by the buyer but not reciprocated by seller | For seller i, i 2 m, and buyer j, j 2 n, ai,j2 A BUR(buyer-unilateral relationship) = ^ i=1 aj,i = 1, ai,j " 1; A is the relationship matrix; m is the total number of sellers; n is the total number of buyers |
| Reciprocated relationships | Total number of bidirectional relationships (seller following buyer and buyer following seller) (Van den Bulte and Wuyts 2007) | Sum of overlapped grids between matrix At and its transpose AtT (where both values from matrix At and matrix AtT equal 1) |
| Study 3 | | |
| Buyer's relational observation | Observing the behavior of others whom the buyer is following, who also follow the seller (i.e., intermediary), prior to relationship formation between buyer and seller | Variable reflecting whether the buyer has any intermediaries with the seller (1 = existence of intermediaries, 0 = no intermediaries) |
| Buyer-reciprocated relationship | In online shopping communities, the buyer follows the party who initiated the relationship, such that the relationship is bidirectional (Van den Bulte and Wuyts 2007) | Buyer following back the seller after the seller has initiated a relationship (1 = buyer reciprocated; 0 = buyer did not reciprocate) |
| Intermediary's reputation | Perceptions held by community members of the intermediary's expertise, knowledge, and credibility | Number of community members following the intermediary |
| Buyer's reputation | Perceptions held by community members of the buyer's expertise, knowledge, and credibility | Number of community members following the buyer |
Control Variables Seller's duration (Study 1) | Time since the seller joined the online shopping community | DURjt = TIME j,t-1, where j's duration (DUR) at time t = number of weeks since j joined the community |
| Common events (Study 1) | Buyer's and seller's participation in the same community events | EVTi,j,t = NJPi,j,t_1, where common events (EVT) between i and j at time t = number of joint participations in community events (NJP) at (t 1) |
| Buyer's followers (Study 1) | Number of people following the buyer | FOLi,t = number of buyer i's followers prior to relationship formation with seller |
| Seller's followers (Study 1) | Number of people following the seller | FOLj,t = number of seller j's followers prior to relationship formation with buyer |
| Seller's product breadth (Study 1) | Total number of items the seller is offering | Total number of items listed on seller j's electronic shop before time t (t 1) |
| Seller's reputation (Study 2) | Signal of seller's quality in reviews left by previous buyers | REPj,t = REVj,t-1, where reputation (REP) of seller j at time t = average score from reviews of previous transactions (1 = lowest, and 5 = highest) |
| Number of members buyer follows (Study 3) | Overall number of people the buyer follows in the community | Total number of people the buyer follows in the community |
| Buyer's activity level (Study 3) | Overall level of activity of the buyer in the community | Total number of posts made by the buyer in the community |
Notes: Sellers all display "shop tags," or embedded hyperlinks to their electronic stores; buyers are those without any such shop tag.
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
In the results reported in Table 3, Model 2 includes the interactions and exhibits a better fit than the main-effects-only model (Model 1). As a robustness test, we add a Gaussian frailty term in each equation to account for unobserved heterogeneity across individual buyers, and the results remain consistent. The Gaussian frailty term is not significant in either Models 3 or 4. The Akaike information criterion values and model coefficients suggest that unobserved buyer heterogeneity is not a significant issue.
TABLE: TABLE 2 Descriptive Statistics and Correlations (Studies 1 and 2)
| A: Study 1 |
| Variable | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
| 1. Buyer relationship formation | .02 | .11 | 1.00 | | | | | | | | | | |
| 2. Bilateral communication | 4.17 | 2.49 | .02 | 1.00 | | | | | | | | | |
| 3. Seller's reputation | 3.77 | 1.21 | .12** | .13** | 1.00 | | | | | | | | |
| 4. Buyer's relational observation | 3.18 | 2.18 | .07* | .23** | .14** | 1.00 | | | | | | | |
| 5. Buyer's experience | 14.83 | 7.38 | .07* | .02 | .07* | .28** | 1.00 | | | | | | |
| 6. Buyer's reciprocal relationship | .01 | .01 | .15** | .10** | .03 | .01 | .07* | 1.00 | | | | | |
| 7. Seller's duration | 10.67 | 5.24 | .05 | .22** | .03 | .03 | .02 | .07* | 1.00 | | | | |
| 8. Common events | 6.98 | 3.09 | .05 | .21** | .12** | .02 | .04 | .12** | .13** | 1.00 | | | |
| 9. Buyer's followers | 11.87 | 12.00 | .06 | .21** | .04 | .04 | .11** | .03 | .26** | .01 | 1.00 | | |
| 10. Seller's followers | 18.38 | 12.46 | .08* | .09** | .12** | .01 | .10** | .05 | .08* | .09** | .12** | 1.00 | |
| 11. Seller's product breadth | 30.03 | 9.66 | .01 | .23** | .00 | .02 | .07* | .73* | .10** | .12** | .03 | .03 | 1.00 |
| B: Study 2 |
| Variable | M | SD | 1 | 2 | 3 | 4 | 5 |
| 1. Seller performance | 33.11 | 13.62 | 1.00 | | | | |
| 2. Seller-unilateral relationships | 12.99 | 9.85 | .08* | 1.00 | | | |
| 3. Buyer-unilateral relationships | 8.02 | 6.83 | .19** | .21** | 1.00 | | |
| 4. Reciprocal relationships | 3.03 | 1.52 | .27** | .02 | .03 | 1.00 | |
| 5. Seller reputation | 3.63 | 1.19 | .05 | .08* | .09* | .11** | 1.00 |
*p < .05. **p < .01.
TABLE: TABLE 3 Study 1 Results: Online Buyer Relationship Formation
| Independent Variables | Hypothesis | Model 1 | Model 2 | Model 3 | Model 4 |
| Main Effects | | | | | |
| Bilateral communication | H1a | .23 (.01)** | .21 (.01)** | .20 (.01)** | .18 (.01)** |
| Seller's reputation | H2a | .33 (.02)** | .27 (.01)** | .35 (.03)** | .25 (.01)** |
| Buyer's relational observation | H3a | .13 (.01)** | .18 (.01)** | .11 (.02)** | .18 (.01)** |
| Buyer's experience | | .01 (.03) | .02 (.12) | .03 (.14) | .04 (.08) |
| Buyer's reciprocal relationship | | -.12 (.01)** | -.12 (.01)** | -.09 (.01)** | -.14 (.01)** |
| Interactions | | | | | |
| Bilateral communication x Buyer's experience | H1b | | -.15 (.01)** | | -.15 (.01)** |
| Seller's reputation x Buyer's experience | H2b | | .25 (.01)** | | .26 (.02)** |
| Buyer's relational observation x Buyer's | H3b | | -.43 (.04)** | | -.40 (.03)** |
| experience | | | | | |
| Buyer's relational observation x Bilateral | H4a | | .13 (.01)** | | .13 (.01)** |
| communication | | | | | |
| Buyer's relational observation x Seller's | H4b | | .24 (.01)** | | .26 (.02)** |
| reputation | | | | | |
| Bilateral communication x Buyer's reciprocal | H5a | | .13 (.01)** | | .14 (.01)** |
| relationship | | | | | |
| Seller's reputation x Buyer's reciprocal | H5b | | .13 (.01)** | | .14 (.01)** |
| relationship | | | | | |
| Buyer's relational observation x Buyer's | H5c | | .18 (.00)** | | .18 (.01)** |
| reciprocal relationship | | | | | |
| Controls | | | | | |
| Seller's duration | | .02 (.04) | .13 (.17) | .22 (.19) | .17 (.24) |
| Common events | | .02 (.27) | .02 (.38) | .01 (.06) | .03 (.31) |
| Buyer's followers | | .34 (.03)** | .26 (.02)** | .31 (.04)** | .26 (.02)** |
| Seller's followers | | .38 (.02)** | .26 (.01)** | .31 (.03)** | .23 (.01)** |
| Seller's product breadth | | .02 (.23) | .04 (.28) | .12 (.34) | .03 (.21) |
| Frailty | | N.A. | N.A. | .14 (.21) | .19 (.33) |
| Sample size | | 1,074 | 1,074 | 1,074 | 1,074 |
| R2 | | .28 | .30 | .27 | .28 |
| Adjusted R2 | | .27 | .28 | .26 | .27 |
| Log-likelihood | | -15,987.56 | -15,747.13 | -16,185.42 | -15,993.50 |
| Wald c2 | | 1,459.14** | 1,561.33** | 1,351.53** | 1,442.62** |
| Akaike information criterion | | 29,939.02 | 29,709.02 | 30,106.84 | 29,958.12 |
| Bayesian information criterion | | 29,984.85 | 29,769.01 | 30,296.04 | 29,993.40 |
*p < .05. **p < .01.
Notes: N.A. = not applicable. This table shows the standardized coefficients. Standard errors are in parentheses. Model 1 is the main-effects-only model, Model 2 is the final model, Model 3 is the main-effects model with afrailtyterm, and Model 4 includes main and interaction effects with the frailty term.
As we predicted in H1a, communication positively affects buyer relationship formation with a seller (β = .21, p < .01), and the buyer's experience diminishes this effect (γ = -.15, p < .01), in support of H1b. The seller's reputation positively affects buyer relationship formation (β = .27, p < .01), in support of H2a. However, contrary to H2b, the buyer' s experience does not diminish but rather enhances this effect (γ = .25, p < .01). In support of H3a, the buyer's relational observation positively affects relationship formation (β = .18, p < .01), and this effect weakens as the buyer's experience increases (γ = -.43, p < .01), as we predicted in H3b. The buyer's relational observation enhances the positive effect of communication (γ = .13, p < .01) and reputation (γ = .24, p < .01) on buyer relationship for mation, in support of H4a and H4b. Finally, H5 is fully supported; communication (γ = .13, p < .01), seller's reputation (γ = .13, p < .01), and relational observation (g = .18, p < .01) have stronger effects on buyer relationship formation when the buyer is reciprocating a seller-initiated relationship rather than forming a unilateral relationship.
Study 1 supports the notion that buyers use seller-, buyer-, and community-generated signals (e.g., communication, relational observation, reputation) to identify suitable relationship partners and reduce online shopping risk. These signals generally diminish in importance (with the exception of seller's reputation) as buyers gain experience because they develop and use their own knowledge to make decisions. The signals become even more important when buyers form more committed reciprocal relationships in response to a seller-initiated relationship, compared with when they form unilateral relation ships on their own. In addition, relational observation appears to be the most critical signal for buyer relationship formation, with the combination of its strong direct effect and its enhancing effects on the seller's reputation and communication. Study 1 thus improves understanding of the factors that increase the likelihood that a buyer will form a relationship with a seller, based on the premise that a buyer-seller relationship is a critical precursor to a purchase decision. In Study 2, we evaluate this premise by testing the sales payoffs earned from unilateral and reciprocal online relationships.
In Study 1, we examined the factors affecting buyer relationship formation in online shopping communities. In Study 2, we focus instead on the dynamic payoffs to sellers when they form such online relationships. Extant research has focused mostly on the indirect effects of social networks, such as Facebook and Twitter, on seller performance (Curty and Zhang 2011); we instead examine the direct payoffs that sellers experience from their portfolios of online relationships. Specifically, we investigate the dynamic effects of both unilateral buyer-to-seller relationships and seller-to-buyer relationships and reciprocal relationships on sales performance, then evaluate whether the more committed reciprocal relationships outperform the unilateral relationships (Figure 1, Panel B). Another key difference is the unit of analysis: in Study 1, we considered the buyer, or individual buyer's relationship formation over time, whereas in Study 2, the unit of analysis is the seller—or the effect of the seller's relationship portfolio, spanning many buyers—on that seller's sales performance over time. The dependent variable in this study thus is seller performance, or the seller's daily sales revenue from all buyers.
Effect of unilateral relationships on seller performance. According to a recent study of a retailer-sponsored online community, joining an online community and forming relationships with other customers increases customers' spending (Manchanda, Packard, and Pattabhiramaiah 2015). We advance this research stream by investigating how the relationships between buyers and sellers influence sales in a non-firm sponsored online shopping community. Consistent with extant research (Ha 2004), we argue that forming a relationship, whether initiated by buyers or sellers, indicates some interest, involvement, and engagement and is a precursor to purchase. Extensive relationship marketing research has also shown that offline relationships increase sellers' performance (Palmatier et al. 2006). When a seller initiates a unilateral relationship with a buyer, it signals the seller's belief in the buyer's quality and likely puts the seller on the buyer's radar, increasing awareness. Overall, a seller initiating a relationship represents a relational investment to engage with the buyer, which should increase the buyer's likelihood to purchase from that seller (Rust and Chung 2006). We expect that sellers with more seller unilateral relationships (relationships initiated by the seller but not reciprocated by the buyer) outperform sellers with fewer such relationships. Similarly, buyers form relationships with sellers to reduce information asymmetry and risk, thereby enhancing trust, so they should be more likely to buy from sellers with whom they have relationships (Palmatier 2008). We therefore expect that sellers with more buyer-unilateral relationships (initiated by the buyer but not reciprocated by the seller) outperform sellers with fewer such relationships.
H6: (a) Seller and (b) buyer-unilateral relationships positively affect seller performance.
Effect of reciprocal relationships on seller performance. In addition to the effect of one-sided, unilateral relationships initiated by either a seller or a buyer, we examine the effect of reciprocal relationships (bidirectional relationship between the buyer and seller) on seller performance. Reciprocation is a critical step in relationship formation because it signals that both parties are motivated, increasing mutual trust and commitment, and prompting exchange norms, all of which increase performance (Dahl, Honea, and Manchanda 2005). Extant research has suggested that reciprocity is a key structural characteristic of social networks that operate in an online channel (Ansari, Koenigsberg, and Stahl 2011). Reciprocal relationships may be more valuable to the seller than unilateral relationships because reciprocity leads to relationship growth, loyalty, and a desire to reward a partner directly through more sales and indirectly through positive word of mouth (Palmatier et al. 2009). Therefore, reciprocal relationships more accurately represent the strength of the seller's relationship portfolio than unilateral relationships because, bilaterally, strongly committed customers likely have a higher propensity to make repeated purchases, expand into other product categories, and serve as advocates for new customers (Reinartz and Kumar 2003).
H7: Reciprocal relationships positively affect seller performance.
H8: Reciprocal relationships have a greater positive effect on seller performance than do unilateral (a) seller and (b) buyer relationships.
Sample and measurement. Study 2 focuses on the dynamic payoffs to sellers of building a portfolio of seller and buyer unilateral and reciprocal relationships in an online shopping community. We use the same context and time frame for the longitudinal data collection in Study 2 that we used in Study 1. Rather than analyzing an individual buyer's relationship for mation, however, we evaluate the impact of these relationships on seller sales. With a web crawler program, we recorded daily transaction for each item listed in each seller's electronic shop, then calculated daily total revenue for each seller. Because we were unable to identify a specific buyer's sales from Study 1, we captured all buyers' purchases from the same 336 sellers across the same time frame. The final sample includes 5,231 buyers and 336 sellers. The seller (buyer) unilateral relationship measure represents the number of relationships initiated by the seller (buyer) that are not reciprocated. Reciprocal relationships are the number of bidirectional relationships between a seller and buyers in the online shopping community. As a control variable, we also include the seller's reputation, as an endogenous variable based on average customer reviews (i.e., same as in Study 1). Table 1 provides the definitions and operationalizations, and Table 2 contains the descriptive statistics and correlations.
Estimation and results. To account for the dynamic nature and potential endogeneity among the variables in our conceptual model, we use a VARX method (Stephen and Toubia 2010) that captures the interdependent evolution of the variables. By treating each variable as potentially endogenous, the VARX model reveals dynamic, complex interdependence among the variables. It also captures the cumulative effects of relationships on sales (Dekimpe and Hanssens 1995). We follow a four step approach for estimating VARX models (Fang et al. 2015).
First-differencing indicates that all the variables are stationary, in support of our choice to estimate a VARX model in difference. To determine an appropriate number of lags, we used the Schwarz Bayesian information criterion (SBIC). A single period emerged as an appropriate lag (SBIC = 5.42). Thus, we estimated a VARX system to capture the dynamic interactions among the three types of relationships in a seller' s portfolio, reputation, and sales revenue, written as follows:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
where Yt indicates seller performance, St is the number of seller-unilateral relationships, Bt stands for the number of buyer-unilateral relationships, RBt refers to the number of reciprocated relationships, and REt is seller reputation, all at time t; j denotes the lagged period used in the VARX model.
The vector of the exogenous variables includes, for each endogenous variable, an intercept that is a deterministic trend variable that captures the impact of the omitted, gradually changing trend of the variables (Fang et al. 2015). Consistent with Joshi and Hanssens (2010), we took a log-transformation of all variables so that the coefficients could be interpreted as elasticities. We derived the impulse response functions (IRFs), which trace the impact of a unit shock to any endogenous variable on other endogenous variables over time. Following Dekimpe and Hanssens (1995), we use generalized IRFs (or simultaneous shocking) to ensure that the order of the variables in the system does not affect the results and to account for contemporaneous effects. The duration of the shock is equal to the last period in which the IRF value had a |t|-statistic greater than 1. We accumulated IRFs until lag k to reflect the cumulative effect of the unexpected shock in the impulse variable on the response variable. Table 4 contains these results.
In support of H6a and H6b, seller-unilateral relationships (elasticity = .10, p < .01) and buyer-unilateral relationships (elasticity = .19, p < .01) positively affect seller performance. Reciprocal relationships also positively influence seller sales (elasticity = .30, p < .01), in support of H7. Both H8a and H8b also receive support because the effect on sales performance is stronger for reciprocal relationships than for unilateral seller relationships (difference = .20, p < .01) or unilateral buyer relationships (difference = .11, p < .05), according to the pairwise difference tests.[ 2] A VARX model also provides insights into the dynamic reach of the three types of relation ships. Changes in seller-unilateral relationships have the shortest reach; they significantly affect seller sales for only one day, whereas buyer-unilateral relationships affect sales for four days. Reciprocal relationships have the longest reach, with an effect on sales for seven days. Reciprocal relationships lift sales (in dollars) approximately 60% more than do buyer and three times more than do seller-unilateral relationships (Table 4).
These results strongly support the premise that building a portfolio of reciprocal relationships is very important to growing sales in online shopping communities. The effect of reciprocal relationships on seller sales is three times greater and lasts many times longer than that of seller-initiated unilateral relationships; it is 60% greater and also lasts longer than buyer-initiated unilateral relationships. Thus, reciprocation represents a key process for online relationship building and an important precursor to purchase decisions. Sellers can indirectly influence buyer relationship formation by signaling their value as a partner (building a stronger reputation), or they can initiate relationships with potential buyers directly. However, our results suggest that seller relationship building has limited effectiveness for sales unless sellers get buyers to reciprocate (follow back). Reciprocation generates a substantial multiplier effect for both sales and dynamic reach. Thus, in Study 3, we consider a managerially important research question that emerges from these results: how can sellers get buyers to reciprocate their seller-initiated unilateral relationship?
In Study 1, we show that relational observation is the most impactful signal for buyers' online relationship formation be cause it ( 1) has a direct effect on buyer relationship formation and ( 2) enhances the positive effects of both communication and reputation on buyer relationship formation. Furthermore, these relational observation effects are twice as strong when buyers form reciprocal versus unilateral relationships. Combining these insights with the results from Study 2, which show elevated payoffs from reciprocal relationships compared with unilateral relationships, we design Study 3 as a field experiment to isolate the effect of relational observation on the buyer' s reciprocation of the seller' s relational efforts and identify factors that can enhance its effectiveness (see Figure 2). Thus, Study 3 provides managerially actionable strategies for using relational observation to grow online sales.
TABLE: TABLE 4 Study 2 Results: Dynamic Payoffs from Unilateral and Reciprocal Online Relationships
| Path Tested | Hypothesis | Elasticity Estimate | Number of Days | Dollar Valuea |
| Seller-unilateral relationships ^ Seller performance | H6a | .10** | 1 | $3.31 |
| Buyer-unilateral relationships ^ Seller performance | H6b | .19** | 4 | $6.29 |
| Reciprocal relationships ^ Seller performance | H7 | .30** | 7 | $9.93 |
| Pairwise difference (Reciprocal relationships Seller-unilateral relationships) | H8a | .20** | | $6.62 |
| Pairwise difference (Reciprocal relationships Buyer-unilateral relationships) | H8b | .11* | | $3.64 |
| Seller reputation ^ Seller performance | | .04 | | |
*p < .05. **p < .01.
aDollar value generated from one additional relationship.
Building on the powerful effect of relational observation on buyers' natural relationship formation, we investigate whether sellers can improve their relationship-building efforts by pro actively identifying buyers that are more likely to reciprocate. As in Study 1, we assert that relational observation occurs when a buyer follows another community member who follows a specific seller. The fellow community neighbors whom the buyer follows and who also follow the focal seller represent intermediaries. An intermediary's choice of seller thus sends a signal to buyers that this seller is reliable, credible, and a good fit with the buyer who follows that intermediary (Chen, Wang, and Xie 2011). Seller initiated relationship efforts in turn should be more successful for buyers who engage in relational observation than for those who do not.
H9: A buyer is more likely to reciprocate a seller-initiated relationship if the buyer observes an intermediary following that seller (i.e., buyer relational observation).
When relational observation occurs, the reputations of both the intermediary and the buyer should determine the effectiveness of relational observation for promoting reciprocation. The effect of reputation can be understood from a signaling perspective, because signals have more weight according to the relative credibility of the source and the receiver (Cialdini 2009). The intermediary's and buyer's reputations both refer to general beliefs about these actors' expertise, knowledge, and credibility. Reputation or status can be inferred in online contexts by the number of followers, which "serves as a quality indicator for users of the community-generated content" (Labrecque et al. 2013, p. 258). Buyers are more likely to reciprocate seller-initiated relationship efforts when they observe an intermediary with a higher reputation because they judge the intermediary's choice as more credible. However, the intermediary's reputation effects are suppressed for buyers with reputations that are stronger than the intermediary's, because buyers give less weight to a source with a similar or lower level of perceived expertise or knowledge (Adjei, Noble, and Noble 2010). For example, a new buyer with few followers, observing an intermediary with many followers, likely perceives that intermediary as more credible and knowledgeable than would a buyer who already has even more followers.
H10: During relational observation, (a) an intermediary's reputa tion positively affects the buyer's likelihood to reciprocate (i.e., buyer-reciprocated relationship), and (b) these effects diminish as the buyer's reputation increases.
We conducted a field experiment on an online social media platform (Weibo.com, known popularly as "China's Twitter") to provide more confidence in the validity of our arguments, test the effectiveness of the important constructs identified in Studies 1 and 2 in a different online context, and address possible endogeneity concerns. In cooperation with a large seller of food and beverage products with approximately 1 mil lion followers, we implemented a field experiment.
In the field experiment, we manipulated the groups of potential buyers with which the seller initiated a relationship: ( 1) the first group—relational observation group of potential buyers—consisted of only those community members who also had an intermediary following the seller, and ( 2) the second group of potential buyers—control group—consisted only of those who did not know any intermediary following the seller, meaning they could not engage in relational observation.
To construct the sample, we identified 4,000 members (in termediaries) who recently started following the seller. We then identified all followers of these 4,000 members, using a web crawler. After we removed potential targeted buyers who already were following the seller, we obtained a sample pool of 98,704 potential targeted buyers, none of whom were following the seller and all of whom were following an intermediary that was fol lowing the seller (relational observation condition). We randomly selected 386 potential buyers from this sample (we started with 400, but removed 14 observations with outliers on non manipulated variables that were ±3 SDs from the mean). The control group consisted of a randomly chosen 2,400 members who were not following either the seller or any intermediary that was following that seller. To ensure that the samples matched on all other attributes, we adopted a propensity matching process and generated the same number of observations. Mean comparisons of the nonmanipulated variables confirmed that no significant differences existed between the two groups (see the Web Appendix).
The managerial strategy of using a seller-initiated relationship was implemented with all 772 potential buyers over an eight-hour period. That is, using this seller's account, we initiated relationships with (followed) potential buyers in both groups. After seven days, we identified all buyers in both groups who had reciprocated the seller-initiated relationship. This window is reasonable; 96% of the reciprocal follows happened within three days. Thus, our dependent variable, buyer reciprocated relationship, equals 1 if the buyer followed back and 0 if the buyer did not. For each buyer, we also capture reputation (number of followers), number of members the buyer follows, and the buyer' s activity level (number of posts).
The second goal of the experiment was to test H10, which posited that when relational observation occurs, the reputations of both the intermediary and the buyer determine the effectiveness of relational observation for promoting reciprocation. Because this hypothesis involves only the relational observation group, the analysis examined only this group of buyers. Thus, testing H10 required no additional manipulations; rather, we just measured the reputations of each intermediary and each of the 386 buyers, using the number of followers for each.
To test our hypotheses, we use logistic regression, consistent with our binary dependent variable. In Panel A of Table 5, we include the buyer's relational observation (1 = relational observation, 0 = control) and control variables such as reputation, activity level, and the number of members the buyer follows to predict whether each buyer will reciprocate the seller initiated relationship. A log-transformation of all control variables corrects for skewness. The results support H9; a buyer is more likely to reciprocate a seller-initiated relationship if (s)he observes an intermediary following the seller (i.e., there is buyer relational observation) (p = .59, p < .05).[ 3]
To evaluate H10, we estimated the model only for the relational observation group and tested whether the intermediary's reputation increased buyer reciprocation and whether the buyer's reputation suppressed this effect. Panel B of Table 5 includes the intermediary' s reputation, buyer' s reputation, intermediary's reputation X buyer's reputation interaction, number of members that the buyer follows, the buyer's activity level, and the buyer's number of intermediaries, which we use to predict buyer reciprocation. In support of H10a, the intermediary's reputation significantly increases the buyer's reciprocation (p = .32, p < .01) when relational observation exists. As predicted by H10b, the interaction of the intermediary' s reputation X the buyer's reputation was negative and significant (p = -.16, p < .01); intermediary's reputation effects are sup pressed as the buyer's reputation increases.
TABLE: TABLE 5 Study 3 Results: Effect of Relational Observation and Reputation on Buyer's Reciprocation
| Variable | Hypothesis | Parameter Estimate |
| Relational Observation Enhances Buyer Reciprocation (vs. Control Group) | | |
| Constant | | -2.40 (.69)** |
| Buyer's relational observation (0 = no relational observation, 1 = relational observation) | H9 | .59 (.29)* |
| Buyer's reputation | | -.10 (.13) |
| Number of members buyer follows | | -.03 (.10) |
| Buyer's activity level | | -.05 (.06) |
| Sample size | | 772 |
| Pseudo R2 | | .02 |
| Likelihood ratio | | 6.54 |
| Moderating Effects of Reputationsa | | |
| Constant | | -6.41 (1.68)** |
| Intermediary's reputation | H10a | .32 (.13)** |
| Intermediary's reputation x Buyer's reputation | H10b | -.16 (.06)** |
| Buyer's reputation | | -.05 (.14) |
| Number of members buyer follows | | .30 (.14)* |
| Buyer's activity level | | .13 (.09) |
| Buyer's number of intermediaries | | .24 (.08)** |
| Sample size | | 386 |
| Pseudo R2 | | .11 |
| Likelihood ratio | | 22.98 |
*p < .05. **p < .01.
aThese estimates feature the relational observation subgroup, because the intermediary's reputation is not defined in the no relational observation group (i.e., control group).
Notes: Standard errors are in parentheses.
The results of Study 3 provide insights into managerially actionable strategies that sellers can use to increase the percentage of potential buyers who would reciprocate their relationship building efforts. First, 8.8% of the participants in the relational observation group and 5.2% in the control group reciprocated the seller-initiated relationship, representing an approximately 70% lift in the buyer's likelihood to reciprocate in the treatment group. Thus, when deciding which potential buyers to follow in hopes of converting them into customers, rather than randomly following anyone, sellers could review their existing followers and follow those members of the community who follow those intermediaries. These potential buyers are much more likely to reciprocate, and as Study 2 shows, reciprocal relationships significantly outperform unilateral relationships. Second, sellers can identify targeted buyers among the followers of intermediaries with high reputations, who signal expertise and credibility and thus increase the likelihood that potential buyers who follow them will reciprocate with the seller. Third, the balance between the reputation of the intermediary and the reputation of the potential buyer is important to consider; signals have more weight with the greater relative credibility of the source to the receiver.
Although online relationship building is rather inexpensive, sellers should work to maintain a higher proportion of reciprocal versus unilateral relationships because this ratio signals the quality of their relationships. Some community members "unfollow" others who do not reciprocate after some period of time. As one Twitter user noted, "I unfollow if they have shown no interest in interacting with me" (Schaefer 2013).
Although some may argue that online relationships function in the same way as do offline relationships, we suggest that several fundamental differences must be accounted for to understand and effectively execute online relationship marketing strategies. As a first step in supporting this effort, we describe the unique characteristics of online relationships, outline evidence from extant as well as this research, and discuss implications for building and executing online relationship marketing strategies. Three tenets parsimoniously capture these insights and inform the emerging theory of online relationships that we summarize in Table 6.
First, online relationships are more anonymous than offline ones. Offline partners typically know the identity (e.g., name, job) of potential partners, with some confidence. Online partners instead tend to have limited information about or confidence in the identity of a partner, such that "the relative anonymity of e-commerce provides a basis for opportunism that does not exist in more traditional forms of business exchange" (Rotman 2010, p. 59). The lack of geographical proximity adds anonymity in terms of location, beyond the identity anonymity that characterizes computer-mediated exchanges. Consequently, the added risk of opportunism from unknown or distant partners and the scarcity of other cues make any available risk-reducing signals highly impactful on online relationship formation. Our research shows that observing other community members, receiving seller communication or follow-back, and reading reviews are all critical signals that give a buyer confidence to build a relationship or make a purchase. Thus, managers need to carefully identify and control the limited number of online risk-reducing signals they transmit. We know the importance of reviews, but other functions, such as seller "likes," customized communication, and other community-based signals, are less well understood and demand further research.
TABLE: TABLE 6 Emerging Theory of Online Relationships: Research Tenets
| Unique Online Characteristics | Source of Unique Characteristics | Supporting Evidence |
| Tenet 1: Online anonymity makes any risk-reducing signals highly influential for relationship formation, allows online relationships to form and end quickly, and supports relationship formation and influence among dissimilar partners. |
| Online relationships are more anonymous: Partners have limited information or certainty regarding the identity of potential online partners (Rotman 2010). | • Online relational partners can be located anywhere in the world. • Online relationships lack rich, face-toface interactions and other nonverbal cues about trustworthiness of an online relational partner (Rovie 2013). • 96% of reciprocal relationships formed in only 3 days (Study 3). • Studies show increased risk of opportunism (Rotman 2010). • Social norms are weaker online (Wallace 1999). |
| Tenet 2: The ease of forming and maintaining online unilateral relationships allows customers to develop an extensive and diverse portfolio of unilateral relationships, which represents an important source of insight for their decision making. |
| Unilateral relationships are easier to form and maintain online: many online relationships have a stable, unilateral structure, in which a relationship partner never reciprocates but remains in the unilateral relationship as a follower (Trier and Richter 2015). | • Unilateral relationships have lower formation and maintenance costs (effort, time, emotion) online. • Offline unilateral relationships become either bilateral as social norms make partners reciprocate relational advances, even when not desired (Cialdini 2009), or else disintegrate if one partner's failure to reciprocate causes the other partner to avoid future interactions. • A typical online user has more unilateral than reciprocated relationships (Study 2). • There is less social pressure to reciprocate relational advances in a computer-mediated environment (Trier and Richter 2015). • There are fewer barriers to relationship formation and termination online (McKenna, Green, and Gleason 2002). |
| Tenet 3: Reciprocated online relationships have a strong effect on customers' psychological commitment and financially relevant behaviors. |
| Tenets 1 and 2 outline key differences between online and offline relationships; Tenet 3 highlights a commonality that is not widely acknowledged but appears to be fundamental to building relationships online (i.e., reciprocity). | • Because most online relationships are unilateral, reciprocation may take on added significance; it helps a buyer differentiate a particular relationship among the vast number of unilateral relationships. • Feelings of reciprocity are fundamental and represent a hardwired social rule; they translate across cultures and can be felt even toward inanimate objects, such as computers (Nass and Yen 2010). • Impact of risk-reducing signals is enhanced when reciprocating a seller's outreach versus initiating a relationship (Study 1). • Reciprocal relationships lift sales (in dollars) about 60% more than do buyer and three times more than do seller-unilateral relationships (Study 2). • Reciprocal relationships have the longest impact on sales: seven days versus one and four days for seller- and buyer-unilateral relationships, respectively (Study 2). |
Anonymity also allows online relationships to form and end quickly. When relational partners know that they can end a relationship and are very likely to never "run into" the person again while also having few common acquaintances, it promotes both risky trial and easy termination. For example, in Study 3, 96% of the observed reciprocal relationships formed in the first three days. Managers should be aware of the high rate of change and short decision windows in online contexts and develop processes to support a nearly real-time response to relational outreach. Otherwise, sellers may lose an opportunity to build a relationship with significant financial ramifications. As Study 2 shows, missing an opportunity to reciprocate a buyer's outreach can reduce sales by approximately 40%, as well as ruining the chances of benefiting from a long-term relationship. More research is needed to understand the optimal response time for online reciprocation that can signal interest but not that the response is automated or without any partner discernment.
In addition, anonymity supports online relationship formation and influence among dissimilar people because of the fewer visual cues and lower pressure from social norms (Wallace 1999). Sellers can use online anonymity to make vertical moves in products and brands by building communities in which on line shoppers provide information, testimonials, and relational observation for dissimilar groups of potential customers that would typically not interact in an offline context. The strong role of relational observation in promoting relationships and sales can work across very dissimilar groups in an anonymous online context (Studies 1 and 3), but this would be atypical in the offline context as shoppers often ignore input from people dissimilar to themselves (Yaniv, Choshen-Hillel, and Milyavsky 2011). This discussion of anonymity leads to our first tenet:
Tenet 1: Online anonymity makes any risk-reducing signals highly influential for relationship formation, allows online relationships to form and end quickly, and supports relation ship formation and influence among dissimilar partners.
Second, unilateral relationships are much easier to form and maintain online than offline. The lower cost (effort, time, emotion) and continuous temporal connectivity (24/7) of online relationships allow users to reach out and build relationships with many buyers and sellers. In an offline context, exchange partners typically must be colocated in space and time for initial relationship building, and this process requires more cognitive and emotional effort in rich, face-to-face, offline environments to build and maintain the relationship. In addition, offline relationships over time tend to either become bilateral, if social norms pressure partners to reciprocate relationship advances (Cialdini 2009), or disintegrate, because one partner's failure to reciprocate over time will cause the other partner to feel spurned and avoid future interactions, thus limiting the size of offline relational portfolios.
In contrast, many online relationships have a stable unilateral structure, in which a relationship partner never reciprocates but remains in the unilateral relationship as a follower (Trier and Richter 2015). It is common for online partners not to reciprocate relational advances, because the computer-mediated environment reduces the social pressure to do so. This allows people to build extensive, easy-to-maintain, unilateral relationships that would be virtually impossible in an offline setting. For example, reality television star Kim Kardashian has 66 million unilateral relationship followers on Instagram, but only 104 of them are the more effortful bilateral or reciprocated online relationships. Thus, the ease of forming and maintaining unilateral online relationships allows customers to develop an extensive and diverse portfolio of unilateral relationships, which is important—and sometimes even essential—for decision making (e.g., identifying products or trustworthy sellers). For example, for buyers considering forming a unilateral relationship, relational observation had strong direct and lever aging effects on relationship formation. Managers need to recognize that most of their potential online customers are going to turn to this pool of partners for information and insight. Thus, sellers should use strategies such as entering a new cluster of customers and leveraging their interconnections rather than targeting different customers with few common intermediaries.
Tenet 2: The ease of forming and maintaining online unilateral relationships allows customers to develop an extensive and diverse portfolio of unilateral relationships, which represents an important source of insight for their decision making.
Third, while the previous two tenets outlined key differences between online and offline relationships, this last tenet highlights a commonality that is not widely acknowledged but appears to be fundamental to building online relationships (i.e., reciprocity). Reciprocity appears to be as important online as it is in an offline context. Many managers involved in our research were surprised at the significant difference in sales coming from buyer-unilateral relationships (i.e., buyer is following a seller) versus reciprocal relationships (i.e., seller is also following the buyer), with sales from the latter being 60% higher and having nearly twice the dynamic reach (Study 2). But why do buyers care if the seller follows them back, if they get the same information and access? Research has suggested that people use the same psychological processes to manage their online and offline relationships, and reciprocity promotes relationship formation, encourages positive behaviors, and enhances performance (Zhu et al. 2012). Therefore, managers should realize that reciprocating a link is a critical relationship-building step, even online, with psychological significance beyond the seemingly trivial action involved.
In particular, our results show that buyers seem to make a meaningful commitment when they reciprocate a seller's out reach because the impact of risk-reducing signals is enhanced when they do so, relative to initiating a relationship (Study 1). The sales performance also is greater for reciprocal than for either type of unilateral relationship (Study 2). In this sense, managers must not only reciprocate their customers' outreach efforts (which many sellers in our sample failed to do) but also design strategies to promote customer reciprocation as a means to build stronger online relationships and enhance sales. Reciprocation may even take on added significance in an online context because it helps the buyer differentiate a particular relationship among the vast number of unilateral relationships; however, more research is needed.
Tenet 3: Reciprocated online relationships have a strong effect on customers' psychological commitment and financially relevant behaviors.
Academic research on the drivers and payoffs of online relationship formation is scarce. In response, we aim to increase understanding of online relationship formation, the performance payoffs of different types of online relationships, and the most effective relationship-building strategies. Our three studies pro vide both theoretical and managerial implications.
As discussed in depth in the previous section, we identify fundamental differences and commonalities in offline and online channels that can affect relationship formation and build on these insights to offer three research tenets as a first step in developing a theory of online relationships. This emerging theory attempts to prevent academics and managers from simply extending results from offline relationships to an online context with little regard for its unique characteristics. We highlight the theoretical implications of a few other findings next.
Our results show that in the online context, being able to validate the choice of a relational partner by observing the behavior of other people (i.e., relational observation) is a critical buyer strategy. Relational observation signals the quality of the seller directly, increasing the likelihood of relationship formation, but it also enhances the positive effects of both communication and the seller's reputation. Online e-retailer Overstock.com agrees that "observing the behavior of other people" was key to its overall success (Bradley et al. 2011, p. 12). Buyers also use other signals, such as communication or reputation, to reduce information asymmetry with sellers and reduce the risks of relationship formation.
However, ignoring the dynamic nature of online relation ships can mask the differential effectiveness of observable signals over time. The effects of communication and relational observation diminish as the buyer gains experience in the community, but the positive effect of the seller's reputation increases with more buyer experience. This latter finding conflicts with our prediction. Perhaps when buyers gain experience in the community, they learn and identify which sellers have strong reputations, follow them to keep track of these "leading" sellers, and reward them by returning to purchase more. This investigation of the dynamic effects of various signals for buyers extends previous research. For example, Katona, Zubcsek, and Sarvary (2011) suggest that network duration has no effect on the growth rate of a user' s network. We confirm the lack of a direct effect, but we also show that duration has a significant, moderating effect on relationship formation across many trust-inducing signals. Finally, our results are consistent with previous online research on the importance of reputation; they also extend these findings by revealing the key role of the relative difference in reputations among members when observing their behaviors.
As our three studies show, when it comes to online relationship formation and its payoffs, three aspects are critical: ( 1) risk reducing signals, such as communication, reputation, and relational observation; ( 2) the level of the buyer's experience; and ( 3) the relationship type (unilateral vs. reciprocal). In a post hoc analysis designed to derive managerial insights, we inte grated the Study 1 results (Table 7, Panel A) with the elasticity results from Study 2 to obtain takeaways about the most effective strategies at different levels of buyer experience (±1 SD) and relationship type (unilateral vs. reciprocal), as summarized in Table 7, Panel B. With this analysis, we determine that, independent of buyer experience, bilateral communication, seller reputation, and relational observation all have greater effects on buyer relationship formation when a buyer is reciprocating versus initiating a unilateral relationship. The effects on reciprocal relationships range from approximately 50% greater for relational observation with inexperienced buyers to five times greater for bilateral communication with experienced buyers. These signals are critical to get buyers to form higher-performing reciprocal bonds with a seller. Study 2 shows that reciprocal relationships have three times more impact on seller performance than seller-unilateral relationships, indicating that seller strategies leading to higher levels of reciprocal relationship formation will be most effective.
TABLE: TABLE 7 Managerial Insights into Online Relationship Formation Strategies A: Post Hoc Analysis for Study 1
| Hazard Probability of Relationship Formation |
| Low Buyer's | High Buyer's |
| Experience (-1 SD) | Experience (+1 SD) |
| Bilateral Communication | | |
| Buyer's reciprocal relationship = 0 (unilateral relationship) | .36 | .06 |
| Buyer's reciprocal relationship = 1 (reciprocal relationship) | .62 | .32 |
| Seller's Reputation | | |
| Buyer's reciprocal relationship = 0 (unilateral relationship) | .13 | .63 |
| Buyer's reciprocal relationship = 1 (reciprocal relationship) | .39 | .89 |
| Buyer's Relational Observation | | |
| Buyer's reciprocal relationship = 0 (unilateral relationship) | .80 | -.06 |
| Buyer's reciprocal relationship = 1 (reciprocal relationship) | 1.16 | .30 |
| B: Managerial Takeaways |
| • For new buyers, relational observation is the most effective online risk-reducing signal, followed by communication and the seller's reputation. |
| • For experienced buyers, seller's reputation is the most effective online risk-reducing signal, followed by communication and relational observation. |
| • Buyer-seller reciprocal relationships consistently outperform unilateral relationships, regardless of the level of the buyer's experience. Reciprocal relationships lift sales (in dollars) about 60% more than do buyer-unilateral relationships and three times more than seller-unilateral relationships. |
| • To increase the likelihood of potential buyers reciprocating seller-initiated relationships rather than randomly following anyone, sellers should review their existing followers (i.e., intermediaries) and follow those members of the community who are following the intermediaries (vs. following members who lack any intermediaries following the seller). |
| • Sellers should identify who, among their followers, has the highest reputations and then initiate relationships with their followers, because buyers are more likely to reciprocate seller-initiated relationship efforts when they observe an intermediary with a higher reputation than their own. |
In addition, the effectiveness of online risk-reducing signals depends on the extent of the buyer' s experience in the com munity and whether that buyer is initiating or reciprocating a relationship. For new buyers in the community (-1 SD in experience) forming reciprocal relationships, relational observation is most effective for increasing relationship formation (approximately 90% more effective than the second best strategy, communication). The pattern of results for new buyers in unilateral relationships initiated by the seller is similar. Relational observation leads to the biggest increase in buyer relationship formation; it is twice as effective as communication and six times more effective than seller's reputation. New buyers thus appear to value signals they observe from the actions of close others. As one community buyer notes, "I care about what my friends like. I have added [followed] many of their favorites to my list of favorites and have bought several things based on that activity feed that I otherwise wouldn't have known about" (Auman 2010). Searching through a vast number of sellers may seem overwhelming to buyers when they first join a community. Thus, observing the sellers that their "friends" follow may determine the buyer's early consideration set of sellers.
In contrast, the pattern for experienced buyers (+1 SD) shows that for buyers in reciprocal relationships, the seller's reputation is the most effective means to increase the likelihood of buyer relationship formation (almost three times as effective as communication or relational observation). The ranking stays the same for experienced buyers in unilateral relationships initiated by the seller. Thus, relational observation, which was most effective for new buyers, is the least effective for expe rienced buyers. Seller's reputation is the least effective for new buyers, but it leads to the largest increases in buyer relationship formation for experienced buyers. Regardless of the buyer's experience level, however, our analysis shows that reciprocal relationships consistently generate higher returns than unilateral ones.
Overall, the biggest bang for the buck for sellers comes from building reciprocal relationships. The elasticity analysis in Study 2 shows that reciprocal relationships generate payoffs that are 60% to 200% greater than those of unilateral relationships. Study 3 identifies some strategies that increase the likelihood of forming reciprocal relationships, such as the presence of an intermediary that follows that seller (relational observation). Thus, it would be more effective for sellers to initiate rela tionships with those buyers whom they have identified by looking through the members who already follow them.
This research has several limitations. We test our conceptual framework in a goods context, but emerging online market places also focus on selling services. Further research could investigate whether the online risk-reducing signals in services oriented online communities differ from those we investigated. We examine relationships between buyers and sellers in an online shopping community who could have simultaneous relationships with members of other communities in the same online marketplace. Additional research could investigate the impact of relationships outside a focal community on the relationships within that community.
We faced some restrictions due to the available data. In Study 2, the private nature of the transactional data prevented us from isolating which transactions came from which specific buyer. Thus, it is accounted for at an aggregated level, with seller performance measured as sales from all buyers in a given time period. We also did not have access to private communications between buyers and sellers. Further research might study how communication may make buyers more aware of specific sellers, as another mechanism for enhancing relationship for mation. In addition, because Study 2 focused on the payoffs of formed relationships to sellers, the sample consisted of sellers with whom buyers had formed relationships in Study 1, who may be more effective than average for the entire community.
Reciprocity also played an important role. Further research might aim to deepen this understanding by investigating whether buyers and sellers choose to reciprocate for different reasons, as well as whether or how their performance outcomes depend on which party initiates versus reciprocates the rela tionship. Consistent with our focus on relationship formation, our studies address "early-stage" relationships; further research could investigate how to maintain or refresh more mature relationships and assess long-term payoffs to sellers.
The authors thank the Marketing Science Institute for their helpful comments on an early version of this paper (Marketing Science Institute Working Paper Series 15-126). The authors acknowledge the financial support of the National Natural Science Foundation of China (grant #71602064; 71672132) and the Fundamental Research Funds for the Central Universities, China (grant #2662016QD052). Satish Jayachandran served as area editor for this article.
A: Understanding Online Relationship Formation (Study 1)
B: Dynamic Payoffs from Unilateral and Reciprocal Online Relationships (Study 2)
A: Utilizing Relational Observation to Enhance Buyer's Reciprocation (vs. Control Group)
B: Moderating Relational Observation's Effect with Intermediary and Buyer's Reputations
Endnotes 1 We thank the Editor in Chief and an anonymous reviewer for their helpful suggestion to expand our examination of reciprocal relationships.
2 In a post hoc analysis, we separated the reciprocal relationships variable into seller-initiated and buyer-initiated reciprocal rela tionships to test for possible asymmetry in the effects on sellers' sales. Both variables significantly increased sales, but we found no significant pairwise difference.
3 As a robustness check, we operationalized relational observation as a continuous variable (i.e., number of intermediaries a buyer has) and still found support for H9 (P = .19, p < .01). We thank an anonymous reviewer for this suggestion.
DIAGRAM: FIGURE 1 Conceptual Models for Formation and Payoffs of Online Relationships
DIAGRAM: FIGURE 2 Field Experiment Testing Seller Strategies for Forming Reciprocal Relationships (Study 3)
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Irina V. Kozlenkova is Assistant Professor of Marketing, Broad College of Business, Michigan State University.
Robert W. Palmatier is Professor of Marketing and John C. Narver Chair in Business Administration, Michael G. Foster School of Business, University of Washington.
Eric (Er) Fang is Visiting Professor of Marketing, University of Hong Kong, and Professor of Marketing and James Tower Faculty Fellow, University of Illinois at Urbana-Champaign.
Bangming Xiao is Lecturer in Marketing, College of Economics and Management, Huazhong Agricultural University.
Minxue Huang (corresponding author) is Professor of Marketing, School of Economics and Management, Wuhan University.
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Record: 129- Online Shopping and Social Media: Friends or Foes? By: Zhang, Yuchi; Trusov, Michael; Stephen, Andrew T.; Jamal, Zainab. Journal of Marketing. Nov2017, Vol. 81 Issue 6, p24-41. 18p. 1 Diagram, 6 Charts, 5 Graphs. DOI: 10.1509/jm.14.0344.
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Online Shopping and Social Media: Friends or Foes?
As social network use continues to increase, an important question for marketers is whether consumers’ online shopping activities are related to their use of social networks and, if so, what the nature of this relationship is. On the one hand, spending time on social networks could facilitate social discovery, meaning that consumers “discover” or “stumble upon” products through their connections with others. Moreover, cumulative social network use could expose consumers to new shopping-related information, possibly with greater marginal value than the incremental time spent on a shopping website. This process may therefore be associated with increased shopping activity. On the other hand, social network use could be a substitute for other online activities, including shopping. To test the relationship between social network use and online shopping, the authors leverage a unique consumer panel data set that tracks people’s browsing of shopping and social network websites and their online purchasing activities over one year. The authors find that greater cumulative usage of social networking sites is positively associated with shopping activity. However, they also find a short-term negative relationship, such that immediately after a period of increased usage of social networking sites, online shopping activity appears to be lower.
Online Supplement: http://dx.doi.org/10.1509/jm.14.0344
In recent years, social media has become an important part of people’s daily lives and, therefore, firms’ marketing programs. Although no longer new, social media still presents marketing professionals with challenges as they struggle to understand how it is changing the business environment and attempt to identify opportunities for effectively leveraging online social media channels (e.g., Facebook, Twitter) in ways that drive value. In parallel with the practitioners, academic researchers have been exploring a variety of topics related to social media, including word-of-mouth (WOM) propagation, online social influence, the role of user-generated content and product reviews in stimulating sales, and factors related to the “virality” or diffusion of online content (e.g., Berger and Schwartz 2011; Chevalier and Mayzlin 2006; Kumar et al. 2013; Kumar and Rajan 2012; Moe and Trusov 2011; Stephen and Galak 2012; Toubia and Stephen 2013; Trusov, Bucklin, and Pauwels 2009; for a comprehensive review, see Lamberton and Stephen 2016).
The foremost players in the social media landscape are social networking sites. In essence, social networks are online communities that allow users and firms to share content with people. Social network use among consumers is high and continues to increase. As of September 2016, approximately 1.79 billion people were active Facebook users (Facebook 2016). In the United States, 30% of online time spent is spent on social networking and social media sites (Mander 2016). Given the widespread use of social networks, it is necessary to understand how this is related to another popular and economically important online activity: e-commerce. Althoughfirms are increasingly using social networks in their marketing strategies, research by IBM suggests that social media has little impact on e-commerce, with only .34% of online sales referred by social media websites (Del Rey 2013; Gara 2012). However, other industry studies indicate a positive link between sales and consumers’ engagement with social media sites such as Twitter, Pinterest, and Facebook (Bercovici 2013).
Despite the growing body of marketing literature on social networks, researchers have paid little attention to the interplay between social network usage and e-commerce activity. In particular, extant research has not examined how consumer engagement in social networks coexists with and is related to consumers’ e-commerce activities. In this article, we aim to shed some light on this question.
We argue that social networks can play a dual role in consumers’ e-commerce buying behaviors. On the one hand, using social networks could be positively associated with purchasing because consumers on social networks are frequently exposed to information about products and consumption-related activities, ranging from product ads by brands to friends’ conversations and opinions about recent shopping experiences (e.g., Chevalier and Mayzlin 2006; Moe and Trusov 2011; Stephen and Galak 2012). Both individuals and firms use social networks to share information that can be broadly described as “consumption related.” For example, on their social network accounts, consumers often post photos of recent purchases, share stories about shopping experiences, and describe products that they want to purchase in the future. Firms also frequently use their social network channels to post information about their various offerings.
Intuitively, a person is more likely to buy while browsing an e-commerce site such as Amazon (e.g., while actively searching for product information) than when interacting with friends on Facebook. However, over time, a person who is more active in social networks might also be exposed to a greater variety of shopping-related content posted by firms and friends than a person whose interactions with shopping content are limited to goal-driven product searches across the e-commerce sites with which (s)he is already familiar. Thus, for the former shopper (with higher social network exposure), “buying” might be less costly than it is for the latter shopper (without much social network exposure) in terms of reduced costs of information search. We argue that repeated exposure to consumption-related content on social networks ( 1) informs consumers about consumption opportunities (purchase options), which reduces search costs and, as such, makes buying more appealing (Hauser, Urban, and Weinberg 1993) and ( 2) may be associated with consumption through social and peer referral mechanisms (e.g., Katona, Zubcsek, and Sarvary 2011; Kumar, Petersen, and Leone 2010; Trusov, Bodapati, and Bucklin 2010). We therefore expect a positive correlation between consumers’ social network engagement and their online shopping behaviors.
On the other hand, the use of social networks may substitute for time spent on e-commerce sites, thus potentially having a negative relationship with online shopping activities in a more immediate, short-term sense. On average, consumers spend approximately 30% of their Internet time on social media and 8% on online shopping (Mander 2016). According to theories of time allocation (e.g., Becker 1965), how people allocate their time tends to be fixed, and there are costs associated with time that is allocated to activities. Logically, because there are a fixed number of hours in a day, allocating time to activities, including online shopping versus social network use, may take on a “zero-sum” nature. In other words, if a consumer spends more time using social networks on a given day, (s)he may spend commensurately less time doing other online activities such as online shopping.1
This duality in how social network usage could be correlated with online shopping activity might explain an interesting and seemingly paradoxical empirical pattern that we observe in our data: during periods of elevated activity on social networking sites, people appear to be less likely to buy online—a result that is alarming to online retailers. However, on a more positive note, we also observe that people who have been engaged in social networking sites for an extended period of time (i.e., cumulative use) tend to buy more and, importantly, tend to buy from a larger number of online retailers. Thus, it appears that cumulative social network use may be associated with an increase in overall shopping behavior in the long run but a decrease in overall shopping behavior in the short run.
With these competing forces in mind, we develop a conceptual framework that builds on classic time-allocation literature (Becker 1965; Hauser, Urban, and Weinberg 1993; Ratchford, Lee, and Talukdar 2003) to explain these trends. We then provide empirical evidence in support of our conceptualization using a unique real-world data set of complete online browsing and purchase activity over one year for more than 10,000 people. Our data enable us to investigate the associations between social network usage and buying behavior over time.
To preview our empirical results, we find evidence of both positive and negative relationships between social network usage and online shopping activities. Notably, the nature of these relationships depends on the time horizon of past social network usage (i.e., immediate vs. cumulative) and, to a lesser extent, product category. Specifically, our main findings are as follows:
- Social network usage is both positively and negatively associated with shopping activity: social network engagement over time (i.e., cumulative usage) is positively correlated with shopping activity, whereas immediate usage is negatively correlated with shopping activity.
- Social network referrals are predictive of shopping activity, but only in the immediate term.
- The positive relationship between cumulative social network usage and online shopping activity is stronger for product categories that tend to be shared on social networks and/or are often bought as unplanned purchases (e.g., chocolates) and weaker for products that are less typically socially shared and/or are usually planned purchases (e.g., automotive parts, gift cards).
This research makes several important contributions. First, to the best of our knowledge, this is the first article to empirically associate individual-level e-commerce activities with the social network usage behavior of a large number of people. While extant studies have suggested that electronic WOM (eWOM) can significantly affect sales (e.g., Chevalier and Mayzlin 2006; Chintagunta, Gopinath, and Venkataraman 2010; Godes and Mayzlin 2004; Kumar et al. 2013; Stephen and Galak 2012), have assessed the impact of social web links and recommendations on online behavior (Goldenberg, Oestreicher-Singer, and Reichman 2012; Mayzlin and Yoganarasimhan 2012; Oestreicher-Singer and Sundararajan 2012), and have linked exposure to another form of media (television advertising) with online shopping (Liaukonyte, Teixeira, and Wilbur 2015), our research fills an important gap in the literature by offering insights into how both immediate and cumulative overtime usage of online social networking sites are related to people’s e-commerce buying behaviors. Second, this study answers recent calls for more research to consider how social network usage is related to consumers’ behaviors outside social media channels (Lamberton and Stephen 2016; Stephen 2016). Finally, we propose an original theoretical framework (building on classic time-allocation literature) that can help managers resolve some ambiguity in the relationship between social network usage and sales. In doing so, we also contribute to the time-allocation literature by exploring more modern uses of time.
As we have noted, our conceptual framework is based on theories of time allocation. Becker (1965) proposes that people allocate their time to various activities in line with the utility impact of the commodities they derive from those activities.2 For example, a consumer can choose to allocate his or her time to playing golf or to exercising on a treadmill. Both activities create commodities such as entertainment value and health value, although golf may offer greater entertainment value and exercise may yield more health benefits. The consumer then allocates a portion of his or her time to golf, the treadmill, or a combination of both to produce both entertainment and health-related value. Inherent in this decision is a trade-off between engaging in one activity or another, because of the fixed amount of available time and the marginal values gained.
The trade-offs that people must make when allocating time to different activities has been studied extensively in the literature. Gronau (1973, 1977) examines the decision to spend leisure time at home and spouses’ allocation of household time. Consumers can engage in different leisure and nonleisure activities and tend to make their time-allocation decisions as a function of the value they potentially derive from the time invested in each. Hauser, Urban, and Weinberg (1993) provide evidence that consumers allocate their time across different sources when searching for information to be used for future purchase decisions. These time-allocation decisions are based on the marginal value people derive from the time dedicated to each source. In these previous examples, allocating time to one activity reduces the time available to other activities. Therefore, certain commodities are gained (e.g., entertainment) at the expense of others (e.g., fitness).
We apply the classic theory of time allocation to our specific context. The Internet offers consumers a variety of sources that provide a wealth of shopping- and entertainment-related content. For the sake of simplicity, we focus on only two types of sites, e-commerce and social networks, and two types of value “commodities” that these sites provide, informational value and entertainment value. Note that entertainment value can be construed broadly and can be considered “all other value” (i.e., noninformational) to some extent to reflect the diversity of experiences and types of value that Internet users might derive.3 For convenience and brevity, however, in developing our theory, we distinguish informational and entertainment value.
Consistent with Becker (1965), these commodities enter a consumer’s utility function, which the consumer then maximizes by allocating a fixed amount of Internet time available to social networking sites, shopping sites, or some combination of the two. Thus, the benefits of obtaining informational value and entertainment value from these websites must be balanced with the cost (in terms of time) of using those sites. More formally, in line with Becker (1965), Hauser, Urban, and Weinberg (1993), and Gronau (1977), consumers maximize their utility subject to their time constraint:
max Utility = Entertainment value + Informational value Entertainment value = f ðEsh, tsh, Esn, tsnÞ
Informational value = g ðIsh, tsh, Isn, tsnÞ s. t. T = tsh + tsn,
where E and I represent the specific entertainment and informational content available on shopping and social networking websites (subscripted with sh and sn, respectively), t is the time input needed to realize the content on the two types of websites, and T is the total time dedicated to these activities. We assume that both types of websites provide some levels of entertainment and informational content, with social networking sites providing relatively more entertainment content and e-commerce sites providing relatively more informational content (Isn < Ish and Esh < Esn).
We also assume a diminishing marginal return functional form for f ð$Þ and gð$Þ. This means that marginal returns in either shopping or social networking sites diminish with more time spent on the site. This reasoning follows previous research that has proposed diminishing returns to the value of information when a consumer spends more time with a given information source (Ratchford, Lee, and Talukdar 2003). Diminishing marginal returns of resource allocation are also well established in the literature not only with respect to time allocation (Gronau 1977; Hauser, Urban, and Weinberg 1993) but also in other contexts, including marketing resource allocation (Dorfman and Steiner 1954; Fischer et al. 2011; Morey and McCann 1983) and customer relationship management (Venkatesan and Kumar 2004). For example, researchers find that marketing budgets should be allocated to different products as a function of incremental sales (Fischer et al. 2011) or proportional to demand elasticities (Dorfman and Steiner 1954). Diminishing returns also occur when firms decide how to allocate their marketing resources to maximize customer lifetime value using consumers’ contribution margin (Venkatesan and Kumar 2004).
Following Gronau (1977) and Hauser, Urban, and Weinberg (1993), we specify a log functional form to reflect the customary diminishing marginal returns of time allocated to either shopping sites or social networking sites. We then solve for the time spent on shopping sites and social networking sites to show the trade-off effect4:
The result implies that the optimal time spent on social networking sites is proportional to the ratio of contributions to the entertainment and information values the two sources produce.
Next, we use our time-allocation model to help explain consumers’ buying behavior, which is central to our empirical study. To demonstrate the association between informational value accumulated from using the Internet and online purchasing behavior, we draw on a well-established premise that choice decisions are a function of the amount of product-related information to which a person is exposed. For example, Meyer (1982) finds that choice decisions may stem from a sequential elimination process that consumers work through by using the information they gather through search. This search process helps people form preferences and leads to purchase activity (Meyer and Sathi 1985). In addition, consumers commonly need to search for product-related information before they make purchase decisions (Branco, Sun, and Villas-Boas 2012). Information search may also increase the utility people gain from their brand choice decisions (Hagerty and Aaker 1984), and it helps consumers reduce their time cost during shopping sessions and enables them to select better-matched products (Ratchford, Lee, and Talukdar 2003). Researchers also find that the availability of product information online (e.g., price, attributes, recommendations) contributes to purchase activity (De, Hu, and Rahman 2010; Klein and Ford 2003; Ratchford, Lee, and Talukdar 2003).
In summary, informational value accumulated through the use of social networks (e.g., exposure to other consumers’ experiences, recommendations, and stated preferences) and e-commerce sites (e.g., exposure to brands and products) can lower consumers’ search and information costs. Accordingly, this may make it easier to buy products, thus increasing consumers’ purchasing probability. We model the amount of informational value as follows:
We first consider the negative relationship between social network usage and e-commerce. In the absence of time allocation considerations, the usage of both social networking and shopping sites is likely to be associated with an increase in online purchasing (through the accumulation of information), with a smaller buying lift for social network usage and a greater buying lift for shopping site usage. However, when this relationship is subject to time-allocation constraints, a greater immediate usage of social networking sites will reduce the usage of shopping sites, implying less purchase activity. A consumer may choose to spend more time on social networking sites because the entertainment value is higher on these sites. According to our proposed model, when a consumer spends e more time on social networking websites, (s)he reduces the corresponding portion of Internet time spent on shopping sites. This may arise because the higher entertainment value found on social networking sites may decrease the attractiveness of subsequent shopping sessions, especially for product categories associated with greater entertainment value (e.g., clothing, children’s products).5 Because the informational value obtained from spending e more time on social networking sites is less than what could have been obtained from spending time on e-commerce websites (Isn < Ish), this substitution with respect to time allocation leads to lower informational value—even with diminishing marginal returns—accumulated in the given period, and thus it is correlated with less purchase activity.6
We illustrate this mechanism in Figure 1. All else equal, the information extracted per unit of time spent on shopping sites yields higher returns than that per unit of time spent on social networks: Îsh > Îsn (left side of graph). Thus, purchase probability increases less rapidly when the user is on social networking sites. This leads to our first hypothesis:
H1: In the short run, usage of social networking sites is negatively correlated with buying behavior (short-term substitution).
Although we argue that the immediate use of social networking sites is correlated with less e-commerce activity, we also propose that cumulative usage of social networks—defined as spending more (vs. fewer) days within any given time period on social networking sites—is positively correlated with buying behavior. This follows from Becker’s (1965) theory and is in line with the common assumption of diminishing marginal returns from the informational value obtained from browsing websites.
Drawing on research on the theory of time allocation, Gronau (1977) and Hauser, Urban, and Weinberg (1993) posit that incremental time spent on a given resource or effort can result in diminished marginal returns. Similarly, others find evidence that diminishing marginal returns occur when allocating marketing resources (Dorfman and Steiner 1954; Fischer et al. 2011; Morey and McCann 1983; Venkatesan and Kumar 2004). Drawing on this theory, we reason that although social networks may provide less informational value than shopping sites, the initial incremental daily time t spent on social networks (see Figure 1) may result in greater informational value (Îsn in Figure 1) than if the incremental time were spent on the shopping sites that have already received a substantial amount of consumers’ attention (^Ish in Figure 1). This implies that spending more days on social networking sites can result in greater total informational value extracted (which is correlated with greater purchase activity) than spending fewer days.
Using the model described in Equation 1, we can assess the impact of cumulative social network usage (e.g., daily usage) by comparing the accumulation of informational value under N and n days of social network usage, where N > n. In the latter scenario, there are N - n days with no social network usage. The expected informational value on each of these nosocial-network days is always Ish · ln ðTÞ. On days with social network usage, we calculate (in Web Appendix A) the expected informational value as follows:
If we compare these two cases, using social networking sites over N days (compared with n days) generates more informational value under the following condition:
In other words, as long as there is informational content available on social networks, allocating a small e amount of time to social networking sites increases the amount of informational value, which is correlated with increased purchase activity. It follows that if e is measured in smaller units (i.e., seconds), T becomes large (total available seconds), and 1/[ln(T) – 1] becomes small.
We note that this is different from the substitution effect proposed previously. In the prior example, we assume that consumers already spend time on social networking sites and that the variation in immediate time spent (greater or less) is correlated with differences in e-commerce activity. Thus, conditional on a person’s already allocating time to social networking activities on a particular day, more time spent on these sites results in less informational value because less time is spent on shopping sites. Here, with respect to cumulative social network usage, we are comparing variation in day-to-day social network usage incidence across an extended period of time (days or even months). Our assumption is that consumers can obtain new information on social networking sites on a day-to-day basis, providing them with greater marginal value relative to their initial daily time allocation to social networking activities. Thus, a consumer who frequently engages in social networking activities each day of the week, compared with a consumer who visits these sites only once per week, will potentially generate greater marginal informational value. Overall, this suggests that, in the long run, there is a positive correlation between social networking usage and shopping activities.
Of course, this outcome relies on the premise of new informational value on social networking sites across different days. We argue that this is indeed the case. Consumers are often exposed to product- and consumption-related content as well as opinions and eWOM posted by other consumers (e.g., Chen, Wang, and Xie 2011; Chevalier and Mayzlin 2006; Chintagunta, Gopinath, and Venkataraman 2010; Godes and Mayzlin 2004; Kumar et al. 2013; Liu 2006; Moe and Trusov 2011).7 As such, consumers can discover new products to purchase through the content that others (e.g., friends) share. Importantly, consumers are exposed to a wide variety of products and consumption-related activities on social networks. Thus, cumulative exposure, even in low amounts, could be correlated with greater e-commerce activity. We provide the formal derivation of this outcome in Web Appendix A.
In addition, we recognize that in social networking sites, consumers are also exposed to negative content that might dissuade them from purchasing particular products (e.g., negative eWOM).8 However, we argue that two key factors might contribute to the long-term positive impact of consumption-related information. First, it is generally acknowledged that the majority of user-generated content tends to be positive (Chevalier and Mayzlin 2006; Moe and Schweidel 2012). For example, more than half of the reviews on Amazon are 5 stars, with the average being 3.9 (Woolf 2014). In addition, there is a tendency among social network users to present themselves and their experiences in a positive light (Toubia and Stephen 2013). In other words, the “share of voice” for positive consumption-related content is larger than that of negative content. Second, and arguably more important, negative consumption-related information is typically associated with a particular product or service experience (i.e., negative reviews). Clearly, such information might be influential in driving other consumers away from making certain purchases (East, Hammond, and Lomax 2008, Ho-Dac, Carson, and Moore 2013). However, because such reviews are product focused, it is unlikely to suppress consumers’ general shopping interest for an entire product category. On the contrary, negative information may actually help consumers find a better match for their preferences, thus increasing overall satisfaction from their shopping experience. Accordingly, we hypothesize that consumption-related content is positively associated with purchase decisions in the long run.
H2: In the long run, cumulative social network usage is positively correlated with buying behavior.
In addition, we propose that product category moderates the relationships we have described. If social network engagement is positively correlated with online shopping activity because social networking sites continually expose people to new information about products and brands, then that correlation should be stronger for product categories that are more commonly mentioned in people’s posts on social networking sites. This is due to, for example, the tendency of social network users to project highly positive self-images to others through their posts on these sites and to feature themselves using certain products, particularly higher-status items or products that signal something favorable about oneself or one’s family, as a way to signal a positive self-image (Gonzales and Hancock 2011; Wilcox and Stephen 2013). For example, we would expect a stronger positive correlation in categories such as chocolate (an indulgence) and children’s products (indicating one’s status as a parent) but not in categories such as automotive parts (which are, for most consumers at least, unglamorous). We also expect a stronger correlation in categories that have a higher incidence of unplanned purchases, as this is associated with casual retail browsing (Moe and Fader 2004; Stilley, Inman, and Wakefield 2010). Finally, we expect the proposed negative correlation between immediate social network usage and online shopping activity also to be moderated by product category. If consumers are getting more entertainment value from social networks, they may be less inclined to also shop for products that provide them with entertainment value in the shopping process. This may be correlated with a reduction in sales for product categories such as clothing, children’s goods, and jewelry. Thus, we advance our final hypothesis:
H3a: The correlation between cumulative social networking usage and online purchase incidence is moderated by the product category of the purchased item.
H3b: The correlation between immediate social network usage and online purchase incidence is moderated by the product category of the purchased item.
To explore the associations between social network usage and online shopping activity, we take advantage of a rich, individual-level data set provided by a major global market information and measurement company. The data set features a panel of 10,192 people whose online activities were tracked during the full year of 2007, and it has three components: individual online purchase records, full web-browsing history, and demographic information. The data provider selected these panelists to be a representative sample of the U.S. population.9
Because the web-browsing data include details on each site visitation, we investigate the data at the session level.10 Each Internet session, as defined by our data provider, begins when a user first opens a website, continues when the user opens other websites, and ends when the user closes all opened website pages or is inactive for more than 30 minutes (see Figure 2). This is slightly different from a website session (which begins when a user opens a website and ends when the user leaves the website by closing the page, progressing to a different domain on the same browser page, or remaining inactive for more than 30 minutes) because it accounts for visits to multiple websites in one sitting. We choose to analyze our data at the Internet session level rather than at the website session level because the latter raises simultaneity concerns. Specifically, once a user opens multiple websites, we are unable to identify the order of use within these concurrent sessions. In contrast, we are able to identify distinct Internet sessions without overlap in usage activity because, by definition, Internet sessions are separated by periods with no Internet usage.
The data set includes all online purchases made by the panelists between January 1 and December 31, 2007. For each purchase, we know who made the purchase, the purchase date, the retailer (i.e., website domain) where the purchase was made, and the category of purchase. We observe 140,291 distinct purchase transactions made during this period by 7,402 unique users. Figure 3 shows a distribution of purchase activity by user. Of our panelists, 73% purchased at least once during the observation period, and conditional on having made a purchase, each person made an average of 19 purchases over the course of the year.11
We show the aggregate purchasing activity for our panel in Figure 4. As expected, we observe seasonal fluctuations (e.g., spikes in shopping activities around the holiday season). This is especially pronounced in the months of November and December, with approximately 25% and 31% greater shopping activities compared with the other months. Therefore, in our subsequent empirical analysis, we control for these seasonal variations using time-specific random effects.
From the purchase records, we create three variables to measure panelists’ online shopping activities: ( 1) purchase observation, ( 2) the number of purchases, and ( 3) the number of retailers purchased from. The first variable, PURCHASEit, records, on a session basis, whether individual I made a purchase in session t. The second variable, NUMPURCHASEit, records the number of purchases made in session t, which represents a panelist’s shopping activity intensity for that session. The third variable, NUMRETAILERSit, records the number of retailers from which a panelist made purchases in each session. We measure each of these variables at the individual session level. Table 1 provides the summary statistics of these variables.
The second component of our data set is a detailed history of website visitation for all members of the panel during the observation period. This includes records of the domain (e.g., www.facebook.com) and domain pages, a time stamp of each website visit, the website category, and duration of the visit. The majority of Internet usage by our panelists in 2007 was related to entertainment, e-commerce, news, search, social communities, and Internet services (for a full list of categories and subcategories, see Web Appendix C). In our analysis, we focus on two specific subcategories of website visit (i.e., browsing) observations: mass merchandiser and member communities. Mass merchandisers include browsing visits to online retailers. Thus, we have detailed information on each user’s shopping browsing activities, captured through shopping duration and shopping sessions. The member communities subcategory includes visits to domains for social networking websites. Given our focus on social networks and online shopping activities, we do not differentiate among other website domain categories that cover the rest of our sample’s Internet usage, but we do use this information as a baseline for all other nonsocial/non-shopping-related online activity.
The first set of measures derived from web browsing includes activities related to social networks.12 In our data, the social networking sites are Facebook and Myspace, which together account for nearly half of all social network browsing sessions.13 We combine the two and refer to them as our “social networking” sites because they place a heavy emphasis on the creation of and interaction with social ties (i.e., “friends”).14 The two social networking sites facilitate a rich variety of content exposure through social posts. As a result, we expect that consumers are exposed to a significant variety of new consumption- and shopping-related experiences in their social networks.
TABLE: TABLE 1 Summary Statistics of Shopping Activities
| Variable | M | SD | Min | Max |
|---|
| PURCHASE | .008 | .090 | 0 | 1 |
| NUMPURCHASE | .020 | .357 | 0 | 49 |
| NUMRETAILERS | .009 | .098 | 0 | 4 |
We created variables to measure social network usage and engagement as follows. First, for each person, we record the incidence and total duration of social network sessions during each Internet session. The variables SN-SESSIONit and SN-DURATIONit record the incidence of social network usage and the usage duration for user I during session t.
Second, we created SN-ACTIVEit to denote whether an individual was participating in social networks before session t. Because our data were left-censored, we do not have information on when each person first visited a social networking site. Instead, we infer this action from the data. Figure 5 plots the first day of observed usage of social networking websites for our panel in January 2007. Many users visited social networking sites from the beginning of our data set, which suggests that they may have been active users of social network before January 1, 2007. We also observe a kink in Figure 5 at about 13 days, suggesting that the initial group of people may have been previously active and different from the users who we observe arriving later.15 Thus, we assume that users we first observe on social networking sites after two weeks are new users and were not active in social networking websites previously. For these “new” users, we set SN-ACTIVEit to 0 before they first visited a social networking website and to 1 after they made their first visit. For all other users, we set SN-ACTIVEit to 1 for all sessions in our data set.16 To further control for users who were active in the first month, we include a dummy variable, SN-FIRSTMONTHi, which equals 1 if we observe an individual on a social networking website during the first month.
Third, we created SN-ENGAGEMENTit to measure the cumulative engagement in social networking sites. This variable records the total number of days that user I has been active on social networks before session t.17 For example, we set this variable to 1 during the first day a user actively visits social networking websites. For the second day with an observed visit, we set this variable to 2, and so on. We use this variable to capture cumulative usage of social networking sites.
Fourth, we created measures related to users’ shopping browsing behavior. We created two variables to account for this: shopping session count and shopping duration. SHOPSESSIONit records the number of unique visits user I made to e-commerce websites in session t, and SHOP-DURATIONit records the total time (minutes) user I spent on e-commerce websites in session t. We also record the total Internet session count and duration for user I in session t, exclusive of shopping or social networks, as covariates (NET-SESSIONit and NET DURATIONit). These variables are important because they allow us to control for individual i’s Internet activity in session t. In other words, they capture the variation in a user’s Inter-net activity over time. In addition, we include cumulative measures of shopping and Internet usage (days that user I has visited the respective site before session t), labeled SHOPCUMULATIVEit and NET-CUMULATIVEit.
Fifth, in addition to social network and shopping metrics, our web-browsing history data enable us to infer referral metrics that identify which domains help lead consumers to online retailers. Although we do not have clickstream data to identify referral sources precisely, we use our web history data to infer referrals to e-commerce websites. Specifically, we define a referral site as one that a user visited immediately before going to an e-commerce website if the referral session ended less than five minutes from the start of the shopping session. Therefore, if we do not observe any activity within five minutes before a shopping session, we assume that the shopping session was not initiated by a referral. The referral activity allows us to assess the relationship of the previously visited website with purchase activity. Thus, this helps us identify potential referral effects of social networks, where consumers may discover a new product or referral link and immediately transition to an e-commerce website, from the cumulative effects of social networks. We separately identify three different categories of referral websites: social network, search, and shopping (see Table 2). There are many referrals from both e-commerce websites and search engines (i.e., Google and Yahoo). In contrast, social network visits precede onlyabout2%ofallshoppingsessionsasreferrals. Usingthisreferral information, we created three variables: ( 1) SN-REFERRALit, ( 2) SEARCH-REFERRALit, and ( 3) SHOP-REFERRALit. These are dummy variables equal 1 if we observe a referral to an e-commerce website for individual I in session t.
The final part of the data set comprises demographic information for each panelist and several control variables. Table 3 provides summary statistics on age, gender, household size, income, and number of working members in a household, as well as an indicator for children in the household. Note that we do not have a continuous measure for income, which we instead measure as one of four categories (see Table 3). We use these demographic variables to control for observed heterogeneity in our model. Note that the average age is greater than what might be expected on social networks because our data set captures a representative sample of the U.S. population.
We now describe how we model the relationship of people’s immediate usage of and engagement with social networking sites with their online shopping activities. As we mentioned previously, shopping activity is indicated by three variables:
( 1) a binary session-level purchase action, ( 2) the number of purchases made each day, and ( 3) the number of retailers purchased from each day. Thus, our empirical analysis is composed of these three components.18
TABLE: TABLE 2 Statistics on Referrals to Shopping Sites
| Referral Type | Number of Sessions | Percentage of Total |
|---|
| Referrals from social networks | 37,834 | 2.1% |
| Referrals from search | 109,276 | 6.0% |
| Referrals from other shopping | 168,845 | 9.3% |
| No/other referrals | 1,491,060 | 82.5% |
| Total shopping sessions | 1,807,015 | 100.00% |
We use a hierarchical binary probit model to assess the relationship between user i’s social network activities and his or her decision to purchase from an online retailer in session t. Let ypit = 1 if individual I makes a purchase in session t and ypit = 0 if we do not observe any purchase activity during that session. The superscript p indicates the parameters and data pertaining to the purchase decision model. We specify individual i’s latent purchase utility in session t as follows:
where each set of variables is defined as follows: yi, t-1 is the purchase decision in the previous session, which accounts for state dependence in purchasing; SOCIALNETWORKi, t-1 is a set of lagged variables describing social network activity; INTERNETi, t-1 contains lagged nonsocial Internet activity (with separate measures for shopping and nonshopping, nonsocial sites); REFERRALit is the three types of referrals before a shopping session (referrals from search, social, and shopping sites); and DEMOGRAPHICSi is the demographic information for user I (see Table 4). We note that for each individual, bp i2, bp i3, bpi4, and dp are vectors of parameters that represent the coefficientsforeachset ofvariables(e.g., SOCIALNETWORKi, t-1 is a matrix that contains the eight variables listed in Table 4, and bp i2 are the coefficients for those variables).
Our goal in Equation 7 is to examine the relationship of social network usage and engagement with purchase activity. Of course, we need to control for many factors that may confound our results, such as unobserved individual-level heterogeneity, correlated unobservables, and simultaneity. First, unobserved differences across consumers could potentially bias our results. For example, some consumers may be particularly savvy Internet users and thus more active on both social networking and e-commerce websites. Our data may also reflect different types of consumers, such that some prefer to participate in social networks, whereas others do not. This is an issue if different types of consumers also exhibit differences in online purchase patterns.
TABLE: TABLE 3 Demographic Summary Statistics
| Variable | M | SD | Min | Max |
|---|
| Age | 47.73 | 14 | 17 | 99 |
| Gendera | 0.43 | 0.5 | 0 | 1 |
| Household size | 2.64 | 1.23 | 1 | 5 |
| Incomeb | 1.55 | 0.9 | 0 | 3 |
| Working members | 1.46 | 0.91 | 0 | 5 |
| Childrenc | 0.37 | 0.48 | 0 | 1 |
Second, correlated unobservables are factors related to both social network engagement and online purchase activities but are unobservable to the researcher. One example in our setting is activity bias, wherein consumers who are active online in a given session might be more likely to visit social networking websites and shop online.19 This becomes a concern if there is high variation in day-to-day Internet use because the relationship between social network usage and online shopping could be driven by general Internet activity (e.g., “lumpy” Internet usage; see Lewis, Rao, and Reiley 2011). Another example is time-varying marketing-mix and web page content. Online retailers may run time-sensitive promotions on social networking websites, which may result in increased social network usage and online purchase for certain days.
To control for unobserved individual-level heterogeneity and correlated unobservables, we follow Hartmann et al. (2008) and Nair, Manchanda, and Bhatia (2010) by including individual-level random effects. Specifically, we decompose the error term in the Equation 7 into the following:
In addition, ai is a random effect specific to individual I and controls for time-invariant unobservable customer heterogeneity (e.g., Kumar et al. 2013) and correlated unobservables. For example, potential customer-level heterogeneity could include a person’s general preference for online shopping or whether (s)he tends to browse websites with particular purchase decisions in mind. For activity bias, Equation 8 enables us to control for unobservable individual-specific factors such as a person’s tendency to go online and, as a result, shop and visit social networking websites more (we also capture observable Internet activity using NET-SESSIONi, t-1 and NET-DURATIONi, t-1, which are time-varying and individual-specific to control for activity bias by capturing any variation in individual i’s Internet activity across time). Furthermore, zt is a random effect specific to day t and controls for time-varying unobservable heterogeneity and time-varying correlated unobservables. These include seasonal effects, such as those found in Figure 4 during the holiday shopping period. In addition, given the early stage of social network adoption in 2007, zt also controls for time-varying changes in consumer behavior with regard to social network usage and online shopping. For identification purposes, we assume that zt is independent of the other error terms in the model and is normally distributed with a mean fixed at 0 (zt ~ Nð0, sÞ, where s is the variance hyperparameter for the prior distribution of zt).
TABLE: TABLE 4 Description of Variables
| Category of Variables | Variables | Description |
|---|
| Social network | SN-SESSION | Indicator for visit to social network websites during the previous session |
| SN-DURATION | Total duration during visits to social network websites during the previous session |
| SN-ENGAGEMENT | Variable indicating the number of days the user has visited a social networking site |
| SN-ACTIVE | Dummy variable indicating whether user has ever visited a social networking site |
| SN-FIRSTMONTH | Dummy variable indicating whether user has visited the social networking site during the first month |
| Internet | SHOP-SESSION | Indicator for visit to shopping websites during the previous session |
| SHOP-DURATION | Total duration during visits to shopping websites during the previous session |
| SHOP-CUMULATIVE | Variable indicating the number of days the user has visited a shopping site |
| NET-SESSION | Indicator for visit to any website (exclusive of shopping or social networks) during the previous session |
| NET-DURATION | Total duration during visits to all websites (exclusive of shopping or social networks during the previous session |
| NET-CUMULATIVE | Variable indicating the number of days the user has visited any non-social-media, nonshopping site |
| Referral | SN-REFERRAL | Dummy variable indicating whether the referral website was from social networks |
| SEARCH-REFERRAL | Dummy variable indicating whether the referral website was from search |
| SHOP-REFERRAL | Dummy variable indicating whether the referral website was from other shopping sites |
| Demographics | GENDER | Gender of user |
| HOUSEHOLD-SIZE | Number of people in user’s household |
| AGE | Age of user |
| INCOME | Income of user |
| WORKING-MEMBERS | Number of working members in user’s household |
| CHILDREN | Dummy indicating whether user has children |
Finally, simultaneity may be a concern. Simultaneity occurs if consumers concurrently use social networking websites and shop online. We control for this issue by including lagged social network activity in the model (i.e., lagged duration and sessions) and examine the relationship of these lagged variables with a consumer’s current online purchase activity.20 Thus, we assume that a person’s social network usage at time t – 1 is not affected by that person’s future shopping activity at time t.
To assess the effects of social network usage on other shopping-related activities, we also examine the relationship between social network activity and both the number of purchases made and the number of retailers purchased from. The number of purchases and the number of different retailers purchased from are count-dependent variables (i.e., nonnegative integers) and are modeled taking this into account using conditional Poisson distributions. Let yrit = k if individual I makes k purchases (r = 1) or makes a purchase at k different retailers (r = 2) during session t and yrit = 0 if we do not observe any purchase activity. Here, the r superscript refers to the parameters and data used in the models assessing the number of purchases (r = 1) and the number of retailers purchased from (r = 2). Because we observe many instances in which yrit = 0 (i.e., purchase activity is relatively uncommon at the daily level for any given individual), we use a zero-inflated model. Specifically, we use a finite mixture between a Poisson distribution (with probability Prit) and a degenerate distribution concentrated at zero (with probability 1 - Prit) to model the number of purchases or the number of retailers purchased from (Lambert 1992). We specify the likelihood of individual i’s purchase activity as follows:
where Prit is the probability that individual i’s behavior follows a Poisson distribution with parameter lrit. We model lrit as follows
The variables entered into Equation 10 for the conditional mean of the Poisson distribution are the same as those used in the previous hierarchical binary Probit model, with one exception: we do not include the referral variables, because we are modeling the number of purchases and shopping activities during a given session rather than a decision to purchase.21 Again, we used a random-coefficients specification to capture unobserved heterogeneity, correlated unobservables, and activity bias: Thus, the models for number of purchases and number of retailers purchased from are hierarchical zero-inflated Poisson models. Note that the hierarchical nature of the model allows for overdispersion, thus permitting distributional flexibility without using a more complex count-data distribution (e.g., negative binomial, double Poisson).
Although we control for many confounding factors, such as activity bias in the previous models, there may still be other potential endogeneity concerns, such as time-varying unobservables. To address other potential sources of endogeneity, we estimate Equations 7–10 using the latent instrumental variables (LIV) approach (Ebbes et al. 2005; Rutz, Bucklin, and Sonnier 2012; Rutz and Trusov 2011; Zhang, Wedel, and Pieters 2009).22 We also allow for the possibility of correlation of purchase, number of purchases, and number of retailers purchased from using a multivariate normal copula (e.g., Danaher and Smith 2011; Kumar, Zhang, and Luo 2014; Stephen and Galak 2012). The details are provided in Web Appendix F.
Next, we discuss the specific parameter estimates pertaining to the relationship between social network usage and purchase incidence. We report our main results in Table 5, which shows how social network activity is associated with consumers’ purchase decisions. We organize this discussion to first focus on the key social network variables. Then, we report the results for the other Internet usage variables and control variables.
The parameter estimates for the social network variables are consistent with our theory and support H1 and H2, suggesting that social network usage and engagement are correlated with online shopping activities. Two specific sets of results are worth noting. First, we find that immediate (or recent) time spent on social networking websites is associated with a decrease in purchase activity (Table 5, Column 1: SN-SESSION = –.3850, SN-DURATION = –.0516). We also report the results from the two other shopping activity measures that corroborate this finding: the number of purchases and the number of retailers purchased from. Columns 2 and 3 in Table 5 the report results for these measures. Higher immediate usage of social networking websites is associated with making fewer purchases (Column 2: SN-SESSION = –1.2704, SN-DURATION = –.1893). This is in line with H1. Similarly, more immediate usage of social networking websites is associated with making purchases from fewer websites, which is also consistent with our theory (Column 3: SN-SESSION = –1.8318, SNDURATION = –.2108).
Second, we find a positive association between cumulative social network usage and shopping activities, which is consistent with H2. Greater cumulative usage of social networking sites is correlated with a higher probability of purchasing (Table 5, Column 1: SN-ENGAGEMENT = .0846). This is corroborated by the other two shopping measures available in our data. More social network engagement (i.e., cumulative use) is also positively associated with the number of purchases made (Column 2: SN-ENGAGEMENT = 1.2768) and the number of retailers purchased from (Column 3: SN-ENGAGEMENT = 1.3623).
TABLE: TABLE 5 Main Results
TABLE:
| Model | Y 5 Purchase (Probit Model) | Y 5 Number of Purchases (ZIP Model) | Y 5 Number of Retailers Purchased from (ZIP Model) |
|---|
| Social Network Variables |
| SN-SESSION | -.3850a [-.2644, -.5174] | -1.2704a [-1.1119, -1.4051] | -1.8318a [-1.5796, -1.9349] |
| SN-DURATION | -.0516 [.0791, -.0908] | -.1893a [-.1632, -.2143] | -.2108a [-.1933, -.2574] |
| SN-ENGAGEMENT | .0846a [.1329, .0446] | 1.2768a [1.3108, 1.2356] | 1.3623a [1.3863, 1.3362] |
| SN-REFERRAL | .007 [.0162, -.0098] | N.A. N.A. | N.A. N.A. |
| SN-ACTIVE | .0339a [.0443, .0266] | .0463a [.0899, .0025] | .0973a [.1580, .0338] |
| SN-FIRSTMONTH | .0303a [.0391, .0228] | .0116 [.0196, -.0068] | .0021 [.0167, -.0125] |
| Other Variables |
| LAG-Y | .0354a [.0467, .0277] | .0641a [.0982, .0316] | .1903a [.2312, .1554] |
| NET-SESSION | -.0132a [-.0035, -.0204] | -.2324a [-.1657, -.3102] | -.3704a [-.2831, -.4229] |
| NET-DURATION | .0016 [.0111, -.0074] | .0492a [.0694, .0257] | .0510a [.0735, .0288] |
| SHOP-SESSION | .0118a [.0175, .0054] | .4887a [.5535, .4165] | .7140a [.82, .6115] |
| SHOP-DURATION | .0078a [.0116, .0039] | .1692a [.1973, .1444] | .1387a [.1629, .1102] |
| NET CUMULATIVE SESSION | .0006 [.0378, -.0358] | -.8839a [-.7629, -.9691] | -.8839a [-.7789, -.9719] |
| SHOP CUMULATIVE SESSION | .0006 [.0161, -.0140] | .1284a [.1803, .0629] | .1638a [.2122, .0865] |
| SEARCH-REFERRAL | .0075a [.0153, .0020] | N.A. N.A. | N.A. N.A. |
| Demographic Variables |
| AGE | .0010a [.0013, .0007] | .0719a [.0845, .0595] | .0157a [.0258, .009] |
| GENDER | -.0059 [.002, -.013] | -.0613a [-.0553, -.0683] | -.0431a [-.0306, -.0649] |
| HOUSEHOLD-SIZE | .0035a [.0085, .0005] | .0262a [.0314, .0195] | -.0627a [-.0527, -.0735] |
| INCOME | .0261 [.0381, -.0124] | -.0047 [.0004, -.0107] | .0762a [.0837, .071] |
| WORKING-MEMBERS | .0111a [.0161, .0066] | -.0082 [.0004, -.014] | -.1082a [-.0999, -.1167] |
| CHILDREN | .0118a [.021, .0048] | .0893a [.097, .0855] | -.0478a [-.0329, -.0575] |
| INCOME2 | .0101a [.0133, .0054] | .0435a [.0517, .0279] | .0238a [.0291, .0016] |
| N | 2,134,689 | 2,134,689 | 2,134,689 |
| Log-likelihood | -1,242,133 | -248,315 | -202,977 |
| DIC | 1,740,305 | -20,363 | 7,726 |
| Hit rate (holdout) | .562 | .929 | .987 |
| MSE (holdout) | .438 | 1,963.448 | 161.488 |
| MAD (holdout) | .430 | .185 | .014 |
aIndicates that 0 is not contained in the 95% Bayesian credible interval.
Notes: The 95% Bayesian credible interval is reported in brackets. N.A. 5 not applicable; DIC 5 deviance information criterion; MSE 5 mean squared error; MAD 5 mean absolute deviation.
These findings demonstrate that the immediate and cumulative usage of social networking websites may play two distinct, opposing roles in their relationship with shopping activities. First, these results point to an immediate, short-term negative relationship, such that time spent on social networking is negatively correlated with online shopping, which suggests a substitution effect. Second, our results indicate that there is a cumulative, longer-term positive relationship, such that cumulative usage of or engagement with social networking websites over time is associated with increased purchasing activity. In line with our theory, this may be because people who have been engaged in social networks for an extended period of time are better informed about new shopping options that they learned from their networks. In other words, continuous exposure to new consumption-related content over time may provide greater marginal benefit than if that time were spent on shopping websites.23
We also consider the relationship of other Internet-related variables with purchase activity in Table 5. These variables allow us to control for short-term effects (e.g., eWOM) that are correlated with immediate purchase. We find that search referrals (i.e., Google and Yahoo web search) have a positive relationship with the probability of purchase (e.g., SEARCH-REFERRAL = .0075). This means that search activity is associated with subsequent purchase activity, which intuitively makes sense (e.g., Hauser and Wernerfelt 1990; Ratchford and Srinivasan 1993).
We also control for several other important factors. For example, NET-DURATION captures a person’s session-level Internet activity and, along with the individual-level random effect, controls for possible activity bias. Our results suggest a positive relationship between the duration of a person’s Internet session usage and both the number of purchases made (NET-DURATION = .0492) and the number of retailers purchased from (NET-DURATION = .0510). SHOP-SESSION and SHOP-DURATION control for the propensity to engage in shopping-related activities. As we expected, these shopping-related activities are positively associated with shopping activities. In the purchase incidence model shown in Table 5, greater shopping duration is related to greater purchase incidence, which means that consumers who visit shopping websites or spend more time shopping are more likely to make a purchase (Column 1: SHOP-SESSION = .0118, SHOP-DURATION = .0078). We also find (Table 5, Column 2) that, consistent with our expectation, more visits and more time spent on e-commerce websites are correlated with the number of purchases (Column 2, SHOP-SESSION = .4887, SHOP-DURATION = .1692). Finally, in Column 3 of Table 5, we find that more time spent on e-commerce websites is associated with purchasing from a greater variety of retailers (Column 3: SHOP-SESSION = .7140, SHOP-DURATION = .1387).
As for the other demographic control variables, we also find that older consumers and those with more income are associated with greater online purchase activity.24 Higher-income people may have more disposable income, which is correlated with greater online spend. We also find that women are more likely than men to make more purchases and buy from a wider variety of retailers. Our results also suggest a notable relationship of household size and number of children with purchase activity. Having a larger household and/or more children are positively associated with greater purchase incidence but negatively associated with the number of retailers purchased from. We speculate this might be a result of larger households combining their purchases within one retailer. We also find that households with more working members are more likely to purchase online but buy from fewer retailers. Finally, as a check, the time-specific estimates are consistent with our expectations, with higher baseline purchase activity occurring during the December holiday season.
TABLE: TABLE 6 Category-Specific Average Partial Effects for Social Network Variables
| Category Description | SN-ENGAGEMENT | SN-SESSION |
|---|
| Audio CD | .345 | -2.568 |
| Auto Parts | .915 | -21.592 |
| Bath and Beauty | .051 | -2.198 |
| Books | .126 | -2.129 |
| Children | .700 | -21.681 |
| Chocolate/Candy | .370 | -2.299 |
| Clothing | 5.079 | -211.046 |
| Computer | .151 | -2.165 |
| Computer Software | .021 | -2.297 |
| Electronics | .127 | -2.178 |
| Flowers | 1.812 | -21.811 |
| Gift Cards | .018 | -2.127 |
| Jewelry | .428 | -21.891 |
| Magazines | .072 | -2.588 |
| Photo | .181 | -.543 |
| Shoes | .097 | -2.259 |
| Sports/Outdoors | .355 | -2.499 |
| Theater/Entertainment | .276 | -2.951 |
| Tools/Hardware | .132 | -2.573 |
| Toys and Games | .081 | -2.240 |
| Video | .639 | -2.637 |
Notes: Each row consists of a separate model, with the dependent variable specified as whether a purchase was made (or not) in that particular category. We report the partial effects (scaled by 1,000 for presentation purposes) for the social network engagement and session variables. Boldfaced values indicate that 0 is not contained in the 95% Bayesian credible interval. The differences between the most affected product categories (i.e., clothing, jewelry, and children’s products) and the least affected categories (i.e., computers, books, and gift cards) are statistically significant.
We also empirically examine product category data to provide further evidence that the positive correlation between social network engagement and purchase activities may arise from greater consumption-related exposure.25 In accordance with H3a, we expect that product category moderates this positive correlation because consumers are more likely to be exposed to certain categories of products on social networks. In addition, product category might moderate the negative correlation between immediate social network usage and purchase incidence (H3b). To test for this, we identify the product category for each purchase observation in our data. There are 21 categories under which the purchases fall (see Table 6). We use this information to create a new set of purchase incidence dependent variables, which we denote as yitc = 1 if consumer I purchased a product in category c during session t. For each category, we separately estimate the binary probit model using yitc as the dependent variable.
In Table 6, we report the average partial effects for the key social network variables—both immediate social network usage (SN-SESSION) and cumulative social network usage (SNENGAGEMENT).26 In Table 6, each row shows the partial effects from one purchase incidence model with the respective category purchase incidence described in Column 1 as the dependent variable. Consistent with H3a, our results (Table 6, Column 1) suggest that product category moderates the positive correlation between social network engagement and purchase incidence. Specifically, categories such as clothing, children products, jewelry, and chocolates have significant, positive coefficients (Column 1: 5.079, .700, .428, .370, respectively). In contrast, categories such as tools/hardware, auto parts, or gift cards have nonsignificant effects (Column 1: .132, .915, .018, respectively). This suggests that the positive correlation between social network engagement and purchase likelihood is more pronounced for products that are more likely to be shared on social networks.
Similarly, consistent with H3b, we find that product category moderates the negative correlation between immediate social network usage and purchase incidence. Consumers with higher previous period social network usage may have satisfied their need for entertainment value, thus exhibiting lower purchase activity for entertainment-related products. While all the partial effects are significantly negative, consistent with H1, we observe variation across the categories. The negative relationships in Column 2 of Table 6 appear stronger for categories that provide greater entertainment value (e.g., –11.046 for clothing,–1.891 for jewelry, –1.681 for children’s products). While not perfect, to large extent, these results are in line with our theoretical framework.
Our findings have two important managerial implications. First, our study offers strong evidence of the correlation between social network usage and online sales, which is a hotly debated topic among both marketing academics and practitioners. While some preliminary evidence—for example, that provided by IBM—suggests that social networks are not effective in driving e-commerce sales (Del Rey 2013; Gara 2012), our results suggest that that firms should not be discouraged by weak (immediate) sales performance of their social network campaigns. Rather, the positive association between cumulative social network usage and sales suggests that the expected payoff might be more of a longer-term phenomenon. This finding also suggests that popular performance metrics such as click-through rates on social networking sites (i.e., direct referrals) are likely to underestimate the total effects of firms’ social network marketing campaigns. Thus, managers should also consider cumulative consumer interactions with their brands on social networking sites and not simply referrals.
Second, managers can benefit from learning that social network engagement is related to people’s propensity to shop. Importantly, this in not necessarily tied to exposure to the firm’s brand. Rather, continuous social network usage is associated with an increase in shopping activities in general. Building on this insight, managers could target specific groups of individuals on social networks who display more positive associations between cumulative social network usage and shopping activities. They could also advertise specific product categories (e.g., chocolate) on social networks to these individuals.27
Using our model, managers can identify this group of consumers by assessing heterogeneity across consumers in their individual-level estimates to determine any correlation between social network engagement and purchase activity.28 Figure 6 plots the bi estimates for SN-ENGAGEMENT and suggests that, for our set of consumers, there is significant variation in the correlation between social network engagement and shopping activities (in this instance, the decision to purchase). Thus, firms (e.g., Facebook) could apply our model to their set of users to identify consumers who have larger bi estimates on the SNENGAGEMENT variable and target these people on social networks.
Despite the growing interest of both academics and managers in online social networks, little is known about the individual-level relationships of immediate and cumulative usage in social networks with online shopping activities. In this article, we study the relationship between consumers’ immediate usage of and cumulative engagement with social networks and their shopping activity. Our individual-level social network and shopping data allow us to directly investigate how different aspects of social networks may have varying correlation with the decision to purchase, the number of purchases made, and the number of websites purchased from.
In summary, our results provide support for both positive and negative relationships across three key indicators of online shopping activities. First, we find that more immediate time spent on social networking websites is associated with a lower purchase probability, a lower number of purchases, and a lower number of websites purchased from. Drawing on Becker’s (1965) theory of time substitution, we attribute this to immediate time spent on social networks cannibalizing time spent on online shopping. Second, we find that engagement in social networks has a positive relationship with all three shopping activities. We hypothesize that this positive association is driven by exposure to new consumption-related information aiding the shopping search process. Social networks facilitate exposure to a wide variety of content from friends or other network ties, and consumers may encounter new information as they discover the products that their social network buys or discusses.
Our research is not without limitations. First, while we have individual-level data on web-browsing and purchase activity, we can only infer that social network usage is associated with shopping activities. Although our experiment in Web Appendix B provides support for causal effects, we do not make any causal claims based on our empirical analysis. In addition, because we do not observe the content that consumers were exposed to on these websites (largely due to privacy reasons), we are unable to determine whether exposure to product-related information definitely occurred. Instead, our data and empirical analysis allow us to identify effects that are consistent with these theorized mechanisms. Further research could investigate whether social discovery or priming is taking place. Second, in a similar sense, we capture the immediate and cumulative usage of social networks with two proxies: previous session/duration spent on the website and cumulative daily website visitation. Although we expect that these variables reflect actual usage and engagement, there is the possibility that people are not actively engaged with the website but simply have the page open in the background. Third, our purchase data only capture online shopping activity and do not include offline purchases. While we expect to find similar positive and negative relationships between social network usage and offline shopping, we are unable to explicitly test for this. Finally, our results for the estimates of search, social network, and shopping referrals are subject to the “last-click bias.” A richer data set may provide more insight into the various attributions of purchase, whether driven by social network usage or search that occurred over a longer period of time.
Informational Value Acquired
Footnotes 1 Consistent with this logic, model-free evidence in our data reveals a negative correlation between time spent on social networks and online e-commerce websites of -.54 (conditional on overall Internet usage activity).
2 We thank the area editor for this suggestion.
3 We thank an anonymous reviewer for this suggestion. We note that our model can be expanded to accommodate multiple types of value (i.e., other than entertainment and information) that consumers derive online. For example, we also explored a three-dimensional extension to our model (the results are available on request) and found that it does not substantially change our key takeaways.
4 For the sake of brevity, we provide a summary of our analytical results here. Full details are in Web Appendix A. Our analytical model assumes diminishing marginal returns with respect to the information and entertainment value gained from time spent on social network and shopping sites. Our choice of a log functional form is driven mainly by analytical convenience and popularity across previous studies. Our hypotheses are also robust under a more general functional specification: ðxÞ = xr, (0 < r < 1), where r is the rate of diminishing marginal returns in entertainment or informational value acquired. We thank the area editor for helpful guidance here.
5 We explore these category-specific effects empirically subsequently.
6 At the optimal time allocation for social networks, the incremental benefit of spending more time on social networking sites with respect to informational value is lower than that of spending more time shopping, even when we consider diminishing marginal returns. This result occurs because people allocate time to maximize both informational and entertainment value. Thus, the optimal time allocation for social networks is greater than that if people allocated time purely on the basis of informational value. This means that a reduction in immediate social network usage will increase informational value (for additional details, see Web Appendix A).
7 We tested the assumption that the use of social network websites correlates with purchase activity (as a result of exposure to informational value) with a simple experiment (details of the experiment appear in Web Appendix B). The results of this experiment suggest that consumers browsing a social network website (vs. a control website) are more likely to be exposed to consumption-related content, which is associated with greater shopping intentions. Thus, our assumption is supported through experimental data.
8 We thank an anonymous reviewer for bringing this point to our attention.
9 To provide a representative panel of online users, our data provider recruits an online panel using random digital dialing and online technologies. The exact method is proprietary. However, as a check, we compare statistics of our data sample with that from the general U.S. population (retrieved from the 2010 U.S. Census [U.S. Census Bureau 2010]). In our data (U.S. Census data in parentheses), the average age is 47 years (U.S. Census: 47.5 years when counting only adults), average income is approximately $52,000 (U.S. Census: $53,400), male-to-female ratio is .48 (U.S. Census: .484), and percentage of families with children under the age of 18 years is 37% (U.S. Census: 40%). This suggests that our sample is representative of the U.S. population on those characteristics.
We also analyze our data at the daily level. The results are available from the authors on request and are consistent with respect to our findings.
The top 10 retailers and their distribution of purchase and shopping activities appear in Web Appendix C.
We also investigate blog browsing activities. The results appear in Web Appendix D.
We note that Facebook launched its mobile app in July 2008 (after our observation period). Therefore, we expect our data to capture the majority of usage because people are limited to web browser-based interactions.
We note that social networks in 2007 (our data time frame) are different from the dominant platforms in the present. In 2007, Facebook contained a news feed on individual profiles with photos and information about groups, interests, updates, and other user-generated content. Myspace was similar in that it included a profile and also allowed users to share posted information with friends. At present, Facebook contains an aggregated news feed that combines postings of all friends. We expect that the current format exposes users to a greater variety of consumption-related content (e.g., sponsored product placement) than the format in our data set.
One possible way to deal with left censoring is to drop the early observations in our data. We run a robustness check by dropping the left-censored users from the analysis. The results are consistent with our main results and are available in Web Appendix C.
To explore the validity of this classification of new versus existing users, we measure the average interarrival time to social networking websites. We find that, on average, consumers visit these social networking websites about once a week. For social networking websites, the mean number of days between usage, across individuals, is 6.8 (SD = 5.67). Therefore, we expect that the average user is a new user if (s)he has not visited a social networking website during the first two weeks. We also calculate new users as those for whom we observe a first usage on social networking websites four weeks after the beginning of our observational period. The results of this robustness check (available on request) are equivalent in terms of sign and significance.
To test robustness, we also calculate this variable as the number of sessions and duration on social networking sites. The results are consistent with our main results and are available on request.
These models are similar in nature to models in the customer relationship management literature (e.g., Venkatesan, Kumar, and Bohling 2007; Verhoef et al. 2010).
Activity bias occurs when, on some days, users spend a lot of time online (engaging in various activities) and, on other days, spend very little time online. We thank an anonymous reviewer for highlighting this important factor.
As a check, we run a robustness test with same-day social network variables, and the findings are consistent.
Given that users can purchase multiple products across various websites, it is less obvious how to attribute the number of purchases or the number of sites with purchase activity to one specific referral site.
Before using the LIV approach, we first ran a test to check for potential endogeneity for all “suspect” predictors individually using the Hausman-LIV (HLIV) test statistic (Ebbes 2004). The HLIV statistics for SN-ENGAGEMENT and SN-SESSION are 6.18 and 5.89, respectively. This suggests the need to correct for endogeneity. The validity of the LIV application is also subject to some assumptions (i.e., normality of the error term and non-normality of the endogenous regressor). We test for these in Web Appendix E.
We also find that number of shopping sessions mediates the relationship between cumulative social network usage and purchase incidence. Thus, our findings suggest that the correlation between increased engagement on social networking websites and the higher likelihood of making online purchases is due in part to increased shopping browsing activity. The results of this analysis are available on request.
We also include interactions between the demographic variables and the other variables in the model to test whether demographic characteristics moderate any of the effects. The one interaction that was significant was income · yi, t-1 (lag purchase), and it was positive. This suggests that higher-income consumers tend to have a higher purchase probability if they purchased during the previous day. The other interactions were not significant.
We thank an anonymous reviewer for this suggestion.
Partial effects show the effect on the probability of purchase for a unit change in the respective social network variable. We report partial effects for comparison across models. The estimated coefficients and other variables are consistent with our main results and are available on request.
This is akin to product placement at checkout, when consumers are already in a shopping mindset (Rook 1987). That is, firms could increase sales by strategically marketing products in categories that tend to be shared on social networks to consumers who have this positive relationship.
An alternative way is to identify characteristics of people whose shopping sessions are more likely to mediate the relationship between social network engagement and sales. We ran an individual-level mediation analysis for each user in our sample. For 52% of the users, shopping sessions partially mediated the relationship between social network engagement and purchase incidence. Using a logistic model, we then regressed the demographic variables for each person on a binary variable indicating whether mediation was found for each person. The results (available on request) suggest that younger consumers, consumers from larger households, and lower-income consumers are more likely to have shopping session mediation, meaning that social network usage is more likely to be associated with shopping activities. Thus, a firm could also target social network users who are younger, part of a larger household, or have lower income.
GRAPH: Notes: All elseequal, the informationextractedper unit of time spent on shopping sites yields higher returnsthan time spent on social networks:Îsh > Îsn (left side of graph). Over the long run, the incremental daily time t spent on social networking sites could result in greater informational value (Îsn) than if that time t were spent on the shopping sites that have already received a substantial amount of the consumer’s attention (the right side of the graph: ^Ish).
GRAPH: FIGURE 4 Daily Purchase Activity (Aggregate)
GRAPH: FIGURE 5 Day of First Observed Social Network Usage
GRAPH: FIGURE 6 Distribution of Individual-Level Estimates for Engagement
GRAPH
DIAGRAM: FIGURE 3 Distribution of Purchase Activity Per User
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Record: 130- Optimizing a Menu of Multiformat Subscription Plans for Ad-Supported Media Platforms. By: Kanuri, Vamsi K.; Mantrala, Murali K.; Thorson, Esther. Journal of Marketing. Mar2017, Vol. 81 Issue 2, p45-63. 19p. 1 Diagram, 5 Charts, 1 Graph. DOI: 10.1509/jm.15.0372.
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Optimizing a Menu of Multiformat Subscription Plans for Ad-Supported Media Platforms
Online Supplement: http://dx.doi.org/10.1509/jm.15.0372 istribution of paid content to end users by publishers, record companies, and film studios has changed exDtensively in the past decade. Paid content that was once distributed through traditional formats such as print, radio, and television is now also available through contemporary digital formats such as websites, smartphone apps, and tablet apps. For instance, ESPN and Netflix content, which was previously accessible only on television, is now available through websites and apps, with access from all digital devices (e.g., laptop, smartphone, tablet). Moreover, content within each format can now be delivered in multiple versions. For example, digital paywalls have helped newspapers to deliver digital news content through a “restricted-access” version (e.g., up to 20 free articles per month before charges) and an unrestricted version (full access to subscribers). Similarly, music streaming websites such as Spotify are delivering digital music through versions with and without advertising. Other format–version combinations, for example, free versus premium access to content delivered in specific formats, are offered by content providers such as magazines (e.g., TIME, McKinsey Quarterly), academic book and journal publishers (e.g., Springer, Harvard Business School Publishing), radio broadcasting companies (e.g., Sirius XM), and online video streaming websites (e.g., Hulu). Hereinafter, we refer to all such information or entertainment content providers as “media firms.”
While the proliferation of formats and versions is presenting advertising-supported media firms with unprecedented opportunities to distribute and monetize content, practitioners lament the increasing complexity of determining the most profitable options (e.g., Newspaper Association of America 2012). For example, one of the difficult questions facing media firms concerns packaging of media content access. Specifically, it is not clear whether bundling access to all the formats and offering a single package (e.g., Hulu) is more or less profitable than unbundling format–version combinations and offering each component or a subset of components at a separate price (e.g., New York Times). Moreover, while it might seem that media firms should offer “menus” of multiformat subscription bundles in order to address heterogeneity in content consumption preferences, there is little theoretical or practical guidance to help media firms design their subscription menus.
In general, designing a profit-maximizing menu for a heterogeneous customer group requires methodically evaluating several assortments of subscription bundles. While a for
midable task in itself (Kohli and Sukumar 1990; Luo 2011;
Venkatesh and Chatterjee 2006), the problem is even more complex for media firms because their business model typically involves not just one but at least two distinct customer
groups with varying needs but interrelated demand functions. Specifically, most media firms are audience-building platforms (Evans and Schmalensee 2007) that are “two-sided” markets. That is, they serve two groups of customers with
distinct preferences and interests: one group that is primarily interested in consuming the content produced by the firm, and a second group, advertisers, that values the firm’s provision of access to the first group (hereinafter, we refer to such media firms as “media platforms”). Because one group’s demand affects the demand of the other group (referred to as “crossmarket network effects” [CMNEs]), strategies developed to maintain or grow the demand of one group without
accounting for the repercussions on the demand of the other group are unlikely to maximize the media platform’s total profit from both groups (Sridhar et al. 2011). In particular, media platforms must account for demands of both content
consumers and advertisers if they aim to design an overall profit-maximizing menu of multiformat subscription plans.
This menu-design problem poses several significant conceptual and operational challenges for media platforms. First, these platforms have only recently begun offering various “new to the world” configurations of multiformat subscription plans and charging consumers for accessing digital content. However,
the novelty of such offerings implies little or no previous data are available on content consumers’ preferences and willingness to pay (WTP) for many multiformat subscription plan options
that are now possible. Such data are essential for optimally configuring and pricing “new to the world” subscription plans. Second, determining the optimal menu that maximizes total aggregate profit of an advertising-supported media platform requires ( 1) estimates of content consumer market potentials by format under different possible menus, and ( 2) estimates of interrelated aggregate content consumer and advertiser demand function elasticities by format. This is because advertisers pay for exposures to content consumers by format, whatever the particular menu or set of choices of multiformat subscription plans offered to content consumers. Third, firms need a mathematical model–based approach, or optimizer, for integrating the data on content consumers’ WTP for different plans with the aggregate format-level demand functions of content consumers and advertisers, and using this combined information to determine the total profit-maximizing menu of subscription plans from a very large number of possibilities.
To address these challenges, we propose a theory-driven implementable model-based approach for multiformat subscription menu design. Our approach consists of three key steps (see Table 1). The first step entails assessing content consumers’ WTP for various multiformat plan configurations. The second step entails calibrating a two-sided market-level model of content consumer and advertiser demands by format. The last step entails determining the profit-maximizing menu of multiformat subscription plans, using WTP information derived in the first step and the calibrated two-sided market demand model from the second step. The proposed three-step approach builds on classic consumer and economics theories such as consumer utility maximization (McFadden 1986), WTP assessment (Kohli and Mahajan 1991), two-sided markets (Rochet and Tirole 2006), and market segmentation and consumer self-selection (Moorthy 1984). Methodologically, the three steps involve developing and using mathematical choice and econometric and optimization models that we demonstrate in an application involving a daily newspaper firm. Specifically, we collect and leverage primary data from the collaborating newspaper’s content consumers (i.e., readers) as well as aggregate historical data on content consumer demand and advertising revenues by format to estimate the models and obtain inputs necessary to optimize subscription plan menus. Our proposed optimizer also allows us to determine optimal menus and predict total profits under various business models and format strategies of interest and relevance to the firm. As a result, we generate a number of useful insights into the merits of alternative business models and format strategies for our collaborating firm and other similar advertising-supported media platforms.
TABLE 1 A Three-Step Approach for Deriving a Profit-Maximizing Menu of Multiformat Subscription Plans
TABLE:
| Step | Theoretical Underpinnings | Method | Key Outcomes |
|---|
| 1. Measuring content consumers’ WTP for various possible multiformat subscription plans | Individual utility maximization theory (McFadden 1986) | HB CBC analysis of individual choice data | • Individual partworth data for various formats, versions, and prices • WTP for all possible multiformat plan combinations • Assessing content consumers’ market potential |
| 2. Calibrating two-sided marketlevel response model including content consumer and advertiser demands by format | Theory of two-sided markets (Rochet and Tirole 2006) | Market response modeling of archival data | • Calibrated market response model for content consumers and advertisers and by various formats |
| 3. Determining profit-maximizing menu of multiformat subscription plans using outcomes of Steps 1 and 2 | Theory of self-selection with incentive compatibility and individual rationality (Moorthy 1984) | Mixed integer nonlinear program | • A heuristic to solve a complex discrete combinatorial optimization problem • Profit-maximizing menus under various business models and format strategies |
This article offers several contributions to research aimed at integrating normative theory and practice in marketing (e.g., Fischer et al. 2011; Kannan, Pope, and Jain 2009; Kumar et al. 2013; Luo 2011). First, we offer a theory-based three-step approach for solving a topical, pressing, and complex problem of designing an optimal menu of novel multiformat subscription plans for an advertising-supported media platform. Second, we propose an optimizer that can effectively integrate individual-level WTP data for new subscription plans with aggregate-level archival data-based demand elasticities. Third, we present a novel mathematical programming model that determines a profit-maximizing subscription menu subject to accounting for the demands of both advertisers and consumers, as opposed to just considering one-sided market demand that characterizes the traditional product line design literature. Fourth, because our mathematical programming model is a complex discrete combinatorial optimization problem, we provide an efficient heuristic approach that can solve this problem in a reasonable amount of time and can be easily scaled up to handle more design alternatives and segments. Fifth, policy simulations enabled by our optimizer allow us to offer interesting insights into the efficacy of various media platform business models and format strategies. Sixth, empirical results from the estimation of the proposed models augment several findings previously documented in the twosided platform, marketing, and media economics literatures.
In the next section, we review and elaborate on our contributions relative to past research. We then detail how we executed our three-step approach in the context of a newspaper partner.
Literature Review
Various studies in the marketing, economics, and management science literatures provide helpful directions for solving the media platform’s contemporary menu-design problem. While a comprehensive review of these literatures is beyond the scope of this article, in this section, we discuss literature relevant to our work and highlight the key points of departure.
First, our work is motivated by previous literature on pricing in two-sided markets. Research in this domain has primarily focused on illustrating ( 1) how standard pricing norms designed under the one-sided market assumption change when CMNEs are incorporated (for a review, see Rochet and Tirole 2006), and ( 2) how pricing policies devised for monopolist platforms differ from those for competing platforms (for a review, see Armstrong 2006). While extant research in this domain provides helpful directions for modeling marketlevel demand functions of two sides, for example, content consumers and advertisers, the majority of these studies are largely analytical in nature. The few studies that do empirically estimate demand models of two-sided firms either are limited to cases in which platforms offer products through a single format (e.g., Lambrecht and Misra 2016; Sridhar et al. 2011) or are aimed at explaining some observed market phenomenon (e.g., Pattabhiramaiah, Sriram, and Sridhar 2014) as opposed to utilizing estimated models in a management decision aid for designing a menu of multiformat subscription plans.
Next, studies in bundling and versioning literatures have also added to our knowledge on optimal menu design. Beginning with Adams and Yellen (1976), scholars have sought to understand conditions when firms can benefit from a purecomponent, pure-bundle, mixed-bundle, or partial-mixedbundle strategy in a dual-product (analogous to dual-format) setting. Venkatesh and Mahajan (2009) provide a comprehensive review of this literature. Similarly, scholars have also studied optimal bundling strategies in dual-version settings, wherein firms offer a high-quality and a low-quality version of the same format (e.g., Bhargava and Choudhary 2008; Bhargava, Kim, and Sun 2013). However, two limitations in these literatures motivate our work. First, existing bundling results are derived largely for one-sided markets, where the goal of the firm is to design and price products (or formats) and versions for only one group of customers. Second, the need for analytical tractability has limited most bundling and versioning studies in two-sided markets to cases involving a monopolist firm producing only two product formats (e.g., Chao and Derdenger 2013; Derdenger and Kumar 2013) or two versions of a single product format (e.g., Bhargava, Kim, and Sun 2013). While the insights from these works certainly benefit media platforms with only two dominant formats or two versions of a format (e.g., seven-day and Sunday versions of the print newspaper format), the contemporary increase in the number of delivery formats (e.g., online, tablet, smartphone) and the number of versions per format (e.g., seven-day print, three-day print, weekend print) calls for new model-based solutions for optimally configuring and pricing multiformat and multiversion offerings.
Finally, research on product line design and pricing is also relevant to our work. Studies in this line of research have mainly focused on developing analytical procedures and heuristics that can leverage consumer preference data to determine optimal design and pricing of product lines. For example, Moorthy (1984) used “self-selection” theory to analytically illustrate a theoretical model-based algorithm for how one-sided product firms can build multiattribute product lines for markets made up of heterogeneous consumer segments. Subsequently, more applied work using simulated data by Kohli and Sukumar (1990) has demonstrated the effectiveness of various heuristics for designing multiattribute product line offerings using conjoint analysis. While this literature stream offers valuable insights into modeling consumers’ plan selection process and using conjoint data to design and price product lines, past research has been largely limited to one-sided markets. More important, the product line design heuristics proposed so far do not offer any guidance for integrating individual WTPs for new subscription plans with aggregate-level transaction data in order to determine the profit-maximizing menu.
In summary, our work is differentiated from all past research in the three streams of relevant literature because it addresses a new but prevalent practical problem facing contemporary media platform firms—the optimal design of a menu of multiformat and multiversion offerings in a two-sided market—using an approach that effectively integrates individual WTP with aggregate data–based estimates of demand functions facing a two-sided media platform.
Method and Application
In this section, we elaborate on the institutional details of our application. We then present model specifications and estimations in each of the three steps in our decision support framework.
Institutional Context
This research was conducted in collaboration with a prominent U.S. West Coast daily newspaper firm. Like many daily newspapers in the United States, the collaborating firm is effectively a local monopoly (Sokullu 2015) in a specific citycentered geographic region, with three main revenue sources at the time we commenced our collaboration: print subscriptions, print advertising, and digital advertising. Moreover, the firm derived 90% of its total revenue from subscriptions to the seven-day and Sunday-only versions of the print format. Historically, the newspaper had not charged its content consumers (i.e., readers) for accessing digital content. However, declining print advertising revenues had forced it to consider charging consumers for access to digital content, which was currently available on its website and planned to be made available through smartphone and tablet apps. Consequently, the firm’s goal was to develop a profit-maximizing menu of multiformat subscription plans. Next, we describe how we accomplished this objective following the steps outlined in Table 1.
Step 1: Estimating Content Consumers’ WTP for Multiformat Subscription Plans
Model. If historical transaction data for all newspaper versions in print and digital formats were available, we could potentially estimate content consumer preferences and price sensitivity across formats and versions using econometric timeseries methods. However, because the collaborating newspaper had never previously monetized its digital formats, useful archival data were not available to estimate preferences. Therefore, we utilized a choice-based conjoint (CBC) approach (Rao 2011) to measure content consumers’ preferences for various formats and, subsequently, use the preferences to compute WTP for various plan combinations.
More specifically, we consider a CBC study setting with N subjects, Q choice sets, and G subscription plans, where a reader i’s utility function can be stated as follows:
where xgq = a vector of 1s and 0s representing multiformat versions available in plan g and choice set q; pgq = weekly where ai is the constant term representing the utility of the no-choice option for reader i. We use standard hierarchical Bayesian (HB) estimation available in the Sawtooth software (see a technical note on HB CBC in Sawtooth Software 2009) to obtain partworth estimates of various formats for each respondent.
Upon obtaining individual partworths for content consumers, we use a point estimation technique described by Kohli and Mahajan (1991) to derive WTP of a plan configuration j using the partworths of format versions (bix), price (bip), and the no-choice option (ai) for a content consumer i. The prescribed technique is a piecewise linear approach that treats WTP as a maximum price at which the reader is indifferent between subscribing and not subscribing to a newspaper offering. This can be represented as follows:
challenge here is to find the right price p for a reader i such that the sum of the reader’s utility for that price (UiðpÞ) and his/her utility for the plan configuration (Uijj-p) is equal to the utility of the no-choice option (ai). We refer to price p as the WTP of reader i for plan configuration j. The specifics of the algorithm
used to compute WTP are outlined in Web Appendix W1. The
resulting WTP matrix is a critical input to Step 3 in our outlined
approach.
Estimation. To estimate our choice model and derive WTPs, we recruited participants from two sources: ( 1) an online intercept on the home page of the newspaper firm’s website, and ( 2) a research pool made up of the collaborating firm’s readers who had expressed interest in participating in the firm’s research activities. Participants were screened according to their frequency of news consumption through the newspaper’s media formats (i.e., print and digital) and smartphone and tablet device ownership. This recruitment process ensured that survey respondents were knowledgeable about the key design elements (i.e., format–version components) in the conjoint survey. Our final sample comprised 1,144 readers (for descriptives of the sample, see Web Appendix W2).
Each CBC profile consisted of four key design attributes and a price attribute. The design attributes comprised news delivery formats being considered by the collaborating newspaper, namely, print, website, smartphone app, and tablet app. A group of managers from the newspaper’s circulation and research departments iteratively reviewed and modified the formats and their definitions until they reached consensus. Each format had distinct versions (or levels, in conjoint analysis terminology). The print format had three levels: ( 1) home delivery of print copies seven days a week, ( 2) home delivery on Sundays only, and ( 3) a “print delivery of news unavailable” (i.e., no print delivery) option. The website format had five levels: ( 1) unlimited online access, where content on the website is optimized for viewing on any device (e.g., computer, smartphone, tablet); ( 2) limited free online access to up to 20 stories per week, where content is optimized for viewing on any device; ( 3) unlimited online news access on a smartphone; ( 4) limited free access to only 20 stories per week on a smartphone device; and ( 5) an “access to online news unavailable” (i.e., no online access) option. Finally, both smartphone app and tablet app formats had two levels each: ( 1) unlimited access to news content through the respective device, and ( 2) a “device delivery of news unavailable” option (i.e., no access on the given device).
To check whether the range of price levels selected affects respondents’ evaluation of format–version combinations, we created two versions of the conjoint survey. The two survey versions differed only in the price points assigned to plan alternatives in choice tasks and were randomly assigned to respondents. The managers picked weekly subscription price levels: $.99, $1.99, $3.49, $4.99, and $6.99 for the first version and $1.49, $3.49, $4.99, $6.99, and $8.99 for the second version.
The stimuli (i.e., profiles) for the CBC conjoint analysis section of the survey were generated using the OPTEX macro in SAS. Design constraints were imposed such that the plan configurations presented in a choice task were managerially relevant. Subsequently, a saturated fractional factorial design that met three efficient experimental design criteria (minimal overlap in plan configurations, level balance, and orthogonality) was obtained using the OPTEX macro. The final conjoint survey comprised 13 choice tasks with three plan combinations and a no-choice option per choice task. Two additional choice tasks were added to assess predictive validity of the estimated partworths. To control for order effects, plans within a choice task as well as the choice tasks themselves were randomized.
The stimuli were pretested on 17 employees at the collaborating newspaper firm to assess face validity of the choice tasks and cognitive load. Some changes with respect to wording of the attributes and levels were suggested, which were subsequently incorporated into the final design. Finally, we incentive-aligned subjects by repeatedly informing each respondent at multiple phases of the survey that (s)he would be entered into a drawing to win one of ten $250 rewards and, if (s)he won, their total reward would be split between a cash reward and a threemonth subscription to a plan that reflected his/her preferences in choice tasks.1
Because our goal is to prescribe a menu solution for predetermined segments within the newspaper’s content consumer market, which predominantly guide the platform’s marketing efforts, we segmented our respondents according to the a priori segmentation information obtained from the collaborating newspaper. More specifically, once every three years, the newspaper firm employs a reputable third-party research company to identify segments among its existing readers in the newspaper’s demographic metropolitan area (NDMA). In the most recent study, conducted in 2014, the research firm identified 22 questions covering media preferences, news and information needs, and demographic information with which to segment the NDMA. Using k-means clustering on these 22 variables, the research firm uncovered seven “strategic segments” (for details, see Web Appendix W3). The cluster weights assigned to the 22 variables for each segment were shared by the firm to help us determine segment memberships of survey participants.
The CBC/HB module in Sawtooth was used to estimate partworths. To improve fit, we included segment membership information in the Gibbs sampler (Allenby, Arora, and Ginter 1995). We used 20,000 iterations to obtain stable partworths, with a burn-in of 10,000 iterations to achieve convergence. The algorithm converged with excellent fit statistics. The percentage certainty was over 75%, the root likelihood was over 70%, and the root mean square was less than 4 for both the versions. Average partworths of various levels by reader segment and survey version are presented in Table 2. Out-of-sample predictions were performed to assess the robustness of the partworths. The results demonstrate excellent first-choice hit rates for both holdout tasks in both survey versions (74% and 77.6% in version 1 and 83.4% and 86% in version 2). In addition, to ensure the reliability of partworths, we performed several other robustness checks, which we outline in Web Appendix W4.
TABLE 2 Average Partworths of Design Attributes and Interactions by Reader Segment and Survey Version
TABLE:
| | Segment 1 | Segment 2 | Segment 3 | Segment 4 | Segment 5 | Segment 6 | Segment 7 |
|---|
| | Version 1 | Version 2 | Version 1 | Version 2 | Version 1 | Version 2 | Version 1 | Version 2 | Version 1 | Version 2 | Version 1 | Version 2 | Version 1 | Version 2 |
|---|
| Print Format |
| Seven-day print access | -1.41 | -3.29 | 1.00 | 1.71 | -1.72 | -1.65 | -.16 | -4.41 | 2.25 | 2.75 | -2.16 | -6.10 | 4.23 | 5.73 |
| Sunday-only print access | -.44 | .46 | .16 | .08 | -.08 | -.37 | .52 | 2.44 | -1.49 | -.97 | .65 | 1.46 | -2.05 | -1.24 |
| No print access | 1.85 | 2.83 | -1.16 | -1.79 | 1.79 | 2.03 | -.36 | 1.97 | -.77 | -1.78 | 1.52 | 4.64 | -2.18 | -4.49 |
| Online Format |
| Unlimited online access, optimized for each device | 3.39 | 3.64 | 1.83 | 3.91 | 2.40 | 2.58 | 2.65 | 3.68 | 2.79 | 2.87 | 3.50 | 2.56 | .76 | 1.94 |
| Access limited to 20 stories per week, optimized for each device | -.75 | -.05 | -.14 | .48 | -.68 | -.55 | -.38 | -.07 | -.47 | .55 | -1.06 | -.72 | -.97 | .16 |
| Unlimited access on smartphone only | -1.98 | -1.02 | -.74 | -.53 | -.43 | .15 | -.88 | -.72 | -1.52 | -1.28 | -2.66 | 1.05 | -1.77 | -.91 |
| Access limited to 20 stories per week on smartphone only | -2.04 | -3.17 | -1.11 | -1.29 | -.04 | -1.42 | -1.77 | -1.35 | -1.23 | -2.30 | -4.66 | -2.83 | -.50 | -1.50 |
| No online access | 1.37 | .60 | .15 | -2.57 | -1.25 | -.77 | .39 | -1.54 | .44 | .16 | 4.87 | -.06 | 2.48 | .30 |
| Smartphone App Format |
| Unlimited app access with ads | -1.70 | -1.53 | -.77 | 1.27 | .13 | .00 | -1.84 | -.71 | -1.32 | -1.17 | -.92 | -1.35 | -2.13 | .05 |
| No app access | 1.70 | 1.53 | .77 | -1.27 | -.13 | .00 | 1.84 | .71 | 1.32 | 1.17 | .92 | 1.35 | 2.13 | -.05 |
| Tablet App Format |
| Unlimited app access with ads | -1.80 | -.19 | -.74 | .80 | 1.92 | 1.49 | -.51 | .62 | -.49 | -.08 | -2.97 | .51 | -1.40 | -1.02 |
| No app access | 1.80 | .19 | .74 | -.80 | -1.92 | -1.49 | .51 | -.62 | .49 | .08 | 2.97 | -.51 | 1.40 | 1.02 |
| Format Interactions |
| Print format X online format | .41 | .51 | .14 | .30 | .78 | .93 | .43 | .20 | .88 | .34 | .48 | .72 | .83 | .22 |
| Print format X smartphone app format | -1.26 | .03 | -.09 | -.14 | -.50 | -.21 | .06 | -.51 | .15 | .11 | -1.73 | -.29 | .99 | .62 |
| Print format X tablet app format | .90 | -.06 | 1.06 | .75 | .68 | .41 | .62 | .03 | .71 | .40 | 1.92 | 1.44 | .54 | .14 |
| Online format X smartphone app format | 2.98 | 1.44 | 1.56 | -.67 | 1.10 | .37 | 2.02 | 1.03 | 1.61 | .81 | 3.94 | 2.44 | 1.99 | -1.09 |
| Online format X tablet app format | 1.84 | .34 | .45 | -.43 | -.96 | -.35 | .63 | -.87 | .22 | -.05 | 3.91 | -1.26 | .45 | 1.66 |
| Smartphone app format X tablet app format | .04 | 1.21 | -.37 | -.57 | -.38 | .70 | .37 | 1.04 | .41 | 1.10 | -2.57 | .10 | .40 | .43 |
| None (no choice option) | 10.13 | 9.85 | 5.25 | 7.55 | 8.83 | 8.48 | 7.58 | 10.23 | 5.61 | 5.67 | 13.40 | 9.98 | 3.60 | 4.07 |
A total of 59 distinct plan combinations are feasible with the design attributes and their corresponding levels. We computed WTP for every plan combination and individual, using the algorithm outlined in Web Appendix W1. Subsequently, the values were averaged by segment to obtain two 7 • 59 matrices of WTPs (one for each version). Next, a nonparametric Kolmogorov–Smirnov test for two samples confirmed that the distributions of WTP values for respective plan configurations are identical across segments in both versions of the conjoint survey. Specifically, at a 95% (99%) confidence level, 83.1% (92.4%) of plan configurations were similarly distributed. Because of this reasonable evidence of similarity in WTP distributions, we merged data from both versions of the conjoint survey to form one 7 • 59 WTP matrix. This matrix of WTP estimates is a critical input for Step 3 in our outlined approach.
Step 2: Estimating Two-Sided Demand Functions of Content Consumers and Advertisers
Model. In this step, we propose and subsequently estimate a two-sided aggregate model of demands by format of content consumers and advertisers. In specifying these demand functions, we incorporate five key effects:
- Cross-market network effects (CMNEs): In advertisingsupported media platforms, demand from readers may affect the demand from advertisers, and vice versa (Armstrong 2006; Rochet and Tirole 2006).
- Marketing investment effects: Reader demand is typically affected by news quality (Chen, Thorson, and Lacy 2005) and distribution investments (Mantrala et al. 2007), and advertising demand is affected by sales force investments (Sridhar et al. 2011).
- Installed-base and carryover effects: Advertising and reader demands are also affected by consumers who have subscribed to the newspaper’s offering (i.e., content or ad space) in the previous period (i.e., installed bases) through a variety of social effects (Narayanan and Nair 2013), word of mouth, tradition, habit persistence, and past experience (Erdem, Imai, and Keane 2003).
- Cross-format effects: Past research has shown that the introduction of print and digital formats for content delivery may have complementary or substitution effects on reader demand (Chyi and Lasorsa 2002; Koukova, Kannan, and Kirmani 2012), which could subsequently affect advertisers’ allocation of their budgets across different formats (Sridhar and Sriram 2015).
- Market potentials’ effects: Content consumers’ aggregate demand is affected by changes in their market potentials due to factors such as population growth, government reforms, technological advances, digital literacy, and employment opportunities. In turn, changes in the number of potential content consumers in the NDMA can make the NDMA more or less attractive for advertisers, thereby affecting advertising demand (Talukdar, Sudhir, and Ainslie 2002).
We incorporate these five effects in the following four-equation response model system:
where PAt and OAt = print and digital advertising demand, respectively, at time period t; PRt and ORt = print and digital reader demand at time period t; PAMM and OAMM = marketing investments that affect print and digital advertiser demand; PRMM and ORMM = marketing investments that affect print and digital reader demand; PMP and OMP = number of potential print and digital readers in the NDMA; bPA and bOA = installed-base effects of print and digital advertiser demand; bPR and bOR = installed-base effects of print and digital reader demand; bRP and bRO = CMNE of reader demand on advertiser demand in print and digital formats; bAP, and bAO = CMNE of advertising demand on reader demand in print and digital formats; bPAMM and bOAMM = effects of marketing investments on advertiser demand in print and digital formats; bPRMM and bORMM = effects of marketing investments on reader demand in print and digital formats; bOAPA and bPAOA = cross-format effects of advertiser demand in print and digital formats; and bORPR and bPROR = cross-format effects of reader demand in print and digital formats.
The multiplicative model specification of these demand equations extends the one adopted by Sridhar et al. (2011) to multiple formats and captures two important realworld characteristics of the newspaper business. First, it accounts for the nonlinear relationship between key independent variables and the dependent variable. For example, we are able to specify that an increase in marketing mix will increase advertising revenue at a diminishing rate. Next, it also allows for implicit interactions among independent variables. In addition, we incorporate another real-world characteristic wherein advertiser demand is 0 when reader demand is 0 (but not vice versa) by specifying (1 + PA) and (1 + OA) in Equations 5 and 7.
Estimation. To calibrate the proposed market response model, we use nine years of monthly transaction data from the collaborating newspaper. We operationalize print and digital advertising demand using advertising revenue because this metric comprehensively captures several important ad features that are regularly scrutinized by ad managers in determining their ad budgets, including advertising rates, number of advertising inches, shape of advertisements, location of advertisements on a page, frequency of appearance, and location of ads within a content area. Next, we operationalize print reader demand using total number of newspaper subscriptions and digital reader demand using total number of unique visitors to the newspaper’s website from the NDMA. It is worth noting that we use penetration rate (PRt-1 and ORt-1) to capture the CMNEs of reader demand on advertiser demand in Equations 4 and 6 because managers noted that penetration rate provides advertisers with a better sense of market coverage than raw reader demand. We derive print and online penetration rates as follows:
where pass-along rate is the number of people who see each issue, including subscribers and every other person subscribers’ copies are passed to before they are discarded. We obtain PMP and OMP data from a third-party firm that was hired by the collaborating newspaper to provide market intelligence. Last, the collaborating platform has one sales force selling both print and digital ad space.
The time-series plots of readership volume and advertising revenue indicate that the two variables exhibit strong trend, seasonality, and cyclicality. In addition, we also observed the effect of recession in the data (for descriptives of all variables used in our analyses, see Web Appendix W5). To account for these effects, we log-transformed Equations 4–7 and augmented the equations with trend, seasonality, cyclicality, and recession variables:
where gt captures the effect of the linear trend variable t. Similarly, gs9 captures the year-end seasonality effect of the Thanksgiving and Christmas holiday season (s9t), where s9t is
defined as
TABLE 3 Model Comparison
TABLE:
| | | Model Parameters | Forecast Accuracy | Model Fit Statistics |
|---|
| | Trend, Seasonality, Cyclicality, and Recession | Marketing Investments | Cross-Market Network Effects | Carryover/Installed-Base Effects | Accounting for Unobservable Effects | MAPE (Across Equations) | MAD (Across Equations) | BIC | Durbin– Watson Statistic |
|---|
| Notes: First 77 observations and last 30 observations were used as estimation and holdout samples for assessing forecast accuracy. |
| Model 1 | ✓ | | | | | 2.828% | .403 | -1,006.218 | .287 |
| Model 2 | ✓ | ✓ | | | | 2.763% | .395 | -1,513.297 | .718 |
| Model 3 | ✓ | ✓ | ✓ | | | 2.838% | .406 | -1,489.757 | .709 |
| Model 4 | ✓ | ✓ | ✓ | ✓ | | 1.638% | .239 | -1,710.718 | 1.456 |
| Model 5 | ✓ | ✓ | ✓ | ✓ | ✓ | 1.560% | .228 | -1,755.812 | 2.252 |
It is worth noting that the key parameter estimates from the econometric analyses of content consumer and advertiser demands (presented in Table 4) also shed light on some interesting patterns, which validate and augment previous marketing literature:
- We find reinforcing (as opposed to countervailing) CMNEs between readers and advertisers in both print and digital formats. This finding augments previous literature that demonstrates this pattern in the print format (Sridhar et al. 2011).
- The analysis confirms a robust positive association between product quality and readership in print as well as digital formats. This finding augments previous work on the effect of product quality on readership in the print format (Mantrala et al. 2007).
- While advertising revenue at the collaborating firm declined during the economic downturn, the newspaper’s subscription numbers remained unaffected. This is counterintuitive because scholars have assumed that consumers generally cut spending during recession (Srinivasan, Lilien, and Sridhar 2011).
- Digital advertising revenues are more elastic to sales force investments than print advertising revenues. This result validates previous literature on sales force elasticity in traditional selling environments (Albers, Mantrala, and Sridhar 2010).
- We find that advertisers are more elastic to readership than readers are to advertising.
- Consistent with the literature that documents “service” (e.g., distribution) as one of the key factors affecting likelihood of repatronage (Tokman, Davis, and Lemon 2007), we find that investments in distribution indeed have a positive elasticity on content consumer demand.
However, distribution elasticity is smaller than quality and sales force elasticities.
In summary, our calibrated market response model demonstrates high forecast accuracy and consistency with the previous literature. Therefore, we use it in Step 3 of our approach.
Step 3: Computing a Profit-Maximizing Menu of Multiformat Subscription Plans
In the last step, we propose an optimization model (which we refer to as an “optimizer”) that integrates WTP estimates for various plan configurations from Step 1 with the calibrated market response model from Step 2 in a mathematical program to determine a profit-maximizing menu of multiformat subscription plans. We implement this math program using a novel heuristic that efficiently and rapidly determines the optimal solution from the very large number of possibilities. Figure 2 demonstrates the logical flow of our optimizer.
TABLE 4 Key Parameter Estimates in the Market Response Model
Optimizer overview. The primary objective of the newspaper is to design an optimal menu of subscription plans that maximizes its total profit derived from both content consumers and advertisers. Therefore, to evaluate the total profit from every possible plan within a given menu, the optimizer first determines the plan selections of each content consumer segment on the basis of their WTPs, obtained in Step 1 of our approach (Table 1). Then, the optimizer will compute the content consumers’ market potential by format by aggregating the marketlevel demand of all segments that picked a plan containing access to the corresponding format. These potentials serve as inputs to the calibrated aggregated content consumer demand and advertising revenue functions obtained in Step 2, leading to forecasts of the content consumer demand and advertising revenue for the subscription menu in question. Subsequently, the optimizer uses the market segment proportions to break down the content consumer demand forecasts by segment to obtain segment-level demand forecasts. Then, for each content consumer segment, the optimizer multiplies the segment’s corresponding demand forecasts by format with the price of the plan chosen by that segment to obtain the subscription revenues from that segment. As a result, the total profits from subscription as well as advertising revenues under the menu being evaluated are derived by multiplying content consumer and
advertiser revenues with corresponding gross margins. The optimizer executes this procedure for various menu configurations determined by our heuristic to find the total profitmaximizing menu.3
Model. We now present the mathematical programming model followed by an explanation of each equation:
where J = index of subscription plans, j = 1, …, J; K = index of customer segments, K = 1, …, K; Npk = number of print readers in a customer segment K in the NDMA; Nko = number of digital readers in a customer segment K in the NDMA;
sRfocPrrmikpjtai=to, n0repsoleatrnhv;earltwiojpins=e;p1rlicjoief
of the the jth = 1 if
kth segment for the jth subsubscription plan has a print the jth subscription plan has
a digital format, 0 otherwise; jk = proportion of segment K in
the newspaper’s NDMA; Ma = margin on print and digital
advertising revenue; and Mpr and Mor = margins on print and
digital subscription revenues, respectively.
Decision variables are Pj = price assigned to bundle j, P = (P1, …, PJ); and Bj = 1 if the newspaper is offering the jth subscription plan, 0 otherwise. Auxiliary variables are PFj = subscription profit from the jth subscription plan; Xj = total
number of readers subscribing to the jth subscription plan; Skj = surplus derived to the kth customer segment from the jth
subscription plan; dkj = 1 if the kth customer segment selects the jth subscription plan, 0 otherwise; MP = potential number of
readers subscribing to print formats (print market potential);
MO = Potential number of readers subscribing to digital for
mats (digital market potential); PA and OA = forecasted print
and digital advertising revenues, respectively; and PR and OR =
forecasted print and digital readers subscribing to newspaper’s
plans.
Self-selection constraints. Equations 14–16 capture selfselection among readers (Moorthy 1984). In particular, Equation 14 implies that a reader within a segment K will select a plan j only if ( 1) the surplus (s)he derives from subscribing to plan j is strictly positive, and ( 2) the surplus (s)he derives from plan j is greater than the surplus (s)he derives from all the other plans offered in the menu. Equation 15 determines the surplus derived by a reader in segment K for plan j, which is computed as the difference between the reader’s reservation price (i.e., WTP) for plan j and the price at which the firm is offering plan j. Next, Equation 16 ensures that readers in segment K pick at most one plan from the menu offered by the firm. In sum, these constraints ensure that the platform’s choice of an optimal plan takes into account the consumer’s individual rationality and
incentive compatibility constraints (Moorthy 1984).
Determining market potential. Equations 17–19 achieve
the objective of deriving the number of potential readers of
print and digital formats required for forecasting advertising re
venues and content consumer demand by format corresponding
to any alternative menu of subscription plans. First, Equation 17 determines the total demand for a subscription plan j ð“j2JÞ.
Note that Equation 17 ( 1) allows multiple reader segments to
choose the same plan (i.e., dkj could be 1 for multiple segments k, resulting in aggregation of demand from multiple segments
for the same subscription plan j), and ( 2) recognizes that, within a reader segment, demands for print (Npk) and digital (Nok) formats are heterogeneous. Therefore, depending on the formats included in the subscription plan j (i.e., depending on ljp and loj), the equation will determine the appropriate demand from segpm1oeapnnutdlka.tliIojofna=sp(l1ia.)en,., itnhmcelauxedqfeuNsabpkti, ooNtnhokpgwr)iinlaltssatenhldeecddtiegtmhiteaalnlfadorrgfmoerratposlfa(in.tehj.e, flrtowjpm=o segment k. Next, Equations 18 and 19 determine the total num
ber of potential content consumers in the print and digital formats
by aggregating the include print (ljp)
demand for all and digital (ljo
plans (Xj)) formats,
within the menu respectively.
that
Forecasting advertising and reader demand. The print
reader potential (MP) and digital reader potential (MO) are then used in the calibrated market response model (Equations 20–23)
to forecast advertiser revenue and content consumer demand
within the two formats.
Computing reader profit. Equation 24 is used to compute gross subscription profit from all reader segments. Gross profit from reader segments is simply the product of margin (Mr) and total revenue from the reader segment. Revenue from each
segment can be determined by multiplying the demand from the
respective segment by the price of the subscription plan (Pj) determined by the newspaper firm. However, the projected reader demand (PR and OR) obtained from Equations 20 and
23 represents aggregate demands by format. Therefore, to compute reader revenue, we first retrieve segment-level demand
by format by multiplying aggregate demand from Equations 21 and 23 by the proportion of each segment in the firm’s existing reader base (jk) and price of the subscription plan chosen by the segment (Pj). Note that this will yield the demands of all seven segments for both print and digital formats. However, a segment
may subscribe to ( 1) a pure-print plan, ( 2) a pure-digital
plan, or ( 3) a plan with both print and digital formats. Therefore, in case ( 1), the gross profit for segment K is determined as PRt • Pj • Mpr • jk. Similarly, in case ( 2), the gross profit for segment K is determined as ORt • Pj • Mor • jk. However, in case ( 3), because the newspaper has never previously offered print + digital bundles, in concurrence with the management, we determine the gross profit for segment K as maxfPRt, ORtg • Pj • Mpr • jk. Last, the margins for delivering print and digital advertising at the focal newspaper are equal. Therefore, gross profit from advertising is computed as ðPA + OAÞ • Ma.
TABLE:
| Effect | Print Format Elasticity Estimate | Digital Format Elasticity Estimate |
|---|
| **p < .05. |
| ***p < .01. |
| n.s.Not significant. |
| CMNE of readership on advertising revenue | .413*** | .327*** |
| CMNE of advertising revenue on readership | .028*** | .031*** |
| Effect of quality investments on readership | .047*** | .112*** |
| Effect of distribution investments on readership | .031** | |
| Effect of sales force investments on advertising revenue | .194*** | .350*** |
| Effect of market potential on advertising revenue | 1.843*** | .288n.s. |
| Effect of market potential on readership | .181*** | .524*** |
| Carryover/installed-base effect of previous period advertising revenue | .293*** | .761*** |
| Carryover/installed-base effect of previous period readership | .272*** | .235*** |
A Heuristic for Solving the Menu-Optimization Problem
The proposed optimization model presents a discrete combinatorial optimization challenge for the newspaper. Given K market segments, the newspaper must decide ( 1) the number of subscription plans to offer in its menu, ( 2) the composition of subscription plans offered in the menu, and ( 3) the price of each subscription plan, such that the chosen menu maximizes its profit from readers as well as advertisers. This is a complex, multidimensional nonlinear optimization problem. Consider a simple case in which the newspaper is trying to design a subscription plan menu for two segments with 59 distinct plan combinations available to the firm. Ideally, the firm could offer one or two of the 59 plans. Therefore, the total number of plans that the newspaper must evaluate before finding the profitmaximizing menu is 59C1 + 59C2 = 1,770. In addition, if the newspaper were to test 10 different price points for each plan, the number of combinations to search over before finding the profit-maximizing menu would rise to (59C1 • 10) + (59C2 • 10 • 10), resulting in 171,690 cases. Generalizing this to J plans, K segments, and P price points, the total number of combinations needed to be tested before arriving at a solution is
As we show in Web Appendix W7, the problem space increases exponentially even with small increases in the number of segments and plan combinations. Specifically, in the case of our collaborating newspaper, when there are seven segments, the optimizer must evaluate 3.45706E + 15 menu combinations before finding the solution. A simple software application written in Excel will take more than 5 million minutes to parse these iterations. Therefore, while elaborative “brute-force line search” techniques can guarantee a global solution by sequentially parsing every single combination in the discrete problem
space, they can prove to be extremely time intensive and
computationally expensive. Therefore, we propose a heuristic to solve the newspaper’s optimization problem within a reasonable amount of time. k’s economic surplus for the assigned plan but also depends on all the plans assigned to segments prior to segment k. Therefore, global optimality using the proposed heuristic is not guaranteed. This, however, is a common problem in the extant product line literature that uses heuristic techniques (Belloni et al. 2008; Luo 2011). We partly mitigate this problem by running our algorithm numerous times with various segment orders. Additionally, to determine whether the line search technique and our proposed heuristic produce the same results, we reduced the dimensionality of the problem by restricting the number of segments to two4 and then ran both the algorithms. The results confirmed that both techniques produce the same results.
Profit-Maximizing Solutions Under Different Business Models
Initial values for the optimizer. We use the 59 • 7 WTP matrix obtained in Step 1 and the parameter estimates of the calibrated market response model obtained in Step 2 to derive a profit-maximizing menu of subscription plans. A complete list of input values for the optimizer is provided in Web Appendix W8. Note that we rounded all WTP estimates to the nearest 25¢ to mimic newspaper subscription pricing in the real world. We obtained margin information from the collaborating firm. Currently,5 the newspaper is making a margin of about .547 on ad revenue (Ma) and .077 on print subscription revenue. Because the newspaper was not monetizing its digital content, we surveyed several newspaper practitioners to obtain a benchmark margin on digital subscription revenue of .20.
Establishing the baseline: current newspaper performance. We will describe the status quo with respect to subscription plans and gross profit at the collaborating newspaper. This information will serve as the baseline for assessing the effectiveness of menus derived under the proposed model and other business models and format strategies. The newspaper is currently offering two paid-subscription plans to its readers: seven-day print at $5.00/week and Sunday-only print at $2.75/week. The current set of reader offerings is identical to a purecomponent strategy because all plans (i.e., seven-day, Sunday, and digital) are offered separately to the readers. As reported by the newspaper, the firm made $3,393,886 through print subscriptions, $7,252,415 through print advertising, and $706,142 through digital advertising in the current period. Using margins described in the previous section, the newspaper’s gross profit in the current period is (.077 • $3,393,886) + .545 • ($7,252,415 + $706,142) = $4,599,082. This gross profit marks the baseline for all further analyses. We will evaluate outcomes from various newspaper strategies with respect to total gross profit ($4,599,082), gross profit from subscriptions (.077 • $3,393,886 = $261,669), and gross profit from advertising (.545 • [$7,252,415 + $706,142] = $4,337,414) in the current period. Next, we briefly describe optimal menus along with their profits under each of four common business model and organizational management scenarios prevailing in the United States.
Siloed business model strategy. Historically, newspaper circulation and advertising departments have focused on their own objectives in budgeting and resource allocation decisions (Willis and Willis 1988), that is adhered to a “siloed” business model. Such decision making by circulation departments is still pervasive in the United States (Newspaper Association of America 2012). To capture this situation, we modified the objective function of the proposed general optimization model M1 to maximize gross profit
from subscriptions alone: maximize å Bj ðPFjÞ. Consequently,
our heuristic determined a menu ofj subscription plans that maximized gross profit from subscriptions without accounting for the consequences for advertising revenues. Then, the heuristic computed and added the gross profits from advertisers using the reader demand generated by the subscription profit–focused subscription menu. Table 5 summarizes the results.
Three interesting results emerge from the analysis of the siloed business model:
- The subscription profit-maximizing menu is a “partial mixed bundle” of print and digital formats (i.e., seven-day + digital, Sunday + digital, and digital-only plans).
- Gross profit from subscriptions in the siloed model ($744,935) is higher than that in the current scenario ($261,668). This result is contrary to the traditional notion that charging for digital content will lead to a decline in subscription revenues.
- Gross profit from advertising in the siloed model ($3,207,713) are lower than those in the current scenario ($4,337,413). This result is interesting because it shows that the newspaper will generate more gross profit from advertising by operating under a siloed business model and charging only for print content (i.e., the current situation) than it would by operating under a siloed business model and charging for both print and digital content.
TABLE 5 Profit-Maximizing Menus Under Various Business Model and Format Strategies
TABLE:
| | Siloed Business Model | Integrated Business Model | Reduced-Print-Frequency Format Strategy | Digital-Only Format Strategy |
|---|
| Menu composition | • Seven-day print + all digital at $5.25/week • Sunday print + all digital at $3.00/week • Sunday print + unlimited online access + unlimited tablet app access at $2.25/week • All digital at $2.25/week | • Seven-day print + all digital at $6.25/week • Sunday print + all digital at $2.25/week • Sunday print + unlimited online access + unlimited tablet app access at $2.00/week | • Sunday print + all digital at $2.25/week • Sunday print + unlimited online access + unlimited tablet app access at $2.00/week | • All digital at $2.00/week • Unlimited online access + unlimited smartphone app access at $1.50/week • Unlimited online access at $1.00/week |
| Gross profit from print advertising | $2,699,808 | $4,267,835 | $4,267,835 | $.00 |
| Gross profit from digital advertising | $507,905 | $517,420 | $517,420 | $445,469 |
| Gross profit from print bundles | $534,450 | $606,709 | $429,512 | $.00 |
| Gross profit from digital-only circulation | $210,486 | $.00 | $.00 | $397,572 |
| Total gross profits from advertising | $3,207,713 | $4,785,255 | $4,785,255 | $445,469 |
| Total gross profits from circulation | $744,936 | $606,709 | $429,512 | $397,572 |
| Percentage increase in gross profit from baseline scenario | -14% | 17% | 13% | -82% |
Integrated business model strategy. In contrast to a siloed model, an integrated business model entails the circulation and advertising departments working together. Specifically, managers overseeing both departments devise subscription plans together to maximize the sum of gross profits derived from subscriptions as well as advertising. While practitioners have speculated that designing multiformat subscription plans by taking consumers’ and advertisers’ preferences into account could yield more profitable outcomes (Newspaper Association of America 2012), there has been no empirical evidence, to the best of our knowledge, in support of this claim. Therefore, to test this claim, we obtained a profit-maximizing menu using the objective function specified in Equation 13 and the algorithm outlined in the Appendix. Three interesting observations can be made from the output obtained under integrated business model strategy (see Table 5):
- The profit-maximizing menu is a “pure bundle” of print and digital formats (i.e., seven-day + digital and Sunday + digital plans).
- While gross profit from subscription in the integrated model ($606,709) is higher than that in the baseline scenario ($261,669), it is lower than that in the siloed model scenario ($744,935). This result is interesting because ( 1) it reaffirms that charging for digital content can increase subscription profit, and ( 2) it shows that offering a pure bundle results in lower subscription profits than offering a partial mixed bundle.
- Gross profit from advertising in the integrated model ($4,785,254) is higher than that in either the baseline scenario ($4,337,413) or the siloed model ($3,207,713). This result is also interesting because ( 1) it demonstrates that charging for digital content can increase profit from advertising, and ( 2) it shows that a pure-bundle offering results in higher advertising profit than pure-component or partial-mixed-bundling strategies.
Reduced-print-frequency format strategy. A decline in print circulation and advertising revenues in the past decade has forced newspapers to consider the option of cutting back on their print frequency as a means to stabilize their finances and restore profitability. While there have been numerous debates on this topic in the popular press, there is very little scientific evidence demonstrating the financial viability of the reducedprint-frequency strategy. Therefore, to glean insight into the effectiveness of such a strategy in the context of the newspaper studied here, we restricted the plan alternatives to Sunday + digital bundles and pure-digital plans (i.e., we eliminated bundles that included seven-day print from the plan alternatives) and subsequently derived profit-maximizing plans within the integrated business model framework.
As shown in Table 5, the profit-maximizing menu under this strategy comprises two Sunday + digital bundles. While the total gross profit obtained under the reduced-print-frequency strategy ($5,214,766) is 3.4% lower than that obtained when the firm operates without any reductions in print frequency ($5,391,964), the total gross profit is still 13.39% higher than that in the current scenario, in which the firm is operating under the print-only strategy ($4,599,082). Therefore, this analysis confirms the financial viability of the reduced-print-frequency strategy in the short term.
Digital-only format strategy. Another strategy pondered by some newspapers to stabilize their financial situation is the complete elimination of the print publication. We refer to this as the “digital-only format strategy.” A small number of newspapers, such as the Ann Arbor News, have implemented the digital-only strategy in recent times. To determine the implications of adopting a digital-only strategy within the context of the collaborating firm, we restricted plan alternatives in the optimization model to pure-digital plans (i.e., we eliminated bundles that contained seven-day and Sunday print options from the plan alternatives) and subsequently ran our optimizer.
The results in Table 5 demonstrate that the profitmaximizing menu under this strategy comprises three variants of the digital plans. While the gross profit from circulation in the digital-only strategy ($397,527) is higher than that in the baseline scenario ($261,668), gross profit from advertising ($445,468) and overall gross profit ($843,041) in the digitalonly strategy are substantially lower than those in the baseline scenario. In summary, this analysis suggests that our collaborating newspaper would face substantial profit declines in the short term under the digital-only scenario.
Accounting for Variance (or Risk) in Content Consumers’ WTP
Thus far, we have evaluated profit-maximizing menus under various business models and format strategies assuming average segment-level WTP estimates. These mean estimates are point
estimates that do not capture the variability of WTP among the
respondents. A manager could be interested in knowing how
variance affects the menu composition and profit because
variance determines the risk involved in offering a plan at its
mean price. Take, for instance, a plan j with high variance. A
manager would be less certain about offering plan j at its mean
price because (s)he would have less confidence in the number of
readers within a segment who would derive positive surplus
from subscribing to plan j at that price. In contrast, a manager
would be more certain about offering a plan m at its mean price if
plan m exhibited smaller variance in WTP because the prob
ability of readers who would derive positive surplus from
subscribing to plan m at mean WTP would be higher.
Therefore, to account for managers’ risk preferences with
respect to variance in segment-level WTP estimates, we added
the following constraint to the proposed optimization model:
sj2k £ s~2, where within segment K
sanj2 diss~2thise
variance in WTP for plan j the risk tolerance of the manager.
We simulated the algorithm for various values of s~2 and obtained
profit-maximizing menu parameters. Several interesting results
emerged from this analysis (for details, see Web Appendix W9):
- Profit-maximizing menus for all risk levels were “pure bundles” comprising seven-day + digital and Sunday + digital plans, which is consistent with the result using point estimates.
- Gross profit increased with an increase in s~2. This finding is consistent with the notion that high risk yields high reward. Additionally, total gross profits at all risk levels are still higher than those in any other alternative business model.
- Gross profit from advertising remained constant for all risk levels. This finding demonstrates that there exists a portfolio of print + digital bundles from which the manager can choose, depending on his/her risk level, without affecting the demand from readers or gross profit from advertising.
- Price dispersion (computed as maximum price – minimum price) among plans within the menu decreased as the risk decreased. This indicates that a manager will price the plans more similarly as (s)he becomes more risk-averse.
In summary, while profits varied in magnitude, substantial findings with respect to menu composition and profitability under an integrated strategy remained unaltered even after we accounted for variance in WTP. This robustness check helped us obtain buy-in from the management (for managers’ responses, see Web Appendix W10), which was crucial to ensure continued adoption of the decision tool (Van Bruggen and Wierenga 2010). In addition, to enhance the firm’s adoption of this tool for future decision making, we coded the heuristic using Visual Basic and implemented the framework in the Microsoft Excel application.
Conclusion
Media platforms such as newspapers have been facing a steady decline in revenues from traditional formats such as print, radio, and television for over a decade (Pew Research Center 2016). At the same time, traditional formats are still the dominant revenue sources at these firms. Furthermore, proliferation of formats and versions is presenting media platforms with unprecedented opportunities as well as challenges with respect to packaging their media content. Consequently, media platforms are confronted with the problem of designing their offerings such that they can sustain revenue growth from contemporary digital offerings while continuing to maintain revenues from their legacy formats (e.g., print, radio, television). To that end, they need a decision tool that can provide them with intelligence on designing profit-maximizing offerings.
Our research addresses this topical menu-design problem confronting the modern-day media platforms. Specifically, leveraging individual format and version preference data from content consumers and a firm’s aggregate transaction data, the study builds a novel mixed-integer, nonlinear programming algorithm that can effectively determine the optimal menu of subscription plans for readers.
Contributions to Marketing Theory and Practice
From a theoretical standpoint, this study augments literatures on pricing in two-sided markets, format bundling, format versioning, and product line design in multiple ways. While the majority of research in marketing and economics has addressed various aspects of the firm’s product offering and pricing problem in a onesided market, a holistic solution that addresses the contemporary media platform’s menu-design problem has not yet been offered. More generally, a product line design and pricing problem that incorporates CMNEs and installed-base effects for multiformat and multiversion media content has not been addressed before in the literature. The three-step approach proposed in the present research offers clear directions for negotiating the conceptual and practical challenges and solving this type of problem. In the process, we also extend Moorthy’s (1984) self-selection theory to two-sided markets. Moreover, this study adds to a very nascent literature that examines asymmetric CMNEs (Gomes and Pavan 2013; Veiga and Weyl 2010) by proposing and calibrating a multiformat market response model for a media platform firm that allows for such CMNEs. From a methodological standpoint, the proposed approach offers a novel and implementable means by which newspapers can integrate their aggregate-level market data with disaggregate-level reader data and a novel heuristic for solving the complex optimization problem efficiently and rapidly. From a managerial viewpoint, to the best of our knowledge, this research is the first to use actual market data-based models to evaluate profitability of various newspaper business models and format strategies for stabilizing newspaper finances that have been debated in the popular press. The insights from our proposed optimizer are thereby valuable not just for newspaper firms but also for other ad-supported media platforms.
Limitations and Directions for Future Research
As with any study, this research is also not without limitations. However, these limitations offer numerous opportunities for future research. First, while our proposed decision support framework is generalizable to all ad-supported media platforms, the analysis was customized to incorporate subtleties of the newspaper business. Therefore, future research could extend the proposed framework by incorporating unique features of other media platforms. For instance, Hulu provides its content consumers with an option to select the type of ads, number of ads, and length of ads. Similarly, television broadcasting stations such as ESPN are now experimenting with dynamic metering strategies in which there are different free-access limits (i.e., meters) in different content categories (e.g., baseball, hockey, soccer) (e.g., Lambrecht and Misra 2016). Moreover, leveraging the fact that multiformat touchpoints could enhance ad recall and effectiveness, many media platforms such as television broadcasting stations are starting to offer a menu of plans to advertisers. Likewise, media platforms may start offering personalized plans to content consumers on the basis of “cookie” data, while others may allow their content consumers to build their own personalized bundles. Consequently, we urge future researchers to explore how these unique characteristics affect menu configurations.
Similarly, future research could also incorporate more upand-coming advertising trends into the proposed menu-design framework.6 For instance, programmatic advertising is eliminating the need for human interaction in the digital ad buying process. A recent article in Ad Age notes that programmatic advertising will soon make up $14.88 billion of the approximately $58.6 billion digital advertising market (Kantriwitz 2015). This trend could result in platforms shifting their marketing investments from outside sales forces to inside sales forces and information technology in order to deliver seamless digital ad buying experience to their advertising customers. Similarly, with the rise of “geo-targeting” and “geo-conquesting,” advertisers are now demanding more precise target audiences. This could significantly affect how pure-digital platforms such as Hulu can use the proposed framework. For instance, the aggregate reader demand variable in the advertising response models would have to be replaced with spatial, demographic, and location information. More along the lines of mobile advertising, industry experts have also predicted an increase in the use of “beacon proximity signals” for geo-targeting (Moores 2015). If so, this will result in significant changes in marketing-mix and IT expenditures at media platforms. Moreover, there are numerous other developments on the horizon in the digital ad delivery space. For instance, just as web pages are adaptable to mobile devices, Google’s new responsive AdSense program adjusts web ads to different screen sizes. Similarly, “snap banners,” which were first invented by People magazine, provide a more fluid and interactive ad experience to mobile users. Digital content delivery platforms are incorporating such developments into their product design. Consequently, these developments could present media platforms with new ways to monetize their content. For example, platforms can offer a base version of the app and a more “native” mobile app that presents an enhanced viewing experience for an additional price. Future research could incorporate such developments into our proposed menu-design framework.
Another consideration is that this research presents a singleperiod static optimization framework that does not incorporate learning among content consumers and advertisers in future periods. One would need observed data to empirically examine multistage dynamic decisions. Moreover, while readers may frequently switch plans, newspaper firms do not change their menu of offerings frequently. This is because a change in offerings would require newspapers to make necessary changes in other marketing-mix instruments such as advertising, promotion, and distributions, which is very expensive. Hence, managers at our collaborating newspaper were willing to accept a static solution. However, as more newspapers update their menus with multiformat and multiversion subscription plans, scholars, should be able to leverage our framework to propose dynamic multiperiod optimization frameworks. Similarly, while our framework uses profit as the performance metric, it can be easily adapted to long-term performance metrics such as customer equity, retention, and acquisition. Future research could propose such model extensions and validate whether or not the proposed menu solutions under various business models change with performance metrics.
Another worthwhile future research direction is simultaneous optimization of menus for both content consumers and advertisers. While our collaborating newspaper was primarily interested in designing content consumer menus and not advertising menus, other media firms could be interested in simultaneously designing content consumer and advertiser menus. Such a simultaneous menu-design problem is much more complicated because an analyst would also need to account for numerous other attributes that influence advertising rates, such as number of advertising inches, the shape of the advertisement (e.g., rectangular vs. square), location of the advertisement on the page or website (e.g., top, in the middle of text, bottom), frequency of appearance (i.e., the number of times the ad appears in a newspaper in a given month), and location of the advertisement within a content area (e.g., sports section, lifestyle section). Future research could extend our model to propose a decision aid that can address the simultaneous multiside menu-optimization problem.
Appendix: Algorithmic Representation of the Proposed Optimizer
Outer Loop
For each segment K and for each plan configuration j, repeat the following.
1We believe that splitting the total reward into monetary and nonmonetary components encourages truth telling among the survey respondents because preferring a plan that is more (less) expensive, when the respondent is truly interested in another plan, would not only result in less (more) monetary compensation but would also result in a three-month subscription to a plan that the respondent did not truly like. For example, a respondent that chose the print + digital plan, when she truly preferred the digital-only plan, would get a smaller dollar reward (because, per the study’s design, the print + digital plan is more expensive than the digital-only plan) and a three-month subscription to a plan that was not her preference. Therefore, the respondent would be better off stating her true preference.
2An augmented Dickey–Fuller test can be used to check for stationarity in error terms. However, heteroskedasticity in the error terms could bias the results of this test.
3It is worth noting that the optimizer we propose is static in nature. That is, it does not capture the dynamic nature of content consumers’ WTP and content consumers’ and advertisers’ demands. This model characteristic is in line with the real-world observation that media platforms do not change their menus frequently. Several marketing, sales, finance, accounting, and IT activities hinge on the composition and price of the content subscription plans put forth by the platform.
Therefore, media platforms typically refrain from modifying their
subscription offerings frequently.
4We could have picked a higher number of segments, but the amount of time it took the line search algorithm to go over all the possible scenarios and find a profit-maximizing plan was abnormally high. Thus, we restrict out robustness check to a two-segment case. We repeated the process for multiple two-segment pairs to rule out any segment selection bias.
- 5“Current period” is the period following the last period in the archival data set. We do not reveal the exact years of data collection for confidentiality purposes (we are bound by a nondisclosure agreement with the newspaper).
6We thank an anonymous reviewer for suggesting this future research direction.
GRAPH: FIGURE 1 Fit Between the Predictions of Market Response Model Calibrated Using the Full Sample and Observed Data
DIAGRAM: FIGURE 2 A Framework to Design Multiformat Subscription Menu for Ad-Supported Media Platforms
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Record: 131- Organizational Multichannel Differentiation: An Analysis of Its Impact on Channel Relationships and Company Sales Success. By: Fürst, Andreas; Leimbach, Martin; Prigge, Jana-Kristin. Journal of Marketing. Jan2017, Vol. 81 Issue 1, p59-82. 24p. 3 Diagrams, 7 Charts. DOI: 10.1509/jm.14.0138.
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Organizational Multichannel Differentiation: An Analysis of Its Impact on Channel Relationships and Company Sales Success
This article examines whether and how a company’s division of segment- and task-related responsibilities among multiple sales channels affects the relationships in the multichannel (MC) system and, ultimately, the company’s sales success. Building on open systems theory, the authors develop an overarching framework of organizational MC differentiation that distinguishes between two generic approaches: segment differentiation and task differentiation. They predict that these two approaches affect key relationship and performance outcomes of an MC system, but do so differently and contingent on key characteristics of the company’s customers. Drawing on a multi-informant survey in a business-to-business context as well as on objective performance data, the authors find that segment differentiation tends to mitigate horizontal conflict and inhibit cooperation, while task differentiation reduces primarily vertical conflict and promotes cooperation. Moreover, depending on customer characteristics, segment differentiation may damage channel relationships overall and, in turn, limit company sales success, whereas task differentiation unambiguously promotes channel relationships and thus drives company sales success. These findings offer novel insights into the relationship and performance impact of MC systems’ organizational structure and provide useful guidance on how managers should allocate segment- and task-related responsibilities among multiple sales channels.
Today, the use of multiple sales channels has become the rule rather than the exception (Ka¨uferle and Reinartz 2015). Multichannel (MC) systems have proliferated
largely because they permit companies to divide responsibilities among channels, thereby capturing the advantages of channel specialization (Neslin and Shankar 2009).
Specifically, in extreme cases, a company could design the organizational structure of its MC system to make downstream channels responsible for different customer segments, rather than, at the other extreme, for all the same customer segments (Cespedes and Corey 1990). For example, the Industrial Division of the Schaeffler Group (which manufactures bearings, linear guides, and direct drives for firms in industries such as aerospace, printing machinery, and mining) typically uses its own sales force to handle large to medium-sized customers, whereas it relies on sales partners to deal with smaller customers. In contrast, the Wu¨rth Group (which manufactures assembly and fastening material for industries such as wood and metalworking, garages, and construction) typically uses all its channels–including its own website, sales force, brick-andmortar branches, and call center–to deal with the same customer segments (i.e., small accounts, big accounts, classic craft businesses, and large craft businesses).
Moreover, a company could design downstream channels to focus on different customer-related tasks rather than having each channel perform the entire range of tasks (Stone, Hobbs, and Khaleeli 2002). For example, in addition to using the channels to close sales, the Wu¨rth Group uses its website mainly for lead generation, its sales force and brick-and-mortar branches mainly for advising, and the call center for aftersales service. Conversely, in the Industrial Division of the Schaeffler Group, both the own sales force and the sales partners perform the entire range of tasks.
By its very nature, such organizational MC differentiation is likely to enable companies to better adapt an MC system to customer-related demands and, thus, to foster customer relationships (Coelho and Easingwood 2003). However, beyond this obvious favorable effect on relationships external to the MC system, organizational MC differentiation may also affect relationships internal to the system and may do so not only in beneficial but also in deleterious
Journal of Marketing Vol. 81 (January 2017), 59-82 ways (Moriarty and Moran 1990). For example, by assigning channels to different customer segments, companies may avoid deleterious between-channel fights for the same customer but, at the same time, may reduce channels’ motivation to work together when acting in the market. Therefore, organizational MC differentiation may both promote and damage the relationships in an MC system and, in turn, the company’s sales success. A thorough understanding of these outcomes is thus important for advancing theory on the organizational design of “[MC] arrangements [that in many cases] haven’t been working as planned” (Cespedes and Corey 1990, p. 67) and for providing practical advice on how best to exploit the unique potential of using multiple channels.
TABLE:
| Study | Focus on Centralization | Focus on Formalization | Focus on Specialization/Differentiation | Focus on Outcomes | Focus on MC Context | Unit and Type of Analysis/Sample |
|---|
| Dwyer and Oh (1987) | | | | | | Single channel, SEM/n = 133 dealers (single-industry, single-informant) |
| Dwyer and Welsh (1985) | | | | | | Single channel, MANOVA/MANCOVA/n = 426 retailers (multi-industry, singleinformant) |
| John (1984) | | | | | | Single channel, SEM/n = 151 dealers (single-industry, single-informant) |
| Kabadayi, Eyuboglu, and Thomas (2007) | | | | | | MC system, cluster analysis/n = 291 companies (singleindustry, multi-informant) |
| Our study | | | | | | MC system, SEM/n = 329 companies (multi-industry, multi-informant), objective performance data |
Despite the theoretical and practical importance of organizational MC differentiation and its outcomes, research on this topic is scarce (see Table 1). Previous research on the organizational structure of channels has focused on the centralization and formalization of a single channel (Dwyer and Oh 1987; John 1984) or has only touched on specialization/differentiation as part of a broad range of structural dimensions, thereby concentrating on the interplay of the various dimensions and their determinants (Dwyer and Welsh 1985; Kabadayi, Eyuboglu, and Thomas 2007).1
1The terms “specialization” and “differentiation” are closely
Thus, the literature lacks research on organizational MC differentiation and its outcomes. This research gap is striking given its potential for gaining novel theoretical insights into whether and how the division of responsibilities among multiple channels affects a company’s channel system and sales performance and for providing managerial advice on the allocation of responsibilities between channels.
Our goal is to address this research gap by focusing on whether and how organizational MC differentiation affects relationships in the MC system (i.e., among channels and between channels and the company’s sales management) and, ultimately, company sales success. It contributes to the literature in several ways. First, as the first in-depth investigation of this issue, the article introduces an overarching framework of organizational MC differentiation, which distinguishes between two generic approaches: MC segment differentiation, or the degree to which channels differ in responsibilities for customers, and MC task differentiation, or the degree to which channels differ in responsibilities for functions. In addition, it identifies potentially critical outcomes that refer to the relationships in the MC system (MC conflict and MC cooperation) and the performance of the MC system (company sales success). By linking constructs related to the neglected issue of organizational MC differentiation with company sales success through littleor never-examined relationship outcomes of MC systems, this framework provides the basis for novel theoretical insights, useful advice for managerial practice, and helpful guidance for further research.
TABLE:
| Construct Labels | Construct Definitions | Main References for Scale Development |
|---|
| Organizational MC Differentiation |
| MC segment differentiation | Degree to which the channels differ in responsibilities for customers and thus focus on different segments | Neslin and Shankar (2009); Sa Vinhas and Anderson (2005) |
| MC task differentiation | Degree to which the channels differ in responsibilities for customer-related functions and thus focus on different tasks | Moriarty and Moran (1990); Stone, Hobbs, and Khaleeli (2002) |
| MC Relationship Outcomes |
| Horizontal MC conflict | Degree of disputes in the MC system that occur among the channels, as reflected by their frequency, intensity, and importance | Brown and Day (1981); Coelho and Easingwood (2004) |
| Vertical MC conflict | Degree of disputes in the MC system that occur between the channels and the company’s sales management, as reflected by their frequency, intensity, and importance | Brown and Day (1981); Webb and Lambe (2007) |
| MC cooperation | Degree to which the channels work together with one another and the company’s sales management in a close and constructive manner when acting in the market | Anderson and Narus (1990); Frazier (1983b); Zhang et al. (2010) |
| MC Performance Outcome |
| Company sales success | Sales achievements of the company relative to the competition in terms of effectiveness and efficiency | Kumar, Stern, and Achrol (1992) |
| MC Customer Characteristics |
| Customer heterogeneity | Degree to which the company’s customers have diverse needs in terms of product type and features, price and quality, and service | Jindal et al. (2007) |
| Customer cross-channel buying | Propensity of the company’s customers to switch among different channels for purchases | Neslin and Shankar (2009); Verhoef, Neslin, and Vroomen (2007) |
| Control Variables (Company Characteristics) |
| Number of channels | Total number of channels of theMCsystem | Jindal et al. (2007); Kabadayi, Eyuboglu, and Thomas (2007) |
| Directness of channels | Degree to which the channels are company-owned, relative to the total number of channels of the MC system | Jindal et al. (2007); Kabadayi, Eyuboglu, and Thomas (2007) |
| Firm size | Overall size of the company in terms of annual revenues | O’Sullivan and Abela (2007) |
| Control Variables (Market Characteristics) |
| Market growth | Degree to which the market of the company is characterized by high growth | Achrol and Stern (1988); Kabadayi, Eyuboglu, and Thomas (2007) |
| Market competition | Degree to which the market of the company is characterized by intense competition | Jindal et al. (2007); Kumar, Stern, and Achrol (1992) |
Second, drawing on a multi-informant sample of manufacturing companies operating as suppliers in a businessto-business context and on objective performance data, this article reveals novel theoretical insights into the impact of organizational MC differentiation on key relationship and performance outcomes of MC systems. It shows that the two generic approaches differ significantly in how they affect these outcomes. Multichannel segment differentiation, targeted at reducing channel overlap in customers, tends to mitigate horizontal conflict between channels but to inhibit MC cooperation. In contrast, MC task differentiation, targeted at reducing channel overlap in functions, tends to reduce primarily vertical conflict between channels and sales management and to promote MC cooperation.
Third, the article highlights the importance of key MC customer characteristics (customer heterogeneity and cross-channel buying) for determining whether and how organizational MC differentiation ultimately influences company sales success. The findings show that, depending on these characteristics, MC segment differentiation can damage channel relationships overall and, in turn, have a detrimental impact on company sales success, whereas MC task differentiation unequivocally promotes channel relationships, thus having a beneficial impact. As such, researchers’ and managers’ assumptions about the benefits of organizing channels around customers or functions must be somewhat curbed and put into context.
Development of Conceptual Framework
Our unit of analysis is a company and its system of multiple sales channels. We define a sales channel as the means by which the company makes its products available for customer purchase (Shervani, Frazier, and Challagalla 2007).
Structure of Conceptual Framework
We draw on open systems theory (Katz and Kahn 1978; Von Bertalanffy 1968), which adopts a system-structural view of organizational design (Ruekert, Walker, and Roering 1985). According to this theory, a social system, such as an MC system, is embedded in its external environment as an “organized, unitary whole composed of two or more … subsystems” (Kast and Rosenzweig 1985, p. 15). The subsystems, such as the channels of an MC system, can be organized according to their responsibilities for dealing with the external environment. The division of these responsibilities among the subsystems determines the extent of organizational differentiation (Von Bertalanffy 1968). Accordingly, the greater the division of responsibilities among the channels of an MC system, the greater is the extent of organizational MC differentiation. Because the theory also stresses that for the successful functioning of a social system, it is crucial to ensure good relationships in the system (Katz and Kahn 1978), we select MC relationship outcomes for our framework, which reflect the quality of relationships in the MC system. Moreover, as the performance of a social system determines its probability of survival (Katz and Kahn 1978), we also consider MC performance outcome, which refers to the success of the MC system.
Open systems theory also indicates that because “a system [is] in exchange of matter with its [external] environment” (Von Bertalanffy 1968, p. 149), researchers should consider the external environment’s characteristics when investigating the appropriateness of the system’s organizational structure (Kast and Rosenzweig 1985). Thus, we consider characteristics of MC systems’ customer-related environment, referred to as MC customer characteristics. Finally, the theory suggests taking into account additional characteristics of a system’s internal and external environment when examining the outcomes of the system’s organizational structure (Katz and Kahn 1978). Therefore, our framework contains additional company and market characteristics that serve as control variables.
Constructs in Conceptual Framework
Open systems theory is also helpful for selecting key constructs in the five categories previously identified. For each category, we draw on this theory and prior literature to introduce, define, and justify these constructs (see also Table 2), which serve as the building blocks for our framework (see Figure 1).
Organizational MC differentiation. According to open systems theory, modes of organizational differentiation include by clientele and functions (Lawrence and Lorsch 1967b). For our study, these modes suggest organizational MC differentiation by customer segments and tasks.
We define MC segment differentiation as the degree to which the company’s channels differ in customers for which they are responsible in the MC system. Under high MC segment differentiation, the channels (C) focus on different segments (S), such as C1 on S1, C2 on S2, and C3 on S3. Under low MC segment differentiation, they focus on the same and, thus, on all the company’s segments, such as C1, C2, and C3 each dealing with S1, S2, and S3.2 In a business-to-business context, the segments could, for example, comprise customers of different sizes or industries. Prior research has highlighted the importance of this approach by indicating that if channels focus on different segments, each channel could specialize in the demands of its respective customers (Cespedes and Corey 1990; Sa Vinhas and Anderson 2005).
We define MC task differentiation as the degree to which the company’s channels differ in customer-related functions for which they are responsible in the MC system. With high MC task differentiation, channels (C) concentrate on different tasks (T), such as C1 on T1, C2 on T2, and C3 on T3. With low MC task differentiation, they perform the same–and thus, all–respective
2We draw on prestudy interviews for two examples from business tasks, such as C1, C2, and C3 each executing T1, T2, and T3.3 Tasks may, for example, include lead generation, advising, and after-sales service. Previous research has emphasized the relevance of this approach by highlighting that if channels concentrate on different tasks, each channel could focus on the specific requirements associated with performing its respective tasks (Moriarty and Moran 1990; Stone, Hobbs, and Khaleeli 2002).
MC relationship outcomes. Open systems theory stresses that the relationships in a system can have both destructive (e.g., conflict) and constructive (e.g., cooperation) properties (Katz and Kahn 1978). In line with prior research describing channel systems as “characterized by the dual elements of conflict and
3Drawing on prestudy interviews, we provide two examples from cooperation” (Etgar 1979, p. 61), we thus include these constructs in our framework.
We define horizontal MC conflict as the degree of disputes in an MC system that occur among the company’s channels, as reflected by their frequency, intensity, and importance
(Brown and Day 1981; Coelho and Easingwood 2004). For example, horizontal MC conflict could emerge from channels competing for the same customer, causing disputes about who owns the customer. Vertical MC conflict refers to the degree of disputes in an MC system that occur between the channels and the company’s sales management, as reflected by their frequency, intensity, and importance (Brown and Day 1981; Webb and Lambe 2007). For example, vertical MC conflict
could arise when channels are unaware or uncertain of their
role in the MC system, leading to disputes between them and the company’s sales management. Confirming the importance of these constructs, prior research has emphasized that “the top issue for many business-to-business firms today is channel conflict” (Webb 2002, p. 95) and that the emergence of conflict is a significant obstacle to exploiting the potential of multiple channels (Rosenbloom 2007).
We define MC cooperation as the degree to which the company’s channels work with one another and the company’s sales management in a close and constructive manner when acting in the market (Anderson and Narus 1990). Previous research has emphasized the importance of considering this construct (Frazier 1983a; Zhang et al. 2010), suggesting that cooperation in an MC system can exist only in the case of collaboration among all members of the MC system. Only in this case is a corresponding coordination of sales efforts and achievement of superordinate goals feasible in the MC system, whereas cooperation either among the company’s channels or between the channels and the company’s sales management is not a sufficient condition. Therefore, in contrast with conflict, distinguishing between horizontal and vertical cooperation in an MC system is neither warranted nor fully appropriate.
MC performance outcome. We consider the performance of an MC system (Katz and Kahn 1978) by including company sales success in our framework. This construct reflects the firm’s sales achievements relative to the competition’s and is related to both effectiveness, such as opening up new markets, and efficiency, such as increasing sales profitability (Kumar, Stern, and Achrol 1992).
MC customer characteristics. Open systems theory suggests considering characteristics of the external environment, such as its heterogeneity and variability, when examining the appropriateness of a system’s organizational structure (Kast and Rosenzweig 1985). In support of this notion, single-channel research has used these constructs (Klein, Frazier, and Roth 1990), and MC research has stressed the importance of examining customer heterogeneity and volatility (Coelho and Easingwood 2005; Neslin and Shankar 2009). Thus, consistent with our differentiation approaches’ reference to customer-related issues (i.e., segments and tasks), our framework treats characteristics of an MC system’s customer-related environment as moderators and includes customer heterogeneity and crosschannel buying.
We define customer heterogeneity as the degree to which the company’s customers have diverse needs (Jindal et al. 2007), including those related to product type and features, price and quality preferences, and service needs. Prior research has shown that customers often use several channels for purchasing (Kumar and Venkatesan 2005). In this context, customer cross-channel buying refers to the propensity of the company’s customers to switch channels within purchases (Verhoef, Neslin, and Vroomen 2007)–for example, by researching a product online and then buying it at a brick-and-mortar branch.
Control variables. Motivated by open systems theory (Katz and Kahn 1978) and prior research, our framework considers additional company and market characteristics that may influence the outcomes of an MC system. Specifically, it includes number of channels, directness of channels (Coelho and Easingwood 2004; Stern and Reve 1980), and firm size (Geyskens, Gielens, and Dekimpe 2002) as well as market growth and market competition (Achrol and Stern 1988; Webb and Hogan 2002).
Pattern of Effects in Conceptual Framework
Drawing on open systems theory, we subsequently offer an overview of our overarching theoretical reasoning for the effects among the constructs of our framework. Figure 2 illustrates the constructs and the basic logic related to the hypothesized main and moderating effects.
Pattern of main effects. Open systems theory suggests that by purposefully reducing channel overlap in customers and functions, organizational MC differentiation influences channel competition, interdependence, and role ambiguity in the MC system (Katz and Kahn 1978; Lawrence and Lorsch 1967b). Through these underlying mechanisms, the two generic approaches are likely to affect MC relationship outcomes. We also assume that MC relationship outcomes influence each other and the MC performance outcome. To ensure parsimony, our hypotheses focus on the main effects most central to our study. Therefore, our framework distinguishes between hypothesized main effects (H1-H6) and additional main effects, for which we provide no explicit hypotheses but only a brief rationale (see Figure 1).
Pattern of moderating effects. Open systems theory also suggests that the characteristics of external stakeholders cause a system’s subsystems to behave in a specific way, which makes the subsystems become either more similar or dissimilar (Lawrence and Lorsch 1967a; Thompson 1967). Thus, MC customer characteristics affect the likelihood to which the channels would generally (i.e., in the absence of any governance of responsibilities by the company) strive for similar customers and functions. Therefore, these characteristics are likely to influence the potential of organizational MC differentiation to purposefully reduce channel overlap in the MC system and, thus, to ultimately influence MC relationship outcomes. Consequently, we assume that MC customer characteristics moderate the impact of organizational MC differentiation on MC relationship outcomes (H7-H10) (see Figure 1). The underlying logic of the two generic approaches clearly indicates that customer heterogeneity is related to the impact of MC segment differentiation, as is customer cross-channel buying to the impact of MC task differentiation.
Pattern of control effects. Our framework also accounts for the potential influence of additional company and market characteristics on relationship and performance outcomes of an MC system (see Figure 1). Drawing on prior findings, we assume that the number and directness of channels (Coelho and Easingwood 2004; Stern and Reve 1980), firm size (Geyskens, Gielens, and Dekimpe 2002), and market growth and competition (Achrol and Stern 1988; Webb and Hogan 2002) influence these outcomes.
Hypothesis Development
Main Effects of Organizational MC Differentiation on MC Relationship Outcomes
Organizational MC differentiation on MC conflict. Regarding horizontal MC conflict, open systems theory emphasizes that conflict among subsystems may occur when they collide in their quest for scarce resources (Katz and Kahn 1978). Consistent with this reasoning, prior research has indicated that channel conflict is nourished by an overlap in requirements for the same limited resources (e.g., customers) and a resulting competition for these resources (Etgar 1979; Sa Vinhas and Anderson 2005). Thus, channel competition for customers is likely to be a key driver of horizontal MC conflict.
The theory also argues that by dividing corresponding responsibilities among subsystems, a company can reduce the subsystems’ overlap in requirements for the same limited resources (Lawrence and Lorsch 1967b). In turn, the reduced overlap in these requirements is likely to decrease the subsystems’ competition for these resources (Katz and Kahn 1978). Therefore, with high MC segment differentiation characterized by highly different segment-related responsibilities of channels, the company may purposefully reduce channel overlap in customers that would otherwise be prevalent in the MC system, thus diminishing interchannel competition for customers. Overall, high MC segment differentiation may serve as a bureaucratic governance device that leads to lesser horizontal MC conflict than low MC segment differentiation, in which channels are likely to fight for the same customers. Thus,
H1: MC segment differentiation reduces horizontal MC conflict.
Regarding vertical MC conflict, open systems theory posits that conflict between subsystems and management may arise from a lack of clear roles for the different subsystems (Katz and Kahn 1978). Therefore, and supported by prior findings in a single-channel context (Etgar 1979; Frazier 1983a), we argue that channel role ambiguity is likely to be a key driver of vertical MC conflict.
Moreover, by dividing task-related responsibilities among subsystems, a company can establish a role structure in which “a role consists of only one activity located in a single subsystem” (Katz and Kahn 1978, p. 197). By reducing subsystems’ overlap in functions, the company is likely to reduce the subsystems’ perceived role ambiguity (Lawrence and Lorsch 1967b). Thus, through high MC task differentiation characterized by channels’ highly different task-related responsibilities, the company may purposefully decrease
channel overlap in functions otherwise prevalent in the
MC system, thereby reducing role ambiguity. Overall,
high MC task differentiation may serve as a bureaucratic governance device that leads to lesser vertical MC conflict than low MC task differentiation. In support of this notion, prior single-channel research has suggested that “initiating structure … reduces a channel member’s role ambiguity and
[thus] allows for a … less conflictual relationship” (Schul, Pride, and Little 1983, p. 32) between a channel and sales management. Thus,
H2: MC task differentiation reduces vertical MC conflict.
We also consider cross-effects of the two approaches on vertical and horizontal MC conflict that are, however, not central to our study. Thus, we develop no explicit hypotheses but, nevertheless, test for them.4
Organizational MC differentiation on MC cooperation. Open systems theory emphasizes that collaboration among subsystems and between the subsystems and management increases when they are interdependent regarding goal achievement (Thompson 1967). Consequently, and consistent with similar findings in a single-channel context (Gundlach and Cadotte 1994; Lusch and Brown 1996), we expect channel interdependence to be a key driver of MC cooperation.
By dividing segment-related responsibilities among subsystems and thus decreasing channel overlap in customers, a company can reduce the necessity for the channels to exchange among each other and with sales management about joint customer-related issues needed for goal achievement, such as whether and how to serve a specific customer (Lawrence and Lorsch 1967b). This, in turn, may decrease interdependence in the MC system (Katz and Kahn 1978). Overall, high MC segment differentiation should lead to lesser MC cooperation than low MC segment differentiation, in which channels target the same customers and, thus, face a strong need for exchange among each other and with sales management and, in turn, high interdependence. Consistent with this notion, previous research has emphasized that a low degree of exchange limits cooperation in a channel (Anderson and Narus 1990; Frazier 1983a). Thus,
H3: MC segment differentiation reduces MC cooperation.
Moreover, by dividing task-related responsibilities among subsystems, which splits the entire range of tasks among channels and thus reduces channel overlap in functions, a company increases the necessity for the channels to interact with each other and with sales management about issues required for goal achievement, such as relevant information (Katz and Kahn 1978; Lawrence and Lorsch 1967b). When the output of one member becomes the input for another, interdependence in the MC system is high (Greenberg and Baron 1996). For example, when channels focus on different tasks, a channel specializing in advising requires prospects’ contact data from the channel responsible for lead generation, as well as information about the purchase history from sales management. Conversely, the latter two members need resources from the former member, such as
4Specifically, our framework includes a ( 1) presumably negative information about the interaction with the prospect. Overall, by increasing interdependence in the MC system, high MC task differentiation should result in greater MC cooperation than low MC task differentiation, in which each channel performs all tasks and, thus, has little need for interaction with other channels and sales management and, in turn, little interdependence. In support of this notion, previous research has indicated that channel members’ need for exchange with others increases interdependence in a channel (Frazier 1983b), which inhibits opportunism and fosters commitment (Kumar, Scheer, and Steenkamp 1995; Lusch and Brown 1996). Thus,
H4: MC task differentiation increases MC cooperation.
Main Effects of MC Relationship Outcomes on MC Performance Outcome
To establish a continuous causal chain, we also consider effects of horizontal and vertical MC conflict on MC cooperation and the effect of MC cooperation on company sales success.5 Open systems theory argues that conflict among subsystems reduces motivation in a system for exchanging about joint issues needed for goal achievement (Kast and Rosenzweig 1985). Prior work has also shown that disputes among channel members lead to “refusing to refer leads, hiding information, and withholding any form of assistance” (Sa Vinhas and Anderson 2005, p. 509), thus harming cooperation.
In addition, conflict between subsystems and management decreases motivation to cooperate in a system (Katz and Kahn 1978). In support of this notion, prior research has indicated that disputes between a channel and sales management reduce satisfaction, commitment, and disposition for collaboration in the MC system (Andaleeb 1995; Frazier, Gill, and Kale 1989). Thus,
H5: (a) Horizontal and (b) vertical MC conflict reduce MC cooperation.
Finally, cooperation in a system is a prerequisite for longterm survival (Katz and Kahn 1978). Consistent with this assertion, previous research has suggested that fostering collaboration in an MC system helps in acquiring, satisfying, and binding customers, thus increasing sales success (Achrol and Etzel 2003; Stern and Reve 1980). Therefore,
H6: MC cooperation increases company sales success.
Moderating Effects of MC Customer Characteristics
Previously, we argued that MC relationship outcomes are driven by channel competition, interdependence, and role ambiguity in the MC system, which result from channel overlap in customers and functions. We also argued that a company can use organizational MC differentiation for bureaucratic governance to purposefully reduce such channel overlap prevalent in the MC system, thus influencing channel competition,
5We also take into account the possibility that horizontal MC conflict may influence vertical MC conflict and assume a positive effect, because channels are likely to blame the company when interchannel conflict occurs, which increases disputes between the channels and sales management (Webb and Didow 1997). Although we develop no explicit hypothesis, we empirically control and test for this additional main effect. interdependence, and role ambiguity and, in turn, MC relationship outcomes.
In addition, we now contend that the corresponding impact of a company’s use of organizational MC differentiation depends on key MC customer characteristics, as they affect the likelihood to which the channels would generally strive for similar customers and functions, thus determining the actual potential for purposefully reducing channel overlap. In support of this reasoning, open systems theory posits that characteristics of external stakeholders cause a system’s subsystems to behave in a way that makes the subsystems become either more similar or dissimilar (Thompson 1967). Moreover, previous research has indicated that the favorability of task-related differentiation depends on the characteristics of actors in the external environment (Kabadayi, Eyuboglu, and Thomas 2007). In the following subsections, we provide a more detailed discussion of our reasoning for each MC customer characteristic and differentiation approach.
Customer heterogeneity on impact of MC segment differentiation. Open systems theory suggests that subsystems, such as channels, tend to develop capabilities to meet the needs of external stakeholders, such as customers (Thompson 1967). The more a company’s customers have similar needs–that is, the lower their heterogeneity–the more likely a channel can develop capabilities that meet the needs of most of them and, thus, the more likely each channel targets most of the company’s customers (Dwyer and Welsh 1985; Lawrence and Lorsch 1967a; Webb and Lambe 2007). Consequently, the lower the customer heterogeneity, the higher the likelihood that in the absence of any governance of responsibilities by the company, the channels would target similar customers.
We previously argued that through MC segment differentiation, a company can purposefully reduce channel overlap in customers in a bureaucratic manner, thus reducing interchannel competition for customers and, in turn, horizontal MC conflict (see H1; Lawrence and Lorsch 1967b; Sa Vinhas and Anderson 2005). Moreover, the lower the customer heterogeneity, the higher the likelihood that the channels would generally strive for similar customers (Thompson 1967; Webb and Lambe 2007). In turn, the higher this likelihood, the greater the actual potential of MC segment differentiation to purposefully reduce channel overlap in customers, and thus the more the extent of MC segment differentiation will make a difference in reducing interchannel competition.
Therefore, the extent of MC segment differentiation will have a greater negative impact on horizontal MC conflict in the case of low customer heterogeneity than in the case of high customer heterogeneity. In other words, the extent of MC segment differentiation will have a weaker negative impact on horizontal MC conflict under high customer heterogeneity than under low customer heterogeneity. Thus,
H7: Increasing customer heterogeneity weakens the negative effect of MC segment differentiation on horizontal MC conflict.
Moreover, as we have previously argued, through MC segment differentiation, a company can purposefully decrease channel overlap in customers in a bureaucratic manner. In turn, this should reduce the necessity in the MC system to exchange information about joint customer-related issues, thus lowering interdependence and, in turn, reducing cooperation in the MC system (see H3; Anderson and Narus 1990; Lawrence and Lorsch 1967b). Furthermore, the lower the customer heterogeneity, the higher the likelihood that the channels would generally target similar customers (Thompson 1967; Webb and Lambe 2007). In turn, the higher this likelihood, the greater the actual potential of MC segment differentiation to purposefully decrease channel overlap in customers, and thus the more the extent of MC segment differentiation will decrease the need to discuss joint customer-related issues and lower interdependence in the MC system.
Consequently, the extent of MC segment differentiation will have a greater negative impact on MC cooperation in the case of low customer heterogeneity than in the case of high customer heterogeneity. In other words, the extent of MC segment differentiation will have a weaker negative impact on MC cooperation under high customer heterogeneity than under low customer heterogeneity. Therefore,
H8: Increasing customer heterogeneity weakens the negative effect of MC segment differentiation on MC cooperation.
Customer cross-channel buying on impact of MC task differentiation. Open systems theory suggests that subsystems must take measures to prevent external stakeholders from switching and to win over new external stakeholders (Katz and Kahn 1978). In support of this idea, prior research has shown that customers typically decide to switch from one channel to another when their current channel cannot perform all the tasks they expect or want for their purchase (e.g., advising; Verhoef, Neslin, and Vroomen 2007). Thus, the more a company deals with customers who have a propensity to switch channels when purchasing, the more each channel is likely to offer all the tasks required to prevent them from switching and to attract new customers from other channels (Neslin and Shankar 2009). Therefore, the more customers are inclined toward cross-channel buying, the higher the likelihood that in the absence of any governance of responsibilities by the company, the channels would take over similar functions.
We previously argued that through MC task differentiation, a company can purposefully decrease channel overlap in functions in a bureaucratic manner, thus reducing channel role ambiguity and, in turn, vertical MC conflict (see H2; Katz and Kahn 1978; Schul, Pride, and Little 1983). In addition, the greater customers’ propensity for cross-channel buying, the higher the likelihood that the channels would generally strive for similar functions (Katz and Kahn 1978; Verhoef, Neslin, and Vroomen 2007). In turn, the higher this likelihood, the greater the actual potential of MC task differentiation to purposefully reduce channel overlap in functions and, thus, channel role ambiguity.
Therefore, if customers show a high propensity for cross-channel buying, the extent of MC task differentiation is likely to have a greater negative impact on vertical MC conflict than if customers show little propensity for crosschannel buying. Thus,
H9: Increasing customer cross-channel buying strengthens the negative effect of MC task differentiation on vertical MC conflict.
Finally, as argued previously, through MC task differentiation, a company can purposefully decrease channel overlap in functions in a bureaucratic manner. In turn, this increases the necessity for exchanging information about joint customer-related issues in the MC system, thus raising interdependence and cooperation (see H4; Frazier 1983b; Lawrence and Lorsch 1967b). In addition, the greater customers’ propensity for cross-channel buying, the higher the likelihood that the channels would generally take over similar functions (Katz and Kahn 1978; Verhoef, Neslin, and Vroomen 2007). In turn, the higher this likelihood, the greater the actual potential of MC task differentiation to purposefully reduce channel overlap in functions, and thus the more the extent of MC task differentiation will increase the need for exchanging information about customer-related issues and the interdependence in the MC system.
Consequently, if customers are characterized by a high differentiation is likely to have a greater positive impact on MC cooperation than if customers show little propensity for crosschannel buying. Stated formally,
H10: Increasing customer cross-channel buying strengthens the positive effect of MC task differentiation on MC cooperation.
TABLE:
| A: Data Collection Procedure for Sample Used for Hypothesis Testing |
|---|
| A1. Size of initial sample of managers highly involved in MC decisions | 1,825 |
| A2. Number of usable questionnaires returned by these managers (=number of primary informants) | 333 (response rate: 18.2%) |
| A3. Number of primary informants who provided name of a potential secondary informant (same company; highly involved in MC decisions) | 247 |
| A4. Number of questionnaires sent to potential secondary informants | 247 |
| A5. Number of questionnaires returned by these managers (=number of secondary informants) | 157 (response rate: 63.6%) |
| A6. Number of primary informants left after checking for informant competency | 329 |
| A7. Number of secondary informants left after checking for informant competency | 153 |
| A8. Number of matched multiple-informant pairs (i.e., primary and secondary informant) whose responses passed the consistency checks (ADM < 1 and acceptable ICC values) | 152 |
| A9. Overall number of cases used for hypothesis testing (=final sample size) | 329, consisting of: • 152 multiple-informant cases (=two informants per company; responses were averaged) • 177 single-informant cases (=one informant per company; includes cases for which responses of two informants were not available [n1 = 329 – 153 = 176] or responses of the first and secondary informant did not pass the consistency checks [n2 = 153 – 152 = 1]) |
| B: Composition of Final Sample |
|---|
| I. Industry | II. Annual Revenues | III. Respondentsa |
|---|
| Machinery/Metal Processing 28% | V25 million 18% | VP Sales/Head of Sales 54% |
| Electronics 27% | V25–V49 million 16% | General Manager 19% |
| Chemicals/Pharmaceuticals 18% | V50–V99 million 19% | VP Sales & Marketing/Head |
| of Sales & Marketing 12% | Textile/Paper 10% | V100–V199 million 17% |
| Automotive 9% | V200–V499 million 15% | VP Marketing/Head of Marketing 6% |
| Others 8% | >V500 million 15% | Other 8% |
Methodology
Sample Derivation and Composition
Data collection procedure. We conducted a multi-industry, multi-informant study (see Table 3). To ensure sufficient sample homogeneity, we focused on manufacturing industries. With the help of a commercial provider, we obtained an initial sample of 1,825 companies, for which we identified the manager primarily responsible for MC issues. We invited these managers twice to fill out a questionnaire on organizational MC differentiation as subsequently, on MC relationship and performance outcomes. We received 333 usable questionnaires, for a response rate of 18.2%. Moreover, following the advice and practices in the literature (Ernst, Hoyer, and Ru¨bsaamen 2010; Philips 1981), we aimed to rely on multiple informants per company, thus improving the quality of data and the validity of reported relationships (Van Bruggen, Lilien, and Kacker 2002). For this purpose, we requested from each primary respondent the contact information of a secondary respondent who was also highly involved in MC decisions; we received 247 responses. In most cases in which we did not obtain information, the primary informant had the sole responsibility for MC issues. From the secondary respondents, we received 157 usable questionnaires, for a response rate of 63.6%.
Checks on nonresponse bias and informant competency. Armstrong and Overton’s (1977) test revealed no indication of nonresponse bias. We also ensured that the responding companies did not differ in size or industry from those we initially addressed. To check informant competency, we asked respondents how well they knew their company’s channel system, how strongly they were involved in MC decisions, and how competent they felt about answering the questions (Kumar, Stern, and Anderson 1993). We discarded four primary and four secondary respondents who answered at least one of these items with a score of four or lower on a seven-point scale; the remaining 329 primary and 153 secondary respondents answered these items with an average score of 6.1. On average, respondents had been working for their company for 9.3 years and in their current position for 4.0 years.
Handling of multiple informants. To use multiple informant data, we applied the common approach of averaging informants’ responses per company on the item level (Van Bruggen, Lilien, and Kacker 2002). To ensure that the respective preconditions were met, we tested for response consistency between primary and secondary informants of one company using two methods. First, we calculated the ADM index, which measures the average deviation of responses of one informant from the mean of responses of all informants of one company (Burke and Dunlap 2002). Applying the suggested cutoff value of 1, which corresponds to a difference in responses to two points on a rating scale, led to the removal of one secondary informant with an (Kumar, Stern, and Anderson 1993). We were thus left with 152 multiinformant and 177 single-informant cases (329 cases altogether). Second, we explored the interrater reliability of primary and secondary informants by comparing the within- and between-company variance of responses on the basis of h2 and intraclass correlation coefficient (ICC) ( 1) (James 1982). Our h2 values range from .63 to .83 and ICC ( 1) values range from .21 to .64 (see Table 4), which are satisfactory (Homburg and Fu¨rst 2005; McFarland, Bloodgood, and Payan 2008). Overall, our final sample includes 329 cases consisting of 152 multi-informant and 177 single-informant responses (see Table 3).
TABLE:
| Construct Measures | Correlations (Squared Correlations) |
|---|
| Construct | CA | CR | AVE | M | SD | h2 | ICC | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
|---|
| 1. MC segment differentiation | .81 | .81 | .52 | 5.47 | 1.26 | .64 | .23 | - | | | | | | | | | | | | |
| 2. MC task differentiation | .82 | .82 | .53 | 4.31 | 1.50 | .65 | .25 | .20** (.04) | - | | | | | | | | | | | |
| 3. Horizontal MC conflict | .88 | .88 | .72 | 2.89 | 1.36 | .64 | .23 | -.14* (.02) | -.18** (.03) | - | | | | | | | | | | |
| 4. Vertical MC conflict | .90 | .90 | .75 | 2.35 | 1.12 | .63 | .22 | -.10 (.01) | -.33** (.11) | .61** (.37) | - | | | | | | | | | |
| 5. MC cooperation | .89 | .89 | .63 | 4.81 | 1.16 | .65 | .25 | -.13* (.02) | .26** (.07) | -.36** (.13) | -.35** (.12) | - | | | | | | | | |
| 6. Company sales success | .91 | .91 | .63 | 4.68 | .90 | .65 | .26 | .13* (.02) | .12* (.01) | -.14* (.02) | -.19** (.04) | .29** (.08) | - | | | | | | | |
| 7. Customer heterogeneity | .84 | .85 | .66 | 3.78 | 1.42 | .64 | .24 | .09 (.01) | .06 (.00) | .04 (.00) | -.04 (.00) | -.03 (.00) | .17** (.03) | - | | | | | | |
| 8. Customer cross-channel buying | .85 | .86 | .60 | 3.15 | 1.27 | .63 | .21 | .07 (.00) | .07 (.01) | .25** (.06) | .11* (.01) | .00 (.00) | .15** (.02) | .18** (.03) | – | | | | | |
| 9. Number of channels | –a | –a | –a | 4.18 | 1.85 | .70 | .36 | – | .15* (.02) | .05 (.00) | .16** (.03) | .05 (.00) | -.05 (.00) | .19** (.04) | .09 (.01) | .29** (.08) | – | | | |
| 10. Directness of channels | –a | –a | –a | .55 | .23 | .68 | .32 | -.13* (.01) | .07 (.00) | -.22** (.05) | -.15* (.02) | .19** (.04) | -.07 (.00) | -.04 (.00) | -.14* (.02) | -.31** (.10) | – | | | |
| 11. Firm size | –a | –a | –a | 5.52 | 2.02 | .83 | .64 | .08 (.01) | .02 (.00) | .15** (.02) | .12* (.01) | -.08 (.01) | .09 (.01) | .04 (.00) | .02 (.00) | .07 (.00) | .00 (.00) | – | | |
| 12. Market growth | .96 | .96 | .89 | 3.62 | 1.51 | .66 | .27 | .03 (.00) | .07 (.00) | .03 (.00) | -.07 (.00) | .05 (.00) | .04 (.00) | -.01 (.00) | .08 (.00) | -.11* (.01) | .02 (.00) | .01 (.00) | – | |
| 13. Market competition | .82 | .82 | .61 | 4.95 | 1.15 | .64 | .23 | .08 (.01) | -.02 (.00) | .07 (.00) | .02 (.00) | .09 (.01) | .03 (.00) | .11 (.01) | .33** (.11) | .22** (.05) | -.09 (.01) | .10 (.01) | .08 (.01) | – |
Scale Development and Assessment
Procedure for scale development. We followed standard scale development procedures (Gerbing and Anderson 1988). First, from a literature review, we identified relevant scales and other input, such as notes and indications of prior research, and generated a set of items for each construct, which amounted to 39 items. Second, on the basis of prestudy interviews with 12 practitioners, we pretested and refined these scales. We refined scales by clarifying wordings and, in the case of MC cooperation and customer heterogeneity, considering further aspects by adding two items and one item, respectively. Third, on the basis of the results of confirmatory factor analysis, we purified the scales of two constructs that had indicator reliabilities far below .40. These scales referred to company sales success, for which we had to drop one item about the increase in customer satisfaction, and market competition, for which we had to eliminate one item about the intensity of price competition. The final scales appear in the Appendix, along with their main literature sources.
Scales for main constructs. Because the literature lacks empirical studies on organizational channel differentiation, we could not draw on existing scales but had to rely on notes and indications in open systems theory and prior research. Resulting scales for MC segment and MC task differentiation encompass four items each. Similarly, owing to the lack of scales on cooperation in an MC context, we had to draw on open systems theory, prior research, and prestudy interviews, resulting in five items. We adapted the scales for horizontal and vertical MC conflict, which include three items each from prior research, and assessed company sales success with six items inspired from existing scales. We measured customer heterogeneity and cross-channel buying with three and four items, respectively, adapted from prior research and, in the case of customer heterogeneity, complemented by prestudy interviews.
Scales for control variables. To measure the number of channels, we presented a list of potential channels and asked informants to mark those used by their firms. To measure directness of channels, which refers to the vertical integration of the MC system, we also relied on this list and calculated the ratio of company-owned channels to the total number of channels. To capture firm size, we relied on annual revenues. To assess market growth and competition, we used three items each, adapted from prior research.
Scale reliability and validity. Applying confirmatory factor analysis, we included all constructs in one multifactorial measurement model, obtaining a satisfactory fit to the data (c2 = 1,268.34, d.f. = 704, c2/d.f. = 1.80; comparative fit index [CFI] = .93; Tucker-Lewis index [TLI] = .92; root mean square error of approximation [RMSEA] = .05; standardized square root mean residual [SRMR] = .04). We also assessed scale reliability and validity for each reflective construct (fixing the first factor loading to 1) and found good psychometric properties. Construct measures, including means and standard deviations as well as correlations among constructs, appear in Table 4 and the Appendix. For all constructs, coefficient alpha and composite reliability exceed .7 and average variance extracted is above .50, surpassing recommended thresholds (Bagozzi and Yi 1988; Nunnally 1978). Moreover, with one
Organizational Multichannel Differentiation / 69 exception, indicator reliabilities are above .40 (Bagozzi and Baumgartner 1994). Finally, for each pair of constructs, we found discriminant validity using Fornell and Larcker’s (1981) criterion (see Table 4) and the chi-square difference test (Bollen 1989).
Results
Results Related to Main Effects Using maximum likelihood in Mplus 4.1, we applied covariancebased structural equation modeling (SEM), finding a good
TABLE:
| | Dependent Variables |
|---|
| Predictors | MC Horizontal Conflict (h1) | MC Vertical Conflict (h2) | MC Cooperation (h3) | Company Sales Success (h4) |
|---|
| *p < .05. |
| **p < .01. |
| Main-Effects Model |
| MC segment differentiation (x1) | g11 = -.18**(H1) | g21 = .04 | g31 = -.16**(H3) | |
| MC task differentiation (x2) | g12 = -.14** | g22 = -.22**(H2) | g32 = .19**(H4) | |
| MC horizontal conflict (h1) | | b21 = .57** | b31 = -.29**(H5a) | |
| MC vertical conflict (h2) | | | b32 = -.13*(H5b) | |
| MC cooperation (h3) | | | | b43 = .34**(H6) |
| Customer heterogeneity (x3) | g13 = -.08 | g23 = -.01 | g33 = -.10* | g43 = .13* |
| Customer cross-channel buying (x4) | g14 = .20** | g24 = .11* | g34 = .07 | g44 = .08 |
| Number of channels (x5) | g15 = .11* | g25 = -.12** | g35 = .03 | g45 = .19** |
| Directness of channels (x6) | g16 = -.18** | g26 = -.03 | g36 = .09* | g46 = -.06 |
| Firm size (x7) | g17 = .16** | g27 = .05 | g37 = -.02 | g47 = .09* |
| Market growth (x8) | g18 = .05 | g28 = -.09* | g38 = .03 | g48 = .04 |
| Market competition (x9) | g19 = -.05 | g29 = -.05 | g39 = .11* | g49 = -.08 |
| Model fit: c2 = 1,275.47, d.f. = 708, c2/d.f. = 1.80; CFI = .93; TLI = .92; RMSEA = .05; SRMR = .05 |
| Interaction-Effects Model |
| MC segment differentiation (x1) | g11 = -.17** | g21 = .03 | g31 = -.17** | |
| MC task differentiation (x2) | g12 = -.14* | g22 = -.23** | g32 = .19** | |
| MC horizontal conflict (h1) | | b21 = .56** | b31 = -.32** | |
| MC vertical conflict (h2) | | | b32 = -.15* | |
| MC cooperation (h3) | | | | b43 = .35** |
| Customer heterogeneity (x3) | g13 = -.12* | g23 = -.01 | g33 = -.17** | g43 = .13* |
| Customer cross-channel buying (x4) | g14 = .20** | g24 = .11* | g34 = .07 | g44 = .08 |
| Number of channels (x5) | g15 = .12* | g25 = -.12* | g35 = .04 | g45 = .19** |
| Directness of channels (x6) | g16 = -.18** | g26 = -.02 | g36 = .09* | g46 = -.06 |
| Firm size (x7) | g17 = .16** | g27 = .04 | g37 = -.02 | g47 = .09* |
| Market growth (x8) | g18 = .06 | g28 = -.05 | g38 = .06 | g48 = .04 |
| Market competition (x9) | g19 = -.05 | g29 = -.05 | g39 = .09 | g49 = -.08 |
| MC segment differentiation x | g110 = .14* | | g310 = .22** | |
| Customer heterogeneity (x10) | (H7) | | (H8) | |
| MC task differentiation x Customer | | g211 = -.14** | g311 = .03 | |
| cross-channel buying (x11) | | (H9) | (H10) | |
| Model fit: c2 = 1,748.80, d.f. = 986, c2/d.f. = 1.77; CFI = .91; TLI = .90; RMSEA = .05; SRMR = .05 |
Discussion
Implications for Research
First, this study provides the first in-depth investigation of organizational MC differentiation, which represents an important topic specific to the context of multiple channels. It introduces two generic approaches to organizational MC differentiation–MC segment differentiation and MC task differentiation–that refer to the decisions necessary for every MC company on how best to organize channels around customers and functions. Moreover, the study shows that these externally oriented decisions may also have effects on relationships internal to the MC system because they may promote or damage channels’ relationships with one another and with the company’s sales management. For this purpose, we identified key MC relationship outcomes that have received little (if any) attention in previous research and link them to the two approaches to organizational MC differentiation as well as to an MC system’s ultimate performance. In doing so, we add to the literature novel theoretical insights into the design and performance impact of the organizational structure of MC systems, thus helping answer the question, “What organization structure best enhances the potential gains from [MC] … management?” (Neslin and Shankar 2009, p. 75).
Second, our study empirically answers the questions of whether and how organizational MC differentiation influences an MC system’s ultimate performance (sales success) by affecting MC relationship outcomes (MC conflict and cooperation). The findings reveal that both MC segment and MC task differentiation have an impact but differ considerably in their pattern of influence. In particular, we find that MC segment differentiation reduces horizontal conflict but inhibits cooperation in the MC system, whereas MC task differentiation reduces primarily vertical MC conflict and promotes cooperation in the MC system. Owing to the lack of previous dependence analyses of the impact of differentiation (see Table 1), we cannot directly compare these findings with those of prior research. Yet comparison with prior findings of a generally unfavorable impact of centralization and formalization on channel relationships (Dwyer and Oh 1987; John 1984) reveals some similarities with regard to MC segment differentiation but dissimilarities with regard to MC task differentiation. Our findings also indicate that whereas MC task differentiation has a positive overall impact on sales success, in general MC segment differentiation neither drives nor impairs sales success, because its favorable effect through reducing horizontal MC conflict tends to be counterbalanced by its unfavorable effect through inhibiting MC cooperation.9
Third, our study also identifies contingency factors that affect organizational MC differentiation’s pattern of influence and impact on sales success. The findings indicate that the pattern of influence and the impact of both approaches depend significantly on the customer-related environment (see Table 5 and Figure 3), which confirms a presumption of Frazier (1999). With significant customer heterogeneity, MC segment differentiation has neither a favorable effect through reducing horizontal MC conflict nor an unfavorable effect through inhibiting MC cooperation and thus does not affect sales success through MC relationship outcomes. In contrast, with little customer heterogeneity, MC segment differentiation has a particularly strong favorable effect through reducing horizontal MC conflict; however, a particularly strong unfavorable effect through inhibiting MC cooperation overcompensates. Thus, in this case, such differentiation even seems to impair sales success overall. Moreover, with significant customer cross-channel buying, MC task differentiation has a particularly strong favorable effect on sales success because it not only enhances MC cooperation but also strongly reduces vertical MC conflict. With little customer cross-channel buying, however, such differentiation drives sales success solely through enhanced MC cooperation. Therefore, MC task differentiation generally seems to promote channel relationships, thus driving sales success, whereas MC segment differentiation may, under certain circumstances, damage channel relationships and thereby even harm sales success. Overall, these findings complement the configurationtheoretic work of Kabadayi, Eyuboglu, and Thomas (2007), which finds that the ideal structure of MC systems depends on environmental complexity and dynamism.
Finally, we reveal that assigning different responsibilities to channels may lead to either decreased or increased MC cooperation. In this context, a decisive factor is whether such differentiation reduces interdependence in the MC system and, thus, the necessity of working together (as in the case of MC segment differentiation), or whether it raises interdependence in the MC system and, thus, the motivation for collaboration (as in the case of MC task differentiation). Moreover, our findings that MC segment and MC task differentiation differently affect horizontal and vertical MC conflict help counteract the lack of knowledge due to “little academic research that examines conflict in [an MC] context” (Webb and Lambe 2007, p. 29) and respond to calls for respective research (Neslin et al. 2006; Rangaswamy and Van Bruggen 2005). As our study also shows, not only can channel management instruments (e.g., conflict resolution mechanisms; Ganesan 1993) or the design of the physical channel structure (e.g., the number and directness of channels; Sa Vinhas and Anderson 2005) mitigate channel conflict, but the design of the organizational channel structure can do so as well because it serves as bureaucratic governance, which avoids conflict in the first place. Thus, we can affirmatively answer the question of whether channel conflict can be “designed out” (Rosenbloom 2007, p. 7).
Implications for Managerial Practice
Our study offers valuable guidance to managers on how to design the organizational structure of their company’s MC system in terms of whether and how to divide responsibilities among the multiple channels.
First, although managers may be tempted to divide responsibilities among channels to increase sales success by capturing the advantages of channel specialization, they should also assess the consequences for their company’s MC system. We show that such division can also have a significant impact on sales success by affecting channel relationships and that this impact may or may not be favorable, thus dampening undifferentiated expectations of or fears about the use of multiple channels. Specifically, some managers may rely heavily on multiple channels because doing so could foster customer relationships, whereas others may not “engage [at all] in [MC] selling as this could jeopardise [channel] relationships” (Coelho and Easingwood 2004, p. 22). Our study offers advice to both groups of managers. For the former group, it delivers the warning that when designing their company’s MC system, managers should be aware that dividing responsibilities among channels will also affect channel relationships and may even impair sales success. For the latter group, our study provides justification for managers’ concerns about jeopardized channel relationships. However, at the same time, it gives them cause for hope, because division of responsibilities among channels can also contribute to strengthening channel relationships. Overall, our study suggests that for relationships internal to a company’s MC system, dividing responsibilities among channels is a double-edged sword, as it can either damage or promote channel relationships depending on how this division is done and on the characteristics of the company’s customers.
Second, our study introduces two generic approaches for dividing responsibilities among channels: segment differentiation and task differentiation. These approaches constitute powerful bureaucratic means to purposefully reduce the degree of channel overlap prevalent in a company’s MC system, which influences competition, interdependence, and role ambiguity of channels and, thus, the relationships in the MC system. In this context, managers should realize that for every channel, they need to decide in any case which customer segments to serve and which tasks to perform. In most companies, this decision likely occurs separately for each channel, and the degree of channel overlap in customers and functions simply results from a bottom-up aggregation of the outcomes of each decision. The danger, however, is that the resulting degree of channel overlap is subject to local (i.e., single-channel) optimization rather than meeting a global purpose of the company’s entire channel system. Therefore, we advise managers to rely on a top-down procedure, which involves a decision at the channel-system level about the extent of differentiation in segments and tasks. Managers should then break down the resulting degree of channel overlap to the single-channel level, considering the strengths and weaknesses of each channel. Thus, our study encourages managers to change from a lower-level, bottom-up procedure to a higher-level, top-down procedure for deciding channel responsibilities.
Furthermore, managers must be aware that the two generic approaches differ in how they affect channel relationships, resulting in different practical implications. A greater use of task differentiation is primarily effective in reducing conflict between the channels and sales management and in fostering cooperation in the channel system. To further enhance cooperation, managers of companies concentrating on this approach, such as the Wu¨rth Group, should support channel system members in their collaboration efforts. For example, these companies could establish software tools or platforms for interaction (Mohr, Fisher, and Nevin 1996) that enable a seamless flow of information, a smooth migration of customers between channels, and coordinated operations on the market. For example, the Wu¨rth Group could use a customer relationship management tool that helps MC system members stay informed about each channel’s sales activities for every customer. Wu¨rth’s sales force and brickand-mortar branches, which focus on advising, could then easily obtain prospects’ contact data through the Wu¨rth website, which concentrates on generating leads, as well as information about the prospects’ purchase history from its sales management.
In contrast, a greater use of segment differentiation is primarily helpful for reducing conflict among the channels. In this context, managers of companies focusing on this approach, such as the Schaeffler Group, must understand that they also bear the risk of inhibiting cooperation in the channel system, such as between their own sales force and sales partners. Therefore, when relying on segment differentiation, managers may want to use channel management instruments, such as rewards (e.g., double compensation; Sa Vinhas and Anderson 2008) or instructions (e.g., guidelines; Kistner, Di Benedetto, and Bhoovaraghavan 1994), that promote collaboration among channels and between channels and sales management and thus help counteract this risk. For example, Schaeffler could offer financial compensation to its sales force, which targets large to medium-sized customers, when this channel proactively migrates a prospect that is too small to Schaeffler’s sales partners and, subsequently, this prospect becomes a customer of the latter channel. Moreover, Schaeffler could formalize such proactive customer migration from one channel to another through respective standard operating procedures.
Moreover, we advise managers to include two key characteristics of their company’s customers in their MC design decisions. When customers, such as those of Wu¨rth, are highly homogeneous in terms of product-, price-, quality-, and service-related needs and tend to alternate between channels depending on the specific purchase situation, relying on task differentiation is particularly beneficial, whereas segment differentiation is detrimental. In contrast, when customers, such as those of Schaeffler, differ significantly in such needs and prefer using the same channel within purchases, managers should still rely on task differentiation. However, in this case, even a focus on segment differentiation is, at least, not detrimental to channel relationships and may even be advisable when taking into account presumably favorable effects on customer relationships. In general, our checks on endogeneity indicate that managers so far do not systematically consider these customer characteristics when designing the organizational structure of their company’s MC system and thus should heed our recommendations to improve their suboptimal corresponding decision making.
In addition, an important phenomenon associated with both generic approaches involves the impact of their use on MC cooperation, which is a key indicator for the internal functioning of an MC system and a key driver of sales success. Our study indicates that the greater the use of segment differentiation and the lesser the use of task differentiation–and thus the stronger the channels’ focus on different customers and the entire range of tasks–the less channels depend on one another for achieving their goals and the less they cooperate. Therefore, high levels of segment differentiation and low levels of task differentiation may foster the creation of self-contained channels and, thus, a channel system in which each channel may inherently act without reference to others. Similar notions by organization theory (March and Simon 1993) and previous research (Valos 2009) lend support to this assumption and suggest that these “channel silos” bear the risk of developing their own narrow view and leaving cross-channel synergies unexploited (Greenberg 2012). Thus, managers should also be aware of these dangers when allocating responsibilities across channels.
Finally, managers should understand that the two generic approaches are not necessarily mutually exclusive but, to a certain extent, can also be used in combination. Thus, when analyzing and designing the organizational structure of their company’s MC system, they should also consider whether and how to combine segment and task differentiation in an overall approach to organizational MC differentiation. Figure 4 illustrates that depending on the extent of a company’s use of segment and task differentiation, different prototypical overall approaches to organizational MC differentiation representing extreme cases that score low or high on one or both dimensions exist.
Specifically, in the case of both low segment and task differentiation, a company does not differentiate its channels at all (”No Organizational MC Differentiation”). Instead, all channels are responsible for all segments and tasks. In the case of high segment and low task differentiation, such as in the example of the Schaeffler Group, the channels concentrate on different segments, but all perform the entire range of tasks (”Pure MC Segment Differentiation”), thus acting as segment specialists in the MC system. In the case of low segment and high task differentiation, such as in the example of the Wu¨rth Group, the channels focus on different tasks but all concentrate on the same segments (”Pure MC Task Differentiation”) and thus serve as task specialists in the MC system. Finally, a company may also use a combination of high differentiation with respect to segments and some differentiation with respect to tasks (”Hybrid Organizational MC Differentiation”). In this case, the channels are responsible for different segments and take the lead on different tasks while covering the entire range of tasks. Thus, they primarily act as segment specialists in the MC system but also serve a support function for the other channels with respect to a specific task.
Avenues for Further Research
Our study also offers helpful guidance for further research. First, we focus on the two generic approaches to organizational MC differentiation and their related outcomes, thus adopting a cause-effect perspective. Further research might instead adopt a configurational perspective on this topic (Homburg, Fu¨rst, and Kuehnl 2012; Vorhies and Morgan 2003). In the previous section, we used a priori conceptual distinctions to develop a typology of overall approaches to organizational MC differentiation, thus providing a good starting point for researchers to empirically derive and examine a taxonomy of such approaches. Second, our study adopts a company perspective on organizational MC differentiation. Additional research could gain novel theoretical insights by taking a customer perspective on this topic (Homburg, Fu¨rst, and Prigge 2010; Keller 1993). Third, while our study concentrates on customer characteristics as moderators, other types of contingency factors, such as company, channel, market, and product characteristics, may also be viable moderators. Finally, we concentrate on how organizational MC differentiation affects relationships internal to the MC system, whereas further research could focus on how it affects relationships external to the MC system, such as relationships with customers and competitors.
Thomas 2007; Ruekert, Walker, and Roering 1985). Consistent with the distinction in organization theory (Blau 1970), we use “specialization” to refer to the perspective of a single channel and “differentiation” to refer to the perspective of the MC system.
TABLE:
TABLE:
TABLE:
| Construct | Items | IR | FL | RV |
|---|
| MC Segment Differentiationa (Neslin and Shankar 2009; Sa Vinhas and Anderson 2005) | Please indicate your agreement with the following statements about the responsibilities of sales channels in your company’s sales system: Our sales channels strongly differ as they … | | | |
| | • … sell to different customer segments. | .58 | .76 | .42 |
| | • … address different target groups. | .57 | .75 | .43 |
| | • … serve different market segments. | .48 | .69 | .52 |
| | • … focus on different customer groups. | .44 | .66 | .56 |
| MC Task Differentiationa,b (Moriarty and Moran 1990; Stone, Hobbs, and Khaleeli 2002) | Please indicate your agreement with the following statements about the responsibilities of sales channels in your company’s sales system: Our sales channels strongly differ as they … | | | |
| | • … execute different customer-related tasks. | .53 | .73 | .47 |
| | • … perform different tasks for customer cultivation. | .73 | .85 | .27 |
| | • …are not all focused on the same customer-related tasks. | .44 | .66 | .56 |
| | • … focus on different customer-related tasks. | .43 | .66 | .57 |
| Horizontal MC Conflicta (Brown and Day 1981; Coelho and Easingwood 2004) | Among our sales channels … | | | |
| | • … frequent conflict occurs. | .74 | .86 | .26 |
| | • … intensive conflict occurs. | .86 | .93 | .14 |
| | • … conflict over important issues occurs. | .55 | .74 | .45 |
| Vertical MC Conflicta (Brown and Day 1981; Webb and Lambe 2007) | Between our sales management and sales channels … | | | |
| | • … frequent conflict occurs. | .81 | .90 | .19 |
| | • … intensive conflict occurs. | .84 | .92 | .16 |
| | • … conflict over important issues occurs. | .61 | .78 | .39 |
| (Anderson and Narus 1990; Frazier 1983b; Zhang et al. 2010) | Our sales channels … |
| | • … regularly exchange relevant information. | .78 | .88 | .22 |
| | • … work closely together when acting in the market. | .83 | .91 | .17 |
| | • … and their relationships between each other can be characterized as cooperative. | .70 | .84 | .30 |
| | • … and our sales management regularly share relevant information. | .41 | .64 | .59 |
| | • … and our sales management work closely together when operating on the market. | .44 | .66 | .56 |
| Company Sales Successc (Kumar, Stern, and Achrol 1992) | Please indicate the success of your company’s sales system compared to your competition with regard to… | | | |
| | • … opening up of new customers or markets. | .56 | .75 | .44 |
| | • … increase in turnover. | .77 | .88 | .23 |
| | • … increase in market share. | .79 | .89 | .21 |
| | • … increase in sales efficiency. | .55 | .74 | .45 |
| | • … increase in sales profitability. | .56 | .75 | .44 |
| | • … decrease in relative sales overhead costs. | .52 | .72 | .48 |
| Customer Heterogeneitya (Jindal et al. 2007) | Our customers are very diverse in terms of… | | | |
| | • … type of products and product features they like to consider. | .40 | .63 | .60 |
| | • … price and quality preferences. | .88 | .94 | .12 |
| | • … service needs. | .70 | .84 | .30 |
| Customer Cross-Channel Buyinga (Neslin and Shankar 2009; Verhoef, Neslin, and Vroomen 2007) | Our customers … | | | |
| | • … frequently switch among sales channels when purchasing. | .73 | .85 | .27 |
| | • …oftenmigrate to another sales channel during a purchase. | .54 | .73 | .46 |
| | • … typically use several sales channels when they buy from our company. | .49 | .70 | .51 |
| | • … often switch among sales channels within purchases. | .66 | .81 | .34 |
| Number of Channels/Directness of Channelsd (Jindal et al. 2007; Kabadayi, Eyuboglu, and Thomas 2007) | Please mark the sales channels used by your company: –e >Company-Owned Channels: | | | |
| | • Company-owned web sales (e.g., online shop) | | | |
| | • Company-owned catalog sales | | | |
| | • Company-owned telephone sales (e.g., call center) • Company-owned sales force | | | |
| | • Company-owned stationary sales (e.g., brick-and-mortar branch) | | | |
| | • Other company-owned sales channel: | | | |
| | Third-Party Channels: |
| | • Third-party web sales (e.g., online portal) | | | |
| | • Third-party catalog sales (e.g., catalog merchant) | | | |
| | • Third-party telephone sales (e.g., telephone agency) | | | |
| | • Third-party sales facilitator (e.g., commercial agent, commission agent, broker) | | | |
| | • Third-party stationary wholesale | | | |
| | • Third-party stationary retail | | | |
| | • Other third-party sales channel: __________ Number of channels = Total number of marked check boxes | | | |
| | Directness of Channels = Number of company-owned channels Number of channels | | | |
| Firm Size (O’Sullivan and Abela 2007) | Please indicate your annual revenues. | | | |
| Market Growtha | Our market(s)… | | | |
| (Achrol and Stern 1988; Kabadayi, Eyuboglu, and Thomas 2007) |
| • … show(s) high growth. | .92 | .96 | .08 |
| • … show(s) significant increase in volume. | .91 | .95 | .09 |
| • … offer(s) great potential for sales growth. | .85 | .92 | .15 |
| Market Competitiona (Jindal et al. 2007; Kumar, Stern, and Achrol 1992) |
| In our market… |
| • … the competition is very tough. | .39 | .62 | .61 |
| • … one frequently hears of a new competitive move. | .81 | .90 | .19 |
| • … the competitors react very quickly in response to new market activities. | .64 | .80 | .36 |
DIAGRAM: Organizational Multichannel Differentiation: An Analysis of Its Impact on Channel Relationships and Company Sales Success
DIAGRAM: Organizational Multichannel Differentiation: An Analysis of Its Impact on Channel Relationships and Company Sales Success
DIAGRAM: Organizational Multichannel Differentiation: An Analysis of Its Impact on Channel Relationships and Company Sales Success
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Record: 132- Organizing for Marketing Excellence. By: Moorman, Christine; Day, George S. Journal of Marketing. Nov2016, Vol. 80 Issue 6, p6-35. 68p. 4 Diagrams, 7 Charts. DOI: 10.1509/jm.15.0423.
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Record: 133- Path to Purpose? How Online Customer Journeys Differ for Hedonic Versus Utilitarian Purchases. By: Li, Jingjing; Abbasi, Ahmed; Cheema, Amar; Abraham, Linda B. Journal of Marketing. Jul2020, Vol. 84 Issue 4, p127-146. 20p. 2 Diagrams, 5 Charts, 2 Graphs. DOI: 10.1177/0022242920911628.
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Path to Purpose? How Online Customer Journeys Differ for Hedonic Versus Utilitarian Purchases
The authors examine consumers' information channel usage during the customer journey by employing a hedonic and utilitarian (H/U) perspective, an important categorization of consumption purpose. Taking a retailer-category viewpoint to measure the H/U characteristics of 20 product categories at 40 different retailers, this study combines large-scale secondary clickstream and primary survey data to offer actionable insights for retailers in a competitive landscape. The data reveal that, when making hedonic purchases (e.g., toys), consumers employ social media and on-site product pages as early as two weeks before the final purchase. By contrast, for utilitarian purchases (e.g., office supplies), consumers utilize third-party reviews up to two weeks before the final purchase and make relatively greater usage of search engines, deals, and competitors' product pages closer to the time of purchase. Importantly, channel usage is different for sessions in which no purchase is made, indicating that consumers' information channel choices vary significantly with the H/U characteristics of purchases. The article closes with an extensive discussion of the significant implications for managing customer touchpoints.
Keywords: customer journey; hedonic and utilitarian products; information sources; path to purchase; touchpoint management
With the proliferation of electronic commerce, examining the role of various information channels during the customer journey is becoming increasingly important. A "customer journey" is the series of actions a customer takes to arrive at the moment of purchase ([47]). Importantly, these actions include an examination of various information sources and the evaluation of alternatives before the purchase decision. Online information channels, which conveniently provide a variety of pertinent information ([71]), significantly affect purchase decisions ([ 7]; [48]). As retailers' spending on online marketing continues to grow, understanding how to best allocate resources across various touchpoints necessitates a "360-degree view" of how customers interact with and leverage multiple information channels throughout the customer journey ([35]). Accordingly, several path-to-purchase information channels have garnered considerable attention, including search engines ([26]), social media ([68]), review sites ([11]), deal sites ([44]), and retailer product pages ([31]).
Given the nuances of customer journeys on the internet, identifying the mechanisms behind information channel choices and usage remains challenging. Channel choices and usage are contingent on diverse retailer- and product-category characteristics as well as heterogeneous consumer preferences. For example, retailers differ considerably in their product and service offerings, brand equity, and target customer segments, potentially influencing consumers' information search behaviors. Moreover, consumers' shopping characteristics ([45]), prior shopping experience ([23]), trust ([ 5]), and demographics ([33]) may also lead to significantly different channel choices. Furthermore, channel choices are interdependent and could vary at various stages of the customer journey ([18]; [55]; [78]). Consequently, marketing managers continue to wrestle with how to best allocate resources for a variety of product offerings across an array of online touchpoints at different stages of the customer journey ([ 1]; [ 7]).
In addition to a utility-based perspective popular in the extant literature, recent studies have called for a social and psychological angle (e.g., [45]) to investigate information channel usage patterns and customer journeys. Purpose has been identified as an important consideration. A recent study published by Google's Zero Moment of Truth ([77]) finds consumer search behaviors to be driven by six needs: the need for surprise, help, reassurance, education, thrill, or the need to be impressed. These needs and purposes are shaped by not only the product category but also by where consumers are in their journey, namely their "path to purpose."
In this study, we use a hedonic–utilitarian (H/U) perspective—a purpose-oriented categorization of consumption extensively studied in the marketing literature ([29])—to explore information channel usage patterns across customer journeys. The H/U characteristics of purchases reflect affective and instrumental motives, which could provide a richer picture of consumers' perceptions toward purchases as well as consumers' information search behaviors. For example, emotions such as fun and guilt have emerged as important considerations for hedonic purchases, with implications for preferences of certain information channels ([55]; [69]). Furthermore, many customer-centric measurement scales have been proposed to quantify the characteristics of product categories or brands based on the H/U dimensions ([ 3]; [ 6]; [80]), which has the potential for large-scale analysis of individual customer journeys across a myriad of retailers and product categories.
Therefore, we contribute to the literature by examining how the usage of a rich set of digital information channels—search engines, social media, third-party reviews, deal sites, and product pages of target retailers and competing retailers—during the customer journey differs by the retailer-product-level H/U characteristics. The specific research questions we answer are the following:
- Do consumers use digital information channels differently for H/U purchases?
- How does this usage vary over the customer journey?
- Does this usage vary between converted and unconverted sessions?
We examine these research questions using both primary and secondary data. We first survey the H/U characteristics of 20 product categories sold by 40 retailers—a total of 115 retailer–product combinations. To understand the H/U effect on actual channel usage, we analyze a large volume of comScore clickstream data that includes all internet activities from 4,356 consumers with 22,751 purchases that account for $1.2 million sales during a 24-month period. We use a hierarchical Bayesian approach to consider channel interdependency, retailer, product, and individual heterogeneity in online information channel usage.
We find that consumers making hedonic purchases tend to utilize social media and product page views on the target retailer's website more extensively than people engaging in utilitarian purchases. By contrast, consumers making utilitarian purchases tend to use search engines, third-party reviews, deal sites, and product page views on the competing retailer's website more frequently than those engaging in hedonic purchases.
Furthermore, we explore the dynamics of channel usage between hedonic and utilitarian purchases throughout the customer journey. We find the H/U effect on the usage of social media and third-party review sites to be stronger earlier in the customer journey. Conversely, the effect of H/U differences on search engines and deal site usage is stronger closer to the point of purchase. While the H/U effect on product page views is significant throughout the customer journey, the magnitude decreases toward the end of the customer journey.
Finally, for unconverted hedonic purchases, consumers visit social media sites less often, visit deal sites more, and are more likely to benchmark with competing retailers' product pages compared with converted sessions, suggesting a potential guilt-justification effect. For unconverted utilitarian purchases, the four channels that facilitate information search are less utilized, indicating an insufficient information search for purchase decisions.
Our research aims to make at least four important academic contributions. First, we extend the customer journey literature by complementing the utility-centric perspective with a social/psychological angle. We analyzed the H/U effect on six prepurchase information channels for 20 product categories across 40 retailers. An examination of this interplay is conceptually and theoretically significant because it provides a new angle to understand the role of affective mechanisms such as amusement seeking, guilt justification, and brand affect during the shopping process. Second, we uncover the dynamics of H/U effects throughout the customer journey, which allows more actionable insights for marketing managers and adds to the nascent research on the temporal effect of the customer journey ([ 7]; [47]). Third, we highlight the importance of considering H/U characteristics at a more granular level. Unlike the existing H/U literature, which mostly focuses on product-level differences, our survey shows a considerable variation of H/U characteristics for similar product categories across retailers, calling for a retailer-category vantage point for future H/U research. Finally, we contribute to the literature regarding the use of Big Data for deriving marketing insights in complex digital environments ([ 9]; [41]; [76]; [82]). We demonstrate the benefits of conducting innovative Big Data marketing research by combining a variety of research methods and data sources. Our research utilizes survey analysis, clustering, text mining, and Bayesian modeling and seamlessly combines survey-based primary data and large-scale secondary clickstream. As a result, we provide a more comprehensive view of customer journey across channels and stages.
Our work offers several actionable implications for marketing managers' digital spend allocation and online marketing strategies. We suggest that marketing managers collect consumers' H/U perceptions of their product offerings relative to their competitors. Leveraging our empirical model, marketing managers can use the obtained H/U characteristics of their products to understand the shopping purposes of their customers, most valuable information channels, and the most common sequences of touchpoint prospects at different stages of the customer journey. Accordingly, they can design their marketing strategies on the basis of not only a utility-centric view but also the social/psychological needs of their customers ([ 7]), thereby enhancing the consumer experience on the path to purchase ([47]).
The importance and impact of consumer goals on the purchase process have been emphasized extensively in the prior literature. Different purpose-oriented categorizations of consumption have been employed, with search–experience (S/E) and H/U perhaps being the two most prevalent. In line with differences in the cognitive processes related to the acquisition of alternative forms of information, the S/E perspective highlights the different information-seeking behaviors associated with search goods and experience goods ([31]). By contrast, the H/U perspective emphasizes the bidimensional consumer attitudes toward brands and consumption that stem from affective and instrumental motives ([29]). Hedonic consumption is based on the consumer's experience of shopping, emotional attachment, focusing on fun, playfulness, enjoyment, excitement, and the need for surprise ([ 2]; [ 3]). By contrast, utilitarian consumption is often more goal-directed and pertains to the need to complete specific tasks efficiently and effectively ([13]; [53]). Recent studies demonstrate the importance of H/U characteristics for purchases. For example, [45] show that consumers who make hedonic purchases are likely to utilize multiple purchase options. Moreover, [66] find that gamers' social network ties on an online gaming platform significantly influence the spending for hedonic products.
The H/U perspective affords at least three opportunities to enrich and enhance insights gained through the S/E vantage point of purchases. First, the theoretical underpinnings for H/U draw from cognitive/social psychology—particularly in consideration of both affective and cognitive attitudes ([ 6]; [72]). These affective–cognitive trade-offs have the potential to complement the utility-centric information-seeking view adopted by the S/E perspective ([59]). For instance, emotions such as pleasure and guilt have emerged as important considerations for certain forms of consumption, with implications for path-to-purchase channels such as social media ([69]). Furthermore, when processing information about the product (e.g., the product name), consumers process hedonic products more holistically than utilitarian products ([54]). Second, in contrast to the S/E's narrow focus on the product categories, the H/U perspective enables customer-centric thinking by quantifying the H/U characteristics of product categories or brands from the customer's perspective. One of the most popular scales (from [80]) allows measurement of the customer's perceived H/U characteristics of purchases at both the product-category and retailer levels, making it highly conducive to a large-scale examination of purchases from different customers on various retailers' product categories.
Third, the hedonic and utilitarian dimensions are independent ([80]). As [ 6], p.161) observe, hedonic and utilitarian "motivations for consumption need not be (and usually are not) mutually exclusive: a toothpaste may both prevent cavities and provide pleasure from its taste." Thus, the bidimensional analysis allows for granular assessment of the role of purpose in the customer journey. For these three reasons, we use H/U as our primary perspective to examine path-to-purchase channels. However, to be more holistic in our operationalization of purpose, we also include S/E as a control variable in our model.
Despite the tremendous potential of the H/U perspective to enhance our understanding of path-to-purchase tendencies, prior studies have typically not considered the role of H/U characteristics on consumers' channel usage during the purchase funnel. In addition, previous studies of customer journeys focus on a few channels or on a single retailer site, which calls for a more comprehensive view with multiple channels, product categories, and retailers. Moreover, H/U characteristics are not unique to the product (category) level but also manifest at the retailer (brand) level ([80]). Furthermore, there is potential for differences in consumers' behavior between product-category-level and retailer-level H/U characteristics. For example, independent of the product, consumers demonstrate greater affective involvement with hedonic retailers and relatively more cognitive involvement with utilitarian retailers ([83]). In addition, retailer-level characteristics are usually associated with brand positioning ([64]). These findings suggest the potential for a simultaneous retailer- and product-category-level effect that may influence H/U motivations.
To illustrate this effect, we conducted a survey involving 3,250 Amazon Mechanical Turk (MTurk) participants to report their H/U perceptions toward 115 common retailer categories. Each participant randomly evaluated the H/U characteristics for six retailer-category combinations, resulting in approximately 100 responses for each retailer-category combination. Figure 1 shows the H/U plots for some common product categories at Walmart, Home Depot, and Amazon (Panel A), and within Amazon (Panel B). Looking at the Panel A, we see that the same product category is perceived differently across retailers. For the electronics category, Home Depot is positioned low on utilitarian and in the middle for hedonic, while Amazon and Walmart are high on both utilitarian and hedonic dimensions. Similarly, Amazon's jewelry and sports products are considered more hedonic relative to Walmart's. Panel B shows differences in consumer H/U perceptions across many of Amazon's product categories.
Graph: Figure 1. H/U plots for Amazon, Home Depot, and Walmart for selected categories and for various product categories at Amazon.Notes: x- and y-axes are calibrated as absolute deviations from the mean (0, 0).
In summary, these charts highlight the notions that ( 1) the same product category can have varying H/U perceptions across different retailers and ( 2) the same retailer can have different H/U characteristics for its product categories. Collectively, to account for these important variations, the plots underscore the value of examining H/U at the "retailer-category" level. In addition, the plots reinforce the potential value of considering the hedonic and utilitarian dimensions separately to allow for more nuanced analysis between retailer categories in the four quadrants, as well as between the ones along the same diagonals. Next, we discuss how cross-channel customer journeys may vary across hedonic and utilitarian retailer-category combinations.
Drawing from research on hedonic and utilitarian purchases, utilitarian purchasing is a relatively more goal-directed cognitive process, while hedonic purchasing is a comparatively more goal-ambiguous, emotional experience. This cognitive and affective dichotomy not only defines the goals of online shopping but also influences channel preferences. Consumers evaluate the outcome of an exchange process with another entity (e.g., channel, retailer) by comparing the relevant perceived benefits against perceived costs ([ 4]). These benefits and costs include economic utility and social and psychological returns, such as enjoyment, trust, and respect. In the context of hedonic and utilitarian purchases, we expect that these inherent cognitive and affective differences would result in varying benefits and costs associated with different digital channels. Moreover, channel usage would likely differ depending on where consumers are on their journey, time-wise. Thus, we also explore how the H/U effects change dynamically as the consumer progresses through the purchase funnel. Finally, prior studies (e.g., [45]) have shown that the choice of information channels could affect conversion outcomes. Accordingly, we also examine the H/U effect on the path to nonpurchases. Next, we discuss how information channel usage may differ across hedonic and utilitarian purchases. A review of relevant literature is included in Table 1.
Graph
Table 1. Summary of Prior Research on Hedonic/Utilitarian Purchases and Online Information Channel Usage.
| Study | Search | Social | Review | Deal | ProdPage_ Target | ProdPage_ Competitor | Study Design | Data | Multiple Products | Multiple Sites |
|---|
| Ghose and Yang (2009) | U+ | | | | | | Empirical | Secondary | Yes | No |
| Chiang and Dholakia (2003) | U+ | | | | | | Experiment | Primary | Yes | No |
| Kim and LaRose (2004) | U+ | | | | | | Survey | Primary | No | No |
| Lin and Lu (2015) | | H+ | | | | | Survey | Primary | No | No |
| Schulze, Schöler, and Skiera (2014) | | U− | | | | | Empirical | Secondary | Yes | Yes |
| Park et al. (2018) | | H+ | | | | | Empirical | Secondary | Yes | Yes |
| Sen and Lerman (2007) | | | H− | | | | Experiment | Primary | Yes | No |
| Kushwaha and Shankar (2013) | | | H+ | | | H+, U− | Empirical | Primary + secondary | Yes | Yes |
| Khan and Dhar (2010) | | | | H+ | | | Experiment | Primary | Yes | No |
| Wakefield and Inman (2003) | | | | H− | | | Experiment | Primary | Yes | No |
| O'Curry and Strahilevitz (2001) | | | | H+ | | | Experiment | Primary | Yes | No |
| Okada (2005) | | | | H+ | | | Experiment | Primary | Yes | No |
| Moe (2003) | | | | | U+, H− | | Empirical | Secondary | Yes | No |
| Moe and Fader (2001) | | | | | H− | | Empirical | Secondary | No | No |
| Novak, Hoffman, and Duhachek (2003) | | | | | H+ | | Experiment | Primary | Yes | No |
| Sloot, Verhoef, and Franses (2005) | | | | | | H− | Interview | Primary | Yes | Yes |
| Noble, Griffith, and Weinberger (2005) | | | | | | U+ | Survey | Primary | Yes | No |
| Chaudhuri and Holbrook (2001) | | | | | | H+ | Survey | Primary | Yes | No |
| Van Trijp, Hoyer, and Inman (1996) | | | | | H+ | | Experiment | Primary | Yes | No |
| Heitz-Spahn (2013) | | | | | | U+ | Survey | Primary | Yes | Yes |
| Mallapragada, Chandukala, and Liu (2016) | | | | | H− | | Survey + empirical | Primary + secondary | Yes | Yes |
| Hughes, Swaminathan, and Brooks (2019) | | H+ | | | | | Experiment | Primary + secondary | Yes | Yes |
| Current study | U+ | H+ | U+ | U+ | H+ | U+ | Survey + empirical | Primary + secondary | Yes | Yes |
1 Notes: H = hedonic; U = utilitarian; PPT = ProdPage_Target; PPO = ProdPage_Competitor; + = positive effect; − = negative effect.
Utilitarian purchases are rational and goal-driven, with the objective of making the best purchasing decision ([61]). Therefore, they often require deeper information processing across concrete, predefined purchase attributes in an efficient manner ([53]; [66]). Moreover, utilitarian purchases are often deliberate and planned, with well-defined dominant attributes that are easy to compare. Accordingly, this ease of comparison reduces brand differentiation and increases price sensitivity ([60]). Consequently, consumers purchasing utilitarian products tend to prefer information channels that allow for convenient and efficient searches and comparisons for product attributes and prices across various alternatives so as to optimize purchasing decisions.
According to prior literature, certain channels could be more effective for utilitarian purchases. First, search engines promote efficiency-oriented shopping by allowing customers to easily and quickly find products through specifying attributes of interest via search queries ([12]; [26]). The list-wise, clear, and condensed format of the search results and the large-scale indexed content enable consumers to quickly navigate alternatives and compare product offerings from different retailers ([40]). Second, third-party review sites provide quantitative and qualitative information about product attributes for comparison, making them more conducive for utilitarian purchases that usually have well-defined and searchable attributes. Similarly, deal sites allow consumers to search for the best deals efficiently and conveniently, which could be useful for consumers to optimize their spending. Finally, because brand differentiation in utilitarian purchases is less extensive, consumers are more likely to browse product pages across multiple retailers to optimize their time, place, and possession needs ([60]). As a result, they could adopt a "cross-channel free-riding" behavior where one retailer's channel is used to prepare a purchase that is eventually completed at another retailer ([27]). In this regard, their product page browsing on competing retailers' websites could be more extensive.
Consumers making hedonic purchases seek surprise, adventure, fun, and variety during their shopping process ([ 2]; [61]). These goals imply a unique set of perceived benefits that consumers may consider when seeking and attaining information pertaining to hedonic purchases. Due to the affective nature of hedonic purchases, consumers are more likely to rely on simple cues and heuristics rather than deeper information processing to reach their purchase decision ([66]). Instead of trying to find the best alternatives, consumers could have a strong "affective attachment" to brands ([10]) and may process information more holistically ([54]). Consequently, consumers making hedonic purchases could spend less time on searching and comparing. However, prior research has also found that consumers buying hedonic products may engage in guilt-reducing justification behaviors ([42]; [62]; [63]) by spending more time in the search process. Consumers could also engage in a variety-seeking behavior ([45]; [61]) due to considerable product differentiation in hedonic purchases ([79]). Therefore, the complex nature and multiple mechanisms of hedonic purchases could have different implications for information channel search under various contexts.
Regarding the information channels for hedonic purchases, social media has emerged as an influential channel, with 70%–80% of study respondents reporting that their purchases are affected by the social media posts of companies and friends ([22]; [28]; [43]). Previous studies show that consumers find fun- and entertainment-oriented social media to be a more suitable information source for hedonic purchases ([32]; [50]; [66]; [69]). However, for third-party reviews, prior research has not been definitive regarding their implications for hedonic purchases. On the one hand, the abstract attributes of hedonic products are less conducive to comparisons through review aspects and dimensions. On the other hand, the lack of concrete attributes also results in uncertainty for hedonic purchase ([45]), which might drive greater usage of qualitative comments.
Similarly, the prior literature on deal websites has been ambivalent regarding their implications for hedonic purchases. Deals have been found to be more effective for hedonic purchases ([37]), supporting the notion that users favor guilt-alleviation mechanisms to justify hedonic consumption ([63]). However, deals could also be less helpful because consumers are less price-sensitive due to the difficulty of comparing hedonic products ([81]). Finally, the findings for product page views for hedonic purchases are also mixed. The experiential nature of hedonic purchases is more closely aligned with hedonic browsing behavior, which is characterized by a leisurely examination of fewer product pages and often results in impulse purchases ([52]; [57]; [65]). Hedonic consumption is also associated with greater variety-seeking behaviors, which could potentially extend page views across multiple retailers ([45]; [61]; [79]). Consequently, due to the mixed findings in the prior literature, there remains a need to formally examine the effect of H/U characteristics on information channel usage.
To provide a comprehensive picture of how consumers' utilization of different online information channels throughout a customer journey varies with the H/U purchases, we operationalized the H/U characteristics at the retailer-category level through a survey. We then combined primary (survey) and secondary (clickstream) data to demonstrate the H/U effect on the usage of different information channels prior to purchases. The data analysis process and the conceptual model are depicted in Figure 2.
Graph: Figure 2. Identification strategy and conceptual model.
Our retailer-category analysis covers 20 product categories from 40 online retailers. To select appropriate retailers, we started with 500 top internet retailers from 2014 sales rankings (https://www.internetretailer.com/top500) and narrowed that list down to 336 that have easily discernible product page URL patterns. Combined with a comScore data set from 2013 to 2014, we found that these retailers' number of transactions shows a Pareto-like distribution, with the top 40 retailers accounting for over 91% of all transactions. We also examined the main products sold by these 40 retailers and found that they cover a wide range of hedonic and utilitarian product categories. Thus, these retailers were included in our study (for details, see Web Appendix W1).
For product categories, we initially used the 22 categories proposed by [45], which adequately captured the major product categories on the H/U spectrum. Drawing from the product purchases on these retailers on a comScore data set, we found 115 unique retailer-category combinations (e.g., Amazon apparel). We then mapped all the extracted categories to the initial 22 and removed 2 that were absent, resulting in a final set of 20 product categories.
We collected approximately 1 terrabyte of the U.S. comScore web clickstream data between January 2013 and December 2014. The data recorded all online clicking behaviors in the form of URLs and timestamps from approximately 100,000 randomly selected households each month. The clicked URLs and timestamps were grouped into clickstream sessions. The end of a clickstream session is determined when clicking behaviors are inactive for a certain period of time. For the clickstream sessions involving a purchase, extra information (e.g., purchased product categories) was provided by comScore. The unit of analysis of our study is a purchase session (converted or unconverted), which captures a consumer's decision-making process toward an actual or intended purchase. A converted session denotes an online purchasing cycle for a consumer, starting with an information search across various information channels and ending with a purchase. Due to the complexity of information search, this purchase cycle can last for several days.
Determining the length of the purchasing cycle is often challenging. Existing literature has mixed findings regarding cycle lengths; depending on the research context and product categories, it could range from several days to one month ([19]; [34]). We derived the purchase cycle length for each of the 20 product categories from the comScore data. The intuition for our method is that consumers may start a purchase cycle by browsing product pages related to an intended product category on any channel. Thus, our algorithm tracked their first encounter with these pages. Web Appendix W2 documents the process in detail. The key to this method is to ensure that product page URLs could be accurately mapped to our 20 product categories. Many retailer websites embed product names or categories in the product page URLs. Therefore, we developed a text-mining algorithm (described in Web Appendix W3) to extract product categories from these URLs. This method is conducive for all retailers except Amazon and Walmart, whose product URLs are sometimes coded with product IDs. Accordingly, we used their product application programming interface to map product IDs to our focal product categories. The average purchase cycle for each product category is presented in Table W2.1 of Web Appendix W2. We found that while some product categories exhibit shorter or longer cycles (e.g., office, music), most of the cycles last around 14 days. Consequently, we used 8–14, 2–7, and 0–1 day windows to capture consumers' information channel usage during the early, middle, and late stages, respectively, of the path to purchases.
Each consumer could have multiple purchases during the two-year period, which creates an opportunity for us to account for variations at both the session and consumer level. To cleanly attribute channel usage to a unique purchase cycle, we removed the purchases that overlapped within a 14-day window (20.78% of the sessions). Furthermore, we found that approximately 23.3% of those purchases involve multiple product categories, which could complicate our analysis because these multicategory purchases could have more than one H/U characteristic. Therefore, we also removed these purchases from our study.
To identify whether the H/U effect on information channel usage is only restricted to converted sessions, we also included unconverted sessions of the same consumers identified previously. Note that we only examine online unconverted sessions for these consumers because our data set is limited to online web clickstream—it is possible that a consumer did not purchase online but purchased through other channels (e.g., offline). Specifically, an online unconverted session denotes a website visitation with an intended purchase (i.e., focused product browsing) but exit before completion (e.g., cart abandonment or leaving the website before adding products to the cart). While the comScore data nicely flags clickstream sessions without purchases, we don't know whether these sessions have product purchasing intentions. Thus, the H/U characteristics of this session could not be determined directly from the comScore data. Fortunately, we can use the text mining method described above to infer the intended products from browsed URLs. Specifically, we identified the product categories from all the browsed URLs during the day of a clickstream session. The category that receives the highest presence is considered as the intended product category. We found this method to be reasonable because people rarely browse product pages unless they have a purchase intention—only 4.6% of the unconverted clickstream sessions have product page views during the session day. Therefore, we include only this subset of sessions with purchase intentions in our unconverted data set. Furthermore, for each consumer, we removed all the unconverted sessions that tap into the converted sessions (approximately 28% removed) or overlap with each other on a 14-day window (approximately 43% removed). Similarly, we derived 8–14, 2–7, and 0–1 day windows to examine the early, middle, and late stages, respectively, of the path to nonpurchases.
We conducted a survey on MTurk to derive our key independent variables related to the H/U aspects of a product category purchased on a specific retailer. Details of the survey are provided in Web Appendix W4. Following [45], we calculated a mean composite hedonic (utilitarian) score by averaging the scores of the five hedonic (utilitarian) scale items. Because H/U have a low correlation of.16, we used separate H/U scores for each retailer-category combination. To account for the heterogeneity of H/U perceptions across consumers, we imputed the H/U scores for the clickstream data using the insights from the MTurk survey. Consequently, consumers with varying demographic characteristics in the clickstream will have different H/U scores despite the same retailer-category purchases. Finally, both scores are mean-centered before being included in the study.
Information channel usage is defined as the number of visited channel URLs corresponding to a search engine, social media, third-party reviews, deals, product page views on target retailers, and product page views on competing retailers. Following prior work, the visited URLs were mapped to six channels using URL token matching ([55]). Web Appendix W1 shows the product categories for identifying competing retailers (i.e., retailers offering the same product categories are considered as competitors), and Web Appendix W5 presents the set of social media, third-party reviews, and deal sites included.
We included several control variables to address the selection bias that commonly occurs in secondary data analysis. Drawing on this principle and previous studies ([31]; [45]), we identified five types of control variables:
- Retailer-specific controls: It is important to control for retailer heterogeneity because the key independent variable H/U scores are at the retailer-category level, and retailers' diverse marketing strategies could affect channel usage ([78]). Thus, we included five types of retailer-level controls. First, we used the top 500 Internet Retailer sales ranking in 2014 to approximate each retailer's popularity rank. Second, we incorporated the visit volume across the comScore clickstream to account for retailer popularity rank specific to the comScore panel. Third, we used the number of page views per user obtained from Alexa (www.alexa.com) to control the level of consumer engagement with different retailers. Fourth, we collected the number of likes for each retailer's Facebook page to control for their social media presence. Because all these controls are highly positively skewed, we log-transformed them before inclusion. Finally, we used a 20-dimensional product category vector to represent the product assortment of each retailer. Specifically, each dimension corresponds to a product category, and the dimensional value denotes the proportion of this category purchased during a two-year period. The higher the proportion, the greater the likelihood that it is a primary category for this retailer.
- Product category control: We included an S/E dummy variable to control for category-level characteristics other than H/U. The S/E assignment for each category is based on the prior literature (e.g., [31]). For example, home and garden products are search goods (S/E = 1), and beauty and automotive products are experience goods (S/E = 0). By including this control, we also intend to empirically illustrate how the two divergent perspectives complement one another, as alluded to in the conceptual development section.
- Prior purchase experience: According to [78], channel usage may be determined by state dependence: how many purchases a consumer has previously made. Therefore, we included the number of prior purchases, as well as the number of purchases specific to the intended product category prior to the current session, to control for a potential systematic shift in channel usage over time.
- Price: Prior research shows that H/U characteristics might be correlated with the dichotomy of luxuries and necessities ([38]). Thus, we included price to control for channel usage driven by other category- and retailer-specific factors. Note that this variable is only available for converted sessions.
- Demographics: Prior studies (e.g., [33]) have shown that demographics such as age, family size, and education play an important role in determining channel usage. Therefore, we included user demographic information accompanying the comScore clickstream as consumer-level controls. Specifically, we incorporated household size, age, gender, education, income level, and the presence of children.
To account for unobserved consumer heterogeneity in information channel usage (in addition to demographics), we adopted a hierarchical Bayesian approach to estimate the parameters of interest. Given the complexity of the proposed model, it is challenging to estimate the entire data set—the computational time increases significantly with the number of consumers. We noticed that 55.70% of the consumers made only two purchases in a two-year period but only occupied 19.60% of the total transactions. We removed these consumers without losing generalizability, resulting in 22,751 converted sessions and 30,550 unconverted sessions from 4,356 consumers in a two-year period, generating approximately $1.2 million in total sales.[ 5] Note that only 3,854 of these consumers have qualified unconverted sessions. A detailed description of all variables included in our model appears in Table 2. Summary statistics for the 14-day sample appear in Web Appendix W6. These statistics show that most channel usage and control variables are different in hedonic and utilitarian conditions.
Graph
Table 2. Operationalization of Variables in the Clickstream Data.
| Variable | Operationalization |
|---|
| Dependent Variables | |
| Search | Search engine visits (e.g., Google, Bing, Yahoo) |
| Social | Social media site visits (36 websites including Facebook, Twitter, Pinterest, etc.) |
| Deal | Deal site visits (31 websites including Slickdeals, eBates, Coupons, etc.) |
| Review | Third-party review site visits (32 websites including Consumer Reports, Epinions, Yelp, etc.) |
| ProdPage_Target | Product page views on the target retailers |
| ProdPage_Competitor | Product page views on the competing retailers (retailers sharing the same product categories) |
| Independent Variables | |
| Hedonic | Hedonic composite score at the retailer-category level (centered on the grand mean) |
| Utilitarian | Utilitarian composite score at the retailer-category level (centered on the grand mean) |
| Control Variables | |
| Rank | Log-transformed average sales rank for each retailer in 2014 |
| VisitCS | Log-transformed visit volume for each retailer from comScore |
| PageViews/User | Log-transformed page viewers/user for each retailer from Alexa |
| Like | Log-transformed number of likes at each retailer's Facebook page |
| ProductOffering | A 20-dimensional vector representing proportional product offerings of each retailer |
| S/E | Dummy variable representing S/E for each category (search = 1) |
| PurExp | Prior purchases before the current session |
| CatExp | Prior purchases related to the focal product category before the current session |
| Price | Price of the purchased product (not available for unconverted sessions) |
| Age | Age of a customer |
| Gender | Dummy variables representing the customer's gender (female = 1) |
| Income | Seven-level ordinal representing income level of the household |
| HHSize | Five-level ordinal variable representing the household size |
| Education | Five-level ordinal variable representing the education level of the customer |
| Child | Dummy variable for the presence of children in the household (has child = 1) |
We developed a multivariate multilevel model to explain how the channel usage patterns differ from H/U characteristics and other factors. Consider a consumer i who has made a purchase at retailer j of category k on occasion t. This consumer can utilize M channels to gather necessary information throughout the customer journey. The channel utilization is denoted by the number of URL visits to each channel Yijktm. Because the distribution of URL visits is heavily positively skewed, we log-transformed the visits. We add one to observations where the number of visits is zero ([16]). Thus, our model becomes a semilog model and the 100 × slope parameters measure the percentage change of the information channel usage given a one-unit absolute change of the explanatory variables. Given the time allocated to shopping, consumers make trade-offs between the usage of different channels. To catch the potential interdependencies among the six channels in our study, we let the channel utilization follow a multivariate normal distribution. Following previous studies (e.g., [48]), we also consider consumer heterogeneity in channel utilization. Thus, we develop a multilevel setting to allow every consumer to have a unique channel usage intercept.
Level 1:
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Level 2:
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In the Level 1 Model, aim is a random intercept that allows for variation in baseline channel usage across consumers. Hedonicijk and Utilitarianijk are grand-mean-centered hedonic and utilitarian scores for a product category k purchased at a retailer j, which can vary depending on consumer i's characteristics. We controlled for product prices Pricejk, prior purchase experiences PurExpit and CatExpikt, and product category heterogeneity S/Ek. The model for unconverted sessions is estimated separately and does not have the control Pricejk. The variables Retailerj help control for the retailer heterogeneity, such as retailers' sales ranking (Rank), visit volume (PurchaseCS), Alexa traffic (PageViews/User), number of likes on retailer's social media page (Like), and proportional offerings of 20 product categories for each retailer (ProductOffering). Because a consumer in our data, on average, has five sessions, but retailer controls consist of 24 variables, we took an alternative route to identify the Level 1 model. Specifically, we performed K-means clustering to construct three retailer groups and include two cluster dummies into our model. Clustering details are discussed in the "Retailer Clustering" subsection. Therefore, cm7 is a vector of parameters corresponding to retailer cluster variables. Finally, because ln(Yijktm) follows a multivariate normal distribution MVN(µ, ∑∊), the error term follows MVN(0, ∑∊), where ∑∊ is a variance–covariance matrix that allows the error terms to be correlated across channels. Due to a lack of prior knowledge about the correlation pattern, we allow ∑∊ to be unstructured to allow flexible variance–covariance matrix.
For the Level 2 model, to account for heterogeneity across consumers, we assume aim ∼ N(am, σ2), where am and σ2 measure the mean effect and dispersion of aim across consumers, respectively. am can be further decomposed into an intercept am0 and the effect of demographic controls Demoi, such as Age, Gender, Income, Education, household size (HHSize), and whether a child is present in the household (Child).
Incorporating all 24 retailer-level controls, we performed K-means clustering to categorize the 40 retailers into a more manageable set of clusters. Using the appropriate evaluation criteria, three clusters emerged. In the main model, we omit Cluster 1 as the base cluster and introduce two dummy variables to the Level 1 model to account for retailer heterogeneity. Web Appendix W7 describes the clustering details, the specific clustering assignment for each retailer, and a cluster centroid table that depicts characteristics of the three clusters.
We estimated six models corresponding to 8–14, 2–7, and 0–1 day windows for converted and unconverted sessions. We conducted a Kolmogorov–Smirnov test on the parameter samples obtained from converged Markov chain Monte Carlo iterations to assess the significance of slope difference for hedonic and utilitarian scores between converted and unconverted sessions. We conducted this estimation of the multivariate multilevel model using a Gibbs sampler programmed in JAGS ([67]), with uninformative priors for all parameters. To promote the efficiency of estimation and the ease of interpretation, we median split all the ordinal variables, including income, household size, and education. A robustness check on the 0–1 data found that median splitting these variables does not change the sign and significance of the H/U effect. The final estimates are posterior means based on 40,000 Markov chain Monte Carlo iterations with a thinning factor of 4, after 40,000 burn-ins. To assess the convergence of the model estimates, we use three diagnostic methods, including the [25] diagnostics, the [24] diagnostics, and the effective sample size ([36]). The Geweke statistics for all the parameters are less than 1.96, confirming that all the parameters have reached the stationary posterior distributions. We run two additional chains with different sets of initial values with the same number of burn-ins. The potential scale reduction factors are approximately 1.001 (<1.2) for all parameters, supporting the convergence of all three chains. The effective sample size is above 500 for all parameters, suggesting that previous samples are not highly autocorrelated with the samples from the posterior distribution.
We compare the proposed model with a model that does not allow for variation in baseline channel usage across consumers (i.e., a single-level model without uim term) and a univariate model (i.e., no off-diagonal elements for ∑∊), using the deviance information criterion (DIC; [74]) on the 0–1 window data. Our model (DICproposed = 236,788) is substantively better than the two benchmarking models (DICfixed = 261,370; DICunivar = 485,641), suggesting substantial consumer heterogeneity and channel interdependencies. We also estimated a multilevel multivariate Poisson log-normal model ([21]) on the 0–1 window data and found consistent results.
Tables 3 and 4 summarize the posterior means of all the parameters for the converted and unconverted sessions with 8–14, 2–7, and 0–1 day windows (early, middle, and late stages, hereinafter). We used highest posterior density (HPD) intervals to evaluate the significance of the model parameters. Consistent with the prior literature (e.g., [31]), we found that usage of at least some information channels differs across S/E, purchase sequence, retailer characteristics, and demographics. In the following subsections, we discuss the changes in the baseline channel usage across the customer journey. Subsequently, we discuss the channel-specific H/U effects using a typical hedonic product (toys) and a representative utilitarian product (office supplies).
Graph
Table 3. Retailer-Category H/U Effects on Information Channel Usage for Converted Sessions.
| Variables | Search | Social | Review | Deal | ProdPageTarget | ProdPageCompetitor |
|---|
| 8–14 days (early) | Intercept | 3.0489*** | 2.7999*** | .1960*** | .2195*** | .4172*** | .2025*** |
| Hedonic | .0014 | .0043*** | −.0006* | −.0005 | .0017** | −.0011*** |
| Utilitarian | .0012 | −.0001 | .0011** | .0004 | .0004 | −.0014*** |
| Cluster2 | −.0532 | −.0559 | −.0096 | −.0257* | .1923*** | −.0714*** |
| Cluster3 | .0603 | .0442 | −.0056 | .0429** | −.1004*** | −.1301*** |
| S/E | −.0420 | −.0408 | −.0067 | −.0026 | −.0444** | .0174* |
| PurExp | .0027 | −.0085*** | .0018*** | .0020*** | −.0018 | .0008 |
| CatExp | −.0030 | .0142*** | −.0020 | −.0012 | .0113*** | −.0032*** |
| Price | −.0002** | −.0002 | 4.4E-05 | −2.93E-05 | −.0001 | −7.11E-05 |
| Age | −.0004 | .0001 | .0007** | .0002 | .0004 | −1.22E-05 |
| Gender | −.0623 | −.1350*** | −.0101 | −.0073 | −.0042 | −.0098 |
| Education | −.0561 | .0337 | .0020 | −.003 | .0303 | .0163 |
| Income | .0372 | .0529 | −.0112 | −.0186 | −.0283 | −.0110 |
| Size | −.0451 | −.0141 | −.007 | −.0026 | −.0378* | .0085 |
| Child | .0899* | .0653 | .0228** | .0029 | .0319 | .004 |
| RMSE | 2.0850 | 2.3596 | .5317 | .5894 | 1.0115 | .4976 |
| 2–7 days (middle) | Intercept | 3.0581*** | 2.7666*** | .1654*** | .2200*** | .5738*** | .2068*** |
| Hedonic | .0011 | .0032** | −.0007** | −.0004 | .0017** | −.0010*** |
| Utilitarian | .0016 | −.0003 | .0007 | .0009** | .0006 | −.0006 |
| Cluster2 | −.0552 | −.0694 | .0013 | −.0399*** | .0951*** | −.0838*** |
| Cluster3 | .0608 | .0198 | −.0069 | .023 | −.1393*** | −.1292*** |
| S/E | −.0602* | −.0603* | −.0012 | −.0095 | −.0090 | .0196** |
| PurExp | .0003 | −.0089*** | .0016*** | .0012* | −.0026** | .0013** |
| CatExp | −.0016 | .0101** | −.0032*** | .002 | .0090*** | −.0040*** |
| Price | −.0001 | −.0003** | −2.57E-05 | −1.05E-05 | .0002*** | 2.56E-05 |
| Age | −.0005 | .0002 | .0003 | −7.42E-06 | .0008 | 3.01E-05 |
| Gender | −.0453 | −.0993** | −.0069 | −.0012 | −.0218 | −.0089 |
| Education | −.0896* | .0074 | .0061 | −.0168 | .0090 | −.0117 |
| Income | .0564 | .0790 | −.0011 | .0043 | −.0004 | .0085 |
| Size | −.0621 | −.0738 | −.0198* | −.0058 | −.0445** | .0135 |
| Child | .0850* | .0982* | .0282*** | .0082 | .0420* | −.0007 |
| RMSE | 2.0126 | 2.2993 | .5053 | .5615 | 1.0472 | .4735 |
| 0–1 days (late) | Intercept | 2.0804*** | 1.7121*** | .0342*** | .1910*** | 1.5028*** | .1270*** |
| Hedonic | −.0001 | .0039*** | −.0001 | −.0005** | .0013* | −.0006*** |
| Utilitarian | .0023* | −.0012 | .0004* | .0004 | .0022** | −.0009*** |
| Cluster2 | −.0070 | .0019 | .0144** | −.1131*** | −.3883*** | −.0675*** |
| Cluster3 | .0200 | −.0192 | .0069 | −.0512*** | −.5002*** | −.1089*** |
| S/E | −.0362 | −.0462* | −.0028 | −.0019 | .0201 | .0178*** |
| PurExp | −.0017 | −.0072*** | .0010*** | .0008* | −.0088*** | .0007* |
| CatExp | −.0019 | .0037 | −.0023*** | −.0014 | −.0022 | −.0029*** |
| Price | −.0001 | −.0001 | 1.11E-05 | 8.45E-06 | .0003*** | 4.65E-05** |
| Age | −.0006 | −.0010 | 4.95E-05 | −.0002 | .0007 | −1.86E-05 |
| Gender | −.0417 | −.0904** | .0015 | −.0045 | −.0257 | −.0034 |
| Education | −.1034*** | −.0271 | −.0034 | −.0225*** | −.0025 | −.0102 |
| Income | .0342 | .0363 | .0035 | .0122 | .0077 | .0033 |
| Size | −.0651* | −.0569 | −.0054 | −.0055 | −.0276 | .0094 |
| Child | .0610* | .0628 | .0059 | .0087 | .0469** | .0010 |
| RMSE | 1.5849 | 1.7677 | .2392 | .3649 | 1.0463 | .3274 |
- 2 *90% of the HPD interval does not contain 0.
- 3 **95% of the HPD interval does not contain 0.
- 4 ***99% of the HPD interval does not contain 0.
- 5 Notes: Hedonic = mean-centered hedonic score; Utilitarian = mean-centered utilitarian score; RMSE = root mean squared error.
Graph
Table 4. Retailer-Category H/U Effects on Information Channel Usage for Unconverted Sessions.
| Variables | Search | Social | Review | Deal | ProdPageTarget | ProdPageCompetitor |
|---|
| 8–14 days (early) | Intercept | 3.3920*** | 3.0655*** | .1679*** | .2556*** | .2575*** | .1494*** |
| Hedonic | −.0011 | −.001 | −.0004 | −.0002 | −.0022*** | −.0005* |
| Utilitarian | −.0012 | −.001 | .0005 | −.0007 | −.0012 | −.0021*** |
| Cluster2 | .2878*** | .3196*** | .0768*** | −.0025 | .5789*** | .0204 |
| Cluster3 | .1228** | .1828*** | .0139 | .0063 | .0194 | −.0626*** |
| S/E | −.0147 | .0011 | .0063 | .0018 | −.0234 | .0134* |
| PurExp | .0192*** | −.0036 | .0001 | .0048*** | .0323*** | .0055*** |
| CatExp | −.0337*** | −.0485*** | −.0045 | −.0063** | −.0375*** | −.0055** |
| Age | .0019 | .0002 | −.0004 | −.0002 | −.0003 | .0005 |
| Gender | .0911** | .1747*** | .004 | .0145 | .0042 | .0093 |
| Education | .0481 | .0202 | −.008 | .0028 | .0110 | .0190 |
| Income | .0195 | .0292 | .0192 | .0185 | .0197 | .0044 |
| Size | .0420 | .1431** | .0057 | .0217 | .0172 | .0091 |
| Child | −.0833* | −.1543** | −.0044 | −.0673*** | −.0357 | −.015 |
| RMSE | 1.7080 | 2.1904 | .6161 | .6609 | 1.1497 | .5308 |
| 2–7 days (middle) | Intercept | 2.9275*** | 2.6771*** | .1241*** | .1414*** | .1451*** | .0748*** |
| Hedonic | −.001 | −.0005 | .0003 | .0002 | −.0017*** | −.0001 |
| Utilitarian | −.0023 | −.0032** | −.0002 | −.0013*** | −.001 | −.0017*** |
| Cluster2 | .2486*** | .2742*** | .0297** | .0046 | .4328*** | .0365*** |
| Cluster3 | .1070*** | .1118*** | .0064 | −.0025 | .0879*** | −.0540*** |
| S/E | −.0114 | .0131 | .0106* | .0097 | .0049 | .0152*** |
| PurExp | .0100*** | −.0105*** | −.0004 | .0030** | .0157*** | .0011 |
| CatExp | −.0280*** | −.0268*** | −.002 | −.0028 | −.0327*** | −.0028 |
| Age | .0004 | .0002 | −.0003 | −.0001 | −.0003 | −.0002 |
| Gender | −.1520*** | −.0551** | −.0316 | .0045 | −.0196 | .0015 |
| Education | .1955*** | .0241 | .0377 | .0216 | .0385 | −.0119 |
| Income | .013 | −.0476 | .0079 | .0108 | −.0257 | −.0057 |
| Size | .0042 | −.0246 | −.0019 | −.0005 | −.0162 | −.0107 |
| Child | .0022 | .0103 | −.0194 | −.0161 | −.0216 | −.0013 |
| RMSE | 1.6488 | 2.1038 | .4697 | .5086 | .9949 | .4044 |
| 0–1 days (late) | Intercept | 2.3640*** | 1.9460*** | .0623*** | .1132*** | 1.6675*** | .1569*** |
| Hedonic | .0005 | .0025*** | −.0002 | −.0002 | −.0005 | −.0010*** |
| Utilitarian | .0008 | −.0010 | .0009*** | .0003 | .0025*** | −.0001 |
| Cluster2 | .2212*** | .2593*** | .0217*** | −.0170* | −.0716*** | −.0635*** |
| Cluster3 | .1163** | .0934 | .0216* | −.0026 | −.1985*** | −.1073*** |
| S/E | .0016 | −.0279 | −.0058 | −.0022 | −.0462*** | .0220*** |
| PurExp | .0029 | −.0133*** | −.0002 | .0022*** | .0131*** | .0033*** |
| CatExp | −.0125** | −.0118* | −.002 | −.0049*** | −.0159*** | −.0086*** |
| Age | −.0007 | −.003 | 3.74E-05 | −.0003 | −.0007 | .0001 |
| Gender | .0450 | .1198*** | −.0061 | .0071 | .0057 | .0046 |
| Education | .0085 | −.0356 | −.0008 | .0205 | .0374 | −.0014 |
| Income | .0023 | .0328 | .0054 | −.0018 | −.0077 | −.0085 |
| Size | .0307 | .0708 | −.0010 | .0039 | .0064 | −.0029 |
| Child | −.0656* | −.1101** | −.0015 | −.0192** | .0043 | .0028 |
| RMSE | 1.3285 | 1.7685 | .3033 | .3521 | .7641 | .3713 |
- 6 *90% of the HPD interval does not contain 0.
- 7 **95% of the HPD interval does not contain 0.
- 8 ***99% of the HPD interval does not contain 0.
- 9 Notes: Hedonic = mean-centered hedonic score; utilitarian = mean-centered utilitarian score; RMSE = root mean squared error.
We derived the average daily number of visits in each of the purchase windows (i.e., the intercepts from early, middle, and late-stage models) for the six digital channels, as presented in Figure 3. The daily channel usage (intercept) varies significantly across different time windows, confirming prior studies ([34]; [56]; [78]) showing that consumers utilize channels at differing intensities throughout the customer journey. Specifically, all six channels' usage increases toward the final purchase, validating that they are important information search channels for online shopping. From the middle to the late stage of the journey, the increase in the rate of product page views on the target retailer's website (ProdPage_Target) is greater than that of product page views on competing websites (ProdPage_Competitor), suggesting a consumer lock-in, consistent with prior findings that consumers gradually narrow their consideration set throughout the customer journey ([34]; [56]). Finally, the covariance among channel usage is all significant, verifying considerable interdependency among channel usage. Specifically, for converted sessions, search engine and social media usage are highly correlated with each other (e.g., correlation =.54 for the late stage), representing high co-usage of these two information channels. In addition, toward the end of the purchase, product page views on target websites are the most correlated with rest of other channels (average correlation =.21) but product page views on competing websites are the least correlated (average correlation =.09), indicating a funneling effect of the information search on target websites.
Graph: Figure 3. Average daily channel usage (intercept) for three stages of the customer journey.Notes: We calculate the average daily channel usage by exponentiating the intercepts of the early- (8–14 days), middle- (2–7 days), and late- (0–1 day) stage models divided by the number of days in each time window. The y-axis is in logarithmic scale. The x-axis depicts the early, middle, and late stages of the customer journey.
For the converted sessions described in Table 3, the utilization of six information channels varies significantly with the H/U characteristics of purchases, and across different stages. Because the H/U score follows a 0–100 scale, one unit change of the H/U score on a 100-point scale would equal to a 1% change of the H/U score. In our semilog model setting, given that b is the parameter for the H/U score, a 1% change of H/U score, conditional on a focal consumer's other purchase characteristics, will correspond to a 100 × b percentage change of information channel usage. For instance, the significant parameter.0039 (p <.01) of the Hedonic score on social media during the late (0–1 day) window indicates that a 1% increase in the Hedonic score in the late stage of the journey leads to a.39% increase in social media usage. In general, we find that a 1% increase in the Hedonic score involves a greater usage of social media (early:.43%, middle:.32%, late:.39%) and more product page views on the target retailers' site (early:.17%, middle:.17%). Furthermore, a 1% increase in the Utilitarian score is associated with heavy usage of search engines (late:.23%), third-party reviews (early:.11%, late:.04%), and deal sites (middle:.09%).
In contrast to the converted sessions, the H/U effects are different for the unconverted sessions depicted in Table 4. For example, the Hedonic score's effect on social media is attenuated (unconv_late:.25% vs. conv_late:.43%; p <.05) based on a Kolmogorov–Smirnov test, and the signs for deal site usage (unconv_mid: −.13% vs. conv_mid:.09%) and product page views on the target retailers (unconv_early: −.22% vs. conv_early:.17%; unconv_mid: −.17% vs. conv_mid:.17%) are flipped, suggesting that the H/U effects could be potentially related to conversion. To compare the differential H/U effects across different stages of customer journeys for converted and unconverted sessions, we selected a typical hedonic product—toys (Hedonic = 10.82; Utilitarian = −5.15)—and a typical utilitarian product—office supplies (Hedonic = −15.92; Utilitarian = 6.55)—and visualize their percentage change in channel usage relative to a product with the average H/U scores (H = 59.86; U = 66.35)[ 6] in Figure 4, Panels A–D. Drawing on this selection, we next discuss the important channels for hedonic and utilitarian purchases at different stages of the customer journey. Table 5 presents a summary of these results.
Graph: Figure 4. Channel usage percentage differences for hedonic and utilitarian purchases.Notes: Hedonic and Utilitarian are mean-centered H/U scores; H and U are the original H/U scores. Toys represent a typical hedonic product (Hedonic = 10.82; Utilitarian = −5.15); Office represents a typical utilitarian product (Hedonic = −15.92; Utilitarian = 6.55). For all panels, the y-axis reflects the percentage change of channel usage for toys and office relative to a product with mean original H/U scores (H = 59.86; U = 66.35). The x-axis depicts the early (8–14 days), middle (2–7 days), and late (0–1 day) stages of a 14-day customer journey.
Graph
Table 5. Summary of Results: Information Channels Utilized at Different Stages of the Customer Journey Vary by Product Characteristics and Purchase Conversion.
| Stage of Customer Journey | Hedonic Product | Utilitarian Product |
|---|
| Purchases | Nonpurchases | Purchases | Nonpurchases |
|---|
| Early (8–14 days) | Social Media ProdPage_Target | | Reviews | |
| Middle (2–7 days) | Social MediaProdPage_Target | Social MediaDeals | ReviewsDealsProdPage_Other | |
| Late (0–1 days) | Social Media | Social Media | Search EngineDeals | ReviewsDealsProdPage_Other |
For converted sessions, toys (hedonic) purchases utilize a greater level of social media than office supplies (utilitarian) purchases, with an average of 10% more channel utilization throughout the purchase cycle (early toys: 4.65% vs. early office: −6.85%; middle toys: 3.46% vs. middle office: 5.09%; late toys: 4.22% vs. late office: −6.21%). Furthermore, the hedonic effect for social media uses increases slightly toward the end of the customer journey (middle toys: 3.46% vs. late toys: 4.22%), indicating that people may utilize the social media channel more when they have a relatively clear purchase intention. This finding nicely complements the social media literature ([39]; [51]; [58]) by showing that, in addition to soliciting impulse buying, social media might have become an information channel that consumers use to proactively search for information (e.g., finding product pictures on Instagram). However, the positive hedonic effect on social media use is significantly smaller for unconverted sessions than for converted sessions (p <.05), suggesting that a greater level of social media usage might be more useful for realizing hedonic purchases.
These findings reinforce the importance of emotive and social aspects during the hedonic shopping process highlighted by prior research in the H/U domain ([ 2]; [61]) and support the notions that social media is more effective for viral marketing of hedonic products ([ 8]) and that online social connections are more influential for hedonic spending ([66]). Furthermore, given that social media is utilized by hedonic purchases for both converted and unconverted sessions (with different effect sizes), social media marketing might be more effective to reach potential consumers and improve conversion.
It is important to note that our results regarding social media use are correlational in nature, and causal inferences should be made with caution. Because of privacy considerations, we cannot see what people are browsing on social media. While differences in social media usage across products and over time give us some confidence in making prescriptive recommendations, and these results are reinforced by other studies ([15]; [14]; [32]), the aforementioned caveat remains.
In addition, hedonic purchases involve more product page views on the target retailers up to two weeks before the conversion, with as much as a 4.55% difference between toys and office supplies purchases (early toys: 1.84% vs. early office: −2.71%), presenting a funneling effect toward to the final purchases. However, this effect is reversed for unconverted sessions, (early toys: −2.38% vs. early office: 3.50%), suggesting that consumers seeking hedonic products might browse more product pages on competing retailers and make purchases there, leading to nonconversion on the focal retailer site. This finding reveals that sufficient on-site product page views up to two weeks before conversion are crucial for realizing hedonic purchases, in support of the notion that consumers with hedonic purchases exhibit a greater level of "affective attachment" ([10]) toward retailer brands and are less likely to engage in a brand-switching behavior ([39]).
Finally, while deal sites are utilized less for hedonic purchases than for utilitarian purchases toward the end of the customer journey for converted sessions (middle toys: −.46% vs. middle office:.59%; late toys: −.54% vs. late office:.80%), this effect is reversed for unconverted sessions (middle toys:.67% vs. middle office: −.85%). Prior research has suggested that consumers buying hedonic products can engage in guilt-justification behavior (e.g., [37]; [63]) by looking for deals. Were consumers doing so during the middle stage of the customer journey in the unconverted sessions? Because our data set does not contain information about the types of deals consumers viewed, we are unable to investigate the exact association between the deal visits and nonconversion (e.g., is this nonconversion due to unsatisfying discount level?). Future research with more granular data could investigate this guilt-justification mechanism in the customer journey.
As illustrated in Figure 4, Panel B, consumers making utilitarian purchases such as office supplies utilize more third-party reviews (early office: 1.68% vs. early toys: −1.21%; middle office: 1.11% vs. middle toys: −.76%) at the beginning and the middle of the customer journey. They also visit product pages on competing retailers more often (middle office: 1.59% vs. middle toys: −1.08%) in the middle of the journey. In addition, consumers demonstrate greater usage of search engines[ 7] (late office: 1.51% vs. late toys: −1.18%) and deal sites (middle office:.59% vs. middle toys: −.46%; late office:.80% vs. late toys: −.54%) toward the end of the customer journey.[ 8]
These findings are consistent with the cognitive mechanisms discussed in the H/U literature ([61]). Utilitarian purchases are often rational and goal-driven, with the objective of optimizing the purchase decision. With more tangible and well-defined utilitarian attributes, information channels that facilitate flexible and direct search and allow for convenient comparisons among alternatives, such as search engines and third-party review sites, might be particularly useful. In addition, the less extensive differentiation associated with utilitarian products makes consumers easier to benchmark across retailers ([60]). Thus, utilitarian purchases involve more product page views on competing retailers than hedonic purchases.
For the unconverted sessions illustrated in Figure 4, Panel D, the patterns observed for converted sessions are attenuated at the early and middle stages of the customer journey. In fact, we find that the overall late-stage channel usage patterns for the unconverted sessions are more like the early-stage channel usage patterns of converted sessions. Thus, analyzing channel usage in sessions that have not led to purchase is important and could be viewed as information search in the early stage of a shopping funnel. In summary, we found that search engines, reviews, deal sites, and competing retailers' product pages are important for utilitarian purchases, and we speculate that the nonconversion related to utilitarian purchases might be due to insufficient information search and alternative comparisons.
This study makes several important contributions to the established literature on hedonic and utilitarian consumption, the emerging research on customer journey, and the nascent literature on Big Data marketing. First, we extend the prior customer journey research on online information channel usage (e.g., [45]; [48]; Neslin and Shankar 2009) by introducing a social/psychological angle. In addition to the utility-centric perspective of prepurchase information channels, we find that affective mechanisms such as pleasure seeking and affective attachment documented in the H/U literature ([ 2]; [10]) have significant implications for information channel usage ([46]). For example, we find that hedonic purchases (e.g., toy products) utilize social media up to 10% more than their utilitarian counterparts (e.g., office supplies) throughout the purchase cycle and are less likely to switch retailer brands by browsing on competing retailers' websites. Thus, we employ new angles to study information channel choices related to online purchases and extend the H/U literature by testing its predictions in the customer journey context ([ 7]).
Second, our study provides a more nuanced view of the dynamic channel usage patterns during the paths to purchase. Our results highlight the importance of considering the temporal effect in the customer journey literature. By examining the early, middle, and late stages of the customer journey, we can derive more actionable insights. For example, we find that product page views on the transacting retailers are different between hedonic and utilitarian purchases up to two weeks before the conversion, and that deal sites are visited by consumers with utilitarian purchases one week before the final purchase. These nuanced findings indicate that when deploying marketing-mix, advertising, and promotion strategies, managers might want to leave a longer window for these strategies to be effective. In summary, we contribute to the customer journey literature (e.g., [34]; [48]) by demonstrating the necessity of considering the temporal dimensions in studying the purchase funnel and the customer journey ([47]).
Third, we provide an actionable framework for incorporating H/U scales in the online purchasing context. Unlike previous studies that have operationalized the H/U characteristics at the product-category level, we provide a more nuanced retailer-category vantage point. From our survey, we found that similar product categories sold at different retailers (e.g., electronics at Home Depot vs. Amazon) receive different H/U scores due to the characteristics of the retailer brands. Managers can leverage our scales to understand their H/U positions in relation to their competitors (see Figure 1 as an example for Home Depot vs. Amazon). Subsequently, these retailer-category-level H/U scores could be plugged into our multivariate multilevel model to identify effective touchpoints among different product categories (as shown in our toys vs. office example in Figure 4) or across different retailers. Therefore, we contribute to the H/U literature by highlighting the necessity of considering retailer brand differences in the product category's H/U perceptions and bringing the H/U scales to the context of touchpoint management ([35]).
Finally, we contribute to the growing literature of Big Data marketing ([ 9]; [41]; [76]; [82]) by demonstrating the great potential unlocked by "Big Data" through a multidata, multimethod approach. By combining primary and secondary data, we illustrate how primary data remain an important complement to large-scale clickstream data by providing critical perceptual enrichment. To integrate these data sources and harness the rich insights embedded in terabytes of data, we employ survey analysis, text mining, machine learning, and Bayesian modeling. Our multivariate multilevel model carefully considers the channel interdependency as well as the customer, retailer, and product heterogeneity through a hierarchical Bayesian approach, providing a viable framework for future research on customer journey. As a result, the study covers six channels and 20 product categories sold on 40 top internet retailers with 115 retailer-category combinations, with over $1 million in sales in a two-year period. To the best of our knowledge, this is the first study that incorporates such a comprehensive set of channels, product categories, and retailers in the customer journey literature. Future Big Data marketing research could use our approach as a framework to integrate multiple data sources encompassing both primary and large-scale secondary data and derive "big" insights accordingly.
Our results have several actionable implications for marketing managers. By identifying varying H/U effects on six information channels across customer journey for a total of 115 retailer-category combinations, our model offers a more principled, theoretically driven inductive approach for tailoring marketing strategies to consumers' shopping needs throughout their journey. In the following, we discuss general marketing strategies as well as Black Friday (and Cyber Monday) marketing ideas for retailers selling hedonic and utilitarian products. Multicategory retailers can customize their marketing strategies based on their product types accordingly.
First, for retailers selling hedonic products such as toys, we provide two actionable insights: ( 1) embrace social media and ( 2) monitor on-site product page views. Our study shows that social media is being used extensively throughout the customer journey and is increasingly becoming a channel for proactive information search ([22]). Therefore, marketing managers should consistently invest in social media marketing to entice more consumers to visit their websites. In addition, we find that there is a potential guilt-justification need for consumers who failed to complete hedonic purchases. Because social media is extensively used at the beginning of the journey, retailers could deploy social coupons with features that serve both the experiential and justification needs of hedonic purchases ([44]). Furthermore, we find that on-site product pages are leveraged extensively at the beginning of the journey and start to reduce one week before the purchase. Given the affective nature of hedonic purchases, retailers should constantly improve the experiential features of the product pages on their sites to convert more hedonic purchases. Moreover, retailers can monitor their page views and reach out to heavy browsers with promotions with a longer redemption time (e.g., two weeks).
Second, for retailers selling utilitarian products such as office supplies, we offer two prescriptions: ( 1) benchmark price and product and ( 2) prioritize search engine marketing (SEM). Our study shows that consumers tend to optimize their utilitarian purchase by visiting third-party review sites, exploring deal sites, and browsing product pages on competing retailers' sites. Therefore, retailers should employ price and product benchmark analysis to understand whether their price is above or below the market price and what potential customers see and experience when searching for similar products. Given the rise of competitive intelligence, managers could invest more in automated benchmarking tools to monitor, listen, and analyze the key competitive metrics (e.g., price, live deals, Yelp reviews) in real time. In addition, we find that consumers making utilitarian purchases tend to use search engines more toward the end of the journey. Because search engine optimization is more powerful in driving organic traffic at the top of the funnel, and SEM is more effective in driving conversions at the bottom of the funnel, retailers should prioritize SEM over search engine optimization. In addition, they should choose paid keywords that are more related to product features and benefits, provided that utilitarian purchases usually involve more product comparisons.
Finally, our dynamic view of channel usage across customer journey could offer specific guidelines for a Black Friday (and Cyber Monday) marketing strategy. Retailers selling hedonic products could market their promotional content on social media and send reminder emails inviting on-site traffic two weeks before Black Friday, when their customers start to engage in social media and on-site product pages. Retailers selling utilitarian products could extend their sales because consumers start to visit deal sites one week before they make purchases. In addition, they could optimize their SEM strategy during Black Friday or Cyber Monday to enhance the conversion rate.
Admittedly, this study has several limitations that future work could address. First, we examined observed channel usage behaviors mostly at the URL level. We do not have data on the specific types of information searches that consumers performed—for example, seeing what a friend "liked" on a social networking site, as well as the content about the products available at these channels. Although such data are difficult to collect and raise significant privacy concerns, analyzing them could offer additional insights regarding the H/U effect on specific types of touchpoints as well as how this effect interacts with product availability. In a similar vein, although we track usage of review sites, we do not have measures of consumers' use of product reviews within a retailer's site because on-site product reviews are often embedded within the product pages, thereby lacking explicit URL patterns to help discern when a consumer has read a review. These limitations make it difficult to make unique causal attributions from social media, search engine, and review site browsing to product purchases.
Second, although we use three time-windows to capture the dynamic channel usage effect at the early, middle, and late stages of the customer journey (8–14, 2–7, and 0–1 days, respectively), we do not consider the order of channel usage within each time window. The temporal proximity of channel usage might affect the final purchase. While acknowledging this limitation, our goal was to highlight the interplay between retailer-category-level H/U scores and online channel usage leading up to consumer purchases. We believe the work on multichannel attribution models (e.g., [48]) represents an important related body of literature that could be incorporated into future studies.
Similarly, we did not analyze search depth, which is often represented as total time spent on each URL. Search depth could be important for the H/U effect, as suggested by prior studies ([45]; [63]). Moreover, we did not employ category-specific cycle length because we could not differentiate product-based searches from baseline channel usage for certain channels. Future studies with detailed search log and clickstream data could solve this problem.
Finally, due to data limitations, we did not analyze other important information channels. These include email, referrals, television (see, e.g., [18]; [20]; [75]) and visits to brick-and-mortar stores (showrooming). Given the increasing importance of social media ([22]; [28]), further research could investigate differential effects of various types of social media during the customer journey: examples include firm-generated content ([15]), social influencers ([32]), and the consumers' individual expression on social media ([30]; [46]). In a similar vein, because our data set is restricted to desktop clickstream data, we did not observe purchases that occurred offline or through mobile sites. With the surge in mobile usage, cross-device channel usage could constitute an increasingly important future direction of research (e.g., [17]).
Our results show that consumers' utilization of various path-to-purchase channels differs across the retailer-category hedonic and utilitarian characteristics of purchased products. Specifically, consumers making hedonic purchases seek fun, enjoyment, and pleasure in their shopping process; prefer social media; and are more likely to browse product pages on the target retailers' website. By contrast, consumers making utilitarian purchases prefer channels that facilitate convenient and efficient search across alternatives. Therefore, they prefer leveraging search engines, reading more reviews on the third-party review sites, comparing prices on deal sites, and browsing more product pages on competing retailers' websites than hedonic purchasers.
Because channel usage changes dynamically throughout the customer journey, the H/U effect also varies. Specifically, for hedonic purchases, social media is used as early as two weeks before the final purchase. Conversely, for utilitarian purchases, third-party review sites are engaged two weeks before the final purchase, and this effect is attenuated toward the purchase day. Search engines and deal sites are utilized to a greater extent closer to the day of a utilitarian purchase. We also find that the H/U effect on product page views decreases over time, suggesting a consideration set narrowing process.
The analysis of unconverted sessions demonstrates a different H/U effect. For hedonic purchases, social media is only used by hedonic purchases closer to the end of the journey. Deal site visits and product page views on competing retailers' sites increase, indicating a possible guilt-justification demand commonly shown in hedonic consumption. Conversely, channels used by consumers making utilitarian purchases are not employed at the same level in the unconverted sessions, indicating that the nonconversion might be due to insufficient information search.
As digital marketing and monitoring spending continue to grow, our findings provide important implications for marketing managers who want to better allocate resources across digital channels. We also believe this study constitutes an important step toward examining the interplay between hedonic and utilitarian characteristics of online purchases and their implications for digital path-to-purchase channels—a direction on which we hope future research can continue to build.
Supplemental Material, jm.16.0391-File003 - Path to Purpose? How Online Customer Journeys Differ for Hedonic Versus Utilitarian Purchases
Supplemental Material, jm.16.0391-File003 for Path to Purpose? How Online Customer Journeys Differ for Hedonic Versus Utilitarian Purchases by Jingjing Li, Ahmed Abbasi, Amar Cheema and Linda B. Abraham in Journal of Marketing
Footnotes 1 Associate EditorP.K. Kannan
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242920911628
5 1The original data set before any matching, pruning, and sampling contains 13,805 consumers making 50,479 purchases on 40 websites, with approximately $2.7 million sales.
6 2The Hedonic and Utilitarian scores are mean-centered scores; the H and U scores are original scores.
7 3Search engine usage for utilitarian purchases is marginally significant, potentially indicating that with increasing experiential features and ever-improving search engine marketing (SEM) strategies, the importance of search engines for hedonic purchases is rising, thus shrinking the H/U effect.
8 4Similar to social media, privacy considerations prevent us from observing what people are searching for on search engines. As a result, these attributions of search effects are correlational in nature.
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By Jingjing Li; Ahmed Abbasi; Amar Cheema and Linda B. Abraham
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Record: 134- Paywalls: Monetizing Online Content. By: Pattabhiramaiah, Adithya; Sriram, S.; Manchanda, Puneet. Journal of Marketing. Mar2019, Vol. 83 Issue 2, p19-36. 18p. 14 Charts, 1 Graph. DOI: 10.1177/0022242918815163.
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Paywalls: Monetizing Online Content
In recent years, many news providers have begun monetizing online content through paywalls. While the premise behind paywalls is that the subscription revenue can be a new source of income, the externalities that might arise from this pricing change are unclear. The authors study two potential externalities of newspaper paywalls: ( 1) the effect of a paywall on the engagement of its online reader base and ( 2) the spillover effect on the print version of the newspaper. The engagement effect considers how the paywall altered the various engagement metrics among light and heavy readers of online news. The spillover effect is likely to arise if readers view print and online versions of a newspaper as substitutes, implying that increasing the price of the latter is likely to increase the demand for the former. Moreover, many newspaper paywalls offer bundles wherein print subscribers are provided free access to the online newspaper. Therefore, the value that a reader derives from the print subscription could be higher after the erection of the paywall. As a result, paywalls are likely to have a positive spillover effect on print subscription and, consequently, circulation. The authors document the sizes of the two externalities for the New York Times paywall and compare them with the direct subscription revenue generated. They comment on implications for newspapers and online content providers that are seeking mechanisms to monetize digital content.
Keywords: paywalls; newspaper industry; monetization; digital engagement; cross-channel spillovers
The movement of content (news, music, TV, etc.) to digital media has had a large and (mostly) negative impact on the economics of multiple industries over the last 15 years. This impact is largely a function of the fact that while consumption of digital content is growing by leaps and bounds, the willingness to pay for this content is very low. As a result, firms in these industries have been exploring different options to monetize this content. Because access to the online version of newspapers has traditionally been free, revenue from the online channel has come solely from advertising. Although online ad revenues have been growing steadily, this growth has not been sufficient to compensate for the offline ad revenue losses (industry estimates suggest that for every advertising dollar gained online, newspapers lose $16 in advertising offline; [38]). Thus, in recent years, newspapers have tried to tap into a new source of online revenue by imposing an access and/or a consumption fee, commonly implemented as a "paywall." As a result, several high-profile national newspapers such as the New York Times (NYT), the Los Angeles Times (LAT), and the Washington Post (WP) have implemented paywalls. However, the expectation that paywalls provide an additional source of revenue should be evaluated in the light of the fact that newspapers are complex businesses, with multiple interconnected parts. Specifically, newspapers are platforms that bring readers and advertisers together, with the two sides having a direct impact on each other. This effect is even more nuanced because online user types (loyal vs. casual) may react differentially to any changes ([29]; [31]; [40]). In addition, for (legacy) newspapers, the online and offline properties do not exist in isolation.
The heterogeneity in terms of user type and the interconnectedness of these various parts leads to multiple externalities that arise when a paywall is implemented, making it difficult to pinpoint its overall impact. In this article, we do this by explicitly considering two externalities. The first externality is the "engagement effect," or the impact of the paywall on reader engagement (measured through activity and consumption patterns on its website) for both loyal and casual readers ([14]).[ 6] Note that both sets of readers are important to the newspapers. Broadly speaking, loyal users bring in more subscription revenue, whereas casual readers (typically a majority of traffic) bring in more advertising revenue while also increasing the "footprint" of the newspaper. Any change in reader engagement is likely to have an impact on online advertising revenues. Second, and perhaps more importantly, we consider the effect of the online paywall on print (offline) readership and subscriptions. We call this the "spillover effect." The spillover effect can arise because of ( 1) a substitution effect: readers view print and online versions of NYT as substitutes, and thus, an increase in the price of the online version can increase the demand for print, or ( 2) a bundling effect: subscribing to the print version provides free access to the online content, and thus the erection of the paywall increases the value of print subscription. While there is some discussion in the popular press regarding the first externality and its corresponding role in influencing newspaper advertising revenues, there is little documentation about its direction and magnitude. In addition, the second externality, the spillover effect, has been virtually ignored. A useful baseline to examine the impact of these externalities is the incremental online subscription revenue from paywalls, which we call the "direct effect." Thus, our approach provides a holistic assessment of the implementation of the paywall. This is in contrast to popular press reports (highlighted previously) that, while mentioning the direct effect, do not provide estimates of its direction and magnitude. More importantly, they typically ignore the spillover effect completely.
We investigate the two externalities for the paywall commissioned by NYT in March 2011. Ideally, there are two possible research designs that could help identify the causal impact of the paywall. In the first such design, if there are a large number of newspapers serving independent markets, we could assign a random sample of these newspapers to the treatment condition by introducing a paywall and compare the quantities of interest for the treated newspapers with those experienced by the control newspapers. For the second ideal design, we could treat a random sample of readers of NYT to the paywall and compare their subsequent response to those of the untreated readers. However, the implementation of either design is unrealistic. We therefore attempt to infer the effect of the paywall by leveraging nonexperimental behavioral/market data by considering the change in online readership metrics and print circulation for NYT subsequent to the commission of the paywall. To parse out temporal trends in online news consumption that are common to all newspapers, we compare these changes for NYT with those experienced by national newspapers of similar popularity (i.e., USA Today [USAT], the Washington Post, the Wall Street Journal [WSJ], Chicago Tribune [CT], and New York Daily News [NYDN]). The availability of granular data on news consumption at the individual-user level allows us to test the validity of the key identifying assumption behind such a design—that there be no impact of the treatment on the control units. We do this in two ways. First, we verify in the raw data that reader substitution between NYT and the control newspapers was minimal. In addition, we compare the results of an analysis based on data from a subsample of users who are exclusive users of NYT or the control newspapers with those for our full sample.
A further potential concern with such an analysis is that the control newspapers may not be strictly comparable to NYT because they experienced different temporal trends prior to the erection of the paywall. Under such a scenario, it would be difficult to parse out the effect of the paywall from the naturally occuring differences in readership over time. To address this issue, we employ the synthetic control method ([ 1]; [ 2]). Intuitively, this approach creates a "synthetic control unit," computed as a weighted combination of all the control units. The weights are chosen such that the synthetic control closely matches the treated unit in terms of preperiod trends and other covariates. Thus, the synthetic control method naturally satisfies the parallel trend assumption required for inferring the causal effect of the paywall. As [ 3] note, the synthetic control methodology is one of the most prominent advances in causal identification and policy evaluation over the last 15 years. It has also been receiving increased attention in the marketing literature ([18]; [26]; [39]).
To infer the engagement effect of the paywall, we use web analytics data from comScore, which tracks the visitation behavior of panelists to NYT and the control newspapers. The availability of granular information on newspaper consumption at the individual level (as noted previously) enables us to rule out the possibility that the effect of the NYT paywall on its online visitation was driven by cross-newspaper substitution of readers. For the spillover effect, we obtain the weekday and weekend print circulation data for NYT, WP, and USAT from the annual audit reports published by the Alliance of Audited Media. All our analyses are conducted at the designated market area (DMA) level. We control for cross-sectional differences in tastes for newspaper readership by including controls in the form of DMA-specific fixed effects for each newspaper. In addition, we include controls for temporal evolution in preference for consuming online and print news. Therefore, our identification relies on how the differences in the variables of interest between NYT and the control newspaper changed subsequent to the paywall, after controlling for cross-sectional differences between these newspapers as well as broader temporal trends in news consumption. Furthermore, the variant of the synthetic control method that we employ allows us to leverage the rich variation in readership available at the DMA level for each of our control newspapers, to improve the identification of the paywall's effect on the treated newspaper.
Our results reveal that the number of unique visitors decreased by 16.8% as a result of the paywall. This drop was also accompanied by a significant reduction in engagement metrics such as visits, pages consumed, and duration per visitor. In addition, while heavy users of the NYT website (defined on the basis of their prepaywall usage) reduced their visits and pages viewed significantly subsequent to the paywall, the corresponding effect on the behavior of light users was not pronounced. Thus, the adverse effect of the paywall was driven mainly by the behavior of heavier users. Furthermore, we find that this effect is dampened among heavy users who are likely to have subscribed. Together, these results suggest that the quantity of advertising impressions that could be served at the NYT website decreased as a result of the paywall.
With regard to the spillover effect, we find that the introduction of the paywall arrested the decline of print subscriptions for NYT. As a result, compared with the counterfactual scenario of no paywall, the newspaper witnessed between a 1%–4% lift in readership in both weekday and weekend subscriptions when we use national newspapers such as USAT and WP as the control group. Furthermore, we attempt to parse out the substitution and bundling explanations for the positive spillover effect by exploring the differences in the spillover effect for subscriptions (which come with free digital access) versus single-copy sales (with no free digital access). Our results suggest that the positive spillover effect was more likely driven by the bundling effect than the substitution effect.[ 7]
While related work (e.g., [24]) has investigated the impact of paywalls on unique visitors, our research differs on multiple dimensions. First, in addition to the number of unique visitors, we investigate the impact of the paywall on industry-standard metrics of user engagement such as as pages consumed, visits per visitor, pages per visitor, and duration per visitor. Second, we exploit the disaggregate nature of our data to pinpoint the heterogeneity in the effect of the paywall on usage segments. Third, we isolate and quantify the spillover effect of the paywall on the legacy product (print subscriptions) and discuss the plausible mechanism driving this effect. The last aspect (i.e., the spillover effect) is a hitherto unexplored consequence of paywalls.
There are two broad implications of our findings. First, the results suggest that monetization of online content, especially in the form of metered paywalls, might suppress usage among loyal consumers. This can have implications for future growth potential of the firm. The loss of heavy users might hamper user-generated content creation, which might be detrimental to platforms such as newspapers, which may rely on such content in the future. Second, for media firms, a surprising, and usually overlooked, insight from our research is that the monetization of online content can have positive spillover effects for offline consumption. In situations where the offline channel is significantly more lucrative than its online counterpart (which is the case for newspapers and television), charging a fee for online content might arrest the erosion of offline revenues. In summary, our article proposes a framework that will help managers evaluate the various implications of monetizing digital content.
The rest of the article is organized as follows. First, we investigate the effect of the paywall on online readership. Next, we consider the spillover effect of the paywall on print readership and discuss the possible mechanisms behind the observed effect. We conclude with some comments regarding the implications of these findings for the broad issue of monetizing online content.
The first externality of the paywall that we consider is the engagement effect.[ 8] This effect is likely to arise because erection of the paywall can adversely affect the number of visitors to a newspaper's website ([ 9]). For example, prominent national (San Francisco Chronicle, Dallas Morning News) and international (the Sun [United Kingdom], Toronto Star) newspapers have withdrawn their paywalls reportedly because of large losses in traffic ([ 5]). Similarly, several local U.S. newspapers (e.g., Memphis Commercial Appeal, Columbia Tribune) also witnessed considerable decrease in traffic with the erection of the paywall ([ 6]). Moreover, as some paywalls (e.g., NYT's) limit the number of articles that can be viewed for free, they can also reduce reader engagement by lowering both a visitor's average number of pages and visit duration.
We use the web analytics data collected by comScore from January 2010 through May 2013 for our investigation. Given that NYT launched its paywall on March 28, 2011, our data span a reasonably wide window before and after the intervention. The web analytics data track the online activities of comScore panelists and include information on the websites visited by each panelist, date and time of the visit, number of pages viewed, and the time spent on each website. In addition, we have information on the zip code where each panelist resides. To provide a benchmark for inferring the effect of the paywall on traffic to the news website, we extracted information regarding activities on six websites: NYTimes.com, WashingtonPost.com, USAToday.com, WSJ.com, ChicagoTribune.com, and NYDailyNews.com. This resulted in a sample of 75,174 representative individuals identified by comScore's sampling strategy.[ 9]
Our primary interest is in studying how news consumption at NYT changed after its paywall was erected. To parse out any changes in consumption that might have occurred as a result of the general trend in news consumption, we use WP, USAT, WSJ, CT, and NYDN as part of the control group. We chose these five newspapers as reasonable "controls" because, like NYT, they are national newspapers. Moreover, the readership bases of the six newspapers are comparable (see Table 1) and represent the top set of U.S. news websites in terms of online traffic ([34]). In addition, WP, USAT, WSJ, CT, and NYDN did not undergo any changes in the pricing of online content during the period of our analysis. While WSJ always had a paywall, the other newspapers in our control group also commissioned paywalls in the time period after our analysis window. Table 2 lists the paywall launch dates for each newspaper in our analysis set.[10] Thus, we intend to use the readership trends for WP, USAT, WSJ, CT, and NYDN after March 2011 to project the trend that NYT would have experienced had it not instituted the paywall. This, in turn, would help us understand the causal effect of the paywall instituted by NYT.
Graph
Table 1. Top Newspapers in the United States by Circulation: Comparison of NYT, LAT, and Control Newspaper Readership (Print + Online).
| Newspaper | Rank in 2010 | Rank in 2011 | Rank in 2013 | Circulation in 2010 | Circulation in 2013 |
|---|
| WSJ | 1 | 1 | 1 | 1,752,693 | 2,378,827 |
| USAT | 2 | 2 | 3 | 1,671,539 | 1,674,306 |
| NYT | 3 | 3 | 2 | 1,086,293 | 1,865,318 |
| LAT | 4 | 6 | 4 | 1,078,186 | 653,868 |
| WP | 5 | 5 | 8 | 763,305 | 474,767 |
| CT | 7 | 7 | 10 | 657,690 | 414,930 |
| NYDN | 6 | 6 | 6 | 701,831 | 516,165 |
10022242918815164 Notes: Source: AAM's annual Newspaper Audit Reports and http://www.thepaperboy.com/usa-top-100-newspapers.cfm.
Graph
Table 2. Paywall Launch Dates for the Newspapers in our Sample.
| Newspaper | Paywall Launch Date |
|---|
| NYT | March 2011 |
| WSJ | April 1996 |
| WP | June 2013 (enforced December 2013) |
| CT | February 2016 |
| USAT | October 2017 |
| NYDN | February 2018 |
We begin by aggregating the web visitation data across panelists within a geographic market (DMA) to a monthly level. Our analysis includes the panelists residing in the top 25 DMAs (see Table 3)—these comprise over 70% of NYT's readership base.[11] Following industry practice and prior research (e.g., [25]), we use the following four metrics of online news consumption to measure and capture engagement: number of unique visitors, number of visits per visitor, pages viewed per visitor, and the average time spent per visitor on the website.
Graph
Table 3. Top 25 DMAs for NYT.
| Rank | DMA |
|---|
| 1 | New York |
| 2 | Los Angeles |
| 3 | Chicago |
| 4 | Boston |
| 5 | Philadelphia |
| 6 | Washington, DC |
| 7 | San Francisco/Oakland/San Jose |
| 8 | Dallas/Fort Worth |
| 9 | Atlanta |
| 10 | Seattle/Tacoma |
| 11 | Minneapolis/St. Paul |
| 12 | Phoenix |
| 13 | Detroit |
| 14 | Houston |
| 15 | Portland |
| 16 | Tampa/St. Petersburg/Sarasota |
| 17 | Orlando/Daytona Beach/Melbourne |
| 18 | Indianapolis |
| 19 | Denver |
| 20 | Hartford/New Haven |
| 21 | Cleveland |
| 22 | Pittsburgh |
| 23 | Miami/Fort Lauderdale |
| 24 | Sacramento/Stockton/Modesto |
| 25 | Charlotte |
Recall that we propose to study the effect of the NYT paywall by using a basket of five newspapers—USAT, WP, WSJ, CT, and NYDN—as controls. To address the concern that these three newspapers may have different temporal trends than NYT, we employ the synthetic control method. The synthetic control method permits the pooling of a combination of untreated units to create a composite control against which the treated unit can be compared. The central idea behind the synthetic control method is that the outcomes of the control units can be weighted so as to construct the counterfactual treatment-free outcome for the treated unit. The weights are chosen such that the treated unit and synthetic control have similar outcomes and covariates over the pretreatment period. Therefore, intuitively, the synthetic control method projects the treated units into a multidimensional space spanned by the control units in a way that they are matched on pretreatment outcomes. Thus, the treated and control units are rendered "more comparable" by adjusting the loadings on each of the dimensions (also referred to as "factors" herein).
More technically, a synthetic control for a single treated unit is formed by finding the vector of weights that minimizes [ ] subject to the weights in being positive and summing to 1, where and contain the pretreatment outcomes and covariates for the treated unit and control units, respectively, and captures the relative importance of these variables as predictors of the outcome of interest. Intuitively, the coefficient of interest (the parameter governing the treatment effect) is estimated by choosing , which forces the synthetic control to be as close to the treated unit as possible.
A primary benefit of a synthetic control estimator is that it reduces the reliance of the results on the parallel trends assumption that difference-in-difference/panel estimators are predicated on ([ 1]; [39]; [43]). Thus, our identification of the effect of the paywall on NYT's online visitation does not rely on the control newspapers necessarily following a similar trend. Our model specification can incorporate strict nonparametric controls in the form of newspaper-market fixed effects to account for idiosyncratic differences in tastes for each newspaper in each market. In addition, they also include fixed effects for each month in the data so as to capture the influence of any common (across newspaper) time trends.
We study the effect of the paywall on multiple treatment units, with each DMA constituting a different treatment unit. In our analysis, we employ the generalized synthetic control estimator ([43]), which is a variant of the synthetic control estimator. A key advantage of employing a generalized synthetic control method is the ability to handle data with multiple treated units (DMAs in our case). The generalized synthetic control method leverages information on differences in control newspaper readership at different markets to construct a synthetic control unit for NYT's readership in each market, which effectively enhances the reliability of inference by increasing the size of the control group from (number of control brands) to [(number of control brands) × (number of markets)]. ([43]). For ease of exposition, we omit subscript , which indexes DMAs, though, unless indicated otherwise, we estimate the model using DMA-level data on newspaper readership. Thus, each newspaper has additional DMA data points.
We specify the model for online newspaper readership as follows:
ln(Qknt)=γknDknt+x′nt α+λ′n ft+εknt,1
where indexes the online metric (visits, pages, duration, etc.) for newspaper in month . is an indicator variable that turns on for all months following the introduction of the paywall for only the treated newspaper. The term is the coefficient of interest and captures the heterogeneous treatment effect of the paywall along metric on newspaper at time . The term is a vector of observed covariates and is the corresponding vector of unknown parameters. The term is a vector of unobserved common factors, while denotes the corresponding factor loadings.[12] Although the treated and control units are influenced by the same set of factors, and the number of factors is fixed throughout the analysis period (t = month 1 through month 48), each newspaper × DMA combination can have a different set of loadings on the factors. Note that cross-sectional controls in the form of newspaper × DMA fixed effects and time (month) fixed effects can be considered two special cases of the unobserved factors by setting and , respectively. In all our model specifications, we impose additive two-way fixed effects, a very strict nonparametric way of accounting for the possibly evolving nature of unobservables specific to treated and control units ([43], p. 60). We discuss the steps involved in the generalized synthetic control estimation in the next subsection. Because the dependent variable is specified in logarithms, we can compute the percentage change in the readership metric for NYT as a result of the paywall as .[13]
An additional advantage of the generalized synthetic control method is its ability to report readily interpretable uncertainty estimates around the treatment effect. Traditional inference in the synthetic control method is performed through placebo tests, which involve a procedure of "synthetically" assigning treatment to control units, chosen one at time at random from the donor pool (i.e., the set of untreated newspapers) to compute a distribution of treatment effects. This enables us to assess whether the estimated treatment effect is larger than the collection of simulated treatment effects in placebo tests where no effect should exist. The generalized synthetic control method "automates" this procedure of running placebo tests and provides readily interpretable uncertainty estimates in the form of standard errors and confidence intervals around the estimated treatment effect, while preserving the efficiency of the estimation algorithm ([43]). In addition, the generalized synthetic control method has built-in safeguards to ensure that the results are robust in the presence of serial correlation. The estimator obtains uncertainty estimates around the treatment effect, using a parametric bootstrap procedure through resampling of the residuals, conditional on observed covariates and unobserved factors and factor loadings. This method allows for the preservation of the serial correlation within the units, thus avoiding underestimation of the standard errors from serial correlation. The detailed algorithm describing the implementation of the parametric bootstrap procedure is available in [43], p. 65.
Next, we discuss a few possible threats to the validity of our estimation of the causal effect of the paywall on online news consumption. The first issue is whether treatment was anticipated—that is, whether consumers anticipated the launch of the paywall in a way that motivated either ( 1) elevated levels of news consumption on the site right before the paywall went up or ( 2) avoidance of the website in this period on account of NYT's decision to commission a paywall. To test both of these possibilities, we performed simple checks by comparing trends in NYT visitation patterns over a narrow window immediately preceding paywall launch. A paired (across-DMAs) two-tailed t-test comparing NYT page consumption levels per visitor (visits per visitor), during March 2011 through the month before NYT's paywall commission, with its preceding month of February 2011, had a t-value of −.99 (−.84), with d.f. = 165, while the analogous t-values for the tests comparing these measures for February 2011 with January 2011 numbers were.72 (.62). Thus, we find limited evidence of anticipatory effects in visitation/news consumption behavior at NYT in advance of the paywall.
A second issue to consider is whether the design of the NYT paywall was chosen strategically by the firm. Specifically, we are unable to separate out the effect of the paywall's introduction from the effect of changes to the newspaper's quality that might have been prompted by the paywall. As [12] notes, there is little evidence that the quality of NYT's online offering changed concomitantly with the paywall. This gives us confidence that the rigorous temporal controls included in the model are appropriate to account for changes in our dependent measures witnessed over time unrelated to paywall commission. Furthermore, it may be important to parse out the effects of any purely coincidental strategic actions by NYT at the time of the paywall (i.e., these actions would have occurred even if the paywall were not introduced). If this were to be an issue, the observed effects of the paywall should be unlikely to hold for other similarly sized national newspaper(s) that also launched paywalls. To explore this possibility, we use a similar research design to evaluate the effect of the paywall that LAT launched in March 2012. To the extent that the estimated effects for the LAT paywall (commissioned in a completely different time window) are similar to those for NYT, we can gain some confidence that the results both are unlikely to be contaminated by coincidental strategic changes unrelated to the paywall and can be generalized to similar newspapers. Nevertheless, similar to the majority of empirical research focused on inferring causal effects from nonexperimental data (e.g., [ 8]; [16], [17]), we are unable to rule out completely the possibility that there may be factors unobservable to us that played a role in NYT's paywall launch decision.
We present the results of the model (Equation 1) intended to explore the paywall effect on online readership in Table 4. These results suggest that the NYT paywall had a negative and statistically significant effect on engagement metrics (i.e., number of visits, pages visited, and the duration per visitor). In addition, the number of unique visitors decreased by 16.8% as a result of the paywall.
Graph
Table 4. Effect of the Paywall on NYT Online Visitation, Aggregate Data, and Generalized Synthetic Control.
| ln(Unique Visitors) | ln(Pages) | ln(Visits per Visitor) | ln(Pages per Visitor) | ln(Duration per Visitor) |
|---|
| Est. | SE | Est. | SE | Est. | SE | Est. | SE | Est. | SE |
|---|
| NYT × Paywall | −.184** | .029 | −.428** | .073 | .010 | .125 | −.104 | .127 | −.112 | .148 |
| # Observations: treated | 1,025 |
| # Observations: control | 5,125 |
- 20022242918815164 **p <.01.
- 30022242918815164 Notes: Standard errors are obtained from a placebo test and are bootstrapped with 1,000 replications. Two-way fixed effects for DMA × newspaper and month are included. The treatment effect is evaluated at the mean counterfactual.
In terms of the differential effects on light versus heavy consumers of online news, there are two alternative views. On the one hand, [31] suggests that paywalls are likely to deter casual visitors and/or readers with low willingness to pay for online content.[14] On the other hand, a metered paywall such as the one erected by NYT is likely to impose a constraint only for heavy users. Therefore, the paywall is likely to have an adverse effect on the more engaged readers of NYT. Notwithstanding the ambiguity regarding whether the paywall is likely to have a greater effect on visits among heavy or light users, the debate highlights the importance of considering the effects on these groups separately.
We investigate whether the paywall had a differential impact on light versus heavy users by dividing panelists into two groups in line with their prepaywall usage. Specifically, we classify a panelist as a heavy user if his or her prepaywall average number of pages accessed at NYT was higher than the median value of 4.1 pages. We first examine the impact on unique visitors: the results in Table 5 reveal that the paywall adversely affected the number of unique visitors by 11.3% among light users and 57.2% among heavy users. We then turn to the engagement metrics: these suggest that the impact of the paywall was more negative for the heavy users.
Graph
Table 5. NYT Paywall on Online Visitation: Breakup by Activity Level, Median Split, Aggregate Data, and Generalized Synthetic Control.
| ln(Unique Visitors) | ln(Pages) | ln(Visits per Visitor) | ln(Pages per Visitor) | ln(Duration per Visitor) |
|---|
| Est. | SE | Est. | SE | Est. | SE | Est. | SE | Est. | SE |
|---|
| Light × NYT × Paywall | −.120† | .065 | .023 | .125 | .022 | .044 | .086 | .084 | .289* | .113 |
| Heavy × NYT × Paywall | −.858** | .150 | −3.560** | .660 | −.632** | .180 | −.884** | .316 | −.390 | .362 |
| # Observations: treated | 1,025 |
| # Observations: control | 5,125 |
- 40022242918815160 †p <.10.
- 50022242918815160 *p <.05.
- 60022242918815160 **p <.01.
- 70022242918815160 Notes: Standard errors are obtained from a placebo test and are bootstrapped with 1,000 replications. Two-way fixed effects for DMA × newspaper and month are included. The treatment effect is evaluated at the mean counterfactual.
We further verify the sensitivity of the results to alternative characterizations of heavy versus light usage. Rather than classifying panelists into heavy versus light users on the basis of a median split, we perform this classification drawing on their actual usage. Specifically, we classify a panelist as a heavy user if his or her average number of visits to NYT is greater than a certain number of pages. The paywall imposed a limit of 20 articles per month that could be accessed without payment. Because our data contain information on the number of pages accessed by each panelist, and not the number of articles, we try different page thresholds under the assumption that a typical NYT article has approximately 1,200 words, ranging from one to two pages ([19]; [33]).
In our empirical analysis, we use three thresholds: 20, 30, and 40 pages. Note that the definition of heavy usage becomes more stringent as we move from a threshold of 20 pages to 40 pages. Therefore, comparing the results across alternative thresholds will help us assess how the effect of the paywall changes with the degree of heavy usage. We present the results from this analysis in Table 6. Overall, we find consistent results (across these thresholds) that the paywall instituted by NYT had an adverse effect on engagement among heavy users.
Graph
Table 6. NYT Paywall on Online Visitation of Users with Varying Activity Levels, Aggregate Data, and Generalized Synthetic Control.
| ln(Unique Visitors) | ln(Pages) | ln(Visits per Visitor) | ln(Pages per Visitor) | ln(Duration per Visitor) |
|---|
| Est. | SE | Est. | SE | Est. | SE | Est. | SE | Est. | SE |
|---|
| 20 Pages | | | | | | | | | | |
| Light × NYT × Paywall | −.117* | .058 | .095 | .131 | −.04 | .05 | .142 | .097 | .302** | .115 |
| Heavy × NYT × Paywall | −.751** | .115 | −3.110** | .373 | −.788** | .165 | −1.508** | .31 | −1.806** | .393 |
| 30 Pages | | | | | | | | | | |
| Light × NYT × Paywall | −.149** | .06 | −.691** | .297 | −.019 | .045 | .109 | .088 | .283* | .111 |
| Heavy × NYT × Paywall | −.571** | .086 | −2.677** | .26 | −1.082** | .168 | −2.010** | .27 | −2.106** | .284 |
| 40 Pages | | | | | | | | | | |
| Light × NYT × Paywall | −.154** | .061 | −.063 | .132 | −.039 | .045 | .06 | .082 | .237* | .113 |
| Heavy × NYT × Paywall | −.148 | .138 | −1.994** | .202 | −.988** | .133 | −1.667** | .171 | −1.783** | .198 |
| # Observations: treated | 1,025 |
| # Observations: control | 5,125 |
- 80022242918815170 *p <.05.
- 90022242918815170 **p <.01.
- 100022242918815170 Notes: Standard errors are obtained from a placebo test, and are bootstrapped with 1,000 replications. Two-way fixed effects for DMA × newspaper and month are included. The treatment effect is evaluated at the mean counterfactual.
There are two potential explanations for the stronger adverse effect of the paywall among heavy users.[15] First, the paywall can deter the ability of engaged users to share their content with others because the recipients may find it harder to read this content under the paywall ([28]). Although we cannot formally test whether this mechanism is indeed driving our results, previous research (e.g., [32]) has documented a reduction in online word-of-mouth activity pertaining to popular newspaper articles in the periods following the paywall. Second, as noted previously, heavy users are more likely to be constrained by the limit on the number of free articles imposed by the paywall, leading to the asymmetric reduction in engagement. However, note that the number of articles constraint should not apply to heavy users who subscribe to NYT (either online or offline).
To understand whether the constraint imposed by the paywall on nonsubscribers drove the decrease in engagement among heavy users, we need to study how subscribers and nonsubscribers responded to the erection of the paywall. However, the comScore data do not contain information on whether a panelist was a subscriber to NYT. Typically, only the publisher is privy to the proprietary information on subscription status. Therefore, we adopt an alternative approach by inferring subscribers on the basis of users' postpaywall usage. As we have noted, the NYT paywall allows a user to view 20 articles a month without subscription. Thus, we can identify a panelist as a subscriber by noting whether they accessed more than 20 articles in a month. Using the same logic as before, we translate this article limit to page limits: 20 and 40 pages. However, this count needs to be adjusted for traffic that came in through social media sites such as Facebook and/or search engines such as Google—the paywall's "leaky" design did not include these visits in the 20-article limit.[16]
Fortunately, the comScore data contain information on the source (referring) website from which a user accessed NYT. This enables us to identify the number of accessed pages that would be counted toward the limit for each user. In other words, we drop all page views from referrals (from search engines, news aggregators, and social media sites) and consider only the number of directly accessed pages (20 or 40). Once a user crosses this page limit in a given month, we classify him or her as a subscriber for all subsequent months.[17] It is important to note that classifying subscribers according to this strategy is likely to be noisy. However, by considering a wide range of thresholds to define subscribers, we are able to assess the robustness of our results to this noisiness in classification. We aggregated the data to the newspaper level for all months for this analysis, as we encountered estimation challenges with the synthetic control method with DMA-level data. [ 1] note that, to prevent its applicability where inappopriate, the synthetic control method employs a safeguard in that it fails to provide a result when the counterfactual units (i.e., the weighted combination of untreated units) fall outside an acceptable region (the convex hull) as governed by the treated units—the DMAs, in our case. We thus aggregate up to the newspaper level for this analysis to avoid this problem.
This analysis allows us to examine whether the adverse effect of the paywall is restricted to nonsubscribers. Specifically, we focus on the effect of the paywall for four groups of customers: (heavy vs. light users, defined by their prepaywall usage) × (subscribers vs. nonsubscribers, defined by their postpaywall activity, counting only direct visits to the NYT website). The results from this analysis appear in Table 7. As the table illustrates, the adverse effect of the paywall is not pronounced among subscribing users. Overall, we consistently find that among heavy users, nonsubscribers reduce their activity on NYT more than subscribers, in line with the intuition that either the act of subscribing attenuates the drop in engagement for heavy users or users who anticipate using NYT more tend to subscribe to the paywall.
Graph
Table 7. NYT Paywall on Online Visitation: Tracking Behaviors of Subscribers and Nonsubscribers, Aggregate Data, Generalized Synthetic Control.
| ln(Unique Visitors) | ln(Pages) | ln(Visits per Visitor) | ln(Pages per Visitor) | ln(Duration per Visitor) |
|---|
| Est. | SE | Est. | SE | Est. | SE | Est. | SE | Est. | SE |
|---|
| 20 Pages | | | | | | | | | | |
| Light × Nonsubs. × NYT × Paywall | −.170** | .061 | −.144 | .126 | −.039 | .043 | .003 | .089 | .082 | .118 |
| Light × Subs. × NYT × Paywall | −.548** | .219 | −3.142 | 2.662 | .223 | .533 | −.941 | 1.157 | −4.815 | 2.846 |
| Heavy × Nonsubs. × NYT × Paywall | −.520** | .079 | −2.158** | .204 | −.629** | .130 | −1.640** | .250 | −1.803** | .269 |
| Heavy × Subs. × NYT × Paywall | −.032 | .021 | −.152† | .082 | −.108† | .061 | −.321* | .147 | −.230 | .243 |
| 40 Pages | | | | | | | | | | |
| Light × Nonsubs. × NYT × Paywall | −.164** | .058 | −.236† | .133 | −.052 | .041 | −.067 | .083 | .025 | .115 |
| Light × Subs. × NYT × Paywall | −1.124 | .727 | −.373 | 5.351 | 1.825 | 1.048 | 2.682 | 1.792 | 3.888 | 2.723 |
| Heavy × Nonsubs. × NYT × Paywall | −.136 | .096 | −1.334** | .181 | −.638** | .101 | −1.238** | .129 | −1.301** | .145 |
| Heavy × Subs. × NYT × Paywall | .004 | .018 | −.035 | .059 | −.013 | .032 | −.050 | .054 | −.008 | .081 |
| # Observations: treated | 1,025 |
| # Observations: control | 5,125 |
- 110022242918815170 †p <.10.
- 120022242918815170 *p <.05.
- 130022242918815170 **p <.01.
- 140022242918815170 Notes: Standard errors are obtained from a placebo test and are bootstrapped with 1,000 replications. Two-way fixed effects for DMA × newspaper and month are included. The treatment effect is evaluated at the mean counterfactual.
An underlying assumption behind the synthetic control method is that contributors to the donor pool (i.e., the untreated newspapers) should not have experienced treatment during the analysis period. However, as we discussed previously, WSJ had launched a paywall in 1996, well before the period of our analysis. Because the WSJ's paywall was in place well before the erection of NYT's paywall in 2011, it is unlikely that WSJ's paywall operations interfered with how the NYT paywall influenced the engagement of its users. Nevertheless, to be conservative, we reestimated all our models using the generalized synthetic control method by omitting WSJ from the donor pool. We present the results in which we examined the effect of the NYT paywall on light and heavy users, for a cutoff of 40 pages, in Web Appendix A. These results suggest that the key findings remain unaltered when we excluded WSJ from the donor pool.
A potential concern with our analysis is that the limits imposed by the NYT paywall might have induced some of its readers to substitute to the control newspapers. If such substitution exists, we would be double-counting the effect of the paywall in our analyses wherein we treat the control newspapers as being unaffected by the treatment.
We verify whether substitution is bound to be problematic in our context in two ways. First, we examine whether there is model-free evidence of substitution by considering how users change their online reading habits of the control newspapers when they modify their online usage of NYT after the paywall. If NYT and the control newspapers are substitutes, we should observe that a decrease (increase) in usage of NYT should be associated with an increase (decrease) in usage of the control newspapers. In Table 8, we present a two-way frequency tabulation of the number of individuals in our sample who demonstrated an increase, decrease, or no change (within 5%) in their consumption levels (as measured by the number of pages consumed) from before and after the paywall period across treated and control newspapers. The numbers indicate that the majority of users did not change their usage of the control newspapers even when they changed their consumption of news content at NYT after the paywall. This gives us confidence that substitution is unlikely to have affected our results.
Graph
Table 8. Two-Way Frequency Table of Change in Newspaper Visitation from Prepaywall to Postpaywall: Exploring Substitution Across Treated and Control Newspapers.
| # Users |
|---|
| NYT Pages |
|---|
| Control Group | Increased | No Change | Decreased |
|---|
| USAT | Increased | 62 | 142 | 1 |
| No change | 630 | 67,588 | 3,745 |
| Decreased | 4 | 1,823 | 1,179 |
| WSJ | Increased | 48 | 85 | 2 |
| No change | 643 | 69,045 | 4,282 |
| Decreased | 5 | 423 | 641 |
| NYDN | Increased | 55 | 109 | 4 |
| No change | 635 | 68,358 | 4,240 |
| Decreased | 6 | 1086 | 681 |
| WP | Increased | 77 | 206 | 2 |
| No change | 609 | 68,361 | 3,874 |
| Decreased | 10 | 986 | 1,049 |
| CT | Increased | 9 | 41 | 1 |
| No change | 685 | 68,670 | 4,537 |
| Decreased | 2 | 842 | 387 |
Second, we employed the generalized synthetic control method on a restricted sample of exclusive users in our data set who accessed either NYT or one of the control group newspapers in either period (i.e., users who used NYT and any one of the control group newspapers in either pre- or postpaywall periods are excluded from the analysis). To construct our data set for this analysis, we aggregated the individual level newspaper consumption data to the DMA-month level for each newspaper. Approximately 27% of users in our sample accessed both treated and control newspapers in either the pre-/postpaywall periods. We present summary statistics in Table 9, comparing the full sample with the exclusive sample; the two data sets are alike on our key measures of interest in the preperiod. If the results are robust for this set of exclusive consumers of each newspaper for whom we can rule out substitution, we can infer that substitution is unlikely to have biased the results from the analysis based on the broader sample of users.
Graph
Table 9. Summary Statistics for Full Sample and Exclusive Sample (Users Who Accessed Either NYT or One of the Control Newspapers, but Not Both).
| Full Sample | Nonoverlapping Users |
|---|
| (Preperiod, per Quarter) | NYT | Control | NYT | Control |
|---|
| Visits | 16.06 | 8.30 | 16.16 | 8.39 |
| Pages | 37.82 | 27.53 | 38.11 | 27.89 |
| Duration | 102.83 | 35.97 | 103.24 | 36.20 |
We present these results based on this sample of exclusive users in Web Appendix A, which suggest that the paywall adversely affected engagement among heavy users of NYT. Therefore, we contend that our key results are not driven by users substituting between newspapers as a result of the paywall instituted by NYT.
A potential concern with the analysis is that the estimated treatment effect of the paywall includes the effect of the structural change in price as well as any associated promotions that NYT might have initiated comcomitantly to recruit subscribers. To the extent that these additional promotional efforts (if they exist) are a result of NYT introducing the paywall, the estimated treatment effect may be interpreted as a consequence of the implementation of the paywall. Nevertheless, we examine the role of the newspaper's advertising by including acquisition focused promotion (with subscriber-acquisition-focused ad expenditure as a proxy) as a covariate in our analysis. To this end, we collect time-series data on advertising expenditures focusing on subscription drives by these newspapers at the national level, from Kantar Media's Ad$pender database. We test for the robustness of our results on users classified into light/heavy on the basis of their activity levels, to the inclusion of ad spending as a covariate. We find that the results are substantively unaffected (see Web Appendix A). In addition, we find that the effect of promotion- (subscriber-acquisition) focused advertising spending by the newspapers is not significant in all cases, after incorporating rigorous controls for time trends, and so on. This is intuitive because we already include rich nonparametric controls in the form of two-way fixed effects effects while estimating our generalized synthetic control models.[18]
Our results suggest that the paywall instituted by NYT adversely affected engagement among its heavy users. However, because our analysis is based on data from one newspaper, it is not clear whether our results can be generalized to other contexts. To explore whether similar results are likely to hold for other national newspapers, we consider the paywall instituted by LAT in March 2012. To this end, we adopt a research design similar to the one discussed previously by using data on online visitation to LAT's website among comScore panelists. We stratify users into light and heavy users, based on a median (4.4 pages) split, based on their preperiod activity levels on LAT's website, similar to our approach for NYT. We use USAT, WP, WSJ, CT, and NYDN as part of the donor pool. We present the results from this analysis in Web Appendix A. These results suggest that the key results that the paywall adversely affected engagement among heavy users of NYT are replicated for LAT (see Web Appendix A). This provides us some confidence that the key findings documented herein may not be unique to NYT. Furthermore, they also enhance our confidence that the observed effect of NYT's paywall on its visitation patterns is unlikely to have accrued on account of factors that merely coincided with the paywall rollout but were unrelated to the paywall launch decision (such as the newspaper's decision to change its font size on the website or invest in its newsroom with an objective of improving the general quality of its news offerings).
The spillover effect of the online paywall on print readership can arise through two possible mechanisms. First, if readers view print and online versions of a newspaper as substitutes, increasing the price of the latter is likely to increase demand for the former. Second, many newspaper paywalls, including the one instituted by NYT, offer print subscribers free access to the online newspaper. Such a bundled pricing strategy suggests that the value a reader derives from print subscription is likely to have increased subsequent to the erection of the paywall. As a result, paywalls can have a positive spillover effect on print subscription and, consequently, circulation. In addition to the positive benefit from generating revenue from readers, the paywall may allow newspapers to boost their print ad revenues by projecting a higher circulation to its advertisers. This is especially important given that an average print reader brings in 16 to 228 times more in advertising revenue than an online reader ([ 7]; [38]). Thus, the effect of a positive spillover on readership will be larger than the additional revenue generated from the online side.[19]
We obtained data on print circulation from the Alliance for Audited Media's (AAM) annual Audit Reports for 2005 through 2013. As in the case of online visitation data, we collected this information for NYT and three other newspapers with similar circulation (USAT, WP, and WSJ).[20] The AAM reports the circulation data at the annual level. Therefore, we have six years of data prior to the erection of the paywall (i.e., 2005–2010) and three years after the paywall (i.e., 2011–2013). We collected these data at the DMA level for 202 DMAs in the United States. The circulation data are further broken down by weekdays versus weekends. Next, we also collected these circulation data for the most popular local newspaper in the 25 largest DMAs. The idea is to verify the robustness of the results by treating local newspapers (as opposed to the national newspapers listed previously) as the control group.
We present the average circulation numbers before (2005–2010) and after (2011–2013) the paywall for NYT and the three other national newspapers in Table 10. When we used preperiod circulation as an evaluation metric, USAT was the most popular newspaper, with 2.45 million subscribers, followed by WSJ (1.92 million) and NYT (1.69 million). Table 3 provides a list of the top 25 DMAs for NYT by circulation. These DMAs account for approximately 75% of NYT's print circulation, in the average year in our data. Across the 202 DMAs in our sample, the average DMA had about 11,091 ( 7,734) USAT subscribers compared with 5,146 ( 4,080) NYT subscribers in the pre- (post-) period.
Graph
Table 10. English Print Circulation Trends for Each Newspaper.
| Print Circulation for Each Newspaper |
|---|
| NYT Weekend | NYT Weekday | USAT Weekend | USAT Weekday | WSJ Weekend | WSJ Weekday |
|---|
| Prepaywall | 1,686,020 | 1,034,263 | 2,454,332 | 2,207,041 | 1,919,427 | 2,039,218 |
| Postpaywall | 1,407,170 | 819,372 | 1,742,403 | 1,554,420 | 1,474,160 | 1,502,907 |
| Percentage change (pre- to postpaywall) | −16.54% | −20.78% | −29.01% | −29.57% | −23.20% | −26.30% |
| Avg. year-on-year percentage change (2005–2013) | −3.28% | −1.89% | −6.54% | −6.19% | −4.39% | −5.20% |
| Avg. year-on-year percentage change (2005–2010) | −3.18% | −4.19% | −4.41% | −3.47% | −2.59% | −1.59% |
| Avg. year-on-year percentage change (2011–2013) | −3.44% | 1.93% | −10.11% | −10.72% | −7.37% | −11.23% |
Turning to the temporal pattern, paid circulation of U.S. print newspapers decreased consistently during our analysis period.[21] Our data from the four national newspapers—NYT, WP, USAT, and WSJ—exhibit a similar pattern. In Table 10, we present the average annual (i.e., year-on-year) growth rates for these newspapers. These data suggest that, on average, the three newspapers experienced a decline in circulation figures of 3.0% to 7.9% during the period of our analysis.
To examine the extent of possible substitution between print versions of NYT and control newspapers, we exploit the fact that we have circulation and subscription data for the print newspapers across many DMAs. Using these data, we perform analyses in the same spirit as in the case of online readership to understand if there is any substitution between print versions of these newspapers. The premise is that different DMAs varied in the extent to which the print readership of NYT changed over time. If there is indeed substitution between the various newspapers, markets that saw a steep decline in NYT print readership should also have experienced a steep increase in the readership of the control newspapers. Overall, change in NYT readership is not associated with a concomitant change in the readership of other newspapers (see Web Appendix A). This suggests that there is limited concern about substitution between newspapers as a result of the paywall, contaminating our characterization of the spillover effect.
Next, we consider the average annual growth rates for these newspapers before and after the NYT paywall (see Table 10). These results highlight two aspects of the print circulation data. First, prior to the erection of the paywall, NYT circulation decreased at rates similar to those experienced by other national newspapers. Second, we find that between 2011 and 2013, WP, USAT, and WSJ experienced steeper declines in their circulation than during the 2005–2010 period. This pattern is consistent with the steep decline in print circulation experienced by the U.S. newspaper industry in the last decade ([13]). Contrary to this pattern, the results in Table 10 imply that NYT experienced lower declines during this period. Together, these data patterns are suggestive of a positive spillover effect of the paywall on print circulation. In Figure 1, we present a histogram of the average percentage year-on-year change in weekday print circulation for NYT over our analysis duration to illustrate the cross-sectional variation in the data—the plot indicates that the majority of markets experienced a small percentage decline in circulation over time (the average percentage change across markets in NYT's weekday print circulation ranges between 2%–3%, as shown in Table 10).[22] Thus, the effect is unlikely to be driven by the presence of outliers. Next, we formalize the analysis by including controls for potentially differential rates of evolution of print circulation across the newspapers. Specifically, we include DMA-specific linear and quadratic time trends to account to differential temporal evolution in readership across the DMAs in our data. Thus, we estimate the effect of the paywall intervention on print readership by exploiting the residual variation in shares after accounting for those motivated by changes in seasonal changes to print readership at the market level.
Graph: Figure 1. Change in NYT weekday print circulation.Notes: Figure 1 presents a histogram of the percentage of year-on-year change in NYT weekday circulation.
We employ a combination of approaches to estimate the effect of the NYT paywall on its print circulation. First, we use the generalized synthetic control method to estimate the effect of the NYT paywall, which is very similar in spirit to the models discussed thus far for online visitation, with the exception that we use annual data in this case. However, results from the generalized synthetic control can be less reliable when the pretreatment observation window is rather short (T < 10 periods). In such cases, [43] suggests that results from the generalized synthetic control method should be validated against alternative estimation methods that are less dependent on the need for long observation windows. Therefore, as a robustness check, we specify a panel model using the same data set, to examine the effect of the paywall on print readership of newspaper in market in year :
Rnjt=λn+λj+μIτ+δIn=NYT × Iτ+ϑpnjt+℘j t+ϒj t2+εnjt,2
where is a time-indicator signifying pre-/postpaywall launch and takes on the value of 1 postpaywall and 0 otherwise. We use print readership of USAT and the WSJ to establish a baseline/control for the effect of NYT's paywall (we do not use WP because DMA-level circulation data for this newspaper were not available in the AAM database). We use two dependent variables (as part of the vector ) consisting of the weekday and weekend newspaper print circulation share (i.e., the percentage of readership in each market, which is constructed by dividing the market-level circulation by the number of households) for the analysis. The terms and capture reader preference for newspaper consumption in market and for newspaper , respectively (controlling for differences in taste for readership of different newspapers and in different markets), while captures any time specific effects of the postperiod (common to both newspapers). is our coefficient of interest and measures the causal average effect of the paywall on NYT print newspaper readership. We also control for market-specific time trends in newspaper readership using a parametric function (the term in Equation 2).
The basic premise behind the difference-in-differences specification is that the temporal trends in print circulation for the control newspapers postpaywall will inform us about the corresponding trends for the NYT had the paywall not been instituted. We therefore verify whether NYT and the control newspapers experienced similar temporal trends prior to the erection of the paywall. To do this, we regress preperiod Sunday readership for all newspapers on a newspaper-specific linear year-time trend, after including DMA and newspaper fixed effects and clustering standard errors across DMAs to account for any serial correlation in readership. We do not see significant differences in the annual circulation trends for NYT versus USAT (F( 1, 200) =.04, p =.83) or for NYT versus WSJ (F( 1, 200) = 1.05, p =.31).
We present the results from the method of generalized synthetic control in Table 11 for the full sample of 202 DMAs, as well as only the top 25 DMAs, for comparison. Overall, the results indicate that the effect of the paywall is positive and statistically significant, with the effect on circulation share ranging from.38–.52 share points.[23]
Graph
Table 11. Effect of Paywall on Print Readership, Generalized Synthetic Control.
| All DMAs | Top 25 DMAs |
|---|
| Weekday Circulation Share (%) | Weekend Circulation Share (%) | Weekday Circulation Share (%) | Weekend Circulation Share (%) |
|---|
| DV = | Est. | SE | Est. | SE | Est. | SE | Est. | SE |
|---|
| NYT × Paywall | .35** | .02 | .34** | .03 | .40** | .11 | .32* | .14 |
| N (treated) | 202 | 25 |
| N (control) | 404 | 50 |
- 150022242918815170 *p <.05.
- 160022242918815170 **p <.01.
- 170022242918815170 Notes: All models include two-way (newspaper × market and year) fixed effects. Newspaper sample: NYT (treated), USAT and WSJ (donor pool).
We report the results from the panel regression with individual newspapers as controls in Table 12. The results reveal a significantly positive coefficient on the NYT × paywall interaction term for both weekday and weekend circulation shares. This implies that the paywall had a positive effect on the offline readership of NYT, in terms of either a slower rate of decline compared with the control newspapers or growth. This result is consistent with the model-free evidence presented in the previous section. The estimates suggest that the NYT paywall had a positive effect on its print circulation to the extent of.18–.68 share points, representing between a 1.05%–3.98% lift in print subscriptions compared with the counterfactual scenario without the paywall. Thus, we see the impact of firm actions online on the behavior of its customers offline (similar results for a nonmedia market have been also been documented in [41]).
Graph
Table 12. Robustness Check: Effect of Paywall on Print Readership, Panel Regression.
| All DMAs (USAT as Control Group) | All DMAs (WSJ as Control Group) |
|---|
| Weekday Circulation Share (%) | Weekend Circulation Share (%) | Weekday Circulation Share (%) | Weekend Circulation Share (%) |
|---|
| DV = | Est. | SE | Est. | SE | Est. | SE | Est. | SE |
|---|
| NYT × Paywall | .46** | .02 | .50** | .03 | .26** | .02 | .20** | .02 |
| DMA dummies, DMA-specific linear and quadratic time trends | ✓ | ✓ | ✓ | ✓ |
| .63 | .49 | .70 | .67 |
- 180022242918815170 **p <.01.
- 190022242918815170 Notes: Standard errors are clustered by DMA.
To assess the robustness of our estimates to the choice of control group, we consider an alternative analysis with the most popular local newspaper in each market as the baseline/control. Because we could not obtain credible circulation numbers for local newspapers in each of the 202 DMAs, we restrict our analysis to the top 25 DMAs. We present the results from this analysis in Table 13. Consistent with the results from the analysis with national newspapers used as controls, we find that the NYT paywall had a positive effect on the offline circulation of NYT. However, note that the magnitude of this effect is larger, at 2.27 circulation share points, when we use the local newspapers as controls (for reference, the effect of the paywall for the top 25 DMAs, using USAT as the control newspaper, was.62–.55 share points for weekday and weekend circulation, when we estimated a panel regression model; see Web Appendix A). This larger effect can perhaps be rationalized by the steeper drop in print circulation witnessed by local newspapers in relation to national newspapers ([35]).
Graph
Table 13. Effect of the Paywall on Print Readership: Using Local Newspapers as the Control Group, Panel Regression.
| Top 25 DMAs (Most Popular Local Newspaper in Each Market as Control Group) |
|---|
| Weekday Circulation Share (%) | Weekend Circulation Share (%) |
|---|
| DV = | Est. | SE | Est. | SE |
|---|
| NYT × Paywall | 2.28** | .70 | 2.27* | 1.06 |
| Time trend | −.60 | .20 | −.89 | .32 |
| DMA dummies | ✓ | ✓ |
| .86 | .87 |
- 200022242918815170 *p <.05.
- 210022242918815170 **p <.01.
In summary, we find that the print readership of NYT benefited from the paywall, potentially in the form of lower attrition relative to other similar newspapers. In other words, NYT experienced a positive and significant spillover effect of the online paywall on its print edition. There are two possible mechanisms governing this finding. The first mechanism is substitution—that is, readers might have viewed the print and online versions of NYT as substitutes. As a result, increasing the price of the online version by erecting a paywall might have had a positive effect on the demand for the print version of the newspaper. The second mechanism is bundling arising from the fact that NYT offered bundled versions of the newspaper wherein print subscribers received free access to the digital content. In fact, this bundle was priced very close to the digital-only subscription, thereby rendering it more attractive than digital-only subscription.[24] This bundling might also have increased the demand for print subscription after the paywall was erected, thereby also positively influencing the newspaper's ability to attract print advertising.
Interestingly, the bundling mechanism only works for print subcriptions sales but not for single copy sales. As a result, any spillover effect on single-copy sales should be solely attributable to substitution between print and online versions of NYT, while the spillover effect on subscription sales should be a composite of both the substitution and bundling mechanisms. Therefore, by comparing the spillover effects for single-copy sales versus subscriptions, we can comment on whether the substitution or bundling mechanism drove the spillover effect. To this end, we collected quarterly national-level data on single-copy sales and circulation of the print version for the newspapers in our sample from AAM's semiannual publisher's statements. We use these data in a panel regression to investigate the prevalence of these two mechanisms.
The results from this analysis (Table 14) show an overall positive effect of the paywall amounting to 29.3% of the newspaper's total circulation (i.e., a combination of subscription and single-copy sales). However, when we consider subscription and single-copy sales separately, we find an overall positive and significant effect of the paywall on subscription sales but an insignificant effect on single copy sales (p =.459). This suggests that the primary driver of the positive spillover effect of the paywall on print readership was the bundling mechanism, though we are unable to conclusively demonstrate that substitution did not play a role.[25]
Graph
Table 14. Exploring the Mechanism Behind the Spillover Effect of the NYT Paywall on Its Print Circulation.
| Total Print Circulation Considering Subscription and Single-Copy Sales | Subscription Sales | Single-Copy Sales |
|---|
| Effect of the paywall | 29.3% | 23.31% | n.s. |
| Controls (fixed effects for each newspaper, year fixed effects specific to each newspaper, seasonality controls in the form of quarter of the year fixed effects included) | ✓ | ✓ | ✓ |
220022242918815170 Note: n.s. = not significant.
As in the case of our analysis of online visitation, these results are based on data from one newspaper. Therefore, we explore whether these results are generalizable to other newspapers of similar size. To this end, we compiled zip code–level print circulation data for LAT, in the period surrounding its paywall commission (May 2012), and for a control newspaper, WP, as it did not operate a paywall during our analysis window. We were unable to perform this analysis using USAT as an alternative control group because USAT data are not available at the zip code level. We chose to collect zip code–level data both because zip code was the lowest level of aggregation reported in the AAM Audit Reports for LAT and also because the relatively smaller national coverage of the LAT restricted the DMA-level data to fewer than ten DMAs. We present the results of this panel regression analysis in Web Appendix A. We find a significant positive spillover effect (4.5–4.8 share points, higher in magnitude than the corresponding number for NYT) for the paywall erected by LAT. These results are similar in spirit to our finding for the NYT paywall and help us place more confidence in our documentation of a positive spillover effect of the NYT paywall on its print circulation.
Overall, these results suggest a positive effect of the paywall on print newspaper circulation for NYT—a positive, significant spillover effect of the paywall. Thus, our results are consistent with the view that the paywall may be serving a very important objective for this industry, viz., stemming the decline in print readership. As discussed previously, 65%–80% of revenues for newspapers such as NYT and LAT are obtained from the print edition of the newspaper. In addition, preserving a print reader is believed to be at least 16 times as valuable, in revenue terms, than an online reader. Thus, the spillover effect of newspaper paywalls may play a large role in preserving a legacy source of revenue by slowing the decline in print readership.
Our article proposes a framework that will help managers in evaluating the various implications of monetizing digital content. Specifically, we document that, in addition to considering the obvious direct effect of paywalls on subscription revenue, managers need to consider ( 1) how such a monetization approach would alter user engagement and ( 2) the spillover effect of the paywall on the offline channel. Of these, the insight that managers need to consider the spillover effect of digital monetization on legacy media channels is a surprising and often overlooked implication. Overall, our empirical analyses highlight three key findings of relevance to managers:
- The paywall instituted by NYT drove away some readers, as evidenced by a decline in the number of unique visitors to its website after the paywall.
- In the period following the paywall, previously heavy readers of the NYT visited the website less often and also spent less time on the website. Although these adverse effects are attenuated among readers who are likely to have subscribed to the newspaper, these findings imply that paywalls might pose a challenge to the greater objective of increasing engagement among online readers.
- There is a positive significant effect of the paywall on the newspaper's print circulation, indicating that the spillover effect serves as a sizeable benefit.
In summary, there are two positive consequences of the paywall: ( 1) the incremental online subscription revenues generated by the paywall and ( 2) the arrested decline in print circulation and the corresponding benefits from circulation and advertising revenue. However, decrease in engagement might have had an adverse effect on the newspaper's digital advertising revenues. In what follows, we discuss the overall implications of these findings for the overall financial performance of NYT.
First, let us consider the direct effect of the paywall in the form of increased subscription revenues. In each quarter following the launch of its paywall in March 2011, NYT experienced a steady increase in the number of paid subscribers ([10]). The newspaper is reported to be successful in amassing a sizable base of over 500,000 digital subscribers in just 18 months after the paywall was set up ([21]). In addition to this direct effect, the paywall may also influence the newspaper's online advertising revenues indirectly in multiple ways. As discussed previously, the paywall resulted in lower engagement in online content. Lower engagement and traffic leads to a lower quantity of ad impressions that can be served on the newspaper's website. Thus, relative to the period before the paywall, this will lead to lower advertising revenue. However, as a result of the paywall, the newspaper is likely to have richer information on subscribing visitors, increasing its ability to serve targeted ads. Moreover, subscribing visitors, by virtue of their revealed willingness to pay for digital content, are likely to be more attractive to advertisers. In the absence of the paywall, advertisers would not have been able to directly identify such high willingness to pay users. Therefore, the paywall can potentially help a newspaper charge higher ad rates per impression (typically measured in terms of cost per mille) as a result of the improved quality of the served ad impressions. There are early indications from the results of survey-based journalism research that advertisers are willing to pay higher advertising rates for their ads in paid online newspapers.[26] Therefore, the net effect of paywalls on online advertising is likely to depend on the relative magnitudes of the changes in the quantity and quality of ad impressions subsequent to the paywall.
If online advertising revenues did indeed decline as a result of the paywall, this resulting lower cash flow can hamper the newspaper's ability to invest in quality. Consequently, the decline in quality can lead to a further decline in readership, thereby driving the quality-driven circulation spiral ([15]).[27] While our data do not allow us to comment on the circulation spiral, media reports have lauded the the paywall as a net positive contributor to NYT's revenues ([12]). Therefore, any adverse changes in the quality of online content because of the paywall are unlikely.
Next, we consider the spillover effect of the paywall on the print newspaper. This spillover effect can have positive revenue implications both from the reader and advertiser sides. Nevertheless, recall that we discussed two plausible mechanisms behind the spillover effect: substitution effect, wherein readers abandon the online version and switch to print as a result of the paywall, and the bundling effect, wherein readers who would have otherwise subscribed to the online paywall instead find print subscription with free online access more attractive. Note that even if it was not labeled as such, a similar "bundled option" of consuming print and online news was available to the reader before the paywall, though the online newspaper was free at that time. Thus, the benefits to the newspaper arising from such a bundled pricing plan should be attributed to the paywall, as the bundle would not have existed otherwise. Given our finding that bundling was probably the main driver of the spillover effect of the paywall, the positive benefit from the reader side is likely to have been somewhat limited. If the spillover effect of the paywall were to be driven by the substitution explanation, all the increase in print readership would be deemed as incremental, likely implying a larger revenue gain. Therefore, we can view our analysis as a conservative assessment of the magnitude of the spillover effect.
In the scenario in which the spillover effect is mostly driven by bundling, most of the positive benefit would be derived from increase in print advertising. Given that an average print reader generates $126 in print advertising, this increase can be sizable (for a rough calculation of the revenue gain from the spillover effect, see Web Appendix B). However, there are two potential caveats to this positive outlook. First, if advertisers are actively switching between print and online versions of the newspaper, it is possible that some of the calculated increase in print advertising might be a result of advertiser substitution away from online advertising at NYT. However, as [37], [36], and [20] note, such cross-channel substitution is likely to be small.
Second, if bundling is the main driver of the spillover effect, it could be argued that readers who subscribe to the print plus online bundle may, in reality, end up throwing away the print newspaper and consume only digital news. If this is true, advertisers might not view the corresponding subscriptions numbers as credible, thereby calling into question any corresponding gains in print advertising revenues. However, comparison of the options suggests that the price of even the cheapest print option was greater than that of a digital-only access plan. Therefore, it is unlikely that a NYT reader who is interested only in its online content would have subscribed to the print newspaper just to gain access to the digital version (i.e., with no intention of consuming the print version). Rather, bundling is likely to have helped in retaining some marginal print subscribers who were contemplating moving away from the print version. Furthermore, advertisers still continue to rely on the readership numbers for print newspapers that are compiled and audited by the AAM ([23]; [30]). Therefore, in the short run, newspapers are strictly better off by projecting a larger print readership base to advertisers even if there are questions regarding the extent to which their readers actually consume the print newspaper. Nevertheless, advertisers may view these circulation numbers differently in the long run.
In conclusion, this discussion highlights that the managerial implications span outcomes related to subscriptions, product design (e.g., bundling), pricing, and advertising revenues. More importantly, an often-ignored consequence is that these implications encompass both the digital and offline channels.
This article advances a framework that can help managers in evaluating the various implications of monetizing digital content. The notion of monetizing online content is a problem that extends beyond the context of newspapers. Recently, television content providers and educational institutions have been grappling with the issue of designing appropriate monetizing strategies for their online content.
The first key insight from our work is that one needs to consider the spillover effect of online monetization on offline content consumption. This is especially critical if, as in our setting, there is a positive spillover of charging for online content on offline revenues. In our case, the analysis suggests that this positive spillover was due primarily to the bundling of online and offline content. We conjecture that a bundling strategy that provides free access to online content with the subscription to offline content might be reasonable when ( 1) the marginal cost of online content delivery is relatively low, ( 2) the offline channel is significantly more lucrative in terms of ad revenues, and ( 3) it is important to prevent channel partners for offline content (e.g., cable companies for television content) from feeling threatened by the online content.
Second, a digital monetization strategy might indirectly facilitate the creation of a broader and more comprehensive "view" of audience engagement with both online and offline product offerings. This will happen when offline subscribers start linking their email ID with their online subscription accounts to authenticate their status. The ability to create such a comprehensive database may offer various long-term benefits for firms implementing various digital monetization strategies.
Finally, the design of metered plans brings to surface the debate regarding whether heavy users should be "penalized" by the platform, as is typically done in freemium pricing plans. This might motivate heavy users to migrate from the platform or curtail their usage, thereby having deleterious consequences for overall engagement as well as sharing and propogation of content. Providing some value-added services exclusively to subscribers might be a viable strategy to circumvent this problem. We hope that our findings and this discussion engender future investigation in this area.
Newspaper paywalls are becoming an increasingly prevalent phenomenon, with nearly 75% of newspapers in the United States either having implemented a paywall or actively considering setting one up. The popular belief is that paywalls may provide a welcome new source of revenue: online subscriptions. However, as suggested by various surveys of newspaper readers, newspapers stand the risk of driving away readers who are not willing to pay for online news. As online ad revenues are heavily linked to newspaper readership, newspapers also stand to put these revenues at risk if the paywall leads to heavy reader attrition. Thus, the overall impact of setting up newspaper paywalls is far from obvious. In this study, we employ data on online and print readership of NYT to assess the overall impact of the paywall it instituted in March 2011. We find that NYT's paywall appears to have driven away some readers, as evidenced by a decline in the number of unique visitors to its website after the paywall. In addition, our results suggest that, following the paywall, previously active readers of NYT visit the website less often and also spend less time on the website, implying that paywalls may pose a challenge with the greater objective of generating increased engagement of online readers. We find a positive significant effect of the paywall on the newspaper's print circulation, indicating that the spillover effect serves as a sizeable benefit, in addition to the incremental online subscription revenues generated by the paywall. Overall, this research is the first of its kind to offer empirical evidence for positive overall economic returns accrued to information media firms from the decision to charge readers for access to online content.
However, our work does have a few limitations. First, without access to individual-level data that include payment (subscription) status online and offline as well as advertising revenues, we cannot delve deeper into the reasons for the increased revenue. Second, as discussed previously, the aggregate nature of our data permits us only to offer logical conjectures regarding the mechanism governing the spillover effect, versus providing a precise quantification of the role played by all the plausible alternative mechanisms. Third, our analysis is unable to offer normative/prescriptive guidelines for setting up paywalls or managing their timing, as our estimates are conditional on the firm's decision to charge readers for online news content. Fourth, while we show that our results are consistent across two large national newspapers, they may not extend directly to other media properties. We hope that future work can overcome these limitations.
Supplemental Material, DS_10.1177_0022242918815163 - Paywalls: Monetizing Online Content
Supplemental Material, DS_10.1177_0022242918815163 for Paywalls: Monetizing Online Content by Adithya Pattabhiramaiah, S. Sriram, and Puneet Manchanda in Journal of Marketing
Footnotes 1 Authors' NoteThis paper is based on the second essay of the first author's doctoral dissertation at University of Michigan.
2 Associate EditorMichel Wedel served as associate editor for this article.
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
5 Online supplement: https://doi.org/10.1177/0022242918815163
6 1Note that our conceptualization of the engagement effect corresponds better to the industry standard for measuring digital engagement—that is, consumer engagement with the content (product-level engagement) versus broader measures of firm-level engagement (e.g., number of user referrals, app downloads).
7 2We thank an anonymous reviewer for encouraging us to further explore the mechanism behind the spillover effect.
8 3Note that the engagement effect captures the effect of the paywall on digital consumers' (including both subscribers and nonsubscribers) engagement with the online newspaper, whereas the direct effect is based on the behavior of only subscribers.
9 4For details on the sampling strategy, see http://www.comscore.com/Media/Files/Misc/comScore-Unified-Digital-Measurement-Methodology-PDF.
5The WP announced a paywall in June 2013, though it was not effectively enforced until December 2013 ([42]).
6[1] highlight that the synthetic control method works well with balanced panels where there is no missing data. When we considered the full set of 202 DMAs, they included gaps in our dependent measures, which caused the synthetic control method not to work. Therefore, we restrict our analysis to the top 25 DMAs. When we estimated the effect of the paywall using a panel regression (differences-in-differences) model using the full set of 202 DMAs available in our data set, the estimates were very close to the estimates for only the top 25 DMAs (see Web Appendix A), and this was true for all of our dependent variables of interest.
7The terms "factors" and "factor loadings" in the generalized synthetic control method are borrowed from the literature on interactive fixed effects models in economics ([4]). The time-varying coefficients are also referred to as (latent) factors while the unit-specific intercepts are labeled as factor loadings.
8We add a small constant term to get around instances of zeroes in our dependent variable as we take logs.
9Casual visitors may especially perceive the pop-up reminders notifying them of the remaining number of free articles (before encountering the paywall) as detrimental to their experience.
10A third explanation has to do with the "hassle cost" of having to repeatedly log in to verify subscription status. However, modern web browsers allow for the saving of login credentials (typically through cookies), so this is unlikely to play a large role.
11A 2013 study found that nearly 66% of users reported social media as their primary source of news, with 47% of users surveyed identifying Facebook as their main source of news ([27]), highlighting the importance of accounting for the referring medium while analyzing page visits.
12The premise behind treating a user as a subscriber for all months after reaching the 20-article limit is that subscribers do not necessarily need to surpass the 20-article limit every month. However, we acknowledge that this definition assumes that users do not terminate their subscription subsequently.
13These results also reinforce our confidence in the ability of the strict nonparametric controls in the form of month dummies in all other model specifications to serve as reasonable controls for any coincidental subscriber-acquisition related promotions by newspapers.
14Given the proliferation of digital devices on which news content can be consumed, the paywall could induce switching behavior within online channels (e.g., the website vs. a mobile app; see [11]).
15Because of the sparsity of print circulation data at the DMA level, we are unable to include a broader basket of newspapers for our analysis of the spillover effect. Note that it is more likely for national newspapers outside of the top five (such as even CT and NYDN) to have online visitors from a broader set of DMAs than print subscribers living in regions farther out from their core circulation markets (Chicago and New York, respectively).
16See http://www.marketingcharts.com/traditional/global-newspaper-circulation-and-advertising-trends-in-2013-43338/attachment/wan-ifra-newspaper-circs-ad-trends-in2013-june2014/.
17Although this plot indicates an overall year-on-year drop of 2%–4% in print circulation, a few specific DMAs (e.g., Bend, Oregon) seem to experience large percentage gains because of their very low circulation base.
18The results presented here do not account for the role played by subscription prices in determining circulation share. Across all our specifications, the size of our estimates of the spillover effect computed with subscription price as a covariate were identical (within.01–.02 share points) with their counterparts with price omitted. We chose to omit price as a covariate because it was statistically insignificant in all cases and did not add materially to model fit.
19For details on the various subscriptions offered by NYT around the time of the paywall, see Web Appendix A.
20This conclusion is based on the assumption that although subscription and single-copy sales address different segments of the newspaper's readership base, they responded similarly to the erection of the paywall in terms of their print readership.
21See http://sabramedia.com/blog/newspapers-battle-between-paywall-and-advertising.
22We thank an anonymous reviewer for pointing out the role of declining circulation/advertising in driving the circulation spiral.
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By Adithya Pattabhiramaiah; S. Sriram and Puneet Manchanda
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Record: 135- Pleasant Ambient Scents: A Meta-Analysis of Customer Responses and Situational Contingencies. By: Roschk, Holger; Hosseinpour, Masoumeh. Journal of Marketing. Jan2020, Vol. 84 Issue 1, p125-145. 21p. 1 Diagram, 7 Charts, 2 Graphs. DOI: 10.1177/0022242919881137.
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Pleasant Ambient Scents: A Meta-Analysis of Customer Responses and Situational Contingencies
To prevail in the fierce competition of in-store experiences, some firms have focused on providing pleasant ambient scents. However, equivocal results on scent effects make generalizations and managerial guidance uncertain. While efforts to consolidate research findings have been conducted, a comprehensive quantitative integration is notably lacking. In this meta-analysis, the authors integrate 671 available effects from ambient scent experiments and show that exposure to pleasant ambient scents on average produces a substantial increase in the level of customer responses (3%–15%). The effects of ambient scent depend on situational contingencies and are, for example, positively related to congruency, unidimensional aroma structure, ascribed familiarity of a scent, service exchange, proportion of female participants in the sample, and imagined (vs. fictitious) offering. Thus, the authors estimate expenditures would increase by 3% and 23% for an average and a most favorable condition, respectively. The authors also examine effect patterns, identifying, for example, ambient scent as more cognitive than affective and nonlinear effects of perceived concentration. Using the insights, they develop a research agenda and provide clear strategic guidance to leverage ambient scent effects.
Keywords: pleasant ambient scents; meta-analysis; expenditures; in-store customer experience; atmospheric stimuli
Contemporary in-store experiences have become highly competitive. A driving force behind this is e-commerce, which has pushed established firms (e.g., Book World, formerly the fourth-largest bookstore chain in the United States) out of business and opened markets for new, disruptive players, such as Airbnb, which has caused hotel revenue losses of up to 10% ([71]; [77]). Internet-born firms now see the value of a physical presence ([ 2]), making competition fiercer. Amazon recently expanded its brick-and-mortar strategy with a $13.4 billion investment ([63]). Up to 67% of large e-commerce brands have done the same, opening physical stores so that customers can marry the best of the online and offline worlds ([14]). This decision seems well founded: even among digital natives, 60% prefer to purchase offline ([14]). Thus, to prevail in the market, firms must perfect their in-store experience because that physical presence allows for a sensory and social interaction that customers do not or cannot get elsewhere, virtually or at a competitor ([10]; [37]).
In executives' search for new ways to create a distinct and irreplaceable in-store experience, some focused on ambient scent ([48]). This is not surprising. Scents have flavored our daily lives since ancient times, when pharaohs adorned themselves with lavish fragrances ([46]). In a world of digital reproducibility, a custom-made scent that offers an olfactory identity has become a thriving luxury business ([70]). The interest in ambient scent is related to the unique features of the olfactory system, which is a powerful stimulus that evokes strong emotional memories ([33]). Ambient scent has created various success stories: Hyatt Place enhanced its brand memorability, Novotel increased its breakfast sales, and Samsung found that customers underestimated shopping time by 26% ([48]; [62]).
Despite these successes, research has also presented opposite findings, showing that the presence (vs. absence) of a pleasant ambient scent increases or decreases expenditures by up to 60% ([45]; [51]). This pattern is observed across customer responses, with findings being unclear about the degree to which ambient scent influences customer responses. In addition, little is known about how situational contingencies influence ambient scent effects and whether there is an effect of gender. Findings are also inconclusive about a suggested but unproven nonlinear effect of scent intensity ([67]). Given these equivocal results, key managerial questions remain difficult to answer. For instance, how powerful are pleasant ambient scents in shaping customer responses, particularly expenditures? What situational contingencies account for variations in the strength of effects? What is the nature of these effects?
In a large body of literature that spans over 30 years, the contradictions can be partly attributed to the large variety of study contexts. By summarizing the results and taking this diversity into account, a meta-analysis resolves certain inconsistencies and so answers key managerial questions. These insights may increase return on firms' scent investments and provide researchers with ways to build stronger tests for future findings ([57]). In addition, by taking stock of what is empirically known, a meta-analysis represents an important step for a field's knowledge development ([44]). Previous efforts to consolidate research findings are largely qualitative (e.g., [ 8]; [50]; [56]). For atmospheric cues in general, [59]; RLB hereinafter) provide a quantitative attempt. Our analysis is focused on ambient scent and includes more variables than RLB, as indicated in Figure 1. This, combined with a larger database, enables us to provide a thorough summary with rich and distinctive insight into ambient scent effects. The objectives of this investigation are the following.
Graph: Figure 1. Meta-analytic framework of ambient scent effects.aNot tested, as scent selection is usually a function of the other scent characteristics and the scent perceptual properties.bIn relation to RLB newly tested variables.cResearch has also shown that effect sizes of customer responses are influenced by these variables ([23]; [51]). Due to few empirical results, however, these cannot be integrated and thus were not tested.
First, we provide a comprehensive review of the effects of pleasant ambient scent on customer responses. Our analysis is based on 71 samples from 64 articles, covering 15,447 respondents and 671 effects (compared with 34 samples and 57 effects in RLB). Our results reveal positive and robust effects of ambient scent on mood, evaluations, memories, intentions, and behaviors, yielding a 3%–15% average increase in the level of the response variables. The results indicate larger effects on evaluation and memory than on mood, contributing insight as to whether ambient scent serves as a cognitive or an affective stimulant. Furthermore, the first insight into the causal relationships indicates that ambient scent links to expenditures through mood and evaluation responses, which work along parallel rather than sequential pathways.
Our second objective is to better understand how situational contingencies account for variations in findings. Results revealed that the following factors influence the effect strength of ambient scent on customer responses: congruency, dimensionality, familiarity, and perceived concentration of the scent; service versus nonservice exchange; presence versus absence of incongruent music; controlling versus not controlling for extraneous influences; imagined versus experienced offering; and the proportion of females in a sample. Furthermore, we provide evidence for nonlinear effects of perceived concentration and interactions among the factors (pleasantness with other scent-related factors, imagined vs. experienced offering with scent-related factors).
Finally, pleasant ambient scents may be considered an unobtrusive way to stimulate in-store behavior. We analyze whether ambient scent influences customers' expenditures and predict the strength of the effect under more and less favorable conditions. Our findings indicate that the presence (vs. absence) of ambient scent results in a 3% increase in expenditures in an average setting. In assessing how sensitive this effect is to the influence of the situational contingencies, we predict a theoretical 23% increase in expenditures for the most favorable condition and a 17% decrease for the least favorable condition. Similar results are obtained for lingering of customers in the environment, rendering a substantial increase in both outcomes achievable.
We use the theoretical framework shown in Figure 1. We lay out the relationship between ambient scent and customer responses. We then explain how situational contingencies influence the effect sizes of customer responses (i.e., the standardized relationship between two variables, which we measured using the Pearson correlation coefficient; [25]).
"Ambient scent" is defined as a scent present as a part of the retail or service environment, and its effect is measured by comparing customers' responses in an unscented condition with those in a scented condition ([23]). The literature discusses three functions of ambient scent—attracting attention, priming affect, and facilitating information retrieval—which trace back to the unique biological features of olfaction. One such feature is that basic olfactory processing, such as odor detection, occurs in more primitive areas of the brain, requiring little to no cognitive effort ([33]). Thus, ambient scent may attract attention because of an unprompted processing of olfactory information. Another feature is the privileged neural link that the olfactory nerve shares with the neural area for emotional memory ([32]). Thus, a scent may trigger the remembrance of positive emotions and memories and so prime affect ([33]). It may also serve as an especially good cue for retrieving information stored under its presence ([33]).
Studies on pleasant ambient scents test their effects in experiments on numerous customer responses, such as mood activation, mood valence, and mood control; product evaluations, environmental quality, and shopping satisfaction; recall and time elusiveness; purchase intentions and intention to recommend; and expenditures and lingering. Their organization follows [50] and [56]. Table 1 provides the operational definitions used to integrate the constructs of the individual studies.
Graph
Table 1. Operationalizations of the Customer Responses to Integrate Individual Study Constructs.
| Customer Responsesa | Definitions | Common Aliasesb | Representative Papers |
|---|
| Mood | A general affective state (Nibbe and Orth 2017) that is typically measured by self-reports and conceived as arousal, pleasure, and dominance dimensions (Bone and Ellen 1999), which we define in more general terms as: |
Activation
| The degree of felt stimulation in terms of arousal or alertness. | Arousal, alertness, activeness | Chebat and Michon (2003); Doucé and Janssens (2013); Mattila and Wirtz (2001) |
Valence
| The degree to which the felt affective state is positive (vs. negative or neutral), in terms of pleasantness or unpleasantness or feeling good or bad. | Pleasure, positive affect, mood valence, cheerful | Chebat and Michon (2003); Doucé and Janssens (2013); Mattila and Wirtz (2001) |
Control
| The degree of felt power over the situation in terms of dominance or feelings of independence. | Dominance, control, independence | Spangenberg, Grohmann, and Sprott (2005) |
| Evaluations | An attitude toward a product or an issue and/or the extent to which an individual likes or dislikes a certain thing (Spangenberg, Crowley, and Henderson 1996), often measured as self-reports, including the following: |
Product evaluations
| Evaluations about a store's products and service offerings (common dimensions are style, selection, quality, and attitudes). | Product quality, evaluations of merchandise, service excellence | Chebat and Michon (2003); Doucé and Janssens (2013); Spangenberg, Crowley, and Henderson (1996) |
Environmental quality
| Evaluation about the attractiveness of a store's environment (typically captured with the Fisher scale). | Environmental (affective) quality (Fisher scale and its items, e.g., liveliness, brightness), evaluations of the environment, mall atmosphere | Doucé and Janssens (2013); Mattila and Wirtz (2001); Morrin and Chebat (2005); Spangenberg, Crowley, and Henderson (1996) |
Shopping satisfaction
| Overall judgment about the shopping experience as satisfying. | Satisfaction, satisfaction with the shopping experience, store evaluations and attitudes, shopping enjoyment | Mattila and Wirtz (2001); Morrison et al. (2011); Spangenberg, Grohmann, and Sprott (2005) |
| Memories | Retrieved information that was encoded while ambient scent was present or absent, often measured as consumers' remembrance of information and time for the shopping episode, including the following (Krishna, Lwin, and Morrin 2010; Morrin, Chebat, and Chebat 2010): |
Recall
| Remembered attributes about the product or service offering. | Recalled product attributes, recalled brands | Mitchell, Kahn, Knasko (1995); Morrin and Ratneshwar (2003) |
Time elusiveness
| Failure to remember what happened during a given time, resulting in the perception that less time has passed (i.e., "time flies" phenomenon) and in a smaller overestimation or a larger underestimation of actual time. | Perceived and estimated time in relation to actual time, perceived distance traveled in relation to actual distance. | Morrin, Chebat, and Chebat (2010); Spangenberg, Crowley, and Henderson (1996) |
| Intentions | An individual's readiness to perform a given behavior (Motyka et al. 2014), often measured as self-reports indicating willingness to behave in a certain way, including the following: |
Purchase intentions
| Intentions that reflect willingness to engage in business transactions with the firm, such as the acquisition of products, the price willing to pay, or the usage of its offered services. | Purchase intentions, intention to (re)visit, purchase intent, price willing to pay, shorter acquisition times. | Doucé and Janssens (2013); Herrmann et al. (2013); Spangenberg, Crowley, and Henderson (1996) |
Intention to recommend
| Willingness to spread positive word of mouth and encourage others to do business with the firm. | Intended word of mouth | Adams and Doucé (2016) |
| Behaviors | Acts performed by the customer (Motyka et al. 2014), often measured by observing customers during the shopping episode, including the following: |
Expenditures
| The amount of money spent during the shopping episode. | Various aliases referring to the amount of money spent, number of items or products purchased, impulsive buying | Morrin and Chebat (2005); Morrison et al. (2011); Herrmann et al. (2013) |
Lingering
| Behaviors that reflect a lengthy shopping episode or a more extensive stay. | Shopping duration, dwell timings, retention time, chatting with personnel, number of products examined or picked up | Doucé et al. (2013); Morrison et al. (2011); Spangenberg, Crowley, and Henderson (1996) |
1 aAuthors analyzed further customer responses, such as variety seeking (Mitchell, Kahn, and Knasko 1995), ease of search (Morrin and Chebat 2005), and estimated price (Fiore, Yah, and Yoh 2000). These were not integrated because only a few studies analyzed them and most were unique in our data set. Thus, in line with prior meta-analytic research and to ensure a meaningful number of integrated effects, our analysis includes those responses for which at least ten study effects from two different articles were available (Kirca et al. 2005; Rubera and Kirca 2012).
2 bSome measures can be seen as proxies for the focal customer response measure, such as items purchased for the amount of money spent or number of items examined for the amount of time spent. We therefore checked if the effect sizes for the proxy measures differed from the other measures. Including a dummy variable marking the proxies in Model 3 from Table 5 indicated that this was not the case (β =.006, p =.820).
As a starting point for our framework (Figure 1), we propose that the presence (vs. absence) of a pleasant ambient scent positively influences customer responses. RLB found scent-facilitated mood valence, satisfaction, and behavioral intentions, and physiological evidence suggests that scents attract attention, prime affect, and facilitate information retrieval ([32]; [33]). However, scholars also caution about scent effects on customer responses, because the underlying processes are poorly understood and the evidence to date offers inconsistent results, partially depending on the type of response ([56]). We thus also examine the relative effect sizes of affective (mood) and cognitive (evaluations and memory) responses. For mood, research describes the results as mixed and not always clear; the results for evaluations are comparatively more compelling and robust ([ 8]; [50]; [56]). The results also seem to favor memory effects, though they are less frequently studied ([56]). Overall, the evidence appears stronger for cognitive than for affective responses. Thus,
- H1: The presence (vs. absence) of pleasant ambient scent (a) has a positive effect on customer responses from Table 1 and (b) produces larger effect sizes for evaluation and memory responses than for mood responses.
We next discuss how situational contingencies influence the effect sizes of customer responses (Figure 1). Situational variation may explain why the influence of ambient scent varies across studies, providing insight into scent selection and usage in different settings and the expected change in outcomes. That insight allows reflection on industry reports, such as by Nike, claiming a scent-elicited 80% increase in purchase intent ([62]).
Scent characteristics describe morphological aspects of a pleasant ambient scent and include quality (in-kind description), congruency (fit with the environment and its products and services), and structure (single vs. multiple aroma dimensions). Quality characterizes the perception of the scent in kind and so differentiates it to others ([24]). While people distinguish well among many scents they have previously smelled, they have difficulty providing a verbal or semantic label for them and thus often experience a feeling of recognizing a scent, without being able to identify it (tip-of-the-nose effect; [50]). Because scents act as a memory cue even without identification ([33]), the specific pleasant ambient scent used is often based on other scent characteristics and perceptual properties.
One such characteristic is congruency. Our conceptualization of it follows suggestions that people respond not necessarily to discrete elements but rather to their total configuration ([ 6]). We therefore combine prior fit considerations that use the environment and particular offerings as reference points ([ 9]; [31]). Congruency may facilitate effect sizes, as people are positively predisposed to it ([32]) and incongruent ambient scents interfere with information processing ([49]). In terms of structure, whether pleasant ambient scents contain a single aroma dimension (unidimensional) or multiple aroma dimensions (multidimensional) represents a version of [31] simple versus complex differentiation to allow for greater cross-study generalizability. [31] find that the ambient scent orange-basil with green tea (multidimensional) is less effective than orange (unidimensional) because it is more challenging to process. Thus,
- H2: The effect sizes of pleasant ambient scent for customer responses are (a) positively related to congruency and (b) smaller for multidimensional versus unidimensional ambient scents.
Scent perceptual properties refer to the hedonic perception of a scent, which is an affective evaluation, centering on whether someone likes it or not ([32]). Liking a scent is due to acquired emotional associations learned over time and carries hedonic meaning ([32]). Thus, in contrast to scent characteristics, which are analytical and unrelated to acquired associations, the perceptual properties reflect feelings and meanings that are linked to and result from the sensation of an ambient scent. Because scent liking is learned, it is also more subjective and emphasized as culture bound ([50]). The cultural context is relevant because most studies were conducted in North America and Europe. Researchers use pleasantness, activation, familiarity, and intensity, which are not necessarily independent and refer to scent liking in terms of meaning and physiological perception ([56]).
We propose that pleasantness, familiarity, and activation are positively related to effect sizes. In theory, a scent must be perceived as pleasant (i.e., enjoyable) to positively prime affect, because scent-based associations and hedonic perceptions are contingent on each other ([33]). To stimulate customers through emotionally potent memories, a scent also needs to be familiar ([33]). Familiarity is not about identifying a scent by name but rather being acquainted with its aroma. If a scent is unfamiliar, no meaningful associations that can be remembered and elicit affect could have been formed ([33]). Familiarity also leads to liking ([58]), which may transfer to the environment and its elements. Finally, certain ambient scents can induce greater activation (i.e., their perception as stimulating; [47]) than others do. Activating customers may amplify the positive in-store experience ([67]) and result in responses that are more positive. Thus,
- H3: The effect sizes of pleasant ambient scent for customer responses are positively related to (a) pleasantness, (b) familiarity, and (c) activation.
Across studies, scent intensity is typically assessed on a perceptual level. To examine its effects, we therefore use perceived concentration, defined as the proportion of respondents who detected (i.e., realized the presence of) the scent in the environment ([53]). Perceived concentration reflects perceptual detection on a group level, which is a required condition for scents to have any effect; there are no subliminal effects of odors ([32]). Higher intensities also generally extend the time until the sense of smell is blunted by too much exposure (olfactory adaptation; [32]). In addition, research suggests an inverted U-shaped relationship: with increasing intensity, a pleasant scent is evaluated as more positive up to a point where it overpowers and becomes aversive ([56]). Because the value range of perceived concentration is limited to 100%, when every respondent detects the presence of an ambient scent, it may not tap into the area where a scent overpowers. Thus,
- H4: The effect sizes of pleasant ambient scent for customer responses increase at a decreasing rate with higher perceived concentrations.
The nonlinear relationship is part of an interplay between pleasantness and intensity. We apply this logic to the interactions of pleasantness with congruency, dimensionality, familiarity, and activation. This interplay for scents in general (not only pleasant ones) is described as follows: while intensity makes pleasant scents more positive (up to a point), it decreases the hedonic value for scents that may only be acceptable, such as a weak fishy smell ([32]). Thus, the inverted U-shaped curve occurs because pleasantness shifted the turning point, allowing people to experience scents at higher intensities ([67]). Similarly, pleasantness may buffer against ( 1) less congruent scents by increasing tolerance for a perceptual misfit, ( 2) multidimensional scents by easing their processing, ( 3) less familiar scents by evoking (less but) additional positive emotional associations, and ( 4) less activating scents by making them more attractive. Thus, we propose that pleasantness buffers against levels in scent characteristics and perceptual properties that are associated with smaller effect sizes. Thus,
- H5: Pleasantness weakens the positive effect of (a) congruency, (b) familiarity, and (c) activation and weakens the negative effect of (d) dimensionality.
Given the wide applicability in retail stores, entertainment venues, and medical facilities, we are next interested in the following factors proving (un)favorable ambient scent conditions: service versus nonservice exchange, multi- versus single-store environment, and presence versus absence of (in)congruent music ([47]; [51]; RLB).
Service exchanges refer to activities performed on or for the customer (e.g., by a spa). Compared with nonservice exchanges, service exchanges may favor pleasant ambient scents for two reasons. First, service exchanges have a high degree of intangibility ([ 5]). If there are no tangible features, environmental cues may gain importance as decision criteria. Such a shift is reflected in the stronger reliance on word of mouth when consumers cannot try an offering ([76]). Second, service exchanges include a high degree of person-to-person interactions, where scent is shown to facilitate peer perceptions and helping behaviors ([ 3]; [ 5]).
Multistore environments, such as a mall or a bookstore with a café, contain multiple single stores. A pleasant ambient scent may render an environment distinct and so attract attention by setting off the particular object from its environment (e.g., a single store from other stores). [41] argue that the capability to enhance an object's contextual distinctiveness lies not in the uniqueness of the scent itself but in the number of objects with which it is associated. Thus, by being associated with multiple stores (e.g., a mall) compared with a single store (e.g., a florist shop), an ambient scent's capability to render the environment distinct is stretched over many objects, making it less effective.
Finally, we consider whether music is playing in the environment and, if so, whether it matches the pleasant ambient scent. Such a cross-modal congruency goes back to findings showing that customers respond more positively when the music tempo matches the ambient scent in terms of being activating (fast) or relaxing (slow), compared with a mismatch ([47]). In addition, [68] found similar results for the cross-modal congruency of music theme (Christmas) with scent quality (pine). The presence of incongruent music likely interferes with ambient scent effects because customers respond holistically to an environment ([47]). For congruent music, no prediction can be made. Its presence may amplify, attenuate, or fail to change the effect of ambient scent ([69]). We explore this constellation without a hypothesis. Thus,
- H6: The effect sizes of pleasant ambient scent for customer responses are (a) larger for service than nonservice exchanges, (b) smaller in multistore than in single-store environments, and (c) smaller when incongruent music is present than when it is absent.
In their experiments, researchers obtain control over extraneous factors through statistical control (controlling vs. not controlling for extraneous influences) and the design of the research setting (fictitious vs. actual) and the stimuli (imagined vs. experienced offering). To assess a statistical method of isolating factors besides the ambient scent manipulation, we distinguish whether the absence–presence comparison of ambient scent controls for effects from other variables or not (based on raw means). These are controlled for when the effect size is extracted from a multivariate model or the reported univariate statistic itself is adjusted for covariates. Raw means may lead to larger effect sizes, as they capture other variables with which they are collinear. However, controlling for other effects may also lead to larger effect sizes, as variance from extraneous factors is partialled out ([11]). We follow the latter argument because it reflects scholars' attempts to achieve precision in results.
The design of research settings can be differentiated as based on fictitious (artificial laboratory) or actual (field or in-store) environments. Fictitious environments may allow researchers to better isolate extraneous factors ([64]) and so may lead to larger effect sizes than actual environments do. The design of the experimental stimuli may provide control over response errors. Photographic or video materials require respondents to imagine (parts of) the product or service offering. In such cases, respondents must make projections about reality, which translate into overstated reactions ([74]). In contrast, simulated store environments or field settings allow respondents to actually sense and experience the offering. Thus,
- H7: The effect sizes of pleasant ambient scent for customer responses are larger (a) when they are controlled for extraneous influences than when not, (b) for fictitious than actual settings, and (c) for imagined than experienced offerings.
Age and gender represent the final set of variables. First, physiological evidence shows women outperforming men in odor response (detection, discrimination, identification, and memory) and being emotionally more sensitive and responsive to scent than men are ([21]; [33]). In the atmospherics domain, [42] find support for this and show that women exhibited a more positive mood from ambient scent than men. Other tests could not detect gender differences in responses to ambient scent (e.g., [ 3]; [41]), which may be due to lower power than in an aggregated analysis. Second, [15] observed scent-facilitated expenditures for younger (<35 years) but not for older (≥35 years) respondents. Differences in individuals' susceptibility to ambient scent trace back to olfactory capabilities that change during a life span. Loss of olfactory function in old age is well established ([19]). Research indicates that olfactory performance peaks between 20 and 40 years of age and notably declines afterward, with a rapid decrease after age 70 ([19]). Thus,
- H8: The effect sizes of pleasant ambient scent for customer responses are related (a) positively to the proportion of females and (b) negatively to the mean age of the respondents.
To ensure extensive and complete coverage, we first searched electronic databases (EBSCO, Science Direct, Emerald, ABI/INFORM, and PsycINFO), using keywords such as "ambient scent," "scent," and the general term "atmospheric stimuli" combined with "customer/consumer behavior," and manually reviewed leading journals of ambient scent research (Journal of Marketing, Journal of Consumer Research, Journal of Business Research, Journal of Retailing, Journal of Service Research, Psychology & Marketing, and Environment and Behavior). Second, we consulted the references of major research summaries on pleasant ambient scents (e.g., [ 8]; [50]; [56]; RLB). Third, we searched the Social Science Citation Index and Google Scholar for articles referring to these summaries. Fourth, to address the "file drawer" problem, we searched the internet (e.g., Google Scholar, SSRN database, key authors' web pages) to retrieve unpublished work. We also emailed the authors of each study deemed appropriate for inclusion and asked for unpublished material and, to minimize study exclusion due to missing data, for additional statistics. Finally, for each study appropriate for inclusion, we performed steps two through four until no further work was found.
Inclusion criteria were that ( 1) ambient scent had to be manipulated experimentally, ( 2) the data reported on a sample needed to be independent (i.e., if the results of two different studies were derived from the same sample, the study that provided more details was used), ( 3) the measurement item(s) accurately reflected our construct specifications for customer responses, and ( 4) either an effect size could be directly derived or sufficient data (e.g., Student's t, η2, F-ratios; [17]; [43]) were reported so that we could calculate an effect size. An exclusion criterion was when the ambient scent manipulation was collapsed with another factor (e.g., presence of ambient scent and product display compared with the absence of both; [29]) so that the effect is not due to ambient scent alone.
The constructed database contained 71 independent samples reported in 64 articles from 1989 to 2018 (May) referring to a combined sample of 15,447 respondents. The average sample had a mean age of 32.6 years and a mean proportion of female respondents of 60%. For those samples that had an origin available, 91% were from Western countries (North America 48%, Europe 41%, and Australia 2%) and 9% from others (Asia 7%, Africa 2%). Theme 1 in the Web Appendix provides a list of included samples, and the bibliography is available from the authors. The accumulated data across the samples allowed for the extraction of 671 effect sizes. Of these, 11% are based on unpublished material (working papers, dissertations, and one data set).
In accordance with guidelines for the meta-analysis of experimental work ([43]), we calculated effect sizes as standardized mean differences (Cohen's d) converted into Pearson correlation coefficients using formulas provided by [17], pp. 23, 82). A positive (negative) correlation indicates that the presence versus the absence of ambient scent increases (decreases) the value of the customer response variable. Although the correlation coefficient is widely used and easy to interpret (e.g., [75]), scholars have criticized it and its squared expression as explained variance. For instance, a correlation of.15 corresponds to an explained variance of 2%, leaving 98% unaccounted for, a potentially misleading reflection of the importance of an effect relative to the percentage change in the outcome ([28]).
We assessed the percentage change in two supplementary ways. First, we calculated it based on the raw means by (value of the customer response variable in the ambient scent presence condition/the value in the absence condition) − 1. The values typically represent scale-based scores (mood, evaluations, and intentions) or observed quantities (recalled information, spent time and money), reflecting the level of the customer response (e.g., degree of positive mood, amount of expenditures) in the respective ambient scent condition. For 515 effect sizes, the raw means were available and the percentage change could be calculated. To avoid bias due to outliers, we excluded 15 values (2.9%) that were larger than ±60%. These cases had values more than three times the interquartile range (the middle 50% of the records). Second, we calculated it by converting effect sizes, using Cohen's improvement index. For example, a correlation of.15 moves from the median of a normal distribution to its 60th percentile and represents a 10% change in the outcome in standardized terms ([25]). The advantage of the raw mean assessment is its direct reflection of the study findings. In comparison, converting effect sizes presents a standardized and so more precise approach that also leverages the full data. We use both to provide a more comprehensive picture than would be possible with either alone.
We prepared two coding forms (Table 2). The first form specified the coding of the environmental, research operational, and individual factors, following their theoretical conceptualizations. The second form specified the coding of ambient scents. We used nine-point scales to rate the scent characteristics and perceptual properties, as they enabled the capture of nuanced differences among the scents ([49]). We made an exception for the distinction between multidimensional and unidimensional scents, because a continuous scale would not be adequate. In total, 80 different pleasant ambient scents were used across the samples, and 61 were rated by the coders. The remaining 19 represented compounds of some of the other scents (e.g., a floral complex made of rose and jasmine), requiring the following adjustments. We defined these compounds as multidimensional in aroma structure and calculated their perceptual properties as averages from the coded values of the ambient scents they contained. Finally, perceived concentration was measured as the proportion of respondents in the ambient scent presence group who detected the scent.
Graph
Table 2. Coding Scheme and Statistical Properties of the Situational Contingencies.
| Level: Variable | Coding Scheme | M (SD) |
|---|
| Scent Characteristics | |
| 1: Congruency | How well does the ambient scent fit the environment and the products and services included therein? (1 = "not at all," and 9 = "very much") | 4.99 (1.21) |
| 1: Dimensionality | Does the aroma structure of the ambient scent contain a single dimension or multiple dimensions? (1 = multiple dimensions, 0 = single dimension) | .36 (.47) |
| Scent Perceptual Properties | |
| 1: Pleasantness | How enjoyable is the ambient scent, in general? (1 = "not at all," and 9 = "very much") | 6.02 (1.19) |
| 1: Familiarity | How familiar is the ambient scent, in general? (1 = "not at all," and 9 = "very much") | 6.44 (2.00) |
| 1: Activation | How stimulating is the ambient scent, in general? (1 = "not at all," and 9 = "very much") | 6.44 (1.54) |
| 2: Perceived concentration | Proportion of respondents in the ambient scent presence group who detected the scent in the environmenta | .58 (.29) |
| Environmental Factors | |
| 2: Service exchangeb | Was a service exchanged in the environment?2/3 = service/single-store −1/3 = nonservice/single-store −1/3 = nonservice/multistore
| −.16 (.38) |
| 2: Multistore environmentb | Does the environment contain multiple stores, or does it represent a single store?1/2 = multistore/nonservice −1/2 = single-store/nonservice 0 = single-store/service
| −.20 (.41) |
| 2: Incongruent musicc | Proportion of effect sizes within a sample when cross-modally incongruent music was present | .08 (.23) |
| 2: Congruent musicc | Proportion of effect sizes within a sample when cross-modally congruent music was present | .05 (.19) |
| Research Operational Factors | |
| 1: Statistical control | The absence–presence comparison of ambient scent was or was not statistically controlled for effects from other variables (extraneous influences)? (1 = controlled for, 0 = not controlled for [based on raw means]) | .30 (.46) |
| 2: Fictitious settingb | Did the research design use a setting that is fictitious (laboratory experiments) or actual (field experiments)?1/2 = fictitious setting/experienced offering −1/2 = actual setting/experienced offering 0 = fictitious setting/imagined offering
| −.13 (.40) |
| 2: Imagined offeringb | Did the respondents need to imagine the product or the service offering, or did they experience it?2/3 = imagined offering/fictitious setting −1/3 = experienced offering/fictitious setting −1/3 = experienced offering/actual setting
| −.05 (.45) |
| Individual Factors | |
| 2: Proportion of female participantsd | The proportion of female participants in a sample | .60 (.18) |
| 2: Mean aged | The average age of respondents in a sample (in years) | 31.7 (5.89) |
- 3 aIn their question whether an ambient scent was detected, some researchers attempted to avoid a direct reference to smell, such as by asking for anything special (or this information was not available). We checked via a dummy, marking if a reference to smell was present and influenced the results, which it did not (B =.014, p =.576).
- 4 bThese variables used contrast codes according to [18], as service exchanges were exclusively studied in single-store environments and imagined offerings in fictitious settings.
- 5 cCaptured as proportion of effect sizes within a sample as the presence versus absence of (in)congruent music varied on the sample and effect-size level. For music's match with ambient scent, we followed [47] and assessed whether the music was more or less arousing (based on volume and tempo) and matched this to whether the scent was more or less activating (defined as scoring above or below average on the activation factor). For [68], we made an exception and followed their study conditions.
- 6 dMissing values were imputed with the sample-size-weighted mean ([13]).
We coded the data according to the definitions in Table 2. The first author initially coded all data, then the second author independently coded all data ([60]). Intercoder reliability was calculated according to [61] and [65] for the categorical and continuous variables, respectively. The reliability values ranged from.89 for congruency of ambient scent to 1.00 for fictitious (vs. actual) settings and were comparable to those in other analyses (e.g., [26]; [57]). We resolved inconsistencies for the categorical and continuous data by discussion and by averaging the ratings between coders, respectively. In addition, we compared our codings of the two perceptual properties, pleasantness and activation, with the values provided by [67]. For a subset of 27 ambient scents, the pleasantness and activation values correlated with.87 and.84 between both data sets, respectively.
We adjusted the effect sizes for reliability to correct for attenuation from random measurement error ([35]). If reliability indices were not available, we used the mean sample-size-weighted reliability across all studies. Next, within samples, multiple effect sizes for the same relationship could be extracted. This was the case when a response was broken down into single facets and/or repeatedly measured (e.g., product evaluations were assessed in terms of style, selection, and quality and/or for different product categories). We kept the effect sizes separate and assigned each a weight of 1/number of effect sizes for this relationship ([27]). Thus, when all available effect sizes for a relationship are averaged, multiple effects within one sample do not lead to its overrepresentation. We then computed the sample-size-weighted means (r) of all available effect size estimates for each relationship ([35]). Theme 2 in the Web Appendix presents the formulas for these calculations. Because the effect sizes depend on the situational contingencies, we provide an adjusted value for r (ra), which would be expected when all situational contingencies were at their mean and, to represent field conditions, when an experienced offering was assumed (i.e., more conservative estimates). In interpretational terms the ras represent the integrated rs under average field conditions. Theme 3 in the Web Appendix provides the technical details of these calculations.
We investigated how the situational contingencies influence effect sizes of customer responses. The analysis is justified when there is sufficient variation among effect sizes. This variation is assessed by the I2 statistic, which indicates the percentage of total variation in effect sizes due to heterogeneity rather than chance ([34]). Values less than 30% represent mild, between 30% and 50% represent moderate, and more than 50% represent severe heterogeneity ([34]). Table 3 shows the I2 values, which indicate mild to severe heterogeneity, justifying our analysis.
Graph
Table 3. The Presence (vs. Absence) of Ambient Scent Influences Customer Responses.
| Dependent Variables | Number of Effects | Total Sample Size | Adjusted ra | % Changeb | Availability Biasc | I2 (%) | Raw Means |
|---|
| Sample Size | % Changed |
|---|
| Mood | | | | | | | | |
| Activation | 75 | 6,469 | .097** | 6.2 | 1,304 | 60 | 3,504 | 7.1 |
| Valence | 120 | 8,099 | .088** | 5.6 | 1,727 | 34 | 3,980 | 7.4 |
| Control | 12 | 1,433 | .073 | 4.7 | — | 74 | 1,042 | 4.9 |
| Evaluations | | | | | | | | |
| Product evaluations | 132 | 6,878 | .121** | 7.7 | 3,018 | 0 | 4,579 | 7.6 |
| Environmental quality | 105 | 6,709 | .122** | 7.8 | 2,261 | 29 | 3,642 | 7.9 |
| Shopping satisfaction | 40 | 3,095 | .144** | 9.2 | 913 | 67 | 2,213 | 7.8 |
| Memories | | | | | | | | |
| Recall | 29 | 1,299 | .177** | 11.3 | 328 | 37 | 401 | 15.4 |
| Time elusiveness | 13 | 1,336 | .111* | 7.1 | 81 | 56 | 844 | 9.5 |
| Intentions | | | | | | | | |
| Purchase intentions | 49 | 2,849 | .105** | 6.7 | 533 | 36 | 2,227 | 8.4 |
| Intention to recommend | 11 | 486 | .098** | 6.3 | 49 | 0 | 356 | 12.4 |
| Behaviors | | | | | | | | |
| Expenditures | 52 | 8,150 | .051 | 3.2 | — | 76 | 6,714 | 3.9 |
| Lingering | 33 | 2,762 | .078** | 5.0 | 230 | 21 | 2,023 | 3.8 |
- 7 *p <.01.
- 8 **p <.001.
- 9 aBecause the "simple" rs are affected by the situational contingencies, we provide an adjusted value for the rs, which would be expected when all situational contingencies are at their mean (with the exception that an experienced offering was assumed to represent field conditions). The calculations used the estimation results from Model 3 in Table 5. The unadjusted rs are provided in Theme 2 in the Web Appendix and present the data input for p, availability bias, and I2. Further statistics are available from the authors.
- 10 bStandardized percentage change in the outcome as indicated by Cohen's Improvement Index.
- 11 cAvailability bias refers to the fail-safe N-statistic, which allows finding the average number of discarded null results that would render a relationship nonsignificant ([43]).
- 12 dSample-size-weighted mean of the individual values.
- 13 Notes: Cells without asterisks have p-values greater than.05.
To examine the influence of the situational contingencies, we regressed the reliability-corrected effect size estimates on the scent characteristics, the scent perceptual properties, and the environmental, research operational, and individual factors. Because the variables (see Table 2) vary at the effect size (Level 1: individual effect size) and sample (Level 2: experiment[s] within an article) levels, we used a two-level hierarchical linear model (HLM) to account for within-sample error correlation between estimates. Theme 3 in the Web Appendix presents the calculation details. Analogous to effect size integration, we weighted effect sizes to correct multiple counts within a sample and represent sample size.
Before calculating the HLM, we checked data properties. First, we assessed whether the data formally required a hierarchical approach. The amount of variance in effect sizes due to sample membership (intraclass correlation coefficient) equaled 36.9%, indicating that the HLM was necessary. Second, we assessed multicollinearity as a major threat to the robustness of results. Theme 4 in the Web Appendix shows the correlations between the situational contingencies. Because no direct diagnostic is available for multicollinearity in the HLM, we regressed the reliability-corrected effect size estimates on the situational contingency variables in a conventional model that applied the same weights as the hierarchical model ([60]). The results yielded a sufficiently low degree of multicollinearity, with a maximum variance inflation factor of 1.74 ([60]).
We constructed a meta-analytic correlation matrix to inform about the correlations among the customer responses. The matrix has a reduced set of responses due to data availability and the way it is constructed, which we explain in Theme 9 in the Web Appendix. We used the meta-analytic correlation matrix and the median sample size of N = 1,632 across the matrix's effect size estimates as model input for our path analysis, in which we explore the links from ambient scent to expenditures and the causal priorities for mood and evaluations. Theme 9 in the Web Appendix presents the analysis. We discuss the results in the final section.
Table 3 shows the results from the meta-analytic effect size integration. The presence (vs. absence) of ambient scent produced significant (p <.01) and positive effects on customer responses, except for mood control and expenditures. Of the significant effects, the ras ranged from.078 to.177 and the fail-safe N-values from 49 to 3,018. The results largely supported H1a. In regard to H1b, the ras show that ambient scent caused larger effects on evaluation and memory responses (ra =.131) than on mood responses (ra =.094). Contrasting both response types through a dummy variable yielded a significant result (p =.023), in support of H1b. Furthermore, the presence (vs. absence) of ambient scent increased the level of the customer responses by 3.2% to 15.4%, with the standardized values tending to be somewhat lower than the raw mean values. Table 4 presents the meta-analytic correlation matrix, providing a first indication that the presence (vs. absence) of ambient scent exhibits downstream links to expenditures.
Graph
Table 4. Meta-Analytic Intercorrelations Among Constructs.
| 1 | 2 | 3 | 4 | 5 |
|---|
| 1. Presence (vs. absence) of ambient scent | — | 195 (8,099b) | 277 (9,269b) | 49 (2,849) | 52 (8,150) |
| 2. Mood | .092* | — | 16 (1,634) | 2 (388) | 6 (855) |
| 3. Evaluations | .125* | .293* | — | 10 (624) | 4 (592) |
| 4. Purchase intentions | .105* | .309* | .452* | — | N.A.a |
| 5. Expenditures | .051 | .106 | .187 | .320*a | — |
- 14 *p <.05.
- 15 aApproximated via the meta-analytic correlation between intention and observed behavior as determined by [39].
- 16 bDue to the combination of two or more customer responses, the total sample size is adjusted for double counts and therefore does not correspond to the values given in Table 3.
- 17 Notes: N.A. = not applicable. Off-diagonal entries in the lower left contain the average sample-size-weighted mean correlations (rs). For the relationships between ambient scent and customer responses, the ras are provided. Off-diagonal entries in the upper right show the number of effect sizes and, in parentheses, the total sample sizes from which the mean correlations were derived. Theme 9 in the Web Appendix presents the path analysis.
Table 5 presents the HLM results for the impact of the situational contingencies on effect sizes of customer responses. We calculated three models. Model 1 represents our baseline model, explaining 13.1% of the within-sample variance (Level 1) and 40.2% of the across-sample variance (Level 2). In support of the respective hypotheses (p <.05), larger effect sizes were caused by congruency (β =.053; 2a), familiarity (β =.018; 3b), service (vs. nonservice) exchange (γ =.131; 6a), controlling (vs. not controlling) for extraneous influences (β =.075; 7a), imagined (vs. experienced) offering (γ =.085; 7c), and proportion of female participants (γ =.287; 8a). We observed smaller effect sizes for multidimensional compared with unidimensional scents (β =.064; 2b) and the presence (vs. absence) of incongruent music (γ = −.083; 6c). Contrary to our expectations, effect sizes were not larger for more (vs. less) pleasant scents (3a), more (vs. less) activating scents (3b), multistore (vs. single-store) environments (6b), and fictitious (vs. actual) settings (7b). Effect sizes also were not negatively related to the mean age of the respondents (8b). The results were inconclusive for congruent music, indicating a nonsignificant effect in the more comprehensive Model 3.
In Model 2, we added the scent interaction terms to the baseline model, which increased the explained Level 1 variance by 2.1%, to 15.2%. As expected, pleasantness weakened the positive effect of familiarity (β = −.012, p =.031; 5b) and the negative effect of dimensionality (β =.075, p =.004; 5d). Figure 2, Panels A and B, show the interactions. More pleasant ambient scents yielded a smaller reduction in effect sizes due to unfamiliarity and multidimensionality than less pleasant scents did. We found no support for H5a and H5c, which suggested weaker positive effects of congruency and activation due to pleasantness.
Graph: Figure 2. Interaction effects of perceived scent pleasantness with familiarity and dimensionality.Notes: The interactions are plotted according to suggestions by [18] at ±1 SD above and below the mean of the variable (see also Theme 3 in the Web Appendix regarding scaling of these variables).
In addition to the hypothesized effects, we explored the data for other interactions among the situational contingencies. We found three interactions, shown in Model 3 and plotted in Theme 5 in the Web Appendix, which increased the explained Level 2 variance by 12.0%, to 52.2%. For a higher (lower) proportion of female participants, the positive effect of familiarity on effect sizes was stronger (weaker) (γ =.096, p =.070). Furthermore, for imagined compared with experienced offerings, the positive effect of congruency on effect sizes was weaker (γ = −.069, p =.075) and that of familiarity stronger (γ =.039, p =.039).
Finally, Models 1 through 3 included a set of dummy variables to control for the type of customer response. The dummy variables accounted for 2.6% of Level 1 variance, indicating that the situational contingencies in Models 2 and 3 explained 12.6% of additional Level 1 variance (15.2% − 2.6%). Omitting the dummies did not alter the result pattern, with one exception. When they were removed from Model 3, the significance value of the proportion of female participants × scent familiarity interaction decreased from.070 to.045. Moreover, using a dummy that marks the mood responses, we checked whether mood interacted with any of the 14 situational contingencies. We tested the interactions separately and thus adjusted the p-value for multiple comparisons, following [ 4]. Pleasantness was positively related to effect sizes for mood but not for the other responses (p =.056), as plotted in Theme 6 in the Web Appendix. This interaction did not influence the result pattern, nor did mood interact with any of the scent interactions. Proceeding likewise, we found no interactions (ps >.10) with the other responses.
Testing scent intensity effects required a different approach. Information on perceived concentration was available for a subset of 21 samples and 165 effect size estimates, which were selected for analysis. To keep the information from the full data set, we corrected each effect size for the influence of the situational contingencies, sample affiliation, and measured customer response as obtained from Model 3 (Table 5). Theme 3 in the Web Appendix provides the technical details of these calculations. In a conventional model, we regressed the corrected effect sizes on perceived concentration and its quadratic term, applying the same weights as in the hierarchical models. The model explained 10.4% of variance in effect sizes. Figure 3, Panels A and B, show the resulting curve, with an increase in effect sizes that levels off toward the upper range of the perceived scent concentration values (Blinear =.466, p =.006; Bquadratic = −.287, p =.048), in support of H4. When not controlling for the type of customer response, the result pattern remained.
Graph
Table 5. Sensitivity of Ambient Scent Effects to Situational Contingencies.
| DV: Effect Sizes of Customer Responses | Expected Direction | (1) Baseline | (2) Scent Interactions | (3) Exploratory Interactions and Sensitivity Analysis |
|---|
| Coeff. (SE) | Coeff. (SE) | Coeff. (SE) | Improvement in Expenditures (3.2%)a |
|---|
| Intercept (Level 2) | | −.132 | (.091) | −.132 | (.091) | −.116 | (.099) | | | |
| Scent Characteristics | | | | | | | | | | |
| Congruency | + | .053 | (.016)*** | .064 | (.013)*** | .065 | (.013)*** | −.9% | vs. | 7.3% |
| Dimensionality | − | −.064 | (.020)*** | −.079 | (.024)*** | −.079 | (.024)*** | 4.8% | vs. | 1.6% |
| Scent Perceptual Properties | | | | | | | | | |
| Pleasantness (PL) | + | −.019 | (.016) | .008 | (.016) | .008 | (.016) | | | |
| Familiarity | + | .018 | (.006)*** | .013 | (.006)** | .013 | (.006)** | 2.2% | vs. | 4.3% |
| Activation | + | −.003 | (.014) | .008 | (.012) | .008 | (.012) | | | |
| Scent Interaction Effects | | | | | | | | | |
| PL × Congruency | − | | | −.020 | (.015) | −.020 | (.015) | | | |
| PL × Dimensionality | + | | | .075 | (.026)*** | .076 | (.026)*** | | | |
| PL × Familiarity | − | | | −.012 | (.006)** | −.012 | (.006)** | | | |
| PL × Activation | − | | | −.005 | (.008) | −.005 | (.008) | | | |
| Environmental Factors | | | | | | | | | | |
| Service exchange | + | .131 | (.039)*** | .131 | (.039)*** | .137 | (.040)*** | 1.8% | vs. | 10.5% |
| Multistore environment | − | −.017 | (.029) | −.016 | (.029) | .002 | (.031) | | | |
| Incongruent music | − | −.083 | (.034)** | −.084 | (.034)** | −.120 | (.051)** | 3.9% | vs. | −3.8% |
| Congruent music | ? | −.114 | (.027)*** | −.114 | (.027)*** | −.043 | (.041) | | | |
| Research Operational Factors | | | | | | | | | |
| Statistical control | + | .075 | (.037)** | .076 | (.037)** | .089 | (.032)*** | 2.3% | vs. | 4.1% |
| Fictitious setting | + | −.003 | (.038) | −.003 | (.038) | .026 | (.038) | | | |
| Imagined offering | + | .085 | (.034)** | .086 | (.034)** | .086 | (.031)*** | | —a | |
| Fictitious × congruency | ? | | | | | .006 | (.032) | | | |
| Imagined × congruency | ? | | | | | −.069 | (.038)* | | | |
| Fictitious × familiarity | ? | | | | | −.006 | (.014) | | | |
| Imagined × familiarity | ? | | | | | .039 | (.018)** | | | |
| Individual Factors | | | | | | | | | | |
| Proportion of female participants | + | .287 | (.096)*** | .287 | (.096)*** | .315 | (.085)*** | −2.2% | vs. | 8.7% |
| Proportion of female participants × Familiarity | ? | | | | | .096 | (.052)* | | | |
| Mean age | – | .002 | (.002) | .002 | (.002) | .002 | (.002) | | | |
| Model Fit | | | | | | | |
| ΔDeviance (df) | | 132.0 (25) | 146.2 (29) | 160.0 (34) | | | |
| Intraclass correlation | | .369 | .369 | .369 | | | |
| R2 | | | | | | | |
| Level 1: Effect size | | .131 | .152 | .152 | | | |
| Level 2: Sample | | .402 | .402 | .522 | | | |
- 18 *p <.1.
- 19 **p <.05.
- 20 ***p <.01.
- 21 aPredicted values are shown for significant predictors and assume that the offering was experienced. For Level 1 variables, values at ±1.5 SD from the mean are based on the group-mean-centered distribution of values. For proportion of female participants, values are at ±1.5 SD from the mean. Theme 3 in the Web Appendix provides further information.
- 22 Notes: DV = dependent variable. The 11 dummy variables, representing the type of customer response, are included in all models but omitted here for the sake of brevity. The differences in deviance and df values between two models indicate that the models are predictive over each other (ps for all comparisons are <.05). Furthermore, sampling frame (consumer vs. student) was not included due to lack of explanatory power.
Graph: Figure 3. Nonlinear effect of perceived concentration.Notes: The perceived concentration values do not start at zero and so cause a negative intercept that is theoretically not supported. This inaccuracy is likely due to the measures' perceptual nature. Respondents indicated detecting an ambient scent, though there was none (e.g., [54]). We also provide a curve estimation with a more conservative progression (dotted line in Panel B), which is based on the unweighted sample means of the effect sizes.
Drawing on the parameter estimates of Model 3 (Table 5), we calculated the percentage change of customer expenditures through the ras (standardized approach) that the model predicts for different levels of the significant situational contingencies. Theme 3 in the Web Appendix provides the statistical details for this analysis, and Table 5 shows the results in the last column of Model 3. On average, when all situational contingencies are at their mean, the presence (vs. absence) of ambient scent produced a 3% increase in expenditures. Combining more favorable conditions, the presence (vs. absence) of a congruent, unidimensional, and familiar ambient scent resulted in a 10% increase. If a service exchange without incongruent music and a female-dominant sample were also predicted, the increase was 23%. If the respective less favorable conditions were combined, the presence of ambient scent caused a reduction of 17%, compared with its absence. Set in relation to the distribution of the raw mean values, the estimated range of +23% to −17% falls within the average deviation from the mean (±1 SD), rendering the estimates adequate to gauge the percentage change. We also obtained similar results for lingering and for an average across all customer responses, summarized in Theme 7 in the Web Appendix.
From the empirical evidence, we provide more definite conclusions on the existence and magnitude of ambient scent effects, identify situational contingencies that explain the variations in study findings, and provide insights into the nature of these effects. Table 6 summarizes our findings, compares them with those of RLB, and offers implications. Next, we discuss key findings, suggest directions for future research, and conclude on ambient scent's role in meeting current market challenges.
Graph
Table 6. Summary of Findings and Implications.
| Area | Findings from RLB | Findings from the Present Study | Research Implications | Managerial Implications |
|---|
| Customer responses | Positive effect sizesa on pleasure, shopping satisfaction, and behavioral intentions | Positive effect sizesa on mood activation and valence, product evaluations, environmental quality, shopping satisfaction, recall, time elusiveness, purchase intentions, intention to recommend, and lingering | Results provide more definite conclusions for ambient scent effects, taking situational contingencies into account. | Ambient scent may enhance the in-store experience, potentially providing a competitive edge in a fierce market. |
| Effect sizes for evaluation and memory responses are larger than for mood responses, indicating that ambient scent serves more as a cognitive than an affective stimulant. | Concentration may shift from the traditional affective toward a cognitive perspective on ambient scent. | Cognitive responses may be more promising to achieve; effects such as lingering may not always be desired. |
| Initial evidence confirms the links from ambient scent to expenditures and indicates that mood and evaluations work along parallel rather than sequential paths.b | Mood and evaluation responses may be studied as a space, not necessarily as a sequence. | Ambient scent moves consumers down the purchase funnel, indirectly fostering expenditures. |
| Presence (vs. absence) of ambient scent increases customer responses between 3% and 15%. Predicted changes in expenditures range from −17% to +23% across least and most favorable conditions. | First calibration, especially of the percentage changes, renders ambient scent effects substantial in magnitude. | Scent investments are promising and should be judged relative to the context. Negative effects are possible. |
| Scent characteristics | Congruency is positively related to effect sizes (qualitative account). | Effect sizes are related positively to congruency and negatively to dimensionality. | Results provide empirical informed guidance for scent selection. Congruent, unidimensional, and familiar scents may be favored over their counterparts. | Selection of the right ambient scent is important to ensure its effectiveness. For less obvious criteria, such as familiarity, pre-testing is recommended. |
| Scent perceptual properties | | Scent familiarity is positively related to effect sizes. |
| Perceived concentration increases effect sizes at decreasing rates. The curve flattens when around 60%–80% of respondents detected the ambient scent. | Results support an unclear nonlinear relation, with a first guidance for calibrating concentration levels. | Ambient scent needs to meet perceptual detection and should not overpower. |
| Scent pleasantness buffers small effect sizes from multidimensional and unfamiliar ambient scents. | Pleasantness may exert a supporting role among the scent factors in general. | If unidimensionality or familiarity cannot be met, pleasantness should be ensured. |
| Environmental factors | There is a tendency for larger effect sizes in service (vs. retail) environments. | Ambient scent exhibits larger effect sizes in service (vs. retail) environments. | Results provide novel evidence for an effect so far observed only in tendency. | Service environments appear favorable for leveraging on ambient scent effects. |
| Presence (vs. absence) of incongruent music leads to smaller effect sizes; results for congruent music are inconclusive. | Both stimuli should match, but if they generally should be combined remained an open question. | Both stimuli's match may be established via their arousing properties. |
| Research operational factors | | Effect sizes are larger when researchers statistically control for factors outside of the ambient scent manipulation than when not. | Factors outside the experiment's control should be captured and raw effects validated for their influence. | Field data may show weaker effects due to other factors apart from ambient scent. |
| | Imagined (vs. experienced) offerings yield larger effect sizes. In imagined offerings, congruency is (in tendency) weaker and familiarity is more strongly related to effect sizes. | Experienced offerings may be favored over imagined ones. For the latter, results may be verified and scent selection then retested. | If an ambient scent strategy is pretested in the lab, this should be with experienced offerings (tentatively similar to the field). |
| Individual differences | Effect sizes for mood valence were larger for samples with higher (vs. lower) proportion of female participants. | Effect sizes are larger for samples with a higher (vs. lower) proportion of female participants. Among samples with a higher female proportion, familiarity is (in tendency) more strongly related to effects sizes. | Slight gender differences from the sample to the target group are of less concern for inferences; larger differences are of concern. | Spatially separated areas where women represent the main response group are especially suited for ambient scent. |
- 23 aEffect sizes for the presence versus absence of ambient scent on customer responses.
- 24 bResults provided in Theme 9 in the Web Appendix.
Opposite effects, different study settings, and limitations in response and data coverage made it difficult for prior work to deduce general inferences about the existence of ambient scent effects ([56]; RLB). The present findings overcome these challenges and allow us to conclude that pleasant ambient scents positively influence consumer responses. More importantly, we provide insight into the magnitude of effects, especially the percentage change as a managerially relevant metric. We found an average increase in the level of the responses between 3% and 15%, which is why we consider the magnitude of ambient scent effects as substantial. The findings also illustrate the value of percentage change assessments and, though uncommon, of Cohen's Improvement Index.
Prior work has referred to various situational contingencies that presumably influence ambient scent effects. However, there are few actual tests of these factors and scant empirical generalizations (Table 6). Extending prior evidence, we found eight factors that account for variations in findings.
One area is the scent-related factors—congruency, dimensionality, familiarity, pleasantness, and activation—that guide researchers' scent selection (for an overview, see Theme 8 in the Web Appendix). Our findings substantiate the general tenet that congruency drives effect sizes and is thus used as a selection criterion. We also found dimensionality and familiarity, the least-often-considered criteria, to influence effect sizes, while pleasantness and activation, the most-often-considered criteria, did not. This unexpected result may be due to the absence of explicit tests of these factors (for an exception, see [31]]), making it reasonable to select scents based on pleasantness and activation, as established by [67]. We also discuss additional reasons that may account for the absence of significance and how pleasantness and activation may be relevant in different contexts. Overall, we advise a stronger reliance on the factors (even congruency was pretested in only 31% of samples), with the present findings providing insight and guidance.
Combining ambient scent and music presents two questions. Should they match? Does their combination eventually lead to larger or smaller effects of ambient scent? Our results confirm prior findings that both should match ([47]). They further indicate that the stimuli's arousing properties are useful for establishing their cross-modal congruency. This may explain why [51] observed negative sentiments—shoppers' expenditures dropped by an average of 40%—when slow-tempo music was combined with a citrus (i.e., activating) scent compared with when either stimulus or both were absent. Whether both stimuli should generally be combined was not conclusive, requiring further research.
Many findings were obtained in fictitious settings (51% of samples), and it remains unclear how their differences from actual (field) settings influence ambient scent effects. When fictitious settings allowed respondents to experience the offering (as naturally happens in field settings), we could not detect response differences, making both tentatively comparable. However, fictitious settings that required respondents to imagine the offering yielded inflated responses compared with experienced offerings (in fictitious and actual settings). We corrected our results to represent an experienced offering. Researchers may favor experienced over imagined offerings in their laboratory experiments, or they may wish to verify their results accordingly.
Our results also offer insight that allows for a more detailed description of the effects in terms of effect patterns, nonlinear relationships, and interactions. Drawing on the response pattern, we corroborate qualitative findings that ambient scent serves more as a cognitive than affective stimulant ([ 8]; [50]). Typically, affective-based arguments, such as the emotional power of scent-evoked memories, speak to ambient scent effects ([32]). In contrast, our result suggests supplementing the affective perspective with cognitive-based frameworks, which has been advocated in previous research (e.g., [16]; [50]). In addition, initial results from a path analysis (see Theme 9 in the Web Appendix) showed that ambient scent links to expenditures through mood and evaluation responses and indicated that these responses work along parallel rather than sequential pathways. In light of different frameworks (e.g., [16]; [54]), the findings provide a first empirical consolidation of the observed causalities and suggest studying mood and evaluation responses as a space in which ambient scent is positioned.
Our results also revealed a gender effect, which remained largely undetected in individual studies, and allow a first estimation of its magnitude. Effectively 60% of extant research is based on a female respondent perspective, which is associated with larger effect sizes. Set in relation to an equal gender split (50%), the deviation accounts for a change of.03 in ra (∼2%). Slight discrepancies in the gender composition between the sampled respondents and the target group are not of serious concern; however, researchers should be cautious about making inferences when deviations are larger. It is inconclusive whether these results indicate a general gender bias in the research stream. The higher proportion of female participants may, in the early stages of a field's development, be needed to offset other (unknown) unfavorable conditions.
For scent intensity and a nonlinear relationship with effect sizes, prior research provided inconclusive results ([67]). Capturing scent intensity through perceived concentration, we found support for a nonlinear relationship: effect sizes increased at a decreasing rate. The curve indicates that effect sizes level off when around 60%–80% of respondents detect the ambient scent, which may provide scholars initial guidance in calibrating scent concentration on a perceptual level.
The nonlinear relationship follows from predictions for scents in general (not only pleasant ones), according to which pleasantness creates a larger tolerance to scents at a higher intensity. We observed an extension of this: pleasantness buffered against larger reductions in effect sizes when the ambient scent was unfamiliar or multidimensional. To the best of our knowledge, this is a newly observed behavior, giving initial support to the idea that pleasantness may generally play a supporting role for scent characteristics and perceptual properties.
Both increases and decreases in the levels of the responses span up to 60%; success stories from the industry claim even larger positive changes ([62]). We estimated that the presence (vs. absence) of ambient scent yields a 3% increase in expenditures for an average setting and a 23% increase for a most favorable condition, with similar results for lingering and an average across the response variables (see Theme 7 in the Web Appendix). Thus, industry reports such as increased breakfast sales (Novotel) and a 16% rise in store traffic (Dunkin Donuts) lie within our predictions, whereas claims of an 80% higher purchase intent (Nike) do not ([62]). Moreover, the success stories inevitably portray the positive side. While a less favorable condition may often lead to diminished effects, our predicted 17% decrease for a least favorable condition and the negative percentage changes in the data also show the possibility for negative effects.
The predictions need to be set in context. Although they combine a large volume of data, they represent forecasts, which are not free from error. They inform about the potential of ambient scent and its situational sensitivity according to currently available evidence. Larger numbers are typically more attractive. However, situational considerations (such as wide reach or great leverage on profit) may make even small changes desirable. Overall, ambient scent is able to facilitate expenditures, either by doing so directly or by moving consumers down the purchase funnel, thus supporting the business press belief "smell sells" ([ 8]).
Ambient scent positively affected most responses. Nevertheless, recommendations are not straightforward, because scent-facilitated responses may not always be desirable or occur for alternative mechanisms. Undesirable responses may include scent-enhanced recall and activation intensifying negative experiences (e.g., complaint handling, waiting) and scent-facilitated lingering worsening performance indicators (e.g., the number of processed consumers). Responses occurring for alternative mechanisms may include that ambient scent reduces cognitive efforts ([52]), which can be responsible for better evaluations and the perception that less time has passed than is actually true.
To leverage ambient scent effects, marketing executives should consider situational aspects, guided by two questions. First, which pleasant scent should be selected? Using lavender in a French florist shop ([36]) may illustrate a best-practice example. Lavender is a fit to the store, is familiar to most French people (who were interviewed), and contains a single aroma. Other cases may be less obvious, especially regarding familiarity, which is not about identifying a scent but rather being acquainted with its aroma and the memories and emotions attached to it. Popular scents may be known but are not necessarily familiar. Thus, firms may need to test the responses from their target group.
Second, which contexts are beneficial? Service exchanges favor ambient scent effects, making the 1 Hotels group a positive example ([48]). Scent also appears to be beneficial when female consumers represent the main group in the facility or in a spatially separated area (e.g., women's sections in retailing and spa facilities). In either context, the scent needs to be perceived, which is different from consumers often being unaware that scents affect them ([32]). If ambient music is present, it should match the ambient scent (via both stimuli's arousing properties). We also saw the possibility of negative effects. Even a pleasant ambient scent may create negative sentiments, such as when it leads to overstimulation ([51]), is so strong that it turns aversive ([32]), or distorts the perception of the environment. Because also other as-yet-undiscovered individual differences may account for ambient scent effects, it is again advisable to use field tests.
A special strategy of firms is to use bespoke fragrances, such as Hyatt Place's unique "Seamless" scent ([48]). Because people can distinguish between many thousands of scents they have previously smelled, signature scents appear suited to build an olfactory identity. At the same time, positive associations have yet to be formed. Thus, there is little margin for failure, a challenge for services ([30]), and they may elicit effect only after repeated exposure. Furthermore, because they are often compounds, unidimensionality appears more difficult to achieve, which pleasantness can partially compensate for.
Meta-analyses have strengths but also inherent limitations. First, they are constrained by the available primary data. Thus, our framework should be considered a summary of the most commonly studied variables. Data constraints are also reflected in the focus of this meta-analysis on pleasant scents and its geographic focus on North America and Europe. The scent perceptual properties are learned and carry culture-bound meaning. In consequence, generalizations may be more difficult, which probably accounts for the absence of main effects for scent pleasantness and activation, and our results may not extend to other cultures. Furthermore, the results on multistore versus single-store distinction and mean respondent age were not predictive. The store distinction may be too coarse to reflect the idea that ambient scent creates distinctiveness as a function of the number of associated objects. The absence of significance for age is likely because olfactory deficits become salient after 70 years old ([19]), an age group that is only barely covered by the average sample (mean age 32.6 years). The second limitation is that situational contingencies accounted for 12.6% of the within-sample variance in effect sizes, indicating that variability could not be explained fully and heterogeneity remained. To explain variability, the model assumed linearity. However, the situational contingencies may exhibit nonlinear effects, leading to positive but not negative effect sizes or vice versa. They may also exert response-specific effects that our analysis could not reveal. Third, though we attempted to address publication bias, studies that failed to establish ambient scent effects may not have been available, positively biasing our summarized results.
Table 7 offers a compilation of six areas for future research and various questions on meeting market challenges. While firms attempt to perfect their in-store experience with pleasant ambient scents, unpleasant smells may cause detrimental effects, likely to a larger extent than pleasant scents yield positive effects ([32]). Given the lack of research and the various conceivable sources for malodors (e.g., kitchens, building sites), an exploration and understanding of unpleasant ambient scents is encouraged (area 1). To stand out in the crowd, marketers also use ambient scents to create an olfactory identity in the form of signature scents. While such trends illustrate the markets' interest in ambient scents, we have little understanding of why we observed our reported effects. Thus, insights into process explanations (area 2) and relations with other stimuli (area 3) are needed to fully leverage ambient scent effects and expand its fields of application (areas 4–6).
Graph
Table 7. A Research Agenda.
| Areas | Research Gap and Questions |
|---|
| 1. Unpleasant ambient scents | While firms attempt to perfect their in-store experience with pleasant ambient scents, many sources for unpleasant scents exist (e.g., kitchens, chemicals, animals) that may counter such efforts. As virtually all studies focused on pleasant ambient scents, questions remain. Where do malodors occur? Are they detected in the environment at lower intensities than their pleasant counterparts? To what extent do they elicit ill effects, and are the effect magnitudes larger than the ones we found for pleasant ambient scents? Across pleasant and unpleasant ambient scents, do the situational contingencies exhibit nonlinear effects, so that some factors yield negative but not positive effects and vice versa? How can presumed elicited ill effects be mitigated?
|
| 2. Process explanations | To meet the strong interest in ambient scent and expand its usage to other fields, we need a better understanding of why we observe the effects we report herein. So far, the research stream offers little evidence for needed process explanations. How do the biological idiosyncrasies of the sense of smell link to consumer responses? Do other biological features, such as odor adaptation (saturation of receptor cells) and intermittent dispense (desaturation), account for consumer responses? Do other factors, such as processing fluency, olfactory imagery, or distinctiveness perceptions, explain ambient scent effects?
|
| 3. Cross-modal effects | For similar reasons, a theoretical advancement and insight into ambient scent's relation with music and other stimuli, such as temperature (Sinha and Bagchi 2019), is desirable. Because little evidence is yet available and consumers perceive the environment as a whole (Mattila and Wirtz 2001), questions remain. According to which criteria can the match among ambient scent and other stimuli be ensured, what cross-modal congruency facets prove viable for this, and are there other criteria than congruency conceivable? In consequence, does the integration of ambient scent with other stimuli lead to more positive responses that exceed or fall behind the sum of the unisensory effects? May the integration overstimulate and cause negative effects? Which conditions account for the different constellations?
|
| 4. Ambient scent on international markets | Most studies were conducted in North American and European contexts. Scent-attached meanings do not travel across cultures, and specific scents, such as Arabian rose water and Indian oud, carry strong positive connotations in their cultural heritages. Thus, various cross-cultural questions emerge. To which extent should ambient scents be differentiated across countries and cultures? Can ambient scents similar to, for instance, numbers (Westjohn, Roschk, and Magnusson 2017) serve as symbols to convey a local image? Can they, like other cultural symbols (e.g., the "8" in China), be used to trigger positive superstitious beliefs that translate into willingness to pay a higher price? Do cross-cultural differences exist in the extent to which consumers are moved by ambient scents?
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| 5. Ambient scent for social betterment | Marketing may serve social objectives (White, Habib, and Hardisty 2019). By leveraging its effects, ambient scent may do the same and be seen as a behavioral nudge. Does scent-facilitated lingering translate to other areas, such as more time spent with physical activities and the processing of health-related information? Do the mood and evaluation responses translate into enhancing environments with social purposes, such as public transport and museums? Given its time-related effects, does ambient scent lead to an underestimation of traveled time with buses and trains? In which contexts do such effects have undesired consequences?
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| 6. Ambient scents in the digital age | We discussed ambient scents from the perspective of the physical world, but are they doomed to it? Basic questions can be asked about the role of ambient scents in the digital age and especially for digital environments. Given that we are capable of olfactory imagery, to which extent can we re-experience an ambient scent from the physical store in a digital environment, and does this imagination evokes similarly positive effects? Given that ambient scent is able to facilitate human interactions, can it also facilitate human–computer interactions (with avatars or robots), such as by reducing technological anxiety, facilitating "peer" perceptions, or putting consumers in a more positive mood?
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Hyatt Place, for example, is currently active in 23 countries. However, cross-cultural knowledge on ambient scent is scarce (area 4). Because scent preferences are culture bound, should Hyatt Place adapt its "Seamless" scent to local preferences, or can it use its scent to target a multinational audience? Like using music to cue healthier choices ([20]), ambient scent may also be viewed as a nudge for social betterment (area 5). For instance, by making time pass faster, it may promote health-related behaviors, such as spending more time in physical activities. For prudent use, it is also necessary to understand when this leads to undesired consequences. Finally, are ambient scents doomed to be relevant only in the physical world (area 6)? People are capable of imagining a scent, so scents provide a sensory appeal that can be reexperienced in a digital environment ([ 7]; [40]). Scholars also see potential solutions to problems in artificial intelligence through the way olfactory information is processed ([12]). Because ambient scent facilitates social interactions ([ 3]), might it also support human–computer interactions, such as with robots?
Overall, the idea of using pleasant ambient scents to connect to consumers is well founded. Scent positively influences consumer responses. More importantly, the magnitude of its effects appears substantial, as gauged by the percentage changes. However, it requires judiciously considering the various situational contingencies and the nature of the effects because they are eventually decisive for the success of an ambient scent strategy. This is reflected in the sensitivity of expenditures, for which we predicted an increase between 3% and 23% across an average and a most favorable condition, respectively; however, negative effects are also possible. Exciting questions for future research also make ambient scent a promising topic. Although it seems unlikely that a pleasant ambient scent can turn a poor in-store experience into a great one, we see it as an enhancement that, in the fierce competition for the perfect in-store experience, may be the decisive factor in a firm's ability to thrive and prevail in the market.
Supplemental Material, jm.18.0334-File003 - Pleasant Ambient Scents: A Meta-Analysis of Customer Responses and Situational Contingencies
Supplemental Material, jm.18.0334-File003 for Pleasant Ambient Scents: A Meta-Analysis of Customer Responses and Situational Contingencies by Holger Roschk and Masoumeh Hosseinpour in Journal of Marketing
Footnotes 1 Associate EditorDonald R. Lehmann
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 ORCID iDHolger Roschk https://orcid.org/0000-0003-2521-8367
5 Online supplement: https://doi.org/10.1177/0022242919881137
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By Holger Roschk and Masoumeh Hosseinpour
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Predicting Mobile Advertising Response Using Consumer Colocation Networks
Building on results from economics and consumer behavior, the authors theorize that consumers’ movement patterns are informative of their product preferences, and this study proposes that marketers monetize this information using dynamic networks that capture colocation events (when consumers appear at the same place at approximately the same time). To support this theory, the authors study mobile advertising response in a panel of 217 subscribers. The data set spans three months during which participants were sent mobile coupons from retailers in various product categories through a smartphone application. The data contain coupon conversions, demographic and psychographic information, and information on the hourly GPS location of participants and on their social ties in the form of referrals. The authors find a significant positive relationship between colocated consumers’ response to coupons in the same product category. In addition, they show that incorporating consumers’ location information can increase the accuracy of predicting the most likely conversions by 19%. These findings have important practical implications for marketers engaging in the fast-growing location-based mobile advertising industry.
Online Supplement: http://dx.doi.org/10.1509/jm.15.0215
In 2013, for the first time, Americans spent more time consuming digital media (online and mobile) than television (eMarketer 2013), and consumers’ use of their mobile devices has continued to increase at a rapid pace, reaching almost three hours a day in 2015 (eMarketer 2015a). This shift of consumer attention to the mobile channel has also triggered changes to marketers’ allocation of their advertising budgets: worldwide mobile ad spending is set to grow from $19.2 billion in 2013 to over $100 billion in 2016 (eMarketer 2015b), with mobile ads taking up the majority of global digital advertising spending for the first time.
The proliferation of smartphone and tablet devices has led to entirely new types of interactions between consumers and marketers: mobile technology not only enables consumers to access digital content on the go but also allows marketers to collect information on consumers’ location and target their advertising according to these data (Luo et al. 2014; Shankar et al. 2010). Tapping into this opportunity, many apps in the mobile advertising ecosystem collect subscribers’ locations: a recent study found that the apps on a typical smartphone user’s mobile device collectively report the subscriber’s location up to several thousand times per week (Almuhimedi et al. 2015). After an app obtains consent to access a user’s location, it periodically collects the user’s geographic coordinates and transmits them to a server. From these data, marketers can construct consumers’ dynamic location profiles, which can in turn be used to enhance the customization and targeting of the marketers’ offers.
Traditionally, marketers’ knowledge of consumers’ location was limited to their place of residence. The main uses of such static location data were to partition consumers’ locations into contiguous geographic regions representing market segments, or to consider spatially correlated preferences depending on the proximity of consumers (due to either location-specific shocks or the nature of observational word of mouth; e.g., Bell and Song 2007; Yang and Allenby 2003). Early attempts to monetize mobile location data applied a similar logic, dynamically mapping consumers into segments characterized by a static partitioning of geographic locations. The best-known such approach is geofencing, sending consumers promotional offers when they enter a retailer’s vicinity (Jagoe 2003; Schiller and Voisard 2004).
Targeting mobile coupons by distance to stores has been confirmed to be quite effective in certain contexts (Danaher et al. 2015; Molitor et al. 2015). However, Luo et al. (2014) show that store distance effects are only prevalent for same-day offers and that next-day coupons are in fact more effective in nonproximal targeting. This is good news for marketers because longer offer validity allows them to cast a wider net and target more prospects. However, it remains unclear whether collecting consumers’ location data may improve the targeting of such longer-validity offers beyond the extent that can be based on consumers’ past behavior (Reinartz and Kumar 2003; Rossi, McCulloch, and Allenby 1996). In contrast, it is known that collecting sensitive information such as location data may reduce prospects’ willingness to opt in to receive advertising (Banerjee and Dholakia 2008; Barkhuus and Dey 2003).
Here, we present a new general method to improve the dynamic segmentation of consumers according to their past responses to marketing activities and their location histories. Our work builds on theoretical advancements in economics and consumer behavior suggesting that consumers’ location choices may be indicative of their product preferences (Bettman, Luce, and Payne 1998; McFadden 2001). In particular, we propose that consumers who attend the same venues may exhibit commonalities in their tastes. To test this theory, we construct a dynamically evolving network of colocation events, that is, events wherein two or more participants are at the same place at the same time. Our work combines the technique employed by Bell and Song (2007), who create a static network of consumers through clustering them by zip codes, as well as the dynamic methods proposed by Hui, Fader, and Bradlow (2009), to study consumers’ movement patterns on a map.
Specifically, we propose that the likelihood of a consumer’s undertaking of a particular activity (e.g., redeeming a coupon in a particular product category) is positively related not only to the consumer’s past rate of engaging in the same activity but also to the corresponding past rate of each of his or her colocation network neighbors, that is, those consumers whose mobility trace overlaps with that of the focal consumer. We note that our goal is not to separate the economic and behavioral explanations linking consumers’ location behavior to their product choices; rather, in the spirit of Leone and Schultz (1980) and Bass (1995), we document an important empirical pattern that is relevant for subsequent theory building.
We empirically test our proposition with data from a pilot program of a mobile operator that, over several months, provided smartphone user participants (independent of their current or past locations) with digital coupons in four product categories, while collecting their location data up to a few dozen times each day. For every coupon offered in the program, we compute the colocation network corresponding to the day before the offer was launched. We then simultaneously estimate the effects of network position in the colocation network, referral network effects, and the impact of demographics and psychographics on coupon redemption in a fixed-effects logit model. Our key empirical findings include the following:
• We discover a significant positive link between the colocation of consumers and their response to coupon-based promotions, even after controlling for coupon characteristics, demographic and psychographic differences, referral network effects, and unobserved individual heterogeneity. This suggests that consumers who frequent the same locations have correlated preferences (even if they do not know each other). Constructing dynamic networks from colocation events appears to be an effective method to capture such preference similarities and thereby enhance marketers’ segmentation and targeting efforts.
• Whereas consumer-level demographic or psychographic variables may also be used to uncover correlations between consumers’ preferences, we find that variables derived from consumers’ location history may be more effective predictors of their purchase behavior than traditional variables. In particular, we perform out-of-sample estimations to show an approximately 19% increase of prediction accuracy at identifying the most valuable prospects. Given the high opportunity cost of inaccurately targeted ads—overloading consumers with ads might cause them to opt out from receiving any ads in the future—this improvement of targeting accuracy is of utmost importance to managers.
These are fundamental results confirming that location history can be effectively used to assess consumer preferences and improve the targeting of next-day mobile coupons. Therefore, our method may successfully complement current location-based advertising methods, which typically rely on the geofencing approach. More important, our work highlights the opportunity in capturing the dynamic interdependencies in prospects’ location behavior. In today’s world, where rich mobility data on consumers is abundant, marketers must look beyond methods that attempt to monetize consumer location data using only a static paradigm.
Location Data and Consumer Choice
For most of the twentieth century, location-aware empirical methods were constrained by the scarce availability of data. In the early days of marketing, most firms could at best observe data on consumer activities (e.g., sales) aggregated at the store level. The introduction of individual loyalty cards and credit cards (Guadagni and Little 1983) made individual-level observation possible, but beyond loyal customers’ residential address, most marketers could still only learn consumers’ precise location upon their visit to a brick-and-mortar touch point (e.g., retail store) of the brand. Consequently, early applications of spatial models in marketing focused on grouping locations into geographic market segments (Anderson and De Palma 1988; Ter Hofstede, Steenkamp, and Wedel 1999; Huff 1964; Zoltners and Sinha 1983) and on the implications of the segment boundaries to the optimal pricing, promotion, and distribution strategies of the firm (Bronnenberg 2005; Bronnenberg and Mahajan 2001; Greenhut 1981). More recently, the impact of consumers’ relative locations to stores on online purchases has also been examined within this paradigm. For example, Forman, Ghose, and Goldfarb (2009) demonstrate that when a store opens locally, people substitute away from online purchasing. Similarly, Ghose, Goldfarb, and Han (2012) find that Internet users accessing a microblog are more likely to browse for stores in their vicinity.1
Location Data in Mobile Marketing
The near-ubiquitous adoption of smartphones and tablets has dramatically improved marketers’ ability to collect rich location data on consumers. To monetize the location data collected via smart devices, marketers initially employed methods reminiscent of traditional location-aware marketing models. In the most popular application of location data, geofencing, prospects are targeted with promotional offers when they enter the vicinity of the retailer (De Reyck and Degraeve 2003; Schiller and Voisard 2004). In essence, this approach creates a dynamic segmentation of consumers based on one static partitioning of all physical locations in the market (where the surroundings of each store correspond to a specific partition). Confirming the results known from the literature on the redemption of nondigital coupons (Chiou-Wei and Inman 2008), many studies have independently demonstrated the increased promotion sensitivity of mobile consumers closer to the physical location of the store (Danaher et al. (2015); Fong, Fang, and Luo 2015; Luo et al. 2014; for a comprehensive review, see Andrews et al. 2016). In the context of location-aware pull advertising, wherein consumers search for available offers in their vicinity, the results are similar (Ghose, Goldfarb, and Han 2012; Molitor et al. 2015).
These results are not without caveats. Combining field experiments (conducted at grocery retailers) with simulations, Hui et al. (2013) highlight that once the marketer accounts for unplanned purchases, mobile advertising that requires shoppers to travel farther away from their planned path may be more profitable than offers for an unplanned category near shoppers’ planned path. Furthermore, both Luo et al. (2014) and Fong, Fang, and Luo (2015) show that store-proximity effects may wear out by the day after the coupon is delivered. This may indicate that most consumers plan their shopping trips in advance (Dellaert et al. 1998; Popkowski Leszczyc, Sinha, and Sahgal 2004) or that consumers taking care of business unrelated to shopping find mobile advertising intrusive (Shankar et al. 2010). Making consumers opt in to mobile promotions (Barwise and Strong 2002; Danaher et al. 2015) dampens the intrusiveness of mobile advertising, but even so, a store engaging in geofencing cannot send promotional messages to prospects whose typical movement patterns do not intersect the fence. Put differently, targeting based on distance should be more effective when consumers have already revealed their shopping intentions (Hui, Bradlow, and Fader 2009; Hui et al. 2013).
Dynamic Location Behavior and Consumer Preferences
The aforementioned models take location data as input to predict consumers’ likelihood of choosing from a set of options (which may, for product purchase behavior, include the outside option) that is the same for the entire population of consumers. However, marketers are often also interested in understanding consumers’ location choices (Bradlow et al. 2005).
The assumptions of standard economic theory state that each location choice is made to maximize some underlying utility function (McFadden 2001). Under this paradigm, consumers’ choices of location reveal their (static) underlying preferences, which in turn may allow marketers to better predict their future choices. For instance, Bellovin et al. (2013) and De
Montjoye et al. (2013) demonstrate how even the aggregation of noisy location data (collected from cell towers) may be used to predict individuals’ demographic attributes and frequently attended future locations.
In contrast, the literature on constructive consumer choice (Gregory, Lichtenstein, and Slovic 1993) argues that location choices, instead of revealing well-articulated preferences, may reveal situational factors such as the activated goals of the decision maker (Bettman, Luce, and Payne 1998). Moreover, exposure to particular contextual factors present at consumers’ chosen locations (Kirchner et al. 2012) may influence the accessibility of their preferences, ultimately affecting their future choice behavior in nonlocational domains (Berger and Fitzsimons 2008; Feldman and Lynch 1988). Examples for such context effects in mobile marketing include the effect of weather on advertising response (Li et al. 2015), the differential effect of being exposed to ads at home or at work (Reinaker et al. 2015), and the effect documenting higher ad response from passengers traveling on more (vs. less) crowded trains (Andrews et al. 2015).
Importantly, the approaches of both the economics and consumer behavior literature streams acknowledge the possibility that consumers’ dynamic location choices carry information about their (possibly dynamically changing) product preferences. When the locational context (e.g., points of interest) at the place of colocation is known to the marketer, it is indeed straightforward to link consumers’ locations to their subsequent product choices. However, even in the absence of such contextual information, marketers may be able to monetize the information captured in consumers’ location histories. To demonstrate this, we present a novel method that uses networks to capture commonalities between consumers’ preferences.
Using Networks to Predict Consumer Behavior
Besides the emergence of rich data on consumer locations, the technological advancements of the twenty-first century have also led to the emergence of large databases on the structure of consumer interactions, for example, online friendship networks, telecommunication networks, and networks based on financial transactions (Sundararajan et al. 2013). Marketers have long recognized the powerful impact of word of mouth on consumer choice (Bass 1969; Brooks 1957; Herr, Kardes, and Kim 1991), and researchers have later shown how microlevel data can enhance models of product or service adoption (Katona, Zubcsek, and Sarvary 2011; Manchanda, Xie, and Youn 2008) and disadoption (Nitzan and Libai 2011).
Much of the empirical social network literature has focused on empirically separating social influence from the inherent similarity (or homophily; McPherson, Smith-Lovin, and Cook 2001) of connected individuals in the network (Durlauf and Ioannides 2010; Iyengar, Van den Bulte, and Valente 2011), which is an often-difficult task due to the reflection problem (Aral, Muchnik, and Sundararajan 2009; Manski 1993). However, in the typical scenario wherein the marketer cannot control the (mostly static) structure of the consumer network, the agnostic approach of naively quantifying the relationship between network characteristics and consumer choice (Hill, Benton, and Van den Bulte 2013; Reingen et al. 1984) can provide a major contribution to the firm’s bottom line. In a recent example, Goel and Goldstein (2014) take this route and show how incorporating the structure of communication links from a very large instant messaging network can improve the accuracy of predictions for a variety of consumer choice behaviors, including response to advertising and retail purchases.
In the same spirit, we propose a novel method to monetize location data. In particular, we propose to use network theory for characterizing commonalities between consumer location patterns, and we use the so-derived network characteristics to improve the prediction accuracy of traditional consumer choice models. We present our proposed approach next.
Predicting Consumer Choice Using a Network of Colocations
Traditionally, marketers have assessed consumer choice likelihood using demographic variables and data on consumers’ past purchase behavior (Rossi, McCulloch, and Allenby 1996). However, the literature linking location behavior to consumer preferences suggests that consumers’ dynamic location choices may also be indicative of their product preferences.2 In particular, to the extent that consumers’ location choices are indicative of their preferences in a product category, the choices of individuals who often attend the same venues may exhibit similarities. To quantify these similarities, we take an approach reminiscent of Goel and Goldstein (2014). Specifically, we propose to construct a dynamic network to capture the correlations of consumer preferences reflected by colocation events, that is, events wherein consumers are at the same place at approximately the same time. Furthermore, we propose that the likelihood of a consumer’s engaging in a particular activity (e.g., redeeming a coupon in a particular product category) is then positively related not only to his or her past rate of engaging in the same activity but also to the corresponding past rate of each of the consumer’s colocation network neighbors, in other words, those consumers whose mobility trace recently overlapped with that of the focal consumer.
Thus, our proposed method is a form of naive collaborative filtering (Herlocker, Konstan, and Riedl 2002) based on colocation patterns.3 It presents a novel means of dynamically mapping geographic locations to consumer segments, wherein the role of specific locations may exhibit variation both across time and, via others’ location choices, across consumers. In this regard, we improve on Andrews et al. (2015) and Hui, Bradlow, and Fader (2009) by considering multiple location observations to determine colocation between consumers. Table 1 contrasts our approach with existing work that considers dynamic location information to predict consumer choice.
Importantly, our method does not aim to disentangle the economic and behavioral explanations linking consumers’ location behavior to their product choices. To identify specific drivers of consumer choice, one would have to run causal experiments (Cooke and Zubcsek 2017; Lurie et al. 2016). Rather, in the spirit of Leone and Schultz (1980) and Bass (1995), we document an important empirical pattern that is relevant for later theory building. We proceed with an empirical test of our approach on a location-enhanced data set of mobile coupon response.
Application: Improving Mobile Coupon Targeting via Subscriber Colocation Networks
Smartphone app–based advertising platforms provide a natural context in which to empirically test our modeling approach. Relying on the GPS sensors in participants’ mobile devices, these platforms can collect more precise consumer location data than cell tower–based methods, as well as collecting data outside the vicinity of participating retailers’ store locations. In addition, the reach of the smartphone channel has grown past 2 billion consumers (eMarketer 2016). Mobile devices are becoming consumers’ personal companions (Shankar and Balasubramanian 2009)—a trend witnessed by the increasing amount of time consumers spend on their mobile devices (eMarketer 2015a). Marketers’ reallocation of advertising budgets from the online to the mobile channel (eMarketer 2015b) highlights the importance of further studying this application area.
In-app display ads constitute a rapidly emerging format of mobile advertising (Bart, Stephen, and Sarvary 2014; Grewal et al. 2016; Shankar et al. 2010). Whereas the display advertising category is often thought of as just banner and poster ads on the mobile web, it increasingly also includes other formats, such as video ads, sponsored stories in social media newsfeeds, and rich media advertising. Recent research has demonstrated the effectiveness of these marketing vehicles in a variety of contexts (Burns and Lutz 2006; Katz 2014; Rosenkrans 2009).4
The most advanced in-app display advertising techniques attempt to leverage the targeting opportunities arising from the high level of portability and personalness of mobile devices (Barwise and Strong 2002; Ghose and Han 2011; Shankar and Balasubramanian 2009). Inter alia, data on online consumer behavior can be complemented with rich data on offline consumer behavior (e.g., time and location data), and offline purchase incentives can be delivered in an online way (e.g., mobile coupons, or “m-coupons”).
Our study falls into the rapidly emerging topic in mobile advertising that closely builds on the mobile nature of the advertising medium; it examines the impact of subscriber location on mobile coupon redemption (Danaher et al. 2015; Luo et al. 2014; Molitor et al. 2015). Importantly, seminal work on mobile coupons (Banerjee et al. 2011; Danaher et al. 2015; Dickinger and Kleijnen 2008; Fong, Fang, and Luo 2015; Luo et al. 2014; Reichhart, Pescher, and Spann 2013) has presented findings and coupon redemption rates consistent with the literature that has analyzed traditional coupon formats (Bawa and Shoemaker 1987; Inman and McAlister 1994; Lichtenstein, Netemeyer, and Burton 1990). This suggests that, pending data availability, better understanding of the link between consumer location choices and offer redemption behavior in the context of mobile coupons may also carry managerial relevance for marketers who use traditional coupon formats.
Data
Our data came from a mobile operator in a Pacific country. The operator ran a pilot program in two major metropolitan areas to study consumer response to in-app mobile advertising from January to early April 2012. Subscribers invited to the pilot program—either by the operator or by one of the program members—had to respond to a short demographic survey and were asked to install a new app on their smartphone. During the program, the operator distributed digital coupons (hereinafter, “offers”) from participating retailers to every participant currently in the program. In practice, for each offer, the mobile app received the coupon information from the server and displayed a notification on participants’ smartphones. When a participant clicked through the notification, the app asked a lead-in question indicating the category of the offer, such as “Are you interested in an offer for groceries?” At this point, participants could either discard the offer or see more details, including the participating brand(s) and (if applicable) product(s),5 the discount value of the coupon and the “when-you-spend” amount.6 After viewing these details, participants could hit “accept” anytime during the validity period of the offer and receive the in-store discount after making the required purchase and showing the accepted coupon. Unfortunately, the exact time of each participant’s redemption was not recorded. We only have data on whether and how a participant responded to any given offer over the duration of its validity. Our panel contains a total of 15,353 observations on 96 offers sent to 217 participants. The panel is incomplete, however, because some participants signed up after certain offers had already expired (registration was open throughout the period of the pilot).
Participants signing up for the pilot also agreed to have their GPS location information regularly transmitted to the mobile operator by the app. However, location data could only be captured this way when both the location services were enabled on participants’ smartphones and the device was able to detect the necessary satellite signals. Whereas the app was set up to submit location information about four times every hour, the actual rate of transmission was lower. To correct for this dispersion and balance the data, in the data set that the operator provided, the coordinates of all observations within the same hour are averaged, resulting in up to one observation per hour. For each hour during which there was no successful transmission, there is no location observation in the data set. We have an average of 11.29 hourly location observations per day per participant in our panel. However, for about 40% of the observations, there is no location information on the participant.
The data set does not contain information about potential points of interest (POIs) in the geographic region where the study was conducted. Obvious POIs would include the stores that participated in the coupon program, but unfortunately, the operator collecting the data did not record location information for the overwhelming majority of advertisers. Moreover, in some cases, the coupons were valid for a whole network of stores (including several dozen store locations in town), while other campaigns were specific to a store, making store-location data difficult to work with. We are also missing information about participants’ home address, which was not collected for confidentiality reasons. To gauge the impact of these data issues, we conducted a variety of robustness analyses. For instance, in one of our validity tests, we attempted to infer participants’ home location from the GPS data to make sure that colocation did not simply reflect cohabitation (e.g., membership in the same family).
Methods
We modeled consumers’ offer redemption behavior using a logistic regression. To control for unobserved heterogeneity, we included offer fixed effects and participant random effects in our main models. Estimating our models with offer fixed effects was a natural choice for two reasons. First, the individual offers in the program were qualitatively very different, and henceforth the baseline response rate also exhibited a large variation. Second, the cardinality of offers is much lower than that of participants in our panel. Concerning participant random effects, we note that some participants did not redeem any offer during the period studied, so we could only include fixed effects at the cost of throwing away all the observations for these participants.
We formally specified our model as where Yij is an indicator of consumer i’s response to offer j. If consumer I redeemed offer j, then Yij = 1, and 0 otherwise; Wi is a set of covariates measuring consumers’ individual characteristics; Xij are variables that are both consumer- and offer-specific (e.g., the length of time an offer was available for a particular consumer); xi ~ Nð0, r2Þ represent participant random effects and hj represent offer-specific fixed effects; and Zij are variables that capture the network effects between pairs of consumers for a given offer. Central to our interest is the network based on consumers’ colocation. In addition, to control for the otherwise unobserved similarity of consumers, we also constructed a network connecting consumers according to whether one referred the other to the program. Table 2 describes the variables used in our main regressions. We detail these next.
Consumer-Level and Offer-Level Covariates (W I and X ij )
The data contain numerous demographic and psychographic variables. Upon signing up, participants answered 24 profile questions. Dropping all profile variables that had more than two possible answers and did not translate to an ordinal scale left us with nine profile variables (see Table 2). In addition, for each consumer–offer pair, we included three other variables.
First, we included the number of days the offer could be used by the participant (Offer_Length) (this only varied for participants who joined the program while the particular offer was available). Second, we added the number of days the participant had been in the program prior to the first day the offer was available (Days_Since_Joined). Finally, we added Category_Redeem_ Rate, the rate at which the participants had been responding to prior offers in the category of the offer considered (i.e., consumer packaged goods, food and beverages, retail, or recreation). Table 3 reports the summary statistics for these variables per offer category. Whereas it is straightforward to also calculate overall response rates, due to the high (.41) correlation between the overall and within-category response rates, we decided to include only the category-specific variable in our model.
Network Variables (Z ij )
To study how similarities between individuals’ location behaviors may correspond to similarities in coupon redemption, while controlling for the similarity of socially connected individuals, we introduced variables derived from two participant networks, colocation and referral. In both cases, our network-based variables were constructed in two steps. We first constructed a dynamic network between consumers; then we defined a neighborhood effect for each offer on the basis of the network structure a day before the launch of the offer and neighboring consumers’ response to prior offers in the same category.
We defined both networks as time-varying undirected simple graphs over participants as nodes. In the colocation network (denoted by the matrix Cj), two participants were connected to each other if they had been at the “same location” according to at least one of the hourly GPS observations during the last day preceding the launch of offer j. The GPS coordinates of the hourly location observations (where available) gave us the latitude and longitude of each participant. For computational reasons, we used this information to determine colocation the following way. We defined a rectangular grid of .002° spacing and determined which grid cell each observation belonged to. (The grid spacing was chosen to be approximately equal to the lowest reported GPS signal accuracy of 250 m.) Two participants were then considered colocated when their observation for the same hour fell into the same grid cell (see Figure 1). The colocation matrix was thus formally defined as
A positive effect of colocation would indicate similarity of the colocated consumers’ preferences. Importantly, however, Pan, Aharony, and Pentland (2011) point out that such an effect may be driven by the presence of social relationships between most colocated actors. In our main model, we attempted to control for such relationships both through participant random effects and by adding information about referrals between participants to the model.
To control for referrals, we considered the referral network, denoted by Rj. This network was also defined as a time-varying simple network, wherein two participants were related if one of them had invited the other to the program. (Although referral itself assumes asymmetric roles, we defined an undirected referral network to capture not just word-of-mouth influence but any correlation between the advertising response behavior of two linked participants.) Formally,
Note that the variation in the referral network was monotonic in time, that is, the network kept growing because for each referral relationship in our data, we included the network link starting from the day when the second of the two participants joined the pilot.
The final step was to construct the Zij neighborhood effect variables for each offer in each network. In line with Goel and Goldstein (2014) and Provost, Martens, and Murray (2015), we expected that a participant would be more likely to engage in a certain behavior if they were connected to someone who had previously engaged in that behavior. To capture this phenomenon, in both networks, for each participant and offer, we took the sum of the within-category response rates of all network neighbors prior to the launch of the offer, and included zeroes where this was not applicable. Formally, for offer j, in the Cj graph,
For the reference network, we defined the variable Referral_ Category_Redeem_Ratei, j in the exact same way, using the Rj network instead of Cj.
We predicted that colocation relationships would correspond to correlated coupon redemption rates. The network variables constructed previously provide a simple way to capture this main effect and also to control for the effect of referrals. Importantly, however, the colocation variable contained positive values for fewer than 20% of the program participants per offer. Furthermore, we needed to make sure that positive values of Colocation_Category_Redeem_Rate were not simply more prevalent for participants whose location was observed more often. Therefore, in our regressions we controlled for GPS activity, that is, the number of nonzero location observations available to us during the period in which colocation was studied (i.e., the day preceding the launch of the offer in question).
Results
The results of our estimations are presented in Table 4. Model 0 was our baseline model that estimated redemption likelihood in terms of only the simple offer-related variables. Model 1a added traditional predictor variables (demographics and psychographics). In contrast, Model 1b augmented Model 0 by incorporating the location variables. Models 2a and 2b extended Models 1a and 1b, respectively, by controlling for referrals. Model 3, the full model, included all demographic, psychographic, location, and referral variables in addition to those included in Model 0. (Thus, Models 0–1a–2a–3 and 0–1b–2 b–3 were nested in each other.)
The effects on the category- and participant-specific variables show strong face validity and are generally consistent across all models. For instance, the data confirm the intuition that the longer an offer was available to a participant, the more likely that participant was to use it. In addition, there appears to be weak evidence (after we control for for offer-specific mean response rates) for the notion that the tenure of participants in the program increased their response rate. We speculate that this may be due to many less adventurous participants joining the program toward its end; if it took these participants some time to get familiar with the system, a large number of them could lead to such an effect.
Our next set of observations focuses on the effect of central interest: that of colocation. Models 1b, 2b, and 3 all estimated the colocation network effect, varying the degree of control through demographic variables and the referral network. Confirming our predictions, we found that colocation had a significant effect over and above other variables. Specifically, in all three models, Colocation_Category_Redeem_Rate was significant at the p = :01 level (Table 4), which provides evidence of the positive relationship between the coupon redemption likelihood of participants who were colocated on the day prior to the launch of the offer.
Turning to the control variables, we first note that we found referral effects to be quite strong. This result, consistent with the results of Goel and Goldstein (2014), underscores the importance of controlling for participant heterogeneity via the referral network. Concerning demographics, the results provide consistent support for the pattern that female participants and participants with more education had higher coupon redemption rates. The results in Table 4 thus indicate that there were some younger (and consequently less educated) male participants whose engagement at redeeming offers was lower than that of the average user. A closer investigation revealed that these participants were generally less engaged with the program. In the Web Appendix, we report additional tests wherein we controlled for this respondent heterogeneity in multiple ways. We found our main results to be robust to alternative specifications.
Demographics and Psychographics Versus Colocation
Our findings demonstrate how predicting coupon redemption behavior becomes more accurate as colocation is incorporated in the model. These results are reminiscent of those in Goel and Goldstein (2014). Albeit only for age and gender, Goel and Goldstein also demonstrate (see their Figures 6b and 7) that using (social) network variables in the absence of demographic predictors may achieve better results than using only demographic information in the estimations. It is therefore natural to investigate the same issue in our data and compare the performance of the location variables with that of demographics.
It is paramount to highlight that such questions carry tremendous importance to mobile marketers. For example, in many emerging markets, the vast majority of mobile subscriptions are prepaid (Castells 2007). In a prepaid customer relationship, the operator often does not possess reliable demographic information about the customer. However, location data may be captured either at the cell tower or at the GPS level (Bellovin et al. 2013), and customer relationship management databases can store prior customer response to marketing offers.
In-sample model fit. To compare the predictive power of demographic and psychographic variables with that of location variables, we first studied the fit of Models 0–3. All six models included the offer-specific variables because it is fair to assume that these would naturally be available to marketers. Further, in the models with colocation (Models 1b, 2b, and 3), we included the GPS Activity variable to ensure that “participant activity” did not contribute to any network effects identified by the estimation. Generally, adding the demographic and psychographic variables to a model improved the model’s fit less than adding the location variables: LR(Model 1a, Model 0) = c2ð9Þ = 16:76, p = :053; LR(Model 3, Model 2b) = c2ð9Þ = 14:69, p = :100; LR(Model 1b, Model 0) = c2ð2Þ = 24:50, p < :001; LR(Model 3, Model 2a) = c2ð2Þ = 18:89, p < :001. Comparing the log-likelihoods of Models 1a and 2a with those of Models 1b and 2b, respectively (Table 4), we found that despite using fewer predictors, the model incorporating location variables achieved a higher model fit. In accordance with this, on both the Akaike and Bayesian information criterion tests (Table 4), Model 2b performed best.
Out-of-sample estimations. Next, we also performed outof-sample estimations. First, we split the data in time such that about 75% of the observations would be used as training data, and we tested the accuracy of model predictions on the remaining 25% of observations. The receiver operating characteristic (ROC) values were .7177 for Model 0, .7285 for Model 1a, .7453 for Model 1b, .7346 for Model 2a, .7443 for Model 2b, and .7469 for Model 3. None of the pairwise differences between the ROC values were significant at p = :05. This is not surprising given that when we accounted for the offer fixed effects, even the baseline model included over 100 variables, to which we added a total of nine demographic and psychographic and up to three location and referral variables.
It is important to note, however, that marketers should care more about the sensitivity than the specificity of the model. The reason for this is that in most mobile advertising platforms, consumers may limit their daily exposure to a small number of ads, thereby vastly increasing the opportunity cost of inaccurate targeting. To assess the specificity of the model at such low rates of targeting, we next devised and carried out a round-robin test. Specifically, we randomly divided our observations into ten approximately equal sets and performed ten tests. In each test, we pooled nine of the sets to form the training data and used the tenth (denoted by T in this paragraph) to evaluate the predictions of the models calibrated on the training data. In each test, we ranked the predicted probabilities and defined our prediction list as the list containing the m nodes with the highest predicted adoption probabilities (for various values of m). To test predictive power, we calculated the proportion of offer redemptions in the prediction list as a function of the fraction of observations included in our sample. (We expressed m as a fraction of jTj to balance the slight variation of the number of observations across the ten evaluation data sets.) Finally, we averaged the ten success rate functions corresponding to different choices of the training set.
We carried out the round-robin prediction test for the six models from our main analysis. In line with our previous argument, we focused on the success rate of the models when no more than 10% of the participants (according to their high estimated redemption probability) were predicted to redeem the offer. The average lift of Model 1b over Model 1a was 18.82% and 8.81% in the range of predicting up to 5% and 10% of redemptions, respectively. When we controlled for referrals (Model 2b vs. Model 2a), the average improvement of prediction success was 19.09% and 9.57%, respectively. These differences provide evidence that location variables may be better predictors of the most likely coupon redemptions than demographic and psychographic variables. Because calculating the colocation variables is a straightforward task even for firms with moderately sophisticated computational infrastructure, we believe our method can be transformed into an effective and practical tool to improve targeting accuracy.
Naturally, it is likely that a smoother definition of the networks used in the estimations could slightly alter the range in which the location-enhanced models outperform their “location-free” counterparts. For instance, including variables derived from a social network based on communication interactions (voice calls, texts, etc.) would likely increase the discriminating power of our model over most participants, not only those few who were involved in referrals. Unfortunately, we do not possess such additional data. However, our location data are rich enough to allow for some flexibility in our approach. In the next sections, we explore various relaxations of the definition of colocation to study the trade-off between the number of relationships in the so-derived network and the ability of these relationships to uniquely reflect common underlying shocks affecting the related consumers’ behavior.
Eliminating Store Distance Effects
Mobile subscribers may be colocated for a wide variety of reasons. They may dine or shop at the same location, pass through the same public transportation hub, and so on. Following the rational and constructive consumer choice literature streams, we theorized that such co-occurrences may reflect similar preferences. Importantly, however, our network definitions will also deem participants related if they live in the same household or work in the same office (building or district). Whereas such instances should clearly correspond to similar preferences, this also admits the possibility that our results are driven by the main effect identified in Molitor et al. (2015): if two people live in the same household (or next to each other), then any store may be the same distance from each of them. (Recall that because we do not know the exact time at which offers were delivered, we also do not possess the location of participants at the time of coupon delivery.)
Including the referral network variables in our main models did account for such social effects. However, given that our referral network sample may only contain the strongest social relationships between participants, we performed further tests to study the impact of multiple participants in or near the same household or at the same workplace, respectively. In particular, for various cut-off thresholds :2 £ Fi £ :5, we defined two participants as cohabiting if they were colocated at the same grid cell between 1 A.M. and 5 A.M. on at least an Fi fraction of the days that they spent in the program. (We note that this is consistent with the method of Reinaker et al. [2015], which corresponds to Fi :33 in our approach.) For example, in the case of Fi = :5, this search returned two connected pairs of subscribers with no overlap between the pairs. Removing all four participants from our data decreased the average degree in the colocation network by over 15%, indicating that other colocation relationships had a greater offer-to-offer variability.
Similar to cohabitation, for any threshold Fi, we defined two participants as being in a coworking relationship on the basis of colocation observations in the morning between 9 A.M. and 12 P.M. and in the afternoon between 2 P.M. and 5 P.M. Because the two analyses yielded similar results, herein we focus on the results of the cohabitation analysis. (The results for the coworking analysis are presented in Table WA5 in the Web Appendix.) The results of our estimations on the reduced samples of participants are presented in Table 5. We found that the relationship between the location network variables and coupon redemption remained significant even after we removed the potentially cohabiting individuals from the sample. We conclude that our results cannot be merely driven by store distance effects.
Hot Spots Versus “Cooler” Locations
Could busy urban hubs drive the colocation effect? In principle, it is possible that a few popular locations, such as shopping malls visited by many users, reveal something context-specific about the preferences of consumers who appear at those locations. Clearly, the presence or absence of such effects would have very different implications for marketers planning to target consumers with mobile coupons. To this end, we examined whether popular places or less frequently visited locations reveal more about consumer preferences.
Apart from the obvious managerial takeaways, this problem is also interesting from a theoretical perspective. In the related prior literature, Hui, Bradlow, and Fader (2009) find that consumers in (currently) busier areas of the supermarket are, in general, less prone to make a purchase than those in less busy areas. In contrast, Andrews et al. (2015) show that coupons sent by text message (which could be converted by sending the appropriate response code in a reply message) had a higher take-up rate in more-crowded subway trains than in lesscrowded ones.
In both of those studies, the nature of the physical environment formed a small natural upper bound on the number of jointly colocated consumers. Relaxing the strict simultaneity constraint and focusing on the aggregate visitation count of locations over ten days, Provost, Martens, and Murray (2015) find mixed support for the inverse relationship between location popularity and the strength of the colocation effect on publisher choice. We conducted a similar test to determine whether frequently attended hot spots or “cooler” (i.e., lessvisited) locations explained more of consumers’ coupon redemption behavior. Just like Provost, Martens, and Murray (2015), we proxied the popularity of a given location (here, a grid cell) by counting the total number of visitations during the observed period. As an alternative definition, we counted only the aggregate number of distinct users at any location (counting even frequently visiting consumers only once). Figure 2 displays a heat map of one metropolitan area, defining the popularity of any grid cell as the total number of GPS observations in that cell during the period studied. (The heat map for the alternative definition of location popularity is displayed in Figure WA1 in the Web Appendix.)
For both definitions of location popularity, we conducted the following test. We split the locations into two groups and derived two separate colocation networks according to colocation events that occurred at hot spots and at lessvisited locations. To obtain the maximum power, we created the split so that our 15,353 coupon redemption observations were represented in the two resulting networks as evenly as possible. For the heat map in Figure 2, the 97 locations, each with more than 595 location observations in our sample, became the hot spots. For the heat map shown in Figure WA1, cells that were visited by at least ten participants became the hot spots. For both definitions of location popularity, we then created the Colocation_High-Frequency_Category_Redeem_ Ratei, j and Colocation_Low-Frequency_Category_Redeem_ Ratei, j variables, just as in Equation 4, and estimated Model 3 replacing the colocation variable with the aforementioned two variables.
For brevity, we report the detailed results of our analysis in Table WA7 in the Web Appendix. Concerning the variables of interest, we found that the colocation effect was statistically significant only in the network representing colocation events at infrequently visited locations (b = 4:912, z = 3:20 for the split based on total observation count, and b = 3:498, z = 3:11 for the split based on the count of distinct users) but not for the network capturing frequently-visited locations (b = 1:916, z = 1:67 and b = 2:990, z = 1:53, respectively). We conclude that our effects are not driven by colocation at hot spots such as business districts, transportation hubs, and so on. Instead, the increased predictive power of Models 2 and 3 in our main analysis stems from capturing the colocation events that occurred at less-visited places on the map.
Alternative Definitions of Colocation
This section explores the robustness of our results to alternative definitions of colocation. To this end, we first relax the definition of colocation in geographical space and time, respectively. We then look at aggregating more (days’ worth of) colocation events into the colocation network. Finally, we prune the colocation network to retain only links between participants who met at more than one location during the program. The summary statistics of the so-created networks (we present these along with the characteristics of the networks used in our main models) are reported in Table 6, and the results of our alternative models are shown in Table 7. We detail our methods and findings next.
“Almost colocation”: space. In our main model, we defined colocation as being in the same cell on a grid of .002° spacing. Due to the accuracy limits of our GPS data (captured by smartphones), further precision was not possible. However, one may question whether even such a strict definition is required to study participants’ preferences. To examine this issue, we redefined our colocation network so that it admitted relationships between any two participants who were at the same time in cells that could be blocks of the same cell on a grid of .004° spacing (see Figure 3). We then estimated Model 3 using the so-derived colocation variables.
Intuitively, making the definition of colocation more inclusive should induce two effects. On the one hand, the density of the colocation network should increase. On the other hand, each relationship in the colocation network should be a weaker reflector of the similarities between the preferences of the two linked participants. We observed both of these effects in our data. In particular, Table 6 shows that the average degree in the colocation network almost precisely quadrupled from the average degree derived from the basic definition of colocation. In contrast, we found that whereas Colocation_Category_Redeem_Rate was significant at the p = :05 level (b = 1:411, z = 2:47), its coefficient was substantially lower than the corresponding equivalent reported in Table 4.
“Almost colocation”: time. In our main model, we defined being colocated as being in the same cell on the grid during the same hour. It is natural to ask how the model performs with the time constraint relaxed. To that end, we created an alternative colocation network in which two participants were connected if they were at the same location (i.e., inside the same grid cell) during any two-hour window during the day preceding the launch of the offer. We then estimated Model 3 using the so-derived colocation variables.
We expected the same effects as in the previous section, and again, we observed both trends in the data. The average degree in the colocation network increased by about 70%. The effect of colocation remained significant at the p = :05 level (b = 2:342, z = 2:86), but both its coefficient and the significance level decreased relative to the values reported in Table 4.
Varying window length. In our main model, two participants are neighbors in the colocation network if they were colocated at least once during the last day preceding the launch of the offer. Table 7 reports the results for the models wherein the colocation network is defined according to such colocation events for w = 2, 3, and 4 days before the launch of the offer. The results are similar to those discussed in the two previous sections. As w increased, more distant events were incorporated in the network, leading to a higher degree (see Table 6). However, these distant events were less revealing about participants’ current preferences, ultimately weakening the effect of the Colocation_Category_Redeem_Ratei, j variable.
Requiring multiple locations of co-occurrence. Given the positive link between colocation and coupon redemption behaviors, it is interesting to consider whether multiple colocations shortly preceding the launch of an offer indicate more commonalities between the corresponding consumers’ preferences. Such events, however, were very rarely observed in our data, and thus we are unable to answer this question. To focus on participants who co-occurred at different locations, we therefore defined a colocation network that factored in participants’ location behavior during the entire program. Specifically, we pruned the colocation network (defined in Equation 2) such that we only kept connections between colocated participants who, over the entire period observed, co-occurred at a minimum of two different locations. (We did remove connections between individuals who colocated very often but did so always at the same location.)
The results (presented in Table 7) are nearly identical to those of our baseline model. The directions of the coefficients are the same, and, except for some demographic variables, the significance levels also do not change. These results lend additional robustness to our main findings. In particular, since we do not observe the physical location(s) associated with each offer, one could argue that our colocation effect merely captures the fact that different consumers regularly return to the same location near a given store, and that this is driving our results.7 Defining colocation on the basis of multiple distinct locations alleviates this concern.
Additional Tests of Validity
Besides the coworking analysis and the additional results concerning hot spots versus less-visited locations, we also detail additional validity tests we performed. Specifically, we estimated our model (1) using alternative means to control for participant heterogeneity, (2) with category-specific slopes to capture the impact of past coupon redemption behavior, and (3) using data only on those offers that were viewed by participants (to the end of testing the impact of their coupon selection behavior). We note that for the variables of interest, the results of these tests are consistent with those reported herein. Details of these additional validity tests are in the Web Appendix.
Discussion and Concluding Remarks
As digital media consumption and the time consumers spend on mobile devices steadily rise, marketers are reallocating their budgets to reach consumers increasingly through their smartphones. However, marketers’ use of dynamic location data is mostly limited to geofencing—targeting consumers when they are in the vicinity of the retailer (Jagoe 2003). Recent academic research (Fong, Fang, and Luo 2015; Luo et al. 2014) points out that such methods ignore many valuable prospects outside the geofence, leaving money on the table. The core objective of this study is therefore to explore a novel use of dynamic consumer location data, with applications to segmenting consumers and targeting mobile advertisements. On the basis of results from economics (McFadden 2001; Rossi, McCulloch, and Allenby 1996) and consumer behavior (Bettman, Luce, and Payne 1998;
Feldman and Lynch 1988), we theorize that consumers’ movement patterns are informative of their product preferences and propose to exploit this relationship by constructing a network of colocations, that is, events when two or more consumers appear at the same place at approximately the same time.
Applying the proposed new theory, we study the behavior of 217 smartphone users who participated in a pilot advertising program run by a mobile operator in the Pacific region. Over three months, participants received about 100 mobile coupons in four different categories. Although participants received the offers irrespective of their locations, the data also contained hourly GPS records for the participants, allowing us to reconstruct their mobility patterns. We created a dynamically evolving colocation network, wherein any two participants were connected if they had appeared at the same place at about the same time during the period studied. We then modeled the joint effects of network position in the so-derived colocation network, offer characteristics, demographics, psychographics, and referral network position on advertising response.
Our results show that location history can be a relatively effective predictor of promotion response behavior. Specifically, we find that the past coupon response rate of a consumer in a given category positively correlates with the coupon response likelihood of another consumer with whom he or she has colocated, and that this effect on mobile coupon response is present over and above the effects of both traditional variables, such as demographic and psychographic data, and those of referral network position. Moreover, our validity tests demonstrate that the effect of colocation was not driven by cohabitation or coworking (individuals residing or working in the same area), supplying further evidence that consumers’ location patterns provide information about their preferences beyond what is revealed by their social relationships. In addition, comparing colocations at frequently visited “hot spots” with all other colocation events indicates that trips to unusual destinations contain more information on consumers’ preferences than do trips to busy locations visited by many people. These results lend further credibility to the idea that consumers’ location choices can be indicative of their product preferences.
Finally, we estimate the value of information captured by the location variables in scenarios with high costs of false positives. An example for such a setting is an advertising platform that gets commission from all ads that lead to sales while being constrained (e.g., legally) by user-defined limitations such as delivering a maximum of three ads per day to any participating consumer’s smartphone. In such environments, marketers’ primary goal is to identify prospects who are the most likely to respond to each ad. Using out-of-sample estimations, we demonstrate the practical value of our methods: we find that even after we control for participant and offer heterogeneity, incorporating location data in the models increases the sensitivity of selecting the top 5% of prospects by as much as 19%.
Limitations and Avenues for Future Research
Our results suggest that managers need to respond to the new data challenges emerging in the mobile marketing ecosystem by embracing new methods in the area of location-based marketing. To illustrate the potential of such methodological developments, it is important to remember that our analysis was based on location information only representing abstract coordinates. We did not have information about the POIs in the geographic region covered by consumers’ movements.8 Notwithstanding these restrictions, just by studying the colocation network of consumers, we were able to demonstrate significant effects while controlling for individual and offer characteristics and even for social relationships via the referral network between participants. One can only speculate that the presence of POI information, let alone purchase history data, could greatly enhance the effect of location measures.
Specifically, combining our data with the exact time of coupon redemptions and detailed information on the network of participating stores could allow us to separately test colocation effects on same-day conversions and on all other coupon redemptions. In line with Luo et al. (2014), we anticipate store distance effects to be prevalent for same-day conversions, potentially weakening the colocation effect. Furthermore, obtaining the home and work locations of participants could provide further insights into the specific circumstances and specific types of offers that lead consumers to plan trips further off their usual path of travel, a key question in shopper marketing (Shankar et al. 2011). Similarly, observing the list of items purchased by an individual upon redeeming a coupon could improve our model by providing context to the purchase occasion. Understanding customers’ shopping goals could in turn allow marketers to “contextualize” the various points on consumers’ geographic path to purchase, using the techniques of Hui et al. (2013), leading to a deeper understanding of the cognitive processes leading to coupon conversions (Cooke and Zubcsek 2017). Finally, obtaining data on participants’ voice and short messaging communications could allow us to differentially account for strong and weak social ties in our models. Admittedly, most referrals in our data likely indicate strong ties, but, as Pentland (2014) points out, weak ties may be more central to the spreading of behaviors (such as redeeming a specific offer).
It is clear from this discussion that there may be several variables that correspond to boundary conditions for the colocation effect documented in this article. Notwithstanding these limitations, in the spirit of Leone and Schultz (1980) and Bass (1995), our work aims to take an important step toward establishing an empirical generalization linking consumers’ preferences to their dynamic movement patterns. In addition, we note that the proposed methodology is robust and relatively easy to implement in a practical setting.9 By matching consumers’ locations in space and time, a simple algorithm can create the colocation network variables, which can then be used in standard statistical models. It is also straightforward to generate a colocation measure based on our methods that can be used in scoring models. These models can compare the effect of colocation with those of other covariates in the particular context and investigate further the interactions with other factors. Although our work does not uncover the details of the process through which colocation is related to consumer behavior, we demonstrate that this is not necessary for identifying and targeting the best prospects. In summary, we believe that our proposed methodology can be particularly useful for marketing practice and that it is an important step toward understanding the role of location dynamics in mobile marketing.
1The aforementioned models constrain the direct interdependency between consumers’ actions mostly to within their geographic regions. A common way this problem has been addressed in the literature is by assuming that the geodesic distance of decision-making individuals moderates the likelihood of them engaging in the same behavior as each other, for example, due to network externalities, geographically varying common shocks, or simply a higher chance of observational word of mouth (Bell and Song 2007; Bronnenberg, Dhar, and Dube´ 2007; Dekimpe, Parker, and Sarvary 2000; Jank and Kannan 2005; Mittal, Kamakura, and Govind 2004; Yang and Allenby 2003).
2Demonstrating a positive link between consumers’ web browsing and location behaviors over a span of ten days, Provost, Martens, and Murray (2015) provide initial evidence for this claim.
3Specifically, we define the dynamic location-based similarity in a dichotomous fashion, and we use equal weights to aggregate network neighbors’ past behavior. We thank two anonymous reviewers for highlighting this connection.
4Mobile banner ads have also been shown to bear many similarities to online banner ads (Bart, Stephen, and Sarvary 2014; Rosenkrans and Myers 2012). For instance, there is a consensus that digital banner ads primarily impact consumer behavior via memory and recall rather than leading to instant click-throughs directing traffic to the advertiser’s website (Dreze and Hussherr 2003; Manchanda et al. 2006; Zhang, Wedel, and Pieters 2009).
5The applicability of the offers varied from a single product of a given brand to any purchase at any coffee shop in town.
6According to data from the mobile operator, participants saw 85.66% of the offers. Not-seen offers were mostly due to initial technical difficulties or to participants losing interest in the program altogether. Within observations on participants who had not redeemed any coupons yet, the rate of seen offers varied between 76.9% and 78.9% in each category. For observations on participants who had redeemed at least one coupon, the “seen” rates were between 97.6% and 97.8% for each category. Together, these results indicate a limited impact of coupon category selection. Nonetheless, for completeness, we also estimated the model excluding the observations corresponding to not-seen coupons. The results of interest, reported in Table WA1 in the Web Appendix, are essentially the same as those for the full set of observations.
7We thank an anonymous reviewer for pointing out this possibility.
8The type of POIs can be quite varied, depending on the context of the marketing campaign or study. In most cases, the store locations make a natural candidate. However, more general POIs may also prove to be useful. For instance, in the context of a political campaign, visiting historical sites could be a good indicator of political affiliation.
9Note that the general methodology used in our estimations could just as easily be replicated on other data from which consumer networks can be generated. The point is that such networks may be based on the recording of a variety of consumer behaviors. The advantage of such “agnostic” approaches is that they need not rely on strong behavioral assumptions. Instead, they may benefit from the well-documented pattern of homophily that generally characterizes behavior-based networks.
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TABLE 1 Comparison of Consumer Choice Models Using Dynamic Location Data
TABLE:
| | Factors Moderating the Impact of Location on Consumer Choicea | |
|---|
| Study | Time | Idiosyncrasy | Structure of Colocation | Location History | Geography Covered | Time Period Covered (Days) | Location Observations per Participant |
|---|
| Andrews et al. (2015)b | Yes | Other consumers’ locations | Clusters (passengers on same train) | No | Subway in an Asian city | 30 | 1 |
| Danaher et al. (2015) | No | No | N.A. | No | Shopping mall (~400 stores) | 654 | 16.87 |
| Fong, Fang, and Luo (2015) | Yes | No | N.A. | No | 4.5 km strip connecting two cinemas | 7 | 1 |
| Ghose, Goldfarb, and Han (2012) | No | No | N.A. | No | South Korea | 98 | 34.22 |
| Hui, Bradlow, and Fader (2009) | No | Other consumers’ locations | Clusters (shoppers in same zone) | Via latent variables | Supermarket (96 zones) | 21 | 99.80 |
| Li et al. (2015) | Yes | No | N.A. | No | China | 32 | 1 |
| Luo et al. (2014) | Yes | No | N.A. | No | 2km circle around a cinema | 3 | 1 |
| Molitor et al. (2015) | No | No | N.A. | No | Large Western European country | 98 | 3.26c |
| Reinaker et al. (2015) | No | Home/work | Clusters (coworkers) | Home location | Hospitals in an Asian country | 30 | N.A. (1 used) |
| This study | Yes | Other consumers’ locations | Network | 24–96 hours | Two major cities in a Pacific country | 84 | 1202.94 |
aA few other moderators have been considered in specific papers. Ghose, Goldfarb, and Han (2012) consider different location effects for computers and mobile devices, while Molitor et al. (2015) allow the effects to reflect which order the coupons were presented in and whether the distance of the store was displayed on the coupon. bIn Andrews et al. (2015), six-car subway trains are considered as the location of a consumer. cOn average, the redemption behaviors of 27.43 offers belong to a single location observation (session). Notes: N.A. = not applicable.
TABLE 2 Description of Variables
TABLE:
| Variables | Description | M | SD |
|---|
| Offers |
| Redeemedi, j | The dependent variable, equals 1 if participant I redeemed offer and 0 otherwise | 0.049 | 0.22 |
| Offer_Lengthi, j | The number of days for which offer j was available to participant (may be less than the duration of offer j if I joined after offer j was launched) | 3.84 | 0.9 |
| Days_Since_Joinedi, j | The number of days that participant I had been in the program before offer j became available | 28.87 | 19.52 |
| Category_Redeem_Ratei, j | The fraction of offers that participant I redeemed from those that were in the same category as offer j and that had expired before offer j became available | 0.026 | 0.07 |
| Location |
| GPS_Activityi, j | The number of hours with location observations from participant during the day before offer j became available to i | 11.31 | 9.87 |
| Colocation_Category_Redeem_Ratei, | The sum of Category_Redeem_Rate for the neighbors of participant I in the colocation network defined by “same time, same place” events on the day before offer j became available to i; or 0 if not applicable | 0.0065 | 0.04 |
| Individual Characteristics (Profile Variables) |
| Agei | Age of participant I (in years) | 34.95 | 9.75 |
| Genderi | Gender of participant I (1 = male; 2 = female) | 1.4 | 0.49 |
| Educationi | Highest degree completed by participant I (1 = primary school; 6 = PhD or above) | 3.35 | 1 |
| Experiencei | Professional experience of participant I (1 = has not started career; 4 = established in career) | 3.09 | 1.09 |
| Incomei | Income bracket of participant I (1 = lowest; 4 = highest) | 2.91 | 1.34 |
| Hedonici | Participant i’s tendency to consume hedonic products (1 = lowest; 3 = highest) | 2.25 | 0.6 |
| Well-readi | Participant i’s tendency to read books (1 = lowest; 4 = highest) | 2.36 | 0.86 |
| Hard-workingi | Participant i’s ideal work-week length (1 = lowest; 4 = highest) | 1.93 | 1.15 |
| Spenderi | Participant i’s tendency to spend money (1 = lowest; 5 = highest) | 2.12 | 0.76 |
| Referral (Control) |
| Referral_Category_Redeem_Ratei, j | The sum of Category_Redeem_Rate for the neighbors of participant I in the referral network on the day before offer j became available to i; or 0 if not applicable | 0.0064 | 0.03 |
TABLE 3 Descriptive Statistics of Offers by Category
TABLE:
| Offer Category | Observations | Offer Length | After-Coupon Minimum Pricea | Overall Redemption Rate |
|---|
| Consumer packaged goods | 3,534 | 3.89 (.38) | 12.78 (10.44) | .06 (.24) |
| Food and beverage | 4,296 | 3.95 (.83) | 9.57 (4.98) | .03 (.18) |
| Retail | 5,136 | 4.07 (.83) | 20.34 (13.24) | .04 (.19) |
| Recreation | 2,387 | 3.85 (.47) | 3.25 (6.10) | .08 (.28) |
aEquals the “when-you-spend” sum minus the value of the coupon. Notes: Statistics are means, with standard deviations in parentheses.
FIGURE 1 Creating Colocation Networks from Consumers’ Mobility Traces
Notes: Panel A illustrates our definition of colocation on a grid; Panel B shows the colocation network derived from this information. The three shapes and the numbers inside them represent three consumers and the same five consecutive time periods, respectively. The dark gray grid cell in row 3, column 3, witnessed a colocation (between the circle and pentagon shapes) in time period 3. This relationship is indicated by the solid line between these symbols in the network shown below the grid. However, the light gray grid cell in row 3, column 5, does not correspond to such an event because the pentagon and the square were in the same cell at different times. Thus, the network derived from these movement patterns of consumers would only have one relationship, as shown.
TABLE 4 Parameter Estimates, Main Models
TABLE:
| Variable | Model 0 | Model 1a | Model 1b | Model 2a | Model 2b | Model 3 |
|---|
| Offer_Length | .366** (3.14) | .371** (3.18) | .291* (2.50) | .353** (3.02) | .280* (2.39) | .286* (2.44) |
| Days_Since_Joined | .015 (1.68) | .012 (1.42) | .016 (1.76) | .016 (1.78) | .018* (2.06) | .016 (1.79) |
| Category_Redeem_Rate | 1.054 (1.71) | 1.014 (1.65) | .845 (1.36) | .861 (1.39) | .717 (1.15) | .693 (1.11) |
| GPS Activity | | | .016* (2.48) | | .016* (2.46) | .016* (2.45) |
| Colocation_Category_ | | | 3.858*** (3.99) | | 3.225** (3.26) | 3.267** (3.30) |
| Redeem_Rate | | .040 (1.91) | | | | |
| Age | | .785* (2.30) | | .035 (1.72) | | .036 (1.75) |
| Gender | | .359* (2.13) | | .748* (2.21) | | .716* (2.13) |
| Education | | -.154 (-.74) | | .351* (2.10) | | .349* (2.10) |
| Experience | | -.103 (-.67) | | -.134 (-.65) | | -.133 (-.65) |
| Income | | -.171 (-.62) | | -.090 (-.59) | | -.104 (-.69) |
| Hedonic | | -.224 (-1.23) | | -.182 (-.67) | | -.163 (-.61) |
| Well-read | | -.175 (-1.27) | | -.206 (-1.14) | | -.214 (-1.20) |
| Hard-working | | .242 (1.08) | | -.144 (-1.05) | | -.158 (-1.16) |
| Spender | | | | .175 (.78) | | .178 (.81) |
| Referral_Category_ | | | | 5.971*** (3.65) | 5.380** (3.16) | 4.769** (2.81) |
| Redeem_Rate | | | | | | |
| Observations | 15353 | 15353 | 15353 | 15353 | 15353 | 15353 |
| Participants | 217 | 217 | 217 | 217 | 217 | 217 |
| Offers | 96 | 96 | 96 | 96 | 96 | 96 |
| Log-likelihood | -2,082.15 | -2,073.77 | -2,069.90 | -2,067.09 | -2,065.00 | -2,057.65 |
| Akaike information criterion | 4362.3 | 4363.54 | 4341.8 | 4352.19 | 4333.99 | 4337.3 |
| Bayesian information criterion | 5118.57 | 5188.56 | 5113.34 | 5184.84 | 5113.18 | 5194.88 |
*p < .05.
**p < .01.
***p < .001.
Notes: The models include offer fixed effects and participant random effects. Parameter estimates are followed by z-scores in parentheses.
TABLE 5 Parameter Estimates, “Cohabiting” Individuals Removed
TABLE:
| | Maximum Frequency of Nights Colocated |
|---|
| Variables | 50% | 40% | 30% | 20% |
|---|
| Offer_Length | .284* (2.44) | .273* (2.35) | .269* (2.31) | .263* (2.26) |
| Days_Since_Joined | .015 (1.77) | .015 (1.73) | .016 (1.76) | .015 (1.60) |
| Category_Redeem_Rate | .691 (1.09) | .647 (1.01) | .688 (1.07) | .643 (1.00) |
| GPS_Activity | .016* (2.42) | .016* (2.42) | .016* (2.40) | .014* (2.03) |
| Colocation_Category_Redeem_Rate | 3.258** (3.25) | 3.165** (3.14) | 3.253** (3.13) | 2.904** (2.67) |
| Age | .036 (1.72) | .038 (1.80) | .037 (1.74) | .034 (1.53) |
| Gender | .744* (2.21) | .762* (2.23) | .723* (2.06) | .757* (2.13) |
| Education | .318 (1.88) | .319 (1.87) | .333 (1.92) | .311 (1.76) |
| Experience | -.134 (-.65) | -.144 (-.69) | -.145 (-.69) | -.163 (-.77) |
| Income | -.101 (-.66) | -.117 (-.75) | -.123 (-.77) | -.092 (-.57) |
| Hedonic | .-217 (-.79) | -.216 (-.78) | -.210 (-.74) | -.123 (-.42) |
| Well-read | -.204 (-1.14) | -.202 (-1.11) | -.215 (-1.17) | -.173 (-.93) |
| Hard-working | -.154 (-1.13) | -.156 (-1.13) | -.146 (-1.03) | -.114 (-.79) |
| Spender | .177 (.79) | .192 (.85) | .205 (.90) | .086 (.34) |
| Referral_Category_Redeem_Rate | 4.951** (2.78) | 4.924** (2.76) | 4.761** (2.65) | 4.386* (2.21) |
| Observations | 15042 | 14890 | 14668 | 14116 |
| Participants | 213 | 211 | 208 | 200 |
| Offers | 96 | 96 | 96 | 96 |
| Log-likelihood | -2,021.02 | -1,988.18 | -1,954.24 | -1,859.99 |
*p < .05.
**p < .01.
***p < .001.
Notes: The models include offer fixed effects and participant random effects. Parameter estimates are followed by z-scores in parentheses.
FIGURE 2 Heat Map of All GPS Observations in One of the Metropolitan Areas Covered by the Program
TABLE 6 Descriptive Statistics of Networks Considered
aMean per offer (across the 96 offers considered). Notes: Standard deviations are shown in parentheses.
TABLE:
| | Degree | Clustering |
|---|
| Network | Mean | Mean Number of Nonisolate Nodesa | Mean of Nonisolate Nodes | Mean Number of Nodes for Which Coefficient Is Defineda | Mean of Nodes Defined |
|---|
| Colocation | .164 (.49) | 20.33 | 1.292 (.64) | 4.38 | .401 (.45) |
| Colocation, overlapping cells of size .004 | .655 (1.55) | 41.19 | 2.543 (2.14) | 22.46 | .481 (.37) |
| Colocation, overlapping twohour intervals | .280 (.74) | 28.16 | 1.589 (1.00) | 9.97 | .358 (.41) |
| Colocation, two-day look-back | .263 (.66) | 29.23 | 1.439 (.84) | 8.35 | .314 (.41) |
| Colocation, three-day lookback | .344 (.80) | 35.25 | 1.564 (.99) | 11.8 | .279 (.38) |
| Colocation, four-day look-back | .420 (.92) | 40.29 | 1.666 (1.12) | 14.77 | .272 (.37) |
| Colocation, at multiple locations (in entire sample) | .090 (.33) | 12.46 | 1.152 (.44) | 1.57 | .192 (.40) |
| Referral | .096 (.39) | 12.24 | 1.249 (.76) | 2.66 | 0 (0) |
TABLE 7 Parameter Estimates, Alternative Definitions of Colocation
TABLE:
| | Considering Colocations That Preceded Offer Launch By… |
|---|
| Variable | .004 Cells | Two-Hour Window | Two Days | Three Days | Three Days | Multiple Locations |
|---|
| Offer_Length | .285* (2.44) | .286* (2.45) | .314** (2.65) | .323** (2.72) | .323** (2.72) | .243* (2.20) |
| Days_Since_Joined | .016 (1.80) | .016 (1.79) | .015 (1.77) | .015 (1.75) | .015 (1.74) | .020* (2.27) |
| Category_Redeem_ | .762 (1.23) | .734 (1.18) | .706 (1.13) | .695 (1.11) | .698 (1.12) | .730 (1.17) |
| GPS_Activity | .016* (2.40) | .016* (2.42) | .0089 (1.23) | .0073 (.95) | .0078 (.97) | .018** (2.75) |
| Colocation_Category_Redeem_Rate | 1.411* (2.47) | 2.342* (2.86) | 2.364** (2.75) | 1.579* (2.02) | 1.321 (1.87) | 3.202** (2.92) |
| Age | .037 (1.79) | .036 (1.77) | .036 (1.76) | .036 (1.77) | .036 (1.78) | .018 (.93) |
| Gender | .723* (2.15) | .716* (2.13) | .726* (2.16) | .732* (2.18) | .730* (2.17) | .405 (1.27) |
| Education | .336* (2.02) | .342* (2.06) | .354* (2.13) | .351* (2.11) | .350* (2.11) | .223 (1.37) |
| Experience | -.130 (-.63) | -.134 (-.65) | -.136 (-.66) | -.138 (-.67) | -.139 (-.68) | -.136 (-.66) |
| Income | -.100 (-.67) | -.103 (-.68) | -.100 (-.67) | -.097 (-.64) | -.096 (-.64) | -.124 (-.82) |
| Hedonic | .-156 (-.57) | -.162 (-.60) | -.165 (-.61) | -.166 (-.61) | -.162 (-.60) | -.356 (-1.34) |
| Well-read | -.218 (-1.22) | -.216 (-1.21) | -.208 (-1.16) | -.207 (-1.16) | -.208 (-1.16) | -.325 (-1.83) |
| Hard-working | -.165 (-1.21) | -.159 (-1.17) | -.151 (-1.11) | -.151 (-1.11) | -.152 (-1.12) | -.244 (-1.81) |
| Spender | .180 (.81) | .180 (.82) | .180 (.81) | .180 (.81) | .180 (.81) | .0088 (.04) |
| Referral_Category_Redeem_Rate | 5.152** (3.08) | 5.092** (3.03) | 4.855** (2.85) | 5.106** (3.01) | 5.180** (3.06) | 5.531** (3.32) |
| Observations | 15353 | 15353 | 15353 | 15353 | 15353 | 15353 |
| Participants | 217 | 217 | 217 | 217 | 217 | 217 |
| Offers | 96 | 96 | 96 | 96 | 96 | 96 |
| Log-likelihood | -2,060.06 | -2,059.09 | -2,062.26 | -2,064.46 | -2,064.72 | -2,063.04 |
*p < .05.
**p < .01.
***p < .001.
Notes: The models include offer fixed effects and participant random effects. Parameter estimates are followed by z-scores in parentheses.
FIGURE 3 Relaxed Definition of Colocation
Notes: The different shapes on the grid represent the locations of three consumers at a given hour. Although no two consumers are in the same cell (resulting in no colocation event considered in our main estimations), under the relaxed definition, the circle is colocated with both the pentagon and the square. However, there is no 2 · 2–cell grid rectangle that contains the latter two symbols, so the pentagon and the square are not colocated.
DIAGRAM: A: Definition of Colocation
DIAGRAM: B: Colocation Network
DIAGRAM
DIAGRAM
PHOTO (BLACK & WHITE)
PHOTO (BLACK & WHITE)
PHOTO (BLACK & WHITE)
PHOTO (BLACK & WHITE)
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Record: 138- Price No Object!: The Impact of Power Distance Belief on Consumers' Price Sensitivity. By: Lee, Hyejin; Lalwani, Ashok K.; Wang, Jessie J. Journal of Marketing. Nov2020, Vol. 84 Issue 6, p113-129. 17p. 2 Diagrams, 2 Charts, 3 Graphs. DOI: 10.1177/0022242920929718.
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Price No Object!: The Impact of Power Distance Belief on Consumers' Price Sensitivity
The role of culture in consumers' price search and behavior has received limited attention in the literature. In the present research, the authors examine how the cultural dimension of power distance belief (PDB)—the extent to which people accept and endorse hierarchy—influences consumers' price sensitivity. The authors propose that consumers high (vs. low) in PDB are less price sensitive because they have a higher need for closure, which motivates them to "seize and freeze" on a current offer and quickly arrive at the final purchase decision rather than search for a better price. Accordingly, the relationship between PDB and price sensitivity is moderated by variables that alter consumers' need for closure, such as social density. Six studies (and five more summarized in the Web Appendices) using a variety of operationalizations of the key variables provide robust support for the relationship between PDB and price sensitivity and shed light on the underlying mechanisms and boundary conditions. Theoretical and managerial implications are discussed.
Keywords: power distance belief; price sensitivity; need for closure; price search; social density
Sometimes, it just comes down to figuring out who has pricing power: who can raise prices and who can't.
—Jim Cramer, host of Mad Money (as cited in [48]])
A multitude of factors—including the recent economic slowdown, sluggish income growth, and the rise of price comparison tools online—have conspired to erode corporate pricing power ([ 2]; [ 4]). Consequently, many firms are finding it difficult to raise or even maintain prices. For example, Netflix recently faced a huge uproar when it tried to raise prices ([63]). However, the importance of price in today's fiercely competitive environment cannot be understated. Indeed, businesses are increasingly realizing that they can only go so far by cutting costs, optimizing expenses, or improving efficiencies and that fighting with competitors over price never ends well ([48]). Thus, there is a renewed interest in understanding factors that influence consumers' price sensitivity and in identifying consumer segments that are more or less price sensitive (e.g., [17]).
Despite the importance of understanding factors that influence consumers' price sensitivity, the role of culture has been underexplored. Some previous research has examined ethnic group or country differences in price sensitivity. For example, German consumers have been found to be more price sensitive than those from the Netherlands ([60]; see also [ 1]). However, these studies suffer from several limitations. First, because these studies used ethnic or national groups as a proxy for culture, they are unable to shed light on the drivers of the effect. Indeed, ethnic and national groups differ on cultural, economic, and psychological factors. Second, they do not provide a theoretical framework linking cultural constructs to price sensitivity. Thus, they are unable to shed light on underlying mechanisms and boundary conditions. Third, they provide little guidance to managers on which cultural segments are likely to be more or less price sensitive and on marketers' actions that may increase or decrease consumers' price sensitivity.
In the present research, we explore how the cultural orientation of power distance belief (PDB)—defined as the extent to which people accept and endorse hierarchy and inequality in society ([23])—influences consumers' price sensitivity. Issues related to societal inequality have garnered considerable attention in recent years. The stubbornly increasing income gap between the rich and the poor has fueled unrest across the world, from Europe's ongoing Brexit crisis to the recent elections in India ([21]). These trends indicate that inequality is rising and that PDB levels are shifting. It is important to understand the implications of these shifts for marketers. However, little is known about how consumers' beliefs about hierarchy and inequality influence their reliance on price in purchase decisions, especially their price sensitivity.
We propose that consumers high (vs. low) in PDB are less price sensitive due to a higher need for closure, which is defined as a desire for a quick solution to an ambiguous situation or problem and an aversion to uncertainty (Kruglanski [34]). Fundamental tenets of individuals high (vs. low) in PDB include a discomfort with ambiguity ([23]), a need for structure ([39]); a desire for clear, unambiguous, and well-specified situations ([ 7]; [23]); and closed-mindedness ([ 7]), all of which lead to a higher need for closure ([33]; [72]). In turn, the need for closure makes consumers "seize and freeze" on a given offer and reduces the tendency to search for better priced offers, thereby reducing price sensitivity. Furthermore, we identify an important boundary condition based on social density theory which increases confidence in our proposed mechanism.
Although PDB is one of the five cultural dimensions developed by [23], our analysis suggests that the role of other cultural dimensions on price sensitivity is nonsignificant or inconsistent across studies (for details, see Web Appendix A). Examining the role of PDB in price sensitivity also offers considerable potential for contribution to marketing theory and practice. First, as noted, PDB is highly relevant to modern society socially, economically, and politically ([ 7]; [20]). Second, the current research is one of the first to bring PDB to the pricing literature (see also [39]). Third, we identify both a novel antecedent (PDB) and consequence (price sensitivity) of need for closure, and deepen understanding of this motivational variable. As such, our research not only expands the nomological network, but also links need for closure with the literatures on culture and price perceptions, which may enable future researchers to gain insights on allied areas. Just to take one example, based on our finding that PDB increases need for closure, as well as the previous finding that need for closure reduces consumers' tendency to update their investment portfolios ([13]), one may predict that PDB also influences such a tendency. These interesting questions should be formally examined by future research. Fourth, shedding light on the underlying role of need for closure also enables us to identify theoretically meaningful boundary conditions (such as social density) that influence the strength of the relationship between PDB and price sensitivity.
For marketers, our findings suggest that consumer segments characterized by high PDB (e.g., politically conservative [Paharia and Swaminathan 2019] or religious [[ 7]] consumers) may be less price sensitive. These findings can aid managers in their pricing, segmentation, and targeting decisions. Furthermore, our findings suggest that managers can reduce the price sensitivity of low-PDB consumers by activating high PDB using marketing stimuli such as ads, point-of-purchase material, or slogans (e.g., "you deserve to reach the top"); by evoking a high need for closure (e.g., via time pressure or environmental cues); or by increasing social density (the perception of crowdedness) in stores via pictures and decorative items. We elaborate on these strategies in the "General Discussion" section.
Price sensitivity is defined as the relative change in consumers' purchase likelihood or willingness to pay after a price change ([68]). Price sensitivity can be lowered by investing heavily in advertising ([51]), building brand credibility ([14]), or fostering brand loyalty ([32]). However, if marketers can identify consumer segments that are low in price sensitivity, they can target these consumers without having to spend vast amounts of money. For example, [17] demonstrated that consumers with a local (vs. global) identity are less price sensitive because they have a greater sacrifice mindset. In the current research, we examine the role of PDB and that of need for closure on price sensitivity, rule out alternative explanations, and examine boundary conditions to the relationship. Moreover, as we demonstrate subsequently, the effect of PDB on price sensitivity is independent of both local–global identity and sacrifice mindset. Next, we explore how the cultural dimension of PDB and its relationship with need for closure may enable marketers to identify segments low (vs. high) in price sensitivity.
Power distance belief refers to the extent to which people in a culture accept hierarchy and inequalities in power ([23]). In high-PDB societies, individuals endorse inequality and hierarchy, whereas in low-PDB societies, individuals believe in equality and in the absence of hierarchy. Examples of high-PDB cultures include Mexico, China, and Indonesia, whereas examples of low-PDB cultures include New Zealand, Ireland, and the Scandinavian countries ([23]). Recent research suggests that PDB can be fruitfully studied at the individual level and can also be temporarily heightened ([20]).
We propose that high (vs. low) PDB individuals have a higher need for closure—the desire for a quick solution to an ambiguous situation or problem and an aversion to uncertainty ([34])—for several theoretical reasons. Although the need for closure is a latent variable, it manifests itself via five dimensions: closed mindedness, a discomfort with ambiguity, a need for structure, greater decisiveness, and a desire for predictability ([72]). In [37], p. 1009) terms, "a useful way of thinking of these facets is as representing heterogeneous potential sources of the need for closure." That is, individuals who score high on these five aspects have a high need for closure ([33], [34]; [37]).
Previous research from different domains implies that PDB may increase these five aspects of need for closure (for a visual representation of the proposed relationship between PDB and need for closure, see Web Appendix B). First, high- (vs. low-) PDB individuals tend to be closed minded. For example, people in high- (vs. low-) PDB cultures tend to be rigid minded, are less open to changing their minds, and have fixated views and firm opinions ([23]). They also prefer homogeneity and have less favorable attitudes toward diversity ([ 7]). Indeed, sharing divergent views and accepting diversity can promote equality among people and threaten hierarchies. Accordingly, high-PDB cultures are less likely to accept immigrants, embrace different values, and share divergent views ([ 7]). Similarly, employees in organizations high in power distance—such as the army and military—tend to be closed minded because complete obedience to superiors is almost a prerequisite for success in the operations of such entities ([18]).
Second, high- (vs. low-) PDB individuals have a greater desire for clarity and unambiguity ([ 7]; [23]). For example, individuals high (vs. low) in PDB prefer certainty and definitiveness (rather than ambiguity; [ 6]), have clear-cut boundaries between relationships, prefer fixed (not malleable) societal structures, and discourage movement across social classes, leading to less malleable and less ambiguous societal structures. Research also suggests that PDB is negatively associated with role ambiguity ([ 7]).
Third, high- (vs. low-) PDB individuals have a greater need for structure ([39]). For example, high-PDB cultures tend to have more structured and well-ordered websites and physical office structures compared with low-PDB cultures ([39]; [66]). Furthermore, people who endorse hierarchy value order and structure ([50]). Across several studies, [16] found that a preference for hierarchy (vs. equality) elicited greater structure, order, and stability, and that individuals with a greater need for hierarchy had a higher need for structure. Other research suggests that order and structure reduces the number of alternatives considered in the decision task, and facilitates closure ([61]).
Fourth, research also suggests that PDB may also increase decisiveness—the tendency to reach decisions rapidly, without deliberation or delay ([23]; [39]). High- (vs. low-) PDB individuals shun decision processes that bog them down or compel them to protractedly consider decision criteria. For example, high-PDB cultures are more likely to engage in stereotyping and use heuristics that allow them to make decisions quickly ([12]; [39]). Indeed, [39] found that high- (vs. low-) PDB individuals are more likely to use the price–quality heuristic.
Fifth, high- (vs. low-) PDB individuals are more likely to endorse predictability and have clearer rules and expectations on behavior ([ 6]; [39]). For example, in high- (vs. low-) PDB cultures, the seating system for a banquet dinner is based on clear criteria such as the person's social standing ([74]). For individuals who favor predictability, not reaching a conclusive solution is stressful and unnerving, which suggests that high-PDB individuals are more motivated to arrive at a conclusion, demonstrating a higher need for closure.
Additional evidence for the link between PDB and need for closure comes from work on leadership in groups. The evidence suggests that individuals who prefer autocratic (rather than egalitarian) leaders who can sustain hierarchy and inequality in society ([54]), those who favor less egalitarian participation during group discussions ([11]), and those who are less likely to discuss individual views but are more likely to share time unequally by group members ([28])—all of which characterize high- (but not low-) PDB individuals—tend to have a high (rather than low) need for closure.
It is important to note that although the need for closure has multiple sources, the individual components separately often do not have the predictive power of the aggregate ([37]). This occurs because there exists a single latent variable of true interest, which has several distinct surface manifestations ([ 8]), or the superordinate construct is often more important than its discrete components because it represents a more appropriate level of abstraction compared with the individual facets ([27]). In [37], p. 1009) terms, "Of course, a person may desire closure for more than one reason, hence we conceived of the different facets as additive in their impact on the total need for closure." Following Kruglanski et al., we pool these motivations together because we are interested in consumers' need for closure itself, not the sources of this motivation ([72]; see [37]).
Although we acknowledge that PDB and need for closure may influence each other, we propose that PDB—being a cultural variable—is more likely to influence the need for closure, which is a motivational variable. Previous research is replete with examples of how cultural variables (e.g., independence–interdependence, PDB) influence motivational variables (e.g., regulatory focus [[39]], regulatory mode [[42]], self-regulation [[39]], familiarity seeking [[29]], socially desirable responding [[39]]). In contrast, we are not aware of any examples of motivational variables influencing cultural variables.
We propose that high- (vs. low-) PDB individuals' need for closure (mediator 1) increases the tendency to "seize and freeze" on a given option, which reduces the motivation to search for better prices (mediator 2) and lowers price sensitivity. In other words, price sensitivity is one outcome of a truncated search process of high- (vs. low-) PDB individuals' need for closure. For this serial mediation relationship, see Figure 1.
Graph: Figure 1. Conceptual framework.
Indeed, individuals high (vs. low) in need for closure tend to allocate a restricted pool of cognitive resources to the activity at hand ([31]), engage in less information search, and strive to quickly reach a conclusion ([73]). For example, individuals high (vs. low) in need for closure tend to adopt noncompensatory rules (i.e., search based on attributes rather than alternatives), which enables them to arrive at a decision more quickly ([ 9]). Furthermore, individuals who are high (vs. low) in chronic levels of need for closure experience increased physiological (e.g., increased systolic blood pressure and heart rate) and psychological distress when no conclusive solution is obtained ([57]), which may increase their motivation to make a purchase decision quickly rather than keep searching for a better priced option. Research also suggests that need for closure decreases the amount of information considered, leads to snap conclusions, and increases the speed of decision making ([26]). It also increases the use of simple and clear-cut evidence to reach a quick decision to satisfy the need to "seize and freeze" ([77]). People high (vs. low) in need for closure also exhibit urgency in wrapping up tasks ([56]) and disproportionately prefer temporally proximal options over temporally distal ones ([59]).
When applying this quick decision making tendency in the price search context, we expect that individuals high (vs. low) in need for closure (mediator 1) may be less likely to search for better priced options, and purchase the first available option (i.e., exhibit a lower price search tendency; mediator 2), thereby lowering price sensitivity. Moreover, research points to a direct association between participants' price search tendency and their price sensitivity ([55]). Indeed, the commonly used price sensitivity scale developed by [47] includes items such as "I will grocery shop at more than one store to take advantage of low prices" and "I am not willing to go to extra effort to find lower prices" (reverse scored). Moreover, people who are more (vs. less) likely to search for better prices are more price sensitive ([ 3]). Thus, we expected high- (vs. low-) PDB individuals, due to their higher need for closure, to be more likely to "seize and freeze" on a given offer and be less likely to look for other offers that may be better priced and, thus, be less price sensitive (i.e., serial mediation through need for closure [mediator 1] and less likely to price search [mediator 2]).
- H1: When PDB is high (vs. low), people are less price sensitive.
- H2: The relationship between PDB and price sensitivity is serially mediated by need for closure and price search tendency.
We also aimed to push the boundaries of our phenomenon by examining a contextual and managerially relevant moderator, namely social density—the number of people in a given space. We propose that people are more uncomfortable in high (vs. low) social density situations—such as in a crowded store—because of lesser control over one's movements and a higher likelihood of being pushed and jostled by others ([24]). Indeed, research suggests that socially dense environments reduce people's perceived control over the environment and increase one's discomfort. For example, students living in apartments with high (vs. low) person-per-room density report greater feelings of crowding, perceive less control over room activities, express more negative interpersonal attitudes, and experience a more negative room ambience ([ 5]). Therefore, we predict that the discomfort stemming from high social density should motivate people to attain closure in that context and move on to areas where the traffic is less severe and where there is more room to breathe freely. For example, shopping in crowded stores or in conditions that appear to be packed or crammed may motivate people to make quick purchase decisions and increase the need for closure.
Research confirms that uncomfortable situations such as environmental noise ([39]), time pressure, fatigue, or task unattractiveness ([36]; [72]) increase the need for closure. For example, [34] manipulated the need for closure using environmental noise and found that people have a higher need for closure when a printer located nearby was running versus when it was quiet, without the printing noise. Thus, we suggest that a high social density (compared with a control condition wherein social density is not contextually influenced) would increase the need for closure (and consequently, lower the price sensitivity) of low-PDB individuals, whose baseline need for closure is low and has greater potential for increase. However, it should not affect the need for closure (and consequently, price sensitivity) of high-PDB individuals, whose baseline need for closure is high and has lower potential for increase (ceiling effect; [41]). See Figure 1 for the full model.
- H3: A high social density reduces the effect of PDB on price sensitivity, compared with baseline social density conditions. Specifically, a high social density reduces the price sensitivity of low (but not high) PDB individuals.
Several alternative explanations can also account for the negative effect of PDB on consumers' price sensitivity. First, individuals high (vs. low) in PDB are more concerned about their status and the quality of products ([39]; [70]). Therefore, high- (vs. low-) PDB individuals may be less sensitive to price due to the need for status. Second, and relatedly, high-PDB individuals may be less price sensitive because of their greater tendency to use price–quality judgments ([39]).
Third, high- (vs. low-) PDB individuals tend to have a local (vs. global) identity, which has been shown to reduce price sensitivity ([17]). Fourth, high-PDB individuals often sacrifice their own welfare to maintain a hierarchical structure ([23]; [70]), and a sacrifice mindset has been associated with price sensitivity ([17]).
Fifth, research suggests that PDB increases risk aversion ([23]), which in turn may decrease consumers' price sensitivity because searching for better priced options entails the risk that the search may not materialize. Sixth, high-PDB individuals have fixed roles and obligations in society and therefore they may see little variation and more homogeneity in the world. If high- (vs. low-) PDB individuals perceive lower variance in product prices, they may be less price sensitive ([75]). Seventh, due to their rigid and inflexible structures, high-PDB individuals may have lower perceptions of self-efficacy because they are less able to change things. In turn, the lower perceptions of self-efficacy may lead to the belief that they will be unable to find a better deal even if they try and should accept the currently available offer, thereby reducing price sensitivity. Finally, consumers high (vs. low) in PDB have greater self-regulatory resources ([78]), which can increase the ability to search for better prices and lead to higher price sensitivity. Although this prediction is opposite to our core hypothesis (H1), we tested it for the sake of completeness.
We used a multimethod approach to ascertain the generalizability and robustness of the hypothesized relationships. Study 1a provided evidence of the negative relationship between PDB and price sensitivity using A.C. Nielsen scanner panel data that tend to have high external validity. Study 1b provided convergent evidence for external validity using a consequential measure to assess consumers' actual purchasing behavior which was driven by their price sensitivity. Study 1c provided evidence of the relationship via a field study at a small local grocery store. Study 2a directly assessed the mediating role of need for closure and ruled out the role of other cultural variables and all alternative explanations proposed previously. Study 2b provided evidence for serial mediation through need for closure (mediator 1) and price search tendency (mediator 2) using four established measures of price sensitivity. Study 3 revealed that a high social density reduces the price sensitivity of low- (but not high-) PDB consumers.
Study 1a was designed to test the relationship between PDB and price sensitivity using scanner panel data set for two different product types (carbonated beverages and dried fruits) collected by A.C. Nielsen and state-level cultural variables including PDB. We focused on these two products because their price fluctuates often, which enabled us to examine people's purchasing behavior as a function of price. Moreover, both carbonated beverages and dried fruits can be inventoried, which may encourage price-sensitive people to purchase a greater quantity when the price is low (vs. high), enabling the effect to emerge (unlike, say, for perishables, which cannot be inventoried). We focused on the most recent year of the Nielsen data set at the point of analysis, which is 2016. Panelists were geographically dispersed and demographically balanced (for detailed demographics, see Table WC1 in Web Appendix C). The data set included detailed transaction information for each product purchased, price of the purchase, number of units purchased, and demographic information of panelists.
Consistent with previous research, we focused on the top ten brands and controlled for the effect of the brand name in subsequent analyses ([25]; [46]). We included all 1,028,551 transactions of the product purchases from the Nielsen panel in 2016 in the analysis. We then imposed state-level cultural orientation values (PDB, uncertainty avoidance, long-term orientation, masculinity, independence–interdependence, local–global identity) for each data point depending on the state code information provided by Nielsen. The cultural orientation values for the 50 states were obtained from [69], who collected these data from a panel of Amazon Mechanical Turk (MTurk) or Qualtrics members (minimum N per state = 60; total N = 3,103; for details see Table WC2 in Web Appendix C).
We predicted that low- (vs. high-) PDB consumers would purchase more units of carbonated beverages and dried fruits when the price is low (vs. high; i.e., they will be more price sensitive; H1). The data supported this hypothesis. A linear regression with quantity purchased entered as the dependent variable and unit price, all cultural variables (PDB, uncertainty avoidance, long-term orientation, masculinity, independence–interdependence, and local–global identity), their interactions with unit price, the number of units in a multipack, size of the product, all demographic variables,[ 5] and brand name entered as independent variables revealed a significant interaction between PDB and unit price (β =.11, t( 1,028,503) = 3.25, p <.005, d =.006). To explore this interaction further, we used the Johnson–Neyman technique (i.e., floodlight analysis; [64]). The results revealed a significant negative effect of unit price on the quantity purchased for consumers whose PDB score was less than 4.23 ( = −.13, SE =.07, p =.05), indicating that low-PDB individuals purchased more units when the price was low (vs. high), exhibiting higher price sensitivity. The effect was not significant above the PDB score of 4.23, suggesting that high-PDB individuals did not differ in quantity purchased based on price (see Figure 2; for detailed results on the effect of all cultural values, see Table WC3 in Web Appendix C). The interaction remained significant after controlling for multiple purchases, suggesting that that factor did not alter the results. In addition, we tested whether PDB influenced price paid per unit and found a significant effect (β =.01, t( 1,028,510) = 5.80, p <.001, d =.011), suggesting that high- (vs. low-) PDB individuals tend to pay a higher unit price (i.e., they are less price sensitive).
Graph: Figure 2. The effect of PDB on quantity purchased when the unit price is high versus low (Study 1a).Notes: The figure shows the region of significance of the simple effect of unit price on quantity purchased at different levels of PDB, such that there is a significant negative effect of unit price when PDB is lower than 4.23. The dotted rectangle represents this region of significance.
Using Nielsen scanner panel data set for two different product types, Study 1a provided initial support for H1 by showing that low- (but not high-) PDB consumers are more likely to purchase carbonated beverages and dried fruits when the price is low (vs. high), suggesting that they are more price sensitive, after controlling for other cultural variables, the number of units in a multipack, size of the product, demographic variables, and brand name.
In Study 1b, we measured consumers' real choice, which was driven by their price sensitivity, so as to further assess the external validity of the findings. We predicted that individuals low (but not high) in PDB would be less likely to choose a high (vs. low) price option because they are more price sensitive.
Participants were 141 members of MTurk (47.5% female; Mage = 38 years)[ 6] who participated in exchange for $1.00. The study utilized a price (high, low; between subjects) × PDB (continuous) mixed design.
We assessed price sensitivity by measuring participants' real purchasing behavior (i.e., whether they actually chose to rent a movie at different price points) from a purportedly real website called "e-Entertainer," which sells and leases movies, songs, albums, and so on via instant downloading. At the beginning of the survey, all participants were informed that an online retailing company is launching a website called "e-Entertainer.com" and that we have been asked to conduct a survey to gauge customer responses prior to its launch. Next, participants were asked to list a movie title that they may be interested in renting (for a full description of the procedure, see Web Appendix D). Thereafter, they were given the opportunity to rent that movie for either $.30 (low-price condition) or $1.00 (high-price condition). In the low-price condition, participants were told that if they chose to rent the movie, we would pay e-Entertainer.com $.30 from the amount that they would have otherwise received for completing the survey ($1.00) and reimburse them the remaining $.70. In the high-price condition, they were told that if they chose to rent the movie, we would pay e-Entertainer.com the amount that they would have otherwise received for completing the survey ($1.00) and they would not get any monetary remuneration at the end of the survey. Each participant was asked to indicate whether they would like to rent the movie (yes/no). We measured PDB via a three-item, seven-point scale (α =.97; 1 = "social equality is important," and 7 = "social hierarchy is important") developed and validated by [78]. For all the items we used across the studies, see Web Appendix E. Thereafter, we debriefed participants and informed them that the questions related to e-Entertainer.com were a part of our survey and that e-Entertainer.com is not a real website. All participants were paid $1.00 regardless of their choice.
A logistic regression with movie rented (or not) as the dependent measure (dummy-coded: 0 = not rented, 1 = rented) and PDB, price condition (dummy-coded: 0 = low price condition, 1 = high price condition), and their interaction as independent variables revealed a significant effect of price condition ( ( 1) = −2.90, Exp( ) =.06, Wald = 7.71, p =.005, d = −.48), a nonsignificant effect of PDB ( ( 1) =.15, Exp( ) = 1.16, Wald = 1.42, p >.23), and a significant interaction between PDB and price condition ( ( 1) =.52, Exp( ) = 1.68, Wald = 4.32, p <.04, d =.36), as predicted. To identify the range of values of PDB for which the simple effect of price condition was significant, we used the Johnson–Neyman technique (i.e., floodlight analysis; [64]). The results revealed a significant negative effect of price condition on the movie rental behavior for participants whose PDB score was less than 4.08 ( = −.79, SE =.40, p =.05), indicating that low-PDB individuals were less likely to rent a movie in the high price condition, compared with the low price condition, showing a higher price sensitivity. The effect was not significant above the PDB score of 4.08, suggesting that high-PDB individuals did not differ in their rental behavior based on price and were thus less price sensitive (Figure 3).
Graph: Figure 3. The effect of PDB on the likelihood of renting a movie when the price is high versus low (Study 1b).Notes: The figure shows the region of significance of the simple effect of price condition at different level of PDB such that there is a significant effect of price condition when PDB is lower than 4.08. The square represents this region of significance.
Study 1b provided further support for the hypothesis that individuals high (vs. low) in PDB are less price sensitive using real purchase behavior. An alternative explanation of this study is that high- (vs. low-) PDB individuals may have been more committed to the movie they listed and thus were more likely to rent it even at the high price ([23]). However, this account cannot explain the data of Study 1a and the subsequent studies.
In Study 1c, we examined the relationship between PDB and price sensitivity via a field study at a local grocery store to assess the generalizability of our findings in the real world.
Seventy shoppers in a local grocery store participated in exchange for a soup voucher worth $2.99 (70% female; Mage = 45 years). The manager of a small grocery store in our neighborhood informed us in advance that Brown Cow brand yogurt will be discounted from April 15, 2019 to April 30, 2019. We collected data for 10 days from Mondays through Fridays between April 1, 2019, and April 14, 2019 (i.e., before the discount was implemented), and for 12 days from Mondays through Fridays between April 15, 2019, and April 30, 2019 (i.e., when the discount was in effect). The regular price of Brown Cow brand yogurt was $5.25/16 oz, and it was on sale for $4.59/16 oz, which is a 13% discount. The study utilized a price (high, low; between subjects) × PDB (high, low; between subjects) design.
A researcher approached shoppers as they entered the grocery store. Each shopper first answered a prequalification question on whether they were considering purchasing yogurt that day. Those who responded "yes" were invited to participate in a short survey in exchange for a soup voucher worth $2.99. To manipulate PDB, the researcher wore two different T-shirts on alternate days. The slogan on the T-shirt that primed low (high) PDB read, "Social equality should be desired by all" ("Hierarchy protects society and enables it to work properly"; for pictures, see Web Appendix F). The slogans were on both sides of the T-shirts. A pretest revealed that participants in the high- (vs. low-) PDB condition scored higher on the state PDB scale used in Study 1b (α =.94; Mlow PDB = 1.94, Mhigh PDB = 2.64, t(61) = −2.14, p <.05, d = −.55), implying that the manipulation was effective.
Each participant was given a brief form to record whether and how much of the yogurt they purchased, their gender, and their age. At the end of the shopping trip, they returned the form back to the researcher and received the soup voucher.
A regression analysis with unit purchases of the yogurt as the dependent variable and PDB (dummy-coded: 0 = low PDB, 1 = high PDB), price condition (dummy-coded: 0 = low price condition, 1 = high price condition), and their interaction as independent variables revealed significant effects of price condition (β = −.85, t(66) = −2.88, p <.006, d = −.71) and PDB (β = −.77, t(66) = −2.68, p <.01, d = −.66), and more importantly, a significant interaction between price condition and PDB (β =.44, t(66) = 2.53, p <.02, d =.62), as predicted. Follow-up spotlight analyses suggested that low-PDB individuals were significantly more likely to purchase the yogurt when it was discounted, compared with at the regular price (Mdiscounted =.21, Mregular price =.04, β = −.40, t(66) = −2.94, p <.005, d = −.72). However, high-PDB consumers did not purchase more units because of the price reduction (Mdiscounted =.13, Mregular price =.14, β =.05, t(66) =.40, p >.68). Furthermore, in the reduced-price condition, low- (vs. high-) PDB individuals purchased more units of yogurt (β = −.32, t(66) = −2.46, p <.02, d = −.61). However, this effect was not significant in the regular price condition (β =.13, t(66) = 1.06, p >.29).
Using a field experiment with real purchase behavior, Study 1c further supported the hypothesis that PDB negatively influences price sensitivity. This study has high external and internal validity. Moreover, the PDB prime used in this study can be readily used by managers (Study 4 in Web Appendix G also validated the causal role of PDB on consumers' price sensitivity using a different product category, which provides evidence of generalizability). Given the importance of field studies in demonstrating external validity, we conducted another field study using a different product category and a different operationalization of PDB. This study, which is summarized in Study 5 in Web Appendix G, also supported the negative association between PDB and price sensitivity. In the next two studies (Studies 2a and 2b), we tested the mechanism underlying the link between PDB and price sensitivity (H2).
The purpose of Study 2a was fivefold. First, we aimed to demonstrate that PDB is positively associated with the need for closure, which in turn reduces price sensitivity. Second, we tested the mediating role of need for closure (H2). Third, we assessed generalizability by using a different product type. Fourth, we assessed if the effect of PDB holds after controlling for other cultural variables. Fifth, we tested the role of all alternative explanations proposed previously.
Respondents were 79 members of MTurk[ 7] who participated for a small monetary remuneration (48.1% female; Mage = 39 years). We measured PDB via a five-item, nine-point scale (α =.91) validated by [76]. A sample item included "People in higher positions should make most decisions without consulting people in lower positions" (for all items, see Web Appendix E). After a filler task in which participants were asked to write down as many U.S. states as they could recall, we measured participants' need for closure via a 41-item, six-point scale (α =.96) developed by [56]; modified to assess state, rather than chronic tendency). This scale taps into all five dimensions of need for closure. A sample item included "At the present time, I believe that I dislike unpredictable situations" (for all items, see Web Appendix E).
Following [17]; Study 5), participants were asked to imagine that they were looking for a new backpack and found one they liked. However, when they visited the store a week later, they found that the price had gone up by 15%. They were then asked their willingness to purchase the backpack at the current (higher) price with a five-item scale developed by [19]; α =.98; see Web Appendix D). A higher purchase likelihood after a price increase indicates lower price sensitivity.
We also measured participants' independence-interdependence via a 16-item, seven-point scale (α =.67; sample item: "I would rather depend on myself than others.") developed by [62], uncertainty avoidance via a three-item, seven-point scale (α =.68; sample item: "Security is an important concern in my life") developed by [15], long-term orientation via a three-item, seven-point scale (α =.86; sample item: "I plan for the long term") developed by [30], and masculinity–femininity via a four-item, five-point scale (α =.94; sample item: "I feel as though I am very masculine") developed by [65]. For all items, see Web Appendix E.
We tested need for status, risk aversion, perceptions of self-efficacy, local (vs. global) identity, sacrifice mindset, price–quality judgments, self-regulation, and perceived price variance as alternative explanations in this study. For details of these measures, see Web Appendix H. The sequence of the scales in the study was price sensitivity → PDB → need for closure → perceptions of self-efficacy → sacrifice mindset → need for status → perceived price variance → local–global identity → price–quality judgments → self-control → risk aversion → long-term orientation → uncertainty avoidance → masculinity → individualism.
As predicted, PDB was significantly positively associated with the need for closure (r =.36, p =.001, d =.77) and with purchase likelihood (r =.57, p <.001, d = 1.39), suggesting that PDB increased the need for closure and reduced price sensitivity. Furthermore, need for closure was significantly positively associated with purchase likelihood (r =.41, p <.001, d =.90), suggesting that it increased the tendency to purchase the product at the higher price. Finally, a bootstrapping procedure (10,000 iterations) with 95% bias-corrected confidence estimates (Model 4, [22]) suggested that the indirect effect of need for closure on the link between PDB and purchase likelihood was significant ( =.07, SE =.04, 95% confidence interval [CI95] = [.0158,.1592]), indicating that the effect of PDB on purchase likelihood is mediated by need for closure.
Next, we conducted another regression equation using purchase likelihood as the dependent variable and all cultural dimensions (PDB, individualism, uncertainty avoidance, long-term orientation, and masculinity) as independent variables. The effect of PDB (β =.58, t(73) = 5.79, p <.001, d = 1.36) remained significant, whereas the effect of all other cultural variables was not significant (all ps >.13). All variance inflation factor values were less than 1.29, which indicated that multicollinearity was not an issue in the data. For details, see Table 1.
Graph
Table 1. Regression on Purchase Likelihood (Study 2a).
| Variables | Standardized β | t-Statistic | Cohen's d | VIF |
|---|
| Power distance belief | .578* | 5.791 | 1.356 | 1.128 |
| Individualism | −.010ns | −.096 | −.022 | 1.282 |
| Uncertainty avoidance | .160ns | 1.501 | .351 | 1.281 |
| Long-term orientation | .028ns | .268 | .063 | 1.233 |
| Masculinity | .013ns | .123 | .029 | 1.210 |
1 * Significant at p <.001.
- 2 nsNonsignificant effect at p >.10.
- 3 Notes: VIF = variance inflation factor.
Next, we conducted another regression equation using purchase likelihood as the dependent variable and all alternative explanation variables as independent variables. Results suggested that PDB (β =.50, t(69) = 4.50, p <.001, d = 1.08) was significantly associated with purchase likelihood after controlling other variables, suggesting that other variables were not responsible for our effect.[ 8] For details, see Web Appendix H. Study 2a provided evidence for the mediating role of need for closure using a correlational approach. We acknowledge that the sample size in this study was relatively small. Moreover, because the PDB scale was administered before the other variables, its effect may have been inordinately influenced by its placement. However, we have no reason to believe that these factors influenced the results because, in other studies, we obtained similar results using larger sample sizes and by varying the placement of the PDB measure. In a follow-up study, we obtained evidence of the mediating role of need for closure using an experimental approach by manipulating instead of measuring need for closure (see Study 6 in Web Appendix G).
In the next study, we sought additional, more refined, evidence of the underlying mechanism. Specifically, we have argued that the greater need for closure of high- (vs. low-) PDB consumers disrupts their tendency to search for better prices which, in turn, reduces price sensitivity. If so, the relationship between PDB and price sensitivity should be mediated by need for closure and price search tendency (i.e., a serial mediation model).
The goals of Study 2b were fourfold. First, to assess the tendency to search for prices, we provided participants with an opportunity to search for better prices in an interactive shopping simulation. Second, we assessed the serial mediation model and tested if the relationship between PDB and price sensitivity is driven by both need for closure (mediator 1) and price search tendency (mediator 2). Third, we ascertained the generalizability of the effect by using a different product type: a coffeemaker. Fourth, we ascertained the robustness of our findings by using four different established measures of price sensitivity from the literature.
Respondents were 230 members of MTurk[ 9] who participated for a small monetary remuneration (57% female; Mage = 40 years). PDB was measured as in study 2a (α =.92). We also measured participants' need for closure via a 15-item, 6-point scale validated by [58] scale (this scale is an abridged version of the 41-item scale developed by [56]], and was modified to assess state, rather than chronic tendency; α =.89). A sample item included "At the present time, I believe that I dislike unpredictable situations" (see Web Appendix E for all items).
Price search tendency was assessed via the number of stores each participant visited before they made a final purchase decision. For this, we developed an interactive shopping simulation. Participants were asked to assume that they were shopping for a coffeemaker and had finalized their choice to the model Ultra Pro from Coffee-Smart. Then, they found out that the price of the model at store A has changed from $54.99 to $65.99. Then, each participant had two options: ( 1) to buy Coffee-Smart Ultra Pro at the current (higher) price from store A or ( 2) to check out other stores to possibly find a lower price for Ultra Pro from Coffee-Smart. If they chose the second option, they were then shown ten different stores to visit and informed that each store carries Coffee-Smart Ultra Pro (and other brands) at the same or different price (prices ranged from $56.86 to $74.23). To visit a particular store and to find out the prices at that store, participants had to click 30 times on that store. This ensured that there was some cost in the form of time and effort to visit each store. Participants were allowed to visit ten different stores for up to 20 times in total. Once they finalized the store to purchase the coffeemaker from, the simulation was over and we calculated the number of stores each participant visited. For details, see Web Appendix D.
Price sensitivity was measured in four ways. Following [68], the first measure was the price of the coffeemaker at the store that the participant finally decided to purchase it from. A higher final price represents lower price sensitivity. Second, following [19], we assessed the purchase likelihood at the price offered by store A via a five-item scale (α =.97; sample item: "If I were going to buy a coffeemaker, the probability of buying this coffeemaker at the price offered by store A [which was $65.99] is..."). A higher purchase likelihood represents a lower price sensitivity. Third, we administered a five-item price sensitivity scale developed and validated by [47]; α =.94; sample item: "I am not willing to go to extra effort to find a lower price for a coffeemaker" [reverse-coded]). Fourth, we administered a three-item price sensitivity scale developed by [68]; α =.87; sample item: "I am willing to make an extra effort to find a low price for a coffeemaker"). For the latter two scales, higher scores indicate greater price sensitivity. For all items, see Web Appendix E.
As we predicted, PDB significantly positively predicted the final price (r =.19, p <.005, d =.39), the purchase likelihood at the price offered by the first store (r =.30, p <.001, d =.63), but negatively predicted the two price sensitivity scales ([47]]: r = −.33, p <.001, d = −.70; [68]]: r = −.13, p =.052, d = −.26), suggesting that PDB reduced price sensitivity. These results suggest that the negative association between PDB and price sensitivity is robust across measures. Furthermore, a bootstrapping procedure with 10,000 iterations (Model 6, [22]) revealed that the indirect effect of need for closure (mediator 1) and price search (mediator 2) on the link between PDB and the four different price sensitivity measures were all significant (see Table 2), indicating that the effect of PDB on price sensitivity is mediated by both need for closure and price search tendency (for the serial mediation on final price as the dependent variable, see Figure 4).
Graph: Figure 4. The serial mediation by need for closure and price search on the effect of PDB on price sensitivity (Study 2b).aIndicates the beta coefficient before controlling for the mediators (i.e., need for closure and price search).Notes: A higher purchase likelihood after a price increase indicates lower price sensitivity.
Graph
Table 2. Serial Mediation Results (Study 2b).
| Dependent Variable | CI95 |
|---|
| Final price | (−.0712, −.0008) |
| Purchase likelihood | (−.0213, −.0003) |
| Price sensitivity scale (Lichtenstein, Ridgway, and Netemeyer 1993) | (.0006,.0277) |
| Price sensitivity scale (Wakefield and Inman 2003) | (.0002,.0228) |
Across four measures of price sensitivity, Study 2b provided converging and robust evidence that PDB reduces price sensitivity. It also showed, using a shopping simulation that PDB, via need for closure, reduces the tendency to search for better prices. In a different study (Study 7 in Web Appendix G), we used the same shopping simulation using a different product—a portable charger—to ascertain the generalizability of our findings. In yet another study (Study 8 in Web Appendix G), we ascertained the mediating role of price search tendency in the relationship between PDB and price sensitivity using yet another product category (wine) as well as the role of other cultural variables and alternative explanations. Both studies provided robust support for our hypotheses.
Study 3 was designed to test H3—that social density increases the need for closure of low- (but not high-) PDB individuals, making them less price sensitive.
Respondents were 91 students at a large Midwestern university who participated in exchange for partial class credit and 163 respondents from MTurk[10] who completed the survey for a small monetary remuneration (44.9% female; Mage = 31 years).[11] We measured PDB as in Study 1b (α =.95). The study utilized a social density (high, control; between subjects) × PDB (continuous) mixed design.
Next, all participants were shown five pictures and asked to arrange them "in a way you see fit" and to describe how each of the images made them feel. In the high-social-density condition (N = 119), all five images depicted very crowded stores. In the control condition (N = 135), the five images depicted different fruits (see Web Appendix I).
As in Study 2b (as well as [17]], Study 4), participants were asked to assume that they had decided to buy the model Ultra Pro coffeemaker from Coffee-Smart, but its price had increased from $54.99 to $65.99. Participants' willingness to purchase the coffeemaker at the current (higher) price was assessed via a three-item scale adapted from [19]; α =.96; sample item: "If I were going to buy a coffeemaker, the probability of buying this coffeemaker at the current price is..."). A higher purchase likelihood after a price increase indicates lower price sensitivity. For details, see Web Appendix D. Next, participants completed a 15-item, seven-point state need-for-closure scale (sample item: "At this moment, I like to solve a problem as quickly as possible to move onto the next one"; α =.89) adapted from [58].
We predicted that the high-social-density (vs. control) condition would significantly increase low- (but not high-) PDB individuals' likelihood of purchasing the coffeemaker at the higher price (i.e., it would reduce their price sensitivity). A regression with purchase likelihood as the dependent variable revealed significant effects of PDB (β =.83, t(250) = 4.95, p <.001, d =.63) and social density (β =.30, t(250) = 2.65, p <.01, d =.34) and, importantly, a significant interaction between the two (0: control condition, 1: high-social-density condition; β = −.23, t(250) = −2.10, p <.04, d = −.28).[12] Johnson–Neyman technique (i.e., floodlight analysis) revealed a significant positive effect of social density on the purchase likelihood of participants whose PDB score was less than 3.03 ( =.38, SE =.19, p =.05), but a nonsignificant effect above the PDB value of 3.03. Thus, social density increased the purchase likelihood of low-, but not high-, PDB individuals, in support of H3 (Figure 5).
Graph: Figure 5. The moderating role of social density in the relationship between PDB and price sensitivity (Study 3).Notes: Higher purchase likelihood values indicate lower price sensitivity; higher PDB values indicate greater PDB.
Our theorization rests on the assumption that social density increases low- (but not high-) PDB individuals' need for closure. A regression analysis with state need for closure as the dependent variable and PDB, social density condition (0: control, 1: high-social-density condition), and their interaction as the independent variables revealed significant main effects of PDB (β =.60, t(250) = 3.19, p <.003, d =.40) and social density manipulation (β =.29, t(250) = 2.25, p <.03, d =.28) and, importantly, a significant interaction between the two variables (β = −.52, t(250) = −2.38, p <.02, d = −.30). The significant and positive main effect of social density suggested that participants in the high-social-density (vs. control) condition had a higher need for closure.
Furthermore, Johnson–Neyman technique (i.e., floodlight analysis; [64]) revealed a significant positive effect of social density condition on state need for closure for participants whose PDB score was less than 1.38 ( =.31, SE =.16, p =.05), indicating that the social density manipulation increased need for closure for low-PDB individuals. The effect was not significant above the PDB value of 1.38, suggesting that social density manipulation did not increase state need for closure for high-PDB individuals, thus validating our assumptions.
Next, we also tested if state need for closure mediated the interactive effect of PDB and social density on price sensitivity (i.e., a mediated moderation model; [22]; Model 8). We used PDB as the independent variable, social density as the moderator, need for closure as the mediator, and purchase likelihood (i.e., price sensitivity) as the dependent variable. A 95% bias-corrected bootstrap (based on 10,000 samples) confidence interval revealed that the indirect effect of the interaction between PDB and social density on consumers' price sensitivity through need for closure was significant (β = −.06, SE =.03, CI95 = [−.1446, −.0108]). Specifically, the indirect effect of PDB on consumers' price sensitivity through need for closure was significant in the control condition (β =.06, SE =.03, CI95 = [.0222,.1232]), but not in the high-social-density condition (β =.00, SE =.02, CI95 = [−.0417,.0454]). Moreover, ancillary analyses suggested that in the control condition, high- (vs. low-) PDB individuals were less price sensitive. However, in the high-social-density condition, the strength of the relationship was significantly reduced, which suggests that social density reduced the effect of PDB on price sensitivity through need for closure. For ancillary analyses, see Web Appendix J.
Study 3 demonstrated that a high social density increases low- (but not high-) PDB individuals' need for closure and, consequently, reduces their price sensitivity. This finding offers significant implications for marketers and suggests that if stores are overcrowded (or if atmospherics increase the perception of social density), low- (but not high-) PDB consumers are more likely to accept higher prices.
Our goals in the current research were to examine the link between PDB and consumers' price sensitivity, as well as the underlying mechanisms and boundary conditions. A series of six studies (and five more studies summarized in Web Appendix G) provided converging and robust evidence that individuals high (vs. low) in PDB are less price sensitive because of a greater need for closure. Using A.C. Nielsen scanner panel data, Study 1a indicated that consumers in states characterized by high (vs. low) PDB are less price sensitive, after controlling for multiple cultural and demographic variables and brand-name-related effects. To further assess the external validity, in Study 1b we replicated the effect using consumers' actual movie rental behavior. Study 1c was conducted in a small grocery store and extended the findings to the field.
Study 2a shed light on the mediating role of need for closure and ruled out the role of other cultural variables and all alternative explanations we proposed previously. Study 2b provided support for the serial mediation model through need for closure (mediator 1) and price search tendency (mediator 2) in the relationship between PDB and price sensitivity. Study 3 revealed that a high social density reduced the price sensitivity of low- (but not high-) PDB consumers. Five additional studies reported in Web Appendix G provided further support for the effect of manipulated PDB (Study 4) and showed that the effect generalizes to the field using different products (Study 5), that situationally increasing the need for closure reduces the price sensitivity of low- (but not high-) PDB individuals (Study 6), and that the link between PDB and price sensitivity for other products is also mediated by price search tendency (Studies 7 and 8). For a summary of the results of the 14 studies (6 studies reported in the manuscript, 5 studies reported in Web Appendix G, and 3 unreported studies), see Web Appendix K.
Our data also point to the greater relevance of PDB as a cultural factor in influencing price sensitivity. Indeed, the effect of other cultural factors is mixed or nonsignificant (for theoretical and empirical evidence for how all the other cultural orientations influence price sensitivity, see Web Appendix A). In contrast, the effect of PDB robustly and consistently emerged across numerous data sets utilizing field studies, lab experiments, and secondary data (for a summary of findings across data sets, see Web Appendix K). A meta-analysis we conducted across all our studies (as well as [17]] data and three studies not reported in this paper but summarized in Web Appendix K) suggested that the relationship between PDB and price sensitivity is highly significant (r = −.004, p <.001, d = −.008). Moreover, file drawer analyses revealed that it would require a massive 377 studies to nullify the relationship. Because the effect size is highly susceptible to sample size, we ran the meta-analysis again after excluding the data of Study 1a (N = 1,028,551) but including the other 14 studies, and we found a stronger effect (r = −.207, p <.001, d = −.423).
We expect this article to contribute to several different areas of literature. First, our research is one of the first to introduce PDB to the pricing literature (for an exception, see [39]]). This introduction is important because someone relying solely on the dominant cultural orientation (independence–interdependence; [40]) may conclude that culture does not influence price sensitivity (see Web Appendix A). Moreover, one may expect consumers in high-PDB countries to be more price sensitive. However, the existing literature provides mixed evidence on Chinese and Indians' (vs. Americans') price sensitivity. For example, some research suggests that Chinese consumers are more price sensitive than American consumers ([ 1]). However, others found that Chinese are less price sensitive than Americans ([79]). Similarly, Indians, compared with Americans, have been found to be more willing to pay a premium for products, suggesting that they may be more price sensitive ([10]). These findings further attest to the importance of isolating the distinct aspect of culture (e.g., PDB) rather than treating culture via a broad variable such as nationality.
Moreover, we contribute to the need for closure literature by identifying both an antecedent (PDB) and a consequence of it (price sensitivity). This finding enables future researchers to make predictions about allied areas. For example, one may predict that variables that are influenced by need for closure (e.g., group-centrism [[36]], style of information search [attribute-based vs. alternative-based; [ 9]]) may also be influenced by PDB. Furthermore, we integrate our findings with the allied discipline of social density by showing that social density increases the need for closure and reduces the price sensitivity of low- (but not high-) PDB individuals. Finally, we also ruled out the role of several variables that provide an alternative account for the relationship between PDB and price sensitivity (in particular, for the role of need for structure, see Web Appendix L).
As we have noted, managers in diverse industries are finding it difficult to raise prices. First, our findings may enable managers to identify more or less price-sensitive segments, which can aid in their segmentation and targeting decisions. For instance, high- (vs. low-) PDB individuals have been shown to be more conservative, less liberal, and more religious ([ 7]; [52]). Thus, based on our findings, one may expect conservative and religious consumer segments to have a lower price sensitivity, compared with liberal and less religious segments. Thus, there may be relatively more scope of raising product prices in the former segments.
Second, managers can also activate high- or low-PDB by using slogans or quotes strategically placed in ads, point-of-purchase material, or store displays. For example, the slogan "You deserve to reach the top" can trigger a high-PDB and reduce consumers' price sensitivity, whereas the slogan "Everyone is born equal" can trigger low PDB and increase price sensitivity. The latter can be particularly useful for generic brands that tend to be priced lower than their competitor, national brands. However, as noted previously, increasing PDB can also cause negative societal consequences, which is a "side effect" of this tactic. Thus, to be socially responsible, marketers should first attempt natural approaches to reduce consumers' price sensitivity, such as identifying high-PDB segments, or prime consumers' need for closure or social density, before activating a high PDB. As for priming high-PDB, marketers should assess the benefits (in terms of lowering price sensitivity) versus costs (e.g., negative social ramifications) of the endeavor.
Third, marketers can also directly increase their target consumers' need for closure to make them less price sensitive. Research suggests that need for closure can be increased by reducing task attractiveness, time pressure, or environmental factors ([72]). Thus, marketers can incentivize their customers to participate in a boring word puzzle or crossword tasks inside the store in exchange for a chance to win a prize. Marketers may also increase consumers' need for closure (and reduce their price sensitivity) by offering products or promotions for a limited amount of time (as in Study 6 in Web Appendix G). Fourth, retailers can reduce the price sensitivity of low- (but not high-) PDB consumers by increasing social density (e.g., via crowded stores; via atmospheric cues such as lighting, music, width of aisles, placement of merchandise; see [67]). For example, marketers can make a certain section of the store more spatially crowded by placing some physical obstacles ([49]). Perhaps stores such as T.J. Maxx are onto something by displaying products densely rather than sparsely.
[17] found that the effect of PDB on consumers' price sensitivity to food products is not significant, which, on the surface, appears counter to our core findings. However, a deeper examination yields a different story. First, [17] results are correlational rather than experimental. As is well known, correlational relationships are vulnerable to numerous alternate explanations. We address this issue by examining the link between PDB and price sensitivity using multiple operationalizations of both variables, including experimental approaches. Moreover, their sample size was too small (N = 59) and they did not attempt to replicate their result. Thus, the relationship they observed may be unreliable or suffer from low power. Across the studies, our sample size is 1,030,503. Furthermore, as noted previously, a meta-analysis revealed a highly significant effect of PDB on price sensitivity.
We acknowledge that it is possible for high-PDB individuals, due to their higher need for closure, to simply pay less attention to all information (including price). However, because price is a common feature across all products, is one of the most salient attributes used by consumers and is an alignable attribute that readily enables comparison between brands ([53]), we believe that consumers high in need for closure are more likely to "seize and freeze" on price, compared with other attributes, such as quality. For this reason, we focus on price sensitivity rather than sensitivity to other attributes (such as quality), although we do not rule out the possibility that, under some conditions (e.g., when consumers have enough resources so that they do not have to pay a lot of attention to price), consumers high (vs. low) in need for closure may also be more sensitive to differences in other attributes (such as quality).
In addition, it is possible that mechanisms other than need for closure (e.g., self-regulation) drive consumers' price sensitivity in other situations. Future research should systematically examine these variables. For example, it is possible that self-regulation dominates consumers' price sensitivity when they buy expensive products, whereas need for closure drives consumers' price sensitivity when they buy inexpensive products. Moreover, although the effect of uncertainty avoidance is nonsignificant in Studies 2a and 8, its effect was significant in Study 1a. Interestingly, although both participants high and low in uncertainty avoidance significantly reacted to price changes (i.e., both are price sensitive), participants high (vs. low) in uncertainty avoidance were more price sensitive (for details, see Figure WC1 in Web Appendix C). This is contrary to what one would expect if one assumes that the uncertainty avoidance component of need for closure is driving the effect. This result also suggests that the effect of need for closure on price sensitivity cannot be accounted for by uncertainty avoidance.
Supplemental Material, PDB-PS-Web_Appendices-29April2020-AKL_PDF - Price No Object!: The Impact of Power Distance Belief on Consumers' Price Sensitivity
Supplemental Material, PDB-PS-Web_Appendices-29April2020-AKL_PDF for Price No Object!: The Impact of Power Distance Belief on Consumers' Price Sensitivity by Hyejin Lee, Ashok K. Lalwani and Jessie J. Wang in Journal of Marketing
Footnotes 1 Associate Editor Dhruv Grewal
2 Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors are grateful for funding from the Carolan Research Institute, San Antonio, as well as from the Dean's Office at the Kelley School of Business, Indiana University, Bloomington.
4 Online supplement: https://doi.org/10.1177/0022242920929718
5 1 The demographic control variables (covariates) were household income, household size, type of residence, household composition, age and presence of children, female head age, male head age, female head employment situation, male head employment situation, male head education, female head education, male head occupation, and female head occupation.
6 2 Seven participants did not complete the study and were thus excluded from the analyses.
7 3 Nine participants from MTurk did not complete the study and were thus excluded from the analyses.
8 4 When we controlled for all alternative explanations and all cultural variables, the results were similar. Only PDB was significant (β =.52, t(65) = 4.53, p <.001, d = 1.12).
9 5 Seventeen participants from MTurk did not complete the study and were thus excluded from the analyses.
6 Due to the limited number of participants available in our subject pool, we augmented the data with participants from MTurk.
7 Sixteen participants from MTurk did not complete the study and were thus excluded from the analyses.
8 Within the student sample, a linear regression with purchase likelihood as the dependent variable revealed nonsignificant effects of PDB (β =.19, t(87) =.57, p >.57), social density (β =.18, t(87) =.72, p >.47), and their interaction (β = −.12, t(87) = −.28, p >.78, d = −.06). Floodlight analysis in the student sample did not reveal a significant effect either. However, within the MTurk sample, a similar regression revealed significant effects of PDB (β =.96, t(159) = 5.01, p <.001, d =.79), social density (β =.19, t(159) =.57, p >.57), and their interaction (β = −.45, t(159) = −2.04, p <. 05, d = −.32). Floodlight analysis in the MTurk sample revealed a significant positive effect of social density on purchase likelihood for participants whose PDB score was less than 3.14 ( =.45, SE =.23, p =.05), indicating that the social density manipulation reduced price sensitivity for low-PDB individuals. The effect was nonsignificant above the PDB value of 3.14, suggesting that the social density manipulation did not influence price sensitivity for high-PDB individuals. Although the results from MTurk sample support our hypotheses, we did not exclude the student sample because we had already collected those data.
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Project Customization and the Supplier Revenue–Cost Dilemmas: The Critical Roles of Supplier–Customer Coordination
This study examines customization as a coordination problem in transactions with business customers. Marketing research has investigated challenges associated with customized offers from the customer side; however, scant research has examined the supplier’s challenges and their performance implications. The authors distinguish between project revenues and costs to reveal a fundamental dilemma that suppliers face during customization. Analyses of dyadic survey data collected from a software supplier and its business customers, as well as objective revenue and cost data, reveal a tension between project revenues and costs. The outcomes of customization depend on factors that relieve the coordination problem, such as customer demand ambiguity, customer participation, product modularity, project teamtechnological capability, and relational embeddedness. These findings provide a basis to assess the value of customization as a tool to implement a customer-oriented business-to-business marketing strategy.
Customization is a key practice for implementing customer-oriented marketing strategies geared toward creating unique value for individual customers (Dellaert and Stremersch 2005; Franke, Keinz, and Steger 2009). Particularly in business-to-business (B2B) markets, customization is an important route to building and strengthening relationships with customers (Ghosh, Dutta, and Stremersch 2006). However, firms face challenges in providing customized solutions that are also profitable (Tuli, Kohli, and Bharadwaj 2007). Customization can increase sales revenue by better serving customer needs, but it can also lead to higher costs associated with the complex coordination processes required to define the specific needs of individual customers and make relevant adaptations to existing products (Piller, Moeslein, and Stotko 2004). For example, to satisfy the unique needs of health care start-ups such as Preventice, Samsung customized its hardware and software, which prompted higher revenues from its customers but also huge customization costs (Burrows 2012). Suppliers even may lose money if the added costs of customization exceed the increased sales revenue (Stanley and Wojcik 2005). Managing
Yonggui Wang is Professor of Marketing and Strategy, University of Inter-this balance is crucial to enable suppliers to profit from their customization projects. Accordingly, we attempt to address two research questions:
RQ1: What effects does project customization have on a supplier’s project revenues and costs?
RQ2: Given the tension between project revenues and costs, how should the supplier manage its customization to increase revenues and reduce costs?
We conceptualize customization as a coordination problem between the supplier and its customer, whereby coordination refers to the deliberate and orderly alignment or adjustment of partners’ actions to achieve jointly determined goals (Gulati and Singh 1998; Gulati, Wohlgezogen, and Zhelyazkov 2012). Drawing on customization research across disciplines, including marketing and operations management, we propose that a supplier’s project revenues and costs depend on the mechanisms it can use to coordinate customer interactions during the customization process. First, we posit that the product attributes and developers’ technological capability define the supplier’s ability to implement customization for customers (Ghosh, Dutta, and Stremersch 2006; Tu et al. 2004), such that we investigate product modularity and the project team’s technological capability as mechanisms that might relieve coordination problems associated with customization. Second, frequent interactions or prior relationships tend to increase mutual understanding and cooperative routines (Gulati 1995; Hoang and Rothaermel 2005), so we consider customer participation and relational embeddedness as another coordination mechanism. Finally, information of customer demands is a critical input for customization, and prior research has highlighted ambiguous demand as a
Journal of Marketing Vol. 81 (January 2017), 136–154 critical challenge during customization (Duray et al. 2000; Franke, Keinz, and Steger 2009). We therefore examine customer demand ambiguity as a factor that worsens coordination during customization.
With dyadic survey data from 134 pairs of B2B software suppliers and customers, as well as objective revenue and cost data, we analyze sales projects that involve varying degrees of customization. Our results confirm the presence of a tension between revenue and cost; customization does not always lead to greater profitability. Instead, project outcomes depend on factors that worsen or relieve coordination problems. In particular, low customer demand ambiguity, high product modularity, and high relational embeddedness magnify profitability by increasing project revenues or reducing project costs due to customization, whereas high customer participation magnifies both project revenues and costs.
These results represent several contributions. First, most prior research has focused on challenges from the customer side, such as unclear customer preferences, complexity, or mass confusion (e.g., Dellaert and Stremersch 2005; Franke, Schreier, and Kaiser 2010). We focus on the supplier’s perspective and thereby reveal the tension between project revenues and costs that results from customization. The boundary conditions associated with this tension in turn provide a basis for assessing customization as a strategic tool that suppliers can implement to ensure the customer orientation of their B2B marketing strategies.
Second, prior research has considered various types of coordination in vertical relationships, such as information sharing, resource integration, joint development, or formal governance structures (e.g., Fang 2008; Grewal, Chakravarty, and Saini 2010; Kumar, Heide, and Wathne 2011; Lee, Hoetker, and Qualls 2015); we extend this understanding by investigating customization as a coordination problem, as well as its interactions with coordination mechanisms, to determine the financial performance of a customization project.
Third, by undertaking a scenario analysis, our study provides specific guidance to B2B managers to implement customization projects: Aligned, strong coordination mechanisms
(i.e., low customer demand ambiguity and high customer participation aligned with high product modularity and project team technological capability, together with high relational embeddedness) lead to the highest profitability (26%), approximately 8 percentage points greater than an average project in our sample (18%). Alignments in the opposite direction instead lead to the lowest profitability (14%), approximately 4 percentage points lower than average. These results help clarify when and how firms should involve their B2B customers in developing customized offers.
Literature Review
Customization involves interactions between customers and suppliers to coordinate specific offerings. Marketing researchers have highlighted the challenges customers face in determining the value of customized products, despite their potential benefits compared with off-the-shelf products (see Table 1). Customization tends to increase the complexity of product con
figurations and designs, and customers also might experience
Project difficulties expressing their specific preferences, choosing product designs, or recognizing the value of customized products (Dellaert and Stremersch 2005). From this perspective, prior research has examined the contingent value of customization to customers, which depends on customers’ involvement, expertise, trust in the supplier, or awareness as a creator (Franke, Keinz, and Steger 2009; Franke, Schreier, and Kaiser 2010; Simonson 2005). Prior operations management research has also revealed that the value of product customization depends on the supplier’s production operations, including its customization capability and efficient, flexible practices (Liu, Shah, and Babakus 2012; Squire et al. 2006; Tu, Vonderembse, and Ragu-Nathan 2001). As such, prior research has shown that customization does not always lead to greater value for customers, but it mainly is realized through the efforts of both suppliers and customers.
For the supplier, customization represents a relationship strategy that can be combined with joint development, customer participation, or customer education (Stump, Athaide, and Joshi 2002). Suppliers make various customization-related decisions, including their level of control over the customization (Ghosh, Dutta, and Stremersch 2006), market entry, and the amount of customized production (Novshek and Thoman 2006; Syam and Kumar 2006; Zhang, Zhao, and Qi 2014). Customization provides an effective means to continue or maintain a B2B relationship and improve relationship satisfaction (Stump, Athaide, and Joshi 2002). To be successful though, prior research has shown that customization requires the supplier to possess customization capabilities, which might stem from product attributes, close relationships with customers, or effective learning processes (Huang, Kristal, and Schroeder 2008, 2010; Tu et al. 2004).
Although these findings indicate the contingent value of customization, prior research has paid scant attention to the potential financial outcomes of such customization for suppliers. In particular, we know little about the outcomes at the project level, at which customization is a specific B2B transaction type. Research into broader, buyer–seller, vertical relationships has considered product-level performance, using measures of product innovativeness or development speed (Bonner and Walker 2004; Fang 2008) as well as firm-level performance (Chan, Yim, and Lam 2010; Fang, Lee, and Yang 2015). Yet such measures cannot capture the financial outcomes of customization projects, which would reflect how well customization works for the supplier. That question requires consideration of project-specific outcomes such as project costs, revenues, and profitability.
Theoretical Framework
Coordination Problems and Mechanisms for Customization
Customization can be regarded as a coordination problem between a buyer and a supplier that aims to implement adaptations to meet the specific needs of individual customers. Customized offers, achieved through coordination with the customer, have greater potential than standardized offers to increase supplier revenues, but the challenges of
customization suggest that it also can impose costs on the supplier, creating a tension between revenues and costs. As Figure 1 illustrates, we develop a contingency model to predict boundary conditions that might magnify or relieve this inherent tension, according to the coordination mechanisms. Coordination may reside in the unilateral efforts of individual firms and bilaterally in the properties of the relationship itself between the two firms.
TABLE:
| References | Context | Customization Outcomes | Independent Variables | Findings |
|---|
| Marketing Studies of Customization |
| Dellaert and Stremersch (2005) | Customization of personal computers | Customer’s utility | Product utility, complexity, expertise | Product utility and complexity affect the utility that consumers derive from mass customization. |
| Simonson (2005) | Customization for one-to-one marketing | Customer’s responses to customization | Customer’s preference development, trust in marketer | Customers’ response to customized offers includes preference development before evaluation and actions. |
| Franke, Keinz, and Steger (2009) | Customization of newspaper | Customer’s benefit | Fit between preferences and product attributes, customer’s product involvement | The customer benefit from customization depends on customers’ insight into their own preferences, ability to express their preferences, and their product involvement. |
| Franke, Schreier, and Kaiser (2010) | Customer participation in mass customization | Value for customer | Preference fit, design effort, awareness of being the creator | “I designed it myself” effect creates economic value for the customer. |
| Ghosh, Dutta, and Stremersch (2006) | Customization of complex products for original equipment manufacturing customers | Supplier’s control over customization | Product modularity, technological unpredictability, customer’s knowledge, knowledge mobilization | Contracting parties choose the level of vendor control over customization to enhance the benefits from customization for both parties. |
| Operations Management Studies of Customization |
| Novshek and Thoman (2006) | A new entrant’s decision for customization | Amount of customized production | Customer’s valuations, market structure | The entrant is unconcerned about the impact of its custom production on the incumbent’s market and may supply more custom products than is socially desirable. |
| Liu, Shah, and Babakus (2012) | Mass customization in manufacturing plants | Customer satisfaction | Mass customization ability, demand uncertainty | Mass customization ability is more critical for customer satisfaction when customer demand uncertainty is higher. |
| Tu, Vonderembse, and Ragu-Nathan (2001) | Manufacturing practices for mass customization | Value for customer | Time-based manufacturing practices, mass customization | The supplier’s efficient, flexible production practices play important roles in enhancing the customer value of customization. |
| Zhang, Zhao, and Qi (2014) | Customization by manufacturing plants | Supplier’s customization capability | Product modularity, coordination, organizational flatness | Product modularity and crossfunctional/supply chain coordination improve customization capability, and organizational flatness enhances coordination practices. |
First, the product attributes and technological capability might relieve or worsen the coordination problem associated with customization. Product modularity reflects the degree to which functional components of a product interact in standardized, specified ways, such that they allow for the substitution of components without requiring changes to the product design ensures flexibility in product modifications (Zhang, Zhao, and Qi 2014), so this product attribute should affect the coordination problem during customization (Srikanth and Puranam 2014). Because this form of coordination also relies on the supplier’s capability to accommodate the specific needs of the customer, we consider project team technological capability, or the project team’s ability to develop technological competencies and processes that enable it to transform innovative ideas into new products (Dutta, Narasimhan, and Rajiv 1999).
Second, interactions or relationships between the two
firms in the customization process may affect coordination efforts to determine the scope and specific form of adaptation. Customer participation, or the extent to which a customer engages directly in the supplier’s customization activities, facilitates customer-oriented product development activities (Carbonell, Rodríguez-Escudero, and Pujari 2009; Fang 2008) in that it entails intensive interactions and thus can affect the benefits of customization. At the same time, an existing relationship between a buyer and a seller can help them develop mutual understanding and routines, which in turn allow for more effective coordination (Gulati 1995; Hoang and Rothaermel 2005). Relational embeddedness is the extent to which relationships among firms are reciprocal and close (Rindfleisch and Moorman 2001).
Third, poorly defined customer demands create a significant hurdle to coordinated efforts for effective customization (Franke, Keinz, and Steger 2009). Customer demand ambiguity refers to the extent to which the customer lacks an accurate assessment of its own product attribute needs (Franke, Keinz, and Steger 2009; Kramer 2007). This can be due to customers’ lack of knowledge about the product category or rapid technological changes, factors that make it difficult for customers to describe their needs as specific product attribute demands (Ghosh, Dutta, and Stremersch 2006).
Effects of Customization on the Supplier’s Project Revenues and Costs
A higher level of customization can increase the value offered to individual customers by better solving the specific problems that any particular customer faces (Tuli, Kohli, and Bharadwaj 2007), which in turn generates more sales revenue for the supplier. Through customization, the supplier better defines its offerings to match its customers’ specific needs. Unlike standard product offerings, customization requires the two firms to share product and demand information so they can arrive at optimal product offerings tailored to the customer’s needs (Simonson 2005). Thus, customization reflects a supplier’s coordination efforts with its customer to leverage their respective capabilities.
Customized solutions also may entail coordinated efforts and further investments, such as new solutions or adaptations of standard products (Hallén, Johanson, and Seyed-Mohamed 1991). For example, to satisfy unique customer requirements, Toyota reconfigured its organizational systems to pursue continuous improvement (Pine and Victor 1993). When it involves coordinated information sharing and partner-specific investments, customization enables suppliers to design product features that better fit their customers’ preferences. The improved fit also increases the expected benefits of customization to the customer and, thus, the price the customer is willing to pay, leading to greater revenue for the supplier.
However, if we conceive of customization as a coordination problem, it must also invoke costs. First, the process is complex because of the need for intensive interactions with individual customers (Simonson 2005). A customized project demands more setup costs before being initiated, including efforts to determine the project scope or product attributes to be customized, the role of each party, and the criteria for evaluating outputs (Piller, Moeslein, and Stotko 2004). Customization also increases organizational complexity, in that it requires cross-functional coordination to integrate the different resources and capabilities (e.g., hardware, software, services) necessary to develop customized solutions (Zhang, Zhao, and Qi 2014). Second, the investment required to implement customized solutions can make it difficult to achieve economies of scale, such that the unit cost of each offering is higher (Piller, Moeslein, and Stotko 2004). The supplier’s sales activities
Customization and the Supplier Revenue–Cost Dilemmas / 139 then are less cost efficient by definition. Because a higher level of customization incurs higher costs, we propose a baseline hypothesis regarding the inherent tension between supplier revenues and customization costs arising from customization.
H1: For the supplier, customization (a) increases project revenues and (b) increases project costs.
Moderating Effects of Unilateral Coordination Mechanisms
Product modularity. Modular products provide flexibility that allows for product adaptation and can meet the specific needs of individual customers (Sanchez and Mahoney 1996). Integrative, nonmodular products instead increase the complexity of and uncertainty about customization because changes to any one part create a need for further changes in other parts. Because modular products support “mixing and matching” of the components, they can result in a wide range of product variations on a single, existing platform, with less uncertainty (Ghosh, Dutta, and Stremersch 2006; Sanchez 1999). The supplier has more product options available to respond to a customer’s specific needs, such that the coordination effort required to align the supplier’s products and the customer’s needs becomes more efficient. Tu et al. (2004) thus show that modular product design improves the supplier’s capability for customization. Furthermore, modular products enable the supplier to apply common parts across its product range, reducing the investments required to make changes to existing products (Sanchez 1999). Thus, the supplier can achieve economies of scale while still serving specific customer needs. Because modular products increase the value of customization through increased product variety while also mitigating the costs of customization, we predict the following:
H2: When product modularity is higher, customization has (a) a more positive effect on the supplier’s project revenues and
(b) a less positive effect on the supplier’s project costs.
Project team technological capability. A project team with greater technological capabilities will be more effective in coordinating with customers because the team can identify and interpret customers’ needs accurately and incorporate them explicitly into product specifications (Cohen and Levinthal 1990). A team with greater technological capability also can implement innovative solutions more readily (Dutta, Narasimhan, and Rajiv 1999), which likely helps the supplier accommodate customers’ specific needs. These elements in turn should increase the revenue generated through customization.
Although building technological capability requires additional investments over time, a project team with this capability can coordinate and invest in customization efficiently, with less trial and error, as well as reduce coordination complexity with individual customers during the customization process (Xiong and Bharadwaj 2011). Building on its technological capability, the project team also can invest more efficiently in designing customized solutions. Thus,
H3: When project team technological capability is higher, customization has (a) a more positive effect on the supplier’s project revenues and (b) a less positive effect on the supplier’s project costs.
The Moderating Effect of Bilateral Coordination Mechanisms
Customer participation. Coordination requires the active participation of the customer. A more involved customer makes the customization process more effective, in terms of ensuring fit between the supplier’s products and the customer’s needs (Duray et al. 2000; Franke, Keinz, and Steger 2009). Through their active participation, customers directly define and adapt products to meet their specific needs; they also provide the supplier with privileged access to downstream information and resources, which can facilitate the invention of new solutions (Klein 2007; Uzzi 1997). Tuli, Bharadwaj, and Kohli (2010) argue that connections between the supplier and the customer boost information sharing, which helps the supplier acquire valuable information about customer needs. By facilitating coordination, customer participation enables the supplier to customize its products better, which should increase the value of project customization for the customer and improve the supplier’s sales performance in turn (Carbonell, Rodŕ?guez-Escudero, and Pujari 2009).
Customer participation also may make interactions more efficient, such that the costs decrease. There might be some additional costs of customer participation, because it requires more interactions, but the supplier can avoid costs associated with miscommunication by involving customers directly in the customization process. Furthermore, investments might be more efficient by targeting the precise needs of individual customers who participate. Therefore,
H4: With greater customer participation, customization has (a) a more positive effect on the supplier’s project revenues and
(b) a less positive effect on the supplier’s project costs.
Relational embeddedness. Relational embeddedness entails trust, overlapping identities, and closeness resulting from lasting, durable relationships (Nahapiet and Ghoshal 1998). It reflects the quality of social relations and determines firms’ willingness to share their resources and capabilities with partners (Moran 2005). For example, prior research has examined various aspects of relational embeddedness, such as the concentration of transactions (Uzzi 1997), the duration or repetition of relationships (Fischer and Pollock 2004; Kraatz 1998), and the types of relationships that require resource commitment (Rowley, Behrens, and Krackhardt 2000). Embedded relationships can act as coordination mechanisms that facilitate robust, collective actions between the project team and the customer by aligning their motivations. In particular, when the project team and customer are closer, they likely are more cooperative in terms of sharing information to define a specific form of customization (Tu et al. 2004), and they commit more resources to implementing the customized solution. These embedded relations reduce the uncertainty surrounding the exchange as well by facilitating exchange-inducing norms and sanctions against partners’ opportunistic behaviors (Coleman 1990; Moran 2005). Thus, embedded relations improve the customization process, which increases the value of customized offers and produces more revenues from customization. The project team also can minimize some of the costs of customization, such as the time and resources they need to devote to developing background knowledge about the customer and its specific needs.
H5: When relational embeddedness is higher, customization has
(a) a more positive effect on the supplier’s project revenues and
(b) a less positive effect on the supplier’s project costs.
Customer Demand Ambiguity Worsens the Coordination Problem
The value of customized offers depends on the extent to which the customer can define its needs accurately (Franke, Keinz, and Steger 2009). Extant research has suggested that accurate customer input is vital for effective customer-oriented marketing activities (Fang, Palmatier, and Evans 2008; Lee, Naylor, and Chen 2011). Product modifications face a greater risk of losing their direction or relevance during customization in the presence of high demand ambiguity. For example, Huffman and Kahn (1998) show that customization based on poorly defined demands can cause customer confusion and diminish the overall value. Thus, customer value and the revenue created from adaptation is limited to the extent that the customer’s demand can be clearly defined.
Demand ambiguity also increases the cost of customization to the supplier. Ambiguous needs are likely tacit and difficult to transfer, so customization requires more interactions with the customer to clarify the ambiguous demand and to transfer this information from the customer to the supplier (Hansen 1999). Ambiguous demands also make an investment in customization less efficient because the customer’s requirements likely change as the customization materializes. Customers with unclear demands may construct more specific or altered demands along the way, such that the customization process becomes increasingly complicated and costly (Simonson 2005; Slovic 1995). Offering more customized solutions thus may be more expensive when customer demand is ambiguous.
H6: When customer demand ambiguity is higher, customization has (a) a less positive effect on the supplier’s project revenues and (b) a more positive effect on the supplier’s project costs.
Method
Research Setting
We test our hypotheses with dyadic data collected from one of the largest enterprise resource planning (ERP) software suppliers in China and its business customers. Because ERP software supports various operational activities—such as planning, purchasing, financing, human resources, marketing, and customer services—this context provides some important advantages for testing our hypotheses. First, ERP software sales and deployment typically involve varying customization levels (Dittrich, Vaucouleur, and Giff 2009). In interviews with managers, we confirmed that customized products were key features of the firm’s product offerings. Even if the corefunction ofthe software remains unchanged, the unique needs of the supplier’s business customers prompts it to adopt different levels of customization involving various product components, implementation methods, software upgrades, and maintenance contracts to help customers integrate various business operations, including supply chain planning,
Project purchasing, manufacturing, sales and marketing, distribution, accounting, and customer service.
Second, project-based customization to implement the enterprise software provides an ideal context for testing project-level outcomes (Shanks 2000), such as project revenues and costs. The focal supplier has a dedicated project team and manager for each B2B customer, and the project team manager determines the level of customization, through interactions with the customer. Some customers choose the precise specifications of the system and contract with the supplier for their supply, with little customization. In other cases, the supplier teams work proactively with customers to develop software specifications or design products that fulfill particular functional criteria.
Third, B2B customers in various industries use ERP software products widely. The customers in our data set thus represent various industries, including information technology, manufacturing, construction, real estate, telecommunication, and hotels. Our test of customization involves business practices that occur across diverse industries.
Fourth, the supplier’s products feature various components, assembled in different ways to fit customers’ needs. Depending on the components, different products exhibit distinct levels of modularity. Furthermore, the different teams contain members with varying expertise, so each team has unique technical capabilities. These features should ensure sufficient variation in the constructs of this study.
Fifth, depending on the level of customization, the customer and supplier agree ex ante about the minimum price a customer will pay and contractually specify any contingencies, such as more development time, greater investments in the features required by the customer, or more training sessions, that might invoke additional payments.
Data Collection
We collected dyadic survey data from both customers and suppliers, as well as objective revenue and cost data from the supplier. The supplier reasonably should have more information about project characteristics, such as costs and revenues, customization, product modularity, and project team technological capabilities. In contrast, the customer should be more knowledgeable about its own demand, environment, and relationship with the supplier. The survey respondents represented diverse teams and possessed sufficient knowledge to answer the survey questions. To ensure that these key informants were qualified to respond to the questionnaire on behalf of their respective firms, we also included questions about the extent to which they were knowledgeable about the customization project, using a seven-point Likert scale. The mean values were 5.75 (SD = .69) for buyers and 6.09 (SD = .39) for the supplier.
Business customers. We started the data collection with the customers. Using the complete customer list provided by the software supplier, we randomly selected 500 B2B customers, to avoid any sampling bias that might arise if the supplier chose customers (e.g., bias toward the most important or successful projects). Ghosh, Dutta, and Stremersch (2006) suggest that customers should be end users of software products, not resellers or distributors that sell standard products to mass customers without customization. In addition, we required that the supplier team deal directly with the customer, without any third-party agent (e.g., project consultants, system integrators), because direct B2B interactions are less likely in the presence of a third-party agent.
We e-mailed the survey to the customers, then reminded them about it in telephone calls one week later. The unit of analysis refers to the project level, so we asked respondents to identify the most recently completed project involving a purchase from the focal software provider. The informants in the customer firms were senior executives directly responsible for the software implementation. We offered a 100 RMB (US$15) telephone bill credit for each respondent as an incentive. Within two months, we received 197 customer responses (after dropping 3 responses for which the informant scored lower than four on a seven-point Likert scale measuring knowledge about the customization project); of these, 18 contained too much missing data and were discarded, leaving 179 valid responses (effective response rate of 36.2%).
Among the 179 responses, 100 came from state-owned firms, in which the government owns more than 50% of shares
(55.25%), and the rest were private firms. In terms of size, the firms employed approximately 1,200 people on average, and their average contract value was 2 million RMB (approximately US$40 million). Nine firms represented the information technology industry (4.97%), 68 were manufacturers (38.12%), 31 firms were in the construction or real estate industries (17.8%),
37 firms came from telecommunications (20.67%), and 20 firms were in other industries (11.17%). To check for nonresponse bias, we compared firm size, project contract value, project duration, and industry (manufacturing or nonmanufacturing) between the responding and nonresponding firms. None of the t-statistics for these comparisons was significant, suggesting that response bias was not a threat in our sample.
Suppliers. In the questionnaire distributed to customers, we asked them to provide contact information for a team manager they knew from the supplier firm. Then we distributed 179 supplier questionnaires, referring to the specific project chosen by the customer. We first contacted the team manager by telephone or in person, then sent the survey by e-mail. One week later, we made telephone calls to remind these respondents of the survey. We received 136 questionnaires; we dropped 2 responses that scored lower than four on the seven-point Likert scale of respondents’ knowledge about the customization project. The 134 valid responses from suppliers represented an effective response rate of 75.1%. Ultimately, we had 134 matched supplier–customer dyads for our analyses.
Measurement
We developed the questionnaires using Churchill’s (1979) and Gerbing and Anderson’s (1988) recommended procedures. Initially, we conducted several interviews with executives from the supplier teams and their business customers. These early interviews, which lasted approximately ten hours in total, helped us develop the measurement scales and were instrumental in our attempts to craft the pretest survey. On the basis of these interviews and an extensive review of previous studies, we developed preliminary versions of the questionnaires. When possible, we adapted the existing scale items to our context. We initially developed the questionnaire in English, translated it into Chinese, and then back-translated it into English. This procedure ensured that the English and Chinese versions contained identical measures. We pretested the questionnaires with a sample of 16 pairs of supplier teams and business customers to verify the appropriateness of the terminology used and the clarity of the instructions. These results indicated that the survey instrument was generally sound, though we modified a few items for clarity. We provide the measurement items in Appendices A and B.
Project revenue and cost. The project revenue and cost data came from the supplier’s archival records. This supplier keeps detailed records of the revenues and costs generated by each project. The costs derive from design, development, delivery, after-sales service, and other administrative activities. For parsimony, we only included direct costs during the contract period, such as materials and labor dedicated to product development, sales, and after-sales service. Labor costs reflect compensation paid to developers, salespeople, and after-sales service representatives for the length of the contract. Customization requires more labor inputs to coordinate customization, make product adaptations according to the customer’s specific needs, or provide additional training on customized features for the customer’s employees. Although relatively minimal for software, some customization projects encounter material costs too (e.g., personal computers, servers). We did not include indirect costs, such as when customized projects use components developed for prior customization projects, because it is very difficult (if not impossible) to track such indirect costs accurately. Other costs also might accrue after the contract expires, such as additional service requests from the customer, but it would be difficult to determine whether such service requests were due to customization or other factors. To avoid ambiguity, we focused on direct costs during the contract period. Both revenue and cost data are skewed, so we took the log transformation; after the transformation, both variables were normally distributed.
Project customization. We obtained measures of project customization from the supplier teams. We used a three-item, seven-point Likert scale to measure the extent to which each supplier team delivered standardized versus customized software to customers. We adapted this measure from Homburg, Müller, and Klarmann (2011). The coefficient alpha was .76.
Customer participation and customer demand ambiguity. We obtained measures of customer participation from the customer side. We used a four-item, seven-point Likert scale, based on Fang (2008), to measure the extent to which the customers were involved in the software development and delivery process on a limited versus extensive basis. The coefficient alpha was .801. For customer demand ambiguity, we developed a four-item, seven-point Likert scale to measure the extent to which the customers were clear about their demands for the software when purchasing it from the supplier. The coefficient alpha was .77.
Product modularity and project team technological capability. We obtained both these measures from the supplier teams. For product modularity, we followed Tu et al. (2004) and used a five-item, seven-point Likert scale to measure the extent to which the software could be easily decomposed into separate modules. The coefficient alpha was .80. For project team technological capability, we obtained the measure from the supplier teams and developed a four-item, seven-point Likert scale to measure how knowledgeable the supplier teams were about the development and delivery of
the software. The coefficient alpha was .77.
Relational embeddedness. Relational embeddedness measures the degree of closeness between customers and the supplier (Uzzi 1997); we used a four-item, seven-point Likert scale adapted from Fang (2008). The coefficient alpha was .77.
Control Variables
We included several control variables that could affect project revenues and costs. At the project level, we controlled for project duration, measured as the number of months from the start to the end of the project, which reflects the amount of time devoted to customization. We also controlled for project type and complexity. For project type, we used a series of dummy variables (project type_1 = 1 if the project involves customer relationship management, project type_2 = 1 if it involves financial planning, and project type_3 = 1 if it involves production and order planning; 0 if not in each case). Project complexity captures the extent to which the development project entails complicated processes and technical knowledge, with measures adapted from Fang (2008). We also controlled for customer type (i.e., state-owned vs. private customers) because employees of state-owned firms have different incentives and objectives. We defined firms as state-owned if the government owned more than 50% of their shares.
At the relationship level, a firm’s dependence on a partner can affect its bargaining power and ability to retrieve more revenues or projects from the relationship (Heide and John 1988). We included supplier dependence on customers, obtained from the supplier, and customer dependence on the supplier, obtained from customers (Fang, Palmatier, and Evans 2008). Furthermore, we controlled for the number of prior projects the supplier had undertaken with each specific customer, which might affect the level of customization or customization effi-ciency because firms grow to understand each other and develop routines for effective interactions through their past relationship experience (Anand and Khanna 2000).
At the industry level, we controlled for industry dynamism, or the degree of change and unpredictability in the market environment, with four items from Jaworski and Kohli (1993). A dynamic environment can induce additional costs by increasing the uncertainty associated with rapid, unexpected changes in the market (Teece 2007). Finally, we controlled for customer size, measured as the log of the number of employees of the customer firms.
Assessment of Measurement Models
We estimated two separate measurement models with the data sets from the supplier and customers, respectively. We restricted each scale item’s loading to its a priori specified factor and allowed for correlations among factors. The fit indices for each model were good. Specifically, for the measurement model from the suppliers, we obtained c2 = 68.13 (p > .10), comparative fit index (CFI) = .93, normed fit index (NFI) = .92, and root mean square error of approximation (RMSEA) = .04. The values for the measurement model with the customers were c2 = 201.49 (p > .10), CFI = .92, NFI = .91, and RMSEA = .05. All factor loadings were positive and significant (p < .01), and the composite reliabilities were greater than .70 (see Appendices A and B). The average variance extracted by each construct was greater than the square of the latent correlation between it and all other constructs in the measurement model (Fornell and Larcker 1981). We conducted pairwise chi-square difference tests for each pair of constructs in the overall model (Bagozzi, Yi, and Phillips 1991) to test for discriminant validity, which exists if the unconstrained model demonstrates significantly better fit than a constrained model in which we constrain the correlation between those constructs to 1 (Dc2 significant at p < .01). These analyses suggest discriminant validity among the constructs. We descriptive statistics and correlations in
Model Estimation
Model Setup
We estimated two models using seemingly unrelated regressions to control for other unobserved factors that could affect the revenue and cost simultaneously (Fang 2008):
Project revenuei = ba0 + ba1Project customizationi
+ ba2Product modularityi
+ ba3Project team technological capabilityi + ba4Customer participationi
+ ba5Relational embeddednessi
+ ba6Customer demand ambiguityi
+ ba7Project customizationi
• Product modularityi
+ ba8Project customizationi
• Project team technological capabilityi + ba9Project customizationi
• Customer participationi
+ ba10Project customizationi
• Relational embeddednessi
+ ba11Project customizationi
• Customer demand ambiguityi + Control variables + na,i, and
( 1)
( 2)
Project costi = bb0 + bb1Project customizationi + bb2Product modularityi
+ bb3Project team technological capabilityi + bb4Customer participationi
+ bb5Relational embeddednessi
+ bb6Customer demand ambiguityi
+ bb7Project customizationi
• Product modularityi
+ bb8Project customizationi
• Project team technological capabilityi + bb9Project customizationi
• Customer participationi
+ bb10Project customizationi
• Relational embeddednessi
+ bb11Project customizationi
• Customer demand ambiguityi + Control variables + nb,i:
Self-Selection Correction
When estimating the two models, we addressed the potential for endogeneity; factors not included as covariates in Equations 1 and 2 but that appear in the error term could influence the level of customization. For example, the supplier team’s unobserved, personal preferences about customization might affect the level of customization. The failure to address the influence of such unobserved factors could bias the estimation results from Equations 1 and 2. We therefore applied the control function approach (Petrin and Train 2010; Sridhar and Srinivasan 2012), for which the basic logic is that we include a new control variable in the choice regression equation (Equation 3). After accounting for the influence of the control variable on customization as a dependent variable in the choice regression equation, customization as an independent variable no longer correlates with the error term in the performance regression equation (Equations 1 and 2). Adding the control variable to the regression equation enables us to establish the independence assumption between project customization and the error term. Specifically, after accounting for its influence on project revenues and costs (dependent variables), project customization as the independent variable does not correlate with the error term (Sridhar and Srinivasan 2012).
TABLE:
| Variable | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |
|---|
| 1. Project customization | 3.897 | 1.201 | 1 | | | | | | | | | | | | | | | |
| 2. Project cost | 1.942 | .442 | .172 | 1 | | | | | | | | | | | | | | |
| 3. Project revenue | 2.070 | .478 | .168 | .678 | 1 | | | | | | | | | | | | | |
| 4. Project profitability | .182 | .070 | .036 | .187 | .289 | 1 | | | | | | | | | | | | |
| 5. Customer demand ambiguity | 2.585 | 1.140 | -.020 | .045 | .016 | -.068 | 1 | | | | | | | | | | | |
| 6. Customer participation | 5.816 | .986 | .234 | .111 | .004 | .018 | -.087 | 1 | | | | | | | | | | |
| 7. Product modularity | 5.725 | 1.017 | .201 | -.302 | -.201 | -.048 | -.097 | .014 | 1 | | | | | | | | | |
| 8. Industry dynamism | 4.463 | 1.302 | -.206 | .001 | -.003 | .004 | .097 | -.037 | .140 | 1 | | | | | | | | |
| 9. Supplier dependence | 4.914 | .990 | -.153 | -.017 | -.158 | -.032 | -.133 | -.147 | .300 | .181 | 1 | | | | | | | |
| 10. Customer dependence | 5.015 | .810 | -.090 | -.089 | -.067 | -.024 | -.069 | .151 | .081 | .159 | .142 | 1 | | | | | | |
| 11. Project team technological capability | 5.934 | .958 | .226 | -.018 | -.011 | -.025 | -.072 | -.010 | .114 | .104 | .021 | -.091 | 1 | | | | | |
| 12. Customer size | 4.272 | 1.062 | .219 | .255 | .267 | .144 | .044 | -.054 | -.193 | .003 | .182 | -.077 | -.020 | 1 | | | | |
| 13. Relationship embeddedness | 5.603 | .978 | .110 | .229 | .243 | .121 | -.182 | .211 | .000 | .178 | -.011 | .062 | -.019 | .129 | 1 | | | |
| 14. Project duration | 13.070 | 9.087 | .216 | .411 | .358 | .164 | .222 | .004 | -.279 | -.002 | -.149 | -.122 | .094 | .082 | -.051 | 1 | | |
| 15. Prior relationship | 2.297 | 1.582 | -.056 | .132 | .141 | .126 | -.087 | .099 | .028 | .101 | .046 | -.062 | .024 | .110 | .187 | .084 | 1 | |
| 16. Project complexity | 4.578 | 1.139 | .046 | .231 | .103 | -.087 | .106 | -.067 | -.276 | .039 | .087 | -.046 | .115 | .267 | .006 | .199 | -.043 | 1 |
Using Petrin and Train’s (2010) approach, for each endogenous variable, we performed an estimation using as a covariate at least one exogenous (excluded) variable that affects the endogenous variable (project customization) but is not related to either project revenues or costs. The predicted residual provides effective control variables to address endogeneity concerns. We used customer industry_dummy (=1 for manufacturing, =0 for services), because customization generally is greater in service than manufacturing industries, due to their experiential nature (Gwinner et al. 2005). We found no evidence that project costs, revenues, or profitability differed systematically across these two industry groups. In addition, we included all independent variables in Equations 1 and 2 as control variables, because customer demand ambiguity, customer participation, product modularity, project team technological capability, and relational embeddedness could affect the decision to adopt different project customization levels. Thus,
Project customizationi = ga0 + ga1Product modularityi
( 3)
+ ga2 Project team technological capabilityi + ga3Customer participationi
+ ga4Relational embeddednessi
+ ga5Customer demand ambiguityi
+ ga6Prior experiencei
+ ga7Project durationi
+ ga8Customer typei
+ ga9Customer sizei
+ ga10Supplier dependencei
+ ga11Customer dependencei
+ ga12Project complexityi
+ ga13Cusotomer industry dummyi
+ ga14Project type dummy1i
+ ga15Project type dummy2i
+ ga16Project type dummy3i + da;i:
After estimating this model (see Appendix C), we put the predicted residuals in the performance equations (Equations 1 and 2) to control for self-selection concerns.
Results
Table 3 presents the results of our empirical analysis. Regarding the main effects, Model 1 indicates that customization increases project revenues (b = .054, p < .05), and Model 3 indicates that it also increases project costs (b = .065, p < .05). These findings provide support for H1 and confirm the tension between the revenues and costs of customization.
In terms of the moderating effects of unilateral coordination mechanisms, we find in Models 2 and 4 that product modularity decreases project costs resulting from customization (b = –.155, p < .01), in support of H2b, but it has no significant effect on project revenues resulting from customization (b = –.019, n.s.). Low product modularity makes customization less efficient and increases customization costs. Furthermore, project team technological capability increases project revenues due to customization (b = .096, p < .05), with no significant effect on project costs (b = .058, n.s.). Consistent with H3a, technological capability is a coordination mechanism that can facilitate the benefits of customization.
For the moderating effect of bilateral coordination mechanisms, customer participation increases both project revenues and costs due to customization (b = .083, p < .05; b = .091, p < .05, respectively), such that it further heightens the focal tension. Although customer participation can enhance the project revenues associated with customization, in support of H4a, it also is accompanied by higher costs to accommodate the specific needs of actively engaged customers. We find that relational embeddedness increases project revenues resulting from customization (b = .091, p < .05) but has no significant effect on project costs (b = .027, n.s.). Consistent with H5a, these findings confirm the role of relational embeddedness in relieving coordination problems associated with customization and improving project revenues.
Finally, Models 2 and 4 indicate that customer demand ambiguity decreases project revenues resulting from customization (b = –.077, p < .05), but it has no significant effect on project costs (b = –.051, n.s.). In support of H6a, customization is less effective for sales in the presence of high demand ambiguity.
Robustness Analyses
To enhance confidence in our results, we conducted two robustness tests to evaluate the results: ( 1) including a squared term of customer participation to assess its possible curvilinear moderating effect on the customization–outcome relationships and ( 2) using an alternative measure of customer participation from the supplier side (i.e., supplier’s perception of customer participation). None of the squared effects on revenues or costs was significant. In addition, the measure based on supplier perceptions of customer participation yielded results consistent with those in Table 3.
TABLE:
| | | Project Revenue | Project Cost |
|---|
| | | Model 1 | Model 2 | Model 3 | Model 4 |
|---|
| Variable | Hypotheses | b | SE | b | SE | b | SE | b | SE |
|---|
| *p < .10. |
| **p < .05. |
| ***p < .01. |
| Intercept | | 1.492 | .377*** | 1.329 | .465*** | 1.052 | .328*** | 1.182 | .354*** |
| Main Effects |
| Project customization | H1a–1b | .054 | .028** | .037 | .035 | .065 | .035** | .059 | .037* |
| Product modularity | | -.010 | .021 | -.038 | .039 | -.046 | .027* | -.039 | .035 |
| Project team technological capability | | .013 | .024 | .010 | .038 | .030 | .031 | .011 | .037 |
| Customer participation | | .047 | .026* | .046 | .032 | .087 | .033*** | .079 | .039** |
| Relational embeddedness | | .091 | .045** | .082 | .045* | .084 | .036** | .082 | .039** |
| Customer demand ambiguity | | .051 | .038 | .048 | .045 | .106 | .048** | .097 | .055** |
| Two-Way Interactions |
| Project customization • Product modularity | H2a–2b | | | -.019 | .044 | | | -.155 | .059*** |
| Project customization • Project team technological capability | H3a–3b | | | .096 | .040** | | | .058 | .050 |
| Project customization • Customer participation | H4a–4b | | | .083 | .039** | | | .091 | .042** |
| Project customization • Relational embeddedness | H5a–5b | | | .091 | .051** | | | .027 | .055 |
| Project customization • Customer demand ambiguity | H6a–6b | | | -.077 | .036** | | | -.051 | .056 |
| Control Variables |
| Project type_1 | | -.089 | .074 | -.116 | .075 | -.095 | .094 | -.114 | .097 |
| Project type_2 | | -.003 | .097 | -.036 | .096 | .187 | .123 | .132 | .123 |
| Project type_3 | | .028 | .057 | .032 | .055 | .016 | .073 | .001 | .074 |
| Customer type | | .197 | .064*** | .199 | .065*** | .148 | .081* | .139 | .083* |
| Customer size | | .079 | .031*** | .080 | .032*** | .085 | .039** | .099 | .042** |
| Industry dynamism | | .053 | .041 | .048 | .047 | .042 | .032 | .037 | .037 |
| Prior experience | | .042 | .020** | .044 | .021* | .053 | .026** | .044 | .027* |
| Supplier dependence | | -.057 | .041 | -.050 | .043 | -.035 | .052 | -.058 | .053 |
| Customer dependence | | .003 | .022 | .000 | .022 | .024 | .028 | .029 | .029 |
| Project duration | | .011 | .003*** | .011 | .003*** | .012 | .004*** | .015 | .004*** |
| Project complexity | | – | – | – | – | .083 | .038** | .083 | .041** |
| Self-selection correction | | -.399 | .224* | -.356 | .240 | -.141 | .324 | -.156 | .341 |
| Adjusted R2 | | .215 | | .279 | | .168 | | .233 | |
Additional Analyses
Impact on Profitability
Noting the tension between revenues and costs of customization, we conducted an additional analysis with profitability as the dependent variable to identify scenarios that led to the greatest or least profitability. After obtaining project revenue and cost information, we took the difference and divided it by project revenues to obtain a measure of project profitability. The results indicate no significant effect of customization on project profitability (b = .003, n.s.), as detailed in Table 4, Model 5. Model 6 shows that customer demand ambiguity decreases project profitability resulting from customization (b = -.033, p < .01), whereas project modularity and relational embeddedness increase it (b = .019, p < .05; b = .017, p < .10, respectively). These findings are consistent with the main findings for project revenues and costs in Table 3. Using these results, we conducted a scenario analysis to find alignments among the various moderating factors that induce the greatest and least profitability.
We developed 32 scenarios with varying combinations of high and low levels of customer demand ambiguity, customer participation, product modularity, project team technological capability, and relational embeddedness. The high conditions were one standard deviation above the mean values; the low conditions were one standard deviation below them. Low customer demand ambiguity and high customer participation from the customer side, aligned with both high product modularity and high project team technological capability from the supplier side and high relational embeddedness (i.e., high coordination), led to the greatest profitability (around 26%)— approximately 8 percentage points higher than the profitability of an average project in our sample (around 18%). High customer demand ambiguity and low customer participation, aligned with low product modularity, low project team technological capability, and low relational embeddedness (i.e., low coordination), instead produced the lowest profitability (14%)—about 4 percentage points lower than the average project in our sample.
TABLE:
| | Project Profitability |
|---|
| | Model 5 | Model 6 |
|---|
| Variable | b | SE | b | SE |
|---|
| *p < .10. |
| **p < .05. |
| ***p < .01. |
| Intercept | .096 | .081 | .081 | .098 |
| Main Effects |
| Project customization | .003 | .006 | .001 | .009 |
| Product modularity | -.008 | .005* | -.008 | .009 |
| Project team technological capability | .002 | .005 | .004 | .007 |
| Customer participation | .002 | .006 | -.007 | .007 |
| Relational embeddedness | .019 | .010** | .022 | .010** |
| Customer demand ambiguity | -.015 | .008** | -.022 | .012** |
| Two-Way Interactions |
| Project customization X Product modularity | | | .019 | .009** |
| Project customization X Project team technological capability | | | .013 | .009 |
| Project customization X Customer participation | | | .004 | .008 |
| Project customization X Relational embeddedness | | | .017 | .010* |
| Project customization X Customer demand ambiguity | | | -.033 | .012*** |
| Control Variables |
| Project type_1 | .030 | .016* | .031 | .016* |
| Project type_2 | -.019 | .021 | -.029 | .020 |
| Project type_3 | -.010 | .012 | -.012 | .012 |
| Customer type | .017 | .014 | .028 | .014** |
| Customer size | .012 | .007* | .016 | .006** |
| Industry dynamism | .003 | .005 | .002 | .006 |
| Prior experience | .008 | .004* | .008 | .004* |
| Supplier dependence | .001 | .009 | .001 | .009 |
| Customer dependence | .009 | .005** | .009 | .005* |
| Project duration | -.001 | .001 | .001 | .001* |
| Project complexity | -.002 | .007 | -.004 | .007 |
| Self-selection correction | .021 | .053 | .019 | .057 |
| Adjusted R2 | | .157 | | .211 |
Tension Associated with Customer Participation
Although customer participation is an important part of customization, it appears to worsen the tension between project revenues and costs by increasing both of them. Therefore, we examined the potential moderating role of other coordination mechanisms (i.e., product modularity, project team technological capability, and relational embeddedness) and customer demand ambiguity with regard to the revenue–cost tension induced by customer participation in project customization. Empirically, we included relevant three-way interactions of
Project customization and customer participation with other moderators in both the revenue and cost models. Table 5 presents the results.
As Model 7 shows, we found no significant three-way interactions that affected project revenues, though the two-way interactions (cf. product modularity) were significant. In contrast, Model 8 shows that three-way interactions influenced project costs, except for project team technological capability. Thus, the paths through which coordination mechanisms affect project revenues versus costs differ: customer demand ambiguity and relational embeddedness directly moderate project revenues resulting from customization, but they affect project costs indirectly by moderating the customization–customer participation interactions.
TABLE:
| | Project Revenue | Project Cost |
|---|
| | Model 7 | Model 8 |
|---|
| Variable | b | SE | b | SE |
|---|
| *p < .10. |
| **p < .05. |
| ***p < .01. |
| Intercept | 1.104 | .488** | 1.467 | .476*** |
| Main Effects |
| Project customization | .023 | .046 | .027 | .076 |
| Product modularity | -.056 | .056 | -.095 | .057 |
| Project team technological capability | .047 | .062 | .013 | .086 |
| Customer participation | .064 | .058 | -.076 | .056 |
| Relational embeddedness | .079 | .058 | .099 | .051** |
| Customer demand ambiguity | .039 | .095 | .020 | .071 |
| Two-Way Interactions |
| Project customization x Product modularity | -.016 | .048 | -.115 | .066** |
| Project customization x Project team technological capability | .086 | .046** | -.013 | .062 |
| Project customization x Customer participation | .077 | .044** | .075 | .056 |
| Project customization x Relational embeddedness | .090 | .053** | .023 | .064 |
| Project customization x Customer demand ambiguity | -.096 | .048** | -.042 | .076 |
| Three-Way Interactions with Customer Participation |
| Project customization x Customer participation x Product modularity | -.002 | .013 | -.029 | .012*** |
| Project customization x Customer participation x Project team technological capability | .002 | .013 | .003 | .011 |
| Project customization x Customer participation x Relational embeddedness | .020 | .015 | -.032 | .014** |
| Project customization x Customer participation x Customer demand ambiguity | .021 | .017 | .039 | .017** |
| Control Variables |
| Project type_1 | -.114 | .077 | -.112 | .099 |
| Project type_2 | -.039 | .098 | .133 | .122 |
| Project type_3 | .031 | .057 | .001 | .072 |
| Customer type | .199 | .068*** | .134 | .081 |
| Customer size | .084 | .032*** | .102 | .044** |
| Industry dynamism | .040 | .047 | .035 | .037 |
| Prior experience | .042 | .022* | .049 | .028* |
| Supplier dependence | -.052 | .043 | -.051 | .053 |
| Customer dependence | -.001 | .024 | .031 | .027 |
| Project duration | .012 | .005*** | .015 | .005*** |
| Project complexity | – | – | .083 | .041** |
| Self-selection correction | -.337 | .238 | -.153 | .310 |
| Adjusted R2 | .259 | | .267 | |
The significant effects of these three-way interactions on project costs might arise because with greater customer demand ambiguity, customer participation increases the risks of cost increases due to customization. Coordination with an actively demand is unclear. Such a customer creates a strong risk of leading the customization process in the wrong direction, ultimately resulting in higher coordination costs for the supplier (Anderson, Chu, and Weitz 1987). In contrast, with less customer demand ambiguity, customer participation can facilitate the process of customization by guiding its direction (Weiss and Heide 1993), which should reduce project costs resulting from high customer participation during customization.
Mechanism for interfirm collaboration (Gulati 1995; Moran 2005) and also improve interactions with customers in customization projects. Mutual understanding and cooperative routines in embedded relationships (Tu et al. 2004; Uzzi 1997) help reduce coordination problems in interactions with actively participating customers and thus reduce project costs. Similarly, modular products provide more flexibility to coordinate with actively participating customers more efficiently during customization.
Using a simple-slope analysis with the results of the three-way interaction, we illustrate the effects of customization on project costs when customer participation is high (Aiken and West 1991). The high and low levels of the moderators represent one standard deviation above and below the mean, respectively. As we illustrate in Figure 2, customization leads to more project costs with high levels of customer participation, but in some scenarios, this effect is smaller or even insignificant. Specifically, customization with high customer participation does not lead to cost increases if customer demand ambiguity is low (b = .058, n.s.) or relational embeddedness is high (b =
.019, n.s.). In other words, clearly defined customer demand or close relationships between the project team and its customer can resolve the tension between project revenues and costs caused by customer participation in project customization. Similarly, high product modularity, compared with low levels, can reduce cost increases as a result of customization with high customer participation (b = .067, p < .10 vs. b = .118, p < .05, respectively).
Discussion
This study investigates customization as a key marketing practice in B2B markets. Previous empirical research on customization has focused primarily on the customer side; we provide important implications for the supplier. In particular, we clarify the challenges that suppliers face when offering customized solutions to business customers and present conditions that can help them overcome these challenges and thereby gain positive returns from their investments in customization. The findings have both theoretical and managerial implications.
Theoretical Implications
Prior customization research has highlighted the challenges to customers associated with customized products, such as confusion or difficulties defining their preferences ex ante (Dellaert and Stremersch 2005; Franke, Keinz, and Steger 2009). However, we present unique challenges that suppliers confront with their customization efforts. We identify the tension that arises between project revenues and costs when suppliers implement customized solutions for individual customers. Although customization fosters more sales opportunities by better serving individual customers’ needs, it also comes at a significant cost, associated with the complexity of coordination and the loss of economies of scale due to customer-specific investments (Homburg, Müller, and Klarmann 2011; Simonson 2005). The presence of this tension makes the benefits of customization indeterminate; our contingency model contributes to prior customization research in marketing and operations management by offering a theoretical basis for identifying methods to cope with the tension.
Specifically, clearly defined customer demands, project team technological capability, and relational embeddedness enable suppliers to improve the sales revenues they earn from customization projects, with little additional cost. Modular products also help reduce the costs of customization, though they do not directly increase project revenues. Customization projects that meet these conditions enhance the supplier firm’s is possible to evaluate the value of customization to suppliers by considering not just the tension between revenues and costs but also the moderating factors that can intensify or help relieve this tension.
We contribute to literature on interfirm relationship coordination by regarding customization as a coordination problem and identifying factors to relieve it. Prior research has examined various types of coordination, including information sharing, resource integration, joint development, or governance structures (e.g., Fang 2008; Grewal, Chakravarty, and Saini 2010; Kumar, Heide, and Wathne 2011; Lee, Hoetker, and Qualls 2015). Our study advances understanding of coordination in a customization project and thereby sheds light on specific attributes related to the customer (demand ambiguity and participation), supplier (product modularity and technological capability), and their relationship (relational embeddedness) as mechanisms for relieving or worsening the coordination difficulty associated with customization.
The findings also inform the customer participation literature. Customer participation is a double-edged sword that can increase both costs and revenues. Prior research has emphasized the value of customer inputs for developing and implementing marketing activities more effectively (e.g., new product development, new market entries) and coping with environmental uncertainty (Fabrizio and Thomas 2012; Fang 2008; Griffin and Hauser 1993). We further explicate a dilemma that firms face when they involve their customers in developing customized solutions. Even though customer participation is vital for generating sales opportunities by facilitating access to downstream information and the transfer of tacit market knowledge (Fang, Lee, and Yang 2015), it also creates significant costs for the supplier, intensifying the customization tension between project revenues and costs. Our supplementary analyses suggest some boundary conditions that can relieve this tension by mitigating the project cost increases associated with customer participation. Clearly defined customer demand, modular product designs, and embedded relationships can help the supplier coordinate with active customers in customization projects.
Finally, our findings complement prior research from the customer side; together, they provide a more comprehensive understanding of customization as a dyadic process. The difficulties the customer faces with regard to customized products also become challenges for the supplier. Specifically, customer demand ambiguity, which can lead to customer confusion (Franke, Keinz, and Steger 2009), is a hurdle to the supplier, in that it increases project costs without contributing to project revenues. In this sense, our study extends previous findings from the customer side and derives implications for suppliers. We show that the challenges customers face when coordinating to develop customized products have direct impacts on the supplier, in terms of both project revenues and costs. This study accordingly enhances understanding of the dynamics between the supplier and the customer during customization projects.
Managerial Implications
Customization is an important practice for implementing customer-oriented marketing, and the findings of this study provide new insights into when firms should use customization to serve their business customers, as well as how to manage it. Broadly, suppliers should pay close attention to project revenues and costs when developing and implementing customized solutions. More specifically, when firms offer customization to customers, they need to identify and implement mechanisms to coordinate it, such as low customer demand ambiguity, high product modularity, project team technological capability, and relational embeddedness.
In addition, suppliers should pay special attention to the dilemma that customer participation creates, increasing both project revenues and costs, as well as the boundary conditions that can enable firms to benefit from customer participation. Our findings suggest that customer participation is viable only when customer demands are clearly defined, products have modular designs, or the project team and customer already have a close relationship. Our scenario analysis provides even more specific guidance by simulating the profitability of customization in various conditions. Suppliers can increase the potential value by offering customization to actively participating customers with clearly defined demand, in a modular product design, with high technological capability, and with high relational embeddedness.
Limitations and Further Research
This study is subject to several limitations that suggest directions for research. First, we focused on customization projects in the software industry, though the B2B customers represent various industries. This specific context helped us test our conceptual arguments, and it is an ideal research setting because of the popular use of customization for software products. Still, further research in different industrial contexts would provide more generalizable insights into the pertinent revenues and costs. We also recognize the need for research that examines diverse suppliers, to reduce the common factors that may exist across projects by a single supplier. Along similar lines, the revenue and cost of customization likely depend on the contract type, which defines how costs are paid. We did not test for any implications of contact type, because the single supplier used the same type of contract for all the customers in our sample. Additional research that explicitly investigates various contract types, such as fixed-price or cost-based versions (Corbett, Zhou, and Tang 2004), could provide further insights into customization decisions and outcomes.
Second, we found a limited role of product modularity, without any significant impact on project revenue. Nor did it interact with customer participation to produce more revenue or reduce costs. However, the flexibility of modular product designs in serving diverse customer needs (Sanchez 1999) suggests the need for more research that investigates product modularity from varied perspectives. For example, researchers might identify boundary conditions for the benefits of product modularity in developing customized solutions. Other studies could examine product characteristics (e.g., complexity) or industry environments (e.g., stage in the product life cycle, dynamism, growth). More complex products, fast-growing industries, or competitive market environments might require more flexibility to implement customized solutions, such that they would benefit more from modular product designs.
Third, we focused on direct costs during the contract period to avoid measurement ambiguity or inconsistency. Further research might test a more comprehensive set of cost components, such as after-sales service or administrative
Fourth and finally, prior research has examined the customer side of customization, whereas we focus on the supplier side. It also would be worthwhile to test a model that integrates both approaches. As a dyadic process between the supplier and the customer, customization could have interrelated effects on each party. Therefore, we suggest research that evaluates the outcomes for both supplier and customer, to gain further in-
TABLE 2
**p < .05.
***p < .01.
Notes: Unstandardized coefficient; one-tailed test of significance for hypotheses, and two-tailed tests of significance for other variables.
*p < .10.
**p < .05.
***p < .01.
Notes: Unstandardized coefficient; two-tailed tests of significance for all variables.
<
***p < .01.
Notes: Unstandardized coefficient; two-tailed tests of significance for three-way interactions.
FIGURE 2
APPENDIX A
TABLE:
| Constructs and Items | Loadings |
|---|
| aRated on a scale from 1 (“highly standardized”) to 7 (“highly customized”). |
| bReverse-coded. |
| Project Cost |
| The direct costs (dollar amount) incurred during the duration of the project (e.g., project design, project development, project delvery, after-sales service, other administrative costs) |
| Project Revenue |
| The total revenues (dollar amount) generated from the customer during the duration of the project |
| Project Customization |
| Please answer questions related to the software provided to this customer. |
| 1. We delivered a ______ software to this customer.a | .79 |
| 2. We extensively customized the software to meet unique customer needs. | .78 |
| 3. The software provided to this customer has a lot of features that are NOT available in the standard version. | .79 |
| Product Modularity |
| Please answer questions related to the software provided to this customer. |
| 1. This software can be easily decomposed into separate modules. | .82 |
| 2. This software is composed by several standardized modules. | .80 |
| 3. We can make changes in key components of this software without redesigning others. | .84 |
| 4. The software modules can be reconfigured into different forms and functions. | .80 |
| 5. The software modules can fit together with little adjustment. | .83 |
| Project Team Technological Capability |
| Please answer questions related to the team that delivers the software to this customer. |
| 1. Our team is very knowledgeable about the development and delivery of the software. | .87 |
| 2. Our team is very knowledgeable about the technologies used in the software. | .80 |
| 3. Our team has diverse expertise across multiple technological domains. | .81 |
| 4. Our team has different technological skills pertinent to the development and delivery of the software. | .79 |
| Product Complexity |
| Please answer questions related to the software provided to this customer. |
| 1. Compared with other software in the industry, this software is complex. | .80 |
| 2. Compared with other software in the industry, this software is very technical. | .78 |
| 3. The development of this software spans across wide range of functions. | .80 |
| Supplier Dependence on Customer |
| 1. If this customer stopped buying from us, we could easily find another customer with approximately equal size.b | .72 |
| 2. It would be relatively easy for us to find another buyer for the software.b | .79 |
| 3. If the relationship with this customer were terminated, our business bottom line would not be significantly affected.b | .70 |
Notes: Fit indices: c = 68.13; goodness-of-fit index (GFI) = .92; NFI = .92; CFI = .93; RMSEA = .04. Respondents answered on a scale from 1 (“strongly disagree”) to 7 (“agree”), unless specified otherwise. aRated on a scale from 1 (“very limited”) to 7 (“very extensive”).
bRated on a scale from 1 (“very low”) to 7 (“very high”).
cRated on a scale from 1 (“not taken very seriously”) to 7 (“taken very seriously”).
Notes: Fit indices: c2 = 201.494; GFI = .88; NFI = .91; CFI = .92; RMSEA = .05. Respondents answered on a scale from 1 (“strongly disagree”) to 7 (“agree”), unless specified otherwise.
TABLE:
| Constructs and Items | Loadings |
|---|
| aRated on a scale from 1 (“very limited”) to 7 (“very extensive”). |
| bRated on a scale from 1 (“very low”) to 7 (“very high”). |
| cRated on a scale from 1 (“not taken very seriously”) to 7 (“taken very seriously”). |
| Customer Participation |
| 1. We were involved in the software development and delivery process on a _____ basis.a | .69 |
| 2. We had a ______ level of influence over the software development and delivery process.b | .71 |
| 3. Our suggestions were ________ by the supplier.c | .79 |
| 4. Generally, our level of participation on the software purchased from the supplier was _______.b | .74 |
| Customer Demand Ambiguity |
| 1. When purchasing the software from the supplier, our demand of the software was not clear. | .80 |
| 2. When purchasing the software from the supplier, we did not clearly know how the software can help our business. | .82 |
| 3. There were ambiguities about the software’s specifications when we purchased the software. | .83 |
| 4. It was hard to specify the features and technologies to be incorporated into the software when we purchased the software. | .80 |
| Industry Dynamism |
| 1. In the industry of the software, customers’ product preferences change frequently. | .87 |
| 2. In the industry of the software, industry demand and consumer tastes have been unpredictable. | .82 |
| 3. In the industry of the software, the evolution of customer preference is difficult to predict. | .79 |
| 4. In the industry of the software, the technologies are changing rapidly. | .89 |
| 5. In the industry of the software, price competition is very common. | .82 |
| Relational Embeddedness |
| 1. We feel indebted to this supplier for what they have done for us. | .78 |
| 2. Our relationship with this supplier can be defined as “mutually gratifying.” | .71 |
| 3. Keeping a long-term relationship with this supplier is important to both parties. | .73 |
| Customer Dependence |
| 1. If we decided to stop purchasing the software from the supplier, we could easily find a replacement. | .73 |
| 2. There are many suppliers in the market selling similar software. | .79 |
| 3. Our operation system can be easily adapted to software from different suppliers with little adjustment. | .81 |
TABLE:
| | Project Customization |
|---|
| Variable | b | SE |
|---|
| *p < .10. |
| **p < .05. |
| ***p < .01. |
| Intercept | 3.899 | 1.083*** |
| Product modularity | .134 | .054** |
| Project team technological capability | .187 | .072*** |
| Customer participation | .198 | .081** |
| Relational embeddedness | .092 | .051* |
| Customer demand ambiguity | -.053 | .073 |
| Prior experience | -.023 | .068 |
| Project duration | .034 | .011*** |
| Customer type | .365 | .167** |
| Customer size | .211 | .098** |
| Supplier dependence | -.053 | .067 |
| Customer dependence | .062 | .071 |
| Product complexity | .089 | .091 |
| Customer industry_dummy | .564 | .201*** |
| Product type_1 | .500 | .241** |
| Product type_2 | .218 | .317 |
| Product type_3 | -.018 | .188 |
| Adjusted R2 | .413 | |
GRAPH: of Customization on Cost Under High Customer Participation
GRAPH: C: High and Low Relational Embeddedness
GRAPH: Results of Self-Selection Model
DIAGRAM: Project Customization and the Supplier Revenue–Cost Dilemmas: The Critical Roles of Supplier–Customer Coordination
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Record: 142- Providers Versus Platforms: Marketing Communications in the Sharing Economy. By: Costello, John P.; Reczek, Rebecca Walker. Journal of Marketing. Nov2020, Vol. 84 Issue 6, p22-38. 17p. 3 Diagrams, 2 Graphs. DOI: 10.1177/0022242920925038.
- Database:
- Business Source Complete
Providers Versus Platforms: Marketing Communications in the Sharing Economy
Peer-to-peer (P2P) business models have become increasingly prevalent in the marketplace. However, little is known about what factors influence consumer perceptions of purchases from firms using these models. The authors propose that features inherent to the P2P model lead consumers to perceive high provider–firm independence, where providers are viewed as relatively independent from the platform on which they offer goods/services. Across a series of studies, the authors show that when P2P brands use provider-focused (vs. platform-focused) marketing communications, consumers perceive a purchase as helping an individual provider to a greater extent, which increases consumers' willingness to pay and their likelihood of both making a purchase and downloading the brand's app. This is because provider-focused marketing communications in this context lead consumers to think about their purchase from the provider's perspective, thus adopting an "empathy lens." The authors further show that this effect does not extend to other business models. This work thus identifies provider- (vs. platform-) focused marketing communications as a way for marketing managers of P2P brands to drive important purchase-related outcomes.
Keywords: empathy; P2P business model; peer-to-peer; prosocial behavior; sharing economy
Over the past decade, a growing number of firms have found success using a peer-to-peer (P2P) business model ([15]; [54]; [57]). The prototypical P2P model consists of a platform that mediates good and service flow between providers and consumers (e.g., Uber, Lyft, Airbnb, TaskRabbit). P2P brands are expected to drive much of the sharing economy's future growth, with experts projecting a market size for the overall sharing economy of $335 billion by 2025, up from $14 billion in 2014 ([56]). Despite this growth, as well as calls for research on this topic ([20]; [42]), relatively little academic study to date has explored what factors influence consumers' perceptions of P2P purchases. In this research, we propose that a P2P firm's decision to focus on either the platform or peer provider in marketing communications can shape consumers' perceptions of P2P purchases and subsequently drive purchase-related outcomes (e.g., willingness to pay [WTP], purchase likelihood, likelihood of downloading the brand's app).
Consumers interact with two distinct entities in a P2P purchase. The first is the platform, a typically for-profit firm that acts as an intermediary for exchange between consumers and providers of goods and services. The second is a peer provider, an individual who offers a good or service and connects with the consumer through the platform ([46]). We propose that when a P2P brand's marketing communications focus on the provider, consumers perceive a potential purchase from the brand as helping an individual to a greater extent, thus increasing purchase-related outcomes. This is because focusing on individual providers in marketing communications in this context leads consumers to think about their purchase from the provider's perspective, thereby considering how the money exchanged in the transaction helps that individual (and not the firm). This prediction is consistent with recent work on the sharing economy suggesting the importance of both social connection and empathy in P2P transactions ([24]; [46]). We label the tendency to think about how one's purchase affects the provider as an "empathy lens." However, consumers will not view all P2P purchases through this lens. When a P2P brand's marketing communications focus on the platform instead, we propose that consumers adopt an "exchange lens" in which the reciprocal exchange of money with a firm in return for a good or service is the primary focus ([ 2]; [30]).
We further propose that the helping perceptions and empathy lens elicited by provider-focused P2P marketing communications also increases consumers' likelihood of purchasing from the P2P firm, which is consistent with research in cause marketing showing increased sales when purchases are connected to helping a cause ([ 4]; [39]). We predict that provider-focused communications will lead to increased purchase likelihood for P2P firms relative both to using platform-focused marketing communications and to traditional firms' marketing communications (regardless of focus). We test our predictions both in the lab and in a field study conducted in collaboration with a real P2P firm. Finally, while marketing communications are one way to influence whether providers or platforms are top of mind, we also show that specific features of alternate P2P business models can also make either the provider or platform "naturally salient" to consumers. We show that our effects are attenuated for brands that utilize business models that make providers naturally salient in the minds of consumers such that focusing marketing communications on provider versus platform has little impact on helping perceptions or purchase-related outcomes.
From a theoretical perspective, our work contributes to the burgeoning literature focused on understanding how consumers interact with the sharing economy ([ 8]; [20]; [27]; [41]; [46]; [48]; [57]). To date, much of this work has focused on issues related to access and shared ownership (e.g., [ 8]; [41]; [48]). Our research instead provides insight into how consumers perceive P2P platforms as a function of the platform's marketing communications and business model, thus expanding our understanding of consumer response to different models within the broader sharing economy. Our work also offers practical insights for P2P marketers who face an increasingly competitive landscape with challenges from both traditional firms and other sharing economy brands ([20]). We show that using provider-focused (vs. platform-focused) marketing communications drives important brand outcomes, including willingness to pay, purchase likelihood, and likelihood of downloading the brand's app. We close with a discussion of the implications of these findings for marketers of P2P brands, public policy makers, and consumers.
There are multiple business models associated with the sharing economy. In this research, we focus on peer-to-peer (P2P) platforms; that is, platforms that mediate goods and services flow between providers and consumers. The P2P model tends to be most associated with the types of firms [46] classify as "matchmakers" (e.g., Uber, TaskRabbit, Lyft, Airbnb), brands characterized by high degrees of both platform intermediation (i.e., the extent to which a platform is involved in an exchange) and consociality (i.e., copresence and interaction with a social other). Although other variants of the P2P model do exist (a topic we return to later), we remain consistent with prior research ([57]) and use the more generic terms "peer-to-peer" or "P2P" to refer to for-profit brands that use the matchmaker P2P model. We refer to firms that do not use sharing economy models as "traditional firms." Past research in marketing has demonstrated that a firm's business model influences consumer perceptions of the firm ([ 1]; [43]). We build on this work by exploring how consumer perceptions of firms differ as a function of business model and how the unique aspects of the P2P model affect consumer response to marketing communications from a P2P brand.
What then is unique about the P2P business model? One key difference from traditional models lies in consumers' perceptions of the relationship between the providers of a good or service and the firm facilitating the transaction. We suggest that features inherent to the P2P model lead consumers to perceive relatively high "provider–firm independence," in which consumers view providers as relatively independent from the platform(s) on which the providers offer goods or services. In contrast, we propose that consumers perceive higher "provider–firm convergence" for traditional firms because such firms spend time and other resources to ensure that employees serve as representations of the brand (e.g., using brand mantras, uniforms; [28]; [34]; [45]; [51]).
To test our predictions about provider–firm independence, we conducted four pilot studies (total n = 1,003) on MTurk with individuals in the United States who had completed over 100 HITs and had an approval rating over 95%, a criterion used for all our MTurk studies. We randomly assigned participants in each study to one of two conditions (business model: P2P vs. traditional) in a between-subjects design. Each replicate study (n = approximately 250) used a different purchase type (e.g., purchasing a ride, renting a textbook). We told participants that the company was either a P2P firm or a firm that uses a traditional business model, with all other information held constant. Participants then completed the dependent variable, which was a scale depicting the relationship between the provider and company with increasing overlap as the scale increased (see Figure 1), modeled after the Inclusion of Other in the Self scale ([ 5]). Analysis revealed that participants perceived significantly greater provider–firm independence for the P2P firm across all four studies (means in Figure 1; all ps <.01; see Web Appendix A).
Graph: Figure 1. Pilot study dependent measure and results.Notes: The options are presented in two rows for space reasons here but were presented in a single row in the actual study.*p <.01.Error bars: ± 1 standard error.
Given the perceived independence between the provider and firm for P2P firms, an important question for P2P marketing managers is whether it is more effective to focus on the role of the provider or the platform in marketing communications.[ 5] Although the term "marketing communications" encompasses a variety of tools, including advertising, sales promotions, personal selling, and digital marketing ([ 9]), our empirical studies focus on advertising and other forms of digital promotions commonly used by P2P firms to reach consumers (e.g., information on the brand's website, in-app communications). Marketing communications have become increasingly essential for P2P platforms: Uber spent $500 million on ads in 2018 ([26]), and brands like Airbnb and Turo regularly advertise on TV and online.
We propose that making one of the two entities with which consumers interact in a P2P transaction (i.e., provider or platform) salient in marketing communications changes consumers' perceptions of a P2P transaction. Specifically, we predict that when P2P marketing communications predominantly focus on the role of the provider in a P2P exchange, a strategy we refer to as "provider focus," consumers will think about their purchase from the provider's perspective. We label this tendency to think about how one's purchase in a P2P transaction has an impact on the individual provider as an empathy lens. This is consistent with past research on empathy, which defines empathy as being cognitively aware of another person's internal states and/or putting oneself in the place of another ([32]).
A primary driver of empathy for providers in the P2P business model is the ease with which individuals in the sharing economy can easily become "prosumers," or consumers that also serve as product or service providers in the sharing economy ([20]). This ability to play both roles (i.e., as both a provider and a consumer) makes taking the perspective of providers an easier task for consumers. Second, for the specific type of P2P firms we primarily explore (i.e., for-profit P2P brands like Uber or Lyft), consociality is also high ([46]), meaning that consumers and providers often share highly social experiences (e.g., getting a ride in the driver's own car and having a conversation). We argue that these empathy-inducing features of the P2P purchase experience become salient when marketing communications focus on the provider. The perspective taking encouraged by this empathy lens leads consumers to consider how the money exchanged in the transaction helps the individual provider. In other words, when consumers adopt an empathy lens, they are more likely to think about how their purchase contributes to the individual P2P provider's well-being because they have put themselves in that provider's proverbial shoes.
This perspective aligns with two bodies of literature. First, recent sharing economy research suggests that consumers view interactions with P2P providers as more personal and communal than typical marketplace exchanges ([22]; [24]; [49]; [50]). Second, our theorizing regarding the empathy lens is consistent with past research in marketing that explored communal ([ 2]) and social market relationships ([30]) in which concern for others' needs and perspectives are considered relevant and helping is typical, and with research on more prototypical forms of sharing in which individuals adopt other-focused perspectives ([13]).
However, consumers will not always adopt an empathy lens when considering a P2P transaction. We predict that when P2P marketing communications focus on the role of the platform instead of provider in a P2P exchange, a strategy we refer to as "platform focus," consumers will adopt an exchange lens in which the reciprocal exchange of money with a firm in return for a good or service is the primary focus (consistent with exchange relationships and monetary markets; [ 2]; [30]). Focusing on the platform rather than the provider therefore encourages consumers to adopt a perspective based on strict reciprocity rather than empathy because it makes salient the other entity involved in the transaction: the for-profit firm. Thus, when P2P consumers focus on the platform and adopt an exchange lens, a P2P purchase does not feel like a helping behavior to as great an extent as it does when consumers adopt an empathy lens.
The consequences of consumers adopting one of these lenses are significant, as we predict that a consumer perceiving that their purchase will help an individual can influence purchase-related outcomes, from purchase likelihood to downloading a P2P app to willingness to pay.[ 6] Perceptions of helping are a strong driver of purchase behaviors, in part because they provide hedonic "warm glow" benefits ([ 3]) associated with the pleasure of helping others ([52]). To illustrate the impact of feeling like a purchase helps someone on actual purchases, consider research on transactional cause marketing showing that when companies link a product purchase to a donation to a specific charitable cause, sales of the product increase ([ 4]; [ 6]; [39]). We propose that a consumer viewing a P2P purchase as a helping behavior will result in a similar increase in purchase-related outcomes:
- H1: Provider-focused (vs. platform-focused) marketing communications will increase purchase-related outcomes for a P2P firm.
- H2: The effect predicted in H1 will be mediated by an increase in consumers' perception that their purchase helps an individual.
We note that to obtain the effects we predict in H1, consumers must view a transaction through an empathy lens. Our theorizing therefore implies that focusing on individual providers in marketing communications will not produce the same benefit for traditional business model firms as it does for P2P firms, both because provider–firm independence is not present and because consumers are unlikely to adopt an empathy lens for a firm that uses a traditional business model. We therefore predict that P2P firms using provider-focused marketing communications will increase purchase-related outcomes relative to both P2P firms using platform-focused marketing communications (as predicted in H1) and to traditional firms (regardless of marketing communications focus). More formally:
- H3: Provider-focused marketing communications will increase purchase-related outcomes for P2P firms (as hypothesized in H1) but not traditional firms.
Thus far we have focused on the prototypical P2P business model (i.e., for-profit matchmakers). P2P matchmakers are characterized by high consociality (i.e., a shared provider–consumer social experience) and high platform intermediation (i.e., the platform is involved in the exchange; [46]). This model is used by many prominent P2P brands such as Airbnb and Lyft. These characteristics ensure that both the provider and the platform play an important role in any purchase, increasing the importance of marketing communications that can be used to shift consumers' attention to one entity or the other. However, there are other P2P business models that we believe will influence our predictions and thus limit the role of marketing communications.
We theorize that although marketing communications are one way to influence whether providers or platforms are top-of-mind, specific features of alternate P2P business models can also shift the relative salience of the provider versus the platform. For example, because salience is determined by the attention paid to an entity relative to competing entities ([31]), if a P2P business model features a minimal role for the platform due to low platform intermediation, providers will inherently be top-of-mind or "naturally salient." If providers are naturally salient, marketing communications will do little to shift focus from provider to platform and thus will have minimal impact on helping perceptions and purchase-related outcomes. We next discuss two P2P business model variants for which we predict providers will be naturally salient and therefore focusing marketing communications on providers versus platform will have little impact: forums ([46]) and community-based P2P cooperatives ([20]; [41]).
Perren and Kozinets define forums as platforms in which consociality is high (like matchmakers) but platform intermediation is low. Low levels of platform intermediation mean that forums typically only do one thing—connect providers and consumers—and do not engage in the variety of activities (e.g., payment processing, ratings systems) that are typical of a matchmaker P2P firm ([46]). For example, although Uber (a matchmaker) and Carpool World (a forum) provide a similar basic service and levels of consociality, the Uber platform plays a significantly larger role in a given transaction than the Carpool World platform. Considering the minimal role of the platform in an exchange involving a forum, we propose that providers will be naturally salient when consumers consider forum purchases, meaning that a provider (vs. platform) focus in marketing communications will not influence helping perceptions or purchase-related outcomes.
We also suggest that providers will be naturally salient for another variant of the P2P business model: the community-based cooperative (e.g., rideshare and food cooperatives; [20]; [41]). These cooperatives are created and operated exclusively by peer providers with profits being shared among them, meaning that the platform represents an extension of the providers themselves, rather than an independent entity. Providers will therefore be naturally salient when consumers consider cooperatives, mitigating any effect provider-focused marketing communications would have on helping perceptions and purchase-related outcomes. We therefore predict:
- H4: The effect of provider (vs. platform) focus in marketing communications on purchase-related outcomes (as hypothesized in H1) will be attenuated when the P2P business model makes the provider naturally salient (e.g., forums and cooperatives).
In Study 1, we partnered with a real P2P company to run a field study that provides initial evidence that consumers are more likely to take promotional materials for a P2P firm when marketing communications focus on providers versus the platform (H1). In Study 2a, we provide evidence that the increased effectiveness of provider- versus platform-focused marketing communications for P2P firms is due to increased perceptions that a purchase helps an individual (H2). Study 2a also demonstrates that provider-focused marketing communications do not increase helping perceptions or purchase likelihood for firms using a traditional business model (H3). In Study 2b, we show that this is the case for traditional firms both high and low in provider–firm independence. In Study 3, we demonstrate that when consumers are focused on the provider, increases in empathy toward the provider and helping perceptions serially mediate greater willingness to pay for a gift card for a P2P (vs. traditional) ride service. In Studies 4a and 4b, we test H4, comparing the prototypical for-profit matchmaker P2P model to P2P forums (4a) and cooperatives (4b).
Study 1 tested H1, which predicts that consumers are more likely to take promotional materials for a P2P firm when its marketing communications focus on providers (vs. the platform).
We partnered with Borrow'd, a real, new-to-the-market P2P company (available by smartphone app) for the buying and renting of textbooks between students, to conduct a field experiment over the course of a week at a large public university in the United States. This field study mimicked the brand's promotions held at other universities: setting up a booth on campus and providing potential users with information about the app.
The study employed two conditions (promotion focus: provider vs. platform) that varied the imagery and text in the promotional appeals (see Figure 2). The platform-focused condition featured a cartoon individual paying for a book through an app and the slogan "Buy or rent your textbooks on Borrow'd." The imagery and text centered on an individual's ability to make a book purchase through the platform's app, but it did not highlight the peer provider. In contrast, the provider-focused condition featured two cartoon individuals exchanging a book through the app. Therefore, unlike the platform-focused condition, the provider-focused condition included an image representing the peer provider. The slogan featured at the top of the provider-focused condition was "Buy or rent your classmates' books."
Graph: Figure 2. Study 1 stimuli.
To ensure that the field study stimuli were not unintentionally manipulating other consumer perceptions (e.g., price, reliability, quality), we conducted a between-subjects pretest of these stimuli during a different academic year than the one in which we conducted the field study. Detailed descriptions of the pretest and the procedure of the main field study are available in Web Appendix C. In short, there were no significant differences across conditions on these perceptions (all ps >.19). In the main field study, the stimuli were used in two ways. First, the image and text were printed on a 2.5′ × 4′ banner affixed to the front of the tables where the promotion was occurring. Second, the stimuli were printed on business card–sized download cards. These cards were offered to students to take with them as a reminder to download the app. The number of download cards taken served as the dependent variable. Each day during the study, two research assistants who were blind to the hypotheses sat at the table where the banner was hung. Research assistants wore t-shirts with the company's logo to look like brand ambassadors and, when students approached, provided more information about the app and offered interested students a download card that matched the day's experimental condition. The research assistants recorded how many download cards were taken.
As predicted, more cards were taken in the provider-focused condition (379) than the platform-focused condition (281). Because the data consisted of the total count of cards taken in both conditions (where both were run for the same number of hours), we ran a Poisson model to test whether the total number of cards taken in the provider-focused condition (coded as 1) was significantly different than the total number taken in the platform-focused condition (coded as −1). The omnibus test for the overall model was significant (χ2 ( 1) = 14.61, p =.0001), as was condition (β =.299, SE =.079, Wald 95% confidence limits:.145 to.454, Wald χ2 ( 1) = 14.44, p =.0001), demonstrating that the total number of cards taken in the two conditions was significantly different. In percentage terms, the 98 additional cards taken in the provider-focused condition resulted in 35% (i.e., 98 / 281) more download cards being taken by new potential users compared to the platform-focused condition.
Technical limitations prevented us from directly observing how each condition influenced the acquisition of new users, but our partners at Borrow'd shared data with us that showed how many users had been acquired on each day that we ran the field study as compared to the prior year. For confidentiality reasons, we cannot report the raw data of how many new users Borrow'd acquired during these days, but the app saw a 60.71% increase in new users in the days on which we ran the provider-focused condition as compared to the prior year and a smaller 19.61% lift in new users for days on which we ran the platform-focused condition. These figures should be interpreted with caution, as users may have become interested in downloading the app due to the stimuli in a particular experimental condition but actually downloaded the app on another day. Still, the pattern of results provides additional evidence consistent with our primary finding.
The results of the field study provide support for H1 using real behavior and a real P2P company new to the market: promotions that focused on the role of the peer provider in a transaction were more effective than those that featured the role of the platform. Despite providing initial support for H1, this study has several limitations. First, we did not have direct evidence that the effect was driven by greater interest in using the app for purchasing/renting (vs. selling) textbooks. Second, we did not have a control condition focused either on the platform or the provider, which left us unable to determine whether the provider-focused condition was increasing brand interest or if the platform-focused condition was decreasing interest.
To address these concerns, we ran a follow-up lab study (n = 253 undergraduates) using the same two conditions as in the field study plus a third (control condition) in which the promotional material was focused on the primary benefit of the app (i.e., getting books) but not the platform or provider. In this study (reported in full in Web Appendix D), the provider-focused condition led to significantly higher purchase likelihood than the platform-focused and control conditions. This study also ruled out the possibility that the effect in Study 1 was driven by people assuming they can sell textbooks in the provider-focused but not platform-focused condition, both by telling participants across all conditions that the platform allows buying and selling and by including a purchase likelihood measure specific to buying/renting.
Study 2a tested H2 by measuring perceptions that a purchase helps an individual. It also tested H3, which predicts that the beneficial effect of provider-focused marketing communications on purchase likelihood does not extend to firms using a traditional business model.
Two hundred fifty-nine undergraduates (50.6% male, Mage = 20.4 years) completed Study 2a in exchange for extra credit. The study employed a 2 (ad focus: provider vs. firm) × 2 (business model: P2P vs. traditional) between-subjects design in which we showed participants an advertisement for either "Reliable Rideshare" (in the P2P condition) or "Reliable Cab" (in the traditional business model condition). The advertisement featured four individuals who were either identified as drivers or corporate employees, depending on condition. The ad also reinforced that the business had either a P2P or traditional business model (see Figure 3 for example stimuli and Web Appendix E for full stimuli).
Graph: Figure 3. Reliable rideshare stimuli for studies 2a, 4a, and 4b.Note: Corporate titles in the platform-focused conditions are Chief Operating Officer, Chief Technology Officer, VP of Engineering, and Founder & Executive Chairwoman.
We asked all participants to imagine they were visiting a town with which they were unfamiliar and needed a ride to the airport but did not know what options were available. To control for potential differences in price perceptions between traditional and P2P options, all participants read the following: "The cost of a Reliable Rideshare [Cab] ride to the airport is $13 for a 10-mile ride. Based on your research, this is similar to other ride services like taxis [peer-to-peer rideshares]" and were then asked to indicate how likely they would be to choose the advertised service as their ride to the airport (1 = "very unlikely," and 7 = "very likely"). All participants then indicated their likelihood of downloading the app featured in the advertisement (1 = "very unlikely," and 7 = "very likely"). We combined these two measures to form an index of purchase likelihood (r =.74, p <.0001), which served as our dependent variable. All participants then answered three items intended to measure their perceptions that a purchase from this platform would help someone ("I would feel like I helped someone if I spent money on a Reliable Rideshare [Cab] ride," "If I chose to make a Reliable Rideshare [Cab] purchase I would feel like I supported a member of the local community," and "I would feel good about who got the profits from a Reliable Rideshare [Cab] purchase"), which we combined to form an index of helping perceptions (α =.90). We also asked participants to rate their perceptions of the ride's price (1 = "cheap," and 7 = "expensive") and to provide demographic information.
To rule out the possibility that price perceptions varied across conditions, we ran a between-subjects ANOVA with ad focus and business model as independent variables and price perceptions as the dependent variable. There were no main or interactive effects (all ps >.48). We then ran the same ANOVA with the combined purchase likelihood measure as the dependent variable. There was not a significant main effect of business model (F( 1, 255) =.21, p =.643, =.001) or ad focus (F( 1, 255) = 2.46, p =.118, =.010). However, as predicted, the interaction between these factors was significant (F( 1, 255) = 4.76, p =.030, =.018). Analysis of simple effects revealed that, consistent with H1, consumers were significantly more likely to make a purchase from the P2P brand when the ad focused on providers (M = 4.25) versus the platform (M = 3.47; F( 1, 255) = 6.95, p =.009, =.027). Consistent with H3, there were no differences in purchase likelihood for the traditional firm as a function of whether the ad focused on providers (M = 3.70) or the firm (M = 3.83; F( 1, 255) =.19, p =.664, =.001). We note that these results conceptually replicate when likelihood of choosing the app and of downloading the app are analyzed separately.
We next conducted a moderated mediation analysis using PROCESS model 7 ([29]). In this model, business model condition moderated the "a path" between the independent variable (ad focus) and the mediator (helping perceptions) with download likelihood serving as the dependent variable. Results indicated that the moderated mediation model was significant (index of moderated mediation =.209; 95% CI:.005 to.520). For a P2P firm, the indirect effect of ad focus on purchase likelihood via helping perceptions was significant (ab =.228; 95% CI:.069 to.477), providing support for H2. However, this indirect effect was not significant for a traditional firm (ab =.019; 95% CI: −.142 to.187).
Study 2a demonstrates that consumers' perceptions that a purchase helps an individual mediate purchase likelihood for P2P firms when marketing communications focus on the provider, which is consistent with our theory that P2P purchases are viewed through an empathy lens after exposure to provider-focused marketing communications. The results of Study 2a also demonstrate that provider-focused marketing communications do not increase purchase likelihood or helping perceptions for traditional firms. However, the traditional firm used in the stimuli was characterized by provider–firm convergence. In Study 2b, we tested whether this effect holds when a traditional firm uses a business model that features provider–firm independence. We predicted that even when provider–firm independence is encountered in a traditional business model (e.g., a non-P2P firm uses independent contractors to complete work for its customers) and marketing communications focus on these independent providers, this context will not evoke increased perceptions that a purchase is a helping behavior nor increased purchase likelihood because the purchase will still be viewed through an exchange and not an empathy lens due to the business model.
In Study 2b, we manipulated provider–firm independence for a traditional business model firm by telling participants that a service provider was either an employee working on behalf of the firm or an independent contractor that the firm coordinates with but does not employ directly.
Participants for Study 2b were 301 Amazon Mechanical Turk (MTurk) workers (Mage = 37.87 years, 51.8% female) who were assigned to one of two conditions (provider–firm independence: high vs. low) in a between-subjects design. Participants saw an ad for a national hardware store called Thompson's Hardware (see Web Appendix G), which promoted its home installation services. In the low provider–firm independence condition, the ad stated that these service providers were employees of Thompson's, whereas in the high provider-firm independence condition, they were independent contractors.
After viewing the ad, participants were asked to imagine they needed to have a home installation job done but did not know how to do it themselves and answered two purchase-related outcome measures. First, they indicated how likely they were to choose Thompson's Hardware to complete the installation (1 = "very unlikely," and 7 = "very likely"). Next, they were told they got a quote of $2,000 from a competing store and were asked to indicate how much they would be willing to pay Thompson's to complete the job using a slider that ranged from US$0 to US$2,000. Participants then answered the same three helping perceptions measures from Study 2a adapted to this context (α =.89). Participants also answered a question that measured their perception of provider–firm independence using the same interlocking circles measure used in the pilot studies and, finally, provided demographic information.
A between-subjects ANOVA revealed that our manipulation was successful (Memployee = 4.29; Mcontractor = 3.15; F( 1, 299) = 35.62, p <.0001, =.107). To test our predictions, we then ran three between-subjects ANOVAs with provider–firm independence condition as the independent variable and helping perceptions, WTP, and likelihood of choosing the service as dependent variables. As expected, greater provider–firm independence did not lead to greater helping perceptions (Memployee = 4.70, Mcontractor = 4.69; F( 1, 299) = 0, p =.957, = 0), WTP (Memployee = $1,568.11, Mcontractor = $1,564.25; F( 1, 299) =.01, p =.942, = 0) or purchase likelihood (Memployee = 5.19, Mcontractor = 5.04; F( 1, 299) =.99, p =.321, =.003).
Study 2b provides evidence consistent with our theory: Focusing on a provider in marketing communications when provider–firm independence is present is not sufficient to lead to greater perceptions of helping and purchase likelihood/WTP for a traditional business model firm. We speculate this is the case because a consumer views such a purchase through an exchange and not an empathy lens due to the business model. In Study 3, we measure the extent to which participants consider the internal states of and adopt the perspective of providers as a function of business model to provide more direct evidence that consumers adopt an empathy lens when P2P (but not traditional) firms focus on individual providers in their marketing communications.
Study 3 tested whether consumers adopt an empathy lens when considering P2P (but not traditional) purchases when marketing communications focus on individual providers, which leads to increased helping perceptions and, ultimately, greater increases in purchase-related outcomes. We also use incentive compatible WTP as our dependent variable and measure potential differences in perceptions of P2P versus traditional employee service providers that could serve as alternative explanations for our effect.
Three hundred eighty-seven undergraduates (56.3% male, Mage = 20.6 years) completed Study 3 in exchange for extra credit. The study was part of a larger lab session in which participants completed several unrelated studies and provided demographic information at the beginning of the session. The study employed a between-subjects design (business model: P2P vs. traditional) in which we told all participants they could win a gift card for a ride service. We told participants in the P2P condition that this study was about Lyft, a real P2P transportation company. In the traditional business model condition, we told participants that the study was about Yellow Cab, a real local taxicab company. We made the provider (i.e., the driver) salient by including a description of each company that focused on the role of drivers and their status as independent contractors or employees—much like what a consumer might find on a brand's website—depending on condition (stimuli in Web Appendix H).
We next elicited participants' WTP on a sliding scale ranging from US$0 to US$25 for a gift card with US$25 worth of rides from either Lyft or Yellow Cab (depending on condition). We made this WTP measure incentive compatible by employing the procedure used by [23], which involves an initial lottery followed by the standard Becker-DeGroot-Marschak procedure ([12]). We told participants they were being entered into a lottery to win US$25 and that the winner of this lottery would be subject to the Becker-DeGroot-Marschak procedure on the basis of their stated WTP, which we explained using an adapted version of Fuchs, Schreier, and [23] stimuli. After the study was completed, a winner was selected and she received her prize. All participants then answered the same three helping perceptions measures from prior studies (α =.87), modified for the study context.
Next, to measure the extent to which participants had adopted an empathy lens, we asked all participants to indicate to what extent they would think about the following while taking a ride with Lyft/Yellow Cab on a seven-point scale (1 = "I wouldn't think about this at all," and 7 = "I would think quite a bit about this"): "what it's like to live a day in my driver's shoes," "what the driver is feeling," "what my driver is like as a person," "how my interaction with this driver impacts his/her day," and "whether I can have a genuine connection with this person" (α =.83). We developed these items to measure either the extent to which participants considered the provider's perspective (e.g., "what it's like to live a day in my driver's shoes") or internal state (e.g., "what the driver is feeling") ([32]).
We also included additional measures to test alternative mediators related to dimensions on which consumers might believe drivers differ as a function of business model, perceived driver similarity, perceived driver politeness, and perceived driver helpfulness. Specifically, we asked participants to respond to the following: "drivers for this service are a lot like me" (1 = "strongly disagree," and 7 = "strongly agree"), "how polite do you think a driver for this service would be?" (1 = "not at all polite," and 7 = "very polite"), and "how helpful do you think a driver for this service would be?" (1 = "not at all helpful," and 7 = "very helpful"). Finally, to use as covariates in our analysis, we asked participants to indicate how frequently they get cab rides and rides from P2P services like Lyft and Uber (1= "never," and 7 = "frequently") and whether they have the Lyft app installed on their phone.
We first conducted a between-subjects ANOVA with business model as the independent variable and WTP as the dependent variable. As expected, this analysis demonstrated that participants were willing to pay significantly more for the Lyft (M = $11.30) than the Yellow Cab gift card (M = $9.47; F( 1, 385) = 6.12, p =.0138, =.016). The effect remained robust (MP2P= $11.36, Mtraditional= $9.40; F( 1, 382) = 7.05, p =.008, =.018) when frequency of cab use (F( 1, 382) =.31, p =.57, =.0008), P2P ride service use (F( 1, 382) = 6.95, p =.009, =.02), and whether the participant has the Lyft app installed (F( 1, 382) =.00, p =.97, =.00) were included as covariates in an ANCOVA.
We next tested our prediction that when consumers are focused on an individual provider, the P2P business model promotes adoption of an empathy lens, which leads to greater helping perceptions and WTP. To do so, we employed a serial mediation model using PROCESS model 6 ([29]). In this initial analysis, business model served as the independent variable, the extent to which the consumer adopts an empathy lens served as the first mediator, helping perceptions served as the second mediator, and WTP served as the dependent variable. This analysis indicated significant serial mediation (ab =.16; 95% CI:.035 to.332), consistent with our theory that focusing on the P2P provider leads consumers to adopt a more empathetic perspective, which in turn drives perceptions that the purchase has helped an individual. We ran a second serial mediation that reversed the order of the mediators while keeping all other elements of the analysis the same, but the model was not significant. This result is consistent with the direction of our theorizing (ab =.01; 95% CI: −.028 to.069).
We next ran simple and serial mediation analyses to test whether alternative explanations of perceived differences in driver politeness, helpfulness, and similarity could explain our results (see Web Appendix H for details). In short, even when controlling for perceptions of driver similarity, politeness, and helpfulness, these analyses indicated that the P2P model leads consumers to take the perspective of the provider, which, in turn, increases both the perception that their purchase helps an individual and, ultimately, WTP. Furthermore, when we ran separate mediation analyses for each potential alternate mediator, we found that these alternative mechanisms did not significantly mediate the effect we observed on WTP (helpfulness of driver 95% CI: −.065 to.350; politeness of driver 95% CI: −.088 to.398; similarity of driver 95% CI: −.019 to.761).
Study 3 shows that consumers demonstrate greater empathy toward P2P providers when marketing communications focus on providers and, as a result, are more likely to perceive a purchase as helping an individual, leading to greater WTP. These effects do not extend to traditional business model firms. In Studies 4a and 4b, we compare the prototypical matchmaker P2P business model with two P2P variants for which we predict providers will be naturally salient.
Study 4a tested H4 by comparing matchmakers to forums (i.e., brands that are high in consociality [like matchmakers] but low in platform intermediation). Low platform intermediation means that the platform plays a significantly reduced role in transactions for forum brands as compared to matchmakers, making the role of the provider top-of-mind. Because providers would be naturally salient to consumers in the context of forums, we therefore predicted that, when considering a forum purchase, ad focus would do little to shift consumers' helping perceptions and likelihood of downloading the promoted brand's app.
Participants in this study were 600 MTurk workers (Mage = 39.13 years, 52.2% female) who we randomly assigned to an experimental condition in a 2 (ad focus: provider vs. firm) × 2 (P2P business model: matchmaker vs. forum) between-subjects design. We told participants they would see an ad for a ride service called Reliable Rides.
In the matchmaker condition, we told participants that "Reliable Rides is like other P2P organizations like Uber and Lyft and has a very similar business model." In the forum condition, we modeled the description of the platform after [46] description of CarPool World, a brand that provides the same basic service and level of consociality as businesses like Uber and Lyft but offers a lower level of platform intermediation. Specifically, we told participants that "the forum operates much like a Craigslist specifically for ride services and carpooling" and that the forum plays no role in the ongoing "communication, coordination, and payment between drivers and passengers" but instead serves one purpose: letting drivers and consumers "post and coordinate rides/carpools and payment together on the forum."
Participants then viewed a provider-focused or platform-focused ad that was presented as part of an upcoming campaign for the brand (see Figure 4). After viewing the ad, participants provided their likelihood of downloading the platform's app on the basis of the advertisement they viewed (1= "very unlikely," and 7 = "very likely") and completed the same helping perceptions measures from previous studies (α =.92). We also included additional measures to rule out alternative explanations based on other perceptions that might differ as a function of business model and/or ad focus, including perceptions of driver similarity, perceptions of driver financial need, the participant's own experience with the business model, and the participant's expected comfort with consociality. These were, "How similar do you think the average driver on Reliable Rides would be to you?" (1 = "not at all similar," and 7 = "very similar"); "How high do you think the financial need is for a driver on the Reliable Rides app?" (1 = "very low," and 7 = "very high"), "How experienced are you as a customer with businesses that use the same model as Reliable Rides?" (1 = "not at all experienced," and 7 = "very experienced"), and "How comfortable would you be sharing a space with a driver for Reliable Rides?" (1 = "not at all comfortable," and 7 = "very comfortable"). Finally, as has been recommended as a recent best practice for MTurk data collection ([17]; [18]), we included a data validity question to identify fraudulent responses and bots from virtual private severs (see Web Appendix J for additional details) and asked demographic details. Seventeen responses failed this check, leaving a final sample of 583 usable responses.
Graph: Figure 4. Study 2a results.*p <.01.Error bars: ± 1 standard error.
We ran a between-subjects ANOVA with ad focus and P2P business model condition as independent variables and download likelihood as the dependent variable. There was a significant main effect of ad focus (F( 1, 579) = 5.96, p =.0149, =.010), such that likelihood of downloading the app was higher in the provider-focused (M = 4.23) than the platform-focused condition (M = 3.86). The main effect of business model was not significant (F( 1, 579) =.31, p =.578, =.0005). However, the interaction between these factors was significant (F( 1, 579) = 4.65, p =.0315, =.008), as predicted in H4. The simple effects of this interaction reveal that, consistent with H1 and our prior studies, when a P2P brand is a matchmaker, provider-focused ads (M = 4.34) lead to significantly higher download likelihood as compared to platform-focused ads (M = 3.65; F( 1, 579) = 10.78, p =.0011, =.018). However, when a P2P brand is a forum, ad focus did not significantly affect download likelihood (Mprovider = 4.11, Mplatform = 4.06; F( 1, 579) =.04, p =.84, =.0001).
We next conducted a moderated mediation analysis using PROCESS model 7 ([29]). In this model, P2P business model moderated the path between the independent variable (ad focus) and the mediator (helping perceptions), with download likelihood as the dependent variable. The results of this analysis indicate that the moderated mediation model was significant (index of moderated mediation =.427; 95% CI:.153 to.590, see Figure 5). For a matchmaker P2P brand, the indirect effect of ad focus on download likelihood via helping perceptions was significant (ab =.268; 95% CI:.166 to.379), supporting H2. However, this indirect effect is not significant when the P2P brand is a forum (ab =.039; 95% CI: −.059 to.136). These findings support our theorizing that provider- (vs. platform-) focused marketing communications will only be more effective in boosting helping perceptions and download likelihood for a P2P brand when the P2P organization employs a business model in which the provider is not naturally salient.
Graph: Figure 5. Studies 4a and 4b moderated mediation results.
We next ran a follow-up parallel moderated mediation analysis to test whether alternative explanations of perceived differences in driver similarity, perceived financial need of the drivers, participants' experience with the business model, or comfort with sharing a physical space with the driver could explain our results. The index of moderated mediation remained significant for helping perceptions even when controlling for these alternative explanations (index of moderated mediation =.27; 95% CI:.093 to.461). Furthermore, none of the other indices of moderated mediation were significant for any of the other alternative mediators (see Web Appendix J for details).
Study 4b tested H4 by manipulating whether a P2P organization was described as a typical for-profit matchmaker or as a community-based cooperative. We predicted that provider-focused ads would increase helping perceptions and download likelihood for for-profit matchmaker P2P brands but not cooperative P2P matchmakers.
Participants in this study were 600 MTurk workers (Mage = 37.28 years, 52.2% female) who were randomly assigned to an experimental condition in a 2 (ad focus: provider vs. firm) × 2 (P2P business model: for-profit matchmaker vs. cooperative matchmaker) between-subjects design. The setup of the study was similar to Study 4a, in which participants were told they were going to see an ad for a P2P ride service (called Reliable Rides in all conditions).
Before seeing the ads, we described the P2P organization as either a typical for-profit P2P matchmaker firm or a community-based P2P cooperative. In the cooperative condition, we told participants that "Reliable Rides is a bit different from other P2P organizations like Uber and Lyft, as the platform is operated as a community-based cooperative rather than as a corporation. This means that all of the profits from a ride go to the driver. The platform is owned and operated by drivers for Reliable Rides and its technologies are supported by volunteers that are part of the co-op." In the for-profit condition, we provided participants with the same information as in the matchmaker condition in Study 4a. Participants then viewed the same ads from Study 4a, indicated their likelihood of downloading the platform's app, completed the same helping perceptions measures (α =.93), and answered demographic questions. We again included a data validity measure (see Web Appendix K for details). Twenty-three participants failed this check, leaving a final sample of 577 usable responses.
We ran a between-subjects ANOVA with ad focus and business model condition as independent variables and download likelihood as the dependent variable. There was a significant main effect of ad focus (F( 1, 573) = 3.99, p =.046, =.007), such that likelihood of downloading the app was higher in the provider-focused (M = 4.17) than the platform-focused condition (M = 3.86), and a significant main effect of business model (F( 1, 573) = 10.03, p =.0016, =.017), such that participants indicated they were more likely to download the app when the company was a cooperative (M = 4.26) than when it was a for-profit (M = 3.77). Importantly, and as predicted in H4, the interaction between these factors was significant (F( 1, 573) = 4.29, p =.0388, =.007). The simple effects of this interaction reveal that, consistent with H1, when a P2P matchmaker firm is for-profit, provider-focused ads (M = 4.09) lead to significantly higher download likelihood as compared to platform-focused ads (M = 3.45; F( 1, 573) = 8.09, p =.0046, =.014). However, consistent with H4, when the P2P matchmaker uses a cooperative model, ad focus does not significantly affect download likelihood (Mprovider = 4.26, Mplatform = 4.27; F( 1, 573) =.00, p =.956, = 0).
We next conducted a moderated mediation analysis using PROCESS model 7 ([29]), which mirrored the analysis described in Study 4a. The moderated mediation model was significant (index of moderated mediation =.284; 95% CI:.021 to.590, see Figure 5). For a for-profit P2P matchmaker, the indirect effect of ad focus on download likelihood via helping perceptions was significant (ab =.244; 95% CI:.044 to.486), supporting H2. However, this indirect effect was not significant when the P2P matchmaker was a cooperative (ab = −.041; 95% CI: −.220 to.127). These findings support our theorizing that provider- (vs. platform-) focused marketing communications will only be more effective in boosting helping perceptions and brand interest for a P2P organization when the P2P provider is not already naturally salient.
The P2P business model is used by a growing number of firms that have become ubiquitous in the lives of many consumers. Experts predict continued growth of the P2P model in the coming decade (Yaraghi and Ravi 2016) and suggest that this business model will become increasingly common in a diverse number of industries ([54]). However, the growth of this model has also coincided with heightened competition among sharing economy firms and between P2P and traditional firms operating in similar industries ([20]). This increasingly competitive environment means that it is more important than ever for P2P marketers to consider what strategies are most effective in convincing consumers to download their apps and ultimately make purchases on their platforms.
We argue that what P2P marketers decide to focus on in their marketing communications is one important factor in driving these brand outcomes. Marketing communications represent the "voice" of the company to consumers considering a brand and its competitors ([35]) and offer a key way to shift consumers' purchase perceptions and behaviors. Although P2P brands face a number of important decisions regarding their marketing communications, in this research we explore one specific but consequential decision facing P2P marketers: whether to focus on the platform or the provider in marketing communications. The results of our pilot studies suggest that consumers view these entities as relatively independent from each other in the prototypical P2P context, which means that P2P brands may choose to focus on either entity when designing marketing communications. Accordingly, P2P brands in the marketplace use both strategies (see Web Appendix B), but existing research offers no insight into whether platform- or provider-focused communications are more effective and why.
We investigate these questions across five experiments and a field study conducted in collaboration with a real P2P company. We demonstrate that provider- (vs. platform-) focused marketing communications are more effective in driving purchase-related outcomes for P2P brands and show that this is the case because provider- (vs. platform-) focused marketing communications lead consumers to view these purchases through an empathy (vs. exchange) lens. This lens leads to greater perceptions that one's purchase helps an individual and ultimately increases purchase-related outcomes. More generally, our research provides insight into the importance of provider (vs. platform) salience, demonstrating how consumers' purchase likelihood, app download likelihood, and WTP is influenced by both a P2P brand's business model and marketing communications focus. We also show that provider versus platform focus does not influence purchase-related outcomes for traditional business model firms, even when the provider and firm are perceived as relatively independent.
Our work makes contributions to a number of important literatures in both marketing and consumer behavior. First, our research contributes to the growing literature that investigates how consumers interact with and perceive firms associated with the sharing economy. To date, much of the work on the sharing economy has focused on business models like Zipcar, in which the firm owns a resource that is shared among consumers (e.g., [ 8]; [41]; [48]). Our research instead provides insight into how consumers perceive purchases from P2P firms. [46] caution that findings from one sharing economy model may not be applicable to other sharing economy models. Our results support this contention, as we find important differences in consumer response to marketing communications from for-profit P2P matchmakers versus P2P forums (Study 4a) and P2P cooperative matchmakers (Study 4b). These studies highlight that both the features of the business model and the focus of marketing communications can shift the relative salience of P2P providers versus platforms in the minds of consumers, thus influencing perceptions of whether the purchase helps an individual provider or the company.
More generally, our research contributes to the literature on consumer helping behaviors and prosocial behaviors ([11]; [14]; [52]), as well as past research in the marketing literature on empathy (e.g., [ 7]). Although much of the literature on prosocial behavior focuses on charitable giving ([52]), consumers can feel like they are helping others in a variety of less explicitly prosocial ways, perhaps even in ways that may not be especially substantial in an objective sense (e.g., token shows of support for a cause; [40]). Our work demonstrates that consumers' sense that their purchase has helped someone varies as a function of the firm's business model and the focus of its marketing communications. Our conceptualization of empathy versus exchange lenses also contributes to the literature on communal versus exchange norms ([ 2]) and social market versus monetary relationships ([30]). Our research extends this work by exploring how business model and marketing communications influence the perspective consumers take when making a purchase.
Finally, our work also contributes to the literature on the associations consumers have with different types of businesses models. Prior work in this area has investigated consumers' perceptions of firms based on their nonprofit or for-profit status ([ 1]) or a hybrid for-profit social venture business model ([43]). Our work contributes to this literature by demonstrating how other distinctions between business models (i.e., P2P vs. traditional, P2P matchmaker vs. P2P forum, and for-profit P2P matchmaker vs. cooperative P2P matchmaker) shape consumer perceptions and response to marketing communications. We also identify the source of these perceptions by introducing the constructs of ( 1) provider–firm independence versus convergence, ( 2) provider versus platform focus in marketing communications, and ( 3) empathy versus exchange lens.
Our findings have clear practical implications for marketing managers of P2P brands, public policymakers, and consumers. From a managerial perspective, our work is the first to identify the importance of provider-focused marketing communications as a way for marketers of P2P brands to drive important brand outcomes, including willingness to pay, purchase likelihood, and likelihood of downloading the brand's app. Although we focused specifically on these outcomes, we expect that our effects likely extend to other key performance indicators, like click-through rate for digital ads. To illustrate, Borrow'd, our partner company from Study 1, shared secondary data with us from one of its Facebook advertising campaigns that support this proposition. While testing different creative treatments of its Facebook ads with the same budget over the same time period, the company found that an ad featuring a peer provider resulted in a significantly higher click-through rate than an ad focusing on the platform (.25% vs..10%, χ2 = 4.66, p =.031).
Furthermore, although we believe that our findings will be useful for marketers of all matchmaker P2P brands, they will perhaps be particularly so for those working on behalf of a growing number of newer start-up P2P brands (Yaraghi and Ravi 2016). These brands face increased spending from established P2P brands ([26]) and a relatively high failure rate ([20]), making informed decision making around marketing communications particularly important. Our findings provide key insights for these marketers into the benefits of focusing on providers (vs. platforms) in marketing communications. However, Studies 2a, 2b, and 3 provide evidence that the benefit of focusing on providers does not generalize to traditional firms, and Studies 4a and 4b show that these effects do not generalize to P2P forums or P2P cooperative matchmakers. Collectively, these findings suggest that it is also important for for-profit P2P matchmaker brands to educate consumers about the business model the organization uses prior to investing in provider-focused marketing. This is especially true for firms with which consumers are initially unfamiliar.
Our findings also suggest that for-profit P2P matchmakers—even established brands—should be careful when managing their public relations, attempting to minimize narratives that make consumers think more about the underlying for-profit business and less about peer providers. Although firms should be careful to not deceive consumers or misrepresent their business models, they should also be strategic when formulating public relations messaging. Uber made headlines in 2017 as a result of its aggressive corporate culture and remained in the news as the company put forth an effort to change this culture ([16]). Although stories about dedication to improvement in culture have largely cast the organization in a positive light, this PR also provided ongoing reminders about the platform and its associated underlying corporation, potentially making consumers view a purchase from the firm through an exchange lens rather than an empathy lens. In contrast, a more effective provider-focused PR strategy has been the "Uber Presents" short film series, which features drivers' stories and perspectives. This series offers an example of how to use PR to move from platform to provider focus through the brand's effort to "shift the conversation away from Uber corporate in favor of talking about the raw and authentic lives of the men and women behind the wheel" ([37]).
Although our research explores the prototypical for-profit matchmaker P2P model and some other related variants (i.e., forums and cooperatives), we believe our findings offer a more generalizable contribution that may be useful as the sharing economy changes. An important consideration for sharing economy firms is how technological developments related to both platforms and providers may change consumers' perceptions of a sharing economy purchase. Specifically, one of the biggest future shifts will likely involve more sharing economy brands moving from the use of human providers to autonomous cars, drones, and delivery robots ([20]; [55]). As this shift occurs, our research suggests that firms that retain human providers rather than AI-driven nonhuman providers should highlight these individuals in marketing communications. These appeals may be particularly effective when compared to purchases involving machines that are literal extensions of the firm (representing the ultimate provider–firm convergence) and that consumers may view as eerie and threatening entities ([44]; [47]) rather than as individuals who elicit empathy.
Finally, our studies suggest that there may be an opportunity for policymakers to educate consumers about how their perceptions regarding P2P purchases may not match economic reality. Our studies show that consumers often view purchases from for-profit matchmakers as helping an individual provider, which benefits these firms. However, this perception could have negative consequences for providers. For example, if consumers already believe that they are helping through their purchases, they may be less willing to support regulations that help to protect these individuals financially or may be less willing to provide other support (e.g., tipping). This is particularly consequential when considering that P2P providers often receive little compensation for their work. Researchers have proposed that the median pretax profit earned by Uber and Lyft drivers is $3.37 per hour ([19]), significantly below minimum wage.
Our research has limitations that may be future research opportunities. Because we conducted all our studies using U.S.-based samples, future research might explore how national and regional differences may have an impact on our findings ([36]). As P2P models are being employed in increasingly diverse industries ([55]), future research might also explore how product or service type affects consumers' response to provider-focused marketing communications.
Another important avenue for future research is understanding how the characteristics of the specific providers featured in provider-focused marketing communications influence our effects. First, we do not expect our empathy mechanism to operate when providers evoke very negative images, such as a deadbeat dad ([33]), or if the provider shows only a weak need for financial help. Past research on empathy suggests that an individual's need is a primary determinant of empathy ([10]). Thus, we suggest that our effects may not hold if the provider shown in a provider-focused ad has only a weak need for financial help (i.e., a purchase may not feel like a helping behavior if the consumer knows the P2P provider makes a six-figure salary). Provider need may be difficult to communicate to consumers with a single image shown in an ad; however, some observable characteristics do affect consumers' inferences of need ([21]). Because perceptions of need are often driven by competing beliefs and normative expectations related to demographics ([11]) that may contradict or enhance each other (e.g., the interaction of race and gender), we propose that need perceptions are likely driven by an amalgamation of provider characteristics. Future research might consider how different observable attributes have an impact on and interact in the formation of need inferences and how the text used in P2P marketing communications might also communicate provider need.
Future research should also consider the role of perceived similarity between the provider featured in a provider-focused ad and the target consumer segment. Although past research has indicated that individuals may be more motivated to help others similar to oneself (e.g., [38]), the extent to which the same action is perceived as helping when it benefits similar versus dissimilar others likely depends on the variable(s) on which similarity is evaluated. For example, if a consumer with a secure financial situation perceives a featured P2P provider to have a worse financial situation than their own, the consumer likely would perceive a purchase from this provider as helping to a greater extent than purchasing from a P2P provider with a similar or better financial situation than their own. Making predictions about the impact of other types of similarity on helping perceptions is less straightforward ([10]). We propose that future research explore how a variety of important domains of demographic similarity (e.g., gender, age, race) may interact to affect perceptions that a purchase helps a P2P provider. The observable demographics of the specific P2P provider featured in a P2P ad may also affect consumers' purchase likelihood directly—not just through helping perceptions—for reasons related to the consociality (i.e., copresence and interaction with a social other) of a purchase. Consociality can elicit potential safety concerns from consumers ([46]), and a dissimilar provider may heighten such anxieties ([53]). For this reason, consumers may prefer making consocial purchases from a similar other. Whether such an effect occurs also likely depends on whether consumers make inferences that most or many of the providers on a platform are likely to share demographics with the provider or providers featured in a provider-focused ad for a P2P firm. Whether consumers make this type of inference (and what cues in marketing communications might suggest a heterogeneous vs. homogenous provider pool) is also an important avenue for future research.
Finally, although we have focused on one positive outcome of an empathy lens (i.e., purchase-related outcomes), future work might examine negative downstream consequences of consumers adopting an empathy lens. For example, might consumers be more likely to seek revenge or hold a grudge ([25]) against a brand when they are disappointed or rejected by a provider for whom they felt empathy? Future work could also examine how other outcomes important to managers are affected by provider–firm independence and/or consumers adopting an empathy lens, like trust in the firm or provider.
Supplemental Material, jm.19.0011-File003 - Providers Versus Platforms: Marketing Communications in the Sharing Economy
Supplemental Material, jm.19.0011-File003 for Providers Versus Platforms: Marketing Communications in the Sharing Economy by John P. Costello and Rebecca Walker Reczek in Journal of Marketing
Footnotes 1 Associate Editor Vanitha Swaminathan
2 Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work is supported in part by grants from the Fisher College of Business and the Decision Sciences Collaborative at The Ohio State University.
4 Online supplement: https://doi.org/10.1177/0022242920925038
5 1 Evidence from the marketplace suggests that real P2P firms use both approaches, focusing on either peer providers or the platform in their advertisements (see Web Appendix B for examples from Lyft, TaskRabbit, and Turo).
6 2 We use the term "purchase-related outcomes" in our hypotheses to describe the various behaviors/intentions that indicate greater likelihood of consumers buying from the brand. We operationalize this construct in a variety of ways across our studies.
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Record: 143- Provision of Optional Versus Standard Product Features in Competition. By: Balachander, Subramanian; Gal-Or, Esther; Geylani, Tansev; Kim, Alex Jiyoung. Journal of Marketing. May2017, Vol. 81 Issue 3, p80-95. 16p. 1 Diagram, 8 Charts, 1 Graph. DOI: 10.1509/jm.15.0208.
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Provision of Optional Versus Standard Product Features in Competition
Competing brands differ in the extent to which they offer a given feature as standard or optional in their product lines. In this article, the authors study the competitive basis for this difference in brands' product line strategies. Specifically, they analyze the relationship between a brand's quality image and its propensity to offer a wider product line, from a relatively stripped-down base model to a more feature-rich model. They develop a conceptual framework and hypotheses by considering an analytical model with two vertically differentiated firms: They show that a low-quality firm would offer a feature as optional—that is, it would offer both a feature-added product and a stripped-down base product—if it chose to add the feature to its product. In contrast, a high-quality firm would offer the feature as a standard component unless the cost of the feature was high. This asymmetry in the propensity of high- and low-quality firms to offer optional and standard features with their products is tested using data from the U.S. passenger car market; the authors find empirical support for their model.
Online Supplement : http://dx.doi.org/10.1509/jm.15.0208
When choosing among competing brands of a product such as automobiles, customers consider inherent quality of the brands as well as available product features (Purohit 1992; Sullivan 1998). Typically, a brand or subbrand may offer several variants with differing levels of features in its product line to appeal to different customer segments. For example, the 2014 Toyota Avalon car model (where Toyota is the brand and Avalon is the subbrand, or model) is offered in the United States in four variants ("trim levels," in industry parlance), namely, XLE, XLE Premium, XLE Touring, and Limited. These four product variants offer an increasingly richer set of features at higher prices, starting from the basic XLE variant, which we term the "base product." Although consumers can customize each car model variant to a minor extent (e.g., adding a remote engine starter), a consumer who desires additional features typically needs to upgrade to a more expensive variant. For example, a consumer who desires ventilated front seats has to choose the highest Limited variant because this feature is not available on the other variants. In contrast, a consumer who does not value the steering wheel-mounted audio control cannot purchase a variant without this feature because it is standard on all variants in Toyota Avalon's product line.
In general, competing brands differ in the extent to which they offer a given feature selectively on some product variants versus making the feature a standard part of their product. For example, heated seats are a standard feature on all 2014 Avalon variants. In contrast, the Chrysler 300 and Dodge Charger, American competitors to the Avalon in the large sedan category, offer heated seats only on their most expensive, feature- rich model variants. Because of such standard features, Toyota Avalon appears to have a narrower product line than Chrysler 300 and Dodge Charger. While the 2014 Toyota Avalon comes in only four variants, the 2014 Chrysler 300 and Dodge Charger have six and seven variants, respectively (Cars. com 2013a, b, c).
In this article, we study the competitive basis for such differences in brands' product line strategies. Specifically, we analyze the relationship between a brand's quality image and its propensity to offer a wider product line, from a relatively stripped-down base model variant to a more feature-rich variant. For example, does Toyota's stronger quality image in comparison to Dodge make it more likely that Toyota offers a narrower product line loaded with standard features? More generally, we investigate whether and why high-quality brands offer narrower product lines in comparison to low-quality brands. For the purposes of this article, we define the product line of a brand/subbrand to be the set of all possible unique variants of the brand/subbrand that a consumer can purchase by adding optional features to the base product, which refers to a variant that does not include any added features.[ 1]
TABLE: TABLE 1 Theoretical Contribution in Relation to the Literature
| Theoretical Papers on Quality Choices by Firms | Firm(s) Offer Quality-Differentiated Product Line? | Firm(s) Compete on Price? | Asymmetric Prior Quality Positioning of Competitors? | Provision of Optional or Standard Features by Competitors? |
| Moorthy (1984); Mussa and Rosen (1978); Villas-Boas (2004) | Yes | No | No | No |
| Moorthy (1988); Shaked and Sutton (1982) | No | Yes | No | No |
| Champsaur and Rochet (1989); Gilbert and Matutes (1993); Johnson and Myatt (2006); Katz (1984); Schmidt-Mohr and Villas-Boas (2008) | Yes | Yes | No | No |
| Desai (2001) | Yes | Yes | No | No |
| Johnson and Myatt (2003) | Yes | Noa | Yes | No |
| The current study | Yes | Yes | Yes | Yes |
aFirms compete on quantity supplied, not prices.
We answer this research questions by first developing a theory for why high- and low-quality firms may differ in the widths of their product lines. Subsequently, we test the hypotheses derived from this theory using an empirical analysis of data from the U.S. passenger car market. Our theoretical framework is a game-theoretic model in which two vertically differentiated firms, whose base products have different inherent qualities, have the ability to add a feature to their base products. Our equilibrium results show that the high- quality firm offers the feature as a standard component of its product except when the cost of the feature is high, in which case it makes the feature optional. In contrast, the low-quality firm offers the feature only as an optional component, and it does so only when the cost of the feature is low. In other words, the low-quality firm also offers the base product whenever it offers the feature-added product variant. This asymmetry in the behavior of the firms is due to the quality positioning of the firms. By offering the base product as an alternative to the feature-added variant, the low-quality firm avoids pricing low-valuation consumers out of the market. In contrast, if the high-quality firm adds the base product to its offering along with the feature-added variant, price competition is intensified as the differentiation between the firms reduces. Therefore, the high-quality firm avoids offering the base product except when the feature cost is high. These differing incentives of the two firms imply that the propensity to offer the base product as part of its product line, either as an option or as a stand-alone product, is higher for the low-quality firm. An empirical analysis of the U.S. passenger car market is consistent with this and other predictions of our theoretical model, thus offering support for our theory.
In Table 1, we show our theoretical contribution is positioned in the literature on vertical, or quality, competition. By considering product line competition between firms, our study takes into account both the price competition effect of product differentiation, identified in Shaked and Sutton (1982) and Moorthy (1988), and the benefit of market segmentation, as in Mussa and Rosen (1978) (see also Moorthy 1984 and VillasBoas 2004). Unlike Shaked and Sutton (1982), we find that the differentiation between firms' products decreases when firms compete on product features and have the option of offering different variants.
Among studies that consider vertical product line competition, Champsaur and Rochet (1989) show that the quality ranges offered by the firms do not overlap in equilibrium (we assume in our model that they do not). However, they do not characterize conditions under which the gap in the quality ranges is greater or smaller, which is the focus of our analysis. Moreover, firms choose continuous quality intervals in Champsaur and Rochet' s abstract model, whereas competing firms in our theoretical model choose in correlated, discrete quality levels, allowing us to model product features. Katz (1984) and Gilbert and Matutes (1993) are similar to Champsaur and Rochet (1989) in finding that competing firms may not offer full product lines and may instead prefer to specialize. In contrast, Johnson and Myatt (2003, 2006) and Schmidt-Mohr and Villas-Boas (2008) find that competing firms may opt to go head-to-head, with multiple quality offerings, rather than specializing. Desai (2001) studies the effect of heterogeneity of consumer taste preferences for competing firms' products on the qualities served to different segments. Our analysis, unlike all the aforementioned studies on product line competition, sheds light on how value to consumers and cost of added product features affect competitors' product line width through optional or standard features. Moreover, our study is the only one (with the exception of Johnson and Myatt 2003) to consider the effect of differences in competitors' prior quality positioning (inherent quality) on a firm's product line strategies.
We develop a theory of why brands with different inherent qualities may offer a more or less differentiated product line, with a view to formulating some hypotheses. For this purpose, we use a stylized, analytical, game-theoretic model of competition between firms, wherein we use the term "firm" generically to denote a brand or subbrand. We are interested in strategies relating to firms that add "vertical" product features, that is, features that every car buyer would value in a car, although perhaps to different degrees, such as antilock brakes. We do not examine firms' strategies on "horizontal" features, such as the size of a car, where different car buyers may have different ideal sizes for the car they want. In the context of the introductory example of the Toyota Avalon, we are thus interested in decisions made on vertical product features at the subbrand or model (Avalon) level, because at the brand (Toyota) level, Toyota offers different subbrands, like Avalon and Corolla, to cater to divergent size (horizontal) preferences.
We consider a market in which there are two firms, firm L and firm H, differentiated in the inherent quality of their products. Firm H's inherent quality (mh) is higher than firm L's (mi), with mh — ml = d > 0. The inherent quality represents the quality of a base product offered by a firm. We assume that firms are endowed with their products' inherent qualities and that these qualities cannot be changed in the short run. In making their purchase decisions, consumers may use brand names to assess products' inherent qualities (Sullivan 1998). For example, an inherent quality of BMW cars is their driving performance, which has been a characteristic of BMW's cars for a long time. To focus on demand effects of quality differentiation, we assume that firms L and H have the same marginal cost for their respective base products, and we normalize this common marginal cost to zero for simplicity. Each firm, however, has the ability to add a feature to its base product. In what follows, we let lb and hb denote the base products and lf and hf denote the feature-added products of firms L and H, respectively.
Consistent with our research focus on vertical features, we assume that addition of a feature enhances the perceived quality of the base product of either firm by an amount k. Thus, the quality of a base product from firm H or firm L that incorporates the feature becomes mh + k or ml + k, respectively. The assumption that the feature increases the perceived qualities of the base products of both firms by the same amount is parsimonious, while being general enough to allow the feature to be valued differently by customers of the two firms. For example, a feature may be an "eco" setting for a car that improves gas mileage of a car by 5 miles per gallon. In this example, k refers to the improvement in the gas mileage of each firm's base product from the feature and will consequently be identical for the two firms. However, in equilibrium, the high-quality firm would serve consumers whose value for a marginal unit quality would be higher, effectively making the value of the feature higher when offered on the higher-quality product.[ 2] We reparameterize k = ad, where a represents relative value of the feature with respect to the quality difference between the two base products. We assume that 0 < a < 1, and therefore k < d. In other words, this assumption implies that the quality enhancement from the incorporation of the feature is smaller than the inherent quality differences between the base products. Such an assumption appears reasonable for new features introduced in many durable-product markets because many of the new features represent incremental innovation. Without loss of generality, we set the quality of firm L to be 1 (i.e., ml = 1). We assume that incorporating the feature would increase the marginal cost of the base product of either firm by the same amount, which we denote by c. This assumption of a common marginal cost of the feature for both firms enables us to focus on the demand effects of adding a feature to the base product.[ 3]
There is a unit mass of consumers who demand at most one unit of the product. Consumers differ in their value, x, for a unit of quality, with x being uniformly distributed in [0, 1]. Consumers purchase the product that offers the highest net surplus, provided the surplus is nonnegative, where net surplus is measured by the value of the product minus its price. Thus, given the prices pi of the products in the market, a consumer with a value of x per unit of quality derives the following utility from purchasing product i or from not purchasing, as follows:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
Though stylized, our model is general enough to accommodate uncertainty of consumer quality perceptions at the individual consumer level. In this case, the utility function Ui(x) can be thought of as the von Neumann-Morgenstern expected utility function for the consumer indexed by x (Hauser 1978; Roberts and Urban 1988). In this interpretation, mH and mL can be interpreted as the certainty equivalent of the uncertain quality (e.g., Kreps 1990, p. 84; Rust et al. 1999). Likewise, if consumers are heterogeneous in their perception of a firm's quality (or certainty equivalent), we can transform the parameters to yield a model similar to ours. Thus, if consumers with taste parameter x perceive the quality of a firm to be mx, we can rewrite the consumer' s utility mxx from the product equal to mx*, where m is the common perception across all consumers and x* is the new taste parameter, with an appropriate distribution.
We focus our analysis in this article on the interesting case where c < k, or equivalently, c < αd. In other words, the interesting case occurs when at least the highest-valuation consumer in the market values the feature more than the amount the feature costs the firm.
We investigate a product line length game between the two firms with the following sequence of decisions. First, firms decide through their product line strategies whether they will give consumers flexibility in choosing between a base product and a product variant that combines the base product with a feature, where the role of the feature is to lift quality. In other words, each firm has three alternatives: ( 1) Sell only the base product (strategy B); ( 2) Make a feature optional, thereby giving consumers flexibility in choosing between the base product and the feature-added product (strategy BF); ( 3) Make a feature standard on its product, thereby selling only the feature-added product (strategy F). In the second stage, firms announce prices for individual products in the product lines. We assume prices are chosen after the product line decisions because prices can be more easily changed than the product line itself. Our assumption about consumers' valuation distribution implies that in equilibrium, the market is not covered. The lack of complete market coverage—that is, a market in which not everyone purchases the product—is likely a more appropriate assumption for durable-goods markets. Given the three product line strategies for each firm, there are nine possible subgames at the end of the first stage, as shown in Table 2. We solve for the subgame perfect equilibrium of the game. Proofs, model extensions, and additional details of our analysis are presented in the Web Appendix.
Strategic effects of feature additions: insights from a simpler model. The main model described earlier combines firms' choices of whether or not to add the feature and of whether to make it a standard or an optional feature, that is, choice of product line length. To disentangle the effects of these two choices in a clear manner, we initially analyze a simpler version of our main model. In this benchmark case, firms can only offer one product, which could be either a base product or a feature-added product (i.e., feature is offered as standard). We present detailed results of this analysis in the Web Appendix. This analysis establishes that when the marginal cost c of adding a feature is sufficiently small, both firms make the feature standard (F-F) in equilibrium, whereas neither firm offers the feature when c is large (B-B). In the intermediate range of c, only firm H offers the feature-added product (B-F).
The interesting insight from this result is that there cannot be an F-B equilibrium, that is, one in which only the low-quality firm offers the feature in the product. In particular, the result that the high-quality firm would offer the feature either as the only firm doing so or along with the low-quality firm suggests a bias in favor of the high-quality firm in offering the feature. The intuition for this bias is the difference in the strategic effects on price competition when firm H offers the feature versus when firm L offers it. Specifically, when firm L offers the feature, the price competition intensifies as the quality differentiation between the firms reduces (Moorthy 1988; Shaked and Sutton 1982). This increase in price competition reduces the potential profits to firm L from adding the feature. However, when firm H offers the feature, the quality differentiation between the firms increases, relaxing the price competition between the firms and increasing the profit from adding the feature. This difference in the strategic effects for the two firms implies that if firm L finds it profitable to add the feature in spite of the accompanying intensification of price competition, firm H would certainly find it profitable to do the same, because its offer of the feature would have the added benefit of reducing competition. On the other hand, it is possible that firm H finds it profitable to offer the feature while firm L does not. Thus, this benchmark analysis establishes that there is a bias in favor of firm H offering the feature because of the strategic effects on price competition.
TABLE: TABLE 2 Subgames after Stage 1
| Firm L's Product | Firm H's Product Line Strategy |
| Line Strategy | B (hb) | BF (hb, hf) | F (hf) |
| B (lb) | B—B | B—BF | B—F |
| BF (lb, lf) | BF—B | BF—BF | BF—F |
| F (lf) | F—B | F—BF | F—F |
Notes: Product lines are in parentheses.
Technical results from the theoretical model. The equilibrium results for the various product line subgames are presented in Table WM of the Web Appendix. Because the conditions for equilibrium are quite complex for general d, we assume d = 1. While the intuition for the results described next also holds for general d, the main difference in the general case is that BF-BF can be an equilibrium for d sufficiently greater than 1, whereas it fails to be an equilibrium when d = 1. The rationale is that the four products offered by the two firms in a BF-BF equilibrium are sufficiently differentiated and profitable to sustain the equilibrium only when the difference between firms' intrinsic quality is sufficiently large (i.e., large d).[ 4] Proposition 1 characterizes the equilibrium assuming d = 1.
Proposition 1: When firms can offer a product line with the feature being optional or standard, the equilibrium strategies are as follows: BF-F when 0 < c <X1; B-F when X1 < c < X2; B-BF when X2 < c < a, where X1 and X2 are as given in the Web Appendix.
The equilibrium profits, prices, and quantities are given in Table WM of the Web Appendix.
Proposition 1 shows that the low-quality firm always offers its base product in equilibrium but does not offer the feature-added product except when the cost is low. Even in this case of low marginal cost for the feature, the feature-added product is offered as an optional variant in firm L's product line. In contrast, the high-quality firm always offers the feature-added product. Furthermore, the feature is included as a standard part of firm H' s product, except when the cost is high, when the feature is offered as an option.
What is the reason for this asymmetry in the equilibrium strategies of the high- and the low-quality firms? The intuition is a market segmentation argument. Consider, for example, why F-B cannot be an equilibrium. In the case of an F-B equilibrium, the market is segmented as in Figure 1, Panel A, with consumers between x1 and x2 buying the feature-added product (lf) of firm L, while consumers between x1 and 1 buy the base product (hb) offered by firm H. Consumers between 0 and x2 do not buy either product. If firm L were to offer a base product (lb) as well in its product line, the market segmentation in the resulting BF-B equilibrium would be as in Figure 1, Panel B, with the segment to the left of x2 being split into two segments: those buying firm L's base product (lb) and those opting to not buying any product. As a result of such segmentation, firm L serves with its base product some consumers who were previously priced out of the market and did not buy any product. Thus, firm L's market share increases in the BF-B equilibrium in comparison to the F-B equilibrium. In addition, firm L's addition of the base product to its product line does not increase the intensity of price competition, as evident from the unchanged equilibrium price of its feature-added product between the F-B and BF-B equilibria. The rationale is that the marginal consumer choosing between firm L' s products and firm H's product in both equilibria (consumer at x1 in Figure 1, Panels A and B) is choosing between the feature-added product of firm L and the base product of firm H in either equilibrium. Thus, when firm L adds a base product to move from an F-B equilibrium to a BF-B equilibrium, it increases its market share through better market segmentation without affecting the intensity of price competition, thus increasing its profit. Therefore, firm L always deviates from an F-B equilibrium. For the same reason, F-F and F-BF cannot be equilibrium strategies.
An analogous situation obtains in a B-B equilibrium when firm H extends its product line by offering a feature-added product (hf) in addition to its base product (hb). In this case, the consumers between x1 and 1 are split into two segments, as shown in Figure 1, Panel C, for the B-BF equilibrium: those buying product hf and those buying product hb. Note from Figure 1, Panel C, that product hf appeals to consumers with the highest valuation for quality, and therefore it is firm H's base product, hb, that competes directly with firm L for the marginal consumer at x1. Thus, the addition of the feature-added product as a second product helps firm H discriminate between high- valuation consumers without intensifying price competition, thereby increasing profit.[ 5] Therefore, firm H finds it profitable to deviate from a B-B equilibrium. For the same reason, equilibria such as BF-B and F-B do not exist. To summarize, the asymmetry in the equilibrium strategies of the high- and low- quality firms is a result of the strategic effect of product offerings on price competition and the benefits of market segmentation. Thus, firm L always offers the base product because this product minimizes the intensity of price competition when firm L offers only one product in its line. Furthermore, in the event that firm L offers the feature-added product, the addition of the base product to the product line provides market segmentation benefits without intensifying price competition. Cannibalization is less of a concern to firm L in this case because the base product appeals to customers who would not have otherwise purchased any product in the market. Conversely, firm H prefers not to offer the base product to avoid the negative effects of increased price competition and consequent cannibalization of its feature-added product. We capture these results on the asymmetry in strategies between high- and low-quality firms in the offering of a base product through the following hypothesis:
H1: As the inherent quality of a firm increases, its propensity to offer a base product as part of its product line decreases.
Proposition 1 and this discussion on the asymmetry between the strategies of the high- and low-quality firms can also be summarized from the perspective of which type of firm tends to offer a feature-added product. In this respect, firm H always offers the feature-added product because this is the product that would minimize price competition when firm H offered a single product in its line. Furthermore, even if firm H offered the base product because of high feature costs, the addition of the feature- added variant would increase profit due to market segmentation without increasing price competition. On the other hand, firm L is concerned about increasing price competition by offering the feature-added product, and thus it does not offer such a product unless the feature cost is sufficiently low. Thus, we have the following hypothesis:
H2: As the inherent quality of a firm decreases, its propensity to offer a feature-added product as part of its product line decreases.
Proposition 1 suggests that when the cost of the feature is sufficiently low, firm L finds that the profit from a feature- added product is sufficiently attractive to offset the increased price competition that will result from adding this product to its line. On the other hand, the low cost of the feature makes the segmentation benefits firm H receives from adding a lower-cost base product smaller than the negative effects of increased price competition and cannibalization of its feature- added product that such a move would engender. Thus, firm H prefers not to add a base product when the cost of the feature is low, and we have a BF-F or a B-F equilibrium. However, when the cost of the feature is sufficiently high, firm H realizes less profit from the feature-added product and therefore finds that the segmentation benefits from offering a lower-cost base product dwarf the negative effects of higher price competition and cannibalization of the feature-added product. In contrast, adding the feature-added product is not attractive to firm L when the cost is high because the profit margin from this product is not attractive enough to overcome the negative effects of greater price competition from offering such a product. Thus, we have a B-BF equilibrium when the cost of the feature is sufficiently high. For intermediate cost levels, both firms are content to offer a single product to minimize price competition and avoid cannibalization in their product lines, and we have a B-F equilibrium.[ 6] One way of summarizing the above discussion and Proposition 1 is that a low-quality firm offers the base product under all cost conditions. In contrast, the high-quality firm does not offer the base model unless the cost of the feature, c, is high in relation to its value, a. Accordingly, we have the following hypothesis:
H3: As the cost of a feature decreases, the propensity to offer a base product without the feature decreases more for firms with higher inherent quality.
If we frame Proposition 1 and our preceding discussion from the perspective of how cost influences firms' offering of a feature-added product rather than a base product, we can generate the following hypothesis:
H4: As cost of a feature increases, the propensity to offer a feature- added product decreases less (more) for firms with higher (lower) inherent quality.
Our stylized, theoretical model assumes that firms have a single feature available to them that they can add to the base product. We may, however, interpret the results in Proposition 1 to infer firms' product line strategies when multiple features are available, some with lower cost than others. In such a case, Proposition 1 suggests that, in addition to the base product, lower-quality firms would offer variants based on low-cost features, effectively following a BF product line strategy with respect to such low-cost features. On the other hand, high- quality firms would offer such low-cost features as standard components in their product, effectively following an F strategy with respect to such features. However, for high-cost features, low-quality firms would choose not to offer them on any of its variants (effectively following a B strategy with respect to these features), while high-quality firms might offer such features as optional, thus offering consumers a choice between variants that would include the high-cost feature and those that would not (effectively following a BF strategy with respect to these features). This interpretation of the results from Proposition 1 for a multifeature context results in the following hypothesis:
H5: Product line variants for low- (high-) quality brands are based on low- (high-) cost features.
The preceding hypothesis has implications for empirically testing our theory. Comparing product line lengths across brands can be misleading because variants may be based on different features for high- and low-quality brands. Thus, we need to keep the feature constant across brands when comparing variants offered by them, as we do in the empirical application.
Proposition 1 can also be interpreted to draw implications for how firms' product line strategies may evolve over time. Two important sources for evolution in firms' product strategies are consumer learning and acceptance of new product features, and declining costs of offering the features. It can be shown that in Proposition 1, the minimum cost, c2, of the feature needed to induce firm H to offer the base product increases with a, the relative value of the feature. Likewise, the maximum cost, C1 ,of the feature at which firm L would offer the feature-added product also increases with a. Both of these analyses suggest that as consumer's value of the feature increases over time because of learning and greater consumer acceptance, low- quality firms would offer a product variant with the feature, while higher-quality would make the feature standard for their product line. Similarly, we can draw implications of cost dynamics from Proposition 1. Many studies document that costs decrease over time because of learning by doing (see Lieberman 1984). Proposition 1 indicates that as the cost of the feature decreases over time, a high-quality firm would make the feature a standard part of its product, while a low-quality firm would offer a feature-added product as a variant in its product line. While this conclusion is similar to H3, the implication here is that we would observe such changes in firms' strategies over time because of traditional cost dynamics. A different way of inferring the influence of cost dynamics through Proposition 1 is to conclude that firms, both of high and low qualities, would be offering the feature in all their products or in some of their product variants as time progresses because of cost reductions. Because we do not have direct temporal data on consumer value for features in our empirical study, we do not formalize these conclusions on dynamics as a hypothesis. However, we do include a control for calendar time in our empirical model, where possible, to control for such dynamic effects. In other case, we include cost information in our empirical model, where the source of cost variation is temporal. This cost term helps account for some of the dynamics in product line strategies in our empirical setting.
Data on optional and standard product features. We investigate whether H1-H5, derived from our theoretical framework, are supported by data from the U.S. passenger car market for the period 2001-2010. We obtain data on technical specification of cars sold in the U.S. passenger car market during this period as reported by Ward's Automotive Yearbook. For a particular year and car model, the specifications are available for each variant represented by the trim level of the car and its body style. For example, in 2001, specification data for the car model Ford Focus is available for each of its trim levels, such as LX, SE, and ZTS, and for each available body style, such as a three-door hatchback or a four-door sedan. Available specifications include car characteristics such as weight, engine horsepower, and gas mileage as well as whether two features, antilock brake system (ABS) and traction control (TC), were each offered as optional equipment or as standard equipment for that car model variant.[ 7] Our empirical analysis tests the propensity of these features to be offered as optional or standard features by a car variant as represented by a car model and body style, with the body style classified as one of four kinds: two- door sedan, three-door hatchback, four-door sedan, and four- door wagon. We use such a variant as the unit of analysis because consumers may have sufficiently strong preferences for a body style to induce them to confine their purchases to cars with their preferred body styles. Note that we may consider the combinations of a car model and different body styles to be "horizontal" variants. However, in this study, we are primarily interested in additional "vertical" variants that arise out of making ABS or TC optional for each of these horizontal variants. For each analysis unit represented by a car model and body style (e.g., Ford Focus four-door sedan), if a feature such as ABS is standard on all trim levels (e.g., LX, SE, and ZTS for the Ford Focus), we consider this feature standard for that unit. If this feature is optional for any of the trim levels associated with the analysis unit, we consider the feature to be optional for that unit.
We exclude hybrid cars from our analysis, leaving us with a sample of 221 distinct car models over the ten-year period. Between 2001 and 2010, the annual number of analysis units representing combinations of car models and body styles ranged from 146 to 181, with an average of 167.8 for the time period. Of the 1,678 analysis units offered over this period, Table 3 shows that 27.2% offer a stripped-down product lacking both ABS and TC. In contrast, the proportions of analysis units that offer a less stripped-down product, such as one without TC but with ABS as standard, or one without ABS but with TC as standard, are 22.2% and 0%, respectively. The remaining 50.6% of analysis units offer both TC and ABS as standard features.
TABLE: TABLE 3 Offer of Base Product Without Features Among Car Models/Body Styles
| Offer Car Variant | |
| Offer Car Variant | Without TC? | |
| Without ABS? | No | Yes | Total |
| No | | | |
| Frequency | 849 | 372 | 1,221 |
| Percentage of total | 50.6% | 22.2% | 72.8% |
| Percentage within row | 69.5% | 30.5% | |
| Percentage within column | 100.0% | 44.9% | |
| Yes | | | |
| Frequency | 0 | 457 | 457 |
| Percentage of total | 0% | 27.2% | 27.2% |
| Percentage within row | 0% | 100.0% | |
| Percentage within column | 0% | 55.1% | |
| Total | 849 | 829 | 1,678 |
| Overall percentage | 50.6% | 49.4% | 100% |
Data relating to inherent quality. To derive the inherent quality of the car models as relevant to our hypotheses, we start from Automotive Performance, Execution and Layout (APEAL) ratings of a car model's performance as provided by J.D. Power and Associates. Derived from surveys of new vehicle owners, the APEAL ratings range from 1 to 5 for each car model, with a rating of 5 indicating the best score on the attribute. Furthermore, according to J.D. Power, the APEAL ratings of a car model' s performance are "based on owner satisfaction with the vehicle's powertrain and suspension systems, including acceleration, fuel economy, handling stability, braking performance, and shift quality." From a mechanical standpoint, the optional car features of ABS and TC in our study should be strongly related to handling stability and braking performance, which are part of the APEAL performance ratings. Therefore, we use these ratings as a starting point to derive a measure of inherent quality to be consistent with our analytical model, in which consumer valuations of inherent quality and the optional feature are positively correlated (APEAL does not separately break out ratings for safety or handling). A regression of performance ratings against car characteristics and the availability of ABS and TC as standard or optional features confirms that these features are positively related to a car model's performance ratings (see Table G1 in the Web Appendix). Furthermore, we find that ABS and TC account for only 23.2% of the variation in performance ratings of car models. Therefore, offering these features is unlikely to drastically change perceived performance of a car model, consistent with the assumption of our theoretical analysis that k < d.
The challenge in deriving the inherent quality measure from the APEAL performance ratings is that these ratings reflect the performance attributable to the inherent quality of the car as well as the performance due to the optional features of ABS and TC. As we discuss in the next subsection, we obtain the residual information in the form of car model-specific intercepts from a regression of the APEAL performance rating on car characteristics and the ABS and TC variables. We then test our hypotheses by using these intercept parameters as the intrinsic quality estimates for the individual car models.
Data relating to feature costs. Testing H3-H5 requires information on the marginal cost of the features ABS and TC. However, information on the cost of ABS and TC during the data period is limited, with data generated as part of regulatory analysis conducted in the United States in 2006 (National Highway Traffic Safety Administration 2006), pursuant to making Electronic Stability Control (ESC) mandatory for all passenger cars, beginning in the 2012 model year (after our data period). Approximately speaking, ESC combines some form of TC with an ABS system. Because the term "ESC" is often used interchangeably with "TC," we use the cost estimates for ESC in National Highway Traffic Safety Administration (2006) as the cost for TC. Furthermore, because ABS is a subsystem of TC (or ESC), a firm incurs an additional marginal cost to provide TC on top of ABS. Data from U.S. and Canadian regulatory analyses, such as the ones discussed earlier, generated two data points each for the cost of ABS and TC at different points in time. In both cases, the cost declines over time are similar to those documented in other industries (Rapping 1965). Following Rapping (1965), we fit a cost function that follows an exponential decline over time to the two data points that we have for ABS and TC. From this fitted cost function, we interpolate the cost for each of the calendar years in our data period of 2001-2010 and use it in our analysis. The Web Appendix provides further details of how we estimate this cost function. Figure F1 therein plots estimated cost functions for ABS and TC. We also test the robustness of our empirical results to fitting alternative cost functions, namely, a power function or linear function, to the cost data. We find that our conclusions are robust to fitting these alternative cost functions (see Web Appendix for details).
To test the hypotheses, we estimate a system of two simultaneous equations. The first equation has some discrete dependent variable depending on the hypothesis being tested. The second equation regresses the APEAL performance ratings on variables related to the optional or standard product features of interest, namely, ABS and TC, along with car model dummies and car characteristics such as gas mileage, the ratio of horsepower to weight, and size of the car. The estimated car model dummy parameters (car model intercepts) in the second equation, representing residual car model-specific performance that is not explained by ABS, TC, and other car characteristics, serve as measures of intrinsic quality for the car models. These car model intercepts appear in the first equation as an explanatory variable, representing the intrinsic quality of the car model. Thus, the empirical simultaneous equation model is as follows:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
In Equation 2, for car model i with body style b in year t; y2ibt is the APEAL performance rating; X2ibt is the vector of explanatory variables relating to whether ABS and TC are optional or standard features, and to car characteristics such as gas mileage, as detailed later; Xi is the car model-specific intercept parameter; and β2 is a parameter vector. In Equation 1, y*1ibt is a latent variable whose value determines the observed discrete variable, y1ibt, using a probit or ordered probit link function depending on y1ibt. Further, g(ξ i) is, in general, a function of the car model-specific parameter representing intrinsic quality, as estimated from Equation 2 with associated coefficient βq; and X1ibt with associated parameter vector β1 is a vector of variables that control for car segment, competition, body style, cost of ABS or TC, and time trends, as described in detail later. The function g(Xi) could simply equal Xi or could involve interaction of xi with other variables, depending on the model being tested. The terms e1ibt and e2ibt are i.i.d. error terms with the following distribution assumptions:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
where
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
For estimating the preceding simultaneous equation system, we use Bayesian estimation with the help of Markov chain Monte Carlo (MCMC). This estimation method allows us to efficiently account for the estimation error with respect to the xi estimates from Equation 2, when used as an explanatory variable in Equation 1. More details are provided in the Web Appendix.
H1 and H3. H1 and H3 concern the propensity to offer a base car variant devoid of additional features. In our empirical context, a base product would be one that has no ABS or TC. Thus, a natural way to model offering of such a base product is to define y1ibt in Equation 1 such that y1ibt = 1ifa fully stripped- down base product with no TC and no ABS is offered, with y1ibt = 0 otherwise. This formulation of y1ibt results in a probit equation, and for ease of differentiating this model from others, we use the alternate term "BASEMOD" for y1ibt defined in this fashion. However, note that this formulation makes no distinction between a car variant that offers a partially stripped- down product without one of the two features and a car variant in which both features are standard equipment, setting y1ibt = 0 in both cases. We therefore estimate an alternative ordered probit model in which y1ibt is defined as follows: y1ibt = 1 if both ABS and TC are offered as standard features (see northwest cell of Table 3); y1ibt = 2 if ABS is a standard feature but a product variant without TC is offered (see northeast cell of Table 3); and y1ibt = 3 if a base product without ABS or TC is offered (see southeast cell of Table 3). For ease of exposition, we refer to y1ibt defined as previously with the term "PRODLINE." Note that we do not have a value for PRODLINE for an analysis unit that offers TC as a standard feature but without ABS because we observe no such units in our data (see Table 3). Note further that an ordered probit model is appropriate for this formulation of y1ibt because the degree to which the base product is stripped down increases as y1ibt increases discretely from 1 to 3.
H2 and H4. In testing H2 and H4, we can distinguish between analysis units that offer only one feature, ABS, in any of its products from those that offer both ABS and TC or neither of these features. Table G2 in the Web Appendix presents a cross- tabulation of analysis units with these feature offerings. Thus, an ordered probit specification for Equation 1 is appropriate. Accordingly, we define the following for car model b at time t: y1ibt = 1 if both ABS and TC are not offered (3.8% of observations); y1ibt = 2 if ABS is offered on some product variant but TC is not offered on any variant (24.4% of observations); and y1ibt = 3 if ABS and TC are offered together or separately on some product variant or variants (71.8% of observations). As discussed before, technological constraints prevent TC from being offered without ABS on a car, whereas ABS can be offered separately. For convenience, we refer to y1ibt as defined here as "YESFEAT."
H5. To test H5, note that in our empirical context, ABS represents the low-cost feature, while TC is the high-cost feature, because TC combines the cost of ABS and the incremental cost of TC, as shown in Figure F1 in the Web Appendix. Thus, this hypothesis predicts that an analysis unit of higher intrinsic quality offers product variants with and without the high-cost feature, namely, TC, while the lower-quality unit offers product variants with and without the low-cost feature, ABS. In other words, it predicts that ABS is more likely to be optional for the lower-quality unit, while TC is more likely to be optional for a high-quality unit, with ABS being standard for such units. To test this hypothesis, we set up Equation 1 as a probit equation in which y1ibt = 1 if ABS is standard while TC is optional or standard and y1ibt = 0 otherwise, which includes cases in which ABS is optional and TC is not offered, ABS and TC are both not offered, or ABS and TC are both optional. To facilitate exposition, we refer to y1ibt for this hypothesis as "HICOSTVAR."
Inherent quality. Variables based on the inherent quality of a car model, Xi (INHQUAL), are used as explanatory variables in Equation 1. As explained earlier, ξ i is estimated as the car model-specific intercept from the regression in Equation 2.
Costs. The marginal costs of features are relevant for testing H3 and H4. Therefore, we set up an explanatory variable, COST, for Equation 1 in both cases. To test H3 and H we interact COST with the intrinsic quality, ξ i, in Equation 1. We describe next how we create the COST variable for our hypotheses. There are three costs available to us the cost of ABS, the incremental cost of adding TC functionality to an ABS system, and the total cost of a TC feature (which always includes ABS). It would appear that managers might consider all these costs in making the decision on what features to include in their product and whether each included feature will be standard or optional. Because all three costs are highly correlated, we cannot include all three in the model. So we set COST to be the cost of an ABS system because it appears to be the kernel of the TC system and the biggest cost component.
Car characteristics in Equation 2. In the performance regression of Equation 2, we use the explanatory variables TRACT_OPT (=1 when TC is optional, 0 otherwise) and TRACT_STD (=1 when TC is standard, 0 otherwise). In addition, we use the corresponding variable for ABS, ABS_STD, which is defined similarly. We do not use ABS_OPT in Equation 2 because the correlation between ABS_STD and ABS_OPT is somewhat high, at -.9, and using both these variables in the equation may result in higher standard errors for the car model intercept parameters in Equation 2. As additional explanatory variables, we also use the car model' s average miles per gallon (MPG), the ratio of horsepower to weight (HPWT), and the logarithm of size (LNSIZE), where size is measured as the product of the car's height, width, and length in inches.
In the models to test all the hypotheses, we include the following additional explanatory variables in Equation 1 to control for other factors that may influence a car model's propensity to offer a base product, added feature, or feature-added product variant. These control variables include the following car segment classifications, derived from Ward's Automotive Yearbook: small, small specialty, lower middle, upper middle, middle specialty, large, lower luxury, upper luxury, luxury specialty, and luxury sport. We include nine dummy variables to capture a car's membership in one of these segments, with the luxury sport segment acting as the baseline. We also include the control variables OWNMODEL and COMPMODEL to capture the number of other car models that the firm and competitors, respectively, offer in the same segment and body style. Dummy variables TWODR, HATCH, and WAGON, indicating the body styles of a two-door sedan, a three-door hatchback, and a four-door wagon, respectively, are additional control variables in the model, with the four-door sedan style acting as the baseline. We also include a TIME variable (years since 2001) in the models testing H1, H2, and H5, to control for time trends in performance ratings. We do not include TIME in the models testing hypotheses involving cost of features (i.e., H3 and H4), because TIME is highly correlated with cost. The Web Appendix provides additional details on the control variables.
Table 4 provides descriptive statistics of key variables in the model, with the Web Appendix providing the same for other variables. We checked the correlations between variables and found none of them to be high enough to raise concerns about multicollinearity. Figure 2 presents a simple plot of the data to show that the percentage of car model styles offering a base product decreases with APEAL performance rating (inherent quality), consistent with H1. As noted earlier, the performance ratings of cars in the APEAL survey reflect the performance attributable to the inherent quality of the car as well as that due to car characteristics. Thus, this plot is only suggestive of the support for H1; our empirical model is designed to isolate the intrinsic quality from the performance ratings, as noted earlier, for a more rigorous test. Figure 2 also shows that the probability of offering of a base product by higher-performance cars is more sensitive to cost, consistent with H3, although this sensitivity disappears for cars with the highest performance in the data period. The likely reason for the latter observation is that cars with the highest performance are already at a point of not finding it worthwhile to offer a base product at the highest cost for ABS observed in the data period. Likewise, Figure G1 in the Web Appendix seems to support H2, with high-performance cars more likely to offer feature-added products even when the cost is high (H4).
TABLE: TABLE 4 Descriptive Statistics of Key Model Variables
| Variable | Explanation | M | SD | Min | Max |
| BASEMOD | Offers base product? | .272 | .445 | 0 | 1 |
| PRODLINE | Offers optional ABS and/or TC? | 1.765 | .850 | 1 | 3 |
| PERFORMANCE | | 3.727 | .944 | 2 | 5 |
| YESFEATURE | Offers ABS and/or TC? | 2.680 | .542 | 1 | 3 |
| HICOSTVAR | Offers high-cost feature and standardizes low-cost feature? | .716 | .451 | 0 | 1 |
| OWNMODEL | Number of same-segment offerings by firm | 4.895 | 4.286 | 0 | 24 |
| COMPMODEL | Number of same-segment offerings by competitors | 24.666 | 17.227 | 0 | 76 |
| COST | Cost in dollars | 365.000 | 36.900 | 308.2 | 424.0 |
| ABS_STD | | .728 | .445 | 0 | 1 |
| TRACT_OPT | | .212 | .409 | 0 | 1 |
| TRACT_STD | | .506 | .500 | 0 | 1 |
We estimate the empirical model in Equations 1 and 2 using a Bayesian approach employing fairly diffuse priors for the parameters. Because the posterior distribution for the parameters does not have a closed form, we simulate from the posterior distribution using an MCMC approach. Details of our MCMC estimation algorithm are in the Web Appendix. We run the MCMC algorithm for a total of 250,000 iterations, with the first 200,000 iterations serving as the burn-in to allow convergence of the Markov chain. We use the last 50,000 iterations to obtain mean estimate of the parameters and 95% probability intervals (highest posterior density intervals). Note that for the probit model used for BASEMOD, we specify y1ibt = 1 if y1ibt > 0 and y1ibt = 0 otherwise. In the case of the ordered probit model used to model PRODLINE and YESFEAT, y1ibt = k if μ k-1 < y*1ibt ≤ μk (for k = 1,2, 3), where μ0 = -∞, μ3 = and μk (for k = 1, 2) are parameters to be estimated. Given that we estimate an intercept term in the ordered probit model, only one parameter from μ1 and μ2 can be separately identified. Therefore, we set μ1 = 0, with μ2 to be estimated. In the error matrix, ∑, σ1 cannot be separately identified from the coefficients in Equation 1. Therefore, we normalize by setting σ 1 = 1, with σ 2 and σ 12 to be estimated.
H1. Table 5 presents the parameter estimates of the simultaneous equation models for our empirical test of hypothesis H1. One of the models uses a probit equation for Equation 1 with BASEMOD as the dependent variable, while the second model uses an ordered probit equation with PRODLINE as the dependent variable. The coefficient of INHQUAL (ξ i) is negative and significant in both models. This result is consistent with H1, lending support to our theoretical finding that propensity to offer a base product is negatively related to the inherent brand or firm quality. The similar results with the alternative empirical models of probit and ordered probit indicate robustness of this finding. We also estimated alternative models in which the propensity to introduce a base product was measured at the brand or company level rather than at the car model level. Such an analysis may capture the notion that brands or firms may consider their decision to offer a base product in the context of their entire portfolio of products. These analyses yielded similar results.
In both models, the coefficient of TIME is negative and significant, suggesting that car models increasingly offer ABS and/or TC. Such a strategy by car manufacturers may reflect increased consumer learning about the features' benefits, resulting in greater recognition of their value (higher a or k in our theoretical model) and consumers' expectations that their ideal car should have these features. Note also that the parameter estimates for the performance equation (Equation 2) in Table 5 and in other subsequent tables do not show the 220 car model intercepts (ξ i), which become the INHQUAL measures used in Equation 1. Lastly, we find that s12 is negative and significant, suggesting that any unaccounted performance in Equation 2 is negatively related to the propensity to offer a base product, consistent with H1
TABLE: TABLE 5 Test of H1
| Probit Model (DV: BASEMOD) | Ordered Probit Model (DV: PRODLINE) |
| Estimate | 95% HPD Interval | Estimate | 95% HPD Interval |
| Probit Equation and | | | | |
| Ordered Probit Equation | | | | |
| INHQUAL | -.902** | (-1.089, -.714) | -.775** | (-.916, -.64) |
| INTERCEPT | -2.309** | (-3.44, -1.262) | .220 | (-.193, .632) |
| OWNMODEL | -.032** | (-.056, -.007) | -.019** | (-.037, -.002) |
| COMPMODEL | .006 | (-.002, .013) | .003 | (-.002, .009) |
| Car Segment Variables | | | | |
| Small | 2.734** | (1.741, 3.805) | 1.429** | (1.1, 1.765) |
| Small specialty | 1.846** | (.684, 3.058) | .930** | (.485, 1.377) |
| Lower middle | 1.882** | (.842, 2.989) | .328 | (-.063, .715) |
| Upper middle | 1.737** | (.759, 2.826) | .289* | (.001, .567)a |
| Middle specialty | 2.707** | (1.68, 3.785) | 1.395** | (1.061, 1.719) |
| Large | 1.100** | (.04, 2.286) | .027 | (-.431, .494) |
| Lower luxury | .925* | (.031, 1.843)a | -.518** | (-.84, -.194) |
| Upper luxury | .645 | (-.456,1.815) | -.925** | (-1.29, -.548) |
| Luxury specialty | 1.379** | (.086, 2.702) | -.621** | (-1.219, -.052) |
| Body Style Variables | | | | |
| WAGON | -.203 | (-.503, .097) | -.131 | (-.366, .106) |
| TWODR | -.291** | (-.578, -.004) | -.162 | (-.376, .049) |
| HATCH | -.309 | (-.781, .135) | -.438** | (-.798, -.071) |
| TIME (years since 2001) | -.147** | (-.178, -.114) | -.146** | (-.17, -.121) |
| 12 | | | 1.138** | (1.056, 1.219) |
| Performance Equation | | | | |
| INTERCEPT | -22.785** | (-30.872, -14.932) | -23.506** | (-31.43, -15.717) |
| ABS STD | -.071 | (-.167, .032) | -.061 | (-.157, .037) |
| TRACT_OP | .112** | (.005, .219) | .122** | (.013, .227) |
| TRACT_STD | .117** | (.028, .209) | .076 | (-.018, .172) |
| MPG | .009 | (-.003, .021) | .009 | (-.003, .022) |
| HPWT (horsepower/weight) | 7.630** | (4.511, 10.79) | 7.577** | (4.456, 10.681) |
| LNSIZE (log of car size) | 1.934** | (1.34, 2.532) | 1.988** | (1.398, 2.571) |
| σ22 | .152** | (.142, .163) | .153** | (.141, .165) |
| σ12 | -.060** | (-.099, -.02) | -.069** | (-.106, -.034) |
*90% of posterior mass away from zero.
**95% of posterior mass away from zero.
a90% HPD interval.
Notes: Car model-specific intercept parameters in performance equations are omitted from table. HPD = highest posterior density.
H2. In testing this hypothesis, we use an ordered probit model for Equation 1 with YESFEAT as the dependent variable, as defined earlier. Table 6 presents the results for our test of this hypothesis using our simultaneous equation model. The coefficient of INHQUAL is positive and significant, in support of H2, suggesting that the propensity for a car model to offer a feature-added product increases with its inherent quality. In terms of the other statistically significant parameters, the number of car models/styles offered by the firm in the same segment increases the propensity to offer a feature-added product. This result is understandable as a way for the firm to differentiate its product offerings in the segment. The coefficient of TIME is positive, indicating a trend among car models to offer the added features of ABS and TC. In this model, s12 is positive and significant, suggesting that performance not accounted for by the explanatory variables in Equation 2 is positively related to offering of a feature-added product.
H3. Table 7 presents the parameter estimates for our empirical test of H3. As before, we use the probit and ordered probit models, respectively, for Equation 1, when BASEMOD and PRODLINE are the dependent variables. As noted earlier, we do not use TIME as a control variable in Table 7 because COST is highly negatively correlated with TIME. As expected, the coefficient of COST is positive, suggesting that the propensity to offer a stripped-down product increases (decreases) with higher (lower) cost. To examine the interaction of intrinsic quality and cost, we categorized the estimated car model intercepts, INHQUAL, into high and low values. This new derived variable, INHQUALC, equals 1 for the top 90% of INHQUAL values and 0 otherwise.[ 8] We discretize INHQUAL in this fashion for two reasons. The sensitivity of BASEMOD to costs across performance ratings, shown in Figure 2, is nonlinear due to ceiling effects at the highest performance values. This ceiling effect arises because cars with the highest performance ratings are already at a point of not offering a base product at the highest cost values in the data. Thus, a linear interaction term, INHQUAL X COST, that uses directly the estimated INHQUAL values is not statistically significant when estimated. Second, our theoretical results suggest that the propensity to offer a base product will be insensitive to cost for only the lowest-quality firms because of market segmentation benefits. Therefore, we use the INHQUALC variable to separate the car models with the lowest intercepts from the rest. Table 7 incorporates the interaction term INHQUALC X COST with this new categorical intrinsic quality variable. The interaction term is positive and significant in both models. This result implies that for products with higher INHQUAL values, the propensity to offer a stripped-down product increases more (decreases more) with higher (lower) cost, consistent with H3. We obtain similar results when INHQUALC is set to 1 for the top 80% of INHQUAL values.
TABLE: TABLE 6 Test of H2
| Ordered Probit Model (DV: YESFEAT) |
| | 95% HPD |
| Estimate | Interval |
| Ordered Probit Equation |
| INHQUAL | .396** | (.241, .543) |
| INTERCEPT | 1.862** | (1.437, 2.279) |
| OWNMODEL | .076** | (.052, .098) |
| COMPMODEL | .0004 | (-.005, .006) |
| Car Segment Variables | | |
| Small | -1.113** | (-1.468, -.755) |
| Small specialty | -.046 | (-.562, .48) |
| Lower middle | .012 | (-.406, .449) |
| Upper middle | -.146 | (-.504, .218) |
| Middle specialty | -.656** | (-.998, -.308) |
| Large | .430 | (-.095, .963) |
| Lower luxury | 1.083** | (.648, 1.527) |
| Upper luxury | .791** | (.348, 1.218) |
| Luxury specialty | .362 | (-.191, .925) |
| Body Style Variables | | |
| WAGON | .205* | (.008, .402)a |
| TWODR | .017 | (-.198, .245) |
| HATCH | .318* | (.012, .621)a |
| TIME (years since 2001) | .110** | (.084, .138) |
| m2 | 1.736** | (1.588, 1.883) |
| Performance Equation | | |
| INTERCEPT | -24.163** | (-32.278, -16.278) |
| ABS_STD | -.025 | (-.121, .07) |
| TRACT_OP | .047 | (-.068, .163) |
| TRACT_STD | .075 | (-.022, .171) |
| MPG | .010 | (-.002, .022) |
| HPWT (horsepower/weight) | 7.348** | (4.259, 10.533) |
| LNSIZE (log of car size) | 2.036** | (1.441, 2.632) |
| s2 | .152** | (.141, .163) |
| S12 | .064** | (.025, .103) |
*90% of posterior mass away from zero.
**95% of posterior mass away from zero.
a90% HPD interval.
Notes: Car model-specific intercept parameters in performance equations are omitted from table. HPD = highest posterior density.
H4. Table 8 presents the results in this case, with YESFEAT again being the dependent variable of an ordered probit model in Equation 1. The coefficient of COST is negative and significant, consistent with our theoretical results showing that a firm may choose not to offer a feature added product if the cost is sufficiently high. However, consistent with H4, the propensity of a firm with higher inherent quality to offer a feature-added product is less negatively affected by a higher cost (INHQUAL X COST is positive). Thus, H4 is supported.
H5. Table 8 also presents the parameter estimates for the model used to test this hypothesis. In this simultaneous equation model, we use HICOSTVAR, which was defined earlier, as the dependent variable in a probit regression representing Equation 1. Consistent with H5, the coefficient of INHQUAL is positive and significant, indicating that products with higher inherent quality are more likely to offer product variants with the higher- cost feature of TC than with the lower-cost feature of ABS. Thus, H5 is supported. The propensity for car models to offer the higher-cost feature of TC is increasing over time, as evidenced by the positive coefficient for TIME.
TABLE: TABLE 7 Test of H3
| Probit Model (DV: BASEMOD) | Ordered Probit Model (DV: PRODLINE) |
| Estimate | 95% HPD Interval | Estimate | 95% HPD Interval |
| Probit Equation and Ordered | | | | |
| Probit Equation | | | | |
| INHQUAL | -1.088** | (-1.32, -.861) | -.910** | (-1.078, -.739) |
| INTERCEPT | -7.115** | (-8.574, -5.659) | -4.594** | (-5.434, -3.754) |
| OWNMODEL | -.030** | (-.054, -.005) | -.017 | (-.035, .001) |
| COMPMODEL | .006 | (-.001, .014) | .004 | (-.002, .009) |
| Car Segment Variables | | | | |
| Small | 2.513** | (1.593, 3.536) | 1.342** | (1.009, 1.686) |
| Small specialty | 1.689** | (.603, 2.89) | .888** | (.443, 1.345) |
| Lower middle | 1.640** | (.654, 2.7) | .215 | (-.183, .61) |
| Upper middle | 1.470** | (.52, 2.491) | .149 | (-.2, .497) |
| Middle specialty | 2.527** | (1.585, 3.566) | 1.322** | (.99, 1.669) |
| Large | .896* | (.008, 1.809)a | -.045 | (-.531, .413) |
| Lower luxury | .734 | (-.285, 1.797) | -.580** | (-.916, -.25) |
| Upper luxury | .484 | (-.559, 1.593) | -.997** | (-1.38, -.628) |
| Luxury specialty | 1.238* | (.189, 2.299)a | -.661** | (-1.244, -.071) |
| Body Style Variables | | | | |
| WAGON | -.133 | (-.432, .179) | -.084 | (-.328, .156) |
| TWODR | -.242 | (-.537, .05) | -.122 | (-.338, .101) |
| HATCH | -.263 | (-.748, .191) | -.412** | (-.786, -.051) |
| COST | .010** | (.007, .013) | .010** | (.008, .012) |
| INHQUALC x COST | .002** | (.0003, .003) | .001** | (.0002, .003) |
| m2 | | | 1.146** | (1.038, 1.239) |
| Performance Equation | | | | |
| INTERCEPT | -22.713** | (-30.622, -14.763) | -23.333** | (-31.367, -15.427) |
| ABS STD | -.065 | (-.164, .033) | -.058 | (-.153, .041) |
| TRACT_OP | .112** | (.006, .222) | .121** | (.013, .226) |
| TRACT_STD | .118** | (.026, .208) | .081* | (.004, .162)a |
| MPG | .009 | (-.004, .021) | .009 | (-.003, .021) |
| HPWT (horsepower/weight) | 7.638** | (4.521, 10.809) | 7.599** | (4.447, 10.699) |
| LNSIZE (log of car size) | 1.928** | (1.323, 2.504) | 1.974** | (1.376, 2.563) |
| σ22 | .151** | (.14, .162) | .152** | (.142, .164) |
| σ12 | -.052** | (-.089, -.014) | -.062** | (-.096, -.028) |
*90% of posterior mass away from zero.
**95% of posterior mass away from zero.
a90% HPD interval.
Notes: Car model-specific intercept parameters in performance equations are omitted from table. HPD = highest posterior density.
We now discuss issues concerning the robustness of our empirical results. As in the case of Hi, the results for the other hypotheses remain robust in alternative models, wherein the propensity to introduce a base product is measured at the brand or company level rather than at the car model level. The Ward Automotive data does not include any information on bundles in which ABS/TC are offered. However, such bundles would not affect the conclusions from our empirical analysis, as long as ABS and TC are correctly coded in the data as being optional or standard, whether or not they are part of a bundle. In our data, we define a base product as one without ABS and TC, consistent with our theoretical analysis. One might consider analyzing these features individually, wherein a base product would be defined as one without TC (given the technology, it is not possible to have a product that has ABS but not TC). Our analysis using such a definition of the base product yields similar results (see Web Appendix).
Our data do not support the notion that R&D capabilities, rather than competitive conditions, determine whether a firm offers the feature of ABS or TC—for example, over the data period, 13 of 17 companies offer TC for some products but not others in their product line. Our empirical analysis, however, cannot rule out the possibility that brands may offer optional or standard features for other reasons, such as cost reduction. One may try to control for such explanations by using brand dummies in Equation 1. However, such dummies completely predict firms' feature strategies, in some cases causing model degeneracy in the probit models. Furthermore, the interpretation of these brand dummies is somewhat unclear for the other brands. Nevertheless, we estimated the ordered probit models with the brand dummies and found similar results consistent with our hypotheses. However, given the aforementioned issues with using brand dummies in Equation 1, we present the results without such dummies in this article. Moreover, omitting brand dummies leads to a more parsimonious model (there are 35 brands in the data), allowing more of the variation to be explained by other variables of interest.
TABLE: TABLE 8 Tests of H4 and H5
| Test of H4 | Test of H5 |
| Ordered Probit Model (DV: YESFEAT) | Probit Model (DV: HICOSTVAR) |
| Estimate | 95% HPD Interval | Estimate | 95% HPD Interval |
| Ordered Probit Equation and Probit Equation INHQUAL | | | .696** | (.528, .867) |
| INTERCEPT | 5.227** | (4.298, 6.14) | -.456** | (-.928, -.003) |
| OWNMODEL | .076** | (.053, .099) | .090** | (.062, .117) |
| COMPMODEL | .001 | (-.005, .006) | -.0003 | (-.007, .007) |
| Car Segment Variables | | | | |
| Small | -1.114** | (-1.472, -.758) | -.861** | (-1.245, -.478) |
| Small specialty | -.039 | (-.547, .494) | .241 | (-.331, .828) |
| Lower middle | .030 | (-.39, .459) | .243 | (-.204, .693) |
| Upper middle | -.138 | (-.495, .226) | .661** | (.256, 1.067) |
| Middle specialty | -.647** | (-.991, -.301) | -.222 | (-.607, .169) |
| Large | .445* | (.004, .892)a | 2.002** | (1.268, 2.752) |
| Lower luxury | 1.089** | (.656, 1.531) | 1.329** | (.882, 1.78) |
| Upper luxury | .805** | (.366, 1.239) | .970** | (.534, 1.395) |
| Luxury specialty | .373 | (-.179, .926) | .632* | (.08, 1.158)a |
| Body Style Variables | | | | |
| WAGON | .210* | (.016, .41)a | .152 | (-.126, .439) |
| TWODR | .024 | (-.199, .238) | .270** | (.015, .52) |
| HATCH | .329* | (.023, .629)a | .324 | (-.113, .748) |
| COST | -.008** | (-.01, -.006) | | |
| INHQUAL x COST | .001** | (.0007, .0015) | | |
| m2 | 1.784** | (1.633, 1.926) | | |
| TIME (years since 2001) | | | .174** | (.14, .207) |
| Performance Equation | | | | |
| INTERCEPT | -24.265** | (-32.242, -16.392) | -24.051** | (-32.381, -16.241 |
| ABS STD | -.025 | (-.118, .073) | -.057 | (-.157, .041) |
| TRACT_OP | .042 | (-.075, .156) | .075 | (-.04, .185) |
| TRACT_STD | .072 | (-.025, .168) | .061 | (-.048, .17) |
| MPG | .010 | (-.003, .022) | .008 | (-.004, .02) |
| HPWT (horsepower/weight) | 7.317** | (4.192, 10.454) | 7.729** | (4.537, 10.821) |
| LNSIZE (log of car size) | 2.044** | (1.462, 2.645) | 2.032** | (1.443, 2.649) |
| σ22 | .153** | (.142, .164) | .152** | (.141, .163) |
| σ12 | .068** | (.024, .106) | .052** | (.006, .099) |
*90% of posterior mass away from zero.
**95% of posterior mass away from zero.
a90% HPD interval.
Notes: Car model-specific intercept parameters in performance equations are omitted from table. HPD = highest posterior density.
In this article, we present a conceptual framework using a theoretical model to analyze how firms that differ in their quality image or intrinsic product quality may decide whether to include additional product features in their base products. Specifically, should a firm offer additional product features as an optional component or as a standard component in its base product, and how would this decision depend on the firm's quality positioning? Our theoretical results suggest that a low-quality firm would offer the feature as optional, that is, offer a feature-added product as well as a base product, if it chose to add the feature to its product. On the other hand, a high-quality firm would offer the feature as a standard component unless the cost of the feature was high. These results point to an asymmetry in the propensity of high- and low-quality firms to offer stripped-down versions (i.e., the base product without the added feature) of the product, in that a low-quality firm generally prefers to offer a stripped- down product in its product line. Furthermore, a high-quality firm becomes less likely to offer a stripped-down product in its product line as the cost of the feature decreases. This asymmetry arises because offering the stripped-down product can help the low-quality firm appeal to those marginal consumers who are deciding whether or not to make a product purchase. In contrast, the high-quality firm offering a stripped-down product would aggravate price competition and cannibalization, making such a strategy less preferred for such a firm.
Conversely, our theoretical results suggest that a high- quality firm would generally prefer to offer a feature-added product while a low-quality firm may not. The reason is that from the perspective of the high-quality firm, a feature-added product relaxes price competition with the low-quality firm and can also facilitate market segmentation when offered in conjunction with the base product. However, because a feature- added product offered by the low-quality firm would increase price competition, such a firm would not offer this product unless the feature cost was sufficiently low. Generalizing from the above results leads to the implication that a high-quality firm would be likely to standardize across its product line on low-cost product features while offering high-cost features as optional additions to its base product. In contrast, a low-quality firm would be likely to offer low-cost features as optional additions to its base product, while refraining from offering high-cost features at all.
Firms' strategies on offering optional or standard feature additions to their base product are likely to change over time because of consumers learning the value of feature additions. Our theoretical results suggest that an increase in consumer value due to learning is likely to induce high-quality firms to standardize the product feature and to persuade low-quality firms to offer the feature as an optional addition. Likewise, dynamics in firms' strategies can arise because of reductions in the cost of product features over time, stemming from learning-by-doing efficiencies. Such cost reductions would have the same effect on firms' strategies as increased value perceptions among consumers due to learning would have.
We test several of the implications and hypotheses arising from our theoretical results using data from the U.S. passenger car market. Specifically, we look at the propensity of car models with different inherent perceived performance qualities to offer the product features of ABS and TC as optional or standard features in their product lines. The inherent performance qualities of car models are gleaned from consumer ratings of car models as measured by J.D. Power. Our empirical analysis offers support for our hypotheses about firms of different inherent performance qualities offering product features as optional or standard equipment. Thus, our empirical results offer support for our theoretical propositions and implications.
The managerial implications of our research are as follows. If a firm' s product is perceived to be of a higher quality than those of its competitors, then unless the cost of added features is very high, such a firm should refrain from offering stripped- down versions of its product, to avoid aggravating price competition in the market and not to cannibalize its feature- added products. Alternatively, if the cost of added features is high, a high-quality firm may offer a stripped-down product for cost reasons. However, while doing so, this firm should also offer a fully loaded product with added features in its product line to appeal to high-end customers, thus profiting from market segmentation. Conversely, if a firm's product is perceived to be of a lower quality than those of its competitors, such a firm should generally offer a stripped-down version of its product to appeal to price-sensitive users who are unwilling to pay much for higher quality. However, if such a firm were to also offer a more fully featured product in its product line, it should be cautious about going head-to-head on the added features with high-quality brands, because doing so might lead to increased price competition and lower profits. Nevertheless, if the variable cost of the added features is low, a low-quality firm might find it profitable to add a fully loaded product to its product line because the low cost of the features might offset the effect of lower prices that result from higher price competition and the effect of cannibalization from its stripped-down version.
As in any study, our work is not without limitations. Our theoretical model considers only vertically differentiated firms and features (i.e., quality differentiation). It is possible that consumers may have different tastes for firms' base products and features. As a result, it may be interesting to extend our model to study product line decisions with horizontally differentiated firms and features. Our empirical analysis tests the propensity of only two features to be offered as optional or standard features by car manufacturers. It would be useful to examine the implications of our model using other features. Testing our model's predictions in other markets, such as household appliances, could also strengthen our understanding of this problem.
Authors are listed alphabetically. The authors thank the JM review team for several helpful suggestions. They also acknowledge the comments of participants at the 2012 Marketing Science Conference. Michael Haenlein served as area editor for this article.
Endnotes 1 Note that making a single feature optional introduces two variants in the product line—one variant with the feature added and another without it. In contrast, a feature that is standard requires only one product in the line. A wider product line does not, however, imply greater product line differentiation; even with only a few models, a firm can offer a differentiated product line when these variants are very different from one another.
2 While our model thus assumes perfect correlation between consumer valuations of the base product and the feature, we anticipate that our main results would be unchanged as long as this correlation was not too low. For additional discussion of this point, see the Web Appendix.
3 In the Web Appendix, we relax the assumption of equal marginal costs for the high- and low-quality products. We also analyze the situation in which offering a narrow product line enhances a firm's quality perception. Our analysis shows that our main results continue to hold with these alternative assumptions.
4 This result is similar to that of Gilbert and Matutes (1993), who show that both firms may offer a full product line when the level of differentiation between firms is sufficiently large.
5 Consistent with a lack of intensification of price competition, the price of firm H's base product, hb, stays the same between the B-B and B-BF equilibria.
6 Note that we ignore for simplicity increases in system-wide production, inventory, and marketing costs that result from firms offering optional features. If such costs were included, the parameter range for the B-F equilibrium in Proposition 1 would expand.
7 Besides ABS and TC, the data included information about only one other feature, manual (such as M6) versus automatic transmission (such as A5), that could be optional or standard equipment for a car model. However, casual research reveals that consumer preference for manual or automatic transmission is usually idiosyncratic (see, e.g., Consumer Reports 2015). Thus, this feature does not constitute a vertical feature in our conceptual model.
8 This categorization is done at each iteration of the MCMC estimation algorithm.
GRAPH: FIGURE 2 Propensity to Offer a Base Product Against ABS Cost for Performance Rating Categories
DIAGRAM: FIGURE 1 Consumer Segments in Equilibrium
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~~~~~~~~
Subramanian Balachander is Professor of Marketing and Albert O. Steffey Chair, School of Business Administration, University of California, Riverside.
Esther Gal-Or is Professor of Business Administration and Glenn Stinson Chair in Competitiveness, Katz Graduate School of Business, University of Pittsburgh.
Tansev Geylani is Associate Professor of Business Administration, Katz Graduate School of Business, University of Pittsburgh.
Alex Jiyoung Kim (corresponding author) is Assistant Professor, Graduate School of International Studies, Ewha Womans University.
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Record: 144- Reflections on “Transformative Marketing: The Next 20 Years”. By: Meyer, Robert. Journal of Marketing. Jul2018, Vol. 82 Issue 4, p13-14. 2p. DOI: 10.1509/jm.82.42.
- Database:
- Business Source Complete
Reflections on “Transformative Marketing: The Next 20 Years”
There is an old adage that the more things change, the more they remain the same. In my MBA marketing class, one of the things students like to hear me say is that they are special people living in special times, how technology is completely transforming almost every known aspect of markets, from how consumers shop to how channels operate. New rules, new games. And, indeed, it is not hard to find supporting examples. In the mid-2000s, the ubiquity of Internet access made possible the rise of an exotic new species called the “digitally native vertical brand”—vertically integrated consumer retailers that live exclusively on the web without a physical store presence. But, while the specifics of change are certainly new, the more jaded among us are quick to point out that we have been here before in history—and often. In the mid1800s the ubiquity of railroads in the United States made possible the rise of mail-order retailing businesses (e.g., Sears, Montgomery Ward) that, like digitally native vertical brands, had no physical stores, something that similarly allowed them to conveniently deliver products directly consumers at a low cost. The introduction of WATs lines in the 1960s also gave birth to the benefits (and social costs) of telemarketing. While it is hard to equate eras, some might argue that mail order did as much to revolutionize retailing in the 1800s as Amazon is doing today and that telemarketing (both inbound and outbound) in the 1960s was no less important an innovation for business at that time than the web would be three decades later.
Yet, despite this, it is not hard to argue that the changes we are experiencing today feel different—if not in substance at least in speed, which brings me to V. Kumar’s editorial. In his essay, Kumar (2018) offers a comprehensive road map to this changing landscape, one that suggests not just how academic marketing may evolve, but also, and perhaps most critically, how the practice of marketing may evolve. But it is also an intimidating read in this sense: for anyone who is not at the cutting edge of technology and lacks a crystal ball, it is hard to know how best to respond to this change—for academics, for whom the vast array of new analytic tools is the most critical to learn, as well as for managers, who must know how to adapt their firms to the changing landscape. Of course, for academics the costs of failing to adapt are modest—a tenured professor whose research has lost its relevance is still a tenured professor. But for firms, the stakes are clearly much higher. One need not look far to find vivid examples of the costs of placing wrong bets on the future. In the 1930s and 1940s, Montgomery Ward faltered as a mail-order retailer because it was slow to see the synergistic benefits of brick-and-mortar retail outlets that Sears had seen. Then, decades later, Sears fell into the same trap, faltering by failing to adapt to the changing landscape of the 1990s. In the early 2000s, Blockbuster’s investments in brick-and-mortar stores crippled its ability to respond to the competitive changes that were rapidly transforming video entertainment. The firm lacked Netflix’s acumen in delivering entertainment online and Redbox’s ability to provide physical products at cheaper prices with greater convenience.
How should firms in different industries optimally adapt to the changes that Kumar (2018) lays out? Well, needless to say, that’s the hard part. Because firms have limited resources, they are forced to place focus bets on the specific form that the future will take: which technologies to invest in, how much, and when. And therein lies a conundrum: while the general form of the future may be clear—for example, Kumar (2018) is surely right about the rise of personalization—market evolution is, by definition, a stochastic dynamic process that makes investment in specific tactics a roll of the dice. Even the firms with the deepest pockets and can diversify easily—the Amazons and Googles—know that with a wrong guess about direction they could end up this generation’s Montgomery Wards.
To illustrate the planning challenge this poses, let me take up the first of the research questions that Kumar (2018) poses in his editorial: “What are the future implications for businesses regarding the technical design of devices concerning interconnectivity and information collection?” A few months ago I attended the Shoptalk convention in Las Vegas, where I had a chance to speak to a number of retail firms who are on the front line of dealing with this very question. Consider, for example, the enormous potential of smartphones as a marketing channel. The conventional approach to mobile marketing has been to treat such devices as a web-access point—a device that serves as a portable ordering platform for consumers and a way to target them when they are on the go (e.g., Lamberton and Stephen 2016; Shankar and Balasubramanian 2009). But there is emerging evidence that consumers interact with these devices in a way that is quite unlike how they interact with other webaccess devices such as PCs and tablets, something that has enormous implications for marketing. For example, Melumad, Inman, and Pham (2018) have uncovered evidence that that not only does the content that people produce on smartphones differ from that on PCs, but many consumers engage with them as “digital friends” that serve as a source of trust and comfort in times of stress. At Shoptalk, I sensed a widespread appreciation for the marketing opportunities this new opportunity posed but also very different views on how it might be leveraged. Moby Mart and Amazon Go, for example, envision future retail stores without human assistants; customers are recognized by their smartphones as soon as they enter a store, and all interactions from aiding search to making recommendations to handling payment are done through natural-language interactions on their devices—which consumers rely on with as much trust as they would a human assistant. But, another view is that the primary future battle of retailing is not who can develop the best unmanned store, but universal apps that oversee multiple facets of consumer lives, from texting with friends to shopping—with early examples being China’s WeChat and voice-based assistants such as Amazon’s Alexa. If Alexa is accepted as the member of the family who knows the most about the weather, why not trust her suggestions for where to buy a car? In the future, such apps and devices may thus be the ultimate market gatekeepers: the most successful firms may not be the ones who can develop the bets products and services, but rather those that are most successful in persuading WeChat or Alexa to recommend them.
Here is the uncertainty: we are not there yet, and perhaps may never be. If the data-privacy restrictions currently being implemented in the European Union take broader hold, or if consumers simply do not want virtual “shopping friends” living on their smartphones and in their homes, the billions being invested in such ventures may not be worth much. Radical changes in retailing will certainly occur, but it may take a very different form.
So, if firms cannot predict the future of technology, how can they plan? While Kumar’s (2018) editorial does not provide a precise answer to this difficult question, it does provide at least a starting point. In essence, his advice is that to be good planners one has to be a good student of why and how business transformations occur in the first place. His editorial lays out, if not a theory of transformation, at least a set of guiding principles. One of its implications is that successful firms do not take an immediate look around, see what’s hot, and jump on it, but rather develop a deep understanding of the forces that are driving change and use that as a basis for formulating informed conjectures about where the best investments lie. In essence, to be successful in this rapidly changing world, firms need to become better historians. In other words, avoid the trap captured thus: firms that ignore history are the most likely to be doomed to repeat it.
REFERENCES 1 Kumar, V. (2018), “Transformative Marketing: The Next 20 Years,” Journal of Marketing, 82 (4), 1–12.
2 Lamberton, Cait, and Andrew T. Stephen (2016), “A Thematic Exploration of Digital, Social Media, and Mobile Marketing: Research Evolution from 2000 to 2015 and an Agenda for Future Inquiry,” Journal of Marketing, 80 (6), 146–72.
3 Melumad, Shiri, Jeff Inman, and Michel Pham (2018), “Selectively Emotional: The Effect of Smartphone Usage on User-Generated Content,” working paper, the Wharton School, University of Pennsylvania.
4 Shankar, Venkatesh, and Sridhar Balasubramanian (2009), “Mobile Marketing: A Synthesis and Prognosis,” Journal of Interactive Marketing, 23 (2), 118-29.
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Record: 145- Reflections on Publishing in the Journal of Marketing. By: Kumar, V.; Mittal, Vikas; Morgan, Neil. Journal of Marketing. Nov2018, Vol. 82 Issue 6, p1-9. 9p. DOI: 10.1177/0022242918805485.
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Reflections on Publishing in the Journal of Marketing
Academic research and publication hold vital positions in an academic's career. Publishing research serves a critical purpose in disseminating knowledge to information seekers, contributing to the body of knowledge, showcasing scholarship, and securing tenure/promotion. Although it is necessary to an academic's work and career, the academic publication process can present daunting challenges. For a premier journal such as the Journal of Marketing (JM), which publishes only the best work in the field of marketing and accepts only 7%–8% of all submissions, navigating the peer review process becomes all the more demanding. Amid these high stakes, it is essential that JM's marketing scholarship is captured in its entire essence and presented to the readers in a timely manner. In this regard, it is necessary to nurture a positive and constructive approach to scholarship so that the body of knowledge can experience incremental gains. This editorial is a step in this direction.
In this editorial, we review the recurring feedback that emerged from the decision letters during our editorial term. Our goal is to offer a perspective that is based on our reflection and not what an "ideal" JM submission ought to be. We hope that such a perspective will benefit future contributors in better designing their submissions to JM. In doing so, we provide a synthesis of this review and organize this editorial as follows. First, we present a set of guidelines for crafting manuscripts for JM that are truly compelling and therefore can survive the "desk rejection" stage. In addition, we present a set of "survival tips" for authors to avoid some common mistakes that surface during the initial submission. Then, we offer guidelines on how to address reviewer comments. Finally, we provide directions for how to prepare the manuscript for resubmission.
At the outset, it is important to communicate what we mean by "crafting" manuscripts. We do not mean that the manuscript is merely well-written (i.e., linguistically proficient). It should go beyond that to include the essential ingredients of a submission: a combination of a well-positioned core idea, the incorporation of related theory/theories, the use of rigorous and relevant study methods, and the exposition of meaningful insights. While we recognize that writing is a critical element that can help or hinder the initial submission, it is not the ultimate touchstone by which to evaluate a manuscript, because it is possible for a well-written manuscript to hide errors in the study. Therefore, as long as the aforementioned essential ingredients of a submission are clearly discernible in the initial submission, the writing can be fixed and enhanced during the review process. So, what are the essential elements of an initial JM submission?
To obtain a better grasp of crafting manuscript submissions for JM, we performed a content analysis of all decision letters to JM submissions during our tenure. Specifically, we focused on the "desk reject" letters. This exercise provided us the opportunity to identify the essential items in an initial JM submission that would likely enable it to perform well throughout the review process. Another way of looking at this exercise is to identify the weaknesses in a manuscript that, when avoided, would most likely enable it to progress past the desk reject stage. In this regard, we identify five essential elements exhibited by all submissions that were successful in passing this stage. We present these five elements in a constructive manner aimed toward helping readers craft initial submissions in the future, rather than using a passive, reporting style. We hope this will better serve future contributors to JM.
It is critical that the manuscript clearly identify its positioning within the marketing literature. This can be made evident in two areas. First, the "introduction" section of the manuscript serves a vital role. When the introduction does not adequately situate the study within the body of knowledge and practice of marketing, the clarity of the study can be called into question. As a result, the introduction must be precisely organized to present the objectives within the perspective of the marketing discipline and relative to marketing literature overall. Second, the "literature review" or "conceptual framework development" section becomes important in this regard. Often, manuscripts may cite several studies, including studies from other disciplines. However, if what was learned from this literature is not sufficiently established with respect to marketing, the manuscript rests on a shaky foundation. Furthermore, listing or mentioning all the literature that was reviewed is not informative; rather, a synthesis of the literature reviewed and how it is relevant to the current study is required. As a result, the literature review should be covered in a manner that enhances and informs the subsequent sections of the manuscript such as the hypothesis development, study design, and discussion of results.
The practice of formulating good research questions and the outcome of generating impactful insights can be viewed as two sides of the same coin. The research questions addressed must be clearly articulated because they focus on particular study dimensions in the chosen line of inquiry and convey an understanding of the phenomenon and its evaluative approach. Therefore, the research questions need to be presented alongside an accurate and complete description of the research problem and its context for the maximum study impact. Furthermore, the motivation for the study has to be firmly established in a manner that naturally leads to the research question(s). A required precursor to correctly identifying the research questions is to understand the research problem. [11] defines the research problem as "any problematic situation, phenomenon, issue, or topic that is chosen as the subject of an investigation" (p. 73). Many studies that were desk rejected during our tenure suffered from a lack of understanding of the research problem, which led to poor framing of the research questions. For instance, in addition to other issues, when the authors do not effectively grasp existing discrepancies or incongruities and/or when the authors' conceptualization and understanding of the study phenomenon is at odds with the extant theory/practice, the submitted manuscript invariably gets desk rejected.
Manuscripts must clearly state their purpose early on to clarify the goals of the study and set expectations. This is important both to inform readers about the topic and to keep them engaged as they read through the study. For instance, authors might state the purpose of the study toward ( 1) theoretical, ( 2) conceptual, or ( 3) empirical focus. Such an identification up front attunes readers to take note of the study's contribution. In communicating the study's purpose, it is important to present as complete a picture of the research problem as possible. One way to do this is to identify the foreground, background, focal area, and study context ([ 1]). For instance, consider a submission in the area of customer engagement. In this case, the customers are in the foreground, as this construct is firmly based on how customers engage with brands and/or firms. The other stakeholders (firms, suppliers, employees, etc.) would compose the background. The focal area in a customer engagement study would be, for instance, theory development, its role in managing customers, and its measurement. Furthermore, the regulatory framework, culture, technology, industry type (service vs. manufacturing), and the nature of the business setting (contractual vs. noncontractual), among others, would be considered the study context. When the purpose of the study is presented in such a manner, readers get a holistic view of the proposed study.
Rather than leave it to the readers' inference, it is important that authors clearly identify the study's contributions. Consider a key goal for JM during our tenure: to encourage "papers that address the most relevant managerial problems and propose the most appropriate and actionable method, while ensuring rigor to resolve it" ([ 6], p. 1). Of course, given such a broad ambit, not all studies are likely to make the same type of contribution. In this regard, when evaluating whether to issue a desk reject decision, among other things, we considered identifying manuscripts along the lines of ( 1) contributing to theory development in understanding a phenomenon that affects the marketplace (this includes advancing theories that challenge existing theories), ( 2) developing new methods/models that perform better than existing approaches, ( 3) conceptualizing a business phenomenon or process to understand how it works, ( 4) identifying evidence-based moderating and/or mediating variables that determine future managerial actions, and ( 5) developing evidence-based strategies and/or tactics that firms can readily implement. Furthermore, a precursor to identifying the contribution is to present the extant knowledge in the focal area of research in an accurate manner. This helps substantiate the importance/validity of the proposed contribution.
The benefits of a study are ultimately what hooks people into reading the manuscript. Depending on the type of article, during the desk reject decision evaluation we considered benefits that might include, but are not limited to, ( 1) problems that are relevant and timely to the academic and business communities, ( 2) a study approach that involves all applicable forms of rigor (empirical, conceptual, and analytical), ( 3) conceptual/substantive insights and findings that make an incremental addition to the existing knowledge base, ( 4) actionable implications that will capture the attention of the practitioner community, ( 5) a study that closes gaps in the literature, and ( 6) any new investigation that furthers the marketing discipline. Moreover, because articles in JM typically address ( 1) relevant/pertinent problems regarded as such by JM's audiences (i.e., practitioners and academics), ( 2) areas of marketing that have not received sufficient investigation, and ( 3) changes in businesses' social/political/cultural environment that affect the present and future of marketing, we also consider these features when evaluating a submission for the desk reject decision.
The mere presence of all the aforementioned essential elements of a JM submission offers a manuscript the chance of advancing to the review process. Yet flaws or holes in the research that render the study less impactful or even questionable can be uncovered during the initial round of review. Drawing on a content analysis of first-round rejection letters, we present a few tips that can guide authors in crafting manuscripts that have a solid foundation and, therefore, a better chance of getting past the first round.
A construct is defined as a conceptual term used to describe a phenomenon of theoretical interest, and a measure is defined as an observed score of a construct ([ 4]). Because constructs are used to describe a phenomenon, they are best understood through the variables that affect/explain the phenomenon. Given this contextual nature, it is crucial that the constructs are clearly defined. In the case of conceptual articles, in which the measurement of constructs may not be demonstrated, the importance of construct definition is even more apparent. In studies that propose and measure constructs, precise definition helps readers more easily understand the construct and compare the measurement with the intended meaning of the construct. Overall, well-defined constructs serve a vital need in the understanding, creation, transfer, and advancement of knowledge regarding a specific phenomenon. In this regard, prior studies on construct development (e.g., [ 3], [ 7], [ 8], [10]) can serve as good resource materials for scholars.
All types of papers submitted to JM (i.e., conceptual, analytical, and empirical) must have at least some level of analysis. Clearly, all empirical and analytical papers lend themselves to the presence of a section on analysis and discussion on the focal area of study. However, the notion that conceptual studies can be devoid of analysis and discussion pertaining to their real-world implications is not consistent with JM's standards. Typically, if the analysis/discussion section is written from a descriptive viewpoint (as opposed to using analytical argumentation), restates the proposed ideas/hypothesis, lacks well-thought-out reasoning of the real-world implications of the findings, or fails to address possible alternative interpretations, then the submitted paper may be considered weak by JM's standards. A strong paper for JM would be one that highlights, through analytical means, the novel implications of the study that are of direct relevance to marketing practice, without stating the obvious/already-known inferences.
Authors must devote significant attention to the presentation and discussion of insights. Of course, insights that generate maximum impact tend to be unique, novel, and managerially relevant. In this regard, the insights must be discussed articulately while adding more clarity and nuance to the overall goal(s) and findings of the study. Insights are better served when presented alongside a discussion of the broader implications of the findings. This not only gives readers the appropriate perspective through which to understand the insights but also provides food for thought for practitioners regarding the future applications of the suggested insights. It is important to note that the review process can play a vital role in the generation and enhancement of insights. However, the review process can be helpful in this regard only if the initial submission contains some insights to begin with.
Although poor writing may not be a fatal flaw, it can be a major distraction from the fundamental contribution(s) of the study. Writing well does not necessarily imply the use of sophisticated language. In fact, in most cases, good prose and simple but effective sentence construction can work wonders for the manuscript. In this regard, especially in the case of nonnative speakers of English, it is essential to have the manuscript professionally copy edited before submission.
Academic scholarship extends beyond simply engaging in original research to include bridging theory and practice, integrating ideas from across disciplines, and effectively communicating research findings to the academic community ([ 2]). In this regard, it is essential to clearly lay out the salient contributions of the study to showcase the paper's impact potential. Specifically, when the list of contributions is parsed on theoretical, methodological, and substantive grounds, the paper gains greater impact. Despite the importance of this aspect in any paper, the lack of significant contributions is a prominent reason cited by the reviewers for unfavorable decisions in the review process. So, how can authors better identify the contributions? One way is to get as much feedback from colleagues as possible throughout the course of planning, executing, and writing the study. Colleagues who are active researchers can offer valuable directions in identifying and eliciting study contributions. Feedback regarding how much interest the study's topic generates, the study's approach, the newness of the findings, and the implications of the findings for theory and practice can help the authors identify contributions.
The generalizability of findings is important for two key reasons. First, evaluating the law-like quality of the finding helps advance marketing knowledge. Second, practitioners benefit from such generalizations, as future implementations and managerial actions can be safely planned on the basis of this knowledge. When studies progress toward generating generalizable results, they contribute to the body of knowledge regarding a particular phenomenon. However, authors are also advised to recognize the limits to generalizations, the conditions that might apply, and the exceptions to generalizations. Such an approach would provide practitioners a reliable context on which to base their managerial actions and provide scholars a credible knowledge point to configure their research.
Studies submitted to JM go through a rigorous review process. The reviewers, among other things, pay keen attention to the data and data-related aspects of the study. As a result, authors need to discuss the data used for the study in the initial submission. A clear depiction of the data may also facilitate the identification of additional analysis in the review process that the authors can pursue as they revise the manuscript. JM has experienced a steady and significant increase of submissions and readership from outside the United States that use data from non-U.S. settings. This development also necessitates the accurate description of data so that the reviewers understand the data that is being used and thereby appropriately evaluate the study and its conclusions. In other words, authors who submit to JM must be aware that they are addressing a global audience and must prepare the manuscript accordingly, so as to not leave any segment of readers behind.
The test of significance is an important validation tool for the authenticity of the study findings. Essentially, the statistical significance provides the right perspective to understand the hypothesis formulation, the study method(s) used, and the reported results, with respect to the topic of inquiry. Despite the importance of statistical significance, submissions to JM occasionally report findings without the levels of significance. This places an undue stress on the review team in ascertaining the validity of the study. In addition, some scholars have also called for more education on the understanding and correct usage of statistical significance in marketing research ([ 5]; [ 9]). Therefore, authors are strongly advised to include the levels of significance (with standard errors) when reporting the results.
During our review of the decision letters, we were able to discern several recurring issues for rejected manuscripts, and we present them next. While the JM review team does an excellent job of identifying the issues, the overall review process would be well-served if authors proactively acknowledge and avoid these issues. As mentioned previously, an informal review of the study by the authors' peers (before the paper is submitted to JM) would help identify these issues. We categorize these issues by type of study.
All studies submitted to JM—whether conceptual, empirical, or analytical—are expected to adhere to the most rigorous methodological guidelines in the field of marketing scholarship. However, empirical studies that claim primarily empirical contributions are expected to be especially methodologically airtight, and, ideally, to break new ground in empirical research. Unfortunately, there are a few major empirical shortcomings that recur frequently in JM submissions, each of which researchers should work especially hard to avoid. They are as follows:
When conducting a survey, it is important to collect a sample of respondents that is free of bias and, to the best of the researchers' ability, truly representative of the target population. Most commonly, survey research is undermined by the problem of self-selection bias, wherein participation in the survey is determined by the participants themselves. Effectively, this results in a skewed and nonrepresentative sample, given that the group of people who choose to opt out of the survey will not be accounted for. It is imperative that researchers work to eliminate self-selection bias through a rigorous survey procedure and by running checks on the sample after it has been collected.
When modeling a specific market research phenomenon, it is important that authors account for the differences among the cases being studied. Differences that are accounted for by the covariates can be described by the term "observed heterogeneity." However, any heterogeneity that is unaccounted for—chiefly, unmeasured variation among cases or among relevant but omitted variables—is known as "unobserved heterogeneity." When authors do not adequately control for unobserved heterogeneity, the likelihood of erroneous inferences from the analysis increases.
Endogeneity can arise from unobserved heterogeneity, and this refers to an instance in which some unobserved variable also correlates with the independent variable in the statistical model. Such a condition implies that something other than the independent variable was responsible for driving the observed effects. Sometimes, solving an endogeneity problem is simply a matter of identifying the missing variable, measuring it, and incorporating it into the model. Other sources of endogeneity can be more difficult to account for (e.g., it might arise from causal simultaneity, in which case no amount of control variables can correct for the issue).
It is always commendable for researchers to try their hands at ambitious field experiments, which, unlike conventional laboratory experiments, are conducted in real-world settings and presume to capture the naturalistic behavior of participants. However, field experiments are prone to several pitfalls that must be avoided to meet JM's methodological standards. For instance, conducting experiments outside of a controlled lab environment makes it difficult to control for extraneous variables, which may result in a study with invalid results.
All studies submitted to JM are expected to be conceptually and theoretically rigorous. The conceptual framework, the nomological validity of the constructs, and the theoretical grounding conferred by the literature review must all be cogently and thoroughly established up front. However, a study that primarily aims to make a compelling conceptual contribution must demonstrate particularly fastidious logical development of its ideas and comprehensive familiarity with the relevant extant literature. The most common issues in these studies are as follows:
It is essential that any rigorous conceptual research paper clearly define and justify its focal constructs. If the foundational constructs are not properly motivated, then the proposed theoretical framework of the manuscript is likely to fall apart. The embeddedness of the proposed constructs within a clearly organized theoretical stream or schema is referred to as that construct's "nomological validity." This extends not only to conceptual definition but also to operationalization. This may correspond to, for instance, ascertaining whether the dimensions and subdimensions of the construct are properly defined and measured. When JM submissions are reviewed, the soundness of the constructs is one of the key criteria on which each study is evaluated.
When formulating hypotheses, it is important that the predictions are properly justified. This means that the hypotheses should be compelling, logically sound, and demonstrably testable. However, it is all too common that hypotheses are written tautologically, in an ad hoc manner, as if the results were determined in advance of the actual predictions. Moreover, studies may also suffer from ( 1) hypotheses that are redundant, and ( 2) proposed effects that have already been established by the relevant extant literature. In effect, a conceptual study submitted to JM is likely to fare better in the review process when the hypotheses are reasonably surprising and not simply intuitive.
As noted previously, it is fundamental that the constructs are clearly measured in terms of their composite dimensions and subdimensions covered in the literature. In addition, an operational consistency must be established between the proposed measure and precedent measures from the extant literature. It is one thing to be able to conceptually define a construct—it is entirely another to operationalize a construct such that it can be empirically modeled.
The most important hurdle that must be cleared by all JM submissions is that of a clear and compelling contribution. A paper may yield one or more novel insights or incrementally advance the extant understanding of a given topic, but if the contribution is not sufficiently extensive, it may not pass the muster of JM reviewer team. Often, it is evident that a given topic or data set has the potential to yield more insights than the authors illumined. It is always important to consider the full scope of one's research area and optimize its contributive value.
It is important, especially in conceptual papers, to pinpoint one's intended audience (i.e., who stands to gain the most from the research). If the authors undertake the effort to position compelling conceptual ideas within an extant theoretical stream, then they should be able to explain the implications of the research within the same stream and point to future research directions. Furthermore, the practitioner implications must be clearly delineated in the study.
Analytical studies broach both conceptual and empirical approaches, but they are primarily leveraged toward the solving of specific managerial problems using a rigorous analytical framework. These strategic papers involve the application of research concepts and methodology to real-world marketing problems, bridging the divide between theory and practice. True to their name, these studies absolutely hinge on the strength and rigor of the analysis and problem solving. The common issues with analytical papers include:
Many authors, in developing the conceptual stories of their respective manuscripts, resort to assumptive or conjectural language that requires substantiation. Without proper validation, assumptions become weak links in the explanatory chain of the narrative—often, reviewers will counter assumptions with possible alternative ideas or explanations, demonstrating the flimsiness of unsubstantiated supposition. In light of this, it is recommended that authors back their research assumptions with robust theoretical support.
One of the key attributes of JM is its status as a top managerial journal with a pronounced focus on practical relevance. It is one thing for a paper to propose theoretical insights and build on the academic literature in a given field, but it is another thing to solve the managerially relevant problems that face marketing firms on a day-to-day basis. It is important that there are actionable managerial implications that can be gleaned from the findings of any given analytical manuscript.
Many submitted analytical studies that aim to solve a managerial problem may overlook the real-world contingencies of the focal industry or international market and propose solutions that realistically cannot be implemented. Many of the reviewers for JM have considerable real-world industry experience and aim to nudge these authors into a more comprehensive understanding of managerial practice. Remember, a solution that is theoretically sound does not necessarily mean that it can be implemented.
In addition to the empirical shortcomings noted previously, analytical papers are often beset by experimental designs and simulations that fail to adequately align with the real-world circumstances of the research context. The parameters for a research simulation might be too constricting and specific, and therefore the observed effects may not prove generalizable or managerially meaningful. Moreover, it is always a possibility that the hypothesized solution is not supported by the empirical portion of the manuscript, in which case the research usually needs to be reformulated.
In analyzing a managerial problem and proposing a solution in the form of a new metric, it is important that the computational rigor is adequately nuanced and complex. Many papers submitted to JM feature analytical models that are overly simple and lacking in variables and/or equations that might capture additional nuances and contingencies. Looking out for such missteps is likely to result in a more accurate and comprehensive metric. Furthermore, JM reviewers often request robustness checks. Authors must bear this in mind and proactively provide such checks in the initial submission.
To this point, we have presented our observations and suggestions on how to craft initial JM submissions that can pass the desk rejection stage and the first-round review. In the next section, we discuss how to effectively address reviewer comments from the review process.
An invitation to revise a manuscript is a privilege extended to the authors of a small subset of manuscripts that survive the initial round of processing. It implies that more than one person involved in the review process—the Editor, Coeditor (CoE), Area Editor (AE), and reviewers—are supportive of the manuscript. When evaluating the revised manuscript, reviewers often provide a candid assessment of the revision effort in their private comments to the CoE and AE. Our thematic assessment of these comments reveals guidelines for successfully addressing reviewer comments.
The opening section of the reviewer notes should provide a one- to two-page page summary of all the major changes in response to the reviewer comments. This summary should start with a bulleted list of the specific changes in the positioning, contribution, and theory of the paper. This section should also highlight any new practical or managerial insights that emerged as a result of the revision. Finally, this summary should catalog major changes in the analysis, such as addition and deletion of studies in behavioral papers, additional models estimated, robustness checks for empirical/modeling papers, and a brief description of any appendices added. The goal of this summary should be to provide the review team—especially the CoE and the AE—with a road map for how they should approach the revised paper and what they can expect from it.
Rather than act as a fourth reviewer, the AE typically synthesizes the reviews and focuses the authors' attention to the critical issues that should be addressed. Most AE letters will not cover every point raised by the reviewers. That a specific point is covered in the AE letter is an indication of its importance to the revision. Therefore, be sure to summarize your specific response to that point in your response to the AE and specify where in the reviewer response and/or manuscript you provide a more detailed answer. It is not a good idea to cut and paste answers for specific reviewer comments in the response to the AE. While the response to the reviewer comment can be more detailed, typically the response to the AE is a shorter summary.
A typical JM review will have comments that are numbered. Authors should reproduce each comment in bold and then provide their answers beneath each comment. Though this may sound tedious, it is helpful to the reviewers who do not have to flip back and forth between the reviewer comments, the authors' response, and the manuscript. The answers should be specific and should cover the "spirit of the comment." As an example, if a reviewer asks authors to elaborate on the practical implications of the findings, it is not enough to respond by saying, "We have expanded the implications in the 'Discussion' section." Rather, the response should provide the page number and section along with a précis of the implications in the manuscript. This level of specificity helps the reviewers connect the responses to the manuscripts.
Reviewers raise a lot of points and may seek information that may seem obvious or nitpicky to the authors. Rather than getting vexed by such requests, the authors should remember that members of the review team are neither as familiar nor as embedded in the paper as the authors. Many aspects of the paper that seem clear and obvious to the authors may seem ambiguous, perplexing, and even occluded to the reviewers—they are reading and reacting to the manuscript for the first time. As such, it is possible for reviewers to miss, misread, or misinterpret some of the information. In such a case, the authors should not only provide the specific information but also elaborate on the larger backdrop of the information context.
For example, suppose a reviewer asks for the correlation between measures of two constructs that are posited to be distinct. In addition to providing the correlation between the measures of the two constructs, the authors may also want to run checks on discriminant validity to help assuage the reviewers' concern. If a reviewer asks for robustness checks for an empirical/modeling paper, the authors may want to report all the different models in tables appended to the reviewer notes. This level of responsiveness signals openness, a willingness to go above and beyond the minimum ask, and a desire to seek additional feedback from the reviewers. Reviewers appreciate such a stance; it smooths the process of interacting with the authors and helps the reviewers better evaluate the revised manuscript.
Authors typically spend a lot of time fine-tuning their manuscript, but not the reviewer notes. This can be a mistake. Reviewers will spend just as much, if not more, time on understanding and reading the reviewer notes as they will on the revised manuscript. Inconsistent responses, spelling/grammatical errors, and other signs of carelessness can negatively affect reviewer evaluations of the revised manuscript. Finally, authors should always maintain a respectful, professional, and courteous tone in their responses addressing each reviewer in the first person, without using a passive voice (in other words, adopt a conversational tone). Once the revision notes are finished, authors should set the document aside for a few days. Then, revise it again to ensure it embodies all the aforementioned features.
Because the reviewer's task is to provide a critical evaluation, the review can sometimes come off as overly negative to authors. Rather than respond to the review emotionally, the authors should ensure that the responses are factual, logical, and respectful. If the authors disagree with an issue raised by a reviewer, it is not enough to simply argue with the reviewer. Rather, the authors should try to understand why the reviewer is raising a particular issue—perhaps the explanation or logic in the manuscript is unclear, perhaps the constructs need to be defined better, and so forth. The authors' response should clarify their understanding of the issue, how it has been addressed, and the resulting change(s) in the manuscript (rewriting, additional analysis, new data, improved discussion, etc.).
Reviewing for JM is a privilege that is extended to thought leaders and experts in our field. Reviewers and AEs volunteer their time in service of the discipline. While no review is perfect, almost all reviews are helpful. Authors of published manuscripts, after a few years, almost always attest that the review process, though arduous, improved the manuscript in many different ways. To benefit from the review process—improving the manuscript and increasing the chances of its acceptance—authors submitting their work to JM should keep these guidelines in mind when addressing reviewer comments. Having discussed how reviewer comments can be addressed, in the next section we explain how authors can prepare their manuscripts for resubmission.
Resubmission is the next stage in a manuscript's development. After the first round of reviews, it is not unusual for a revised manuscript to have changed radically from that which was initially submitted and reviewed. The direction of the AE and CoE and the questions, comments, and ideas from the reviewers frequently lead to significant changes in the manuscript. It is important that authors therefore not only rethink what goes into (and is omitted from) the revised manuscript but also treat it as an entirely new version of the paper. Many problems with resubmitted manuscripts are related to "legacy" problems originating in the initial submission that survive the authors' manuscript revisions. In addition, although a paper can survive an initial submission in spite of not being very well written, it rarely survives multiple rounds without substantial improvements in writing and presentation. From this perspective, authors may benefit by keeping several things in mind as they revise their manuscript for resubmission.
As with the initial submission, the authors must spark the reader's interest in the research question they aim to answer in the study. In doing so, however, it is important that the revised manuscript does not "bury the lede." It is generally a good idea to foreshadow the key results in the introduction and then elaborate on these later in the manuscript. In motivating the study, examples can be useful and often help establish the relevance of the research early in the manuscript. However, authors need to be very sure of the facts surrounding the example and their interpretation of them. In telling any story, internal consistency in the logic of arguments is key, so this requires particular care and attention in revising the manuscript. It is also generally true that a "picture is worth a thousand words" (and sometimes more). Figures can be very useful in succinctly communicating a great deal about any research study. It is also helpful in effective communication if the manuscript is not written defensively but rather presents a positive framing of any arguments. Most sentences in a revised manuscript, even in theory development and hypothesis argumentation sections, generally require no reference support. However, limiting the number of references in a single sentence or argument can be helpful in this respect. No manuscript needs more than three references to support a point—and then only if the point is particularly counterintuitive.
As with the first round of review, failure to build and communicate a clear and compelling set of contributions from a study is one of the most common reasons for second-round rejection. From this perspective, authors should answer the following questions in the revised manuscript. To what specific phenomenon/theory does the study contribute? What new knowledge emerges as a result of the study, and why is it important? To whom and under what conditions is it important? What should not be inferred from the study's results? Articulating the answers to these questions is key to a successful revision effort. In addition, authors would also be well-advised to pay greater attention to the "future research" implications of their study. Too many revisions treat this as a "throwaway" section that is "boilerplate" in nature (and remember that that "limitations" ≠ "future research"). JM is seeking high-impact research (generally viewed through the lens of future citation potential), and setting up others' future research is a great way to help achieve that. As a result, the revised manuscript should try to directly answer the following: What new research questions are now worth studying as a result of this study?
Producing an advanced draft should include revisiting everything about the new version of the paper. Not just the implications section—who should care, why they should care, and what they should do/think about differently as a result—but also the "mechanics" of the paper. For example, it is often worth revisiting the paper's title and abstract not only to reflect changes made during the revision but also to evaluate its ability to communicate to the broadest set of readers in the event that the paper is accepted. It is also essential to make sure that there is a clear flow of logic from paragraph to paragraph and from section to section. Consistency in terminology is key, and this is often a cause of confusion and frustration when terms used in the original submission are inadvertently left in the revised manuscript. Again, to ensure clarity in communication, authors are well-served to think about the revised manuscript as an entirely new paper.
There is a reason that Editors and senior academics talk about "crafting" vs. simply "writing" a manuscript. A finished article is not merely a case of the authors writing down in the revised manuscript what they have done in their study; rather, it is the building of a compelling case for the revised research idea, its execution, and implications. All of this is judged by readers (initially the review team) and therefore must be written with the audience in mind. This is easy to say, but often difficult for authors to accomplish because they effectively "know too much" about the study, its details, and its history. Thus, when the authors have an advanced draft of their revised manuscript it can be very helpful to ask others to read the draft and highlight the top three "bumps" (i.e., things that do not flow well from one sentence, paragraph, and section to the next). Once these have been identified and smoothed out, it is often a good idea to then set aside the revised manuscript for a week or more.
Finally, authors should consider answering a checklist of questions from a reader's perspective. Get a colleague (preferably more than one, and those with some experience of publishing in top journals—preferably JM) to read the revised paper and then explain to you:
- What is the research question addressed, who cares about this question—and why?
- What does the study reveal about what phenomenon/theory that was not known before, why is that important, and to whom?
- Who should do what differently, and why, as a result of the study?
- What new questions does the study suggest are now important/worthwhile for future research that were not considered so before?
- If this manuscript were to be rejected by the JM review team in this round, what do you think the most likely reason would be?
If the revised manuscript passes the acid test questions from seasoned readers, and the answers to the last question do not provoke a further rewrite of at least some of the paper, then the revised manuscript is likely to be ready for resubmission. As a final task, the authors should revisit the response notes to the reviewers, AE, and CoE and ensure that page numbers and any quotes from the revised manuscript are consistent with the final version.
We thank the marketing community and the AMA for giving us the opportunity to serve our field. We could not have done our jobs without the support of the authors, reviewers, AEs, Guest Editors of Special Issues, the Senior Editor, the Vice President of Publications, the AMA staff, and, last but not least, our respective universities. While our sole intention was to serve the community in a conscientious manner, the lift in the impact factor during the four-year term from 3.8 to 7.3 has been personally enriching and rewarding. We owe big thanks to the authors and the review team for working toward a common goal of producing papers of high impact, thus causing the impact factor to rise sharply. JM is a flagship journal in the field, and it has been an honor to serve in an editorial capacity of this prestigious publication. We offer our salute to the entire marketing community as we pass on the editorial responsibilities to the new editorial team. We wish them grand success in continuing the great legacy of JM.
Footnotes 1 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
2 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
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By V. Kumar; Vikas Mittal and Neil Morgan
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Relative Strategic Emphasis and Firm-Idiosyncratic Risk: The Moderating Role of Relative Performance and Demand Instability
Firms may allocate scarce resources to two fundamental strategic processes: value creation and value appropriation. The relative investment in these processes (i.e., a firm’s relative strategic emphasis) may be associated with firmidiosyncratic risk. Empirically, a firm’s relative strategic emphasis is represented by the difference between its advertising expenditure and its research-and-development expenditure. Using data from 2,403 firms over the period of 2000–2014, the authors find that firms’ relative strategic emphasis on value appropriation versus value creation reduces firm risk, though in a contingent manner. This association is weaker when firms have larger positive or negative relative performance. Furthermore, these contingent associations are stronger when demand instability in an industry is higher. Overall, the results demonstrate that a firm’s strategic emphasis should be examined in light of its relative performance, as well as in the context of current market conditions, when making judicious resource allocation decisions.
Online Supplement: http://dx.doi.org/10.1509/jm.15.0509
To create a sustainable competitive advantage, firms allocate their scarce resources through two fundamental processes: value creation and value appropriation (Fang, Palmatier, and Grewal 2011; Mizik and Jacobson 2003). Value-creation activities—typically research and development (R&D)—enable a firm to develop new sources of economic rents through activities that create value for customers (Lepak, Smith, and Taylor 2007). Analogously, valueappropriation activities—advertising and branding—enable a firm to appropriate greater value by increasing profits from existing customers (Rao, Agarwal, and Dahlhoff 2004).
Within marketing, scholars have viewed R&D and advertising investments as being central and representative activities signifying a firm’s focus on value creation and value appropriation, respectively (Chakravarty and Grewal 2011; Kashmiri and Mahajan 2010; Krasnikov and Jayachandran 2008; Luo and Bhattacharya 2009; Srinivasan et al. 2009). Table 1 summarizes studies in marketing that have examined value creation and value appropriation separately as well as jointly (i.e., as relative strategic emphasis). Table 1 suggests three conclusions. First, the majority of studies that have examined the association of value-creation (R&D) and valueappropriation (advertising) activities with firm shareholder value have investigated the impacts of value creation and value appropriation separately rather than jointly. Second, these studies have mostly focused on firm return, rather than on firm risk, as an outcome variable. Third, the few studies that have focused on the association between relative strategic emphasis and risk have not examined factors that may moderate this association. By examining the association between relative strategic emphasis and firm risk in the presence of two moderators—relative performance and demand instability—this study contributes to the extant literature in three specific ways.
First, the current study investigates value-appropriation and value-creation activities in a joint, rather than separate, manner by focusing on a firm’s relative strategic emphasis. Examining the association of a firm’s relative strategic emphasis with critical firm outcomes is important because resource limitations may preclude firms from simultaneously pursuing both value-appropriation and value-creation strategies in an unconstrained manner.
Second, in contrast to most studies focused on firm return, we examine the association between relative strategic emphasis and firm risk. Executives today have a keen interest in reducing financial risk rather than focusing exclusively on return maximization (Srivastava, Shervani, and Fahey 1998). In many cases, focusing only on maximizing returns while ignoring risk may mislead managers into adopting pernicious strategies (Bowman 1980; Fiegenbaum and Thomas 1986). By examining how a firm’s relative strategic emphasis on value appropriation versus value creation may be associated with idiosyncratic risk, our study can enhance the quality of managerial decisions.
TABLE 1 Research in Marketing on the Association Between Advertising, R&D, Relative Strategic Emphasis, and Firm
TABLE:
| | VA, VC, or Relative Strategic Emphasis | Dependent Metric(s) | Contingency Approach |
|---|
| Advertising (VA) | R&D (VC) | Relative Strategic Emphasis on VA | Risk | Return | Moderator(s) |
|---|
| Focus on Advertising, R&D, or Relative Strategic Emphasis | | | | | | |
| Cooil and Devinney (1992) | ✔ | ✔ | | ✔ | ✔ | Market turbulence |
| Jindal and McAlister (2015) | ✔ | ✔ | | ✔ | ✔ | |
| Josephson, Johnson, and Mariadoss (2016) | ✔ | ✔ | ✔ | | ✔ | |
| Joshi and Hanssens (2010) | ✔ | | | ✔ | | |
| Joshi and Hanssens (2009) | ✔ | | | ✔ | ✔ | Profit |
| Kim and McAlister (2011) | ✔ | | | | ✔ | |
| Luo and Bhattacharya (2009)a | ✔ | ✔ | | | ✔ | Corporate social performance |
| Luo and De Jong (2012) | | ✔ | | | | |
| Luo and Donthu (2006)a | ✔ | ✔ | | ✔ | ✔ | |
| Malshe and Agarwal (2015) | ✔ | ✔ | ✔ | | ✔ | |
| McAlister et al. (2016) | ✔ | | | ✔ | ✔ | 3Firm strategy (differentiation, cost leadership) |
| McAlister, Srinivasan, and Kim (2007) | ✔ | ✔ | | | ✔ | |
| Mizik and Jacobson (2003) | ✔ | ✔ | | | ✔ | |
| Osinga et al. (2011) | ✔ | ✔ | | | ✔ | • ROA |
| Peterson and Jeong (2010) | ✔ | ✔ | | | ✔ | • Past strategic emphasis |
| Rao, Agarwal, and Dahlhoff (2004)a | ✔ | ✔ | | | ✔ | Relaxation of regulation |
| Saboo, Chakravarty, and Grewal (2016)a | ✔ | ✔ | ✔ | | ✔ | Brand value (mediator) |
| Simon and Sullivan (1993) | ✔ | | | ✔ | ✔ | |
| Singh, Faircloth, and Nejadmalayeri (2005) | ✔ | | | | ✔ | Branding strategy (corporate branding, of brands, mixed) |
| Sridhar et al. (2016) | ✔ | | | | ✔ | Myopic marketing behavior |
| Sridhar, Narayanan, and Srinivasan (2014) | ✔ | ✔ | | | ✔ | 3Advertising vehicles (national, regional, and online) |
| Srinivasan, Lilien, and Sridhar (2011) | ✔ | ✔ | | | ✔ | |
| Srinivasan et al. (2009)a | ✔ | | | | ✔ | • Recession • Market share • Leverage • Industry type |
| Wang, Zhang, and Ouyang (2009) | ✔ | | | | ✔ | Innovations |
| Xiong and Bharadwaj (2013)a | ✔ | ✔ | | | ✔ | Positive/negative news reports |
| Current study | ✔ | ✔ | ✔ | ✔ | ✔ | • Relative performance • Demand instability |
Third, unlike previous studies, we examine how specific factors may moderate the association between a firm’s strategic emphasis and financial risk. Specifically, our study focuses on two factors, relative firm performance and industry demand instability. Theoretically, these factors represent critical constraints on resource allocation decisions; specifically, they may constrain resource allocation decisions by framing executives’ perception of firm performance (Qualls and Puto 1989) and focus on minimizing losses or maximizing gains (Palmer and Wiseman 1999). Relative performance can frame an executive’s perception of firm performance as exceeding or falling short of a reference point (March and Shapira 1987; Qualls and Puto 1989), thus affecting subsequent managerial decisions. In a similar vein, demand instability may cause an executive to focus on the potential downside or upside of a firm’s environment (Palmer and Wiseman 1999). A differential focus on the upside or downside associated with demand instability may affect managerial decisions among executives (Deephouse and Wiseman 2000). This is especially the case in decisions pertaining to marketing, such as organizational search (Weiss and Heide 1993), vendor management (Heide and Weiss 1995; Puto, Patton, and King 1985), and customer selection by salespeople (Ross 1991).
TABLE:
| | VA, VC, or Relative Strategic Emphasis | Dependent Metric(s) | Contingency Approach |
|---|
| Advertising (VA) | R&D (VC) | Relative Strategic Emphasis on VA | Risk | Return | Moderator(s) |
|---|
| Advertising and/or R&D as Covariates | | | | | | |
| Aaker and Jacobson (1994) | ✔ | | | | ✔ | |
| Anderson, Fornell, and Mazvancheryl (2004) | ✔ | | | | ✔ | |
| Fang, Palmatier, and Grewal (2011) | | ✔ | | ✔ | ✔ | |
| Grewal et al. (2008) | | ✔ | | | ✔ | |
| Gruca and Rego (2005) | ✔ | ✔ | | ✔ | ✔ | |
| Hsu, Fournier, and Srinivasan (2016) | ✔ | | | ✔ | ✔ | |
| Kang, Germann, and Grewal (2016) | ✔ | ✔ | | | ✔ | |
| Krasnikov, Mishra, and Orozco (2009) | ✔ | ✔ | | ✔ | ✔ | |
| Luo, Homburg, and Wieseke (2010) | ✔ | ✔ | | ✔ | ✔ | |
| Luo, Wieseke, and Homburg (2012) | ✔ | | | | ✔ | |
| Mani and Luo (2015) | | ✔ | | ✔ | ✔ | |
| Morgan and Rego (2006) | ✔ | ✔ | | | ✔ | |
| Morgan and Rego (2009) | ✔ | | | ✔ | ✔ | |
| Sorescu and Spanjol (2008) | ✔ | | | ✔ | ✔ | |
| Srinivasan (2006) | ✔ | | | | ✔ | |
| Tuli and Bharadwaj (2009) | | ✔ | | ✔ | | |
| Wang, Gupta, and Grewal (2016) | | ✔ | | | ✔ | |
aStudies also examine the moderating role of advertising, R&D, or relative strategic emphasis. For these studies, the main independent variable is in the “Moderator(s)” column. Notes: VA = value appropriation; VC = value creation. For a further review, see, for example, Edeling and Fischer (2016) and Rubera and Kirca (2012).
We posit that firms with higher strategic emphasis on value appropriation (vs. value creation) experience lower idiosyncratic risk. Moreover, we argue that positive and negative relative performance moderates the association between relative strategic emphasis and idiosyncratic risk such that firms with larger positive and/or negative relative performance are less affected by their level of strategic emphasis than firms with smaller positive and negative relative performance. We also posit that industry demand instability amplifies the interactive effect of strategic emphasis and relative performance on firmidiosyncratic risk. We use a data set of 13,880 firm-year observations that includes 2,403 firms operating in 59 industries over 15 years (2000–2014) to test our theoretical hypotheses.
Theory and Hypotheses
Relative Strategic Emphasis and Firm-Idiosyncratic Risk
From a marketing perspective, value-creation activities typically include new product development and R&D, whereas value-appropriation activities include advertising and brand enhancement. Previous studies examining the association of value creation and value appropriation with firm risk have yielded conflicting findings. Chan, Lakonishok, and Sougiannis
(2001) show that increases in R&D intensity (an indicator of value creation) are positively associated with higher stock return volatility, whereas McAlister, Srinivasan, and Kim (2007) demonstrate that increases in R&D intensity decrease systematic risk. Similarly, Osinga et al. (2011) find that value appropriation (as measured by consumer and physician advertising) increases idiosyncratic risk. This finding differs from previous results showing that value appropriation, as measured by advertising, mitigates idiosyncratic and systematic risk (Luo and Bhattacharya 2009; McAlister, Srinivasan, and Kim 2007).
Such mixed findings may emanate from two sources we address in this research. First, separately examining the effect of value creation and value appropriation may obfuscate the risk implications of examining relative strategic emphasis on value appropriation versus value creation. Second, ignoring the effect of moderating factors—relative firm performance and demand instability—may overshadow the extent to which the contextual factors may amplify or mitigate the association between relative strategic emphasis and firm risk.
At their core, value-creation processes are explorative (e.g., inventing a new technology), whereas value-appropriation processes are exploitative (e.g., refining and implementing an existing technology). March (1991, pp. 71–73) explains this distinction in his seminal paper:
Exploration includes things captured by terms such as search, variation, risk taking, experimentation, play, flexibility, discovery, and innovation. … Exploitation includes such things as refinement, choice, production, efficiency, selection, implementation, and execution. … Compared to returns from exploitation, returns from exploration are systematically less certain, more remote in time, and organizationally more distant from the locus of action and adaption.
Accordingly, firm investments in R&D tend to be riskier than those in advertising. Research and development represents a long-term horizon for returns with more volatile and less certain payoffs (Hauser, Tellis, and Griffin 2006), because the goal is to maintain or increase an existing revenue stream from customers (Tellis 2004). Failures in R&D also carry a downside risk: the loss of potential customers and alienation of current customers (Frohlich 2014). In contrast, the downside risk of valueappropriation activities that are focused on current and existing customers is lower because advertising and brand building typically accentuate benefits to customers. To the extent that firm-idiosyncratic risk reflects firm-specific financial volatility, we expect firm-idiosyncratic risk to be higher when a firm places more emphasis on value creation. Stated differently, a firm with a relatively higher emphasis on value appropriation should have relatively lower idiosyncratic risk. Using this baseline argument, we next articulate how a firm’s relative performance may moderate the effect of its relative strategic emphasis on idiosyncratic risk. We also discuss how this association may further be moderated by industry demand instability.
Relative Performance
Reference points play an important role in decision making by marketing managers (Qualls and Puto 1989; Ross 1991). Executives use reference points to draw inferences about a firm’s relative performance. Empirical research has shown that in many cases, it is the relative level of performance that influences decision making rather than the absolute level of performance (Greve 2003; March and Shapira 1992; Palmer and Wiseman 1999). Managers may experience positive relative performance when the firm succeeds in meeting or exceeding a reference point; they may experience a negative relative performance when performance falls below its reference point. We define positive (negative) relative performance as the degree to which a firm’s performance exceeds (falls short of) a reference point (Greve 2003). To be sure, relative performance can be assessed against different reference points including the following:
• Reference points based on self: Managers may compare their
current performance with their past performance (i.e., selfappraisal) to ascertain relative performance (Miller and Chen 2004).
• Reference points based on social comparison: Managers may use
performance level of social cohorts, such as comparable firms operating in the same industry or even other industries, to ascertain their relative performance (Fiegenbaum and Thomas 1988).
• Hybrid reference points: Managers may make comparisons
drawing on both self- and social appraisals to determine relative performance. A weighted average of self- and socialreference points may be used to ascertain relative performance (e.g., Greve 2003).
Studies have also shown that managers systematically switch between reference points according to their situation (e.g., Bromiley 1991; Deephouse and Wiseman 2000). However, the goal of this article is not to ascertain the utility of one class of reference points over another; rather, our goal is to examine how firm relative performance may moderate the association between firm relative strategic emphasis and firm risk.
The Interactive Effect of Strategic Emphasis and Relative Performance
We propose that positive relative performance moderates the association between a firm’s relative strategic emphasis on value appropriation (vs. value creation) and its level of idiosyncratic risk. Specifically, two reasons exist as to why the negative association between a firm’s relative strategic emphasis on value appropriation and a firm’s idiosyncratic risk will be weaker when firms have a larger (vs. smaller) positive relative performance.
First, a firm with a large positive relative performance can accumulate surplus tangible resources that are beyond those needed to maintain basic organizational functions (Sharfman et al. 1988). These surplus, accumulated resources should enhance a firm’s flexibility and ability to execute strategies during good or bad times—thus, reducing its risk (Bourgeois 1981). They should also enable management to implement strategies with fewer negative effects (Fang, Palmatier, and Steenkamp 2008). A larger—rather than smaller—reserve of resources improves a firm’s relative flexibility to engage in innovative strategies, such as introducing new products, entering new markets, or adopting new technologies even though such strategies tend to be risky (Lee and Grewal 2004). The firm does this by insulating itself from the potential repercussions of failure of such activities (Sharfman et al. 1988).
Second, a large positive relative performance may help a firm accumulate market-based intangible resources. These accumulated market-based resources (e.g., a high level of satisfaction [Fornell et al. 2006], brand equity [Keller and Lehmann 2006]) can help the firm enhance customer loyalty (Fornell et al. 2006; Mittal and Kamakura 2001), which strengthens and stabilizes the firm’s customer base. A stable customer base may ensure that the marginal impact of a firm’s strategic emphasis on idiosyncratic risk is mitigated for larger,
relative to smaller, positive relative performance. This should occur because customers experiencing a stronger brand equity or higher customer satisfaction are more likely to respond favorably to the firm’s strategies and are less vulnerable to competitors’ actions (Fornell et al. 2006; Rego, Billett, and Morgan 2009). In turn, the differential level of risk emanating from different strategies will be smaller when firms have more market- and customer-based resources.
In summary, our core argument is that a firm with a large positive relative performance will likely have accumulated more tangible and intangible resources than a firm with a relatively smaller positive performance. These resources should insulate the firm from fluctuations emanating from different strategic emphases (Bourgeois 1981; Srivastava, Shervani, and
Fahey 1998). Therefore, a large, positive relative performance should mitigate the effect of strategic emphasis on a firm’s idiosyncratic risk. However, when a firm has a small and positive relative performance, it is likely to have less flexibility in terms of using resources to absorb fluctuations that come from different strategic emphases. As such, the firm should engender relatively more volatility depending on the specific strategic emphasis it implements. Thus,
H1: The negative association between relative strategic emphasis on value appropriation versus value creation and firmidiosyncratic risk is weaker when firms have a larger positive relative performance than a smaller positive relative performance.
Next, we examine the moderating role of a large and negative relative performance on the association between a firm’s strategic emphasis and its idiosyncratic risk. A negative relative performance should diminish the firm’s buffer against internal fluctuations because of lower levels of accumulated tangible and intangible resources (Voss, Sirdeshmukh, and Voss 2008). Therefore, a firm with a negative relative performance will likely be forced to leverage its limited accumulated resources to implement a specific strategy (Sharfman et al. 1988). This leveraging is likely to restrict the firm’s strategic choices (Cheng and Kesner 1997) and increase its vulnerability to the negative consequences of those choices (O’Brien 2003).
A firm with a smaller negative relative performance may still need to leverage its accumulated resources to adapt, but not as much as a firm with a relatively larger negative relative performance. Furthermore, a firm with a small and negative relative performance may still be able to overcome the deleterious situation depending on the specific strategy that the firm implements. If the firm’s choice requires fewer resources and has a lower level of failure (i.e., value appropriation rather than value creation; Hauser, Tellis, and Griffin 2006; March 1991), the firm is likely to incur lower risk. In contrast, when a firm has realized a large and negative relative performance, its accu
mulated resources are likely to be depleted and will not insulate it against any fluctuations, regardless of its strategic choices (Bourgeois 1981). Therefore, we expect the effect of strategic emphasis on firm-idiosyncratic risk to be weaker when the firm has a larger (vs. smaller) negative relative performance. Formally, we postulate the following:
H2: The negative association between relative strategic emphasis on value appropriation versus value creation and firmidiosyncratic risk is weaker when firms have a larger negative relative performance than a smaller negative relative performance.
The Moderating Role of Industry Demand Instability
Glazer and Weiss (1993, p. 510) characterize turbulent environments as having “(1) high levels of inter-period change (in magnitude and/or direction) in the ‘levels’ or values of key environmental variables and (2) considerable uncertainty and unpredictability as to the future values of these variables.” One source of turbulence is demand instability, defined as the unpredictability of consumer preferences (Grewal and Tansuhaj 2001). Demand instability can shape executives’ and managers’ decisions regarding risky activities.1 Higher demand instability may focus managers toward avoiding potential losses by engaging in fewer risky activities such as R&D, innovation, and diversification (Palmer and Wiseman 1999; Wies and Moorman 2015). Adding to this idea, we examine how demand instability may moderate the interactive association of strategic emphasis and relative performance with firm-idiosyncratic risk.
Over time, firms operating in an industry with high demand instability are more likely to have new customers whose needs differ from those of existing customers, as well as existing customers whose needs may frequently change (Hanvanich, Sivakumar, and Hult 2006). The higher the industry demand instability, the less likely a firm’s existing offerings will be able to match and meet customers’ needs (Kohli and Jaworski 1990); existing value propositions quickly become obsolete for a firm in an industry with high demand instability. Therefore, a firm must continuously use resources and engage in activities that generate new value propositions or modify existing value propositions to meet fluctuating customer needs in industries with high demand instability (Jaworski and Kohli 1993).
Accumulated tangible resources enable a firm to absorb the negative effects of environmental fluctuations manifesting as less financial risk for a firm (Bourgeois 1981). Therefore, when the environment changes at a faster rate, and when those changes are larger, a relatively higher level of accumulated resources may help the firm adapt better (Sharfman et al. 1988). As an example, a firm with more tangible resources may be able to continually modify its offerings (e.g., products and services) to meet constantly changing customer needs.
Accumulated intangible resources such as higher customer satisfaction and brand equity insulate a firm from competitive actions (Anderson 1994; Keller and Lehmann 2006) as well as market-level shocks (Rego, Billett, and Morgan 2009). These accumulated intangible resources may enable a firm to cope with existing demand instability as well as enrich the firm’s knowledge about its customer base. Consequently, a firm can better understand and meet customer needs even when the customer base is heterogeneous and dynamic (Morgan, Anderson, and Mittal 2005).
To the extent that firms with resources can better manage the external fluctuations emanating from demand instability, the moderating role of positive relative performance on the association between strategic emphasis and idiosyncratic risk (i.e., H1) will be amplified under high demand instability. Why? The accumulated resource base of a firm with a large and positive relative performance should enable the firm to better cope with external fluctuations associated with customer demand as well as with any internal fluctuations associated with varying strategic emphases. In comparison, in an environment of low demand instability, accumulated resources emanating from a large and positive relative performance may be less important for a firm trying to cope with internal fluctuations. As such, the levels of idiosyncratic risk associated with different strategic emphases may not differ significantly from each other. Therefore, we posit the following:
H3: Demand instability moderates the joint effect of relative strategic emphasis and positive relative performance, such that the interactive effect of relative strategic emphasis and positive relative performance on firm-idiosyncratic risk is stronger under high demand instability than under low demand instability.
Similarly, the moderating role of negative relative performance on the association between strategic emphasis and idiosyncratic risk (i.e., H2) will be amplified under high demand instability. A firm with a large and negative relative performance will be more likely to leverage its accumulated resources than a firm with a small and negative relative performance. Furthermore, a firm with a large and negative relative performance is likely to have an insufficient amount of accumulated resources to leverage. The negative effects of these constraints are even more likely to be amplified if the firm with a large and negative relative performance operates in an environment where demand is highly unstable. This is because high demand instability should make it more difficult for a firm to manage both internal and external fluctuations with a small amount of resources that are a result of a large negative relative performance. Thus,
H4: Demand instability moderates the joint effect of relative strategic emphasis and negative relative performance, such that the interactive effect of relative strategic emphasis and negative relative performance on firm-idiosyncratic risk is stronger under high demand instability than under low demand instability.
Data
This study uses a data set from 2000 to 2014, assembled from three sources. First, firms’ annual operational and financial information comes from Standard & Poor’s Compustat. Second, firm stock information is from the University of Chicago’s Center for Research in Security Prices. Third, daily risk factors for the factor model (i.e., market, size, value, and momentum factor) are from Kenneth French’s Data Library (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data%5flibrary.html).
Dependent Variable: Firm-Idiosyncratic Risk
Prior research has decomposed firm stock risk into two components: systematic and idiosyncratic risk (Srinivasan and Hanssens 2009). Systematic risk reflects the portion of firm stock risk that reacts to market-wide shocks. Idiosyncratic risk reflects the portion of risk associated with firm-specific shocks. The finance literature has shown that idiosyncratic risk accounts for approximately 80% of the total stock risk (Goyal and SantaClara 2003). As such, it is widely used as a measure of risk in the marketing literature (e.g., Chakravarty and Grewal 2011).
The empirical measure of idiosyncratic risk for each firm is based on daily stock returns. First, we estimated Carhart’s (1997) four-factor model as follows:
where Rid is the stock return for firm I on day d, Rfd is the riskfree return on day d, Rmd is the market return on day d, SMBd is the size factor on day d, HMLd is the value factor on day d, and UMDd is the momentum factor on day d. Next, we calculated the standard deviation of the residuals, eid, for each firm-year to measure idiosyncratic risk for firm I at time t (Tuli and Bharadwaj 2009).2 We also tested our hypotheses using different operationalizations of firm risk. We report the results in the “Robustness Checks” subsection.
Independent Variables
Consistent with previous research, we used lagged independent variables to rule out reverse causality issues (McAlister, Srinivasan, and Kim 2007).
Industry classification. We define industry classification at the two-digit Standard Industrial Classification (SIC) code level when measuring industry-level variables (e.g., Srinivasan, Lilien, and Sridhar 2011).
Strategic emphasis. Following Mizik and Jacobson (2003), relative strategic emphasis on value appropriation (compared with value creation) is measured as follows:
(2) Strategic Emphasisit = Advertising Expendituresit – R&D Expendituresit, Total Assetsit
for firm I at time t. On this measure, a positive value indicates a firm’s relative strategic emphasis on value appropriation. A negative value indicates a firm’s relative strategic emphasis on value creation.
Positive and negative relative performance. Following Lant and Montgomery (1987), we first calculated a firm’s relative performance as the difference between a firm’s performance and its reference point as follows:
(3) Relative Performanceit = Performanceit - Reference Pointit,
for firm I at time t. We used a firm’s return on assets (ROA) as its performance measure (i.e., Performanceit = ROAit) and the prior year’s industry-median ROA as the proxy for its reference point (i.e., Reference Pointit = Industry-Median ROAit-1) (Fiegenbaum and Thomas 1988; Miller and Chen 2004). Next, we separately operationalized positive and negative relative performance, based on Greve (2003), as follows:
(4)
for firm I at time t.
Demand instability. For each period, we used Keats and Hitt’s (1988) model to measure demand instability for each industry. First, we calculated industry sales by aggregating sales for all firms operating in the same industry. Next, for each industry, we ran a regression using industry sales for the prior five years as follows:
where is the natural log of industry sales for industry j at time t, and t is the year. Industry demand instability is measured as the antilog of the standard error of the slope coefficient (i.e., ). This denotes the demand instability in the industry over the prior five-year period.
TABLE 2 Description, Data Source, and Literature Source for the Control Variables
TABLE:
| Variable | Description | Data Source | Literature Source |
|---|
| ROA | The ratio of net income to total assets | Compustat | Kashmiri and Mahajan (2010) |
| Financial leverage | The ratio of long-term debt to total assets | Compustat | Luo and Bhattacharya (2009), Tuli and Bharadwaj (2009) |
| Liquidity | The current ratio (i.e., the ratio of current assets to current liabilities) | Compustat | McAllister, Srinivasan, and Kim (2007), Tuli and Bharadwaj (2009) |
| Dividend payout | The ratio of cash dividends to firm market capitalization | Compustat | McAllister, Srinivasan, and Kim (2007), Tuli and Bharadwaj (2009) |
| Service ratio | Ratio of sales from service business segments to total sales | Compustat | Fang, Palmatier, and Steenkamp (2008) |
| Firm scope | Entropy it where salesikt is the ratio of sales from segment k to the total sales of firm I at time t | Compustat | Groening, Mittal, and Zhang (2016) |
| Firm size | The natural log of a firm’s total market capitalization | Compustat | Luo and Bhattacharya (2009), McAllister, Srinivasan, and Kim (2007) |
Control Variables
Following prior research on firm-idiosyncratic risk (e.g., Luo and Bhattacharya 2009; Rego, Billett, and Morgan 2009), we included a set of firm-level variables associated with firm-idiosyncratic risk. As Table 2 shows, these include firm ROA, financial leverage, liquidity, dividend payout, service ratio, firm scope, and firm size.
Sample
The sample includes publicly traded firms listed on three U.S. markets. (i.e., AMEX, NYSE, and NASDAQ) for the 15-year period spanning 2000–2014. Because of missing values, the final sample has 13,880 firm-year observations. This represents
2,403 firms in 59 two-digit SIC code industries. The summary statistics and the correlation matrix appear in Table 3.
Method
Model Specification
The data include observations for multiple firms, with each firm operating over multiple periods. For this type of time-series, cross-sectional panel data, several issues should be considered during estimation. First, the Wooldridge test rejected the null hypothesis of no serial correlation in the errors (p < .01). Second, the Breusch–Pagan test revealed that heteroskedasticity exists in our data by rejecting the null hypothesis (p < .01). To control for serial correlation and heteroskedasticity, we used clusteradjusted robust standard errors (Jindal and McAlister 2015). Third, the Hausman test rejected the null hypothesis favoring a fixed-effects specification over the random-effects specification (p < .01). Therefore, we included industry- and year-specific dummy variables as controls. The specific model is as follows: where indicates the vector of predictors for firm I in industry j at time t.
We centered all the continuous independent and control variables using the grand mean. We also calculated the variance inflation factor (VIF) and the condition index: the maximum VIF was 6.90 and the condition index was 13.93, after excluding industryspecific dummy variables that typically have high VIF values.3
TABLE 1 Research in Marketing on the Association Between Advertising, R&D, Relative Strategic Emphasis, and Firm
aStudies also examine the moderating role of advertising, R&D, or relative strategic emphasis. For these studies, the main independent variable is in the “Moderator(s)” column. Notes: VA = value appropriation; VC = value creation. For a further review, see, for example, Edeling and Fischer (2016) and Rubera and Kirca (2012).
TABLE 3 Summary Statistics and Correlation Matrix
| Variable | M | SD | Min | 25% | 50% | 75% | Max |
|---|
| 1. Idiosyncratic riskt | 0.035 | 0.023 | 0.003 | 0.02 | 0.028 | 0.043 | 0.275 |
| 2. Strategic emphasist-1 | -0.038 | 0.121 | -3.322 | -0.085 | -0.02 | 0.014 | 2.089 |
| 3. Positive relative performancet-2 | 0.065 | 0.097 | 0 | 0 | 0.024 | 0.092 | 1.662 |
| 4. Negative relative performancet-2 | 0.104 | 0.446 | 0 | 0 | 0 | 0.054 | 25.261 |
| 5. Demand instabilityt-1 | 1.016 | 0.01 | 1.002 | 1.01 | 1.014 | 1.02 | 1.104 |
| 6. ROAt | -0.076 | 0.996 | -100.014 | -0.064 | 0.03 | 0.081 | 1.676 |
| 7. Leveraget | 0.127 | 0.199 | 0 | 0 | 0.031 | 0.202 | 4.394 |
| 8. Liquidityt | 2.985 | 2.799 | 0.014 | 1.45 | 2.222 | 3.539 | 84.36 |
| 9. Dividend payoutt | 0.01 | 0.087 | 0 | 0 | 0 | 0.004 | 6.72 |
| 10. Service ratiot | 0.342 | 0.464 | -0.011 | 0 | 0 | 1 | 1 |
| 11. Firm scopet | 0.408 | 0.713 | -21.346 | 0 | 0 | 0.684 | 8.134 |
| 12. Firm sizet | 5.958 | 2.265 | -1.387 | 4.337 | 5.883 | 7.426 | 12.954 |
| 13. Industry strategic emphasist-1 | -0.089 | 0.261 | -9.982 | -0.124 | -0.086 | 0.006 | 1.493 |
| 14. Industry advertising disclosuret-1 | 0.444 | 0.185 | 0.016 | 0.314 | 0.417 | 0.5 | 1 |
| 15. Industry R&D disclosuret-1 | 0.702 | 0.194 | 0.002 | 0.598 | 0.756 | 0.86 | 1 |
TABLE:
| Variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 |
|---|
| 1. Idiosyncratic riskt | | | | | | | | | | | | | | |
| 2. Strategic emphasist-1 | 2.121 | | | | | | | | | | | | | |
| 3. Positive relative performancet-2 | 2.179 | 0.037 | | | | | | | | | | | | |
| 4. Negative relative performancet-2 | 0.229 | 2.136 | 2.156 | | | | | | | | | | | |
| 5. Demand instabilityt-1 | 2.033 | -0.011 | 2.036 | 0.003 | | | | | | | | | | |
| 6. ROAt | 2.236 | 0.082 | 0.065 | 2.11 | -0.001 | | | | | | | | | |
| 7. Leveraget | 2.07 | 0.091 | 2.062 | -0.001 | 0.027 | -0.006 | | | | | | | | |
| 8. Liquidityt | 0.016 | 2.112 | 0.104 | 0.016 | 2.02 | 0.028 | 2.177 | | | | | | | |
| 9. Dividend payoutt | 2.019 | 0.018 | 0.009 | -0.01 | 0.022 | 0.009 | 0.022 | -0.016 | | | | | | |
| 10. Service ratiot | 0.088 | 2.07 | 2.02 | 0.071 | 2.023 | 2.042 | -0.012 | 2.111 | 0.002 | | | | | |
| 11. Firm scopet | 2.159 | 0.051 | 0.005 | 2.061 | 0.046 | 0.035 | 0.049 | 2.104 | 0.012 | 2.022 | | | | |
| 12. Firm sizet | 2.622 | 0.066 | 0.211 | 2.147 | 2.022 | 0.143 | 0.113 | 2.095 | -0.005 | 2.028 | 0.257 | | | |
| 13. Industry strategic emphasist-1 | 2.082 | 0.206 | 2.064 | 2.082 | 2.056 | 0.027 | 0.058 | 2.05 | 0.007 | 2.13 | -0.005 | 0.041 | | |
| 14. Industry advertising disclosuret-1 | 2.152 | 0.293 | 2.203 | 2.071 | 2.174 | 0.056 | 0.004 | 2.174 | -0.001 | 0.039 | 2.112 | 0.073 | 0.223 | |
| 15. Industry R&D disclosuret-1 | 2.02 | 2.131 | 0.081 | -0.013 | 2.256 | 0.005 | 2.058 | 0.112 | -0.013 | 2.484 | 2.077 | -0.014 | -0.016 | 0.048 |
Notes: Correlations significant at p < .05 are in boldface.
This indicates that multicollinearity is not an issue. To address reverse causality, we used lagged independent variables relative to the dependent variable (Luo and Bhattacharya 2009). We used two-period lagged values for positive and negative relative performance because our interest is to ascertain how a firm’s strategic emphasis (at time t - 1) is associated with firmidiosyncratic risk (at time t) in the presence or absence of positive or negative relative performance (at time t - 2). The idea is that firm management may choose or modify their strategies (e.g., resource allocations) in accordance with the relative performance that has already been realized due to their past strategies.
TABLE 1 Research in Marketing on the Association Between Advertising, R&D, Relative Strategic Emphasis, and Firm
aStudies also examine the moderating role of advertising, R&D, or relative strategic emphasis. For these studies, the main independent variable is in the “Moderator(s)” column. Notes: VA = value appropriation; VC = value creation. For a further review, see, for example, Edeling and Fischer (2016) and Rubera and Kirca (2012).
TABLE 4 Results from the Auxiliary Regressions
TABLE:
| | Model 1: Selection Model | Model 2: Selection Model |
|---|
| Coefficient | SE | Coefficient | SE |
|---|
| Intercept | -3.844*** | 0.585 | -.356*** | 0.106 |
| Positive relative performancet-2 | -.074* | 0.039 | .076*** | 0.011 |
| Negative relative performancet-2 | -.160*** | 0.012 | -.021*** | 0.002 |
| Demand instabilityt-1 | .966* | 0.566 | .418*** | 0.107 |
| ROAt | .003** | 0.001 | .008*** | 0.001 |
| Leveraget | -.517*** | 0.029 | .058*** | 0.005 |
| Liquidityt | -0.002 | 0.001 | -.004*** | 0 |
| Dividendt | -.137* | 0.077 | .021* | 0.011 |
| Service ratiot | .241*** | 0.015 | -.011*** | 0.002 |
| Firm scopet | 0 | 0 | .007*** | 0.001 |
| Firm sizet | .081*** | 0.003 | -.002*** | 0.001 |
| Industry strategic emphasist-1 | -0.015 | 0.019 | .081*** | 0.004 |
| Industry advertising disclosuret-1 | 1.926*** | 0.039 | | |
| Industry R&D disclosuret-1 | 1.499*** | 0.033 | -.041*** | 0.003 |
| Inverse Mills ratiot c2 | 9,948.038*** | | 55.267*** | |
| Model F n | 64,429 | | 13,880 | |
*p < .10. **p < .05. ***p < .01. Notes: Both models include year fixed effects.
Addressing Endogeneity
The model specified in Equation 6 is susceptible to two potential sources of endogeneity—omitted variables and sample selection bias in the independent variable, strategic emphasis. Following Germann, Ebbes, and Grewal (2015) we address these two issues in the following subsections.
Control function approach. Managers may allocate resources to advertising and R&D in anticipation of idiosyncratic risk or other possible factors that drive such decisions and are correlated with the error term. If this is the case, then a firm’s strategic emphasis may be correlated with the error term in Equation 6, thus biasing the estimates. We include various firmlevel control variables (for details, see Table 2) as well as industry- and year-fixed effects to address this potential source of bias. However, other omitted variables may affect the correlation of firm strategic emphasis with the error term.
To address this issue, we use the control function approach (Petrin and Train 2010; Wooldridge 2010). In the first stage of the control function approach, we estimate an auxiliary regression of the endogenous variable on the instrument and the exogenous variables. In the second stage, we estimate the focal model (i.e., Equation 6 in our case) with the estimated residuals from the first stage, which controls for the endogenous variable, as a predictor. The control function approach requires additional variables (i.e., instruments) that satisfy two conditions. These instruments should be (1) correlated with the endogenous variable (i.e., strategic emphasis in our case), yet, (2) uncorrelated with the error term (Wooldridge 2010). One variable that may satisfy these conditions is the level of industryaverage strategic emphasis (e.g., Gurun and Butler 2012; Jindal and McAlister 2015; Sridhar et al. 2016). Arguably, resourceallocation decisions of competitors in the same industry may reflect industry norms. Firm managers may make their decisions on the basis of industry norms to utilize the knowledge among industry competitors (Cohen and Levinthal 1989). Therefore, it may be reasonable to argue that industryaverage strategic emphasis is positively associated with the focal firm’s strategic emphasis (i.e., it satisfies the first condition as an instrument).
At the same time, it is highly unlikely that industry-average strategic emphasis will correlate with the error term—that is, to significantly affect idiosyncratic risk beyond the effects of the focal firm’s specific strategic emphasis and other control variables in our study. For this to occur, competitors would have to jointly adjust their level of strategic emphasis in anticipation of the focal firm’s strategies, which are omitted from our study. This is unlikely to happen because competitors rarely observe specific strategies implemented by firms. Moreover, the large number of firms (i.e., 2,403) in our data set that operate in a relatively small number of industries (i.e., 59) precludes the possibility that all competitors cooperate to engage in such behavior with respect to a single firm. The set of control variables as well as the industry- and year-fixed effects included in our model also mitigates the possibility of industry-average strategic emphasis being associated with the potential omitted variables, which are correlated with the error term. Therefore, the use of industry-average strategic emphasis also satisfies the second condition as an instrument.4
We estimate the following auxiliary model by regressing firm strategic emphasis on industry-average strategic emphasis and the exogenous variables: where Controlsijt indicates the vector of control variables (listed in Table 2). We also include the inverse Mills ratio, explained next, to control for potential selection bias in the endogenous variable (Wooldridge 2010). Finally, we use the predicted residuals from Equation 7 as a control function in the final model (i.e., Control Functionijt = n^ijt-1).
TABLE 5 The Association of Strategic Emphasis, Relative Performance, and Demand Instability with Firm-Idiosyncratic
TABLE:
| | Model 1: Controls | Model 2: Main Effects | Model 3: Two-Way Interactions | Model 4: Three-Way Interactions |
|---|
| Coefficient | SE | Coefficient | SE | Coefficient | SE | Coefficient | SE |
|---|
| Intercept | .033*** | 0.003 | .056*** | 0.001 | .056*** | 0.001 | .056*** | 0.001 |
| ROAt | -.003** | 0.001 | -0.002 | 0.003 | -0.002 | 0.003 | -0.002 | 0.003 |
| Leveraget | 0.001 | 0.001 | .005** | 0.002 | .005*** | 0.002 | .005*** | 0.002 |
| Liquidityt | .000*** | 0 | .000*** | 0 | .000*** | 0 | .000*** | 0 |
| Dividend payoutt | -.007** | 0.003 | -0.004 | 0.003 | -0.004 | 0.003 | -0.004 | 0.003 |
| Service ratiot | 0.001 | 0.002 | -0.001 | 0.002 | -0.002 | 0.002 | -0.002 | 0.002 |
| Firm scopet | -.001** | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Firm sizet | -.005*** | 0 | -.006*** | 0 | -.006*** | 0 | -.006*** | 0 |
| Inverse Mills ratiot | | | 0.012 | 0.009 | 0.013 | 0.008 | 0.013 | 0.008 |
| Control functiont | | | -.012*** | 0.003 | -.011*** | 0.003 | -.011*** | 0.003 |
| Strategic emphasist-1 | | | -.020** | 0.009 | -.027*** | 0.009 | -.028*** | 0.008 |
| Positive relative performancet-2 | | | -.012*** | 0.003 | -.011*** | 0.003 | -.010*** | 0.003 |
| Negative relative performancet-2 | | | .006*** | 0.002 | .007*** | 0.002 | .008*** | 0.002 |
| Demand instabilityt-1 | | | -0.006 | 0.023 | -0.002 | 0.022 | 0.018 | 0.023 |
| H1: Strategic emphasist-1 × Positive relative performancet-2 | | | | | .037*** | 0.014 | .042*** | 0.014 |
| H2: Strategic emphasist-1 × Negative relative performancet-2 | | | | | .015*** | 0.004 | .015*** | 0.004 |
| Strategic emphasist-1 × Demand instabilityt-1 | | | | | | | -.729*** | 0.225 |
| Positive relative performancet-2 × Demand instabilityt-1 | | | | | | | -.167 | 0.163 |
| Negative relative performancet-2 × Demand instabilityt-1 | | | | | | | -.211* | 0.128 |
| H3: Strategic emphasist-1 × Positive relative performancet-2 × Demand instabilityt-1 | | | | | | | 4.297*** | 1.651 |
| H4: Strategic emphasist- × Negative relative performancet- × Demand instabilityt- | | | | | | | 1.099*** | 0.406 |
| R2 | | | | | | | 0.581 | |
| Adjusted R2 | | | | | | | 0.578 | |
| Incremental R2 | | | | | | | .002*** | |
*p < .10. **p < .05. ***p < .01. Notes: n = 13,880. All models include industry and year dummies.
Sample selection. Given the measure of relative strategic emphasis, only firms with nonmissing values for both advertising and R&D expenditures can be included in the final sample. Potential selection bias exists because the Compustat database has a large proportion of missing values in advertising and R&D expenditures. Specific to our data set, 21.54% of the observations are excluded because of these missing values. It is likely that the missing values may not be random, as there may be systematic reasons for a firm to choose to report or not report its advertising and R&D expenditures.
To address sample selection, we use the approach proposed by Heckman (1979). We estimate a probit model in the first stage. Specifically, among all possible firm-year observations in the full data, we coded observations for firms listed on the major U.S. stock markets and had a nonmissing value of relative strategic emphasis as 1 (Sijt = 1). We coded the other observations as 0 (Sijt = 0). Next, we ran the following probit model:
In addition to the exogenous variables, we included two additional variables as suggested by Wooldridge (2010). These include the proportion of firms operating in the same industry that have nonmissing values for (1) advertising expenditures (i.e., Industry Advertising Disclosureijt) and (2) R&D expenditures (i.e., Industry R&D Disclosureijt). Finally, we calculated the inverse Mills ratio using the estimates from Equation 8 as follows: (9)
We include the inverse Mills ratio and the control function estimated from Equations 7–9 in our model (i.e., Equation 6). The resulting model is written as follows:
(10)
Firm Idiosyncratic Riskijt
= b0 + b1Strategic Emphasisijt-1
+ b2Positive Relative Performanceijt-2
+ b3Negative Relative Performanceijt-2
+ b4Demand Instabilityijt-1
+ b5Strategic Emphasisijt-1
· Positive Relative Performanceijt-2
+ b6Strategic Emphasisijt-1
· Negative Relative Performanceijt-2
+ b7Strategic Emphasisijt-1
· Demand Instabilityijt-1
+ b8Positive Relative Performanceijt-2
· Demand Instabilityijt-1
+ b9Negative Relative Performanceijt-2
· Demand Instabilityijt-1
+ b10Strategic Emphasisijt-1
· Positive Performanceijt-2
· Demand Instabilityijt-1
+ b11Strategic Emphasisijt-1
· Negative Relative Performanceijt-2
· Demand Instabilityijt-1
+ b12Control Functionijt + b13Inverse Mills Ratioijt
+ G9Controlsijt +
t=1dtYeart
We correct the standard errors from Equation 10 because the control function and inverse Mills Ratio included in the model are estimates from Equations 7–9. To correct the standard errors, we bootstrap Equations 7–10 together and estimate the standard errors of the coefficients from 1,000 bootstrap samples
(Petrin and Train 2010). We also clustered the standard errors at the firm level to account for heteroskedasticity and serial correlation in the error terms. <pb/>Results <pb/>Table 4 provides the results from the two auxiliary models (i.e., Equations 7 and 8). As we expected, Model 1 in Table 4 shows that the two additional variables—industry advertising disclosure (d = 1.926, p < .01) and industry R&D disclosure (d = 1.499, p < .01)—are significant predictors of the selection probability. Likewise, Model 2 in Table 4 shows that the instrument for the control function approach (i.e., industry-average strategic emphasis) is significantly and positively associated with firm strategic emphasis (p = .081, p < .01). <pb/>Hypothesis Tests <pb/>As we show in Table 5, we started with a baseline model that includes only control variables (Model 1). Model 2 adds the main effects of the explanatory variables. Model 3 includes the two-way interactions between the explanatory variables proposed in H1 and H2. Finally, Model 4 includes the threeway interactions (H3 and H4). Incremental R2 tests indicate that adding the explanatory variables and the interaction terms improve the model’s ability to explain firm-idiosyncratic risk (ps < .01). All hypotheses are tested based on Model 4 in Table 5. <pb/>The main effects in the model are consistent with previous findings. Specifically, relative strategic emphasis on value appropriation is negatively associated with firm-idiosyncratic risk (b = -.028, p < .01). Positive relative performance is negatively associated with firm-idiosyncratic risk (b = -.010, p < .01), while negative relative performance is positively associated with firmidiosyncratic risk (b = .008, p < .01). Demand instability has no association with firm-idiosyncratic risk (b = .018, p > .10), a finding consistent with Fang, Palmatier, and Grewal (2011). <pb/>The joint effect of strategic emphasis and positive relative performance (H1). H1 predicts that a firm’s strategic emphasis on value appropriation, relative to value creation, will have a weaker (stronger) negative association with firm risk when firms have larger (smaller) positive relative performance. Model 4 in Table 5 shows that the interaction between strategic emphasis and positive relative performance is statistically significant (b = .042, p < .01), in support of H1. We visually depict the interaction in Figure 1, following Aiken and West (1991). Specifically, we used the predicted values at one standard deviation above and below the mean of strategic emphasis and positive relative performance.5 Figure 1 shows that the negative association between strategic emphasis and firm risk is weaker when firms have a large positive relative performance than when they have a small positive relative performance. The simple slopes show negative and statistically significant coefficients of strategic emphasis for both large (b = -.023, p < .01) and small (b = -.030, p < .01) positive relative performance. Importantly, the slope for small positive relative performance is steeper than the slope for large positive relative performance (t = 2.949, p < .01). These results support H1.
The interactive effect of strategic emphasis and negative relative performance (H2). H2 predicts an interaction between strategic emphasis and negative relative performance. In support of H2, Model 4 in Table 5 shows that the interaction between strategic emphasis and negative relative performance is statistically significant (b = .015, p < .01). Figure 2 depicts this interaction. The slopes are both statistically significant and negative (large negative relative performance: b = -.021, p < .05; small negative relative performance: b = -.029, p < .01). The difference in the slopes is also statistically significant (t = 3.651, p < .01): the slope is steeper when negative relative performance is small than when it is large. These results support H2.
The moderating role of demand instability for positive relative performance (H3). H3 predicts that the interactive association of relative strategic emphasis on value appropriation and positive relative performance with idiosyncratic risk will be stronger under high demand instability than under low demand instability. To test H3, we examined the three-way interaction among strategic emphasis, positive relative performance, and demand instability. As Model 4 shows, the three-way interaction is statistically significant (b = 4.297, p < .01), in support of H3. We visually depict the effect in Figure 3 using Aiken and West’s (1991) method. Specifically, we separately plot the two-way interaction between strategic emphasis and positive relative performance when demand instability is low (one standard deviation below the mean; Figure 3, Panel A) and high (one standard deviation above the mean; Figure 3, Panel B).
Figure 3, Panel A, shows that, under low demand instability, strategic emphasis is negatively associated with firm-idiosyncratic risk for both small and large positive relative performance. The slopes are statistically significant for both small positive relative performance (b = -.020, p < .05) and large positive relative performance (b = -.020, p < .05). However, the difference between these two slopes is statistically nonsignificant (t = -.091, p > .10). Figure 3, Panel B, shows that, under high demand instability, strategic emphasis has a statistically significant and negative association with firm-idiosyncratic risk for both small (b = -.041, p < .01) and large (b = -.027, p < .01) positive relative performance. More importantly, the slope for small positive relative performance is significantly steeper than the slope for large positive relative performance (t = 3.527, p < .01). These results support H3.
The moderating role of demand instability for negative relative performance (H4). H4 predicts that the joint association of relative strategic emphasis and negative relative performance with idiosyncratic risk will be stronger under high demand instability than under low demand instability. Model 4 shows that the three-way interaction of strategic emphasis, negative relative performance, and demand instability is statistically significant (b = 1.099, p < .01).
Figure 4 illustrates the pattern of results. Figure 4, Panel A, shows the pattern under low demand instability. Strategic emphasis has a negative association with firm-idiosyncratic risk for both small (b = -.021, p < .05) and large (b = -.018, p < .05) negative relative performance. However, the slopes are not statistically different from each other (t = .641, p > .10). Figure 4, Panel B, shows the pattern for high demand instability. Under high demand instability, the association between strategic emphasis and firm-idiosyncratic risk is negative for both small (b = -.038, p < .01) and large (b = -.023, p < .01) negative relative performance. Furthermore, the slope is steeper for small negative relative performance than large negative relative performance (t = 4.711, p < .01). Therefore, H4 is supported.
Robustness Checks
We ran additional analyses to assess the robustness of our results. We summarize these in Table W2 in the Web Appendix and describe them next.
Goods versus service firms. It may be that a firm’s strategic emphasis varies systematically between service and goods firms. Furthermore, unlike in goods firms, R&D expenditures in service firms may be primarily aimed at creating intangible customer value. To test these differences, we ran two additional regressions including (1) a goods industry dummy that takes the value of 1 (0) for the goods (services) industry, and (2) the service ratio of firms.6
Specifically, we tested the three-way interaction between strategic emphasis, relative performance, and the goods industry dummy (or service ratio). The three-way interactions between strategic emphasis, (positive or negative) relative performance, and the goods industry dummy were statistically nonsignificant (ps > .10). The three-way interactions with service ratio were also statistically nonsignificant (ps > .10). In summary, we did not find any systematic difference between goods and service firms.
Results invariant to mean-centering. In our main analyses, we measured a firm’s strategic emphasis by scaling the difference between advertising and R&D expenditures using the firm’s total assets. Therefore, the range of possible values for the variable is between negative infinity and positive infinity. The possibility of strategic emphasis taking positive or negative values, and the moderator (i.e., relative performance) split in the positive and negative domain, may raise questions about the interpretation of the two- and three-way interactions. Furthermore, the grand mean-centering before the analyses may also complicate the interpretation.
To clarify the results and to assess whether the marginal effects of the hypothesized relationships are invariant to such issues, we added an arbitrary value (i.e., 4) to strategic emphasis—so that the variable takes only positive values for all observations—and tested the hypotheses with noncentered variables.7 With these changes, all the variables of interest (i.e., strategic emphasis, relative performance, and demand instability) take on only positive values. Reassuringly, the coefficients and the standard errors of the hypothesized effects as well as the marginal effect of strategic emphasis remained unchanged.
The capital asset pricing model (CAPM). Research on firm stock risk has used factor models different than the fourfactor model (i.e., Equation 1). The CAPM is one of the most widely used models to derive firm stock risk (e.g., McAlister, Srinivasan, and Kim 2007) in which stock returns are regressed only on the market factor. We tested our hypotheses using firm-idiosyncratic risk calculated from the CAPM. All four hypotheses were supported.
Fama–French three-factor model. We also tested our model using the three-factor model proposed by Fama and French (1992). This model regresses stock returns on the market factor, the size factor, and the value factor. Again, the results remained unchanged.
Total stock risk. Using firm-idiosyncratic risk based on Carhart’s (1997) four-factor model may be overly restrictive (Tuli and Bharadwaj 2009). Therefore, we tested the hypotheses using firm total stock risk (i.e., the standard deviation of firm stock returns) that is not derived from a factor model. The results were, again, robust.
Downside and upside idiosyncratic risk. Recent research in marketing decomposes firm stock risk into upside and downside (e.g., Rego, Billett, and Morgan 2009). The idea is that downside risk may be relatively more consequential than upside risk (Kahneman and Tversky 1979). We separately tested our hypotheses using downside and upside idiosyncratic risk. We calculated downside idiosyncratic risk as the standard deviation of the residuals from Equation 1 when firms have negative excess returns—that is, ðRid - RfdÞ<0. We calculated upside idiosyncratic risk as the standard deviation of the residuals from Equation 1 when firms have positive excess returns—that is, ðRid - RfdÞ > 0. The results for downside and upside idiosyncratic risk were consistent with those from our main analyses.
Removing potential outliers. As Table 3 shows, some of our variables have a large range of values that may reflect potential outliers. To check whether potential outliers drive our results, we reran our analyses by Winsorizing the variables at the 1% and 99% levels (Jindal and McAlister 2015). The results remained unchanged.
General Discussion
Because of resource limitations, firms cannot spend an infinite amount of resources on value creation and value appropriation; rather, they need guidance to understand the relative allocation of resources to value creation and value appropriation. This research improves our understanding of the association between relative strategic emphasis on value appropriation (vs. value creation) and firm risk. Relative to studies that separately estimate the association of value-appropriation and valuecreation processes (e.g., advertising and R&D) with firm risk, our results serve as a reminder that firm resources are scarce. In addition, this research also recognizes conditions—relative firm performance and demand instability—under which managers make these resource allocation decisions to manage firm risk.
Our study focuses on resource allocation decisions in terms of minimizing idiosyncratic risk, an important source of value for firms. Lower idiosyncratic risk can benefit all stakeholders by lowering the cost of debt (Anderson and Mansi 2009), promoting stability (Groening et al. 2014), and helping to improve overall returns (Srivastava, Shervani, and Fahey
1998). Theoretically, our results encourage scholars to develop insights on how marketing activities can go beyond return maximization to also address risk management. Prior research on the association between risk and return is inconclusive. Some scholars have argued for a positive association (e.g., Bowman 1980), while others have argued for a positive or a negative association between risk and return depending on firm and industry characteristics (e.g., Fiegenbaum and Thomas 1986, 1988). A simultaneous examination of both risk- and return-related outcomes of relative strategic emphasis may further shape our understanding of resource allocation decisions among executives.
Another key contribution of this research is to supplement prior research that has basically examined relative performance as a direct antecedent of risk taking by managers (Fiegenbaum and Thomas 1986; March and Shapira 1992; Shinkle 2012). The current research extends these studies by investigating relative performance as a moderator. In this regard, our findings suggest that relative performance can frame managerial actions by indicating the resources available to them. If a firm experiences larger positive relative performance, its managers may have the luxury to allocate resources to value creation and still experience a smaller increase in idiosyncratic risk than those at firms with smaller positive relative performance (see Figure 1). Moreover, if the firm experiences larger negative relative performance, the difference in risk from a riskier strategy (i.e., value creation) and a less risky strategy (i.e., value appropriation) decreases (see Figure 2). Within marketing, relative performance may further be assessed on a variety of nonfinancial dimensions such as brand equity, customer satisfaction, customer retention, and social media activity. Measuring and examining the role of these constructs in marketing is a key research area.
A key component of relative performance is determining which of the many available reference points managers use in evaluating performance. Within marketing, research is needed to ascertain how managers’ reference points are determined (Bromiley and Harris 2014) and when managers decide to use one type of reference point over another. Both theoretical and empirical research is needed to explicate how marketing managers in various capacities (e.g., chief marketing officers, communications officers, salespersons, R&D managers) develop and utilize reference points. More specifically, we need a richer theoretical conceptualization on the specific circumstances under which managers use different reference points in assessing their performance and implementing different strategies.
Our results also suggest that a firm’s managerial choice with regard to emphasizing value creation versus value appropriation and its relative performance affect firm risk depending on the nature of demand faced by the firm. Although a firm may not have the luxury to pick the industry in which it competes, our research suggests the importance of cultivating a stable customer base. Demand instability may be managed by attracting and retaining customers selectively as well as by understanding the nature and sources of changes in customer preferences and needs. By understanding customers—the people who affect demand instability—as a contingency factor influencing the impact of advertising and R&D decisions, research can take a broader perspective. To date, most studies have examined advertising and R&D as antecedents of customer perceptions, preferences, and behaviors. Our results suggest otherwise and should become the basis of future research scholarship. Finally, in addition to demand instability, other forms of instability such as those in supply, manufacturing, and currency could be investigated in future studies.
Implications for Practice
Rather than making advertising and R&D decisions in a vacuum, this research provides guidance to managers for better contextualizing their decisions. To the extent that the moderating context may vary by industry, the overall association between relative strategic emphasis and firm-idiosyncratic risk is also likely to vary as well. Figure 5 shows this association by industry. The association is relatively stronger for industries such as lumber and wood products (-.058), but weaker for general merchandise stores (-.019). Even within the same broad industry, such as insurance, there are differences: the association is stronger for insurance carriers (-.057) than for insurance agents, brokers, and service (-.020). In summary, managers can incorporate the context of their industry in their decision process.
Even within an industry, our results can provide firmspecific guidance to managers. Our results suggest that managers who carefully monitor and manage their firm’s reference points may benefit through decreased firm risk. As an example, consider Figure 6, which shows the average marginal effect of strategic emphasis over the 15-year period (i.e., 2000–2014) for 20 firms operating in two industries with low and high demand instability (MSIC56 = 1.010 vs. MSIC36 = 1.019; t = 4.200, p < .01). We show ten firms each in the apparel and accessory stores industry (i.e., SIC code 56) and the electronic/electrical equipment industry (i.e., SIC code 36). In both industries, the marginal effect of strategic emphasis is larger for firms with smaller positive and negative relative performance than for firms with either larger positive and/or negative relative performance. More importantly, there are differences between firms that managers can factor into their decision process. For example, Figure 6, Panel A, shows that the marginal effect of strategic emphasis is larger for Nordstrom (i.e., a firm with small positive and negative relative performance) than for Destination XL (i.e., a firm with large negative relative performance), Buckle (i.e., a firm with large positive relative performance), or Christopher & Banks (i.e., a firm with large positive and negative relative performance). Panel B shows that the marginal effect of strategic emphasis is larger for Sony or Whirlpool (i.e., firms with small positive and negative relative performance) than for Intel (i.e., a firm with large positive relative performance) or Cirrus Logic (i.e., a firm with large positive and negative relative performance).
Previously, it had been assumed that managers at firms tend to show risk-averse behaviors when they have larger positive
relative performance (e.g., March and Shapira 1987; Qualls and Puto 1989). In contrast, our results demonstrate that managers at firms with larger positive relative performance, compared with managers at firms with smaller positive relative performance, have the opportunity to be less risk averse and increase the level of investments to value-creation activities. More specifically, managers at firms with larger (vs. smaller) relative performance could exercise more discretion when allocating resources to riskier value-creation processes.
Managers will also benefit from understanding how they may be more or less similar to their industry peers. Within a specific industry, there is value to understanding interfirm variability. For example, the difference in the marginal effect of strategic emphasis across firms is more striking for firms operating in the electronic/electrical equipment industry (i.e., industry with high demand instability; Figure 6, Panel B) than the apparel and accessory stores industry (i.e., industry with low demand instability; Figure 6, Panel A). In this regard, Figure 6 suggests that firms operating in an industry with higher demand instability (e.g., electronic/electrical equipment industry) should be particularly more prudent in tracking their relative performance and setting the appropriate level of relative strategic emphasis.
Finally, extrapolating these results to individual firms can also be instructive for executives and managers. Using our results and data, we investigated the relative level of idiosyncratic risk a firm may expect to realize in the future (i.e., in 2014) if the firm sets its strategic emphasis at a 1% (i.e., .01) higher level in the current period (i.e., in 2013). For example, for Microsoft—a firm that operates in a relatively stable industry (business services) and has realized a large positive relative performance of 12.104%—a 1% increase in strategic emphasis is associated with a decrease in expected idiosyncratic risk of 2.093 basis points. Likewise, for HP—a firm that operates in a relatively unstable industry (industrial machinery and equipment) and has realized a large negative relative performance of 16.429%—a 1% higher strategic emphasis is associated with a decrease of 3.218 basis points in expected idiosyncratic risk. Managers at either firm may be able to use these estimated decrements in expected risk to make more concrete resource allocation decisions for managing cost of debt, bond ratings, and other downstream effects of firm-idiosyncratic risk.
1A review of marketing literature on demand instability appears in Table W1 of the Web Appendix.
2We used firm-year observations that included stock return data for all the trading days in the focal year (e.g., 252 days for the year 2000).
3The results pertaining to our hypothesis tests were not affected by the inclusion/exclusion of these industry-specific dummy variables. Therefore, we included all the industry-specific dummy variables for our hypothesis tests.
4We also estimated a model with industry-average advertising intensity and industry-average R&D intensity as the instruments for strategic emphasis. The results remained unchanged.
5Note that interactions were plotted at the minimum (maximum) value of a variable if one standard deviation below (above) the mean was smaller (larger) than the minimum (maximum) value of the variable. For example, low-positive relative performance had the value of .000, because one standard deviation below the mean (i.e., -.032) is smaller than the minimum value of the variable (i.e., .000).
6We thank an anonymous reviewer for this suggestion. 7We thank an anonymous reviewer for this suggestion.
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Shareholder Value
Risk
FIGURE 1 The Joint Effect of Strategic Emphasis and Positive Relative Performance
Notes: Numbers in parentheses are the values at which the interaction is plotted.
FIGURE 2 The Joint Effect of Strategic Emphasis and Negative Relative Performance
Notes: Numbers in parentheses are the values at which the interaction is plotted.
FIGURE 3 The Joint Effect of Strategic Emphasis, Positive
Relative Performance, and Demand Instability
B: High Demand Instability (1.026)
Note: Numbers in parentheses are the values at which the interaction is plotted.
FIGURE 4 The Joint Effect of Strategic Emphasis, Negative
Relative Performance, and Demand Instability
B: High Demand Instability (1.026)
Notes: Numbers in parentheses are the values at which the interaction is plotted.
FIGURE 5 Average Marginal Effect of Strategic Emphasis on Idiosyncratic Risk by Industry
FIGURE 6 Average Marginal Effect of Strategic Emphasis on Idiosyncratic Risk by Firm
B: Electronic/Electrical Equipment (High Demand Instability)
Average marginal effect of strategic emphasis Average positive relative performance Average negative relative performance
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Record: 149- Return on Engagement Initiatives: A Study of a Business-to-Business Mobile App. By: Gill, Manpreet; Sridhar, Shrihari; Grewal, Rajdeep. Journal of Marketing. Jul2017, Vol. 81 Issue 4, p45-66. 22p. 18 Charts, 3 Graphs. DOI: 10.1509/jm.16.0149.
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Return on Engagement Initiatives: A Study of a Business-to-Business Mobile App
Firms are increasingly offering engagement initiatives to facilitate firm–customer interactions or interactions among customers, with the primary goal of fostering emotional and psychological bonds between customers and the firm. Unlike traditional marketing interventions, which are designed to prompt sales, assessing returns on engagement initiatives (RoEI) is more complex because sales are not the primary goal and, often, direct sales are not associated with such initiatives. To assess RoEI across varying institutional contexts, the authors propose and empirically implement a methodological framework to investigate a business-to-business mobile app that a tool manufacturer provides for free to engage its buyers. The data include sales by buyer firms that adopted the app over 15 months, as well as a control group of buyers that did not adopt. The results from a difference-in-differences specification, together with selection on observables and unobservables, show that the app increased the manufacturer’s annual sales revenues by 19.11%–22.79%; even after accounting for development costs, it resulted in positive RoEI. This RoEI was higher when buyers created more projects using the app, so customer participation intensity appears to underlie RoEI. This article contributes to engagement literature by providing a methodological framework and empirical evidence on how the benefits of engagement initiatives materialize.
Firms continually find new ways to interact with customers through myriad touch points in multiple channels and media (Lemon and Verhoef 2016). Often, the primary goal of these interactions is not to close a sale. For example, “the Sherwin-Williams’ ColorSnap app allows users to capture desired colors on their mobile devices and then matches the colors to specific paint colors they can purchase at the paint store” (Urban and Sultan 2015, p. 33). In another setting, the milling, turning, tapping, and drilling calculators embedded in Sandvik Coromant’s mobile apps1 help engineers and machinists in buying firms perform machining and cost calculations and compare solutions for various parameters. During an annual fitness exposition, the protein supplement seller Nutrabolt helps runners and cross-training enthusiasts gauge their fitness levels through a CrossFit competition while also providing health and wellness tips.2
Two common themes underlie these organizational initiatives. First, they create customer value by providing product/service information, knowledge, and relevant assistance. For example, the Coromant app reduces the time that buyers spend selecting the right tools and machining processes. Second, firms use these initiatives primarily to foster interactions with customers, not to make sales. Sherwin-Williams does not obligate ColorSnap app users to buy its paint. Because these initiatives create value for customers but are not meant to prompt sales, we refer to them as “engagement initiatives.” Consistent with a definition of engagement by Kumar and Pansari (2016), we define engagement initiatives as organizational initiatives that facilitate firm–customer interactions or interactions among customers, with the primary goal of fostering an emotional and psychological bond between customers and the firm.
Such initiatives have proliferated with the growth of the Internet and mobile devices, which intensify interactions between firms and customers (Manchanda, Packard, and Pattabhiramaiah 2015). Furthermore, with their complex products and services, long sales cycles, and varying interpurchase times, business-to-business (B2B) manufacturers frequently introduce mobile apps to interact with and engage buyers: one survey indicates that 80% of U.S. manufacturing companies have developed B2B apps (International Data Group
Market Report 2013).3 These mobile apps can be effective engagement tools because they create multiple nonpurchase customer touchpoints (e.g., ColorSnap, Coromant), distinct from apps that function primarily as a sales channel (e.g., Amazon, Chipotle). Because B2B engagement apps are costly to develop and offered for free, and because B2B buying processes are not instantaneous or individual-specific, assessing returns to these apps is complex and difficult (e.g., Beebee 2013). Recent work has suggested that engagement initiatives might invoke nonsales outcomes such as trust, commitment, or loyalty (Brodie et al. 2011; Shiri, Beatty, and Morgan 2012), but scholars have called for explicit links to economic outcomes (Lemon and Verhoef 2016) to affirm the viability of such initiatives.
We accordingly propose a methodological framework for assessing economic returns on engagement initiatives (RoEI) and assess RoEI for a B2B mobile engagement app that a manufacturer provides for free to its buyers. We utilize the framework with novel data from a leading U.S. manufacturer (pseudonym: XYZ) that sells tools and industrial materials. XYZ devoted significant resources to launching its free manufacturing app, which can be downloaded on mobile devices and provides both product recommendations and processdesign assistance (e.g., customized machining suggestions). This context is pertinent and typical of engagement initiatives for several reasons. First, the app is offered for free, creating direct economic (development and maintenance) costs, with no direct economic benefit. However, it might produce indirect economic benefits (e.g., increased revenues outside the app), which need to be quantified. Second, even if adopters generate indirect revenue benefits, to determine their value, we must safeguard against self-selection biases (i.e., revenue-generating buyers that adopt the app strategically) to avoid confounding the causal assessments of RoEI. Third, we aim to establish the RoEI mechanism to understand the source of the returns as well as develop managerial insights about which in-app interactions generate RoEI.
We use difference-in-differences specification and matching estimators to address these objectives. Specifically, we use objective sales data from a sample of buyer firms that downloaded the app (treatment group) and compared sales of these firms in the 15 months after the app’s launch with sales in the 15 months before its launch. In turn, we utilize data from a random sample of buyers that did not download the app (control group), over the same time intervals. To avoid a self-selection bias related to buyers that adopt the app strategically, we estimated the treatment effects using different methods, reflecting distinct perspectives on how to obtain the focal differencein-differences comparison: (1) selection on observables through regression and matching estimators and (2) selection on unobservables through a formal selection equation with appropriate instrumental variables.
We therefore offer two key contributions. First, we make theoretical and empirical contributions to engagement literature. Unlike most extant research that has focused on the definition or scope of customer engagement, we address the firm’s economic benefits that result from an engagement initiative. We offer a methodological framework to address identification issues and provide a causal estimate of RoEI. Furthermore, in support of RoEI, we find that buyers that adopted the free app generated additional annual sales of 19.11%–22.79% for XYZ (relative to the preadoption period and benchmarked against nonadopters), even in the presence of alternate estimators, matching strategies, and data transformations. Because XYZ’s RoEI is higher for buyers that create more projects using the app, our findings also indicate the importance of participation intensity in an engagement initiative as an RoEI-generating mechanism. Second, our empirical findings contribute to emerging literature on apps, which largely overlooks B2B apps and focuses primarily on intermediate outcomes, such as customer visits to a firm’s mobile website or attitudes toward the firm (e.g., Urban and Sultan 2015; Xu et al. 2014). Our findings should prove useful to B2B firms trying to develop profitable ways to engage with their buyers through mobile apps.
We next discuss some relevant literature and present our conceptual arguments pertaining to B2B sellers’ payoffs from free mobile apps. Then, we describe the institutional setting and data, model setup, and identification strategies. Finally, we present the results and discuss their implications.
Conceptual Background
Conceptualizing Engagement Initiatives
Engagement initiatives have two salient differences from traditional marketing-mix interventions. First, unlike traditional marketing interventions, engagement initiatives do not intend to induce a sale but primarily aim to build strong, long-term relationships with customers. Second, unlike conventional forms of one-way communication from the firm to the customer, engagement initiatives tend to be interactive and elicit participative experiences. The growth of engagement initiatives might stem from the growth of customer relationship management and its underlying philosophy that customers may interact valuably with the firm without necessarily making a purchase. These interactions need to be measured and managed to build stronger relationships, which then can lead to profitable value extraction. Engagement initiatives also grant firms their own touch points, which they can use to monitor and improve firm–customer and customer–customer interactions.
Components of an Engagement Initiative
A typical engagement initiative begins with customers’ interactive participation with the firm. For example, customer participation in the Sherwin-Williams ColorSnap app is inherently interactive because customers search for their desired paint colors and interact with the app to choose a specific color. Buyers in the tooling industry first provide their machining parameters to the Sandvik machining calculator to obtain the desired machining processes and tolerance levels. Customers with greater participation intensity generally develop stronger emotional bonds and higher perceived interconnectedness with the firm, even when they do not engage in explicit purchasing activities (Van Doorn et al. 2010).
Heightened customer participation ideally leads to value creation for the customer, which constitutes the second component of the engagement initiative. Depending on their nature, engagement initiatives could provide value to customers at different purchase stages, and this value might be intrinsic, justifying the end unto itself, or extrinsic, by enabling a customer to perform a task related to the product or service (e.g., customization, designing a service solution) (Shiri, Beatty, and Morgan 2012). In a B2B context, increased interactions with the seller enable buyers to (1) articulate their business/product needs, (2) specify how they want the process customized, and (3) learn how to use the seller’s products and solutions to fit their evolving needs (Sawhney 2006). The ubiquity and ease of access of mobile apps may be particularly valuable, in that they can alleviate the difficulty of acquiring static product information from a physical catalog as well as provide a platform that helps buyers create their own, customized solutions (Xu et al. 2014).
The last component of the engagement initiative is the firm’s appropriation of value created for the customer. Engagement initiatives help firms strengthen their bonds with customers, and these bonds could lead to economic benefits in the future. A customer that learns about the firm through an engagement initiative may develop more favorable attitudes toward the firm, which should produce favorable economic outcomes. Increased perceived value fosters trust and loyalty, which also may increase purchase volumes (Reinartz and Ulaga 2008). Moreover, when buyers derive more value from participating in an engagement initiative, they might start to rely on seller-provided knowledge that otherwise would be costly or impossible to obtain, and the seller likely becomes a preferred supplier. Value extraction thus can follow from interactive participation through several routes, including increased purchasing behaviors, referrals, and influences on other customers (Kumar et al. 2013). Each behavioral outcome would signal the appropriation of value from an engagement initiative by providing clear pathways to incremental customer demand.
Assessing RoEI
Two challenges impede our ability to obtain causal estimates of RoEI. First, engagement initiatives lead to no direct economic benefits, and thus RoEI stems from indirect economic benefits, or net revenue increase generated from the value created by the engagement initiatives. Quantifying these indirect economic benefits is challenging; it requires a causal assessment of the impact of the initiative in the presence of multiple confounds, such as other environmental trends or marketing efforts, which co-occur with the engagement initiative.
Second, a firm’s decision to offer the engagement initiative is strategic, as is the customer’s decision to participate. That is, firms likely offer engagement initiatives to customers they believe will produce positive economic returns, and customers likely self-select into engagement initiatives according to their strategic evaluation of expected benefits. Buyers might adopt a tooling calculator app because they anticipate economies of scale and improved buying processes, for example, which would create a self-selection confound in assessing RoEI. Because information on all the reasons that firms use decide to launch an initiative or criteria that customers use to decide to participate is not observable to researchers, the omitted variables could lead to endogeneity in the RoEI estimates.
In Table 1, we summarize four potential approaches to assessing RoEI: event studies, seller-level observational inference designs, customer-level randomized experiments, and customer-level observational inference designs. We discuss each approach in turn next.
In an event study method, researchers could treat the announcement of a seller’s engagement initiative as an economic event and estimate the impact of the event on the creation of the seller’s shareholder wealth. Thus, data would be needed on the announcement dates of a sufficient sample of engagement initiatives across different sellers4 over the study’s time horizon (e.g., two years). Subsequently, researchers could assess the event’s impact on the seller’s shareholder value by obtaining a measure of the abnormal returns on the seller’s stock price and testing the significance of the abnormal returns in an appropriate event window (for a discussion of the steps to define a market event and estimate abnormal returns, see Srinivasan and Bharadwaj [2004]). Estimates of RoEI using event studies constitute the market’s belief about the potential economic value of an initiative. Although this method does not directly assess self-selection by sellers into such initiatives, the abnormal return attributable to the announcement would help adjust for the returns that stem from other variables causing price fluctuations in the market.
With seller-level observational inference designs, the primary objective is to establish the causal link between the presence of engagement initiatives and seller performance. Data are required on relevant aggregate seller-level outcomes (e.g., sales, firm value, profitability) from multiple sellers (ideally across several industries) during a time frame before and after each of the sellers launches an engagement initiative. Subsequently, researchers would estimate RoEI as the estimate of how much a seller’s performance would change as a result of the introduction of the engagement initiative and would assess heterogeneity in RoEI estimates using both seller-type and industry-type moderators. However, the researchers would need to control for the notion that sellers likely possess private knowledge about whether, when, or how to launch a profitable engagement initiative. This private knowledge about the perceived efficacy of introducing an engagement initiative is unobserved to the researchers, is correlated to the eventual outcome of the engagement initiative, and could lead to endogeneity bias in RoEI estimates. To correct for this bias, we might rely on corrections such as instrumental variables, control functions, or precise knowledge about the institutional rules governing sellers’ launch of engagement initiatives (for a similar discussion in the context of a firm’s decision to have a chief marketing officer in the C-suite, see Germann, Ebbes, and Grewal [2015]).
The final two approaches, customer-level randomized experiments and customer-level observational inference design,
TABLE 1
Comparison of Frameworks to Assess RoEI
TABLE:
| Method | Aggregation | Data | Measure of RoEI | Solving Endogeneity from Selection |
|---|
| Event studies | Seller level | Data on the announcement dates of a large sample of engagement initiatives across different sellers | Abnormal market returns that a firm experiences on the day it announces an engagement initiative | Adjust for price fluctuations from the entire market on the same day |
| Seller-level observational inference designs | Seller level | Data on relevant aggregate seller-level outcomes (e.g., sales, firm value, profitability) from multiple sellers (ideally across several industries) during a time frame before and after each of the sellers launches an engagement initiative | Compare a relevant sellerlevel outcome (e.g., sales, firm value) across sellers, before and after the firm introduces engagement initiatives | To control for strategic selection by seller firms into engagement initiatives, use instrumental variables, control functions, and seller firms’ decision rules |
| Customer-level randomized experiment | Single seller, customer level | Data from one seller on relevant aggregate economic outcomes (e.g., sales, margins, revenue) from multiple customers during a time frame before and after the seller’s launch of the engagement initiative | Compare a relevant customer-level outcome (e.g., purchase order, quantity) across consumers, before and after the seller introduces an engagement initiative | Offer an engagement initiative to a random preselected treatment group but not to the control group |
| Customer-level observational inference design | Single seller, customer level | Data from one seller on relevant aggregate economic outcomes (e.g., sales, margins, revenue) from multiple customers during a time frame before and after the seller’s launch of the engagement initiative | Compare a relevant consumer-level outcome (e.g., purchase order, quantity) across consumers, before and after the seller introduces an engagement initiative | Selection on observables, selection on unobservables |
4In some cases, sellers could announce multiple engagement initiatives over the duration of the study, and these announcements would be included as separate events with appropriate statistical corrections. focus on one seller’s engagement initiative and use acrosscustomer variation to estimate RoEI. Thus, in each of these two cases, one would need data from one seller as well as relevant aggregate economic outcomes (e.g., sales, margins, revenue) from multiple customers during a time frame before and after the seller’s launch of the engagement initiative.
In customer-level randomized experiment setup, researchers would infer the incremental economic benefit using customers’ revealed purchase behaviors after the introduction of the engagement initiative. To prevent customer self-selection into the initiative, exposures to the engagement initiative would be randomized so that some preselected customers (treatment group) would have access, while other preselected customers (control group) would not. The random assignment implies that the difference in the average economic outcomes across treatment and control groups represents the treatment effect, or RoEI. To control for existing purchasing patterns in both groups, this method compares the change in economic outcomes (rather than levels) before and after the launch, across both groups. This robust version of RoEI would be the difference in the change in economic outcomes (difference-indifferences) across the treatment and control groups, after controlling for permanent differences across groups and time shocks common to both (for a discussion of the steps to infer causal economic effects of interventions using a randomized experimental design, see Athey and Imbens [2016]).
Finally, a customer-level observational inference designs is useful when, for pragmatic or fairness-related reasons, a seller cannot randomize the engagement initiative offering to its customers (as in our data). Thus, it might be possible that customers self-select into the engagement initiative, so RoEI estimates must modify the difference-in-differences estimate from the randomized design case, using empirical strategies that control for self-selection by customers (for a discussion in the context of online communities, see Manchanda, Packard, and Pattabhiramaiah [2015]). Because our data fall in this category, we subsequently describe three such strategies to overcome self-selection bias.
Engagement Initiatives in a Mobile App Context
Nascent but burgeoning literature on mobile apps (see Table 2) reflects the proliferation of mobile apps in the marketplace, with two main streams relevant for our research: demand for and effectiveness of mobile apps. In particular, research into app demand denotes the influences of customer characteristics, such as age, gender, and education (Han, Park, and Oh 2016). Younger users exhibit more affinity toward social networking, gaming, and photo apps relative to seniors; women express more preference for communication and entertainment apps than men. Younger customers also have low satiation for social networking and gaming apps, such that their usage appears to mimic uses of habit-forming substances such as alcohol (e.g., Kwon et al. 2016). Garg and Telang (2013) find that app demand also increases because of app characteristics such as its age, platform, version, and rank. According to Ghose and Han (2014), in-app advertisements negatively influence demand, whereas Carare (2012) shows that app demand increases with the valence and volume of customer reviews. An implicit assumption in this stream is that higher app demand is better for the app developer, but the return on investment remains unexplored.
The second research stream relates to the effectiveness of mobile apps. Firms use mobile apps to engage customers and obtain a competitive edge; for example, branded mobile apps (e.g., eBay, Amazon) allow for continuous interactions with customers and thereby strengthen customer attitudes and purchase intentions (Bellman et al. 2011). Urban and Sultan (2015) argue that engagement apps foster fondness over repeated customer usage, which could strengthen mindset metrics such as brand attitudes, brand consideration, and purchase intentions. According to Xu et al. (2014), customer adoption of news apps increases their probability of visiting the newspaper’s mobile website. Sales apps also can increase overall sales for an omnichannel retailer (Einav et al. 2014). A consistent argument thus holds that firms may use apps to engage their customer base and thereby create several important nonsales outcomes, such as trust, commitment, and loyalty (Brodie et al. 2011; Shiri, Beatty, and Morgan 2012). However, we find no explicit approaches for estimating RoEI.
Data
We obtained data from a leading manufacturer (XYZ) of tooling and industrial materials (e.g., automatic lathes, cutting tools) on its free mobile app5 launched in September 2013. XYZ’s annual sales are more than $1 billion, with buyers on six continents. Like other manufacturers in this industry, XYZ provides detailed print and online catalogs to buyers, specifying appropriate tools for performing simple machining jobs as well as machining sequences (and assembly layouts) required to execute more complex processes. Buyers often find it time consuming to review print catalogs, and generational turnover and a general lack of interest in manufacturing jobs among younger workers led XYZ to expect this knowledge gap to widen. Furthermore, XYZ believed that younger buying managers might be comfortable using Internetenabled technologies in design environments. Accordingly, the firm chose to digitize its existing product information in a mobile app, which would reduce the time required for buyers to identify optimal tools and create machining sequences while also creating a touch point between the buyer and XYZ’s offerings.
Buying firm managers typically use the app at their own machining plants for either product search or more complex product assembly designs. As a product search enabler, the app collects information from the buyer, such as a specification of the focal machining operation, then returns optimal tool recommendations. Buyers can select tools across a range of customizable attributes built into the app; they also can bookmark product recommendations and share their search results with other buying managers. As a product assembly platform, the app allows a buyer to draw an entire manufacturing process, comprising a series of tooling operations, together with the specific tools and tolerance levels associated with that process. The app reviews the overall manufacturing process and provides usage recommendations to make the process more efficient (e.g., better tolerance levels with the current tools) or product improvements for the same operation (e.g., to reduce manufacturing time).
Because XYZ provided the app for free and it was not designed to stimulate direct sales, XYZ employed virtually no targeted marketing efforts to increase adoption, except for an e-mail to all buyers around the time of app launch, followed by a short press release highlighting its features. The sales force also operated independently of the app, and the app remained solely under the purview of the new product development and marketing functions. However, XYZ believed that existing buyers might purchase more tools because of this engagement initiative. When a manager from the buying firm downloads the app, the user must provide the buying firm’s name and a unique sales identifier that XYZ provides each buying firm following its first transaction. Multiple managers within the same buying firm can use the app, but the unique sales identifier consistently refers to the buying firm level. We obtained data from 550 unique buyer firms that downloaded XYZ’s app; however, for confidentiality reasons, we cannot disclose the total number of adopters.
We obtained all (offline) transaction sales data for these buyers for a 15-month period from September 2013 (launch month) to November 2014, as well for a 15-month period preceding the launch. Thus, we create a two-period customerlevel observational inference design with a control and treatment group. We aggregate each set of 15 months of data for two reasons. First, interviews with the app director and sales managers at XYZ indicated that buying firms’ purchase cycles are generally long (5 months on average) but also vary significantly (2–12 months). Disaggregation thus could lead to misrepresentations of sales changes due to organic differences in the purchase cycles across the buying firms. Second, we did not observe the exact date when buyers downloaded the app. Some might have done so early in the postlaunch period, whereas others did so later. By treating the entire 15-month period as the postlaunch period, we assume that buyers who downloaded the app did so right after its launch, which offers a conservative assessment because it limits the treatment effect for later adopters, which effectively must start at the moment of the app launch to indicate business benefits to XYZ. Manchanda, Packard, and Pattabhiramaiah (2015) use a similar conservative assumption; Bertrand, Duflo, and Mullainathan (2004) also recommend such an aggregation to help mitigate potential issues related to serial correlation and grouped error term effects.
Next, we obtained transactional sales data from a randomly drawn sample of 700 unique buyers that did not download the app. For the comparison, we consider a subsample of buyers that purchased at least once in both pre- and postlaunch periods, to mitigate potential endogenous entry or exit effects. We thus have data from 522 buyers that downloaded the app and 626 buyers that did not.
Identification Strategy
Our goal is to assess if XYZ’s introduction of the free app increases sales revenue from buyers that adopted the app. In an experimental sense, XYZ exposes app-adopting buyers to a treatment, and we aim to infer the treatment effect, as represented by the incremental sales revenue from these buyers resulting from their adoption of the app. In an ideal setting, we could randomize the treatment, then observe sales from buyers that did not get the app (S0) and sales from buyers that obtained it (S1). With such a random assignment, the difference in these average sales, or S1 - S0, represents the treatment effect—that is, the incremental economic benefit of introducing the app. However, for fairness, XYZ’s app was available to all buyers. Thus, in our data (as in most observational data settings), buyers’ app adoption is not random, and we need to account for buyers self-selecting into the treatment group. Not all their adoption reasons are observable; for example, we cannot observe improvements in the buying process that result from app adoption. Omitted variables that drive strategic app adoption could correlate with the sales XYZ earns from these buyers, which would involve an endogeneity bias. Therefore, we consider three potential solutions that vary in the extent to which they correct for selection bias to establish the causal link between app adoption and sales: (1) difference-indifferences,
Difference-in-differences, augmented with selection on observables, and (3) difference-in-differences, augmented with selection on unobservables. Difference-in-differences. The difference-in-differences approach compares the sales differential (posttreatment sales pretreatment sales) of buyers in the treatment group with buyers in the control group. Thus where Sijt is buyer i’s sales from group j at time t, and eijt is a random error term, clustered across buyers and the two periods. Our data set contains two groups j (treatment and control) and two time periods t (pre- and postlaunch periods). Then the indicator variable Ij picks up mean differences in the sales between the treatment group and the control group, referred to as group fixed effects and indicated by the coefficient b1. The indicator variable It indicates the mean differences in postrelative to prelaunch period sales, similar to time fixed effects and indicated by the coefficient b2. Finally, b3 captures the difference in the change in sales outcomes (difference-indifferences) across the treatment and control groups, after controlling for permanent differences across groups and the time shocks common to both groups. Thus, b3 is the estimate of the treatment effect, given as
From Equation 2, b3 can also be viewed as the incremental economic benefit to XYZ of introducing the app, or RoEI. A key identifying assumption of the difference-in-differences approach is that the treatment and control groups are identical, so the time trends in sales for the treatment and the control group buyers are also identical (parallel trends assumption), apart from the treatment itself. Using this assumption, the deviation in the difference in sales for the treatment group from that of the control group provides a causal estimate of the treatment effect. Group fixed effects also eliminate time-invariant, buyer-specific unobservable variables—and, thus, self-selection—to the extent that this bias is driven by group-specific, time-invariant omitted variables.
However, the critical parallel trends assumption could be violated in our study context because buyer-specific unobservable variables (which influence both buyers’ adoption decisions and sales) could vary across buyers, resulting in heterogeneous, dissimilar groups. The group fixed effects, meant to smooth out the permanent differences between groups, then would not eliminate buyer unobservable variables that are distinct from the group-specific, time-invariant unobservable variables. Failing to account for them in the difference-indifferences analysis could make our control group an inappropriate counterfactual for the treatment group because of the violation of the parallel time trends assumption. Thus, we augment the difference-in-differences analysis.
Difference-in-differences with selection on observables. Compositional differences between the control and treatment groups (thus violating the parallel trends assumption) arise because buyer firms self-select into the app due to unobservable variables that also correlate with their sales. For example, the app may offer more cost savings for some buyers, which could affect their unobserved preference for XYZ’s offerings more in the treatment group than in the control group. Selection on observables corrects for this self-selection by assuming that the researcher observes all variables that buyers consider while deciding to adopt.
In our study setting, buyers’ motivations to adopt the app may be due to cost-related advantages. A selection-onobservables strategy uses buyer-specific observables to proxy for cost advantages, such that the treatment and control groups look similar and the parallel trends assumption is preserved. Then, the outcome (i.e., sales) is independent of the treatment (i.e., app adoption); formally,
where S is sales, T is an indicator of app adoption, Z indicates the observables, and ’ is an orthogonality operator. We operationalize this approach by augmenting our differenceindifferences model from Equation 1 with all the observed buyer firm variables (e.g., Angrist and Pischke 2009) as follows:
where the added vector Zij captures the set of observables, the effects of which are estimated through the coefficient vector b4. The treatment effect thus is given as
Difference-in-differences with selection on unobservables. The assumption that we can observe all the important variables is a strong one, so we also need to account for unobservable variables. We combine the difference-in-differences analysis with a formal Heckman-style selection model, in which the errors in the selection equation (required to model the buyer’s decision to adopt) and the errors in the outcome equation (i.e., difference-in-differences model) correlate and follow a bivariate normal distribution. In turn, we can derive the inverse Mills ratio (IMR) to account for unobservable variables in the outcome equation (Heckman 1979). Adding this ratio to the outcome equation accounts for omitted unobservable variables, so this strategy is called selection on unobservables (see Appendix A).
We first model buyers’ decision to adopt the app as a function of all the observable variables with a probit model, which we use to calculate the IMR for the buyer firms in the treatment and control groups. Then, we augment our differencein-differences model in Equation 4 as follows:
Results
Model-Free Evidence
As we show in Figure 1 and Table 3, raw mean total sales (scaled in $10,000s) in the control and treatment groups were not statistically different in the prelaunch period (treatment = 80.11, control = 75.75, n.s.). Treatment group sales were higher in the postlaunch than in the prelaunch period (post = 93.44, pre = 80.11, p < .05), whereas the sales in the control group stayed approximately the same in both periods (post = 73.07, pre = 75.75, n.s.). Sales also remained about the same for buyers in the control group, but they increased for buyers in the treatment group, indicating the need for a more formal comparison.
Selection of Covariates
From detailed interviews with the app program director, the app marketing team, and the app developer at XYZ, we learned that buyers’ strategic motivation to adopt the app was cost savings. Specifically, app adoption can reduce the time buyers expend on product searches and help streamline their entire assembly design process because the app provides a common platform for all buying units to create machine assemblies. Thus, we include a set of buyer-specific observable variables that proxy for buyers’ strategic motives to reduce costs by adopting the app, described in the following subsections.
Buyer power. We measured buyer power as the ratio of the buyer’s total sales in the prelaunch period T1 to the sum of total sales by XYZ to all buyers in the same industry division. Buyers that transact often with XYZ likely would enjoy cost advantages by adopting the app because of the efficiency gains of using a single app to design all offerings. Moreover, buyers that transact more often with a seller tend to value relationship-specific investments by the seller because they observe these investments during every transaction.
Buyer’s industry competitiveness. We measured competitiveness in the buyer’s industry by obtaining the buyer’s industry concentration ratio from U.S. Census reports, which reflects the ratio of the sales of the top 20 firms in an industry to total sales in the industry.6 We subtracted the concentration ratio from 1 to measure competitiveness in the buyer’s industry (Lee et al. 2015). Buyer industry competitiveness ranges from 0 to 1, where 0 refers to highly monopolistic industries and 1 implies highly competitive industries. Prior B2B technology adoption literature has shown that greater competitive intensity induces significant heterogeneity in firms’ new technology adoption speed (Lee and Grewal 2004). Firms anticipate costrelated gains from adopting early (and being first movers) because they can limit the losses that might accrue from unsubstantiated early adoptions. Lee and Grewal (2004) show that as competition increases, heterogeneity in adoption (vs. nonadoption) increases; firms decide quickly whether to move early or not adopt at all (and wait for the benefits to trickle down), so it becomes crucial to control for competitive intensity.
Buyer firm size. We measured buyer size using the number of employees in the buying firm. Larger buyer firms might have more cost savings from app adoption than smaller firms because of scale advantages of reduced product search time and product assembly guidance.
Buyer T0 period patterns. We controlled for buyers’ intrinsic preference in transacting with XYZ by including buyers’ past sales (buyer T0 sales) and purchase frequency (buyer T0 frequency) in the period five months before the preperiod T1, which we denote as T0. The T0 period provides the baseline reference period for the study.
Other cultural and industry factors. We controlled for the location (continent) and the industry classification code (using the manufacturer’s internal industry classification code) of the buyer firm, which might induce heterogeneity in buyers’ perceptions of the cost savings achieved from using the app. In Table 4, we show that the composition of buyers in the control and treatment groups is similar. Nearly half the buyers are located in developing economies (Asia, South America, Africa), and the z-statistic shows that the composition between groups is statistically indistinguishable. The composition of buyers across transportation and aerospace, heavy equipment, and general engineering industries across treatment and control groups also is statistically similar. In Table 4, we also report the mean values of the buyer power, buyer firm size, buyer industry competitiveness, and buyers’ intrinsic preference to transact with the manufacturer (captured as T0 sales and purchase frequency) across treatment and control groups. Buyers’ average transaction share, firm size, industry competitiveness, and T0 sales do not differ statistically across groups. However, buyers’ T0 purchase frequency in the treatment group is significantly higher than that of firms in the control group.7
Model-Based Results
Difference-in-differences. We begin by presenting the estimates for Model 1 (Table 5), without the control group. For buyers in the treatment group, sales in the 15-month postlaunch period were higher than sales in the 15-month prelaunch period by $133,300, or an annual increase of $106,640. The average annual sales of a buyer in the treatment group ($640,880) thus reveals a sales increase of 16.64% as a result of adoption of the app.
Next, we added the control group and estimated the treatment effect from the difference-in-differences specification (b3) without any buyer-specific characteristics (Table 5, Model 2). The treatment effect was significant (b3 = 16:01, p < :05Þ, indicating a statistically significant economic impact for XYZ when the buyer adopts the app. The average annual sales of a buyer in the treatment group in the prelaunch period ($640,880) enabled us to calculate a percentage sales increase of 19.99% in the postlaunch period.
Difference-in-differences with selection on observables. We augment the simple difference-in-differences model with buyer-specific characteristics in Model 3 in Table 5. Again, the treatment effect was significant (b3 = 16:01, p < :05Þ, indicating a statistically significant economic impact of buyers’ adoption. According to the average annual sales of a buyer in the treatment group ($640,880), the annual sales increase was 19.99%.
Difference-in-differences with selection on unobservables. To correct for buyers’ potential self-selection into adoption, we used a two-stage Heckman (1979) correction. In the first stage, we model the app adoption choice using key drivers and a probit specification. Buyer power, industry competitiveness, firm size, T0 period patterns (i.e., T0 sales and purchase frequency), and cultural and industry factors can all proxy for buyers’ strategic motives to reduce costs by adopting, so we included these covariates as predictors of app adoption in the first-stage model.
For identification, the covariate set driving the app adoption choice should contain at least one variable that provides an exclusion restriction, such that it affects app adoption but does not directly influence buyer sales. We used the number of buyer firm buying units; as the number of buying units increases, the chances that a buyer has its own centralized product search or assembly unit should increase too, so its reliance on a manufacturer to provide this service is lessened, and more buying units should decrease the probability of app adoption. However, there is no reason to expect a priori that the number of buying units exerts any effect on the change in total buyer sales. The results of the first-stage (probit) model in Table 5 (Model 4a) that predicts app adoption according to buyer characteristics confirms that the number of buying units decreases the probability of adoption (b = - :389, p < :05). Then, we added the IMR as a selection correction term in the second-stage sales equation.
The main results in Table 5, Model 4 (no covariates), reveal that the selection correction term is significant, and the treatment effect remained statistically significant (b3 = 16:01, p < :05). Model 5a (all covariates) confirms these results (b3 = 16:01, p < :05), except that the selection correction term is statistically nonsignificant. Thus, the selection-on-unobservables strategy indicates a positive economic benefit to XYZ: the treatment effect increase translated into a 19.99% annual sales increase.
For robustness, we considered another instrument: the number of buyer firms in the focal firm’s industry that have adopted the app. This instrument passes the validity criterion, because the number of buyer firms that have adopted the app should correlate positively with the decision of a focal firm to adopt. However, there is no reason that peer firms’ adoption decisions should correlate with the focal firm’s sales, conditional on industry competitiveness and time fixed effects. We modeled a buyer firm’s app adoption as a function of various covariates and two instruments (i.e., number of buying units and the number of buyer firms in the focal firm’s industry that have adopted the app), using a probit model.
Accordingly, we obtained unobserved factors capable of influencing the buyer firms to adopt and their sales in the IMR, which we included in the outcome model with the other covariates. Our results further bolstered our claim regarding the treatment effect, which again turned out to be statistically significant (b3 = 16:01, p < :05)8 and implied a 19.99% sales increase for the buyer firms that adopted the app (see Models 5b and 4b, Table 5). the sample of firms to yield the average treatment effect (ATE). However, it is not possible to estimate ATE; we observe only one potential outcome for each buyer. Instead, we use various methods to impute missing potential outcomes and then calculate the ATE as an average of individual treatment effects in the sample. Specifically, we relied on nearestneighbor matching and its variants (i.e., regression adjustment, inverse probability weighting, and inverse probability weighting with regression adjustment). We provide the estimates from these methods and their details in Appendix B.
Role of outliers. We estimated a significant treatment effect (b3 = :151, p < :05) with log-transformed sales (see Table 6) because the log transformation mitigates the threat of outliers. We ideally sought to demonstrate the effect with untransformed data and thus only used a log-transformed model to confirm outlier-related robustness.
Definition of competitive intensity. Buyer firm size and buyer transaction share are objective measures not prone to design choice variations; we also considered alternative measures of competitive intensity. Rather than subtracting 1 from the industry concentration ratio of the sales of the top 20 firms to total sales in the industry, we used the top 4, top 8, and top 50 firms’ sales. As Table 6 reveals, the significance of the treatment effect remained unchanged when we used these alternative measures of competitiveness in the buyer’s industry.
Novelty effects and falsification. We checked whether the positive economic impact for XYZ might stem from the buyer’s early (potentially fleeting) interest in using the app. To rule out novelty effects, we considered a 12-month postlaunch period, starting in the 4th month after the app launch to the end of the 15th month. The prelaunch period then started nine months before the launch date and ran until three months after its launch. Thus, we moved the three-month period after the launch to the prelaunch period, which should be adequate time for the novelty perceptions to wear off. In Table 7, the treatment effect’s significance (b3 = 14:42, p < :05) did not change much as a result of this adjustment. Thus, the economic impact of a buying firm’s adoption seems persistent and not necessarily subject to novelty effects.
We also designed a falsification test to check whether the increase in sales to buyers in the treatment groups was due to the launch of the app. Because there was no app before September 2013, the treatment effect in the prelaunch period should be zero. Accordingly, we performed a difference-in-differences analysis of the prelaunch period data, treating months 2–8 as the prelaunch period and months 9–15 as the postlaunch period. The results in Table 7 confirm our intuition that no treatment effect existed prior to the launch; the effect is not significant (b3 = - 3:33, p > :05).
Quarterly aggregation. We used a two-period model to minimize heterogeneity in sales cycles across buyers in the sample, but in this robustness check, we reestimated the model using quarterly data. Buyer fixed effects account for buyer-specific, time-invariant factors that contribute to differences in sales. We also use quarterly fixed effects to account for time period–specific factors (e.g., seasonality) that might induce changes to sales patterns. The interaction term (postlaunch period · treatment group) captures the treatment effect, identified as within-buyer and over-time variation in sales, after controlling for stable firm and timespecific factors that contribute to changes. As Table 8 reveals, we retrieve a significant treatment effect (b3 = 3:19, p < :05) even with data disaggregated to quarterly time units (see also Jin and Leslie 2009). The average annual economic benefit to XYZ in this model was $127,600, or an increase of 19.91% resulting from the introduction of the app.
In summary, across all identification strategies (selection on observables, selection on unobservables, estimator from potential outcomes framework) and temporal aggregations (two-period, quarterly), the annual economic benefit to XYZ due to the app featured sales increases in the range of 19.11%–22.79%, or an annual RoEI of $122,480–$146,080. We summarize the models and results in Table 9.
Sources of RoEI
Having established the presence of a RoEI, through increased sales, we examine whether the sales increases indicate more frequent purchases, larger quantities, or a broader variety of product purchases. Thus, we use purchase frequency, purchase volume, and purchase breadth as dependent variables. Sales increases and RoEI mainly resulted from purchase frequency and purchase breadth (see Appendix C). Using median splits of the sample, based on buyers’ T0 sales, we also identify a low– and a high–T0 sales group. Purchase frequencies increased marginally for the low–T0 sales group, but it did not show any increases in purchase breadth. Instead, we observe significant increases in purchase frequency, purchase breadth, and sales for the high–T0 sales group (Appendix C). Next, we split the sample according to (1) firm size (i.e., small and large firms), (2) the industries to which buyers belong, and (3) the economic region (developing vs. developed) in which buyers are situated. We find significant sales increases for small (relative to large) firms, firms that belong to the general engineering industry category, and those in developing economies (see Appendix C). These analyses suggest heterogeneity in RoEI across buyers, depending on their size, industry, and location.
Participation Intensity
In keeping with our previous arguments, buyers’ participation intensity is manifest through their repeated activity with the app, and it likely creates more value over time, such that it might expand the value appropriation opportunities for XYZ (Brodie et al. 2011; Kumar and Pansari 2016). We operationalize participation intensity as the number of machining assembly projects created by buyers through the app. Of the 522 buyers that adopted, 63 used it solely as a product search provider, but the remaining 459 firms used the process platform. We plot the histogram of the resulting projects in Figure 2, which reveals varying levels of buyer participation intensity in the treatment group. This variation is unique to the treatment group in the postlaunch period, so we use it to identify engagement mechanisms that likely drive the economic impact for XYZ.
A new variable, participation intensity, is a continuous variable that captures the total number of projects created by buyers. We incorporate this measure to reflect economic impacts, effectively scaling the difference-in-difference coefficient to capture the economic impact of app adoption as follows9:
(8) Sijt = b1Ij + b2It + b3mech Ij · It · Participation Intensityi + b4Zij + eijt:
The interpretation of the scaled difference-in-difference coefficient (b3mech) thus changes. It still measures the change in sales in the treatment group (pre- vs. postlaunch period) with respect to the control group, but with our definition of participation intensity, we anticipate an increase in treatment effect size as participation intensity increases.
As the results in the first column of Table 10 show, we find a statistically significant coefficient (b3mech = 1:709, p < .05); the economic impact is increasingly positive for XYZ as participation intensity increases.10 To verify the robustness of the results, we estimated separate difference-in-difference coefficients for lower– and higher–participation intensity buyers. Low participation intensity refers to buyers who downloaded the app but did not create any projects (n = 63). The high–participation intensity buyers instead downloaded the app and created at least one project (n = 459). In the second column of Table 10, we find a significant focal coefficient for high–participation intensity buyers (b3mech = 15:210, p
TABLE 10 Participation Intensity as Mechanism
TABLE:
| Variables | Continuous | Nonparametric | Linear 1 Quadratic | Log Form | Square Root |
|---|
| Time dummy | -4.290 (3.492) | -2.682 (5.123) | -4.515 (3.835) | -10.86** (5.049) | -12.44** (4.852) |
| Treatment dummy | -20.63*** (5.735) | -20.33*** (6.543) | -20.84*** (5.681) | -28.39*** (6.105) | -29.64*** (5.925) |
| DD Participation intensity | 1.709*** (.241) | | 1.781** (.701) | | |
| DD Low participation intensity | | 21.83 (17.35) | | | |
| DD High participation intensity | | 15.21** (6.678) | | | |
| DD Participation intensity2 | | | -.00033 (.00219) | | |
| DD Log (Participation intensity) | | | | 21.92*** (5.771) | |
| DD Participation intensity. | | | | | 15.94*** (3.463) |
| Buyer firm size | .597*** (.215) | .599*** (.217) | .597*** (.215) | .580*** (.216) | .582*** (.215) |
| Buyer power | .224*** (.0660) | .224*** (.0666) | .224*** (.0660) | .224*** (.0662) | .224*** (.0661) |
| Buyer industry competitiveness | -.286 (.183) | -.290 (.184) | -.285 (.184) | -.291 (.184) | -.287 (.184) |
| Buyer T0 sales | .0526 (.0368) | .0533 (.0383) | .0526 (.0368) | .0535 (.0378) | .0533 (.0374) |
| Buyer T0 purchase frequency | .403*** (.0462) | .435*** (.0489) | .402*** (.0470) | .412*** (.0476) | .402*** (.0469) |
| Constant | 80.20*** (25.23) | 76.83*** (24.98) | 80.27*** (25.24) | 80.67*** (25.37) | 82.48*** (25.47) |
| Observations | 2,296 | 2,296 | 2,296 | 2,296 | 2,296 |
| R-square | 0.486 | 0.466 | 0.486 | 0.473 | 0.479 |
| Division fixed effects | Yes | Yes | Yes | Yes | Yes |
| Continent fixed effects | Yes | Yes | Yes | Yes | Yes |
Next, to understand nonlinearity in the participation intensity mechanism, we reestimated Equation 8 using linear quadratic (Column 3), logarithmic (Column 4), and square root (Column 5) functional forms instead of the linear functional form (Table 10). The results suggest a significant focal coefficient for the linear term of participation intensity (b3mechðlinearÞ = significant effect 1:781, for its p intensity (b3mech = 21:92, p < .05) is significant, indicating diminishing returns as participation intensity increases. This result is substantiated by the results showing a significant focal coefficient of the square root of participation intensity (b3mech = 15:94, p < .05), in support of the diminishing returns that occur as participation intensity increases. In Figure 3, we plot the marginal impact of the number of projects on sales with linear, logarithmic, and square root functional forms (we omitted the linear quadratic model because the quadratic term was not statistically significant). Each of these plots demonstrates visual evidence that RoEI increases with increasing participating intensity but also exhibits diminishing returns, as is common with marketing-mix interventions.
We thus uncover that the true economic impact on XYZ of offering the app stems from its ability to induce buyers to create projects. This source of continuous interaction between the buyer and seller seemingly enables indirect economic benefits to XYZ. Moreover, our results suggest a nonlinear impact of the number of projects on economic benefits to XYZ; an increasing treatment effect emerges as the number of projects increase, but with diminishing returns.
Discussion
Business-to-business firms use various touch points to interact with customers and maintain deep, continuous relationships. With firm-offered engagement initiatives, these firms aim to increase their interconnectedness with customers, even if the initiatives do not provoke any immediate sales outcomes. Engagement initiatives have been lauded for their ability to connect firms to their customers, but they also invoke direct economic costs, without direct economic benefits. We argue that despite these direct economic costs, engagement initiatives can create indirect economic benefits (e.g., increased revenues) by providing customer value through customer participation intensity, which can be appropriated in the form of RoEI. Our methodological framework, which provides suitable self-selection corrections, applied to novel, observational data from a manufacturer that launched a B2B app, confirms RoEI presence for the manufacturer. We leverage buyer-level variation in app adoption to establish buyer participation intensity as a driving mechanism. Our findings in turn have implications for both theory and practice.
Theoretical Implications
Foremost, our theoretical arguments have implications for customer engagement literature, which thus far has focused primarily on customer engagement (Kumar and Pansari 2016) as opposed to engagement initiatives, their definition, their role in enhancing customers’ experiences (Lemon and
Verhoef 2016), and their impact on psychological outcomes such as trust and referrals (Shiri, Beatty, and Morgan 2012). We instead take a return-on-marketing view, questioning whether and how engagement initiatives pay off for firms. Unlike traditional marketing interventions that work to stimulate sales outcomes, RoEI lacks a straightforward return-onmarketing-outcomes path. With our proposed methodological framework, we show that gauging RoEI requires researchers to test for indirect economic returns and overcome several econometric challenges, including controlling for self-selection by firms and customers. By doing so, we also add to the nascent but burgeoning research on the use of observational inference to document causal effects of strategic marketing decisions (e.g., Germann, Ebbes, and Grewal 2015; Shi et al. 2016). We find empirical evidence that RoEI increases nonlinearly with increased customer participation intensity, suggesting a rich link between immediate manifest outcomes of engagement initiatives and their economic returns
Furthermore, our empirical findings have implications for app research. From a substantive standpoint, the empirical evidence about the efficacy of engagement apps complements extant research that has focused mainly on sales apps (e.g., Einav et al. 2014; Ghose and Han 2014). Engagement apps can yield positive economic returns too, through indirect effects. This proposition has been discussed conceptually (Urban and Sultan 2015), but we add empirical evidence, obtained using objective sales data that pertain to the causal economic returns to engagement apps. Research designed to assess the efficacy of mobile apps (e.g., Einav et al. 2014; Urban and Sultan 2015; Xu et al. 2014) thus should account for both direct (e.g., nonzero app prices) and indirect effects when calculating overall marketing effectiveness.
Managerial Implications
Given the exponential rise of engagement initiatives, there is an urgent need to assess their returns to justify effective marketing spending. For managers, we provide an implementable methodological framework to test the hypotheses of positive RoEI, thus eliminating conjecture surrounding whether engagement initiatives generate indirect benefits. Moreover, we propose an approach that uses data on sales transactions and firmographic variables, both of which are ubiquitous in marketing organizations. Firms offering free mobile apps could use our approach to estimate the causal impact of free apps with a sample of buyers. Subsequently, they could extend the average sales increase from a sample to all buyers to arrive at the estimated gross economic benefit of the app, which can then be compared with the development cost of the app. Thus, our analysis had direct, tangible, managerial benefits for firm’s product development efforts.11
Furthermore, in the context of mobile apps, our research provides evidence that RoEI can be positive, with participation intensity as the underlying mechanism. As increase in buyer participation intensity increases economic returns, app designers should incorporate features in the app that are specific to the institutional context under consideration to increase user participation intensity. For example, for business-to-customer (B2C) contexts, app designers might consider including features such as social sharing, product reviews, and instructional videos provided that these features would enhance participation intensity in their specific B2C context. Moreover, we find that RoEI is heterogeneous across customers, such that it varies substantially by buyer size, industry type, and region. Our results suggest that managers could consider customizing RoEI across customer segments to maximize the overall benefit from engagement initiatives.
Limitations
We close by noting the primary limitations of our study, which present avenues for further research. First, we used only one context to assess RoEI; thus, assessments of RoEI in other contexts (e.g., health care, B2C apps) are likely to be beneficial; eventually, a meta-analysis on the magnitude of RoEI would be useful. Second, we provided initial evidence for heterogeneity in RoEI across buyers, depending on their size, industry, and location. Conceptual understanding of engagement would likely improve with the development of formal hypotheses about whether and when to expect differences in RoEI. Third, although we focused on establishing the effectiveness of RoEI from the introduction of a free app, we did not try to explain substitution patterns across channels that resulted from the introduction of the app; future studies could examine this issue. Fourth, a fruitful avenue for further research would be in identifying the profit-maximizing level of engagement initiative spending, which could be possible using data on the cost structure of engagement initiatives. Fifth, whereas we use observational inference methods to establish the existence of RoEI, further research could leverage event studies or randomize field experiments to validate and augment our findings. Sixth, we focused on accounting performance (revenue) in our study, but future researchers could document the impact of engagement initiatives on customer mindset (e.g., satisfaction), product-market performance (e.g., market share), and financial market performance metrics (e.g., investor returns) (Katsikeas et al. 2016). Finally, we did not focus on employee engagement in our research. It would be worthwhile to determine how RoEI affects employee engagement and subsequent performance (Kumar and Pansari 2016).
Appendix A: Selection on Unobservables Strategy
We account for potential unobserved factors affecting buyer firms’ decision to adopt the mobile app and sales by the manufacturer by including unobserved factors, obtained in the form of IMRs in the difference-in-differences model. We calculated the IMR for the firms in the treatment group and the control group using the expressions provided in Equations A1 and A2.
where y1 is the outcome when firm I adopts the mobile app. Similarly
where y0 is the outcome when firm I does not adopt the mobile app. The treatment effect then is
The unobserved component will not equal zero if the errors in the selection equation and the errors in the outcome equation are correlated. However, if the unobserved component is not equal to zero (i.e., if Eðe1ijzi > - PiqÞ - Eðe0ijzi < - PiqÞ 0), our estimate of the treatment effect would be biased. One way to overcome biased estimations of the treatment effect is to use parametric assumptions to model the unobserved component and include them along with the other covariates; conditional on the observed covariates and the unobserved component (i.e., selection on unobservables), the treatment effect should be unbiased. Thus, using the Heckman (1979) model, we assess unobserved component by assuming that the errors in the selection model and those in the outcome model are bivariate normally distributed, such that the unobserved component can be obtained as follows:
Appendix B: Potential Outcomes Framework
The treatment effect of app adoption represents the difference in the change in sales of a buyer due to app adoption (treatment) from the change in its sales without app adoption; when averaged across the population of firms, it represents the ATE, or mathematically:
The sample equivalent of ATE (or ATE is estimated from a sample of size N) is
Collectively, yi1 and yi0 are potential outcomes for a firm, and we must observe both outcomes to estimate the firm’s treatment effect. However, it is not possible to estimate a firmlevel treatment effect, because each firm either receives a treatment or does not. We only observe the outcome of a firm when it receives a treatment (denoted as yi1) or does not receive a treatment (denoted as yi0). The potential outcomes framework argues that a firm-level treatment effect could be estimated by treating the nonavailability of one of the potential outcomes as a missing data problem, then imputing the missing data using the methods available in treatment effects literature. We discuss some of these methods here.
Nearest-Neighbor Matching
In the nearest-neighbor matching method, the imputed value of the missing potential outcome of a buyer in the treatment or control condition is the outcome of the buyer that is most similar to the focal buyer, but present in a condition different from that of the focal buyer. A buyer could be similar to the focal buyer if its distance from the focal buyer—calculated using the observed covariates and employing Euclidean, Ivariance, or Mahalanobis distance metrics—is smaller than that of the other buyer s. Formally, let xi = fxi1, xi2, xi3, :::, xipg be a vector of observed covariates of firm i. Then the distance between firm I and firm j is given as
where S is a symmetric positive definite matrix, determined by the type of distance metric used for the nearestneighbor matching. That is, S is an identity matrix when Euclidean distance serves to calculate the distance between two buyers; S can be a diagonal matrix consisting of the variance of all the covariates to account for the variation in covariates while calculating the distance between two firms; and S could be a variance-covariance matrix when using the Mahalanobis distance to account for the variance in each covariate and the correlation between the covariates. Thus, according to the distance metric used, the set of neighbor mi firms, formally represented in Equation B4, could be considered similar to firm i. Both j and l refer to firms, but firms other than i.
where t indicates the treatment. The potential outcomes then could be imputed as
In Table B1, we provide the results of the treatment effect estimated from a variety of nearest neighbor models.
Model-Based Imputation of Potential Outcomes
Unlike nearest-neighbor matching, model-based imputation relies on regression methods to impute potential outcomes by modeling the outcome (outcome model), the treatment (treatment model), or both. In the outcome model, separate regressions are first estimated (i.e., sales are regressed on the observed covariates) for buyers in the treatment group and buyers in the control group. Then the model-based estimates from the treatment group impute potential outcomes for a buyer in the control group. Estimates from the control group similarly function to impute potential outcomes for the treatment group. After obtaining all the potential outcomes, the ATE is calculated as ATE
The inverse probability weighting method is a treatment model that accounts for the missing potential outcome problem by weighting the observations in the treatment and control groups by the inverse of the probability of receiving the treatment and not receiving the treatment, respectively, which can be estimated using various covariates in either probit or logit models.
Finally, in inverse probability weighting with regression adjustment, we would use a combination of regression adjustment and inverse probability weighting methods, with both outcome and treatment models used to obtain the treatment effect. The inverse probability weights come from estimating the treatment model using logit or probit. These estimated weights then reveal the weighted regression coefficients required to impute the potential outcomes, as in the regression adjustment method. After all the potential outcomes are imputed, the treatment effect can be estimated using ATE
In Table B2, we provide the results of the treatment effect estimated from the regression adjustment, inverse probability weighting method, and inverse probability weighting method with the regression adjustment method.
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TABLE C5
Treatment Effect by Economy Type
Notes: Robust standard errors are in parentheses.
TABLE:
| Variables | Developing Economies | Developed Economies |
|---|
| Treatment effect | 70.28*** | 7.166 |
| (25.42) | (6.171) |
| Time dummy | -34.32** | 1.888 |
| (15.16) | (5.430) |
| Treatment group dummy | -20.13 | -18.45*** |
| (13.56) | (6.785) |
| Buyer firm size | 2.987 | .650*** |
| (1.826) | (.222) |
| Buyer power | .459*** | .240*** |
| (.130) | (.0583) |
| Buyer industry competitiveness | 1.198** | -.386** |
| (.597) | (.185) |
| Buyer T0 sales | 2.427*** | .495 |
| (.571) | (.357) |
| Buyer T0 purchase frequency | -.0685 | .433*** |
| (.156) | (.0461) |
| Constant | -1.104 | 44.16*** |
| (24.80) | (13.15) |
| Observations | 324 | 1,972 |
| Adjusted R-square | .473 | .491 |
| Division fixed effects | Yes | Yes |
| Continent fixed effects | Yes | Yes |
Table C6
Average Firm Size by Industry Type and Economy Type
| Industry | Avg. Employees |
|---|
| Transportation and Aerospace Industryr | 7.38 |
| Heavy Equipment | 10.21 |
| General Engineering | 2.79 |
TABLE:
| Industry | Avg. Employees |
|---|
| Developing | 4.16 |
| Developed | 5.08 |
TABLE B1
Nearest-Neighbor Matching Model Results
TABLE:
| | Nearest-Neighbor Matching |
|---|
| Euclidian Distance | Ivariance Distancea | Mahalanobis Distance | Two-Neighbors | Three-Neighbors |
|---|
| Treatment effect | 18.26** | 15.31** | 16.27*** | 16.98** | 16.93** |
| (7.64) | (6.27) | (6.41) | (6.82) | (6.91) |
| Observations | 1,148 | 1,148 | 1,148 | 1,148 | 1,148 |
**p < .05.
***p < .01.
aInverse diagonal sample covariate covariance. Notes: Robust standard errors are in parentheses.
Table B2
Model-Based Imputation of Potential Outcomes
TABLE:
| | (1) | (2) | (3) |
|---|
| RA | IPW | IPWRA |
|---|
| Treatment effect | 16.20*** | 16.25*** | 16.65** |
| (6.28) | (6.20) | (6.48) |
| Observations | 1,148 | 1,148 | 1,148 |
**p < .05.
***p < .01.
Notes: Robust standard errors are in parentheses. RA = regression adjustment; IPW = inverse probability weighting; IPWRA = inverse probability weighting with regression adjustment.
Appendix C: Source of RoEI
TABLE C1 Source of RoEI
TABLE:
| Variables | Sales | Purchase Frequency | Purchase Volume | Volume Per Purchase | Purchase Breadth |
|---|
| Treatment effect | 16.01** | 22.90** | 4,046 | -45.68 | .407** |
| (6.523) | (11.24) | (3,982) | (54.59) | (.162) |
| Time dummy | -2.682 | -2.337 | 1,151 | 51.47 | -.0895 |
| (5.122) | (5.920) | (1,343) | (53.55) | (.102) |
| Treatment group dummy | -20.33*** | 18.74 | -2,669 | -89.68*** | 3.050*** |
| (6.542) | (14.56) | (4,702) | (28.65) | (.266) |
| Buyer firm size | .597*** | .393** | 410.8 | .127 | .0117*** |
| (.217) | (.199) | (279.0) | (.843) | (.00349) |
| Buyer power | .224*** | .128** | 67.05*** | .274** | .00311*** |
| (.0667) | (.0630) | (21.53) | (.135) | (.00107) |
| Buyer industry competitiveness | -.290 | .258 | 212.5** | -.746 | -.000328 |
| (.184) | (.402) | (101.4) | (1.144) | (.00760) |
| Buyer T0 sales | .0533 | -1.433*** | 158.2 | 1.002 | -.00151 |
| (.0383) | (.206) | (174.3) | (.825) | (.00224) |
| Buyer T0 purchase frequency | .435*** | 5.086*** | 225.5*** | -.409*** | .0194*** |
| (.0489) | (.109) | (24.08) | (.141) | (.00182) |
**p < .05.
***p < .01.
TABLE:
| Variables | Sales | Purchase Frequency | Purchase Volume | Volume Per Purchase | Purchase Breadth |
|---|
| Treatment effect | 18.26** | 15.31** | 16.27*** | 16.98** | 16.93** |
| (7.64) | (6.27) | (6.41) | (6.82) | (6.91) |
| Observations | 1,148 | 1,148 | 1,148 | 1,148 | 1,148 |
Notes: Robust standard errors are in parentheses. Purchase frequency is operationalized as the number of invoices by the buyer firm with the manufacturer. Purchase volume is the total number of units purchased. Purchase breadth is the number of distinct products purchased by buyer firms.
TABLE C2
Sales, Purchase Frequency, and Purchase Breadth Based on Prior Buyer Sales
TABLE:
| Variables | Sales | Purchase Frequency | Purchase Volume | Volume Per Purchase | Purchase Breadth |
|---|
| Treatment effect | 16.01** | 22.90** | 4,046 | -45.68 | .407** |
| (6.523) | (11.24) | (3,982) | (54.59) | (.162) |
| Time dummy | -2.682 | -2.337 | 1,151 | 51.47 | -.0895 |
| (5.122) | (5.920) | (1,343) | (53.55) | (.102) |
| Treatment group dummy | -20.33*** | 18.74 | -2,669 | -89.68*** | 3.050*** |
| (6.542) | (14.56) | (4,702) | (28.65) | (.266) |
| Buyer firm size | .597*** | .393** | 410.8 | .127 | .0117*** |
| (.217) | (.199) | (279.0) | (.843) | (.00349) |
| Buyer power | .224*** | .128** | 67.05*** | .274** | .00311*** |
| (.0667) | (.0630) | (21.53) | (.135) | (.00107) |
| Buyer industry competitiveness | -.290 | .258 | 212.5** | -.746 | -.000328 |
| (.184) | (.402) | (101.4) | (1.144) | (.00760) |
| Buyer T0 sales | .0533 | -1.433*** | 158.2 | 1.002 | -.00151 |
| (.0383) | (.206) | (174.3) | (.825) | (.00224) |
| Buyer T0 purchase frequency | .435*** | 5.086*** | 225.5*** | -.409*** | .0194*** |
| (.0489) | (.109) | (24.08) | (.141) | (.00182) |
*p < .10.
**p < .05.
***p < .01.
Notes: Robust standard errors are in parentheses. T0 is prior sales, and Low T0 and High T0 sales are sales above/below median, respectively.
TABLE C3
Treatment Effect by Firm Size
| | (1) | (2) | (3) |
|---|
| RA | IPW | IPWRA |
|---|
| Treatment effect | 16.20*** | 16.25*** | 16.65** |
| (6.28) | (6.20) | (6.48) |
| Observations | 1,148 | 1,148 | 1,148 |
***p < .01.
Notes: Robust standard errors are in parentheses. We operationalized firm size as the number of employees in a firm.
TABLE:
| High T0 Sales |
|---|
| Buyer industry competitiveness | -.347 | .147 | 413.4** | -.404 | -.00450 |
| (.346) | (.843) | (184.1) | (2.261) | (.00434) |
| Buyer T0 sales | .458 | -1.442*** | 125.5 | .787 | -.000506 |
| (.328) | (.226) | (147.3) | (.654) | (.000396) |
| Buyer T0 purchase frequency | .380*** | 5.212*** | 202.7*** | -.650*** | .00432*** |
| (.0434) | (.120) | (24.19) | (.191) | (.000493) |
| Constant | 112.3*** | 3.630 | 30,970 | 536.5 | 5.276*** |
| (40.79) | (101.2) | (20,289) | (334.3) | (.570) |
| Observations | 1,148 | 1,148 | 1,148 | 1,148 | 1,148 |
| Division and continent fixed effects | Yes | Yes | Yes | Yes | Yes |
TABLE:
| High T0 Sales |
|---|
| Buyer industry competitiveness | -.347 | .147 | 413.4** | -.404 | -.00450 |
| (.346) | (.843) | (184.1) | (2.261) | (.00434) |
| Buyer T0 sales | .458 | -1.442*** | 125.5 | .787 | -.000506 |
| (.328) | (.226) | (147.3) | (.654) | (.000396) |
| Buyer T0 purchase frequency | .380*** | 5.212*** | 202.7*** | -.650*** | .00432*** |
| (.0434) | (.120) | (24.19) | (.191) | (.000493) |
| Constant | 112.3*** | 3.630 | 30,970 | 536.5 | 5.276*** |
| (40.79) | (101.2) | (20,289) | (334.3) | (.570) |
| Observations | 1,148 | 1,148 | 1,148 | 1,148 | 1,148 |
| Division and continent fixed effects | Yes | Yes | Yes | Yes | Yes |
TABLE C4
Treatment Effect by Industry Type
TABLE:
| Variables | Transportation and Aerospace | Heavy Equipment | General Engineering |
|---|
| Treatment effect | 19.02 | 36.79 | 11.30* |
| 14.39) | (36.21) | (6.030) |
| Buyer firm size | 1.178 | -23.97 | -1.115 |
| (8.005) | (35.62) | (4.873) |
| Buyer power | -7.125 | -50.22* | -4.120 |
| (6.937) | (27.66) | (7.111) |
| Buyer industry competitiveness | .189** | .392 | .164 |
| (.0822) | (.335) | (.206) |
| Buyer T0 sales | .534*** | .00434 | .173*** |
| (.141) | (.105) | (.0585) |
| Buyer T0 purchase frequency | -.251 | .0165 | -.352 |
| (.209) | (.443) | (.266) |
| Constant | 1.807*** | 3.835*** | .317 |
| (.483) | (.599) | (.203) |
| Treatment effect | -.109 | .382*** | .460*** |
| (.140) | (.134) | (.0390) |
| Treatment effect | 39.14*** | 212.2* | 71.64** |
| 14.43) | (112.0) | (27.75) |
| Observations | 674 | 252 | 1,370 |
| Adjusted Rsquare | .500 | .665 | .520 |
| Division fixed effects | Yes | Yes | Yes |
| Continent fixed effects | Yes | Yes | Yes |
*p < .10.
**p < .05.
***p < .01.
Notes: Robust standard errors are in parentheses.
11Our results, provided two years after the launch of the app (i.e., 6 months after the data period) convinced the program director that XYZ’s app produced a positive RoEI within 15 months of its launch. After the chief marketing officer reviewed these data, XYZ approved further app development and marketing efforts. This prompted increased internal marketing efforts by XYZ to educate its salespeople about the app’s functionalities, so marketing and sales force efforts could jointly encourage adoption.
FIGURE 3
Marginal Effects from Various Transformations of Number of Projects
Notes: Themarginal effect when the number of projects is log transformed is dðSalesÞ=dðProjectsÞ = 21:92=# of projects, the marginal effect when the number of projects is square root transformed is dðSalesÞ=dðProjectsÞ = 15:94=ð2 · and the marginal effect when number of projects remains the same is dðSalesÞ=dðProjectsÞ = 1:709.
9Equation 8 includes only the three-way interaction term. We deliberately excluded lower-order interaction terms because their inclusion would induce perfect collinearity and fail to identify the impact of a one-unit increase in participation intensity on the buyer firm’s purchases from the manufacturer. Note that this perfect collinearity arises because customers that do not adopt the app cannot have participation intensity.
10Participation intensity, operationalized as the number of machining assemblies, could be highly correlated with sales, so it might not qualify for a true mechanism. However, we find that participation intensity is not highly correlated with sales (.40). A high correlation likely would arise only if buyer firms had to purchase the products they used to create the project assemblies in the app.
Participation Intensity as Mechanism
*p < .10.
**p < .05.
***p < .01.
Notes: DD = product of treatment dummy and time dummy. Robust standard errors are in parentheses. For buyer firms that adopted the app, the participation intensity variable is nonzero in the postadoption period, but it is zero in the preadoption period. Participation intensity is also zero for the buyer firms that did not adopt.
TABLE 9
Range of Estimates
TABLE:
| Estimation Method | Estimation Method | Implied Annual Sales (%) |
|---|
| Selection on observables | 16.01 | 19.98 |
| Selection on unobservables | 16.01 | 19.98 |
| Nearest-neighbor matching (Mahalnobis distance) | 16.27 | 20.31 |
| Nearest-neighbor matching (Ivariance distancea) | 15.31 | 19.11 |
| Nearest-neighbor matching (Euclidean distance) | 18.26 | 22.79 |
| Nearest-neighbor matching (two neighbors) | 16.98 | 21.2 |
| Nearest-neighbor matching (three neighbors) | 16.93 | 21.13 |
| Regression adjustment | 16.2 | 20.22 |
| Inverse probability weighting | 16.25 | 20.28 |
| Inverse probability weighting: regression adjustment | 16.65 | 20.78 |
Distribution of the Number of Projects Created by Buyer Firms
Novelty and Falsification Tests
TABLE:
| Variables | Novelty | Falsification |
|---|
| Treatment effect | 14.42*** (5.402) | -3.332 (4.181) |
| Time dummy | -3.974 (4.170) | 3.516 (3.772) |
| Treatment group dummy | -16.89*** (5.897) | -8.071** (3.547) |
| Buyer firm size | .539*** (.163) | .332*** (.101) |
| Buyer power | .177*** (.0507) | .122*** (.0389) |
| Buyer industry competitiveness | -.226 (.165) | -.143 (.0968) |
| Buyer T0 sales | .0248 (.0333) | .0270 (.0180) |
| Buyer T0 purchase frequency | .363*** (.0427) | .198*** (.0235) |
| Constant | 66.86*** (22.41) | 24.12** (9.630) |
| Observations | 2,255 | 2,178 |
| Adjusted R-square | 0.396 | 0.437 |
| Division fixed effects | Yes | Yes |
| Continent fixed effects | Yes | Yes |
**p < .05.
***p < .01.
Notes: Robust standard errors are in parentheses.
Robustness Analyses
TABLE:
| Variables | Quarterly Aggregation |
|---|
| Treatment effect | 3.19** (1.55) |
| Observations | 10,148 |
| Adjusted R-square | 0.5 |
| Firm fixed effects | Yes |
| Quarter fixed effects | Yes |
Potential outcomes framework. For our selection-onobservables strategy, we used a classic regression estimator to estimate the treatment effect. Specifically, after adding the set of observables Zij, the treatment effect can be estimated directly from observations for which j and t are 0 and 1, respectively, using a conditional independence assumption. Thus, the regressionbased approach conditions on the covariates to compare all members of the control and treatment groups. As an alternative selection strategy, we use a potential outcomes framework (Guo and Fraser 2010). The change in the buyer’s outcomes when it adopts versus does not adopt the app (denoted as =Si0 and =Si1, respectively) are potential outcomes. Their difference represents the firm-level treatment effect, averaged over
Disaggregate Analysis
**p < .05.
Notes: Robust standard errors are in parentheses. A similar analysis is available in Jin and Leslie (2009).
8Although the treatment effects estimates are similar across Models 2–5, their standard errors (reported in Table 5) are different. We conjecture that the similarity of the treatment effects could stem from the stability of the identification strategies.
TABLE 6
Robustness Assessment
TABLE:
| Variables | Outliers: ln (Total Sales) | Competition Intensity Ratio 50 | Competition Intensity Ratio 8 | Competition Intensity Ratio 4 |
|---|
| Treatment effect | .151** (.0741) | 16.01** (6.523) | 16.01** (6.523) | 16.01** (6.523) |
| Time dummy | .000761 (.0572) | -2.682 (5.122) | -2.682 (5.122) | -2.682 (5.122) |
| Treatment group dummy | .699*** (.125) | -20.87*** (6.580) | -19.77*** (6.502) | -19.59*** (6.485) |
| Buyer firm size | .826*** (.205) | .594*** (.215) | .612*** (.220) | .623*** (.221) |
| Buyer power | .200*** (.0678) | .224*** (.0667) | .224*** (.0667) | .224*** (.0667) |
| Buyer industry competitiveness | -.779** (.361) | | | |
| Buyer T0 sales | .000297 (.000324) | .0532 (.0382) | .0534 (.0383) | .0534 (.0383) |
| Buyer T0 purchase frequency | .00682*** (.000719) | .437*** (.0490) | .432*** (.0488) | .431*** (.0488) |
| Competition intensity_50 | | -0.051207 | | |
| Competition intensity_8 | | | -.199 (.195) | |
| Competition intensity_4 | | | | -.129 (.220) |
| Constant | 12.62*** (.539) | 74.31*** (24.48) | 75.35*** (25.97) | 72.85*** (27.80) |
| Observations | 2,296 | 2,296 | 2,296 | 2,296 |
| Adjusted R-square | 0.293 | 0.461 | 0.46 | 0.46 |
| Division fixed effects | Yes | Yes | Yes | Yes |
| Continent fixed effects | Yes | Yes | Yes | Yes |
*p < .10.
**p < .05.
***p < .01.
Notes: Robust standard errors are in parentheses. In the model used to assess robustness against outliers, firm size was scaled down by 10,000, and power is in %.
Treatment Effect Estimation Results
TABLE:
| Economy | Control Group | Treatment Group | z-Stat |
|---|
| Buyer firm size | 4.51 | 5.48 | .70 |
| Buyer industry competitiveness | 55.57 | 53.80 | 1.59 |
| Buyer power | 60.05 | 81.23 | 1.18 |
| Buyer T0 sales | 18.59 | 20.11 | -.40 |
| Buyer T0 purchase frequency | 50.95 | 97.20 | -5.91*** |
| Number of observations | 626 | 522 | |
*p < .10.
**p < .05.
***p < .01.
Notes: Robust standard errors are in parentheses. Dependent variable (sales) is in $10,000. In the selection model, buyer firm size was scaled down by a factor of 100,000; number of buying units was scaled down by a factor of 100; buyer T0 sales was scaled down by a factor of 10,00,000; buyer power (originally measured in %) was scaled down by a factor of 10. Unlike Models 2–5, which consist of 2,296 observations (1,148 buyer firms over two time periods), the selection Model 4a has 1,148 observations, consisting of 522 treated buyer firms and 626 control buyer firms. “Firms that adopted the app” is the second instrument that captures the number of buyer firms, other than the focal firm, that adopted the app in the industry.
7The significant difference in the T0 purchase frequency between the treatment and control groups suggests a covariate imbalance, which violates the parallel trends assumption in difference-in-differences analysis. Accordingly, the use of a selection-on-observables strategy is warranted. We implement this strategy by (1) including all the covariates (including T0 purchase frequency) in the differencein-differences model and (2) matching firms in the treated group to firms in the control group as a function of the covariates. After matching (i.e., nearest neighbor), the difference in T0 purchase frequency between the treated and control firms becomes statistically insignificant. We discuss the treatment effect estimates obtained by using nearest neighbor matching in Appendix B.
TABLE 4
Mean Differences (Covariates) Between Control and Treatment Groups at T1
| Percentage of Observations from Emerging and Emerged Economies |
|---|
| Economy | Control Group | Treatment Group | z-Stat |
|---|
| Developing economies | .13 | .16 | 1.58 |
| Developed economies | .87 | .84 | 1.58 |
| Percentage of Observations from Different Industry Divisions |
|---|
| Economy | Control Group | Treatment Group | z-Stat |
|---|
| Transportation and Aerospace | .30 | .28 | .81 |
| Heavy Equipment | .10 | .12 | 1.27 |
| General Engineering | .60 | .60 | .05 |
TABLE:
| Economy | Control Group | Treatment Group | z-Stat |
|---|
| Buyer firm size | 4.51 | 5.48 | .70 |
| Buyer industry competitiveness | 55.57 | 53.80 | 1.59 |
| Buyer power | 60.05 | 81.23 | 1.18 |
| Buyer T0 sales | 18.59 | 20.11 | -.40 |
| Buyer T0 purchase frequency | 50.95 | 97.20 | -5.91*** |
| Number of observations | 626 | 522 | |
***p < .01.
Notes: Buyer firm size was scaled down by a factor of 100, buyer industry competitiveness and buyer power (originally measured in %) were scaled up by a factor of 100. Buyer T0 sales (scaled down by a factor of 10,000) represents the total sales of the firms in the five months before the start of the data window. Buyer T0 purchase frequency represents the total purchase frequency of the firms in the five months before the start of the data window. We use this scaling throughout the article, except as explicitly noted.
6The U.S. Census reports are available at https://www.census. gov/econ/concentration.html.
FIGURE 1
Model-Free Evidence
TABLE 3
Mean Differences (Total Sales in $10,000) Between Control and Treatment Groups
TABLE:
| | Control Group | Treatment Group | Difference |
|---|
| T1: 15 months pretreatment | 75.75 | 80.11 | 4.36 |
| T2: 15 months posttreatment | 73.07 | 93.44 | 20.37** |
| Number of observations | 626 | 522 | |
5We refer to smartphones and tablets when we use the use the term “mobile device.”
TABLE 2
Summary of Literature on Mobile Apps
TABLE:
| Study | Focus Area | Data and Context | Key Finding |
|---|
| Bellman et al. (2011) | Mobile app effectiveness | Pre-/posttest experiment with general public from Australia (69) and United States (159); survey-based data. | Branded mobile phone apps increase attitude toward the brand and purchase intentions. |
| Xu et al. (2014) | Mobile app effectiveness | Repeated cross-sectional data (Q4 2009 and Q2 2010) from comScore MobiLens on 5,600 smartphone users; survey-based data. | Adoption of the news app significantly increases the probability of visiting the mobile website. |
| Einav et al. (2014) | Mobile app effectiveness | Mobile and nonmobile activity (including purchases) of users of eBay’s shopping app and website. | Adoption of eBay’s mobile application increased total platform purchases. |
| Urban and Sultan (2015) | Mobile app effectiveness | App to assist users who intend to move and “dream mover” app to help users purchase or rent new homes; survey data. | Benevolent apps increase app users’ trust, brand consideration, and purchase likelihood. |
| Carare (2012) | Factors affecting mobile app demand | Daily download rankings for 166 days of top 100 paid and free apps in the United States available through Apple’s app store. | Apps’ past sales ranks affect their current sales. |
| Garg and Telang (2013) | Factors affecting mobile app demand | Daily app ranking and pricing data for two months, on 200 paid and 200 free apps for iPad and iPhone. | Method to estimate apps’ demand using publicly available data on apps’ ranks and prices. |
| Ghose and Han (2014) | Factors affecting mobile app demand | App characteristics and daily panel data on 4,706 iOS-based and 2,624 Android-based smartphone apps’ sales for a period of four months. | App demand increases with the app version, app age, number of apps developed, positive user reviews, number of platforms on which app is released, and the presence of in-app purchase option. |
| Han, Park, and Oh (2016) | Factors affecting mobile app demand | Individual-level weekly data on usage of Android mobile apps. | Consumer utility from app usage varies by product category and consumers’ demographic characteristics. |
| Kwon et al. (2016) | Factors affecting mobile app demand | Individual-level weekly panel data on Facebook and Anipang apps. | Consumers are rationally addicted to social and gaming apps. |
3App downloads increased from 4.5 billion in 2010 to 138.8 billion in 2014 (a 2,984.4% increase), with consumers spending an average of 2 hours and 19 minutes a day using mobile apps in 2014.
1Available at http://www.sandvik.coromant.com/en-gb/knowledge/calculators%5fand%5fsoftware/apps%5ffor%5fdownload (accessed October 2016).
Manpreet Gill is Assistant Professor of Marketing, Darla Moore School of Business, University of South Carolina (e-mail: msgill.usc@gmail.com). Shrihari Sridhar is Center for Executive Development Professor and Associate Professor of Marketing, Mays Business School, Texas A&M University (e-mail: ssridhar@mays.tamu.edu). Rajdeep Grewal is Townsend Family Distinguished Professor of Marketing, Kenan-Flagler Business School, University of North Carolina (e-mail: grewalr@unc.edu). The authors thank the Institute for the Study of Business Markets, Customer Analytics Program for providing access to the data. They thank Pranav Jindal, Gary Lilien, Vidya Mani, Chris Parker, Kapil Tuli, Lisa Scheer, and participants at the ISBM Customer Analytics Workshop 2015 for their helpful comments on previous drafts. Werner Reinartz served as area editor for this article.
2Available at http://www.fit360bcs.com/(accessed November 2016).
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GRAPH: Return on Engagement Initiatives: A Study of a Business-to-Business Mobile App
GRAPH: Return on Engagement Initiatives: A Study of a Business-to-Business Mobile App
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Record: 150- Role of Ambient Temperature in Influencing Willingness to Pay in Auctions and Negotiations. By: Sinha, Jayati; Bagchi, Rajesh. Journal of Marketing. Jul2019, Vol. 83 Issue 4, p121-138. 18p. 3 Charts. DOI: 10.1177/0022242919841595.
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Role of Ambient Temperature in Influencing Willingness to Pay in Auctions and Negotiations
While temperature's effects on human physiology have been well studied, its effects in decision-making contexts are still relatively unknown. The authors investigate the role of ambient temperature in one important decision-making context: consumer purchase. More specifically, they examine how ambient temperature influences consumers' willingness to pay in different kinds of purchase contexts, such as in auctions and in negotiations. The authors show that whereas higher (vs. moderate) temperatures elicit higher willingness to pay in auctions, they lead to a lower willingness to pay in negotiations, and temperature-induced discomfort and aggression underlie these effects. The authors also study the effects of lower temperatures and extend these findings to more general competitive settings. They report findings from six studies and discuss theoretical, managerial, and policy implications.
Keywords: aggression; ambient temperature; auctions; negotiations; willingness to pay
The decisions we make are often influenced by our environment. One factor endogenous in any decision-making context is the ambient temperature. While its effects on human physiology have been examined ([79]), the study of its effects in decision-making contexts is relatively more nascent ([23]; [45]; [81]). We investigate how ambient temperature influences a core tenet of market behavior: consumers' willingness to pay (WTP) in selling/consumer purchase contexts, such as in auctions and negotiations.
Auctions and negotiations provide an important conduit for exchanging goods and services. Companies such as eBay, which allow consumers to partake in auctions and negotiations, earn billions of dollars in revenue every year ([71]). Similarly, the real estate market, which uses auctions and negotiations, is worth over $25 trillion in the United States alone ([43]). Given the popularity of these mechanisms, interventions that influence consumers' WTP can have a large influence on firms' bottom lines, and should be of interest to managers. We propose that ambient temperature (high [e.g., 77°F] vs. moderate [e.g., 67°F–70°F]) is a critical factor that affects consumers' WTP, and its effects are contingent on the type of selling mechanism.
We draw from two literature streams—on temperature effects and selling mechanisms—to derive our thesis. A large body of literature suggests that higher (vs. moderate) temperatures induce greater aggression (e.g., [ 4]). We propose that higher (vs. moderate) temperatures will also induce greater aggression in selling contexts, which are subtle yet competitive. To explain what the patterns of effects might be, we draw from [ 9] research on hue-induced aggression. These authors suggest that red (vs. blue) hues induce greater aggression. However, the effect of aggression on WTP is contingent on the selling mechanism. In auctions, an aggressive bidder attempts to "win" the auction by outbidding other competitors and therefore bids higher. In contrast, in negotiations, an aggressive negotiator attempts to get the best deal possible from the seller and therefore offers lower bids. Like red (vs. blue) hues, higher (vs. moderate) temperatures also increase aggression; thus, we posit that higher temperatures will elicit higher WTP in auctions but lower WTP in negotiations.
To provide preliminary evidence, we analyzed two years of data from Miami-Dade County, Florida, foreclosure home auctions.[ 6] As expected, higher (vs. moderate) temperatures elicited higher maximum bids (i.e., WTP). Given that these are secondary data, they are amenable to alternative explanations, yet these findings provide some real-world evidence. Moreover, we also subject our theory to more stringent tests in rigorous laboratory experiments conducted under controlled conditions, and our results replicate.
Our main contribution, thus, is to extend understanding of how temperature affects decision making in auctions and negotiations. We also show that while our pattern of effects emerges in selling contexts with a competitive element (i.e., auctions and negotiations), such effects do not emanate in noncompetitive (i.e., fixed-price) settings. We also generalize these findings to other competitive market settings and show how temperature affects persistence. We also provide nuanced process support, detailing why these effects emerge. Thus, we provide a holistic perspective on how temperature affects decision making in selling contexts and beyond.
These findings also have important managerial implications, because ambient temperatures can be manipulated. For example, higher temperatures during auctions may generate higher profits for sellers but lower surplus for buyers. In contrast, higher temperatures may have a detrimental effect on negotiators—lowering WTP for buyers and leading to protracted negotiations (more offers/counteroffers) and lower agreement likelihoods. We elaborate on these and other considerations in the "General Discussion" section. Next, we discuss our theoretical underpinnings followed by our empirical investigation. We conclude with a discussion of implications.
Temperature influences perceptions and behaviors in a variety of contexts, ranging from product valuations and preference ([45]; [81]) to variety seeking ([75]) and adoption ([73]). Temperatures also influence stock returns ([22]), customer impulsivity ([57]), performance ([23]; [76]), behavior ([72]), and even economic productivity ([62]). See Table 1 for details.
Graph
Table 1. Research on Effect of Temperature on Consumer Decision Making.
| Source | Setting | Area | Measure | General Temperature Effect | Temperature Measures | Behavioral Mechanism Measured | Overall Purchase DV | Findings |
|---|
| Cheema and Patrick (2012) | Experiment | Performance | Cognitive performance | Y | 67°F and 77°F | Y | Y | Warm (vs. cool) temperatures deplete resources, increase System 1 processing, and influence performance on complex choice tasks. |
| Cohn (1990) | Field study | Crime rate trends | Criminal behavior | Y | High temperature | N | N | Weather could significantly influence crime rates and criminal behavior. |
| Cohen and Rotton (1997) | Field study | Assault | Crime rate | Y | Range: −18°F–104°F | N | N | Assault rates in Minneapolis decrease once temperatures increase beyond 75°F (although until 75°F assault rates increase with temperature). |
| Huang et al. (2014) | Experiment | Valuations and preference for products | Product preferences, stock price forecasts, and betting | Y | 61°F–63°F and 75°F–77°F | Y | Y | Warm (vs. cool) temperatures dispose consumers toward using others' opinions as the basis for product preferences, stock price forecasts, and betting. |
| Kolb, Gockel, and Werth (2012) | Experiment | Interpersonal performance | Customer oriented behavior and higher discount | Y | 68.4°F and 78.6°F | Y | N | Participants in rooms with a low temperature showed more customer-oriented behavior and gave higher customer discounts than participants in rooms with a high temperature. |
| Metcalfe and Mischel (1999) | Experiment | Impulsivity | Delayed gratification | N | Hot/cool system | Y | N | The balance between the hot and cool systems is determined by stress, developmental level, and individual's self-regulatory dynamics |
| Park and Heal (2014) | Country-level data | Economic performance | Income per capita and labor productivity | Y | Annual average temperature data at country level | Y | N | Hotter-than-average years are associated with lower income and total factor productivity for countries in hot climates and higher output per capita for countries in cold ones. |
| Tellis, Stremersch, and Yin (2003) | Field study | Adoption of innovative products | Market penetration | Y | Cool and warm climate by monthly high temperature | N | Y | The time to takeoff varies substantially across countries and categories. It is four times shorter for entertainment products than for kitchen and laundry appliances. It is almost half |
| Tian, Zhang, and Zhang (2018) | Field study | Consumer variety seeking | Brand switching | Y | The daily temperature ranged from 1°C–40°C. | N | Y | Weather conditions are associated with greater variety-seeking behavior. |
| Tong et al. (2018) | Experiment | Complex choices | Performance | Y | Range: 77°F–78.8°F and 69.8°F–71.6°F | Y | Y | Warm (vs. cool) temperatures prompt affective processing, which then leads to better performance in complex choices. |
| Steinmetz and Posten (2017) | Experiment | Response behavior | Memory test, response bias, and affirmation bias | Y | Range: 16°C–18.7°C and 22.4°C–24.2°C, temperature perception, people's imagined warm/cold environment | Y | N | Temperature affects general response behavior by fostering affirmation. |
| Zwebner, Lee, and Goldenberg (2013) | Field study, experiment | Product valuation | Purchase intention, recommendation, WTP | Y | Range: 6.3°C–31.6°C, therapeutic pad | Y | Y | Exposure to physical warmth activates the concept of emotional warmth, eliciting positive reactions and increasing product valuation. |
| The current research | Field study, experiment | Auction and negotiation | Purchase intention, WTP, persistence | Y | Range 63°F–82°F | Y | Y | The influence of ambient temperature (higher vs. moderate) on consumers' WTP is contingent on the selling mechanism (auctions vs. negotiations) such that higher (vs. moderate) temperatures elicit higher WTP in auctions but lead to a lower WTP in negotiations. This occurs because of the effect that ambient temperature has on aggression. These findings are also extended to more general competitive settings. |
1 Notes: DV = dependent variable; Y = yes; N = no.
A large body of literature also suggests that higher (vs. moderate) temperatures incite aggression ([29]; [47]). We posit that this higher aggression will elicit higher (lower) WTP in auctions (negotiations). We also demonstrate that a specific facet of aggression, hostile aggression, is responsible for our effects. Delving deeper, we argue that this occurs because higher temperatures are uncomfortable—it is this discomfort that elicits aggression. Our theoretical section follows this sequence: we first review extant literature on ambient temperature and aggression and then discuss the current research: how temperature-induced aggression affects WTP in our contexts and why this occurs.
One robust finding, dating back a few centuries, is that higher temperatures induce aggression. In the mid-1700s, Montesquieu noted that crime rates were higher in warmer climates ([58]). Other studies have reported similar findings. [ 4] classify these into three categories of studies: geographic domain, time period, and concomitant measures. Geographic domain studies compare regions that vary on temperature but are similar on other aspects; findings suggest that similar-sized cities with higher (vs. lower) temperatures have higher crime rates (e.g., [ 2]). Time period studies, comparing violence as a function of temperature in the same area but during different times of the year, find that assault rates are higher during summer months versus at other times (e.g., [ 3]). In concomitant studies, temperature and aggressive behaviors are measured simultaneously. These findings also suggest that as temperatures increase, so do aggressive behaviors. Results document excessive honking of horns ([47]), aggression demonstrated by trainee police officers ([78]), and increased probability of baseball pitchers hitting batters ([30]; [53]).
While a large body of research has suggested that higher temperatures induce aggressive acts, some question certain specific aspects of these findings. For example, consider the geographic domain studies, which assert that warmer Southern U.S. cities have higher crime rates. These effects can be explained by [26] theory of Southern culture of violence (SCV), which posits that Southerners are more aggressive than Northerners. However, [ 2] argue that SCV may have in fact evolved because of the warmer Southern climates. Indeed, temperature does influence aggression-related cultural differences (e.g., cultural masculinity; Van de [77]). Moreover, SCV alone cannot explain findings from the time period and concomitant studies.
In recent years, researchers have expanded on this early work by demonstrating how temperature affects broader constructs, such as conflict, and not only affects individual-level decision making but also has societal impact. For example, [44], p. 1235367-1) define conflict as "individual-level violence and aggression to country-level political instability and civil war." After conducting a meta-analysis of 60 quantitative studies, they concluded that a one-standard-deviation increase in temperature increases violence by 4% (for related work, see also [20]).
Thus, different approaches (e.g., geographic domain studies, time period studies) and methodologies (e.g., secondary data, lab studies, meta-analyses), extending across time (several centuries), geographic locales and cultures (e.g., Europe, United States), measures (e.g., violence, crime, rape, murder, riots, baseball injury), and scope (individual level and societal) provide converging evidence that increase in temperature induces greater aggression (for a summary of this literature, see Table A1 in Web Appendix A). Next, we discuss how this temperature-induced aggression may influence WTP in market settings.
To derive our hypotheses, we draw from [ 9] research on hue-induced aggression. Drawing from a large literature in psychology (e.g., [ 7]; [42]), [ 9] argue that red (vs. blue) hues induce greater aggression (for a summary of this literature, see Table A2 in Web Appendix A). More germane to our research, they contend that this hue-induced aggression would have contingent effects in auctions and negotiations. In auctions, a consumer competes with other consumers with the goal to acquire a product. Therefore, an aggressive consumer is willing to pay more to outbid the competition and win the auction. Indeed, aggressive consumers often make higher bid jumps ([ 8]; i.e., make a bid larger than necessary to be the highest bidder; [33]; see also [31]). Thus, we expect temperature-induced feelings of aggression to have similar effects—that is, higher (vs. moderate) temperatures will increase aggression, leading to higher WTP.
A different pattern of effects may emerge in negotiations. Negotiators hold the belief that they can only win at the expense of others—if one gets a larger piece of the pie, the opponent gets a smaller piece. Thus, negotiators try to maximize their individual surplus. This occurs even when opportunities for joint maximization exist and is referred to as the "fixed-pie bias" ([61]; see also [74]). When using aggressive tactics, negotiators treat the negotiation as a zero-sum game and try to maximize individual surplus by retaining most of the profits (for a discussion, see [35]; see also [ 9]; [34]; [56]). The goal of an aggressive strategy is also to derive unilateral concessions from the counterpart ([35]; [64]). Consequently, [ 9] argue that an aggressive buyer would try to get the best deal possible by paying the lowest price. Thus, we expect higher (vs. moderate) temperatures to induce greater aggression, which should then lower WTP. Taken together, we posit the following:
- H1: The effect of higher (vs. moderate) temperature on WTP is influenced by the selling mechanism. In auctions, a higher (vs. moderate) temperature induces higher WTP. In contrast, in negotiations, a higher (vs. moderate) temperature lowers WTP.
While the effect of temperature on aggressive behaviors has been shown, the underlying process (i.e., which facet of aggression impacts behavior) is not well documented. Aggression comprises four factors: physical, verbal, anger, and hostile feelings ([21]; [19]; [32]). The context and the pathway through which aggression is induced might play a role in influencing which of these factors affects decision making. Given our marketing context, it is unlikely that the first three factors would play a role. Physical and verbal aggression relate to overt forms of aggression (e.g., hitting another person) and are unlikely to influence WTP in our settings. Anger "represents the emotional or affective component of aggressive behavior" ([65], p. 280). Though tenable, it is also unlikely to be a major driver. For example, it is difficult to envision an angry consumer willing to pay more in an auction. Instead, we believe that hostility might be the primary driver.
Next, consider the route through which temperature induces aggression—this will help us shed light on why hostile feelings might be at play. We believe that it is discomfort that links temperature and aggression. Indeed, researchers (e.g., [ 4]; [11]) often equate higher temperatures with discomfort. Others (e.g., [11]; 1977; for a review, see [ 2]) use discomfort as a manipulation check of high temperature but do not use it as a process measure. This suggests that higher temperatures are uncomfortable. This is also consistent with our own lived experiences. But why should discomfort induce aggression? Researchers often link the two constructs together. For example, [11], p. 830) start their classic paper with the statement that "uncomfortably hot environmental conditions tend to facilitate overt aggression," yet like others do not provide process support.
Although the link between temperature-induced discomfort and aggression is not as well documented, models of aggression, such as the general affective aggression model ([ 1]; [54]) assert that, in general, discomfort induces aggression. For instance, [54] (see also [ 3]) show that discomfort induced through minor pain (e.g., holding an arm up; Study 1) leads to hostile feelings of aggression. At a deeper level, according to [15], [16], [18]) "cognitive-neoassociationistic" model, aversive incidents evoke "angry feelings, hostile thoughts and memories, and aggressive motor reactions automatically" because these components have strong associative ties ([18], p. 278). Thus, aversive incidents that cause discomfort lead to aggressive responses. Even caged animals exposed to aversive stimuli (e.g., heat, noise, physical abuse) become aggressive over time (for a review, see [18]). Similarly, we expect temperature-induced discomfort to also induce feelings of aggression.
Furthermore, it is well known that discomfort activates hostility-related schema ([ 6]; [17]), which comprises "thoughts, feelings, memories, and behavioral scripts" ([ 6], p. 436). For example, experiencing an uncomfortable situation, such as seeing the name of a foe, not only activates memories of negative acts perpetrated by that person but also "reinstate(s) hostile feelings" and activates the entire hostility-related schema, even influencing responses (e.g., plotting revenge; [ 6], p. 436). Uncomfortably high temperatures then may also elicit hostile feelings and influence how contextual cues are interpreted. Furthermore, according to [21], p. 454), hostility leads to feelings of injustice, whereby one feels more disenfranchised relative to others who are fortunate. Injustice is measured by items such as "others seem to get all the breaks," while one "has gotten a raw deal out of life," leading to "feelings of bitterness." These hostile feelings are likely to enhance the desire to "one-up" others, especially in competitive settings. Indeed, hostility is known to increase opposition ([69]). We believe that these subjective feelings of hostility will also influence consumers' responses in competitive market settings and influence WTP. Taken together, we propose that hostile aggression underlies our effects and that these effects emerge because of discomfort. Formally:
- H2: The effect of higher (vs. moderate) temperature on WTP is mediated by (a) subjective feelings of hostile aggression and (b) discomfort and subjective feelings of hostile aggression in sequence.
We test our hypotheses in six studies. The main objective of our studies is to demonstrate that higher (e.g., 77°F–82°F) versus moderate (67°F–70°F) temperatures lead to higher bids in auctions but to lower WTP in negotiations. The studies demonstrate robustness using a set of real (Studies 1 and 2c), incentivized (Study 4), and hypothetical (Studies 2a and 2b) scenarios. Two studies that use realistic settings (Studies 1 and 3) provide support on a variety of dependent measures. We also provide process evidence and rule out alternative explanations (in Studies 2a–2c). Figure 1 provides a graphical overview of our studies and key outcome measures. Table 2 provides a succinct summary of our study designs and key results. Scenario details, instructions, and individual scale items are included in Web Appendix B. Supplementary analyses (e.g., correlations among constructs, covarying effects of control variables) are reported in Web Appendix C. We discuss Study 1 next.
Graph: Figure 1. Conceptual model and operationalization of key constructs.
Graph
Table 2. Summary of Study Design and Results.
| Studies | Context | IV Measures | DV Measures | Key Test | Selling Mechanism | Fixed Price |
|---|
| Auction | Negotiation |
|---|
| Pilot StudyN = 11,530 | Foreclosure home auction | Measured (continuous) | Average daily temperature: 67°F and higher | Winning bid ($) | Regression | β =.03 | | |
| t = 2.95 | | |
| p =.003 | | |
| Study 1(N = 70, undergraduate students) | Five pieces of university memorabilia | Manipulated | 77°F | WTP | Regression | $42.70 ($11.34) | | |
| 67°F | $29.36 ($5.20) | | |
| 77°F | Number of offers | Regression | 32.14 (14.44) | | |
| 67°F | 14.86 (6.80) | | |
| Study 2a(N = 160, undergraduate students) | Cruise vacation package | Manipulated | 77°F | WTP | ANOVA | $1,214.33 ($86.78) | $868.85 ($79.92) | |
| 67°F | $1,067.14 ($67.06) | $911.96 ($84.02) | |
| 77°F | Purchase likelihood | ANOVA | 4.60 (1.83) | 3.15 (1.61) | 3.68 (1.80) |
| 67°F | 2.46 (1.55) | 4.12 (1.81) | 3.36 (1.50) |
| Study 2b(N = 280, undergraduate students) | Cruise vacation package | Manipulated | 82°F | WTP | ANOVA | $1,357.86 (225.63) | $729.71 (125.73) | |
| 77°F | $1,199.14 (130.23) | $768.14 (141.91) | |
| 70°F | $1,120.71 (91.11) | $872.83 (98.28) | |
| 63°F | $1,250.89 (228.06) | $795.71 (119.51) | |
| 82°F | Purchase likelihood | ANOVA | 5.31 (1.71) | 2.17 (1.49) | |
| 77°F | 4.63 (2.10) | 3.17 (2.11) | |
| 70°F | 3.69 (2.07) | 5.46 (1.67) | |
| 63°F | 4.74 (1.87) | 3.09 (1.85) | |
| Study 2c(N = 129, undergraduate students) | Monogrammed pen | Manipulated | 82°F | WTP | ANOVA | $2.35 (.62) | $1.23 (.63) | |
| 67°F | $1.03 (.55) | $1.88 (80) | |
| Study 3 | Buyer–seller role play (used car purchase) | Manipulated | 77°F | Concessions | ANOVA | | $718.09 ($543.67) | |
| 67°F | | $1,027.46 ($471.72) | |
| 77°F | Number of offers | ANOVA | | 8.09 (3.69) | |
| 67°F | 5.20 (2.82) | |
| (N = 140, undergraduate students) | 77°F | Agreed price | ANOVA | | $8,439.29 ($472.27) | |
| 67°F | $7,938.82 ($283.16) | |
| 77°F | Agreement reached | ANOVA | | 60% | |
| 67°F | 94.28% | |
| | Competitiveness (mins) | |
| Competitive | Control | |
| Study 4(N = 125, undergraduate students) | Deal search | Manipulated | 77°F | Time spent | ANOVA | 11.26 (4.03) | 9.00 (2.70) | |
| 67°F | 9.41 (2.92) | 9.45 (2.57) | |
2 Notes: IV = independent variable; DV = dependent variable.
Seventy undergraduate students (Mage = 23.1 years; 48.6% female) participated in this study and were randomly assigned to either a higher (77°F) or a moderate (67°F) temperature condition. This study was conducted in a medium-sized room (with 35 participants per condition). Participants learned that they would be taking part in a real auction and would have to pay their bid amount if they won (payment would occur later). The proceeds from this auction would be given to a charity called Lotus House (and the proceeds were indeed donated). A brief description of the charity and its mission was provided.
We auctioned five pieces of university memorabilia: a printed lanyard, a cell phone card holder, an espresso mug, a key tag, and a mascot stuffed animal. These items' actual prices were $4.48, $8.98, $12.98, $12.98, and $19.98, respectively (these prices were not shown to bidders). Participants were shown pictures of each of the five items and learned that they would be bidding on these items momentarily. They were then asked to indicate the price they would be willing to pay. These prices were not binding and only indicated preauction valuations (participants were made aware of this). This was done to ensure that respondents had a chance to evaluate all the products carefully before the auction.
Each participant was given a bidder card with an assigned number (1–35) and a booklet. We auctioned the five products in order, and each product started with an initial minimum bid (lanyard: $1.00, cell phone card holder: $3.00, expresso mug: $4.00, key tag: $4.00, and stuffed mascot: $10.00). The auctioneer then increased this price; the increments differed for the products ($.50 for the two lower-priced items [lanyard and card holder] and $1 for the remaining products). Participants were required to do two things. First, they were to indicate whether they would be willing to buy the product at each price. If yes, they had to raise their bidder card (for details, see Web Appendix B). From the booklet entries, we assessed how many offers each respondent made. Participants also reported perceptions of the room temperature ("How cold or hot are you feeling right now?"; 1 = "Very cold," and 7 = "Very hot") and demographic information.
An analysis of variance (ANOVA) with temperature perception as the dependent measure and temperature (higher, moderate) as predictor elicited a significant main effect of temperature (F( 1, 68) = 50.85, p <.0001). As expected, participants in the higher temperature condition felt hotter (Mhigher = 5.51) than those in the moderate condition (Mmoderate = 3.74).
Because participants' preauction valuations and during-auction WTP were not skewed (ranging from.72 to 1.95), we use untransformed measures (skewness between −2 and +2 is normal; [36]).
We treated the five products as a within-subjects factor and temperature as a between-subjects factor and conducted a mixed-model regression. A main effect of temperature emerged (F( 1, 68) = 9.34, p <.005). As expected, respondents' valuations were higher when the temperature was higher (sum of average values for all products: Mhigher = $33.53 vs. Mmoderate = $28.05). A main effect of product also emerged, such that valuation increased from products 1–5 (F( 4, 272) = 282.78, p <.0001). This was expected, as the products were displayed in order of increasing price (the mean valuations were $2.46, $5.21, $5.91, $5.02, and $12.20). The interaction of temperature and product was not significant (F( 4, 272) =.77, p >.54). The pattern of results for each of the products was also consistent with our hypotheses; valuation was higher when temperature was higher (all ps <.05 except key tag: p =.0507).
We first analyzed each participants' maximum auction WTP. We treated the five products as a within-subjects factor and temperature as a between-subjects factor and conducted a mixed-model regression. A main effect of temperature emerged (F( 1, 68) = 40.07, p <.0001); WTP was higher when temperature was higher (sum of average values for all products: Mhigher = $42.70 vs. Mmoderate = $29.36). A main effect of product also emerged, such that WTP increased from products 1–5 (F( 4, 272) = 474.84, p <.0001). This was expected, as the products were presented in order of increasing price (the means were $3.02, $5.45, $7.57, $6.14, and $13.84). The interaction of temperature and product was also significant (F( 4, 272) = 7.82, p <.0001), such that the effect of temperature on WTP increased as the value of the product increased. Furthermore, WTP was higher for each product when temperature was higher versus moderate (all ps <.0001).
We then analyzed number of offers made using a similar mixed-model regression. A main effect of temperature emerged (F( 1, 68) = 41.05, p <.0001). As expected, respondents made more offers when the temperature was higher (sum of average values for all products: Mhigher = 32.14 offers vs. Mmoderate = 14.86 offers). The main effect of product (F( 4, 272) = 17.74, p <.0001) and the interaction between temperature and product (F( 4, 272) = 5.01, p <.005) were both significant. Because the offer increments varied across products ($.50 for the first two products and $1 thereafter), it is difficult to interpret these results unambiguously. However, it is possible to compare the number of offers for each product as a function of temperature. As expected, the number of offers was higher for each product when temperature was higher versus moderate (all ps <.0001), suggesting that our results are robust.
Study 1 provides support for H1. In auctions, higher (vs. moderate) temperatures elicit higher WTP and lead to more bids. Thus, these effects are not restricted to hypothetical laboratory studies.
Next, we consider negotiations and fixed-price settings. Consistent with [ 9] findings, we expect our results to emerge in competitive settings, such as in auctions and negotiations, but not in noncompetitive fixed-price settings. This is because only in competitive settings is aggression likely to influence WTP, and aggressive consumers are likely to drive prices up (in auctions) or down (in negotiations). In contrast, in fixed-price settings, the price is given, and consumers are not competing with others; therefore, aggression is unlikely to influence behaviors.
This study also enables us to rule out other explanations. While a large literature, spanning at least two centuries, suggests that higher temperatures have a negative effect (i.e., lead to aggression and violence), some works suggest the opposite—that higher temperatures lead to positive emotional reactions ([13]; [52]), as they elicit feelings of emotional warmth. Taking this a step further, [81] propose that warmer temperatures increase individuals' valuation of products. This occurs because physical warmth triggers positive emotional reactions, which increases valuation. Though related, our research is different. First, our focus is on understanding behavior in auctions and negotiations; in their studies, the selling mechanism was not defined, and only valuation was sought. Furthermore, if their findings were to be extended to our contexts, the prediction might be that warmer temperatures should elicit higher WTP for both auctions and negotiations, and in the fixed-price condition, they should lead to higher purchase likelihoods. This is not what we expect.
Second, [81] primary manipulation (Studies 2a, 3, and 4) involved touching a warm or a cold surface (they do change room temperature in Study 2b). We focus on ambient temperature and not touch, which may elicit different processes and behaviors. Third, although these authors argue for a temperature-positive emotional reaction/affect link, if (uncomfortably) high temperatures evoked positive emotional reactions, people would not commit heinous acts during hot weather, as a large body of research has documented. We, however, acknowledge this as a possibility and measure emotional reactions to rule them out.
Higher temperatures also affect cognitive performance negatively ([39]) and can lead to attention deficiencies ([38]; [66]). However, the negative effects of higher temperatures on performance occur for more complex tasks ([23]; [40]; [80]). Because our tasks are not complex or cognitively taxing, we do not expect depletion to affect our results; nonetheless, we measure depletion in Study 2a.
One-hundred sixty undergraduate students (Mage = 22.03 years; 52% female) participated in exchange for course credit and were randomly assigned to either the higher (77°F) or the moderate (67°F) temperature conditions.
After participating in a few unrelated studies, participants took part in this study. The scenario, adapted from [ 9], indicated that participants were considering purchasing a five-day Southern Caribbean Cruise vacation package (including accommodations, meals, beverages, and entertainment) over spring break (see Web Appendix B). While searching online they find a reasonably priced vacation package listed for $1,000. We then manipulated the selling mechanism—auctions, negotiations, or fixed price—between subjects. In auctions, participants were asked to report their best offer (above the list price), akin to a sealed bid, while in negotiations, participants reported their best price (below the list price), akin to a take-it-or-leave-it offer. However, in the fixed-price condition, participants saw only the list price and did not report their best price. Thus, this study employed a 2 (temperature: higher [77°F], moderate [67°F]) × 3 (selling mechanism: auction, negotiation, fixed price) between-subjects design.
Next, all participants reported the likelihood of purchasing the vacation package at the listed price of $1,000 (1 = "Not likely at all," and 7 = "Very likely"). This enabled us to compare the effect of temperature across the three selling mechanisms. Participants also reported current mood (1 = "In a bad mood," and 7 = "In a good mood") and current aggression on 12 items ([19]; [21]; see Web Appendix B). They responded to the 24-item arousal scale ([55]) that captured emotion-related items (e.g., cheerful, contented, satisfied, happy, dissatisfied, depressed, sad, sorry; 1 = "Not at all," and 7 = "Very"; for items, see Web Appendix A). Participants then rated five items from the depletion scale (e.g., "I feel drained"; see Web Appendix B) adapted from [25]; 1 = "Very slightly or not at all," and 5 = "Very much"), indicated perceptions of temperature (as in Study 1), and responded to demographic questions.
An ANOVA with temperature perception as the dependent measure and temperature and mechanism as the predictors elicited a main effect of temperature (F( 1, 154) = 19.94, p <.0001). As expected, participants in the higher temperature condition felt hotter (Mhigher = 4.10 vs. Mmoderate = 2.93). The effects of mechanism (F( 2, 154) =.33, p =.72) and the temperature × mechanism interaction (F( 2, 154) = 1.40, p =.25) were not significant.
Because stated offers in the auctions and negotiations (N = 110) were not skewed (skewness =.15; [36]), we use untransformed dollar values. An ANOVA with this WTP as the dependent measure revealed a significant main effect of temperature; participants in the higher temperature condition were willing to pay more (Mhigher = $1,041.59 vs. Mmoderate = $989.55; F( 1, 106) = 11.64, p <.001). A main effect of mechanism also emerged; WTP was higher in the auction than in the negotiation (Mauction = $1,140.74 vs. Mnegotiation = $890.41; F( 1, 106) = 269.34, p <.0001). This was not surprising, because participants in the auction condition bid higher than the listed price ($1,000), whereas those in the negotiation condition bid lower than this price.
Importantly, the temperature × mechanism interaction was significant (F( 1, 106) = 38.91, p <.0001). Consistent with H1, in auctions, bidders in the higher (vs. moderate) temperature condition offered higher bids (Mhigher = $1,214.33 vs. Mmoderate = $1,067.14; F( 1, 106) = 49.25; p <.0001). In contrast, negotiators in the higher (vs. moderate) condition made marginally lower offers (Mhigher = $868.85 vs. Mmoderate = $911.96; F( 1, 106) = 3.79; p =.054).
We subjected the aggression measures to a factor analysis and found that a four-factor solution was reasonably acceptable (see Web Appendix C). A multivariate analysis of variance (MANOVA) with the four factors regressed on temperature and mechanism elicited only a main effect of temperature (Wilks' lambda F( 4, 151) = 7.65, p <.0001); mechanism (p >.55) and the temperature × mechanism interaction (p >.85) were not significant. An ANOVA after aggregating all 12 items (α =.92) also only elicited a significant main effect of temperature (F( 1, 154) = 26.36, p <.0001); as expected, the higher (vs. moderate) temperature induced greater aggression (Mhigher = 3.03 vs. Mmoderate = 2.15). None of the other effects were significant (p >.20).
Four separate ANOVAs with each measure of aggression as the dependent variable elicited a main effect of temperature in each case (all ps <.0001), with the higher (vs. moderate) temperature eliciting greater aggression. None of the other effects were significant (ps >.18; see means in Table C1 of Web Appendix C). Thus, regardless of the mechanism, higher temperatures induce greater aggression.
The ANOVAs with these variables did not elicit significance (main effects or interactions). Analyzing the three subscales of the 24-item arousal scale (energetic, tense, and hedonic tone) separately also did not elicit any significant effects. These factors also did not mediate our results (see Web Appendix C).
We conducted several sets of mediation analyses to test H2a's prediction that hostile aggression explains the effect of temperature on both WTP and purchase likelihood.
Given that mechanism type moderates the effect of temperature on WTP, we conducted a moderated mediation analysis (Model 14; [41]) where temperature was the independent variable, hostile aggression was the mediator, WTP was the dependent variable, and selling mechanism (auctions, negotiations) moderated the relationship between hostile aggression and WTP. This moderated mediation was significant (β = 42.47, SE = 12.50, 95% confidence interval [CI] = [19.03, 67.37]). Furthermore, the indirect effect through hostile aggression was significant for both auctions (β = 21.35, SE = 6.26, 95% CI = [9.73, 33.65]) and negotiations (β = −21.13, SE = 6.73, 95% CI = [−35.11, −8.81]). Moderated mediation with the other factors of aggression did not yield significance (physical: 95% CI = [−.80, 14.52]; verbal: 95% CI = [−.81, 16.92]; anger: 95% CI = [−3.22, 12.81]). A similar pattern emerged for purchase likelihood and is reported in Web Appendix C.
We replicate findings of Study 1 for auctions and extend our findings to two other selling mechanisms: negotiations and fixed prices. Consistent with H1, higher (vs. moderate) temperatures elicit higher bids in auctions but lead to lower offers in negotiations. Ambient temperature did not elicit any differences in the fixed-price conditions. Notably, in the auctions and negotiations, purchase likelihoods were elicited after WTP, which could have tainted these responses. However, the key reason to ask for purchase likelihoods was to assess how temperature affects fixed-price conditions, and these responses were unaffected (as WTP was not elicited in this condition).
This study also provides process support; higher (vs. moderate) temperatures induce greater overall aggression. However, of the four factors of aggression, only subjective feelings of hostile aggression mediate our results and lead to our paradoxical results (H2a). In auctions, a more aggressive bidder attempts to win the auction by outbidding other bidders, which leads to higher WTP. In contrast, in negotiations, a more aggressive negotiator tries to get a better deal—leading to lower offers. We also rule out other alternative explanations. For example, we did not find support for either [81] temperature-premium effect or their process (positive emotional reactions)—instead, the pattern of results were consistent with our hypotheses. Likewise, depletion also cannot account for our results.
The goal of the next study is three-fold. First, we use four different temperature conditions to demonstrate robustness and generalizability. Second, we investigate the influence of lower temperatures (63°F). If temperature-induced discomfort incites aggression, then lower temperatures (e.g., 63°F), which are uncomfortable, should also increase aggression and, therefore, elicit results that cohere with those elicited by higher temperatures. Finally, we rule out two other potential mechanisms for our effects: self-esteem and emotionality.
Two-hundred eighty undergraduate students (Mage = 22.5 years; 46% female) participated in exchange for course credit and were randomly assigned to one of eight conditions: 4 (temperature: 82°F, 77°F, 70°F, 63°F) × 2 (selling mechanism: auction, negotiation) between-subjects conditions. Our main goal was to assess how lower and higher temperatures influence behavior.
After participating in a few unrelated studies, participants took part in our focal study. We used the same five-day Southern Caribbean Cruise vacation package scenario as in Study 2a (see Web Appendix B). In auctions, participants reported their best offer (above the list price), akin to a sealed bid, while in negotiations, participants reported their best price (below the list price), akin to a take-it-or-leave-it offer. All participants then reported their likelihood of purchasing the package at the listed price of $1,000 (1 = "Not likely at all," and 7 = "Very likely").
Participants also responded to the ten-item self-esteem scale ([68]; 1 = "Strongly agree," and 4 = "Strongly disagree") and rated five items measuring emotionality (e.g., "I have an uneasy, upset feeling"; 1 = "The statement does not describe my present condition," and 5 = "The condition is very strong; the statement describes my present condition very well"; for all items, see Web Appendix B) adapted from [59]. Participants also indicated perceptions of temperature ("How cold or hot are you feeling right now?"; 1 = "Very cold," and 7 = "Very hot") and responded to a few demographic questions.
An ANOVA with temperature perception as the dependent measure and temperature and mechanism as the predictors elicited a main effect of temperature (F( 3, 272) = 61.79, p <.0001). Participants in the higher temperature condition felt hotter (M82°F = 5.30 vs. M77°F = 4.80 vs. M70°F = 3.44 vs. M63°F = 2.73; all ps ≤.02; for details, see Table C2 in Web Appendix C). The effects of mechanism (F( 1, 274) =.08, p =.78) and the temperature × mechanism interaction (F( 3, 272) = 1.36, p =.26) were not significant.
Because WTP was not skewed (skewness =.59; [36]), we use untransformed dollar values. An ANOVA with WTP as the dependent measure revealed a marginal main effect of temperature (F( 3, 272) = 2.16, p =.093) and a significant effect of mechanism (F( 1, 272) = 577.90, p <.0001; WTP was higher for auctions: Mauction = $1,232.15 vs. Mnegotiation = $791.60). The temperature × mechanism interaction was also significant (F( 3, 272) = 18.02, p <.0001).
In auctions, the very high temperature condition elicited higher bids (M82°F = $1,357.86) relative to all the other conditions, including those in the high (M77°F = $1,199.14; p =.000), moderate (M70°F = $1,120.71; p [70°F vs. 82°F] =.000), and low (M63°F = $1250.89; p =.004) conditions. However, no differences emerged between the high (77°F) and the low (63°F; p =.16) temperature conditions, and both conditions elicited higher bids relative to the moderate condition (p [77°F vs. 70°F] =.033; p [63°F vs. 70°F] =.000). Thus, these results demonstrate that high and low temperatures have a similar effect on bids. A different pattern of effects emerged for negotiations. Those in the very high temperature condition (M82°F = $729.71) had a lower WTP relative to those in the high (M77°F = $768.14; p =.295), moderate (M70°F = $872.83; p =.000), and low (M63°F = $795.71; p =.073) temperature conditions. However, no differences emerged between the high (77°F) and the low (63°F; p =.45) temperature conditions, and both conditions elicited lower WTP relative to the moderate condition (p [77°F vs. 70°F] =.005; p [63°F vs. 70°F] =.036). A similar pattern of results emerged for purchase likelihood (see Web Appendix C).
The ANOVAs with these variables did not elicit any significance (main effects or interactions). These factors also did not mediate our results (see Web Appendix C).
If temperature-induced discomfort incites higher aggression, then lower temperatures (e.g., 63°F), which are also uncomfortable, should also increase aggression and, therefore, elicit results similar to those elicited by higher temperatures. This is what we find.
We also rule out other explanations. We did not find that higher (vs. moderate) temperature leads to higher emotionality or self-esteem, and these items did not mediate our results. The next study extends our investigation in three ways. First, we replicate our effects in a real product purchase context. Participants take part in either an auction or a negotiation to purchase a university monogrammed pen. Second, whereas in Study 2b we manipulated temperature at four levels, in Study 2c, we compare a very high condition (82°F) with a moderate condition (67°F). This allows us to show robustness. Finally, we also provide support for H2b and demonstrate that higher temperatures lead to feelings of discomfort, which evokes hostile feelings of aggression, which then influences behavior in auctions and negotiations.
We recruited 129 students (Mage = 20.7 years; 61.2% female) in exchange for course credit. We used two temperature conditions: very high (82°F) and moderate (67°F). Participants were randomly distributed across these conditions. Participants learned that they would earn an additional $3 for participating, and this was theirs to keep. However, if they wished, they could use some or all of this money to purchase a product: a university monogrammed pen. Depending on the condition, this purchase would occur through an auction or a negotiation.
In auctions, the starting bid was $0, and participants were asked to enter their highest bid, akin to a sealed bid. At the conclusion of the auction, participants would be notified of the outcome (i.e., whether they won the auction), and, if they won, they would then have to remit the payment and purchase the product. If their offer were higher than $3, they would have to pay the difference out of pocket. Then, participants reported their highest bid ($ amount).
In negotiations, the seller's initial price offer was $3, and participants were asked to enter their highest offer, akin to a take-it-or-leave-it offer. The seller would then decide if they accepted this offer or not. At the conclusion of the negotiation, participants would be notified of the outcome (i.e., whether the seller accepted their price offer), and, if the seller accepted their price, they would then have to remit the payment and purchase the product. Then, participants reported their highest offer ($ amount). In summary, this study employed a 2 (temperature: high, moderate) × 2 (selling mechanism: auction, negotiation) between-subjects design.
Participants reported their current aggression, arousal, and depletion (as in Study 2a; for items, see Web Appendix B). Participants also reported their comfort level ("How comfortable do you feel right now?"; 1 = "Not comfortable at all," and 7 = "Very comfortable") and perceptions of room temperature on two items ("How cold or hot are you feeling right now?"; 1 = "Very cold," and 7 = "Very hot," and "How is the temperature setting in this room?"; 1 = "It is very cold," and 7 = "It is very hot").
Participants also responded to a few other questions, including their current mood (1 = "In a bad mood," and 7 = "In a good mood") and how sociable, generous, caring, and powerful they felt and whether the room felt crowded (1 = "Not at all sociable/generous/caring/powerful/crowded," and 7 = "Very sociable/generous/caring/powerful/crowded"). We included these questions to rule out alternative explanations. Participants also responded to a few demographic questions.
We averaged our two temperature items (r =.92) to create a temperature perception index. An ANOVA with this index as the dependent measure and temperature and mechanism as predictors only elicited a main effect of temperature (F( 1, 125) = 125.32, p <.0001). Participants in the higher temperature condition felt hotter (5.50) than those in the moderate conditions (3.54).
Because these data were not positively skewed (skewness =.30), we use the untransformed measure. An ANOVA with WTP as the dependent measure revealed a significant main effect of temperature (F( 1, 125) = 8.47, p <.001). Participants in the higher (vs. moderate) temperature condition were willing to pay more (Mhigher = $1.79 vs. Mmoderate = $1.46). The main effect of mechanism was not significant (F( 1, 125) = 1.39, p =.24). However, the temperature × mechanism interaction was significant, (F( 1, 125) = 72.76, p <.0001). Consistent with H1, in auctions, bidders in the higher (vs. the moderate) temperature condition offered higher bids (Mhigher = $2.36 vs. Mmoderate = $1.03; F( 1, 125) = 65.88; p <.0001). In contrast, negotiators in the higher (vs. moderate) temperature condition made lower offers (Mhigher = $1.23 vs. Mmoderate = $1.88; F( 1, 125) = 15.68; p <.001).
We subjected the 12 aggression items to a factor analysis, and a four-factor solution emerged (see web appendix C). We conducted a MANOVA with the four factors regressed on temperature and mechanism. None of the effects were significant (ps >.23). We also computed an overall aggression score by averaging all 12 items (α =.85). An ANOVA with aggression as the dependent measure only elicited a significant main effect of temperature (F( 1, 125) = 4.32, p <.05); the higher temperature induced greater aggression (Mhigher = 2.74 vs. Mmoderate = 2.38). More germane to our theorizing, we found that a main effect of temperature emerged for hostile feelings (F( 1, 125) = 4.83, p <.05), with higher temperature inducing greater hostility (Mhigher = 3.10 vs. Mmoderate = 2.57; mechanism and the temperature × mechanism interactions were not significant). None of the other factors of aggression elicited significant main effects of temperature (p >.10; for details, see Web Appendix C). The four factors were not very highly correlated with each other. This could be one reason why the MANOVA analysis did not elicit significant effects of temperature, while the aggregate ANOVA did.
An ANOVA with comfort elicited a significant main effect of temperature (F( 1, 125) = 43.81, p <.0001). As expected, participants in the higher temperature condition felt less comfortable (Mhigher = 2.75 vs. Mmoderate = 4.69). Mechanism (F ( 1, 125) =.10, p =.75) and the temperature × mechanism interaction (F( 1, 125) = 1.94, p =.17) were not significant.
Independent ANOVAs with positive emotional reactions, arousal, depletion, and other measures did not elicit any significant effects. They also did not mediate our results (see Web Appendix C).
To provide support for H2b, we tested a moderated mediation model (Model 87, [41]), where comfort and hostile aggression mediated the effects of temperature on WTP. Furthermore, because the effect of hostile aggression on WTP is contingent on the selling mechanism, we included mechanism as the moderator of the relationship between hostile aggression and WTP (see Table 3). The model was significant (β =.16, SE =.06, 95% CI = [.07,.30]). Specifically, the indirect effect was significant for both auctions (β =.09, SE =.04, 95% CI = [.03,.17]) and negotiations (β = −.07, SE =.04, 95% CI = [−.16, −.02]). We also conducted similar moderated mediations using the other factors of aggression (instead of hostile aggression). None of these models were significant for 95% CI (physical = [−.002,.09]; verbal = [−.01,.06]; anger = [−.01,.05]) or for 90% CI (physical = [−.0002,.08]; verbal = [−.01,.05]; and anger = [−.004,.04]).
Graph
Table 3. Moderated Mediation Model Results-Study 2C.
| Antecedents | Consequent Outcomes |
|---|
| Comfort | Hostile Aggression | WTP |
|---|
| Coeff. | SE | t | p | Coeff. | SE | t | p | Coeff. | SE | t | p |
|---|
| Temperature (X) | −.97 | .15 | −6.62 | <.0001 | −.06 | .13 | −.44 | .65 | .16 | .08 | 2.13 | .04 |
| Comfort (M1) | | | | | −.33 | .07 | −4.98 | <.0001 | .03 | .04 | .64 | .53 |
| Hostile aggression (M2) | | | | | | | | | .02 | .05 | .30 | .76 |
| Selling mechanism (W) | | | | | | | | | −.65 | .15 | −4.25 | <.0001 |
| M2 × W | | | | | | | | | .25 | .05 | 5.11 | <.0001 |
| Model Summary | R2 =.26 | R2 =.20 | R2 =.22 |
| F(1, 127) = 43.80, p ≤.0001 | F(1, 126) = 15.27, p ≤.0001 | F(1, 123) = 6.78, p ≤.0001 |
This study makes several contributions. First, we demonstrate our findings in a real product purchase context. Second, we consider higher temperatures. Consistent with H1–H2, higher (vs. moderate) temperatures elicit higher bids in auctions but lead to lower offers in negotiations, and these effects occur because of discomfort and feelings of hostility.
We also rule out alternative explanations. We find that depletion, emotional reactions, and arousal do not affect our results. We also rule out other pathways, including generosity, caring, feelings of power, and social considerations, among others. Furthermore, in Studies 2a and 2b, we used the same starting price ($1,000) in both auctions and negotiations. However, in Study 2c we used different starting prices ($0 vs. $3). We also used different starting points in our auctions in Study 1. Together, these studies suggest that anchoring alone cannot explain our effects. These studies also demonstrate robustness, as some auctions have a fixed lower starting point, whereas others start from $0.
The next study extends our investigation in at least four ways. First, we demonstrate these effects in a protracted negotiation, where two parties (buyer and seller) participate in an extended "to-and-fro" negotiation. Second, we demonstrate effects on other measures—for example, if higher (vs. moderate) temperature elicits more aggressive negotiating, negotiators should also be less likely to reach agreement; in addition, the negotiation should also be more protracted: more rounds of offers and counteroffers, and negotiators should offer lower concessions. Third, we made the negotiation incentive compatible. Finally, we include more questions tapping into emotional reactions from [81] to rule these out.
We recruited 140 undergraduate students (Mage = 21.78 years; 51.4% female) in exchange for course credit, and we randomly assigned them to either the higher (77°F) or the moderate (67°F) temperature conditions. After participating in a few unrelated studies, they participated in a car negotiation study.
We then randomly assigned participants to play the role of a buyer or a seller and then paired them up with another participant, who played the role of a seller or a buyer, respectively. Participants were seated in individual rooms and so did not meet their counterpart. We had 70 dyads with 35 dyads negotiating in each of the two temperature conditions.
We then explained the process: each party could make an offer, which the other party could accept or reject (a research assistant would ferry these offers). If the other party agreed, then the negotiation would end at this agreed price. However, if they rejected the offer, then they could offer a counter. Negotiators could not negotiate in bad faith—a buyer (seller) could not lower (increase) their buying (selling) price in future rounds. The negotiation would continue until an agreement was reached or would terminate without an outcome if no agreement could be reached. Negotiators would earn an amount based on performance; the amount earned would be a function of the agreed price (lower buying [higher selling] price for buyers [sellers] will yield greater return) and the number of rounds (amount earned decreases with the number of rounds to reach agreement; we used a discount factor of.9). The procedure was adapted from [70] and is described in Web Appendix B.
The buyers (sellers) were a manager of a car rental agency (car dealership). Buyers were considering buying a used Ford Focus from the seller (2012 Focus, 45,000 miles, with factory-installed options). All participants learned that ( 1) such cars sell for between $7,500 and $9,000, ( 2) this car was listed by the seller for $9,000, and ( 3) the buyer had made an offer of $7,500. Then the seller and the buyer negotiated back and forth. They could each accept or reject the other party's offer. If they rejected the offer, they could make a counteroffer. We allowed for a maximum of 12 rounds (including the two initial offers). The negotiation terminated if they reached agreement or ended without an outcome if they could not reach agreement within these 12 rounds.
In summary, this study employed a 2 (temperature: higher, moderate) × 2 (negotiation agent: buyer, seller) between-subjects design. After the negotiation, participants responded to a few questions, including competitiveness ("How competitive were you during the negotiation?"; 1 = "Not competitive at all," and 7 = "Very competitive") and goal directedness, which is a combination of motivation ("How motivated were you during the negotiation?"; 1 = "Not motivated at all," and 7 = "Very motivated"), planning ("How carefully did you plan your offers?"; 1 = "Not carefully at all," and 7 = "Very carefully"), goal importance ("How important was it for you to purchase the car?"; 1 = "Not important at all," and 7 = "Very important"), and goal achievement importance ("How important was it for you to purchase the car at the best price possible?"; 1 = "Not important at all," and 7 = "Very important"). We created these questions on the basis of [37]. We also measured control using three items (adapted from [50], [51]]; "How would you rate the amount of control you had over your negotiation performance?," "How would you rate the amount of control you had over the outcome of the negotiation?," and "How much did you feel that the outcome you wanted was in your own hands?" the first two items: 1 = "No control at all," and 7 = "Complete control"; last item: 1 = "Not at all," and 7 = "Completely"). Participants indicated emotions; these included the 24-item arousal scale along with a few other questions on current feelings adapted from [81] to measure warmth (e.g., interested, moved, captivated, delighted, enthusiastic, appealed, amused; 1 = "Not at all," and 7 = "Very").
Participants also indicated how hot or cold they were feeling (1 = "Very cold," and 7 = "Very hot"), and provided information about prior negotiation experience (yes/no) as well as whether they owned a car (yes/no), and provided demographics. We initially included car ownership to control for its effects, but this variable was not significant and did not change the pattern of results in any of our analyses and will thus be omitted from future discussion.
We expect negotiators to negotiate harder when the temperature is higher (vs. moderate). Consequently, we expect agreement likelihoods to be lower but number of offers and counteroffers to be higher when the temperature is higher. We also expect negotiators to make fewer concessions when the temperature is higher.
An ANOVA with temperature perception as the dependent measure and temperature and negotiation role (buyer, seller) as predictors elicited only a main effect of temperature (F( 1, 136) = 8.08, p <.01). Participants in the higher temperature condition felt hotter (4.53) than those in the moderate conditions (3.71).
A logistic regression with agreement likelihood (yes/no) as the dependent variable and temperature as the independent variable elicited a main effect of temperature (χ2( 1) = 8.85, p <.003). A smaller proportion of negotiations reached agreement when temperature was higher (60%; 21 out of 35) versus moderate (94.28%; 33 out of 35).
We computed the number of offers made for each negotiating dyad. The number of offers was higher when the temperature was higher (8.09) versus moderate (5.20; F( 1, 68) = 13.53, p =.0005).
We computed initial concessions offered by each negotiator. For buyers, we subtracted $7,500 (their starting offer) from the buyer's first actual offer. So, if the buyer made an offer of $8,000, then the concession was $500 (i.e., $8,000–$7,500). For sellers, we subtracted the seller's actual first offer from $9,000 (initial selling price). So, if the seller made an offer of $8,750, then the concession would be $250 (i.e., $9,000–$8,750). An ANOVA indicates that negotiators offer lesser initial concessions when the temperature is higher (Mhigher = $718.09 vs. Mmoderate = $1,027.46; F( 1, 68) = 6.47, p <.02).
We also compared the final agreement price across our two temperature conditions—we were only able to do so for the negotiations that reached agreement (N = 54). Because both buyers and sellers were exposed to the same temperature manipulations, a priori we did not expect any difference—as both buyers and sellers should be influenced by temperature to the same extent. However, the main effect of temperature was significant (F( 1, 52) = 32.79, p <.0001), with the higher temperature eliciting a higher agreement price (Mhigher = $8,439.29 vs. Mmoderate = $7,938.82). These results seem to suggest that sellers are more reluctant to make concessions than buyers when the temperature is higher.
Separate ANOVAs with competitiveness and motivation as dependent measures elicited a significant and a marginally significant main effect of temperature, respectively (competitiveness: F( 1, 68) = 6.25, p <.02; motivation: F( 1, 68) = 2.89, p =.094), with higher temperature eliciting higher values (Mhigher = 5.44 vs. Mmoderate = 4.60) and increased motivation (Mhigher = 5.54 vs. Mmoderate = 4.81). However, neither variable mediated our effects.
The items on goal directedness and control did not elicit any significant effects either individually or combined. They also did not mediate our results. Likewise, independent ANOVAs with all the positive emotional items (including arousal, its subdimensions, and the additional items from [81]) did not elicit significant effects and did not mediate our results (see Web Appendix C).
Study 3 extends our findings by demonstrating that negotiations conducted in higher temperature conditions are likely to lead to aversive outcomes—less agreement, more protracted negotiations, and lower concessions. These results are consistent with our theory and demonstrate robustness. Although not predicted, for the negotiations that reached fruition, we found a main effect of final agreement price, suggesting that sellers were less willing to make concessions relative to buyers when temperatures were higher versus moderate. However, this result needs to be interpreted cautiously, as it is based on a small number of negotiations that reached fruition. We encourage future researchers to provide more insights on this.
Although our findings suggest that higher temperatures evoke more aggressive behaviors in competitive market contexts (auctions and negotiations, but not fixed-price settings), a natural question may be, Would higher (vs. moderate) temperatures lead to greater persistence in other competitive settings, such as when consumers search for deals? We investigate this issue next.
We recruited 127 undergraduate students (Mage = 22.05 years; 58.3% female) in exchange for course credit. Participants were informed that the purpose of the study was to understand how consumers make grocery purchases. Participants were given a list of ten products to buy at their local grocery store. We also provided participants with a bucket containing coupons. We informed participants that there were four coupons for each product on their shopping list, and that their goal was to find the best coupon for each product so that they paid the lowest overall price for all their products. We manipulated competitiveness (yes, control) between subjects. In the competitive condition, we told participants that the top 10% performers would receive a reward of $5. In the control condition, no such incentives were provided. We also manipulated temperature (higher [77°F] or moderate [67°F]) between subjects.
Participants could take as much time as they wanted and could also stop anytime. Time spent (number of minutes) finding the best deals to purchase ten products at the lowest possible price served as our main dependent measure. After the grocery purchase task, participants provided perceptions of temperature, rated their spending habits on four items (tightwad–spendthrift scale; [67]; see Web Appendix A), and indicated frequency of searching for deals (1 = "Not often at all," and 7 = "Very often"), and importance of getting best deals possible (1 = "Not important at all," and 7 = "Very important"), and responded to a few demographic questions. In summary, this study employed a 2 (temperature: higher, moderate) × (competitiveness: competitive, control) between-subjects design. Two participants did not follow instructions and were removed, leaving us with 125 respondents (Mage = 21.99 years; 57.6% female).
An ANOVA with temperature perception as the dependent measure and temperature (higher, moderate) and competitiveness (competitive, control) as predictors only elicited a main effect of temperature (F( 1, 121) = 16.95, p <.0001). Participants in the higher temperature condition felt hotter (M = 4.61) than those in the moderate condition (M = 3.84).
An ANOVA with total time spent (in minutes) revealed a significant main effect of competitiveness (F( 1, 121) = 3.97, p =.049). Participants in the competitive condition spent more time finding the best possible deals than those in the control condition (Mcompetitive = 10.33 vs. Mcontrol = 9.23 minutes). The main effect of temperature was not significant (F( 1, 121) = 1.59, p =.21). The temperature × competitiveness interaction, however, was significant (F( 1, 121) = 4.30, p <.05). In the competitive condition, participants spent significantly more time searching for deals when the temperature was higher (Mhigher = 11.26 vs. Mmoderate = 9.41 minutes; F( 1, 121) = 5.61; p <.02). No such difference emerged in the control condition (Mhigher = 9.00 vs. Mmoderate = 9.45 minutes; F( 1, 121) =.33; p >.6). Results remain significant after controlling for other factors (see Web Appendix C). Together, these support our predictions.
Although the main focus of our investigation is to shed light on how temperature influences behaviors in auctions and negotiations, this study extends our findings to more general settings. Consistent with our predictions, higher (vs. moderate) temperatures lead to greater persistence in competitive market settings.
We investigate the role that ambient temperature plays in affecting WTP in different selling contexts—such as in auctions, negotiations, and fixed-price settings. We find that higher (vs. moderate) temperatures increase WTP in auctions but lower WTP in negotiations. This occurs because of temperature-induced subjective feelings of hostile aggression. We also provide nuanced support for our process by demonstrating why temperature incites aggression—the root cause is discomfort.
We report findings from six studies. In Study 1, using a real auction and with multiple products, we provide support for the core effect: higher (vs. moderate) temperature elicits higher WTP. In Study 2a, we also include negotiations and fixed-price settings. As expected, higher (vs. moderate) temperatures lead to an increase in WTP in auctions but a decrease in WTP in negotiations. Temperature does not affect intentions in fixed-price contexts. In Study 2b, we replicate our effects using multiple temperatures, including a lower (63°F) temperature. If temperature-induced discomfort is responsible for higher aggression, then uncomfortable lower temperatures should also increase aggression and elicit results similar to those elicited by higher temperatures; this is what we find. In Study 2c, we replicate these effects by using a real product purchase in the laboratory.
In Study 3, respondents partook in an extended to-and-fro negotiation with a counterpart, which allows us to demonstrate effects on other measures—for example, if higher temperatures elicit more aggressive negotiating, they should lower agreement likelihoods and lead to more rounds of offers and counteroffers. Finally, in Study 4, we generalize to other contexts and show that when the task is incentivized to be competitive, consumers spend more time searching for coupons to get best deals. We also demonstrate process support in several studies (e.g., 2a and 2c) and rule out other possible pathways (e.g., depletion, positive emotional reactions, arousal, self-esteem, emotionality, control, goal-directedness).
We make several contributions. Our work is at the intersection of two broad literature streams on selling mechanisms and temperature effects. We contribute to the auctions and negotiations literature by demonstrating the effect of ambient factors. This literature can be segmented into two areas: how consumers should normatively behave and how they actually behave ([10]; [48]). Our results defy normative expectations as there is no reason to expect an exogenous factor (ambient temperature) to affect WTP.
However, even in the literature investigating effects of psychological factors in auction and negotiation behaviors, the primary focus is on individual difference variables or on factors integral to the setting ([10]; [14]; [24]; [48]). Rarely are ambient/incidental factors investigated (for an exception, see [ 9]]). Given that ambient factors are ubiquitous, it is imperative that these factors be considered. Although we study the effects of temperature, other factors (e.g., noise levels, scents, crowding) are also likely to play a role and could be investigated.
We also extend understanding of temperature effects in many ways. For example, in confrontational contexts, evidence of temperature-induced aggression emerges when individuals are provoked, and the goal is to harm the target ([ 5]). We show that these effects also emerge in subtle (decision-making) situations, such as in auctions, negotiations, and other competitive contexts. We provide process support by demonstrating that feelings of hostile aggression are responsible. These feelings arise from the belief that one has been disenfranchised and that others seem to "get all the breaks." Delving deeper, higher temperatures induce greater levels of discomfort, which induces greater aggression. We draw from the general affective aggression model ([54]) and cognitive-neoassociationistic model ([18]) to provide insights. We make other nuanced and potentially important contributions. For example, while naturalistic studies use higher temperatures (e.g., >84°F; [12]; [29]) or use a wider range (e.g., [ 4]), we use ranges that are commonly encountered in ambient settings (67°F and 77°F; [23]), though, admittedly, we do expand this range (63°F–82°F). We find robust effects even within this range.
Some caveats and questions for future researchers are also in order. Although our research is related to [ 9] work, it also differs in at least four ways (see also Web Appendix A, in which we review literature on hues and aggression). First, studying background hues (red vs. blue), they contend that red induces greater aggression and leads to differential effects in auctions and negotiations. Their research does not explain how temperature affects behaviors. Second, the first step of their process relies on arousal (red induces greater arousal), while ours does not. This is because the effects of temperature on arousal are not always robust ([ 4]; [46]). For instance, [46] finds that temperature-induced arousal affects performance for more complex cognitive tasks (e.g., solving math problems), but not easy tasks (see also [63]). Given that our tasks are not expected to be cognitively taxing, we do not expect our effects to be driven by arousal. We, do, however, measure arousal to rule out its effects. Instead, our mediational process relies on temperature-related discomfort. Third, their process relies on generic aggression. We provide a nuanced understanding of which subdimension of aggression affects behaviors in auctions and negotiations (hostile vs. the other three). Finally, we demonstrate how temperature affects behaviors in other nonselling (albeit competitive) contexts, whereas they do not. However, both studies (theirs and ours) together document the effect of aggression in selling contexts; future researchers could investigate the influence of other sources of discomfort—contextual (e.g., noise, crowding) or individual differences—on aggression and WTP.
Second, while we find that discomfort affects hostile aggression, would such effects emerge in other contexts? Understanding these effects may be instructive. Interestingly, discomfort did not affect mood and emotionality. One reason could be the engaging nature of our context—although the competitive nature allowed discomfort to be channeled through hostile aggression, the engaging nature of the task did not allow mood and emotionality to be influenced. Future research could provide more insights about this.
Third, an alternative argument could be that our effects emerge not because of our manipulated temperatures but because of the temperature differential between external (i.e., outside) and internal (i.e., room) ambient temperatures. We do not believe this can fully account for our results, as our studies were conducted at different times of the year in a southern U.S. state. Furthermore, even if these effects emerge because of systematic differences in temperatures, it is difficult to explain the full range of our findings for auctions, negotiations, and fixed-price settings. However, understanding the effects of temperature differential may be instructive and worthy of investigation. Fourth, although we study the effects of ambient temperature in thermally enclosed settings (i.e., indoors), it may be worthwhile to assess whether such effects also emerge in outdoor settings. Such an investigation might need to control for the effects of sunlight, as sunlight also influences valuations ([60]).
Our findings also have important managerial implications. In auctions, higher (vs. moderate) ambient temperatures will induce higher bids. Thus, increasing temperatures may lead to higher profits for sellers. However, higher temperatures have a detrimental effect on negotiators: they lower WTP for buyers and lead to protracted negotiations and lower agreement likelihoods. But how might online sellers benefit from this? For auctioneers, perhaps conducting auctions during warmer summer (vs. moderate) months may be more beneficial: although many consumers may bid from thermally controlled settings, not all will have their thermostats set at 70°F. In contrast, those offering negotiable online prices should offer more products for sale during the seasons when temperature is moderate. Furthermore, these effects are likely to be more pronounced in developing economies, such as India and China, where many consumers do not have the luxury of thermally insulating their surroundings and often haggle when purchasing products. Indeed, consumers still live without heat and air conditioning in many parts of the world. It may also be interesting to assess whether our effects emerge in outdoor settings, such as when consumers negotiate prices of cars or the purchase price of a rug on the streets of Turkey and Egypt, or when buying vegetables from a street vendor. We suspect that our effects would generalize to such settings and these might indeed provide fertile avenues for future research.
Because discomfort and hostile aggression underlie our effects, manipulating these factors independently could also influence behaviors. For example, other ways to induce discomfort (e.g., uncomfortable seating, noise, crowding) could also incite hostile aggression, which could have similar effects. Therefore, using other ways to lower consumers' level of comfort could increase surplus for auction houses but lower profits when selling products through negotiations. Likewise, inducing hostile aggression directly by highlighting how others are more fortunate might increase bids in auctions but lower offers in negotiations. Managers could highlight this in their advertising and communication. Nonprice negotiations also occur frequently, such as when individuals negotiate workplace responsibilities or divide chores at home. In such instances, concession making is required to arrive at optimal solutions. With higher (vs. moderate) temperatures, individuals may be more aggressive and less likely to concede, which leads to more disagreement. Thus, setting more moderate temperatures in the workplace and at home may lead to more integrative decision making, in which individuals compromise. This may also be a fruitful avenue for future research.
Supplemental Material, DS_10.1177_0022242919841595 - Role of Ambient Temperature in Influencing Willingness to Pay in Auctions and Negotiations
Supplemental Material, DS_10.1177_0022242919841595 for Role of Ambient Temperature in Influencing Willingness to Pay in Auctions and Negotiations by Jayati Sinha and Rajesh Bagchi in Journal of Marketing
Footnotes 1 Author ContributionsThe authors contributed equally to this research.
2 Associate EditorDhruv Grewal served as associate editor for this article.
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
5 Online supplement: https://doi.org/10.1177/0022242919841595
6 1We analyzed 11,530 Miami-Dade County foreclosure home auctions that took place during January 2011 through December 2013 (http://www.miamidadeforeclosures.com). We analyzed data for auctions that took place on days when temperature was moderate (67°F+) or higher (92.7% of our data set). A regression with log-transformed winning bids (originally in USD; skewness = 7.15) on average temperature on the day of the auction (67°F and above, N = 10,692) elicited a main effect of temperature (β =.03, SE =.002, t = 2.95, p =.003), suggesting that, as the temperature increased, maximum bids (i.e., WTP) also increased. This effect remains significant after controlling for other factors (e.g., dwelling type, zip code, humidity, season). Because the bids were entered online, the data are amenable to alternative explanations. Nonetheless, they provide some initial evidence for our contention.
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By Jayati Sinha and Rajesh Bagchi
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Record: 151- Sales Force Downsizing and Firm-Idiosyncratic Risk: The Contingent Role of Investors' Screening and Firm's Signaling Processes. By: Panagopoulos, Nikolaos G.; Mullins, Ryan; Avramidis, Panagiotis. Journal of Marketing. Nov2018, Vol. 82 Issue 6, p71-88. 18p. 1 Diagram, 4 Charts. DOI: 10.1177/0022242918805059.
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Sales Force Downsizing and Firm-Idiosyncratic Risk: The Contingent Role of Investors' Screening and Firm's Signaling Processes
Although sales force downsizing represents a challenging marketing resource change that can signal uncertainty about future firm performance, little is known about its impact on financial-market performance. Drawing from information economics, the authors address this knowledge gap by developing a comprehensive framework to ( 1) examine the impact of the size of a firm's sales force downsizing on firm-idiosyncratic risk, ( 2) uncover investors' screening processes that influence this relationship, and ( 3) identify firms' mitigating signaling processes that can alleviate investor uncertainty linked to downsizing. The authors draw from several secondary sources to assemble a longitudinal data set of 314 U.S. public firms over 12 years and model their framework using a robust econometric approach. Findings show that larger sales force reductions are associated with greater firm-idiosyncratic risk. Furthermore, this increase in risk is amplified when firms face high levels of future competitive threats and lack transparency in financial reporting. However, chief executive officers can mitigate these deleterious moderating effects by signaling a commitment to growth (i.e., increasing advertising expenditures) and formally communicating an external strategic focus to Wall Street.
Keywords: sales force downsizing; firm risk; investors; chief executive officer; information economics
The sales force is among a firm's most significant resources given its strong influence on customer relationships ([38]) and future profitability ([31]). Accordingly, the sales force constitutes a critical driver of current and future cash flows—strong signals of firm value to investors ([48]). Despite these inherent links between salespeople and firm value, little is known about the financial-market value of strategic decisions that affect the sales force, such as sales force downsizing. This knowledge gap is surprising given that salespeople are consistently one of the top positions laid off each quarter ([ 6]). Indeed, empirical research on the link between sales force downsizing and financial-market performance has been remarkably scant (see Table 1.1, Web Appendix 1). Specifically, three notable research gaps remain unaddressed.
First, current decision frameworks for sales force downsizing have examined operational outcomes, such as costs or profits (e.g., [55]), with little focus on financial-market performance. This is an important distinction, given prior work showing that downsizing increases customers' uncertainty ([26]), which can lead to vulnerability in cash flows. Similarly, when sales force layoffs occur, investors' uncertainty grows, owing to their limited knowledge regarding the firm's ability and intentions to secure future cash flows, thus increasing firm-specific risk.
Second, although the extant literature has contributed a great deal of insight into the marketing–finance interface, there is still a limited understanding of how investors evaluate firm marketing actions. This knowledge gap is significant because the information available to investors strongly influences their interpretation of the impact of the firm's marketing actions on firm value. Indeed, [47]) emphasize the importance of understanding how investors evaluate marketing actions that influence firms' cash flows. Moreover, prior literature has shown that information surrounding layoffs has a major influence on investors ([12]), thus reinforcing the need to identify which investor information cues influence the relationship between sales force downsizing and firm risk.
Finally, recent research has highlighted the need for more managerially relevant factors to improve leaders' influence on firm value ([24]). This is clearly evidenced as many chief executive officers (CEOs) struggle to provide a positive outlook for investors during sales force layoffs. For example, despite the CEO's promises of improved performance, sales force layoffs at Pfizer were still perceived with skepticism ([15]). Managers thus need an expanded toolbox to leverage firm-related factors that influence the relationship between sales force downsizing and investors' uncertainty.
We address these research gaps by conceptualizing the size of a firm's sales force downsizing (i.e., the extent of planned reductions to a firm's sales force) as a signal of market risk information that influences investors' uncertainty regarding a firm's future performance. Accordingly, we examine the influence of sales force downsizing on firm-idiosyncratic risk. We focus on idiosyncratic risk for three reasons. First, we expect that investors' interpretation of sales force downsizing will vary, owing to investors' imperfect information. Thus, firm-idiosyncratic risk, a second-order statistic of stock returns, represents a clear overall measure of investors' uncertainty, which stems from the differing signal interpretations.[ 6] Second, we draw on information economics as the theoretical foundation of our work. Idiosyncratic risk is particularly relevant in this context, given risk's interpretation as a measure of stock price informativeness ([17]). Third, firm-idiosyncratic risk represents a key metric that is widely followed by managers, financial analysts, and investors ([ 3]), and it accounts for up to 80% of total stock risk ([47]).
We build from information economics (e.g., [36]; [49]) to identify two mechanisms—screening and signaling—to contextualize the link between the size of a firm's sales force downsizing and firm-idiosyncratic risk. These processes help us understand how investors evaluate the risk linked to sales force downsizing and provide managerial strategies to buoy investors' outlooks even while signaling potential losses of customer relationships.
We draw from a variety of secondary sources to assemble a longitudinal data set of 314 U.S. public firms over 12 years and model our framework using a robust econometric approach. Our findings indicate that larger reductions in the sales force are associated with greater investor uncertainty as evidenced by increased firm-idiosyncratic risk. Furthermore, we illustrate that investors' screening cues, such as a "firm's product market fluidity" (the degree of future competitive threats a firm faces in its product markets; [25]) and a "firm's accruals management" (the degree of a firm's earnings management; [27]), amplify the harmful link between downsizing and firm-idiosyncratic risk. Our findings also show the beneficial effects of a firm's mitigating signaling cues through the "firm's advertising intensity" (the level of advertising targeted to a firm's customers; [43]) and the "CEO's external focus" (the amount of executive attention devoted to entities outside the firm, such as customers and competitors; [53]). These latter cues provide C-suite leaders with strategies to weaken the link between downsizing and idiosyncratic risk, in light of harmful screening cues, by signaling greater firm quality and clearer intentions.
Our research makes several contributions. First, we offer the first study that links sales force downsizing to increases in investor uncertainty. This is especially salient given recent work highlighting that "understanding how marketing actions...can affect the risk profile of the firm is crucial" ([50], p. 64). Accordingly, we provide greater guidance for marketing resource changes and subsequent firm-risk valuations. In addition, by providing theory to account for investor uncertainty, we extend prior downsizing studies that focus on customer uncertainty ([26]).
Second, we draw on information economics to conceptualize and test the measures investors use to screen firms, which are novel to marketing. Specifically, a firm's product market fluidity is reflective of future market share, making it distinct from measures of static competition, such as the Herfindahl–Hirschman index (see [25]). We show that product market fluidity increases investors' uncertainty during sales force downsizing. Furthermore, our inclusion of a firm's accruals management not only is new to marketing but also helps illustrate the "investor community as a customer" ([24], p. 115). Specifically, we posit that insufficient information in financial reporting prevents investors from making optimal capital allocation decisions. As a result, marketing resource changes (e.g., layoffs) may be misinterpreted because investors are not receiving enough information about the firm's intentions. Our results confirm this contention, showing that firms' use of greater accruals management during sales force layoffs heightens investors' uncertainty.
Third, whereas prior downsizing studies have shown that firm-controlled factors, such as open communication ([26]), can help improve operational performance, we lack strategic moderators that can improve financial-market performance outcomes (see Table 1.1, Web Appendix 1). Our focus on the firm's mitigating signaling cues helps illustrate how to reduce investor uncertainty through ( 1) advertising, which provides a quality signal during sales force downsizing and ( 2) CEO external focus, which plays a key role in both signaling a CEO's strategic intentions and clarifying the reasoning behind layoffs.
As a critical marketing resource change, we view the size of a firm's sales force downsizing as a market information signal that can create uncertainty for external parties. We thus anchor our study in two key tenets of information economics: ( 1) information asymmetry, and ( 2) screening and signaling processes. We elaborate on these two tenets next.
Exchange parties often have differing amounts of information, creating information asymmetry ([49]). In our study, information asymmetry occurs because a firm, compared with investors, has more and better information regarding a decision to downsize the sales force. We expect that investors' lack of information can trigger their uncertainty regarding a firm's future financial outlook, thereby increasing firm-idiosyncratic risk. We also expect that information asymmetry makes it difficult for investors to evaluate the unobservable attributes of a firm's "quality" and "intentions" related to the size of sales force downsizing.
In information economics, quality refers to the unobservable ability of the more informed party to fulfill the needs of an outsider ([11]). Because an investor's primary need relates to wealth maximization through investing in firms that yield positive financial outcomes, here we view quality as the unobservable ability of the firm to effectively compete in the market. Investors are also concerned about the firm's future intentions ([11]; [49]). In our context, investors are concerned with the firm's intentions to downsize and whether this action maximizes their future wealth. To resolve uncertainty about the unobservable attributes of quality and intentions, two remedial, information-based processes can take place: screening and signaling ([36]; [49]).
Screening, which is enacted by the least informed party, occurs when investors vigilantly scan the environment to collect and examine observable cues that help to draw inferences about a firm's quality or intentions ([11]). Because investors desire to minimize the risk associated with their investments, they search for signals of the firm's quality to increase their certainty regarding the firm's future prospects ([40]). We consider a firm's product market fluidity as an observable cue reflecting firm quality. Because a firm's product market fluidity captures changes in rival firms' products relative to its own products ([25]), the construct constitutes a forward-looking measure of the potential competitive threats a firm faces, rather than current or historical threats ([ 4]). As such, firm product market fluidity reflects the firm's ability to compete in the market against potential competitive threats and thus fulfill investors' demands.
In addition, because managers might engage in deceitful behavior when information asymmetry is present ([11]; [14]), investors will look for information that indicates the firm's intentions. We consider the degree of a firm's financial disclosure as an observable cue of the firm's propensity to obscure its intentions. Specifically, prior evidence has indicated that a firm's accruals management—delaying the recognition of losses to increase net income, or vice versa—can indicate management's propensity to hide information about firm fundamentals from capital markets, thus clouding investors' ability to ascertain the true state of future cash flows ([27]). Consequently, greater use of accruals management should hinder investors' screening of the firm's intentions about future plans and prospects in light of downsizing, thus leading investors to form different opinions about future firm performance (e.g., [14]).
Signaling, which is enacted by the more informed party, occurs when a firm projects cues to reduce investors' uncertainty related to the firm's imperceptible quality and intention attributes ([11]; [14]). Unlike screening cues, signaling cues must be credible to help investors separate firms that truly possess the attributes of high quality and transparent intentions from "impostor" firms. A signal is credible when it is both observable and costly for other firms to imitate ([11]). Observability refers to the extent to which investors see relevance in and can detect the emitted signal. Cost reflects the notion that some firms are in a better position than others to absorb the costs associated with a particular signal. Thus, lower-quality firms or those that do not have strong intentions cannot emit "false" signals for fear of losing credibility among investors or receiving legal class actions ([44]).
We consider signals of the unobservable attributes of firm quality and intent that satisfy the criterion of credibility. First, we consider a firm's advertising intensity as a highly visible quality signal that provides information about the firm's ability to effectively compete in the market, thus attracting investors' attention. Conceptually, advertising intensity parallels the firm's product market fluidity in the screening process in that both reflect the firm's ability to address competitive actions in the marketplace. For example, prior work has found that advertising spending gives investors a signal of the firm's future financial well-being and competitive viability because advertising is a major contributor to a firm's effort to build brand equity, appropriate value from customers, and differentiate from competition ([19]; [48]). Indeed, prior studies have shown that investors tend to favor stocks with recognized brand names built from advertising activity ([18]). In addition, because cash flow shortfalls are associated with lower levels of future investment in advertising ([35]), it is costly for firms with lower cash flow to imitate their highly capable counterparts.
Second, regarding a firms' intent signals, we consider CEO external focus because it offers investors an observable cue of senior managers' strategic emphasis ([53]). We draw CEO external focus from the 10-K reports submitted to the Securities and Exchange Commission, which requires that all statements are certified by the CEO. As such, CEO external focus is both visible and costly to imitate because 10-Ks ( 1) reflect observable attributes to the investor community ([54]) and ( 2) are formal documents that require board approval, thus making it costly for low-quality firms to emit such signals.
Our conceptual framework (Figure 1) builds directly from the aforementioned tenets. First, we expect that the size of a firm's sales force downsizing represents a market-information signal that influences firm-idiosyncratic risk. Second, we identify cues that fall under each contingency process: ( 1) investors' screening process and ( 2) the firm's mitigating signaling process. We expect that cues related to the screening process will moderate the influence of the size of a firm's sales force downsizing on firm-idiosyncratic risk (i.e., two-way interactions), and we posit that the firm enacts a signaling process that mitigates the interactive effects of the screening process with the size of a firm's sales force downsizing on firm-idiosyncratic risk (i.e., three-way interactions). Next, we delineate the specific hypotheses in our conceptual framework.
Graph: Figure 1. Conceptual framework.
Investors incorporate information about important marketing resource changes (e.g., sales force downsizing) that affect the future outlook of the firm (see [24]). However, firms often have more information than investors about these changes, creating an information asymmetry that increases investors' uncertainty about the firm's ability and intentions to create value for them, thus resulting in markets with higher stock volatility ([47]). As such, idiosyncratic risk reflects volatility in stock returns that result from firm-specific actions, thereby representing investors' uncertainty regarding future cash flows ([ 3]). Accordingly, we expect that larger sales force downsizing increases investors' uncertainty and thus increases firm-idiosyncratic risk as a result of increased volatility and vulnerability of future cash flows.
Specifically, prior work has illustrated that the volatility and vulnerability of cash flows increases when a firm's customer relationships are destabilized ([48]). This suggests that strong customer relationships create competitive barriers that can insulate a firm's cash flows from competition. Although firms can employ a variety of means to acquire and retain customers, the sales force constitutes a vital marketing resource in this context. Specifically, because the sales force represents the primary vehicle through which many firms interact with customers ([38]), customer relationships may be severed after larger cuts to the sales force, thus leading to cash flow volatility. In addition, laying off a larger number of salespeople may damage customers' satisfaction ([22]) or increase customers' relationship uncertainty ([26]) because the firm has fewer salespeople to call on customers. Consequently, larger sales force reductions should increase investors' uncertainty regarding the firm's ability to satisfy customers. Finally, larger cuts in the sales force increase the workload for "surviving" salespeople, thus making new customer acquisition increasingly difficult ([10]). Taken together, these arguments suggest that investors view larger sales force reductions as a threatening signal to the stability and predictability of future cash flows, thus leading to higher firm-idiosyncratic risk.
- H1: The larger the size of a firm's sales force downsizing, the larger the increases in firm-idiosyncratic risk.
As mentioned previously, larger reductions in sales force size increase firm-idiosyncratic risk due to investors feeling uncertain about the stability and predictability of future cash flows. This suggests that factors that influence investors' uncertainty regarding a firm's cash flows should influence the effects of sales force downsizing on risk. We expect that higher levels of a firm's product market fluidity amplify the uncertainty of future cash flows signaled by larger reductions in the sales force, thus further increasing firm-idiosyncratic risk. In particular, because fluidity relates to changes in a firm's product space owing to moves made by the firm's competitors, it captures the extent to which a firm faces rapid changes in its product market ([25]). Thus, compared with firms facing lower product market fluidity, firms with higher levels of product market fluidity signal an inability to maintain the superiority of their products relative to current and emerging rivals' offerings and, thus, differentiate themselves from competition. This vulnerability to competitive actions should increase the volatility of a firm's future cash flows and prompt greater investor uncertainty regarding a downsizing firm's economic future. In other words, higher levels of future product market competition serve as an observable cue that amplifies investors' uncertainty that stems from larger sales force reductions, thereby strengthening and solidifying their disbelief in the downsizing firm's ability to ward off competitive threats or generate demand. In summary, for firms with higher levels of product market fluidity, larger sales force reductions are more likely to cause greater investor uncertainty regarding downsizing firms' ability to secure future cash flows and, therefore, increase firm-idiosyncratic risk.
- H2: The higher a firm's product market fluidity, the stronger the positive effect of the size of a firm's sales force downsizing on firm-idiosyncratic risk.
We posit that a firm's advertising intensity reduces information asymmetry by helping a firm signal its ability to effectively compete in the market and separate itself from competition. Our expectation draws on the work of [28]), who point out that advertising intensity influences investors through a quality-signaling mechanism: increased levels of advertising signals a firm's financial well-being or competitive viability to investors. Indeed, prior work has established advertising's positive moderating impact on stock market responses to firm events (see [19]). We therefore anticipate that advertising intensity can enact a quality-signaling process to counteract the quality-screening process that occurs when firms downsize the sales force under higher product market fluidity. Specifically, the heightened information asymmetry emanating from larger sales force reductions motivates investors to look for proactive, firm-initiated signals to alleviate their concerns regarding the firm's future earnings. As noted in H2, however, higher product market fluidity should cause investors to experience greater uncertainty regarding downsizing firms' ability to secure future cash flows, thereby increasing firm-idiosyncratic risk. But when a firm increases advertising expenditures, investors may interpret the action as a positive signal that larger sales force reductions do not threaten the firm's ability to compete in the future. Rather, increased advertising signals the firm's competitive viability and commitment to growth (e.g., [28]) and should, therefore, decrease investors' uncertainty regarding the firm's ability to counter competing product entries and secure future cash flows. In summary, we expect that improving investors' confidence in the firm's quality through increased advertising spending should reduce the information asymmetry regarding larger sales force reductions amid higher firm product market fluidity, thus reducing firm-idiosyncratic risk.
- H3: The positive moderating effect of a firm's product market fluidity on the relationship between the size of a firm's sales force downsizing and firm-idiosyncratic risk is weakened at higher levels of firm's advertising intensity.
Drawing from the notion of screening explained previously, we also expect that investors will screen for firms' strategic intentions to reduce their uncertainty regarding the influence of the size of a firm's sales force downsizing on future cash flows. Specifically, firms possess private information about the reasoning for strategic decisions such as sales force layoffs ([11]). This suggests that investors' uncertainty of the firm's intentions to secure future cash flows should also influence the effects of sales force downsizing on risk. Accordingly, we expect that the heightened information asymmetry created by larger sales force downsizing will motivate investors to ascertain the intentions surrounding the reductions (e.g., boosting short-term profits, restructuring to meet long-term needs) by closely examining the firm's financial reports. However, a firm's use of accruals management—delaying the recognition of losses to increase net income, or vice versa ([27])—can cloud investors' ability to ascertain the current state of cash flows or to discern how future cash flows will be secured following larger reductions in the sales force. This type of earnings smoothing (i.e., less transparency for investors) makes it more likely that investors will be unable to discern the firm's intentions for future cash flows in light of downsizing. As a result, the firm's use of discretionary accruals is more likely to produce differences in opinion about the firm's future earnings and stock prices ([27]). In other words, higher use of accruals management serves as an observable cue that increases investors' uncertainty regarding the firm's intentions to secure future cash flows following larger sales force cuts, thus further increasing firm-idiosyncratic risk.
- H4: The greater a firm's use of accruals management, the stronger the positive effect of the size of a firm's sales force downsizing on firm-idiosyncratic risk.
We expect that CEO external focus reduces the information asymmetry between a firm's top management and investors by helping firms signal their intentions to effectively compete in the market and, thus, secure future cash flows. Specifically, top management plays an important role in communicating the firm's strategy and viability of plans to investors (e.g., [44]). Although firms communicate with investors through many means, 10-Ks represent an important vehicle CEOs employ to communicate their attentional focus on specific entities such as customers or competition and to elaborate on firm intentions ([37]). The information contained in these reports, regarding the firm's focus, represents an unobtrusive manifestation of organizational strategic intent and mindset, which helps investors evaluate the firm's future prospects ([44]). In particular, an external focus implies high levels of firm preparedness in terms of formulating and implementing strategic actions designed to cater to customer needs or defend against competitive threats ([53]). In this way, external focus can help investors interpret the intent of larger sales force reductions as a long-term strategy that takes emerging customer needs and competition into account. This improved understanding of the firm's plan to create value should help resolve uncertainty stemming from accruals management by strengthening confidence in the firm's intentions to leverage downsizing for future investor wealth. Accordingly, we expect that greater CEO external focus will counteract the intent-screening process that occurs when firms downsize their sales force under high levels of accruals management and, thus, will mitigate investors' uncertainty regarding future cash flows. In summary, we expect that increases in firm-idiosyncratic risk—as a result of larger sales force reductions under higher levels of accruals management—will be lowered for firms with greater CEO external focus.
- H5: The positive moderating effect of a firm's accruals management on the relationship between the size of a firm's sales force downsizing and firm-idiosyncratic risk is weakened at higher levels of CEO external focus.
Consistent with recent firm-level sales force research ([32]), our sampling frame comprises a list of U.S.-based, publicly traded firms with the largest sales forces that is reported by Selling Power magazine. We assemble data for these firms from a variety of secondary sources (see Table 1).
Graph
Table 1. Variables, Measures, and Data Sources.
| Variable | Notation | Operationalization | Data Source |
|---|
| Size of firm's sales force downsizing (%) | SFSFDSP | The percentage change (absolute value) in sales force size between the year of the publication date (year t) and the year that precedes that date (year t − 1), over the sales force size at year t − 1, when we observe a sales force reduction (i.e., negative change). For firm-year observations with sales force upsizing (i.e., positive change) or no change, we set the downsizing variable equal to zero. | Selling Power |
| SFSFDAR/10-Ks | Annual Reports/10Ks |
| Firm-idiosyncratic risk (%) | FIR | The standard deviation of the firm's unexpected return—that is, the standard deviation of the residual term in the Fama–French–Carhart four-factor model. | Center for Research in Security Prices |
| Firm's accruals management | FAM | The absolute value of annual discretionary accruals (relative to total assets) calculated using the modified Jones model, augmented to include net income, as well as the lagged total accruals, firm and year fixed effects, and a correction for serial correlation. | COMPUSTAT |
| Firm's advertising intensity | ADV | Ratio of the firm's reported advertising expenditures to sales. | COMPUSTAT |
| CEO external focus | EXT | Counts of specific keywords (see Web Appendix 3) contained in the MD&A section of firms' 10-K filings. | 10-Ks |
| Firm's product market fluidity | FPMF | The overlap between words in a firm's product description from its 10-k filing and the vector of aggregate absolute change in usage of each word in the product market universe. | Hoberg and Phillips Data Librarya |
| Firm's R&D intensity | R&D | Firm's ratio of R&D expenditures to sales. | COMPUSTAT |
| Firm's ROA | ROA | Firm's ratio of profit before tax to total assets. | COMPUSTAT |
| Firm's financial leverage | LEV | Firm's ratio of total liabilities to total assets. | COMPUSTAT |
| Firm's available liquidity | LIQ | Firm's ratio of current assets to current liabilities. | COMPUSTAT |
| Firm's size | SIZE | Firm's natural log of total assets (in $ '000s). | COMPUSTAT |
| Industry volatility | IVOLAT | Standard deviation of the sales of all firms in the industry using four-digit SIC codes. | COMPUSTAT |
| Industry growth | IGROW | Total sales growth of all firms in the industry using four-digit SIC codes. | COMPUSTAT |
10022242918805060 ahttp://hobergphillips.usc.edu/.
First, to formally test our hypotheses, we estimate our independent variable (i.e., size of a a firm's sales force downsizing) using the annual sales force size data reported in Selling Power magazine, which employs a multiple-informant data collection approach.[ 7] Second, we take an additional estimate of the independent variable from two official mechanisms for transmitting firm information to investors ([37]; [54]): firms' annual reports to shareholders (drawn from Mergent Archives) or 10-Ks[ 8] (drawn from EDGAR, Morningstar, and firms' websites). We use this second estimate to verify the veracity of the Selling Power data (see the "Independent Variable" subsection) as well as the robustness of our hypothesis testing results to a different data source (see the "Results" section). Third, daily stock price data for computing annual firm-idiosyncratic risk are supplied from the Center for Research in Security Prices. Fourth, data on firms' product market fluidity come from the Hoberg and Phillips Data Library. Fifth, we obtained data on CEO external focus from textual analysis of the Management Discussion and Analysis (MD&A) section of firms' 10-Ks. Finally, we use annual financial data from COMPUSTAT's Fundamentals Database to compute firms' accruals management, firms' advertising intensity, and firm- and industry-level covariates.
Our primary objective is to model investors' reactions to information regarding firms' sales force size. Because firms publish their annual reports to shareholders (or file 10-Ks for the year) a few months before Selling Power's release date, it is possible that information on sales force size reaches investors before that date. Accordingly, to establish the time alignment between information on sales force downsizing and investors' reactions, we set the annual report publication date (or 10-K filing date) as the publication date of reference in our operationalization of both the independent and dependent variables (discussed subsequently).
Our main analysis is based on a cross-sectional time series (panel) data set representing 2,349 firm-year observations from 314 firms over a period of 12 years (2001–2012) to account for different cycles in the U.S. economy (for sample composition, see Table 2.1, Web Appendix 2). Our panel is unbalanced in that we do not have complete data available for all firms over the entire 12-year period and across all data sources. Thus, following previous work (e.g., [29]), we employ different sample sizes across estimated models.
We estimate the size of a firm's sales force downsizing using the data reported in Selling Power. We operationalize our independent variable as the percentage change (absolute value[ 9]) in sales force size between the year of the publication date (year t) and the year that precedes that date (year t − 1), over the sales force size at year t − 1, when we observe a sales force reduction (i.e., negative change). For firm-year observations with sales force upsizing (i.e., positive change) or no change, we set the downsizing variable equal to zero.
By construction, this operationalization compares firms that downsized with firms that upsized or made no change in their sales force. However, after we conducted a simultaneous test of downsizing versus upsizing versus no-change, using a spline specification ([20]) that measures the effect of each sales force sizing group separately, we concluded that upsizing and no change have different effects on firm-idiosyncratic risk (see "Additional Analysis: The Impact of Sales Force Upsizing" subsection for details). Thus, upsizing and no change should not be grouped together. Accordingly, we exclude the firm-year observations with sales force upsizing from the main analysis to isolate the effect of downsizing.
Furthermore, we implement two verification tests to assess the information availability to investors and veracity of our independent variable that relies on Selling Power data.[10] First, we create a second estimate of the independent variable by employing the same operationalization and by using the size of a firm's sales force reported in firms' annual reports/10-Ks. The high correlation between the two estimates (see Table 2) suggests that the two sources essentially measure the same construct (Pearson's r =.726, p <.001). Second, we calculate the bivariate correlation between the sample firms' total number of employees reported in COMPUSTAT and the total number of employees published in Selling Power (reported alongside sales force size). The high correlation between the number of employees given by the two sources suggests that the two sources almost overlap (Pearson's r =.910, p <.001). Because COMPUSTAT employee data is gauged to be a reliable data source (given its widespread adoption in prior work), we conclude that the employee data Selling Power collects is also reliable.
Graph
Table 2. Variable Intercorrelations.
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
|---|
| 1. FIR | | | | | | | | | | | | | | | |
| 2. SFSFDSP | .126*** | | | | | | | | | | | | | | |
| 3. SFSFDAR/10-Ks | .143*** | .726*** | | | | | | | | | | | | | |
| 4. FPMF | .113*** | .020 | .068*** | | | | | | | | | | | | |
| 5. FAM | .262*** | .074*** | .090*** | .127*** | | | | | | | | | | | |
| 6. EXT | .017 | .043** | .078*** | .112*** | .014 | | | | | | | | | | |
| 7. ADV | –.091*** | –.011 | –.001 | –.002 | –.013 | .080*** | | | | | | | | | |
| 8. R&D | .013 | .040* | .064*** | .431*** | .108*** | .143*** | –.007 | | | | | | | | |
| 9. ROA | –.413*** | –.096*** | –.112*** | –.069*** | –.027 | –.008 | .157*** | –.013 | | | | | | | |
| 10. LEV | .189*** | –.015 | –.030 | –.156*** | .061** | –.125*** | .040** | –.291*** | –.148*** | | | | | | |
| 11. LIQ | –.029 | .034* | .056*** | .105*** | .033 | .057*** | –.067*** | .226*** | .144*** | –.552*** | | | | | |
| 12. SIZE | –.409*** | –.105*** | –.111*** | .180*** | –.151*** | –.006 | .133*** | .130*** | .079*** | .088*** | –.185*** | | | | |
| 13. IVOLAT | –.154*** | –.032 | –.042** | –.418*** | –.111*** | –.072*** | .079*** | –.366*** | .049** | .086*** | –.026 | .036* | | | |
| 14. IGROW | –.112*** | –.039*** | –.021 | .031 | –.006 | –.001 | .016 | .015 | .087*** | –.041** | –.003 | .045** | –.049** | | |
| 15. IMSSFD | .086*** | .085*** | .116*** | .041* | .056*** | .079*** | –.020 | .025 | –.032 | –.034 | –.031 | –.125*** | –.047** | –.018 | |
| 16. ISFSD | –.055*** | –.051** | –.055*** | .123*** | –.022 | –.066*** | –.005 | .133*** | .075*** | .011 | –.040** | .195*** | –.096*** | .001 | –.114*** |
- 20022242918805060 *p <.10.
- 30022242918805060 **p <.05.
- 40022242918805060 ***p <.01.
- 50022242918805060 Notes: We used two-tailed significance tests. FIR = firm-idiosyncratic risk (%); SFSFDSP = size of firm's sales force downsizing from Selling Power (%); SFSFDAR/10-Ks = size of firm's sales force downsizing from annual reports/10-Ks (%); FPMF = firm's product market fluidity; FAM = firm's accruals management; EXT = CEO external focus; ADV = firm's advertising intensity; R&D = firm's research and development expenditures to firm's sales; ROA = firm's return on assets; LEV = firm's financial leverage; LIQ = firm's available liquidity; SIZE = firm size; IVOLAT = industry volatility; IGROW = industry growth; IMSSFD = industry's mean size of sales force downsizing; ISFSD = industry sales force size disclosure.
Our dependent variable is the idiosyncratic component of a firm's stock daily return volatility. For every firm-year observation, we use the publication date of the annual report (or filing date of the 10-K) as the starting date and include all daily closing prices over the next 12 months following this date. Equation 1 provides the observed daily return:
ri, s= ln[Pi, s/Pi, s − 1],1
where Pi,s is the closing price of firm i's common stock at the end of day s and Pi, s − 1 is firm i's closing price at the end of previous day s − 1 (prices are adjusted for capital changes such as stock splits and stock dividends).
We estimate the expected returns of the firm's stock E(ri,s) using Fama–French–Carhart's four-factor model ([47]), based on daily data from French's website (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data%5flibrary.html). In particular, the expected stock return is the predicted value from the following regression model:
ri, s−rf, s=β0, i+β1, i(rm, s−rf, s)+β2, iHMLs+β3, iSMBs+β4, iUMDs+ui, s,2
where rf, s is the risk-free rate; rm, s is the value weighted return on all stocks listed on NYSE, AMEX, and NASDAQ; is the book-to-market risk premium factor; is the size-based risk premium factor; is the returns momentum factor; and ui, s is the residual term. Following Luo and Bhattacharya (2009), we define firm-idiosyncratic risk as the standard deviation of the firm's unexpected return—that is, the standard deviation of the residual term in Equation 2.
We use data made available by [25]). Specifically, they used a text-based algorithm that captures information from product descriptions of firms' product spaces and rival moves contained in firms' 10-Ks to measure firms' product market fluidity. The algorithm employed involves "measuring the overlap between words in a firm's product description from its 10-K filing and the vector of aggregate absolute change in usage of each word in the product market universe" ([ 4], p. 110). Theoretically, the lowest value that a firm's product market fluidity can get is zero, which indicates no potential competitive threats in its product markets, as such higher values indicate higher levels of competitive threats. We take the two-year moving average of this measure to match the timeframe of the other moderators.
To distinguish between unbiased and discretionary accruals, we employ the modified Jones model, augmented to include net income, as well as the lagged total accruals, firm and year fixed effects, and a correction for serial correlation ([30]). Specifically, we estimate the following fixed-effect first-order autoregressive model:
TAccrualsit=β0i+φTAccrualsit−1+β11Assetsit−1+β2(ΔSalesit−ΔRecit)+β3PPEit+β4ROAit+yt+uit,3
where TAccruals is income before extraordinary items minus cash flow from operating activities, adjusted for extraordinary items and discontinued operations and scaled by lagged total assets; 1/Assets is the inverse total assets of the firm reported in fiscal year t − 1; ΔSales is the annual change in sales scaled by lagged total assets; ΔRec is the annual change in total receivables scaled by lagged total assets; PPE is the firm's property, plant, and equipment scaled by lagged total assets; ROA is net income on lagged total assets; yt is year t effect; and uit is the residual term.
To control for year- and firm-specific effects that can induce model misspecification, we employ the estimation procedure proposed by [30]). In particular, each firm's annual total accruals are subtracted from the cross-sectional mean for that year. Next, the deviation of each firm's annual total accruals from the cross-sectional mean is subtracted from the corresponding deviation in the previous year. The explanatory variables in the model are also subtracted in the same manner. The estimation of the model yields a time series of residuals for each firm. We subtract from each firm-year residual the mean value of the residual across all years for the corresponding firm to obtain the discretionary accruals estimate.
According to [13]), the large, positive discretionary accruals recorded by firms are followed by large, negative discretionary accruals instead of the positive cash flow realizations that would tend to follow unbiased accrual practice. We therefore adopt the two-year moving average of the absolute value of annual discretionary accruals. The higher the accruals management, the more the firm is managing reported earnings, thus revealing less firm-specific financial information to investors.
Given prior work showing a long-term, year-to-year carryover effect of advertising ([45]), we measure a firm's advertising intensity using a two-year moving average of the ratio of the firm's reported advertising expenditures to sales ([43]).
We measure CEO external focus by using counts of specific keywords contained in the MD&A section of firms' 10-K filings. We employed a multistage procedure. First, we collected 10-K filings[11] for each firm-year. Second, we trained two graduate research assistants on 10-K filings, software use, and the keyword dictionary developed by [53]) (see Web Appendix 3). Third, research assistants manually pulled out the MD&A sections from the 10-Ks. We employed a manual procedure because firms often incorporate the MD&A section by reference to an exhibit that is part of the filing but is often merged with other material, thus making automated parsing inaccurate. Finally, each MD&A file was imported into the Hermetic Word Frequency Counter Advanced software to automatically scan documents and record the frequency of keywords in our dictionary for each firm-year. All MD&A files and word counts were checked for accuracy by the first author and the two assistants. The final measure of CEO external focus is the ratio of keyword counts over the total number of words in the MD&A section, scaled by 1,000 ([16]). We take the two-year moving average of this ratio to match the timeframe of the other moderators.
We account for firm- and industry-specific variables in our analyses. With regard to firm-specific variables, leverage increases the volatility of earnings and stock returns; therefore, we control for firms' financial leverage measured by the ratio of total liabilities to total assets. Stock prices are driven by the firm's available liquidity, measured by the ratio of current assets to current liabilities. We control for the relationship of research and development (R&D) with firm risk by using the ratio of R&D expenditures to sales (R&D Intensity). Furthermore, a proportion of firms report R&D and advertising together under the same accounting item of other operational expenses. This creates two significant issues. First, missing data implies a large reduction in the sample size. Second, if R&D and advertising are not missing randomly (i.e., firms with high or low values tend not to report them separately), this would create selection bias. Thus, we follow recommendations in the extant literature (e.g., [33]) and replace all missing values of R&D and advertising expenses with zero. To distinguish firms that do not separately report these expenses from peers that do, we create two dummy variables—the R&D dummy and the ADV dummy—which take a value of one if R&D (or advertising) expenses were not reported and zero otherwise. These dummies capture any hidden effect of missing values as described previously. Moreover, because a firm's earnings affect stock volatility, we take into account the firm's profitability measured by the return on assets (ROA) ratio. Finally, [17]) show that firm size has a negative relation with firm-idiosyncratic risk; we therefore control for firm size measured by the natural log of total assets (in thousands of dollars).
At the industry level, defined using the four-digit Standard Industrial Classification (SIC) scheme, we consider two covariates. Firm stock volatility is positively related to industry volatility ([ 8]), whereas a firm's earnings and stock returns are further driven by industry growth ([21]). We thus include industry volatility, gauged by the standard deviation of the sales of all firms in the industry, and industry growth, measured by the total sales growth of all firms in the industry. Table 2 provides variable intercorrelations (for summary statistics, see Table 4.1, Web Appendix 4). Variance inflation factors indicate no evidence of multicollinearity in our data (see Table 5.1, Web Appendix 5).
We specify a panel data regression model to test hypotheses. Our model specification offers many benefits. First, to eliminate potential simultaneity bias, we employ one-period lagged covariates. Second, in addition to firm- and industry-specific covariates, we account for any unobservable firm-specific heterogeneity by applying a fixed-effects model at the firm level. Third, we account for time trends by adding year effects. Fourth, we use robust clustered estimates of errors ([52]) to curb possible biases of error heteroskedasticity and within-cluster (firm) correlation. Fifth, we include ROA as a covariate to address the firm-level endogeneity from firm financial performance that might affect the relationship between the size of a firm's sales force downsizing and firm-idiosyncratic risk. Sixth, to rule out alternative explanations due to major corporate events, we omit firms from our sample that were involved in consolidation (i.e., mergers/acquisitions), or restructuring (i.e., business closures/exits) during the study period. Finally, we winsorize the data at the 1st and 99th percentiles to curb the impact of spurious extreme values on our findings.
Equation 4 shows the resulting complete model. This model tests ( 1) the impact of the size of a firm's sales force downsizing (SFSFDi,t) on firm-idiosyncratic risk (FIRi, t); ( 2) the two-way interactions between the firm's product market fluidity (FPMFi, t − 1) and accruals management (FAMi, t − 1) with the size of a firm's sales force downsizing (SFSFDi, t); and ( 3) the three-way interaction between the firm's advertising intensity (ADVi, t − 1), product market fluidity (FPMFi, t − 1), and the size of a firm's sales force downsizing (SFSFDi, t) as well as the three-way interaction between the firm's accruals management (FAMi, t − 1), CEO external focus (EXTi, t − 1), and the size of a firm's sales force downsizing (SFSFDi, t).
Graph
where θt is year dummies' effects, Yt is the set of mutually exclusive year dummies, ηi is time-invariant firm's unobservable fixed effects, and ∊i, t is the error term.
Firms experiencing financial troubles or undergoing transformations are more likely to downsize their sales force as part of cost management considerations. In this case, an increase in firm-idiosyncratic risk may be due to the additional uncertainty caused by the deterioration of firm fundamentals rather than to sales force downsizing. Alternatively, sales force downsizing may be caused by increased firm-idiosyncratic risk rather than the other way around. Although we exclude firm-year observations in which a major corporate event has taken place and control for the observed firm's fundamentals and the time-invariant unobserved firm-specific factors, there may be some unobserved time-varying variable that is omitted and could bias our results. Moreover, the sample includes only firms with available sales force data. Nonrandomly selected samples may lead to erroneous conclusions owing to sample selection bias in sales force downsizing. Accordingly, following [23]), we address potential omitted-variable and sample selection biases by running two auxiliary regression models.
To address omitted-variable bias, we use the control function approach, in which a correction term is added to the regression model so that the potentially endogenous variable is no longer correlated with the error term ([23]; [52]). In particular, we estimate an auxiliary regression of the endogenous variable (i.e., sales force downsizing) on an instrument, plus the exogenous controls. We draw from the extant literature (e.g., [ 5]) to identify the instrument using industry-year categorization: the average size of sales force downsizing among peer firms in the industry (two-digit SIC). When calculating the instrument, we omit the contribution of the focal firm, and therefore, the instrument varies across firms even within the same industry and year. Because the peer-based instrument is unlikely to be a determinant of the focal firm's idiosyncratic risk and is correlated with sales force downsizing, the variable meets the conditions of a valid instrument ([52]). We fit the panel regression model in Equation 4 using the estimated residuals from this first auxiliary regression model as an additional covariate.
To address sample selection bias, we run a Heckman selection model using full information maximum likelihood. Specifically, we augment the firm-year observations in our data set with observations for firms listed in the major U.S. stock markets and for which we have no information about their sales force. Drawing on the augmented sample, we estimate an auxiliary probit model using the exogenous controls as well as an exclusion variable. We follow [23]) and define the exclusion variable as the proportion of firms in the same industry (two-digit SIC) that disclose their sales force size. From this second auxiliary regression model, we calculate the inverse Mills ratio (i.e., the ratio of the estimated probability density function to the cumulative distribution function), which we include as an additional covariate to the panel regression model (Equation 4).
Because the control function residuals and the inverse Mills ratio included in the model are estimates rather than true values, we need to take this extra source of variation into account. Therefore, we follow [39]) and correct the standard errors by bootstrapping simultaneously the auxiliary regressions (the control function and the Heckman selection) and the main model (Equation 4). Thus, reported standard errors of the coefficients are derived from 1,000 bootstrap samples.
As shown in the Heckman selection model (see Table 6.1, Web Appendix 6), the industry's percentage of firms that disclose their sales force is a significant predictor (γ = 4.659, p <.01) of the selection probability. Likewise, the control function model (Table 6.1, Web Appendix 6) shows that the average size of sales force downsizing among peer firms in the industry is positively associated with sales force downsizing (γ = 2.187, p <.05).
As mentioned previously, we offer two tests of our hypotheses. First, we formally test hypotheses by employing the size of a firm's sales force downsizing estimate that draws on Selling Power data. Table 3 provides results for three models: Model 1 (covariates, main effect); Model 2 (covariates, main effect, two-way interactions); and Model 3 (covariates, main effect, two- and three-way interactions), which is the complete model used to test our hypotheses. The incremental R2 tests indicate that adding the proposed moderators and interaction terms improves the model's explanatory power.
Graph
Table 3. Hypothesis Testing Results (Based on Selling Power Data): Ordinary Least Squares Fixed-Effects Regression Models of Firm-Idiosyncratic Risk.
| Model 1:Main Effects | Model 2:Two-Way Interactions | Model 3:Three-Way Interactions | Hypothesis |
|---|
| Main Variables | | | | | | | |
| SFSFDSP | .092*** | (.026) | .076*** | (.025) | .076** | (.032) | H1 |
| FPMF | | | .041*** | (.014) | .044*** | (.014) | |
| SFSFDSP × FPMF | | | .064*** | (.019) | .075*** | (.023) | H2 |
| ADV | | | | | .145*** | (.038) | |
| SFSFDSP × ADV | | | | | .027 | (.043) | |
| FPMF ×ADV | | | | | –.010 | (.011) | |
| SFSFDSP × FPMF × ADV | | | | | –.100*** | (.031) | H3 |
| FAM | | | .099*** | (.030) | .102*** | (.030) | |
| SFSFDSP × FAM | | | .108*** | (.031) | .149*** | (.029) | H4 |
| EXT | | | | | .013 | (.013) | |
| SFSFDSP × EXT | | | | | .035* | (.018) | |
| FAM × EXT | | | | | .041 | (.029) | |
| SFSFDSP × FAM × EXT | | | | | –.101** | (.038) | H5 |
| Covariates | | | | | | | |
| R&D | –1.832*** | (.554) | –2.111*** | (.590) | –2.015*** | (.615) | |
| R&D_i | –.218** | (.099) | –.297*** | (.105) | –.264** | (.106) | |
| ADV_i | .019 | (.074) | .050 | (.080) | .218*** | (.082) | |
| ROA | –3.399*** | (.334) | –3.441*** | (.316) | –3.209*** | (.327) | |
| LEV | .713*** | (.216) | .625*** | (.192) | .565*** | (.197) | |
| LIQ | –.071*** | (.022) | –.084 | (.023) | –.081** | (.023) | |
| SIZE | –.225*** | (.046) | –.218*** | (.048) | –.208** | (.048) | |
| IVOLAT | .129 | (.245) | .010 | (.243) | –.158 | (.241) | |
| IGROW | –.077 | (.057) | –.073 | (.054) | –.057 | (.057) | |
| CF | .018 | (.016) | .026 | (.016) | .027* | (.015) | |
| IMR | .444*** | (.137) | .262* | (.143) | .230 | (.152) | |
| Constant | 4.472*** | (.414) | 4.538*** | (.417) | 4.391*** | (.422) | |
| Firm fixed effects | Yes | Yes | Yes | |
| Year effects | Yes | Yes | Yes | |
| Observations | 2,338 | 2,338 | 2,338 | |
| R-squared | .377 | .391 | .416 | |
| Adjusted R-squared | .371 | 383 | .405 | |
- 60022242918805060 *p <.10.
- 70022242918805060 **p <.05.
- 80022242918805050 ***p <.01.
- 90022242918805050 Notes: We used two-tailed significance tests. The first entry within each cell corresponds to estimated coefficients, followed by robust standard errors in parentheses. Reported effects for the main variables are standardized to facilitate interpretation of interaction effects. SFSFDSP = size of firm's sales force downsizing from Selling Power (%); FPMF = firm's product market fluidity; ADV = firm's advertising intensity; FAM = firm's accruals management; EXT = CEO external focus; R&D = firm's research and development expenditures to firm's sales; R&D_i = firm's research and development dummy; ADV_i = firm's advertising dummy; ROA = firm's return on assets; LEV = firm's financial leverage; LIQ = firm's available liquidity; SIZE = firm size; IVOLAT = industry volatility; IGROW = industry growth; CF = residual term of control function model; IMR = inverse Mills ratio estimated using Heckman's selection model.
Consistent with our predictions in H1, we find that larger reductions in the sales force increase firm-idiosyncratic risk (γ1 =.076, p <.05). In support of H2, the two-way interaction of the size of a firm's sales force downsizing with a firm's product market fluidity is positive and significant (γ2 =.075, p <.01), indicating that firms with larger sales force reductions under highly competitive product market conditions experience a stronger positive impact on their idiosyncratic risk. In support of H3, the effect of the three-way interaction between the size of a firm's sales force downsizing, the firm's product market fluidity, and the firm's advertising intensity is negative and significant (γ3 = –.100, p <.01). Thus, although a firm's product market fluidity amplifies the positive influence of the size of a firm's sales force downsizing on firm-idiosyncratic risk, this influence is mitigated by advertising intensity. As predicted in H4, we find that the effect of the two-way interaction between the size of firm's sales force downsizing and a firm's accruals management on firm-idiosyncratic risk is positive and significant (γ4 =.149, p <.01). In other words, accruals management amplifies the positive influence of the size of a firm's sales force downsizing on firm-idiosyncratic risk. Finally, in support of H5, we find that the effect of the three-way interaction between the size of a firm's sales force downsizing, accruals management, and CEO external focus is negative and significant (γ5 = –.101, p <.05). Thus, although a firm's accruals management amplifies the positive influence of the size of a firm's sales force downsizing on firm-idiosyncratic risk, this influence is mitigated by CEO external focus.
Second, we conduct a verification test of our hypotheses by employing the sales force downsizing estimate that draws on data from annual reports/10-Ks. The results confirm our findings for all hypotheses, given that estimated coefficients maintain their expected signs and are statistically significant (see complete Model 3 in Table 4).
Graph
Table 4. Verification of Hypothesis Testing Results (Based on Annual Reports/10-Ks Data): Ordinary Least Squares Fixed-Effects Regression Models of Firm-Idiosyncratic Risk.
| Model 1:Main Effects | Model 2:Two-Way Interactions | Model 3:Three-Way Interactions | Hypothesis |
|---|
| Main Variables | | | | | | | |
| SFSFDAR/10-Ks | .141*** | (.040) | .039 | (.032) | .144*** | (.043) | H1 |
| FPMF | | | .028** | (.013) | .028** | (.013) | |
| SFSFDAR/10-Ks × FPMF | | | .063* | (.038) | .070* | (.041) | H2 |
| ADV | | | | | .145*** | (.042) | |
| SFSFDAR/10-Ks × ADV | | | | | .191*** | (.035) | |
| FPMF ×ADV | | | | | .002 | (.011) | |
| SFSFDAR/10-Ks × FPMF × ADV | | | | | –.101** | (.050) | H3 |
| FAM | | | .077*** | (.028) | .072*** | (.028) | |
| SFSFDAR/10-Ks × FAM | | | .110** | (.043) | .211*** | (.037) | H4 |
| EXT | | | | | .009 | (.013) | |
| SFSFDAR/10-Ks × EXT | | | | | –.022 | (.022) | |
| FAM × EXT | | | | | .054* | (.029) | |
| SFSFDAR/10-Ks × FAM × EXT | | | | | –.142*** | (.041) | H5 |
| Covariates | | | | | | | |
| R&D | –1.466*** | (.532) | –1.816*** | (.567) | –1.810*** | (.605) | |
| R&D_i | –.285*** | (.103) | –.349 | (.109) | –.327*** | (.111) | |
| ADV_i | –.001 | (.074) | .018 | (.078) | .147* | (.080) | |
| ROA | –3.362*** | (.357) | –3.422*** | (.347) | –3.207*** | (.350) | |
| LEV | .645*** | (.229) | .571*** | (.208) | .500** | (.208) | |
| LIQ | –.051** | (.023) | –.058** | (.024) | –.063*** | (.024) | |
| SIZE | –.189*** | (.048) | –.179*** | (.049) | –.184*** | (.051) | |
| IVOLAT | .030 | (.237) | –.048 | (.239) | –.186 | (.235) | |
| IGROW | –.086 | (.059) | –.093* | (.055) | –.072 | (.056) | |
| CF | .034** | (.017) | .063*** | (.018) | .048** | (.020) | |
| IMR | .456*** | (.140) | .320** | (.144) | .265* | (.149) | |
| Constant | 4.179*** | (.412) | 4.200 | (.402) | 4.210*** | (.412) | |
| Firm fixed effects | Yes | Yes | Yes | |
| Year effects | Yes | Yes | Yes | |
| Observations | 2,303 | 2,303 | 2,303 | |
| R2 | .401 | .414 | .440 | |
| Adjusted R2 | .394 | .405 | .429 | |
- 100022242918805050 *p <.10.
- 110022242918805050 **p <.05.
- 120022242918805050 ***p <.01.
- 130022242918805050 Notes: We used two-tailed significance tests. The first entry within each cell corresponds to estimated coefficients, followed by robust standard errors in parentheses. Reported effects for the main variables are standardized to facilitate interpretation of interaction effects. SFSFDAR/10-Ks = size of firm's sales force downsizing from annual reports/10-Ks (%); FPMF = firm's product market fluidity; ADV = firm's advertising intensity; FAM = firm's accruals management; EXT = CEO external focus; R&D = firm's research and development expenditures to firm's sales; R&D_i = firm's research and development dummy; ADV_i = firm's advertising dummy; ROA = firm's return on assets; LEV = firm's financial leverage; LIQ = firm's available liquidity; SIZE = firm size; IVOLAT = industry volatility; IGROW = industry growth; CF = residual term of control function model; IMR = inverse Mills ratio estimated using Heckman's selection model.
We estimate the counterfactual event (i.e., what would have happened if a firm did not downsize its sales force) by employing the nearest-neighbor matching procedure (see Web Appendix 7). The results confirm our findings for the main-effect hypothesis (i.e., H1).
We repeat the analysis using firm random-effects specifications with industry fixed effects to capture any unobserved industry characteristics that may influence our findings. The results confirm our findings for all hypotheses (see Model 1, Table 8.1, Web Appendix 8).
It may be overly restrictive to use firm-idiosyncratic risk based on the four-factor model ([51]). Therefore, we tested our hypotheses using firm total stock risk (i.e., standard deviation of a firm's daily stock returns), which is a descriptive statistic and is free of any factor model error. The results confirm our findings for all hypotheses (see Model 2, Table 8.1, Web Appendix 8).
Given that investors are monitoring whether marketing expenditure cuts or increases are optimal ([34]), we use a spline specification ([20]) to explore the possibility that the effect of sales force upsizing on firm-idiosyncratic risk is equivalent to (i.e., symmetric) or distinct from (i.e., asymmetric) the effect of sales force downsizing ([46]). We construct the size of a firm's sales force upsizing in the same way that we constructed the size of a firm's sales force downsizing. Specifically, we operationalize the size of a firm's sales force upsizing as the percentage change in sales force size between the year of the publication date (year t) and the year that precedes that date (year t − 1), over the sales force size at year t − 1, when we observe a sales force increase (i.e., positive change). For firm-year observations with sales force downsizing (i.e., negative change) or no change, we set the upsizing variable equal to zero. Accordingly, because the size of firm's sales force downsizing is also included as a separate variable in this set of analysis, we apply a simultaneous three-level formulation—that is, sales force increase, decrease, and no change.
The additional analysis results show that the main effect of the size of a firm's sales force upsizing on firm-idiosyncratic risk is positive and significant (see Model 3, Table 9.1, Web Appendix 9). The F-test of the estimated downsizing (γdownsizing =.072, p <.01) and upsizing (γupsizing =.050, p <.01) slopes reveals no statistical difference between sales force downsizing and upsizing effects on firm-idiosyncratic risk. Furthermore, none of the interactions between sales force upsizing and hypothesized moderators were significant. Importantly, all main and interaction effects concerning the size of a firm's sales force downsizing remain significant and equivalent to our main hypothesis testing results (see Table 3), providing additional evidence of the robustness of our main findings. We discuss these findings in the "Managerial Implications" subsection.
There is growing recognition that sales force downsizing constitutes a challenging marketing resource reduction with major implications for firms' operational outcomes ([22]; [26]). However, prior work has provided little guidance on its compelling impact on financial-market performance. Here, we develop a novel conceptual framework to ( 1) establish the link between the size of a firm's sales force downsizing and firm-idiosyncratic risk, ( 2) highlight investors' screening cues that influence the impact of downsizing on risk, and ( 3) offer managerial levers to alleviate the effects of downsizing actions on risk.
We build from information economics theory to illustrate the financial-market value of strategic decisions affecting the sales force. Specifically, we propose that reducing the sales force jeopardizes customer relationships and demand opportunities, thereby increasing cash flow volatility and vulnerability—major drivers of investors' uncertainty about a firm's future performance and, thus, firm risk. Our work supports this argument and provides the first evidence that larger reductions of the sales force increase firm-idiosyncratic risk. This finding is important for multiple reasons. First, we expand the substantive domain of the marketing–finance interface by illuminating the role of the sales force as a strategic firm value lever, establishing new avenues that marketing influences shareholder value. Second, we broaden the theoretical framework of sales force strategy by conceptualizing the influence of sales force size changes on cash flow volatility. Establishing this theoretical mechanism should encourage future research to envision sales force decisions as financial-market investments. Theory development in this area can also provide a deeper understanding of a sales force's financial-market value by addressing the trade-offs and synergies of sales force actions, which accelerate, increase, or enhance the value of cash flows. Third, our findings establish the first empirical linkage between sales force downsizing and market performance metrics. This finding is especially critical given that the extant literature on sales force downsizing does not include market performance metrics (see Table 1.1, Web Appendix 1).
We also contribute to the marketing–finance literature by identifying information sources that affect investors' uncertainty regarding future cash flows. In particular, we introduce two key investor-focused screening variables—the firm's product market fluidity and the firm's accruals management—as information cues that intensify market fluctuations linked to sales force downsizing. On the one hand, product market fluidity informs investors about a downsizing firm's ability to compete in the future. A real-world instance of this phenomenon is apparent in the following quotation from an investor analyst after sales force downsizing at Novell ([ 2]): "My concern for the company is that others, such as Sun Microsystems and Microsoft, now have a broad range of software products that compete against Novell, and those companies have bigger sales forces. It's going to be a real uphill battle." Compared with measures of static competition (e.g., Herfindahl–Hirschman index), the more granular and forward-looking measure of product market fluidity improves our understanding of marketing's strategic impact across contexts. For example, if investors uncover a high degree of rival product entries—threatening future market share—downsizing will be perceived as a riskier strategy.
On the other hand, our focus on firms' accruals management as an intent-screening cue is also new to marketing, as extant research has primarily focused on investors' uncertainty regarding unobservable quality (e.g., [28]). In contrast, because accruals management entails a firm behavior, we highlight investors' uncertainty about the firm's unobservable behavior and reasoning behind sales force downsizing. This is an important extension given that intent-signaling research in management has demonstrated adverse effects for firms that do not disclose influential information (e.g., [ 1]). We find that a lack of transparency in financial statements (i.e., greater accruals management) clouds investors' reasoning about managers' decision to downsize the sales force, making it difficult to accurately evaluate such strategic marketing actions in financial markets. Thus, even if a sales force reduction aims to improve efficiency, investors may still feel uncertain regarding the firm's intentions. This uncertainty may have even more distal consequences, because inaccurate capital allocation in financial markets may lead managers to respond with suboptimal internal investment decisions ([47]).
The findings on both these screening cues help illustrate how investors' evaluations influence the relationship between strategic marketing decisions and firm value. To our knowledge, we are among the first in the marketing–finance literature to identify investors' screening cues. This opens up an exciting area of research because it provides a theoretical basis to understand which information elements can influence investors' uncertainty and, thus, firm value. Furthermore, by examining investors' screening cues, we answer calls for research to examine investors as "customers" in the marketing–finance interface ([24]), providing an important step forward in developing theoretical insights on the financial value of marketing strategy decisions. It is also important to note that while this focus on investors' screening cues helps us untangle the mixed findings often associated with downsizing (see [12]), future research could extend our conceptualization into other marketing–finance domains by including investor cues within a screening framework.
Finally, our study extends marketing theory by offering strategies for firms to address investor uncertainty following a major firm change. Specifically, we shed light on two firm-controlled mechanisms—the firm's advertising intensity and CEO external focus—that can mitigate the effects of sales force layoffs on stock return volatility. Recent research has demonstrated that advertising expenditures constitute an impactful managerial action that helps signal firm quality during negative events such as product recalls ([19]). We broaden this theoretical rationale by showing that advertising, in the face of high rival product entries, sends a positive quality signal to investors during sales force downsizing. Furthermore, we break new theoretical ground by introducing the firm's intent signals as a mechanism to clarify the strategic direction of the firm during a downsizing. Given that the investment community closely follows many CEOs for insights into their thinking ([53]), the intentions signaled through MD&A reports should have a critical impact on how marketing resource changes are interpreted. Our findings support this notion by showing that CEO external focus, in the face of high accruals management, provides clarity to investors during sales force downsizing.
Our findings deepen managers' understanding on the relevance of sales force decisions for Wall Street. [51]) point out that idiosyncratic risk is a key metric used by financial analysts to issue the risk ratings of stocks. Idiosyncratic risk also matters to investors because it lowers subsequent risk-adjusted returns. As a result, identifying sales force changes that influence idiosyncratic risk is important because managers can use the sales force as a lever within the firm's risk-management and financing strategy. For example, managers can quantify the economic significance (i.e., cost of capital) of downsizing by including risk in their decision metrics. From our study, a one-standard-deviation decrease in sales force size (7.5%) increases daily firm-idiosyncratic risk by.076%. Relative to the variability of idiosyncratic risk (SD = 1.141), this represents a 6.7% influence, which is substantial enough to significantly influence a firm's cost of capital—a significant burden for most firms ([42]). To illustrate, previous research in finance finds that a 1% increase in daily idiosyncratic risk increases annualized corporate bond yields by 222 basis points ([ 9]), meaning that a 7.5% sales force downsizing would translate into an increase of 17 basis points (.076% × 222) in yearly borrowing rates for the firm. Moreover, our counterfactual analysis shows that if a downsizing firm elected not to reduce its sales force, it would have avoided a.46% increase in risk (i.e., an increase of 102 basis points in yearly borrowing rates).
Managers can use these findings to make more informed resource allocation decisions for managing cost of capital as well as other downstream effects of firm-idiosyncratic risk. In particular, our findings suggest that sales force management decisions driven by cost management concerns actually have unintended consequences that will offset any potential cost efficiency gains. This is because laying off the sales force increases the firm's cost of capital. To explore this issue, we collected wage data for the sales roles that were most represented in our sample—that is, sales roles in advertising, insurance, financial services, manufacturing, other services, and sales engineering ([ 7]). Using a mean annual wage of $68,247 for salespeople across our sample in these roles, we find that a one-standard-deviation reduction (566 salespeople) would provide $38.6 million in short-term cost savings. As mentioned previously, a one-standard-deviation sales force reduction translates into a 17-basis points increase in cost of borrowing. Thus, for the average firm in our sample, with total liabilities equal to $10,016 million, the 17 basis points translate to an increase of $17.03 million in financial expenses. As such, the $38.6 million in cost savings are actually only $21.57 million, without taking into account other important downstream effects (i.e., cost of equity). Obviously, managers who make decisions about sales force changes without considering these nuanced effects would significantly overestimate cost savings from sales force layoffs. Accordingly, marketing leaders are advised to communicate the expected increase in cost of capital with other C-suite leaders (e.g., CEOs, chief financial officers) as important input into management decision making related to sales force layoffs.
We also urge firm leaders to conduct rigorous research to ascertain investor perceptions about future product market competition by utilizing the publicly available data on the product market fluidity metric (see [25]). This data can help leaders ascertain whether there are cues that signal an inability to differentiate from competition, and, thus, map out specific strategies to mitigate risk during downsizing. In particular, marketing leaders can coordinate advertising campaigns during sales force downsizing to address investors' concerns, especially when the firm faces high levels of rapid changes in its product market. Using data from our sample, we find that, for firms experiencing high rival product entries and low advertising spending, a 7.5% sales force reduction increases risk by.20%, versus only.10% for firms experiencing high rival product entries and high advertising spending. This translates into a savings of 22 basis points in borrowing costs (44 vs. 22). Interestingly, the risk-mitigating role of advertising fades when product fluidity is not high, indicating that there are specific conditions when advertising offers a tangible managerial benefit.
Our findings also suggest that CEOs must focus on the intent signals communicated through MD&As, given that such signals have a critical impact on how downsizing is interpreted. Specifically, elaboration of a strategic focus directed at external entities (e.g., customers, competitors) through the MD&A is a critical process that sends investors key information that helps them ascertain the current and future state of cash flows, thereby dampening their uncertainty linked to downsizing in the face of high accruals management. In our sample, for instance, we find that for firms with greater accruals management but low CEO external focus, a 7.5% sales force reduction increases risk by.19% (42 basis points), versus a risk increase of only.13% (29 basis points) for firms with high CEO external focus. These findings are noteworthy and confirm prior work suggesting that the investment community closely follows CEOs for insights into their thinking ([53]).
Finally, we highlight to managers that while marketing investments (e.g., sales force upsizing) are often implicitly assumed to increase sales, expected cash flows, and consequently shareholder wealth, this intuition is not always valid ([41]). In contrast, our findings indicate that upsizing increases idiosyncratic risk, albeit through different mechanisms than downsizing. Specifically, results from our additional analysis indicate that a 7.5% increase in sales force size increases idiosyncratic risk by.050% (11 basis points). Yet these findings also show that none of the interactions for sales force upsizing were significant, indicating that upsizing influences risk through different mechanisms than downsizing. This is because marketing investments often change the probability distribution of a firm's sales revenues and, thus, its working capital (cash) needs, which together determine the expected cash flows for investors ([41]). We therefore caution managers to account for the influence of upsizing on risk and recognize that the signaling cues we propose for downsizing do not mitigate risk when growing the sales force.
Our study is subject to some limitations that present fruitful avenues for future research. First, we do not specify causes of sales force downsizing, yet these causes may bias investors' evaluations of downsizing. Future research could delineate between different drivers of sales force downsizing (e.g., economic environment, firm strategy) to understand the market performance tied to these strategic decisions. Second, we studied firms in the Selling Power list, which limits our findings to firms with larger sales forces. Thus, future research should confirm the robustness of our findings for firms with smaller sales forces. Third, while our study substantiates the effect of sales force upsizing on firm risk, there is potential for future research to identify signaling cues that mitigate upsizing's influence. Drawing on the results of our additional analysis, we believe that the signaling mechanisms that help reduce investors' uncertainty from upsizing are different from those for downsizing and may, for instance, refer to corporate governance or cost efficiency strategies. Fourth, while we draw on prior theoretical work (e.g., [11]) to focus on firm signals that mitigate investors' screening cues (i.e., three-way interactions), it would be useful to understand which firm signals act independently of investor evaluations (i.e., as two-way interactions) to influence the relationship between sales force size changes and firm risk. Finally, future research should examine the differences in firm risk between early and later rounds of downsizing, given that a single downsizing decision in a given year may involve multiple rounds of downsizing.[13] In conclusion, we hope that our work will spark scholarly interest for future research in this vital area of marketing practice.
Supplemental Material, DS_10.1177_0022242918805059 - Sales Force Downsizing and Firm-Idiosyncratic Risk: The Contingent Role of Investors' Screening and Firm's Signaling Processes
Supplemental Material, DS_10.1177_0022242918805059 for Sales Force Downsizing and Firm-Idiosyncratic Risk: The Contingent Role of Investors' Screening and Firm's Signaling Processes by Nikolaos G. Panagopoulos, Ryan Mullins, and Panagiotis Avramidis in Journal of Marketing
Footnotes 1 Author ContributionsAll authors contributed equally, and their names appear in reverse alphabetical order.
2 Area EditorChristine Moorman served as area editor for this article.
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
5 Online supplement: https://doi.org/10.1177/0022242918805059
6 1Given that investors' interpretation of the downsizing signal may not necessarily be unanimous, the use of stock returns cannot capture the resulting variability in interpretation. We thank the area editor and two anonymous reviewers for contributing this insight.
7 2According to Gerhard Gschwandtner (Selling Power's founder/CEO), Selling Power connects with firms at multiple levels (e.g., human resources, communications, Vice President of Sales) to get the most accurate data on sales force sizing counts (email communication on April 6, 2017; available upon request).
8 3We employ a firm's 10-K report whenever an annual report is not available.
9 4We use the absolute value (i.e., exclusion of the negative sign) of change to facilitate the interpretation of the results.
5We are thankful to the area editor and one anonymous reviewer for their insightful suggestions on this topic.
6In some cases, the 10-K was not available, and thus we collected the 10-K405 filing, which is not different in substance from a 10-K (see https://help.edgar-online.com/edgar/formtypes.asp).
7We are thankful to the JM review team for suggesting this set of analyses.
8We are thankful to an anonymous reviewer for contributing this insightful suggestion.
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By Nikolaos G. Panagopoulos; Ryan Mullins and Panagiotis Avramidis
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Record: 152- Sales Representative Departures and Customer Reassignment Strategies in Business-to-Business Markets. By: Shi, Huanhuan; Sridhar, Shrihari; Grewal, Rajdeep; Lilien, Gary. Journal of Marketing. Mar2017, Vol. 81 Issue 2, p25-44. 20p. 1 Diagram, 12 Charts, 4 Graphs. DOI: 10.1509/jm.15.0358.
- Database:
- Business Source Complete
Sales Representative Departures and Customer Reassignment Strategies in Business-to-Business Markets
Online Supplement: http://dx.doi.org/10.1509/jm.15.0358 ales representatives (reps), as the faces of the selling firm to buyers, are crucial links between buyers and sellers
Sin business-to-business (B2B) markets. Their ability to link the needs of potential customers (accounts) with the offerings or solutions provided by their firm is a key determinant of the financial performance of B2B firms (Ahearne et al. 2010; Kumar, Sunder, and Leone 2014). Zoltners, Sinha, and Lorimer (2012, p. 521) estimate that U.S. B2B firms spend approximately $800 billion annually on sales forces, or roughly 7% of sales, entrusting these salespeople “with a company’s most important asset: its relationship with its customers. Often
salespeople have considerable control over this relationship; to some customers, the salesperson is the company.” Thus, when a sales rep leaves voluntarily, the potentially adverse financial consequences for the firm could be significant (Bendapudi and Leone 2002; Palmatier, Scheer, and Steenkamp 2007). Sales rep turnover is not only problematic but also relatively common; according to the U.S. Bureau of Labor Statistics (2013), the annual turnover rate among B2B sales reps is 22%, exposing approximately $1.6 trillion1 in customer sales to the risk of sales rep turnover. This risk arises because sales managers must reassign the customers of a departing sales rep to one or multiple replacements in the hopes that these replacement reps can reestablish and grow the customer relationships—a deeply challenging task.
Considering the seriousness and prevalence of the issue of sales rep turnover, we address two key questions in this research: ( 1) What is the magnitude of the causal effect of sales rep departures on customer-level revenue? And ( 2) What relative effectiveness do alternative sales rep replacement strategies offer? The answers can assist firms that need effective reactive strategies to find appropriate replacement sales reps. We focus on voluntary turnover, such that the sales rep leaves on his or her own accord, as opposed to reps who are terminated by the firm. Specifically, we establish a causal link between sales rep transition and customer sales by conducting a difference-in-differences analysis of sales changes one year after versus one year before a sales rep’s turnover. The transition refers to the combination of the exit of a previous sales rep and the reassignment of the affected customers to other sales reps. As a benchmark, we consider any change in sales among matched customers that did not experience any sales rep transition. We control for the endogeneity induced by the nonrandom departure and nonrandom reassignment strategy undertaken by the sales manager. In addition, to quantify heterogeneity in sales rep transition effects, we differentiate the causal effects in situations in which managers reassign the customers of departing sales reps to either new hires or existing sales reps. Finally, for reassignment to an existing sales rep, we study how the transition is moderated by the replacement reps’ similar customer base (proxy for similar industry experience) and past performance level (proxy for selling ability), given sales managers’ likely beliefs about what constitutes a good replacement (e.g., Gardner 2005; Groysberg, Lee, and Abrahams 2010).
We test our proposed model with customer-level data from a leading U.S. distributor of electrical component products. Using information about departing and assigned sales reps for a subset of customers who experienced sales rep transition and comparable data for a large set of customers who experienced no sales rep turnover, we find a 13.2%–17.6% annual decease in customer sales, on average, from sales rep transition in the firm in question. Customers reassigned to new hires exhibited a 21.6% sales loss, and those reassigned to existing sales reps exhibited an 11.0% sales loss. The firm in question thus could expect sales rep transitions to lead to $10.65 million–$14.20 million in sales losses. However, over a longer time window (i.e., ten quarters after departure), the sales losses among customers reassigned to both new hires and existing sales reps begin to diminish (e.g., for new hires, 12.5% sales loss over ten quarters vs. 21.6% for one year). of sales rep transition and the effectiveness of replacement strategies might not apply directly to other organizations. Yet our approach can be generalized to other sales organizations with different selling processes (e.g., team selling), and sales managers can use our method to evaluate the impacts of sales rep transitions and thereby design better replacement strategies.
In the next section, we discuss the conceptual background for our work and present our research questions. Then, we describe the institutional setting and data, model setup, and identification strategies. Finally, we present the results and discuss their implications.
Conceptual Background and Research Questions
Our goal is to quantify the effects of sales rep transition on customer-level performance. We define sales rep transition as a combination of two events: the departure of a sales rep from a firm (and customer account), often referred to as turnover, and the reassignment of the customer account to a replacement sales rep. We first summarize the literature on financial cost of sales rep departure and sales rep assignment. Subsequently, we draw from the literature on sales rep capabilities, as well as multilevel trust between buyers and sellers (i.e., interfirm and interpersonal trust), to develop research questions concerning the effects of sales rep transition on customer-level revenues.
Review of Relevant Literature
Financial cost of sales rep departure. The financial cost associated with sales rep turnover consists of both direct (e.g., recruiting and training new or replacement sales reps; Chandrashekaran et al. 2000; Churchill et al. 1985) and indirect (e.g., loss of full realization of future revenues from customers served by departing sales reps; Bendapudi and Leone 2002; Boles et al. 2012) costs. Direct costs are associated with real cash outflows and are easy to quantify; indirect costs are intangible (O’Connell and Kung 2007; Richardson 1999). To quantify these indirect costs, we turn to Darmon (1990), who assesses direct costs at the sales rep level using accounting data but measures indirect costs using managerially estimated data. Darmon identifies differential skills (potential sales loss when higher performers are replaced by low performers) as the largest cost component. However, because the indirect costs are based on managers’ subjective estimation, this quantification cannot be verified, thus offering limited causal inferences. To address these limitations, we rely on objective sales data and perform our analysis at the customer level, which strengthens the causal inference because we can match and control for customer characteristics.
Organizational behavior research has also investigated the financial impact of turnover and the relationship between employee turnover rates and performance. As a general finding, turnover rates are negatively associated with firm performance (e.g., Kacmar et al. 2006; Shaw, Gupta, and Delery 2005; Subramony and Holtom 2012; Ton and Huckman 2008). However, such literature aggregates the unit of analysis (i.e., store or firm level) and cannot address the causal impact of sales rep departure on individual customers.
Sales rep reassignment research. After a sales rep departs, the firm usually reassigns existing customers to other sales reps (e.g., Bendapudi and Leone 2002; Richardson 1999), either by assigning all of them to one replacement rep or by splitting the customer base of the departing sales rep and assigning customers to multiple replacement reps. We focus on the latter approach, which matches the strategy adopted by the focal firm in our empirical setting. The multiple replacement reps might currently work for the firm or could be new to the firm (new hires). Finding appropriate reassignments is vital for a smooth relationship transition, yet most research in this area is conceptual or anecdotal (e.g., Bendapudi and Leone 2001, 2002). Sales managers seem to assume that effective replacement sales reps should have a similar industry background, as demonstrated in the widespread practice of hiring from competitors (Gardner 2005). They also prefer candidates who have demonstrated high past performance, leading to the practice of reassigning accounts to top performers (Groysberg, Lee, and Abrahams 2010). However, the effectiveness of these customer assignment strategies has not been empirically examined.
Furthermore, sales rep effectiveness literature has uncovered some explanatory variables related to sales rep performance (e.g., Farrell and Hakstian 2001; Weitz, Sujan, and Sujan 1986). Most work in this area has focused on the characteristics of ongoing customer–sales rep relationships (e.g., Farrell and Hakstian 2001; Weitz, Sujan, and Sujan 1986), but sales reps’ characteristics (e.g., domain knowledge, selling skills) may matter in relationship transition contexts as well. Ahearne and Lam (2012) even call for more dynamic views of customer–sales
rep relationships. Thus, we empirically examine the effectiveness of alternative customer assignment strategies (new hires vs. existing sales reps) and investigate how sales outcomes vary by newly assigned sales reps’ observable characteristics (e.g., past performance, similarity to departing sales reps). We summarize these research gaps and our attempts to address them in Table 1.
TABLE:
| | Prior Research | Current Research |
|---|
| Sales Rep Turnover Research |
| Focus | Most research has focused on antecedents (e.g., Brown and Peterson 1993; Johnston et al. 1990; Trevor 2001) | The current research focuses on quantifying the economic impact |
| Level of analysis | Firm- or business unit–level (Subramony and Holtom 2012; Ton and Huckman 2008), regional-level (Richardson 1999), or sales rep–level (Darmon 1990) analyses | Customer-level analysis, which enables us to derive causal inferences by matching and controlling for customer characteristics |
| Data source | Managerially estimated data (Darmon 1990) | Objective customer-level sales data and objective estimates of the effect of sales rep departures on customer sales |
| Sales Rep Replacement Research |
| Approach | Primarily conceptual or anecdotal (Bendapudi and Leone 2001, 2002) | Empirical analysis |
| Scope | To recognize the importance of selecting and hiring replacement sales reps (Bendapudi and Leone 2002; Darmon 1990) | To examine and differentiate the effects of customer assignments to new hires versus existing sales reps |
| Replacement/assignment strategies | Conceptual discussions of the goal of replacement strategies (e.g., increase acceptability of replacement employees; Bendapudi and Leone 2002); no examination of specific replacement strategies (i.e., how to select appropriate replacement reps) | We propose two dimensions to describe assignment strategies–sales reps’ performance and similarity–and empirically differentiate the effects of the two dimensions. |
| Performance outcome | No performance outcomes examined | We investigate how different assignment strategies affect objective sales performance. |
Quantifying the Causal Effect of Sales Rep Transition on Customer Sales
Research in interorganizational relationships has provided theoretical perspectives on the effect of sales rep transitions on firm performance. That is, interfirm trust and commitment drive strong interfirm relationships, which lead to enhanced sales and profits (Morgan and Hunt 1994; Palmatier et al. 2006). Interfirm trust and commitment also operate at several levels, so Doney and Cannon (1997) and Zaheer, McEvily, and Perrone (1998) consider the firm level (e.g., buyer and seller firms) and the interpersonal level (e.g., sales reps and buying personnel). Fang et al. (2008) identify three levels of trust—firms’ mutual trust, agency trust between a firm and its own representatives, and the intraentity trust between firms’ representatives (similar to interpersonal trust in Zaheer, McEvily, and Perrone [1998])— and establish the differential influences of these three levels of trust in international joint venture performance.
Applying the multilevel trust–commitment framework to B2B sales rep departures, we suggest that a sales rep transition as a result of turnover will alter the trust between a buyer and its sales rep, which in turn will cause a change in customer sales. Interpersonal trust between a departing sales rep and buying personnel also tends to be cultivated through multiple interactions over time (Zaheer, McEvily, and Perrone 1998), so it is difficult to replicate quickly by replacement reps. When an equally or a less-qualified sales rep is assigned as the replacement, (s)he cannot achieve the same relational strength with customers immediately. The loss of the longterm contact point thus may lower commitment, trust, reciprocity norms, and exchange efficiency (Palmatier 2008; Zoltners, Sinha, and Lorimer 2011), resulting in decreased sales. Conversely, if the replacement is a highly qualified sales rep, capable of surpassing the relationship quality that the previous sales rep had maintained with customers (Bendapudi and Leone 2002; Darmon 1990), customer sales might increase.
Furthermore, the departing sales reps’ ability to exploit customer trust before departure may affect customer sales changes too. In particular, if they are subject to commissionbased compensation plans, departing sales reps might leverage the trust they have built up with customers to pull orders from the future and earn a higher commission before they leave (Steenburgh 2008). This borrowing from the future lowers sales levels in the postdeparture periods, assuming customer purchasing needs are stable. However, if departing sales reps have not been able to maintain trusting relationships with their customers, customers may anticipate a more qualified replacement and hold their purchases until the replacement arrives, which could produce a sales increase after the transition. Multilevel trust–commitment theory thus predicts changes in customer sales, according to the capabilities of the departing and replacement sales reps, but few empirical assessments quantify the causal impact of sales rep departure on firm performance. Therefore, we ask,
RQ1: To what extent are customer sales affected by sales rep transitions?
Effectiveness of new hires versus existing sales reps as replacements. A replacement rep will likely take over the interpersonal relationship between the buyer and the departed sales rep, in the hope of mitigating any sales loss from the transition or even increasing sales by realizing the additional customer potential. Therefore, the sales changes induced by a transition likely differ according to the identity of the replacement. In particular, several factors may put new hires at a performance disadvantage, relative to existing reps, during sales rep transitions. First, new hires generally have less customerspecific sales competence than existing sales reps. They might gain product and procedural knowledge through training, but they are unlikely to be immediately equipped with an understanding of the firm’s customers’ unique needs. Second, new hires face higher pressure to prove themselves than existing sales reps; therefore, they are more likely to engage in shortterm, sales-oriented behaviors (Boichuk et al. 2014). Such behaviors may have damaging effects on the development of long-term trust with customers. Third, new hires suffer from low agency trust within their own hiring firm (Fang et al. 2008): Relative to existing sales reps, new hires have weaker relationships with peers, sales managers, and other firm functions. Therefore, they may receive less support or resources for their relationship-building activities. In Fang et al.’s (2008) trust– commitment framework, new hires tend to display lower interpersonal and agency trust levels than existing sales reps, suggesting the threat of poorer sales outcomes.
However, new hires also could be at an advantage in some transition situations (Cron 1984; Zoltners and Lorimer 2000). First, if a firm’s sales processes change as a result of the transition, new hires recruited for the specific needs of the new business are more competent than existing reps who are accustomed to the old processes. For example, media companies’ transition from print-focused to digital-focused media requires sales reps to be equipped with extensive digital knowledge so that they can convey the value of digital media platforms to customers (Sridhar and Sriram 2015). New hires who already have such knowledge may be more effective at earning customer trust than existing sales reps. Second, Zoltners and Lorimer (2000) note that a new sales rep approaches the customer’s needs with a fresh perspective, which may reveal some new ways to gain customer trust and increase sales. Third, the greater performance pressures on new hires could encourage them to exert more effort to build trusting relationships with customers (Cron 1984). The reasonable arguments on both sides thus lead us to propose the following research question:
RQ2a: Are sales rep transitions handled more effectively by new hires or by existing sales reps?
Existing reps’ similarity. The similarity of the customer bases maintained by the departing and the existing replacement sales reps should affect the sales changes that result from a sales rep transition. In B2B settings, a sales rep’s customer base is a proxy for domain-specific knowledge about the selling situations (Weitz, Sujan, and Sujan 1986). For example, customers from various industries differ in their decision processes, purchasing needs, and buying frequency. When a sales rep previously has worked with customers from a particular industry, that experience offers a good indicator of the rep’s knowledge of associated selling situations. Most B2B firms also use the customer’s industry as a key segmentation variable, which supports the appropriateness of using the industry composition of the customer base to represent a sales rep’s domain knowledge. Accordingly, and in line with the multilevel trust–commitment framework, we predict that similar customer bases maintained by the departing and assigned sales reps help mitigate sales losses because the competency of the newly assigned sales reps is evident and the knowledge transfer is more efficient across these entities with their similar domain knowledge (Argote and Ingram 2000; Lane and Lubatkin 1998). In contrast, if the replacement sales rep is dissimilar to the departing sales rep, (s)he may succeed better with crossselling by leveraging his or her unique knowledge structure. Therefore, we test empirically whether customer base similarity affects sales changes during transition. Formally,
RQ2b: Are sales rep transitions handled more effectively by replacement sales reps who have customer bases that are similar or dissimilar to those of departing reps?
Existing reps’ selling ability. Prior research has confirmed the crucial role of selling ability for improving selling effectiveness in ongoing relationships (e.g., Baldauf and Cravens 2002; Weitz, Sujan, and Sujan 1986). Because a sales rep’s past performance is a good indicator of his or her selling capability (Leigh et al. 2014; Verbeke, Dietz, and Verwaal 2011), we use this proxy for selling ability. Sales reps with high selling ability usually can build strong customer trust—whether through their strong selling skills or enhanced selling activities—relative to average sales reps, which then should lead to better selling performance. However, motivation also can affect customerspecific selling performance (Sabnis et al. 2013), such that a lowperforming sales rep might be more motivated to devote effort to serving new accounts and thus could achieve greater trust and higher sales than a more skilled sales rep. Considering these arguments on both sides, we propose the following:
RQ2c: Are sales rep transitions handled more effectively by higher- or lower-performing replacement sales reps?
Data
Empirical Context
We obtained data from a leading U.S.-based distributor of electrical component products. The firm uses a field-based sales force to sell to customers in six industry segments: construction, industrial, utility, commercial and government, original equipment manufacturers, and other. Inexperienced sales reps first serve as inside staff in the sales department for two years, providing administrative support to seasoned sales reps but not actively involved in prospecting or closing sales. Externally hired sales reps must have at least two years’ field sales experience. The selling task is individual; each sale is attributed to one sales rep, who receives significant commission-based compensation above the base salary.
We interviewed seven sales managers and the vice president of sales to learn how the firm deals with its sales rep turnover, which is approximately 15%, close to the industry average. Turnover occurs through both termination and voluntary departure, though the firm terminates sales reps mainly before or at the end of their two-year probation period and rarely fires seasoned sales reps. Instead, the firm believes that the commission structures it uses helps retain successful sales reps and incentivizes them to perform.2 The sales managers also indicated that the firm’s relationships with its customers are built and maintained through contacts with sales reps. A noncompete agreement prevents departing sales reps legally from taking any customers with them if they are hired by a competitor.
When a sales rep departs, the regional sales manager reassigns the affected customer accounts to other sales reps, who can be either new hires or existing sales reps. No formal guidelines dictate the account reassignment process. Sales managers might reassign customers to several existing sales reps within the same regional office3 who likely have the technical know-how required to serve customers. In this case, sales managers attempt to reassign customers to existing sales reps whose customer industry portfolio is similar to that of the departing sales reps or who have demonstrated strong sales performance. To avoid overloading these existing sales reps, sales managers also replace the departing sales rep and hire a new rep who has similar industry exposure and an acceptable performance history. The reassignment and implementation process usually occurs one to four weeks after a departing sales rep informs the firm of the departure decision.
From the human resources department, we obtained identifiers and departure dates for 129 sales reps who left the firm in 2011. We combined this information with customerlevel sales transactional records from 2008 to 2013. A single transaction record contains identifiers for the customer, industry, sales region, and sales rep, as well as the invoice date and sales amount. The 830 customers served by the 129 departing sales reps generated an average of $73,206 per year per customer for the firm during 2008–2013. We also obtained data about 1,615 customers who transacted with the firm in the same period and were served by 550 sales reps, all of whom stayed with the firm through 2011. The latter group generated an average of $98,958 per year, per customer. Thus, we have data about customers who experienced sales rep transitions, including information from both before and after the sales rep departure (and reassignment), as well as data about a control group of customers who did not experience any sales rep transition.
Sample
The treatment group includes 830 customers who transacted with the firm during 2008–2013 and experienced a sales rep transition in 2011 (approximate data midpoint). Because we know the exact date of each sales rep’s departure, we constructed the predeparture period T1 as one year before that date and the postdeparture period T2 as one year after it. This oneyear pre- and postdeparture duration is long enough to absorb any interim customer sales shocks that might occur immediately after the transition (i.e., reassignment can take up to four weeks) and allow the replacement sales rep time to establish stable customer relationships. The effects also were robust across different lengths of the pre- and postdeparture periods.4 The control group consisted of customers whose sales reps did not depart during T1 or T2 but who engaged in at least one transaction in each period. To create our control group, we drew a stratified random sample of customers from 186 strata, constructed according to 31 sales regions and 6 industry segments. In each stratum, we randomly drew a sample that was approximately twice the size of the treatment group in that same stratum to ensure a sufficiently large sample for the matching estimator, which we present subsequently. The resulting sample included 1,615 control group customers. Thus, our final sample consisted of 2,445 customers: 830 in the treatment group and 1,615 in the control group. These 2,445 customers span 273 branches in 31 regions.
We collapsed invoice-level customer transaction data into two periods, T1 and T2, instead of using a more granular time frame (e.g., monthly, quarterly), for two reasons. First, disaggregation would lead to misrepresentations of sales changes because of the customers’ heterogeneous purchase cycles (see Figure 1). Second, multiple-period difference-in-differences specifications suffer from inconsistent standard error estimates as a result of serial correlation, which we address by collapsing the data into the pre- and postdeparture periods (Bertrand, Duflo, and Mullainathan 2004).
Measures
TABLE:
| Variables | Description |
|---|
| aA customer’s first successful transaction can be tracked to January 1, 2006. |
| bA sales rep’s first successful transaction can be tracked to April 1, 2008. When we used discretized measures (£4 quarters, 4–8 quarters, >8 quarters), the results in our model estimations remained similar. |
| cWe did not use sales rep sales performance measures in 2010 or afterward, to avoid potential simultaneity issues when the explanatory variables (i.e., sales performance) are constructed by dependent variables (i.e., customer sales). |
| Sales | Natural logarithm of total customer sales in period t (t = T1, T2) |
| Customer Relationship Tenurea | The number of quarters since the first transaction to the end of T1 |
| Customer Purchase Size | Regional share of customer sales in 2009, calculated as total sales to a customer in 2009 divided by the total annual sales in the region in which the customer is located |
| Sales Rep Tenureb | Number of quarters from the sales rep’s first transaction in the data to the end of T1 |
| Sales Rep Performancec | Sales rep’s (treatment group’s departing sales reps and control group’s sales reps) regional sales share in 2009, calculated as the sales rep’s annual sales divided by the total annual sales in the region in which the sales rep is located |
| Customer Sales Change 1 | Sales change between the first and second quarters of T1 (log sales of Q2 – log sales of Q1) |
| Customer Sales Change 2 | Sales change between the second and third quarters of T1 (log sales of Q3 – log sales of Q2) |
| Customer Sales Change 3 | Sales change between the third and fourth quarters of T1 (log sales of Q4 – log sales of Q3). |
| Sales Rep Performance Trend | Change of sales reps’ (treatment group’s departing sales reps and control group’s sales reps) regional sales share from 2009 to 2010. |
We define our measures in Table 2. The dependent variable Salesit is the natural logarithm of total sales in period t (t = T1, T2); the logarithmic transformation helps reduce the skewness, which is common when sales volume is the dependent variable, and gives us support points in the range (-‘, +‘). In Figure 2, we illustrate how T1 and T2 were constructed. We also include the following covariates to control for endogeneity (which we discuss in more detail in the “Method” subsection): ( 1) customer relationship tenure, measured as the number of quarters between the first transaction and the end of T1; ( 2) customer purchase size, reflecting the regional share of customer sales; ( 3) sales rep tenure, or the number of quarters between a sales rep’s first transaction to the end of T1; ( 4) sales rep performance, according to the sales rep’s regional sales share; ( 5) three customer sales trajectory variables that reflect the changes between different quarters of T1 (customer sales change 1 = log of Q2 sales - log of Q1 sales, customer sales change 2 = log of Q3 sales - log of Q2 sales, and customer sales change 3 = log of Q4 sales - log of Q3 sales); ( 6) sales rep performance trend, or the change in sales reps’ regional sales shares from 2009 to 2010; ( 7) customer industry dummies; ( 8) branch dummies; and ( 9) departurequarter dummies.
Descriptive Statistics and Model-Free Evidence
TABLE:
| | Correlations |
|---|
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|
| Notes: Entire sample n = 2,445. |
| 1. Sales (log of sales) | 1.00 | | | | | | | | |
| 2. Customer Relationship Tenure (quarters) | .18 | 1.00 | | | | | | | |
| 3. Customer Purchase Size (%) | .19 | .14 | 1.00 | | | | | | |
| 4. Sales Rep Performance (%) | .12 | .11 | .65 | 1.00 | | | | | |
| 5. Sales Rep Tenure (quarters) | .09 | .34 | .07 | .27 | 1.00 | | | | |
| 6. Customer Sales Change 1 | .03 | -.09 | .00 | .02 | -.03 | 1.00 | | | |
| 7. Customer Sales Change 2 | .02 | -.10 | -.01 | -.01 | -.04 | -.43 | 1.00 | | |
| 8. Customer Sales Change 3 | .01 | -.08 | -.02 | .01 | -.07 | -.04 | -.43 | 1.00 | |
| 9. Sales Rep Performance Trend | -.04 | -.07 | -.03 | -.20 | -.50 | .00 | .01 | .03 | 1.00 |
| Summary Statistics |
| Mean | 10.45 | 14.24 | .15 | 2.65 | 11.11 | .25 | .48 | .78 | 1.32 |
| SD | 1.43 | 7.84 | .94 | 4.77 | 3.70 | 5.48 | 5.88 | 6.08 | 3.76 |
| Min | 8.01 | 2.00 | .00 | .00 | 2.00 | -12.98 | -12.79 | -13.02 | -14.44 |
| Max | 15.96 | 24.00 | 20.28 | 74.34 | 15.00 | 13.00 | 13.52 | 13.34 | 15.08 |
TABLE:
| Industry | Treatment Group | Control Group | z-Statistic |
|---|
| **p < .05. |
| Construction | 31.64 | 30.84 | .36 |
| Industrial | 31.15 | 31.20 | .03 |
| Utility | 16.04 | 16.02 | .01 |
| Government and commercial | 6.56 | 6.39 | .98 |
| Original equipment manufacturer | 13.62 | 13.25 | .25 |
| Others | .99 | 2.29 | 2.56** |
| N | 830 | 1,615 | |
Table 3 contains descriptive statistics for the measures. As Table 4 shows, the treatment and control groups are similar in their industry composition. Table 5 then shows the mean differences in the covariates between the control and treatment groups in T1. The standardized mean differences of all variables fall below the threshold of .25, indicating a good balance between the two groups (Ho et al. 2007).
As we have mentioned, we included a sales rep’s past sales performance and selling tenure as covariates of customer sales to control for nonrandomness in sales rep departure. We reveal that these covariates are associated with declines in customer sales, indicating the face validity of their selection (see the Web Appendix). Table 6 also contains the definitions and descriptive statistics for the similarity between a departing and an existing sales rep and the past performance of the existing sales rep. These statistics are based on the subgroup of customers assigned to existing sales reps in the treatment group.
TABLE:
| | Control Group | Treatment Group | | |
|---|
| | M | SD | M | SD | Mean Differencea | Standardized Mean Differenceb |
|---|
| **p < .05. |
| ***p < .01. |
| aStatistical significance of group mean difference t-test. |
| bDifference in means between the treatment and control groups divided by the standard deviation of the treatment group. Better balance across groups is required if this value is greater than .25 (Ho et al. 2007). |
| Sales | 10.412 | 1.455 | 10.591 | 1.301 | -.180*** | -.138 |
| Total Number of Transactions | 11.420 | 30.306 | 10.576 | 15.047 | .845 | .056 |
| Customer Relationship Tenure | 13.819 | 7.936 | 15.064 | 7.589 | -1.245*** | -.164 |
| Customer Purchase Size | .136 | .703 | .186 | 1.284 | -.05 | -.039 |
| Sales Rep Performance | 2.746 | 4.368 | 2.510 | 5.495 | .236 | .043 |
| Sales Rep Tenure | 10.993 | 3.851 | 11.340 | 3.378 | -.347** | -.103 |
| Customer Sales Change 1 | .032 | 5.345 | .688 | 5.702 | -.656*** | -.115 |
| Customer Sales Change 2 | .545 | 5.795 | .350 | 6.039 | .195 | .032 |
| Customer Sales Change 3 | 1.086 | 6.184 | .192 | 5.845 | .894*** | .153 |
| Sales Rep Performance Trend | 1.483 | 3.958 | .988 | 3.292 | .495*** | .150 |
| Number of observations | 1,615 | 830 | | |
To obtain model-free evidence, we first compare the sales trends of the treatment and control groups. As Figure 3 shows, the mean sales for the treatment group (natural logs) in the predeparture period (T1) is 10.59, higher than the 10.42 value in the postdeparture period (T2), which is a statistically significant difference (t = 2.59). For the control group, sales in T1 and T2 were 10.41 and 10.44, respectively, so the difference was not significant (t = .50). That is, customers who experienced a sales rep transition exhibited a downward sales trend. When we shortened the window to two quarters before and after departure, the sales trend patterns were similar (t-test statistics are reported in Web Appendix).5
TABLE:
| Variable Name | Definition | M | SD |
|---|
| Notes: Number of observations = 320. |
| Similarity | The similarity between a departing and an existing replacement sales rep is computed as the cosine of the angle of two 6 . 1 vectors. One vector represents the departing sales rep’s sales shares in each of six industries; the other vector represents the replacement sales rep’s shares in each of six industries. The value is bounded between 0 and 1, where 1 represents matching sales shares across six industries. | .723 | .374 |
| Performance | The past performance of an existing sales rep is measured as the regional sales rank in predeparture period (T1). The value is bounded between 0 and 1, where 1 represents the top performer. | .502 | .298 |
Method
Empirical Strategy to Estimate Causal Effects
To estimate the causal effect of a sales rep transition on customer sales, an ideal experiment would feature an event in which a randomly selected sales rep departs from the firm and a randomly assigned sales rep fills the void. In reality, however, sales rep departures tend to be nonrandom, such that a rep may leave for reasons related to his or her performance; moreover, replacement processes also are nonrandom because managers try to reassign customer accounts strategically. Therefore, to approximate the ideal experiment, we use a difference-in-differences estimate of a customer’s sales change, assuming random departure and assignment (vs. before departure), which we compare with the change in sales of a similar customer who does not experience a transition. To augment the specification, we also control for the nonrandomness of sales rep departure and account for nonrandomness in the sales rep replacement decision.
TABLE:
| | (1) | (2) | (3) | (4) | (5) |
|---|
| Variables | Without Covariates | Customer Characteristics | Sales Rep Characteristics | Predeparture Sales Trend | Heckman Correction |
|---|
| *p < .10. |
| **p < .05. |
| ***p < .01. |
| aThe treatment effect can be interpreted as follows: a treatment effect of -.194 means that effect size is a –17.6% sales loss using the following transformation formula: e(-.194) –1, owing to our use of log-transformed sales as outcome variables. Notes: Robust standard errors are in parentheses for Columns 1–4; bootstrapped standard errors are in parentheses in Column 5. For the randomeffects model, Sigma_u represents the standard deviation of the random intercept. Sigma_e represents the standard deviation of the errorterm. Rho represents the explained percentage of the total variance of the random intercept and error term by random intercept (Sigma_u2)/(Sigma_u2 + Sigma_e2). |
| Treatment Dummy | .180*** (.058) | .178** (.071) | .187** (.073) | .196*** (.073) | .193*** (.057) |
| Post_Period Dummy | .026 (.033) | .022 (.034) | .023 (.034) | .023 (.034) | .023 (.034) |
| Post_Period Dummy X Treatment Dummy | -.193*** (.053) | -.193*** (.054) | -.194*** (.054) | -.194***, a (.054) | -.194*** (.059) |
| Customer Relationship Tenure | | .029*** (.004) | .029*** (.004) | .033*** (.004) | .033*** (.003) |
| Customer Purchase Size | | .525*** (.115) | .491*** (.115) | .489*** (.118) | .490*** (.118) |
| Sales Rep Tenure | | | .002 (.012) | .017 (.015) | .018 (.012) |
| Sales Rep Selling Performance | | | .020** (.010) | .020* (.010) | .020* (.012) |
| Customer Sales Change 1 | | | | .019*** (.005) | .019*** (.004) |
| Customer Sales Change 2 | | | | .022*** (.005) | .022*** (.004) |
| Customer Sales Change 3 | | | | .018*** (.005) | .018*** (.004) |
| Sales Rep Performance Trend | | | | .016 (.011) | .016 (.010) |
| Constant | 10.412*** (.036) | 8.808*** (.504) | 8.725*** (.521) | 8.537*** (.553) | 8.453*** (.516) |
| IMR | | | | | -.067 (.089) |
| Branch Fixed Effects | No | Yes | Yes | Yes | Yes |
| Industry Fixed Effects | No | Yes | Yes | Yes | Yes |
| Quarter Fixed Effects | Yes | Yes | Yes | Yes | Yes |
| Sigma_u2 | – | .973 | .971 | .965 | .965 |
| Sigma_e | – | .901 | .899 | .899 | .899 |
| Rho | – | .538 | .538 | .535 | .535 |
| Observations | 4,890 | 4,890 | 4,890 | 4,890 | 4,890 |
| (Adjusted) R-square | .002 | .219 | .221 | .227 | .227 |
Difference-in-Differences Specification
We use a two-period, difference-in-differences specification to mimic the experimental ideal:
where the subscript i pertains to a customer, and the subscript
t refers to the period (predeparture period T1 or postdeparture period T2); Salesit is the log-transformed sales to customer i in period t; Treatmenti is the treatment group dummy that equals 1 if customer i is in the treatment group and 0 otherwise; Post_Periodt is the pre–post dummy that equals 1 if t is in T2 and 0 if it is in T1; and eit is a random error term. The coefficient b0 measures average sales from the control group in the predeparture period, b1 indicates the group mean difference of sales between the treatment and control groups, b2 reveals the mean difference of sales in T2 relative to T1, and b3 measures the causal effect of the sales rep transition. Equation 1 also controls for average sales trends and stable customer characteristics that may differ across groups.
Controlling for Nonrandomness in Sales Rep Departure
There are three main sources of nonrandomness in a sales rep’s departure decision. First, sales reps may leave for past performance reasons (e.g., Jackofsky 1984; Johnston et al. 1990). The poorest performers likely experience negative job satisfaction or low compensation; the best performers instead may be attracted to superior external career opportunities. A sales rep’s job satisfaction and market value are not observable (part of eit) but contribute to the treatment (i.e., departure), so failing to control for them would induce a correlation between the treatment and eit and bias the treatment effect. However, after controlling for sales reps’ past performance, their job satisfaction and market value should be distributed randomly. Therefore, we added covariates related to a sales rep’s past sales performance and tenure (Cotton and Tuttle 1986) to control for this source of nonrandomness. We also include sales reps’ past performance trends, which may correlate with their expectations about their future overall performance.6
TABLE:
| Variables | Dependent Variable: New Hire Dummy |
|---|
| **p < .05. |
| ***p < .01. |
| Local Unemployment Rate | .008 (.025) |
| Number of Existing Sales Reps | -.030*** (.012) |
| Percentage of Assignments to New Hires in Peer Branches of Same Region | .300** (.145) |
| Customer Relationship Tenure | -.004 (.007) |
| Customer Purchase Size | -.071 (.081) |
| Sales Rep Tenure | -.086*** (.022) |
| Sales Rep Performance | .007 (.019) |
| Customer Sales Change 1 | -.013 (.010) |
| Customer Sales Change 2 | -.013 (.010) |
| Customer Sales Change 3 | -.007 (.009) |
| Sales Rep Performance Trend | -.038** (.018) |
| Constant | 1.493*** (.365) |
| Industry Fixed Effects | Yes |
| Quarter Fixed Effects | Yes |
| Observations | 830 |
| (Adjusted) R-square | .080 |
Second, expected future performance is another predictor of sales rep turnover (Pilling and Henson 1996). Sales reps may leave if they anticipate low purchasing potential among their customers, which would lead to low commissions. The forecast of these future sales again is not observed and could bias the treatment effect, so we enrich our model with customer characteristics, including past purchase sizes, relationship tenure with the firm, and customer sales trajectory in the predeparture period. To the extent that customers’ purchasing timing exhibits cyclical patterns across industries, we also add quarter fixed effects. These variables capture information that sales reps use to form their expectations of customers’ purchasing power in the future. Conditional on these variables, forecasts of a customer’s future sales should be distributed randomly across sales reps.
Third, sales reps may leave for unobserved, sales branch– specific reasons. For example, sales reps may be dissatisfied with the sales branch manager’s supervisory ability or branch rules. This variable is part of eit in Equation 1 and may cause correlation between the treatment and eit. To account for this source of nonrandomness, we include branch fixed effects. Accordingly, we augment Equation 1 with covariates to control for these three sources of nonrandomness in sales rep departure, such that where Xit captures the time-invariant and time-variant control variables, including sales reps’ past observed sales performance and tenure; idiosyncratic customer characteristics such as size and relationship tenure; the customer sales trajectory (Customer Sales 1, 2, and 3); sales reps’ past performance trend; and fixed effects for the industry, branch, and quarter. Finally, ai is a customer-specific random error that captures unobserved customer-level effects.
TABLE:
| | Propensity Score Matching | Minimum Mahalanobis Distance Matching |
|---|
| | RR1 Nearest Neighbor (1) | RR2 Nearest Neighbor (2) | RR3 Kernel Matching | RR4 Nearest Neighbor (One Neighbor) |
|---|
| **p < .05. |
| ***p < .01. |
| aDenotes estimates from propensity score matching methods, calculated on the basis of the weighted difference between the outcome variables of treatment group and matched control group. |
| bTo interpret these values, an average treatment effect of -.165 means that effect size is a –15.2% sales loss using the following transformation formula: e(-.165) – 1, owing to our use of log-transformed sales as outcome variables. |
| Average treatment effecta | -.165***, b (.064) | -.133** (.060) | -.167*** (.054) | -.141** (.624) |
| Observations | 2,445 | 2,445 | 2,445 | 2,445 |
Controlling for Nonrandomness in Sales Rep Replacement
As discussed previously, when a sales rep departs, the regional sales manager is responsible for reassigning customer accounts to replacement reps, who could be internal (i.e., existing sales reps) or external (i.e., new hires). This assignment is a strategic choice made by the sales manager, with the intent of limiting any deleterious impact resulting from sales rep departure, and is thus nonrandom.
We account for the nonrandomness of assignments (existing sales reps vs. new hires) that result from unobserved factors by using a two-stage Heckman (1979) correction. In the first stage, we model the choice of replacement sales rep (new hire vs. existing sales rep) according to several drivers of this decision with a probit specification. For identification purposes, the covariate set driving replacement choice needs to contain some variables (i.e., exclusion restrictions) that affect this choice but do not directly affect customer sales. We specify three such exclusion restrictions:
TABLE:
| | (1) | (2) | (3) |
|---|
| Variables | New Hire | Existing | Similarity vs. Performance |
|---|
| *p < .10. |
| **p < .05. |
| ***p < .01. |
| Notes: DD = Post_Period Dummy X Treatment Dummy interaction. Bootstrapped standard errors are in parentheses. For the random-effects model, Sigma_u represents the standard deviation of the random intercept. Sigma_e represents the standard deviation of the error term. Rho is the explained percentage of the total variance of the random intercept and error term by random intercept, (Sigma_u2)/(Sigma_u2 + Sigma_e2). |
| Treatment Dummy | .155 (.096) | -.107 (.111) | .077 (.107) |
| Post_Period Dummy | .023 (.034) | .024 (.035) | .024 (.035) |
| DD | -.243*** (.066) | -.116 (.083) | -.285* (.152) |
| DD X Similarity | | | .359** (.172) |
| DD X Performance | | | -.182 (.220) |
| Customer Relationship Tenure | .034*** (.004) | .032*** (.004) | .033*** (.004) |
| Customer Purchase Size | .517*** (.134) | .495*** (.162) | .495*** (.162) |
| Sales Rep Tenure | .015 (.013) | .017 (.015) | .016 (.015) |
| Sales Rep Performance | .016 (.012) | .018 (.013) | .017 (.013) |
| Customer Sales Change 1 | .019*** (.005) | .015*** (.005) | .015*** (.005) |
| Customer Sales Change 2 | .023*** (.005) | .019*** (.005) | .020*** (.005) |
| Customer Sales Change 3 | .021*** (.004) | .013*** (.004) | .014*** (.005) |
| Sales Rep Performance | .009 | .008 (.013) | .007 (.013) |
| Trend | (.012) | | |
| Constant | 8.459*** (.517) | 8.464*** (.511) | 8.424*** (.530) |
| IMR | .138 (.199) | .188 (.150) | .231 (.153) |
| Branch Fixed Effects | Yes | Yes | Yes |
| Industry Fixed Effects | Yes | Yes | Yes |
| Quarter Fixed Effects | Yes | Yes | Yes |
| Sigma_u | .967 | .989 | .989 |
| Sigma_e | .906 | .915 | .915 |
| Rho | .533 | .539 | .539 |
| Observations | 4,250 | 3,870 | 3,870 |
| Adjusted) R-square | .242 | .247 | .247 |
1. Local unemployment rate7: This variable refers to the county or city where the branch is located, because it may affect the supply and availability of sales reps in external labor markets. If the local unemployment rate is high, sales managers likely can hire replacement sales reps because there is a higher proportion of unutilized workers in the local workforce.8 However, the local unemployment rate should not affect customer sales directly, because sales in the B2B sector are driven mainly by business needs and the selling capability of the incumbent sales force.
- 2. Ratio of new hires to existing hires in other branches of the same sales region: This variable reflects common practices within a sales region. A sales manager likely adopts the prevalent practice in the sales region, but these peer branches’ assignment choices do not directly affect sales outcomes for the focal sales manager’s branch.
- 3. Supply of existing sales reps in the focal branch: This variable indicates the availability of existing sales reps. With many existing sales reps, sales managers likely assign customers to them, yet the number of existing sales reps should not directly affect sales outcomes for customers in the treatment group, because they are served by just one sales rep each.
Thus, the first-stage selection equation is where NewHireit is a dummy variable (equal to 1 if replacement rep is a new hire, and 0 if replacement rep is an existing sales rep); Unemploy is local unemployment rate; NumExisting represents the number of existing sales reps in the same branch; PeerNewHire is the percentage of assignments to new hires in peer branches of same region; Xit is a vector of departing sales rep characteristics and other controls (customer relationship tenure and purchase size; departing sales rep tenure, performance level, and performance trend; customer sales trend variables, industry fixed effects, and quarter fixed effects); and eit is the error term.
In the second step, we follow a Heckman correction procedure9 and calculate the inverse Mills ratio (IMR) from the first-stage selection equation, then include it as a covariate in Equation 2 to control statistically for the endogeneity of the sales rep replacement decisions. where IMRi is the IMR obtained from the first-stage selection equation, and b5 is the coefficient capturing its impact on customer sales.
TABLE:
| | (1) | (2) | (3) |
|---|
| Variables | New Hire | Existing | Similarity Versus Performance |
|---|
| p < .10. |
| **p < .05. |
| ***p < .01. |
| Notes: DD = Post_Period Dummy . Treatment Dummy interaction. Bootstrapped standard errors are in parentheses. For the random-effects model, Sigma_u represents the standard deviation of the random intercept. Sigma_e represents the standard deviation of the error term. Rho is the explained percentage of the total variance of the random intercept and error term by random intercept, (Sigma_u2)/(Sigma_u2+ Sigma_e2). |
| Treatment Dummy | .106 (.108) | -.107 (.134) | .260** (.119) |
| Post_Period Dummy | -.179*** (.039) | -.178*** (.039) | .012 (.035) |
| DD | -.134* (.081) | -.092 (.081) | -.327** (.138) |
| DD X Similarity | | | .443** (.176) |
| DD X Performance | | | -.154 (.240) |
| Customer Relationship Tenure | .044*** (.004) | .042*** (.004) | .031*** (.004) |
| Customer Purchase Size | .497*** (.114) | .485*** (.151) | .475*** (.161) |
| Sales Rep Tenure | .008 (.015) | .009 (.016) | .009 (.016) |
| Sales Rep Performance | .015 (.013) | .014 (.014) | .018 (.016) |
| Customer Sales Change 1 | .016*** (.005) | .010* (.006) | .010* (.006) |
| Customer Sales Change 2 | .023*** (.006) | .016** (.006) | .015** (.005) |
| Customer Sales Change 3 | .016*** (.004) | .007*** (.005) | .011** (.005) |
| Sales Rep Performance Trend | .014 (.013) | .011 (.014) | .012 (.014) |
| Constant | 7.002*** (.457) | 6.999*** (.434) | 8.553*** (.501) |
| IMR | .098 (.214) | .104 (.213) | -.186 (.202) |
| Branch Fixed Effects | Yes | Yes | Yes |
| Industry Fixed Effects | Yes | Yes | Yes |
| Quarter Fixed Effects | Yes | Yes | Yes |
| Sigma_u | .952 | .971 | 1.008 |
| Sigma_e | 1.010 | 1.010 | .913 |
| Rho | .470 | .480 | .549 |
| Observations | 3,554 | 3,330 | 3,870 |
| (Adjusted) R-square | .260 | .263 | .250 |
Robustness Assessment for RQ1
Propensity score matching. We verified the robustness of our results to different estimation strategies by using propensity score matching instead of a regression-based approach. Whereas the regression-based approach conditions all the members of the control and treatment groups using the covariates to obtain the treatment effect, propensity score matching attempts to identify a control group of customers with a similar probability of being selected into the treatment condition; it only compares “similar” pairs of customers in the control and treatment groups who have numerically similar probabilities of receiving the treatment. In the first stage, we obtained a propensity score from a probit model in which we regressed the matching variables on the treatment dummy. We used predeparture sales patterns (four quarters of sales) and customer and sales rep variables (customer relationship tenure, customer
2000). Thus, we include sales past volatility as an additional influence, measured according to the standard deviation of customer quarterly sales in T1. We then estimated a differencein differences specification (Equation 4) with sales volatility as an additional control variable. The results remained similar (for details, see the Web Appendix).
Second, individual dissimilarity from peers (tenure, age, sex, etc.), group diversity (Jackson et al. 1991; O’Reilly, Caldwell, and Barnett 1989), and peers’ turnover (Boles et al. 2012) all could affect turnover decisions. To test for the potential effect of dissimilarity, we conducted another robustness check.
Peers are sales reps in the same region. The variables we developed included individual dissimilarity from peers in terms of customer industries, individual dissimilarity from peers in terms of tenure, peer turnover, tenure diversity (i.e., standard deviation of selling tenure of peers), and sales diversity (i.e., standard deviation of sales of peers). None of these variables had significant effects on departure decisions in our sample, and the results remain consistent (for details, see the Web Appendix).
Thus, we modify Equation 4 by including interactions of the treatment effect with Similarity, or customer industry similarity between existing sales reps and departing sales reps, and with Performance, or existing sales reps’ past performance level. That is, in Equation 5, we estimate the moderating effects of similarity and past performance using heterogeneous treatment effects.12
We computed similarity between a departing and an assigned sales rep in two steps. First, we computed the share of customer sales that the departing and assigned sales reps achieved in each of the six industries, obtaining two 6 • 1 vectors of customer sales shares. Second, we computed the cosine of the angle between the two vectors. A cosine similarity metric is appropriate when each vector component is bounded between 0 and 1, as is the case for the share of customer sales in each industry (Boran and Akay 2014; Hoberg and Phillips 2010). To illustrate, the depicted heterogeneity in selling experience for six representative sales reps in Figure 4 reveals considerable variability: some sales reps sell to only one industry (Sales Rep 3), but others sell to all industries (Sales Rep 6). We computed the past performance of an existing sales rep as the regional sales rank in the predeparture period (T1).
TABLE:
| Four Quarters Pre- and Postdeparture | N | T1 Predeparture | T2 Postdeparture | Difference T2 2 T1 |
|---|
| aThe difference in T1 to T2 sales changes of the two groups was -.04, which was not statistically significant. |
| Existing sales reps’ customers | 1,258 | 10.46 | 10.45 | -.01 (.26) |
| Control group | 1,615 | 10.41 | 10.44 | .03 (.50) |
| Difference of differencesa | | | | -.04 (.86) |
Additional Analyses
Long-term effects of assignment. We extended our investigation of the effectiveness of reassignment strategies to a postdeparture period of ten quarters to determine whether the effects evolve over time.13 If replacement sales reps build relationships with customers, the loss in sales should diminish or even reverse. In Table 11, we present these long-term effects, revealing that customers reassigned to new hires improved their
Musical chair effects? Assigning additional customers to existing sales reps might have negative impacts on sales to existing customers. That is, if an existing sales rep suffers time constraints already, any additional tasks could undermine the quality of service (s)he provides to existing customers or the attention (s)he devotes to newly assigned customers. We therefore investigated the potential change in sales to current customers; in Table 12, we show that sales by 107 existing sales reps to 1,258 active customers did not decrease with any statistical significance from T1 (10.46) to T2 (10.45; t = .26). The sales change for customers in the control group was not statistically significant (T1 = 10.41, T2 = 10.44; t = .50), nor was the formal difference-in-differences coefficient (coefficient = -.041, p .41). Thus, assigning additional customers to existing sales reps did not have a negative impact on sales to existing customers, at least within the range of observation for our sample.
Discussion
Some sales rep departure is inevitable; reassigning customers to other sales reps thus is a crucial part of the sales force management process for B2B firms. Using data from a B2B firm, we have evaluated the impact of sales rep transition on customer sales and explored the heterogeneous effects of reassignment strategies. Our difference-in-differences approach causally quantifies the impact of a sales rep’s departure on customer sales, such that customer sales drop 13.2%–17.6% one year after the departure. We also exploit the heterogeneity in reassignment decisions to show that customers reassigned to new hires exhibit a 21.6% sales loss, whereas those reassigned to existing sales reps exhibit an 11.0% sales loss. Replacement sales reps with industry experience similar to that of the departing reps appear to have a mitigating effect on sales losses. If the similarity index is above .6 and the replacement sales rep is an average performer, the sales losses could even be eradicated. Over a longer investigation window (i.e., ten quarters), the sales losses among customers served by new hires are also attenuated, so short-term sales losses appear to be due to the learning curve that new hires undergo.
Theoretical Implications
First, our results highlight the causal effects of sales losses from sales force transition, and they have implications for designing customer reassignment strategies after sales rep departure. Our approach and results not only estimate the indirect costs to the firm when sales rep transitions occur but also show that customer reassignment might be managed effectively by using assigned sales reps’ observable characteristics (e.g., industry experience). Accordingly, sales rep management and customer management theories need to move beyond day-to-day management and develop approaches that incorporate the indirect costs of sales rep transitions.
Second, we contribute to relationship marketing and interorganizational relationship literature by quantifying the relationship value generated at the interpersonal level (sales reps and customers). Prior literature has established sales rep– owned loyalty as distinct from firm-owned loyalty (e.g., Kumar, Sunder, and Leone 2014; Palmatier, Scheer, and Steenkamp 2007), with the prediction that the loss of sales rep–owned loyalty may harm customer sales. Our results confirm the existence of this type of loyalty using secondary source data
and show that sales rep transitions induce losses in customer
sales.
Third, we contribute to sales rep effectiveness literature (Farrell and Hakstian 2001; Weitz, Sujan, and Sujan 1986). Industry experience and performance are both indicators of a sales rep’s selling effectiveness, but their effects for mitigating sales losses from sales rep transitions differ: industry experience offers a better indicator of effective loss mitigation than performance. In addition, our finding that sales losses attenuate over time, especially for new hires, underscores the importance of a dynamic view of sales rep effectiveness, which remains underresearched in the sales force performance literature (Ahearne and Lam 2012).
Managerial Implications
Our study is useful for managers who want to evaluate the economic impact of sales rep transitions and improve their customer reassignment practices. First, by estimating the average effects of sales rep transitions on their sales, sales managers can assess the effectiveness of their current reassignment practices.
Sales managers in the firm we studied should expect sales rep transitions to lead to losses of $10.65 million–$14.20 million,15 based on the firm’s annual sales of $80.67 million in the predeparture period. Therefore, sales managers can forecast future sales better using predictions of sales reps’ departure rate as well as select more effective retention practices.
Second, the heterogeneous effects of reassignment strategies offer insights into how sales managers might adjust their customer reassignment and hiring practices to improve performance. The loss in customer sales that results from transitions can be mitigated by reassigning customers to existing sales reps rather than new hires. The short-term opportunity costs of assigning customers to new hires thus are worth noting, even if new hires can overcome these losses over time. These insights can help sales managers trade off the benefits and costs of reassigning customers to various sales reps.
Third, existing reps differ in their effectiveness as replacement reps. Industry experience similarity between assigned and departing sales reps (but, surprisingly, not the sales rep’s past performance) has significant loss-mitigating effects. This evidence indicates that domain knowledge similarity is key to managing the relationship transition process. Sales managers should assign customers to sales reps who have industry experience that is similar to the departing rep. The results in our study suggest that when the similarity level is less than .6, the sales losses are significant, but when it is greater than .6
(keeping the performance level constant), sales losses become nonsignificant and approach zero.
Limitations, Generalizability, and Further Research
This study relies on data from one large B2B distributor. The methods can be applied readily to other sales organizations with similar data, but applying the proposed approach to other selling situations requires some adaptation. For example, our approach might be extended to three other contexts, which suggests ideas for further research. First, “one-to-one” account reassignment could be an alternative strategy, such that all customers of a departing sales rep are assigned to a single replacement, rather than to multiple sales reps, as in our study context. Greater customer heterogeneity (e.g., dispersed geographic locations) may make it cost effective to assign a group of similar customers to one replacement sales rep.16 The approach of quantifying customer-level sales changes following a sales rep transition still should apply to the one-to-one reassignment strategy, as a special case of the strategy we investigate. However, it also suggests a research opportunity to study sales changes at the sales rep level, which might reveal how the single replacement sales rep’s characteristics affect performance within the departing sales rep’s customer portfolio. A new hire case is similar to what we have studied; an existing rep re
assignment raises new questions about how the existing rep
can handle the spike in the number of customer accounts. Second, in our study’s empirical context, customers are
mainly served by one key contact sales rep. For team selling contexts, our method can be modified to account for the team characteristics related to a sales rep’s departure decision and the replacement decision. For example, individual dissimilarity from team peers, team diversity, and peers’ turnover might be significant predictors of individual turnover decisions, and the replacement’s similarity with team peers or adaptiveness to new teams might be factors that managers should consider when
choosing replacement sales reps. Further research could incor
porate these variables into our proposed approach and thereby
correct for endogenous departure and replacement decisions. Third, cross-selling is another important B2B selling con
text that research could investigate. How does sales rep transition affect cross-selling performance? A modified version of our approach could include cross-selling sales volume as an outcome variable and also control for sales reps’ cross-selling ability. In a team selling context, researchers also might incorporate team-level cross-selling characteristics and identify how a change for an individual member (i.e., departure and arrival of team members) affects cross-selling functions.
Our study also has a few other limitations. We consider voluntary sales rep departure, which reflects the situation in our study context. However, sales reps who leave involuntarily may exhibit different behaviors depending on how firms handle their dismissal. It would be worthwhile to study whether the form of the sales rep’s departure (voluntary vs. involuntary) affects firm–customer relationships and customer sales. For example, departing sales reps might leave voluntarily in response to the private information they have about future sales trends, which we addressed by assuming that future sales were a function of past sales in our analysis. Yet further research might use exit survey data to address this question more directly. Our study also does not account for differences in new hires’ past industry experience or selling performance; additional research could quantify the trade-offs in these background variables for new hires. Finally, a true test-and-control experiment might provide more definitive answers to our research questions than are possible with our quasi-experimental analysis. Even with these caveats, we hope our work stimulates more research in this area that can continue to provide useful insights and guidelines for sales managers faced with the constant challenge of reassigning customer accounts after a sales rep leaves.
1This calculation is based on a 22% turnover rate and $7,399 billion in B2B sales in 2013 (http://www.census.gov/wholesale/index.html).
2The company does not proactively fire field sales reps; it relies on its compensation scheme to filter out incompetent sales reps, such that they leave voluntarily because they cannot earn sufficient performance-based income. Thus, as we noted previously, our data involve only voluntary turnover.
3Our data show that 96.5% of internal reassignments came from the same sales region. 4For example, we shortened the window to two quarters and
5In Figure 3, we also provide the purchase frequency (number of transactions) difference between the treatment and control groups. The treatment group shows a significant loss in transaction frequency, from 10.57 in T1 to 9.75 in T2 (t = 2.10); the control group shows significant growth, from 11.42 to 12.58 (t = 3.13). Thus, the treatment group also exhibits decreased purchase frequency after a
sales rep departure.
6We also include the volatility of customer sales to control for sales reps’ nonrandom departure decisions in our robustness analyses (see the “Additional Robustness Tests” subsection).
7We obtained annual unemployment rate data at the county level from the Bureau of Labor Statistics (www.bls.gov) for U.S. branches and at the city level from Statistics Canada (www.statcan.gc.ca) for branches in Canada.
8When managers pursue new hires, they seek salespeople with a minimum of two years of experience (in keeping with the company’s policy). Thus, the unemployment level in a geographic area two years before the focal decision likely represents a shock to the supply of new hires in the geographic area. Therefore, we tested the robustness of our results to the use of two-, three-, and four-year lagged unemployment rates. All the lagged unemployment rates correlate positively with the new hire dummies, consistent with our theoretical prediction, and the results hold as well (for details, see the Web Appendix).
9Although we follow common practices to identify exclusion restrictions, there is little consensus about how to assess the appropriateness of exclusion restrictions (Certo et al. 2016).
10The estimated treatment effect (-.193) is equivalent to a 17.6% sales loss according to the transformation formula: e(-.193) – 1 = -.176, which we apply due to our use of log-transformed sales as outcome variables. We applied the same transformation to translate coefficient estimates into percentage changes.
11We also investigated new customer acquisition activities by new hires and existing sales reps in T2. New hires acquired 7.3 customers on average, existing sales reps acquired 11.5 customers, and the difference is statistically significant. Thus, new hires might exhibit poorer new customer acquisition performance than existing sales reps.
12We consider only the interactions of Similarity and Performance with Treatment • Post_Period, because only observations from the treatment group in the postdeparture period vary in these levels. That is, Similarity and Performance matter only when Treatment = 1 and Post_Period = 1. Observations in the control group and in the treatment group in the predeparture period are not assigned to existing sales reps and therefore not affected by their characteristics. This specification is standard in difference-in-differences analyses (e.g., Manchanda, Packard, and Pattabhiramaiah 2015).
13We adjusted sales in the ten quarters by multiplying them by .4, so the sales magnitude in the postdeparture window was comparable to that in the predeparture window.
14The difference between the ten- and four-quarter postdeparture effects were as follows: new hires’ performance ([-12.5%] – [-21.6%] = 9.1%) and existing sales reps’ performance ([-8.8%] – [-11.0%] = 2.2%).
15The total sales of the treatment group in T1 were $80.67 million. Estimated sales losses of 13.2%–17.6% (Table 9, Model RR2; Table 8, Column 4) imply sales losses of $10.65–$14.20 million.
16When service areas are dispersed and discontinuous, it is not feasible to assign customers in one area to sales reps in other areas because of the long travel distance. A common practice thus is to assign all customers of a departing sales rep to a new hire or to another existing sales rep who works in the same area.
DIAGRAM: FIGURE 1 Examples of Customer Monthly Purchasing Patterns, Showing Great Heterogeneity
DIAGRAM: FIGURE 2 Temporal Illustration for Key Variable Construction
DIAGRAM: FIGURE 3 Differences of Treatment and Control Groups (Model-Free Evidence)
DIAGRAM: FIGURE 4 Examples of Sales Reps’ Selling Industry Experience
DIAGRAM: FIGURE 5 Treatment Effects of Similarity (Conditional on Performance 5 .5)
PHOTO (BLACK & WHITE)
PHOTO (BLACK & WHITE)
PHOTO (BLACK & WHITE)
PHOTO (BLACK & WHITE)
PHOTO (BLACK & WHITE)
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Record: 153- Salesperson Solution Involvement and Sales Performance: The Contingent Role of Supplier Firm and Customer–Supplier Relationship Characteristics. By: Panagopoulos, Nikolaos G.; Rapp, Adam A.; Ogilvie, Jessica L. Journal of Marketing. Jul2017, Vol. 81 Issue 4, p144-164. 21p. 1 Diagram, 7 Charts. DOI: 10.1509/jm.15.0342.
- Database:
- Business Source Complete
Salesperson Solution Involvement and Sales Performance: The Contingent Role of Supplier Firm and Customer–Supplier Relationship Characteristics
Salespeople play a crucial role in their firms’ efforts to provide customer solutions. However, little research has examined how salesperson involvement in customer solutions can be conceptualized, whether it pays off, and what boundary conditions might heighten its performance effects. This study addresses these gaps and offers a conceptualization of salesperson solution involvement by focusing on the set of salesperson-related activities that enact the four relational processes inherent in customer solutions. The authors collect a unique data set that includes a wide range of firms, industries, and countries, as well as the perspectives of both salespeople and customers, across five studies. Results validate the stability of the conceptualization across contexts. They also reveal that salesperson solution involvement is systematically related to increases in both subjective and objective, time-lagged measures of sales performance. Finally, results show that the performance effects of salesperson solution involvement are amplified under higher levels of firm’s product portfolio scope, sales unit cross-functional cooperation, and customer–supplier relationship tie strength. Surprisingly, customer adaptiveness is not found to moderate the performance effects of salesperson solution involvement.
Firms around the globe—including the majority of Fortune 100 firms—have assigned top priority to building a sales force that is effective in providing customer solutions (Guido 2012; Koivuniemi 2016). According to the Bureau of Labor Statistics (2015), salespeople involved in solution provision are the highest-paid sales professionals employed by U.S. firms. Prior studies have also highlighted the role of salespeople in providing customer solutions (e.g., Grewal et al. 2015; Kumar, Petersen, and Rapp 2014). Although extant research has contributed important insights on solutions (Tuli, Kohli, and Bharadwaj 2007; Ulaga and Reinartz 2011), three critical questions remain unaddressed.
First, how can salesperson involvement in customer solutions be conceptualized? Tuli, Kohli, and Bharadwaj (2007) have made an important contribution by introducing a conceptualization of customer solutions that highlights the role of four customer–supplier relational processes in the provision of a solution. However, this work examines customer solutions as firm-level processes, thus placing individuals who are involved in and enact these processes in the background. While understanding the creation and delivery of solutions at the firm level is important, the limitation inherent in this view is that the activities through which individuals enact these processes are ignored, thus offering little guidance for how managers can finetune these processes. Previous marketing and strategy research has suggested that understanding firm-level processes, and thus what is done and by whom, requires decomposing processes into a more granular set of activities that are carried out by individuals (Crowston 1997; Srivastava, Shervani, and Fahey 1998). While employees from different functional units are involved in solutions (Tuli, Kohli, and Bharadwaj 2007), the salesperson plays a crucial role in the interactions that take place at the customer interface throughout each of the relational processes (e.g., La Rocca et al. 2016; Murtha, Bharadwaj, and Van den Bulte 2014). An example is seen in the case of IBM salespeople, who are required to engage in activities that give them deep customer knowledge to effectively provide solutions (Guido 2012). Yet, very little research has examined the solution-related activities salespeople need to perform. This neglect is surprising given that failure to realize benefits from providing solutions is systematically linked to salespeople engaging in the wrong sales activities (Koivuniemi 2016; Ulaga and Loveland 2014).
Second, does salesperson solution involvement pay off? Anecdotal reports highlight that even world-renowned firms struggle to realize benefits from their salespeople’s engagement in solution-related activities (e.g., Koivuniemi 2016). Yet, prior work has not paid attention to empirically substantiating whether salesperson involvement in customer solutions pays off (Grewal et al. 2015).
Third, what boundary conditions influence the effectiveness of salesperson solution involvement? Prior theoretical work has pointed out that the influence of a salesperson’s activities on performance is affected by characteristics of both the supplier and the customer relationship (Weitz 1981). However, empirical research providing managerially relevant conditions that facilitate salesperson solution involvement is surprisingly limited.
Our study takes an initial step toward addressing these three key limitations and makes the following contributions. First, we offer a conceptualization of salesperson solution involvement, which we define as the degree to which a salesperson engages in activities that help his/her firm provide end-to-end solutions to the salesperson’s customers1 (see Table 1 for definitions of key constructs). This study thus overcomes the first key limitation of previous research. In addition, leveraging a unique data set involving five studies across firms, industries, and countries (see Figure 1), we develop and validate a scale of salesperson solution involvement that shows stability regardless of the source of measurement (i.e., salespeople vs. customers).
Next, we examine the direct effect of salesperson solution involvement on sales performance—that is, the financial results achieved from a salesperson’s solution-related activities among her/his customers. Accordingly, we surmount the second key limitation of prior research. Our analyses provide managers a basis for informed decisions: results show that an increase in salesperson solution involvement by one standard deviation leads to a 61.95% increase in objective solution-based salesperson performance against quota (Study 1); a 34.92% increase in subjective salesperson performance (Study 2); and a 22.14% increase in objective, solution-based net profits (V) contributed by a customer (Study 3).
Finally, we address the third key limitation of previous research by looking at a range of moderating conditions. Regarding conditions related to the supplier firm, we propose that the effect of salesperson solution involvement on sales performance is influenced by (1) sales unit cross-functional cooperation—that is, the quality of interactions between salespeople and employees in other functions regarding
value-creating activities for customers (Im and Nakata 2008); and (2) firm’s product portfolio scope—that is, the extent or the breadth (number of product lines) and depth (number of product variants in each product line) of a firm’s product portfolio (Sorescu, Chandy, and Prabhu 2003). Furthermore, we consider two conditions related to customer–supplier relationship characteristics: (1) relationship tie strength—that is, the customer’s perceived degree of closeness and reciprocity in the relationship with the supplier (Rindfleisch and Moorman 2001); and (2) customer adaptiveness—that is, the degree to which a customer is willing to modify or adapt its internal routines/processes to accommodate a supplier’s solution (Tuli, Kohli, and Bharadwaj 2007). Results show that both supplier firm characteristics amplify the performance effects of salesperson solution involvement. Regarding the customer–supplier relationship characteristics, results reveal that relationship tie strength (but, interestingly, not customer adaptiveness) matters in amplifying the relationship between salesperson solution involvement and sales performance.
Theoretical Background and Hypothesis Development
Salesperson Solution Involvement
We anchor our concept of salesperson solution involvement in the work of Tuli, Kohli, and Bharadwaj (2007), who define a customer solution as a “set of customer–supplier relational processes comprising (1) customer requirements definition, (2) customization and integration of goods and/or services and (3) their deployment, and (4) postdeployment customer support, all of which are aimed at meeting customers’ business needs” (p. 5). It follows, then, that involvement in customer solutions entails enactment of these firm-level relational processes. But how are processes enacted in the first place?
Prior work in marketing and strategy documents that a process existing at the firm level refers to a measured set of interrelated activities that need to be carried out by individuals for the process to be enacted (e.g., Srivastava, Shervani, and Fahey 1998). This means that the first step toward understanding how a firm-level process is enacted requires its decomposition into a set of micro activities performed at the employee level (e.g., Crowston 1997; Srivastava, Shervani, and Fahey 1998). But what activities do salespeople engage in to enact these relational processes?
Prior studies in the organizational and strategy literature have pinpointed that the activities individuals engage in depend on the functional unit they belong to and which entails different roles, goals, and specialized skills (e.g., Crowston 1997). So, although multiple functions are involved in relational processes (Tuli, Kohli, and Bharadwaj 2007), salespeople will perform distinct sets of activities according to their function’s role in the creation and delivery of a customer solution. Prior research has suggested that the salesperson plays a prominent role in all four relational processes by interacting with every buyer role in the customer firm across the entire solution cycle (e.g., La Rocca et al. 2016; Murtha, Bharadwaj, and Van den Bulte 2014; Storbacka, Polsa, and Sa¨a¨ksja¨rvi 2011; To¨llner, Blut, and Holzmu¨ller 2011). It therefore stands to reason that salespeople, along with employees in other functions, will be involved in the enactment of the relational processes. However, because of their unique role, salespeople will perform a distinct set of activities within each process. Given that the focus of our study is on the individual salesperson rather than on multiple functions, we examine the salespersonrelated set of activities performed to enact each relational process. Also, because customer solutions are manifested as relational processes (Tuli, Kohli, and Bharadwaj 2007), it follows that the conceptual domain of salesperson solution involvement should involve activities that are directed toward the customer relationship. We elaborate on these activities next.
TABLE 1 Definition and Operationalization of Key Constructs
TABLE:
| | Operationalizationa |
|---|
| Construct | Definition | Study 1 | Study 2 | Study 3 |
|---|
| Salesperson solution involvement | Degree to which a salesperson engages in activities that help his/her firm provide end-to-end solutions to the salesperson’s customers | Second-order reflective construct with four first-order dimensions; 18 reflective items (salesperson) | Second-order reflective construct with four first-order dimensions; 18 reflective items (salesperson) | Second-order reflective construct with four first-order dimensions; 18 reflective items (customer) |
| Sales unit cross-functional cooperation | Quality of interactions between salespeople and employees in other functions regarding value-creating activities for customers | Three reflective items (manager) | – | – |
| Firm’s product portfolio scope | Extent of a firm’s product portfolio | – | Two formative items (manager) | – |
| Relationship tie strength | Customer’s perceived degree of closeness and reciprocity in the relationship with the supplier | – | – | Six reflective items (customer) |
| Customer adaptiveness | Degree to which a customer is willing to modify or adapt its internal routines/processes to accommodate a supplier’s solution | – | – | Five reflective items (customer) |
| Sales performance | Financial results achieved from a salesperson’s solutionrelated activities among her/his customers | Objective index of solutionbased salesperson performance against quota at time1 (archival) | Subjective measure of salesperson performance with five formative items (salesperson) | Objective measure of solutionbased net profits (V) contributed by a customer at time1 (archival) |
Being the primary customer touch point (Murtha, Bharadwaj, and Van den Bulte 2014), the salesperson initiates the process of customer requirements definition by engaging in activities such as probing the customer firm’s employees, uncovering the customer’s broader business needs/objectives, and understanding what goods/services the customer buys from other suppliers to define a solution that creates value for the customer (Aarikka-Stenroos and Jaakkola 2012; Haas, Snehota, and Corsaro 2012; Steward et al. 2010; To¨llner, Blut, and Holzmu¨ller 2011). The salesperson proceeds to enact the process of customization/integration of goods/services by getting involved in the process of assembling and modifying the best possible combination of goods/services, while making sure that this bundle will meet the identified customer requirements (La Rocca et al. 2016; Steward et al. 2010; To¨llner, Blut, and Holzmu¨ller 2011). During the deployment process, whereas technical specialists perform activities of solution delivery or technical installation, the salesperson manages the “people aspects” by personally taking care of and monitoring the quick delivery and installation of the proposed solution (Tuli, Kohli, and Bharadwaj 2007). Deployment also requires salespeople to adapt the solution to needs that may arise during installation. This is done by understanding the capabilities of the employees who are going to use the solution and by providing all the information required for the customer firm to optimize the value derived from a solution (La Rocca et al. 2016; Steward et al. 2010; Storbacka 2011; To¨llner, Blut, and Holzmu¨ller 2011). Finally, enactment of the postdeployment customer support process may involve nonsales activities like conducting routine maintenance, especially in solutions involving capital goods (Ulaga and Reinartz 2011). However, postdeployment support also entails that solutions are viewed as ongoing relationships rather than as one-off projects (Tuli, Kohli, and Bharadwaj 2007). As such, the salesperson is involved in postdeployment customer support by staying available and maintaining a dialogue with the customer to verify that needs are met (Ulaga and Loveland 2014). Because emergent situations may require refinement of the offering’s value (Haas, Snehota, and Corsaro 2012), the salesperson also performs activities to diagnose new needs and propose new solutions and, thus, further cement the quality of interactions with the customer (e.g., To¨llner, Blut, and Holzmu¨ller 2011).
Conceptual Model Overview
Our conceptual model (Figure 1) is informed directly by Weitz’s (1981) contingency framework for understanding salesperson performance. In particular, Weitz proposes that salesperson activities directly influence his or her performance. Accordingly, we posit that the degree to which a salesperson engages in solution-related activities is linked to sales performance (see H1).
Our model also considers theoretically selected moderators that influence the effectiveness of salesperson solution involvement. Specifically, Weitz (1981) describes two types of boundary conditions that influence the relationship between salesperson activities and sales performance: (1) supplier-firm resources made available to the salesperson and (2) the characteristics of the relationship with the customer. These boundary conditions have also been highlighted in prior work that points out that the value created from solutions depends on resources the supplier firm makes available to salespeople (Haas, Snehota, and Corsaro 2012; Steward et al. 2010) and that customer solutions are embedded within customer relationships (Tuli, Kohli, and Bharadwaj 2007). Accordingly, we consider the moderating influence of supplier firm characteristics—that is, “sales unit cross-functional cooperation” (see H2) and “firm’s product portfolio scope” (see H3)—and customer– supplier relationship characteristics—that is, “relationship tie strength” (see H4) and “customer adaptiveness” (see H5)—on the relationship between salesperson solution involvement and sales performance.
Effect of Salesperson Solution Involvement on Sales Performance
As mentioned previously, central to the concept of customer solutions are the four interrelated processes through which salespeople generate and deliver novel solution configurations that meet customers’ needs (Tuli, Kohli, and Bharadwaj 2007). Thus, salespeople create value for customers by effectively deciphering unique customer requirements, strategically combining goods/services in a firm’s product portfolio, integrating products with valuable resources and technical expertise, and deploying/supporting the delivery of these solutions to customers (Haas, Snehota, and Corsaro 2012; To¨llner, Blut, and Holzmu¨ller 2011). Specifically, involvement in solution provision allows a salesperson to better understand current and future customer needs and can catalyze customers’ perceptions of the salesperson as an expert with the unique know-how and competence to add value to their business (Murtha, Bharadwaj, and Van den Bulte 2014), which, in turn, improves sales performance. Moreover, salesperson activities within solution processes can align supplier offerings with pertinent customer requirements and foster an ongoing exchange that generates value for the customer (Storbacka 2011). By investing relational capital throughout the solution processes, the salesperson makes the exchange valuable for the customer, thus catalyzing superior sales performance (Palmatier, Scheer, and Steenkamp 2007). Thus:
H1: Salesperson solution involvement is positively related to sales performance.
Moderating Effects of Supplier Firm Characteristics
Sales unit cross-functional cooperation. We expect that higher levels of sales unit cross-functional cooperation enhance the value generated from solution-related activities, thus increasing the financial results achieved from salesperson solution involvement. First, better quality of interfunctional interactions regarding value-creating activities facilitates salespersons’ access to key specialized skills and expertise required to configure product applications that solve customer-specific problems (Steward et al. 2010; Storbacka, Polsa, and Sa¨a¨ksja¨rvi 2011). Second, prior work suggests that high-quality cross-functional cooperation facilitates better processing and usage of customer-related information (Jaworski and Kohli 1993). By cooperating with employees in other functions, salespeople can supplement the knowledge they have generated with different functional insights that are necessary for creating customer value (Atuahene-Gima 2005). This diversity of information provides multiple lenses into product applications, which enables salespeople in the process of accurately identifying and matching customers’ requirements to value-creating offerings (Steward et al. 2010; Storbacka, Polsa, and Sa¨a¨ksja¨rvi 2011). Third, through high-quality interactions with other functions, salespeople ensure that the identified customer requirements can be enacted through the firm’s resources (Sleep, Bharadwaj, and Lam 2015). Accordingly, the value created through activities such as customization of goods/services or solution deployment is increased. In summary, the combination of high levels of salesperson solution activities and high levels of cross-functional cooperation increases the value created for customers, which, in turn, catalyzes the financial results achieved. Thus:
H2: The higher the sales unit cross-functional cooperation, the stronger the positive relationship between salesperson solution involvement and sales performance.
Firm’s product portfolio scope. A broad and deep product portfolio provides flexibility with regard to the different product versions and variations made available to the salesperson (Saxe and Weitz 1982). This flexibility aids salespeople in adapting solutions to the specific customer requirements which increases the value created for customers, thus improving sales performance. Prior work has shown, for instance, that product portfolio breadth increases customer satisfaction (Bowman and Narayandas 2004). Conversely, a narrow product portfolio limits the options available for accurately identifying customer requirements and the actions needed to fulfill them (Tuli, Kohli, and Bharadwaj 2007), such that engagement in solution-related activities becomes less efficient, thereby hurting sales performance. Furthermore, when a salesperson has access to a broader/deeper product portfolio, (s)he can better adapt the solution to emerging customer needs, thus improving the value of postdeployment support activities for the customer. Finally, a larger product scope entails that salespeople have more opportunities to develop broader levels of expertise and multifaceted knowledge across a variety of specialized applications (Sorescu, Chandy, and Prabhu 2003). Because knowledge diversity improves problem solving (Schilling et al. 2003), a broader product portfolio should increase the value that solutionrelated activities create for customers and improve sales performance in turn. Thus:
H3: The larger the firm’s product portfolio scope, the stronger the positive relationship between salesperson solution involvement and sales performance.
Moderating Effects of Customer–Supplier Relationship Characteristics
Relationship tie strength. Relationship tie strength entails norms of trust, closeness, and mutual reciprocity that provide exchange parties with increased transaction efficiency (Kaufman, Jayachandran, and Rose 2006). We posit that strong relational ties act as mechanisms that increase the value offered to customers through involvement in solution provision, thus enabling the salesperson to achieve better financial results. First, strong relational ties foster the timely exchange of private, complex, and even tacit information (Ganesan, Malter, and Rindfleisch 2005; Rindfleisch and Moorman 2001), such as information about the customer firm’s broader business needs. Further, strong ties increase learning, because they ensure that only required information is transferred (Uzzi and Lancaster 2003). This situation accelerates a salesperson’s capacity to learn about unique aspects of a customer’s operations that may allow either for a deeper understanding of customer requirements or for anticipating changes in future needs (Tuli, Kohli, and Bharadwaj 2007). Second, because information sharing decreases uncertainty (Kaufman, Jayachandran, and Rose 2006), customers can focus their attention on the value created rather than on negotiating and monitoring (Dyer and Singh 1998; Rindfleisch and Moorman 2003). This allows the salesperson to more quickly tap into the specialized systems and knowledge of the customer firm and, thus, acquire a deeper understanding of customer requirements, configure and implement better solutions, and better understand the skills of those who will be using the solution. Third, because strong relational ties induce joint problem solving as well as the pursuit of new ideas (Dyer and Singh 1998), salespeople can offer more value to customers by engaging in solution-related activities, such as designing a bundle of goods/services or deploying the solution to fit the customer’s environment. Thus:
H4: The stronger the relationship ties, the stronger the positive relationship between salesperson solution involvement and sales performance.
Customer adaptiveness. Effective solution provision requires customer adaptation (Tuli, Kohli, and Bharadwaj 2007). This is because customer solutions require customers to make concerted efforts in adjusting their processes and to tolerate heightened levels of risk as a result of unforeseen contingencies (Aarikka-Stenroos and Jaakkola 2012). We anticipate that higher levels of customer adaptiveness will improve the value that salesperson solution activities generate for customers, thus improving the financial results achieved. Specifically, because adaptation inspires “what-if” dialogues with the salesperson (Tuli, Kohli, and Bharadwaj 2007), customers can explore a wide range of potential product configurations during the customer requirements definition process. Salespeople can simultaneously explore avenues to broaden the applicability of existing products within existing routines or to modify customer routines to allow a seamless and quick integration of the solution with the customer environment. Furthermore, when adaptiveness is high, the salesperson enjoys greater latitude in the number and types of goods/services combinations that can be used to solve customer’s problems (Bonney and Williams 2009). Adaptiveness also implies that customers accommodate emergent situational constraints that often occur during or after implementation, such as complying with new training requirements (Tuli, Kohli, and Bharadwaj 2007), thereby enabling salespeople to deploy solutions or provide postdeployment support in ways that create more value for customers. Conversely, if the customer is unwilling to adapt to the requirements of the solution, frictions and tensions appear that may disorganize, prolong, or even damage the process of value creation, thus hurting profits (Cannon and Homburg 2001). For instance, salespeople may need to make more modifications to products/services, a situation that makes the process of customization and integration less efficient (Tuli, Kohli, and Bharadwaj 2007). Thus:
H5: The greater the customer adaptiveness, the stronger the positive relationship between salesperson solution involvement and sales performance.
Overview of Studies
We implemented five studies over two phases (Figure 1). In Phase I, we develop and validate the salesperson solution involvement scale through two prestudies that capture the supplier and customer perspectives. Initial scale development focuses on in-depth insights about salesperson solutionrelated activities gleaned from executives in supplier firms (Qualitative Prestudy), whereas scale validation focuses on customer-side reports of salesperson solution activities with regard to a specific customer–supplier relationship (Quantitative Prestudy). In Phase II, we test hypotheses in three field studies (Studies 1–3). Study 1 is conducted within a major firm and is designed to test the effect of salesperson-reported solution involvement on archival salesperson performance data. In addition, this study tests for the moderating effect of sales unit cross-functional cooperation, which manifests at the firm level and which is captured by managers across countries to allow for variation. Study 2 builds on Study 1 by focusing on salesperson-reported solution involvement and its impact on subjective sales performance. Because this study examines the moderating effect of a firm’s product portfolio scope (i.e., a firm-level construct), it is conducted across firms, industries, and contexts (to allow for variation in product portfolio scope, which is captured by manager data). Finally, Study 3 builds on Studies 1 and 2 by examining salesperson solution involvement with regard to a specific customer– supplier relationship and, thus, focusing on a customer-level measure of sales performance. This study is designed to test hypotheses involving customer–supplier relationship characteristics (i.e., relationship tie strength and customer adaptiveness).
Phase I: Scale Development and Validation
Qualitative Prestudy
We followed established procedures for scale development (Churchill 1979). First, we developed the definition for salesperson solution involvement and its four underlying dimensions (i.e., relational processes) based on the work of Tuli, Kohli, and Bharadwaj (2007). Our definition highlights that salesperson solution involvement can be thought of as a second-order construct reflected in the four first-order dimensions, each of which is reflected in a set of items (i.e., activities). On the basis of the definition as well as a literature review of 170 articles, we generated an initial pool of 30 items. Second, we conducted in-depth interviews with a judgment sample of 27 senior executives who possessed extensive experience with customer solutions and who were working for subsidiaries of major multinational firms operating in Greece. Industries represented in the sample include health care (19%), information technology (37%), electronics (15%), industrial equipment (19%), and consulting services (11%). Participants had an average of 18.1 years of industry experience and 24 years of work experience. Interviews lasted 40–90 minutes ( = 54 minutes). Participants reviewed the list of 30 items, as well as definitions of the construct and its four dimensions, and rated each item on how well it reflected its corresponding dimension (content validity), using a threepoint scale (1 = “not representative,” and 3 = “clearly representative”). Subject matter experts also assessed whether items (1) might be related to the other three relational processes (face validity), (2) were worded appropriately, and (3) were generalizable across contexts. Finally, on the basis of the preceding item analysis, we retained 20 items.
Quantitative Prestudy
Sample. Data were collected in the subsidiary of a U.S.based Fortune 500 information technology (IT) firm in Greece that provides mobile platform, cloud computing, and security solutions. The firm provides high-end, complex solutions that aim to boost customer efficiency by performing processes such as managing payroll and customer relationship management. The firm assigns a salesperson (i.e., account manager), who orchestrates all activities and serves as the primary contact point, to each customer firm. The firm provided us with the contact details of the key buyer who is the primary decision maker and is most knowledgeable about the solution purchasing process in the customer firm. We asked buyers to rate the focal firm’s salesperson—who had recently interacted with them during the purchase of an IT solution—relative to competing salespeople from firms the buyer had considered for the purchase of this solution (see Table 2). We used standard forward- and backward-translation procedures to translate the survey into Greek. We sent an online survey to a random sample of 440 buyers. After two reminders, we received 104 usable responses (23.63%).
TABLE 1 Definition and Operationalization of Key Constructs
TABLE:
| Items | CFA-Based Standardized Loadingsa | Dimension |
|---|
| Quantitative Prestudy and Study 3b The following statements refer to the activities that the salesperson from [Supplier Name] performed during the process of offering you an IT solution. Please rate this salesperson, relative to competing salespeople from companies you considered for this solution, in each of the following activities. Compared to competing salespeople, [Supplier Name]’s salesperson | | |
| … has a deep understanding of our needs. | .88/.72 | Customer requirements definition |
| … has a deep understanding of the broader objectives of my firm. | .83/.82 | Customer requirements definition |
| … asks the right questions to identify our needs. | .96/.82 | Customer requirements definition |
| … has a deep understanding of our firm. | .94/.77 | Customer requirements definition |
| … has a deep understanding of the goods/services we buy from other suppliers. | .92/.82 | Customer requirements definition |
| …designs goods and services that can work together as a solution. | .90/.91 | Customization and integration |
| …modifies goods and services so that they can work together as a solution. | .94/.77 | Customization and integration |
| …selects goods and services that can work together as a solution. | .90/.75 | Customization and integration |
| … personally takes care of the quick delivery of the proposed solution. | .88/.74 | Deployment |
| …personally monitors the installation of the proposed solution. | .88/.81 | Deployment |
| …knows the capabilities of the users of the proposed solution. | .92/.71 | Deployment |
| … provides us with the necessary information about the solution. | .90/.79 | Deployment |
| … keeps us updated about new developments after solution implementation. | .88/.88 | Postdeployment support |
| …always has a new solution to offer to satisfy our new needs. | .93/.89 | Postdeployment support |
| … develops a long-term relationship with us after solution implementation. | .91/.85 | Postdeployment support |
| … stays available after solution implementation. | .81/.83 | Postdeployment support |
| …maintains a continue dialogue with us after solution implementation. | .96/.84 | Postdeployment support |
| … calls on us after solution implementation to verify that our needs have been met. | .93/.82 | Postdeployment support |
| Studies 1 and 2d The following statements refer to the activities you perform during the process of selling a solution to your customers. Please rate yourself relative to salespeople from companies that are directly competing with you in offering solutions to the same customers, in terms of … | | |
| … having a deep understanding of customer needs. | .92/.62 | Customer requirements definition |
| … having a deep understanding of the broader objectives of customer firms. | .85/.76 | Customer requirements definition |
| … asking the right questions to identify customer needs. | .88/.72 | Customer requirements definition |
| … having a deep understanding of customer firms. | .91/.66 | Customer requirements definition |
| …having a deep understanding of the goods/services customers buy from other suppliers. | .73/.71 | Customer requirements definition |
| … designing goods and services that can work together as a solution. | .91/.86 | Customization and integration |
| Items | CFA-Based Standardized Loadingsa | Dimension |
|---|
| … modifying goods and services so that they can work together as a solution. | .93/.88 | Customization and integration |
| … selecting goods and services that can work together as a solution. | .94/.77 | Customization and integration |
| … personally taking care of the quick delivery of the proposed solution. | .82/.62 | Deployment |
| … personally monitoring the installation of the proposed solution. | .80/.68 | Deployment |
| … knowing the capabilities of the users of the proposed solution. | .87/.66 | Deployment |
| … providing customers with the necessary information about the solution. | .88/.64 | Deployment |
| … keeping customers updated about new developments after solution implementation. | .82/.64 | Postdeployment support |
| … always having a new solution to offer to satisfy customers’ new needs. | .78/.50 | Postdeployment support |
| …developing a long-term relationship with customers after solution implementation. | .90/.63 | Postdeployment support |
| … staying available after solution implementation. | .87/.74 | Postdeployment support |
| … maintaining a continue dialogue with customers after solution implementation. | .90/.79 | Postdeployment support |
| …calling on customers after solution implementation to verify that their needs have been met. | .77/.63 | Postdeployment support |
| Items | CFA-Based Standardized Loadingsa |
|---|
| Study 1e |
| Objective index of solution-based salesperson performance against quota at time1 | N.A. |
| Study 2f |
| The following statements refer to aspects of your performance. Please indicate the extent to which you agree with each of the following: |
| I am effective in contributing to my firm’s market share. | N.A. |
| I am effective in generating a high level of euro sales. | N.A. |
| I am effective in identifying major accounts in my territory and selling to them. | N.A. |
| I am effective in exceeding annual sales targets. | N.A. |
| I am effective in assisting my supervisor meet his or her goals. | N.A. |
| Items | CFA-Based Standardized Loadingsa |
|---|
| Study 1g |
| Please indicate the degree of your agreement with each of the following statements. Concerning interactions between salespeople that sell solutions in my sales unit, and members from other functional units, my salespeople … …and other business functions are integrated in serving the needs of our target markets. | .63 |
| Items | CFA-Based Standardized Loadingsa |
|---|
| … understand how other employees in our business can contribute to creating customer value. | .88 |
| … work hard with employees in other functions to solve customer problems thoroughly and jointly. | .93 |
| Items | CFA-Based Standardized Loadingsa |
|---|
| Study 2h |
| The following statements refer to the goods/services comprising the product lines your salespeople draw on to sell solutions to customers. Please indicate the extent to which you agree with each of the following: We offer many different product lines to our customers. | N.A. |
| Our salespeople promote product lines comprising a large number of products each. | N.A. |
TABLE:
TABLE:
| Items | CFA-Based Standardized Loadingsa |
|---|
| Study 3i |
| The following statements refer to your firm’s relation with [Supplier Name]. Please indicate the extent to which you agree with each of the following: |
| We feel indebted to [Supplier Name] for what they have done for us. | 0.79 |
| Our interactions with [Supplier Name] can be defined as “mutually gratifying.” | 0.81 |
| Our employees share close social relations with employees from [Supplier Name]. | 0.78 |
| We expect to be interacting with [Supplier Name] far into the future. | 0.83 |
| Maintaining a long-term relationship with [Supplier Name] is important to us. | 0.8 |
| Our business relationship with [Supplier Name] could be described as “cooperative” rather than an “arm’s-length” relationship. | 0.69 |
Measure assessment. We subjected the salesperson solution involvement scale to a confirmatory factor analysis (CFA). On the basis of modification indices, factor loadings, and model fit statistics (c2166 = 346.78, p<.01; root mean square error of approximation [RMSEA] = .10; normed fit index [NFI] = .97; nonnormed fit index [NNFI] = .98; comparative fit index [CFI] = .98), we deleted two items,2 reducing the scale to eighteen items (see Table 2). The modified model fit the data well: c2131 = 258.36, p<.01; RMSEA = .09; NFI = .97; NNFI = .98; CFI = .98. Composite reliability (CR) coefficients for the second-order construct and first-order dimensions exceed the cutoff value of .70. Convergent validity is evident; average variance extracted (AVE) values all exceed .50. Factor loadings are significant, with the lowest standardized loading equal to .81 (p<.01). The results suggest that significant systematic variance in the individual indicators can be attributed to the underlying latent construct, thereby providing empirical support for the second-order structure of our scale.
Phase II: Hypotheses Testing
Study 1
Sample. Study 1 was set in a Fortune Global 500 electronics firm that provides energy and electronics solutions to enterprise customers. We sent an online survey in English to the entire solutions sales force (168 sales engineers) and their managers (39) across the firm’s subsidiaries in 15 European Union countries: Austria, Belgium, Czech Republic, France, Germany, Greece, Hungary, Italy, Netherlands, Poland, Portugal, Romania, Spain, Sweden, and United Kingdom. Because of salesperson attrition, the sample dropped to 157. We sent weekly reminders over a six-week period and received 120 usable salesperson (76.4%) and 38 manager (97.4%) responses. We matched survey responses (time0) to objective, archival data on sales performance one year after the survey (time1).
Salesperson-reported measures. Given that salespeople were the respondents in this study, we made minor modifications to the introductory text and items in the solution involvement scale to fit with the context (see Table 2). Specifically, salespeople rated themselves relative to salespeople from firms that directly competed with them in offering solutions to the same customers (1 = “much worse,” and 7 = “much better”). Given the results of prior meta-analytic work suggesting that personal and internal/external environmental variables explain sales performance (e.g., Verbeke, Dietz, and Verwaal 2011), we consider several covariates to avoid model misspecification (see Study 1, Theme 1, in the Web Appendix). Specifically, we consider domain-specific expertise (mean number of hours spent weekly inside customer firms), organizational expertise (years working for the current firm), general sales expertise (years of selling experience across firms), activity control (i.e., four items from Miao and Evans 2013) and competitive intensity (i.e., three items from Jaworski and Kohli 1993). Finally, we employ coaching (six items from Heslin, Vandewalle, and Latham 2006) and salesperson’s job engagement (six items from Schaufeli et al. 2002) in our subsequent endogeneity procedure (see Study 1, Theme 1, in the Web Appendix).
Manager-reported measures. We measured sales unit cross-functional cooperation with three items adapted from Im and Nakata (2008) to fit our solution context (Table 2).
Objective, archival measures. We measure sales performance with objective data for the fiscal year following that of the survey (Table 2). Specifically, the firm employs a composite index to assess solution-based salesperson performance against quotas on seven key performance indicators, using a five-point scale (1 = “unsatisfactory performance against quotas,” and 5 = “excellent performance against quotas”). In addition, the firm employs an archival composite index to assess salesperson capabilities along five equally weighted items (i.e., creative thinking, proactive action, effective communication, teamwork, and ethical conduct of business) on a five-point scale (1 = “unsatisfactory,” and 5 = “excellent”). We employ this latter index in our subsequent procedure for ruling out endogeneity.
Measure assessment. To maintain a healthy ratio of sample size to number of estimated parameters while providing a stringent test of discriminant validity, we estimated two CFA models (with maximally similar constructs each) in the salesperson sample. The results of a CFA with salesperson solution involvement and job engagement exhibit good fit (c2248 = 388.17, p < .01; RMSEA = .07; NFI = .95; NNFI = .97; CFI = .98). Likewise, the fit of the second CFA with coaching, activity control, and competitive intensity fit the data well (c622 = 95.48, p < .01; RMSEA = .07; NFI = .95; NNFI = .97; CFI = .98). Composite reliability coefficients (AVEs) exceed .70 (.50) for all study constructs (see Table 2; see also Study 1, Theme 1, in the Web Appendix). These results provide evidence of reliability and discriminant validity. In addition, all factor loadings are significant (p < .01) and have standardized values ranging from .54 to .94, thus offering evidence of the convergent validity of the constructs (for construct intercorrelations, see Table 3, Study 1).
Model estimation. We employ hierarchical linear modeling (HLM) with full maximum likelihood estimation to account for the nested structure of data (Table 4). Despite the high response rate, our model considers the possibility that salesperson solution involvement is endogenous because salespeople with significant levels of engagement in solution activities might have selected themselves to respond to the survey. If so, the relationship between salesperson solution involvement and performance might be an artifact of selfselection-based endogeneity. We employ Garen’s (1984) procedure to correct for this type of endogeneity. We augment our model with the structural residual and interaction term obtained from this procedure to obtain consistent coefficients (see Study 1, Theme 2, in the Web Appendix). We group-mean-centered level 1 predictors and grand-meancentered level 2 variables (except for the dummy variables, which are employed to capture any unobserved business-unit effects). We used change in deviance scores (-2 · loglikelihood) to compare the fit of nested models. We fit Model 1, which contains main effects and covariates. In Model 2, we add the main effect of the moderator variable. Model 3 includes the hypothesized cross-level interaction (for model specifications, see Study 1, Theme 3, in the Web Appendix).
Results. As predicted in H1, we find a significant effect of salesperson solution involvement on objective sales performance (g40 = .59, p < .01; Model 1), after controlling for endogeneity and several covariates. Furthermore, sales unit cross-functional cooperation (H2: g41 = .25, p < .01; Model 3) positively interacts with salesperson solution involvement to influence objective sales performance. Specifically, salesperson solution involvement enhances sales performance when cross-functional cooperation is high (vs. low). This study advances beyond the Quantitative Prestudy by focusing on the salesperson’s overall solution activities over his/her entire portfolio of customers targeted for solutions, rather than customer-specific activities, and by employing salesperson-level performance data. It also shows that the factor structure of salesperson solution involvement developed at the customer–salesperson level—by focusing on what a salesperson does with a specific customer (Quantitative Prestudy)—also holds when focusing on the more general salesperson level of solution activities. Finally, the results demonstrate a positive effect of overall salesperson solution involvement on objective sales performance, and that this effect is amplified when the sales unit cooperates well with other functions during value-creating activities for customers.
TABLE 3 Descriptive Statistics and Construct Intercorrelations Across Studies
| | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|
| 1. Salesperson solution involvementa | 5.19 | 0.84 | | | | | | | |
| 2. Domain-specific expertise (hours)a, b | 15.89 | 12.02 | 0.12 | | | | | | |
| 3. Organizational expertise (years)a, b | 4.79 | 3.22 | -.01 | -.10 | | | | | |
| 4. General expertise (years)a, b | 13.14 | 7.27 | -.04 | 0.06 | .27** | | | | |
| 5. Activity controla | 3.39 | 0.7 | .27** | 0.14 | 0.07 | -.04 | | | |
| 6. Competitive intensitya | 5.28 | 1.01 | .20* | .18* | -.02 | -.02 | 3 | | |
| 7. Sales performance – quotab, c | 3.29 | 0.8 | .25** | 0.03 | -.03 | -.04 | 0.12 | -.11 | |
| 8. Sales unit cross-functional cooperationd | 5.45 | 1.11 | .20* | -.02 | -.08 | -.07 | .20* | 0.03 | 0.04 |
| | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
|---|
| 1. Salesperson solution involvementa | 5.86 | 0.58 | | | | | | | | | | | | | |
| 2. Sales performance (subjective)a, e | 5.5 | 0.93 | .53** | | | | | | | | | | | | |
| 3. General sales expertise (months)a, | 118.1 | 72.83 | .33** | .35** | | | | | | | | | | | |
| 4. Organizational expertise (months)a, | 84.47 | 61.04 | .15* | .22** | .52** | | | | | | | | | | |
| 5. Firm’s product portfolio scoped, e | 3.95 | 0.62 | .19** | 0.02 | -.05 | 0.06 | | | | | | | | | |
| 6. Firm’s customer orientationd | 4.56 | 0.4 | 0.08 | -.02 | -.12 | 0.09 | .29** | | | | | | | | |
| 7. Salespeople’s empowermentd | 3.85 | 0.42 | .21** | .13* | 0.1 | 0.03 | .40** | .18** | | | | | | | |
| 8. Competitive intensityd | 3.81 | 0.7 | 0.06 | -.03 | -.05 | 0.02 | .60** | .17** | .52** | | | | | | |
| 9. Technological turbulenced | 3.82 | 0.8 | 0.03 | -.05 | -.08 | -.03 | .47** | .33** | .35** | .32** | | | | | |
| 10. Environmental dynamismd | 3.37 | 0.77 | 0.06 | -.10 | -.08 | 0.01 | .59** | .31** | .43** | .59** | .62** | | | | |
| 11. Buyer negotiating powerd | 3.16 | 0.68 | -.03 | 0.03 | 0.09 | -.12 | 16** | -.13* | -.00 | 7 | -.01 | -.07 | | | |
| 12. Manufacturing dummyb, f, g | 0.38 | 0.49 | 0.07 | 0.07 | 0.1 | .13* | 0.11 | -.10 | 0.12 | .15* | -.24** | -.10 | -.22** | | |
| 13. Firm’s age (years)b, | 25.07 | 15.06 | 0.09 | 0.02 | 0.05 | .14* | .30** | .15* | .16* | .17** | -.15* | -.02 | -.24** | .30** | |
| 14. Firm’s size (number of employees)b, f | 129.14 | 114.88 | -.00 | -.04 | 0.06 | .13* | 0.07 | 0 | .14* | 0 | -.06 | 0.07 | -.36** | .21** | .45** |
TABLE:
| | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|
| 1. Salesperson solution involvementh | 5.17 | 0.91 | | | | | | | | | |
| 2. Sales performance – net profits (V)b, c | 41640.94 | 55933.03 | 0.14 | | | | | | | | |
| 3. Relationship tie strengthh | 5.1 | 0.92 | 0.05 | -.29** | | | | | | | |
| 4. Value received by the customerh | 5.21 | 1.05 | .53** | -.06 | .29** | | | | | | |
| 5. Customer know-how of solutionsh | 5.33 | 1.03 | .20** | .21** | -.13 | 0.01 | | | | | |
| 6. Customer adaptivenessh | 4.09 | 1.15 | 0.12 | -.22** | .29** | .21** | -.09 | | | | |
| 7. Solution importanceh | 5.02 | 0.98 | -.08 | .20** | -.34** | -.12 | -.01 | -.26** | | | |
| 8. Supplier firm relationship length (years)b, h | 11.25 | 8.12 | -.04 | -.02 | -.02 | -.01 | -.19* | .23** | 0.09 | | |
| 9. Salesperson relationship length (years)b, h | 4.65 | 4.05 | 0.05 | 0.02 | -.04 | 0.07 | -.20* | 0.08 | 0.07 | .63** | |
| 10. Number of sales callsb, c | 2.24 | 2.02 | 0 | 0 | 0.1 | 0.01 | -.02 | .16* | -.14 | 0.12 | 0.08 |
TABLE 4 HLM Results: Effect of Sales Unit Cross-Functional Cooperation (Study 1)
TABLE:
| Predictors (time0) | Dependent Variable: Objective Sales Performance (Solution-Based Salesperson Performance Against Quota at time1) |
|---|
| Main Effects and Covariates (Model 1) | Direct Effect of Moderator (Model 2) | Hypothesized Interaction (Model 3) |
|---|
| ga | SE | ga | SE | ga | SE |
|---|
| Level 1 |
| Intercept (g00) | 3.63** | 0.24 | 3.64** | 0.24 | 3.67** | 0.25 |
| Covariates |
| Domain-specific expertise (hours) (g10) | -0 | 0 | -0 | 0 | -0 | 0 |
| Organizational expertise (years) (g20) | 0 | 0.02 | 0 | 0.02 | 0 | 0.02 |
| General expertise (years) (g30) | -.01* | 0.01 | -.01* | 0.01 | -.01* | 0.01 |
| Activity control (g50) | 0.01 | 0.07 | 0.01 | 0.07 | 0.03 | 0.07 |
| Competitive intensity (g60) | -0.06 | 0.05 | -0.06 | 0.05 | -0.04 | 0.05 |
| ^e (g70)b | -.42** | 0.1 | -.42** | 0.1 | -.48** | 0.09 |
| Salesperson solution involvement (g80) | 0.05 | 0.05 | 0.05 | 0.05 | 0 | 0.05 |
| Hypothesized Effect |
| Salesperson solution involvement (g40) | .59** | 0.12 | .59** | 0.12 | .65** | 0.12 |
| Level 2 |
| Covariates |
| DummyBusiness Unit 1 (g01) | -0.03 | 0.68 | 0.02 | 0.69 | 0.02 | 0.71 |
| DummyBusiness Unit 2 (g02) | -0.28 | 0.29 | -0.29 | 0.29 | -0.27 | 0.3 |
| Direct Effect of Moderator | | | -0.06 | 0.14 | -0.05 | 0.15 |
| Sales unit cross-functional cooperation (g03) |
| Hypothesized Interaction |
| Salesperson solution involvement × Sales unit cross-functional cooperation (g41) |
| -2 × log-likelihood (d.f.) | 197.99 (13) | | 197.84 (14) | | 186.62 (15) | |
| Change in fit index | | | .15 (Dd.f. = 1) | | 11.22 (Dd.f. = 1)** | |
Robustness checks. We assessed the robustness of our results against (1) multicollinearity and (2) endogeneity due to omission of level 2 fixed effects. Results show that our findings are robust against such biases (see Study 1, Theme 4, in the Web Appendix).
Study 2
Sample. Study 2 involves a large-scale survey involving multilevel, multisource data across firms and industries. We selected industries that were systematically noted as solutions providers in business articles, academic studies, and discussions with senior executives in Phase I. This process resulted in the initial set of 660 firms, all subsidiaries of major multinational firms operating in Greece. A trained team of three research assistants approached firms to verify their involvement in offering solutions, explore their willingness to participate, and identify an appropriate manager with overall responsibility for providing solutions. A total of 199 managers from an equal number of firms agreed to participate (30%). Due to missing values, we retained 183 responses for subsequent use. Participating managers were instructed to
distribute research packets to a random sample of their business-to-business salespeople involved in providing solutions. In total, we distributed 1,700 surveys, and 402 were returned directly to us (24%). We next created a multilevel data set by matching 247 salespeople’s responses to 58 managers’ responses; this was done only for firms for which we had received at least 3 salespeople’s responses. Finally, we matched this data set to a third source of secondary, objective firm data containing variables used either as covariates or as input to our endogeneity checks (discussed subsequently).
Data quality checks. First, we assessed respondent fatigue by asking managers to indicate the extent of their agreement with the question “the survey contained interesting questions.” The mean response was 3.8 on a five-point scale. Second, managers had enough knowledge to provide us with meaningful responses to the survey, as indicated by a mean of 4.3 on a five-point scale. Finally, given managers’ hierarchical positions (about 68% held a Sales Director title, whereas the balance held titles like Managing Director or CEO) and tenure (about 68% had been with the firm for more than six years), their responses were assumed to be reliable.
Manager-reported measures. We developed two new items to measure each firm’s product portfolio scope, drawing on prior work (Sorescu, Chandy, and Prabhu 2003). Because the items were not expected to correlate theoretically, we treated the measure as a formative index (Table 2). A firm, for instance, may promote multiple product lines but not necessarily have many variants of each product. In line with a recent meta-analysis (Verbeke, Dietz, and Verwaal 2011), we also considered a number of covariates related to the internal/external environment to rule out omitted variable biases (see Study 2, Theme 1, in the Web Appendix): firm’s customer orientation (all six items from Narver and Slater 1990), salespeople’s empowerment (six items from Hartline, Maxham, and McKee 2000), competitive intensity (three items from Jaworski and Kohli 1993), technological turbulence (all four items from Jaworski and Kohli 1993), environmental dynamism (six items adapted from Homburg and Jensen 2007), and buyer negotiating power (four new items based on Narver and Slater 1990).
Salesperson-reported measures. We measured salesperson solution involvement as in Study 1 (Table 2). We indexed subjective sales performance with five formative indicators adapted from Sujan, Weitz, and Kumar (1994).3 Consistent with prior meta-analytic work (e.g., Verbeke, Dietz, and Verwaal 2011), we used salespeople’s general sales expertise (months of selling experience across firms) and organizational expertise (months working for the current firm) as covariates.
Secondary, objective data. We collected objective data on firm type (manufacturing vs. services), age (years in business), and size (number of employees) for the year of survey, as well as earnings before taxes (EBT), return on assets (ROA), and return on sales (ROS) one year after the survey (for construct intercorrelations, see Table 3, Study 2).
Measure assessment. A CFA on the full manager’s sample (n = 183) exhibits good model fit (c3262 = 676.50, p < .01; CFI = .92; RMSEA = .07; NFI = .85; NNFI = .91). Standardized factor loadings are significant and high, providing evidence of convergent validity (see Study 2, Theme 1, in the Web Appendix). Discriminant validity is established given that all AVEs exceed or are very close to .50, and CR values exceed .70. A CFA on the full salesperson sample (n = 402) exhibits good model fit (c2131 = 427.52, p < .01; CFI = .95; RMSEA = .08; NFI = .94; NNFI = .95). Standardized factor loadings of the salesperson solution involvement scale are significant and high, providing evidence of convergent validity. The scale shows evidence of discriminant validity, with the AVE exceeding .50, whereas CR exceeds .70 (Table 2).
Common method variance assessment. We employed the unmeasured latent method factor technique to assess the extent of common method variance (CMV) bias in the salesperson sample. We partitioned the variance in our measures to trait, method, and random error variance by running two CFAs: one with traits and a method factor and one with traits only. We find that (1) the median effect of CMV on items’ variances is 13%, well below the median amount of CMV found across studies (Carlson and Kacmar 2000); and (2) all relationships remain significant after we include the method factor (p < .01). To account for any systematic effects of CMV on our results, however, we include the method factor scores as a covariate in our model estimation.
Model estimation. Our analysis is based on a subsample of the firms that provided at least three salespeople’s responses. This nonrandom selection process, however, might lead to biased estimates because excluded firms might be different than included firms. To address this issue, we adopt Heckman’s (1979) selection model (for details, see Theme 5 in the Web Appendix). Using the estimates from this model, we calculated the inverse Mills ratio (l^), which we include in our model to control for the effect of unobserved firm characteristics related to the selection process and thus to obtain unbiased estimates.
We employ HLM with full maximum likelihood estimation to test hypotheses (Table 5). We group-mean-centered level 1 predictors and grand-mean-centered level 2 variables (except for the level 2 dummy). We first fit Model 1, which contains main effects and covariates only. In Model 2, we add the main effect of the moderator variable. Model 3 includes the hypothesized cross-level interaction (for model specifications, see Study 2, Theme 3, in the Web Appendix).
Results. Replicating results of Study 1, we find that salesperson solution involvement positively relates to subjective sales performance (H1: g40 = .56, p < .01; Model 1), after we control for CMV, selection biases, and several covariates. We also find that product portfolio scope (H3: g41 = .22, p < .05; Model 3) positively interacts with salesperson solution involvement to influence sales performance. Specifically, salesperson solution involvement enhances sales performance when product portfolio scope is high (vs. low). Study 2 replicates the positive effect of salesperson solution involvement on sales performance found in Study 1 and extends it by examining a supplier firm–level moderating condition (i.e., product portfolio scope) across solution contexts.
Robustness checks. We assessed the robustness of our results to (1) multicollinearity, (2) endogeneity due to omission of important level 2 fixed effects, and (3) CMV. Results of these analyses alleviate concerns with respect to such biases (see Study 2, Theme 4, in the Web Appendix).
TABLE 5 HLM Results: Effect of Firm’s Product Portfolio Scope (Study 2)
TABLE:
| Predictors (time0) | Dependent Variable: Subjective Salesperson Performance |
|---|
| Main Effects and Covariates (Model 1) | Direct Effect of Moderator (Model 2) | Hypothesized Interaction (Model 3) |
|---|
| ga | SE | ga | SE | ga | SE |
|---|
| Level 1 |
| Intercept (g00) | 5.77** | 0.15 | 5.76** | 0.16 | 5.76** | 0.16 |
| Covariates |
| General sales expertise (months) (g10) | 0 | 0 | 0 | 0 | 0 | 0 |
| Organizational expertise (months) (g20) | .00* | 0 | .00* | 0 | .00* | 0 |
| Common method factor (g30) | .44* | 0.2 | .44* | 0.2 | .45* | 0.19 |
| Hypothesized Effect |
| Salesperson solution involvement (g40) | .56** | 0.12 | .56** | 0.12 | .56** | 0.12 |
| Level 2 |
| Covariates |
| Mills inverted ratio (^l) (g01) | .81* | 0.4 | .76* | 0.41 | .76* | 0.41 |
| Manufacturing dummy (g02) | -.60* | 0.31 | -.56* | 0.32 | -.56* | 0.32 |
| Firm’s age (years) (g03) | -0.01 | 0.01 | -0.01 | 0.01 | -0.01 | 0.01 |
| Firm’s size (number of employees) (g04) | -0 | 0 | -0 | 0 | -0 | 0 |
| Firm’s customer orientation (g05) | -0.27 | 0.24 | -0.25 | 0.25 | -0.25 | 0.25 |
| Salespeople’s empowerment (g06) | .35* | 0.2 | .36* | 0.2 | .36* | 0.2 |
| Buyer negotiating power (g07) | 0 | 0.1 | 0.01 | 0.1 | 0.01 | 0.1 |
| Competitive intensity (g08) | -0.19 | 0.12 | -0.2 | 0.12 | -0.2 | 0.12 |
| Technological turbulence (g09) | -0.11 | 0.08 | -0.12 | 0.08 | -0.12 | 0.08 |
| Environmental dynamism (g010) | -0.08 | 0.12 | -0.1 | 0.13 | -0.1 | 0.13 |
| Direct Effect of Moderator |
| Firm’s product portfolio scope (g011) | | | 0.05 | 0.12 | 0.05 | 0.12 |
| Hypothesized Interaction |
| Salesperson solution involvement × Firm’s product portfolio scope (g41) | | | | | .22* | 0.12 |
| -2 × log-likelihood (d.f.) | 561.34 (17) | | 561.20 (18) | | 558.39 (19) | |
| Change in fit index | | | .14 (Dd.f. = 1)n.s. | | 2.81* (Dd.f. = 1) | |
Study 3
Sample. Study 3 was set in Germany in a major pumping system manufacturer that sells energy, water management, and building services solutions consisting of a combination of sophisticated goods/services. Solutions aim to improve the energy/building efficiency of clients by performing processes such as energy monitoring and life cycle cost management. The firm assigns a salesperson (i.e., account manager), who orchestrates all activities and serves as the primary contact point, to each customer firm. The firm provided us with the contact details of key, knowledgeable buyers who are the primary decision makers in their firms. As with the Quantitative Prestudy, we asked buyers to rate the focal firm’s salesperson solution activities (Table 2).
We administered the survey in German. We pretested the survey with two key salespeople and buyers and modified it to fit the firm context. We sent surveys to 510 buyers and received 190 responses (37.25%) after sending three reminders. Respondents averaged 11.30 years of firm tenure and 3.72 years with the salesperson. To mitigate concerns over CMV bias as well as endogeneity due to simultaneity, we separated measurement of salesperson solution involvement (time0) from sales performance, which was operationalized with objective data obtained one year after the survey (time1).
Data quality checks. We asked buyers to indicate whether the survey contained interesting questions; the mean was 3.33 (five-point scale), suggesting that respondent fatigue was not a concern. We also asked buyers to rate the degree of their knowledgeability on the survey questions; the mean was 3.94 (five-point scale), strengthening confidence in data quality.
Customer-reported measures. As in the Quantitative Prestudy, customers assessed the salesperson’s engagement in solution activities specific to the customer firm (Table 2). Relationship tie strength was measured with six items adapted from prior work (Ganesan, Malter, and Rindfleisch 2005;
Rindfleisch and Moorman 2001). We developed a new fiveitem scale to capture customer adaptiveness, drawing on the work of Tuli, Kohli, and Bharadwaj (2007). In line with the extant literature showing that financial results achieved in a customer relationship depend on multiple factors (e.g., Palmatier, Scheer, and Steenkamp 2007), we consider several control variables to avoid omitted variable bias (see Study 3, Theme 1, in the Web Appendix). Specifically, we control for (1) customer–supplier relationship effects by employing value received by the customer (all three items from Palmatier, Scheer, and Steenkamp 2007) and relationship length with the supplier firm (number of years the customer firm has worked with the focal supplier); (2) customer characteristics by employing customer know-how of solutions (all five items from Ghosh, Dutta, and Stremersch 2006) and solution importance to the customer (three items from Cannon and Homburg 2001); and (3) salesperson–buyer relationship effects by employing relationship length with the salesperson (number of years the buyer has worked with the focal salesperson).
Objective, archival measures. Sales performance is captured with an objective measure of solution-based net profits (V) contributed by a customer at the end of the year following the survey (Table 2). We use the natural log of this measure in our analyses. To rule out any potential effects of sales effort (Bowman and Narayandas 2004), we control for the objective number of sales calls the salesperson made to the customer firm during the period of study.
Measure assessment. Multi-item measures were subjected to a CFA with adequate fit: c12;041 = 2,121.67, p < .001; RMSEA = .07; NFI = .90; NNFI = .94; CFI = .94. Standardized factor loadings are significant and high, providing evidence of convergent validity (see Table 2; see also Study 3, Theme 1, in the Web Appendix). Discriminant validity is established given that all AVEs exceed .50, whereas reliability coefficients exceeded or are very close to the value of .70 (for construct intercorrelations, see Table 3, Study 3).
Model estimation. Model estimation was based on 185 out of the 190 customer firms for which we were able to match survey to objective data.4 We adopted a partial least squares (PLS) approach to test hypotheses. We consider the possibility that salesperson solution involvement is endogenous because customers that are either (1) serviced by salespeople with low levels of salesperson solution involvement or (2) not considered important to the focal firm (and thus are systematically subject to differing sales efforts) could have self-selected themselves out of responding to the survey. In both cases, the relationship between salesperson solution involvement and performance might be an artifact of self-selection-based endogeneity bias. We thus employ Garen’s (1984) procedure (see Study 3, Theme 2, in the Web Appendix). We augment our PLS model with the structural residual and interaction term obtained from this procedure to obtain consistent coefficients. We fit Model 1 to test main-effect hypotheses (Table 6). Next, we estimate Model 2, which includes the direct effects of moderators. Finally, we fit Model 3 to test the hypothesized interaction terms, using the product indicator approach. <pb/>Results. Replicating findings from Studies 1 and 2, we find support for the positive effect of salesperson solution involvement (at time0) on objective sales performance (at time1) (H1: g = .20, p < .05; Model 1). Importantly, this positive effect holds true after we control for self-selectionbased endogeneity bias, as well as a set of covariates. Further, in predicting sales performance, the interaction between salesperson solution involvement and relationship tie strength is significant and positive (H4: g = .22, p<.01; Model 3). In particular, results show that the positive effect of salesperson solution involvement on net profits is amplified under stronger relational ties. In contrast, strong relational ties do not compensate for low salesperson solution involvement, as evidenced by reduced levels of performance. However, in predicting sales performance, we find the interaction between salesperson solution involvement and customer adaptiveness to be nonsignificant (H5: g = .10, p > .10; Model 3). Study 3 contributes by extending the results of Studies 1 and 2 and by showing that stronger customer–supplier relational ties moderate the effect of salesperson solution involvement on customer-level sales performance data. <pb/>General Discussion <pb/>Salespeople play a prominent role in customer solution provision, a crucial strategy to generating revenue in business markets. To date, however, three critical research questions remain unanswered: (1) How can salesperson involvement in customer solutions be conceptualized? (2) Does a salesperson’s involvement in solution-related activities pay off? and (3) What boundary conditions influence the effectiveness of salesperson solution involvement? Our study addresses these questions and advances academic knowledge and managerial practice in several ways. <pb/>Research Contributions <pb/>First, previous studies have contributed to our understanding of the relational processes underlying customer solutions only at the firm level (Tuli, Kohli, and Bharadwaj 2007), thus leaving managers puzzled about how the salesperson should go about enacting these processes. This paucity of research is surprising considering prior theoretical work that has established the importance of looking at individual salesperson activities for some time (Weitz 1981) or the multiple calls for solutionspecific research at the individual level (e.g., Evanschitzky, Wangenheim, and Woisetschla¨ger 2011; Grewal et al. 2015; Ulaga and Loveland 2014). Our study fills this void by acknowledging and highlighting the specific role salespeople play during customer solution processes. Taking an individuallevel approach allows us to decompose firm-level solution processes into a measurable set of activities performed by the salesperson—that is, the employee who is primarily responsible for interacting with customers during solution provision (e.g., Murtha, Bharadwaj, and Van den Bulte 2014; Steward et al.
2010). In doing so, our work offers a new conceptualization of the individual-level activities that constitute salesperson involvement in customer solutions.
TABLE 6 Results of Structural Equation Analyses: Effects of Customer–Supplier Relationship Characteristics
TABLE:
| Dependent Variable: Objective Sales Performance (Solution-Based Net Profits [V] Contributed by a Customer at time1) |
|---|
| Predictors (time0) | Main Effects and Covariates (Model 1)a | Direct Effects of Moderators (Model 2)a | Hypothesized Interactions (Model 3)a |
|---|
| Hypothesized Effects |
| Salesperson solution involvement | .20* | .18* | .18* |
| Salesperson solution involvement | | | .22** |
| Relationship tie strength | | | 0.1 |
| Salesperson solution involvement | | | |
| Customer adaptiveness | | | |
| Direct Effects of Moderators |
| Relationship tie strength | | -.17* | -.18* |
| Customer adaptiveness | | -.17** | -0.09 |
| Covariates |
| Value received by the customer | -0.13 | -0.04 | -0.04 |
| Customer know-how of solutions | .17* | .13* | 0.1 |
| Solution importance | .19* | 0.1 | 0.04 |
| Supplier firm relationship length (years) | -0.06 | -0.01 | -0.05 |
| Salesperson relationship length (years) | 0.09 | 0.06 | 0.04 |
| Number of sales calls | 0.05 | .08* | .12* |
| ^eb | -0.09 | -0.08 | -.10* |
| ^e × Salesperson solution involvement | -0.07 | -0.09 | -.14* |
| Explained variance (R2)c | 0.13 | 0.18 | 0.23 |
| Q2d | 0.12 | 0.21 | 0.21 |
In addition, we leverage a unique data set to operationalize a salesperson solution involvement scale that aligns with prior work (Tuli, Kohli, and Bharadwaj 2007). We find support that salesperson solution involvement is a second-order construct comprising four dimensions that reflect the customer–supplier relational processes of defining customer requirements, customizing/integrating goods/services, deploying goods/services, and providing postdeployment customer support. The scale is validated across studies, firms, and countries and is adaptable to different contexts (i.e., measured with salesperson self-reports or customer assessments). We therefore contribute a measurement instrument to scholars aiming at deepening understanding of the critical mediating, moderating, and antecedent variables of salesperson solution involvement.
Second, our study contributes to knowledge on whether salesperson involvement in customer solutions pays off. Our findings provide evidence that salesperson solution involvement is systematically related to improvements in sales performance, including objective, time-lagged measures of quota achievement and net profits. Consequently, we answer calls for research into the effectiveness of solution provision (e.g., Grewal et al. 2015; Tuli, Kohli, and Bharadwaj 2007). The magnitude of effects is suggestive of a construct that warrants more attention in future research.
Third, this study demonstrates that the payoffs from salesperson solution involvement are contingent on a set of previously unexamined boundary conditions. We discover that understanding the performance implications of salesperson solution involvement is a complex endeavor involving conditions related to characteristics of both the supplier firm and the customer–supplier relationship. Specifically, the influence of salesperson solution involvement on sales performance is strengthened when (1) the sales unit’s cooperation with other functions is high, (2) a firm’s product portfolio is broad and deep, and (3) the customer–supplier relationship is characterized by high levels of closeness and reciprocity. These findings demonstrate that supplier-firm resources made available to a salesperson (e.g., quality of interactions with employees in other functional units during value creation for customers; availability of options in the product portfolio) as well as the characteristics of the customer–supplier relationship (i.e., strong relational ties) influence the effectiveness of salesperson solution involvement. Interestingly, the hypothesized moderating effect of customer adaptiveness is not supported. One plausible explanation is the industry examined in Study 3, which is described by a highly complex and risky purchasing process and, as such, is suggestive of a “customer is king” form of relationship. In this form of relationship, although supplier customization is at very high levels, the customer may not be reciprocating with similar levels of adaptation to their own processes (Cannon and Perreault 1999).
Managerial Implications
Our study helps managers answer three critical questions. The following subsections elaborate on these managerial implications.
What do salespeople do during customer solution provision? First, what appears to be lacking is a common understanding among practitioners of the set of salesperson activities related to offering customer solutions (e.g., Evanschitzky, Wangenheim, and Woisetschla¨ger 2011). Our work offers managers a comprehensive definition of salesperson solution involvement that can help them avoid communication problems when designing and rolling out training or strategic initiatives related to solution provision within their firms.
Second, drawing on the notion of relational processes (Tuli, Kohli, and Bharadwaj 2007), we conceptualize salesperson solution involvement as the degree to which a salesperson engages in activities that help his or her firm provide end-to-end solutions to the salesperson’s customers. Managers should pay attention to the underlying activities in all four relational processes as they try to improve solution involvement among their salespeople. Specifically, initiatives such as training, incentives, or the design of selling strategies need to cover all four processes rather than selectively focusing on some of them. For example, training initiatives should aim at improving salespeople’s activities related to (1) asking the right questions to uncover broader business objectives in customer firms, (2) selecting goods/services from a firm’s product portfolio that can work together as a solution, (3) understanding the capabilities of users within customer firms, and (4) keeping customers continuously updated about new developments.
Third, we offer a scale, tested across contexts and countries, to measure the salesperson’s involvement in solution provision. Given the interest of firms in the topic, managers can use this scale to perform internal (by administering it to salespeople) or competitive benchmarking against rival sales forces (by administering it to customers), thereby providing useful insights to sales leaders. The items in the scale are easy to comprehend and it takes little time to administer the scale internally to salespeople or externally to customers. It is encouraging to note that the Fortune Global 500 firm that participated in Study 1 has incorporated the scale into its planning initiatives, thereby providing some evidence of its managerial usefulness.
Does salesperson solution involvement pay off? Across samples and contexts, we found that salesperson solution involvement is positively related to increases in sales performance. Importantly, in two of our studies (i.e., Studies 1 and 3), performance impact is assessed with archival data, which were captured one year after the administration of the salesperson solution involvement scale. Thus, it is possible to argue with some degree of confidence that investing in salesperson solution involvement (as conceptualized here) should relate positively to improvements in future sales performance. This finding is encouraging, given anecdotal reports lamenting over firms struggling to wrench their salespeople away from engaging in the wrong activities when providing solutions (Koivuniemi 2016).
What should managers do to make salesperson solution involvement more effective? Although understanding salespeople’s solution-related activities is key to success, managers can support salespeople in additional ways. We find evidence that conditions related to the characteristics of both the supplier firm and the customer–supplier relationship can amplify the effectiveness of salesperson solution involvement. Firms should work to implement initiatives that help salespeople get the most out of their solution-related efforts. For instance, managers might want to make sure that salespeople are provided with an adequately broad and deep product portfolio when performing activities related to the configuration and customization of valuable customer solutions. A second initiative might be to improve the sales unit’s interactions with other functions when executing value-creating activities such as those involved in customer solution processes. This can be done, on one hand, by enhancing salespeople’s understanding of how nonsales employees can contribute skills and knowledge critical to the creation of value for customers, and, on the other hand, by improving working relations with nonsales functional units.
Regarding customer–supplier relationship characteristics, our results suggest that firms need to be careful with their segmentation strategies in that not all customers should be targeted for solutions. Specifically, we find evidence that firms will benefit more from supporting salesperson solution involvement with customers that are willing to invest in a longterm, cooperative relationship with the supplier. This means that when firms build and maintain strong ties with their customers, salespeople will see more effective reinforcement of activities associated with requirements definition, customization and integration, deployment, and postdeployment support. However, we do not find support for an effect of customer adaptiveness on the relationship between salesperson solution involvement and sales performance; this might be because customers examined in Study 3 do not generally reciprocate the high levels of supplier customization with similar levels of adaptation (Cannon and Perreault 1999).
Limitations and Future Research Directions
Our study is an initial foray into an exciting area of research, which could be expanded in several ways. First, our focus in this study is on the activities salespeople perform during solution processes. Bringing the salesperson to the forefront was required to unveil the role a key employee group plays in customer solutions. This point notwithstanding, we acknowledge that multiple functional units engage in activities that enact the relational processes inherent in customer solutions. Research that will expand our perspective and examine the activities of both sales and nonsales employees is necessary to provide a
complete picture of customer solutions. Doing so implies that the unit of analysis changes from the individual to the firm level. Future investigators can build on our approach—that is, decompose the relational processes into a granular set of activities that employees from nonsales units perform—and, together with the sales-related activities identified in our study, measure relational processes as firm-level processes.
TABLE 7 Generalizability of Results: Comparison of Countries Studied with North American and BRIC Countries
TABLE:
| | UN Rankinga | OECD Rankingb | IMF Rankingc | World Bank Rankingd |
|---|
| Countries Studied |
| Austria | Very high human development | Developed economy | Advanced economy | High-income economy |
| Belgium | Very high human development | Developed economy | Advanced economy | High-income economy |
| Czech Republic | Very high human development | Developed economy | Advanced economy | High-income economy |
| France | Very high human development | Developed economy | Advanced economy | High-income economy |
| Germany | Very high human development | Developed economy | Advanced economy | High-income economy |
| Greece | Very high human development | Developed economy | Advanced economy | High-income economy |
| Italy | Very high human development | Developed economy | Advanced economy | High-income economy |
| Netherlands | Very high human development | Developed economy | Advanced economy | High-income economy |
| Portugal | Very high human development | Developed economy | Advanced economy | High-income economy |
| Spain | Very high human development | Developed economy | Advanced economy | High-income economy |
| Sweden | Very high human development | Developed economy | Advanced economy | High-income economy |
| United Kingdom | Very high human development | Developed economy | Advanced economy | High-income economy |
| Hungary | Very high human development | Developed economy | Emerging market | High-income economy |
| Poland | Very high human development | Developed economy | Emerging market | High-income economy |
| Romania | High human development | Emerging economy | Emerging market | Upper-middle-income economy |
| Comparison Countries |
| United States | Very high human development | Developed economy | Advanced economy | High-income economy |
| Canada | Very high human development | Developed economy | Advanced economy | High-income economy |
| China | High human development | Emerging economy | Advanced economy | Upper-middle-income economy |
| Brazil | High human development | Emerging economy | Emerging market | Upper-middle-income economy |
| Russian Federation | High human development | – | Emerging market | Upper-middle-income economy |
| India | Medium human development | Emerging economy | Emerging market | Lower-middle-income economy |
aUnited Nations Development Programme (2015).
bOrganisation for Economic Co-operation and Development (2017).
cInternational Monetary Fund (2016).
dWorld Bank (2017).
Notes: All sources were accessed February 7, 2017.
Second, given that customer solutions require cross-functional coordination (Steward et al. 2010), such a firm-level measure should also include activities that serve to manage the various
dependencies between functional activities and critical resources residing within the supplier and customer firms (Crowston 1997). For instance, given the key role of salespeople in customer solu
tions, such a measure should capture the activities salespeople perform to coordinate intrafirm and interfirm interactions taking place when a team of buyers interacts with a salesperson or selling
team (Kumar, Petersen, and Rapp 2014).
Third, despite our extensive validation efforts, future research should further refine the salesperson solution involvement scale. Specifically, we acknowledge that four of the five items in the “customer requirements definition” assess the extent to which a salesperson has developed an understanding of customer needs
rather than the activities performed to develop this understanding.
Thus, future research should improve this aspect of the scale by explicitly measuring such activities.5 Doing so, however, may be challenging given that the activities the salesperson engages in to learn about customer needs might involve actions that are not observable by the customer (e.g., salespeople working with external industry experts). In addition, future research should consider items that explicitly capture the aspect of “customization” in the “customization and integration” dimension.
Fourth, we build on the work of Tuli, Kohli, and Bharadwaj (2007) to conceptualize salesperson solution involvement. Our approach needs to be supplemented with or contrasted to alternative conceptualizations such as that of Ulaga and Reinartz (2011).6
Fifth, our studies are based on firms operating in advanced economies/societies, as evidenced by the most recent country rankings (see upper portion of Table 7). Thus, the samples allow generalization of our findings to North American countries (see lower portion of Table 7). The generalizability of our results to the North American context is further supported by U.S. Department of Labor (2017) data that show that salespeople promoting solutions engage in activities such as arranging the delivery of goods/services. However, we acknowledge that the countries studied here are less representative of BRIC (i.e., Brazil, Russia, India, and China) countries (see lower portion of Table 7). Accordingly, future research needs to examine the extent to which country characteristics might explain, for instance, the degree to which salespeople are involved in the deployment or postdeployment processes.
Sixth, given the knowledge-based nature of sales in a solution context (Verbeke, Dietz, and Verwaal 2011), future researchers should examine the antecedents of salesperson solution involvement, such as salesperson knowledge brokering or tacit knowledge transfer skills.
Finally, because a solution-specific sales performance measure that can be applied across industries and contexts is not currently available, the performance measure we employed in Study 2 does not completely isolate the sales of solutions. Given firms’ increasing interest in customer solutions, development of such a measure could make a significant contribution to the field. We hope that our work will stimulate additional research efforts in this vital area for marketing theory and practice.
1We thank the editor and an anonymous reviewer for their assistance with construct definition.
2Deleted items were “Integrates goods and services from various sources into a total solution” in the “customization and integration” dimension and “Makes sure that customers receive the appropriate training about the solution” in the “deployment” dimension. We note that these items refer to activities that may only be necessary in specific situations, although they could be important in other situations in which appropriate training is necessary or the supplier must work with partners and integrate goods/services from various
sources. We thank an anonymous reviewer for this helpful elaboration on our findings.
3We thank an anonymous reviewer for suggesting that we employ a formative specification for this construct. Also, although we had originally included all seven indicators from the work of Sujan, Weitz, and Kumar (1994) in this index, at the request of an anonymous reviewer, we deleted two indicators that referred to selling individual products.
4The five firms excluded from the matched sample required that they remain anonymous. As such, no links could be made to archival data. A series of Mann–Whitney U-tests between included versus excluded firms showed no significant differences across any of the predictors or covariates included in model estimation (p > .10).
(Study 3)
5To validate our results and guide future research, we ran a posttest among a convenience sample of 100 American salespeople wherein we examined a new version of the items comprising the “customer requirements definition” dimension (for details, see Theme 6 in the Web Appendix). Results provide evidence that increase confidence in our original itemization of this dimension. We thank two anonymous
reviewers for their insights on this aspect of our scale. 6We thank an anonymous reviewer for contributing this insight.
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DIAGRAM: FIGURE 1 Conceptual Model and Studies Layout
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Record: 154- Sales-to-Marketing Job Transitions. By: Johnson, Jeff S.; Matthes, Joseph M. Journal of Marketing. Jul2018, Vol. 82 Issue 4, p32-48. 17p. 2 Charts. DOI: 10.1509/jm.17.0279.
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- Business Source Complete
Sales-to-Marketing Job Transitions
Careers evolve over time and can take many paths as they develop. Within marketing and sales, a common variant of career progression is to begin in a sales position and then advance internally into a marketing role. Doing so provides employees with unique but complementary sets of skills, experiences, and perspectives that may increase their efficacy as marketers. However, sales-to-marketing job transitions (SMJTs) can also be suboptimal and result in adverse outcomes. Although the sales–marketing interface literature has examined how the two functions work together, the SMJT process is unclear. To provide an understanding of this phenomenon, the authors conduct in-depth interviews across a host of different companies and industries with 56 informants who successfully transitioned intraorganizationally from sales to marketing, informants who transitioned but did not remain in marketing, and executives. They develop a theoretical model consisting of transition motivation, acquisition, preparation, and encounter. They also advance individual and organizational facilitators of SMJTs and discuss SMJTs’ potential positive and negative effects on the organization.
Careers represent long-term journeys of preparation and selection of one or more occupations (Cron and Slocum 1986). When selecting new career paths, professionals experience a job transition, or a “major change in work role requirements or work context” (Nicholson and West 1989, p. 182). A commonly occurring type of job transition is the sales-to-marketing job transition (SMJT). People may be attracted to sales positions early in their careers for the benefits they provide, though many also enter the selling profession to later move into marketing roles (Johnston and Marshall 2013), as sales jobs can offer initial conduits into the organization (Cron and Slocum 1986). Many companies such as Dow AgroSciences, Union Pacific Railroad, and Stanley Black & Decker expect employees to begin first in a sales role before pursuing marketing opportunities in the firm. Given that a common route for obtaining a marketing role in an organization is through an initial sales role, understanding this form of job transition is important for both academics and practitioners. Furthermore, insights gleaned from the SMJT process can assist in better cognizing job transitions as a whole.
Understanding SMJTs is also important because substantial differences may exist between sales and marketing roles. Despite marketing and sales being integral to each other’s and the firm’s success, such differences affect the degree and quality of cooperation between the two functions (Homburg and Jensen 2007). Furthermore, marketing and sales can be configured differently and be represented in a variety of forms across organizations (Homburg, Jensen, and Krohmer 2008). Marketing is often responsible for big-picture tasks related to the firm’s product mix, strategies, and promotional efforts (Rouzie`s et al. 2005), whereas sales is often more tactical in nature, focusing on continuous, day-to-day activities including customer relationship management (Cespedes 1995; Rouzie`s et al. 2005). Although both marketing and sales affect many marketing activities (Krohmer, Homburg, and Workman 2002), marketing often has a greater influence on activities such as advertising content, satisfaction management, and product development, while sales often has a greater influence on geographic market expansion, customer service, and unit pricing (Homburg, Workman, and Krohmer 1999). Beyond differences in job duties, marketers and salespeople can also differ in terms of customer versus product perspectives and short- versus long-term orientations (Homburg and Jensen 2007). Such complexity, uniqueness, and interdependency between functions make investigating SMJTs critically important.
Research on intraorganizational forms of job transitions encompasses a variety of mobility types, such as job rotations (e.g., Ortega 2001), lateral moves (e.g., Feldman and Ng 2007), and promotions (e.g., Kalleberg and Mastekaasa 2001). The process of salespeople transitioning into marketing roles—a specific context of intraorganizational mobility—can be understood, in part, through existing job transition frameworks. Nicholson and West (1989) discuss a process model in which employee transitions occur in four stages: ( 1) preparation (expectations and anticipation occurring before the change), ( 2) encounter (emotional impact and sense making occurring when first starting the new job), ( 3) adjustment (personal development occurring to better fit the requirements of the new job), and ( 4) stabilization (steadiness being achieved between the employee and the new role). Additional research has examined a variety of job transition aspects, including socialization (e.g., Batistic and Kase 2015), employee retention (e.g., Verbruggen, Cooman, and Vansteenkiste 2015), and career development (e.g., Campion, Cheraskin, and Stevens 1994). Furthermore, firms can facilitate effective job transitions by providing training, mentoring, and social support to transitioners (Kraimer et al. 2011; Moyle and Parkes 1999; Verbruggen, Cooman, and Vansteenkiste 2015).
Effective job transitions can yield multiple benefits to organizations, and as such, their use should be strategically considered by marketing, management, and human resources. Intraorganizational mobility helps employees develop crossfunctional skills and an enhanced understanding of other firm functions (Guenzi and Troilo 2006; Xie, Song, and Stringfellow 2003), and it can lead to increased individual and organization capabilities (Nicholson and West 1989). Job transitions can also improve employees’ job satisfaction (Kalleberg and Mastekaasa 2001) and organizational commitment (Anderson, Milkovich, and Tsui 1981). Furthermore, job transitions can strengthen the integration and cohesion of disparate functions in the firm (Matthyssens and Johnston 2006). Finally, job transitions can be an important part of corporate strategy, given the benefits they may impart to employee development (Baruch and Peiperl 2000). However, some drawbacks are also associated with job transitions. Personnel changes can be costly in terms of time lost while employees learn their new roles (Campion, Cheraskin, and Stevens 1994). Job transitions can also cause increased stress in the workforce, due to the novelty of the change (Nicholson and West 1989; Rudisill and Edwards 2002). Finally, the potential exists for job transitions to be mismanaged and overused, leading to negative outcomes for the firm, such as loss of functional identity and impaired performance (Rouzie`s et al. 2005).
Despite the prevalence of SMJTs and the potential magnitude of their impact, knowledge germane to these phenomena is limited. Research has discussed job rotation, one form of intraorganizational mobility, as a way to increase functional collaboration between marketing and sales (Cespedes 1995). Within the SMJT context, research has found that job rotation reduces prejudices by providing insights into a variety of tasks (Matthyssens and Johnston 2006). Extant conceptualizations have discussed job rotation as an antecedent to outcomes such as increased competencies, skills, and understanding between counterparts (Guenzi and Troilo 2006; Matthyssens and Johnston 2006) but fail to explore its process or the facilitators of its efficacy.
Several gaps also exist in the understanding of job transitions overall. Although research has advanced job transition process model insights (Nicholson and West 1989), no studies have assessed the comprehensiveness of this model, and findings within model stages have been fragmented. In addition, this process model begins at the preparation stage, providing valuable opportunity to expand understanding of motivations leading to job transitions as well as approaches for acquisition. Moreover, opportunity exists to learn more about individual-and organizational-level facilitators that may be essential to efficacious transitions. Finally, job transition literature has focused relatively more on the benefits of transitions than on providing a balanced perspective of commensurate drawbacks. As such, we aim to answer the following research questions: What is the nature of the job transition process model? What motivates transition? What approaches do salespeople use to acquire a job in marketing? How do transitioners prepare for their new role? What challenges must transitioners cope with after the transition has occurred? What role do individual- and organizational-level factors play in facilitating job transitions? What are the benefits and drawbacks of SMJTs to the organization? Answers to these questions can provide valuable insights for employees as well as the sales, marketing, management, and human resource functions of the firm.
To answer these questions and provide an understanding of the factors affecting job transition incidence and efficacy, we examine intraorganizational mobility through SMJTs. Because an exploration of SMJTs is novel to academic inquiry and addresses a complex, nuanced issue, this topic is ideally suited to a qualitative research design (Creswell 2007; Johnson 2015b). Through theoretically exhaustive in-depth interviews, we advance insights into and propositions of the SMJT process, facilitators of SMJTs, and positive and negative outcomes to the organization. The findings from this research not only provide insight into the marketing literature but also extend what is known in the broader job transition literature in management and other related areas, as many of the results are new across domains.
We engage in a multifirm, grounded theory design to gain insights into SMJTs. Grounded theory helps researchers advance explanations, models, and theory of underexplored research areas using the views of participants familiar with the phenomena of interest (Corbin and Strauss 2008). Consistent with other marketing articles investigating complex, novel topics (e.g., Johnson and Sohi 2016; Ulaga and Reinartz 2011), we use grounded theory to explore SMJTs.
Sample and Data Collection
To acquire the necessary data for our inquiry, we employed theoretical sampling, a commonly used technique in marketing research (e.g., Johnson and Boeing 2016; Tuli, Kohli, and Bharadwaj 2007). In theoretical sampling, researchers select participants knowledgeable in their topic of interest (Corbin and Strauss 2008). Investigating SMJTs required a specific subset of marketers—namely, those who had transitioned from an initial sales role to a marketing role within the same company. We recruited informants using a variety of contact mechanisms, including postings on LinkedIn and local chapters of the American Marketing Association, discussions with multiple marketing advisory boards, solicitations of former MBA students, and meetings with personal connections. In addition to successful transitioners, we interviewed executives and “failed” transitioners (i.e., those who did not remain in marketing, but rather moved on to another function or company) from the same companies as the “successful” transitioners to provide balance to our inquiry.
We used a semistructured interview protocol to gain informant insights through open-ended questions. We asked several questions related to the SMJT experience and its facilitation and outcomes. Respondents shaped the flow of the interviews by expounding on key areas of interest and expertise. We used probing questions to follow up on insights and encourage elaboration. The interviews occurred over the telephone and in person and were all recorded for transcription and analysis. We continued to interview respondents until we achieved theoretical saturation—the point at which no new insights or understandings are gleaned from the procurement of additional data (Creswell 2007). We were able to obtain a sample with substantial individual, organizational, and industrial diversity to ensure robustness of the data (Creswell 2007). Respondents came from organizations in a variety of industries, including aeronautics, agricultural/food products, business services, construction, entertainment, financial services, health care, logistics, media, pharmaceuticals, publishing, telecom, and transportation. Thirty-two of the interviews were with successful transitioners (4.09 years’ experience in sales, 6.22 years’ experience in marketing; 37.5% female), 11 were with failed transitioners (3.82 years’ experience in sales, 3.45 years’ experience in marketing, 5.91 years’ experience in other; 36.4% female), and 13 were with executives (20.92 years’ experience; 38.5% female).
Data Analysis and Reliability
We analyzed the data by initially coding informants’ quotations in descriptive, low-level codes based on their language used and then subsequently classified all codes into higher-order categories (Corbin and Strauss 2008). For example, an informant’s discussion of the desire to stay home more was initially coded as such (i.e., stay home more), subsequently classified into work–life balance, and then classified into its motivation component of the model (for a coding structure visual depiction, see Maitlis and Lawrence 2007, p. 65). We structured and coded our data using the qualitative analysis software NVivo 11 in the interest of comprehensive data treatment. After importing our verbatim transcripts of informant interviews, we developed a tree-node structure in NVivo, which allowed us to easily display quotations by case and by code. This process enabled us to assess the refutability of our findings by simultaneously examining data from multiple cases to determine whether any systemic difference of contextual factors (e.g., industry, experience, gender) influenced the qualitative insights. Doing so also allowed us to challenge our assumptions and analyses. While overall the findings were quite consistent, we uncovered certain codes that varied depending on experience, which we discuss in the “Findings” section. We also performed member checks to establish the validity of our findings (Corbin and Strauss 2008). We gave our study results to informants, asking them to critically assess our interpretations and findings and to comment on the veracity of our conclusions. Members provided positive feedback regarding the accuracy and relevance of the findings. We also reinterviewed many of the initial respondents in a second wave of collection to obtain their relative assessments of the advanced model’s components. Finally, we conducted an interrater examination to further establish the reliability of our coding. In line with extant qualitative examinations (Tuli, Kohli, and Bharadwaj 2007, p. 3), we provided two independent judges with the interview data to “verify the accuracy of the themes we identified from the field data” germane to the SMJT process from 30 randomly selected cases. We then assessed interrater reliability using the proportional reduction in loss approach advanced by Rust and Cooil (1994). The resulting proportional reduction in loss for our examination was .93, which exceeds the threshold recommended for qualitative examinations (Rust and Cooil 1994).
The findings reveal many new insights relevant to intraorganizational transitions from sales to marketing roles, as well as broadly to job transitions overall, regardless of firm function. Figure 1 shows novel insights germane to the SMJT process, the facilitators affecting the efficacy of SMJTs, and SMJTs’ organizational outcomes—both positive and negative. We focus on the findings that are novel to the job transition literature overall, thus informing the SMJT context while also providing valuable insights for management, human resources, and the wider base of theory related to job transitions as a whole.
Respondents provided clarity on how the SMJT process unfolds. Specifically, they identified six facets of transition in the SMJT process: motivation, acquisition, preparation, encounter, adjustment, and stabilization. Many interesting and novel elements and themes were provided for transition motivation, acquisition, preparation, and encounter. However, respondents’ comments on the final two stages provided largely generic insights reinforcing existing research on how employees develop and achieve stability over time, and thus, we do not discuss them further. Overall, in addition to informing the SMJT context, process findings provide valuable extensions to job transition insights generalizable beyond marketing (e.g., operations transitioning to marketing, finance transitioning to strategic planning).
SMJT Motivation
Respondents reported that they transitioned from sales to marketing for many reasons, including implicit expectations, marketing power, work–life balance, strategic involvement, organizational altruism, and educational application. These reasons varied from extrinsic (e.g., work–life balance) to intrinsic (e.g., educational application) motivations. Respondents also noted some motivations already advanced in the broader job transition literature (e.g., career development, obtaining new skills and experiences); however, we focus on the more novel motivations.
Extrinsic motivations. Extrinsic motivations for SMJTs refer to the external or instrumental reasons respondents made the change to marketing. For example, some respondents noted that they were motivated to make the SMJT because doing so was an implicit expectation in the organization. Organizations have many written and unwritten rules for expected behavior, especially regarding career advancement. As a senior business manager for a transportation company noted, his organization had an unwritten policy about the necessity of an SMJT for employees wanting to advance their careers:
I think it’s implicit from the time you hire into the organization [when] they lay out the org chart [that SMJT] is encouraged…. You need to have a diverse background which includes both a marketing role and a field sales role…. That seems to be the perspective that you get from most folks, especially people that are within high-level leadership positions.
In addition, respondents noted that the level of marketing power affected their motivation to make the transition. In some organizations, sales holds the power, as it is directly responsible for bringing revenue into the firm. In others, marketing is considered more strategic and important than sales and, thus, more powerful. As a marketing director in the publishing industry reflected, marketing power can incentivize salespeople to make the transition:
If you’re in a sales position where it’s seen as marketing has more of the power, it would seem like more of a progression or a promotion, I would think, or just taking on more responsibility to move to the position where you have the power.
Another reason salespeople made the move to marketing was for work–life balance. While sales roles can provide job flexibility, they also may require significant travel and time away from family. Marketing, however, is generally more of a headquarters-based position. A senior business manager in the transportation industry was motivated to make the SMJT because of work–life balance considerations:
A big part of the marketing job benefit for me is sort of a little bit personal in the sense that I was getting my MBA at the same time, and I could stay home more, and I had a six-month-old baby…. A lot of the sales jobs back then were really out in the field.
Intrinsic motivations. Intrinsic motivations for SMJT entail respondents making the change to marketing for self-actualizing or altruistic reasons. Respondents were motivated to make the SMJT for the strategic involvement it provided. Some respondents felt too tactical in their sales role and thus desired a more involved, strategic role in marketing. A senior vice president in the telecom industry noted that he finds participating in his organization’s strategic decisions exciting:
It’s personal for me with respect to being involved in strategic direction of not only where the company is headed, but why it’s heading there and then how we’re going to get there…. It’s that ability to participate in vision, strategy, what the organization’s doing … that excite[s] me.
Furthermore, some respondents were motivated to make the SMJT out of organizational altruism. These respondents classified their marketing departments as underperforming and unresponsive; however, rather than being deterred by this poor performance, they saw an opportunity to help the organization by improving the marketing department. A marketing program manager at a logistics brokerage realized she could make a positive impact on a subpar marketing department:
I reached out to [marketing], and they gave me kind of our standard presentation, which was [dated]…. I ended up redoing the entire thing myself and putting it into a different format…. That’s when I thought that maybe I should be in marketing, and not so much in sales.
Finally, some respondents were motivated to make the transition for the educational application of what they had learned while earning their degree in marketing. Some sales roles are devoid of traditional marketing activities, and salespeople who have obtained a marketing degree may want to employ this learning in their jobs. A product manager at a medical devices company noted that she enjoyed her marketing education, valued what she had learned, and wanted to apply her education through a marketing role:
Definitely being able to use my education more. So, like I said, I took those classes for a reason, and I was really excited to use all the things that I’ve learned to … update the website or [send] out mass emails and stuff like that [and] … embrace my marketing education.
Relative motivation proposition. In our second wave of interviews, we asked respondents to reflect on the types of motivators that prompted the SMJT and to assess which had a stronger impact on the decision. Of the two primary types of motivators, most respondents indicated that intrinsic motivations had a greater impact on prompting the transition than extrinsic motivations. They noted that because SMJTs may entail certain decreased extrinsic factors (i.e., less money), intrinsic factors are more important in prompting the transition. A senior vice president in a telecom company discussed that extrinsic (lower-order) motivations are not usually the focal point; rather, more salespeople make the transition out of intrinsic (higherorder) motivations:
It would be the higher-order things in terms of personal growth and the ability to participate in where the organization is headed and help the business mature. Those lower-end things aren’t why most people make that transition. They’re doing it for the opportunity to transform a business, to change a business, to grow a business.
P1: Intrinsic motivations prompt SMJTs to a greater extent than extrinsic motivations.
Respondents recognized that their intraorganizational acquisition of the marketing role varied drastically from their sales position acquisition. They advanced different foci in their interviews for the marketing position. Specifically, they employed sales achievement, strategy, and analytical foci to acquire their marketing roles.
Sales achievement focus. Salespeople transitioning to marketing roles have an advantage over applicants from outside the organization because they are a known entity. By employing a sales achievement focus (highlighting their success within the organization in the sales role), salespeople aimed to positively position themselves to hiring managers in marketing. Sales achievement is especially valuable because it is germane to and verifiable within the organization, as opposed to experience an external candidate may have obtained. A senior brand manager in the publishing industry noted that having sales achievement data allowed her to illustrate her accomplishments and convince the hiring manager she was the best person for the job:
I think a big thing that I was able to do, having the sales experience, is to be able to have actual performance data, to be able to say, “This is my territory, this is the growth that I achieved.” Just having metrics that I was able to refer to, it’s a big thing. What’s your record, and do you have a record that you can point to?
Strategy focus. Respondents also noted the need to include a strategy focus in which they communicate their ability to see the big picture for the organization. A vice president of marketing in the security services industry recognized that the attributes he highlighted in acquiring his sales role were less relevant to his acquisition of the marketing role, so instead he emphasized his ability to strategize and capitalize on opportunities in the market:
I have a different approach when I interviewed for sales jobs [than] when I interviewed for marketing jobs…. When I interviewed for the marketing roles, it’s all about strategy. It’s all about looking into the marketplace, solving problems, identifying the gaps in the market.
Analytical focus. In addition, transitioners used an analytical focus to highlight their ability to make logical, datadriven decisions in the marketing job acquisition process. Many marketing jobs require advanced analytical capabilities. A senior business manager in a transportation firm incorporated this focus into his interview by highlighting the analytical experiences he engaged in as part of his sales role:
My responses in that interview were more analytically driven because I know that that’s what the focus of those jobs were, and so I had to try to demonstrate that from the sales experiences that I had. Probably a little bit more deep-dive than I normally would have done.
Relative acquisition propositions. Our transitioners and executives revealed consistencies and differences in the mechanisms salespeople use to transition to a marketing role based on the experience level of the transitioner. Most deemed a sales achievement focus as least effective in increasing the likelihood of a salesperson being hired for a marketing role. Respondents also noted that achievement in sales differs from that in marketing (i.e., high-performing salespeople may not necessarily make the best marketers), and thus, this focus may be less desirable. For early-career salespeople, it is most important to show hiring managers that they possess the necessary abilities to make informed, data-based decisions in their marketing role. Early-career marketing jobs often require direct application of analytical skills, and thus this skill is prized to a greater extent. In addition, early-career transitioners may not possess enough strategic experience to highlight in interviews. Later-career salespeople often want to transition to higher-level marketing jobs, and focusing on their strategic ability is more persuasive in interviews. These transitioners may not engage in ground-level analytics but, rather, set the strategic direction for their teams. A senior human resources director in the transportation industry noted how a salesperson’s focus should vary depending on tenure:
For younger salespeople, I’d probably take the analytical approach. You know, for that immediate transition over, analytical is the skillset that’s required. If you’re making the transition at a higher level, like a director level or executive level, then absolutely strategy.
P2: An analytical focus increases the likelihood of SMJT acquisition for early-career salespeople.
P3: A strategy focus increases the likelihood of SMJT acquisition for later-career salespeople.
SMJT Preparation
Respondents noted the criticality of the period between their notification of acquiring the marketing role and their actual transition date into the role. Respondents indicated the need for soon-to-be transitioners to prepare themselves to make the jump. Respondents advanced two primary ways of preparing for their transition into marketing: broad and targeted preparation.
Broad preparation. Transitioning salespeople recognized that their marketing role would be quite different from their sales role and tried to prepare for the move by gaining general marketing information. Some respondents attempted to augment their marketing knowledge base by reading current marketing texts. A marketing program manager in the logistics brokerage industry noted that she read marketing books extensively to help her transition:
I actually bought a bunch of books about marketing and about brands and about that kind of stuff, and I tried to self-educate as much as I could. I tried to absorb as much as I could. Before my transition, I just really tried to read. I just kind of looked everywhere I could to learn.
Another way transitioners increased their broad-based knowledge of marketing was by taking marketing courses. Some salespeople come into organizations with majors other than marketing and thus have never received any formal marketing education. Others recognize that the marketing profession is highly dynamic and that current courses can supplement their understanding. A vice president of marketing in the printing industry discussed how he would have his transitioners take a “crash course” in marketing at a local university:
There were some classes that people would go to. I used to have my product managers go to the University of [state]. They used to have a product management class. Even when I started, I actually went and did this too; we have people go do that.
Targeted preparation. Some respondents took a different approach, focusing more on ways to increase their knowledge in areas specific to the job. One such means of gathering this specific knowledge was to probe current marketers. Some soonto-be transitioners realized that a great deal of insight could be obtained before their transition by engaging current marketers within their organization. A senior brand manager in a publishing organization recalled her interactions with future colleagues:
I had phone calls with other members of the team that I was going to be on, just learning from them, their thoughts and their experiences of the products and markets. Just trying to learn as much as I could from them before I actually got back and started doing the job.
Another focused manner of learning in which transitioning salespeople engaged was data diving specific to their impending marketing role. Respondents noted that organizations often had analytic systems they could access. Transitioners capitalized on this resource by working to understand the nature of their specific market before the transition. A market analyst in the transportation industry used this approach to improve his understanding of his market:
We have some analytics tools at [company] that let us grab data in terms of customer shipments by certain business segments and things like that…. I remember going into [software program] and pulling some data to see what the business looked like. There was a little bit of legwork I did in advance to know and understand about things.
Relative preparation proposition. When asked to provide an evaluation on which form of preparation—broad or targeted—was the superior approach in preparing themselves for a marketing role, some respondents indicated that a broad approach was the optimal route. These respondents believed that having a better-rounded, deeper understanding of marketing was more valuable in making the transition than was specific, focused knowledge that could be obtained after the transition had been made. However, most respondents believed that a targeted approach was better, as such preparation provides more immediately relevant content to help in their new role. A senior vice president in a telecom company explained the relative value of targeted (focused) versus broad preparation:
I would definitely say focused…. You can read the textbook, but you need to understand the specifics of how the company does marketing and what the role is within marketing, because it changes based on the company. I mean reading books will help, but not nearly as much.
P4: Targeted preparation helps salespeople prepare for their transition to marketing to a greater extent than broad preparation.
SMJT Encounter
Respondents noted that their SMJT came with some challenges. After their transition, these newly minted marketers needed to acclimate to their new job reality. Respondents spoke about two primary types of issues they needed to deal with in their new marketing role: the loss of certain benefits associated with their former sales role and the gain of new challenges arising from their new marketing role.
Loss of benefits. While sales roles are not without their challenges, they also possess certain benefits that may be absent in marketing roles. Salespeople had to come to terms with the loss of these benefits in their new marketing role, such as decreased compensation. Salespeople are often compensated through commission, and their earnings can exceed those of their marketing counterparts. A marketing director at a computer software firm noted that his level and control of pay diminished when making the transition to marketing:
With commission and everything else, usually sales [people] within an organization are going to reap the highest salaries. So, in terms of thinking about a ceiling, at least with this organization, my ceiling was probably lowered going to marketing over sales.
Salespeople also dealt with decreased freedom associated with the new marketing role. Sales positions often come with high levels of flexibility in which salespeople can set their own schedules. Furthermore, when field salespeople transition to marketing, they may be required to work at headquarters. A senior business manager in the transportation industry noted how transitioning to marketing reduced the freedom of his job:
You’re back in the headquarters, you’re back in what we call the “glass palace,” you are under the microscope at all times. You lose freedom of being out in the field sales position. You are kind of under the thumb of executive management back in headquarters.
Respondents also needed to adjust to decreased customer interaction in their marketing roles. Salespeople have extensive interactions with customers and frequently engage in many social activities with them. In moving to marketing, however, they often lose this contact and these activities. A senior director of integrated marketing in the broadcasting industry lamented the loss of these job attributes:
I miss the hospitality aspect of it, the wining and dining clients, and the lunches, and all of that, because there’s a lot more of that on the sales side than on the marketing side, so I miss that. I do miss interacting with clients, because right now my role, I’m very internally facing.
Finally, some respondents found decreased excitement in their marketing roles as compared with their sales roles. In marketing, projects can be long-term in nature, with the results of actions taken being somewhat opaque. By contrast, salespeople have more immediate and direct exposure to success or failure. A vice president of global innovation in a construction products organization noted that sales offers more excitement than marketing:
How can I put it, the emotions of working in a marketing role were much more middle of the grass, whereas in sales the highs are higher, and the lows are lower. There wasn’t kind of the thrill of the hunt that when you close the big account, or you were able to take share away from a competitor at a big account, that really felt great. There just [weren’t] those opportunities.
Gain of challenges. In addition to coming to terms with their lost benefits, transitioners needed to deal with new challenges in their marketing role. For example, salespeople may find increased pressure associated with the marketing role. Marketing places additional challenges on the plate of the transitioner, which can be quite daunting. A senior business manager in the transportation industry noted that he knew the expected duties before joining marketing, but he did not anticipate the significant increase in collective pressure:
The marketing jobs are a little bit more challenging, they’re more of a meat-grinder, and all of the people coming from the field, myself included, the challenge can be pretty extreme…. I didn’t realize how much pressure can actually be in some of these positions…. The task was a lot more intense than I originally probably anticipated.
Another new challenge for transitioned salespeople was the increased job ambiguity that came from moving to marketing. Sales roles often have relatively clearer goals and means for achieving those goals than marketing. A marketing director in the print media industry recognized this disparity between roles, with the marketing role being more ambiguous:
Sales was a little bit more defined. It was like, “Here’s your sales objective, here are the things you’re going to sell, here’s your territory.” It was more, I don’t want to say black and white, but it was more defined versus marketing. [Marketing] was much more ambiguous.
Exposure to increased company politics was another hurdle in the SMJT process. The remote nature of sales roles often insulates salespeople from the politicking that may occur at headquarters. However, after making the transition to marketing, exposure to organizational politics increases dramatically. A general manager in the printing industry noted how this occurred in his transition:
I would say you’re closer to the internal crap. You start seeing the political animals. You start seeing the different ways that people conduct themselves within an organization. You really don’t see it as much when you’re out there in a remote field situation.
Relative encounter propositions. The relative weighting of the difficulty of encounter factors (i.e., loss of benefits or gain of challenges) was contingent on the respondents’ duration of employment in the sales role. Long-tenured salespeople had often become fully entrenched in their sales role and accustomed to its many benefits. It was difficult for these people to give up the perks of the sales role. As a marketing manager in the agricultural industry noted, the loss of freedom from his former sales role was the most salient challenge he encountered:
Honestly, the hardest part was moving into a structured office every day…. I came from working out of my house as a sales guy. So, my day was kind of my day. [When] moving into a structured office … it took me a good eight months to get used to that environment again.
Early-career salespeople, however, had less time to become accustomed to the benefits of the sales role and entrenched in their perspectives, so they were less affected by the loss of these benefits. However, because these respondents were less established in the organization than their senior colleagues and wanted to make positive organizational impressions, they were very much affected by the gain of new challenges associated with their new roles in marketing. A senior marketing manager in the publishing industry explained:
The new challenges. The loss of benefits is real for sure, but I feel like that loss of benefit piece, while that is an adjustment, I knew going in 100% what those things were. I mean, the challenges piece is harder to anticipate because you can’t really know until you know.
P5: The gain of new challenges associated with the new marketing role is more difficult to cope with than the loss of benefits associated with the former sales role for early-career transitioning salespeople.
P6: The loss of benefits associated with the former sales role is more difficult to cope with than the gain of new challenges associated with the new marketing role for later-career transitioning salespeople.
All salespeople encountered challenges in their transition to marketing. However, certain factors espoused by respondents made the transition more efficacious. Respondents reported many different facilitators of SMJT efficacy on both individual and organizational levels.
Individual-Level Facilitators
Respondents advanced certain individual-level factors that can affect the efficacy with which salespeople transition into marketing. These factors came in two varieties according to respondents: who the transitioners already are (traits) and what the transitioners do after the transition (actions).
Traits. Respondents identified several trait factors (i.e., more stable, enduring orientations and characteristics) affecting the efficacy with which salespeople make the transition to marketing. For example, successful transitioners need to have a marketing mindset. Passionate marketers see the world differently from the rest of the population. Transitioners possessing a marketing mindset instinctually think about marketing as part of their daily lives. A vice president of marketing in the aeronautics industry noted the importance of a transitioner’s propensity to view the world through a marketing lens:
It’s not natural for everyone to look at things from a marketing mindset. Some of us walk into a restaurant and simply take it for what it’s worth. Others of us will walk into a restaurant and say, “If they would just do these things a little different.” It’s that creative, strategic, almost intuitiveness that a person might have and legitimately says, “I’m wearing a marketing hat.”
Successful transitioners also need to possess a team orientation. Sales positions can be individually focused, making team emphasis less critical. However, as a marketer, possessing a team orientation is important. Marketing roles are often marked by a significant number of team-based projects. A senior brand manager in a publishing company noted that some salespeople find working on a team challenging; however, to effectively make the transition, they need to successfully work with many other internal stakeholders:
Being a sales rep is a very isolated job…. You are responsible for doing everything that you need to do. A lot of times in marketing, it’s much more of a team-based job…. You have to collaborate, be able to work on a team. For some people in sales, that’s very challenging.
The type of sales experience the transitioners possessed also had an impact on the efficacy of the transition. Specifically, those with inside sales experience had an advantage over those with field sales experience. Inside sales experience gave transitioners direct access and exposure to marketers with whom they are now working. A product manager in a medical devices firm noted how such experience allowed her to accrue more knowledge because she did not need to engage in mediated communication but, rather, could ask questions directly:
Being on inside sales, I was at [headquarters] where outside sales reps usually aren’t. Because I was able to walk down the hall and talk to the product manager and ask them a technical question where an outside sales rep has to pick up the phone and try [to] get a hold of that person or send an email. I think that made a huge difference, at least for me.
Actions. Respondents also noted that action-based factors (i.e., more transient activities) affected the efficacy with which salespeople transitioned to marketing. For example, transitioners who practiced proactive feedback solicitation were able to increase the efficacy of their SMJT. Former salespeople may be unsure of their performance given the different nature of the marketing role. As such, soliciting performance feedback can help them identify both strengths and opportunities for improvement more expeditiously. A marketing specialist at a logistics brokerage proactively scheduled meetings with his boss to make sure he stayed on course and corrected any deficiencies:
I took it upon myself to set up something where [I] and my direct boss meet once a month…. I wanted to make sure I was getting feedback…. That was all self-generated. By setting up that meeting with my boss, she basically had an every four-week way of telling me, “Hey, you’re doing good at this stuff. We need to get you there on this stuff.”
Respondents also noted that extensive information gathering facilitated efficacious SMJTs. Transitioners undergo a necessary learning curve before they match the knowledge of their peer marketers. The gathering of information from their peers can help them get up to speed more quickly. A sales and marketing leader in an agricultural products organization was relentless about asking his peers about how they acquired relevant market information. Doing so helped him build relevant knowledge stores more rapidly than if he had attempted to learn everything on his own:
I just started asking questions…. When I hear about a trend in our industry that I wasn’t aware of, I’m like, “Well how can you know that?” and then they’d show me in the report. I [found] three people I could really trust and say, “I don’t know this stuff, could you help me?”
Another action respondents discussed in facilitating the SMJT was outsourcing of certain tasks in the new role. Delegating key tasks at which the transitioner does not excel was an important attribute noted for separating effective from ineffective SMJTs. The new marketing role may contain elements that involve both strengths and weaknesses of the transitioner. Outsourcing certain activities can prevent transitioners from becoming mired in their limitations. A business director in an agricultural products company was highly skilled in marketing communications but was not a good copywriter. Instead of wasting his valuable time on this activity, he outsourced the activity to free himself up for tasks at which he could excel:
I think I’m a great marketing person and a very good marketing communications person but I’m not an excellent writer. I’m a bad copywriter, so I had to learn very quickly either I can spend hours doing crappy copy or I can invest some time, get a network of either in-house or third-party people that I can go to. I can manage some of these things that, yes, on paper I can do it, but I can’t do everything, so some things you outsource.
Relative individual-level facilitator proposition. While both traits of and actions by transitioners can affect the efficacy of the SMJT, respondents indicated that traits are more important. Respondents discussed that the makeup of the transitioner is the key component to a successful transition. Marketing roles often require certain abilities that may diverge from those necessary to perform in a sales role. As such, regardless of what actions they may take, if transitioners are ill-suited for their new job, they are unlikely to perform at a high level in the new role. A vice president at a print media firm described this notion:
I think it comes down to the foundation, and the biggest predictor of success is who you are. Then you can optimize your performance based on feedback loops and your actions.
P7: Transitioner traits are a better predictor of transition efficacy than transitioner actions.
Organizational-Level Facilitators
Organizational factors also have the potential to increase the efficacy with which salespeople transition to marketing. Some of these facilitators are outside the organization’s control, whereas others are directly controllable. Cognizance of demographic, instructional, and cultural factors can help firms aid salespeople in effectively making the move to marketing.
Demographic factors. Respondents noted several demographic factors about firms that affect people’s efficacy of transitioning into marketing roles. One such factor is the organizational age of the firm. As organizations evolve, institutional knowledge accrues among their members. A component of this knowledge is how to effectively transition employees across various roles, including salespeople to marketing roles. A marketing manager in a telecom company noted that older companies are better suited for helping people transition because of their increased experience with the task:
You need one of those old [companies] that have been around forever, and they’ve accumulated enough resources … required to train the workforce [for] transitions that are recurring within the workplace, and really assisting people on their journey from point A to point B within the company. I think older companies are able to do that more effectively.
The organization’s products can also affect transition efficacy depending on their degree of product differentiation. Specifically, when a firm’s products are highly differentiated, SMJTs are likely to be more effective because the salespeople making these transitions bring deeper understanding of the products’ value propositions and how customers perceive them. A director of sales and marketing in the building materials industry noted that salespeople’s continual delivery of the value proposition for differentiated products helps them be more effective in their marketing role:
I think when you have situations where it is an active value-based sales process, that salesperson, a successful salesperson, will make a better marketer because they have to truly understand and communicate the value proposition. In a commodity-type product, that salesperson is not going to have that same set of skills.
Another product trait that affects the efficacy of SMJTs was whether the firm’s core products were primarily services or goods. The inseparability and intangibility of services make sales experience more vital to marketers. Whereas new marketers without sales experience may be better able to understand and market goods sold by their firms, services may require increased understanding of customer needs and wants. As a director of marketing in a facilities services company noted, services have more complicated value propositions for which sales experience benefits the marketer:
I would say, particularly in a service industry, I think [SMJT] is relevant. Consumer packaged goods for example—do you really need to know how to sell them to market them? Probably not, because I think that you can develop familiarity just by using the product…. But in the service industry you have to. I think it’s much more complicated.
Instructional factors. The respondents also noted many facilitators that organizations can directly control through their provision of learning opportunities. One important consideration for firms is to provide transitioners with a marketing academy. Firms may erroneously assume that salespeople can seamlessly transition to marketing roles without proper training, as they already have experience with the firm and its products. However, the marketing role can vary drastically from the sales role and require specific training for the transitioner to be most effective. Whereas other job transition examinations have discussed training, our respondents discussed a highly formal, academy-like structure. An executive marketing director in a pharmaceutical firm has a formal marketing academy that transitioners attend to ensure that they are properly schooled and know how to market within the organization:
It’s called [company name] Marketing Academy…. It’s kind of a schooling, if you will, on marketing and inner workings. You’re educated and learn everything about marketing and all different facets. That’s what they do to prepare you on that side.
Firms can also improve their SMJTs by using embedded marketing cross-training with their salespeople. Participating in traditional marketing functions (e.g., strategy making, pricing, promotional content) not only helps salespeople gain new perspectives and understanding but also increases SMJT efficacy. More exposure to marketing activities allows salespeople to increase their marketing familiarity, so that when the transition occurs, they already know many elements of the marketing role. A general manager in the printing industry indicated that because he was heavily involved with marketing in his field sales role, he came into his marketing role with a good grasp of what he needed to do to be effective:
I was involved with marketing even when I was in the field. I was involved in quite a bit of test marketing, and I was involved in new product development counsels, sales counsels that were focused on some of the things that marketing was doing. Yeah, I was definitely not coming in blind. I definitely had a sound understanding of what I was getting into.
Cultural factors. Cultural elements within organizations can also facilitate SMJTs. One such element is SMJT openness. While SMJTs were uncommon in some organizations, other firms displayed widespread cultural acceptance, and even expectation, of this practice. Under an open culture to SMJTs, which in turn creates a critical mass of transitioned employees, subsequent transitioners were better able to learn and integrate into their new marketing role. A marketing program manager in a logistics brokerage noted that cultural negativity can occur in the transition if there are not others who have previously made the same transition, whereas SMJT openness lends to a supportive environment:
I would say, it all depends on the organization culture as far as how many previous salespeople are in the marketing group, because you don’t want to end up in a situation where there is a marketing group of no previous salespeople from that organization, and someone transfers over, because there is going to be some culture shock there.
Another cultural aspect respondents indicated as a facilitator of the SMJT is the role formality of the marketing position. Marketing positions vary in their level of formality, with some being highly structured and regimented and others unstructured and open. The activities and goals of a marketing position can be relatively opaque in comparison with a sales position. An assistant vice president in the transportation industry noted that firms with highly structured marketing roles facilitate the SMJT:
A structured marketing role helps. For a salesperson that is going into a marketing role for the first time, I think that structure would be important for that person to understand the path…. I mean I can tell ten different people what a marketing job is, but how they actually approach and do that job may be ten different ways, and some may be on the path and some may be off.
Firms also varied in the degree of customer integration they employed to create a “culture of the customer” in their marketing departments. Some firms had a strong customer-focused culture and required that marketers directly call on at least one customer in their marketplace. In addition to facilitating a reality check for subsequent marketing actions, this requirement provided the transitioning salespeople an aspect of the new job with which they were already familiar. As a vice president of continuous improvement in the transportation industry noted, this customer-integrated culture provides comfort to transitioning salespeople:
Marketers had to [directly] own one customer in their marketplace.… And so, that gave you somewhat of a kind of a calming point moving over [to marketing].
Relative organizational-level facilitator proposition. Respondents noted that the impact of demographic, instructional, and cultural factors on transition efficacy differs and that cultural factors are the most important in facilitating the transition. In some organizational cultures, transitioners were encouraged, supported, and welcomed into their new roles with open arms, providing an environment conducive to transition. These factors had a marked impact on their transition. However, respondents noted that some cultures were instead unconducive, hostile, and negative toward transitioners, thus drastically inhibiting transitioners in their new role. A marketing agronomist in the agriculture industry identified the hierarchy of impact as cultural, instructional, and demographic factors:
The biggest driver of those three is cultural. Looking at an organization and fitting into a marketing department that already had some balance in [it], and so … it was not a culture shock to jump in. Then instructional would be behind that. Demographics would be the least effective of those three from my experiences.
P8: Cultural factors affect the efficacy with which the SMJT occurs to a greater extent than instructional or demographic factors.
Outcomes of SMJTs
Respondents provided many positive outcomes associated with SMJTs that have already been discussed in the job transition literature, including employee development and learning, new insights and perspectives in roles, understanding of the relationships with other firm functions, employee satisfaction and commitment, and improved collaboration between functional areas. Similarly, they discussed known drawbacks of SMJTs, such as increased cost to the organization and higher stress for employees. However, as we discuss in the following subsections, respondents also noted several benefits and drawbacks of transitions that were novel to the literature.
Benefits of SMJTs. Developing effective strategies for the marketplace is a perennial challenge for marketers. Accordingly, firms are always searching for ways to improve the quality of their marketing strategies. Respondents noted that a key result of firms embracing SMJTs is superior marketing strategies. As an assistant vice president of strategic planning in the transportation industry indicated, transitioners are better able to deliver strong products and marketing content given their enhanced understanding of customer needs:
Having a customer perspective makes people more effective marketers. It gives you better products and better services, [improves] how you serve your external market. Really, the better you understand the customer, [the better you are] able to tailor your products and approaches to those needs. And the better you understand your markets, the more effective you are.
Another way SMJTs benefit the organization is through improved strategic implementation. Organizations that support SMJTs have a better understanding of salesperson needs during implementation as transitioners have lived the sales experience. In turn, these marketers have more credibility in the eyes of salespeople. A marketing manager in the publishing industry discussed how, as a marketer, her sales experience allowed her to better convince salespeople about the value of implementing her strategies:
I think you can bring more to the table if you do have that sales experience because you can relate [more] to your previous experience. For example, I’ve sent out a marketing campaign based on what I experienced when I was in sales. I think that being able to do that gives you a lot of credibility…. I feel like I have an easier time communicating with [salespeople].
In addition, SMJTs can reduce tension between sales and marketing. Sales and marketing do not always get along well or engage in constructive interactions. Instead, tension and hostility can arise between functional members. Respondents noted that SMJTs help firms alleviate this strain. As a president in a transportation company noted, SMJTs helped improve the relationship between sales and marketing:
I think [SMJT] reduces tension. I think when you have people rotating in and out more frequently, it helps reduce that tension.
Respondents also noted that SMJTs can serve as an aspirational goal in the organization. In contrast with a siloed sales and marketing organization in which salespeople are limited in their ability to move across functions, organizations with a clear SMJT pathway can encourage enhanced performance from their salespeople. Salespeople may appreciate the developmental flexibility SMJTs provide and increase their effort level in their role in the hopes of achieving the transition. As a marketing agronomist in the agriculture industry noted, SMJTs exert a positive influence on the performance and motivation of salespeople in the organization:
A lot of times, entry-level positions are sales positions, where you get college grads or people that are learning the system come in as a sales opportunity. As they work their way through the company, [SMJT] gives them something to look forward to once [they] gain that sales experience to become more involved in the marketing side of it. You’re hiring from within, which gives drive to very competitive people, as opposed to just hiring from without.
Drawbacks of SMJTs. While SMJTs confer benefits to organizations, they also have potentially deleterious influences. Respondents noted that sales experience could result in overreactive marketing. Salespeople tend to have a shorter-term perspective of their role, which can be beneficial in sales. However, this perspective in transitioned marketers can result in a “react first, think second” mentality in the marketing department. As a marketing manager in the music industry noted, SMJTs can have a negative impact because of this issue:
There is a potential to defer too much to that kind of anecdotal news-of-the-day kind of model. When the organization is so incredibly sales-driven, there is a tendency sometimes to react and not be as mindful about the bigger picture stuff, so that is the danger I think that you have.
Another potential pitfall of SMJTs is suboptimal pricing. While a transitioned salesperson’s level of customer understanding and empathy can be beneficial in the marketing role, it can be a double-edged sword. Specifically, the customer perspective–taking ability transitioned marketers possess from their time interacting with customer in the sales role may result in instances in which they take the customer’s feedback on pricing over what the market would dictate. As a vice president of marketing in an aeronautics company noted, marketers’ sales experience can lead them to unnecessarily back off of price increases:
The cons are that you have empathy toward the customer. I am just saying that you have the perspective of not being onesided. Like a 10% price increase on a line. If the market says that it could support it but the customer does not want to do it, I could see how a [transitioned] marketing person might be willing to go, “Hey, you know what, I understand, let’s sort of split the difference and go 5% or 6%.” If that marketing person maybe didn’t have that experience in the sales role, they could just be hell-bent on getting that 10%, as that’s what the numbers say.
Another downside of SMJTs is the loss of customer relationships. When the sales force is the primary conduit for hiring marketers, the turnover rate in sales positions can increase substantially. As a result, the customer knowledge accrued by the salesperson dissipates. The loss of the customer relationship can adversely affect the organization through reduced sales and customer satisfaction. A vice president of marketing in the printing industry discussed how SMJTs may affect customer relationships and organizational performance:
You get those deep relationships and now, all of a sudden, I pluck you out of sales and I bring you into marketing, you have all of that great customer knowledge, and now I got to put somebody new out there. That hurts those long-term relationships and that can negatively affect your sales as a company. It can blow up those relationships.
Finally, when organizations explicitly or implicitly encourage SMJTs, a misallocation of human resources can result. High-performing salespeople who would better serve the company by remaining in sales may move to marketing because they view doing so as essential to advancing their careers. This action depletes the sales force of a high-performer and can result in lost revenue and increased costs in hiring and training to the firm. Furthermore, the transitioner may be poorly suited to the marketing role and hurt company performance in this manner as well. As a vice president of continuous improvement in a transportation company noted:
The drawback would be if you force naturally inclined salespeople and put them behind the scenes in marketing, do you hurt the company? There are some people that in their heart only want to be a salesperson. If only in your heart you want to be a salesperson, but I tell you [that] you have to be a marketing person, you might not arrive with the same passion for the company. Maybe you could provide the greatest value to us as an ongoing hunter.
Relative SMJT benefits and drawbacks proposition. Respondents carefully considered the pros and cons that SMJTs bring to the organization. Factors such as improved marketing strategies and strategic implementation were noted as important; however, considerations such as adverse human resources ramifications weighed on respondents’ assessments as well. Overall, respondents believed that the positive aspects of SMJTs outweigh the drawbacks. The knowledge, experience, perspective, and skills that sales-transitioned people possess make them better marketers overall. Thus, SMJTs are beneficial, insofar as organizations with better marketers can realize superior performance in the marketplace. As a product manager in the medical industry noted, SMJTs provide the organization positive value:
I definitely think the pros outweigh the cons. I think from a relationship perspective, you have more respect from the sales rep[s] and you get more engagement from them. I feel like the pros are definitely higher than the cons.
P9: The benefits of SMJTs outweigh the drawbacks; thus, SMJTs improve organizational performance.
TABLE: TABLE 1 Prior Research and Contributions of the Current Study
| | Prior Research | Current Study |
|---|
| Job transition process | Conceptualized as a four-stage process of preparation, encounter, adjustment, and stabilization (Nicholson and West 1989). | Advances two additional job transition process categories that improve understanding of the job transition process: transition motivation and acquisition. |
| Motivation | Understood broadly as a function of factors such as career stage, control seeking, organizational mandate, and personal growth/novelty needs (e.g., Anderson, Milkovich, and Tsui 1981; Campion, Cheraskin, and Stevens 1994; Cron 1984; Feldman and Ng 2007; Nicholson 1984; Verbruggen, Cooman, and Vansteenkiste 2015). | Provides two macro categories through which transition motivation manifests: intrinsic and extrinsic. The components of these categories are also novel to job transition research. |
| Acquisition | Enabled through factors such as past position quantity and type, functional background, and early-career stage (e.g., Bruderl, Diekmann, and Preisend orfer 1991; Forbes 1987). | Reveals the importance of three foci potential transitioners employ to successfully obtain the transition position in the SMJT context: sales achievement, strategy, and analytical. |
| Preparation | Conceived as general socialization into a new organization or as a passive process in which the transitioner sets internal expectancies for the impending transition (e.g., Batistic and Kase 2015; Morrison 1993; Nicholson 1987). | Provides two active forms of learning transitioners engage in to prepare themselves for their upcoming move: broad and targeted. |
| Encounter | Conceptualized as initial coping and sense making by the transitioner to transition-related stressors, demands, and uncertainty (e.g., Latack 1984; Louis 1980; Moyle and Parkes 1999). | Delineates two primary categories of difficulty for initial transitioners with which the transitioner must contend: loss of benefits from the former role and gain of challenges from the new role. Provides specific insight into SMJTs. |
| Facilitators | Focused largely on organizational facilitators such as training, mentoring, and social support (e.g., Kraimer et al. 2011; Moyle and Parkes 1999; Verbruggen, Cooman, and Vansteenkiste 2015). | Specifies many individual-level facilitators comprised of transitioner traits and actions. Also provides myriad organizational-level facilitators along demographic, instructional, and cultural lines. Some facilitators apply to job transitions overall, while others are specific to the SMJT context. |
| Outcomes | Focused largely on the positives of job transitions such as satisfaction, commitment, collaboration, and development (e.g., Anderson, Milkovich, and Tsui 1981; Baruch and Peiperl 2000; Campion, Cheraskin, and Stevens 1994; Guenzi and Troilo 2006; Kalleberg and Mastekaasa 2001; Matthyssens and Johnston 2006; Ortega 2001), with less emphasis on negatives such as increased cost, stress, and perceptions of inequity (e.g., Burke and Moore 2000; Rouzi`es et al. 2005; Rudisill and Edwards 2002). | Advances a balanced perspective of both novel benefits to SMJTs and potential dark sides of SMJTs. Some outcomes are specific to the SMJT context, while others can be extrapolated to other job transition contexts. |
This article explores the common reality of job transitions and, specifically, intraorganizational mobility through SMJTs. We extend a conceptual framework of the SMJT process, identify factors facilitating transitions, and delineate outcomes (both benefits and drawbacks) to organizations. The findings add theoretical insight to the marketing and broader job transition literature domains. We next discuss the theoretical contributions to job transitions overall and to the SMJT context in particular, as well as managerial implications, limitations, and future research avenues. Table 1 summarizes each component of the framework, presenting findings from prior research alongside contributions from the current study.
Theoretical Contributions
Prior research on job transitions has advanced a process model for understanding the transition process through the stages of preparation, encounter, adjustment, and stabilization (Nicholson and West 1989). Building on this model, we demonstrate that the framework should be extended to also consider motivational factors prompting job transitions and the acquisition strategies used to secure the new role. Understanding motivational aspects of the job transition process is an important component, considering that while certain job transitions are compulsory, others are not (e.g., Eriksson and Ortega 2006). Thus, it is important to recognize and delineate the factors that motivate employees to initiate the job transition process. Furthermore, many job transitions must be competitively obtained (Ng et al. 2005; Rosenbaum 1984). Extant literature has focused largely on trait factors germane to employees’ ability to acquire a new job, such as prior positions, background, and career stages (Bru¨derl, Diekmann, and Preisendo¨rfer 1991; Forbes 1987). Our findings add to this knowledge by providing valuable insights into the mechanisms employees use to successfully secure their transition.
Our research also contributes to these novel and existing job transition process categories by revealing thematic groups of variables affecting the process. Research has often conceptualized motivation as a function of intrinsic and extrinsic factors (e.g., Miao, Evans, and Zou 2007). Our findings explicate how these motivational elements extend to job transitions. In addition, while previous research recognizes the importance of preparation in job transitions, it mostly conceptualizes preparation as either general socialization into a new organization (Batistic and Kase 2015; Morrison 1993) or a passive process in which the transitioner sets internal expectations for the transition (Nicholson 1987; Nicholson and West 1989). Our findings advance two active forms of pretransition learning—broad and targeted—that transitioners use to prepare themselves for their impending change. Furthermore, transitions can be difficult and put strain on the transitioning party (Verbruggen, Cooman, and Vansteenkiste 2015). Current perspectives in the encounter stage focus on the strains employees confront in the new job, such as stressors, demands, and uncertainty (Latack 1984; Louis 1980; Moyle and Parkes 1999). Our findings show that in addition to the challenges from the new role that transitioners must contend with, they cope with the loss of benefits from their former role. These thematic classifications can be generalized across functional areas and provide utility to researchers when conceptualizing other types of job transitions. For example, transitions from sales to finance or from marketing to human resources would still involve both loss of benefits and gain of challenges within the encounter phase; however, the specific benefits and challenges would likely change given the different transition contexts.
In addition, we collected a second wave of data from respondents to provide propositional guidance on the relative impact of each of these categories on their stage within the job transition process. With these insights, we propose that transition motivation comprises more intrinsic than extrinsic factors. As SMJTs can at times have decreased extrinsic factors (i.e., less earning potential in the new role), the intrinsic factors prove more salient in motivating the transition. This finding adds to extant research examining salespeople’s intrinsic and extrinsic motivations (e.g., Miao, Evans, and Zou 2007) and provides insight into which factors are most critical. We also propose that targeted preparation helps transitioners to a greater extent than broad preparation. This finding is in line with tangential literature germane to interorganizational job transitions showing that employees new to organizations try to learn from their peers to socially integrate into the company (Morrison 1993). We build on this concept by applying peer socialization to an intraorganizational mobility context that requires attaining rolespecific information rather than general socialization and, importantly, that occurs before the transition. Furthermore, in our assessment of refutability—questioning assumptions related to our analysis and exploring whether systemic differences in our respondents influence the findings—we discovered experiencebased nuances for acquisition and encounter and thus contribute to career stages literature in marketing (e.g., Cron and Slocum 1986). For acquisition, the most efficacious mechanism was an analytical focus for early-career salespeople to obtain the job. However, for later-career salespeople, a strategic focus was superior. As members progress through the organizational hierarchy, a big-picture vision becomes increasingly more important than specific, tactical application (Mumford, Campion, and Morgeson 2007). Finally, the nature of the difficulty salespeople experience after making the transition also differed as a function of their tenure in the sales role. Early-career salespeople found it more difficult to deal with the added challenges of the new role, while later-career salespeople found the loss of benefits a more significant issue. The longer a person experiences a benefit, the more difficult it is to have it removed, and early-career organizational members value external appraisals of their performance to a greater extent than later-career members (Johnston and Marshall 2013).
We provide additional contributions with the lower-level codes shown in Figure 1. Some codes provide insights into job transitions overall as well as marketing, while others are marketing specific. In particular, motivational factors such as organizational altruism may motivate job transitions in myriad other contexts, such as transitioning from finance to strategic planning or supply chain to operations. However, factors such as marketing power, work–life balance, strategic involvement, and marketing educational application are specific to marketing. For example, the overall job transition literature conceptualizes work–life balance in an adverse manner, as transitions can be taxing, potentially requiring additional work from the transitioner (Verbruggen, Cooman, and Vansteenkiste 2015). However, given that the sales role can also require long and late hours as well as heavy travel (Johnston and Marshall 2013), work–life balance was perceived as a motivator in the SMJT context.
Furthermore, the encounter elements are highly specific to the SMJT context. Compensation decrease is common in SMJTs but rare in voluntary job transitions in other contexts. Similarly, excitement represents a positive aspect of transitioning in the broader job transition literature given the exposure to new experiences and skills (Xie, Song, and Stringfellow 2003). However, in the SMJT context, salespeople miss the “thrill of the hunt” associated with closing a sale. The loss of interacting with customers that occurs in the SMJT is also specific, as is the gain of challenges from the new role, as job ambiguity and pressure may or may not increase with other forms of job transitions as they do in the SMJT context. Finally, the exposure to company politics a salesperson experiences when transitioning from a field role to the home office is a drastic change that does not exist in an intraheadquarters job transition.
As Table 1 shows, the literature has advanced certain factors as facilitators of job transition efficacy. Training, mentoring, and social support all affect an organizational member’s ability to effectively transition jobs (Kraimer et al. 2011; Moyle and Parkes 1999; Verbruggen, Cooman, and Vansteenkiste 2015). Our research contributes to theory by identifying additional individual- and organizational-level facilitators that affect job transition efficacy. Furthermore, we provide thematic categorization of the facilitators that is generalizable across job transition contexts (i.e., transitioning between functional areas other than sales and marketing). For individual facilitators, enduring traits and transient actions both affect the efficacy of job transitions. Respondents’ relative assessment, however, was that traits have a greater impact on job transition efficacy than actions. Respondents noted that the similarity of the transitioned job greatly affects the relative impact of traits versus actions. When employees transition into a job that requires substantively different abilities and orientations (e.g., SMJTs), their individual traits are more predictive of success. These findings add to research asserting the value of trait factors in career success (Judge et al. 1999). For organizational facilitators, demographic, instructional, and cultural factors all have an impact on the efficacy with which the transition is made. Of these three factors, respondents noted that cultural factors are the most important in ensuring an effective transition. Cultural receptivity of new members is a critical component of successful adaptation and integration into the organization (Batistic and Kase 2015), and factors such as openness and cultural fit shape the transition experience.
The lower-level components of the facilitators also contain both generalizable and SMJT-specific elements. For example, at the individual level, the actions of proactive feedback solicitation, information gathering, and outsourcing apply to the broader job transitions domain in addition to the SMJT context. However, the traits of marketing mindset and inside sales experience contribute solely to the SMJT context. For organizational-level facilitators, factors such as organizational age, embedded crosstraining, and role formality may hold across transition contexts. However, factors such as level of product differentiation, services versus goods, and customer integration are SMJT specific. In addition, we provide nuanced understanding in the SMJT context of aspects noted previously in the literature, such as training. Given the magnitude and frequency of SMJTs, training in this context may be more formalized and stable than in other, less commonly occurring job transition contexts.
As Table 1 also shows, prior research has discussed benefits associated with job transitions, including collaboration (e.g., Guenzi and Troilo 2006), competencies and skills (e.g., Campion, Cheraskin, and Stevens 1994), job satisfaction (e.g., Kalleberg and Mastekaasa 2001), and organizational commitment (e.g., Anderson, Milkovich, and Tsui 1981), as well as drawbacks in the form of stress (e.g., Rudisill and Edwards 2002), cost (e.g., Campion, Cheraskin, and Stevens 1994), and perceived inequity (e.g., Burke and Moore 2000). However, there is a relative imbalance, with more focus on the pros than the cons. Although our respondents indicated that the positives of SMJTs outweigh the negatives, they expressed a commensurate number of potential pitfalls of transitioning. As such, our results provide a balanced perspective of this phenomenon to the literature.
The lower-level outcomes provide many marketing-specific contributions in our exploration of SMJTs. The sales and marketing functions may be fraught with tension that strains their relationship and results in a dysfunctional interface (Malshe, Johnson, and Viio 2017). Respondents revealed that SMJTs can reduce tension and thus improve this critical interface within the organization, in part because of transitioners’ enhanced ability to understand and communicate with salespeople. In addition, motivating salespeople is a key concern in sales management (Miao, Evans, and Zou 2007). As a form of continued career progression, SMJT opportunities encourage salespeople to increase their effort and thus can improve selling performance for the organization. Furthermore, scholars have noted the importance of the effective development and execution of marketing strategies in promoting optimal marketing performance (Morgan 2012) and the impact of sales and marketing on these desirable activities (Malshe and Sohi 2009a, b). Transitioners’ enhanced knowledge of customers and other salespeople increases their ability to create high-potential strategies and encourages their implementation by the sales force.
Despite these many benefits, respondents also noted that marketing organizations should be cognizant of the drawbacks of SMJTs. Salespeople can possess more short-term thinking and be tactical owing to their sales role (Homburg and Jensen 2007). These traits can transition with them to the marketing role and result in overreactive marketing in which the marketing function acts rashly on idiosyncratic information. In addition, salespeople’s desire to please their customers may make them prone to sacrifice margins in the sales role (Dolan and Simon 1996), which can also transfer with them to their marketing role in the form of suboptimal pricing. Furthermore, long-term relationships between salespeople and customers are vital to organizations (Friend and Johnson 2014), and SMJTs can have a disruptive influence on these relationships. Finally, salespeople may differ from marketers on key characteristics, abilities, and orientations that originally attracted them to the sales role (Homburg and Jensen 2007; Homburg, Jensen, and Krohmer 2008). Implicitly or explicitly requiring SMJTs could result in a top-performing salesperson transitioning into a mediocre marketer, depleting the sales force and weakening the marketing function at the same time.
Managerial Implications
In addition to extending theory, the findings are particularly relevant to managers, as they provide valuable guidance for how managers can strategically utilize, position, and guide human resources within their firm. Furthermore, improving the development and implementation of marketing strategies, reducing the tension between marketing and sales, and increasing salesperson motivation are common managerial goals. Our results reveal that managers can use SMJTs to reach these goals. However, our results also reveal that these benefits can come with a price—namely, overreactive marketing, suboptimalpricing, loss of customer relationships, and misallocation of human resources. As such, it is imperative for managers to know how to optimize SMJTs to help maximize the benefits and minimize the drawbacks. Our results provide guidance to these ends.
Proper selection is critical to ensuring that the right salesperson is chosen to make the transition to marketing, as respondents noted that not all salespeople are a correct fit for the marketing role. As such, hiring managers can assess individual factors such as marketing mindset in the interview process by having salesperson applicants answer marketing-scenario questions and team orientation by having them explain how they have contributed to teams in the past. Hiring managers can also assess whether the potential transitioner is likely to engage in transition-facilitating activities, such as proactive feedback solicitation and information gathering, by evaluating the quality of the applicant’s questions to the interview team.
Beyond proper selection, managers can also increase efficacy by providing a supportive culture and necessary instructional support to the transitioned salesperson. Cultural aspects of the organization such as openness to internal mobility and establishing roles that are culturally conducive to transition should be encouraged to enable successful transitions. Moreover, managers should be cognizant of the difficulties salespeople experience in the encounter phase of the transition and help ameliorate these issues as much as possible. Later-career salespeople will need more help overcoming potential losses in compensation, freedom, customer interaction, and excitement, whereas early-career salespeople will need more help overcoming challenges resulting from increased pressure, job ambiguity, and company politics. Finally, managers can encourage targeted preparation to soon-to-be transitioned employees by encouraging open communication with current marketers and providing easy access to relevant company data.
Limitations and Future Research Opportunities
Considering the limited research investigating SMJTs, a grounded theory approach was ideal for the discovery-oriented investigation of this topic. This type of exploratory, qualitative research is vital for expanding understanding of an underrepresented phenomenon and from this work, several potential future research opportunities using quantitative methods exist, such as statistically testing the conceptual model provided (Johnson 2015a). For example, researchers could test the propositions advanced to understand the relative impact of factors in the transition process as well as the individual- and organizational-level facilitators to determine the degree to which they increase transition efficacy and marketing performance. Furthermore, the lower-level components of the various categories could be assessed empirically. For example, researchers could test which of the transition-associated losses affects transitioning salespeople to the greatest extent. In addition, scholars could test organizations’ levels of SMJTs on dependent variables, such as marketing strategy quality, strategy implementation efficacy, and business-unit performance, as well as the contingencies on which these relationships depend.
Another future research opportunity is to examine in greater detail the three interview foci—sales achievement, strategy, and analytical—that respondents used to differentiate themselves in the interview process. Both theory and practice could benefit from a better understanding of which focus is most effective in terms of securing the new role and what factors lead to a given focus being emphasized by employees. One way to pursue this opportunity is through experimental manipulations. For example, marketing managers could be provided hiring scenarios in which skills, experience, and other factors are identical, while the employees’ interview focus varies among sales achievement, strategy, and analytical foci. The managers could then provide an intention to hire score for the transitioner and potential significant differences between foci may be evidenced.
Additional qualitative samples could also be pursued and analyzed to understand other job transitions occurring in sales and marketing as well as in other domains of inquiry. For example, scholars have indicated that the transition from salesperson to sales manager can be problematic (Anderson, Mehta, and Strong 1997). Furthermore, examining transitions between other firm functions, such as marketing to strategic planning or sales to human resources, would provide valuable insights. These alternative contexts may follow the same underlying job transition process but contain different lower-level elements for each facet. For example, many of the benefits and drawbacks included in our model are unique to the SMJT context, and it is likely that transitions between other firm functions would result in different positive and negative outcomes. Further research could provide an exposition in these contexts converging on elements from the current inquiry that generalize and uncover new insights specific to these types of transition contexts. New insights could also be gleaned by examining interorganizational transitions from sales at one company to marketing at another company. While our research is specific to intraorganizational SMJTs within a multitude of industries and firms, some employees may experience SMJTs across two companies from firms poaching salespeople from competitors or channel partners to fill marketing roles. Investigating this process could provide unique insights that would add depth to the understanding of SMJTs.
DIAGRAM: FIGURE 1 SMJT Model
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Record: 155- Satisfaction (Mis)pricing Revisited: Real? Really Big? By: Bharadwaj, Sundar G.; Mitra, Debanjan. Journal of Marketing. Sep2016, Vol. 80 Issue 5, p116-121. 6p. DOI: 10.1509/jm.16.0236.
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Record: 156- Savoring an Upcoming Experience Affects Ongoing and Remembered Consumption Enjoyment. By: Chun, HaeEun Helen; Diehl, Kristin; MacInnis, Deborah J. Journal of Marketing. May2017, Vol. 81 Issue 3, p96-110. 15p. 1 Diagram, 1 Chart, 4 Graphs. DOI: 10.1509/jm.15.0267.
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Savoring an Upcoming Experience Affects Ongoing and Remembered Consumption Enjoyment
Five studies, using diverse methodologies, distinct consumption experiences, and differentmanipulations, demonstrate the novel finding that savoring an upcoming consumption experience heightens enjoyment of the experience both as it unfolds in real time (ongoing enjoyment) and when it is remembered (remembered enjoyment). This theory predicts that the process of savoring an upcoming experience creates affective memory traces that are reactivated and integrated into the actual and remembered consumption experience. Consistent with this theorizing, factors that interfere with consumers' motivation, ability, or opportunity to form or retrieve affective memory traces of savoring an upcoming experience limit the effect of savoring on ongoing and remembered consumption enjoyment. Affective expectations, moods, imagery, and mindsets do not explain the observed findings.
Online Supplement : http://dx.doi.org/10.1509/jm.15.0267
Consumers spend considerable time and money on ordinary (e.g., a trip to the movies, video games), special (e.g., vacations, anniversary parties), and extraordinary (e.g., skydiving, whitewater rafting) consumption experiences (Bhattacharjee and Mogilner 2014; Hirschman and Holbrook 1982). Indeed, the contribution of experiential purchases to the gross domestic product of developed economies is significant. In 2015, for example, U.S. consumers spent $182 billion on club memberships and theater, amusement park, and museum experiences alone (U.S. Bureau of Economic Analysis 2016). Consumers also value the opportunity to enhance their enjoyment of consumption experiences (Clarkson, Janiszewski, and Cinelli 2013). Recent research has further suggested that, compared with material purchases, experiential purchases lead to greater consumer satisfaction and well-being (e.g., Dunn and Norton 2013; cf. Nicolao, Irwin, and Goodman 2009).
It is vital for marketers to maximize consumers' real-time (ongoing) and remembered enjoyment of consumption experiences (Schmitt 1999). The more enjoyable such experiences are, the more likely consumers are to repeat them (increasing revenue) and to share their experiences with others (reducing marketing costs; Moore 2012). Creating positive consumption experiences also protects companies against commoditization and heightens their profits (Pine and Gilmore 2011). Indeed, marketers from Disney (Cockerell 2008; The Disney Institute and Kinni 2011) to Starbucks (Michelli 2007) spend significant organizational and monetary resources to create consumption experiences that are maximally pleasurable, whether it is by reducing services lines, perfecting experiential product attributes, designing ideal consumption environments, or training employees on the best delivery of experiences (Schmitt 1999).
We add to this substantive domain by identifying a novel and managerially implementable factor—specifically, savoring an upcoming consumption experience. Five studies show that savoring an upcoming experience positively affects ongoing and remembered consumption enjoyment. We observe these effects using ( 1) real and meaningful consumption experiences that participants actually live through (e.g., spring break, movies, online video games, hotel stays), ( 2) diverse research methodologies (e.g., field study, lab experiments, secondary data), ( 3) different consumer populations (students, online participants, and a J.D. Power traveler panel), and ( 4) diverse manipulations of savoring.
Our findings offer important marketing implications because marketers might encourage consumers to savor a consumption activity in advance of its occurrence. For example, Viking River Cruises and Lindblad Expeditions encourage savoring prior to upcoming trips by sending destination-specific interactive presentations, reading lists, or language videos of useful phrases for different countries along the cruise. Automakers, such as BMW, create tools that enable buyers to follow the assembly of their car and to watch its progress as the car is being shipped across the Atlantic (Boeriu 2009). Sensory-rich brand names, preannouncements of new products or product variants, reminders of upcoming events and experiences, evocative ads, and even explicit instructions to savor are other tactics that may encourage savoring. Blogs, product/ service reviews, and media stories can also encourage consumers to savor an upcoming consumption experience (e.g., a restaurant, a movie, a sporting event).
Our findings also make novel theoretical contributions: ( 1) we conceptually differentiate savoring an upcoming experience from potentially related constructs, such as anticipation, imagery, moods, mindsets, and affective expectations; ( 2) we examine how savoring an upcoming experience affects ongoing and remembered enjoyment of that experience; ( 3) we show that savoring produces effects that differ from and go beyond those of affective expectations; and ( 4) we develop novel theory to explain when and why savoring an upcoming experience produces the predicted effects on enjoyment.
We formally define savoring as a cognitive process involving awareness of current pleasure from a target-specific consumption experience. One can savor an upcoming experience, an ongoing experience, or a past experience (Bryant and Veroff 2007). We focus on the former and define savoring an upcoming experience as a cognitive process involving awareness of current pleasure from a target-specific, future consumption experience. When people savor an upcoming consumption experience, they are aware (in the moment) that they feel pleasure from this upcoming experience. Thus, while the content of savoring is affective (involving pleasure), the process of savoring is cognitive (involving awareness). Moreover, savoring does not reference just any positive feelings—only feelings linked to the savored consumption experience itself. In this way, savoring is target specific.
Prior research on savoring consumption experiences is quite limited. Researchers have studied how savoring an ongoing experience (e.g., savoring the experience of eating a cake vs. eating while distracted) heightens enjoyment of that experience (Le Bel and Dube 2001). Other research has suggested that savoring an upcoming positive experience can make waiting for the experience more pleasant (see Chew and Ho 1994; Kumar, Killingsworth, and Gilovich 2014; Loewenstein 1987; Lovallo and Kahneman 2000). These findings explain why consumers might choose to delay positive future experiences (e.g., knowing whether they have won the lottery): the wait itself may be experienced positively (Chew and Ho 1994; Loewenstein 1987).
We ask a novel question: Does savoring an upcoming experience affect how much people enjoy the experience as it unfolds and how enjoyable they remember it to have been? Before describing our theory, we first distinguish savoring from potentially similar constructs.
Anticipation. Savoring an upcoming experience likely involves anticipation. However, the two constructs are distinct. First, not all anticipated experiences are judged to be positive. Anticipating unpleasant experiences would evoke dread rather than pleasure. Second, although savoring involves anticipation, savoring's focus is on the current state of pleasure (i.e., the here and now), not on the anticipated experience. Furthermore, anticipating a future experience can lead to impatience, whereas savoring lessens the pain of waiting (e.g., Chew and Ho 1994).
Imagination. Relatedly, whereas savoring an upcoming experience might involve imagining an upcoming future, savoring is more specific. One can imagine many things, both real and fantasy based (Martin 2004). Savoring emphasizes real- world outcomes, not fantastic ones, and it is target specific. It involves not just any imagined future experience, but a specific upcoming experience. Finally, imagery can occur without awareness of one's current state of pleasure. Savoring, by definition, requires such awareness.
Moods and mindsets. The notion that savoring is target specific also distinguishes savoring from moods and mindsets. Savoring might induce a positive affective state by making consumers aware of their current feelings. However, unlike moods, savoring references the upcoming consumption experience. Research has suggested that a mindset carries over to affect judgments of unrelated situations (Xu and Wyer 2007). Thus, positive moods and mindsets should affect enjoyment of any future movie experience. In contrast, savoring an upcoming experience (e.g., watching a Minions movie) should only affect enjoyment of that particular experience, not any experience (e.g., watching a different movie).
Affective expectations. Finally, savoring is distinct from "affective expectations," defined as predictions about how good a consumption experience will make one feel (e.g., Wilson et al. 1989). Although savoring might induce affective expectations, affective expectations emphasize how a consumption experience is likely to make one feel in the future. In contrast, savoring emphasizes the pleasure one feels in the present moment. Moreover, affective expectations can enhance how good consumers remember an experience to have been without necessarily changing the enjoyment of the ongoing experience (e.g., Wirtz et al. 2003). As we explain next, we predict that savoring an upcoming experience will affect not just remembered enjoyment, but also ongoing enjoyment.
Predicted effects on consumption enjoyment. Why might savoring an upcoming consumption experience affect ongoing and remembered consumption enjoyment? When people savor, they must use information processing resources and prior knowledge to elaborate on aspects of the upcoming experience (see Schacter and Addis 2007). Prior research has suggested that elaborative processing strengthens memory traces; the greater the elaboration, the stronger the memory traces (Bradshaw and Anderson 1982). If memory traces are strong, they may be reactivated when the experience actually occurs. Reactivation of these memories may then be integrated into one's reaction to the ongoing and remembered consumption experience. The process invoked here is similar to that in information integration models, which specify how information is combined (e.g., Anderson 1981; Kardes and Kalyanaram 1992). In summary, we expect that when consumers savor an upcoming experience, memory traces of this previously savored experience are reactivated and integrated into the ongoing and remembered consumption experience. This theorizing leads us to predict the following:
H1: Savoring an upcoming positive experience enhances consumption enjoyment of the experience (a) while it is ongoing and (b) retrospectively.
Savoring an upcoming experience is likely to use elaborative processing that involves anticipation (Schlosser and Shavitt 2002) and imagery processing (MacInnis and Price 1987). Thus, savoring requires that consumers have sufficient motivation, ability, and opportunity (Greenwald and Leavitt 1984; MacInnis and Jaworski 1989) to engage in elaborative processing (Schacter and Addis 2007). For example, limited knowledge of a future consumption experience or limited time or processing resources to savor the upcoming experience should limit its effect on consumption enjoyment. Furthermore, distractions during the actual experience may be detrimental to retrieving memory traces created during savoring, thus hampering the effect of prior savoring on enjoyment. Formally, we predict:
H2: Reducing consumers' motivation, abilities, or opportunities to elaborate on an upcoming consumer experience or to reactivate affective memories of the previously savored experience should dampen the effect of savoring on ongoing and remembered consumption enjoyment.
If savoring creates affective memory traces that are subsequently retrieved and integrated into the actual consumption experience, savoring should enhance not just remembered but also actual consumption enjoyment. This prediction differs from prior research on affective expectations (e.g., Schwarz and Xu 2011; Wirtz et al. 2003). Prior research has found that remembered enjoyment aligns with one' s expectations of the experience, but ongoing consumption enjoyment is not affected. We predict that savoring an upcoming experience causes memory traces of pleasure from the savored experience to be reactivated and integrated into both the ongoing and remembered consumption experience. This also implies that the effect of savoring on enjoyment should endure over time, unlike shortlived mood and mindset explanations. Our prediction is also different from marketer-induced false memories (e.g., Braun 1999), in which postexperience advertising information becomes incorporated into and alters what people remember. We suggest that consumers' own affective memory traces created from preconsumption savoring are reactivated when the experience actually unfolds and when consumers remember it.
Overview of the studies. Next, we report five studies designed to test H1 and H2 and to rule out alternative explanations (see Table 1). Study 1 uses a field study involving a multiday experience. Results show that savoring an upcoming experience enhances enjoyment only of that particular experience, not of a different experience (as predicted by moods and mindsets). We further demonstrate that the effect is different from imagery by showing that savoring increases enjoyment of the actual experience, whereas merely imagining the experience does not. Study 2 replicates Study 1 using a different consumption experience and a more naturally occurring manipulation of savoring. Study 3 supports H1 even when affective expectations are taken into account. Consistent with H2, Study 3 also shows that the effect of savoring on ongoing and remembered consumption enjoyment is dampened when people lack the opportunity to retrieve affective memory traces. Study 4 provides further evidence of the affective memory trace reactivation perspective using yet a different consumption experience. We replicate H1 and find support even when affective expectations are manipulated and when the quality of the ongoing experience varies. Study 5 replicates H1 and supports H2 using real-world (secondary) data, in which alternative explanations (e.g., demand effects, carryover effects, self- generated validity effects, moods, mindsets) cannot account for the results.
Companies such as Walt Disney World Resort Hotels routinely distribute multiple upcoming trip reminders before consumers arrive on site (e.g., "Your Disney experience starts right now!"). Prior research has suggested that such reminders make waiting times less painful. We are interested in whether such reminders also enhance both ongoing and remembered enjoyment of the consumption experience, as predicted by H1. We suggest that reminders that encourage savoring of an upcoming experience should enhance opportunities for consumers to be aware of and encode their current state of pleasure from contemplating the upcoming experience. Such processing should, in turn, make memory traces more accessible when the consumption experience unfolds in real time and when it is remembered.
Study 1 manipulates whether participants ( 1) savor the upcoming experience, ( 2) merely imagine its future occurrence, or ( 3) savor a different future experience. The imagery condition enables us to demonstrate that savoring is distinct from imagining while also controlling for mood effects (either imagining or savoring an upcoming vacation should induce positive mood). The third condition, which asks participants to savor a different focal experience (specifically, summer break), enables us to test the idea that unlike moods and mindsets, the effect of savoring is target specific. Consequently, and as predicted by H1, savoring an upcoming summer break should not enhance enjoyment of spring break.
We randomly assigned 141 students who participated in the study for course credit to three conditions (savor spring break N = 48, savor summer break N = 47, imagine spring break N = 41). Five participants did not complete remembered enjoyment ratings, resulting in a final sample of 136 participants. For seven days before spring break, participants received a daily e-mail message asking them to briefly write about their upcoming spring break or summer break in a daily journal. Participants in the savoring conditions were asked to "write about your thoughts and feelings regarding how much you'll enjoy your upcoming Spring (or Summer) Break. Turn your attention to the good feelings you have now about enjoying your break." Participants in the "imagine spring break" condition were asked to "think about the activities you may be involved in during the Spring Break" and to write about what they imagined they might do during the break (for all instructions, see Section A in the Web Appendix). Participants provided an average of six to seven journal entries. The number of entries provided did not vary by condition.
TABLE: TABLE 1 Overview of Studies
| Study 1 | Study 2 | Study 3 | Study 4 | Study 5 |
| Consumption experience | Spring break | Movie | Movie | Video game | Hotel stay |
| Method | Field study | Online experiment | Online experiment | Lab experiment | Secondary data: J.D. Power Hotel Guests Satisfaction Survey |
| Design/independent variables | (1) Savor an upcoming spring break (opportunities to savor an upcoming experience), (2) savor summer break (opportunities to savor a different experience), and (3) imagine upcoming spring break | Savor actual movie (vs. a different movie) x Movie replicate | (1) Savoring the upcoming experience only, (2) savoring the ongoing experience only, (3) savoring the upcoming experience plus savoring the ongoing experience, and (4) savoring the upcoming experience plus distraction during consumption | Savoring ability (high vs. low) x Affective expectations (more positive vs. less positive) x Experience quality (higher vs. lower) | Traveler type (motivation to savor) x Booking window (opportunity to savor) |
| Dependent variables | Remembered enjoyment of spring break | Ongoing and remembered enjoyment of the movie | Ongoing and remembered enjoyment of the movie | Remembered enjoyment of the game | Remembered enjoyment of hotel stay |
| Findings | Savoring an upcoming experience enhances remembered consumption enjoyment only when the savored and experienced events are the same; Imagery alone does not produce the effects. | Savoring an upcoming experience enhances ongoing and remembered enjoyment only when the savored and experienced events are the same. | Savoring an upcoming experience induces as much enjoyment as savoring the ongoing experience; distraction during the experience (which interferes with an ability to retrieve affective memory traces of savoring) eliminates the effect of savoring the upcoming experience. | Savoring an upcoming experience enhances remembered enjoyment regardless of the quality of the experience, except when consumers have less positive expectations to begin with. | Enjoyment was greatest for consumers who had both the motivation to savor (leisure vs. business travelers) and the opportunity to savor (longer booking window). |
| Unique study advantages | Supports Hi using a real- life, multiday experience, distinguishes savoring from imagery; tests the target- specific property of savoring, and addresses mood and mindset accounts | Supports Hi using a short experience, provides a subtle manipulation of savoring (limits demand explanation), and addresses mood and mindset accounts | Supports Hi and H2, controls for affective expectations | Supports Hi with a different consumption experience, manipulates (controls for) affective expectations, manipulates the quality of the experience, and addresses mood and mindset accounts | Supports Hi and H2 using real-world secondary data; there is no potential for demand effects, self-generated validity, or carryover effects of measurement to explain the results |
The day after spring break ended, participants took part in an ostensibly different study conducted by a different researcher on "college students' leisure activities and experiences." We also disguised the link between the two studies by including numerous, unrelated items. Other researchers have used similar procedures to reduce potential demand biases (Klaaren, Hodges, and Wilson 1994). We excluded three participants (N = 1 in the imagine spring break condition; N = 2 in the savoring spring break condition) who reported being ill during spring break. We report results that include these participants in footnote 1.
Four items assessed remembered enjoyment of spring break: the extent to which participants liked spring break (1 = "disliked it very much," and 9 = "liked it very much") and the extent to which spring break was fun, enjoyable, and good (1 = "not at all," and 9 = "a great deal"). The four items were highly correlated (α = .93), and a principal component analysis suggested one underlying factor that explained 83.20% of the variance. We averaged the four items to form a composite measure of remembered enjoyment. Although the current research emphasizes only results that pertain to H1 and H2, Section B in the Web Appendix provides all questions included in all studies.
Given our predictions, we tested H1 using planned contrasts. In support of H1, participants in the savor spring break condition remembered the experience as more enjoyable than did those in the imagine spring break condition (Msavor spring = 7.88, SD = 1.12 vs. Mimagine spring = 7.19, SD = 1.44; F( 1, 130) = 5.10, p = .026) and the savor summer break condition (Msavor summer = 7.26, SD = 1.58; F( 1, 130) = 4.59, p = .034).[ 1]
Study 1 supports H1. Savoring an upcoming consumption experience enhances (remembered) consumption enjoyment. Neither purely imagining the future consumption experience nor savoring a different consumption experience (summer break) produces the same effects. These effects occur only when the savored and the focal event are the same; memory traces created when savoring a different event are not recruited during the focal event. The results also rule out mood and mindset accounts, which would predict positive carryover effects regardless of the target event. Given the procedure, demand effects are also unlikely to explain the pattern of results.
In Study 1, the actual consumption experience varied by respondent. In Study 2, we keep the consumption experience constant. We also examine whether the effects observed in Study 1 are replicated for a short (vs. a multiday) experience. Finally, we use a more subtle manipulation of savoring.
In Study 2, the focal consumption experience involved watching a film clip. We manipulated savoring by showing a movie trailer that was either directly related or unrelated to the film clip. A trailer, or more generally a product preview, may be a managerially relevant vehicle to induce people to savor an upcoming consumption experience. Previews should enhance consumers' abilities to savor the upcoming experience because they provide concrete information about that experience. Given the target- specific nature of savoring, we predict that enjoyment of the clip will be greater for those who watch a related (vs. unrelated) trailer. Only the related target experience should activate memory traces of prior savoring.
Six hundred seventeen participants recruited from Amazon Mechanical Turk participated in a study about watching movie trailers and movies online. Participants were told that they would watch a movie trailer and then a movie, with the computer randomly determining which trailer and movie they would watch (for stimuli, see Section C in the Web Appendix). The actual movie was either a Toy Story or Minions short movie. Movie served as a between-subjects replicate. We manipulated savoring the upcoming movie by randomly assigning participants to first watch a trailer that was either related (savoring condition) or unrelated (control) to the target movie. That is, if the target movie was Toy Story, those in the savoring condition saw a Toy Story trailer, and those in the control condition saw a Minions trailer (and vice versa). A pretest (n = 83) indicated that both trailers (Minions and Toy Story) evoked a similar positive mood (MMinions = 6.76, SD = 1.44, M^ story = 6.79, SD = 1.56; F( 1,81) = .006, p = .94), were equally well liked (MMinions = 6.71, SD = 2.25, MToy story = 6.68, SD = 1.66; F( 1,81) = .043, p = .84), and were perceived to be similar in quality (MMinions = 7.11, SD = 1.80, MToy story = 7.18, SD = 1.57; F( 1, 81) = .038, p = .85).
Next, participants watched the movie clip and provided ongoing enjoyment ratings using a sliding scale displayed at the bottom of the computer screen (see Section C in the Web Appendix). Participants were told to drag the slider to record moment-by-moment enjoyment of the film. The slider scale was anchored by "not at all enjoyable" and "very enjoyable." Following Andrade and Cohen (2007), enjoyment levels were recorded every three seconds, producing 65 data points per participant. After watching the clip, participants rated their enjoyment of the film using the same itemsasinStudy1(a= .98). Because prior experience can facilitate savoring, participants indicated whether they had previously seen any Minions or Toy Story movies (yes/no). We use this variable as a covariate and report least-square means and standard errors in subsequent analyses. Respondents then completed a manipulation check measure, which involved the average of two indicators of savoring ("I was feeling joy at the thought of watching this movie" and "I was aware that I felt good from the prospect of watching the movie"; anchored at 1 = "strongly disagree," and 9 = "strongly disagree"; a = .96).
Three participants had critical technical/audio problems while watching the film and were excluded from the analysis (N = 614). We report results including these participants in footnotes 2-4. Ongoing ratings were not recorded for six participants, and three participants failed to provide ongoing ratings; thus, analyses of ongoing enjoyment are based on only 605 observations.
Manipulation check. The savoring manipulation was successful. A 2 (movie replicate: Toy Story vs. Minions) X 2 (movie trailer: related vs. unrelated) analysis of covariance revealed a significant effect of whether participants had seen a movie of the franchise before (Myes = 6.43, SD = 2.13, Mno = 4.97, SD = 2.47; F( 1, 609) = 50.95, p < .001). The manipulation of savoring was also significant (Msavor = 5.91, SE = .13, Mcontrol = 5.46, SE = .14; F( 1,609) = 6.15, p = .013, wp2 = .01). Neither the main effect of movie replicate (F( 1,609) = .24, p > .6, wp2 < 0) nor the interaction (F( 1,609) = .97, p > .3, wp2 < 0) were significant.[ 2]
Ongoing enjoyment. We used prior exposure to Minions or Toy Story movies, respectively, as a covariate, which had a strong positive effect on ongoing enjoyment (Myes = 65.06, SD = 21.69, Mno = 52.13, SD = 24.87; F( 1, 600) = 33.49, p < .001). Importantly, beyond this effect, participants in the savoring condition reported greater enjoyment while watching the movie compared with those in the control condition (Msavor = 61.35, SE = 1.32 vs. Mcontrol = 56.51, SE = 1.37; F( 1, 600) = 7.03, p < .01; see Figure 1).[ 3]
Remembered enjoyment. Prior exposure to Minions or Toy Story movies also had a positive effect on remembered enjoyment (Myes = 7.10, SD = 1.95; Mno = 5.78, SD = 2.45; F( 1, 609) = 48.01, p < .0001). Yet here, too, we observed the predicted effect of savoring on remembered consumption enjoyment (Msavor = 6.63, SE = .12 vs. Mcontrol = 6.22, SE = .13; F( 1, 609) = 5.75, p = .017).[ 4]
Study 2 further supports H1 using a common, short-lived consumption experience, a naturally occurring (nonintrusive) savoring manipulation, two replicates, and a different (nonstudent, online) sample. Savoring an upcoming experience heightens ongoing as well as remembered consumption enjoyment. Consistent with Study 1, only savored experiences, not any positive experience, induced these effects. Notably, this study induced savoring simply by exposing participants once to commonly used marketing materials (i.e., trailers), suggesting that there is potential for marketers to manipulate savoring similarly in the real world.
One may wonder whether remembered enjoyment is based on participants' preceding assessment of their ongoing enjoyment (Hastie and Park 1986; Lynch, Marmorstein, and Weigold 1988). However, the pattern of results for remembered enjoyment in this study replicates that of Study 1, in which we did not assess ongoing enjoyment. We return to this issue in the "General Discussion" section and provide meta-analytic evidence further ruling out this alternative explanation. Study 3 builds on prior studies by testing whether H1 is supported even when affective expectations are taken into account. Study 3 also tests H2 by examining whether factors that interfere with the retrieval of affective memory traces reduce the effect of savoring an upcoming experience on ongoing and remembered enjoyment.
If savoring the upcoming experience creates affective memory traces, anything that interferes with consumers' abilities to retrieve such traces should reduce savoring's effects (H2). To test this prediction, we added a condition that distracts participants during the consumption experience. We predict that participants who are prevented from reactivating memory traces as a result of distraction will enjoy the consumption experience less than those who savored but did not face distraction.
We also added a different control condition. Instead of comparing savoring with not savoring, we examine whether effects of savoring an upcoming experience are comparable to savoring an ongoing experience, which has been shown to heighten consumption enjoyment (Le Bel and Dube 2001). For our proposed intervention to be of interest, the effect we study should be comparable in size to that of other interventions that may enhance the enjoyment of an experience directly and concurrently.
We also added a condition in which participants both savor the upcoming experience and the ongoing experience to test whether there is an additive effect of savoring an upcoming experience beyond the effect of savoring an ongoing experience. Finally, we examine whether savoring heightens affective expectations of the upcoming experience and, if so, whether its effects on consumption enjoyment persist even after controlling for affective expectations.
One hundred fifty-three students participated in Study 3 in exchange for course credit. Participants were randomly assigned to one of four conditions: ( 1) savoring the upcoming experience only, ( 2) savoring the ongoing experience only, ( 3) savoring the upcoming experience plus savoring the ongoing experience, and ( 4) savoring the upcoming experience plus distraction during consumption.
Savoring manipulations. All respondents were sent an e-mail reminder about their participation in the study the day before the study began. Participants in the three "savoring the upcoming experience" conditions were told that they would watch a short animated movie based on the Toy Story movies. They were encouraged to enjoy the prospect of watching the movie. Those in the "savoring the ongoing experience only" condition were told that they would watch a short animated movie. The name of the movie was not mentioned and there were no instructions to savor the upcoming movie.
At the lab, participants, who sat at individual computer workstations equipped with headphones, were told that they would watch and then report their opinions of an animated short film. We showed the film's title and provided a short description of the clip (see Section D in the Web Appendix).
Before watching the movie, participants in the three "savoring the upcoming experience" conditions were given 30 seconds to savor the prospect of watching it. We instructed these participants to be aware of how the thought of watching the movie clip soon makes them feel at the moment and to pay attention to their thoughts and feelings. Participants in the "savoring the ongoing experience only" condition spent 30 seconds answering questions on their general consumption behaviors. Manipulation check measures were identical to those used in Study 2 (a = .95).
We also created an index of affective expectations of the film by averaging standardized responses to the items ("How much fun do you expect the movie will be?" anchored at 1 = "not at all fun," and 9 = "very fun"; "Compared to other movies I watch, I expect this movie to be…" anchored at 1 = "below average," and 5 = "above average"; a = .74). Prior to watching the movie, we told participants in the "savoring the ongoing experience only" and the "savoring the upcoming experience plus ongoing experience" conditions to be aware of how the movie makes them feel and to pay attention to their thoughts and feelings as they watched the clip.
Distraction manipulation. For those in the "savoring the upcoming experience plus distraction during consumption" condition, we used a well-known task (e.g., Shiv and Fedorikhin 1999) to manipulate distraction. Participants were given the number 154 and were asked to subtract 3 from this running total each time one of the movie characters said the word "you."
Evaluations of the experience. All participants then watched the film clip and rated their ongoing and, afterward, their remembered (a = .95) enjoyment, using the same measures as in Study 2. Those in the "savoring the upcoming experience plus distraction" condition reported the final running number from their mental calculation task before providing remembered enjoyment ratings. Participants also indicated whether they had previously seen any Toy Story movies. Figure 2 depicts the sequence of the manipulations and enjoyment measures.
Across the four conditions, three participants had headphones that did not work, two ignored the distraction instructions, and three provided ongoing and/or remembered enjoyment responses that were more than three standard deviations from the mean. We exclude these eight participants (final N = 145) and report the analyses that do not exclude these participants in the footnotes. One participant did not respond to the covariate, and four participants did not provide online ratings.
Given our predictions, we estimated planned contrasts controlling for whether participants had seen any Toy Story movies before to ensure consistency with Study 2 (Simmons, Nelson, and Simonsohn 2011). We report least-square means and standard errors.
Ongoing consumption enjoyment. Prior exposure to a Toy Story movie did not affect ongoing enjoyment (Myes = 68.47, Mno = 52.81; F( 1, 135) = 2.34, p > .12). Importantly, participants in the "savoring the upcoming experience only" condition (M = 64.17, SE = 4.23) reported similar levels of enjoyment during the clip as did those in the "savoring the ongoing experience only" (M = 65.89, SE = 4.22; F( 1, 135) = .15, p > .70) and "savoring the upcoming experience plus ongoing experience" (M = 68.85, SE = 4.49; F( 1, 135) = 1.07, p > .30) conditions, which were not different from each other (F( 1,135) = .45, p > .50). These results indicate that the retrieval of affective memory traces results in enjoyment ratings that are not different from savoring the ongoing experience—a result that supports the managerial impact of encouraging consumers to savor an upcoming consumption experience. Moreover, asking consumers to savor both the upcoming and the ongoing experience yields no additional benefit beyond of that of asking them to savor in advance, suggesting that marketers can influence consumption enjoyment by encouraging people to savor an experience either in advance or while it is ongoing. In support of H2, those in the "savoring the upcoming experience plus distraction" condition enjoyed the clip less (M = 57.02, SE = 3.36) than did those in the other three savoring conditions ("savoring the upcoming experience only": F( 1, 135) = 5.88, p < .02; "savoring the ongoing experience only": F( 1, 135) = 8.10, p < .01; "savoring the upcoming plus ongoing experience": F( 1, 135) = 11.65, p = .001; see Figure 3).[ 5]
Remembered consumption enjoyment. Prior exposure to a Toy Story movie did not affect remembered consumption enjoyment (Myes = 7.21, Mno = 6.73; F( 1, 139) = .00). As was the case with ongoing enjoyment, participants in the "savoring the upcoming experience only" condition (M = 7.21, SE = .31) remembered having enjoyed the clip as much as those in the "savoring the ongoing experience" (M = 7.43, SE = .31; F( 1, 139) = .44, p > .5) and "savoring the upcoming experience plus ongoing experience" (M = 7.54, SE = .32;F( 1,139) = 1.01, p > .3) conditions. In addition, remembered enjoyment for participants in the "savoring the ongoing experience only" condition was the same as for participants in the "savoring the upcoming experience plus ongoing experience" condition (F( 1,139) = .12, p > .73). Finally, in support of H2, those who had savored the upcoming movie clip but were distracted (M = 6.41, SE = .28) remembered having enjoyed the movie less than did those in the other three savoring conditions ("savoring the upcoming experience only": F( 1, 139) = 5.46, p = .02; "savoring the ongoing experience only": F( 1, 139) = 8.94, p < .01; "savoring the upcoming plus ongoing experience": F( 1, 139) = 10.67, p = .001). These results replicate H1 and support H2.[ 6]
Affective expectations. We first analyzed the effect of the savoring manipulation on affective expectations. Note that we measured affective expectations after the savoring manipulation but before any other manipulation. Thus, at the point of measurement, participants in the three "savoring the upcoming experience" conditions had the same experience. Therefore, we pooled their results for this measure. Participants in the "savoring the ongoing experience only" condition had not received any manipulation at this point and represent a true baseline control. Whether participants had seen a Toy Story movie before as a covariate had a marginal positive effect on affective expectations (Myes = .10, Mno = -.35; F( 1,141) = 3.49, p = .06). Moreover, we find that savoring the upcoming experience heightens affective expectations of watching the movie clip (M = -.03, SD = .13) relative to the baseline (M = -.42, SD = .18; F( 1, 141) = 6.09, p = .015). This finding is novel because prior research has not examined the effect of savoring on affective expectations. Furthermore, affective expectations significantly predict both ongoing (b = 10.63; t(139) = 5.98, p < .001) and remembered (b = .68, t(143) = 5.17, p < .001) consumption enjoyment.[ 7]
Next, we examined whether the effect of savoring an upcoming experience on remembered enjoyment is still significant when accounting for affective expectations. Using measured savoring as the independent variable, the model treats remembered enjoyment as the dependent variable and affective expectations and prior exposure to Toy Story movies as control variables. This analysis excludes the "savoring the upcoming experience plus distraction" condition because distraction hampered savoring. The covariate did not have a significant effect (t(106) = .96, p > .30); however, savoring did affect remembered enjoyment (b = .26, t(106) = 2.92, p < .01) even after we controlled for affective expectations (b = .37, t(106) = 2.56, p = .01).
Study 3 replicates H1 and supports H2. Furthermore, although affective expectations influence consumption enjoyment, savoring's effect on consumption enjoyment goes beyond that explained by affective expectations. We designed Study 4 to provide further evidence of H1 and H2 using yet another consumption experience. In addition, whereas Study 3 controls for affective expectations, Study 4 manipulates them.
Study 4 manipulates affective expectations orthogonally from savoring and focuses on a different consumption experience (playing a video game). We also examine the robustness of the effect by asking whether the effects of savoring are limited to high-quality experiences. If so, the managerial impact of savoring would be limited to situations in which experience quality is high. Study 4 also manipulates the amount of information consumers had about the game beforehand to further test H2. We expect savoring's effects on enjoyment to diminish if consumers are unable to savor an upcoming experience because they have limited information about it. If affective memory traces cannot be formed, they cannot be retrieved when the actual experience unfolds. This manipulation is actionable for marketers who can encourage savoring by providing more information about the savored entity. Study 4 does not measure ongoing enjoyment so as to rule out the possibility that the effects on remembered experiences are due to carryover effects from measuring ongoing enjoyment.
Participants and design. One hundred thirty-eight undergraduate students participated in Study 4 for course credit. The study used a 2 (savoring) X 2 (affective expectations) X 2 (quality of the experience) between-subjects design. Participants were randomly assigned to one of the eight conditions. The focal experience was an air hockey video game. Sections E and F in the Web Appendix provide study details.
Manipulation of savoring. We manipulated savoring by varying how much information was provided about the game (limited vs. extensive) and by framing the time before they played the game as either savoring time or waiting time. Consumers in the high-savoring condition were given extensive information about the experience and were told to use the delay period to savor the future experience. Those in the low-savoring condition were given limited information about the future experience and were told to wait for it to begin.
Manipulation of expectations. We manipulated affective expectations about the future experience using a methodology adapted from prior studies (e.g., Geers and Lassiter 2002; Patrick, MacInnis, and Park 2007). Before they played the game, participants read a review that described the game as being either liked or disliked by most students.
Manipulation of the quality of the experience. We used two games that were similar in content, rules, and scoring system but that differed in graphics quality. An earlier pretest had indicated that the higher-quality graphics game provided a better experience than did the lower-quality graphics game.
Procedure. Once participants were seated at a computer, we told them that the research concerned video game preferences and that they would play a game during the study. Participants received either more or less information about the game before playing it. All participants experienced a 50 second time delay before starting the game. Those in the low-savoring condition were asked to wait for the game to begin. Those in the high-savoring condition were asked to think about how much they would enjoy the game. Following this delay, participants completed a manipulation check measure of savoring (α = .93).
Participants then read product reviews, which were designed to create either positive or less positive expectations about the game. They rated expected enjoyment of the game ("How enjoyable [fun] do you expect the experience of playing the video game will be?"; 1 = "not at all enjoyable [fun]," and 9 = "very enjoyable [fun]"; a = .92). They then played either the higher- or lower-quality video game and indicated how much they enjoyed playing using two items from our prior studies (1 = "not at all enjoyable [fun]," and 9 = "very enjoyable [fun]"; a = .96).
Manipulation check of savoring. We took the measure of savoring before any other manipulation. As such, we estimated a one-way analysis of variance (ANOVA) with the manipulation of savoring as the only independent variable. As we expected, savoring was greater in the high- (vs. low-) savoring condition (Mhigh sav = 6.18, SD = 1.40 vs. Mlow sav = 4.98, SD = 1.80; F( 1, 136) = 19.40, p < .001, wp = .12).
Manipulation check of expected enjoyment. A 2 (savoring) X 2 (expectations) ANOVA on expected enjoyment supports the manipulation of expectations (f( 1, 134) = 49.84, p < .001). Participants in the more positive expectations condition expected the experience to be more positive than did those in the less positive expectations condition (Mmore pos = 6.11, SD = 1.67 vs. Mless pos = 4.09, SD = 1.67; wp = .26). As with Study 3, participants in the high-savoring conditions had more positive expectations about the game than did those in the low-savoring conditions (Mhigh sav = 5.55, SD = 1.76 vs. Mlow sav = 4.61, SD = 2.04; F( 1, 134) = 8.17, p < .01, wp = .05). The interaction was not significant (F( 1, 134) = .17, p > .60).
Remembered consumption enjoyment. A 2 (savoring) X 2 (expectations) X 2 (experience quality) ANOVA on remembered consumption enjoyment yielded main effects of savoring (F( 1, 130) = 7.59, p < .01) and experience quality (F( 1, 130) = 15.06, p < .001). Participants in the high-savoring condition (Mhigh sav = 5.52, SD = 1.94 vs. Mlow sav = 4.56, SD = 2.16) and those who played a higher-quality game (Mhigher qual = 5.74, SD = 1.90 vs. Mlower qual = 4.44, SD = 2.09) enjoyed the game more than their counterparts. These main effects were qualified by a two-way interaction of expectations by outcome (F( 1, 130) = 3.82, p = .05) and a three-way interaction of savoring, expectations, and experience quality (F( 1,130) = 4.06, p < .05). To interpret our findings, we decomposed the three- way interaction into the two two-way ANOVAs depicted in Figure 4, Panels A and B.
For the higher-quality experience, a 2 (savoring) X 2 (expectations) ANOVA revealed only main effects of savoring (F( 1, 63) = 5.86, p < .02) and expectations (F( 1, 63) = 5.19, p < .03) but no interaction (F( 1, 63) = .25, p > .60). Specifically, those in the more positive expectations conditions remembered the experience as more enjoyable than did those in the less positive expectations conditions (Mmore pos = 6.26, SD = 1.82 vs. Mless pos = 5.17, SD = 1.84). These results replicate prior findings in the literature and Study 3 by showing that affective expectations can enhance remembered enjoyment of positive experiences. We also replicate our previous studies, showing that enjoyment of the video game was greater among participants in the high-savoring conditions (Mhigh sav = 6.26, SD = 1.69 vs. Mlow sav = 5.10, SD = 1.97). As in our previous studies, when the experience is generally high in quality, savoring enhances enjoyment, and it does so independent of expectations.
For the lower-quality experience, a 2 (savoring) X 2 (expectations) ANOVA revealed only a significant interaction between savoring and expectations (F( 1, 67) = 5.24, p < .03). When expectations were high, participants in the high-savoring condition enjoyed the video game more than did those in the low-savoring condition (Mhigh sav = 5.18, SD = 2.17 vs. Mlow sav = 3.34, SD = 2.49; F( 1, 67) = 7.20, p < .01), in support of H1. When expectations were low to begin with, however, there was no effect of savoring (Mhigh sav = 4.36, SD = 1.55 vs. Mlow sav = 4.72, SD = 1.79; F( 1, 67) = .29, p > .50).
Study 4 supports H1 using a novel experience (playing a video game) without asking participants to rate their ongoing enjoyment. Furthermore, while savoring affects expectations, its effects on remembered consumption enjoyment remain significant even after we account for expectations, which we manipulated independently. The effects are also robust across conditions that vary in the quality of the experience. We again observe an effect of savoring on enjoyment of higher-quality experiences. This effect replicates for lower-quality experiences when expectations are high, but not when they are low. Therefore, savoring may still have an effect even for lower-quality experiences, albeit on a more limited basis. Notably, we find that enjoyment in the low- quality/no-savoring condition is greater when expectations were low vs. high (4.7 vs. 3.3, respectively). This finding seems to reflect a well-known effect of expectation/disconfirmation in the absence of savoring.
Our final study further tests H2 in a real-world context involving enjoyment with a hotel experience. We use secondary data in which demand effects, self-generated validity, or carryover effects of measurement cannot explain the results.
Study 5 tests H1 and H2 using data from the J.D. Power Hotel Guests Satisfaction Survey. The survey assessed the purpose of respondents' hotel stay (i.e., leisure, business, or conference travel) and how much they enjoyed it. We reasoned that leisure (vs. business or conference) travelers would be more motivated to savor their upcoming trip because it is pursued for pleasure (vs. work). The 2009 and 2010 surveys also measured how far in advance hotel guests booked their stay (i.e., the "booking window"). We argue that the longer the booking window, the greater the opportunity respondents had to savor this experience. While leisure travelers may be more motivated to savor an upcoming trip than business travelers are, savoring may affect enjoyment only when these consumers also have sufficient opportunity to savor (i.e., have a longer booking window). We predict an interaction, such that enjoyment for leisure travelers (those with the motivation to savor) will increase as the booking window increases (as the opportunity to savor increases), but we do not predict such an increase for business travelers.
The analysis utilized hotel enjoyment ratings of 71,929 hotel guests (53,258 leisure travelers and 18,671 business/conference travelers) who booked the hotel themselves. The survey tracked when consumers booked their hotel room (1 = "within a week prior," 2 = "one-two weeks prior," 3 = "three-four weeks prior," 4 = "one-two months prior," 5 = "three-six months prior," 6 = "seven-nine months prior," 7 = "ten-twelve months prior," and 8 = "more than one year prior"). Remembered enjoyment of their hotel experience was measured on a ten-point scale anchored by "unacceptable" and "outstanding" using the item "Thinking back through your entire experience staying at this hotel, how would you rate your overall hotel experience?" We focus on travelers who booked less than a year prior to the trip (total N = 71,783; leisure travelers N = 53,135, coded as 1; non-leisure travelers N = 18,648, coded as 0). We treated booking window as a continuous variable (M = 2.42, SD = 1.33). Note that the median booking window was the same for both leisure and business travelers (Mdn = 2), though the mean was slightly higher for leisure travelers (M = 2.43, SD = 1.31) compared with business travelers (M = 2.37, SD = 1.29, t = 5.62, p < .0001), meaning leisure travelers booked slightly earlier (for details, see Section G in the Web Appendix). We center the booking window at the scale point of 4 ("one-two months prior") so that 0 represents time at this effective midpoint of the scale (Spiller et al. 2013). All results also hold when controlling for per- night hotel cost.
Regressing the hotel enjoyment score on the type of travelers, the booking window, and their interaction revealed an effect of traveler type (b = .158, t = 6.12, p < .0001). Leisure travelers (M = 7.82, SD = 1.91) enjoyed the hotel stay more than business travelers did (M = 7.74, SD = 1.84). Consistent with H2, we observed the predicted interaction between traveler type and booking window (b = .055, t = 4.43, p < .0001; Figure 5). To explore the interaction, we examined the slopes of the booking window at each level of traveler type. The slope was significant and positive for leisure travelers (b = .066, SE = .006, t = 10.60, p < .0001) but insignificant for business travelers (b = .011, SE = .011, t = 1.02, p > .30). Thus, leisure travelers (who had more motivation to savor) enjoyed their stay more if they booked their trip earlier (had greater opportunity to savor). Business travelers, who are likely less motivated to savor a trip in advance, did not benefit from a longer booking window.[ 8]
Study 5 supports H1 and H2 using real-world (secondary) data; in addition, demand effects, self-generated validity, or carryover effects of measurement cannot explain the results. Although we cannot completely rule out that those who are enthusiastic about the trip may book further in advance of the trip and end up enjoying the trip more, our findings support our theory that savoring an upcoming experience affects consumption enjoyment only when consumers have motivation (they are leisure travelers) and opportunity (they have a longer booking window) to savor. Note that although leisure travelers book on average slightly earlier than business travelers did (i.e., had slightly more opportunity to savor), this is just another aspect that differs between business and leisure travelers in general. Importantly, the interaction of time and traveler type—specifically, the significant linear trend of booking window for leisure but not business travelers—supports the claim that motivation, not just opportunity, is necessary for savoring to occur. Although the size of the predicted interaction is relatively small, small effects at an individual level may lead to large effects in the aggregate. Furthermore, these hotel guests were not likely to have been exposed to marketing tactics that encouraged them to savor their upcoming trip. Marketing actions (such as those noted in the introduction) that explicitly encourage savoring might intensify the effect we observe.
Five studies using diverse methodologies, different types of consumers, varying experiences, and diverse savoring manipulations show that savoring an upcoming consumption experience heightens ongoing and remembered enjoyment of the consumption experience (H1). These effects cannot be explained by moods or mindsets. Whereas savoring can induce positive affective expectations of the experience (itself a novel finding), savoring affects consumption enjoyment even after affective expectations are taken into account. Consistent with our theorizing, we find that factors that interfere with consumers' motivation, ability, or opportunity to either savor the upcoming experience or retrieve affective memories of the previously savored experience limit the effect of savoring on ongoing and remembered consumption enjoyment (H2).
Because we examined the effect of savoring across a variety of contexts and diverse savoring manipulations, we estimated the average effect size of savoring on remembered enjoyment by conducting a mini meta-analysis (Goh, Hall, and Rosenthal 2016). In this meta-analysis, we focused on Studies 1-4, which manipulated savoring, and compared the focal savoring condition with the most appropriate control condition (e.g., in Study 3 we compare the "savoring the upcoming experience only" condition with the "savoring the upcoming experience plus distraction" condition).[ 9] For each study, we used the means and standard deviations of each condition to compute Hedges' g (Hedges and Olkin 2014). We then calculated a weighted mean, using inverse variance weights to assign more weight to studies with larger samples (Lipsey and Wilson 2001). Overall, we find that savoring the upcoming experience heightens remembered enjoyment, with a mean effect size of .265 (95% confidence interval [CI] = [.134, .396]).[10] We expanded this meta-analysis to include three additional studies not reported in the article. Two were earlier versions of Studies 1 and 2, and one is similar to Study 4, in which a delay prior to playing a video game was framed as either waiting time or savoring time, and the length of delay was manipulated. The mean effect size across all these studies is .30 (95% CI = [.194, .407]). Average effect sizes did not differ between included versus all studies (Q = .8472, p > .35, calculated using Wilson's [2005] macro).
One may wonder whether asking participants to provide online measures in some of our studies heightened the effect of savoring on remembered enjoyment. However, we did not find any evidence for such an effect. The mean effect size for studies using online measures is .246 (95% CI = [.128, .363]) in the expanded meta-analysis and .192 (95% CI = [.042, .342]) for the studies reported in the article. The mean effect size for studies that did not use online measures was larger: .544 (95% CI = [.296, .792]) across all studies and .491 (95% CI = [.225, .758]) for those reported here. The difference in effect size was significant across all studies (Q = 4.538, p = .033) but only marginally significant for the studies we report in this article (Q = 3.668, p = .056). As such, there is no evidence that taking online measures heightened the effect of savoring on remembered enjoyment and, indeed, some evidence indicates that it reduced the effect.
Beyond their theoretical implications, our findings are managerially significant. Positive consumer experiences and subsequent satisfaction have been linked to increased word of mouth (Moore 2012), customer retention (Gustafsson, Johnson, and Roos 2005), return on investment (Anderson, Fornell, and Rust 1997), and earnings (Yeung and Ennew 2000). As such, tactics that encourage consumers to savor the prospect of using their product or service may positively affect marketers' bottom-line performance.
Encouraging consumers to savor an upcoming experience would seem to be relevant to marketers in several industries: inducing consumers to savor can make waiting time more pleasurable (as previous research on savoring shows) and can heighten enjoyment of the actual experience (as we show). Moreover, various tactics might induce savoring. For example, Illumination Entertainment set up the Minions Facebook page nine months before the movie' s release, alerting consumers about the trailer release and continuously posting new pictures of the Minion characters. The World of Coca-Cola museum in Atlanta plays a preview video that provides consumers with information about an upcoming movie they will watch in the "4-D" theater. Similarly, the Simpsons ride at Universal Studios uses a "preshow" by showing queuing customers cartoons of the Simpsons family taking the same ride. Universal Studios' new Harry Potter and the Escape from Gringotts ride encourages savoring by gradually building a storyline while waiting, taking queuing consumers into various rooms that resemble different parts of the Hogwarts Castle (as featured in the Harry Potter movies). The World of Coca-Cola museum in Atlanta also plays a preview video that provides consumers with information about an upcoming movie they will watch in the "4-D" theater. Starbuck's Roastery and Tasting Room in Seattle is also designed to encourage consumers to savor pleasurable, multisensory coffee experience in advance of enjoying it (roaster.starbucks.com). Such tactics might not only help firms manage service delays but also make the focal experience more enjoyable. Firms with access to consumers' contact data (e.g., Viking River Cruises) can use advertising, e-mail marketing, social media, or other communications such as those we used in Study 1 to encourage savoring and its positive downstream consequences on consumption enjoyment (see also Chun 2011).
Our findings differ from those of Ofir and Simonson (2001), who find that expecting to evaluate an upcoming experience reduces satisfaction because negative aspects of the experience are more likely to be noticed. Instead, savoring an upcoming experience creates positive affective traces, which are later retrieved and integrated into the ongoing consumption experience, increasing consumption enjoyment. Although expecting to evaluate an experience and savoring involve fundamentally different processes, the parallel between these two phenomena is notable and worthy of further research.
Future studies might test the underlying process mechanism involving affective memory traces using mediation, rather than through moderation as was done in this article. Researchers should also explore whether different aspects of savoring (e.g., the time between savoring and the event, the number of savoring occasions) affect consumption enjoyment. It is possible that encouraging savoring too early before a consumption experience is ineffective because consumers are less able to retrieve those memory traces created during savoring. In addition, there may be contexts in which the pain, displeasure, or impatience associated with a wait outweighs the positive effect of savoring. For example, an externally imposed wait may create not only pleasurable anticipation but also an aversive wait experience, eventually detracting from remembered enjoyment in the long run (Nowlis, Mandel, and McCabe 2004). Further research could also examine situations in which consumers may experience complex emotions and explore whether an active effort to savor an upcoming experience could transform an otherwise aversive experience to a positive one.
Notes: Enjoyment was measured every three seconds on a 0-100 scale.
Notes: Enjoyment was measured every three seconds on a 0-100 scale.
A: Higher-Quality Experience
B: Lower-Quality Experience
Notes: Error bars denote standard errors. Enjoyment was measured on a 1-9 scale.
Notes: Error bars denote standard errors. Enjoyment was measured on a 1-10 scale.
Endnotes 1 The results hold when excluded participants are added. Those in the savor spring break condition remembered the experience as more enjoyable than did those in the imagine spring break condition (Msavor spring = 7.80, SD = 1.16 vs. Mimagine spring = 7.14, SD = 1.47; F(1, 133) = 4.79, p = .03) and those in the savor summer break condition (Msavor s"mme-=7.26, SD = 1.58; F(1,133) = 3.50,p = .06).
2 These effects were replicated when data from all respondents are included (prior exposure effects: Myes = 6.44, SD = 2.13, Mno = 4.97, SD = 2.48; F(1, 612) = 50.60, p < .0001; savoring effects: Msavor = 5.90, SE = .13, Mcontrol = 5.48, SE = .14; F(1, 612) = 5.22, p = .023, wp2 = .01). No other effects were observed.
3 These effects were replicated when data from all respondents are included (prior exposure effects: Myes = 65.06, SD = 21.69, Mno = 52.14, SD = 24.81; F(1, 604) = 33.20, p < .0001; savoring effects: Msavor = 60.95, SE = 1.33 vs. Mcontrol = 56.35, SE = 1.38; F(1, 604) = 6.23, p = .013).
4 These effects were replicated when data from all respondents are included (prior exposure effects: Myes = 7.08, SD = 1.97 vs. Mno = 5.78, SD = 2.45; F(1, 612) = 47.92, p < .0001; savoring effects: Msavor = 6.63, SE = .12 vs.Mcontrol = 6.19, SE = .13;F(1,612) = 6.52, p = .011).
5 We observed similar effects when data from excluded respondents were included. Prior exposure had no effect (Myes = 66.80, Mno = 48.01; F(1, 147) = 3.28, p = .072). Participants in the "savoring the upcoming experience only" condition (M = 57.58, SE = 4.70) reported similar levels of ongoing enjoyment as did those in the "savoring the ongoing experience only" condition (M = 62.98, SE = 4.73; F(1, 147) = 1.19, p > .20) and slightly lower levels than did those in the "savoring the upcoming plus ongoing experience" condition (M = 67.66, SE = 4.96; F(1, 147) = 4.19, p = .04). "Savoring the ongoing experience only" and "savoring the upcoming plus ongoing experience" yielded similar levels of online enjoyment (F(1, 147) = .89, p > .30).
6 Not excluding participants weakens the results, though the pattern remains the same. Prior exposure had no effect (Myes = 7.03 vs. Mno = 6.73; F(1, 147) = 0). Participants in the "savoring the upcoming experience only" condition (M = 6.85, SE = .36) remembered having enjoyed the clip as much as those in the "savoring the ongoing experience only" condition (M = 7.31, SE = .36;F(1,147) = 1.50) and marginally less than those in the "savoring the ongoing experience" condition (M = 7.54, SE = .38; F(1, 147) = 3.31, p = .07). Remembered enjoyment for participants in the "savoring the ongoing experience only" condition was the same as for participants in the "savoring the upcoming experience plus ongoing experience" condition (F(1, 147) = .35, p > .5). Finally, those who had savored the future movie clip but were distracted (M = 6.45, SE = .32) remembered having enjoyed the movie less than did those in the "savoring the upcoming experience only" condition (F(1, 147) = 1.08, p = .3).
7 Prior exposure to a Toy Story movie had no effect on affective expectations (Myes = .02, Mno = -.35; F(1, 149) = 2.0, p = .16). Savoring the upcoming experience had a marginal effect on affective expectations (M = -.09, SD = .14) relative to this baseline control (M = -.40, SD = .19; F(1, 149) = 3.43, p = .07). Affective expectations predict ongoing (b = 12.46, t(151) = 6.98, p < .0001) and remembered (β = .93, t(151) = 7.06, p < .0001) enjoyment.
8 The critical interaction was replicated when we treated booking window as a seven-level categorical (vs. continuous) variable (F(6, 71,769) = 3.87,p < .05). We estimated both linear and quadratic trends for each traveler type, assuming, for simplicity, equal spacing of booking window. For leisure travelers, the linear trend was significant (b = .38, SE = .08, t = 5.01, p < .0001) but the quadratic trend was not (b = .07, SE = .07, t = 1.06, p > .28). For business travelers, neither the linear (b = .15, SE = .12, t = 1.22, p > .2) nor the quadratic (b = .11, SE = .11, t = 1.03, p > .30) trend was significant.
9 Note that, given our interest, we did not include conditions that tested the robustness of our effect (i.e., low-quality experience in Study 4).
Note that Studies 2 and 4 were factorial studies. A separate analysis used adjusted effect sizes for these studies (Morris and DeShon 1997), which led to slightly different estimates (effect size = .260, 95% CI = [.129, .391]).
GRAPH: FIGURE 1 Ongoing Enjoyment Ratings of the Movie (Study 2)
GRAPH: FIGURE 3 Ongoing Enjoyment of the Movie by Condition (Study 3)
GRAPH: FIGURE 4 The Effect of Savoring and Expectations on Remembered Enjoyment (Study 4)
GRAPH: FIGURE 5 2009 and 2010 J.D. Power Hotel Guests Survey: Enjoyment with the Hotel Stay by Traveler Type and Booking Window (Study 5)
DIAGRAM: FIGURE 2 Sequence of Manipulations and Enjoyment Measures (Study 3)
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HaeEun Helen Chun is Assistant Professor of Marketing, School of Hotel Administration, S.C. Johnson College of Business, Cornell University.
Kristin Diehl is Associate Professor of Marketing, Marshall School of Business, University of Southern California.
Deborah J. MacInnis is Charles L. and Ramona I. Hilliard Professor of Business Administration and Professor of Marketing, Marshall School of Business, University of Southern California.
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Record: 157- Scheduling Content on Social Media: Theory, Evidence, and Application. By: Kanuri, Vamsi K.; Chen, Yixing; Sridhar, Shrihari (Hari). Journal of Marketing. Nov2018, Vol. 82 Issue 6, p89-108. 20p. 1 Color Photograph, 1 Diagram, 5 Charts, 1 Graph. DOI: 10.1177/0022242918805411.
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Scheduling Content on Social Media: Theory, Evidence, and Application
Content platforms (e.g., newspapers, magazines) post several stories daily on their dedicated social media pages and promote some of them using targeted content advertising (TCA). Posting stories enables content platforms to grow their social media audiences and generate digital advertising revenue from the impressions channeled through social media posts' link clicks. However, optimal scheduling of social media posts and TCA is formidable, requiring content platforms to determine what to post; when to post; and whether, when, and how much to spend on TCA to maximize profits. Social media managers lament this complexity, and academic literature offers little guidance. Consequently, the authors draw from literature on circadian rhythms in information processing capabilities to build a novel theoretical framework on social media content scheduling and explain how scheduling attributes (i.e., time of day, content type, and TCA) affect the link clicks metric. They test their hypotheses using a model estimated on 366 days of Facebook post data from a top 50 U.S. newspaper. Subsequently, they build an algorithm that allows social media managers to optimally plan social media content schedules and maximize gross profits. Applying the algorithm to a holdout sample, the authors demonstrate a potential increase in gross profits by at least 8%.
Keywords: circadian rhythms; content strategy; decision support system; genetic algorithm; social media
More than 1.8 billion users worldwide spent an average of 118 min a day on social media in 2016 ([47]; [47]), and 77% of them actively engaged with social media content through likes, comments, shares, and link clicks ([62]). Following this remarkable consumer trend, content platforms (e.g., newspapers, sports websites, magazines) frequently use social media to disseminate content rapidly to their audiences ([36]). ESPN.com, for example, has more than 34 million Twitter page fans and posts 24 times per day, on average. People has approximately 6.8 million followers on its dedicated Facebook page and posts 28 stories per day, on average.
Building a social media following enables content platforms to generate traffic on their own websites and increase their online advertising revenue from impressions channeled through link clicks of social media posts. However, content platforms are struggling to develop profitable social media schedules to maximize website traffic originating from their social pages ([10]; [11]). To develop a profitable social media schedule, a content platform must begin with the question, What is the best time to post content on social media (i.e., timing)? Moreover, social media websites allow content platforms to advertise content in consumers' social media news feed. Such paid targeted content advertising (TCA) helps attract a new audience base outside of a content platform's current reach. This raises a second question: When should content platforms schedule advertised posts in correspondence with free posts (i.e., timing of TCA)? Furthermore, content platforms aim to design content that better engages targeted users and drives users to click on the posted stories (e.g., [40]). In addition, when should content platforms schedule specific types of content (i.e., timing of content type)?
Existing social media management software platforms (e.g., Hootsuite, CoSchedule, Buffer, Tailwind, Post Planner, Sprout Social) do not offer a holistic solution to these questions.[ 5] Moreover, phone interviews with 15 social media professionals of major content platforms (e.g., Dallas Morning News, Newsday, Baltimore Sun, Texas Tribune) indicated that they currently use simple rules of thumb to overcome the complexity in social media content scheduling and indicated skepticism about the profit-maximizing ability of their heuristics. In addition, barring anecdotal discussions (e.g., [11]; [74]), social media scheduling has not been systematically addressed in the academic literature, highlighting the urgency to understand the drivers of effective social media scheduling to justify return on social media investments ([ 9]; [50]).
This study aims to address these shortcomings. Drawing on the chronopsychology literature that shows that, for most people, working memory availability is highest in the morning, lowest in mid-afternoon, and moderate in the evening ([46]), we hypothesize that consumers' desire to engage with content is highest in the morning, moderate in the evening, and lowest in the afternoon. Moreover, because the scarcity of working memory activates various actions intended to preserve working memory efficiency, we also hypothesize that the use of TCA and content type (content with high-arousal emotions and content requiring high cognitive processing) differentially affect link clicks by time of day (morning, afternoon, and evening).
To test our hypotheses, we use data pertaining to 5,706 posts on the Facebook page of a U.S. newspaper between December 31, 2014, and December 31, 2015. For robust identification of our hypothesized effects, we consider strategic (nonrandom) post allocation to consumers and account for endogeneity in content platforms' strategic decisions of content timing, content type, and TCA. We find strong support for our hypotheses, thus empirically validating our framework.
Finally, we build and test an optimizer that incorporates estimates from our econometric model to simultaneously determine the profit-maximizing mix of scheduling attributes (i.e., timing, content type, and TCA) over a given posting horizon. We use a genetic algorithm to solve the implied multiobjective large-scale optimization problem across several holdout periods. Our results indicate that the proposed solution can improve the content platform's profitability from its own digital advertising by at least 8%.
Together, we make four contributions to marketing theory and practice. First, we augment the burgeoning literature on the drivers of social media content engagement (e.g., [ 2]; [ 6]; [67]) by proposing time of day as a crucial driver of social media content engagement. Our results imply that factoring time-of-day effects into content scheduling is critical; for example, posting content in the morning results in an 8.8% (11.1%) increase in link clicks than doing so in the afternoon (evening).
Second, we build a robust and replicable identification strategy to demonstrate the impact of time of day, TCA, and content type on link clicks. Specifically, we leverage ( 1) exogenous shocks to content timing because of the nature of breaking news and the institutional knowledge of the different functional personnel responsible for crafting the Facebook message and news article, and ( 2) the latent instrumental variables approach to control for endogeneity. The combination of these methods contributes to robust estimation of social media content effectiveness.
Third, we show that time of day interacts with content type and TCA to influence social media post performance and thus add to the literature on paid social media advertising (e.g., [23]). For example, we show that employing TCA in the afternoon generates 21% more link clicks compared with doing so in the morning, and posting content that contains high-arousal negative emotions in the afternoon is 1.6% less effective in generating link clicks than in the morning. These findings serve as guidelines for effective content scheduling and allocation of marketing communication resources.
Fourth, in the spirit of contributing to both the rigor and relevance of marketing literature ([37]), we present a novel optimizer that works as a decision-support tool for social media managers to profitably schedule content on social media. Furthermore, we coded our algorithm using the genetic algorithm feature in Microsoft Excel's Solver, which greatly enhances the managerial appeal of our proposed optimizer.
Next, we outline a theoretical framework to link key social media scheduling attributes to postlevel performance metrics. Subsequently, we describe our data and institutional context and build an econometric model to validate our conceptual framework. After discussing the results of our econometric analysis, we describe our normative model as it pertains to profit-maximizing social media schedules and illustrate an application for our collaborating content platform. We conclude with a discussion of the key managerial takeaways and possible extensions.
Extant research on social media content effectiveness has largely focused on how social media content characteristics and TCA affect content engagement. For instance, prior research has demonstrated that online content that evokes high-arousal emotions leads to more virality ([ 6]) because it increases activation and elicits action-related behaviors such as sharing and consumption ([18]). As such, content that elicits positive (e.g., awe, amusement) or negative (e.g., anger, anxiety) high-arousal emotions is more viral than content that does not. Likewise, content with high information value has been shown to perform well online ([63]) because it elicits higher cognitive processing, which in turn fulfils consumers' self-enhancement goals ([73]) and ability to generate social exchange value ([29]).
Similarly, TCA is known to increase content engagement by allowing content platforms to promote specific posts to broader audiences on the basis of demographics, interests, and location ([50]). As such, TCA is a form of tailored marketing communication that matches content with consumers' preferences and needs. Because content customization increases the relevance of social media posts, TCA improves content effectiveness by enhancing consumers' propensity to engage with social media content.
However, prior research has not explained how the efficacy of psychological and cognitive traits embedded in social media content can change during the day—a necessary input to understanding how to schedule content on social media. Similarly, literature on TCA also falls short of explaining how the effectiveness of TCA changes during the day. These limitations motivate us to develop a novel framework around how diurnal fluctuations in the psychological and cognitive traits embedded in social media content and content targeting affect engagement.
What determines time-of-day effects in social behavior among human beings? Research in chronopsychology has attributed time-of-day effects to diurnal variation in an individual's working memory availability and has found activation of inhibitory processes to increase working memory efficiency during periods of low working memory availability. Working memory is a "brain system that provides temporary storage and manipulation of the information necessary for such complex cognitive tasks as language comprehension, learning, and reasoning" ([ 4], p. 556). It provides the necessary capabilities of storing, retrieving, and processing immediate information. For most people, working memory availability is highest when they wake up in the morning, lowest in mid-afternoon, and moderate in the evening ([46]).
The availability of working memory affects an individual's psychological states and cognitive capabilities. For instance, extant research has shown that high availability of working memory in the morning is likely to make consumers more alert ([68]), less creative ([20]), less innovative ([15]), and less pessimistic ([43]) in the morning. More generally, diurnal variations in working memory can cause sinusoidal cycles (or circadian rhythms) in the level or intensity of people's psychological states and cognitive capabilities ([72]). Such cycles can, in turn, influence the perception of stimuli, judgments, and preferences ([30]) and dictate consumers' social behavior ([16]).
Research in chronopsychology has also attributed time-of-day effects in social behavior to diurnal variation in inhibitory processes that increase working memory efficiency. When working memory availability decreases, the human brain automatically activates several inhibitory processes to increase working memory efficiency ([25]). First, the brain gives preferential treatment to favorable information triggered by external cues that can be easily referenced from previously stored information ([52]). For example, "you may be looking around your apartment for your car keys and your phone simultaneously, holding templates of both in your working memory as you scan your surroundings. Suddenly the phone starts ringing, so you prioritize finding the phone" ([51], p. 450). Second, the brain selectively inhibits processing new information that will further drain working memory usage ([14]). For example, when cortisol levels rise as a result of anxiety, the human brain impairs the processing of visuospatial information because it can further deteriorate working memory availability ([59]). Third, the brain minimizes distracting tasks and tries to direct all cognitive resources to the focal task ([25]; [76]). For example, when working memory availability is reduced, "inhibitory mechanisms prevent irrelevant, off-task information from entering working memory, thus limiting access [of the working memory] to purely goal-relevant information" ([76], p. 91).
In the context of social media, consumers encode, process, and decode social media posts in their working memory. Consequently, consumers' social media engagement (e.g., link clicks) is reliant on their ability to process information in their working memory. However, because the availability and efficiency of working memory exhibit diurnal variations, we purport that social media post performance is likely dependent on diurnal variations in working memory.
We leverage the arguments on time of day, working memory availability, and working memory efficiency to build a novel theoretical framework for scheduling content on social media (Figure 1). First, drawing on time of day and working memory availability arguments, we hypothesize the main effect of time of day and link clicks (H1a–c). Next, drawing on time of day and working memory efficiency arguments, we posit that the main effect of time of day on link clicks is moderated by content that elicits positive and negative high-arousal emotions (H2a–c), content that requires higher cognitive processing (H3a–c), and TCA (H4a–c).
Graph: Figure 1. Conceptual model.
Conceptually, a day can be divided into four parts—morning, afternoon, evening, and night—which we refer to as dayparts. Because the majority (∼98% in our empirical context) of social media content is posted in the morning, afternoon, and evening dayparts, we limit our theoretical discussion to these three dayparts. Next, we present the arguments for our hypotheses (for an overview of the logic employed, see Web Appendix W1).
For most social media content consumers, the availability of working memory peaks in the morning. Higher availability of working memory makes individuals more alert ([68]), attentive ([64]), curious ([ 8]), deliberative ([ 3]), and information seeking in electronic environments ([49]). However, as the day progresses, people take on more tasks or accumulate more stress. Stress causes cortisol levels to increase, which then impairs working memory availability ([45]). Limited availability of working memory limits people's ability to process new information and impairs their desire and ability to engage with social media content.
Consequently, because working memory availability is highest in the morning, lowest in mid-afternoon, and moderate in the evening for most individuals ([46]), we theorize that the desire to engage with content will likely be highest in the morning, moderate in the evening, and lowest in the afternoon. As such, we posit:
- H1 : Ceteris paribus, (a) posting content in the afternoon results in fewer link clicks than in the morning, (b) posting content in the evening results in fewer link clicks than in the morning, and (c) posting content in the afternoon results in fewer link clicks than in the evening.
Previous research has concluded that online content that elicits positive high-arousal emotions (e.g., awe, amusement) and negative high-arousal emotions (e.g., anger, anxiety) receives increased engagement owing to the activation of psychological states ([ 6]). However, how does the effectiveness of content with high-arousal emotions vary across dayparts? As we have discussed, working memory availability decreases from morning to afternoon and is moderate in the evening. Therefore, in the evening (afternoon), when working memory is more resource deprived than in the morning (evening), the brain selectively inhibits information that will further drain working memory availability ([51]). Specifically, it focuses only on critical tasks achievable with current working memory and filters out information that could hinder it ([14]). As such, inhibitory mechanisms, which are responsible for the suppression of irrelevant, off-task information, are activated when working memory is struggling to process new information ([25]).
In the social media context, because content with high-arousal emotions could further increase stress and cortisol levels ([ 1]; [35]; [66]),[ 6] which are known to deplete working memory, we theorize that working memory will deprioritize content with high-arousal emotions during periods of already constrained working memory. Because working memory is most constrained in the afternoon, moderately constrained in the evening, and least constrained in the morning, we conjecture that people will be less able to consumer content with more high-arousal emotions when their working memory is more depleted. As such, we theorize the following:
- H2 : Ceteris paribus, social media content with positive high-arousal emotions (e.g., awe, amusement) and negative high-arousal emotions (e.g., anger, anxiety) accumulate fewer link clicks (a) in the afternoon than in the morning, (b) in the evening than in the morning, and (c) in the afternoon than in the evening.
Previous research has demonstrated that online content that requires higher cognitive processing (e.g., insight, reason) receives increased engagement because of its increased level of cognitive involvement ([63]). However, how does the effectiveness of such content vary across dayparts? As we have discussed, in the evening (afternoon), when working memory is more resource deprived than in the morning (evening), it inhibits irrelevant information by minimizing distracting tasks and directing all available cognitive resources to the focal task ([25]; [76]). Inhibition fulfills two crucial tasks in enhancing cognitive processing. First, it prevents irrelevant and off-task information from entering the working memory. Second, it deletes marginally relevant information from working memory. Both tasks together minimize competition from distracting information during information encoding, retrieval, and processing in the working memory, thereby increasing attention on focal information ([76]) and improving analytical and cognitive processing capabilities ([75]).
Within the social media context, individuals have better inhibitory capabilities because the working memory is more constrained. Thus, we theorize that the likelihood of people consuming content (i.e., clicking on links) that requires higher cognitive processing is highest in the afternoon, moderate in the evening, and lowest in the morning. As such, we posit the following:
- H3 : Ceteris paribus, social media content requiring higher cognitive processing accumulates more link clicks (a) in the afternoon than in the morning, (b) in the evening than in the morning, and (c) in the afternoon than in the evening.
Targeted content advertising enables content platforms to promote specific posts to broader audiences on the basis of demographics, interests, and location ([50]). Therefore, consistent with prior research, we expect a positive association between TCA and link clicks.
However, how does TCA's effectiveness vary across dayparts? As we have discussed, working memory availability decreases from morning to afternoon and then moderately increases in the evening. Therefore, when working memory is more resource deprived in the evening (afternoon) than in the morning (evening), the brain prioritizes preferential information and diverts available cognitive resources to this information by biasing the receptive fields of neurons in the information's favor ([26]; [27]). In neuropsychology literature, this is commonly referred to as the "biased competition principle" ([14]). However, to activate preferential information processing, the working memory needs an external cue that can be easily referenced and retrieved from long-term memory.
In the social media context, we theorize that TCA can serve as an effective cue for the preferential processing of a social media post. An individual's interests and preferences are typically stored in his or her long-term memory and easily referenced and retrieved to the working memory on demand ([60]). When an individual is exposed to TCA in the afternoon or evening, the working memory picks it up as an external cue because TCA is sufficiently differentiated from regular content in the news feed.[ 7] Subsequently, the working memory prioritizes the advertised content over other information in the news feed. Because TCA, by design, aligns well with the individual's interests and preferences, (s)he will likely pull the template of the information from the long-term memory, give it preferential processing, and engage with the content (e.g., click on the post to read further). Thus, we expect TCA to be most effective in the afternoon, moderately effective in the evening, and least effective in the morning:
- H4 : Ceteris paribus, TCA on social media results in higher link clicks (a) in the afternoon than in the morning, (b) in the evening than in the morning, and (c) in the afternoon than in the evening.
We use data from a top 50 U.S. newspaper (we refer to this as the "content platform" hereinafter) that generates revenue through print subscriptions, print advertising, and digital advertising. The content platform has been a local monopoly for several decades. It has a daily circulation of ∼230,000 and weekend circulation of ∼336,000 and attracts ∼5.3 million monthly unique visitors to its website. The content platform reaches seven out of ten adults with annual household incomes of $100,000 or more in the two largest counties in its state.
Like most U.S. news organizations, the content platform views social media as a key strategic lever to increase website traffic and digital advertising revenue. The content platform has more than 350,000 fans on its dedicated Facebook page and currently allocates more than 90% of its social media budget to Facebook. Each Facebook post on the platform's dedicated social media page includes a web link to a corresponding full news story on the platform's website. Increasing website traffic through social media link clicks helps the platform increase its digital advertising revenue, as most advertisers pay for impressions. Digital advertising currently accounts for approximately 30% of the content platform's overall revenue and constitutes its fastest-growing revenue source.
In-depth interviews with the content platform's social media manager, advertising director, and content editor revealed that the firm currently employs ad hoc rules of thumb, such as prioritize posting lifestyle and sports news in the morning and waiting at least 30 min between posts, to make daily scheduling decisions. While the content platform realizes that arbitrary rules alleviate complexity, it desires a model-based approach to maximize its digital advertising revenues from impressions channeled through Facebook.
We discuss two social media metrics that drive traffic to the content platform's website, organic reach and link clicks, and comment on our choice of one metric over the other. Organic reach is the total number of unique social media users viewing the content platform's posts in their news feed for free. Maintaining a strong fan base helps maximize the platform's likelihood of engaging with its customers through an unpaid distribution channel, in turn affecting brand equity and word of mouth ([36]; [53]). However, owing to increased competition in news feed visibility, businesses have been experiencing a steady decline in organic reach on Facebook ([ 7]). Specifically, a high influx of posts from friends and other businesses a user follows has pushed older posts to the bottom of the news feed, making them less likely to gain exposure. Consequently, Facebook instituted a relevance-based algorithm, EdgeRank, in 2014 to increase the exposure of relevant content to each Facebook user. EdgeRank prioritizes stories on the basis of post type (e.g., photo, video, link), affinity score between businesses' dedicated Facebook page and users who view the posted stories, and post recency ([12]; [40]).
However, because EdgeRank is a proprietary algorithm, firms cannot determine whether their organic reach is due to an individual's choice to consume content or the algorithm's decision to show content to that individual. Thus, we do not study organic reach but rather use total link clicks garnered by each Facebook post on our collaborating content platform's dedicated page as our dependent variable. Unlike organic reach, a link click is a deliberate action and reflects an individual's revealed content preference. It also demonstrates an instantaneous effect of post scheduling, thereby allowing the firm to influence the metric.
Content platforms can also improve social media post performance through TCA, commonly known as boosting ([50]). Facebook provides a content platform with the opportunity to pay to reach users who are not subscribed to the platform's dedicated page on the basis of these users' demographics, interests, and location. When the content platform boosts a post, it appears as an inline-ad on the news feed of Facebook users who fit the targeting criterion. Thus, TCA increases post engagement by reaching a wider audience ([44]). The higher engagement level that results from TCA then prioritizes the post in the news feed of Facebook users who are currently fans of the content platform, further increasing link clicks.
Our dependent variable "link clicks" refers to the total number of clicks on the content platform's link associated with each Facebook post. Because link clicks are strictly positive, we use the logarithm of link clicks as the dependent variable to alleviate distributional violations and account for posts that receive abnormally high link clicks.
We specify four indicator variables to capture time of day (dayparts) effects. Night (Daypart1) refers to the period between 12:00 a.m. and 5:59 a.m., morning (Daypart2) captures the period between 6:00 a.m.–11:59 a.m., afternoon (Daypart3) is the period between 12:00 p.m. and 5:59 p.m.; and evening (Daypart4) refers to the period between 6:00 p.m. and 11:59 p.m. The respective indicator variable is equal to 1 if a story's posting time belongs to the daypart and 0 otherwise. The baseline daypart is morning (i.e., Daypart2). Our collaborating content platform is located in Pacific Time Zone, so our time stamp corresponds to that time zone.[ 8]
We use an indicator variable to capture whether a post is advertised on Facebook (1 = yes, 0 = otherwise). The content platform uses the same set of targeting filters (country = United States, age range = 22–65 years) and an identical TCA budget of $100 across all advertised posts. Because we do not observe variation in these two dimensions, we are only able to assess the first-order effect of TCA (i.e., whether [=1] or not [=0] a post was boosted) on link clicks.
Following [ 6]), we use an automated text analysis tool to quantify high-arousal positive emotions (e.g., awe, amusement) and high-arousal negative emotions (e.g., anger, anxiety) in Facebook posts. The Linguistic Inquiry and Word Count (LIWC) program provides the scale score of these two dimensions using LIWC2015 Dictionary, which contains a list of 6,400 words, word stems, and selected emoticons ([54]).
Following [13]) and [55]), we use LIWC to calculate the cognitive processing scale score (e.g., insight, causation). The LIWC2015 Dictionary is an appropriate tool because it can accommodate numbers, punctuation, short phrases, and informal languages, allowing us to read the "netspeak" common in social media posts, and its internal reliability and external validity are well supported in the literature ([54]; [65]). A sample of words and word stems of three content types is available in Web Appendix W2.
We include several control variables to account for content- and environment-level heterogeneity. First, we control for news topic categories. Our content platform classifies its stories into eight categories: business, entertainment, life, local, national, opinion, other, and sports. Each content topic represents a substantive domain for the content platform, with dedicated resources (e.g., editors, journalists), and generates distinct costs per impressions from advertisers on its website. Next, we control for the linear and quadratic terms of interpost duration, operationalized as the minutes elapsed between two subsequent posts. In addition, we include month dummies and cluster standard errors by day of the week to capture the unobserved temporal heterogeneity that might influence a post's link clicks (e.g., growth of the social media platform, changes in external market conditions, popularity of the newspaper industry). Finally, we control for content features that might affect consumers' perceptions, including message length (i.e., word count) and text readability, which is measured as the FOG index.[ 9] We present the notations of variables, measures, and data sources in Table 1.
Graph
Table 1. Variables, Notations, Measurements, and Data Sources.
| Variable | Notation | Measurement | Data Source |
|---|
| Dependent Variables | | | |
| Link clicks | log(Link Clicki) | Log of total number of clicks on content platform's link associated with each Facebook post | Facebook Insights |
| Independent Variables | | | |
| Time of day | Nighti, Afternooni, Eveningi | 1 if a story is posted in the corresponding daypart, 0 otherwise | Facebook Insights |
| Targeted content advertising | TCAi | 1 = yes, 0 = no | Facebook Insights |
| High-arousal negative emotions (message) | Negemo_arousali | LIWC scale score quantifying extent to which each message evokes high arousal from negative emotions (e.g., anger) | Facebook Insights |
| High-arousal positive emotions (message) | Posemo_arousali | LIWC scale score quantifying extent to which each message evokes high arousal from positive emotions (e.g., amusement) | Facebook Insights |
| Cognitive processing (message) | Cog_processi | LIWC scale score quantifying extent to which each message demands cognitive processing (e.g., insight) | Facebook Insights |
| Control Variables | | | |
| Message length | | Number of words in Facebook message | Facebook Insights |
| Message readability | | FOG index =.4 × (average sentence length + 100 × proportion of difficult words) | Facebook Insights |
| Interpost duration | | Minutes elapsed between two subsequent posts | Facebook Insights |
| News topic | | Categorical variable denoting eight topics: business, entertainment, life, local, national, opinion, other, sports | Collaborating Content Platform |
| Month | | Categorical variable with 12 values | Facebook Insights |
| Organic reach | log(Organic Reachi) | Log of total number of unique people shown post through unpaid distribution | Facebook Insights |
| Excluded Variables | | | |
| Breaking tweets | Breakingij | Average number of breaking tweets posted by Associated Press and CNN Breaking News in each daypart | Twitter |
| High-arousal negative emotions (description) | D_negemo_arousali | LIWC scale score quantifying extent to which each description evokes high arousal from negative emotions | Facebook Insights |
| High-arousal positive emotions (description) | D_posemo_arousali | LIWC scale score quantifying extent to which each description evokes high arousal from positive emotions | Facebook Insights |
| Cognitive processing (description) | D_cog_processi | LIWC scale score quantifying extent to which each description demands cognitive processing | Facebook Insights |
10022242918805412 Notes: "Message" refers to the text of the Facebook message; "description" refers to the text describing the news story.
Our data set comprises 5,706 individual posts from our content platform's dedicated Facebook page between December 31, 2014, and December 31, 2015. Our data are a snapshot of all posts and the corresponding engagement on the content platform's Facebook page collected in June 2016. Therefore, all posts in our data set reach their maximum lifetime engagement. For each Facebook post, we observe the time stamp; original link (a URL to the specific story on the content platform's website); message, title, and description of the post (for an example, see Figure 2); whether the post is advertised; and key performance indicators (e.g., link clicks).
Graph: Figure 2. News story content.Notes: This figure is an example from the Associated Press (accessed January 22, 2018).
Graph: 10.1177_0022242918805411-fig2.tif
On average, a post reaches 18,706 fans and obtains 967 link clicks (for detailed descriptive statistics, see Web Appendices W3 and W4). Both metrics exhibit considerable variation, with organic reach ranging from 0 to 173,043 and link clicks ranging from 0 to 152,448. On average, the interpost duration between two posts is 68 min. Figure 3 shows noteworthy patterns in the independent variables. Panel A shows that 2,040 stories (36%) were posted in the morning, while 2,404 (42%), 1,135 (20%), and 127 (2%) were posted in the afternoon, evening, and night dayparts, respectively. Panel B shows that the majority of posts are on local news (N = 1,721, 30%), followed by sports (N = 1,026, 18%), and life (N = 841, 15%). Next, among 518 targeted posts, we observe that 188 stories (36%) were posted in the morning daypart, while 211 (41%), 111 (21%), and 8 (2%) were posted in the afternoon, evening, and night dayparts, respectively. Panel B also illustrates that sports stories (N = 164) are among the most advertised, followed by local (N = 141) and life (N = 64) stories. As Panel C shows, posted stories have the highest level of positive high-arousal emotions at night (4.53) and lowest level of positive high-arousal emotions in the evening (2.85). However, posted stories have the highest level of negative high-arousal emotions in the afternoon (.49) and lowest level of negative high-arousal emotions at night (.25). Finally, in Panel D, we see that posted stories have the highest level of cognitive processing content in the morning (7.67) and lowest level of cognitive processing content in the evening (7.31).
Graph: Figure 3. Data distribution. A: Distribution of Facebook posts across dayparts. B: Distribution of topic categories. C: Arousal level of content across dayparts. D: Level of cognitive processing required across dayparts.Notes: Black bars refer to all posts; gray bars refer to advertised posts.
Graph: 10.1177_0022242918805411-fig3.tif
We detail several empirical challenges that inhibit robust model identification and subsequently present our corresponding solutions (for a summary, see Web Appendix W5).
As we have discussed, organic reach is the total number of unique social media users who view the content platform's posts in their news feed for free, and link clicks capture the number of users who clicked on the post. However, the EdgeRank algorithm strategically determines whether users see the stories in their news feed and is responsible for the organic reach a post obtains. Accordingly, we model how our focal drivers affect link clicks by conditioning out this strategic behavior in two ways. First, we control for organic reach in the link clicks equation. By doing so, we model the direct outcome of the EdgeRank algorithm (i.e., the number of users who actually see the stories posted by the content platform through unpaid distribution). Second, Facebook's EdgeRank algorithm might display posts to consumers to induce link clicks on the basis of characteristics other than those included in organic reach. Therefore, we account for news topic, month, and content features (i.e., message length and text readability) to capture factors that induce strategic nonrandomness in allocating posts. Thus, we have the following:
log(Link Clicki)=β0+β11Nighti+β12Afternooni+β13Eveningi+β2TCAi+β3Negemo_arousali+β4Posemo_arousali+β5Cog_processi+β61Nighti×TCAi+β62Afternooni×TCAi+β63Eveningi×TCAi+β71Nighti×Negemo_arousali+β72Afternooni×Negemo_arousali+β73Eveningi×Negemo_arousali+β81Nighti×Posemo_arousali+β82Afternooni×Posemo_arousali+β83Eveningi×Posemo_arousali+β91Nighti×Cog_Processi+β92Afternooni×Cog_Processi+β93Eveningi×Cog_Processi+β10log(Organic Reachi)+Θ′Controls+∊i,1
where i is the subscript for the Facebook post. β11, β12, and β13 capture the effect of time of day on link clicks (with morning as the baseline); β2 captures the effect of TCA on link clicks; β3, β4, and β5 capture the effects of content types on link clicks; β61, β62, and β63 capture the three interactions between the time of day dummies and TCA; and β71–β93 capture the nine interactions between each of the time-of-day dummies and content type. β10 captures the effect of nonrandom post allocation on link clicks, and a vector of covariates (Controls) is included.
The social media manager of the content platform is likely to decide the posting daypart strategically drawing on private knowledge (e.g., expected number of clicks), which we do not observe. This private knowledge creates a correlated unobservables problem because it influences the posting daypart but resides in the error term. To alleviate endogeneity bias from a correlated unobservables problem, we use the control function approach ([56]). Specifically, we estimate an auxiliary regression for posting decisions in each daypart (i.e., the first stage). As a predictor in the auxiliary regression, we need an excluded variable that meets the relevance criterion (i.e., the excluded variables should be correlated to the endogenous variable daypart) and the exclusion restriction criterion (i.e., the excluded variables should not be correlated to the shock in the dependent variable).
We use breaking news to identify our excluded variable. The timing of breaking news is typically exogenous (e.g., the AirAsia crash), and content platforms such as newspapers push out stories on such events as soon as possible to inform their audiences. Thus, we collect all breaking news Twitter posts (tweets) in 2015 reported by the Associated Press (@AP) and CNN Breaking News (@cnnbrk), which receive a significantly larger number of replies, shares (retweets), and likes compared with regular tweets (p <.01) (for details, see Web Appendix W6).
The average number of breaking tweets posted by the Associated Press and CNN Breaking News in a given daypart meets the relevance criterion because more breaking events in a given daypart (e.g., afternoon) affects the probability that our collaborating partner will post regular stories in the same daypart. In other words, the original post planning in a given daypart is more likely to be disrupted if the supply of breaking news in the same daypart is higher. In Web Appendix W7, we present an example showing how breaking news interrupts local newspapers' social media schedules. Here, our collaborating content platform's reporting of the AirAsia crash has pushed "life" news that is unrelated to the crash and typically scheduled in a given daypart down to the next time slot.
We validated this argument in interviews with a group of social media professionals who work for content platforms, including the Dallas Morning News, Newsday, Baltimore Sun, Texas Tribune, and others. Sample responses are as follows: "We push breaking news out immediately and move the schedule around as appropriate," "We prioritize breaking news ahead of the schedule," and "We have a team of editors being ready to get out urgent news at all times." The first-stage results confirm these intuitions (see Web Appendix W8).
The average number of breaking tweets in a corresponding daypart also meets the exclusion restriction criterion because breaking news events are external exogenous shocks (e.g., terror attacks, unexpected moves by North Korea) and are likely uncorrelated with the anticipated link clicks of a news story originally planned for the given daypart. Therefore, we estimated the following first-stage model for each daypart:
Daypartij*=α0+α1Breakingij+Λ1Controls+μij,and2a
Daypartij=1 if Daypartij* > 0,2b
where Daypartij is a binary variable indicating whether the story i is posted in daypart j (j = 1, 3, or 4 for night, afternoon, or evening, respectively). Breakingij is the average number of breaking news tweets posted by the Associated Press and CNN Breaking News in daypart j for each day in 2015. All other covariates are as defined in Equation 1 to explain the likelihood of posting in a given daypart. We then compute the inverse Mills ratios (λ1i, λ3i, λ4i) derived from each probit specification and add them to Equation 1 to control for selection bias.
Similarly, social media manager is likely to design each Facebook message strategically to induce a larger number of link clicks drawing on private knowledge (e.g., content types that elicit higher engagement) unobserved us. This private knowledge creates a correlated unobservables problem because it influences the content type of the Facebook message but resides in the error term. For example, if the social media manager receives a piece of relatively unbiased news to be scheduled, (s)he may try to increase the arousal level in the news by adding an anxiety-inducing spin to the content to increase link clicks.
To address the endogeneity concern, we again use the control function approach ([56]). We seek an excluded variable that directly affects each of the three Facebook message content types—the level of positive or negative high-arousal emotions and level of cognitive processing required—but only indirectly affects link clicks. We use each of three content types in the story description as the excluded variable for the corresponding content type in the Facebook message (recall the difference between the Facebook message and story description described in Figure 2).
Content types of story description meet the relevance criterion because the Facebook message should carry the essence of the story description, which is a summary of the original article. In other words, the content of story description will explain, at least partially, the content type of the Facebook message. We confirmed this intuition by verifying the first-stage results (see Web Appendix W8). Story description content types also meet the exclusion restriction criterion because the story description in the original article is not written by social media managers, who might have expectations when engaging their audience, but exogenously given by journalists or editors to social media managers. Thus, we specify the following equations:
Negemo_arousali=γ10+γ11D_negemo_arousali+Λ2Controls+τ1i,3a
Posemo_arousali=γ20+γ21D_posemo_arousali+Λ3Controls+τ2i, and3b
Cog_processi=γ30+γ31D_cog_processi+Λ4Controls+τ3i,3c
where D_negemo_aoursali, D_posemo_aoursali, and D_cog_processi are scale scores of the three content types in the story description, respectively. All other covariates are as previously defined in Equation 1. The predicted residuals of , , and from Equations 3a, 3b, and 3c serve as effective control variables to address the endogeneity concern.
Finally, social media managers make TCA decisions strategically in anticipation of a higher clicking probability or other factors unobservable to us. This strategic behavior could render TCA endogenous to link clicks, because correlated unobservables (e.g., expected future post performance) drive both TCA decisions and content engagement.
Because of the lack of a clean exogenous TCA shifter in our data set, we use a latent instrumental variables approach to correct for possible endogeneity ([17]; [41]; [57]). That is, we correct for the endogenous regressor by introducing a discrete, unobserved latent instrumental variable with m categories (m > 1) that partitions its variance into endogenous (possibly correlated with the error term) and exogenous (uncorrelated with the error term) components. Accordingly, we specify the following equation:
TCAi=θZi+τ4i,4
where i is the subscript for the post; TCAi denotes the endogenous TCA decision for post i; Zi is the unobserved discrete instrument (uncorrelated with the error term in Equation 1); and τ4i refers to the error term, which is correlated with the error term in Equation 1.
To obtain , we follow [71]) and perform a latent class clustering, which splits TCAi into a manifest variable from a finite mixture of distributions. For an m-cluster model, we can then predict every value of TCA as
TCAi^=∑k=1mθkp(Ci=k|TCAi),5
where θ1, θ2,..., θm is the latent cluster mean vector that makes up TCAi; is the predicted probability that a value TCA belongs to cluster k. Using the Akaike information criterion, we retain a two-cluster model. Because latent class mixtures, by definition, are computed by assuming that Zi is uncorrelated with the error term in Equation 1, Zi is the unobserved discrete instrument. Finally, we replace TCAi in Equation 1 with the predicted values of TCA from Equation 5 ( ) and add the error residual as an additional control variable ([71]). After correcting for endogeneity of time of day, content type, and TCA, the full model is specified as follows:
log(Link Clicki)=β0+β11Nighti+β12Afternooni+β13Eveningi+β2TCAi^+β3Negemo_arousali+β4Posemo_arousali+β5Cog_processi+β61Nighti×TCAi^+β62Afternooni×TCAi^+β63Eveningi×TCAi^ +β71Nighti×Negemo_arousali+β72Afternooni×Negemo_arousali+β73Eveningi×Negemo_arousali+β81Nighti×Posemo_arousali+β82Afternooni×Posemo_arousali+β83Eveningi×Posemo_arousali+β91Nighti×Cog_Processi+β92Afternooni×Cog_Processi+β93Eveningi×Cog_Processi+β10log(Organic Reachi)+Θ′Controls+δ1λ1i+δ2λ3i+δ3λ4i+δ4τ1i^+δ5τ2i^+δ6τ3i^+δ7τ4i^+∊i,6
where and are terms correcting for endogeneity, and all other covariates are as defined in Equation 1.
Table 2 presents the estimation results of Equation 6. We report results from the auxiliary equations (Equations 2a–b, and 3a–c) in Web Appendix W8. To compare afternoon with evening (as opposed to using the morning daypart as the baseline), we conducted a statistical test on the difference between β12 and β13, where the β12 compares the effectiveness of afternoon with morning, and β13 compares the effectiveness of evening with morning. Similarly, we also conducted statistical tests on the differences between β62 and β63, β72 and β73, β82 and β83, and β92 and β93.
Graph
Table 2. Scheduling Attributes and Post Performance.
| Log (Link Clicks) |
|---|
| Without Endogeneity Correction | With Endogeneity Correction |
|---|
| Coef. | SE | Coef. | SE |
|---|
| Night (12:00 a.m.–5:59 a.m.) | −.137 | .100 | −.107 | .106 |
| Afternoon (12:00 p.m.–5:59 p.m.) | −.113*** | .016 | −.104*** | .014 |
| Evening (6:00 p.m.–11:59 p.m.) | −.154*** | .035 | −.152*** | .037 |
| TCA (1 = yes) | .798*** | .069 | 2.109*** | .196 |
| Night × TCA | −.097 | .126 | −.208* | .098 |
| Afternoon × TCA | .211** | .062 | .370*** | .080 |
| Evening × TCA | .040 | .063 | −.187 | .260 |
| Negative emotions (message) | .016** | .005 | .035 | .022 |
| Positive emotions (message) | −.002 | .001 | .029** | .010 |
| Cognitive processing (message) | −.002 | .001 | .013 | .011 |
| Night × Negative emotions (message) | −.076** | .024 | −.082** | .025 |
| Afternoon × Negative emotions (message) | −.016** | .005 | −.015* | .006 |
| Evening × Negative emotions (message) | −.026* | .013 | −.023 | .015 |
| Night × Positive emotions (message) | .004 | .003 | .003 | .002 |
| Afternoon × Positive emotions (message) | −.001 | .003 | −.001 | .003 |
| Evening × Positive emotions (message) | .001 | .004 | .000 | .004 |
| Night × Cognitive processing (message) | .003 | .003 | .003 | .004 |
| Afternoon × Cognitive processing (message) | .005** | .002 | .005** | .002 |
| Evening × Cognitive processing (message) | .007* | .003 | .007* | .003 |
| Log(organic reach) | 1.742*** | .061 | 1.740*** | .063 |
| Message length | −.003 | .002 | −.003* | .002 |
| Message readability (FOG index) | −.007*** | .001 | −.005** | .002 |
| Interpost duration | .000 | .000 | −.001 | .001 |
| Interpost duration2 | .000 | .000 | .000 | .000 |
| Local news dummy | 1.275*** | .159 | .579*** | .129 |
| Business news dummy | 1.310*** | .177 | .611*** | .138 |
| Sports news dummy | 1.320*** | .136 | .662*** | .109 |
| Entertainment news dummy | 1.413*** | .176 | .874*** | .147 |
| Life news dummy | 1.454*** | .177 | .847*** | .123 |
| Opinion dummy | 1.098*** | .191 | .369** | .138 |
| National news dummy | 1.108*** | .158 | .465*** | .088 |
| TCA (LIV_error term) | | | .100 | .132 |
| λnight (inverse Mills ratio) | | | .585 | .615 |
| λafternoon (inverse Mills ratio) | | | 1.889 | 1.753 |
| λevening (inverse Mills ratio) | | | −.031 | .390 |
| Negative emotions (residuals) | | | −.021 | .020 |
| Cognitive processing (residuals) | | | −.017 | .011 |
| Positive emotions (residuals) | | | −.031** | .011 |
| Intercept | −11.945 | .646 | −13.883*** | 1.917 |
| Day-of-week and month effects | Yes | | Yes | |
| Pseudo-R2 | .765 | | .773 | |
| N | 5,706 | | 5,706 | |
- 20022242918805412 Notes: Standard errors in parentheses.
- 30022242918805412 *p <.1.
- 40022242918805410 **p <.05.
- 50022242918805410 ***p <.01.
Our results suggest that posting content in the afternoon results in fewer link clicks than in the morning (β = −.104, p <.01), lending support to H1a. Furthermore, posting content in the evening results in fewer link clicks than in the morning (β = −.152, p <.01), lending support to H1b. However, we do not find support for H1c, concerning the differential impact of posting content in the evening and afternoon (F = 1.13, n.s.).
Consistent with prior research ([ 6]), we find that content with high-arousal positive emotions is associated with higher link clicks (β =.029, p <.05). However, we do not find an association between high-arousal negative emotions and link clicks (β =.035, n.s.).
We find that content with high-arousal negative emotions garners fewer link clicks in the afternoon than in the morning (β = −.015, p <.10), but we find no such evidence for high-arousal positive emotions (β = −.001, n.s.). Therefore, we find partial support for H2a. In addition, we do not find support for H2b; that is, neither negative (β = −.023, n.s.) nor positive (β =.000, n.s.) high-arousal emotions garner fewer link clicks in the evening than in the morning. In general, our lack of support for positive high-arousal emotions could be because the brain may not activate preferential treatment of information when it encounters content with positive high-arousal emotions because these emotions are less threatening to the working memory than content with negative high-arousal emotions. Finally, with regard to the difference in the effectiveness between emotion-filled content in the afternoon and evening dayparts (H2c), we do not find significant differences for negative (F =.750, n.s.) and positive (F =.170, n.s.) high-arousal emotions.
We find evidence for a significant interaction between timing and content that requires higher cognitive processing. First, social media content based on higher cognitive processing draws a larger number of link clicks in the afternoon than in the morning (β =.005, p <.05). This finding supports H3a. Second, such social media content elicits higher link clicks in the evening than in the morning (β =.007, p <.10), lending support to H3b. However, we do not find support for H3c, concerning the differential impact of such social media content in the afternoon and evening (F =.660, n.s.).
We find that TCA is more effective in the afternoon than morning (β =.370, p <.01), lending support to H4a. However, we do not find support for H4b, which states that TCA is more effective in the evening than morning (β = −.187, n.s.). In addition, we observe that TCA is less effective at night than in the morning (β = −.208, p <.10), likely because majority of the audience is inactive at night. Finally, we do not find support for H4c, concerning the differential effect of TCA in the evening and afternoon (F = 3.36, n.s.).
There could be several plausible explanations for the lack of support for differences in reactions to content in the evening versus in the afternoon. For instance, the stress levels among the social media users in our sample could be consistent across the afternoon and evening dayparts, resulting in identical working memory availability. Moreover, the difference in working memory availability between afternoon and evening could be less than the difference in working memory availability between morning and afternoon, and morning and evening, respectively.
There might be heterogeneity in how consumers view dayparts. Alternatively, we redefine evening (daypart 4) to be between 6:00 p.m. and 9:59 p.m. and night (daypart 1) to be between 10:00 p.m. and 5:59 a.m. (i.e., sleep hours). Our results are robust to this alternative operationalization (see column 1, Web Appendix W9).
To further control for time-invariant unobserved heterogeneity, we add a lagged error term ([31]). Note that we observe only one instance of performance metrics for each of the 5,706 posts, so the lagged error term captures unobserved heterogeneity that is time invariant and affects all the posts uniformly. Results are robust to the addition of the lagged error term (see column 2, Web Appendix W9).
Interpost duration might also represent a strategic decision by the social media manager. For a story posted at a given time stamp, we use the number of breaking tweets in the previous hour as the excluded variable for interpost duration. Similar to our arguments in the identification section, the planned schedule is likely to be disrupted if the supply of breaking news in the previous period is higher. We confirm our intuition with the first-stage results. Our results are robust to accounting for endogeneity in the interpost duration term (see column 3, Web Appendix W9).
Currently, we use the organic reach metric to account for unobservable patterns in the exposure of social media content induced by the EdgeRank algorithm. Instead of organic reach, one could also use number of impressions (i.e., the number of times when the content is displayed in a user's news feed) to account for patterns in the exposure of social media content ([40]). Thus, we use the log of impressions (instead of the log of organic reach) as an alternative measure to correct for the Facebook algorithm. Again, our results are robust to this alternative measure (see column 4, Web Appendix W9).
The primary purpose of the econometric model was to illustrate the theoretical linkages between the time of day, TCA and content type, and link clicks. However, managers need a practical scheduling tool that recommends when to post (time of day), whether to engage in TCA, and which content topic to post at a certain time (e.g., sports, life, entertainment). We are able to reconcile both the need for theory and practice in Equations 7–15, which contain estimates pertaining to time of day, TCA, content type, content topic, and interpost duration.
However, from a social media manager's standpoint, it is not pragmatic to optimize the emotional and cognitive levels of a post. Therefore, we deemphasize content type in the optimizer and hold the emotional and cognitive levels at their respective median values.[10]
Accordingly, in the normative model, we view the social media manager's objective as simultaneously determining time of posting, interpost duration, and whether to employ TCA with a capacity constraint on content topics and a constraint on the number of posts that can be advertised. The objective of this discrete optimization problem is represented as
max{i,j}π={∑∀i∑∀j(CPIi×Link Clicksij)×Sj}−cTCA.7
The objective function in Equation 7 represents the difference between revenue from social media scheduling and the cost of TCA (cTCA). Revenue is obtained by multiplying link clicks to the platform's website from social media by the cost per impression to advertise on the ith content topic of the platform's website (i = index of content topics 1–7) and Sj, an indicator variable capturing the decision to post a certain content topic in time slot j.
The cost of TCA is determined using the following equation:
cTCA=∑∀j∑∀iTCAj×Content Topicij×CPCi×Link Clicksij,8
where TCAj is an indicator variable capturing the decision to advertise a post in slot j, Content Topicij represents whether the social media manager has allocated content topic i in slot j, CPCi indicates the average cost per click charged by Facebook for topic i, and Link Clicksij denotes the link clicks garnered by topic i when posted in slot j.
Next, the social media manager must account for several constraints as follows:
∑∀jContent Topicij=Ci,9
∑∀iContent Topicij≤1,10
TCAj ≤ ∑∀iContent Topicij,11
Interpost Durationij={0 if k∈{1, 0} (TSk−1− TSk)×30, otherwise,12
log(Link Clicki)=β0+β11Nighti+β12Afternooni+β13Eveningi+β2TCAi^+β3Negemo_arousali+β4Posemo_arousali+β5Cog_processi+β61Nighti×TCAi^+β62Afternooni×TCAi^+β63Eveningi×TCAi^ +β71Nighti×Negemo_arousali+β72Afternooni×Negemo_arousali+β73Eveningi×Negemo_arousali+β81Nighti×Posemo_arousali+β82Afternooni×Posemo_arousali+β83Eveningi×Posemo_arousali+β91Nighti×Cog_Processi+β92Afternooni×Cog_Processi+β93Eveningi×Cog_Processi+β10log(Organic Reachi)+Θ′Controls+δ1λ1i+δ2λ3i+δ3λ4i+δ4τ1i^+δ5τ2i^+δ6τ3i^+δ7τ4i^,13
∑∀jTCAj≤TCA Boosted, and14
Sj∈{0, 1} Content Topici∈{0, 1} TCAj∈{0, 1}.15
Equation 9 ensures that the total number of posts within a content topic i across all time slots sum to the number of stories selected by the editor within the corresponding news topic. Equation 10 ensures that the optimizer posts only one story per time slot. Equation 11 ensures that the total number of stories advertised is less than or equal to the total number of stories available to be posted across all content categories. Equation 12 computes interpost duration. In particular, interpost duration is assigned a value of 0 for the first post within the schedule; otherwise, it is computed as the difference between the time slot (TS) of the previous post and current post. Because each time slot lasts 30 min, we multiply the difference by 30. Equation 13 uses the interpost duration, time of day, content topic, daypart, and whether the firm decides to advertise the post (i.e., TCA), along with their respective regression weights, to predict link clicks. We hold all other controls at their median values. Equation 14 ensures that the total number of stories advertised is less than or equal to the number of stories advertised in the observed data.
The proposed optimizer presents a multidimensional, discrete, nonlinear optimization problem for the social media manager. For posts, the social media manager must decide which time slots to select for each post, which posts to advertise, and how many posts to advertise. For instance, assuming that there are 25 30-minute slots (from 6 a.m. to 6 p.m.), the number of ways the slots can be filled with r stories without replacement is given by 25!/(25 – r)!. If the content platform decides to post one story from each content topic (seven stories in all), there are more than 2.4 billion permutations. This is a conservative estimate, as it excludes permutations for selecting stories to be advertised. Consequently, although complete enumeration can guarantee a global optimum solution, it is impractical and computationally expensive. In fact, most discrete nonlinear combinatorial problems, such as product-line design problems in marketing (e.g., [32]), belong to a special class of problems that are classified as NP-hard. The global optimum to these problems is difficult to obtain within polynomial time.
Therefore, we resort to heuristic techniques. Heuristics can help with shrinking the problem space by applying well-defined rules so the near-optimal solution can be found within polynomial time. Depending on the formulation and complexity within the Lagrange functions, one could use heuristics in the attribute space, such as coordinate ascent, genetic algorithm (GA), or simulated annealing; methods in the product space, such as greedy heuristics, divide-and-conquer, or product-swapping heuristics; or methods that evaluate partially formed solutions, such as dynamic programming, beam search, or nested partition heuristics. [ 5]) provide a comprehensive review of these techniques. We choose the GA technique to implement our optimizer; GA offers a superior ability to quickly arrive at a near-optimal solution. Specifically, previous research has noted that GA has a "higher probability of convergence to global optimum solutions when data points are less, number of parameters is large, the parameter space is multimodal, and the model is inherently nonlinear" ([69], p. 453). Because our parameter space is multidimensional, nonlinear, and discrete-continuous, with gross profits changing with content categories and time slots, GA is ideal. Moreover, the availability of GA in Microsoft Excel enhances its appeal, as one of our research goals is to develop a decision support tool using a familiar interface for social media managers. We provide additional details on the GA approach in Web Appendix W10.
We use the coefficients of the estimated model in Equation 6 to forecast link clicks. We obtain cost per click and cost per impression values from our collaborating content platform. The content platform's costs per impression for local, business, sports, entertainment, life, opinion, and national stories are.06,.08,.08,.12,.10,.08, and.12 dollars, respectively. The historical costs per click charged by Facebook for local, business, sports, entertainment, lifestyle, opinion, and national stories posted by our content platform are.04,.07,.04,.05,.06,.03, and.07 dollars, respectively.
We use posting and TCA schedules from December 21–30, 2015, as the baseline for assessing proposed optimizer's performance. The baseline data, which include 123 posts from seven content categories and 14 boosted posts, constitutes our holdout sample. Table 3 illustrates the distribution of posts across content categories. Cumulatively, the posts in our holdout sample garner 49,920 link clicks, which generates a gross profit of approximately $3,518 for the content platform. This gross profit is a conservative estimate, as it represents profit per advertisement on the firm's website and assumes a page depth (i.e., the number of pages a consumer visits before exiting the website) of 1. Discussions with the firm's data analysts revealed that its webpages carry at least five ads per page on average, and each visitor from Facebook is believed to visit at least six pages before exiting. Factoring in these average values would result in a gross profit of approximately $105,540. However, because we do not have accurate information on the total number of ads on a webpage for each day, we restrict our comparison to gross profit per ad with the assumption that page depth = 1. Consequently, the observed gross profit on each day between December 21 and December 30 serves as the baseline for evaluating the performance of the content schedules predicted by our optimizer.
Graph
Table 3. Optimizer Input.
| | Observed Posts Across Topic Categories in the Holdout Sample |
|---|
| | Local | Business | Sports | Entertainment | Life | Opinion | National | Total # of Posts | # of Posts Advertised |
|---|
| Monday | 21-Dec | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 3 | 1 |
| Tuesday | 22-Dec | 8 | 1 | 2 | 2 | 1 | 1 | 1 | 16 | 1 |
| Wednesday | 23-Dec | 5 | 1 | 2 | 2 | 1 | 0 | 1 | 12 | 1 |
| Thursday | 24-Dec | 3 | 2 | 2 | 2 | 2 | 2 | 1 | 14 | 1 |
| Friday | 25-Dec | 2 | 0 | 3 | 1 | 0 | 0 | 1 | 7 | 1 |
| Saturday | 26-Dec | 2 | 6 | 5 | 1 | 0 | 0 | 0 | 14 | 2 |
| Sunday | 27-Dec | 2 | 2 | 7 | 1 | 0 | 1 | 2 | 15 | 1 |
| Monday | 28-Dec | 3 | 2 | 5 | 0 | 2 | 0 | 0 | 12 | 0 |
| Tuesday | 29-Dec | 4 | 2 | 1 | 1 | 3 | 1 | 3 | 15 | 2 |
| Wednesday | 30-Dec | 4 | 2 | 5 | 0 | 2 | 1 | 1 | 15 | 4 |
We use the same starting values and stories as those available to the social media manager between December 21, 2015, and December 30, 2015. We mimic the daily schedule of a social media manager at our collaborating firm by allowing the optimizer to create a schedule between 6 a.m. and 6 p.m., with 30-minute intervals. In addition, we restrict the optimizer to the same number of TCAs as observed in the holdout sample (see Table 3). Subsequently, we run the optimizer one day at a time and document the predicted advertising revenue, advertising cost, and gross profits for each day in the holdout sample.
Tables 4 and 5 summarize our results. As we have discussed, we illustrate results from three scenarios in which the emotional and cognitive levels of each post are held at their respective low, median, and high values. The proposed optimizer is able to find a schedule that increases gross profits on every day in the holdout sample. Across the ten-day period, the proposed schedules generate $810.04, $901.86, and $4,004.91 in total gross profits, which represent, on average, a 7.84%, 9.04%, and 9.87% increase in daily gross profits from the baseline, respectively.
Graph
Table 4. Optimizer Results.
| Low (HANM, CP) | Median (HANM, CP) | High (HANM, CP) |
|---|
| % Increase in Profits from Observed Data | % Increase in Profits from Observed Data | % Increase in Profits from Observed Data |
|---|
| 21-Dec | 29.66% | 23.01% | 27.78% |
| 22-Dec | 18.46% | 12.25% | 13.34% |
| 23-Dec | 11.41% | 1.41% | 8.42% |
| 24-Dec | .27% | 7.91% | 6.92% |
| 25-Dec | 1.11% | 5.19% | .82% |
| 26-Dec | 3.70% | 1.70% | 16.63% |
| 27-Dec | 1.28% | 4.31% | 11.15% |
| 28-Dec | 1.74% | .38% | 1.89% |
| 29-Dec | 2.73% | 24.67% | 7.86% |
| 30-Dec | 8.02% | 9.57% | 3.93% |
| Ten-day average | 7.84% | 9.04% | 9.87% |
60022242918805410 Notes: HANM = high-arousal negative emotions; CP = cognitive processing.
Graph
Table 5. Sample Posting Schedule Predicted by Optimizer (December 30, 2015).
| Current Schedule (Baseline) | Proposed Schedule |
|---|
| Low (HANM, CP) | Median (HANM, CP) | High (HANM, CP) |
|---|
| 7:04:41 a.m. | Local | 6:00:00 a.m. | Local | 6:00:00 a.m. | Local | 10:00:00 a.m. | Sports |
| 7:27:11 a.m. | National | 8:30:00 | Local | 11:30:00 a.m. | National | 11:30:00 a.m. | National |
| 7:55:19 a.m. | Business | 9:00:00 a.m. | Sports | 12:00:00 | Opinion | 12:00:00 p.m. | Opinion |
| 8:34:41 a.m. | Sports | 11:30:00 a.m. | National | 12:30:00 | Life | 12:30:00 p.m. | Life |
| 9:53:26 a.m. | Local | 12:00:00 p.m. | Opinion | 1:00:00 p.m. | Life | 1:00:00 p.m. | Life |
| 10:32:49 a.m. | Sports | 12:30:00 p.m. | Life | 1:30:00 p.m. | Sports | 1:30:00 p.m. | Sports |
| 11:29:04 a.m. | Opinion | 1:00:00 p.m. | Life | 2:00:00 p.m. | Sports | 2:00:00 p.m. | Sports |
| 12:00:00 p.m. | Local | 2:00:00 p.m. | Sports | 2:30:00 p.m. | Sports | 2:30:00 p.m. | Sports |
| 12:33:45 p.m. | Sports | 2:30:00 p.m. | Sports | 3:00:00 p.m. | Sports | 3:00:00 p.m. | Local |
| 1:30:00 p.m. | Business | 3:00:00 p.m. | Sports | 3:30:00 p.m. | Sports | 3:30:00 p.m. | Sports |
| 2:00:56 p.m. | Sports | 4:00:00 p.m. | Business | 4:00:00 p.m. | Business | 4:00:00 p.m. | Business |
| 2:29:04 p.m. | Life | 4:30:00 p.m. | Business | 4:30:00 p.m. | Business | 4:30:00 p.m. | Business |
| 3:02:49 p.m. | Life | 5:00:00 p.m. | Local | 5:00:00 p.m. | Local | 5:00:00 p.m. | Local |
| 4:44:04 p.m. | Local | 5:30:00 p.m. | Local | 5:30:00 p.m. | Local | 5:30:00 p.m. | Local |
| 5:45:56 p.m. | Sports | 6:00:00 p.m. | Sports | 6:00:00 p.m. | Local | 6:00:00 p.m. | Local |
| Ad revenue | $197.35 (low), $214.82 (median), $922.34 (high) | $211.65 | $228.03 | $1,020.01 |
| Cost of TCA | $100.93 (low), $109.88 (median), $470.73 (high) | $107.50 | $113.04 | $550.64 |
| Gross profit | $96.42 (low), $104.94 (median), $451.60 (high) | $104.15 | $114.98 | $469.36 |
| % increase in profits from baseline | 8.02% | 9.57% | 3.93% |
70022242918805410 Notes: Boldfaced values represent TCA posts. HANM = high-arousal negative emotions; CP = cognitive processing.
The profit-maximizing schedules determined by our optimizer look different from those in the baseline scenario. Table 5 compares the posting schedule predicted by our optimizer on the last day in the holdout sample with the observed schedule. As we can see, simply rearranging the posts without expending additional resources can help the firm increase gross profits. In summary, the optimizer increases profitability by reorganizing the social media schedule to align content topic and timing with performance and exploiting the benefit–cost trade-off to enable simultaneous determination of TCA, along with content topic and time of day.
Content platforms have experienced a dramatic decline in print advertising revenue and seek new practices to generate online advertising revenue ([38]). One such practice is to leverage social media channel to engage customers and direct traffic to websites. However, as we have discussed, a formidable challenge is to design a systematic framework that enables social media managers to design profit-maximizing social media schedules. This need is urgent given practitioners' call for effective scheduling strategies (e.g., [11]), sparse literature on scheduling content on social media, and need for new knowledge in media scheduling.
Accordingly, we fulfill this need in three steps. First, building on circadian rhythms literature, we provide novel insights into how content effectiveness varies by the time of day, which has typically been studied within the purview of behaviors such as variety-seeking ([24]), decision quality ([42]), and risk-taking behavior ([70]). Moreover, we offer a coherent theoretical framework by theorizing how known drivers of social media engagement (i.e., TCA and content type) interact with the time-of-day effect to contribute to post performance. Second, we develop, estimate, and validate a response model that simultaneously considers attribute-based social media schedules involving time of day, TCA, and content type using post-level data from a major content platform. Third, we build a decision-support tool to assist social media managers in profit-maximizing social media content scheduling, and we show the profitability implications over a finite planning horizon.
The estimates allow us to evaluate marginal effects of scheduling attributes and thus conduct a set of what-if calculations. From the calculations, we offer several key managerial takeaways (note that we assume that each post attracts 967 link clicks, the mean value in our data):
Our estimates on time-of-day effects suggest that, ceteris paribus, posting stories in the morning generates approximately an 8.8% (11.1%) increase in link clicks compared with posting stories in the afternoon (evening). Assuming that page depth is 1 and cost per impression is $0.06 (i.e., lowest observed return among the seven categories), the 8.8% (11.1%) increase translates into a gross profit of $25,529 ($32,201) for a content platform that posts 5,000 free stories per year.
In the afternoon, on average, TCA accumulates approximately a 21% increase in link clicks compared with TCA in the morning. This 21% increase translates into a $60,921 increase in advertising revenue for a content platform that posts 5,000 stories per year. In contrast, TCA at night, on average, decreases link clicks by approximately 9.7% compared with TCA in the morning, leading to a loss of $28,140 in advertising revenue. These findings contribute to the knowledge on boundary conditions of online advertising effectiveness, such as personalization ([39]), obtrusiveness ([22]), and purchase funnel stage ([28]).
Posting social media content with negative high-arousal emotions in the morning, on average, leads to a 1.6% (7.6%) increase in link clicks compared with that in the afternoon (night). This 1.6% (7.6%) increase translates into $4,642 ($22,048) increase in gross profits for the content platform that posts 5,000 stories per year. Thus, we offer implications for online content virality ([ 2]) by underscoring the need to account for content type depending on the time of the day. Specifically, we suggest managers to weigh in on the interactions between various content characteristics and day parts while designing their social media message.
Simply rearranging the posts without allocating additional budget for TCA can help the firm increase gross profits by at least 8% on average over a ten-day horizon. This suggests that our optimizer could be used as a decision-support tool to profitably schedule content on social media without adding additional resources. In fact, the managerial appeal of our scheduling tool, which is developed in Microsoft Excel, significantly lowers the hurdle of adoption of our prescriptive model within content platforms. As such, 73% of managers we have interviewed have explicitly expressed an interest in using our scheduling tool in their operations. We provided an overview of an implementation guide for managers in Web Appendix W11.
Our analysis reveals a nonlinear association between advertising spending (i.e., TCA costs) and gross profits. For instance, as we observe in Web Appendix W12, additional spending on TCA will result in only a marginal increase in gross profits, suggesting a concave relationship between TCA and gross profits. Indeed, prior research has shown that the relationship between increased budgets on traditional media and optimized profits (conditional on optimal allocation) is concave (e.g., [48]). Managers can use this finding to allocate budgets effectively across multiple marketing communication instruments including the TCA.
Our work has some limitations that offer promising future research avenues. First, our collaborating firm did not induce variation in targeting filters while advertising content. Thus, we could not estimate heterogeneity in TCA effectiveness with respect to those filters. Future research could explore the role of targeting filters on TCA effectiveness. Second, future research could explore the effectiveness of TCA on the basis of topics discussed within the content. Such fine-grained analysis could provide managers with important guidelines on the allocation of TCA through the textual characteristics of social media posts. Third, post-level data preclude us from modeling how individuals allocate their time within a daypart between Facebook browsing and other browsing activities. As individual data becomes increasingly available, future research could address how other browsing options can affect working memory allocation to Facebook content. Finally, we hope managers and researchers use our econometric and optimization model to generate empirical generalizations for other content platforms (e.g., magazines, video sharing websites).
Supplemental Material, DS_10.1177_0022242918805411 - Scheduling Content on Social Media: Theory, Evidence, and Application
Supplemental Material, DS_10.1177_0022242918805411 for Scheduling Content on Social Media: Theory, Evidence, and Application by Vamsi K. Kanuri, Yixing Chen, and Shrihari (Hari) Sridhar in Journal of Marketing
Footnotes 1 Area EditorP.K. Kannan served as area editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242918805411
5 1Existing software can simultaneously post a firm's content on multiple social media platforms and allow managers to set up an inventory of posts at their chosen time in the future, thereby saving significant time and increasing efficiency. However, it lacks the prescriptive capability of suggesting what content to post when and when to schedule TCA to maximize post link clicks and implied advertising revenue.
6 2Working memory identifies irrelevant information through textual cues (e.g., [34]). When working memory processes information, it can identify the emotions embedded within the content. Thereby, it can differentiate between high-arousal and low-arousal information. As high arousal content increases anxiety and cortisol levels, which further hinder the function of the working memory when it is resource deprived, working memory signals the brain to move away from such information ([33]). This natural mechanism improves working memory efficiency.
7 3Social media sites are required by law to highlight advertised content within the news feed. For instance, Facebook and LinkedIn explicitly identify TCA as "sponsored" within an individual's news feed. Such explicit identification attracts attention and thus serves as an external cue ([58]).
8 4In our context, we have multiple sources of evidence to support that the majority of the target audience (both readers and advertisers) are in the same time zone. First, 99% of the subscribers to the print and online newspapers come from one state located in the Pacific Time Zone. Second, Google Trends data show that among the top 30 cities where searches of our collaborating content platform are most popular, 27 cities are located in the Pacific Time Zone. Third, 98.5% of the print and online advertising revenue (in part generated by redirecting to the online website from the Facebook page) comes either from advertisers who are located only in the state or from local advertising spend within the purview of local subsidiary of a national brand. Finally, Audit Bureau of Circulation reports and the sales force pitch documents of the content platform confirm that it competes locally by way of its indicated presence in designated market areas. We thank an anonymous reviewer for requesting this clarification.
9 5The FOG index is the most commonly used metric to evaluate the lexical complexity of texts. It indicates the number of years of formal education a reader of average intelligence needs to understand text. Our results hold using alternative measures such as the Flesch reading ease and Flesch–Kincaid grade-level scores ([19]; [61]).
6For illustrative purposes, we also run the optimizer by holding the emotional and cognitive levels of each post at low and high values, respectively.
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By Vamsi K. Kanuri; Yixing Chen and Shrihari (Hari) Sridhar
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Record: 158- Seeding as Part of the Marketing Mix: Word-of-Mouth Program Interactions for Fast-Moving Consumer Goods. By: Dost, Florian; Phieler, Ulrike; Haenlein, Michael; Libai, Barak. Journal of Marketing. Mar2019, Vol. 83 Issue 2, p62-81. 20p. 7 Charts, 1 Graph. DOI: 10.1177/0022242918817000.
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Seeding as Part of the Marketing Mix: Word-of-Mouth Program Interactions for Fast-Moving Consumer Goods
Seeded marketing campaigns (SMCs) have become part of the marketing mix for many fast-moving consumer goods (FMCG) companies. In addition to making large investments in advertising and sales promotions, these firms now encourage seed agents or microinfluencers to discuss brands with friends and acquaintances to create further value. It is thus critical to understand how an FMCG seeding program interacts with traditional marketing tools when estimating the effectiveness of such efforts. However, the issue is still underexplored. The authors present the first empirical analysis of this question using a rich data set collected on four brands from various European FMCG markets. They combine advertising and sales promotion data from FMCG brand managers with sales and retail variables from market research companies as well as firm-created word-of-mouth variables from SMC agencies. The authors analyze the data using several approaches, confronting challenges of endogeneity and multicollinearity. They consistently find that firm-created word of mouth through SMC programs interacts negatively with all tested forms of advertising but positively with promotional activities. This phenomenon has significant implications for understanding the utility of SMCs and how they should be managed. The analysis implies that SMCs may increase total sales by approximately 3%–18% throughout the campaigns.
Keywords: advertising; marketing-mix modeling; product seeding; sales promotion; seeding program
A fundamental development in the marketing landscape of the past two decades is the realization that word of mouth (WOM) should be viewed as part of the marketing mix and managed accordingly ([21]; [71]). Consequently, a significant literature stream has emerged to explore issues such as the motivation for WOM and its effect on the audience, what brands people talk about, and how WOM can affect customer profitability ([ 9]; [52]; [55]; [57]). Scholars have paid increasing attention to firms' emerging efforts to create programs that generate amplified WOM through tools such as seeding programs, influencer marketing, and referral reward programs ([35]; [41]; [53]).
Given that consumers are more likely to engage in social interactions on high-ticket, complex, high-involvement products ([ 9]), one might wonder how relevant WOM programs are to fast-moving consumer goods (FMCG) such as groceries, household products, and beauty and health products. Firms seem to believe they are very relevant: in the past two decades, various FMCG marketers have, with the help of specialized agencies, regularly employed seeded marketing campaigns (SMCs), which use thousands of individuals who "buzz" about the product ([17]; [34]). The idea is to select a certain number of customers as "seed agents" and to equip those agents with the product to be marketed (in the form of either actual products or samples) as well as additional brand information. The customers are then encouraged to engage with the product while also telling their peers about it. A handful of academic articles that address the effectiveness of such SMCs have demonstrated a positive impact on sales and the potential for substantial return on investment ([19]; [26]; [34]).
The move toward integrating WOM into the FMCG marketing mix should be considered in light of the shifts these trillion-dollar markets have been experiencing in recent years, including significant changes in consumer tastes, changes in the effectiveness of media channels, and the move to e-commerce. With the aim of adapting their brands to a changing world, FMCG marketers have been moving budgets to digital advertising and social media, although many open questions remain regarding the efficacy of such efforts ([65]; [76]). Thus, it is unsurprising that many FMCG brands experimenting with additional ways to acquire customers have aimed to integrate WOM into the marketing mix through SMCs. As a result, according to a proprietary competition analysis that covered 348 campaigns across Europe during 2007–2011, more than 80% of all commissioned SMCs are for FMCG brands.
Still, the question of how to manage SMCs is largely open, and a source of confusion, as we discovered in a series of interviews with managers who run SMCs for consumer packaged-goods firms. One manager we interviewed ran three different SMCs of similar design, size, and cost for the same brand but saw estimated sales effects that differed by more than 150%. Yet she could not identify any apparent differences in those SMCs or the amplified WOM generated that could have explained these significant variations. We ultimately discovered that what differed among these campaigns were other planned marketing activities to which the SMC had been added. Another manager suspected that as the marketing plan becomes more complex, advertising increasingly cannibalizes firm-created WOM effects. Yet she did not have sufficient insights on the question of potential interaction effects between SMCs and traditional marketing communication measures, such as advertising and sales promotions, to explicitly take account of these effects in her decision making.
The question of the interaction among marketing-mix elements is critical to marketing planning in general ([63]; [64]) and specifically for FMCG marketers who are considering introducing an SMC program in addition to the current marketing mix. Whereas some marketing campaigns use WOM as the main vehicle to drive new product growth and seeding campaigns as a main tool to initiate the process ([56]), SMCs for supermarket brands must integrate into a world in which massive budgets are spent on existing products using both advertising and promotion ([59]). Their profitability will thus depend on how they interact with existing media campaigns. The fundamental question is whether the additional WOM complements or substitutes for traditional marketing efforts such as advertising ([ 4]). The answer is vital to the ability to plan and justify SMC campaigns.
However, brand managers who aim to plan an informed use of SMCs have little research knowledge to draw on. One reason is that in FMCG environments, firm-created WOM tends to take place offline ([82]). For example, one SMC agency we interviewed in the context of this study reported that over 90% of all measured conversations occur face-to-face. Yet given the prevalence of social media and the online influence in consumer markets, as well as researchers' ability to collect data on customer interactions through electronic means, the vast majority of recent knowledge about WOM and its effectiveness, including WOM programs, has come from programs geared toward online (notably social media) environments (Babić [ 5]; [30]; [90]). In addition, prior research has focused extensively on organic WOM, and firm-created WOM has received substantially less attention. For an overview of the few studies analyzing SMC in an FMCG setting, see Table 1.
Graph
Table 1. Overview of Selected Studies Analyzing Seeding Marketing Campaigns for FMCGs.
| Research | Firm-Created WOM Channel | Dependent Variable | Marketing Plan Interaction | Major Findings |
|---|
| Chae et al. (2017) | Online | WOM volume | — | Seeding increases WOM about the focal brand among nonseeds and decreases WOM about other brands in the same category |
| Berger and Schwartz (2011) | Offline | WOM volume | — | More interesting products create immediate WOM, publicly visible or cued products create immediate and ongoing WOM |
| Toubia, Stephen, and Freud (2011) | Offline | WOM volume | — | More social seed agents generate more WOM |
| Groeger and Buttle (2014) | Offline | Reach | — | Reach is lower than total WOM volume due to multiple exposures and channel overlap |
| Groeger and Buttle (2016) | Offline | Reach | — | Only half of firm-created WOM reaches its target group, but this share is higher when embedded in everyday conversation |
| Dost, Sievert, and Kassim (2016) | Offline | Sales | — | SMCs show sales effect; seeding through high-value customers or reaching high-value peers increases sales effect for unknown products |
| Godes and Mayzlin (2009) | Offline | Sales | — | SMCs show sales effect; WOM from nonloyal seeds drives incremental sales, because it reaches more unaware peers |
| Current study | Offline | Sales | Advertising (TV, digital, print)Promotions (point-of-sale, direct email) | For SMC WOM volume, consistent negative interaction with advertising and positive interaction with promotion is evident across different environments |
The issue is particularly challenging given consistent findings on the differences between online and offline WOM behavior, which may stem from the diverse nature of oral versus written communication channels and the size and makeup of the audience ([ 9]). For example, online and offline environments differ with regard to people's motivations to share information ([10]), willingness to retransmit WOM ([ 7]), and the role of customer loyalty in WOM transmission ([28]). Furthermore, researchers have found that people tend to talk about different types of products online and offline ([29]) and, unsurprisingly, people tend to talk offline rather than online regarding low-involvement, less status-based supermarket goods ([ 9]; [10]; [57]). Given these constraints, findings from the online-dominant world, such as those dealing with the integration of social media and traditional advertising ([45]; [54]; [83]), may be of limited help to FMCG managers planning an SMC.
Some FMCG brand managers may have considered conducting an independent analysis of interaction effects for SMC campaigns—a nontrivial task. For such an analysis, diverse data across brands, campaign types, and media outlets are required to generalize the phenomenon. In addition, estimating sales and interaction effects of SMCs is challenging because of the inherently unplannable nature of the campaigns. The seed agents may self-select or be selected nonrandomly into the campaign or react to unobserved market dynamics. The result is a potential endogeneity bias in sales model estimates, which demands a level of statistical analysis often not available to brand managers.
Taking up this challenge, we present the first empirical analysis on the integration of SMCs with other marketing-mix activities that dominate the world of FMCG, namely, mass advertising and sales promotions. For our study, we collected a rich data set that combines advertising and promotion plans from FMCG brand managers with sales and retail variables from market research companies, as well as firm-created WOM variables from SMC agencies. In addition, our data set comprises different market situations to represent the wide variety of both FMCG products and SMCs. We focus on SMCs for four products (instant coffee, sensitive toothpaste, anti-age cosmetics, and organic chocolate) from three European markets. The cases we analyze feature different types of advertising (e.g., TV, digital, print) and/or promotional activities (e.g., point-of-sale stoppers and coupons, direct mail coupons) in their respective marketing plans. Furthermore, the data differ in their structure (i.e., weekly or monthly measurement and number of cross-sectional units) and operationalization of several variables (e.g., measures for amplified WOM).
To address endogeneity concerns, we collected additional external SMC data and population statistics and employed an instrumental variable (IV) modeling approach ([33]; [88]). We compare the estimates against several robustness models with panel internal instruments ([44]; [85]). For additional robustness, we consider variable transformations, measurement error, and collinearity in interaction effects. We also employ an equivalent control function approach ([70]), which allows for an endogeneity correction in all estimated robustness models.
We find that despite the variation in data and models, the results still converge to consistent interaction effects between firm-created WOM from SMCs and other marketing variables, which suggests generalizability. First, our results indicate that firm-created WOM from SMCs incrementally increases sales in all cases. Second, we observe managerially relevant interaction effects between firm-created WOM and advertising/sales promotions. Specifically, for the FMCG products we analyzed, firm-created WOM consistently interacts negatively with all tested forms of advertising. In contrast, it consistently interacts positively with promotional activities. To put these empirical results into perspective, we calculate effect sizes and meta-analytically integrate them to compare them with extant meta-analytic findings. Sales elasticities and sensitivity analyses help us explore the role and magnitude of marketing-mix interactions, such that managers can use these findings to optimize their marketing plan.
These results enrich our understanding of the integration of emerging WOM tools into the FMCG world. For managers, they provide insight into what to expect when introducing SMCs into this environment. Our findings support the importance of SMCs to FMCG marketers: SMCs may increase total sales by approximately 3%–18% over the course of the campaigns, and sales elasticities are comparable to or stronger than those previously reported for tools such as electronic WOM. Yet these elasticities decrease with a higher level of advertising, which calls for brand-specific analyses to determine the optimal investment. Our analysis provides guidance to managers and consulting firms on how to conduct a rigorous analysis, possibly simulating specific brand conditions.
On the theoretical level, we provide evidence of how new tools such as SMCs integrate with more established tools of the marketing mix, which can help not only shed light on the dynamics of interactions but also provide some indication, as we subsequently discuss, of the mechanism by which SMCs contribute to the firm. In the context of FMCG, seeding campaigns may be better considered as substitutes for advertising, rather than complements, at least for certain consumers. Whereas in the prevailing view of WOM, which has been shaped in many cases by organic WOM, and largely in new product and social media contexts, advertising ignites the WOM process that later dominates sales, in the context of the supermarket it may be markedly different.
The increasing connectivity of customers through online means, the realization of the power of online reviews, and the ability to track online connectivity have led to a rise in general interest in WOM activity. A significant body of research has examined WOM in consumer markets, investigating issues such as individuals' motivations to talk and listen ([ 9]), which products people talk about ([11]), and WOM effects on individual customer profitability ([52]) and on sales in general ([90]).
A notable change during the past two decades is the growing realization that WOM not only is an organic part of customer interactions but can also be amplified through WOM programs ([35]). Indeed, cross-industry surveys among managers reveal that most plan to use campaign formats that leverage WOM, driven by the belief that WOM marketing is more effective than traditional marketing activities ([87]).
[41] highlight three types of such WOM programs: referral programs, which encourage and incentivize current customers to contribute to customer acquisition by helping to acquire new customers; online recommendation programs, which encourage individuals to spread the word to their close social network or a broader network such as in an online review website; and seeding programs, which aim to get products into the hands of some individuals (seeds) in the hope that the consequent social influence will help accelerate and expand the growth process. Seeding programs are our focus here, specifically SMCs in the FMCG industry. To establish the context, we next describe the main insights from qualitative interviews regarding major European providers' planning and setup of SMCs.
When a marketing manager commissions an SMC at a specialized agency (e.g., BzzAgent, The Insiders, TRND), three key questions must be answered: First, how many seed agents should take part in the campaign (typically ranging from 100 to 20,000)? Second, when and how long should the campaign run (typically between 4 and 12 weeks)? And third, what exactly should be given to the seed agents (typically the full focal product, several smaller samples to share, and a brand information booklet)? Using these specifications, the SMC agency invites potential seed agents from its seed panel to apply for the campaign.
Subsequently, the agency selects the requested number of seed agents among the applicants using a set of proprietary selection criteria and sends the product to them. The SMC agencies put considerable effort into determining which and how many seed agents to choose, which incentives to offer them, and the contents of the campaign material they receive. Selection criteria typically include demographics, prior campaign participation and performance, stated preferences for products and brands, stated personality measures, and perceived motivation judged from the open-text application form. Selection criteria deemed more critical—for example, demographics that match the intended target group, very high stated liking of the brand, or a proven record of reported WOM volume in prior campaigns—are often balanced with selection criteria intended to maintain an agency's panel health. For example, seeds may be rotated such that every applicant regularly gets the opportunity to participate in a campaign.
Over the course of the campaign, the agency engages the seed agents and manages the evolving campaign process through a campaign-specific online platform. Typically, such SMC platforms include a project blog to facilitate interagent communication; messaging tools to contact the seed agents directly; and survey tools to track responses, requests, and WOM behavior. Seed agents then test the products; engage with the agency, the brand, and one another; and recommend the product to their peers. These incremental recommendations, or firm-created WOM, are the main intended outcome of the campaign. Such firm-created WOM predominately takes place offline (with some estimating that the share of offline conversations for these products exceeds 90%) and is overwhelmingly positive. One of our interview partners analyzed 43,000 receiver surveys from 36 SMCs and found that less than 3% of all WOM incidents were negative and over 90% positive. Therefore, WOM measurement focuses on volume, not valence, similar to traditional types of marketing communication (e.g., TV advertising gross rating points [GRPs]).
Issues of disclosure are pivotal to the ability to operate and benefit from SMCs, in particular given growing concerns on the ethical behavior of brands. The agencies we collaborate with operate under comparable ethical terms that are made explicit to the agents. Specifically, seed agents ( 1) participate voluntarily, ( 2) are encouraged share their honest opinion about the products (though, through phrasing, the agency primes positive WOM), ( 3) do not receive additional financial rewards or payment besides what is included in the starter packages, and ( 4) should disclose their participation. In particular, given the European General Data Protection Regulation, agencies are making additional efforts by highlighting, and even verifying, where possible, appropriate disclosure. Interestingly, disclosure may even help the SMC efforts. Research in this area has suggested that when disclosure occurred, agents were rated as more credible and the conversation partner had fewer negative feelings about the agent's corporate affiliation and told more people about the brand being discussed ([18]).
An interesting issue relates to the relationship between customer loyalty and seeding effectiveness. [34] suggest that loyal customers may be less effective as seeds than nonloyal ones, who are more likely to know equally unaware and untapped peers. However, this phenomenon depends on the type of product and may be different for really new products ([26]). Research has provided some insights on the expected characteristics of agents who will engage more in WOM ([82]) and how agents choose conversation partners ([38]). Other research has addressed the effect of spillover to other categories on brand-level WOM ([19]) and the importance of product interest and cuing from the environment as drivers of WOM ([11]).
Yet most of the research on WOM seeding has been done in the context of networks and their effect on optimal seeding. Labeled in computer science and related fields as the "influence maximization problem," the issue is generally considered one of the most important in network science in general, as evidenced by numerous efforts to develop efficient algorithms for a given network ([49]; [61]). Academic work in marketing and related fields typically takes a network-based approach, mostly examining markets for new products with a given network, in which organic WOM is expected to complement the seeding effort that will ideally ignite a further contagion process. Among the network issues related to seeding effectiveness are the importance of degree or betweenness centrality ([46]), assortativity in customer lifetime value ([40]), seed size ([ 3]), competition ([56]), and network characteristics such as relationship type or homophily ([20]; [66]).
The question remains to what extent the network-related insights are relevant for seeding in FMCG markets. Although SMC agencies commonly look for individuals who are more connected and socially active when screening potential candidates, beyond that, the SMC operators we interviewed had little empirical knowledge about the social networks that existed in their markets or even about how information spreads further after the seeds communicate. Although some industry efforts have been made to follow information spread, these analyses are not easy to conduct, and estimations may be biased ([17]).
What emerges as a key managerial concern is the need to better understand the interaction between SMCs and other marketing-mix elements—particularly the two mostly widely used types for these products: advertising and sales promotions ([59]). In the FMCG industry, SMCs are typically conducted in an environment of mature categories, in which the seeding campaign joins larger-scale efforts of advertising and sales promotion. Indeed, firms commonly decide whether to conduct an SMC after the marketing plan for advertising and promotion has been finalized. Because SMCs are typically cheaper than traditional advertising activities, they are often added (or not) with the remains from a larger budget for a marketing plot or pulse. To justify the use of SMCs, managers must understand the extent to which seeding interacts with current efforts and whether it complements or substitutes them.
Scholars and practitioners widely agree on the need to manage marketing-mix tools to increase brand equity and sales ([50]). Yet parts of the mix may interact and affect each other, and firms should consider interactivity trade-offs in planning their marketing-mix strategies ([64]). Much attention in this respect has been given to media synergies. [ 8] show that coordinated media campaigns among channels may lead to more favorable attitudes toward the brand. For example, in an attempt to take advantage of media-mix synergy, marketers may increase the media budget and allocate more funds to the less effective activity ([63]).
Adding WOM to the marketing mix adds complexity and challenges to managing marketing-mix interactions. Given the availability of data and the rising importance of social media, recent examinations in this area have centered nearly exclusively on the context of mostly organic, online social interactions. The challenge is to untangle an "echoverse" in which online and offline social interactions and offline and online WOM affect each other ([45]). What emerges from this growing literature stream is recognition of both the power of online WOM and the compound effects associated with it. These effects vary across platform, product, time, and metric factors (Babić [ 5]; [54]; [90]). Despite the potential heterogeneity underlying those findings, it is clear that WOM and advertising are expected to affect each other ([83]). Thus, ignoring WOM when planning marketing campaigns can lead to suboptimal spending ([91]).
The overall picture that emerges from previous work is that WOM mainly complements, rather than substitutes for, advertising ([ 4]). Researchers view WOM as a more effective and persuasive tool that can convince people to close a purchase, following the awareness created by advertising ([42]). From another angle, advertising is viewed as a first step of customer acquisition that will be followed by a ripple of customers acquired by WOM ([47]). Furthermore, advertising stimulates conversation, both online and offline ([81]). Industry studies suggest that approximately 25% of talks about brands involve discussions of an ad for that brand ([48]).
If WOM complements advertising, then a positive interaction between them may be expected. Managers may be encouraged to invest more in SMCs in tandem with a larger investment in advertising. Yet it is not clear that this is indeed the case for FMCG SMCs, as there may be substantial differences in the power of WOM, and so in the dynamics of interactions. First, much of the literature on WOM effectiveness focuses on the effect of organic, and not firm-driven, WOM. Organic WOM may be more relevant for complex, novel, exciting, and risky products but less so for the supermarket goods SMCs often aim to promote ([ 4]; [ 9]).
For new and complex products, a higher level of organic WOM helps drive profitability because it follows the early adoption of the seeds, accelerating adoption and increasing customer equity. Nevertheless, high levels of organic WOM also imply a fast penetration without the seeding processes ([40]). For FMCG, in contrast, organic WOM that follows the seeding may not be that large. However, the seeding process itself may be quite effective, as the organically occurring alternative is not that strong.
It should also be noted that much of the recent knowledge regarding WOM stems from an analysis of dynamics within social media, in which the intensity of WOM may be much stronger because of the audience size and large scale effects. Even though research has shown referral effectiveness that is an order of magnitude stronger than advertising ([83]), this finding may be less relevant to FMCG brand-related communication.
In the FMCG environment, advertising plays a major role in creating product awareness and familiarity ([84]), and much information can be retrieved in front of the shelf. The seeding campaign may help more in creating awareness among customers than reducing risk and decreasing uncertainty. As a result, the incremental effect of firm-created WOM on awareness can be expected to be lower.
Prior research on SMCs has provided some support to this expectation: targeting firm-created WOM toward peers already aware of the product results in smaller sales effects ([26]), which explains why less loyal seed agents can achieve greater sales effects than more loyal ones ([34]). In addition, traditional advertising and firm-created offline WOM do not occur at the point of purchase, which limits any expected interaction effect from recognition. This differs from online settings, in which advertisements may directly link to an e-commerce website such that they benefit from improved familiarity ([69]).
Thus, the combined information from advertising and firm-created offline WOM is likely to be more substitutive than complementary. Substitutive information has a cannibalizing effect that reduces positive interactions and can even result in negative interaction effects across communication elements ([16]; [68]). This phenomenon has been shown both in the movie industry, in which the positive interaction between online reviews and advertising disappears over time ([13]), and in the negative interaction effects between publicity and print advertising for video games ([15]). For the same reason, the integrated communication synergy potential is generally more limited for FMCG ([54]) than for more complex products such as cars ([62]). Given these factors, we expect to find negative interaction effects between firm-created WOM and advertising in an FMCG setting.
Will the interaction direction with sales promotion be the same as with advertising? In the context of FMCG, advertising and sales promotions are perceived to contradict each other. Whereas sales promotions are focused on creating short-term sales, advertising is aimed at establishing long-term brand equity. Firms must thus find the optimal combination to drive long-term profits and be careful not to invest too much in the short run ([78]).
Sales promotions may create different interaction dynamics with SMCs. Although most types of promotions (e.g., coupons, in-store displays, features) provide little additional awareness, cues such as point-of-sale promotions can trigger the retrieval of SMC-induced memories in a purchase decision context and thus improve recognition and affective/heuristic choice for already-familiar brands and products ([69]). Similar effects have been observed for traditional advertising, in which point-of-sale displays have been shown to be more effective when accompanied by a TV advertising campaign ([25]). We therefore expect firm-created WOM and point-of-sale promotions to exhibit similar recognition synergies at the point of purchase.
Furthermore, firm-created WOM provides information and reduces uncertainty about the product and the consumer's own preferences, which should lead to a steeper, more price-sensitive individual price response curve ([27]) and to more price-sensitive demand ([80]). In combination with the price discount that accompanies most promotions, the increase in demand (i.e., sales) from firm-created WOM should be more pronounced. Therefore, based on rational choice, we expect a positive interaction effect between firm-created WOM and promotions.
In combination, the mechanisms for recognition and information suggest a mutually supportive interaction between firm-created WOM and promotions. In our empirical study, we thus expect to find positive interaction effects between firm-created WOM and promotions.
The data set used for our analysis represents four SMCs for four FMCG products in three European countries: instant coffee, sensitive toothpaste, anti-age cosmetics, and organic chocolate. We use sales as the dependent variable in our model. We compiled this data set by combining information from the SMC clients on the overall marketing plan (advertising and promotions) and other market variables (sales, distribution, price, and competitive advertising) with data from SMC agencies on firm-created WOM volumes.
Table 2 provides details on the specific product context, SMC setup and measures, marketing plan variables, and other market variables for each SMC. This overview shows that the data sets are heterogeneous on several dimensions, which enables us to obtain tentative insights into the generalizability of our findings. The seed agents used in our study were recruited from four SMC agency panels in Europe, two of which were from the same European country. All campaigns were run by specialized SMC agencies between 2011 and 2014. The campaigns differed in size (between 1,500 and 7,500 seed agents) and duration (between eight and nine weeks). As specified previously, agent screening procedures in the SMC agencies are not a function of the client's marketing-mix spending.
Graph
Table 2. Case and Data Description.
| SMC Case | Instant Coffee | Sensitive Toothpaste | Cosmetics | Premium Chocolate |
|---|
| Product and campaign context | Major coffee brand, reintroduction of a variant from a product family in a southern European country | Well-known dental care brand, support for mature product in a western European country | High-end cosmetics brand, support for mature product in brand-owned retail stores in a central European country | Small premium organic chocolate brand, support for stable retail sales in a western European country |
| SMC characteristics | 7,500 seeds from SMC agency panel, 8 weeks campaign durationWOM data over 12 weeks | 7,500 seeds of WOM agency panel, 9 weeks campaign durationWOM data over 9 weeks | 1,500 seeds of WOM agency panel, 8 weeks campaign durationWOM data over 13 weeks | 5,000 seeds of WOM agency panel, 8 weeks campaign duration,WOM data over 3 months |
| WOM variables | SMC platform visits
| Seed reports
| SMC platform visits Seed reports
| SMC platform visits Seed reports
|
| Marketing plan variables | Advertising (TV): GRPs Promotion (point of sale): Number of supermarkets with tasting events and discounts Promotion (direct email)a: Online coupons, emails sent (in thousands) Promotion (sampling)a: Product samples in newspapers, copy (in thousands)
| Advertising (digital): large (€500,000) digital banner campaign, banner views (in thousands) Promotion (point-of-sale): percentage of supermarkets with stopper displays and promotion shelves
| Advertising (print): brand–owned magazine, estimated circulation with focal product support Promotion (direct email): Promotion coupons sent by email, emails sent (in thousands)
| Promotion (point-of-sale): Supermarkets with promotional activities, percentage points
|
| Other variables | Distribution: weighted Price: euros (per pack) Competitive advertising (TV): TV advertising for other variant of the brand family, GRPs
| Distribution: weighted Price: euros (per pack) Competitive advertising (TV): TV advertising for major competitor, GRPs
| Price: euros (per unit)
| Distribution: weighted Price: EUR (per SKU)
|
| Variables under brand control | Preplanned marketing plan activities, including competitive advertising for variant | Preplanned marketing plan activities | All observed variables under full brand control due to brand-owned retail stores | Preplanned marketing plan activities |
| Dependent variables | Volume sales, packs sold | Volume sales, packs sold | Volume sales, units sold | Volume sales, packs (per SKU) |
| Data structure | Panel (national level): 84 weeks × 8 regions: N = 672 | Panel (national level): 36 weeks × 9 regions: N = 324 | Panel (national level): 37 weeks × 7 cities: N = 259 | Panel (national level): 12 months × 20 regions × 19 variants: N = 4,560 |
| Descriptive statistics | Web Appendix B.1 | Web Appendix B.2 | Web Appendix B.3 | Web Appendix B.4 |
10022242918817000 a Not concurrent with WOM.
Brand marketing plans were set well in advance (typically 6–12 months ahead) and contained different forms of advertising and sales promotions to which the SMC had been added. In addition, the instant coffee brand provided advertising data for its other independently positioned product variants as a competitive advertising control variable. The sensitive toothpaste brand obtained its estimated GRPs through a media data provider. Three of the products were sold through third-party retailers (which makes the level of distribution and average retail price relevant control predictors), while one (the cosmetics line) was sold in the brand's own stores, giving the brand full control over not only advertising and sales promotions but also distribution and price. All brands obtained retail sales (and price) data either through a scanner panel or directly from their own stores.
To measure firm-created WOM volume in the sensitive toothpaste, the anti-age cosmetics, and the organic chocolate cases, we use WOM conversations as reported by the seeds and captured by the agency's survey tools during the campaign. This is the standard measure for firm-created WOM volume used by most SMC agencies and in prior research. For the instant coffee case, we use the tracked number of seed agent visits to the campaign platform as a measure for firm-created WOM. Although the relationship between seed agent activity on the platform (which may include mere reading and lurking) and the actually created WOM volumes is only correlational, the advantage of using this measure is that, compared with seed reports, the tracked platform activity does not require seed agents' attention and diligence, which may decrease over the course of the campaign. In addition, platform activity extends beyond the duration of the campaign: seed agents often remain active on the platform—and, presumably, in the actual market. To ensure comparability, we also obtained information on platform visits for the anti-age cosmetics and organic chocolate cases, which allows us to compare both measures. Web Appendix A summarizes both commonly used measures, their advantages and disadvantages, as well as their use in our data set and extant studies.
We determined the data set structure for the four cases using the dependent variable (sales), which provides a panel structure of regions measured over time. The chocolate data comprise several separate flavors as different stockkeeping units (SKUs) in addition to the regions and month. Sampling intervals range from weekly (coffee, toothpaste, and cosmetics) to monthly (chocolate), and we matched all variables to their respective data set structure. Web Appendices B.1–B.4 include information on measurement units, descriptive statistics, and correlations. For our model, we use standardized variables to allow better comparison between cases.
We start by presenting some model-free evidence for potential interaction effects between firm-created WOM and advertising and sales promotions. Figure 1, Panels A–D, shows sales over WOM against a backdrop of high or low levels of concurrent advertising and sales promotions for one of our cases (sensitive toothpaste). We scale the marketing variables to range from 0 to 100 and normalize the sales to a maximum of 100 in every regional cross-section of the data set. Three findings are of importance. First, sales for low levels of advertising (low WOM: 54.5, high WOM: 59.9) are higher than for higher levels of advertising (low WOM: 48.3, high WOM: 53.0), consistent with our expectation of a negative interaction effect between firm-created WOM and advertising. Second, sales for high levels of promotions (low WOM: 50.5, high WOM: 59.9) are higher than for lower promotion levels (low WOM: 48.3, high WOM: 52.3), consistent with our expectation of a positive interaction effect between firm-created WOM and sales promotions. Third, for the same level of advertising and sales promotions, sales are higher for high levels of WOM than for lower levels, indicating a direct positive impact of firm-created WOM on sales. This picture remains consistent when running linear regressions on the high/low marketing backdrop subsets of all points in the sales over WOM scatterplots (Figure 1, Panels C and D). To test these effects more formally, we next specify a comprehensive sales model to isolate and estimate the various effects.
Graph: Figure 1. Model-free evidence for interactions between firm-created WOM and other marketing.Notes: Sales, advertising, promotion, and firm-created WOM are scaled by region to a regional maximum of 100. Panels A and B show scaled sales mean values per WOM quartile, and Panels C and D show scatterplots and linear regression lines.
Consistent with models used for similar questions in prior research ([34]; [63]), we rely on a sales model that controls for carryover effects using a lagged dependent variable and includes fixed effects for seasonality and regional unobserved effects. In our short data sets, which feature mostly stable sales, the seasonal dummies capture time trends without an additional linear trend. Marketing activity and control variable effects are additional predictors, as well as the (multiplicative) interaction effects of interest. This base model can be formalized as follows:
Salesit=Constant+λitSalesit−1+β0itWOMit+β1itAdvertisingit+β2itPromotionsit+κ1itWOMit×Advertisingit+κ2itWOMit×Promotionsit+γjitControlsit+δiRi+δtSt+∊it,1
where Salesit represents volume sales of region i in time period t, Salesit–1 is the lagged dependent variable, WOMit is the firm-created WOM volume, and Advertisingit and Promotionsit comprise the advertising and the sales promotions, respectively, concurrent with WOM in region i and time t. The vector Controlsit includes other marketing plan elements (e.g., distribution, price, competitive advertising); regional effects appear within the vector Ri; and seasonal effects are in the vector St. Finally, ∊it denotes the error term.
Two points that require closer attention are a potential bias from endogeneity concerns and problems resulting from (multi)collinearity. The following subsections explain how we addressed those issues. Table 3 provides detailed information on the relevant robustness checks.
Graph
Table 3. Robustness Checks.
| Model | Description | Reason | Related Literature |
|---|
| Main model External typical SMC IV | Instrumental variable approach, 2SLS, IV: Average firm-created WOM of similar, but unrelated SMCs (Table 4) | Correcting for a potential endogeneity bias stemming from unobserved variables on firm-created WOM | Angrist and Pischke (2009), Germann, Ebbes, and Grewal (2015), Wooldridge (2010) |
| Fixed effects | Main model with seasonal and regional dummies (no instruments) (Web Appendix C) | Controlling for unobserved regional and seasonal effects | Papies, Ebbes, and Van Heerde (2017) |
| Panel internal regional IV | Instrumental variable approach, 2SLS, IV: average firm-created WOM of similarly populated regions within the panel data (Web Appendix D1) | Correcting for a potential endogeneity bias stemming from unobserved variables on firm-created WOM | Hausman and Taylor (1981) |
| Panel internal temporal IV | Instrumental variable approach, 2SLS, IV: two-week lag of WOM activity (Web Appendix D2) | Correcting for a potential endogeneity bias stemming from unobserved variables on firm-created WOM | Villas-Boas and Winer (1999) |
| Kalman filter | Main model applying Kalman filtering with additional control function (Web Appendix E) | Controlling for potential endogeneity bias stemming from measurement error | Naik and Raman (2003); Petrin and Train (2010) |
| Month × Region | Main model with interacting monthly and regional effects (Web Appendix F) | Controlling for unobserved effects specific to a certain time and region | Papies, Ebbes, and Van Heerde (2017) |
| Square root | Main model with squared advertising and WOM variables (Web Appendix G) | Accounting for diminishing effects of advertising and WOM variables | Bruce, Peters, and Naik (2012) |
| Random effects | Main model allowing for seasonal and regional individual effects, with additional control function (Web Appendix H) | Addressing potential unobserved heterogeneity | Papies, Ebbes, and Van Heerde (2017) |
| Ridge regression | Main model with ridge parameter penalizing model fit and coefficient size, with additional control function (Web Appendix I) | Addressing potential collinearity by shrinking estimate errors | Amemiya 1985; Cule and Iorio (2012) |
| Sequentially adding interactions | Adding single interaction terms to the direct effects only model (Web Appendix J1–J4) | Controlling for potential collinearity by sequentially adding single interaction terms to direct effects–only model | |
It is widely known that endogeneity in marketing models can lead to biased coefficient estimates ([33]; [67]). Broadly speaking, endogeneity can result from reverse causality in observational data (or simultaneity), unobserved variables, or measurement error. A common way to correct for endogeneity involves using IVs that, in an ideal case, allow for unbiased estimates, which can be implemented through either two-stage least squares (2SLS) models that use estimated values from a first-stage regression for the possibly endogenous variable ([ 2]; [33]; [88]) or an equivalent control function approach that includes the first-stage regression residuals as control variables in the main model ([70]).
Current recommendations stress that researchers should first carefully exploit control variables and panel structures in the data sets (to control for unobserved effects) before deciding to use IVs ([33]; [67]; [72]). Our data set comes with a rich set of control variables: all cases are complete in their respective available marketing plan variables. Because the marketing plans in all cases were planned and commissioned in advance, they are independent of the later concurrent variations in the market. In addition, three cases (instant coffee, sensitive toothpaste, and premium chocolate) control for distribution and price, and two cases (instant coffee and sensitive toothpaste) control for some form of competitive advertising. In the cosmetics case, the data come from the brand's own stores, which means distribution and price are under causal control of the brand. Therefore, a rich-data, fixed-effects regression approach that leverages the panel structure of the data sets to control for unobserved regional and seasonal effects should account for a significant part of the unobserved effects in the marketing plan variables ([33]; [67]). We provide these estimates in Web Appendix C.
However, it is still conceivable that firm-created WOM volumes may be subject to endogeneity bias, for the following three reasons. First, before the start of the SMC, invited prospective seed agents might self-select, and more agents might apply in regions where a brand is already perceived positively. As a result, we may observe more WOM in regions where the product already sells better. Second, SMC agencies may select seed agents with respect to the success measurement of the client brand, which creates an incentive to select more and better seed agents in regions where the brand focuses on. Third, the selected seed agents might react in their activity to some unobserved dynamics, resulting in a correlation between WOM or seed activity measure and the unobserved variables. We correct for such possible endogeneity bias in the WOM variable using an instrument as described next.
A suitable instrument must be correlated with the focal variable (regional weekly WOM volumes) but independent of the dependent variable (regional weekly sales). To obtain such a variable, we collected similar WOM volume variable levels over time from similarly sized SMCs, run by the same agency but at different times and with different products. The weekly averages of these WOM volume levels provide typical aggregate WOM dynamics for comparable SMCs, which are unrelated to the focal SCM and do not affect the respective product sales, nor are they affected by the respective unobserved conditions. We weigh these typical WOM levels regionally by the regional share of population in the country. As a result, our instrument represents a typical firm-created WOM pattern, as would be expected if seed agents apply and are selected proportional to the general population. We use such an instrument for each case and each type of WOM volume measure in a 2SLS regression as our main model.
As a robustness check, we also design two panel internal instruments ([33]). As a regional panel internal instrument, we construct the weekly WOM averages of the three regions most similar in population size to the target region and reestimate the models in Web Appendix D.1. This approach is similar to [44], in that it assumes that comparable cross-sections of the data set may be less affected by an unobserved variable in a focal section. As a temporal panel internal instrument, we use the two-week lag of the focal, regional WOM variable in Web Appendix D.2. This approach is similar to [85] in that it assumes that the potential unobserved variables are uncorrelated over time. We use a two-week lag because we have already included lagged sales in the main model. As a robustness check against endogeneity issues from measurement error, we employ a Kalman filter estimation in Web Appendix E, in which we correct for unobserved variables using the main model instrument and the control function approach ([70]).
We also reestimate the main models with interacting seasonal (e.g., monthly) and regional dummy variables to control for unobserved effects specific to a time and region (e.g., a local retailer reacting to the SMC) in Web Appendix F. We run additional models with square root–transformed advertising, promotion, and WOM variables to check for robustness to diminishing communication effectiveness at high levels of advertising pressure in Web Appendix G, although in fact none of our cases exhibits particularly high advertising pressures (i.e., the largest single TV advertising volume in all our data amounts to just 58 GRPs).
Models that include complex marketing plans run the risk of highly collinear variables, which can distort coefficient estimates—although they remain unbiased as the sample size approaches infinity. All our variables show low pairwise correlations, the strongest being r = −.69 between sales promotions and price for the toothpaste case (see Web Appendix A.2), suggesting generally low levels of collinearity. Adjusted generalized variance inflation factors (GVIF; [31]) also signal low collinearity in models with only direct effects (average GVIF = 3.50, single largest GVIF = 5.02). Still, adding interaction effects in the main models will increase collinearity (average GVIF = 3.57, single largest GVIF = 12.01) with a potential increase in standard errors.
To address this problem, we estimate the main models with a random-effects instead of a fixed-effects specification and the control function approach (see Web Appendix H). Random-effects models are more efficient (i.e., smaller errors in the estimates) but do not correctly account for endogeneity from unobserved effects ([67]). We also apply a ridge regression (and control functions) with an automatically selected ridge parameter ([23]) that penalizes model fit and shrinks the estimated coefficients ([ 1]) in Web Appendix I. These models trade smaller errors for biased estimates and lower model fit. Finally, we sequentially add all interaction terms to a direct-effects-only model in Web Appendices J.1–J.4 to determine whether the estimated parameters remain stable when adding possibly collinear interaction variables.
Table 4 lists the estimated main model for the four cases. All cases show a good model fit, explaining over 80% of the variance in the respective sales data. The direct effect estimates are in the expected directions—positive for planned marketing activities and distribution levels, negative for price—and mostly significant. For firm-created WOM, the direct effect results indicate a consistent positive effect on sales. These direct WOM effects are larger in the main models that include interaction effects than in corresponding models with direct effects only (see the "Robustness Checks" section and Web Appendices J1–J.4). These results provide a first indication that the WOM effects on sales may be weaker on a backdrop of advertising.
Graph
Table 4. Estimation Results of Main Model (2SLS: External Typical SMC IV).
| Instant Coffee | Sensitive Toothpaste | Cosmetics | Premium Chocolate |
|---|
| Weekly Sales (Units) | Weekly Sales (Units) | Weekly Sales (Units) | Monthly Sales (SKU) |
|---|
| Visits | Seed Reports | Visits | Seed Reports | Visits | Seed Reports |
|---|
| Constant | .456*** | .964*** | −.385** | −.321* | −.009 | .011 |
| (.137) | (.233) | (.142) | (.153) | (.051) | (.051) |
| DVt − 1 | .373*** | .234*** | .100 | −.012 | .645*** | .644*** |
| (.033) | (.052) | (.070) | (.097) | (.011) | (.011) |
| WOM | .090* | .450** | .237*** | .399*** | .034* | .081*** |
| (.046) | (.161) | (.049) | (.084) | (.015) | (.016) |
| Advertising (TV) | .055† | | | | | |
| (.029) | | | | | |
| Advertising (digital) | | .237*** | | | | |
| | (.061) | | | | |
| Advertising (print) | | | .148** | .159** | | |
| | | (.050) | (.059) | | |
| Promotion (point-of-sale) | .075** | .547*** | | | .022** | .029*** |
| (.026) | (.067) | | | (.008) | (.008) |
| Promotion (direct email) | .139*** | | .002 | .099 | | |
| (.027) | | (.054) | (.062) | | |
| Promotion (sampling) | .094*** | | | | | |
| (.019) | | | | | |
| Distribution | .270*** | .142*** | | | .123*** | .130*** |
| (.060) | (.037) | | | (.011) | (.011) |
| Price | −.453*** | −.295*** | −.009 | −.022 | −.060*** | −.061*** |
| (.036) | (.035) | (.033) | (.036) | (.014) | (.014) |
| Competitive advertising (TV) | −.118* | .022 | | | | |
| (.046) | (.057) | | | | |
| WOM × Advertising (TV) | −.115* | | | | | |
| (.045) | | | | | |
| WOM × Advertising (digital) | | –.343*** | | | | |
| | (.080) | | | | |
| WOM × Advertising (print) | | | −.228*** | −.300** | | |
| | | (.051) | (.090) | | |
| WOM × Promotion (point-of-sale) | .097* | .237*** | | | .082*** | .080*** |
| (.040) | (.068) | | | (.008) | (.008) |
| WOM × Promotion (direct email) | | | .071* | —a | | |
| | (.028) | —a | | |
| Observations | 672 | 324 | 259 | 259 | 4,560 | 4,560 |
| R2 | .832 | .953 | .924 | .910 | .898 | .897 |
- 20022242918817000 †p <.1.
- 30022242918817000 *p <.05.
- 40022242918817000 **p <.01.
- 50022242918817000 ***p <.001.
- 60022242918817000 a Seed report WOM measure does not overlap with promotions in the cosmetics case.
- 70022242918817000 Notes: Standard errors are in parentheses. All variables are standardized. Seasonal and regional effects are not shown for brevity.
The interaction effects between firm-created WOM and either advertising or sales promotion consistently support our expectations. Firm-created WOM and advertising interact negatively, and all advertising-related interaction effects have a negative sign, irrespective of whether the advertising is TV, digital, or print. In contrast, all promotion-related interaction effects are positive and significant, again irrespective of whether promotions are point of sale or direct email. In addition, the chocolate model confirms that significant positive interactions between firm-created WOM and point-of-sale promotions exist even when no advertising is present, providing evidence that the effect does not result from more complex higher-dimensional interactions.
When comparing different measures of firm-created WOM, we observe that direct WOM effects as well as most interaction effects show larger coefficients when using seed agent reports than when using platform visits. This is particularly obvious in the cosmetics and the chocolate cases, in which both measures are available. This can be explained by WOM-unrelated seed activities, which are included in the platform visits measure but not in the seed reports.
We used several checks to test the robustness of our findings over different model choices. Table 5 summarizes the directions and significance levels of the interaction effect results and their robustness checks across all four cases.
Graph
Table 5. Direct and Interaction Firm-Created WOM Effects Across Modeling Choices.
| Model | Instant Coffee | Sensitive Toothpaste | Cosmetics | Premium Chocolate | Summary |
|---|
| Visits | Seed Reports | Visits | Seed Reports | Visits | Seed Reports |
|---|
| Direct effect firm-created WOM | Direct effects only model—external IV (Web Appendix J1–J4) | .017n.s. | .307*** | .054n.s. | .239*** | .046** | .098*** | + |
| Main model—external IV (Table 4) | .090* | .450** | .237*** | .399*** | .034* | .081*** | |
| Firm-created WOM | Fixed effects—no IV (Web Appendix C) | −.175*** | −.072n.s. | −.222*** | −.156*** | | | − |
| × Advertising | Main model—external IV (Table 5) | −.115* | −.343*** | −.228*** | −.300** | | | |
| Panel internal regional IV (Web Appendix D1) | −.164*** | −.176* | −.205*** | −.140** | | | |
| Panel internal temporal IV (Web Appendix D2) | −.086n.s. | −.531* | −.131n.s. | −.502*** | | | |
| Kalman filter (Web Appendix E) | −.159* | −.065n.s. | −.308* | −.243* | | | |
| Month × Region (Web Appendix F) | –.072† | −.268*** | −.283*** | −.413*** | | | |
| Square root (Web Appendix G) | −.082n.s. | −.290*** | −.155*** | −.187† | | | |
| Random effects (Web Appendix H) | −.140*** | −.268*** | −.238*** | −.278*** | | | |
| Ridge regression (Web Appendix I) | −.060* | −.060** | −.140*** | −.118*** | | | |
| Sequentially added interaction (Web Appendix J1–J4) | –.081* | –.380*** | –.234*** | –.300** | | | |
| Firm-created WOM | Fixed effects – no IV (Web Appendix C) | .071** | .158** | .075** | —a | .048*** | .048*** | + |
| × Promotions | Main model—external IV (Table 5) | .097* | .237*** | .071* | —a | .082*** | .080*** | |
| Panel internal regional IV (Web Appendix D1) | .076** | .200** | .071* | —a | .032* | −.071n.s. | |
| Panel internal temporal IV (Web Appendix D2) | .118*** | .300*** | .146*** | —a | .058*** | .073*** | |
| Kalman filter (Web Appendix E) | .086* | .135* | .090* | —a | .078* | .082* | |
| Month × Region (Web Appendix F) | .044n.s. | .101† | .008n.s. | —a | .018† | .040** | |
| Square root (Web Appendix G) | .078† | .082n.s. | .070* | —a | .083*** | .073*** | |
| Random effects (Web Appendix H) | .100** | .244*** | .070** | —a | .083*** | .080*** | |
| Ridge regression (Web Appendix I) | .072* | .160*** | .065** | —a | .082*** | .080*** | |
| Sequentially added interaction (Web Appendix J1–J4) | .072* | .242*** | .081** | —a | .082*** | .080*** | |
- 80022242918817000 †p <.10.
- 90022242918817000 *p <.05.
- 100022242918817000 **p <.01.
- 110022242918817000 ***p <.001.
- 120022242918817000 n.s.Not significant.
- 130022242918817000 a Seed report WOM measure does not overlap with promotions in the cosmetics case.
A comparison of the main model interaction effects with the results from a fixed-effects only model without IV correction (Web Appendix C) shows similar directions and significance levels of the negative interactions with advertising and the positive interactions with promotions. This finding indicates that our instrument does not fundamentally change the estimated coefficients. However, all estimated interaction coefficients are larger when using an instrument, pointing to the possibility of underlying endogeneity in the firm-created WOM that goes uncorrected when not using an instrument.
Comparing the panel external instrument used in the main models with the two panel internal instruments (Web Appendices D.1–D. 2) shows that overall, both alternative instrument robustness models have largely the same pattern of negative interactions of WOM with advertising and positive interactions with promotions. The results from the temporal panel internal instruments model (using two-week lag as the instrument) show inconsistent differences in parameter sizes for the interaction effects with advertising: the models with platform visits as WOM measure show insignificant coefficients, whereas the models with seed reports show much larger estimated interaction coefficients. Presumably, some of the advertising effects may not be contained within a single week, thereby violating the temporal independence assumption for the temporal panel internal instrument.
We also test for instrument strength and estimation consistency. All employed instruments can be considered strong as evidenced by significant partial F-tests ranging from F = 5.93 (cosmetics) to F = 24,289.29 (premium chocolate) ([12]). Furthermore, Wu–Hausman tests ([43]; [89]) reject consistency of fixed effects estimates in most cases (with minimum p <.10), except in three instances (main and panel internal regional instrument for visits for anti-age cosmetics, and panel internal regional instrument for seed reports), which indicates that instrumented WOM estimates are consistent in the presence of endogeneity and should be preferred over fixed-effects estimates.
As a robustness check against endogeneity from measurement error, we also estimate the main models with a Kalman filter in Web Appendix E ([63]), including our panel external instrument with the control function approach ([70]). Again, the interaction effects show largely similar directions and strengths, although one interaction with advertising in the toothpaste model remains insignificant. In summary, we are confident that the interaction effects exist as expected and that they are not an artifact of the instruments used to correct for potential endogeneity in the WOM variables.
Using square root–transformed WOM, advertising, and promotion variables to account for possible diminishing marginal direct effects does not substantially alter the directions of the interaction effects when reestimating the model with 2SLS (see Web Appendix G). Qualitatively, these results indicate interaction effects sufficiently strong to remain super- or subadditive, even after reducing the influence of the larger marketing levels in the model.
Controlling for interacting monthly and regional unobserved effects when reestimating the main model (see Web Appendix F) confirms that interaction effects remain in the same directions, providing evidence of robustness against an unobserved dynamic, specific to select regions (e.g., a regional retailer with special promotions). Only the positive interactions between WOM and promotions seem weaker compared with the main model, possibly indicating some unobserved regional retailers' actions. However, given that we find these weaker results mainly in the cases with the smallest sample sizes and fewest overlapping weeks with the WOM variables (coffee and cosmetics), the result might be an artifact of the higher collinearity from interacting control dummies and the resulting larger estimation errors. Recalling that the cosmetics case data come from the brand's own stores, which rules out unobserved retailer activity, we deem this explanation likely.
We confirmed this speculation using random effects models (Web Appendix H) and ridge regression models with control functions (Web Appendix I), both of which are more efficient than the fixed-effects models when faced with collinearity and produce smaller standard errors. None of these models shows substantially different results, only some changes in coefficient size for specific variables. Finally, sequentially adding the interaction effect of interest to a direct-effects-only model (Web Appendices J.1–J.4) again confirms that the expected negative interactions with advertising and positive interactions with promotions are all significant when estimating models with reduced collinearity.
The question of how SMC effects behave in the presence of traditional communication tools and how these effects compare with the effects of advertising or sales promotion remains unanswered, especially in an FMCG context. Our study aims to provide an initial empirical analysis in this direction. The four product markets we analyze demonstrate converging evidence for consistent interaction effects. We demonstrate consistent negative interaction effects between firm-created WOM and various types of advertising (TV, digital banner, and print).
To put our estimated sales effects into perspective with prior research, we calculate partial correlation effect sizes, r = √[t2/(t2 + d.f.)], using the t-statistics of the estimated coefficients in our main models ([22]). Table 6, Panel A, lists all estimated effect sizes. Integrating all three WOM × advertising interactions—using the seed report measure where possible—with a random-effects meta-analysis ([24]), we identify an overall effect size of r =.187 (95% confidence interval [CI] = [.083,.291]). We also demonstrate consistent positive interaction effects between firm-created WOM and various kinds of promotions (point-of-sale and direct email), with an overall integrated effect size of r =.143 (95% CI = [.117,.169]). Such strong interactions of SMCs with other marketing effects can help explain the wide range of firm-created WOM sales effects perceived by our interview partners and the WOM industry ([86]).
Graph
Table 6. Effect Sizes and Marketing Variable Elasticities.
| A: Estimated Direct and Interaction Effect Sizes |
|---|
| Instant Coffee(Visits) | SensitiveToothpaste(Seed Reports) | Cosmetics(Visits) | Cosmetics(Seed Reports) | Premium Chocolate(Visits) | Premium Chocolate(Seed Reports) |
|---|
| r | r | r | r | r | r |
|---|
| Firm-created WOM | .080 | .167 | .316 | .312 | .034 | .076 |
| Advertising (TV) | .076 | | | | | |
| Advertising (digital) | | .229 | | | | |
| Advertising (print) | | | .202 | .182 | | |
| Promotion (point-of-sale) | .117 | .443 | | | .043 | .058 |
| Promotion (direct email) | .208 | | .002 | .109 | | |
| Promotion (sampling) | .196 | | | | | |
| Distribution | .182 | .227 | | | .160 | .168 |
| Price | .459 | .456 | .019 | .043 | .063 | .064 |
| Competitive advertising (TV) | .103 | .023 | | | | |
| WOM × Advertising (TV) | .103 | | | | | |
| WOM × Advertising (digital) | | .253 | | | | |
| WOM × Advertising (print) | | | .296 | .224 | | |
| WOM × Promotion (point-of-sale) | .099 | .206 | | | .152 | .143 |
| WOM × Promotion (direct email) | | | .175 | | | |
| B: Marketing Variable Elasticities |
| InstantCoffee(Visits) | SensitiveToothpaste(Seed Reports) | Cosmetics(Visits) | Cosmetics(Seed Reports) | PremiumChocolate(Visits) | PremiumChocolate(Seed Reports) |
| ∊ | ∊ | ∊ | ∊ | ∊ | ∊ |
| Firm-created WOM | .028 | .201 | .066 | .149 | .068 | .138 |
| Advertising (TV) | .005 | | | | | |
| Advertising (digital) | | .002 | | | | |
| Advertising (print) | | | .094 | .104 | | |
| Promotion (point-of-sale) | .072 | .282 | | | .124 | .128 |
| Promotion (direct email) | .234 | | .027 | .145 | | |
| Promotion (sampling) | .175 | | | | | |
| Distribution | .825 | .852 | | | .326 | .344 |
| Price | −2.205 | −2.070 | −.051 | −.131 | −.662 | −.680 |
| Competitive advertising (TV) | −.111 | .014 | | | | |
140022242918817000 Notes: r represents the partial correlation effect sizes of the estimated variables; ∊ indicates sales elasticities (signup elasticity for online service), based on marginal changes in marketing variables (including interaction effects).
Generally, we see that the effect sizes illustrated in Table 6 are small to medium, ranging from r =.034 to r =.316. These values are in the range of the known sales effect sizes in prior studies with stand-alone SMCs of r =.148 (N = 180; [34]) and r =.262 (N = 88; both calculated from Cohen's d in [26]]). Integrating the effect sizes from our four cases and the two extant offline SMC effect sizes with random-effects meta-analyses, we identify an overall r =.156 (95% CI = [.080,.232]). Note that this effect is only slightly stronger than the sales effect of electronic WOM volume on sales identified by prior meta-analyses (r =.091 overall; r =.141 for WOM volume; Babić [ 5]). We attribute this slightly stronger result to the rich face-to-face nature of most SMC-created WOM communication.
Given the negative interaction between firm-created WOM and advertising, one might wonder about the contribution of SMCs, given that FMCG companies typically employ large-scale investment in media. Our interviews with SMC users indicate that, unlike when promoting new products, which can be largely pushed in various social influence channels, SMCs for supermarket goods are not aimed to replace traditional communication methods but rather to add to them. For example, SMCs are conducted when marketers want to inform users about a new development in a current product line such as a line extension (e.g., a new flavor or form of the product). In this context, one can imagine that some FMCG consumers can be more easily reached by advertising than others. Here, SMCs can be an efficient method to reach consumers who are less exposed to traditional media or are skeptical of its content. The larger this group is, the greater the contribution of SMC. If in such a context a firm increases advertising spending, some (but not necessarily all) of the individuals may be affected even before the SMC starts, which may explain the negative interaction between advertising and SMCs.
For promotion, the story is different. Marketers invest in promotions to convince buyers to take advantage of price deals. The higher the intensity of the price deals, the better the ability of the SMC to contribute to sales—thus the positive interaction with promotion efforts.
One of the interesting aspects of these results relates to how researchers approach amplified WOM programs in comparison to organic WOM. While originally the term "word of mouth" referred to organic talks among individuals, the growing involvement of firms in managing their customer interactions has blurred the distinction such that WOM effects may refer to both organic and amplified forms ([11]; [35]). Yet it may be necessary to differentiate among various forms of WOM: firm-created WOM from SMC programs for supermarket goods may work as a substitute to advertising, whereas organic WOM for complex produces may work best instead of, or in addition to, advertising. Examining the interactions of WOM with other marketing tools can help in the quest to understand the role of WOM in specific markets.
It is the central assumption of all research on integrated marketing communication that different types of marketing communication and activities interact with one another ([ 8]; [63]; [77]; [79]). For a marketing manager commissioning an SMC, it is important to know how the additional firm-created WOM interacts with the firm's other advertising and promotional activities. Our results offer managers the opportunity to compare firm-created WOM sales effects from SMCs with sales effects from other marketing activities. To facilitate comparisons, we calculate elasticities of each marketing variable for each period and region with a nonzero value and then average them. For the calculation, we increase the variables by 1% and all affected interaction effects accordingly. In addition, we simulate how sensitively the firm-created WOM elasticities would react to marginal changes in advertising or promotion variables. We base the following managerial recommendations on these calculations and comparisons with elasticities from extant literature.
Similar to previous SMC studies, we find evidence for a positive effect of SMCs on FMCG sales. Our analysis implies that SMCs may increase total sales by approximately 3%–18% over the course of the campaigns. The firm-created WOM elasticities ∊ shown in Table 6, Panel B, range from ∊ =.03 to ∊ =.20. These values are comparable to or stronger in size than extant meta-analytic sales elasticities for electronic WOM volume of ∊ =.026, when including advertising in the sales model, and ∊ =.014, when including a lagged sales variable in the sales model ([90]). They are also comparable in absolute values to the average sales elasticity (∊ =.12) and the median (∊ =.05) advertising elasticity for TV advertising in prior meta-analyses ([74]). These numbers thus indicate that SMCs, which frequently cost well below €100,000, likely generate incremental sales that are greater than their costs. It is important to note, however, that these numbers reflect relatively small sized SMC with no known simultaneous SMC by competitors. As the use of SMC widens and the market matures, we expect these elasticities to decline over time.
Table 7 shows how firm-created WOM elasticities change as a result of marginal increases or decreases in advertising or promotion. The relative changes can be interpreted as cross-elasticities between media. The results show that firm-created WOM elasticities decrease by −.6% to −2.3% for every 1% increase in concurrent advertising activities. Over the course of an SMC, similar reductions in total sales effects can be expected, which means that by reducing other advertising in the marketing plan, an SMC could substantially increase its total sales impacts, with the optimal mix being a function of media cost. An obvious implication of this finding is that firms should refrain from adding SMCs to the "big bang" marketing plan, as one of our interviewees put it. When temporally disentangling SMCs and advertising, we anticipate an order effect ([73]) in favor of running the SMC first, followed by advertising later, because SMCs have smaller reach but likely richer information. In theory, this allows high-reach advertising to still inform unaware consumers and possibly recall SMC-induced memories of those already informed. In contrast, SMCs seem to combine well with promotion activities. The sensitivity analyses demonstrate higher firm-created WOM sales elasticities of +.3% to +1.1% for each 1% increase in promotional activities.
Graph
Table 7. Sensitivity of Firm-Created WOM Elasticities to Advertising and Promotion Changes.
| InstantCoffee(Visits) | SensitiveToothpaste(Seed Reports) | Cosmetics(Visits) | Cosmetics(Seed Reports) | PremiumChocolate(Visits) | PremiumChocolate(Seed Reports) |
|---|
| ∊ | Relative Change | ∊ | Relative Change | ∊ | Relative Change | ∊ | Relative Change | ∊ | Relative Change | ∊ | Relative Change |
|---|
| WOM elasticity | .028 | | .201 | | .066 | | .149 | | .068 | | .138 | |
| With +1% advertising (TV) | .028 | −2.16% | | | | | | | | | | |
| With +1% advertising (digital) | | | .200 | −.61% | | | | | | | | |
| With +1% advertising (print) | | | | | .064 | −2.31% | .147 | −1.40% | | | | |
| With +1% promotion (point-of-sale) | .029 | +1.06% | .201 | +.29% | | | | | .069 | +.47% | .138 | +.29% |
| With +1% promotion (direct email) | | | | | .066 | +.39% | | | | | | |
150022242918817000 Notes: ∊ indicates elasticities based on marginal changes in marketing variables.
Our study is limited to the analysis of offline firm-created WOM in the FMCG industry. Still, other forms of SMC do exist, and it is unclear whether our findings also hold in those cases. Online reviews as a form of electronic WOM, for example, occur at the (electronic) point of purchase. As such, they may benefit from easier recall due to familiarity from paid media or firm-created WOM. Digital advertising forms that directly link to a shopping site, such as search ads, may benefit from SMC or advertising-induced familiarity ([69]). Relatedly, forms of organic or firm-created WOM that are less information rich, such as ratings on review sites ([60]) or short social media network posts ([54]), may offer more potential for complementary information and positive interaction effects with other marketing communication than face-to-face firm-created WOM. Future research could systematically vary the closeness of firm-created WOM or other marketing communication to points of consumer activity and reinvestigate the impacts on cross-media interaction effects.
Further, more complex products may allow for more information complementarity between marketing communications and thus promise more potential for synergy ([62]). In such cases communication may also be more focused on sales promotions than the more brand-related advertising in FMCG. In a business-to-business marketing environment with more customized and complex product solutions, personal selling—which ought be information rich and adaptive, similar to face-to-face WOM—exhibits positive instead of negative synergies with TV advertising ([32]; [36]). Future research could reinvestigate WOM-related media interaction effects in various product, market, or media channel contexts. The results could explain systematic differences between online and offline forms of marketing.
Future research may enhance our results with individual-level analysis to understand the process that creates the interactions between SMCs and advertising/promotion in the context of FMCG. Customer heterogeneity is of particular interest. The interaction of SMCs with the marketing-mix elements can partly be explained by the existence of segments that react differently to the communication tools. If such segments can be identified, and possibly targeted, then managers can use the more precise targeting offered by SMCs to mitigate the negative interaction with advertising. Field experiments may be a promising tool to disentangle segment-dependent reactions to SMCs and the other communication tools.
A common assumption is that advertising spawns organic WOM, which then amplifies the sales effect ([47]). Although researchers have analyzed this finding in the context of organic WOM, it has not yet been studied in the context of firm-created WOM. In this light, failing to consider the downstream consequences of interacting SMCs and advertising on organic WOM might represent a limitation on our effect estimates, because some portions of the interaction effect may be attributable to organic WOM from advertising. Although recent research has challenged the notion that advertising effects are mediated by organic WOM ([58]), it would be worthwhile to analyze the relationship in more detail.
Our study does not consider the qualities or differences in communication content, and it is conceivable that matching content in firm-created WOM and advertising could affect their interaction effects. In this light, a shift to "strategic" rather than "tactical" content integration could be beneficial for marketers ([75]). This may be more complicated than it appears, considering that consumer-created content is inherently uncontrollable and even SMC seed agents do not just parrot the information that a brand or agency gives them ([51]). One option to design complementary content that propagates firm-created WOM is to offer trustworthy, unique, valuable stories ([ 9]). Another avenue to create complementary content is to monitor a wide variety of ongoing organic conversations and then suggest careful additions between, not within, the separate topics, such that the content bridges separate conversations. Such content bridging then helps form a comprehensive communication "trellis" ([ 6]) across the separate topics and increases information complementarity—and synergy.
Interacting in a market environment in which consumers pay increasingly less attention to traditional media and are notoriously difficult to reach through previously effective channels represents a challenge for most companies. Although firms increasingly realize that managing customer relationships in this context is substantially different from doing so in the past ([39]), they still struggle with the day-to-day implementation of new strategies. If WOM programs are to become part of the marketing mix, we should understand their applicability to different types of markets and their interaction with other tools. The consistent results we found across scenarios suggest that, at least for FMCG, one can form expectations in the direction of the effects. We believe such analysis can help FMCG firms, as well as other markets, continue to explore the opportunity of SMCs and manage them as another marketing-mix tool used in the marketing plan.
Supplemental Material, DS_10.1177_0022242918817000 - Seeding as Part of the Marketing Mix: Word-of-Mouth Program Interactions for Fast-Moving Consumer Goods
Supplemental Material, DS_10.1177_0022242918817000 for Seeding as Part of the Marketing Mix: Word-of-Mouth Program Interactions for Fast-Moving Consumer Goods by Florian Dost, Ulrike Phieler, Michael Haenlein, and Barak Libai in Journal of Marketing
Footnotes 1 Associate EditorRobert Meyer served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors thank the Dynamic Capabilities and Relationships (DCR) doctoral program for financial support.
4 Online supplement: https://doi.org/10.1177/0022242918817000
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By Florian Dost; Ulrike Phieler; Michael Haenlein and Barak Libai
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Record: 159- Self-Selected Sales Incentives: Evidence of their Effectiveness, Persistence, Durability, and Underlying Mechanisms. By: Bommaraju, Raghu; Hohenberg, Sebastian. Journal of Marketing. Sep2018, Vol. 82 Issue 5, p106-124. 19p. 3 Diagrams, 11 Charts, 1 Graph. DOI: 10.1509/jm.17.0002.
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Self-Selected Sales Incentives: Evidence of their Effectiveness, Persistence, Durability, and Underlying Mechanisms
Drawing on goal-setting theory, this study develops a new self-selected incentive scheme. Within this scheme, a sales employee chooses an individualized goal–reward level combination from a menu the firm proposes given the employee’s past performance. To test the effects of the self-selected incentive scheme, the authors conducted two field experiments at two <italic>Fortune</italic> 500 companies. Results of both experiments show that, compared with two equivalent quota systems, sales employees’ performance increased substantially under the self-selected incentive scheme. In addition, findings reveal that the performance increase induced by this scheme is substantially greater for sales employees with a high variation in past performance and for employees with a low past-performance level. Moreover, the authors find that the effects of the self-selected incentive scheme not only are durable when offered again but also persist after the scheme is discontinued. Through two additional online experiments, the authors extend the findings of the field studies, isolate the self-selected incentive scheme’s three underlying mechanisms, and examine each mechanism’s relative strength.
difference-in-differences; field experiment; goal-setting theory; incentives; sales force
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By Raghu Bommaraju and Sebastian Hohenberg
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Record: 160- Selling the Premium in Freemium. By: Gu, Xian; Kannan, P.K.; Ma, Liye. Journal of Marketing. Nov2018, Vol. 82 Issue 6, p10-27. 18p. 1 Color Photograph, 9 Charts, 1 Graph. DOI: 10.1177/0022242918807170.
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Selling the Premium in Freemium
The success of a freemium model depends on the number of customers who purchase the premium version in the presence of the free version. The authors investigate the strategy of extending the premium product line to spur demand for the existing premium version. Extending the results of the standard product line model is insufficient in such cases because of the conceptual nuances in a freemium context. The authors conduct a randomized field experiment with an online content provider that offers book titles in a PDF version for free and sells the paperback version for a premium. The authors show that paperback titles accompanied by an additional premium version, either in e-book or hardcover format, have higher sales than those in the control condition. The positive impact on paperback sales is stronger for titles that are more popular or cheaper, and the effect of introducing the e-book version is higher when the e-book price is closer to the paperback price. By analyzing individual customer choices, the authors identify the existence of the compromise effect and the attraction effect in the extended product line setting, a significant contribution not only in the freemium context but also to the product line literature.
Keywords: freemium; product line; compromise effect; attraction effect; randomized field experiment
Freemium has become an increasingly popular business model over the past decade. The term "freemium" is a combination of "free" and "premium" and refers to the strategy in which customers can get a basic version of a product or service for free and switch to a premium version with additional features by paying a price. For example, Dropbox offers 2 GB of free storage capacity in the cloud, which is generally adequate for text documents. However, if users exceed that storage limit, they have the option to upgrade to 1 TB (i.e., 1,000 GB) by paying a monthly subscription fee of $9.99 or an annual subscription fee of $99. According to the latest Securities and Exchange Commission filings by [ 8]), the freemium model has enabled Dropbox to acquire over 11 million paying users (more than 500 million registered users in total) through 2017. These paying customers generated approximately $1 billion in revenue for Dropbox in 2017. Other popular examples of the freemium model include voice and video call services by Skype, social networking services by LinkedIn, news content by the New York Times, and mobile apps with both free and paid versions. Sometimes, firms use freemium models to get customers into their stores (the "foot-in-the-door" strategy) by giving away free products and cross-selling other offerings from the firm. For example, join.me uses free conference calls to spur demand for its other offerings such as webinars and video conferences.
The appeal of the freemium model for companies is that it can generate high volumes of traffic and provide direct access to potential paying customers without making heavy investment in marketing promotions ([21]). The freemium strategy serves as a marketing tool that can draw in potential customers through channels such as word of mouth and organic searches. From a customer's perspective, the freemium strategy provides a costless way to sample and experience a new product or service. Companies are able to generate revenues from these customers by selling a premium product to those who value using the product enough that they are willing to pay for additional or different functionality. In such a context, one key challenge for companies that utilize the freemium model is determining how to increase the number of customers who purchase the premium version in the presence of the free version. If a company is unable to convert an adequate fraction of its free customers to paying customers, the freemium model is likely to fail ([21]).
Although extant research has focused on the optimal design of the free product, the pricing of the premium product (e.g., [24]), and the conditions for providing free content in a freemium model (e.g., [23]), such analyses have not studied the premium product line that many implementations of the freemium model involve (e.g., Dropbox offers a premium version for individual consumers and a premium version for businesses). Understanding the impact of such line extensions can be critical for the success of the freemium model. In this research, we focus on the impact of extending the product line to spur demand for the existing premium version and to increase overall revenues.
Prior research in product lines has shown that extension of a product line is often associated with higher customer utilities because customers are more likely to find a product alternative closer to their respective ideal points (e.g., [26]). For example, if the addition of the new alternative is of appropriate (high) quality level, it can lead to higher brand evaluations of the entire product line ([13]), which can lead to higher willingness to pay for the premium alternatives in a freemium context and, thus, higher revenues. However, the freemium context is different from a standard product line context because of the zero-price effect ([31]). This effect predicts that customers perceive the benefits of the free product to be higher than what a cost–benefit model would predict, indicating a stronger preference for the zero-priced alternative. Under such conditions, instead of the whole line benefiting from the positive brand evaluations, customers may be disproportionately attracted to the free version. This, in turn, may lead to nonrealization of the expected positive impact as the product line extension literature would predict. As this example illustrates, the conceptual nuances introduced by the presence of the free version in the freemium model necessitates more than an application of standard product line models to this context and requires a closer examination of the demand outcomes driven by the potential effects of different behavioral mechanisms. This is one of our key objectives in this article.
The subject of our research is a publisher of scholarly, research-oriented content, the National Academies Press (NAP), which offers free online PDF versions for all of its book titles and sells a paperback version of the same titles for a price. We conducted a randomized field experiment that examined customer downloads and purchases on NAP's website (www.nap.edu) between January 2016 and August 2016. All book titles that were randomly selected in our field experiment had a free PDF version for download and a paperback version available for purchase on the publisher's website before the start of experiment. We manipulated the treatment for book titles by randomly assigning them to one of the following three conditions: ( 1) a treatment condition under which an additional premium version of e-book format is introduced, ( 2) a treatment condition under which an additional premium version of hardcover format is introduced, and ( 3) a control condition under which no additional premium versions are made available.
We apply the difference-in-differences (DID) method to the data generated from the field experiment, which covers a seven-month period before the start of the experiment and a seven-month period after the start of the experiment. Overall, we show that the titles accompanied by an additional premium version, either the e-book or the hardcover format, have higher paperback sales than those in the control condition. The positive impact on paperback sales is stronger for titles that are more popular or lower in price. In addition, our results reveal that when the e-book price is close to the paperback price, the increase in paperback sales is more pronounced. To further understand the underlying mechanisms driving the results of our randomized field experiment, we analyzed customer choices at the individual level using choice models. Interestingly, our analysis indicates the existence of the compromise effect and the attraction effect in the extended product line setting as the reason for the observed results. Drawing on our findings, we provide specific recommendations on premium versions and prices for managers to improve the effectiveness of their freemium model implementations.
The contributions of this paper are two-fold. First, this study establishes the impact of product line extensions on enhancing the effectiveness of freemium models. Previous literature has discussed how to improve the performance of freemium models by focusing on the design of the free product/service and its price. For example, [24]) explore the optimal design of the free product in the context of cloud-based storage service. They estimate a dynamic structural model characterizing consumers' plan choice, usage, and referral. Specifically, they analyze the impact of a referral incentive on customers' likelihood of upgrading to the premium product and find that the referral program contributes to 65% of the value of free consumers. [27] compare two freemium models in the software industry, feature-limited freemium and uniform seeding, with a conventional business model under which software is sold as a bundle without free offers. Their analytical model suggests that the feature-limited freemium strategy outperforms the conventional model when consumers have either a relatively low or high prior preference for premium functionality. [23]) propose a revenue model for online content providers and suggest that it is optimal for firms to offer more free content during periods of high demand but charge for content during periods of low demand. Our article examines the impact of introducing additional versions to the product line and investigates how product line extensions improve the freemium model performance.
Second, our research adds to the substantive findings in the product line literature. Specifically, our study focuses on the conditions under which a product line extension can overcome the potential zero-price effects ([31]) and result in increased demand for the premium versions. In addition, our results show how behavioral mechanisms such as the compromise effect (e.g., [32]) and the attraction effect (e.g., [14]) can explain the demand patterns seen in a product line extension context, providing evidence for these alternative explanations in our field study. While these effects have been well established in the laboratory setting, this is the first time, to our knowledge, that these effects have been empirically identified in a product line setting. Consistent with the compromise effect, our results suggest that when a higher-quality hardcover version is introduced to the product line, thus making paperback the middle option, the sales of the paperback version increase. In addition, when an e-book version is introduced to the product line and its price is set close to the paperback version, the sales of paperback version increase because the paperback version tends to dominate the high-priced e-book version, leading to an attraction effect. Thus, our paper makes substantive contributions to both the freemium model literature and the product line literature. In addition to offering useful managerial implications for the publisher we studied, our research provides a better understanding of the behavioral mechanisms leading to the success of freemium models as well as product lines.
The basic question we are aiming to answer in our research is how to increase demand for the premium version in a freemium model by adding another premium version as a product line extension—a version that could be either of higher quality and higher price or lower quality and lower price than the existing premium version. We also want to identify the underlying mechanism(s) driving the results in this context. The obvious starting point for such research is the extant work in product line extensions. In the following sections, we discuss this literature and the associated conceptual underpinning and argue why a direct application of the results from extant product line research is inadequate in the freemium context.
In previous research, the addition of products in a product line is often associated with higher customer utilities, as customers are more likely to find an option close to their respective ideal points, resulting in larger demand for the firm offering the line. While a monopolist can price these products appropriately and extract all the surplus from customers, in competitive markets the return from a longer product line depends on the pricing power, the costs of producing the extended line, and the associated expenses in supporting the line ([ 2]; [ 7]; [18]; [26]). Depending on the distribution of customer valuation for quality and taste preferences, the introduction of lower-quality, lower-priced alternatives in the product line can lead to cannibalization and decreased revenue and profits (e.g., [ 6]). Focusing on brand evaluations, [13]) find that introduction of higher-quality, higher-priced alternatives in the line can lead to increases in brand evaluations of larger magnitude than the decreases in brand evaluation when lower-quality, lower-priced alternatives are introduced. The underlying reason for such effects is that an extended product line provides more options for customers, which mitigates the negative evaluations that result from the lower-quality products, whereas higher-quality products set the overall quality expectations from the brand, leading to higher evaluations. In contrast, [ 4] show that adding a higher-quality, higher-priced alternative to the line can lead to decreased overall demand, as the addition of the alternative could lead to consumers' reassessment of their perceptions of the brand in a competitive setting, especially when the attributes of the products match competitor brands. Although these results are mixed, we can, however, expect the positive impact of higher-quality, higher-priced alternatives to exist in a monopolistic setting.
In extending the aforementioned findings to the freemium context, we examine two cases: the introduction of a higher-quality, higher-priced alternative and the introduction of a lower-quality, lower-priced alternative. In the first case, if there are enough customers with ideal points close to the high-quality alternative, we can expect these customers to substitute the existing premium alternative and/or the free alternative with the new premium alternative, thereby increasing revenues (e.g., [26]; [37]). In addition, one could argue that the introduction of a higher-quality option can increase brand evaluations of the freemium brand (e.g., [13]), making the entire product line more attractive. If this leads to higher willingness to pay for the brand, then the entire product line can benefit through increased demand for the free alternative as well as increased sales of the existing premium and the newly introduced high-quality premium alternatives. In the second case of introducing the lower-quality, lower-priced premium alternative as compared with the existing premium alternative, the insights from the product line research would suggest cannibalization of the existing premium alternative by the lower-priced alternative (e.g., [ 6]). However, the provision of more alternatives could make the product line more attractive, and thus some customers could switch from the free alternative to the lower-priced premium alternative (as it could be closer to their ideal point). The net impact of these effects depends on the magnitude of these effects. Revenues may decline if the cannibalization effect on the existing premium alternative outweighs the revenue increase or the other positive impacts generated from the increase in demand for the newly introduced lower-quality, lower-priced premium alternative.
The freemium context is different from a standard product line context because of the role of zero price of the free alternative influencing customers' choices through the zero-price effect ([31]). The traditional cost–benefit perspective predicts that customers choose the alternative with the highest cost–benefit difference, but the zero-price effect predicts that customers perceive the benefits of the free product to be higher than what a cost–benefit model would predict, indicating a stronger preference for the zero-priced alternative. Depending on the magnitude of the zero-price effect, when a higher-quality, higher-priced alternative is introduced, the positive brand evaluations suggested by [13]) can occur, but instead of the whole line benefiting from it, customers may be disproportionately attracted to the free version, leading to nonrealization of the expected positive impact of adding the higher-quality, higher-priced alternative as predicted by the product line literature. In the case of adding a lower-quality, lower-priced alternative to the product line, the existence of zero-price effect could significantly dampen the switch away from the free alternative, leading to a net negative effect, and so the results from the product line research may not directly translate to the freemium context.
In addition, the literature on the anchoring effect (e.g., [33]) would suggest that customers are likely to use the price of the premium alternative as an anchor to evaluate the value of the free alternative, mentally coding the difference between the price of the premium and the price of the free alternative (zero) as a "gain" in obtaining the free alternative. After the introduction of a higher-quality, higher-priced premium alternative, the anchor could shift from the existing premium alternative to the higher-priced premium alternative, leading to a larger gain in obtaining the free alternative and making the free alternative even more attractive. Under this scenario, the introduction of the higher-quality, higher-priced premium alternative could lead to decreased overall revenue as customers could shift from buying premium alternatives to obtaining the free version. This highlights why the freemium context requires a more nuanced analysis than what a direct application of product line research would suggest.
Moreover, other behavioral effects in the freemium context, hitherto untested in the product line context, could play an important role in the monopolistic setting we consider. Behavioral research in the context of choice set variations has shown that introducing a new alternative into a choice set can affect the choice probabilities of the original alternatives in two important ways. For example, as per the compromise effect ([32]), when a higher-quality, higher-priced premium alternative is introduced to the choice set, the existing premium alternative could become a compromise option between the higher-quality, higher-priced premium alternative and the free alternative, which could result in customers switching from the free alternative to the compromise option. This could lead to higher sales of the existing premium option and thus increase revenues, essentially moving customers from the free alternative to the premium alternative. Analyzing the case when a lower-quality, lower-priced alternative (with quality higher than the free alternative) is introduced to the existing product line, the newly introduced alternative could become the compromise option between the existing premium and free alternatives, and its sales could increase. If the extent of the cannibalization of the existing premium by this alternative is lower than the increase in revenue from customers switching from the free alternative to the newly introduced alternative, the new impact on revenues could still be positive. However, if the newly introduced alternative cannibalizes the existing premium alternative to a greater degree than the gains in revenues, total revenue could decrease.
In addition to the compromise effect, this setup could also make salient the attraction effect ([14]; [15]; [32]), whereby the existing premium alternative dominates the higher-quality, higher-priced premium alternative in one case and dominates the lower-quality, lower-priced alternative in the other case. It is conceivable that if the higher-quality alternative is priced close to the existing premium product, the newly introduced alternative could dominate the existing premium alternative. The existence of the attraction effect would make the dominating alternative (the newly introduced alternative) more attractive, and this could lead customers to switch from the free alternative and the existing premium option to the newly introduced alternative, leading to increased revenues. In the second case, if the newly introduced lower-quality alternative is very low on the quality dimension compared with the existing premium but priced close to the premium option, it could be dominated by the existing premium option and could lead customers to switch from the free option to the existing premium option, again leading to overall revenue increase. The zero-price effect could coexist with the these behavioral effects of compromise and attraction and could dampen the demand for the existing premium alternative under both conditions. It could also have an impact on the utilities determined for the various alternatives in the product line design, even in the absence of other behavioral effects.
In summary, the outcome of including another premium alternative to the offerings in the freemium model on the existing premium alternative's demand and overall revenue depends on the relative strengths of these opposing effects of the underlying mechanisms (see Table 1). There is a notable difference between the market expansion effects predicted by the product line literature (e.g., the quality association effect and the positive variety effect) and the choice set effects predicted by the behavioral literature (the compromise effect and the attraction effect). The market expansion effects predict that the brand and the product line as a whole are evaluated higher by customers. However, although more customers could be attracted to the brand, they are much more likely to be attracted to the free alternative because of the zero-price effect. In contrast, the compromise effect and the attraction effect target a specific alternative in the choice set. Therefore, even if the compromise effect and the attraction effect could be dampened by the zero-price effect, firms can still focus on making the premium alternative attractive to realize a positive impact on the demand for the premium alternatives.
Graph
Table 1. The Expected Impacts of Adding a Premium Alternative on Sales.
| Expected Impact on Sales |
|---|
| Underlying Mechanisms | Free Alternative | Existing Premium | Overall Revenue |
|---|
| Strategy 1: Adding a Higher-Quality, Higher-Priced Premium Alternative |
| Market expansion | | | |
| Variety effect (e.g., Berger, Draganska, and Simonson 2007) | Positive | Positive | Positive |
| Quality association effect (e.g., Heath, DelVecchio, and McCarthy 2011) | Positive | Positive | Positive |
| Cannibalization effect (e.g., Tversky 1972; Moorthy 1984) | Negative | Negative | Positive |
| Compromise effect (e.g., Simonson 1989) | Negative | Positive | Positive |
| Attraction effect (e.g., Huber, Payne, and Puto 1982) | Negative | Negative | Positive |
| Strategy 2: Adding a Lower-Quality, Lower-Priced Premium Alternative |
| Market expansion | | | |
| Variety effect (e.g., Berger, Draganska, and Simonson 2007) | Positive | Positive | Positive |
| Quality association effect (e.g., Heath, DelVecchio, and McCarthy 2011) | Negative | Negative | Negative |
| Cannibalization effect (e.g., Desai 2001; Tversky 1972) | Negative | Negative | Ambiguous |
| Compromise effect (e.g., Simonson 1989) | Negative | Negative | Ambiguous |
| Attraction effect (e.g., Huber, Payne, and Puto 1982) | Negative | Positive | Positive |
10022242918807170 Notes: The zero-price effect will attenuate the negative effect on the free PDF downloads and strengthen the positive effect on the free PDF downloads ([31]).
Our objective in this article, therefore, is to determine the outcome of adding the alternatives—either a higher-quality, higher-priced alternative or a lower-quality, lower-priced alternative—using a randomized field experiment. We analyze the data on sales at the aggregate title level obtained from the field experiment to determine the outcome and identify the mechanisms that could be at play (see Table 1) as well as analyze data at the individual customer level to confirm specific mechanisms at work that lead to the results.
National Academies Press is a nonprofit publisher of scholarly content on a wide range of topics in the areas of pure sciences, social sciences, and education. Motivated by its dual mission of dissemination and self-sustainability, NAP is committed to enhancing its reach to audiences all over the world while remaining self-sufficient. Most of NAP's sales revenue comes from its website, which began selling books in print format in 1996. Around 2003, NAP started selling the book titles as PDFs that could be directly downloaded from the website. The prices of the PDF versions were set at approximately 75% of the print book prices (see [16]). In addition, NAP also provided all the content of its book titles on the website in a lower-quality PDF version (called "open book") that could be browsed page by page but could not be downloaded as a full document. This eliminated any uncertainty about the content of any book a customer purchased. Customers could simply browse the content and then purchase the book in print format and/or PDF. More recently, under the directive of the board of the National Academies, NAP changed its business model to a freemium model, wherein the PDF version of any book title could be downloaded for free, while a paperback version of the title could be purchased online for a price. The free PDF content attracted significant traffic to the website through organic searches. Most customers tended to sample the open book for content and then download the free content, whereas a fraction switch to purchasing the premium paperback version. Although the website traffic increased due to the freemium model, revenue decreased significantly compared with the pre–freemium model period (when the PDF version was sold at a price).
Similar to any content seller, NAP's demand for the titles it sells is characterized by a long-tail distribution. A few titles tend to be bestsellers in any given year while the majority of titles have low demand, both in terms of free PDF downloads as well as paperback sales. Given the pressures on revenues in the context of the freemium model, NAP contemplated adding additional versions of the book titles to their current offerings of the free PDF version and the premium paperback version to increase revenue. Although the free PDF version has an acceptable image resolution, the paperback version has a much higher-quality resolution and production quality. To this, NAP wanted to add a hardcover version with superior production quality and a more durable cover and binding. However, the higher cost of producing a hardcover meant that the price for this format would be much higher than that of a paperback. NAP also considered adding an e-book version, with resolution quality somewhat similar to the PDF version but with additional features such as font size customization, notes features, and compatibility with Kindle and iPhone/iPad devices. Because customers' price expectations for electronic formats are generally lower than those of print formats, the e-book's prices are set lower than those of the paperback versions. It is in this context that NAP wanted to research the impact of extending the product line of premium products on the revenues of NAP—either through the higher-quality, higher-priced hardcover option or the lower-quality, lower-priced e-book option.
The NAP context is similar to the more well-known freemium model implementations (e.g., the New York Times, Spotify, Dropbox) on all the salient dimensions. The free alternative or the free PDF content generates a market for the paperback version, which generates the revenues to enable the nonprofit to be self-sustaining, much in the same way free content or free membership or free cloud storage generates revenues for the premium version for the New York Times, Spotify, or Dropbox, respectively. The free alternative allows the firm to generate a potentially large number of customers through word of mouth in all these examples. Spotify and the New York Times earn advertising dollars through the use of the free service and free content, while NAP earns utility through the dissemination of the content of the books in free PDF versions (which the academic authors value). The freemium models also range from being service subscription (Dropbox) to content subscription (Spotify, New York Times). Although the content purchase is in product form in the NAP context, the utility for users is derived from the repeated use of the book content (which is mainly academic, reference-type material) much in the same way that Spotify users or New York Times users derive utility from repeated use of the service. Although the time frame of using the free version and switching to the premium version may be longer in the other examples, in the NAP context a customer samples the free content to determine whether to continue using the free version or switch to the premium version in a short amount time. This is because other freemium models involve experience products, whereas the value of the content from NAP can be determined quickly.
There are two advantages of conducting this research in this context. The first advantage is that in the category of books, the product line varies only in its formats (i.e., the content remains the same across different formats). Therefore, the free and premium versions will differ only in their formats and prices, which are the attributes that we want to manipulate in the field experiment. Second, NAP can be considered a monopolist in the market for its specialized and authoritative content because it has the exclusive right to publish studies of the National Academies. This frees us from the potential omitted variable problem caused by unobserved competitive factors from other publishers. These two characteristics help ensure a clean experimental setting, which improves the validity of our results.
Next, we provide a detailed description of the formats to clarify the attributes of the products making up the freemium product line. The free alternative is the PDF version, which is a single PDF file with an image resolution lower than that of the existing premium version (the paperback), but with the advantage of being electronically transferable. The paperback version has all the advantages of a print copy: it can be leafed through quickly and can be stacked on a bookshelf for easy retrieval. The higher-quality, higher-priced alternative is the hardcover version, which has a thicker protective cover, a better-quality binding, and acid-free pages, all of which make the hardcover version more durable than the paperback version. The lower-quality, lower-priced alternative is the e-book version, which is optimized for e-reader devices and apps that offer a much better digital reading experience than a PDF. The e-book version allows readers to resize fonts, bookmark pages, make notes, highlight specific passages, and save selected text. The e-book version also automatically scales for the content, whereas the PDF version has a fixed width and height. The paperback and hardcover versions can be viewed as vertically differentiated, as are the PDF version and the e-book version. In comparing the PDF with the paperback on features such as image quality, the formats are vertically differentiated—the paperback being clearly better—but on other dimensions they could be horizontally differentiated. Nevertheless, customers could choose between the two formats given the strength of their preferences for each and the products' relative prices. Regardless, the important question in our context is how the addition of the new formats affects the demand for the original existing premium product and overall revenue.
The field experiment includes a total of 2,051 randomly selected book titles that NAP was already selling in paperback on its website before the start of the experiment; the content of these titles was also available for free download in PDF version on its website. Given that the demand distribution (PDF downloads as well as paperback sales) is long tail and NAP wanted the results to be generalizable to all its titles, we sampled titles from the entire distribution, including bestsellers. Our design accounts for the heterogeneity in the titles, as we describe next. Our field experiment began on January 10, 2016, when all new formats were introduced on the same day, and lasted for 32 weeks until August 20, 2016. Our data set covers the 32 weeks before and after the start of the experiment, totaling 64 weeks.
Because the prices and the popularity (proxy for potential demand) of the titles vary significantly, we use a restricted randomized design that uses the price of the paperback version as one blocking variable with three levels (low, medium, and high) and title popularity as the other blocking variable with three levels (low, medium, and high). This creates nine cells, within which, the titles are similar on the two blocking variables. According to the price distribution of the paperback version, the price level is defined as "low" if it is among the lowest 25% of prices (in our setting this lower quartile has a price ≤$29), as "high" if it is among the highest 25% of prices (in our setting, this higher quartile has a price > $47.25), and as "medium" if the price falls between these (i.e., $29 < price ≤ $47.25). Popularity is measured by the count of page views of a book title's catalog page (webpage where the details of the titles are provided), using the number of views as a metric of popularity among customers. The threshold for each level of popularity is determined in a similar way on the basis of the distribution of the metric: popularity is defined as low if counts are less than or equal to 136 (the bottom 25%), as high if counts exceed 953 (the top 25%), and as medium if the counts are between the two (136 < counts ≤ 953; the middle 50%). In summary, cells 1–9 contain 269, 208, 38, 218, 557, 250, 40, 250, and 221 titles, respectively.
In each cell, titles are randomly assigned to one of the following three conditions: the treatment condition, TH, in which a hardcover version was added to the existing product line; the treatment condition, TE, in which an e-book was added to the existing product line; and the control condition, C, in which no changes were made to the existing product line. As we show in Figure 1, Panel A, the existing product line before the start of the experiment comprised two versions: the free version (the PDF version for download) and the premium version (the paperback version at a specific price). At the start of the experiment, no changes were made to control condition (C) titles; a hardcover version was added for the TH titles (see Figure 1, Panel B); and an e-book version was added to the existing product line for the TE titles (see Figure 1, Panel C). The presentation order of the new premium version (Figure 1) was also randomized within the treatment condition. Overall, 716 titles were assigned to the control condition, 668 titles were assigned to the hardcover condition, and 667 titles were assigned to the e-book condition (see Table WA1 in the Web Appendix).
Graph: Figure 1. Examples of titles under control and treatment conditions.
The prices of the titles paperback were kept at their existing levels, with the corresponding PDF versions being available for free. One objective of our research in varying the relative prices of the new versions is to examine when paperback sales increase. For the e-book version, in line with the pricing practices of publishing industry ([ 5]; [35]), the titles were priced lower than the price of paperback version. We generated an equally spaced sequence that consisted of 20 levels ranging from 25% and 80% of the price of the titles in the paperback version. Similarly, hardcover prices were set between the range of 160% and 790% of the paperback version prices, which covers the usual range of hardcover prices encountered in NAP as well as in the publishing industry. We also chose the wide range of the specific pricing levels to cover all the possibilities that NAP was considering with regard to the pricing strategy of e-book and hardcover versions. The e-book or hardcover prices were displayed in absolute dollar terms on the website. Finally, the prices for paperback, e-book, and hardcover versions of all titles were held constant throughout the experiment.
Our data set contains book title characteristics including the price of different versions, the copyright year, the number of pages, and the dummy variables identifying the different experimental condition. We also observe the sale quantities and the revenues of paperback, e-book, and hardcover versions; the number of free PDF downloads; and the number of email campaigns.[ 5]Table 2 shows the summary statistics for the data, and Table 3 compares the mean value of variables across the control and treatment conditions.
Graph
Table 2. Summary Statistics.
| Variables | Mean | SD | Min | 25% | 75% | Max |
|---|
| Paperback price ($) | 39.28 | 17.96 | 5.00 | 29.00 | 47.25 | 475.00 |
| Copyright year | 2008 | 4 | 2000 | 2004 | 2011 | 2015 |
| Number of pages | 181 | 121 | 20 | 96 | 236 | 1,357 |
| Before Experiment 32 Weeks (Average per Title) |
| Free PDF quantity | 188.47 | 844.25 | 4 | 33 | 149 | 26,066 |
| Paperback quantity | 1.77 | 26.96 | 0 | 0 | 0 | 1,035 |
| Paperback revenue ($) | 47.57 | 464.91 | 0 | 0 | 0 | 13,602 |
| Total revenue ($) | 47.57 | 464.91 | 0 | 0 | 0 | 13,602 |
| Email campaigns | .05 | .33 | 0 | 0 | 0 | 8 |
| Experiment Period 32 Weeks (Average per Title) |
| Free PDF quantity | 173.32 | 699.63 | 5 | 34 | 134.5 | 23,961 |
| Paperback quantity | 1.57 | 25.71 | 0 | 0 | 0 | 837 |
| Paperback revenue ($) | 42.38 | 558.09 | 0 | 0 | 0 | 21,600.77 |
| Total revenue ($) | 43.69 | 568.89 | 0 | 0 | 0 | 22,061.87 |
| Hardcover quantity | .01 | .09 | 0 | 0 | 0 | 1 |
| Hardcover revenue ($) | 1.19 | 15.73 | 0 | 0 | 0 | 357.00 |
| E-book quantity | .16 | 1.06 | 0 | 0 | 0 | 17 |
| E-book revenue ($) | 2.82 | 20.94 | 0 | 0 | 0 | 461.10 |
| Email campaigns | .06 | .33 | 0 | 0 | 0 | 6 |
Graph
Table 3. Comparing Mean Values Across Conditions.
| Variables | All Titles | Hardcover Condition | E-Book Condition | Control Condition |
|---|
| Paperback price | 39.28 | 40.51 | 38.86 | 38.53 |
| Copyright year | 2008 | 2007 | 2008 | 2008 |
| Number of pages | 181.09 | 187.62 | 180.19 | 175.83 |
| Before Experiment 32 Weeks (Average per Title) |
| Free PDF quantity | 188.47 | 166.83 | 248.08 | 153.13 |
| Paperback quantity | 1.77 | 1.58 | 2.93 | 0.86 |
| Paperback revenue ($) | 47.57 | 49.90 | 67.57 | 26.77 |
| Total revenue ($) | 47.57 | 49.90 | 67.57 | 26.77 |
| Email campaigns | .05 | .04 | .07 | .03 |
| Experiment Period 32 Weeks (Average per Title) |
| Free PDF quantity | 173.32 | 162.36 | 205.08 | 153.96 |
| Paperback quantity | 1.57 | 1.43 | 2.79 | .57 |
| Paperback revenue ($) | 42.38 | 41.34 | 64.80 | 22.48 |
| Total revenue ($) | 43.69 | 42.53 | 67.62 | 22.48 |
| Email campaigns | .06 | .05 | .06 | .06 |
As Table 2 shows, there is a positive number of PDF downloads for every title (ranging from 4 to 26,066) while the paperback sales are positive for a much smaller number of titles. This is the characteristic of freemium models, in which the demand for free products are much higher than that for the premium version. For example, for Dropbox the percentage of premium users in its customer base is around 4%.
The average number of paperback sales (quantity) per title before the start of experiment was 1.77 and dropped to 1.57 during the experimental period. Our DID model accounts for the potential decreasing trend over time (due to seasonal effects, NAP typically has higher sales in the last four months of a calendar year, which was part of our pre-experiment period). Along with the drop in paperback sales quantity, both paperback revenue and total revenues decreased during the experiment. Also consistent with the seasonal effects, the free PDF downloads declined from 188.5 to 173.3 over time.
In addition, the hardcover version generated average revenue of $1.19 per title under the hardcover treatment condition. This primarily reflects the very low sales quantity of the hardcover version. The e-book version generated average revenue of $2.82 per title under the e-book treatment condition. We note that there is a great amount of heterogeneity in sales quantity and revenues across book titles, as their standard deviations are much larger than the means, which explains our decision to use block design by dividing book titles into nine cells to reduce the variance.
Comparing the sales performance across titles under the control and treatment conditions, we highlight several notable patterns in Table 3. First, the sales quantity and revenues of the paperback version decrease across all three conditions from the pre-experimental period to the postexperimental period. Therefore, it is essential to control for the time trend in our analysis. Second, the total revenue of titles under the e-book condition increased slightly after the start of the experiment. Third, free PDF downloads increased slightly for titles under the control condition but decreased for titles under the treatment conditions. This provides model-free evidence that adding a new version (hardcover version or e-book version) may lead to fewer downloads of the free PDF version.
Finally, there may seem to be differences across treatment and control conditions in terms of quantities downloaded and sold before the start of the experiment. For example, titles under the e-book condition had the most downloads of the PDF version and the highest sales quantity and revenues of the paperback version among the three conditions before the start of experiment. However, in conducting a one-way analysis of variances to compare the mean values of the three conditions before the start of experiment, we find no statistical difference in means at a significance level of.05 (see Table WA2). This result confirms that our random assignment ensures that the titles are similar across different experimental conditions before the experiment started.
To analyze the effect of adding a hardcover version to the freemium product line, we apply the DID method to the data of titles across the two periods of pre-experimental and experimental durations under the control and hardcover conditions. Similarly, we estimate the effect of adding an e-book version by using the data for the titles under the control and e-book conditions. The DID model enables us to account for unobserved factors that are common to all book titles, such as economic shocks, seasonal effects, and firm-level marketing campaigns that are common to all book titles. It also eliminates the impact of the fixed effects of the titles because we take differences across pre-experiment and experimental periods. If these two factors are left unaccounted for, they can seriously invalidate the results from any market response model.
Our dependent variable is the sales quantity of the paperback version of a book title, because the paperback version is the only premium (nonfree) format present in all conditions and the dominant revenue source. We combine the DID model with a negative binomial distribution because the dependent variable, sales quantity of paperback, follows a long right-tail distribution with nonnegative values. Using a negative binomial distribution also diminishes the impact of variances in sales quantity across titles while accounting for the fact that title demand variances differed from the means. For book title i in period t,
E[Yit=y|Xit,ϵit]=exp{Xitβ+ϵit}, t=0,1 ,1
and
Xitβ=α+θ⋅Treati+γ⋅Postt+δ⋅(Treat×Post)it+ρ⋅Zit+Wiλ ,2
where is the dependent variable, sales quantity of paperback version. There are two periods: denotes the period before the experiment started, and denotes the period during the experiment. if title i is assigned to the treatment condition; if period t happens after the start of experiment; measures the number of email campaigns for title i in period t; and represents the book title characteristics, including the copyright year and the number of pages. Because the randomized block design captures the variations on the dimensions of price and popularity, we use the copyright year and the number of pages to control for additional variations among book titles that have not been captured by the randomized block design. The copyright year describes the recency of publication, and the number of pages captures the production cost of the paperback version.[ 6]
In addition, is an idiosyncratic error term that captures all determinants of that our model omits, with following a one-parameter gamma distribution . Furthermore, we allow the error terms of the same title to be correlated before and after the experiment started (i.e., ). This corrects for a potential bias in the estimation of standard errors due to serial correlation.
For parameters, θ is the treatment condition–specific effect that captures the average permanent differences between the control and treatment conditions. If our random assignment was successful, the estimate of θ should not be significantly different from zero. γ accounts for the time trend during the sample period that is common to all titles; most importantly, the DID estimator is δ, which captures the true effect of introducing the e-book or hardcover version on the sales quantity of the paperback version:
δ^=(log{E[Y¯1treat]}−log{E[Y¯0treat]}) −(log{E[Y¯1control]}−log{E[Y¯0control]}) .3
While the magnitude of is not equal to the marginal effect of the treatment, the sign of δ and its marginal effect are identical. That is, being positive indicates that the introduction of a new version has a positive impact on paperback sale quantity.
Table 4 presents our DID model results estimating the effects of ( 1) introducing a hardcover version to the product line on the sales quantity of paperback versions and ( 2) introducing an e-book version to the product line on the sales quantity of paperback version. In Table 4, Columns 1 and 2 show the estimates of coefficients and marginal effects at means (MEM) for titles under the control condition and the hardcover condition, respectively, and Columns 3 and 4 show the estimates of coefficients and MEM for titles under the control condition and the e-book condition, respectively.
Graph
Table 4. The Effects of Introducing Hardcover or E-Book Version on Paperback Sales Quantity.
| Hardcover Versus Control | E-Book Versus Control |
|---|
| (1) | (2) | (3) | (4) |
|---|
| Variables | Coefficient | MEM | Coefficient | MEM |
|---|
| Treat | −.511 | −.338 | −.483 | −.383 |
| (.380) | (.297) | (.355) | (.313) |
| Post | −.734** | −.495* | −.550* | −.441 |
| (.306) | (.292) | (.282) | (.274) |
| Treat × Post | 1.207** | 1.156 | 1.795*** | 2.582 |
| (.551) | (.931) | (.688) | (2.262) |
| Email campaigns | 1.649*** | 1.089** | 2.878*** | 2.282** |
| (.502) | (.503) | (.657) | (1.038) |
| Copyright year | .135*** | .0893*** | .111** | .0877** |
| (.0468) | (.0281) | (.0432) | (.0375) |
| Number of pages | .344** | .227*** | .0719 | .0570 |
| (.138) | (.0768) | (.0930) | (.0617) |
| Constant | −1.810** | — | −1.294* | — |
| (.872) | — | (.702) | — |
| Observations | 2,768 | 2,768 | 2,766 | 2,766 |
| Log-likelihood | −2,243 | — | −2,321 | — |
| AIC | 4,502 | — | 4,658 | — |
| BIC | 4,549 | — | 4,706 | — |
| Pseudo-R2 | .0406 | — | .0560 | — |
- 20022242918807170 *p <.1.
- 30022242918807170 **p <.05.
- 40022242918807170 ***p <.01.
- 50022242918807170 Notes: AIC = Akaike information criterion; BIC = Bayesian information criterion. Robust standard errors are in parentheses.
First, the estimate for the dummy variable Treati is insignificant, therefore indicating that titles assigned to the control and hardcover conditions are comparable. This confirms that our random assignment has been successful. Second, the sales quantity of the paperback version shows a decreasing trend over time as the estimate for dummy variable Postt is negative and statistically significant. This indicates a seasonal effect or decreasing trend for NAP titles in general. More interestingly, the coefficient estimate for Treat × Post is positive and significant after controlling for the aforementioned effects. This shows that introducing a new premium version—the hardcover version, in this case—increases demand for the paperback version. More precisely, introducing the hardcover version can increase sales of the paperback version by 1.2 copies per title, which can be translated into an increase in total sales of 772 paperback copies. In addition, the introduction of hardcover version is able to offset the negative time effect, as the marginal effect's magnitude of Treat × Post is greater than that of Postt. Finally, as we expected, marketing titles using email campaigns increased paperback sales; book titles that were published more recently and with more pages had higher paperback sales.
When the e-book version is added to the freemium product line, we find similar results. First, there is no significant difference between titles under the control condition and those under the e-book condition. Second, the sales quantity of the paperback version decreased over time. Furthermore, introducing the e-book version to the product line approximately tripled the average sales quantity of the paperback version. In particular, the sales quantity of the paperback version increased by 2.6 copies per title as a result of the introduction of the e-book version, which can be translated into an increase in total sales of 1,722 paperback copies. In addition, email campaigns for book titles significantly increased paperback sales. Moreover, book titles published more recently had higher paperback sales.
As we have indicated, the demand distribution for an NAP title follows a long tail. Given that a few bestsellers could make up a significant portion of revenue for the firm in a given year, our sampling frame included all those bestsellers to ensure that the results of the analysis were representative of all titles that NAP carries. The use of the DID model already accounts for the fixed effects of each title. In addition, our use of the negative binomial distribution specification in the DID model ensures that these demand variations do not skew the DID results. Yet there might still be a concern that the estimated positive effect is driven by a few titles that have the largest quantity of paperback sales (the blockbusters).
We address this concern by applying our DID analysis to ( 1) the sample without blockbuster titles, ( 2) the 500 subsamples constructed by randomly drawing 90% of all titles, and ( 3) the 500 bootstrap samples. We find consistent results from all robustness checks. We identified the blockbuster titles on the basis of the distribution of paperback sales quantity (see Table WA3 in the Web Appendix). After removing two blockbuster titles—one title under the hardcover condition and one title under the e-book condition—the mean values across the three conditions become much closer and the standard deviations of titles under the hardcover condition and the e-book condition become much smaller. Furthermore, we evaluate the pre-experiment time trends for the control and treatment customers through a graphical inspection: Figure 2 suggests that the time trends of the paperback quantity of titles under the treatment and control conditions were similar before the start of experiment, which thus supports the validity of identification for treatment effect using the DID approach.
Graph: Figure 2. The graphical test of common-trend assumption (blockbuster titles excluded).
After removing the blockbuster titles, we find that adding a hardcover version has a significant positive impact on paperback sales quantity. In particular, introducing the hardcover version can increase sales of the paperback version by.5 copies per title, which can be translated into an increase in total sales of 335 paperback copies. Similarly, we find that introducing the e-book version to the product line can increase sales of the paperback version by.4 copies per title, which can be translated into an increase in total sales of 287 paperback copies (see Tables WA4, WA5, and WA6 in the Web Appendix).
To obtain a better understanding of the influence of introducing new versions to the freemium model, we also analyze the effect of extending the product line on paperback sales quantity using subsamples defined by paperback price, popularity, and copyright year. We find that the positive impact on paperback sales is stronger for titles that are more popular or lower in price. In addition, adding a hardcover version increased the paperback sales of titles published more recently while adding an e-book version increased the paperback sales of titles published earlier (see Table WA7, WA8, and WA9 in the Web Appendix).
We also investigate how price levels of the new premium versions affect the increase in paperback sales quantity. In Table 5, the estimates show that introducing the hardcover increased the sales of paperback. The increase is statistically significant when the hardcover price is set at 160%: 230% and 440%: 790% of the paperback price.
Graph
Table 5. The Influence of Hardcover Pricing on Paperback Sales Quantity.
| (1) | (2) | (3) | (4) | (5) |
|---|
| Variables | 160%–230% | 230%–300% | 300%–370% | 370%–440% | 440%–790% |
|---|
| Hardcover | −1.269*** | −1.349*** | .125 | .294 | −.713 |
| (.370) | (.408) | (.433) | (.523) | (.510) |
| Post | −.583** | −.576** | −.572** | −.602** | −.557** |
| (.264) | (.257) | (.256) | (.263) | (.251) |
| Hardcover × Post | .800* | .424 | .345 | 1.305 | 2.109** |
| (.419) | (.442) | (.469) | (.815) | (.999) |
| Email campaigns | 1.710*** | .952*** | .907*** | 1.686*** | 1.056*** |
| (.395) | (.248) | (.253) | (.607) | (.183) |
| Copyright year | .0866 | .0828 | .114** | .0992* | .0842 |
| (.0562) | (.0556) | (.0538) | (.0568) | (.0592) |
| Number of pages | .148 | .162 | .137 | .165 | .124 |
| (.118) | (.111) | (.116) | (.131) | (.109) |
| Constant | −1.216 | −1.183 | −1.385 | −1.330 | −1.145 |
| (.849) | (.833) | (.847) | (.880) | (.850) |
| Observations | 2,744 | 2,744 | 2,744 | 2,744 | 2,744 |
| Log-likelihood | −6,850 | −6,850 | −6,850 | −6,850 | −6,850 |
| AIC | 13,780 | 13,780 | 13,780 | 13,780 | 13,780 |
| BIC | 14,017 | 14,017 | 14,017 | 14,017 | 14,017 |
- 60022242918807170 *p <.1.
- 70022242918807170 **p <.05.
- 80022242918807170 ***p <.01.
- 90022242918807170 Notes: AIC = Akaike information criterion; BIC = Bayesian information criterion. Robust standard errors are in parentheses.
In Table 6, the results of titles under the e-book and control conditions suggest that the introduction of the e-book version increased paperback sales. The increase was statistically significant when the e-book price was set at the level of 45%: 55% and 65%: 80% of the paperback price. We note that the sales quantity of the paperback version increased when the e-book price was high and, thus, close to the paperback price. We provide additional insights into this result in the next section, in which we examine the underlying mechanisms for the results.
Graph
Table 6. The Influence of E-book Pricing on Paperback Sales Quantity.
| (1) | (2) | (3) | (4) | (5) |
|---|
| Variables | 25%–35% | 35%–45% | 45%–55% | 55%–65% | 65%–80% |
|---|
| E-book | −1.100*** | −.465 | .168 | −.0327 | −1.305*** |
| (.320) | (.461) | (.400) | (.521) | (.368) |
| Post | −.570** | −.539** | −.535** | −.579** | −.626** |
| (.253) | (.246) | (.267) | (.254) | (.280) |
| E-book × Post | .344 | .00466 | 2.592*** | .0586 | 1.031*** |
| (.357) | (.556) | (.827) | (.616) | (.396) |
| Email campaigns | .970*** | .865*** | 2.764*** | 1.052*** | 2.694*** |
| (.257) | (.200) | (.825) | (.280) | (.695) |
| Copyright year | .0917 | .0841 | .0898 | .0667 | .0948* |
| (.0558) | (.0545) | (.0566) | (.0545) | (.0564) |
| Number of pages | .147 | .105 | .0650 | .167 | .196 |
| (.114) | (.107) | (.0890) | (.124) | (.123) |
| Constant | −1.233 | −1.110 | −1.133 | −1.065 | −1.357 |
| (.845) | (.814) | (.802) | (.829) | (.869) |
| Observations | 2,758 | 2,758 | 2,758 | 2,758 | 2,758 |
| Log-likelihood | −6,891 | −6,891 | −6,891 | −6,891 | −6,891 |
| AIC | 13,861 | 13,861 | 13,861 | 13,861 | 13,861 |
| BIC | 14,098 | 14,098 | 14,098 | 14,098 | 14,098 |
- 100022242918807170 *p <.1.
- 110022242918807170 **p <.05.
- 120022242918807170 ***p <.01.
- 130022242918807170 Notes: AIC = Akaike information criterion; BIC = Bayesian information criterion. Robust standard errors are in parentheses.
We also use our model to investigate the impact of extending the product line on the total revenues for NAP. Given that the revenues follow a long right-tail distribution, which cannot be appropriately captured by the normally distributed error terms, we ran a DID model with the negative binomial distribution as in Equations 1 and 2. The results in Table 7 show that the total revenues increase under both the hardcover treatment condition and the e-book treatment condition.[ 7]
Graph
Table 7. The DID Estimates for the Effects of Introducing Hardcover or E-Book Version on Total Revenues.
| Hardcover | E-Book |
|---|
| (1) | (2) |
|---|
| Full sample | .859** | 1.246** |
| (.403) | (.514) |
| Paperback price: low | 2.427*** | 5.185*** |
| (.483) | (1.033) |
| Paperback price: medium | −.148 | .183 |
| (.482) | (.526) |
| Paperback price: high | 5.185*** | −.195 |
| (1.033) | (.339) |
| Popularity: low | −.496 | 1.052 |
| (1.514) | (1.361) |
| Popularity: medium | −.00523 | −.683 |
| (.509) | (.616) |
| Popularity: high | .653* | .917** |
| (.393) | (.379) |
| Copyright year: 2000–2004 | .378 | 2.046*** |
| (.624) | (.578) |
| Copyright year: 2005–2010 | −.0802 | 1.399 |
| (.586) | (.989) |
| Copyright year: 2011–2015 | 1.172*** | .441 |
| (.407) | (.419) |
- 140022242918807170 *p <.1.
- 150022242918807170 **p <.05.
- 160022242918807170 ***p <.01.
- 170022242918807170 Notes: Robust standard errors are in parentheses.
The DID results show that introducing a new version, either the hardcover or e-book version, to the product line of book titles has a positive impact on the sales quantity of the existing premium version (the paperback version) as well as overall revenue. There could be several explanations as presented in Table 1 for these results. For example, the market expansion during the experiment duration could increase paperback sales. With the control group acting as a control for seasonality, one could argue that the market expansion is due to the addition of new versions leading to increased traffic (as a result of the quality association effect or variety effect), which could affect the demand under the experimental conditions more positively than in the control condition. Indeed, the DID analysis on free PDF downloads shows an increase under the treatment conditions of hardcover and e-book, showing that market expansion effects exist. However, this effect could be confounded with the compromise effect in the hardcover case and the attraction effect in the e-book case. Therefore, to investigate the underlying behavioral mechanisms cleanly, we must formally control for this confound. In particular, we use the individual-level choice analysis, which we discuss next.
The increase in demand for the paperback version under the hardcover treatment condition can be explained by the compromise effect ([32]). The addition of the higher-quality and higher-priced hardcover version to the already existing paperback option renders the paperback version as the compromise option in the choice set. A survey of NAP's customers showed (see [20], p. 105) that the image quality of the paperback version is generally perceived to be of higher quality than the PDF; the mean ratings of overall quality on a nine-point scale (1 = "low," and 9 = "high") were 7.81 (SD = 1.3) for paperback and 6.11 (SD = 1.8) for PDF, positioning the paperback version quality (and price) between that of the PDF version and the hardcover version. Thus, the compromise effect predicts that adding the hardcover version will increase the choice probability of the paperback version because the paperback version becomes a middle option in the product line of hardcover, paperback, and PDF versions.
The situation with respect to the e-book treatment condition is slightly more complex. On the one hand, e-books are generally cheaper than paperback books and are perceived to be of lower quality by the majority of NAP customers. From this perspective, an e-book would become the compromise option once added to the existing choice set of the free PDF version and the more expensive paperback version. Because the e-book version was not offered prior to the experiment, this compromise effect will be confounded with the baseline quality of the e-book and, thus, cannot be separately identified. Furthermore, and more importantly, this compromise effect does not explain the change in paperback sales, which is positive.
Instead, with regard to the change in sales quantity of paperback titles under the e-book treatment condition, we argue that, in specific situations, the main underlying driver would be the attraction effect ([14]; [15]; [32]). E-books are commonly sold at noticeably lower prices than paperback books,[ 8] because a significant number of consumers consider the quality and costs of a paperback title to be higher.[ 9] In this context, and given consumers' expectations, if an e-book is priced too closely to its paperback counterpart, some consumers may find the e-book price "unfairly high" even if the price is still technically lower than that of the paperback. In this situation, these consumers may perceive the e-book option to be dominated by the existing paperback option. This generates the attraction effect, whereby the introduction of a dominated option increases the attractiveness of the dominating one. When an e-book is introduced with a relatively high price, then, the sales of the paperback version can increase because of this attraction effect.
Finally, given that one of the options in the product line is free PDF, we expect the presence of zero-price effect ([31]) as the free PDF may play a special role in influencing customers' choices. We cannot identify the zero-price effects from our study. However, because we expect the zero-price effect to have a negative impact on the demand for the paperback option in both the treatment conditions, thus countering the impacts of the compromise effect and the attraction effect, we argue that our tests in the main results are conservative tests in showing the evidence for these effects. We leave the estimation of zero-price effects in such contexts for future research.
To empirically examine the extent of the compromise effect and the attraction effect, we use the individual-level purchase data to analyze customers' choice decisions between different versions. We use a multinomial logit model for this analysis. Each choice incidence corresponds to one customer purchasing from a choice set consisting of {PDF, paperback, hardcover} or {PDF, paperback, e-book}. In other words, we analyze the choice among different versions, conditional on the customer purchasing or downloading the book. We model this conditional choice for two reasons. First, we do not observe the nonpurchase incidences, in which a customer browsed through the different versions but decided not to buy. Second, as discussed previously, when a new version is introduced, there could be a market expansion effect that lifts the utility of all options relative to the outside option. Modeling the conditional choices controls for this market expansion effect and helps in identifying the compromise effect and attraction effect, which change only the relative appeal of different versions.
Overall, there are 742,991 choice incidences; in most of these, customers chose the free PDF. Moreover, customers chose the paperback version in 1,024 incidences before the experiment and 1,049 incidences during the experimental period. After the introduction of new versions, customers chose the hardcover version in only six choice incidences, whereas they choose the e-book version in 115 incidences. In addition, we observe 338 choice incidences in which customers purchased multiple copies of the same title in paperback version. There is no incidence in which customers purchase multiple copies of the same title in hardcover, e-book, or PDF version. Finally, we observe only two cases in which a customer purchased different versions of the same title at the same time. Therefore, for modeling purposes, we consider the purchases of different versions of the same title as separate choice incidences. In each choice incidence, a customer purchases one version of one title.
In choice incidence i across customers and time, the customer's utility of choosing version j, is
uij=vij+ϵij=αj+γ⋅Dijtreat+δ⋅priceij+ϵij.4
In this equation, is a dummy variable that equals 1 if ( 1) the title in choice incidence i is assigned to the treatment condition, ( 2) the choice incidence i happens after the experiments started, and ( 3) version j is paperback. All three conditions must be satisfied for the variable to be 1. As a result, captures the change in the utility of the paperback version by adding hardcover or e-book version to the product line. In addition, priceij is the purchase price of version j of treplacitle at choice incidence i. Finally, is the error term clustered by customers.
For the hardcover treatment group, we expect that the compromise effect exists, and thus we expect to be positive. For the e-book treatment group, we expect that the attraction effect exists if the e-book price is close enough to the paperback price to allow the paperback version to dominate the e-book version. In this model setup, the attraction effect for that subset of book titles will be loaded onto the parameter at the entire treatment group level, and thus we also expect to be positive. To identify the attraction effect with better precision, we also extend the model to separate the high-price situations of the e-book from the non-high-price situations. For titles under the e-book condition, we further add a dummy variable, , which equals 1 if equals 1 and the price level of the e-book version is relatively high. We set a threshold level of 65%; that is, the dummy variable is set to 1 if the price of the e-book version is higher than 65% of the price of paperback price, and 0 otherwise.
uij=vij+ϵij=αj+γ⋅Dije-book+λ⋅Dijhigh.e-book+δ⋅priceij+ϵij.5
Customers perceive the e-book to be more similar to the paperback version than the PDF version when the e-book price is high and close to the paperback price, creating dominance over the e-book version by the paperback version. Because we expect the attraction effect to be present, we expect the coefficient of dummy variable to become smaller in magnitude and insignificant after adding the dummy variable . Furthermore, and more importantly, we expect the coefficient for to be positive and significant.
For each choice incidence, we use free PDF as the baseline version. Assuming that follows a type I extreme value distribution, the choice probability function is given by
Pr(yi=j)=exp{vij}∑k=1Jiexp{vik},6
where is the number of total versions in choice incidence i. The log-likelihood function is then given by
In L(y|x; β) = ∑i=1N∑j=1Ji {dij⋅In Pr(yi=j)},7
where if and otherwise, N is the total number of choice incidences, and Ji is the number of product versions in choice incidence i.
Columns 1 and 2 of Table 8 present our estimation results of the multinomial logit model to identify the underlying factors. First, the estimates for dummy variable and are statistically significant and positive, indicating that the paperback version gains more preference as a result of the new premium version, which is consistent with our DID estimates. Drawing on the model specification in Column 1, introducing a hardcover version to the product line increase the logarithmic of the odds of purchasing a paperback relative to downloading a PDF by 16.2%. Drawing on the model specification in Column 2, introducing an e-book version to the product line increase the log odds of purchasing a paperback relative to downloading a PDF by 22.6%. Second, the coefficients for the paperback price are significantly negative, showing that customers are more likely to download a free PDF if the paperback prices are higher (the prices of hardcover and e-book versions are also higher). In general, the estimation results of the choice model are consistent with results of the aggregate analysis.
Graph
Table 8. Multichannel Logit Estimates for Identifying the Underlying Factors.
| Hardcover and Control Titles | E-Book and Control Titles Spec. I | E-Book and Control Titles Spec. II | All Titles Spec. I | All Titles Spec. II |
|---|
| Variables | (1) | (2) | (3) | (4) | (5) |
|---|
| Dhard | .162** | — | — | .179** | .179** |
| (.0751) | — | — | (.0736) | (.0736) |
| De-book | — | .226*** | .0809 | .167*** | .0229 |
| — | (.0634) | (.0786) | (.0629) | (.0783) |
| Dhigh.e-book | — | — | .344*** | — | .340*** |
| — | — | (.0973) | — | (.0972) |
| Price | −.0211*** | −.0194*** | −.0191*** | −.0204*** | −.0202*** |
| (.00261) | (.00204) | (.00205) | (.00184) | (.00184) |
| Constant (hardcover) | −7.294*** | — | — | −7.359*** | −7.383*** |
| (.535) | — | — | (.505) | (.505) |
| Constant (e-book) | — | −6.650*** | −6.657*** | −6.628*** | −6.633*** |
| — | (.106) | (.106) | (.104) | (.104) |
| Constant (paperback) | −5.050*** | −5.190*** | −5.204*** | −5.094*** | −5.104*** |
| (.120) | (.0916) | (.0921) | (.0838) | (.0840) |
| Observations | 987,496 | 1,181,517 | 1,181,517 | 1,729,337 | 1,729,337 |
| Log-likelihood | −8,151 | −10,565 | −10,559 | −15,139 | −15,133 |
| AIC | 16,309 | 21,139 | 21,128 | 30,291 | 30,280 |
| BIC | 16,356 | 21,187 | 21,188 | 30,365 | 30,367 |
- 180022242918807170 *p <.1.
- 190022242918807170 **p <.05.
- 200022242918807170 ***p <.01.
- 210022242918807170 Notes: AIC = Akaike information criterion; BIC = Bayesian information criterion. Clustered standard errors are in parentheses.
Moreover, the results in Column 3 confirm our expectation. On the one hand, the coefficient for becomes insignificant after adding the new dummy variable and its magnitude changes from.226 (Column 2) to.0809 (Column 3). On the other hand, the coefficient for is positive and significant. Taken together, the results in Columns 2 and 3 are strong evidence that the increase of paperback sales quantity for titles under the e-book condition is due to the attraction effect.
Additional analyses support the robustness of these results to alternative specifications of choice model. First, we use a binary logit model to estimate the probability of purchasing a paperback version given the product line of book titles. Second, we use a nested logit model to relax the assumption of independence of irrelevant alternatives. We test two nest structures with data of titles under the e-book and control conditions: ( 1) free (i.e., PDF) versus nonfree (paperback and e-book) and ( 2) digital (PDF and e-book) versus nondigital (paperback) (see Tables WA10 and WA11 in the Web Appendix). Finally, we use an alternative specification of multinomial logit model, which allows us to include the data of all titles.
uij=αj+γ1⋅Dijhard+γ2⋅Dije-book+γ3⋅Dijhigh.e-book +δ· priceij+ϵij,8
where is for the hardcover condition and and are for the e-book condition.[10] The results are reported in columns 4 and 5 in Table 8.
In summary, our results consistently demonstrate that introducing a hardcover or e-book version has a positive impact on paperback sales quantity. Moreover, when a hardcover version is added to the product line, the choice probability of the paperback version increases relative to that of a PDF version. Consistent with the compromise effect, the paperback version becomes a compromise option in the choice set and thus becomes more attractive to customers. When an e-book version is added to the product line and its price is set close to the paperback price, the relative choice probability of the paperback version to that of a PDF version also increases. This is because the paperback version tends to dominate the e-book version, thus leading to an attraction effect.
Our analyses at the aggregate level and the individual customer level have confirmed that there is increased demand for the existing premium alternative as well as an increase in overall revenue when a new premium version (hardcover or e-book) is introduced. The aggregate analysis may seem to imply that the increased demand for the existing premium version, overall revenue, and the free version is due to the market expansion effect. However, the individual-level analysis shows that the increase in the sales of the existing premium version comes at the expense of the free version: the compromise effect plays a role when the higher-quality, higher-priced alternative is introduced, and the attraction effect plays a role when the lower-quality but similarly priced alternative is introduced. The confirmation of these effects in the context of product line extension research is a significant contribution because it highlights conditions and mechanisms under which a product line extension can be successful in increasing revenue. In the case of the freemium model, the results show that such an line extension strategy can even overcome the zero-price effect and lead to higher overall revenues if the new premium alternatives are appropriately priced. This is a significant contribution in the freemium context as this is the first time such strategies have been shown to have positive impact.
In the case of NAP, the results imply that an extended premium product line can be more attractive and can lead customers to the website as a result of the variety effect and, at the same time, increase paperback sales by moving customers from downloading the free PDF to purchasing the existing premium alternative. In addition, our subsample analyses using the aggregate data and DID model reveal that the aforementioned effects are more pronounced for book titles that are more popular or lower in price. These results make intuitive sense—the lower-priced paperbacks are more likely to be within consumers' threshold of willingness to pay for the titles, and thus the compromise effect or the attraction effect manifest themselves more easily than in the case of higher-priced paperbacks, which may be beyond many consumers' threshold of willingness to pay. Similarly, the more popular the titles are, the higher consumers' willingness to pay is, leading to significant compromise or attraction effects. The subsample analyses also highlight that adding a hardcover version has a greater impact on paperback sales of those titles published more recently. Given that the hardcover option becomes more salient for newer titles and people's willingness to pay could be higher for newer titles, it is easier for the compromise effect to be manifest under these conditions. In the case of older titles, this threshold could be lower, and the lower-priced e-book version becomes more salient, leading to the observed result. Overall, these results help NAP determine which titles to focus on for its initial rollout of the additional premium version.
If NAP were to introduce an additional version, which version should they introduce, and at what price? To help answer these questions, we performed a scenario analysis using the results of the DID models. As Table 9 shows, NAP could offer hardcover versions, but given that hardcover versions did not sell well in the experiment and that the impact of increase in demand for the paperback version is lower than when an e-book version is introduced, the hardcover option is not ideal. The e-book version, in contrast, sells more and, when priced closely to the paperback version, has a positive impact on paperback sales. Table 9 provides the impact of relative prices of the sales of e-books as well as the impact on paperback sales. When the e-book version is priced lower, its impact on paperback sales is neutral (or negative), resulting in a trade-off in the pricing decision. Taking the trade-offs into account, we find that pricing the e-book version at 70%–80% of the paperback price is optimal for NAP.
Graph
Table 9. The Impact of Pricing Level of New Versions on Revenues.
| Hardcover Condition | 160%−220% | 220%−300% | 300%−330% | 330%−360% | 360%−390% | 390%−440% | 440%−790% |
|---|
| % revenue (hardcover) | 0.26% | .25% | .00% | .30% | .00% | 1.57% | .00% |
| % revenue (paperback) | 1.38% | 3.24% | 6.22% | −9.00% | −6.96% | 8.90% | 3.58% |
| % revenue (total) | 1.64% | 3.49% | 6.22% | −8.71% | −6.96% | 10.47% | 3.58% |
| E-Book Condition | 25%–34% | 34%–40% | 40%–49% | 49%–54% | 54%–61% | 61%–70% | 70%–80% |
| % revenue (e-book) | .44% | .62% | .20% | .64% | .60% | .23% | 1.45% |
| % revenue (paperback) | 1.73% | −3.73% | 1.60% | −4.35% | −6.86% | 5.53% | 21.45% |
| % revenue (total) | 2.17% | −3.11% | 1.80% | −3.71% | −6.26% | 5.75% | 22.90% |
Although the results of our freemium study are set in the context of books, the findings should extend readily to other freemium implementations, as we discussed previously. Streaming video or audio services, such as YouTube or Pandora, are quite similar to the NAP context. Currently, the free version and the premium version of these services are differentiated on the basis of the presence of advertisements as an annoyance in the free version, with the quality, in terms of resolution, being almost the same between the two versions. One could conjecture that adding another premium version and differentiating the two versions in terms of resolution or speed (given the relaxation of net neutrality rules) could lead to similar compromise effects. Another context where extending the product line of premium versions could be fruitful is for software and online services, such as Skype or LinkedIn. Firms could differentiate the quality of the premium versions and the free version on the basis of the availability of different functions and features. Finally, the findings would also extend to contexts such as newspapers and other ad-supported content platforms (e.g., [17]; [29]; [34]). That is, our findings should apply to instances in which premium services dominate the free option on the quality dimension. We note, however, that in our context, we defined quality in terms of resolution and production quality. This dimension was also very salient for NAP's customer base, which consists mainly of academics and highly educated readers. However, this may not be the case for other types of content and other customer bases. For example, in the case of newspapers, quality dimension based on resolution and production quality may not be a salient differentiator. Instead, quality could be the portability of the content/device, which could enable easy and ubiquitous access. Thus, it is important for firms in different contexts to understand the dimensions on which they should vertically differentiate their premium versions on the basis of the dimensions customers consider salient (e.g., [22]). The magnitude of demand increase through such product line extensions is, of course, dependent on the particular context of the freemium model, which could be tested using a field experiment. Nevertheless, our research should be very encouraging for firms trying to increase revenues using their freemium model implementations.
With firms increasingly resorting to the freemium model to sell their products and services, it is important that research focus on understanding how firms can make this model profitable and sustainable. While extant research has focused on the design and pricing issues in such models, it has mainly been in the context of a single premium product. We extend this literature by focusing on the product line problem and use a field experiment to highlight how such strategies can lead to increased sales. From a substantive viewpoint, our results should be very valuable to both academics and practitioners. We not only show how firms can increase their sales in the freemium models but also uncover possible behavioral mechanisms that contribute to such increases. From the perspective of product line research, our research highlights the role of the behavioral mechanisms such as the compromise effect and attraction effect as alternative explanations for the demand patterns observed in the field experiment. As a testimony to the usefulness of our research, NAP has already implemented the introduction of e-books at suggested price levels and has been successful in increasing the purchase rates of paperbacks as well as in selling e-books.
We, however, need to point out several limitations of our work so that we do not oversell the identification of the behavioral mechanisms. After all, the mechanisms have been teased out of a study in which the major focus was on the aggregate data, and as such, the study was not specifically designed to rule out other possible effects. For example, extant research ([10]) has shown that the attraction effect is highly context dependent and may be contingent on how the items are presented to the customers. In addition, we show these effects to be present under conditions of low price and high popularity. Although we provide a possible reason for such boundary conditions, more research is needed to understand why these become boundary conditions. Such research may require field experiments specifically designed for such retail conditions (e.g., [28]).
Another limitation of this research is that we cannot clearly identify the extent to which the zero-price effect has dampened the compromise and attraction effects. This identification is needed to more fully understand how they affect the demand patterns in a product line setting. Ideally, future empirical studies would separately estimate the zero-price effect. In addition, there is a possibility that the customers in the field experiment viewed different experimental conditions. The experimental design could be improved in the future to avoid such problems. Moreover, our field experiment was conducted for one category; however, additional examinations may extend to a larger set of categories such as those of hedonic or luxury products, which might introduce other moderating effects. We leave these questions for future research. Our effort will hopefully encourage other scholars to research these issues.
Supplemental Material, DS_10.1177_0022242918807170 - Selling the Premium in Freemium
Supplemental Material, DS_10.1177_0022242918807170 for Selling the Premium in Freemium by Xian Gu, P.K. Kannan, and Liye Ma in Journal of Marketing
Footnotes 1 Area EditorPraveen Kopalle served as area editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242918807170
5 1NAP uses email campaigns directed at its customers to advertise its titles. Our data show that the number of email campaigns increased somewhat after the start of the experiment, but this was mainly in the control condition and not others, thus posing less of a threat to validity of our results.
6 2We also ran the DID model with the price of paperback versions as an additional explanatory variable. The results remain qualitatively the same.
7 3The results are robust when the blockbuster titles are excluded.
8 4Drawing on a web survey of prices at Amazon, Barnes & Noble, Books-a-Million and Apple during 2012 and 2013, [5] find that e-book prices are approximately 50% of the list prices of print books.
9 5A survey of 55 NAP customers who had purchased paperback/e-book or downloaded PDF copies in the previous year provided an overall quality rating on a nine-point scale (1 = "low," and 9 = "high") of 8.02 (SD = 1.7) for paperback and 7.1 (SD = 2.1) for e-books, with 40% of customers indicating that paperbacks had a higher overall quality than e-books. Eighty-two percent estimated the costs to be higher for paperbacks than for e-books.
6To account for heterogeneity, we have also run the random coefficient model allowing the parameter for to be random. The results are robust.
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By Xian Gu; P.K. Kannan and Liye Ma
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Record: 161- Selling to Barricaded Buyers. By: Chase, Kevin S.; Murtha, Brian. Journal of Marketing. Nov2019, Vol. 83 Issue 6, p2-20. 19p. 1 Diagram, 6 Charts. DOI: 10.1177/0022242919874778.
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- Business Source Complete
Selling to Barricaded Buyers
In business-to-government and business-to-business transactions, suppliers often have limited access to buyers during the buying process. The authors term these buyers "barricaded buyers." Despite the prevalence of barricaded buyers in practice, research has remained largely silent on the topic. Therefore, the authors combine insights from eight organizational purchasing case studies and individual interviews with signaling theory to advance a conceptual framework that highlights ways a supplier can increase its competitiveness (and, correspondingly, its selection likelihood) when selling to barricaded buyers. The framework reflects three distinct ways in which signaling occurs or influences the barricaded buying process: the seller signals to buyers (e.g., through novel solutions, explicit responding), the seller signals to competing sellers (e.g., through peacocking), and the buyer signals to sellers whose meaning is jammed (e.g., through supplier-specific capabilities and language). The framework invokes barricade restrictiveness as an important contingency variable that lends nuance to when the signaling activities are most likely to affect suppliers' competitiveness.
Keywords: barricaded buyers; business to government; case studies; organizational buying; request for proposals; signal jamming; signaling theory
From the issue date of the solicitation [request for proposal] and until a supplier is selected for contract award and the selection is made public, suppliers are not allowed to communicate for any reason with any state staff regarding the solicitation."
—[21], Sec 4.4.2 (emphasis added)
Business-to-government (B2G) suppliers can face significant restrictions on their access to buyers during the buying process. Indeed, a comprehensive review of the procurement practices of each of the 50 U.S. states finds that 48 substantially restrict suppliers' access to buyers during the buying process (the exceptions appear to be Alaska and Wyoming; for U.S. federal policy and 10 largest U.S. states, see Table 1; for the complete list, see Web Appendix A). Similar restrictions exist in the U.S. federal buying process and in those of several foreign countries (e.g., Australia, United Kingdom; see Web Appendix B). Although the procurement practices of firms in the business-to-business (B2B) sector are not publicly available, our private-sector interviews nevertheless indicate that similar restrictions arise in this sector as well (Table 2).[ 5] Given suppliers' difficulty in accessing them, we refer to the buyers bound by these restrictive policies as "barricaded buyers"—individuals involved in a purchasing process who are difficult to communicate with, usually during a certain period in the buying process.
Graph
Table 1. Barricaded Buying Language in U.S. Purchasing Policies (U.S. Federal and Top Ten Largest U.S. States).
| Municipality | Timing of Barricade | Policy Language | Source |
|---|
| U.S. Federal Government | Post-RFP | "From the issue date of this Solicitation until a Contractor is selected and a contract award is made, Respondents are not allowed to communicate about the subject of the RFP with any [organization employees] except:The Purchasing Department representative, any University Purchasing Officer representing the organization, or others authorized in writing by the Purchasing Office and organization representatives during Respondent presentations."
| State RFP Section 5.13 |
| California | Post-RFP | "All questions must be submitted in writing to the individual listed on the RFP." | State RFP Template |
| Texas | Post-RFP | "All communication with potential respondents should be made only through the Purchasing Department or other designated staff. The program staff should not have contact with potential respondents outside of pre-solicitation conferences. Likewise, a respondent that contacts someone other than authorized staff in regards to a solicitation maybe disqualified. While the Purchasing Staff or other designated staff may not be able to answer all of the technical questions asked by potential respondents, they will ensure that the information is provided to all potential respondents." | State Contract Management Guide, p. 71 |
| Florida | Post-RFP | "Stage Four of the Public Procurement Process is the Solicitation process. The purpose of this Stage is to publicly release the competitive solicitation to the vendor community and collect responses that are submitted by the date and time listed in the solicitation Timeline of Events. During stage four, the Procurement Officer will serve as the sole point of contact for the solicitation." | State Procurement Manual, Section 4.1 - Introduction to the Solicitation Process |
| New York | Post-RFP | "State Finance Law §§139-j and 139-k impose certain restrictions on communications between an agency and an offerer/bidder during the procurement process. An offerer/bidder is restricted from making "contacts" (defined in the law as communications intended to influence the procurement) from the date of the earliest notice of intent to solicit offers/bids through the date of the final award, and, if applicable, approval of the contract by the Office of the State Comptroller, to other than designated staff (as identified by the agency). The interval between these points is known as the "restricted period." Certain exceptions to this restriction are set forth in State Finance Law §139-j (3) (a). An example of an exception would be communication during contract negotiations." | State Procurement Manual - Section III Guidelines for Solicitations, Section F |
| Illinois | Post-RFP | "The sole point of contact in this Commonwealth for this RFP shall be the Issuing Officer [procurement officer]." | State RFP Template |
| Pennsylvania | Pre- and Post-RFP | "SOLICITATION CONTACT: The individual listed below shall be the single point of contact for this solicitation. Unless otherwise directed, Offerors should only communicate with the Solicitation Contact. The State/Agency/University shall not be held responsible for information provided to any other person." | State RFP Template |
| Ohio | Post-RFP | "During the evaluation phase, offerors may not initiate any communication with the evaluation team." | State Procurement Manual: Section 5.3.4 |
| Georgia | Post-RFP | "From the issue date of the solicitation and until a supplier is selected for contract award and the selection is made public, suppliers are not allowed to communicate for any reason with any state staff regarding the solicitation except through the issuing officer (or his/her designee) named in the solicitation. Prohibited communication includes all contact or interaction, including but not limited to telephonic communications, emails, faxes, letters, or personal meetings, such as lunch, entertainment, or otherwise." | State Procurement Manual, Section 4.4.2.: Restrictions on Communications |
| North Carolina | Post-RFP | "During the period of evaluation and prior to award, only the information provided in the tabulation is public record. Possession of offers, including any accompanying information submitted with the offers, shall be limited to persons in the agency who are responsible for processing and evaluating the offers and accompanying information. Vendor participation in the evaluation process shall not be permitted." | State Procurement Manual: Section 5.3 |
| Michigan | Post-RFP | "Once the solicitation is published, communication with vendors regarding the content of the solicitation must be limited. Strict State and vendor communication protocol is essential to ensure a fair and competitive purchasing environment. State and vendor communication protocol is as follows: The Solicitation Manager is the individual responsible for leading and facilitating all aspects of the solicitation process through contract award, and will serve as the point of contact for potential vendors during this period. Once the solicitation is released, all communication with vendors must be through only the Solicitation Manager." | State Procurement Manual: Section 7.3 |
Graph
Table 2. Examples of Barricaded Buying in the Private Sector.
| Job Title | Industry | Description of Barricaded Process |
|---|
| Global Sourcing Manager | Office equipment manufacturing | "They're [suppliers] allowed to talk to me [after they submit their RFP]. I do not like them talking to stakeholders. And if they've got any questions, my stakeholders—and a lot of times they do. They'll try to communicate straight with the stakeholder. A lot of times they know who they are right? And my stakeholders are really good about either asking me for permission. "Can I answer this?" Or they'll just say, "You know what? I need you to talk to Global Sourcing." And the reason for that is we don't want one supplier to get advantage over another one....I've trained my stakeholders to really use us, use the Global Sourcing as kind of the head of all of it [RFP Process]. I just don't want any influence one way or another." |
| Procurement Manager | Auto manufacturing | "Once the RFQ is issued up until that Q&A due date, that's when it's basically [procurement] probably could have daily communication with suppliers. And one thing we do is we request all Q&A to come through an electronic system, so like SAP Ariba has the ability for suppliers to ask questions through that portal. Before we were using that, we requested it to come through in email and then we would distribute it electronically as well." |
| Supply Chain Manager | Auto manufacturing | "The [initiator of the purchase] would create the budget and he would create the specs for what he wanted. We would agree on the supply base. I would typically host a bid meeting. We would, again this is for larger projects, we would bring those suppliers in. We would go over the terms and conditions, the bid sheet, how we wanted it broken down. In there, it also says that if you have questions you must submit those in writing [to me] so that when I answer it, I would reply to everybody....In the bid documents, it would say all of it [communication] needed to go through me." |
| Procurement Consultant | Varied | "So the rule's very clear. You can only talk to the RFP coordinator. Now, like I said, if they're an incumbent supplier and they're talking about other pieces of business or other projects, totally fine. But once the conversation turns towards the RFP that's in flight, they're knocked out of that RFP process....That's a pretty general rule and that is written down on the RFP itself. So the document does contain that language, so it's made very clear. Usually, if it's a physical document like a Microsoft Word file that's sent, it's highlighted with bold font to say, 'Hey, this is how you need to conduct yourself during this process.'" |
| Sourcing Manager | Construction supply | "I try to keep all of the communication through the RFP tool because I kind of feel like it's part of the record. So if there's four vendors in the RFP and they all issue questions, I take their question and I wipe out any reference to that specific company and I have my stakeholders answer them and then I issue them back out to the entire field....So you'll get stakeholders that will email you directly or you'll get vendors that will sort of circumvent the RFP platform for communication. If it's a quick question, I'll probably fire back but let them know, 'Hey, future correspondence, can you run it through here?'.... But I try to encourage, again, all the communication to go through this platform because I feel like it becomes part of the record. There's been vendors who have been removed from RFPs mostly because they go around, not only the RFP platform, they also go around the sourcing manager." |
Despite the pervasiveness of these restrictive policies in practice, research on how to sell to barricaded buyers in the B2G and B2B sectors remains scarce. Indeed, in their comprehensive review of the B2B literature, [45] suggest that marketing to the government "is almost barren of academic research. This is amazing considering that sales to the government represents a significant amount of business in business markets" (p. 138). Similarly, [36], p. 544) states, "There is virtually no academic work on sales to the military or to federal, state and local governments in the US or elsewhere....Yet such sales are of enormous financial importance and represent the predominant business for many firms." Although recent research has examined the performance implications of selling to the government ([30]), there remains a need to better understand the bidding process through which suppliers compete to win government contracts. Furthermore, research on B2B sales tends to focus on interpersonal interactions when buyer and supplier communication is unimpeded (e.g., [25]). The present research, therefore, aims to address these voids by advancing a conceptual framework on how to sell to barricaded buyers.
Prior research on gatekeepers (i.e., individuals who control the flow of information into or out of the buying team/center; e.g., [33]; [55]) has shed some light on the issue of restricted access to buyers. However, a thorough review of the gatekeeping literature indicates an almost exclusive focus on individuals who restrict the flow of information between buyers and suppliers by controlling access to or distribution of supplier information (Web Appendix C). In contrast, policy gates (or barricades) are the key means of restricting access to buyers in many purchasing processes. Given their impersonal nature, unlike gatekeepers, policy barricades are not susceptible to direct interpersonal influence. Thus, we complement the literature by examining a pervasive means of restricting access, which is largely absent from the extant literature but pervasive in B2G and B2B settings.
In this research, we integrate our extensive field work across eight case studies with signaling theory ([32]; [47]) to develop a conceptual framework of selling to barricaded buyers. Signaling theory is a useful guide because barricades restrict actual communication between buyers and sellers. Our findings indicate that there are three distinct ways in which signaling occurs or influences the barricaded buying process: the seller signals to buyers, the seller signals to competing sellers, and the buyer signals to sellers whose meaning is jammed ([18]). Our emergent framework identifies key variables that provide signals across different phases of the buying process that influence a supplier's competitiveness (i.e., buyers' perceptions of the value provided by a supplier's offering[s] relative to other suppliers) and, therefore, its selection likelihood. Accordingly, the present research makes the following contributions.
First, our research addresses recent calls in the organizational buying and selling literature to take a more holistic view of the buying process ([17]; [24]). More specifically, in addition to examining how a supplier's actions affect buyers (e.g., [40]), we illustrate how a supplier's actions can affect its competitors. For instance, we introduce the notion of "relationship peacocking" (i.e., the degree to which a supplier signals the strength of its relationship with the buyer to competitors) and describe how these signals can enhance a supplier's competitiveness by demotivating its competitors from responding to a request for proposal (RFP).
Second, we advance the field's understanding of RFPs, which are the primary sales tools and chief signaling devices in selling to barricaded buyers. Although prior research has documented the importance of RFPs in the buying process ([29]; [35]) and examined how buyers use RFPs for both evaluation ([14]; [52]) and learning tools ([42]), the literature remains limited in a couple of ways. For instance, research on how suppliers can shape buyer RFPs ex ante remains largely unaddressed. The present research identifies variables (e.g., supplier-specific capabilities and language) a supplier can use to influence buyers to craft RFPs in its favor. By successfully embedding its supplier-specific capabilities or language into the buyer's RFP, a focal supplier can effectively "jam" or interfere with the fairness signal buyers try to send to all competing suppliers about the purchasing process.[ 6] By jamming the buyer's fairness signal to competing suppliers, a focal supplier can increase its competitiveness by decreasing competing suppliers' motivation to respond to the RFP. Thus, we highlight the important role signal jamming can play in the RFP process, which, to our knowledge, has not been advanced in prior sales research.
Furthermore, the implicit view in prior research is that RFPs are evaluated in an objective fashion ex post (e.g., lowest price, fastest delivery; [52]). Our research suggests that subjective factors (e.g., positive tone, explicit responding) are also important signals in determining suppliers' competitiveness. We find that buyers use these subjective factors as important indicators of underlying supplier solution and/or relationship quality that are harder to obtain by barricaded buyers due to limited supplier interactions. As a result, we add greater clarity and nuance to the way in which buyers evaluate suppliers in this restricted setting.
Third, we identify barricade restrictiveness as an important contingency variable. For instance, our research indicates that a supplier's ability to embed its supplier-specific capabilities into the buyer's RFP and its inclusion of supplemental solutions in its RFP response will have a more positive impact on its competitiveness when the barricade is more rather than less restrictive. Thus, we lend further clarity to when several of our framework's independent variables are likely to affect competitiveness.
Due to the limited research on barricaded buyers, we use a qualitative research approach to build our conceptual framework (e.g., [20]; [51]). Our findings and contributions are based on a longitudinal participant observation of eight organizational purchases within a large public organization as well as subsequent interviews with buyers and suppliers. In total, our data collection occurred over approximately 22 months and encompassed 22 observations of buyer meetings and 72 depth interviews with buyers and suppliers both during and after supplier selection across the eight case studies. We complemented our case study data collection with seven interviews with private-sector B2B purchasing executives not involved in the case studies.
The present research develops a conceptual model of selling to barricaded buyers through a qualitative, grounded-theory approach ([22]; [49]) using case study methodology ([15]). The case study approach is often utilized when researchers are interested in describing a phenomenon or generating theory ([15]). For instance, marketing scholars have used this approach to explore how buyers and suppliers build and sustain relationships ([41]) and how firms include customers in the innovation process ([13]) as well as to understand market scoping for early-stage technologies ([39]).
A case is typically an entity that is naturally bound by domain specific criteria. For example, previous marketing research has defined cases in terms of individual consumers (e.g., [16]), projects (e.g., [20]; [39]), and specific relationships (e.g., [41]). Developing theory from cases is generally conducted using theoretical sampling and constant comparative analysis. Theoretical sampling is the process by which cases are chosen specifically to develop theory rather than to enhance statistical validity. The process of comparative analysis involves analyzing each case individually, followed by comparison between cases to determine similarities and differences ([15]). Case selection and comparison stops when theoretical saturation is achieved (i.e., when new observations yield small increases in model improvement; [49]).
We determined that a grounded-theory approach using case studies to examine the barricaded buying process was appropriate for three main reasons. First, such an approach is best utilized in domains where there is little existing knowledge or when significant knowledge gaps exist ([15]). Our extensive review of the gatekeeping literature indicated a significant gap on how formal policy barricades affect the buying process (Web Appendix C). Second, academic research on selling to barricaded buyers is sparse. Third, to our knowledge, real-time (rather than retrospective) observations of the buying process have not been forthcoming in the literature. As such, the inductive case-based approach to studying barricaded buyers provided an opportunity to explore and develop rich insights into these previously understudied areas.
We begin by describing the case selection for our B2G field study and follow this with a complementary study involving interviews with both B2G and B2B buyers and sellers.
We followed an approach similar to [19], which begins with developing a data collection strategy and ends with a grounded model based on constant comparative analysis and theoretical sampling. After reviewing the organizational buying literature, we developed a case selection strategy based on buy class (new purchase vs. modified rebuy) and purchase type (product vs. service). This strategy ensures variation across cases to facilitate the generation of new theory on the barricaded buying process ([15]).
Our next step was to partner with an organization that uses a typical barricaded buying process (see Web Appendix A) while making purchases diverse enough to satisfy the aforementioned case selection criteria. Our partner, a large state organization in the United States that employs over 14,000 people and has procurement contracts totaling $2 billion annually, met these criteria. The organization operates departments responsible for education, health care, construction, agriculture, and transportation, among others. Our first several meetings with the buying organization involved the chief purchasing officer and the two managers who oversaw the procurement team. We confirmed with the chief purchasing officer that the organization followed protocols typical of public procurement. After these initial meetings, the first author spent several days shadowing managers within the procurement department to increase our familiarity with the process and to identify potential cases for observation.
Once familiar with the purchasing process, we identified three initial cases for data collection: pest control (modified rebuy/service), general office software (new purchase/product), and waste management (modified rebuy/service). Data collection across these first three cases occurred over eight months and provided foundational insights for subsequent case selection. Over the next ten months we continued to add cases to replicate and extend our understanding of selling to barricaded buyers ([15]). We continued theoretical sampling for case selection until our model reached theoretical saturation ([49]). In total, we collected data on eight purchase cases spanning approximately 18 months (for the characteristics of all cases and interviews used in our analysis, see Tables 3, 4, and 5).
Graph
Table 3. Case Descriptives.
| Buying Project | Product/Service | Buy Class | NAICS | Decision Makers | Supplier RFP Responses | Number of Interviews | Average Interview (Mins.) | Total Interview (Hours) | Meetings Observed | Post-RFP Duration (Months) |
|---|
| 1. Pest control | Service | Modified rebuy | 56 | 3 | 1 | 4 | 32 | 2.13 | 4 | 2 |
| 2. General office software | Product | New purchase | 51 | 5 | 4 | 11 | 25 | 5.00 | 2 | 3 |
| 3. Waste management | Service | Modified rebuy | 56 | 10 | 2 | 9 | 45 | 7.50 | 4 | 9 |
| 4. Specialized software | Product | New purchase | 51 | 8 | 4 | 7 | 22 | 2.57 | 5 | 10 |
| 5. Veterinary services | Service | Modified rebuy | 54 | 6 | 2 | 6 | 20 | 1.67 | 2 | 2 |
| 6. Network hardware | Product | New purchase | 42 | 6 | 8 | 8 | 22 | 2.93 | 2 | 2 |
| 7. Transportation rental services | Service | New purchase | 53 | 6 | 2 | 0 | 0 | .00 | 2 | 4 |
| 8. Commercial printing | Service | Modified rebuy | 32 | 5 | 6 | 1 | 20 | .33 | 1 | 2 |
| Totals | | | | 49 | 29 | 46 | 23.25 | 17.83 | 22 | 4.25 |
1 Notes: NAICS = North American Industry Classification System.
Graph
Table 4. Initial and Complementary Case Study Informants.
| | Buyers | Suppliers |
|---|
| Case | RFP | | Title | Initial | Complementary | | Title | Complementary |
|---|
| 1 | Pest control | Buyer 1 | Manager (Chair) | ✓ | | Supplier 3 | Owner | ✓ |
| Buyer 2 | VP | ✓ | | | | |
| Buyer 3 | Manager | ✓ | | | | |
| 2 | General office software | Buyer 1 | Director (Chair) | ✓ | | Supplier 1 | Proposal Director | ✓ |
| Buyer 2 | Assistant VP | ✓ | | Supplier 2 | Sales Consultant | ✓ |
| Buyer 3 | Director | ✓ | ✓ | | | |
| Buyer 4 | Director | ✓ | ✓ | | | |
| Buyer 5 | Director | ✓ | | | | |
| 3 | Waste management | Buyer 1 | Manager (Chair) | ✓ | | Supplier 1 | VP of Operations | ✓ |
| Buyer 2 | Manger | ✓ | | Supplier 3 | Sales Consultant | ✓ |
| Buyer 3 | Director | ✓ | | | | |
| Buyer 4 | VP | ✓ | ✓ | | | |
| Buyer 5 | Manager | ✓ | ✓ | | | |
| Buyer 6 | Manager | ✓ | ✓ | | | |
| Buyer 7 | Coordinator | ✓ | | | | |
| Buyer 8 | Manager | ✓ | ✓ | | | |
| 4 | Specialized software | Buyer 1 | Director (Chair) | ✓ | ✓ | Supplier 1 | Sales Consultant | ✓ |
| Buyer 2 | Manager | ✓ | | Supplier 2 | Proposal Consultant | ✓ |
| Buyer 3 | Administration | ✓ | | | | |
| Buyer 4 | Manager | ✓ | ✓ | | | |
| 5 | Veterinary services | Buyer 1 | Director (Chair) | ✓ | | | | |
| Buyer 2 | Manager | ✓ | | | | |
| Buyer 3 | Specialist | ✓ | | | | |
| Buyer 4 | Specialist | ✓ | | | | |
| Buyer 5 | Manager | ✓ | | | | |
| 6 | Network hardware | Buyer 1 | Director (Chair) | ✓ | | Supplier 1 | Sales Consultant | ✓ |
| Buyer 2 | Manager | ✓ | ✓ | Supplier 2 | President | ✓ |
| Buyer 3 | Executive Director | ✓ | | Supplier 3 | Sales Consultant | ✓ |
| Buyer 4 | Director | ✓ | ✓ | | | |
| Buyer 5 | Manger | ✓ | ✓ | | | |
| 7 | Transportation rental services | Buyer 1 | Executive Director | | ✓ | | | |
| Buyer 2 | Manager | | ✓ | | | |
| 8 | Commercial printing | Buyer 1 | Coordinator (Chair) | ✓ | | Supplier 1 | VP of Sales | ✓ |
| | | | Supplier 2 | Owner | ✓ |
| | | | Supplier 3 | Sales Manager | ✓ |
| Totals | | | 31 | 13 | | | 13 |
Graph
Table 5. Complementary Study Interviews with Private Sector Informants.
| Respondent | Title | Experience | Function | Industry |
|---|
| 1 | Purchasing Manager | 17 years | Supply chain | Electronics manufacturing |
| 2 | Procurement Manager | 12.5 years | Supply chain | Mattress manufacturing |
| 3 | Procurement Manager | 19 years | Procurement | Automotive manufacturing |
| 4 | Procurement Consultant | 34 years | Procurement | Consumer packaged goods |
| 5 | Traffic Manager | 13 years | Supply chain | Electronic component wholesale |
| 6 | Sourcing Manager | 5 years | Procurement | Construction supply |
| 7 | Supply Chain Manager | 29 years | Supply chain | Automotive manufacturing |
To collect our data, the first author worked extensively in the field as a nonvoting member of the buying center for each case. As a buying center member, the first author spent approximately 45 days in the field observing and collecting data. Data collection involved on-site meetings, conference calls, face-to-face and phone interviews, and review of buyer RFPs and supplier responses. By immersing himself in the field, the first author was able to engage in informal discussions with buyers as well as conduct formal, semistructured interviews. Formal interviews were recorded and transcribed verbatim, while detailed notes and audio-recorded thoughts and observations were taken for other field activities as recommended by [49]. This process enabled us to analyze data on an ongoing basis, so we could use constant comparative analysis to identify themes as they emerged from the data. Our strategy was to speak with buyers at different phases of the buying process to gain understanding of buyers' theories-in-use as the purchasing process unfolded. We conducted interviews with buyers during buyer development of RFP specifications, after the RFP had been completed and released to suppliers, and during evaluation of supplier RFP responses. This gave us rich insights into supplier behaviors that affected buyers' selection likelihood as the purchasing process unfolded.
Consistent with prior research (e.g., [10]; [39]), we analyzed the data using open, axial, and selective coding ([49]). The purpose of open coding is to identify concepts and relationships with other concepts within the barricaded buying process. We coded each line of all interview transcripts and field notes for variables that positively and negatively affected buyer perceptions of supplier competitiveness. Once we identified initial concepts, we began the axial coding process whereby lower-level concepts are related to higher-order categories through identification of their properties and dimensions. For example, we identified that suppliers influence the development of buyers' RFPs by promoting their supplier-specific capabilities and language, resulting in the higher-order category of RFP shaping (see Table 6). Finally, we conducted selective coding, which is the process of integrating and refining theory to identify the central theme. The result of this process was an initial grounded model highlighting variables that can increase a supplier's competitiveness in the barricaded buying process.
Graph
Table 6. Sample Grounded Theory Coding Process.
| First-Order Code | Definition | Source | Data Sample | Second-Order Code |
|---|
| Supplier-specific language | Terminology that is distinctive to a specific supplier | Buyer | "Buyers don't know much about the particular products they're buying. So we've found that [we] often will sort of request sample proposals from vendors and then actually use some of that language in the RFP itself." | RFP Shaping |
| Supplier | "I think there are situations where a buying organization may actually, even though it's not supposed to happen, want to favor a vendor. I mean, sometimes you can read right within the RFP, you can almost tell who they're looking for. There are certain things about product development philosophy where one vendor may differ from another, so you can kind of tell, sometimes, what they're going after." | |
| Supplier-specific capabilities | Firm-specific capabilities and/or skills that distinguish it from others | Buyer | "I think our RFP evolved into so much more than what [Supplier 2] expected. We added the 24/7 365 emergency call requirement offered by [Supplier 1], which probably drove [Supplier 2] away. We requested that they are available all the time which most suppliers likely can't or don't want to do. Companies don't want to handle unique situations, it takes a special person to do that." | |
| | Supplier | "Purchasing people, or whoever's putting this document together, doesn't really fully understand that because of the specs [from the sample RFP] and the way the wording is, that they are potentially locking out other people." | |
As part of the buying center, the first author signed confidentiality agreements that limited his ability to speak with any suppliers during the original data collection. Once the purchasing contracts were fully executed, however, this restriction expired. Therefore, we aimed to conduct postpurchase decision interviews with both buyers and suppliers involved in the eight original cases. For inclusion in this sample, we again chose individuals using several criteria. First, we wanted to speak with suppliers who had won, lost, and chosen not to bid in the cases we observed. Second, we wanted to speak with buyers and suppliers who had experience with the RFP process in both the public and private sectors to understand how our model relates to B2B contexts. Using these criteria, we conducted an additional 26 postpurchase interviews with 13 buyers and 13 suppliers involved in the eight case studies.
Finally, to enhance the generalizability of our findings, we conducted additional interviews with seven managers involved in purchasing in the B2B sector (Table 5). These managers represented companies in the automotive, electronics manufacturing, mattress manufacturing, electronics component wholesale, and construction supply. Taken together, these complementary efforts resulted in an additional 33 interviews with 20 buyers and 13 suppliers and an additional 16 hours of interview data (see Tables 4 and 5).
As with the original sample, we recorded interviews and transcribed them verbatim to facilitate line-by-line analysis using the open, axial, and selective coding process. We again used comparative analysis to examine insights generated from this sample with those generated from the original sample to further refine the model. The result of the two-phase data collection process is a grounded theoretical model that delineates actions suppliers can take to increase their competitiveness when selling to barricaded buyers.
In a barricaded buying context, communication between buyers and suppliers is limited. Correspondingly, it is more difficult for buyers and suppliers to exchange information or to gain experience with one another to reduce information asymmetry than in unbarricaded buying contexts.
Signaling theory addresses issues associated with information asymmetry (i.e., when one party lacks information that another party has; e.g., [32]), which is a key aspect of many purchasing processes ([12]). For instance, information asymmetry arises because buyers have information about their needs (e.g., willingness to pay) that suppliers do not have, while suppliers have information about their true capabilities that buyers do not have (e.g., product quality). Aside from using/experiencing a product or service, a key means of reducing information asymmetry is through the use of signals ([32]). Signals are "actions that parties take to reveal their true types (e.g., skill level)" ([32], p. 66) and are widely used when communication between two parties is limited ([ 4]; [32]; [38]). Examples of signals include nonsalvageable assets such as signs and logos ([38]), warranties (e.g., [ 6]), brand names ([44]), and advertising expenditures ([ 3]). We draw on and extend signaling theory in the barricaded buying environment.
We divide our discussion of results into the following sections: competitor perception shaping and RFP shaping (which fall in the pre-RFP phase), RFP response content and delivery (which occur in the post-RFP phase), and the moderating role of barricade restrictiveness. In the pre-RFP phase, suppliers have relatively broad access to buyers because buyers are collecting information to develop their RFP specifications. The post-RFP phase begins once the RFP is released to the public; restrictiveness typically increases at this stage of the process. Within each section, we identify novel variables under suppliers' control that affect their competitiveness (i.e., buyers' perceptions of the value provided by a supplier's offering(s) relative to other suppliers (e.g., [ 2]) and, ultimately, their selection likelihood. We begin with competitor perception shaping, which generally affects competitiveness in the pre-RFP phase.
Competitor perception shaping is the degree to which a supplier influences its competitors' beliefs about its connection to the buying organization. A common practice in complex/large purchases is to have a pre-RFP meeting or series of meetings with potential suppliers. These meetings may include facilities walk-throughs and/or inspections that serve to clarify complex information that may be included in the RFP. They are attended by most, if not all, competing suppliers, which provide unique opportunities to shape competitors' perceptions.[ 7] A B2B procurement manager in the automotive industry noted,
So if I'm expanding the [production] facility, for example, I want all three, four, five general contractors on that site, and I want them to hear the message at the same time. I want them to receive the documents at the same time. I want them to participate in the same walk-through. And they are free to ask questions, and we document those questions, and we issue the answers in a documented format or method. So that's when you see that [pre-RFP meetings] a lot in our side of the business.
Our research identified two signals suppliers use to shape competitor perceptions: information peacocking and relationship peacocking.
Information peacocking is the degree to which a supplier signals the strength of its knowledge about the buying firm to competitors. For example, the incumbent supplier in the pest control case demonstrated its superior knowledge of the buying firm to other potential suppliers during the buyer's two-hour initial facility walk-through. The supplier even corrected the purchasing manager about information that was incorrect:
Purchasing Manager: As you will note on page six of the proposal, we are requesting service for seven buildings.
Supplier 1: It says seven, but the current service is for eight buildings. I think that needs to be corrected.
Purchasing Manager: Oh, you are correct, I don't think that number got updated.
The supplier also made several comments about the level of service the buying organization was receiving throughout the walk-through. He mentioned that the buying organization received service every 30 days for each building and often required "spot checks" for areas that could be potential future problems. These comments made a strong impression on the buyers who were present. Several months later, when the buying center met to evaluate RFP responses, the buyers recalled:
[Supplier] was "Johnny-on-the-spot" with his walk-through. You can definitely tell that [they] know our account inside and out.
We also conclude that these comments also made a strong impression on competitors because seven of the eight suppliers chose to not even respond to this RFP. In support of this contention, a sales consultant involved in the general office software case explained:
I've seen that,...where it was mandatory to be on-site [for a pre-RFP meeting]. Then you can definitely see people positioning at the table to try and impress other people about what they know....I can imagine it does drive people away if they're doing an RFP, especially if there is clearly an incumbent there.
In these examples, a supplier's information peacocking provides a signal to buyers that it has the requisite knowledge to identify needs and provide appropriate solutions. Information peacocking also signals to competitors that their (lack of) knowledge may put them at a competitive disadvantage, which can demotivate them from responding to the buyer's RFP ([26]). As a result, information peacocking can benefit a supplier through more positive buyer perceptions and lower competition.
Relationship peacocking is the degree to which a supplier signals the strength of its connection to individuals within the buying firm to competitors. For instance, during a pre-RFP meeting in the waste management case, the incumbent supplier confidently discussed his chances of keeping the account. When we subsequently asked a member of the buying team why the supplier was so confident, he offered:
I think it's just arrogance and entitlement on the part of the CEO of the [incumbent supplier]. He has connections inside the [buying organization] that go beyond his business connections, and he's just kind of resting on some of those laurels since they [competitors] know about it.[ 8]
We later spoke with one of the competing suppliers who was present at the meeting. She acknowledged the demotivational role relationship peacocking can have:
The incumbent is a little bit more smug than everybody else....You see the comments and the sideways smiles [with buyers], it might intimidate [competitors]. I've probably done it myself from time to time.
The preceding discussion relates to the interpersonal relationship literature on mate-retention tactics that prevent a partner from becoming involved with other parties ([ 7]; [ 8]). By using a retention tactic such as relationship peacocking, a supplier signals the strength of its customer relationship to competitors. Similar tactics used to protect interpersonal relationships have been shown to reduce a competitor's motivation to try to "poach" a mate ([ 8]). Our observations in an organizational buying and selling context seem congruent with these findings. Therefore, we propose the following:
- P1: A supplier can increase its competitiveness through greater use of (a) information peacocking and (b) relationship peacocking.
Request for proposal shaping is the degree to which a supplier influences the content in the buyer's formal RFP. Our data indicate two key ways suppliers can shape the RFP in their favor: by ( 1) embedding their firm-specific capabilities and ( 2) embedding their firm-specific language into the buyer's RFP. Supplier-specific capabilities are those that distinguish one supplier from competitors ([37]; [50]). Supplier-specific language refers to terminology that is distinctive to a specific supplier. Its inclusion in the buyer's RFP increases the odds that the supplier will win because the specifications reflect its strengths. Notably, however, the inclusion of such language may also signal to competing suppliers that the buyer is biased toward a particular supplier or solution. As a result, competitors' motivation and capability to develop effective RFP responses may be diminished ([11]; [46]).
Our research suggests that suppliers can enhance their competitiveness by embedding their specific capabilities into the buyer's RFPs. For example, in the pest control case, Supplier 1 promoted its round-the-clock service (a capability specific to it) during a meeting before the development of the buyer's RFP. This service requirement was subsequently included in the formal RFP. Consequently, a competitor [Supplier 2] alerted the purchasing manager that it would not be submitting an RFP response due to its inability to offer the requested solution. The buying committee chair provided additional insights:
I think our RFP evolved into so much more than what [Supplier 2] expected. We added the 24/7/365 emergency call requirement offered by [Supplier 1], which probably drove [Supplier 2] away. We requested that they are available all the time, which most suppliers likely can't or don't want to do. Companies don't want to handle unique situations; it takes a special person to do that.
Reflecting on similar purchasing occasions, the president of an information technology (IT) supplier involved in the network hardware case confirmed how embedding firm-specific capabilities in a buyer's RFP can eliminate some suppliers from the buyer's consideration set:
So to say that the network switch has to have X, Y, or Z. Well, depending on what that X, Y, Z is, whatever that specification is could lock out six vendors right there, which narrows you down to those two vendors, or two manufacturers that you really want anyhow. So there's certain key features that every manufacturer has in their products. It's what makes them unique. Right?...So you see a lot of things like that.
A key account manager involved in the network hardware case discussed how this practice can also be demotivating:
It's frustrating because when you know in your heart that you have equally as good a solution that a customer has written an RFP spec that potentially locks you out, it does damper the whole situation, and it is discouraging.
Thus, the inclusion of supplier-specific capabilities in the buyer's RFP can "lock out" competitors thereby enhancing its competitiveness by reducing not only competitors' capability to respond effectively but also their motivation to respond to the RFP ([46]).
Embedding its specific language into the buyer's RFP can also enhance a supplier's competitiveness. Doing so can send a powerful signal to competitors because they tend to notice its unique language. Not only can this be demotivating, but it also makes it difficult for competitors to respond to the buyer's RFP because they are less familiar with the supplier's idiosyncratic language. A B2B procurement manager in the automotive industry illustrates how impactful supplier-specific language can be:
We have a situation where we bid a large piece of equipment and it has lots and lots of parts to it....[The incumbent] rebadged the part number for their own purposes....So we always have to buy it from them because nobody else knows what that [part] is.
The preceding insights suggest that RFP shaping can be an effective way to demotivate competing suppliers because the supplier that is able to get the buyer to include its capabilities and language into the RFP appears to have a competitive advantage. In addition to this advantage, the ability to shape the RFP also appears to "jam" any fairness signals that buyers try to convey to suppliers about the RFP process. Signal jamming is the extent to which one interferes with another's inference of a signal ([18]). In general, our discussions with buyers in both the public and private sector indicated that a key purpose of the barricaded buying process is to send a signal that the process will be fair and competitive. For example, the state of Michigan explicitly indicates that "strict State and vendor communication protocol is essential to ensure a fair and competitive purchasing environment" (2019 Michigan State Procurement Manual, Section 7.3, p. 2). Furthermore, Apple's sourcing process "is designed to ensure equal and fair treatment of suppliers so that they can fully participate in a competitive procurement process" (https://www.apple.com/procurement; see other examples in Tables 1 and 2). When a supplier is able to instill its specific capabilities or language into the buyer's RFP, this fair and competitive signal is effectively jammed because it interferes with competing suppliers' inference that the buying process is fair. Our results indicate that competitors infer that RFPs that contain specific capabilities and specific language from a supplier are biased toward that supplier. The result is that competitors are less willing to invest the time and resources necessary to develop a quality RFP response. Thus,
- P2: A supplier can increase its competitiveness by embedding its (a) supplier-specific capabilities and (b) supplier-specific language into the buyer's RFP.
How may suppliers embed their supplier-specific capabilities and language into buyer RFPs? Although buyer–supplier meetings and product demonstrations play important roles, our research indicates that providing facilitating documentation (i.e., marketing materials or sample RFPs that ease the buyers' RFP development efforts) can be a key means of transmitting a supplier's specific capabilities and language into buyer RFPs. Because buying center members often come from various positions in the buying organization and have little purchasing experience (especially in B2G settings), sample RFPs can substantially simplify buyers' RFP development efforts. The buying committee chair in the general office software case (who included the brand name for a customer service portal that was specific to one supplier in his buying team's RFP) described the process he used to develop the RFP:
I had never done an RFP before so I started piecing it together from multiple sources....The supplier demo, the sample RFP from the supplier...
Although more efficient, sample RFPs may lead buyers to unintentionally include a supplier's specific capabilities and language into their RFP. An IT director for the buying organization in the network hardware case noted,
The reason for using the language is buyers are trying their best not to reinvent the wheel on RFPs....Once in a while something will sneak in there that's pretty—it might tee up toward a particular vendor, and we definitely don't do that on purpose.
A networking hardware and software salesperson elaborated,
I have one example with a public-sector customer here in [state] where the RFP document that was released was one that we have seen repeatedly in two other situations here in the state. And so what that tells me is this particular competitor has a document that they've gone to either the decision makers or purchasing or whomever, and said, "Look, this will save you a lot of time. This meets the criteria if you ask for these things." And the purchasing people, or whoever's putting this document together, doesn't really fully understand that because of the specs and the way the wording is, that they are potentially locking out other people. It's an easy track to take because it minimizes the investment of time. I mean there's just not a lot of resources that are available, and so it just makes their job easier and they're like, "Okay." So they take that document and then they put that as part of their larger RFP document, again, not realizing that what they're doing is potentially digging themselves into a hole that eliminates other potential suppliers.
Suppliers can also shape the selling process even after the RFP has been issued. Specifically, while suppliers need to comply with the requirements of the RFP, our research suggests that those suppliers who provide additional value beyond the buyer's requirements in the bidding process are ultimately more competitive. Two types of content emerged as effective signals at this stage: novel solutions and supplemental solutions. While exceeding expectations is a well-known way to increase buyer attractiveness, the timing of these signals—which occurred in the post-RFP phase—precludes competitors from responding to them because they are often unaware of them.
Novel solutions are supplier offerings that comply with the requirements of the RFP in a new or imaginative way. Buyers deemed a supplier more competitive when, in addition to providing solutions that complied with the RFP requirements as requested by buyers, it offered novel solutions in its RFP response. To illustrate, a question in the waste management RFP asked if suppliers could follow the current waste pickup schedule. Each supplier's RFP response affirmed that the schedule could be met (i.e., they provided compliant solutions); however, one supplier also offered a novel approach to route optimization using radio-frequency identification (RFID) technology to pick up trash only when dumpsters were full. A manager involved in the buying center elaborated on how this novel solution affected the supplier's competitiveness:
You can use that information to optimize your routes and determine if you're tipping air or if you can cut your frequencies. And we currently don't have that. And unfortunately, we didn't write that in our RFP to begin with to require that. We did ask for collaborative processes and so this other company offered that as one possible thing. But because they offered that and the other company didn't, that was one of the things, in my opinion, that contributed to a wide gap in them.
Group evaluations and follow-up discussions with buyers illustrated that offering novel solutions increases a supplier's competitiveness in two ways. First, the more obvious rationale is that offering the novel solution may provide value to buyers by saving them time or money. Second, novel solutions convey positive signals about the supplier's partnership potential. For instance, although buyers were not interested in purchasing the RFID technology for the additional cost, they repeatedly commented on how the supplier's extra effort signaled its innovativeness and willingness to go the extra mile that would be beneficial in a future partnership.
Supplemental solutions are supplier offerings that address customer needs that were not explicitly identified in the RFP (and are provided in addition to solutions requested by buyers). For instance, several suppliers felt they knew what buyers needed better than the buyers themselves. A sales consultant who was successful in the network hardware case described his company's approach to RFPs as follows:
Typically, the way that we go about RFP development is we will draft, I'll call it response number one. We'll draft an RFP that meets their specs exactly....And then we will basically draft a second response and apply that as an alternate response....We take the time to put an alternate number two, because we may truly feel that it is what is best for them.
Furthermore, in the general office software case, an assistant vice president (VP) involved in the buying center mentioned the value added from a supplier who submitted an alternative proposal offering a supplemental solution:
I thought one thing that was helpful was [Supplier] provided an alternate proposal. The alternate proposal was pretty much take two steps back and think about your overall needs, not just your needs for these two systems. I actually, when [procurement manager] said there was an alternate proposal from [Supplier], I was like, "That's weird," but actually it was valuable in the overall thinking about what we were doing. No one else did that.
Both novel and supplemental solutions are discretionary efforts provided by suppliers, which prior research suggests customers appreciate (e.g., [31]). Buyers also appear to view novel and supplemental solutions in terms of exceeding expectations (i.e., positive disconfirmation; e.g., [43]), which signals that the potential supplier is likely to be a good partner.[ 9] Formally,
- P3: A supplier can increase its competitiveness by offering (a) novel solutions and (b) supplemental solutions in its RFP response.
Our fieldwork surfaced additional characteristics associated with the way suppliers convey their RFP responses provides important signals to buyers. Through our analysis, we identified reference matching, positively toned responding, explicit responding, and tailored responding as qualitative characteristics buyers emphasized in their evaluation of supplier responses.
Reference matching refers to the degree of similarity between the buying organization and the organizations provided as references in the supplier's RFP response ([34]). Buyers repeatedly alluded to reference matching during their supplier RFP evaluation meetings. When a supplier provided references different in size and scope from the buying firm, buyers perceived it as a signal that the supplier lacks the adequate experience and knowledge to service their needs. A director on the buying team for the general office software case suggested,
One thing I noticed is that [Supplier 1] provided a reference for [Public Organization 1] which is small compared to us, [Supplier 2] gave [Public Organization 2] as a reference, which feels more like us.
References that were more prominent than the buying organization often signaled likely inattentiveness to the buyer's account. For instance, a manager on the buying team for the network hardware case expressed his concerns:
I feel that [Supplier 1] and [Supplier 2] aren't focused on this project. For example, [Supplier 2] gave [very prestigious organization] as a reference. So if [very prestigious organization] calls, I feel we would get bumped to the bottom of the queue. I think we'd be a small fish in a large pond with [Supplier 1] and [Supplier 2].
Thus, the references suppliers include in their RFP responses signal relevant understanding to buyers, which is likely to affect their competitiveness. Formally,
- P4: A supplier can increase its competitiveness through greater reference matching in its RFP response.
Tone refers to the supplier's attitude conveyed in its RFP response. Although buying center members frequently discussed the attitudes reflected in suppliers' RFP responses, suppliers were surprisingly inattentive to its importance. As a director on the buying team for the networking hardware case noted,
Someone who didn't provide a good RFP response was [Supplier]. [Supplier]'s response was pretty much abysmal. They seemed mad in their response.
In the general office software case, supplier tone was brought up immediately during the initial buying center meeting to evaluate supplier RFP responses. The assistant VP's reaction to the negatively toned response was as follows:
[Supplier] is eliminated in my mind....They came across as really arrogant in their RFP [response]. For example, for our question about bankruptcy they just put "we're not going to answer that." What type of response is that?
Because buyers are forced to rely heavily on the supplier's RFP responses to infer the potential quality of a relationship—and its potential to have an open and constructive nature—it is not surprising that tone is used as a signal for this quality. Our follow-up discussions with buyers revealed they perceive the RFP response to be a proxy for suppliers "on their best behavior." Consequently, several buyers mentioned that if a supplier cannot present itself well in the RFP, how is it going to behave when issues ultimately arise? Formally,
- P5: A supplier can increase its competitiveness through a positive tone in its RFP response.
Explicit responding refers to the clarity with which suppliers convey their offerings to buyers. Like tone, the importance of responding clearly would seem apparent, but suppliers varied greatly on the clarity of their RFP responses. Suppliers make their RFP responses more explicit in several ways. For instance, clarifying visuals (such as screenshots for software), detailed descriptions of processes, and clearly formatted responses to RFP specifications enhance the explicitness of suppliers' responses. An IT manager in the specialized software case provided the following insights:
The ones that were hard to follow and understand, were messy, were the ones that ended up on the bottom of my list....I couldn't get a true sense at what they were trying to say in the RFP. Whereas those that were laid out with tables and examples and formatted nicely were the ones that rose to the top because I could clearly see and understand what they were trying to say in the narrative.
A manager in the waste management case reacted negatively to an RFP response that lacked explicitness:
I think I commented on it in a couple of the meetings. It's not really an acceptable response...to say, "Oh, well. See previous experience," or, "No details needed. We'll keep doing it the way we've been doing it because..." That immediately sent up a red flag for me because it sort of showed that they didn't necessarily even recognize who their audience was, that half of the group who was reviewing their proposal was not familiar, necessarily, with their past practices, their history, how they had been delivering services to [us] over the past seven years. So it seemed sort of a minimal effort to earn or retain the business."
Thus, RFP responses that are less explicit not only undermine buyers' understanding but also provide negative signals about suppliers' effort. The information processing literature reinforces the importance of explicit responding. For instance, research suggests that easy-to-understand information results in more favorable product evaluations (e.g., [ 1]). When information is difficult to process, individuals may switch preferences to those that are easier to understand ([28]). Therefore, we propose the following:
- P6: A supplier can increase its competitiveness through greater explicitness in its RFP response.
Another common theme pervading discussions with and among buying team members was the perceived level of tailoring in suppliers' RFP responses. Tailored responding refers to the degree to which suppliers customize their RFP response to the buying organization. Buying center members often objected to untailored or "canned" responses because they seemed generic and unhelpful (e.g., [29]). Consequently, buyers viewed canned responses negatively because they signaled a lack of effort and attention to detail, which raised concerns about the quality of the supplier's solution and the importance the supplier placed on the (potential) relationship with the buyer. A manager in the waste management case reflected,
When you read an RFP, and it just sounds like a standard response that they cut and paste from something else. And sometimes you can tell because they didn't change a word that identifies the other [public entity] or something. And that's minor, but that kind of shows lack of attention to detail or that you're just one of many.
Similarly, a manager in the network hardware case elaborated,
You take [Supplier], for instance...they did a lot of copying and pasting. When they don't know some of the nuances, that's kind of a warning....In the case of the [Supplier] response, you get stuff where they literally, it's like they didn't read the RFP, they were just doing cutting and pasting.
After speaking with suppliers, it is not surprising buyers perceived a great number of "canned" responses. Indeed, all 13 suppliers we spoke to mentioned they had a database of RFP responses. For example, a supplier in the general office software case (who was identified as providing a canned RFP response by the committee chair) discussed his approach to developing RFP responses:
[What] we found that works the best is a lot of the responses and answers, we'll put in something like a OneNote that lets you just do a quick search of a couple of keyword phrases and then that will pop up a response somewhere that you've done on a previous one or in a library. That tends to be about the best we've found for just trying to help in some of that cut-and-paste approach.
Although more efficient for suppliers, using canned responses may come at the expense of lower competitiveness. Corresponding prior research suggests that tailoring messages enhances the receivers' perceptions of the sender ([23]; [29]). Untailored and generic responses appear to signal a lack of effort and attention to detail to buyers, which raises concerns among buyers about the potential supplier.
- P7: A supplier can increase its competitiveness through greater tailoring of its RFP response.
Although there are exceptions (e.g., Alaska, Wyoming), barricades generally arise once the RFP is released to the public. At this point, communication between buyers and suppliers becomes more limited; however, our research suggests that the extent to which such communication is limited can vary across a "barricade restrictiveness" continuum. Barricade restrictiveness is defined as the degree to which a purchasing policy limits communication between buyers and sellers. For instance, some U.S. states allow limited contact (e.g., New Jersey, Oklahoma) and communication by email (but not in person or by phone) (e.g., Iowa), whereas others do not allow any communication (e.g., Mississippi). Some states restrict access to a single point of contact (e.g., Colorado, Georgia), whereas others allow exceptions to this rule (e.g., Alabama, Arizona) (Web Appendix A).
Barricade restrictiveness also varies across B2B settings. For example, a B2B purchasing consultant with deep experience in managing numerous RFPs across industries establishes very explicit and strict rules against communication with anyone other than an RFP coordinator with his clients (see Table 2). Alternatively, a global sourcing manager of an office equipment manufacturer uses a less rigid process which relies more on norms that govern supplier and stakeholder communication (Table 2).
In general, barricades tend to be more restrictive in B2G than in B2B settings. This is due, in large part, to the formalization of the barricaded process in B2G settings (e.g. Table 1). The more formalized the process, the more emphasis there is on following and enforcing rules placed on communication barriers (e.g., disqualification for breaking rules; e.g., [27]). Thus, there is less discretion for buyers and suppliers to communicate in B2G than in B2B settings. For instance, the sales manager of a commercial printing firm with extensive experience in selling to both public and private organizations noted:
Once the RFP comes out, you're really constrained under those government rules to be working with that purchasing agent....The rules are clearly more stringent in public RFP work than they are in private RFP work....Certainly, you feel freer to communicate in the private world than you do in the public government. Most of our selling is done through relationships in the private sector..., but it's not formalized the way it is in government RFP work.
Correspondingly, the IT sourcing manager for a construction supply company in the private sector described barricade restrictiveness as more informal at his firm:
We don't have a written policy, but I advise all my stakeholders to run any communications with vendors during an RFP through me. I ask the same of the vendors. We do limit contact during an active RFP and I will ensure that if one vendor happens to obtain more information or gets an opportunity to communicate with stakeholders, that information/opportunity is given to the rest of the field. Not having a written policy is a challenge as some stakeholders love to talk to vendors and give away leverage.
Thus, not only does barricade restrictiveness vary within B2G and B2B settings, but it also varies across B2G and B2B settings. In the sections that follow, we shed light on the moderating impact of barricade restrictiveness on the relationships we outlined previously.
Recall that information and relationship peacocking are signals to competing suppliers about the strength of a focal supplier's knowledge about and connection to the buying firm, respectively. Such signals can reduce the motivation of competing suppliers to respond to the buyer's RFP; however, we argue the that impact of such signals is likely to depend on the restrictiveness of the barricade. When the barricade is less restrictive, competing suppliers have more opportunities to communicate with buyers to evaluate the veracity of a focal supplier's peacocking signals. Should the signals be accurate, competing suppliers have more opportunities to offset a supplier's informational or relational advantages with relationship development efforts of their own; should the signals be false, competing suppliers are likely to be more motivated to respond to the buyer's RFP. In either case, a less restrictive barricade should undermine the efficacy of a focal supplier's peacocking signals. Alternatively, fewer opportunities like these are likely to arise for competing suppliers when the barricade is more restrictive and communication is more difficult. Formally,
- P8: The relationship between a supplier's (a) information peacocking behaviors and (b) relationship peacocking behaviors and its competitiveness becomes more positive as barricade restrictiveness increases.
As the barricade becomes more restrictive, communication between buyers and suppliers grows more difficult. Correspondingly, it becomes more difficult for competitors to clarify meaning (e.g., about a competing supplier's specific language that was embedded in the RFP) or to discuss with buyers alternative ways to comply with the RFP's requirements (e.g., requirements reflecting a competing supplier's specific capabilities) once the RFP is released. Thus, when the barricade is more restrictive, a supplier can more effectively jam the buyer's intended fairness signal about the purchasing process by embedding its specific language and capabilities into the buyer's RFP. Consequently, competitors are likely to be less motivated to expend the time and effort required to respond to the RFP.
Alternatively, with a less restrictive barricade, it is easier for buyers and suppliers to communicate after the release of the RFP. As such, competing suppliers can clarify meaning and discuss alternative ways to comply with the RFP's specifications. Thus, the advantage gained by a supplier that might have had its specific language or capabilities included in the buyer's RFP is diminished. Shaping the buyer's RFP ex ante, therefore, becomes even more impactful on a supplier's competitiveness when the barricade is more restrictive. Formally,
- P9 : The relationship between embedding (a) supplier-specific capabilities and (b) supplier-specific language into the buyer's RFP and its competitiveness becomes more positive as barricade restrictiveness increases.
With a more restrictive barricade, suppliers' supplemental and/or novel solutions and the positive signals they convey are shielded against competitive responses ([11]). However, buyers who would like to pursue one supplier's novel or supplemental solutions can more freely discuss them with competing suppliers when the barricade is less restrictive. Thus, the impact of the originating supplier's novel and supplemental solutions and the quality signals they convey is diminished.
- P10: The relationship between offering (a) novel solutions and (b) supplemental solutions and competitiveness becomes more positive as barricade restrictiveness increases.
When barricades are more restrictive, buyers rely more on suppliers' signals because there are fewer opportunities to reduce information asymmetries. Thus, the importance of RFP response document characteristics is greater as the barricade becomes more restrictive. Accordingly, reference matching, positively toned responding, explicit responding, and tailored responding become more important because buyers use them as signals about supplier quality. When the barricade is less restrictive, a supplier has a greater ability to respond to potentially negative signals it sends in its RFP response (in terms of, e.g., [poor] tone, [lack of] explicitness, [lack of] tailoring). Formally,
- P11: The relationship between a supplier's (a) reference matching, (b) positively toned responding, (c) explicit responding, and (d) tailored responding and its competitiveness becomes more positive as barricade restrictiveness increases.
Our emergent framework integrates insights from our fieldwork and signaling theory to identify key variables across different phases of the barricaded buying process that influence a supplier's competitiveness and, correspondingly, its selection likelihood (see Figure 1). The framework reflects three distinct ways in which signaling occurs or is influenced in the barricaded buying process—seller signals to buyers, seller signals to competing sellers, and buyer signals to sellers that are jammed in their meaning. The results of our research suggest that while suppliers may be restricted from accessing buyers directly, they may nevertheless have several opportunities to influence the buying process in their favor through the types of signals we uncovered. In the next sections, we provide implications for theory and practice, discuss the limitations of the research, and offer future research directions.
Graph: Figure 1. Emergent framework of selling to barricaded buyers.
The present research advances the literature in the following ways. First, we shed light on the concept of barricaded buying in the B2G and B2B sectors. In doing so, we complement extant research on gatekeeping that tends to focus on personal "gates" or "barricades" by shedding light on the pervasive impersonal policy barricades found in the public and private sectors. An upshot of our approach is that we also lend insights into the underdeveloped area of B2G selling (e.g., [36]) and some of the differences involved in B2B and B2G selling.
Second, we contribute to scarce research on RFPs (e.g., [29]). Although extant research has tended to focus on objective factors, such as price and delivery terms that affect proposal selection ([14]; [52]), our research emphasizes the importance of more subjective content (e.g., novel and supplemental solutions) and delivery (e.g., reference matching, positively toned, explicit and tailored responding) signals. Making positive impressions through these signals is particularly important in barricaded buyer settings because ( 1) competitors are less capable of responding to novel and supplemental solutions (if provided in the post-RFP phase), and ( 2) buyers rely more heavily on their subjective perceptions of suppliers based on their RFP responses. Thus, this research moves the literature on RFPs beyond its traditional focus on objective factors to one that also includes more subjective factors that are important to buyers. These findings lay the groundwork for future research to examine the qualitative aspect of supplier proposals, such as tone, utilizing emerging text analytics technologies ([ 5]).
Third, our research contributes to our understanding of signal jamming and complex signaling networks in B2G and B2B buying. In particular, our research suggests that a focal supplier who can successfully embed its specific capabilities and/or language into the buyer's formal RFP can gain an advantage. Doing so effectively jams or interrupts the buyer's fundamental fairness signal about the purchasing process to competing suppliers (e.g., [18]; [48]). The fairness signal is jammed because competing suppliers are likely to infer the buying process is skewed in one supplier's favor and, therefore, they will be less motivated to provide a quality RFP response of their own. To the best of our knowledge, we are the first to introduce the concept of signal jamming to the B2B and B2G buying literature.
Our research also sheds light on the complex web of signaling within organizational purchasing. Given the information asymmetries involved, which may be particularly pronounced in a barricaded buying setting, it is not surprising to see an array of signals sent by buyers and suppliers (e.g., [32]). More specifically, suppliers send signals to competitors (e.g., via peacocking) and to buyers in terms of the content of (e.g., novel solutions) and manner in which they write their RFP responses (e.g., explicit responding). Such a perspective takes a more holistic view of supplier competitiveness in exchanges—one that depends not only on creating positive interactions with buyers but also on the consideration of competitors and their responses.
Fourth, prior research has implicitly assumed that information exchange between buyers and suppliers tends to be beneficial across phases of the buying cycle. However, our research highlights the importance of knowing when to convey what types of information in barricaded buying environments. For instance, our research lends nuance to when a supplier should communicate different types of solution information to a prospective buyer. In the pre-RFP phase, suppliers should identify solutions that highlight their supplier-specific capabilities, while withholding novel and supplemental solutions that may not reflect their supplier-specific capabilities until the post-RFP phase. Thus, the present research takes a first step in understanding how suppliers can use buyer barricades to their advantage, which, until now, has remained largely unexamined.
Fifth, we introduce and explore the concept of barricade restrictiveness. As discussed, as barricade restrictiveness increases, buyers are less able to share competitive solution information with competing suppliers ([11]; [46]). Therefore, solutions providers can gain an advantage by offering novel and/or supplemental solutions after the barricade arises. In addition, as barricade restrictiveness increases, buyers rely more on the written RFP for cues about suppliers ([53], [54]). Thus, the present research begins to develop theory on how barricade restrictiveness can affect buyer and supplier behaviors in the organizational buying process.
The results of our research have important implications for suppliers that sell to barricaded buyers. Although suppliers could find it frustrating to be restricted from buyers in barricaded settings, our research suggests they can use the barricade strategically to increase their competitiveness and selection likelihood. Next, we provide insights to managers on the roles of pricing, context (B2B, B2G), signaling, complexity of the RFP, and supplier size in the barricaded buying process.
Although price is certainly an important factor in selling to barricaded buyers, our research suggests that its importance is largely situational. For instance, price-driven decision making is generally the norm in request for quote (RFQ) processes, which are used "[to] obtain simple, common, or routine services that may require personal or mechanical skills. Little discretion is used in performing the work"; in contrast, RFP processes (as in the present research) are generally used "[to] obtain complex services in which professional expertise is needed and may vary" ([ 9], p. 54). Correspondingly, procurement managers repeatedly mentioned that the use of an RFP process was specifically for creating fair and competitive buying for more innovative solutions. When asked specifically about the relative importance of price, respondents indicated its importance ranged from 0% (for some services) to 75% (for some commodity products) (with the average around 30%). This is consistent with data that a large public organization (different from the participating organization) shared with us in which the decision weight for price averaged 29% across all its RFP scorecards in 2018. As a result, suppliers engaging in the barricaded buying process should acknowledge not only the important role of price but also the important role that the variables in our framework can have on their competitiveness.
Another important consideration for suppliers is how to deal with barricaded buyers in the public sector versus those in the private sector. Our research shows that a key difference between the two is that barricaded buying tends to be more formalized in the public than in the private sector. Without formal penalties for violating implicit or explicit rules of engagement in the private sector, suppliers may be tempted to circumvent the barricades to acquire information and influence decision makers. However, procurement managers in the private sector mentioned that doing so can nevertheless have negative repercussions. For instance, although an explicit punishment policy (e.g., disqualification) may not be in place, buyers noted that circumventing the barricade was a negative signal that raised flags about a potential future relationship. Furthermore, buyers mentioned that it was common to take suppliers off their supplier lists for future RFPs as a result of these behaviors. As a result, it may behoove suppliers to treat informal barricades in the private sector more like formal barricades in the public sector.
Our research also has important implications for the signaling strategies of suppliers. For instance, suppliers should consider the potential benefits of "peacocking" to their competitors by signaling their tight connections and relationships to the buyer; however, they should balance these benefits with the potential negative impressions this might create among buying team members. Suppliers should also seek opportunities to inject their firm's idiosyncratic terminology and capabilities into buyer's RFPs (e.g., by increasing their use of facilitating documentation). Doing so can effectively "jam" or interfere with the fairness signal buyers intend to send to competing suppliers about the purchasing process.[10] Adopting these strategies can decrease competitor motivation and capability to respond to the RFP, which can enhance a supplier's competitiveness and selection likelihood.
Our research also provides several means by which suppliers can enhance their competitiveness through the RFP response document itself. For instance, suppliers need to resist the urge to take a merely compliant approach when developing their RFP responses. Although developing RFP responses is a difficult and time-consuming process, our research suggests that providing extra effort by offering outside-the-box solutions (i.e., novel solutions) and solutions that go beyond what is expected (i.e., supplemental solutions) can pay off in greater competitiveness. Moreover, suppliers should resist the urge to share their novel and supplemental solutions until the RFP response. Doing so prevents a competitor from offering the same or better version of the solution to the buyer, thereby negating the supplier's opportunity to differentiate itself. Furthermore, suppliers should be more strategic in providing references to buyers. Specifically, we recommend providing references that approximate the prospective buyer (e.g., in terms of size and scope). Doing so signals that suppliers are more likely to understand the customer's needs, offer suitable solutions, and provide apt levels of customer service.
In addition to these signaling strategies, suppliers should understand that the importance of particular signals may depend on the complexity of the RFP and potential solutions. In general, signaling is likely to be more important in purchases that are more complex because information asymmetry (e.g., about product features and supplier capabilities) between buyers and suppliers is likely to be greater (e.g., [32]). Less complex products (e.g., commodities, furniture) are easier for buyers to understand; thus, in these situations, suppliers may be better served by developing RFP responses that meet the RFPs specifications in a cost-effective way. Furthermore, although peacocking and instilling supplier-specific capabilities and language may be less important with less complex RFPs, suppliers who offer novel and supplemental solutions in their RFP responses and do so in a tailored, explicit, and positively toned way can nevertheless benefit from the positive signals these measures convey to buyers.
Finally, although both small and large suppliers were selected by buyers across our case studies, our research revealed some notable differences between them. For instance, the importance of a transaction is likely to vary between smaller and larger suppliers. Larger firms, for whom the transaction may be less important than for a smaller firm, may run the risk of appearing inattentive to customer needs by providing untailored (or "canned") RFP responses. Such inattentiveness signals to buyers that their account may not be that important to them if they were to win the RFP. This can open the door for smaller suppliers to win the buyer's business, so long as they take a more conscientious approach in doing so. Furthermore, uncertainty about the underlying abilities of smaller and larger suppliers is likely to vary. For instance, buyers are likely to infer that large suppliers are more capable of handling large and complex solutions. Smaller suppliers, therefore, may need to lean more heavily on sending signals of their abilities to buyers (e.g., via innovative solutions) and/or shaping RFPs in the pre-RFP phase.
As with most research, the present research has several limitations. While our data collection efforts included eight diverse cases with buyers and suppliers that have extensive experience across different organizations and industries, our buying cases were within one large U.S. state–based public organization. Future research, therefore, should examine different types of purchases across different government levels (local, state, and federal) and across different types (e.g., public and private) and sizes of organizations (e.g., small and large organizations).
The present research reflects policy-based, impersonal barricades that suppliers frequently encounter. As noted, however, there are other types of barricades that suppliers may face, such as gatekeepers, which affect buyer and supplier interactions. As such, it would be beneficial to determine how different types of barricades might interplay to affect the buying process. Such an endeavor could provide valuable insights to sales managers and salespeople about the challenges they face in different settings.
Additional research on the nature and implications of peacocking could also provide interesting insights. For instance, respondents indicated that peacocking can enhance supplier competitiveness; however, some buyers were irritated by supplier peacocking. Thus, future research should try to tease out these potentially competing effects on performance.
Finally, like [51], our focus was on variables that emerged from our research that are less frequently discussed in the literature. Future research, therefore, should integrate additional variables that may impact competitiveness and/or selection likelihood (e.g., price, trust, quality, delivery terms, value, incumbency). Doing so will shed light on the relative effects of those variables presented here.
Supplemental Material, DS_10.1177_0022242919874778 - Selling to Barricaded Buyers
Supplemental Material, DS_10.1177_0022242919874778 for Selling to Barricaded Buyers by Kevin S. Chase and Brian Murtha in Journal of Marketing
Footnotes 1 Associate EditorChristian Homburg
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242919874778
5 1For example, firms such as Walmart, Exxon, and Apple are restricting personal access to suppliers by way of procurement portals (see, e.g., https://supplier.walmart.com/, https://corporate.exxonmobil.com/procurement/ supplier-collaboration, https://www.apple.com/procurement/).
6 2For instance, California explicitly states that the RFP communication practices are to create "fair and competitive [buying] practices" (2013 California Institute for Local Government, p. 1).
7 3In many cases, pre-RFP meetings require attendance by suppliers to submit an RFP response later in the process. For instance, respondents indicated that between 5%–20% of their RFPs (usually for larger and more complex purchases) involve pre-RFP meetings. We also reviewed government contracting manuals in all 50 states and the District of Columbia and confirmed that pre-RFP meetings are used in each of these municipalities. We also confirmed this practice with buyers in the private sector that pre-RFP meetings occur when the product/service is more complex.
8 4In this case, the incumbent won the deal despite the negative impression that relationship peacocking evoked from the buying team. Also, although peacocking is most likely to come from an incumbent, this need not always be the case. For instance, a supplier might have established a good relationship with a buyer outside of work, or from previous sales encounters for other products at the current buying organization. In some cases, a supplier might have developed a relationship with a buyer when the buyer was at a different organization.
9 5To be more competitive, our research suggests that suppliers should disclose information about novel and supplemental solutions in their RFP response, rather than in the pre-RFP phase. For instance, in the waste management case, a supplier offered a novel RFID solution to trash pickup in its RFP response document. Had it disclosed this solution in the pre-RFP phase, the buyer could have included it as a requirement in its RFP. A competitor likely could have provided a similar, or perhaps, better solution. Thus, the supplier created an advantage by strategically disclosing a novel solution when its competitors were less capable of responding to it (i.e., post-RFP phase).
6From a buyer's standpoint, this means being vigilant to avoid including a supplier's specific language and capabilities into its RFP. Such vigilance can help guard against sending unfairness signals to suppliers.
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Record: 162- Serial Position Effects on Native Advertising Effectiveness: Differential Results Across Publisher and Advertiser Metrics. By: Wang, Pengyuan; Xiong, Guiyang; Yang, Jian. Journal of Marketing. Mar2019, Vol. 83 Issue 2, p82-97. 16p. 2 Diagrams, 3 Charts, 1 Graph. DOI: 10.1177/0022242918817549.
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Serial Position Effects on Native Advertising Effectiveness: Differential Results Across Publisher and Advertiser Metrics
The advertising industry has recently witnessed proliferation in native ads, which are inserted into a web stream (e.g., a list of news articles or social media posts) and look like the surrounding nonsponsored contents. This study is among the first to examine native ads and unveil how their effectiveness changes across serial positions by analyzing a large-scale data set with 120 ads. For each ad, the authors use separate "natural experiment" studies to compare the ad's performance as its serial position varies. Subsequently, they conduct a meta-analysis to generalize the results across all studies. The results reveal vastly asymmetric effects of native ad serial position on publishers' metrics (click-based) versus advertisers' metrics (conversion-based). As serial position lowers (i.e., from rank 1 to a lower rank), there are only modest changes in publishers' metrics, but drastic reductions in advertisers'. This pattern is unique to native ads and has not been indicated by prior research on ad serial position. Moreover, the authors show the moderating effects of audience gender and age. The findings provide new and timely implications for researchers and marketers.
Keywords: contingency effects; meta-analysis; native advertising; serial position
Because of the extremely low click-through rate (CTR, the rate of click per impression) of traditional online display ads (.05% on average according to DoubleClick's Display Benchmarks Tool 2017), publishers (i.e., websites) cannot charge advertisers high fees for such ads and thus are often forced to sell more ad slots to ensure revenue. Doing so leads to clogged web pages with an excessive number of banners and pop-ups, decelerating page loading and damaging viewer experience. Consumers often filter or avoid such ads or view them skeptically ([10]), and ad blocking surged by 30% globally in 2016, with a total of 615 million devices using this software ([39]). As a result, advertisers began seeking alternatives for interruptive display ads, and native ads seemed an ideal candidate.
Native advertising, also referred to as sponsored content or streaming advertising, is an increasingly popular form of disguised online display advertising wherein ad experience matches the format/function of user experience on the platform on which it is displayed. It "camouflages the marketing messages so that they look and sound like editorial (organic) content" ([34]). From 2017 to 2018, spending on native advertising was projected to increase by 31% to a total of $32.9 billion, which makes up 58% of all display ad spending ([17]). In comparison, in 2010, 63% of all display ad spending was on interruptive banner ads ([16]). There has been limited research on native advertising, despite its recent proliferation.
Sponsored listings (native ads) are inserted into a stream of listings such as articles (e.g., sponsored articles inserted in Yahoo! News), social media postings (e.g., sponsored posts on Facebook) and online video titles (e.g., sponsored titles on YouTube). Given the positions available for sponsored items on a website, where an ad would appear is determined by its serial/rank position (we use "serial position" and "rank position" synonymously), which can in turn influence ad performance. How quickly does a native ad's performance change as its serial position lowers (i.e., from rank 1 to a lower rank)? How does the rate of change vary for different performance metrics and viewer groups? This study addresses these questions.
Following the online ad literature (e.g., [19]; [59]), we focus on CTRs and conversion rates (CVRs, the rate of conversion per click) as key performance metrics. The stream of listings (including both organic and sponsored listings) is loaded bit by bit as viewers scroll down the web page. An impression is counted when the listing is loaded into the web page and displayed to the viewer. After an impression occurs, the viewer might (or might not) notice it and even click on it. Under the prevalent "pay per click" scheme in the online advertising industry, advertisers do not pay a fee for an ad impression unless the ad is clicked. Thus, for a publisher, CTR is a key metric that determines how much revenue it can generate by displaying ads on the web page. Meanwhile, an advertiser is primarily concerned with CVR or how many conversions occur after clicks, because its business success depends on the number of conversions (e.g., purchases). The CTR and CVR correspond to different stages in the online sales funnel. The classical sales funnel is a multistage process through which a consumer moves toward a purchase (i.e., from attention to evaluation and attitude formation to decision and action; [29]; [56]). In the online advertising context, the sales funnel is a web viewer's journey from ad impression to click to conversion.[ 5] Thus, CTR is the transition rate at the upper funnel (i.e., from impression to click), while CVR is the transition rate at the lower funnel (i.e., from click to conversion). In addition to CTR and CVR, we conduct supplemental analyses on additional performance metrics to make potential financial implications.
Marketers and researchers have long been concerned that the serial position in ad placement can influence ad effectiveness (e.g., [50]; see Table W1.1 of Web Appendix W1). According to the literature, the serial position effect is different across various types of ads, and there are inconsistent findings within each ad type/media platform. Moreover, previous studies mostly examine undisguised and interruptive ads, and thus their findings may not apply to native ads, which viewers may not recognize as an ad until after clicking on it. As we theorize subsequently, this disguised nature predicts a distinct role of serial position for native ads compared with conventional ads—that is, it may have a modest impact on ad click but a radical impact on postclick conversion. Furthermore, prior research has provided limited insight regarding the contingent effects of ad serial position across consumer groups.
This study is among the first to examine native ad performance across serial positions. Its main contributions are threefold. First, we theorize and empirically investigate a relatively new type of advertising, native advertising, which is of immense managerial and economic importance but rarely studied by marketing researchers. Second, based on large-scale field data, we find that as serial position lowers, the performance of a native ad drops only moderately for publishers (in terms of CTR and revenue per impression) but acutely for advertisers (in terms of CVR and conversion per ad dollar spent). Such vastly asymmetric effects of serial position on publishers' versus advertisers' metrics are unique to native ads and have not been documented or implied by prior research. Managerial insights derived from these findings may encourage potential revolution in the native ad industry (e.g., our findings show that native ad advertisers overpay for lower rank positions and are thus at a disadvantage). Third, we unveil important contingency factors that moderate the serial position effect, including audience gender and age. By taking a contingency perspective, this study not only adds to the literature by providing a more thorough understanding of the serial position effect (i.e., the effect is not homogeneous across audience groups) but also enables practitioners to better optimize native ad performance under various conditions.
Prior research has studied the effect of serial position for interruptive ads on conventional media and certain online ads. However, this literature has provided mixed findings (summarized in Table W1.1 of Web Appendix W1).
Research on the serial position of TV ads has mostly examined viewer memory (e.g., brand recall) as the outcome using lab experiments. Some studies (e.g., [27]; [41]) find support for a primacy effect on memory (i.e., viewers tend to better remember the ads in the first position of a sequence), while others (e.g., [53]) document recency effect (the tendency to better remember the last ad in a sequence). Regarding print ads, research based on Starch scores has provided ambiguous results (e.g., [18]). Using an eye-tracking lab study with 88 consumers reading two magazines, [55] show stronger recency effect than primacy effect on memory. In contrast with the findings in conventional offline media contexts, [32] show that online video ads (in-stream ad clips inserted in a YouTube video) in the middle positions lead to higher recall than those in the first or last position, based on experiments with 240 college students.
There is also a growing literature on keyword-based sponsored search ads on online search engines. Like most research on online ads, this literature focuses on CTRs and CVRs instead of memory as outcome variables. The impact of ad serial position is inconclusive from this literature. For example, some studies show that CTR and CVR constantly decrease as rank position lowers ([44]), some find nonlinear effect of rank position ([ 2]), and others report no significant change in CVR across rank positions ([37]). Note that a potential consumer will not see search ads unless (s)he has intentionally searched for a related keyword. Therefore, consumers exposed to them are likely to have already developed interest or purchase intent to some extent ([12]). Because of such preexisting interest, viewers of search ads are more likely to look through lower-ranked ads to find the best match to their interest. Thus, there are still substantial chances for conversions to occur with search ads at lower rank positions (e.g., [19]). Similar motivations to scan through lower rank positions are unlikely to exist for other types of ads (including native ads), whose exposures are more random and coincidental.
In summary, there is continuous debate on the significance and magnitude of ad serial position effect, which appears to vary for different types of ads and different outcome metrics of interest. In addition, although "serial position" shares one basic meaning in both offline and online contexts (i.e., the ordinal position; e.g., first, second,...) of an ad in a series of sequentially presented ads (which determines whether an ad would appear earlier or later in the sequence), the way the ads are inserted in the media content could vary. For instance, a series of TV or radio ads often run back to back within an ad pod (i.e., a block of ads clustered right next to one another), a magazine ad appears every few pages, and each online ad (including native ad) is placed at a different slot between the organic contents on a web page or in a video.[ 6] Such differences further highlight the importance of examining the serial position effect in each context because results from one context may not directly apply to the others. More importantly, compared with the other ad types discussed previously, native ads are more disguised and less interruptive by design ([42]).[ 7] Viewers are likely to click on a native ad without recognizing that it is in fact an ad ([57]). Because of this unique nature, the serial position effect for native ads can significantly differ from that for traditional, undisguised ads. Considering the recent proliferation of native ads, our study is meaningful and timely.
Native advertising is a form of online display ad. Thus, we also review the online display ad literature and highlight the uniqueness of our study. Unlike ads in conventional media (e.g., TV, print), online ads allow and encourage users to take immediate actions (e.g., clicks and purchases) on the same device/platform where the ad is displayed. Moreover, for advertisers, instead of pay-per-slot (e.g., time slot on TV or pages/sections on print media), the pay-per-click scheme dominates online ad platforms. Thus, online ad studies focus on CTRs and CVRs as key outcome metrics (e.g., [10]; [59]). Consistent with the literature, we also examine these metrics.
Our study differs from prior research on four major fronts. First, most previous studies examine banner ads (see Table W1.2 of Web Appendix W1), which are interruptive and trigger negative connotations upon impression ([20]; [33]). In contrast, we focus on native ads, which are designed to counter this nature of banner ads and better blend into the surrounding organic contents ([12]). Only until recently have researchers begun to pay attention to native ads: using lab experiments, [57] and [ 8] examine some drivers (e.g., disclosure format and companion banner ad) of viewers' recognition of and attitude toward native ads, and [45] conduct field experiments in the context of a mobile app for restaurant search to assess the level of consumer deception. Second, although web pages rarely display only one single ad, and viewers are typically exposed to multiple online display ads in a sequence, previous studies (including the three on native ads) have not examined the effect of online display ads' serial positions. Third, because of data restrictions, prior research has typically treated consumer features (e.g., gender, age) as unobservable or has ignored them and thus cannot speak to their moderating impact on ad effectiveness. Fourth, most prior studies use data about one particular advertiser, limiting the generalizability of the results.
Prior research has suggested two major theoretical mechanisms that explain the impact of online ads' placement positions on their performances: ( 1) the annoyance effect and ( 2) the attention effect.[ 8]Table 1 lists the key differences in these effects between disguised native ads and undisguised ads. Undisguised ads (e.g., banner ads, pop-up ads) are interruptive and thus cause annoyance upon ad impression before click. In contrast, in the case of native ads, viewers can mistake a sponsored listing for an organic listing (e.g., a news article or a regular Facebook post) and thus might not be annoyed until after clicking on the ad ([57]). Thus, native ads may "postpone" annoyance to later stages of the online sales funnel (from preclick to postclick). As we elaborate next, because of this uniqueness of native ads, the impact of ad serial position may be asymmetric on click-related metrics (e.g., CTR) versus conversion-related metrics (e.g., CVR).
Graph
Table 1. Theoretical Distinctiveness of Native Ads Versus Undisguised Ads.
| A: Undisguised Online Ad (Hypothetical Context for Theoretical Comparison) | B: Disguised Native Ad (Focal Context of the Present Study) |
|---|
| Changes in CTR (Clicks/Impressions) Across Rank Positions | Changes in CTR (Clicks/Impressions) Across Rank Positions |
|---|
| Annoyance effect | A lower-ranked ad triggers greater annoyance (because, prior to the current ad, the viewer has already been interrupted by other ads and thus becomes less patient with yet another ad) and thus has lower CTR. | Annoyance effect | Native ads look like organic listings and viewers may not recognize them as ads. Thus, annoyance does not significantly increase as ad rank lowers, because the higher ranked ads presented before the focal ad have not caused significant interruption of the viewing experience. |
| Attention effect | A lower-ranked ad may be less noticeable and attract less attention and thus has lower CTR. | Attention effect | A lower-ranked listing may be less noticeable and attract less attention and thus has lower CTR. |
| | Conclusion: CTR drops relatively slowly as a native ad's rank position lowers because of the lower preclick annoyance effect. |
| Changes in CVR (Conversions/Clicks) Across Rank Positions | Changes in CVR (Conversions/Clicks) Across Rank Positions |
| Annoyance effect | A lower-ranked ad can trigger greater annoyance. However, the fact that the viewer has clicked on the ad means that (s)he might be interested in the product advertised. The viewer's interest in the product could mitigate the annoyance effect on CVR. | Annoyance effect | Although viewers are less likely to be annoyed by native ads before clicking on them (because of the ads' disguised nature), they may be annoyed after realizing that they have been "tricked" (i.e., after clicking on an ad that they thought was an organic listing). Such annoyance swells as ad position lowers (because of increasing chance of repeated exposures and greater time constraint as they browse through the website, both of which amplify annoyance). Thus, native ad CVR can decrease rapidly as its rank position lowers due to the annoyance effect. |
| | Conclusion: CVR drops relatively quickly as the rank position of a native ad lowers because of the stronger postclick annoyance effect. |
30022242918817548 Notes: Other theoretical mechanisms proposed by prior research on ad serial position include ( 1) the primacy and recency effect (i.e., viewers tend to better remember the first and the last ads in a sequence), ( 2) the perceived quality effect (i.e., an ad in a lower position might be associated with lower quality), and ( 3) the fatigue effect. These theories are relatively less relevant to our context. First, the primacy and recency effect explains the effect of serial position on memory, which is not the focal outcome of our study. Like most studies on online ads, we model clicks and conversions instead of memory. Second, prior research on the perceived quality effect typically considers products that are direct competitors (e.g., [19]). For example, in the context of keyword-based search ads, when a consumer searches for the keyword "car insurance," all the ads displayed are from car insurance companies that bid for this keyword. In contrast, native ads inserted into a web stream are "random": products advertised in neighboring native ads are often from different product categories (e.g., car insurance ad in rank 1 followed by video game ad in rank 2). Therefore, product qualities in neighboring native ads cannot be directly compared. Moreover, because native ads are disguised, a viewer is less likely to know whether (s)he has seen other ad(s) before seeing the current lower-ranked ad and, thus, is less likely to develop strong association between ad rank and product quality. Third, the literature generally agrees that visual and psychological fatigue tends to be insignificant within 20 minutes of web browsing ([11]), and the length of typical viewing sessions on the focal website is shorter than 20 minutes.
For regular undisguised ads displayed in a sequence, an ad impression tends to trigger greater annoyance as its rank position lowers. This is because viewers become more annoyed after repeatedly seeing interruptive ads ([ 4]). Increasing preclick annoyance in turn results in decreasing CTR. Moreover, ads in lower positions may be less noticeable and attract less viewer attention ([25]), further reducing the CTR of lower-ranked ads.
In contrast, for disguised native ads, preclick annoyance may not significantly increase as ad rank lowers. Native ads look similar to the organic listings surrounding them, and viewers may not recognize them as ads ([45]). Thus, viewers may not realize whether or how many higher-ranked ads have been presented before a lower-ranked ad. Therefore, a lower-positioned native ad may not be associated with considerably higher annoyance than the top-positioned one. Meanwhile, as viewers scroll down, they are unlikely to experience greater attention reduction on a web page with native ads than on a web page with traditional ads. For these reasons, we propose that, unlike regular display ads, as a native ad's rank position lowers, its CTR only drops at a moderate pace.
Although viewers are less likely to be annoyed by native ads before clicking on them, they tend to be annoyed after realizing that they have been "tricked" (i.e., after clicking on an ad which they initially thought was an organic listing) ([ 8]; [57]). A viewer may not encounter lower ranked listings unless they scroll down and spend a longer time on the website. Given the limited time available for each website visit, a viewer typically has greater time constraint and exhibits increased impatience as (s)he scrolls down. Thus, a lower-positioned ad may be perceived as a greater disruption or waste of time, causing greater postclick annoyance than a top-positioned one. Moreover, when the topmost ad is clicked, the viewer has typically just started the viewing session and has not clicked on any other ads; in comparison, by the time a lower-ranked ad is displayed, the viewer might have already clicked on an ad and will thus be highly annoyed if (s)he is tricked by yet another ad. This greater annoyance negatively affects the likelihood of conversions after clicks. Therefore, we expect the CVR of a native ad to decrease rapidly as its rank lowers as a result of the increasing postclick annoyance effect.
In contrast, the effect of postclick annoyance on CVR may not be so severe for regular online display ads. Because of their undisguised nature, web users actively attempt to avoid clicking on them ([10]; [20]). When viewers do click on such ads, it is often because they are interested in the products advertised, and thus they are less likely to be annoyed after clicking.
Therefore, we expect differential effects of serial position on native ad's CTR versus CVR:
- H1 : As a native ad's serial position lowers from rank 1 to rank 2 (or 3), (a) CTR drops at a moderate speed while (b) CVR drops at a high speed.
Although prior researchers have realized the importance of examining serial position effect from a contingency perspective, the moderators they identified are mostly specific to the type of ad under study and do not apply to native ads.[ 9] The classic persuasion theory (e.g., [40]) indicates that the nature of the recipient (i.e., the ad viewer) is a key factor that moderates advertising effectiveness. Thus, we examine the moderating roles of two main demographic factors that constantly interest marketers and researchers, namely, gender and age (e.g., [ 6]; [24]; [58]). These moderators fit in the conceptual framework because they can mitigate or amplify the two main theoretical mechanisms (i.e., attention effect and the annoyance effect) and thus can moderate how native ad performance varies across rank positions (summarized in Figure 1).
Graph: Figure 1. Conceptual framework.
Biological differences (e.g., brain lateralization, chromosomes, hormones) across genders result in cognitive and behavioral differences ([24]), one of which pertains to visual attention. The literature suggests that women's visual attention spreads over a wider range, whereas men's is more focused. For example, [54] indicates that women are more discovery-oriented than men when browsing a website. Consistently, using eye tracking, [47] find that women and men orient their visual attention differently during listening tasks (e.g., women's saccades are often "distracted" toward background scene elements), [23] show that women scan more and extract a wider array of visual information than men, and [26] find that women attend visually to more areas than men in the context of online shopping. According to mental health researchers, "women are just biologically wired to pay attention to different things than men are" and thus have widespread attention ([31]). Thus, we expect that women are more likely to notice and click on lower-positioned listings than men. Therefore, as a native ad's position becomes lower, its rate of decrease in CTR can be slower among women than men.
However, there can be a faster reduction in CVR among women, whose postclick annoyance may increase more quickly as ad position lowers. After clicking on a disguised ad, viewers may consider the ad deceiving if they find themselves tricked. Compared with men, women consider deception in communication less acceptable and more annoying ([30]; [38]). Moreover, women are, in general, more sensitive to time constraints in their spare time because of the maternal instinct (e.g., [46]), and thus their postclick annoyance may increase more quickly as ad position lowers (because the time constraint increases as they scroll down the web page). In addition, when viewers associate an advertiser with an intent to mislead or cheat, the perceived risk of transaction is increased, and women exhibit stronger risk aversion than men ([ 5]). As ad rank lowers and the chance of viewer exposure to preceding ads increases, this negative effect is further amplified because women exhibit stronger reactance to persistent annoyance or repetitive provocation ([36]). Therefore, we expect the following:
- H2 : (a) CTR drops faster across ranks for men than for women, whereas (b) CVR drops faster across ranks for women than for men.
While both older and younger adults are comparably capable of processing information from accessible sources (Gaeth and Heath 1987), younger viewers have less concentrated and more diffuse attention spread ([35]) and constantly "jump to the next thing" in the online context ([43]). Thus, younger viewers may not pay significantly less attention to lower-positioned listings than the topmost listing, thus mitigating the rate of change in CTR as a native ad's serial position lowers. In contrast, older viewers' attention and clicks drop more quickly as ad position lowers.
However, serial reduction in CVR can be slower among older viewers than younger ones. When viewers realize that what they have just clicked on is in fact not an organic listing but a disguised ad, they may perceive the ad as misleading and manipulative. Manipulative ads trigger psychological reactance ([13]), inducing people to "do just the opposite," reducing conversions. As a native ad's serial position lowers, web viewers tend to be more irritated or annoyed after being tricked by it, leading to a further increase in reactance and decrease in CVR. This tendency can be weakened as viewer age increases because psychological reactance to such external stimuli tends to decrease with age ([24]). Prior research has also documented that younger adults report greater exposure to daily stressors (hassles) than older ones ([48]) and are thus likely to have lower tolerance for and higher annoyance with unwanted distractions, especially under time constraint. Because perceived time constraint increases as viewers scroll down the web page, the postclick annoyance effect can be augmented for younger viewers. Consequently, age is likely to have asymmetric moderating effects for CTR versus CVR:
- H3 : (a) CTR drops faster across ranks for older than for younger audiences, whereas (b) CVR drops faster across ranks for younger than for older audiences.
We obtained data from a leading global web portal headquartered in the United States. The list of all articles, including both organic and sponsored articles (native ads), is called a "web stream." Sponsored articles are "native" because they are inserted into the web stream and blend in with the surrounding organic articles (for examples, see Figure 2). To better engage users, publishers typically do not display sponsored articles in the first position of the stream. The topmost (rank 1) ad is at the third position of the web stream (i.e., the first and second positions are filled with organic articles); then, there are four positions for organic articles in between every two neighboring ads onward (i.e., the rank 2 ad is inserted at the 8th position of the web stream, rank 3 ad is at the 13th position, and so forth). Our data set includes 120 distinct native ads randomly selected from the portal's database in March 2016, covering approximately 180 million page views. To avoid selection bias caused by targeting, we focus only on nontargeted ads.
Graph: Figure 2. Examples of native ads in web stream.Notes: In the screenshots, the listings marked with "}" are examples of native ads inserted in a web stream. The ad on the left was for Home Chef, a meal kit and food delivery company, and the ad on the right was for Verizon, promoting a new smartphone.
We examine two key metrics, CTR and CVR, following the literature (e.g., [19]; [59]). For a publisher, the number of ad impressions it can sell is not infinite, and too many ad slots may jeopardize perceived website quality and, thus, viewer experience. Because publishers attempt to maximize revenue from the limited number of ad slots available and an impression would not contribute any revenue until viewers click on it, CTR = clicks/impressions is a vital metric. For an advertiser, who typically aims to enhance conversions to maximize business success, CVR = conversions/clicks is a key metric. To provide further managerial insights, especially potential financial implications, we also discuss three additional outcome metrics in a subsequent section—namely, conversion per impression (CPI), publisher's revenue per impression (RPI), and advertiser's conversion per ad dollar spent (CPD).
The focal contingency factors are viewer age and gender (1 for female and 0 otherwise). In addition to the focal moderators, we include digital access device (desktop, tablet, or mobile), location (country), operating system (OS; Windows, Apple OS, or Android), web page types,[10] and categories of the products advertised as control variables.
Our data set has several unique features compared with those used by prior empirical research on ad serial position. First, previous studies mostly employ data that is aggregated or averaged by ad and/or by time ([ 1]), such as the average daily ad ranks and performances (e.g., on each day t, this ad's average rank position, total impressions, total clicks, and total conversions). Such data cannot identify the CTR or CVR of each ad at a particular rank. Because of this data restriction, prior studies have treated average daily rank position as a continuous independent variable and further assume a linear relationship between average daily ad rank and average daily CTR or CVR (e.g., [44]). Such data also introduces measurement error and aggregation bias ([ 1]). In contrast, our data are disaggregated, and we observe the exact CTR and CVR for each ad each time it was displayed at a specific rank position. Thus, we can directly compare each ad's performance at each rank position and find nonlinear rate of change in ad effectiveness across ranks.
Second, native ads are not triggered by a search query, and thus, their appearances are not determined by their relevance to the viewer. Neighboring native ads are often irrelevant to one another and rarely belong to the same product category. This is in sharp contrast with the context of sponsored search ads, in which neighboring ads are typically direct competitors. Moreover, unlike search ads, a native ad in rank m is not inherently more relevant than that in rank n (m < n). For these reasons, in our data, the native ads displayed to each viewer on each web page are random in nature.
Third, while viewer characteristics (e.g., demographics) were typically unobservable in prior studies (e.g., [44]), we explicitly account for viewer characteristics in the model and examine how they moderate the effectiveness of native ad at each rank position.
Each of the 120 sample ads has its own advertiser and creative content. It is thus unwise to simply aggregate the ranking effect across different ads. Therefore, we conduct separate studies for each ad and then use meta-analysis to conduct an "analysis of analyses" across the separate studies. Specifically, we first conduct a separate study to examine the relative performance of a particular ad in rank r (r ≥ 2) versus the same ad's own performance in rank 1 (the topmost rank position) under each unique "campaign scenario" (CS). A CS means a particular native ad viewed by a particular type of viewer under a particular circumstance. In other words, each CS is a unique combination of all possible variations in the native ad itself, viewer characteristics, web page type, and other contextual factors (e.g., viewing device and location). Within each CS, the same ad's rank positions are "manipulated"/altered while all other relevant factors are held constant. Thus, each CS can be considered an individual "natural experiment" with ad rank as the focal treatment. Meta-analysis then integrates the results from all individual natural experiments to obtain an overall estimate of the relative effectiveness of native ad at each rank position. As we specify subsequently, the dependent variable in meta-analysis is the ratio of ad effectiveness (e.g., CTR, CVR) on rank r relative to rank 1 under each CS. To test the moderating effects, we further fit a meta-regression model using the contingency factors as explanatory variables.
Meta-analysis has been extensively applied in such areas as medical research and biostatistics to combine results from various scenarios or studies ([ 3]). The fixed-effect meta-analysis assumes that, for study s (s = 1, 2,..., N), one observes the effect size or outcome measure , which often takes the form of log-odds-ratio or log-relative-risk in frequency analysis ([52]). Assuming in each study s, where is the squared standard error (SE), meta-analysis provides an overall estimation of the outcome measure across all N studies as
θ=∑sθsws/∑sws,1
where ws = 1/vs. Thus, meta-analysis estimation is a weighted average of the outcomes from various individual studies, and the weight is proportional to the inverse of the variance. Such "inverse-variance weighting" naturally assigns higher weight to a study with lower variance (i.e., higher confidence in the outcome). However, the fixed-effect model assumes no interstudy variability and is thus prone to various sources of heterogeneity.
In comparison, the random-effect meta-analysis model (Equation 2) has less restrictive assumptions and is more suitable to our context considering the heterogeneity across different ads/scenarios. It assumes that the included studies represent a random sample from a population of studies addressing the focal research question. Here, the true effect/outcome of each study is sampled from a normal distribution (i.e. ; [22]).
ys=μ+us+es, where us∼N(0,σθ2),es∼N(0,vs).2
This approach also uses inverse-variance weighting, that is, each study is weighted by , where vs is again the squared SE of ys and is replaced by its estimator in practice.
Thus, we apply the random-effect model in Equation 2 to test H1. Specifically, by estimating of the following model, we can estimate a meta-analysis weighted average of the relative CTR at each rank r (r ≥ 2) compared with rank 1:
log(RRi, CTRr,S)=log(Bir,SAir,SBi1,SAi1,S) =μrCTR+ui, CTRr,S+ei,CTRr,Swhere ui, CTRr,S∼N(0,σθ, CTRr 2), ei, CTRr,S∼N(0,vi, CTRr,S).3
is the observed CTR of rank r relative to rank 1 (i.e., the "risk ratio" in meta-analysis research; [52]) for each native ad i (i = 1, 2,..., 120) in each possible scenario S = {g, a, d, p, os, l} defined by a unique combination of viewer gender (g), age (a), device (d), web page type (p), OS (os), and location/country (l); A and B represent impressions and clicks, respectively; the squared SE of is estimated as following [ 7]. Each experiment (CS) is then weighted by .
When there is a need to test moderating effects, researchers employ the mixed-effect meta-regression model (e.g., [ 5]; [60]). It is a generalized form of the random-effect meta-analysis model that allows inclusion of regressors/moderators. Suppose that each study has a vector of contingency factors ; the mixed-effect meta-regression model is specified as
ys=β0+ XsTβ+us+es, where us∼N(0,σθ2),es∼N(0,vs).4
The coefficient vector denotes the impact of the moderators on the studies, and each study is again weighted by . Notably, while captures observed heterogeneity, us captures the unobserved heterogeneity across studies.
Accordingly, we specify the following model to test the effects of the moderators (H2–H3):
log(RRi, CTRr, S)=log(Bir,SAir,SBi1,SAi1,S)=β0,rCTR+XiSβrCTR+ui, CTRr,S+ei,CTRr,Swhere ui, CTRr,S∼N(0,σθ, CTRr 2), ei, CTRr,S∼N(0,vi, CTRr, S),5
where is a vector of the focal moderators and control variables as specified in the "Data and Variables" section, and represents the impact of the moderators on the log relative CTR of rank r. Similarly, we can specify the models for CVR and additional outcome metrics (details in Web Appendix W2). Web Appendix W3 discusses more about the appropriateness of using meta-analysis models in this study.
Table 2, Panel A, presents the estimation results from the random-effect meta-analysis model (Equation 2) to describe the overall relative effectiveness of each rank across all CSs. For ease of interpretation, we convert each estimate of μ into a percentage (by taking its exponential) with rank 1 as baseline (100%) and report the results in Table 2, Panel B. Notably, as the rank position of a native ad becomes lower, CVR decreases at a much higher speed than CTR. For example, CVR in rank 2 is only 15.9% of that in rank 1, on average, whereas CTR in rank 2 is 97.0% of that in rank 1. This pattern is consistent with our expectation in H1 and can be attributed to the unique nature of native ads. Prior research on the serial position of other types of online ads has not documented similar effects. Also in Table 2, Panel B, we present the estimates based on naive aggregation and weighted least square (WLS) regression (with logged CTR or CVR as dependent variable, rank indicators as independent variables, and the inverse of squared SE of CTR or CVR as weights) to compare with meta-analysis estimates. Their differences further confirm the importance of using random-effect meta-analysis that accounts for the heterogeneity and differential estimation uncertainties across various scenarios (for further discussion, see Web Appendix W3). That said, the results from all three methods indicate vastly asymmetric effect of native ad serial position on CTR versus CVR (i.e., CVR drops much more rapidly than CTR as serial position lowers).
Graph
Table 2. Results on Native Ad CTR and CVR Across Serial Positions.
| A: Results of Random-Effect Meta-Analysis Without Moderators (H1a and H1b) |
|---|
| DV: Relative CTR at Rank r | DV: Relative CVR at Rank r |
| log(CTRrank = 2/CTRrank = 1) | log(CTRrank = 3/CTRrank = 1) | log(CVRrank = 2/CVRrank = 1) | log(CVRrank = 3/CVRrank = 1) |
| μ | SE | μ | SE | μ | SE | μ | SE |
| −.030** | .003 | −.057** | .003 | −1.837** | .009 | −3.111** | .013 |
| B: Results of Meta-Analysis Converted into Percentages (Rank 1 as Baseline; Rightmost Two Columns Present Estimates from Alternative Methods for Comparison) |
| Relative CTR | Estimates Based on Random-Effect Meta-Analysis | Estimates Based on Naive Aggregation | Estimates Based on WLS |
| Rank 1 | 100.0% | | 100.0% | 100.0% |
| Rank 2 | 97.0% | 90.5% | 95.9% |
| Rank 3 | 94.5% | 86.0% | 91.1% |
| Relative CVR | Estimates Based on Random-Effect Meta-Analysis | Estimates Based on Naive Aggregation | Estimates Based on WLS |
| Rank 1 | 100.0% | | 100.0% | 100.0% |
| Rank 2 | 15.9% | 21.1% | 22.7% |
| Rank 3 | 4.5% | 10.8% | 13.0% |
| C: Results of Mixed-Effect Meta-Regression with Moderators (H2 and H3) |
| DV: Relative CTR at Rank r | DV: Relative CVR at Rank r |
| log(CTRrank = 2/CTRrank = 1) | log(CTRrank = 3/CTRrank = 1) | log(CVRrank = 2/CTRrank = 1) | log(CVRrank = 3/CTRrank = 1) |
| Variable | Coef. | SE | Coef. | SE | Coef. | SE | Coef. | SE |
| Constant | −.085** | .008 | −.148** | .011 | −2.231** | .022 | −5.566** | .063 |
| Age | −.0010** | .0002 | −.0016** | .0002 | .017** | .001 | .065** | .004 |
| Gender (male as baseline) | | | | | | | |
| Female | .011* | .005 | .013 | .007 | −.123** | .015 | −.157** | .038 |
| Device (desktop as baseline) | | | | | | | |
| Tablet | −.098 | .069 | −.282 | .177 | .829 | 1.095 | Tablet | −.098 |
| Phone | −.051 | .032 | −.105 | .065 | .451** | .137 | Phone | −.051 |
| Web page content (mixed as baseline) | | | | | | | |
| Leisure | .011 | .009 | −.018 | .017 | .733** | .046 | 1.943** | .540 |
| Nonleisure | .025 | .024 | .021 | .026 | −.868* | .417 | −1.218* | .560 |
| OS (Windows as baseline) | | | | | | | |
| Apple | .058** | .006 | .121** | .008 | −.444** | .024 | .011 | .067 |
| Android | .017 | .039 | .029 | .056 | −.408 | .228 | −1.480* | .716 |
| Locations | Included in the model as control variables |
| Product categories | Included in the model as control variables |
- 40022242918817550 * Significant at.05.
- 50022242918817550 ** Significant at.01.
- 60022242918817550 Notes: Two-tailed tests of significance. Location indicators include Australia, Canada, and the United Kingdom, with the United States as baseline. Product category indicators include retail, health and beauty, travel, financial services, and automobile and digital products, with others as baseline.
The results of the mixed-effect meta-regression with moderators (Equation 4) are reported in Table 2, Panel C.
With log(CTRrank = r/CTRrank = 1) as the dependent variable, the positive coefficient of viewer gender (female indicator) is significant for r = 2 but nonsignificant for r = 3. The results provide partial support for H2a and indicate that the speed of reduction in CTR from rank 1 to rank 2 is mitigated among women. With log(CVRrank = r/CVRrank = 1) as the dependent variable, the negative coefficient of female indicator is significant for both r = 2 and r = 3. The results support H2b, indicating that women's CVR decreases at an even faster rate than men's as rank position lowers.
We find strong support for H3a and H3b because age has negative and significant coefficients in CTR models and positive and significant coefficients in CVR models. Thus, as native ad rank lowers, the speed of reduction in CTR is amplified while that in CVR is mitigated for older audiences.
Drawing on the model coefficients, we plot Figure 3 with both the relative (plotted in bars, with rank 1 as baseline 100%) and absolute values (plotted in lines) of estimated CTR and CVR at each rank under each condition. We would like to point to a discrepancy between the conclusion based on relative values and that based on absolute values in one particular case related to H2b: while the relative values indicate that the CVR change from rank 1 to rank 2 is larger for women (100% − 14.76% = 85.24%) than for men (100% − 16.70% = 83.30%) and support H2b, the absolute values indicate otherwise (.0800 −.0118 =.0682 for women and.0822 −.0137 =.0685 for men). This discrepancy is due to the inherently higher level of overall CVR (and, thus, higher baseline value) for men than for women.[11] Depending on managerial needs, practitioners may decide whether to focus more on relative or absolute changes in this case.[12] Nevertheless, both relative and absolute values plotted in Figure 3 and the meta-regression coefficients lead to consistent conclusions regarding each corresponding hypothesis except for H2b.
Graph: Figure 3. Changes in native ad performance across ranks contingent on each moderator.Notes: The bars are plotted based on proportional values (with rank 1 as baseline 100%) and correspond to the left axis. The lines are plotted based on absolute values and correspond to the right axis. Age is treated as a continuous variable in the model. In Panels C and D, for demonstration purposes, the younger and older groups are divided at the mean.
Table 3 reports the results from supplemental analyses of additional outcome metrics. First, we consider the likelihood of conversion per ad impression (CPI = conversions/impressions = CVR × CTR). Because of the drastic change in CVR but only modest change in CTR across rank positions, the result pattern of CPI largely mirrors that of CVR. Specifically, there is a fast drop in CPI across ranks (as reported in Table 3, Panels A and B), and the moderating effects of gender and age on CPI are similar to those on CVR (as reported in Table 3, Panel C). Second, we analyze two metrics that could potentially generate more direct profitability implications: ( 1) publisher's revenue generated from each ad impression (RPI = CTR × CPC, where CPC represents cost per click) and ( 2) number of conversions per ad dollar spent (CPD = CVR/CPC), which is directly proportional to the advertiser's return on investment (ROI). Because the variations across rank positions in CPC are much smaller than those in CTR or CVR, the result patterns of RPI are dominatingly determined by CTR, and CPD results are largely consistent with CVR results. As we report in Table 3, Panel B, there is a faster drop across ranks in CPD than in RPI. We discuss the potential financial implications of these results in the "General Discussion" section.
Graph
Table 3. Results on Additional Metrics.
| A: Results of Random-Effect Meta-Analysis Without Moderators |
|---|
| DV: Relative RPI at Rank r | DV: Relative CPD at Rank r | DV: Relative CPI at Rank r |
| log(RPIrank = 2/RPIrank = 1) | log(RPIrank = 3/RPIrank = 1) | log(CPDrank = 2/CPDrank = 1) | log(CPDrank = 3/CPDrank = 1) | log(CPIrank = 2/CPIrank = 1) | log(CPIrank = 3/CPIrank = 1) |
| μ | SE | μ | SE | μ | SE | μ | SE | μ | SE | μ | SE |
| −.049** | .003 | −.087** | .004 | −1.818** | .010 | −3.081** | .013 | −1.900** | .009 | −3.233** | .011 |
| B: Meta-Analysis Estimates Converted into Percentages (with Rank 1 as Baseline) |
| | Relative RPI | Relative CPD | Relative CPI | | | | | | | | |
| Rank 1 | 100.0% | 100.0% | 100.0% | | | | | | | | |
| Rank 2 | 95.2% | 16.2% | 15.0% | | | | | | | | |
| Rank 3 | 91.7% | 4.6% | 3.9% | | | | | | | | |
| C: Results of Mixed-Effect Meta-Regression with Moderators |
| DV: Relative RPI at Rank r | DV: Relative CPD at Rank r | DV: Relative CPI at Rank r |
| log(RPIrank = 2/RPIrank = 1) | log(RPIrank = 3/RPIrank = 1) | log(CPDrank = 2/CPDrank = 1) | log(CPDrank = 3/CPDrank = 1) | log(CPIrank = 2/CPIrank = 1) | log(CPIrank = 3/CPIrank = 1) |
| Variable | Coef. | SE | Coef. | SE | Coef. | SE | Coef. | SE | Coef. | SE | Coef. | SE |
| Constant | −.116** | .008 | −.192** | .013 | −2.203** | .021 | −5.526** | .063 | −2.430** | .020 | −5.764** | .056 |
| Age | −.000** | .000 | −.001** | .000 | .016** | .001 | .064** | .004 | .017** | .001 | .063** | .003 |
| Female | .011* | .005 | .006 | .008 | −.092** | .014 | −.134** | .034 | −.069** | .013 | −.094** | .034 |
| Tablet | −.136** | .049 | −.201 | .134 | .983 | 1.044 | 1.935 | 1.141 | .575 | 1.085 | 1.709 | 1.188 |
| Phone | −.029 | .021 | −.114** | .040 | .443** | .131 | 1.566** | .365 | .420** | .135 | 1.343** | .382 |
| Leisure page | .027 | .019 | .018 | .020 | .712** | .044 | 1.988** | .489 | .724** | .042 | 1.806** | .447 |
| Nonleisure page | −.000 | .024 | −.037 | .030 | −.725 | .397 | −1.001* | .507 | −.692 | .413 | −.947 | .542 |
| Apple OS | .077** | .006 | .158** | .009 | −.459** | .023 | −.072 | .061 | −.423** | .023 | .148* | .063 |
| Android OS | .015 | .039 | .065 | .065 | −.380 | .218 | −1.810** | .648 | −.334 | .226 | −1.053 | .683 |
70022242918817550 Notes: In Panel C, all other control variables are included in the model.
Web Appendix W4 presents several robustness checks. First, we explicitly account for potential sources of endogeneity (e.g., bid, other ads on the same web page) and employ alternative modeling approaches such as the copula method, WLS model, and mixed-effect linear model with random-coefficient specification. Second, we explain why our model and empirical setting allow us to avoid other potential biases pointed by prior research. Third, we rerun the analyses with a subsample of gender-balanced native ads. Fourth, we control for carryover effects by including each ad's prior daily number of impressions and prior average rank position. Fifth, we include the gender × age interaction term as an additional moderator in the model. The results remain consistent with the main analysis.
Recently, online advertising platforms are making an active shift toward native ads. For example, an increasing amount of revenue for Facebook comes from sponsored posts and streaming ads inserted in pages ([12]). Our study is among the first to study native ads, unveiling how the key success measures for publishers and advertisers change across rank positions and identifying important contingency factors, based on unique data on 120 native ads.
Although the literature on ad serial position has a long tradition, the change in ad performance across serial positions is nonobvious depending on ( 1) the type of ad and ( 2) the outcome metrics studied. It is necessary to study the effect of serial position for native ad because ( 1) it is highly unique (disguised and less interruptive compared to the types of ads previously studied), ( 2) different parties involved (e.g., publishers, advertisers) are concerned about different outcome metrics, and ( 3) it is an economically important and fast-growing multi-billion-dollar industry ([16]). We develop a theoretical framework that predicts a distinct pattern of serial position effect for native ads (as opposed to disguised ads) and empirically test it with large-scale data from the field. The results demonstrate vastly asymmetric effects of native ad serial position on publishers' metrics (click-based) versus advertisers' metrics (conversion-based). Such result pattern has not been documented or implied by prior research.
It is also imperative to take one step further and understand under what conditions native ads' effectiveness changes more quickly (or slowly) across serial positions. Prior research on the serial position effect has offered limited insight regarding its contingency factors, especially about how it may vary across viewer groups (see Table W1.1 of Web Appendix W1). Most empirical studies on online ads have treated viewer features as unobservable and thus have rarely examined how they may moderate ad effectiveness (see Table W1.2 of Web Appendix W1). In comparison, we take a contingency perspective in theoretical development and empirical testing and demonstrate that ( 1) the relative speed of cross-rank change in native ad effectiveness is conditional on viewer gender and age and ( 2) these viewer demographics exhibit asymmetric moderating effects on publishers' metrics versus advertisers' metrics.
Our study is based on large-scale behavioral data from the field. As commented by [49], while lab experiments and surveys unveil novel psychological processes behind a phenomenon, empirical insights from field data are also valuable because they reveal the relative economic magnitude of the effect, and thus, practitioners can directly apply them to make marketing decisions. Moreover, unlike most empirical studies on online advertising, which analyze one particular online advertiser, we use meta-analysis to conduct "analysis of analyses" of a large number of ads across various product categories to better generalize the results.
Our findings on the asymmetric and contingent effects of native ad serial position on CTR versus CVR provide new and timely managerial insights for marketers. Moreover, we provide supplemental analyses of additional metrics (e.g., RPI, CPD) to proffer potential financial implications.
We show that, in the native ad context, advertiser's metrics reduce drastically from the topmost rank to lower ranks. Such radical reduction has not been documented by prior empirical research on online ads' serial positions, which has mostly been conducted in the context of search ads. For example, using search ad data from an online retailer, [19] find that CVR of the lowest rank is still over half of that in rank 1; similarly, based on search ads for a retailer of consumer durables, [37] report no significant decrease in CVR as rank position decreases (except from rank 5 to 6). In contrast, we find that, for native ads, the CVR in rank 2 and rank 3 is only 15.9% and 4.5%, respectively, of that in rank 1. Furthermore, research based on search ads suggests that prominent rank positions are not necessarily more profitable. For instance, [19] and [ 2] find higher profits at the middle position than the top position. In contrast, in the context of native ads, advertisers' CPD (proportional to ad profit or ROI) in the topmost rank position is dominantly higher than any lower position (CPD at rank 1 is over 5 times as much as that at rank 2 and over 20 times of that at rank 3; see Table 3, Panel B). In other words, for each dollar spent at rank 2, the advertiser gets less than one-fifth of the conversions it would have gotten for the same dollar spent at rank 1. Therefore, native ad advertisers overpay for lower rank positions.
Currently, in the online advertising industry, ad platforms for both search ads and native ads (including our focal ad portal) share very similar bidding and ad ranking systems (e.g., [14]), which do not allow advertisers to preselect the rank position for the ad (i.e., an advertiser cannot place a separate bid for each rank position).[13] Under such systems, an ad could incur similar costs per click at two neighboring rank positions. These systems might be fair to search ad advertisers but not to native ad advertisers, because a native ad's value to the advertiser (CVR) decreases disproportionally faster than its cost does as rank position lowers. For advertisers, the ideal bidding system should allow them to place a separate bid for each rank position, and the ideal bid at each rank should be proportional to the expected CVR.
Meanwhile, the publishers (e.g., YouTube, Yahoo!, Facebook) face a dilemma. On the one hand, they want to enhance revenue by selling more ad slots; on the other hand, too many ads destroy user experience. Thus, a publisher may be motivated to eliminate unprofitable ad slots (e.g., it can consider removing lower-position slots if its revenue per ad insertion drops significantly as the position lowers). Recall that an ad impression does not contribute any revenue to the publisher unless it is clicked on, and thus, a publisher's revenue depends on CTR. We find only modest reduction in CTR as native ad position lowers. Moreover, under the bidding system that is currently dominating the industry, publishers' RPI also remains relatively stable across rank positions (e.g., RPIs in rank 2 and rank 3 are 95.2% and 91.7% of that in rank 1, respectively). Thus, it may be unwise for publishers to eliminate lower-rank ad positions, which can serve as an important source of ad revenue.
Our results indicate that the cross-rank change in native ad effectiveness varies across viewer groups. The findings point to the importance for marketers to adjust their practices in accordance with the contingency factors such as viewer gender and age. For example, for advertisers to optimize conversion and ROI, it is more imperative to get their ads on the top rank position when the audience includes women and younger customers (compared to men and older customers). On the other hand, publishers may consider increasing the density of native ads in the upper portion of the web page for men and older viewers. These implications are readily applicable because of the increasing feasibility of precision targeting.
Finally, while native ads proliferate in the online ad industry, they may be subject to stricter regulations in the future. Advertisers may face legal consequences when using disguised ads on certain user groups (e.g., children), and are under increasing pressures to use more salient disclosures ([ 9]). [57] suggest that native ad disclosures could influence its level of disguise or the rate of ad recognition (e.g., when using alterative disclosures such as "sponsored content," "advertisement," "brand-voice," and "presented by [sponsor]," the percentage of participants who can recognize a native ad ranges from 2% to 13%). We could expect that, when practitioners have to adopt more salient disclosures and make native ads less disguised/more easily recognizable, the result pattern that we propose in Table 1, Panel B, would become weaker, and the serial position effect of native ads could become more similar to that of conventional disruptive ads (Table 1, Panel A).
In this study, we theorize and empirically demonstrate native ads' serial position effects using large-scale field data. Future researchers could use lab experiments to further explore the uniqueness in consumer psychology/behavior in the context of native ads, which has rarely been studied by prior research. In addition to serial positions, future research could examine other drivers of native ad performance, such as consumer mood states and adjacent articles,[14] or compare the performances of native ads inserted in user-generated content (e.g., social media posts) versus professionally generated content. Native ads inserted in web streams with clear rank orders are prevalent in leading native ad platforms such as Facebook and Yahoo!. However, just like search ads, there can be variation across publishers in terms of how native ads are displayed. For example, some websites may display ads in a cluster of small thumbnails without clear rank orders (in fact, such clustered ads are not strictly "native" or disguised because they are not seamlessly inserted into organic listings and thus are easier for web viewers to identify). Similar to the literature on search ad ranks, we focus only on in-stream native ads with rank orders and leave the other possible formats of native ad displays for future research. Finally, native advertising can be considered one form of disguised marketing. Practitioners might employ alternative ways to disguise the source of promotion, such as sponsored influencer marketing ([28]). The theory that we propose in this study (e.g., regarding the annoyance effect) might apply to some other forms of disguised marketing, which future research could further explore and empirically test.
Supplemental Material, DS_10.1177_0022242918817549 - Serial Position Effects on Native Advertising Effectiveness: Differential Results Across Publisher and Advertiser Metrics
Supplemental Material, DS_10.1177_0022242918817549 for Serial Position Effects on Native Advertising Effectiveness: Differential Results Across Publisher and Advertiser Metrics by Pengyuan Wang, Guiyang Xiong, and Jian Yang in Journal of Marketing
Footnotes 1 Associate EditorTimothy Heath served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242918817549
5 1The online sales funnel is consistent with the central idea of the classical sales funnel: after exposure to an ad, a viewer may or may not pay attention to or click on it; after clicking, the viewer may or may not perceive the ad positively and convert.
6 2In other words, serial position in our context describes whether an ad is inserted earlier or later (relative to the other ads) in a stream of web content (e.g., listings, articles). For more details, see the "Data and Variables" section.
7 3Search ads are also more interruptive than native ads per industry standard. For instance, similar to other search engines, every search ad on Google "is clearly marked and set apart from the actual search results" ([21]).
8 4Other arguments from the literature include primacy/recency effect, perceived quality effect, and fatigue effect, which are less relevant to our context as discussed in the note for Table 1.
9 5As Table W1.1 shows, studies on TV ads focused on such moderators as ad duration, commercial break length, and channel switching, whereas those on keyword-based search ads focused on keyword features (e.g., specificity).
6The focal portal hosts various types of web pages. We include indicators for leisure (e.g., entertainment) and nonleisure (e.g., finance) web pages, with mixed pages as baseline. This is because, conceptually, leisure and nonleisure web page contents lead to unequal levels of cognitive load ([51]), which can influence the perceived annoyance of the ads inserted in the web page ([15]) and, thus, ad effectiveness. As a robustness check, we further classify the leisure/nonleisure web pages into more specific subcategories and use their identifiers as controls instead (Web Appendix W4), and the coefficients of the key variables remain consistent.
7We focus on the serial position effect and the moderating role of gender; the baseline difference across genders (i.e., the main effect of gender) is beyond the scope of this study. By examining relative values, we can tease out the baseline difference across consumer groups and pinpoint the effect of serial position within each consumer group.
8For example, if they are targeting on a particular group of consumers (e.g., women only) and need to understand the relative importance of getting the ad placed at the top (e.g., given that an advertiser's focal audience is women, how much is rank 2 worth compared with rank 1?), they can refer to relative values.
9Each advertiser submits only one bid for each keyword (in the context of search ads) or for each ad (in the context of native ads). Then, the ad portal's algorithm will rank the ad based on its bid and other factors as explained in Appendix W4. If the ad is clicked on, the focal advertiser pays the adjusted bid of the advertiser that is ranked right below it, capped by the focal advertiser's own bid.
10The placements of adjacent articles are nonstrategic in our context and can be considered randomized in each CS.
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Record: 163- Service Satisfaction–Market Share Relationships in Partnered Hybrid Offerings. By: Becerril-Arreola, Rafael; Zhou, Chen; Srinivasan, Raji; Seldin, Daniel. Journal of Marketing. Sep2017, Vol. 81 Issue 5, p86-103. 18p. 1 Diagram, 6 Charts. DOI: 10.1509/jm.15.0537.
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Service Satisfaction–Market Share Relationships in Partnered Hybrid Offerings
Many goods manufacturers and service providers jointly deliver partnered hybrid offerings to achieve competitive advantage and superior performance. In such cases, service providers may emphasize different aspects of service in their offerings. Do service providers and goods manufacturers benefit equally from emphasizing service satisfaction? Do the performance effects of emphases on different aspects of service satisfaction vary across different goods? The authors examine the effects of emphases on two aspects of service satisfaction, relational service (interactions with the service provider’s staff) and service environment (service provider’s facilities), on the market shares of service and goods components of partnered hybrid offerings. Using multiple secondary data sources from the U.S. automobile industry between 2009 and 2015, the authors find that emphasizing relational service satisfaction increases service market share but decreases goods market share. Counterintuitively, emphasizing service environment satisfaction decreases service market share. Furthermore, the vertical quality of the good moderates these relationships. The findings generate actionable guidelines to improve market shares by adjusting relational service satisfaction and service environment satisfaction in the partnered hybrid offerings context.
Online Supplement: http://dx.doi.org/10.1509/jm.15.0537
Hybrid offerings that combine goods and services are a key mechanism by which firms can improve their profits (Ulaga and Reinartz 2011) and firm value (Fang, Palmatier, and Steenkamp 2008). Because many manufacturers either lack the capability or are not legally allowed to offer services with their goods (e.g., the U.S. automobile industry; see Lafontaine and Scott Morton 2010), they partner with thirdparty service providers to offer “partnered hybrid offerings” (distinct from hybrid offerings—i.e., combined goods and services offerings from a single firm). A good example of the partnered hybrid offerings context is the automobile industry, in which car manufacturers produce and market the cars and car dealers sell, distribute, and service them. Other examples of partnered hybrid offerings are home appliances (service provider: department stores), automobile tires (tire dealers’ repair shops), and computer systems (system integrators).
Even if the partnered service provider and goods manufacturer do not consider their partnered hybrid offering as one offering, consumers do view it as one (Mittal, Kumar, and Tsiros 1999; Verhoef, Langerak, and Donkers 2007; Ulaga and Reinartz 2011). Thus, consumers’ service satisfaction in the partnered hybrid offerings context will affect their service and goods purchase behaviors and, in turn, market outcomes (Mohr et al. 2014).
Service plays distinct roles for the two partners in the partnered hybrid offerings context. For the service provider, it is the main product, but for the goods manufacturer, it is the postpurchase support to ensure that the goods component delivers promised benefits. Consequently, satisfaction with the service component of partnered hybrid offerings may differentially affect consumers’ service and goods purchase decisions. This results in challenges of coordinating service investments between the service provider and goods manufacturer (Hirsh 1996; Rindfleisch and Heide 1997). For example, in the U.S. automobile industry, to achieve competitive advantage, car manufacturers require dealers to invest in service facilities and service staff (Mohr et al. 2014). Manufacturers expect enhancements in service to increase the sales of both services and cars. Some dealers, however, contend that improved service satisfaction does not increase sales and that the only effect of improved satisfaction with service facilities is increased competition and lower performance (Mercer 2013). As a result, some dealers have engaged car manufacturers in costly lawsuits (Csere 2012) hurting both partners. Yet the marketing literature provides no guidance to managers on this issue. Addressing this gap, we examine the relationship between service satisfaction and market shares of services and goods components of partnered hybrid offerings.
We focus on market share, a key indicator of the effectiveness of customer satisfaction and a metric that has been studied in the literature (e.g., Anderson, Fornell, and Lehmann 1994; Rego, Morgan, and Fornell 2013). There is contradictory evidence in the literature on the relationship between customer satisfaction and market share. Some research has suggested that increased customer satisfaction increases customer retention and acquisition (through positive word of mouth), both of which increase market share (e.g., Kamakura et al. 2002; Morgan, Anderson, and Mittal 2005). However, other research has suggested the opposite (Fornell 1995). Using firm-level American Customer Satisfaction Index data, Rego, Morgan, and Fornell (2013) find that customer satisfaction cannot explain market share except when the firm is benchmarked against its nearest rival and consumers’ switching costs are low.
The importance of different aspects of service depends on the goals of consumers and, therefore, differs between services and goods (Mittal, Kumar, and Tsiros 1999). To the extent that various aspects of service facilitate or hinder the fulfillment of consumer goals, emphasizing different aspects of service may either please or displease consumers (see Dixon, Freeman, and Toman 2010) and therefore affect market shares differently. In the partnered hybrid offerings context, the prominence of relational service (i.e., consumers’ interactions with the service provider’s staff) and service environment (i.e., service provider’s facilities) (Bitner 1990) raises the question: How do emphases on relational service satisfaction and service environment satisfaction affect market shares of services and goods in partnered hybrid offerings?
Modeling the relationship between customer satisfaction and market share must account for heterogeneity of consumer preferences (Rego, Morgan and Fornell 2013), which is associated with product differentiation. The vertical quality of a good—that is, the aggregate of the good’s attributes that elicit homogeneous preferences across consumers (attributes for which all customers prefer either higher performance or lower performance; Golder, Mitra, and Moorman 2012)—is a key source of differentiation that moderates the effects of firms’ service strategies (Verhoef, Langerak, and Donkers 2007). Thus, we examine whether the vertical quality of the goods component moderates the relationships between emphases on relational service and service environment satisfaction and market shares of service and goods components of hybrid offerings.
We address these research questions in an economically substantive context, the U.S. automobile industry between 2009 and 2015. We assemble a panel of car make-models (e.g., Toyota Camry, Ford Focus) using multiple secondary sources that include two large-scale consumer surveys from J.D. Power and Associates: the Customer Service Index (CSI) study of automobile dealer services and the Initial Quality Study (IQS) of new car owners.
We find that when the service provider emphasizes relational service satisfaction, the market share of the service component increases, whereas the market share of the goods component decreases. Counterintuitively, when service environment satisfaction is emphasized, the market share of the service component decreases. Furthermore, we find evidence for moderation effects of the vertical quality of the goods component on the relationships between emphasis on relational service satisfaction and market shares of both the service and good components. Specifically, the vertical quality of the good and an emphasis on relational service satisfaction are substitutes for increasing service market shares but complements for increasing goods market shares.
This research’s findings extend the marketing literature in multiple ways. First, the findings provide novel insights on the contingent nature of market share rewards to emphasis on service satisfaction for the two partners in the partnered hybrid offerings context. In doing so, the findings extend, to the product-level and the partnered hybrid offerings context, prior research on performance rewards to customer satisfaction at the firm level (e.g., Anderson, Fornell, and Lehmann 1994; Rego, Morgan, and Fornell 2013) or to service satisfaction in pure services (e.g., Kamakura et al. 2002). Second, the findings on the asymmetric market share rewards that result from emphasizing relational service satisfaction and service environment satisfaction demonstrate their different roles and the need to study disaggregate aspects of service satisfaction in partnered hybrid offerings, an issue hitherto unexplored in the literature. Finally, the moderating role of the vertical quality of the good on the relational service satisfaction–market share relationships highlights the importance of consumer preference heterogeneity when assessing the rewards to service satisfaction.
Our findings also generate actionable managerial implications. First, our findings reconcile opposing views in business practice on whether to emphasize noncore aspects of service (Dixon, Freeman, and Toman 2010; Hirsh 1996; Mercer 2013; Savitz and Beninato 2011). We show that there is no simple answer to this question. Instead, managers need to consider the vertical quality of the goods component and the asymmetry of benefits that service providers and goods manufacturers receive by emphasizing different aspects of service. Second, the moderating effects of the vertical quality of the goods component on the service satisfaction–market share relationships indicate that, in contrast to some prevalent beliefs (e.g., Kurylko 2012), the rewards to service satisfaction are not always higher for goods of higher vertical quality. Finally, the asymmetry of market share rewards to the service provider and goods manufacturer indicates the need to coordinate service provision in partnered hybrid offerings. We illustrate how partnered firms can identify opportunities to increase service and good market shares by adjusting their emphases on relational service and service environment for certain offerings.
Theoretical Framework
Partnered hybrid offerings create consumption systems, in which goods purchase and consumption occur in multiple episodes at different points in time (Mittal, Kumar, and Tsiros 1999; Rust and Oliver 1994). We first provide a brief overview of the differential role of goods and service components in the partnered hybrid offerings context. Then, we develop hypotheses of the effects of emphasizing different aspects of service satisfaction on market shares of services (relative to service providers not partnering with the goods manufacturer) and goods (relative to other goods in the same category).
Roles of Goods and Services in Partnered Hybrid Offerings
In partnered hybrid offerings, consumers initially purchase and pay for the goods component and subsequently purchase service as needed. For example, a consumer may buy a car once and take it for maintenance and repairs multiple times. Likewise, a consumer may buy a set of car tires once and have them serviced (rotated, inflated) multiple times before replacing them. The purchase of the service component of partnered hybrid offerings is salient only after the purchase (and usage) of the goods component. Consequently, the service component may be perceived differently by consumers in the service purchase decision than in the goods purchase decision. In the service purchase decision, the service component is the core offering, whose purpose is to ensure the effective functioning of a good that consumers already own. In addition, when consumers purchase (and use) services of partnered hybrid offerings, they personally interact with the service provider at the provider’s facilities, experiencing different aspects of the service provision. Consequently, consumers make the service purchase decision with both a focus on their preferences for the service and a good understanding of the performance of the service provider.
In the goods purchase decision, in contrast, consumers may be uncertain about their preferences for service providers and instead rely on their perceptions of the service component to determine their preference for the good (Chernev 2006). Thus, in the goods purchase decision of partnered hybrid offerings, the service component is an abstract attribute, ancillary to consumers’ consumption goals. Although preferences for service are not focal when purchasing the goods component of partnered hybrid offerings, consumers may use the information on the service component, a signal of future service, to do a cost–benefit analysis of the hybrid offering before they choose the good. Thus, overall, service satisfaction may have different effects on the market shares of the service and goods components of partnered hybrid offerings. This and the following three developments in the extant literature support our conceptual framework and hypotheses.
Crossover effects in consumption systems. The consumption system perspective of hybrid offerings suggests that the goods and service components not only influence their own market shares but also have crossover effects on the market shares of the service and goods components, respectively, because satisfaction with partnered hybrid offerings is determined by both the goods and the service attributes (Mittal, Kumar, and Tsiros 1999). Given our interest on the returns to service, we focus on studying how the firm’s emphases on two key aspects of service satisfaction affect the service and goods market shares of partnered hybrid offerings.
Key roles of relational service and service environment.
Developments in the literature suggest that the firm’s emphases on satisfaction with two key aspects of service, relational service and service environment, influence customers’ purchase behaviors (e.g., Baker et al. 2002; Bitner 1990). Relational service is the aspect of service related to personal interactions between a service provider’s service staff and consumers (Baker et al. 2002; Scott, Mende, and Bolton 2013; Sirianni et al. 2013), and service environment is the aspect of service related to the service provider’s facilities where the service fulfillment occurs (Baker et al. 2002; Bitner 1992; Turley and Milliman 2000).
A moderating role for vertical quality of the good. Goods may be described by two types of attributes: those for which consumers have homogeneous preferences (e.g., reliability), used for vertical differentiation, and those for which they have heterogeneous preferences (e.g., color), used for horizontal differentiation (Golder, Mitra, and Moorman 2012). We define the vertical quality of a good by the level of its combined attributes associated with homogeneous preferences. The vertical quality of a good influences consumers’ responses to its marketing mix. For example, consumers are more likely to switch to upper-tier brands (vs. lower-tier brands) following price discounts (Blattberg and Wisniewski 1989), advertising, and promotions (Lemon and Nowlis 2002).
Using these three theoretical building blocks, we propose a conceptual framework (Figure 1) and hypotheses on the effects of emphases on relational service satisfaction and service environment satisfaction on the market shares of the services and goods components of the partnered hybrid offerings. We further hypothesize that these effects will be moderated by the vertical quality of the good. Next, we discuss the effects on service market shares, followed by the effects on goods market shares.
Emphases on Service Satisfaction and Service Market Share
Emphasis on relational service satisfaction. Relational service arises from consumers’ interactions with the service provider’s staff. Superior relational service is characterized by service staff’s courteous treatment of consumers, thoroughness of staff’s explanations, and responsiveness to consumers’ requests. We define the firm’s emphasis on relational service satisfaction by the extent of consumers’ satisfaction with relational service, relative to their satisfaction with all other aspects of service.1
Enjoyable, satisfying interactions between consumers and the service providers’ staff increases consumers’ trust (Sirdeshmukh, Singh, and Sabol 2002), affect (Yim, Tse, and Chan 2008), and engagement (Kumar et al. 2010). By strengthening consumers’ personal connections (Gremler and Gwinner 2000), satisfying interactions improve consumers’ perceptions of (Gummesson 1987; Yim, Tse, and Chan 2008) and loyalty to (Price and Arnould 1999) the service provider. Moreover, consumers attribute satisfying interactions with the service provider’s staff to the management of the service provider, giving it credit for the superior relational service (Bitner 1990). Thus, ceteris paribus, the firm’s increasing emphasis on relational service satisfaction will improve consumers’ perceptions of the service relationship, motivating consumers to revisit the service provider and increasing service market share.
Emphasis on service environment satisfaction. The quality of a service environment depends on different aspects of its facilities, such as layouts and amenities. Superior service environments involve (1) efficient layouts that reduce both service time and consumer effort and (2) user-friendly amenities that consumers can use without help from service staff. We define the firm’s emphasis on service environment satisfaction as the extent of consumers’ satisfaction with service environment, relative to their satisfaction with all other aspects of service.
Service providers who emphasize service environment satisfaction excel in the design, efficiency, and convenience of their service facilities. While all of these may increase consumer satisfaction, they may also decrease both the number and duration of personal interactions with the service staff (Bitner 1992). Interactions between consumers and service staff provide opportunities for bonding (Price and Arnould 1999) and building consumer loyalty (Yim, Tse, and Chan 2008). Thus, ceteris paribus, the firm’s increasing emphasis on service environment satisfaction may weaken the personal bonds between the service provider’s staff and consumers, decreasing loyalty and, therefore, the service’s market share.
Integrating these ideas suggests that, in the partnered hybrid offerings context, an increasing emphasis on relational service satisfaction will increase service market share, and an increasing emphasis on service environment satisfaction will decrease service market share. Thus, we offer H1a and H1b.
H1a: In partnered hybrid offerings, the higher the emphasis on relational service satisfaction, the higher the service’s market share.
H1b: In partnered hybrid offerings, the higher the emphasis on service environment satisfaction, the lower the service’s market share.
Moderation by vertical quality of the good. Goods with high vertical quality are purchased more often by high-income consumers (Allenby, Garratt, and Rossi 2010) who have high time costs and low price sensitivity (Cooil et al. 2007). In the partnered hybrid offerings context, there is some evidence that service quality improves consumers’ loyalty for mid-tier brands and has mixed effects for low-tier brands (Verhoef, Langerak, and Donkers 2007).
When there is a high emphasis on relational service satisfaction, the staff are trained to personalize service by engaging with consumers in small talk (Surprenant and Solomon 1987). Such friendly small talk may impose additional cognitive and time costs on consumers by forcing them to reciprocate and sustain the warm conversations (Surprenant and Solomon 1987). In addition, an emphasis on relational service satisfaction involves the service staff providing thorough, time-consuming explanations of the service provided. These cognitive and time costs of an emphasis on relational service satisfaction may be particularly burdensome for discerning, time-sensitive owners of high–vertical quality goods. Furthermore, they may also perceive negatively a service provider who invests excessively in staff training to delight consumers rather than to provide services more efficiently and effectively (Surprenant and Solomon 1987). Thus, as the firm’s emphasis on relational service satisfaction increases, consumers of high–vertical quality (vs. low–vertical quality) goods may have less positive perceptions of the service provider, decreasing their propensity to visit it and thus decreasing service market share.
However, we expect the opposite effect with respect to the firm’s emphasis on service environment satisfaction, which would be reflected in high service efficiency. High-income consumers of high–vertical quality (vs. low–vertical quality) goods are time-sensitive and have a high premium on their time (Cooil et al. 2007). Thus, they may respond positively to the high convenience and efficiency associated with a service provider’s emphasis on service environment satisfaction. This should increase their preferences for the service provider, increasing service market share.
Integrating these ideas, we propose, for consumers of goods with higher vertical quality, a negative relationship between the firm’s emphasis on relational service satisfaction and service market share and a positive relationship between emphasis on service environment satisfaction and service market share. Thus, we offer H2a and H2b:
H2a: In partnered hybrid offerings, the higher the vertical quality of the good and the higher the emphasis on relational service satisfaction, the lower the service’s market share.
H2b: In partnered hybrid offerings, the higher the vertical quality of the good and the higher the emphasis on service environment satisfaction, the higher the service’s market share.
Emphases on Service Satisfaction and Goods Market Shares
When purchasing the good, consumers are unlikely to have an up-to-date experience with the service of the provider because the probability of brand switching for durables is high (McCarthy et al. 1992; Verhoef, Langerak, and Donkers 2007). Furthermore, even if consumers have owned a good of the provider’s brand in the past, the interpurchase time for durable goods is long enough (often a few years) for the quality of service to have changed significantly. Consumers may have not evaluated the provider’s service quality in the interim period. Thus, consumers may need additional information on the service component of the partnered hybrid offering to assess the offering’s cost and benefits. Consumers may consequently rely on aspects of service that can be observed without experiencing the service, such as the service provider’s emphases on relational service satisfaction and service environment satisfaction, to infer costs (Erickson and Johansson 1985) and quality (Zeithaml 1981) of future service.
Emphasis on relational service satisfaction. The service provider’s emphasis on relational service satisfaction, manifest as warm and responsive service staff, may lead consumers to infer not only high service benefits but also high investments in service staff hiring and training, which have to be recovered through high service prices (Baker et al. 2002). While benefits and costs are inferred from the same information, they may be weighted differently when consumers evaluate the overall value of the service component. Specifically, consumers may weight service costs more than they do service benefits because service quality is not guaranteed but costs are more certain (Zeithaml 1981). Thus, in partnered hybrid offerings, as the service provider’s emphasis on relational service increases, we expect goods market share to decrease.
Emphasis on service environment satisfaction. Because a superior service facility is expensive to build (high fixed cost) and maintain (high variable cost), emphasis on service environment satisfaction may also lead consumers to infer high future service costs, which the service provider has to recover by charging high service prices. As with superior relational service, consumers’ perceptions of high service price arising from the superior service environment may increase the perceived total cost of owning the good, decrease its perceived value (Baker et al. 2002), and thereby decrease the market share of the goods component.
Integrating these ideas, we propose negative relationships between emphasis on relational service satisfaction and goods market share and between emphasis on service environment satisfaction and goods market share. Thus, we offer H3a and H3b:
H3a: In partnered hybrid offerings, the higher the emphasis on relational service satisfaction, the lower the good’s market share.
H3b: In partnered hybrid offerings, the higher the emphasis on service environment satisfaction, the lower the good’s market share.
Moderation by the vertical quality of the good. We propose that time discounting by consumers (Zauberman and Lynch 2005) may cause the moderating effects of the vertical quality of the good to differ across the goods and service purchase decisions. For consumers, the outcomes of service visits are temporally more distant from the goods purchase decision than from the service purchase decision. Therefore, service satisfaction may be subject to time discounting in the goods purchase decision but not in the service purchase decision. This causes consumers to weight time costs and financial costs of service differently across the service and goods purchase decisions. When choosing a service provider, the absence of time discounting may, in general, lead consumers to weight inferred time and financial costs equally. In the goods purchase decisions, in contrast, consumers may be influenced by time discounting and may underestimate their future time constraints more than their future financial constraints (Zauberman and Lynch 2005), attributing lower weights to time costs than to financial costs.
As we discussed previously, an emphasis on relational service satisfaction may lead consumers to infer high costs and benefits of the service component. Consumers of low–vertical quality goods are sensitive to price but are less sensitive to time costs and service quality benefits, which may lead them to weight the financial costs of an emphasis on relational service satisfaction more than its benefits. Consumers of high– vertical quality goods, in contrast, are sensitive to time costs (Cooil et al. 2007) and to service quality benefits but not as much to prices (Allenby, Garratt, and Rossi 2010). When purchasing the good, these consumers may underestimate the time costs
associated with relational service because of time discounting. The discounted time costs and low sensitivity to price may lead these consumers to weight the benefits of an emphasis on relational service satisfaction more than its time and financial costs.
When considering a service provider that emphasizes service environment satisfaction, consumers may underestimate the time savings associated with efficient facilities more than they underestimate the financial cost of the emphasis during the good’s purchase. For consumers of low–vertical quality goods, the financial costs of an emphasis on service environment satisfaction may be more important than its benefits. For consumers of high–vertical quality goods, although the time-saving benefits of an emphasis on service environment satisfaction may not be influential because of time discounting, consumers’ preferences for the high quality of service may outweigh their sensitivity to potential financial costs.
Integrating these arguments, we expect the effects of emphases on relational service satisfaction and service environment satisfaction on goods market share to be more positive for consumers of high–vertical quality goods than for consumers of low–vertical quality goods. Thus, we offer H4a and H4b:
H4a: In partnered hybrid offerings, the higher the vertical quality of the good and the higher the emphasis on relational service satisfaction, the higher the good’s market share.
H4b: In partnered hybrid offerings, the higher the vertical quality of the good and the higher the emphasis on service environment satisfaction, the higher the good’s market share.
TABLE: TABLE 1 Summary Statistics
| Statistic | Mean | SD | Min | Max |
|---|
| a Original scores. |
| b Measurements obtained through partial correlation methods to deal with halo effects. |
| CARSHARES | .051 | .069 | .000 | .626 |
| SERVSHARES | .836 | .173 | .000 | 1.000 |
| RELSERV0a | 7.958 | .537 | 5.333 | 10.000 |
| SERVENV0a | 7.815 | .504 | 5.333 | 10.000 |
| RELSERVSATb | .000 | .040 | -.122 | .139 |
| SERVENVSATb | .000 | .034 | -.134 | .140 |
| VERQUAL | .000 | 1.894 | -1.798 | 3.197 |
| OVSERVSAT | .786 | .041 | .560 | .965 |
| CARSAT | 8.178 | .512 | 5.587 | 9.938 |
| CARPRICE | 34,111.960 | 25,06.160 | 10,675.000 | 500,500.000 |
| ADSPEND | 62.431 | 454.332 | .000 | 12,878.500 |
| PROMO | 21.335 | 21.383 | .000 | 213.953 |
| NDEALERS | 1,107.490 | 1,028.930 | .000 | 3,463.000 |
| RECALLS | 3.305 | 9.699 | .000 | 211.000 |
| CARAGE | 3.496 | .404 | 3.000 | 5.750 |
| FREESERVICE | .422 | .336 | .000 | 1.000 |
| NPROBLEMS | .772 | .418 | 3.000 | 3.000 |
| WARRANTY | 1.804 | 1.673 | .000 | 7.000 |
Data and Measures
Data Sources
We assembled a panel data set of passenger cars and light trucks in the United States between 2009 and 2015, using seven waves of the J.D. Power and Associates’ CSI study.2 We collected data on cars’ sales, marketing mix, and other control variables from AutoData Corp., Kantar ad$pender, WardsAuto, the J.D. Power and Associates’ IQS, and other sources (e.g., World Bank, the Bureau of Labor Statistics).
The CSI study is a nationwide annual survey of vehicle owners (who have owned a new vehicle for between one and five years) that examines their satisfaction with the maintenance and repair services for their cars. In this study, consumers evaluate various aspects of dealer services including relational service and service environment. In addition, the survey collects information on the amount spent on service and the number of service visits to new car dealers (NCDs) and to nondealer service facilities (NDSFs; e.g., Jiffy Lube, Sears) as well as respondents’ demographic information. The IQS study is a nationwide annual survey of new vehicle owners (i.e., sent during the first 90 days of ownership). The survey assesses vehicle owners’ satisfaction with their new vehicles.
The unit of analysis is the unique combination of car makemodel (e.g., Ford Focus, Toyota Corolla) and year. We index car make-models with j = 1, …, J and years with t = 1, …, T. After accounting for missing data across the various data sources, we have an unbalanced panel of 1,149 observations for 270 car make-models, which account for 82% of the sales of the 504 car make-models that constitute the population.
Measures
We next discuss the key variables in the study. We describe all variables, including the control variables, in more detail in Web Appendix WA1. We provide the summary statistics of the key variables in Table 1 and their correlations in Web Appendix WA2. Most correlations are statistically significant but below .50.
Service satisfaction. The CSI study was conducted between October and December of the year (e.g., 2009) before the year of the study (e.g., 2010). Each CSI wave includes over 80,000 respondents.3 The analysis excludes respondents who reported receiving free service and those who owned their vehicles for two years or less as they may be locked in by service contracts. This results, on average, in 49,432 respondents per year.
We define RELSERVSAT and SERVENSAT as the extent to which dealers emphasize relational service satisfaction and service environment satisfaction, respectively, over all other aspects of service satisfaction. High average ratings of relational service satisfaction and service environment satisfaction may arise simultaneously because of a strong dealership service
TABLE 1 Summary Statistics
aOriginal scores. bMeasurements obtained through partial correlation methods to deal with halo effects.
1We note that because services may involve aspects other than relational service and service environment, it is possible for a service provider to emphasize these two simultaneously while deemphasizing others. Thus, an emphasis on relational service satisfaction does not preclude an emphasis on service environment satisfaction, and vice versa. Furthermore, simultaneous investments in relational service and service efficiency could be made. We thank an anonymous reviewer for drawing our attention to this point.
2In response to the area editor’s suggestion, we aggregated the measures of service satisfaction from the CSI study to the brand level and compared these aggregates with the ACSI data used in previous research on customer satisfaction. The data from these two sources correlate highly (correlation = .682, p < .0001), indicating that the CSI data are reliable and capture much of the variation in the ACSI data.
3J.D. Power and Associates addresses concerns of nonresponse bias by conducting a phone follow-up of nonresponders. In a followup survey to a recent CSI study, the findings indicated that the responders and nonresponders were generally very similar in profiles, and their responses to key questions were not statistically different. This suggests that there is no evidence of a threat to the validity of the data because of nonresponse bias.
4Residuals have been interpreted analogously in the work of Geyer and Steyrer (1998) (as relative performance beyond that explained by predictors) and in the work of Agrawal and Kamakura (1999) (as a price premium beyond the price predicted by product attributes).
5We note that oil and filter change visits are economically important in the United States. For example, in 2015, these services generated $6.3 billion in revenues and $750 million in profits (Peters 2015). For new car dealers, the profit margins on services are higher than margins on sales of new and used cars (Peters 2016).
6An additional analysis (available from the authors on request) revealed that the negative sign of the autoregressive term is explained by dealers’ marketing actions and their interplay with market-wide shocks.
7The alternative definitions of VERQUAL are constructed by letting the value of the constant k range over k = 2, …, 7. The alternative measures of emphasis can be grouped into two sets. The first set is computed by subtracting the level of overall service satisfaction (as directly reported by survey respondents) from the raw measures of relational service satisfaction and service environment satisfaction. The differences thus computed are the levels of satisfaction with relational service and environment satisfaction beyond overall service satisfaction. The second set of measures of emphasis are as the original measures, except that the measure SERVENVSAT excludes survey items not related to time efficiency (comfort of waiting areas and cleanliness of dealership). The alternative measure includes two items: ease of driving in/out of facility and convenience of parking.
8We thank the anonymous reviewers for suggesting these control variables.
9Some automobile dealers create off-site quick-service facilities in addition to those in the main dealership complex (Mercer 2013).
10We thank the area editor for this suggestion. orientation. This correlation among the measures of service satisfaction may induce multicollinearity, hindering identification of their effects on market shares. We thus use a partial correlation method (Landy et al. 1980) to separate the variation specific to each aspect of service satisfaction from the variation that is common to all of them. Specifically, we regress consumers’ satisfaction ratings of relational service and service environment on the independent ratings of all other service aspects and use the residuals of these regressions as the new measures of service satisfaction. The residuals are independent from any halo effect but also from important information on satisfaction with all other aspects of service satisfaction (see discussions by Dillon, Mulani, and Frederick 1984; Murphy et al. 1993). The residuals can thus be considered measures of emphases on different aspects of service satisfaction.4 We define overall service satisfaction, OVSERVSAT, as respondents’ reported overall satisfaction and use it as a control variable in the estimation (discussed subsequently).
Market share. The dependent variables are service market share for dealers and car market share for car manufacturers, both measured at the make-model level. To compute service market share, we use the items in the CSI study on the amounts spent on services at NCDs and at NDSFs. We include service visits for maintenance and oil and filter changes but exclude repair visits because having repairs addressed at NDSFs may void a car’s warranty. We also exclude from our analysis visits for free service and product recalls.5 We compute service market share (SERVSHARE) by averaging across respondents the ratio of expenditure at NCDs to the total expenditure at both NCDs and NDSFs.
Because demand substitution occurs primarily within vehicle categories (e.g., Albuquerque and Bronnenberg 2012), we measure a car make-model’s market share within its category. This enables us to compare car make-models against their closest competitors and accurately model the relationship between service satisfaction and market share (Rego, Morgan, and Fornell 2013). We define vehicle categories combining vehicle type (car, sport utility vehicle, or pickup truck) and vehicle supersegment (a combination of subcompact, compact, midsize, or large, with either mainstream or premium) as defined by J.D. Power and Associates. We use the yearly unit sales of new car make-models sold in the United States to compute the category-level market share (CARSHARE) for each car make-model.
Vertical quality of cars. Car manufacturers typically offer vertically differentiated goods, with higher-quality vehicles having higher prices. The price ranking of a good within a brand is important because consumers recall price rankings better than they do actual prices (Mazumdar and Monroe 1990). To judge the vertical quality of the good, consumers also rely on the brand’s tier (Leclerc, Hsee, and Nunes 2005). Thus, the vertical quality of a good includes (1) its price ranking within its brand and (2) the tier of its brand. We measure the vertical quality of each car make-model by the ratio of the ranking of its price within its brand to the number of car make-models in the brand (because the number of car make-models varies across brands). To account for the difference between brand tiers, we add a constant k to this ratio for upper-tier brands. Thus, we obtain the variable VERQUAL, which ranges from 0, for lower-tier (mainstream) entry-level car make-models, to k + 1, for upper-tier (luxury) top-of-theline car make-models. The constant k determines the separation between lower-tier and upper-tier brands and is set at 4 as consumers perceive larger quality differences across than within brand tiers (Isaac and Schindler 2014). In Web Appendix WA3, we discuss the choice of k and related robustness tests.
Empirical Approach
We integrate multiple econometric methods to jointly estimate the relationships between emphases on service satisfaction and market shares of service and goods components of partnered hybrid offerings, accounting for simultaneity, reverse causality, unobserved heterogeneity, serially correlated errors, endogenous service satisfaction and prices, and the challenges posed by autoregressive processes. We propose a system of simultaneous equations in which market share depends on service satisfaction and, at the same time, service satisfaction depends on lagged market share (Rego, Morgan, and Fornell 2013). Because the effect of market share on service satisfaction is lagged, the system of equations is triangular and can be estimated sequentially using a control function approach (Blundell and Matzkin 2014).
In implementing the control function approach, we ensure that the exclusion conditions for identification are met by using instruments that influence service satisfaction (through car dealer behavior) but do not directly affect market shares. The control function approach allows the residuals to be correlated across multiple equations (Web Appendix WA4 provides the covariance matrix of the system residuals). We address heterogeneity using make-model-specific fixed effects and a set of controls. Because the data do not include all car make-models (i.e., market shares do not add up to 1), we include year fixed effects to account for time trends or other forms of nonstationarity (Hahn and Moon 2006). Finally, we control for the inertia of demand by specifying the equations of market shares and service satisfaction to be dynamic.
Model Development
As our focus is on the relationships between service satisfaction and market shares of services and car make-models, we first discuss these models; then, we discuss the models for service satisfaction and prices. We first present the model of the relationship between service satisfaction and service market share (SERVSHAREjt):
Here, ttSS and djSS are fixed effects specific to year and car makemodel, respectively. RELSERVSATjt, SERVENVSATjt, and OVSERVSATjt are emphasis on relational service satisfaction, emphasis on service environment satisfaction, and overall service satisfaction, respectively. These variables are assumed to affect service market shares contemporaneously because consumers’ service purchase decisions may be influenced by their recent experience with the service. For completeness, we include OVSERVSATjt and its interaction with VERQUALjt as controls. SERVPRICEjt is the average service price paid by owners of car make-model j at year t. The vector XSjtS contains other components of the marketing mix and controls (described next). The terms ljStP, xjRtS, xSjtE, xjOt S, and hSjtS are control functions that address the endogeneity of service prices, emphasis on relational service satisfaction, emphasis on service environment satisfaction, overall service satisfaction, and the lagged term of service shares, respectively. The term ejStS is the equation’s residual and is assumed to be normally but not necessarily independently distributed.
The model of the relationship between service satisfaction and the car make-model market share (CARSHAREjt) is given by:
where most variables are as defined previously, with five exceptions: (1) CARSHAREjt-1 replaces SERVSHAREjt-1; (2) SCreEApRlRaVcPePRsRIClICSjEtPEj;t, jt(;t4h()e3)XlitsCjhttSeprrcieocpneltaorcofeltshfueXncSjctaStr;iomannadfkoer(-5mc)aorhdpCjetrSli, crreeespp, llaalccjCteePss, hSjtS. In addition, we lag the service satisfaction variables (RELSERVSATjt, SERVENVSATjt, and OVSERVSATjt) because they may affect the sales of new car make-models through word of mouth (Rust, Zahorik, and Keiningham 1995).
Service satisfaction variables are modeled as follows: where ZjSt is a vector of instrumental variables (described in the Appendix) and XjAt S is a vector of control variables. We include CARSHAREjt-1 in this model because past market shares embody the heterogeneity of consumer preferences, which may reduce customer satisfaction (Rego, Morgan, and Fornell 2013).
Finally, car prices and service prices are modeled as where ZCjtP is a vector of instruments specific to car prices and ZjStP is a vector of instruments specific to service prices (described in the Appendix).
The vectors of controls XjStS, XjCtS, and XjAt S include subsets of the following variables: advertising spending (ADSPENDjt), promotions (PROMOjt), number of car dealers (NDEALERSjt), vertical quality of the car make-model (VERQUALjt), satisfaction with the car make-model (CARSATjt), the number of product recalls (RECALLSjt), the number of problems reported for the car make-model (NPROBLEMSjt), and the average age of the car make-model (CARAGEjt), which may affect the demand for service. We also include interactions of the consumer sentiment index (SENTIMENTjt) with dummies that indicate whether the car is a truck (VEHTYPEjt) or a luxury vehicle (LUXURYjt) because the general economic environment may differentially affect different cars (note that the main effect of the economic environment is absorbed by the year fixed effects).
Estimation
We first estimate the price equations and obtain the control functions lSjtP and ljCtP, which we then use to equations and obtain the control functions estimate the service xjRt S, xjStE, and xjOt S. The control function hjCtS is obtained using CARSHAREjt-2 as instrument for CARSHAREjt-1 (Hsiao 2014). The computation of the control function hSjtS is analogous to that of hjCtS. We use these control functions to estimate the market share equations.
We test for unit roots using the Harris–Tzavalis unit-root test developed for fixed T (the number of periods in the data) and large N (the number of car make-models). The tests reject the hypotheses of unit roots for SERVSHARE (p = .00) and CARSHARE (p = .03). Endogenous lagged dependent variables complicate estimation and may be addressed by generalized method of moments or bias-correction approaches (Hsiao 2014, Chapter 4). We combine control functions and bias corrections to address the endogeneity of lagged market shares while allowing for time fixed effects (Hsiao 2014, p. 122). Specifically, we retain time-specific fixed effects but apply a within transformation to the data to remove car make-model fixed effects, rather than differencing over time, so that we can use all observations in the unbalanced panel. We then use feasible generalized least squares (FGLS) to account for serial correlation of the residuals without assuming any particular error structure. We note that this estimator is consistent, but the estimate of the autoregressive term is biased (Hsiao 2014, pp. 122, 128). Because maximum likelihood estimates and FGLS estimates are equivalent in this context (Hsiao 2014, p. 127), we apply a bias-correction method for dynamic panels (Hahn and Moon 2006) that involves computing where f is a placeholder for the estimates of fCS and fSS, and j is the bias-adjusted estimate. We address nonnormality concerns through Box–Cox transformation of the dependent variables (we add the constant .0001 to these variables so that they are always positive). Thus, for each market share variable y, we estimate l, the power parameter, such that the model is as close as possible to the normality assumptions of FGLS. The value of l determines the skewness of y(l) such that small values of l strongly amplify small values of y, while large values of l strongly amplify large values of y. This transformation, together with the fixed effects for the year and car make-model, mitigates concerns of heteroskedasticity.
Results
Parameter Estimates
In Table 2, we report the estimates of the relationship between emphasis on service satisfaction and service market share. In Column 1, we present regression results with only control variables. In Column 2, we include overall service satisfaction in the model. In Column 3, we include emphasis on relational service satisfaction and emphasis on service environment satisfaction. In Column 4, we include the interaction effects of the three service satisfaction measures with the vertical quality of the car make-model.
We compare the performance of the models using multiple comparison tests because different tests may lead to different conclusions in small samples (Engle 1984). We report adjusted R2, log-likelihood, the corrected Akaike information criterion (AICc, described in Web Appendix WA5), F tests (not robust to heteroskedasticity), and Wald tests (robust to heteroskedasticity). We compute the root mean squared error (RMSE) of prediction by reestimating the model on years t = 2, …, T and using the estimates to predict market shares in year t = 1. We also compute the RMSE of a K-fold validation test with 10 folds. The adjusted R2 and log-likelihood increase through Columns 1–4 of Table 2. The nonincreasing RMSE suggests that model complexity does not cause overfitting. In general, all the tests support the model specification in Column 4 of Table 2.
The signs of the various control variables are as expected. We find a negative relationship between the price of service and service market share (b = -.049, p < .01). The number of problems of the car make-model is positively related to service market share (b = 2.697, p < .01) and past service market share is negatively related to current servicemarket share (b=-.146, p < .01).6 Overall service satisfaction is positively related to service market share (b = .544, p < .05), and this relationship is negativelymoderated by the vertical quality of the car makemodel (b = -.126, p < .01).
In Table 3, we report the results of the relationship between emphasis on service satisfaction and car make-model market share. The adjusted R2, the RMSE of prediction, and the K-fold RMSE suggest that models in Columns 3 and 4 are equivalent and slightly better than those in Columns 1 and 2. The log likelihood supports the model in Column 4, but the AICc is lowest for Column 3. Thus, the interaction terms add explanatory power to the model, but their effects are dominated by the AICc’s penalty on the number of parameters. Yet the interactions of service satisfaction with VERQUAL cannot be ruled out. An underspecified model may have superior predictive ability than the true model (Shmueli 2010).Moreover, a version of themodel in Column 4 without the nonsignificant interaction of SERVENVSAT and VERQUAL (not reported here in the interest of brevity) has an AICc of -3,455.682 (not materially different from the AICc in Column 3) and is marginally superior in terms of adjusted R2 and log-likelihood, while retaining the substantive results. Thus, we interpret the results in both Columns 3 and 4.
TABLE: TABLE 2 Relationship Between Service Satisfaction and Service Market Share
| | Model Specification |
|---|
| 1 | 2 | 3 | 4 |
|---|
| RELSERVSAT (H1a) | | | .279*** (.107) | .196* (.102) |
| SERVENVSAT (H1b) | | | -.468*** (.131) | -.427*** (.130) |
| VERQUAL × RELSERVSAT (H2a) | | | | -.080*** (.026) |
| VERQUAL × SERVENVSAT (H2b) | | | | .009 (.036) |
| OVSERVSAT | | .689*** (.192) | .612*** (.228) | .544** (.223) |
| VERQUAL × OVSERVSAT | | | | -.126*** (.034) |
| SERVSHAREt–1 | -.177*** (.026) | -.168*** (.028) | -.166*** (.028) | -.146*** (.024) |
| SERVICEPRICE | -.044*** (.007) | -.053*** (.008) | -.053*** (.008) | -.049*** (.008) |
| ADSPEND | -.710 (.528) | -.627 (.550) | -.676 (.563) | -.564 (.563) |
| NDEALERS | .912 (.822) | .188 (.905) | .427 (.948) | .398 (.859) |
| VERQUAL | -.011 (.011) | -.001 (.012) | -.006 (.012) | -.003 (.011) |
| CARSAT | -.001 (.006) | -.002 (.006) | -.003 (.006) | -.001 (.006) |
| RECALLS | .115 (1.123) | 1.083 (1.194) | 1.288 (1.221) | 1.357 (1.182) |
| NPROBLEMS | 14.236*** (5.390) | 22.160*** (6.194) | 18.903*** (6.652) | 2.697*** (6.389) |
| CARAGE | .575 (.502) | .641 (.539) | .624 (.527) | .379 (.472) |
| LUXURY × SENTIMENT | .001*** (.0003) | .001*** (.0003) | .001*** (.0004) | .003*** (.001) |
| TRUCK × SENTIMENT | -.0004* (.0002) | -.0004* (.0002) | -.0004* (.0002) | -.0004* (.0002) |
| λSP | .025*** (.007) | .039*** (.009) | .035*** (.009) | .032*** (.009) |
| ηSS | .00003 (.014) | -.0001 (.015) | .010 (.015) | .011 (.014) |
| ξRS | | | -.130 (.101) | -.088 (.094) |
| ξSE | | | .388*** (.122) | .337*** (.120) |
| ξOS | | -.714*** (.194) | -.582** (.232) | -.540** (.225) |
| Adjusted R2 | .814 | .815 | .818 | .820 |
| Log-likelihood | 1,716.320 | 1,719.565 | 1,729.609 | 1,736.559 |
| AICc | -4,398.004 | -4,396.847 | -4,401.502 | -4,403.698 |
| F p-value | | .042 | .001 | .004 |
| Wald p-value | | .001 | .000 | .000 |
| Holdout RMSE | .083 | .083 | .084 | .080 |
| K-fold RMSE | .027 | .027 | .027 | .027 |
| Observations | 895 | 895 | 895 | 895 |
| Model parameters | 245 | 247 | 251 | 254 |
The results in Column 3 of Table 3 indicate that, in support of H3a, the car dealer’s emphasis on relational service satisfaction is negatively related to car make-model market share (b = -.458, p < .05). However, the car dealer’s emphasis on service environment satisfaction is not related to car makemodel market share (i.e., H3b is not supported). Because this coefficient becomes marginally significant in multiple robustness tests (see the “Additional Analyses” subsection), we conjecture the lack of significance is due to the effect being relatively weak. The results in Column 3 indicate that past market share (b = .956, p < .01), satisfaction with the car (b = .139, p < .01), and advertising spending (b = 4.127, p < .01) are positively related to car make-model market share. Promotion (b = -.025, p < .01) and the car make-model’s price (b = -.044, p < .05) are negatively related to car make-model market share. Car make-model market share is also positively related to overall service satisfaction (b = .371, p < .10).
The key effects are consistent in Columns 3 and 4 in Table 3, indicating that the results are robust to alternative model specifications. In support of H4a, the higher the vertical quality of the car make-model and the higher the emphasis on relational service satisfaction, the higher the car make-model market share (b = .122, p make-model market share is not contingent on the vertical quality of the good (i.e., H4b is not supported, perhaps for the same reasons that we propose that H2b is not supported). We also find support for a positive moderation effect of the vertical quality of the good on the effect of overall service satisfaction on car make-model market share (b = .172, p < .05).
Finally, we briefly discuss the estimation results for the models of the endogenous variables. In Table 4, Columns 1–3 present estimates of the models that explain emphasis on relational service satisfaction, emphasis on service environment satisfaction, and overall service satisfaction, respectively. Column 4 presents estimates of the model that explains prices of services. Column 5 presents estimates of the model that explains prices of car make-models. We assessed the strength of the instruments by performing a series of regressions for each endogenous variable. In a first set of regressions, we regressed the endogenous variables on only the instruments. In a second set, we regressed the endogenous variables on instruments and other covariates. Table 4 reports the adjusted R2 of the full models (“total adj. R2”) and the adjusted R2 of the models with only instrumental variables (“adj. R2 of IVs”). For each of the five models, the instruments explain a substantial proportion of the variation in the dependent variables, and the F-statistics of the regressions with instruments only are significantly larger than the recommended value of 10. Therefore, we conclude that the instruments are sufficiently strong to identify the effects of service satisfaction, prices of services, and prices of cars on market shares.
TABLE: TABLE 3 Relationship Between Service Satisfaction and Car Market Share
| | Model Specification |
|---|
| | 1 | 2 | 3 | 4 |
|---|
| RELSERVSAT (H3a) | | | -.458** (.196) | -.356* (.199) |
| SERVENVSAT (H3b) | | | -.299 (.239) | -.285 (.239) |
| VERQUAL · RELSERVSAT (H4a) | | | | .122** (.055) |
| VERQUAL · SERVENVSAT (H4b) | | | | -.073 (.061) |
| OVSERVSAT | | .404** (.189) | .371* (.198) | .429** (.198) |
| VERQUAL · OVSERVSAT | | | | .172** (.072) |
| CARSHAREt–1 | .985*** (.016) | .974*** (.016) | .956*** (.018) | .953*** (.018) |
| CARPRICE | -.016 (.014) | -.024 (.015) | -.044** (.018) | -.045** (.018) |
| ADSPEND | 4.303*** (1.315) | 4.134*** (1.338) | 4.127*** (1.417) | 3.799*** (1.419) |
| PROMO | -.028*** (.002) | -.027*** (.002) | -.025*** (.003) | -.025*** (.003) |
| NDEALERS | -4.071* (2.271) | -4.892** (2.365) | -4.192 (2.590) | -3.637 (2.605) |
| VERQUAL | -.002 (.047) | -.012 (.048) | -.016 (.052) | -.016 (.052) |
| CARSAT | .127*** (.014) | .129*** (.014) | .139*** (.016) | .140*** (.016) |
| LUXURY · SENTIMENT | .001** (.0004) | .001* (.0004) | .0002 (.0005) | -.002** (.001) |
| TRUCK · SENTIMENT | -.001*** (.0003) | -.001*** (.0003) | -.001** (.0004) | -.001** (.0004) |
| lCP | .014 (.016) | .025 (.017) | .047** (.020) | .046** (.020) |
| hCS | .127*** (.016) | .124*** (.016) | .113*** (.017) | .114*** (.017) |
| xRS | | | .544*** (.193) | .480** (.193) |
| xSE | | | .194 (.228) | .210 (.228) |
| xOS | .140 (.234) | .139 (.243) | .148 (.243) |
| Adjusted R2 | .955 | .955 | .956 | .956 |
| Log-likelihood | 1,046.028 | 1,050.219 | 1,062.954 | 1,064.135 |
| AICc | -3,447.318 | -3,448.529 | -3,459.558 | -3,451.002 |
| F p-value | | .016 | .000 | .511 |
| Wald p-value | | .000 | .066 | .004 |
| Holdout RMSE | .141 | .142 | .142 | .142 |
| K-fold RMSE | .061 | .060 | .061 | .061 |
| Observations | 1,164 | 1,164 | 1,164 | 1,164 |
| Model parameters | 292 | 294 | 298 | 301 |
Additional Analyses
We test the robustness of our results to address concerns related to measurements, data sample selection, and model specification. Regarding measurements, this analysis assumes that the vertical quality of luxury car make-models is that of mainstream car make-models plus a constant. In addition, we interpret our measures of satisfaction as measures of emphasis on satisfaction rather than absolute levels of satisfaction. We also focus on the efficiency aspect of the measure of service environment satisfaction. To ensure that the results are robust to the construction of the vertical quality (VERQUAL) variable; that our measures of emphasis do, in fact, measure emphasis; and that SERVENVSAT measures efficiency, we test the model with alternative measures of VERQUAL, RELSERVSAT, and SERVENVSAT.7
TABLE: TABLE 4 Estimates of Models of Service Satisfaction, Service Prices, and Car Prices
| Variable | Audi R8 | Mazda 3 |
|---|
| Current level of … |
| Relational service | -.01 | -.04 |
| Service environment | -.01 | -.04 |
| Overall service | .08 | -.01 |
| Vertical quality | 3.03 | -1.66 |
| Elasticity of service market share with respect to … |
| Relational service satisfaction | .45** (.21) | .86*** (.30) |
| Service environment satisfaction | -.08 (.70) | -.01*** (.00) |
| Overall service satisfaction | -.26*** (.03) | -.51*** (.11) |
| Elasticity of car make-model market share with respect to … |
| Relational service satisfaction | -.78*** (.11) | .14*** (.01) |
| Service environment satisfaction | -2.48** (1.19) | .15*** (.12) |
| Overall service satisfaction | -.16* (.09) | -.01*** (.00) |
Because the definition of car make-model market share depends on the classification of car make-model categories, we also use an alternative classification of car make-model categories to demonstrate robustness of the results. Regarding sample selection, our main analysis uses a sample from 2009 to 2015. To ensure that the results are robust to the 2008 recession, which may have affected the demand for cars and services in 2009, we estimate the model with an alternative sample (2010–2015). Regarding model specification, we consider measuring service satisfaction relative to the satisfaction with the service of competing offerings. We estimate an alternative model akin to an attraction model but found that, while the focal effects are preserved, the original model performs better. We likewise consider a more complex model structure in which we allow for an effect of CARSHAREt-1 on SERVSHAREt. However, we found no evidence for such an effect. Finally, to examine the robustness of the results to omitted variables, we include additional control variables (i.e., past service price, variances of the aspects of service satisfaction, average length of maintenance warranty of the car model, and the number of free service visits associated with the car make-model in the year8). Web Appendix WA3 presents the results of all these alternative analyses, which are consistent with the results in Tables 2 and 3. Overall, the empirical findings indicate robust support for the hypotheses and the model specification.
Discussion
In this article, we examine the effects of emphasizing relational service satisfaction and service environment satisfaction on service and goods market shares and how these relationships are moderated by the vertical quality of the goods component of partnered hybrid offerings. We conclude with a discussion of the article’s theoretical contributions, managerial implications, limitations, and opportunities for further research.
Theoretical Contributions
The findings indicate that in partnered hybrid offerings, (1) the service provider’s emphasis on relational service satisfaction is positively related to service market share, and this relationship is weakened by the vertical quality of the good; (2) an emphasis on relational service satisfaction is negatively related to goods market share, and this relationship is weakened by the vertical quality of the good; and (3) an emphasis on service environment satisfaction is negatively related to service market share and does not affect goods market share. Overall service satisfaction is positively related to service market share, and this relationship is weakened by the vertical quality of the good. Finally, overall service satisfaction is positively related to the goods market share, and this relationship is strengthened by the vertical quality of the good. The main effects of emphases on relational service satisfaction and service environment satisfaction parallel those of Chiou and Droge (2006), who report a positive and significant effect of relational service on loyalty, as well as a negative, nonsignificant effect of service environment.
The pattern of our findings suggest that, for service providers, the vertical quality of the good is a substitute both for overall service satisfaction and for emphasizing relational service satisfaction in increasing service market share. In contrast, emphasizing service environment satisfaction hurts service market share regardless of the vertical quality of the good. For goods manufacturers, the vertical quality of the good and an emphasis on relational service satisfaction are complements in increasing the
Notes: IV = instrumental variable.
TABLE: TABLE 5 Elasticities: Service Satisfaction and Service and Car Market Share
| A: Service Market Share |
|---|
| Car Segment | Relational Service Satisfaction | Service Environment Satisfaction | Overall Service Satisfaction |
|---|
| Mainstream | (-1.57, .05, 1.55) 42%, 12%, 46% | (-.18, .00, .17) 31%, 35%, 34% | (-1.47, -.05, 1.51) 49%, 11%, 40% |
| Luxury | (-.52, .02, 1.00) 44%, 19%, 37% | (-.01, .00, .01) 13%, 78%, 9% | (-.93, -.01, 1.22) 48%, 12%, 40% |
| B: Car Market Share |
| Car Segment | Relational Service Satisfaction | Service Environment Satisfaction | Overall Service Satisfaction |
| Mainstream | (-.7, -.01, .86) 41%, 18%, 41% | (-.75, .01, .78) 42%, 18%, 40% | (-.17, .00, .16) 35%, 31%, 34% |
| Luxury | (-2.45, .09, 2.97) 45%, 13%, 42% | (-2.88, -.12, 1.98) 47%, 16%, 37% | (-.16, .00, .17) 23%, 37%, 40% |
Notes: The figures in parentheses are the minimum, mean, and maximum elasticities, respectively. The percentages in the second row in each cell are the percentage of car make-models with negative, zero, and positive elasticities, respectively.
TABLE 6
Illustration of Elasticities: Audi R8 and Mazda 3 goods market share. Likewise, the vertical quality of the good and overall service satisfaction are complements.
First, these findings extend, in a novel way, prior research on the rewards of customer satisfaction at the firm level (Anderson, Fornell, and Lehmann 1994; Rego, Morgan, and Fornell 2013) and of service satisfaction in the pure services domain (Kamakura et al. 2002; Rust and Zahorik 1993) to the product level in the more complex partnered hybrid offerings context. While these insights on the effects of service satisfaction on service and goods market shares in partnered hybrid offerings context are novel, they reiterate the contingent rewards to customer satisfaction, which have been previously documented in the literature at the firm level (Rego, Morgan, and Fornell 2013). Furthermore, these findings confirm that the inherent tension in the effects of customer satisfaction on market share, identified in pure service contexts, also extend to the partnered hybrid offerings context. The insights also raise questions about how service satisfaction may affect the profits of the service provider and goods manufacturer. Further research on this issue may consider linking this work and Kamakura et al.’s (2002) work on service-profit chain relationships in the partnered hybrid offering context.
Second, in a novel extension to past work on (overall) service satisfaction, we consider the emphases on two disaggregate aspects, relational service satisfaction (Baker et al. 2002; Scott, Mende, and Bolton 2013; Sirianni et al. 2013) and service environment satisfaction (Baker et al. 2002; Bitner 1992; Turley and Milliman 2000), in the partnered hybrid offerings context. The pattern of findings on the asymmetric effects of emphasizing relational service satisfaction and service environment satisfaction on service and goods market shares highlight their different (and sometimes opposing) roles in the partnered hybrid offerings context. A comprehensive theory of the disaggregate aspects of service satisfaction may benefit from further research on the different effects of these aspects of service satisfaction on other outcomes such as loyalty, share of wallet, and referral value.
Finally, the findings on the moderation effects of the vertical quality of goods extend prior work on the effects of vertical quality on market responses (Blattberg and Wisniewski 1989; Lemon and Nowlis 2002; Verhoef, Langerak, and Donkers 2007) to the hybrid partnered offerings context. The key role of vertical quality of the good suggests that consumer preference heterogeneity is important not only at the level of overall service satisfaction (Rego, Morgan, and Fornell 2013) but also at the level of disaggregate aspects of service satisfaction. The lack of evidence for consumer preference heterogeneity in the effect of emphasizing service environment satisfaction is intriguing. We conjecture that this may be because, for consumers of high–vertical quality cars, the financial costs of superior service environments may counterweigh their benefits, so that the two effects cancel out. Further research on the effects of service environment satisfaction (emphasis or the absolute level) on other outcomes will shed more light on this issue.
Managerial Implications: Model Estimates
Our results address three major controversies faced by service providers of partnered hybrid offerings. The first controversy is the one motivating a debate between those proposing that noncore aspects of service should be emphasized (e.g., Kurylko 2012; Savitz and Beninato 2011) and others arguing that the core service should be prioritized (Dixon, Freeman, and Toman 2010; Hirsh 1996; Mercer 2013). Our findings indicate that there is no simple answer to this question; instead, managers must consider the vertical quality of the good, the differential effects of various aspects of service, and the asymmetry of the benefits to service providers and goods manufacturers from emphasizing service satisfaction.
The second controversy clarified by our findings is whether emphasizing relational service satisfaction or service environment satisfaction increases both service and goods market shares in partnered hybrid offerings (Csere 2012; Mercer 2013; Mohr et al. 2014). We find that this is not always the case and that conflicts between service providers and goods manufacturers are justified by asymmetries in the rewards to service satisfaction. We recommend two solutions here. First, partners may opt not to differentiate their services through an emphasis on either aspect of service satisfaction. Instead, partners can focus on achieving superior overall service satisfaction through efforts on the core service (as recommended by Hirsh [1996] and Dixon, Freeman, and Toman [2010]). Second, partners can coordinate their data analysis efforts to find the optimal levels of service investments that benefit both parties.
The third controversy that our findings address is the common belief that consumers of high-end goods should be the key target of an emphasis on relational service satisfaction (Kurylko 2012). While we find that, indeed, the effects of service satisfaction are moderated by the vertical quality of the goods component of hybrid offerings, our results indicate that, in the case of goods of high vertical quality, an emphasis on relational service satisfaction benefits goods market shares but not service market shares. To leverage the effects of different aspects of service provision on market share, service providers should tailor their offerings for different consumers. This does not mean that firms should discriminate consumers through the quality of service but merely that consumers be provided the level of service that they prefer. Service providers can offer a menu of services at different prices for consumers to choose from. Alternatively, service providers may customize service offerings in line with the price rank of a good. For example, service providers can assign consumers to different service facilities and staff depending on the price of the goods component.9 These managerial implications may apply to other partnered hybrid offerings in the transportation industry, such as motorcycles and boats, as well as to partnered hybrid offerings in different industries, such as high-end jewelry products and watches.
TABLE: TABLE 6 Illustration of Elasticities: Audi R8 and Mazda 3
| Variable | Audi R8 | Mazda 3 |
|---|
| Current level of … |
| Relational service | -.01 | -.04 |
| Service environment | -.01 | -.04 |
| Overall service | .08 | -.01 |
| Vertical quality | 3.03 | -1.66 |
| Elasticity of service market share with respect to … |
| Relational service satisfaction | .45** (.21) | .86*** (.30) |
| Service environment satisfaction | -.08 (.70) | -.01*** (.00) |
| Overall service satisfaction | -.26*** (.03) | -.51*** (.11) |
| Elasticity of car make-model market share with respect to … |
| Relational service satisfaction | -.78*** (.11) | .14*** (.01) |
| Service environment satisfaction | -2.48** (1.19) | .15*** (.12) |
| Overall service satisfaction | -.16* (.09) | -.01*** (.00) |
Managerial Implications: Elasticity Analysis
We next use elasticity analysis to derive further insights and to illustrate how firms can identify offerings for which service adjustments can increase market share. We quantify the benefits of changing current levels of emphasis on service satisfaction by computing the elasticities of emphasis on relational service satisfaction, emphasis on service environment satisfaction, and overall service satisfaction on market shares of service and car make-models in our sample (we describe the procedure to compute the elasticities in Web Appendix WA6; when variable cost data are available, managers could also use these elasticities to optimize their resource allocation, following procedures such as the one proposed by Sethuraman and Tellis [1991]).
We define elasticity as the percentage change in service and car make-model market shares following a 1% increase in emphasis on relational service satisfaction, emphasis on service environment satisfaction, and overall service satisfaction of each car make-model. We present the averages and ranges of these elasticities (for mainstream and luxury car make-models) and the percentages of car make-models with negative, zero, and positive elasticities (statistically significant at p < .10) in Table 5.
Service market share. The average elasticities of an emphasis on service environment satisfaction on service market shares, especially for mainstream car models (.00), are smaller (p < .12 and p < .04, respectively) than the average elasticities of emphasizing relational service satisfaction (.05) and overall service satisfaction (-.05). Furthermore, 46% of mainstream car make-models have positive elasticities of an emphasis on relational service satisfaction, which is greater than the 40% of mainstream car make-models with positive elasticities of overall service satisfaction. Overall, these findings highlight that car dealers can emphasize relational service for mainstream car makemodels to increase their service market share.
Car make-model market share. The average elasticities of emphases on relational service satisfaction and service environment satisfaction on car make-model market shares are stronger (p < .1 and p < .25) for luxury brands (.09 and -.12) than for mainstream brands (-.01 and .01). This finding suggests that there are more opportunities to improve car makemodel market shares of luxury brands than of mainstream brands.
The numbers of mainstream car make-models with positive elasticities of emphases on relational service satisfaction and service environment satisfaction are both higher than that of overall service satisfaction (41% and 40%, respectively, as opposed to 34%). This suggests that while, on average, an emphasis on relational service satisfaction may hurt goods market shares, some goods manufacturers may actually benefit from increasing their emphasis on relational service satisfaction or on service environment satisfaction than from improving all aspects of service (i.e., overall service satisfaction).
Finally, comparing the ranges of elasticities on service market share and car make-model market share, we find that (1) for mainstream car make-models, the variations in elasticities of emphasis on relational service satisfaction and overall service satisfaction for service market share are larger than those for car make-model market share; (2) for luxury car make-models, the variations in elasticities of an emphasis on relational service satisfaction and an emphasis on service environment satisfaction on service market share are smaller than those for car make-model market share. In other words, adjusting the emphasis on relational service satisfaction and overall service satisfaction of mainstream car make-models is likely to benefit car dealers, whereas adjusting the emphasis on relational service satisfaction and the emphasis on service environment satisfaction of luxury car make-models may benefit the car manufacturer.
An illustrative example: elasticities and service investments. We next use the elasticity estimates to identify service provision opportunities that may prevent conflict and/or create benefits for both partners. We focus on two car make-models, Audi R8 and Mazda 3. In Table 6, we present the current levels of their emphasis on relational service satisfaction, emphasis on service environment satisfaction, overall service satisfaction, and vertical quality, and their elasticities on service market shares and car make-model market shares.
For Audi R8, the elasticities of an emphasis on relational service satisfaction on market shares of service and car makemodels have opposite effects. Thus, emphasizing relational service satisfaction may generate conflict between the service provider and the car manufacturer. In contrast, reducing the emphasis on service environment satisfaction may improve the goods market share of Audi R8 (-2.48, p < .05) but such a deemphasis on service environment satisfaction need not be opposed by Audi dealers, because it does not negatively affect service market share. Therefore, Audi car dealers and manufacturer may undertake efforts to de-emphasize service environment satisfaction.
We next discuss an example of elasticities with similar signs for both service and car make-model market shares (i.e., Mazda 3), which offers an opportunity to benefit both partners. Mazda 3 may improve both service and car market shares (.86, p < .01 and .14, p < .01, respectively) through a greater emphasis on relational service satisfaction. The elasticity of emphasis on relational service satisfaction on service market share is higher than on goods market share, which suggests that car dealers may benefit more by emphasizing relational service satisfaction for consumers of Mazda 3. Therefore, Mazda car dealers and Mazda may undertake efforts to improve relational service satisfaction, possibly with car dealers covering more of these costs.
Limitations and Opportunities for Further Research
This research has some limitations that present opportunities for further research. Although our data set includes industry-wide measures of service satisfaction which extend into 2015, the data set spans only seven years. J.D. Power and Associates’ survey instruments changed in some early years, precluding the inclusion of these data in the analysis. Moreover, because car sales data are aggregated at the national level, we are unable to examine the effects of geographic differences on the returns to service satisfaction (Mittal, Kamakura, and Govind 2004). In addition, the cost data on service provision are not available, which precludes a study of profitability and resource allocation across different aspects of service satisfaction. Further research in other partnered hybrid offering contexts where cost data are available can examine the financial returns to various aspects of service satisfaction.
Second, the proprietary data set that we use along with the multiple data sources enabled the estimation of market shares of both the service and the goods component of the partnered hybrid offering. A worthwhile question is whether the relationships that we find support for occur in other contexts (e.g., ACSI data).10 Unfortunately, we were unable to obtain disaggregated ACSI data, but this is an opportunity for further research.
Third, the lack of consistent data on satisfaction with the service of nondealer facilities prevented us from normalizing the measures of service satisfaction with respect to those of competitors. Although we show that our model of goods market shares fits the data better than an equivalent model with normalized measures, we are unable to verify whether this holds for the model of service market share. The availability of a more complete data set is another opportunity for further research.
Fourth, the secondary data in our study precluded the empirical testing of theoretical mechanisms underlying the effects. Future studies can examine the mechanisms using lab experiments. Likewise, the degrees of freedom are not sufficient to estimate heterogeneous responses to service satisfaction, so we estimate average effects only.
Finally, our empirical findings are restricted by the measurements in J.D. Power and Associates’ surveys. For example, consumers’ ratings of the service environment are based on efficiency of the facility design, cleanness of facility, and comfort of the waiting area. These measurements prevent us from examining the potential effects of innovations in service environment, such as Honda’s Green Dealership program or fast wireless Internet. The availability of more inclusive and innovative measurements on various aspects of service satisfaction offers opportunities for further research.
In summary, we take a first step toward generating insights on the asymmetric effects of emphasizing two types of service satisfaction, relational service satisfaction and service environment satisfaction, on service and goods market shares in the partnered hybrid offerings context. Given the substantive economic size of partnered hybrid offerings and its anticipated future growth, we hope that our research stimulates further work in the area.
Appendix: Instrumental Variables
In this Appendix, we discuss the instruments used to address the endogeneity of service satisfaction and prices of services and cars. We collected these instruments from multiple sources and used them, together with car attributes and marketing-mix variables, to compute additional instruments. These additional instruments are category-level and make-level averages of car attributes and the original instruments. We identify these new instruments with the suffixes “CAT” or “MAKE” (e.g., RECALLS.CATjt is the average of RECALLSjt across other car models in the category of car make-model j).
Instruments for Service Satisfaction
The instruments for emphasis on relational service satisfaction, emphasis on service environment satisfaction, and overall service satisfaction include shifters for the cost of capital investments, including IMPORTjt, the fees levied to import a 20-foot container into the country of final assembly of the car make-model; MINWAGEjt, the minimum wage at the country of final assembly; WAGESjt, a proxy for dealer spending on total annual wages; and REALESTATEjt, a proxy for dealer spending on real estate investment. In addition, we include TAXABLEWAGESjt, a proxy for the annual taxable wages paid by dealers, and EMPLOYMENTjt, the annual average of monthly employment levels associated with the make of the car make-model. We expect the cost shifters of goods (MINWAGEjt and IMPORTjt) to affect the quality of service provision through the cost of parts, which affects capital investment and therefore, capital available for service equipment and labor. REALESTATEjt determines the cost of service environment, being higher for large, convenient, and well-located facilities. We expect WAGESjt and TAXABLEWAGESjt to directly influence the cost and quality of service provision as higher wages attract more capable technicians and service staff. EMPLOYMENTjt should also affect the cost of services. None of these figures are known to consumers and thus should not directly affect service satisfaction. Thus, they are valid instruments for service satisfaction.
We obtained the variable IMPORTjt from the World Bank and variables related to the cost and quality of services (WAGESjt, TAXABLEWAGESjt, EMPLOYMENTjt, and REALESTATEjt) from the J.D. Power and Associates CSI study, the U.S. Census Bureau, and the Quarterly Census of Employment and Wages as follows. Using the Zip-3 codes of dealers in the CSI study, we obtained the number of dealers of each car make-model in each U.S. county. We matched the number of dealers in each county to the number of employees, wages, and taxable wages of new car dealers in the same county. We then regressed the number of employees and wages on the number of dealers of each car make-model/year combination, such that the coefficients quantify the average contribution of each dealer of each car make-model to the county levels of dealership employment and salaries across the U.S. Thus, the coefficients of the regression are the cost instruments WAGESjt, TAXABLEWAGESjt, and EMPLOYMENTjt. To create the instrument REALESTATEjt, we multiplied the average value of homes in each county by the number of dealers of the car make-model in the same county to assess how much the dealers of the car make-model invest in real estate.
Instruments for Service Prices
The instruments for service prices include INVDOSUPPLYjt-1, the yearly average of the number of days that current inventories can meet demand at current sales rates; INVUNITSjt-1, the yearly average of the units in inventory; IMPORTjt, described previously; TAXRATESjt, the tax rates on manufacture at the country of final assembly; and EXRATEjt, the yearly average of the exchange rate between the U.S. dollar and the currency of the country of final assembly. In addition, we include WAGESjt, and REALESTATEjt (described previously). We use INVDOSUPPLYjt-1 and INVUNITSjt-1 as instruments because high inventory levels translate into high capital investments that may strain the finances of dealers, inducing them to raise service prices. However, we do not expect inventory of cars to directly affect service sales. The cost shifters IMPORTjt, TAXRATEjt, MINWAGEjt, EXRATEjt may correlate with the cost of parts and repairs. These variables should, however, not influence service sales directly. WAGESjt and REALESTATEjt are cost shifters that determine the service prices.
Inventory variables (INVDOSUPPLYjt and INVUNITSjt) were obtained from AutoData. Variables related to the cost of cars (IMPORTjt, TAXRATEjt, MINWAGEjt, and EXRATEjt) were obtained from the World Bank and USForex.
Instruments for Car Prices
The instrumental variables for the prices of car make-model include INVDOSUPPLYjt-1, INVUNITSjt-1, IMPORTjt, TAXRATEjt, and EXRATEjt. These variables determine the cost of the cars. The variables INVDOSUPPLYjt-1 and INVUNITS jt-1 are valid instruments for the car make-models’ list prices because they are lagged and do not affect availability at time t. Furthermore, Cachon and Olivares (2010) show that in the U.S. automobile industry, inventories of cars are more strongly correlated with production flexibility and product variety than with demand variability. We also expect that IMPORTjt, TAXRATEjt, and EXRATEjt will not directly affect sales of cars because they are not known to consumers.
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TABLE 4 Estimates of Models of Service Satisfaction, Service Prices, and Car Prices
TABLE 3 Relationship Between Service Satisfaction and Car Market Share
DIAGRAM: FIGURE 1 Conceptual Framework: Relating Service Satisfaction to Service and Good’s Market Shares in Partnered Hybrid Offerings
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Record: 164- Share Repurchases and Myopia: Implications on the Stock and Consumer Markets. By: Bendig, David; Willmann, Daniel; Strese, Steffen; Brettel, Malte. Journal of Marketing. Mar2018, Vol. 82 Issue 2, p19-41. 23p. 1 Diagram, 12 Charts. DOI: 10.1509/jm.16.0200.
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Share Repurchases and Myopia: Implications on the Stock and Consumer Markets
Investor demand has promoted share repurchases to the dominating payout instrument for U.S. firms. However, critics worry that the repurchase boom leads to firms neglecting long-term investments. Even worse, scholars have shown that investor pressure also motivates firms to cut marketing investments with the aim of boosting short-term income, a practice called myopic marketing management. Extant theory still lacks an understanding of whether and how the cooccurrence of share repurchases and myopic marketing affects firm stakeholders such as investors and consumers. Using a large-scale cross-industry sample, the authors reveal that there is a higher share of firms cutting marketing investments among repurchasing firms than among nonrepurchasing firms. Furthermore, investors immediately respond negatively to myopic firms that also repurchase shares. Finally, repurchases and myopic marketing are also associated with an increase in product recalls. This first study to assess share repurchases through a marketing lens hence reveals negative effects on both the stock and the consumer markets.
Over the last two decades, the practice of buying back own shares traded on capital markets—that is, share repurchases—has become the most important payout instrument for U.S. firms. In 2013, firms in the S&P 500 index spent $500 billion on repurchases, which accounted for almost 60% of total cash returns (Economist 2014). While share repurchases should be shareholder-friendly in theory, in practice it is increasingly emphasized that they "may exact a long-term toll" (Brettell, Gaffen, and Rohde 2015). Critics worry that the repurchase boom "can create perverse incentives to pay out too much cash, damaging firms' … ability to invest" (Economist 2014, p. 68) and reducing their focus on "innovations that can drive growth" (Trainer 2016).
Although investments in future growth initiatives are crucial for marketers (e.g., Rust et al. 2004), top managers give in to increasing investor pressure and spend considerable resources on share repurchases. To make things worse, the marketing- finance literature has revealed another symptom resulting from investor pressure: a substantial number of firms cut their marketing and innovation budgets to boost short-term results (Chakravarty and Grewal 2011), a practice known as myopic marketing management (Mizik and Jacobson 2007). According to the theory of market-based assets (Srivastava, Shervani, and Fahey 1998), such a shortsighted practice harms a firm's potential to generate future cash flows. Indeed, prior research demonstrates that myopic marketing is detrimental to long-term shareholder value (Mizik 2010; Mizik and Jacobson 2007; Saboo, Chakravarty, and Grewal 2016). Against expectations, however, these studies find that investors are not able to detect myopic behaviors immediately. Instead, investors "appear temporarily fooled by myopic managers" (Mizik and Jacobson 2007, p. 362), and it takes them one to two years to respond negatively to a myopic budget cut. Prior research argues that this delay of an appropriate investor response is rooted in inefficiencies that can exist in financial markets (Mizik and Jacobson 2007). However, the assumption that investors learn about managerial behaviors over time (Fama 1970) requires a better understanding of why they are not able to detect myopic practices when they occur. Given that investors cannot perfectly monitor managers' hidden actions and information (Akerlof 1970; Jensen and Meckling 1976), they may need additional, more directly observable information that helps detect myopic behaviors when they occur. The present study hence proposes the following: ( 1) Share repurchases provide signals that help investors uncover managers' myopic behavior in a timely manner. ( 2) Both myopic marketing management and share repurchases can represent shortsighted behaviors that result in observable negative implications for consumers.
First, corporate financing programs such as share repurchases, which are publicly announced, provide signals that increase investor scrutiny (Grullon and Michaely 2004). Investors should thus learn from recent negative opinions on buybacks among scholars and journalists (Almeida, Fos, and Kronlund 2016; Chan et al. 2010). Marketers also concern themselves with such financing events because they are an integral component of firm strategy. Research in marketing, however, largely focuses on equity offerings (Luo 2008; Mizik and Jacobson 2007; Saboo, Chakravarty, and Grewal 2016). Hence, there is a lack of marketing research on repurchases—even though "it would be worthwhile to examine how … share repurchases (capital outflow) affect marketing strategy and how investors respond to changes in firms' marketing expenditures around these events" (Kurt and Hulland 2013, p. 72). Unlike issuing new equity, repurchasing shares diminishes resources and leaves myopic firms with even fewer means to invest in market-based assets (Almeida, Fos, and Kronlund 2016). Repurchases accompanied by myopic budget cuts might thus raise investor concerns regarding long- term implications and root them to respond to myopic marketing practices in a timely manner. Yet, in the context of share repurchases, we lack both a theoretical and an empirical understanding of the prevalence of myopic marketing management and the corresponding stock market reaction.
Second, a failure to invest in market-based assets can diminish resources needed to sustain customer relationships and product offerings (Srivastava, Shervani, and Fahey 1998). The effects of absent marketing investments often only surface in the future (Mizik 2010). A product recall—the withdrawal of a defective product from the consumer market—might represent an example for such a delayed and observable consequence (Chen, Ganesan, and Liu 2009). Evidence exists that product recalls affect a firm' s stock returns as investors factor in signals about product quality (Gao et al. 2015; Srinivasan and Hanssens 2009). Yet, the determinants that provoke product recalls "have received little attention in the literature" (Wowak, Mannor, and Wowak 2015, p. 1083). Market-based assets theory suggests that marketing resources are needed to ensure high product and service reliability (Gao et al. 2015; Thirumalai and Sinha 2011); myopic marketing management is hence likely related to later product recalls. Similarly, share repurchases, which reduce the resources available to deliver reliable products and services, might relate to recalls as well. For instance, toy producer Mattel initiated a massive $500 million repurchase program in 2003 and increased it by an additional $250 million in 2005 (Mattel 2005). Two years later, Mattel suffered one of the most expensive product recall programs in history, which affected nearly 9 million toys (Bloomberg 2013). It is natural to conjecture that the firm could have limited the damage by investing more resources in product safety (Rhee 2009); marketing scholars, however, lack an understanding of whether share repurchases relate to such negative consequences.
To address these gaps, this study aims to answer the following research questions. First, do firms combine myopic marketing practices with share repurchases? Second, how does the stock market respond to myopic marketing practices coinciding with share repurchases? Third, are myopic marketing and share repurchases associated with product recalls? To address these questions, we build on secondary data on listed firms, stock returns, and consumer product recalls. The results show that myopic marketing practices are indeed more common among repurchasing firms. Our econometric analyses suggest, further, that firms combining myopic marketing with share repurchases directly experience negative stock market reactions, while firms only pursuing myopic marketing do not. Finally, we observe that both repurchases and myopic marketing practices are linked to a subsequent increase in product recalls, but the co-occurrence of both practices has no effect.
This study substantially contributes to the marketing literature. First, it advances the literature on myopic marketing practices as it explains why prior studies find investors to respond positively to myopic marketing practices in the short term (Mizik 2010; Mizik and Jacobson 2007; Saboo, Chakravarty, and Grewal 2016). We achieve this by introducing and assessing the role of share repurchases empirically—and this study, to the best of our knowledge, is the first to do so in marketing research. Repurchases provide signals that spur investors to evaluate hard-to-detect myopic marketing management negatively and in a timely manner. This emphasizes how important contingencies are in research on myopia, as they help demonstrate that investors do value marketing investments. Furthermore, this study adds share repurchases—a resource- diminishing corporate financing event—as a novel context in which managers behave myopically. Prior studies, in contrast, focus on the context of resource-increasing financing events (e.g., Luo 2008; Mizik and Jacobson 2007).
Second, this study advances the theory of market-based assets (Srivastava, Shervani, and Fahey 1998). By conceptually integrating repurchases, we demonstrate how diminished investments in market-based assets result in negative implications for the consumer market. We show that product and service reliability suffer from managers' shortsightedness. This also offers novel insights to the marketing-finance literature on product recalls, which lacks theory and evidence on precursors of recalls other than operational issues. Even though share repurchases dominate boardroom discussions (Sanders and Carpenter 2003) and have generally been praised by finance theory (e.g., Ikenberry, Lakonishok, and Vermaelen 1995), critics continue to warn of the dark side of the repurchase boom, which manifests itself in "underinvestment and the maximisation of short term returns" (Das 2016). This study offers support for the existence of this dark side and, in turn, demonstrates the importance of the marketing function (Rust et al. 2004).
According to the theory of market-based assets, marketing activities result in market-based assets that increase a firm's future cash flows and contribute to shareholder value (Sri- vastava, Shervani, and Fahey 1998). Market-based assets are largely intangible off-balance sheet assets that are formed by delivering value to customers through, for instance, a better service quality or product reliability (Srinivasan and Hanssens 2009). Investments in marketing thus offer potential for sustainable earnings (Rust et al. 2004). However, with shareholders exerting intense pressure on firms to increase short-term shareholder value (Chakravarty and Grewal 2011), managers are tempted to act myopically at the expense of long-term firm value (Mizik and Jacobson 2007). They might not only use resources to buy back shares to stimulate their firm's current share price (Hribar, Jenkins, and Johnson 2006)—they might also cut marketing budgets to increase current earnings and outperform investor expectations, a practice defined as myopic marketing management (Mizik 2010).
Given the information asymmetry between managers and investors, agency models (Akerlof 1970; Jensen and Meckling 1976) provide explanations why managers have incentives to act myopically instead of investing in market-based assets and why investors may have difficulties in detecting such practices (for detailed reviews, see Grant, King, and Polak 1996; Mizik 2010). First, hidden-action models refer to situations in which the manager can shift future earnings to the present without the principal (i.e., the shareholder) being able to discern which parts of the reported earnings are "true" or "distorted" information (Narayanan 1985; Stein 1989). Managers are hence motivated to cut investments in future market-based assets to increase current earnings (Mizik and Jacobson 2007). Consistent with this, prior studies demonstrate that investors positively respond to such misleading earnings reports (Mizik 2010; Mizik and Jacobson 2007; Saboo, Chakravarty, and Grewal 2016). Second, hidden-information models refer to situations in which the principal can observe the manager' s actions but the manager possesses private information not accessible to investors (Akerlof 1970). Here, three specific models explain myopia. Signaling models suggest that firms can reduce information asymmetry by choosing certain projects that indicate positive prospects to investors (Spence 1973). Managers who strongly focus on the current share price can be inclined to engage in signal-jamming behavior that results in nonoptimal myopic investments (Mizik 2010). According to information-neglect models, investors use public information to form their opinion about a firm's set of actions. Because managers cannot credibly reveal private information about projects exceeding investors' expectations, they tend to choose projects that fulfill investors' expectations (Brandenburger and Polak 1996). Finally, the "lemons market" mechanism described by Akerlof (1970) explains that, at the extreme, firms with good prospects can completely forgo profitable projects when managers cannot credibly signal the quality of the firm (Mizik 2010).
For marketers, top management' s myopia represents a threat. Managers have incentives to cut marketing expenditures because they are discretionary and primarily yield long-term benefits. In addition, Mizik (2010) argues that managers who are approaching retirement or have stock options may have manipulation incentives. Saboo et al. (2016) empirically find that financial bubbles reduce and venture capital ownership increases the pressure to cut marketing expenses during initial public offerings (IPOs). In turn, evidence regarding investor reactions indicates that myopic marketing may be hard to detect for investors. Empirical studies (summarized in Table 1) reveal that myopic firms achieve positive same-year stock returns (Mizik 2010; Mizik and Jacobson 2007; Saboo, Chakravarty, and Grewal 2016). However, further quantitative evidence shows that myopic spending cuts result in a delayed negative stock market performance in the years following IPOs or seasoned equity offerings (SEOs) (e.g., Kothari, Mizik, and Roychowdhury 2016; Saboo, Chakravarty, and Grewal 2016). Overall, scholars agree that myopia has a negative impact on stock returns in the long run (e.g., Mizik 2010). The delayed response reveals that investors tend to adjust their misevaluation and learn when they gain new insights (Fama 1970). This, in turn, suggests that additional information or signals accompanying myopic practices should enable investors to discern risks to long-term prosperity earlier on. This study argues that share repurchase programs represent such observable signals: they are publicly announced and increase investor scrutiny (Grullon and Michaely 2004). Yet, as shown in Table 1, extant research connecting myopic practices with repurchases is scarce and focuses on selected cases in which firms might increase spending (Cooper, Downes, and Rao 2017). This lack of research is surprising because repurchases are also of interest to marketers: they further reduce myopic firms' resources (Almeida, Fos, and Kronlund 2016; Kurt and Hulland 2013), which can result in even lower investments in market-based assets (Rust et al. 2004). Investors evaluating myopic firms that repurchase shares might thus be aware of the strained resource setup. In addition, having fewer marketing resources might affect consumers because it might lead to lower-quality customer services or products (Rubera and Kirca 2012; Rust et al. 2004). Hence, myopic marketing and repurchases can have an impact on the consumer market. However, as shown in Table 1, there is little research on such palpable implications of myopic marketing practices.
TABLE: TABLE 1 Selected Research on Implications of Discretionary Spending Adaptations in Corporate Financing Contexts
TABLE: TABLE 1 Selected Research on Implications of Discretionary Spending Adaptations in Corporate Financing Contexts
TABLE 1 Selected Research on Implications of Discretionary Spending Adaptations in Corporate Financing Contexts
| Study | Corporate Financing Context | Spending in Focus | Earnings Impact in Focus | Dependent Variables | Empirical Findings |
| Luo (2008) | Equity issuance | Marketing | Spending increase | IPO underpricing, trading volume | • Pre-lPO marketing expenditures lower firms' IPO underpricing and increase IPO trading volume. |
| Saboo, Chakravarty, and Grewal (2016) | Equity issuance | Advertising and R&D | Spending cut, earnings increase | Stock returns | • Myopic firms that cut advertising and R&D expenses experience positive short-term and inferior long-term post- IPO stock market performance. • The inferior long-term effects increase with more strategic alliances and a strategic emphasis on value creation but decrease in the presence of key customer relationships. |
| Kurt and Hulland (2013) | Equity issuance | Marketing | Spending increase | Stock returns | • Firms that increase marketing expenditures experience positive short- and medium-term post-IPO and post- SEO stock market performance when competing against rivals with less flexibility. |
| Mizik and Jacobson (2007) | Equity issuance | Marketing | Spending cut, earnings increase | Stock returns | • Myopic firms that cut marketing expenditures experience positive short- term and inferior long-term post-SEO stock market performance. |
| Kothari, Mizik, and Roychowdhury (2016) | Equity issuance | R&D and SG&A | Spending cut, earnings increase | Stock returns | • Myopic firms that cut R&D and SG&A expenses experience inferior post-SEO stock market performance. |
| Cooper, Downes, and Rao (2017) | Share repurchase | Advertising, R&D, SG&A, and production | Spending increase, earnings decrease | Discretionary spending, production, stock returns | • Firms increase discretionary spending before stock repurchases with high repurchase volumes and underproduce inventory before nonconsecutive repurchases. • Firm that reduce income-increasing real earnings management before nonsignaling stock repurchases experience positive stock returns in the subsequent period. |
| This study | Share repurchase | Marketing and R&D | Spending cut, earnings increase | Stock returns, product recalls | • Firms demonstrate an increased prevalence of myopic cuts in marketing and R&D investments at the time of a share repurchase. • Firms that combine myopic marketing practices with share repurchases experience negative stock market reactions. • Firms that conduct myopic marketing practices and firms that repurchase shares are associated with subsequent product recalls. |
| Ge and Kim (2014) | Debt issuance | Advertising, R&D, SG&A, sales manipulation, and production | Spending cut, earnings increase | Bond yield spreads, credit ratings | • Overproduction lowers credit ratings; sales manipulation and overproduction are related to higher bond yield spreads at the issuance of new bonds. • No significant effects identified regarding discretionary expenditures (advertising, R&D, SG&A). |
| Crabtree, Maher, and Wan (2014) | Debt issuance | Advertising, R&D, SG&A, sales manipulation, and overproduction | Spending cut, earnings increase | Bond yield spreads, credit ratings | • Sales manipulation, overproduction, and cuts to discretionary spending (advertising, R&D, SG&A) result in lower bond ratings and higher market yields at issuance due to higher perceived credit risks. |
| Mizik (2010) | No specific corporate financing context | Marketing and R&D | Spending cut, earnings increase | Stock returns | • Myopic firms cutting marketing and innovation expenditures experience positive short-term and inferior long-term stock returns. |
| Chakravarty and Grewal (2011) | No specific corporate financing context | Marketing and R&D | Earnings increase | Unanticipated R&D and marketing spending changes | • In response to past stock market returns and volatility, most myopic firms decrease R&D but increase marketing expenditures. |
| Moorman et al. (2012) | No specific corporate financing context | Introduction of innovations | Earnings increase over time | Stock returns, revenue growth, ratchet strategy | • Firms ratcheting introductions of innovations experience higher stock market performance than firms without such a myopic strategy, but lower revenue growth. • Public firms are more likely to adopt a ratchet strategy. |
To advance existing theory, this study develops a conceptual framework (see Figure 1) that examines the implications of myopic management and share repurchases on both the stock and consumer markets. We draw on the theories of market- based assets and myopic management (Mizik 2010; Rust et al. 2004; Srivastava, Shervani, and Fahey 1998), on finance theory (Chan, Ikenberry, and Lee 2004; Peyer and Vermaelen 2009), and on product recall literature (Gao et al. 2015; Thirumalai and Sinha 2011). First, this study argues that and empirically examines whether managers have incentives to engage in myopic marketing in the context of share repurchases. Both practices increase the earnings-per-share ratio. Because this ratio is an essential determinant of the actual return shareholders receive (Grullon and Ikenberry 2000), managers might combine both practices to affect a firm's stock market performance. Second, the actual direction of the immediate investor response to such behaviors (illustrated on the left side of Figure 1) depends on whether or not investors are able to detect myopic behaviors (Hribar, Jenkins, and Johnson 2006). We propose that share repurchases provide signals that make investors skeptical about the firm' s prospects and thus help them respond to myopic behaviors in a timely manner. Third, as illustrated on the right side of Figure 1, our framework posits that both myopic marketing and share repurchases are associated with product recalls. Both practices reduce the resources available for marketing activities, which can result in increased product quality risks (Mitra and Golder 2006; Thirumalai and Sinha 2011). Marketing resources ensure that firms deliver value to their customers, which includes activities to enhance product features as well as to explain them to customers (Srivastava, Shervani, and Fahey 1998). Advertising can help clarify product functionalities, and customer service ensures immediate remedies when quality issues arise. As investors react to product recalls with prompt share price adjustments (Chen, Ganesan, and Liu 2009), this might also explain the delayed investor response to myopia identified by prior studies (e.g., Mizik 2010). Next, we introduce share repurchases and then derive our hypotheses.
Shareholder value and corporate financing topics strongly influence boardroom decisions on resource allocation (Garmaise 2009). Share repurchases, for which firms use substantial means, represent a potential rival to marketers regarding resource claims (Farrell, Unlu, and Yu 2014; Skinner 2008). Hence, they likely affect not only a firm's stock market performance but also its marketing activities targeting the consumer market (see Figure 1). More than 90% of all share repurchases in the United States are open-market repurchase programs (Grullon and Michaely 2004), meaning that firms buy back own shares on the stock exchange. In these programs, firms are not obliged to disclose the time, price, or extent of the repurchases; they are free to decide if, when, and how much they buy back (Chan et al. 2010; Hribar, Jenkins, and Johnson 2006). A main reason for managers' interest in repurchases is their impact on the pivotal earnings- per-share ratio through a reduction of its denominator (i.e., shares outstanding). Assuming that firms increase their productivity with the remaining assets (Grullon and Ikenberry 2000), a repurchase can affect stock returns (Hribar, Jenkins, and Johnson 2006). Evidence shows that firms buy back shares to meet analysts' earnings-per-share forecasts or their own targets (Bens et al. 2003; Hribar, Jenkins, and Johnson 2006), or to prevent a decrease in earnings per share (Myers, Myers, and Skinner 2007). Accounting scholars thus consider buybacks a way to influence stakeholders' perception of a firm's economic situation (Healy and Wahlen 1999), which can be misleading because buybacks can result in lower future operating performance (Chan et al. 2010).
The most cited managerial motive behind share repurchases refers to the undervaluation hypothesis (e.g., Brav et al. 2005; Chan et al. 2010; Comment and Jarrell 1991;Dann 1981).Firms buy back shares "to signal to the market that their shares are undervalued" (Rau and Vermaelen 2002, p. 249). Managers possess private information about the firm's prospects and may be motivated to signal the degree of the perceived undervaluation: the spread between the actual stock price and a firm's intrinsic value (Louis and White 2007). In theory, buying back undervalued shares indeed equals a positive net present value transaction that transfers wealth from the former to the continued shareholders (Dittmar 2000). However, using resources to buy back shares instead of investing them in marketing activities that create market-based assets may destroy value (Rust et al. 2004). In this vein, some scholars argue that managerial opportunism can be another motive for repurchases as managers use them to boost share prices and sell their own shares afterward (Fried 2001; Kahle 2002). Other motives for repurchases, such as the distribution of excess cash to shareholders (Jensen 1986) or the balancing of a firm's debt-to-equity ratio, receive only limited empirical support (Chan, Ikenberry, and Lee 2004). In general, finance research has often argued in favor of repurchases (e.g., Comment and Jarrell 1991; Dann 1981). Critics, in contrast, have recently argued that buybacks are rather a form of risky stock price manipulation (Economist 2014). For instance, "of the five companies that reduced share count the most via buybacks since 2011, four … subsequently generated subpar returns" (Derousseau 2017). However, we still lack a full picture of the impact of repurchases that includes the marketing perspective (Kurt and Hulland 2013).
Stein (1989) demonstrates that even in fully efficient stock markets, the more important the current share price, the higher the incentives for managers to behave myopically (Mizik and Jacobson 2007). A firm's share price is of utmost importance to managers, particularly in corporate financing phases such as share repurchases (Jagannathan, Stephens, and Weisbach 2000). Following Stein's (1989) logic, this share price obsession should provide managers with strong incentives to engage in myopic marketing practices when repurchasing.
Repurchases reduce the number of shares outstanding and myopic marketing increases earnings; hence, both practices hike earnings per share. Managers might thus use both practices together to send reinforced signals to investors (Graham, Harvey, and Rajgopal 2005). Exceeding earnings-per-share benchmarks and focusing on short-term results are major motives for increasing current-period earnings (Chakravarty and Grewal 2011; Graham, Harvey, and Rajgopal 2005, 2006). If the hike is higher than investors expect, share prices tend to rise as investors react to unforeseen information (Fama 1970). Myopic budget cuts enable managers to report positive earnings surprises that "mislead at least some stakeholders into believing certain financial reporting goals have been met" (Roychowdhury 2006, p. 337). In line with the hidden-information logic, it is hard for investors to observe directly whether investment cuts are justified. Therefore, managers use myopic practices to signal that a firm performs better than it does. In a similar vein, managers buy back shares to correct for negative signals sent by analysts, such as downgraded earnings-per-share forecasts for their firm (Peyer and Vermaelen 2009). Thus, managers are likely tempted to reinforce their signals through both buybacks and positive earnings surprises (Chan et al. 2010). This should increase the proportion of firms conducting myopic marketing among buyback firms compared with firms that do not buy back.
Previous research supports this notion. First, Chan et al. (2010) reveal that firms under pressure to boost share prices use both income-increasing accounting options and share repurchases. In contrast to myopic marketing management, such accounting options do not affect a firm's real marketing actions. Yet, they similarly result in higher reported earnings. The findings of Chan et al. (2010) suggest that managers inflate (i.e., increase) earnings to enhance the signaling credibility of their repurchase programs. Second, while some evidence exists that firms decrease instead of increase earnings prior to share repurchases in selected cases (Cooper, Downes, and Rao 2017), finance scholars conclude that "managers who intend to signal favorable private information are unlikely to manage earnings down" (Gong, Louis, and Sun 2008, p. 952). Third, the findings of Louis and White (2007) indicate that managers may prefer the combination of share repurchases and positive earnings surprises to press releases or conference calls to convey optimism to investors. Finally, managers are also motivated to sacrifice discretionary expenditures prior to equity offerings, with the aim to send positive signals to investors (Mizik and Jacobson 2007; Saboo, Chakravarty, and Grewal 2016). A higher share price leads to greater proceeds, and it thus seems trivial that managers might be motivated to inflate earnings before an equity issuance. In our context, in contrast, one could argue that a higher share price would make a repurchase more expensive. However, given that the vast majority of repurchase programs are open-market repurchases that are not obligatory (Chan et al. 2010), managers can flexibly decide whether and when they buy. This flexibility, in turn, provides even more incentive to increase the credibility of their repurchase program to ensure that investors believe their undervaluation signal (Rau and Vermaelen 2002). Hence, we expect that managers have strong incentives to apply myopic marketing practices, particularly in the context of share repurchases. We thus propose our baseline hypothesis:
H1: Myopic marketing practices are more common among firms that repurchase shares than among firms that do not.
While managers appear to be tempted to employ myopic marketing practices in the context of share repurchases, the question arises how investors respond to such a behavior. As prior research has demonstrated how investors respond to myopic marketing practices and share repurchases individually (e.g., Hribar, Jenkins, and Johnson 2006; Mizik 2010), we concentrate on the novel interaction effect between both practices. We focus on short-term reactions to detect whether repurchases help investors identify myopic practices in a timely manner.
Information asymmetry makes it difficult for investors to discern unjustified cuts in marketing and R&D expenditures that lead to positive earnings surprises (Mizik 2010). Prior studies find that investors evaluate such practices negatively only in the long run (see Table 1). In the short term, however, inefficiencies in financial markets result in investors' inability to evaluate the negative consequences of myopic practices correctly and in a timely manner (Mizik and Jacobson 2007). Yet, we argue that investors react earlier when myopia coincides with contingencies that raise their doubts. Buying back shares results in fewer firm resources after the transaction (or higher risks, if financed with debt) (Almeida, Fos, and Kronlund 2016). Thus, firms that myopically cut marketing investments and consume resources for a repurchase have even fewer funds to invest in marketing and R&D. This exacerbates the negative impact of myopia for a firm's potential to create market-based assets (Rust et al. 2004), a mechanism investors should be aware of. Moreover, recent evidence on repurchases, including the repurchase boom of the last two decades, shows that firms invest less in employment, capital expenditures, and R&D after repurchases (Almeida, Fos, and Kronlund 2016). Firms also experience a significant decline in operating performance in the years after open-market share repurchase announcements (Grullon and Michaely 2004). Although finance scholars agree that "it is impossible to identify the true motivation … behind a given buyback announcement," at least some firms announce buybacks to mislead investors (Chan et al. 2010, p. 155). While investors tend to respond with positive returns very shortly after the announcement—which might also be related to the short-term interests of some investors—they negatively respond later, when information about actually repurchased shares and firm performance becomes public (Chan et al. 2010). This suggests that investors are not naive and learn about managerial gaming over time. In turn, repurchases thus provide signals that increase investor scrutiny regarding a firm's investment activities. This likely facilitates the early detection of myopic practices.
Reporting both positive earnings surprises and buying back shares likely raises investor doubts. Prior research suggests that "if managers are deliberating trying to send a message, the market appears to be reacting with skepticism" (Grullon and Ikenberry 2000, p. 38), which several studies confirm. First, investors respond with disbelief to repurchase announcements by firms with higher volumes of exercisable employee stock options (Kahle 2002). In this case, managers buy back shares to offset an earnings dilution caused by newly issued employee stocks. Kahle' s (2002) findings suggest that investors are indeed able to detect the consequences of managerial signals sent around repurchases. Second, when investors are making evaluations, they immediately factor in components of earnings surprises when realizing that these are managed (DeFond and Park 2001). Since both myopic marketing and share repurchases are tools used to manipulate earnings per share (Chakravarty and Grewal 2011; Hribar, Jenkins, and Johnson 2006), the reaction of the stock market to the co-occurrence of both practices is more likely to be negative than positive. Third, there is also evidence that investors can discern managerial behavior aimed at deliberately trying to exceed earnings-per- share benchmarks when repurchasing shares (Hribar, Jenkins, and Johnson 2006). In conclusion, investors are more likely to detect harmful practices behind myopic marketing when these co-occur with repurchases; hence, the stock returns in the year of occurrence will be negative. We hypothesize:
H2: The interaction between myopic marketing management and share repurchases is associated with negative abnormal stock returns.
While managers have incentives to combine myopic marketing with repurchases, such behaviors can affect consumers if managers have difficulties balancing stakeholder interests (Von Werder 2011). Unsafe products can represent such implications; recalls are issued to protect consumers against these flawed products (e.g., hazardous materials, products with risks from improper use) (Dawar and Pillutla 2000). When a recall is announced by a government agency, such as the U.S. Consumer Product Safety Commission (CPSC), the firm is obliged to withdraw the product and provide a refund, replacement, repair, or discount for a future purchase (Liu, Liu, and Luo 2016). Evidence in marketing shows that product recalls can severely damage a firm' s brand equity (Dawar and Pillutla 2000) or lead to market-share losses (Van Heerde, Helsen, and Dekimpe 2007). As investors react to observable signals, recalls result in an immediate drop in a firm's share price (e.g., Davidson and Worrell 1992; Gao et al. 2015). Despite these ethical and economic risks, 2,363 consumer products, pharmaceuticals, and medical devices were recalled in the United States in 2011, representing a 62% increase from 2007 (Advisen 2012).
Marketing scholars have thus far focused on actions such as advertising or promotions that mitigate the economic damage of a recall (Gao et al. 2015; Rubel, Naik, and Srinivasan 2011). Haunschild and Rhee (2004) show that automakers learn from prior voluntary product recalls, thereby lowering the likelihood of future recalls. Thirumalai and Sinha (2011) add that firms with smaller product portfolios have relatively more resources to develop, produce, and monitor individual product lines, which decreases the likelihood of recalls. This notion is in line with the theory ofmarket-based assets, according to which more resources for marketing increase the reliability and quality of products and services (Rubera and Kirca 2012; Rust et al. 2004). Hence, a myopic cut in a firm' s marketing and R&D expenditure is likely related to subsequent product recalls. Yet, research on the drivers of recalls is rare and mainly focuses on operational issues (e.g., Steven, Dong, and Corsi 2014).
Managers who myopically cut marketing[ 1] budgets might provoke product recalls for several reasons. First, although recalls represent risks to a firm's long-term prosperity (e.g., Chen, Ganesan, and Liu 2009), hidden-action models suggest that managers have incentives to reduce resources needed for reliable product and service offerings (Narayanan 1985; Stein 1989). Because investors cannot immediately observe the positive impact of marketing activities, managers are motivated to take risks to deliver on the short-term interests of shareholders (Wowak, Mannor, and Wowak 2015). When there is an unexpected cut in the R&D budget for testing new and current products, the organization has fewer resources to apply proper quality controls and preventive systems (Chiarini 2015; Kiani et al. 2009). This increases the risk that design flaws, material faults, and packaging errors remain undiscovered (Thirumalai and Sinha 2011). Reduced product development budgets may also lead firms to continue selling products with outdated safety standards. In addition, lower sales personnel, advertising, and service levels may lead to product safety issues. When a firm decreases its sales force, the remaining staff has less time to explain complex products or risks due to improper use and hazardous materials to customers. Goldsmith (2016) argues that advertising also plays a vital role in that addressing the wrong target group (e.g., children) can jeopardize consumer safety. Firms with resource constraints in such situations may have insufficient service levels, which impedes them from learning from customers and quickly phasing out affected products. Still, managers appear not to fear the downsides of product recalls. Bromiley and Marcus (1989, p. 248) demonstrate in the automotive industry that "for a large majority,… the production of defective automobiles appears to be a profitable activity" when considering savings originating in unfixed defects, income from earlier product introductions, and legal liabilities. In sum, we expect myopic marketing to relate positively to product recalls.
Second, managers buying back shares to signal perceived undervaluation care about the current share price (Brav et al. 2005; Peyer and Vermaelen 2009). In such situations, information-neglect models suggest that managers tend to fulfill investors' expectations by buying back shares at the expense of product and service reliability. The pressure to send positive signals to investors is increased by the fact that it is difficult for investors to evaluate a firm' s marketing and innovation activities properly (Cohen, Di- ether, and Malloy 2013; Srinivasan and Hanssens 2009). Managers therefore choose observable repurchases rather than nonobservable projects enhancing product and service quality to meet investor demand in a credible way. Moreover, to deliver on the undervaluation signal sent by repurchases, managers overlook possible disadvantages and aggressively push their marketing function to introduce new products earlier (Moorman et al. 2012). Such a strategy increases the risk of unreliable products with higher recall probability (Thirumalai and Sinha 2011). Finally, following the mechanism of the lemons market, managers might use available cash to fund their share repurchases if they believe the increased risk of not investing in reliability is worth the payoff that the repurchase offers to investors. Evidence shows that intense investor pressure impedes investments in long term-oriented R&D projects (He and Tian 2013) and leaves firms with more risky projects regarding innovations, existing products, and distribution channels (Markovitch, Steckel, and Yeung 2005). Hence, we expect that repurchasing firms experience an increase in product recall incidences.
Finally, empirical studies indicate a significant increase in the number of product recalls in the United States (Gao et al. 2015). As products have become more complex, customers more demanding, and product safety laws more stringent (Dawar and Pillutla 2000), the risk of recalls has grown (Chen and Nguyen 2013). Against this backdrop, reducing a firm's resources with both myopic budget cuts and share repurchases should even further enhance the threat of product recall situations. Thus, we also expect the interaction between myopic marketing and share repurchases to result in a higher risk of product recalls. In sum, we hypothesize:
H3: (a) Myopic marketing management, (b) share repurchases, and (c) the interaction of both practices are positively associated with subsequent product recalls.
We merge several archival sources for this study: Compustat for the annual accounting data, the University of Chicago's Center for Research in Security Prices (CRSP) database for the stock market data, and the Kenneth French Data Library for the portfolios needed to calculate abnormal returns. In line with prior research, we restrict our data set as follows. First, we only consider U.S. firms in the years from 1983 to 2013, as in other repurchase studies (Brav et al. 2005; Farrell, Unlu, and Yu 2014). Second, we exclude firms in highly regulated industries (SIC codes 4400-5000 and 6000-6500) to ensure that corporate policies are not driven by regulatory requirements (Leary and Roberts 2005; Roychowdhury 2006). Third, we remove all observations with asset values below $10 million to avoid results being driven by small firms (Kurt and Hulland 2013; Leary and Roberts 2005). Fourth, to ensure comparability of annual return data, we only consider firms whose fiscal year ends in December (Mizik 2010). Finally, we exclude firm-years with missing data regarding our main variables. This results in an unbalanced time- series panel of 19,869 firm-year observations.
For the analyses regarding product recalls, we add recall announcement data from the CPSC for the period from 2008 to 2013 and analyze CPSC-regulated consumer goods manufacturing firms (Chen, Ganesan, and Liu 2009; Zavyalova et al. 2012). This ensures that trading firms do not dilute the sample because they mainly recall products manufactured by others. To identify CPSC-regulated firms, we requested a list of regulated product classes from the CPSC. We compared this list with Hoover's company profiles (www.hoovers.com) and with firm websites to confirm that firms' primary products match these product classes (Zavyalova et al. 2012). We thus minimize sample-selection bias and include firms with and without recalls. We also obtained Exhibit 21 reports from the SEC website for all regulated firms to assign subsidiary recalls with diverging names to their respective parent. In our sample, we chose large firms (S&P 500 and S&P Midcap 400) to warrant a minimum level of customer scrutiny as firms and customers themselves can initiate recalls by the CPSC (Chen, Ganesan, and Liu 2009; Zavyalova et al. 2012). After matching the CPSC sample with our initial sample, we obtained 804 firm-year observations for our recall analysis.
Identifying myopia. We use proven methods to identify myopic marketing management. Firms reporting greater than normal earnings, lower than normal marketing expenses, and lower than normal R&D expenses at the same time are more likely to act myopically (Cohen and Zarowin 2010; Mizik 2010; Mizik and Jacobson 2007). We thus calculate unexpected changes in earnings Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. marketing expenses Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. and R&D expenses Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. as the spread between actual and expected levels based on the whole Compustat database. The fixed-effect autoregressive panel data forecast models in Equations 1-3 are used to estimate the three error terms Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. (Anderson and Hsiao 1982; Mizik 2010):
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 1)
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 2)
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 3)
where ROAit, Mktgit, and R&Di t are return on assets (ROA), marketing intensity, and R&D intensity of firm i in period t and ROAi t-1, Mktgi t-1, and R&Di t-1 represent the lagged values, respectively. Table 2 shows the detailed definition of these and other variables. The term YEARt is a set of time dummies, and Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. and Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. are firm-specific intercepts, whereas Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. and Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. constitute the estimates of persistence. We allocate our firm-year observations into eight different groups according to the conditions shown in Table 3. Group 1 firms are classified as "potentially myopic" because they report unexpected increases in earnings and unexpected decreases in marketing and R&D expenses. We define a binary variable (Myopicit) taking the value of 1 if firm i is a Group 1 firm in year t and 0 otherwise.
TABLE: TABLE 2 Variable Definitions
TABLE: TABLE 2 Variable Definitions
TABLE 2 Variable Definitions
| Variable | Definition | Indicative References |
| Marketing intensity (Mktg) | Ratio of selling, general, and administrative (SG&A) expenses reduced by research and development expenses, scaled by total assets. SG&A comprises expenses such as those for sales force, sales commissions, and advertising. | Kurt and Hulland (2013); Mizik (2010); Mizik and Jacobson (2007) |
| R&D intensity (R&D) | Ratio of research and development expenses scaled by total assets. | Kothari, Mizik, and Roychowdhury (2016); Mizik (2010); Roychowdhury (2006) |
| Return on assets (ROA) | Ratio of operating income before depreciation scaled by total assets. | Kurt and Hulland (2013); Mizik (2010); Rego, Billett, and Morgan (2009) |
| Repurchase | Binary variable that takes a value of 1 if the repurchase volume is larger than 0, and 0 otherwise. Repurchase volume is determined by the purchase of common and preferred stock reduced by preferred stock/redemption value. | Banyi, Dyl, and Kahle (2008) |
| Myopic management (Myopic) | Binary variable that takes a value of 1 if a firm acts myopically (Group 1 firm), and 0 otherwise (for categorization, see Table 3). | Mizik (2010); Mizik and Jacobson (2007) |
| Compounded abnormal stock returns (CAR) | CARi,t = log Π [1 + (RETi,m — eRETi,m)], where m is month; RETi,m is raw stock return in month m; eRETi,m is expected monthly return, where eRETi,m = ßi χ (RETMARKET^ — RETRISKFREE^) + si χ SMBm + hi χ HMLm + rni χ MOMm, where (RETMARKET,m — RETRISKFREE,m)is market premium; SMBmis the return on a value-weighted portfolio of small stock minus the return on big stock; HMLmis the return on a value-weighted portfolio of high-book-to-market stock minus the return on low-book-to-market stock; and MOMm is momentum factor. We get the portfolio returns from the Kenneth French Data Library and apply the following equation to estimate ßi, si, hi and mi: (RETi,m — RETRISKFREE,m) = ai + ßi χ (RETMARKET,m — RETRISKFREE,m) + si χ SMBm + ßi χ HMLm + mi χ MOMm + ei,m. | Carhart (1997); Fama and French (1996); Grullon and Michaely (2004); Mizik (2010) |
| Buy-and-hold abnormal stock returns (BHAR) | BHARi,t = RETi,t — exp RETi,t = ei,t is equal tc e,t = RETi,t — Σ=1 a1,t χ YEARt + ΣΤ=1 a + Σ,=1 as,t χ log (BMVi,t-1 ) χ YEAR, 2,t χ log(MVi,t-1 ) χ YEAR | Daniel and Titman (1997); Mizik (2010) |
| where YEARi is a year dummy equal to 1 if it equals t and 0 otherwise; MVi,t-1 is lagged market capitalization; and BMVi,t-1 is lagged book-to-market ratio. | |
| Recall | Number of product recalls within a year. | Wowak, Mannor, and Wowak (2015) |
| Firm age | Number of years since the firm was first reported in Compustat. | Malmendier and Tate (2005); Saboo, Chakravarty, and Grewal (2016) |
| Net income | Ratio of total earnings after depreciation, interest, and tax, divided by firm sales. | Saboo, Chakravarty, and Grewal (2016) |
| Firm size | Log transformation of number of employees. | Fang, Palmatier, and Grewal (2011); Saboo, Chakravarty, and Grewal (2016) |
| Market-to-book ratio | Log transformation of stock market value divided by book value of common stock. | Kurt and Hulland (2013); Mizik and Jacobson (2007); Rego, Billett, and Morgan (2009); Saboo, Chakravarty, and Grewal (2016) |
| Asset growth | Percentage change in total assets from t — 2 to t — 1. | Cooper, Gulen, and Schill (2008) |
| Technological turbulence | Ratio of R&D expenses to sales within an industry. | Saboo, Chakravarty, and Grewal (2016) |
| Market turbulence | Ratio of SG&A expenses to sales within an industry. | Saboo, Chakravarty, and Grewal (2016) |
| Competitive intensity | Herfindahl-Hirschman index: sum of squared market shares for each industry. Market shares are calculated as the ratio between firm sales and industry sales. | Fang, Palmatier, and Grewal (2011); Kurt and Hulland (2013); Luo, Homburg, and Wieseke (2010); Saboo, Chakravarty, and Grewal (2016) |
TABLE: TABLE 3 Group Categories
TABLE 3 Group Categories
| Group Number | Category | εROAi,t | εMktgit | εR&Di,t |
| 1 | Potentially myopic | >0 | <0 | <0 |
| 2 | Nonmyopic | >0 | >0 | <0 |
| 3 | Nonmyopic | >0 | <0 | >0 |
| 4 | Nonmyopic | >0 | >0 | >0 |
| 5 | Nonmyopic | <0 | <0 | <0 |
| 6 | Nonmyopic | <0 | >0 | <0 |
| 7 | Nonmyopic | <0 | <0 | >0 |
| 8 | Nonmyopic | <0 | >0 | >0 |
Identifying repurchases. We follow Banyi, Dyl, and Kahle (2008) to identify share repurchases. We approximate the repurchase volume by subtracting "preferred stock/redemption value" from "purchase of common and preferred stock." Then, we define a binary variable (Repurchasei t) taking the value of 1 if the repurchase volume is larger than 0 and 0 otherwise. Table 4 exhibits the distribution of repurchases by industry, indicating they are common across many sectors.
TABLE: TABLE 4 Industry Distribution of Share Repurchases
| | Firm-Year | No | | Repurchases |
| Industry | SIC Code(s) | Observations | Repurchases | Repurchases | in % |
| Oil and gas extraction | 13 | 244 | 153 | 91 | 37.30% |
| Food and kindred products | 20 | 281 | 137 | 144 | 51.25% |
| Apparel and other textile products | 23 | 6 | 5 | 1 | 16.67% |
| Paper and other wood products | 24, 25, 26, 27 | 723 | 359 | 364 | 50.35% |
| Chemicals and allied products | 28 | 2,435 | 1,313 | 1,122 | 46.08% |
| Manufacturing | 30, 32, 33, 34 | 1,604 | 942 | 662 | 41.27% |
| Computer equipment and business services | 35, 73 | 4,775 | 2,781 | 1,994 | 41.76% |
| Electronic and electric equipment | 36 | 2,922 | 1,725 | 1,197 | 40.97% |
| Transportation and miscellaneous manufacturing industries | 37, 39 | 1,152 | 655 | 497 | 43.14% |
| Measuring instruments | 38 | 2,478 | 1,490 | 988 | 39.87% |
| Durable goods trade | 50 | 482 | 284 | 198 | 41.08% |
| Retail | 53, 54, 56, 57, 59 | 607 | 347 | 260 | 42.83% |
| Automotive dealers and gasoline services | 55 | 88 | 27 | 61 | 69.32% |
| Eating and drinking establishments | 58 | 477 | 253 | 224 | 46.96% |
| Hospitality and entertainment | 70, 79 | 317 | 194 | 123 | 38.80% |
| Health services | 80 | 354 | 198 | 156 | 44.07% |
| All others | 22, 29, 51, 52, 87, 99 | 924 | 586 | 338 | 36.58% |
Measuring stock returns. To ensure the robustness of our results, we build on two methodologies to calculate a firm's abnormal stock return (ASRi,t+kit). First, we apply the four-factor model proposed by Fama and French (1996) and Carhart (1997) to calculate compounded abnormal stock returns (CAR). We use the stock market portfolios from the Kenneth French Data Library in panel regressions to calculate the four risk factor loadings (i.e., market, size, book-to-market, and momentum) and use them to estimate the expected returns for each firm. We then subtract the expected returns from the realized returns retrieved from CRSP to calculate monthly abnormal returns. Finally, we generate the annual CAR by taking the logarithm of the compounded monthly abnormal returns. Second, we apply the time-varying risk characteristic approach proposed by Daniel and Titman (1997) to calculate the buy-and-hold abnormal stock returns (BHAR). We regress realized returns retrieved from CRSP against the log of lagged firm risk characteristics (i.e., size and book-to-market) that are allowed to vary by year. The obtained residuals it are the BHARs for each year and firm. Table 2 provides details on both variables.
Measuring recalls. Product recalls are widely used as a metric for product reliability in marketing research (e.g., Gao et al. 2015). In line with Wowak, Mannor, and Wowak (2015), we measure our recall variable (Recalli t) based on the number of recalls of firm i in period t.
Control variables. On the firm level, we control for firm size and firm age to ensure that maturity and life cycle stage do not dilute our results. We measure firm size as the log transformation of the number of employees (Fang, Palmatier, and Grewal 2011) and firm age as the years since a firm's first report in Compustat (Malmendier and Tate 2005). We control for firm profitability by using net income scaled by firm sales because profitability can influence firm discretion (Rego, Billett, and Morgan 2009). On the industry level, we control for the firm's industry environment (Saboo, Chakravarty, and Grewal 2016). We define the industries at the two-digit SIC code level (Kurt and Hulland 2013). Technological turbulence accounts for industry dynamics regarding R&D investments and is defined as the ratio of R&D expenses to sales within an industry. Market turbulence accounts for the dynamics regarding marketing investments and is calculated by dividing selling, general, and administrative (SG&A) expenses by sales within a firm's industry. Finally, we account for an industry's competitive intensity according to the Herfindahl-Hirschman index (Luo, Homburg, and Wieseke 2010). We incorporate additional capital market-related controls in our stock return analysis: We include the market-to-book ratio to control for investor growth expectations measured by using the log transformation of stock market value divided by the book value of common stock. We enter asset growth in our models because Cooper, Gulen, and Schill (2008) identify it as a strong predictor of abnormal returns, and we follow their calculation approach. We Winsorize the input variables for our myopic measure and all continuous control variables at the one-percent level to account for outliers (e.g., Kim and McAlister 2011). Our econometric stock return models, moreover, employ firm- cluster robust standard errors to account for heteroskedasticity.
To test H1, we split our sample into two subsamples based on firms' share repurchase behavior in year t (i.e., Repurchasei,t = 1 and Repurchasei,t = 0). We calculate the relative distribution of firm-years for each subsample and run t-tests to assess differences among groups. In line with H1, we would expect a higher prevalence of myopic firm-years at the time of share repurchases. This testing procedure has been applied in comparable tests in previous myopia research (e.g., Mizik and Jacobson 2007; Saboo, Chakravarty, and Grewal 2016).
To test H2, we investigate the abnormal stock returns associated with myopic marketing and share repurchases with time-series regressions (Kothari, Mizik, and Roychowdhury 2016; Mizik 2010). We analyze abnormal stock returns in year t to identify the immediate reaction of the stock market to the cooccurrence of marketing myopia and repurchases. Specifically,
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 4)
where ASRit is the abnormal stock return for firm i in year t; Myopici,t and Repurchasei,t refer to the binary variables identifying myopic and repurchasing firms; and Controls refers to the control variables. To confirm H2, we would expect the abnormal returns associated with the interaction effect between myopic marketing and repurchases to be negative (i.e., χ3 < 0). Although it was not hypothesized, we additionally assess ( 1) whether myopic marketing has a lagged effect on stock returns, similar to prior studies (e.g., Mizik 2010), and ( 2) whether the co-occurrence of myopic marketing and repurchases is indeed only associated with a short-term stock market reaction. We thus analyze lagged abnormal stock returns for three years:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 5)
where ASRi,t+k|t is the k-periods-ahead abnormal stock return for firm i for the periods k = 1 — 3 following year t; and Myopici,t, Repurchasei,t, and Controls are as defined previously. If the stock market reaction to myopic firms in the context of repurchases materializes only in the short term, we would expect λ3 in k = 1 — 3 to be insignificant.
To test H3a-c, we follow prior research and assess whether our variables of interest are related, using lagged product recall incidences. Lagging product recalls is necessary because management actions need time to materialize in reduced product reliability (Kalaignanam, Kushwaha, and Eilert 2013; Wowak, Mannor, and Wowak 2015). The period until a recall materializes varies between studies, so we observe a time span of three years:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 6)
where Recallit+k|t is the k-periods-ahead number of product recalls for firm i for the periods k = 1-3 following year t; and Myopicit, Repurchase^, and Controls are as specified previously. We would expect an increased number of product recalls for firms conducting myopic marketing (H3a) and for repurchasing firms (H3b). H3c holds if the interaction effect between myopic marketing and repurchases is positively associated with recalls.
Our hypotheses build on the assumption that management decisions on myopia and share repurchases are exogenous. However, there may be other factors driving the choice to engage in myopic marketing and to repurchase shares. We correct for such potential endogeneity in several ways. First, our stock return models include firm fixed effects to capture time-invariant unobserved firm characteristics that may correlate with independent variables and time fixed effects that account for overall time-series trends.
Second, we control for the endogeneity of myopic marketing, similar to Saboo, Chakravarty, and Grewal (2016), by using Heckman's (1978) treatment effects model correction. In step 1, we estimate a probit regression with exogenous factors associated with a firm's choice to behave myopically. We use firm-level antecedents (i.e., ROA, firm assets, marketing intensity, R&D intensity, firm size, and firm age) as well as industry-level antecedents (i.e., market and technological turbulence) to predict the decision for myopic marketing management. We regress our myopic variable (Myopicit) on these antecedents. All antecedents except technological turbulence are significantly related to myopic marketing (p < .01). In step 2, we use the probit estimates to calculate the inverse Mills ratio (Hamilton and Nickerson 2003). This ratio (Myopic Correction Termi,t) is included as a control in Equations 4-6 to correct our final model for the potential endogeneity of myopia.
Third, because the choice to repurchase shares could also be endogenous, we perform an additional Heckman correction. In a first-stage probit regression, we use antecedents that relate to firms' investment decisions and hence potentially influence repurchase decisions (i.e., industry growth, technological and market turbulence, market share, rival strategic flexibility, investment cash flow, capital expenditures, and cash and short-term investments) and firm-level characteristics (i.e., total assets, firm age, and firm size) (e.g., Saboo, Chakravarty, and Grewal 2016). Regressing our repurchase variable on these antecedents reveals that investment behavior and cash are not significantly associated with repurchases. Industry growth (p < .10), technological and market turbulence, competitive positioning, and firm-level characteristics are significantly related to repurchases (all ps < .01). We compute an inverse Mills ratio (Repurchase Correction Termi,t) using the resulting estimates from the probit regression. We add this correction term as an additional control variable to Equations 4-6 .
Table 5 shows the descriptive statistics of our stock return sample and the corresponding correlation matrix. Table 6 presents the persistence estimations for the forecast models presented in Equations 1-3, exhibiting significant persistence across all equations.
TABLE: TABLE 5 Descriptive Statistics and Correlations for Stock Return Sample
| Variables | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| 1. Mktg | .260 | .200 | 1.000 | | | | | | | | | | | |
| 2. R&D | .060 | .080 | .209 | 1.000 | | | | | | | | | | |
| 3. ROA | .100 | .140 | -.166 | - .448 | 1.000 | | | | | | | | | |
| 4. Firm age | 16.750 | 9.780 | -.166 | -.212 | .183 | 1.000 | | | | | | | | |
| 5. Net income | -.050 | .660 | -.082 | -.298 | .546 | .097 | 1.000 | | | | | | | |
| 6. Firm sizea | 11.076 | 30.353 | -.294 | -.355 | .406 | .446 | .212 | 1.000 | | | | | | |
| 7. Market-to-book ratio | .700 | .790 | .118 | .229 | .140 | -.010 | -.040 | .023 | 1.000 | | | | | |
| 8. Asset growth | .060 | .210 | -.138 | -.121 | .233 | -.050 | .094 | .118 | .107 | 1.000 | | | | |
| 9. Technological turbulence | .050 | .030 | -.033 | .439 | -.165 | -.050 | -.102 | -.230 | .178 | .003 | 1.000 | | | |
| 10. Market turbulence | .210 | .070 | .206 | .350 | -.119 | -.126 | -.076 | -.263 | .199 | .016 | .715 | 1.000 | | |
| 11. Competitive | .070 | .050 | .045 | -.237 | .103 | .056 | .055 | .117 | -.102 | -.017 | -.532 | -.393 | 1.000 | |
| intensity | | | | | | | | | | | | | | |
| 12. CAR | -.030 | .470 | -.042 | -.073 | .272 | .029 | .130 | .063 | .341 | -.046 | -.017 | -.002 | .009 | 1.000 |
aFigures for mean and standard deviation are reported in thousands without log. Notes: N = 19,869. Boldface indicates correlations significant at least at the 5% level.
TABLE: TABLE 6 Estimations for the Fixed-Effects Autoregressive Panel Data Forecast Models
| ROA (Equation 1) | Mktg (Equation 2) | R&D (Equation 3) |
| f | .458*** (39.21) | .375*** (21.37) | .301*** (16.52) |
| Standard error | .01 | .02 | .02 |
| Firm-year observations | 132,839 | 67,367 | 74,041 |
***p < .01.
Notes: Values in parentheses are z-values.
H1 proposes that myopic marketing management is more common among firms that repurchase shares than among firms that do not. To test this hypothesis, we compare groups on their myopic marketing (according to the classification in Table 3) and repurchasing behavior. Table 7 presents the results. Among firm- years without repurchases, 22.93% are classified as potentially myopic, whereas among firm-years with repurchases, 26.69% are classified as potentially myopic. The difference of 3.76% is statistically significant (two-tailed t-test, p < .01) and represents the largest difference between repurchasing and nonrepurchasing groups. Furthermore, the results reveal that the increase in the portion of myopic firm-years among repurchasing firms of nearly 4% corresponds to a 4% decrease in nonmyopic firm-years with negative earnings surprises (i.e., Groups 7 and 8). The results support H1.
TABLE: TABLE 7 The Prevalence of Myopic Marketing at the Time of Share Repurchases
| Number (Percentage) of Firms in Year Without Share Repurchase | Number (Percentage) of Firms in Year With Share Repurchase | Group Differences |
| Firms With Repurchase — Firms Without Repurchase | t-Statistic |
| Group 1: Potentially myopic firms | 2,625 (22.93%) | 2,247 (26.69%) | 3.76%*** | -6.04 |
| Group 2: Nonmyopic firms | 1,505 (13.15%) | 1,297 (15.40%) | 2.26%*** | -4.48 |
| Group 3: Nonmyopic firms | 781 (6.82%) | 479 (5.69%) | -1.13%*** | 3.28 |
| Group 4: Nonmyopic firms | 1,231 (10.75%) | 926 (11.00%) | .25% | -.55 |
| Group 5: Nonmyopic firms | 1,546 (13.50%) | 1,139 (13.53%) | .02% | -.05 |
| Group 6: Nonmyopic firms | 1,077 (9.41%) | 732 (8.69%) | -.71%* | 1.74 |
| Group 7: Nonmyopic firms | 971 (8.48%) | 601 (7.14%) | -1.34%*** | 3.51 |
| Group 8: Nonmyopic firms | 1,713 (14.96%) | 999 (11.86%) | -3.10%*** | 6.38 |
| Firm-year observations | 11,449 | 8,420 | | |
*p < .10. ***p < .01.
Notes: Two-tailed significance; groups are based on the categories in Table 3.
H2 proposes that the interaction effect between myopic marketing and share repurchases is negatively associated with abnormal stock returns in the year of occurrence. We conduct time-series regressions to test H2. We use Equation 4 as basis for our econometric models including control variables, correction terms, firm and time fixed effects, and firm-cluster robust standard errors. Table 8 presents the results. As in prior research, the association between myopic marketing and abnormal returns is positive and significant (CAR: χ1 = .057, p < .01). We find support for H2 since the interaction effect is negative and significantly related with abnormal returns in the year of occurrence (CAR: χ3 = -.053, p < .01). The results are similar for BHAR.
TABLE: TABLE 8 Relationship Between Myopic Marketing, Repurchases, and Stock Returns
| CAR | BHAR |
| Myopic | .057*** (4.82) | .064*** (5.72) |
| Repurchase | -.031*** (-3.53) | -.036*** (-4.38) |
| Myopic χ Repurchase | -.053*** (-3.63) | -.053*** (-3.76) |
| Firm age | -.016 (-1.48) | -.011 (-.93) |
| Net income | .043*** (2.93) | .069*** (3.60) |
| Firm size | -.014 (-1.36) | -.036*** (-3.43) |
| Market-to-book ratio | .405*** (39.17) | .477*** (44.24) |
| Asset growth | -.405*** (-18.86) | -.454*** (-21.74) |
| Technological turbulence | -1.287 (-1.54) | -.786 (-.93) |
| Market turbulence | -.450* (-1.71) | -.802*** (-3.02) |
| Competitive intensity | .083 (.42) | -.033 (-.16) |
| Myopic correction term | -.535*** (-15.15) | -.561*** (-15.59) |
| Repurchase correction term | -.265*** (-5.77) | -.304*** (-6.37) |
| Firm-year observations | 19,869 | 19,711 |
| R2 | .29 | .36 |
*p < .10. ***p < .01.
Notes: Values in parentheses are t-statistics.
In addition, to assess the lagged stock return implications, we use Equation 5 as a basis for lagged econometric models. Table 9 presents the results. In line with Mizik (2010), the association between myopic marketing and abnormal returns is negative in the years after the action (CAR: λι,ι = -.046, p < .01; λι,2 = -.022, p < .10; λι,3 = -.013, n.s.). The interaction effect between myopic marketing and share repurchases is insignificantly related to abnormal returns in the consecutive years (CAR: λ31 = .010, n.s.; λ3,2 = .007, n.s.; λ3,3 = .004, n.s.). This indicates that the co-occurrence of both practices does not have a lagged relationship with stock returns. We find a negative relationship between repurchases and returns in the same year (CAR: χ2 = -.031, p < .01) as well as two years after the repurchase year (CAR: λ22 = -.024, p < .01). Similar to the results of Saboo, Chakravarty, and Grewal (2016), the coefficient estimates for the myopic correction term are significant. The repurchase correction term is significant for all years except k = 3. This confirms the need to correct for endogeneity. When we repeat the regressions without the endogeneity correction, we obtain similar results, indicating the robustness of our findings.
TABLE: TABLE 9 Lagged Relationship Between Myopic Marketing, Repurchases, and Stock Returns
| CAR | BHAR |
| k = 1 | k = 2 | k = 3 | k = 1 | k = 2 | k = 3 |
| Variables of Interest in k = 0 |
| Myopic | -.046*** (-3.71) | -.022* (-1.67) | -.013 (-.96) | -.051*** (-4.30) | -.025* (-1.91) | -.009 (-.67) |
| Repurchase | -.013(-1.38) | -.024*** (-2.62) | -.008(-.85) | -.013(-1.45) | -.026*** (-2.91) | -.014(-1.48) |
| Myopic χ Repurchase | .010(.62) | .007(.39) | .004(.21) | .023(1.50) | .009(.56) | .005(.32) |
| Controls in Return Year |
| Firm age | -.004(-.40) | -.008(-.71) | -.010(-.85) | .001(.12) | -.006(-.53) | -.010(-.77) |
| Net income | .111*** (5.79) | .088*** (5.23) | .091*** (5.09) | .135*** (5.96) | .109*** (5.52) | .116*** (5.04) |
| Firm size | -.018* (-1.72) | -.009(-.77) | -.022* (-1.71) | -.034*** (-3.12) | -.025** (-2.20) | -.039*** (-3.12) |
| Market-to-book ratio | .414*** (34.67) | .412*** (31.54) | .414*** (30.37) | .483*** (38.67) | .481*** (35.49) | .484*** (33.99) |
| Asset growth | -.239*** (-9.83) | -.298*** (-11.26) | -.269*** (-9.98) | -.281*** (-11.73) | -.341*** (-13.46) | -.313*** (-11.80) |
| Technological turbulence | -2.632*** (-2.96) | -1.546* (-1.67) | -1.557(-1.58) | -2.003** (-2.25) | -.895(-.97) | -.903(-.92) |
| Market turbulence | .268(.95) | .161(.56) | .209(.68) | .009(.03) | -.038(-.14) | .062(.20) |
| Competitive intensity | -.031(-.16) | .207(1.02) | .198(1.02) | -.125(-.64) | .086(.43) | .050(.26) |
| Myopic correction term | .138*** (3.04) | 141*** (3.40) | .106** (2.51) | .165*** (3.45) | .197*** (4.89) | .105** (2.47) |
| Repurchase correction term | .168*** (3.26) | .088* (1.65) | .040(.74) | .184*** (3.59) | .105** (2.02) | .028(.53) |
| Firm-year observations | 16,998 | 14,846 | 13,041 | 16,868 | 14,737 | 12,945 |
| R2 | .26 | .26 | .26 | .32 | .32 | .32 |
*p< .10. **p < .05.
***p < .01.
Notes: Period k refers to the year relative to year t in which myopic management and share repurchases take place. Values in parentheses are t-statistics.
In addition, there might also be a potential simultaneity issue between myopic marketing and repurchases. Therefore, we conduct an additional test following the two-stage least squares approach. We use an instrumental variable that influences repurchases but is not related to abnormal returns except through repurchases. We employ the total quantity of repurchases in a firm's industry and lag it by one period to obtain our instrument (Industry_Repurchasei,t). In the first-stage regression, we find that Industry_Repurchase has significant power to explain our repurchase variable (p < .01). In the second stage, we use the predicted values of Repurchase from the firststage regression to replace our original repurchase variable in Equation 4. The results remain comparable to those shown in Table 8 (CAR: χ! = .290, p < .01; χ2 = -.517, p < .01; χ3 = -.428, p < .01), indicating the robustness of our findings.
One could argue that some firms with unexpected cuts in marketing and R&D that also repurchase shares simply lack growth opportunities. Without promising market prospects, firms might stop investing in customer offerings and prefer to distribute their resources to shareholders through repurchases. Two key determinants of future growth are a firm' s realized growth and the overall growth in its industry (Bharadwaj, Clark, and Kulviwat 2005; Penrose 1959). We therefore assess whether these determinants also unduly affect our model. We derive measures for firm sales growth and industry growth following Homburg, Vollmayr, and Hahn (2014); split the data set at the annual Compustat median for each growth variable; repeat our regression based on Equation 4 in separate estimations for each subsample; and test for significant differences regarding coefficients among groups as shown by Faulkender and Wang (2006). The results are shown in Table 10. We observe that the coefficients for myopia, repurchases, and the interaction effect remain significant and similar to our main results in Table 8. Furthermore, group differences are insignificant for all three effects across both growth splits. Hence, our model is not biased by firms' limited growth opportunities.
TABLE: TABLE 10 Subgroup Comparison for High- and Low-Growth Firms
| CAR |
| Industry Growth | Firm Sales Growth |
| p-Value (Low — High Φ 0) | Low | High | Low | High | p-Value (Low — High Φ 0) |
| Myopic | .069*** (4.20) | .057*** (3.78) | .725 | .058*** (3.02) | .043** (2.48) | .572 |
| Repurchase | -.026** (-1.99) | -.032*** (-2.69) | .637 | -.043*** (-2.79) | -.027** (-2.10) | .419 |
| Myopic χ Repurchase | -.051** (-2.33) | -.058*** (-2.85) | .817 | -.076*** (-2.76) | -.044** (-2.08) | .355 |
| Firm age | -.011 (-.63) | -.002 (-.13) | | .013 (.35) | -.035* (-1.74) | |
| Net income | .020 (1.57) | .057*** (4.81) | | .051*** (3.07) | .075*** (3.32) | |
| Firm size | -.025* (-1.82) | -.011 (-.90) | | -.031* (-1.83) | -.015 (-1.03) | |
| Market-to-book ratio | .411*** (42.61) | .423*** (49.34) | | .464*** (39.90) | .333*** (32.89) | |
| Asset growth | -.313*** (-12.57) | -.479*** (-21.08) | | -.499*** (-16.72) | -.282*** (-9.52) | |
| Technological turbulence | -1.494 (-1.56) | -.481 (-.56) | | -3.173*** (-2.87) | .633 (.77) | |
| Market turbulence | .448 (1.25) | -.978*** (-3.03) | | -.842* (-1.91) | -.291 (-.90) | |
| Competitive intensity | .170 (.65) | .296 (1.24) | | -.049 (-.12) | -.185 (-.85) | |
| Myopic correction term | -.526*** (-13.67) | -.521*** (-15.55) | | -.554*** (-12.84) | -.294*** (-4.87) | |
| Repurchase correction term | -.316*** (-4.89) | -.281*** (-4.81) | | -.304*** (-3.91) | -.121* (-1.90) | |
| Firm-year observations | 8,929 | 10,940 | | 6,926 | 6,668 | |
| R2 | .50 | .50 | | .55 | .48 | |
*p < .10. **p < .05. ***p < .01.
Notes: Values in parentheses are t-statistics.
Finally, we assess consumer market implications. Table 11 reports the descriptives and correlations for our CPSC-regulated consumer goods firms subsample. Following previous studies using longitudinal recall data (Rhee 2009; Rhee and Haunschild 2006), we employ generalized estimating equations (GEE) based on Equation 6 to test H3. GEE models are usually preferred to alternative random- and fixed-effect models in such a setting because the method accounts for nonindependence of recalls (Liang and Zeger 1986). We specify our GEE models based on a negative binomial recall distribution with a natural log-link function and Huber-White robust standard errors (Wowak, Mannor, and Wowak 2015). Table 12 shows the results.
TABLE: TABLE 11 Descriptive Statistics and Correlations for Product Recall Sample
| Variable | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| 1. Mktg | .170 | .130 | 1.000 | | | | | | | | | |
| 2. R&D | .050 | .050 | -.134 | 1.000 | | | | | | | | |
| 3. ROA | .140 | .070 | .301 | -.038 | 1.000 | | | | | | | |
| 4. Firm age | 30.130 | 12.040 | -.050 | -.210 | -.008 | 1.000 | | | | | | |
| 5. Net income | .060 | .150 | -.026 | -.112 | .411 | .080 | 1.000 | | | | | |
| 6. Firm sizea | 25.291 | 38.478 | -.059 | -.375 | .097 | .370 | .118 | 1.000 | | | | |
| 7. Technological turbulence | .050 | .030 | -.123 | .395 | -.096 | -.007 | -.026 | -.162 | 1.000 | | | |
| 8. Market turbulence | .210 | .060 | .214 | -.067 | .068 | -.045 | .035 | -.050 | .472 | 1.000 | | |
| 9. Competitive intensity | .060 | .050 | .385 | -.168 | .156 | -.056 | -.006 | -.019 | - .535 | -.212 | 1.000 | |
| 10. Product recalls | .180 | .620 | .105 | -.113 | .034 | .158 | .013 | .227 | .021 | .045 | .040 | 1.000 |
aFigures for mean and standard deviation are reported in thousands without log. Notes: N = 804. Boldface indicates correlations significant at least at the 5% level.
TABLE: TABLE 12 Lagged Relationship Between Myopic Marketing, Repurchases, and Product Recalls
| Product Recalls |
| k = 1 | k = 2 | k = 3 |
| Variables of Interest in k = 0 | | | |
| Myopic | .076 (.16) | .890** (2.28) | .232 (.59) |
| Repurchase | .161 (.45) | .738** (1.99) | .097 (.23) |
| Myopic χ Repurchase | -.109 (-.24) | -.732 (-1.44) | -.024 (-.05) |
| Controls in Recall Year | | | |
| Firm age | .173*** (3.23) | .199*** (4.31) | .200*** (4.56) |
| Net income | .930** (2.03) | .934* (1.94) | 1.004** (2.37) |
| Firm size | .441 (1.41) | .346 (1.38) | .336 (1.38) |
| Technological turbulence | -7.854 (-.90) | -3.309 (-.32) | -5.660 (-.58) |
| Market turbulence | 5.875* (1.80) | 5.658* (1.70) | 5.899* (1.74) |
| Competitive intensity | -.462 (-.12) | 1.699 (.39) | -.022 (-.01) |
| Myopic correction term | 5.380** (2.09) | 6.392*** (2.91) | 6.450*** (3.00) |
| Repurchase correction term | 2.313* (1.85) | 2.301* (1.82) | 2.239* (1.71) |
| Firm-year observations | 804 | 791 | 777 |
| Wald χ2 | 147.53*** | 181.81*** | 171.57*** |
*p < .10. **p < .05. ***p < .01.
Notes: Period k refers to the year relative to year t in which myopic management and share repurchases take place. Values in parentheses are z-values.
H3a predicts that myopic marketing is positively related with the lagged number of product recalls. We observe a significantly positive relation between myopia and recalls two years after the action (δι,2 = .890, p < .05) and insignificant interactions one year (δχ,χ = .076, n.s.) and three years after the action (δι,3 = .232, n.s.). Thus, we find support for H3a. H3b suggests that share repurchases are positively associated with subsequent recalls. We find a positive and significant relationship between repurchases and subsequent recalls two years after the repurchase (δ2 2 = .738, p < .05), so H3b is supported. Surprisingly, the interaction term between marketing myopia and repurchases is insignificant across all years. Hence, H3c is not supported. In a sensitivity analysis, we repeat the analysis with the extended control set used in the aforementioned stock return regressions. We obtain similar results in these nondisplayed regressions.
To explore further whether there are certain managerial setups that foster myopic marketing and repurchases, we conduct a post-hoc analysis. We draw on ExecuComp and top management team (TMT) data for the S&P 500 firms in our sample from 2007 to 2013 (Menz and Scheef 2014). We then regress our dummy variable of the co-occurrence of marketing myopia and repurchases on TMT characteristics using logit regressions. The results reveal that CFO presence (β = .684, p < .10) and a TMT's mean age (β = .078, p < .10) positively relate to the cooccurrence of both myopic marketing and repurchases. This supports Mizik's (2010) argument that managers who are closer to retirement and influenced by finance advocates may have a particular affinity for myopia.
Share repurchases are prominent on the agendas of top managers. Yet, the popularity of this practice may exacerbate the consequences of managerial short-term zeal. The aim of this study is to shed light on myopic marketing practices in the context of share repurchases and to assess their implications on the stock and the consumer markets. We find that the portion of firms acting myopically is higher among firms that repurchase shares than among firms that do not. Our results also indicate that when firms engage in both myopic marketing and share repurchases, investors respond with immediate negative returns, while they show a delayed reaction when firms pursue myopic marketing alone. Finally, we find that share repurchases and myopic marketing are positively related with product recall incidences. With this, the present study offers several implications for both theory and practice.
This study helps extend the literature on myopic marketing practices and the theory of market-based assets (Mizik 2010; Srivastava, Shervani, and Fahey 1998). First, we offer an explanation for the inability of investors to detect marketing myopia in the short term, which has remained an intriguing conundrum in prior work (Mizik 2010; Mizik and Jacobson 2007; Saboo, Chakravarty, and Grewal 2016). Because marketing-finance research assumes that investors value marketing investments (Luo 2008; Srinivasan and Hanssens 2009), the question arises: Why do investors not negatively respond to myopic marketing practices in a timely manner? Prior research argues that financial markets are not entirely efficient because information asymmetry between managers and investors hinders the latter to discern correctly the potential negative long-term consequences of such practices (Mizik and Jacobson 2007). This study advances the literature on myopic marketing by demonstrating the importance of a contingency perspective in this respect. We are the first to integrate repurchases conceptually, thereby highlighting a contingency that has been criticized in practice as also setting myopic incentives (Das 2016; Trainer 2016). We explain and empirically demonstrate that investors, with additional, observable signals such as a repurchase, are better able to evaluate in the short term whether a reduction of marketing investments is justified or not. In other words, the co-occurrence of positive earnings surprises and repurchases seems too good to be true, making investors immediately skeptical as do other contingencies (Kahle 2002). This skepticism is rooted in learning over time that creates awareness that a firm's prosperity is not only at risk due to limited investments in marketing but also due to the diminished resources available after a repurchase. Thus, when share repurchases and myopic practices co-occur within a short time horizon, investors will likely seek further information to be better able to understand and evaluate managers' intentions about to their marketing activities.22 We thank an anonymous reviewer for pointing this out.
In addition, we provide an explanation for the underlying mechanisms regarding the myopia-return relationship. Prior research posits that investors correct their initial positive response to myopic marketing when additional information is published (Mizik 2010). Yet, manifestations of this logic have not been examined, and Mizik (2010, p. 609) calls for "research focused on developing a better understanding of the mechanisms driving the future-term underperformance of myopic firms." We show that product recalls—embodying negative outcomes—represent such a mechanism that explains the delayed underperformance.
Second, we add share repurchases as a novel corporate financing context in which managers have incentives to opt for myopic marketing practices. Prior work has highlighted that firms conducting equity offerings more often engage in myopic practices (Kothari, Mizik, and Roychowdhury 2016; Mizik and Jacobson 2007; Saboo, Chakravarty, and Grewal 2016). This study reveals that firms also draw on marketing myopia in the context of repurchases. The incentives set by investors' pressure can motivate managers to distribute their resources to shareholders and to cut investments in customer offerings. Extending recent evidence that marketing budgets can conflict with firm financial policy (Malshe and Agarwal 2015), our findings imply that marketing needs to demonstrate its accountable impact more strongly.
Third, by integrating share repurchases for the first time, this study adds to the theory of market-based assets (Srivastava, Shervani, and Fahey 1998). We respond to a call for an investigation of repurchases using a marketing angle because prior work has thus far focused on how resource-increasing financing events interact with marketing strategy (Kurt and Hulland 2013). Marketing theory emphasizes the importance of marketing investments for the generation of firm value, and practitioners increasingly criticize repurchases; many finance scholars, however, argue in favor of resource-diminishing repurchases. Our study helps bridge this discrepancy. Share repurchases consume financial means and leave fewer spare resources to invest (Almeida, Fos, and Kronlund 2016). A potential reduction of marketing budgets aggravates this situation. As the first to look at repurchases through a marketing lens, we unveil evidence on the dark side of the repurchase boom for shareholder wealth and consumers alike, both of which are subject to negative consequences. This also helps increase our understanding of the "seemingly contradictory behaviors" of caving in to short-term investor pressure and engaging in "effective strategic marketing spending" (Srinivasan and Hanssens 2009, p. 308).
What is more, we extend the consumer perspective in market-based assets theory that has typically focused on the positive side of more investments in such assets. This study explores the much less addressed negative outcomes by revealing that reduced investments lead to severe implications on the consumer market, such as product failures. This also adds novel insights on product recalls to the marketing-finance literature, which lacks a deep understanding of the antecedents of product reliability risks. Recalls are critical for marketers because they affect brand equity and customer behaviors (e.g., Dawar and Pillutla 2000), and it is difficult for managers to balance various stakeholder interests (Von Werder 2011). Since there are often early signs for a recall (Bromiley and Marcus 1989), a devil's advocate might argue that some managers even risk negative consequences to satisfy shareholders. Interestingly, we do not find the co-occurrence of myopic marketing and repurchases to be significantly related to product recalls. This result suggests that other factors need to be considered to explain why the resulting high resource constraints do not affect product safety. For instance, one might speculate that at the time of the co-occurrence, those firms had less complex product and service offerings. This would be in line with Thirumalai and Sinha's (2011) finding that a narrower product scope of medical device firms limits the likelihood of product safety issues. Because a higher cut of resources in such firms should be less harmful regarding product reliability, further analyses with more detailed data are needed. In sum, our study strengthens the theory of market-based assets as it helps develop our "long-sought understanding of the impact of the billions of dollars that are spent every year on marketing activities" (Rust et al. 2004, p. 86). We demonstrate that a lack of such spending threatens both shareholder wealth and consumers—a finding marketers can leverage to shed further light on the value of marketing in the repurchase era.
As investors urge firms to increase shareholder value, share repurchase volumes break one record after another. Skeptics, however, seem to receive only limited attention in the C-suite. This article builds a case for identifying "black sheep" among repurchasing firms. The results emphasize that myopic managerial practices are particularly detrimental in the context of a share repurchase because the latter leaves firms with even fewer resources to invest in customer offerings—a notion that critics increasingly emphasize (Brettell, Gaffen, and Rohde 2015; Denning 2015; Economist 2014; Trainer 2016). In a group of CFOs interviewed in a survey by Graham, Harvey, and Raj- gopal (2006), 80% would cut discretionary spending such as marketing and R&D expenses to meet investor targets despite the awareness that this practice can destroy value. Our study draws marketers' attention to repurchases and thus helps CMOs to underline the value of marketing activities in boardroom discussions and of securing resources otherwise distributed through repurchases.
Regulators and consumer advocates may also devote more attention to firms' spending and financing decisions, as this study reveals a relationship with product recalls. A decade after the largest recall ever by Mattel, the firm is still on the forefront, with a $493 million repurchase in 2013, accounting for 111% of its available cash flow (Marder 2014). At the same time, one subsidiary of Mattel was a "leader" in recalls of kids' products between 2009 and 2014, which involved about 9.3 million units and 828 incidents, with 130 injuries (Lipka 2015). It would be naive to assume that managers with strong incentives to act myopically will change their behavior; governance mechanisms are needed that tie top management pay to product quality issues and their delayed costs. The fatal consequences of managerial actions surface years later—and this is grist to the mill for campaigners in favor of delayed bonus payouts. If managerial compensation were strongly linked to product reliability and long-term value creation, managers might be more careful with decisions favoring short-term goals.
Finally, competitors of myopic firms may also benefit from the implications of this study. If a rival firm is resource- constrained due to cuts in marketing budgets and repurchases, competitors can gain market share by setting up aggressive marketing programs, as shown by Kurt and Hulland (2013). Moreover, nonmyopic firms can reduce their information asymmetry with investors by providing explicit information on customer-oriented investments (e.g., new product announcements). This is important because most market-based assets do not appear on the balance sheet, and it may prove crucial around repurchases to prevail against misleading signals sent by myopic competitors. Finally, it seems worthwhile to support repurchase programs with extensive investor communication similar to, for instance, an IPO prospectus. If a firm conducts repurchases on a healthy basis, it should be able to convince investors that this does not limit its marketing and innovation capabilities. Here, marketers should be of great value.
The findings and limitations of our study open avenues for further research. Although our myopia proxies build on strong theoretical and empirical validation (McAlister, Srinivasan, and Kim 2007; Mizik 2010), they are subject to potential inaccuracy due to the use of Compustat data. The measure for marketing intensity also includes other expenditures (e.g., other overhead), and the measure for R&D intensity is imprecise for firms that do not disclose their R&D expenses. The measures also do not allow differentiation between expenditures directed toward new versus existing products or incremental versus radical changes, which would be particularly interesting regarding the effects on product recalls. Scholars have indicated that there is a trade-off between ( 1) using approximated measures with real large-scale data across many decades and industries, as provided by Compustat, and ( 2) obtaining more accurate measures for smaller samples, thereby increasing sample selection bias (Kurt and Hulland 2013). Future research could thus focus on the latter, with more detailed measures in smaller samples.
Furthermore, we cannot infer the true managerial intentions from our data. We classify firms as "potentially myopic" on the basis of unexpected earnings surprises and cuts in marketing and R&D budgets. Future research could thus investigate the timing regarding cutoffs of individual marketing projects or collect survey data from managers regarding their myopic intentions. In contrast to prior studies, it would be particularly worthwhile to survey CMOs. Our data also do not allow us to examine the true investor motivation that likely influences the identified stock market implications. As investors learn about managerial behaviors over time, future research should differentiate between investor types because some investors might be more focused on the short run than others (Bushee 1998). Such differences might additionally explain the positive short- term stock returns of myopic practices identified by our and prior studies.
In conclusion, this study provides a first step toward revealing how important share repurchases are for marketing strategy. Given the rising importance of repurchases, future works should further explore the implications for marketing theory and practice in this context.
Endnotes 1 We use the umbrella term "marketing" to summarize product development and improvement, advertising, and sales efforts.
DIAGRAM: FIGURE 1 Conceptual Framework
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David Bendig is Assistant Professor, Innovation and Entrepreneurship Group (WIN)-TIME Research Area, RWTH Aachen University
Daniel Willmann is Research Associate and Lecturer, Innovation and Entrepreneurship Group (WIN)-TIME Research Area, RWTH Aachen University
Steffen Strese is Assistant Professor, Innovation and Entrepreneurship Group (WIN)-TIME Research Area, RWTH Aachen University
Malte Brettel is Full Professor and Head of Group, Innovation and Entrepreneurship Group (WIN)-TIME Research Area, RWTH Aachen University
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Record: 165- Should Anthropomorphized Brands Engage Customers? The Impact of Social Crowding on Brand Preferences. By: Puzakova, Marina; Kwak, Hyokjin. Journal of Marketing. Nov2017, Vol. 81 Issue 6, p99-115. 17p. 1 Diagram, 1 Chart, 1 Graph. DOI: 10.1509/jm.16.0211.
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Should Anthropomorphized Brands Engage Customers? The Impact of Social Crowding on Brand Preferences
Anthropomorphizing a brand (i.e., imbuing a brand with humanlike features) serves as an important brand positioning strategy for marketing managers. This research identifies a key brand anthropomorphization strategy—positioning a brand as either oriented to interact with consumers or not. Managers generally rely on this brand interaction strategy to enhance consumer brand engagement regardless of the social context. However, given that consumers often experience brands in a social context, this research demonstrates that social crowdedness moderates the positive impact of interaction-oriented anthropomorphized brands on consumer brand preferences. Specifically, the authors show that consumers’ inferences of an anthropomorphized brand’s intentionality to interact with them in a socially crowded context trigger greater social withdrawal, thereby resulting in lower preferences for the brand. The authors further demonstrate that the core negative effect of social crowdedness is contingent on the type of crowding (goal-related vs. goal-unrelated). In particular, a goal-related crowding decreases social withdrawal reactions, which, in turn, leads to greater preferences for interaction-oriented anthropomorphized brands relative to brands with other positioning strategies. In contrast, the effect of social crowdedness on consumer preferences for interaction-oriented anthropomorphized brands remains negative in goal-unrelated crowded settings.
Marketers frequently use anthropomorphism (i.e., endowing a nonhuman entity with humanlike characteristics, intentions, emotions, beliefs, and mind; Aggarwal and McGill 2007) as a brand positioning strategy. That is, to facilitate a brand’s meaning as humanlike, practitioners rely on anthropomorphic advertising imagery (e.g., Pom-Bear, Mr. Peanut), represent their products with human-like shapes (Romero and Craig 2017), or adopt first-person communications (Puzakova, Kwak, and Rocereto 2013). However, one of the important anthropomorphic brand strategies that has been overlooked in prior work is to position these humanized brands as either interacting with customers or not. For example, “Better with M” campaign from the M&M’s brand incorporates anthropomorphized candies into M&M’s consumers’ everyday moments and activities. Because people are generally willing to engage with others (MacIntyre, Babin, and Clement 1999) and are more attracted to others who are highly sociable (Ilmarinen et al. 2015), positioning a humanized brand as oriented to interact with others should prove to be an effective brand strategy.
However, consumers frequently experience brands in physical proximity to others. Given that social crowdedness (the extent of social presence of and proximity to others in consumption contexts; Maeng, Tanner, and Soman 2013) has been shown to decrease consumers’ desires for interpersonal contact (Baum and Koman 1976; Valins and Baum 1973) and that people apply social schemas to anthropomorphized brands (Aggarwal and McGill 2007), the positive impact of positioning anthropomorphized brands as interaction-oriented on consumer brand preferences may be reversed in socially crowded contexts. An examination of this potentially negative effect of social crowdedness on branded communications is particularly important because marketers frequently place strategic out-of-home branded communications (e.g., digital signage, billboard ads) in areas of high foot traffic, including busy urban street intersections, active transportation systems, and shopping malls. The cost of this out-of-home branding significantly increases with more impressions in socially crowded spaces (e.g., renting a billboard space in Times Square, the most socially crowded core of Manhattan, recently reached the unprecedented cost of $2.5 million; bMedia Group 2015) because placing brand communications in socially crowded areas generally leads to greater brand awareness and significant effects on sales (Woodside 1990).
In this research, we provide our key contribution by demonstrating that the negative effect of social crowdedness on consumers’ preferences for anthropomorphized brands is driven by consumers’ inferences of an anthropomorphized brand’s intentionality to interact, rather than by anthropomorphism per se. Elaboration of this novel relationship between social crowdedness and interaction-oriented anthropomorphized brands provides three major contributions to the brand anthropomorphization and social crowdedness literature. First, our research demonstrates the impact of a prevalent environmental factor (i.e., social crowdedness) in the context of brand anthropomorphization. Second, most prior studies examine the downstream consequences of anthropomorphizing an entity (Aggarwal and McGill 2012; Hur, Koo, and Hofman 2015; Puzakova, Kwak, and Rocereto 2013), with very few studies considering the nature of an anthropomorphized target (Aggarwal and McGill 2012; Kim and Kramer 2015). Thus, we contribute to anthropomorphism work by establishing the existence of negative consumer reactions to a specific type of anthropomorphized brand, namely, an interaction-oriented anthropomorphized brand. By demonstrating these relationships, we also extend previous research on the downstream consequences of social crowdedness. Past work establishes the negative impact of social crowdedness on evaluations of and reactions to other people in general (Evans et al. 2000; Stokols et al. 1983); however, it does not take into consideration an individual’s explicit intention to interact with other people in the crowd. In our research, we demonstrate that a humanized entity’s perceived intention to interact with consumers (i.e., an interaction-oriented anthropomorphized brand) drives the negative impact of social crowdedness on brand preferences. In doing so, we offer a more nuanced understanding of the mediating role of social withdrawal in the core negative effect of social crowdedness on consumer choice and purchase intentions of interaction-oriented anthropomorphized brands. That is, whereas prior work demonstrates that social crowdedness itself enhances social withdrawal (Evans et al. 2000), we show that an anthropomorphized brand’s intentionality to interact with consumers in a crowded setting augments consumers’ reactions of social withdrawal.
Finally, we contribute to research on both brand anthropomorphism and social crowdedness by identifying an important and theoretically relevant moderator of the overall negative effect of social crowdedness, namely, goal-related vs. goal-unrelated social crowdedness. Specifically, we show that social crowdedness that elicits shared consumption goals (i.e., goal-related crowdedness) reverses the negative effect of crowdedness on consumer preference for interaction-oriented anthropomorphized brands. Overall, our findings can help guide managerial decisions on what types of anthropomorphic branding strategies should be used in highly crowded (vs. uncrowded) consumer environments.
Prior research has established that environmental factors are significant determinants of customers’ feelings, thoughts, and reactions across a variety of consumption settings. For example, Hui and Bateson (1991) demonstrate that consumer density decreases the pleasantness of a service experience and facilitates consumers’ avoidance responses toward a service encounter. Similarly, highly crowded waiting areas in restaurants decrease consumer service satisfaction by triggering customers’ loss of control over their environment and causing perceived intrusions into people’s personal space (Hwang, Yoon, and Bendle 2012). Social crowdedness has also been shown to reduce shopping satisfaction, increase negative emotions related to shopping (Machleit, Eroglu, and Mantel 2000), attenuate consumers’ exploratory behaviors, and reduce inclination to engage with other shoppers and the store sales force (Harrell, Hutt, and Anderson 1980). Further lines of research identify several important moderating factors influencing relationships between retail crowding and shopping satisfaction. For instance, the negative effect of crowding on shopping satisfaction is especially pronounced for shoppers under time pressure and those with lower tolerance levels for social crowding (Eroglu and Machleit 1990).
Furthermore, most recent work has begun to examine the effect of crowding on consumer choice and product preference. For instance, Xu et al. (2012) reveal that physical proximity to others threatens a consumer’s distinctiveness goals, thus triggering more unique product choices as a way of reaffirming differentiation. In turn, Maeng, Tanner, and Soman (2013) establish that being socially crowded induces a prevention-oriented focus, thus leading to greater consumer preference for safety-oriented product options (e.g., pharmacy). In sum, prior research on social crowdedness shows its crucial marketplace consequences for both consumers and marketers. Our research adds a critical contribution to this body of work by investigating whether social crowdedness impacts consumer preferences for brands with different brand positioning strategies.
In this research, we investigate particular types of brand positioning strategies: interaction-oriented anthropomorphized and nonanthropomorphized brands. Here, we propose that consumers interpret brand messages as relational communication signals that either connote or do not communicate one’s orientation to interact. In particular, we define an interaction-oriented brand positioning strategy as a relational brand communication that connotes inclusion (openness and accessibility to a relational partner) and the intensity of engagement in mutual experiences and activities with consumers (i.e., any shared activities that include an active involvement of both a consumer and a brand). For example, Radio Shack designed several branding campaigns, including “Do It Together” and “Let’s Play,” to stress interaction between the consumer and the brand. In summer 2015, Oreo launched a global ad campaign, “Play with Oreo,” where brand communications invited consumers to interact with an anthropomorphized Oreo cookie (e.g., “Discover with Oreo,” “Dream with Oreo,” “Dunk with Oreo”). We choose to focus on interaction-oriented brands because this type of brand positioning has a particular theoretical relevance to the context of social crowdedness that makes consumers especially sensitive to cues signaling greater interaction. Specifically, we rely on a framework that delineates the relational function of communication as a particular theoretical lens for our conceptualization of this brand positioning strategy. Past work in this area is concordant that people send different relational messages (i.e., verbal and nonverbal expressions that suggest how people regard each other within the framework of relationships; Hale, Burgoon, and Householder 2014) during instances of their communications with others.
One of the critical relational dimensions on which these messages differ is the extent to which individuals desire to engage or interact with others (Burgoon and Hale 1987; Mashek and Sherman 2004). Interaction has been defined in various ways. For instance, Berscheid, Snyder, and Omoto (1989) define interaction as the sharing of information and interests, participation in joint activities, and spending time together. Resonating with these findings, Thomas (2011) also identifies interaction as engagement and participation in mutual activities. From a sociobiological perspective of behavioral displays, the inclusion dimension of interpersonal behaviors is one of the dimensions in a larger group of bonding, interactive behaviors and is a subcomponent of a broader dimension of greater engagement (Burgoon and Hale 1984). Similarly, relevant work on interpersonal attraction defines the construct of interaction as a relational communication message that conveys one’s desire to engage in joint tasks with others (Burgoon and Hale 1984). These multiple research perspectives indicate that relational communication messages entail a variety of themes (e.g., desire to engage in mutual activities, to be included, to be involved with another individual) that, in combination, bolster the broader dimension of a relational communication message that signals one’s desire to interact. We bring this idea to the context of our research and propose that brand communications can also be perceived as messages that signal a brand’s orientation to interact with consumers.
Because individuals encode relational messages as signaling a desire for a particular type of relationship, it is important to note the nature of a brand’s interaction-orientation signal. In this regard, Burgoon and Hale (1984) point out that cues indicating desire and interest for mutual experiences and joint activities escalate relationships to more intimate levels. Similarly, Aron et al. (2000) argue that shared activities in which participants might engage together are likely to involve cooperation, thus facilitating a sense of affection and closeness. Doing things together tends to intensify the perception of “we-ness” in the relationships, resulting in greater partner inclusion and enhanced perceptions of relational closeness (Sela, Wheeler, and Sarial-Abi 2012). Along the same lines, social penetration theory (Altman and Taylor 1973), which delineates the movement of relationships toward greater intimacy, identifies cues such as relational involvement and inclusion as expressions of greater relational depth. Relying on these lines of research, we propose that a brand’s interaction-orientation communication is likely to be interpreted as the signal of consumer–brand relational closeness.
An interaction-oriented anthropomorphized brand should prove to be an effective brand positioning strategy because prior research indicates that people seek to engage with other humans (McCroskey and Richmond 1990) and are attracted to others who explicitly communicate an interest to interact (Ilmarinen et al. 2015). However, a more nuanced analysis of research elucidates the fact that people’s desire to interact with others depends on a myriad of individual difference, cultural, situational, and individual state (e.g., mood, arousal, or anxiety) factors (McCroskey and Richmond 1990). In this research, we put forward an idea that an important situational factor, social crowdedness, has a negative impact on consumers’ preferences for anthropomorphized brands that show an intention to interact with consumers.
This postulate is born from prior research showing that social crowdedness generally triggers defensive avoidance responses (Evans et al. 2000). These responses include “fight or flight” reactions, increased hostility, higher anxiety, elevated arousal, social overstimulation, and social overload (Desor 1972; Valins and Baum 1973). According to Altman’s (1977) boundary control model of privacy, people either actively seek or prevent social interactions to sustain an appropriate level of social stimulation. In this regard, socially crowded environments inhibit individuals’ abilities to control the nature and frequency of interpersonal interactions (Evans et al. 2000). To reduce unwanted social encounters and to maximize meaningful social experiences at the expense of less important or new social interactions, people in crowded spaces tend to respond with coping strategies that regulate their psychological or physical distance to others (Regoeczi 2008). One of these strategies in response to social crowdedness involves the avoidance of further interpersonal contact, in other words, social withdrawal (Baum and Koman 1976).
In this research, we go beyond the possibility that social withdrawal evoked by social crowdedness can lead to negative evaluations of others (or humanized brands) to argue that consumers’ inferences of an anthropomorphized brand’s intentionality to interact in socially crowded situations facilitate greater social withdrawal tendencies, resulting in lower preferences and purchase intentions for the brand. Several important lines of evidence are pertinent to this conjecture. First, although prior work on social crowdedness does not directly examine whether people in socially crowded situations react more negatively to explicit intentions to interact with others, there is important evidence that alludes to this possibility. That is, Baum and colleagues (Baum and Koman 1976; Baum, Singer, and Baum 1981; Baum and Valins 1979) examine reactions of two groups of participants: corridor residents who lived in more crowded areas of dormitories and suite residents who lived in noncrowded conditions. These studies find that corridor residents (vs. suite residents) were particularly likely to withdraw from (e.g., chose more distant seats, avoided eye contact) and respond negatively toward another research participant when both participants were waiting in a room for an experimental session to begin. One possibility that this research stream offers is that corridor residents could potentially infer a greater probability of interacting with another research participant. In a different study (Baum and Valins 1979), half of respondents were told that they would be cooperating with a confederate, and the other half were told that they would be competing. The results of that study showed that there were no differences in comfort levels or other withdrawal tendencies between corridor (i.e., people who live in crowded environments) and suite residents during the competitive task. In contrast, corridor residents reacted more negatively to the presence of a confederate (e.g., avoiding eye contact, less facial regard) only in a cooperative task condition. Additional work reports similar effects, namely, that participants attribute more negative traits to others in crowded cooperative (but not competitive) situations (Stokols et al. 1983). Although these studies do not directly measure participants’ perceptions of others’ intentionality to engage in interaction, more negative reactions toward others among crowded participants might have evolved from the greater possibility of interpersonal involvement. In this regard, when people are directly instructed not to interact or converse with each other in a crowded setting, research finds that the negative impact of social crowdedness on participants’ interpersonal judgments dissipates (Dooley 1978).
Prior research on social withdrawal induced by stressors other than social crowdedness (e.g., work overload) also indicates that greater likelihood of involvement with significant others or interaction initiation from family members both result in lower positive responses and even greater social withdrawal from household members (Repetti 1989). Similarly, a related work directly stresses the idea that engagement-seeking peer groups generate greater social avoidance from children who are already predisposed to social-withdrawal tendencies (Rubin and Burgess 2001).
These prior findings, overall, point out that sometimes human interactions can be overwhelming and, thus, interaction-oriented anthropomorphized brands in crowds are less desirable. That is, we propose that consumers’ inferences of an anthropomorphized brand’s intentionality to interact with them are likely to facilitate greater social withdrawal in a crowded setting compared with inferences of a non-interaction-oriented anthropomorphized brand or both an interaction-oriented and a non-interaction-oriented nonanthropomorphized brand. Because prior research indicates that consumers view humanized brands’ actions as having intentions (Puzakova, Kwak, and Rocereto 2013) and the ability to enact these intentions (Kervyn, Fiske, and Malone 2012), we argue that consumers exposed to an anthropomorphized brand’s message that signals desire to interact with them in social crowds are likely to interpret this message as a relational signal of further undesirable engagement with this humanized entity. Because consumers’ abilities to regulate the nature and frequency of their social interactions are inhibited in crowded environments, the relational message of further engagement is likely to trigger an even greater social-withdrawal response. In turn, greater social withdrawal is likely to decrease consumers’ preferences for interaction-oriented anthropomorphized brands in the crowded environment.
Accordingly, one might argue that consumers may react negatively to even objectified interaction-oriented entities (i.e., nonanthropomorphized brands) in socially crowded situations. However, prior research points out that nonanthropomorphized brands are unlikely to be perceived as intentional social entities and unlikely to elicit consumer reactions resembling interpersonal experiences (Aggarwal and McGill 2012; Fournier 1998). Because nonanthropomorphized brands do not evoke quasi-social experiences (Kim and Kramer 2015) and they lack intentionality attributed to humanized entities (Puzakova, Kwak, and Rocereto 2013), we expect that interaction-oriented nonanthropomorphized brands are unlikely to trigger social-withdrawal responses, thus attenuating the negative impact of social crowdedness on consumer preferences for the brand. Finally, we also hypothesize that both a non-interaction-oriented anthropomorphized and a non-interaction-oriented nonanthropomorphized brand are unlikely to elicit greater social withdrawal because the core factor that drives this response (i.e., interaction orientation) is absent.
H1a: Social crowdedness moderates the interaction effect of brand anthropomorphization and a brand’s interaction orientation.
H1b: In a socially crowded context, consumers develop lower preferences for interaction-oriented anthropomorphized brands (vs. non-interaction-oriented anthropomorphized brands and interaction-oriented and non-interaction-oriented nonanthropomorphized brands).
H1c: In a socially uncrowded context, consumers develop higher preferences for interaction-oriented anthropomorphized brands (vs. non-interaction-oriented anthropomorphized brands and interaction-oriented and non-interaction-oriented nonanthropomorphized brands).
H2: The negative impact of social crowdedness on consumer preferences for interaction-oriented anthropomorphized brands (vs. non-interaction-oriented anthropomorphized brands and interaction-oriented and non-interaction-oriented nonanthropomorphized brands) is mediated by social withdrawal.
So far, our conceptualizations have focused on the negative impact of social crowdedness on consumer preferences for interaction-oriented anthropomorphized brands. However, prior research indicates that the extent of people’s negative reactions to social crowdedness depends on a variety of factors (e.g., personal level of crowding tolerance, degree of interference with the individuals’ goals and objectives, behavioral constraints; Loo and Ong 1984; Pons, Laroche, and Mourali 2006; Westover 1989). As such, it is likely that social crowdedness has a differential impact on consumers’ brand perceptions depending on the characteristics of the crowded environment. In this regard, prior research indicates that, for example, if a recreational event in a park does not deviate from norms and expectations or does not limit one’s ability to pursue individual goals, the negative effect of crowding may diminish. Furthermore, people with different characteristics tend to react to the same environmental features in distinct ways due to the differences in their past experiences, frequency of exposure to particular situations, and familiarity with a setting (Ulrich et al. 1991). For example, because certain cultures prefer smaller distancing and higher levels of contact (e.g., Arab, Mediterranean, Latin American) compared with other cultures (e.g., Northern European), people from these high-contact cultures tend to have more favorable experiences in crowded situations (Pons, Laroche, and Mourali 2006). Several studies in service contexts also identify that the nature of the service experience can moderate the negative effect of crowding on customer experiences. For instance, Hui and Bateson (1991) establish that social crowdedness contributes to pleasant emotional experiences in hedonic service settings (e.g., bar, concert, sporting event).
Past research also documents that the characteristics and types of other people in a setting, their behaviors, and the extent of common goal sharing in a crowded context play important roles in influencing the directionality of crowding influence (Westover 1989). Earlier studies show that people report greater feelings of crowding when encountering even a few others who are perceived to have values and status that conflict with their own (Gramann and Burdge 1984). Other streams of research suggest that in crowded leisure settings, people diminish the importance they place on personal freedom and, in contrast, value sharing space with others (Goulding, Shankar, and Elliot 2002). This is because people in such a situation consider others an integral part of their own experiences (Pons, Laroche, and Mourali 2006), as sharing common experiences with others increases people’s sense of interpersonal connectedness (Walton et al. 2012). For example, due to the communion aspects present during baseball games (generally characterized by dense settings), as Holt (1995) posits, privacy and individualism are replaced by happiness and pleasure arising from sharing excitement with a group. Along the same lines, prior research concurs that the more focused and mutually shared consumers’ experiences in a crowded setting are, the more positive the effect of crowding on consumers’ evaluations of these experiences (Westover 1989). Overall, as people have greater interest in and desire to interact with others due to common shared goals of consumption in a goal-related crowd, they should have more favorable evaluations of others (including anthropomorphized entities) who show an explicit intention and interest in interaction. Therefore, we propose that when consumers share common goals with others present in a crowded setting, they are more likely to have greater preferences for an interaction-oriented anthropomorphized brand compared with brands with different positioning strategies (i.e., non-interaction-oriented anthropomorphized brands and interaction-oriented and non-interaction-oriented nonanthropomorphized brands).
H3a: The type of social crowdedness interacts with brand anthropomorphization and a brand’s interaction orientation.
H3b: In a goal-related socially crowded context, consumers have greater preferences for interaction-oriented anthropomorphized brands (vs. non-interaction-oriented anthropomorphized brands and interaction-oriented and non-interaction-oriented nonanthropomorphized brands).
H3c: In a goal-unrelated socially crowded context, consumers have lower preferences for interaction-oriented anthropomorphized brands (vs. non-interaction-oriented anthropomorphized brands and interaction-oriented and non-interaction-oriented nonanthropomorphized brands).
H4a: The negative impact of goal-unrelated social crowdedness on consumer preferences for interaction-oriented anthropomorphized brands (vs. non-interaction-oriented anthropomorphized brands and interaction-oriented and non-interaction-oriented nonanthropomorphized brands) is mediated by social withdrawal.
H4b: The positive impact of goal-related social crowdedness on consumer preferences for interaction-oriented anthropomorphized brands (vs. non-interaction-oriented anthropomorphized brands and interaction-oriented and non-interaction-oriented nonanthropomorphized brands) is mediated by desire to engage in interactions with others.
We conduct four studies to test our hypotheses. Studies 1 and 2 provide support for our baseline hypotheses of the negative effect of social crowdedness on consumer preferences for interaction-oriented anthropomorphized brands. Studies 3 and 4 demonstrate processing mechanisms of these relationships. Finally, Study 4 shows an important boundary condition of the core negative effect of social crowdedness (i.e., goal-related vs. goal-unrelated crowding).
The main objective of Study 1 was to investigate an actual consumer choice behavior between (non)humanized brands with different positioning messages in crowded and uncrowded situations. With this goal in mind, we conducted two sub studies. Study 1a investigates consumers’ choices between an anthropomorphized and a non-anthropomorphized brand that do not provide cues of interaction orientation. In Study 1b, we examine consumers’ choices between an interaction-oriented anthropomorphized and a non-anthropomorphized brand. In Study 1a, we did not expect any differences in consumers’ choices between a humanized and a nonhumanized brand either in crowded or uncrowded situations, as we predict that the effect of social crowdedness on consumer preferences for anthropomorphized brands is not driven by anthropomorphism itself but rather by the anthropomorphized brand’s humanlike intention to interact with consumers. In contrast, in Study 1b, in which the brand was interaction-oriented, we expected consumers in crowded environments to decrease their preferences for an interaction-oriented anthropomorphized (vs. non-anthropomorphized) brand.
The target branded product that we used in Study 1 is a fictitious brand of sport bottles, called Aqin. We custom-designed and ordered four different versions of real sport bottles from a well-known online company specializing in personalizing and manufacturing various types of consumer merchandise (see Figure 1 for the actual sport bottles used). All four versions of the sport bottle were BPA-free and identical in size (20-oz high-density polyethylene plastic bottle) and color, with a black color imprint on both sides of the bottle. The only difference among the four versions was the content of the imprint, according to our intended manipulations. Anthropomorphism was manipulated through a visual and a verbal cue. Furthermore, brand’s interaction orientation was manipulated consistent with our defi-nition, through a communication message that connoted the brand’s engagement in mutual activities with consumers (e.g., “Let’s get active together!”).
To ensure that our manipulations of brand anthropomorphization and interaction orientation were successful, we conducted a pretest (n = 139). Participants were informed that a new brand of sport bottles, Aqin, was interested in how consumers react to different communication messages. A manipulation check on the extent of anthropomorphism was measured with four items adopted from prior research (Hur, Koo, and Hofman 2015; Puzakova, Kwak, and Rocereto 2013), for example, “It seems almost as if Aqin has a mind of its own” (1 = “strongly disagree,” and 7 = “strongly agree”) and “To what extent does Aqin remind you of some humanlike qualities?” (1 = “not at all,” and 7 = “very much”; a = .97). Consumer perceptions of a brand’s interaction orientation were measured with three items: “It seems almost as if Aqin wants to interact with me,” “It seems almost as if Aqin wants to be part of my everyday activities,” and “It seems almost as if Aqin expresses interest in doing things together” (a = .96). The results of a 2 (anthropomorphized vs. nonanthropomorphized brand) · 2 (interaction-oriented vs. non-interaction-oriented brand) between-subjects analysis of variance (ANOVA) on the perception of the brand as a human revealed a main significant effect of brand anthropomorphization (F( 1, 135) = 12.67, p .17). The details of all manipulation check analyses are presented in Web Appendix W1. The brand was perceived as more humanlike when it was anthropomorphized (vs. non-anthropomorphized) in both interaction-oriented (MAB = 3.54, MNAB = 2.46; F( 1, 135) = 7.31, p < .05) and non-interaction-oriented (MAB = 3.13, MNAB = 2.06; F( 1, 135) = 5.59, p orientation revealed a main effect of our manipulation of interaction orientation (F( 1, 135) = 10.35, p < .05), with no main or interaction effects being significant (ps > .10). In conditions of both an anthropomorphized brand (Minter-orient = 4.16, Mnon-inter-orient =3.21; F( 1, 135) = 4.06, p < .05) and a nonanthropomorphized brand (Minter-orient = 3.74, Mnon-inter-orient = 2.55; F( 1, 135) = 6.44, p < .05), participants reported that they perceived a brand as intending to interact with them more when we manipulated interaction-oriented (vs. non-interaction-oriented) brands. We also conducted a pretest (n = 101; for details, see Web Appendix W2) to ensure that consumers interpreted interaction-oriented brands as sending a relationship closeness signal (measured with three items, e.g., “The brand seems to be initiating a sense of closeness between us”; a = .90). As we expected theoretically, the results showed that participants perceived interaction-oriented brands as sending a signal of greater relationship closeness compared with non-interaction-oriented brands (Minter-orient = 4.91, Mnon-inter-orient = 3.93; F( 1, 97) = 11.89, p
Study 1 involved 274 consumers as participants. A small table was placed near the entrance to an on-campus cafeteria at a private East Coast university. Two different versions of Aqin sport bottles were displayed on the table, along with a 20 · 30-inch poster that read, “BPA-Free Sport Bottle! Get one free and take a simple survey.” The data collection lasted for one week and occurred at different times of the day, in order to ( 1) ensure different levels of social crowdedness and ( 2) minimize the impact of potential confounding variables (e.g., hunger). Although estimating and manipulating crowd size in the real world is challenging, we relied on the Herbert Jacobs method (Time 1967). That is, we treated the environment as socially crowded when one person per approximately 4.5 (or fewer) square feet was observed (coded as 1), and the environment as socially uncrowded when one person per approximately 10 (or more) square feet was observed (coded as 0). For example, two time frames of the day between 11:50 A.M. and 12:30 P.M. and between 2:40 P.M. and 3:10 P.M. met the criteria above and represented highly socially crowded environments. In contrast, the time slots between, for example, 11 A.M. and 11:30 A.M. or 6 P.M. and 7 P.M. represented socially uncrowded environments. In both Studies 1a and 1b, participants were given an opportunity to examine two versions of Aqin sport bottles (anthropomorphized vs. nonanthropomorphized Aqin bottles for Study 1a, and interaction-oriented anthropomorphized vs. nonanthropomorphized Aqin bottles for Study1b) and to make a choice of one free Aqin bottle to take with them. After making their choices, they were asked to complete a brief survey. First, participants responded to a social crowdedness manipulation check: “How crowded do you feel the space around you is at the moment?” (1 = “not at all crowded,” and 7 = “very crowded”). Next, they were presented with the images of two bottles and asked to place a check mark next to the sport bottle that they had just picked up. Finally, they provided brief demographic information and were thanked for their participation.
The manipulation check on social crowdedness demonstrated that in the crowded situations, consumers perceived the surrounding space to be more crowded than in the uncrowded situations (Study 1a: Mcrowd = 4.27, Muncrowd = 1.97; F( 1, 131) = 109.05, p < .05; Study 1b: Mcrowd = 4.19, Muncrowd = 2.18; F( 1, 139) = 59.05, p .10). An almost equal percentage of participants chose an anthropomorphized (vs. nonanthropomorphized) brand in both crowded (53.7%; 36 out of 67) and uncrowded (57.6%; 38 out of 66) situations. In contrast, consistent with our expectations, in Study 1b there was a significant association between social crowdedness and brand anthropomorphization (c2( 1) = 28.32, p < .05). In greater detail, the results showed that in the crowded situation, only 38.8% of participants (26 out of 67) chose an interaction-oriented anthropomorphized brand, whereas 82.4% of participants (61 out of 74) chose an interaction-oriented anthropomorphized brand in the uncrowded condition. Overall, the results of Studies 1a and 1b demonstrate that social crowdedness does not influence consumer preferences for anthropomorphized brands per se, but, rather, crowding has a negative impact on preferences for anthropomorphized brands that signal intentions to interact with consumers. Thus, this field study with actual consumer choices shows that humanizing an interaction-oriented brand reduces consumers’ desires to own a product when the space is socially crowded.
In Study 2, we sought to extend the findings of Study 1 in three ways. First, Study 2 presents a full test of the effects by crossing two factors—brand anthropomorphization and interaction orientation—within the same experimental design. This design provides a more robust test of our prediction that the negative impact of social crowdedness on consumer preferences for anthropomorphized brands is specifically driven by an anthropomorphized brand’s intentionality to interact with consumers and not by anthropomorphism itself. Second, Study 2 aims to enhance the internal validity of our findings by manipulating social crowdedness in a controlled experimental setting. Finally, Study 2 investigates the impact of social crowdedness on consumers’ intentions to purchase a brand in a different product category (i.e., coffee maker).
Study 2 respondents were 244 undergraduate students at a private East Coast university who participated in exchange for course credit. To manipulate social crowdedness in an experimental setting, we followed a procedure used in prior research (Maeng, Tanner, and Soman 2013). In greater detail, in the crowded condition, respondents participated in groups of approximately 12 per session, whereas in the uncrowded context, the sessions took place in groups of 3–4 students, with all study sessions conducted in the same small laboratory room. These numbers of participants are in accord with prior work that manipulates social crowdedness in the laboratory setting (Baum and Koman 1976; Maeng et al. 2013).
After being seated, participants were given a study booklet in which they were exposed to one of the four versions of an advertisement for a fictitious brand of coffee maker, Aroma, and indicated their intentions to purchase the product. Next, they responded to manipulation check questions. The survey ended with demographic information and a demand probe; none of the participants correctly identified the true research question.
Consistent with prior research (Kwak, Puzakova, and Rocereto 2015), brand anthropomorphization was manipulated through visual and verbal advertising aspects: ( 1) by rearranging the buttons of the coffee maker to enhance an impression of a human face, and ( 2) by using a first-person (vs. third-person) communication in accompanying advertising copy (e.g., “I am Aroma, I will perfectly complement any occasion!”). The ad copy in the nonhumanized brand condition was similar to that of the humanized brand; however, it used third-person language, while the coffee maker image did not evoke associations of a human face. The brand’s interaction orientation was manipulated with advertising copy. In particular, consistent with our definition of interaction-oriented brands, the ad copy suggested opportunities for a consumer and a brand to engage in mutual experiences (e.g., “Together, you and I will explore a variety of coffee drinks” in the humanized brand condition). When brands were non-interaction-oriented, the ad copy did not contain any cues of interaction and delivered a more brand-focused communication (e.g., “I will create a variety of coffee drinks and make every cup irresistibly delicious”). All manipulations are presented in Web Appendix W3.
As a measure of purchase intentions, participants indicated their intentions to purchase an Aroma coffee maker on three semantic differential items: 1 = “unlikely,” “uncertain,” “definitely would not buy,” and 7 = “likely,” “certain,” “definitely would buy” (a = .70). Manipulation checks on the extent of anthropomorphism (a = .90), perceived brand interaction orientation (a = .87), and social crowdedness were measured with the same items used in Study 1.
Manipulation checks. The results of a 2 (social crowdedness: crowded vs. uncrowded) · 2 (anthropomorphized vs. nonanthropomorphized brand) · 2 (interaction-oriented vs. non-interaction-oriented brand) between-subjects ANOVA on the perception of the brand as humanlike showed that respondents in the anthropomorphized (vs. nonanthropomorphized) brand condition perceived the brand more as humanlike (MAB = 3.18, MNAB = 2.50; F( 1, 236) = 12.59, p .13). A similar three-way ANOVA with the extent to which participants perceived a brand as interaction-oriented as the dependent variable showed the main significant effect of the manipulation of interaction orientation (Minter-orient = 4.06, Mnon-inter-orient = 3.50; F( 1, 236) = 6.74, p .11). Finally, a three-way ANOVA with the same three factors and perceptions of social crowdedness as the dependent variable showed that participants perceived a crowded (vs. uncrowded) study room as more crowded (Mcrowd = 4.87, Muncrowd = 2.06; F( 1, 236) = 201.96, p < .05), with no othermain or interaction effects being significant (ps > .23).
Hypothesis tests. We analyzed our predictions using a 2 (social crowdedness: crowded vs. uncrowded) · 2 (anthropomorphized vs. nonanthropomorphized brand) · 2 (interaction-oriented vs. non-interaction-oriented brand) between-subjects ANOVA with purchase intentions as the dependent variable. The results revealed a significant two-way interaction between social crowdedness and a brand’s interaction orientation (F( 1, 236) = 4.24, p < .05). In support ofH1a, we also found a significant three-way interaction between social crowdedness, brand anthropomorphization, and a brand’s interaction orientation (F( 1, 236) = 6.93, p < .05). Follow-up planned contrasts revealed that in the uncrowded condition, participants reported greater purchase intentions for an interaction-oriented anthropomorphized brand compared with ( 1) an interaction-oriented nonanthropomorphized brand (Minter-orient, AB = 3.46, Minter-orient, NAB = 2.70; F( 1, 236) = 4.86, p < .05); ( 2) a non-interaction-oriented anthropomorphized brand (Minter-orient, AB = 3.46, Mnon-inter-orient, AB = 2.67; F( 1, 236) = 5.44, p < .05); and ( 3) a non-interaction-oriented nonanthropomorphized brand (Minter-orient, AB = 3.46, Mnon-interorient, NAB = 2.86; F( 1, 236) = 2.86, p = .09).However, consistent with H1, in the socially crowded room, participants indicated lower purchase intentions for an interaction-oriented anthropomorphized brand compared with ( 1) an interaction-oriented nonanthropomorphized brand (Minter-orient, AB = 2.43, Minterorient, NAB = 3.08; F( 1, 236) = 4.86, p < .05); ( 2) a noninteractionoriented anthropomorphized brand (Minter-orient, AB = 2.43, Mnon-inter-orient, AB = 3.11; F( 1, 236) = 5.44, p < .05); and ( 3) a non-interaction-oriented nonanthropomorphized brand (Minter-orient, AB = 2.43, Mnon-inter-orient, NAB = 3.05; F( 1, 236) = 2.86, p < .05). Thus, H1b and H1c are supported.
Study 2 provides further support for H1 by demonstrating that the experience of social crowdedness decreases consumer evaluations of anthropomorphized brands that are positioned as interaction-oriented (vs. brands with other positioning strategies). Interestingly, consistent with prior research indicating that people are generally willing to engage with others (MacIntyre et al. 1999), Study 2 reveals that consumers exhibit greater purchase intentions for interaction-oriented anthropomorphized brands (vs. brands with the three other types of positioning). Our findings contribute to prior work on anthropomorphism by demonstrating that anthropomorphism does not always result in the liking of a humanized entity, and its effects depend on the specific nature of an anthropomorphized target (Hur, Koo, and Hofman 2015; Kim and Kramer 2015).
Study 3 builds on the previous studies in several ways. First, in Study 3, we manipulate social crowdedness utilizing an alternative picture priming procedure, adopted from previous research (Maeng, Tanner, and Soman 2013). Second, Study 3 aims to investigate the underlying processes of our core effects. Our main prediction is that exposure to an interaction-oriented anthropomorphized brand (vs. brands with other types of brand positioning) engenders greater feelings of social withdrawal, which, in turn, lead to lower brand purchase intentions in crowded situations. Here, we also delve deeper into the psychological process of social withdrawal. In particular, prior work identifies two different types of social withdrawal: a more active avoidance of interaction with others (i.e., social avoidance) and a more passive response manifested in low social approach motivation (i.e., social disinterest) (Harrist et al. 1997; Leary, Herbst, and McCrary 2003; Rubin and Burgess 2001). Although previous research establishes that social crowdedness induces social withdrawal in general (Evans et al. 2000), social-withdrawal responses induced by crowding can manifest in active social avoidance (distancing from others; Valins and Baum 1973) or a more passive social disinterest (preference for solitary activities; Loo 1979). Prior work is silent with respect to which contexts might result in one or another type of social withdrawal, although previous studies find that crowding may elicit social disinterest while not evoking any active responses of social avoidance (Loo 1979). We expect here that an anthropomorphized brand’s intentionality to interact is more likely to induce greater active seeking to avoid further interactions (vs. social disinterest), as social crowdedness makes people particularly sensitive to interaction-seeking cues from others.
The third objective of Study 3 is to rule out possible alternative explanations of the negative effect of social crowdedness. Although we predict that the negative effect of social crowdedness on consumer preference for interaction-oriented anthropomorphized brands is driven by greater social withdrawal, there could be other psychological mechanisms that can explain these outcomes as well. More specifically, prior work points out that perceived violation of personal space is another major outcome of being socially crowded, which in some studies is found to be correlated with social withdrawal (Baum and Koman 1976; Jeffrey and Mark 1998). Past work indicates that feelings of being crowded induced by physical proximity of other people are characterized by experiences of personal space violations, which, in turn, arouse discomfort and anxiety (Baum and Valins 1979). Research also points out that adding individuals to a physical environment influences one’s perceptions of the amount of personal space (Jeffrey and Mark 1998). As such, it is possible that humanizing a brand and imbuing it with interaction-oriented cues will make the brand appear to be a part of the crowd and, thus, enhance consumers’ feelings of crowdedness. If this is the case, then consumers might perceive a socially crowded situation as more violating of their personal space when they are exposed to communications from an interaction-oriented anthropomorphized brand (compared to communications from a brand with other types of brand positioning).
Furthermore, prior research indicates that social crowdedness influences the way people form impressions of others’ personality traits. For example, several prior studies establish that people in high-density conditions evaluate others as more aggressive and unfriendly (Dooley 1978; Worchel and Teddlie 1976). Along the same lines, Stokols et al. (1983) show that participants perceive others’ jokes and comments as more hostile and attribute greater overall aggressiveness to others in crowded (vs. uncrowded) experimental sessions. Since consumers also attribute personality characteristics to brands (Aaker 1997), the relationships between social crowdedness and less favorable personality impressions led us to consider the possibility that greater consumer attributions of aggressive personality traits to interaction-oriented anthropomorphized brands may explain our core negative effect of social crowdedness. Overall, the goal of Study 3 includes examining alternative mediating mechanisms of the impact of social crowdedness through perceived personal space violation and attributions of a brand’s aggressiveness.
Study 3 respondents were 392 undergraduate students who participated in exchange for extra credit. Consistent with prior work (Maeng, Tanner, and Soman 2013), we presented participants with one of two versions of images that included either a crowded or an uncrowded city scene (see Web Appendix W4). Respondents were instructed to imagine how they would feel in the pictured scene and then describe their thoughts and feelings in the space below. Embedded in the city scenes were several billboard advertisements, which is typical for metropolitan areas.
The focal billboard ad promoted a fictitious brand of luggage, KalPak. We manipulated brand anthropomorphization by using first-person (vs. third-person) pronouns in the ad copy and by creating a product design in a form reminiscent of a human smile. Product design in the nonhumanized brand condition was similar, albeit modified such that it did not resemble a smile. Perceived brand interaction orientation was manipulated similar to that of Study 2, namely, by using brand communication that suggested a consumer and a brand engaging in mutual experiences (e.g., “Together, you and I will explore new places!”; Web Appendix W5). In the non-interaction-oriented brand positioning strategy, communication was focused on brand rather than on consumer–brand interaction (e.g., “With my new features, every trip is organized!”). After viewing the city scene with billboard advertisements, consumers provided their purchase intentions, responded to several questions that measured mediating variables, and answered the manipulation check questions. The survey ended with demographic information and a demand probe; none of the participants correctly identified the true research question.
Purchase intentions (a = .76), the manipulation check on brand anthropomorphization (a = .88), and a perception of the brand as interaction-oriented (a = .85) were measured with the same items as in Study 2. Next, we measured two types of social withdrawal process variables. To gauge social avoidance, we asked participants to respond to three questions: ( 1) “How interested would you be in interacting with people around you?” (reverse-coded); 2) “Is this a place in which you feel talkative to a stranger who happens to be near you?” (reverse-coded); and ( 3) “Is this a place where you might try to avoid other people?” (1 = “not at all,” and 7 = “very much”; a = .70). The questions were constructed by adopting items from previous studies that delineate social avoidance as an active seeking to elude social interactions (Harrist et al. 1997; Leary, Herbst, and McCrary 2003) and modifying them for the context of our research. As a measure of social disinterest, participants responded to two questions. First, consistent with prior research (Vohs, Mead, and Goode 2006), they indicated on a seven-point semantic-differential scale in which activity they would rather engage right now (1 = “going to a movie with others,” and 7 = “staying at home and watching a movie alone”). Second, they indicated their agreement with the statement “I would rather be alone than with others at the moment” (1 = “strongly disagree,” and 7 = “strongly agree”). We averaged responses to these two questions to create a social disinterest index (r = .44, p < .05), with higher scores indicating a greater social disinterest. Next, the violation of personal space was measured with three items on a Likert-type scale (i.e., “I would feel my personal space is violated,” “I would feel restricted,” and “I would feel constricted”; a = .94; Harrell et al. 1980). We measured brand personality by asking participants to evaluate a brand on two items (“aggressive,” “pushy”; 1 = “not at all descriptive,” and 7 = “very descriptive”; r = .72, p
Manipulation checks for brand anthropomorphization and interaction orientation. We conducted a 2 (social crowdedness: crowded vs. uncrowded) · 2 (anthropomorphized vs. nonanthropomorphized brand) · 2 (interaction-oriented vs. non-interaction-oriented brand) between-subjects ANOVA on the perception of the brand as human. As we expected, participants in the humanized (vs. nonhumanized) brand condition perceived the brand as more humanlike (MAB = 3.33, MNAB = 2.89; F( 1, 384) = 10.76, p < .002), with no other main or interaction effects being significant (ps > .16). A similar threeway ANOVA with the perceived brand’s interaction orientation as the dependent variable revealed the main significant effect of interaction orientation (Minter-orient = 4.44, Mnon-inter-orient = 3.86; F( 1, 384) = 16.20, p < .05), with no other main or interaction effects being significant (ps > .054). Finally, the same ANOVA model with the perceptions of social crowdedness as the dependent variable showed that participants perceived a crowded (vs. uncrowded) scene as more crowded (Mcrowd = 6.00, Muncrowd = 3.02; F( 1, 384) = 467.88, p < .05), with no other main or interaction effects being significant (ps > .064).
Hypothesis tests. Our predictions were analyzed using a 2 (social crowdedness: crowded vs. uncrowded) · 2 (anthropomorphized vs. nonanthropomorphized brand) · 2 (interactionoriented vs. non-interaction-oriented brand) ANOVA with purchase intentions as the dependent variable. The results revealed the main effect of social crowdedness (Mcrowd = 2.64, Muncrowd = 2.94; F( 1, 384) = 6.23, p < .05), a two-way interaction between social crowdedness and a brand’s interaction orientation (F( 1, 384) = 5.54, p < .05), and a three-way interaction between social crowdedness, brand anthropomorphization, and a brand’s interaction orientation (F( 1, 384) = 11.73, p < .05), further supportingH1a. To examine the nature of a three-way interaction, we conducted planned contrast tests. As expected, participants in the uncrowded condition had higher purchase intentions for an interaction-oriented anthropomorphized brand compared with ( 1) an interaction-oriented nonanthropomorphized brand (Minter-orient, AB = 3.38, Minter-orient, NAB = 2.88; F( 1, 384) = 3.89, p < .05); ( 2) a non-interaction-oriented anthropomorphized brand (Minter-orient, AB = 3.38, Mnon-inter-orient, AB = 2.60; F( 1, 384) =
9.71, p < .05); and ( 3) a non-interaction-oriented nonanthropomorphized brand (Minter-orient, AB = 3.38, Mnon-inter-orient, NAB = 2.87; F( 1, 384) = 4.32, p < .05). However, in the crowded condition, consumers had lower purchase intentions for an interaction-oriented anthropomorphized brand compared with ( 1) an interaction-oriented nonanthropomorphized brand (Minter-orient, AB = 2.17, Minter-orient, NAB = 2.93; F( 1, 384) = 10.07, p < .05); 2) a non-interaction-oriented anthropomorphized brand (Minter-orient, AB = 2.17, Mnon-inter-orient, AB = 2.78; F( 1, 384) = 7.15, p < .05); and 3) a non-interaction-oriented nonanthropomorphized brand (Minter-orient, AB = 2.17, Mnon-inter-orient, NAB = 2.67; F( 1, 384) = 4.25, p < .05). These outcomes are consistentwithH1b andH1c. The interaction plot is presented in Figure 2.
Next, we examined the role of social withdrawal and alternative process variables. We first ran a 2 (social crowdedness: crowded vs. uncrowded) · 2 (anthropomorphized vs. nonanthropomorphized brand) · 2 (interaction-oriented vs. non-interaction-oriented brand) multivariate ANOVA (MANOVA) with social avoidance, social disinterest, personal space violation, and perceptions of brand aggressiveness as the dependent variables. We found a significant main effect of social crowdedness (Wilks’ l = .83, F( 4, 381) = 20.03, p < .05). That is, social crowdedness enhanced social avoidance (Mcrowd = 4.97, Muncrowd = 4.71; F( 1, 384) = 4.40, p < .05) and perceptions of personal space violation (Mcrowd = 4.31, Muncrowd = 2.88; F( 1, 384) = 76.65, p < .05). We also found a significant main effect of a brand’s interaction orientation on perceived brand aggressiveness (Minter-orient = 3.37, Mnon-interorient = 3.09, F( 1, 384) = 4.94, p < .05). There were no other significant main or interaction effects (ps > .077). Further planned contrasts revealed that in the crowded situation, consumers’ social avoidance was higher when a brand was anthropomorphized and interaction-oriented compared with the three other types of brand positioning strategies (Minterorient, AB = 5.37, Minter-orient, NAB = 4.74, Mnon-inter-orient, AB = 4.91, Mnon-inter-orient, NAB = 4.85; ps < .05). Web Appendix W6 presents the correlation matrix.
To further examine the underlying process of the effect of social crowdedness, we tested regression models with brand anthropomorphization, a brand’s interaction orientation, social crowdedness, and all two-way and three-way interactions among them as the independent variables; social avoidance, social disinterest, brand aggressiveness, and perceived personal space violation as the mediators; and purchase intentions as the dependent variable (Hayes 2013, Model 12), using a bootstrapping approach. Consistent with H2, we found that in the crowded condition and when a brand was perceived as interaction-oriented, social avoidance mediated the effect of brand anthropomorphization on purchase intentions (a point estimate of the effect = –.14; 95% confidence interval (CI) = [–.30, –.04]). In the crowded condition and when a brand was anthropomorphized, the results also revealed a significant indirect path from perceived brand interaction orientation to purchase intentions through social avoidance (a point estimate of the effect = –.11; 95% CI = [–.26, –.005]). All other indirect paths in other conditions and for the other three mediators were nonsignificant, as the CIs for indirect paths through these mediators included zero (Web Appendix W7). These results revealed that the negative impact of social crowdedness is specifically driven by enhanced social avoidance responses.
Study 3 replicates the findings of Study 2 in a different product category and utilizing a different manipulation of social crowdedness. Study 3 also shows that a more active response of social withdrawal (i.e., social avoidance) drives the negative impact of social crowdedness on consumers’ purchase intentions of interaction-oriented anthropomorphized brands (vs. brands with other positioning strategies). Finally, Study 3 also rules out alternative explanations of the effect of social crowdedness through both consumers’ attributions of brand aggressiveness and perceived violation of personal space. One might interpret these findings as indications that consumers perceive an anthropomorphized brand to be sufficiently distinct from the crowd (e.g., because the brand and crowd belong to different social groups, such as sellers vs. buyers) and, as a result, humanized brands are not perceived as part of the crowd that violates consumers’ personal space. It is also likely that when the presence of other people in crowded spaces induces perceptions of personal space violations, adding one more person (or a humanized brand) into the already crowded environment has no further significant impact on perceived intrusion into personal space.
The main objective of Study 4 is to examine the moderating role of the type of social crowdedness—goal-related vs. goal-unrelated—on consumer preferences for brands with different types of brand positioning strategies. Our expectations are that an anthropomorphized brand’s intentionality to interact with consumers will enhance consumers’ social-withdrawal response in the goal-unrelated social crowdedness, thus decreasing their preference for the brand. In contrast, we predict that a humanized brand’s interaction orientation in the situation of goal-related social crowdedness will significantly diminish consumers’ social withdrawal, thereby increasing their preference for the brand.
Furthermore, in our Studies 1–3, social crowdedness was irrelevant to the target product. In Study 4, we examine the situation wherein the crowd appears to be there to buy the target product and, thus, might signal the popularity of the target brand. In this regard, prior research indicates that individuals who are highly sociable and express interest in interacting with others tend to be preferred as interaction partners and are perceived as more popular (Ilmarinen et al. 2015). As such, it is also possible that a humanized brand signaling intention to interact with consumers might enhance consumers’ perceptions of the brand’s popularity in crowded environments. Because popular brands enjoy greater consumer demand (Zhu and Zhang 2010), greater perceived popularity of an interaction-oriented humanized brand (vs. the other three types of brand positioning) might enhance consumers’ preferences for the brand in a crowded setting. Hence, Study 4 examines this potential impact of crowding as the signal of product popularity.
We recruited 424 consumers (average age = 35.6 years, 42.5% female) from Amazon’s Mechanical Turk to participate in Study 4. The context we examined in Study 4 was a food truck festival. We chose this context for two reasons. First, being an effective way to introduce new customers to a business, food truck festivals are gaining traction with consumers (Keene 2015). Second, with thousands of people attending these festivals, understanding the impact of crowds on food truck preferences becomes an important question for food truck owners.
The procedure in Study 4 was similar to that of Study 3. The only modification in the crowded condition was that we incorporated a manipulation of goal-related and goal-unrelated social crowdedness before presenting the crowded image. After participants examined the image and listed their thoughts evoked by the image, they reported their intentions to try food from the Big Melt food truck and responded to mediating, manipulation, and demographic questions.
In the goal-related crowding condition, participants were asked to imagine that they were food lovers who decided to go to a local food festival to share their experiences and exchange opinions about different food options with other people. In the goal-unrelated crowding condition, respondents were prompted to imagine that they happened to drive by a local food festival, and it was lunchtime. Thus, they decided to stop to grab food for their lunch at this festival and to leave immediately. In the uncrowded condition, we provided only a statement: “Please imagine that you decided to go to the local Food Truck Festival.”
We conducted a pretest (n = 53) to ensure that ( 1) the goal-related (vs. goal-unrelated) social crowdedness was perceived to be more relevant to consumers’ goals of visiting the festival, and ( 2) there were no differences in consumers’ perceptions of the extent to which they voluntarily joined people at the festival, as voluntary proximity might indicate consumers’ affiliation motivations (Xu, Shen, and Wyer 2012). As intended, the results of the pretest showed that participants viewed goal-related (vs. goal-unrelated) crowding as more relevant to their goal of visiting the festival (Mgoal-related = 4.46, Mgoal-unrelated = 3.35; F( 1, 51) = 4.71, p Mgoal-related = 5.65, Mgoal-unrelated = 5.28; F( 1, 51) = .83, p > .36). The results of a separate pretest (n = 57) also indicated that our manipulation of goal-related vs. goal-unrelated social crowdedness did not induce any perceived differences in how pleasant the crowd was (Mgoal-related = 5.34, Mgoal-unrelated = 5.16; F( 1, 55) = .31, p > .10; 1 = “not at all nice,” “very unpleasant,” and 7 = “very nice,” “very pleasant”; r = .75, p < .05).
After reading instructions, participants viewed the image of the festival scene (see Web Appendix W8) and were asked to describe how they would feel in the scene. Embedded in the scene were several food trucks. Our focal food truck was called the Big Melt, offering grilled cheese. Brand anthropomorphization and interaction orientation were manipulated as in Study 3 (see Web Appendix W9).
We measured intentions to try food from the Big Melt food truck with three items (1 = “very unlikely,” “definitely would not try,” “certainly would not try,” and 7 = “very likely,” “definitely would try,” “certainly would try”; a = .97). Social withdrawal (a = .82), brand anthropomorphization (a = .96), brand interaction orientation (a = .94), and extent of social crowdedness were gauged with the same items as in Study 3. We measured brand popularity by asking respondents to indicate how they viewed the Big Melt food truck (1 = “has very low demand among consumers,” and 7 = “has very high demand among consumers”; Zhu and Zhang 2010). As the manipulation check on the type of crowding, participants responded to two questions: “To what extent do you and people at the food festival share the same goals?” and “At the food festival, to what extent are people’s goals in close alignment with yours?” (1 = “not at all,” and 7 = “very much”; r = .81, p < .05).
Manipulation checks. The results of a 3 (social crowdedness: goal-related crowded vs. goal-unrelated crowded vs. uncrowded) · 2 (anthropomorphized vs. nonanthropomorphized brand) · 2 (interaction-oriented vs. noninteractionoriented brand) ANOVA showed that consumers viewed an anthropomorphized (vs. nonanthropomorphized) brand as more humanlike (MAB = 3.17, MNAB = 2.68; F( 1, 412) = 8.01, p < .05; Web Appendix W1). Similar analysis revealed that our manipulation of an interaction-oriented brand was successful (Minter-orient = 3.90, Mnon-inter-orient = 3.14; F( 1, 412) = 17.67, p < .05). Results also confirmed that consumers viewed goal-related crowded (Mcrowd = 6.09, Muncrowd = 3.03; p < .05) and goal-unrelated crowded (Mcrowd = 6.27, Muncrowd = 3.03; p < .05) festival scenes as more crowded than the uncrowded scene. Respondents also indicated that they perceived themselves to be sharing the same goal with the people at the food festival more when we manipulated goal-related (vs. goal-unrelated) social crowdedness (Mgoal-related = 5.16, Mgoal-unrelated = 4.70; F( 1, 269) = 8.88, p < .05).
Hypothesis tests. We tested our predictions using a 3 (social crowdedness: goal-related crowded vs. goal-unrelated crowded vs. uncrowded) · 2 (anthropomorphized vs. nonanthropomorphized brand) · 2 (interaction-oriented vs. noninteractionoriented brand) between-subjects ANOVA with intentions to try food as the dependent variable. The results revealed a significant main effect of a brand’s interaction orientation (Minter-orient = 5.27, Mnon-inter-orient = 5.00; F( 1, 412) = 3.93, p < .05), a significant interaction effect between social crowdedness and brand anthropomorphization (F( 1, 412) = 7.32, p < .05), a significant interaction effect between a brand’s interaction orientation and social crowdedness (F( 1, 412) = 4.23, p < .05), and amarginally significant three-way interaction effect between social crowdedness, brand anthropomorphization, and a brand’s interaction orientation (F( 1, 412) = 2.85, p = .059). Further planned contrasts showed that in the goalunrelated crowded festival condition, consumers had lower intentions to try the food fromtheBigMelt food truckwhen the brand was interaction-oriented and anthropomorphized compared with ( 1) when the brand was interaction-oriented and nonanthropomorphized (Minter-orient, AB = 4.16, Minter-orient, NAB = 5.38; F( 1, 412) = 9.00, p < .05); ( 2) when the brand was noninteractionoriented and anthropomorphized (Minter-orient, AB = 4.16, Mnon-inter-orient, AB = 5.00; F( 1, 412) = 4.43, p < .05); and ( 3) when the brand was non-interaction-oriented and nonanthropomorphized (Minter-orient, AB = 4.16, Mnon-inter-orient, NAB = 5.16; F( 1, 412) = 6.88, p < .05). However, consumers indicated higher intentions to try food in the goal-related crowded condition when the brand was interaction-oriented and anthropomorphized compared with ( 1) when the brand was interaction-oriented and nonanthropomorphized (Minter-orient, AB = 5.96, Minter-orient, NAB = 5.18; F( 1, 412) = 4.55, p < .05); ( 2) when the brand was non-interaction-oriented and anthropomorphized (Minter-orient, AB = 5.96, Mnon-inter-orient, AB = 5.10; F( 1, 412) = 5.64, p < .05); and ( 3) when the brand was non-interaction-oriented and nonanthropomorphized (Minter-orient, AB = 5.96, Mnon-inter-orient, NAB = 4.47; F( 1, 412) = 15.44, p < .05). These outcomes support H3b and H3c. The results for the uncrowded condition were consistent with the findings of Studies 2 and 3. The interaction plot is presented in Figure 2.
Next, we examined the mediating role of social withdrawal in our core effects. First, the results of a 2 (social crowdedness: goal-related crowded vs. goal-unrelated crowded) · 2 (anthropomorphized vs. nonanthropomorphized brand) · 2 (interaction-oriented vs. non-interaction-oriented brand) between-subjects ANOVA with social withdrawal as the dependent variable showed that in the goal-unrelated crowded situation, consumers’ social withdrawal was higher when a brand was interaction-oriented and anthropomorphized compared with the three other types of brand positioning strategies (Minter-orient, AB = 5.12, Minter-orient, NAB = 4.14, Mnon-inter-orient, AB = 4.11, Mnon-inter-orient, NAB = 4.11; ps < .05). In contrast, in the condition of goal-related social crowdedness, the results revealed that consumers’ social withdrawal was lower when a brand was interaction-oriented and anthropomorphized compared with the three other types of brand positioning strategies (Minter-orient, AB = 3.19, Minter-orient, NAB = 4.04, Mnon-inter-orient, AB = 4.17, Mnon-inter-orient, NAB = 4.04; ps < .05). Second, we used similar regression analyses as in Study 3. In the goalunrelated crowded condition, when a brand was perceived as interaction-oriented, we found a significant indirect path from brand anthropomorphization to consumers’ intentions to try food through social withdrawal (a point estimate of the effect = –.24; 95% CI = [–.59, –.04]). Similarly, when a brand was anthropomorphized, there was a significant indirect path from perceived brand’s interaction orientation to purchase intentions through social withdrawal (a point estimate of the effect = –.25; 95% CI = [–.58, –.06]). In contrast, in the goalrelated crowded condition, when a brand was perceived as interaction-oriented, reduced social withdrawal explained the impact of brand anthropomorphization on consumers’ intentions to try food (a point estimate of the effect = .21; 95% CI = [.04, .49]). In the same goal-related crowded context, when a brand was anthropomorphized, we find that lowered social withdrawal also mediated the impact of perceived brand’s interaction orientation on purchase intentions (a point estimate of the effect = .24; 95% CI = [.07, .52]). These outcomes are in line with our conceptualizations and support H4a and H4b.
Social crowdedness as the signal of product popularity. To examinewhether brand popularity underlies our core effects, we entered both social withdrawal and brand popularity as the mediating variables in the regression model containing social crowdedness, brand anthropomorphization, interaction orientation, and the interactions between them as the independent variables, and intentions to try food as the dependent variable (Hayes 2013, Model 12). The results showed that in the goal-unrelated crowded condition and when the brand was interaction-oriented, the only significant path from brand anthropomorphization to intentions to try food was through social withdrawal (a point estimate of the effect = –.21; 95% CI = [–.51, –.04]), with the indirect path through brand popularity being nonsignificant (a point estimate of the effect = –.13; 95% CI = [–.49, .15]). Social withdrawal also remained the only mediating variable in the relationships between interaction orientation and intention to try food when a brand was anthropomorphized in the goal-unrelated crowded condition (social withdrawal: a point estimate of the effect = –.21; 95% CI = [–.49, –.05]; product popularity: a point estimate of the effect = .01; 95% CI = [–.31, .33]). However, we find that in the goal-related crowded condition, the indirect paths from both social withdrawal and brand popularity were significant in predicting the impact of brand anthropomorphization on the intentions to try food when a brand was positioned as interaction-oriented (social withdrawal: a point estimate of the effect = .16; 95% CI = [.04, .43]; brand popularity: a point estimate of the effect = .23; 95% CI = [.007, .52]) and in predicting the impact of interaction orientation on the intention to try food when a brand was anthropomorphized (social withdrawal: a point estimate of the effect = .20; 95% CI = [.06, .45]; brand popularity: a point estimate of the effect = .36; 95% CI = [.12, .71]). Thus, the positive impact of goal-related social crowdedness on consumer preferences for interaction-oriented anthropomorphized brands is driven not only by lower social avoidance (or greater desire to engage in interactions with others) but also by greater consumer inferences of the brand’s popularity.
The results of Study 4 support H3. In accord with our theoretical arguments that social crowdedness is experienced negatively only when it violates consumers’ goals (Westover 1989), Study 4 finds that when people in a crowd share a common goal, social crowdedness leads to greater preferences for an interaction-oriented anthropomorphized brand (vs. other types of brand positioning strategies). In contrast, Study 4 shows that even leisure crowds (such as that at a food truck festival) and crowds that converge in order to buy the target brand could be experienced negatively when a consumer and the crowd do not share a common consumption goal. In this situation, the effect of this type of social crowdedness is one of inducing a negative effect on consumer preferences for an anthropomorphized interaction-oriented brand (vs. the other three types of brand positioning strategies). Importantly, because consumers’ inferences of a humanized brand’s intentionality to interact in a goal-unrelated crowd facilitates greater social-withdrawal responses, the same type of brand positioning in a goal-related crowded setting instead evokes the opposite consumer reactions. In addition, the results of Study 4 establish that exposure to interaction-oriented anthropomorphized brands facilitates consumer inferences of the brand’s popularity, thereby enhancing consumer preferences for the brand. These latter findings resonate well with prior research in social psychology demonstrating that people expressing interest in interaction with others tend to be generally more likeable (Ilmarinen et al. 2015). However, Study 4 shows that this effect holds only in the crowded settings in which consumers and brands share similar consumption goals.
The primary objective of strategic branding is to position brand messages in optimal settings that reach the maximum number of viewers. However, our four studies, in distinct crowded settings and with different product categories, demonstrate that certain branding messages can backfire, depending on the type of environmental setting. In particular, our studies show that the experience of social crowdedness decreases consumer evaluations of interaction-oriented anthropomorphized brands (vs. brands with other positioning strategies; Table 1). These results provide important evidence demonstrating that the negative impact of social crowdedness on consumer preferences for interaction-oriented anthropomorphized brands is not driven by anthropomorphism per se but, rather, by an anthropomorphized brand’s intentionality to engage in interactions. Furthermore, Study 3 establishes that an interaction-oriented humanized brand triggers greater social withdrawal, thus leading to lower consumer preferences for the brand. Finally, Study 4 shows that the composition of the crowd serves as a crucial moderator of our core effect. That is, while goal-unrelated crowding triggers less favorable reactions to interaction-oriented anthropomorphized brands, goal-related crowding reverses this effect. That is, a goal-related crowd induces greater desire to interact with people and enhances consumer inferences of product popularity, thus leading to greater preferences for interaction-oriented anthropomorphized brands (vs. brands with the other types of positioning strategies).
TABLE: TABLE 1 Cell Means in Studies 2, 3, and 4
| | Socially Crowded Context | Socially Uncrowded Context |
|---|
| Interaction-Oriented Brand | Non-Interaction-Oriented Brand | Interaction-Oriented Brand | Non-Interaction-Oriented Brand |
|---|
| NAB | AB | NAB | AB | NAB | AB | NAB | AB |
|---|
| Study 2 |
| Purchase intentions | 3.08 | 2.43 | 3.05 | 3.11 | 2.70 | 3.46 | 2.86 | 2.67 |
| Study 3 |
| Purchase intentions | 2.93 | 2.17 | 2.67 | 2.78 | 2.88 | 3.38 | 2.87 | 2.60 |
| Social avoidance | 4.74 | 5.37 | 4.85 | 4.91 | 4.82 | 4.74 | 4.67 | 4.62 |
| Social disinterest | 3.39 | 3.47 | 3.14 | 3.39 | 3.47 | 3.08 | 3.19 | 3.00 |
| Perceived personal space violation | 4.34 | 4.52 | 4.28 | 4.07 | 2.97 | 3.12 | 2.98 | 2.47 |
| Brand aggressiveness | 3.35 | 3.05 | 3.09 | 3.15 | 3.58 | 3.50 | 3.26 | 2.88 |
| Study 4 |
| Intentions to try food | 5.38a (5.18b) | 4.16 (5.96) | 5.16 (4.47) | 5.00 (5.10) | 5.05 | 5.88 | 4.98 | 5.06 |
| Social avoidance | 4.14 (4.04) | 5.12 (3.19) | 4.11 (4.04) | 4.11 (4.17) | 3.07 | 3.26 | 3.26 | 3.27 |
| Product popularity | 5.47 (5.36) | 5.20 (5.73) | 5.62 (5.25) | 5.24 (4.93) | 5.22 | 4.84 | 4.83 | 5.01 |
aMean scores in the goal-unrelated socially crowded condition.
bMean scores in the goal-related socially crowded condition.
Notes: AB = anthropomorphized brand; NAB = nonanthropomorphized brand.
From a theoretical point of view, the present research makes several contributions to work on the downstream effects of social crowdedness. Overall, prior research on crowding in the marketplace has shed light on behavioral outcomes in retail and service settings (Machleit, Eroglu, and Mantel 2000). Researchers have also begun to uncover the impact of crowding on decision making and product choice (Maeng, Tanner, and Soman 2013). The current research extends this body of work by demonstrating important implications of the impact of social crowdedness on consumer reactions toward particular types of brand messages. Furthermore, although prior research has uncovered different pathways through which social crowding influences social responses (Worchel and Teddlie 1976), this prior work does not examine the impact of explicit intentions to interact with other people in the crowd on individuals’ downstream reactions. Our work establishes that a particular factor—an anthropomorphized brand’s intentionality to interact with consumers in the crowded setting—induces greater social-withdrawal responses, thereby reducing consumer preferences for interaction-oriented anthropomorphized brands (vs. brands with other positioning strategies). Restated, prior work establishes the impact of social density per se on downstream consequences; our work adds important new insights regarding the impact of individuals’ (or humanized brands’) intentions in the crowd on the social decisions of other individuals. In addition, our findings provide an important contribution by shedding light on the crucial moderating role of goal-related vs. goal-unrelated crowding in its impact on consumer brand preferences. This enhances understanding of the differential effects of social crowdedness on consumer brand preferences, which largely depend on alignment with the consumers’ consumption goals.
Next, our findings add new dimensions to existing work on brand anthropomorphism. Specifically, prior work has primarily focused on motivational forces (e.g., sociality) and cognitive antecedents of anthropomorphism (Aggarwal and McGill 2007; Epley, Waytz, and Cacioppo 2007), as well as on the downstream evaluations of humanized entities. The current research contributes to this important body of work by establishing the impact of a key environmental factor (i.e., social crowdedness) on consumer reactions to anthropomorphized brands. Our findings further contribute to the growing body of work showing that anthropomorphism by itself does not always contribute to the liking of a humanized entity (Puzakova, Kwak, and Rocereto 2013) and that its effects depend on the nature of an anthropomorphized target (Aggarwal and McGill 2007). That is, we show that simply humanizing a brand does not lead to changes in consumer preferences compared with a nonanthropomorphized brand. In contrast, we show that explicitly positioning a humanized brand as interaction-oriented leads to negative consumer preferences in instances of social crowdedness. In sum, we show that anthropomorphizing a brand does not always automatically transform a brand into a relationship partner seeking to engage with consumers, but it allows for quasi-social experiences to evolve and communicates specific meaning for a brand’s social role, such as a potential interaction partner (vs. a mere bystander).
As marketing practitioners have control over their highly targeted specific messages, our research offers potentially significant practical guidelines for updating branding messages, depending on the level and type of social crowdedness. Our results imply that while it is generally advantageous to activate human schemas and provide interaction cues in branded communications, doing so while targeting consumers in high-traffic areas (e.g., busy urban street intersections) might backfire. In other words, it might be beneficial to display brand communications emphasizing nonsocial (i.e., low in interaction likelihood) brand elements when an environment becomes socially crowded, especially when the crowd is unrelated to individuals’ consumption goals. Our research also shows the importance of considering not only the level of social density but also the structure and configuration of social crowdedness. For example, it might be more effective to emphasize brand anthropomorphization with the engagement cues in socially crowded environments designed for shared consumption experiences and festivals, and it might be detrimental to use such branding strategies in more mundane crowded spaces.
This research raises several interesting questions that merit further inquiry. Future research should examine distinct types of brand relational messages. We address brand interaction cues, while prior work elucidates additional interpersonal relational messages that connote various themes of dominance, equality, exclusion, or control (Burgoon and Hale 1984). These different relational brand messages might have distinct effects in the context of social crowdedness. For example, prior work sheds light on different brand roles, such as working for (i.e., brand-as-servant) vs. working with (i.e., brand-as-partner) a consumer (Kim and Kramer 2015). Consumers might infer that a partner brand intends to actively interact with them (i.e., equal relationship), or they might infer that a servant brand intends to serve them (i.e., hierarchical relationship; Kim and Kramer 2015). In these instances, not only could the brand’s intentionality to interact with consumers play an important role in determining the direction of the crowding effect, but the degree of power over a brand could also become an important explanatory factor. In addition, prior work identifies task orientation as a relational theme independent of dimensions that have affiliative connotations (Burgoon and Hale 1987). It is possible that when consumers perceive that a brand wants them to participate in a specific task (e.g., come up with a new menu), they might interpret that the brand treats them as an instrument to contribute to the company. This potential brand dominance cue may have important consequences in crowded contexts, as crowding has been shown to reduce consumer perceptions of control (Hui and Bateson 1991).
While our focus here is on the impact of social crowdedness on consumer preferences for interaction-oriented humanized brands, there is a plethora of additional factors that may exert their influence on preferences for brands suggesting interaction (e.g., anxiety, self-esteem, mood). Consumers may also develop expectations about being crowded that might induce effects similar to those of experienced crowding (Baum 1975). In addition, as our research shows that goal-related crowding reverses the negative impact of crowding on consumer preferences for interaction-oriented humanized brands due to the goal-sharing nature, future work might examine whether voluntarily joining the crowd may qualify this negative effect. There are further opportunities to investigate the impact of spatial crowding, which has been shown to trigger feelings ofpsychological space violation (Levav and Zhu 2009). Why and under what conditions would spatial crowding induce the effects obtained in our research? Effects similar to those we observe in our work may occur because spatial crowding can lead to more aggressive reactions and not necessarily to social withdrawal (Aiello, Nieosia, and Thompson 1979). Relatedly, would our core effect of social crowdedness hold in different cultures? For example, people from collectivistic cultures strive for proximate social interactions (Evans et al. 2000); this indicates a possibility that in these cultures, social crowdedness may facilitate consumer attraction to interaction-oriented humanized brands. Furthermore, people may have different individual tolerance levels for crowded environments. For example, as prior research identifies an association between social crowdedness and extraversion (Miller and Nardini 1977), it is possible that extroverts (vs. introverts) may be more tolerant of crowding and, thus, may not exhibit lower preferences for interaction-oriented anthropomorphized brands in socially crowded contexts. Overall, this work presents multiple opportunities for further consideration of a variety of cultural and individual factors in qualifying the impact of social crowdedness on brand communications.
GRAPH: FIGURE 2 A Three-Way Interaction Between Social Crowdedness, Brand Anthropomorphization, and Interaction Orientation in Studies 3 and 4
DIAGRAM: FIGURE 1 Manipulations of Brand Anthropomorphization and Interaction-Oriented Brands in Study 1
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Record: 166- Social Comparison in Retailer–Supplier Relationships: Referent Discrepancy Effects. By: Lee, Hannah S.; Griffith, David A. Journal of Marketing. Mar2019, Vol. 83 Issue 2, p120-137. 18p. 2 Diagrams, 6 Charts, 1 Graph. DOI: 10.1177/0022242918823542.
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Social Comparison in Retailer–Supplier Relationships: Referent Discrepancy Effects
Social comparisons among suppliers connected through a common retailer pose significant management challenges for the retailer. For instance, a focal supplier's social comparison can result in upward or downward referent discrepancy, decreasing or increasing perceptions of distributive fairness, respectively, subject to the tie strength of the relationship. Because decreasing perceptions of distributive fairness can be harmful to the retailer–supplier relationship, the authors examine the use of tie strength and timing of explanations as actions a retailer can take to mitigate such perceptions. They test their hypotheses with a two-study, multimethod design conducted in Japan. Study 1 employs a survey of suppliers in a store-within-a-store context as well as objective performance data. The results indicate that upward (downward) referent discrepancy decreases (increases) a focal supplier's perceptions of distributive fairness. Study 2 employs an experiment using brand/store managers. The results show that when upward referent discrepancies are present, retailers can mitigate the invidious effects of decreased perceptions of distributive fairness by developing strong ties and enacting procedurally fair policies such as proactively providing explanations.
Keywords: distributive fairness; procedural fairness; retailer–supplier relationships; social comparison; upward/downward referent discrepancy
In the summer of 2011, Italian fashion brand Gucci learned that Shilla, operator of the duty-free retail facility located in the Incheon International Airport, offered Louis Vuitton concession terms that were more generous than its own ([48]). Gucci perceived that it was not being treated fairly and requested the same terms as Louis Vuitton. Shilla denied the request. In protest of the alleged unfair treatment, Gucci withdrew two of its locations from Shilla's facilities and moved to Shilla's main competitor, Lotte Duty Free ([11]). Gucci viewed Louis Vuitton as a comparative referent, even though the firms had achieved different performance levels, creating a referent discrepancy (i.e., when a focal firm selects a referent firm that is not comparable based on specified criteria, such as sales performance).
A retailer's ability to manage multiple supplier relationships is challenging, as differential treatment of partners can be inevitable. For example, retailers such as Shilla operate a store-within-a-store (SWS) format with multiple suppliers operating within a single retail facility. Because each supplier is unique (e.g., varying in sales performance), the retailer develops concession agreements with different terms (e.g., commission rate) for each supplier. Similarly, in the consumer packaged goods industry, grocery retailers (e.g., Kroger) set distinct contract terms (e.g., fees) for each supplier according to specified criteria. As these dyadic business relationships are within the retailer's larger supplier network, they are subject to extradyadic effects ([35]; [36]). [55] note that within business networks, due to increased access to private information, firms engage in external social comparisons—that is, when one partner of a dyadic relationship evaluates its treatment and outcomes in relation to referents outside the focal dyad. Differential treatment of suppliers can result in referent discrepancies with detrimental relationship consequences, such as increased perceptions of unfairness ([55]) and relationship termination, as was the case in Gucci's relationship with Shilla.
Scholars note the importance of social comparison and fairness in retailer–supplier relationships (e.g., [55]; [57]); however, an understanding of how referent discrepancy influences attitudinal outcomes, such as perceptions of distributive fairness (i.e., the supplier's perception of the fairness of earnings and other outcomes it receives from its relationship with the retailer; [40]), is limited. Furthermore, there is little guidance on how a retailer can manage perceptions of distributive fairness in a context in which differential treatment of suppliers is inevitable. This work addresses these gaps through a two-study examination of retailer–supplier relationships in an SWS format in Japan and, in doing so, makes three important contributions to the literature.
First, drawing on the distributive fairness literature and social comparison theory (e.g., [ 6]; [16]), we extend understanding of social comparison's influence on perceptions of distributive fairness, providing evidence of referent discrepancies. Notably, we advance the literature by demonstrating differential effects of upward and downward referent discrepancy (i.e., when a focal supplier views another supplier performing at a higher or lower level than it is as a comparable referent) on the focal supplier's perception of distributive fairness. Decomposing referent discrepancy into upward and downward referent discrepancy and demonstrating differential outcomes advances our understanding of the effects of social comparison, similar to the advancements in the literature brought forth by the study of positive and negative inequity outcomes (e.g., [25]; [57]).
Second, given the importance of the relationship context to its operation and management, we examine the important role of tie strength (i.e., the potency of the bond between parties in a relationship; [47]; [54]) in the context of social comparisons. We find that a focal supplier's tie strength to a retailer dampens the negative effect of upward referent discrepancy and enhances the positive effect of downward referent discrepancy on a focal supplier's perceptions of distributive fairness, respectively. These findings extend the literature, underscoring the importance of strong retailer–supplier bonds. This finding is important, as the SWS context stimulates retailers to view supplier relationships as arm's-length economic transactions governed solely by the concession agreement.
Third, by drawing on the procedural fairness literature (e.g., [37]; [62]), we provide guidance on the timing of actions that retailers can employ to more effectively manage multiple supplier relationships. Specifically, we demonstrate the efficacy of proactive explanations regarding outcome distributions provided well in advance (vs. explanations offered concurrently to or after the delivery of the concession agreement) to mitigate the invidious effects of upward referent discrepancy. Thus, we provide theoretically founded guidance to retailers on how they can mitigate detrimental relationship consequences resulting from decreased perceptions of distributive fairness.
The SWS format consists of multiple suppliers[ 5] connected through a single retailer. The retailer rents space to suppliers at a commission rate and allows these suppliers to operate relatively autonomously ([35]; [49]). Suppliers control both staff and store space. The SWS environment closely connects suppliers in a context of social exchange, presenting unexplored management challenges ([36]; [70]). Specifically, variance in supplier performance makes differential treatment of suppliers (e.g., commission rates established in the concession agreement) inevitable.
Theoretically, [ 6] argues that a characteristic of social exchange is that the rule of justice serves as a mechanism sustaining connections among actors. He states that the rule of justice is a social norm of fairness. Group members at least partly share these generally accepted rules of behavior ([31]), as continuing relationships are founded on fairness ([ 4]; [37]). The consequences of violating the norms of fairness are greater than typically acknowledged, because people go out of their way to punish actions they perceive as unfair, even at a cost to themselves ([15]; [66]). As such, identifying the factors that influence perceptions of fairness is important.
[40] add clarity to the literature, noting that fairness perceptions can derive from the distribution of outcomes (i.e., distributive fairness) or processes (i.e., procedural fairness). We begin by focusing on distributive fairness, as such perceptions can influence exchange partners' attitudes and behaviors, such as relationship quality ([40]), relational behavior and long-term orientation ([24]), and satisfaction and conflict ([ 8]).
Fairness perceptions are conceptually linked to social comparison ([17]). Social comparison theory (e.g., [16]) contends that actors compare themselves with those they perceive to be similarly situated, to assess their own social and economic worth. This comparison process influences their sentiments and behaviors ([50]). For example, [ 4], arguing that as it is not uncommon for discussions of issues such as work effort or bonuses to be generally known within a sales force, find that salespeople use referents (i.e., other salespeople) in forming fairness judgments. Applied within the context of a retailer–supplier relationship, we argue that members of a retailer's supplier network informally share information, resulting in a focal supplier comparing its economic outcomes with other suppliers of the retailer. For example, Procter & Gamble may view Unilever as a referent. As such, it will judge the contract terms it is offered by Kroger to the terms it believes Kroger has offered to Unilever. This argumentation is consistent with [55], who find that a focal buyer evaluates the consistency of its economic payoffs with those of other buyers in the supplier's network, ultimately influencing the buyer's satisfaction. Building on this literature, we contend that a focal supplier compares itself with other suppliers and that this referent comparison influences a fairness judgment.
In many retail contexts, including the SWS format, sales performance is a primary driver of contract terms.[ 6] As such, sales performance often serves as the basis of social comparison of referents. However, the environment is not always conducive to effective social comparisons ([73]), because relevant information may not be available ([55]). When making social comparisons in a retailer's network, a focal supplier knows its own performance. However, it may not be able to accurately assess the performance of other suppliers, as firms are reluctant to share detailed accounting information ([34]). By contrast, the retailer, which establishes the contract terms, has full knowledge of the performance of all its suppliers. Given the limited-information environment of a focal supplier (in terms of both treatment by the retailer and the specific performance of other suppliers), its social comparison may result in a referent discrepancy, influencing perceptions of fairness. For example, Gucci considered itself similar to Louis Vuitton; however, Louis Vuitton reported a 19% increase in revenue in the prior year, whereas Gucci had recorded a loss. Shilla's election of concession terms based on objective performance data and Gucci's referent discrepancy evoked Gucci's perceptions of unfairness.
Suppliers are often assessed by retailers on sales performance. As such, we use sales performance to clarify the relationship of social comparison and referent discrepancy. In this context, social comparisons can result in one of three referent discrepancy outcomes. First, a focal supplier can select a supplier with the same level of sales performance. In this case, no referent discrepancy exists. Second, a focal supplier can select a supplier with a higher level of sales performance as a referent. This case, termed "upward referent discrepancy," varies in the magnitude of the discrepancy. Third, a focal supplier can select a supplier with a lower level of sales performance as a referent. This case, termed "downward referent discrepancy," also varies in the magnitude of the discrepancy. Given that a specific referent sales performance is often unknown to the focal supplier, we expect referent discrepancy to occur.
Furthermore, we argue that social comparisons are not symmetric in direction ([13]; [72]), and thus upward and downward referent discrepancy (of varying magnitude) not only occurs but also is likely to occur at different rates. Research has suggested that when engaging in social comparisons, there is a bias toward upward comparisons ([44]) wherein individuals tend to identify referents who are economically better off ([75]). This is argued to be due to inflated perceptions of personal contribution or performance ([45]; [75]). The combination of upwardly biased self-assessment and propensity to compare performance outcomes to those earning more leads to increased engagement in upward social comparisons ([50]). Thus, we expect a greater occurrence of upward referent discrepancy as opposed to downward referent discrepancy. More formally,
- H1: Focal suppliers of a common retailer are more likely to (a) exhibit referent discrepancy than no referent discrepancy and (b) exhibit upward referent discrepancy than downward referent discrepancy.
Upward and downward referent discrepancy have differential effects on a focal supplier's perceptions of distributive fairness (see Figure 1). First, we theorize that greater upward referent discrepancy negatively influences the focal supplier's perceptions of distributive fairness. A better-performing referent supplier receives more favorable terms from the retailer than the focal supplier, because the retailer bases its evaluation of each supplier on objective performance data. Because the focal supplier receives less favorable terms, relative to what it perceives to be a comparable referent—but what is actually a better-performing referent—the focal supplier will perceive its treatment (i.e., contract terms) as increasingly unfair. This argumentation is consistent with the literature that finds that inflated self-assessments tend to be invidious ([50]). More formally,
Graph: Figure 1. Conceptual model in Study 1.
- H2a: Upward referent discrepancy in a retailer–supplier relationship negatively influences the focal supplier's perceived distributive fairness.
Second, we theorize that greater downward referent discrepancy positively influences the focal supplier's perceived distributive fairness. Downward referent discrepancy occurs when the focal supplier compares itself with a supplier that is less skilled or in a worse position. As the retailer sets the terms of its contracts according to objective performance data, the greater the difference between the objective performance of the focal supplier and its referent, the more favorable its treatment will be compared with its lower-performing referent. Because its distributive outcomes are greater than those of its comparative referent, the focal supplier will perceive its treatment by the retailer as increasingly fair. More formally,
- H2b: Downward referent discrepancy in a retailer–supplier relationship positively influences the focal supplier's perceived distributive fairness.
The influence of social comparison on distributive fairness perceptions is determined within the context of the relationship. Research has demonstrated that tie strength is central to understanding the operation of business relationships ([54]; [74]). Tie strength refers to the potency and depth of the dyad relationship and reflects the closeness and intensity of the two parties. We theorize that tie strength of the retailer–supplier relationship will moderate the influence of referent discrepancy on a focal supplier's perceived distributive fairness. Strong-tie relationships embody greater trust, inclusive of credibility and benevolence ([14]; [47]). A focal supplier with a strong tie to the retailer operates from a baseline assumption that the retailer is treating it in good faith. Partners in high-trust relationships maintain positive feelings toward their partners by discounting negative elements in ways that confirm their positive trusting attitudes ([32]). The trusting parties do not naively ignore negative elements in relationship issues, but they make fewer negative attributions ([21]).
Consistent with [20], who find a buffering effect of commitment on the relationship between a supplier's ethical violations and a buyer's switching behavior, we argue that a focal supplier with a stronger tie to the retailer puts greater trust in the retailer with respect to the establishment of contract terms, thereby creating a buffering effect on the negative relationship between upward referent discrepancy and the focal supplier's perception of distributive fairness. By contrast, tie strength enhances the positive influence of downward referent discrepancy on the focal supplier's perceived distributive fairness. The focal supplier's trust in the retailer is higher with stronger ties, thus operating as both a relationship "safeguard" in which negative effects are reduced and an "amplifier" in which positive effects are enhanced. Thus, tie strength moderates the two types of referent discrepancies in opposite ways. More formally,
- H3a: The negative effect of upward referent discrepancy in a retailer–supplier relationship on the focal supplier's perceived distributive fairness is dampened when the supplier's tie strength to the retailer is strong (vs. weak).
- H3b: The positive effect of downward referent discrepancy in a retailer–supplier relationship on the focal supplier's perceived distributive fairness is enhanced when the supplier's tie strength to the retailer is strong (vs. weak).
Upward referent discrepancy not only is more common than downward referent discrepancy ([27]; [50]) but also poses significant problems for retailers managing multiple supplier relationships. As illustrated previously, Gucci severed ties with Shilla after upward referent discrepancy caused it to believe that it was being unfairly treated. This led us to question what Shilla could have done to mitigate the negative relationship outcome of Gucci exiting the relationship, and we focused our effort, and the remaining hypotheses, on understanding how retailers can manage the effects of upward referent discrepancy.
We draw on our previous argumentation of tie strength and introduce the notion of procedural fairness, a concept closely tied to distributive fairness (e.g., [24]; [40]). Procedural fairness refers to processes that allocate resources and resolve disputes ([37]). Procedural fairness, which [24] label as the "social policy" of relationship management, focuses on the decision-making process itself and on the attitudes of those directly involved in or affected by those decisions. The provision of explanations is particularly relevant to the study of procedural fairness ([62]) given its relationship management role.
Explanation strategies can be an effective means of mitigating perceptions of unfairness in interactions between parties ([61]; [64]). Explanations (i.e., the act or process of making something clear or understandable) reveal the reason for or the cause of an event that is not immediately obvious. Research has indicated that the provision of explanations reduces negative attitudes and conflictual behavior ([62]; [64]). [26] argue that explanations and feedback in channel relationships are essential for effective relationship management and positive relationship outcomes.
We contend that retailers can use explanations to provide the rationale for outcome distributions, enhancing perceptions of distributive fairness when a supplier engages in upward social comparison (i.e., upward referent discrepancy). When a retailer provides explanations for why it has set specific contract terms, it influences suppliers' perceptions of distributive fairness. We argue that the effectiveness of such explanations depends on the context of the relationship within which an explanation is provided and the timing of explanation delivery. Thus, building on our prior argumentation, we theorize that both tie strength (i.e., relationship context) and the timing of the explanation (i.e., relationship management) will influence a supplier's perception of distributive fairness, when considered in the context of upward referent discrepancy (see Figure 2).
Graph: Figure 2. Conceptual model in Study 2.
As noted previously, a strong-tie relationship reflects a high level of potency of the bond between parties in a relationship ([47]). A strong tie increases trust ([23]). Interorganizational trust facilitates relationships ([41]), reducing conflicts and enhancing satisfaction ([ 2]), and thereby operates as a governance mechanism ([ 7]; [30]). High-trust parties maintain positive feelings (i.e., priors) toward their partners ([21]; [32]). As such, we argue that positive priors (trust) built through a strong-tie relationship insulate the partner from the negative effects (i.e., lower perceived distributive fairness) that can arise from perceptions of differential treatment. Explanations from a trusted source (i.e., the retailer) are more effective in persuasion ([10]; [56]), favorably influencing the focal supplier's perception of distributive fairness. By contrast, when tie strength is weak, trust is lower and the positive prior is not present, provoking resistance to the retailer's explanation. More formally,
- H4: In a retailer–supplier relationship exhibiting upward referent discrepancy, (a) the focal supplier's perceived distributive fairness is greater when an explanation is offered in a relationship with a strong tie than in a relationship with a weak tie due to (b) higher levels of the focal supplier's trust in the retailer.
The timing of an explanation refers to when the explanation occurs in an outcome distribution event (e.g., the establishment of a commission rate level in a new concession agreement). Research on timing of explanations has indicated that the later the delivery, the less effective it will be in ameliorating negative responses ([64]). More timely explanations influence the perceived adequacy of the rationale ([61]). Proactive explanations are viewed as justifications ([59]), positively framing the information to follow. Explanations provided later (e.g., either at the time of the concession agreement or after) may be viewed as excuses and thus are less effective in ameliorating negative responses following an outcome distribution event ([64]).
We contend that a retailer's explanations given in advance of the new concession agreement (i.e., proactive) will positively influence the perceived adequacy of the explanation and the resultant perceived distributive fairness of a focal supplier exhibiting upward referent discrepancy. This argument is based on the contention that information received before an event more strongly influences judgments and information provided after an event is discounted ([67]). Furthermore, explanations offered in advance of a negative outcome provide a positive frame of reference for interpreting the event ([71]). Explanations given at the time of (i.e., reactive-concurrent) or after (i.e., reactive-subsequent) the new concession agreement will be less effective at stimulating perceived adequacy, being viewed rather, as an excuse for an allocation that has already been determined. Reactive explanations create a negative frame of reference that impedes the consideration of additional information and the explanation's effect on perceptions of fairness. More formally,
- H5: In a retailer–supplier relationship exhibiting upward referent discrepancy, (a) the focal supplier's perceived distributive fairness is greater when an explanation is provided proactively than reactively (either concurrent or subsequent) due to (b) higher levels of perceived explanation adequacy.
To test the hypotheses, we used a multimethod research design consisting of a survey of suppliers operating within a common retailer (i.e., an SWS format), complemented with objective performance data (i.e., Study 1), and a scenario-based experiment with brand/store managers in the retail industry (i.e., Study 2). We conducted both studies in Japan. In Study 1, we began by testing the presence and prevalence of referent discrepancy of focal suppliers. We then empirically tested the effects of upward and downward referent discrepancy on a focal supplier's perceptions of distributive fairness, subject to the tie strength of the retailer–supplier relationship. Retailer participation enabled us to obtain objective performance data to determine referent discrepancy. In Study 2, we constrained our effort to focal suppliers experiencing upward referent discrepancy. In this context, we tested the role of tie strength and the timing of the retailer's explanation on focal supplier perceptions of distributive fairness perceptions.
To develop the survey for Study 1, we conducted interviews with both retailers and suppliers operating in the SWS format in Japan. The survey was translated into Japanese, reviewed by managers, assessed for form and meaning equivalence, and back-translated into English.
To facilitate data collection, we secured the participation of a large Japanese retailer using an SWS format under a nondisclosure agreement. The retailer identified the senior managers for 280 suppliers, assigning each a numeric code. The identified managers interacted with the retailer and reported directly to the supplier. The retailer invited the identified managers to participate, indicating that the study was being conducted by academic researchers to better understand retailer–supplier relationships. The retailer distributed the survey and a sealable envelope and asked that the survey be returned to the main office. Of the 280 surveys distributed, 212 were returned (an effective response rate of 75.7%).
Responding suppliers operated in the retailer's SWS format for an average of 7.9 years. Respondents (62.7% female) were 45.2 years of age on average and had an average of 11.85 years of retail experience. The product categories represented include women's apparel (54.2%), men's apparel (14.6%), sports/casual wear (22.6%), cosmetics (4.7%), and miscellaneous fashion (3.8%, which includes shoes, leather goods, and various accessories).
Questions that rely on retrospective recall ([39]) can increase method bias. To minimize retrospective biases, the structural design of the study followed the remedies [43] suggest. To make it easier for respondents to recall the information necessary to answer the questions accurately, ( 1) they were asked to think back to the most recent concession agreement (see [65]); ( 2) the survey was designed to facilitate temporal retrieval, starting with the first event and then moving forward chronologically ([58], p. 15); and ( 3) each question was clearly prefaced with the timing of the measured item (e.g., "the time preceding your brand's signing its most recent concession agreement," "immediately after signing concession agreement").
Nonresponse bias testing compared objective sales performance (drawn from the retailer and based on the numeric code of suppliers) of respondents and nonrespondents. No significant differences (p <.05) emerged, indicating that nonresponse bias is minimal.
We assessed upward and downward referent discrepancy with an approach adapted from a measure [68] recommend and similar to those applied by [57], [ 4], and [25]. Component measures permit the construction of variables that capture both the magnitude and the direction of discrepancy (i.e., upward and downward referent discrepancy) between the supplier and its identified referent. In the survey, managers identified another supplier they regarded as performing at an equivalent sales level before signing their most recent concession agreement. The retailer then provided sales performance data (overall sales in the previous contract year multiplied by a common factor to protect supplier privacy) for each respondent and each identified referent. To determine referent discrepancy, we subtracted the referent's sales performance from the focal supplier's sales performance:
Focal supplier's sales performance−Referent supplier's sales performance.1
The absolute value represents the level of referent discrepancy. Among the newly created spline variables, we categorized scores that fell within a.5 standard deviation point from a zero sales difference as equally performing (i.e., no referent discrepancy), as the performance difference between a focal supplier's own sales and the chosen referent's sales is unlikely to equate exactly to the zero level. This approach allows for a more realistic range of the discrepancy scale when the difference in performance is considered equal. Outside this range, if the sign is negative, the focal supplier is engaging in upward social comparison, and an upward referent discrepancy exists. If the sign is positive, the focal supplier is engaging in downward social comparison, and a downward referent discrepancy exists.
The variable referent discrepancy measured with Equation 1 includes both upward and downward referent discrepancy. It is necessary to separate the two forms in different monotonic components, called "spline variables" (for applications of this method, see [25]; [57]). We recoded the referent discrepancy into two new variables (upward and downward referent discrepancy) in the following manner: If the value of the difference in sales performance is less than −.5 standard deviations from zero, the upward referent discrepancy equals the absolute value of discrepancy (the negative sign is reversed to positive) and the downward referent discrepancy equals zero. If the value of the difference in sales performance is greater than.5 standard deviations from zero, the downward referent discrepancy equals the value of discrepancy and the upward referent discrepancy equals zero. In this way, we created two spline variables. We then introduced these variables as independent variables in regression ( 2), called "spline regression," on perceived distributive fairness:
Perceived distributive fairness=α0+β1TIE+β2UPRDIS+β3DNRDIS+β4UPRDIS×TIE+β5DNRDIS×TIE+β6INFO+β7DEP+β8SAT+β9LENG+e.2
where
TIE = tie strength to retailer,
UPRDIS = upward referent discrepancy,
DNRDIS = downward referent discrepancy,
INFO = information exchange,
DEP = dependence,
SAT = relationship satisfaction, and
LENG = relationship length.
Such "spline" variables arguably create a more objective assessment of the discrepancy in referents than can be gathered through an attitudinal measure. In this sense, the relationship between spline measures and attitudinal outcome variables can be considered more conservative than linking one self-reported outcome to another.
We conceptualized perceived distributive fairness as the focal supplier's perception of the fairness of the distribution of outcomes relative to its effort. Respondents were asked to recall their thoughts immediately after signing their last concession agreement and respond to a five-item, seven-point Likert scale adapted from [40] (α =.97).
We conceptualized tie strength as the potency of the bond between the focal supplier and its retailer. Respondents were asked to think back to the time before signing their last concession agreement and respond to a three-item, seven-point Likert scale adapted from [47] (α =.96).
To minimize spuriousness, as covariates we included the relationship characteristics of relationship length and the focal supplier's dependence on the retailer (a two-item, seven-point Likert scale adapted from [42]]; α =.73), relationship satisfaction (a one-item, seven-point Likert scale), and information exchange among the suppliers (a four-item, seven-point Likert scale adapted from [ 3]]; α =.81). For the focal supplier's dependence on the retailer, relationship satisfaction, and information exchange measures, respondents were asked to think back to the time before signing their last concession agreement. All measurement items are available in Appendix A.
We estimated the measurement model, consisting of the reflective multi-item latent constructs of tie strength, perceived distributive fairness, the focal supplier's dependence on the retailer, and information exchange, using confirmatory factor analysis with Mplus Version 8. Appendix A presents the results, together with item loadings and average variance extracted (AVE) values. Descriptive indicators, composite reliabilities, and correlations appear in Table 1.
Graph
Table 1. Study 1: Measure Statistics and Correlation Matrix.
| M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|
| 1. Perceived distributive fairness | 3.70 | 1.06 | .972 | | | | | | | |
| 2. Information exchange | 5.90 | .53 | −.131 | .785 | | | | | | |
| 3. Focal supplier's dependence on retailer | 6.22 | .40 | −.060 | .044 | .727 | | | | | |
| 4. Relationship length | 7.91 | 2.78 | .040 | .084 | .078 | N.A. | | | | |
| 5. Relationship satisfaction | 4.43 | 1.18 | .374** | −.167* | .030 | .108 | N.A. | | | |
| 6. Tie strength to retailer | 4.47 | 1.17 | .393** | −.185** | −.003 | .080 | .943** | .958 | | |
| 7. Upward referent discrepancy | 2.96 | 1.49 | −.435** | .026 | .111 | −.044 | −.075 | −.063 | N.A. | |
| 8. Downward referent discrepancy | 4.08 | 1.75 | .409** | −.005 | −.191** | −.036 | −.026 | −.022 | −.388** | N.A. |
- 80022242918823530 *p <.05.
- 90022242918823530 **p <.01.
- 100022242918823530 Notes: Composite reliabilities are on the diagonal. N.A. denotes single-item and categorical scales.
We assessed the reliability of individual items by examining the loadings of the items on their respective latent construct; loadings of less than.50 may represent poorly worded or inappropriate items. All measurement items exceeded this threshold and loaded significantly on the expected constructs (ranging from.71 to.96). Constructs had acceptable reliability, with the composite reliability coefficients ranging from.73 to.97, well exceeding the.70 recommended threshold ([51]). Convergent validity was also evident, with the AVE for each construct ranging between.51 and.88, exceeding the.50 recommended benchmark ([19]). We assessed discriminant validity by following [19] recommended procedure. All constructs demonstrate discriminant validity.
The chi-square goodness-of-fit index for the model is 95.216, based on 72 degrees of freedom. We deemed the measurement fit indexes adequate given the model measures (comparative fit index [CFI] =.991, root mean square error of approximation [RMSEA] =.039, standardized root mean square residual [SRMR] =.036).
Common method variance (CMV) is often a concern with survey-based research ([53]). However, the independent variables in H1–H3b (i.e., referent discrepancy) derive from objective data, thereby minimizing common method concerns. Furthermore, H3a and H3b involve interactions and therefore "cannot be artifacts of CMV" ([63], p. 456). With that said, to increase the validity of our findings, we test for CMV using a marker variable. We selected "job autonomy" because it is theoretically unrelated to at least one of the study constructs and is measured consistently with other survey measures. We used a three-item, seven-point Likert scale adapted from [28] (see Appendix A). To estimate CMV, we identified the lowest positive correlation between the marker variable and one of the criterion variables (ρ =.02). We then partialed out this correlation from all bivariate correlations. The zero-order correlations of the other variables remained significant. The objective data, interaction effects, and the marker variable testing suggest that CMV is not a concern.
We performed a chi-square goodness-of-fit test to determine the presence of referent discrepancy. In support of H1a, we found that 70.8% of the focal suppliers exhibited a referent discrepancy (χ2( 1, 212) = 36.528, p =.000) while 29.2% of the focal suppliers selected an equally performing supplier and thus exhibited no referent discrepancy. In support of H1b, we found that among the focal suppliers exhibiting a referent discrepancy, 66.7% selected a comparative referent with greater objective sales performance (i.e., upward referent discrepancy) and 33.3% selected a referent with lower performance in terms of sales (i.e., downward referent discrepancy) (χ2( 1, 150) = 16.667, p =.000).
We tested H2a–H3b using a spline regression analysis. To increase the ease of interpretation, we mean-centered the moderating variable of tie strength to the retailer ([12]) to avoid analyzing individual effects at the zero level of the moderator, which may be outside the relevant range of interest. We examined the variance inflation factors to determine the existence of multicollinearity. All factors were less than 9.32, suggesting that multicollinearity does not significantly influence the analyses.
Table 2 presents the results. In support of H2a, upward referent discrepancy negatively influenced the focal supplier's perceived distributive fairness (β = –.293, p <.001). In support of H2b, downward referent discrepancy had a significant, positive effect on the focal supplier's perceived distributive fairness (β =.309, p <.001).
Graph
Table 2. Least Squares Regression Results Dependent Variable: Perceived Distributive Fairness.
| Model 1a | Model 1b |
|---|
| Predictors | β | SE | β | SE |
|---|
| Information exchange | −.053 | .114 | −.043 | .107 |
| Focal supplier's dependence on retailer | .031 | .146 | .013 | .141 |
| Relationship satisfaction | −.001 | .148 | −.050 | .143 |
| Relationship length | .011 | .021 | .026 | .020 |
| Tie strength to retailer (TIE) | .372* | .149 | .438*** | .144 |
| Upward referent discrepancy (UPRDIS) | −.293*** | .035 | −.298*** | .034 |
| Downward referent discrepancy (DNRDIS) | .309*** | .033 | .306*** | .032 |
| UPRDIS × TIE | | | .120* | .029 |
| DNRDIS × TIE | | | .273*** | .028 |
| Adjusted R2 | .385 | | .430 | |
| ΔF-value | | | 9.116 | |
- 110022242918823530 *p <.05.
- 120022242918823530 **p <.01.
- 130022242918823530 ***p <.001.
- 140022242918823530 Notes: Standardized coefficients are reported. Two-tailed significance tests.
In support of H3a, tie strength to the retailer had a significant, positive effect on the upward referent discrepancy–perceived distributive fairness relationship (β =.120, p <.05). In support of H3b, tie strength to the retailer had a significant, positive effect on the downward referent discrepancy–perceived distributive fairness relationship (β =.273, p <.001). The addition of the interaction effects produced a significant change in the variance explained (ΔR2 =.049, ΔF = 9.116, p =.000).[ 7]
We conducted simple slope tests to examine the significant interactions ([ 1]). The results in Panel A of Figure 3 indicate that at the mean level of tie strength to the retailer, upward referent discrepancy negatively influences the focal supplier's perceived distributive fairness. The negative effect is larger in magnitude when tie strength to the retailer is low (1 SD below the mean; perceived distributive fairness simple slope = −.249, p <.000) rather than high (1 SD above the mean; simple slope = −.103, p =.04). Panel B of Figure 3 indicates that at the mean level of tie strength to the retailer, a downward referent discrepancy positively influences the focal supplier's perceived distributive fairness. The positive effect is larger in magnitude when tie strength to the retailer is high (1 SD above the mean; perceived distributive fairness simple slope =.307, p <.000) rather than low (1 SD below the mean; simple slope =.031, p =.53). Downward referent discrepancy does not influence the focal supplier's perceived distributive fairness of the retailer unless the tie strength of the retailer–supplier relationship is sufficiently strong. Thus, although the direction of H3b is as expected, the results indicate a more nuanced effect than theorized. Taken together, in support of H3a and H3b, tie strength operates as a buffer of the negative effect of upward referent discrepancy and an amplifier of the positive effect of downward referent discrepancy on the focal supplier's perception of distributive fairness.
Graph: Figure 3. Study 1: Graphical interpretation of the moderating effects of tie strength to retailer.Notes: Solid lines indicate a significant slope; dotted lines indicate a nonsignificant slope.
We tested H4a–H5b using an online, scenario-based experiment. Respondents consisted of Japanese managers in the retail industry (i.e., supplier or retail managers), incentivized by a market research firm. Respondents were informed that the purpose of the study was to understand retailer–supplier relationships in an SWS format and were asked to imagine themselves in the presented managerial position and situation.
The study used a 2 (tie strength: strong, weak) × 3 (timing of explanation: proactive [one month prior], reactive [concurrent and subsequent]) between-subjects experimental design. Respondents acted as the focal supplier in the context of upward referent discrepancy (for ordering of the experiment, see Appendix B).
We developed the scenario using sample averages from Study 1 to control for length of relationship (i.e., eight years). Consistent with the SWS formats in East Asian markets, which are characterized by a power imbalance in favor of the retailer, and consistent with our sample in Study 1 (i.e., high dependence on the retailer), the scenario indicated that the retailer is one of the largest in the country. Furthermore, as information exchange occurs with other suppliers in a SWS format, we indicated that the manager had heard of the commission rates of other suppliers. The operationalization of tie strength was based on the measurement employed in Study 1. The experiment treatments and all measures were translated into Japanese, checked for form and meaning equivalence, and then back-translated into English.
Six hundred forty-four panel participants employed in the retail industry were invited to participate in the online survey. Qualification to take the survey required that respondents indicate being employed as a brand/store manager. Respondents who met the screening criteria proceeded to the survey. We randomly assigned respondents treatments. Sampling continued until 350 managers agreed to participate. Ninety questionnaires were unusable because the answers were incomplete, the respondent sped through the survey, and so on, which left us with 255 usable questionnaires (an effective response rate of 39.6%): Scenario 1 (strong tie, proactive): n = 40; Scenario 2 (strong tie, reactive: concurrent): n = 46; Scenario 3 (strong tie, reactive: subsequent): n = 40; Scenario 4 (weak tie, proactive): n = 43; Scenario 5 (weak tie, reactive: concurrent): n = 37; Scenario 6 (weak tie, reactive: subsequent): n = 49.
Respondents (95.3% male) were 55.3 years of age on average and had an average of 12.9 years of retail experience. Product categories represented in the sample include women's apparel (6.3%), men's apparel (5.1%), sportswear (5.1%), miscellaneous fashion (18.4%), cosmetics (2.4%), and other (62.44%, which includes electronics, supplies, books, etc.). Nonresponse bias testing compared early and late respondents (mean comparisons repeated for the first 25%, 33%, and 50% of respondents versus the last 25%, 33%, and 50%) on the characteristics of managerial experience, age, gender, and job autonomy. We identified no significant differences (p <.05), indicating that nonresponse bias is minimal.
Consistent with Study 1, perceived distributive fairness was captured with a five-item, seven-point Likert scale (α =.94). Trust in the retailer was conceptualized as the perceived credibility and benevolence of the partnering retailer and was captured with a three-item, seven-point Likert scale adapted from [14] (α =.88). Perceived explanation adequacy was conceptualized as the focal supplier's perception of the adequacy of a given explanation. The measure captured the perceived satisfaction, adequacy, and sufficiency of the explanation given by the retailer on a three-item, seven-point Likert scale adapted from [61] (α =.89). To control for individual factors, we included respondents' retail experience (years), age, and gender (for all measures, see Appendix C).
We conducted a confirmatory factor analysis using AMOS Graphics 22. Appendix C reports the results of the measurement model, together with item loadings and AVE values. Descriptive indicators, composite reliabilities, and correlations appear in Table 3. The fit was reasonable (CFI =.994, RMSEA =.036, SRMR =.035; [ 5]), and all composite reliabilities were greater than or equal to.88. All indicators loaded significantly on the intended latent constructs, demonstrating convergent validity and reliability. All other indicators showed AVE values greater than or equal to.72, exceeding the threshold of.50 and thus demonstrating discriminant validity ([19]).
Graph
Table 3. Study 2: Measure Statistics and Correlation Matrix.
| M | SD | 1 | 2 | 3 | 4 | 5 | 6 |
|---|
| 1. Perceived distributive fairness | 4.09 | 1.15 | .942 | | | | | |
| 2. Trust in retailer | 4.39 | 1.29 | .305** | .883 | | | | |
| 3. Perceived explanation adequacy | 4.00 | 1.31 | .775** | .348** | .897 | | | |
| 4. Age | 55.30 | 7.83 | .106 | .193** | .094 | N.A. | | |
| 5. Retail experience | 12.9 | 10.25 | .082 | .097 | .076 | .429** | N.A. | |
| 6. Gender | 1.05 | .212 | −.086 | −.048 | −.074 | −.279** | −.033 | N.A. |
- 150022242918823550 *p <.05.
- 160022242918823550 **p <.01.
- 170022242918823550 Notes: Composite reliabilities are on the diagonal. N.A. denotes single-item and categorical scales.
An analysis of covariance tested the effects of tie strength and timing of explanations on perceived distributive fairness in relationships exhibiting upward referent discrepancy (H4a and H5a). Table 4 reports the results.
Graph
Table 4. Perceived Distributive Fairness.
| A: Analysis of Covariance Results |
|---|
| F | d.f. | Significance | Partial η2 | Observed Power |
|---|
| Covariate | | | | | |
| Retail experience | .516 | 1 | .473 | .002 | .11 |
| Age | 1.144 | 1 | .286 | .005 | .187 |
| Gender | .787 | 1 | .376 | .003 | .143 |
| Factor | | | | | |
| Tie strength | 4.133 | 1 | .043 | .017 | .526 |
| Explanation timing | 11.094 | 2 | 0 | .083 | .991 |
| Tie strength × Explanation timing | .391 | 2 | .677 | .003 | .113 |
| B: Estimated Marginal Means |
| | | | 95% Confidence Interval |
| Factor | | M | SE | Lower | Upper |
| Tie strength | Weak | 3.962 | .098 | 3.77 | 4.154 |
| Strong | 4.244 | .098 | 4.05 | 4.438 |
| Explanation timing | Reactive: subsequent | 3.877 | .118 | 3.645 | 4.11 |
| Reactive: concurrent | 3.864 | .122 | 3.624 | 4.105 |
| Proactive | 4.567 | .121 | 4.329 | 4.806 |
| C: Indirect Effects of Relationship and Explanation Factors |
| | Effect | Bootstrap SE | Bootstrap LLCI | Bootstrap ULCI | |
| Indirect effect of tie strength through trust in retailer | .0982 | .0495 | .0194 | .2169 | |
| Indirect effect of explanation timing through perceived explanation adequacy | .2541 | .063 | .1359 | .3821 | |
180022242918823550 Notes: Bias-corrected bootstrap confidence intervals are based on 1,000 resamples. Indirect effects: 95% confidence intervals. LLCI = lower limit confidence interval; ULCI = upper limit confidence interval.
We theorized that perceived distributive fairness would be greater when an explanation was offered in a relationship with a strong tie than a weak tie. The results indicate that tie strength to the retailer significantly influenced the focal supplier's perceived distributive fairness (Mweak = 3.96 vs. Mstrong = 4.24; F = 4.133, p <.05). Thus, H4a is supported.
In addition, we posited that perceived distributive fairness would be greater when an explanation was provided proactively rather than reactively (either concurrently or subsequently). In support of H5a, the results indicate that the timing of explanation significantly influenced perceived distributive fairness (Mreactive-subsequent = 3.88 vs. Mreactive-concurrent = 3.86 vs. Mproactive = 4.57; F = 11.094, p <.001). Planned contrasts results revealed that reactive-concurrent (p =.000, 95% confidence interval [CI] [−1.041, −.364]) and reactive-subsequent (p =.000, 95% CI = [−1.023, −.356]) explanations resulted in lower levels of perceived distributive fairness perceptions than a proactive explanation.
To further explore the hypothesized mediating effects of the focal supplier's trust in the retailer (H4b) and perceived explanation adequacy (H5b), we conducted mediator testing using [29] SPSS 20 Macro. In support of H4b, tie strength was positively related to the focal supplier's trust in the retailer (b =.387, p =.015), In turn, the focal supplier's trust in the retailer was positively related to perceived distributive fairness (b =.254, p =.000). The bias-corrected bootstrap confidence intervals derived from 1,000 samples (Table 4, Panel C) indicate that the indirect effect coefficient for tie strength on perceived distributive fairness was significant (b =.098, SE =.495, CI = [.019,.217]). These results provide support for the indirect effect of tie strength on a focal supplier's perceived distributive fairness through trust in the retailer (H4b).
In support of H5b, proactive explanation timing was positively related to perceived explanation adequacy (b =.384, p =.000). In turn, perceived explanation adequacy was positively related to perceived distributive fairness (b =.662, p =.000). The bias-corrected bootstrap confidence intervals derived from 1,000 samples indicate that the indirect effect coefficient for proactive explanation on perceived distributive fairness was significant (b =.254, SE =.063, CI = [.136,.382]). These results provide support for the indirect effect of the timing of explanation on perceived distributive fairness through perceived explanation adequacy (H5b).
This research was motivated by a desire to understand the challenges manifested in retailer–supplier relationships caused by social comparisons. The findings provide significant implications for theory and practice, as well as a foundation for future research.
Interorganizational scholars have begun examining social comparison, given that many dyadic relationships function within a business network (e.g., [55]; [69]). Building on prior research, we demonstrate that the concept of referent discrepancy of focal suppliers is pronounced (with approximately 70% of focal suppliers in a state of upward or downward referent discrepancy). These findings, based on objective performance data, should caution interorganizational researchers investigating social comparison in taking the validity of comparative referents identified by a respondent at face value.
Furthermore, our findings present a foundation on which a greater understanding of upward and downward social comparison in a retailer–supplier context can be advanced. Importantly, the majority of referent discrepancy is upward, negatively influencing perceptions of distributive fairness. Downward referent discrepancy, though less common, results in increased perceptions of distributive fairness. These findings contribute to the literature by extending related work pertaining to the attitudinal and behavioral effects resulting from an advantageous or a disadvantageous perceived distribution situation (e.g., [25]; [57]). Specifically, while being in a disadvantageous position results in an expected negative attitude (i.e., lower perception of distributive fairness), a divergence from a balanced state (i.e., referent discrepancy) that benefits the focal supplier does not always produce relationally harmful effects.
Building on interorganizational research on embedded ties (e.g., Rindfleisch and Moorman; [74]), we found that tie strength interacts with the two types of referent discrepancies in opposite ways. Our results, extending [20] work on buffering effects of commitment, reveal that tie strength can "limit" the invidious effects of upward referent discrepancy on a focal supplier's perceptions of distributive fairness. By contrast, tie strength "amplifies" the positive influence of downward referent discrepancy on a focal supplier's perceived distributive fairness. In particular, we found that downward referent discrepancy does not have a positive influence on the focal supplier's perceived distributive fairness of the retailer unless the tie strength of the retailer–supplier relationship is sufficiently strong. A possible explanation for the noneffect in low–tie strength relationships could be that trust is low in the relationship, and the focal supplier is more likely to carefully scrutinize and monitor the retailer's behavior to guard against potential opportunism ([21]). Such vigilance could cancel out the positive effects of downward referent discrepancy on perceived distributive fairness, leading to a noneffect in weak tie relationships.
We further demonstrated the importance of strong ties when examining retailer explanations intended to remedy the negative effects of upward referent discrepancy. The literature on procedural fairness in organizations has shown that in many cases, the effort to convince employees of the procedural justice of management practices is neglected, poorly executed, or undermined by the organization's informal social system ([56]). Extending the literature on explanations (e.g., [61]; [64]) to the context of a retailer operating with multiple suppliers under differing tie strength, we show that the investment in strong ties and use of proactive explanations can be effective management tools in enhancing a focal supplier's perceptions of distributive fairness decreased by upward referent discrepancy. When a retailer sets procedurally fair policies, such as proactively providing suppliers with explanations for its specific policies, and works to continuously develop strong tie relationships, the focal suppliers' perceptions of distributive fairness increase. We theorized that perceived distributive fairness would be greater when the explanation was offered proactively in a relationship with a strong tie than in a relationship with a weak tie. Consistent with the literature asserting that positive priors insulate a partner from the negative effects of potential differential treatment ([47]) and that explanations coming from a source of positive priors are more effective in persuasion ([10]), our findings are suggestive of insular effects of tie strength on a focal supplier's perceptions of distributive fairness. In particular, a strong tie facilitated higher levels of trust in the retailer, resulting in greater effectiveness of the explanations coming from the trusted partner in enhancing perceptions of distributive fairness decreased by upward referent discrepancy.
A proactive explanation provides a positive frame of reference and is perceived as useful foretelling ([71]), whereas reactive explanations can be perceived as an excuse or a "quick-fix" attempt ([59]). Our research demonstrates that respondents viewed a proactive explanation as more adequate than a reactive explanation. This suggests a framing effect of a proactively given explanation in which individuals interpret subsequent information (including the differential treatment that influenced variations in perceptions of distributive fairness) under a positive light and perceive it as an adequate justification. These nuanced findings contribute to the literature delineating procedural fairness as the "social policy" of relationship management ([24]) and provides an important avenue for further research.
From a managerial standpoint, this research presents several insights for retailer–supplier relationships, such as customer business development ([33]) and category management ([22]). First, retailers need to recognize that social comparison occurs among partnering suppliers. Our findings, derived from retailer–supplier relationships, indicate that suppliers can engage in either upward or downward social comparisons, resulting in upward or downward referent discrepancy. While upward referent discrepancy can result in deleterious supplier attitudes, downward referent discrepancy can have positive relationship effects. Given the differential effects, we recommend that a retailer identify whom a supplier is using as a comparative referent. This can be gleaned during a retailer's regularly scheduled meetings with its suppliers. The retailer can then determine, from its criteria of evaluation, whether the supplier is engaging in upward or downward referent discrepancy. This assessment is important because it can ( 1) help the retailer understand whether its criteria of evaluation is clear and observable by its suppliers (possibly suggesting the need for greater transparency to suppliers), ( 2) identify which suppliers are potentially in situations of upward or downward referent discrepancy (and by what magnitude), and ( 3) aid in developing effective relationship management strategies based on a supplier's specific situation.
Second, managers need to engage with suppliers with a long-term mindset toward the relationship. This is important because the autonomous nature of supplier agreements (e.g., when employing a SWS format) can lead to an arm's-length relationship with the retailer that is perceived as increasingly economic and stipulated solely by the contract. However, as not all suppliers are equally valuable to the retailer in the long run, we recommend that retailers exert effort in those supplier relationships that have the highest value to the retailer's long-term positioning to build strong ties and maintain procedurally fair policies, regardless of the exhibition of and type of referent discrepancy. Frequent and continuous communication is one way to build strong ties with a supplier, ensuring that the retailer and supplier see eye to eye. In addition, proactively engaging suppliers as long-term partners and exerting effort to develop emotional closeness will help increase the potency of the retailer–supplier bond.
Third, there is a tendency for managers to avoid or delay discussing aspects that could be viewed negatively (e.g., delaying the delivery of bad news, failing to explain rationale behind a change; [56]; [62]). Yet failure to proactively provide an explanation (i.e., foreseeing possible differential treatments that can cause decreased levels of perceived distributive fairness and providing justification in advance) with substantive content and specificity (i.e., providing directive information on similarly performing comparative referents) will most likely result in negative attitudes, potentially damaging the relationship. Our results indicate that providing proactive explanations of differential treatment across partners can enhance perceptions of distributive fairness when upward referent discrepancy exists. This finding makes us wonder whether Shilla could have saved its relationship with Gucci had it proactively provided an explanation.
Although this work advances the literature, it is not without limitations. We address the limitations with future research directions. First, although this study demonstrates referent discrepancy effects on perceived distributive fairness, greater theoretical insight is needed to understand referent discrepancy itself. For example, our findings raise the theoretical question as to what may be motivating respondents to engage in upward or downward social comparisons. Is their referent discrepancy due to the lack of information in the environment in which the firm operates? Could it be inflated perceptions of personal contribution or performance ([45]; [75])? Or could underlying cognitive biases ([73]) be at play?
Second, although we recognize that power in the dyadic relationship could play a role in social comparisons and perceptions of distributive fairness, a richer examination of power dependence would be a fruitful research avenue. Elements of brand power and tenure with the retailer could be introduced. We focused on the supplier's assessment of its performance and value relative to another supplier external to its relationship with the retailer. However, it is also important and relevant to consider how the retailer assesses and values the supplier (e.g., relative dependence, magnitude of interdependence in the relationship).
Third, construct measurement could be improved. For example, the low reliability of the measurement of information exchange in Study 1 is a limitation of this study. Further research could consider scale development of constructs specifically tailored to the study context of retailer–supplier relationships in the SWS format in which information exchange among parties occurs. In addition, having multiple indicators for each construct (e.g., satisfaction of relationship, relative dependence) would produce stronger measures beyond the current study.
Fourth, perceptions of distributive fairness can result in negative behaviors and relationship performance consequences. It would be fruitful to examine behaviors (e.g., voicing, withdrawal, exiting the relationship) and objective relationship performance effects when retailers engage in strategies to mitigate the effects of negative attitudes caused by social comparison. The literature suggests that procedural fairness ([40]) can affect distributive fairness perceptions. A deeper examination of procedural fairness aspects (e.g., courteous behavior, knowledgeability, refutability) across supplier–retailer relationships would advance understanding of the fairness effects of referent discrepancy.
Fifth, we examined the effects of tie strength and explanation timing on perceived distributive fairness in relationships exhibiting upward referent discrepancy through an experimental scenario. Although this research design accounts for many theoretically relevant factors, it is limited in its (narrow) scope. Additional elements of explanations (e.g., sincerity/empathy of delivery, format of delivery) can influence explanation efficacy (e.g., [61]; [64]). Thus, an examination of these elements would be fruitful. Related to external social comparisons, this study restricts the number of comparative referents, whereas in practice, suppliers refer to multiple referents simultaneously ([ 4]; [52]). Thus, research could also explore how explanations operate under the simultaneous impact of multiple referents.
Sixth, retailers would benefit from having a better understanding of when and how (i.e., cognitive processes) suppliers form initial referents. The literature has suggested that similarity bias (i.e., actors look to other similar actors/entities for reference; [38]), cohesiveness and interpersonal ties ([60]), and peer position ([ 9]) all influence aspects of referent formation. However, evaluations are not always unbiased ([73]), and many goals, other than accurate self-evaluation, may underlie social comparisons. Individuals can harbor unrealistic views of themselves due to self-serving biases ([46]; [50]). Further research could examine underlying biases that influence referent selection decisions and social comparison as well as the actions a retailer could take to help establish a supplier's initial comparative referent at the onset of the relationship.
Finally, researchers contend that culture is a lens through which people interpret fairness ([18]). Our results derive from data gathered within a single national culture (i.e., Japan). We selected this context because of the prevalence of SWS format usage. However, research indicates that culture-based differences exist in how interorganizational exchange members react to situation effects, such as positive and negative inequity ([25]; [57]). Given this, it would be worthwhile to examine whether the effects of referent discrepancy, tie strength, and explanation strategies are similar in business environments within different national cultures.
Supplemental Material, DS_10.1177_0022242918823542 - Social Comparison in Retailer–Supplier Relationships: Referent Discrepancy Effects
Supplemental Material, DS_10.1177_0022242918823542 for Social Comparison in Retailer–Supplier Relationships: Referent Discrepancy Effects by Hannah S. Lee, and David A. Griffith in Journal of Marketing
Graph
| Construct and Items | |
|---|
| Tie Strength to Retailera(adapted from Mittal, Huppertz, and Khare 2008; AVE =.88, α.=.96) | |
We had close relations with the retailer.
| .92 |
We felt close to this retailer.
| .96 |
We had strong ties with the retailer.
| .95 |
| Perceived Distributive Fairnessa(adapted from Kumar, Scheer, and Steenkamp 1995; AVE =.88, α.=.97) | |
| Did your brand believe your outcomes with the retailer were fair given... | |
| ...what the retailer earned from its sales through our brand's business. | .92 |
| ...the roles and responsibilities the retailer assigned to your brand. | .94 |
| ...what other brands in the retail location earned. | .96 |
| ...what the retailer store earns from sales through our brand. | .94 |
| ...the contributions your brand made towards your relationship with the retailer. | .93 |
| Relationship Length | |
| How many years has your brand engaged in operations with the retailer? (______) years | N.A. |
| Focal Supplier's Dependence on the Retailera(adapted from Lusch and Brown 1996; AVE =.57, r =.57) | |
| We were dependent on this retailer.b | |
| This retailer would have been difficult to replace. | .77 |
| This retailer would have been costly to lose. | .74 |
| Relationship Satisfactiona | |
| Generally, we were very satisfied with our overall relationship with this retailer. | N.A. |
| Information Exchangea(adapted from Antia and Frazier 2001; AVE =.51, α =.81) | |
| We interacted considerably with other brands in this location. | .72 |
| We frequently discussed common problems with other brands in this location. | .72 |
| Information on how other brands in this location were treated is readily available to us. | .72 |
| We communicated frequently with the other brands in this location. | .71 |
| Marker Variable (Job Autonomy)a(adapted from Hackman and Oldham [1976]) | |
| I have significant autonomy in determining how I do my job. I can decide on my own how to go about doing my work. I have considerable opportunity for independence and freedom in how I do my job. | N.A. |
1 a Measured on a scale of 1 = "strongly disagree," and 7 = "strongly agree."
- 2 b Deleted item due to low loading.
- 3 Notes: N.A. = not applicable. χ2 = 95.216 (d.f. 72), CFI =.991, RMSEA =.039, SRMR =.036.
This study uses an imaginary scenario to understand how a supplier feels about its retailer when it considers how other suppliers are treated by the retailer. Your responses will remain anonymous. Thanks in advance for your help! We wish you good fortune and health.
You are the brand manager responsible for your brand's operations as well as your relationship with the retailer in which you operate. The retailer is one of the largest in the country and all brands within the retail location pay commission charges (set as a percentage of sales) to the retailer to operate within their location. During the previous years, the commission rate for your brand product category has been on average around 30%. Your brand has worked with this retailer for about 8 years under annual contracts and will continue to unless either party moves to terminate the agreement.
[Insert Tie Strength Treatment]
[Measurement of dependent variable: trust in benevolence of retailer]
Given your role at the retail location, you have been able to observe and communicate with other brand managers that work with the same retailer. Among the other suppliers operating within the retail location, you consider "Brand A" to be performing at an equivalent sales level as your brand, and "Brand B" to be performing at a lower sales level than your brand.
[Insert Timing of Explanation Treatment]
[Measurement of dependent variables: perceived explanation adequacy and perceived distributive fairness]
Your supplier has a stable, close relationship with the retailer and maintains a strong tie with them.
Your supplier does not have a stable, close relationship with the retailer and maintains a weak tie with them.
For ongoing agreements, the retailer usually provides a renewal concession agreement outlining the new terms in December of each year, which is effective as of January 1st of the next contract period.
In November 2017, a month prior to receiving your renewal contract, the retailer sent a letter explaining that suppliers that meet sales goals in the prior year typically receive a commission charge around 30%; with those exceeding sales goals receiving a lower commission charge and those that did not reach sales goals receiving a higher commission charge.
In the renewal contract received from the retailer in December 2017, you found that your brand received a 30% commission charge for the next contract period. You found this interesting, as you had heard from other suppliers that Brand A, a brand that you had considered to be performing equivalent to your brand, received a much lower and favorable commission charge at 20%, and Brand B, a brand that you considered to be performing at a lower level than your brand, received a similar commission charge to your brand at 30%.
For ongoing agreements, the retailer usually provides a renewal concession agreement outlining the new terms in December of each year, which is effective as of January 1st of the next contract period.
In December of 2017, you receive the new contract. The cover letter of the contract explained that suppliers that meet sales goals in the prior year typically receive a commission charge around 30%; with those exceeding sales goals receiving a lower commission charge and those that did not reach sales goals receiving a higher commission charge.
In the contract itself, you found that your brand received a 30% commission charge from the retailer for the next contract period. You found this interesting, as you had heard from other suppliers that Brand A, a brand that you had considered to be performing equivalent to your brand, received a much lower and favorable commission charge at 20%, and Brand B, a brand that you considered to be performing at a lower level than your brand, received a similar commission charge to your brand at 30%.
For ongoing agreements, the retailer usually provides a renewal concession agreement outlining the new terms in December of each year, which is effective as of January 1st of the next contract period.
In December of 2017, you receive the renewal contract and found that your brand received a 30% commission charge from the retailer for the next contract period. You found this interesting, as you had heard from other suppliers that Brand A, a brand that you had considered to be performing equivalent to your brand, received a much lower and favorable commission charge at 20%, and Brand B, a brand that you considered to be performing at a lower level than your brand, received a similar commission charge to your brand at 30%.
You requested an explanation regarding the commission charge arrangement. In response to your request, the retailer sent a letter explaining that suppliers that meet sales goals in the prior year typically receive a commission charge around 30%; with those exceeding sales goals receiving a lower commission charge and those that did not reach sales goals receiving a higher commission charge.
Graph
| Construct and Items | |
|---|
| Perceived Distributive Fairnessa(adapted from Kumar, Scheer, and Steenkamp 1995; AVE =.76, α.=.94) | |
| Given the retailer's explanation, I feel the commission charge arrangement was fair compared to... | |
| ...what the retailer earns from its sales through our brand's business. | .89 |
| ...the roles and responsibilities the retailer assigns to our brand. | .85 |
| ...what other brands in the retail location earn. | .88 |
| ...what the retailer store earns from sales through our brand. | .89 |
| ...the contributions our brand makes towards our relationship with the retailer. | .87 |
| Trust in Retailera(adapted from Doney and Canon 1997; AVE =.72, α.=.88) | |
| My supplier trusts the retailer's promises. | .89 |
The retailer is honest and trustworthy.
| .81 |
My supplier has confidence in the retailer.
| .84 |
| Perceived Explanation Adequacya(adapted from Shapiro, Butner, and Barry 1994; AVE =.75, α. =.89) | |
I am satisfied with the retailer's explanation regarding the commission charge arrangement.
| .81 |
The retailer provided an adequate explanation for the commission rate arrangement.
| .90 |
The retailer provided a sufficient explanation for the commission rate arrangement.
| .88 |
| Age | |
| What is your age? | N.A. |
| Retail Experience | |
| How many years of have you been in this [current job] position? (__) years | N.A. |
| Gender | |
What is your gender? Male Female Prefer not to answer
| N.A. |
- 4 a Measured on a scale of 1 = "strongly disagree," and 7 = "strongly agree."
- 5 Notes: N.A. = not applicable. χ2 = 54.632 (d.f. 41), CFI =.994, RMSEA =.036, SRMR =.0347.
Footnotes 1 Associate EditorAric Rindfleisch served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242918823542
5 1We use the term "suppliers" because not all brand owners are manufacturers. Some brand owners focus on research and development, product development, and marketing and contract out manufacturing.
6 2Interviews with retail managers using the SWS format in Japan and South Korea indicated that sales performance (e.g., sales, sales growth) is a key driver of concession agreement terms. However, managers also noted that other criteria were used in the evaluation, such as market share and brand strength, depending on specific circumstances.
7 3We conducted robustness testing of our model using an alternative sales performance measure (i.e., sales growth). The estimation results mirrored the original model results based on sales data, providing confidence in the hypothesized differential effect of tie strength on the relationships between upward and downward referent discrepancy and focal suppliers' perceptions of distributive fairness. The Web Appendix presents the results.
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Specialist Competitor Referrals: How Salespeople Can Use Competitor Referrals for Nonfocal Products to Increase Focal Product Sales
Intuition suggests that a salesperson should not refer consumers to a competitor for products that they both sell. However, myriad examples reveal salespeople doing just that. The authors study specialist competitor referrals, a sales strategy by which one increases consumers’ purchase likelihood of a focal product (e.g., a painting at an art gallery) by ( 1) referring consumers to a competitor (e.g., a frame warehouse store) that offers a nonfocal product (e.g., a frame) at a lower price, while ( 2) stating that the stores differ in their specializations (i.e., the stores concentrate their efforts on different goods). Using a study and survey with salespeople, experimental studies, an incentivized negotiation experiment, and a field study, the authors show that specialist competitor referrals can indeed benefit sellers. Specifically, they build on equity theory to show that specialist competitor referrals increase focal product sales by reducing consumers’ perceived overpayment risk for the focal product via increasing perceived equity in the exchange. The authors also show that competitor referrals for nonfocal products that do not justify the price difference on the nonfocal product are ineffective.
Online Supplement: http://dx.doi.org/10.1509/jm.16.0269
If your competitor isn’t really competing with your direct market, you can refer business to each other without anyone losing customers (Zwilling 2011).
Imagine a consumer shopping for an unframed painting at a local art gallery. After finding one he/she likes, this consumer is uncertain about the price of this focal product and whether the price is fair (Haws and Bearden 2006) and so decides to postpone the purchase, hoping to find a lower price at some later time or with a different seller (Dutta 2012). To encourage the consumer to buy the painting, a gallery salesperson might make a specialist competitor referral: ( 1) inform the consumer that a competitor sells a nonfocal product (e.g., a frame for the painting) at a lower price, while ( 2) highlighting that the two competitors have different specializations (i.e., they concentrate on different subsets of goods).1 Although the idea of recommending any lower-priced competitor might seem unreasonable, it may be that when the salesperson provides a credible justification for the price differential of the nonfocal products (e.g., the frame), the referral can substantially improve the odds of selling the focal product (e.g., the painting). In this article, we investigate whether specialist competitor referrals can be an effective strategy to influence consumers to purchase, and why.
Salespeople’s beliefs about the efficacy of specialist competitor referrals are mixed, but the use of such referrals is common. Noting a lack of in-depth research on specialist competitor referrals in marketing, we conducted an exploratory study with 145 salespeople across multiple industries to gain some preliminary insights. The results revealed that 71% of salespeople have used specialist competitor referrals for a vast range of products (and nonfocal products), including a $695 sculpture ($100 base), $300–$1,000 beds ($20–$100 sheets), $1,000 televisions ($50 audiovisual wires), and $50 shoes ($15 socks). These salespeople also reported wide-ranging and varying beliefs about the effects of such referrals on sales of focal and nonfocal products and, thus, their potential impact on profits.
The uncertainty regarding the effectiveness of specialist competitor referrals is not surprising. On the one hand, receiving information about the price of a nonfocal product may not affect consumers’ likelihood of purchasing the focal product because it does little to assuage their concerns about the focal product’s price. It also could reduce nonfocal product sales for the seller if the information prompts consumers to buy nonfocal products from the competitor (Park 2005). On the other hand, specialist competitor referrals provide valuable information to consumers, including not only different purchase options but also justifications for price discrepancies, on the basis of different specializations (e.g., the seller is an art gallery, the competitor is a frame warehouse store, and both sell frames). Such information could influence consumers, especially when consumers are uncertain about the lowest price for the focal product (i.e., they perceive overpayment risk; Dutta 2012). Because individuals are concerned not only about the value of a product to them but also about equity in the exchange (Darke and Dahl 2003; Kahneman, Knetsch and Thaler 1986), specialist competitor referrals are likely effective, as some salespeople believe them to be, to the extent that they can increase perceived equity in the exchange.
Previous applications of equity theory to salespeople– consumer interactions indicate that increasing consumers’ perceived equity positively influences their perceptions of the product, the salesperson, and the seller (e.g., Swan and Oliver 1991). In what follows, we argue that specialist competitor referrals increase consumers’ likelihood of purchasing the focal product by improving consumers’ perceived equity in the exchange and this shift in perceived equity functions as an informational cue about the store’s pricing that helps reduce consumers’ overpayment risk for the focal product.2 Furthermore, we argue that specialist competitor referrals are effective because the justification they offer for the price disparity (i.e., a difference in specialization) is credible and thus increases consumers’ perceived equity. That is, competitor referrals for nonfocal products without any justification are likely not as effective. To test these predictions, we conduct experiments in different purchase settings (paintings, mattresses), as well as in the field (consumers donating money in exchange for pumpkins).
Our findings contribute to marketing literature in several ways. First, whereas most research on referrals examines consumers who refer other consumers to sellers (i.e., word of mouth; for a review, see Berger 2014), we investigate situations in which salespeople refer consumers to competing sellers. As our salesperson survey shows, specialist competitor referrals are prevalent in the industry but not well understood; therefore, a better understanding and effective use of this sales strategy can improve seller outcomes. We thus contribute to a substantive research domain and offer further insights into how sellers can take control of referrals to benefit directly (Hada, Grewal, and Lilien 2014; Kumar, Petersen, and Leone 2010).
Second, we contribute to equity theory applied to salespeople and consumers. Most research addresses price fairness as an indicator of consumers’ perceived equity, because “consumers usually do not know either seller’s cost structure or other pertinent information to determine seller’s input accurately” (Xia, Monroe, and Cox 2004, p. 3). We show that specialist competitor referrals can increase consumers’ perceived equity by providing a credible information cue about the seller’s structure. As perceived equity has been consistently shown to improve consumers’ purchase likelihood in literature (e.g., Haws and Bearden 2006; Morales 2005), this finding can be efficacious to sellers. Furthermore, by directly providing a credible information cue, sellers do not need to rely on comparing consumers’ transactions with other consumers’ transactions to establish equity in the exchange. This finding is especially important for purchase situations where such comparatives are not easily available, such as when a product is unique or infrequently purchased.
Third, our research provides managerial insights for helping salespeople close deals and create win-win situations for sellers and consumers (Dixon, Spiro, and Jamil 2001). Insights into consumer behavior that can be applied easily are critical to marketing (Grewal and Sharma 1991; Puccinelli et al. 2009), suggesting that these managerial implications are highly pertinent. We also conduct a profitability analysis (see Appendix) that shows the conditions under which specialist competitor referrals are a profitable strategy for salespeople.
We organize the remainder of the manuscript as follows: First, we present a brief literature review on referrals and salespeople’s influence strategies. Second, we develop our hypotheses. Third, we present our studies, starting with an exploratory study and survey of salespeople, followed by the experimental studies, and concluding with a field study. Fourth, we discuss the results, account for the profitability conditions of specialist competitor referrals and alternative explanations, and present some implications.
Interactions between salespeople and consumers influence consumers’ purchasing behavior, including how they process product-related information (Sujan, Bettman, and Sujan 1986), their product performance expectations (Grewal and Sharma 1991), and satisfaction (Oliver and Swan 1989). However, these interactions can be challenging because consumers associate salespeople with profit motives, view their actions with skepticism (Brown and Krishna 2004), and actively take steps to evade them (Kirmani and Campbell 2004). Noting such perceptions, as well as unethical uses of influence tactics by some salespeople (Cialdini 1999), research suggests salespeople focus on adapting selling tactics to meet consumers’ needs (Kohli, Shervani, and Challagalla 1998; McFarland, Challagalla, and Shervani 2006) and assist consumers by providing expert information or alternatives (Goff et al. 1997; Harris and Spiro 1981).
To influence consumer decisions, salespeople must follow these suggestions in the context of consumers’ motivations and the information salespeople already have (Mallalieu 2006). However, despite vast amounts of available price information, considerable price dispersion for products may leave consumers uncertain about whether they should purchase a product at a specific price (Salop and Stiglitz 1982). Prices for the same product may vary due to sales promotions, even when provided by the same seller (Darke and Dahl 2003), so consumers come to recognize a range of market prices, which may create uncertainty about where a specific seller lies in that range. As Srivastava and Lurie (2001) show, consumers are influenced by their perception of whether the price they are getting represents a good deal (see also Biswas et al. 2002). This uncertainty about getting the “best” or “lowest” price (for a value of a product) evokes consumers’ concerns that they will pay too much and suffer a financial loss in the exchange (Grewal et al. 1994). This overpayment risk (Dutta 2012) influences consumers’ perceptions of offers by sellers and purchase decisions (Biswas, Dutta, and Pullig 2006) and affects their perception of the value of the offer (Nowlis 1995).
Situations in which consumers perceive overpayment risk are common. For example, in interactions with salespeople, consumers might suffer uncertainty if ( 1) the product is unique (e.g., a painting), ( 2) the product has so many variants that it is difficult for consumers to compare prices across stores (e.g., mattresses, watches; Bergen, Dutta, and Shugan 1996; Srivastava and Lurie 2001), ( 3) consumers believe they can negotiate for a lower price (Blanchard, Carlson, and Hyodo 2016), or ( 4) consumers are focused on the equitable amount they should exchange for a product (Briers, Pandelaere, and Warlop 2007).
Consumers search for information to reduce their price uncertainty (Mehta, Rajiv, and Srinivasan 2003), and referrals are important sources of information. A referral occurs if source A makes a recommendation to recipient B to purchase from source C (Hada, Grewal, and Lilien 2010; Spurr 1987). Most of the research to date investigates consumer-to-consumer referrals, often in the form of word of mouth, to understand a source’s motivations for granting the referral (Anderson 1998; Berger and Schwartz 2011) and how the information influences the recipient’s perceptions and behaviors (Chevalier and Mayzlin 2006; Villanueva, Yoo, and Hanssens 2008). Consumer-toconsumer referrals can effectively reduce consumers’ uncertainty and risk in purchasing situations (e.g., Murray 1991; Trusov, Bucklin, and Pauwels 2009). Referrals have been shown to be critically important in sales of new products and innovations as they can significantly reduce consumers’ risk associated with the product’s characteristics and performance (e.g., Berger and Schwartz 2011). Notably, research has also found that referrals can have greater impact on the sales of riskier, more expensive products (e.g., Dellarocas 2003). Referrals effectively reduce consumers’ risk, so sellers can benefit from finding ways to generate positive referrals for themselves.
Not all referrals are provided by consumers, however; the source A and the beneficiary C also could be sellers. Further, in such horizontal referrals (Hada, Grewal, and Lilien 2010), the two sellers are not necessarily competitors. Hada, Grewal, and Lilien (2010) note the example of a contract lawyer who refers a client to another lawyer who specializes in personal injury law. Reingen and Kernan (1986) also note the case of a piano tuner who benefits from referrals from music stores. When the source and recipient (consumer) have no existing exchange relationship, the referral does not cost the source any potential business and also may create expectations of potential future rewards (Lakhani and Von Hippel 2003).
Little marketing research has addressed competitor referrals for a product that the seller could offer (but see Mayzlin and Yoganarasimhan 2012); but these referrals have been considered in economics research that seeks to resolve the “matching” problem (Spurr 1987) whereby sellers sort which customers they should serve if sellers know they are not the most efficacious (i.e., specialists) at providing consumers a solution. Specialization refers to a seller’s focus or concentration on a subset of products/services, to gain greater degree of efficiency (e.g., McConnell, Brue, and Flynn 2012). Such awareness by sellers about relative specialization in the market can result from various sources. For instance, a mattress store salesperson should be more familiar with the prices of bed frames in other stores than consumers, who infrequently shop for such products. Competitor referrals may be particularly likely in industries in which specialists diagnose the problem and refer customers elsewhere if needed, such as “in various types of consulting/advisory services, or in repair services of durable goods such as houses and automobiles” (Park 2005, p. 391).
In such competitor referrals, the salesperson has more information than the consumer does and decides whether to attempt to serve the consumer, refer the consumer to a competitor, or simply decline service without providing information about a competitor. Research suggests that, on average, salespeople should not recommend consumers to competitors (e.g., Garicano and Santos 2004), and instead should serve consumers themselves, even if they lack specialization or wind up misleading consumers (Arbatskaya and Konishi 2012; Bolton, Freixas, and Shapiro 2007; Park 2005). That is, when the seller lacks specialization and knows of a competitor that could do better, it faces a trade-off between “honestly advising clients to build a good reputation, and reaping a quick profit at the client’s expense” (Grassi and Ma 2016, p. 938).
In summary, prior literature regarding salespeople’s influence and competitor referrals offers several insights for our study. First, consumers’ perceived overpayment risk can hinder salespeople’s efforts to conclude a successful purchase. Second, due to specialization differences, salespeople know that competitors may be able to offer better prices on similar products. Third, sellers likely do not benefit in the short term by recommending a competitor for a product that they themselves sell.
In competitor referrals, the focal product or service may not the only one discussed during the interaction between the salesperson and consumer. For example, a mattress salesperson may believe that a consumer looking for a mattress also needs a bed frame. Such nonfocal products are offered by the focal seller but are not necessarily a purchase target for the consumer (DelVecchio 2005; Janakiraman, Meyer, and Morales 2006). Salespeople often are aware of the price ranges at which competitors offer their products, so they might use the information regarding the prices of nonfocal products to make a specialist competitor referral. We argue that doing so is to the seller’s benefit if the consumer perceives overpayment risk and the competitor referral contains a credible justification for a lower price available from the competitor.
When consumers are uncertain about whether they can obtain a better price (or whether the price offered for the product is a good deal; Srivastava and Lurie 2001), they often question whether the transaction is equitable; equity theory suggests that parties to exchange relationships always compare their outputto-input ratios to determine whether the exchange is equitable (Adams 1965). As Haws and Bearden (2006) elaborate, a perception of equity reflects the judgment of the overall merits of an offer. In exchanges with salespeople, consumers thus evaluate their output-to-input ratio (product value-to-price) with the seller’s output-to-input ratio (profit-to-cost) to determine whether the exchange is equitable (Oliver and Swan 1989). However, purchase likelihood also depends on whether consumers believe their outcome is proportional to the seller’s outcome. If consumers sense that the seller is earning too much profit, they may find the sale inequitable and try to restore balance by negotiating a lower price or simply not buying. Such perceptions are especially relevant in consumer relationships because consumers expect sellers to bear the bulk of exchange costs and consider positive inequity in their own favor as fair (Bower and Maxham 2012; Lapidus and Pinkerton 1995).
Accordingly, perceived (in)equity reflects consumers’ perceptions, not objective inputs and outputs (Adams 1965); information about actual inputs and outputs often is not available to both parties. Consumers’ perceptions of their own input and output are straightforward, such that they must establish an expected value for a focal product and understand what the price means to them. However, they face considerable uncertainty about the seller’s outputs and inputs. Consumers know the price but lack information about the seller’s costs and thus its profits. Most research studies consumers’ perceived equity in sales exchanges as judgments of price fairness or price equity, derived from comparative transactions that involve different parties because consumers do not have the pertinent information to determine sellers’ input directly (Xia, Monroe, and Cox 2004). However, in situations in which such comparative information is difficult to access, such as when the product is unique, infrequently purchased, or on sale (which requires consumers to determine if the discounted price is “fair”; Darke and Dahl 2003), their uncertainty and overpayment risk increase. Instead, consumers might turn to other cues (e.g., loyalty status of another customer) to infer perceptions of equity (Darke and Dahl 2003). That is, if consumers could rely on information about the seller’s benefits, it would enable them to estimate the equity in the exchange better.
Swan and Oliver (1991) find that consumers are sensitive to what they receive in an interaction with salespeople and to salespeople’ efforts on their behalf; this “summary concept” of equity (p. 16) influences their purchase intentions and satisfaction. When a salesperson gives a specialist competitor referral, the salesperson may increase this summary concept of consumers’ perceived equity in two ways. First, assuming consumers want the nonfocal product, the referral enables them to obtain it at a lower price from the competitor, which reduces their total expected cost (i.e., total input) for any consumer who wants both products. Second, specialist competitor referrals affect consumers’ perceptions of the seller’s output-to-input ratio. Equity theory suggests that many tangible and intangible factors determine consumers’ perceived equity, including salespeople’s effort to help consumers (Morales 2005). By offering consumers an opportunity to forgo profits from the sale of a (nonfocal) product, the salesperson appears willing to reduce his/her own output, possibly by forgoing a sale (Grassi and Ma 2016). As such, even though the salesperson may not actually be giving up the sale of nonfocal products (e.g., if their purchase rate is low), the perception that the salesperson is willing to reduce his/her own output may be sufficient to improve consumers’ perceived equity, which in turn should improve evaluations of the seller (Campbell 2007; Swan and Oliver 1991). Therefore, specialist competitor referrals should increase consumers’ perceived equity in an exchange with the seller, and perceived equity should increase consumers’ likelihood of purchasing the focal product:
H1: Specialist competitor referrals for nonfocal products increase consumers’ likelihood of purchasing the focal product.
H2: Specialist competitor referrals increase consumers’ perceived equity in the exchange with the seller.
In addition to providing information about the price for a nonfocal product available from a competitor (which increases perceived equity), a specialist competitor referral also offers information about price differentials. That is, a salesperson informs the consumer that a competitor sells the nonfocal product at a lower price, one that is better than what the seller offers. As previously mentioned, the literature on competitor referrals notes the lack of information on how consumers should be allocated to sellers of different specializations which vary in their ability to satisfy consumer needs3 (Spurr 1987). Consumers might not know all potential sellers in a market or which is best suited for certain products, but they understand both that inherent differences exist in sellers’ specialization and that differences in costs may justify price differences among sellers (Bolton, Warlop, and Alba 2003). If consumers believe that stores specialize differently in the nonfocal product, a natural inference may be that the stores differ in their specialization in the focal product, too. This inference likely increases consumers’ confidence not only about a bad price for the nonfocal product (which seller does not specialize in) but also about a good price for the focal product (which seller does specialize in).
Indeed, research in equity theory indicates that consumers perceive whether the price is “right” not only on the basis of the actual price offer but also from the procedure and seller interaction that lead to the offer (Herrmann et al. 2007). Consumers believe that salespeople are motivated to increase their sales (Cialdini 1999; Kirmani and Campbell 2004), so they may require an equitable exchange before they will consider information about the specialization differential as credible evidence (even if indirect) that the seller can offer the “right” price for the focal product. Blanchard, Carlson, and Hyodo (2016) show that in the context of an equitable exchange, information provided by salespeople can increase consumers’ confidence in product prices. Similarly, a specialist competitor referral might reduce perceived overpayment risk (and increase purchase likelihood) only if that referral successfully increases perceptions of equity. Thus, in addition to the direct effect of increasing perceived equity, we expect an indirect effect through both perceived equity and reduced perceived overpayment risk on purchase likelihood. Formally, we hypothesize that:
H3: The increase in perceived equity due to specialist competitor referrals also increases consumers’ likelihood of purchasing the focal product by reducing their perceived overpayment risk for the focal product.
We have suggested that specialist competitor referrals, whereby a salesperson informs a consumer that a nonfocal product is available at a lower price from a competitor, can increase sales of focal products. Moreover, we have argued that justifying the discrepancy between the competitors in the nonfocal product’s price on the basis of specialization can be credible. As previously mentioned, specialization refers to a seller’s focus or concentration on a subset of goods to gain efficiency (McConnell, Brue, and Flynn 2012). When salespeople mention that the seller (e.g., mattress store) concentrates its efforts on products like the focal product (e.g., mattresses) but not the nonfocal product (e.g., bed frame) that the competitor concentrates on, they are explaining why the seller has gained efficiency in one product (the focal product) at the expense of another (the nonfocal product). In doing so, salespeople simultaneously explain why the nonfocal product is more expensive at their store and provide evidence that the price of the focal product likely reflects the seller’s gains from specializing in products like the focal product.
There is precedence for the idea that whereas consumers are generally naive about sellers’ costs, they do acknowledge store differences and they do expect prices to differ by store as a function of their cost differences (Bolton, Warlop, and Alba 2003). That is, consumers consider the cost of goods sold as an acceptable reason for price differences. Without a justification, however, informing the consumer that the price of the nonfocal product is lower at a competitor may not increase consumers’ perceived equity because consumers may not intuit why the seller cannot offer the nonfocal product as the same price as the competitor. In that context, and as consumers tend to suspect that sellers seek to keep the most profit for themselves (e.g., Verlegh et al. 2004), we do not expect that a competitor referral for a nonfocal product without a justification for the price discrepancy would positively affect consumers’ perceptions of equity in the exchange or increase their likelihood of purchasing the focal product. We hypothesize:
H4: Specialist competitor referrals do not increase consumers’ likelihood of purchasing the focal product if they fail to justify the nonfocal product price difference according to the different specializations of the competing sellers.
We begin with an exploratory study and survey of salespeople, in which we ask salespeople to imagine selling a focal product (painting) and see what they would do with the knowledge that a competitor offered a nonfocal product at a lower price (frame). We also assess whether salespeople have used specialist competitor referrals, and for which products, in practice. We then test H1, in the context of a painting gallery with posted prices for its painting (Study 1)4 and with an incentivized negotiation experiment in which consumers must negotiate a price for a mattress, with real financial stakes (Study 2). In Study 3, we investigate the proposed process through which specialist competitor referrals operate (H2 and H3). Then, to test H4, in Study 4, we consider whether mentioning the difference in specialization is necessary for competitor referrals for nonfocal products to increase consumers’ purchase likelihood for focal products. Finally, in Study 5, we replicate the effect of specialist competitor referrals in a distinct context, namely, raising funds for UNICEF by asking people for donations in exchange for pumpkins, with pumpkin carving accessories acting as the nonfocal product. Table 1 summarizes our studies.
We started with an exploratory study to determine whether salespeople engage in specialist competitor referrals and what their beliefs are about their effectiveness as a sales strategy. To avoid recall bias and demand effects, we developed a scenario that mimics the choices a salesperson would have to make in a sales interaction (i.e., which strategy to use), reviewed their choices, and then administered a survey about their experiences. To reach a sample of respondents with real sales experience, we hired a market research company, Research Now Inc., which manages dedicated panels preselected by their members’ professions, to which we paid $21 per completed response. We offered the online survey to 1,007 salespeople in a wide range of industries (e.g., health care, software, real estate, financial loans, insurance, retail, travel) and received completed responses from 145 participants, with an average of 22 years of experience in sales and 13 years in their current firm. Web Appendix A provides additional sample details.
For the scenario-based portion of this exploratory study (see Web Appendix A), we sought a sales context that required little technical knowledge, so that salespeople from various backgrounds could relate. Specifically, we wanted a context in which there would be a natural potential for the presence of focal and nonfocal products; and for which our pretest indicated that consumers perceived high overpayment risk. We thus asked salespeople to imagine working at an art gallery that primarily sells unframed paintings, along with some nonfocal products such as frames. Next, the participants read, “A customer walks in and spends some time looking at the paintings. After taking some time to look around, they seem to settle in front of a painting, with a price of $220. Your intuition tells you that they like the painting. But, you also see them looking at the price tag and seem unsure.” They were told they could assume that the consumer would likely need a frame but that they might have one at home. As a salesperson, they also knew that the gallery’s frames sold for $70 but that a frame warehouse store down the block sold an equally nice frame for $45. We described two selling strategies the salespeople could use—( 1) suggesting the frame sold by the gallery after the consumer bought the painting or ( 2) making a specialist competitor referral5—and asked them which strategy would have more positive implications for the long-term sales of paintings (item 1) and frames (item 2) (responses on a fivepoint scale; see Web Appendix A). With this question, we could discriminate between participants who believe that the outcome of a specialist competitor referral will be strictly positive (i.e., better for sales of at least one product and no harm to the other), strictly negative (i.e., worse for sales of at least one product and no benefit to the other), equivalent (i.e., no difference between the two selling strategies), or mixed (e.g., beneficial for paintings but harmful for frames).
In the survey section, we then asked salespeople how often they use each strategy (end points “all the time” and “never”). Among those who indicated that they had used specialist competitor referrals, we asked for descriptions of the selling situation, including the focal and nonfocal products and their price ranges. We concluded with questions about their sales experience and demographic items.
Salesperson beliefs. We find substantial heterogeneity in salesperson beliefs. Whereas 13.10% (19/145) believe that the two strategies will sell an equivalent number of paintings and frames, most salespeople do not hold this belief; they differ considerably in their expectations of the outcome. That is, 29.66% (43/145) believe that the effect of specialist competitor referrals will be strictly negative, but a similar 33.10% (48/145) predict that it will be strictly positive. The remainder are more nuanced, believing that specialist competitor referrals will help one of the products (15.86% painting; 8.28% frame) but hurt the other (see Table WA1 in Web Appendix A). Salespeople who expect positive effects do not differ from the other respondents in their experience, whether in total years or in whether they are actively selling products in their current role.
Industry and product descriptions. The survey reveals that 71% of salespeople have offered specialist competitor referrals for various products, across both consumer and business-to-business industries (20% of the sample). In consumer industries, examples included selling $50 diapers while recommending a local pharmacy for a $60 baby commode; selling $1,000 televisions at an electronic store while recommending Amazon.com for $50 audiovisual wires; selling travel packages while recommending online websites for flight tickets; and selling floor tiles for $5 per square foot while recommending Home Depot for the $25 installation materials. Table 2 contains a sample of specialist competitor referrals mentioned in the survey.
This preliminary study illustrates that specialist competitor referrals are widely used in practice. However, salespeople vary widely in their beliefs about the strategy’s effectiveness for selling focal and nonfocal products. In this study, we put salespeople in a setting in which they worked for an art gallery and had information that a competitor offered a frame at a lower price than the gallery. For the following series of studies, we reverse the perspective of the scenario by focusing on consumer reactions to the use of specialist competitor referrals.
In Study 1, we ask consumers to imagine themselves in a scenario in which they seek a focal product. We manipulate the salesperson’s strategy to isolate the effect of specialist competitor referrals on consumers’ likelihood of purchasing the focal product (H1) and nonfocal product.
We recruited 157 participants from Amazon’s Mechanical Turk (MTurk) to complete a three-minute study, in exchange for financial compensation. Participants were assigned to a twocondition (specialist competitor referral, control) betweensubjects design, in which they read a scenario that asked them to take the role of consumers who wanted to buy a painting for their living room, had walked into a local art gallery, and liked one. All participants were informed of the price of the painting: “The painting is listed, on sale, at a price of $215. You like the painting, but remain uncertain about its selling price. You continue discussing with the store owner.” Then, on a second screen, we presented the owner’s statement: “You’ve found a really nice painting. It is from a local artist who is popular with many of my customers. $215 is a good price too, only available for the current sale.” All participants were told that they had an old frame at home that they could use to display the painting but that the cost of the painting was still a concern.
TABLE: TABLE 1 Summary of Studies
| Study | Findings | Product: Focal (Nonfocal) | Pricing Type |
|---|
| Exploratory study and survey of salespeople | Salespeople use specialist competitor referrals as a sales strategy and consider it effective for influencing customers to purchase focal products. | Painting (frame); salespeople provided various contexts in which they use specialist competitor referrals | Posted price |
| Study 1: Main effect study | Specialist competitor referrals increase customers’ purchase likelihood for focal products (H1). | Painting (frame) | Sale price |
| Study 2: Incentivized negotiation study | The effect of specialist competitor referrals on customers’ purchase likelihood holds even when customers are incentivized to reach the lowest possible price (H1). | Mattress (bed frame) | Negotiation |
| Study 3: Mediation mechanism | Specialist competitor referrals influence customers’ purchase likelihood through perceived equity and reduced overpayment risk (H2 and H3). | Mattress (bed frame) | Negotiation |
| Study 4: Need for specialist justification | A competitor referral is ineffective if the seller does not justify the price discrepancy by emphasizing the difference in specialization (H4). | Mattress (bed frame) | Negotiation |
| Study 5: Field study, donation context | Replicates the effects in the field with consumers who donate in a low-price exchange setting. | Pumpkins (pumpkin carving accessories) | Suggested donation |
| Web Appendix |
| Study W1 | Accounts for the effect of salesperson trustworthiness on specialist competitor referrals. | Mattress (bed frame) | Negotiation |
| Study W2 | Accounts for the effect of consumers’ prior knowledge on specialist competitor referrals. | Painting (frame) | Negotiation |
Conditions. In the specialist competitor referral condition, participants read that as they continued to look at the painting, before they decided whether to buy it, the store owner added:
About half of the time, people who buy a new painting will buy a new frame to go with it. We specialize in getting the best prices in art, not frames, but we do carry some nice ones. In fact, I have a $70 frame that will nicely hold this painting. That said, I’ll mention that just down the block is a frame warehouse store, where you can get an equally nice frame for $45.
In the control condition, participants were not told about the seller’s offer of a frame (at this stage, before making the purchase decision). These conditions, before the consumer’s purchase decision, were identical to the sales strategies presented to salespeople in our exploratory survey.
Purchasing the focal product. Participants indicated whether they would buy the painting (yes/no).
Purchasing the nonfocal product. After deciding whether to buy the painting, participants were asked about the frame. All participants knew that they needed a frame and had a suitable one at home (i.e., the need for the frame was salient in both conditions), but they possessed varying knowledge about the frames available. In the specialist competitor referral condition, consumers knew that the seller sold frames for $70 and a competitor sold a similar frame for $45. If they also had indicated they would buy the painting, we asked if they would buy the frame from the gallery, buy the frame from the frame warehouse, or not buy a frame from either store. In the control condition, if participants chose to buy the painting, the study indicated that the owner added, “About half of the time, people who buy a new painting will buy a new frame to go with it. We do carry some nice ones. In fact, I have a $70 frame that will nicely hold this painting.” We asked whether they would buy the frame from the gallery or not.
In support of H1, participants in the specialist competitor referral condition were more likely to purchase the painting (55.7%; 44/79) than participants in the control condition (39.7%; 31/47; c2( 1) = 4.00, p = .05). However, the specialist competitor referral did not significantly influence the likelihood of buying the nonfocal product, such that participants in the referral condition were no less likely to buy the frame from the gallery (9.1%) than those in the control condition (16.1%; c2( 1) = .32, p = .57).
TABLE: TABLE 2 Examples of Specialist Competitor Referrals Reported by Salespeople
| Salespeople’s Industry | Focal Product (Price Range) | Nonfocal Product (Price Range) |
|---|
| Automotive | Car ($20,000–$50,000) | Car starter ($700) |
| Biotechnology | Genetic testing ($2,000–$2,500) | Drug testing ($500–$800) |
| Crystals | Sphere ($200) | Stand ($25) |
| Electronics | Televisions ($1000) | Audiovisual wires ($50) |
| Floor and wall covering installation | Porcelain tile ($5 per sq. ft.) | Installation materials ($25) |
| Floral | Flowers ($35–$50) | Vase ($5–$10) |
| Furniture | Beds ($300–$1,000) | Sheets/mattress pads ($20–100) |
| Jewelry at department store | Jewelry ($300) | Jewelry repair ($100) |
| Metals distribution | Titanium ($10,000) | Grinding to size ($1,000) |
| Media sales | Ad campaign ($50,000) | Production ($1,500) |
| Mobile hydraulic components | Valve products ($1,000) | Valve controller software ($200) |
| Office products | Printer ($600–$900) | Printer cartridge ($125–$175) |
| Industrial | Optical fiber ($10,000) | Generator ($5,000) |
| Plumbing | Sink ($500) | Disposer ($150) |
| Real estate | First mortgage loan | Construction loan |
| Real estate artwork | Sculpture ($695) | Base ($100) |
| Retail | Ingrown hair serum ($25) | Eyebrow powder ($10–$20) |
| Sports medicine | Diaper ($50) | Commode ($60) |
| Trade shows | Booth at trade show ($850) | Table ($50–$75) |
| Tourism | Prepackaged tours and cruises | Airline tickets |
| Wholesale | Shoes ($50) | Socks ($15) |
| Wholesale | Dresses ($70) | Undergarments ($45) |
| Wine | Wine ($20) | Wine bottle opener ($5) |
Study 1 shows that a specialist competitor referral increases consumers’ likelihood of purchasing the focal product from the seller, providing support for H1. Even when consumers receive information that a nonfocal product is available for less from a competitor, they are not less likely to purchase the nonfocal product from the seller.
With Study 2, we assess support for H1 in a different purchasing situation, a different product category, and a different setting, such that consumers believe they can negotiate on the price of the focal product. Such a negotiation setting is pertinent for studying specialist competitor referrals. First, consumers typically negotiate if they believe that the listed price is not the lowest price (Blanchard, Carlson, and Hyodo 2016) or that the seller is keeping too much profit for itself (Schurr and Ozanne 1985). That is, negotiations likely take place in purchasing situations in which consumers perceive overpayment risk, and a negotiation should induce that perception. Second, by engaging in price negotiations, consumers signal their belief that the seller’s output-to-input ratio is higher than their own, implying low perceived equity (Balakrishnan, Patton, and Lewis 1993). Therefore, we investigate the effect of specialist competitor referrals on consumers’ likelihood of purchasing a focal product in a situation in which consumers are incentivized to achieve better financial outcomes. We adopt a negotiation paradigm (Srivastava and Oza 2006) in which participants obtain a performance bonus based on the price they negotiate with the seller.
Scenario. The study started by explaining that participants would be involved in a purchase price negotiation with a salesperson and would receive $.20 as base payment. They could obtain a performance bonus if the “negotiations end in a sale.” The magnitude of the bonus depended on their performance, so obtaining the lowest possible price through negotiations would lead to a bonus of $.50. Participants were also told that the seller would likely reject extremely low offers and terminate the negotiation process if the participant did not make any concessions. The second screen introduced the shopping scenario: They were moving to a new residence and needed a mattress and bed frame for their guest room. They found a mattress they liked at a price of $1,800 and a bed frame listed at $500 at the seller’s store.
Offers and counteroffers. To start, participants indicated how much they would offer for the mattress (up to $1,799) and had to justify, in a text box, this offer to the store owner. We wanted to ensure that participants were paying attention to the scenario and interested in reaching an agreement, so if they offered less than $1,000 for the mattress, we dismissed them and indicated that the extremely low offer was rejected by the store owner, who terminated the negotiation. If they offered at least $1,000 (but less than $1,799), participants received a counteroffer from the owner, corresponding to 25% of the difference between the list price and the consumers’ first offer ($X). For example, if the consumer offered $1,000, the counteroffer ($Y) would be $1,600 (= $1,800 – [$1,800 – $1,000]/4). The owner also provided a reason for rejecting the participants’ first offer: “Your offer of $X isn’t acceptable. This mattress is backed by a 10-year warranty, which should last the realistic lifespan of any mattress. I think it’s a great choice, which is popular with many consumers, and it goes well with the bed frame at $500. I am willing to lower the price to $Y, but you need to know that $1,800 was already a very good price for this high-quality mattress.” After seeing the counteroffer, participants indicated whether they wanted to accept the offer, make a counteroffer, or reject the seller’s offer and walk away from the exchange. If participants rejected the offer or accepted the counteroffer, we excluded them from any further analysis, because they could not be exposed to the manipulation.
Next, participants who decided to proceed provided a second offer ($W) and a second justification. Any participants whose second offer was equal to or lower than their original offer ($X) were rejected for failing to make a concession. The remaining participants, who provided the usable sample for our analyses, were informed that the owner agreed to sell them the mattress for $Z (>second offer of $W), with the comment that this offer was the seller’s absolute lowest price. This offer price of $Z represented another 25% reduction between the consumer’s second offer of $W and the seller’s last offer of $Y.
Participants were then randomly assigned to one of two conditions. Participants in the specialist competitor referral condition were told that as they were pondering the offer, the owner added: “Unfortunately, we do not specialize in bed frames. But I’ll just add that if you go a few blocks away to the nearby furniture store, you should be able to get a nearly identical bed frame for $350.” No further information was given in the control condition. Participants chose to accept, reject, or provide another counteroffer. Because of our interest in determining whether specialist competitor referrals increase consumers’ likelihood of purchasing the focal product, we used acceptance versus rejecting/countering the offer as our dependent variable. Participants who chose “another counteroffer” were informed that the owner rejected the idea of any further negotiation and walked away. Finally, all participants answered questions about their expertise with negotiations, level of comfort with negotiations, and ability to picture their interaction with the owner, along with demographic questions about their age, gender, and race.
We sought to obtain responses from approximately 80 participants per condition. Therefore, we collected initial data from 189 MTurk participants, in exchange for a $.20 fee and a potential $.50 bonus (112 men, 77 women, mean age = 31.93 years), of which 30 exited before the second counteroffer from the seller. We were left with 159 participants for analysis. We found no differences in experience, comfort, difficulty imagining the scenario, or age between respondents who reached the manipulation stage and those who exited before it.
In the second stage of the negotiation, after assigning participants to the conditions, we found a positive effect of the specialist competitor referral on participants’ decision to purchase the mattress (63.3% vs. 48.8%; c2( 1) = 3.42, p = .06), a 29.71% increase. The presence of the referral did not affect the probability that the consumer would walk away (8.8% vs. 11.4; c2( 1) = .31, p = .58). These findings provide stronger support for H1 because specialist competitor referrals increased purchases of the focal products even when consumers faced real financial stakes.
Thus, Study 2 provides further support for H1. Although our focus is not on the effect of the price, excluding price from our model could create omitted variable bias. In Web Appendix B, we show that the effect of specialist competitor referrals is robust to controlling for the negotiated price. Next, we investigate the mechanism underlying this effect.
Using the same context as in Study 2 (without the financial incentives), we seek evidence that specialist competitor referrals increase consumers’ likelihood of purchasing the focal product by increasing perceived equity and thus reducing overpayment risk (H2 and H3).
We recruited 201 MTurk participants who completed a mattress-shopping scenario similar to Study 2. Participants were randomly assigned to a 1 • 2 between-subjects conditions (control vs. specialist competitor referral) and read, “After spending some time trying different mattresses at the mattress store, you find a mattress that is appealing to you. The mattress is listed at a sales price of $1,120. The store also has a bed frame that you like, listed at $500.” The scenario indicated that they were fairly certain the owner would be open to reducing the price of the mattress, so they initiated a negotiation. A second screen presented the owner’s response:
You’ve found a very nice mattress. It is backed by 10-year warranty, which should last the realistic lifespan of any mattress. I think it’s a great choice, which is popular with many customers, and it goes well with the bed frame on special $500. For this mattress, you should know that $1,120 was already a very good price. It’s at a sales price we only offer a few times in the year.
After deliberating and exchanging for a while, the owner agrees to sell the mattress for $1,000. For the manipulation, we did not provide any further information to participants in the control condition but told those in the specialist referral condition that as they were pondering the offer, the owner also added:
Unfortunately, we do not specialize in bed frames. But I’ll just add that if you go a few blocks away to the nearby furniture store, you should be able to get a nearly identical bed frame for $350.
After making their decisions, participants completed scales to measure their perceived equity and overpayment risk. For
perceived equity, we used three items (seven-point scale): “Overall with the owner, there is a balance in our dealings,” “Overall with the owner, we provided each other with equal benefits,” and “Overall with the owner, the benefits we provide and receive even out over time” (a = .91) (Pervan, Bove, and Johnson 2009). For perceived overpayment risk, we used four measures from Dutta (2012) (seven-point scale): “I am confident that I was offered the mattress at the lowest possible price by the owner,” “The final offer for the mattress is probably the lowest price available in the market for this item,” “I did not risk paying too much if I bought the mattress,” and “I am not likely to find a lower price for this mattress from another store” (a = .87). We also assessed perceived salesperson expertise (White 2005; a = .86) and trust in the salesperson (Tax, Brown, and Chandrashekaran 1998; a = .82). For each participant, the order of items within each scale was random. Finally, partic
ipants answered demographic questions.
Specialist competitor referrals significantly increased consumers’ likelihood of purchasing the focal product (69.4% vs. 49.0%; c2( 1) = 8.51, p < .001), in line with the results from our previous studies. To assess support for H2 and H3, we must consider the effect of an increase in consumers’ perceived equity on their perceived overpayment risk and whether both factors explain the increase in the likelihood of purchasing the
focal product. First, as we show in Figure 1, the specialist competitor referral increases consumers’ perceived equity (path a: b = .47, t(194) = 3.13, p < .01), and this increase in perceived equity affects the likelihood of purchasing the focal product, even when we control for all other variables (path c2: b = .87, Z = 3.75, p < .01), in support of H2. Second, as we also show in Figure 1, consumers’ perceived equity increases purchase likelihood indirectly, through reduced overpayment risk (path b1: b = .54, t(193) = 6.71, p < .01), which thereby increases purchase likelihood (path b2: b = 1.13, Z = 5.60, p < .01), as we predicted in H3. Third, if we control for perceived equity, we no longer find a significant effect of the specialist competitor referral on reduced overpayment risk (path c1: b = .15, t(193) = .91, p = .36) or the focal product’s purchase likelihood (path c9: b = .47, Z = 1.23, p = .22).
We then estimated the three indirect effects and their biascorrected and accelerated 95% confidence intervals. We find a significant indirect effect of the specialist competitor referral through increased perceived equity alone (a • c2 = .4117 [.1503, .8594]), even while controlling for reduced overpayment risk for the focal product. Perceived equity operates through overpayment risk also; the indirect effect of increased perceived equity and reduced overpayment risk for the focal product (a • b1 • b2) is significant (.2790 [.1052, .5714]), in support of H3.
We performed several robustness checks. First, inverting the two mediators provides a marginal effect through perceived equity (.14 [–.0078, .4048]); specialist competitor referrals do not directly reduce perceived overpayment risk (b = .11, t(191) = .76, p = .45). Second, our results are robust to controlling for salespeople’s expertise and trustworthiness, as detailed in Web Appendix C.
Study 3 shows that when consumers receive specialist competitor referrals, it increases perceived equity between themselves and salespeople, which influences their purchase likelihood directly (H2). It also reduces their overpayment risk for the focal product (H3).
Our previous studies all feature specialist competitor referrals that include two components: the salesperson notes the difference in specialization between stores (i.e., specialization justification) and refers the consumer to a competitor that sells the nonfocal product at a lower price (i.e., competitor referral). In Study 4, we investigate the effect of failing to provide a justification when making a competitor referral for a nonfocal product (H4), with the expectation that the absence of justification will moderate the identified effect. However, it also may be that even if both pieces of information are necessary, they do not operate through consumers’ perceived equity. For example, purchase intentions for the focal product might increase for merely economic reasons, such that the referrals prompt consumers to shift their attention to the purchase of the pair of focal and nonfocal products and seek the best price for the combined offer (Hsee and Leclerc 1998). Alternatively, the economic component might not be necessary, if citing the seller’s lack of specialization or the simple gesture of offering a competitor referral creates a social connection that encourages the consumer to prefer to buy from the seller (Dahl, Honea, and Manchanda 2005). With Study 4, we also seek to rule out these alternative explanations.
We recruited 207 MTurk participants and assigned them to four conditions (control, specialist information, competitor referral, specialist competitor referral) in a between-subjects design, using the same context as in Study 3. The scenario presentation was similar, such that they were confident the owner would reduce the price and thus initiated a negotiation. They read a statement in which the owner talked about the mattress and the bed frame and agreed to sell the mattress for $975. Participants in the control condition received no further information before making their decision. Participants in the specialist competitor referral condition read that the owner added, “Unfortunately, we do not specialize in bed frames. But I’ll just add that if you go a few blocks away to the nearby furniture store, you should be able to get a nearly identical bed frame for $350.”
Participants in the specialist information condition only saw the first part of this statement: “Unfortunately, we do not specialize in bedframes.” Those in the competitor referral condition (without justification) only saw the second part: “I’ll just add that if you go a few blocks away to the nearby furniture store, you should be able to get a nearly identical bed frame for $350.” All participants chose whether to accept, reject and counter, or reject the offer.
We analyzed the probability that participants would accept the offer using logistic regression, with three condition indicators (and control as the default). Consistent with our prior findings, participants in the specialist competitor referral condition were more likely to accept the discounted offer for the mattress (68.6%) than participants in the control condition (49.0%; b = .822, p = .05). Providing only the specialist information (54.0%; b = .20, p = .62) or providing a competitor referral without justification (50.9%; b = .08, p = .85) did not increase participants’ likelihood of purchasing the focal product compared with the control condition, in support of H4 (Figure 2).
This study provides support for H4. It also shows that the effect cannot be solely attributed to consumers’ expectation of decreased overall costs for their joint purchase of both products, nor did it result from salespeople simply creating a social connection with consumers by giving them a competitor referral or acknowledging a lack of specialization in the nonfocal product. It is the combination of a competitor referral for the nonfocal product and of a justification based on specialization differences that produces the positive effect of specialist competitor referrals.
The preceding studies offer evidence that specialist competitor referrals are effective in posted price settings and in negotiations, even when consumers are incentivized. However, we have yet to provide causal evidence that they work in the field. In Study 5, we seek such evidence and also shift to a different purchase context, that of soliciting donations. This setting offers an interesting test of the effectiveness of specialist competitor referrals because charities often solicit donations by offering a small token gift, so the exchange forces consumers to estimate an “appropriate” amount to donate (De Bruyn and Prokopec 2013). However, consumers lack information about what is equitable (i.e., cost of the good) (Bolton, Warlop, and Alba 2003), such that “asking for a lot of money in exchange for a worthless token might be perceived as unfair” (Briers, Pandelaere, and Warlop 2007, p. 17). As such, donations solicited through such exchange can elicit equity concerns. We leverage a real-life setting, namely, UNICEF’s annual Halloween fundraiser, Trick-orTreat for UNICEF. In support of this initiative, we obtained small painted pumpkins from a pumpkin farm, which we offered in exchange for a small donation to the charity. Thus, the pumpkins were the focal product, and we offered them for a suggested donation of $10, higher than their retail price of $4.50.6 In addition to creating uncertainty about the donation amount, our Halloween fundraiser introduced a nonfocal product that was readily available elsewhere at a lower price: a “pumpkin carving accessory kit” to be sold for $4. The grocery store behind the fundraising table sold the same item for $2.
TABLE: TABLE 3 Study 5: Field Study Results
| Condition | Visitors Who Donated | Visitors Who Did Not Donate | Proportion Who Donated | Donations per Visitor |
|---|
| Specialist competitor referral | 15 | 6 | 71.43% | $6.48 |
| Control | 8 | 11 | 42.11% | $4.05 |
We set up a table on a sidewalk in front of a grocery store (competitor), around the corner from its entrance (see Web Appendix D). A poster featured the logo of the Trick-or-Treat for UNICEF program, and on the table, we placed the small painted pumpkins, pumpkin carving accessory kits, and a sign that read, “Pumpkins for Halloween; Suggested Donation $10.” When a potential participant approached the table, a research assistant explained,
We are raising funds for UNICEF trick-or-treat through these pumpkins that a pumpkin farm donated to us. We take donations for the pumpkins, with a suggested amount of $10. If you need some, we also have these carving tools for $4 extra.
In the specialist competitor referral condition, the assistant added:
I’ll tell you, though, we obtained these pumpkins through a donation from a pumpkin farm, but not the carving tools. In fact, I just saw that they are available at this grocery store for $2.
This latter information was not provided in the control condition. By flipping a coin, we randomly determined which condition to assign to people at the start. We conducted this study on Friday evening (4:00–6:00 P.M.) and Saturday (11:00 A.M.–4:00 P.M.) (it was canceled Sunday due to rain), and because of the rate of traffic changes throughout the day, we switched the conditions after every hour. Specifying that the pumpkins were donated by a pumpkin farm established the difference between the source of the focal and nonfocal products (and a pumpkin farm is definitely a specialist in pumpkins) and provided a justification for why the pumpkin carving kit would be more expensive at the fundraising table than at the grocery store.7
During the sessions, 40 people interacted with the research assistants, so this number constitutes our sample size. We assigned 2 participants who donated without hearing the scripts to the control condition, because all the information shared in the control condition also was on display. Overall, 21 people were in the specialist competitor referral condition and 19 in the control condition.
Among the participants, 10 people donated $10 in exchange for pumpkins (6 referral, 4 control), 11 people donated but opted not to take a product in exchange (8 referral, 3 control), and 2 people paid $4 in exchange for the carving kit (1 in each condition). Participants randomly assigned to the specialist competitor referral condition were more likely to donate (71.43%) than participants in the control condition (42.11%; c2ð1Þ = 3:56, p = :06Þ. As Table 3 shows, giving a specialist competitor referral increased the odds of receiving a donation by 69.64% and increased the amount donated by 59.80%. Overall, this study raised $213 for UNICEF: $136.08 from the specialist competitor referral condition and $76.95 from the control condition.
With Study 5, we demonstrate the effect of specialist competitor referrals in the context of consumers donating money to a charity in exchange for a small gift. This study thus replicates the effect in the field, in a distinct situation and price point ($10).
We show that salespeople who offer specialist competitor referrals for nonfocal products can increase the likelihood of their focal products’ sales. Building on equity theory, we determine that this effect functions by increasing consumers’ perceived equity and reducing perceived overpayment risk for the focal product. Without a credible justification for the price differential though (e.g., that the seller and competitor differ in their specializations), a competitor referral for nonfocal products is not sufficient to increase focal product sales.
An important practical concern is that specialist competitor referrals may result in losses of nonfocal product sales that could damage seller profitability as a whole. We do not find any such evidence in our experiments or field study (Studies 1 and 5), but it is important to delineate the conditions in which the net effect of specialist competitor referrals on sellers’ profits might be negative. Therefore, in the Appendix, we detail the profit equations for the specialist competitor referral and control conditions and investigate situations in which the former are profitable. Across various levels of relative profit contributions by focal and nonfocal products and different baseline probabilities of purchasing the nonfocal products, we find that minimal increases in the focal products’ sales can be sufficient for specialist competitor referrals to increase sellers’ net profits. In particular, we suggest three factors to consider when trying to maximize main ways to maximize the effectiveness of the specialist competitor referral on total profits. First, as long as the probability of purchase of the nonfocal product does not decrease in the presence of the referral, any increase in purchase probability of the focal product will increase profits. Second, if the probability of purchase of the nonfocal product does decrease, profitability depends on ( 1) the ratio of the dollar margin of the focal and nonfocal products and ( 2) the decrease in odds of purchase of the nonfocal product. This implies that it is easiest to benefit from specialist competitor referrals when the dollar margin of the focal product is not much smaller than that of the nonfocal product, and when the odds that a consumer purchases the nonfocal product are relatively low in the absence of the referral.
Finally, we highlight that the positive effect of specialist competitor referrals is generalizable. Our study shows that salespeople use this strategy in practice, and it can increase the seller’s profitability in various situations (see Appendix). With experiments, we show that the strategy significantly increases consumers’ purchase likelihood for focal products across varied categories (paintings, mattresses, shoes,8 pumpkins), price conditions (posted price, negotiations, donations), and methodologies (online, field, with financial incentives). Taken together, our research identifies specialist competitor referrals as a useful sales strategy across many contexts.
Referrals. Our main contribution is to the domain of referrals. First, we contribute to marketing literature on referrals by moving beyond consumer-to-consumer/word-of-mouth referrals and considering a setting in which both the source and the beneficiary are sellers (i.e., competitor referrals). For both consumer-to-consumer and competitor referrals, the purpose is to influence potential consumers. Thus, several factors that determine the influence of consumer-to-consumer referrals also could have a bearing on our findings, such as source trustworthiness and expertise (which moderates the influence of the source on the consumer; Gilly et al. 1998) and recipients’ prior price knowledge (which affects whether recipients need the referral; Hada, Grewal, and Lilien 2010). In Study 3, we find that even if we control for perceived seller trustworthiness and expertise, our proposed mechanisms hold (see also Web Appendix C). However, perceptions of the trustworthiness of the salesperson and perceptions of equity appear intertwined. Kickul, Gundry, and Posig (2005) note that perceptions of equity relate strongly to trust; in a salesperson interaction, some level of trust may be necessary before consumers will regard the information as credible, even if the salesperson possesses relevant expertise (Liu and Leach 2001). In that spirit, we conducted an additional study and manipulated perceived trust in the salesperson (see Study W1 in Web Appendix E). Consistent with the idea that some trust is necessary, at low levels of trust, specialist competitor referrals appear ineffective.
As we noted, the effectiveness of specialist competitor referrals could also depend on the knowledge consumers already have about the price of the focal product at the seller’s store. However, when we provide participants with additional information about typical discounts at the retailer (see Study W2 in Web Appendix F), we fail to find that consumers’ prior knowledge moderates the influence of specialist competitor referrals. This study provides further evidence that our effect operates through changing consumers’ perceived equity. That is, even if consumers know what a good input (price) would be, they remain uncertain about equity, because they lack information about the seller’s inputs and outputs, so specialist competitor referrals likely remain effective. Thus, we also contribute by integrating equity theory into the domain of referrals.
Equity theory. Reducing consumers’ perceived inequity leads to positive outcomes for sellers (Oliver and Swan 1989). However, as Xia, Monroe, and Cox (2004, p. 3) note, “equity theory uses buyer and seller input and output ratio as comparatives, [because] consumers usually do not know either seller’s cost structure or other pertinent information to determine seller’s input accurately.” In turn, most research studying equity in seller– consumer interactions has focused on consumers’ perceived price (un)fairness, which develops according to comparative transactions that involve different parties (Morales 2005). We show that a specialist competitor referral can directly influence consumers’ perceptions of sellers’ benefits without relying on actual comparatives, which are not always available or always in the seller’s favor. We also show that even in a negotiation setting, which tends to evoke an initial sense of inequity and incentivizes participants to reach the lowest possible price, specialist competitor referrals can increase the likelihood that a consumer accepts an offered price.
Relatedly, Bolton, Warlop, and Alba (2003) reveal that consumers have a poor appreciation of the costs faced by sellers, such that they ignore anything other than the cost of goods, causing them to regard most sales transactions as inequitable. A competitor referral for nonfocal products, without the specialization justification, does not improve consumers’ likelihood of purchasing the focal product, which may signal consumers’ general disbelief about the seller’s cost justifications (Bolton, Warlop, and Alba 2003). That is, providing a credible explanation for a price differential helps counter consumers’ natural insensitivity to most price differences that these authors observed.
Overpayment risk. Related to the contributions to referrals domain and equity theory are our contributions to understanding of consumers’ overpayment risk and how sellers can reduce it. First, referrals research has not explicitly studied the effect of referrals on consumers’ price-related risk assessments; it mainly focuses on risk or uncertainty about product choices. By studying a situation in which overpayment risk is present, we show how specialist competitor referrals can effectively reduce overpayment risk. Second, Darke and Dahl (2003, p. 337) call for research into whether consumers look for equity in their purchases because “they may suspect that they typically pay too much for regular priced items.” We respond that consumers seek equity in exchanges in which they perceive overpayment risk. Thus, we contribute to literature that investigates ways to reduce consumers’ overpayment risk, such as by posting higher prices and having salespeople offer lower, sale prices (Grewal, Monroe, and Krishnan 1998) or by providing lowprice guarantees (Dutta 2012). Because perceived overpayment risks can lead consumers to increase their search for the best price or postpone their purchase (Biswas, Dutta, and Pullig 2006), reducing these risks offers clear managerial benefits, as we elaborate next.
Our exploratory survey shows that although 71% of salespeople have used specialist competitor referrals, there is little consensus about whether the strategy is profitable. Our studies confirm that specialist competitor referrals can be effective at increasing sales of focal products. We also fail to find any evidence that they harm the sales of nonfocal products.
In the Appendix, we specify the conditions in which specialist competitor referrals are most likely to be profitable. In conjunction with our experimental studies and the mathematical profitability analysis, we offer some key takeaways for managers. First, even with conservative assumptions (e.g., a nonfocal product whose conditional purchase probability of 30% is reduced by half; equal dollar contribution margins for the focal and nonfocal products), increasing focal product sales by small amounts (e.g., 13%) can still make the strategy profitable. In our experiments, the average increase in focal product sales was typically much greater than would be necessary (40% in Study 1, 30% in Study 2, 69% in Study 5). Second, in the worst-case scenario (i.e., if the seller entirely loses the sale of the nonfocal product to the competitor), the profitability of the strategy depends on the relative margins of the focal and nonfocal products. Third, the nonfocal product does not have to be one that is a complement to the focal product. Specialist competitor referrals are effective even when the nonfocal product is likely to be sold with the focal product (as our field study shows; pumpkin carving kits are not a complement to painted pumpkins), and profitable.
The strategy also can be helpful when engaging in negotiations. Consumers increasingly negotiate with sellers, for products ranging from Chelsea Clocks priced at several hundred dollars to Jos. A. Bank shirts; as one consumer commented, “I know these things are significantly marked up. I said ‘I’m buying three; I’d like 15 or 20 percent off’” (Clifford 2012). Specialist competitor referrals help sellers assure consumers that they are getting the lowest price possible and encourage the sale. Our field study also showcases how charities soliciting donations could use specialist competitor referrals to enhance their chances of success.
Finally, by granting specialist competitor referrals, salespeople can use the information they have about competitors and their prices to increase their sales in a manner that does not require them to reduce their prices, as long as they can justify the discrepancy. That is, in our studies, the seller did not offer a discount on the nonfocal product, even after admitting that the competitor offered a lower price. We did not explore the long-term benefits of this strategy, though their potential is clear; in particular, giving specialist competitor referrals might lead to repeat business and stronger relationships with consumers.
Specialist competitor referrals might operate in other contexts. Economics research that investigates referral fees (Arbatskaya and Konishi 2012) and other incentives for competitor referrals (Park 2005) suggests that it is not in the best interest of the seller to provide these referrals. However, salespeople already use this sales strategy (as our exploratory study shows), and we highlight the conditions under which it can benefit the seller. Further research accordingly might address different situations in which specialist competitor referrals help sellers, such as in business-tobusiness industries or contexts marked by relationships between the seller and the competitor.
In addition, we identify a credible justification based on the difference in specialization between the seller and the competitor as a moderator of the effect of competitor referrals on consumers’ purchase likelihood. This difference in specialization might manifest in differences in prices between the two competitors (as we study), or it could manifest as a difference in the quality of the products being offered. That is, if the consumer was price insensitive but uncertain about quality levels, then the seller could benefit by giving a specialist competitor referral for a competitor who sells higher-quality nonfocal products at a higher price (e.g., a frame store doing custom framework). Future research could consider these different kinds of competitor referrals for nonfocal products.
We have attempted to set widely different price levels (from a $10 pumpkin to a $1,000 mattress), but the effect still could differ at other price points or levels of involvement. Higher price points tend to incur greater price fluctuations (e.g., buying a car), so specialist competitor referrals may have particularly substantial impacts on these consumers, who likely are involved and concerned about both equity and overpayment risk (Viswanathan et al. 2007). We do not necessarily expect that the effect would be stronger (or weaker) as the price changes or for all situations in which consumers are highly involved. Indeed, although the price is probably the most commonly used proxy for involvement, in many situations, prices can be low when consumers are involved (Laurent and Kapferer 1985). We purposefully focused on situations marked by at least some minimal level of involvement due to perceived overpayment risk; such additional research might manipulate levels of involvement to test their effects.
Finally, a specialist competitor referral could have longterm consequences beyond the short-term outcome of purchase likelihood of the focal product. In this sense, our research can be considered in conjunction with studies that identify other benefits of greater perceived equity, such as increased consumer satisfaction. Traditional word of mouth and referrals offer sellers excellent long-term benefits (e.g., Kumar, Petersen, and Leone 2010). Accordingly, to the extent that the salesperson is truthful when making a specialist competitor referral, the effect should remain positive. However, we would be remiss if we did not acknowledge the potential for negative consequences. For example, the repeated use of specialized competitor referrals could reduce their effectiveness, or consumers might start their search process at the referred competitor for their next occasion.
These potential long-term consequences provide ample opportunity for further research.
Competitor referrals come in many forms; the most famous example is probably from the classic movie Miracle on 34th Street, when Kris Kringle refers Macy’s consumers to a toy store competitor just before Christmas. The premise of such a competitor referral is to sacrifice the first sale in hopes of longterm benefits. We show that competitor referrals can even be profitable without sacrificing a focal sale, if the referral is for a lower-priced nonfocal product that the competitor specializes in. Furthermore, in giving such a competitor referral, Kris Kringle would not only have increased Macy’s sales but might have also gained loyal customers.
Specialist competitor referrals for nonfocal products can increase focal product sales, but it is possible that in some conditions, specialist competitor referrals decrease nonfocal product sales. Although we find no statistical evidence for this in our studies, a decrease in nonfocal product sales could decrease profits. We therefore consider the parameters associated with the profitable use of specialist competitor referrals as a sales strategy, such that they produce a net gain in sellers’ profits. We formulate profit equations for both the control and specialist competitor referral (SCR) conditions to determine the minimum focal product sales increase needed for the seller to profit.
Let ϕF and ϕNF be the profit contributions from each sale of a focal and nonfocal product, respectively. Then let PðFÞ and PSCRðFÞ reflect the probabilities that a consumer buys the focal product in the control and specialist competitor referral conditions, respectively, and let PðNFÞ and PSCRðNFÞ reflect the probabilities that a consumer purchases the nonfocal product in each condition. Assuming that ϕF and ϕNF remain the same across both conditions (i.e., the seller does not provide a dis
count on the nonfocal product after providing a specialist competitor referral), the equations for expected profits PC and PSCR are
(A1)
PC = ϕFPðFÞ + ϕNFPðNFÞ and
PSCR = ϕFPSCRðFÞ + ϕNFPSCRðNFÞ.
The consumer might purchase only a focal product, but it is safe to assume that a consumer seeking the focal product would not purchase the nonfocal product from the seller without also buying the focal product (i.e., PðNFjF = 0Þ = PSCRðNFjF = 0Þ = 0). This possibility is particularly unlikely when the nonfocal product’s purchase depends on the purchase of the focal product (e.g., a frame is unlikely to be useful without a painting), which is the focus of the consumer’s interest in the first place. With these assumptions, we can simplify the expected profit Equation A1 to
To identify the necessary increase in the probability of purchasing of the focal product to guarantee an increase in profits, we next compute the difference in expected profits:
from which we obtain the following insights. Profitability of SCR when the probability of purchasing the nonfocal product does not decrease. If
PSCRðNFjF = PðNFjF, as we observed (i.e., the specialist competitor referral does not affect the probability of purchase of the nonfocal product), then any increase in the probability of purchase of the focal product increases profits. To see this point, let PSCRðNFjF = PðNFjF = p be that probability. In this case, Equation A3 simplifies to
which will be positive for any PSCRðF > PðF. Profitability of SCR when the probability of purchase of the focal product increases but the probability of purchase of the nonfocal product decreases. In situations in which PSCRðF > PðF and PSCRðNFjF < PðNFjF, the profitability difference between the two strategies depends on the relative contribution of the two product sales (fNF=fF) and the decrease in the probability of the purchase of the nonfocal product between conditions (PSCRðNFjF=PðNFjF) that would thus occur.
To illustrate, let PSCRðF=PðF be the lift in purchase probability of the focal product obtained due to the specialist competitor referral. After we set Equation A3 to 0, isolating for this ratio provides us the minimum increase of PSCRðF over PðF, such that the difference in profits remains positive conditional on nonfocal product purchase probabilities and relative contributions:
Equation A5 provides several observations. When fF fNF (i.e., the focal productmakes amuch higher contribution to profits than the nonfocal product), any decrease of nonfocal product sales (PSCRNFjF relative to PNFjF) has little impact on the required lift for the probability of purchase of the focal product to achieve an increase in profit.
In the worst-case scenario, NFjF = 0, and the specialist competitor referral completely eliminates sales of the nonfocal product. This situation is highly unlikely, considering the presence of hassle costs (e.g., having to travel to another store) and the perception of equity generated by the referral, but in this case, the required increase in purchase probability of the focal product to achieve an increase in profits (Equation 5)would fall to 1 + from 0 would reduce the lift needed for SCR to ensure a profit. And, to ensure a profit, the lift required in the sales of the focal product increases with the margin of the nonfocal products and PðNFjFÞ; the lift required decreases as the margin from the focal product decreases.
Furthermore, the probability of purchase of the nonfocal product PðNFjFÞ limits the effect of any lost sales of the nonfocal product on potential profit. At PðNFjFÞ = 1, the specialist competitor referral ruins a guaranteed nonfocal product sale, and the necessary lift to achieve a profit is given by 1 + ðfNF=fFÞ. If product sales contribute equally to profits (i.e., fF = fNF), the probability of purchase of the focal product would have to double. However, at PðNFjFÞ = :1, the necessary lift in purchase probability of the focal product will be 10% of the ratio of their contributions to profits, so even here, the necessary lift would be 1.2. Highlighting nonfocal products whose sales probability are low in specialist competitor referrals thus increases the seller’s profits.
We present some illustrations in Figure A1. Given the relative contributions of the focal and nonfocal products ðfF=fNFÞ, and the purchase probability for the nonfocal product under the SCR (PSCRðNFjFÞ) and control (PðNFjFÞ) conditions, Figure A1 presents the lift in focal product purchase probability needed to achieve additional profits under SCR. Notably, even in the case that ( 1) the nonfocal product has a 30% probability of purchase (PðNFjFÞ = :30) (that is, high complementarity9), ( 2) this probability is reduced by half (PSCRðNFjFÞ=.15), and ( 3) the nonfocal product contributes as much to profit as the focal product ðfF=fNF = 1Þ, the minimum lift in the purchase profitability of the focal product would be 1.1304 (13%; from 30% to 33.91%).
We illustrate the seller’s possible profits with examples from our experiments. In the art gallery context (Study 1), the seller offered the painting at $215 and the frame at $70. It is difficult to obtain margin information for art, but industry insights10 suggests that art gallery margins are typically around 50% of the selling price. Assuming conservatively that the same margin applies to frames and that the probability of purchase of the nonfocal product would decrease from 16.1% to 9.6%, as observed, the art gallery would have to increase the probability of purchase of the focal product by 1.02 to be profitable. In Study 1, we estimated the increase to shift from 39.7% to 55.7%, which implies a lift of 1.403, greater than 1.02.
For mattresses, the margins are similar (approximately 50%).11 For the mattress sold at $1,125 and the bed frame priced at $500 (Study 2), we did not gather data on the percentage of people who would buy the bed frame from the mattress store in both conditions. However, if the lift occurs at an increase of 1.30 (63.3% vs. 48.8%), and in the specialist competitor referral condition the purchase rate for the bed frame is 5%, then the purchase rate of the bed frame in the control condition would have to be at least 90% to compensate for the lower purchase rate of the mattress. Even with these very conservative assumptions for nonfocal product margins (matching the focal product) and purchases (only 5%), the increase in purchase probability due to the mattress is sufficient for specialist competitor referrals to be a profitable sales strategy.
TABLE 3 Study 5: Field Study Results
TABLE 2 Examples of Specialist Competitor Referrals Reported by Salespeople
Footnotes 1 For example, the salesperson could explain, “We sell very nice frames for $70. However, I will just add that we are not specialists in frames, and the frame warehouse two blocks down offers equally nice frames for $45.” Thus, the salesperson has informed the consumer that the frame (nonfocal product) is available for less from a competitor, due to the difference in what the stores concentrate on, while still attempting to sell the painting (focal product).
2 One reason cited in industry articles and blogs for using specialist competitor referrals is that sellers might lose the current sale but reap benefits in the long term (future purchases, word of mouth). However, we anticipate that sellers can benefit even in one-time transactions.
3 Our argument relies on a difference in specialization, not on whether the effect is driven by a stated specialty in the seller’s focal product (direct statement of specialty: “We are specialists in art”) versus mentioning that they are not specialists in the nonfocal product (indirect statement of specialty on the focal product: “We are not specialists in frames”). We assess both the specialty in the focal product (Studies 1 and 2, field study) and only the lack of specialty regarding the nonfocal product (Studies 2 and 3). We find the effect both ways.
4 In a pretest, we assessed consumers’ perceived overpayment risk in ten purchasing situations, where each involved a focal product sold at a specific kind of store. We find that consumers’ perceived overpayment risk was significantly higher for purchasing a painting in an art gallery than any other situation except purchasing a car from a dealership. The findings also confirm that overpayment risk is not product-specific; for example, respondents perceived higher overpayment risk for buying running shoes from a local running store than from a large sporting goods store. Buying a mattress from a mattress store represented the midpoint for perceived overpayment risk-significantly higher than buying a toaster from a department goods store, running shoes from a large sporting goods store, or silverware from a dollar store. Therefore, we conducted our experiments across distinct purchasing contexts: buying a painting at an art gallery (Study 1), buying a mattress at a mattress store (Studies 2-5), and buying running shoes from a local running store (available on request). Note that as we focus on a purchasing situation for a specific product, in effect, we keep product performance constant.
5 The stimuli also included a potential strategy in which the salesperson mentioned the frame sold by the gallery prior to the purchase decision. However, we did not obtain all measures for that scale (i.e., we did not compare it with the two other strategies); the limited results we have are available on request.
6 In a separate sample, we assessed whether consumers would perceive overpayment risk for a painted pumpkin in our field study setup. We asked 80 respondents on MTurk whether they would expect to overpay for a painted pumpkin in the described setup (1 = “a great deal,” and 5 = “not at all”); we find that they would (M = 2.55; significantly different from the midpoint of scale, p < .01).
7 To assess whether consumers saw a difference between UNICEF volunteers and the grocery store chain as differently able to obtain good prices on the carving tools (the specialization difference), we conducted an online test with 120 respondents on MTurk. We showed them the donation table setup, provided the information related to the specialist competitor referral condition, and asked them the focal question, “Which of the two stores do you think would be able to acquire the carving tools at a better price?” They responded on a mean-centered nine-point scale (-4 = “The grocery store chain should be able to acquire carving tools at a cheaper price than UNICEF,” and 4 = “UNICEF should be able to acquire carving tools at a cheaper price than the grocery store chain”). We found that the average evaluation on this scale was less than the midpoint (M = -.71, t(125) = -2.90, p < .01) suggesting that people were more likely than not to see the grocery store as being able to get a better price on the carving tools than the UNICEF volunteers.
8 Available from the authors.
9 When the purchase probability of a nonfocal product given the purchase of a focal product, PSCRðNFjFÞ, is high, it indicates complementarity between the two products. Note that we do not consider complementarity when we study the effectiveness of a specialist competitor referral, and the strategy should still be effective (as Study 5 shows; pumpkin carving tools are not necessarily complementary to painted pumpkins).
See https://www.quora.com/What-percentage-cut-do-art-dealersgalleries-make-on-selling-art.
See https://www.themattressunderground.com/mattress-forum/index/13826-average-markup-in-a-showroom.html.
GRAPH: FIGURE 2 Study 4: Purchase Likelihood by Condition
DIAGRAM: FIGURE A1 Minimal Lift in PSCR(F) over P(F) Necessary to Maintain PSCR PC
DIAGRAM: FIGURE 1 Study 3: Process Measures for the Effect of Specialist Competitor Referrals on Likelihood of Purchase of the Focal Product
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Record: 169- Speed Up, Size Down: How Animated Movement Speed in Product Videos Influences Size Assessment and Product Evaluation. By: Jia, He (Michael); Kim, B. Kyu; Ge, Lin. Journal of Marketing. Sep2020, Vol. 84 Issue 5, p100-116. 17p. 1 Diagram, 3 Graphs. DOI: 10.1177/0022242920925054.
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Speed Up, Size Down: How Animated Movement Speed in Product Videos Influences Size Assessment and Product Evaluation
Digital ads often display video content in which immobile products are presented as if they are moving spontaneously. Six studies demonstrate a speed-based scaling effect, such that consumers estimate the size of an immobile product to be smaller when it is animated to move faster in videos, due to the inverse size–speed association they have learned from the domain of animate agents (e.g., animals, humans). Supporting a cross-domain knowledge transfer model of learned size–speed association, this speed-based scaling effect is ( 1) reduced when consumers perceive a product's movement pattern as less similar to animate agents' movement patterns, ( 2) reversed when a positive size–speed association in the base domain of animate agents is made accessible, ( 3) attenuated for consumers who have more knowledge about the target product domain, and ( 4) mitigated when explicit product size information is highlighted. Furthermore, by decreasing assessed product size, fast animated movement speed can either positively or negatively influence willingness to pay, depending on consumers' size preferences.
Keywords: animated movement speed; digital video ad; knowledge transfer; size assessment; visual perception
Fueled by advances in telecommunication technologies, video advertising has penetrated various digital channels and plays an increasingly crucial role in marketing communications ([66]). Online banners, which previously displayed only static images, now frequently show video content. Vivid product animations are commonly employed on retailing sites and products' official sites to provide information to consumers. In-stream video ads on online streaming sites and in-app video ads on mobile devices frequently appear before, during, or after consumers watch videos, use apps, or play games. On Instagram, more product ads are featured with videos than static images ([70]). Even in on-site and outdoor displays, dynamic presentations have begun to widely replace static images, with large LED signs and billboards constantly presenting product videos to consumers. Undoubtedly, videos have become an important medium through which consumers learn about the outside world, particularly about objects with which they are not familiar, such as new technologies and products ([40]; [57]), as evidenced by the fact that YouTube now serves as the second-largest search engine ([71]). In response to these trends, spending on digital video advertising among major global brands has increased by more than 50% in recent years ([45]).
Video advertising often involves dynamic presentations of products that are displayed to move spontaneously. For instance, an audio speaker can be animated to flash in, bounce, turn around, or spin actively in video ads, although it is not able to move freely in reality (i.e., an immobile product). Products in video ads cannot be shown as their actual physical sizes. When no explicit size information or point of reference is provided in video ads, the size of a product may look unclear, so that different consumers may have different assessments of the product's physical size, which is an important attribute in consumer decision making about many products ([17]; [18]; [22]; [31]; [52]; [65]).
Given the potential ambiguity regarding product size in video ads and the importance of size assessment in consumer preference formation across various circumstances, it is highly beneficial for marketers to be aware of what dynamic visual cues consumers might draw on to infer the physical size of a product shown in video ads. To shed light on this issue, we examine whether and how the animated movement speed of a product (i.e., the speed at which a product is animated to rotate, vibrate, or bounce as a whole; move its parts; or transform its shape) displayed in video ads can influence consumers' size assessment as well as their willingness to pay (WTP) for the product.
Developing a cross-domain knowledge transfer model of learned association ([27]; [28]; [48]), we propose that consumers overgeneralize the inverse size–speed association learned from the base domain of free-moving, animate agents (e.g., animals, humans) to the target domain of immobile, inanimate products (e.g., consumer goods) that are animated to move in video ads. Specifically, we propose a speed-based scaling effect, such that consumers estimate the size of a product to be smaller when the product is animated to move faster in video ads. Across six studies, we demonstrate this speed-based scaling effect, reveal the knowledge-transfer nature of this effect, and show the marketing-relevant downstream consequences of this effect in terms of WTP. Our research contributes to the literature on product display and size perception and provides important managerial implications. By examining animated movement speed easily controllable by marketers, testing stimuli adapted from real-world video ads, and identifying consumers more susceptible to the speed-based scaling effect, we aim to improve the effectiveness of video ads.
Broadly defined, product size has multiple facets, including dimensions (e.g., height, length), weight (or mass), and volume ([12]). On the one hand, depending on product categories, a certain facet may be more commonly used than the others to communicate product size to consumers, such as dimensions for consumer electronics, weight for packaged goods, and volume for drinks. On the other hand, holding density and shape constant, different facets of size are correlated. In this research, we adopt a broad definition of product size and examine dimensions, weight, and volume as interchangeable indictors reflecting the same underlying product size construct. Across divergent product categories and with different relevant facets of size being examined, the marketing literature has convergently demonstrated that consumers' assessment of product size is malleable and susceptible to the influences of various visual cues (for a review, see Table WA 1 in the Web Appendix). Whereas prior research has focused on how static visual cues affect product size assessment (e.g., [18]; [31]), the present research explores a dynamic visual cue unique to video ads that influences size assessment of a product—that is, the movement speed of a product that is animated to move spontaneously in video ads.
When creating a video ad, marketers can choose how fast or slow a product is animated to move, and the speed of the same product's animated movement can vary widely across different video ads. Given that immobile, inanimate products do not move in reality, there is no objective standard for consumers to use for evaluating animated movement speed. Nevertheless, through frequent exposure to various video ads displaying animated product movements in daily life, consumers could have developed subjective reference points for judging animated movement speeds as relatively fast or slow.
In this research, movement speed refers to how fast or slow an entity's parts or whole body moves on a pivot point or around an axis (i.e., the speed at which an entity rotates, vibrates, or bounces as a whole; moves its parts; or transforms its shape). Movement speed differs from locomotion speed, which represents how fast or slow an entity as a whole travels a distance between two locations. Experimental biologists have documented abundant evidence that in the natural world, an animal's physical size is associated with its movement speed ([ 6]): hummingbirds flap their wings invisibly fast, whereas eagles do so in a relatively slower manner ([59]); the fins of small fish beat much faster than those of large fish ([20]); small mammals (e.g., squirrels) move their jaws rapidly when gnawing, whereas large mammals (e.g., cows) chew slowly ([21]).[ 6] Drawing on observations of various species, experimental biologists have concluded that movement speed (but not locomotion speed) and body size are in general negatively related, such that movement speed decreases as body size increases ([59]). This inverse size–speed association can also be found in observations in popular culture. For instance, shorter basketball players (e.g., guards) spin much faster than taller basketball players (e.g., centers; [34]).
Because consumers are frequently exposed to the negative relationship between movement speed and physical size among animate agents (e.g., animals, humans) in daily life, we propose that this size–speed association may have been easily learned and deeply rooted in people's minds. Although other sources could also provide a ground for consumers to learn an inverse size–speed association, the domain of animate agents is more relevant to our focal research context of animated product movements shown in video ads, on which we elaborate next.
The knowledge transfer literature has suggested that consumers often transfer their internal knowledge from familiar base domains to novel target domains ([27]; [28]; [47]). Knowledge transfer often occurs across related domains. For instance, consumers can use their knowledge about film-based cameras (i.e., the base domain) to facilitate their comprehension of digital cameras (i.e., the target domain; [47]). Knowledge transfer can even take place across remote domains, such as from the domain of animate agents to the domain of inanimate entities. For example, consumers tend to imbue products ([53]), brands ([26]), or firms ([ 1]) with personalities (e.g., rugged, sophisticated) or traits (e.g., competent, warm) uniquely possessed by humans. Relatedly, consumers also rely on norms grounded in interpersonal relationships to guide their interactions with products, brands, and firms (e.g., whether consumers treat them as partners or servants; [ 2]; [ 3]). In these cases, animate agents (e.g., humans) serve as the base domain from which consumers have formed certain beliefs or learned certain principles, and inanimate entities (e.g., products, brands, firms) are target domains to which consumers apply these beliefs and principles.
By similar logic, we expect that consumers will apply the inverse size–speed association they have learned from behaviors of free-moving, animate agents (e.g., animals, humans) to their size assessment of immobile, inanimate products (e.g., consumer goods) when the latter are animated to move lively and spontaneously in video ads in the way animate agents move. Consequently, consumers will use a product's animated movement speed displayed in a video ad as a heuristic cue to assess the product's size. Following this rationale, we propose a speed-based scaling effect, such that consumers will assess a product's size to be smaller when the product's animated movement speed is faster.
- H1: Consumers assess the size of a product to be smaller when the product is animated to move faster in a video ad.
Drawing on the proposed knowledge transfer process, we further derive a set of moderators for the speed-based scaling effect. First, knowledge transfer theory maintains that knowledge transfer occurs when the base domain and the target domain share common attributes that are relevant to the focal judgment ([27]; [28]). This tenet implies that a basis for the speed-based scaling effect is that consumers perceive a product's movement pattern in the target domain as to some extent similar to animate agents' movement patterns in the base domain. Animate agents move with changes in speed, direction, and manner, which signal agency or spontaneity of movements ([ 7]). Indeed, when products (e.g., consumer electronics) are animated to vibrate, bounce, turn around, and spin spontaneously in videos, their overall movement patterns may look similar to various movements that insects or birds perform in the air, fish perform in the water, dancers perform on the stage, or superheroes perform in movies. When some products (e.g., Swiss Army knives) are animated to unfold their moveable parts or transform their shapes, these movements may also to some extent resemble the way animals move. In these cases, observing a product's movements can activate nodes about animate agents' movements and, consequently, the size–speed association in consumers' memories, which facilitates the knowledge transfer from the base domain of animate agents to the focal target product domain. In the meanwhile, this rationale implies that the speed-based scaling effect could be attenuated if we lower consumers' perception of the similarity between a product's animated movement pattern and animate agents' movement patterns, for example, through inducing consumers to think about how the focal product moves in a way similar to the movements of other objects (e.g., gears). In such cases, consumers may be more inclined to attribute the observed movement speed to other external factors rather than physical size because objects do not move spontaneously, and instead their movement speeds are determined by external forces imposed on them, preprogramed by their designers, or specified by their users. Thus, we predict that when consumers' attention is explicitly directed to the similarity between a product's animated movement and other objects' movements instead of the resemblance to animate agents' movements, consumers will be less likely to rely on animated movement speed as a basis for product size assessment.
- H2: The speed-based scaling effect is reduced for consumers who perceive low similarity of movement patterns between the focal product and the base domain of animate agents.
Second, another tenet of knowledge transfer theory is that knowledge about the base domain determines consumers' evaluation of objects in the target domain ([47]). This tenet implies that the proposed speed-based scaling effect in the target domain could be reversed when a positive association between physical size and movement speed in the base domain of animate agents (e.g., animals, humans) is made accessible in consumers' minds ([ 8]). Specifically, if consumers are induced to learn that, in the base domain, smaller agents (e.g., smaller animals) move more slowly than larger ones, this knowledge about the positive association between physical size and movement speed would transfer to the evaluation of products in the target domain. As a result, consumers will assess faster-moving products shown in video ads to be larger in size.
- H3: The speed-based scaling effect is reversed when consumers learn a positive association between body size and movement speed in the base domain of animate agents.
Third, knowledge transfer is more likely to occur when consumers are trying to understand a novel, less familiar target domain ([27]; [28]; [47]). Presumably, such transfer is less important for those who already have considerable expertise in the target domain. When watching a video ad of a new product, those who are more knowledgeable about the target product domain will be better at processing and comprehending product-related information (e.g., the shape or the intended use of the product). Thus, these consumers are able to base their assessment of the product's size on such product-related information rather than on display-related information (e.g., animated movement speed). For instance, when evaluating a novel consumer electronics product displayed in a video ad, consumers with more target-domain knowledge may search their memories for another familiar consumer electronics product with a similar intended use ([47]). They may use the size of this familiar product retrieved within the same target domain as a proxy to assess the size of the focal, novel product displayed in the video ad, instead of relying on animated movement speed as a heuristic cue for size assessment. Thus, the speed-based scaling effect should be weaker for consumers with more knowledge about the target product domain.
- H4: The speed-based scaling effect is reduced for consumers who have more knowledge about the target product domain.
Moreover, consistent with the rationale that knowledge transfer is less necessary for those who know the target domain well ([27]; [28]; [47]), if consumers already have a clear idea of the size of the focal product being presented, they do not need to rely on animated movement speed as a heuristic cue for size inference, either. Thus, we expect that a product's animated movement speed will no longer influence consumers' assessment of its size when explicit information about the product's actual size is highlighted in video ads.
- H5: The speed-based scaling effect is reduced when explicit product size information is highlighted in video ads.
Animated movement speeds of products in video ads have important downstream consequences on product evaluation via their influence on size assessment. For consumer packaged goods, such as edible products (e.g., drinks, snacks) and household supplies (e.g., detergents, cleaner sprays, shampoos), a larger product size implies a greater quantity, resulting in a perception of greater value ([74]). For these products, faster animated movement speed will cause consumers to assess the product as smaller in size and, consequently, decrease their valuation of the product. Under other circumstances, however, a small product size may be a desirable attribute ([18]; [31]). For instance, consumers may look for a small product when they care about its portability or have limited storage space. In this case, faster animated movement speed will cause consumers to perceive the product to have a smaller size and thus enhance their valuation of the product.
- H6a: When a small product size is desirable, faster animated movement speed leads to higher willingness to pay than slower animated movement speed.
- H6b: When a large product size is desirable, faster animated movement speed leads to lower willingness to pay than slower animated movement speed.
- H7: Size assessment mediates the effect of animated movement speed on willingness to pay.
Six studies test our conceptual framework (see Figure 1). We show that consumers overgeneralize the size–speed association they have learned from the domain of animate agents (e.g., animals, humans) to size assessments of products that cannot move in reality but are animated to move spontaneously in video ads (e.g., consumer goods). We demonstrate the robustness of the speed-based scaling effect across different product categories and for different facets of size assessment (e.g., dimension, weight), depending on their relevance for specific product categories (Studies 1–6). Importantly, in support of the proposed cross-domain knowledge transfer model of learned size–speed association, we demonstrate that the speed-based scaling effect is weakened when a product's movement pattern is perceived as less similar to animate agents' movement patterns (Studies 2 and 6), reversed when a positive size–speed association in the base domain is made accessible (Study 3), less pronounced for consumers with more knowledge about the target product domain (Study 4), and attenuated when explicit product size information is highlighted (Study 5). The final set of studies further reveal marketing-relevant downstream consequences. Faster animated movement speed leads to higher WTP when a small product size is desirable (Study 5), but it results in lower WTP when a large product size is preferable (Study 6). Moreover, size perception mediates the effect of animated movement speed on WTP (Studies 5 and 6). Due to the subjective and relative nature of speed perceptions about immobile products, we manipulated movement speeds differently for each group of video stimuli adopted in different studies.
Graph: Figure 1. Conceptual framework.
Using animations of an audio speaker, Study 1 aims to provide initial evidence for the speed-based scaling effect, such that a product will be assessed to be smaller when it is animated to move faster (H1). This study focuses on the dimension facet of size, given that dimensions are an important size indicator for consumer electronics, such as audio speakers. To calibrate the speed-based scaling effect, we compared multiple movement speeds with static images.
We recruited 352 U.S. residents (162 women; Mage = 36.13 years) from Amazon Mechanical Turk (MTurk) for Study 1. The online setting represents a typical context in which consumers are exposed to video advertising, making MTurk an ideal platform for conducting online experiments given the specific focus of the current research on video ads. To minimize the possibly unintended influence of screen size, we prevented participants working on mobile devices from taking all studies reported in this article. All participants completed our studies on either desktop or laptop computers. Study 1 adopted a single-factor between-subjects design with five product-animation conditions varying in movement speed and two static-image conditions.
We manipulated movement speed based on a video ad, in which an audio speaker is animated to flash in and out and rotate spontaneously. We altered the original playback speed to create a series of new videos. In total, there are five videos displaying the product moving at different speeds, including the animated movement speed displayed in the original video: 50%, 100%, 150%, 200%, or 250% of the original movement speed (see the Web Appendix for the stimuli and a pretest that confirmed the effectiveness of the speed manipulation).
To control for product presentation duration as well as simulating a real online advertising setting, we presented product animations to participants as graphics interchange format (GIF), which is widely used in online advertising. In a GIF, product movements are displayed repeatedly and continually while consumers browse the webpage on which the GIF is embedded. In a daily online browsing scenario, before consumers leave a webpage, it is very common that they are exposed to a product animation repeatedly when the animation is short, or part of the animation is skipped when the animation is long. Under this circumstance, the potential difference in viewing time across different product animations could be minimized.
In addition to the five product-animation conditions, we created two static-image conditions. In one static-image condition, we presented four screenshots of the audio speaker video in a slide-show mode. In the other static-image condition, we combined the same four screenshots into a single picture (see the Web Appendix for the stimuli). These two static-image conditions served as baselines for the five product-animation conditions.
After participants viewed the product stimulus, we asked them to provide their estimates of the audio speaker's height between 0 and 40 inches, after seeing a simple drawing that explained the meaning of height (for the illustration, see the Web Appendix). To control for potential confounds, we first measured perceived normalness of speed on the same scale used in the pretest. We further measured the extent to which the audio speaker looked close (i.e., perceived distance) and the extent to which participants perceived the audio speaker as "high-quality" (i.e., perceived quality), "durable" (i.e., perceived durability), "reliable" (i.e., perceived reliability), and "beautiful" (i.e., perceived aesthetics). Moreover, participants indicated the extent to which they paid attention while they viewed the image (i.e., attention to the image), the audio speaker captured their attention (i.e., attention to the product), and they felt "alert," "active," "aroused," and "energized" while they viewed the image (adapted from [ 5]]), which formed an arousal index (α =.81). We presented all items on a seven-point scale (1 = "not at all," and 7 = "very much").
First, we focused on the five product-animation conditions and regressed size assessment on movement speed (coded as a continuous variable with five values that represent proportions of the original speed:.5, 1, 1.5, 2, and 2.5). The regression analysis generated a negative effect of movement speed on size assessment (B = −2.12, t(248) = −2.86, p =.005). Specifically, as movement speed increased from 50% to 250% of the original speed, size assessment decreased linearly (M50% = 15.14, SD = 7.65; M100% = 14.08, SD = 9.50; M150% = 13.39, SD = 8.70; M200% = 12.60, SD = 8.81; M250% = 10.62, SD = 6.44), as depicted in Figure 2. This linear effect supports H1 and demonstrates the relative nature of the speed-based scaling effect, wherein an incremental increase in movement speed leads to a proportional decrease in size assessment.
Graph: Figure 2. Size assessment as a function of animated movement speed (Study 1).Notes: Error bars = ± 1 SEs.†p <.10. *p <.05. **p <.01.
In addition, movement speed did not influence perceived distance, specific product perceptions (quality, reliability, and aesthetics), attention to the product, attention to the image, or arousal (|t|s <.81, ps >.42). Although movement speed directionally affected perceived durability (B = −.20, t(248) = −1.67, p =.10), it still significantly decreased size assessment (B = −1.98, t(247) = −2.67, p =.008) when perceived durability was added as a covariate. Moreover, there was a directionally inverted U-shaped relationship between movement speed and perceived normalness of speed (the squared term of movement speed: B = −.32, t(247) = −1.60, p =.11), such that perceived normalness of speed first increased and then decreased as movement speed increased from 50% to 250% of the original speed. However, the speed-based scaling effect cannot be simply attributed to a deviation from normal speed, given that the relationship between movement speed and size assessment was linear rather than inverted U-shaped. Importantly, the negative linear effect of movement speed on size assessment remained unchanged (B = −2.03, t(247) = −2.69, p =.008) when perceived normalness of speed was added as a covariate. Taken together, all these control variables cannot explain the observed speed-based scaling effect.
We conducted two additional regressions by using either the slide-mode condition or the image-combination condition as a baseline (coded as 0) and dummy-coding each of five different speed conditions (coded as 1). Among all the pair-wise comparisons, we were in particular interested in those involving the fastest speed (i.e., 250%) or the slowest speed (i.e., 50%). Specifically, there were significant differences between the 250% speed condition (M250% = 10.62, SD = 6.44) and the slide-mode condition (Mslide mode = 15.63, SD = 9.52; B = −5.02, t(297) = −3.04, p =.003) as well as between the 250% speed condition (M250% = 10.62, SD = 6.44) and the image-combination condition (Mimage combination = 14.51, SD = 8.71; B = −3.89, t(293) = −2.35, p =.02). In contrast, the differences between the 50% speed condition (M50% = 15.14, SD = 7.65) and any of these two static-image conditions were not significant (|t|s <.37, ps >.71). Taken together, these results suggest that the speed-based scaling effect seems to result from fast animated movement speed decreasing product size assessment when static product displays are adopted as baselines for gauging the effect of animated movement speed.
By comparing multiple animated movement speeds with two types of static images, Study 1 provides initial support for the speed-based scaling effect, that is, consumers estimate the same product to have a smaller size when the product is animated to move faster in a video ad. The following studies examine a set of moderators that support the proposed cross-domain knowledge transfer model of learned size–speed association. To simplify experimental designs, make data analyses easier to interpret, and better procedurally control for perceived normalness of movement speed, in the following studies we mainly tested fast-movement videos against slow-movement videos instead of creating a series of videos at multiple movement speeds. Specifically, we sped up (vs. slowed down) the original speeds of the same videos to create fast- (vs. slow-) movement videos to rule out an alternative explanation that the observed effect is driven by a deviation from the original speed rather than the proposed relative change from a slower speed to a faster speed. Furthermore, while in Study 1 the regression analysis revealed that size assessment linearly decreased when movement speed increased from 50% to 250% of the original speed, pair-wise contrasts among different speed conditions (see Table WA2 in the Web Appendix) showed that the effects of other conditions against the 50% speed condition became significant when movement speed shifted from 200% to 250% of the original speed (i.e., shifting from four times to five times of the 50% speed). Thus, in most of our following studies, we generally set the speeds of the fast-movement videos to be at least as four times fast as those of the slow-movement videos.
According to the cross-domain knowledge transfer model, perception of similarity of movement patterns between an inanimate product and animate agents serves as a basis for cross-domain knowledge transfer of the learned size–speed association that drives the speed-based scaling effect. The main purpose of Study 2 is to examine the moderating role of perceived cross-domain movement similarity. We aim to show that the speed-based scaling effect will be reduced when consumers' attention is directed away from the similarity between a product's movement pattern and animate agents' movement patterns so that they perceive low cross-domain movement similarity (H2). Moreover, whereas Study 1 examines dimension assessment (e.g., height), Study 2 aims to generalize the speed-based scaling effect to weight assessment.
Four hundred three U.S. residents (176 women; Mage = 35.66 years) were recruited from MTurk and randomly assigned to one of the four conditions in a 2 (movement speed: fast vs. slow) × 2 (perceived movement similarity: control vs. low) between-subjects design.
In the survey, participants watched a video, in which a Swiss Army knife is animated to unfold its various tools (e.g., blade, corkscrew), glide, and spin spontaneously. We set the playback speed of the product video to be 60% of its original speed in the slow-movement condition and 240% of its original speed in the fast-movement condition. As in Study 1, we presented the videos in GIF format, in which the knife keeps moving unceasingly, to control for playtime difference (for the stimuli and the pretest results for the speed manipulation, see the Web Appendix).
After watching the product animation, participants completed a choice task, which manipulated perceived cross-domain movement similarity. In the control condition, participants were shown three pictures of animate agents, including a fish, a dog, and a bird, and indicated which of the three animals was relatively the most similar to the Swiss Army knife they viewed previously in the way they are moving. The setup of this control condition is consistent with our theorization that exposure to animated product movements could activate nodes about animate agents' movements in consumers' memories.
In the low-similarity condition, participants examined three pictures of objects, including a gear, a tire, and a fan, and indicated which of the three objects was relatively the most similar to the knife in terms of the movement pattern. We expect that directing participants' attention to the similarity between the knife's movement pattern and other objects' movement patterns would suppress the perceived similarity between the knife's movement pattern and animate agents' movement patterns (for the pretest results for the perceived movement similarity manipulation, see the Web Appendix). Under this circumstance, the learned size–speed association from the base domain of animate agents would not be activated and consequently transfer to the target product domain. Thus, the speed-based scaling effect would be attenuated.
Given that Swiss Army knives' dimensions have limited variation, we asked participants to estimate the knife's weight between 0 and 10 ounces after the choice task. We also measured perceived distance, specific product perceptions (i.e., quality, durability, reliability, and aesthetics), attention to the animation, attention to the product, and arousal on the same scales from Study 1 as control variables.
A two-way analysis of variance (ANOVA) on weight assessment generated a movement speed × perceived movement similarity interaction (F( 1, 399) = 6.12, p =.01). Neither movement speed nor perceived movement similarity produced a main effect (Fs <.29, ps >.59). Simple contrasts showed that in the control condition in which participants compared the knife's movements to animate agents' movements, we replicated the results of Study 1, such that participants estimated the knife to have a smaller size in the fast-movement condition than in the slow-movement condition (Mfast = 5.27, SD = 2.48 vs. Mslow = 6.07, SD = 2.63; F( 1, 399) = 4.58, p =.03). In contrast, in the low-similarity condition in which participants compared the knife's movements to objects' movements, this effect was attenuated (Mfast = 5.79, SD = 2.76 vs. Mslow = 5.27, SD = 2.80; F( 1, 399) = 1.86, p =.17). These results support H2.
Moreover, additional two-way ANOVAs showed that there were no movement speed × perceived movement similarity interactions on perceived quality, perceived durability, perceived reliability, perceived aesthetics, attention to the animation, attention to the product, and arousal (Fs < 2.48, ps >.12). There was an unexpected directional movement speed × perceived movement similarity interaction on perceived distance (F( 1, 399) = 2.80, p =.10). Nevertheless, when perceived distance was added to the model as a covariate, the movement speed × perceived movement similarity interaction on weight estimate remained significant (F( 1, 398) = 5.79, p =.02). Thus, these control variables cannot explain the results of Study 2.
The moderating role of perceived cross-domain movement similarity provides support for the proposed cross-domain knowledge transfer model of learned size–speed association. If a product's movement pattern is perceived as less similar to animate agents' movement patterns, the learned size–speed association will be less likely to transfer from the base domain of animate agents to the target product domain, inhibiting the speed-based scaling effect. Moreover, given that we manipulated perceived cross-domain movement similarity after participants watched product animations, the results of this study suggest that consumers' perception of animated product movement could be subject to contextual influences that occur and shape the speed-based scaling effect at the time of judgment formation. In the next study, we provide further evidence for the cross-domain knowledge transfer of learned size–speed association.
Study 3 aims to demonstrate that the size–speed association learned from the base domain determines the speed-based scaling effect in the target domain. If the association between physical size and movement speed consumers have learned from observing animate agents (e.g., animals, humans) does indeed drive the speed-based scaling effect for inanimate products, this effect should be reversed when consumers learn a positive size–speed association that is opposite to the previously learned negative association in the domain of animate agents. Thus, we predict that participants primed with a positive association between physical size and movement speed when observing animals' movements would estimate a faster-moving product to have a larger size rather than a smaller size (H3). We tested this prediction in Study 3.
We randomly assigned 163 U.S. participants (68 women; Mage = 31.47 years) recruited from MTurk to one of the four conditions in a 2 (movement speed: fast vs. slow) × 2 (learned association: default/negative vs. reversed/positive) between-subjects design.
At the beginning of the study, as a cover story, we told participants that they were going to rate several short video clips in terms of image quality. We sequentially presented three video clips. The first two video clips manipulated the accessibility of the relationship between physical size and movement speed. Although the relationship between animal size and movement speed within similar species is generally negative, a comparison of animals across vastly different species reveals a few exceptions to this negative relationship, making the priming of a positive relationship between animal size and movement speed possible. In the default-association cells, participants watched a video that showed a small, fast-moving animal (a hummingbird) and another video that showed a large, slow-moving animal (an elephant). In the reversed-association cells, participants watched a video that showed a small, slow-moving animal (a snail) and one that showed a large, fast-moving animal (a cheetah; for the pretest results for the association manipulation, see the Web Appendix). The durations of the four videos were the same (30 seconds), and the presentation order of the two videos in the same cell was counterbalanced.
The third video clip was the target video for size assessment. In the target video, a mobile WiFi device performs various lively movements, such as spinning and zooming out, so that it appears to move spontaneously. We altered the playback speed of the original video to create a fast-movement video and a slow-movement video. The movements of the mobile WiFi device in the fast-movement cells (video playtime = 12 seconds) were displayed to be, on average, seven times faster than those in the slow-movement cells (video playtime = 39 seconds; for the stimuli and the pretest results for the speed manipulation, see the Web Appendix). To be consistent with the first two videos that were presented only once in video format and the cover story that was about video quality evaluation, we also presented the third target video in video format once instead of GIF format used in our previous studies. To ensure that the observed effects result purely from visual cues of movement speed, and not from auditory cues associated with the alternations of video playback speed, we muted all the videos in this and other studies that presented product animations in video format.
Consistent with our cover story, participants rated all three videos in terms of image quality on a seven-point scale (1 = "bad," and 7 = "good"). After having seen a simple drawing that explained the meaning of length (for the illustration, see the Web Appendix), participants provided their estimates of the size of the mobile WiFi device in terms of its length in inches and then indicated their perceived durability and reliability of the mobile WiFi device.
A two-way ANOVA revealed no main effects of either movement speed or learned association (Fs <.37, ps >.54). Only a movement speed × learned association interaction emerged (F( 1, 159) = 7.04, p =.009). Specifically, in the default-association cells, replicating the results of our previous studies, participants estimated the same mobile WiFi device to be marginally smaller when it moved faster than when it moved slower (Mfast = 5.99, SD = 1.63 vs. Mslow = 6.85, SD = 2.64; F( 1, 159) = 2.95, p =.09). In contrast, in the reversed-association cells, the effect of movement speed was reversed, such that participants estimated the size of the mobile WiFi device to be larger when it moved faster than when it moved slower (Mfast = 6.73, SD = 2.71 vs. Mslow = 5.68, SD = 2.14; F( 1, 159) = 4.13, p =.04). These results support H3 and provide further evidence for the proposed cross-domain knowledge transfer account.
We further ran two-way ANOVAs on perceived image quality, durability, and reliability, respectively. These variables were not influenced by the movement speed × learned association interaction (Fs <.60, ps >.44) and therefore cannot explain the results of Study 3.
Study 3 demonstrates that the speed-based scaling effect is reversed when consumers learn a positive association between physical size and movement speed that is opposite to the default negative association in the domain of animate agents. These findings provide convincing support for our proposed cross-domain knowledge transfer model of learned size–speed association that explains the speed-based scaling effect. Although the video playback time differed across the fast- and slow-movement videos in this study, such a difference cannot explain the speed-based scaling effect. If the difference in playback time did in fact drive the speed-based scaling effect, we would have observed participants consistently estimating the mobile WiFi device to have a smaller size in the fast-movement condition than in the slow-movement condition regardless of the learned association manipulation. In contrast to the prediction based on differences in playback time, we found that the speed-based scaling effect fully depended on the learned association manipulation.
Study 4 aims to test the prediction that the speed-based scaling effect will be reduced for consumers with more knowledge about the target product domain (H4). For this purpose, we examined consumers' size assessment of a dehumidifier and measured their knowledge about the broad target product domain, that is, home appliances.
We randomly assigned 200 U.S. residents (90 women; Mage = 39.14 years) recruited from MTurk to either a fast-movement condition or a slow-movement condition.
Participants watched a short video, in which a dehumidifier is animated to slide, turn around, and move its handle in a lively and spontaneous manner. We set the playback speed of the dehumidifier video to be 40% of its original speed in the slow-movement condition and 200% of its original speed in the fast-movement condition, so that the fast movements are as five times fast as the slow movements (for the stimuli and the pretest results for the speed manipulation, see the Web Appendix). In this study, we used another method to control for playback time difference. Specifically, the fast-movement video repeats the humidifier's movements five times, so that the playback time is 66 seconds for both the fast-movement video and the slow-movement video.
After participants watched the video, they estimated the size of the dehumidifier in terms of its height between 0 and 40 inches. For control purposes, we also measured participants' perceived distance to the dehumidifier; perceived quality, durability, reliability, and aesthetics of the dehumidifier; attention to the animation; attention to the dehumidifier; and arousal level by using the same scales as in Study 1. At the end of the survey, we measured participants' knowledge about the broad target domain by asking them "How familiar are you with home appliances?" and "How knowledgeable are you about home appliances relative to the people around you?" on a seven-point scale (1 = "not at all," and 7 = "very much"). These two items were adapted from [51] and averaged to form a target-domain knowledge index (α =.75).
We regressed size assessment on movement speed (−1 = slow, 1 = fast), target-domain knowledge (mean-centered), and their interaction. The regression analysis generated a main effect of movement speed (Mfast = 22.02 vs. Mslow = 24.71; B = −1.36, t(196) = −2.16, p =.03), which replicated the results of Studies 1–3, and a marginally significant main effect of target-domain knowledge (B =.93, t(196) = 1.66, p =.10).
More importantly, these main effects were qualified by a significant movement speed × target-domain knowledge interaction (B = 1.21, t(196) = 2.16, p =.03). To decompose this interaction effect, we conducted a spotlight analysis ([ 4]; [24]) by using PROCESS Model 1 ([32]). Fast movement speed decreased size assessment only for participants who had less knowledge about the target domain (defined as one SD below the mean; Mfast = 19.60 vs. Mslow = 25.03; B = −2.71, t(196) = −3.03, p =.003). In contrast, for those who had more knowledge about the target domain (defined as one SD above the mean), movement speed did not influence size assessment (Mfast = 24.44 vs. Mslow = 24.38; B =.03, t(196) =.03, p =.98). As Figure 3 shows, an additional floodlight analysis ([62]) revealed that 50.2% of participants whose target-domain knowledge rating was below the Johnson–Neyman point (J-N point = 5.17 vs. sample mean = 5.09). These results support H4.
Graph: Figure 3. The moderating role of target-domain knowledge (Study 4).
In addition, another set of moderated regression analyses confirmed that there were no movement speed × target-domain knowledge interactions on perceived distance; specific perceptions of the dehumidifier regarding its quality, durability, reliability, and aesthetics; attention to the animation; attention to the product; and arousal (|t|s < 1.28, ps >.20). Thus, these control variables cannot account for the results of this study.
Study 4 shows that the speed-based scaling effect is reduced for consumers with more knowledge about the target product domain. Such results are consistent with the cross-domain knowledge transfer model, which suggests that more target-domain knowledge makes knowledge transfer from the base domain less important for understanding a product in the target domain. In two final studies, we further demonstrate marketing-relevant downstream consequences of the speed-based scaling effect while continuing exploring boundary conditions for this effect.
The purpose of Study 5 is threefold. First, we examine another boundary condition for the speed-based scaling effect. We expect that the speed-based scaling effect will be reduced when explicit product size information is highlighted (H5). In this case, cross-domain knowledge transfer is no longer necessary because consumers do not need to base their size assessment on animated movement speed. Second, if fast animated movement displayed in a video ad does indeed decrease consumers' size assessment of the product, it should elicit a higher WTP when a small product size is desirable for consumers (H6a), and decreased size assessment should mediate this effect (H7). Study 5 demonstrates this downstream consequence of the speed-based scaling effect by examining consumers' responses to a mobile WiFi device, for which a small size contributes to greater portability and convenience and thus is a preferred attribute for consumers. Third, whereas in Studies 1–4 participants provided numerical estimates of product size, Study 5 adopted another operationalization of size assessment in terms of subjective size perception indicated on a rating scale to generalize the speed-based scaling effect.
Three hundred one U.S. residents recruited from MTurk (134 women; Mage = 35.57 years) participated in Study 5, which adopted a 2 (movement speed: fast vs. slow) × 2 (explicit size information: absent vs. highlighted) between-subjects design.
We presented participants with a product animation in GIF format, showing a mobile WiFi device flash in and out and turn around. We altered the playback speed of the original video to be 50% to create a slow-movement video and to be 250% to create a fast-movement video (for the stimuli and the pretest results for the speed manipulation, see the Web Appendix).
In the size-information-absent cells, only product movements are shown in the GIFs. In the size-information-highlighted cells, a description of the mobile WiFi device's dimensions and weight is constantly highlighted beneath product movements in the GIFs (for the stimuli, see the Web Appendix).
After viewing the product animations, participants imagined that they were looking for a mobile WiFi device that is convenient and easy to carry and then indicated the highest price they were willing to pay for the mobile WiFi device they viewed between $0 and $100. Then, they rated the perceived size of the mobile WiFi device with two items on an eight-point bipolar scale (1 = "large/heavy," and 8 = "small/light"; α =.66). Finally, we measured a set of control variables on the same scales used in our prior studies, including perceived distance, specific product perceptions (i.e., quality, durability, reliability, and aesthetics), attention to the animation, attention to the product, and arousal.
A two-way ANOVA on size perception revealed a marginally significant main effect of size information (F( 1, 297) = 3.63, p =.06) but not movement speed (F( 1, 297) =.41, p =.52). More central to our prediction, there was a movement speed × size information interaction (F( 1, 297) = 5.62, p =.02). When actual size information was not shown, we replicated the speed-based scaling effect, such that participants perceived the mobile WiFi device as smaller in size at a faster speed (Mfast = 6.53, SD = 1.05 vs. Mslow = 6.09, SD = 1.37; F( 1, 297) = 4.60, p =.03). In contrast, when explicit product size information was constantly highlighted in product animations, movement speed no longer influenced size perception (Mfast = 5.91, SD = 1.39 vs. Mslow = 6.16, SD = 1.16; F( 1, 297) = 1.48, p =.23). These results support H5.
Another two-way ANOVA on WTP generated no main effects (Fs < 1.15, ps >.29) but only a marginally significant movement speed × size information interaction effect (F( 1, 297) = 2.95, p =.09). When product size information was absent in product animations, participants indicated a higher WTP for the mobile WiFi device when movement speed was faster (Mfast = 62.88, SD = 29.13 vs. Mslow = 53.99, SD = 26.08; F( 1, 297) = 3.94, p =.05). These results confirm H6a such that fast animated movement enhances WTP when a small product size is desirable. In contrast, when product size information was highlighted, WTP did not change as movement speed increased (Mfast = 56.75, SD = 27.21 vs. Mslow = 58.83, SD = 28.24; F( 1, 297) =.21, p =.65).
We used the PROCESS macro (Model 7) developed by [32] to test a moderated mediation model (with 5,000 resamples). Size perception mediated the effect of movement speed on WTP only when explicit product size information was absent (95% confidence interval [CI]: [.19, 3.64]), in support of H7, but not when such information was highlighted to participants (95% CI: [−2.92,.34]).
Finally, an additional set of two-way ANOVAs revealed no movement speed × size information interactions on perceived distance, specific product perceptions, attention to the animation, attention to the product, and arousal (Fs < 1.03, ps >.31) and thus ruled out these alternative explanations for the results of Study 5.
Study 5 demonstrates another boundary condition for the speed-based scaling effect, that is, explicit external reference for product size will override this effect. More importantly, this study illustrates a downstream consequence of the speed-based scaling effect. When a small size is a preferred product attribute, fast animated movements of a product shown in video ads can increase WTP through decreasing size assessment of the product.
Study 6 has two main purposes. First, in contrast to Study 5 focusing on a small product size as a preferred attribute, Study 6 is aimed at examining WTP when a large product size is a desirable attribute. For this purpose, we adopted a bottled blueberry drink as the focal stimulus for Study 6 because a larger quantity contained in a product package represents greater product value for edible products. With this stimulus, Study 6 also generalizes the speed-based scaling effect to beverages, a completely different product category than those examined in Studies 1–5. When a large product size is desirable, fast animated movement should lead to a lower WTP (H6b) through decreasing size assessment (H7). Second, Study 6 provides supplementary evidence for the moderating role of perceived cross-domain movement similarity (H2). Whereas in Study 2 we manipulated perceived cross-domain movement similarity, in Study 6 we measured participants' perception of similarity in movement patterns between the bottled blueberry drink and animate agents as a continuous moderator.
We randomly assigned 203 U.S. participants from MTurk (76 women; Mage = 33.56 years) to either a fast-movement condition or an original-movement condition.
We showed participants a bottled blueberry drink that performs various movements, including spinning, gliding, and zooming in and out actively in a video ad. We altered the playback speed of the original video to be 250% to create a fast-movement video and compared it with the original video (for the stimuli and the pretest results for the speed manipulation, see the Web Appendix). Both videos were presented in GIF format.
After viewing the product ad, participants indicated the highest price they were willing to pay for one bottle of the blueberry drink shown in the ad between $0 and $10. Then, they rated the product size on two items (1 = "small/light," and 8 = "large/heavy"; α =.64). To measure perceived cross-domain movement similarity, we asked participants to rate the extent to which "the way the blueberry drink moves is similar to the way an animal (e.g., insect, bird, fish) moves" (1 = "not at all," and 7 = "very much"). Finally, we measured a set of control variables, including perceived normalness of speed, perceived distance, attention to the animation, attention to the product, and arousal, on the same scales used in our previous studies. To measure specific product perceptions, we kept the items "high-quality" and "beautiful" we used previously and replaced "durable" and "reliable" with "healthy" and "tempting," given that durability and reliability are less relevant to drinks.
First, we tested the two-way interaction between movement speed (−1 = original, 1 = fast) as a dichotomous variable and perceived cross-domain movement similarity (mean-centered) as a continuous variable in a regression model. Replicating our prior studies, we found a main effect of movement speed (Mfast = 4.59 vs. Moriginal = 4.92; B = −.16, t(199) = −1.96, p =.05). There was also a main effect of perceived movement similarity (B =.15, t(199) = 3.25, p =.001). More importantly, these main effects were further qualified by a two-way interaction between movement speed and perceived movement similarity (B = −.09, t(199) = −2.06, p =.04).
We conducted a spotlight analysis to understand this interaction ([ 4]; [24]) with PROCESS Model 1 ([32]). Only for participants who perceived a high level of cross-domain movement similarity (defined as one SD above the mean), size perception decreased as movement speed increased (Mfast = 4.69 vs. Moriginal = 5.36; B = −.34, t(199) = −2.82, p =.006). In contrast, this difference disappeared (Mfast = 4.49 vs. Moriginal = 4.47; B =.01, t(199) =.09, p =.93) for those who perceived a low level of cross-domain movement similarity (defined as one SD below the mean). As shown in Figure 4, Panel A, a floodlight analysis ([62]) indicated that 44.8% of participants whose perceived movement similarity score was above the Johnson–Neyman point (J-N point = 2.88 vs. sample mean = 2.87). These results provide convergent support for H2 regarding the moderating role of perceived cross-domain movement similarity by operationalizing both size assessment and perceived cross-domain movement similarity in a different way.
Graph: Figure 4. The moderating role of perceived cross-domain movement similarity (Study 6).
Consistent with our prediction, a moderated regression on WTP generated a main effect of movement speed, such that fast movement marginally decreased WTP for the bottled blueberry drink (Mfast = 4.12 vs. Moriginal = 4.89; B = −.39, t(199) = −1.79, p =.08). Perceived movement similarity also had a main effect (B =.99, t(199) = 8.48, p <.001). More importantly, these main effects were qualified by a movement speed × perceived movement similarity interaction (B = −.39, t(199) = −3.38, p =.001). A spotlight analysis ([ 4]; [24]) based on PROCESS Model 1 ([32]) showed that fast movement decreased WTP for participants who perceived high cross-domain movement similarity (defined as one SD above the mean; Mfast = 5.23 vs. Moriginal = 7.47; B = −1.12, t(199) = −3.63, p <.001). These results support H6b such that fast animated movement decreases WTP when a large product size is desirable. In contrast, for those who perceived low cross-domain movement similarity (defined as one SD below the mean), movement speed did not influence WTP (Mfast = 3.01 vs. Moriginal = 2.31; B =.35, t(199) = 1.15, p =.25). A floodlight analysis ([62]) found that 44.8% of participants whose perceived movement similarity score was above the Johnson–Neyman point (J-N point = 2.98 vs. sample mean = 2.87), as shown in Figure 4, Panel B.
A moderated mediation test with 5,000 resamples (PROCESS Model 7; [32]) demonstrated that size assessment mediated the effect of movement speed on WTP only for participants who perceived high cross-domain movement similarity (defined as one SD above the mean; 95% CI: [−.91, −.07]), in support of H7, but not for those who perceived low cross-domain movement similarity (defined as one SD below the mean; 95% CI: [−.29,.34]).
Finally, regression analyses did not reveal movement speed × perceived movement similarity interactions on perceived normalness of speed, perceived distance, specific product perceptions (i.e., quality, healthiness, temptingness, and aesthetics), attention to the animation, attention to the product, and arousal (|t|s < 1.10, ps >.27), which could not explain the results of Study 6.
Study 6 provides convergent evidence for the moderating role of perceived cross-domain movement similarity and further increases the robustness of our findings by showing that the speed-based scaling effect also applies to consumer packaged goods, such as bottled drinks, which cannot move spontaneously in reality either. The results also point to another important marketing implication by indicating that fast animated movements of a product displayed in video ads decrease consumers' size assessment of the product and consequently lower their WTP for it when a large product size is a desirable attribute.
Six studies provide convergent support for the speed-based scaling effect. A product that is animated to move spontaneously in video ads but incapable of moving in reality is judged to have a smaller size when it is shown moving faster. We demonstrate this speed-based scaling effect across various product categories (consumer electronics, hand tools, home appliances, and consumer packaged goods) and for different facets of size measurement (length and weight). Intriguingly, for such immobile, inanimate products, animated movement speed systematically influences consumers' assessment of product size, which consequently affects their WTP, depending on their specific product size preferences. These findings add to the literature on the malleable nature of product size perceptions ([17]; [18]; [31]; [43]; [60]).
Extant studies in the marketing literature have mainly demonstrated that consumers employ a learned association to evaluate targets within the same domain in which the association was initially learned (e.g., [ 9]; [10]). More recent research has started to examine consumers' cross-domain application of learned associations ([13]). The current research adds to this emerging stream of research by illustrating consumers' application of the size–speed association learned from the domain of free-moving, animate agents (e.g., animals, humans) to the domain of immobile, inanimate products (e.g., consumer goods).
The cross-domain knowledge transfer model maintains that similarity between the target domain and the base domain should facilitate cross-domain knowledge transfer ([27]; [28]). We demonstrate that a basis for cross-domain application of the learned size–speed association is that a product's movement pattern in the target domain is perceived as at least to some extent similar to animate agents' movement patterns in the base domain (Studies 2 and 6). Furthermore, the cross-domain knowledge transfer model suggests that knowledge about the base domain determines people's evaluation of objects in the target domain ([27]; [28]; [48]). In line with the notion that people have primarily learned the relationship between physical size and movement speed from the base domain of animate agents, we find that the sign of the speed-based scaling effect was reversed after participants were exposed to a positive size–speed association about animals (i.e., the base domain; Study 3). Finally, prior research has suggested that cross-domain knowledge transfer occurs particularly when people are trying to understand a novel target domain ([47]). Consistent with this account, we show that participants with less knowledge about home appliances were more likely to generalize the size–speed association they had learned from the domain of animate agents to their size assessment of a dehumidifier (Study 4). In addition, when explicit size information was highlighted in video ads, participants no longer needed to rely on cross-domain knowledge transfer to form product size assessment (Study 5).
Product display research has mainly focused on how "static" display features influence product judgment and evaluation ([54]; [58]). Yet none of these "static" factors, such as display location and position ([ 9]; [10]; [11]; [16]; [18]; [36]; [64]), display orientation ([19]; [23]; [55]; [68]), number of units ([44]; [69]), camera angle ([46]; [76]), color ([31]; [42], [41]), contrast ([35]), density ([63]), shape ([12]; [13]; [25]; [38]; [56]; [60]; [73]; [75]), realism ([39]), and visual concealment ([61]), is uniquely related to animated product movements in video ads.
Despite the increasing importance of video ads ([40]; [57]; [67]), research on how dynamic visual cues related to movements of a product shown in video ads can shape consumers' perception of the product has been scant until recently. For instance, [49] showed that a nonhuman target is more likely to be perceived as having human-like intentions when its movement speed resembles the natural speed of human movements; [33] demonstrated that, in general, spatially approaching objects induce a negative reaction from viewers; [40] showed that consumers perceive products that move in a more lively pattern as more novel; [57] found that dynamic product presentations induce a more favorable evaluation of hedonic products than do static product displays; [50] demonstrated that subtle changes of background colors elicit favorable responses. In addition, a related stream of research has explored the impacts of dynamic visual cues related to signs and logos on evaluation and behavior ([ 7]; [14], [15]; [29]). We contribute to this emerging literature on dynamic visual cues by demonstrating a cross-domain application of the specific association between movement speed and physical size in shaping consumers' judgments and by bringing to light important perceptual and marketing-relevant consequences of animated movement speeds displayed in video ads.
In the contemporary digital era, consumers are constantly exposed to product videos from pop-up ads and banners when they browse online, from in-stream ads when they stream music and movies, from in-app ads when they play games on their smart devices, and from large LED signs when they walk in commercial districts or inside shopping malls. In video ads, products are often displayed in a dynamic manner as if they can fly or rotate spontaneously, even though they may not be able to move freely in reality. Given the complex nature of movements displayed in video ads, it is imperative for marketers to gain a systematic understanding of how dynamic visual cues can shape product perceptions. Based on this understanding, they will be in a better position to make use of these dynamic cues and present their products to stimulate more click-throughs and higher purchase conversion rates or increase consumers' interest in the products or store visits. Addressing this important issue, our research suggests that how fast or slow products are depicted as moving can systematically affect consumers' product judgments.
For products for which a small size is preferred by consumers due to considerations of portability or storage constraint (e.g., mobile devices), marketers can animate products' movements to be fast in video ads to convey a small product size. In contrast, for edible products (e.g., food, drinks) and household products (e.g., detergents), a large product size is generally a desirable product attribute to consumers. Our findings suggest that marketers should avoid displaying the movements of these products to be fast in video ads if they adopt a value-based positioning (i.e., greater quantity for the same price).
The current research has important implications for advertising of new products in particular. Firms often use video ads to introduce their new products. Because consumers will generally be less familiar with new products (especially with really new products), their assessment of the size of a new product will be more susceptible to the influence of product movement speeds shown in video ads, especially when explicit product size information is not highlighted. To shape consumers' expectation about the actual size of a new product, marketers should carefully determine the product's animated movement speed when designing video ads.
To leverage the speed-based scaling effect, marketers could animate a product to move in a pattern that is similar to animate agents' movement patterns. Our findings also suggest that consumers' perception of animated product movement—and, consequently, the speed-based scaling effect—could be shaped at the time of judgment formation. Thus, marketers who aim to strengthen the speed-based scaling effect can direct consumers' attention to animate agents that remind consumers of the inverse relationship between movement speed and physical size, for instance, by showing a bird flying around the focal product that is animated to move in a video ad. Moreover, online advertisers can strengthen the speed-based scaling effect by carefully targeting the right audience. Whenever possible, online advertisers are advised to identify and target consumers who are less familiar with the broad target product domain through tracking their prior purchase or browsing histories to maximize the effectiveness of animated movement speed in influencing their product size assessment, given that this group of consumers are more susceptible to the speed-based scaling effect.
Although in our studies the speeds of fast-movement videos were generally set to be four or five times fast as the speeds of slow-movement videos, we would like to clarify that we adopted this setup primarily to maximize speed differences across conditions to detect a statistically meaningful sizeable effect. We do not intend to suggest that marketers and designers must animate products to move at speeds employed in our studies to communicate specific product sizes. Instead, the implication of this research is that a relative increase in animated movement speed can lead to a corresponding decrease in size assessment as shown in Study 1. Our recommendation for marketers and designers is that they should be aware of the general negative relationship between movement speed and size assessment. Guided by this principle, they are advised to determine the ideal animated movement speeds for their products through speed calibration tests tailored to their products' natures.
This research demonstrates a cross-domain transfer process of the learned size–speed association and identifies several conditions in which the cross-domain knowledge transfer is more or less likely to occur. We discuss several limitations of the current research and venues for future research. First, we did not investigate whether the cross-domain knowledge transfer process occurs spontaneously or not. Addressing this issue in future studies would further broaden our understanding of the speed-based scaling effect. Second, we develop our theoretical framework based on the domain of animate agents due to its relevance to animated product movements and argue that the size–speed association has been primarily learned through observations of animals or other humans. Within the domain of animate agents, people also have ample opportunities to vividly experience this size–speed association from one's own bodily sensations. For example, the same person is able to move faster in a compact and light sports shirt than in a bulky and heavy winter jacket. Outside the domain of animate agents, similar laws of physics regarding the relationship between movement speed and physical size may also apply to objects in motion, although in this case consumers could be more likely to attribute observed movement speed to other factors, such as external forces or preprogrammed configurations. Further research could explore how these various grounds differ or converge in shaping consumers' learning about the size–speed association and consequently the speed-based scaling effect. Third, future studies can examine whether a product's animated movement speed could affect consumers' judgment of other product attributes, such as prestige ([13]) or innovativeness ([40]). Researchers may also extend the scope of research to the domain of logo animations by establishing associations between different logo movement speeds and different dimensions of brand personalities ([ 7]; [14]; [30]; [37]; [64]; [72]). Further investigations of dynamic visual cues will enrich our understanding of how video ads can shape consumers' learning and perception in the modern video-rich environment.
Supplemental Material, jm.18.0539-File003 - Speed Up, Size Down: How Animated Movement Speed in Product Videos Influences Size Assessment and Product Evaluation
Supplemental Material, jm.18.0539-File003 for Speed Up, Size Down: How Animated Movement Speed in Product Videos Influences Size Assessment and Product Evaluation by He (Michael) Jia, B. Kyu Kim and Lin Ge in Journal of Marketing
Footnotes 1 Associate EditorGita Johar
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors gratefully acknowledge the financial support from the Early Career Scheme of the Research Grants Council of Hong Kong (27503517), the HKU-Fudan IMBA Joint Research Fund (16170704), the HKU Seed Fund for Basic Research (201811159194), and the HKU Teaching Development Grant (101000726) awarded to the first author.
4 ORCID iDHe (Michael) Jia https://orcid.org/0000-0003-4690-9490
5 Online supplement: https://doi.org/10.1177/0022242920925054
6 1Experimental biologists use "movement frequency" to describe "movement speed" defined in this research and operationalize body size as body mass given the infeasibility of gauging dimensions across species with morphological differences on a unified scale ([59]).
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By He (Michael) Jia; B. Kyu Kim and Lin Ge
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Record: 170- Status Games: Market Driving Through Social Influence in the U.S. Wine Industry. By: Humphreys, Ashlee; Carpenter, Gregory S. Journal of Marketing. Sep2018, Vol. 82 Issue 5, p141-159. 19p. 2 Diagrams, 4 Charts. DOI: 10.1509/jm.16.0179.
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Status Games: Market Driving Through Social Influence in the U.S. Wine Industry
Research on market orientation finds that market-driven firms succeed by identifying and appealing to consumer needs. Yet many technologically innovative firms achieve remarkable success by taking a market-<italic>driving</italic> approach. The ways that firms drive markets without disruptive innovation, however, remain unclear. Adopting a market-systems perspective, the authors conduct an ethnographic analysis of producers, distributors, retailers, critics, and consumers in the U.S. wine market. They find that firms drive the market by playing a status game. Firms pursue a vision and advance that vision among influencers inside and outside the industry to gain recognition. Winners of the status game influence and drive social consensus by setting benchmarks and shaping consumer preferences to the firm’s advantage. High status is difficult to imitate, creating an advantage that can endure for years or decades.
market orientation; status; competitive advantage; social influence; market driving
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By Ashlee Humphreys and Gregory S. Carpenter
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Record: 171- Stock Returns on Customer Satisfaction Do Beat the Market: Gauging the Effect of a Marketing Intangible. By: Fornell, Claes; Morgeson III, Forrest V.; Hult, G. Tomas M. Journal of Marketing. Sep2016, Vol. 80 Issue 5, p92-107. 16p. 1 Diagram, 8 Charts, 3 Graphs. DOI: 10.1509/jm.15.0229.
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Record: 172- Strategic Information Transmission in Peer-to-Peer Lending Markets. By: Caldieraro, Fabio; Zhang, Jonathan Z.; Cunha, Marcus Jr.; Shulman, Jeffrey D. Journal of Marketing. Mar2018, Vol. 82 Issue 2, p42-63. 22p. 7 Charts, 3 Graphs. DOI: 10.1509/jm.16.0113.
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Strategic Information Transmission in Peer-to-Peer Lending Markets
Peer-to-peer (P2P) marketplaces, such as Uber, Airbnb, and Lending Club, have experienced massive growth in recent years. They now constitute a significant portion of the world's economy and provide opportunities for people to transact directly with one another. However, such growth also challenges participants to cope with information asymmetry about the quality of the offerings in the marketplace. By conducting an analysis of a P2P lending market, the authors propose and test a theory in which countersignaling provides a mechanism to attenuate information asymmetry about financial products (loans) offered on the platform. Data from a P2P lending website reveal significant, nonmonotonic relationships among the transmission of nonverifiable information, loan funding, and ex post loan quality, consistent with the proposed theory. The results provide insights for platform owners who seek to manage the level of information asymmetry in their P2P environments to create more balanced marketplaces, as well as for P2P participants interested in improving their ability to process information about the goods and services they seek to transact online.
The Internet and information technology increasingly produce more disintermediated and democratized industries by connecting individual actors in unprecedented ways. Such development fostered the explosive growth of the peer-to-peer (P2P) economy and enabled the rise of many successful P2P platforms. Uber has quickly become the world's largest driving service; Alibaba is now the most valuable retailer; and Airbnb offers more rooms than any other hospitality service (Weed 2015). Similarly, Lending Club, a P2P lending platform, is now the world' s largest online marketplace connecting individual borrowers and investors.
Deservedly, the P2P economy and its major societal impacts have attracted substantial research interest as well as calls for more studies that apply decision-making perspectives to these consumer-to-consumer interactions (Kumar 2015; Yadav and Pavlou 2014). In response, a few recent articles in marketing and economics have studied peer-influenced consumer decisions in industries such as music (Sinha, Machado, and Sellman 2010), video games (Landsman and Stremersch 2011), used cars (Lewis 2011), lending (Lin, Prabhala and Viswanathan 2013), and retailing (Backus, Blake, and Tadelis 2015).
Even as P2P platforms expand in various industries, information asymmetry remains a challenge for both participants and P2P platform managers. Without the signals of brand power and other reputational heuristics that consumers often use as proxies for quality, participants in P2P platforms need to make decisions with limited information, causing transaction risks to be higher than those in traditional business settings. On the one hand, buyers need to decide how to interpret the information provided by sellers to infer quality and minimize risk; on the other hand, sellers can strategically reveal or withhold information about themselves to increase their chances of a favorable outcome. In turn, platform managers likely need to weigh the information provided by sellers and create mechanisms to reflect transactional risk accurately and engender more trust in the platform (Schlosser, White, and Lloyd 2006).
In this research, we examine the issue of information asymmetry in P2P markets by studying a social lending platform. We center attention on the Lending Club platform because it is an exemplar P2P marketplace that is gaining substantive importance and in which the effects of asymmetric information can lead to significant consumer losses. P2P lending is a multibillion dollar industry that has experienced staggering 100% annual growth since 2010 (Economist 2014) and is expected to reach $150 billion in size by 2025 (PWC 2015). It has democratized capital markets by allowing people to bypass traditional banking roadblocks and enabling them to become customers and suppliers of their own financial products. Lending platforms provide verifiable information about borrowers, but a considerable degree of information asymmetry remains, causing lenders to bear significant risk because loans are unsecured. For instance, as of March 2016, Lending Club reports that across all loans, 7.8% of the amount issued to borrowers is charged off. Because lenders shoulder the default losses, it is optimal for them to minimize default risk by looking for borrowers who are most likely to repay their loans.
We consider the Lending Club platform and examine whether the strategic transmission of nonverifiable information by a borrower, represented by the length of the description of the reasons for the loan, offers signaling content that complements other verifiable information, and helps lenders distinguish the likelihood of repayment of each loan. Any mechanism on a P2P lending platform that can further distinguish borrowers more likely to repay a loan has the potential to improve the lending market for both borrowers and lenders. To the best of our knowledge, this is the first study to consider how the mere presence of a description and the length of the description might help borrowers strategically transmit information about their repayment prospects.
According to extant theories on "cheap talk," if the provision of nonverifiable information is costless, then it should not affect a buyer's decision. If the provision has costs in terms of effort, then such information carries a signal that might affect the buyer' s decision in a monotonic manner: the higher the effort, the stronger the signal. In contrast with these traditional perspectives, we recognize that nonverifiable communication in P2P platforms is a more complex phenomenon because there are multiple sources of information. Thus, we propose to study the P2P market under the lenses of a theory of countersignaling as the potential major force governing P2P interactions under information asymmetry.
Specifically, within the same creditworthiness class, as measured by verifiable information, loan applicants who provide no loan description (i.e., choose not to transmit non- verifiable information) are expected to have a higher likelihood of getting funded and a lower likelihood of delinquency, according to the countersignaling argument. However, when borrowers decide to write descriptions, applications featuring longer descriptions have a greater likelihood of getting funded and a lower likelihood of delinquency than those with short descriptions, a result that is consistent with an effort-asa-signal argument. We contrast the predictions of counter- signaling with those of competing theories. Using a data set of loan applications from the P2P platform Lending Club over three years, we find support for our theory: lenders' funding decisions are influenced by strategic countersignaling by borrowers, and these decisions are confirmed by the borrower' s subsequent likelihood of delinquency.
This research thus contributes to information transmission and consumer decision-making literature in several ways. Theoretically, we show that the countersignaling mechanism is present in the P2P transaction setting, and it helps resolve information asymmetry. This is a novel finding in light of competing theories based on signaling, cheap talk, persuasion, and psycholinguistics. Empirically, we provide evidence that is consistent with the countersignaling theory and inconsistent with the competing mechanisms. In particular, we show that individuals indeed strategically transmit information to other individuals in an online P2P environment through the effort they exert to write a loan description. This strategic transmission provides an informative signal about loan quality, as evidenced by subsequent loan performance. We show that in equilibrium, individuals on both sides of the platform are sophisticated actors, capable of sending and interpreting quality signals.
Our research also adds to literature on consumer lending decisions, a stream of research that has increasingly received attention from marketing scholars. For instance, research has investigated lenders' reaction to race and appearance of an applicant' s uploaded photograph (Galak, Small, and Stephen 2011; Ravina 2012), the number and roles of the members of an applicant's friendship group (Lin, Prabhala, and Viswanathan 2013), lender herding behaviors (Herzenstein, Dholakia, and Andrews 2011; Zhang and Liu 2012), and the impact of type of media on microlending (Stephen and Galak 2012). Our study adds to the literature by showing how borrowers can use loan descriptions to signal quality to lenders. The findings have implications for designers of P2P lending platforms, who should consider countersignaling behavior when they seek to fine-tune their risk/return algorithms. The findings provide guidance to borrowers regarding when to countersignal; for lenders, they reveal how to weight information that goes beyond the verifiable information provided by the platform in order to better identify true risks. A growing industry of hedge funds and algorithm-based services (e.g., Lending Robot) promise that their risk assessments are more comprehensive than those from existing platforms; they select loans on the basis of a borrower's nonverifiable information to boost returns. Our finding reveals an area of information that these companies could productively exploit. Our work not only informs the growing number of P2P marketplaces but also provides new insights into consumer financial decision making in the age of data prevalence.
More generally, our work has managerial implications for various platforms (e.g., eBay, Etsy, Airbnb, Upwork) on which sellers may wish to communicate their quality credibly to buyers. Given the evidence that countersignaling can indeed convey information about quality, managers of P2P platforms should consider opening this avenue of information exchange and incorporate it in their composite seller rating score presented to the buyer in order to reduce information asymmetry and increase efficiency, which, in turn, would build trust among participants. As the P2P economy keeps growing, information asymmetry and trust in the platform will continue to be notable issues. Platforms that can fine-tune their rating system to better reflect risk and to resolve information asymmetry more effectively will instill more confidence among participants and thus gain advantages over their rivals. Sellers (in our case, borrowers) on P2P platforms can use the insights of this research to decide when to rely exclusively on verifiable information provided by the platform and when it is worthwhile to produce nonverifiable information. Buyers in P2P marketplaces can learn how to aggregate platform-provided with participant-provided information about the products and services marketed on the platform. Buyers who are adept at picking up informational cues might achieve higher returns (e.g., buying high-quality products at a lower price) while mitigating risks.
Our work brings together two research streams: asymmetric information in P2P platforms and P2P lending specifically, and the mechanism of countersignaling. We now briefly discuss related research in each stream and our contributions.
It is well understood that for markets to work efficiently, buyers and sellers need to possess symmetric information. In the presence of information asymmetry, the market will not allocate resources efficiently and may even collapse (Akerlof 1970).
Thus, research on P2P platforms has largely focused on information disclosure and signaling to alleviate asymmetric information. For example, Lewis (2011) studies the P2P marketplace eBay Motors and finds that the disclosure of some degree of verifiable information by a seller, such as pictures and text with specifications of the automobile, can serve to reduce adverse selection, provided sellers are contractually obligated to fulfill products that match the information they provide. Backus, Blake, and Tadelis (2015) identify how participants bargaining on eBay's "Best Offer" listings can signal their level of impatience by posting round-number prices. Li, Tadelis, and Zhou (2016) investigate how sellers can signal quality by offering incentives for consumers to leave feedback in the online P2P marketplace. Taobao, Tadelis, and Zettelmeyer (2015) use a field experiment to investigate how information disclosure about the quality of objects can improve the efficiency of markets. The authors find that the disclosure decreases search costs and thus helps bidders better match their preferences with the quality of the products being offered in the marketplace.
All of these studies find that sellers can alleviate asymmetric information by either voluntarily revealing verifiable information about quality types or sending a costly signal. Our work adds to the literature by recognizing that strategic information transmission in P2P platforms can be a more complex phenomenon in situations in which the P2P platform can serve as an additional source of information. In such cases, the sellers can resolve additional information asymmetry by ( 1) the voluntary disclosure of unverifiable information (even if unrelated to quality types) and ( 2) the effort of providing lengthy disclosures. These two elements together can result in a nonmonotonic relationship between the degree of the seller's disclosure and the buyer's interpretation of quality.
Likewise, P2P lending platforms also experience asymmetric information. Research in this domain has primarily focused on investigating how factors beyond borrower's creditworthiness can influence lender behavior.
Freedman and Jin (2011) show that some of the asymmetric information and adverse selection can be reduced through a learning-by-doing process in which the entire market learns about the risk level of the financial products being offered in the market and gradually excludes low-quality borrowers in favor of higher- quality borrowers. The likelihood of a loan application getting funded can be affected by the race and appearance of an applicant's uploaded photograph (Duarte, Siegel, and Young 2012; Pope and Sydnor 2011; Ravina 2012), the number and roles of the members of an applicant's friendship group (Lin, Prabhala, and Viswanathan 2013), and lender herding behaviors (Herzenstein, Dholakia, and Andrews 2011; Zhang and Liu 2012).
Kawai, Onishi, and Uetake (2014) also study the issue of adverse selection, using data from an earlier version of the Prosper P2P platform, where potential borrowers posted public reserve interest rates to signal their creditworthiness. In our framework and data, interest rates are set by the lending platform according to the borrower's verifiable risk profiles, which is similar to the situation proposed by Milde and Riley (1988). As a result, our borrowers cannot use interest rates to signal their quality and instead rely on unverifiable information to signal and countersignal. When the borrower is required to provide a loan description, Sonenshein, Herzenstein, and Dholakia (2011) demonstrate that, using the perspectives of persuasion, borrowers with poor credit history can improve funding likelihood by explaining and taking responsibility for their financial mistakes. Similarly, the number and content of borrower identity claims influence lenders' decisions (Herzenstein, Sonenshein, and Dholakia 2011; Michels 2012).
Our current research differs from previous P2P lending contributions in two dimensions. First, previous work has only looked at lender's funding decision. We take it a step further and examine whether these decisions are correct in the long run, as measured by loan performance. Second, whereas previous platforms require borrowers to provide loan descriptions, Lending Club offers borrowers the option not to do so. The proposed framework allows us to investigate the differential signaling values of the description length (and its related effort) as well as the value from the mere presence of (or lack of) the description.
Therefore, we can corroborate prior findings that nonverifiable information such as the purpose and the content of the loan description affect loan funding. However, we uniquely provide theory and evidence suggesting that ( 1) the mere presence and ( 2) the length of the loan description are signaling mechanisms that can attenuate information asymmetry between the borrower and lender regarding the borrower's ability to repay the loan. Our framework contributes to the P2P lending literature by offering a unified theory of countersignaling, explaining both borrower and lender behaviors, that is likely sustainable in the long run. We show that in equilibrium, individuals on both sides of the platform are sophisticated actors, capable of sending and interpreting quality signals.
In traditional signaling models, all the information originates from the sender, and the effect of the signaling instrument is monotonic. Research in signaling has examined a broad range of contexts, from education choice (Spence 1973) to advertising decision (Milgrom and Roberts 1986) and pricing (Desai 2000).
The P2P lending context features a mix of verifiable information, screened and provided by the platform (e.g., credit score, debt level, public records), and nonverifiable information provided by the borrower (e.g., loan purpose, loan description). When borrowers prepare their applications, they do not know with certainty how their verifiable information (compiled by the P2P lending platform) will appeal to lenders, according to the platform's underwriting model. This reality more closely relates to the counter- signaling theory, in which there are two sources of information: one provided by the sender and the other provided by a trusted third party (Feltovich, Harbaugh, and To 2002).
Prior research theorizes that when additional sources of information are available, high-quality senders countersignal by choosing not to provide information about course grades Feltovich et al. (2002), by using low-quality packaging (Clements 2011), spending less on advertising (Orzach, Overgaard, and Tauman 2002) or engaging in either image or informative advertising (Mayzlin and Shin 2011). Countersignaling creates a nonmonotonic relationship between sender quality and signaling effort, and this relationship can hold even if there is heterogeneity in how consumers process information (a common phenomenon, as noted by Bart et al. [2005] and Zhu and Zhang [2010]).
Our research is one of the very few studies to empirically investigate countersignaling. We do so in the growing field of P2P lending. We now describe our theory, followed by an empirical test of the theory.
In this section, we put forth a theory in which loan descriptions serve as an instrument for countersignaling, formalize hypotheses associated with this theory, and contrast its predictions with competing theories regarding the role that loan descriptions serve on peer-to-peer lending platforms. In the P2P marketplace, informational asymmetry exists regarding a borrower's type. Borrowers' inherent quality may not be expressed perfectly in the available verifiable information (e.g., credit scores), and borrowers have superior knowledge about their own likelihood to pay back a loan, relative to lenders. Lenders may attempt to infer the true quality of the loan from the information that borrowers provide on their applications. Thus, a borrower might try to use the loan request or description as a signaling instrument, to facilitate the exchange of information from the prospective borrower (sender) to the potential lenders (receivers). Countersignaling theory offers predictions about a lender's behavior in response to a borrower's communication of such nonverifiable information.
Consider first a stylized P2P social lending situation in which there are three types of borrowers for a given asset class: high-quality, medium-quality, and low-quality, where quality indicates borrowers' unobservable likelihood to repay the loan. Each loan has a level of risk and an interest rate that compensates the lender for taking that risk. The lender's god is to choose the asset classes that match his or her portfolio objectives and within each class identify the quality of the loans. The signaling mechanism (i.e., loan description, in our case) does not differentiate applicants across verified credit grades; this classification already has been done by the credit grade itself. Instead, the loan description functions to differentiate among the loans within the same credit grade. All potential lenders know that a loan with credit grade A (best), priced at an interest rate of 5%, has a lower risk of default than a loan with credit grade G (worst), priced with an interest rate of 21%. The informational problem is the differentiation among loans within a range of similar credit grades and interest rate combinations. Thus, high-, medium-, and low-quality types refer to the types within the same credit grade (e.g., among three A-grade borrowers or three G-grade borrowers).
It is important to note that Lending Club loans are unsecured personal loans, so their creditworthiness is supported only by and is a direct function of the creditworthiness of the borrower. The credit grade, however, is not a completely deterministic measure of creditworthiness (e.g., 95% of A-grade borrowers honor their loans, but 5% do not), nor is it the only verifiable information reported by the Lending Club. The platform also reports additional verifiable information such as FICO score and number of previous hard credit inquires. Credit grades largely depend on FICO scores, though, as we demonstrate empirically later in this article.
In our data set, Lending Club borrowers apply for loans and may write loan descriptions before the platform performs the formal creditworthiness assessment (e.g., checking credit scores, employment, public records) and reveals this third-party-verified information to lenders on the platform. At that point, borrowers know that the third-party-verified information correlates positively with their identified type, but they do not know the exact information.
Borrowers seek to maximize their payoff when choosing whether to write a loan description and how long to make it.[ 1] A borrower is willing to incur the cognitive cost (effort) of creating a loan description if the benefits outweigh the costs. Thus, the loan description can facilitate a lender's inference of borrower quality. Medium-quality borrowers cannot be confident about whether the information provided by the third party will be viewed positively. Thus, they exert effort to send a signal to differentiate themselves from low-quality borrowers and write long descriptions. Low-quality borrowers recognize that the verifiable information is unlikely to benefit them, so it is unprofitable to attempt to overcome the negativity of this information by exerting a high level of effort to provide a lengthy loan description. They thus exert less effort describing the loan than medium-quality borrowers do. High-quality borrowers recognize that the third-party information has a high probability of distinguishing them from a low-quality borrower. That is, they have a low probability of being confused with low-quality borrowers, and by providing no loan description, they can profitably draw a distinction from medium-quality borrowers.
Assume that, in accordance with their previous portfolio allocation decisions,[ 2] two lenders on the platform wish to invest some of their money into a certain risk-reward category of loans offered in the platform. Because each risk-reward category has fixed interest rates, it is optimal for lenders to infer the quality of the loans within each asset class and to allocate higher shares of their budget to the loans that are less likely to default (i.e., have higher quality).[ 3]
The quality of the loans within a risk-reward category is linked to the quality of the borrower, which can be represented by a parameter θ€[θ, θ], with 0 < θ < 1. Higher values of θ represent higher-quality borrowers in terms of the likelihood of on-schedule loan repayment. Both borrowers and lenders have common knowledge about the distribution of borrowers in the market, but ex ante, only a particular borrower knows his or her own true quality.
The independent platform compiles verifiable information and sends a noisy signal, x = θ + ε, where ε is a random variable distributed uniformly in the interval [-a, a]. This signal represents a measure of all the verifiable information provided by the lending platform, such as monthly income, delinquencies, credit inquiries, and so on. Ex ante, the borrower knows his or her own type θ but not the realization of x. Without loss of generality, we assume that the interval [-a, a] is such that θ — a > 0 and θ+ a < 1.[ 4]
Before x is revealed on the platform, borrowers can write descriptions of a length s (with s > 0) to send a signal to the market. To send the signal, borrowers experience a cognitive cost of effort s, where k is a cost parameter. We consider that a proportion λ of lenders are sophisticated and make decisions based on the verifiable information x and on their own inferences from the signal s. The likelihood these sophisticated lenders will fund a loan is equal to μ8 = Φ(s, x), where Φ (s, x) represents the lenders' belief function as they rationalize both the verifiable and nonverifiable information. As discussed above, this assumption is consistent with the optimal portfolio allocation of rational lenders who consider a mean-variance trade-off, as these lenders should allocate higher shares of their budget to higher-quality loans and consequently be more likely to fund higher-quality loans.
We also allow for a proportion 1 — λ of lenders to be naive lenders who myopically believe in the nonverifiable information. Our treatment of naive individuals is similar to Inderst and Ottaviani (2012), who also allow for individuals who do not properly account for the strategic incentives behind the information they receive. For these lenders, their belief of borrower quality increases directly with the amount of nonverifiable information and with the quality of the platform' s provided noisy signal (subsequently, we discuss the ensuing outcomes when these types of lenders are absent from the market).
The likelihood that naive lenders will fund a loan is equal to μ n = [s/(1 + s)]xy, where xy reflects the importance of verifiable information to the naive lenders and captures the effect that borrowers who have good objective information have a better basis to write enticing descriptions (for instance, a borrower who has a prestigious job or high income can write a description that highlights these facts). The parameter g allows for a nonlinear interaction. Whereas this functional form nicely captures the possibility that naive lenders are more easily swayed by nonverifiable information when the verifiable information is positive, we note the countersignaling equilibrium result is robust to modifications to this function.[ 5] Our assumption about the likelihood that a naive lender funds a loan is also in line with portfolio allocation decisions of lenders who consider a mean-variance trade-off. In the same vein as sophisticated lenders, the naive lenders should be more likely to fund the loans that they perceive to be of higher quality. The difference between these two types of lenders is the way they form beliefs about the quality of the loans.
Notice that the presence of naive lenders may provide an incentive for some borrowers to convince these lenders via description length. As will be seen later, in the section on equilibrium results, for any type θ, both too little and too much effort investment in the description can be costly. If borrowers invest too much, their disutility for writing descriptions overpowers the benefit of convincing lenders; if borrowers invest too little, they leave too much "money on the table" from naive lenders.
Assuming that borrowers get a value V from obtaining a loan,[ 6] and recalling that x is a function of θ, we can write the borrowers' expected utility as
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 1)
Given that x is the only random variable, we can rewrite this expression as
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 2)
Since we are interested in whether the amount of non- verifiable information can signal the quality of the borrower, we will be looking for an informational equilibrium that satisfies the following perfect Bayesian equilibrium conditions:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
The first condition states that each borrower type θ sends a signal sp that maximizes his or her own utility. The second condition states that the sophisticated lenders' beliefs about the borrowers' types are confirmed in equilibrium. In other words, Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. will capture the derived belief supports our countersignaling equilibrium.
Because E[Φ(s, χ(θ))] is ultimately a function of s and θ, we can define Φ x(s, θ) " E[Φ (s, χ(θ))]. In addition, we can compute the expectation E[x(θ)y] by integrating over the random noise e:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
Hence, we define the function
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
to be this expectation.
Following Condition i, we maximize Expression 2 with respect to s. We take derivatives with respect to s and find the first-order condition
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 3)
where s* denotes the equilibrium signaling effort.
By solving the ordinary differential equation given by Expression 3, we find that Φx(s*, θ) can be expressed as
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 4)
where C is a constant to be determined by the appropriate boundary condition.
As in Milgrom and Roberts (1982) and Daughety and Reinganum (1995), we consider that a Pareto-efficient outcome requires that in a separating equilibrium, the lowest quality borrower θ has no incentive to distort his or her optimal amount of nonverifiable information. Hence, we rewrite the borrower's expected utility function in Expression 1 as
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 5)
By maximizing this expression with respect to s, we find that the optimal (undistorted) amount of nonverifiable information sent by a θ borrower is
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 6)
By using Expression 6 as a boundary condition and solving the equality
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
we can determine the constant C and rewrite Expression 4 as
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 7)
By observing Condition ii in our signaling equilibrium that Φ (s*, θ) = θ, we solve
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
for the optimal signaling amount s*. Inverting the equation, we find that the curve
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 8)
can represent the optimal amount of nonverifiable information s* for a borrower of type θ.
Finally, we prove that a countersignaling equilibrium is possible in this model. By observing Condition i in our signaling equilibrium, we find that a countersignaling equilibrium is possible if the higher types find it optimal to switch from the function described in Expression 8 to the curve s = 0 (i.e., to provide no description). Hence, we compute the higher-quality- type borrowers' expected outcome when following the belief function given by Expression 7:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 9)
On the other hand, the higher-quality-type borrowers' expected outcome when they exert zero signaling effort is simply
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. (10)
The belief Φx(0, θ) = θ is rational only for types that prefer the outcomes given by Expression 10 over those given by Expression 9.
To verify existence, consider that the difference in utilities Us* — Us0 (Expressions 9 and 10) must be positive for the lowest-quality borrower θ. This difference is
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
Next, consider that the difference in utilities Us* — Us0 for the highest-quality borrower θ must be negative. The difference is
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
Countersignaling occurs when the two conditions above are satisfied. They can be combined in the condition
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. (11)
One can verify that because s*(θ) > θ, there are parameter values that satisfy Condition 11. Furthermore, because both ξ (θ) and s* (θ) are strictly increasing in θ, there exists a cutoff borrower-quality level θ* such that types θ > θ* find it better to countersignal by sending s* = 0 because Us |θ>θ* — Us0 |θ>θ* < 0, whereas types θ < θ* find it unprofitable to do so because Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. (the types θ < θ* thereby optimally choose to send a signal s* according to Equation 8).
Thus, it follows that the equilibrium amount of non- verifiable information as a function of borrower type is
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. (12)
Notice, however, that if there were no naive consumers, and thus (1 — λ) = 0, it would be impossible to find any values for the parameters that satisfy Condition 11. In such a case, a countersignaling equilibrium could not be sustained, and the optimal for all players would be to write no description. The intuition is that if the benefit of writing a description cannot be rationalized and the cost of writing a description still exists, then the optimal is for all borrowers to simply avoid the cost of writing a description (thus setting s*no_naives = 0).
Figure 1 illustrates our theory's prediction about the relationship between the equilibrium amount of nonverifiable information sent by the borrowers and the likelihood that a loan will be funded. The figure shows that loan requests with no description are interpreted as being sent by higher-quality borrowers and are rewarded by a high likelihood of being funded. For the remaining borrowers, the likelihood that a loan request is funded is increasing in the amount of signaling effort.
In summary, in the P2P lending setting, in which information asymmetry regarding the quality of the borrowers plays a significant role, if potential lenders are able to account for countersignaling when inferring the quality of a loan, the effect of loan description on funding will be nonmonotonic. Specifically, the absence of a loan description should increase the likelihood that the loan gets funded relative to the presence of a loan description. However, once a borrower provides a loan description, the likelihood of getting funded should be higher when the description is lengthy than when the description is short. Furthermore, no borrower has an incentive to pretend to be of a different quality by mimicking the strategy of other borrowers. The implications of the analytical model are syn- thetized in four testable hypotheses:
H1: The absence of a loan description has a positive effect on the likelihood of a loan being funded.
H2: The absence of a loan description has a negative correlation with the ex post likelihood of a loan being delinquent in repayment.
H3: Given that a loan has a borrower-provided description, the likelihood of a loan being funded increases with description length.
H4: Given that a loan has a borrower-provided description, the ex post likelihood of a loan being delinquent in repayment correlates negatively with its description length.
At this point, it is useful to compare our hypotheses with competing theories. If providing a lengthy loan description were costless to borrowers, a model of cheap talk would predict that it would have no effect on lender behavior (e.g., Crawford and Sobel 1982). Thus, if loan descriptions are costless to borrowers, H1-H4 would not be supported, and we would not be able to reject the null hypotheses.
Literature on persuasion and compliance (Langer, Blank, and Chanowitz 1978) would predict that a lender is more likely to comply with a funding request when a reason is offered, whether that reason is legitimate or not. In contrast with H1, this account would predict a negative effect of the absence of a loan description on loan funding. Moreover, we would not be able to reject the null version of H2. Research that predicts an effect of the number of persuasive arguments (e.g., Petty and Cacioppo 1984) predicts that more arguments tend to improve persuasion. This prediction is consistent with H3, but it implies the opposite of H1. Moreover, we would not be able to reject the null hypotheses associated with H2 and H4.
Finally, we compare our theory' s predictions with psy- cholinguistics theory, which predicts that in asynchronous computer communications, liars produce more words when lying than when telling the truth (e.g., Hancock et al. 2008). This theory would predict that ex post loan delinquency increases monotonically with description length, in conflict with H4, and if lenders rationally infer this relationship, loan funding mono- tonically decreases with description length, in contrast with H3.
In summary, in the P2P lending context that we study, the countersignaling model generates four testable hypotheses that capture the nonmonotonic effects of loan descriptions on funding and ex post delinquency. Competing theories based on models of cheap talk, persuasion, and psycholinguistics generate one or more predictions that conflict with H1-H4. Thus, we can test whether countersignaling governs how borrowers and lenders use the loan description or whether this relationship instead can be explained better by one of the competing theories.
In this section, we describe the institutional settings that govern the theory construction and the empirical model. We first discuss the data used to test our hypotheses and provide some model free evidence, then present the tests of the impact of borrowers' provision of nonverifiable loan information on attracting funding and the ensuing relationship between non- verifiable loan information and ex post loan performance.
We examine loan requests on Lending Club. The two dependent variables of interest are whether the loans are funded by lenders and their subsequent performance. The loan application process is as follows: First, the borrower fills out an online form with his or her name, address, date of birth, annual income, and requested loan amount. Second, Lending Club immediately makes an initial nonbinding offer that includes information about the monthly payment. This offer is based on the information provided as well as publicly available information about the borrower; it is known as a "soft credit pull." Borrowers are reminded to be truthful in providing the information because false information will lead to denial of the loan. Third, once the borrower agrees with the initial terms, he or she fills out another online form, providing additional information that can be used for credit verification and displayed to lenders (e.g., employment, loan description, home ownership, Social Security number). At this point, the borrower also can write the loan description. All of this occurs before Lending Club performs the final creditworthiness verification. Fourth, after the borrower decides whether to write nonverifiable information in the form of a loan description, Lending Club conducts further identity verification by checking additional paperwork (e.g., recent tax returns), conducting phone call verifications, and making inquiries into the borrower's credit history. Finally, if the borrower passes Lending Club's underwriting review, the loan request will be posted online, along with the verified information (credit score, debt-to-income ratio, number of delinquencies) and any borrower-provided information about the purposes and description of the loan. Once the loan is funded, Lending Club deposits the money in the borrower' s bank account.
Several details make Lending Club unique relative to other P2P platforms (e.g., Prosper.com), such that it offers a cleaner venue in which to study countersignaling. First, borrowers are anonymous and are unable to communicate with lenders offline; lenders, in turn, have no capability to identify whether a particular borrower is a first-time or repeat borrower. In our data set, we observe the same information that is available to lenders when they make funding decisions; it seems implausible that lenders could gain any private, unobserved information about borrowers. Second, some factors included in prior research, such as applicant photos, borrower groups, and borrower history, are not available to lenders on this platform. Lenders thus cannot be influenced by heuristics based on borrowers' looks, group associations, or prior repayment history. Third, borrowers cannot set their own interest rates, which are set solely at Lending Club's discretion. The platform's website states that interest rates are a result of Lending Club's base rate plus an adjustment for risk and volatility, depending on creditworthiness scores.[ 7] Borrowers thus cannot signal their quality using interest rates.
The sequence of events in this loan application process may motivate borrowers to engage in signaling/countersignaling: Borrowers need to provide loan descriptions prior to Lending Club performing the verification and publishing the verifiable information. Borrowers have private information about their true quality, which they expect will correlate positively with Lending Club's published, verifiable information. However, they do not know this with certainty or a priori. Because the interaction is anonymous, and borrowers cannot communicate with lenders or entice lenders with higher interest rates, the only action they can perform is writing a loan description.
The source of data is Lending Club archives, which feature all loan applications from 41 consecutive months, May 2007- September 2010, providing a total of 26,314 applications. During this period, the platform was open only to individual instead of institutional lenders.[ 8] To be considered for a loan, borrowers must have a valid bank account, valid Social Security number, a sufficiently high credit score (640 or above), and a debt-to-income ratio below 25% (excluding mortgage). After the verifiable information is authenticated, the loan request is listed on the site for two weeks or until it gets funded, whichever happens first.
The Lending Club platform provides verifiable credit history information collected from the major credit bureaus and reports. The following information thus is reported about each application: the borrower's FICO credit score range, number of open lines of credit, earliest credit line, credit line utilization, revolving credit balance, number and timing of delinquencies, home ownership status, physical location of the applicant (state), number of credit inquiries in the last six months, and other relevant public records such as public records on file, months since last record, and months since last major derogatory report. Potential borrowers are also required to select a purpose of the loan (e.g., debt consolidation, home improvement) and have the option to provide a loan description and state why lenders should lend them money (for samples of loan descriptions of varying lengths, see Web Appendix 2), which is nonverifiable information. Interested lenders can fund a portion (minimum of $25) or the entirety of the loan request. Any defaults are managed by collection agencies commissioned by the platform. The key dependent variable of interest is the loan funding outcome, which equals 1 if the loan is fully funded and 0 if it is not funded.[ 9]
The platform allows lenders to diversify across different loans. Paravisini et al. (2016), using both Lending Club data from its early days and private third-party data on lender identification, observe diversification decisions and estimate a risk-aversion parameter for different lenders on the platform. While we do not observe lender identification or lender's other investment vehicles and hence do not explicitly model the lenders' diversification decisions, our model does speak to the lenders' selection of loans to invest once a risk-balance allocation has been made and investors have decided how much to allocate to each loan asset class. As such, the lending decisions we model are in line with the modern portfolio theory prescription of selecting loans such that the expected return is maximized for a given level of risk. A loan that is fully funded implies that a sufficient number of lenders deemed it to have low probability of default and therefore worthy of funding (see the aforementioned discussion of loan selection).
As researchers, we observed exactly the same information that lenders did. In what follows, we classify the information available to the lenders as verifiable or nonverifiable.
Verifiable information. Lending Club collects information to verify borrowers' identity and assess creditworthiness. Using key indicators of creditworthiness, the lending platform classifies each potential borrower into seven grade categories, A-G, where A is the best and G the worst credit grade. These credit grades are determined by the lending platform as a function of the verifiable creditworthiness indicators. Interest rates are not set by the borrowers but instead are reflected by the credit grades; the relationship between the interest rate and risk is made salient by the lending platform. More specifically, much of the verifiable information (FICO score range, number of delinquencies in the past two years, number of credit inquiries in the past six months, revolving balance utilization, debt-to- income ratio, total credit lines, number of derogatory public records, number of public record bankruptcies, zip code, income, length of employment, employment title, etc.) is disclosed by the platform to potential lenders. As expected, the correlation between credit grade and interest rate in our data is .933, implying that credit grades are determined almost entirely by verifiable credit risks.
Nonverifiable information. While all borrowers are required to provide a "loan purpose" by selecting one of the categories offered by the platform, the thesis of our article focuses on the provision of the optional, open-ended loan description. We hypothesize that the presence and length of the description serve as a proxy for the effort a borrower uses to signal. Accordingly, we create a "no_description" indicator variable that is equal to 1 when borrowers provide no description and 0 otherwise, and a "description_length" variable that measures the number of words in the description. Of the 24,594 applications, 6,372 have zero words (no loan description).
We create several other variables to control for variations in the content of the descriptions. With the classification and linguistic processing algorithms provided by SPSS software,[10] we identify the top concepts that appear in loan descriptions. Automatic classification helps control for the content coding biases that might arise with a researcher-developed classification scheme. The top six categories account for more than 95% of the observations and account for both concreteness (e.g., budget) and attitudes of the description. Overall, the "budget" category represents the largest number of observations (65.7%). The most frequently used words in the descriptions in this category are "loan," "pay," "payment," "paying," "money," and "rate." Immediately following the budget category in magnitude are the "positive" category, with 36.4% of the observations (e.g., "excellent," "good," "timely"), and then the "negative" category (e.g., "problem," "bad," "difficult"). For each of the top six categories, we create a dummy variable that indicates whether an observation falls into that category. A single loan description can belong to multiple categories. We use these six dummy variables to control for variations across the content of loan descriptions.
Finally, the data include the date the application was posted on the platform. We create a time trend variable to account for potential changes in loan funding behavior over time due to macroeconomic environments.
We first present some model-free evidence consistent with our theory, highlighting the effects the optional description might have on the funding decision. Table 1, Panel A, shows, for each grade, the percentage of loans with no description, the overall loan funding percentage rate, and the funding percentage for those loans with no description. Table 1, Panel B, shows, for those loans with descriptions, the mean, median, and standard deviations of description length for funded and nonfunded loans. Table 2 examines the overall funding percentages for loans with different description lengths.
TABLE: TABLE 1 Model-Free Evidence
TABLE 1 Model-Free Evidence
| A: Percentage of Loans Funded with and Without Description |
| Credit Grade | Percentage of Loans with No Description | Overall Loan Funding Percentage | Funding Percentage for Loans with No Description |
| A | 29% | 66% | 71% |
| B | 27% | 57% | 62% |
| C | 25% | 54% | 63% |
| D | 25% | 53% | 58% |
| E | 23% | 50% | 56% |
| F | 21% | 48% | 52% |
| G | 16% | 45% | 51% |
| B: Description Length (Words) for Funded and Nonfunded Loan Applications |
| Credit Grade | Funded | Nonfunded |
| M | Mdn | SD | M | Mdn | SD |
| A | 65 | 43 | 68 | 46 | 28 | 56 |
| B | 72 | 49 | 75 | 55 | 35 | 66 |
| C | 73 | 48 | 77 | 54 | 34 | 64 |
| D | 76 | 51 | 80 | 62 | 36 | 75 |
| E | 78 | 53 | 78 | 63 | 38 | 78 |
| F | 90 | 59 | 102 | 78 | 40 | 94 |
| G | 76 | 47 | 74 | 73 | 38 | 84 |
TABLE: TABLE 2 Percentage of Loans Funded by Description Length
TABLE 2 Percentage of Loans Funded by Description Length
| | Length Percentile |
| 0 Words | 25th | 50th 75th | 90th |
| Length of description (words) | 0 | 6 | 28 | 72 | 139 |
| Percentage of loans funded | 62.2 | 53.2 | 55.1 | 55.4 | 58.3 |
The evidence shows that ( 1) loans with no descriptions are funded with higher probability than those with descriptions, and ( 2) if a description is provided, higher length improves the probability of funding, although the probability is still less than those for loans with no description. These nonparametric analyses point to a correlation between description length and loan funding. Finally, we see that borrowers with high credit grades are more likely to provide no description. This pattern is consistent with our analytical model, which states that unobservable loan quality drives the signaling effort and is also imperfectly yet positively correlated with the verifiable information.
Table 3 shows the summary statistics for our data set. The length of the description is relevant to the countersignaling account because it can serve as a proxy for signaling effort. For loans with descriptions, the average length is 54 words, and the 90th percentile is 139 words.
TABLE: TABLE 3 Descriptive Statistics for Loan Applications
TABLE: TABLE 3 Descriptive Statistics for Loan Applications
TABLE 3 Descriptive Statistics for Loan Applications
| A: Summary Statistics |
| M | SD | 10th Percentile | Median | 90th Percentile |
| Loan amount applied ($) | 10,541 | 6,755 | 3,000 | 9,000 | 21,000 |
| |
| Loan description length (words) | 54 | 73 | 6 | 28 | 139 |
| B: Loan Proportions by Characteristic |
| Proportion of Loans |
| Percentage Funded | |
| 0% | 35.0% |
| 100% | 65.0% |
| Credit Grade | |
| A | 16.8% |
| B | 27.8% |
| C | 23.9% |
| D | 16.8% |
| E | 9.1% |
| F | 3.7% |
| G | 2.0% |
| Loan Length | |
| 36 months | 88.7% |
| 60 months | 11.3% |
| Home Ownership | |
| None | 1.2% |
| Mortgage | 39.3% |
| Own | 9.8% |
| Rent | 49.7% |
| Description Content | |
| Contains "currency" | 8.9% |
| Contains "buying" | 12.1% |
| Contains negative words | 18.4% |
| Contains positive words | 36.4% |
| Contains date | 4.4% |
| Contains "budget" | 65.7% |
| Loan Purpose | |
| Car | 3.5% |
| Credit card | 11.1% |
| Debt consolidation | 38.2% |
| Educational | 3.1% |
| Home improvement | 7.2% |
| House | 1.5% |
| Major purchase | 6.5% |
| Medical | 2.0% |
| Moving | 1.6% |
| Other | 14.0% |
| Renewable energy | .2% |
| Small business | 7.7% |
| Vacation | .8% |
| Wedding | 2.5% |
| C: Borrower Characteristics |
| Mean Value |
| Number of delinquencies in past two years | .17 |
| Number of credit inquiries in past six months | 1.59 |
| Revolving balance utilization | 45.2% |
| Monthly income ($) | 6,017 |
| Debt-to-income ratio | 12.1% |
| Total credit lines | 9 |
Notes: Total observations = 26,314.
Before we present the empirical analysis, we address the matter that description provisions are nonrandom decisions and can be influenced by the factors that also influence the verifiable information. Recall that in our analytical model, borrowers first make description decisions based on their knowledge about their type, their belief about how their verifiable information will be presented to the potential lenders by the platform, and their expectation about how lenders will react to their description efforts. Borrowers who are confident about the later realization of their verifiable information (the high-quality types) will choose to countersignal, and those who are less confident will want to bolster their chances by providing a description, with the length varying depending on the cost and benefit of the description-writing effort for the borrower. Subsequently, lenders will observe the information provided by the borrowers and the platform and make their investment decisions.
To model this two-step process and to control the non- randomness of the description writing decisions, we use a two- stage regression approach. In the first stage, we use a zero-inflated Poisson (ZIP) regression to model borrowers' description decisions as a function of the creditworthiness information most salient to the borrower (i.e., FICO score). In the second stage, we use the residuals from the first-stage regression as a control for the lenders' funding decisions. This modeling approach not only provides us with empirical evidence of whether salient credit information would impact description decisions as we have hypothesized, but it also controls in the second stage for any potential biases arising from endogenous description decisions.
We model borrowers' description decisions (number of words) as ZIP (Bohning et al. 1999; Greene 1994; Lambert 1992), which we believe is appropriate because a large proportion of the loans lack descriptions. ZIP is a two-component mixture model combining a point mass at zero with a count distribution. It assumes that the excessive zeros (i.e., loans that lack descriptions) are generated by a separate process from the count values and that the excess zeros are modeled separately. Therefore, it has two parts: a Poisson count model and the logit model for predicting excess zeros.[11] Specifically, the first stage contains the following explanatory variables:
description decision = f (FICO_scorei, loan_lengthi, loan_amounti).
We use FICO score in our data set because we believe it is the most salient to borrowers and therefore will have the most impact on their decisions to provide descriptions. We exclude FICO score in the second stage.[12] We include loan amount and loan length because large amounts or lengths might drive borrowers to offer explanations. We perform this analysis within each credit grade.
The results, shown in Table 4, suggest that for each of the credit grades, borrowers with higher FICO scores are less likely to offer a description, but for those who do write a description, a higher FICO score correlates with a longer description. The result is consistent with our theory for borrowers.
TABLE: TABLE 4 Model for Description Decision
TABLE 4 Model for Description Decision
| Grade A | Grade Β | Grade C | Grade D | Grade Ε | Grade F | Grade G |
| Estimate | SD | Estimate | SD | Estimate | SD | Estimate | SD | Estimate | SD | Estimate | SD | Estimate | SD |
| Poisson Count Model | | | | | | | | | | | | | | |
| Intercept | 5.409 | .032 | 4.746 | .075 | 4.395 | .032 | 3.124 | .032 | 2.567 | .042 | 1.835 | .052 | 1.707 | .092 |
| FICO | .002 | .001 | .001 | .001 | .000 | .001 | .002 | .001 | .003 | .001 | .004 | .001 | .004 | .001 |
| Loan length | .147 | .017 | .201 | .006 | .205 | .008 | .164 | .006 | .302 | .008 | .027 | .010 | .142 | .019 |
| Loan amount | -.018 | .001 | -.013 | .004 | -.010 | .000 | -.006 | .001 | -.004 | .001 | -.003 | .001 | -.009 | .001 |
| Zero-Inflation Model (Binomial with Logit)° | | | | | | | | | | | | | | |
| Intercept | -2.503 | .924 | -1.622 | .743 | 1.902 | .992 | -1.431 | 1.153 | -3.223 | 1.859 | -2.248 | 3.095 | -5.194 | .527 |
| FICO | -.002 | .000 | .000 | .001 | -.006 | .001 | -.005 | .002 | -.004 | .002 | -.005 | .002 | -.003 | .001 |
| Loan length | .483 | .194 | .663 | .085 | .989 | .099 | .734 | .098 | 1.050 | .111 | 1.115 | .184 | 1.416 | .303 |
| Loan amount | -.003 | .009 | -.017 | .005 | -.011 | .005 | -.011 | .006 | -.001 | .008 | -.004 | .011 | .017 | .017 |
aO = no description.
Notes: Boldface indicates significance at the .05 level.
In the second stage of our empirical analysis, we model the loan funding outcome and test for the existence of counter- signaling from the lender's perspective. Specifically, we wish to answer the following question: Do lenders infer quality within a risk-reward class by considering the borrower's decision to exert effort to provide unverifiable loan descriptions? To answer this question, we include all information about the borrower that was available to the lender, to avoid omitted variable biases. We performed a variance inflation factor (VIF) analysis to address potential multicollinearity. The resulting model specification exhibited VIFs of less than 5 for all variables, so this model is unlikely to suffer from multicollinearity concerns. Interest rates correlated highly with credit grade (.933), so we could only include one of these two variables. After testing, the specification that provided the best model fit was the one that used credit grade instead of interest rates. Both models share the same qualitative results.
In summary, for the ith loan request, the model we test is
loan_funding_outcomei = f (loan_amounti, loan_lengthi,
#_open_credit_linesi,
#_delinquencies_past_2_yearsi,
#_total_credit_linesi,
revolving_balance_utili,
monthly_incomei,
debt-to-income_ratioi,
home_ownership_statusi,
state_residencei, #_credit_inquiriesi,
currencyi buyingi, negativei,
positivei datei, budgeti, credit_gradei,
description_lengthi, description_length2,
no_descriptioni, time trendi, residuali).
We estimate the model for loan funding outcome using logistic regression on funding, and we compare our proposed model against two benchmark models that vary in their degrees of signaling. The first benchmark model assumes that the funding outcomes are based solely on verifiable information (controlling for loan amount, loan length, and date of the loan request, as captured in the "time trend" variable). Then, the second benchmark model accounts for the length of description but ignores the effect of countersignaling, so it excludes the "no_description" variable. For ease of presentation, we first run an aggregate model (using credit grades as dummies) for model comparison to assess model fit with and without countersignaling. We present the result in Web Appendix 3 (see Table W1). Then, because our model is within credit grade, we present the loan funding result for each individual credit grade in Table 5.
TABLE: TABLE 5 Model of Lending Decisions by Credit Grade
TABLE: TABLE 5 Model of Lending Decisions by Credit Grade
TABLE 5 Model of Lending Decisions by Credit Grade
| Grade A | Grade Β | Grade C | Grade D | Grade Ε | Grade F | Grade G |
| Estimate | SD | Estimate | SD | Estimate | SD | Estimate | SD | Estimate | SD | Estimate | SD | Estimate | SD |
| Intercept | 2.856 | .759 | 2.194 | .568 | 2.265 | .647 | 2.374 | .727 | 1.374 | .827 | -.285 | .312 | -.575 | .311 |
| Number of open credit lines | .007 | .019 | .006 | .014 | -.013 | .014 | .019 | .016 | .056 | .022 | -.025 | .035 | -.034 | .034 |
| Number of delinquencies in past two years | -.199 | .234 | -.018 | .104 | -.180 | .031 | .053 | .082 | .099 | .113 | -.408 | .176 | -.286 | .060 |
| Revolving balance utilization | -.337 | .328 | -.908 | .183 | -1.185 | .178 | -.726 | .201 | -1.152 | .299 | .205 | .465 | .472 | .596 |
| Monthly income | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 |
| Debt-to-income ratio | -.380 | .964 | -1.657 | .715 | -1.129 | .756 | -1.964 | .885 | -1.239 | 1.250 | -4.736 | 1.831 | -5.013 | 2.547 |
| Total credit lines Home Ownership (vs. None) | .010 | .008 | .006 | .005 | .009 | .006 | .012 | .007 | -.013 | .010 | .017 | .015 | .004 | .019 |
| Mortgage | .680 | .357 | .820 | .249 | 1.141 | .298 | .399 | .304 | .440 | .462 | 1.850 | .585 | .211 | .776 |
| Rent | -.275 | .364 | .271 | .258 | .715 | .307 | .027 | .318 | -.354 | .484 | .929 | .836 | .676 | .859 |
| Own | .538 | .156 | .738 | .248 | 1.069 | .296 | .460 | .300 | .083 | .457 | .987 | .783 | -.238 | .771 |
| Inquiries in past six months | -.138 | .045 | -.022 | .028 | -.043 | .022 | -.021 | .024 | -.059 | .021 | -.068 | .045 | -.059 | .050 |
| Loan amount | -.128 | .010 | -.042 | .004 | -.154 | .005 | -.046 | .006 | -.056 | .008 | -.059 | .013 | -.037 | .017 |
| Term (60 months = 1 ) | -.665 | .208 | -.558 | .092 | -1.302 | .110 | -.800 | .110 | -.233 | .147 | -.704 | .251 | .417 | .460 |
| Time Trend Y2008 | -2.965 | .603 | -3.198 | .469 | -3.757 | .522 | -3.400 | .596 | -4.033 | .324 | -3.285 | .421 | -1.769 | .485 |
| Y2009 | -2.603 | .599 | -3.039 | .466 | -3.740 | .519 | -3.544 | .592 | -4.120 | .324 | -3.332 | .485 | -1.797 | .422 |
| Y2010 | -2.898 | .599 | -3.214 | .466 | -3.765 | .520 | -3.685 | .593 | -4.550 | .355 | -4.112 | .522 | -1.837 | .442 |
| Loan Purpose | | | | | | | | | | | | | | |
| Credit card | .648 | .181 | .848 | .163 | .781 | .190 | .574 | .242 | .113 | .372 | -.505 | .719 | 2.036 | 1.283 |
| Debt consolidation | .740 | .158 | .848 | .148 | .720 | .172 | .943 | .223 | .132 | .337 | .056 | .665 | 2.014 | 1.236 |
| Educational | -.282 | .228 | .014 | .203 | -.111 | .224 | -.155 | .300 | -.413 | .429 | -1.019 | .870 | 1.387 | 1.344 |
| Home improvement | .748 | .185 | .824 | .166 | .528 | .197 | .707 | .262 | -.345 | .377 | -.364 | .729 | .934 | 1.297 |
| House | .104 | .345 | .580 | .253 | -.207 | .283 | -.320 | .352 | -.425 | .520 | .085 | .874 | 2.578 | 1.547 |
| Major purchase | .110 | .167 | .284 | .169 | .270 | .199 | .415 | .255 | -.365 | .391 | -.743 | .766 | .478 | 1.401 |
| Medical | -.459 | .242 | -.010 | .227 | -.016 | .259 | .159 | .325 | -.286 | .475 | -.931 | .966 | -.205 | 1.512 |
| Moving | .152 | .272 | .232 | .246 | -.126 | .276 | .316 | .367 | -.303 | .498 | -.237 | .884 | .982 | 1.635 |
| Other | .392 | .163 | .445 | .155 | .337 | .178 | .408 | .234 | -.225 | .352 | -.184 | .694 | 1.578 | 1.265 |
| Renewable energy | 1.306 | .323 | .337 | .500 | -.742 | .576 | .588 | .425 | -1.786 | 2.226 | -1.572 | 3.956 | -.163 | .205 |
| Small Business | -.133 | .223 | -.176 | .177 | -.337 | .198 | -.051 | .241 | -.846 | .358 | -.960 | .679 | 1.255 | 1.245 |
| Vacation | -.170 | .307 | .221 | .333 | .164 | .381 | .027 | .469 | -.778 | .645 | -1.988 | 1.175 | 1.041 | 1.645 |
| Wedding | .462 | .269 | .287 | .207 | .589 | .246 | .825 | .302 | -.013 | .460 | -.655 | .861 | -.332 | .398 |
| Description Content | | | | | | | | | | | | | | |
| Contains "currency" | -.105 | .145 | -.047 | .106 | -.071 | .108 | .204 | .127 | -.539 | .485 | -.359 | .307 | -.134 | .361 |
| Contains "buying" | .240 | .119 | -.066 | .094 | .039 | .102 | .176 | .121 | -.016 | .167 | -.249 | .273 | .068 | .318 |
| Contains negative words | -.015 | .115 | -.075 | .078 | -.089 | .082 | .017 | .095 | -.075 | .135 | -.288 | .229 | -.072 | .266 |
| Contains positive words | .048 | .091 | -.013 | .065 | .068 | .069 | -.025 | .081 | -.108 | .116 | .335 | .190 | .342 | .536 |
| Contains date | -.144 | .205 | -.053 | .154 | -.170 | .160 | .319 | .181 | -.209 | .255 | .012 | .402 | -.274 | .305 |
| Contains "budget" | .325 | .104 | .192 | .078 | .242 | .085 | .240 | .101 | .363 | .143 | .377 | .236 | .003 | .008 |
| Description Length | | | | | | | | | | | | | | |
| Description length | .010 | .003 | .011 | .002 | .009 | .002 | .008 | .002 | .018 | .003 | .009 | .005 | .006 | 1.44E-04 |
| Description length2 | -1.34E-05 | .000 | -1.41E-05 | .000 | -1.48E-05 | .000 | -1.17E-05 | .000 | -2.07E-05 | .000 | -9.30 E-05 | .000 | -9.14E-04 | 4.35E-01 |
| Countersignaling | | | | | | | | | | | | | | |
| No description | 1.378 | .290 | 1.415 | .213 | 1.275 | .254 | .832 | .300 | .651 | .214 | .426 | .120 | .522 | 3.82E-03 |
| Interaction Terms: | | | | | | | | | | | | | | |
| Description Length χ … | | | | | | | | | | | | | | |
| Number of open credit | 3.00E-04 | 2.15E-05 | 2.18E-04 | 3.40E-05 | 1.75 E-04 | 3.44E-05 | 1.77E-04 | 4.50 E-05 | 7.62E-04 | 2.27E-04 | 4.84 E-04 | 2.60E-04 | 1.82E-04 | 3.82E-04 |
| lines |
| Number of delinquencies in past two years | -1.22E-03 | 3.54E-03 | -1.35E-03 | 8.32E-04 | 2.80E-05 | 8.88E-04 | -1.08E-03 | 1.01 E-04 | -2.01 E-03 | 3.69E-04 | -2.22 E-03 | 5.41 E-04 | -2.52E-03 | 2.05E-04 |
| Revolving balance utilization | -1.62E-03 | 3.90E-03 | 7.99E-05 | 1.74E-03 | -3.79E-03 | 1.73E-03 | -1.83E-03 | 1.76E-03 | -5.75E-03 | 2.65E-03 | -1.50E-03 | 3.40E-03 | 1.96E-03 | 5.72E-03 |
| Monthly income | 5.68E-08 | 1.31E-07 | 4.23E-08 | 5.20E-08 | -1.10E-07 | 8.33E-08 | 3.60E-08 | 7.56E-08 | -6.03E-08 | 1.63E-07 | -8.98E-08 | 1.90E-07 | 5.42E-06 | 3.14E-07 |
| Debt-to-income ratio | -5.47E-03 | 1.18E-02 | 4.41 E-04 | 7.25E-03 | -5.92E-03 | 7.63E-03 | -8.30E-03 | 8.02E-03 | -7.30E-03 | 1.16E-02 | -2.19E-02 | 3.69E-03 | -1.42E-02 | 2.87E-02 |
| Total credit lines | 1.34E-04 | 9.69E-05 | 4.82E-05 | 5.38E-05 | 8.61 Ε-05 | 5.71 E-05 | 4.01 E-05 | 6.40 E-05 | 2.84E-04 | 2.04E-04 | -2.24E-04 | 1.22E-04 | 4.93E-05 | 1.89E-04 |
| Inquiries in past six months | -5.92E-04 | 5.38E-04 | -2.48E-05 | 2.73E-04 | 8.53E-05 | 2.47E-04 | 1.72E-04 | 2.54 E-04 | -6.46E-04 | 3.06E-04 | 5.69E-04 | 3.88E-04 | -3.55E-05 | 5.76E-04 |
| Residual from first stage | .066 | .016 | .090 | .030 | .022 | .025 | .071 | .018 | .065 | .022 | .045 | .027 | .075 | .022 |
Notes: Bold estimates indicate significance at the .05 level.
Table W1 shows the estimated parameters for the lending decision across the proposed and benchmark models. According to the Bayesian information criterion, which compares nested models and penalizes model complexity, the best- fitting model is the proposed model, which accounts for both the effect of nonverifiable information and countersignaling. All the parameters from the proposed model, as well as those of the two benchmark models, are in the expected direction and support our theory. For instance, borrowers with lower credit grades are less likely to be funded, and we find a residual impact of verifiable information after controlling for credit grade (e.g., borrowers with higher debt-to-income ratios, more past delinquencies, and more inquiries in the past six months are less likely to be funded). These results provide face validity for our analyses and show that lenders base their decisions on multiple pieces of information in addition to the summary credit grade. Loans of larger amounts and longer terms also are less likely to be funded, but we find no impact of the borrower's state of residence on funding decisions. For time trend, we use the year indicator[13] and find that compared with 2007, all subsequent years result in lower funding likelihood, which reflects increasing lender caution as the economy proceeded deeper into recession after the bankruptcy of Lehman Brothers.
Several interesting insights stem from these model comparisons. First, the two models that account for the effects of nonverifiable information (proposed model and benchmark model 2) fit the data better than the model that accounts only for verifiable information (benchmark model 1), Therefore, non- verifiable information influences loan funding decisions and is not viewed by lenders as uninformative cheap talk (Crawford and Sobel 1982). For instance, mentions of "budget" in the description might signal concreteness of financial planning and thus increase funding likelihood. Second, in our proposed model, the parameter estimate for no_description is positive and statistically significant. In Table 5, we show this effect to hold across all credit grades, showing that within each credit grade and conditional on verifiable information, borrowers who do not provide a loan description are more likely to have their loans funded than those who provide a loan description, in support of H1.
We also find a positive, statistically significant parameter value for "description_length" and a negative, statistically significant parameter value for "description_length." Once borrowers decide to provide a reason for the loan request, their chances of getting funded increase with the number of reasons, in a concave manner (i.e., decreasing returns to the number of words), in support of H3.
We then run separate models for each credit grade, and the same patterns emerge. We present these results graphically in Figure 2, using the parameter estimates from the separate regressions. The patterns in Figure 2 are consistent with the prediction in our analytical model. Specifically, not providing a loan description is a countersignal of high quality and is rewarded by lenders with greater funding likelihood. When borrowers provide descriptions, however, longer descriptions are perceived more favorably than shorter ones. Together, these results are consistent with the proposed countersignaling theory. The statistical significance in several interactions between description length and verifiable borrower creditworthiness information is in line with our theory that some consumers naively use the combination of description length and verifiable creditworthiness information to assess the quality of the loan requests in the platform.
A critical element of our theory is that lenders correctly infer loan descriptions as a mechanism for strategic information transmission. We next consider ex post loan performance to test H2 and H4.
The relationship between lending decisions and the optional borrower-provided nonverifiable information empirically supports countersignaling theory. In this section, we provide further support for countersignaling as an explanation of loan funding outcomes in P2P lending. An integral part of countersignaling theory is that the interpretation of the signal should be consistent with the strategy and the type of sender. Otherwise, lenders would learn not to trust the signal, and the descriptions would become uninformative. We therefore turn our attention to testing the observed ex post performance of the loans (i.e., long- term performance, as measured by payment delinquency). With this analysis, we can determine whether, within a given credit grade, borrowers who countersignal are of higher quality, as indicated by a lower likelihood of delinquency.
Because high-quality borrowers should countersignal and refrain from providing a description, loans with no description should be less likely to be delinquent than loans with descriptions. Countersignaling theory also suggests that once a description is provided, a longer description should be negatively correlated with loan delinquency. We describe the loan performance measurement and specify the model to test our loan performance prediction based on countersignaling theory. We perform the analysis using the funded applications from our data set.
Of the funded loans in our data set, 6% are noncurrent, exhibiting one of the following four statuses: "late (16-30 days)," "late (31-120 days)," "charged off," or "default," which represent various stages of delinquency. We pool these four categories, due to the sparseness of these data points, and create a binary "delinquency" variable that is equal to 1 if the loan is delinquent and 0 otherwise. We present in Table 6 model-free evidence showing delinquency percentage by credit grade and compare the percentages across various description lengths. Preliminary evidence suggests that having no description results in the lowest delinquency rate, followed by long descriptions, with short descriptions having the highest rate.
TABLE: TABLE 6 Loan Delinquency by Credit Grade
TABLE 6 Loan Delinquency by Credit Grade
| Grade A | Grade B | Grade C | Grade D | Grade E | Grade F | Grade G |
| Number of delinquencies | 187 | 275 | 229 | 174 | 87 | 35 | 29 |
| Delinquency percentage for all loans | 6.0% | 6.4% | 6.5% | 6.9% | 7.8% | 8.3% | 12.0% |
| Delinquency percentage for loans with no description | 2.9% | 2.6% | 3.6% | 3.9% | 3.9% | 5.8% | 7.5% |
| Delinquency percentage for loans with descriptions of below-median length | 11.2% | 13.9% | 12.2% | 15.7% | 14.0% | 14.1% | 20.8% |
| Delinquency percentage for loans with descriptions of below-median length | 4.7% | 5.5% | 4.8% | 5.4% | 5.3% | 6.3% | 8.8% |
To examine the effect of countersignaling on the probability of delinquency, we model the delinquency of loan i with the same two-step approach, with ZIP as the first stage. The second stage is a logistic regression with delinquency as the dependent variable:
delinquencyj = f (loan_amountj, loan_lengthi,#_open_credit_linesi, #_delinquencies_past_2_yearsi,
#_total_credit_linesj,
revolving_balance_utili, monthlyjncomei,
debt-to-income_ratioi, home_ownership_statusi,
state_residencei, #_credit_inquiriesi, currencyi,
buyingi, negativei, positivei, datej, budgeti,
credit_gradei, description_lengthi,
description_lengthi2, no_descriptioni,
time_trendj).
Similar to the loan funding results, we present in Web Appendix 3 (Table W2) the model comparisons results based on aggregate analysis using credit grade as dummies. In Table 7, we present our full results, broken down by credit grade.
TABLE: TABLE 7 Model of Delinquency by Credit Grade
TABLE 7 Model of Delinquency by Credit Grade
| Grade A | Grade Β | Grade C | Grade D | Grade Ε | Grade F | Grade G |
| Estimate | SD | Estimate | SD | Estimate | SD | Estimate | SD | Estimate | SD | Estimate | SD | Estimate | SD |
| Intercept | -.264 | 1.615 | .270 | .920 | .269 | 1.172 | 1.396 | 1.378 | -.168 | .102 | -.149 | .192 | -.160 | .220 |
| Number of open credit lines | .100 | .037 | .078 | .030 | .007 | .029 | .044 | .038 | .075 | .023 | .126 | .009 | .073 | .010 |
| Number of delinquencies in past two years | .434 | .431 | -.329 | .282 | -.119 | .203 | .242 | .171 | .061 | .129 | 143 | .537 | .501 | .513 |
| Revolving balance utilization | -.719 | .603 | .052 | .387 | .321 | .397 | .448 | .048 | -1.003 | .724 | -.141 | 1.321 | 2.458 | 2.025 |
| Monthly income | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 |
| Debt-to-income ratio | -.739 | 2.051 | 1.097 | 1.578 | -1.935 | 1.694 | -1.565 | 2.081 | -2.175 | 2.741 | -2.931 | 4.688 | -5.837 | 7.318 |
| Total credit lines | .027 | .014 | -.001 | .012 | .016 | .011 | .021 | .015 | .031 | .025 | .028 | .042 | -.012 | .053 |
| Home Ownership (vs. None) | | | | | | | | | | | | | | |
| Mortgage | .549 | 1.045 | -.554 | .523 | -.274 | .778 | .194 | .779 | .152 | .122 | -.158 | .592 | -.190 | .420 |
| Rent | .877 | 1.062 | -.110 | .550 | -.045 | .804 | -.406 | .847 | .163 | .113 | .140 | .392 | -.180 | .412 |
| Own | .599 | 1.046 | -.385 | .517 | -.163 | .775 | .110 | .774 | .153 | .132 | .154 | .142 | .189 | .420 |
| Inquiries in past six months | .000 | .066 | .014 | .038 | .024 | .030 | -.030 | .031 | -.007 | .035 | -.058 | .078 | .052 | .062 |
| Loan amount | .024 | .023 | -.009 | .012 | .036 | .014 | -.027 | .017 | .004 | .025 | -.047 | .039 | .120 | .058 |
| Term (60 months = 1) | -.619 | 1.072 | -.231 | .444 | -.866 | .612 | -.585 | .640 | -.224 | .848 | -.522 | 1.176 | -1.582 | 2.125 |
| Time Trend | | | | | | | | | | | | | | |
| Y2008 | -1.020 | .315 | -.975 | .245 | -.499 | .242 | -.851 | .265 | -.416 | .323 | -.878 | .579 | .968 | .739 |
| Y2009 | -1.716 | .300 | 1.653 | .241 | -.597 | .232 | -1.625 | .269 | -1.415 | .382 | -2.091 | .685 | -2.365 | .586 |
| Y2010 | -2.570 | .328 | -2.673 | .268 | -.844 | .275 | 1.875 | .349 | -1.972 | .667 | -2.674 | .944 | -1.213 | 1.799 |
| Loan Purpose | | | | | | | | | | | | | | |
| Credit card | .439 | .428 | .701 | .519 | -.414 | .507 | .643 | .800 | .295 | 1.195 | .201 | 1.458 | -.883 | 1.895 |
| Debt consolidation | .074 | .407 | .513 | .501 | -.315 | .473 | .430 | .774 | 1.023 | 1.120 | .388 | 1.353 | -2.733 | 1.795 |
| Educational | .164 | .617 | .901 | .579 | -.502 | .615 | .744 | .898 | .286 | 1.281 | 1.540 | 1.614 | .981 | 2.246 |
| Home improvement | .034 | .461 | .265 | .555 | -.111 | .524 | .109 | .865 | .430 | 1.279 | 1.067 | 1.666 | -1.443 | 1.989 |
| House | .377 | .852 | 1.026 | .660 | .250 | .729 | -.402 | 1.315 | 2.031 | 1.379 | 3.532 | 2.081 | -.204 | .202 |
| Major purchase | .483 | .451 | .774 | .537 | -1.039 | .643 | .097 | .869 | 1.340 | 1.280 | 2.344 | 1.902 | -.182 | .359 |
| Medical | .105 | .672 | .829 | .652 | -.138 | .687 | .569 | .945 | -.047 | 1.534 | .131 | .185 | -.184 | .300 |
| Moving | -.085 | .725 | 1.207 | .665 | -.774 | .872 | .078 | 1.094 | -.138 | .807 | -.135 | .152 | -.187 | .449 |
| Other | .255 | .414 | .536 | .511 | -.117 | .484 | -.175 | .808 | .737 | 1.146 | -.971 | 1.679 | 4.542 | 1.850 |
| Renewable energy | 2.382 | 1.244 | 1.221 | 1.182 | -.135 | .103 | -.124 | .213 | 1.307 | 1.182 | 2.206 | 1.445 | 1.221 | 1.182 |
| Small business | -.143 | .659 | -.204 | .631 | -.748 | .592 | .153 | .842 | -2.540 | 1.605 | -2.955 | 2.620 | 3.912 | 1.186 |
| Vacation | 1.170 | .650 | .035 | 1.153 | -.141 | .468 | -.142 | 1.100 | 1.454 | 1.373 | 1.990 | 1.865 | .035 | 1.153 |
| Wedding | -.045 | .654 | .380 | .643 | .317 | .591 | -.142 | .432 | .380 | .643 | .555 | .461 | -.197 | .432 |
| Description Content | | | | | | | | | | | | | | |
| Contains "currency" | .282 | .279 | .133 | .270 | .136 | .277 | .040 | .308 | .194 | .473 | .587 | 1.116 | 2.401 | 1.518 |
| Contains "buying" | .363 | .275 | .355 | .218 | -.179 | .278 | .044 | .311 | -.561 | .504 | -.029 | .824 | 1.996 | 1.343 |
| Contains negative words | .123 | .248 | .051 | .189 | -.126 | .207 | .264 | .224 | .396 | .329 | 1.026 | .702 | .239 | .938 |
| Contains positive words | -.182 | .192 | -.118 | .156 | .068 | .170 | -.141 | .203 | -.139 | .309 | -.255 | .525 | -.332 | .752 |
| Contains date | .300 | .437 | -.532 | .426 | .444 | .383 | -1.048 | .753 | -.093 | .790 | .138 | 1.057 | 1.695 | 1.772 |
| Contains "budget" | -.212 | .213 | -.386 | .175 | -.223 | .202 | -.359 | .238 | .078 | .357 | -.306 | .690 | -.561 | .707 |
| Description Length | | | | | | | | | | | | | | |
| Description length | — 1.00E-02 | 5.06E-03 | -1.34E-02 | 4.29E-03 | —7.66E-03 | 2.58E-03 | — 1.98E-02 | 6.75E-03 | —9.84E-03 | 8.77E-03 | -1.17E-02 | 1.56E-03 | — 1.23E-02 | 4.70E-03 |
| Description length2 | 2.50E-05 | 9.47E-06 | 3.31 E-05 | 6.46E-06 | 2.93E-05 | 7.38E-06 | 3.48E-05 | 2.16E-06 | 2.15E-05 | 3.78E-06 | 2.50E-05 | 5.49E-06 | 2.89E-05 | 6.66E-06 |
| Countersignaling | | | | | | | | | | | | | | |
| No description | — 1.96E+00 | 6.99E-01 | -1.51E+00 | 6.10E-01 | —7.35E-01 | 2.81 E-01 | -1.98E+00 | 4.44E-01 | —8.86E-01 | 3.56E-01 | — 8.26E-01 | 2.48E-01 | —1.99E+00 | 4.87E-01 |
| Residual from first stage | .022 | .003 | .019 | .004 | -.030 | .020 | .032 | .005 | .004 | .002 | .072 | .009 | .035 | .019 |
Notes: Boldface indicates significance at the .05 level.
Table W2 shows the estimated parameters for the likelihood of the loan being delinquent. We compare the proposed model with the same benchmark models used in the funding outcome analysis in Table W1 to examine the extent of the impact of countersignaling on loan performance. Overall, the results confirm the convergence between the funding decisions and delinquency rates. The proposed model provides the best fit for the data, according to the Bayesian information criterion, in support of the predicted, nonmonotonic relationship between the number of words in the loan description and the borrower's quality (in terms of delinquency).
As Table 7 shows, within each credit grade, the coefficient for the "no_description" variable is negative and statistically significant, providing evidence that borrowers who provide no description in their loan requests are less likely to be delinquent in their payment than those who provide short descriptions, in support of H2. The coefficient for "description_length" is negative and statistically significant, which demonstrates that borrowers who provide longer descriptions are less likely to be delinquent than borrowers who provide shorter descriptions, in support of H4.
The depiction of the results in Figure 3 uses the parameter estimates from Table 6. Combined with the loan funding results, these findings support the four hypotheses predicted by coun- tersignaling theory with respect to how lenders correctly discriminate borrowers' types using the signal associated with the length of their loan descriptions.
Recall that our theoretical model assumes there is some proportion of naive lenders, and the analytical results hold even for a very small proportion of such lenders. Our empirical evidence supports our theory for the existence of some naive lenders. In reality, even in the world of institutional lending, the fact that some funds perform better than others indicates that there are variations in the processing of financial and creditworthiness information.
To provide further evidence for the theory, we performed the following robustness checks:
- Tested various specifications of no_description and description_ length.
- Checked whether the effect of no_description is unique to zero words.
- Tested for the influence of repeat borrowers.
- Tested whether there exists evidence of other signal mechanisms (e.g., loan terms, loan amounts).
- Checked the potential effect of description content and quality.
First, in test 1, we tested various combinations of no_ description and description_length in the model. The best-fitting model is one that includes "no_description," "description_ length," and "description_length2." Because all three variables are significant, excluding one would result in a worse fit. The cubed term "description_length3" is not significant, which indicates that the probability will not invert and increase after certain number of words. Interaction terms of "no_description" and "description_ length" with credit grades are not significant, suggesting that although no_description is important within each credit grade, its effects do not differ significantly across credit grades. We also discretized "description_length" in three to five groups and reran the model using these indicator variables (e.g., short, medium, long). We also tested an empirical model in which description lengths were classified as high-quality (H), medium-quality (M), or low-quality (L) type instead of using a continuous classification. More specifically, we lumped descriptions shorter than the median length as L type, descriptions longer than the median length as M type, and absent descriptions as H type. The substantive results hold with these discretization efforts (that is, M type results in higher likelihood of funding than L type), albeit with a slightly worse fit compared with using a continuous variable for number of words, as in the proposed model. This result is expected because we do not a priori know the cutoffs for the discretization, and the continuous type offers more flexibility. Finally, we included interaction between credit grades and the description variables, as well as interaction terms between description variables and other credit information of borrowers, both as continuous length in words and as discretized buckets. We find no effect of the interaction.
Next, in test 2, we investigated whether the proposed model correctly captured a unique zero-word effect of no_description rather than an alternative effect caused by a length below some other threshold. We compared the proposed model with models including dummy variables that reflected whether the descriptions contained fewer than X number of words, where X = 3,5, or 10 words, while holding everything else the same. We find that as X increases from 0, the effect size of the dummy variables "X_words_or_less" decreases and the model fit worsens. Therefore, providing no loan description is a unique signal. This phenomenon holds for both the loan funding decision and the ex post likelihood of loan delinquency. The result of this check is available in Web Appendix 4.
As mentioned earlier, Lending Club keeps lenders and borrowers anonymous to each other, and the platform does not provide information about repeat borrowers or past performance indicators on the platform. Nevertheless, in test 3, we empirically investigated the presence of repeat borrowers by performing exact matches on time-invariant demographic and behavioral information such as income, credit grade, state of residence, earliest credit line opened, and home ownership. We found 120 borrowers who met the criteria, suggesting they may have applied twice. This group represented less than .5% of applications, and there were no matches to indicate any borrowers applied three times or more. When we exclude these 120 observations from the data, the same results hold.
In test 4, we considered two situations. First, we investigated whether borrowers signal through loan amount or loan terms (durations) and whether certain lenders might treat loans of varying magnitudes differently (e.g., larger loans might signal a more responsible borrower, with strong repayment ability and consequently easy access to outside lending markets). To do so, we divided the loan amounts into quintiles and created a dummy variable for each of the following quintiles: amount < $500; $500 < amount < $5,000; $5,000 < amount < $9,000; $9,000 < amount < $15,000; and $15,000 < amount < $25,000. We included these indicator variables in addition to the amount and found that, relative to the baseline of the first quintile (amount < $500), the other four quintile dummy variables are nonsignificant. This indicates that, aside from the fact that larger loan amounts tend to decrease funding probability, there is no significant signaling value from loan amounts that would lead to lenders to behave differently. The same nonsignificant result also holds on the loan performance side, confirming that loan magnitude has no significant effect on borrowing and lending behavior in the platform we study.[14]
In addition, to rule out potential signaling via loan length, we run the model on a subset of the data from the period when Lending Club did not allow borrowers to request 60-month loans (thus, all loans were 36 months in duration). All the substantive results are similar between the subset of the data and the full data set.
Finally, test 5 assessed whether shorter or longer descriptions contain specific words that drive decisions. We modeled description length as a dependent variable on content dummies and found no significant differences between the effects of different content dummies. Correlations between description lengths and various content dummies ranged from .16 to .22; we observed no particular content variable that had a systematically higher impact on description_length.
To assess whether different description lengths exhibit different qualities of writing, we randomly selected 40 descriptions from each of the following groups: less than 33rd percentile, or 12 words (short group); between 33rd and 66th percentile, or 13-39 words (medium group); and greater than 66th percentile, or 40+ words (long group). We conducted a survey of quality with 153 Amazon Mechanical Turk respondents, who were asked to rate the quality of the descriptions and the effort exhibited in them. The correlations are .7 between description_length and quality, .88 between description_length and perceived effort, and .92 between effort and quality. Regressing perceived effort on description_length yields an R2 of .7, and regressing quality on description_length yields an R2 of .5. This shows that longer descriptions are seen as both more effortful and of higher quality, and that people perceive that it takes more effort to write high-quality descriptions.[15]
In summary, the robustness analyses lend support for H1-H4. We now discuss how our countersignaling theory and results compare with other prior theories.
Following our theoretical and empirical analysis of counter- signaling in P2P lending, we discuss candidate explanations for the observed phenomena. We describe how these theories do not explain the totality of our results.
Persuasion. According to persuasion theory, the provision of descriptions might be interpreted as a compliance issue. Individuals and companies acting as persuaders often attempt to increase compliance with their requests by offering reasons why others should behave accordingly (Langer et al. 1978; Petty and Cacioppo 1984). These predictions could accommodate the behavior of naive consumers in the analytical model, but they cannot accommodate the effect of no_description. These predictions also cannot account for the observed relationship between loan description and ex post loan delinquency.
Preemptive behavior. In mortgage applications, if there is something negative in a borrower's verifiable information, the lender often asks the borrower to write an explanation for this potential blemish in the credit record. Borrowers in this setting might act preemptively and, without being asked, write a de- scriptiontoexplain anynegativeverifiable information. However, if such preemptive behavior were successful in attracting lenders, it could explain the positive relationship between ex post loan defaults and the presence of a description, but it would not explain the negative relationship between funding and the presence of a description. Moreover, this explanation cannot address the nonmonotonicity of both borrower and lender behavior.
Sufficiency of verifiable information. A final alternative explanation is that some borrowers do not provide descriptions because their applications get funded without any further information. That is, similar to the countersignaling explanation, only borrowers who are truly confident that positive information about their profile would be revealed by the platform can afford not to send a description, so they forgo the opportunity to persuade some naive lenders. However, in this explanation, the empirical model accounting for all other information would reveal no effect of description length because it has no signaling effect. Such an explanation is not consistent with the empirical findings.
In summary, none of these explanations can explain the totality of the nonmonotonic pattern of behavior we observe and the ex post efficiency of borrowers' and lenders' decisions.
In this research, we find that the strategic transmission of nonverifiable information by borrowers is an important influence on P2P lending platforms. Lenders make decisions on loan investments on the basis of both verifiable information and the optional nonverifiable information provided by borrowers, and such decisions are validated by subsequent loan performance.
We show that the presence of an optional loan description and its length both influence loan funding, so the provision of such information is not viewed as uninformative cheap talk. Borrowers and lenders use nonverifiable information in a way that is consistent with the properties of a countersignaling equilibrium. Specifically, our empirical evidence suggests that lenders view those who provide no loan description as high-quality borrowers. Moreover, medium-quality borrowers can distinguish themselves from low-quality borrowers by exerting greater effort to provide more nonverifiable information (i.e., longer descriptions) than low-quality borrowers do. It is important to note that we are not suggesting the act of writing requires so much effort that someone might not undertake it. Rather, the marginal benefit of writing a description (or not) is the probabilistic improvement of obtaining funding, and this improvement depends on the quality of the borrower. Thus, the marginal effort cost of writing need not be so great as to offset the value of the loan completely; rather, borrowers weigh the effort costs, however small, relative to the associated marginal improvement in funding probability.
The evidence from loan funding and ex post loan performance suggest that countersignaling is a robust explanation of lending behavior in a P2P environment. Even after we control for all the information that lenders encounter when making funding decisions, loan applications with no loan description turn out ex post to be higher-quality loans within a given credit grade, as measured by their lower delinquency rates. Applications that provide longer descriptions are less likely to be of low quality than applications with short descriptions. Thus, lenders are correct in interpreting the signaling effect of nonverifiable information when making loan funding decisions, because their funding decisions correspond with ex post loan quality.
The convergence between lenders' view of nonverifiable information and ex post loan performance suggests that non- verifiable information serves as a mechanism to attenuate information asymmetry regarding unobserved borrower quality. Borrowers who shrewdly recognize the signal/countersignal properties of nonverifiable information are rewarded, on average, by lenders. Lenders who correctly identify the signals will be rewarded with better-performing loans. An essential characteristic of marketing is understanding the marketplace and the factors that encourage consumers to engage in it (Bradford 2015). We identify a robust factor that affects both consumer exchange behaviors and the makeup of the marketplace. As P2P lending becomes a big part of the current financial landscape, our work contributes to a growing body of research that provides empirical insights into the intricacies of consumer lending decisions as the result of availability of large- scale data sets (Galak, Small, and Stephen 2011; Stephen and Galak 2012), something that was difficult to do a few years ago.
Substantively, we provide several insights that can be broadly contextualized to the parties involved in P2P transaction platforms such as Lending Club, eBay, Upwork, and Airbnb. First and foremost, designers of the platforms should realize that verifiable information is not enough to eliminate asymmetric information and reveal the true nature of a seller. Although offline communication between buyer and seller could reduce the information asymmetry, the platform runs the risks of prolonging the transaction time and the risks of the two parties transacting outside of the platform, which would reduce platform revenue. Thus, platforms always need to balance the benefits of allowing parties to transmit information with the risks of potentially losing platform revenue (e.g., Amazon and eBay both block email addresses in communication and cancel accounts that try to do business outside the platforms). Because countersignaling behavior has quality content, platforms that disallow communication (such as Lending Club) should improve their proprietary seller-quality metrics by incorporating countersignaling behavior in their computations (e.g., those who countersignal by not providing a description could get a score of 3, long descriptions a 2, short descriptions a 1). This scoring algorithm should remain proprietary so participants cannot game the system (akin to the review filtering algorithm on Yelp or the "portfolio health meter" by Lending Robot). As P2P platforms become more important, platforms that can fine-tune their rating systems to better reflect true risks and to resolve information asymmetry more effectively without communicating with each other offline would ( 1) ensure the revenue stays on the platform and ( 2) instill more confidence among participants.
For sellers who are reading this article, those who are confident in the quality of their profile as a provider should abstain from providing unsolicited information about their superior capabilities (e.g., repayment ability, reliability in terms of packaging quality and speed of delivery, quality of their freelance work) when verifiable information about their quality as a provider is available. For these sellers, volunteering nonverifiable information can lower their probability of making a deal (e.g., getting a loan funded, renting their apartment, making a sale). To illustrate, high-quality sellers on eBay should refrain from providing superfluous information and instead let their "power-seller" status do the talking; similarly, professionals who market their services in freelancing marketplaces (e.g., Upwork) should let their references, performance records, and professional portfolios speak for themselves, instead of providing nonverifiable descriptions of their skills or the quality of their services.
We note some limitations and suggest avenues for future investigation. During the time frame in our analysis, the platform was open only to individual, as opposed to institutional, investors. Starting in 2012, the platform was opened to institutional investors, which by December 2015 made up 33% of the funding. One can examine how asymmetric information is resolved differently in P2P versus peer-to-business (P2B). As the percentage of sophisticated investors is likely to be higher for institutional than for individual investors, our model suggests that the impact of countersignaling would be even stronger for institutional investors. Further, in light of the recent Lending Club scandal in the P2B domain,[16] we performed an analysis in which we excluded data from the month of December 2009, and we observed that the results do not change (which is to be expected, given the small proportion of loan transactions relative to all the transactions in the platform). Nevertheless, one can examine how the lender decision model and the weights they place on various pieces of information evolve. We suspect that now that the scandal has revealed that certain variables within the loan can be manipulated, lenders may deemphasize these variables and shift weight to other variables to form their decisions. Even so, we believe that as long as the public company and the industry as a whole conduct business in a legitimate fashion, they can provide value to both lenders and borrowers. During the time frame of our analysis, buyers and sellers could not communicate offline, and the platform's disclosure rules have not changed. Further research could examine a period when there is change in the platform's communication rules (e.g., change of sequence, opening or closing avenues of disclosure), which could shed light on how signaling mechanisms shift to alleviate asymmetric information. Finally, further research could examine whether characteristics specific to other P2P contexts moderate or replicate the signaling/countersignaling dynamics present in P2P lending.
Endnotes 1 Prior research reveals the cognitive cost of writing text. For example, Greiner and Wang (2010) report that prospective borrowers need to invest effort to write high-quality loan requests. Shavell (2010) also asserts that people usually experience some disutility for writing well-crafted works, and Liebowitz and Margolis (2005) even suggest that the act of writing may be subject to opportunity costs.
2 Fabozzi (2013, p. 464) states that the first decision portfolio managers should make is the asset allocation decision, that is, the decision of how much to invest in each asset class. Brus (2010) provides the example of the Oklahoma Teachers Retirement System, which has an executive directive to invest 70% in the equity market and 30% in the bond market. Bodie, Kane, and Marcus (2009, p. 218) report that even sophisticated investment companies may adopt a multistage decentralized approach by first deciding between asset-class allocations and then performing security section within each class, because of the overwhelming complexity of optimizing an organization's entire portfolio decision in one stage. Our analysis is abstracting from higher-level asset-class allocation and concentrates on the security selection of loans within the same risk-reward category.
3 A straightforward portfolio analysis in which loans have fixed interest rates and differ in their likelihood of default will conclude that lenders should be more likely to fund loans they believe to be less likely to default. Such an analysis is available from the authors upon request.
4 The intervals for the values of the borrower quality θ and the noise ε can be constructed from a simple normalization in which alternative param eters θ ' and a' are members of R+, with θ' > a'. The normalization is formed by choosing a large enough number M, (M = θ' + a' is sufficient) and making θ = θ'/M and a = a'/M.
5 The countersignaling equilibrium that arises in our model is robust to other specifications that preserve the standard single- crossing property. For instance, the effect of signaling on naive borrowers can be independent of borrower type, and the signaling cost can be a function of borrower type, as in Feltovich, Harbaugh, and To (2002).
6 In an extension of this model in Web Appendix 1, we show that results are preserved even if the value V is dependent on the borrower's type θ, provided that the single-crossing property is preserved.
7 Details are available from http://www.lendingclub.com/public/how-we-set-interest-rates.action.
8 See https://help.lendingclub.com/hc/en-us/articles/215437958-How-has-Lending-Club-s-investor-base-changed-.
9 The majority (58.8%) of the loans were fully funded, 34.7% of the loans received no funding, and 6.5% received partial funding. For our empirical analysis, we have excluded the partially funded loans from our data set. Comparing estimates with these loans included and coding them as 1 if >.5 funded and 0 if <.5 funded yields the same substantive results. This results in 24,594 observations.
We use the software package SPSS Text Analytics for Surveys 3.0, which is designed to extract and categorize free-text responses using natural language processing capabilities. For more information, see the IBM Software Business Analytics white papers "Analyzing Survey Text: A Brief Overview" and "IBM SPSS Text Analytics for Surveys," available at http://www.ibm.com/software/ analytics.
For robustness check to assess dispersion, we also ran a zero- inflated negative binominal regression. The results are similar, but the fit is slightly worse, suggesting that overdispersion is not an issue.
We choose FICO as an instrument for the following institutional reasons: (1) It is the most salient credit information that forms the borrower's belief of his/her quality, hence impacting his/her description decision. (2) The econometrist point of view needs to consider the lender's decision. The platform assigns credit grades largely according to FICO score, so credit grade already soaks up most of the effect of FICO score. The residual information should then be captured by other verifiable credit-related information, such as debt-to-income ratio, past delinquency, and so on. Thus, conditional on credit grades and other verifiable information, FICO score should have negligible residual effect.
We also ran models using month/year and quarter/year indicators. Both models give insignificant results for these indicators.
It is worth mentioning that even if some degree of signaling by amount was operating in the platform, undetected by our empirical analysis, such signaling effort would not invalidate our counter- signaling results. As discussed in the literature (Feltovich, Harbaugh, and To 2002), signaling and countersignaling can coexist in the same strategic interaction of economic agents.
We also note that the respondents are all likely to have limited lending experience, so in absence of other cues, they make take the length of description as a proxy for effort and quality of writing, thus behaving in the way we theorize naive lenders would do.
In April 2016, an investigation indicated that the now-ousted CEO Renaud Laplanche and three of his family members had taken 32 inside loans (totaling $720,000) to inflate growth, a practice Silicon Valley insiders call "growth hacking." Lending Club shares plunged 51% the week after the reporting of the scandal as institutional investors suspended debt purchases and the U.S. Justice Department announced a probe. For more information, see http://www.bloomberg.com/news/features/2016-08-18/how-lending-club-s-biggest-fanboy-uncovered-shady-loans.
GRAPH: FIGURE 1 Countersignaling Theoretical Prediction for the Relationship Between Signaling Effort (Description Length) and Probability of Funding
GRAPH: FIGURE 2 Effect of Countersignaling on Loan Funding Probability
DIAGRAM: FIGURE 3 Effect of Countersignaling on Delinquency Probability
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~~~~~~~~
Fabio Caldieraro is Associate Professor of Marketing, Brazilian School of Public and Business Administration, FGV/EBAPE
Jonathan Z. Zhang is Assistant Professor of Marketing, Michael G. Foster School of Business, University of Washington, Seattle
Marcus Cunha Jr. is Professor of Marketing, Terry College of Business, University of Georgia
Jeffrey D. Shulman is Marion B. Ingersoll Associate Professor of Marketing, Michael G. Foster School of Business, University of Washington, Seattle
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Record: 173- Strong Anxiety Boosts New Product Adoption When Hope Is Also Strong. By: Lin, Yu-Ting; MacInnis, Deborah J.; Eisingerich, Andreas B. Journal of Marketing. Sep2020, Vol. 84 Issue 5, p60-78. 19p. 2 Diagrams, 4 Charts. DOI: 10.1177/0022242920934495.
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- Business Source Complete
Strong Anxiety Boosts New Product Adoption When Hope Is Also Strong
New products can evoke anticipatory emotions such as hope and anxiety. On the one hand, consumers might hope that innovative offerings will produce goal-congruent outcomes; on the other hand, they might also be anxious about possible outcomes that are goal-incongruent. The authors demonstrate the provocative and counterintuitive finding that strong anxiety about potentially goal-incongruent outcomes from a new product actually enhances (vs. weakens) consequential adoption intentions (Study 1) and actual adoption (Studies 2 and 3) when hope is also strong. The authors test action planning (a form of elaboration) and perceived control over outcomes as serial mediators to explain this effect. They find that the proposed mechanism holds even after they consider alternative explanations, including pain/gain inferences, confidence in achieving goal-congruent outcomes, global elaboration, affective forecasts, and motivated reasoning. Managerially, the findings suggest that when bringing a new product to market, new product adoption may be greatest when hope and anxiety are both strong. The findings also point to ways in which marketers might enhance hope and/or anxiety, and they suggest that the use of potentially anxiety-inducing tactics such as disclaimers in ads and on packages might not deter adoption when hope is also strong.
Keywords: new product adoption; emotions; hope; anxiety; elaboration; perceived control
When consumers consider a product that is new and previously untried, it is possible that they experience varying degrees of hope and/or anxiety about consequences that may occur when they buy or use the new product. For example, a consumer might hope that a new skincare product can improve her acne. However, she might also be anxious about whether it will irritate her skin. Hope is defined as a positive emotion experienced in the present in regard to a goal-congruent future outcome (e.g., clear skin). Conversely, anxiety is defined as a negative emotion also experienced in the present but in regard to a goal-incongruent future outcome (e.g., skin irritation). In a new product adoption context, hope and anxiety are emotions that are integral to (vs. incidental to) decisions about whether to adopt the new product ([ 7]). That is, they are evoked in response to information about the new product, and they are directly related to consumers' decisions about whether to adopt it.
Although one might surmise that feeling anxious about possible goal-incongruent outcomes should dampen new product adoption, we make the counterintuitive prediction that strong anxiety actually heightens new product adoption when hope is also strong. We document this interactive effect of hope and anxiety on new product adoption in three studies that use different products and different consumer populations and when both measuring and manipulating hope and anxiety. We also address a novel processing mechanism that underlies this effect. Specifically, we argue that strong feelings of hope and anxiety cause consumers to engage in action planning—that is, to mentally elaborate on how hoped-for outcomes can be achieved while anxiety-evoking ones can be avoided. For example, a consumer who feels strong hope and strong anxiety about a new skincare product might plan to read usage instructions carefully, to ask friends if they have tried it, and to look up the product's ingredients and potential side effects. We anticipate that by engaging in such action planning, consumers feel more in control over the outcomes that the new product might bring, thus heightening their adoption intentions. Studies 2 and 3 support this prediction. If action planning and perceived control over outcomes indeed mediate the effect of hope and anxiety on new product adoption, factors that make action planning easier should enhance the effects of strong hope and strong anxiety. One such factor is prior experience with the product category. Specifically, consumers with more (vs. less) experience with skincare products might find it easier to engage in action planning because their prior experience with other products in the category enhances their ability to identify actions that support hoped-for outcomes and avoid anxiety-arousing ones. We find evidence for this moderating effect of product category experience in Study 3.
Overall, our research makes several important contributions to the literature. We are the first to document that when products are new, strong feelings of anxiety about potential outcomes from new product adoption can actually boost (vs. dampen) adoption decisions when hope is also strong. These findings augment prior studies that show that that anxiety can cause individuals to reject new products (e.g., [24]; [30]), as well as studies that document methods to reduce anxiety when new products are incongruent with expectations (i.e., [47]). Our findings suggest that, in some situations, anxiety can induce an approach (vs. avoidance) response, motivating information seeking and effortful information processing. We also add to the research on the effects of hope (e.g., [ 9]; [29]) by finding that hope for goal-congruent outcomes can drive action planning, perceived control over outcomes, and adoption, particularly when it is accompanied by strong anxiety about possible goal-incongruent outcomes. Beyond documenting this effect and theoretical bases for it, our results suggest that practitioners might shape new product adoption by activating the anticipatory emotions of hope and anxiety in marketing communications.
In addition, we add to the literature on planning and perceived control over outcomes. To date, research has examined planning in the context of self-regulation and/or resource planning (e.g., [28]). Our research extends this work by examining planning in the context of new product adoption and by introducing the novel construct of action planning. It also examines strong hope and strong anxiety as facilitators of action planning. Our research also augments work on perceived control. Prior work in marketing has studied threats to perceived control (e.g., [25]), feelings of low personal control (e.g., [ 8]), and internal locus of control ([12]; [36]). Research has also studied perceived control over objects ([49]) as well as perceived control over life, time, and physical space (e.g., [18]). We contribute to the literature by introducing a different control construct—perceived control over outcomes from new product adoption. We show that hope and anxiety drive such perceptions because they encourage action planning.
As noted previously, hope is defined as an anticipatory emotion that reflects the extent to which an individual appraises an uncertain but possible future outcome as goal-congruent ([29]), whereas anxiety is defined as an anticipatory emotion that reflects the extent to which an individual appraises an uncertain but possible future outcome as goal-incongruent ([23]). Thus, consistent with appraisal theories of emotion ([ 3]; [23]), two critical appraisal dimensions constitute the defining properties of hope and anxiety: goal (in)congruence and uncertainty. Goal (in)congruence is defined as the extent to which an outcome is regarded as positive (negative) because it is appraised as good (bad) in light of activated goals. Uncertainty is defined as the extent to which an outcome is unknown in the present because whether and how it occurs is realized only in the future. Consumers experience stronger (vs. weaker) feelings of hope when the outcome is appraised as being more (vs. less) goal congruent (positive). Likewise, they experience stronger (vs. weaker) feelings of anxiety when the outcome is appraised as being more (vs. less) goal incongruent (negative).
We differentiate hope and anxiety from other emotions because they are anticipatory in nature. Specifically, consumers experience hope and anxiety when they contemplate an uncertain but possible future outcome as opposed to an outcome that has already happened. When outcomes have already occurred, consumers experience different emotions such as joy, excitement, sadness, or anger. Hope and anxiety are also different from affective forecasts (also called anticipated emotions). The former are emotions that consumers experience in the present regarding uncertain and goal (in)congruent future outcomes, whereas the latter are emotions that consumers anticipate they will experience in the future once a particular outcome is realized. Thus, a consumer might hope (in the moment) that she will have clear skin after using a new skincare product and anticipate that she will feel happy (in the future) if her skin actually does clear up. Research has also differentiated anxiety from fear; anxiety is experienced in response to an unknown future, whereas fear is experienced in response to a known present ([34]). For example, when hiking, one might be anxious about whether one might see a bear, whereas actually seeing a bear evokes fear.
Although they are distinct emotions, hope and anxiety can co-occur. For example, consumers might naturally hope that a new product can produce outcomes that are congruent with their goals ([29]). However, because they are new and untried, consumers might also be anxious about potentially goal-incongruent outcomes. Notably, research on the joint effect of varying levels of hope and anxiety is, to our knowledge, quite limited.
We predict that strong hope coupled with strong anxiety regarding a new product induces consumers to engage in a form of elaboration that we call "action planning." [39] define planning as "the use of knowledge for a purpose; the construction of an effective way to meet some future goal" (p. 145). Just as hope and anxiety are future-oriented, planning is engaged with a future goal in mind. We define action planning as a form of planning that involves mentally elaborating on behaviors (actions) that one can enact so that the goal-congruent outcomes can be attained while goal-incongruent outcomes can be avoided.[ 5]
We predict that action planning will be greatest when hope and anxiety are both strong. Several factors motivate this logic. First, the act of mentally elaborating on actions that are aligned with goal-congruent and goal-incongruent outcomes involves cognitive effort ([13]; [41]). Prior work has suggested that when hope is strong, people expend and sustain effort to attain what they hope for ([11]; [43]). Individuals also sustain effort to avoid what they are highly anxious about because such outcomes can have considerable significance to the self. Consistent with this notion, prior research has found that strong anxiety encourages attentional vigilance (e.g., [19]), as well as selective attention to and processing of anxiety-related stimuli ([31]; [37]). Strong anxiety also motivates effort in seeking information ([26]) and soliciting advice from others ([16]).
Strong hope and strong anxiety should also motivate an aspect of action planning called pathways thinking—that is, identifying the various paths that might support the occurrence of hoped-for outcomes or prevent the occurrence of anxiety-arousing ones. Whereas prior research has emphasized the role of strong hope in pathways thinking ([44]; [45]), pathways thinking also includes contemplating actions that can avoid the outcomes about which individuals may be anxious ([ 4]).
Strong hope and strong anxiety are also linked with problem-solving behavior ([ 6]; [21]; [45]). Considering which actions will be most effective at attaining (avoiding) goal-congruent (goal-incongruent) outcomes is a form of problem solving. Pertinent to strong hope, strong anxiety, and problem solving, [ 6] found that students who were dispositionally high in hope reported thinking through possible solutions to highly anxiety-evoking academic situations. They also engaged in less avoidant coping in response to anxiety-evoking situations. In short, when hope and anxiety are both strong, individuals should be inclined to elaborate on pathways and solutions (i.e., action plans) that can attain the outcomes they hope for and avoid those about which they are highly anxious.
Action planning should be more limited when hope is strong but anxiety is weak. When anxiety is weak, goal-incongruent outcomes are less "problematic." Thus, action plans should include only pathways that pertain to those outcomes that consumers hope to attain. There is less need to elaborate on pathways and solutions when anxiety is weak because the goal-incongruent outcomes are less negative and entail lower significance to the self.
We also expect that when hope is weak, consumers' most natural response is to avoid the new product, regardless of whether anxiety is strong or weak. Specifically, when hope is weak but anxiety is strong, effortful action planning should be limited because the new product offers little benefit from a goal-congruity perspective. Even if anxiety-evoking outcomes could be avoided, the ultimate outcome from the new product is not one that consumers regard as highly goal congruent. As such, weak hope should not motivate individuals to confront potential anxiety-inducing outcomes by thinking through how they can be avoided. In short, weak hope does not provide the motivational energy for individuals to engage in action planning. Thus, when hope is weak, we expect to confirm prior findings that have shown that anxiety creates an avoidance response (e.g., [10]; [24]; [30]). Similarly, action planning should be limited when hope and anxiety are both weak. Because the outcomes are neither highly goal-congruent nor highly goal-incongruent, individuals should devote limited effort to developing action plans.
We also predict that the process of engaging in action planning increases individuals' perceived control over realizing (avoiding) goal-congruent (incongruent) outcomes. Perceived control is defined here as a subjective belief that one has the ability to influence desired and undesired outcomes ([41]).[ 6][41] argues that the planning process should "lead individuals to view themselves as capable of (and hence in control over) implementing effective responses" (p. 280). She also argues that such planning may be particularly prevalent when individuals believe that the environment may not fully support the goals they wish to attain (as might be true when strong anxiety regarding outcomes is present). Extending this logic, we predict that by virtue of engaging in action planning and determining what actions will support attaining (avoiding) goal-congruent (goal-incongruent) outcomes, individuals come to believe that they can influence these outcomes by virtue of their volitional actions. Thus, they perceive that control over outcomes resides with them and their actions.
If individuals feel that they have control over outcomes from a new product, they should be more likely to adopt that new product when perceived control is high. This latter conclusion is consistent with work by [40], who find that perceptions of control over outcomes predict when an intention translates into an actual behavior (in this case, new product adoption). It is also consistent with the theory of planned behavior ([ 1]), which suggests that control beliefs regarding actions that can facilitate a given outcome can influence actual behavior.
Building on this logic, we predict that:
- H1: New product adoption intentions are greatest when hope and anxiety are both strong.
- H2: Action planning and perceived control over outcomes serially mediate the effects of strong hope and strong anxiety on new product adoption.
If our logic is correct, the effects predicted in H1 and H2 should be particularly strong when other aspects of the environment beyond hope and anxiety facilitate action planning. We presume that elaborating on potential actions requires some prior knowledge related to past experiences, such as those that consumers have encountered with other products in the product category. Work in psychology supports the notion that prior experience facilitates planning abilities (e.g., [13]; [39]). We expect that when hope and anxiety are both strong, individuals with more (vs. less) product category experience should be better equipped to draw on their past experiences to mentally simulate action plans associated with the occurrence of hoped-for and anxiety-inducing outcomes. These ideas are consistent with [ 2] claim that individuals with more versus less expertise, such as those who have more experience in the product category, are "more likely to appreciate the potential complexities of a problem and are better equipped to deal with them" (p. 427). Individuals with less product category experience should have less ability to engage in action planning because their prior knowledge is more limited. In short, greater experience with the product category as a whole should provide more knowledge about potential action plans that align with the outcomes that consumers hope for and those that avoid the outcomes about which consumers are anxious. We also expect less action planning among people with more product category experience when hope is strong but anxiety is weak (vs. when both hope and anxiety are strong) because weak levels of anxiety require less action planning (see H2). Finding that the effect of strong hope and strong anxiety on action planning is moderated by prior experience with products in the product category would thus provide additional evidence of the (action planning) processing mechanism that underlies H2.
Next, we report three studies designed to test H1 and H2. We also test the moderating role of prior knowledge to provide further support for the processing mechanism proposed in H2. All studies use real products. We summarize the studies in Table 1.
Graph
Table 1. Overview of the Studies.
| Studies | Study 1 | Study 2 | Study 3 |
|---|
| Product | PrEP: Medication designed to prevent HIV/AIDS | Skin peel product designed to create younger-looking/healthier skin | Energy drink product designed to enhance mental clarity and offer a healthy source of energy |
| Population | Individuals in poverty-stricken areas in Africa, South America, Eastern Europe, and Asia (N = 1,861) | Highly educated managers from the Americas, Europe, Africa, Middle East, and Asia participating in an executive teaching program at a university (N = 475) | Part-time postgraduate university students (N = 557) |
| IVs | Measured hope and anxiety related to taking the medication | Manipulated strong vs. weak hope and strong vs. weak anxiety via online product reviews by other users | Manipulated strong vs. weak hope via online product reviews and manipulated strong vs. weak anxiety by providing disclaimers about risks from use of the product |
| Product described | By health care workers | By social media post | By marketing communications |
| DV | Adoption intentions | Actual adoption (number of sample units adopted) | Actual adoption (number of sample units adopted) |
| Process | N.A. | Measured action planning, perceived control over outcomes | Measured action planning, perceived control over outcomes |
| Moderator related to the process | N.A. | N.A. | Moderating role of prior product category experience. Prior product category experience moderates the influence of hope × anxiety → action planning such that action planning and new product adoption are greatest for consumers with prior category experience |
| Control variables (including alternative process mechanisms) | Gender, age group, country, fear, embarrassment | Measured confidence in attaining (avoiding) goal-congruent (incongruent) future outcomes, affective forecasts, pain-gain inferences, global elaboration, motivated reasoning, concerns about appearance, product relevance, brand familiarity, gender, age | Measured confidence in attaining (avoiding) goal-congruent (incongruent) future outcomes, affective forecasts, pain-gain inferences, global elaboration, motivated reasoning, valence of prior experience with similar product, product relevance, brand familiarity, gender, age |
| Findings | H1 supported, H2 not tested | H1 and H2 supported | H1 and H2 supported |
Study 1 was a field study (N = 2,084) sponsored by the Bill & Melinda Gates Foundation. Our goal was to test H1 in a meaningful real-world context. Specifically, Study 1 involved respondents' intentions to adopt a real medication called "preexposure prophylaxis" (PrEP) designed to protect individuals from contracting the human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS). The study included respondents from eight countries (Thailand, India, South Africa, Botswana, Uganda, Kenya, Peru, and Ukraine) who were deemed to be at high risk of contracting HIV/AIDS. Respondents included injecting drug users, men who have sex with men, female sex workers, individuals whose partners have HIV/AIDS, and young women who were at risk due to high rates of HIV/AIDS and rape in their country. Due to missing data or incomplete answers, 223 cases were deleted, resulting in an effective sample size of 1,861 individuals.
In this study, hope is relevant because the medication can potentially protect individuals from contracting HIV/AIDS. Taking the medication might also generate anxiety, as respondents were informed that the medication could create goal-incongruent physical (headache, drowsiness, and bloating) and performance outcomes (it might not be effective if not taken as directed).
Ipsos MORI, a large international market research company, gathered the data using surveys.[ 7] The questionnaire was translated into 16 languages by local marketing research teams and back-translated by professional translators to ensure content consistency. The final translation was approved by consensus among the professional translators. Respondents provided written consent and responded to questionnaires in their native languages. Participants were offered a monetary incentive (the local equivalent of US$5) for participating in the study, except in South Africa, where the ethics committee prohibited the use of incentives.
After being given a description of PrEP, respondents indicated their feelings about it and their willingness to use it ("Would you take PrEP as soon as it becomes available, or not?"; 1 = "no, definitely not," 2 = "no, probably not," 3 = "yes, probably," and 4 = "yes, definitely"). We measured hope by asking respondents, "How much hope, if any, does PrEP give you for new possibilities for you in life?" (1 = "no hope at all," 2 = "not much hope," 3 = "some hope," and 4 = "a lot of hope"). We measured anxiety by asking, "How anxious, if at all, does the thought of taking PrEP make you feel?" (1 = "not at all anxious," 2 = "not very anxious," 3 = "fairly anxious," and 4 = "very anxious").
We included several measures to differentiate anxiety from other negative high-arousal emotions. Fear of contracting HIV was measured by asking, "How afraid are you of contracting HIV/AIDS, if at all?" (1 = "not at all afraid," 2 = "not very afraid," 3 = "fairly afraid," and 4 = "very afraid"). We measured embarrassment by asking, "How embarrassing, if at all, would you find taking PrEP to be?" (1 = "not at all embarrassing," 2 = "not very embarrassing," 3 = "fairly embarrassing," and 4 = "very embarrassing"). We included these measures to differentiate the anticipatory emotion of anxiety from the anticipated emotions of fear and embarrassment. Feeling anxious about the health consequences of taking PrEP was modestly correlated with both fear of contracting HIV/AIDS (r =.12, p <.001) and embarrassment over using PrEP (r =.23, p <.001).
Finally, respondents indicated their gender (1 = male, 2 = female, and 3 = transgender), age group (in years; 1 = up to 15, 2 = 16–18, 3 = 19–24, 4 = 25–30, 5 = 31–35, 6 = 36–40, 7 = 41–45, 8 = 46–50, 9 = 51–55, 10 = 56–60, and 11 = 61+), and country of origin (1 = Thailand, 2 = Ukraine, 3 = India, 4 = Peru, 5 = South Africa, 6 = Botswana, 7 = Uganda, and 8 = Kenya). The results reported below do not change when controlling for fear of contracting HIV, embarrassment over using PrEP, age groups, gender dummies, and country dummies.
The Web Appendix shows the correlations among constructs (Table WA1) and the number of respondents in each country (Table WA2). Because the independent and dependent variables were both measured and continuous, we ran a regression analysis using the full sample of respondents. All measured variables were mean-centered ([20]). The analysis modeled the main effect of hope, the main effect of anxiety, and their interaction. As we expected, the main effect of hope was significant and positive (see Model 1 in Table 2). The more respondents hoped that PrEP would offer goal-congruent life outcomes, the more positive were their intentions to adopt it (β =.40, SE =.03, t = 13.76, p <.001). There was no main effect of anxiety (β =.01, SE =.01, t =.64, p >.51); however, the predicted interaction between hope and anxiety was significant (β =.07, SE =.03, t = 2.54, p =.011).
Graph
Table 2. Study 1: The Impact of Hope and Anxiety on Adoption Intention.
| Model 1Base Model(The Effect of Hope, Anxiety, and Their Interaction) | Model 2Base Model with Controlsfor Fear and Embarrassment | Model 3Base Model with Controlsfor Demographics | Model 4Base Model withAll Controls |
|---|
| DV: Adoption Intentions | B (SE) | T | B (SE) | T | B (SE) | T | B (SE) | T |
|---|
| Constant | 3.45 (.02) | 215.81*** | 3.58 (.10) | 35.45*** | 2.82 (.07) | 40.37*** | 2.91 (.12) | 25.98*** |
| Hope | .40 (.03) | 13.76*** | .38 (.03) | 12.92*** | .36 (.02) | 14.59*** | .33 (.03) | 13.23*** |
| Anxiety | .01 (.01) | .64 | .03 (.02) | 1.81 | .02 (.01) | 1.01 | .03 (.02) | 1.95† |
| Hope × Anxiety | .07 (.03) | 2.54* | .06 (.03) | 2.13* | .07 (.02) | 2.87** | .06 (.02) | 2.45* |
| Fear | | | .00 (.02) | .21 | | | .02 (.02) | 1.01 |
| Embarrassment | | | −.10 (.02) | −4.62*** | | | −.11 (.02) | −5.49*** |
| Gender | | | | | −.02 (.03) | −.72*** | −.03 (.03) | −.92 |
| Age groups | | | | | .04 (.01) | 4.02*** | .04 (.01) | 3.69*** |
| Country (Base: Thailand) | | | | | | | | |
| India | | | | | .72 (.06) | 11.39*** | .76 (.06) | 11.98*** |
| South Africa | | | | | .64 (.06) | 10.21*** | .66 (.06) | 10.57*** |
| Botswana | | | | | .47 (.07) | 7.19*** | .46 (.06) | 7.17*** |
| Uganda | | | | | .43 (.07) | 6.41*** | .46 (.07) | 6.92*** |
| Kenya | | | | | .47 (.07) | 7.08*** | .49 (.07) | 7.49*** |
| Peru | | | | | .47 (.06) | 7.23*** | .47 (.06) | 7.28*** |
| Ukraine | | | | | .65 (.06) | 10.05*** | .64 (.06) | 9.91*** |
| R2 =.12 | R2 =.13 | R2 =.21 | R2 =.23 |
| F(3, 1857) = 70.72*** | F(5, 1855) = 50.11*** | F(12, 1848) = 41.76*** | F(14, 1846) = 38.58*** |
1 †p =.051.
- 2 *p <.05.
- 3 **p <.01.
- 4 ***p <.001.
- 5 Notes: Variables are mean-centered. SEs are heteroskedasticity-consistent.
To better understand this interaction, we conducted a floodlight analysis ([46]) to observe the range of hope for which the effect of anxiety on adoption intentions was significant. As predicted by H1, we found that as hope becomes stronger, stronger anxiety has a positive impact on adoption intentions. In Table 3, we see that the effect of anxiety on adoption intentions switches from being negative and significant at weak levels of hope (<−.97) to positive and significantly greater than zero at strong levels of hope (>.27). Figure WA1 in the Web Appendix depicts the interaction between hope and anxiety at ±1 standard deviation, showing that adoption intentions were greatest when hope and anxiety were both strong. Figure WA2 in the Web Appendix depicts floodlight points, showing that the effect of anxiety on adoption was negative when hope was at −.97 and positive at when hope was at.27. Combined, these results support H1.
Graph
Table 3. Study 1: PrEP, Conditional Effect of Anxiety at Values of Hope.
| Hope | Effect Anxiety | SE | t | p | Lower-Level 95% CI | Upper-Level 95% CI |
|---|
| −2.49 | −.16 | .06 | −2.58 | .0100 | −.2875 | −.0391 |
| −2.34 | −.15 | .06 | −2.55 | .0108 | −.2704 | −.0354 |
| −2.19 | −.14 | .06 | −2.52 | .0117 | −.2532 | −.0318 |
| −2.04 | −.13 | .05 | −2.49 | .0128 | −.2361 | −.0281 |
| −1.89 | −.12 | .05 | −2.45 | .0143 | −.2191 | −.0244 |
| −1.74 | −.11 | .05 | −2.41 | .0161 | −.2020 | −.0206 |
| −1.59 | −.10 | .04 | −2.35 | .0187 | −.1850 | −.0168 |
| −1.44 | −.09 | .04 | −2.29 | .0222 | −.1681 | −.0130 |
| −1.29 | −.08 | .04 | −2.21 | .0272 | −.1513 | −.0090 |
| −1.14 | −.07 | .03 | −2.11 | .0348 | −.1345 | −.0050 |
| −.99 | −.06 | .03 | −1.99 | .0469 | −.1179 | −.0008 |
| −.97 | −.06 | .03 | −1.96 | .0500 | −.1148 | .0000 |
| −.84 | −.05 | .03 | −1.83 | .0677 | −.1015 | .0036 |
| −.69 | −.04 | .02 | −1.62 | .1059 | −.0853 | .0082 |
| −.54 | −.03 | .02 | −1.34 | .1817 | −.0696 | .0132 |
| −.39 | −.02 | .02 | −.96 | .3393 | −.0543 | .0187 |
| −.24 | −.01 | .02 | −.45 | .6545 | −.0398 | .0250 |
| −.09 | .00 | .02 | .20 | .8414 | −.0264 | .0324 |
| .06 | .01 | .01 | .94 | .3453 | −.0144 | .0412 |
| .21 | .02 | .01 | 1.67 | .0953 | −.0042 | .0517 |
| .27 | .03 | .01 | 1.96 | .0500 | .0000 | .0572 |
| .36 | .03 | .02 | 2.25 | .0244 | .0044 | .0639 |
| .51 | .04 | .02 | 2.65 | .0080 | .0116 | .0775 |
6 Notes: Figures in boldface highlight Johnson–Neyman points, identifying the range of hope for which the effect of anxiety on adoption intentions was significant.
Models 2–4 in Table 2 present robustness checks by incorporating various control variables in a stepwise fashion. All measured variables were mean-centered using Hayes's PROCESS Model ([20]).[ 8] The regression analysis modeled the main effect of hope; the main effect of anxiety; their interaction; and several control variables, including high-arousal emotions (i.e., fear and embarrassment), gender, age groups, and country dummies. Importantly, the positive interaction remained unchanged (β =.06, p =.014), even when including these controls (see Model 4 in Table 2). These results also support H1.
The results of Study 1 support H1, indicating that the combination of strong hope and strong anxiety about outcomes from taking PrEP led to more positive adoption intentions. We designed Study 2 to see if these results would be replicated when using a different population and a different product, when using multi-item measures for hope and anxiety, and when measuring actual behavior. We also wished to manipulate (vs. measure) hope and anxiety so as to rule out the potential for reverse causality.[ 9] Finally, Study 2 tested H2.
Four hundred seventy-five managers (53.3% female) attending an executive education program at a research university agreed to participate in a study about an actual new product. Participants' average age was 47 years (SD = 6.49). Managers were from different parts of the world, including North America (N = 125; 26.3%), Africa and the Middle East (N = 68; 14.3%), Latin America (N = 43; 9.1%), Europe (N = 121; 25.5%), and Asia (N = 118; 24.8%).
The product was an extra-strength skin peel product called Dr Dennis Gross Skincare. Web Appendix Figure WA3 shows an image of the product's package to which participants were exposed. The package claimed that the product can produce balanced, clear, bright, and smooth skin. We manipulated (vs. measured) strong versus weak hope and strong versus weak anxiety by asking participants to read social media comments about potential goal-congruent and goal-incongruent outcomes from using the new product. These ostensible social media comments were presented as being from two users of the product. One user commented on the product's potential goal-congruent outcomes (clear and beautiful skin), while the other commented on potential goal-incongruent outcomes (e.g., negative skin reactions). We created two versions of the social media comment that pertained to the product's goal-congruent outcomes; one designed to evoke strong hope and the other designed to evoke weak hope in consumers, as shown here.
Strong [weak] hope: I had amazing [limited] results from using this product! I needed a product that made my skin look radiant, minimized the size of my pores, and reduced some fine lines and wrinkles. This product did everything I had hoped that it would do. [I did have some marginal improvements in skin brightness and clarity. But I didn't see any effect on fine lines and wrinkles. The effects were clearly not as dramatic as I had hoped for.]
Likewise, we created two versions of the social media comment that pertained to the product's goal-incongruent outcomes; one designed to evoke strong anxiety and the other designed to evoke weak anxiety.
Strong [weak] anxiety: This product is very strong! Although I didn't have any burning or allergic reactions to it, other users might be very anxious about such outcomes. If not used properly, or if used on sensitive skin, this product could produce some worrisome results. [The product is labeled "extra strength" because it dissolves older layers of skin. But I had no burning or allergic reactions to it. It's hard to imagine that it would produce any worrisome results, even if used improperly or on sensitive skin.]
We randomly assigned participants to one of four conditions representing a 2 (strong vs. weak hope) × 2 (strong vs. weak anxiety) between-subjects design. Next, we told participants that they could purchase one or more sample units of the product at the end of the study. One sample unit included two sachets of the skin peel product. Each sample unit cost 4 USD. The instructions emphasized that this was a real purchase decision. Participants read that as compensation for their participation they were eligible to receive US$4, which they could use to buy one sample unit of the product if they wished to do so. They could also purchase additional sample units with their own money if they desired. Participants then indicated on a notecard how many sample units, if any, they would like to purchase at the study's conclusion. They then responded to a set of items designed to measure all focal constructs. Web Appendix Table WA3 shows all constructs, items, scale reliabilities, and descriptive statistics for all measures used in Study 2. Unless otherwise indicated, all items were evaluated on seven-point scales, with scale endpoints anchored at "not at all" ( 1) and "very much" ( 7). Finally, all participants were debriefed. During the debriefing, we told respondents that while the product was real, the social media comments to which they had been exposed were fictitious.
The main dependent variable was the number of sample units that respondents opted to buy, as indicated by the index card they completed after reading the media comments.
We then assessed perceived control over outcomes, asking participants to indicate the extent to which they felt that they had control over attaining the positive outcomes they wanted from the product and the extent to which they felt that they had control over avoiding the negative outcomes that they did not want from the product (r =.78).
Next, we assessed action planning. We asked respondents to indicate the extent to which they had mentally considered actions that they could engage in so as to facilitate goal-congruent outcomes and thwart goal-incongruent ones. Questions included "I am thinking about what I can do so that I can achieve the positive outcomes I hope this new product will bring me" and "I am thinking about what I can do so that I can avoid the negative outcomes that I am anxious about from using this new product." We also asked participants to indicate the extent to which these statements characterized their planning about actions: "Considering what might go wrong with this new product helps me plan for how I can avoid any negative outcomes from using it," "I am actively planning what I should do so that I can get the best outcomes possible from using this new product," and "I am actively planning what I should do so that I can avoid the worst outcomes from using this new product." These items loaded cleanly on a single factor (α =.90).
We next asked participants questions designed to rule out alternative explanations for our proposed effects. We initially treat these alternative processes as control variables because our primary objective is to test H1 and H2. In subsequent analyses, we ask if one or more processes could act as parallel mediators. This objective is secondary and is designed to foster future theory building as opposed to theory testing.
First, it is possible that confidence in attaining hoped-for outcomes could account for the results, as strong hope might incline individuals to be overconfident in attaining outcomes. Two items (r =.63) measured confidence: "I'm confident that I will experience positive improvements to my skin from using this product" and "I'm confident that I can avoid negative outcomes to my skin from using this product." Second, affective forecasts could play a role in influencing the observed results ([15]). That is, one might argue that when thinking about the new product, individuals might anticipate that they will experience positive and/or negative emotions following their purchase or use of the new product. Such anticipated emotions could drive their choices. Thus, we asked respondents to indicate the extent to which they would feel excited, dissatisfied, disappointed, delighted, pleased, regretful, and upset after using the product (α =.91 after reverse coding the negative items). Third, because the social media comments noted goal-congruent (hoped-for) and goal-incongruent (anxiety-evoking) outcomes, it is possible that the former would be interpreted as gains, while the latter would be interpreted as pains ([22]). Thus, we asked participants about the extent to which they believed that to obtain gains they must experience pains. Specifically, respondents indicated to what extent they felt that "experiencing the product could be painful, but it's worth it" and "experiencing this new product could involve some suffering, but I am willing to endure it to obtain the result I want" (r =.74). Fourth, one could argue that because action planning is a form of elaboration, any form of elaboration might explain the results. Thus, we asked respondents to indicate the extent to which the reviews made them think a lot about the new product and the extent to which the reviews made them consider a lot of things (r =.76). Fifth, we assessed motivated reasoning based on the work of [ 9], who postulated that when hope is strong and goal-congruent outcomes are threatened, consumers engage in motivated reasoning. It is possible that anxiety constitutes a threat to hoped-for outcomes. Thus, we asked respondents to indicate to what extent they believed that the skin peel's downsides were not that bad and that outcomes were unlikely to occur to them personally (r =.69). Web Appendix Table WA4 shows the correlations among Study 2's constructs. A factor analysis shown in Web Appendix Table WA5 indicates that action planning, perceived control over outcomes, and the aforementioned alternative process measures were empirically discriminable, with each item loading cleanly on its respective factor.
In Study 1, hope and anxiety were indicated by single-item measures. To reduce the potential for measurement error, we included these same measures in Study 2 while including three additional items for each construct. For hope, respondents indicated the extent to which they agreed with the following: "I really hope that the new product can help me improve the appearance of my skin," "The new product gives me hope for experiencing bright and clear skin," and "I yearn to have clear skin—free of the types of skin imperfections—that the new product can bring me." For anxiety, items included "I am worried that this new product could temporarily harm my skin," "I am anxious that I could have an allergic reaction to this product," and "I am nervous about whether this new product will burn my skin or give me a rash." Both multi-item measures proved to be high in reliability (hope: α =.87; anxiety: α =.90). Moreover, a set of factor analyses indicated that the indicators for hope and anxiety construct loaded cleanly on their respective factors.
Finally, respondents completed several additional control variables; specifically, brand familiarity, interest in skin peel products, concern over one's appearance, and gender. The conditions did not differ on any of these control variables.
Table 4 shows cell means for all variables collected in Study 2.
A set of 2 (hope: strong vs. weak) × 2 (anxiety: strong vs. weak) analyses of variance (ANOVAs) on the manipulation checks confirmed that the manipulations were successful. Specifically, the manipulation check for hope showed only a main effect of hope. As we expected, respondents in the strong hope condition felt stronger hope for having clear and bright skin from the new product (Mstrong = 4.65) than did those in the weak hope condition (Mweak = 2.90; F( 1, 471) = 162.70, p <.001). The manipulation check for hope showed neither a main effect of anxiety (F( 1, 471) =.003, p >.95) nor a significant interaction between hope and anxiety (F( 1, 471) =.26, p >.61).
Likewise, the manipulation of anxiety worked as expected, revealing only a main effect of anxiety. Respondents in the strong anxiety condition reported feeling stronger anxiety about the potential for temporary harm to their skin from the new product than did those in the weak anxiety condition (Mstrong = 4.65 vs. Mweak = 2.84; F( 1, 471) = 182.40, p <.001). Neither the main effect of hope (F( 1, 471) = 1.23, p >.26) nor the interaction between hope and anxiety was significant (F( 1, 471) =.16, p >.69).
The overall mean of the focal dependent variable (number of units adopted) was 3.27 (SD = 6.00), with a range from 0 to 40 units. Web Appendix Figure WA4 shows a histogram of the number of units purchased. A 2 × 2 ANOVA with the number of units adopted as the dependent variable revealed a main effect of hope (Mstrong = 5.43 vs. Mweak = 1.11; F( 1, 471) = 71.56, p <.001), a main effect of anxiety (F( 1, 471) = 5.09, p =.025), and the predicted hope × anxiety interaction (F( 1, 471) = 5.77, p =.017). In support of H1, respondents in the strong hope/strong anxiety adopted more units of the product (Mstrong hope/strong anxiety = 6.62) than did those in the other three conditions (Mstrong hope/weak anxiety = 4.24; t(236) = 2.34, p =.02; Mweak hope/weak anxiety = 1.15; t(235) = 6.26, p <.001; Mweak hope/strong anxiety = 1.08; t(234) = 6.32, p <.001). Also as anticipated, respondents adopted the fewest units when hope was weak, regardless of the levels of anxiety (see Table 4). These results support H1.
Graph
Table 4. Study 2 Main Study Cell Means.
| Means | Strong Hope | Weak Hope |
|---|
| Strong Anxiety(N = 118) | Weak Anxiety(N = 120) | Strong Anxiety(N = 118) | Weak Anxiety(N = 119) |
|---|
| Manipulation Checks | | | | |
| Hope | 4.69a | 4.61a | 2.87b | 2.93b |
| Anxiety | 4.70a | 2.93b | 4.60a | 2.73b |
| Dependent Variable | | | | |
| Units adopted | 6.62a | 4.24b | 1.07c | 1.15c |
| Proposed Process Mechanisms | | | | |
| Action planning | 4.28a | 3.38b | 2.32c | 2.26c |
| Perceived control over outcomes | 3.84a | 3.38b | 2.25c | 2.21c |
| Alternative Process Mechanisms | | | | |
| Confidence in hoped-for outcome | 3.02b | 3.50a | 2.12d | 2.55c |
| Affective forecasts | 3.56b | 4.26a | 2.08d | 2.65c |
| Pain-gain inferences | 3.53a | 3.28a | 2.52b | 2.30b |
| Global elaboration | 3.34a | 3.07a | 2.33b | 2.17b |
| Motivated reasoning | 2.92b | 4.10a | 1.88c | 2.85b |
| Covariates | | | | |
| Interest in skin peel products | 3.68a | 3.77a | 3.49a | 3.66a |
| Concerns about skin appearance | 4.09a | 4.14a | 3.85a | 3.82a |
| Brand familiarity | 1.99a | 1.99a | 1.97a | 1.97a |
| Gender | .51a | .53a | .56a | .54a |
| Age | 47.74a | 47.51a | 47.77a | 45.22b |
7 Note: Means with different superscripts are significantly different at p <.05.
A 2 × 2 ANOVA on the measure of action planning revealed a main effect of hope (Mstrong = 3.83 vs. Mweak = 2.29; F( 1, 471) = 171.86, p <.001), a main effect of anxiety (Mstrong = 3.30 vs. Mweak = 2.82; F( 1, 471) = 16.81, p <.001), and the predicted hope × anxiety interaction (F( 1, 471) = 12.78, p <.001). A 2 × 2 ANOVA on the measure of perceived control over outcomes also revealed a main effect of hope (Mstrong = 3.61 vs. Mweak = 2.23; F( 1, 471) = 124.62, p <.001), a main effect of anxiety (Mstrong = 3.04 vs. Mweak = 2.80; F( 1, 471) = 4.06, p =.044), and a marginally significant hope × anxiety interaction (F( 1, 471) = 2.86, p =.092). The means in Table 4 show that action planning and perceived control over outcomes were significantly greater in the strong hope/strong anxiety condition than in any of the other three conditions. As we anticipated, action planning and perceived control were lowest when hope was weak, regardless of whether anxiety was strong or weak (see the means in Table 4). We also examined whether gender[10] and brand familiarity[11] might serve as proxies for prior product category experience and thus moderate the results. However, neither served as a significant moderator.
To examine whether action planning and perceived control over outcomes serially mediate the effect of strong hope and strong anxiety on adoption (H2), we used bootstrapping with repeated extraction of 5,000 samples ([20], PROCESS v3.3, Model 6). The mediation analysis used the hope and anxiety manipulations and a serial sequence of action planning and perceived control over outcomes in mediating the relationship between hope × anxiety and the number of units adopted. The analysis also controlled for all other potential process explanations (i.e., confidence, affective forecasting, pain-gain inferences, motivated reasoning, and elaboration) as well as interest in skincare products, concerns about appearance, brand familiarity, gender, and age.[12]
The results in Figure 1, Panel A, showed that the interaction between hope and anxiety predicted action planning (β =.66, p <.001). The sign of the effect indicated that action planning was greatest when hope and anxiety were both strong. Greater action planning, in turn, enhanced perceived control over outcomes (β =.39, p <.001). Perceived control over outcomes, in turn, significantly predicted the number of units adopted (β =.88, p <.001). Evidence for sequential mediation was confirmed (indirect effect =.224, bootstrap SE =.074, confidence interval [CI]: [.094,.382]). Notably, switching the order of the mediators resulted in a nonsignificant mediation (CIs include 0), suggesting that our proposed sequence of mediators is more appropriate. In short, the results support H2 when we use manipulated independent variables and control for alternative factors. Notably, the results also indicated that confidence, pain-gain inferences, and interest in skincare products were also significant as control variables. However, the interactive effect of hope and anxiety on the number of units adopted was significant even after accounting for these alternative factors (β = 1.21, p <.001). Furthermore, the results indicated a significant direct effect of hope and anxiety on the number of units adopted, suggesting that additional mechanisms beyond those noted in Figure 1, Panel A, are operative. We discuss this issue in the "General Discussion" section.
Graph: Figure 1. Study 2 mediation results (DV = number of sample units adopted).
To test whether any of the alternative mechanisms could also serve as parallel mediators (which could prove useful in future theory building), we first conducted separate sets of bootstrap mediation analyses ([20], PROCESS v3.3, Model 4). These results, which are reported in Web Appendix Table WA6, showed that the largest effect was for perceived control over outcomes (indirect effect =.541, bootstrap SE =.146, bootstrap 95% CI [CI95]: [.279,.845]) and then action planning (indirect effect =.298, bootstrap SE =.156, bootstrap CI95: [.003,.606]), followed by pain-gain inferences (indirect effect =.209, bootstrap SE =.087, bootstrap CI95: [.060,.396]), affective forecasts (indirect effect =.145, bootstrap SE =.070, bootstrap CI95: [.029,.305]), and confidence (indirect effect =.116, bootstrap SE =.071, bootstrap CI95: [.011,.285]). Global elaboration, motivated reasoning, and general interest showed no effects.
To determine whether these alternative mediators were still significant when modeled as parallel mediators along with our proposed serial mediators, we ran a structural equation model (SEM) analysis. The results in Web Appendix Figures WA5 show that our proposed explanation (action planning and perceived control over outcomes), as well as confidence and pain-gain inferences, emerged as significant parallel mediators. Positive affective forecasts were only marginally significant. Notably, though, including these additional mediators did not result in a better-fitting model (χ2(182) = 366.36; comparative fit index [CFI] =.97, Tucker–Lewis index [TLI] =.97, root mean square error of approximation [RMSEA] =.046, 90% CI [CI90] = [.039,.053]; see Web Appendix Figure WA5) than the more parsimonious model that included only our proposed serial mediators (χ2(24) = 71.91; CFI =.98, TLI =.97, RMSEA =.065, CI90 = [.048,.082]; see Web Appendix Figure WA6). A model that used our proposed mediators and only pain-gain inferences shows that both parallel mediators were significant (see Web Appendix Figure WA7), though again, the model fit was not superior to our simpler model (χ2(39) = 73.64; CFI =.99, TLI =.98, RMSEA =.043, CI90 = [.028,.058]). A final model that used action planning and perceived control along with pain-gain inferences and confidence as parallel mediators showed support for both action planning/perceived control and pain-gain inferences and confidence as parallel mediators. The fit of this model was similar to, but not significantly better than, our more parsimonious model (see Figure 1, Panel B; χ2(58) = 112.64; CFI =.98, TLI =.98, RMSEA =.041, CI90 = [.030,.052]).
Study 2 replicated Study 1 in a controlled lab study that ( 1) manipulated and used multi-item indicators of hope and anxiety, ( 2) used a different product, ( 3) sampled a different population, and ( 4) used actual adoption as the dependent variable. The results support the idea that new product adoption is greatest when hope and anxiety are both strong (H1). They also suggest that strong levels of these emotions affect adoption intentions through the serial mediational effects of action planning and perceived control over outcomes (H2). We observed these effects even when accounting for other potential drivers of purchase and other control variables. Among the alternative processing mechanisms, only pain-gain inferences and confidence over outcomes also served as parallel mediators.
Although the results of Study 2 are encouraging, we wanted to determine whether the proposed effects were replicated with yet another product, a younger population, and a different operationalization of anxiety (manipulating anxiety via product disclaimers vs. social media comments). We also aimed to determine whether prior product category experience, which should enhance consumers' abilities to engage in action planning, would provide additional evidence for the proposed action planning mechanism. We conducted Study 3 with these objectives in mind.
Five hundred fifty-seven part-time postgraduate college students (49.40% = female, 49.70% = male,.20% = other,.70% = prefer not to disclose) participated in this study about an actual new energy drink called Neuronade, whose product package promised mental clarity and healthy energy. The average age of participants was 31.18 years (SD = 6.54).
We manipulated strong versus weak hope via an online product review, purportedly from an individual who had used the product and posted the review on social media. We created two versions of the review: one designed to evoke strong hope and the other designed to evoke weak hope.
Strong hope: I felt energized and clear-headed even hours after drinking it. The mental focus the product gave me allowed me to accomplish a lot more in my day than I typically accomplish. This product did everything I had hoped that it would do.
Weak hope: I did have some marginal improvements in energy and clear-headedness, but only for a very short time. Any improvements in mental focus were marginal, though, as it didn't allow me to accomplish too much more than what I typically accomplish. The effects were clearly not as dramatic as I had hoped for.
We manipulated strong versus weak anxiety by using product disclaimers in an ad. The disclaimers were as follows:
Strong anxiety: Use with care. Can cause rapid heartbeat, gastric distress, and sour breath odor.
Weak anxiety: Use with care. Can cause slight increase in heart rate, mild stomach flutters, and slight breath odor.
Web Appendix Figure WA8 shows the ad to which respondents were exposed. Participants were randomly assigned to one of four conditions representing a 2 (strong vs. weak hope) × 2 (strong vs. weak anxiety) between-subjects design.
The procedure for Study 3 closely followed that of Study 2. Participants were endowed with the equivalent of US$2 as a token of appreciation for participating in the study. They were informed that they would see information about a real energy drink product that was new to the market and that they could purchase one or more sample units of the product at the end of the study if they chose to do so. Each sample unit cost the equivalent of.50 USD. After reading the social media comment and seeing the ad participants indicated on an index card how many sample units they would like to purchase. Participants then answered a set of questions related to perceived control over outcomes and action planning. They also responded to a set of questions regarding the same alternative mediators examined in Study 2. They then responded to a set of manipulation check measures of hope and anxiety.
Participants then indicated the extent to which they had prior experience with the product category of energy drinks (1 = "not at all," 7 = "very much"). Note that product category experience is measured (not manipulated). This measure of prior category experience theoretically maps onto one's ability to engage in action planning. As noted previously, if H2 is correct and hope and anxiety do encourage action planning, we should find that the interaction between hope and anxiety is strongest for people who have the greatest ability to engage in action planning (i.e., those with more vs. less product category experience). We also asked about the valence of one's prior category experiences as a control variable. Respondents indicated whether their experiences were "positive," "negative," "mixed," or "not applicable; I have not used these types of products before."
As in Study 2, we asked participants to indicate their interest in the product category, brand familiarity, gender, and age. We debriefed participants after they paid for the number of sample units they had elected to buy, if any. At that time, we told them that while Neuronade is a real product, the social media comment and the disclaimers were fictitious.
The measures in Study 3 were nearly identical to those used in Study 2, with the exception of prior product category experience and the valence of the experience, which were new to Study 3. Web Appendix Table WA7 shows all items, reliabilities, and descriptive statistics for the measures used in Study 3. Web Appendix Table WA8 shows the correlations among all constructs in Study 3.
A set of 2 (hope: strong vs. weak) × 2 (anxiety: strong vs. weak) ANOVAs on the manipulation checks confirmed that the manipulations were successful. Specifically, the manipulation check for hope showed only a main effect of hope. As we expected, respondents in the strong hope condition felt stronger hope about achieving enhanced mental clarity and healthy energy (Mstrong = 4.32) than did those in the weak hope condition (Mweak = 2.55; F( 1, 553) = 248.89, p <.001). The manipulation check for hope showed neither a main effect for anxiety nor a significant interaction between hope and anxiety. Likewise, the manipulation of anxiety worked as expected, revealing only a main effect of anxiety. Respondents in the strong anxiety condition reported feeling stronger anxiety than did those in the weak anxiety condition (Mstrong = 4.57 vs. Mweak = 2.92; F( 1, 553) = 160.78, p <.001). Neither the main effect of hope nor the interaction between hope and anxiety was significant. Prior experience did not have any effect on the hope and anxiety manipulations. Table 5 shows cell mean results pertinent to all measures collected in Study 3.
Graph
Table 5. Study 3 Main Study Cell Means.
| Means | Strong Hope | Weak Hope |
|---|
| Strong Anxiety(N = 139) | Weak Anxiety(N = 140) | Strong Anxiety(N = 140) | Weak Anxiety(N = 138) |
|---|
| Manipulation Checks | | | | |
| Hope | 4.31a | 4.33a | 2.51b | 2.60b |
| Anxiety | 4.54a | 2.90b | 4.60a | 2.93b |
| Dependent Variable | | | | |
| Units adopted | 1.83a | 1.29b | .33d | .49c |
| Proposed Process Mechanisms | | | | |
| Action planning | 4.46a | 3.24b | 2.10c | 2.28c |
| Perceived control over outcomes | 4.02a | 3.37b | 2.14c | 2.20c |
| Proposed Moderator | | | | |
| Prior product category experience | 3.90a | 3.49a | 3.52a | 3.68a |
| Alternative Process Mechanisms | | | | |
| Confidence in hoped-for outcomes | 3.13b | 3.56a | 2.39c | 2.49c |
| Affective forecasts | 3.60b | 4.30a | 2.47d | 2.83c |
| Pain-gain inferences | 3.09a | 2.94a | 2.54b | 2.37b |
| Global elaboration | 2.54a | 2.61a | 2.31a | 1.90b |
| Motivated reasoning | 2.90b | 3.26a | 1.93c | 2.04c |
| Control Variables | | | | |
| Valence of prior experience | 2.17a | 2.06a | 2.11a | 2.18a |
| Interest in energy drinks | 4.37a | 4.16a | 4.33a | 4.25a |
| Brand familiarity | 1.76a | 1.77a | 1.69a | 1.73a |
| Gender | 1.52a | 1.54a | 1.53a | 1.49a |
| Age (in years) | 31.32a | 31.46a | 30.78a | 31.15a |
8 Notes: Means with different superscripts are significantly different at p <.05; Each participant was given the equivalent of US$2 as a token of appreciation for participating in the study. Energy drink sample units could be purchased for the equivalent of US$.50 each.
The overall mean of the focal dependent variable (number of units adopted) was.99 (SD = 1.53), with a range of 0–16 units purchased. Web Appendix Figure WA9 shows the distribution of the number of units purchased. A 2 × 2 ANOVA with the number of units purchased as the dependent variable revealed a main effect of hope (Mstrong hope = 1.56 vs. Mweak hope =.41; F( 1, 553) = 92.47, p <.001), no main effect of anxiety (F( 1, 553) = 2.40, p >.12), and the predicted hope × anxiety interaction (F( 1, 553) = 8.54, p =.004). In support of H1, those in the strong hope/strong anxiety condition purchased the greatest number of units of the new product (Mstrong hope/strong anxiety = 1.83) relative to those in the other three conditions (Mstrong hope/weak anxiety = 1.29, t(277) = 2.36, p =.019; Mweak hope/weak anxiety =.49, t(275) = 6.43, p <.001; Mweak hope/strong anxiety =.33, t(277) = 7.38, p <.001).
A 2 × 2 ANOVA on the measure of action planning revealed a main effect of hope (Mstrong hope = 3.85 vs. Mweak hope = 2.19; F( 1, 553) = 243.83, p <.001), a main effect of anxiety (Mstrong anxiety = 3.28 vs. Mweak anxiety = 2.76; F( 1, 553) = 24.30, p <.001), and the predicted hope × anxiety interaction (F( 1, 553) = 43.47, p <.001). The means in Table 5 show that action planning was greater in the strong hope/strong anxiety condition than in any of the other three hope/anxiety conditions. Similarly, a 2 × 2 ANOVA on perceived control over outcomes showed that both the main effects of hope (Mstrong hope = 3.70 vs. Mweak hope = 2.17; F( 1, 553) = 257.63, p <.001) and anxiety (Mstrong anxiety = 3.08 vs. Mweak anxiety = 2.79; F( 1, 553) = 9.52, p =.002) and the predicted interaction terms were significant (F( 1, 553) = 14.10, p <.001). The means in Table 5 show that perceived control over outcomes was greater in the strong hope/strong anxiety condition than in any of the other three hope/anxiety conditions.
To test the moderating role of product category experience on mediators and outcome variable, we first ran three sets of moderated mediation moderation analyses using [20] PROCESS Model 3. We observed the predicted three-way interaction for action planning (β =.23, p =.013) and a marginally significant three-way interaction on the number of units adopted (β =.21, p >.086). The three-way interaction for perceived control over outcomes was not significant. The pattern of results remained unchanged when alternative explanations and controls were taken into account.
To further examine whether prior product category experience moderates action planning, which in turn affects perceived control over outcomes and the number of units purchased, we used bootstrapping with repeated extraction of 5,000 samples ([20]; PROCESS v3.3, Model 85). The moderated mediation analysis included a serial sequence of action planning and perceived control over outcomes in mediating the relationship between hope × anxiety and actual purchase, with prior experience moderating the link between hope × anxiety and action planning. The results remain unchanged when we account for the alternative explanations described in Study 2 and the control variables of brand familiarity, gender, age, and the valence of prior experience. We report the full set of results in Figure 2, Panel A. These results are based on the manipulated conditions of hope and anxiety.
Graph: Figure 2. Study 3 mediation results (DV = number of sample units adopted).
The results support H2. Specifically, the interaction between hope and anxiety predicted action planning (β =.62, p <.001). The positive sign of the effect indicates that action planning was greatest when hope and anxiety were both strong. This relationship was significantly moderated by the degree of prior experience with the product category (β =.19, p <.001), such that action planning among those in the strong hope and strong anxiety condition increased as product category experience increased. Action planning, in turn, influenced perceived control over outcomes (β =.28, p <.001). Subsequently, perceived control over outcomes significantly predicted actual purchase of the energy drink (β =.36, p <.001). Conditional indirect effects revealed a more pronounced positive serial moderation for more experience (indirect effect =.098, bootstrap SE =.023, CI: [.058,.147]) than for minimal experience (indirect effect =.025, bootstrap SE =.008, CI: [.011,.044]). Notably, switching the order of the mediators resulted in a nonsignificant moderated mediation (all CIs include 0). The results in Figure 2, Panel A, also show a direct effect of hope and anxiety on the number of units purchased (β =.13, p =.043), suggesting that alternative mediators are operative.
Web Appendix Figure WA10 depicts the effect of hope and anxiety at high versus low levels of prior product category on action planning (for effects on purchase, see Figure WA11). Both figures show that hope and anxiety had the greatest effect at high levels of product category experience, providing further support for H2. We also plotted the Johnson–Neyman turning point of the continuous measure of product category experience on action planning and purchase. Web Appendix Figure WA12 suggests that the conditional effects of hope and anxiety at mean-centered values of experience were −2.473 and −.078, respectively. The greater the experience, the greater effect of hope and anxiety on action planning and purchase.
As in Study 2, we conducted a set of individual PROCESS analyses ([20], Model 4) to assess whether any alternative process mechanisms mediated the relationship between hope × anxiety and adoption and hence could serve as parallel mediators. Although these analyses are not relevant to our hypotheses, testing these effects could be useful for future theory-building purposes. Web Appendix Table WA9 shows the results of a factor analysis that demonstrates the empirical distinctiveness of these potential alternative mediators. Web Appendix Table WA10 shows the indirect effects for the proposed and alternative mechanisms when tested individually. The largest effects were for variables associated with our proposed mechanism (i.e., perceived control; indirect effect =.258, bootstrap SE =.043, bootstrap CI95: [.179,.349]) and then action planning (indirect effect =.147, bootstrap SE =.042, bootstrap CI95: [.072,.234]), followed by pain-gain inferences (indirect effect =.045, bootstrap SE =.020, bootstrap CI95: [.014,.093]), positive affective forecasts (indirect effect =.038, bootstrap SE =.013, bootstrap CI95: [.015,.064]), confidence (indirect effect =.035, bootstrap SE =.017, bootstrap CI95: [.009,.074]), and motivated reasoning (indirect effect =.028, bootstrap SE =.013, bootstrap CI95: [.004,.055]). Neither general elaboration nor general interest in the product category were significant.
To determine whether these alternative mediators were still significant when modeled along with our proposed serial mediators (action planning and perceived control), we conducted a SEM analysis involving hope and anxiety, action planning, perceived control over outcomes, the number of units of the product adopted, and those alternative mediators that were significant when tested individually. The results in Web Appendix Figure WA13 show that action planning (β =.57, p <.001), perceived control over outcomes (β =.41, p <.001), pain-gain inferences (β =.20, p <.001), and confidence (β =.14, p <.001) emerged as significant mediators. These results did not result in a better-fitting model (χ2(221) = 501.54; CFI =.94, TLI =.94, RMSEA =.048, CI90 = [.042,.053]) than the results presented in Web Appendix Figure WA14, which included only our proposed serial mediators (χ2(24) = 58.53; CFI =.99, TLI =.98, RMSEA =.051, CI90 = [.034,.068]) (see also Web Appendix Table WA11).
In Study 2, only pain-gain inferences, confidence, and our proposed serial mediators were significant. For the purpose of further exploration, we ran a SEM model that included our proposed mediators and pain-gain inferences (see Web Appendix Figure WA13). The mediating effect of pain-gain inferences was significant (along with our proposed mediators). However, this model did not fit the data better than the model that included only the mediators noted in H2 (χ2(39) = 76.98; CFI =.99, TLI =.98, RMSEA =.042, CI90 = [.028,.056]). Because pain-gain inferences and confidence affected adoption in both Studies 2 and 3, we ran a SEM model that included our proposed mediators, pain-gain inferences, and confidence (see Figure 2, Panel B). Whereas both pain-gain inferences and confidence served as mediators along with our proposed mediators, the strongest effects were for our proposed mediators. Moreover, that model did not show a better fit (χ2(58) = 190.90; CFI =.96, TLI =.94, RMSEA =.064, CI90 = [.054,.074]) than our original and more parsimonious model shown in Web Appendix Figure WA14.
Study 3 replicated H1 and H2. In addition, Study 3 provided further evidence of the underlying process by highlighting the moderating role of prior product category experience. Specifically, the effect of strong hope and strong anxiety on action planning, perceived control over outcomes, and adoption decisions was greater for consumers who had the greatest ability (by virtue of their prior category experiences) to engage in action planning. In Studies 2 and 3 we also found that pain-gain inferences and confidence in outcomes mediated the relationship between hope × anxiety and actual adoption decisions.
We demonstrated the novel and provocative finding that when hope is strong, anxiety regarding possible outcomes from product use can actually enhance new product adoption intentions (Study 1) and real adoption behavior (Studies 2 and 3). The interaction of strong hope and strong anxiety was driven at least in part by the psychological process of action planning and its effects on perceived control over outcomes. We observed these effects when using respondents from diverse geographical locations and socioeconomic backgrounds, when measuring and manipulating hope and anxiety, when using different emotion-induction methods, when using diverse products, and when accounting for alternative explanations. Our theoretical mechanism was also supported by evidence that prior product category experience, a variable that should enhance consumers' abilities to engage in action planning, moderates the results. As predicted, our effects were stronger for those individuals with more (vs. less) prior experience in the product category. To our knowledge, our research is the first to show the interactive effect of strong hope and strong anxiety on new product adoption decisions. Our findings contribute to both theory and managerial practice as discussed next.
First, our work contributes to research on hope. Whereas prior research has focused hope in the contexts of self-regulation (e.g., [33]) and information search and evaluation of message arguments (e.g., [ 9]), to our knowledge, we are the first to examine the role of hope on new product adoption. Notably, whereas prior research suggests that threats to hoped-for outcomes can foster individuals' motivated reasoning ([ 9]), we found that when examined alone, motivated reasoning had a smaller effect than did other mechanisms in mediating the relationship between of hope and anxiety on new product adoption. One reason why might be that a threat to what consumers hope for is distinct from anxiety about goal-incongruent outcomes. In addition, in [ 9] work, the threat was not related to the product but rather to information external to the product. In our work, anxiety was directly tied to the product.
Moreover, our work contributes to the literature on consumer anxiety. Whereas some work in marketing has considered the effect of incidentally evoked anxiety on information processing (e.g., [38]), our focus is on the information-processing consequences of anxiety that is integral to a decision (here, new product adoption). Outside of marketing, work on the motivational effects of anxiety is empirically mixed. For instance, on the one hand, anxiety can create an avoidance motivation that makes consumers want to distance themselves from anxiety-inducing stimuli (e.g., [10]). This motivation should reduce new product adoption. On the other hand, anxiety has sometimes been found to create an approach motivation, resulting in information seeking and vigilance (e.g., [19]; [26]). Our findings suggest that strong hope provides the motivational energy that spurs individuals to confront their anxieties and use them for adaptive purposes. That is, when hope for a goal-congruent outcome is strong, anxiety may enhance adoption by allowing individuals to engage in action planning, considering what actions they can enact, so as to mitigate the occurrence of anxiety-evoking outcomes and maximize hoped-for ones. The notion that strong hope can play a significant role in understanding when individuals confront versus withdraw from their anxieties is novel and important.
Our work also augments prior work on planning by focusing on the effortful process of action planning—that is, mentally contemplating the set of actions that one will undertake to achieve a goal-congruent outcome and avoid anxiety-inducing ones. Whereas prior research finds that consumers' desires for control can reduce new product adoption ([12]), our research suggests that action planning might enhance new product adoption by increasing perceptions of control. Action planning might be a useful construct for understanding not just new product adoption decisions but also planning in other contexts, such as resource planning. We also contribute to the literature in marketing and consumer behavior on control by emphasizing perceived control over outcomes and demonstrating the role of strong hope, strong anxiety, and action planning in enhancing such perceptions. Notably, whereas some consumer research has studied drivers of low feelings of perceived control, we are the first to study when and why hope and anxiety can enhance perceptions of control over outcomes.
From a managerial perspective, our findings suggest that when bringing a new product to the market, adoption might increase when hope and anxiety are strong. Thus, if market research reveals that consumers already have strong anxiety about outcomes from new product adoption, it would behoove marketers to develop communications that evoke strong hope rather than trying to downplay anxiety. Although we did not test the role of ads in fostering hope, prior research identifies several tactics that marketers can pursue to foster hope in marketing communications ([29]).
Conversely, if market research reveals that consumers have strong hope for the product but low anxiety, our results suggest that marketers might benefit by providing information designed to enhance consumers' anxiety about possible goal-incongruent outcomes of new product adoption. Warning labels, disclaimers, disclosures, and vivid images are potentially valid vehicles for enhancing potential anxiety. Beyond encouraging new product adoption, such communications could also enhance product satisfaction. That is, to the extent that consumers consider goal-incongruent outcomes and plan for how they can be avoided, they may ultimately be more satisfied with the product than would consumers who never considered potential anxiety-evoking outcomes or engaged in action planning.
Our findings also suggest that disclosures or labels that evoke strong anxiety about goal-incongruent outcomes from new product use might encourage more thoughtful decision making when hope is also strong. Whereas marketers may be loath to use such disclaimers, our research suggests that when a new product evokes strong levels of hope, anxiety-inducing disclosures might not harm, and could potentially help, new product adoption.
In Studies 2 and 3, we consistently observed that pain-gain inferences and confidence in outcomes also mediated the effects of hope and anxiety on new product adoption decisions. While including these variables did not result in a better-fitting model, it did not result in a worse-fitting model either. From a theory development standpoint, these results raise the interesting possibility that strong hope and strong anxiety might influence adoption intentions through both thoughtful/systematic processing (e.g., action planning) and heuristic processing (e.g., pain/gain inferences; see [ 5]; [35]). Future research might assess whether these "dual routes" are replicated and examine the conditions under which one or both routes are most likely to occur. Such research could enhance our understanding of the effects of emotions such as hope and anxiety on consumer choices.
Future research might also examine whether our predictions are replicated when hope and anxiety are incidental (vs. integral) to the decision (as was the case here). For example, [27] found that incidentally evoked anxiety enhanced risk avoidance. However, this article did not examine the interaction of incidentally induced hope and anxiety. Nor did it emphasize new product adoption decisions. Overall, the effects of hope and anxiety on intentions to adopt new products represents a rich domain for future research. The notion that consumers might embrace their anxiety when hope is strong is particularly interesting from both academic and pragmatic perspectives.
Supplemental Material, Hope_Anxiety_JM_R4_Web_Appendix_18_May_2020_PDF - Strong Anxiety Boosts New Product Adoption When Hope Is Also Strong
Supplemental Material, Hope_Anxiety_JM_R4_Web_Appendix_18_May_2020_PDF for Strong Anxiety Boosts New Product Adoption When Hope Is Also Strong by Yu-Ting Lin, Deborah J. MacInnis and Andreas B. Eisingerich in Journal of Marketing
Footnotes 1 Associate EditorAndrea Morales
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Bill & Melinda Gates Foundation sponsored Study 1. The funder had no role in the study design, data collection, decision to publish, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242920934495
5 1Action planning differs from elaboration on potential outcomes ([32]) because it is situationally induced (vs. an individual difference factor), is tied to outcomes consumers hope for and feel anxious about (rather than all possible outcomes), and emphasizes behaviors that one might enact prior to (vs. following) consumption. It differs from "process" and "outcome" simulations (e.g., [50]) because it focuses on behaviors one can enact before using or while using the product that will produce hoped-for outcomes and avoid anxiety-inducing ones (vs. the process of using the product or outcomes that follow from its use). Finally, action planning differs from implementation intentions ([17]), because it emphasizes outcomes from new product adoption as opposed to self-regulatory success. Moreover, whereas implementation intentions simulate potential future scenarios (e.g., if the waiter brings a dessert menu) and a response (e.g., "I will refuse it"), action planning focuses on predicted outcomes (e.g., "I might have an allergic response to this product") and actions that support (avoid) goal-(in)congruent outcomes.
6 2Whereas we use the term "control over outcomes," other researchers have referred to the same phenomenon using the term "control beliefs," defined as beliefs about the extent to which an agent can produce desired events and prevent undesired ones ([42]). In the context of aversive (i.e., anxiety-producing) events, the same concept has been called "behavioral control," defined as "a belief that one has a behavioral response available that can affect the aversiveness of the event" and that one "could terminate the event, make it less probable, less intense, or change its duration or timing" ([48], p. 90). We use the term "control over outcomes" because it is more specific regarding the nature of consumer beliefs, whereas the term "control beliefs" is less specific. Moreover, "control over outcomes" references outcomes that are consistent with both the outcomes that consumers hope to attain and the anxiety-evoking ones that they want to avoid, whereas "behavioral control" emphasizes only the latter type of outcome.
7 3The study protocol was approved by numerous agencies, including the Health Research and Development Division, Ministry of Health (Botswana); the Independent Ethics Committee, Bangalore (India); the Kenya Medical Research Institute; Comite Institucional de Etica, Universidad Peruana Cayetano Heredia (Peru); the Human Research Ethics Committee (Medical), University of the Witwatersrand, Johannesburg (South Africa); the Director General Health Services Ministry of Health (Uganda); the Committee of Professional Ethics of the Sociological Association (Ukraine); and the Institute for the Development of Human Research Protections, Ministry of Public Health (Thailand).
8 4We tested whether the data met the assumption of collinearity. The results suggest that multicollinearity was not a concern (hope, tolerance =.99, variance inflation factor = 1.01; anxiety, tolerance =.99, variance inflation factor = 1.01).
9 5Given the correlational nature of our data, it is possible that purchase intentions influence hope and anxiety, as opposed to the reverse sequence. To address this issue, we ran two additional models with purchase intentions as the independent variable (IV) and hope and anxiety as the dependent variables (DVs). Our original model best fits the data because the F-value (F(13, 1,847) = 40.97, p <.001) and adjusted R2 (.22) are relatively higher (hope as DV: F-value = 30.92, adjusted R2 =.17; anxiety as DV: F-value = 34.07, adjusted R2 =.19), suggesting that reverse causality may not be in play. Nonetheless, given the correlational nature of the data, reverse causality cannot be entirely ruled out in this study. Studies 2 and 3 are thus designed to test causality directly.
6One might anticipate that gender is associated with product category experience in the use of skincare products, with women having more category-level experience than men. To determine whether gender acted as a proxy for product category experience, we conducted a 2 (hope) × 2 (anxiety) × 2 (gender) ANOVA on action planning. The results replicated our predicted effects but did not show any effects for gender. We expect that the lack of gender as a moderator is because men are increasingly using skincare products. Industry reports label men's skincare as an emerging disruptor in the beauty industry, with men's skincare products reaching approximately $10 billion by the end of 2019 ([14]).
7One might also wonder whether brand familiarity serves as a proxy for prior product category experience. However, we did not find that brand familiarity moderated the results in either Study 2 or Study 3. These results are perhaps not surprising, because brand familiarity is distinct from product category experience. One might be familiar with a brand because one has merely heard the brand name, not because one has prior experience with it. Even if it did involve prior experience with the promoted brand, familiarity with a specific brand is not isomorphic with experience with the product category broadly defined.
8We also tested whether the effect of control on purchase intentions was mediated by confidence. The results showed that the R2 of the three-mediator model was not significantly different from that of the two-mediator model illustrated in Figure 1, Panel A (R2 = 22.70% for both models when using the manipulation conditions as the IV; R2 = 20.55% for both models when using the manipulation check measures as the IV). We emphasize the two-mediator versus the three-mediator model given its parsimony and its consistency with our theoretical explanation.
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By Yu-Ting Lin; Deborah J. MacInnis and Andreas B. Eisingerich
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Record: 174- Successfully Communicating a Cocreated Innovation. By: Wang, Helen Si; Noble, Charles H.; Dahl, Darren W.; Park, Sungho. Journal of Marketing. Jul2019, Vol. 83 Issue 4, p38-57. 20p. 2 Diagrams, 2 Charts, 3 Graphs. DOI: 10.1177/0022242919841039.
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Successfully Communicating a Cocreated Innovation
Despite the growing popularity of cocreation approaches to innovation, the bottom-line results of these efforts continue to frustrate many firms. Marketing communications are one important tool in stimulating consumer adoption, yet marketers to date have not taken advantage of a unique phenomenon associated with many cocreated innovations: the presence of a genesis story in the words of the creator, which can be combined in different ways with traditional marketing messaging. Using mixed methods, the authors demonstrate a crossover effect in which a "mismatch" of the fundamental motivations behind authentic creation narratives and traditional persuasive messages enhances adoption of the cocreated innovation. This effect is mediated by potential adopters' self-referencing of their own stories about similar experiences or consumption episodes. Furthermore, the effect of a motivation mismatch strategy is attenuated for expert consumers. Finally, this motivation mismatch strategy triggers "takeoff" of cocreated innovations. This research offers substantial implications for research on cocreated innovation, narrative persuasion, and firm-generated and user-generated communication. It provides managers specific guidance on enhancing the success of cocreation programs through an integrated communications strategy.
Keywords: adoption; cocreated innovation; firm-generated content; narrative self-referencing; user-generated content
Innovation through cocreation, in which firms provide a platform to harness customers' creative ideas and then commercialize the most promising ideas into finished products, has been adopted as a key innovation strategy by nearly 78% of large firms ([ 6]; [10]; [11]). However, the majority of cocreated innovations have shown disappointing consumer sales in the marketplace, demonstrating the challenge of stimulating consumer adoption and calling into question the entire cocreation model. For example, from 2009 to 2014, Quirky, formerly one of the largest cocreation-based consumer products firms, withdrew 70% of its 500-plus cocreated innovations because of stagnant sales ([46]). In 2015, Quirky filed for bankruptcy. At Apple's App Store, the world's most vibrant user-design platform, 80% of apps do not generate enough revenue to survive for more than a few months ([ 1]; [ 4]). In the absence of early success, cocreating firms may also terminate potentially promising innovations too early ([16], [17]), which, in turn, may greatly dampen customer-inventors' enthusiasm to participate in the future, putting the engine that fuels the success and sustainability of the cocreation model at risk ([45]).
Marketing communication is often regarded as one of several major influences on innovation adoption ([36]). In the context of cocreated innovations, both cocreating firms and customer-inventors recognize the importance of marketing communication in driving adoption ([ 4]; [55]). In particular, marketing communications are perceived to be fundamental in achieving new product takeoff,[ 5] a critical signal for early adoption success ([36]). Typically, cocreating firms (e.g., Starbucks, Dell, Lego) take conventional approaches in trying to understand potential adopters' motivations; then they craft appropriately matched persuasive messages (i.e., firm-generated content [FGC]) to drive adoption ([ 5]; [13]; [41]). Challenging this conventional approach, our research examines a unique aspect of many cocreated goods, the presence of an authentic creation narrative (i.e., a genesis story in the words of the creator). These stories represent an understudied form of user-generated content (UGC; [31]). This study focuses on examining the interplay of these forms of FGC and UGC to drive the adoption of cocreated innovation.
We contend that the communication of cocreated innovations is interesting and unique for several reasons. First, it often features the dual voices of both the customer-inventor and the cocreating firm. The "customer-inventor" creates the product concept, often with an initial quest to satisfy his or her personal needs and a desire to share the solution with others. The "cocreating firm" offers a platform for the innovation and engages in marketing communication activities. This dual-voice communication environment is different from the communication environment associated with most products, in which no distinction is made between inventor, manufacturer, or other parties. More importantly, these two sources of communication may work separately or interactively to influence adoption of the cocreated innovation.
Second, customer-inventor-generated content, unlike more common forms of UGC such as product/service reviews, often records customer-inventors' real-life stories about their creative motivations and experiences. For example, Thom Jensen, the customer-inventor of a best-selling cocreated innovation, Perfect Bacon Bowl, posted the following story on the online cocreating platform, Edison Nation:
I was cooking breakfast one Saturday morning for my family and was playing around trying to create a bacon turtle. I wove a basket out of the bacon trying to make the body, and through the process, I thought of the Bacon Bowl pan. I made a crude prototype ([12]).
For companies that have embraced the cocreation model (e.g., Lego, Starbucks), their customer idea-generation platforms hold a reservoir of creation stories. We suggest that these stories can be grouped on the basis of their underlying motivation as either "approach-oriented" (i.e., achieving new or desired outcomes) or "avoidance-oriented" (i.e., avoiding unpleasant or undesirable outcomes) (see Web Appendix W1). More formally, we define these narrative accounts of customer-inventors' approach- or avoidance-oriented creative motivations and experiences as authentic creation narratives. As a form of UGC, authentic creation narratives are published content created outside of firms' professional routines and practices and allow consumers to express their creative experiences. Distinct from firm-generated persuasive messages (i.e., well-crafted messages with explicit persuasion intent to promote innovation attributes and reasons for adoption), authentic creation narratives are powerful customer-inventor-generated stories providing a genuine account of consumers' creative motivations and experiences.
Finally, the dual-voice communication environment and the unique properties of authentic creation narratives call for an integrated communication strategy to drive adoption of cocreated innovations. Recent research has shown that firms that strategically integrate UGC with FGC (i.e., more traditional persuasive messaging) produce better business outcomes ([24]; [27]). However, extant FGC–UGC research has mostly focused on how firms respond to UGC that features customers' consumption experiences (e.g., online reviews, tweets; for a review, see [24]]), not the authentic creation narratives that we consider here.
Therefore, in this research, we focus on examining the interactive effect of authentic creation narratives (UGC) and persuasive messages (FGC) on the adoption of cocreated innovations. We develop and test a conceptual model (see Figure 1) to answer three important research questions: ( 1) What is an optimal strategy to integrate firm-generated persuasive messages and customer-inventor-generated authentic creation narratives to drive adoption?; ( 2) What is the underlying mechanism that explains the optimal strategy?; and ( 3) What can we learn about any boundary condition(s) surrounding this phenomenon?
Graph: Figure 1. Conceptual framework.
Our research makes several important contributions to the literature. First, drawing from motivation-creativity theory (e.g., [13]; [23]) and associative storage and retrieval theory ([48]), we posit that a motivation "mismatch" strategy utilizing an approach- (avoidance-) oriented persuasive message and a avoidance- (approach-) oriented authentic creation narrative enhances adoption of cocreated innovations when compared with a matching strategy (i.e., approach-approach or avoidance-avoidance messages). This mismatching hypothesis suggests a new approach to the marketing communications strategy surrounding cocreated innovation and takes advantage of the unique messaging opportunity arising in products created primarily by consumers. We also extend the FGC–UGC literature from simply profiling user content derived from consumption experiences (e.g., product/service reviews) to incorporating UGC related to consumers' creative experiences in product and service development.
Second, we offer a theoretical rationale for the effectiveness of this mismatch strategy by showing that potential adopters' self-referencing of their own needs or wants to the narrative (i.e., narrative self-referencing) mediates the proposed effect. This adoption mechanism challenges the focus on inventor characteristics in previous research (e.g., [11]; [33]; [43]). We also extend previous work in narrative persuasion, which has focused on activating narrative processing using single-source (firm-generated), single-form (narrative) content. Instead, we demonstrate an activation of narrative processing using multisource (FGC and UGC), multiform (persuasive message and authentic creation narrative) content.
Third, we show that consumer expertise attenuates the effect of the motivation mismatch strategy on adoption. Indeed, we find that expert consumers—who, by definition, have a high level of domain-specific knowledge related to a cocreated innovation—are less likely to respond to the motivation mismatch strategy and adopt the innovation under consideration. This important boundary condition offers managers actionable guidelines on when to use our proposed strategy and whom to target.
Finally, we explore adoption at the macro level, where the motivational mismatch strategy leads to an earlier takeoff—the first dramatic adoption sales increase of a cocreated innovation after its introduction ([ 2]; [16]). This finding both validates the fundamental premise of our work and demonstrates its effects with large-scale field data.
The proliferation of UGC has fundamentally changed the dominance of FGC in the communication environment ([24]; [31]). A growing body of literature in marketing suggests that FGC and UGC often interact in influencing product/brand communication, which increasingly requires firms to monitor and track UGC and develop FGC–UGC strategies to achieve desired business outcomes. For example, [50] show that the volume (or number of messages) of online WOM referrals and firm-sponsored marketing activities (events, media coverage) interact to influence new customer acquisition. [24] find that in the banking industry, firms that changed their FGC over time in response to the volume and valence (positive or negative) of social media are more able to drive deposit performance.
In the realm of cocreated innovations, social- and online-based cocreation platforms have given rise to a unique type of customer-inventor-generated narrative content: authentic creation narratives. More formally, we define authentic creation narratives as customer-inventor-generated stories about how their unmet needs or wants motivated their creative ideas. We further reason that in the communication environment of cocreated innovations, authentic creation narratives may interact with persuasive messages—the most prevalent form of firm-generated content in innovation communication—to synergistically influence important adoption outcomes. We define persuasive messages as firm-generated explicit persuasion statements to promote innovation attributes and reasons for adoption.
Cross-disciplinary research offers strong support for our supposition that integrating multisource, multiform communication content will enhance the effectiveness of innovation communications. In linguistic and cognitive research, dual coding theory posits that multiple types of linguistic forms can be used to communicate a concept, whether they are verbal (i.e., concrete words or sentences) or imaginal (i.e., stories that evoke mental imaging) ([34]). Mixing verbal and imaginal linguistic presentations is particularly constructive for understanding novel concepts because it helps audiences make easy references and connections between their own internal information and the information presented ([42]). To identify an optimal FGC–UGC strategy involving persuasive messages and authentic creation narratives, we first draw from motivation-creativity theory ([13]; [23]) and narrative persuasion theory ([14]; [19]) to understand the unique properties of authentic creation narratives.
Motivation-creativity theory ([13]; [23]; [41]) posits that two types of motivations underlie human creative engagement in innovation and its adoption.[ 6] Approach-oriented people engage in inventing or adopting new products to achieve new possibilities and desired outcomes, whereas avoidance-oriented people engage in invention or adoption to avoid unpleasant experiences and undesirable outcomes. Similarly, for cocreated innovations, approach or avoidance can be fundamental motivations for their creation ([44]; [52]). For example, lead-users' invention of the integrated circuit, which integrates large numbers of tiny transistors into a small chip, was motivated by their desire to achieve better mass production capability and reliability (approach oriented) ([51]). In contrast, when a surgeon experiences a serious problem during a specific operation, (s)he is motivated to invent new processes or medical devices to avoid such undesirable situations and solve existing problems (avoidance oriented) ([44]). Therefore, authentic creation narratives that feature a customer-inventors' approach- or avoidance-oriented motivation may provide potential adopters a familiar anchor to relate to their own approach- or avoidance-oriented motivation for adoption. In other words, authentic creation narratives have the potential to connect customer invention and adoption at the motivational level.
The second unique property of authentic creation narratives is their narrative linguistic form. Narrative persuasion theory (for a review, see [53]]) posits that narratives (stories) featuring a character achieving certain outcomes may activate audiences' narrative self-referencing, in which people process incoming stories by relating them to their own life experiences and episodes ([14]; [26]). Narrative self-referencing has been found to be a powerful persuasion mechanism. It persuades by enhancing the realism of audiences' experiences and triggers strong affective responses ([14]; [19]). In contrast, firm-produced persuasive messages often trigger analytical processing in which audiences produce logical arguments and evaluate the merits of incoming messages ([14]; [32]). Importantly, activating narrative self-referencing requires audiences' focused attention on the story ([32]; [53]). Prior research has shown that when audiences are distracted or less motivated to pay attention to a story, narrative self-referencing fails to happen ([19]; [56]). Taken together, the second unique property of authentic creation narratives appears to be that, if given enough attention, they activate a powerful persuasive mechanism: narrative self-referencing.
In light of the aforementioned unique properties of authentic creation narratives, marketers' intuition may suggest that firms should simply design persuasive messages that align with the approach- or avoidance-oriented creative motivation identified in the authentic creation narrative in an effort to enhance the adoption of a cocreated innovation. However, drawing from associative storage and retrieval theory (e.g., [29]; [48]), we propose a communication strategy for cocreated innovations that utilizes persuasive messaging and authentic creation narratives in a somewhat counterintuitive, mismatching fashion. When an authentic creation narrative features an approach- (avoidance-) oriented motivation for an invention, we contend that firms should design persuasive messaging that promotes a mismatched avoidance- (approach-) oriented motivation to enhance the adoption of cocreated innovations.
In support of our proposed strategy, recent research on narrative persuasion suggests that the activation of narrative processing requires consumers to deploy sufficient cognitive resources to process the story ([38]; [54]). Without sufficient cognitive resources to support deliberate cognitive processing, narrative ads are likely to fail in activating consumers' narrative self-referencing and would not be effective in producing the desired persuasive effects ([20]; [32]). We argue that in a cocreated innovation context, a motivation match between persuasive messaging and authentic creation narratives makes the communication much easier to understand. As a result, potential adopters may reduce their cognitive deliberation, which in turn inhibits their activation of narrative self-referencing ([21]).
In contrast, associative storage and retrieval theory suggests that mismatched or incongruent information increases audiences' engagement in information search and retrieval to better comprehend the message ([29]). When an information mismatch happens, people search and retrieve information based on its relevancy ([21]; [48]). In other words, to make sense of a mismatched message, people are more likely to pay attention to information that is more personally relevant and subsequently retrieve similar personal information from their memory to establish an interepisode associative link. The interepisode association is defined as a direct connection between the event described in the incoming message and the event the audience personally experienced and stored in their long-term memory ([48]). In contrast, less relevant information in the mismatched message receives less attention and less elaboration ([48]).
In our research context, we argue that compared with firm-generated persuasive messages, authentic creation narratives may be more personally relevant given their unique property of connecting motivations of invention and adoption. Therefore, when a motivation mismatch occurs between persuasive messages and authentic creation narratives, potential adopters are more likely to pay attention to the authentic creation narrative and retrieve a similar approach- or avoidance-oriented personal experience from their memory. This process provides a favorable condition for activating potential adopters' narrative self-referencing. In contrast, a motivation match strategy is likely to reduce potential adopters' overall cognitive engagement--in particular, their attention to the authentic creation narrative (and related information search and retrieval), which in turn fails to activate narrative self-referencing. Note that a motivation mismatch (vs. match) will motivate potential adopters to deploy more cognitive resources regardless of the specific motivation orientation (approach or avoidance oriented; [32]). Therefore, we do not expect that different types of mismatch strategies will influence narrative self-referencing differentially.
Finally, we believe that adopters' narrative self-referencing will enhance the adoption of cocreated innovations. Narrative persuasion theory shows that the activation of narrative self-referencing greatly reduces critical thoughts about incoming messages ([14]). When audiences process and immerse themselves into their own stories, they are more likely to find the presented assertions convincing and persuasive ([14]; [19]). In our research context, when potential adopters activate narrative self-referencing related to the authentic creation narrative, they retrieve similar information about their own personal experiences or consumption episodes that relate to their unmet wants or needs. This process not only enhances the perceived value of the cocreated innovation but also increases the perceived self-relevancy of the innovation—the key determinant of adoption ([14]; [40]). Taken together, we hypothesize the following:
- H1: A motivation mismatch (vs. a motivation match) between persuasive messages and authentic creation narratives increases the adoption of cocreated innovations. Specifically, when authentic creation narratives feature an approach- (avoidance-) oriented motivation for invention, designing persuasive messages to promote a mismatched, avoidance- (approach-) oriented motivation increases adoption.
- H2: Narrative self-referencing mediates the effect of the motivation mismatch (vs. match) strategy on the adoption of cocreated innovations.
Narrative persuasion theories suggest that when activating narrative self-referencing (or narrative processing in general), consumers tend to match incoming information to their existing stories and experiences in memory ([54]). In contrast, when activating analytical processing, consumers often access both incoming information and their own opinions or knowledge to conduct evaluations and reach verifiable conclusions ([32]). Because narrative self-referencing and analytical processing involve two distinct forms of cognitive processing, we expect that factors that promote analytical processing will suppress the influence of a motivation mismatch strategy on the adoption of a cocreated innovation. In particular, we examine a theoretically interesting and managerially relevant situational factor: consumer expertise.
Consumer expertise refers to a consumer's knowledge in certain product domains or categories, which enables them to perform critical product-related evaluations ([ 3]). Expert consumers often have well-defined, domain-specific knowledge structures, whereas novice consumers tend to lack them ([ 3]; [35]). Prior research has found that expert consumers tend to use analytical processing, in which the goal is to evaluate the attributes of the product/service and arrive at a decision ([30]; [32]). In contrast, novice consumers have limited and unstructured domain knowledge, which constrains them from performing analytical evaluations ([30]). In our research context, we expect that expert consumers, who have a high level of domain-specific knowledge related to the cocreated innovation, are less likely to respond to the motivation mismatch strategy and activate their narrative self-referencing. In contrast, novice consumers are more likely to respond to the motivation mismatch strategy by activating their narrative referencing, making adoption more likely. Formally:
- H3: Consumer expertise moderates the effect of the motivation mismatch (vs. match) strategy on the adoption of cocreated innovations, such that the effect of the motivation mismatch strategy on adoption is attenuated for expert consumers, but not for novice consumers.
The innovation diffusion literature suggests that consumer expertise also determines the timing of adoption, allowing for a simple distinction between experts and novices ([ 7]; [40]). Traditionally, FGC has dominated the communication environment, and expert consumers often adopt at the introduction stage because they possess adequate domain-specific knowledge to enable them to make adoption decisions based entirely on their own private, analytical evaluations of the innovation before technical information or customer feedback is prevalent ([17]; [40]). In contrast, novice consumers follow or imitate other adopters because they lack the necessary domain-specific knowledge about an innovation to make adoption decisions on their own ([ 7]; [17]).
The different adoption timing among expert and novice consumers should have a direct effect on the timing of takeoff—a key metric of innovation diffusion. Takeoff is defined as the first dramatic increase in adoption after the introduction of an innovation ([ 2]; [16]; [36]). Because expert consumers represent only a small number of adopters in the market, their initial adoption alone may not be adequate to trigger takeoff ([17]); thus, persuading novice consumers to adopt early helps secure a sizable initial adoption and drives early takeoff. According to our theorizing, a motivation mismatch strategy should be particularly effective in driving novice consumers' adoption, and thus we expect that in the FGC–UGC communication environment, the motivation mismatch (vs. match) strategy should lead to early takeoff of cocreated innovations. Because, at the aggregate level, an early takeoff is a critical indicator of innovation adoption success, an identification of this pattern of effect would provide further support for H1's prediction that the mismatch (vs. match) strategy leads to better adoption of cocreated innovations.
To test our hypotheses, we conducted five studies using mixed methods of experiments and empirical modeling of real-world, cocreated innovation adoption. In Studies 1a, 2, and 3, we use controlled experiments to validate the effect of the motivation mismatch (vs. match) strategy on the adoption of cocreated innovation (H1), its underlying mechanism (narrative self-referencing; H2), as well as the moderating role of consumer expertise (H3). In Studies 1b and 4, we model the adoption (actual sales) data of real-world cocreated innovations to validate the effect of our proposed strategy and its effect on the takeoff of these innovations.
In Study 1a, we use a field experiment to examine the effect of the motivation mismatch strategy on the adoption of cocreated innovation. This study was conducted outside a Starbucks coffee store at a southwestern U.S. university from 11 a.m. to 5 p.m. for two consecutive weeks. At the time of the study, Starbucks had just launched a cocreated new coffee beverage called "Starbucks Doubleshot Energy Coffee—Mexican Mocha." The original Starbucks online ad for this new coffee beverage contained persuasive messages only, which promoted an approach-oriented motivation for adoption (e.g., "Starbucks Doubleshot Mexican Mocha—what the world desires this winter," "One sip makes you embrace all the winter warmth and joy"). We accessed the Starbucks cocreation platform (see https://ideas.starbucks.com/) and retrieved the customer-inventor's original authentic creation narrative (see Web Appendix W1), which also featured an approach-oriented motivation for invention. Drawing on Starbucks' original approach-oriented persuasive messages, we further developed avoidance-oriented persuasive messages to promote an avoidance-oriented motivation for adoption and to allow for the creation of a motivation mismatch manipulation (to test H1).
Eighty college students were intercepted by a research assistant who was blind to the purpose of the research. We employed a single-factor between-subjects design to manipulate the motivation mismatch (vs. match) strategy between an avoidance-oriented or an approach- oriented persuasive message and an approach-oriented authentic creation narrative as follows (also see Web Appendix W1):
• Persuasive message (approach/[avoidance])
• Starbucks® Doubleshot® Energy Mexican Mocha Coffee Drink
- Match condition
- Starbucks Doubleshot Mexican Mocha—what the world desires this winter
- It begins with the bold taste of Starbucks coffee and a blend of ginseng, cinnamon and B vitamins. Then, we top it all off with Mexican chocolate.
- One sip makes you embrace all the winter warmth and joy
- 15 oz can
- Mismatch condition
- Starbucks Doubleshot Mexican Mocha—[what the world can't miss this winter]
- It begins with the bold taste of Starbucks coffee and a blend of ginseng, cinnamon and B vitamins. Then, we top it all off with Mexican chocolate.
- One sip makes you [say bye-bye to the winter chill and blues]
- 15 oz can
• Authentic creation narrative (approach-only, same for both conditions)
Starbucks Mexican Mocha Coffee is co-created by a real customer like you.
Dale posted at MyStarbucksIdea.com
When I was little, my grandma told me that late night on those cold and gloomy winter days,an ideal drink is some good Mexican hot chocolate, made with creamy chocolate, fresh cinnamon sticks, sugar, and steamy milk. It has the true cocoa flavor that makes me embrace every winter...a bit sweeter than a regular chocolate mocha yet not as sweet as a white mocha, but all delicious!!! One sip fuels me with all the winter warmth and energy I want. Suddenly, I enjoy winter like never before. What if in honor of Mexican Chocolate, we create a Starbucks Mexican Mocha Coffee?
Participants were asked to help review some online information about a newly launched Starbucks beverage on a tablet device and then provide their feedback for a compensation of $1. After reviewing the ad, participants were asked: "Would you like to try out this coffee or receive an equivalent value of $2?" Participants then received the new coffee drink or $2 cash, based on their actual choice. In addition, we collected information about participants' average consumption of coffee and energy drinks ("How often do you drink coffee?," "How often do you drink an energy drink?"; 1 = "not at all," and 7 = "always") to control for their personal preferences with respect to coffee and energy drinks.
Following [41] procedures, we first conducted two separate pretests (n = 143, n = 71) to ensure the success of our manipulations. The first pretest checked the manipulations of the approach- or avoidance-oriented persuasive messages. As expected, participants in the approach (vs. avoidance) condition indicated that "the ad message was promoting innovation adoption to gain more benefits" and "to achieve ideal experiences" (M = 5.25 vs. M = 4.72; F( 1, 141) = 6.50, p <.05), whereas participants in the avoidance (vs. approach) condition reported that "the ad message was focused on promoting innovation adoption to avoid frustrations" and "to prevent undesired experiences" (M = 4.30 vs. M = 3.50; F( 1, 141) = 9.29, p <.01). Across conditions, participants rated the messages as equally credible, persuasive, interesting, and engaging (all ps >.35), and the messages did not evoke different mood states (happy, pleased, cheerful; all ps >.30). The second pretest checked the manipulation of the approach-oriented authentic creation narrative. Participants strongly agreed that "the message told a story about a customer-inventor creating a new product with the goal of gaining more benefits" and "achieving ideal experiences" (M = 5.17, SD = 1.07), which were significantly different from participants' rating on "the message told a story about a customer-inventor creating a new product with the goal of avoiding frustrations" and "preventing undesired experiences" (M = 3.40, SD = 1.40; F( 1, 139) = 27.19, p <.01). Participants rated the authentic creation narrative as "credible," "persuasive," "interesting," and "engaging" (all Ms > 5.28, SDs < 1.50) and triggered a neutral mood (vs. happy, pleased, or cheerful; M = 4.86, SD = 1.33).
We conducted a logistic regression to validate our hypothesis that the motivation mismatch (vs. match) strategy increases adoption of cocreated innovations. We regressed adoption choice (coffee = 1, $2 = 0) on the motivation mismatch (vs. match) variable. Participants' average coffee and energy drink consumption were entered as control variables. The results directly supported our hypothesis (H1) that the mismatched (vs. matched) ad generated a higher level of adoption (b = 1.73, SE =.58, p <.01; 56.1% vs. 26.3% chose adoption option over cash). Participants' average consumption of coffee positively influenced their adoption (b =.42, SE =.14, p <.01), and participants' average consumption of energy drinks marginally increased their adoption (b =.31, SE =.17, p =.06) (see Figure 2, Panel A).
Graph: Figure 2. The effects of the motivation mismatch strategy on adoption.Notes: PM = persuasive message.
Study 1b analyzed 122 cocreated innovations' sales records from an international cocreation firm to further validate H1: that the motivation mismatch (vs. match) strategy leads to more adoption. Founded in 2008, the firm was one of the first cocreation online platforms. It not only provides an online crowdfunding platform to enable customer-inventors to solicit funds for their inventions but also provides a sales platform called "marketplace" to provide marketing support and promote online sales for those successfully funded customer inventions. At the marketplace, each funded customer invention features a customer-inventor-generated video about the invention as well as a cocreating-firm-generated persuasive message about the new product. This type of cocreation model provided an ideal setting to examine the interactive effect of firm-generated persuasive messages and authentic creation narratives on adoption.
To construct the data set for Study 1b, two trained coders reviewed the comprehensive set of all successfully funded customer inventions sold on the firm's online sales platform marketplace over a four-month period from November 2017 through February 2018. Given our research focus on cocreated new products, art projects (e.g., music and films) and social projects (e.g., human rights and environment protection) were excluded from the sample. As a result, we obtained a total sample of 186 cocreated new products covering five major categories (Fashion & Wearables, Food & Beverages, Health & Fitness, Home Improvement, and Travel & Outdoors) and "Others" (which includes minor categories such as Energy & Green Tech, Phones & Accessories, Photography, Spirituality, etc.).
For the dependent variable, we obtained information on the sales revenue and the number of adopters for each new product in the first 60 days after launch and used them as dependent variables measuring the adoption of the cocreated innovation (for descriptive statistics, see Web Appendix W2). A typical online ad of the innovation features two sections: ( 1) a customer-inventor-generated video about the invention and ( 2) a firm-generated persuasive message about the product along with some pictures. We trained two coders blind to our hypotheses to code the narrative form and the motivation orientation of the customer-inventor's video as well as that of the persuasive message, which served as the independent variable.
To identify the motivation of authentic creation narratives, the two coders first reviewed all customer-generated videos and evaluated whether the video was in a narrative form (narrative = 1, otherwise = 0) by following the two-item scale adapted from [14]; "The information has a beginning, middle, and end" and "The information describes the character, a customer-inventor, and the evolution of his/her creative ideas or solutions"; kappa =.80). The results showed that 122 out of 186 new products featured narrative stories about customer invention, whereas the remaining videos were in either descriptive or persuasive form. Given our focus, we excluded products with nonnarrative customer-generated videos from the analysis.[ 7] Next, two coders coded the motivation of authentic creation narratives as either approach or avoidance oriented by following the cocreated innovation literature and motivation-creativity theories ([41]; [44]; [52]). When the video narrative explicitly expressed the quest for an ideal experience, the authentic creation narrative was coded as approach oriented (e.g., "I will never forget the day that I saw clear ice for the first time. It was a three-story tall ice cube that I saw on an expedition to climb Everest. I started to wonder how I can get my hand on some crystal-clear ice cubes and put them into whisky"); conversely, when the video narrative focused on solving existing problems or avoiding unwanted experiences, we coded it as avoidance oriented (e.g., "I used to be a ballerina but now I'm stuck in a chair for 15 hours a week. I bought expensive ergonomic chairs and back support. I tried standing desk and laptop stand but nothing works. Eventually I developed chronic back pain. I had to do something for myself").
To identify the motivation of firm-generated persuasive messages, the two coders coded the persuasive message by following motivation-creativity theories from psychology ([41]). When the persuasive message promoted adopting the innovation to gain more positive benefits, better functions, or better performance, we coded the persuasive message as approach oriented (e.g., "Unlock the potential of your classic cocktails with crystal clear ice. Your guests won't believe it!"); conversely, when the persuasive message promoted adopting the innovation to solve existing problems or to avoid negative outcomes, we coded the persuasive messages as avoidance oriented (e.g., "Experience weightless sitting with the most ergonomic cushion EVER"). Finally, we constructed a binary variable, match (vs. mismatch), by coding the persuasive message motivation and the authentic creation narrative motivation, where match took the value of 1 (31% of the innovations), and 0 otherwise (mismatch). The two coders reached a high level of agreement (kappa =.81).
We controlled for a total of five important variables in our model: ( 1) novelty, ( 2) usefulness, ( 3) unit price, ( 4) product launch month, and ( 5) product category. To measure the novelty and usefulness of each product, two research assistants who were blind to our research hypotheses rated the novelty (α =.76) and usefulness (α =.80) of each product on a seven-point Likert scale (1 = "not at all," and 7 = "very much"). Unit price (in USD) was obtained from the platform. We found that the prices of all products were unchanged during the four-month sample period. We controlled for product launch month to rule out the potential effects of seasonality or holiday shopping on adoption. Finally, to capture unique category-specific effects on the adoption of innovations, we constructed six category dummy variables: Fashion & Wearables (13%), Food & Beverages (8%), Health & Fitness (16%), Home Improvement (13%), Travel & Outdoors (11%) and Others (38%).
To investigate the effect of the motivation mismatch strategy on the adoption of cocreated innovations, we estimate a log-linear model. Note that both of our dependent variables (sales revenue and number of adopters) show large skewness. To minimize the effect of this on our estimation, we specify log-linear models and estimate them using the ordinary least squares method. In incorporating launch month and product category into the model, we use "January" and "Category: Others" as baseline categories and do not include dummies for these in the estimation. Table 1 reports the estimation results using log sales revenue as a dependent variable. The estimated coefficients of "match" are significantly negative in all specifications considered, in support of H1's prediction that the cocreated innovations with a mismatched (vs. matched) motivation strategy yield higher revenue. Specifically, in Model 6 (a full model with all explanatory variables), the estimated coefficient of "match" is −.949, indicating that the sales revenue of cocreated innovations with a motivation mismatch strategy generated 94.9% higher revenue than those using a motivation match strategy.[ 8] We also found that more useful cocreated innovations and innovations with approach-oriented authentic creation narratives generate higher revenue. We observe highly consistent results by using the number of adopters as the dependent variable (see Web Appendix W3). The coefficient of match is significantly negative in all specifications considered. Highly consistent results from two different dependent variables add robustness in validating H1.
Graph
Table 1. Study 1b Results Using log Sales Revenue as the Dependent Variable.
| Predictors | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 |
|---|
| Estimate | Estimate | Estimate | Estimate | Estimate | Estimate |
|---|
| Constant | 10.699*** | 11.540*** | 8.656*** | 8.645*** | 8.514*** | 8.421*** |
| Persuasive message orientationa | .277 | −.202 | .210 | .204 | .188 | .256 |
| Authentic creation narrative orientationa | .809* | 1.569*** | 1.531*** | 1.529*** | 1.466*** | 1.407** |
| Matchb | | −1.307*** | −1.088** | −1.120** | −1.065** | −.949* |
| Noveltyc | | | .067 | .045 | .036 | .013 |
| Usefulnessc | | | .537*** | .542*** | .561*** | .538*** |
| Unit price (continuous in US$) | | | | .001 | .001 | .000 |
| Launch month: November | | | | | .151 | .147 |
| Launch month: December | | | | | .033 | .073 |
| Launch month: February | | | | | .424 | .372 |
| Fashion & Wearables | | | | | | −.327 |
| Food & Beverages | | | | | | .656 |
| Health & Fitness | | | | | | .383 |
| Home | | | | | | .274 |
| Travel & Outdoors | | | | | | .733 |
| R-square | .046 | .099 | .245 | .257 | .260 | .282 |
1 *p <.10.
- 2 **p <.05.
- 3 ***p <.01.
- 4 aDummy: 1 = approach, 0 = avoidance.
- 5 bDummy: 1 = match, 0 = mismatch.
- 6 cInterval: 1 = "not at all," and 7 = "very much."
- 7 Notes: All variables are dummies (1 = yes, 0 = no) unless otherwise noted.
The objectives of Study 2 are twofold. First, we aim to replicate the findings of the effect of the motivation mismatch (vs. match) strategy on adoption (H1) and to examine its underlying mechanism (H2). Second, to enhance generalizability, we examine another real-world cocreated innovation: Pivot Power. Pivot Power originated from a customer-inventor's avoidance-oriented authentic creation narrative about the hassles of traditional electrical power strips. To better isolate the motivation mismatch (vs. match) effect in our hypothesis testing, we develop a full set of mismatch (vs. match) manipulations (see Web Appendix W4).
One hundred ninety participants (Mage = 33 years; 51% male) from Amazon Mechanical Turk took part in the study in exchange for a small monetary compensation. We employ a 2 (persuasive messages: approach- vs. avoidance-oriented motivation for adoption) × 2 (authentic creation narratives: approach- vs. avoidance-oriented motivation of invention) between-subjects design. Participants were asked to review some online information about a real-world cocreated innovation, Pivot Power. Drawing on the original online ad for Pivot Power, we designed two sets of persuasive messages, promoting either an approach- or avoidance-oriented motivation for adoption. Using the customer-inventor's original avoidance-oriented authentic creation narrative retrieved from his personal blog, we developed a new, approach-oriented authentic creation narrative. To create a realistic scenario about the newly launched innovation, we highlighted in the ad that the cocreated innovation had been on the market for just "a week" with no update on "units sold to date" (see also Web Appendix W4 and Figure 3). Following prior research on innovation adoption ([11]; [28]), we did not mention the price of the innovation. The manipulation check in the main study shows that across conditions, our manipulations were equally believable (M = 5.55) and realistic (M = 4.57) (all ps >.28):
• Persuasive message (approach/[avoidance])
- You know what makes your dream come true [You know what makes your frustration go away]? This. Reclaim your outlets with Pivot Power, a flexible surge protecting power strip that bends to fit every sized plug or adapter.
- With Pivot Power, you can fit as many plugs all at once as you dream of. More economic, and more space saving. [no more frustration over plug traffic jams, or blocked outlets —even those big ol' bricks are welcome.]
• Authentic creation narrative (approach/[avoidance])
• Pivot power was invented by real customers like you.
- Chris had a dream of being able to use all the outlets on a power strip, even if they are all giant power bricks (transformers) adjacent to each other. [Chris often found himself in a situation that he can't use an outlet on a power strip because a giant power brick (transformer) in the adjacent outlet is blocking it.]
- Dreaming of an ideal power strip [Sick of such a frustrating problem], he considered how he might create something new. Driving home, the idea for Pivot Power hit him.
Graph: Figure 3. Pictures of manipulations in Study 2 (Pivot Power) and Study 3 (Stem).
Participants were asked to first review the online information about Pivot Power and then rate their likelihood of adoption ("How likely would you be to purchase this new product, if the price were reasonable?"; 1 = "very unlikely," and 7 = "very likely"). On the next survey page, participants were asked to "write down all aspects you have considered in making this decision." We followed [14] procedure and measured participants' narrative self-referencing by inviting two research assistants to code participants' thoughts. Both research assistants were blind to the purpose of the study. The research assistants used a four-item scale of narrative self-referencing adapted from [14] based on the seven-point Likert scale: ( 1) "participants' thoughts were about how they themselves engaged in activities to achieve desired outcomes or solve existing problems"; ( 2) "participants' thoughts provided insights about their personal life, stories, or situations"; ( 3) "participants' thoughts had a well-delineated story with a beginning (initial event), middle (problem or turning point), and end (conclusion)"; and ( 4) "participants' thoughts reflected their thought process of picturing themselves experiencing the same or a similar event to that described by the customer inventor." We averaged the four items to form a narrative self-referencing score for each participant. The two coders' ratings were highly correlated (r =.76, p <.01; α =.84). We averaged both of their ratings to form an average score of narrative self-referencing. For example, the assistants coded a high level of narrative self-referencing when one participant wrote:
This product seems like something that would be quite helpful to me. As I sit on my desktop computer in my evening study now, I have to make a tough choice between turning on my lamp [and] listening to music from my iPhone. I have enough plug-ins on the surge-protecting strip; however, it doesn't fit right to allow both items to turn on. With Pivot Power, I am hopeful I can use both of my belongings.
A low level of narrative self-referencing was coded when participant wrote comments such as "style, design, practicality." In an unrelated study, we collected these same participants' chronic approach/avoidance motivation orientation using the Regulatory Focus Questionnaire (RFQ; [15]). The RFQ, along with age, gender, and household income, were collected and used as control variables.
We first performed separate pretests as in Study 1 to validate the success of our manipulations (for details, see Web Appendix W5). To test H1, the mismatched ad effect on adoption, we ran a 2 × 2 analysis of covariance (ANCOVA), with the likelihood of adoption as the dependent variable. We found a significant interaction effect (F( 1, 182) = 6.87, p =.01, =.05) but no significant main effect of each factor. Follow-up planned contrasts showed that when the authentic creation narrative was approach-oriented, the motivation mismatch (vs. match), in which persuasive messages promoted avoidance- (vs. approach-) oriented motivation for adoption, led to a higher likelihood of adoption (M = 6.11 vs. M = 5.62; F( 1, 182) = 4.51, p <.05). When the authentic creation narrative featured an avoidance-oriented motivation of invention, the motivation mismatch (vs. match), in which persuasive messages promoted an approach- (vs. avoidance-) oriented motivation for adoption, increased adoption (M = 5.82 vs. M = 5.33; F( 1, 182) = 4.28, p <.05). These findings provide support for H1. Specifically, the motivation mismatch (vs. match) strategy increases adoption of cocreated innovations (see Figure 2, Panel B).[ 9]
We hypothesize that narrative self-referencing mediates the motivation mismatch (vs. match) strategy on adoption. To test this underlying mechanism, we first ran a 2 × 2 ANCOVA with narrative self-referencing as the dependent variable. We found a significant interaction effect (F( 1, 182) = 25.45, p <.01, =.12) but no other main effect of either variable. Follow-up planned contrasts revealed that when the authentic creation narrative was approach oriented, a motivation mismatch (vs. match) with an avoidance- (approach-) oriented persuasive message evoked a greater level of narrative self-referencing (M = 4.89 vs. M = 3.64; F( 1, 182) = 10.37, p <.01). Similarly, when the inventor's motivation was avoidance oriented, the motivation mismatch (vs. match) with an approach- (vs. avoidance-) oriented persuasive message increased narrative self-referencing (M = 4.67 vs. M = 3.10; F( 1, 182) = 15.41, p <.01).
To directly test the mediating role of narrative self-referencing, we conducted a mediation analysis using the bootstrapping method ([39]). First, we created a dummy variable for the motivation mismatch (vs. match; mismatch = 1, match = 0) as the independent variable.[10] We specified narrative self-referencing as the mediator and the likelihood of adoption as the dependent variable. We also specified the following variables as control variables in the model: approach- (vs. avoidance-) oriented persuasive message; approach- (vs. avoidance-) oriented authentic creation narrative; and participants' age, gender, income, and RFQ. The results supported H2's prediction that narrative self-referencing mediates the effect of mismatched (vs. matched) ads on adoption (95% confidence interval [CI] = [.03,.25]). Consistent with prior research, we also found that RFQ promotion- (vs. prevention-) focused participants are more likely to adopt the product (b =.34, t = 3.14, p <.05). Moreover, men are more likely to adopt this new electronic product (Pivot Power) than women (b =.42, t = 2.57, p <.05).[11]
One may argue that the effect of the motivation mismatch strategy on adoption is mediated by motivation compatibility. That is, a motivation mismatch (vs. match) strategy combines both avoidance and approach motivations and therefore may have a greater chance of being compatible with potential adopters' chronic approach- or avoidance oriented regulatory focus (RFQ), and this motivation compatibility subsequently influences adoption. To rule out this alternative explanation, we conducted a postanalysis (for details, see Web Appendix W6). The results show that the motivation mismatch strategy did not lead to motivation compatibility (b = 21.92, Wald <.01, n.s.). Furthermore, controlling for the motivation compatibility, the mediating effect of narrative self-referring was still significant (95% CI = [.05,.30]).
We also conducted two poststudies to better establish our underlying process in terms of whether it is the customer as an inventor, the customer as the conveyor of the message, or the content of the message itself that is driving our results (for details, see Web Appendix W7). Poststudy 1 employed a 2 (persuasive messages: approach- vs. avoidance-oriented) × 2 (authentic creation narratives: approach- vs. avoidance-oriented) × 2 (inventor: employee vs. customer) between-subjects design. The results show a significant three-way interaction effect (F( 1, 492) = 3.95, p <.05, =.01). In particular, the motivation mismatch effect holds when a customer is the inventor, but the effect is mitigated when the inventor is a firm employee. This result indicates that customer (vs. employee) as inventor, rather than the content of the message, is fundamental to our effect.
Poststudy 2 employed a 2 (inventor: employee vs. customer) × 2 (communication channel: user-generated vs. firm-generated) × 2 (ad strategy: motivation mismatch vs. match) between-subjects design. The results replicate the finding of Poststudy 1 that the motivation mismatch effect holds for customer as inventor, but not employee as inventor. Furthermore, the motivation mismatch effects hold for both user-generated (customer blog) or firm-generated (official website) communication channels. Thus, the communication channel is not key to our results. In summary, using a controlled experimental design and a different real-world, cocreated innovation (Pivot Power) as our key manipulations, Study 2 provides convergent evidence that the motivation mismatch drives the adoption of cocreated innovation, because it activates narrative self-referencing of authentic creation narratives (H1 and H2).
Study 3 was an experiment designed to examine the moderating role of consumer expertise. To improve the external validity of the research, this study uses a different real-world, cocreated innovation—Stem (a lemon spray) —as our key manipulation. Stem originated from an approach-oriented authentic creation narrative (see also Web Appendix W8). We developed an avoidance-oriented authentic creation narrative to test a full set of mismatched and matched ad conditions. Separate pretests were conducted to validate the success of the manipulations (for details, see Web Appendix W9 and Figure 3):
• Persuasive Message (approach/[avoidance])
- This metal spray is an ideal [a must have] tool for your kitchen. Stem sprays juice directly from any citrus fruit with the press of a finger.
- With stem, more fun, more convenience [no more fuss of cutting and squeezing, no more mess].You can spray where you desire [need]: on slices fruit, on salads, on meat, or even on surfaces for cleaning.
• Authentic Creation Narrative (approach/[avoidance])
• Stem was invented by real customers like you.
- Pat noticed that the most common way to apply lemon juice or any other citrus fruit, to a food product, is by cutting the lemon and squeezing the juice over the dish.
- Dreaming of more fun and the same job easily done [Tired of all the mess in cutting and manually squeezing], Pat came up with the idea for Stem.
Four hundred seven participants (Mage = 40 years; 46% male) from Amazon Mechanical Turk took part in the study in exchange for a small monetary compensation. Study 3 implemented a 2 (persuasive message: approach- vs. avoidance-oriented motivation for adoption) × 2 (authentic creation narrative: approach- vs. avoidance-oriented motivation of invention) between-subjects design, with consumer expertise as a measured variable. Participants first read some online information about the lemon spray and then rated their likelihood of adoption. Next, we asked participants to self-report their narrative self-referencing using the same four-item scale as in Study 2, except changing "participants" to "I" (α =.71; [14]). We also collected a measure of consumer expertise on a three-item seven-point Likert scale (α =.83; "I have a lot of knowledge to evaluate this kind of product," "I am very capable of evaluating this kind of product," and "I am an expert in this kind of product"; [ 9]). Finally, participants evaluated how "believable" and "realistic" the online information was. We again collected RFQ scores through a different study, along with demographic information (i.e., age, gender, and household income) to use as control variables. Because none of the control variables were significant, we do not discuss them further. Across conditions, participants rated that the online information was equally believable (5.00 to 5.29) and realistic (5.04 to 5.37).
We ran a 2 × 2 × continuous consumer expertise ANCOVA on the likelihood of adoption dependent variable. In support of H3, we found a significant three-way interaction effect (F(16, 331) = 3.90, p <.01, =.16). To further explore the interaction effect, we conducted simple slope analyses at one standard deviation unit above (high expertise) and below (low expertise) the mean of consumer expertise ([22], Model 3). The results show that the persuasive message × authentic creation narrative interaction effect is significant and negative for low-expertise consumers (1 SD below M = 3.18; blow = −2.15, t = −4.22, p <.01) but not for high-expertise consumers (1 SD above M = 5.95; bhigh =.94, t = 1.87, p =. 06). By applying the Johnson–Neyman technique, we found that based on a 95% confidence band, the interaction effect is no longer significant and negative when consumer expertise is above 4.47 (Johnson–Neyman point of significance = 4.47[12]; see Figure 2, Panel C).
We expected that consumer expertise moderates the motivation mismatch strategy on adoption, because high (vs. low) consumer expertise attenuates the effect of the motivation mismatch on narrative self-referencing. To validate this theorizing, we conducted a 2 × 2 × continuous consumer expertise ANCOVA on narrative self-referencing. The results showed a significant three-way interaction effect (F(16, 331) = 1.85, p <.05, =.08). Simple slope analyses show that the persuasion message × authentic creation narrative interaction effect is significant and negative for low-expertise consumers (1 SD below M = 3.18; blow = −1.61, t = −4.73, p <.01), but not for high-expertise consumers (1 SD above M = 5.95; bhigh = −.30, t =.90, p =. 37). Analysis using the Johnson–Neyman technique showed that based on a 95% confidence band, the interaction effect on narrative self-referencing is no longer significant when consumer expertise is above 5.43 (Johnson–Neyman point of significance = 5.43).
Next, we tested moderated mediation by using bootstrap analysis. First, we created a dummy variable for the motivation mismatch (vs. match) ad (mismatch = 1, match = 0) as the independent variable. We treated the motivation mismatch (vs. match) as an independent variable, adoption as a dependent variable, consumer expertise as a moderator, and narrative self-referencing as a mediator ([22], Model 7). The results validated the moderated mediation effect. When consumer expertise is low (1 SD below M = 3.18), the effect of the motivation mismatch (vs. match) strategy on adoption was significantly and positively mediated by narrative self-referencing (95% CI = [.25,.86]), but the mediation effect went away when consumer expertise was high (1 SD above M = 5.95) (95% CI = [−.12,.29]).
To recap, in Study 3, we used a different real-world cocreated innovation (lemon spray) to validate the moderating role of consumer expertise. By measuring individual differences in consumer expertise, we find convergent evidence that consumers' high expertise attenuates the effect of the motivation mismatch strategy on adoption. Furthermore, the moderated mediation test demonstrated that narrative self-referencing is the underlying mechanism of the effect.
Study 4 aims to validate the effect of the motivation mismatch (vs. match) strategy on the early takeoff of cocreated innovations. To do so, we obtained 112 cocreated innovations' month-to-month sales records from a U.S.-based cocreated innovation firm. The firm was founded in 2009 and was one of the pioneers in cocreated innovation. Following the typical cocreation model, the firm operates an online platform to collect creative ideas and prototypes from consumers around the world. After a series of screening processes, the firm codevelops the most promising ideas and prototypes with the inventor and the community and brings them to market. By 2014, the firm had sold more than 100 cocreated innovation products, with annual revenues exceeding US$100 million. Most of the cocreated innovations are consumer products that fall into four categories: Electronics; Home and Garden Supply; Kitchen Supply; and Health, Fitness, and Travel Accessories. Because the cocreated innovations are mainly sold through the firm's online website, the sales records of each cocreated innovation provide reasonable control of exogenous variation and can be used as valid data sources to model innovation adoption ([25]). We obtained month-to-month sales records of 112 cocreated innovations launched from December 2009 to June 2014.
Innovation takeoff is the transition point from the introductory stage to the growth stage in the adoption life cycle ([16]). Although prior research on durable consumer products and high-technology innovations has used yearly sales data as the unit of analysis to model takeoff time, recent research has shown that the product life cycle for consumer products has decreased to two to three years ([ 8]). Thus, we use monthly sales data to model time to takeoff for a more precise analysis.
We adopt [ 2] discriminant approach to measure time to takeoff because this method is rigorous and does not require complete information about the four life-cycle phases ([49]). Here, we briefly repeat the key steps in the discriminant approach (for detailed procedures, see [ 2]]). We first used a visual analysis by plotting the month-to-month sales data to generate a graph of potential adoption phases, particularly the introduction and growth phases. Then, from the percentage change in the monthly sales data, we partitioned the series of monthly sales data into three categories. The first and third categories contain the months in which the percentage change in sales clearly reflects the pre- and posttakeoff periods, and the second category contains the remaining "in-between phase" data. Finally, we followed [18] statistical procedure of a generalized version of discriminant analysis based on mean values of the pre- and posttakeoff period sales growth rates to further partition the in-between phase data and pinpoint the takeoff month that reports a dramatic sales growth over the prior month (for sample graphs, see Figure 4 and Web Appendix W10). We excluded ten innovations with a sales period of less than five months, because it is too early to define takeoff timing for them.[13] We also used an alternative method to measure the takeoff time following [16]. The main results are robust to the alternate measure of takeoff time (for details, see Web Appendix W11).
Graph: Figure 4. Study 4 takeoff month examples.
We apply our data to a discrete time hazard model. We define our dependent variable Yit as:
Graph
where TOi denotes the takeoff month of the cocreated innovation i. Yit is a binary variable with the number of observations equal to TOi. We apply a linear probability model using Yit as a dependent variable. The probability of takeoff is specified as[14]:
Graph
1
where Xit is a vector of an intercept and explanatory variables (e.g., price, category), and TLit denotes time (month) elapsed since the launch of product i at t. Modeling duration data in this manner can be regarded as a discrete time hazard model. Specifically, flexibly captures the effect of time since launch on baseline hazard (or takeoff probability). Note that this specification allows for a wide range of different hazard shapes and has been successfully used in discrete time hazard models (see, e.g., [47]], Equation 12, p. 467).
On the cocreating firms' website, typical information about a cocreated innovation consists of three sections: ( 1) the persuasive message, ( 2) information about the customer-inventors and their invention, and ( 3) pictures of the product. We trained two research assistants who were blind to our hypotheses to code the motivation orientations in the first two sections of the actual ads of 102 cocreated innovations. The coding method is consistent with Study 1b. We found that for 15 of the 102 cocreated innovations, the inventor's story did not provide any information about his or her motivation of invention (kappa =.77, and intraclass correlation coefficient =.87). Instead, the inventor's story was about his or her hobbies or identified demographic information. Given our research focus, we excluded these innovations from our analysis. In the remaining sample (n = 87), 40% of the ads of cocreated innovation utilized a match strategy, taking the value of 1 (kappa =.75, intraclass correlation coefficient =.86).[15]
We controlled for the following ten potentially influential variables in our model:
- We controlled for the selling price of each innovation in the takeoff model. In our sample, the selling price of the innovations range from $1.99 to $300, with a mean of $26.50. Following [25], we take the natural log of price (Lnprice) for the estimation (M = 2.86, SD =.89).
- We controlled for the potential influence of the product category on time to takeoff. The company classifies its products into four categories: ( 1) Electronics (41%); ( 2) Home and Garden (29%); ( 3) Kitchen (20%); and ( 4) Health, Fitness, and Travel (10%). Following this categorization, we constructed three category dummy variables (Electronics, Home and Garden, and Kitchen), while the Health, Fitness, and Travel category serves as a baseline.
- Lninfl is the log of the number of community members who participated in the product development (M = 6.95, SD =.91). This control variable helps us control for the influence of the participating customers/community on takeoff.
- Lndvlp is the log of the duration (days) of product development (M = 4.24, SD =1.29). This variable, as a proxy of innovation development cost, may also potentially influence adoption takeoff ([25]).
- Lnbase is the log of the first month's sales (Lnbase). We include this variable to control for the influence of the base level of sales on time to takeoff ([16]). Adding the base level of sales may also help us control for other potential explanatory variables (e.g., competition).
- We control for novelty (avgnovel; α =.94) as in Study 1b.
- We control for usefulness (avguse; α =.88) as in Study 1b.
- PriorSuccess (1 = yes, 0 = no) indicates whether the innovation comes from a customer-inventor who has previously successfully launched an innovation in the same platform.
- DecJan (1 = yes, 0 = no) indicates whether the innovation were launched during December or January to control for the effect of seasonality or holidays.
- Takeoff Month (Month) indicates the takeoff month of the innovation (for descriptive statistics, see Web Appendix W14).
- We also checked the correlations among these variables and found that the largest correlation coefficient is.54, indicating that multicollinearity is not a significant concern.
We modeled the takeoff of 87 cocreated innovations using a discrete time hazard model. In the data, 21 innovations (24% of the total) never took off and 66 innovations (76% of the total) show clear takeoff patterns (M = 4.38 months, SD = 3.00 months, Min = 2 months, Max = 14 months). We estimated the linear probability model specified in Equation 1. Table 2 reports the estimation results. In all the models with Match variable, the estimated coefficients were significant and negative, implying that the motivation match (vs. mismatch) strategy decreases the hazard rate (i.e., probability of its takeoff). The estimated coefficients ranged from −.07 to −.10, and this indicates that the probability of takeoff decreases by 7% to 10% with the motivation match strategy. This result provided support for the notion that the motivation mismatch (vs. match) strategy is more likely to speed up time to takeoff. We also found that usefulness is highly significant and positive in Models 5–7.
Graph
Table 2. Study 4 Results Using Log Total Units Sold as the Dependent Variable.
| Predictors | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 |
|---|
| Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate |
|---|
| Constant | .56*** | .61*** | .63*** | .50*** | .40*** | .38*** | .58*** |
| TL (continuous; months elapsed since the launch of product) | −.62*** | −.62*** | −.61*** | −.60*** | −.57*** | −.58*** | −.59*** |
| TL2 | .19*** | .19*** | .18*** | .18*** | .17*** | .18*** | .18*** |
| 1/TL | −.05*** | −.05*** | −.05*** | −.05*** | −.05*** | −.05*** | −.05*** |
| Authentic creation narrative orientationa | −.05 | −.03 | −.02 | −.04 | −.03 | −.03 | −.03 |
| Persuasive message orientationa | .04 | .03 | .03 | .06 | .05 | .05 | .06 |
| Matchb | | −.07*** | −.10*** | −.10*** | −.10*** | −.10*** | −.10*** |
| Electronics | | | −.01 | .03 | −.03 | −.03 | −.01 |
| Home & Garden | | | .03 | .03 | −.01 | .00 | .01 |
| Kitchen | | | −.07 | −.07 | −.10 | −.10 | −.09 |
| Ln (sales price) (continuous in US$) | | | | −.02 | −.02 | −.02 | −.02 |
| Ln(development days) (continuous) | | | | .02 | .03* | .03* | .03* |
| Ln(number of influencers) (continuous) | | | | .01 | .02 | .02 | .02 |
| Ln(units sold in 1st month) (continuous) | | | | −.01 | .00 | .00 | .00 |
| Inventor prior success | | | | | .11* | .10 | .10 |
| Noveltyc | | | | | −.02 | −.03 | −.03 |
| Usefulnessc | | | | | .04*** | .04*** | .04*** |
| Takeoff month: January | | | | | | .13*** | |
| Takeoff month: February | | | | | | | −.17*** |
| Takeoff month: March | | | | | | | −.16*** |
| Takeoff month: April | | | | | | | −.19*** |
| Takeoff month: May | | | | | | | −.10 |
| Takeoff month: June | | | | | | | −.10 |
| Takeoff month: July | | | | | | | −.19*** |
| Takeoff month: August | | | | | | | −.15*** |
| Takeoff month: September | | | | | | | −.22*** |
| Takeoff month: October | | | | | | | −.27*** |
| Takeoff month: November | | | | | | | −.25*** |
| Takeoff month: December | | | | | | | −.09 |
| R-square | .07 | .08 | .08 | .10 | .11 | .13 | .16 |
- 8 *p <.10.
- 9 **p <.05.
- 10 ***p <.01.
- 11 aDummy: 1 = approach, 0 = avoidance.
- 12 bDummy: 1 = match, 0 = mismatch.
- 13 cInterval: 1 = "not at all," and 7 = "very much."
- 14 Notes: All variables are dummies (1 = yes, 0 = no) unless otherwise noted.
The estimates of τ1, τ2, and τ3 were all significant and have similar values in all models estimated. We illustrate the baseline hazard of Model 7 (the best-fitting model) over the first 20-month period since launch in Web Appendix W16, Figure A. The baseline hazard rate or the baseline probability of takeoff increases in the first 4 months from launch, then slightly decreases until 16 months from launch. We also found that the probability of takeoff shows significant variation over months. Figure 5 graphically represents the estimated coefficients of month dummies in Model 7. Note that the takeoff probability of January is normalized to zero for the model identification. The probability of takeoff is relatively high in January, December, May, and June and is significantly lower in other months. However, after we control for this seasonality effect, our proposed effect of the motivation mismatch (vs. match) strategy still holds.
Graph: Figure 5. Study 4 monthly variation in probability of takeoff.*p <.10.**p <.05.***p <.01.Notes: Probability of takeoff in January is normalized to zero.
In summary, our empirical analyses provide direct support for our prediction that the motivation mismatch (vs. match) between persuasive messages and authentic creation narratives in ads of cocreation innovation is more likely to drive early takeoff. Given that takeoff is an important turning point for innovation adoption ([16]), this study provides convergent evidence for our proposed model at the aggregate level.
Despite their unique value, the low adoption rates of many cocreated innovations challenge both cocreating start-ups and established firms, dampen customers' enthusiastic participation, and threaten the sustainability of the cocreation model ([ 1]; [ 4]). In this research, we explore a new communication strategy and adoption mechanism for cocreated innovations involving an interactive FGC–UGC strategy. Our supported conceptual framework has significant implications for both academia and cocreation managers.
Our research contributes to the marketing literature in several important ways. First, we contribute to the growing body of literature on interactive FGC–UGC strategies and their impact on business outcomes. Extant FGC–UGC literature has focused mostly on UGC-based on retrospectives of consumers' consumption experiences (e.g., product/service reviews). Little is known about how to leverage UGC that describes consumers' creative experiences or how to strategically design FGC and UGC at the content level to drive the adoption of cocreated innovations. Our research shows that in the communication of cocreated innovations, firms can leverage a unique type of creation story that features the creative motivations and experiences of the customer-inventor, in what we label authentic creation narratives. Specifically, creating a motivation mismatch (vs. match) between firm-generated persuasive messages (explicit persuasion statements designed to promote innovation attributes and reasons for adoption) and customer-inventor-generated authentic creation narratives drives the adoption of cocreated innovation. This finding extends FGC-UGC research by furthering our understanding of UGC and developing interactive FGC-UGC strategies that are compelling to consumers.
Second, we offer a novel explanation for the effect of the motivation mismatch strategy by showing evidence that the activation of narrative self-referencing is the underlying mechanism at play in these situations. This finding contributes to both the cocreated innovation literature and narrative persuasion theory. Extant research on cocreated innovation has primarily examined the effect of a "user-designed" labeling strategy (e.g., [11]; [33]; [43]) and its adoption mechanisms by influencing adopters' identification or evaluations of the customer-inventor or cocreating firm. Our work extends research on cocreated innovation by offering a novel and unique adoption mechanism that elicits adopters' actual needs or wants for the innovation itself. It also adds to narrative persuasion theory, which is based mainly on firm-generated narrative materials in isolation, by showing how to manage multisource (FGC and UGC), multiform (persuasive messages and authentic creation narratives) communication materials to activate narrative processing.
Third, we delineate an important moderating factor in our conceptual model. That is, we demonstrate that the effect of the motivation mismatch strategy is attenuated for high (vs. low) consumer expertise. Furthermore, we show that the motivation mismatch (vs. match) strategy predicts takeoff of the cocreated innovation. These findings outline effective boundary conditions as to when and for whom the motivation mismatch strategy should be used to drive the adoption of cocreated innovation. Furthermore, we contribute to the innovation diffusion literature by tying a micro-level marketing communication strategy to innovation adoption at both the individual (adoption) and aggregate (takeoff) levels ([36]).
This research offers important implications for managers and companies aiming to leverage the creative power of the crowd in their innovation development. First, by addressing the key challenge of better managing the development of cocreated innovations ([ 4]), we shed light on new and better ways to communicate cocreated innovations and drive adoption. Although the common practice is to label cocreated innovations as "user designed" and provide information about customer-inventors' demographics or social affiliations, we show that firms can leverage authentic creation narratives—stories about their real-life experiences that motivated their creative ideas—to drive adoption of cocreated innovations. More importantly, contrary to management intuition of promoting an aligned motivation for adoption, our findings show that a motivation mismatch between persuasive messages and authentic creation narratives drives adoption. These findings introduce a valuable new perspective to management thinking that should enhance the success of cocreation programs.
Second, our proposed boundary condition offers firms actionable guidelines on when and to whom to apply the motivation mismatch strategy to influence the adoption of cocreated innovation. Specifically, marketers can identify high- versus low-expertise consumers through segmentation or by tracking consumer behaviors (e.g., high-expertise consumers may work in similar product industries or act as opinion leaders in providing product reviews/YouTube tutorials) and apply the motivation mismatch strategy toward low-expertise consumers. For example, when the authentic creation narrative is approach oriented (e.g., it features a customer-inventor's dream or aspiration of achieving ideal consumption outcomes), and consumers' expertise is low, the cocreating firm should design an avoidance-oriented persuasive message that highlights how the cocreated innovation can help avoid undesired or unwanted consumption outcomes. Marketers can also evaluate the novelty of the cocreated innovation (continuous vs. discontinuous). The mismatched strategy is more likely be effective in driving adoption of discontinuous cocreated innovations because these extremely novel products will, by definition, be unfamiliar to the majority of the market. That unfamiliarity equates to low product category expertise related to the innovation and, per our model, a receptive consumer base for the mismatch approach. Finally, our proposed model is also highly relevant for managing the diffusion of cocreated innovation. Beyond using a pricing strategy or waiting for customer interactions through word of mouth to trigger takeoff ([36]), managers of cocreated innovations can apply the motivation mismatch strategy to follower-adopters (e.g., consumers who often rely heavily on customer reviews for adoption or are less willing to test new products) to drive their early adoption and aggregate takeoff.
This work has limitations that call for further research attention. First, drawing from research on the motivation-creativity model, we focus on examining motivation mismatch between two distinct motivational orientations: approach and avoidance. Although prior research has indicated that these two distinct motivation systems provide fundamental explanations for human creativity, further research could examine the effects of other dimensions in creative motivation systems, such as self-/social-oriented creative motivations. Furthermore, in our work we have sought to highlight the role of self-referencing as a focal mechanism that underlies the motivation mismatch (vs. match) strategy on the adoption of cocreated innovations. Indeed, we believe we have effectively established (empirically and theoretically) that self-referencing is fundamental to the effects we identify. However, we do not want to imply that self-referencing is the sole driver of the phenomenon. We believe that, like most consumption phenomena, this effect is multiply determined by a synergy of elements, and additional complexity surrounds our identification. For example, how does analytical versus affective/emotional processing relate to the mismatch (vs. match) strategy we identify? Does the self-referencing mechanism center on how unmet needs are identified for the consumer or simply motivate the decision to adopt? We hope that future research will deepen our investigation by expanding on the initial conceptualization that we proffer here.
Second, our empirical study on the adoption of newly cocreated innovation focuses on cocreated consumer products. Study 4, which measures the takeoff of cocreated innovations, is based on cocreated innovations developed by one firm. Although our mixed methods of experiments and analytical modeling of real-world adoption/sales data increase our confidence in our conceptualization, future research could expand the examination to industrial, cocreated innovations and diffusion data covering multiple cocreation firms. In addition, in our sampled data in Study 4, we observed that takeoff is followed by only a brief period of growth. This effect may be due to data at the monthly level, the type of sampled product categories, or seasonal effects. Future research could ascertain which of these explanations is the most robust.
Finally, an interesting extension of this research would be the consideration of "inauthentic" creation narratives, or creation narratives that the potential adopter would likely know to be fiction, such as those long offered by the J. Peterman Company. Here, elaborate narratives are offered for the creation of clothing goods, often involving a trek through a dangerous jungle or glamorous gatherings of high society. When combined with traditional persuasive messages, would our mismatch effects persist? If so, this opens great opportunities to expand application of the concept to enhance adoption in broader settings.
Through five studies, we have examined the effects of a motivation mismatch strategy between inventors' authentic creation narratives and firms' marketing messages for cocreated innovations that are delivered in traditional product sales settings, such as product descriptions on a website. Future research could explore how presenting persuasive messages and authentic creation narratives through different channels (e.g., firms' press conferences, customer-inventors' YouTube channels) and in distinctive formats (e.g., persuasive speeches, video stories) could moderate the effect of the motivation mismatch strategy on adoption.
Supplemental Material, DS_10.11770022242919841039 - Successfully Communicating a Cocreated Innovation
Supplemental Material, DS_10.11770022242919841039 for Successfully Communicating a Cocreated Innovation by Helen Si Wang, Charles H. Noble, Darren W. Dahl and Sungho Park in Journal of Marketing
Footnotes 1 Associate EditorPraveen Kopalle served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242919841039
5 1Takeoff refers to the first dramatic increase in adoption after the introduction of an innovation ([2]; [16]; [36]).
6 2The approach-/avoidance-oriented motivations can be consumers' chronic traits or situation based ([13]; [23]; [41]). In this article, we focus on examining these motivations as situation-based message characteristics. Using randomized controlled experiments, Studies 2 and 3 manipulate these motivations as communication characteristics and explicitly control for consumers' approach-/avoidance-oriented chronic traits.
7 3We interviewed executives of the cocreating platform to validate the assumption that the motivation orientation of firm-generated persuasive messages was independent of authentic creation narratives. Executives reported that no thought was given to a potential relationship between messages.
8 4The estimated lift looks somewhat large. Therefore, we investigated the model-free evidence and found that the motivation mismatch strategy generated 58% higher sales revenue than the motivation match strategy. Substantial difference in sales revenue associated with the motivation mismatch strategy might be explained by the innovation takeoff. The motivation mismatch strategy is associated with higher probability of early takeoff. In the data set used in Study 4, we observed that three-month adoption amount (units sold) after takeoff is greater by 688% than those before takeoff.
9 5The results follow the same pattern of effects without the covariates.
6The results follow the same pattern of effects using moderated mediation (Model 7, [39]).
7The identified mediation effect held without the control variables in the model.
8The results follow the same pattern of effects without the covariates.
9In the data, the average time to takeoff is 4.38 months for cocreated innovations that show clear takeoff patterns.
10As a robustness check, we ran two additional models based on different specifications and assumptions (see Web Appendix W12).
11We have assumed that persuasive message and authentic creation narratives are generated independently. In other words, the match and mismatch strategies are exogenously determined. We further validated this assumption by interviewing the chief executive officer of the company and adopting the control function approach using an instrumental variable ([37]). Specifically, we use the developer's prior success as an instrumental variable. After controlling for the effects of various independent variables (e.g., price, novelty, usefulness of the product), the prior success of the developer is unlikely to be correlated with the takeoff timing of the focal innovation, satisfying the exclusion restriction. In addition, we confirmed that the prior success of the developer (the independent variable) satisfies the inclusion restriction (see Web Appendix W13).
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By Helen Si Wang; Charles H. Noble; Darren W. Dahl and Sungho Park
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Record: 175- Swayed by the Numbers: The Consequences of Displaying Product Review Attributes. By: Watson, Jared; Ghosh, Anastasiya Pocheptsova; Trusov, Michael. Journal of Marketing. Nov2018, Vol. 82 Issue 6, p109-131. 23p. 3 Charts, 5 Graphs. DOI: 10.1177/0022242918805468.
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Swayed by the Numbers: The Consequences of Displaying Product Review Attributes
Prior research has shown the independent effects of average product ratings and number of reviews for online purchases, but the relative influence of these aggregate review attributes is still debated in the literature. In this research, the authors demonstrate the conditional influences of these two attributes as a function of the valence of average product ratings and the level of review numbers in a choice set. Specifically, they argue that the diagnosticity of the number of reviews, relative to average product ratings, increases when average product ratings are negative or neutral (vs. positive) and when the level of review numbers in a choice set is low (vs. high). As a result, when consumers choose among the best options on one of the review attributes (average product ratings or the number of reviews), their preference shifts from the higher-rated option with fewer reviews toward the lower-rated option with more reviews. The authors demonstrate this preference shift in seven studies, elucidate the underlying process by which this occurs, and conclude with a discussion of the implications for retailers and brands.
Keywords: average product ratings; number of reviews; online product reviews; online retail; choice
With the rise of Internet shopping, product reviews have gained prominence. Nearly 60% of consumers now say that the average product rating is the most important product attribute in their purchase decisions. Within consumer reviews, 54% of consumers report paying attention to average product ratings, while 46% pay attention to the number of reviews ([ 6]). Because average product ratings and the number of reviews (referred to as "aggregate reviews" hereinafter) play a significant role in consumer behavior, marketing academics have tried to understand the processes by which consumers incorporate aggregate review information into their purchase decisions ([13]; [14]; [16]; [18]; [22]; [28]; [32]; [45]; [52]; [75]; [87]).[ 5]
While the literature has demonstrated the positive influence of both average product ratings and the number of reviews on sales, their relative influence is still debated. Indeed, two recent meta-analyses arrived at opposing conclusions. [26] find support for the claim that average product ratings are more influential than the number of reviews, whereas [83] argue for the greater importance of the number of reviews.[ 6] The goal of this article is to clarify our understanding of the relative influence of average product ratings and number of reviews by specifying the conditions by which the interactive effect of these two attributes on consumer preference takes place.
Some previous research has also explored the interactive relationship between various aspects of aggregate review information. [14] investigate the impact of aggregate movie reviews on box office sales (considering only average product ratings but not the review numbers of competing choice options) and find no interactive effects of average product ratings and number of reviews. [10] investigate the impact of reviews on book sales and find that individual reviews that have received high proportions of "helpful" votes by other consumers are more impactful on sales relative to other reviews, and that this effect is stronger for less popular (vs. more popular) books. Finally, [41] find that ratings dispersion (i.e., the distribution of individual ratings) has a differential impact on sales of negatively and positively rated products, but only when the products have a large number of reviews. A wide ratings dispersion increases evaluations of negatively rated products, whereas a narrow dispersion increases evaluations of positively rated products. Thus, previous literature provides some indication that people might evaluate average product ratings differently when choice options have a different number of reviews (and vice versa), yet the precise nature of when such an interactive effect occurs has not been explored.
To answer this question, we examine consumer choice between multiple products that vary on average product ratings and number of reviews but whose other attributes (e.g., price points, brand recognition, functionality) are similar. This enables us to investigate the relative diagnosticity of these two attributes in consumer decisions and the conditions by which these relative diagnosticities change. Furthermore, whereas prior research has largely focused on preference of individual choice options (for a notable exception, see [14]), in this research, we examine the influence of average product ratings and number of reviews on preferences and choices within a choice set.
Increasingly, retailers provide consumers with product options within a choice set rather than individual options (e.g., product search pages, "recommended for you" lists; for examples, see Web Appendix W1); thus, consumers encounter aggregate review information for multiple choice options simultaneously. Within these choice sets, we specifically examine the conditions by which consumers prefer higher-rated choice options with fewer reviews over lower-rated choice options with more reviews as a function of different levels of average product ratings and review numbers.
For example, consider the following scenario. You are searching online for a new blender and see two comparable choice options that meet your specifications. While one choice option has a higher rating but fewer reviews (e.g., 3.5 out of 5.0 based on 8 reviews), the other choice option has a lower rating but more reviews (e.g., 3.2 out of 5.0 based on 64 reviews). What is the relative diagnosticity of average product ratings and number of reviews as a signal of product quality? Would these diagnosticities—and ultimately, your decision—change if the review numbers were instead 408 and 464, respectively? What if the average product ratings were 4.5 and 4.2, respectively?
Managerially, this is an important investigation, as the choice sets in which consumers make trade-offs between product ratings and number of reviews are commonplace. For example, imagine two products released in January and June, respectively. The product released in January has had six additional months to accrue reviews, resulting in a higher number of reviews but technology that is six months older, thus potentially yielding a lower-quality product relative to the product released in June. Alternatively, consider a low-quality brand that ran a brief steep price promotion. Because of the promotion, this low-quality option could have more reviews than its high-quality competitors, which did not engage in a major price promotion. As such, it is quite likely that consumers are faced with a choice set, as described previously.
To demonstrate the prevalence of this trade-off scenario, we analyzed over 2.5 million products across 24 product categories and their corresponding choice sets using data collected from Amazon ([51]). On average, 79% of the product choice sets in our sample featured at least one other product that was superior in one of the review attributes but inferior in the other (for details of the data set and our analyses, see Web Appendix W2). Thus, we argue that studying these types of consumer choices not only is interesting from a theoretical standpoint but also has direct practical relevance, as these are the decisions consumers face on a regular basis.
In the following sections, we develop our conceptual framework and the hypotheses to test it. We then test our hypotheses in seven studies before concluding with the managerial and theoretical implications of our findings and directions for future research.
Numerous studies have examined how consumers infer product quality from multiple product attributes when making choices ([43]; [64], [65]; [66]; [72]; [73]). [73] propose the concept of attribute diagnosticity in the utilization of attributes in making choices. They argue that the perceived diagnosticity of any attribute is a function of the degree to which it separates the available choice options on perceived quality. More diagnostic attributes take precedence over less diagnostic ones as inputs into judgments. The accessibility–diagnosticity framework further argues that the interpretation of attributes for choice and judgments is context-dependent rather than fixed ([24]; [48]). It suggests that the same attribute can have a different perceived diagnosticity depending on the context. For example, [48] demonstrate that when attributes are easily recalled, they are used as inputs for choice, but when the same attributes are difficult to recall, consumers rely on overall evaluations of the choice rather than individual attributes. Thus, it is not the inherent diagnosticity of an attribute that dictates its use in decisions but the perceived diagnosticity of that attribute at the moment of choice.
Importantly, such perceived diagnosticity of one attribute can be a function of the valence of the other attributes. For example, [63] demonstrate that manufacturer reputation is considered a highly diagnostic cue, whereas warranty is not. As a result, whether warranty is used in product evaluations depends on the valence of reputation: when reputation is positive, a longer warranty improves quality judgments; when reputation is negative, the length of warranty does not affect judgments. Thus, the level of one attribute affects the perceived diagnosticity of the other, leading to joint effects of these attributes on consumer preferences.
Related work on attribute evaluability has further demonstrated that the levels of the same attribute of competing choice options can similarly change the perceived diagnosticity of the attribute and affect preferences ([29]; [33], [34]; [35]; [36]). [33] asks participants to evaluate two dictionaries: one dictionary has 10,000 entries and is in perfect condition, while the other has 20,000 entries and a torn cover. When evaluated independently, the former dictionary is preferred, but when evaluated jointly, preference for the latter dictionary increases. In other words, when evaluated independently, the condition of the dictionary was more diagnostic, but when evaluated jointly, the number of entries was more diagnostic.
Taken together, this literature provides evidence that the diagnosticity of attributes, as signals of product quality and their influence on preference, is not fixed. Rather, consumers often evaluate attributes depending on the values and availability of other attributes in the choice set. Next, we apply these frameworks to consumers' use of average product ratings and the number of reviews in decision making.
Consumer reviews on most sites include aggregate review information (average product ratings and number of reviews) and individual reviews that can include individual product ratings and (often) the textual content of each review. A large body of work has focused on examining the influence of aggregate review information and has demonstrated considerable impact of average product ratings and number of reviews on consumers' online behavior ([13]; [14]; [22]; [45]). Yet, as previously mentioned, the relative effects of these attributes are still debated ([26]; [83]). This ambiguity has encouraged recent academic interest in how disaggregated review information, such as the sentiment of individual reviews, can improve our understanding of online consumer behavior ([47]; [81]). This research demonstrates, for instance, that affective content of individual reviews influences conversion rates above and beyond the effects of aggregate review information; in addition, it demonstrates that aggregate review information (e.g., changes in the number of reviews) still has an impact on conversion rates ([47]).
This article complements these lines of research by aiming to clarify when and how review attributes jointly affect consumers' decisions. We argue that the relative diagnosticity of the average product ratings and the number of reviews in consumer choices is not fixed and may depend on the value of each attribute. We focus on the diagnosticity of the number of reviews and average product ratings relative to each other (and not relative to other attributes, such as textual content of reviews) for several reasons. First, the accessibility–diagnosticity framework posits that attributes that are more easily accessible to consumers are more likely to be inputs into judgments ([24]). When evaluating products online, consumers are likely to first examine the most accessible information. Individual reviews are less accessible than aggregate review information, as the former are often placed at the bottom of a product's page or on a separate page from other product attributes. Second, attributes that are easier to evaluate because they are comparable with each other are more likely to be inputs into judgment ([33]; [49]; [85]).
Prior work has demonstrated that comparisons between different levels of alignable attributes (which would be the case of two numerical attributes, such as average product ratings and number of reviews) are easier to make than comparisons of nonalignable attributes ([15]; [57]; [74]; [82]; [86]). Relatedly, comparisons of quantitative attributes are easier to make than comparisons of qualitative ones ([58]), suggesting that aggregate review information expressed in a numerical way (average product ratings and number of reviews) is more likely to influence consumer judgments.
We propose that, for consumers, average product ratings act as more evaluable signals of quality than the number of reviews. This happens because average product ratings are generally bound by a scale with two endpoints (e.g., 1–5 stars), so consumers can easily compare the average product rating with the scale endpoints to infer the level of absolute quality. By contrast, the number of reviews is presented on an unbound scale; furthermore, while the minimum number of reviews a product can possess is zero, the potential maximum is infinite. We argue that having the number of reviews unbound, at least on one end of the scale, can make the absolute number of the reviews more difficult to interpret as a signal of quality and can lower the perceived diagnosticity of this attribute in consumers' judgments. Consistent with our proposition, recent work has demonstrated that consumers believe that average product rating is the strongest indicator of a product's objective quality, more so than other quality cues, such as product price ([17]).
We build this argument drawing on numerical cognition literature, which has investigated how changing the features of numeric scales of a single attribute influences product evaluations ([ 2]; [53]; [60]; [70]). This literature has found that numbers and calculations that are easier to process positively improve brand evaluations and product promotions ([42]). This finding suggests that attribute values presented on easier-to-evaluate bound scales would lead to greater influence on consumer judgments (although this has not been tested directly in prior work). In one recent study consistent with this view, [ 8] find that consumers make better (i.e., more accurate) judgments when they are estimating decreasing quantities of food (on a scale bound by two endpoints: zero and a maximum possible quantity) as opposed to increasing quantities of food (on a scale bound by only one endpoint, minimum possible quantity, and unbound by infinity on the other). Prior work on evaluability discussed previously ([33]) can also be viewed through the lens of bound and unbound scales. The number of words in a dictionary is more difficult to evaluate because it does not have a well-defined endpoint, whereas the dictionary's condition has implicit endpoints (e.g., "perfect" vs. "completely destroyed"). Thus, the findings that the dictionary's condition is easier to evaluate and does not require an additional anchor (in the form of joint evaluation), as compared with the number of words in the dictionary, supports our view that attributes expressed on bound scales are easier to evaluate and would be judged as more diagnostic than attributes expressed on unbound scales.
Building on these literature streams, we propose that the diagnosticity of average product ratings as a signal of product quality is higher than that of the number of reviews. Furthermore, similar to prior work on attribute diagnosticity ([63]), we argue that whether the number of reviews is incorporated into decisions depends on the valence of a more diagnostic cue (i.e., average product ratings). When making a buying decision, consumers are motivated to avoid choosing an inferior option to avoid postdecisional regret ([77]; [84]). This anticipated regret from making an incorrect (i.e., suboptimal) decision often drives consumers to engage in a more comprehensive assessment of the choice options ([ 4]). Similarly, research on the negativity bias ([ 3]; [38]; [69]) suggests that consumers attend to and elaborate more on information about a judgment in the presence of negative information. Thus, we argue that the diagnosticity of the number of reviews is likely to increase when average product ratings contain negative information (e.g., negative or neutral average product ratings) relative to when they contain positive information (e.g., positive average product ratings).
How might consumers use the number of reviews in their judgments under these conditions? We believe that the difference in the number of reviews between two choice options would appear more diagnostic when the level of review numbers in a choice set is low relative to high. Holding the absolute difference constant in the number of reviews between choice options, the difference between the number of reviews would appear relatively larger in choice sets with fewer (vs. more) reviews. Prior work has demonstrated that people attend more to relative differences than absolute differences in values ([76]; [78]). For example, people are willing to exert more effort to save $5 on a $15 purchase than on a $125 purchase. This happens because of a steeper slope for smaller values and a shallower slope for larger values of the utility function, as argued by the prospect theory ([40]; [78]). Thus, the same absolute difference in numbers (e.g., 10) would loom larger when the numbers being compared are low (e.g., 20 vs. 30) versus high (e.g., 200 vs. 210). Applying this sensitivity in relative differences to the context of the number of reviews, we would expect that, holding the absolute difference in the number of reviews between choice options constant, having a choice set with a low (vs. high) level of reviews would increase diagnosticity of the review numbers attribute.
The proposed change in perceived diagnosticity of the number of reviews (relative to average product ratings) has direct implications for consumer preferences. In a choice set in which one option has a higher rating but fewer reviews and another option has a lower rating but more reviews, an increase in the perceived diagnosticity of the number of reviews would lead to a weaker preference for the higher-rated option. This occurs because when the level of review numbers is low (vs. high), the diagnosticity of the number of reviews increases, leading to joint influence of average product ratings and the number of reviews. As such, when the higher-rated choice option has fewer reviews, it is perceived as superior on one quality attribute and inferior on another quality attribute, thus weakening preference for it. However, this effect of a low level of review numbers on preferences would be attenuated when the average product ratings of both choice options are high, as most consumers will engage in less elaborative decision-making and will be less likely to incorporate the number of reviews into their decision.
Formally, we propose that, given a choice set in which consumers face a trade-off between average product ratings and the number of reviews,
- H1: Preference for a higher-rated choice option with fewer reviews is weaker when the level of review numbers is low (vs. high).
- H2: The influence of the number of reviews on preference for a higher-rated choice option with fewer reviews is attenuated when average product rating is positive (vs. neutral or negative).
- H3: Attribute diagnosticity of the number of reviews (relative to average product ratings) is the highest when the level of review numbers is low (vs. high), and this increase in diagnosticity influences preference between choice options.
We test our predictions in a series of seven studies. Study 1 demonstrates the systematic shift in preference between choice options as a function of the level of review numbers, providing support for H1. Study 2 generalizes this finding by using an expanded choice set while also demonstrating the impact of the number of reviews on choice and choice deferral. Study 3 then tests H2 by examining the interaction of the level of the average product ratings' valence and the level of review numbers on preference between options. Study 4 demonstrates that a large ratings difference between choice options only increases diagnosticity of the average product ratings when the level of review numbers is high versus low. Study 5 investigates how the diagnosticity of average product ratings is increased when one product rating rests at the scale boundary (e.g., 1.0, 5.0). Finally, Studies 6 and 7 test H3's assertion that the difference in the diagnosticity of average product ratings and number of reviews drives the effect of the level of review numbers on preference, using an evaluative measure of attribute diagnosticity (self-reported attribute weights in Study 6) and an attention measure of diagnosticity (eye tracking in Study 7).
In every study, participants were asked to imagine that they were considering the purchase of a new product and had narrowed their choice set to two comparable choice options (four choice options in Study 2). Participants then saw both choice options side by side, with information about the brand name, price, average product rating, and the number of reviews for each choice option presented beneath the product images (for an example, see Web Appendix W3). In each choice set (except for Study 2), choice option A always had a higher average product rating with fewer reviews, and choice option B had a lower average product rating with more reviews. Other product attributes were not significantly different.
Specific values of average product ratings and the numbers of reviews varied between studies and between choice sets within the studies to extend the generalizability of our results (see Table 1). We chose the numbers of reviews by selecting values at the lower and upper limits of the perceived average number of reviews based on a pretest (N = 182) in which we had participants classify various numbers of reviews along a continuum from 1 = "Far fewer than average," and 7 = "Far more than average." In general, consumers found 74 reviews to be about average, independent of any other information (e.g., product category, website).
Graph
Table 1. Design and Measures Summary.
| Product | Option | Average Product Ratings | Review Numbers | Reported Measures |
|---|
| (1.x) | (2.x) | (3.x) | (4.x) | (5.x) | Low | Moderate | Moderately High | High | Absent | Relative Preference | Absolute Choice | Choice Deferral | Process Measures |
|---|
| Study 1 | | | | | | | | | | | | | | | |
| Over-the-ear headphones | A | — | — | .5 | — | — | 8 | 72 | 201 | 456 | N.A. | x | — | — | — |
| B | — | — | .3 | — | — | 64 | 128 | 257 | 512 | N.A. |
| Coffee makers | A | — | — | .4 | — | — | 6 | 64 | 180 | 412 | N.A. | x | — | — | — |
| B | — | — | .0 | — | — | 58 | 116 | 232 | 464 | N.A. |
| Microwaves | A | — | — | .5 | — | — | 9 | 71 | 195 | 443 | N.A. | x | — | — | — |
| B | — | — | .2 | — | — | 62 | 124 | 248 | 496 | N.A. |
| Speaker systems | A | — | — | .4 | — | — | 12 | 86 | 234 | 530 | N.A. | x | — | — | — |
| B | — | — | .1 | — | — | 74 | 148 | 296 | 592 | N.A. |
| Lounge chairs | A | — | — | .7 | — | — | 5 | 103 | 299 | 691 | N.A. | x | — | — | — |
| B | — | — | .4 | — | — | 98 | 196 | 392 | 784 | N.A. |
| Study 2 | | | | | | | | | | | | | | | |
| Camping lamps | A | — | — | .2 | — | — | 61 | — | — | 361 | N.A. | — | x | x | — |
| B | — | — | .6 | — | — | 22 | — | — | 322 | N.A. |
| C | — | — | .8 | — | — | 5 | — | — | 305 | N.A. |
| D | — | — | .4 | — | — | 43 | — | — | 343 | N.A. |
| Study 3 | | | | | | | | | | | | | | | |
| Blenders | A | — | .4 | .4 | .4 | — | 8 | — | — | 408 | — | x | — | — | — |
| B | — | .1 | .1 | .1 | — | 64 | — | — | 464 | — |
| Study 4 | | | | | | | | | | | | | | | |
| Earbud headphones | A | — | — | .8,.6,.4,.2 | .8,.6,.4,.2 | — | 9 | — | — | 409 | — | x | — | — | — |
| B | — | — | .6,.4,.2,.0 | .6,.4,.2,.0 | — | 57 | — | — | 457 | — |
| Study 5 | | | | | | | | | | | | | | | |
| Hand mixers | A | .3 (.4) | — | — | (.9) | .0 | 13 | — | — | 313 | — | x | — | — | — |
| B | .0 (.1) | — | — | .7, (.6) | — | 34 | — | — | 334 | — |
| Study 6 | | | | | | | | | | | | | | | |
| Blenders | A | — | — | .4 | — | — | 8 | — | — | 408 | — | — | x | x | x |
| B | — | — | .1 | — | — | 64 | — | — | 464 | — |
| Study 7 | | | | | | | | | | | | | | | |
| Microwaves | A | — | — | .5 | — | — | 9 | — | — | 443 | — | x | — | — | x |
| B | — | — | .2 | — | — | 62 | — | — | 496 | — |
10022242918805468 Notes: For Study 4, all pairwise comparisons were used for average product ratings where A was greater than B, resulting in ten comparisons. For Study 5, numbers in parentheses were paired together in their respective negative/positive conditions.
After viewing each choice set, participants were asked to indicate their relative preference between choice options on a seven-point scale (1 = "Strongly prefer option A," and 7 = "Strongly prefer option B"; except for Studies 2 and 6, in which we used choice as a dependent measure). This measure anchored preference for the higher-rated choice option with fewer reviews at 1 and the lower-rated choice option with more reviews at 7. As such, a higher number on this measure would indicate a weaker preference for the higher-rated choice option with fewer reviews. For a summary of results across studies, see Table 2.
Graph
Table 2. Means Summary Across Studies.
| Average Product Rating | Level of Review Numbers |
|---|
| Valence | Difference | Low | Moderately Low | Moderately High | High | Absent |
|---|
| Study 1 (n = 250) | Neutral | — | 3.96 | 2.93 | 2.83 | 2.63 | 2.81 |
| Study 2 (n = 144) | Neutral | — | 49 | — | — | 78 | 76 |
| Study 3 (n = 433) | Negative | — | 4.15 | — | — | 3.32 | — |
| Neutral | — | 4.38 | — | — | 3.38 | — |
| Positive | — | 3.04 | — | — | 2.97 | — |
| Study 4 (n = 705) | Neutral | — | 3.98 | — | — | 2.77 | — |
| Positive | — | 3.32 | — | — | 2.66 | — |
| — | Small | 3.62 | — | — | 2.98 | — |
| — | Large | 3.68 | — | — | 2.45 | — |
| Study 5 (n = 410) | Negative | — | 3.58 | — | — | 3.67 | — |
| Negative (extreme) | — | 3.84 | — | — | 3.44 | — |
| Positive | — | 4.08 | — | — | 2.71 | — |
| Positive (extreme) | — | 3.48 | — | — | 2.96 | — |
| Study 6 (n = 143) | Neutral | — | 36 | — | — | 61 | 61 |
| Study 7 (n = 92) | Neutral | — | 4.09 | — | — | 3.68 | — |
20022242918805468 Notes: Studies 1, 3, 4, 5, and 7 use a relative preference measure (1 = "Strongly prefer higher-rated, fewer reviews option," and 7 = "Strongly prefer lower-rated, more reviews option"). Studies 2 and 6 use an absolute choice measure in which the number reported is participants' percentage choosing the higher-rated option with fewer reviews.
The purpose of this study was to test the systematic shift in preference as a function of the level of review numbers outlined in H1. We argue that preference for the higher-rated options with fewer reviews is weaker when the level of review numbers in a choice set is low relative to high or absent. To test this assertion, we examine four increasing levels of review numbers while keeping average product ratings constant across conditions. We also include a fifth condition in which the number of reviews is absent from the information provided to participants. This provides a condition in which preference is based only on average product ratings; furthermore, by comparing this condition with the others, we can conduct an initial test of the dynamic diagnosticity of the review attributes (H3). Finally, to demonstrate the robustness of this effect, we replicate it across five product categories in which the brands, prices, average product ratings, and numbers of reviews all slightly vary for each product to avoid any demand effects from specific values.
We recruited 250 participants (Mage = 31.35 years; 31% female) from Amazon Mechanical Turk (MTurk) in exchange for $.50 and randomly assigned them to one of five levels of review numbers: low (8 vs. 64 reviews), moderate (72 vs. 128 reviews), moderately high (201 vs. 257 reviews), high (456 vs. 512 reviews), or control (i.e., the review numbers were absent), in a between-subjects design. Within-subject, each participant viewed five product choice sets.
For each of the five choice sets (headphones, microwaves, coffee makers, speaker systems, and lounge chairs), participants viewed two products that were nearly identical, with the exception of their average product ratings and number of reviews (see Table 1).
After viewing each choice set, participants were instructed to indicate their preference between the options on a seven-point scale (1 = "Strongly prefer option A" [the higher-rated option with fewer reviews], and 7 = "Strongly prefer option B" [the lower-rated option with more reviews]). Thus, a higher score on this scale indicated weaker preference for the higher-rated option with fewer reviews.
A 5 (level of review numbers: low, moderate, moderately high, high, control) × 5 (product category: headphones, microwaves, coffee makers, speaker systems, lounge chairs) repeated-measures analysis of variance (ANOVA) on preference yielded significant main effects of the level of review numbers (F( 4, 246) = 11.45, p <.001) and product category[ 7] (F( 1, 246) = 18.54, p <.001). The interaction was not significant (p >.10). Because of this, we collapsed across the product category factor to simplify the reporting of results (see Figure 1), though the same directional pattern held for all products. In support of H1, planned contrasts demonstrated that preference for the choice option with a higher rating and fewer reviews was significantly weaker in the low review numbers condition (Mlow = 3.96) compared with all other conditions (Mmoderate = 2.93; t(246) = −4.74, p <.001; Mm-high = 2.83; t(246) = −5.12, p <.001; Mhigh = 2.63; t(246) = −6.15, p <.001; Mcontrol = 2.81; t(246) = −5.27, p <.001). Importantly, not displaying the number of reviews led to no significant difference in preferences relative to when the level of review numbers was high (Mhigh = 2.63, Mcontrol = 2.81; p >.10). This is consistent with our prediction that the number of reviews is less diagnostic and, therefore, less likely to influence preferences relative to average product ratings when the level of review numbers is high (H3).
Graph: Figure 1. Study 1 results: Preference across five levels of review numbers.
Study 1 demonstrated that consumers' preference for the option with higher ratings and fewer reviews is weaker when the level of review numbers is low versus high or absent, consistent with H1. We replicated the effect in a follow-up consequential choice study, in which participants were entered into a raffle to receive their preferred product option (a blender). Using the low and high levels of review numbers from the main study, we observed the same shift in preference away from the higher-rated option with fewer reviews as the level of review numbers decreased (Mlow = 4.68, Mhigh = 3.08; F( 1,104) = 19.10, p <.001), which is consistent with H1.
To test the robustness of this effect, we also examined several potential boundary conditions to the effect of number of reviews on consumer preference (full descriptions of each study are available in the Web Appendix). A large body of work on numerosity ([ 7]; [44]; [60]) makes a different prediction from the one we demonstrate in Study 1. This line of research shows that, in choice options, keeping relative differences between attribute values constant while expanding the scale in which the values are presented (e.g., a warranty described in months vs. years)—thus changing the absolute difference—causes differences between attribute values to appear exaggerated on an expanded scale (e.g., warranty described in months). By contrast, in Study 1 we keep the absolute difference between choice options constant while changing the relative difference between attribute values by changing the scale. We believe that both effects might exist in the context of product reviews, but it is unclear a priori which has a stronger effect on consumer judgment because prior work on numerosity has not studied its effects in the context of multiple numerical attributes, as we do. Indeed, in an additional study (Web Appendix W4), we compare the influence of an increase in relative difference between choice options on the review numbers attribute (as in our main studies) with an increase in an absolute difference between choice options on the review numbers attribute (as demonstrated by prior work). This study provides initial evidence that both effects exist at varying degrees of strength.
In another study (Web Appendix W5), we examine whether aggregate review information would be equally diagnostic in all product categories. Specifically, we examine products with primarily aesthetic or taste value. Prior literature has suggested that consumer responses to reviews differ as a function of the consumer's self-expression goals and whether a product is being evaluated on the basis of taste versus quality ([30]; [68]). Consistent with this literature, in this study, we demonstrate that aggregate review attributes are less diagnostic when consumers make preferences on the basis of taste or self-expression concerns (e.g., artwork).
Finally, in another study (Web Appendix W6), we examine the role of single- versus joint-option evaluations (i.e., viewing option A and then viewing option B vs. viewing both options simultaneously). Prior literature investigating this aspect of choice has demonstrated that joint evaluation increases the diagnosticity of difficult-to-evaluate attributes ([33]) and attenuates the effects of numerosity ([70]). In our research, this would suggest that diagnosticity of review numbers (a more-difficult-to evaluate attribute) could be further attenuated under single-evaluation conditions. We test this proposition in Web Appendix W6 and find that our effect holds for both joint and single evaluations of choice options. Taken together, these studies demonstrate robustness of the effect of number of reviews on preferences.
Study 2 is designed to demonstrate that the effect of the level of review numbers on preferences is robust for larger choice sets. Importantly, it also examines whether the effect of the level of review numbers has an impact on discrete option choice and consumer likelihood to defer choice. Prior research has shown that large choice sets increase the use of noncompensatory decision strategies ([39]; [61]), such that consumers are more likely to choose options that are superior on one of the most important or easiest-to-differentiate attributes rather than incorporating multiple attributes. In the context of this research, we argue that when the level of review numbers is low (vs. high), the diagnosticities of average product ratings and number of reviews are more similar. As such, although consumers are more likely to use a noncompensatory strategy in the multiple–choice option context, the likelihood of consumers using the number of reviews as a diagnostic cue increases. When the level of review numbers is high, most consumers will use average product ratings as their primary diagnostic cue, leading them to choose the highest-rated choice option. In contrast, when the level of review numbers is low, both attributes appear diagnostic, and consumers are less likely to use average product ratings as their primary diagnostic cue, leading to weaker preference for the highest-rated choice option. Thus, we expect a similar pattern of preferences to emerge as we observed in Study 1, in which participants will be less likely to choose the highest-rated option with the fewest reviews when the level of review numbers is low relative to high or absent, even when choosing from a choice set with several options.
In addition, this study tests the effect of the level of review numbers on choice deferral, to provide additional support for H3. We argue that the relative diagnosticity of average product ratings and the review numbers is more similar when the level of review numbers is low (vs. high or absent). The need to make trade-offs between attributes of similar importance increases choice difficulty ([ 9]; [21]), which makes choice deferral more likely ([79]; [19]; [23]). Thus, we expect the rate of choice deferral to be the highest when the level of review numbers is low, where the trade-off between the average product ratings and the review numbers are more salient compared with the conditions where level of review numbers is high or when the review numbers are absent.
We randomly assigned 144 undergraduate students (Mage = 20.91 years; 50% female) to one of three levels of review numbers (low, high, control), in a between-subjects design. In exchange for participating in the study, the students received course credit.
Participants viewed a choice set of four camping lamps. The options were nearly identical except for their average product ratings and number of reviews. While one choice option had the highest rating with the fewest reviews (3.8, 5 reviews), another choice option had the lowest rating with the most reviews (3.2, 61 reviews), and two choice options in the middle were compromise choice options that were neither the highest nor lowest on either attribute but were superior on one relative to the other compromise choice option (3.4, 43 reviews vs. 3.6, 22 reviews) (see Table 1). We manipulated the level of review numbers by either withholding the number of reviews in the control condition or adding 300 reviews to the numbers reported previously to generate the high level of review numbers.
To capture preference among the four choice options, we used a discrete choice measure of the highest-rated choice option with the fewest reviews rather than the relative preference measure used in the previous study. Next, to assess the likelihood of choice deferral, we asked participants, "Are you more likely to purchase one of the available options or defer purchase and look elsewhere?" and analyzed this as a binary measure. Finally, to assess the need for more information, participants were asked, "How would you classify the amount of information provided?" on a seven-point scale (1 = "not enough information," and 7 = "too much information"). A more difficult trade-off would require more information to help participants make a decision; thus, participants in the condition with a low level of review numbers would be expected to require more information relative to those in the other conditions.
A binary logistic regression in which we dummy-coded the level of review numbers yielded an omnibus effect of the level of review numbers (χ2( 2) = 10.84, p =.004). Consistent with H1, when the level of review numbers was low, participants were significantly less likely to choose the highest-rated option with the fewest reviews (Plow = 49%) relative to when the level of review numbers was high (Phigh = 78%; χ2( 1) = 7.09, p =.004) or absent (Pcontrol = 76%; χ2( 1) = 8.24, p =.008). There was no significant difference in choice in the high and control conditions (p >.80).
A binary logistic regression in which we dummy-coded the level of review numbers yielded an omnibus effect of the level of review numbers (χ2( 2) = 6.73, p =.035). When the level of review numbers was high (Phigh = 53%; χ2( 1) = 4.30, p =.038) or absent (Pcontrol = 49%; χ2( 1) = 6.02, p =.014) participants were significantly less likely to defer choice relative to when the level of review numbers was low (Plow = 73%). There was no significant difference between high and control conditions (p >.70). A higher rate of choice deferral under the low level of review numbers is consistent with prior work linking choice difficulty with choice deferral ([19]; [23]; [80]). Consistent with our theorizing, when the level of review numbers is low (relative to high or absent), the diagnosticity of the number of reviews increases (H3), creating a more difficult choice involving the trade-offs, ultimately increasing choice deferral.
A one-way ANOVA of the level of review numbers on the need for additional information yielded a marginal effect of the level of review numbers of reviews (F( 2,144) = 2.98, p =.054). Planned contrasts further demonstrated that participants who encountered a high level of review numbers indicated that they had significantly more information than those who had encountered a low level of review numbers (Mhigh = 3.37, Mlow = 2.73; t(144) = 2.43, p =.016), which is consistent with our prediction. Participants who did not see the review numbers were not significantly different from those of the other groups (Mcontrol = 3.00, p >.15). Although we expected the low level of review numbers to increase the need for additional information, we were surprised to find that when the level of review numbers was absent, participants felt no greater need for additional information than when the level of review numbers was high. This result suggests that withholding the number of reviews from the list of attributes would not have a negative impact on consumers' perceptions of the amount of information they are provided during a decision.
Study 2 provided additional evidence for the effect of the level of review numbers by demonstrating that a low level of review numbers shifts preference away from the higher-rated choice options relative to a high level of review numbers or no review numbers at all, which is consistent with H1. We demonstrate this effect in the context of an expanded choice set while also using a discrete choice measure (rather than the relative preference measure used in Study 1). Furthermore, this study provided additional evidence in support of H3 by demonstrating that the choice deferral rate is highest when the level of review numbers is low relative to high or absent. In addition, the need for additional information was greatest when the level of review numbers was low, suggesting that the trade-off between average product ratings and the number of reviews was most difficult because of the similarity in the diagnosticity of the two attributes in this condition. This is, again, consistent with H3.
Study 3's objective is to test H2. Using relatively neutral average product ratings (e.g., 3.0–3.8), our initial studies demonstrated that consumers are more likely to use both average product ratings and the number of reviews in their choices when the level of review numbers is low. H2 suggests that the effect of the level of review numbers on consumer preferences can be attenuated when consumers choose between positively rated products. We argue that this occurs because positively rated products can reduce consumers' need to elaborate on additional information, thereby increasing the diagnosticity of average product ratings relative to the number of reviews. To test this theory, in this study, we manipulate the valence of average product ratings while holding the ratings difference between options constant.
We recruited 433 undergraduate students (Mage = 20.28 years; 46% female) to participate in this study in exchange for course credit. We then randomly assigned them to a condition in a 2 (level of review numbers: low, high) × 3 (ratings valence levels: negative, neutral, positive) between-subjects design.
Participants saw a choice set of two blenders. Choice options were nearly identical with the exception of their average product ratings and the number of reviews (see Table 1). In the low (high) level of review numbers condition, participants chose between 8 (408) and 64 (464) reviews, respectively. We manipulated ratings valence levels by changing the first digit of the average product ratings for both choice options. Thus, the negative condition presented consumers with 2.x choice options, the neutral condition presented 3.x choice options, and the positive condition presented 4.x choice options. After viewing the choice set, participants indicated relative preference on the same seven-point scale used in Study 1.
A 2 (level of review numbers: low, high) × 3 (ratings valence levels: negative, neutral, positive) ANOVA on preference yielded main effects of the level of review numbers (F( 1, 427) = 17.26, p <.001) and the ratings valence level (F( 2, 427) = 12.68, p <.001), qualified by the predicted interaction (F( 2, 427) = 3.58, p =.029, see Figure 2). Replicating prior studies, in the neutral ratings valence level conditions, preference for the higher-rated option with fewer reviews was weaker when the level of review numbers was low versus high (Mlow = 4.38, Mhigh = 3.38; F( 1, 427) = 14.28, p <.001). As we predicted in H1, a similar effect was present in the negative ratings valence condition (Mlow = 4.15, Mhigh = 3.32; F( 1, 427) = 9.77, p =.002). Furthermore, as we predicted in H2, when the ratings valence level was positive, the effect of the level of review numbers on preference was attenuated (Mlow = 3.04, Mhigh = 2.97; F( 1, 427) =.07, p >.75). As we expected, the effect of the level of review numbers on preference was attenuated when the more diagnostic cue (i.e., average product ratings) was positive.
Graph: Figure 2. Study 3 results: Preference by average product ratings valence and the level of review numbers.
This study provided support for H2 by demonstrating that the effect of the level of review numbers on preference between choice options is attenuated when the choice set features only positive average product ratings. Furthermore, consistent with H1, when consumers encountered non-positive product ratings, preference between choice options was affected by the level of review numbers, thus replicating the results of Studies 1 and 2.
Study 3 demonstrated the moderating role of average product ratings valences, but we also investigated several other potential moderators, reported fully in the Web Appendix. In one study (Web Appendix W7), we examine whether more disaggregated information (i.e., ratings distributions that display individual product ratings) moderates the effect of the level of review numbers on preference. Some literature investigating the role of a ratings distribution has demonstrated a significant effect of skew on product evaluations ([25]; [41]). This work largely differs from ours in that it explores only single-option choice sets. In the context of multi-option choice sets, we argue that it is more difficult to compare and interpret different ratings distributions relative to the aggregate review attributes (i.e., average product ratings and review volumes). This is because the ratings distribution is a more complex-to-process attribute with several values, one for each rating. Thus, we argue and demonstrate that adding a ratings distribution to a multi-option choice sets does not change the effect of the level of review numbers on consumer preferences. We tested this effect using several different distributions (e.g., positively, negatively, and even-skewed), and our findings were robust.
In another study (Web Appendix W8), we examined whether a popularity cue can attenuate the effect of the level of review numbers on preferences. Prior research has suggested that higher review numbers can signal greater popularity, which in turn increases their trustworthiness ([12]; [87]). If the review numbers are perceived solely as a signal of product popularity, their effect should be attenuated in the presence of an alternative popularity cue. In this study, we test the effect of labeling one of the choice options as a "Best Seller" and demonstrate that this does not attenuate the effect of the level of review numbers.
Furthermore, in the study reported in Web Appendix W9, we tested credible reviews as another moderator. Some may argue that low review numbers contain more risk relative to high review numbers because of a higher likelihood of fraudulent reviews affecting average product ratings ([46]; [50]). Thus, one might conclude that if consumers could be certain of the veracity of reviews for the higher-rated choice option with fewer reviews, the effect of the level of review numbers may be attenuated. We test this proposition by labeling the higher-rated option with fewer reviews as "Consumer Reports Verified" and demonstrate again that this label does not attenuate the effect of the level of review numbers.
Finally, in studies reported in Web Appendices W10 and W11, we tested whether the influence of the level of review numbers can be attenuated by providing consumers with a justification of why a product in a choice set might have fewer reviews (for reasons unrelated to product quality). In Web Appendix W10, we examine the role of production years of the goods. One reason that some products have more reviews than others is simply because they have been on the market longer. While this may be a positive signal for some products, for tech products, this signals outdated technology. However, we argue that the effect of review numbers is so strong, it leads consumers to discount the inferiority of older technology if the product has more reviews. We demonstrate that consumers are more apt to choose older products with more reviews (e.g., a 2013 DVD player, Galaxy S6) relative to newer products with fewer reviews (e.g., a 2015 DVD player, Galaxy S7) when the level of review numbers is low versus high. As such, it is quite likely that review numbers may bias consumers' decisions, leading to suboptimal outcomes.
Following the same logic as in the prior study, in Web Appendix W11, we examine the role of a "new arrival" label on the effect of the level of review numbers. Because new products have been on the market for a shorter period than other products, their fewer reviews should be justified. In theory, consumers should be more accepting of "new arrivals" with low review numbers relative to options that do not feature this label, as their low review number may not be justified. However, it is difficult to quantify the diagnostic value of a "new arrival" label and arrive at a proper discount rate for the review numbers relative to simply comparing the review numbers of competing products. As such, we theorize and demonstrate that the effect of the level of review numbers persists in the presence of a "new arrival" label. The studies presented in Web Appendices W7–W11 address several potential moderators identified in prior work, thus demonstrating that the interactive effect of the number of reviews and average product ratings does not appear to be affected by these factors.
Study 4 accomplishes two goals. First, it provides an additional test of H1 and H2 by examining the effect of the level of review numbers at different valences of average product ratings. We expect to replicate the findings of Study 3, in which the effect of a low level of review numbers is attenuated when average product ratings are positive. Second, it investigates the role of the magnitude of difference between average product ratings in the joint influence of review numbers and ratings valence on preference.[ 8] Building on prior work that argues that the difference in attribute values between choice options can increase the salience of this attribute in decision making ([54]; [67]; [76]), we would expect that an increase in magnitude of difference between average product ratings of the options in the choice set would increase preference for the higher-rated product. However, consistent with H3, we believe this effect will be more pronounced when the diagnosticity of average product ratings is high. Thus, an increase in the difference between average product ratings in the choice set is more salient to consumers when both the level of review numbers and the diagnosticity of average product ratings are high than when the number of reviews is low and consumers place relatively less weight on the average product ratings. Examining the influence of the magnitude of a ratings difference between choice options at different levels of review numbers provides another test of H3.
In our prior studies, we used relatively small differences between average product ratings (e.g.,.2–.4); in this study, we expand the magnitude of difference to include.6 and.8. Specifically, we created choice sets with differences between choice options of magnitude of.2 (e.g., 3.8 vs. 3.6),.4 (e.g., 3.8 vs. 3.4),.6 (e.g., 3.8 vs. 3.2), and.8 (e.g., 3.8 vs. 3.0). We did so across two different valences of average product ratings: neutral and positive (e.g., 3.x or 4.x). This resulted in a necessarily unbalanced design because there are more differences of.2 within a level than.8; however, because there was no significant difference in effect within a given distance (for example, a difference of 4.2 and 4.4 led to similar preference as a choice set that had 4.4 and 4.6), we collapsed across those cells for the analysis. Thus, we collapsed across all small difference (.2–.4) and large difference (.6–.8) conditions to generalize our findings across relatively small and large differences in average product ratings.[ 9]
We recruited 705 people from MTurk (Mage = 35.61 years; 47% female) to participate in the study in exchange for $.50. We randomly assigned them to a condition in a 2 (level of review numbers: low, high) × 2 (ratings valence levels: neutral, positive) × 2 (ratings difference size: small, large) between-subjects design.
Participants saw a choice set of two headphones. Choice options were nearly identical except for their average product ratings and number of reviews, as described previously (see Table 1). Relative preference was measured on the same seven-point scale as in Studies 1 and 3.
A 2 (level of review numbers: low, high) × 2 (ratings valence levels: neutral, positive) × 2 (ratings difference size: small, large) ANOVA on preference yielded significant main effects of the level of review numbers (F( 1, 697) = 40.66, p <.001) and ratings valence levels (F( 1, 697) = 6.70, p =.01), qualified by the interaction of the level of review numbers and ratings difference size (F( 1, 697) = 4.12, p =.043) and a marginal interaction of the level of review numbers and ratings valence levels (F( 1, 697) = 3.47, p =.063). The main effect of the level of review numbers demonstrated that preference for the higher-rated option with fewer reviews was greater when the level of review numbers was high (Mlow = 3.63, Mhigh = 2.82), which is consistent with prior studies. The main effect of valence demonstrated that preference for the higher-rated option with fewer reviews was greater when valence was positive relative to neutral (Mneutral = 3.37, Mpositive = 3.08), consistent with Study 3.
In line with our expectations, the level of review numbers by ratings difference size interaction indicated that the effect of the ratings difference size on consumer preferences depended on the level of review numbers (see Figure 3, Panel A). When the level of review numbers was low, consumers were less sensitive to the ratings difference size between options (Msmall = 3.62, Mlarge = 3.68; p >.85), suggesting that any ratings difference was considered a trade-off with the number of reviews. This is consistent with H3, which proposes an increase in relative diagnosticity of the number of reviews when the level of the review numbers is low. By contrast, when the level of the review numbers was high, participants were more sensitive to the ratings difference size between options, thus increasing preference for the higher-rated choice option when the ratings difference size was large versus small (Mlarge = 2.45, Msmall = 2.98; F( 1, 701) = 6.72, p =.01). This finding is also consistent with H3, which proposes that the relative diagnosticity of average product ratings is greater when the level of review numbers is high (vs. low), thus increasing sensitivity to any difference in average product ratings.
The ratings valence levels by level of review numbers is consistent with the findings of Study 3 (see Figure 3, Panel B). Although a low level of review numbers decreased preference for the higher-rated choice option with fewer reviews, the magnitude of this effect was larger in the neutral condition relative to the positive valence condition, consistent with H2 (neutral valence: Mlow = 3.98, Mhigh = 2.77; F( 1, 701) = 37.74, p <.001; positive valence: Mlow = 3.32, Mhigh = 2.66; F( 1, 701) = 19.61, p =.012). Note that the positive valence attenuated, rather than completely eliminated, the effect of the level of review numbers on preference (as we found in Study 3). While this nominal difference could be a result of the specific stimuli used for each study, both studies demonstrate an attenuation of the influence of the level of review numbers, which is consistent with our prediction, albeit to varying strengths.
Graph: Figure 3. Study 4 results.
Study 4 replicates the findings in Study 3 by demonstrating that positive valences attenuate the influence of the level of review numbers on consumer decisions, thus providing additional support for H2. Importantly, it also tests how the size of the difference in average product ratings between choice options changes consumer preference at different levels of review numbers. Consistent with the change in diagnosticity of attributes outlined in H3, our results demonstrate that the ratings difference size is more important to consumers when the level of review numbers is high (vs. low). This happens because under these conditions, the diagnosticity of the number of reviews is low (relative to the diagnosticity of average product ratings). By contrast, consumers are less sensitive to the ratings difference size between choice options when the diagnosticity of the number of reviews increases (e.g., when the level of review numbers is low). This finding underscores the robustness of the effect of the level of review numbers on consumer choices.
Study 5's objective is to provide additional tests of H1 and H2 by examining whether the influence of average product ratings' valence is enhanced at ratings scale boundaries. As we argued previously, ratings scales have defined boundaries (e.g., 1.0–5.0). Relative to unbound scales, bound scales make for easier comparisons of values, thus increasing the diagnosticity of average product ratings.
In this study, we investigate whether the ratings that exist at the endpoints of the scale increase the diagnostics of average product ratings relative to when ratings do not exist at the endpoints. We build this proposition on work by Isaac and Schindler (2013), which demonstrates that consumers often form mental boundaries of ranked lists around the numbers that end in zeroes (e.g., "top 10," "top 100"). Even if a list has more than 10 options, consumers will evaluate those within and outside of the top 10 differently. Because grouping options in this way affects the types of comparisons people make, as well as their final choices ([ 5]), the 10th option is perceived significantly more differently than adjacent options. Isaac and Schindler (2013) demonstrate this effect in the context of student rankings. Imagine students in a classroom who are ranked on performance. The 11th-ranked student is perceived to be significantly worse than the 10th-ranked student; however, there is no difference in evaluation of the 11th- and 12th-ranked students. The "top 10" effect could be explained within the confines of prospect theory ([80]), in which losses are shown to loom larger (i.e., be more important) than gains. Thus, if consumers' point of reference is the top 10, 10th place would meet their standard, while 11th place would be considered a significant loss. However, if the consumers are evaluating 11th and 12th places, both fall below their reference point of the top 10, thus diminishing sensitivity to the loss on this attribute and leading to attenuation of the importance of their rankings.
Similarly, in the context of our research, we expect that when the rating of one product in a choice set lies at the boundary (i.e., has a rating of 1.0 or 5.0), the diagnosticity of average product ratings increases, thus attenuating the influence of the number of reviews. For example, in a choice set featuring ratings of 5.0 versus 4.7, consumers will be more influenced by the ratings rather than by the number of reviews relative to when they view a choice set featuring 4.9 versus 4.6.
While one might expect the same effect to occur at the negative boundary of the scale, the effect is likely to be smaller (i.e., consistent with the Prospect Theory), such that a loss relative to the reference point of 5.0 is more significant than an equivalent gain relative to the reference point of 1.0. Although consumers are unlikely to choose between options with extremely negative ratings, this study extends the examination of review volume effects to the extreme negative boundary of the ratings scale to provide complete analysis of all possible levels of product ratings.
We recruited 410 people from MTurk (Mage = 37.88 years; 51% female) to participate in the study in exchange for $.50. They were randomly assigned to a condition in a 2 (level of review numbers: low, high) × 2 (ratings valence level: negative, positive) × 2 (scale boundary included: no, yes) between-subjects design.
Participants saw a choice set of two hand mixers. Choice options were nearly identical except for their average product ratings and number of reviews (see Table 1). We manipulated valence and scale boundary by the specific values used for average product ratings. The negative valence conditions were 1.3 versus 1.0 (boundary included) and 1.4 versus 1.1 (boundary not included). The positive valence conditions were 5.0 versus 4.7 (boundary included) and 4.9 versus 4.6 (boundary not included).
After viewing the choice options, participants indicated relative preference on the same seven-point scale used in previous studies.
A 2 (level of review numbers) × 2 (average product ratings valence) × 2 (scale boundary inclusion) ANOVA on relative preference between options yielded a significant main effect of valence (F( 1, 404) = 3.98, p =.047), a marginal main effect of the level of review numbers (F( 1, 404) = 3.12, p =.078), and an interaction between the level of the review numbers and scale boundary inclusion (F( 1, 404) = 4.47, p =.035), qualified by the three-way interaction (F( 1, 404) = 12.98, p <.001). To explain the relationship between these factors, we examine the two-way interactions between the level of review numbers and scale boundary inclusion at the negative and positive ends of the rating scale (see Figure 4).
Graph: Figure 4. Study 5 results: Preference by ratings valence, level of review numbers, and scale boundary inclusion.
At the positive end of the scale (4.6–5.0), a 2 (level of review numbers) × 2 (scale boundary inclusion) ANOVA on preference yielded a marginal effect of the level of review numbers (F( 1, 202) = 2.84, p =.093) qualified by a significant interaction of the level of review numbers and scale boundary inclusion (F( 1, 202) = 13.98, p <.001). When the scale boundary is not included (i.e., 4.9 vs. 4.6), a low level of review numbers weakens preference for the higher-rated option with fewer reviews (Mlow = 4.08, Mhigh = 2.71; F( 1, 202) = 14.86, p <.001), which is consistent with the results of our prior studies. By contrast, when the scale boundary is included (i.e., 5.0 vs. 4.7), the effect of the level of review numbers is attenuated (Mlow = 3.48, Mhigh = 2.96; F( 1, 202) = 2.09, p =.15). Consistent with [37] findings, this suggests that when products have perfect ratings (5.0), the ratings become significantly more influential in the decision process relative to when products have near-perfect ratings (4.9).
At the negative end of the scale (1.0–1.4), a 2 (level of review numbers) × 2 (scale boundary inclusion) ANOVA on preference yielded no significant effects (p >.20). Across all negative conditions, preference averaged 3.64, where four would indicate no preference between options. We did not predict this effect a priori, and we discuss it next.
This study demonstrated that the effect of the level of review numbers is attenuated when the diagnosticity of average product ratings is high as a result of the inclusion of the rating scale boundary in the positive-valence choice set. This finding provides additional support to H2. Furthermore, an interesting asymmetric valence effect emerged, such that, at the negative end of the scale, the effect of the level of review numbers was attenuated regardless of whether the scale boundary was included. This could be a function of consumers choosing between two unattractive options ([20]), which is known to increase difficulty of making a choice and likelihood of choice deferral. To further explore this point, in a follow-up study we compared the effect of the level of review numbers across very negative ratings (1.3 vs. 1.0) and somewhat negative ratings (2.4 vs. 2.1). Replicating the findings in Study 5, we found no effect of the level of review numbers in the very negative condition (Mlow = 3.51, Mhigh = 3.14; F( 1, 83) = 1.13, p =.29). Yet, consistent with our findings in Study 3, we replicated the effect of the level of review numbers in the somewhat negative condition (Mlow = 3.89, Mhigh = 3.16; F( 1, 85) = 3.75, p =.056). Furthermore, consistent with the view that choosing between two extremely unattractive options increases choice difficulty, potentially leading to the random choice between low-valence options in the main study, the rate of choice deferral was significantly higher in the very negative condition (45.9%) as compared with the somewhat negative condition (21.8%; χ2( 1) = 10.93, p =.001). This finding suggests that consumer decision processes differ in the context of extremely negative and somewhat negative choice sets.
Having established the robust effect of the level of review numbers on consumer decisions, we next shift our focus to directly measuring the relative diagnosticity of the average product ratings and number of reviews. H3 suggests that the preference between choice options is driven by the difference in diagnosticity of average product ratings and the number of reviews. When the level of review numbers is low (vs. high), the diagnosticity of the number of reviews increases relative to the diagnosticity of average product ratings. To test this proposition directly, in this study we ask participants how important each attribute was to their decision and compute the difference between the importance of average product ratings and the number of reviews to demonstrate the changing diagnosticity of these attributes between conditions.
We recruited 183 people from MTurk to participate in this study in exchange for $.50. We randomly assigned them to a condition with one of three levels of review numbers (low, high, control) in a between-subjects design.
Participants saw a choice set of two blenders. Choice options were nearly identical except for their average product ratings and number of reviews, as described previously (see Table 1). Similar to Study 2, we used discrete choice as our dependent measure. However, this time we integrated the choice and deferral measures into one, so participants were told that they could choose option A or option B; alternatively, they could "defer purchase and look elsewhere," as this more closely mirrors the consumer decision process. Unlike in our other studies, to test H3, we then asked participants to "indicate the importance of each attribute in making your decision" for the five attributes (image, brand, price, average product rating, and review volume) on seven-point scales (1 = "not at all important," and 7 = "extremely important"). As we expected, there were no significant differences across conditions of the perceived diagnosticity of product image, brand, or price (p >.10) because they were relatively comparable across products. Next, we computed a difference score of the review attribute diagnosticities (diagnosticity of average product ratings minus the diagnosticity of the number of reviews) to demonstrate the changing diagnosticities of the review attributes as a function of the level of review numbers. Thus, a positive score indicates that average product ratings are more diagnostic than the number of reviews and vice versa. As the difference score approaches zero, this indicates that consumers would equally weigh average product ratings and the number of reviews in their decisions.
Examining only participants who chose one of the two product options (N = 153), a binary logistic regression in which we dummy coded our level of review numbers yielded an omnibus effect of the level of review numbers (χ2( 2) = 15.07, p =.001). Consistent with our prior studies, when the level of review numbers was high (Phigh = 71%; χ2( 1) = 11.40, p =.001) or absent (Pcontrol = 71%; χ2( 1) = 11.83, p =.001) participants were significantly more likely to choose the higher-rated option with fewer reviews relative to when the level of review numbers was low (Plow = 36%). There was no significant difference in the high and control conditions (p >.95).
A binary logistic regression in which we dummy coded our level of review number yielded an omnibus effect of the level of review numbers (χ2( 2) = 9.42, p =.009). When the level of review numbers was high (Phigh = 13%; χ2( 1) = 4.64, p =.031) or absent (Pcontrol = 8%; χ2( 1) = 7.60, p =.006), participants were significantly less likely to defer choice relative to when the level of review numbers was low (Plow = 29%), replicating results of Study 3. There was no significant difference in choice deferral between the high and control conditions, consistent with our prior studies (p >.40).
A one-way (level of review numbers: low, high, control) ANOVA on the difference in diagnosticity of average product ratings and the number of reviews yielded a marginal omnibus effect (F( 2, 150) = 4.59, p =.061). Consistent with H3, the difference in perceived diagnosticity between the review attributes is smaller when the level of review numbers was low (Mlow =.26) relative to high (Mhigh =.85; t(150) = 2.28, p =.024) or absent (Mabsent =.75; t(150) = 1.89, p =.061). There was no significant difference between the absent and high level of review numbers (p >.65). In other words, average product ratings are considered significantly more diagnostic than the number of reviews when the level of review numbers is absent or high, relative to when the level of review numbers is low (i.e., when the two attributes are more equally diagnostic).
We used mediation analysis (Model 4; [62]) to demonstrate that the effect of the level of review numbers (low vs. high) on consumer preference is driven by the difference in the diagnosticity of average product ratings and number of reviews. As we expected, the model demonstrated that the effect of the level of review numbers on consumer preference was mediated through the difference in perceived diagnosticity of average product ratings and the number of reviews (B = −.49; CI95% = [−1.22, −.10]).
This study provided support for H3 by demonstrating that the effect of the level of review numbers on choice option preference was driven by the difference in diagnosticity of average product ratings and number of reviews: as the diagnosticity of the number of reviews increases (i.e., when the level of review numbers is low), the preference shifts away from the higher-rated option with fewer reviews toward the lower-rated option with more reviews. We further showed, consistent with H3, that average product ratings are considered more diagnostic than the number of reviews, but this difference in diagnosticity is attenuated when the level of review numbers is low. Finally, we demonstrated that as the diagnosticity of average product ratings and the number of reviews become more equal, choice deferral increases, which is consistent with the findings of Study 2 and prior work demonstrating the link between trade-off difficulty and choice deferral ([19]; [23]; [80]).
The objective of Study 7 was to further test H3 by demonstrating how the level of review numbers differentially affects consumers' attention to average product ratings and the number of reviews. An information uptake measure, such as eye movement, has been validated to provide insight into cognitive processes underlying choice ([ 1]; [59]). Specifically, research has shown that attention is often top-down driven and decision makers are more likely to attend to stimuli with higher task relevance ([59]). Thus, in this study we capture consumer attention (measured with eye tracking) to provide further evidence for the differential diagnosticity of average product ratings and the number of reviews in consumer decisions.
We have argued that consumers infer different diagnostic values of average product ratings and number of reviews as a function of the level of the review numbers (H3). Specifically, in choice sets with neutral and low average product ratings, when consumers see that the number of reviews is low, it signals to them that average product ratings may not be as diagnostic of product quality as when the number of reviews is high or when no review number information is displayed. Therefore, in terms of consumers' attention, when examining review attributes, we would expect that consumers would be more likely to reexamine average product ratings after viewing a low (vs. high) number of reviews. This happens because the diagnosticity of the number of reviews increases under these conditions (H3), leading consumers to reappraise the diagnosticity of the average product ratings in light of the information garnered from the number of reviews.
To test this argument, we use eye-tracking measurements to determine not only gaze times (i.e., time spent looking at) for each attribute but also the sequence of fixations (i.e., order looked at) for all attributes to determine whether consumers are more likely to reexamine average product ratings after viewing low versus high levels of review numbers.
Ninety-two undergraduate students participated in the study in exchange for course credit. They were randomly assigned to conditions with one of two levels of review numbers (low, high) in a between-subjects design. Participants were randomly selected two at a time from a larger sample of research participants to participate in the eye-tracking study. After engaging in a short eye-tracking calibration task, participants followed a similar paradigm to prior studies.
Participants saw a choice set of two microwave ovens. Choice options were nearly identical except for their average product ratings and number of reviews as described previously (see Table 1). Relative preference between choice options was measured on the same seven-point scale as in our previous studies.
We defined areas of interest as parts of the screen where corresponding product attributes were displayed and measured the number of eye fixations and gaze times for each attribute. Fixations refer to the frequency with which participants would look at a given attribute, and gaze times refer to the amount of time participants spent looking at the specific attributes. As we expected, there were no significant differences across conditions for fixations or gaze times of product images, brand names, prices, or highlighted information (p >.10); thus, we do not discuss these further.
A one-way (level of review numbers: low, high) ANOVA on preference yielded a significant effect (F( 1, 90) = 10.32, p =.002). Consistent with prior studies, preference for the higher-rated option with fewer reviews was weaker when the level of review numbers was low (Mlow = 4.89) versus high (Mhigh = 3.68).
To provide further support for this process, we also derive transition matrices from the eye-tracking data. Doing so enables us to demonstrate the probabilities of participants transitioning their attention from one attribute to the next. As we discussed previously, we argue that a low level (relative to a high level) of review numbers increases the diagnosticity of the number of reviews, and this causes consumers to reevaluate average product ratings. To demonstrate this, we assessed the differential probabilities of participants shifting their attention from the number of reviews to average product ratings as a function of the level of review numbers (for the complete transition matrices, see Table 3). Consistent with our theory, participants were significantly more likely to return their attention to the average product ratings after viewing the number of reviews when the level of review numbers was low relative to high (Plow =.24, Phigh =.13; z = 3.25, p <.01). This suggests that participants were nearly twice as likely to return their attention to average product ratings when the level of review numbers was low versus high. Importantly, the transition proportions from the number of reviews to all other attributes were not significantly different across conditions (p >.10).
Graph
Table 3. Study 7: Eye-Tracking Attribute Transition Matrices by Level of Review Numbers.
| To Image | To Brand and Price | To Rating | To Number of Reviews | To Additional Information | To End |
|---|
| A: Low Level of Reviews |
| From image | 68.23% | 14.47% | 4.51% | 2.26% | 6.02% | 4.51% |
| From brand and price | 8.94% | 72.81% | 12.23% | 2.37% | 2.92% | 0.73% |
| From rating | 3.50% | 10.07% | 62.58% | 19.69% | 3.72% | 0.44% |
| From volume | 2.65% | 3.41% | 24.24% | 49.62% | 18.94% | 1.14% |
| From additional information | 9.66% | 1.87% | 1.71% | 2.80% | 82.09% | 1.87% |
| B: High Level of Reviews |
| From image | 71.74% | 12.89% | 3.64% | 2.15% | 6.12% | 3.47% |
| From brand and price | 9.35% | 73.28% | 12.02% | 1.53% | 2.48% | 1.34% |
| From rating | 4.26% | 9.31% | 64.10% | 14.63% | 6.12% | 1.60% |
| From volume | 4.69% | 2.17% | 13.36% | 60.65% | 17.33% | 1.81% |
| From additional info | 8.20% | 1.64% | 1.37% | 4.37% | 83.47% | .96% |
Because our variable of interest is the difference in attention paid to average product ratings and the number of reviews, we calculated the difference in fixations between average product ratings and the number of reviews. A one-way (level of review numbers: low, high) ANOVA on the difference in fixation counts yielded a significant effect (F( 1, 90) = 7.05, p =.009). As we expected, the difference in fixations between average product ratings and the number of reviews was greater when the level of review numbers was low (Mlow = 4.29 fixations) relative to high (Mhigh = 2.11 fixations). This is consistent with our view that a low level of review numbers causes consumers to reevaluate the average product ratings attribute, thus increasing overall attention paid to that attribute.
A one-way (level of review numbers: low, high) ANOVA on the difference in gaze times yielded a significant effect (F( 1, 90) = 10.59, p =.002). As we expected, the difference in gaze times between average product ratings and the number of reviews was greater when the level of review numbers was low (Mlow = 12.40 seconds) relative to high (Mhigh = 5.78 seconds). Consistent with our prior findings, consumers seem to pay more attention to average product ratings when the level of review numbers is low, and we argue that this occurs because a low number of reviews causes consumers to reevaluate the inferences from average product ratings.
We argue that gaze times are a more precise measure of attention relative to fixations because they quantify the time spent on an attribute. As such, we demonstrate that consumers are likely to pay more attention to average product ratings when the level of review numbers is low; thus, this difference in gaze times would mediate the influence of the level of review numbers on consumer preference. Using mediation analysis (Model 4; [62]), we find support for the argument that the difference in gaze times between average product ratings and the number of reviews mediates the effect of the level of review numbers on consumer preference between choice options (B =.25; CI95% = [.04,.62]).
This study provided further evidence for H3 by demonstrating that the difference in gaze times for the average product ratings and the number of reviews dictates consumer preference between options. When the level of review numbers is low, it signals to consumers that the average product ratings may be relatively closer in diagnosticity to that of the number of reviews, leading consumers to reevaluate this attribute before reaching a decision. Yet when the level of review numbers is high, there is a hierarchy in diagnosticity between the two attributes, and consumers can reach a decision faster without reevaluating average product ratings. Thus, this study compliments Study 6 in providing a different measure of diagnosticity of average product reviews and number of reviews.
Across seven studies, we find consistent support for our propositions that average product ratings are a more diagnostic cue of product quality than the number of reviews and that the number of reviews can become more influential in consumer decisions when ( 1) average product ratings are negative or neutral and ( 2) the level of review numbers is low. This change in attribute diagnosticity leads to a systematic shift in preference away from higher-rated options with fewer reviews toward lower-rated options with more reviews. Furthermore, when the diagnosticities of these two review attributes are closest to each other, consumers experience trade-off difficulty, as evidenced by increased choice deferral.
This work makes several contributions. Theoretically, we contribute to the literature on numerical cognition, which investigates how consumers process numbers as information; we do so by examining distinctions between interpretations of continuous and discrete variables, scalar variability, and the relationships between absolute and relative number comparisons ([27]). We add to this literature by demonstrating how consumers integrate multiple attributes that use different numeric scales. Specifically, we investigate how consumers integrate average product ratings, which are usually clearly bound on a one- to five-point scale, and numbers of reviews, which are usually unbound. Prior work on attribute diagnosticity ([24]; [31]; [63]) has suggested that consumers weigh attributes in their decisions differently as a function of these attributes' perceived diagnosticity as signals of product quality. Consistent with this research, we demonstrate that average product ratings are more likely to be incorporated in consumer judgments as a signal of product quality than the number of reviews because of the natural differences in the scaling of these attributes. Importantly, we further outline conditions under which the perceived diagnosticity of the number of reviews can increase, leading to joint influences of average product ratings and number of reviews in consumers' choices. Our investigation into these conditions advances the understanding of how consumers incorporate numerical cues on different scales into their judgments.
While we consistently demonstrate the persistent effect of the level of review numbers on consumer preferences across seven studies in the main article and eight in the Web Appendix, several other possible moderators exist that we did not address. Recent work has demonstrated that individual review sentiment has an influence on consumer decisions above and beyond aggregate review information ([47]; [81]), yet this work has not looked at the three-way interactive relationship among product ratings, number of reviews, and textual content of individual reviews. Naturally, we believe there are several purchase decisions in which consumers would be more likely to consult the reviews' text in addition to aggregate measures of reviews. For instance, it would seem that, when average product ratings and the level of review numbers hold similar diagnosticity (e.g., when the level of review numbers is low) consumers would be more likely to seek additional information to differentiate options. This would be consistent with prior work demonstrating that making choices is more difficult when consumers are making trade-offs between attributes of similar importance ([ 9]; [21]), therefore increasing the need for additional information (as we demonstrate in Study 2). If consumers choose to seek more information about product options, such as by reading individual reviews, such information may become more influential in judgments as compared with the previously accessed information ([80]). This could result in an increase in the diagnosticity of textual information relative to numerical information contained in aggregate reviews. Exactly how consumers integrate numerical and textual information to form a single judgment remains an worthwhile avenue for future research.
Next, we provide an illustration of how the results of our study can be incorporated into business practices. We interviewed two managers involved with product review acquisition strategies for their respective brands. One, the chief executive officer of a nutritional supplement company, instructs his team to employ a proactive strategy in which it aggressively pursues reviews from customers through email nudges after purchase and offers them steep product discounts. The discounts are used to increase the level of review numbers as a function of more sales, whereas the email nudges are intended to increase the sales-to-review conversion ratio. The other, an associate brand manager for a leading home electrics company, relies on a reactive strategy in which the company offers free products in exchange for honest reviews. The interviews confirmed our perception that business practitioners appreciate the importance of both average product ratings and number of reviews in driving product sales.
We should note that the aforementioned strategies, while certainly effective in increasing the number of reviews, do not necessarily result in improved average product ratings. Indeed, depending on the review stimulation strategy employed as well as the dynamics in the particular rating environment (e.g., see [52]), the average product rating can increase, decrease, or remain unchanged. Thus, in this section we focus solely on the number of reviews, making a simplifying assumption that the average product rating is not affected by the stimulation. As various stimulation instruments typically carry some associated costs (e.g. reduced earnings from deep discounts or free products), we argue that managers need to trade off the potential benefits of receiving additional reviews against the costs associated with these efforts. Because the detailed financial analysis would call for additional assumptions about the product profit margins and the cost of stimulation, for the sake of generalizability, we explore how the incremental number of reviews may drive sales volume under different choice scenarios. Specifically, we show how the firm could benefit from increasing the number of reviews in early- and mid-stage product life cycles when they face competitors with more reviews.
We conducted a simulation in which the higher-rated good with fewer reviews accrued reviews at the same rate as the lower-rated good with more reviews or at twice the rate (e.g., when a review acquisition strategy was employed). We then crossed this with whether the firm employed a proactive review acquisition strategy early on in the product life cycle (e.g., at 5 reviews) or a reactive strategy later on in the life cycle (e.g., at 15 reviews). The simulation was conducted in MATLAB using choice shares computed from Study 1. We took the relative preference measure used in that study and pooled the numbers that indicated preference for the higher-rated option with fewer reviews (i.e., 1–3) and the numbers that indicated preference for the lower-rated option with more reviews (i.e., 5–7). Participants who indicated no preference (i.e., 4) were excluded from the choice share computation. This resulted in several data points ranging from low to mid- to high in the level of review numbers. Then, we used the MATLAB surface fitting function ("fit") to interpolate choice shares for various combinations of review volume pairs.
Our simulations were agnostic as to what type of review acquisition strategy was employed (e.g., email nudges, price promotions, free product), but we modeled the effect of employing an acquisition strategy by doubling the likelihood of a review being written. We seeded the baseline likelihood of receiving a review after a purchase at 50%, and the likelihood of receiving a review when a review acquisition strategy is employed at 100%. The likelihood of leaving a review has been estimated as low as.001% depending on the product category. Thus, the results we discuss here are extremely conservative estimates of the impact the number of reviews might have on choice shares over time.
In the first two simulations, we investigated a proactive strategy in which the higher-rated product had 5 reviews, whereas the lower-rated product had 10 reviews. In Scenario 1, we held the likelihood of receiving a review constant across both products at 50%. Thus, the first scenario investigated how long it would take for a product with a higher rating, but five fewer reviews, to accumulate more sales than the other products. As we illustrate in Figure 5, Panel A, it would take approximately 240 consumers purchasing products in this category for this to occur. In Scenario 2 (Figure 5, Panel B), we assume that the manager of the higher-rated product employs an active review management strategy and doubles the likelihood of receiving product reviews. In this scenario, the higher-rated product reaches the dominant sales position roughly 33% more quickly—around 160 consumer category purchases.
Graph: Figure 5. Implications simulations for early- and mid-life-cycle products.
In Scenarios 3 and 4 (Figure 5, Panels C and D), we investigate a reactive strategy in which the market has somewhat matured (i.e., 15 and 45 reviews, respectively). Again, in Scenario 3, we assume that each product has a 50% likelihood of receiving a review after it is purchased. Here, it takes approximately 75 additional customer purchases in the product category before the higher-rated product surpasses the lower-rated product in sales. In Scenario 4, we once again assume that the manager of the higher-rated product actively manages its reviews and can double the likelihood of receiving a review after purchase. This results in the higher-rated product surpassing the lower-rated product in sales roughly 20% earlier, or after 60 consumer category purchases.
While these scenarios are just a few of the many possible that exist in the marketplace, they provide additional support for our claim that the number of reviews is highly influential in consumer decisions, and managers would be wise to oversee their growth. As new products with fewer reviews enter against incumbents with more reviews, employing a proactive strategy to spur an increase in product reviews can quickly decrease the disadvantage that a product manager faces when competing against established products. Obviously, a comprehensive optimization of the review stimulation strategy should account for various additional factors (e.g., costs associated with discounted products or additional review nudging, anticipated competitive response, product profit margins); nonetheless, we argue that the results of our study provide an important input to managerial decision making.
This research outlines conditions in which the diagnosticity of the number of reviews increases relative to the diagnosticity of average product ratings (as cues of product quality), potentially leading to suboptimal decisions for consumers and an increase in choice deferral for brands and retailers. Theoretically, we argue that an inherent difference in the types of scales in which these attributes are presented (bound and unbound) leads to the observed difference in their diagnosticity; by demonstrating how consumers integrate attributes on both numeric scales into a single judgment, we contribute to the literature in numerical cognition. Furthermore, we provide clarity to the debate on the relative influence of average product ratings and the number of reviews (see [26]; [83]) by demonstrating the conditional influences of the number of reviews under various average product rating valence conditions.
Supplemental Material, DS_10.1177_0022242918805468 - Swayed by the Numbers: The Consequences of Displaying Product Review Attributes
Supplemental Material, DS_10.1177_0022242918805468 for Swayed by the Numbers: The Consequences of Displaying Product Review Attributes by Jared Watson, Anastasiya Pocheptsova Ghosh, and Michael Trusov in Journal of Marketing
Footnotes 1 Area EditorAlexander Chernev served as area editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242918805468
5 1More recently, academics have begun to investigate how the text content of reviews affects consumers' online behavior ([11]; [55]; [71]). Given that the current research is a systematic investigation of the joint influence of two numerical review attributes, average product ratings and the number of reviews, on consumer choices, the relationship between average product ratings, number of reviews, and review sentiment is outside the scope of our research. We leave this topic for future studies, where the conditions by which consumers incorporate individual review content can be more thoroughly explored.
6 2Similarly, there is no clear consensus from retailers on the importance of these attributes. Some retailers choose to present only average product ratings, whereas others present both ratings and review numbers. In a preliminary exploration of the market, we analyzed the review attribute presentations of over 300 websites and found that although 99 of the 337 websites chose not to display any review information (29%), of the remaining 238 websites, 54% chose to display the number of reviews on the search page, while the others chose instead to display the number of reviews on the product page or reviews page. This preliminary study explored the review attribute presentations for three of the highest-grossing online retail categories (apparel, small electronics, and consumer appliances; [56]) from over 300 of the largest (based on Alexa.com rankings) retailers, providing a conservative estimate of review display, as smaller retailers are less likely to have the functionality for review acquisition. For the retailers in our sample that chose to display review information, the average number of reviews was 390 (SD = 1,241), and the median was 15 reviews for their most popular products. For comparison, the average number of reviews of all products on Amazon is 88 (SD = 64), and the median is 2.
7 3We conducted paired sample t-tests to contrast the product categories. Regardless of the level of review numbers, preference for the higher-rated choice option with fewer reviews was marginally weaker for headphones (M = 3.25) than that for coffee makers (M = 2.96; t(250) = 1.93, p =.055) and speaker systems (M = 3.00; t(250) = 1.71, p =.088) and significantly weaker than for chairs (M = 2.56; t(250) = 4.93, p <.001). Preference for the higher-rated choice option with fewer reviews in the coffee makers choice set was significantly stronger than that for microwaves (M = 3.25; t(250) = −2.25, p =.025) and chairs (t(250) = 3.16, p =.002). Preference for the higher-rated choice option with fewer reviews in the microwaves choice set was significantly stronger than that for speaker systems (t(250) = 2.00, p =.047) and chairs (t(250) = 5.11, p <.001). Preference for the higher-rated choice option with fewer reviews in the speaker systems choice set was significantly stronger than that for chairs (t(250) = 3.65, p <.001). Although there were minor categorical differences between products, we urge the reader to not read too far into this as it could merely be a function of the stimuli. The important takeaway is that the same systematic shift in preference occurred across the various product categories.
8 4We thank an anonymous reviewer for suggesting this study.
9 5Additional results of extensive exploration of various levels of rating dispersion and the level of review numbers are available from the authors on request.
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By Jared Watson; Anastasiya Pocheptsova Ghosh and Michael Trusov
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The Benefit of Becoming Friends: Complaining After Service Failures Leads Customers with Strong Ties to Increase Loyalty
Service firms spend considerable resources soliciting complaints to initiate recovery efforts and improve their offerings. However, managers may be overlooking the fact that complaints serve an equally important role in engendering loyalty. The authors demonstrate that the strength of social ties between customers and service providers influences the degree to which complaining drives loyalty. Paradoxically, while strongly tied customers fear that complaining threatens their ties with the provider, when they are encouraged to complain, their loyalty increases because offering feedback serves as an effective way to preserve social ties. Conversely, for weakly tied customers, complaining has no effect on loyalty. Furthermore, complaints are more effective in driving loyalty for strongly tied customers when the feedback is directed toward the provider who failed, rather than to an entity external to the failure. Finally, when providers signal an authentic openness to feedback, strongly tied customers are more loyal after complaining, whereas authenticity does little to engender loyalty for weakly tied customers who complain. The value of complaints in driving loyalty is promising both for customers who perceive a strong tie to a particular provider within the firm and, more generally, in service industries wherein strong ties naturally occur.
Many firms subscribe to the idea that customer complaints play two important roles: to initiate service recovery and to inform future service offerings. As Bill Gates suggests, “[A firm’s] most unhappy customers are [its] greatest source of learning.”1 Firms such as JetBlue, Starbucks, and T-Mobile rely heavily on social media platforms to solicit and respond to customers after a service failure to implement recovery efforts and learn why failures have occurred (Helmrich 2014). We confirm this perspective in interviews with eleven managers and nine service providers who described their perceptions of what role customer complaints play in service provision. The interviewees revealed that their companies track and review negative feedback so that managers can use the feedback as a source of information about how to rectify and improve service processes.
Contrary to standard practice, we suggest that treating customer feedback solely as a vehicle for rectifying or improving service provision overlooks the other important ways that complaining behavior can enhance relationships between customers and service providers. Specifically, service firms are missing an important opportunity to utilize complaints to strengthen providers’ relationships with customers. Indeed, our interviews revealed that, unfortunately, few managers or service providers had any insight into how complaining could create loyal relationships, nor had they ever received formal instruction about how to solicit complaints effectively after a failure had occurred.
We use the relationship marketing framework (e.g., Morgan and Hunt 1994; Palmatier, Dant, and Grewal 2007; Palmatier et al. 2006) to explore how various managerial levers may be manipulated to strengthen social ties with customers after a service failure. We focus on the role of tie strength, or the potency of the bond between relational partners (Brown and Reingen 1987; Granovetter 1973; Mittal, Huppertz, and Khare 2008; Rindfleisch and Moorman 2001), as a construct that plays a critical role in defining the practical parameters that enable customer complaints to drive future loyalty. Specifically, we emphasize the importance of social ties between customers and service providers, which may be established briefly during an initial service encounter (Jiang et al. 2010) or may result from repeated interactions. Prior research (e.g., Mittal, Huppertz, and Khare 2008) has shown that customers who feel a strong tie to a service provider are less likely to complain after a service failure, due to fears of damaging the tie. However, past research has stopped short of illuminating the relational implications of complaining when strongly tied customers do voice negative feedback. In response to this gap, we add to past work on tie strength by revealing its paradoxical role in customer–provider relationships: while strongly tied customers are often reticent to complain, their loyalty is enhanced when they decide, or are encouraged, to do so.
We provide substantive contributions by recommending that managers inculcate the interpersonal communication approach of “authentic openness to negative feedback” in their employees. We define this idea as both providers’ sincere openness to hearing negative feedback and their signaling a genuine willingness to integrate this feedback into their service provision. When service providers signal an authentic openness to feedback, strongly tied customers who complain about a service failure are more loyal after doing so. In contrast, when weakly tied customers complain, the authenticity of providers’ openness to feedback does little to engender loyalty. Furthermore, while customers may want to vent their dissatisfaction externally (to other customers, friends, competitors), we show that complaints are more effective in increasing loyalty when strongly tied customers direct their feedback toward the provider who failed.
We demonstrate that the value of customer complaints in driving loyalty is promising both for customers who perceive a strong tie to a particular service provider within the firm and, more generally, in service industries wherein close connections between customers and providers naturally occur (e.g., personal services, hospitality, beauty industries). These findings have implications for which relationships and industry contexts managers should target when determining whether to expend resources on generating complaints from dissatisfied customers.
Finally, we demonstrate these effects using a variety of data sources, including controlled scenario-based experiments, behavioral studies with real service interactions, secondary survey data from a large service firm, and field data from Yelp.com. Across contexts and data sources, we consistently find that complaining (vs. withholding negative feedback) after a service failure engenders greater loyalty from customers with strong ties to a service provider, especially if the feedback is solicited in an authentically open and direct manner. Notably, we show that these findings hold in the context of both newly formed social ties and relationships resulting from repeated interactions. However, we do not find evidence of similar benefits of complaining for customers with weak social ties. In what follows, we establish our conceptualization and confirm our predictions in a set of six studies. We conclude with a discussion of the implications and limitations of this work, as well as ideas for future research.
Although service providers strive to please customers, failures inevitably occur, and the question of whether a customer will be loyal after such an event transpires becomes a concern for managers. We draw from the relationship marketing (RM) framework (e.g., Morgan and Hunt 1994; Palmatier, Dant, and Grewal 2007) to understand how service relationships may continue to thrive even after a service failure has occurred. The RM model, which incorporates several theoretical components—trust, dependence, relational norms, and commitment—describes how customer relationships are maintained in a service context (Berry 1983). The basic contention of the framework is that the trust between customers and providers, customers’ feelings of dependence on providers, and both parties’ observation of relational norms work in concert to create a service atmosphere in which customers feel a strong commitment to the providers.
We interpret this framework in situations in which a service failure has occurred and examine the role of complaining in maintaining strongly tied relationships after such an event. We do not directly examine the effect of customer complaining on service recovery efforts, which has been studied extensively in the prior literature (e.g., Voorhees et al. 2006). Rather, we contribute to the existing literature by revealing that the act of complaining alone buttresses customers’ relationships with providers even without the explicit promise of rectifying the failed service.
We suggest that complaining functions like other relationship-preservation strategies (e.g., honesty and investing time and energy), and may act as a signal sent by a customer indicating his/her interest in repairing the threatened social tie and remaining loyal to the provider. While prior research has looked at each of these variables separately, the intersection between all three substantive areas—tie strength, complaining, and loyalty—has not been examined, as we illustrate in Table 1. We address this gap by examining how the strength of the social tie between customers and service providers, whether established in an initial service encounter or cultivated over repeated interactions, influences the degree to which complaining after a service failure increases customer loyalty.
Despite the positive outcomes of complaining (Nyer 2000), offering negative feedback is both time consuming and potentially threatening to the customer–provider relationship. A question arises: Which types of customers (e.g., strongly or weakly tied) are most likely to voice dissatisfaction after a service failure? The RM model indicates that individuals who are “dependent” on their service providers perceive that the providers offer “valued resources for which there are few alternative[s]” (Dwyer 1984). In other words, when the connection between the provider and customer is unique and difficult to replace, the customer is likely to feel a greater stake in the relationship.
To capture the construct of dependence, we consider the role of tie strength in voicing complaints. Tie strength exists as an element of the broader theoretical framework of social belongingness, which is a fundamental human motivation involving the need to form and maintain strong, stable interpersonal relationships (Baumeister and Leary 1995). Social ties may form in mere minutes, resulting from a strong feeling of similarity or emotional connection between individuals. For instance, customers may encounter several providers when choosing a masseuse, manicurist, or aesthetician but may be able to quickly determine whether they relate more strongly to one of the individuals. Furthermore, some social ties can be developed over repeated interactions, resulting in long-standing close relationships. However, repeated interaction is neither a necessary nor a sufficient condition to ensure strong ties between customers and providers. For example, it is plausible that a customer could use the same financial advisor for years without feeling a strong social tie to him/her. Given the many ways that strongly tied relationships emerge, we test our predictions under conditions in which the relational ties were formed in a single interaction and under conditions in which ties have been created over multiple interactions. We show that our predicted effects are robust across both contexts.
TABLE: TABLE 1 Review of Literature on Complaints, Tie Strength, and Loyalty
TABLE:
TABLE:
| Study | Data Sources | Dependent Variables | Independent Variables | Findings | Tie Strength Studied? At What Level? | Complaining Studied? | Loyalty Studied? |
|---|
| Deutsch (1969) | Theoretical model | Relationship outcome | Prior relationship, nature of conflict, individual characteristics, estimation of success, third parties | Whether conflict results in positive or negative outcome depends on how “productive” the conflict is in creating a resolution between the two individuals. | Yes, at the firm level | Yes | No |
| Fornell and Westbrook (1984) | Survey data | Consumer complaints | Active participation of consumer affairs department in firm decisions | The more consumer complaints a firm receives, the more isolated its complaint handling becomes from management decision making, which then causes complaints to increase. | No | Yes | No |
| Fornell and Wernerfelt (1987) | Theoretical model | Customer retention | Investment in encouraging complaints | Whenever revenue loss is greater than the cost of dealing with the complaints, complaints should be encouraged and the issues voiced should be corrected. | No | Yes | Yes |
| Fornell and Wernerfelt (1988) | Theoretical model | Loyalty | Investment in facilitating complaints to resolve customer concerns | Dissatisfied customers do not necessarily plan to desert the firm. Furthermore, effective complaint management systems offer an opportunity to resolve customers’ grievances. | No | Yes | Yes |
| Stephens and Gwinner (1998) | In-depth interviews with customers | Coping response (e.g., complaining) | Coping style | Appraisal process and/or elicited emotion from a bad experience leads customers to problem-focused, emotionfocused, or avoidance coping styles. Problem-focused coping may lead to complaining. | No | Yes | No |
| Blodgett and Anderson (2000) | Secondary data | Redress seeking, repatronage, intended word of mouth | Attitude toward complaining, store loyalty, success likelihood, stability/controllability, perceived justice | Most noncomplainers do not plan to defect but intend to limit their purchases. Study shows support for “service recovery paradox”: a formerly dissatisfied customer may end up being more loyal because of successful complaint handling. | No | Yes | Yes |
| Mittal and Kamakura (2001) | Secondary data | Repurchase | Satisfaction | Satisfaction ratings and repurchase behavior predict each another, depending on other consumer characteristics (e.g., age, gender). | No | No | Yes |
| Homburg and Furst (2005) | Survey data and interviews | Customer justice, satisfaction, evaluations, loyalty | Firm response to consumer complaints (mechanistic vs. organic) | Both mechanistic and organic approaches to complaints significantly influence customers’ assessments, although the mechanistic approach has a stronger impact on the dependent measures. | No | Yes | Yes |
| Mittal, Huppertz, and Khare (2008) | Experimental data | Complaining (intention and behavior) | Tie strength, information control, need for cognition, satisfaction, relationship duration | Complaining is more likely when tendency for information control is stronger and ties between provider and customer are weaker. | Yes, at the individualrelationship level | Yes | No |
| Dunn and Dahl (2012) | Experimental | Attitude toward product, brand, and purchase. | Complaining behavior, selfthreat | When the firm is responsible for the failure, consumers feel better after complaining about it. When consumers are to blame, complaining has a detrimental effect on product reactions. | No | Yes | No |
| Zhang, Feick, and Mittal (2014) | Experimental data | Negative word of mouth (NWOM) transmission likelihood | Tie strength, image impairment, concern for self vs. others, concern prime | For women, the effect of image impairment concern onNWOM transmission is stronger for weak (vs. strong) ties. For men, there is no relationship. When consumers have high (vs. low) concern for others, the effect of image-impairment concern is stronger for weak ties. | Yes, at the individualrelationship level. | No | No |
| Dunn and Dahl (2012) | Experimental | Attitude toward product, brand, and purchase | Complaining behavior, selfthreat | When the firm is responsible for the failure, consumers feel better after complaining. When consumers are to blame, complaining is detrimental to reactions to the product. | No | Yes | No |
| This article | Survey, experimental, behavioral, field data | Loyalty (behavioral, attitudinal, intended, probable, actual) | Tie strength, complaining, providers’ authentic openness to feedback | Although customers with strong ties are less likely to complain, when they do, they are more loyal than those with weak ties, because providing feedback serves as a way to preserve the tie. The effect is strengthened when providers are authentically open to feedback. | Yes, at both the individualrelationship and industry levels | Yes | Yes |
**p £ .05.
***p £ .01.
Although under most conditions, individuals who have strong social ties (vs. weak ties) engage in more communication with one another, in the context of a service failure, the feeling of dependence on and irreplaceability of the relationship lowers the likelihood that a customer will offer negative feedback due to fears of threatening an important relationship (Mittal, Huppertz, and Khare 2008). However, we expect that despite their reluctance, when strongly tied customers are encouraged to complain, their social ties can, in fact, be strengthened. Specifically, we predict that when a relationship is at risk (e.g., after a disagreement has occurred), individuals with strong ties will use “tie-strength preservation” strategies (e.g., directing their time and attention toward the relationship) to signal their interest in repairing and protecting the ties. In the context of service failures, we suggest that offering diagnostic (negative) feedback is one way to signal the importance of a service relationship.
According to the RM model, this investment of personal resources in a relationship leads customers to escalate their commitment. We conceptualize commitment as the belief of an exchange partner (in our model, the customer) that an ongoing relationship with another entity (in our model, the service provider) is so important that s/he is willing to devote effort toward maintaining it to ensure future interaction (Morgan and Hunt 1994). We use customers’ loyalty as a proxy for the construct of commitment described in the RM framework. Although strongly tied customers are less likely to complain, we predict that when they do, offering such feedback will help preserve their relational ties to the provider, and as a result, they will be more inclined to increase their loyalty to the provider. In contrast, for weakly tied customers, complaining is unlikely to influence subsequent loyalty because there is no relational tie to preserve by offering honest feedback after something has gone wrong. Thus, we hypothesize that tie strength will positively moderate the effect of complaining on loyalty because complaining acts as a relational preservation mechanism for customers with strong ties, but not for those with weak ties. More formally:
H1: Strongly tied customers who complain (vs. withhold feedback) after a service failure will increase their loyalty. In contrast, for weakly tied customers, complaining has no effect on loyalty.
H2: Tie-strength preservation resulting from customers’ complaining behavior mediates the relationship between tie strength and loyalty.
Given that customers are wary of threatening social ties by offering negative feedback (Mittal, Huppertz, and Khare 2008) but that complaining is beneficial to service relationships, managers are faced with the predicament of how to encourage strongly tied customers to offer this feedback. Drawing from prior research, we predict that the presence of trust between customers and service providers is likely to ameliorate the threat of complaining and enable customers to voice their feelings of dissatisfaction. In our studies, we operationalize customers’ trust by manipulating the authenticity of providers’ openness to feedback, and we corroborate the prediction of the RM framework by showing that if providers signal that they can be trusted (vis-à-vis authentic expressions of openness), the tie between the provider and customer is more likely to be preserved after a service failure.
The first dimension of authentic openness to feedback is the degree to which service providers use open communication techniques. These techniques are characterized by supportiveness and empathy; they encourage “candid disclosure of feelings” (Redding 1972, p. 30) to appear more approachable and receptive to negative feedback. A second dimension is the degree to which the provider validates the complaint and the importance of the relationship by signaling that the feedback will help inform future service provision (for a review of the related literature, see Blodgett and Anderson 2000; Fornell and Wernerfelt 1987, 1988; Fornell and Westbrook 1984; Homburg and Fürst 2005). Thus, we propose that service providers’ authentic openness to negative feedback possesses two distinct components: ( 1) appearing authentically receptive to feedback and ( 2) signaling the genuine intention to use the feedback to inform future service provision.
Although managers often teach their service providers to use communication techniques that encourage open dialogue between themselves and customers (e.g., smiling, projecting a friendly demeanor), these formalized communication processes may be perceived by customers as inauthentic, forced, or robotic. Indeed, prior research has shown that authenticity enhances customers’ reactions to service encounters, while inauthentic communication styles undermine service interactions (Grandey et al. 2005). In the context of our research, we predict that after a service failure, strongly tied customers who complain (vs. withhold feedback) will be more loyal to providers who authentically express an openness to feedback. Indeed, if both parties engage in authentic, open interactions, the relational norms of reciprocity will be realized, such that both individuals are investing effort into the relationship (Palmatier, Dant, and Grewal 2007, Umashankar, Srinivasan, and Parker 2016). On the other hand, if the service provider appears inauthentic or robotic, complaining is unlikely to result in greater commitment to the relationship by the customer, despite his/her strong social tie to the provider. In this case, the provider has not upheld the norms of the relationship, and the customer is thus unlikely to increase his/her loyalty after complaining.
In the case of weakly tied customers, we predict that irrespective of whether they complain, service providers’ authentic openness to feedback is unlikely to influence their loyalty. Since weakly tied customers are less inclined to remain connected to the provider, the precondition of a strong tie does not exist, and therefore, complaining will not function to preserve the social tie. Consequently, providers’ authentic openness to feedback will not enable or codify the relational connection, and the interaction will be null. In simple terms, we hypothesize the following three-way interaction:
H3a: For strongly tied customers, when the service provider is perceived as authentically open to feedback, complaining (vs. withholding feedback) after a service failure leads to an increase in loyalty. In contrast, when the provider is perceived as inauthentic, complaining has no impact on customers’ loyalty.
H3b: For weakly tied customers, service providers’ authentic openness to feedback has no effect on the relationship between complaining and loyalty.
We investigate these hypotheses in six studies using multiple measures of our key constructs of tie strength, complaining, and loyalty. In Study 1, we use secondary survey data to confirm H1 and show the positive moderating effect of tie strength on the relationship between complaining on loyalty (both attitudinal and behavioral). In Study 2, we test H1 in a controlled setting and show that when strongly tied customers complain after a service failure, they increase their loyalty intentions toward the service provider. In contrast, for weakly tied customers, complaining has no effect on intended loyalty. This finding demonstrates the need for managers to encourage negative feedback from strongly tied customers after a service failure has occurred. In Study 3, a behavioral study with real service interactions, we reveal the process underlying the positive moderating effect of tie strength on the relationship between complaining and loyalty. We show support for H2 and find that customers with strong ties have higher loyalty intentions after complaining (vs. withholding feedback) because voicing negative feedback serves as a mechanism for maintaining relational ties, that is, “tie-strength preservation.” Studies 4a and 4b use a combination of scenario-based and behavioral data to show that when strongly tied customers perceive the provider as authentically open to feedback, complaining after a service failure leads them to increase their loyalty. However, when the provider is perceived as inauthentic, complaining has no impact on loyalty (H3a). For weakly tied customers, complaining has no impact on loyalty, irrespective of the authenticity of the service provider’s openness to feedback (H3b). Finally, we broaden the context in Study 5 and operationalize tie strength at the industry level, using field data from Yelp.com. Again, we find support for H1 in a managerially relevant setting and demonstrate that generating complaints from dissatisfied customers in service industries in which customers and providers naturally forge strong ties is particularly effective in driving both probable and actual loyalty. Conversely, in industries in which weak ties are more common, complaining is not an effective mechanism for driving loyalty.
To investigate our hypothesis that customers with strong (vs. weak) ties to service providers are more loyal after complaining following a service failure, we obtained secondary survey data from a national fitness company. The company, which has 82 fitness locations across the United States, Canada, and the United Kingdom, offers high-end personal training, fitness classes, and spa facilities. We obtained survey data culled from all its facilities from October 2014 to September 2015. The data contain survey responses from 1,911 customers about their most recent fitness experiences, including whether they had a personal training session. Further, the surveyed customers were asked to indicate whether they faced a problem ( 1) or not (0) during their most recent visit. Given the focus in this article on interpersonal social ties and failures, we reduced the data set to one that only included the 280 cases in which a customer experienced a service failure caused by a personal trainer. The survey gave customers the opportunity to provide a written description (i.e., a complaint) of the service failure and explain what could have been done differently. We created a “Complain” measure that was coded as 1 if the customer complained and 0 if s/he chose not to complain. Of the 280 personal trainer–related service failures, 185 customers filed a formal complaint, whereas 95 customers chose not to complain.
To capture tie strength (for a complete description of the focal measures used in each study, see Table 2), we used the measure of personal trainer friendliness (Likert scale from 1 to 10, where 10 = “very friendly”) as a corollary. This is supported by prior research, which has established that in business relationships, service providers’ perceived “friendliness” is one of the key preconditions for bonding and is predictive of tie strength (Witkowski and Thibodeau 1999). The survey also measured two dimensions of loyalty (Umashankar, Bhagwat, and Kumar 2016): customers’ Behavioral Loyalty (intention to remain a member of the gym for the next 6 months) and Attitudinal Loyalty (likelihood to recommend the gym to others; Likert scale from 1 to 10, where 10 = “very likely). We tested these two outcome measures separately to see whether the effects of tie strength and complaining differed. The survey also included measures of how long the customer had been a member of the gym (“Tenure”; translated into days from the customer’s start date) and his/her regularity of using the facility (“Usage Frequency”; Likert scale from 1 to 5, where 5 = “very frequent”).
We estimated two regression models of the effects of tie strength and complaining, each with a different measure of loyalty (behavioral and attitudinal). Specifically, we included the main effects of tie strength and complain, their interaction, and the control variables of tenure and usage frequency. The model of loyalty m (where m is either behavioral or attitudinal loyalty) for customer I was estimated as follows:
The descriptive statistics and correlations (within acceptable limits; r < .46) are presented in Table 3. The results of both estimations are shown in Table 4. The results of the control variables were in the expected directions. The results show that as the tie strength between the customers and personal trainers increased, both customers’ behavioral (b = .180, t = 3.40, p < .001) and attitudinal (b = .246, t = 5.30, p < .001) loyalty increased. The main effect of complaining was nonsignificant in both models (behavioral loyalty: b = .182, t = .45, p = .66; attitudinal loyalty: b = .058, t = .16, p = .87). The marginally significant interaction effects show that for customers who complained after a service failure, increasing tie strength led customers to become more behaviorally (b = .053, t = 1.66, p = .09) and attitudinally (b = .041, t = 1.71, p = .08) loyal, providing preliminary support for H1. Compared with a main-effects model, the incremental variance explained by the interaction was 4% (p < .05).
TABLE: TABLE 2 Variables, Data Sources, and Measures Across Studies
| Variable and Study | Data Source | Operationalization | Measure |
|---|
| Tie Strength |
| Study 1 | Survey | Measured the perceived friendliness of the service provider. | “How friendly did you feel the personal trainer was?” (Likert scale from 1 to 10; 10 = “very friendly”) |
| Study 2 | Scenario experiment | Manipulated the personal nature of the conversation between the respondent and service provider. | “I liked the service provider”; “I felt connected to the provider”; “I felt chemistry with the service provider”; (Likert scale from 1 to 7; 7 = “strongly agree”) |
| Studies 3, 4a, and 4b | Scenario and behavioral experiments | Manipulated incidental similarity between the respondent and service provider. In Study 4, strong ties were established with all respondents using incidental similarity. | “I liked the service provider”; “I felt connected to the provider”; “I felt chemistry with the service provider”; (Likert scale from 1 to 7; 7 = “strongly agree”) |
| Study 5 | Yelp data | Measured whether the industry in which the customer complained is a strong-tie (hair styling) or weak-tie (plumbing) industry. | Hair styling = strong tie = 1; plumbing = weak tie = 0 |
| Complaining Behavior |
| Study 1 | Survey | Measured whether the customer complained after experiencing a service failure. | Chose to complain = 1; chose not to complain = 0 |
| Studies 2, 4a, and 4b | Scenario and behavioral experiments | Manipulated complaining behavior. | Complained = 1; did not complain = 0 |
| Study 3 | Behavioral experiment | Manipulated the recipient of the complaint. | Complained to the service provider = 1; complained to a third party = 0 |
| Study 5 | Yelp data | Measured the extent of complaining. | Number of words in the Yelp complaint |
| Loyalty |
| Study 1 | Survey | Measured customers’ intentions to recommend (attitudinal loyalty) and return to the gym (behavioral loyalty). | Attitudinal loyalty: “How likely are you to recommend the gym?”; Behavioral loyalty: “How likely are you to be a member of this gym in 6 months?” (Likert scale from 1 to 10; 10 = “very likely”) |
| Studies 2, 3, 4a, and 4b | Scenario and behavioral experiments | Measured the respondents’ intentions to use the same service provider again. | “It is very likely that I would schedule my next session with this service provider”; “I would be loyal to this service provider in the future”; “I would consider trying a new service provider next time” (reverse-coded); (Likert scale from 1 to 7; 7 = “strongly agree”) |
| Study 5 | Yelp data | Coded the likelihood of customers returning after a failure from Yelp reviews (probable loyalty) and directly asked them whether they returned (actual loyalty). | Probable loyalty: likely to return = 1; unlikely = 0. Actual loyalty: did return = 1; did not return = 0 |
| Tie-Strength Preservation |
| Study 3 | Behavioral experiment | Measured perceptions about the extent to which act of complaining helps preserve social ties. | “My feedback…” “…shows that I care”; “…is an investment in the relationship”; “…is a display of effort”; (Likert scale from 1 to 7; 7 = “strongly agree”) |
| Authentic Openness to Feedback |
| Studies 4a–b | Behavioral experiment | Measured perceptions of the authenticity of the service provider’s openness to feedback. | “The service provider seemed open to my feedback”; “She uses customer feedback to improve her performance.” |
This study provides initial evidence that increasing tie strength between customers and service providers positively influences the extent to which complaining after a service failure generates both behavioral and attitudinal loyalty. However, we recognize that the effects are only marginally significant, and we seek to replicate this finding in a more controlled setting in Study 2.
In this study, we observe whether tie strength moderates the effect of complaining behavior on loyalty intentions (H1) in a controlled setting. Participants read a scenario in which they imagined a training session with a personal trainer that did not go well. They were instructed to either complain or write about another topic.2 Subsequently, we measured their loyalty intentions.
Participants were 192 individuals from Amazon’s Mechanical Turk (hereinafter, MTurk) who took part in a 2 (tie strength: strong vs. weak) · 2 (manipulated feedback: complain vs. do not complain) study in exchange for $.50. To determine the robustness of our tie-strength measure, we manipulated this variable by varying the degree to which the topics discussed by the customer and provider were personal in nature (Cavanaugh, Bettman, and Luce 2015). Participants read, “Imagine you are at the gym to meet with a personal trainer.” They were thenassigned to one of the following conditions.
Strong tie strength. “After meeting her, you immediately feel a connection. During your workout, you and your trainer discuss your day, things that are going on in your life, and other common interests. You find yourself talking to the trainer as you would a close friend.”
Weak tie strength. “You and the trainer do not talk much and you find that you do not have many common interests. You don’t feel much chemistry with the trainer.”
Service failure. Prior research has established that customers feel that one of the most frustrating service experiences is being ignored or deprioritized by a distracted service provider (Winsted 2000). Thus, we created a service failure using the following description: “During the workout, you notice that the trainer is distracted and the workout is repetitive and boring. The trainer keeps checking her phone and you have to wait several times for the trainer to think of the next exercise. Overall, you feel the trainer did a poor job.”
After reading the scenario, participants were randomly assigned to a “complain” or “no-complain” condition. In the complain condition, they provided some feedback about the training session and described anything that went wrong using an online form we created. In the no-complain (control) condition, participants described their fitness goals. Next, participants indicated their intended loyalty using three measures (see Table 2) cited in prior literature (e.g., Gwinner, Gremler, and Bitner 1998; Umashankar, Bhagwat, and Kumar 2016). The measures were strongly correlated (a = .93), and thus we collapsed them into a single variable of intended loyalty by averaging the items. To confirm that we successfully manipulated tie strength, we asked the participants to indicate the degree to which they perceived that a social tie had been created (Table 2). We collapsed the multi-item scales of tie strength (a = .91). Finally, we measured whether the respondents perceived that a failure had occurred by asking them to rate the degree to which they perceived that “something had gone wrong during the service encounter.”
We confirmed that the tie-strength manipulation was successful. Participants in the strong-tie condition perceived a stronger tie to the trainer than those in the weak-tie condition (MStrong Tie = 4.18, MWeakTie = 1.84; t(190) = 8.69, p < .001). Further, participants indeed experienced a service failure (MService Failure = 4.9, MMidpoint = 4.0; t(191) = 3.07, p < .01).
An analysis of variance (ANOVA) revealed a positive effect of tie strength on intended loyalty (F( 1, 188) = 4.37, p < .05) and a nonsignificant main effect of complaining (F( 1, 188) = .57, p = .45). In support of H1, we found a significant two-way interaction between tie strength and complaining on participants’ intended loyalty (F( 1, 188) = 8.32, p < .01; incremental adjusted R2 = 8%, p < .01). Planned follow-up contrasts (see Figure 1) showed that for participants with strong ties to the trainer, complaining (vs. not) led to higher loyalty intentions (MComplained = 3.35, MDid Not Complain = 2.28; t(92) = 2.57, p < .05). In contrast, for those with weak ties, complaining had no impact on loyalty intentions (MComplained = 1.99, MDid Not Complain = 1.95; t(96) = .02, p = .67). complain, there is no effect on their loyalty intentions. Furthermore, the fact that the results of this study are realized after only a single interaction between a customer and a service provider underscores both how quickly ties can emerge and how powerful they can be in creating loyalty. Next, we observe to what extent it is important that the customer complains directly to the service provider rather than a third party. Although complaining to another person or entity is less threatening to the customer’s relationship tie with the provider, it might fail to preserve the relational connection between them.
TABLE: TABLE 3 Descriptive Statistics and Correlation Matrix (Study 1)
| Variable | Measure | M (SD) | 1 | 2 | 3 | 4 | 5 | 6 |
|---|
| 1. Behavioral Loyalty | 1–10 | 7.27 (3.03) | 1.000 | | | | | |
| 2. Attitudinal Loyalty | 1–10 | 6.87 (2.74) | .731** | 1.000 | | | | |
| 3. Tie Strength | 1–10 | 7.75 (3.16) | .307** | .395** | 1.000 | | | |
| 4. Complain | 0/1 | 66% | .056 | .021 | -.174** | 1.000 | | |
| 5. Tenure | Days | 1,382 (1,052) | .167** | .202** | .168** | -.052 | 1.000 | |
| 6. Usage Frequency | 1–5 | 3.63 (1.16) | .242** | .209** | .147* | -.022 | .193** | 1.000 |
*p < .05.
**p < .01.
TABLE: TABLE 4 Regression Analysis of Effects of Tie Strength and Complaining on Loyalty (Study 1)
| Variable | Behavioral Loyalty Model | Attitudinal Loyalty Model |
|---|
| Tie Strength | .180 (.053)*** | .246 (.046)*** |
| Complain | .182 (.408) | .058 (.360) |
| Tie Strength · Complain | .053 (.032)* | .041 (.024)* |
| Tenure | .0002 (.0015)* | .0003 (.0001)** |
| Usage Frequency | .380 (.138)*** | .225 (.112)* |
*p < .10.
**p < .05.
***p < .01.
Notes: Parameter estimates are reported, with standard errors in parentheses.
In Study 3, we test the contention that the way in which a customer offers his/her negative feedback affects downstream loyalty intentions. Using a behavioral-study format in which real social ties are created between participants and a personal trainer (a hired actor who was trained to look, dress, and behave like a trainer), we test whether tie-strength preservation explains the interactive effect of tie strength and complaining on intended loyalty (H2). We manipulated to whom participants complained: a third party or the service provider who caused the failure. We predict that complaining to the service provider will engender tie-strength preservation, because the customer is actively investing in the tie, as opposed to complaining anonymously and indirectly, which, while cathartic, is unlikely to strengthen a social tie.
Participants were 146 individuals at a southeastern university who participated in a 2 (tie strength: strong vs. weak) · 2 (complaint recipient: service provider vs. third party) between-subjects behavioral study for a payment of $10. Participants were unaware that they were participating in a marketing study and were told the following cover story: “The citywide Fitness Association is trying to improve its fitness programs for university students. They are asking students to sign up for a 15-minute fitness survey for a payment of $10.”
The study was conducted one participant at a time, and each participant was allotted a 20-minute time slot. When each participant entered the research setting, the actor (who was playing the role of a personal trainer) greeted him/her. The actor was trained by an author of this article in how to behave in each of four conditions, but she had no knowledge of the hypotheses. In the strong-tie condition, the actor created some incidental similarities between the participant and herself using a “getting to know you” exercise designed to foster a social tie between the individuals (Jiang et al. 2010). Specifically, the actor tailored her conversations with participants such that her interests and experiences matched the information that participants revealed about themselves. Below is an example of a conversation:
Actor: Before we begin, let’s take a few minutes to get to know one another. Tell me a little bit about yourself. What year are you and what is your major?
Participant: I am a sophomore and I’m a chemistry major. Actor: Oh, that was my major too! I also did my undergrad here and majored in that as well. So, where are you from?
Participant: I’m from San Diego, California.
Actor: I love that area. I have visited a few times. I have friends in the area. So, tell me, what you do for fun?
Participant: I like to go out with my friends and socialize. I also travel a lot and I like to hike and do water sports in the summer.
(The actor nods her head in agreement and smiles.)
Actor: I love doing a lot of the same things! It has been great getting to know you. It sounds like we actually have a lot in common! Now let’s move on to a few additional questions about your fitness goals.
In the weak-tie condition, the actor asked the participant about his/her background but showed no evidence of incidental similarity. The actor then moved on to a fitness assessment, which was ostensibly the focus of the study. The assessment was conducted on a laptop and consisted of questions pertaining to fitness (e.g., medical history, past injuries, fitness goals). The participant first entered his/her demographic and background information to create a temporal separation between the social-tie manipulation and the upcoming service failure. Next, the actor began a series of questions and entered participants’ answers into an online form, thus allowing her to manage the service interaction.
To create a service failure, after two or three minutes of working on the fitness questions, the actor started to appear visibly distracted by her phone. Next, she stopped the assessment to text a friend and then continued to text and laugh at the responses she received from the friend, while ignoring the participant. When she returned to the focal task, she asked the participant (in a rude tone) to repeat him/herself and then appeared disinterested when the participant complied. Finally, after a few more minutes, she told the participant to continue the assessment by him/herself while she took a phone call from a friend. She stepped away from the table and took the call while the participant finished the assessment unaided. When the participant completed the assessment, the actor completed her phone call and told the participant to follow the instructions on the laptop. The actor then left the room.
Participants were directed to provide feedback about their experience with the trainer. The recipient of the complaint was manipulated such that participants either provided written (nonanonymous) feedback for the trainer to read after the session using a standard feedback form, or posted a comment on a third-party website that houses reviews of personal trainers. The feedback form in the trainer-complaint condition asked participants to describe the service experience and whether anything went wrong, and to give their full name (indicating that the feedback was not anonymous). In the third-party-complaint condition, we created a mock website in which the participants complained anonymously and were reminded that the provider had no access to the sentiments they expressed. Thus, participants had no way of remaining connected to the provider visà-vis their feedback. A graduate research assistant (GRA) read the complaints to confirm that in both conditions, participants mentioned some aspect of having experienced bad service (i.e., everyone actually complained).
After filling out their complaints, participants answered questions pertaining to our dependent measures of intended loyalty from prior studies, which were collapsed into a multiitem measure of intended loyalty (a = .85). In addition, participants answered questions about how important giving feedback is to “preserving a social tie” (see Table 2; a = .93), what their perceived tie to the trainer was (Table 2; a = .94), and whether a failure had occurred.
We first confirmed that our manipulations were successful. Specifically, we found a significant difference in participants’ perceived tie strength to the trainer by condition (MStrong Tie = 4.45, MWeak Tie = 2.96; t(145) = 5.22, p < .001) and confirmed that participants felt they had experienced a service failure (MService Failure = 4.97, MMidpoint = 4.00; t(145) = 2.98, p < .01). To test H2, we examined whether the moderating effect of tie strength on the relationship between complaint recipient and intended loyalty is mediated by perceptions of tie-strength preservation, using the PROCESS mediation macro in SPSS (Model 8 in Hayes 2013). First, we found the interaction between tie strength (strong tie = 1; weak tie = 0) and complaint recipient (service provider = 1; third party = 0) on intended loyalty to be significant (b = .46, t = 2.67, p < .01; incremental adjusted R2 = 4.8%, p < .05). Second, to test whether this interaction is mediated by tie-strength preservation, we tested the interaction effect between tie strength and complaint recipient on tie-strength preservation and found it to be significant (b = .35, t = 2.78, p < .01; incremental adjusted R2 = 5%, p < .05). A breakdown of this interaction (see Figure 2) shows that when participants with a strong tie to the trainer complained directly to the trainer (vs. anonymously on the website), they perceived the act of complaining as more important in preserving the social tie (MComplained to Trainer = 5.29, MComplained on Website = 3.93; t(70) = 2.65, p < .01). Conversely, when those with weak ties complained, the recipient of the complaint (trainer vs. anonymous website) had no effect on their perceptions of tie-strength preservation (MComplained to Trainer = 3.15,
MComplained on Website = 3.62; t(72) = -1.02, p = .31). Third, we found that increases in perceived tie-strength preservation from complaining led to increased loyalty intentions (b = .40, t = 3.21, p < .01). Finally, in the presence of tie-strength preservation, the interaction effect between tie strength and complaint recipient on intended loyalty dropped in significance (b = .20, t = 1.71, p < .10), although it did not become nonsignificant. A bootstrapping analysis with 10,000 samples confirmed that the indirect path of tie-strength preservation was significant, according to a model of tie strength · complaint recipient → tie-strength-preservation → intended loyalty (95% confidence interval: [.04, .55]). Thus, we conclude that tie-strength preservation at least partially mediates the moderating effect of tie strength on the relationship between complaint recipient and intended loyalty (see Figure 3).
The results of this study reveal the importance of directing strongly tied customers to complain directly to the service provider who provided the unsatisfactory service, rather than to externalize their disappointment anonymously to an indirect outlet. Voicing one’s feelings directly to the provider enables the customer to preserve the social tie (H2) by taking responsibility for the complaint and investing in a particular tie. Next, we consider communication strategies that service providers can use to fortify the effect of complaining after a service failure on loyalty.
In Studies 4a and 4b, we examine the strategic importance of service providers’ orientation toward negative feedback (H3a–b). We examine the three-way interaction of complaining, tie strength, and the authenticity of providers’ openness to feedback on intended loyalty in a controlled setting (Study 4a). Then, we narrow our focus to strongly tied customers and examine the interaction effect of providers’ authentic openness to feedback and complaining in a behavioral study with real interpersonal interactions (Study 4b).
Study design. Participants were 312 individuals from MTurk who took part in a 2 (tie strength: strong vs. weak) · 2 (service provider feedback orientation: authentic vs. inauthentic) · 2 (manipulated feedback: complain vs. do not complain) between-subjects study for a payment of $.50. Participants read a scenario about a failure that occurred with a personal trainer with whom they felt either a strong tie or a weak tie (using verbiage similar to the stimuli in Study 3). The scenario then manipulated the authenticity of the trainer’s openness to feedback by either indicating that the trainer was authentically open to feedback or simply following a feedback protocol required by management.3 Participants gave feedback either about the quality of the trainer’s service (complain condition) or about their own fitness goals (control [i.e., no-complain] condition). The following text was shown in the complain condition [no-complain condition]:
Authentic openness to feedback: “I would like to ask for your feedback on today’s workout [what your fitness goals are for the next year]. While we are required by regulation to ask for your feedback on today’s session [information on your fitness goals], we are very open to your comments and take your feedback [your fitness goals] very seriously to help us improve our services. Please take a few minutes to fill out this questionnaire on how things went today and whether anything went wrong or wasn’t to your satisfaction [what your fitness goals are]. Again, I appreciate you taking the time to give us feedback on today’s service [on what your fitness plans are in the next year].”
Inauthentic openness to feedback: “I would like to ask for your feedback on today’s workout [what your fitness goals are for the next year]. We are required by regulation to ask you for your feedback on today’s fitness assessment [your fitness goals for the next year]. So, please take a few minutes to fill out this questionnaire.”
Next, participants in the complain condition were instructed to describe the service encounter and anything that went wrong, while those in the no-complain condition were asked to describe their fitness goals. Then, participants answered questions pertaining to our dependent measures of loyalty (a = .85) and manipulation-check questions about perceived tie strength (a = .92) and how open the trainer was to feedback (see Table 2; r = .91). Finally, they indicated whether something went wrong during the service encounter.
Results. We first confirmed that participants in the strong-tie condition perceived stronger ties to the trainer than those in the weak-tie condition (MStrong Tie = 5.27, MWeak Tie = 2.26; t(311) = 7.16, p < .001) and that those in the authentic condition felt that the trainer was more open to feedback than those in the inauthentic condition (MAuthentic = 5.11, MInauthentic = 3.36; t(311) = 5.22, p < .001). We also confirmed that the participants experienced a service failure across all conditions (MService Failure = 5.65, MMidpoint = 4.00; t(311) = 4.88, p < .001).
The results support H3a–b. Specifically, an ANOVA revealed a significant three-way interaction among providers’ authentic openness, tie strength, and complaining (F( 1, 308) = 4.82, p < .05; incremental adjusted R2 = 7%, p < .01). A breakdown of this three-way interaction shows a significant two-way interaction between providers’ expression of openness and complaining behavior on intended loyalty for strongly tied customers (H3a; F( 1, 150) = 5.66, p < .05; incremental adjusted R2 = 5%, p < .01). Specifically, strongly tied participants were more likely to become loyal after complaining (vs. not complaining) to an authentically open trainer (MComplained = 4.79, MDid Not Complain = 3.96; t(76) = 2.64, p < .01). However, they showed no difference in intended loyalty after complaining (vs. not complaining) to an inauthentically open trainer (MComplained = 3.53, MDid Not Complain = 3.85; t(74) = -.63, p = .54). Further, and consistent with H3b, the two-way interaction for weakly tied customers was nonsignificant (F( 1, 158) = 1.23, p = .19; incremental adjusted R2 = -1.7%, p > .10). Thus, for participants with weak social ties, the trainer’s authenticity had no effect on intended loyalty irrespective of whether they complained.
From Study 4a, it is clear that the impact of service providers’ authentic openness to negative feedback emerges in strong-tie (H3a) but not weak-tie relationships (H3b). Next, we conducted a behavioral study to test H3a in a more externally valid service setting with actual interactions with a service provider.
Study design. Participants were 96 individuals who took part in a 2 (service provider feedback orientation: authentic vs. inauthentic) · 2 (manipulated feedback: complain vs. do not complain) between-subjects behavioral study in return for a payment of $5. A hired actor played the role of a personal trainer. The trainer conducted a fitness assessment, one at a time, on each of the 96 participants. Given the results of Study 4a, we ensured that the actor created strong social ties with all the participants by using a scripted conversation incorporating incidental similarity, similar to the one used in Study 3. Then, during the fitness assessment, the actor created a service failure by appearing distracted and rude. After the assessment was completed, the actor varied her openness to feedback about the quality of her service (vs. the participant’s fitness goals), using a manipulation identical to that of Study 4a.
The actor then provided the participant with a feedback form and left the room. The participants in the complain condition filled out a form requesting that they describe the service encounter and anything that went wrong. Those in the no-complain condition filled out a form requesting that they describe their fitness goals. After the participants completed the feedback forms, they exited the room and were greeted by a GRA who provided them with an exit survey. In the survey, participants answered questions pertaining to our dependent measures of intended loyalty (a = .88). Finally, they answered manipulation-check questions about how open the trainer was to feedback (r = .91), the extent to which they perceived strong ties to the trainer (a = .83), and whether they thought something had gone wrong during the fitness assessment.
Results. We first confirmed that participants in the authentic condition felt that the trainer was more open to feedback than those in the inauthentic condition (MAuthentic = 4.81, MInauthentic = 3.16; t(94) = 5.53, p < .001). Further, we confirmed that participants perceived strong social ties to the trainer (MStrong Tie = 5.86, MMidpoint = 4.00; t(95) = 4.91, p < .001) and experienced a service failure (MService Failure = 5.27, MMidpoint = 4.00; t(95) = 4.52, p < .001).
An ANOVA revealed that in the context of strong-tie relationships, providers’ authentic openness to negative feedback (F( 1, 92) = 4.21, p < .05) had a positive effect on intended loyalty, whereas the main effect of complaining was non-significant (F( 1, 92) = 1.56, p = .17). A test of H3a revealed a significant two-way interaction (F( 1, 92) = 5.66, p < .05; incremental adjusted R2 = 5.5%, p < .05). Specifically, participants indicated higher loyalty after complaining (vs. not) when the trainer was authentically open to feedback (MComplained = 4.84, MDid Not Complain = 4.17; t(76) = 2.54, p < .05). Conversely, they showed no difference in intended loyalty after complaining (vs. not) when the trainer was perceived as inauthentic (MComplained = 3.95, MDid Not Complain = 4.01; t(74) = -.06, p = .95; see Figure 4).
Discussion. The results of Studies 4a and 4b support H3a–b and demonstrate the important role that service providers’ authentic (vs. inauthentic) openness to feedback plays in engendering loyalty intentions when customers with strong ties complain, but not when customers with weak ties complain.
To validate the need for providers both to appear authentically open to feedback and to signal the intention to use that feedback, we ran a study to distinguish these two components. Participants were 140 individuals from MTurk who participated in a study for a payment of $.50. In this study, we manipulated authenticity differently than in Studies 4a–b, by removing the statement “I will use your feedback to improve my service.” Thus, the authenticity construct made no mention of using the feedback to improve the service provision. The interaction between simply appearing open and customers’ complaining behavior on their intended loyalty was nonsignificant (F( 1, 136) = .46, p = .45) and, thus, differed from the significant result found when we used both components of authenticity. These findings underscore the importance of training service providers to authentically signal both their openness to feedback from strongly tied customers and their intention to use the feedback to improve their future service provision.
In the previous studies, we tested tie strength within a given customer–service provider relationship; however, we contend that at an aggregate level, service industries differ in the extent to which customers and providers naturally forge strongly tied relationships. Indeed, in certain industries, service providers and customers are more likely to engage in interpersonal interactions (Anderson and Narus 1991). Strong-tie service industries, which are characterized by naturally occurring relational ties between customers and providers (e.g., personal care, beauty, and hospitality services), are generally perceived as higher-touch contexts. In these industries, providers “cocreate” experiences with customers and are frequently informal sources of social support (Cipolla and Manzini 2009). Further, firms in strong-tie industries allow customers to define or express themselves, and, in the process of receiving the service, customers tend to require interpersonal interactions with their providers (Yim, Tse, and Chan 2008).
In contrast, industries characterized by weak social ties (e.g., administrative, maintenance, repair services) are more functional in nature and likely to produce quid-pro-quo (rather than communal) relationships between providers and customers. In such industries, the outcome is deemed more important than the process. Relationships between providers and customers in weak-tie industries exist primarily to facilitate exchange and not relationships (Zhang et al. 2016).
Past research in the RM domain (e.g., Palmatier et al. 2006) has argued that in relational service contexts, factors related to the relationship have a strong impact on loyalty, whereas in transactional service exchanges, the relationship between buyers and sellers has little influence on loyalty. We use a related line of reasoning to propose that in industries naturally amenable to strong social ties, complaining is more likely to preserve the ties between customers and providers than it is in weak-tie industries, wherein relational connections are less likely to form and consequently less likely to be preserved by offering diagnostic feedback. However, for customers who withhold feedback, the industry context is less important in facilitating loyalty because the customer has neither adhered to relational norms of open communication nor put forth the effort to preserve the social tie.
Thus, we expect that the effects hypothesized in H1 will extend beyond specific service relationships to entire service industries. We test this notion using data collected from Yelp.com (hereafter, Yelp). Yelp is one of the most popular review websites for service industries (with over a hundred million reviews) and is widely considered by service providers to be a good source of feedback from their clients. “Yelpers,” individuals who log in to offer their feedback on businesses they have used, post their reviews nonanonymously (e.g., all Yelpers have their first name and initial of their last name explicitly listed on their profile and most include a picture). Indeed, many firms look at Yelp reviews directed at their business on a daily or weekly basis and respond directly to customers via the Yelp interface.
Pretest. In the main study, the type of service industry in which the service interaction takes place behaves as a proxy for the tie strength that exists between the customers and service providers. To identify service industries that are generally characterized by strong versus weak ties, we ran a pretest with 102 participants from MTurk who participated for a payment of $.50. We presented the participants with six professions and asked them to judge (on a Likert scale from 1 to 7) the degree to which each profession could be characterized by strong-tie attributes (connectedness, relational, interpersonal) and weak-tie attributes (transactional, outcome-driven, nonconversational) (Cipolla and Manzini 2009; Zhang et al. 2016; see Table 2). A priori, we classified the professions of bartender, hair stylist, and personal trainer as belonging to strong-tie industries and the professions of electrician, plumber, and mechanic as belonging to weak-tie industries. Our goal in the pretest was to verify empirically these classifications, and we were able to do so.
Specifically, the strong-tie professions were judged as more connected (MStrong Tie = 4.40, MWeak Tie = 2.32; t(100) = 14.35, p < .001), relational (MStrong Tie = 4.97, MWeak Tie = 2.67; t(100) = 12.96, p < .001), and interpersonal (MStrong Tie = 4.96, MWeak Tie = 2.97; t(100) = 9.74, p < .001) than the weak-tie professions. Similarly, the weak-tie professions were judged as more transactional (MWeak Tie = 5.86, MStrong Tie = 3.95; t(100) = 9.13, p < .001), outcome-oriented (MWeak Tie = 6.12, MStrong Tie = 4.97; t(100) = 4.96, p < .001), and nonconversational (MWeak Tie = 4.11, MStrong Tie = 2.14; t(100) = 8.74, p < .001) than the strong-tie professions. We did not find any differences within the strong-tie and weak-tie industry classifications. Participants judged bartending, hairstyling, and personal training to be equally connected (MHair Stylist = 4.45, MPersonal Trainer = 4.65, MBartender = 4.38; F( 2, 48) = .89, p = .42), relational (MHair Stylist = 5.03, MPersonal Trainer = 5.14, MBartender = 4.98; F( 2, 48) = .43, p = .65), and interpersonal (MHair Stylist = 5.37, MPersonal Trainer = 5.64, MBartender = 5.46; F( 2, 48) = .36, p = .77). Likewise, participants judged the professions of electrician, plumber, and mechanic to be equally transactional (MElectrician = 5.81, MPlumber = 6.02, MMechanic = 5.81; F( 2, 46) = .43, p = .65), outcome-driven (MElectrician = 6.18, MPlumber = 6.15, MMechanic = 6.00; F( 2, 46) = .59, p = .56), and nonconversational (MElectrician = 4.91, MPlumber = 4.83, MMechanic = 4.94; F( 2, 46) = .63, p = .60). Thus, we determined that tie strength can be operationalized at the industry level. Given the scope of the data collection required for the Yelp reviews, we chose to focus on two particular industries in the main study: hair styling (strong ties) and plumbing (weak ties).
Main study. The feedback offered by Yelpers is relevant to this study insofar as it corresponds closely to the constructs we are examining. First, contributors give feedback about service experiences across many industry contexts, and, as confirmed in the pretest, service industry type can serve as a proxy for tie strength. Second, using complaints culled from Yelp, we can operationalize complaining behavior according to the length of the complaint. Specifically, the number of words in the complaint can serve as a proxy for the degree to which the customer offers his/her negative feedback (short vs. long complaints). Thus, we can capture both of our key independent measures from Yelp reviews. We restricted our sample to hair salons and plumbers in seven major cities: Atlanta, Chicago, Los Angeles, Houston, San Diego, Austin, and New York. We further restricted our sample to more recent reviews (within the past 12 months) to assess our loyalty measures (described in the “Dependent Variables” section). We needed to identify service failures in general, and in particular, those that were due to the action of a service provider (hair stylist or plumber) and not another cause (e.g., price, poor ambiance, lack of parking). Consistent with past research (e.g., Ho-Dac, Carson, and Moore 2013), we identified whether a service failure had occurred using the reviews’ star ratings (one-, two-, and three-star reviews [out of five] were indicative of a failure). Then, we combed through the content of the reviews to ensure that those with low star ratings were indeed complaints; we kept only those that pertained to provider-related failures. This process culminated in 301 reviews from an original sample of 3,000 reviews.
Independent variables. Consistent with the results of our pretest, we operationalized tie strength at the industry-level by coding reviews that pertained to hair stylists as strong-tie encounters ( 1) and those that pertained to plumbers as weak-tie encounters (0). In our previous studies, our measure of complaining behavior included customers who complained after a service failure and those who did not complain. In the case of Yelp reviews, anyone who has experienced a service failure ( 1, 2, and 3 stars) has invariably complained, by virtue of posting a Yelp review with a low star rating. Therefore, we were unable to capture customers who experienced a failure but chose not to complain. Instead, we measured the extent of complaining as a proxy for complaining behavior by counting the number of words in the complaint.
Control variables. We controlled for several variables at the Yelp review, Yelper, and firm levels using data available on Yelp. At the level of the Yelp review, we controlled for failure severity using the star rating of the review (reverse coded: 1 star = less severe, 2 stars = moderate severity, and 3 stars = severe failure). At the level of the Yelper, we controlled for his/her average ratings, number of friends, tenure at Yelp (in years), and gender (1 = female, 0 = male). Lastly, we controlled for differences in the type of firm by including the firm’s overall rating (1–5 stars) and its total number of reviews.
Dependent variables. Since Yelp reviews do not provide any loyalty information (i.e., there is no explicit indication of whether one would return or has returned to a business), we assessed loyalty in two ways: probable loyalty and actual loyalty. To assess probable loyalty, we hired two GRAs to analyze the reviews in our sample and code whether the Yelper was likely to return ( 1) or not (0). For some of the reviews, probable loyalty could not be assessed because the review failed to hint at any sense of loyalty or disloyalty; as a result, these reviews were dropped from the sample. Examples of Yelpers indicating a willingness to try the service again included, “Overall this place seems like it could be good for a lot of people, but if you do go here, make sure you are clear on what you want and make sure you feel like it will hold through your event,” “I’m not sure if I will be returning although my hair cut came out really nice hence the 3 stars” and “I’ve had a bit of an up and down experience with this place, but overall they’re informative, efficient, and priced for what they are and where they are. If you want the convenience of a trustworthy auto shop in the middle of the city, then this is your best bet.” Examples of Yelpers indicating that they would not return to the service provider included, “I suppose I will have to go elsewhere,” “I walked out frustrated, upset and will never be back. Unbelievable…if you have a lot of hair, I do not recommend,” and “Do not go here unless you for some reason desire to pay someone to waste your time.” The two raters had very similar ratings (r = .92). Any discrepancies were resolved through discussion with one of the authors. This process culminated in a data set of 206 Yelp reviews (104 about hair stylists and 102 about plumbers) with probable loyalty coded from the reviews.
To assess actual loyalty, the GRAs personally messaged the 206 Yelpers for which we had measures of probable loyalty, using a Yelper-to-Yelper messaging function in which anyone with a Yelp account can send a personal message to another Yelper. We messaged the Yelpers in our sample with the following message, “Hello, we found your review of [firm name] very helpful. Did you ever go back or would you go back in the future?” We received 67 responsesfrom Yelpers (34whorevieweda hair stylist and 33 who reviewed a plumber) who indicated whether they returned to the business or intended to in the near future (coded as 1) or never did or would not return (coded as 0).
We ran two logistic regression models, one with probable loyalty and one with actual loyalty as the dependent variable. The independent and control variables were identical in both models: tie strength, extent of complaining, their interaction, and the controls. The m (probable or actual) loyalty models for Yelper I at firm j were estimated as follows:
The descriptive statistics and correlation matrix (r < .50) are reported in Table 5. From the descriptive statistics of both probable and actual loyalty, it is evident that around half of the Yelpers are, to some extent, willing to give a business another try after a failure. The average complaint was 219 words, with length varying dramatically (range: 13–950 words).
The logistic regression results (see Table 6) revealed that industry tie strength has a positive effect on probable loyalty (b = .004, Wald = 8.87, p < .01) but not on actual loyalty (b = .002, Wald = 1.39, p = .24). While the effects are slightly different, this difference is not germane to the central thesis of this article. Furthermore, the extent of complaining alone does not directly impact probable (b = -.379, Wald = 1.22, p = .27) or actual (b = -.405, Wald = .49, p = .48) loyalty. With respect to the hypothesized interaction effect of tie strength and extent of complaining on loyalty, we find support for H1. Specifically, for customers in strong-tie (vs. weak-tie) industries, longer complaints result in higher probable (b = .002, Wald = 3.97, p = .06; incremental adjusted R2 = 5%, p < .05) and actual (b = .005, Wald = 4.93, p < .05; incremental adjusted R2 = 7%, p < .01) loyalty.
We establish that in strong-tie industries—characterized by connectedness, rapport, and close interpersonal relationships—the positive effect of complaining manifests more than in weak-tie industries. Thus, the effect hypothesized in H1 generalizes beyond individual relationships to entire service industries. Yelpers who complained to a greater extent about failures in strong-tie (vs. weak-tie) industries both expressed intentions to return and actually returned to the providers who had failed them. This further supports our contention that more involved disclosure of dissatisfaction behaves as a mechanism for keeping strongly tied customers connected after a failure.
We demonstrate the value of customer complaints beyond simply being a source of information guiding managers whether to initiate service recovery efforts or how to improve their future offerings. We contend that complaints also serve as a relationship-building tool for customers who have formed strong ties to the firm’s service providers, but not for those for whom these ties do not exist. Specifically, we show that the act of complaining may drive strongly tied customers to become loyal toward the very providers who failed them. Our findings generate several theoretical and managerial implications, as well as directions for future research.
TABLE: TABLE 5 Correlation Matrix and Descriptive Statistics (Study 5)
| Variable | Measure | M (SD) | (SD) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
|---|
| 1. Probable Loyalty | 0 = not likely to return; 1 = likely to return | 0.53 | -0.5 | 1 | | | | | | | | | | |
| 2. Actual Loyalty | 0 = did not return; 1 = returned | 0.51 | -0.5 | .426** | 1 | | | | | | | | | |
| 3. Industry Tie Strength | 0 = weak tie; 1 = strong tie | 0.53 | -1 | 0.052 | 0.036 | 1 | | | | | | | | |
| 4. Review Extent of Complaining | 13–950 words | 219 | -172 | -.185* | -0.209 | -0.017 | 1 | | | | | | | |
| 5. Review Failure Severity | 1–3 stars (reverse-coded, where 1 star = severe failure and 3 stars = minor failure) | 2.3 | -0.87 | -.470** | -.274* | -.181* | -0.094 | 1 | | | | | | |
| 6. Yelper Average Rating | 1–5 stars | 3.3 | -0.83 | .183* | 0.138 | -.154* | 0.036 | .237** | 1 | | | | | |
| 7. Yelper No. of Friends | 0–5,000 | 74 | -375 | 0.105 | -0.154 | 0.029 | 0.079 | 0.093 | 0.062 | 1 | | | | |
| 8. Yelper Tenure | 1–11 years | 4.99 | -2.66 | .171* | -0.091 | -.301** | .173* | 0.136 | .274** | .186** | 1 | | | |
| 9. Yelper Gender | 0 = male; 1 = female | 0.73 | -0.44 | -0.004 | -0.194 | .494** | 0.007 | -0.095 | -0.022 | 0.061 | -0.073 | 1 | | |
| 10. Firm Overall Rating | 1–5 stars | 4.13 | -0.8 | -0.005 | -0.016 | .550** | -0.048 | -0.062 | -0.094 | 0.028 | -.197** | .270** | 1 | |
| 11. Firm Total Reviews | 2–1,441 reviews | 183.46 | -198.97 | .152* | 0.041 | .250** | 0.052 | 0.038 | -0.075 | 0.126 | -0.036 | .179* | .153* | 1 |
*p < .05.
**p < .01.
Much of the past work on complaining has focused on what drives customers to complain (e.g., Mittal, Huppertz, and Khare 2008; Mittal and Kamakura 2001; Stephens and Gwinner 1998; Zhang, Feick, and Mittal 2014) and how firms should manage complaints (e.g., Fornell and Wernerfelt 1987; 1988; Fornell and Westbrook 1984). However, little research has focused on how the mere act of complaining can affect loyalty. We show that complaining may not have a direct effect on loyalty but that its influence is contingent on the strength of the social tie between the customer and service provider. Further, we demonstrate the contingent value of complaining at both the level of a single relationship and the level of an entire industry.
TABLE: TABLE 6 Logistic Regression Results of Industry Tie Strength and Extent of Complaining Using Yelp Data (Study 5)
| Variable | Probable Loyalty Model | Actual Loyalty Model |
|---|
| Industry Tie Strength | .004 (.001)*** | .002 (.002) |
| Review Extent of Complaining | -.379 (.343) | -.405 (.577) |
| Industry Tie Strength · Review Extent of Complaining | .002 (.001)* | .005 (.002)** |
| Review Failure Severity | -1.566 (.276)*** | -.883 (.423)** |
| Yelper Average Rating | .233 (.236) | .551 (.535) |
| Yelper No. of Friends | .003 (.002)* | -.005 (.006) |
| Yelper Tenure | .1 (.074) | -.085 (.135) |
| Yelper Gender | -.02 (.244) | -.739 (.416)* |
| Firm Overall Rating | -.015 (.268) | -.746 (.634) |
| Firm Total Reviews | .002 (.001) | 0 (.001) |
*p < .10.
**p < .05.
***p < .01.
Notes: Parameter estimates are reported, with standard errors in parentheses.
We respond to a gap in the literature by showing that, despite strongly tied customers’ reluctance to complain (Mittal, Huppertz, and Khare 2008), when they do so, they are more loyal (vs. weakly tied customers) to the very service provider who failed. Further, we demonstrate a novel underlying process driving the moderating effect of tie strength on the relationship between complaining and loyalty: tie-strength preservation. That is, by investing in the relationship (by offering negative feedback), customers with strong ties preserve, or enhance, their social ties to the provider and, as a result, are subsequently more loyal. However, for weakly tied customers, because there is no social tie to preserve, complaining cannot act as a preservation mechanism and therefore fails to positively influence loyalty. Thus, we add to past work on tie strength by showing its paradoxical role in both dissuading customers from complaining and also enhancing their loyalty when they are encouraged to do it.
We operationalize tie strength at both the individual-relationship and industry levels and show that our predictions are robust across types of social ties. Testing newly formed social ties in initial service encounters is a particularly conservative test of our theory, as the interaction is so brief that there is little time for a customer and service provider to form a meaningful relationship. Given these consistent findings, we suggest that in future work, researchers expand their understanding of when and how social ties develop to include brief encounters, shared social connections, perceptions of incidental similarity, and an overall “vibe” between individuals, in addition to the more long-standing relational characteristics (e.g., tenure, frequency of purchasing, number of ties) used to define social ties in past research (e.g., Mittal, Huppertz, and Khare, 2008; Rindfleisch and Moorman 2001; Zhang, Feick, and Mittal 2014).
Service firms are increasing their focus on customer complaints (Michel, Bowen, and Johnston 2008), but they may not be effectively leveraging all the value embedded in feedback systems and, therefore, may be missing opportunities to convert service failure experiences into customer loyalty. Further, we conclude that service providers are not being trained to use complaints to facilitate strong ties. Anecdotally, we found that none of the managers we interviewed viewed customer complaints as a way to maintain relationships with customers. Instead, managers focus heavily on the content of customers’ complaints to identify problems with current service offerings. We urge managers to recontextualize negative feedback and view complaints as a tool to strengthen relationships that may be at risk.
Across our studies, our key moderator was the degree to which customers felt a strong tie to service providers, either within or across service contexts. Although strongly tied relationships are usually the product of multiple interactions between two individuals (which we test in Studies 1 and 5), we also show evidence that providers can create social ties in a matter of minutes using simple rapport-building techniques. While social ties are often assumed to arise organically, we have identified several techniques that service providers can use to propel these relationships. For instance, by identifying similarities between themselves and the customers, or sharing small bits of personal information about themselves, providers can create a multidimensional identity that customers can connect to emotionally.
We demonstrate to managers that collecting negative feedback is a process that requires a relatively specific protocol to achieve maximal effectiveness. Given that customers who have suffered a service failure may want to externalize their dissatisfaction to other potential customers or peers, the results indicate that providers should concentrate their complaint solicitation efforts on strongly tied customers. By engaging customers in an online forum that the provider is known to read, sending nonanonymous follow-up surveys, or engaging in a casual discussion after the service is rendered, providers may avoid situations in which the customer complains to a third party rather than directly to them. This is relevant given that complaints are effective in increasing loyalty more so when they are directed to the firm, and specifically to the individual who failed. Furthermore, when providers solicit negative feedback with an authentically open demeanor—demonstrating a true interest in hearing negative feedback and a willingness to integrate the feedback into their service provision—strongly tied customers feel more inclined to remain loyal after complaining. While this result may seem intuitive, service providers often do not display consistent openness and come across as inauthentic. This could be because they do not believe that management is open to employee input, so they, in turn, are less open to customer feedback. Likewise, service providers who are not trained to deal effectively with negative feedback may find it threatening to receive, and thus avoid soliciting feedback in a sincere manner. We advocate that managers consider providers’ natural interpersonal skills when making hiring decisions. The ability to solicit feedback in an authentic way is difficult to teach but a powerful aid in creating loyalty among customers with whom providers share strong ties.
Managers should be cognizant of the industry within which they operate when training service personnel. We show evidence that the positive effect of complaining holds in strong-tie industries (e.g., high-touch contexts such as personal care, beauty, therapeutic, and design services) in which customers naturally create social ties with providers. On the other hand, in weak-tie industries (e.g., low-touch administrative and transaction-oriented industries such as maintenance, administrative, and repair), negative feedback is largely ineffective in facilitating loyalty. Given this differential result, we suggest that service providers customize the kind of feedback they solicit depending on the context in which they work. In strong-tie industries, we suggest that managers strongly incentivize providers to garner direct negative feedback using an open demeanor if a service failure has occurred. In contrast, in weak-tie industries, perhaps service providers should elicit “suggestions for improvement” rather than negative feedback after a service failure.
Overall, we urge managers to rethink the way they view customer complaints. In many firms, complaint management is relegated to the customer service department, a cost center. Often, the solicitation for feedback is an automated component of customer relationship management systems, making the process impersonal and unlikely to increase customer loyalty. This work indicates that customer complaints would be better viewed as a means by which customers engage with service providers, rather than as a way by which they vent or demand compensation from a firm. By changing managers’ perspective on negative feedback, we think that service failures will be viewed as an opportunity to grow and nurture relationships rather than as a source of defection.
In this package of studies, we focus on situations in which the service provider caused the service failure. While we think this makes the failure more interpersonally relevant, it would be informative to examine failures caused by the firm (rather than an individual). For instance, it would be interesting to see how customers attribute the failure to an entity (vs. a person) and how an indirect failure by the firm would affect an interpersonal tie with the service provider. Furthermore, although we confirm that the predicted effects are robust across both process and outcome failure types, future research might benefit from examining other aspects of failure (e.g., controllability, attribution) to see whether the tested relationships change.
Due to its natural connection to tie strength and relationship marketing, we chose to focus on tie-strength preservation as the underlying process of interest. However, to delve deeper into additional underlying processes, we urge researchers to examine the roles of accountability and guilt, both of which might influence the degree to which strong-tie customers are willing to commit to the relationship after complaining. Furthermore, we did not directly examine the effect of complaining on providers’ service recovery efforts. Instead, we focused on how the simple act of voicing negative feedback may increase customers’ loyalty, even without the explicit promise of rectifying the failed service. We urge researchers to continue this line of research by examining how service recovery efforts may moderate the relationship between complaining and loyalty in strong-tie relationships.
Finally, we recommend that customers with strong ties provide feedback directly to the provider, rather than externalize their frustration to a friend or complain anonymously (Study 3). If complaints cannot be directed at the provider, then non-anonymous third-party complaints (Yelp data; Study 5) are still valuable in that the degree of engagement, via the extent complaining, in these forums is still meaningful and informative to managers. Still, we urge researchers to collect data on face-toface complaints, which we believe will be even more powerful in preserving social ties, ultimately leading to loyalty.
Footnotes 1 See https://www.upcounsel.com/blog/your-greatest-source-oflearning.
2 We manipulate (rather than measure) complaining behavior to avoid concerns of endogeneity associated with measuring complaining and subsequently using this measure to predict loyalty. To capture the more realistic effect of elective (as opposed to manipulated) complaining behavior on loyalty, we measure actual complaining behavior in Studies 1 and 5.
3 To validate that participants discerned the difference between a provider who portrays an authentic openness to feedback and one who is simply executing a required protocol, we conducted a posttest with 102 participants from MTurk in return for a payment of $.50. Participants read the manipulation used in the main study and subsequently indicated how authentic or inauthentic the provider was in her request for feedback. Using measures adapted from prior research (Grandey et al. 2005), we confirmed that in the authentic condition, participants thought that the provider was more genuine (MAuthentic = 4.81, MInauthentic = 3.85; t(101) = 3.2, p < .002), personally curious about the feedback (vs. fulfilling a professional obligation) (MAuthentic = 3.47, MInauthentic = 2.34; t(101) = 3.3, p < .002), and personally motivated (vs. required) to ask for feedback (MAuthentic = 5.96, MInauthentic = 5.18; t(101) = 2.5, p < .01).
GRAPH: FIGURE 4 The Moderating Effect of Providers’ Authentic Openness to Feedback on the Relationship Between Complaining and Intended Loyalty, for Customers with Strong Ties (Study 4b)
GRAPH: FIGURE 2 The Moderating Effect of Tie Strength on the Relationship Between Complaint Recipient and Tie-Strength Preservation (Study 3)
GRAPH: FIGURE 1 The Moderating Effect of Tie Strength on the Relationship Between Complaining and Intended Loyalty (Study 2)
DIAGRAM: FIGURE 3 Moderated Mediation Analysis of Tie-Strength Preservation (Study 3)
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Record: 178- The Clash of the Titans: On Retailer and Manufacturer Vulnerability in Conflict Delistings. By: Van der Maelen, Sara; Breugelmans, Els; Cleeren, Kathleen. Journal of Marketing. Jan2017, Vol. 81 Issue 1, p118-135. 18p. 1 Diagram, 6 Charts. DOI: 10.1509/jm.15.0282.
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The Clash of the Titans: On Retailer and Manufacturer Vulnerability in Conflict Delistings
The days of dominant manufacturers dictating the game to obedient retailers are long gone. When parties believe they have equal bargaining power, negotiations end in deadlock more frequently and result in conflict delistings wherein the manufacturers’ brands get removed from the retailers’ assortments. This might cause major sales losses as consumers are forced to change stores or brands. The authors study both parties’ vulnerabilities by investigating their market share shifts during a highly publicized real-life conflict delisting executed by a major retailer against a major manufacturer, involving multiple brands and categories. Generally, both parties lost sales, yet the retailer was the most vulnerable party. Manufacturer-brand and retailer-assortment characteristics moderated both parties’ vulnerability: the manufacturer and retailer became respectively less and more vulnerable when a high-equity brand was delisted in a small assortment. Both parties lost more in necessity than in impulse categories. The authors additionally investigate long-term consequences that occurred after the conflict was settled: the retailer’s market share recovered to the predelisting level, whereas the manufacturer’s share underwent a long-term level rise.
Conflict Delistings
The days of dominant manufacturers dictating the game to obedient retailers are long gone. When parties believe they have equal bargaining power, negotiations end in deadlock more frequently and result in conflict delistings wherein the manufacturers’ brands get removed from the retailers’ assortments. This might cause major sales losses as consumers are forced to change stores or brands. The authors study both parties’ vulnerabilities by investigating their market share shifts during a highly publicized real-life conflict delisting executed by a major retailer against a major manufacturer, involving multiple brands and categories. Generally, both parties lost sales, yet the retailer was the most vulnerable party. Manufacturer-brand and retailer-assortment characteristics moderated both parties’ vulnerability: the manufacturer and retailer became respectively less and more vulnerable when a high-equity brand was delisted in a small assortment. Both parties lost more in necessity than in impulse categories. The authors additionally investigate long-term consequences that occurred after the conflict was settled: the retailer’s
Keywords: conflict delisting, bargaining power, retailer-manufacturer negotiations, brand equity, assortment size nly a few decades ago, large and dominant manufacturers could easily impose prices and other buying
O conditions on typically small retailers in a fragmented
retailer landscape (Ailawadi, Borin, and Farris 1995). Over time, retailers have gained bargaining power and have altered the retailer-manufacturer relationships thanks to the rise of store brands, the scarcity in shelf space (product proliferation), and the availability of scanner data (Ailawadi et al. 2010; Farris and Ailawadi 1992; Kadiyali, Chintagunta, and Vilcassim 2000; Messinger and Narasimhan 1995). Instead of being at the beck and call of all-powerful manufacturers, retailers now can stick to their guns during negotiations. As a counterbalance to large manufacturers bringing their highly popular brands into the game (Soberman and Parker 2006), retailers can force manufacturers to cooperate by leveraging their final decision about which items to carry in their assortments (Dukes, Geylani, and Srinivasan 2009). The interdependency, and the often conflicting interests of retailers and manufacturers, can cause major clashes between parties with equal bargaining power that can fairly be called “titans” (Gaski 1984; Rosenberg and Stern 1971).
When a clash of the titans is set on a collision course, retailers as well as manufacturers can decide to delist the manufacturers’ brand(s) from the retailers’ assortment. We refer to these delistings as “conflict delistings.” In case of disagreement, manufacturers can decide to stop distributing their brand(s) via a specific retailer, and retailers can likewise delist all items of a manufacturer’s brand(s) from the assortment (Sloot and Verhoef 2008). Business press articles confirm that conflict delistings frequently occur when parties fail to agree on terms and conditions. For example, in 2009, Colgate-Palmolive decided to remove all of its products from the shelves of the Philippine grocery store SM because of disputes over terms of trade (Isip 2009). In November 2010, Costco, the third-biggest retailer in the United States, decided to cease stocking Coke, Sprite, Fanta, Powerade, and Dasani products because the CocaCola Company did not satisfy Costco’s request to lower its prices (Ritson 2010). Other conflict delisting examples include the decision of toy shop Fun in 2009 to stop offering Mattel’s Barbie because of the manufacturer’s unacceptable price increase (Lecluyse 2011). The media attention typically accompanying conflict delistings triggers consumer awareness, making it very likely that consumers take this information into consideration even before their shopping trip starts.
Draganska, Klapper, and Villas-Boas (2010) point out that if both parties seem to have equal bargaining power, it becomes unclear who gains the upper hand when negotiations run aground. In a conflict delisting, both parties may get hurt because they both risk sales losses. The magnitude of these sales losses, and thus the retailer’s and/or manufacturer’s vulnerability in the conflict, depends on the decisions made by the
Journal of Marketing Vol. 81 (January 2017), 118-135 consumer. When confronted with a delisting, consumers are forced to adjust their purchase behavior by either changing stores or changing brands (Sloot and Verhoef 2008). If the
consumer decides to switch stores in an attempt to buy the delisted brand elsewhere, the retailer becomes vulnerable and loses sales that would have been generated for the delisted brand. If the consumer instead decides to choose another brand from the same category at the afflicted store, the manufacturer becomes vulnerable and loses the sales that would have been generated at the afflicted retailer. These sales losses give an insight in a party’s true bargaining position–a necessary antecedent to bargaining power–which is assumed to be weaker the more a party loses in case of failure of negotiations (Dukes, Gal-Or, and Srinivasan 2006).
In this study, we investigate the sales consequences of a highly publicized real-life conflict wherein a major retailer decided to delist all of a major manufacturer’s brands due to price negotiations turning sour. This unique natural field experiment involved 47 categories, ranging from cleaning to health and beauty care and food. Multiple low- and highequity brands from the manufacturer were offered and were then, consequently, delisted. In total, there were 59 brandcategory cases. The wide range of brands and categories involved in this exciting natural field experiment allows us to tackle three research objectives. The first objective is to shed light on who was more (less) vulnerable during the conflict delisting. To do so, we study the changes in brand and retailer share resulting from the conflict delisting to better understand the retailer’s and the manufacturer’s vulnerability. The second objective is to shed light on the potential factors that explain cross-category and cross-brand variation in manufacturer and retailer vulnerability. Therefore, we investigate whether and how antecedents related to the delisted brands (e.g., brand equity), to the afflicted retailer’s assortment (e.g., assortment size) and to intrinsic category characteristics (e.g., necessity/impulse) may explain the differences in brand and
retailer share changes across the different (brand-) category cases. As a third objective, we assess what happened after the conflict’s settlement by investigating the long-term impact of the conflict delisting for both involved parties.
Even though conflict delistings occur with increasing frequency in practice, our study is, to the best of our knowledge, the first to systematically investigate the consequences of such a power battle on the market positions of both parties during as well as after the delisting period. This research extends prior studies on the power balance between manufacturers and retailers and introduces conflict delistings as a new type of product unavailability in the literature. Previous studies on retailer-manufacturer power in the grocery retailing context have been mainly based on game theory, surveys, or general account data. We are the first to investigate the sales consequences resulting from drastic measures (in this case, delisting) taken by one party in a retailer-manufacturer power battle. We also contribute to the product unavailability literature by systematically investigating drivers that can explain differences across (brand-) category cases in the setting of a conflict delisting. Conclusions regarding drivers confirmed in prior product unavailability studies cannot directly be transferred to a conflict delisting situation because of major contextual differences. Applying these drivers in the unique and distinct context of a conflict delisting allows us to determine under which manufacturer-brand, retailer-assortment, and category conditions the retailer or manufacturer maintains or loses market position relative to the other players in the market. In this way, we increase the generalizability of our findings to other situations in which a retailer or manufacturer decides to carry out a delisting.
Our results will also be beneficial for industry practitioners. Both retailers and manufacturers will gain a better understanding of their market position, and especially of the impact of the moderating characteristics that explain under which conditions each party is more vulnerable during the conflict delisting situation. This, along with the insight into the longterm impact of the conflict delisting, is important information that both parties can bring to the negotiation table or use to design an appropriate strategy when confronted with a delisting.
This article is organized as follows: First, we give an overview of previous literature on retailer-manufacturer power relationships and product unavailability. These literature streams are brought together and form the basis of our conceptual framework as the product unavailability under study results from a power game gone wrong. Next, the models, data set, and results are presented that shed light on the retailer’s and the manufacturer’s vulnerability during the conflict delisting. Subsequently, we focus on the long-term consequences after the clash has come to an end. To conclude, we summarize the findings and managerial implications and discuss our limitations and directions for future research.
The Retailer-Manufacturer Power Balance
The topic of the retailer-manufacturer power balance has attracted marketing researchers from different streams. They have studied the issue from both an aggregated and a disaggregated perspective and have used different methodologies.
Studies that use aggregate industry-level financial and account data, such as trends in relative profitability, financial and stock market measures, and exercised (economic value added) and potential (market value added) market power measures, do not find empirical support for a power balance shift in favor of the retailer (Ailawadi 2001; Ailawadi, Borin, and Farris 1995; Bloom and Perry 2001; Farris and Ailawadi 1992; Messinger and Narasimhan 1995). According to these studies, the retailer performance did not improve over time relative to manufacturer performance, which is interpreted as evidence that retailer power did not increase (Messinger and Narasimhan 1995). However, these financial performance measures on the industry level might be too aggregate to give a clear view of the potential power shift. Environmental factors, such as the competitiveness of the market, may obstruct such aggregate performance measures.
Studies that base their analyses on game-theoretic principles use a more disaggregated view to provide insights into the retailer-manufacturer power balance. Results from these studies are mixed. Kadiyali, Chintagunta, and Vilcassim (2000) find evidence for extensive retailer power in two fast-moving
The Clash of the Titans / 119 consumer goods markets on the basis of the estimated division of channel profits. Draganska, Klapper, and Villas-Boas (2010), in contrast, estimate the bargaining power of six retailers and seven national brands in the German ground-coffee market and conclude that the bargaining power lies mainly with the manufacturer. An important reason for these seemingly contradictory results is the finding that the retailer-manufacturer power balance depends on the characteristics of the retail environment (Draganska and Klapper 2007) and the negotiation partner (Draganska, Klapper, and Villas-Boas 2010). Although these structural analyses have shed a new light on the power balance question, an important limitation of models that use game-theoretical principles is that the results are conditional on the underlying assumptions.
Finally, several marketing channel studies use survey instruments to assess the self-perceptions of the various channel members with respect to power and its performance consequences (e.g., El-Ansary and Stern 1972; Geyskens and Steenkamp 2001; Geyskens, Steenkamp, and Kumar 1999). They develop the definition, dimensions, bases, and measurement of power from a firm-level dyad (individual supplier vs. channel member) (Ailawadi 2001) and establish a clear link between perceived power and satisfaction of the channel members (Geyskens and Steenkamp 2001; Geyskens, Steenkamp, and Kumar 1999). Notwithstanding its lower external validity concerns and much more disaggregated perspective on the power balance between two parties, survey research has a high risk of measurement errors (e.g., social desirability), a feature especially harmful in sensitive situations such as retailer-manufacturer negotiations and conflict delistings.
The conflict delisting situation that we investigate allows us to take yet another perspective on the issue of bargaining power. Given the potential sales risks associated with a delisting, negotiations will probably only result in a conflict delisting when both players believe themselves to be powerful. Hence, studying the sales consequences of a power battle and the resulting conflict delisting situation offers a useful context to assess who is less/more vulnerable and thus who, in hindsight, truly held the strongest position. In this study, we take a disaggregate look at the power balance and focus on the observed consumers’ reactions to the conflict. Thereby, we not only alleviate previously raised concerns about external validity and survey measurement errors but also turn the focus to another–currently neglected–party in the power balance discussion. Indeed, as the final decision maker in the purchasing process, the consumer plays a key role in designating which is the more vulnerable party, taking into account all players in the market.
Product Unavailability
When a conflict delisting results in the removal of all items of a manufacturer’s brand(s) from a retailer’s shelves, consumers have to deal with the unavailability of those products. A conflict delisting therefore shows resemblances with other product unavailability situations, such as an out-of-stock (OOS) situation, a permanent assortment reduction (PAR), or a brand delisting (BD), that have been studied extensively in the literature (e.g., Campo, Gijsbrechts, and Nisol 2000, 2004; Sloot and Verhoef 2008). Nevertheless, findings of these studies are not immediately transferable to a conflict delisting context because of important differences among the different product unavailability types. We summarize these differences in Table 1.
Whereas an OOS is typically not intended by the retailer and usually pertains to one stockkeeping unit (SKU) in one category, conflict delistings are deliberately executed and cover several brands in several categories, depending on the offering of the afflicted manufacturer. This could affect a consumer’s decision to switch stores or brands because the consequences of a conflict delisting might concern multiple products of the consumer’s shopping basket. Conflict delistings further differ from most other forms of product unavailability in that highequity brands get delisted as well. Consumers might more strongly react to the unavailability of such high-equity brands, compared with low-equity brands that are removed from the shelves for operational reasons (which is usually the case for BDs and PARs) and whose absence might be unnoticed by the large majority of consumers (Campo, Gijsbrechts, and Nisol 2000; Sloot and Verhoef 2008).
Moreover, in contrast to other types of product unavailability, the question of who is to blame gets highlighted much more for a conflict delisting. A consumer’s decision to switch stores or brands in such a situation may therefore seem like choosing sides with either the retailer or the manufacturer. Unlike the other product unavailability types, conflict delistings also receive a lot of media attention, which results in a high consumer awareness before they step into the store. This could also affect consumers’ reactions to the conflict delisting.
Lastly, unlike PARs and BDs, conflict delistings are usually not permanent, although the length of the unavailability tends to be unpredictable because it lasts until the conflict is settled. Sloot and Verhoef (2008) find that consumers in a BD situation are triggered to either stay loyal to the brand (and thus switch stores) or stay loyal to the store (and thus switch brands). Primary demand reactions, such as the decision to postpone the purchase of the item until the next shopping occasion or the decision to cancel the purchase, are less likely to happen in BD situations. Other studies on product unavailability have indicated that in BD situations, postponement or cancellation occur less often than brand or store switching (Campo, Gijsbrechts, and Nisol 2000; Sloot and Verhoef 2008; Verhoef and Sloot 2010), and we expect this to be especially true for situations where the unavailability is long and unpredictable, such as when conflict delisting occurs. Moreover, these reactions are not very informative on the question of who is more or less vulnerable because both the retailer and manufacturer may experience negative consequences of postponement and cancellation behavior (Verhoef and Sloot 2010).
Conceptual Framework
To quantify the manufacturer’s and the retailer’s vulnerability following a conflict delisting, we look at the changes in brand and retailer share between the periods during and before the conflict delisting. The threat of losing sales puts both parties in a vulnerable position, and once the conflict delisting is executed, their fate lies in the hands of the consumer. Yet, each party has advantages that may act as a buffer (i.e., minimize potential negative effects) in a conflict delisting, thus making the party less vulnerable. The selection of drivers in our conceptual framework is based on prior literature outlining the power of retailers and manufacturers as well as research on product unavailability reactions, both discussed in the previous sections. The particular context of a conflict delisting offers the opportunity to integrate insights of these literature streams and enables us to study drivers that were previously studied separately or in a completely different context.
TABLE:
| | Out of Stock (OOS) | Permanent Assortment Reduction (PAR) | Brand Delisting (BD) | Conflict Delisting (CD) |
|---|
| Planned vs. unexpected | Unexpected (caused by, e.g., inefficient replenishment) | Planned (caused by, e.g., operational factors) | Planned (caused by, e.g., operational factors) | Planned (caused by a conflict between retailer and manufacturer) |
| Number of categories involved | One category (multiple OOSs can occur within the same store) | One category (multiple PARs can occur within the same store) | One category | Multiple categories (unless manufacturer serves one category only) |
| Level of unavailability | SKU/brand | SKU (usually multiple items of different brands), typically low-equity brands | Brand (all items of the same brand), typically lowequity brands | Brand (all items of the same brand), high-as well as low-equity brands |
| Consumer awareness | In store (consumer recognizes an OOS when encountering an empty shelf in the store) | In store (consumer becomes aware after minimum of one store visit) | In store (consumer becomes aware after minimum of one store visit) | Before store visit (consumer becomes aware before store visit because a CD typically receives extensive media attention) |
| Time span | Short-term unavailability (temporary, typically 1–2 days) | Long-term unavailability (permanent) | Long-term unavailability (permanent) | Term of unavailability unknown (until the conflict is resolved, typically longer than OOS and shorter than PAR or BD) |
| References | Campo, Gijsbrechts, and Nisol (2000); Fitzsimons (2000); Sloot, Verhoef, and Franses (2005); Verhoef and Sloot (2010) | Campo, Gijsbrechts, and Nisol (2004); Dukes, Geylani, and Srinivasan (2009); Sloot, Fok, and Verhoef (2006) | Sloot and Verhoef (2008); Wiebach and Hildebrandt (2012) | This study |
Prior power balance and product unavailability literature (e.g., Ailawadi et al. 2010; Campo, Gijsbrechts, and Nisol 2000; Draganska and Klapper 2007; Sloot and Verhoef 2008) has suggested three characteristics that are pertinent in the context of a conflict delisting (see Figure 1): drivers related to the delisted brand (manufacturer-brand characteristics), drivers related to the set of alternatives available in the affected category at the retail store (retailer-assortment characteristics), and drivers intrinsically related to the category (category characteristics).1
Product unavailability literature has highlighted the importance of brand equity and assortment size (Campo,
1While the selection of characteristics is mainly guided by
Gijsbrechts, and Nisol 2000; Sloot and Verhoef 2008). Indeed, the manufacturer’s strength during a conflict delisting can depend on the popularity of the delisted brand(s), which we refer to as brand equity. Retailers, in contrast, can rely on their product assortment size in a retailer-manufacturer power battle. The assortment size provides insight into how large or how
small the assortment is in terms of brands, which also indicates how easy it may be for the consumer to find an acceptable alternative for the delisted brand at the afflicted retailer. The power balance literature has suggested the retailer’s private label and both parties’ promotional strategies as further potential weapons in the battle between retailers and manufacturers
(Ailawadi et al. 2010). We therefore take into account the assortment’s private label share because store brands may play a major role in the assumed manufacturer-retailer power shift (Ailawadi et al. 2010). In addition, we include the brand’s deal frequency (Nijs et al. 2001) as a manufacturer-brand characteristic and its counterpart, the assortment’s deal frequency, as a retailer-assortment characteristic. Regarding category characteristics, we focus on the “necessity” versus “impulse” nature of the categories (as suggested by Sloot, Verhoef, and Franses 2005). The media attention triggered by a conflict delisting may influence consumers’ decisions before they visit the store and thus may have other repercussions on
categories that are bought in a whim versus categories for which
the purchase is planned in advance. For each of these charac
teristics, we detail below our expectations on their impact on the retailer’s and manufacturer’s vulnerability.
Manufacturer-Brand Characteristics
Brand equity. The equity of a brand, represented by its market share in the category prior to the delisting, indicates how favorably the customer responds to (the marketing of) the brand (Aaker 1996; Keller 1993). Higher brand equity typically results in higher quality perceptions and higher customer preferences, making consumers more likely to purchase the brand (Chandon, Wansink, and Laurent 2000). This is particularly the case in a grocery-shopping context, wherein consumers exhibit limited information processing and search effort (Amato and Amato 2009). Consequently, consumers are more committed to highequity brands (Bansal, Irving, and Taylor 2004), which gives a high-equity brand’s manufacturer a dominant power position over the retailer (Porter 1974). Prior research has confirmed that the unavailability of high-versus low-equity brands leads to less brand switching and more store switching (Sloot and Verhoef 2008; Sloot, Verhoef, and Franses 2005). This insight, combined with the high likelihood of consumers being aware of the conflict delisting before their store visit takes place because of media attention to the situation, leads to following hypothesis:
H1: When a delisted brand holds a high versus a low equity, (a) retailers are more vulnerable, and (b) manufacturers are less vulnerable.
Brand deal frequency. A brand’s deal frequency is defined as the proportion of times a brand is found on sale (Bell, Chang, and Padmanabhan 1999; Nijs et al. 2001) in the period before the delisting. Brands with a high deal frequency are more likely to attract deal-prone customers, who tend to buy the brand because of the promotional deal (i.e., an external cue) rather than its intrinsic brand characteristics (Mazursky, LaBarbera, and Aiello 1987). This observation could affect the retailer’s and manufacturer’s vulnerability positions in the event of a conflict delisting. Retailers might benefit when a delisted brand has a high deal frequency because the consumer is less reluctant to buy an alternative brand (they may have bought the delisted brand previously just because of the sales). Manufacturers, on the other hand, might benefit in categories in which deal frequency is low and consumers do not have monetary incentives to try new brands. Sloot and Verhoef (2008) back this assumption by finding a positive effect of promotional buying on brand switching in the event of a brand delisting. In our setting, we expect the following:
H2: When a delisted brand is on deal more versus less frequently, (a) retailers are less vulnerable, and (b) manufacturers are more vulnerable.
Retailer-Assortment Characteristics
Assortment size. The size of an assortment is denoted by the number of brands offered in a category at the retailer compared with the average number of brands offered by competing retailers (see Sloot and Verhoef 2008), in the period prior to the delisting. A large assortment provides the consumer with a large number of alternatives in case the consumer’s favorite product is unavailable (Fitzsimons 2000; Sloot and Verhoef 2008; Wiebach and Hildebrandt 2012), which favors the retailer. Furthermore, a larger assortment may draw a greater number of customers to the retailer, increasing the power position of the retailer even more (Draganska and Klapper 2007). This idea is supported by negative effects of assortment reductions on category sales (Borle et al. 2005; Sloot, Fok, and Verhoef 2006), as well as the finding that less store switching and more brand switching occurs when a product within a large assortment becomes unavailable (Sloot and Verhoef 2008). On the basis of these findings, we expect the following:
H3: When a delisting takes place in a category with a large versus small retailer’s assortment, (a) retailers are less vulnerable, and (b) manufacturers are more vulnerable.
Share of private label brands. The share of private labels in a retailer’s assortment is defined as the volume sold for all private label brands relative to the total volume sold in the category at the afflicted retailer (Fader and Lodish 1990), in the period before the conflict delisting. In recent years, the introduction of private labels has been suggested as a strong weapon for retailers in their negotiations with manufacturers because of the private labels’ higher retail margins and positive influence on store loyalty (Ailawadi et al. 2010). Meza and Sudhir (2010) conclude that the presence of private labels in the assortment increases the retailer’s bargaining power. Thanks to the increasing value perception of private label brands (Geyskens, Gielens, and Gijsbrechts 2010), combined with their lower price relative to national brands, private labels have become acceptable alternatives to purchase. In other words, when the consumer faces a certain brand’s unavailability, the retailer’s private label could be an attractive alternative and may be bought instead. In conclusion, we expect the following:
H4: When a delisting takes place in an assortment with a large versus small share of private labels, (a) retailers are less vulnerable and (b) manufacturers are more vulnerable.
Assortment deal frequency. Similar to the aforementioned deal frequency of the delisted brand, the deal frequency of the assortment is defined by the proportion of time a deal is issued in an assortment (Bell, Chang, and Padmanabhan 1999), in the period before the delisting. In line with the reasoning for brand deal frequency, we assume that assortments with high deal frequency are more likely to attract deal-prone customers, who tend to buy because of the promotional deal (Mazursky, LaBarbera, and Aiello 1987). Hence, consumers are more likely to have experience with buying competing brands because there have been multiple promotional triggers to do so. As a result, when consumers are confronted with a conflict delisting, they are more willing to substitute brands. Thus, we expect the following:
H5: When a delisting takes place in an assortment with high versus low deal frequency, (a) retailers are less vulnerable and (b) manufacturers are more vulnerable.
Category Characteristics
Necessity/impulse. Impulse categories are categories with a higher tendency to be bought on a whim or when the urge strikes (Assael 1995), whereas purchases in necessity categories tend to be planned in advance (Bell, Chang, and Padmanabhan 1999). That, combined with the fact that consumers generally are aware of a highly publicized conflict delisting before they step into the store, puts retailers as well as manufacturers in a more vulnerable position in necessity versus impulse categories. Consumers are more inclined to consciously adjust their shopping plans according to their needs in necessity than in impulse categories. Prior literature has indeed shown that purchases of necessity products are more likely to happen in a planned and structured way compared with impulse purchases, which are not given a lot of thought prior to the shopping trip (Sharma, Sivakumaran, and Marshall 2010). We expect the following:
H6: When a delisting takes place in necessity versus impulse categories, (a) retailers are more vulnerable and (b) manufacturers are more vulnerable.
Model
We study the change in market position of the retailer and manufacturer resulting from the conflict delisting to measure each party’s vulnerability during the conflict delisting. Hence, our key dependent variables focus on the change in category and brand market position, comparing the period during the delisting with the period before. To adequately capture both dependent variables, our modeling approach should address four issues. First, our model should account for measures that are comparable over different categories, potentially with different volume units. Second, we should select a “before” period that accounts for enough purchases to make a reliable comparison basis. Third, we need to account for potential scaling effects that are caused by absolute differences in market position. Finally, our approach should account for a potential correlation between observations of ( 1) multiple brands that are affected in the same category and ( 2) the same brand that is affected in different categories (both instances occur in our data; see “Data” section). We address these issues as follows.
First, to make our measures for market position comparable over categories with different volume units, we study the manufacturer’s brand share (i.e., volume sales of the afflicted brand in a particular category across all retailers divided by the total volume sales in that category across all retailers) and the retailer’s category share (i.e., volume sales of the afflicted retailer in a particular category divided by the total volume sales in that category across all retailers).2 Not only are these measures unit free, but they also allow us to control for the competition in the market as well as for factors that affect the whole-category demand during the focal time span, such as seasonality and holiday periods (Hanssens, Parsons, and Schultz 2001, p. 50). Second, to obtain a reliable estimate of the market position before the conflict delisting as the basis of comparison, we must use a sufficiently long period before the delisting to ensure that we observe enough purchases in this period. In line with Gielens and Steenkamp (2007), and given that we study frequently purchased consumer goods with different interpurchase times, we use an observation period of one year before the delisting. Third, to account for potential scaling effects that are caused by the fact that absolute and relative changes in market positions are different for large versus small a priori market shares, we divide our change in market share variables by the average of the market share before and during the delisting (for a similar practice, see Cleeren, Van Heerde, and Dekimpe [2013]). Finally, because we measure the change in brand share for a given brand in a given category, we need to account for the fact that the same brand may be involved in different categories and that a particular category may involve different brands. Following Mizik and Jacobson’s (2009) recommendation, we use a robust clustered error-term estimation for our brand share model. Specifically, we estimate robust standard errors accounting for these two potential sources of clustering as specified in Lin (1994) and Cameron, Gelbach, and Miller (2011).
2We do not focus on the share of the afflicted brand at the afflicted
We use a regression framework to assess the impact of manufacturer-brand, retailer-assortment, and category characteristics on the change in retailer-category and manufacturer-brand share. More specifically, we model the (transformed)3 change in retailer share for category c as
( 1) DRetailer Share*c = bR0 S + bR1 SBrand Equityc + bR2 SBrand Deal Frequencyc + bR3 SAssortment Sizec + b4RSPrivate Label Sharec + bR5 SAssortment Deal Frequencyc
+ bR6 SNecessity=Impulsec + b7RSXc + ec, where Xc denotes our control variable, that is, the change in the relative category price. Since we study only one retailer, we cannot test the effectiveness of strategic price actions during the conflict delisting; we use price as a control variable instead.
Similar to the retailer share model, the (transformed) change in manufacturer share for brand b in category c is specified as follows:
( 2) DBrand Share*bc = bB0 S + b1BSBrand Equitybc + bB2 SBrand Deal Frequencybc + b3BSAssortment Sizec + bB4 SPrivate Label Sharec + b5BSAssortment Deal Frequencyc + bB6 SNecessity=Impulsec + bB7 SXbc + ebc, where Xbc denotes the control variable, that is, the change in relative brand price. Given the nature of the dependent variables, it is not possible to model the correlation between the error terms of the two equations because the observations in the two models do not coincide. Still, the errors within the brand share model may be correlated because of clustered observations. Indeed, different brands of the same category may be affected (for categories where the manufacturers holds different brands), and a single brand may be affected in several categories (for brands that are present in multiple categories). As mentioned before, we estimate this brand share model using robust standard errors to allow for the potential two-way clustering (Cameron, Gelbach, and Miller 2011).
Data
We use sales data, delivered by GfK Belgium, that covers a unique natural field experiment concerning a real-life “war” between a major retailer with a 25% market share and a top-five manufacturer in the Belgian fast-moving consumer goods market.4 In February 2009, price negotiations between both
3To account for the bounded range of the changes in retailer and parties turned sour. As a consequence, the retailer decided to not (re)order and thus to delist all of the manufacturer’s items across different categories. The conflict period comprised four subsequent weeks. At the end of the conflict period, both parties reached an agreement, and the brands were reintroduced on the supermarket shelves. The terms of the settlement were not mentioned in the public press.
The power battle was highly publicized and received media attention in all major and small newspapers and several TV and radio journals and documentaries. To illustrate, during the month that the conflict took place, we found more than 130 articles describing the conflict, spread over 14 Belgian journals, and more than 30 articles released by the three main Belgian press agencies. We read 10 conflict-related articles in four Belgian magazines and found more than 50 online articles on blogs, specialized economic websites, retail intelligence websites (e.g., PlanetRetail), and German, Spanish, English, and French news websites. As a result, consumers were well aware of the power battle and the according conflict delisting, even before they visited the involved retailer. The beginning and ending dates of the conflict delisting period were clearly mentioned in the press. All of the aforementioned information channels are clear about the delisting’s executor, namely, the retailer. In contrast, the media did not explicitly blame one of the parties for the conflict but did provide information on its underlying reason: the parties disagreed on new pricing conditions.
The conflict delisting took place in 47 categories, ranging from cleaning (e.g., fabric softeners) to health and beauty care (e.g., body lotion) to food and beverages (e.g., soup, iced tea). In certain categories, multiple brands of the manufacturer had been offered and were subsequently delisted. Likewise, certain brands were present in more than one category. This led to 59 brand-category cases.
Dependent Variables
The change in share of the afflicted retailer in category c during the conflict is the difference between the retailer’s category share during and before the delisting, summarized as
relative to the sum of the volume sales in the same category at all major retailers. In the denominator, we focus on the six retailers with a minimum market share of at least 6% (including the afflicted retailer).5 In this way, we cover 70% of the grocery retail market. As mentioned in the “Model” section, the period before the delisting is a one year period, and the period during the delisting comprises the four subsequent conflict weeks. We divide the change in retailer share by the average of both shares to avoid potential scaling effects. all retailers before conflict
The brand share is calculated as the volume sales of the afflicted brand in the afflicted category across all retailers, relative to the volume sales of the afflicted category across all retailers. We consider the same set of major retailers as in the retailer share measure, use the same time spans for the “before” and “during” periods, and again divide by the average of the share before and during the delisting.
Because the changes in retailer and brand shares, as calculated in Equations 3 and 4, have a bounded range and are constrained to [-2, 2], we apply the logit-type transformation prescribed by Lesaffre, Rizopulos, and Tsonaka (2007) to normalize these variables:
of 0 (see Cleeren, Van Heerde, and Dekimpe 2013). 5The afflicted retail chain consists of dependent (franchised) as
Independent Variables
The variable operationalization is described in detail in Table 2. Brand equity is defined as the total volume sales of the delisted brand in the afflicted category, divided by the total volume sales of all brands in the category (see Aaker 1996), in the period before the conflict delisting. We use the same definition in the retailer share model, except when there are multiple delisted brands in the category. In that case, we use the sum of the delisted brands’ equities.
To operationalize the (brand as well as assortment) deal frequency measures, we combine an additional promotional data set (also delivered by GfK) with our sales data set. The delisted brand’s deal frequency is measured as the average number of SKUs of the delisted brand on sale per week relative to the average weekly number of SKUs on sale for competing brands. We use the same measure for the retailer share model, unless there were multiple afflicted brands in the category. In that case, brand deal frequency is calculated as the market share weighted average of the previously mentioned delisted brand’s deal frequencies (see Raju 1992). The assortment’s deal frequency is measured as the average weekly number of SKUs on sale in the assortment at the involved retailer relative to the average weekly number of SKUs on sale at competing retailers. All deal frequency measures are based on the period before the delisting.
We define the assortment’s size as the relative number of brands offered at the afflicted retailer’s product category relative to the average number of brands offered in the product category by all competing retailers (Sloot, Verhoef, and Franses 2005). The assortment’s private label share is expressed as the sales volume of the involved retailer’s store brands in the category, relative to the total category sales volume at the involved retailer (Fader and Lodish 1990). To avoid endogeneity problems, we again focus on the period before the delisting to construct these measures.
We measure the necessity/impulse nature of the category on a seven-point Likert scale. We asked a convenience sample consisting of 26 participants to indicate the usual nature of their purchases in each category, from 1 = “totally planned” to 7 = “completely impulsive.” This classification is similar to what has been used in previous research (e.g., Campo and Breugelmans 2015; Fok et al. 2006). Finally, the control variable price is measured as the difference in average brand or retailer price between the period during and before the conflict delisting, relative to the weighted average price of the competing brands or retailers, for the brand and retailer share models, respectively.
Results s
The descriptive statistics for the dependent as well as the independent variables are displayed in Table 3. The antecedents have sufficient variation to be investigated. The afflicted brand’s equity equals .19 on average, while the afflicted retail store holds an assortment that tends to be larger than those of the competing retailers (on average, ratio > 1). The private label brands hold a share of about 25% on average.
When looking at the transformed dependent measures (as defined in Equations 5 and 6 and reported in Table 3), we see that the retailer is more vulnerable than the manufacturer. If we compare the change in market share with the market share before the delisting (i.e., using nontransformed measures because of interpretation), we find, over all brand-category combinations, an average shift in brand share of -4.33%, and, over all categories, an average shift in retailer share of -8.75%. So even though we find that both parties lose substantially during the conflict delisting under research, the retailer is more vulnerable given the larger negative shift in retailer share. This is in line with earlier findings of Draganska, Klapper, and Villas-Boas (2010), who find that bargaining power in the ground-coffee market lies mainly with the manufacturer.
TABLE:
| Variable | Operationalization |
|---|
| aIn the retailer share model, we have adapted manufacturer-brand measures for categories with multiple delisted brands. More particularly, the brand equity measure in that case is the sum of the delisted brand’s equities, while brand deal frequency is calculated as the market share weighted verage of the delisted brand’s deal frequencies. |
| Manufacturer-Brand Characteristicsa Brand equity | Sales volume of the delisted brand in the category, relative to the total sales volume in the category |
| Brand deal frequency | Average weekly number of SKUs of the delisted brand on sale in the category, relative to average weekly number of SKUs on sale for competing brands |
| Retailer-Assortment Characteristics Assortment size | Number of brands offered in the product category by the afflicted retailer, relative to the number of brands offered in the category averaged over the competing retailers |
| Private label share | Sales volume of the afflicted retailer’s private label brands, relative to the afflicted retailer’s category sales volume |
| Assortment deal frequency | Average weekly number of SKUs on sale in the category at the afflicted retailer, relative to the average weekly number of SKUs on sale in the category at the competing retailers |
| Category Characteristic Necessity/Impulse | Rating on a seven-point Likert scale, where 1 = “absolute necessity” and 7 = “absolute impulse” |
| Control Price | Difference in the delisted brand (retailer) price per volume unit during and before conflict, relative to the weighted average price of the competing brands (retailers) for the brand (retailer) share model |
The results further indicate that the retailer experiences negative shifts in share for 31 out of the 47 afflicted categories. In these cases, the afflicted retailer lost sales to the competing retailers. The manufacturer, on the other hand, experiences negative shifts in share for 36 out of 59 brand-category combinations. In these cases, the afflicted manufacturer lost sales to the competing manufacturers.
Estimation Results
The estimation results for the retailer share and brand share model are displayed in Table 4. Both models have a reasonable model fit (R2 = .237 for both models). The maximum variance inflation factor values (1.898 and 1.863 for the retailer and brand share model, respectively) are well below 10 (Hair et al. 2010, p. 204), indicating that multicollinearity is not a concern.
With regard to the manufacturer-brand characteristics, the estimation results for brand equity confirm that the retailer is
more vulnerable when a high-versus low-equity brand gets delisted (b = -1.702, p < .05). In contrast, the manufacturer is less vulnerable when a high-versus low-equity brand gets delisted (b = .355, p < .05). Brand equity thus seems to be an effective buffer for brand manufacturers to protect their
share, while at the same time it reduces retailer share, in support of H1a-b. As we hypothesized in H2b, we find that a higher brand deal frequency increases manufacturer vulnerability (b = -.199, p < .05). This result is in line with our expectation that brand switching is more likely to occur when
consumers were offered several incentives to buy (try out)
brands on sale before the delisting (see Sloot and Verhoef 2008). In contrast, we do not find support for H2a; the effect of the brand’s deal frequency on the retailer’s share is nonsignificant (b = .172, p > .10).
Regarding the retailer-assortment characteristics, we find support for the hypotheses on assortment size (H3a-b), indicating that large assortments make the retailer less vulnerable (b = .631, p < .10), whereas they make the manufacturer more vulnerable in conflict situations (b = -.304, p < .01). Large assortments offer more alternatives to consumers in case a
product is unavailable (Fitzsimons 2000) and thus offer protection for the retailer in a conflict delisting. Moreover, large assortments tend to draw a greater number of customers to the retailer, which comes at the expense of the manufacturer’s power (Draganska and Klapper 2007). Even though prior lit
erature has suggested that private labels have contributed to the retailer’s power (Ailawadi et al. 2010), we do not find support for H4a (b = -.950, p > .10), nor for H4b (b = -.260, p > .10). The share of private labels thus neither significantly protects the retailer nor hurts the afflicted manufacturer. Similar to our results with respect to the afflicted brand’s deal frequency, we find that the assortment’s deal frequency only influences the brand share (b = -.005, p > .10 and b = -.844, p < .05 for the retailer and brand share model, respectively). We thus find support for H5b but not for H5a. This again suggests that past promotional incentives may make consumers more comfortable substituting brands in case of unavailability because of their prior experience with different brands.
TABLE:
| | Retailer Share Model (N = 47) | Brand Share Model (N = 59) |
|---|
| | M | SD | M | SD |
|---|
| aStatistics are for the transformed dependent variables as explained in Equations 5 and 6. |
| bEven though the operationalizations of the retailer-assortment and category characteristics are exactly the same for both models, they have slightly different means and standard deviations per model. This is because of the presence of multiple brands in certain categories, resulting in certain brand-category cases repeating the same retailerassortment and category characteristics. |
| Dependent Variablesa | |
| Shift in retailer share | -.24 | .91 | |
| Shift in brand share | | -.09 | .31 |
| Independent Variables |
| Manufacturer-Brand |
| Characteristics |
| Brand equity | .24 | .21 | .19 | .20 |
| Brand deal frequency | .17 | .25 | .15 | .23 |
| Retailer-Assortment |
| Characteristicsb |
| Assortment size | 1.24 | .32 | 1.21 | .29 |
| Private label share | .25 | .20 | .27 | .21 |
| Assortment deal frequency | .38 | .14 | .38 | .16 |
| Category Characteristicb |
| Necessity/impulse | 3.06 | 1.00 | 2.94 | .94 |
| Control |
| DPrice | .07 | .33 | -.03 | .17 |
TABLE:
| Variable | Retailer Share Model (N = 47) | Brand Share Model (N 5 59) |
|---|
| *p < .1 (one-tailed). |
| **p < .05 (one-tailed). |
| ***p < .01 (one-tailed). |
| Intercept | -1.109 | (.750) | .266 | (.163) |
| Brand equity | -1.702** | (.694) | .355** | (.184) |
| Brand deal frequency | .172 | (.562) | -.199** | (.115) |
| Assortment size | .631* | (.421) | -.304*** | (.093) |
| Assortment deal frequency | -.005 | (1.087) | -.844** | (.276) |
| Private label share | -.950 | (.857) | -.260 | (.226) |
| Necessity/impulse | .239* | (.165) | .122** | (.050) |
| Price | -.148 | (.442) | -.208 | (.192) |
Finally, for the category characteristic of necessity/impulse, we find that retailers (b = .239, p < .10) as well as manufacturers (b = .122, p < .05) are less vulnerable when the conflict delisting takes place in impulse versus necessity categories, as we suggested in H6a-b. Indeed, purchases in necessity categories are planned in advance (Bell, Chang, and Padmanabhan 1999), which allows consumers to consider the information on the conflict delisting situation before the shopping trip takes place. The control variable, difference in price, is not significant for the brand or the retailer share model.
Simulations
To quantify the sales losses for both parties during the conflict, we first calculate the impact for an average brand and category, using the parameter estimates for the retailer and brand share model, as displayed in Table 4, and the variables’ averages, displayed in Table 3. Next, to quantify the impact of different brand and category characteristics, we simulate the average change in share for a number of different scenarios wherein we focus on one characteristic pertinent to the manufacturer (brand equity) and one characteristic pertinent to the retailer (assortment size). We use the maximum and minimum values for high and low brand equity and for large and small assortment size.
We find that the average afflicted brand loses 8.4% brand share during the conflict delisting, while the average retailer category share decreases by 21.2%, confirming our result that both parties lose but the retailer loses more during the conflict. For the highest-equity brand, we find that, ceteris paribus, the manufacturer could win 13.2% brand share, whereas the retailer risks losing 69.0% share during the conflict delisting. In contrast, for the lowest-equity brand, the manufacturer can lose up to 14.2% share, whereas the retailer can gain 17.1% share during the delisting period, ceteris paribus. When the assortment size is set to its maximum, manufacturers can lose up to 29.3% and retailers can gain 32.7% share during the conflict delisting, ceteris paribus. When the assortment size is lowest, manufacturers can gain 6.1% and retailers can lose 43.0% share during the conflict delisting.
It is clear from all these insights that, on average, both parties lose share during a conflict delisting. Yet, there are also scenarios in which one party turns out to gain share. The media attention surrounding the delisting may increase the consumer’s awareness of both the manufacturer’s brands and the retailer’s category (Berger, Sorensen, and Rasmussen 2010). Furthermore, the perceived scarcity of the delisted product may make the brand or category look exclusive, which increases its attractiveness and choice probability (Cialdini 1985; Van Herpen, Pieters, and Zeelenberg 2014). This is more likely to occur for high-equity brands and categories for which the retailer is deemed an expert (e.g., because it holds a large assortment).
TABLE:
| Variable | Parameter Estimate | SE |
|---|
| *p < .1 (two-tailed). |
| **p < .05 (two-tailed). |
| ***p < .01 (two-tailed). |
| Retailer Share Model |
| Time Series Operators |
| Trendt | .006 | .004 |
| log Retailer Sharect-1 | .777*** | .074 |
| Dlog Retailer Sharect-1 | -.415*** | .056 |
| Dlog Retailer Sharect-2 | -.228*** | .033 |
| Structural Change Dummies |
| DAftert | -.050 | .060 |
| DPulse Aftert | -.040 | .050 |
| Control Variables |
| Pricect | -.000** | .000 |
| DPulse Duringt | -.184* | .104 |
| Differenced Brand Share Model |
| Time Series Operators |
| Dlog Brand Sharebct, t-1 | -.528*** | .056 |
| Dlog Brand Sharebct, t-2 | -.207*** | .077 |
| Dlog Brand Sharebct, t-3 | -.121** | .052 |
| Structural Change Dummy |
| DPulse Aftert | .152* | .077 |
| Control Variables |
| DPricebt | -.005*** | .001 |
| DDPulse Duringt | .001 | .043 |
Robustness Checks
We ran an extensive number of tests to show the robustness of our results. We first performed a sensitivity analysis on the number of weeks used to determine the period during the conflict delisting. Because the afflicted retailer may still have stock left, a conflict delisting may not directly lead to an out-ofstock situation. We therefore tested the robustness of our results by leaving out the first week of the delisting to control for potential stock effects. We found that our analyses that considered only weeks 2-4 did not improve the results (lower R2 for both models), nor did our substantive findings change. Although sales might not immediately drop to zero because of remaining stock, we did find evidence that sales of the involved brands at the afflicted retailer dropped significantly during the conflict period compared with their levels before (paired sample t-test result: t(57) = -3.82, p < .001).
Second, we verified whether the primary category demand changed as a result of the conflict delisting. Given that our dependent variables (i.e., market shares) do not offer insights
into possible primary demand effects such as purchase post
ponement or cancellation, we compared the average daily
category sales during the delisting period with the average daily
category sales of the one-year period before the delisting across
all retailers. Results across the 47 categories show that there is no significant difference in average daily category sales before and during the conflict delisting (t(46) = -1.13, p > .10), ruling out any primary demand effects. This is in line with prior
literature on product unavailability indicating that primary
demand reactions typically occur less often than brand or
store switching (Campo, Gijsbrechts, and Nisol 2000; Sloot and
Verhoef 2008; Verhoef and Sloot 2010).
Third, we reran our models while controlling for the t-values
of predelisting trends in manufacturer brand share and retailer
category share. This approach, based on Pauwels and Hanssens
(2007), allows us to control for the tendencies of the
(manufacturer-brand or retailer-category) volume shares before the conflict took place. For each of the 47 product categories (and 59 brand-category cases), we regressed the monthly sales volume share of the afflicted retailer (brand) in a category during the 12-month period before the delisting on an intercept and a time trend. We inserted the resulting trend’s t-value in our retailer share (brand share) model as extra control variables. Although these trends before the delisting had a significant impact on retailer and brand share, the substantive results of
both models remained unchanged.
Fourth, apart from assortment size, category assortment
differentiation might affect retailer and manufacturer vulnerability because it influences the substitutability of brands in the category (Bell, Chang, and Padmanabhan 1999). However, none of the five commonly used differentiation or competition measures (i.e., the Herfindahl index, the Rosenbluth-Hall-Tideman index, the entropy measure, the variance of brand shares measure, and the proportion of high
equity brands; see Dhar and Hoch 1997; Raju 1992; Sloot and Verhoef 2008) improved model fit (adjusted R2). Parameter estimates for these measures were always nonsignificant, likely because of multicollinearity issues with the brand
equity or the assortment size variables. We believe this issue
of multicollinearity is linked to the limited degrees of free
dom in our models (because of a limited number of cases),
which underscores the importance of striving for parsimony
in our models.
Fifth, we tested a number of additional category-related drivers. To control for a potential difference between categories that are often and less frequently bought, we included the categories’ purchase frequencies, operationalized as the average number of times that a household bought the category the year before the conflict delisting (Bell, Chang, and Padmanabhan 1999), as an additional variable in the model. Furthermore, we tested for the impact of household penetration, operationalized as the percentage of households that made at least one purchase in the product category (Fader and Lodish 1990). These variables were not significant in either model and did not increase the adjusted R2 of the model. We also accounted for the storable/perishable and the hedonic/utilitarian nature of the product category, according to evaluations by 26 consumers who rated the categories on seven-point Likert scales (Mace´ and Neslin 2004). Parameter estimates for these variables were insignificant, and their inclusion led to a decrease in adjusted R2 in both models. Replacing the necessity/impulse variable with the hedonic/utilitarian measure also resulted in lower adjust R2 for both models. For parsimony reasons, all of these category-related variables were left out of the final models.
Sixth, to rule out the possibility that our brand equity measure, based on the brand’s market share one year before the conflict, reflects a scale effect rather than an equity effect, we replaced it with a measure of brand familiarity, namely, the number of households that bought the delisted brand, relative to the total number of households that bought in the category, one year before the conflict. The results for both models remained similar.
Seventh, a promotion change variable instead of or on top of a price change variable was added to the models to test for the impact of shifts in price promotions before and during the conflict delisting. This did not improve the fit of the models. This might be because we consider only one conflict case, involving the same manufacturer and retailer in all brand and category cases, which substantially reduces the variability in the price promotion variable over brands and categories. Given our degrees of freedom, we did not retain the promotion change variable in the final model, in an attempt to keep it parsimonious.
Finally, inspired by, for instance, Sloot, Verhoef, and Franses (2005), we tested for interaction effects between our explanatory variables. For example, we investigated interactions between brand equity and the necessity/impulse character of the category, and between the size of the assortment and the necessity/impulse category characteristic, to capture whether the positive (negative) effects of brand equity for the manufacturer (retailer) and the positive (negative) effects of assortment size for the retailer (manufacturer) are stronger in particular categories. We did not find evidence for this; neither did we find evidence for other interaction effects.
When the Clash Ends
Whereas the previous sections focus on changes in market performance during the conflict, in this section we explore what happens after the conflict is resolved, that is, once the afflicted manufacturer’s brands are reintroduced to the afflicted retailer’s shelves. Our a priori expectations are mixed and threefold. First, we expect that the negative effects that we observe for both parties will endure, even after the conflict is settled. Indeed, the consumer´s experience with competing brands and stores may have altered their preferences, loyalty, and shopping habits permanently. In contrast, when consumers revert to their “old” (predelisting) habits after the conflict is settled, there will be no long-term consequence of the conflict delisting. Prior literature on reactance theory has even suggested that there could be positive after-conflict effects, stipulating that a perceived reduction of one’s freedom to choose may trigger reactance behavior (Brehm 1966; Verhallen and Robben 1994). This may imply that consumers want a product even more or buy a category in a particular supermarket even more often when they had the impression that the range of available alternatives was limited for a temporary (a priori unknown) period of time. Positive after-conflict effects may also be triggered by increased brand salience coming from the media attention surrounding the conflict delisting (Berger, Sorensen, and Rasmussen 2010). Compared with standard advertising messages, the publicity surrounding a conflict delisting is rather uncommon and may therefore be more memorable (Ahluwalia, Burnkrant, and Unnava 2000). Reactance behavior, as well as atypical media messages, may generate sympathy and awareness for the involved retailer and/or brand, which may translate into increased purchases of the afflicted retailer and/or brand.
To provide a first insight into potential long-term consequences of a conflict delisting, we performed a structuralbreak unit-root analysis on the log-transformed retailer and brand shares,6 as introduced by Perron (1989, 1994). We broaden the observation window to one year before and one year after the conflict and model the log-transformed retailer share for category c and month t as:
( 7) log
Retailer
Share*ct
=
aRS
+
g
RS 1
trendt
+ rRSRetailer Sharec,t-1
+ c1RSD log Retailer Sharec,t-1
+ c2RSD log Retailer Sharec,t-2
+ qR1 SDAftert
+
qR2 SDPulse
Aftert
+
g
RS 2
Pricect
+ gR3 SDPulse Duringt + ect,
better known as the augmented Dickey-Fuller test. As prescribed by Perron (1989, 1994), we tested for the presence of a unit root (rRS = 1) against the alternative hypothesis of a stationary time series (rRS < 1). The resulting test statistic does not reveal a unit root; thus, it indicates that the retailer share model in Equation 7 is stationary (F( 1, 46) = 9.15, p < .01).
6Logit-type transformation of both retailer and brand share, as
Following common practice, we added two lagged first differences of Log Retailer Share for the residuals series to be white noise.7
In the second part of the equation, we include two dummy variables to test for a structural change. DAftert is a step dummy that switches from 0 to 1 in the first month after the delisting and stays 1 thereafter. The accompanying parameter qR1 S indicates the change in the intercept after the conflict delisting and therefore captures the long-term effect in the aftermath of the
delisting. DPulse Aftert is a pulse dummy that is equal to 1 in the first month after the conflict delisting and 0 in all other months. This variable is added to ensure statistical properties (Perron 1994).8 Pricect (the average price at the afflicted retailer in category c of month t) and DPulse Duringt (a pulse dummy variable that equals 1 during the conflict delisting and 0 otherwise) are added as control variables in the last part of the equation.
The results for the structural-break analysis for the afflicted retailer’s share are displayed in Table 5, Panel A. Because the parameter estimate for the step dummy DAftert (q1RS = -.050, p > .10) is nonsignificant, the results show that once the agreement was reached, there was no long-term impact on the afflicted retailer’s volume share. This indicates that the retailer share recovered to the predelisting level.
The log-transformed brand share of month t for brand
b in category c is modeled in a similar fashion as Equation
7, with three lagged differences. Contrary to the retailer
share model, the brand share model does contain a unit root (F( 1, 58) = 1.14, p > .10). Therefore, following Deleersnyder et al. (2002), we continue our structural-break analysis on
the differenced log-transformed brand share as shown in
Equation 8:
( 8) D Log Brand Share*bct = aBS + c1BSD log Brand Sharebc,t-1 + cB2 SD log Brand Sharebc,t-2 + cB3 SD log Brand Sharebc,t-3 + qB1 SDPulse Aftert + gB1 SDPricebct + gB2 SDDPulse Duringt + ebctt,
=. total volume sales in category c across all retailers in month t
Explanatory variables are operationalized in an identical way, except for the price variable, which now is the average price for the afflicted brand b in category c of month t. Using a differenced (log-transformed) brand share as dependent variable also changes the interpretation of the dummy variables that represent the structural change.9 The parameter associated with the pulse dummy DPulse Aftert in this first difference Equation 8 captures the long-term level shifts (and is therefore similar in interpretation to the step dummy DAftert in the retailer share Equation 7). The estimation results are displayed in Table 5, Panel B. The structural change dummy variable, DPulse Aftert, is significantly positive (qB1 S = .152, p < .10). Hence, there is a long-term level rise in the manufacturer’s brand share after the conflict is resolved.10 The finding that the manufacturer’s brand share goes up permanently after the settlement of the conflict is in line with reactance theory (Brehm 1966; Verhallen and Robben 1994) as well as the increased awareness triggered by the media attention (Skurnik et al. 2005). It suggests that the temporary restriction of one’s freedom to choose and the “atypical” messages in the press have increased the sympathy and choice probability for delisted brands once products are available again on all supermarket shelves.
In conclusion, based on these structural-break analyses, the retailer’s share was not affected after the conflict’s resolution and thus returned to the predelisting level. In contrast, the manufacturer’s share was positively influenced after the settlement. The substitution costs connected to a switch of brands may be lower and therefore easier to maintain in the aftermath of the conflict compared with the transaction costs involved in switching stores. This may explain the persistent positive effects on manufacturer brand share and the null effects on retailer share. Simple descriptive statistics comparing the average “before” period share with the average “after” period share support these findings (for retailer share: mean difference = .000, p > .010; for brand share: mean difference = .011; p < .05).
TABLE:
| Category | Our Advice |
|---|
| Decision Framework for the Manufacturer |
| Necessity |
| High deal frequency | Do not engage in a conflict delisting. |
| Low deal frequency | A conflict delisting can be a viable strategy, except when brand equity is low and the retailer’s assortment size is high. |
| Impulse |
| High deal frequency | Do not engage in a conflict delisting, except when brand equity is high and the retailer’s assortment size is low. |
| Low deal frequency | A conflict delisting can be a viable strategy. |
| Decision Framework for the Retailer |
| Necessity | Do not engage in a conflict delisting, except when the manufacturer’ brand equity is low and assortment size is high. |
| Impulse | A conflict delisting can be a viable strategy, except when the manufacturer’s brand equity is high and assortment size is low. |
To test whether our conceptual model for the during-before analysis generalized to the after-before analysis, we specified a model similar to the one in Equations 1 and 2, using the shift in brand (retailer) share one year after versus one year before the conflict as the dependent variable. Interestingly, none of our manufacturer-brand, retailer-assortment, or category variables affected the change in market share after versus before the conflict. We speculate that while unavailability is key in the period during the conflict, the salience effect created by the media attention is more prominent in the after-conflict period. Because media attention was devoted to the conflict as a whole rather than to the specific involved brands, we surmise that the salience effect is spread equally over all affected brands, minimizing the cross-case variation in the long-term effect. We acknowledge, however, that further research involving different conflict cases is necessary to unveil potential variables that influence the long-term effect of a conflict delisting.
Conclusion
Conflict delistings are occurring more and more frequently. Like manufacturers, retailers increasingly tend to hold their ground and are willing to execute a product delisting when negotiations strand. This suggests, as described by Draganska, Klapper, and Villas-Boas (2010), that retailers have more bargaining power today than in the past and that the two parties can be considered equal when it comes to resisting pressure during negotiations. Our study offers a useful context for assessing who is less/more vulnerable and thus who (in hindsight) truly held the strongest position, by studying the sales consequences of a conflict delisting. Given the unavailability of the affected products at the afflicted retailer, both parties can be vulnerable and lose sales as consumers who seek an unavailable product have the choice to switch either brands or stores. The increased frequency and potential important negative consequences of such conflict delistings stand in sharp contrast with the very limited attention devoted to this topic in academia. To provide insights into who gains the upper hand in such a delisting, we use sales data of a large-scale, real-life conflict delisting initiated by a major retailer against a major manufacturer, involving 47 categories and 59 brand-category cases.
Our study shows that a conflict delisting is a no-win situation: both parties lost substantial market share during the conflict across all categories and brands, as well as for the majority of categories and brands. Hence, from the point of view of sales losses, executing conflict delistings does not benefit any party. This is in line with Sharp (2011), who states that always being physically available is an important condition for growing brands. Therefore, manufacturers and retailers need to cautiously weigh the possibility of getting what they want out of a negotiation against the negative sales consequences of a delisting.
At the same time, our results indicate that among both these titans, the retailer is the one that lost relatively more share and therefore was more vulnerable than the manufacturer. We confirm this finding not only during the conflict delisting (in which simulations point to a larger average sales loss for the retailer), but also when the clash ended (in which results point out that the retailer share recovered to the predelisting level, while the manufacturer actually experienced a long-term gain). Business press examples point out that very often, the retailer initiates the delisting and effectively stops ordering the manufacturer’s items (Ritson 2010). This was also the case in our study. Our findings suggest that this is a very dangerous strategy because the retailer turns out to be the most vulnerable party when the voice of the consumers is taken into account. We believe that our results are rather conservative because we focus only on afflicted categories and brands. Negative consequences can be even larger when consumers also shift their purchases from other nonafflicted categories to a competing store, further fostering the vulnerability of the retailer. In conclusion, despite the retailer having gained the power to stand up to the manufacturer (Ailawadi et al. 2010), our results show that in a conflict delisting, the retailer still is the most vulnerable party. Consumers punished the retailer more than the manufacturer during the conflict, and the manufacturer actually benefitted in the aftermath of the delisting. We speculate that these long-term positive results on brand share are caused by the media attention surrounding the conflict.
Although we show that, overall, both parties lose during the conflict delisting, we find a lot of cross-case variability. To create a better understanding of the factors that might moderate the vulnerability of both parties, we investigate the impact of several manufacturer-brand, retailer-assortment, and category characteristics on the change in market positions. A first interesting observation is that both parties are more vulnerable in necessity categories, in which purchases tend to be planned in advance. This can be explained by the fact that conflict delistings commonly receive a lot of media attention, resulting in consumers being well aware of the situation before they enter the store.
In terms of manufacturer brand share, our results confirm prior research findings on the critical role of brand equity (Aaker 1996; Keller 1993; Sloot, Verhoef, and Franses 2005). Building strong brands is key in helping to protect the manufacturer during a conflict delisting because consumers are more likely to still buy the delisted brand elsewhere. Not surprisingly, a manufacturer carrying low-equity brands is in a less powerful position in a delisting. This result underscores the importance of brand equity as a buffer against negative events such as a conflict delisting. In contrast to Sharp’s (2011) disposition that firms should not focus on building brand loyalty, we thus show that building a loyal customer base can have important advantages. In addition to brand equity, deal frequency and the assortment at the involved retailer play important moderating roles in the manufacturer’s brand share position during the conflict. If the category of the afflicted brand was highly promoted in the period before the delisting and/or if the involved retailer has a wide offer of acceptable alternatives, consumers are much more likely to buy a competing brand at the same retailer. While there is a lot of variation in the characteristics we observe, the manufacturer in our case indeed holds several high-equity brands (in 28 of the 59 brand-category cases, the manufacturer’s brand holds an equity of more than 15%). The manufacturer’s brands are not very often promoted; the (delisted) brand’s deal frequency is lower than 10% for 37 of the 59 brand-category cases. This may explain why we observe lower negative sales consequences for the manufacturer than the retailer in our study.
In terms of retailer category share, we find–in line with previous literature on product unavailability (Campo, Gijsbrechts, and Nisol 2000)–that a retailer has more leverage in categories with a large assortment in which consumers can easily find acceptable alternatives. Also from the retailer’s side, the brand equities of the involved manufacturer’s portfolio are important to keep in mind. Hence, upon deciding to engage in a delisting, a retailer needs to balance the brand equity of the involved brands with the retailer’s own strengths in terms of assortment size. In our case, the retailer was left with the short end of the stick, suggesting that its strengths (essentially in terms of assortment size) did not measure up against the strengths of the manufacturer (essentially in terms of brand equity).
To our surprise, and counter to what has been suggested in prior literature as one of the biggest reasons retailers have become more powerful (Ailawadi et al. 2010), we could not confirm that the share of private labels serves as protection against retailer sales losses, nor could we confirm that the manufacturer becomes more vulnerable when the share of private labels in the category is high. These results seem to suggest that the role of private labels in the retailer-manufacturer power conundrum might be less extensive than originally thought. A possible explanation could be that among the categories we study, a high proportion are cleaning or health and beauty categories (16 of 47), which are categories in which private labels are typically considered less acceptable.
Managerial Implications
Thanks to the wide variation in the retailer-assortment, manufacturer-brand, and category characteristics across different categories and brands, our results can inform retailers as well as manufacturers about which type of categories or brands are particularly vulnerable to sales losses during a conflict delisting, information that can be particularly helpful in making the decision to threaten with or execute a delisting. Based on parameter estimations of and simulations for the most influential characteristics, we developed a marketing dashboard that may guide both the manufacturer and the retailer that are involved in tough negotiations (see Table 6). For both parties, we summarize whether it is wise to engage in (or threaten with) a conflict delisting, depending on different circumstances and focusing on the outcomes during the conflict delisting period.
In the top portion of Table 6, we show under which circumstances a conflict delisting may be a viable strategy or one that needs to be avoided from the manufacturer’s point of view. Following the estimation results, category type, deal frequency, assortment size, and brand equity are the decisive factors in whether the brand will lose share during a conflict delisting. Manufacturers with brands that are offered in a necessity category with high deal frequency are advised to not opt for (or threaten with) a conflict delisting because it is likely to lead to a substantial loss of market share. In these categories, consumers tend to plan purchases in advance. Moreover, in the period before the delisting, consumers have had several monetary incentives to try a variety of brands because of the high deal frequency. In these circumstances, conflict delistings do not pay off for the manufacturer because consumers are more likely to a priori (before entering the store) decide to buy one of the (several) other brands they have used before. The opposite holds for brands that are offered in an impulse category with low deal frequency. Because of the unplanned character of purchases in the category, combined with the consumer’s low experience in buying competing brands, the manufacturer’s risk of getting hurt is lower, which leads to the advice that a conflict delisting can be a viable strategy.
In the other two conditions (necessity plus low deal frequency, and impulse plus high deal frequency), the advice not to engage in (or threaten with) a conflict delisting depends on both the manufacturer’s strength and the retailer’s strength. In the case when purchases are planned in advance (necessity) but where consumers have not had multiple incentives to try out brands (low deal frequency), a conflict can be a viable strategy except when the brand has a low equity and the retailer has many alternatives to offer. Then, consumers will not side with the delisted brand (manufacturer) because the retailer offers several acceptable alternatives. In the case when purchases tend to be unplanned (impulse) and consumers have had multiple incentives to try out brands (high deal frequency), a conflict delisting only pays off when the manufacturer’s brand equity is high and the retailer’s assortment size is low. The favorable, well-known brand then stands up against the limited number of acceptable alternatives offered by the retailer.
Our advice to retailers heading for tough negotiations is shown in the bottom portion of Table 6. According to the estimation results, category type, brand equity, and assortment size influence retailer share losses. We advise retailers not to engage in (or threaten with) a conflict delisting in necessity categories, except when they have the power (in the form of a large assortment size) to defeat a weak opponent (a manufacturer with low brand equity). Conflict delistings are a viable strategy for retailers in impulse categories, except when the retailer’s assortment is small and the manufacturer’s brands have high equity. In that case, consumers are likely to switch stores to buy the well-known high-equity brand elsewhere.
In our discussion, we have assumed that a conflict delisting involves one category only. In some conflicts (as in the case under study), however, an involved manufacturer may hold a variety of brands, and a conflict delisting therefore may cover multiple categories. As shown in Table 6, this can result in a case in which a retailer and manufacturer need to be advised about a variety of conditions. Both parties need to carefully assess the portfolio of brands that are involved during tough negotiations and determine the share of impulse versus necessity categories, the share of high versus low deal frequency brands (for the manufacturer), and their own and their opponent’s stance in terms of brand equity and assortment size. It is essential for both parties to balance the negative sales consequences, especially for low-equity brands (manufacturer) and categories with small assortment size (retailer), against the potential gains from the decision not to give in.
Apart from advising whether or not to engage in a conflict delisting, our results are also useful when a conflict delisting is deemed inevitable. Retailers then need to realize that strategic reactions are necessary to attract customers to the store and entice them to buy other products, given that the retailer is more vulnerable than the manufacturer. Although the retailer has the power to communicate with consumers in store (e.g., suggesting alternative SKUs; presenting apologies when consumers are in front of the empty shelf), in-store promotions alone are likely insufficient because of the potentially strong impact of a highly publicized delisting on store visit probability. In these outof-store communications, a retailer may especially want to concentrate effort on protecting itself when the delisted brands have high equity and/or the involved categories have small assortments. Manufacturers, in contrast, could nurture the critical role of brand equity, for instance by reminding consumers of their strong brands in an attempt to endear them. Finally, retailers and manufacturers should pay special attention to necessity categories in their communication and promotion strategies following a conflict delisting, as sales losses for both parties are more severe in these categories.
Limitations and Directions for Further Research
First, although we were able to test the moderating impact of different characteristics on the manufacturer’s and retailer’s vulnerability because of the multitude of brands and categories affected, our results are based on a natural experiment involving one conflict delisting wherein the retailer decided to delist all of the manufacturer’s brands from the shelves at once as a result of price negotiations breaking down. Further research is necessary to determine whether our results generalize to other conflict delisting settings. Future studies involving multiple conflict delisting cases should investigate whether our findings are moderated by the reason for the delisting or the initiating party. The moderating impact of a mass delisting that receives high publicity versus a delisting that is more gradually implemented could be useful to research. Future research could also investigate multiple conflict delisting cases with variation in strategic marketing mix reactions of the involved and competing manufacturers and retailers to shed more light on the impact of such strategic actions.
Second, the single case focusing on a conflict between one retailer and one manufacturer and the lack of perceptual (selfreporting) data, along with our methodological choice for aggregated models, puts limits on characteristics (e.g., consumer or store characteristics) and drivers (e.g., perceived quality of alternatives) that can be explored. We cannot explore possible cross-store differences because we only have data at the retailer-chain level and not the store-outlet level. Also, we cannot mimic what would have happened if the conflict delisting had not taken place, since all brands of the manufacturer were delisted from every store of the afflicted retailer over the entire country and control groups therefore are lacking. Furthermore, we looked only at the categories in which the brands were delisted. Therefore, we do not account for the possibility that consumers also reallocated their purchases in categories that were not directly affected by the conflict. Future research could bring insights into these issues.
Third, a more disaggregated analysis, for example at the weekly level, would provide more insights into the underlying dynamics during the conflict period. A household-level model would lead to further insights into the topic by providing information on potential cross-household differences in the reaction to the conflict delisting. We leave these models as avenues for future research.
Fourth, we focus on the changes in market position during versus before the conflict delisting and explore what happens in terms of market position when the clash ends. Our focus on market shares, however, did not allow us to study other consumer reactions, such as purchase deferral or cancellation. Although the product unavailability literature has demonstrated that these primary demand effects are generally less common (Campo, Gijsbrechts, and Nisol 2000), and our own descriptive results seem to confirm this, future research could investigate these reactions in more detail.
Finally, our first exploratory analysis seems to indicate a small but positive long-term impact of the conflict delisting for the manufacturer. Future research should explore the mechanisms underlying this effect and investigate whether this longterm impact generalizes to other delisting settings.
Despite these limitations, we believe our study offers a number of interesting new insights into the impact of conflict delistings on the involved retailer and manufacturer, a topic that has been largely ignored in academic research so far. We hope that retailers and manufacturers facing the dilemma of whether or not to delist brands in the context of tough negotiations will benefit from our recommendations.
DIAGRAM: The Clash of the Titans: On Retailer and Manufacturer Vulnerability in Conflict Delistings
PHOTO (BLACK & WHITE)
PHOTO (BLACK & WHITE)
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Record: 179- The Color of Support: The Effect of Sponsor–Team Visual Congruence on Sponsorship Performance. By: Henderson, Conor M.; Mazodier, Marc; Sundar, Aparna. Journal of Marketing. May2019, Vol. 83 Issue 3, p50-71. 22p. 3 Diagrams, 6 Charts, 2 Graphs. DOI: 10.1177/0022242919831672.
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The Color of Support: The Effect of Sponsor–Team Visual Congruence on Sponsorship Performance
Brand sponsorship connects brands with large, passionate audiences. The sponsorship literature emphasizes the importance of brand sponsor–team congruence; however, prior research has largely focused on the relevance of the brand to the sport or geographic area. This article offers the first real-world empirical investigation of the effects of visual congruence through color matching on sponsorship performance. A wide-scale study of 703 Major League Baseball fans' evaluations of their team's sponsors, merged with real stadium signage data, offers evidence of the benefits of visual congruence. Two experiments in the contexts of product packaging and online advertising provide converging evidence of the positive effects of created visual congruence on attitudes toward the sponsorship, brand attitudes, and intentions. Brands without an inherent match to a team can enjoy enhanced sponsorship benefits with little additional costs simply by adopting the team's colors in visual displays. However, the viewer's motivation (fan status), opportunity (fan exposure), and ability (lack of color blindness) to process visual congruence moderates its effectiveness. By using the proposed framework, managers can maximize the value of their sponsorship rights.
Keywords: brand alliance; color; sponsorship; sports marketing; visual congruence
During the 2016 National Football League (NFL) season, Bud Light customized beer cans to visually match each team. Shedding its iconic blue color in favor of each sponsored team's colors may have enhanced the effectiveness of its $1.4 billion NFL sponsorship ([45]). However, Bud Light's cobranding redesign violates the established branding principle of maintaining consistent visual cues over time to reinforce a brand's identity ([27]). Furthermore, in asking "What is the proper executional approach to combining brands?" [28], p. 750) lament that although "companies frequently spend considerable sums on the design of logos, little academic research has explored the impact on consumer behavior of logo design or other visual aspects of branding" (p. 743). In response, the current research offers a real-world empirical investigation of the effect of visual congruence achieved through the brand sponsor matching the team's colors on sponsorship performance.
Communicating through visual design is efficient ([30]; [31]) and especially relevant in sports contexts. The brief displays of sponsor logos on or near athletes during an event offer little opportunity to convey the brand's support and activate an association with the team ([39]). As a result, investors often regard sponsorship announcements pessimistically ([38]). However, we argue that optimizing the visual display may improve brand sponsorship efforts. This is important because brand expenditures on sports sponsorships are outpacing general brand advertising in North America (4.3% vs. 2.6%) and exceeding $62 billion annually worldwide ([24]). Sponsorship also remains one of the few avenues to reach mass-market consumers in fragmented, on-demand, commercial-free (e.g., Netflix) entertainment contexts ([19]; [53]).
Managers who aim to maximize the value of their sponsorship expenditures unfortunately encounter conflicting guidance from research on visual design in sponsorship. On the one hand, visual congruence from matching brand–team colors may reduce effectiveness, as eye-tracking experiments have found that sponsorship signage with colors that contrast with surroundings better captures viewers' attention ([ 8]; [ 9]). On the other hand, students evaluate brand advertisements that support a cause more favorably when the brand's and the cause's colors match ([58]). In addition, fans wear their team's colors to signal their support ([16]); perhaps sponsors can communicate their support similarly.
To reconcile the seemingly contradictory best practices emerging from prior research, we posit that visual congruence enhances sponsorship performance, as long as the viewer processes the visual information. Conceptually, we draw on [34] framework of brand information processing to organize viewer characteristics that afford the necessary opportunity (i.e., viewership-based exposures to signage), ability (i.e., no color blindness), and motivation (i.e., fan status) to process the visually congruent sponsorship. With these viewer characteristics as preconditions, we use categorization ([18]; [55]) and attribution ([29]; [56]) theories to predict that fans evaluate visually congruent sponsorships more positively because the brand shares the appearance of a sincere supporter of the team.
Our empirical investigation of the proposed conceptual model takes a multimethod approach and relies on brand sponsor evaluation data from 1,358 participants across three studies in different sports and sponsorship advertising contexts. Study 1 ties actual in-stadium sponsorship signage from every Major League Baseball (MLB) stadium to 703 fans' evaluations of their team's brand sponsors. The resulting 15,289 ratings reveal a positive effect of visual congruence, in which brand colors in the sponsorship signage match the team's colors, on fan attitudes toward the sponsorship. A bootstrap mediation analysis reveals an indirect effect of visual congruence on fan attitudes toward the sponsorship through perceptions of sponsor support, as well as a direct effect. As predicted, the positive effects disappear when fans have not viewed enough games or are color blind. In Studies 2 and 3, we manipulate visual congruence in quasiexperiments. Study 2, set in the context of sponsor product packaging similar to Bud Light's custom NFL cans, verifies that perceived sponsor support mediates the positive effect of visual congruence on attitudes toward the sponsorship. Study 3 features digital advertising that promotes a National Basketball Association (NBA) team along with its main sponsor, and it extends the key effect to several customer-level brand performance metrics that each contribute to brand equity (brand attitudes, visit and purchase intentions, and word of mouth). Because the samples of the first two studies are mostly composed of fans, we recruited both fans and nonfans for Study 3 to examine the role of fan status and find that visual congruence only drives favorable attitudes and behavioral intentions among fans.
The empirical investigation in turn offers several insights for visual design in branding and sponsorship. First, our findings reveal the importance of color, beyond even its connotative meaning or representativeness ([15]; [31]; [55]). Visual congruence signifies a brand sponsor's genuine support of the team, which is critical because fan attributions partly determine sponsorship success ([56]). Despite the strong argument for keeping branding elements consistent ([ 1]; [27]), our findings corroborate calls for cobranding flexibility when one brand communicates support for another ([41]).
Second, this research adds to studies of congruence in sponsorship literature, most of which focus on conceptual or geographic fit between the sponsor and the sponsored entity (e.g., [43]; [48]; [56]). In Study 1, we find positive effects of visual congruence while controlling for conceptual and geographic congruence. These findings are highly consequential for managers, in that visual congruence, as a sponsorship congruence strategy, falls under managers' control, but geographic congruence is inherently limited to local teams and conceptual congruence is limited to certain brands that fit with sports ([39]). Brands without an inherent color match to a team can adopt that team's colors in targeted sponsorship activities, which corresponds to a 12.4% lift in attitudes in Study 1. Targeting is key though, because, as we find in Study 3, a visually congruent sponsorship ad (vs. incongruent ad) prompts fans to rate the brand approximately 17% more favorably on a composite of brand performance metrics, but we find marginal support that nonfans recall the brand less frequently. With this framework, managers can leverage visual congruence strategically to maximize the value of their sponsorship rights.
Sponsorship marketing refers to the organization and implementation of activities to build and communicate an association with a sponsored entity ([12]). Fans watch athletes compete while taking in visual information from sponsor brands' logos, displayed on signage throughout the stadium, on the playing surface, and on athletes' uniforms. Visual congruence in sponsorship, which we define as an expression of unity through matching visible branding elements (e.g., color identity) between a sponsor brand and a sponsored entity (e.g., team), is often achieved when the sponsor brand's signage matches the team's color identity. For example, Toyota's and Budweiser's red signs at the Cincinnati Reds' stadium match the Reds' color identity, but at the New York Mets' stadium, their signs are incongruent with the Mets' orange and blue color identity (for examples, see Figure 1). Although visual congruence in sponsorship has not been examined in empirical field research, the current work benefits from being situated at the intersection of two independent literature streams: ( 1) sponsorship congruence and ( 2) visual design in branding. We first review sponsorship congruence literature and then integrate literature on visual design into our conceptual framework and hypotheses.
Graph: Figure 1. Examples of visual congruence in MLB sponsorship signage.
In general, sponsorship congruence refers to the fit between the sponsor brand and the sponsored entity (e.g., team). Congruence predicts a variety of performance metrics—from attitudes, to recall, to abnormal stock returns. While studies tend to find that congruence enhances performance ([12]; [13]; [37]; [48]; [50]; [56]), exceptions exist in which the effect is nonsignificant or negative ([10]; [11]; [39]; [38]; [42]). The inconsistent findings may partly stem from variations in congruence assessments.
Sponsorship research tends to focus on conceptual congruence, which represents the semantic overlap or logical coherence between the brand and the sponsored entity (for a review, see [43]]). Researchers have operationalized conceptual congruence and related constructs (e.g., generalized fit) with surveys of fan perceptions, experimental manipulations, and coded archival data ([12]; [13]; [26]; [48]; [50]). Conceptual congruence can be further divided by similarity, such as functional relevance when a brand's product is used in a sport (e.g., Nike athletic gear) or cultural relevance when a brand's product is emblematic of fans (e.g., luxury watches and golf; beer and football tailgates) ([13]; [43]; [48]). We view conceptual congruence as encompassing the many shared mental representations of the brand and sponsored entity (e.g., shared mission, personality, status) and distinguish them from other bases of comparison that may be manifest externally from mental representations.
Researchers seeking additional sources of congruence in sponsorship have identified geographic congruence as a unique driver of performance (see Table 1). Operationalized as a shared country of origin or shared hometown within a county, geographic congruence tends to improve performance beyond conceptual congruence ([ 2]; [43]; [56]; [57]), though in one exception, geographic congruence predicted a negative shareholder response to a sponsorship announcement ([11]). Both conceptual and geographic congruence help facilitate affect transfer and make the brand's support appear stronger and more authentic ([20]; [56]).
Graph
Table 1. Review of Research on Multiple Sources of Sponsorship Congruence Beyond Conceptual Congruence.
| Research | Sources of Congruence and Operationalization | Effect on Performance | Methodology | Sponsorship Context |
|---|
| Abril, Sanchez, and Recio (2018) | Conceptual: dummy-coded if sponsor is functionally or culturally relevant to the sport (following Cornwell et al. [2005]) Geographic: dummy-coded if event took place in sponsor's home country Visual: N.A.
| Outcome: abnormal stock return Conceptual: + Geographic: + Visual: N.A. (not investigated)
| Event study methodology | 98 sponsorships of four major international sporting events |
| Cobbs, Groza, and Pruitt (2012) | Conceptual: dummy-coded if sponsor is functionally relevant to the sport Geographic: dummy-coded if team is from the sponsor's home country Visual: N.A.
| Outcome: abnormal stock return Conceptual: n.s. Geographic: − Visual: N.A. (not investigated)
| Event study methodology | 73 Formula One sponsorships |
| Olson and Thjømøe (2011) | Conceptual: participant survey ratings of overall fit (following Speed and Thompson [2000]), functional and cultural relevance, and audience similarity; inverted absolute difference scores in ratings of sponsor/event prominence, prestige, and personality Geographic: inverted absolute difference scores in participant ratings of sponsor/event as international versus domestic on semantic differential scale Visual: N.A.
| Outcome: attitudes and intentions Conceptual: mostly + Geographic: +, n.s. Visual: N.A. (not investigated)
| Cross-sectional: 285 participants completed all measures in a single survey | Fictional press releases for one of two sporting events by one of six brands |
| Woisetschläger, Backhaus, and Cornwell (2017) | Conceptual: participant survey ratings of overall fit (following Simmons and Becker-Olsen 2006) and sponsor industry related (or not) to sport in experiment Geographic: dummy-coded sponsor from headquarter categories of international, national but not from team's city, local if from team's city Visual: N.A. (not investigated)
| Outcome: attitude and loyalty Conceptual: + Geographic: +, indirect Visual: N.A. (not investigated)
| Multisource field study: objective sponsorship data matched with survey of 2,787 consumers. Experiment: 576 participants.
| 44 real sponsorships of 25 German soccer teams (2,997 total evaluations) 48 fictional press releases with varying characteristics.
|
| Zdravkovic, Magnusson, and Stanley (2010) | Conceptual: participant survey ratings of overall fit (following Simmons and Becker-Olsen [2006]) and similarity of brand/cause mission and target market Geographic: participant survey ratings on three-item scale assessing the extent to which the brand and the cause have ties to the same geographic area Visual: participant survey ratings on three-item scale assessing the extent to which the visual presentation and colors of the brand and the cause overlap
| Outcome: attitude Conceptual: mostly + Geographic: n.s. Visual: +
| Cross sectional: 92 participants completed all measures in a single survey. | 12 real advertisements promoting sponsorship of a social cause (9 ads evaluated per participant, 826 total evaluations) |
| This research | Conceptual: dummy-coded if sponsor's product is functionally or culturally relevant to the sport (following Cornwell et al. [2005]) Geographic: dummy-coded if team is from the sponsor's home city Visual: dummy-coded if signage displays sponsor in the team's color identity, either from an natural match (incidental) or because brand adopts team's colors in signage (created). Follow-up experiments randomly assigned participants to view sponsors in team's colors or brand's original colors
| Outcome: perceived support, attitude toward sponsorship, and brand equity indicators Conceptual: + Geographic: + Visual: +
| Multisource field study: objective sponsorship data matched with survey of 703 real fans; organic exposure to sponsorship Experiment: 126 participants Experiment: 338 nonfans, 91 fans
| 646 real sponsorships of 30 MLB teams (15,289 total evaluations) Sports drink product packaging Digital advertising
|
1 Notes: N.A. = not available; n.s. = not significant. Studies focused on a single congruence source not included for brevity. [ 9] only examine visual congruence between sponsor signage and surrounding images (negative effect on attention). [57] only examine geographic congruence (positive effect on joint sponsor/team value). [43] provide a review of studies focused only on factors related to conceptual congruence.
Less is known about other sources of congruence. To inform our study of the effects of visual congruence, alongside conceptual and geographic congruence, we review the three studies that are directly relevant. The first suggests a positive impact of visual congruence. [58] examine the effects of several sources of congruence on students' evaluations of ads promoting sponsorship of social causes. Of ten predictors in the proposed model, visual congruence exerted the strongest effect on attitudes toward the brand. The other two studies highlight a potential risk arising from visual congruence. [ 9] and [ 8] conduct eye-tracking studies of participants viewing sporting events and find that visual contrast, not congruence, between a sponsor's signage color and the sign's surrounding colors captures more viewer attention. This result is consistent with visual salience literature suggesting that visual contrasts stand out in distracting environments, such as retail stores ([30]; [40]). Overall, the real-world effect of visual congruence in determining sponsorship performance remains uncertain ([41]).
How does visual congruence affect sponsorship performance? First, we detail two paths through which visual congruence should enhance fans' sponsorship evaluations and ultimately drive more favorable brand attitudes and intentions. Second, we draw on [ 8] motivation, opportunity, and ability framework of brand information processing to integrate the seemingly contradictory findings detailed in the previous section ([ 8]; [ 9]; [58]). We argue that viewer characteristics map onto their motivation, opportunity, and ability to process the visual congruence favorably, so they represent necessary conditions for the positive effects of visual congruence. We summarize our conceptual framework in Figure 2.
Graph: Figure 2. Conceptual framework: Visual congruence in sponsorship.
Visual congruence should enhance attitudes toward the sponsorship through two paths: ( 1) categorization-based affect transfer and ( 2) inferences of sponsor support for the team. Path 1 reflects categorization theory, in that consumers attempt to classify an object into a certain category on the basis of salient cues. If the categorization is successful, affect associated with the category transfers to the object ([18]). Prior research has indicated that visual appearance acts as a category membership cue ([ 5]) and dominates other types of cues ([49]). Moreover, logo color serves as a category label when color is diagnostic ([55]). Use of the team's color indicates that entities are connected with the team, and thus, visual congruence should lead to a category-based evaluation of the brand. Brands classified into the team category then may benefit from an affect transfer from the team ([37]; [50]). Anecdotal evidence of color-based affect comes from Celtic Football Club fans who were outraged when they received season tickets that featured the colors of the Rangers, a rival team in the Scottish Premiership ([21]).
Attribution theory underlies the second path by which visual congruence might improve sponsorship evaluations. People tend to attribute internal causes to other people's actions and hold more favorable attitudes toward an actor when they infer a benevolent motivation ([29]). Sports fans may attribute sponsorship to a brand's genuine support for the team or calculative self-promotion; only inferences of genuine support lead to favorable attitudes toward the sponsor ([56]). Fans wear their favorite team's color to "express their identification with their team as a unified community and use color as a means to assess support of other fans" ([16], p. 511), and as a result of consistency effects ([23]), this belief should extend to beliefs about a brands.
Together, these two paths should contribute to more favorable evaluations of the sponsorship and translate into enhanced customer-level brand performance (i.e., brand recall, brand attitude, visit and purchase intentions, and word of mouth). Therefore, we formally posit the following:
- H1a : Visual congruence has a positive effect on fans' sponsorship evaluations (attitude toward the sponsorship) and enhances brand sponsor performance (brand recall, brand attitude, visit and purchase intentions, and word of mouth).
- H1b : Perceived sponsor support mediates the positive effect of visual congruence on fans' sponsorship evaluations (attitude toward the sponsorship).
[34] brand information processing framework articulates that the effectiveness of brand advertising fundamentally depends on the viewer's motivation, opportunity, and ability to comprehend, make sense of, and draw meaning from the brand information contained in an ad. To establish H1a and H1b, we assumed that viewers comprehend, make sense of, and draw meaning from the brand sponsor displaying its brand information in colors matching the team, but such an assumption may not hold.
First, opportunity is "the extent to which distractions or limited exposure time affect consumers' attention to brand information in an ad" ([34], p. 34). For our research context, opportunity results from exposure to the visual display of the sponsorship signage. A fan who listens to games rather than watching them would lack a sufficient number of exposures to the visually congruent signage to recognize that the brand uses the team's colors. More exposures to sponsorship signage are important, because contrasting colors are better for grabbing viewer attention ([ 9]).
Second, we consider ability, defined as "consumers' skills or proficiencies in interpreting information in an ad" ([34], p. 34). Reflecting our focus on visual congruence through matching colors, we test for color blindness as a relevant impairment. A fan with color blindness can process all branding information except for the visual congruence achieved through color matching. With this measure, we can effectively isolate any true positive effect of visual congruence from unobserved confounds that should equally influence all fans.
Third, motivation entails the "desire or readiness to process brand information in the ad" ([34], p. 34). We have assumed that viewers are fans, but prior studies of visual congruence sample nonpartisans, which may have at least partially contributed to the negative effects of matching colors on viewer attention ([ 9]). Only fans with affiliation motives seek out and remember brands that show support for the team ([36]). If a sponsor brand tries to create visual congruence by displaying its brand in the team's colors, rather than the brand's original color, a less motivated viewer might fail to recognize the brand in this unfamiliar color ([27]). We capture our expectations about the conditional processing of visual congruence as follows:
- H2a : Fan exposures moderate the effect of visual congruence on sponsorship performance, such that the positive effects of visual congruence on sponsorship performance strengthen as fan exposures increase.
- H2b : Fans' color blindness moderates the effect of visual congruence on sponsorship performance, such that the positive effects of visual congruence on sponsorship performance fail to materialize among fans with color blindness.
- H2c : Viewers' fan status moderates the effect of visual congruence on sponsorship performance, such that positive effects of visual congruence on sponsorship performance materialize only among fans of the team.
We amassed a unique, multisource data set to offer the first empirical test of visual congruence between sponsor and team brands and its effects on real-world sponsorship performance. The compilation of the data set began with coding information about sponsorship signage in every MLB stadium during the 2015 season. We combined this signage information with background information about each sponsor brand and each team to capture three sources of sponsor–team congruence: ( 1) visual congruence, based on the colors of the sponsor brand in the sponsorship signage; ( 2) conceptual congruence, based on the relevance of the brand's category to the sport; and ( 3) geographic congruence, based on shared headquarters locations. At the conclusion of the 2015 MLB season, we surveyed fans of each team, recruited through targeted Facebook ads. Fans rated each brand sponsoring their favorite team, as well as four additional brands that were not sponsors of any team; we included them as a comparison with actual sponsors to check the validity of the data collection method.
With this empirical analysis, we sought four objectives. First, we test whether visual congruence in sponsorship signage has a robust relationship with fans' attitudes toward the sponsorship (H1a). Second, we assess the extent to which the total effect reflects an attribution appraisal–based indirect effect, such that fans perceive sponsors with brand colors matching the team's colors as more supportive and therefore evaluate supportive sponsor brands more positively (H1b). Third, we compare the total effect of visual congruence with that of conceptual and geographic congruence. Fourth, we test the moderation hypothesis, which predicts that the effect of visual congruence is conditional on fans' opportunity and ability to process the visual information (H2a–b).
We recruited respondents through Facebook ads targeted to people identified as fans of a particular MLB team. Our ad for each team featured a picture of the team's players and the request, "[Team] fans: take survey & enter to win $100"; the description further stated, "[Team] fans, please rate sponsors from the 2015 season. Take a short survey and be entered in a drawing for $100 to Amazon. Your opinion supports our research and teaching. Thank you."
The total campaign cost $1,723 and resulted in 703 completed fan surveys (36% female; average age 52 years). Fans of all 30 teams (an average of 23 fans per team, ranging from 11 to 34) evaluated brand sponsors with signage in their teams' stadium (an average of 26 sponsors per team, ranging from 12 to 43), culminating in 15,289 total observations. We did not provide any pictures of the brands or their sponsorship signage in the survey, so any influence of the signage came from the respondents' exposure to it throughout the season.
The fan survey supplied the measures for our dependent variable, representing the attitudinal dimension of sponsorship performance, and our observed mediation variable, representing the attribution process through which sponsorships influence attitudes. Fans rated their attitudes toward each brand's sponsorship ("Use the scale below to indicate your opinion of each brand as a sponsor of [Team name]") on a seven-point scale (1 = "very negative," 4 = "neutral," and 7 = "very positive") and the level of perceived sponsor support ("Use the scale below to indicate the extent to which you believe each brand currently supports the [Team name]") on another seven-point scale (1 = "does not support team," 4 = "moderately supports team," and 7 = "intensely supports team"). We relied on single items to reduce fan fatigue from rating many brands. We chose to have fans evaluate the brand as a sponsor, because this evaluation task is applicable to a wide variety of brands from any industry, whether a fan has prior experience with the product or service category or not.[ 6] After the brand ratings, fans completed [ 7] pictorial color blindness tests (1 = failed any tests, 0 = otherwise; see Web Appendix W2), estimated the percentage of the team's games they watch (0%–100%) for a measure of fan exposure that approximates their opportunities to view sponsorship signage, and provided basic demographics. We explore fan-level heterogeneity in the tests of our moderation hypotheses.
Six undergraduate students served as independent coders and captured information on the sponsor brands, sponsors' stadium signage, and teams. One student watched a home game for every MLB team and took screen shots of sponsor signage in the stadium.[ 7] Two students compiled a list for each team of all the sponsor brands with permanent signage in the stadium, excluding digital signage or signage superimposed through the television broadcast (i.e., optic replacement signage). Every stadium features permanent and nonpermanent signage that changes throughout the season, this rotational signage would add noise to the data and reduce our ability to observe a true effect. Finally, at least two students for each brand searched the internet to record the brand's colors, category, location of its headquarters, and distribution presence. The student coders scored each brand's conceptual congruence according to [13] criteria (1 = product is a component of the athlete or fan experience [e.g., New Era baseball caps, Budweiser beer], 0 = otherwise [e.g., State Farm insurance]). Students scored geographic congruence on the basis of the brand's and team's geographic locations (1 = same metropolitan area, 0 = otherwise). As a control for brand size, they scored the brand's distribution presence (1 = national [large], 0 = otherwise [small]). The coders then returned to the images of the sponsor's signage in the stadium and recorded its location (behind home plate, dugout, infield wall, outfield stands, outfield wall), which we dummy-coded, with outfield wall as the reference category. They also coded the size of the signage relative to what is typical for each location in the stadium (3 = larger, 2 = typical, 1 = smaller). These served as signage-related controls.
Finally, for our key independent variable, the coders captured visual congruence by classifying whether the sponsor's signage colors matched the team's colors. The reference category contained sponsors with incongruent signage that displayed the brand in its original colors, which happened to differ from the team's colors. As contrasts, we also considered incidental visual congruence, such that the brand's colors happen to match the team's colors, and created visual congruence, in which a brand purposefully adopts the team's colors in the sponsorship signage (e.g., Kaiser Permanente and the Baltimore Orioles; see Figure 1). We also observed a third type of visual congruence that served as a quasiplacebo comparison, namely, misplaced visual congruence, which occurred when the brand adopted the colors of the stadium but not the team's colors (e.g., green and white branding on the Green Monster portion of the Boston Red Sox's outfield wall). The coders reported few differences (less than 5%) and discussed the team's context to arrive at final decisions.[ 8] Partial matches (e.g., a brand's signage features two colors, only one of which matches the team) were coded as incongruent. We also gathered team-related control variables for performance, popularity, and the prevalence of sponsor signage. We captured each team's wins for three seasons (2013–2015), average attendance for the 2015 season in thousands, and number of sponsor signs. Table 2 contains the descriptive data and correlations.
Graph
Table 2. Descriptive Statistics of Sponsorship in MLB.
| Variables | N | Min | Max | M | SD | Correlations |
|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 |
|---|
| 1. Attitude toward the sponsorship | 15,289 | 1 | 7 | 4.34 | 1.98 | 1 | | | | | | | | | | | | | | | | | | | | |
| 2. Perceived sponsor support | 15,289 | 1 | 7 | 4.73 | 1.55 | .56 | 1 | | | | | | | | | | | | | | | | | | | |
| Visual Congruence |
| 3. Noncongruence: colors do not match | 646 | 0 | 1 | .68 | .47 | −.04 | −.05 | 1 | | | | | | | | | | | | | | | | | | |
| 4. Incidental: Brand colors already match team | 646 | 0 | 1 | .16 | .37 | .05 | .04 | −.65 | 1 | | | | | | | | | | | | | | | | | |
| 5. Created: Change colors to match team | 646 | 0 | 1 | .03 | .17 | .04 | .02 | −.27 | −.08 | 1 | | | | | | | | | | | | | | | | |
| 6. Misplaced: Colors match stadium, not team | 646 | 0 | 1 | .12 | .33 | −.02 | .01 | −.54 | −.17 | −.07 | 1 | | | | | | | | | | | | | | | |
| 7. Conceptual congruence (component of sport) | 378 | 0 | 1 | .25 | .43 | .14 | .13 | −.01 | .01 | −.04 | .02 | 1 | | | | | | | | | | | | | | |
| 8. Geographic congruence (shared headquarters) | 646 | 0 | 1 | .48 | .50 | .06 | .04 | .00 | .03 | .04 | −.06 | −.21 | 1 | | | | | | | | | | | | | |
| 9. Sponsor brand size (national vs. regional) | 378 | 0 | 1 | .69 | .46 | −.04 | −.03 | .09 | −.04 | −.03 | −.06 | .16 | −.39 | 1 | | | | | | | | | | | | |
| 10. Signage size (small, typical, large) | 646 | 1 | 3 | 1.82 | .64 | .09 | .06 | .04 | .08 | −.01 | −.14 | −.01 | −.03 | .11 | 1 | | | | | | | | | | | |
| Signage Location |
| 11. Behind home plate | 646 | 0 | 1 | .03 | .18 | −.01 | −.02 | −.05 | −.06 | .02 | .13 | −.07 | −.03 | .00 | −.11 | 1 | | | | | | | | | | |
| 12. Dugout | 646 | 0 | 1 | .05 | .18 | .02 | .02 | −.22 | −.05 | .05 | .34 | .11 | −.08 | −.05 | −.28 | −.04 | 1 | | | | | | | | | |
| 13. Infield wall | 646 | 0 | 1 | .04 | .19 | −.01 | .00 | −.06 | .02 | −.04 | .09 | .06 | .02 | .05 | −.13 | −.04 | −.04 | 1 | | | | | | | | |
| 14. Outfield stands | 646 | 0 | 1 | .40 | .49 | .05 | .03 | .04 | .07 | −.02 | −.13 | .12 | .05 | .01 | .28 | −.15 | −.19 | −.16 | 1 | | | | | | | |
| 15. Outfield wall | 646 | 0 | 1 | .48 | .50 | −.05 | −.03 | .10 | −.04 | .00 | −.10 | −.16 | .00 | .00 | −.06 | −.18 | −.22 | −.19 | −.78 | 1 | | | | | | |
| 16. Fan gender (female) | 703 | 0 | 1 | .36 | .48 | −.03 | .02 | −.03 | .00 | .00 | .04 | −.01 | −.01 | .01 | .00 | −.01 | .00 | .01 | −.01 | .01 | 1 | | | | | |
| 17. Fan age | 703 | 19 | 71 | 52.19 | 9.36 | −.03 | −.01 | −.01 | .01 | .00 | .01 | −.01 | .02 | .00 | −.01 | .00 | .00 | .02 | −.01 | .01 | .05 | 1 | | | | |
| 18. Fan color blindness | 703 | 0 | 1 | .26 | .44 | .04 | .03 | .00 | .00 | −.02 | .01 | −.01 | .01 | −.01 | .01 | −.01 | .00 | .00 | .01 | −.01 | −.03 | .05 | 1 | | | |
| 19. Fan exposures to signage (% games watched) | 703 | 0 | 1 | .72 | .27 | .07 | .08 | −.01 | .00 | .03 | .00 | −.01 | .00 | −.01 | .02 | −.01 | .00 | −.01 | .00 | .01 | .15 | .10 | .07 | 1 | | |
| 20. Team wins (total: 2013–2015) | 30 | 207 | 287 | 243 | 22.6 | −.01 | −.02 | −.05 | .02 | .15 | −.04 | −.01 | −.03 | −.04 | −.01 | −.01 | −.03 | .00 | −.09 | .11 | .13 | .06 | −.04 | .14 | 1 | |
| 21. Team attendance (average in thousands) | 30 | 15.4 | 46.5 | 30.35 | 7.36 | −.04 | −.04 | −.01 | −.04 | .10 | .01 | .02 | −.04 | .04 | −.06 | .00 | −.02 | .00 | −.01 | .02 | .05 | .01 | −.07 | .02 | .50 | 1 |
| 22. Team number of stadium sponsors | 30 | 8 | 39 | 21.70 | 6.40 | −.05 | .01 | −.02 | .07 | −.04 | −.03 | −.02 | .06 | .00 | .01 | −.02 | −.01 | −.05 | .04 | .00 | .01 | −.02 | .01 | −.04 | −.15 | .18 |
2 Notes: N = number of unique observations in the sample, Min is minimum observed value, Max is maximum observed value. Visual congruence is a categorical variable, so it has been converted into four binary variables and correlations are point-biserial.
We constructed several models to examine the empirical relationship between sponsor–team congruence and fan attitudes toward the sponsorship. First, we aimed to assess the total effect of each of the three observed sources of congruence (visual, conceptual, and geographic) on the attitude of fan i of team j toward the sponsor brand b. Given our cross-nested structure, multiple brands sponsoring multiple teams, we specified the model as follows:
Graph
where we decompose visual congruence into β1 for incidental visual congruence, in which the brand happens to share colors with the team; β2 for created visual congruence, in which the brand adopts the team's colors in the signage; and β3 for misplaced visual congruence, in which the brand adopts the colors of the stadium but not the team. In our main model, we do not include brand fixed effects, because the effect of conceptual congruence, β4, would be fully captured by brand fixed effects; conceptual congruence varies by brand but does not vary within brand across teams. Instead, we control for observable brand- and signage-specific controls, captured by β6–β11. Because each fan rates multiple brands, we can control for fan/team-specific fixed effects (e.g., fan's demographics and team success) through the coefficient vector β12.[ 9]
Second, we constructed models to assess the extent to which the total effect of each source of congruence might reflect an attribution process, such that fans evaluate congruent sponsor brands more positively because they perceive these brands as more supportive of their team. We adapted Equation 1 to a model in which the outcome is fan i's perception of the level of brand b's support of team j:
Graph
Then, we updated the model for attitude toward the sponsorship to include perceived sponsor support as a predictor of attitude toward the sponsorship:
Graph
The estimates of the indirect effects are γ1 × α1 for incidental visual congruence, γ2 × α1 for created visual congruence, γ4 × α1 for geographic congruence, and γ5 × α1 for conceptual congruence. Because the components of the indirect effects are estimated quantities, we calculate the indirect effects from 5,000 bootstrapped samples and examine the middle 95% of the estimated indirect effects to confirm that the effects differ from 0 ([22]). The fan/team fixed effects help control for common method variance from measuring both the mediator and outcome in the same survey. We also cluster standard errors by fan to account for correlation in the errors from Equations 2a and 2b. To facilitate the tests of the moderation hypotheses, we removed the fan/team fixed effects, which allows for fan-specific heterogeneity; we test whether fan exposures to sponsorship signage amplify (H2a) and fan color blindness suppresses (H2b) the effects of visual congruence on attitudes toward the sponsorship through perceived support.
The estimated model reveals the sponsorship performance implications of each source of sponsor–team congruence (Table 3, Model 1). In support of H1a, fans appear to hold more favorable attitudes when the sponsorship signage displays the brand in colors matching the sponsored team's colors. Both incidental and created visual congruence exhibit significant, positive effects on attitudes toward the sponsorship (incidental b =.22, p <.01; created b =.41, p <.01). The effect of created visual congruence is greater than that of incidental visual congruence (b =.19, p <.05). These positive effects do not seem to be due to visual fluency, however; we find no evidence of a similar benefit from misplaced visual congruence, in which the sponsor signage matches the stadium but not the team (b = −.08, p =.13).
Graph
Table 3. Estimation Results: Total, Indirect, and Direct Effects of Sponsor–Team Congruence on Attitude Toward the Sponsorship in MLB.
| Total Effects | Indirect and Direct Effects |
|---|
| Model 1: Attitude Toward the Sponsorship | Model 2a: Perceived Sponsor Support | Model 2b: Attitude Toward the Sponsorship | Bootstrap Estimates of Indirect Effect |
|---|
| Estimate | SE | Estimate | SE | Estimate | SE | Mean | LLCI | ULCI |
|---|
| Mediator Model 2b: perceived sponsor support | | | | | .65*** | (.02) | | | |
| Sponsor–Team Congruence | | | | | | | | | |
| Visual congruence (vs. noncongruent brand colors) | | | | | | | | | |
| Incidental: Brand colors already match team | .22*** | (.04) | .16*** | (.03) | .12*** | (.03) | .10 | .07 | .14 |
| Created: Change colors to match team | .41*** | (.10) | .18** | (.07) | .29*** | (.08) | .12 | .04 | .20 |
| Misplaced: Colors match stadium, not team | −.08 | (.06) | .02 | (.04) | −.09* | (.05) | | | |
| Conceptual congruence | .70*** | (.04) | .48*** | (.03) | .39*** | (.03) | .31 | .28 | .34 |
| Geographic congruence | .31*** | (.04) | .21*** | (.03) | .18*** | (.03) | .13 | .10 | .17 |
| Controls | | | | | | | | | |
| Sponsor brand size | −.17*** | (.05) | −.08** | (.04) | −.12*** | (.04) | | | |
| Signage size | .28*** | (.02) | .16*** | (.02) | .18*** | (.02) | | | |
| Signage location (vs. outfield wall) | | | | | | | | | |
| Behind home plate | .21*** | (.08) | .04 | (.06) | .19*** | (.07) | | | |
| Dugout | .26*** | (.08) | .21*** | (.06) | .12* | (.06) | | | |
| Infield wall | −.19** | (.07) | .03 | (.06) | −.20*** | (.06) | | | |
| Outfield stands | −.01 | (.03) | −.01 | (.03) | −.01 | (.03) | | | |
| Fan–team fixed effects (703 fans from 30 teams) | Yes | Yes | Yes | |
| Model | | | | |
| R-square | .36 | .36 | .53 | |
| N | 15,289 | 15,289 | 15,289 | |
- 3 *p <.10.
- 4 **p <.05.
- 5 ***p <.01.
- 6 Notes: LLCI = lower level of the 95% confidence interval; ULCI = upper level of the 95% confidence level; estimates of 5,000 bootstrap samples.
Both conceptual congruence (b =.70, p <.01) and geographic congruence (b =.31, p <.01) also significantly predict more favorable fan attitudes toward the sponsorship. To determine the magnitude of the congruence-based performance benefits, we drew 5,000 bootstrap samples and estimated the percentage lift in attitudes from each source (for a display of the density distribution for each source of congruence, see Figure 3). Across all 5,000 samples, each source of congruence always corresponds to more positive attitudes. Conceptual congruence is the most beneficial (22.6% more favorable attitudes), followed by created visual congruence (12.4%), geographic congruence (9.7%), and incidental visual congruence (6.6%). These percentages are encouraging, though the results for conceptual and geographic congruence should be taken with caution because their benefits might exist even without the sponsorship, given that people are already naturally inclined to favor local over distant brands and sports fans prefer sports-related brands over other brands ([56]).
Graph: Figure 3. Density distribution of estimated percentage lift in attitude toward the sponsorship from each source of sponsor–team congruence.Notes: Distribution of estimated percentage lift in attitude obtained from 5,000 bootstrap samples. Endogeneity correction from color-blind fans suggests that true effect of processing visual congruence is 4.9% lift in attitude for incidental and 9.2% lift for created. There was no method for endogeneity correction of conceptual or geographic congruence.
We find evidence of an indirect effect of each source of congruence in the significant, positive effects on perceived sponsor support (Table 2, Model 2a: incidental visual congruence b =.16, p <.01; created visual congruence b =.18, p <.05; conceptual congruence b =.48, p <.01; geographic congruence b =.21, p <.01), along with a positive effect of perceived sponsor support on attitudes toward the sponsorship (Model 2b: b =.65, p <.01). In support of H1b, the bias-corrected 95% confidence interval [CI] based on 5,000 bootstrap samples excludes 0 for both the indirect effect of incidental visual congruence (index =.10; 95% CI = [.07,.14]) and created visual congruence (index =.12; 95% CI = [.04,.20]). The results of Model 2b show that each congruence source also exhibits a significant direct effect on attitudes (ps <.01), beyond the indirect effect through perceived sponsor support. The indirect effect accounts for less than 50% of the total effect of each congruence source, suggesting that congruence's power to shape attitudes extends beyond its influence through fan attributions.
We conducted additional analyses to increase our confidence in the core findings. In Robustness 1 (see Table 4), we reduced the sample to brands that sponsor multiple teams and included brand fixed effects, along with fan/team fixed effects. The significant effect of incidental visual congruence (b =.13, p <.05) suggests that the positive influence of visual congruence extends beyond unobserved managerial actions. These effects hold even after we remove respondents who live in the same city as the team, which we did to reduce concerns about the impact of preexisting local brand biases.[10] We recognize that this positive effect might exist without the sponsorship, such that fans might prefer any brand that shares their favorite team's colors. However, the positive effect of created visual congruence (b =.35, p <.01) suggests unique benefits of visual congruence in the sponsorship itself. Unfortunately, we cannot observe why a brand manager might choose to change colors for one team but not another. A comparison of the magnitude of effects from this more restricted model and the model without brand fixed effects reveals that between 60% (incidental) and 85% (created) of the effect of visual congruence comes directly from visual congruence, but unobserved factors also have a role (e.g., managers might increase sponsorship activations for teams with visual congruence, perhaps reflecting the same visual congruence processes that work on fans). Together, these findings substantiate the performance benefits of visual congruence in sponsorship. We also address the managerial selection issues in Studies 2 and 3.
Graph
Table 4. Robustness Results: Alternative Specifications for Examining Visual Congruence on Attitude Toward the Sponsorship.
| Robustness 1: Including Brand Fixed Effects | Robustness 2: Alternative Measure Visual Congruence | Robustness 3: Including Nonsponsor Brands |
|---|
| Estimate | SE | Estimate | SE | Estimate | SE |
|---|
| Mediator Model 2b: Perceived Sponsor Support | | | | | | |
| Sponsor–Team Congruence | | | | | | |
| Visual congruence (vs. noncongruent brand colors) | | | .04** | (.01) | | |
| Incidental: Brand colors already match team | .13** | (.06) | | | .23*** | (.04) |
| Created: Change colors to match team | .35*** | (.13) | | | .47*** | (.08) |
| Misplaced: Colors match stadium, not team | .00 | (.08) | | | −.06 | (.04) |
| Conceptual congruence | | | .70*** | (.03) | .57*** | (.03) |
| Geographic congruence | .29*** | (.05) | .32*** | (.03) | .27*** | (.03) |
| Controls | | | | | | |
| Sponsor brand size | | | −.17*** | (.04) | −.14*** | (.04) |
| Signage size | .28*** | (.02) | .29*** | (.02) | | |
| Signage location (vs. outfield wall) | | | | | | |
| Behind home plate | .42*** | (.13) | .18** | (.08) | | |
| Dugout | .37*** | (.12) | .23*** | (.07) | | |
| Infield wall | −.14 | (.11) | −.19*** | (.07) | | |
| Outfield stands | .01 | (.05) | .00 | (.03) | | |
| Nonsponsor brand (vs. actual sponsors) | | | | | −.93*** | (.04) |
| Brand fixed effects (82 brands) | Yes | | |
| Fan–team fixed effects (703 fans from 30 teams) | Yes | Yes | Yes |
| Model | | | |
| R-square | .49 | .36 | .37 |
| N | 8,321 | 15,289 | 18,165 |
- 7 *p <.10.
- 8 **p <.05.
- 9 ***p <.01.
- 10 Notes: Robustness 1 includes brands that sponsor multiple teams. Conceptual congruence and sponsor brand size are captured by brand fixed effects. Robustness 2 utilizes a measure of visual congruence derived from the Euclidean distance between the digital scores of hue, saturation, and brightness for the signage and the team. Robustness 3 includes ratings of four brands for each team that did not actually sponsor the team.
For the second robustness analysis, we sought to increase confidence that our findings do not stem from biases or errors introduced when our coders used their judgment to classify visual congruence. Therefore, we constructed an alternative visual congruence measure derived from the Euclidean distance between the digital color for the brand signage and the team's color,[11] in terms of hue, saturation, and brightness ([ 9]). The mean digital color distance score converged with our coders' classification; signs classified as visually incongruent have digital color distance scores twice the magnitude of signs classified as visually congruent (Mcongruent = 73.12, Mincongruent = 155.32; t(708) = 9.53, p <.001). We transformed and normalized this digital color distance score into a congruence metric by the function 1 − (digital color distance/100). The positive, significant effect of this alternative visual congruence measure on attitudes toward the sponsorship (b =.04, p <.05) substantiates the original findings that rely on the coders (see Table 4).
In Robustness 3, we included all fan ratings of the four nonsponsor brands included in the fan survey, despite not having any signage supporting a team (Starbucks, Walmart, IKEA, and Home Depot). We dummy-coded nonsponsors and observe, as expected, a strong, significant, negative effect of nonsponsorship on fans' attitudes toward the sponsorship (b = −.93, p <.01). Even the most zealous fans have trouble recalling which brands actually sponsor their team ([54]), but these results support the notion that sponsorship signage has a general, sustaining influence on fan evaluations.
We removed the fan/team fixed effects to examine whether the effects of visual congruence differ across fans, because their attitudes are shaped by their visual processing of sponsorship signage. We replaced the fixed effects with fan- and team-level controls and ran several moderation analyses of the indirect effect through perceived sponsor support (see Table 5). In support of H2a, fan exposures, measured as the percentage of games the fan watched (mean-centered to ease interpretation of the simple effects of visual congruence), significantly moderate the effect of created visual congruence (b =.72, p <.01). A spotlight analysis ([51]) reveals that created visual congruence lifts attitudes by 17% for fans who watch many games (99% of games = 1 SD above the mean) but only by 7% for fans who watch a more moderate percentage (45% of games = 1 SD below the mean). A floodlight analysis identifies 60% of games watched as the Johnson–Neyman point, above which created congruence has a significant, positive effect (p <.05). More than 70% of fans in our sample reported watching at least 60% of games. Interestingly, we find no statistical support for the prediction that exposures moderate the effect of incidental visual congruence, when a brand's colors happen to match the team's colors. Perhaps the benefits of incidental visual congruence get conveyed immediately, due to fans' prior knowledge of both the brand's and the team's shared colors. In addition, the sponsor brand may be recognized more easily in its familiar color scheme.
Graph
Table 5. Moderated Mediation Models: Individual Differences That Moderate the Linkage Between Visual Congruence and Perceived Sponsor Support.
| Moderation H2a: Fan Exposures | Column AModeration H2b: Fan Color Blindness | Column BModeration H2b: Fan Color Blindness | Bootstrap Estimates of Moderated Mediation |
|---|
| Estimate | SE | Estimate | SE | Estimate | SE | Index | LLCI | ULCI |
|---|
| Visual congruence (vs. noncongruent brand colors) | | | | | .21*** | (.04) | | | |
| Incidental: Brand colors already match team | .16*** | (.03) | .19*** | (.04) | | | | | |
| Created: Change colors to match team | .25*** | (.07) | .33*** | (.08) | | | | | |
| Misplaced: Colors match stadium, not team | .06 | (.04) | .06 | (.05) | .06 | (.04) | | | |
| Conceptual congruence | .48*** | (.03) | .48*** | (.03) | .48*** | (.03) | | | |
| Geographic congruence | .20*** | (.03) | .20*** | (.03) | .20*** | (.03) | | | |
| Moderation | | | | | | | | | |
| Incidental visual congruence × Fan exposures | −.08 | (.13) | | | | | | | |
| Created visual congruence × Fan exposures | .72*** | (.28) | | | | | .50 | .16 | .84 |
| Misplaced visual congruence × Fan exposures | .11 | (.13) | | | | | | | |
| Incidental visual congruence × Fan color blindness | | | −.13* | (.08) | | | | | |
| Created visual congruence × Fan color blindness | | | −.28 | (.17) | | | | | |
| Misplaced visual congruence × Fan color blindness | | | .01 | (.09) | | | | | |
| Visual congruence × Fan color blindness | | | | | −.16** | (.07) | −.11 | −.21 | −.01 |
| Controls | | | | | | | | | |
| Sponsor brand size | −.10*** | (.04) | −.10*** | (.04) | −.10*** | (.04) | | | |
| Signage size | .18*** | (.02) | .18*** | (.02) | .18*** | (.02) | | | |
| Signage location (vs outfield wall) | | | | | | | | | |
| Behind home plate | .02 | (.07) | .02 | (.07) | .02 | (.07) | | | |
| Dugout | .19*** | (.06) | .19*** | (.06) | .19*** | (.06) | | | |
| Infield wall | .02 | (.07) | .02 | (.07) | .01 | (.07) | | | |
| Outfield stands | −.02 | (.03) | −.02 | (.03) | −.02 | (.03) | | | |
| Fan gender (female) | .03 | (.03) | .03 | (.03) | .03** | (.03) | | | |
| Fan age | .00* | (.00) | −.003* | (.001) | −.003* | (.001) | | | |
| Fan color blindness | .07** | (.03) | .10*** | (.03) | .10*** | (.03) | | | |
| Fan exposures | .43*** | (.06) | .46*** | (.05) | .46*** | (.05) | | | |
| Team wins | .00 | (.00) | .00 | (.00) | .00 | (.00) | | | |
| Team attendance | −.007*** | (.002) | −.007*** | (.002) | −.007*** | (.002) | | | |
| Team number of stadium sponsors | .00 | (.00) | .00 | (.00) | .002* | (.002) | | | |
| Intercept | 4.67*** | (.18) | 4.66*** | (.18) | 4.64*** | (.18) | | | |
| Model | | | | | | | |
| R-square | .04 | .04 | .04 | |
| N | 15,289 | 15,289 | 15,289 | |
- 11 *p <.10.
- 12 **p <.05.
- 13 ***p <.01.
- 14 Notes: LLCI = lower level of the 95% confidence interval; ULCI = upper level of the 95% confidence level; estimates of the index of moderated mediation estimated for 5,000 bootstrap samples.
For the second moderation hypothesis (H2b), we initially observed only a marginally significant color blindness × incidental visual congruence interaction (Table 4, Moderation H2, Column A: b = −.13, p <.10) and a directional but nonsignificant color blindness × created visual congruence interaction (b = −.28, p =.11). We found few observations of color-blind fans rating visually congruent sponsors (i.e., approximately 5% of total observations). Therefore, we ran the color-blindness moderation model a second time (Column B), after combining both types of sponsor–team visual congruence. In support of H2b, this second color-blindness moderation model reveals that the positive effect of visual congruence on fans without color blindness (b =.21, p <.01) almost completely disappears among fans with color blindness (interaction b = −.16, p <.05). We consider color-blind fans as a type of placebo control, not influenced by visual congruence but affected by other unobserved factors that potentially correspond with visual congruence, and therefore we consider the conditional effect of visual congruence among fans without color blindness as an endogeneity-corrected estimate. Evidence of moderated mediation through perceived sponsor support comes from an index of moderated mediation, for which the 95% confidence interval from 5,000 bootstrapped samples does not include 0 (Table 5).
This study of sponsor–team congruence in MLB documents a notable effect of visual congruence on sponsorship performance. Sponsor brands' signage, displaying the brand in colors that match the sponsored team's colors, improves attitudes toward the sponsorship. The effect holds whether the congruence is created or incidental, though it is significantly stronger for created visual congruence. We observe an indirect path for each source of congruence through perceived support and a direct path that is not mediated by attribution-based appraisals. Further increasing our confidence in the causal, positive effects from visual processing, we show that the benefits from created visual congruence increase with the number of exposures to the signage, but fans with color blindness are not influenced by visual congruence. Because all respondents are fans, we were unable to test H2c's prediction that the positive effect is conditional on fan status; we address this third moderation hypothesis in Study 3. Our robustness analyses support our coding and our data collection method; they also reduce concerns that the results of visual congruence are simply due to brand-specific effects, because we reveal within-brand effects of incidental visual congruence (e.g., Coca-Cola matches the Cincinnati Reds' colors but not the New York Mets'). Moreover, the effects of created visual congruence imply that the benefit derives from the sponsorship, rather than inherent fan color preferences for any brand.
Despite the convergent results across analyses, the correlational nature of these data mean that we cannot completely rule out the possibility that the effects of created visual congruence result from unobserved factors, related to the brand manager's selection of which colors to use. This concern is unlikely to explain the total effect or the effect of incidental visual congruence, which varies across teams within the same brand. Furthermore, misplaced visual congruence with the stadium also might stem from a brand manager's dedication to the team, as indicated by a willingness to alter the brand's colors to match the stadium, but it does not correspond with more positive fan attitudes. Ultimately, we need experiments to confirm the causal role of visual congruence for enhancing sponsorship performance. Inspired by Bud Light's use of a custom-colored can for each NFL team, we designed Study 2 as a managerially relevant extension.
The objectives for Study 2 were threefold. First, we aimed to confirm experimentally that created visual congruence enhances attitudes toward sponsorships through perceived support, using multi-item measurement scales. Second, we aimed to generalize our findings to another sponsorship context. In Study 1, the sponsor's logo appeared on the team's property (i.e., stadium signage). On product packages, however, the team's logo appears on the sponsor's property (i.e., product), where it does not compete for attention with the game. Third, we examine created visual congruence in a situation similar to Bud Light's custom NFL beer cans, in which Bud Light pursued visual congruence in its sponsorships of several teams simultaneously. Ubiquitous sponsorship usually undermines a brand's ability to convey support for any team ([50]), but if visual congruence through color matching can help boost perceived support, managers may expand their sponsorship efforts and reach more fans. Therefore, Study 2 features a brand that adopts the colors of multiple teams for an ecologically valid, managerially useful manifestation of created visual congruence.
A marketing research firm intercepted 175 people in public and asked them to participate in the study, in exchange for a nominal payment. Participants began the survey on an iPad by reading information about Monster Energy, which included information that Monster was considering launching a sports drink. Cincinnati was a desirable location for the study, because it is Monster Energy's top market by market share.[12] Participants were handed a poster of the sports drink and prototypes of actual bottles that featured a sponsorship of the Cincinnati Bengals on the bottle label, as well as three other bottles featuring three other NFL teams (for pictures, see the Appendix). Conditions were assigned according to the locations in the mall where the firm recruited participants; in one condition, the bottle's visual design featured the Monster logo in its original green color (visually incongruent), and in the other condition, the Monster logo matched each team's color (visually congruent). After evaluating the drink, participants completed the survey. To qualify, the participants had to indicate sufficient brand knowledge to identify Monster's color identity as green (49 lacked this knowledge). The final sample contained 126 adults (65 women, 60 men, 1 preferred not to indicate; median age 26 years; 98 identified as Cincinnati Bengals fans).
Participants evaluated their attitudes toward the sponsorship on a three-item, seven-point Likert scale in which they noted their agreement with the following statements: "I feel positive about Monster's sponsorship that I saw today," "The sponsorship I saw today is great," and "I feel very positive about Monster's sponsorship" (1 = "strongly disagree," and 7 = "strongly agree"; α =.96). Next, participants indicated the extent to which the brand supports the Cincinnati Bengals on three items: "Monster Energy intensely supports the Cincinnati Bengals," "Monster Energy appears to be committed to the Cincinnati Bengals," and "Monster Energy is passionate about the Cincinnati Bengals" (1 = "strongly disagree," and 7 = "strongly agree"; α =.93). We asked whether they were fans of the Cincinnati Bengals (yes/no) and to what extent (1 = "not at all," and 7 = "to a great extent"). Then, participants answered, "What color best represents Monster's brand logo (not the sports drink you evaluated today)?" and completed a manipulation check of the extent to which they noticed visual congruence: "The image of Monster's logo on the bottle matched..." "the Cincinnati Bengals' typical logo color identity" and "Monster's typical logo color identity" (reversed). Finally, participants completed the three-part color blindness test from Study 1 ([ 7]) and provided demographic information.
Confirming the expected differences across visual congruence conditions, an independent sample t-test reveals a significant difference in participants' ratings of visual congruence (Mcongruent = 4.96, Mincongruent = 3.66; t(124) = 5.65, p <.001).
With a mediation analysis, using ordinary least squares path analysis (Model 4; [22]) while controlling for participant characteristics of age, gender, color blindness, and whether they identified as a fan of the team, we find that visual congruence indirectly influences attitudes toward the sponsorship through its effect on perceived support (see Table 6). The effect of visual congruence on perceived support is positive and significant (a =.56, p =.02); in support of the indirect effect (ab =.32), the bias-corrected 95% confidence interval based on 5,000 bootstrap samples excludes 0 ([.07,.65]). We find only marginal support for a direct effect, independent of the effect through perceived sponsor support (c′ =.42, p =.07). The total effect of visual congruence on attitudes toward the sponsorship (c =.74, p =.005) corresponds to an estimated 14% performance increase. The results remain consistent when we include participants who did not know Monster's color identity, though the effect sizes are predictably slightly smaller and the p-values slightly increase (perceived support a =.39, p =.04; attitude c =.50, p =.02).
Graph
Table 6. Effect of Visual Congruence on Sponsorship Performance in Product Packaging and Digital Advertising Quasi Experiments.
| Study 2: Product Packaging | Study 3: Digital Advertising |
|---|
| Perceived Support | Sponsorship Attitude | Brand Attitude | Visit Intentions | Purchase Intentions | Word of Mouth | Brand Recall |
|---|
| Estimate | SE | Estimate | SE | Estimate | SE | Estimate | SE | Estimate | SE | Estimate | SE | Estimate | SE |
|---|
| Primary Predictors | | | | | | | | | | | | | | |
| Visual congruence | .56** | (.23) | .74*** | (.26) | .55*** | (.18) | .88*** | (.28) | .59** | (.27) | .56** | (.26) | .76 | (.74) |
| Fan | −.15 | (.27) | −.03 | (.31) | .47*** | (.14) | .53** | (.22) | .31 | (.21) | .24 | (.20) | 1.02* | (.55) |
| Visual congruence × Fan | | | | | .63*** | (.20) | .65** | (.32) | .48 | (.30) | .45 | (.30) | 1.49* | (.85) |
| Controls | | | | | | | | | | | | | | |
| Age | .00 | (.01) | −.01 | (.01) | .01 | (.00) | .01** | (.01) | .02*** | (.01) | .02*** | (.01) | .00 | (.02) |
| Gender (female) | .58** | (.23) | .73*** | (.26) | −.21** | (.08) | −.42*** | (.13) | −.42*** | (.13) | −.38*** | (.12) | .13 | (.36) |
| Color blindness | −.77*** | (.28) | −.41 | (.32) | −.21* | (.11) | −.14 | (.18) | −.02 | (.17) | −.17 | (.16) | .71 | (.64) |
| Ratings of competitors | | | | | .39*** | (.04) | .70*** | (.07) | .64*** | (.07) | .67*** | (.06) | .43*** | (.14) |
| Intercept | 4.16*** | (.39) | 5.41*** | (.45) | 2.80*** | (.29) | .69 | (.46) | .57 | (.45) | .50 | (.41) | −4.29*** | (1.08) |
| Model | | | | | | | | | | | | | | |
| R-square | .14 | .12 | .22 | .25 | .22 | .26 | .10 |
| N | 126 | 126 | 429 | 429 | 429 | 429 | 429 |
- 15 *p <.10.
- 16 **p <.05.
- 17 ***p <.01
- 18 Notes: Study 2 included too few nonfans (N = 28) for a reliable estimate of the visual congruence × fan interaction. Study 3 displays the direct effect of visual congruence for fans of the team and the direct effect of fan for the visually congruent ad. R-square is Nagelkerke R-square for brand recall logistic regression.
This managerial extension to test visual congruence through color matching in a product package design experiment confirms the correlational findings from our MLB real-world data. Visual congruence positively influences attitudes toward sponsorship through perceived support. We experimentally demonstrate these effects in an ecologically valid product testing environment, similar to the real-world example of Bud Light's sponsorship of the NFL. Unfortunately, fewer than 20 participants did not identify as Bengal fans in each congruence condition, so we could not estimate a congruence × fan status interaction. It is unclear whether visual congruence helps regardless of fandom, perhaps due to fluency, or whether it only works for fans who are motivated to process the sponsorship. We conducted Study 3 to address this question.
With Study 3, we experimentally test the sponsorship performance implications of visual congruence and add two extensions. First, we investigate whether fan status moderates the key effect to test our prediction in H2c that visual congruence improves sponsorship performance only among fans of the sponsored team. Study 1 and 2 relied on samples that did not include a sufficient number of nonfans to examine H2c. Second, we test the performance implications on a broad set of customer-level brand performance metrics that contribute to brand equity ([44]; [48]). Attitudes toward the sponsorship positively influence customer-level brand performance (see the supplemental study in Web Appendix W1; [48]); with this current study, we measure brand performance in a context where visual congruence potentially hinders performance among nonfans who may be less likely to notice visually congruent sponsorship ads. Therefore, we situate our experiment in an online newspaper, in which we embedded a digital ad featuring the team and sponsor brand, displayed either in the team's colors or the brand's original color. If the benefits of visual congruence are conditional on fan status, then brands may target visually congruent digital ads to the sponsored team's fans.
Four hundred twenty-nine residents of California (184 female; median age 29 years) completed an online survey through the Prolific Academic website (www.prolific.ac). We told participants that the survey sought their views about the new layout of a digital newspaper for the website (see the Appendix). At the time of the survey (September 2018), the Golden State Warriors, an NBA team located in California, had completed the first season of a three-season, $60 million jersey sponsorship deal with Rakuten, an e-commerce brand ([46]). The newspaper contained several digital ads, including one for the Golden State Warriors that featured the sponsor Rakuten and that repeated on the first and third pages of the newspaper. Participants were randomly assigned to one of two conditions in which the Rakuten logo appeared in either the Warrior's blue color, to create a visually congruent ad, or Rakuten's original red color.
After reading the newspaper and providing open-ended feedback about the layout, the participants transitioned to an ostensibly separate part of the survey to evaluate several e-commerce brands. As a measure of brand recall, they listed all the e-commerce brands they could recall (1 = Rakuten listed, 0 = otherwise). Then respondents completed the brand performance metrics for four e-commerce brands: Amazon, Rakuten, eBay, and Wish. The different customer-level brand performance metrics each contribute to brand equity ([44]; [48]); they are brand attitude ("How do you feel about the following e-commerce companies?"; 1 = "extremely negative," and 7 = "extremely positive") and behavioral intentions ("I will visit [brand] website or mobile application to buy products online"; "I will purchase products from [brand]"; "I will recommend [brand] to my friends to buy products online"; 1 = "strongly disagree," 7 = "strongly agree"). We analyzed each measure independently but also compiled them into a composite score, computed as an average of the measures (α =.91).
Next, participants indicated if they were a fan of the NBA; if so, they selected their favorite team. Their selections enabled us to create a dichotomous variable of fan status. Finally, participants supplied basic demographics, and completed the color blindness tests from Studies 1 and 2.
We conducted a series of ordinary least squares regressions with visual congruence, fan status, and their interaction as predictors of the customer-level brand performance metrics (i.e., brand attitudes, visit intentions, purchase intentions, word of mouth, and the composite measure). We also controlled for gender, age, and color blindness, as well as for evaluations of the other e-commerce companies (Amazon, eBay, and Wish) to account for baseline variance in comfort with e-commerce, the content of the newspaper, and the ad format. Table 6 provides model estimates, for which we shifted visual congruence (0 = congruent ad, −1 = incongruent ad) and fan status (0 = fan, −1 = not a fan), so the lower-order effect of visual congruence represents the effect among Warrior fans, and the lower-order effect of fan status represents the effect among those who saw the congruent ad.
In partial support of H2c, the results reveal a significant interaction of visual congruence and fan status for the customer-level brand performance metrics that arise earlier in the purchase journey (brand attitudes b =.63, p <.01; visit intentions b =.65, p =.04) but not those that are prominent later (purchase intentions b =.48, p =.12; word of mouth b =.45, p =.13). Strong support for the conditional effect of visual congruence occurs among fans, but not nonfans, for all measures (brand attitudes b =.55, p <.01; visit intentions b =.88, p <.01; purchase intentions b =.59, p =.03; word of mouth b =.56, p =.03). To get a sense of the practical impacts of visual congruence among fans, we also estimated marginal means for each individual performance metric; the estimated improvements due to visual congruence range from 12% for brand attitudes to 24% for visit intentions to 16% for purchase intentions to 15% for word of mouth.
To consider the overall impact on customer-level brand performance, we averaged each item to create a composite brand performance score, and a significant interaction between visual congruence and fan status (b =.58, p =.02) reveals that the effectiveness of visual congruence is conditional on fan status, in support of H2c. That is, the positive effect of visual congruence occurs for fans of the team (b =.68, p <.01) but not among nonfans (b =.10, p =.40). Although the brand had sponsored the Warriors for the entire prior NBA season, mainly in its original red color, fan status exerts a positive and significant effect when Rakuten's brand logo appeared in the Warriors' blue color (b =.40, p =.02) but not when it used its original red color (b = −.18, p =.31), as we depict in Figure 4.
Graph: Figure 4. Study 3: Composite brand performance score after exposure to sponsorship ad.Notes: Bar height represents estimated marginal means and error bars indicate 95% confidence intervals. A greater proportion of respondents are not fan of the sponsored team (n = 338) compared with fans of the sponsored team (n = 91), which is reflected in the tighter confidence intervals for nonfans.
Although we do not observe different attitudes or behavioral intentions among nonfans, some tentative evidence indicates a negative effect of visual congruence on free recall of Rakuten. That is, we submitted participants' free recall of Rakuten as an e-commerce company to a logistic regression, with fan status, visual congruence, their interaction, respondent demographics, and total number of e-commerce companies recalled as predictors. The interaction of visual congruence and fan status is marginally significant (b = 1.49, p =.08), and the conditional effect of visual congruence among nonfans is negative (b = −.73, p =.09), such that an estimated 4% of nonfans recall Rakuten when exposed to the visually congruent ad, less than half the estimate of 9% when exposed to the visually incongruent ad.
Study 3 provides experimental evidence that visual congruence offers the potential to improve brand performance when sponsorships are promoted to fans, though not to nonfans. In this study context (digital advertising), we could achieve a subtle experimental manipulation of visual congruence as part of a task that was unrelated to sports or sponsorship. Digital ads can accurately target audiences according to their interests, so sponsors can maximize the value of their sponsorships by targeting visually congruent sponsorship ads to fans, even after the season ends. This study also enabled us to observe a negative effect of visual congruence among nonfans, with marginal statistical support for the notion that visual congruence undermines recall among nonfans. This preliminary evidence reiterates the importance of targeting digital ads.
This research extends both sponsorship and brand alliance literature by examining the role of visual congruence between a brand sponsor and a team. Without a natural match to the team, a brand sponsor can enjoy greater sponsorship efficacy by adopting the team's colors in its sponsorship efforts. However, the positive effects are conditional on consumers' opportunity (i.e., viewership-based exposures), ability (i.e., lack of color blindness), and motivation (i.e., fan status) to process the visually congruent sponsorship. Color blindness helps identify color as a causal factor because nonvisual dimensions of the sponsorship should have similar effects across visually congruent and incongruent sponsorships. The other moderators indicate that passionate fans are the consumers especially influenced by visual congruence. Therefore, sponsors should target customized product packaging to team's local markets and target customized digital ads to fans. Sports fans represent billions of consumers and their affiliation motives drive them to both pay extra attention to ads featuring sponsorship and recommend sponsors to their friends ([ 3]; [14]; [36]). Accordingly, this research has important implications for marketing scholars and brand managers alike.
In contribution to the branding and design literature, we identify the importance of visual similarities in brand alliances. Literature on the topic of product design has investigated congruence between colors or between color and shape ([ 6]; [15]) and visual similarity to a prototypical design ([32]; [33]), but not between two brands. In a single brand context, research into color effects considers the fit with brand personality ([31]) or product category ([17]). It is worth noting that sponsorship alliances feature a unique power dynamic, in which the team has an existing positive relationship with the customer, and the other brand pays to leverage this established relationship. Further research should investigate whether visual congruence might be less helpful when these power dynamics are more balanced or the direction of the color alteration reverses (e.g., a team adopts the brand's colors). Continued research should also examine aspects of visual congruence other than color (e.g., font, shape and other visual elements of brand logos) and confirm if the effects we find extend to other forms of brand alliances.
This research shows that the boost in attitudes due to visual congruence results from both a direct route of categorization-based affect transfer and an indirect route that relies on fans' appraisals of perceived support. Past research notes that color can influence connotative meaning ([31]), embodied meaning ([52]), arousal ([ 4]), and categorization ([15]; [55]), but it has stopped short of examining attributions. Because one brand (the team) is already loved by the target consumers (fans), it is important that the other brand (the sponsor) is perceived as genuinely supportive. Visual design communicates efficiently ([30]; [31]), which is especially relevant in sports contexts where brand images are briefly displayed. Future research will need to explore if this strategy remains effective in cobranding contexts where the visuals of one brand conflicts with the brand personality of the other.
For brand managers, our findings support calls for flexibility in changing branding elements for sponsorships ([41]), despite arguments for keeping visual elements consistent in a single brand context ([ 1]; [27]). In the fragmented media environment, sponsorship is growing, because major sporting events continue to draw massive attention ([19]) and fans cannot "fast-forward" sponsorship images displayed during the game. In-game displays of sponsor brand signage, however, do not afford brands the opportunity to deliver lengthy messages. Managers can leverage their sponsorship investments by changing the colors in the display of their logo to match those of the team, which should involve few additional costs, especially for digital imagery. Although created congruence requires the brand to abandon its brand color identity in the sponsorship context, adopting a team's colors appears effective (16% increase in purchase intentions in Study 3). Indeed, our results also indicate that brands might adopt visual congruence for multiple teams to capitalize on league-wide sponsorship rights (Study 2). The practice of switching colors to match multiple teams' colors remains fairly novel. Further research should investigate if created congruence continues to make the sponsor brands appear more supportive of teams if many brands change colors for many teams. The categorization-based affect transfer path should remain intact.
Targeted advertising, which refers to any form of advertising that is based on information the advertiser has about the recipient ([47]), increases return on investment ([35]). Targeted advertising has seen explosive growth with the widespread adoption of mobile and social media, with European targeted digital advertising spending expected to reach €21.5 billion in 2020 ([25]). In Study 3, a visually congruent sponsorship that is displayed in a digital advertisement enhances customer-level brand performance metrics. However, Study 3 also indicates that nonfans' recall of the sponsor brand is weaker, at marginally significant levels, when the brand uses the team's colors. Managers can change the digital advertisement image according to the interests of the intended recipient. For in-stadium sponsorship signage, brands could use digital ad replacement technology to change logo colors for specific markets. In this sense, our findings extend prior research that emphasizes targeting sponsorship communications according to fan affiliation motives ([36]), with our research going beyond whom to target by considering what to display.
As a note of caution, Study 1 finds that changing the display of the brand to the team's colors is not statistically superior to using incongruent brand signage among fans who watch fewer than 60% of the games, a finding that aligns with prior research ([ 9]; [30]; [40]) and common sense: If a brand is less recognizable, fans need more exposures to recognize it. Incidental visual congruence (e.g., Budweiser at the Reds' stadium), in contrast, is effective for all levels of fan exposure; consistent with advertising research that color typicality facilitates identification and positive evaluation at brief exposures ([55]). Further research might investigate whether nonsponsors that happen to share the team's colors are also favored by fans, even in contexts in which the team is not salient.
Brands would benefit from knowing if the effects diminish as more brands implement visual congruence tactics or if stadiums demand all signage to be visually congruent to create a consistent aesthetic. Additional complications might arise from competitors' color identities; a brand might avoid a team's color if its rival already has that color identity (e.g., Home Depot and Lowe's). Devoted fans may dislike a brand if it adopts the color of a hated team, compared with when the brand simply sponsors the team. This research represents a step toward a better understanding of the role of visual congruence in sponsorship effectiveness, which appears likely to become increasingly important as consumers lose patience for lengthy commercials ([53]).
Supplemental Material, DS_10.1177_0022242919831672 - The Color of Support: The Effect of Sponsor–Team Visual Congruence on Sponsorship Performance
Supplemental Material, DS_10.1177_0022242919831672 for The Color of Support: The Effect of Sponsor–Team Visual Congruence on Sponsorship Performance by Conor M. Henderson, Marc Mazodier and Aparna Sundar in Journal of Marketing
Graph: Appendix. Stimuli for Studies 2 and 3.
Footnotes 1 Author ContributionsThe authors contributed equally to the article.
2 Associate EditorPradeep Chintagunta served as associate editor for this article.
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partially funded by the Business School of Hong Kong Baptist University, AACSRE, the Warsaw Sports Marketing Center and SPM at the University of Oregon.
5 Online supplement: https://doi.org/10.1177/0022242919831672
6 1A supplemental study with a minor league baseball team whose sponsor switched colors to match the team reveals a strong correlation between this attitudinal measure of sponsorship performance and both word of mouth (r =.65, p <.001) and purchase intentions (r =.60, p <.001). Details are available in Web Appendix W1.
7 2These games were from the last two months of the season. Three students watched highlights from games at the beginning of the season to confirm that more than 89% of the signs in our data set were present at the start of the season. If we exclude signage absent at the start of the season, the sample size decreases from 15,289 to 13,823, but the key effects of visual congruence on attitudes toward the sponsorship remain positive and significant at p <.01.
8 3The coders considered the fans' perspective when determining congruence between shades of colors. Teams differ in the extent to which they adopt various color shades, so the coders considered each team's color history, variety of uniforms and hats for sale on the http://MLBshop.com website, and the similarity of their colors to rival teams' colors. To address concerns about potential biases or errors due to the classification of visual congruence, we conducted an analysis ("Robustness 2") with an alternative measure based on digital scores of hue, saturation, and brightness.
9 4A robustness check uses brand fixed effects, which wholly encompass the effect of conceptual congruence but allow for a more conservative test of visual congruence.
5Qualtrics provides estimates of longitude and latitude. Fans' median distance from the team's stadium was 48 miles, and 25% of fans were at least 180 miles away. If we only include fans who were at least 48 miles away, the brand fixed effects model should provide estimates free of any local brand bias. With this restricted sample (4,135 ratings), we still find positive effects of incidental and created visual congruence on attitudes toward the sponsorship (p <.01).
6For teams with multiple colors, we chose the color that was closest to the predominant color in the signage.
7We thank Monster Energy's Senior Vice President of Marketing for this insight.
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Record: 180- The Commercial Consequences of Collective Layoffs: Close the Plant, Lose the Brand? By: Landsman, Vardit; Stremersch, Stefan. Journal of Marketing. May2020, Vol. 84 Issue 3, p122-141. 20p. 1 Diagram, 6 Charts, 3 Graphs. DOI: 10.1177/0022242919901277.
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The Commercial Consequences of Collective Layoffs: Close the Plant, Lose the Brand?
This article examines the effects of collective layoff announcements on sales and marketing-mix elasticities, accounting for supply-side constraints. The authors study 205 announcements in the automotive industry using a difference-in-differences model. They find that, following collective layoff announcements, layoff firms experience adverse changes in sales, advertising elasticity, and price elasticity. They explore the moderating role of announcement characteristics on these changes and find that collective layoff announcements by domestic firms and announcements that do not mention a decline in demand as a motive are more likely to be followed by adverse marketing-mix elasticity changes. On average, sales for the layoff firm in the layoff country are 8.7% lower following a collective layoff announcement than their predicted levels absent the announcement. Similarly, advertising elasticity is 9.8% lower and price elasticity is 19.2% higher than absent the announcement. Conversely, layoff firms typically decrease advertising spending in the country where collective layoffs have occurred, yet they do not change prices. These findings are relevant to marketing managers of firms undergoing collective layoffs and to analysts of collective layoff decisions.
Keywords: advertising; collective layoffs; difference-in-differences; downsizing; marketing-mix elasticity; price; pricing; sales
Collective layoffs—the simultaneous termination of the labor contracts of a large group of workers—are common in many Western societies ([15]). In Europe alone, 556 collective layoffs were announced between December 2018 and November 2019, involving more than 250,000 employees ([18]). In addition to their societal implications, collective layoff decisions have an immense impact on the firms that initiate them.
Management scholars have studied the financial consequences of collective layoffs for downsizing firms ("layoff firms") as well as for their employees (see, e.g., [21]; [39]; [48]). In marketing, prior research has studied various aspects of customer or investor response to collective layoffs (see Table 1). These studies, which mostly focused on layoffs of customer-facing employees, have shown, for example, that downsizing can increase customer uncertainty, decrease firms' customer orientation and customers' positive perceptions of the brand, and decrease customer satisfaction ([22]; [25]; [51]).
Graph
Table 1. Review: Marketing Studies on Employee Downsizing.
| Outcome Variables | | |
|---|
| Paper | Customer Satisfaction | Customer Uncertainty | Customer Loyalty | Reputation/Image | Performance | Advertising Elasticity | Price Elasticity | Sample | Findings |
|---|
| McElroy, Paula, and Rude (2001) | ✔ | | | | Profit per loan, employee productivity | | | Home mortgages (one-on-one sales setting) | Downsizing has greater and more pervasive adverse effects on satisfaction and profitability (yet not on productivity) than voluntary and involuntary turnovers. |
| Flanagan and O'Shaughnessy (2005) | | | | ✔ | | | | Fortune's "America's Most Admired Companies" | Layoffs have a negative effect on firm reputation; more so for newer firms than older firms, and for smaller firms than larger firms (limited support for the latter). |
| Lewin and Johnston (2008) | ✔ | | ✔ | ✔(perceived performance) | | | | B2B purchasing professionals (survey) | Downsizing suppliers experience lower customer satisfaction and lower loyalty, as compared with nondownsized suppliers. There is a nonlinear pattern between downsizing extent and performance and repurchase intentions. |
| Lewin (2009) | ✔ | | ✔ | ✔(perceived value) | | | | B2B purchasing professionals (survey) | Downsizing is associated with worse quality delivery and lower value for customers, leading to decreased customer satisfaction and customer loyalty. |
| Love and Kraatz (2009) | | | | ✔ | | | | Fortune's "America's Most Admired Companies" | Downsizing has a strong, negative effect on firm reputation that is significantly moderated by factors such as stock market reaction and downsizing prevalence. |
| Williams, Khan, and Naumann (2011) | ✔ | | ✔ | | | | | B2B building services (survey) | Customer satisfaction levels following the downsizing event are lower than those before the event. |
| Subramony and Holtom (2012) | | | | ✔(service brand |image [SBI]) | Organization unit profitability | | | B2B temporary help services offices (survey) | The relationship between downsizing and SBI is fully mediated by customer orientation. The relationship between voluntary turnover and SBI is fully mediated by customers' evaluations of service delivery. |
| Homburg, Klarmann, and Staritz (2012) | ✔ | ✔ | | | Managers' assessment of firm performance | | | B2B supplier firms, bank (survey) | Downsizing size is associated with customer uncertainty. Open communication may increase customer uncertainty depending on customer informal ties with the firm's employees or perceived product importance. Perceived customer uncertainty has a negative effect on perceived customer satisfaction. |
| Habel and Klarmann (2015) | ✔ | | | | Return on assets | | | U.S. firms (different industries) | Downsizing negatively affects customer satisfaction, and more so for companies with specific characteristics or from specific type of industries or product categories. Customer satisfaction mediates the effect of downsizing on return on assets. |
| Panagopoulos, Mullins, and Avramidis (2018) | | | | | Firm idiosyncratic risk | | | U.S. public firms | Sales force reductions are associated with firm idiosyncratic risk and more so when there is higher competitive pressure and lack of transparency in financial reporting. Firm advertising can mitigate the moderating effect of competitive pressure on idiosyncratic risk. |
| The current research | | | | | Sales | ✔ | ✔ | Automotive industry | Sales, advertising elasticity, and price elasticity significantly drop following layoff announcements. Layoff announcement characteristics, moderate the effects of collective layoffs on advertising elasticity and price sensitivity. |
1 Notes: B2B = business to business.
The present research complements this prior work in management and marketing by being the first to empirically demonstrate the effects of collective layoff announcements on demand and the effectiveness of its drivers (i.e., marketing-mix elasticities). Given that termination of employment, particularly of large numbers of people, typically evokes negative connotations, it seems reasonable to expect that layoff announcements should have negative, rather than positive, effects on the layoff firm's demand. Nevertheless, we do not know whether such negative demand effects are universally present (i.e., in how many cases do collective layoffs typically lead to lower demand?) and what the magnitude is of such demand effects (i.e., are these effects typically very large or typically rather small?). Moreover, the measurement of these effects is not straightforward, as the methodology used must control for factors such as production capacity constraints, which are likely to result from staff downsizing, as well as for potentially endogenous relationships between collective layoff announcements and various marketing decisions that might influence demand.
The effects of collective layoffs on the elasticities of marketing-mix components (e.g., advertising and price elasticities) are also unknown at present and are not simple to predict. For instance, consider advertising. On the one hand, a firm that announces a collective layoff may create uncertainty among consumers ([25]); as a result, consumers may rely more heavily on the firm's advertising as a source of information that might mitigate such uncertainty—thereby increasing advertising elasticity. On the other hand, a firm that announces a collective layoff may be viewed as being unfair to workers ([49]), making the firm less likeable and trustworthy—thereby decreasing advertising elasticity ([12], [53]). Given that such opposing forces are at play, the extent to which firm marketing instruments (e.g., advertising) are expected to dampen any adverse demand effects caused by the announcement of a collective layoff is not obvious. Moreover, thus far, the marketing literature has given no empirically validated guidance in this regard. This study aims to provide such insights, toward supporting firms' decision making with regard to marketing instruments in the country where the collective layoffs take place.
Taking a broader perspective, this article complements the scholarly insights provided by prior studies on the commercial consequences of other types of firm crises. For instance, previous research has investigated the impact of product harm crises (e.g., [11]; [33]), firms' violations of ethical or moral norms such as sweatshop operations ([ 4]; [26]), or negative news on celebrities who have endorsed a particular brand ([30]). However, collective layoffs have several unique characteristics that distinguish them from other crisis types, and thus, the commercial consequences of such layoffs warrant specific consideration.
First, while firms do not purposefully initiate most types of brand crisis (e.g., a product harm crisis, negative news on celebrities who have endorsed a brand), firms do initiate collective layoffs themselves and, thus, typically have some level of control over the timing, location, and communication of the collective layoff. Such control may help the firm to contain the potentially adverse outcomes of the layoffs ex ante.
Second, collective layoff announcements differ from other crises in terms of the information they might convey about the performance of the firm. For example, a product harm crisis, by definition, indicates that the quality of a firm's products has decreased and may even endanger users' lives. A collective layoff announcement, in contrast, does not directly reflect on the quality of the firm's products, although the merit of the firm's prior actions, or its prospects, may be called into question. Other crisis types, such as the emergence of bad news about affiliated celebrities, might provide even less concrete information about the firm—as they are not triggered by the firm's actions, let alone the quality of its products—yet nevertheless affect consumers' perceptions of the firm (e.g., owing to the mental association that they have established between the firm and the affiliated celebrity).[ 5]
Third, in estimating the commercial consequences of collective layoffs, one needs to control for potential supply constraints that the firm imposes on itself due to the layoffs. Notably, such supply-side constraints might also come into play during a product harm crisis (e.g., because of production-line shutdowns), yet, to our knowledge, studies in this domain have rarely taken them into account. In other crisis types, supply-side constraints are less likely to affect the estimation of commercial consequences.
With the aim of providing an initial empirical generalization on the commercial consequences of collective layoffs, we study 205 collective layoff announcements in the automotive industry across nine major automotive markets (Austria, Canada, France, Germany, Italy, Japan, Spain, the United Kingdom, and the United States) and 20 major brands, between 2000 and 2015, which led to the termination of the labor contracts of more than 300,000 employees. Because we do not necessarily observe the labor contract termination dates, we consider the announcement as the event whose impact is of interest ([43]). Conceptually, this approach suits our purpose—namely, to examine the commercial consequences that unfold after consumers hear of the firm's decision to lay off employees. Another unique feature of our study is that, in estimating the demand-side effects of interest, we control for production capacity utilization on the supply side (among other factors). In this way, we isolate an obvious potential cause of a decline in sales: a drop in produced supply.
We utilize a hierarchical Bayes estimation technique on a difference-in-differences (DID) model for unit sales. Our model specification enables us to estimate brand-specific elasticities over time and across countries while controlling for car model and time effects on sales, as well as production capacity constraints. The model thus captures the effects of collective layoff announcements on the sales of layoff brands and on their advertising and price elasticities. The DID model addresses the fact that collective layoff events do not occur randomly but rather are endogenous (i.e., result from firm decisions). We use a system of equations together with instrumentation to address the endogeneity of advertising and pricing and to account for common unobserved shocks that may influence sales, advertising, and price levels.
Our rich data together with our modeling framework also enable us to explore the heterogeneity of our main effects of interest (demand, advertising elasticity, and price elasticity) across characteristics of the layoff announcements and to identify boundary conditions. From our analysis of the content of these announcements and the events they cover, we identify three information components that an announcement typically contains and that seem worthy of exploration: ( 1) motive (did the firm motivate the collective layoff by a decline in demand or by other reasons [e.g., a supply-side search for efficiency gains]?), ( 2) nationality (is the firm domestic [and thus considered an in-group actor] or foreign [and thus considered an out-group actor] to the layoff country?), and ( 3) layoff size (how many employees are affected by the collective layoff?). While we do not claim that this is an exhaustive set of factors that might moderate the effects we explore, we believe that investigation of these factors can deliver some first insights that may stimulate further research to shed light on mediation and moderation processes regarding the commercial consequences of collective layoffs.
We report the following findings, which are new to the literature. First, using model-free evidence, we show that for two-thirds of the collective layoff announcements in our sample, the sales of the corresponding brands in the layoff country decreased in the year following the announcements as compared with sales in the year before the announcements. The mean drop in sales across all announcements was −6.6%. Our model estimates enable us to demonstrate that, accounting for all other effects in our model—including changes in marketing-mix elasticities and changes in advertising spending by layoff firms in the layoff country—sales for the layoff brand are 8.7% lower following a layoff announcement than their predicted levels absent the announcement.
Second, we observe that the marginal effects of collective layoff announcements on advertising elasticity and price elasticity are significantly negative, indicating that consumers become less sensitive to the advertising of the firm and more sensitive to its prices. On average, advertising elasticity is 9.8% lower and price elasticity is 19.2% higher (a more negative price elasticity) than absent the announcement. These effects are moderated by the layoff announcement characteristics we investigate.
Third, we show model-free evidence suggesting that firms do not universally adopt a single dominant advertising spending strategy following collective layoff announcements (the median change in spending is about 2%). However, our model estimates reveal that firms typically spend less on advertising (16% less, on average) than they would absent the announcement in the layoff country during the year following a collective layoff announcement.
These findings are relevant to marketing managers in firms that (plan to) announce collective layoffs. First, our findings regarding the commercially adverse effects of collective layoffs suggest that marketing managers should claim their place in the task forces that manage such layoffs, alongside functional representatives of other areas, such as finance and operations. Second, given the adverse effects we find for advertising elasticities, we recommend that marketers in a layoff country should allocate attention to their advertising response. We show that firms typically spend less on advertising following a layoff announcement than what they would have spent absent the announcement. As a result, the adverse effects of collective layoffs on sales in the layoff country loom larger not only because of lower advertising elasticity but also because of lower spending. An alternative response could be to increase advertising spending to compensate for the decreased elasticity and to consider such higher ad spending in the layoff country as a restructuring cost. For analysts, the present research offers a methodological framework to assess commercial consequences of collective layoffs and provides empirical estimates based on a large number of events across multiple countries, though constrained to one industry.
As discussed previously, we focus our analysis on three outcome variables: sales, advertising elasticity, and price elasticity. Figure 1 depicts our conceptual framework. It illustrates how marketing-mix decisions—and specifically, decisions with regard to advertising and price—influence firm sales before and after a collective layoff announcement, and how characteristics of the collective layoff communication affect our outcome variables. We also include several control variables that may affect the sales of the layoff brand (for parallel logic in the context of product-harm crises, see Cleeren, Van Heerde, and Dekimpe [2013]).
Graph: Figure 1. Conceptual framework.
We suggest that the effect of a collective layoff announcement on sales may occur through two primary routes. First, a firm that announces a collective layoff may create uncertainty among consumers ([25]). Such uncertainty might reflect, for example, the consumer's state of doubt about the continuance and the quality of the relationship with the layoff brand. An increase in consumer uncertainty may drive consumers to other brands, leading to a loss of sales. We acknowledge that, in some cases, it is possible that a collective layoff may have the opposite effect, lowering consumer uncertainty and reaffirming consumers' beliefs in the viability of a brand; nevertheless, in line with prior evidence, we expect heightened uncertainty to be the more common response to a layoff announcement ([25]).
Second, a firm that announces a collective layoff may be perceived as treating workers unfairly. First, collective layoffs may represent a broken commitment by a firm to its workers; indeed, decisions to initiate such layoffs are rarely a response to individual employees' failure to perform as expected but, rather, are typically determined by general economic conditions (e.g., labor costs) or firm health (e.g., low sales volumes, financial losses) ([34]; [49]). Second, collective layoffs typically affect the socioeconomic conditions of vulnerable workers, who either become unemployed or, if they remain employed by the firm, have to settle for lower wages with less job security ([49]). In such cases, the announcement of collective layoffs may alienate consumers who sympathize with the affected employees ([27]), making the brand less likeable and trustworthy. Alienated consumers may avoid the brand themselves (i.e., individual action) or encourage others to do so (i.e., collective action) ([ 5]; [24]; [27]), both leading to a loss in brand sales.
Our theorizing on the effect of collective layoff announcements on advertising elasticity is grounded in the informative and persuasive roles of advertising ([ 1]; [ 8]; [40]; [41]). If the announcement of a collective layoff creates uncertainty among consumers (as shown by, e.g., [25]]), advertising may offer a means of learning about the prospects of the layoff firm and their capacity to continue their relationship with the firm ([44]). This informative role of advertising in the presence of consumer uncertainty may lead to an increase in advertising elasticity for the layoff firm in the wake of the collective layoff announcement.
At the same time, if consumers consider collective layoffs to be unfair to workers, making the firm less likable and trustworthy as a communication source, advertising may become less persuasive ([ 9]; [53]). Our empirical tests enable us to determine whether, on average, the increase in the informative role of advertising dominates the decrease in the persuasive role of advertising or vice versa.
We expect collective layoff announcements to increase price elasticity (such that an increase in price has a stronger negative effect on demand). First, as theorized previously, collective layoff announcements may increase uncertainty among consumers regarding the future of their relationship with the firm. Uncertainty regarding future interactions with the firm may lead to higher price sensitivity among consumers ([10]) and, thus, to stronger or more negative price elasticity. Second, we theorized that consumers might consider collective layoffs to be unfair to workers, which may, in turn, decrease the perceived trustworthiness of the firm. Lower trustworthiness of the firm may lead to higher price sensitivity among consumers ([17]) and, thus, to a more negative price elasticity.
The extent to which collective layoff announcements elicit adverse consumer response and influence marketing-mix elasticities may vary across announcements. As discussed previously, we examine three collective layoff announcement characteristics that might have a role in moderating these effects: ( 1) whether the firm announcing the collective layoff is domestic (i.e., has its headquarters in that country) or foreign to the layoff country, ( 2) whether the collective layoff is motivated by a decline in demand or by other reasons (e.g., collective layoffs driven by a supply-side search for efficiency gains; for examples, see Web Appendix A; [20]),[ 6] and ( 3) the number of employees affected by the collective layoff.
We consider the empirical study of these collective layoff announcement characteristics as exploratory. Although there are clear reasons why these characteristics are expected to affect the commercial consequences of collective layoffs (as we elaborate subsequently), it is difficult to postulate the direction and magnitude of said effects ex ante.
Media sources typically provide richer coverage of domestic firms than of foreign firms, such that consumers are likely to be more informed about the former than about the latter. Therefore, consumers may experience less of an increase in uncertainty following a collective layoff announcement of a domestic firm than they would after an announcement of a foreign firm ([46]). Consumers also perceive domestic firms as in-group actors and foreign firms as out-group actors ([14]) and, consequently, typically expect domestic firms to adhere to higher standards of fairness toward domestic workers than foreign firms ([37]). Therefore, consumers may evaluate unfair behavior of domestic firms (as they are in-group members) more negatively than unfair behavior of foreign firms (as they are out-group members) (for a similar logic, see [52]]). Thus, for advertising elasticity we may expect that if it is a domestic firm (rather than a foreign firm) that lays off employees, the adverse effects of a collective layoff are stronger (i.e., due to lower increase in customer uncertainty [informative role] and higher decrease in likability and trustworthiness [persuasive role] compared with foreign firms). For sales and price elasticity, the effect of being a domestic, rather than foreign, firm depends on whether on average the smaller increase in customer uncertainty counteracts the greater decrease in likability and trustworthiness.
When a firm indicates that a collective layoff is motivated by a decline in demand, it may create doubt in consumers' minds regarding whether they will be able to continue their relationship with the firm in the future ([25]). Analysts and critics may magnify and further broadcast the "firm-in-decline" message and frame a perception of an uncertain future for the firm ([34]). Conversely, consumers may consider a decline in demand as a more justified reason for reducing manufacturing capacity than, for instance, the search for cost efficiency (i.e., the desire of the firm to increase profits). Most notably, delocalization of manufacturing to countries with lower labor costs has been the source of hot societal debate and boycotts ([38]). Thus, the likeability and trustworthiness of a firm that announces a collective layoff as being motivated by a decline in demand may decrease less than those of a firm that does not present such motivations for its announcement (e.g., when the motive is the search for efficiency gains). Thus, for advertising elasticity we may expect that if decline in demand is mentioned as a motive for the collective layoffs (rather than another motive), the adverse effects of a collective layoff are weaker (i.e., due to greater increase in customer uncertainty [informative role] and smaller decrease in likability and trustworthiness [persuasive role] compared with nondecline motives). For sales and price elasticity, the effect of a demand-driven motive depends on whether, on average, the greater increase in uncertainty counteracts the smaller decrease in likability and trustworthiness, compared with other motives.
The number of employees being laid off is likely to be related to consumer awareness about, and the salience of, the collective layoff announcement ([25]). Thus, it is likely to moderate the extent to which the collective layoff announcement affects consumer uncertainty and the trustworthiness and likeability of the brand. We may expect that if more employees are laid off, the adverse effects of the layoff announcement on sales and price elasticity will be stronger. For advertising elasticity, the effect of the number of employees being laid off depends on whether, on average, the expected higher increase in uncertainty as more employees are laid off, counteracts the expected stronger decrease in likability and trustworthiness as more employees are laid off.
In our empirical investigation we also control for other factors that may affect sales before and after the collective layoff announcement. In particular, to identify the magnitude of demand-side effects of a collective layoff announcement, our model must contain data on supply-side dynamics that may be affected by such collective layoffs. Thus, as noted previously, we control for production capacity constraints that may drive lower sales for the firm ([ 6]), as reflected in production capacity utilization. We also control for competitive sales, which may affect own-firm sales positively (i.e., capturing overall market trends) or negatively (i.e., capturing market-share stealing).
In the automotive industry, our empirical context, collective layoffs, including plant closures, by major international manufacturers frequently occur both in the United States and in many Western European countries ([ 3]). In North America, many manufacturing jobs have shifted from the United States to Mexico, which has experienced a massive investment in vehicle assembly in recent decades ([28]). In Europe, automotive assembly has shifted from Western Europe to lower-wage Eastern European countries ([28]; [29]). In fact, automotive production in Poland, the Czech Republic, Hungary, and Slovakia reached a record high in 2015 with the production of 3.5 million units, making the region the second-largest automotive hub in Europe, after Germany ([16]).
We combine four unique secondary data sets for this study. First, we utilize data from R.L. Polk Automotive (now IHS) regarding unit sales (i.e., new vehicle registrations) and list prices for 20 major automotive brands between 2000 and 2015 in nine countries. The brands are Alfa Romeo, BMW, Chevrolet, Chrysler, Citroen, Daihatsu, Fiat, Ford, Honda, Mazda, Mercedes, Mitsubishi, Nissan, Opel, Peugeot, Renault, Seat, Suzuki, Toyota, and Volkswagen, and the countries are Austria, Canada, France, Germany, Italy, Japan, Spain, the United Kingdom, and the United States.[ 7] Each brand we analyze is among the top ten car sellers in at least one of the countries we investigate. All the countries are automotive manufacturing locations, and they include the countries of origin of all of the aforementioned automotive brands (Alfa Romeo and Fiat originate in Italy; Seat in Spain; BMW, Mercedes, and Volkswagen in Germany; Chrysler, Chevrolet, and Ford in the United States; Daihatsu, Honda, Mazda, Mitsubishi, Nissan, Subaru, Suzuki, and Toyota in Japan; Citroen, Peugeot, and Renault in France).
Second, we utilize data from Focus Media (Austria), Kantar (Japan and France), and Nielsen (all other countries) on monthly advertising spending for all car models and corporate advertising of the car brands and countries we consider. Third, we use a unique data set, purchased from R.L. Polk Automotive (now IHS), that covers the monthly production levels and the maximum production capacity for all automotive plants of light vehicles between the years 2000 and 2015, globally.
Fourth, for the countries, brands, and time periods we consider, we manually collected data on collective layoff announcements (n = 205) in which a minimum of 90 employees were dismissed.[ 8] We began with an internet search for basic information on the factories that assemble cars of each of our brands. We then built for each brand a list of factories worldwide and noted the current status of each factory (open/closed/sold), along with the year of closure or sale, when applicable. Next, we focused on the countries in our data and obtained detailed monthly information on factory closures (e.g., from press coverage). We carried out an additional search using each factory's name and a range of relevant dates to search for information on collective layoffs that did not involve plant closures. We then validated our data by cross-checking among different sources. Specifically, for the United States, we used a report issued by the Center for Automotive Research ([ 7]) that provided details on closed (and repurposed) U.S. auto-manufacturing facilities. For Europe, we used the European Monitoring Center of Change database ([18]). In addition, we used [ 2] "Guide to Assembly Plants in Europe." Finally, we used the brands' own websites. We scanned their lists of existing factories to ensure that we had not missed any collective layoff announcement and used the "Media Centers" on their websites to obtain press releases on closure and dismissal announcements.
For every collective layoff announcement, we collected information on the announced motive for the collective layoff to code whether the layoffs were driven by a decline in demand (i.e., "demand-driven") or not. We codeed collective layoff as demand-driven if a decline in demand was mentioned as a cause of the collective layoffs. We also coded whether the respective firm announcing the collective layoffs was domestic or foreign in the layoff country. In addition, we gathered the number of employees affected and the date (month and year) of the announcement.[ 9] To check data collection reliability, we employed two independent research assistants to gather the collective layoff announcement data. A third research assistant examined the joint list of announcements gathered by the first two to make sure there was full agreement across the two announcement lists and, in the case of a disagreement, gather the required information to resolve the inconsistency. The level of agreement between the first two research assistants before any disagreement resolution took place was high (95.6%).
The 205 layoff announcements we analyze include 4 collective layoffs in Austria, 15 in Canada, 37 in France, 20 in Germany, 8 in Italy, 13 in Japan, 31 in Spain, 22 in the United Kingdom, and 55 in the United States. The investigated collective layoff announcements involved more than 300,000 employees. In summary, our empirical investigation utilizes 129,919 data points at the model-month-country level. Each data point captures sales, advertising, pricing, and manufacturing information on a specific car model manufactured by a brand that announced a collective layoff in the 12-month period before or after the given month. In 118 announcements, a decline in demand was explicitly mentioned as a motive for the layoffs, and 105 of the collective layoffs were announced by domestic brands.
Table 2 presents descriptive statistics and a correlation matrix of our estimation data. Advertising spending, price, and competitive sales are all measured at the country-model-month level. We attribute corporate advertising spending (defined as advertising spending for the car brand that does not promote any specific car model) to the respective models, according to their relative model-level sales. Competitive car sales include all monthly sales for the respective country except those for the respective car model. For production capacity utilization, we first calculate for every plant of the brand the ratio between actual monthly production and maximum production capacity. Then, we calculate the production-weighted average of this ratio across all plants of the brand in a given region. This averaging is done for every month in our data to get the average monthly regional production capacity utilization for the brand.[10]
Graph
Table 2. Descriptive Statistics and Correlation Matrix (for Model Estimation).
| Label | Unit Sales(at Model Level)c | Advertisinga | Priceb | Competitive Sales | Production Capacity Utilization |
|---|
| Advertisinga | Advmjct | .32** | | | | |
| Priceb | Pricemjct | −.16** | −.09** | | | |
| Competitive sales | CompSalesmjct | .42** | .10** | .06** | | |
| Production capacity utilization | PCUjct | .06** | .05** | .07** | −.01* | |
| Mean | | 961 | 703,954 | 27,802 | 250,056 | .71 |
| SD | | 1,663 | 2,297,922 | 16,978 | 313,430 | .10 |
- 2 *p <.05.
- 3 **p <.01.
- 4 a Expenditures in Euros for the car model.
- 5 b Car model price in Euros.
- 6 c Unit sales (at car-model level) refers to the monthly unit sales of a car model. Competitive sales refer to the sum of unit sales across all other models of all brands.
- 7 Notes: The descriptive statistics and correlation matrix are based on the data we use for model estimation (i.e., the data correspond only to the 12-month periods before and 12-month periods after all layoff announcements in the relevant countries for each collective layoff and across the respective car models for the brand). In total, we use 129,919 data points for model estimation.
In this section, we examine sales, advertising, and price data before and after collective layoff announcements, without specifying a formal model. Such model-free evidence provides a first rough view on how these variables change following collective layoff announcements, albeit without the controls that we incorporate into our formal estimation (such as for endogeneity).
First, for each of the collective layoffs, we calculated the percentage change in the layoff brand's unit sales in the layoff country, comparing postannouncement levels with preannouncement levels. On average, the percentage change between unit sales 12 months before and 12 months after the announcement is −6.6%.[11] For two-thirds of the layoff announcements in our data set, we observe a negative change in sales in the year following the announcement. These findings provide preliminary evidence of the negative effects of collective layoffs on sales. Such evidence is preliminary because it does not control for the nonrandomness of the layoffs (e.g., the collective layoffs may happen precisely because demand for the brand is in decline) or for potential supply-side constraints. Moreover, it does not account for the nonrandomness in marketing efforts (e.g., in advertising spending) before and after the announcement. We address such issues with our formal estimation technique.
Panels A and B of Figure 2 present the distribution of percent change in sales for different collective layoff characteristics. Panel A compares the distributions for domestic and foreign collective layoff firms. We observe that, on average, collective layoffs of domestic firms are associated with a sales decrease of 5.5%, whereas layoffs for foreign firms are associated with a sales decrease of 7.7%. Panel B compares the distributions for demand-driven and non-demand-driven collective layoff announcements. We find that, on average, collective layoffs that are announced as demand-driven are associated with a decrease in sales of 7.1%, whereas layoffs that are non-demand-driven are associated with a decrease in sales of 5.8%.
Graph: Figure 2. Percentage change in sales.Notes: Percentage change is calculated as postevent mean monthly levels over a period of 12 months, minus pre-event mean levels over a period of 12 months, divided by pre-event mean levels for the brand.
Second, we calculated the percentage change in the layoff brand's advertising spending in the layoff country, comparing postannouncement levels with preannouncement levels. We find that the median change in advertising spending is 2%, suggesting that firms do not show a dominant tendency to substantially increase or decrease spending in the year following a layoff announcement.[12] Panels A and B of Figure 3 present the distributions of percent change in advertising spending for domestic and foreign collective layoff firms (Panel A), and for demand-driven and non-demand-driven collective layoffs (Panel B). We observe that a higher percentage of domestic firms, compared with foreign firms, increased advertising spending by up to 20% following a layoff announcement; yet a higher proportion of foreign firms than domestic firms increased advertising spending by more than 40% in the year following the layoff announcement. Similarly, when comparing demand-driven and non-demand-driven layoff announcements, we observe that non-demand-driven announcements were more likely than demand-driven announcements to be followed by an increase in advertising spending of up to 20%, whereas demand-driven announcements were more likely than non-demand-driven announcements to be followed by an increase in advertising spending of more than 40%.
Graph: Figure 3. Percentage change in ad spending.Notes: Percentage change is calculated as postevent mean monthly levels over a period of 12 months, minus pre-event mean levels over a period of 12 months, divided by pre-event mean levels for the brand.
Third, we calculated the percentage change in the layoff brand's car prices in the layoff country one year before and one year after the collective layoff announcement. The average price change across all announcements was 2.7%, with a majority of cases (83%) in the range between −5% and 5% change. We do not observe notable differences in the distribution of price change between layoff announcements of domestic versus foreign firms or between layoff announcements that were non-demand-driven versus demand-driven.
Our econometric model should address three main challenges. First, the collective layoffs are not random events but are decisions that may be driven by expected demand fluctuations. This concern is especially relevant when a drop in demand is given as a motive for the collective layoffs. Second, advertising and pricing are strategic decision variables, which are driven by sales objectives and expectations. Third, unobservable shocks may simultaneously affect sales, advertising spending, and prices.
To address these challenges, we develop a hierarchical Bayesian model consisting of three dependent variables: sales, advertising, and price. Accordingly, our model comprises a system of three equations. To address the first form of endogeneity (nonrandomness of collective layoff announcements), our model adopts a DID approach. In our data, we observe sales before and after collective layoff announcements in a "treatment" country (the treatment being the collective layoff announcement for a given brand in a given country), which we can compare with "control" countries (i.e., all countries other than the treatment country in our data set in which we do not observe a collective layoff announcement for that brand in the 12 months before or after the focal collective layoff announcement).
The appropriateness of this DID approach is contingent on two key assumptions that seem to be realistic in our context. First, we assume that the collective layoff decision is taken at the regional, and perhaps even global, production level, and not in the layoff country in isolation; thus, the treatment is not driven solely by the demand conditions in the treatment country. Second, we assume that the impact of collective layoff announcements on consumer demand is country-specific. Typically, media outlets cover layoff announcements in their own country more intensively than they cover announcements of collective layoffs abroad. Consumers are more likely to be aware of such announcements in their own country than in other countries and to consider workers in their own country as in-group members, compared with workers abroad.
To ease the interpretation of our DID model, we compare a simulated "but-for" world—the world that would have existed had a collective layoff announcement never occurred—to the "actual" world—the world that exists given that the collective layoff has occurred. We adopt this method from the legal and economics literature ([23]); it has also been used previously in marketing ([35]).
Figure 4 presents a stylized example. A line represents the (stylized) actual sales of the BMW 3 Series in a collective layoff country (in this case, the United States) and a bold line represents the (stylized) actual sales of the BMW 3 Series in a control country (in this case, Germany). At T*, BMW announces a collective layoff in the United States. The "actual" world comprises the observed sales of the BMW 3 Series in the United States after T*, while the "but-for" world (depicted by a dashed line) comprises the expected sales of the BMW 3 Series in the United States, absent a collective layoff announcement of BMW in the United States, based on the evolution of the sales of the BMW 3 Series in the United States before T* and on the sales of the BMW 3 Series in Germany before and after T*. The difference between the "actual" sales levels in the United States after T* (i.e., the full line) and the "but-for" sales levels in the United States after T* (i.e., the dashed line) is the DID.
Graph: Figure 4. Stylized example for sales of BMW 3 Series before and after a collective layoff event for BMW in the United States.
To address endogeneity in pricing and advertising spending, we use an instrumental-variable procedure ([47]). We utilize the periodic price and advertising spending for the car model, averaged across the control countries, as instrumental variables for the periodic price and advertising spending of a given car model (see the exact specification next). These variables are correlated with pricing and advertising for the car model in the layoff country, because they may capture the temporal global marketing strategy and cost function for that car model over time, as well as the temporal cost of advertising. However, these variables are not expected to be correlated with that model's unit sales in the layoff country, because potential buyers in that country are not likely to be exposed to prices and advertising in other countries. Finally, we allow for correlation in unobserved temporal shocks of the three dependent variables, by specifying the errors of the three equations in our system to be jointly distributed.
We start with the specification of Model 1, which focuses on the main effects of collective layoff announcements on sales, advertising elasticity, and price elasticity in the collective layoff country. We then proceed to Model 2, which further explores the role of our moderators in these main effects.
The dependent variable in the first equation of Model 1 is the log-transformed unit sales of car model m of brand j in country c at month t (lnSalesmjct), as follows:
Graph
1
We log-transformed all the independent variables such that the respective parameters denote the elasticities of the corresponding variables. Advmjct represents the level of advertising spending for car model m at time t in country c. Pricemjct represents the price of car model m at time t in country c. Accordingly, and represent advertising and price elasticities. As our theoretical expectations regarding advertising and price elasticities are at the brand-country level, we specify these random parameters at the brand-country-time level.
Similarly, represents the baseline sales for brand j in country c at time t, after controlling for the marketing mix and other market conditions (Van Heerde, Helsen, and Dekimpe 2007). The inclusion of base sales allows us to obtain unbiased estimates for advertising and price elasticities. Our model also accounts for past advertising spillovers through the inclusion of lagged advertising levels, captured by . We utilize a grid search for the number of lags. CompSalesmct represents competitive car unit sales in country c at time t. and in Equation 1 are random time and car model effects, respectively. The parameters and are each drawn from a normal distribution.
Following the principles of a DID model, our focal interest is in whether time t is before or after the collective layoff announcement, and whether country c is the treatment or a control country. Accordingly, we specify the baseline sales, as well as the advertising and price elasticity parameters ( , , and , respectively), as follows:
Graph
2
is a vector of dummy variables that indicate whether time t is before (12 months) or after (12 months) a collective layoff announcement of brand j in country c. CLCountryjct is a dummy variable indicating whether c is a collective layoff country, in which case the variable is equal to 1 in the periods surrounding the layoff announcement (from 12 months before until 12 months after) and 0 otherwise. To clarify, assume that Mazda has made a collective layoff announcement in Germany in March 2012. For all Mazda car models, Postjct is 0 for all time periods before March 2012 and 1 for time periods from March 2012 to February 2013. CLCountryjct is equal to 1 for all Mazda car models in Germany between March 2011 and February 2013, and equal to 0 otherwise. Thus, for the collective layoff announcement in question (and, similarly, for any collective layoff announcement) we might see Post/CLCountry combinations of 0/0 (e.g., Austria before the announcement [i.e., between March 2011 and February 2012), 0/1 (Germany before the announcement [i.e., between March 2011 and February 2012), 1/0 (e.g., Austria after the announcement [i.e., between March 2012 and February 2013), and 1/1 (Germany after the announcement [i.e., between March 2012 and February 2013).[13]
PCUjct in Equation 2 is the production-weighted average production capacity utilization of brand j at month t in the region corresponding to country c. The error terms are assumed to be uncorrelated with and jointly distributed as , where .
Next, we further specify an advertising equation and a price equation in our system ([47]). We specify the advertising equation as follows:
Graph
3
The instrumental variable for car model advertising is Advmjc′t, which is calculated as the level of advertising spending for model m at time t, averaged across all countries in which there was no collective layoff announcement for brand j in the 12 months preceding or following the collective layoff announcement.[14]
in Equation 3 represents baseline advertising levels at the brand-country-time level. We allow for the possible influence of the collective layoff announcement and its characteristics on base advertising by specifying this intercept as follows:
Graph
4
All variables in Equation 4 are defined as previously. The error term is assumed to be uncorrelated with and distributed as .
We specify the price equation in our system as follows:
Graph
5
The instrumental variable for car model price is Pricemjc′t, which is calculated as the price of car model m at time t, averaged across all countries where there was no collective layoff announcement for brand j in the 12 months preceding or following the layoff announcement.[15]
in Equation 5 represents the baseline price at the brand-country-time level. Similarly to what we did in the sales and advertising equations and for similar reasons, we specify this intercept as follows:
Graph
6
All variables in Equation 6 are defined as previously. The error term is assumed to be uncorrelated with and distributed as . The parameters , , and are each drawn from a normal distribution. We model the errors of Equations 1, 3, and 5 to be jointly distributed as , where .
Model 1 allows us to test the change in marketing-mix elasticities following collective layoff announcements across all announcement types. To explore the role of our moderators in this variance, we proceeded to specify Model 2. This model is similar to Model 1, with the exception of the second-layer equations for , , , and . These first-level parameters are specified to depend also on the characteristics of the collective layoff announcements as follows:
Graph
7
Domesticjct in Equation 7 is a dummy variable that equals 1 if brand j is a domestic brand in the collective layoff country, and 0 otherwise. MotiveDjct is a dummy variable that equals 1 if the layoff is driven by a decline in demand and 0 otherwise. Employeesjct is the announced number of employees to be laid off. This variable is positive in the 12 months following the layoff announcement, and 0 otherwise.[16]
We jointly estimated the sales, advertising, and price equations of Model 1 using a hierarchical Bayesian estimation technique. We ran the algorithm for 5,000 iterations. The first 4,000 iterations were used for burn-in, and every tenth iteration of the last 1,000 was saved to obtain the posterior parameter estimates. We graphically plotted these estimates to examine their convergence (plots are available on request). Table 3 presents the estimation results of the second-layer parameters of , , , , and .
Graph
Table 3. Estimation Results of Second-Layer Equations, Model 1.
| Variable (Parameter) | | Base Brand Sales | Brand Advertising Elasticity | Brand Price Elasticity | Base Brand Advertising | Brand Prices |
|---|
| Intercept | (θ0) | 6.8[6.03, 7.44] | .09[.08,.10] | −.80[−.86, −.73] | 9.15[8.73, 9.41] | .01[.003,.02] |
| Post period | (θ1) | −1.58[−1.93, −1.18] | .04[.04,.05] | .10[.07,.13] | .04[−.03,.14] | .01[.002,.02] |
| Collective layoff country | (θ2) | −2.56[−2.95, −2.05] | .03[.02,.05] | .26[.21,.29] | .39[.28,.53] | −.03[−.04, −.02] |
| Collective layoff country × Post period | (θ3) | 1.45[.87, 2.15] | −.02[−.03, −.004] | −.11[−.16, −.06] | −.06[−.25,.06] | .02[.00,.03] |
| Production capacity utilization | (θ4) | −2.82[−4.52, −2.14] | −.06[−.08, −.04] | .40[.31,.52] | .82[.65,.98] | .03[.01,.05] |
8 Notes: Boldfaced parameters indicate that 95% of the posterior distribution is above/below zero. The estimation is based on 129,919 observations.
In this article, we focus on the effect of collective layoff announcements on sales, advertising elasticity, and price elasticity. For sales, the effect of such announcements is composed, in part, of their potential effect on marketing-mix variables and marketing-mix elasticities. For this reason, we cannot assess the effect of collective layoffs solely on the bases of changes in the intercept of the sales equation. Therefore, we start by reviewing the estimation results for the effect of collective layoffs on marketing-mix elasticities. That is, the interaction effects between a postannouncement period and the layoff country, θ1,3 and θ2,3, in the second-layer equations of the two elasticity parameters (see Equation 2 and columns 4 and 5 in Table 3). Subsequently, we simulate the overall effect of collective layoffs on sales on the basis of a comparison of "but-for" and "actual' sales.
We find that these DID interaction parameters are negative and significant in both the advertising elasticity and the price elasticity equations, indicating that both elasticities are lower following a collective layoff announcement than absent the announcement ( = −.02; = −.11). These significant changes in advertising and price elasticities represent a −9.8% drop in advertising elasticity, and a −19.2% drop in price elasticity.
While these findings show that more than 95% of the posterior distribution of each of the DID interaction parameters is negative both for advertising elasticity and for price elasticity, we observe substantial variance in both parameter distributions. Next, we investigate the moderating role of the collective layoff communication characteristics in the effects of the DID interaction parameters.
Table 4 presents the estimation results of Model 2. We focus on the estimated interaction parameters between a postannouncement period, a collective layoff country, and the announcement characteristics, for advertising elasticity and price elasticity, θ1,12, θ2,12, θ1,13, θ2,13, θ1,14 and θ2,14 (see columns 4 and 5 in Table 4).
Graph
Table 4. Estimation Results of Second-Layer Equations, Model 2.
| Variable (Parameter) | | Base Brand Sales | Brand Advertising Elasticity | Brand Price Elasticity | Base Brand Advertising | Brand Prices |
|---|
| Intercept | (θ0) | 5.41[4.65, 6.17] | .10[.09,.12] | −.70[−.78 –.64] | 8.81[8.45, 9.11] | .04[.03,.06] |
| Post period | (θ1) | −.18[−1.39, 1.24] | .03[.00,.06] | −.03[−.14,.08] | .20[−.08,.44] | .05[.02,.08] |
| Collective layoff country | (θ2) | −3.30[−4.25, −2.19] | .04[.01,.05] | .29[.20,.39] | −.04[−.24,.16] | .01[−.02,.03] |
| Collective layoff country × Post period | (θ3) | 2.44[−.17, 4.25] | −.06[−.11, −.02] | −.17[−.33,.04] | −.01[−.55,.53] | −.04[−.10,.02] |
| Production capacity utilization | (θ4) | −3.55[−4.60, −2.76] | −.06[−.07, −.04] | .43[.36,.52] | .77[.57,.96] | .03[.00.04] |
| Domestic brand | (θ5) | 1.06[.32, 1.68] | −.02[−.03,.00] | −.11[−.17, −.06] | −.37[−.51, −.25] | 8.46E-04[−.01,.02] |
| Stated motive: demand | (θ6) | .08[−.41,.75] | −.01[−.03,.00] | 1.91E-04[−.04,.06] | −.19[−.19, −.07] | −.01[−.03,.00] |
| Number of employees | (θ7) | −.04[−.19,.12] | −.004[−.007, −.001] | .01[.00,.02] | −.04[−.06,.00] | −.01[−.01,.00] |
| Collective layoff country × Domestic brand | (θ8) | −2.15[−3.30, −.97] | .02[−.01,.05] | .25[.14,.34] | .91[.68, 1.19] | −.05[−.07, −.02] |
| Collective layoff country × MotiveD | (θ9) | 2.86[1.60, 4.10] | −.03[−.05,.00] | −.23[−.35, −.14] | .04[−.16,.42] | −.05[−.07, −.02] |
| Post period × Domestic brand | (θ10) | −1.90[−2.69, −1.26] | .05[.03,.06] | .14[.09,.21] | .07[−.08,.25] | .002[−.01,.02] |
| Post period × MotiveD | (θ11) | −.62[−1.40,.12] | .03[.02,.05] | .02[−.05,.08] | .18[.03,.36] | .02[.00,.03] |
| Post period × Collective layoff country × Domestic brand | (θ12) | 2.32[.52, 3.69] | −.07[−.10, −.04] | −.16[−.27, −.01] | −.27[−.58, −.05] | .02[−.01,.06] |
| Post period × Collective layoff country × MotiveD | (θ13) | −1.29[−2.66,.07] | −.002[−.04,.02] | .12[.03,.26] | −.17[−.48,.16] | .01[−.03,.04] |
| Post period × Collective layoff country × Employees | (θ14) | −.17[−.43,.13] | .01[.00,.02] | .01[−.02,.03] | .02[−.04,.09] | .01[.00,.01] |
9 Notes: Boldfaced parameters indicate that 95% of the posterior distribution is above/below zero. The estimation is based on 129,919 observations.
We find that a collective layoff announcement of a domestic firm is associated with lower postlayoff advertising and price elasticity than a collective layoff announcement of a foreign firm ( = −.07; = −.16). The stronger decrease in advertising elasticity for domestic firms than for foreign firms is as expected. The stronger decrease in price elasticity for domestic firms, is in line with the expectation that domestic firms experience a greater decrease in likability and trustworthiness than foreign firms following collective layoff announcements.
For layoff motive, we find that a collective layoff announcement that is demand-driven is associated with lower postlayoff price elasticities (a less negative elasticity) than a non-demand-driven announcement ( =.12). This finding is in line with the expectation that, following demand-driven layoff announcements, firms experience a smaller decrease in likability and trustworthiness than following collective layoff announcements that mention other motives.
For the announced number of affected employees, we find that a collective layoff announcement that involves more employees is associated with higher postlayoff advertising elasticities than a collective layoff announcement that involves fewer employees ( =.01). This finding is consistent with the expected higher consumer uncertainty following collective layoff announcement the more employees that are laid off as well as the increased informative role of advertising in such situations.
To examine the effect of a collective layoff announcement on advertising spending and prices, we elaborate on the estimation results of Model 2, which incorporates all moderators. Columns 6 and 7 in Table 4 present the (Model 2) estimation results of the second-layer parameters of base advertising spending and base prices ( and ). These results indicate that advertising spending is lower after a collective layoff announcement of a domestic brand than after a collective layoff announcement of a foreign brand ( = −.27). The effect of a collective layoff announcement on car prices, however, does not seem to differ across announcements with different characteristics.
The marginal effects of collective layoff announcements are captured by the second-layer parameters of each elasticity corresponding to a postannouncement period in a collective layoff country (see Equation 7). These marginal effects on and are calculated as follows:
Graph
8
Subscript q takes the value of 1 if it refers to advertising elasticity , and 2 if it refers to price elasticity ( ). To account for layoff characteristics, we plug in Equation 8 all possible value combinations of Domesticjct and MotiveDjct. For layoff size, we utilize the mean number of employees across all layoff announcements we analyze. In line with the Bayesian estimation approach, the calculation must account for parameter uncertainty. We thus utilize all draws from the posterior distributions of the parameters in Equation 8 to calculate posterior draws of the marginal effects.
Table 5 presents the posterior means of the marginal effects on advertising elasticity and price elasticity, across possible values of the layoff announcement characteristics we examine, based on the estimates of Model 2. We find a significant decrease in advertising elasticity only following layoff announcements by domestic firms. We find a significant negative change in price elasticity (i.e., a more negative price elasticity) following all announcement types, with the exception of a collective layoff announcement of a foreign firm that is presented as being demand-driven. We further see that the largest mean marginal change in price elasticity is expected for non-demand-driven layoff announcements of domestic firms.
Graph
Table 5. Mean Change in Advertising Elasticity and Price Elasticity.
| Domestic: Demand | Domestic: Nondemand | Foreign: Demand | Foreign: Nondemand |
|---|
| Advertising elasticity | −.06 | −.06 | n.s. | n.s. |
| Price elasticity | −.16 | −.28 | n.s. | −.12 |
10 Notes: n.s. = not significant. Boldfaced parameters indicate that 95% of the posterior distribution is above/below zero.
Table 6 presents the results of the estimations of the car-model-level parameters in Equations 1, 3, and 5. We find that competitive sales and lagged advertising have positive effects on unit sales ( =.68, =.16) and on advertising spending ( =.17; =.60). Competitive sales also have a significant negative effect on prices ( = −.04). We also find that both instrumental variables have significant positive effects on advertising spending and car price ( =.23, =.30). As we expected, production capacity utilization, which we added as a control variable, has a significant effect on brand sales ( = −3.55; see Table 3).
Graph
Table 6. Estimation Results: Car-Model-Level Parameters.
| Parameter | Sales Equation | Advertising Equation | Price Equation |
|---|
| Competitive sales | | .68[.65,.72] | .17[.13,.17] | −.04[−.04, –.03] |
| Lag advertising | | .16[.15,.17] | .60[.59,.61] | −2.15E-04[−.001,.001] |
| Mean advertising in control countries | () | | .23[.21,.27] | |
| Mean price in control countries | () | | | .30[.27,.31] |
- 11 Notes: Boldfaced parameters indicate that 95% of the posterior distribution is above/below zero.
- 12 For car model effects presented in this table, we report the hyperparameter (i.e., the means across car models).
Next, we examine the economic significance of our statistical findings, using the "but-for" analysis we introduced previously.[17] For this calculation, "actual" sales are the observed sales in our data. "But-for" sales ( ) are the corresponding predicted sales, based on our estimation results, had the collective layoff announcement not occurred. We calculate these predicted values, , as follows:
Graph
9
where are the mean time-varying brand-level parameter estimates in prelayoff periods. These parameters replace the periodic postannouncement first-level parameters to simulate the "but-for" condition[18] and are specified as follows:
Graph
10
and in Equation 9 are predicted after the layoff announcement "but-for" values for advertising and price, respectively, which are calculated as follows:
Graph
11
Graph
12
The values of all other parameters in Equations 9 to 12 are the estimated values of Model 1 parameters (see Table 3). The error terms, , , and , are drawn from a multinomial normal distribution , where is the estimated variance–covariance matrix of the error terms of our three model equations. In line with our Bayesian estimation approach, the calculation of "but-for" values must account for parameter uncertainty. We thus calculate probabilistic "but-for" values using all estimated draws from the parameters' posterior distributions.
We compare the "actual" and "but-for" values for sales, advertising, and prices and calculate the percentage change between the actual, observed values and the calculated, "but-for" values for every postlayoff announcement period in our sample. We average these changes for each collective layoff announcement, across all the car models for the respective brand.
The mean percentage change between "actual" and "but-for" sales across our collective layoff announcements is −8.7%. This indicates that, on average, for the brands in our sample, sales are 8.7% lower in the year following the announcement than their expected level absent the announcement. This drop in sales is somewhat larger than the actual drop of 6.6% that we observed in the model-free section. Across announcements with different characteristics, we find that the mean percentage change between "actual" and "but-for" sales is −8.8%, −8.7%, −7.9%, and −9.9%, for announcements of domestic firms, announcements of foreign firms, demand-driven announcements, and non-demand-driven announcements, respectively.
To investigate whether and how firms make changes in advertising spending and pricing after issuing collective layoff announcements, we also calculate the percentage change between "actual" and "but-for" advertising spending and price levels (see Equations 11 and 12). For advertising spending, we find that the mean percentage change between "actual" and "but-for" spending across the layoff announcements in our data set is −16%.[19] In fact, for 84% of the layoff announcements, we find that actual advertising for the brand is lower than the predicted "but-for" value. These findings indicate that many firms spend less on advertising in the year following collective layoff announcements than they would have been expected to spend absent the announcements.
For pricing, our estimates suggest that the difference between "actual" and "but-for" prices is very low. The mean percentage difference is 1%, indicating that firms do not seem to change their pricing strategy following collective layoff announcements.
We further separately calculated the percentage change compared to the "but-for" scenario due to the lower marketing-mix elasticities, keeping the actual (observed) advertising and pricing levels. We find that the mean drop in sales due to the change in elasticities is −5.1% compared with "but-for" sales. This finding indicates that the elasticity component is responsible, on average, for 58% of the predicted change in sales due to the collective layoff announcement.
To examine the robustness of our findings, we estimated three simple models based on our full model: an ordinary least squares regression for sales; a seemingly unrelated regression model with sales, advertising, and price as dependent variables; and a two-stage least-squares model for sales, where advertising and price are treated as endogenous. While such models have certain limitations—such as the fact that they do not address heterogeneity or endogeneity—they may still provide a sanity check of our DID approach. We compared the estimates obtained with these models (i.e., the main effects of collective layoff announcements on advertising and price elasticities that we obtain as well as the moderating effects of the three layoff characteristics on these main effects) with the estimates of our main model (see Web Appendix C). In total, we corroborated the face validity of eight coefficients (main DID effect and three moderating effects on that main effect, for each elasticity). For advertising elasticity, we find all coefficients to be robust across all estimation methodologies. For price elasticity, two out of four coefficients identified in the main model are not replicated with the alternate simpler models. We therefore recommend that readers interpret our price elasticity findings with more caution than our advertising elasticity findings.
We carried out several additional robustness checks to further test the validity of our results. First, we estimated a model that takes into account the global (instead of regional) average production capacity utilization for the brand. We also checked the production capacity utilization in the plants corresponding to the collective layoff announcements in our sample. For 6 of the 205 announcements, we found that the production capacity utilization of the plant in the year following the announcement was greater than 90%.[20] We estimated our model excluding these six announcements and found all our results to be robust.
Second, we examined the types of laid-off employees. We found that 172 announcements mentioned production workers, 8 mentioned research-and-development (or design) workers, 23 mentioned "headquarters" workers (e.g., management, marketing, sales, finance), and 15 provided no information regarding the types of employees involved. Note that more than one type of employee could be mentioned in a single layoff announcement. These data suggest that although it is common in the automotive industry for production workers to be affected by collective layoffs, other employees might also be involved in such layoffs. As a robustness check, we estimated the model using only events that mention production workers as the type of employee to be laid off and found the same effects.
Third, we varied the total observation window for a collective layoff between 18 months (6 months before and 12 months after the announcement) and 24 months (12 months before and 12 months after the announcement). Again, we found our results to be robust. In summary, our main results show high robustness over all these alternative model specifications (estimation results of these models appear in Web Appendix C).
This article examines the commercial consequences of collective layoff announcements using data on 205 collective layoff announcements that affected more than 300,000 employees. It offers several implications for managers whose firms are considering initiating collective layoffs or are experiencing the commercial consequences of such layoffs, and for market analysts studying collective layoff announcements and their consequences.
Collective layoffs are likely to entail negative demand consequences for the firms that initiate them. We observed that the majority of brands in our data set that issued collective layoff announcements (two out of three) faced a drop in sales, in absolute terms, in the layoff country during the year following the announcement. Using our model estimates, we showed that, on average, sales following collective layoff announcements are 8.7% lower than their expected level absent the announcements. These changes result, in part, from lower advertising elasticity, potentially higher price sensitivity, and lower advertising spending following the layoff announcements. Given these robust findings, we suggest that firms should go beyond supply considerations when they consider downsizing and integrate consumers' response in their decision calculus. Specifically, firms should include marketers in the task forces that manage collective layoffs, beyond functional representatives of other areas, such as operations and finance.
Our findings also provide essential insights to marketers as they ponder whether the marketing instruments they have at their disposal (e.g., advertising, price) may dampen adverse demand effects. We find that advertising elasticity and price elasticity typically decrease following layoff announcements. At the same time, we also find that firms, on average, spend less on advertising in the layoff country following layoff announcements than they would absent the announcements. Given the lower advertising elasticity following collective layoff announcements, it seems likely that decreasing or even merely sustaining advertising spending in the layoff country will lead to lower sales in that country and a loss of market share. Lowering advertising spending as a response to a decrease in advertising elasticity may be considered the optimal solution to a marketing-mix allocation problem ([42]). However, to counteract a negative demand spiral following collective layoff announcements, marketers might consider increasing their investment in advertising in the respective country following the layoff announcement, as long as advertising elasticity remains positive, to correct for lower advertising elasticity. Layoff firms could also consider such a temporary increase in advertising spending as a restructuring cost.
For pricing, we propose that increased price sensitivity cannot universally form a basis for price cuts to support brand share in the affected country. Of course, other reasons may exist for temporary price cuts in the respective country. We do recognize that this article is only a first attempt at addressing this question and that future research is needed to provide more guidance on pricing implications.
Beyond the implications of our results for managers, the analytical framework we have developed is also relevant for internal analysts who study the impact of collective layoffs. The heterogeneity we observed in consumer response suggests that analysts should carefully tailor the sample and variables to suit the specific context that they wish to investigate, in terms of the type of firm that is affected, or the reason for the collective layoff, given the heterogeneity in consumer response we have found. On such a tailored sample, marketing analysts could then utilize our model framework and retrieve simulation results for different scenarios (considering, for instance, different advertising spending levels). As with any prediction tool that deals with a market shock, one should not expect total accuracy; nevertheless, we suggest that such a tool can stimulate important discussions in management teams on the commercial consequences of collective layoffs. From our discussions with practitioners who have been involved in such collective layoff decisions (including representatives of two companies whose brands are included in our data set, i.e., Volkswagen and General Motors), we have learned that decision makers primarily tend to take manufacturing efficiency considerations into account while generally ignoring potential demand consequences. The tools proposed herein have the potential to help marketers in downsizing firms to draw more attention to demand consequences.
Our work can also prove useful to external business analysts. The media often ask such external experts to predict the consequences of collective layoffs on the layoff brand or on its consumers. Similarly, our results could be informative for economists trying to predict broader economic impacts of layoffs. From our findings, three conclusions are worth keeping in mind. First, a negative impact on sales is more likely than no impact at all. Second, the impact on sales is likely to be rather large (−8.7%, on average). Third, the exact magnitude of this effect depends on the characteristics of the announcements. Analysts can code the collective layoff announcement on the characteristics that we have analyzed and make inferences from our results regarding whether the impact of the collective layoff on demand will be more or less severe than the average.
This study opens up many new directions for future research. First, although our data set is rich, spanning 16 years with monthly periodicity, nine countries, and 20 automotive brands, the empirical analysis focused on only one industry. Replication of our results in other industries would be valuable. Moreover, even within the automotive industry, collection of more data could enable researchers to gain additional insights regarding the boundary conditions of collective layoff effects. For instance, the collective layoffs we considered mostly affected factory workers, and thus we were not able to closely examine differential effects of layoffs of different categories of employees. An extensive data set on layoffs of employees in different roles (e.g., customer-facing employees) would contribute toward addressing this gap. Similarly, all firms in our data set were multinational; data on both multinational as well as national companies would allow for an examination of potential contrasts between reducing the overall labor force versus shifting the labor force proportionally from one country to another. We also studied only data on employee downsizing; future research could also study the consequences of upsizing the labor force.
Second, in this article, we studied the effects of collective layoff announcements, rather than their actual execution. Although our data do not permit us to identify potential differences between announcement and execution, we believe that the study of such differences and their consequences, while challenging from a data perspective, would provide additional value.
Third, drawing from prior theory, we were able to identify mechanisms that might underlie consumers' responses to collective layoffs and to firms' marketing-mix decisions in the wake of such layoffs; however, our (secondary, behavioral) data did not enable us to prove that these mechanisms were indeed at play. It would be interesting to explore and prove such mediation mechanisms, potentially utilizing primary data collected before and after collective layoffs are announced. Online chatter that takes place before and after a layoff announcement would be a useful source of such data.
Fourth, our study constitutes a first exploratory step in elucidating the role of announcement characteristics in the commercial consequences of collective layoffs, examining three characteristics of interest. Future research should focus on the multitudes of additional communication characteristics that are likely to be worthy of study. For example, it would be interesting to examine the extent to which a firm's presence on social media or the sentiment of the news coverage about a collective layoff affect its commercial consequences. Such investigations could also offer a tighter connection with the mediation mechanism than the current study offers.
Marketing scholars have started to show an interest in collective layoffs only relatively recently, many years after their colleagues in economics, organizational behavior, and finance began to do so. Accordingly, the knowledge at our disposal remains limited. Our work provides several promising insights regarding the nuanced interplay between the characteristics of the communication of collective layoffs and their marketing outcomes.
Supplemental Material, jm.16.0235-File003 - The Commercial Consequences of Collective Layoffs: Close the Plant, Lose the Brand?
Supplemental Material, jm.16.0235-File003 for The Commercial Consequences of Collective Layoffs: Close the Plant, Lose the Brand? by Vardit Landsman and Stefan Stremersch in Journal of Marketing
Footnotes 1 Associate EditorRebecca Slotegraaf
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Israel Science Foundation and by the Henry Crown Institute of Business Research.
4 Online supplement: https://doi.org/10.1177/0022242919901277
5 1In the case of bad news about affiliated celebrities, one could argue that the firm should have better vetted the celebrities they endorse, yet these are secondary concerns compared with direct performance concerns such as those resulting from a product harm crisis.
6 2Note that [43] use a comparable classification. They classify layoffs as supply-driven layoffs (also called "efficiency layoffs"), which are aimed at, or result from, improved efficiency, and demand-driven layoffs, which evolve from unfavorable market conditions.
7 3For Japan and France, our data set covers the years 2000–2013. Our data set does not cover prices for Canada and Japan prior to 2007. Accordingly, we eliminate from our analysis collective layoff events that occurred during these periods and in these countries.
8 4For each of the events, we also ensure that the brand's models are also sold in at least one of the other sample countries where there is no other collective layoff announcement for that brand in the year before or after the event.
9 5In the empirical tests presented in the following sections, we consider the month in which the collective layoff was announced as the time of "treatment" (rather than the month in which layoffs were expected to take effect). This choice is based on the fact that, at the point of announcement, consumers are exposed to information that may trigger mistrust and/or uncertainty. Moreover, in many cases, the actual layoff date was not clearly conveyed in the layoff announcements. Some indicated a general period within which the collective layoffs would take place (e.g., a coming year or two years), others did not mention the intended date at all, and still others announced effective dates that ultimately differed from the actual effective dates. In some cases, for instance, labor union negotiations or government interventions may shift the effective date of a layoff, impeding the capacity of outside analysts to identify this date, a task that becomes even more complicated across numerous events.
6We use the term "region" to describe the production area to which a given country belongs and in which its supply of cars is likely to be produced. The regions are based on the definition of our production data provider, HIS, and consist of Europe, North America, and Japan/Korea.
7Percent change is calculated as postevent mean monthly levels over a period of 12 months, minus pre-event mean levels over a period of 12 months, divided by pre-event mean levels for the brand.
8Because of the high variance in the percentage change in advertising spending, we find it more informative to present the median and not the mean across the collective layoff events we investigate.
9Web Appendix B contains a description of how we stacked the DID variables for the estimation of our model using a stylized example.
10Because scales of advertising spending levels may vary greatly across countries with different population sizes, for the construction of this variable we first standardize advertising spending at the country level for each car model and then take the monthly average across the relevant countries (i.e., across all control countries).
11For price, the independent variable distribution is very similar to that reported in Table 2 (M = 28,007, SD = 16,849). For advertising, because the independent variable is constructed by first standardizing advertising at the country and car-model level over the 15-year period we consider, the distribution is somewhat different from that of our advertising variable (M = 126.10, SD = 14,645).
12Because this variable is specified as zero in all pre-event months, in Equation 7 we do not include all interaction terms between Employeesjct and Postjct.
13While some scholars view "but-for" causation as a special case of counterfactual analysis used to compare real-world outcomes with those in a world in which a harmful action has not happened ([45]; [50]), others distinguish between counterfactual and potential outcome causation and "but-for" causation ([13]). According to [13], in a typical counterfactual and potential outcome causation test, modeling assumptions derive a hypothetical world in which there is one unit less of some cause variable leading to a certain difference in an outcome variable. The logic behind a "but-for" causation claim is that a cause (collective layoffs in our case) creates a response that would otherwise not have occurred. Such causation can be claimed as long as other conditions are controlled for in the empirical investigation so that the mere cause suffices to create the response. A DID approach is a suitable empirical setting for the investigation of such causation type.
14The prelayoff parameters are used here as a proxy for "but-for" postlayoff parameters. The true "but-for" parameters also account for changes in postlayoff parameters in the control condition.
15Due to the high variance in the "actual" to "but-for" comparisons in advertising spending, for the calculation of average changes across the collective layoff announcements we investigate, we replace all values greater than a 300% increase (13 cases) by a fixed value of 300%.
16The mean production capability utilization in our data is.71 (see Table 2). In only 9% of our observations the production capability utilization is higher than 90%.
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By Vardit Landsman and Stefan Stremersch
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Record: 181- The Differential Impact of New Product Development “Make/Buy” Choices on Immediate and Future Product Quality: Insights from the Automobile Industry. By: Kalaignanam, Kartik; Kushwaha, Tarun; Swartz, Tracey A. Journal of Marketing. Nov2017, Vol. 81 Issue 6, p1-23. 23p. 2 Diagrams, 7 Charts, 1 Graph. DOI: 10.1509/jm.14.0305.
- Database:
- Business Source Complete
The Differential Impact of New Product Development “Make/Buy” Choices on Immediate and Future Product Quality: Insights from the Automobile Industry
This article examines the impact of new product development (NPD) “make/buy” choices on product quality using data from the automobile industry. Although the business press has lamented that NPD outsourcing compromises product quality, there is no systematic evidence to support or refute this assertion. Against this backdrop, this study tests a contingency model of the impact of NPD make/buy decisions on immediate and future product quality. The hypotheses are tested using data on NPD make/buy choices of 173 models of 12 automobile firms in the United States between 2007 and 2014. The authors find that whereas NPD buy has a more positive impact on immediate product quality, NPD make has a more positive impact on future product quality. Furthermore, the immediate product quality impact of NPD buy is stronger when ( 1) technologies are more complex and ( 2) firm NPD capability is higher. In contrast, the future product quality impact of NPD make is stronger when ( 1) there is postlaunch adverse feedback and ( 2) firm NPD capability is higher. The study highlights the complex trade-offs associated with NPD make/buy decisions and offers valuable insights on how firms could manage these decisions.
Online Supplement: http://dx.doi.org/10.1509/jm.14.0305
New product development (NPD) outsourcing has gathered momentum in multiple industries, such as automotive, pharmaceuticals, and consumer electronics. Although the practice of firms outsourcing standardized or routine processes has existed for a long time, NPD outsourcing of complex components is a relatively new phenomenon. A recurring theme in the business press is that product quality problems observed in the marketplace are a result of firms’ increased penchant for outsourcing. Following the Toyota’s high-profile recall in 2009, concerns emerged that NPD outsourcing may be responsible for Toyota’s quality woes. Connor (2010) notes, “Toyota used to buy parts from a small group of Japanese suppliers that were longtime partners. But, like almost all automakers, Toyota more recently has outsourced much of its NPD. Outsourcing NPD may have played a part in the car maker’s problems.” Similarly, Boeing 787’s lithium ion battery problems have also been attributed to the fact that Boeing outsources the NPD of more than 30% of its components from third-party vendors (Plumer 2013). Despite such concerns, firms continue to outsource NPD in a variety of industries.
Although outsourcing decisions are generally driven by considerations of labor cost savings (Raassens, Wuyts, and Geyskens 2012), product complexity should favor organizing NPD internally. Firms’ practice of routinely outsourcing the NPD of complex components raises important questions: When does NPD buy influence product quality? When does NPD make influence product quality? New product development “make” refers to performing product design and manufacturing within the firm, whereas “buy” refers to contracting with external entities to perform product design and manufacturing processes.1 The conclusions about the product quality consequences of NPD make/buy decisions are deeply mixed, if not contradictory. On the one hand, NPD buy decisions could hurt product quality because firms might be unable to adapt to unexpected contingencies. On the other hand, NPD buy decisions could improve product quality because they allow for earlier access to cutting-edge technologies from vendors. The possibility that vendors could learn from product development projects across multiple customers suggests that they might have a distinct advantage of being further along the learning curve. This advantage offers instant product quality benefits to firms. Likewise, NPD make decisions enable firms to exercise greater control and authority over internal product development teams and should therefore be better suited for improving product quality. However, NPD make could also be disadvantageous because firms may not possess the necessary expertise early in the product development life cycle. To fully understand the product quality impact of NPD make/buy choices, one needs to explicitly test the trade-offs at different points in the product development cycle (at and after launch).
Our study makes three contributions to marketing theory and practice. First, our study is one of the first to test the impact of NPD make/buy decisions on product quality. A small but emerging body of literature in marketing has investigated issues related to firm–supplier relationships in an outsourced NPD environment. For instance, researchers have examined whether it is desirable for firms to give up control of suppliers in an outsourced NPD environment if the task involves a certain degree of creativity (Carson 2007). Raassens, Wuyts, and Geyskens (2012) examine the performance implications of NPD outsourcing using the event study methodology, and they test the moderating impact of minority equity and prior ties in influencing this relationship. However, to the best of our knowledge, no study has examined the product quality effects of both NPD modes (i.e., make/buy) in a product development context. Table 1 provides an overview of prior research on NPD make/buy choices, highlights the gaps in the literature, and explains how our study contributes to this stream of research.
TABLE: TABLE 1 An Overview of Previous Research on Make/Buy Choices and Performance Effects
TABLE: TABLE 1 An Overview of Previous Research on Make/Buy Choices and Performance Effects
| Studya | Drivers of Make/Buy | Immediate Product Quality | Future Product Quality | Performance Measure | Key Insights and Gaps in Literature |
|---|
| Walker and Weber (1984) | Cost advantage, technological uncertainty, volume uncertainty, competition | X | X | X | The study finds that cost advantage had the strongest effect in predicting make/buy choices, whereas volume and technological uncertainty had relatively smaller effects. Gap in literature: The study does not test the performance effects of make/buy choices |
| Ulset (1996) | Sunk costs, technical novelty, expected resale value | ✕ | ✕ | ✕ | The study finds a positive relationship between sunk costs and vertical integration. Technical novelty has a negative relationship with vertical integration. Gap in literature: The study does not test the performance effects of make/buy decisions. |
| Leiblein, Reuer, and Dalsace (2002) | Ex ante number of suppliers, volume uncertainty, asset specificity | ✕ | ✕ | Technological performance (a function of transistor density) | The study finds that outsourcing has neither a positive nor a negative impact on firm technological performance. Deviation from the optimal governance form, as predicated by contracting hazards, leads to suboptimal performance. Gap in literature: The study does not test whether performance could be affected differently by make/buy choices or whether the performance effects vary over time. |
| Argyres and Mayer (2007) | ✕ | ✕ | ✕ | ✕ | The study finds that strong technological capabilities help a firm (1) craft better ex ante contracts to clearly define the roles and responsibilities of each party, (2) specify the knowledge to be exchanged, and (3) identify appropriate milestones. Gap in literature: The study does not compare the role of firm capabilities for both make and buy choices and their effects over time. |
| Tiwana and Keil (2007) | ✕ | ✕ | ✕ | Adherence to budgets/schedules, work quality, efficiency/effectiveness | The study finds that firm capabilities/knowledge in an outsourced domain increase outsourced alliance performance and that this performance is stronger for outcome controls in outsourcing (prespecification of the desired interim and final outputs). Gap in literature: The study does not compare the role of firm capabilities for both make and buy decisions and their effects over time. |
| Raassens, Wuyts and Geyskens (2012) | ✕ | ✕ | ✕ | Shareholder value | The study finds that firms can hedge against technological uncertainty and increase NPD outsourcing performance by taking a minority equity stake in the provider. Minority equity positions are inefficient in managing cultural knowledge transfer problems, but these can be lessened by prior tie selection. When both technological and cultural uncertainty are low, the choice of prior tie selection and minority equity stake are irrelevant. Gap in literature: The study does not compare the performance of NPD outsourcing with NPD make decisions. Furthermore, the study does not distinguish between immediate and future performance. |
| Borah and Tellis (2014) | Firm resources, prior makes, prior buys, prior alliances, marketing investments, R&D investments, diversification levels, prior make payoffs, prior buy payoffs, prior ally payoffs, number of patents, number of commercializations | ✕ | ✕ | Shareholder value | Make or ally announcements have positive and higher payoffs than buy announcements. Buy announcements result in negative payoffs. The negative payoffs from buy can be reduced when the acquirer is experienced and the target is related and offers a higher customer benefit. Firms buy when they lack commercialization. Gap in literature: The study examines make/buy/ally in the context of innovation. “Make” refers to setting up R&D units/centers, “buy” refers to acquisitions, and “ally” refers to partnering for technology development. The study does not test performance effects over time. |
| Current study | Volume uncertainty, production cost advantage, sunk costs, technological complexity, product line breadth, R&D intensity | ✔ | ✔ | Product quality | NPD buy choices have a greater impact on immediate product quality (compared with NPD make choices), whereas NPD make choices have a greater impact on future product quality (compared with NPD buy choices). NPD capability is beneficial for NPD buy in the short run (i.e., immediate product quality) and beneficial for NPD make in the long run (i.e., future product quality). The immediate quality impact of NPD buy is positively moderated by technological complexity and the future quality impact of NPD make is positively moderated by PLAF. |
Second, our study tests the impact of NPD make/buy choices on immediate and future product quality and contributes to prior research in important ways. Previous research on NPD outsourcing has examined factors such as ease of evaluating the partner’s performance (Anderson 2008; Raassens, Wuyts, and Geyskens 2012) and the firm’s general knowledge levels (Stremersch et al. 2003) in explaining the variation in performance outcomes at a given point in time. Moorman and Day (2016) note that the emphasis in prior research has been on understanding factors that enhance the effectiveness of NPD buy decisions, and they call for more research on this topic. Our study responds to this call. We distinguish between immediate and future product quality in an effort to provide a more complete picture of the consequences of NPD make/buy choices. The results point to differential quality effects for NPD make/buy choices. Whereas NPD buy choices have greater positive impact on immediate product quality, NPD make choices have greater positive impact on future product quality.
Third, our study contributes to conversations in prior research about the trade-offs inherent in the NPD make/buy choices. New product development outsourcing involves a trade-off between the benefit of gaining faster access to complex technologies versus the disadvantage of poor adaptation. Previous research has noted that although firms are likely to outsource NPD for complex technologies (Singh 1997), increases in transaction costs could diminish product performance (Argyres and Mayer 2007). Our study contributes to this stream of research by showing that NPD outsourcing has a significant advantage (over NPD make) in boosting immediate product quality for complex technologies. However, this initial quality advantage (for NPD buy) diminishes in later years of the product development cycle. Similarly, while prior research has recognized adaptation problems when NPD is outsourced (Ghosh and John 1999; Tadelis 2007), our study explicitly tests the relevance of these concerns for immediate and future product quality. We find that adaptation concerns are not severe enough before product launch to adversely affect product quality from NPD outsourcing. However, NPD make has a significant advantage (over NPD buy) for adjusting and improving product quality when problems emerge after product launch.
Notably, we find that NPD capability is a valuable resource for improving product quality of both NPD make and buy choices. Although firms tend to vertically integrate in domains in which they possess superior NPD capabilities (Argyres 1996; Leiblein and Miller 2003), they outsource in domains in which they have merely adequate capabilities. Our study helps shed light on this paradox. We find that NPD capability is a valuable governance resource in outsourced NPD environments (Argyres and Mayer 2007; Wuyts and Geyskens 2005) and a learning resource for vertically integrated NPD. The results indicate that NPD capability improves immediate product quality for NPD buy and future product quality for NPD make. The implication is that NPD outsourcing is not a panacea for lack of NPD capabilities.
We test the research hypotheses in the U.S. automobile industry using a novel data set assembled from numerous secondary sources. Our data feature NPD make/buy choices for transmission systems of 173 models by 25 makes of 12 major automobile firms (i.e., BMW, Chrysler, Ford, General Motors, Honda, Mazda, Mitsubishi, Nissan, Suzuki, Subaru, Toyota, and Volkswagen) between 2007 and 2014. The unit of analysis is model-year (we elaborate on the rationale for this choice in the “Data” section). In the automotive industry, the NPD contracting mode for vehicle transmission systems does not change frequently. This feature of the automobile industry is well-suited to empirically test the performance effects of a transmission make/buy decision over time. The empirical methodology we employ for hypothesis testing is rigorous and accounts for the endogeneity of NPD make/buy choices. We also account for unobserved model, firm, and temporal heterogeneity and cross-sectional dependence in the data.
Boundary choice decisions are fundamental to the disciplines of marketing, strategy, and economics. There are two streams of research pertinent to understanding the product quality effects of NPD make/buy choices. Transaction cost economics (TCE), a dominant theoretical lens, focuses on transaction as the unit of analysis and argues that transactional attributes play a crucial role in the firm’s make/buy decisions (Williamson 1985). Whereas an extensive body of literature has focused on investigating the drivers of make/buy choices (for reviews, see Geyskens, Steenkamp, and Kumar 2006; Rindfleisch and Heide 1997), studies testing the performance effects of make/buy choices are less common (for an exception, see Raassens, Wuyts, and Geyskens 2012). As noted previously, the key challenge in testing the performance effects of the make/buy decision is the difficulty in observing performance at the transaction level. The relatively limited research on the performance effects of NPD make/buy (as evidenced in Table 1) is surprising given that NPD is a core business process for firms (Hauser, Tellis, and Griffin 2006; Srivastava, Shervani, and Fahey 1998) and understanding heterogeneity in performance is central to marketing strategy research.
Using insights from TCE and organizational learning, we develop a conceptual framework that delineates the determinants of NPD make/buy choices and their impact on immediate and future product quality. Figure 1 depicts the conceptual framework for the study. The model depicts the impact of NPD buy on immediate product quality as moderated by ( 1) technological complexity and ( 2) firm NPD capability. The impact of NPD buy on future product quality is posited to be moderated by ( 1) postlaunch adverse feedback (PLAF) and ( 2) firm NPD capability. The choice of examining these moderating variables is guided by the observation that NPD make/buy decisions involve a fundamental trade-off between access to novel technologies and greater learning and control. These moderating variables bring into sharp focus the conditions that strengthen or exacerbate the benefits and challenges accompanying NPD make/buy decisions.
Previous research has noted two critical advantages of NPD make: the ability to exert control and authority and the ability to adapt to unforeseen contingencies (Williamson 1985). In the case of product development of a complex component, NPD make enables firms to maintain control of internal product development teams through hierarchy. Contract theory, a stream of research related to TCE, contends that internal NPD is a superior mechanism because firms can use subjective performance criteria and motivate product development teams to exert greater effort (Gibbons 1998). The reason is that internal product development teams are long-term employees and can be incentivized to put in effort beyond a single NPD project. This benefit is crucial in the case of NPD of complex components because of the greater need for adaptation or adjustment after product launch. In contrast, NPD buy offers the benefit of using incentives to extract greater performance from vendors (Anderson, Glenn, and Sedatole 2000; Poppo and Zenger 1998; Williamson 2008). However, incorporating numerous contingencies in contracts is costly and difficult to enforce. The implication is that adaptation and adjustment after product launch for NPD buy is relatively more difficult.
The second relevant stream of research for understanding the product quality effects of NPD make/buy choices is the organizational learning literature. A key insight from this stream of literature is that vertical integration or “make” is inherently a superior mode for learning (Levin 2000; Miner, Bassof, and Moorman 2001) compared with external parties or “markets.” Product quality improvement often takes the form of a learning curve. Our conceptualization of learning mirrors the behavioral view of the theory of the firm. Here, learning is represented as emanating from the organization’s experience in a path-dependent way and becoming encoded in routines (e.g., rules, standard operating procedures; Cyert and March 1963; March 1991). Typically, learning in organizations is characterized by the institutionalization of routines and is punctuated by external disruptions. In this tradition, learning occurs when there is a noticeable change in behavior. In the context of product development, improvement in product quality is an indicant of learning (Levin 2000).
Why does NPD make offer superior learning benefits over NPD buy? The logic is related to how firms notice issues of poor quality and solve problems. In the case of product development, most of the improvements in product quality occur after the product is launched (Levin 2000). Internal product development teams should be better equipped to coordinate and engage in trial-and-error learning after product launch. In contrast, improving product quality over time is relatively more difficult with vendors because of the lack of incentives. This learning advantage over time is critical for complex components that require interdependent problem solving (Sorenson 2003).
The preceding arguments highlight the trade-offs associated with NPD make/buy decisions. At the time of product development, NPD buy has a distinct advantage over NPD make because vendors have greater expertise or an “early start” with respect to a particular application or technology. Furthermore, firms could use pay-for-performance contracts to realize significantly higher product quality at the time of product launch (Anderson, Glenn, and Sedatole 2000). Therefore, NPD buy should offer higher immediate product quality compared with NPD make. In contrast, NPD make is superior to NPD buy for future product quality because it allows for greater ability to adjust to contingencies that arise after product launch. New product development buy is not a suitable mode when such contingencies arise because of the difficulty in anticipating these issues and enforcing them through a contract. Furthermore, relative to vendors, firms’ product development teams are motivated to respond to subjective performance criteria. It is difficult to enforce subjective performance criteria when contracting with vendors. Finally, NPD make enables better coordination with employees within firms and helps improve quality over time (Levin 2000; Sorenson 2003). These control, adaptation, and learning benefits of the NPD make mode translate to higher quality after product launch and overcome the quality disadvantage the firm experiences at the time of product launch. Drawing on these arguments, we advance the following baseline hypotheses:
H1: NPD buy has a more positive impact on immediate product quality than NPD make.
H2: NPD make has a more positive impact on future product quality than NPD buy.
An attribute that creates a dilemma for firms when selecting the NPD contracting mode is technological complexity. Technological complexity refers to the design and manufacturing challenges faced when implementing a technology to produce a component (Singh 1997). Although technological complexity increases coordination costs for NPD buy and favors NPD make, firms may not have the expertise to implement complex technologies. Therefore, firms might be less inclined to invest in complex technologies and might prefer to wait and see how the technology fares in the market (Balakrishnan and Wernerfelt 1986). A key benefit of NPD outsourcing is that it provides early access to newer technologies. Therefore, firms might be motivated to outsource NPD and leverage the skills of suppliers in such technological areas. Meta-analytic evidence on make/buy choices supports this view and suggests that technological complexity offers greater product quality advantages under market governance compared with hierarchical governance (Geyskens, Steenkamp, and Kumar 2006). The rationale is that vendors are likely to be better equipped than firms in ensuring product quality for complex technologies because they have acquired knowledge across multiple projects and customers. In contrast, when technologies are less complex, the immediate product quality advantage of NPD buy over NPD make is likely to be suppressed. Given these arguments, we expect the positive relationship between NPD buy choice and immediate product quality to be stronger when the technologies involved are complex.
H1a: The positive impact of NPD buy on immediate product quality is stronger when technological complexity is higher.
NPD capability refers to the ability of firms to generate innovative outcomes efficiently using the resources at their disposal. Firms with superior product development capabilities related to a particular component are able to produce the component or system more efficiently than firms without these capabilities because they possess the appropriate personnel, equipment, and knowledge (Lieberman and Dhawan 2005; Srinivasan, Lilien, and Rangaswamy 2002). Although NPD capabilities are known to be a valuable and inimitable resource, we argue that higher NPD capabilities enable firms to manage risks associated with NPD outsourcing in the short run better than firms with lower NPD capabilities (Argyres and Mayer 2007).
In an outsourced NPD environment, the presence of contractual hazards could limit performance gains. As we have noted, the advantage of outsourcing lies in the firm’s ability to motivate suppliers with incentives, whereas the benefits of vertical integration lie in learning and minimizing the risks associated in dealing with markets (Bajari and Tadelis 2001). The ability to write superior contracts could be a distinct source of advantage for firms (Argyres and Mayer 2007; Wuyts and Geyskens 2005). Firms with strong NPD capabilities have greater ability to specify appropriate incentives, project milestones and deliverables, and extract greater effort from suppliers. Greater effort in the context of product development could imply dedicating appropriate resources to the task (e.g., personnel). Previous research has found that firms with greater capabilities/knowledge in a domain are able to have better outcome control in an outsourced environment (Tiwana and Keil 2007). However, there are limits to the benefits of NPD capability. Anticipating and incorporating unforeseen contingencies is not feasible even if firms possess higher NPD capability. In contrast, the effect of NPD buy on immediate product quality is likely to be suppressed for firms with lower NPD capabilities. Firms with weaker NPD capabilities would be unable to extract similar effort from suppliers because they lack the requisite skills to link incentives to appropriate project milestones. Therefore, we expect that the impact of NPD buy on immediate product quality will be positively moderated by NPD capability of the firm.
H1b: The positive effect of NPD buy on immediate product quality is stronger when firms have higher NPD capability.
Postlaunch adverse feedback refers to feedback after product launch from the market about potential problems. As noted previously, many NPD projects involve quality improvements over multiple time periods. Such projects are characterized by unforeseen contingencies; consequently, contracts between firms and suppliers are inherently incomplete. New product development outsourcing contracts primarily focus on technical and cost objectives that are observable prior to market introduction. Although firms that outsource NPD could potentially include penalties for performance under certain thresholds, such penalties are difficult to enforce if suppliers disagree about the root cause of problems. In other words, problems that emerge after product launch often necessitate costly adjustments and adaptation. In contrast, vertically integrated NPD should be better suited to handle unforeseen contingencies given the firm’s authority and control over internal product development teams (Forbes and Lederman 2009). The rationale for this argument is that internal product development teams (vs. outsourced vendors) are likely to respond better to incentives based on subjective performance criteria. Drawing on these arguments, we hypothesize:
H2a: The positive impact of NPD make on future product quality is stronger when there is PLAF.
NPD Make/Buy Decisions and Future Product Quality: The Moderating Impact of NPD Capability
The future quality impact of NPD make rests on the firm’s ability to learn through trial and error and adapt over the product development life cycle. However, this benefit of NPD make is unlikely to be uniform across firms. We argue that firm NPD capability is an important boundary condition for this relationship. Specifically, we expect that NPD capability serves as a learning resource in the long run (Argyres and Mayer 2007). The benefit of NPD capability originates from deeper and insightful learning because firms already possess the baseline knowledge and understanding in a domain. Firms are therefore likely to be able to more effectively interpret knowledge generated and engage in problem solving under NPD make because of their existing capabilities (Cohen and Levinthal 1990). New product development capability reflects the firm’s knowledge and experience with previous technological generations, and this capability perhaps evolves through previous NPD make decisions (Jacobides and Winter 2005). The marginal benefit of NPD make over time should be contingent on the level of in-house NPD capabilities because learning over a product development cycle is path dependent. In contrast, when firm NPD capability is lower, the learning curve for product quality under NPD make is steeper and, as a result, quality improvements over the life cycle are likely to be suppressed. From these arguments, we hypothesize:
H2b: The positive impact of NPD make on future product quality is greater when firms have higher NPD capability.
We test our research hypotheses on firms’ NPD make/buy choices for transmission systems in the automobile industry. The automobile industry has three features well-suited for testing the research hypotheses. First, new product launches in the U.S. automobile industry follow a schedule that is not synchronous with the calendar year. Figure 2 illustrates the typical sequence of events before and after product launch. The launch of new models for model-year t typically occurs between the summer of calendar year t - 1 and the beginning of calendar year t. The production for new models commences two to three months prior to launch, and NPD make/buy choices are made well in advance of the production schedule. J.D. Power and Associates (JDPA) reports product quality scores in the month of June of calendar year t. This quality score corresponds to the performance of systems for models introduced in that calendar period. Given that JDPA reports product quality scores for systems every year, we are able to track the product quality of a system over time.
Second, in the automobile industry, once firms choose a NPD contracting mode for systems/components, they do not change the contracting mode within a short period. In our data, the NPD contracting mode for transmissions remains unchanged for four years for several models. For example, if firms outsource NPD for a transmission system in a model in 2008, NPD contracting mode remains NPD buy for transmissions incorporated in models in 2009, 2010, and 2011. This feature enables us to test the product quality impact of NPD make/buy choices for four years.
Third, another benefit of the automobile industry is that there is a learning curve for product quality (for reviews, see Levin 2000). In the automobile industry, firms perform quality audits during production runs to identify defects and make necessary changes to improve quality. These quality audits are crucial in minimizing the manufacturer’s liability in the market (e.g., warranty claims, petitions, recalls, fines). Note that although firms are unable to alter design before a new model or redesigned model is launched, ongoing quality checks help in identifying and fixing defects before the vehicle rolls out to dealerships. Thus, automakers experience an immediate product quality that corresponds to the initial product development efforts and a future product quality that corresponds to quality improvements after product launch. Finally, the automobile industry enables us to link NPD make/buy choices to product quality at the transmission system level across models and years.
We assembled the data set from numerous sources. We obtained data on NPD make/buy choices of automobile firms for vehicle transmission systems from MarkLines, a leading vendor that tracks the automobile industry in North America, Europe, and Asia. Whereas NPD make decisions imply that the automaker designs and manufactures transmission systems in-house, NPD buy decisions imply that the vendor designs and manufactures transmission systems.
The data feature NPD make/buy choices for 25 makes (i.e., Acura, Audi, BMW, Buick, Cadillac, Chevrolet, Chrysler, Dodge, Ford, GMC, Honda, Infiniti, Jeep, Lexus, Lincoln, Mazda, Mercury, Mitsubishi, Nissan, Pontiac, Scion, Subaru, Suzuki, Toyota, and Volkswagen) and 173 models of the 12 largest automobile firms (i.e., BMW, Ford, General Motors, Chrysler, Honda, Mazda, Mitsubishi, Nissan, Subaru, Suzuki, Toyota, and Volkswagen) in United States between 2007 and 2014. These 12 automobile firms accounted for approximately 90% of the vehicles sold in United States. The data set comprises NPD make/buy choices of 173 models tracked for four years (166 models for four years and 7 models for eight years). The final data set for empirical analyses features 180 NPD make/buy choices for transmission systems between 2007 and 2014. Note that these 180 NPD make/buy choices correspond to 173 models (i.e., 166 + [7 · 2] NPD make/buy choices).2
We collected data on product quality of transmission systems for makes and models between 2007 and 2014 from JDPA. The product quality measure in our study is akin to quality in use. We collected data on several transactional and firm characteristics. For transactional characteristics, we collected data on the transmission speed (e.g., four-speed, five-speed), type of transmission system (front-wheel drive, rear-wheel drive, and all-wheel drive) and weight of the transmission. Finally, we collected data on the size and the labor efficiency of the transmission plants. With regard to firm characteristics, we collected data on model sales from Ward’s Automotive Yearbook and research and development (R&D) expenditures from Compustat and annual company reports and filings. We also collected data on the breadth of product line (i.e., number of different trims offered for a given model in a given year). Finally, we also collected data on the dry weight (i.e., without transmission fluid) of transmission systems from MarkLines.
Immediate and future product quality. We operationalized product quality in terms of the number of problems experienced by vehicles for transmission systems. J.D. Power and Associates administers detailed surveys from verified owners about the quality of the product or service. Using these measurements, JDPA constructs power circle ratings whereby the product with the highest quality rating is designated a 5 (i.e., “among the best”) and the product with the lowest quality is designated a 2 (i.e., “the rest”), indicating that consumers rate them lower than other companies or models in the survey. Immediate product quality is operationalized as product quality in the first two years of product launch (t and t + 1).
Future product quality is operationalized as product quality of transmission systems of a vehicle model in the third and fourth years after product launch. For example, if a model with a new transmission system is introduced in year t, future product quality refers to the product quality of transmission systems for models produced in years t + 2 and t + 3. The choice of examining years t + 2 and t + 3 after product launch for future product quality is consistent with our goal of examining quality improvement over the product development cycle. For instance, problems emerging after product launch in year t + 1 will require adaptation and learning in subsequent models. If changes need to be incorporated, one could reasonably expect that these changes would have a greater opportunity to be incorporated in years t + 2 and t + 3. Consistent with this logic, we expect immediate product quality—that is, product quality of transmission systems for models produced in years t and t + 1—to reflect initial product development efforts because of the relatively smaller window of opportunity to learn and adapt (for alternate measures for immediate and future product quality, see the “Validation Analyses” subsection).
Technological complexity. Technological complexity in transmission systems arises because firms try to maintain a balance between weight, fuel efficiency, and space constraints. We operationalized technological complexity using three indicants: ( 1) the number of gears in the transmission system; ( 2) whether the transmission is a front-wheel drive, rear-wheel drive, or all-wheel drive; and ( 3) weight of the transmission. The first indicant is operationalized as the count of the number of gears in the system. For instance, the technology underlying an eight-speed transmission system is more complex than a five-speed transmission system. More ratios require more shifting elements, and these add weight, complexity, and drag to the transmission (Colwell 2013). According to industry reports, the technology for all-wheel transmission systems is the most complex, followed by rear-wheel drives and front-wheel drives (O’Dell 2014). Front-wheel drives (transverse mounted engines) are less complex than rear-wheel drives and all-wheel drives because power needs to be delivered only to the front wheels, and there is enough room under the hood to accommodate this design. For rear-wheel drives, the engine is mounted longitudinally, and transmission sits at the back in the form of a rear transaxle for superior traction. The space constraints for this transmission makes the technological design more complex. Finally, all-wheel drives introduce greater complexity to the drive system because they feed power to all four wheels and introduce more power losses (O’Dell 2014). Therefore, we operationalized this indicant by coding front-wheel drives as 1, rear-wheel drives as 2, and all-wheel drives as 3. Finally, we use weight of the transmission system as the third indicant of complexity because greater weight imposes design constraints and increases the degree of difficulty in maintaining the vehicle’s fuel efficiency. Using these indicants, we create a composite measure of technological complexity by extracting principal components. Note that the indicants are standardized before principal components analysis and are therefore not sensitive to different scales for the indicants. The extracted component accounts for 81% of the variation in the three indicants.
NPD capability. We operationalize NPD capability as the efficiency with which firms are able to convert relevant technological resources into valuable technological output. We use an input–output approach to derive NPD capabilities using a NPD transformation function. We reverse-code NPD inefficiency to generate the measure of NPD capability (Dutta, Narasimhan, and Rajiv 2005). We operationalize technological output (TECHOUTPUT) in terms of the number of citation-weighted patents firms receive for vehicle transmission systems. The rationale for using citation-weighted patents as the measure for technological output is that more cited patents are likely to be more innovative. Given the possibility of learning-curve effects in high-technology industries, we expect R&D expenditures (RDEXP) to enable firms to achieve their technological output.3 Similarly, the base of past technological output is likely to be the platform on which firms generate new and innovative technologies. Therefore, we use technological output base (TECHBASE) from previous years as an input in the NPD capability function. Technological base refers to the cumulative citation-weighted patent output from the previous three years after adjusting for decay rates.4 Finally, because technological output is likely to be shaped by the voice of the customer, we include marketing capability (MKCAP) as an input in the NPD transformation function. In summary, we specify technological output that firms seek to maximize, and we specify R&D expenditures, base of technological output, and marketing capability as inputs such that:
where k = firm, t = year, ekt is the random error component, hkt is the time-varying inefficiency term, and ms are the response parameters of inputs in the NPD transformation function. The random error component captures the purely stochastic variation in the firm’s output, while the inefficiency term captures the deterministic component of the firm’s ability to efficiently transform its inputs to outputs. We compute the firm’s NPD capabilities as 1 - hkt (i.e., NPD _ CAPkt = 1 - hkt). Following prior research, we estimate the NPD transformation function using stochastic frontier estimation. The NPDCAP score from this approach serves as the measure for firm NPD capability in Equations 2 and 3.
To estimate marketing capability (MKCAP), consistent with previous research, we again use the input–output approach (Dutta, Narasimhan, and Rajiv 1999; Kalaignanam et al. 2013). We specify sales as the output that firms try to maximize and specify investment in marketing (MKTGSTOCK) and customer relationships (ICR) as inputs:
where k = firm, t = year, MKTGSTOCK is the stock of marketing expenses, ICR is investments made in developing and maintaining relationships with customers, ukt is the random error component, and qkt is the time-varying marketing inefficiency. We operationalize marketing capabilities as 1 - qkt APkt = 1 - qkt). The marketing capability score from this approach serves as the measure of marketing capability in the NPD capability transformation function.
PLAF. We operationalize PLAF in terms of a dummy variable that reflects whether complaints pertaining transmission systems of various model-years are received by the National Highway and Traffic Safety Administration (NHTSA). The NHTSA is a federally governed organization established under the Highway Safety Act of 1970 to enhance and monitor highway and motor vehicle safety. It is enforced by the U.S. Department of Transportation with the goal of establishing and governing safety standards for motor vehicles. The NHTSA passes on these complaints to automobile manufacturers for potential action or remedy. Specifically, we examine whether a model launched in year t received complaints from consumers in year t + 1. As noted previously, we examine future product quality in years t + 2 and t + 3. This temporal separation is consistent with our goal of examining adaptation problems after launch and how firms adjust to this feedback. We coded PLAF as 1 if complaints were received for transmission systems of a given model-year, and 0 otherwise.
A potential concern with the PLAF measure is that it may be correlated with immediate product quality.5 To circumvent this problem, we developed a PLAF measure that is orthogonal to immediate product quality. To do so, we use probit specification to model the impact of initial product quality from year t on PLAF in year t + 1. From this model, we estimate the residuals for the probability of PLAF in years t + 1 (i.e., difference in predicted probabilities and actual outcomes of PLAF). If this difference is greater than .5, PLAFRESIDUAL takes a value of 1, and 0 otherwise. Table 2 summarizes the variables in the study, their operational measures, and descriptive statistics.
TABLE: TABLE 2 Variable Operationalization and Descriptive Statistics
TABLE: TABLE 2 Variable Operationalization and Descriptive Statistics
| Measure | Level of Variation | Operationalization | Data Sources | M | SD |
|---|
| NPD buy | Model i | Indicator variable reflecting the firm’s NPD make/buy choice for a given make and model (1 = buy, 0 = make) | MarkLines | 0.29 | N.A. |
| Immediate product quality | Model I in year t | Product quality rating for transmission systems for models in the first two years of launch (2 = “lowest quality,” and 5 = “highest quality”) | JDPA | 3.44 | 0.94 |
| Future product quality | Model I in year t + 2 | Product quality rating for transmission systems for models two and three years after launch (2 = “lowest quality,” and 5 = “highest quality”) | JDPA | 3.41 | 0.95 |
| Technological complexity | Technology m | Principal component score of (1) number of gears in the transmission system; (2) whether the transmission is front-wheel drive, rear-wheel drive, or all-wheel drive; and (3) weight of the transmission system | MarkLines | 0 | 1.13 |
| NPD capability | Firm k in year t | The efficiency with which firms can convert inputs such as R&D expenditures, technological base, and marketing capability into citation weightedpatent counts (measured in %) | USPTO, Compustat | 64.35 | 18.87 |
| PLAF | Model i, technology m, in year t + 1 | Indicator variable reflecting whether complaints were received by NHTSA for the given transmission system of a model between the years t and t + 2 | NHTSA | 0.21 | N.A. |
| Volume uncertainty | Model i | Coefficient of variation for model’s sales in past three years scaled by the make’s mean sales | MarkLines | 0.18 | 0.12 |
| Production cost advantage | Firm k | Inverse of the number of hours taken by a firm’s plant to build transmission systems | Harbor Reports | 0.04 | 0.02 |
| Sunk costs | Firm k | Total size (in millions of sq. ft) of transmission plants operated by a firm | ELM Analytics | 4.58 | 2.38 |
| Product line breadth | Make j in year t | Count of the number of models of a make in a given year | Ward’s Automotive Yearbook | 1.56 | 0.82 |
| Market performance | Model I in year t | Units of the model sold in a given year (in thousands) | Ward’s Automotive Yearbook | 42 | 71 |
| R&D intensity | Firm k in year t | Annual research and development expenditures of the firm as a percentage of annual revenues | Compustat, Annual Reports | 4.26 | 2.24 |
| Sales | Firm k in year t | Sales of the firm (in millions of US$) | Compustat | 135170 | 55291 |
| Patent output | Firm k in year t | Citation-weighted patents for transmissions systems | USPTO | 20.08 | 35.08 |
| Technology base | Firm k in year t | Cumulative citation-weighted patents over the last three years adjusted by decay rates | USPTO | 19.44 | 32.28 |
| R&D expenditures | Firm k in year t | Annual R&D expenditures of the firm (in million $) | Compustat | 5706 | 2482 |
| Marketing stock | Firm k in year t | Cumulative SG&A expenditures over the last three years adjusted by decay rates | Compustat | 16195 | 5833 |
| Investment in customer relationships | Firm k in year t | Receivables from customers (in millions of US$) | Compustat | 47475 | 40674 |
| Marketing capability | Firm k in year t | The efficiency with which firms can convert inputs such as marketing stock and investment in customer relationships into marketing capability (measured in %) | Compustat | 83.54 | 13.99 |
A rigorous test of our hypotheses requires a close alignment of the theory, measures, and empirical model. We followed three steps to achieve this. First, we model the determinants of NPD make/buy choices on a cross-section data set of 173 models. This choice is consistent with the fact that NPD make/buy choices do not vary over time. Second, we test the impact of NPD make/buy choices (year t) on immediate product quality (years t and t + 1) using 360 model-years (173 models tracked in the first two years of product launch). Third, we test the impact of NPD make/buy choices (year t) on future product quality (years t + 2 and t + 3) using 360 model-years (173 models tracked in years 3 and 4 after product launch).
Endogeneity of NPD make/buy choices. Firms are likely to be aware of the performance frontiers of the make/buy decision and would rationally select the NPD contracting mode that offers higher product quality. If so, examining the product quality outcomes of NPD contracting modes without correcting for this endogeneity would yield biased coefficients and possibly erroneous conclusions. To account for this problem, we model the determinants of the NPD make/buy choice and use predicted NPD make/buy choices in the subsequent product quality equations.
We model a firm k’s (e.g., Ford Motor Company, Toyota Motor Corporation) NPD make/buy choice for technology m (e.g., six-speed, front-wheel drive, 135 pounds) for model I (e.g., Navigator, LS460) of make j (e.g., Lincoln, Lexus) as a function of both transactional and firm characteristics. Consistent with previous research, we include technological complexity, volume uncertainty, sunk costs, and production cost advantage as drivers of the NPD make/buy choice. First, greater technological complexity (TECHCOMPm) is likely to increase the cost of coordination with the vendor, and therefore firms may choose to pursue NPD make. An alternate argument is that firms may not have the requisite expertise for a given technology and may rely on NPD outsourcing to gain earlier access to complex technologies. Second, when volume uncertainty (VOLUNCERTi) is high, firms may not be able to predict demand for a component and therefore may be exposed to costly renegotiations with the supplier (Walker and Weber 1984). Because this uncertainty could increase transaction costs, weexpect volume uncertainty toresult in firms opting to perform NPD in-house. Third, firms with higher sunk costs, such as capital investments in plants, are more likely to make rather than buy NPD (Lieberman 1991). We include size (in millions of square feet) of the firm’s transmission plants as a measure of sunk costs (SCOSTk). Fourth, firms with a greater production cost advantage (PRODCOSTADVk) are more likely to perform NPD in-house. We include the inverse of the number of hours required to build a transmission assembly as a measure of production cost advantage.
We also include firm characteristics such as product variety, market performance, and availability of resources as determinants of the NPD make/buy choice. Firms offering greater product variety need to contain development costs (Bayus and Putsis 1999) and are likely to organize NPD internally. To account for this possibility, we include product line breadth (PLBRDTHj) as a covariate. Next, when market performance (PERFi) is higher, firms may pursue NPD make to exercise greater control. We also expect availability of resources to influence the ability or motivation of firms to make or buy. We include R&D intensity to control for the resource base of a firm: R&D intensity (RDINTENk) is operationalized as R&D expenditures as a percentage of sales. Finally, it is likely that NPD capability may drive firms to organize NPD internally. Accordingly, we include NPD capability (NPD _ CAPkt) as a driver of the NPD make/buy choice. We utilize the following probit specification to model the determinants of NPD make/buy choices.
where d are response parameters, F is the cumulative distribution function of normal distribution, and x ~ N [0, s2 x] are normally distributed random errors. FIRMk is the firm-specific effect and YEARt is the year-specific effect. In this model, sunk costs and production cost advantage are instruments that identify the system. To test the quality of the instrument, we estimated an alternative specification that excludes sunk costs and production cost advantage. The Bayesian information criterion (BIC) for this alternative model is 760.32, which is greater than the model that includes both instruments (BIC = 724.18). Thus, our instruments improve the model fit and successfully predict the NPD make/buy choices. The Sargan–Hansen overidentifying restrictions test for the exogeneity of instruments is 3.89 (p > .10) for immediate product quality and 4.65 (p > .10) for future product quality. Thus, we fail to reject the null hypothesis that the instruments are uncorrelated with the second-stage regression. Finally, the correlation coefficient between sunk costs and immediate (future) product quality is .02 (.01) and that between product cost advantage and immediate (future) product quality is -.09 (-.08). These correlations are relatively small, further indicating that they are exogenous to product quality.
NPD make/buy choice → immediate product quality. To test the immediate product quality impact of NPD make/buy choices, we estimate a linear model that links the hypothesized variables to immediate product quality. We use the following specification:
where IPQit, is immediate product quality for model I for model-year t, as are response parameters, and g ~ N [0, s2 g ] is the normally distributed random error component. The variable NPD _ CAPkt is the NPD capability of firm k in year t.6 We include sales of the model (PERFit), product line breadth (PLBRDTHjt), and R&D intensity (RDINTENkt) as controls. To control for firm-specific and year-specific unobserved heterogeneity, we include firm (FIRMk) and year (YEARt) dummies. Coefficients a1, a4, and a5 test H1, H1a, and H1b, respectively.
NPD make/buy choice → future product quality. Next, we test the impact of NPD make/buy choice on future product quality using the following specification:
where FPQit + 2 is future product quality for model I of model-year t + 2, bs are response parameters, and z ~ N [0, s2 z] is the normally distributed random error component. PLAFRESIDUAL is postlaunch adverse feedback received by model I in year t + 1 (orthogonal to immediate product quality). To control for potential autocorrelation in product quality scores, we include initial product quality in the model. Other variables are as defined before. Coefficients b1, b6, and b7 test H2, H2a, and H2b, respectively.
There are a few econometric issues pertaining to the error structure in Equations 2 and 3 that need to be accounted for. Cameron and Trivedi (2005, p.702) suggest that “NT correlated observations have less information than NT independent observations.” Thus, ignoring the error correlation between cross-sectional units could lead to erroneous conclusions. Firms market vehicles under numerous makes that, in turn, offer several models. This structure leads to clustering of observations and possibly contemporaneous correlation between cross-sectional units. We tested for the presence of cross-sectional dependence and find that the null hypothesis of no spatial dependence is rejected for both the immediate (c2( 8) = 94.73, p < .01) and future (c2(11) = 104.29, p < .01) product quality model. As suggested by Creel and Farell (1996), we use feasible generalized least square estimation (XTGLS in STATA 13) to estimate Equations 2 and 3 with cross-sectional dependence and heteroskedasticity.
Our unit of analysis is model-year (e.g., Acura TSX 2007).7 This choice is related to the fact that we observe considerable variation in the NPD make/buy choices for transmissions across models. Table 3 provides a list of all makes and models in our sample and the corresponding model-years. For example, whereas the NPD buy mean for Toyota is .46, the NPD buy mean of Lexus is .85. In other instances, such as Honda, the NPD contracting mode does not vary across makes (Honda, Acura) and models. It is worth noting that NPD make/buy choice varies at the model level for approximately 65% of the models and is invariant across the remaining 35% of models. Decisions for NPD make/buy do not appear to be made at the make/brand level. We choose model-year as the unit of analysis because this choice is consistent with 65% of the models and because doing so theoretically allows choices to vary at the model level. However, in 35% of the data, the NPD contracting mode is invariant across models of the same firm, and we account for this by including firm-specific or manufacturer-specific fixed effects (e.g., the manufacturer Honda vertically integrates NPD for all makes and models); we also account for cross-sectional dependence (correlation across models) in the immediate and future product quality models.
TABLE: TABLE 3 Make-Specific Descriptive Statistics
TABLE: TABLE 3 Make-Specific Descriptive Statistics
| Make | Models | Model-Years | NPD Buy Mean |
|---|
| Acura | MDX, RL, TL, TSX | MDX: 2007–2010, RL: 2009–2012, TL: 2009–2012, TSX: 2009–2012 | 0 |
| Audi | A4, A6, A7, A8, Q5 | A4: 2009–2012, A6: 2011–2014, A7: 2011–2014, A8: 2011–2014, Q5: 2009–2012 | 1 |
| BMW | 3 Series, 5 Series, 7 Series, X3, X5 | 3 Series: 2007–2010, 5 Series: 2011–2014, 7 Series: 2007–2010, X3: 2011–2014, X5: 2007–2010 | 1 |
| Buick | Enclave, Lacrosse, Lucerne, Regal | Enclave: 2008–2011, Lacrosse: 2010–2013, Lucerne: 2007–2010, Regal: 2007–2014 | 0.5 |
| Cadillac | CTS, CTS-V, DTS, Escalade, Escalade ESV, Escalade EXT, SRX, STS | CTS: 2008–2011, CTS-V: 2009–2012, DTS: 2007–2010, Escalade: 2007–2010, Escalade ESV: 2007–2010, Escalade EXT: 2007–2010, SRX: 2010–2013, STS: 2007–2010 | 0.12 |
| Chevrolet | Avalanche, Aveo, Camaro, Cobalt, Colorado, Corvette, Cruze, Equinox, Express, HHR, Impala, Malibu, Suburban, Tahoe, Trailblazer, Traverse | Avalanche: 2007–2010, Aveo: 2007–2010, Camaro: 2010–2013, Cobalt: 2007–2010, Colorado: 2007–2010, Corvette: 2008–2011, Cruze: 2011–2014, Equinox: 2007–2014, Express: 2009–2012, HHR: 2007–2010, Impala: 2007–2010, Malibu: 2008–2011, Suburban: 2007–2010, Tahoe: 2007–2014, Trailblazer: 2009–2012, Traverse: 2007–2010. | 0.33 |
| Chrysler | A200, A300, Aspen, Pacifica, PT Cruiser, Sebring, Town & Country | A200: 2011–2014, A300: 2011–2014, Aspen: 2007–2010, Pacifica: 2007–2010, PT Cruiser: 2011–2014, Sebring: 2007–2010, Town & Country: 2008–2011 | 0 |
| Dodge | Avenger, Challenger, Charger, Dakota, Durango, Grand Caravan, Journey, Magnum, Nitro, Ram 1500, Ram 2500, Ram 2500/3500, Ram 3500 | Avenger: 2008–2011, Challenger: 2008–2011, Charger: 2011–2014, Dakota: 2007–2010, Durango: 2011–2014, Grand Caravan: 2011–2014, Journey: 2011–2014, Magnum: 2007–2010, Nitro: 2007–2010, Ram 1500: 2009–2012, Ram 2500: 2010–2013, Ram 2500/3500: 2007–2010, Ram 3500: 2007–2010 | 0.07 |
| Ford | Edge, Escape, E-Series Wagon, Expedition, Explorer, F-150, Flex, Focus, Fusion, Mustang, Ranger, Taurus, Transit Connect | Edge: 2007–2010, Escape: 2008–2011, E-Series Wagon: 2007–2010, Expedition: 2007–2010, Explorer: 2011–2014, F-150: 2009–2012, Flex: 2009–2012, Focus: 2011–2014, Fusion: 2007–2014, Mustang: 2011–2014, Ranger: 2007–2010, Taurus: 2008–2011, Transit Connect 2009–2012 | 0.37 |
| GMC | Acadia, Canyon, Sierra 1500, Sierra 2500HD, Sierra 3500HD, Terrain, Yukon | Acadia: 2007–2010, Canyon: 2007–2010, Sierra 1500: 2008–2011, Sierra 2500HD: 2008–2011, Sierra 3500HD: 2007–2010, Terrain: 2007–2010, Yukon: 2010–2013 | 0.42 |
| Honda | Accord, Accord Crosstour, Civic, CR-V, Element, Fit, Odyssey, Pilot, Ridgeline | Accord: 2008–2011, Accord Crosstour: 2010–2013, Civic: 2007–2010, CR-V: 2007–2010, Element: 2007–2010, Fit: 2009–2012, Odyssey: 2011–2014, Pilot: 2009–2012, Ridgeline: 2007–2010 | 0 |
| Infiniti | EX35, FX35, FX45, FX50, G25, G37, M37, QX56 | EX35: 2007–2010, FX35: 2009–2012, FX45: 2007–2010, FX50: 2009–2012, G25: 2009–2012, G37: 2009–2012, M37: 2010–2013, QX56: 2008–2011 | 1 |
| Jeep | Commander, Grand Cherokee, Liberty, Wrangler | Commander: 2007–2010, Grand Cherokee: 2011–2014, Liberty: 2008–2011, Wrangler: 2007–2010 | 0 |
| Lexus | ES350, GS350, GS460, GX460, IS F, IS250, IS350, LS460, SC430 | ES350: 2007–2010, GS350: 2007–2010, GS460: 2010–2013, GX460: 2007–2010, IS F: 2007–2010, IS250: 2007–2010, IS350: 2007–2010, LS460: 2008–2011, SC430: 2007-2010 | 0.85 |
| Lincoln | MKS, MKT, MKX, MKZ, Navigator, Town Car | MKS: 2009–2012, MKT: 2010–2013, MKX: 2007–2010, MKZ: 2007–2010, Navigator: 2007–2014, Town Car: 2010–2013 | 0.83 |
| Mazda | CX-9, Mazda 3, Mazda 5, Mazda 6, MX-5 Miata, Tribute | CX-9: 2008–2011, Mazda 3: 2007–2010, Mazda 5: 2007–2010, Mazda 6: 2007–2014, MX-5 Miata: 2007–2010, Tribute: 2008–2011 | 0.5 |
| Mercury | Grand Marquis, Mariner, Mountaineer | Grand Marquis: 2007–2010, Mariner: 2008–2011, Mountaineer: 2007–2010 | 0.5 |
| Mitsubishi | Eclipse, Endeavor, Galant, Outlander | Eclipse: 2008–2011, Endeavor: 2007–2010, Galant: 2008–2011, Outlander: 2008–2011 | 1 |
| Nissan | A350 Z, Armada, Frontier, Frontier Crew Cab, Pathfinder, Quest, Titan, Versa, Xterra, X-Trail | A350 Z: 2009–2012, Armada: 2007–2010, Frontier: 2007–2010, Frontier Crew Cab: 2008–2011, Pathfinder: 2007–2010, Quest: 2009–2012, Titan: 2008–2011, Versa: 2007–2010, Xterra: 2008–2011, X-Trail: 2007–2010 | 1 |
| Pontiac | G6, Solstice, Vibe | G6: 2007–2010, Solstice: 2007–2010, Vibe: 2009–2012 | 0.4 |
| Scion | Scion tC, Scion xB, Scion xD | Scion tC: 2011–2014, Scion xB: 2008–2011, Scion xD: 2008–2011 | 0 |
| Subaru | Forester, Impreza, Legacy | Forester: 2009–2012, Impreza: 2008–2011, Legacy: 2010–2013 | 0 |
| Suzuki | Equator, SX4, Grand Vitara, Reno | Equator: 2009–2012, SX4: 2007–2010, Grand Vitara: 2009–2012, Reno: 2007–2010 | 1 |
| Toyota | 4Runner, Avalon, Camry, Corolla, FJ Cruiser, Highlander, RAV4, Sequoia, Sienna, Tacoma, Tundra, Venza, Yaris | 4Runner: 2010–2013, Avalon: 2007–2010, Camry: 2007–2014, Corolla: 2009–2012, FJ Cruiser: 2007–2010, Highlander: 2007–2014, RAV4: 2007–2010, Sequoia: 2008–2011, Sienna: 2007–2010, Tacoma: 2007–2010, Tundra: 2009–2012, Venza: 2011–2014, Yaris: 2007–2010 | 0.46 |
| Volkswagen | Beetle, Golf, Jetta, Passat, Tiguan, Touareg | Beetle: 2011–2014, Golf: 2010–2013, Jetta: 2011–2014, Passat: 2007–2010, Tiguan: 2009–2012, Touareg: 2011–2014 | 0.83 |
Table 4 presents the correlation between the key variables in the study. The correlations between the independent variables are within prescribed limits (variance inflation factors < 10, condition indices < 20), suggesting that multicollinearity is not a serious threat in this study.
TABLE: TABLE 4 Correlation Matrix of Key Variables
| | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 |
|---|
| 1. NPDBUY | | | | | | | | | | | | | | | | | | |
| 2. IPQ | 0.26 | | | | | | | | | | | | | | | | | |
| 3. FPQ | -0.12 | 0.46 | | | | | | | | | | | | | | | | |
| 4. TECHCOMP | 0.32 | -0.12 | -0.08 | | | | | | | | | | | | | | | |
| 5. NPDCAP | -0.27 | 0.09 | 0.16 | -0.17 | | | | | | | | | | | | | | |
| 6. PLAF | 0.14 | -0.07 | -0.15 | -0.07 | 0.05 | | | | | | | | | | | | | |
| 7. PLBRDTH | 0.03 | 0.02 | -0.01 | -0.16 | 0.19 | 0.22 | | | | | | | | | | | | |
| 8. VOLUNCER | -0.17 | -0.05 | -0.01 | 0.03 | 0.05 | -0.02 | 0.04 | | | | | | | | | | | |
| 9. PRODCOSTADV | -0.44 | 0.16 | 0.29 | -0.01 | 0.07 | -0.12 | -0.13 | -0.07 | | | | | | | | | | |
| 10. SCOST | -0.17 | 0.12 | 0.12 | 0.14 | -0.11 | -0.05 | 6 | -0.04 | 0.13 | | | | | | | | | |
| 11. PERF | -0.02 | -0.07 | 0.05 | 0.02 | 0.38 | -0.05 | -0.13 | -0.03 | 0.06 | 0.13 | | | | | | | | |
| 12. RDINTEN | 0.05 | 0.09 | 0.08 | -0.02 | 0.36 | 0.07 | 0.14 | 0.02 | -0.07 | -0.14 | 0.23 | | | | | | | |
| 13. SALES | 0.01 | 0.04 | 0.08 | -0.24 | 0.22 | 0.03 | 0.09 | -0.09 | 0.2 | -0.09 | 0.17 | 0.04 | | | | | | |
| 14. TECHOUTPUT | 0.01 | 0.1 | 0.06 | 0.09 | 0.26 | 0.06 | -0.02 | 0.02 | -0.08 | -0.07 | 0.07 | 0.03 | 0.02 | | | | | |
| 15. TECHBASE | -0.05 | 0.04 | 0.08 | -0.03 | 0.18 | 0.01 | 0.06 | 0.07 | 0.07 | 0.08 | -0.05 | -0.02 | 0.01 | 0.31 | | | | |
| 16. RDEXP | 0.1 | -0.02 | 0.09 | -0.07 | 0.14 | 0.09 | 0.07 | 0.01 | 0.05 | -0.04 | -0.05 | -0.09 | 0.14 | 0.23 | 0.08 | | | |
| 17. MKTGSTOCK | -0.06 | 0.05 | -0.04 | 0.04 | -0.07 | -0.1 | 0.09 | -0.08 | -0.09 | -0.06 | 0.03 | -0.05 | 0.24 | 0.04 | 0.09 | 0.06 | | |
| 18. ICR | -0.01 | -0.08 | 0.01 | -0.05 | 0.05 | 0.01 | -0.09 | 0.1 | -0.06 | -0.01 | 0.01 | 0.01 | 0.19 | 0.06 | 0.08 | 0.1 | 0.16 | |
| 19. MKCAP | 0.03 | -0.06 | -0.01 | -0.02 | -0.02 | -0.07 | 0.02 | 0.04 | -0.09 | -0.09 | -0.08 | -0.04 | 0.16 | 0.18 | 0.06 | 0.1 | 0.19 | 0.05 |
Notes: ICR = investment in customer relationships; IPQ = initial product quality.
The results of the determinants of NPD make/buy choices appear in Table 5. To evaluate whether transactional and firm characteristics predict NPD make/buy choices, we estimated two nested models: ( 1) Model 1a, the base model that includes only intercept, firm-specific, and year-specific effects and ( 2) Model 1b, the full model that includes all variables. A comparison of BIC statistics shows that the full model (Model 1b) has a BIC of 728.13 and the base model (Model 1a) has a BIC of 903.48. Model 1b has a significantly better fit for the NPD make/buy choice data. We find that technological complexity is positively related to NPD buy (.4321, p < .05). Therefore, firms are likely to outsource NPD when technological complexity is higher. Consistent with expectations, we find that volume uncertainty is negatively related to NPD buy (-.2278, p < .05). Similarly, we find that sunk costs are negatively related to NPD buy (-.0959, p < .05). As we expected, the results show that production cost advantage is negatively associated with NPD buy (-1.4180, p < .01). With regard to firm characteristics, R&D intensity (-.4263, p < .01) is negatively related to NPD buy.
TABLE: TABLE 5 Random Effects Probit Results: Determinants of NPD Make/Buy Choice
| Dependent Variable: NPDBUYi | Model 1a | Model 1b |
|---|
| Estimate | SE | Estimate | SE |
|---|
| Intercept | -.3707* | .1972 | -1.0242** | .4184 |
| Technological complexity (TECHCOMPm) | | | .4321** | .1978 |
| Volume uncertainty (VOLUNCERi) | | | -.2278** | .1147 |
| Sunk costs (SCOSTk) | | | -.0959** | .0391 |
| Production cost advantage (PRODCOSTADVk) | | | -1.4180*** | .4017 |
| Product line breadth (PLBRDTHj) | | | .2981 | .2343 |
| Market performance (PERFi) | | | -.1971* | .1125 |
| R&D intensity (RDINTENk) | | | -.4263*** | .1514 |
| NPD capability (NPDCAPKT) | | | -.0871 | .0614 |
| Firm effects (FIRMk) | Included | Included |
| Year effects (YEARt) | Two significant | None significant |
| BIC | 903.48 | 728.13 |
| N (sample size) | 180 | 180 |
*p < .10.
**p < .05.
***p < .01.
We do not find the impact of NPD capability on NPD buy to be statistically significant (-.0871, p > .10). This insignificant finding is perhaps because NPD capability varies at the firm level, whereas NPD make/buy choice varies within firms (i.e., firms use both make and buy modes across models). Furthermore, it is plausible that R&D intensity captures some of the effects of NPD capability especially because NPD capability is also correlated with R&D intensity. We find the impact of market performance on NPD buy to be negative (-.1971, p < .10). Finally, we do not find product line breadth to be significantly related to NPD buy.
The results for the effects of NPD make/buy choices on immediate and future product quality appear in Table 6. Model 2 corresponds to results for immediate product quality. Model 3 corresponds to results for future product quality.
TABLE: TABLE 6 Results: The Impact of NPD Make/Buy on Immediate and Future Product Quality
| | Immediate Product Quality (IPQit): Model 2 | Future Product Quality (FPQit12): Model 3 |
|---|
| Estimate | SE | Estimate | SE |
|---|
| Intercept | -0.7492 | 2.9383 | .7883 | 3.1699 |
| NPD buy (NPD?BUYi) | .4110*** | 0.1466 | -.6627** | .3297 |
| Tech complexity (TECHCOMPm) | -.4686** | 0.205 | -.1017* | .0579 |
| NPD capability (NPD?CAPkt) | .0030** | 0.0016 | .0247** | .0109 |
| PLAF (PLAFRESIDUAL | | | -.1561* | .0919 |
| NPD buy. Tech complexity | .6954** | 0.2842 | .1377 | .1071 |
| NPD buy. NPD capability | .0248* | 0.0144 | -.0316** | .0127 |
| NPD buy. PLAF | 0.009 | 0.033 | -.8823** | .4300 |
| Product line breadth (PLBRDTHjt) | | | -.0484 | .0453 |
| Market performance (PERFit) | -.1216** | 0.0565 | .0957 | .0963 |
| R&D intensity (RDINTENkt) | .1558** | 0.0639 | .0593 | .0655 |
| Immediate product quality (IPQit) | | | .3029* | .1816 |
| Firm effects (FIRMk) | Four significant | | One significant | |
| Year effects (YEARt) | None significant | | None significant | |
| BIC | 629.93 | | 604.89 | |
| N (sample size) | 360 | | 360 | |
*p < .10.
**p < .05.
***p < .01.
Notes: Standard errors are robust.
The results in Models 2 and 3 suggest that the impact of NPD buy on immediate product quality is positive and significant (a1 = .4110, p < .01), while that on future product quality is negative and significant (b1 = -.6627, p < .05). This finding indicates that when firms outsource NPD, the impact on immediate product quality is more positive than that from NPD make. However, the impact of NPD make on future product quality is more positive than that from NPD buy. Thus, at average values of the moderating variables, both NPD make and buy decisions have a positive impact on product quality, but the impact manifests differently over time. The baseline hypotheses H1 and H2 are supported.
Next, we examine how technological complexity and NPD capability moderates the relationship between NPD make/buy and immediate product quality. The results from Model 2 suggest that the direct impact of technological complexity on immediate product quality is negative (a2 = -.4686, p < .05). This finding implies that technologically complex transmissions have lower quality than those that are less complex. The interaction of NPD buy and technological complexity on immediate product quality is positive and significant (a4 = .6954, p < .05). H1a is supported.
To gain better insight into the nature of the interaction, we performed spotlight analyses by setting low and high values of technological complexity at two standard deviations below (low) and above (high) the mean, respectively. The spotlight analyses appear in Figure 3, Panel A. Note that we account for uncertainty in the parameter estimates by drawing 1,000 bootstrap samples from the two-standarddeviation asymptotic interval of the main and interaction coefficients. The numbers reported in Figure 3 are the mean predicted values across the 1,000 bootstrapped samples. As we show in Figure 3, Panel A, when technological complexity is high (two standard deviations above the mean), NPD buy is associated with higher immediate product quality compared with NPD make (3.45 > 1.49, p < .10). However, when technological complexity is low (two standard deviations below the mean), we find no significant difference between immediate product quality for NPD make/buy choices (2.53 » 3.66). The difference-in-differences (DDbuy-make = 3.09, p < .05) is significant and suggests that as technological complexity increases, NPD buy choices result in greater immediate product quality compared with NPD make choices.
We find a similar pattern of results for the moderating impact of firm NPD capability. The direct impact of NPD capability on immediate product quality is positive (a3 = .0030, p < .05). That is, firms with higher NPD capability experience greater immediate product quality compared with counterparts with lower NPD capability. The interaction between NPD buy and NPD capability on immediate product quality is positive and significant (a5 = .0248, p < .10). Thus, H1b is partially supported.
The spotlight analysis in Figure 3, Panel A, suggests that when a firm’s NPD capability is high, NPD buy is associated with superior immediate product quality compared with NPD make (4.03 > 2.69, p < .10). However, when NPD capability is low, we find no significant difference between immediate product quality experienced for NPD make/buy choices (1.95 » 2.46). The difference in differences (DDbuy - make = 1.85, p < .10) is significant and suggests that as firm NPD capability increases, NPD buy choices lead to greater immediate product quality compared with NPD make choices. Collectively, the results of H1, H1a, and H1b suggest that NPD buy (vs. NPD make) is associated with greater immediate product quality, and this initial advantage is greater for ( 1) technologically complex transmissions and ( 2) firms with superior NPD capability.
We next examine the moderator results for the impact of NPD make/buy choices on future product quality. The results from Model 3 suggest that the direct impact of PLAF on future product quality is negative (b4 = -.1561, p < .10). Consistent with H2a, we find that the interaction between NPD buy and PLAF on future product quality is negative and significant (b7 = -.8823, p < .05). As we show in Figure 3, Panel B, in the presence of PLAF, NPD make is associated with higher future product quality compared with NPD buy (2.42 > .87, p < .05). However, in the absence of PLAF, the impact of NPD make/buy on future product quality is not significantly different (2.58 » 1.90). The difference-in-differences (DDmake-buy = .88, p < .10) is significant and suggests that if a firm choosing NPD make experiences PLAF, it is in a better position to adapt to negative feedback and improve product quality. However, NPD buy is ill-suited to adapt to negative feedback and improve future product quality because of the difficulty of coordinating with vendors.
Finally, we examine the moderating impact of NPD capability on the NPD buy–future product quality relationship. We find that the direct impact of NPD capability on future product quality is positive (b3 = .0247, p < .05). Therefore, firms with higher NPD capability also experience higher future product quality. Notably, the impact of NPD capability on future product quality is larger than its impact on immediate product quality (b3 = .0247 > a3 = .0030, p < .05). Consistent with H2b, the interaction between NPD buy and NPD capability is negative (b6 = -.0316, p < .05).
Figure 3, Panel B, suggests that when a firm’s NPD capability is high, NPD make is associated with higher future product quality compared with NPD buy (3.53 > 1.66, p < .05).
However, when NPD capability is low, we find no significant difference between future product quality associated with NPD make/buy choices (1.63 » 2.18). The difference-in-differences (DDmake - buy = 2.42, p < .05) is significant and suggests that as NPD capability increases, NPD make leads to higher future quality compared with NPD buy. Thus, the results of H1b and H2b show a differential moderating effect of NPD capability: higher NPD capability strengthens the NPD buy–immediate product quality relationship as well as the relationship between NPD make and future product quality. Collectively, the results of H2, H2a, and H2b suggest that NPD make (vs. NPD buy) decisions are associated with higher future product quality, and this impact is greater when ( 1) there is adverse feedback after product launch and ( 2) firms have higher NPD capability.
The results for the control variables are in the expected direction. Consistent with evidence on the firm performance and organizational change relationship (Greve 1998), we find that models with higher sales experience inferior immediate product quality outcomes (-.1216, p < .05). We note that these results account for firm-specific and year-specific heterogeneity. With regard to firm-specific effects, we find that the immediate product quality of Ford, Toyota, BMW, and Volkswagen is significantly higher (relative to Chrysler), whereas the future product quality of Toyota is significantly higher (relative to Chrysler). Consistent with expectations, we also find that higher levels of investments in R&D is positively related to immediate product quality (.1558, p < .05). We also find that immediate product quality has a positive impact on future product quality (.3029, p < .10).
We performed additional analyses to check the robustness of the study’s main findings. Specifically, we examined the stability of the results ( 1) to two alternate measures of NPD capability, ( 2) when dropping NPD make/buy choices that do not vary by model, and ( 3) to an alternate measure of immediate and future product quality. We discuss the analyses with two alternate measures of NPD capability here and the others (2 and 3) in the Web Appendix.
As noted previously, we used the input–output transformation function to estimate NPD capability. The inputs used in the NPD capability transformation function are ( 1) R&D expenditures, ( 2) technological base, and ( 3) marketing capability. A potential concern is that the use of overall R&D expenditures (as opposed to R&D expenditures of transmission systems) in this function could over- or underestimate NPD capabilities. While it would be ideal to have R&D expenditures for transmissions, these data are unfortunately not available. To check the robustness of the results, we reestimated NPD capabilities after dropping R&D expenditures as an input from the transformation function. Following this, we estimated the immediate and future product quality models with this new measure. The results from this analyses appear in the Web Appendix. We find that the substantive conclusions are unchanged with this alternative NPD capability measure.
Second, although the use of a transformation function and stochastic frontier estimation for NPD capabilities is consistent with previous research in marketing and strategic management (Dutta, Narasimhan, and Rajiv 1999; Mahmood, Zhu, and Zajac 2011), the measure is based on the efficiency of the transformation function (i.e., inputs used to generate outputs). As such, one might wonder whether efficiency is the most important dimension of NPD capability. We performed additional analyses to test the sensitivity of the results by employing an alternate measure that does not rely on the efficiency view of capabilities.8 We used citation-weighted patent scores (adjusted for the number of patents) as a proxy for NPD capability. The results using this alternate measure of NPD capability appear in the Web Appendix. We find that although the main effect of NPD capability on future product quality is significant at p < .10, the interaction effects are robust and the substantive conclusions hold. Thus, the results of the study are not sensitive to the choice of an efficiency view of NPD capability.
New product development outsourcing of complex components/systems is rapidly emerging as a business reality for firms. Although researchers in marketing have often advocated for more research on NPD issues and, more specifically, on how firms should organize NPD activities (Hauser, Tellis, and Griffin 2006; Moorman and Day 2016; Rindfleisch and Moorman 2001), empirical evidence on the performance effects of alternate NPD contracting modes is limited. We respond to calls for more research in this domain by investigating the product quality consequences of both NPD make/buy choices. Next, we discuss the study’s research implications and outline the managerial significance of our findings.
Our study offers valuable implications for marketing research and theory. Although previous studies in marketing and economics have highlighted challenges and benefits associated with make/buy decisions in other settings, such as manufacturing/production (Geyskens, Steenkamp, and Kumar 2006; Rindfleisch and Heide 1997), virtually no empirical research has compared the performance effects of NPD make/buy decisions. Raassens, Wuyts, and Geyskens (2012) examine stock market reactions to NPD outsourcing announcements across a broad cross section of industries. They find that overall reactions to NPD outsourcing is positive, although there is wide variation in the magnitude and direction of abnormal returns. Furthermore, they show that governance mechanisms such as minority stake in the vendor and prior ties are positively related to abnormal returns following NPD outsourcing. Carson (2007) examines the degree of control firms should exert on vendors when the outsourced task involves some degree of creativity. However, neither study investigates the effects of NPD make decisions or tests product quality outcomes.
Our study empirically tests the trade-offs associated with NPD make/buy decisions by focusing on immediate and future product quality. The results reveal that NPD make/buy decisions differentially affect immediate and future product quality. Whereas NPD buy (vs. NPD make) has a positive impact on immediate product quality, NPD make (vs. NPD buy) has a positive impact on future product quality. The implication is that both NPD contracting modes offer benefits (and pose challenges) at different points in the product development cycle. Therefore, research investigating the consequences of NPD make/buy choices by examining product quality outcomes at a single point in time might over- or underestimate the normative value of these choices.
Technological complexity creates a fundamental dilemma for firms. Although complex technologies are likely to be better handled through internal product development, firms in high-technology industries are at a relative disadvantage compared with outsourced vendors with respect to their pace of developing competence in complex technological domains. We find support for this early-stage difference between NPD contracting modes for complex technologies. The results indicate that immediate product quality is higher for NPD buy when technologies are complex. However, this advantage does not carry over to future product quality. In fact, we find that the interaction of technological complexity and NPD make/buy is not statistically significant for future product quality.
Our findings also highlight positive contingency role of NPD capability for NPD buy and immediate product development outcomes and for NPD make and future product development outcomes. We argue that the positive immediate benefit of NPD capability in the context of NPD buy is traceable to the firm’s ability to design effective contracts using appropriate incentives (Argyres and Mayer 2007; Zenger and Lazzarini 2004). However, NPD capability is not effective for NPD buy for improving future product quality because of its limited ability to counter other adaptation challenges that emerge after product launch. In contrast, the positive future benefit of NPD capability in the context of NPD make is traceable to the benefit of providing a baseline stock of product development knowledge and foster path dependent learning (Cohen and Levinthal 1990; Levin 2000). More broadly, our findings are consistent with extant research that has documented the learning advantages and governance mitigation opportunities afforded by NPD capability (Argyres and Mayer 2007; Tiwana and Keil 2007).
We further explored whether governance mechanisms such as percentage of equity in the vendor (Raassens, Wuyts, and Geyskens 2012) are as effective as firm NPD capability in countering the immediate risks of NPD outsourcing. To do this, we tested the main effect of percentage equity stake on immediate and future product quality on a subset of NPD buy choices (after accounting for the strategic nature of the NPD make/buy choice).9 The analysis was restricted to NPD buy choices because percentage equity stake in the vendor is not relevant for NPD make choices. We do not find the main effect of percentage equity stake to be significant in either the immediate or future product quality model (p > .10).
The results also suggest that when problems emerge after product launch, NPD make is a superior mode for improving future product quality compared with NPD buy. This finding is similar in spirit to previous research that has documented a positive relationship between current product recalls and product quality in future time periods (Kalaignanam, Kushwaha, and Eilert 2013). However, we show that NPD make can adapt to unforeseen contingencies better than NPD buy. Again, we tested whether percentage equity stake in the vendor could help the firm in adapting better to adverse feedback. To do this, we tested the interaction of PLAF and percentage equity stake on future product quality for a subset of NPD buy choices. This analysis reveals that percentage equity stake positively moderates the effect of PLAF and future product quality (.14, p < .05). However, because this analysis is limited to NPD buy choices, we are unable to empirically ascertain whether this effect is stronger than the adaptation benefits of NPD make choices. One could speculate that the adaptation benefits of percentage equity stake should lie between those of NPD make and NPD buy choices.
The study offers several valuable managerial insights. The results suggest that NPD make/buy choices have differential impact on immediate and future product quality. Specifically, NPD buy has a positive significant impact on immediate product quality, whereas NPD make has a positive significant impact on future product quality. Is the impact of NPD make on future product quality greater than the impact of NPD buy on immediate product quality? To understand this important managerial question, we performed univariate t-tests comparing the estimates of NPD make/buy on immediate and future product quality, respectively. The results indicate that the estimates are not significantly different (p > .10). The managerial insight is that NPD make/buy decisions involve clear trade-offs that influence product quality differently over time.
Of greater managerial relevance are our findings that the impact of NPD make/buy choices on immediate and future product quality is moderated by transactional as well as firm characteristics. The results reveal that when technologies involved in NPD are complex, the positive impact of NPD buy on immediate product quality is stronger. However, the instant product quality advantage that the NPD buy offers over NPD make for complex technologies disappears for future product quality. Instead, we find that NPD make can overcome the immediate product quality disadvantage with respect to complex technologies and catch up with markets two years after product launch. To quantify the economic benefit of these results, we performed additional analyses (see Table 7).
TABLE: TABLE 7 Post Hoc Analyses on the Economic Significance of NPD Make/Buy Choices
| | Model 1a | Model 1b | Annual Financial Gains for Buy over Make |
|---|
| Estimate | SE | Estimate | SE |
|---|
| Technological complexity | Low | 2.53 (1.03, 4.03) | 3.66 (2.80, 4.53) | 3.09 (.69, 5.50) | $30.88 million ($6.89 million, $54.96 million) |
| High | 3.45 (1.94, 4.96) | 1.49 (.63, 2.36) |
| Change | .92 (-2.04, 3.88) | -2.17 (-3.90, -.44) |
| NPD capability | Lowa | 1.95b (.88, 2.99)c | 2.46 (2.34, 2.59) |
| Higha | 4.03 (2.99, 5.10) | 2.69 (2.57, 2.81) |
| Change | 2.08 (.37, 3.83) | .23 (-.01, .47) | 1.85 (.08, 3.90) | $18.49m ($.80m, $38.97m) |
| | Model 1a | Model 1b | Annual Financial Gains for Buy over Make |
|---|
| Estimate | SE | Estimate | SE |
|---|
| PLAF | No | 1.90 (1.29, 2.52) | 2.58d | .88 (.06, 1.69) | $8.79 million ($.60 million, $16.89 million) |
| Yes | .87 (.01, 1.86) | 2.42 (2.26, 2.59) |
| Change | -1.03 (-.20, -1.86) | -.15 (-.32, .01) |
| NPD capability | Low | 2.18 (.86, 3.50) | 1.63 (.86, 2.40) |
| High | 1.66 (.35, 2.76) | 3.53 (2.98, 4.30) |
| Change | -.52 (-2.86, 1.83) | 1.90 (.36, 3.44) | 2.42 (.65, 4.18) | $24.18 million ($6.49 million, $41.77 million) |
aThe low and high values of the moderators are set at 2 SD above and below the mean.
bThe mean of predicted values from 1,000 draws from the 2 SD asymptotic confidence interval of estimates.
c90% confidence interval of predicted values from 1,000 draws.
dBecause PLAF = 0 and make = 0, there is only intercept adjustment and, thus, no observable range.
We created high and low levels of technological complexity by setting them at two standard deviations above and below the variable mean. Next, we computed the impact of NPD make/buy on immediate product quality for high and low levels of technological complexity. The difference in predicted product quality (immediate) for NPD make between high and low levels of technological complexity is -2.17 (see Table 7). Likewise, the difference in predicted product quality (immediate) for NPD buy between high and low levels of technological complexity is .92. Therefore, the net positive impact of NPD buy on immediate product quality at low and high levels of technological complexity (relative to an NPD make decision) is 3.09.
Next, we compute the impact of change in product quality on change in shareholder value. To compute the economic gains/losses, we turn to previous research finding that product quality lowers future product recalls, which in turn prevents shareholder value from eroding. Kalaignanam, Kushwaha, and Eilert (2013) report that the impact of change in product quality on change in future product recall frequency is -.077. This estimate is appropriate for this analyses because the product quality measure and the empirical setting in Kalaignanam, Kushwaha, and Eilert are the same as in our study. Using this result, we compute that an increase in product quality of 3.09 corresponds to a decrease of .24 in recall frequency. Finally, we computed the dollar impact of change in product recall frequency using the financial loss figures reported in previous research. Barber and Darrough (1996) report that the financial loss in 1990 for a recall event for an automaker is $72.99 million. The 2016 adjusted financial loss for a recall event is approximately $130 million. Accordingly, we estimate that the economic gains associated with NPD buy choices for complex technologies is $30.88 million (relative to NPD make choices). This figure is economically significant given that these dollar gains correspond to a NPD contracting choice of a single vehicle system and model.
We also find that NPD buy has a more positive impact on immediate product quality when firm NPD capability is higher. The implication is that NPD capability acts a valuable buffer against contractual risks when firms outsource NPD. Managers should therefore bear in mind that although NPD buy provides immediate product quality advantages, these advantages are not necessarily a substitute for lack of NPD capabilities. Firms are likely to experience higher immediate product quality from NPD buy if they possess greater NPD capabilities. To quantify the moderating impact of NPD capability, we performed additional analyses. As Table 7 shows, the difference in immediate product quality for NPD buy at high and low levels of NPD capability is 1.85 (relative to NPD make decisions). Following the same procedures as before, we find that economic gains accruing from NPD buy for firms with higher NPD capabilities are $18.49 million (relative to NPD make decisions).
Our results suggest that NPD make (vs. NPD buy) has a more positive impact on future product quality in the presence of adverse feedback after product launch. This finding underscores the downside of NPD buy when problems emerge after product launch. Firms find it relatively easier to shore up product quality when working with internal product development teams than with third-party vendors. The post hoc analyses reveal that the differential product quality impact of NPD make on future product quality in the presence of PLAF is .88 (relative to NPD buy decisions). The dollar impact of NPD make on future product quality in the presence of PLAF is $8.79 million (relative to NPD buy).
We also find that the positive impact of NPD make on future product quality is stronger when firms have higher NPD capability. This finding highlights that NPD capability is also a valuable resource for NPD make over time because of the path-dependent nature of learning during complex product development. The post hoc analyses indicate that the difference in product quality outcomes for NPD make at high and low levels of firm NPD capability is 2.42 (relative to NPD buy decisions). The corresponding dollar value for the interaction effect of NPD make and firm NPD capability is $24.18 million (relative to NPD buy). Collectively, our findings related to the positive moderating role of firm NPD capability for immediate and future product quality imply that managers need to invest in NPD capabilities for both NPD make and buy.
Our study has certain limitations that warrant caution in interpreting the results. We focus on NPD make/buy choices in the automobile industry because our interest was in boosting the internal validity of the study. More research is needed on the product quality effects of NPD make/buy choices in other industries for deriving empirical generalizations. Second, although our article relied on theoretical ideas such as learning and adaptation to motivate the hypotheses, these effects are not directly estimated in the study. The challenge in explicitly measuring learning and adaptation is that there are no reasonable proxies for these constructs. Therefore, it may not be appropriate to directly attribute the study’s findings to reduction to these mechanisms. Third, we treated the NPD make/buy choice as a binary variable to compare the product quality outcomes of these modes. In some situations, it is plausible that the NPD contracting mode is more complex (e.g., codevelopment). Understanding the benefits of such hybrid NPD contracting modes is a fertile area for empirical research. Finally, the product quality measures provided by JDPA rely on consumer surveys for identifying system defects. Although these measures are well-regarded in the automotive industry, it is possible that consumers are not always accurate about identifying problems. If these misattributions are significant, the product quality in use measure may not correspond well with internal measures of product quality. The findings should be interpreted with this caveat in mind.
aThe low and high values of the moderators are set at 2 SD above and below the mean.
bThe mean of predicted values from 1,000 draws from the 2 SD asymptotic confidence interval of estimates.
c90% confidence interval of predicted values from 1,000 draws.
dBecause PLAF = 0 and make = 0, there is only intercept adjustment and, thus, no observable range.
Footnotes 1 We use the terms “NPD make” and “vertical integration” interchangeably and the terms “NPD outsourcing” and “NPD buy” interchangeably.
2 The data include 7 models that switched NPD contracting modes after four years (Buick Regal, Chevrolet Equinox, Ford Fusion, Lincoln Navigator, Mazda 6, Toyota Camry, and Toyota Highlander). We track these seven models for eight years. We exclude 11 models from the data either because these models switched NPD contracting modes within four years or because the NPD contracting mode was available for less than four years.
3 Although it would have been ideal to include R&D expenditures of transmissions as an input in the NPD capability transformation function, disaggregate data on R&D spending in different domains is not available (for an alternate measure for NPD capability, see the “Validation Analyses” subsection.).
4 Technological base is computed as TECHBASEkt = cTECHOUTPUTkt-1 + c2TECHOUTPUTkt-2 + c3TECHOUTPUTkt-3. c is the decay rate or weight assigned to past innovative output.
5 We thank an anonymous reviewer for this insight.
6 NPDCAPkt = 1 - hkt refers to NPD capability estimated using input-output function.
7 In this example, make refers to “Acura,” model refers to “TSX,” year refers to “2007,” and firm refers to “Honda Motors.”
8 We thank an anonymous reviewer for this suggestion.
9 The results are available from the authors on request.
DIAGRAM: FIGURE 1 Conceptual Model of NPD Make/Buy Choices and Product Quality Outcomes
DIAGRAM: FIGURE 3 Spotlight Analyses for the Effect of NPD Make/Buy Choices on Immediate and Future Product Quality
DIAGRAM: FIGURE 2 Timeline of Product Development in the Automobile Industry
PHOTO (BLACK & WHITE)
PHOTO (BLACK & WHITE)
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Record: 182- The Dynamic Interplay Between Recorded Music and Live Concerts: The Role of Piracy, Unbundling, and Artist Characteristics. By: Papies, Dominik; van Heerde, Harald J. Journal of Marketing. Jul2017, Vol. 81 Issue 4, p67-87. 21p. 1 Diagram, 3 Charts, 3 Graphs. DOI: 10.1509/jm.14.0473.
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The Dynamic Interplay Between Recorded Music and Live Concerts: The Role of Piracy, Unbundling, and Artist Characteristics
The business model for musicians relies on selling recorded music and selling concert tickets. Traditionally, demand for one format (e.g., concerts) would stimulate demand for the other format (e.g., recorded music) and vice versa, leading to an upward demand spiral. However, the market for recorded music is under pressure due to piracy and the unbundling of albums, which also entail threats for the traditional demand spiral. Despite the fundamental importance of recorded music and live concerts for the multibillion-dollar music industry, no prior research has studied their dynamic interplay. This study fills this void by developing new theory on how piracy, unbundling, artist fame, and music quality affect dynamic cross-format elasticities between record demand and concert demand. The theory is tested with a unique data set covering weekly concert and recorded music revenues for close to 400 artists across more than six years in the world’s third-largest music market, Germany. The cross-format elasticity of record on concert revenue is much stronger than the reverse elasticity of concert on record revenue. The results show the key role of piracy, unbundling, and artist characteristics on these cross-format elasticities, which have implications for the business model of the music industry.
Online Supplement: http://dx.doi.org/10.1509/jm.14.0473
The market for entertainment goods (e.g., music, movies, video games) has seen remarkable growth in the past years, with global revenue approaching $2 trillion in 2015 (Eliashberg et al. 2016). Accordingly, entertainment goods have attracted considerable academic attention in marketing (e.g., Eliashberg et al. 2016; Saboo, Kumar, and Ramani 2016). One key business in the entertainment sector is the music industry, with global revenues from recorded music alone worth $16.5 billion in 2012 (International Federation of the Phonographic Industry 2013). The music industry is an example of an entertainment good with two major consumption formats: live concerts and recorded music (singles and albums, in either physical or digital form). Traditionally, demand for an artist’s music in one format would stimulate demand for this artist in the other format, leading to an upward demand spiral. For example, an upcoming artist could give concerts, which would lead to more albums sold, which would then lead to a greater attendance at future concerts, and so on.
Technological developments, however, challenge many traditional business models (Eliashberg et al. 2016; Shugan 2004). The advent of technology changes the way consumers consider, purchase, and consume entertainment goods in general and music in particular. A key development is file sharing, or piracy, which is the process by which “individuals who do not own and have not purchased a particular song or movie can nevertheless obtain that song or movie from unknown third parties” (Liebowitz 2006, p. 4). Piracy represents a major threat to recorded music sales, leading to strong decline in revenues (e.g., Browne 2012; Liebowitz 2016).
Another key development is unbundling, wherein firms can “offer individual products that were previously only (or primarily) sold as part of bundles” (Elberse 2010, p. 107). The unbundling of music allows consumers to cherry-pick their favorite songs rather than buy the entire album, with possibly adverse effects on demand for recorded music (Elberse 2010).
Whereas recorded music is under threat, the market for live concert performances has seen remarkable growth (Krueger 2005), and many artists have been able to substitute lost revenues from record sales with revenues from live performances.1 Two examples illustrate the growing importance of concerts. For a concert by Led Zeppelin with a venue capacity of 18,000, millions of fans entered a ballot (at more than V180 per ticket; Cheal 2007). In 2007, Madonna left her music label (Warner) to join Live Nation, a company that was known as a concert agency (Waddell 2007). Industry statistics mirror these examples. Artists in the U.S. music industry earned $4 billion in revenue through concerts and sold 100 million tickets in 2007 (Courty and Pagliero 2011). In the United Kingdom, revenues from live performances exceeded revenues from recorded music for the first time in 2008 (Michaels 2009). These trends suggest that the live experience of music (vs. the consumption of recorded music) has become increasingly important for many consumers. Artists have capitalized on this trend as well. Traditionally, artists such as Madonna or U2 have gone on world tours to promote their albums, triggering cross-buying from concert tickets to records. With concerts becoming an increasingly important source of income, the cross-buying behavior from records to concert tickets may be more relevant (Seabrook 2009). This new focus is illustrated pointedly by the artist Prince, who made albums available for free to enhance concert ticket sales (Paine 2010).
Despite the fundamental importance of recorded music and live concerts for the multibillion-dollar music industry, no prior research has studied the interrelationship of these revenue sources. The objective of this article is to shed new light on dynamic cross-format effects: how demand for one format (e.g., concerts) is affected by demand for the other format (e.g., records). We study the strength of these effects, their symmetry versus asymmetry, and their moderating factors. We estimate cross-format elasticities that capture how much demand for one format (e.g., concerts) is affected by a 1% change in demand for the other format (e.g., records). The main research questions are the following:
• How strong are the dynamic cross-format elasticities between record demand and concert demand and vice versa?
• How are these cross-format elasticities changing due to technological developments?
• How are the cross-format elasticities moderated by artist characteristics?
A fundamental technological development in society in general is the digitization of information. In the music industry, this has led to digital music files that can be transferred via the Internet. This opened key opportunities to develop new business models because it meant that albums could be unbundled and the tracks could be sold separately. It also led to a key threat in the form of illegal file sharing among consumers (i.e., piracy). In this article, we develop and test new theory of how these two key technological developments, piracy and unbundling, affect cross-buying from concerts to recorded music. One crucial untested hypothesis is proposed by Krueger (2005). He speculates that in the pre–file sharing era, a positive stimulus from attending a concert would lead to a record purchase, but that now, consumers can download the music for free through file sharing. This development would imply that the link between concert ticket demand and record demand becomes weaker, that is, cross-buying is inhibited by piracy. We also hypothesize that this link could be weakened by unbundling, which allows consumers to buy just those tracks they enjoyed the most at the concert.
We expect that the cross-buying effects are moderated not only by the external environment represented by technological change but also by characteristics of the artist’s music itself. We distinguish between the quality of the artist’s music, which reflects consumers’ quality assessment and satisfaction with the artist’s current offering, and the artist’s fame, which reflects past billboard successes. We develop and test new theory on how cross-format buying is moderated by music quality and artist fame. We expect that music quality enhances cross-buying in both directions (from records to concerts and vice versa) but that fame especially increases the cross-buy from records to concerts.
We test the hypotheses using a unique weekly panel data set. It covers close to 400 of the best-selling artists in Germany, the third-largest music market worldwide (International Federation of the Phonographic Industry 2013), across more than six years, for a total of 118,000 artist-week observations. The data cover the period 2004–2010, representing a turbulent time in the music industry given the threat of piracy and the rise of unbundling. The data set includes a comprehensive list of concert activities, assembled from PollstarPro and other sources. On the basis of these data, we estimate an econometric model that assesses how the dynamic cross-format elasticities between record revenue and concert ticket revenue are moderated by technological developments and artist characteristics. The econometric model enables us to capture the main and interactive effects of marketing activities (new products, advertising, and airplay, i.e., tracks played on TV and radio) on both formats’ revenues. The model also captures the artist’s decision to give a concert, accounts for unobserved demand shocks as well as endogeneity in advertising and airplay, and allows for error correlations between concert and record revenue.
We contribute to the literature by providing evidence for a self-enforcing spiral of “success breeds success”: if an artist becomes more successful on the record market, this will enhance this artist’s concert revenue, which in turn enhances record revenue. However, the spiral is highly asymmetric, with a much stronger effect of record demand on concert demand than the other way around. In line with our theorizing, we identify technological developments and artist characteristics that affect this spiral. In particular, piracy and unbundling weaken the effect of concert demand on record demand, as these developments allow consumers to substitute the full product (whole album) with a free alternative (pirated files) or a less costly alternative (parts of the unbundled album). We also find that artist fame is a double-edged sword. A more famous artist enjoys a stronger effect of record demand on concert demand but faces a relatively weak effect of concert demand on record demand. Finally, music quality enhances the impact of concert demand on record demand. Our findings allow us to draw new implications for the music industry.
Related Literature and Contributions
Literature on Multiformat Goods
This article contributes to several literature streams (Table 1), including those on multiple formats and cross-buying. Music is an entertainment good that can be enjoyed across different channels or formats (e.g., Elberse 2010; Koukova, Kannan, and Kirmani 2012; Mortimer, Nosko and Sorensen 2012). The major formats are recorded music (e.g., Koukova, Kannan, and Kirmani 2012) and live music (concerts). Similarly, for the movie industry, the major formats are watching a movie in cinema versus watching it at home (e.g., Eliashberg et al. 2006; Hennig-Thurau et al. 2007). We use the term “format” rather than “channel” to highlight the fact that the different formats share some attributes but may differ on other attributes (Koukova, Kannan, and Kirmani 2012). Other examples of multiformat goods or services are restaurant meals (in-restaurant dining vs. at home), education (on-campus or online learning), news (print vs. online) and books (paper copy vs. e-book).2
A key challenge in managing multiformat goods is balancing the relationship between formats to improve overall performance across formats. This can be achieved by reducing substitutability and/or improving complementarity (e.g., Gentzkow 2007; Geyskens, Gielens, and Dekimpe 2002; Koukova, Kannan, and Kirmani 2012). In some industries, the formats are substitutes (e.g., paper book version vs. electronic book version of the same novel), whereas in other industries, the formats are complements (e.g., consumers may purchase a DVD after having seen a movie in a theater). Koukova, Kannan, and Kirmani (2012) show that when the formats are equivalent in quality on salient attributes, consumers are more likely to see them as complementary and will be more likely to purchase both (i.e., a cross-buy will occur). Cross-buying will also be enhanced when the risk associated with the cross-buy is reduced (Kumar, George, and Pancras 2008).
Managing multiformat goods becomes even more complicated when there are dynamic consumption effects. Many multiformat goods cannot be consumed simultaneously. For example, a consumer would typically not attend a concert and listen to recorded music at the same time. Instead, a consumer may buy recorded music, listen to it, and then decide to attend the concert, or vice versa (Krueger 2005), creating dynamic cross-format effects.
Technology affects business models in various industries (e.g., Elberse 2010; Gentzkow 2007; Geyskens, Gielens, and Dekimpe 2002; Shugan 2004). Technological developments such as digitization and interconnectivity due to the Internet fundamentally change the way consumers search for, buy, and consume products and services. This may also affect the dynamic interplay between multiformat goods. As we argue for the case of the music industry, technology may affect crossformat demand effects. As such, a focal contribution of this article is to increase our understanding of how the dynamic interplay between two formats is moderated by technology.
Literature on the Music Industry
The other stream this study contributes to is the literature on the music industry. Given the relevance of the music industry and the richness in research questions it entails, there is an emerging literature on this industry in economics (e.g., Dewenter, Haucap, and Wenzel 2012; Peitz and Waelbroeck 2005; Thomes 2013), in cultural economics (e.g., Liebowitz 2016), and in marketing (e.g., Elberse 2010; Giesler 2008). Mortimer et al. (2012) study the direct effect of piracy on concert demand and record demand by examining the launch of the file-sharing website Napster in 1999. They conclude that while record sales declined in the years after 1999, concert ticket sales increased for smaller artists. Because these authors do not measure piracy, they use the potential structural break that occurred in 1999 with the introduction of Napster to identify the effect of file sharing on demand for both formats.3 Importantly, Mortimer et al. (2012) study record sales and concert sales as separate dependent variables, without linking the two; that is, they do not estimate cross-format elasticities. Consistent with the idea that the two formats may act as complements, our study adds a new perspective by looking at how demand for one format dynamically drives demand for the other format. This allows us to understand the time lag, asymmetries, and moderators of the cross-format elasticities. As far as we are aware, this is the first empirical study to do so.
In a descriptive study on the music industry, Krueger (2005, p. 26) speculates that new “technology that allows many potential customers to obtain recorded music without purchasing a record has severed the link between the two products” (i.e., concert ticket and record demand). Krueger (2005) does not test this proposition empirically. A second key development initiated by technological advancements is unbundling. Elberse (2010) studies how unbundling affects record demand. However, the study does not analyze concert demand or the moderating effect of unbundling on the cross-format elasticity from concert demand to record demand.
In summary, some prior research has studied the main effect of piracy on record and concert demand (e.g., Mortimer et al. 2012) and some research has studied the main effect of unbundling on record demand (e.g., Elberse 2010). Krueger (2005) suggests (but does not test) that piracy severed the link between concert demand and record demand. However, what is unclear is how piracy and unbundling moderate the dynamic cross-format elasticities between concert demand and record demand. We therefore contribute to the literature by developing and testing hypotheses for these moderating effects.
The music literature has identified artist fame and music quality as key demand drivers (e.g., Dewan and Ramaprasad 2012; Elberse 2010). We add to this literature by studying how the cross-format effect is moderated by artist fame and music quality. As we argue in the next section, we expect that the impact of concert demand on recorded music demand and vice versa varies systematically with artist fame and quality of the music.
To keep the wheel of concert and record demand spinning, artists need marketing activities, in particular, new product innovation (new music releases), advertising, and airplay (songs played on TV and radio). Some research has looked at a subset of these activities. For example, Lee, Boatwright, and Kamakura (2003) and Moe and Fader (2001) conclude that record sales peak at the time of a new release and quickly decline afterward, and they find that airplay enhances a record’s market potential. However, no prior study has measured the effects of new product innovation, advertising, and airplay on record demand and concert demand. We do this and test not only for main effect but also for interaction effects.
In sum, this article’s contribution to the literature is a theoretical and empirical assessment of the dynamic cross- format elasticities between record and concert demand. We test new theory on the moderating role of piracy, unbundling, and artist characteristics on these cross-format elasticities, and we assess the effects of marketing activities on record and concert demand.
Conceptual Framework and Hypotheses
The focal outcome variables are the demand for records and concerts for an artist (see Figure 1). We conceptualize these two consumption formats as a wheel or spiral. A main source of power to keep the wheel spinning comes from the marketing variables, which we discuss in more detail after the hypotheses. In addition, artist characteristics drive demand for concerts and records; for example, better artists are likely to produce better records and concerts. As we outline in more detail in the “Model” section, we control for time-invariant artist characteristics by including artist fixed effects and by including timevarying artist characteristics as control variables.
The central feature of the conceptualization is that we expect a positive dynamic effect of concert demand on record demand and vice versa, even after we control for artist characteristics, fixed effects, and marketing variables. These dynamic cross-format effects (visualized in Figure 1 by curved arrows with plus signs along them) mean that an increase in demand for either format at time t will enhance demand for the other format at a time later than t. We expect these positive dynamic effects due to cross-buying effects (Kumar, George, and Pancras 2008) where a consumers’ experience in one format (e.g., recorded music) triggers purchases from the other format (e.g., concerts) from the same artist. The rationale is that entertainment goods such as music are experience goods for which the utility or quality can only be fully assessed during consumption (Dewan and Ramaprasad 2012; Nelson 1970; Saboo, Kumar, and Ramani 2016). This means that consumers experience uncertainty that needs to be resolved before they make a cross-buy purchase (Nelson 1970). If the consumer is satisfied with the consumption of one format, a cross-buy of the other format is more likely.
These arguments are supported by the literature on multiformat goods (e.g., Koukova, Kannan, and Kirmani 2012). This literature has established that when the two formats of a good have unique attributes (as is the case for recorded and live music), these two formats are more likely to be complements because these two different formats are valuable in different usage situations. In particular, recorded music allows consumers to relive the music they may have enjoyed at the concert at any time and in any place. Conversely, when consumers have enjoyed an artist’s recorded music, they may become interested in attending a concert. Seeing the artist live strengthens the human element of music and enhances the bond between the consumer and the artist (Park et al. 2010). A concert also brings a mass social element to enjoying the music, which is absent when listening to recorded music alone or in smaller groups (Raghunathan and Corfman 2006). Thus, we expect a positive effect of record demand on concert ticket demand and vice versa because consumption of one format will reduce the risk associated with purchasing the other format and may even create the desire to purchase the other format.
The Moderating Effects of Technological Developments and Artist Characteristics
The tenet of this study is that the dynamic effects between demand for the two formats (concerts and records) are systematically moderated by technological developments and artist characteristics. We will develop formal hypotheses for these moderating effects in the following sections. As we argued previously, the digitization of music and the rise of the Internet have enabled two developments that continue to shape the market, namely, piracy as a key illegal force, and unbundling as the major change in the legal market. We therefore focus on these two technological developments. There are associated developments we do not study, such as the rise of online radio stations and mobile apps, and the sales of MP3 players and smartphones. We view these as corollaries of the unbundling of music, which is the focal variable representing a new way of buying recorded music. Before we discuss the hypotheses, we clarify that we measure the dynamic demand effects between the two formats using cross-format elasticities: when demand for one format increases by 1% in period t, by how much does the demand for the other format increase across periods t + 1, t + 2, t + 3, and so on? Thus, the hypotheses are about how moderators affect the dynamic cross-format elasticity. Please note that we do not propose formal hypotheses for main (direct) effects.
Piracy
Piracy has led to a sharp decline in recorded music revenue over time (Liebowitz 2016), and we thus expect a negative main effect on record demand. Krueger (2005) speculates (but does not empirically test) that the effect of concert ticket demand on record demand has been severed by file sharing. The reason is that consumers who have attended a concert would in the past have bought a record because of a positive concert experience; in other words, cross-buying would occur (Kumar, George, and Pancras 2008). With the advent of file sharing, however, the desire to savor the experience can also be accommodated by downloading the music from a file sharing network; that is, the cross-buying behavior is inhibited by piracy. We therefore hypothesize the following:
H1: As piracy increases, the impact of concert demand on record demand decreases.
While piracy represents a phenomenon that directly competes with record demand, it may increase concert demand as well because it leads to a greater pool of consumers who are exposed to the artist’s recorded music (Mortimer et al. 2012). Like consumers who have acquired the recorded music legally, consumers who have downloaded tracks illegally may develop a desire to experience a live concert by the artist. Thus, we expect a positive main effect of piracy on concert demand. Because piracy is a phenomenon that shapes the record market and because theory does not provide strong arguments for an interaction effect between record demand and piracy on concert demand, we do not state a hypothesis for this interaction. In a robustness check, we establish that its inclusion does not change the results for the other hypothesis tests.
Unbundling
Unbundling means that consumers no longer have to buy the full album if they are interested in just a few tracks. This may bring in new consumers who otherwise would not have purchased the album, potentially enhancing demand. However, Elberse (2010) finds that the flexibility to purchase individual tracks instead of the entire albums reduces overall record demand, and thus we expect a negative main effect of unbundling on record demand.
Our main interest is the question of how unbundling moderates the dynamic cross-buying effect of concert demand on record demand. As we have discussed, attending a concert is an experience that may trigger a cross-buy of recorded music because the consumer has experienced the product and lowered the risk associated with purchasing additional products from the same artist (Kumar, George, and Pancras 2008). Unbundling, however, gives consumers the flexibility to purchase just those tracks that they enjoyed the most (Elberse 2010), so a cross-buy may still occur, but at a lower rate:
H2: As unbundling increases, the impact of concert demand on record demand decreases.
Unbundling means that recorded music can more easily spread among a wider audience. This may enhance demand for concerts, and thus we anticipate a positive main effect of unbundling on concert demand. However, because unbundling is a phenomenon that shapes the record market and there are no strong arguments for an interaction effect between record demand and unbundling on concert demand, we do not state a hypothesis for this interaction. One of the robustness checks shows that its inclusion does not change the results of other hypothesis tests.
We now discuss the moderating effects of music quality and artist fame. Music quality is an evaluation of how highly the current music is rated, whereas fame represents the artist’s proven track record of producing music with mass appeal, reflecting the artist’s entire oeuvre, possibly going back decades. These constructs are not necessarily correlated; empirically, the correlation between the respective measures (discussed in the next section) is only .02.
Music Quality
Music is an experience good, for which quality is difficult to assess prior to consumption (e.g., Nelson 1970). Therefore, consumers look for ways to reduce their purchase uncertainty, and one way to achieve this is through quality assessments by other consumers (e.g., Liu 2006). These quality assessments reflect and signal peers’ satisfaction with the music. We expect a positive main effect of music quality (as rated through consumer reviews) on both record demand and concert demand.
Once consumers have attended a concert, a cross-buy of recorded music is more likely if consumers are satisfied with the quality of the music. Satisfaction is mirrored in positive quality evaluations (Zhu and Zhang 2010). Similarly, for consumers who have bought the record, the decision to attend a concert (i.e., a cross-buy) is facilitated when their satisfaction with the music quality is higher. Thus:
H3: The impact of concert demand on record demand is stronger for music of higher quality.
H4: The impact of record demand on concert demand is stronger for music of higher quality.
Artist Fame
An artist’s fame serves as a cue to reduce the uncertainty that is associated with purchasing music as an experience good (Dewan and Ramaprasad 2012). This uncertainty will be lower for a more famous artist because the artist has a proven track record of producing music with mass appeal. Thus, consumers conjecture that the artist’s recorded music and live concert present more enjoyable experiences. We therefore expect a positive main effect of fame on both record demand and concert ticket demand.
Consumers who purchase an artist’s recorded music are more likely to cross-buy a concert ticket if they perceive a low consumption risk. This will be the case for famous artists. In other words, more famous artists may leverage their brand strength to enhance cross-buying from records to concerts; thus:
H5: The more famous the artist, the stronger the impact of record demand on concert demand.
For the reverse cross-effect (from concerts to records), we expect that fame will play a different role. We conceptualize fame as previous record market success (i.e., cumulative past chart placements). While the theory implies that the risk associated with cross-buying a record from a famous artist is low, there is an opposing force that does not apply to the concert market: fame implies prior record chart success, which means that consumers who attend a concert of a more famous artist are likely to already own most of the artist’s recorded music. Thus, for a famous artist, there is less potential for converting concertgoers into record sales because these fans probably already own these records.4 This limitation does not apply to concerts because each new concert tour offers consumers new ways to experience the music. The flip side of the argument is that a less famous artist has a bigger potential record market to win over, and one way to do that is by giving concerts, which inspire consumers to start buying records. Thus, while we expect that cross-buying will still occur for famous artists, it will be relatively weak for more famous artists compared with less famous artists:
H6: The more famous the artist, the weaker the impact of concert demand on record demand.
Marketing Variables
We identify new releases, airplay, and advertising as the marketing drivers of music demand. The rationale is that releasing new products and communicating about them will drive demand for both records and concerts. We expect that the marketing variables will have not only main effects but also interaction effects. We primarily anticipate positive interaction effects, or synergistic effects, for example, that advertising enhances the effectiveness of new releases. However, we can also expect antagonistic effects, for example, that advertising becomes less effective for higher levels of airplay due to saturation effects (e.g., Burmester et al. 2015).
Data and Measures
Sample
To test the conceptual model (Figure 1), we analyze the German music market for both domestic and international artists. We focus on artists who earn revenues from both record sales and concert sales and thus exclude artists who no longer perform live (e.g., ABBA, the Beatles). We identified all artists who had given at least one concert and who appeared at least once in the German music charts (album or single top 100) between January 2003 and June 2010. We selected 410 of the most successful artists according to their cumulative chart placements of singles and albums during the observation period. We dropped 16 artists because sales data were unavailable and 7 artists because they did not have at least one full year of observations (i.e., they entered the market at the end of the observation period). For the remaining 387 artists, we collected weekly data on the relevant variables. Importantly, the sample includes international superstars in the top deciles but also new and relatively unknown artists in the bottom deciles. The estimation period covers January 2004–June 2010 because some variables (e.g., Google searches) are only available as of 2004. Table 2 summarizes the data set.
Dependent Variables
We measure both recorded music demand and concert demand in revenue rather than units because this puts both formats on a common denominator, which would not be the case if we used units such as the number of albums or tracks sold for recorded music and the number of tickets sold for concerts. Revenue has also more direct managerial relevance than unit sales. We note that in one of the robustness checks we obtain very similar results if we use unit sales. We also note that for more popular artists, concert tickets and album prices tend to be higher than for less popular artists, which will drive up their revenues compared with those of less popular artists. The analysis employs a log-log model with fixed effects for artists, which means that any revenue-level differences are filtered out and that the cross-format effects are unitless elasticities, which can be compared across artists.
Record revenues. One dependent variable is the revenue earned in Germany from recorded music, observed per artist per week. Through an industry partner that wishes to remain anonymous, we obtained weekly unit sales data for all artists in the sample for all records (i.e., physical records such as CDs, as well as paid downloads through commercial download stores, as tracked by GfK). Following Elberse (2010), we multiply the sales volume by the average prices of the respective formats of recorded music (e.g., average price for a given format of recorded music across all artists per year) to obtain weekly record revenue for each artist across formats.5
Concerts. The other dependent variable is concert revenue earned in Germany per artist per week. Artists tend to choose the capacity of the venue as well as the number of concerts to match the expected demand for their concerts. We undertook a comprehensive search on when each artist gave a concert in which venue (sources included Last.fm, Laut.de, MLK.de, venue and artist websites). This search resulted in a master set of 10,277 concerts performed by the 387 artists in Germany between 2004 and 2010. We believe this represents a (near) census of all concert activity. We observe for each artist, for each week, whether a concert took place and, if so, the capacity of the concert venue. To complement the concert data, we also purchased data from PollstarPro, which collects data on concert activities including capacity, crowd size, and ticket prices. For the sample of 387 artists, PollstarPro has records in the form of artist tour reports for 2,199 concerts in Germany during the observation period, representing just 21.4% of all concerts.
To combine the strengths of the master data set with more than 10,000 concerts (completeness) and the PollstarPro data (revenue information), we fuse the data sets. As we establish in the Web Appendix, the PollstarPro data and the master data set have very similar distributions of observable variables. We also utilize the fact that the size of the venue (that we observe for all concerts) is the primary driver of crowd size, with a correlation of .97 in the PollstarPro data. As explained in more detail in the Web Appendix, using the PollstarPro data, we regress attendance rate on attendance rates per artist, attendance rates per venue, and venue size. We then use this model to calculate predicted attendance rates for concerts not contained in the PollstarPro data. The correlation between the predicted versus actual concert attendance for the PollstarPro data for which we have full data is again .97, confirming the validity of the approach. We proceed in a similar way to calculate the expected ticket prices (details in the Web Appendix). Combining these with the number of tickets sold allows us to calculate concert revenues for each artist for each week.
We acknowledge that ideally, we would have had firsthand records of revenues for all 10,000+ concerts, but these are not available. Given the research questions, we believe that analyzing all concerts that took place is preferable to using just 21% of the concert observations where revenue is observed (the PollstarPro data). Nevertheless, we ran a robustness check in which we included only those artists in the estimation sample that are also included in the PollstarPro data. The results are very similar, as the next section reports.
Independent Variables
Marketing. We obtained advertising information from the market research firm Ebiquity and use weekly advertising expenditures (in euros) per artist across all major vehicles, such as TV, print, radio, outdoor, and Internet. Television accounts for 92% of all advertising expenditure. In line with previous research (e.g., Dinner, Van Heerde, and Neslin 2014), we aggregate the weekly advertising expenditures per artists across advertising vehicles. To account for new product releases, we use a weighted new-release variable. This is the weighted combination of four categories of new releases: (1) new album releases, (2) new single releases, (3) new DVD releases, and (4) rereleases of content that had been on the market before (e.g., “greatest hits” albums).6 We measure airplay by the cumulative number of airplays (on the radio) and video plays (on TV) that an artist received in a given week in Germany, across all radio stations and across the two main music video channels. These data were tracked by Nielsen Music Control. While we do not have access to data on online airplay, we expect it to be strongly correlated with our airplay measure, because songs/artists that are popular on the radio and TV are also likely to be popular online.
Cross-format demand effects. To capture cross-format demand effects, we use the lag of the stock of cross-format revenue as independent variables. These stock variables, which we define in the “Model” section, allow us to directly estimate dynamic cross-format elasticities.
Moderating variables. The moderating variables enter the models for concert demand and record demand as both main effects and interaction effects. For the piracy moderator, we use data from an anonymized survey (“Brennerstudie”; see http://www.musikindustrie.de/sonstige-studien/) conducted by GfK in Germany, which measures the number of people who illegally download music in a given year.7 We operationalize unbundling as a market-level variable by dividing, for a given week, the number of singles sold by the total sales volume (i.e., single plus album sales).
Music quality is the average number of Amazon stars (from 1 to 5; e.g., Chevalier and Mayzlin 2006) across all products for artist I in week t. This measure reflects consumers satisfaction with the artist’s music (Zhu and Zhang 2010), which is mirrored by the fact that retailers such as Amazon send out postpurchase e-mails asking consumers whether they are satisfied with the product and encouraging them to share satisfaction ratings. Similar to Dewan and Ramaprasad (2012) and Elberse (2010), we measure fame as the cumulative number of the inverse of album chart placements until week t for artist i. The measure uses the inverse to ensure that, for example, a number-two hit (inverse = 1/2) counts more than a number-ten hit (inverse = 1/10). As mentioned before, music quality and fame are only weakly correlated, at .02 (see the Web Appendix). This allows us test the hypotheses on the moderating effects of music quality and fame without multicollinearity concerns.
Control variables. Demand for an artist’s products may be influenced by unobserved shocks such as positive or negative publicity. To capture otherwise unobserved time-varying artist demand shocks, we measure the extent to which an artist is subject to online search (Archak, Ghose, and Ipeirotis 2011), in line with the recommended data-rich approach to prevent endogeneity in Rossi (2014) and Germann, Ebbes, and Grewal (2015). We collect the weekly Google Trends search volume in Germany for all artists across the observation period. Google indexes search volume for a keyword (i.e., artist) per week relative to the week with the highest search volume. To account for an upward trend in consumer usage of search engines, we regress the artist-specific search volume on a time trend and use the residuals in the model. We also include a time trend to safeguard against the possibility that the regressors of interest pick up general trends. Quarter dummy variables capture seasonality within the year (e.g., Christmas).
Descriptive statistics. Table 2 displays the descriptive statistics together with the variable definitions. On average per week, an artist earns V22,290 in revenue from record sales and V29,400 from concerts. Both variables have substantial variation, with a maximum of almost V3.5 million for weekly record revenue and V21.5 million for weekly concert revenue. The average advertising budget per week is V2,950, with a range between V0 and V2.2 million. On average, an artist is played on radio and TV 88 times per week. The mean value of the new-release variable is .02, corresponding with an average of one new album per year.
Model
To test the conceptual framework, we need a model that addresses the following challenges. It needs to account for (1) the current and dynamic effects of the marketing variables and the dynamic cross-format revenue effects; (2) the truncated nature of the concert variable, equal to 0 when an artist does not give a concert, positive otherwise; (3) the possible endogeneity in advertising and airplay; and (4) correlated errors across dependent variables to account for shocks induced by omitted variables. To address these challenges, we build a system of equations with correlated errors, using one equation for revenues from recorded music, one for concert revenues, a selection equation (Tobit Type II) to model the artist’s decision to give a concert, and equations to account for potential endogeneity in advertising and airplay.8
To model the dynamic effects of the key independent variables, we use variables that are analogous to AdStock variables (Broadbent 1984; Dinner, Van Heerde, and Neslin 2014). AdStock is a flexible yet parsimonious way of accounting for advertising effects that carry over to future periods. We use this approach to capture the current and dynamic effect of an independent variable x, and we use the term “stock” to convey the analogy with AdStock. We define the stock variable for variable x as
The carry-over coefficient lx, y varies between 0 and .9. If it is 0, the effect of x on y is instantaneous; if it is .9, the effect of x decays very slowly. We allow each stock variable to have its own carry-over coefficient lx, y for independent variable x and dependent variable y. In the calculation of the log values of variables, we add 1 to the original variable to avoid taking the log of 0 (e.g., advertising may be zero in a given week). A benefit of the stock variable as specified in Equation 1 is that its regression coefficient in a model for a log-dependent variable is the long-term elasticity: the cumulative impact across time of a 1% shock in the input variable (Dinner, Van Heerde, and Neslin 2014). When we interact x stockit with a moderator, we directly obtain the moderating effect on the long-term elasticity.9
Model for Record Revenue
We use log-log models to be able to interpret the coefficients as elasticities. The model for log revenue from recorded music for artist I in week t is
(2) ln RecordRevenuesit = b0i + b1 ln ConcertRevenueStockit-1
+ b2NewReleaseStockit
+ b3 ln AdvertisingStockit
+ b4 ln AirplayStockit
+ b5 ln Piracyt
+ b6 ln Unbundlingt
+ b7 ln MusicQualityit
+ b8Fameit
+ b9 ln ConcertRevenueStockit-1
· ln Piracyt
+ b10 ln ConcertRevenueStockit-1
· ln Unbundlingt
+ b11 ln ConcertRevenueStockit-1
· ln MusicQualityit
+ b12 ln ConcertRevenueStockit-1
· Fameit + b13 ln AdvertisingStockit
· ln AirplayStockit
+ b14 ln AdvertisingStockit
· NewReleaseStockit
+ b15 ln AirplayStockit
· NewReleaseStockit
+ b16 ln GoogleStockit + b17Trendt
+ b18Quarter2t + b19Quarter3t
+ b20Quarter4t + b21DRMt + er it, where the variables are as defined in Table 2. The term b0i is an artist-specific intercept that controls for artist differences. The dynamic cross-format elasticity for concert revenue on recorded music revenue is b1. The hypotheses are tested through the interactions between lag of concert revenue stock 0), (2) unbundling (H2: b10 < 0), (3) music quality (H3: b11 > 0), and (4) fame (H6: b12 < 0). The model controls for the main effects of new releases, advertising, and airplay, and their interactions, as well as for the main effects of the moderators and control variables. The term erit is the error term. In the interactions, we mean-center the artist-specific variables by artist means and market-level variables by their grand means before calculating the product term. This allows us to interpret the main effects to hold at the mean level of the independent variables.
Model for Concert Revenue
Equation 3 models concert revenues per artist, conditional on giving a concert:
(3) ln ConcertRevenuesit = g0i + g1 ln RecordRevenueStockit-1
+ g2NewReleaseStockit
+ g3 ln AdvertisingStockit
+ g4 ln AirplayStockit + g5 ln Piracyt
+ g6 ln Unbundlingt
+ g7 ln MusicQualityit
+ g8Fameit
+ g9 ln RecordRevenueStockit-1
· ln MusicQualityit
+ g10 ln RecordRevenueStockit-1
· Fameit + g11 ln AdvertisingStockit
· ln AirplayStockit
+ g12 ln AdvertisingStockit
· NewReleaseStockit
+ g13 ln AirplayStockit
· NewReleaseStockit
+ g14 ln GoogleStockit + g15Trendt
+ g16Quarter2t + g17Quarter3t
+ g18Quarter4t
+ g19IVConcertit + ec it.
In this model, g0i is an artist-specific intercept and eict is an artist i– and time t–specific error term. The dynamic cross-format elasticity for record revenue on concert revenue is g1. The hypotheses are tested through the interaction between lag of record revenue stock and (1) music quality (H4: g9 > 0) and (2) fame (H5: g10 > 0).
Many artists give only a couple of concerts per year, leading to zeroes in the observations for concert revenue (e.g., Courty and Pagliero 2011; Krueger 2005; Mortimer et al. 2012). To accommodate this and to control for selection effects, we model both the incidence of concerts and the revenue conditional on giving a concert with a (Bayesian) Tobit Type II model with correlated errors (e.g., Chib 1992; Van Heerde, Gijsbrechts, and Pauwels 2008). The incidence of artist I giving a concert in week t is captured by a probit model:
(4) zit = 1 if zpit > 0, and zit = 0 otherwise,
where the latent variable zpit is a linear function of a vector of independent variables (Zit):
(5) zipt = y0i + Zity + uit.
The Web Appendix details the full model and the additional independent variables that enter Zit for identification purposes and that are not part of Equation 3 (e.g., Wooldridge 2002, p. 564). This concert incidence equation is a selection equation that accounts for the fact that artists may strategically choose the timing of their concerts. To account for unobserved dependencies, we allow for correlation in the error terms of Equations 2, 3, and 5.
Identification and Model Estimation
Endogeneity. To safeguard against unobserved demand shocks that may be correlated with the predictors, we follow a three-pronged approach. First, we include artist fixed effects in all equations, for example, b0i for recorded music (Equation 2) and g0i for concert revenue (Equation 3). Fixed effects control for endogeneity due to unobserved artist characteristics simultaneously determining demand and the drivers of demand (Germann, Ebbes, and Grewal 2015). Second, as discussed before, we include Google searches to control for otherwise unobserved demand shocks (Archak, Ghose, and Ipeirotis 2011). Third, advertising and airplay may be strategically set or pushed to capitalize on additional, unobserved demand shocks, which makes these variables potentially endogenous. We therefore use a simultaneous equation approach with correlated errors (e.g., Ataman, Van Heerde, and Mela 2010). We model the endogenous regressors as a function of the exogenous variables and instrumental variables (IVs); the specifications are in the Web Appendix. One IV is the log of total advertising expenditure by artists from different labels and from different music genres (for a similar approach, see Cleeren, Van Heerde, and Dekimpe 2013). This variable is unlikely to be correlated with artist-specific unobserved demand shocks, but it is correlated with the focal artist’s advertising behavior because it reflects general advertising drivers (e.g., changing advertising costs). As in other sectors of the entertainment industry, advertising in the music industry typically peaks around the time of a new release (Moe and Fader 2001; Joshi and Hanssens 2009). We therefore include as IVs dummy variables that equal 1 in the week prior to a new album (or single) release. This captures the advertising spike caused by a new release but is unrelated to a potential unobserved demand shock because the new release is not yet on the market. To compute statistical tests on the suitability of the instruments, we construct models in 2SLS that mimic the full model as closely as possible. The multivariate Sanderson–Windmeijer F-test in a model with both advertising and airplay as endogenous variables shows that the IVs are sufficiently strong, with an F-value of 221.30 (d.f.1 = 2; d.f.2 = 118,218; p < .001) for advertising and an F-value of 221.90 (d.f.1 = 2; d.f.2 = 118,218; p < .001) for airplay (Sanderson and Windmeijer 2016). The Sargan test shows that the exclusion restriction is satisfied (c2 = .113, d.f. = 1, p = .74). The Hausman– Wu test rejects the null of no systematic differences in coefficients between a model that corrects for endogeneity and a model that does not (c2 = 170.48, d.f. = 3, p < .001).
Identification of Equations 2 and 3. We also need to establish the identification of the two revenue equations. Models 2 and 3 capture the dynamic cross-format effects by including the lagged stock variable of the other format on the right-hand side. Note that this is different from including a lagged dependent variable (of the same format) on the right-hand side; if that were the case, panel data generalized method of moments estimators would need to be considered (e.g., Baltagi 2013).
Note also that we do not include the current dependent variable of the other format on the right-hand side; rather, we use the lag of a stock variable of the other format, which helps to mitigate simultaneity concerns. Empirically, the cross-format stock variables obtain carry-over coefficients of lx, y = :9, in both Equations 2 and 3 (see next section). This means there is only a 10% weight on lagged concert revenue and a 90% weight on earlier concert revenue. In other words, there is a very much delayed effect of concert revenue on record revenue and vice versa, with a 90% duration interval of 23 weeks. This time separation reduces simultaneity concerns substantially. To be sure that the focal estimates are not affected by simultaneity, we include one identifying (unique) variable on the right-hand side of each revenue equation. We need a variable that affects demand for a given format but that is uncorrelated with unobserved demand shocks in the other format. For the record market, we use a step dummy for the introduction of downloads (tracks and albums) that were freed of restrictions imposed by digital rights management (DRM). This move was initiated by Apple’s Steve Jobs and was widely implemented in the German market on April 1, 2009. Step dummy DRMt in Equation 2 equals 0 before this week and 1 afterward. It has a negative effect (shown in Table 3) reflecting overall adverse effects on physical music sales. For the concert market, there is no equivalent supply shock. Instead, we use the log of concert revenue from all artists from other genres and other labels as an instrument (IVConcertitÞ in Equation 3. Importantly, however, the results do not depend on whether we include or exclude these additional identifying variables, as the robustness checks show.
Hierarchical Bayes estimation. We use hierarchical Bayes with uninformative priors to estimate the system of equations (Chib and Greenberg 1995; Web Appendix). There are 118,627 observations across 387 artists and 337 weeks (some artists enter the market later in the observation period). We run the Gibbs sampler for 100,000 draws, retaining every tenth of the last 50,000 draws. The chain is well converged within the burn-in sample of 50,000 draws (convergence plots are in the Web Appendix).10
Results
Preliminaries
The correlation between actual and predicted log record revenue is high (.88), indicating a good fit for this model. The hit rate for concert incidence is .72 (with a sensitivity of .80 and a specificity of .72). Conditional on an artist giving a concert, the model does an adequate job explaining concert revenue (correlation is .55). Across equations, the system R2 is .823. The correlations between the independent variables are modest (most variance inflation factors are below 2, and all are below 8), mitigating multicollinearity concerns. The error covariances between the equations are nonzero, showing the need to allow for these (see the Web Appendix).
Table 3 summarizes the posterior parameter distribution for the concert model and the record model (the Web Appendix contains the results for the other equations). Throughout the analysis, we discuss the medians of the posterior parameter distributions. We use the 95% posterior density intervals to assess the significance of the estimates (expressed by “p < .05,” even though p-values are not strictly Bayesian).
Hypothesis Tests
As expected, we find evidence for a spiral of success breeding success; that is, if an artist becomes more successful in the concert market, it enhances the artist’s record revenue, and vice versa. However, we find that the cross-format elasticity is much stronger for the effect of records on concert revenue (.237; p < .05) than for the effect of concerts on records (.030; p < .05).
These elasticities do not yet incorporate cross-equation feedback effects; we assess these in the section “Estimating Dynamic Effect Sizes.”
In line with H1, we find that as piracy increases, the effect of concert on record revenue becomes significantly weaker (-.118; p < .05). This offers empirical support for the expectation that the link between these two formats has been weakened by file sharing (Krueger 2005). H2 states that the cross-format elasticity from concert revenue to record revenue is impeded by unbundling. The analysis supports this notion, as we find a significant negative interaction between concert revenue and unbundling (-.068; p < .05). H3 suggests that the cross-format elasticity from concert revenue to record revenue is enhanced by music quality. The significant positive interaction (.200; p < .05) supports this hypothesis. H6 argues that cross-buying from concerts to records may be less effective for more famous artists. Indeed, we find that this part of the spiral works significantly worse for more famous artists (-.005; p < .05).
We now move to the cross-format elasticity from record revenue to concert revenue. H4 posits that this cross-format elasticity should be higher for more famous artists. Indeed, we find a significant positive interaction (.018; p < .05), in line with the prediction that the more famous the artist, the stronger the impact of record revenue on concert revenue. H5 posits that the cross-format elasticity should increase as music quality increases. While the median of the posterior parameter distribution is positive, the 95% posterior density interval includes 0, which means that H5 is not supported. In summary, the data provide support for five out of six hypotheses.
Marketing Variables
Advertising. We estimate a long-term advertising elasticity of .258 (p < .05) for record revenue, which is similar to the metaanalytic long-term elasticity of .24 reported by Sethuraman, Tellis, and Briesch (2011). In the concert equation, we find an insignificant main effect of advertising, which is not unexpected because advertising in this market is typically directed at records and not at concerts. Given that advertising drives record demand and record demand drives concert demand, there is an indirect effect of advertising on concert demand (to be discussed in more detail subsequently).
Airplay. For airplay, we find a long-term elasticity of .224 (p < .05) on record revenues, which is close to the estimate for advertising elasticity. The airplay elasticity (.214; p < .05) on concert demand model is similar in magnitude.
New releases. New releases are a strong driver of record revenues, with a long-term effect of 10.452 (p < .05). If a new album is released, the new-release variable changes from 0 to 1, and the coefficient captures the cumulative long-term increase in ln RecordRevenue after a new album is released. The strong effect is in line with the notion that new releases are the lifeblood of the record market, and without new releases, many artists sell few records. While new releases do not significantly affect concert revenues directly, they do have an indirect effect via record demand.
We also obtain insights on marketing interactions. For the record model, the interaction between new releases and advertising is positive (.481; p < .05), suggesting that informing consumers about new albums has synergistic effects. Interestingly, the interaction between advertising and airplay is negative (-.028; p < .05). This suggests a saturation effect (Burmester et al. 2015): when consumers are exposed to a lot of airplay by one artist, the marginal effectiveness of advertising weakens (and vice versa). We find a similar negative interaction effect (-.013; p < .05) between advertising and airplay and between new releases and airplay in the concert model (-.270, p < .05).11
Control Variables
The effects of the control variables are in line with expectations. The main effect of piracy on record revenue is significantly negative (-.502; p < .05), as expected, and the same applies for the main effect of unbundling (-.323; p < .05) and the general trend (-.001; p < .05). In line with the expectations, fame (.041; p < .05) and music quality (1.148; p < .05) have significant positive effects on record revenue. Google search volume is significantly positively associated with record revenue (1.125; p < .05). The quarterly dummies pick up seasonality in record revenue (e.g., a spike in quarter 4 due to Christmas).
Google searches (1.133; p < .05) are also positively associated with concert revenue, as are fame (.051; p < .05) and the general trend (.002; p < .05). The quarterly dummies capture seasonality in concert revenue (e.g., spikes in quarters 2 and 3 due to outdoor concerts).
Robustness Checks
We conduct five robustness checks. First, we estimate the model on those artists who are part of the PollstarPro data, and we drop all other artists. Despite the substantial loss of data, the results are very similar (for details of all robustness checks, see the Web Appendix). The only difference is that the interaction between piracy and concert demand is insignificant in this robustness check. Second, we estimate the full model but replace revenue with unit sales throughout. The substantive results are very similar to the focal results. Third, we run a model variant in which we exclude the additional identifying variables (DRMt from Equation 3 and IVConcertit from Equation 4), with again very similar results. Fourth, we include the interactions between piracy and unbundling, respectively, and record revenue, as drivers of concert revenue. Both interactions are insignificant, and all other results remain virtually unchanged. Finally, we estimate the focal model excluding all the marketing interactions, which leaves all results with respect to the hypothesis testing unchanged. In the Web Appendix, we demonstrate the strong robustness of the findings. Across six hypotheses and five robustness checks, only one hypothesis in one test is not consistent with the focal model (1 out of 30 cases).
Finally, we estimate the model excluding the last 52 weeks of the data as a holdout sample. We next predict the dependent variables for this holdout sample. In the Web Appendix, we show that the hypothesized model performs similarly well in the estimation sample and the holdout sample, mitigating overfitting concerns.
Estimating Dynamic Effect Sizes
The focus of this study is the cross-format elasticities between concert and record revenue and how they change due to technological developments and artist characteristics. We have established statistical significance for most of the hypotheses (five out of six), but we also would like to assess effect sizes to establish managerial significance using dynamic simulations. The approach enables us to quantify how the effect of the crossformat elasticity changes in response to the moderators, while taking all interactions and potential feedbacks between formats into account, as well as parameter uncertainty.
We start by simulating the impact of a 1% shock in concert revenue for an average observation at the sample mean. To this end, we add .01 to ln Concert Revenue, compute the new value for the corresponding stock variable, and use the model estimates to predict the resulting changes in record revenue. These changes in record revenue then carry over to the concert incidence and concert revenue of the next period, which again affects next period’s record revenue. We then cumulate the effects over 52 weeks to assess the full long-term impact of this shock. For each moderator, we repeat this process for a range of values from low (one standard deviation [SD] below the mean) to high (one SD above the mean). To account for parameter uncertainty, we repeat the simulation for 250 random draws from the Gibbs sampler in the Bayesian estimation and use these to assess the posterior distribution of the effect size.
Figure 2, Panel A, shows how piracy affects the crossformat elasticity from concert revenue to record revenue. The elasticity essentially halves when piracy increases from low to high. This again supports the notion that the cross-buying link between these two formats has been attenuated by the prevalence of online file sharing (Krueger 2005). The attenuating effect is similarly strong due to unbundling (see Figure 2, Panel B). The implication is that while technological developments brought about by the Internet weaken the impact of concert revenue on record revenue, this is due not only to piracy but also to unbundling.
The cross-format elasticity from concert demand to record demand also changes substantially due to artist fame (Figure 2, Panel C). An artist low on fame has a cross-format elasticity (around .08) that is about twice as strong as that for an average artist, and for famous artists, the cross-format elasticity is essentially zero. Thus, a less famous artist can use concerts much more as a driver of record revenue than a more famous artist. Figure 2, Panel D, shows that music quality has a managerially strong positive moderating effect on the impact of concert revenue on record revenue.
The cross-format elasticity of record revenue on concert revenue (Figure 3) has a mean around .27, which is much stronger than the reverse elasticity of concert revenue on record revenue (Figure 2), with a mean of .04. Figure 3, Panel A, shows that as music quality increases, the cross-format elasticity from record revenue to concert revenue increases. However, the confidence interval shows substantial uncertainty for this effect, in line with the lack of support for H5. Interestingly, as fame increases, there is a fivefold increase in the impact of record revenue on concert revenue, from about .1 to .5 (Figure 3, Panel B). Thus, for a famous artist there is a much stronger spillover effect from the record market to the concert market than for a less famous artist.
Discussion and Implications
This study fits in a broader stream of articles looking at marketing issues in the entertainment industry, including movies (e.g., Eliashberg et al. 2006), theme parks (Van Oest, Van Heerde, and Dekimpe 2010), and music (e.g., Dewan and Ramaprasad 2012; Elberse 2010; Saboo, Kumar, and Ramani 2016). The entertainment industry continues to see substantial changes due to new technologies. The empowerment of consumers through new technology has strongly impacted many firms’ business models in several sectors of the industry. In particular, rapid advancements in (Internet) technology have caused changes in how consumers obtain music (legally or illegally) and how they enjoy music. At the same time, concerts are thriving and have surpassed records as the main revenue stream in some key markets, such as the United Kingdom (Michaels 2009), and for the artists we study in the German market.
This research contributes to the literature by providing a theoretical and empirical analysis of the dynamic and changing relationship between recorded music and concert revenue. Our findings represent an important extension of the literature (e.g., Krueger 2005; Mortimer et al. 2012), which has so far assumed that concerts and records have cross-format effects. To the best of our knowledge, ours is the first empirical study to show that these cross-format effects exist, how strong they are, that they are asymmetric, and that they are moderated by technological advancements and artist characteristics.
The analysis of close to 400 musicians across more than six years of weekly data shows evidence for a reinforcing spiral of “success breeds success.” If an artist is successful on the record market, this will enhance concert revenue, which in turn enhances demand for the artist’s recorded music. However, this conclusion comes with several caveats. First, there is a general downward trend in record revenue and a general upward trend in concert revenue, so the cross-format effects we discuss operate at the margin: a marginal increase in the revenue of one format enhances the revenue of the other format.
The second caveat is that this spiral is asymmetric. The asymmetry is that the dynamic cross-format elasticity from record revenue to concert revenue (with a mean of .27) is much stronger than the reverse cross-format elasticity from concert revenue to record revenue (with a mean of .04). Thus, record success breeds concert success, but the reverse is much less the case. At the same time, revenue streams from both sources run into the millions of dollars for some artists, so even small elasticities represent a lot of revenue.
The third caveat is that the demand spiral changes in response to technological advancements. The analysis uncovers severe threats to the cross-format elasticity from concert revenue to record revenue. Piracy allows consumers to download records illegally, weakening this cross-format elasticity, in line with our hypotheses. On top of that, unbundling allows consumers to cherry-pick the tracks they like the most. This again attenuates the cross-format elasticity, consistent with our theorizing. While piracy is a phenomenon that sparked the attention of academic research (e.g., Krueger 2005) and worries many industry players, we find that the attenuating effect is equally strong for unbundling. In addition, we find the moderating effect of unbundling to be very stable in all robustness checks, whereas the interaction with piracy is insignificant in one robustness check. This highlights that while we have evidence that piracy attenuates the link from concerts to records, artists should be at least as concerned about unbundling. This finding is new and contributes to the literature’s understanding of how these two formats interact. Furthermore, this finding extends our knowledge about the role of unbundling. Elberse (2010) introduces this concept into the literature and highlights the negative main effect on demand. We extend this finding by showing that—in addition to the negative main effect—unbundling weakens the cross-format elasticity from concerts to records.
What are the bright spots? One is that piracy seems to be on the decline in some markets, such as Germany (see Figure 4, Panel B), which could undo the negative impact of piracy on the cross-format elasticity from concert revenue to record revenue. The upward movement of unbundling is also slowing down (Figure 4, Panel A), which means that its harmful effect does not get stronger. Figure 4, Panel C, shows the joint impact of piracy and unbundling on cross-format elasticity by calculating this elasticity across the observed levels of piracy and unbundling across the observation period 2004–2010. On the basis of the joint impact of both moderators, we conclude that the cross- format elasticity from concert revenue to record revenue has, on balance, stayed stable across the observation period. The fact that piracy appears to be declining while unbundling is increasing results in two opposing forces, and the cross-format elasticity would be much lower today if piracy were on the level that we saw, for example, in 2004. This substantially extends the findings by Krueger (2005), who does not consider unbundling or foresee a scenario in which piracy might actually decline.
A silver lining also applies for artists who are (still) relatively low on fame. They benefit from giving concerts, which stimulate record revenue to a larger extent than those of more famous artists, in line with the hypothesis. In a sense, a road to success for a relatively new artist is still through giving concerts, although, of course, this is not the only path in today’s online world. Most likely, these findings do not apply for completely new or unknown artists, who are not contained in our sample. Furthermore, we find that quality does pay off for record revenue, not only in a direct way (significant main effect) but also as a facilitator of the cross-format elasticity from concert revenue to record revenue. The better the peer-rated quality of the music, the greater the impact of concert revenue on record revenue, as we hypothesized. The magnitude of the two artistrelated moderating effects (Figure 1, Panels C and D) on the cross-format elasticity from concert revenue to record revenue is sufficiently strong to counter the negative moderating effects of piracy and unbundling (Figure 2, Panels A and B). We do not find support for a moderating role of music quality on the effect of record on concert revenue. One potential explanation could be that we measure music quality as the satisfaction with the recorded music and not as the quality of the concert.
A seemingly conflicting finding is that music quality strengthens the cross-format effect from concert revenue to record revenue, whereas fame weakens it. However, the latter finding is in line with the notion that a more famous artist has experienced past sales success (in line with the artist’s billboard hits, which we use to operationalize fame) as well as current sales success (in line with the positive main effect of fame on record demand). This means that a lot of consumers already own the current and past music of a more famous artist and that cross-buy of the artist’s recorded music after a consumer attends a concert is less likely. Importantly, the correlation between fame and music quality is very low (.02), suggesting that an artist’s ability to create high-quality music is distinct from the artist’s ability to create music that appeals to the masses.
For the other side of the demand spiral, the cross-format elasticity from record revenue to concert revenue, the implications are even more upbeat. Not only is it stronger to begin with than the reverse elasticity, but there is no evidence for technological developments threatening this cross-format elasticity. We find that the more famous an artist, the stronger the cross-format elasticity from record revenue to concert revenue, which is line with the expectation. Thus, we uncover the novel and interesting finding that fame is a double-edged sword: it makes it easier to convert record revenue into concert revenue, but it makes it harder to use concerts as a driver of record revenue.
Managerial Implications
This research has important additional implications for artists and labels. Their attempts to stimulate record revenue (e.g., through new releases or advertising) translate into revenues from the concert market. Concerts capitalize on the base of potential fans, who have learned about the artist through airplay and listening to recorded music. A key insight from this research is therefore that artists across the spectrum should have a strong interest in giving concerts, albeit for different reasons depending on their fame. For famous artists, concerts represent a core format through which to reap what has been sown through selling records and producing hits. For less famous artists, concerts represent a seed that can be harvested through selling records.
This finding also implies that labels, especially the ones promoting famous artists, should intensify their attempts to benefit from revenue earned on the concert market. The fact that records stimulate demand for concerts means that labels’ marketing efforts indirectly contribute to revenues that the labels do not benefit from under standard agreements. From this perspective, labels’ attempts to install deals that cover both record and concert revenues are justified. Madonna is a prominent example of an artist whose activities in both recorded music and concerts are covered by one agency (Live Nation).
Marketing actions: advertising, new releases, and airplay. We find that marketing actions have much more of a direct impact on record revenue than on concert revenue. New releases are the lifeblood of the record market, confirmed by a strong effect on record revenue, but they do not impact concert revenue directly. Advertising in this empirical context is primarily focused on selling recorded music, not on promoting concerts. Accordingly, we find a positive main effect of advertising on recorded revenue but not on concert revenue. We also find that the record revenue impact of a new release is enhanced through advertising. In short, there is little to no direct gain from new releases and advertising on the concert market, whereas the record market does benefit. Airplay is the only variable that has a positive main effect on both record revenue and concert revenue, but it comes with negative interaction effects with advertising.
However, once we consider indirect effects (i.e., via the other format), the picture changes. The strong increases in record revenue that can be accrued due to new releases, advertising, and airplay will benefit the concert market through the cross-format elasticity from records to concerts. Consider the case of a 1% increase in advertising. The estimation results (Table 3) indicate that this 1% increase lifts record revenue by .26%. For the average artist, the cross-format elasticity from records to concerts is .27, which means that concert revenues increase by .07% (.27 · .26% = .07%). This indirect effect is even more pronounced for more famous artists, for whom a 1% increase in advertising leads to a .13% increase in concert revenues. Thus, artists on the concert market can indirectly benefit from their marketing activities. The fact that the direct effects of marketing actions (except airplay) are less beneficial to concerts than the indirect effects means that only artists who are successful in the record market can reap the benefits of marketing in the concert market.
Technology is constantly evolving, so the music industry is by no means in a new equilibrium. Rather, technological developments will continue to shape the relations that we analyze in this article. On the one hand, piracy is on the decline (Figure 4, Panel B), and theory indicates that the link between concert revenue and record revenue should therefore at least not decline any further. On the other hand, music labels use streaming services as a new business model to combat piracy, and streaming can be seen as yet another variant of recorded music that is even further unbundled. In the observation period, streaming music was not yet relevant, but today it is becoming a major phenomenon (e.g., Wlo¨mert and Papies 2016). Hence, streaming may further reduce the positive cross-format elasticity from concerts to record sales.
Other implications for the entertainment industry. Our findings highlight that many artists have found a way of meeting the challenges fueled by technological developments. Concerts become increasingly relevant as a revenue source as record revenues decline. This shows that consumers are strongly interested in the core product of the artists: the music. The consumption mode for music is, however, constantly changing. Similar developments may be relevant for other sectors of the entertainment industry. Consider the book industry, wherein illegal e-books are becoming a threat. The results suggest that consumers will still be interested in the core product—authors and books—but may also seek new ways of consuming. Live events (e.g., authors reading from their books for an audience, in person or online) may be a powerful way of reaching the audience and triggering future sales. The strategic challenge is that firms need to properly define the formats that are relevant for consumer enjoyment, as well as the resulting business model. A music label that defines its business model as promoting and delivering records to consumers will most likely not survive the changes that were fueled by the technological developments analyzed in this study because its business model and competitors will be too narrowly defined (Levitt 1960). When piracy and unbundling started to unfold their impact, the music industry initially struggled to address consumer preferences. The reaction of some artists to these new demands shows how firms can actively seek new opportunities when technological innovations force business models to change. It would have been easy to interpret the decline of record sales as an indication that consumers were losing interest in music and engaging in alternative entertainment instead. However, many artists recognized that the interest in the product was still high, but consumers were looking for new ways to enjoy it. This may serve as an encouraging example for industries that see consumer interest in their products slip, for example, traditional newspapers, video rental stores, and travel agents. The underlying consumer needs are stronger than ever before, but the preferred delivery format is changing. Firms that can recognize the underlying consumer need but anticipate or even orchestrate the change in format will lead the industry.
Limitations
As is true for any research, this study has limitations that offer opportunities for future research. One limitation is that we cannot assess profitability, since we do not have access to cost and profit margin data. Organizing concerts is expensive, especially in the light of ever-increasing expectations for spectacular shows and ever more stringent safety requirements. In addition, artists generate revenue from other sources during concerts, such as merchandising, which we do not observe. Another limitation is that we only observe concert revenue at the moment the concert took place, which is a limitation that this research shares with other research on concerts (e.g., Courty and Pagliero 2011; Krueger 2005; Mortimer et al. 2012). Many concert tickets are sold weeks or months in advance of the concert. In the analyses, this is accounted for through the observations on the drivers in the lead-up period to the concert as drivers of concert demand. In addition, we observe concert revenue only for a subset of all concerts. We therefore impute the attendance rate for the remaining observations. The results are fairly robust even if we estimate the model that only considers artists contained in the PollstarPro sample. However, we acknowledge that it would be desirable to use a data set that contains complete information on all concerts. Furthermore, the sample includes close to 400 of the most successful artists during the observation period, rather than a random sample. All variables show substantial variance (e.g., some artists spend very little on advertising or receive little airplay). Combined with the fact that we consider artist-level heterogeneity through the moderator of fame, we believe the results can be generalized. However, it may be desirable to test the hypotheses on an even larger random sample of artists with more unknown artists.
Finally, we study a specific dual-format industry (music) and moderating effects of technological developments that are most germane to that industry (piracy and unbundling) and cross-sectional characteristics that are key in this context (quality and fame). We cannot generalize all moderators to other multiformat goods, although at a meta-level, quality is an asset that facilitates cross-buying, and the digitization of information represents a major opportunity and threat for many (single- and multiformat) industries. We hope this article stimulates new research on how technological developments impact the business model of other multiformat (entertainment) goods and services. Despite these limitations, we believe this study makes an important step toward an understanding of how the music industry, with its multiple formats, is evolving.
1In this research, we do not differentiate between an artist, the management, and the record label; we use the term “artist” to represent their combination.
2There are some similarities between dual-format markets and dual-sided markets. The key difference is that dual-sided markets serve two markets, (e.g., readers and advertisers), while dual-format markets serve (in principle) the same market (e.g., music consumers).
3In an additional, more disaggregate analysis, the authors group market areas by broadband penetration under the assumption that the effects should be more pronounced in areas with high broadband penetration.
4We emphasize that this reasoning also holds in the presence of a positive main effect of fame on record revenue because the negative interaction only implies that it becomes more difficult for famous artists to convert concert ticket sales into record sales.
5The analysis does not include streaming because it was still at its infancy during the observation period and did not contribute to an artist’s revenue from recorded music.
6We obtain the weights by an auxiliary regression of revenues on the full set of independent variables and the four new-release dummies. The weights are such that a new album gets the highest weight of 1 and the other releases (singles, DVDs, rereleases) obtain lower weights. Table 2 provides details. Using weights to combine the effects of similar independent variables is often done in response modeling to combine the cross-effects of multiple competitors (e.g., Cleeren, Van Heerde, and Dekimpe 2013). This allows for a more parsimonious model. In this case, the alternative (less parsimonious) model would require four separate dummy variables for the four categories, plus eight interactions with the other marketing instruments. In previous analyses, we also estimated models with alternative weights of 1 for albums and .1 for all other categories. There is no material difference in the substantive outcomes.
7Musicmetric is another potential source for measurement of piracy. However, it is not an option for this study because it provides data only as of December 2011, not for the required period 2004–2010.
8Requirements 1, 3, and 4 would naturally lead to a vector autoregressive (VAR) model. However, in this case, we have an additional requirement 2, concerning the distribution of the concert variable because in most weeks, an artist does not give a concert. A VAR model cannot readily accommodate this type of limited dependent variable, which is why we use a Tobit Type II model for the concert revenue equation and integrate it into a system of equations with correlated errors.
9For new releases, we adopt NewReleaseStockit = lx, y NewReleaseStockit-1 + ð1 - lx, yÞNewReleaseit because of the discrete nature of the new-release variable. Its coefficient is a quasilong-term elasticity.
10We account for artist heterogeneity by estimating artist-specific intercepts. We attempted estimating a model with slope parameter heterogeneity, but it was not feasible because for many artists there is insufficient variation in the concert variable to reliably estimate artist-specific slope parameters. One additional option would arise through a finite-mixture modeling approach, in which latent artist segments are uncovered. We leave this as a potential avenue for future research.
11We speculate that the latter finding could be due to a lack-offamiliarity effect: if there is a lot of advertising for the artist’s new music, this could lead to adverse responses among those consumers who primarily attend concerts to experience the artist’s existing music. Future research could address this angle.
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Table 1
Related Literature and the Incremental Contributions of This Study
TABLE:
| Key Issue | Description of Issue | Main Findings | Some Key Publications | Our Incremental Contribution |
|---|
| Demand for multiformat goods | Multiformat goods offer consumers two or more formats of the same or similar good. These formats may be substitutes or complements. The firm’s challenge is to manage demand across formats. | • New digital channels/formats hurt established channels/ formats. • Overall performance implications may be positive or negative. • Equivalent quality on salient attributes leads to products being perceived as complements. • Cross-buying is enhanced by risk reduction. | • Gentzkow (2007) • Geyskens, Gielens, and Dekimpe (2002) • Koukova, Kannan, and Kirmani (2012) • Kumar, George, and Pancras (2008) | Our article contributes to this literature by studying how the dynamic interplay between the demand for two formats is moderated by technology and by cues that consumers can utilize to reduce the purchase risk associated with a crossbuy. |
| Opportunities and threats in the music industry | The digitization of music and the rise of the Internet have led to two key developments: piracy and unbundling. | • Piracy hurts recorded music sales. • Live concerts, especially for smaller artists, have thrived since piracy became a mass phenomenon. • Unbundling reduces revenues from recorded music. | • Elberse (2010) • Krueger (2005) • Mortimer, Nosko, and Sorensen (2012) | Our article adds the perspective that demand for one format (e.g., concert) may affect demand for the other format (e.g., recorded music), captured by dynamic cross-format elasticities. We contribute to the literature by studying (1) how piracy and unbundling moderate the crossformat demand elasticity between recorded music and live concerts; (2) the moderating effect of artist fame and music quality on the cross-format elasticities; and (3) the effects of the marketing variables advertising, airplay, and new releases on record demand and concert demand. |
Figure 1 Conceptual Framework for the Music Demand Spiral Between Concert Revenue and Record Revenue
Table 2 Variables and Operationalizations
TABLE:
| Variable | Definition | Source | M (SD) | Min | Max |
|---|
| Dependent Variables |
| RecordRevenuesit | Record revenues in thousand euros for artist I (i = 1,…, 387) in week t (t = 1,…, 337) | GfK Entertainment | 22.29 | -58.08 | 0 | 3490.49 |
| ConcertRevenuesit | Concert revenues in thousand euros for artist I (i = 1,…, 387) in week t (t = 1,…, 337) | PollstarPro; various websites | 29.4 | 408.39) | 0 | 21549.81 |
| zit | Equals 1 if artist I gives a concert in week t, 0 otherwise | Own calculation from concert observations | 0.04 | -0.2 | 0 | 1 |
| Independent and Moderator Variablesa |
| Piracyt | Number of German Internet users (in millions) having illegally downloaded music from the Internet | GfK Brennerstudie | 3.64 | -0.49 | 2.9 | 4.4 |
| Unbundlingt | Single sales divided by the sum of single and album sales in week t | Own calculation from record revenue (Elberse 2010) | 0.35 | -0.09 | 0.12 | 0.57 |
| Fameit | Cumulative number of album chart placements (=chart position-1) from 1995 until week t for artist i | Offiziellecharts. (Dewan and Ramaprasad 2012) | 4.21 | -6.43 | 0 | 35.81 |
| MusicQualityit | Average number of Amazon review stars across all products for artist I in week t | Amazon.de | 4.26 | -0.63 | 0 | 5 |
| NewReleaseit | AlbumNewReleaseit +:40 · SingleNewReleaseit +:31·ReReleaseit +:25·DVDNewReleaseit, where AlbumNewReleaseit, SingleNewReleaseit, ReReleaseit, and DVDNewReleaseit are dummies for artist I that equal 1 in the week of the new release, and 0 otherwise. | Musicbrainz; Wikipedia | 0.02 | -0.12 | 0 | 1.71 |
| Advertisingit | Thousands of euros spent on advertising on artist I in week t | Ebiquity | 2.95 | -25.34 | 0 | 2199.74 |
| Airplayit | RadioPlaysit + VideoPlaysit for artist I in week t | Nielsen Music Control | 88.08 | 267.43) | 0 | 5777 |
| GoogleSearchesit | Google search volume (index) for artist I in week t | Google.de | 0.56 | -1.78 | 0 | 270 |
| DRMt | Step dummy for the removal of DRM (= 0 before April 1, 2009, and 1 after) | Lischka (2009) | 0.21 | -0.41 | 0 | 1 |
| IVConcertit | Concert revenue from all artists from other genres and other labels | Own calculation from concert revenue | 9842.58 | 21,034.43) | 0 | 191966.18 |
aNonlogarithmic form, excluding interactions.
TABLE 3 Estimation Results
TABLE:
| | Percentiles of Posterior Parameter Draws |
|---|
| Hypothesis | 2.5 | Median | 97.5 |
|---|
| Record Revenue Model | | | | |
| Lagged concert revenue | | 0.021 | 0.03 | 0.038 |
| · Piracy | H1: - | 2.186 | 2.118 | 2.049 |
| · Unbundling | H2: - | 2.095 | 2.068 | 2.040 |
| · Music quality | H3: + | 0.153 | 0.2 | 0.246 |
| · Fame | H6: - | 2.006 | 2.005 | 2.005 |
| Piracy | | 2.599 | 2.502 | 2.406 |
| Unbundling | | 2.352 | 2.323 | 2.295 |
| Fame | | 0.038 | 0.041 | 0.044 |
| Music quality | | 1.108 | 1.148 | 1.187 |
| New release | | 10.224 | 10.452 | 10.686 |
| Advertising | | 0.251 | 0.258 | 0.266 |
| Airplay | | 0.217 | 0.224 | 0.23 |
| New release · Advertising | | 0.328 | 0.481 | 0.633 |
| New release · Airplay | | -.070 | 0.089 | 0.249 |
| Advertising · Airplay | | 2.031 | 2.028 | 2.025 |
| Trend | | 2.001 | 2.001 | 2.001 |
| Quarter 2 | | 2.090 | 2.076 | 2.063 |
| Quarter 3 | | 0.002 | 0.016 | 0.03 |
| Quarter 4 | | 0.111 | 0.127 | 0.142 |
| Google searches | | 1.091 | 1.125 | 1.159 |
| DRM | | 2.101 | 2.083 | 2.066 |
| Fixed effects | | | Included | |
| Concert Revenue Model | | | | |
| Lagged record revenue | | 0.182 | 0.237 | 0.301 |
| · Music quality | H4:+ | -.017 | 0.097 | 0.222 |
| · Fame | H5: + | 0.012 | 0.018 | 0.025 |
| Piracy | | -.424 | 0.394 | 1.209 |
| Unbundling | | -.129 | 0.119 | 0.373 |
| Fame | | 0.041 | 0.051 | 0.063 |
| Music quality | | -.415 | 0.15 | 0.731 |
| New release | | 1.078 | 0.205 | 1.481 |
| Advertising | | -.079 | -.030 | 0.016 |
| Airplay | | 0.124 | 0.214 | 0.284 |
| New release · Advertising | | 2.521 | 2.270 | 2.019 |
| New release · Airplay | | -.370 | 0.318 | 0.971 |
| Advertising · Airplay | | 2.025 | 2.013 | 2.001 |
| Trend | | 0.001 | 0.002 | 0.003 |
| Quarter 2 | | 0.095 | 0.23 | 0.364 |
| Quarter 3 | | 0.058 | 0.192 | 0.322 |
| Quarter 4 | | -.048 | 0.109 | 0.263 |
| Google searches | | 0.801 | 1.133 | 1.343 |
| Other artist’s concert revenue | | 0.346 | 0.384 | 0.413 |
| Fixed effects | | | Included | |
| Number of artists | | | 387 | |
| Number of weeks | | | 337 | |
| Observations | | | 118627 | |
Notes: Boldface indicates the parameters whose 95% highest posterior density excludes 0. New release, advertising, airplay, Google searches, lagged record revenue, and lagged concert revenue are stock variables.
FIGURE 2 Simulated Effects of a 1% Shock in Concert Revenue on Record Revenue
Notes: The heavy black line is the mean across all draws for a given level of the moderator; the gray band indicates the area between the 2.5th and the 97.5th percentile. Low level of moderator = one SD below mean; high = one SD above mean.
FIGURE 3 Simulated Effects of a 1% Shock in Record Revenue on Concert revenue
Notes: The heavy black line is the mean across all draws for a given level of the moderator; the gray band indicates the area between the 2.5th and the 97.5th percentile. Low level of moderator = one SD below mean; high = one SD above mean.
FIGURE 4 The Observed Levels of Unbundling and Piracy over Time and the Resulting Cross-Format Elasticity from Concert Revenue on Record Revenue
Notes: Unbundling is the ratio of unbundled sales (singles) to total record sales (singles + albums). Piracy is the number of German Internet users (in millions) having illegally downloaded music from the Internet in a given year. In Panel C, the heavy black line is the mean across all draws for a given level of the two moderators; the gray band indicates the area between the 2.5th and the 97.5th percentile.
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Record: 183- The Effect of a Data Breach Announcement on Customer Behavior: Evidence from a Multichannel Retailer. By: Janakiraman, Ramkumar; Lim, Joon Ho; Rishika, Rishika. Journal of Marketing. Mar2018, Vol. 82 Issue 2, p85-105. 21p. 13 Charts, 1 Graph. DOI: 10.1509/jm.16.0124.
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The Effect of a Data Breach Announcement on Customer Behavior: Evidence from a Multichannel Retailer
In this study, the authors assess the effects of a data breach announcement (DBA) by a multichannel retailer on customer behavior. They exploit a natural experiment and use individual customer transaction data from the retailer to conduct a detailed and systematic empirical examination of the effects of a DBA on customer spending and channel migration behavior. To identify the effects, the authors compare the change in customer behavior before and after the DBA between a treatment group (customers whose information is breached) and a control group (customers whose information is not breached) using the difference-in-differences modeling framework. They find that although the data breach results in a significant decrease in customer spending, customers of the firm migrate from the breached to the unbreached channels of the retailer. The findings further reflect that customers with a higher retailer patronage are more forgiving because the negative effects of the DBA are lower for customers with a higher level of patronage. The authors propose andempirically test for the role of customer data vulnerability as the behavioral mechanism that drives customer behavior subsequent to a DBA. The authors offer prescriptions for managers on how to engage with customers following DBAs.
In his first major television interview, the former director of the Federal Bureau of Investigation noted, "There's only two types of corporations—big corporations—in America. There are those who've been hacked … and those who don't know they've been hacked" (Roberts 2014). Cybercrimes related to data breaches have exploded in recent years, and more and more businesses—ranging from health and retail (e.g., Anthem, Target) to the financial sector (e.g., JPMorgan Chase)—have reported data breach incidents (Abelson and Goldstein 2015). In 2017, one of the nation's three major credit reporting agencies, Equifax, reported a data breach that could potentially affect the personal information of 143 million consumers (Janofsky 2017). The economic impact of data breach events for business entities could be staggering. According to estimates from the Center for Strategic and International Studies, cyber security issues could lead to a loss of $445 billion and 200,000 jobs for businesses annually (Intel Security-McAfee 2014). In 2014, Target Corporation' s chief executive officer resigned in the aftermath of the retailer's massive data breach during the 2013 Thanksgiving holiday season, when the personal information of approximately 100 million customers was stolen (Trefis Team 2014). It was widely covered in the business press that the acquisition of Yahoo by Verizon was delayed and subsequently the acquisition price was lowered because of breach liability faced by Yahoo (Athavaley and Shepardson 2017).
From a customer' s perspective, breach of data containing personal and financial information can be perceived as a violation of social contract and a service failure (Malhotra and Malhotra 2011) negatively affecting the customer-firm relationship. Thus, from a retailer's perspective, understanding the impact of data breach announcements (DBAs) on customer behavior is vital in developing strategies that can help mitigate long-term negative consequences if the retailer were to experience such an event. Often companies, both large and small, are underprepared for these incidents, and they may not accurately estimate the impact of a data breach (Eversley and Hjelmgaard 2013). Yet there is virtually no study that uses actual customer transaction data and empirically examines the effect of a DBA on customer behavior. Although a few studies have examined the impact of security breaches on firm value (e.g., Schatz and Bashroush 2016), no study has examined the impact of a DBA on customer behavior. We attempt to fill this gap in the literature. Thus, our first objective is to leverage a natural experiment and use a unique customer transaction data set from a multichannel retailer both before and after a (customer-related) DBA by the retailer to examine the effect of the DBA on individual customer spending level.
Many retailers operate through multiple channels, and rarely does a data breach involve all of their channels. For example, in the data breach reported by Home Depot (Winter 2014), the breach was limited to the payment systems at the physical stores' self-checkout lanes. In such a case, a retailer's multichannel strategy (Venkatesan, Kumar, and Ravishanker 2007) can prove to be advantageous and help mitigate negative consequences of a data breach in the breached channel by facilitating channel migration for customers who may feel vulnerable shopping from the affected channel. It is also possible, however, that perceptions of vulnerability (stemming from potential misuse of data) following a data breach may spill over to other unaffected firm channels, multiplying the negative outcomes for the firm. We examine this critical multichannel issue with the aim of shedding light on the effect of negative demand shocks for a multichannel retailer. Thus, our second objective is to examine customers' channel migration (from the breached channel to other unbreached channels) behavior in response to a DBA.
Studies have documented that firm-related negative outcomes are likely to be dampened for customers with greater brand commitment and familiarity (e.g., Ahluwalia 2002). Some customers may perceive greater anxiety about the threat of harm from a data breach and may exhibit a greater response to such an event. Therefore, our third objective is to examine whether customer characteristics moderate the effect of a DBA on customer behavior. In particular, we study how customers with a high level of retailer patronage change their spending and channel migration behavior in response to a DBA as compared with customers with a low level of retailer patronage.
In a digital era when firms create large databases with different types of customer information, from their transaction history to payment information, data breaches and security protocols to prevent data breaches are increasingly garnering attention. Indeed, a single data breach incident has the potential to immediately heighten a customer' s perception of susceptibility to harm (Martin, Borah, and Palmatier 2017), reducing trust and negatively affecting subsequent behavior. Firms often follow a DBA with email communication to customers who are affected by the data breach, which can make the data breach more salient to customers. Research has suggested that customers inclined toward processing information more deeply will engage in a more thoughtful assessment and evaluation of communication messages, leading to stronger attitudes (Mac- Innis, Moorman, and Jaworski 1991). Thus, email communications to the affected customers that follow a public DBA can enhance customers' perceptions of harm or data vulnerability and can further reduce customer trust in the customer-firm relationship. A violation of customer trust is linked to a host of negative outcomes for the firm, such as negative word of mouth, reduced customer satisfaction, and reduced repurchase intentions (e.g., Wang and Huff 2007). Thus, it becomes crucial to gain a better understanding of the underlying mechanism that drives the undesirable customer outcomes in the aftermath of a data breach. Recent studies (e.g., Goldfarb and Tucker 2014) have also argued for the importance of a mechanism check when making causal inference claims. Accordingly, our final objective is to establish the role of customer data vulnerability as the underlying mechanism that drives customer behavior subsequent to a DBA.
Examining customer response to a DBA at the individual customer level has both data and econometric challenges. Firms are often reluctant to disclose data breach events and may not want to reveal the damage or the extent of the damage to avoid negative publicity and potential legal liabilities (Javers 2013). It becomes even more difficult to obtain individual customer-level transaction data after a data breach event because firms strive to install more security protocols and become hesitant to share data with other entities. In addition, firms also want to avoid publicizing the negative effects of the data breach while they are taking steps to undo the damage incurred. This makes it nearly impossible to obtain data from a firm to study the effects of a data breach. We have, however, been fortunate to obtain a unique panel customer transaction data set from a multichannel retailer that reported a data breach in one of its channels. From an econometric perspective, issues of reverse causality and endogeneity can also plague the identification of the data breach effect. To address these issues and to accomplish our objectives, we use transaction data of the same panel of customers before and after a DBA by our focal multichannel retailer.
A key feature of our data set is that while the focal retailer operates through multiple sales channels, the data breach was limited to only one channel (i.e., the customer payment card information from only one channel was compromised). We exploit this "natural experiment" and use the customer transaction data that spans pre- and post-DBA time periods to cast our empirical analyses in the difference-in-differences modeling framework (Angrist and Pischke 2009) that has been employed in recent studies in marketing (Kumar et al. 2016; Shi et al. 2017). In our context, we track the behavior of two groups of customers: namely, the treatment group customers whose information was reported as breached and the control group customers whose data were not breached. Another highlight of our data is that we have information on individual customers' opening of emails that they received from the retailer following the public DBA. We leverage this information to uncover the role of customer data vulnerability in customers' response to DBA.
Our results show that following the announcement of a data breach by the focal multichannel retailer, the affected customers decrease their spending level by 32.45%. We find support for customer data vulnerability as the behavioral mechanism through which a DBA affects customer behavior. We also find significant evidence of customer migration from the breached channel to the nonbreached channels following the DBA by our focal multichannel retailer. However, the negative effects of the DBA are lower for customers with a higher level of retailer patronage as compared with customers with lower patronage. We find that the customers who received and opened emails about the breach respond more negatively to the data breach than customers who did not open the emails. We perform a series of robustness checks with alternative operationalization of variables, model specifications, and study time periods and conduct a battery of falsification tests to validate our findings.
Our study contributes to the literature in the following four ways. First, it contributes to the emerging literature on data breach- and data privacy-related issues. While studies in marketing (e.g., Martin, Borah, and Palmatier 2017) and other fields such as information systems (e.g., Cavusoglu, Mishra, and Raghunathan 2004) have focused on how Wall Street responds to DBAs, no study to our knowledge (perhaps due to data challenges that we described previously) has examined how "Main Street" (i.e., the customers of a firm) responds to DBAs. Our study attempts to fill this gap. Second, our findings related to customer channel migration prove valuable in furthering our understanding of the data breach effects. We find that it is imprudent to assume that customers would stop using both the breached and the unbreached channels. Our results suggest that a multichannel retailing strategy can help absorb some of the negative fallout after a data breach event. Following recent studies that have highlighted the benefits of a multichannel strategy (Venkatesan, Kumar, and Ravishanker 2007), we believe the results of our study provide a new justification for using multiple channels as a strategic tool for firms in creating a sustainable long-term advantage. Third, we show that customer data vulnerability is at play behind the undesirable customer outcomes after a data breach, suggesting that firms must invest in allaying consumer concerns regarding their digital data security features. Finally, as many studies in the area of customer relationship management have extolled the benefits of deep customer relationships (Reinartz, Krafft, and Hoyer 2004; Reinartz and Kumar 2003), our study demonstrates that customers with a stronger retailer patronage are more forgiving and exhibit a weaker negative response to the data breach event, thus highlighting the role of investing in customer relationship management initiatives.
We structure the rest of the article as follows: First, we provide a brief background on data breaches and a review of the existing set of studies that have examined the consequences of a DBA and delineate the contributions of our study. Next, we develop a set of testable hypotheses on the effects of a DBA on customer behavior. Then, we describe our research setting and data and present our econometric modeling approach, followed by the results of our proposed models. We then present supplementary analyses as well as a series of robustness checks and falsification tests, and we conclude the article with a discussion on the implications of our study.
We begin this section by providing a brief background on DBAs and highlight how our study is different from related studies on brand scandals and data privacy issues. We then present our conceptual background and a set of testable hypotheses related to the effects of DBAs on customer behavior.
Data breach announcements often involve firms informing their customers that their security systems that protect customers' payment and other personally identifiable information have been breached by people or entities that may have intentions to use this information in an unlawful way. Although the number of data breaches reported by firms in the United States and across the world has been on the rise (Weise 2014), currently there are no laws that mandate public companies to disclose cyber security in their U.S. Securities and Exchange Commission filings. In 2011, the Securities and Exchange Commission issued guidance advising companies to report cyber threat and security issues.[ 1] However, this guidance was simply advice, not regulation.
Much of the cost associated with a data breach is due to the recovery costs in the form of paying fines, losing revenue, hiring people to fix the problem, paying for credit monitoring services for customers, spending on public relations efforts, and so on. Studies in the area of information systems have investigated the effect of DBAs from the viewpoint of stock market reaction. For example, Cavusoglu, Mishra, and Raghunathan (2004) analyzed the effect of 66 distinct security breaches of public firms and concluded that the breached firms, on average, lost 2.1% of their market value within two days of the announcement of the data breach. From the average market value of the analyzed firms, the authors find that this amounts to a $1.65 billion loss in market capitalization. More recent studies by Malhotra and Malhotra (2011) and Martin, Borah, and Palmatier (2017) have employed event study methodology to understand the effect of privacy breaches on firms' abnormal stock returns and find that data breaches have a significant negative effect on firm performance. Cavusoglu, Mishra, and Raghunathan (2004) argue that the costs firms face as a result of security breaches include the transitory costs from lost business, decreased productivity, and stolen information; the litigation costs of containing the damages of data breach; and the costs from the loss of current customers to competitors. Although these studies document that the stock market views DBAs unfavorably, the impact of such data breaches on customer behavior using objective individual customer-level data has not been examined.
Brand crises and negative news of any type about a brand can erode a brand's image and trust in the eyes of consumers (Dawar and Pillutla 2000). Drawing on attribution theory, scholars have classified crisis responsibility into three types: ( 1) victim crisis, in which the firm has a weak attribution of responsibility; ( 2) accidental crisis, which is an unintended accident in which the firm has minimal attributions of crisis responsibility; and ( 3) intentional crisis, which includes accidents and errors caused by human error and violation of law and other misdeeds by the organization (Coombs and Holladay 2002; Pick et al. 2016). Given the prevalence of digital security and data breach issues, in the absence of employee-caused errors or misdeeds, we believe that customers of a firm would typically view customer data breach as a victim crisis or as an accidental crisis.[ 2]
Prior studies in marketing have established that a brand crisis in the form of a product-harm crisis lowers brand equity and consumers' perceptions of brand quality (Van Heerde, Helsen, and Dekimpe 2007; Zhao, Zhao, and Helsen 2011). Whereas in the case of a product-harm crisis, product consumption may lead to serious and direct harm to an individual consumer, in the case of a data breach, consumers are more likely to form and suffer from perceptions of harm or data vulnerability that stems from a risk of impersonation, fraud, or identity theft (Acquisti, Friedman, and Telang 2006).
Consumers share personal and financial information (e.g., credit card details, mailing address) with retailers for a better shopping experience, and they are likely to view a DBA as a violation of their trust and a breach of the psychological contract that they perceive to have with retailers (Malhotra and Malhotra 2011). From this point of view, data breaches are likely to be construed as a firm service failure. Levitt (1981) argues that a product purchase from a retailer includes not just the tangible product but also the augmented product, which comprises all the other benefits a consumer derives from product purchase and consumption. Consumers expect retailers to safeguard their personal information and consider this service as part of a retailer' s augmented product. A Congressional Research Service report (Cashell et al. 2004) suggests that the negative publicity associated with cyberattack announcements can lead to an erosion of confidence among customers and firms may experience lost sales in the process. Other studies in law (Fisher 2013) and marketing (Martin, Borah, and Palmatier 2017) have also argued that customers will experience anxiety and data vulnerability at the moment of the breach, irrespective of whether data were subsequently misused. Overall, we expect that a DBA will lead to decreased customer spending. Thus, we formulate the following hypothesis:
H1: Following a DBA, individual-customer spending decreases.
Multichannel retailers operate through multiple channels, and sometimes only one of the channels suffers a data breach at a given point of time. For example, in the case of the data breach reported by Home Depot, the breach of data was restricted to customer self-checkout point-of-sale terminals (Winter 2014). In such cases, retailers typically emphasize in their marketing communication that only one of their channels was breached in order to minimize the fallout to other unaffected channels. When a DBA is made, whereas some customers may cease their relationship with the retailer, other customers may choose to modify their behavior and look for alternative ways to shop from the retailer until their trust in the retailer is restored. For such customers, shopping from an alternative channel is a viable option, especially if the alternative channel is not compromised. In recent years, multichannel retailing and customer management have gained increased prominence, with retailers investing in building a seamless transition experience for its customers across different channels of the firm (Neslin et al. 2006). This has created a fluid multichannel environment for customers in which they can easily transition from shopping from one channel to another. Studies have also suggested that retailers invest in a multichannel strategy to enhance customers' experience, increase customer satisfaction, and build customer loyalty (Wallace, Giese, and Johnson 2004). Thus, in the event of a DBA, if a retailer has an efficient multichannel environment, many customers will choose to simply migrate from the breached channel to the unbreached channel(s).
From a consumer behavior perspective, theories in consumer psychology suggest that consumers tend to discount negative information that is inconsistent with their preferences (Klein and Ahluwalia 2005). This suggests that when faced with negative information related to only one retail channel, customers may not immediately transfer the negative associations onto the other channel(s) and end their relationship with the retailer. Some customers may indeed choose to transition to other retail channels of the retailer instead of terminating their relationship with the retailer. In our context, these arguments lead us to expect that many customers will migrate from the breached to the nonbreached channel(s) following a DBA and continue their patronage with the retailer. From these arguments, we propose the following hypothesis:
H2: Following a DBA, customers migrate from the breached channel to the unbreached channel(s).
Several studies have linked retail patronage to favorable attitudes toward the firm (e.g., Eastlick and Liu 1997; Korgaonkar, Lund, and Price 1985). Customers with a high level of retailer patronage have stronger positive attitudes toward the retailer, which can introduce skepticism toward negative information regarding the retailer. Customers with a stronger relationship with the retailer tend to be more familiar with the retailer' s products, prices, and customer service, which can increase their switching costs as well. Such patrons are more likely to discount new negative information that is disconfirmatory with their prior positive experience with the retailer (Dawar and Pillutla 2000). Studies have further argued that customers with favorable prior attitudes will assign a lower weight to new information that may reflect poorly on the firm (Ahluwalia 2002), and such customers may not consider the preference-inconsistent negative news as relevant, because they selectively avoid inconsistent information (Xiong and Bharadwaj 2013).
Drawing on these arguments, we posit that the negative effect of a DBA on customer spending will be less pronounced for customers with a higher level of retailer patronage as compared with customers with a lower level of retailer patronage. We thus propose the following hypothesis:
H3: The impact of a DBA on customer spending is weaker for customers with a higher level of patronage.
Customers' channel choice decision has been characterized as a moving target that can evolve over a customer's lifetime with the firm (Valentini, Montaguti, and Neslin 2011). Among the factors that can induce a customer to migrate to other channels of the firm, an unsatisfying experience has been cited as a critical driving force. It has been long known that attitudes are a precursor to forming behavioral intentions (Ajzen and Fishbein 1980) and they have been shown to affect channel migration behavior (Verhoef, Neslin, and Vroomen 2007). Thus, an exogenous negative shock in the form of a DBA that affects one particular channel of a firm, while inducing channel migration across all customers, may affect customers differentially on the basis of their prior attitudes toward the firm. Customers with a strong patronage with the firm harbor a deeper relationship with the firm and have built more positive and favorable associations with the firm over time. Therefore, they are more likely to continue to shop from their preferred channel and will be less likely to migrate to nonpreferred channels after a DBA. Customers with a weaker patronage with the firm, in contrast, are more likely to exploit alternative channels of the current firm, which may include transitioning to unaffected channels.
Thus, we expect that the effect of a DBA on customer channel migration (from the breached channel to the unbreached channels) will be weaker for customers with a higher level of retail patronage. We propose the following hypothesis:
H4: The impact of a DBA on customer channel migration is weaker for customers with a higher level of patronage.
After the public announcement of a data breach, firms and retailers typically communicate with the individual customers who are affected by the breach by email, outlining the steps that will be undertaken in response to the incident. Retailers' objectives in sending these communication messages are to notify their customers about the data breach and to lay out the steps customers need to follow to minimize the potential harm that they can experience as a result of the data breach. Such communications from the firms in the aftermath of a data breach incident can amplify customers' concerns regarding the breach and misuse of their personal information. In particular, customers who open and read these emails may particularly internalize the DBA, perceive a serious imminent threat to their personal information, and react strongly. Thus, customers who are exposed to email communications from the firm may feel an amplified sense of data vulnerability after a data breach incident has occurred.
Martin, Borah, and Palmatier (2017, p. 37) describe customer data vulnerability as a "customer's perception of his or her susceptibility to being harmed as a result of various uses of his or her personal data." The authors argue that customers' perception of their susceptibility to being harmed can lead to negative outcomes even if they do not become actual victims. We argue that customers' exposure to email communications from a retailer about a data breach would make the data breach more salient, triggering an enhanced perception of harm resulting from the misuse of personal information. This would further invoke a customer's sense of data vulnerability that can exacerbate customers' response to a DBA.[ 3]
A rich stream of literature in the area of consumer information processing helps us understand and predict consumers' attitudinal response to marketing communication (for a review, see MacInnis and Jaworski [1989]). Specifically, we build on the arguments of the elaboration likelihood model (ELM) to understand how customers who receive and open email marketing communications (from the retailer about the data breach) after a DBA will respond to the DBA. The basic tenet of the ELM is that marketing communication information can change consumers' attitudes toward messages through two different information processing routes: the central route and the peripheral route (Petty and Cacioppo 1986). Whereas the central route requires diligent processing of information on the part of consumers, the peripheral route requires less cognitive effort. Prior research in consumer psychology has established that attitudes that are formed through the central route are highly accessible and are more predictive of subsequent behavior (Petty and Krosnick 1995). It has also been argued that people with greater motivation and ability are more likely to process marketing communication through the central route of information processing. A recent study by Sahni, Wheeler, and Chintagunta (2016) suggests that when people have both the motivation and the ability, they are more likely to carefully process email marketing communication. The authors conduct a randomized field experiment and find that customers with a greater level of motivation and ability are likely to engage in deeper processing of messages shared through email communications. In our context, following a DBA, customers who received and opened emails from the breached retailer will use the central route for processing information, which will lead to a more thoughtful and cognitive process resulting in an increase in perceived customer data vulnerability, which, in turn, will lead to a stronger response to the DBA.
While the public announcement of data breach by the retailer is likely to affect all customers whose data are potentially breached, information processing theory based on the ELM suggests that the impact of the DBA will be greater for customers who have more motivation and ability to process information. Such affected customers ( 1) will be more motivated to open the emails from the retailer because of their heightened perceptions of harm resulting from the DBA; ( 2) will process information through the central route (more thoughtfully), which will further increase data breach vulnerability, and ( 3) will therefore exhibit a stronger response in terms of customer spending and customer channel migration (from the breached to the unaffected channels). Drawing on these arguments, we propose the following hypotheses:
H5: The impact of a DBA on (a) customer spending and (b) customer channel migration is stronger for customers who perceive higher data vulnerability.
The data set for this study comes from a publicly owned department store retailer headquartered in the United States. The product categories that the retailer carries include men's apparel, women's apparel, footwear, accessories for men and women, and kids' apparel. The retailer operated through three channels during the data time period: the physical store (multiple brick- and-mortar stores), the Internet, and catalogs. The brick-and- mortar stores and the Internet are the two dominant channels for the multichannel retailer. During the time span of the data set, the focal multichannel retailer suffered a data breach and subsequently announced publicly that customer payment data related to its physical store channel during a specific transaction time period was breached. In particular, customer debit and/or credit card information was compromised for customers who purchased in the physical stores (the "breached" channel, hereinafter) during a certain time window (the "breached" or the "affected" time period).[ 4] Customer information related to other channels of the multichannel firm, the Internet and the catalog channels (the "unbreached" channels, hereinafter), was not affected or compromised in any way. It is also worth noting that customers who shopped through the breached channel but outside of the breached time period were also not affected. The retailer specifically mentioned and clarified these issues in its public announcement and subsequent email communication with the affected customers.
Following the public announcement of the data breach, the retailer also contacted all the customers who purchased through the breached channel during the affected time period and for whom mailing and/or email addresses were available. The retailer also posted details of the DBA on its main website. We have transaction data from the same panel of customers both before and after the DBA. The data specifically include detailed customer-level information on the items purchased, the dollar amount of the purchases, and the channel used for purchasing. In addition to the individual customer-level transaction data, we also have access to data on individual customers' opening of emails from the retailer following the DBA.
Difference-in-differences (DD) approach. In this subsection, we discuss the econometric challenges and the identification strategy that we undertake in examining the effect of DBAs. Examining the effect of DBA using aggregate level sales data across a panel of firms with and without DBAs would be limiting because such data may mask important patterns at the individual customer level. To resolve this, we work with customer-level transaction data that from before and after the DBA by the focal multichannel retailer. However, a simple before-and-after comparison, even if one were access customer- level data, would not be able to rule out the effect of temporal factors (e.g., competitors' actions) that are unobserved to us as researchers. Such factors can make the identification of the DBA challenging. To rule out the effect of such factors, we rely on a natural experiment in which we examine the effect of an exogenous treatment (in our context, the DBA) on customer behavior using the DD approach. The DD approach examines the behavior of customers in the treatment group (the affected customers whose data were breached) and the control group (the unaffected customers whose data were not breached) before and after the treatment (DBA). The DD modeling approach helps account for both time-invariant customer characteristics and any time trend effects and helps establish the causal effect of the DBA on customer behavior (Angrist and Pischke 2009; Huang et al. 2012; Shi et al. 2017; Wooldridge 2002).
The first step in implementing the DD approach involves the construction of the two groups of customers—the treatment and the control group. We define the treatment group as the set of customers whose information was breached (i.e., customers who transacted with the retailer through the "breached" channel at least once during the "affected" time period [T1b in Figure W1.1 in Web Appendix W1]). The control group comprises customers whose data were not breached because they did not transact through the breached channel during the affected time period, and thus their payment information was not compromised. It is worth noting that at the time of purchasing through the breached channel during the affected time period, customers did not know that the channel was going to be hacked subsequently. The firm also did not have any information about the timing of the data breach until after the breach was discovered. In other words, to the extent that the timing of the data breach was exogenous and to the extent that customers and the retailer did not know that a particular channel is going to be hacked at a particular time, the classification of customers into the treatment and the control group is exogenous. In the "Robustness Checks" subsection, we test whether the treatment and the control group customers have similar trends in behavior over time before the DBA. In Web Appendix W2, we elaborate on the motivation of the DD and the setup of the DD approach in regression modeling framework (see Table W2.1 in Web Appendix W2).
DD approach with propensity score matching (PSM). We have argued that because the timing and the channel of breach are exogenous, the assignment of customers into the treatment and the control groups is likely to be random.[ 5] However, we recognize that the construction or definition of a control group in a natural experiment setting is not always apparent. To rule out any customer self-selection issues and to ensure similarity of the treatment and the control group customers, we follow recent studies in marketing (e.g., Huang et al. 2012; Kumar et al. 2016; Shi et al. 2017), operations management (Bell, Gallino, and Moreno 2016), and economics (O'Keefe 2004) in applying a quasi-experimental approach and use the DD approach with PSM.[ 6] Propensity score matching helps mimic a randomized experimental study design (Rubin 2006) by creating matched pairs of treatment and control group customers who are similar on a set of observed characteristics, thus addressing the customer self-selection effect (Rishika et al. 2013). The DD modeling approach helps account for "common shocks" or time trend effects that affect all customers and any inherent differences (not observed by the researcher) between the two groups. In summary, the combination of PSM and DD helps control for selection resulting from both observed and (time-invariant) unobserved factors that could confound the effect of DBA (Gertler et al. 2011). Thus, we rely on two identification strategies: ( 1) the DD and ( 2) the combination of DD and PSM to establish the impact of DBA on customer behavior.
For our analysis, we use customer transaction data over a time period that spans seven months before and after the DBA. From our background research on multichannel retailing and our discussions with managers of the focal retailer, we found that this time period is long enough to observe noticeable changes (if any exist) in customer behavior. To construct our estimation data set, we applied the following data filtering steps: We began with a sample of randomly selected 15,000 customers. We then removed observations with missing values or errors. To keep the treatment and the control group customers comparable, we work with a sample of multichannel customers (i.e., those who shopped through more than one channel) in the pre-DBA period. For the customer spending model, we did not apply any filters in the post-DBA period because we want to include customers who cutback on their spending and those who left the retailer altogether after the DBA.[ 7] However, for the channel migration model, we did not include customers who did not purchase in the post-DBA time period, because we cannot model their channel choice post-DBA if the customers did not shop at the retailer.[ 8] The data cleaning and filtering procedure yielded two separate data sets, one for the customer spending model and the other for the channel migration model. The data set for the spending model consists of5,004 treatment and 6,455 control customers, and the data set for the channel migration model consists of 1,572 treatment and 2,883 control customers.
Our dependent variables of interest are customer spending and customer channel migration behavior. A focal customer's spending level (denoted by Spendingit) is operationalized as the total amount of spending (in USD) by the focal customer i at time period t. We operationalize customer migration from the breached channel to the set of nonbreached channels (CM_Tripit) by the proportion ofpurchase trips undertaken by a focal customer to the unaffected channels (i.e., the Internet and the catalog channels) to the total number of purchase trips (across the retailer's three channels). In other words, CM_Tripit is defined as follows:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 1)
where Tripitaffected is the number of purchase trips undertaken by a focal customer i through the unaffected channels at time period t. Tripit denotes the total number of purchase trips taken by the customer i at time period t.[ 9]
We note that we first aggregate customers' transaction data over seven months in the pre-DBA period and seven months in the post-DBA period and analyze their spending and channel migration behavior with the balanced panel data. The reason for doing so is similar to the reasons expounded in recent studies using similar modeling techniques (Shi et al. 2017). First, customers do not purchase products from a department store every week or every month. The average number of purchase trips for a seven-month time period in our sample is 3.96 (with a standard deviation of 3.39). Second, by collapsing the customer transaction data into two time periods, we can circumvent the inconsistent estimation of standard errors due to a serial correlation in the DD models over multiple time periods (Bertrand, Duflo, and Mullainathan 2004; Shi et al. 2017). We also note that we collapse the Internet and the catalog channels into one "nonbreached" channel for the operationalization of CM_Tripit because customers' use of the catalog channel is extremely low relative to the other two channels (physical stores and the Internet).[10]
Effect of the DBA on customer spending behavior. Following prior studies that have used the DD modeling approach (D anaher et al. 2010 ; Goldfarb and Tucker 2011b), we model the impact of the DBA on customers' spending behavior as follows:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 2)
where HACKEDi is a treatment group indicator that is equal to 1 if the customer i is in the treatment group and 0 if the customer i is in the control group. DBAt is a time period indicator that is equal to 1 if time period t is the post-DBA period and 0 if time period t is the pre-DBA period. We also include customer fixed effects (μ i) that help control for unobserved customer heterogeneity and remove endogeneity bias (Rossi 2014). Because the customer-specific fixed effects would be perfectly collinear with the treatment group indicator (denoted by HACKEDi), we do not include the main effect of the treatment group indicator (for details, see Goldfarb and Tucker [2011b]).[11] eŞp denotes the error term of the model proposed in Equation 2. We note that we transformed the dependent variable, Spendingit, by taking a natural logarithm to reduce right skewness.[12] Our primary coefficient of interest is the interaction coefficient, ßsp, which captures the effect of the retailer' s DBA on the change in the spending of both the treatment group customers and unaffected customers (across the pre- and post-DBA time periods). In line with the model presented in Equation 2, ßsp can be interpreted as the causal effect of DBA on customer spending behavior (subject to the identifying assumptions of the DD modeling approach). We conduct tests to check for the identifying assumptions in a subsequent section.
Effect of the DBA on customer channel migration behavior. Next, we turn our attention to customers' channel migration behavior. We specify the DD model of customers' channel migration as follows:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 3)
The independent variables in Equation 3 are the same as the ones in Equation 2. wi refers to the customer fixed effects. As before, the focal coefficient of interest is ßcm, which captures the impact of the DBA on customer channel migration behavior. We note that given our operationalization of channel migration, CM_Tripit falls in the interval [0, 1]. Because the normality assumption of ordinary least squares (OLS) regression does not hold, we cannot estimate the model in Equation 3 using the OLS technique. We thus work with a logit transformation of CM_Tripit.[13]
Effect of the DBA on high- versus low-patronage customers. Using the pre-DBA time period transaction data, we segment customers into two groups of high- and low- patronage customers by using a median split of their spending level (Tucker, Zhang, and Zhu 2012). We use the first three months of the pre-DBA period to calibrate the pre-DBA customer patronage level; thus, our customer segmentation analysis spans four months before and after the DBA.[14] This helps ensure that customer classification does not confound with the estimation time period and also aids in interpretation. To investigate how the effect of DBA on customer spending and channel migration varies across high versus low levels of customer patronage, we follow recent studies (Danaher et al. 2010; Goldfarb and Tucker 2011b; Shi et al. 2017) in extending our DD models to the difference-in-difference-in-differences (DDD) modeling framework. The DDD models for customer spending and customer channel migration are as follows:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 4)
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 5)
where HIGHi takes the value 1 if a customer i is a highpatronage customer and 0 if the customer i is a low-patronage customer. In Equations 4 and 5, all the other variables are the same as the ones used in Equations 2 and 3, respectively. In Equations 4 and 5, the main coefficients of interest are δsp and δcm. The three-way interaction model in Equations 4 and 5 is commonly referred to as the DDD model or the triple-difference model because it helps examine the variation in the outcome variable specific to the high-patronage customers (relative to low-patronage customers) in the treatment group (compared with the control group) in the time period following the DBA (relative to time period before the DBA). For example, dsp captures the effect of the DBA on the spending behavior of high-patronage customers (relative to low-patronage customers) in the treatment group (relative to the control group) in the post- DBA period (relative to the pre-DBA period).
Effect of customer email opening following the DBA. We have built on the arguments of ELM and suggested that the affected customers who receive email marketing communication following a data breach will perceive a greater sense of data vulnerability. Thus, although all the affected customers whose data were announced as breached may feel apprehensive, we argue that customers who receive and open emails will exhibit a stronger response to the DBA. To empirically test this proposition, we leverage access to unique individual customer-level marketing communication data. More specifically, we have information on the customers who received and opened the retailer's email within one week of the public announcement regarding the data breach. Customers who opened the retailer's email (with information on the data breach) immediately following the announcement of the breach may become more acutely aware of the data breach and feel more threatened that the data breach would affect them personally. We use a customer's email communication exposure within a week of the public DBA as a measure for checking the proposed underlying mechanism of customer data vulnerability.
To empirically examine how customer response to the DBA varies with customer email opening behavior, we leveraged our customer-level email communication data and classified the treatment customers into two groups—treatment customers who received and opened the retailer's email (within one week) after the DBA and treatment customers who received the email but did not open it. Following the technique in recent DD modeling literature (Levine and Toffel 2010), we extended the DD models of spending and channel migration presented in Equations 2 and 3, respectively, to examine the differential response of the two types of treatment group customers:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 6)
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 7)
where HACKEDiEmailOpen (HACKEDiEmailNotOpen) is an indicator variable that equals 1 if a treatment customer i received and opened the retailer's email within a week of the DBA (received the email but did not open it), and 0 otherwise.[15] In Equations 6 and 7, all the other variables are the same as the ones used in Equations 2 and 3, respectively. Comparison of δsp to δsp would help evaluate the effect of the DBA on the spending level of the treatment group customers who received and opened emails versus the treatment group customers who did not open the emails. Likewise, comparison of δcm to δcm would help quantify the differential response in terms of channel migration behavior.
PSM in combination with DD. We note that we follow the procedure in Caliendo and Kopeinig (2008) and recent studies in marketing (e.g., Huang et al. 2012; Kumar et al. 2016; Shi et al. 2017) to implement PSM. Propensity score matching involves the calculation of the propensity score, the probability that a customer purchased through the breached channel at least once during the affected time period, obtained from a logit model of a set of customer-specific factors.[16] We used the following customer-specific observables as the matching variables: the total number of purchase trips to the breached channel prior to the breach period (TRIP_HACKED_CHANNEL), whether a customer used multiple channels or only a single channel to purchase prior to the breach period (MULTICHANNEL_CUSTOMER), age (AGE), gender (FEMALE), marital status (SINGLE) and income level (INCOME). Table 1 presents the descriptions and the summary statistics of these matching variables and Table W3.1 in Web Appendix W3 provides the estimation results of the logistic regression models.[17]
TABLE: TABLE 1 Summary Statistics
TABLE 1 Summary Statistics
| | Spending Model | Channel Migration Model |
| | Treatment (N = 5,004) | Control (N = 6,455) | Treatment (N = 1,572) | Control (N = 2,883) |
| Variable | Description | M | SD | M SD | M | SD | M | SD |
| Spending | Spending amount (in USD) over a period of seven months | .91 | 1.05 | 1.07 1.40 | — | — | — | — |
| CM_Trip | Proportion of the number of purchase trips undertaken by a customer in the unaffected channels to the total number of purchase trips over a period of seven months | | | | .42 | .18 | .40 | .23 |
| TRIP_HACKED_CHANNEL | Number of purchase trips to the breached channel prior to the breach time period | 2.29 | 2.06 | 2.92 | 2.71 | 2.95 | 2.09 | 3.25 | 3.00 |
| MULTICHANNEL_CUSTOMER | Whether a customer used multiple channels prior to the breach time period | .76 | .43 | .93 | .26 | .87 | .33 | .91 | .28 |
| AGE | Customer age | 51.79 | 13.79 | 51.01 | 13.48 | 53.05 | 13.57 | 51.62 | 13.32 |
| FEMALE | Whether a customer is female | .84 | .37 | .82 | .38 | .89 | .31 | .85 | .35 |
| SINGLE | Whether a customer is single | .29 | .45 | .29 | .45 | .28 | .45 | .28 | .45 |
| INCOME | Customer income level, coded as an ordinal variable with 36 categories: 1 indicates under $15,000, 2 indicates $15,000-$19,999, 3 indicates $20,000-$24,999, and so on. | 26.31 | 9.34 | 26.39 | 9.31 | 26.59 | 9.23 | 26.76 | 9.20 |
Notes: We rescae Spending by dividing it by the average of spending amount across all customers in both pre- and post-DBA periods for reasons of confidentiality
We matched the treatment to the control customers with the closest propensity score (which is equal to the estimated probability) using the optimal full matching algorithm. We employ this algorithm because it allows for a more general type of matching of one treatment unit to one or more comparison units and vice versa and thus does not require discarding of any unmatched observations (Hansen and Klopfer 2006; Rosenbaum 2002). Furthermore, the optimal full matching is more robust against violations of the common support region assumption as compared with the conventional greedy matching algorithms (Guo and Fraser 2015).
After matching, we conducted a detailed examination to assess the quality of matching by checking whether the matching variables are well balanced between the treatment and the control groups, where balance refers to the similarity of their covariate distributions. In Web Appendix W3, we present the standardized differences between the treatment and the control groups on the matching variables before and after matching. As Table W3.2, Panel A, shows, whereas most of the standardized difference measures are statistically significant prior to matching, the measures are not significant after matching, which implies that PSM helps achieve covariate balance between the treatment and the control group customers. We obtain significant bias reduction after conducting PSM, which attests to the appropriateness of our matching technique. In addition, we follow Hansen and Bowers (2008) and conduct an omnibus test for balance on all of the matching variables simultaneously (as opposed to comparing the treatment and control groups on each matching variable separately). In Table W3.2, Panel A, we present the results of the omnibus balance test. Large p-value (.8698) of the combined baseline difference statistic (δ2) after matching suggests that we cannot reject the null hypothesis of well-balanced matched sets. The balance between the treatment and control groups on the matching variables also holds for the PSM conducted for the channel migration analysis (Table W3.2, Panel B, in Web Appendix W3). AH these results taken together suggest that we are able to achieve statistical balance between the treatment and the control customers for both the spending and the channel migration analyses. In Figure W3.1 in Web Appendix W3, we present the graphical representations of covariate balance before and after matching shown in Table W3.2.
The final step of our effort in reducing potential selection biases and capturing the causal impact of DBA is to combine PSM with the DD model. We follow the propensity score weighting procedure that has been expounded in statistics and program evaluation literature (e.g., Hirano and Imbens 2001; Hirano, Imbens, and Ridder 2003; McCaffrey, Ridgeway, and Morral 2004; Rosenbaum 1987). A similar procedure has been implemented in recent management science studies as well (e.g., Bell, Gallino, and Moreno 2016). We obtained the propensity scores from the logit model estimation results and used them as sampling weights in the DD model estimation. Weighted regression (with the weights being the customers' propensity scores) involves assigning propensity scores as weights to the treatment and control customers and helps make the two customer groups as similar as possible on their observed characteristics (matching variables). In Figure W3.2 in Web Appendix W3, we summarize the steps of PSM in combination with the DD modeling approach. To facilitate a better understanding of the structure of our analyses, we present the overview of our study timeline and explanation of how we construct key variables on the basis of specific time windows in Figure W1.1 of Web Appendix W1.
Table 1 summarizes the main dependent variables and the matching variables that we use in the customer spending and migration models. In Table 2, we present model-free evidence of the effect of DBA on the two groups—the treatment and the control group—of customers across the two time periods—the pre-DBA and the post-DBA periods. The key takeaway is that the treatment customers (i.e., customers whose data were reported as breached) reduced their spending level more after the DBA as compared with the control customers. More specifically, the average spending amounts for the treatment customers are 1.0739 and .7398 in the pre- and post-DBA periods, respectively. The average spending level decreased by .3341, which is statistically significant (t = 32.41, p < .01).
TABLE: TABLE 2 Raw Difference-in-Differences
TABLE 2 Raw Difference-in-Differences
| Treatment Customers | Control Customers | Difference Between Treatment and Difference-in- Control Customers in Pre-DBA Period | Differences in Differences |
| (1)Pre-DBA | (2)Post-DBA | (2)-(1) Difference | (3)Pre-DBA | (4) Post-DBA | (4)-(3) Difference | (1) — (3) | ((2) — (1)) — ((4) — (3)) |
| Spending | 1.0739(1.0535) | .7398(1.0291) | -.3341*** | 1.0758(1.3134) | 1.0687(1.4868) | -.0071 | -.0019 -.3270*** |
| CM_Trip | .3951 (.1529) | .4509 (.1913) | .0558*** | .4019 (.1348) | .3995 (.3030) | -.0024 | -.0067 | .0581*** |
***p < .01.
Notes: The table compares the means of our focal dependent variables, Spending and CM_Trip, between treatment and control customers during pre- and post-DBA periods. We calculate a "difference" that indicates the change in outcome variable pre-and post-DBAforeachgroupanda "difference-in-differences" measure by subtracting "difference" for control customers from "difference" for treatment customers. In addition, we perform t-tests to confirm whether they are statistically significant. Standard deviations are in parentheses.
The average spending amounts of the control group customers are 1.0758 and 1.0687 in the pre- and post-DBA periods, respectively, and the difference between the two periods is not statistically significant (t = .40). This suggests that the DBA did not have a significant effect on the control group customers. All these results taken together suggest that the DD for spending is negative and significant (-.3270, p < .01), thus providing prima facie evidence of the negative effect of DBA on customers' spending behavior. We find similar results for customers' channel migration behavior. Specifically, we find that the treatment customers show a significant change in their channel preference and prefer the nonbreached channels in the post- DBA period as compared with the control customers. The DD value for channel migration is positive and significant (.0581, p < .01) suggesting a positive effect of DBA on customers' channel migration behavior. We also find that there is no significant difference between the treatment and the control group customers in their spending and channel migration behavior in the pre-DBA time period.[18] This suggests that the two groups are similar (in terms of the outcome variables) prior to the DBA, which in turn implies that customer self-selection is not a major concern in our context. Figure 1 visually presents the model-free evidence of the DBA effects.
We also present the customer spending level for the treatment and control group customers that spans 100 days before and after the DBA (see Figure W4.1 in Web Appendix W4).
The plot suggests that there is no clear difference in the spending level of the control group customers (whose data were not breached) before and after the DBA, which supports the previous finding that the DBA did not have a noticeable effect on the control group customers. However, we can see a large drop in the spending level of the treatment group customers right after the DBA. We also note that there is no significant difference in the spending patterns between treatment and control customers during the pre-DBA period. In other words, the visual analytics based on time series plot are in conformance with the raw DD results we discussed previously (in Table 2). Taken together, the model-free evidence suggests that customers cut back on their spending and increasingly migrate to the non breached channels of the focal retailer in response to the DBA. Next, we present the results of the effect of DBA on customer behavior based on a series of DD models.
In Table 3, we present the estimation results of the DD model of customer spending. We note that the standard errors reported in the table are clustered at the customer level and are hetero- skedasticity robust.[19] We find that ßsp (from Equation 2) is negative and significant, which suggests that the DBA has a negative and significant impact on customer spending behavior. More specifically, on average, we find that the DBA leads to approximately a 32.45% decrease in customer spending (over a period of seven months). Thus, we find support for Hj. With respect to channel migration, we find that ßcm (from Equation 3) is positive and significant, which suggests that customers prefer to purchase through the nonbreached channels after the DBA (see Table 4). More specifically, on average, we find that the ratio of the number of purchase trips to the nonbreached channels to the number of purchase trips to the breached channel for the treatment group customers is 167.54%[20] greater than the corresponding ratio for the control group customers. This suggests evidence of channel migration from the breached channel to the nonbreached channels; thus, we find support for H2.
TABLE: TABLE 3 Impact of DBA on Spending
TABLE 3 Impact of DBA on Spending
| DV: ln(Spending) |
| HACKED × DBA | -.3245*** (.0674) |
| DBA | -1.3292*** (.0459) |
| Customer fixed effects | Yes |
| # of observations | 22,918 |
| # of customers | 11,459 |
| R2 | .6993 |
***p < .01.
Notes: DV = dependent variable. Robust standard errors clustered at the customer level are in parentheses. The focal variable of interest and its statistically significant coefficient estimate (i.e., DD estimate) are highlighted in boldface.
TABLE: TABLE 4 Impact of DBA on Channel Migration
TABLE 4 Impact of DBA on Channel Migration
| DV: logit(CM_Trip) |
| HACKED × DBA | .9841*** (.1030) |
| DBA | -.7561*** (.0977) |
| Customer fixed effects | Yes |
| # of observations | 8,910 |
| # of customers | 4,455 |
| R2 | .5019 |
***p < .01.
Notes: DV = dependent variable. Robust standard errors clustered at the customer level are in parentheses. The focal variable of interest and its statistically significant coefficient estimate (i.e., DD estimate) are highlighted in boldface.
H3 and H4 propose that the effect of the DBA will differ across high- and low-patronage customers. In Table 5, we present the parameter estimates of the DDD models of customer spending and channel migration behavior, respectively.[21] The DDD models show that δsp (from Equation 4) is positive and significant (.3474, p < .05) suggesting that the negative impact of the DBA on customer spending is lower for customers with high patronage as compared with customers with low patronage. With respect to channel migration, we find that δcm (from Equation 5) is negative and significant (-1.8220, p < .01) suggesting that migration from the breached channel to the unbreached channels is less pronounced for customers with high patronage as compared with customers with low patronage. Thus, we find support for both H3 and H4. In summary, while the DD model results suggest that the DBA leads to customer spending reduction and channel migration (from the breached channel) to the unaffected channels in the aftermath of the crisis, the DDD results suggest that the DBA has a weaker impact on customers with a high level of patronage as compared with those with a low level of patronage.
TABLE: TABLE 5 Effect of DBA on High Versus Low Patronage Customers
TABLE 5 Effect of DBA on High Versus Low Patronage Customers
| (1) DV: ln(Spending) | (2) DV: logit(CM_Trip) |
| HACKED × DBA × HIGH | .3474** (.1381) | 21.8220*** (.7053) |
| DBA | -1.2942*** (.0839) | -.6096 (.5044) |
| HACKED × DBA | -1.1138*** (.1043) | 1.6405*** (.5783) |
| DBA × HIGH | .5748*** (.1057) | 1.6854*** (.5914) |
| Customer fixed effects | Yes | Yes |
| # of observations | 14,700 | 5,052 |
| # of customers | 7,350 | 2,526 |
| R2 | .6589 | .4281 |
**p < .05.
***p < .01.
Notes: DV = dependent variable. Robust standard errors clustered at the customer level are in parentheses. The focal variable of interest and its statistically significant coefficient estimate (i.e., DDD estimate) are highlighted in boldface.
TABLE: TABLE 6 Customer Data Vulnerability and Response to DBA: Effect of Email Communication
TABLE 6 Customer Data Vulnerability and Response to DBA: Effect of Email Communication
| (1) DV: ln(Spending) | (2) DV: logit(CM_Trip) |
| HACKEDEmailOpen × DBA | -1.4147*** (.5417) | 1.2668*** (.2022) |
| HACKEDEmailNotOpen × DBA | -.2333** (.0916) | .6052*** (.1119) |
| DBA | -1.3132*** (.0455) | -.7561*** (.0977) |
| Customer fixed effects | Yes | Yes |
| # of observations | 16,212 | 6,460 |
| # of customers | 8,106 | 3,230 |
| R2 | .7074 | .4916 |
| Wald test (H0: 02 = a3) | 28.5642*** | 4.1249** |
**p < .05. ***p < .01.
Notes: DV = dependent variable. Robust standard errors clustered at the customer level are in parentheses. The focal variable of interest and its statistically significant coefficient estimate (i.e., DD estimate) are highlighted in boldface. Wald tests report F- statistics.
In Table6, wepresent theresults of themodelsrelated to therole of opening of email on customer response. We find that both a2p and a3p(from Equation 6) are negative and significant. This suggests that the DBA has a negative impact on the buying behavior of the two groups of treatment customers, the group who received and opened the retailer' s email within a week of the DBA and the group who received the email but did not open it. However, the Wald test suggests that the negative effect of DBA on spending level is greater (more negative) for the treatment customers who opened emails from the retailer as compared with customers who did not open emails from the retailer. We find similar results for channel migration behavior. We argued that customer data vulnerability is the mechanism that drives customers' response to the DBA. These results support our arguments, in line with H5a and H5b. We thus find support for all our proposed hypotheses.
We note that ours is the first study to empirically test for and document the role of customer data vulnerability using actual individual customer-level transaction data and breached retailer' s email communication information. Goldfarb and Tucker (2014, p. 32) argue that "mechanism check is important because it helps support claims of causal inference and because it enhances the likelihood that a paper is remembered." They also suggest that "if the effect is larger when theory suggests it should be, then this helps identify the mechanism" (Goldfarb and Tucker 2014, p. 31). In our context, our results that the DBA effect is larger for customers with greater customer data vulnerability highlight the potential role of customer data vulnerability as the underlying mechanism behind customer behavior following a DBA by a retailer.
To account for possible customer-self-selection-driven confounding factors, we reestimated our proposed DD model using the matched sets of treatment and control customers by using propensity scores as weights (Hirano, Imbens, and Ridder 2003; Khandker, Koolwal, and Samad 2010). The propensity score is the probability that a customer would be in the treatment group (in our context, the group of customers whose data were breached) given a set of covariates (also known as the matching variables). We added matched-set fixed effects to the DD models to account for unobserved heterogeneity at the matched- set level. We present these results of the revised DD models (based on matched samples of treatment and control customers) in Table 7. The key takeaway from the table is that the results of the DD models based on matched samples are in conformance with the results of the proposed DD models presented previously. This suggests that the core set of results related to the effect of DBA on customer behavior are robust to potential customer self-selection issues.
TABLE: TABLE 7 Impact of DBA with PSM
TABLE 7 Impact of DBA with PSM
| (1) DV: ln(Spending) | (2)DV: logit(CM_Trip) |
| HACKED × DBA | -.3007** (.0684) | .9741*** (.0740) |
| DBA | -1.3597*** (.0471) | -.7561** (.0702) |
| Customer fixed effects | Yes | Yes |
| Matched-set fixed effects | Yes | Yes |
| # | of observations | 22,572 | 8,656 |
| # | of customers | 11,286 | 4,328 |
| R2 | .6961 | .5085 |
**p < .05.
***p < .01.
Notes: DV = dependent variable. Robust standard errors clustered at the customer level are in parentheses. The focal variable of interest and its statistically significant coefficient estimate (i.e., DD estimate) are highlighted in boldface. We note that the number of customers in the PSM-based model is different from that of the main model because of missing matching variables for some of the customers.
In this section, we discuss various robustness checks and a series of falsification tests we conducted to examine potential concerns about our empirical strategy and any spurious correlations.
We perform various checks to ascertain that our core results related to the DD models are robust to alternative operation- alization of dependent variables and model specifications. We first checked whether the core results of the effect of DBA on customer spending behavior would hold if we analyze customer behavior in terms of the number of purchase trips (denoted by Tripit) and the number of products purchased (denoted by Quantityit). We examine these two outcomes using a Poisson model specification (Anderson et al. 2010). We present the results of these two alternative dependent variables in Columns 1 and 2 of Table 8, respectively. We find that the pattern of results for the two alternative outcome variables is consistent with the results of the effect of the DBA on customer spending. Specifically, we find that the DBA leads to a decrease in the number of purchase trips undertaken and the number of products purchased by 20.28%[22] and 22.31%, respectively (over a period of seven months).
TABLE: TABLE 8 Robustness to Alternative Dependent Variables and Model Specification
TABLE 8 Robustness to Alternative Dependent Variables and Model Specification
| Original DV: ln(Spending) | Original DV: logit(CM_Trip) |
| (1) | (2) | (3) | (4) | (5) | (6) |
| Alternative DV: Trip Model: Poisson | Alternative DV: Quantity Model: Poisson | Alternative DV: Buy Model: Logistic | Alternative DV: logit(CM_Spending) Model: OLS | Alternative DV: logit(CM_Quantity) Model: OLS | Alternative DV: Channel Model: Logistic |
| HACKED × DBA | 2.2267*** (.0163) | 2.2525*** (.0218) | 2.3676*** (.0256) | .7656*** (.1501) | .6806*** (.1425) | .1621*** (.0494) |
| DBA | -.2019*** (.0112) | -.1640*** (.0157) | -.4514*** (.0187) | -.4821*** (.1195) | -.3601*** (.1147) | -.4563*** (.0334) |
| Customer fixed effects | Yes | Yes | Yes | Yes | Yes | Yes |
| Month fixed effects | No | No | Yes | No | No | Yes |
| Day of week fixed effects | No | No | No | No | No | Yes |
| # of observations | 22,918 | 22,918 | 183,344 | 8,910 | 8,910 | 135,134 |
| # of customers | 11,459 | 11,459 | 11,459 | 4,455 | 4,455 | 4,455 |
| Log-likelihood | -19,428.535 | -43,767.021 | -75,754.300 | - | - | -61,794.035 |
| R2 | - | - | - | .5179 | .5172 | - |
***p < .01.
Notes: DV = dependent variable. Robust standard errors clustered at the customer level are in parentheses. The focal variable of interest and its statistically significant coefficient estimate (i.e., DD estimate) are highlighted in bold.
We further check the robustness of our results by using a DD model of customer purchase incidence. We use and estimate a logistic regression specification of purchase incidence to model the probability of purchase from the retailer in a given month (Goldfarb and Tucker 2011a). In this model of purchase incidence, Buyit is the alternative dependent variable, which is equal to 1 if a customer i makes a purchase in a year- month t, and 0 otherwise. The negative and significant DD estimate (see Column 3 of Table 8) of the proposed model supports our main finding that the DBA negatively affects customers' purchase behavior.
For the customer channel migration analysis, we worked with the ratio of a focal customer' s number of purchase trips to the unaffected channels to the total number of purchase trips (see Equation 1). As a robustness check, we operationalized channel migration in two alternative ways: ( 1) the ratio of spending at the unaffected channels to the total spending (denoted by CM_Spendingit) and ( 2) the ratio of number of items purchased through the unaffected channels to the total number of items bought by a focal customer (denoted by CM_Quantityit). We present the results of these robustness checks in Columns 4 and 5 of Table 8, respectively. We find that the results of models with these alternative channel migration variables are consistent with the main results.
Finally, we develop a simple channel choice model that models the probability that a customer would purchase through one of the unbreached channels. More specifically, we use a logistic regression specification with a dependent variable, Channelit, that is equal to 1 if a customer i shops through the unbreached channels at time (purchase trip date) t, and 0 otherwise. As the results in Column 6 of Table 8 show, we find that the customers are more likely to choose the unbreached channels over the breached channel after the DBA. In summary, all the results from the alternative operationalization of dependent variables and model specifications are consistent with the results of the main DD models.
Our analyses related to customer patronage were based on the median split of customers' spending level during the calibration period (T1a in Figure W1.1 in Web Appendix W1). To check the robustness of the results, we operationalized customer patronage on the basis of the median split of the number of purchase trips (denoted by Tripit) and the number of products purchased (denoted by Quantityit). Table 9 shows that the DDD estimates based on these alternative measures of customer patronage are consistent with the results of the main DDD models for both spending level and channel migration analyses.
TABLE: TABLE 9 Effect of DBA on High- Versus Low-Patronage Customers: Robustness to Alternative Measures of Customer Patronage Level
TABLE 9 Effect of DBA on High- Versus Low-Patronage Customers: Robustness to Alternative Measures of Customer Patronage Level
| DV: ln(Spending) | DV: logit(CM_Trip) |
| Alternative Measure of Patronage Level | Alternative Measure of Patronage Level |
| (1) | (2) | (3) | (4) |
| Trip | Quantity | Trip | Quantity |
| HACKED × DBA x HIGH | .3963** (.1898) | .4636** (.1901) | -1.6738** (.6641) | -1.7574*** (.6716) |
| DBA | -1.2828*** (.0994) | -1.3270*** (.1111) | -.1840 (.4168) | -.0923 (.4232) |
| HACKED × DBA | -1.0914*** (.1270) | -1.1834*** (.1420) | 1.3254*** (.4923) | 1.4251*** (.5083) |
| DBA × HIGH | .8231*** (.1416) | .7274*** (.1449) | 1.2275** (.5435) | .9400* (.5546) |
| Customer fixed effects | Yes | Yes | Yes | Yes |
| # of observations | 14,700 | 14,700 | 5,052 | 5,052 |
| # of customers | 7,350 | 7,350 | 2,526 | 2,526 |
| R2 | .6627 | .6628 | .4258 | .4255 |
*p < .10. **p < .05. ***p < .01.
Notes: DV = dependent variable. Robust standard errors clustered at the customer level are in parentheses. The focal variable of interest and its statistically significant coefficient estimate (i.e., DDD estimate) are highlighted in boldface.
TABLE: TABLE 10 Robustness to Alternative Study Time Period: Short-Term and Long-Term Effects of DBA on Spending
TABLE 10 Robustness to Alternative Study Time Period: Short-Term and Long-Term Effects of DBA on Spending
| Time Period Under Study |
| (1) | (2) | (3) | (4) | (5) | (6) |
| One Month Pre- and | Two Months Pre- and | Three Months Pre- and | Four Months Pre- and | Five Months Pre- and | Six Months Pre- and |
| Post-DBA | Post-DBA | Post-DBA | Post-DBA | Post-DBA | Post-DBA |
| DV: In(Spending) | DV: In(Spending) | DV: In(Spending) | DV: In(Spending) | DV: In(Spending) | DV: In(Spending) |
| HACKED X DBA | -4.2349*** (.1627) | -2.5095*** (.1200) | -1.6572*** (.1013) | -1.0933*** (.0881) | -.7840*** (.0788) | -.5597*** (.0725) |
| DBA | -.8477*** (.1510) | -1.2954*** (.1003) | -1.4291*** (.0787) | -1.4090*** (.0649) | -1.3032*** (.0557) | -1.2555*** (.0503) |
| Customer fixed effects | Yes | Yes | Yes | Yes | Yes | Yes |
| # of observations | 16,048 | 19,558 | 21,114 | 21,960 | 22,458 | 22,720 |
| # of customers | 8,024 | 9,779 | 10,557 | 10,980 | 11,229 | 11,360 |
| R2 | .5929 | .5623 | .5955 | .6335 | .6612 | .6819 |
***p < .01.
Notes: DV = dependent variable. Robust standard errors clustered at the customer level are in parentheses. The focal variable of interest and its statistically significant coefficient estimate (i.e., DD estimate) are highlighted in boldface.
TABLE: TABLE 11 Robustness to Alternative Study Time Period: Short-Term and Long-Term Effects of DBA on Channel Migration
TABLE 11 Robustness to Alternative Study Time Period: Short-Term and Long-Term Effects of DBA on Channel Migration
| Time Period Under Study |
| (1) | (2) | (3) | (4) | (5) | (6) |
| One Month Pre- and | Two Months Pre- and | Three Months Pre- and | Four Months Pre- and | Five Months Pre- and | Six Months Pre- and |
| Post-DBA | Post-DBA | Post-DBA | Post-DBA | Post-DBA | Post-DBA |
| DV: logit(CM_Trip) | DV: logit(CM_Trip) | DV: logit(CM_Trip) | DV: logit(CM_Trip) | DV: logit(CM_Trip) | DV: logit(CM_Trip) |
| HACKED X DBA | 4.0944*** (.6571) | 2.6687*** (.4111) | 1.9037*** (.3110) | 1.4641*** (.2377) | 1.3821*** (.1900) | 1.2895*** (.1444) |
| DBA | -3.9969*** (.4510) | -3.3169*** (.2923) | -2.5933*** (.2259) | -1.9456*** (.1743) | -1.5056*** (.1448) | -1.1016*** (.1192) |
| Customer fixed effects | Yes | Yes | Yes | Yes | Yes | Yes |
| # of observations | 1,448 | 3,120 | 4,680 | 5,984 | 7,026 | 7,904 |
| # of customers | 724 | 1,560 | 2,340 | 2,992 | 3,513 | 3,952 |
| R2 | .5941 | .5591 | .5333 | .5267 | .5027 | .5039 |
***p < .01.
Notes: DV = dependent variable. Robust standard errors that are clustered at the customer level are in parentheses. The focal variable of interest and its statistically significant coefficient estimate (i.e., DD estimate) are highlighted in boldface.
With respect to the role of the email communication on customer response, we defined the treatment group as the customers who not only shopped in the breached channel during the breach period but also received and opened the retailer' s email within a week of the DBA. We then estimated the models of customer spending and customer channel migration presented in Equations 2 and 3 on the redefined treatment group customers and the control group customers. We find that the DBA effects based on the subsample with the redefined treatment customers are stronger than those from the full sample with the original treatment customers.[23] This highlights the role of customer data vulnerability as the underlying mechanism behind customer behavior following DBA by a retailer.
In our main DD analyses, we used customer transaction data that span seven months before and after the focal retailer's DBA. To check the robustness of our DD results to alternative time periods and examine how the effects of DBA change over time, we estimate a series of DD models (Equations 2 and 3) with one month pre- and post-, two months pre- and post-, three months pre- and post-, four months pre- and post-, five months pre- and post-, and six months pre- and post-DBA.[24] This lets us work with balanced data and helps resolve any inconsistent estimation of standard errors (Bertrand, Duflo, and Mullaina- than 2004; Shi et al. 2017). We present the results of these models of customer spending and customer channel migration in Tables 10 and 11, respectively. We find that while the direction of the DD estimates of the six models of both customer spending and channel migration is consistent with that of the original DD estimates, the effects of the DBA attenuate over time. The good news is that the negative effect of DBA on customer spending seems to wane over time, indicating that breached firms need to take immediate action to minimize negative publicity at the early stage of the data breach crisis.
The identifying assumption behind the DD modeling approach is that the treatment group and the control group customers have similar trends in behavior over time before the intervention (in our context, DBA). To check the validity of this identifying assumption, we conduct a "placebo" test that has been used in the economics literature (Carrieri, D'Amato, and Zotti 2015; Kim, Urpelai- nen, and Cooper 2015; Puri, Rocholl, and Steffen 2011) by using only the pre-DBA period data and treating the data from the first half of the pre-DBA period as the new pre- DBA period data and the second half of it as the "fake" post-DBA period data. That is, there is no real treatment between the new pre-DBA period and the fake post-DBA period. Given this setting, we estimate the following DD model:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 8)
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 9)
where HACKEDi is a treatment group indicator that is equal to 1 if the customer i is in the treatment group, and 0 otherwise. DBAplacebo is the placebo indicator variable that distinguishes between the first half of the original pre- DBA period (the new pre-DBA period) and the second half of the original pre-DBA period (the fake post-DBA period). The DD estimates (ßsp and ßcm) will not be statistically significant if the pretreatment parallel trend assumption holds. Table 12 indicates that the DD estimates for spending and channel migration models are not statistically significant, suggesting that the treatment and control customers follow the same trend in the pre-DBA period, and thus the assumption of a parallel trend holds in our study.
TABLE: TABLE 12 Falsification Test 1: Placebo Effect
TABLE 12 Falsification Test 1: Placebo Effect
| (1) | (2) |
| DV: ln(Spending) | DV: logit(CM_Trip) |
| HACKED × DBAPlacebo | .0358 (.0715) | -.1325 (.2015) |
| DBAPlacebo | .6275*** (.0546) | .3753* (.1944) |
| Customer fixed effects | Yes | Yes |
| # of observations | 19,020 | 4,078 |
| # of customers | 9,510 | 2,039 |
| R2 | .6184 | .4361 |
*p < .10. ***p < .01.
Notes: DV = dependent variable. Robust standard errors clustered at the customer level are in parentheses. The focal variable of interest and its statistically significant coefficient estimate (i.e., DD estimate) are highlighted in boldface.
In our analysis, the treatment group consists of customers who used the breached channel during the affected time period and thus suffered a possible breach of their personal information. The control group customers did not use the breached channel during the affected time period and thus are not affected by the data breach. The DD modeling framework builds on this assumption and compares the behavior of the two groups of customers (the treatment and the control group) across the two time periods (before and after the DBA). To assess the validity of the construction of our treatment group, we conduct a falsification test by randomly treating half of our control customers as "fake treatment customers" and the other half as control customers. If the DD estimate based on this fake treatment group is different from zero, the construction of the treatment group would be questionable. To confirm the stability of the estimation results, we repeat the random sampling 5,000 times and report the bootstrap DD estimates in Table 13. We find that the bootstrap DD estimates are not statistically significant, implying that our original construction of the treatment group is valid (see Table 13).
TABLE: TABLE 13 Falsification Test 2: Fake Treatment Group
TABLE 13 Falsification Test 2: Fake Treatment Group
| (1) | (2) |
| DV: ln(Spending) | DV: logit(CM_Trip) |
| HACKEDFake × DBA | -.0010 (.0655) | .0012 (.1371) |
| DBA | -1.3286*** (.0327) | -.7567*** (.0685) |
| Customer fixed effects | Yes | Yes |
| # of observations | 12,910 | 5,766 |
| # of customers | 6,455 | 2,883 |
| R2 | .7019 | .4874 |
***p < .01.
Notes: DV = dependent variable. To confirm the stability of the estimation results, we report the bootstrap coefficient estimates and bootstrap robust standard errors clustered at the customer level (provided in parentheses) based on the randomly selected 5,000 bootstrap samples. We also provide the bootstrap R-squared.
To summarize, our main results regarding the effects of the DBA on customer spending and channel migration behavior survive a battery of additional analyses with alternative oper- ationalization of variables, alternative model specifications, an alternative treatment group, different study time periods, falsification tests, and different estimation strategies such as combining the PSM and the DD approach. Therefore, we conclude that we find compelling evidence of the causal effect of the DBA on customer behavior.
Firms spend vast resources to build reputation and create brand equity, and yet a single data security incident can inflict serious damage to the firm' s reputation, lead to significant customer churn, and increase customer acquisition costs. According to a study by IBM and the Ponemon Institute, a public policy think tank dedicated to privacy and information security policy, the average total cost of a data breach registered an increase of 23% from 2013 to 2015 to $3.79 million (Ponemon Institute 2015). In light of the substantial financial impact of data breach events, our study is the first to document the impact of a DBA on customer behavior and thereby makes significant contributions to the understanding of the phenomenon from both theoretical and managerial perspectives.
A recent white paper suggests that one quarter of all security breaches occur in a retail environment (Retail Perceptions 2014). The report uses survey data and suggests that customers are likely to spend less and visit the retail stores less often following a security violation. While several such business reports suggest the possibility of reduced customer spending following a security incident, virtually no study has used actual customer transaction data to study the effect. Thus, from a theoretical perspective, our study helps make inroads into understanding the impact of DBAs at an individual customer level. Broadly, this study contributes to the growing literature on brand crises (such as product recalls) that can cause revenue and share losses and harm brand equity (e.g., Cleeren, Van Heerde, and Dekimpe 2013; Dawar and Pillutla 2000).
While product-harm crises negatively affect product quality and customer purchase behavior, we find that the mechanism through which a data breach operates is different. Data breach announcements typically contain information about the number of people affected and specify the time period during which a particular breach took place. Such announcements are followed by email communications to the affected customers in which individual customers are informed that their information is stolen and that it can be potentially misused in an unauthorized manner in the future. We argue that a DBA would immediately heighten affected customers' perceptions of data vulnerability, resulting in negative customer outcomes. We argue further that the email communications initiated by the firm to the affected customers would make the incident even more salient in customers' minds, causing them to process the information more deeply (through the central processing route, per ELM), thus strengthening the negative effects of the breach. Therefore, we propose and test for customer data vulnerability as the underlying behavioral mechanism that would help explain the effect of DBAs on customer behavior. We find that customers' perception of the severity of the harm enhances their data vulnerability and negatively affects their subsequent behavior. This is a novel finding that makes new contributions to the emerging literature on data breaches and data vulnerabilities (e.g., Martin, Borah, and Palmatier 2017; Ransbotham et al. 2016).
Increased customer profitability of multichannel shoppers has generated a fair bit of interest in marketing literature and is often cited as a key reason for firms to engage in multichannel strategy (e.g., Montaguti, Neslin, and Valentini 2016; Thomas and Sullivan 2005; Venkatesan, Kumar, and Ravishanker 2007). Our study adds a new dimension to the current multichannel marketing literature by showing that investing in a multichannel strategy is beneficial for firms because it can help absorb the negative impact of DBAs. Using customers' actual transaction data and exploiting a unique natural experiment where an exogenous shock (i.e., data breach) occurs at only one channel of the firm, we are able to compare pre- and post- buying and channel usage behavior of treatment group versus control group customers. Our findings suggest that while customer spending substantially decreased, significant customer migration to the unaffected channels also occurred simultaneously. Thus, from a strategic perspective, shocks that affect customer trust can be mitigated to an extent by diverting resources to the unaffected channels while the firm is recovering from the crises. While existing literature has advocated several benefits of multichannel strategy, such as increasing customer engagement and loyalty leading to increased customer profitability (Venkatesan, Kumar, and Ravishanker 2007), our findings are the first to demonstrate that multiple channels can have a broader strategic purpose in an era of cybercrimes when data breaches are threatening to disrupt the pace of business at a much larger scale.
Our findings also contribute to the literature on relationship marketing and customer relationship management. Customer relationship management literature has suggested that the relationship value to the firm is not generally homogeneous across customers, and thus, it is important to invest in the right relationships (Reinartz, Krafft, and Hoyer 2004). Customer loyalty has occupied a key place in this literature stream, and studies have suggested that cultivating attitudinal loyalty is important creating more profitable customer-firm relationships (Reinartz and Kumar 2003). Our results build on this stream of literature and help furnish a new justification for investing in the right customers. We find that firms have a lot to gain from nurturing relationships with more loyal customers, as these customers tend to support a firm through a crisis. The willingness of loyal customers to withstand negative shocks has rarely been examined before, and therefore, this study takes important strides in adding to the customer relationship management literature.
Data breach events are on the rise, and most firms today face an unprecedented security risk that managers must actively manage. Drawing on the results from our study, we offer the following prescriptions for managers.
Engage actively in damage control and address customer data vulnerability. When online clothing and shoe retailer Zappos suffered a data breach incident that affected 24 million customers in 2012, it took assertive and remedial steps immediately following the discovery of the breach. For example, Zappos reset the customers' passwords promptly and encouraged them to alter their usernames and passwords used in Zappos for any other websites (Goldman 2012). Such an active damage control strategy can be crucial in preventing loss of customer trust. Our results show that the announcement of a data breach leads to a 32.45% reduction in customer spending, 20.28% decrease in the number of purchase trips, and a 22.31% decrease in the number of products purchased by customers (over a period of seven months). These findings suggest that how a company responds to a data breach will determine how well it survives in the wake of a DBA. While prevention is a key element in cyberattacks, new threats keep evolving in the dynamic marketplace, and marketing managers must be prepared to engage in aggressive damage control after a threat to the company's cybersecurity occurs. A data breach response plan is essential for surviving and successfully managing a data breach incident.
It is also crucial not only to work on mitigation of the negative fallout from a data breach incident but also to invest in consumer trust-building initiatives. In particular, managers must address customers' perception of data vulnerability because it influences how they respond to data breaches. To the extent possible, retailers must communicate the steps that they plan to take to assuage consumers' perceptions of vulnerability. It is also worth noting that we find that the negative effect of DBA decreases over time. However, this would imply that the breached firms need to take immediate and concrete actions at the early stage of the crisis. In the months following a DBA, because the traffic comes mainly from high-patronage customers, we suggest that retailers initiate measures that serve to maintain customer loyalty while the retailer is dealing with the crisis.
Invest in multiple channels. An important result of our study, from a managerial perspective, is that customers migrate from the breached channel to the unbreached channels subsequent to the DBA. We find that the number of purchase trips by the affected customers to the nonbreached channels of the firm increases substantially after a DBA as compared with the trips by the unaffected customers. This finding provides a significant justification for pursuing a multichannel strategy. This would also suggest that a multichannel retailer should be prepared for increased traffic to the unbreached channels following the DBA. Because several data breaches involve only one channel, operating through multiple channels can help absorb external shocks that affect consumer attitudes and behavior in only one channel. However, different channels have different operational and logistical challenges, and therefore, firms must invest in multichannel development and management strategies simultaneously such that the unaffected channels can seamlessly integrate excess demand from the affected channel.
Although our study is the first to examine the effect of a DBA using actual customer behavioral data, it is not without its limitations. Our results are based on data from only one multichannel retailer that experienced a data breach in one of its channels and followed the breach with a public announcement. While we leverage the data breach affecting only one of the channels of the retailer as a natural experiment and, thus, control for other firms' actions, we are not able to examine competitor actions specifically. Although we leveraged the natural experiment-based research design and supplemented our core results with analyses based on matching techniques, we caution that any causal interpretation drawing on the results that we report is subject to the identifying assumptions. We believe a great deal of research remains to be done, and future studies could examine how different retailers react to a DBA and how their customers respond to negative publicity associated with different forms of data breaches. Future studies could also examine the role of the severity of data breaches on customer behavior, which we were unable to do because of data limitations. We leveraged data on emails received by individual affected customers following the data breach to shed light on customer data vulnerability. However, future research could examine the role of marketing communication efforts in getting customers back to the stores in the days following a DBA. Despite these limitations, we hope that our study helps convey the direct costs to firms in the form of lost business due to a DBA and spurs more studies in the area of business implications of cyberattacks and data breaches.
Endnotes 1 See http://www.sec.gov/divisions/corpfin/guidance/cfguidance- topic2.htm.
2 In other words, whereas customers are more likely to attribute product harm crisis to the actions of managers, firms that suffer from a data breach are perceived as victims of cybercrimes. In particular, loyal customers or customers who are more familiar with a retailer are less likely to attribute a data breach to the actions of managers and can be more forgiving of the retailer. We explore the differential response to DBA across customers later in this section.
3 We do not know whether a customer faced real harm because of the data breach. Furthermore, our data are not conducive to disentangling attitudinal vulnerability from actual harm or vulnerability. Thus, in our context, customers' perception of data vulnerability is attitudinal or psychological.
4 Due to confidentiality agreements, we are not able to disclose the name of the retailer, the specific time period during which the breach occurred, and other detailed and technical information about the nature of the data breach.
5 We also find no significant difference between the treatment and the control group customers in their purchase behavior during the pre-DBA time period (see the "Descriptive Statistics and ModelFree Evidence" subsection).
6 We thank the area editor for the suggestion.
7 We thank the area editor and the anonymous reviewers for the suggestion.
8 In other words, our channel migration model is conditional on a customer shopping with the retailer. As a result, the composition of the sample for the channel migration model is a subset of the sample for the spending behavior model.
9 We note that the operationalization does not restrict customers' choice of channels and allows for customers' switching back and forth from the breached channel to the unbreached channels. If customers were to prefer or migrate to the unbreached channels from the breached channel, CM_Trip would approach 1. If customers were to prefer or stay with the breached channel (i.e., there is no migration from the breached channel to the unbreached channels), CM_Trip would be closer to 0.
In our sample, purchase trips to the breached channel (i.e., physical stores) and the Internet channel account for 76.16% and 20.32% of the total purchase trips, respectively. Purchase trips to the catalog channel account for only approximately 3.51% of the total purchase trips.
There is no intercept in the model because we have fixed effects for all the customers.
Before applying the log-transformation, we rescaled Spendingit by dividing it by the average spending amount across all customers in both the pre- and the post-DBA time periods for reasons of confidentiality. We also added a small number (.001) to the rescaled dependent variable to handle zero values (Collett 2002).
We add a small number (.001) to both the numerator and the denominator of the variable (Collett 2002).
As a result, the number of observations for the DD analyses and the customer segmentation (DDD-based) analyses are different.
We note that the retailer sent emails to all the affected customers. Thus, the retailer's decision to send emails is exogenous to individual customers' perceptions of vulnerability. We thank the area editor for pointing this out.
We note that because the samples for the spending and the channel migration models are different, we run two logit models for matching, one for spending analysis and the other for channel migration analysis.
In the interest of space, we do not discuss the results of the logistic regression models.
The difference between the mean of Spending for the treatment group before the DBA and that of the control group before the DBA (-.0019) is not statistically significant. In addition, the difference between the mean of CM_Trip for the treatment group prior to DBA and that of the control group prior to DBA (-.0067) is also not statistically significant.
The reported standard errors in all of the DD and DDD models are clustered at the customer level.
100 X ([exp(ßcm)] — 1).
We computed the variance inflation factor (VIF) for each of the independent variables and found that none of the VIFs are larger than ten. Drawing on VIF diagnostics (Hair et al. 2010; Kutner, Nachtsheim, and Neter 2004), we note that multicollinearity is not a concern in our context. We thank an anonymous reviewer for the suggestion regarding this multicollinearity check.
100 X ([exp(-.2267)] — 1).
The results are available from the authors on request.
We set the same time window for pre- and post-DBA periods to make the shopping behavior of interest observed in the two time periods comparable. All the data filtering criteria we applied for the sample of the main analysis hold for these additional analyses. Our analyses of short-term and long-term effects are based on the balanced data construction.
GRAPH: FIGURE 1 Comparison of Shopping Behavior in the Pre- and Post-DBA Periods
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Ramkumar Janakiraman is Professor of Marketing and Business Partnership Foundation Research Fellow, Darla Moore School of Business, University of South Carolina
Joon Ho Lim is Assistant Professor of Marketing, College of Business, Illinois State University
Rishika Rishika is Clinical Assistant Professor of Marketing, Darla Moore School of Business, University of South Carolina
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Record: 184- The Effect of Review Writing on Learning Engagement in Channel Partner Relationship Management. By: Aguirre, Elizabeth; Mahr, Dominik; de Ruyter, Ko; Grewal, Dhruv; Pelser, Jan; Wetzels, Martin. Journal of Marketing. Mar2018, Vol. 82 Issue 2, p64-84. 21p. 1 Diagram, 3 Charts, 3 Graphs. DOI: 10.1509/jm.15.0121.
- Database:
- Business Source Complete
The Effect of Review Writing on Learning Engagement in Channel Partner Relationship Management
To develop the knowledge and skill sets of channel partner firms, manufacturers increasingly introduce learning programs as part of their relationship management strategies. However, the engagement of channel partners in these programs tends to be low. The current research, conducted in collaboration with a Fortune 100 information technology company, examines ways to strengthen learning engagement. In accordance with self-regulated learning theory, the authors propose and demonstrate that when channel partners write reviews of a learning module that they attended, beyond providing ratings, they are better able to reflect on the relevance of their learning experience and are further engaged in learning activities. The audience and focus of these written reviews determine the engagement of the channel partner sales personnel; therefore, review writing is a valuable, informal mechanism to motivate them. These effects are moderated by characteristics of both the channel partner (salesperson's learning orientation and identification with the manufacturer) and the relationship with the manufacturer (length and exclusivity).
Channel partners—defined as independent intermediaries of a supplier's downstream networks that sell products to other channel members or end users (Hughes and Ahearne 2010)—often drive suppliers' bottom lines, accounting for as much as 65% of their total revenue (Accenture 2010). Recognizing the strategic importance of these resellers, many suppliers commit substantial resources to partner relationship management (PRM; Palmatier 2008; Palmatier, Dant, and Grewal 2007).
Such a strategy often prioritizes education, with the aim of helping the resellers and their employees gain a clearer understanding of the suppliers' offerings and build vital competencies that promote sales. The learning management systems that suppliers offer as part of a PRM strategy might include forums and virtual meeting rooms for knowledge exchange, collaborative learning opportunities, social networking, and peer support (Harmon et al. 2013). For example, IBM's "Know Your IBM" program and Partner World University host thousands of online learning modules for its channel partners, related to critical sales and technical skills. In addition, HP Enterprise recently announced plans to add a solutions and competency component to its Partner Ready program, to extend beyond product-focused offerings. It provides various training modules and resources to help develop channel partners' competencies (Haber 2016).
However, PRM programs often suffer from low participation rates and insufficient partner engagement (Hennessy, Powers, and Kirk 2012), such that providers seek effective solutions to the unique challenges of leveraging education as a strategic PRM activity in marketing channels. For any single supplier, it is difficult to motivate salespeople employed by channel partners to devote their time and effort voluntarily to self-directed learning about specific offerings; such salespeople likely receive PRM solicitations and educational offerings from many suppliers (Anderson, Lodish, and Weitz 1987; Hughes and Ahearne 2010). Although some employees might recognize the value of professional development (CSO Insights 2014; Ford et al. 1998) and find participation rewarding (Palmer, Lunceford, and Patton 2012), large segments remain unengaged (Hennessy, Powers, and Kirk 2012). Existing incentive structures that offer cash or soft benefits (e.g., preferred partner status) quickly become ineffective or even counterproductive (Gilliland and Kim 2014).
In response, some innovative learning programs, such as Lenovo' s Expert Achievers Program, a worldwide business partner portal, have added feedback systems to allow participants to rate their experiences and write reviews of the learning modules (Fiorletta 2012). Anecdotal evidence suggests that people reflect on their experiences more when they share information about them (The New York Times 2011). Substantive evidence provided by research on self-regulated learning affirms that when writing prompts elicit reflective thinking, the writers become more aware of their own learning processes (Jacobs and Paris 1987). With such self-reflective assessments, learners plan better, make more effective use of their cognitive resources (e.g., attention), and become more interested in gaining further knowledge (Hulleman and Harackiewicz 2009). Therefore, asking channel partners to write a review of a learning module may activate their reflections on developing their own ability and skills, thereby instigating and encouraging them to engage in more training.
In this sense, review writing is a communication process that makes the salesperson's reflection about his or her learning experience explicit. It comprises three constituent features: sender, recipient, and message. We anticipate influences of all three factors, because they determine the writer's allocation of metacognitive resources. The channel partner's sales personnel (i.e., sender or learner of the module) participates in the learning module and writes a review of it, so their characteristics, such as their willingness to improve, as manifested in their learning orientation (LO; Ford et al. 1998), should be influential. These writers might view the supplier firm or their peers (i.e., recipients) as the audience or beneficiary of the review. Accordingly, we assess whether the audience is similar (peers/ other resellers) or dissimilar (manufacturer/supplier) to the reviewer. The message element reflects the attention focus that the learner adopts in describing his or her experience with the learning module, such as broader versus more narrow focus of the review. Our analysis also includes channel-specific contingencies identified in prior marketing channels literature, such as the channel partner's identification with the supplier, relationship length, and the exclusivity of the relationship (i.e., if the channel partner has only one upstream supplier but the supplier has multiple channel partners).
Research on self-regulated learning theory (Zimmerman and Schunk 2001) has also demonstrated that people vary considerably in their ability to reflect on their experiences or recognize personal advancement (Ford et al. 1998). That is, the motivational force that results from writing a review may be stronger with some people but weaker among others. Furthermore, differences in the frames that prompt people to reflect on their experiences produce distinct outcomes (Sheldon, Dunning, and Ames 2014). Therefore, to assess whether review writing motivates subsequent training uptake by channel partners, we take this granular heterogeneity into careful account. We consider different types of reviews to determine how they drive learning engagement behaviors. A more elaborate review (e.g., written review), relative to a simple, overall rating (e.g., star rating), should trigger more reflective thinking. Furthermore, we probe the mechanism by which review writing affects behavioral engagement, such that we investigate how self-reflection leads people to realize the relevance of learning activities, which then affects their future behavior. Writing a review of a learning module (vs. providing a rating) likely triggers a reflexive realization of the relevance of the supplier-provided training to the reseller' s sales performance, thereby functioning as a motivator of further learning engagement. We present the theoretical framework in Figure 1.
By investigating the concepts and relationships in this framework, we aim to make three main contributions to marketing channel literature. First, this study offers an initial examination of feedback systems as tools for engagement, in line with the shift toward relationship marketing in channel contexts (Palmatier 2008). We propose that channel partners' self-reflection on their learning experiences, triggered by feedback systems, influences their future engagement likelihood. This consideration goes beyond a traditional view of feedback as simple input that the manufacturer can use to improve the quality of its offerings (Golder, Mitra, and Moorman 2012; Voss et al. 2004). Because channel partners with low LO are less able to engage in reflection (Ford et al. 1998), we explore different forms of feedback systems to understand how they drive learning engagement behaviors by different channel partners.
Second, we draw on self-regulated learning theory (Zimmerman and Schunk 2001) and examine its boundary conditions to offer a more fine-grained view of when reviews of a learning experience promote effective engagement in learning programs. We consider two central features of self-reflective evaluations prompted by reviewing activities—the beneficiary (peers vs. manufacturer) and the perspective taken (broad vs. focused)— and how they direct the focus of the reflection, such that they might enable effective metacognition that fosters appreciation for learning. To advance understanding of feedback systems specific to channel contexts, we further identify and analyze channel-specific contingencies related to the channel partners and their relationship with the supplier.
Third, we probe the underlying mechanism by which feedback writing affects engagement by investigating how self-reflection can help resellers increase their learning activities. We explore whether more subtle interventions, such as feedback writing, trigger similar outcomes. In a PRM context, we reveal whether feedback writing about a previous learning experience triggers channel partners to realize the relevance of that experience to their day-to-day business activities, which could increase their behaviors dedicated to learning activities in the future. That is, we identify new mechanisms to strengthen interorganizational relationships, beyond trust and commitment (Palmatier, Dant, and Grewal 2007).
Self-regulated learning strategies put PRM participants (learners) in control over cognitive processes, in that they monitor and reflect on their own knowledge (Zimmerman 2002). Self-evaluative reflection involves comparing one' s own knowledge against goals or standards, so it makes people more aware of their competencies, learning progress, and thought processes (Somuncuoglu and Yildirim 1999). Recognition of these cognitive processes allows people to make regulatory decisions, such as where to allocate attention or cognitive resources, which can facilitate further learning and improve performance (Anseel, Lievens, and Schollaert 2009). Self-awareness helps people recognize their learning strengths and weaknesses, thereby motivating them to engage in activities that help them grow (Grant, Franklin, and Langford 2002; Sheldon, Dunning, and Ames 2014; Sitzmann and Ely 2010).
Self-reflective evaluation may be prompted by feedback systems, such as ratings or review writing, which require learners to assess their personal knowledge and skill acquisition. Both practices ask learners to make subjective assessments, but review writing is more elaborate, demanding that the learner explicate verbal representations of his or her thoughts and make associations among pieces of information. That is, review writing triggers self-reflection, which prompts the reviewer to elaborate on and gain a more precise understanding of her or his thoughts (Li, Liu, and Steckelberg 2010). Through this process, the learner can make sense of and interpret her or his experiences, which influences the way he or she thinks about them (Applebee 1984; Glogger et al. 2012; Klein 1999). Thus, compared with simply clicking a numerical rating or assigning some number of stars, writing a review should trigger metacognitive awareness that helps a learner obtain a deeper understanding of his or her learning experiences (Cho and MacArthur 2011; Klein 1999; Kuhrt and Farris 1990). In particular, as reviewers evaluate their experiences, they make associations between newly acquired and previously known information, then integrate them by finding patterns. The realization of the personal value of the learning experience in turn is critical for the reviewers' motivation to expend effort for further learning (Miller and Brickman 2004).
Accordingly, the process must involve learning that is personally relevant; relevant information is needed to motivate salespeople to engage in the effortful cognitive processing of information required to perform this metacognitive reflection (Zaichkowsky 1994). When information has greater personal relevance, a learner can perform better, because that relevance triggers him or her to synthesize information, recognize his or her strengths and weaknesses, and strive for an improved state of knowledge (Zaichkowsky 1994). This recognition also should influence behavior (Boud, Keogh, and Walker 1985), leading salespeople in marketing channels to pursue learning and performance improvements (Anseel, Lievens, and Schollaert2009). For example, systematic after-action reviews conducted between trainers and trainees can stimulate organizational learning (Ellis and Davidi 2005; Villado and Arthur 2013). Through feedback functions, after-action reviews help organizational learners confirm or update their conceptual and metacognitive knowledge (Ellis and Davidi 2005). Despite these broader insights into the benefits of metacognition in organizational settings, insights into how to use it to motivate channel partner representatives to learn and work better in marketing channel relationships are limited.
Previous marketing studies addressing how to trigger channel partners' activity in channel relationships have identified manufacturer investments, such as tangible assets (e.g., equipment, IT systems, facilities; Kim, Cavusgil, and Calantone 2006), intangible assets (e.g., training, coordination support; Pelser et al. 2015), or monetary and nonmonetary incentives (Kashyap, Antia, and Frazier 2012) as potential solutions (for an overview, see Table 1). Yet channel-related activities, such as review writing, have not been addressed as potential tools to activate channel partner behaviors or enhance the learning engagement of these members of the channel. By studying the effects of the review writing activity, we aim to extend prior research that has identified learning opportunities as motivators, such that rather than the straightforward incentive, we consider how metacognitive efforts related to this incentive exert an impetus for further learning.
TABLE: TABLE 1 Marketing Channels Literature Review: Manufacturer Investments and Channel Partners' Activity
TABLE: TABLE 1 Marketing Channels Literature Review: Manufacturer Investments and Channel Partners' Activity
TABLE 1 Marketing Channels Literature Review: Manufacturer Investments and Channel Partners' Activity
| Source | Summary of Relevant Findings | Industry | Manufacturer Investment | Focus |
| Kim, Cavusgil, and Calantone (2006) | The authors examine the effect of two types of innovations in supply chain communication systems for enhancing channel capabilities and performance and find that administrative innovations provide a competitive advantage by improving the responsiveness of the partnership and firm performance. Applied technological innovation does not affect these outcomes, but it influences channel capabilities through interfirm systems integration. | Various | Tangible assets | Supply chain managers |
| Song, Di Benedetto, and Zhao (2008) | The effectiveness of incentives on cooperation between the manufacturer and the distributor is greater in the United States than in Japan. | Manufacturing firms | Incentives | Manufacturers |
| Richey, Tokman, and Dalela (2010) | The authors investigate collaborative technology categories (communication, customization, and data storage) and their effects on firm and partner performance. | Retailing | Tangible assets | Marketing or supply chain managers |
| Gilliland, Bello, and Gundlach (2010) | According to an investigation of the impact of a manufacturer's relative dependence on the distributor for the efficacy of control-based governance tactics, incentives that stem from both unilateral and bilateral control, through social norms, are less effective when the manufacturer is relatively more dependent on the distributor. | Industrial distribution | Incentives | Resellers |
| Hughes and Ahearne (2010) | Organizational identification strengthens the salesperson's adherence to controls; brand identification increases the salesperson's effort for a specific brand and thus improves brand performance. | Distributor sales | Intangible assets | Distributors |
| Chung, Chatterjee, and Sengupta (2012) | Intangible asset investments by the manufacturer in intermediaries relate positively to its reliance on the intermediary. This finding did not hold for tangible assets. | Various | Intangible assets | Distributors, wholesales, retailers, or manufacturer reps |
| Kashyap, Antia, and Frazier (2012) | Contractual completeness reduces monitoring and enforcement efforts; one-sidedness (favoring the franchisor) is associated with more monitoring but reduced enforcement. Extracontractual incentives are associated with increases in monitoring and enforcement. Different combinations of franchisor monitoring and enforcement efforts affect franchisee compliance and opportunism. | Automotive | Incentives | Franchises |
| Gilliland and Kim (2014) | Two components of incentive evaluation (instrumental and congruence) have differing effects on whether a channel partner complies with contractual obligations and wholeheartedly supports the manufacturer's brand(s). Internal (channel partner's dependence) and external (market turbulence) factors moderate these relationships. | IT, beer/brewing | Incentives | Resellers/ distributors |
| Pelser et al. (2015) | Training programs and incentives can trigger channel partners to feel indebted or gratitude toward the manufacturer, which in turn influences commitment and sales effort. | IT | Intangible assets | Resellers |
| Ramas wam i and Arunachalem (2016) | In their Study 2, these authors investigate two strategies that suppliers can use to motivate dealers to promote their products: by (1) using monetary incentives and (2) enabling the dealer to deliver value (product/process/relationship) and better serve its customers. Customer value strategies result in stronger positive impacts on dealer satisfaction than economic incentives, which ultimately influence the dealer's recommendations. | Equipment financing industry | Incentives | Dealers: principals, general managers, finance managers |
Feedback systems require participants to reflect on and assess their past experiences. Although all feedback systems exhibit this retrospective characteristic, they differ in the extent of deliberation required; verbatim reviews are more elaborate than numerical or star ratings. Ratings are simple and straightforward, such that they do not require cognitive elaboration or dedicated effort.
Unlike ratings, written reviews require the reviewer to reflect, more critically and extensively, on his or her experience by putting it down in writing, which can invoke metacognition (Magnifico 2010). As a reviewer engages in a metacognitive process, (s)he reflects on the experience and seeks to apply what (s)he has learned to other contexts (Boud, Keogh, and Walker 1985). This self-reflective evaluation of the learning experience thereby increases the relevance of the learning to the reviewer and may affect performance outcomes (Anseel, Lievens, and Schollaert 2009; Hulleman and Harackiewicz 2009). In effect, through writing and the ensuing self-regulated learning, a reviewer realizes the value of the knowledge gained, which should make him or her more likely to engage in future learning activities. Thus, review writing, as a metacognitive intervention, helps reviewers become more self-aware about what they have learned, increases learning engagement, and raises the likelihood of future learning (Sitzmann and Ely 2010).
In PRM specifically, review writing should activate the self-regulated learning that triggers channel partner salespeople to reflect on their learning experiences with the supplier-provided learning modules and relate these experiences to their day-today business activities. Because these learning modules are designed specifically to help resellers and their employees, the realization that the modules are relevant could motivate participation in additional learning programs.
Hj: Review writing activates channel partner sales employees to pursue further learning more than rating does.
Salespeople must learn continuously and apply their acquired knowledge and skills to their work tasks (Sujan, Weitz, and Kumar 1994; Wang and Netemeyer 2002), so a strong LO is beneficial in both the short and long run (Harris, Mowen, and Brown 2005; Kohli, Shervani, and Challagalla 1998). It even can become manifest at the organizational level (Beli, Mengüç, and Widing 2010).
We posit that salespeople with lower LO might benefit more from review writing, because it helps them recognize the relevance of the learning programs for their professional development in ways that they would not have recognized otherwise. The prompted self-reflection then could pave the way to purposeful behavioral change (Grant, Franklin, and Langford 2002), including increased participation in learning activities. People with higher LO already display an inherent willingness to improve (Dweck 1986; Dweck and Leggett 1988) and recognize the benefits of learning modules, so they likely monitor their learning progress, regardless of whether they engage in review writing.
People with a lower LO are not innately driven to learn and have difficulty motivating themselves to exert the necessary effort to engage in metacognition (Ford et al. 1998). Thus, they may fail to gain a rich understanding of their own thoughts and personal progress in learning situations, and they may be reluctant to seek activities to improve and develop themselves (Sheldon, Dunning, and Ames 2014). Even though people with lower LO possess poorer innate metacognitive skills, it is possible to activate their metacognition through interventions that prompt reflection (Pintrich 2004; Schunk 2005; Veenman, Van Hout-Wolters, and Afflerbach 2006). Accordingly, we predict that for salespeople with lower LO, encouraging them to reflect on their learning by writing a review has a more salient and powerful effect than simply rating the training module.
H2: Review writing (vs. rating) activates low-LO channel partner sales employees to pursue further learning but does not affect those with a high LO.
Reviewers typically have an audience in mind, which defines the purpose of their writing task (Magnifico 2010). The purpose relates closely to the beneficiary of the review, who might be similar or dissimilar to the reviewer. When reviewers consider an audience that is like themselves, they tend to assimilate their point of view into the review, in a process called social metacognition (Jost, Kruglanski, and Nelson 1998). A review provided for a similar other thus contains information that is personally relevant for the reviewer (Lerouge and Warlop 2006; Naylor, Lamberton, and Norton 2011), which is not the case for a dissimilar beneficiary.
If salespeople employed by channel partners review a learning module for their peers, they might write about the returns of their learning experience, such as how the content helps them complete daily work tasks (Miller and Brickman 2004). If salespeople, as learners and reviewers, are primed to think that similar partners will benefit from their reviews, they may engage in more metacognition, through self-reflection, which then should help them realize the relevance of the learning modules to their sales activities and motivate them to participate in additional learning programs. However, if they review them for the supplier (i.e., dissimilar other), they may not engage as effectively in this metacognitive process, such that they would have a harder time realizing the relevance of the learning modules and ultimately would be less motivated to participate in additional learning programs.
H3: Review writing for an audience of peers (similar others) increases channel partner sales employees' pursuit of further learning more than review writing for a manufacturer (dissimilar others) audience.
Salespeople with high LO already engage in metacognition and may not benefit from further interventions (H2; Schmidt and Ford 2003), but those with low LO could benefit even more from writing for peers (i.e., similar others), which prompts them to probe their personal learning experiences more closely than does writing a review for the benefit of the manufacturer (i.e., dissimilar others). This greater stimulated reflection then should result in increased learning engagement (Sitzmann and Ely 2010).
H4: Review writing for an audience of peers (similar others) increases the pursuit of future learning more among channel partner sales employees who have a low (vs. high) LO.
Moreover, channel partner employees identify with the upstream supplier to varying degrees (Hughes and Ahearne 2010). Identification with an organization implies a sense of connectedness and oneness with it (Mael and Ashforth 1992) because of perceived similarities with that group (Gammoh, Mallin, and Pullins 2014). This perception in turn fosters more intrinsic motivation and behaviors congruent with the organization's interests, reflecting an alignment of organizational and personal goals (Badrinarayanan and Laverie 2011; Hughes and Ahearne 2010). Such identification is common with employers, but it also might arise for other partners in a channel context. For example, a salesperson could describe her or his role as "a salesperson of Manufacturer X's product for Reseller Y." Such channel-based relationships do not require formal associations (Badrinarayanan and Laverie 2011), but they can lead to better job performance (Ahearne, Bhattacharya, and Gruen 2005) and prosocial citizenship behaviors (Bhattacharya and Sen 2003) as well as to the potential for conflict among the multigroup identities (Wieseke, Geigenmüller, and Kraus 2012).
If a channel partner's sales employees identify more strongly with the supplier, they focus on how their reviews can benefit that supplier. This attention to the manufacturer's goals is at odds with their natural assimilation with peers. That is, salespeople who identify closely with the supplier may have a harder time recognizing the value of the learning modules for their own and their peers' sales activities, so they could be less motivated to participate in additional learning programs. Formally, we posit:
H5: The positive relationship between review writing for peers (vs. manufacturer) and the pursuit of future learning diminishes when the channel partner sales employees identify strongly with the manufacturer.
The information included in a review may be broad, offering a bird's-eye view of a topic, or more narrow and detailed. In line with conceptual attention research, whether the message is broad or detailed likely influences how reviewers process the information (Friedman and Förster 2005). Messages in reviews might vary in their breadth of topics, with either a broad or detailed perspective (Applebee 1984). Broad conceptual activations trigger global processing of information and activate more concepts in memory, which should prompt people to think about the bigger picture. Writing a broad review also may trigger global processing, which activates associations in memory that do not relate directly to the topic at hand. A reviewer then would reflect on various issues, such as the fit of the learning module with other learning modules completed previously, rather than on the immediate, specific, personal experience with the learning module itself. Detailed concept activations instead induce local processing that triggers reviewers to focus on the specific subject matter (Forster 2012; Friedman and Forster 2005).
Construal-level theory further predicts that a good fit between the level of information stored (e.g., LO) and the information sought increases metacognitive ease (Kyung, Menon, and Trope 2014).[ 1] People with a higher LO take a keen interest in their own personal development and devote time and cognitive effort to pursuing it, so they likely store each learning opportunity at a higher, abstract level, rather than the concrete, detailed level used by people with less expertise (Chase and Ericsson 1981; Ericsson and Kintsch 1995). Therefore, in response to a request for broad, abstract reviews, reviewers with higher LO, who already store the information at higher, abstract levels, can access it readily, whereas those with lower LO, who store more detailed information, may need to exert significant effort to sift through and connect the details gathered from various modules.
Low-LO reviewers writing a broad review likely struggle to identify the essential information or make sense of their learning experience, whereas reviewers with high LO who write a broad review can readily recognize the value of the module and its fit with their learning development. In contrast, if the prompt requests a focused review, channel partners with lower LO can rely on their local processing and write about concrete concepts immediately associated with their learning experience (Forster and Dannenberg 2010). Because they can easily access and focus on the relevant information, they likely engage in self-reflection, develop a greater understanding of the experience, and enjoy greater benefits of the focused review. Thus, the focus of the review should interact with LO as follows:
H6: Writing a broad (vs. narrow) review increases the pursuit of future learning more among reviewers with a high (vs. low) LO.
In a marketing channel context, the focus of the review also should depend on the relationship between the partners. We consider two determinants of this relationship. First, a reseller and supplier might enter a one-time, discrete interaction or pursue an ongoing relational exchange (Palmatier, Dant, and Grewal 2007). Over time, channel actors tend to develop common expectations, adopt a long-term perspective, and focus on the broader business environment in which both parties operate (Palmatier, Dant, and Grewal 2007). We thus consider the moderating effect of relationship length, which is distinct from identification with the supplier, in that it pertains to the achievement of the reseller's goals within the channel, whereas identification centers solely on the supplier's goals. Accordingly, the consideration of the broader context that stems from greater relationship length may affect the way in which the focus of the review influences the reviewer's future learning participation. Over time, having gained a broad understanding of the business context in which the channel relationship is embedded, the reviewer can relate his or her own strengths and weaknesses to this context and compare the value of the learning module with this extended frame of reference. Therefore,
H7: The positive relationship between writing a broader review and the pursuit of future learning increases with channel relationship length.
Second, exclusivity in a channel relationship determines the power that the channel parties can exert, their motivations to work, and the channel's structures and performance (Antia, Zheng, and Frazier 2013; Gilliland and Kim 2014; Palmatier, Dant, and Grewal 2007). Reliance on one upstream supplier (vs. many) likely increases a reseller's in-depth attention to this specific relationship and this supplier's offerings (Gilliland and Kim 2014), but it also can increase the risk of channel conflicts (Koza and Dant 2007). Accordingly, many channel partners enter contractually exclusive agreements or preferred partnerships, which limits their exposure to the product portfolios of other suppliers. Conversely, nonexclusive channel partners, with their wider consideration sets, gain access to a wider range of information, which may lead to a broader focus on various learning experiences and partner relationships across the board. Therefore, nonexclusive channel partnerships may counteract the influence of taking a narrow focus in review writing and its self-reflective thinking.
H8 : The effect of review focus on the pursuit of future learning diminishes in exclusive (vs. nonexclusive) channel partner relationships.
Several other variables influence channel partners' behaviors as well, beyond the hypothesized communication process variables. Therefore, we account for these general effects in our analyses. For example, sales experience, or the time a salesperson has functioned in this occupation, influences people's attitudes, perceptions, and sales performance (Cron and Slocum 1986). Several studies also note the influence of sales experience on empowerment, effort, and behavior (Ahearne, Mathieu, and Rapp 2005; Ahearne et al. 2010). Because sales experience might be confounded with the effects we predict, we include it as a control variable in all our studies.
We also acknowledge the different types of intermediaries within channels. Our sample comprises salespeople employed by resellers and distributors. Both types of intermediaries are independent of the manufacturer, but they differ in their commitments and investments to the relationship. For example, distributors tend to take more responsibility and ownership for products and likely provide a wider range of services to customers on behalf of the manufacturer. Such stronger commitments increase these channel members' reliance on the manufacturer (Chung, Chatterjee, and Sengupta 2012), which could have an influence on the hypothesized effects. We therefore control for the type of intermediary in all our studies.
Finally, reviews can be used to express affect or emotions (Ludwig et al. 2013). Reseller salespeople who write a positive review about their learning experience may be more likely to engage in future learning opportunities. Conversely, negative reviews may indicate potential for learner dropout. Thus, we consider the effects of review valence on further learning engagement.
These studies involve the global channel partners of one of the world's best-known technology brands and enable us to assess whether reviewing (vs. rating) a learning module increases the total number of subsequent learning modules completed by channel partners and to specify the potential moderating role of LO (Study 1). We also assess the effects of the audience (peer vs. supplier) and the moderating role of audience factors (LO and identification with the supplier) (Study 2). Finally, we examine the role of message focus (broad vs. narrow review) and the moderating effects of LO and two relationship factors (length and exclusivity of relationship) (Study 3).
The Fortune 100 manufacturer that cooperated with us on this research project makes its PRM central to its go-to-market strategy, because it regards its channel partners as extensions of its sales force. Through these resellers, it has been increasing its sales performance each year, and the partners are increasingly critical to its growth. To ensure continued success, learning programs within the PRM enable salespeople employed by the channel partners to operate effectively within the business ecosystem by providing them with resources to expand their capabilities and deliver value-added services.
The learning modules themselves reflect self-directed learning principles. Content appears in an interactive format, rather than in a traditional linear fashion, so that learners may focus on content that they deem important (e.g., conversation starters with clients). The sales-related content generally follows a three-lesson structure (i.e., value propositions and competitive positioning, in-depth offering information, and steps in the sales process). In addition, technology-related modules detail product specifications. Most of the modules pertain to specific offerings (e.g., cloud solutions), but some more generic modules on marketing, social media, analytics, financing, leasing, and pricing are available, too.
Learners usually need just under a half-hour to review the content in a module (though they may review more detailed content by clicking on hyperlinks). In principle, the modules are voluntary, and channel partners/resellers can complete as many modules as they like, such that there are no formal dependencies between modules. However, learning roadmaps offer some guidance, such as revealing which set of modules would enable participants to earn different forms of certification that are specific to the industry (e.g., analytics, cloud, security, storage, financing, social commerce). Approximately 35,000 employees of the supplier's resellers have successfully completed at least one certification track. Other guidelines also identify which modules pertain to a new product line or special theme (e.g., Flash systems boot camp, software-defined storage immersion).
To be counted, the learner must demonstrate comprehension by passing (scoring better than 80%) a brief multiple-choice test at the end of each module. The system also unobtrusively records the number of page visits. Because the information must be current to be relevant, the supplier reviews all the modules it provides frequently and updates them when new products or updates are released. The PRM program also relaunches each year, with new content and (marketing) promotions.
All employees of resellers who participated in these studies were subscribed to the manufacturer's learning program, and we used unique channel partner identifications to ensure that no one participated twice. Prior to the studies, the supplier had not incorporated any feedback system. During the 2012-2013 period we study, it introduced 35 learning modules at different intervals, available to all its channel partners. In total, 61% of the manufacturer-provided modules were technical in focus, and 39% were sales focused.
The implicative value of this study also rests on the conventional premise that when salespeople engage in learning, it benefits the firm in the form of increased revenues. To establish the validity of this assumption in our PRM context, we examined the relationship between learning module uptake and revenues for 657 channel partners in one of the company' s major product categories in the U.S. market across four quarters (2016 Q4 and 2017 Q1, Q2, and Q3). The significant Pearson correlation coefficient (.21, p < .01, two-tailed) between the number of modules completed and total revenue generated by these channel partners confirms this basic premise.
With Study 1, we examine whether the method of providing feedback (i.e., writing about vs. rating the learning module) results in differences in channel partner sales employee (or reviewer) engagement over time (Hj). We also investigate whether this effect is moderated by the LO of the reseller employee (H2). As a measure of engagement, we collected company data about the number of learning modules that each reviewer completed in the three months following his or her review or rating. That is, we asked all the learners in the data set to provide feedback about one learning module they had completed, either by writing a review or by offering a rating, and we observed how this action affected the number of modules they completed after three months.
The design for Study 1 involved 88 participants who had subscribed to the learning program in the manufacturer's PRM; in return for their participation, they earned points in the manufacturer' s incentive program. Participants either wrote a review (e.g., "In the space below, please provide feedback on the module. Please write at least 20 words about the module [the ideal length of feedback is approximately 75 words]") or provided a rating of a learning module (e.g., "Please rate the module on a scale of 1-10 [1-lowest; 10-highest]. Click on the pointer and slide it to the desired rating"). We excluded three participants from the analysis: one who could not write in the language of the study, another who was assigned to the review condition but did not write a review, and a third participant who did not take the study seriously (e.g., inputting an HTML address when asked to indicate age). Thus, the sample consisted of 85 participants (Mage = 38.05 years, SDage = 7.91 years; Msales_experience = 9.53 years, SDsales_experience = 7.13 years; 8.20% women; 83.50% resellers; 30.60% worked exclusively with the manufacturer).
These participants considered a dropdown menu of modules offered by the program and selected one they had completed in the previous six months. This menu ensured that participants had a stable set of modules to select from, could easily recall those they had taken, and could recall the module name. Next, they provided a written review in the space provided or rated the module. Participants also completed an adapted, four-item version of a LO scale (Elliot and Church 1997; " I want to learn as much as possible from the modules that I take," "It is important for me to understand the content of each module as thoroughly as possible," "I always seek to have abroad and deep knowledge of each subject discussed in a module," and "I desire to completely master the material presented in each module I take"; α = .85; M = 6.06, SD = .76; min = 4.00, max = 7.00), measured on a seven-point Likert scale (1 = "strongly disagree," and 7 = "strongly agree"). We averaged the four items, such that higher values indicated higher LO. We also incorporated two control variables for the model estimation, sales experience and user type (reseller or distributor), to control for individual differences that could affect the number of modules taken. Three months after the study, we collected behavioral information about the number of modules each participant had completed. During the study, participants took an average of 1.78 (SD = 3.09) modules. Web Appendix 1 contains further descriptive information and the correlation tables.
We estimated H1 using a zero-inflated Poisson (ZIP) model because the number of modules represented count data, and we found a considerable amount of zero values (50.6%). A Vuong (1989) test confirmed the applicability of this model for our data. The dependent variable was the number of modules completed 90 days after the study; the independent variables were the review format (review = 1; rating = 0), LO, and their interaction. Moreover, we included sales experience and user type as covariates.
As the results in Table 2 show, we uncover a significant main effect for the review format manipulation (β = 4.16, incident rate ratio [IRR] = 63.83, p = .02), as predicted in Hj. The IRR results (Long and Freese 2006) suggest that providing written reviews increases the number of modules subsequently completed, by a factor of 63.83, with all other variables held constant. We find no significant effect of LO (β = .34, IRR = 1.41, p = .08), but the interaction between review format and LO is significant (β = -.66, IRR = .52, p = .03).
TABLE: TABLE 2 Results of Studies 1-3
TABLE 2 Results of Studies 1-3
| A: Study 1 (H1-H2)a |
| Number of Modules |
| Coefficient | SE | IRR |
| Constant | .04 | -1.38 | 1.04 |
| Feedback (rating = 0; reviewing = 1) | 4.16 | (1.83)* | 63.83 |
| LO | .34 | -.20 | 1.41 |
| Feedback χ LO | -.66 | (.30)* | .52 |
| Sales experience | -.08 | (.02)** | .93 |
| User type (distributor = 0; reseller = 1) | -.46 | -.30 | .63 |
| Log-likelihood | -157.39 | | |
| BIC | 345.88 | | |
| AIC | 328.78 | | |
| B: Study 2 (H3-H5)b |
| Number of Modules |
| H3-H4 | H5 |
| Coefficient | SE | IRR | Coefficient | SE | IRR |
| Constant | -1.55 | -.99 | .21 | .77 | (.38)* | 2.15 |
| Purpose (manufacturer = 0; peers = 1) | 6.20 | (1.38)** | 494.70 | 1.49 | (.36)** | 4.46 |
| LO | .37 | (.15)** | 1.45 | | | |
| Purpose χ LO | -.96 | (.23)** | .38 | | | |
| Sales experience | .05 | (.01)** | 1.05 | .04 | (.01)** | 1.04 |
| User type (distributor = 0; reseller = 1) | .44 | (.17)** | 1.55 | .44 | (.17)** | 1.55 |
| Identification | | | | -.03 | -.07 | .98 |
| Purpose χ Identification | | | | -.24 | (.09)** | .79 |
| Log-likelihood | -164.22 | | | -161.04 | | |
| BIC | 356.98 | | | 350.62 | | |
| AIC | 342.44 | | | 336.07 | | |
| C: Study 3 (H6-H8)c |
| Number of Modules |
| H6 | H7 | H8 |
| Coefficient | SE | IRR | Coefficient | SE | IRR | Coefficient | SE | IRR |
| Constant | -1.57 | -1.81 | .21 | .34 | -.34 | 1.4 | .55 | -.33 | 1.73 |
| Perspective (broad = 0; narrow = 1) | 4.57 | (2.10)* | 96.26 | .03 | -.26 | 1.03 | -1.08 | (.28)** | .34 |
| LO | .31 | -.28 | 1.37 | | | | | | |
| Perspective × LO | -.83 | (.34)* | .44 | | | | | | |
| Sales experience | .03 | (.01)** | 1.03 | .02 | (.01)* | 1.02 | .03 | (.01)** | 1.03 |
| User type (distributor = 0; reseller = 1) | 1.24 | (.31)** | 3.46 | 1.01 | (.30)** | 2.75 | 1.10 | (.30)** | 3.01 |
| Relationship exclusivity | | | | | | | -.24 | -.27 | .79 |
| (0 = nonexclusivity; 1 = exclusivity) | | | | | | | | | |
| Perspective × Channel dependence | | | | | | | 1.27 | (.43)** | 3.57 |
| Relationship length | | | | .03 | (.02)* | 1.04 | | | |
| Perspective × Relationship length | | | | -0.06 | (.02)** | .94 | | | |
| Log-likelihood | -126.84 | | | -127.02 | | | -125.08 | | |
| BIC | 282.09 | | | 282.46 | | | 278.59 | | |
| AIC | 267.67 | | | 268.04 | | | 264.17 | | |
*p < .05.
**p < .01.
aN = 85 (nonzero = 42; zero = 43).
bN = 59 (nonzero = 34; zero = 25).
cN = 58 (nonzero = 32; zero = 26).
Notes: BIC = Bayesian information criterion; AIC = Akaike information criterion. Results are unchanged when we exclude the sales experience control variable (the interaction Z-value goes from 2.96 to 2.92).
To explore the moderating influence of LO, we used the margins command in STATA12 (Williams 2012) to obtain estimates of the conditional marginal effects (or simple effects; Spiller et al. 2013) across values of LO, ranging from the observed minimum ( 4) to the observed maximum ( 7). Significant differences arise between the rating and writing formats for LO values between 4.37 and 5.61 (5% significance). At higher values of LO, we find no significant differences between formats, in support of H2. Reviewers with lower LO are more likely to pursue additional modules after reviewing, rather than rating, a learning module, whereas at higher levels of LO, we find no significant differences between the two formats (Figure 2). Web Appendix 2 offers a comparison of the results when we use the number of modules completed within 30 days as the dependent variable; the results are substantively the same, with coefficients that are similar in their direction and significance.
Firms invest substantially in developing learning programs to encourage employees of their channel partners to participate; increase their skills and product knowledge; and, thus, ideally achieve more sales. Study 1 lends support to H by revealing a positive main effect of review writing (vs. rating), which suggests that writing about one's own learning experience can lead to an increase in the number of modules taken. In addition, we find support for H2, such that writing reviews of a learning module can drive resellers who are less motivated learners to complete more modules during the subsequent three-month period than asking them to provide ratings. We explain this finding by positing that the review-writing process activates metacognitive thinking, which triggers participants with lower LO to evaluate their learning experience. However, writing reviews does not affect reviewers with higher LO, potentially due to ceiling effects. With a posttest, we investigate the underlying mechanism.
The posttest involves 100 participants (Mage = 33.2 years; 28% women), gathered from Amazon Mechanical Turk. Before the study, a filtering question checked the eligibility of these respondents—namely, that they had performed a sales function at work within the previous seven years. In welcoming those who qualified for the study, an introduction noted that they would be tasked with evaluating a training program for sales representatives. Nine participants (9%) were excluded, for various reasons: two could not write in English, two did not find the scenario believable, three indicated they had previously seen the video, and two noted that they did not pay attention to the video. The 91 remaining participants were instructed to imagine themselves as sales representatives of a fictitious company, MV Europe (Scheer and Stern 1992). Specifically, the instructions read:
Please read the following text carefully. As you read, imagine that you are in the following situation: You are a sales representative for MV Europe. MV Europe offers a full range of analytical tools and provides support and training to help get clients' analytical projects up and running. MV Europe has decades of project experience across analytical platforms including Alteryx, Lavastorm, and Rapid Minder, as well as with their core business products SAS and IBM SPSS. It is your responsibility to acquire new clients and close sales deals. As one of MV Europe's business partners, IBM offers you voluntary online educational modules to assist you with building your skills and knowledge of IBM's products and solutions portfolio, to make you an even more essential resource for your clients.
A recent set of modules that has been developed in collaboration with the Aberdeen Group is a series of four sales enablement modules. On the following page, you will be exposed to a snippet of one of these modules, entitled "Social Selling: Unleashing the Power of Social Media on B2B Sales Enablement." Please watch the snippet of the module carefully from the perspective of a sales representative for MV Europe. Afterwards, you will be asked to provide feedback about the module. Please put on your headphones now. If you are ready to continue, please click on the arrow button below.
Next, the participants watched a snippet (i.e., last 4 minutes) of a 14-minute video about the importance of social media in business-to-business sales. This snippet summarized the issues discussed in the full-length video. After viewing the video, the participants were randomly assigned to a feedback manipulation, to either review or rate the video, as in Study 1. Because they watched only a snippet, we asked participants whether they were interested in watching the rest of the 14-minute video, which provides the dependent variable to test learning engagement. For the mediator, we assessed relevance using a three-item scale (α = .85, M = 5.81, SD = .95): "I believe that this online learning module offers valuable insights," "How relevant do you feel this online learning module is to your performance as a sales representative of MV Europe?" and "How connected did you feel this type of training module was to help you as a sales representative of MV Europe do your job better?" (Drewery, Pretti, and Barclay 2016).
To test the effect of the review format on interest as the dependent variable, through the mediation of relevance, we used PROCESS Model 4 (Hayes 2013). The mean indirect effect in the bootstrap analysis (bootstraps = 5,000) is positive and significant (a × b = .23), and the 95% confidence interval does not include 0 (.08, .44). In the indirect path, reviewing (vs. rating) increases relevance (a = .24, b = 1.01), so holding the manipulation constant, a unit increase in relevance increases learning engagement. The direct effect of c (.09) is not significant (p = .491), in support of full or indirect-only mediation (Zhao, Lynch, and Chen 2010). That is, writing reviews can trigger metacognitive thinking by prompting participants to reflect on the relevance of the learning to their own experiences. In realizing the relevance of the learning experience, participants become more motivated to engage in further learning opportunities. Study 2 investigates how the audience of a review might affect further learning.
When the audience for the review is perceived as similar (i.e., peers or other resellers), it likely activates self-reflection because the reviewer assumes that his or her thoughts and preferences are in line with those of this audience (Lerouge and Warlop 2006), which is not the case when the audience is perceived as dissimilar. Therefore, prompting participants to write a review for a similar other (e.g., peers) rather than a dissimilar other (e.g., manufacturer) may induce metacognition, which could encourage their participation in new modules (H3). Channel partners with lower LO could benefit from the self-focus that such writing provides, which could also translate into increased participation (H4). Similarly, channel partners with lower identification with the supplier could benefit from the self-focus that such writing provides, which could translate into increased participation (H5).
The 64 participants received points from the manufacturer' s incentive program. They also wrote reviews, for the benefit of either their peers (e.g., "In the space below, please provide feedback on the module. Your feedback will help improve the quality of the learning modules for other [program name] members. Please write at least 20 words about the module [the ideal length of feedback is approximately 75 words]") or the manufacturer that hosted the learning program (e.g., "In the space below, please provide feedback on the module. Your feedback will help [manufacturer] improve the quality of their learning modules. Please write at least 20 words about the module [the ideal length of feedback is approximately 75 words]"). Five participants were removed from the analysis (7.8%): one who wrote incomprehensible gibberish, two identical entries, suggesting the same person participated in the study twice, and two others who simply cut and pasted the description of the module into their review. The remaining 59 entries entered our analysis (Mage = 39.10 years, SDage = 8.85 years; Msales_experience 10.61 years, SDsales_experience = 7.01 years; 18.60% women; 67.80% resellers; 35.60% worked exclusively with the manufacturer).
Participants also completed the four-item LO scale (α= .89; M = 5.87, SD = .88; min = 3.00, max = 7.00). To assess identification with the manufacturer, we use an adapted version of the Inclusion of Other in Self scale (Aron, Aron, and Smollan 1992), which consists of two circles that vary in their degree of overlap. Participants then must "assume that in each pair of circles in the scale, one circle represents you, while the other represents [manufacturer]. Please select the pair of circles that most accurately represents how close you feel to [manufacturer]." Sales experience and user type are covariates, and we again collected behavioral information after three months. During the study, participants took an average of 3.34 (SD = 5.57) modules.
Audience X LO. We estimate a ZIP model, confirmed as appropriate by a Vuong (1989) test. The dependent variable is the number of modules completed 90 days after the manipulation; the independent variables are the audience manipulation (peers = 1; manufacturer = 0), LO, and their interaction. Sales experience and user type serve as covariates.
The results in Table 2 provide support for H3, such that writing for the benefit of peers increases the number of modules that channel partners take in the subsequent three-month period (β = 6.20, IRR = 494.71, p < .01), compared with writing for the supplier. Moreover, LO affects this measure, such that higher LO corresponds to more modules taken (β = .37, IRR = 1.45, p = .01). As we predicted in H4, the interaction between the audience manipulation and LO is significant (β = -.96, IRR = .38, p < .01): low-LO participants who write for peers are more likely than their counterparts who write for the manufacturer to complete more modules, but this difference does not arise among participants with high LO.
We also use the margins command in STATA12 (Williams 2012) to obtain estimates of the conditional marginal effects (Spiller et al. 2013) at LO values ranging from the observed minimum ( 3) to the observed maximum ( 7). Significant differences arise in the audience manipulation for values of LO ranging from 3.26 to 6.12 (5% significance). At higher values of LO, we find no significant differences across conditions, in support of H4. Participants with lower LO pursue more additional modules after providing a written review for the benefit of similar others (peers) than if they write for dissimilar others (manufacturer), whereas those with higher levels of LO show no significant differences (Figure 3).
Supplemental mediation analysis. To confirm that writing reviews for similar others triggers the reviewer to reflect on the relevance of the learning module, two independent coders with professional business experience read the randomized reviews and assessed the relevance of the module to the author of the review on a five-point scale (1 = "not at all relevant," and 5 = "very highly relevant"), similar to the procedure in Krishnamurthy and Sivaraman (2002). Their intercoder agreement revealed a KrippendorfFs alpha value of .82 (above the critical threshold of .80). Under the supervision of one of the authors, the coders discussed any disagreements until they reached consensus (M = 2.25, SD = 1.31).
To understand whether relevance drives the effects, we accordingly conducted a test of mediated moderation, following the procedure recommended by Muller, Judd, and Yzerbyt (2005), in which we estimate three regressions. First, we assess the moderation of the overall treatment effect with a ZIP model that features the number of modules as the dependent variable and the audience manipulation, LO, and their interaction as independent variables, as well as sales experience and user types as covariates. We find consistent evidence for the predicted interaction between the audience manipulation and LO on the number of modules taken (β = -.96, IRR = .38, p < .01). Second, we run a linear regression model that contains relevance as the dependent variable and the same independent variables, interaction, and covariates to investigate the treatment effect on this mediator. The effect of the audience manipulation on relevance is moderated by LO (β = -1.28, p = .01). Third, we estimate a final ZIP model that includes the relevance mediator and its interaction with LO in the model from the first step. The findings reveal a significant effect of relevance on the number of modules (β = -2.83, IRR = .06, p < .01). The residual direct effect of the audience manipulation on the number of modules also is less moderated by LO after we control for relevance and its interaction with LO (β = -1.03, IRR = .36, p < .01), as indicated by the minor yet significant decrease in IRR from .38 in the first step to .36. Thus, relevance mediates the impact of writing for a similar (vs. dissimilar) audience and LO on the number of modules completed. We also test for the effect of alternative mechanisms, such as feelings of identification and ownership, but find no significant treatment effects.
Audience × Identification with manufacturer. With another ZIP model, we assess the effect of identification with the manufacturer. The main effect of identification is not significant (β = -.03, IRR = .98, p = .707), but its interaction with the audience manipulation is (β = -.24, IRR = .79, p < .01). Using the margins command of STATA12, we obtain estimates of the conditional marginal effects at different identification values. Significant differences appear in the audience manipulation for values of identification below 4.68 (5% significance); no significant differences emerge for values of 4.68 or above. Therefore, the less the participant identifies with the manufacturer, the greater the difference of the effect invoked by writing for similar versus dissimilar audiences. In support of H5, the positive relationship between review writing for similar (vs. dissimilar) others and the reviewer's pursuit of future learning weakens when (s)he identifies strongly with the dissimilar other.
The findings related to H3 and H4 are in line with the concept of social metacognition, such that reviewers with lower LO use their self-knowledge to stand in for the thoughts of others who are similar to them (i.e., peers). They share their personal experiences in their reviews, which helps them make sense of their learning experiences and realize the relevance of what they have learned for their day-to-day activities, thereby leading to greater future engagement in learning modules. But if they identify with dissimilar others (i.e., the manufacturer), the positive effect of review writing on this form of engagement is disrupted, as we predicted in H5.
By considering the audience for a review, we clarify how writing a review can lead reviewers to complete additional learning modules. Not all review writing effectively enhances engagement; only that which helps the participant focus on self-relevant information does so. We offer evidence that focusing on self-relevant information triggers reviewers to realize the relevance of the learning module for their work activities in the channel. Study 3 explores the message itself, according to its broad versus narrow focus.
The focus of the review might be broad or detailed, such that it relates to global or local processing styles, respectively. The activation of global processes could trigger more associations in memory that are not directly related to the reviewer's learning experience, which may prevent his or her realization of the relevance of the learning module to day-to-day channel activities. A broad focus also implies accounting for more information, which might overwhelm people with low LO and make it difficult for them to retrieve relevant information related to their learning experience. In contrast, we expect greater participation among channel partners with lower LO who provide a narrow-focused review (H6). When the channel relationship is longer (H7) and involves exclusive contractual arrangements between the reseller and supplier (H8), the impact of the broader review focus also should increase future engagement behavior.
The 65 participants received points from the manufacturer's incentive program. They were instructed to write either a broad review about how the module fit within their overall learning program (e.g., "In the space below, please provide feedback on how the module fits with your overall learning program within [program name]. Please write at least 20 words about the module [the ideal length of feedback is approximately 75 words]") or a narrow review of the module itself (e.g., "In the space below, please provide detailed feedback on the module [e.g., helpfulness, difficulty level, comprehensiveness]. Please write at least 20 words about the module [the ideal length of feedback is approximately 75 words]"). We excluded seven participants (10.80%), because one wrote no reviews, another indicated that he had 100 years of sales experience, one indicated that he could not write in the language of the study, two had incomplete entries, and two simply cut and pasted the description of the module into their review. Thus, the sample contained 58 participants (Mage = 39.91 years, SDage = 10.36 years, Msales_experience = 10.64 years, SDsales_experience = 9.47 years; 20.70% women; 77.60% resellers; 41.40% worked exclusively with the manufacturer).
The procedure was similar to that of Studies 1 and 2. Participants selected a module they had completed from a dropdown menu, then provided a written review in the space provided. They also completed the four-item LO scale (α = .91; M = 6.06, SD = .79; min = 3.50, max = 7.00), indicated whether they sold products exclusively for the manufacturer (as a measure of exclusivity), and provided the length of the relationship with the supplier in years. We use their sales experience and user type (distributor or reseller) as covariates. Three months after the study, we collected the number of modules each participant completed. During the study, they took an average of 2.60 (SD = 4.20) modules.
Focus X LO. A ZIP model again is appropriate (Vuong 1989). Table 2 contains the model estimates: the focus manipulation (1 = narrow review; 0 = broad review), LO, and their interactions, as well as sales experience and user type as covariates. The focus manipulation has a significant main effect (β = 4.57, IRR = 96.26, p = .03), LO does not affect the number of modules (β = .31, IRR = 1.37, p = .26), and the interaction between review focus and LO is significant (β = -.83, IRR = .44, p = .02), in support of H6.
The margins command in STATA12 (Williams 2012) again provides the estimates of the conditional marginal effects (Spiller et al. 2013) across the observed minimum (3.5) and maximum ( 7) values of LO. We find significant differences for lower LO values, ranging between 3.50 and 4.10 (10% significance), such that reviewers with lower LO pursue additional modules after providing a narrowly focused review, rather than a broad one, unlike learners with higher levels of LO. Furthermore, we identify significant differences between the broad and narrow focus manipulations for higher LO values, ranging from 5.94 to 7, at a 5% significance level. Here, higher-LO reviewers who provide broad reviews are more likely to pursue additional modules than if they write a detailed review (Figure 4). These results highlight the importance of the joint effects of the focus of the review and LO.
Mediation analysis. Following a procedure similar to that for Study 2, independent coders rated the relevance of the reviews (Krippendorffs α = .94). First, we find evidence of the predicted interaction between the focus manipulation and LO on the number of modules taken (β = -.83, IRR = .44, p = .02).
Second, a linear regression model contains relevance as the dependent variable, and the rest of the variables remain the same. The effect of the focus manipulation on relevance is moderated by LO (β = -.86, p = .06). Third, a final ZIP model includes relevance and its interaction with LO from the first model and reveals a significant effect of relevance on the number of modules taken (β = -1.86, IRR = .16, p = .04). The residual direct effect of the focus manipulation on the number of modules is less moderated by LO when we control for relevance and its interaction with LO (β = -1.06, IRR = .34, p < .01; i.e., IRR decreases from .44 in the first step to .34).
Focus × Relationship length. To test H7, we investigate the moderating effect of relationship length using another ZIP model. We find a significant interaction between the focus manipulation and a longer relationship, which implies greater sales experience with the supplier's products (β = -.06, p < .01, IRR = .94) after the channel relationship has lasted for at least seven years (5% significance). In support of H7, the impact of review type on the reviewer's pursuit of future learning increases with the length of the channel relationship.
Focus × Relationship exclusivity. With another ZIP model, we assess the influence of relationship exclusivity, as indicated by the presence or absence of an exclusivity agreement between the reseller and the supplier. The main effect of exclusivity is not significant (β = -.24, IRR = .79, p = .372), but its interaction with the review focus manipulation is (β = 1.27, IRR = 3.57, p < .01). When reviewers are employed by firms that do not have exclusive relationships with the supplier, the difference between writing a broad or a narrow review is significant (dy/dx = -2.23, z = -3.23, p = .001). If such exclusive relationships exist though, this difference is not significant (dy/dx = .56, z = .69, p = .489). These findings support H8.
The support for H7 and H8, regarding the moderating influences of relationship length and exclusivity, may reflect the strength of the relationship effects. That is, when salespeople are dedicated solely to the supplier's products or have been selling it for more time, they have stronger relationships with the supplier. This relationship strength likely helps them make mental connections and reflect on various issues related to the product, the supplier, and their job or channel requirements. Such reflection should enable them to recognize the relevance of the learning modules they have taken, especially when they write a broad review.
Further considerations. Reviews might be positive or negative, so valence could also have an impact on future learning engagement. We perform exploratory post hoc analyses to investigate this issue, using the data from Studies 2 and 3. Web Appendix 3 contains the results, including the findings about the effect of negative emotional words on learning engagement. Yet review valence does not affect the reported findings or their interpretation, so we do not discuss it further here.
Regarding the potential interdependencies of the study manipulations in Studies 2 and 3, in a study (reported in Web Appendix 4), we analyze the three-way interaction of both manipulations with salespeople's LO. These results indicate that when they write for the benefits of peers, participants with low LO take more modules if they write narrowly; participants with high LO engage in more modules if they write broad reviews. This finding highlights the influence of focusing on similar peers, as well as the need to consider review perspectives and reviewers' learning abilities to predict outcomes (see Web Appendix 4).
Learning can be an instrument for channel engagement—particularly if learners reflect on their learning experiences, through feedback systems, and develop greater future engagement likelihood. Drawing on self-regulated learning theory, we attribute this drive to review writing, which engages the reviewers, particularly those with low LO, to reflect on the relevance of the learning content to their channel activities, resulting in a heightened pursuit of additional learning (Study 1). Certain types of review writing, for different audiences and with varying levels of focus, also have stronger effects on engagement than others. If reviewers provide a review for similar others, they reflect more on the relevance of the learning, because they believe that peers have similar preferences. This in turn leads to a heightened pursuit of additional learning programs (Study 2). Moreover, if people write with a message focus that matches their LO, it also increases additional learning (Study 3).
This study draws on self-regulated learning theory, yet its theoretical framework, concepts, and implications are grounded in marketing channel literature. In Table 3 we explicate, for each hypothesis, the theoretical perspectives that support our argumentation, highlighting those that are specific to marketing channels.
TABLE: TABLE 3 Overview of Theoretical Perspectives
TABLE: TABLE 3 Overview of Theoretical Perspectives
TABLE 3 Overview of Theoretical Perspectives
| Hypothesis and Key Concept | Theoretical Perspectives | Argument | Results |
| Hi : Review format | Self-regulated learning, involvement (relevance) | Unlike providing ratings, writing reviews requires channel partners to deeply and critically reflect and assess their learning experience. This self-reflection leads to a realization of the experience's relevance for their own work, which motivates further learning. | Supported |
| H2: Review format × LO | Self-regulated learning, involvement (relevance), goal orientation | Channel partners without an innate drive to learn (low LO) benefit particularly from review writing because they come to realize the relevance of the module for their business activities. High-LO learners who possess high metacognitive skills already are able to assess the value of the learning experience. | Supported |
| hls: Review audience | Self-regulated learning, involvement (relevance), social meta-cognition | When channel partners consider a review audience that is like themselves and believe that a peer may benefit from their review, they assimilate this point of view. The similarity allows them to realize the relevance of the learning modules for peers, which is not the case if the audience is the manufacturer, which has different daily tasks and goals. | Supported |
| H4: Review audience × LO | Self-regulated learning, involvement (relevance), social metacognition, goal orientation | Channel partners without an innate drive to learn (low LO) benefit particularly from review writing for peers, because they come to realize the learning module's relevance. High LO learners who possess high metacognitive skills already understanding the relevance of learning for peers' activities. | Supported |
| hls: Review audience × Identification level | Self-regulated learning, channel partner identification | Strong identification with the supplier fosters a sense of connectedness and alignment with its goals. Such identification diminishes the channel partner's assimilation with peers and the stronger learning engagement induced by writing for peers. | Supported |
| H6: Review focus × LO | Self-regulated learning, involvement, construal level, goal orientation | A channel partner's LO determines its ability to engage in global, broad processing of information for metacognition. To realize the relevance of information, high LO reviewers process abstract information linked to the entire learning development and benefit from a broad review focus; low LO reviewers process locally and prefer narrow information related to a specific learning experience. | Supported |
| My: Review focus × Relationship length | Self-regulated learning, construal level, channel partner relationship | Over time, a channel partner is better able to understand the business context in which the relationship is embedded. This understanding supports a broad focus of a review, so the link of a specific learning experience with the entire learning development and its influence on channel partner engagement grows stronger. | Supported |
| Ha: Review focus × Relationship exclusivity | Self-regulated learning, construal level, channel partner relationship | A channel partner that engages in exclusive channel contracts limits its own exposure to the product portfolios and sales approaches of other suppliers. Its limited understanding of the wider business context and other reference points diminishes the effect of review focus on specific learning experiences or the wider learning trajectory, as well as its influence on channel partner engagement. | Supported |
Beyond these theoretical links to extant research, our findings advance this literature stream. First, by examining feedback systems as tools for engagement, in line with the shift toward relationship marketing in channel contexts (Palmatier 2008), we move beyond a traditional sense that feedback functions only as input that the manufacturer leverages to improve its offerings (Golder, Mitra, and Moorman 2012; Voss et al. 2004). Our research highlights how the very process of review writing relates to self-regulated learning, triggering channel partners to reflect on the relevance of their learning experience, which influences their future learning behaviors. By considering whom they write for and how, we also provide additional support for the presence of metacognition, in that reviewers prompted to consider self-relevant information related to their learning experiences display a higher propensity to complete additional learning modules. We introduce previously unexamined, positive consequences of review writing tasks, thereby opening the theoretical realm to include both direct benefits of feedback (e.g., improved service) and its indirect benefits due to behavioral and motivational transformations. These theoretical implications, identified in a channel context, might emerge in customer-firm relationships, such that feedback systems might help embed customers with the organization too (Bhattacharya and Sen 2003). Leading customers to participate actively with the firm and enter a reflective process may help them recognize the benefits of continued participation and engagement with the organization. Motivating channel partners to undertake actual channel-related activity thus represents an alternative to manufacturers' typical investments in tangible assets, intangible assets, or monetary and nonmonetary incentives (see Table 1).
Second, in demonstrating that the self-reflective evaluation of learning experiences drives the effects of review writing on engagement, we extend research on (self-)reflection, beyond considerations of it as a tool to learn (Ellis et al. 2014; Schippers, Homan, and Van Knippenberg 2013). In a channel context, in which manufacturers use training systems to empower sales forces, if salespeople (especially those with low LO) write reviews, they can make better sense of their learning experience and gain a deeper understanding of its benefits. Reflectingonthe relevance of the learning experience makes them more willing to engage in additional learning, because they pursue further benefits for their performance in the channel.
Third, though outside the scope of our study, education research has shown that students who reflect on the personal value of class material exhibit increased interest in the course and their class performance (Hulleman and Harackiewicz 2009). With our channel-based study context, we extend this view by including feedback systems that represent subtle rather than explicit demands that learners make connections. Such insights are beneficial, because among professionals, the latter tactic might create backlash.
Fourth, these findings indicate the importance of including reflections about the relevance of the learning experience as mediating mechanisms. Contingent on the audience and focus, these mechanisms help explain channel partners' engagement and the returns of relationship-specific channel investments on financial outcomes. Investments in training programs can build relationship bonds with channel partners and translate into improved sales (Palmatier et al. 2006). However, channel partners need a clear understanding of the value embodied by these relationship-specific investments. These mechanisms can strengthen interorganizational relationships, offering alternatives to efforts that rely solely on trust or commitment (Palmatier, Dant, and Grewal 2007).
By enabling channel partners to meet dynamic growth opportunities in a market, PRM has a strategic influence on manufacturers' overall success. Our findings suggest several ideas for increasing engagement and promoting learning programs, using feedback systems. First, suppliers should incorporate intrinsic motivators to supplement their existing incentive programs. Extrinsic incentives, such as gifts or vacation destinations, might encourage salespeople to participate in learning modules, but they can be easily matched by competitors (Lane4 2013) and generally cannot create truly engaging, meaningful experiences (Palmer, Lunceford, and Patton 2012). Encouraging salespeople to reflect on what they have learned instead helps them understand the relevance of the lessons to their day-to-day activities. Therefore, we recommend that manufacturers incorporate review systems into their learning modules to prompt participants to review modules after taking them. This simple, powerful means to nurture reflective thinking offers notable benefits for channel engagement over time.
Second, channel partners and their employees are heterogeneous, with varying learning motivations and different evaluations of certain rewards (Palmer, Lunceford, and Patton 2012); these traits can signal their willingness to engage in learning programs. Although reflective thinking brought about by writing reviews is a powerful behavioral motivator, its effectiveness seems to differ according to the extent to which the participant is intrinsically motivated to learn. To optimize the behavioral outcomes of reviewing, manufacturers should find ways to focus on essential information—namely, information that is particularly relevant to the personal experience of lower LO partners—in the task instructions. Similar to suggestions put forward by ZS Associates (2014), our results indicate that review tasks should be customized to appeal to different segments of channel partners with distinct needs. For example, manufacturers could identify learners with low LO through a survey question selected from the LO scale, then invite these participants to review modules and highlight the benefits of doing so for peers. As time passes, reviewers may grow accustomed to the learning modules and begin to take them for granted, so the supplier that provides them should specifically ask them to provide a written review for the benefit of their peers or a broad review. Either prompt should lead them to reflect on the relevance of the modules to their business activities and spur them to complete additional learning modules.
Third, manufacturers should emphasize the immediate relevance of any activities that they develop for channel partners. Within learning modules, they might address issues of immediate relevance to these partners, then provide further information that the salespeople can implement directly in their sales pitches. Furthermore, they should strive to translate theory into action, by providing concrete, usable implementation examples.
This study of how feedback systems can increase individual engagement focuses specifically on engagement in a learning program, which should have cascading effects on objective outcomes, such as sales. However, further research could go beyond a count measure of modules taken to assess learning engagement in different ways, such as the variety of module types taken. It also could address other outcomes that might result from self-regulated learning, such as increased lead generation. This research could be extended to more direct channels as well, wherein firms provide learning programs directly to customers. Beyond learning, other managerially relevant contexts might be considered, such as the impact of feedback systems in business-to-consumer settings (e.g., online customer reviews on ecommerce and social networking sites). Such investigations would enrich theoretical understanding, by providing evidence of whether metacognition drives other engagement behaviors, such as a greater share of wallet or word of mouth.
Continued research might concentrate on motivators other than review writing, such as providing comparative information that benchmarks people's performance on learning tasks against an average or sending trivia questions related to the subject matter to help them think about how much they have learned. We have focused on the effects of review writing after a three-month period; other research might investigate the dynamic effects of these interventions to detail if and when they diminish over time. Comparative studies of multiple interventions also could shed light on which strategies are most effective for engaging partners over time. These insights would provide more delineated understanding of the mechanisms behind the interventions and thus reveal new theoretical knowledge.
Our feedback studies take place within a PRM context. It might be useful to examine whether these effects generalize to other contexts, such as consumer reviews of various service providers (e.g., health portals, restaurants, hotels) and product information provided by retailers and manufacturers. If so, the insights might explain why encouraging review writing (regardless of valence) is beneficial.
Finally, the self-reflective activity of writing reviews drives intrinsic motivations, but in other circumstances, it might trigger external drivers, such as social acceptance, particularly if the review platform enables the writer to showcase him- or herself.
Understanding other motivations associated with writing reviews could expand the theoretical foundations for research in this area, as well as suggest more insightful and sophisticated applications of this simple and effective tool for manufacturers.
Overall, we find that reviewing learning modules can drive learners with low LO to take on additional learning tasks, particularly when they are prompted to consider specific information for the benefit of their peers. We attribute this finding to the activation of metacognition that helps channel partners see the value of the programs. These findings have implications for new methods of engagement, and they also provide a cost-effective solution that managers can implement for their partner relationships.
Endnotes 1 Construal-level theory takes a matching perspective, and because a broad message focus and a narrow message focus match information storing at high and low LO levels, respectively, we do not predict a direct effect of message focus here.
GRAPH: FIGURE 2 Study 1: Interaction Between Feedback Manipulation and LO
GRAPH: FIGURE 3 Study 2: Purpose Interactions and Effects on the Number of Modules
GRAPH: FIGURE 4 Study 3: Perspective Interactions and Effects on Number of Modules
DIAGRAM: FIGURE 1 Organizing Framework: Role of Review Writing in Channel Partner Programs
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~~~~~~~~
Elizabeth Aguirre is a postdoctoral researcher in marketing, Maastricht University
Dominik Mahr is Associate Professor of Marketing and Supply Chain Research, Maastricht University, and Adjunct Professor, University of Waikato
Ko de Ruyter is Professor of Marketing, Cass Business School, City University London
Dhruv Grewal is Toyota Chair in E-Commerce and Electronic Business, Babson College
Jan Pelser is a postdoctoral researcher in marketing, Maastricht University
Martin Wetzels is Professor of Marketing, Maastricht University, and Adjunct Professor, University of Waikato
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Record: 185- The Effectiveness of Customer Participation in New Product Development: A Meta-Analysis. By: Woojung Chang; Taylor, Steven A. Journal of Marketing. Jan2016, Vol. 80 Issue 1, p47-64. 18p. 2 Diagrams, 5 Charts, 1 Graph. DOI: 10.1509/jm.14.0057.
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Record: 186- The Effects of Advertised Quality Emphasis and Objective Quality on Sales. By: Kopalle, Praveen K.; Fisher, Robert J.; Sud, Bharat L.; Antia, Kersi D. Journal of Marketing. Mar2017, Vol. 81 Issue 2, p114-135. 22p. 7 Charts, 3 Graphs. DOI: 10.1509/jm.15.0353.
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The Effects of Advertised Quality Emphasis and Objective Quality on Sales
Many firms emphasize high quality in their advertising but actually deliver lower-quality products and services. A review of Federal Trade Commission (FTC) cases related to advertised quality (FTC 2016) reveals the very prevalent practice of firms across multiple industries—including, but not limited to, food, toys, finance, retailing, pharmaceuticals, electronics, and packaged goods—emphasizing quality in their advertising content particularly when their products are of a lower quality. Emphasizing superior quality in advertisements is also common among firms selling experience goods for which quality is not observable until purchase and use.
Our examination of enforcement actions undertaken by the FTC over a decade reveals that approximately 80% of the advertising enforcement cases involved quality misrepresentations by low-quality brands. This evidence is consistent with our own database of minivan advertisements over two decades. Cases wherein a minivan received a rating of 2.0 on a five-point scale from Consumer Reports yet, for example, used the slogan “Quality Is Our No. 1 Job”1 or stated, “Once again, our commitment to quality and continuous improvement pays off” are by no means atypical exceptions. We find approximately 23% of the observations in our data are for brands with lower objective quality ratings emphasizing quality at a level that is commensurate with that of brands with higher objective quality ratings. Given that consumers value quality and that such advertising content informs consumers’ beliefs about quality, it is not surprising that high-quality brands emphasize quality in their advertising content. What is less obvious is whether firms with lower-quality brands should also follow suit and emphasize quality in their advertising to signal a higher quality. We examine this issue and study the effectiveness of quality-based advertising messages.
Table 1 displays relevant prior research in marketing that either implicitly or explicitly seeks to address the impact of advertising content. Whereas some analytical models imply a pooling equilibrium such that the advertising content is the same for both high- and low-quality brands (Anderson and Renault 2006; Gardete 2013; Mayzlin and Shin 2011), others suggest a separating equilibrium whereby a high-quality brand emphasizes quality in its advertising but a low-quality brand does not (Anderson and Renault 2013; Kopalle and Assunção 2000). To the best of our knowledge, our research is the first to empirically test the effects of advertised quality emphasis and objective quality, as well as their interaction, on sales in a longitudinal field-based study.
TABLE 1 Positioning the Article Amid Studies of Both Advertised and Objective Quality
TABLE:
| Study | Analytical/Conceptual/Empirical | Field Study/Experiment | Longitudinal? | Impact on Actual Sales? | Product Categories |
|---|
| Chang (2004) | Empirical | Experiment | No | No | Beverages |
| Deighton (1984) | Empirical | Experiment | No | No | Ford automobiles |
| Hoch and Deighton (1989) | Analytical | – | No | No | |
| Hoch and Ha (1986) | Empirical | Experiment | No | No | Polo shirts, paper towels |
| Kopalle and Lehmann (1995) | Analytical | Experiment | No | No | Automobile batteries and tires |
| Kopalle and Assunção (2000) | Analytical | – | No | No | |
| Kopalle and Lehmann (2006) | Analytical | – | No | No | |
| MacInnis and Jaworski (1989) | Conceptual | – | No | No | |
| Mayzlin and Shin (2011) | Analytical | – | No | No | |
| This study | Empirical | Field study and experiment | Yes | Yes | Minivans, electric car, repeat-purchase products |
Contrary to the pooling equilibrium results and the generally held (as inferred by its widespread prevalence), yet erroneous belief in the efficacy of low-quality products emphasizing quality in their advertising, we demonstrate through multiple methods—a field study of more than two decades of advertising content and its impact on sales, a regression of parameter values obtained from a category-agnostic numerical simulation, and an experiment—that ( 1) it is not beneficial for a low-quality firm to emphasize quality in its advertising, and ( 2) it is effective for a high-quality firm to do so. That is, we find evidence of a separating equilibrium. We thus provide practical guidance to managers regarding when to emphasize quality in their advertising content and hopefully help steer them away from the popular but demonstrably ineffective practice of qualitybased advertising content when the objective quality is low.
Although prior empirical research on advertised and objective quality has almost exclusively considered consumer perceptions measured in a laboratory setting (Chang 2004; Kopalle and Lehmann 1995) rather than actual sales, it is the latter outcome that is of fundamental interest to managers. Marketing managers need to know the degree to which qualitybased advertising messages affect consumers’ actual choices rather than just attitudes, perceptions, or intentions. Furthermore, most empirical research on the effects of advertising and brand sales has considered advertising spending, which does not help managers determine the advertising content (e.g., Popkowski Leszczyc and Rao 1990; Sethuraman, Tellis, and Briesch 2011). In the spirit of Wertenbroch (1998), our use of multiple methods not only helps us arrive at our conclusions but also enables us to develop an intuition for the observed interaction of advertised and objective quality.
The field study uses data from the minivan category of the U.S. automobile market from the category’s inception in 1983 through 2003. The category is mature enough to observe the effects of advertised and objective quality, but its beginning is recent enough to make it feasible to create a comprehensive longitudinal data set. We model 21 years of monthly data for all major brands in the category. Our data set includes sales of nearly 20 million automobiles, prices, distribution intensity, advertising spending across all media, objective quality ratings from Consumer Reports, a measure of advertised quality emphasis based on a census of 1,876 print ads placed in major magazines, and a long list of control variables. The result is a comprehensive perspective on a major consumer durable category over an extended period. Our analysis of simulated data from Kopalle and Lehmann (2015) corroborates the results of our field study. Finally, our experiment offers a test of the internal validity of quality-based advertising effects on quality perceptions.
In the following section, we present our theory on the contingent effect of advertised quality emphasis and objective quality on sales. We then describe the research method, model, and results for our major field study. Next, we generalize our results by analyzing simulated data from an analytical model and an experiment as further evidence of our result that advertising that emphasizes quality is ineffective when objective product quality is low. We conclude with a discussion of the theoretical and managerial implications of our research.
The Contingent Effect of Advertised Quality Emphasis on Sales
Our research contribution lies in offering the first real-world test of the effectiveness of emphasizing quality in advertising messages and complements the growing body of theoretical/analytical literature on information disclosure in advertising. This literature base yields conflicting results about whether a pooling equilibrium (Anderson and Renault 2006; Branco, Sun, and Villas-Boas 2016; Chakraborty and Harbaugh 2014; Guo and Zhao 2009; Mayzlin and Shin 2011) or a separating equilibrium (Anderson and Renault 2013; Gardete 2013; Kamenica and Gentzkow 2011) exists. Whereas laboratory research has found general support for the effectiveness of quality-based messages and other types of rational appeals (e.g., Kopalle and Lehmann 1995; Petty, Cacioppo, and Schumann 1983), evidence from field studies has been mixed. For example, MacInnis, Rao, and Weiss (2002) report no effect of rational appeals on ad success in their study of television commercials for a wide range of consumer products. Also, drawing on a sample of 90 award-winning ads across various product categories, Hamish and Field (2008) conclude that rational ads are not effective in creating differentiated brand positions that enhance brand performance. In his review of academic research on rational advertising, Tellis (2004, p. 14) concludes that most advertisers (and consumers) falsely believe that “the most effective ad appeal is clear information based on strong arguments.”
For many consumers, accurately assessing the quality of even simple products or services is difficult (Hoch and Deighton 1989; Wilcox, Roggeveen, and Grewal 2011). The problem is even more challenging for complex experience goods. For automobiles, Consumer Reports bases its automobile quality ratings on an assessment of approximately 30 attributes related to cost of ownership, performance, comfort, and safety. Consumers frequently do not have either the inclination or ability to thoroughly evaluate product quality, so they rely on Consumer Reports and other experts instead.
We propose that when consumers are informed about quality, a separating rather than pooling equilibrium occurs due to two significant effects. First, the effectiveness of qualitybased advertising messages becomes contingent on the degree to which these messages are congruent with information from third-party sources. When the information about quality from credible third-party experts is different from what is contained in advertising, consumers discount or ignore the latter because they are skeptical about advertising claims (Friestad and Wright 1994). Consumers understand that advertising is paid for by the firm and is designed to persuade them to purchase the firm’s products and services (Friestad and Wright 1994). In contrast, Consumer Reports has a reputation for being an advocate for consumers rather than for manufacturers. Consumer Reports remains staunchly independent of the manufacturers whose products it rates by not accepting advertising, by conducting policy and advocacy work on behalf of consumers, by tracking and responding to consumer feedback, and by refusing to accept free product samples from manufacturers. Consumer Reports is trusted because its mission is to “unleash the world-changing power of consumers” (Consumer Reports 2015).
Second, we expect that, at least in high-involvement categories, consumers who are ready to buy are more likely to seek out quality information than consumers making low-involvement decisions. As a result, the effects of quality-based advertising on sales should be contingent on a brand’s objective product quality. Although product quality is difficult to judge for products like automobiles, televisions, and major appliances, a significant proportion of consumers have access to information about product quality from credible sources such as Consumer
Reports and J.D. Power before purchase. Consequently, we expect that consumers are responsive to quality-based advertising messages when the advertised level of quality is congruent with information about product quality obtained from credible sources. If the information contained in quality-based advertising messages is contradicted by information from credible thirdparty sources, these messages will be ineffective.
Tying the aforementioned perspective to a signaling viewpoint, if a low-quality product were to signal high quality through a greater emphasis on quality in its advertising, consumers might not repeat-purchase the product after realizing its low quality, due to disconfirmed expectations. On the other hand, if a highquality product places a high emphasis on quality in its advertising, consumers are more likely to buy the brand again in the future, due to confirmed expectations. This implies that ( 1) the benefits for a high-quality product to signal the high quality through a greater emphasis on quality in its advertising are higher than the benefits of not signaling its high quality, and ( 2) the benefits for a low-quality brand to signal a high quality through a greater emphasis on quality in its advertising are lower than the benefits of not signaling a high quality. This leads to a separating equilibrium where a high-quality firm signals quality in its advertising and a low-quality firm does better by not emphasizing quality in its ads (Kirmani and Rao 2000).
Field Study
The research context for our field study is the U.S. minivan market. The minivan automobile category was established in 1983 with the launch of the Dodge Caravan, Plymouth Voyager, and Toyota Van brands. The category is well defined, which facilitates the identification of relevant brands and competitors. The purchase of an automobile is a high-involvement decision that is second only to the purchase of a home in terms of cost. It is therefore an ideal context within which to examine the effectiveness of quality-based advertising messages. Our data indicate that nearly 20 million automobiles were sold in the U.S. minivan category between 1983 and 2003.
Scope of Study and Unit of Analysis
It is common practice for manufacturers to introduce multiple brands within the minivan and other automotive categories. For example, General Motors has had eight brands in the category: the Lumina, Trans Sport, Astro, Montana, Safari, Silhouette, Sportvan, and Venture. Accordingly, our unit of analysis is the individual brand rather than the corporate brand or manufacturer. We collected information on the 20 brands that satisfied the following three conditions: ( 1) the brand attained market share of at least 1% at any point in its life, ( 2) it advertised regularly, and ( 3) it was identified as a member of the category both by other researchers (e.g., Bowman and Gatignon 1996; Kwoka 1996) and by third-party agencies (Consumer Reports and J.D. Power).
Data Collection
We obtained monthly sales volume data, manufacturer’s suggested retail price, and the number of dealerships selling each brand from Ward’s Automotive Yearbook (volumes published from 1980 to 2004). We complemented these data with objective quality ratings for each brand from Consumer Reports (volumes published from 1983 to 2004) and purchased reports of monthly advertising spending from TNS Media Intelligence (2005). Table 2 displays descriptive statistics for each of the 20 brands we track in this study.
Measures
Objective quality. Consistent with prior research, we define objective quality as an overall evaluation of a product’s excellence relative to competing products (e.g., Tellis and Johnson 2007). Our measure of objective quality is the overall rating by Consumer Reports of minivan quality over the years studied (Caves and Greene 1996; Curry 1985; Curry and Riesz 1988; Mitra and Golder 2006; Tellis and Wernerfelt 1987). Prior research has used Consumer Reports ratings as a measure of the quality that consumers experience after purchase in many consumer-durable categories, including appliances (e.g., handheld vacuums, blenders, toaster ovens), consumer electronics (e.g., televisions, DVD players, personal computers), major appliances (e.g., washers, dryers), and automobiles (Caves and Greene 1996; Curry 1985; Curry and Faulds 1986; Curry and Riesz 1988; Mitra and Golder 2006). A meta-analysis of research on the price–quality relationship by Tellis and Wernerfelt (1987) includes more than 1,000 product categories in which objective quality is measured with Consumer Reports data.
TABLE 2 Descriptive Statistics for Minivan Brands
TABLE:
| | Average |
|---|
| Brand | Entry Year | Exit Year | S | P | D | AS | OQ | AQ |
|---|
| Chevrolet Astro | 1985 | >2003 | 12.73 | 1.08 | 4.58 | .50 | -1.22 | -.05 |
| Chevrolet Lumina APV | 1990 | 1997 | 4.31 | 1.16 | 4.56 | .54 | -.28 | -.23 |
| Chevrolet Venture | 1997 | >2003 | 7.77 | 1.26 | 4.29 | 1.68 | -.33 | -.93 |
| Chrysler Town and Country | 1990 | >2003 | 5.77 | 1.67 | 2.95 | 2.01 | .21 | -.23 |
| Dodge Caravan | 1984 | >2003 | 24.63 | 1.04 | 3.13 | 3.27 | .45 | .27 |
| Ford Aerostar | 1986 | 1997 | 17.86 | 1.01 | 4.47 | .53 | -.27 | 1.05 |
| Ford Windstar | 1995 | >2003 | 15.77 | 1.20 | 4.13 | 1.70 | -.39 | .03 |
| GMC Safari | 1985 | >2003 | 3.50 | 1.08 | 4.58 | .15 | -1.22 | -.25 |
| Honda Odyssey | 1996 | >2003 | 7.69 | 1.43 | .99 | 1.46 | 1.60 | -.51 |
| Kia Sedona | 2002 | >2003 | 4.31 | 1.09 | .27 | 1.34 | -.44 | -.29 |
| Mazda MPV | 1989 | >2003 | 2.77 | 1.24 | .80 | .95 | .20 | .03 |
| Mercury Villager | 1993 | 2002 | 4.41 | 1.29 | 2.60 | 1.17 | .48 | -.03 |
| Nissan Quest | 1993 | >2003 | 3.30 | 1.33 | 1.04 | 2.19 | .39 | -.39 |
| Oldsmobile Silhouette | 1990 | >2003 | 2.08 | 1.46 | 2.86 | .30 | -.35 | -.35 |
| Plymouth Voyager | 1984 | >2003 | 16.57 | 1.03 | 2.96 | 1.43 | .44 | .43 |
| Pontiac Montana | 1999 | >2003 | 4.26 | 1.38 | 2.82 | .76 | -.11 | -1.03 |
| Pontiac Trans Sport | 1990 | 1999 | 2.94 | 1.25 | 2.89 | .72 | -.25 | -.05 |
| Toyota Previa | 1991 | 1997 | 2.47 | 1.43 | 1.18 | .71 | 1.48 | .49 |
| Toyota Sienna | 1998 | >2003 | 7.61 | 1.35 | 1.15 | 1.47 | 1.69 | -.06 |
| Toyota Van | 1984 | 1990 | 8.75 | 1.01 | 1.09 | .37 | .30 | .28 |
| Statistics over All Brands and Months |
| M | | | 9.27 | 1.22 | 2.96 | 1.21 | .00 | -.02 |
| SD | | | 8.47 | .22 | 1.35 | 1.75 | 1.00 | 1.00 |
| Min | | | .06 | .72 | .20 | .00 | -2.72 | -2.78 |
| Max | | | 43.27 | 1.99 | 5.12 | 17.95 | 2.23 | 3.69 |
Advertised quality emphasis. Advertised quality emphasis is defined as the degree to which quality is emphasized in a brand’s advertising messages. Brands in our sample emphasized quality in their advertising in a variety of ways, including ad headlines such as “Chrysler’s statement of quality: 1998 Total Quality Award,” and ad taglines such as Ford’s “Quality is Job One.” Examples of ad statements that use other types of
messages that might lead consumers to infer a minivan has high quality include “Chevy Venture has lots of available features” and the “Plymouth Voyager … handles like a car, parks like a car, garages as easily as a car—even gets mileage like a car.”
TABLE 3 Parameter Estimates for Sales Model
TABLE:
| Variable | Parameter Estimate | p-Value |
|---|
| **p < .05. |
| ***p < .01. |
| Price (P) | -.489*** | .001 |
| Number of dealers (D) | -1.216 | .081 |
| Brand goodwill (G) | 1.739*** | .000 |
| Goodwill carryover (d) | .003*** | .000 |
| Objective quality (OQ) | 746.3*** | .007 |
| Advertised quality emphasis (AQ) | -8.388 | .539 |
| OQ X AQ interaction | 9.554*** | .010 |
| Recession (NBER business cycle data) | -47.87 | .781 |
| Inflation (consumer price index) | 4,127.1** | .030 |
| Unemployment rate | -934.4*** | .002 |
| Treasury rate | -499.4*** | .008 |
| Number of observations | 2,631 |
| R2 | .873 |
| Adjusted R2 | .866 |
Model and Results
The data are analyzed using a unit sales model. Our data comprise all U.S. sales of new minivans each month; thus, there is no sampling error. The key predictor variables are price (P), number of dealerships (D), brand goodwill (B), objective quality (OQ), advertised quality emphasis (AQ), and the in
teraction between objective quality and advertised quality emphasis. We employ the standard Nerlove–Arrow (1962) model of brand goodwill with exponential decay (Narayanan,
Desiraju, and Chintagunta 2004). That is, brand goodwill in time t for brand b is represented as Bbt = dAbt + ð1 - dÞBbt-1, where Abt is the advertising expenditure of brand b in time t.
Price is measured as the manufacturer’s suggested retail price minus any cash discounts that were offered. We controlled
for macroeconomic factors such as recession (R, obtained from
National Bureau of Economic Research [NBER] business cycle data), inflation (I, calculated using consumer price index), U.S. unemployment rate (UR), and U.S. treasury rate (TR). The
dependent variable is unit sales (Sbt). We employed brandspecific fixed effects to control for heterogeneity across brands and estimated clustered standard errors to account for homo
geneity of observations within brands (note that a failure to do so would artificially deflate the standard errors). We also included year and month dummies to control for seasonality and
other yearly changes. Finally, to aid interpretation, we stan
dardized the AQ variable (Kopalle and Lehmann 2006), denoted by “Std.AQ.” Thus, the model we estimated is given by
Fourth, we tested a series of models incorporating nonlinear effects, but do not find any evidence thereof. Specifically, we estimated models that incorporated the quadratic terms of objective quality and advertised quality emphasis. Through separate specifications, using both the subjective measure of advertised quality emphasis and its objective measure, we tested their quadratic effects, both individually and along with the quadratic effects of objective quality. Across all the models,
Finally, to provide further support for our result, we present model-free evidence based on our data. This is displayed graphically in Figure 2, in which the x-axis is the average objective measure of advertised quality emphasis, the y-axis is the average unit sales, and the three lines denote different levels of average objective quality, all computed from the data. We note that when the objective quality is high, sales are highest when the advertised quality emphasis is also high. In contrast, when the objective quality is low, unit sales are lower when the advertised quality emphasis is high relative to when the advertised quality emphasis is low.
TABLE 4 Parameter Estimates for Sales Model with Objective Measure of Advertised Quality Emphasis
TABLE:
| Variable | Parameter | Estimate p-Value |
|---|
| **p < .05. |
| ***p < .01. |
| Price (P) | -.467*** | .003 |
| Number of dealers (D) | -1.597 | .058 |
| Brand goodwill (G) | 1.466*** | .000 |
| Goodwill carryover (d) | .004*** | .000 |
| Objective quality (OQ) | 831.0*** | .003 |
| Advertised quality emphasis (objective) (A) (standardized, AQ - AQ=sAQ) | -318.5** | .017 |
| OQ X standardized AQ interaction | 57.03** | .031 |
| Recession (NBER business cycle data) | -102.2 | .572 |
| Inflation (consumer price index) | 4,050.8** | .028 |
| Unemployment rate | -925.3*** | .002 |
| Treasury rate | -478.9*** | .006 |
| Number of observations | 2,652 |
| R2 | .872 |
| Adjusted R2 | .865 |
TABLE 5 Parameter Estimates for Sales Model 2: Competitive Brands’ Advertised Quality Emphasis and Interactions as Instruments for Brand Advertised Quality Emphasis and Interaction
TABLE:
| Variable | Parameter | Estimate p-Value |
|---|
| **p < .05. |
| ***p < .01. |
| Price (P) | -.516*** | .002 |
| Number of dealers (D) | -1.035 | .217 |
| Brand goodwill (G) | 2.087 | .954 |
| Goodwill carryover (d) | .0001*** | .001 |
| Objective quality (OQ) | 825.184*** | .008 |
| Advertised quality emphasis instrument (AQ) (standardized, AQ - AQ=sAQ) | 7,545.811* | .096 |
| OQ X standardized AQ interaction instrument | 3,333.531** | .044 |
| Recession (NBER business cycle data) | -137.252 | .450 |
| Inflation (consumer price index) | 4,655.027** | .016 |
| Unemployment rate | -858.604*** | .002 |
| Treasury rate | -452.598*** | .007 |
| Number of observations | 2,651 |
| R2 | .879 |
| Adjusted R2 | .872 |
TABLE 6 Parameter Estimates for Sales Model 3: Using Lagged Subjective Advertised Quality Emphasis and Objective Quality as Instruments
TABLE:
| Variable | Parameter | Estimate p-Value |
|---|
| **p < .05. |
| ***p < .01. |
| Price (P) | -.489*** | .003 |
| Number of dealers (D) | -1.122 | .174 |
| Brand goodwill (G) | 1.746 | .972 |
| Goodwill carryover (d) | .0001*** | .000 |
| Objective quality (OQ) | 652.505** | .025 |
| Lagged advertised quality (AQ) | 15.152*** | .000 |
| Lagged OQ X AQ interaction | 15.325** | .050 |
| Recession (NBER business cycle data) | -72.893 | .660 |
| Inflation (consumer price index) | 4,307.053** | .015 |
| Unemployment rate | -933.811*** | .002 |
| Treasury rate | -526.650*** | .003 |
| Number of observations | 2,599 |
| R2 | .883 |
| Adjusted R2 | .877 |
Additional Evidence
In this section, we extend our empirical work by providing further evidence using data from results published in related analytical research. We corroborate our empirical result with those from analytical models and provide intuition for the result. Here, we use numerical results reported in Table 3 of Kopalle and Lehmann’s (2015, p. 257) analytic, asymmetric duopoly framework that endogenizes objective quality, advertised quality, and price in a repeat-purchase, experience goods category in which objective quality is revealed upon use. The model incorporates intertemporal dynamics through the effects of satisfaction after purchase. In such contexts a tension exists, from a firm’s perspective, between emphasizing high quality to increase initial acceptance/trial versus promising quality to increase satisfaction and, thus, future sales.
Kopalle and Lehmann’s (2015) Table 3 reports optimal advertised quality, objective quality, and price in cases where one firm (Firm A) has higher consumer preference (“brand equity”) than its competition (Firm B, which could be another known brand or a more generic competitor) for various model parameter values. The simulations provide a total of 22 observations, that is, 22 parameter variations and the corresponding equilibrium advertised and objective quality levels and prices from Firms A and B. Using these data, we ran a seemingly unrelated regression with objective quality and advertised quality emphasis as the two correlated outcomes of interest and a dummy variable identifying Firm A as the main regressor (system weighted R2 = .21). The results suggest that ( 1) Firm A’s objective quality is significantly higher than that of Firm B (slope = 2.23, p = .016) and ( 2) the advertised quality emphasis is indeed significantly higher for Firm A relative to Firm B (slope = 2.32, p = .027). This indicates that there exists a separating equilibrium in advertising messages in which the higher-quality firm, A, emphasizes its higher quality in its advertising, whereas the lower-quality firm, B, does not mimic the higher-quality firm in its advertising messages. In other words, the lower-quality Firm B does not have an incentive to advertise quality at the same level as that of the higherquality Firm A. The intuition behind the result is that if a low-quality firm mimics a high-quality firm, its future sales will be hurt due to the long-term effect of postpurchase consumer dissatisfaction.
This result, which holds when prices are not endogenous to the model, suggests that the advertised quality emphasis should be lower for lower-quality products relative to higherquality products. In summary, our analysis of the numerical results of an analytical model, published in related research, is consistent with the results of our field study. Specifically, the results suggest that firms with lower-quality brands should not emphasize quality in their advertising at the same level as higher-quality brands. In the next section, we further examine the relationship between objective quality and advertised quality emphasis in an experiment.
Experiment
We conducted an experiment to extend our empirical result in two ways. First, we wanted to test our result in a way that enables us to understand the differential effects of quality-based appeals on informed and uninformed consumers. Researchers have argued that consumers are most likely to be influenced by advertised quality emphasis when they are uncertain about the underlying quality of a product (Deighton 1984; Hoch and Ha 1986). We therefore expect that quality-based advertising messages should be particularly effective when consumers do not have access to objective quality ratings from a credible thirdparty source. Second, an experiment offers a stringent test of causality and internal validity.
Our experiment was a 3 (Consumer Reports rating: below average vs. above average vs. absent) • 2 (advertised quality emphasis: strong vs. weak) between-subjects design. We recruited 176 members of a participant pool at a large North American university and introduced them to a study on the Mitsubishi MiEV, a fully electric vehicle that had been recently launched in the local market. We selected this vehicle because pretests indicated that participants were relatively unfamiliar with the new model and its underlying quality, which is in contrast to the field study context. All participants were provided basic information about the vehicle, including its price, specifications, key features, and an image harvested from the Mitsubishi website. They were then randomly assigned to one of the experimental conditions.
Reports ratings and when there was no objective quality information. In contrast, when the vehicle had inferior objective quality, advertised quality emphasis had no effect. Beyond replicating the results of our field study and additional evidence, the positive effect of advertised quality emphasis on quality perceptions when consumers were uninformed about objective quality mirrors the real-world situation of consumers who do not refer to third-party evaluations of objective quality.
Overall Discussion
Our research offers new insights into the effectiveness of qualitybased marketing strategies, a topic of fundamental interest to both academics and practitioners (Rust, Moorman, and Dickson 2002). We provide consistent and complementary evidence across a field study, an analysis of parameter values from a published category-agnostic simulation, and an experiment that the effec
tiveness of quality-based advertising messaging depends on whether the brand’s level of objective quality is high or low, as well as on whether consumers are informed about quality. In particular, we find that brand advertising that emphasizes quality is ineffective unless there is credible information that the brand offers high quality.
For academics, our research contributes to the very limited literature on how real-world consumers respond to quality-based
advertising in high-involvement product categories. The context is important not only because consumer responses are likely to be different in the laboratory and the field but also because while actual choices are consequential, intentions to buy are not (Wells 1993). We examine a high-involvement context to test the effectiveness of quality-based advertising, which is a rational and therefore central route appeal (MacInnis and Jaworski 1989). Our research also contributes to the ongoing debate about when consumers assimilate versus contrast information when making decisions (Hoch and Deighton 1989; Kan et al. 2014; Wilcox et al. 2011). Across our multimethod approach, we find that consumers assimilated or contrasted advertising information depending on whether the quality information in the brand’s advertising and that from a third-party expert were consistent. We infer that if consumers in our field study found that quality-based advertising messages were consistent with third-party ratings, they integrated or assimilated the information. If, however, they found that the two sources offered significantly different information about quality, they discounted the quality-based advertising appeals and relied on the third-party ratings.
Although our field study and experiment test the effectiveness of rational advertising messages for two types of automobile purchases (i.e., minivans and electric cars), recent research has suggested that the effects we found may be generalizable to a wide range of low-involvement purchases. Huang, Lurie, and Mitra (2009) argue and find evidence that a representative sample of U.S. consumers actively sought information on product quality for both search (i.e., shoes, home furniture, and garden and patio implements) and experience (automotive parts and accessories, health and beauty products, and camera equipment) goods. We expected that consumers would be both inclined and able to confirm or disconfirm the veracity of quality-based advertising across these categories in the same way automobile buyers do.
As expected, however, we find that quality-based advertising claims were effective for uninformed consumers. When the participants in our experiment did not have access to information about quality from Consumer Reports, advertising that emphasized quality significantly enhanced their perceptions of quality. When consumers were uninformed either because of the challenges associated with evaluating a complex good or because they did not seek out credible third-party ratings, they were vulnerable to the influence of advertising puffery, and perhaps to deception as well.
Our research offers at least limited empirical support for the principle of marketing consistency, which specifies that the elements of a marketing strategy should be internally consistent and therefore mutually reinforcing to be effective (Erdem and
Swait 1998; Shapiro 1985). Specifically, we found that qualitybased advertising enhanced consumer perceptions and choices only when it communicated a level of quality that was consistent with a brand’s actual or objective quality.
Consistency between a product’s objective quality and other aspects of the marketing mix becomes important only when
quality can be easily measured and compared across brands;
consumers will respond to inconsistencies in the marketing mix
only when they have access to understandable and accurate
information about quality that enables them to recognize when
these inconsistencies exist. Hence, our research suggests not
only that third-party ratings of quality are fundamental to the
success of any quality-based strategy but also that the value of
consistency depends on the availability of these ratings. In our field study, we did not find a significant main effect on
sales of advertising messages that emphasized quality. One explanation for the null result is that the firms in our field study continued to emphasize quality in their advertising after target
consumers became bored with the message, thereby leading to a significant wear-out effect (Bass et al. 2007). Advertising messages that have not changed may become ineffective, which may produce an insignificant effect of advertised quality emphasis on sales that we observed. It may be that over time, minivan consumers
became less responsive to quality-related messages and more
responsive to other types of appeals, such as those for innovations related to entertainment systems, flip-fold seats, or additional sliding passenger doors. The coefficient for quality messaging may be insignificant because consumers tend to believe that all major minivan brands offer sufficient quality, which, over time, has made them less responsive to this type of benefit. A clear implication of our results is that firms should set their quality-based advertising strategies only after understanding their brands’ objective quality.
Our results also have implications for firm-level theory. In particular, depending on the benefits and costs of a high emphasis on quality in advertising, signaling theory (Kirmani and Rao 2000) suggests either a “separating equilibrium” or a “pooling equilibrium.” Our results support the idea that a separating equilibrium is more effective.
Managerial Implications
As an early field test of quality-based advertising messages for a major consumer durable, our research has important implications for managers. First, managers should not expect to change consumers’ beliefs about product quality through persuasive advertising. Rather, the focus should be on informing target consumers about actual quality. Our research demonstrates that consumers who have accurate and easy-to-use quality ratings use this information to confirm or disconfirm quality-based advertising messages.
Second, firms have the opportunity to capitalize on the significant influence of third-party experts on consumers’ assessments of quality. Firms might be better off basing their quality improvement strategies on the metrics and procedures used by third-party experts rather than those used by consumers. Whereas typical consumers of complex durables such as automobiles, televisions, and major appliances might be unwilling or unable to share their reasons for brand preferences, the metrics and procedures used by third-party experts are public knowledge. Consequently, firms have a clear understanding of how to invest to influence the quality ratings of third-party experts compared to researching the quality ratings of consumers.
Third, our research suggests that firms benefit from designing and manufacturing their products to maximize quality ratings as defined by third-party experts such as Consumer Reports. These ratings not only shape what is deemed to be important by consumers through an education function, but they are also influential because they are based on rigorous processes, are simple and therefore easy to use and understand, and are provided by independent experts.
Finally, our research offers guidance to managers on when to emphasize quality in their advertising. It is recommended that managers provide a lesser (greater) emphasis on quality when their objective quality is low (high). This suggests that claims by low-quality firms of being as good or almost as good as high-quality firms are likely to be ineffective. For regulators attempting to rein in deceptive advertising, the implication is that they should focus their efforts on product categories in which quality is difficult to assess, ratings are not available, or consumers lack knowledge. Where consumers are knowledgeable, regulators can focus on brands with high objective quality for detecting deceptive advertising practices. Furthermore, managers must ensure that their quality-based messages are consistent with the objective quality that is delivered to consumers as assessed by third-party experts. Our research also indicates that managers need to understand exactly how third-party experts define and measure quality because of the power of these ratings in shaping consumers’ perceptions and choices (Yubo and Xie 2005).
Limitations and Future Research Directions
As with any research, this study has limitations that provide avenues for future research. Although we conducted a census of minivan advertising in national magazines and included advertising spending across all media, we are unable to measure and incorporate advertising messages for television, radio, or online media. Future research on the effects of advertising messages related to quality across all media would be desirable. A limitation of our empirical analysis includes potential omitted variable bias in the model specification. However, we note that
( 1) the results of our empirical analysis are corroborated with additional evidence in published research (where both objective quality and advertised quality emphasis, as well as price, are endogenous variables) and a laboratory experiment (where advertised quality emphasis has been manipulated), and ( 2) the R2 is relatively high (R2 = .873; adjusted R2 = .866).
Consistent with prior field research on advertising message effect in the service sector (Chandy et al. 2001; Tellis, Chandy, and Thaivanich 2000; Tellis et al. 2005), we based our field study on a single product category. We argued that it is only in highinvolvement contexts that consumers would have the motivation to search for quality information from third parties and to process quality-based advertising appeals. Huang et al. (2009), however, offer compelling evidence that the Internet has blurred the distinction between search and experience goods by making it easy to interact with products before purchase. Given that the Internet has lowered search costs, it seems likely that the distinction between low- and high-involvement products is blurred as well. We therefore encourage future researchers to examine the extent who which our results can be extended to lower-involvement goods and services in which credible third-party quality ratings are available.
Finally, we offer two additional questions that arise from our research. First, consumers have access to a wide array of choices when it comes to third-party experts, and each one uses a different metric. For example, J.D. Power is known for its initial quality studies (i.e., product defects arising within 90 days of purchase), whereas Consumer Reports uses overall quality ratings. It would be interesting to examine how consumers respond to different metrics or what happens when the rankings or ratings from different experts are inconsistent. Second, it would be beneficial to assess the relative effectiveness of different types of quality-based advertising appeals (including those in which quality is not mentioned in the ad), as advertisers could emphasize quality in various ways, such as noting awards received; making comparative claims; highlighting specific attributes (e.g., performance, comfort, cost of ownership, entertainment system); or making more general claims, such as “best in class” quality. Thus, although work remains to be done, examining the effectiveness of quality-based advertising content strategies in a high-involvement category seems viable and worthy of the effort required to understand it more fully.
1Advertisement of the 2001 Ford Aerostar used multiple times in Better Homes and Gardens, People, Newsweek, Sports Illustrated, and Good Housekeeping.
DIAGRAM: FIGURE 1 Sales Effects of Advertised Quality Emphasis at Different Levels of Objective Quality
DIAGRAM: FIGURE 2 Model-Free Evidence
DIAGRAM: FIGURE 3 Advertising Experiment Stimuli
DIAGRAM: FIGURE 4 Effects of Advertised Quality Emphasis on Perceived Quality by Quality Rating and Availability
DIAGRAM: FIGURE 4 Effects of Advertised Quality Emphasis on Perceived Quality by Quality Rating and Availability
PHOTO (BLACK & WHITE)
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The Faces of Success: Beauty and Ugliness Premiums in e-Commerce Platforms
Given the positive bias toward attractive people in society, online sellers are justifiably apprehensive about perceptions of their profile pictures. Although the existing literature emphasizes the "beauty premium" and the "ugliness penalty," the current studies of seller profile pictures on customer-to-customer e-commerce platforms find a U-shaped relationship between facial attractiveness and product sales (i.e., both beauty and ugliness premiums and, thus, a "plainness penalty"). By analyzing two large data sets, the authors find that both attractive and unattractive people sell significantly more than plain-looking people. Two online experiments reveal that attractive sellers enjoy greater source credibility due to perceived sociability and competence, whereas unattractive sellers are considered more believable on the basis of their perceived competence. While a beauty premium is apparent for appearance-relevant products, an ugliness premium is more pronounced for expertise-relevant products and for female consumers evaluating male sellers. These findings highlight the influence of facial appearance as a key vehicle for impression formation in online platforms and its complex effects in e-commerce and marketing.
Keywords: attractiveness; beauty premium; e-commerce; social selling; ugliness premium
The role of attractiveness in social judgments and the beauty premium have been well documented in various social settings such as dating, hiring, selling, and advertising, especially when the task or product is related to appearance ([ 3]; [10]; [31]). A few studies have found opposite results when a product is not relevant to appearance, but they have not provided coherent explanations for these findings ([25]; [53]). Moreover, most researchers have compared attractive models or endorsers with those who are less attractive, largely ignoring people who are unattractive altogether. Recent studies indicate a potential ugliness premium: unattractive people are perceived as more intelligent and earn significantly more than their attractive counterparts (e.g., [17]; [26]), which suggests that the effect of attractiveness is nonlinear. Thus, researchers have yet to identify the precise underlying mechanisms and contexts for the beauty premium or that for the ugliness premium, if it exists.
Unlike conventional marketing that relies on celebrities or salespeople promoting a specific product, customer-to-customer (C2C) e-commerce involves large numbers of ordinary people as sellers pitching a variety of products, making seller credibility a critical issue ([36]). While online sellers exhibit a wide range of attractiveness, their profile pictures, as an integral part of seller identity, serve as a key vehicle for impression formation and evoke feelings that affect buyer decisions ([16]). Most people, however, are not endowed with perfect facial symmetry and proportions. In light of the increasing popularity of social selling, how one's attractiveness or lack thereof affects the sales of various products is of much concern among online sellers and of great interest to marketing researchers and practitioners.
Drawing from the literature on impression formation, the match-up hypothesis, and evolutionary psychology, we argue that both attractive and unattractive online sellers command more attention and source credibility than plain-looking sellers, resulting in a U-shaped effect of attractiveness on sales. In contrast to previous studies, we go beyond consumer attitudes toward advertisements and products and focus on trait inferences to explore the underlying mechanisms of beauty and ugliness premiums and their effect on source credibility and purchase intention. We find that while attractive faces fare better in sociability than both plain-looking and unattractive people, they are not considered more competent than unattractive people, who are perceived as more competent than plain people, resulting in a plainness penalty. These relations are moderated by product relevance (appearance vs. expertise) and a cross-gender effect for women looking at male sellers.
The remainder of this article is organized as follows. We first provide a succinct review of the relevant literature and present a conceptual framework for the effect of facial attractiveness on consumers. We extract the geometric features of facial images and adopt a machine learning approach to score large samples of online seller portraits. Next, we investigate beauty and ugliness premiums using a multimethod approach involving large data sets from two e-commerce platforms and two online experiments to assess the potential mediators and moderators. Finally, we discuss the key findings and implications for e-commerce and internet marketing.
Faces are known to bias decisions ([57]). We form first impressions of others and make judgments about their social traits almost instantaneously on the basis of face perceptions ([47]; [52]; [58]). The neural mechanism underlying trait impressions of faces involves the amygdala, a subcortical brain region crucial in coding the value of stimuli (e.g., [11]). In functional magnetic resonance imaging (fMRI) studies, the amygdala has been observed to be more sensitive to unusual rather than to neutral stimuli, suggesting that our response to both attractive and unattractive faces may be stronger than to plain-looking ones (e.g., [46]).
In addition, the amygdala response to facial attractiveness triggers rapid automatic inferences about people's dispositions, which in turn affects subsequent information processing and decisions ([11]). Greater attention to an eye-catching face makes it more likely that people process additional information associated with the face, which may weaken but not change the nature of the relation between inferences from faces and decisions ([52]; [54]). Thus, advertisers find it effective to use either attractive or unattractive models to present certain products ([21]).
Studies in many fields have concluded that beauty has a premium and ugliness is penalized ([10]; [31]). According to evolutionary psychology (e.g., [38]), an attractive face indicates good health and prospect for survival and reproduction. Beauty is also correlated with perceived intelligence and social skills ([10]; [23]). Attractive solicitors can obtain twice as much in donations as their unattractive counterparts ([44]), and a good-looking salesperson enhances customer evaluation of a product simply by touching it ([ 3]). Although attractiveness is valued in both men and women, men are more responsive to the physical attractiveness of women ([33]). Meanwhile, studies have found that attractiveness sometimes fails to work, for instance, when helping children in need or selling an embarrassing product ([14]; [55]).
Several recent studies show that unattractive faces are associated with certain positive outcomes. [17] find that students rate unattractive professors as better scientists than attractive professors. A study of Nobel laureates reinforces the pervasive stereotype that scientists sacrifice physical appeal for intellectual pursuits ([13]). [26] indicate that very unattractive executives earn significantly more than their attractive counterparts, although the study does not consider perceptions of competence. These findings support the popular belief that unattractive people exert greater effort to compensate for their disadvantaged appearance; however, these studies fall short of offering plausible explanations for the ugliness premium.
Online forums and social media have aggravated people's concern with appearance and greatly affected social and consumption behaviors ([19]). The advantages of anonymity and lack of immediate social censoring may make such biases more prevalent online ([20]). Online transaction platforms (e.g., Uber, Airbnb) typically require sellers to upload real photos as their profile pictures and to display them in prominent positions. These profile pictures provide impression-bearing information that affects source credibility and behavioral outcomes ([16]; [36]).
Studies of the attractiveness effect have mostly used a small number of pictures in experimental settings rather than assessing real-world situations, leaving the robustness and generalizability of their findings open to question ([31]). It is not clear from the literature whether social stereotypes based on attractiveness extend to the C2C e-commerce context. Researchers usually adopt a linear model or compare only two levels of attractiveness (i.e., attractive vs. less attractive), neglecting any potential nonlinear effect. Thus, C2C e-commerce platforms involving ordinary people provide an excellent setting to explore the effect of beauty and ugliness premiums and their underlying mechanisms.
To explore the potential nonlinear effect of facial attractiveness on product sales, we focus on the profile pictures of ordinary people in C2C platforms who display a wide range of attractiveness (i.e., attractive, plain-looking, and unattractive). To investigate the mechanism underlying the beauty and ugliness premiums, we conduct online experiments to assess the effect of seller attractiveness on perceptions of sociability and competence, which in turn affect source credibility and purchase intention. With these objectives in mind, we present our conceptual framework in Figure 1 and elaborate the hypotheses in the ensuing sections.
Graph: Figure 1. Conceptual framework.
Faces have a special advantage over other stimuli in terms of visual processing and the attention-orienting mechanism ([54]). On e-commerce platforms that involve information overload, unusual faces (i.e., both attractive and unattractive) have high arousal values compared with plain-looking faces, and thus their messages are more likely to pass through the attention gate rather than being ignored. Recent studies using fMRI have found that the amygdala, the part of the brain responsible for visual attention and processing, exhibits nonlinear responses to human faces as both attractive and unattractive faces elicit quicker and stronger responses than plain-looking ones ([40]; [46]; [59]).
Moreover, people instantaneously assign a set of personality-like traits and judgments to faces, particularly along the dimensions of warmth and competence ([15]). Research suggests that good-looking people are regarded as more sociable, likable, intelligent, and persuasive ([23]). Unattractive people may obtain positive judgments derived from inferences of competence ([17]). Thus, while the beauty premium will be apparent in online platforms, we also expect that unattractive sellers elicit positive perceptions in certain contexts, which we elaborate in the next section. We propose that compared with plain-looking faces, both attractive and unattractive sellers command greater consumer attention and desirable trait inferences, which in turn leads to a greater likelihood of a sale.
- H1: Holding other things constant, there is a U-shaped relationship between product sales (or purchase intention) and facial attractiveness of online sellers in that both attractive and unattractive people perform better than plain-looking people.
However, gaining more attention cannot solely justify the advantages of attractive and unattractive faces over plain faces. In line with the implicit personality theory, trait inference is the key mechanism underlying the effect of attractiveness ([10]). In addition to the primary messages such as product quality and price, source credibility is a key factor that influences consumer decisions ([18]). Consumers use nonverbal cues (e.g., face attractiveness) to infer the perceived trustworthiness and expertise of a source (i.e., two determinants of source credibility), which in turn affects their perceptions of the products ([43]).
Previous research has suggested that a salesperson's attractiveness does not directly affect sales performance but, rather, influences some aspects of the customer's impression of the salesperson such as sociality or competence ([ 2]; [ 9]). In online platforms, the pictorial and aesthetic features of profile pictures have a profound influence on consumers' assessments of source credibility ([ 6]). Meanwhile, attractiveness has been found to be moderately correlated with perceived sociability, less so with competence, and almost not at all with honesty ([10]; [19]). Thus, it is plausible that beauty and ugliness premiums operate under different mechanisms in terms of sociability and competence.
Beauty, as an endowment, has many benefits. Because of beauty's halo effect, attractive faces lead to a higher level of arousal and inferences of sociability and competence ([31]). Strong empirical evidence suggests that attractive individuals are perceived to possess more socially desirable traits and exhibit greater persuasive power in selling products with which they are associated ([10]; [43]). Thus, because attractive individuals are perceived as more likable and competent, they are considered more credible than plain-looking and unattractive ones.
- H2: Compared with plain-looking faces, attractive faces enhance (a) perceived sociability and (b) competence, which in turn affect (c) source credibility and (d) purchase intention.
For unattractive faces, attention alone may not be sufficient to induce a positive effect. In light of the overwhelming beauty premium for attractive people and the ugliness penalty in sociability, unattractive people have an advantage only over plain-looking people in perceived competence for several reasons. Compensatory adaptation is a widely held perception that unattractive people often work harder to compensate for their disadvantaged appearance, leading to a perception of greater competence than those with plain-looking faces ([30]). There is an ingrained perception that whereas attractive people obtain everything more easily, particularly in settings that require social skills, unattractive people must exert greater effort to compensate for their disadvantaged appearance and often shift to areas that do not demand social skills, such as scientific pursuits ([13]).
Moreover, the "ugly Einstein" effect suggests that the stereotypical expert may be an impartial truth seeker with limited personal appeal ([ 8]; [17]). The stereotypical belief that attractiveness and intelligence are negatively associated is also prevalent, particularly for women ([24]). This argument is used to explain the "dumb blonde" stereotype, in which attractive women rely on their looks to advance rather than intelligence ([45]). Not surprisingly, much anecdotal evidence exists on the perceived creativity and extraordinary characteristics of unattractive people ([21]; [28]). Our research extends these notions and proposes that the ugliness premium operates through perceived competence, which in turn enhances source credibility and purchase intention.
- H3: Compared with plain-looking faces, unattractive faces enhance (a) perceived competence, which in turn affects (b) source credibility and (c) purchase intention.
Studies in labor economics and human resource management suggest that based on perceived fit, people of various degrees of attractiveness may self-select or be selected into occupations and positions that are "suitable" for their appearance ([ 4]; [24]). Attractive people are perceived as more fitting for positions in which a pleasing appearance and sociability are appreciated, whereas unattractive people are regarded as more competent in professions for which technical or professional expertise matters ([17]; [32]). Likewise, the beauty premium has been found to accrue in situations centered on social interactions ([ 1]), whereas the ugliness premium plays a role in assessing professional competence ([17]; [26]). Therefore, the context of evaluation influences the effect of beauty and ugliness premiums on trait perceptions and outcomes.
Product relevance is well grounded in the existing literature on attractiveness in marketing and serves as a key moderator on how the attractiveness of an endorser or salesperson affects their performance ([53]). According to the match-up hypothesis, endorsers of various degrees of attractiveness are more effective when their perceived ability and credibility are relevant for presenting and interpreting the products ([25]). Following this logic, we expect that the advantages of attractive and unattractive faces are greater when they are aligned with a product relevant to the positive traits derived from their appearances. Whereas attractive people are more effective in presenting appearance-relevant products that enhance sociability, unattractive faces bring an advantage over plain-looking faces when they are associated with a product related to technical or professional expertise (e.g., [ 5]; [27]).
- H4: Product relevance moderates the mediating effect of sociability (competence) between beauty (ugliness) premium and source credibility. (a) The mediating effect of sociability is stronger for attractive people selling appearance-relevant products, whereas (b) the mediating effect of competence is stronger for unattractive people selling expertise-relevant products.
Gender greatly influences perceptions based on appearance, and gender bias can be regarded as a subset of attractiveness bias ([ 1]). Unlike dating or hiring, online shopping does not involve social decisions or represent a competitive environment, so the negative vigilance toward an attractive person of the same sex found in previous studies is unlikely (e.g., [39]). Due to the opposites attract principle, studies have found that people are more subject to the influence of attractiveness in the opposite sex ([28]; [33]). Thus, we expect that beauty and ugliness premiums are stronger in a cross-gender context than in a same-gender one.
Moreover, attractiveness affects men and women differently. Studies of evolutionary psychology indicate that men place greater importance on female attractiveness, so male consumers are more likely to award a beauty premium to attractive female sellers ([ 1]). However, although women may value attractiveness in men, they are more likely to prioritize other considerations and place greater emphasis on competence and favor status and intelligence in men because these qualities indicate the ability to acquire resources and provide security ([50]). Thus, the ugliness premium in competence is likely to be stronger for female consumers looking at male sellers.
- H5: Compared with a same-gender setting, (a) the mediating effect of sociability for attractive female sellers is stronger for male buyers and (b) the mediating effect of competence for unattractive male sellers is stronger for female buyers.
In the following sections, we use field data from two transactional sites to provide empirical evidence for the U-shaped relationship between seller attractiveness and product sales (Study 1). As social traits and source credibility are not directly observable online, we examine the different mechanisms underlying the beauty and ugliness premiums and the moderating effects of product relevance and gender in two online shopping experiments (Study 2).
Airbnb and 5miles are the research settings for our field studies. These C2C e-commerce platforms provide information on sellers, including their photos, which serve as a means of identity verification and narrow the social distance between buyers and sellers ([36]). Airbnb is a sharing economy platform in which travelers are matched with hosts who have properties for rent. We examine how an Airbnb host's facial attractiveness affects their listing's occupancy rate. 5miles is a mobile app that connects buyers with sellers of different products, and it enables us to assess the effect of a seller's facial attractiveness on the likelihood of a sale.
Determining the facial attractiveness of profile pictures on Airbnb and 5miles is a challenging task, as these sites have over 1 million hosts and 100,000 sellers, respectively. Because standards of facial attractiveness are universal across cultures, ethnic groups, sexual orientations, and ages, facial attractiveness is a quantifiable trait that can be assessed by both people and computer algorithms ([31]; [38]). We apply a machine learning method to process a large quantity of profile pictures with a high level of accuracy, making cumbersome human coding of all the portraits unnecessary.
First, we retrieved a random sample of 32,386 profile pictures from Yelp and recruited ten male and ten female raters between 19 and 25 years old. Each image was randomly assigned to five raters (two men and three women or three men and men women), who scored it on a five-point scale from 1 ("very unattractive") to 5 ("very attractive"). The final attractiveness score is the average of the five ratings. We randomly divided the raters into two groups and consistently obtained correlations of.87 to.96 for the average ratings between groups. The insignificant t-statistic confirms that the raters used similar criteria to assess facial attractiveness. A chi-square test on the distribution of ratings between male and female raters also revealed no significant differences.
Second, we used image processing techniques to retrieve key pictorial features. Substantial evidence from computing and aesthetics research suggests that symmetry and proportional facial features (e.g., distance between the eyes, cheek width, size of nose and forehead) are good predictors of facial attractiveness ([22]). We used a set of 68 facial landmarks to extract these features and compute various facial ratios and proportions. For example, the aesthetic standard of the golden ratio can be obtained by comparing the distance between the eyes and mouth to the distance between the mouth and chin.
Third, we applied several machine learning methods (linear regression, Bayesian ridge regression, Gaussian regression, support vector machine regression, random forest regression, and convolutional neural networks) to learn the relationship between facial geometrics and the attractiveness scores from the human raters. We used 80% of the portrait data as the training set for model fitting and the remaining 20% for validation and model selection. Random forest regression achieved the best performance in terms of the Pearson correlation, the mean absolute errors, as well as the computational cost. We thus applied random forest regression to predict the facial attractiveness of all the profile pictures in the Airbnb and 5miles data sets as follows:
Graph
1
where rn (X, Θm, Dn) refers to the randomized base regression tree. Θ1, Θ2,..., Θm are identically and independently distributed outputs of the randomized variables. EΘ denotes the expectation with respect to the random parameters, conditional on the data set Dn and X. The predicted scores are highly correlated with those of the raters (r =.71). In addition to the cross-validation using the training data set, we adopt two other procedures[ 7] to assess the accuracy of machine learning. The validation tests suggest that the algorithm works well for faces from different genders, age, and ethnicity, and that the results from human raters are highly consistent with those from random forest regression.
To rule out potential confounding factors, we control for several pictorial features including photographic quality, face proximity, and smiling expressions. Profile pictures vary in resolution, brightness, and quality and range from headshots with high facial prominence (close-up) to full-body shots with low facial prominence (distant). We used the vision libraries available in OpenCV to derive the hue, saturation, and value (HSV) color spectrum for each picture and then aggregated these measures into a single index of photographic quality using principal component analysis. We measured face proximity as the ratio of the area of a face to the whole picture, ranging from 0 to 1. The higher (lower) the facial proximity ratio, the more (less) prominent the face is in the picture. In addition, a smiling face may be equally effective as the attractiveness of the seller because it can evoke a sense of familiarity and increase the positive evaluations of viewers ([48]). We used a random forest regression model to predict the likelihood of smiling for each profile picture in the main sample.[ 8]
We collected all publicly available data for Airbnb listings in Los Angeles through June 15, 2017. We then appended the annual occupancy data from AirDNA (a major supplier of data on worldwide Airbnb listings) covering the same period. We combined the data from these sources and excluded properties without complete information (e.g., the ones less than one year in operation). We downloaded host profile pictures and used image processing techniques to extract the pictorial features. The final sample consists of 17,935 Airbnb properties from 10,979 hosts. Of these listings, 17,749 have a profile picture and 9,953 use a single portrait. We controlled for ( 1) host characteristics (e.g., identity verification, reputation), ( 2) listing characteristics (e.g., accommodation type, price, postal code, age of listing, quality of the listing photos), and ( 3) review characteristics (e.g., number of reviews, property ratings by reviewers, review sentiment). These three groups of variables are critical to rule out potential confounds. For instance, postal codes are frequently used to control for socioeconomic differences such as housing quality across geographic areas, which may affect the outcome variable. This is also true for review volume and sentiments. The dependent variable is the annual occupancy rate, which is a proxy for sales performance. Table 1 provides the variable definitions and summary statistics.
Graph
Table 1. Summary Statistics of Airbnb Data (Study 1a).
| Variable | Definition | N | M | SD | Min | Max |
|---|
| Pictorial Characteristics |
| Presence of picture | Presence of profile picture | 17,935 | .990 | .101 | 0 | 1 |
| Human portrait | Presence of human portrait | 17,749 | .711 | .454 | 0 | 1 |
| Photographic quality | Aggregated measure of HSV (hue, saturation, value) and picture resolution | 17,749 | .303 | .154 | 0 | .962 |
| Facial attractiveness | Face attractiveness score determined by the machine learning approach | 9,953 | 3.05 | .433 | 1.91 | 4.26 |
| Smiling expression | Likelihood of smiling expression determined by the machine learning approach | 9,953 | .645 | .257 | 0 | 1 |
| Face proximity (%) | Ratio of the area of a face to the whole picture | 9,953 | .111 | .090 | .001 | .820 |
| Host Characteristics |
| Superhost | Binary indicator of whether the host has a superhost badge representing a good reputation | 10,979 | .206 | .404 | 0 | 1 |
| Verified home email | Binary indicator of whether the account is verified by home email | 10,979 | .972 | .166 | 0 | 1 |
| Verified work email | Binary indicator of whether the account is verified by work email | 10,979 | .136 | .342 | 0 | 1 |
| Verified government-issued ID | Binary indicator of whether the account is verified by government-issued ID | 10,979 | .445 | .497 | 0 | 1 |
| Verified phone number | Binary indicator of whether the account is verified by phone number | 10,979 | .996 | .060 | 0 | 1 |
| Verified selfie with ID | Binary indicator of whether the account is verified by selfie with ID | 10,979 | .023 | .149 | 0 | 1 |
| Linked Facebook account | Binary indicator of whether the account is linked to Facebook account | 10,979 | .275 | .446 | 0 | 1 |
| Linked Google account | Binary indicator of whether the account is linked to Google account | 10,979 | .065 | .247 | 0 | 1 |
| Linked LinkedIn account | Binary indicator of whether the account is linked to LinkedIn | 10,979 | .029 | .168 | 0 | 1 |
| Listing Characteristics |
| Response rate | % of new inquiries and reservation requests the host responded to within 24 hours in the past 30 days | 17,935 | .939 | .158 | 0 | 1 |
| Average daily rate | The average rate paid for rooms booked | 17,935 | 148.76 | 164.386 | 6.670 | 4,290 |
| Annual occupancy rate | % of total available days in the year with a confirmed booking | 17,935 | .590 | .256 | .032 | 1 |
| Apartment | Binary indicator of whether the listing is an apartment | 17,935 | .576 | .494 | 0 | 1 |
| House | Binary indicator of whether the listing is a house | 17,935 | .291 | .454 | 0 | 1 |
| Shared apartment/house | Binary indicator of whether the apartment/house is a shared unit | 17,935 | .350 | .477 | 0 | 1 |
| # of listing photos | Number of property photos shown | 17,935 | 18.765 | 13.886 | 1 | 255 |
| Quality of main listing photo | Aggregated measure of HSV (hue, saturation, value) and picture resolution | 17,935 | .506 | .261 | .049 | .950 |
| Listing postal code | A series of dummy variables indicating the listing location, where X = 1 (X ∈ (all postal codes in Los Angeles)) if the listing is located in the ZIP code tabulation areas X; 0 otherwise. |
| Joined year (age of listing) | The year when the listing joined Airbnb |
| Review Characteristics |
| Property rating | Average star rating by reviewers | 17,935 | 4.663 | .435 | 1 | 5 |
| Ln (# of reviews) | Log of the total number of reviews received | 17,935 | 2.424 | 1.375 | 0 | 6.084 |
| Review subjectivity | Average subjectivity of customer review | 17,935 | .625 | .039 | .17 | .95 |
| Review polarity | Average polarity of customer review | 17,935 | .411 | .065 | −.344 | 1 |
| Review readability | Average readability of customer review | 17,935 | 67.92 | 7.57 | .92 | 100 |
1 Notes: The number of reviews is incremented by one before the log transformation. We assess the review polarity from −1 (negative) to 1 (positive), the subjectivity score from 0 (objective) to 1 (subjective), and readability using Flesch reading ease scale from 0 to 100.
We used a hierarchical framework to assess the effect of host attractiveness on occupancy rate. Approximately 24% of the hosts own more than one listing, and thus the unit of observation is a listing. We estimated the model in a stepwise fashion. The baseline econometric model is as follows:
Graph
2
where Occupancyhl is a measure of the annual occupancy rate of listing l owned by host h. The parameter of interest is β1, the effect of pictorial features including facial attractiveness. Xhl represents a set of listing characteristics and review characteristics. Yh denotes a set of host characteristics. The random intercept, uh, is a host-specific error component that accounts for unobserved heterogeneity across hosts, and ehl is a listing error component that varies between listing l and host h.
As Table 2 shows, the coefficients for pictorial features are as expected across all specifications. The presence of a profile picture has a positive effect, resulting in an approximately 4.1% increase in occupancy rate (Spec. 1). Better photographic quality (Spec. 2) and smiling expression (Spec. 3) are positively related to occupancy rate. The results of Spec. 3 show that a one-unit increase in a host's facial attractiveness can increase the occupancy rate by approximately 1.3%, suggesting that the beauty premium is prevalent. We introduced the quadratic term in Spec. 4 and use a three-step procedure to test the U-shaped relationship between facial attractiveness and occupancy rate ([34]). First, the results of Spec. 4 in Table 2 show the joint significance and expected signs of the direct effect (b = −.911, p <.01) and the squared term effect (b =.150, p <.01). Second, as shown in Figure 2, Panel A, the slope of the lower end is significantly negative (−.341, p <.01), and the slope of the higher end is significantly positive (.368, p <.01). Third, the turning point (3.04, p <.01) is significant and located well within the data range. Thus, these results support H1. To account for potential social interactions, we controlled for rental type (room vs. whole unit) by assuming that guests expect to meet their hosts face-to-face when renting a room in the unit. While shared apartment/house has a significant negative effect, the interaction between shared unit and facial attractiveness is found to be insignificant, and thus no concern of social pressure from the expectation of meeting an attractive host is present.
Graph
Table 2. Estimation Results for Occupancy Rate (Study 1a).
| Spec. 1 | Spec. 2 | Spec. 3 | Spec. 4 | Spec. 5 (SIMEX) | Spec. 6(SIMEX) |
|---|
| Pictorial Characteristics |
| Presence of picture | .041** | – | – | – | – | – |
| (.020) | | | | | |
| Photographic quality | – | .059*** | .062*** | .059*** | .052*** | .044** |
| (.014) | (.018) | (.018) | (.016) | (.017) |
| Human portrait | – | .025*** | – | – | – | – |
| (.005) | | | | |
| Smiling expression | – | – | .024** | .044*** | .024*** | .064*** |
| | | (.011) | (.011) | (.009) | (.009) |
| Face proximity | – | – | .221*** | .228*** | .193*** | .211*** |
| | | (.032) | (.032) | (.028) | (.029) |
| Facial attractiveness | – | – | .013** | −.911*** | .014** | −1.899*** |
| | | (.007) | (.082) | (.006) | (.150) |
| Facial attractiveness | – | – | – | .150*** | – | .310*** |
| | | (.013) | | (.024) |
| Listing Characteristics |
| Response rate | .151*** | .149*** | .162*** | .161*** | .169*** | .167*** |
| (.014) | (.015) | (.020) | (.020) | (.018) | (.015) |
| Average daily rate | −2.26e-04*** | −2.24e-04*** | −2.59e-04*** | −2.62e-04*** | −2.85e-04*** | −2.81e-04*** |
| (2.25e-05) | (2.26e-05) | (3.90e-05) | (3.73e-05) | (3.62e-05) | (3.38e-05) |
| Apartment | .024*** | .024*** | .014* | .012* | .019** | .019** |
| (.006) | (.006) | (.008) | (.008) | (.007) | (.007) |
| House | .022*** | .022*** | .020** | .019** | .015* | .014* |
| (.007) | (.007) | (.009) | (.009) | (.008) | (.008) |
| # of listing photos | 1.61e-04 | 1.86e-04 | 1.95e-04 | 1.90e-04 | −1.71e-04 | −1.23e-04 |
| (1.56e-04) | (1.56e-04) | (2.26e-04) | (2.24e-04) | (2.20e-04) | (2.20e-04) |
| Quality of main listing photo | −.003 | −.002 | .003 | .003 | −.005 | −.002 |
| (.006) | (.006) | (.008) | (.008) | (.008) | (.009) |
| Shared apartment/house | −.068*** | −.068*** | −.072*** | −.072*** | −.076*** | −.076*** |
| (.005) | (.005) | (.007) | (.007) | (.006) | (.006) |
| Postal code fixed effect | Yes | Yes | Yes | Yes | Yes | Yes |
| Joined year fixed effect | Yes | Yes | Yes | Yes | Yes | Yes |
| Host Characteristics |
| Superhost | .019*** | .018*** | .018*** | .017** | .012** | .011* |
| (.005) | (.005) | (.007) | (.007) | (.005) | (.006) |
| Host identity variables | Yes | Yes | Yes | Yes | Yes | Yes |
| Review Characteristics |
| Property rating | .015*** | .015*** | .014* | .014** | .022*** | .025*** |
| (.005) | (.005) | (.007) | (.007) | (.007) | (.007) |
| ln (# of reviews) | .063*** | .063*** | .058*** | .057*** | .063*** | .062*** |
| (.002) | (.002) | (.002) | (.002) | (.002) | (.002) |
| Avg. review polarity | −.060* | −.062** | −.031 | −.034 | −.020 | −.027 |
| (.031) | (.031) | (.041) | (.040) | (.047) | (.043) |
| Avg. review subjectivity | .081 | .076 | .115 | .128* | .170** | .192*** |
| (.054) | (.054) | (.071) | (.071) | (.072) | (.071) |
| Avg. review readability | 1.14e-03*** | 1.17e-03*** | 1.26e-03*** | 1.25e-03*** | 1.56e-03*** | 1.55e-03*** |
| (2.27e-04) | (2.29e-04) | (3.11e-04) | (3.08e-04) | (3.31e-04) | (3.64e-04) |
| # of observations | 17,935 | 17,749 | 9,953 | 9,953 | 9,953 | 9,953 |
2 Notes: Host identity/verification information is also included in estimation but not reported for brevity. Heteroskedasticity consistent robust standard errors are reported in parentheses. *p <.10; **p <.05; ***p <.01.
Graph: Figure 2. Relationship between facial attractiveness and sales performance.Notes: This curve is drawn at the average level for all other variables.
In Specs. 5 and 6, we used the simulation extrapolation method (SIMEX) to account for measurement errors in the machine learning approach. SIMEX is a data-driven approach to correcting measurement errors and requires relatively fewer assumptions and information than alternative methods ([60]). We followed its diagnostic procedure to assess the measurement error using the known attractiveness scores rated by human coders in a random sample of host pictures from 2,750 listings. Compared with the naive model, the parameter estimates of facial attractiveness using the SIMEX corrected model are larger in magnitude, suggesting that the naive model may underestimate the effects. The other variables, however, change little in the presence of measurement error.
To validate the findings of Study 1a, we tested the model using data from 5miles. We tracked a random sample of product listings on a daily basis for 60 days (January 31 to March 31, 2019) in three product categories—beauty products (11,842 items), electronics (7,171 items), and bags (7,215 items)—resulting in a sample of 26,228 items from 11,115 sellers. Approximately 46% of the products received at least one offer during the observation period. We used the same method as in Study 1a to extract facial features from seller profile pictures. We controlled for seller characteristics (e.g., trust level, star rating, gender, identity verifications), and product characteristics (e.g., product category, number of product photos, length of product description, price). We used a topic modeling approach[10] (guided latent Dirichlet allocation) to classify the products on the basis of the degree of relevance to either appearance or expertise. Drawing on the topic dominance in product descriptions, we classify 5,977 listings as expertise-relevant, 8,841 listings as appearance-relevant, and the other listings for which neither appearance nor expertise topics are dominant serve as the baseline group. Table 3 provides the variable definitions and summary statistics.
Graph
Table 3. Summary Statistics of 5miles Data (Study 1b).
| Variable Definition | N | M | SD | Min | Max |
|---|
| Pictorial Characteristics |
| Presence of picture | Presence of profile picture | 26,228 | .853 | .354 | 0 | 1 |
| Human portrait | Presence of human portrait | 22,371 | .453 | .498 | 0 | 1 |
| Photographic quality | Aggregated measure of HSV and picture resolution | 22,371 | .295 | .162 | 0 | .982 |
| Facial attractiveness | Face attractiveness score determined by the machine learning approach | 8,184 | 3.08 | .425 | 1.99 | 4.35 |
| Smiling expression | Likelihood of smiling expression determined by the machine learning approach | 8,184 | .543 | .254 | .020 | 1 |
| Face proximity (%) | Ratio of the area of a face to the whole picture | 8,184 | .188 | .142 | .001 | .949 |
| Seller Characteristics |
| Female | Binary indicator of the seller gender: female = 1, otherwise = 0 | 11,115 | .523 | .498 | 0 | 1 |
| Trust level of seller | Seller's trust level determined by the platform | 11,115 | 2.48 | 2.19 | 0 | 11 |
| Seller star rating | Average star rating by reviewers | 11,115 | .707 | .431 | 0 | 1 |
| Log (# of seller followers) | Log number of followers the seller has | 11,115 | 2.81 | 1.30 | 0 | 7.89 |
| Verified email | Binary indicator of whether the account is verified by email | 11,115 | .736 | .441 | 0 | 1 |
| Verified phone number | Binary indicator of whether the account is verified by phone number | 11,115 | .953 | .211 | 0 | 1 |
| Linked Facebook account | Binary indicator of whether the account is linked to Facebook account | 11,115 | .395 | .489 | 0 | 1 |
| Product Characteristics |
| # of product photos | Number of product photos shown | 26,228 | 2.99 | 2.23 | 0 | 12 |
| Log length of listing description | Log of the total number of words in the product description | 26,228 | 2.46 | 1.15 | .69 | 7.06 |
| Price of the product | Listing price | 26,228 | 107.36 | 236.06 | 1 | 7,000 |
| Offer made by buyers | Binary indicator of whether an offer is received | 26,228 | .460 | .498 | 0 | 1 |
3 Notes: The number of seller followers and length of listing descriptions are incremented by one before the log-transformation.
We specify the utility that affects the sale of product j as follows:
Graph
3
where PDij is the picture decision of seller i who lists product j, Xjt represents a vector of product characteristics, and Si represents seller characteristics. To accommodate unobserved seller heterogeneity, we split the error term (eijt = μi + εijt) into μi ∼ N (0, σ2), which is specific to seller i, and εijt, which is unique for each listing.
Most sellers have only one item to sell, and this item may be requested by multiple buyers at different times. While a sale is made to one of the offers, we cannot observe which offer received a sale. Thus, the time to receipt of the first offer is one of the most important outcomes that can be attributed to facial appearance, among other factors. Given this dynamic process, we model the time-to-offer using a discrete-time proportional hazard model:
Graph
Graph
4
where h(dijt, PDij, Xjt, Si) is the hazard rate for product listing j receiving an offer in time period t given that it has not received an offer before time t, and T is a stochastic representation of the time duration. h0(dijt) is the baseline hazard rate capturing the likelihood of receiving an offer. The hazard rate depends on both the independent variables and the length of time a listing is at risk. We estimated the model using a binary choice model with time fixed effects, as it is equivalent to a piecewise exponential hazard model when the data are observed at discrete time points. We thus adopted the probit specification and Equation 4 as a discrete time duration model.
Graph
5
where y1 = 1 if Uijt > 0, and kt − t0 represents a set of temporal dummy variables.
The Kaplan–Meier survival curves in Figure 3 show that at any point in time, sellers with profile pictures of themselves are more likely to receive offers from buyers sooner (i.e., the lowest survival rate) than those with nonhuman pictures or without profile pictures. The survival curves for the three groups show that plain-looking sellers are associated with a higher survival rate, suggesting that their listings (compared with either attractive or unattractive sellers) have a longer sales cycle. Again, the results of Spec. 1 and Spec. 2 in Table 4 suggest that the mere presence of a profile picture (b =.255, p <.01) and a human portrait (b =.118, p <.01) are positively related to sales performance. After controlling for smiling expressions in Spec. 3, the coefficient of facial attractiveness remains significantly positive (b =.086, p <.01). We include the quadratic term of facial attractiveness in Spec. 4, and the result is consistent with that of Study 1a, in that both attractive and unattractive sellers are more likely to receive offers sooner than plain-looking sellers (slope: b = −1.001, quadratic term: b =.177; p <.01). Thus, H1 is again supported. In Spec. 5, we introduce the interaction between facial attractiveness and product relevance. Compared with less attractive sellers, attractive sellers perform better for appearance-relevant products (b =.082, p <.10) but worse for expertise-relevant products (b = −.138, p <.05). These results provide support for H4.
Graph: Figure 3. Kaplan–Meier survival curves (Study 1b).
Graph
Table 4. Estimation Results from Duration Model (Study 1b).
| Spec. 1 | Spec. 2 | Spec. 3 | Spec. 4 | Spec. 5 |
|---|
| Estimate | Hazard Ratio | Estimate | Hazard Ratio | Estimate | Hazard Ratio | Estimate | Hazard Ratio | Estimate | Hazard Ratio |
|---|
| Pictorial Characteristics |
| Presence of picture | .255*** | 1.29 | — | | — | | — | | — | |
| (.018) | | | | | | | | | |
| Photographic quality | — | | .254*** | 1.29 | .218*** | 1.24 | .212*** | 1.24 | .215*** | 1.24 |
| | | (.036) | | (.063) | | (.063) | | (.063) | |
| Human portrait | — | | .118*** | 1.13 | — | | — | | — | |
| | | (.012) | | | | | | | |
| Smiling expression | — | | — | | .120*** | 1.13 | .148*** | 1.16 | .122*** | 1.13 |
| | | | | (.037) | | (.037) | | (.037) | |
| Face proximity (%) | — | | — | | .177*** | 1.19 | .181*** | 1.20 | .173*** | 1.19 |
| | | | | (.067) | | (.066) | | (.067) | |
| Facial attractiveness | — | | — | | .086*** | 1.09 | −1.001*** | .37 | .072*** | 1.08 |
| | | | | (.020) | | (.252) | | (.022) | |
| Facial attractiveness2 | — | | — | | — | | .177*** | 1.19 | — | |
| | | | | | | (.041) | | | |
| Facial attractiveness × ER | — | | — | | — | | — | | −.138** | .87 |
| | | | | | | | | (.061) | |
| Facial attractiveness × AR | — | | — | | — | | — | | .082* | 1.09 |
| | | | | | | | | (.044) | |
| Seller Characteristics |
| Female | −.002 | 1.00 | −.016 | .98 | −.044** | .96 | −.042** | .96 | −.043** | .96 |
| (.011) | | (.012) | | (.019) | | (.019) | | (.019) | |
| Trust level of seller | .071*** | 1.07 | .063*** | 1.07 | .065*** | 1.07 | .063*** | 1.07 | .064*** | 1.07 |
| (.005) | | (.005) | | (.008) | | (.008) | | (.008) | |
| Seller star rating | .023*** | 1.02 | .023*** | 1.02 | .023*** | 1.02 | .023*** | 1.02 | .023*** | 1.02 |
| (.001) | | (.001) | | (.001) | | (.001) | | (.001) | |
| ln (# of seller followers) | .011 | 1.01 | .021*** | 1.02 | .021* | 1.02 | .022* | 1.02 | .022* | 1.02 |
| (.007) | | (.007) | | (.012) | | (.012) | | (.012) | |
| Seller identity variables | (Included in estimation) |
| Product Characteristics |
| Price of the product | −.015*** | .99 | −.014*** | .99 | −.014*** | .99 | −.014*** | .99 | −.014*** | .99 |
| (.000) | | (.000) | | (.000) | | (.000) | | (.000) | |
| # of product photos | .016*** | 1.02 | .017*** | 1.02 | .023*** | 1.02 | .023*** | 1.02 | .023*** | 1.02 |
| (.003) | | (.003) | | (.005) | | (.005) | | (.005) | |
| Log length of listing description | .013** | 1.01 | .015*** | 1.02 | .023** | 1.02 | .022** | 1.02 | .022** | 1.02 |
| (.005) | | (.005) | | (.009) | | (.009) | | (.009) | |
| Product categories | (Included in estimation) |
| # of observations | 26,228 | 22,371 | 8,184 | 8,184 | 8,184 |
| Log likelihood at convergence | −40,756.18 | −36,820.86 | −13,531.48 | −13,522.19 | −13,523.17 |
- 4 *p <.10.
- 5 **p <.05.
- 6 ***p <.01.
- 7 Notes: Seller verification information and product categories are included in estimation but not reported for brevity. Heteroskedasticity consistent robust standard errors are reported in parentheses.
We tested the robustness of results in a number of ways. We obtained the variance inflation factors for all the covariates in Study 1a (see Table W2-6 in Web Appendix 2). They are all below the conventional threshold of 4, indicating that multicollinearity does not appear to be a concern. We then explore the potential problem arising from outliers. For example, we excluded observations within the top 5% of the average daily rate (Spec. 2). We also excluded listings in the top 5% of the distribution of occupancy (Spec. 3). The reestimated results remain robust in terms of sign, magnitude, and statistical significance.
For Study 1a, the use of linear regression may be inappropriate if the dependent variable is not normally distributed. The residuals of the model fit are approximately normal, suggesting that the possible violation of nonnormality is not likely. We also took the log-transformation of the occupancy rate and rerun the model in Spec. 4 and the results remain consistent. In addition, the use of a percentage as a dependent variable (i.e., occupancy rate) in ordinary least squares regression may cause predictions that are nonsensical (below 0 or above 1). We thus rerun the model using beta regression, which is appropriate for a response variable that is restricted to the interval (0, 1), and find that the parameter estimates remain robust. To explore the alternative specifications of the U-shaped relationship. We used the inverse form rather than the quadratic form to specify the relationship between facial attractiveness and sales. The results of the parameter estimates are robust, as both the direct and inverse terms are significant. In addition, including the cubic term of facial attractiveness does not improve model fit, thus further supporting the U-shaped relationship. For Study 1b, in addition to the duration to receiving an offer, we used the seller's offer (i.e., a sale dummy) as an alternative dependent variable and find the parameter estimates to be consistent with the duration survival model (Figure 2, Panel B).
Finally, sellers' uploading portraits with varying degrees of attractiveness may affect the accuracy of the parameter estimates. We examined the distribution of attractiveness scores for both data sets and find them to be normally distributed. For Airbnb data, we found an insignificant correlation between hosts' facial attractiveness and property ratings (r =.0016). We also adopted the propensity score matching approach to examine the sample with and without profile pictures and find them to be comparable in terms of products, seller, and review characteristics.
To investigate the mechanism underlying the beauty and ugliness premium, we first conduct online experiments to examine the mediating roles of perceived sociability and competence in the relationship between seller attractiveness and source credibility and purchase intention.
We selected seller photos from Chicago Face Database, which provides high-resolution, standardized photographs of male and female faces. Extensive norming data are available for each individual photo including physical attributes as well as subjective ratings by independent judges (e.g., attractiveness, trustworthy, feminine/masculine). The manipulation of attractiveness, while successful, may influence the perception of seller trustworthiness. Following previous research (e.g., [25]; [51]), we avoided this problem by choosing sellers who vary in attractiveness yet are of equivalent trustworthiness. To control for facial expressions and gender, we chose three male and three female photos with attractive, plain-looking, and unattractive faces, all with neutral expressions. Except for the photos, the scenario for the shopping task was identical across conditions.
We randomly assigned 350 participants (187 men; Mage = 36.76 years, SD = 12.83) recruited from consumer panelists on Amazon's Mechanical Turk (MTurk) to one of the three (attractive, plain-looking, unattractive) between-subject conditions. They were first instructed to read the materials describing a hypothetical shopping task for a digital camera and then asked to investigate the seller and their product carefully. They had to click the "next" button to go to the questions. Then they were asked to first indicate their purchase intention on a scale from 1 ("I definitely would not buy") to 5 ("I definitely would buy"). Next, they assessed the seller's credibility on a four-item scale ("To what extent do you think the source is credible/reliable/trustworthy/an expert?" [ 7]). The responses were averaged to form a composite score of source credibility (α =.91). The participants then rated the perceived sociability ("The seller is easy to like/a fun person to be around/like a good friend/a very nice person"; α =.92; [37]) and competence ("The seller is competent/intelligent/capable/skillful"; α =.91; [56]) of the seller. All these measures use a five-point scale from 1 ("strongly disagree") to 5 ("strongly agree"). To rule out potential confounds, we also measured face familiarity (1 = "does not look familiar at all," and 5 = "looks very familiar") and perceived trustworthiness ("The seller is someone I feel I can trust/never tries to mislead me/is always honest in his/her dealing with others"; α =.89; [49]).
The participants rated the attractive sellers (Mattractive = 3.55) as significantly more attractive than the plain-looking (Mplain = 3.03; p <.01) and unattractive (Munattractive = 2.57; p <.01) sellers. All pairwise comparisons between conditions are significant at the.01 level, and there is no significant difference in attractiveness between male and female sellers within the same condition. The differences in perceived trustworthiness turn out to be insignificant among the three groups (Mattractive = 3.37, Mplain = 3.43, Munattractive = 3.54; F( 2, 347) =.84, p =.43).
To examine whether unattractive and attractive faces on the first page receive more attention from participants, we recorded the browsing time between when a seller picture is completely loaded and when the "next" button is clicked. We find that the participants take more time (in seconds) to browse the pages of either attractive or unattractive faces than those of plain-looking faces (Mattractive = 34.29, Mplain = 26.75, Munattractive = 33.03; F( 2, 347) = 3.22, p <.05). Given that everything except for the picture is identical across the groups, this finding confirms the U-shaped relationship between attractiveness and attention.
Consistent with the findings from field studies, seller attractiveness has a U-shaped relationship with purchase intention (F( 2, 347) = 4.18, p <.05), in support of H1. Changing from unattractive to plain-looking decreases purchase intention (Munattractive = 3.86 vs. Mplain = 3.65; F( 1, 229) = 3.44, p <.10). Beyond that point, however, additional attractiveness increases the purchase intention (Mplain = 3.65 vs. Mattractive = 3.95; F( 1, 238) = 9.44, p <.01). There is no difference in purchase intention between unattractive and attractive conditions (F < 1). As for source credibility, we observe a significant difference among the three conditions (F( 2, 347) = 5.97, p <.01). Both attractive sellers (Mattractive = 4.07 vs. Mplain = 3.74; F( 1, 238) = 12.87, p <.01) and unattractive sellers (Munattractive = 3.91 vs. Mplain = 3.74; F( 1, 227) = 2.87, p <.10) are perceived as more credible than plain-looking sellers. There is no significant difference in perceived credibility between unattractive and attractive faces (p >.10). Source credibility is highly correlated with purchase intention (r =.80).
There is a significant difference in perceived sociability (F( 2, 347) = 9.04, p <.01) and competence (F( 2, 347) = 3.81, p <.05) among the three conditions. Attractive sellers are perceived as more sociable than plain-looking ones (Mattractive = 3.63 vs. Mplain = 3.34; F( 1, 238) = 7.08, p <.01) and unattractive ones (Mattractive = 3.63 vs. Munattractive = 3.15; F( 1, 227) = 18.03, p <.01). The results reveal no significant difference in sociability between plain-looking and unattractive sellers (p =.105). Perceived competence was significantly higher for unattractive and attractive faces than for plain-looking faces (Munattractive = 3.94 vs. Mplain = 3.69; F( 1, 229) = 6.73, p <.05; Mattractive = 3.87 vs. Mplain = 3.69; F( 1, 238) = 3.88, p <.10). There is no difference between the unattractive and attractive conditions (F < 1). Perceived sociability/competence were positively correlated with source credibility (r =.46/.58, p <.01) and purchase intention (r =.45/.57, p <.01).
We took a bias-corrected bootstrapping approach with 5,000 samples to simultaneously test sociability and competence as mediators, generating a 95% confidence interval around the following paths: ( 1) from attractive faces to sociability to source credibility to purchase intention and ( 2) from unattractive faces to competence to source credibility to purchase intention. The path coefficients from serial multiple mediated models are presented in Figure 4, Panel A. It is worth noting that the direct effect of attractive faces on sociability is much greater than that on competence (b =.29 vs. b =.18). The indirect effect of attractive faces on purchase intention via sociability/competence and source credibility is significant and positive (b =.037/.065, SE =.017/.034, 95% bootstrap confidence interval [BCI] = [.003,.071]/p =.06). The indirect effect of unattractive faces on purchase intention through competence and source credibility is also significant (b =.092, SE =.036, 95% BCI = [.022,.164]). These results support H2 and H3. We conducted a test of the alternative causal chain by reordering the mediators and testing the following pathways: ( 1) from attractive faces to source credibility to sociability to purchase intention and ( 2) from unattractive faces to source credibility to competence to purchase intention. However, the confidence intervals for these alternative mediation model contain zero (sociability: b =.010, SE =.007, 95% BCI = [−.004,.025]; competence: b =.014, SE =.010, 95% BCI = [−.006,.034]). Thus, we concluded that the causal chain occurs only in the predicted directions.
Graph: Figure 4. Mediation path diagram (Study 2a).*p <.10.**p <.05.***p <.01.
We recruited 479 participants from MTurk and randomly assigned them to one of four between-subject conditions (no picture, attractive, plain-looking, and unattractive). Except for the shopping task for a sunscreen, everything else is identical to the original study. The presence of a picture is found to have a positive effect on source credibility (Mpicture = 3.84 vs. Mno picture = 3.63; F( 1, 477) = 5.35, p <.05) and purchase intention (Mpicture = 3.84 vs. Mno picture = 3.60; F( 1, 477) = 5.05, p <.05). The results from mediation analysis on the sunscreen setting (shown in Figure 4, Panel B) are largely consistent with those on the digital camera setting. In particular, the indirect effect of attractive faces on purchase intention via sociability/competence and source credibility is significant and positive (b =.051/.054, SE =.022/.031, 95% BCI = [.010,.093]/p =.076). The indirect effect of unattractive faces on purchase intention via competence and source credibility is also significant (b =.066, SE =.035, p =.056). Thus, we found consistent beauty and ugliness premiums and the mediating mechanisms via sociability and competence for both digital camera and sunscreen.
Following the recommendations of [61], we examined potential mediators simultaneously alongside sociability and competence. We performed serial mediation analyses on visual attention and test whether it is a potential mediator driving the results. Although attractive and unattractive faces attract greater attention (battractive = 7.54, SE = 3.17, p <.05; bunattractive = 6.28, SE = 3.19, p <.05), visual attention does not significantly affect source credibility (b =.001, SE =.001, p >.10), which influences purchase intention. The 95% BCI [−.015,.03] of its indirect effect also includes zero. These results confirm our conjecture that attention is only the starting point for perceptions but not sufficient to induce a positive effect on the outcomes. The potential mediating effects of trustworthiness and face familiarity are also found to be insignificant.[12] While source credibility is an inference of expertise and trust based on the perception of all available cues (with attractiveness being just one of them), visual-based trustworthiness is the trustworthiness judgment based on an online profile photo ([12]). Thus, it is independent of a purchase context. In contrast, source credibility is more context-specific, especially relevant for evaluating products for purchase. That explains why visual-based trustworthiness does not play a significant mediating role between facial attractiveness and source credibility.
We recruited 556 participants (306 men; Mage = 37.15, SD = 10.57) from MTurk and randomly assigned them to a 3 (unattractive, plain-looking, and attractive faces) × 2 (product relevance: appearance vs. expertise) × 2 (seller gender: male vs. female) between-subjects conditions. The experiment simulates online shopping for a cookbook. To rule out potential confounds from the difference between products in terms of features, prices, and so on, we followed the practice of using one product positioned to be different in its relevance, as it is possible that a product may be relevant to appearance or expertise to varying degrees ([ 5]; [53]). Thus, unlike some studies that only used product type as a measure of product relevance (e.g., [53]), we manipulated product relevance by inserting a positioning message: "This cookbook contains many beauty secrets in its recipes that will give you a healthy and radiant appearance" in the appearance-relevant (AR) condition and "this cookbook can help you spend less time preparing nutritious meals and provide better cooking through science" in the expertise-relevant (ER) condition. Participants went through the same procedure as described in Study 2a. We also asked questions regarding the manipulation check of product relevance: "This book would improve the appearance of an unsatisfactory physical feature" and "this product would improve the efficiency of cooking through scientific methods." Participants responded using a five-point scale (1 = "does not describe at all," 5 = "describes completely"). At the end of the study, we collected the genders of the participants to examine the cross-gender effect.
Participants viewed attractive sellers as significantly more attractive than plain-looking and unattractive sellers (Mattractive = 3.36 vs. Mplain = 2.71 vs. Munattractive = 2.24; F( 2, 553) = 58.2, p <.01). Those in the AR condition believed that the cookbook could help improve appearance more than those in the ER condition (MAR = 2.65 vs. MER = 2.15; F( 1, 554) = 24.34, p <.01). In addition, participants in the ER condition believed that the cookbook could improve the efficiency of cooking more than those in the AR condition (MAR = 3.09 vs. MER = 3.75; F( 1, 554) = 49.10, p <.01).
Consistent with previous studies, seller attractiveness has a U-shaped relationship with purchase intention (F( 2, 553) = 5.12, p <.01) and source credibility (F( 2, 553) = 6.77, p <.01). Moving from unattractive to plain-looking sellers decreases purchase intention (Munattractive = 3.91 vs. Mplain = 3.72; F( 1, 366) = 4.42, p <.05) and source credibility (Munattractive = 3.84 vs. Mplain = 3.69; F( 1, 366) = 3.02, p <.10). Beyond that, however, additional attractiveness increases purchase intention (Mplain = 3.72 vs. Mattractive = 3.98; F( 1, 371) = 9.81, p <.01) and source credibility (Mplain = 3.69 vs. Mattractive = 3.99; F( 1, 371) = 13.71, p <.01). Source credibility is highly correlated with purchase intention (r =.74).
First, we performed separate mediation analyses for the AR and ER conditions (Figures 5, Panels A and B), simultaneously testing perceived sociability and competence as mediators. For the AR condition, the indirect effect of attractive sellers on purchase intention via sociability and source credibility is significant and positive (b =.053, SE =.028, p =.052) whereas the path via competence is not significant (b =.02, SE =.055, p =.72). For the ER condition, the effect of unattractive faces on purchase intention via competence and source credibility is significant and positive (b =.147, SE =.057, 95% BCI = [.034,.261]). H4a and H4b are largely supported.
Graph: Figure 5. Moderated mediation path diagram (Study 2b).*p <.10.**p <.05.***p <.01.
Second, a moderated mediation analysis yields similar results (Figure 5, Panel C). In particular, the AR product moderates the sensitivity to sociability (b =.31, SE =.12, p <.01), and sociability is positively related to source credibility (b =.13, SE =.04, p <.01). The conditional indirect effects show that perceived sociability matters more in the AR condition (b =.049, SE =.021, 95% BCI = [.008,.089]) than in the ER condition (b =.017, SE =.012, 95% BCI = [−.007,.041]). We also found that the ER product moderates the sensitivity to competence (b = −.20, SE =.12, p <.10) and that competence is positively related to source credibility (b =.58, SE =.04, p <.01). The conditional indirect effects show that perceived competence matters more in the ER condition (b =.140, SE =.045, 95% BCI = [.052,.228]) than in the AR condition (b =.054, SE =.048, 95% BCI = [−.043,.228]).
Finally, by examining their relative values across AR versus ER conditions, we further assessed how perceived sociability and competence together influence source credibility, which in turn affects purchase intention (Figure 6). For both conditions, attractiveness increases perceived sociability. When a seller is attractive, perceived sociability is significantly higher in the AR condition than in the ER condition (MAR = 3.44 vs. MER = 3.13; F( 1, 186) = 6.42, p <.05). When a seller is unattractive, perceived competence is significantly lower in the AR condition than in the ER condition (MAR = 3.71 vs. MER = 3.91; F( 1, 181) = 3.11, p <.10). These results confirm that product relevance affects the attractiveness–purchase relationship by influencing perceived sociability and competence, respectively.
Graph: Figure 6. The interaction between attractiveness and product relevance (Study 2b).
We created two dummy variables to test the moderating effect of cross-gender: MBFS takes a value of 1 if a male buyer faces a female seller, FBMS takes a value of 1 if a female buyer faces a male seller, and both take 0 for pairs of the same gender. To test H5a, we conducted a moderated mediation analysis (from seller attractiveness to sociability to source credibility to purchase intention, with MBFS as the moderator) with 5,000 bootstrapped samples. For attractive sellers, there is no evidence of moderated mediation for the MBFS group from sociability to source credibility to purchase intention (b =.16, SE =.11, p =.139). The conditional indirect effect also suggests that perceived sociability does not matter more in the MBFS condition than in the other conditions (p >.10). Thus, H5a regarding a stronger beauty premium in the MBFS setting is not supported. For unattractive faces, a similar moderated mediation analysis (from seller attractiveness to competence to source credibility to purchase intention with FBMS as the moderator) suggests that unattractive men moderate the sensitivity of female buyers to perceived competence (b =.30, SE =.11, p <.01), and perceived competence is positively related to source credibility (b =.59, SE =.04, p <.01), which in turn affects purchase intention (b =.78, SE =.04, p <.01). A bootstrapping test with 5,000 resamples indicates a significant indirect effect (95% BCI = [.089,.307]). Thus, H5b regarding a stronger ugliness premium in the FBMS setting is supported.
Unlike previous studies of attractiveness that focus on social selections in experimental settings, our field studies examine the effect of facial attractiveness among large numbers of sellers and buyers in an e-commerce context, in which profile pictures serve as a primary vehicle for impression formation and trait inference. Although the literature has documented a beauty premium in a variety of settings and occasionally found an ugliness premium, our analyses of tens of thousands of seller profile pictures from two websites provide converging evidence of a U-shaped relationship between facial attractiveness and sales. As for the underlying mechanisms, our experimental results support previous findings of a beauty premium and of an ugliness penalty when evaluating sellers' sociability. We also find an ugliness premium in perceived competence for unattractive sellers over plain-looking people. Thus, whereas attractive faces signal sociability and competence, unattractive faces elicit an enhanced perception of competence over sellers with plain looks, even slightly more so than the attractive people. Thus, contrary to the notion of the curse of ugliness, our findings indicate that plain-looking faces are caught in the middle without any real advantage, as they are considered less sociable than attractive people and less competent than unattractive people. As such, when consumers make online purchases, sellers' faces serve an important discriminating function to encode sellers' characters, sometimes in unexpected ways.
In addition, the effects of attractiveness and inferred traits are mediated by source credibility and are subject to the influence of important contextual variables—that is, product relevance (to appearance or expertise) and gender. Our results reveal that the mediating role of sociability on the relationship between attractive sellers and source credibility is significantly stronger for products relevant to appearance. In contrast, the mediating effect of competence is more associated with products for which expertise is more important than appearance. Finally, we find a greater ugliness premium for unattractive male sellers in perceived competence awarded by female consumers. However, male respondents do not reciprocate a greater beauty premium on attractive female sellers, perhaps because online purchases do not involve social selection like dating or hiring. It is not uncommon for attractive women to be viewed negatively for certain products or professions ([24]; [45]; [47]) or to draw suspicion for their appearance in online forums ([35]).
The role of attractiveness in human interactions is complex. Although most studies indicate a prevailing beauty premium, there are many exceptions and counterexamples (e.g., [10]). Our findings of a U-shaped relationship and the different mechanisms and contexts underlying the beauty and ugliness premiums highlight the complex relations between facial attractiveness and outcomes in C2C e-commerce and, to some extent, reconcile the previous disparate findings. Previous studies of the beauty premium have mainly considered mass media or interpersonal and face-to-face situations. Although social pressure is of lesser concern in C2C e-commerce, attractive individuals retain the beauty premium in sociability and competence, whereas their plain-looking counterparts suffer a penalty. Meanwhile, we find consistent evidence that even unattractive individuals have an edge in perceived competence over plain-looking people. More importantly, we shed light on the different mechanisms and conditions for the beauty and ugliness premiums, that is, social trait inferences, product relevance, and gender interactions. While the marketing and advertising literatures have emphasized the halo effect of beauty, our findings suggest that the effect of attractiveness is more complicated and subject to the influence of these factors, which researchers and practitioners must consider when assessing the effect of seller attractiveness on consumer responses.
Our findings provide meaningful implications for both online sellers and platform operators who want to leverage seller profile pictures to enhance business performance. Posting a photo of oneself instead of an avatar or landscape makes a difference. Having said that, loading a profile picture is not a task to be taken lightly. Similar to the beauty and ugliness premiums in earnings found by studies of labor market (e.g., [ 4]; [26]), our results indicate that one's attractiveness level has a tremendous effect on sales performance in C2C e-commerce platforms. Figure 2, Panel A, suggests that the beauty premium over plainness in the annual occupancy rate on Airbnb is, on average, 6% (62% vs. 56%) and as high as 22% (i.e., 78% vs. 56%) for perfect faces. Thus, everything being equal, good looks sell more. Meanwhile, the ugliness premium over plain-looking hosts is approximately 4%, on average, (60% vs. 56%) and up to 16% (72% vs. 56%) for the most unattractive hosts. Thus, such premiums are much higher for the extreme cases, whether it is extremely attractive or unattractive. Likewise, findings from the 5miles study show that both attractive and unattractive sellers are more likely to make a sale than their plain-looking counterparts (predicted probability: 44% for attractive, 38% for plain-looking, and 41% for unattractive; Figure 2, Panel B). Our experimental results suggest that while the beauty premium of female sellers does not hold true for male buyers, the ugliness premium only applies to unattractive men seen by female buyers, revealing the inequality in the cross-gender effect of beauty and ugliness premiums.
While the marketing literature is not short of studies emphasizing the effect of attractiveness in sales and customer service encounters ([29]; [42]), our nuanced findings of the curvilinear relationship between attractiveness and performance and the underlying mechanisms are particularly relevant for today's social selling on e-commerce platforms. First, like candidates in political campaigns who often enhance their images ([41]), aspiring entrepreneurs in social selling and C2C e-commerce should be mindful of their self-presentation; attractive appearances help create a favorable impression and gain the trust of shoppers. A professional photographer can produce a quality portrait to enhance attractiveness, and sellers can pretest the effect of a portrait on their perceived sociability and competence using services such as photofeeler.com. As consumers often choose between many sellers pitching similar products online, sellers with different degrees of attractiveness must be cognizant of their source of credibility, that is, sociability and/or expertise, as well as the type of products they are selling. A small perceptual difference based on appearance or credibility can have a nonnegligible effect.
Although e-commerce platform operators have no control over how people take pictures, they should provide guidance and suggestions and encourage sellers to provide attractive portraits of themselves. In addition to a good-quality photograph (i.e., in brightness and pixels), taking a photo from a particular angle may enhance attractiveness to avoid the plainness penalty. While attractive sellers enjoy an advantage, especially for appearance-related products, people without perfect facial symmetry and proportions should not shy away from displaying their true appearance. Emphasizing expertise in technical products can enhance their credibility and performance. Thus, on e-commerce platforms, both attractive and unattractive sellers can increase their performance by enhancing their perceived sociability or competence, especially when they are matched with products associated with the particular strengths derived from appearances. Because a product may be relevant to both appearance and expertise in varying degrees, our treatment of product relevance goes beyond mere product type and is based on product positioning with additional information. For online marketers, this means that given the positioning of a product (as relevant to appearance or expertise), they may select attractive or unattractive sellers as promoters and achieve similar results. Conversely, sellers with attractive or unattractive faces may find themselves better off presenting a product depending on its relevance to appearance or expertise.
With respect to the cross-gender interactions, existing studies in marketing have pointed to the potential positive effect of mismatched gender in service counters (e.g., [42]) as well as its precarious pitfalls in other cases ([55]). Our findings of the inequality in the cross-gender effect of attractiveness and ugliness premiums suggest that attractive female sellers do not have an advantage over their less attractive counterparts in appealing to male buyers, who may not succumb to the female beauty in online purchase given the reduced social pressure. However, female buyers tend to consider unattractive men as more competent than the Average Joes, perhaps perpetuating the stereotype of the tech-savvy nerd. Social sellers and e-marketing managers may heed such complex cross-gender interactions when attempting to leverage the effect of seller appearances in online selling. Altogether, these implications regarding the relevance of product and cross-gender effect of beauty and ugliness premiums are not limited to profile pictures of online sellers and may be pertinent to advertising and marketing aesthetics in general. Thus, researchers should consider a broader range of attractiveness, traits inferred from appearance, and its complex interactions with product relevance and gender.
Recent trends in collaborative consumption increase the already large number of selection decisions facing consumers; this could further contribute to information overload and potentially increase reliance on the physical and facial appearances of sellers. Although poor-quality pictures may dampen consumer confidence, attempts by sellers to make themselves appear more attractive may backfire if they appear otherwise incompetent or suspicious ([35]; [47]). Although consumers may consider the attractiveness of sellers in their decision making, they should not allow a seller's appearance to cloud their judgment of source credibility and product quality. Due diligence in confirming the veracity of sellers and product information is necessary, as platforms often provide indicators of sellers' reputations, track records, and social media connections.
Methodologically, this study is the first to explore the effect of facial attractiveness using large data sets of real profile pictures in online transaction platforms. This further validates the generalizability of studies based on laboratory methods using a limited number of facial stimuli. The use of online field data and actual sales overcomes the limitations of perceptual measures and strengthens the validity of our findings. The machine learning approach to assessing facial attractiveness proves to be reliable and robust and provides a useful tool for future studies using large data sets for facial recognition and deep learning in online settings.
People make individual choices when uploading a profile picture and selecting the type of products they sell. Future researchers could collect more data from other e-commerce sites to address potential self-selection bias and to validate our findings, particularly the U-shaped relationship between facial attractiveness and sales and the disadvantages for plain-looking people. This research focuses on facial geometrics to assess attractiveness. Characteristics of attractiveness other than faces could be examined, such as expressions and head tilt, which can affect perceptions of attractiveness. Although they are beyond the scope of this research, extrafacial features such as clothing and body posture, biometric data such as skin tone, color, race, gender, and enhancement by cosmetics and accessories may affect social attributions and provide rich data and broad avenues for future studies. For instance, attractiveness enhanced by cosmetics or perceived expertise from eyeglasses can augment or alter social perceptions.
Greater insight is needed regarding how other dimensions, such as cultural or dispositional variables, may moderate the relationship between seller attractiveness and consumer reactions. Online social interactions, such as messaging and chat, and offline face-to-face meetings between sellers and buyers may influence the effect of seller attractiveness. Software is now commonly used to enhance self-presentation, but excessive manipulation in portraits may be deceptive, raise suspicion, and lead to consumer dissonance and discontent. Thus, how consumers perceive and react to enhanced portraits and facial features warrants investigation. Finally, more tests are necessary to validate the mechanisms through which sociability and competence judgments are derived from facial cues and carried over to decision making. Innovative methods such as neuroscience and fMRI scans may help to reveal how these evaluation processes influence consumer perceptions and purchase decisions.
Supplemental Material, jm.18.0569-File003 - The Faces of Success: Beauty and Ugliness Premiums in e-Commerce Platforms
Supplemental Material, jm.18.0569-File003 for The Faces of Success: Beauty and Ugliness Premiums in e-Commerce Platforms by Ling Peng, Geng Cui, Yuho Chung and Wanyi Zheng in Journal of Marketing
Footnotes 1 Author ContributionsLing Peng, Geng Cui, and Yuho Chung share equal authorship.
2 Associate EditorRaj Venkatesan
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge the financial support of Lingnan University, Hong Kong for this research (FRG 102016/DB18B1).
5 ORCID iDLing Peng https://orcid.org/0000-0002-6034-4580
6 Online supplement: https://doi.org/10.1177/0022242920914861
7 1Web Appendix 1 presents the procedure and results about inviting MTurkers to code a random sample of profile photos. Web Appendix 2 reports the details from using human coders to score 2,750 host pictures required by SIMEX approach to assess measurement errors.
8 2We searched for a "smiling human face" and "neutral human face" in a Google image search. After extracting facial geometrics, we used the random forest regression to predict the likelihood of smiling in each of the profile pictures. Web Appendix 1 presents the details.
9 3Web Appendix 2 reports the details for the correction of measurement errors using simulation extrapolation.
4Web Appendix 2 provides details of the topic modeling approach with guided latent Dirichlet allocation.
5Web Appendix 2 reports the detailed results of robustness checks including multicollinearity, outliers, alternative DVs, and alternative U-shaped specifications for Study 1a and 1b. It also provides details for the propensity score matching method to address potential selection bias.
6Web Appendix 3 reports the mediation analysis and results for other potential mediators such as trustworthiness and face familiarity.
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By Ling Peng; Geng Cui; Yuho Chung and Wanyi Zheng
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Record: 189- The Impact of Advertising and R&D on Bankruptcy Survival: A Double-Edged Sword. By: Jindal, Niket. Journal of Marketing. Sep2020, Vol. 84 Issue 5, p22-40. 19p. 8 Charts. DOI: 10.1177/0022242920936205.
- Database:
- Business Source Complete
The Impact of Advertising and R&D on Bankruptcy Survival: A Double-Edged Sword
Advertising and research and development (R&D) benefit firms by increasing sales and shareholder value. However, when a firm is in bankruptcy, the cumulative effects of its past advertising and R&D can be a double-edged sword. On the one hand, they increase the firm's expected future cash flow, which increases the likelihood that the bankruptcy court will decide the firm can survive. On the other hand, they increase the liquidation value of the firm's assets, which decreases the likelihood that the bankruptcy court will decide that the firm can survive. The author argues that the ability of advertising and R&D to either increase or decrease bankruptcy survival is contingent on the influence that the firm's suppliers have, relative to other creditors, on the bankruptcy court's decision. Advertising and R&D increase (decrease) bankruptcy survival when suppliers have a high (low) level of influence. Empirical analyses, conducted on 1,504 bankruptcies, show that advertising (R&D) increases bankruptcy survival when at least 35%−38% (18%−21%) of the bankrupt firm's debt has been borrowed from suppliers, whereas it decreases bankruptcy survival below this point. Out-of-sample machine learning validation shows that the ability to predict whether a bankrupt customer will survive is substantially improved by considering the firm's advertising and R&D.
Keywords: advertising; bankruptcy; credit; R&D; suppliers; survival
Bankruptcy survival (i.e., emerging from bankruptcy instead of being liquidated) is critical to not only the bankrupt firm but also stakeholders such as suppliers, customers, partners, investors, and creditors. For example, a supplier suffers if its bankrupt customer does not survive, as the supplier loses the future revenue stream from the customer, loses the value of any relationship-specific investments it has made in the customer, incurs increased costs associated with trying to replace the customer, and loses any payments that were due from the customer.
Because marketing assets comprise a large portion of the value for many firms, understanding how investments in marketing assets affect bankruptcy survival is critically important for managers. Given the prominent role that advertising and research and development (R&D) play in building a firm's shareholder value (e.g., [33]; [52]) and in protecting a firm from the risk of entering bankruptcy ([32]), in this article I study the impact of advertising and R&D on bankruptcy survival.[ 6] I show that, after controlling for the financial predictors from extant bankruptcy survival research, advertising and R&D have an additional impact on bankruptcy survival.
Although one might expect advertising and R&D to increase bankruptcy survival in the same way they increase shareholder value, I argue that this is not the case. This is because, in contrast to shareholder value, bankruptcy survival is a function of the relative value that creditors expect to receive if the bankrupt firm survives versus gets liquidated. Because suppliers have greater noncontractual revenue and generally do not require collateral whereas banks have less noncontractual revenue and generally do require collateral, the value received if the bankrupt firm survives versus gets liquidated differs for suppliers versus banks. I show that the cumulative effects of a firm's past advertising and R&D are a double-edged sword that can either increase or decrease bankruptcy survival and that this difference depends on the influence that the bankrupt firm's suppliers have, relative to other creditors, on the bankruptcy court's decision of whether to allow the firm to emerge from bankruptcy. If the suppliers have a large influence on the bankruptcy court's decision, advertising and R&D increase the probability that the bankrupt firm will survive. In contrast, if the suppliers have a small influence on the bankruptcy court's decision, advertising and R&D decrease the probability that the bankrupt firm will survive.
This research makes several contributions to marketing theory and generates actionable insights for managers. First, it contributes to the discipline's knowledge on the value of advertising and R&D. It adds to theory on how advertising and R&D create shareholder value by showing how they can either increase or decrease bankruptcy survival. To explain how and why their impact on bankruptcy survival differs from their impact on shareholder value, I develop a conceptual framework that shows how advertising and R&D influence the bankruptcy court's decision of whether the bankrupt firm survives versus gets liquidated.
Second, this research sheds additional light on the relationship between a firm's marketing and finance decisions ([44]). It provides managers with insight into how their firm's financing decisions (i.e., deciding the extent to which the firm borrows from suppliers vs. banks) influence the impact of their firm's marketing decisions (i.e., deciding how much to spend in advertising and R&D) on bankruptcy survival.
Third, in addition to providing insight for a bankrupt firm's marketing managers, this research provides actionable insight for marketing managers who are managing a relationship with a bankrupt customer or partner. Because the value of any relationship-specific investments would be lost if the bankrupt customer or partner does not survive, the bankruptcy filing triggers marketing managers to reassess the extent to which their firm should maintain, reduce, or enhance their investments in the relationship. For example, if marketing managers do not expect the bankrupt customer to survive, they may reduce or eliminate their investments that are specific to that customer and redeploy critical resources (e.g., sales forces) to other customers. In contrast, if marketing managers expect the customer to survive, they may maintain or even strengthen their investments in the relationship with the customer.
A key challenge marketing managers face in these scenarios is predicting whether the bankrupt customer or partner will survive. To provide insight for marketing managers in these scenarios, I use random forests machine learning to predict bankruptcy survival. The out-of-sample validation results indicate that marketing managers can improve their ability to predict whether their bankrupt customer or partner will survive by considering the firm's advertising, R&D, and suppliers' influence on the bankruptcy court's decision, in addition to the usual financial predictors.
This research lies at the intersection of two streams of marketing research: the impact of advertising and R&D on a firm's financial performance and marketing's role in the context of bankruptcies. In the following subsections, I review these streams of research and discuss how the current study contributes to them.
Marketing research shows that the cumulative effects of a firm's past advertising and R&D generally improve the financial performance of the firm (e.g., [20]; [32]; [53]). As summarized in Table 1, marketing literature has shown that advertising and R&D generally increase a firm's sales and market share (e.g., [53]; [61]) as well as shareholder value (e.g., [20]; [52]). Marketing research has found mixed results for advertising's effect on stock return risk, with some studies finding that advertising decreases stock return risk (e.g., [26]) and others finding that advertising increases stock return risk ([47]). Marketing literature has also shown that R&D decreases stock return risk (e.g., [43]) and that advertising and R&D reduce a firm's risk of entering bankruptcy ([32]).
Graph
Table 1. Marketing Literature on Financial Outcomes of Advertising and R&D.
| Financial Outcome | Effect of Advertising and R&D | Representative Studies |
|---|
| Sales, market share | Positive | McAlister et al. (2016); Rubera and Kirca (2012); Sethuraman, Tellis, and Briesch (2011); Steenkamp and Fang (2011) |
| Shareholder value | Positive | Edeling and Fischer (2016); Erickson and Jacobson (1992); Jindal and McAlister (2015); Joshi and Hanssens (2010); McAlister et al. (2016); Osinga et al. (2011); Rubera and Kirca (2012); Sridhar et al. (2016); Srinivasan et al. (2009); Srinivasan and Hanssens (2009); Srinivasan, Lilien, and Sridhar (2011); Srivastava, Shervani, and Fahey (1998) |
| Positive (negative) advertising effect above (below) response threshold | Kim and McAlister (2011) |
| Stock return risk | Mixed for advertising, negative for R&D | Frennea, Han, and Mittal (2019); McAlister, Srinivasan, and Kim (2007); Osinga et al. (2011) |
| Risk of entering bankruptcy | Negative | Jindal and McAlister (2015) |
| Bankruptcy survival | Positive (negative) for high (low) supplier influence | Current study |
Marketing theory argues that advertising and R&D increase a firm's shareholder value by enhancing, accelerating, reducing the volatility of, and reducing the vulnerability of the firm's expected future cash flows ([60]). In the current research, I build on this theory to develop conceptual arguments for how advertising and R&D affect the relative value creditors expect to receive if the bankrupt firm survives versus gets liquidated and, consequently, creditors' votes on whether the bankruptcy court should allow the bankrupt firm to survive.
In contrast to the wide body of marketing literature on the financial outcomes of advertising and R&D, there has been relatively little marketing research on bankruptcies. A contribution of the current article is that it builds on an emerging stream of research that studies marketing's role in the context of bankruptcies (Table 2). Research in this area has shown how marketing variables can influence a firm's risk of entering bankruptcy and how a rival entering bankruptcy affects marketing reactions of other firms in the industry. This research has shown that the mechanisms through which advertising and R&D protect a firm from the risk of entering bankruptcy differ from the mechanisms through which they build shareholder value ([32]), that ability- and motivation-influencing governance mechanisms influence bankruptcy risk spillover between franchisors and franchisees ([ 2]), that prices increase after a rival enters bankruptcy ([49]), and that sales decrease after a rival enters bankruptcy ([48]).
Graph
Table 2. Marketing Literature on Bankruptcy.
| Bankruptcy Variable | Marketing Variable | Key Insight | Study |
|---|
| Risk of entering bankruptcy | Advertising, R&D | The mechanisms through which advertising and R&D protect a firm from the risk of entering bankruptcy differ from the mechanisms through which they build shareholder value. Consequently, market turbulence moderates the impact of advertising and R&D on the risk of entering bankruptcy but not on shareholder value. | Jindal and McAlister (2015) |
| Risk of entering bankruptcy | Governance mechanisms | Franchisee bankruptcies spill over to franchisors and other franchisees. Ability- and motivation-influencing governance mechanisms have main and interaction effects on the risk of a franchisee entering bankruptcy. | Antia, Mani, and Wathne (2017) |
| Rival enters bankruptcy | Price | After Chrysler entered bankruptcy in 2009, the prices of automobiles from other dealerships increased approximately 1%. The price increase was lower at dealerships closer to a Chrysler dealership. | Özturk, Venkataraman, and Chintagunta (2016) |
| Rival enters bankruptcy | Sales | After Chrysler entered bankruptcy in 2009, the unit sales of automobiles from other dealerships decreased 28% on average. This negative effect was driven by an increase in consumer uncertainty about the viability of Chrysler's rivals following Chrysler's bankruptcy filing. | Özturk, Chintagunta, and Venkataraman (2019) |
| Bankruptcy survival | Advertising, R&D | The impact of advertising and R&D on bankruptcy survival is contingent on the influence that the bankrupt firms' suppliers have, relative to other creditors, on the bankruptcy court's decision. Advertising and R&D increase bankruptcy survival when suppliers have a high level of influence, whereas they decrease bankruptcy survival when suppliers have a low level of influence. | Current study |
Whereas extant marketing research has focused solely on antecedents and consequences of entering bankruptcy, the current article adds to this stream of research by studying marketing's role in exiting (i.e., surviving) bankruptcy. Because the risk of entering bankruptcy is very different from surviving bankruptcy, marketing's role in these two scenarios differs. The risk of entering bankruptcy is the risk that the firm will not generate enough cash in the near term to pay its financial and operating commitments ([ 5]; [14]). Advertising and R&D have been shown to reduce this risk by enhancing a firm's expected near-term cash flow ([32]).
In contrast, bankruptcy survival is the probability that the bankruptcy court will decide to approve the bankrupt firm to emerge from bankruptcy. Unlike the risk of entering bankruptcy, bankruptcy survival is a function of the relative value that creditors expect to receive if the bankrupt firm survives versus gets liquidated, and these expected values differ for creditors that are suppliers versus banks ([23]). In the next section, I review the bankruptcy process, and in the following section I develop conceptual arguments for how advertising and R&D affect bankruptcy survival. I show that the cumulative effects of a bankrupt firm's past advertising and R&D are a double-edged sword that can either increase or decrease bankruptcy survival and that this difference depends on the influence that the bankrupt firm's suppliers have, relative to other creditors, on the bankruptcy court's decision.
A firm that does not have sufficient funds to pay its financial commitments can enter bankruptcy (i.e., file for Chapter 11 in a U.S. bankruptcy court) to seek court protection from creditors' collection efforts. After a firm enters bankruptcy, it submits a debt reorganization plan to the court that outlines how the firm will pay back its creditors. If the bankruptcy court approves the plan, the firm survives (i.e., it emerges from bankruptcy and pays back its creditors from the proceeds of its future earnings according to the approved debt reorganization plan). If the bankruptcy court does not approve the plan, the firm does not survive (i.e., its assets are liquidated and the creditors are paid back from the proceeds of the liquidation sale).
The bankruptcy court's decision of whether to approve the firm's debt reorganization plan is based on the court's judgment of whether the creditors will receive more if the firm survives rather than is liquidated. To aid the court's decision, the bankruptcy court requests each creditor to vote on whether the court should accept the plan (and let the firm survive) or reject the plan (and order the firm to be liquidated). The details of this process, which are specified in the United States Bankruptcy Code, are summarized in the "Chapter 11 Bankruptcy" section in the Web Appendix. In brief, creditors play the primary role in influencing the court's decision.
Creditors who will receive less than the full value owed to them or whose payment terms are modified under the debt reorganization plan are entitled to vote on the plan. Creditors who will receive the full value owed to them under the original payment terms are deemed to have accepted the plan. After the votes are tallied, the bankruptcy court determines whether to approve the plan. The court considers whether the plan is feasible, legal, not likely to be followed by liquidation, and not likely to need further debt reorganization. Furthermore, the bankruptcy court assesses whether the expected value creditors receive under the plan is at least as large as the expected value that would be received if the bankrupt firm's assets were liquidated.
If the plan meets these criteria and all classes of creditors (e.g., banks, suppliers) have accepted the plan, the bankruptcy court will approve the plan, and the bankrupt firm will emerge from bankruptcy. Otherwise, the bankruptcy court generally orders a liquidation sale of the bankrupt firm's assets. Although the law allows the bankruptcy court to approve the debt reorganization plan even if one or more classes do not accept the plan (as long as the plan does not unfairly discriminate and is fair and equitable), this is uncommon in the cases of large public firms ([39]).
Not all creditors are treated equally in bankruptcy. A creditor's priority determines the payment it is entitled to if the bankrupt firm is liquidated. Bankruptcy law favors creditors that have a secured debt contract (i.e., a debt contract that was secured with one or more of the bankrupt firm's assets as collateral). These creditors receive the full proceeds from liquidating the assets used as collateral. Bankruptcy law places a lower priority on claims from an unsecured debt contract (i.e., a debt contract that was not secured with an asset as collateral). A creditor that has an unsecured debt contract is entitled to a portion of the liquidation value of only the bankrupt firm's assets that were not used as collateral with other creditors. In practice, the proceeds from liquidation received by creditors that have an unsecured debt contract are generally substantially lower than the amount due to the creditor ([23]).
Creditors have two primary roles in bankruptcy. Their first role is to work with the bankrupt firm to develop the debt reorganization plan. This includes specifying the terms, time frames, and conditions for how the debt will be repaid. In some instances, this may also include a plan that forgives a portion or all of the debt. The bankrupt firm has the incentive to work closely with its creditors to increase the likelihood that the creditors will vote to approve the plan and let the firm survive. Creditors have the incentive to work closely with the bankrupt firm to negotiate a plan that best benefits them.
The second role of creditors in bankruptcy is to vote on whether they accept the bankrupt firm's debt reorganization plan. Each creditor's vote is influenced by their expected contractual payment under the plan and their noncontractual revenue from the bankrupt firm ([23]; [62]). For example, if a supplier had provided the bankrupt firm 30 days to make payment on a $100 purchase, the supplier's contractual payment due is $100. The debt reorganization plan may state that the bankrupt firm will pay this $100 over the next 18 months after the firm emerges. When forming expectations of their contractual payment under the plan, the supplier would assess the risk that the bankrupt firm will not be able to make some or all of this $100 payment and the risk that the payment will be delayed past 18 months. The supplier would also consider its noncontractual revenue from the bankrupt firm, which is the future cash flow the supplier expects to receive if the bankrupt firm emerges that is above and beyond the $100 payment stipulated in its credit contract (often referred to as "quasi-rents" [[23]]). The supplier's noncontractual revenue is influenced by its expectations of the bankrupt firm's future cash flows.[ 7]
Advertising raises customer awareness of a firm's offerings and communicates the firm's point of difference, which support the development of the firm's intangible assets (e.g., brands, customer relationships, channel relationships). For example, the cumulative effect of a firm's past advertising contributes to the firm's brand equity by building a network of strong, favorable, and unique associations in customers' minds that differentiate the firm and its offerings ([34]). The intangible assets built through advertising enhance the firm's expected future cash flows and increase the firm's shareholder value ([42]; [60]).
R&D enables a firm to develop processes, technologies, and products (goods and services) that meet customer preferences and differentiate the firm. The cumulative effect of a firm's past R&D supports the development of intangible assets such as intellectual property and patents. The intangible assets built through R&D enhance the firm's expected future cash flows and increase the firm's shareholder value (e.g., [52]).
Advertising and R&D affect bankruptcy survival by influencing creditors' votes on whether the bankrupt firm should survive or be liquidated. Because the cumulative effects of a firm's past advertising and R&D increase the firm's expected future cash flows, they increase the noncontractual revenue that creditors expect to receive if a bankrupt firm survives. Furthermore, by enhancing the value of a bankrupt firm's assets that would be sold off in a liquidation (e.g., brands, trademarks, customer lists, intellectual property, patents),[ 8] advertising and R&D also increase the proceeds that creditors expect to receive if the bankrupt firm is liquidated. Because advertising and R&D increase the value to creditors both if the bankrupt firm survives and if the bankrupt firm is liquidated, their impact on creditors' votes—and consequently bankruptcy survival—depends on the relative value that advertising and R&D create for creditors if the bankrupt firm survives versus is liquidated.
Suppliers differ from banks on two key attributes that are relevant to the impact of advertising and R&D on bankruptcy survival. First, suppliers tend to have greater noncontractual revenue with a business customer than do banks ([62]). Suppliers receive revenue from a business customer primarily by selling products to the customer, not by collecting interest from the supplier debt contract with the customer. In contrast, banks primarily receive revenue from a business customer from the interest that is specified in the bank debt contract with the customer, not by selling products to the customer.
Second, suppliers generally do not require a business customer to provide assets as collateral, whereas banks generally do ([23]).[ 9] Suppliers do not require collateral because a supplier can protect itself by threatening to stop the supply of goods if the business customer does not meet the terms of its credit contract ([50]). In contrast, a bank provides the full amount of the loan to the customer upfront and therefore is unable to protect itself in a similar manner. A bank requires collateral to give it the protection to seize those assets that were pledged as collateral if the business customer does not meet the terms of its contract with the bank. Consequently, banks generally require that their business loans are secured with collateral ([ 8]; [12]).
Because of these differences between suppliers and banks, advertising and R&D have a differential effect on whether suppliers and banks vote to accept the bankrupt firm's debt reorganization plan. In the following subsections, I first develop conceptual arguments for how advertising and R&D influence suppliers' votes on the bankrupt firm's debt reorganization plan and then develop arguments for how they influence banks' votes. I then propose hypotheses for how the impact of advertising and R&D on bankruptcy survival is contingent on the suppliers' influence on the bankruptcy outcome (Figure 1).
Graph: Figure 1. Conceptual framework for the impact of advertising and R&D on bankruptcy survival.
Advertising and R&D increase the likelihood that suppliers will vote for the bankrupt firm to survive (i.e., vote to accept the bankrupt firm's debt reorganization plan) for three reasons. First, advertising and R&D increase the noncontractual revenue that suppliers expect to receive if the firm emerges from bankruptcy. If the bankrupt firm is not approved to survive, suppliers would lose this expected future revenue.
Second, although advertising and R&D also increase the liquidation values of the bankrupt firm's assets, suppliers generally receive minimal payment from liquidation due to their unsecured debt contracts having a lower priority than banks' secured debt contracts ([23]). Therefore, the impact of advertising and R&D on the payment that suppliers expect to receive is generally smaller if the bankrupt firm is liquidated than if it survives. Third, advertising and R&D increase a firm's point of differentiation. To the extent that the bankrupt firm's differentiation is due in part to products (goods, services, raw materials, or other inputs) from suppliers, suppliers are better positioned than banks to enforce any repayment agreements in the debt reorganization plan. Unlike banks, suppliers can credibly enforce payment through the threat of stopping the supply of products that are critical for the firm's differentiation ([50]).
All else being equal, advertising and R&D increase the likelihood that banks will vote for the bankrupt firm to be liquidated (i.e., vote to reject the bankrupt firm's debt reorganization plan) for three reasons. First, advertising and R&D increase the liquidation values of the bankrupt firm's assets. Because banks' secured debt contracts have a higher priority in liquidation, advertising and R&D increase the likelihood that banks will receive the full amount of payments that are due to them if the firm is liquidated. Second, although advertising and R&D increase the noncontractual revenue that suppliers expect to receive if the bankrupt firm survives, banks tend to have less (and in some cases no) noncontractual revenue that would be lost if the bankrupt firm does not survive. Third, unlike its effect on suppliers, advertising and R&D do not improve banks' ability to enforce repayment agreements in the debt reorganization plan. Banks face a higher risk from letting the bankrupt firm make repayments from future earnings and a lower risk from receiving repayments from the immediate liquidation of the bankrupt firm's assets.
Because advertising and R&D have differential effects on suppliers' and banks' votes, they create a conflict between them in the bankruptcy process. This conflict results in the suppliers and banks either ( 1) negotiating a compromise and working with the bankrupt firm to develop a debt reorganization plan they both approve or ( 2) submitting opposing votes to the bankruptcy court. The resolution of the conflict is contingent on the portion of the bankrupt firm's debt that is owed to its suppliers (what the finance literature refers to as the firm's "supplier debt ratio," or the ratio of the firm's supplier debt to the firm's total debt from suppliers and banks [[27]]).
If a large portion of the bankrupt firm's debt is due to suppliers, then suppliers have a large influence on the bankruptcy outcome because they can influence banks' votes by making sufficient concessions to banks when negotiating the debt reorganization plan. For example, the bankrupt firm's supplier debt may be large enough that suppliers can agree to a plan that allows the banks to be fully paid in cash immediately, to reduce their risk, while suppliers get paid over a longer period of time after the firm emerges from bankruptcy. Indeed, research shows that suppliers are more likely than banks to allow a bankrupt firm to reduce or delay its debt repayments ([23]; [62]). However, if suppliers have a small influence (i.e., only a small portion of the bankrupt firm's debt is due to suppliers), then suppliers do not have the ability to influence banks' votes because they are not owed large amounts that enable them to make sufficient concessions to the banks when negotiating the debt reorganization plan.
In summary, advertising and R&D have differential effects on suppliers' and banks' votes. Advertising and R&D increase the likelihood that suppliers will vote to accept the plan. Their impact on the banks' vote, however, is contingent on the suppliers' influence. If suppliers have sufficient influence to negotiate a favorable plan for the banks, advertising and R&D increase the likelihood that suppliers will make concessions so that banks will vote to accept the plan and allow the bankrupt firm to survive. If the suppliers do not have sufficient influence to negotiate a favorable plan for the banks, advertising and R&D increase the likelihood that the banks will vote against the plan and that the bankrupt firm will be liquidated. Thus,
- H1: Advertising's impact on bankruptcy survival is contingent on supplier influence. Advertising increases bankruptcy survival when supplier influence is high, whereas it decreases bankruptcy survival when supplier influence is low.
- H2: R&D's impact on bankruptcy survival is contingent on supplier influence. R&D increases bankruptcy survival when supplier influence is high, whereas it decreases bankruptcy survival when supplier influence is low.
To test my hypotheses, I specify bankruptcy survival models that are functions of the bankrupt firm's advertising, R&D, suppliers' influence, and control variables found to influence bankruptcy survival in prior literature. In this section, I first present the measures I use for each of the variables. Then, I specify the bankruptcy survival models and describe the data set assembled to estimate the models.
The variable Survivali is a nominal variable that is set to 1 if bankrupt firm i survived bankruptcy (i.e., emerged) and 0 if bankrupt firm i did not survive bankruptcy (i.e., it was liquidated). The variable Survivali is set to −1 if the court dismissed bankrupt firm i's case without either approving the debt reorganization plan or ordering liquidation and −2 if bankrupt firm i's case is still ongoing (i.e., its outcome is not yet known).[10] As subsequently discussed, I model bankruptcy survival using a logit analysis and a competing risks analysis. The logit analysis uses only the two cases in which Survivali equals 1 (survived) or 0 (liquidated).
Consistent with extant bankruptcy survival research, the predictor variables are measured using data from the firm's most recent financial report prior to its bankruptcy filing ([15]; [18]).
The variables Advertisingi and R&Di represent the cumulative effects of bankrupt firm i's past advertising and R&D, respectively. Following extant marketing literature, I calculate Advertisingi as advertising stock divided by assets and R&Di as R&D stock divided by assets for bankrupt firm i ([32]; [41]). Advertising stock is measured as a Koyck-type (i.e., geometric) distributed lag function of annual advertising expenditures with a decay parameter of.60 and R&D stock is measured as a Koyck-type distributed lag function of annual R&D expenditures with a decay parameter of.80.[11]
Because the U.S. Security and Exchange Commission requires firms to disclose material advertising expenditures and the FASB (Financial Accounting Standards Board) requires firms to disclose material R&D expenditures, I assume that when a firm does not disclose its spending in these areas, its expenditures in advertising or R&D are, or are close to, zero.[12] Consequently, I set missing values of advertising and R&D expenditures to zero to reduce sample selection bias. I test the robustness of this assumption by subsequently reporting results in which missing values are set to alternative values and in which the models are estimated on the subset of the data that does not include missing values for advertising and R&D.
The variable Supplier influencei represents the suppliers' influence on the bankruptcy court's decision of whether to allow the firm to emerge from bankruptcy. Because the suppliers' influence depends on the amount the bankrupt firm owes to suppliers versus other creditors, Supplier influencei is measured as the percentage of bankrupt firm i's debt that is owed to suppliers. Following extant finance literature, I calculate Supplier influencei using the supplier debt ratio, which is accounts payable divided by total liabilities for bankrupt firm i ([22]; [27]). A Supplier influencei value of.10 indicates that 10% of bankrupt firm i's debt is owed to suppliers. The value of Supplier influencei that is sufficient to affect the bankruptcy court's decision is not a predetermined fixed value (e.g., 50%) but is an empirical question.
Extant bankruptcy survival research has shown that a bankrupt firm's leverage, liquidity, profit, size, and industry affect bankruptcy survival (see the "Bankruptcy Survival Literature" section in the Web Appendix). Therefore, I control for each of these variables in my models. I summarize each of the variables and their measures in Table 3.
Graph
Table 3. Variable Measures, Descriptive Statistics, and Correlation Matrix.
| Variable | Measure | Mean | SD | Min | Max | (2) | (3) | (4) | (5) | (6) | (7) |
|---|
| 1. Survival | Survived = 1, liquidated = 0, dismissed = −1, ongoing = −2 | Nominal variable with the following frequency distribution: 1 (56%), 0 (35%), −1 (8%), −2 (1%) | | | | | | |
| 2. Advertising | Advertising stock divided by assets | .04 | .11 | .00 | .78 | | | | | | |
| 3. R&D | R&D stock divided by assets | .23 | .68 | .00 | 4.47 | .10 | | | | | |
| 4. Supplier influence | Account payables divided by liabilities | .16 | .15 | .00 | .71 | .19 | .10 | | | | |
| 5. Leverage | Long-term debt divided by assets | .33 | .41 | .00 | 1.99 | −.08 | −.04 | −.31 | | | |
| 6. Liquidity | Working capital divided by assets | −.36 | 1.14 | −7.61 | .75 | −.12 | −.23 | .11 | .08 | | |
| 7. Profit | EBITDA divided by sales | −1.57 | 6.72 | −52.69 | .49 | .00 | −.28 | −.02 | −.02 | .17 | |
| 8. Size | Natural logarithm of assets in 1980 million dollars | 3.99 | 2.09 | −1.43 | 8.78 | −.17 | −.39 | −.39 | .14 | .33 | .27 |
1 Notes: Correlations greater than |.04| are significant (p <.05). EBITDA = earnings before interest, taxes, depreciation and amortization.
To test my hypotheses, I model bankruptcy survival as a function of advertising, R&D, suppliers' influence, firm control variables that have been used in prior bankruptcy survival research, industry dummy variables, and year dummy variables.
Because bankruptcy survival can be viewed as a binary outcome (survival or liquidation), recent research on bankruptcy survival uses logit or probit models ([10]; [11]; [15]; [18]; [38]).[13] Therefore, I model a bankrupt firm's survival probability using the following logit model specification.[14]
Graph
where pi ≔ Pr(Survivali = 1) is the probability that bankrupt firm i will survive, and Controlsi is a vector of control variables that includes Leveragei, Liquidityi, Profiti, Sizei, Supplier influencei, industry dummy variables, and year dummy variables. A positive and significant estimate for β1 would support H1 and a positive and significant estimate for β2 would support H2.
While the logit model distinguishes between bankrupt firms that survive versus those that are liquidated, it has two limitations in the context of bankruptcy survival. First, bankruptcy cases that were dismissed are eliminated from the analysis because they neither survived nor were liquidated (e.g., [18]).[15] Second, although the logit model distinguishes between bankrupt firms that survive versus those that are liquidated, the model treats all bankrupt firms that survive as having the same outcome. However, because the duration of time that a firm spends in bankruptcy can be costly to the firm, surviving bankruptcy after a shorter amount of time in bankruptcy may be more valuable than after a longer amount of time ([ 9]; [25]). In my data set, there is substantial variation in the length of time a firm spends in bankruptcy, with durations ranging from less than six months to over eight years (see the "Bankruptcy Duration" section in the Web Appendix). Therefore, in the next subsection I also model a bankrupt firm's hazard of surviving, which accounts for both whether the bankrupt firm survived and the duration of time the firm spends in bankruptcy ([ 7]).[16]
In a context such as bankruptcy survival, which has three different outcomes (survived, liquidated, and dismissed), traditional hazard models treat the cases in which the bankrupt firm does not survive as censored, which biases results. That is, a bankrupt firm that is liquidated or dismissed would be incorrectly treated as a firm that would later survive bankruptcy, even though that is impossible. Therefore, I use a competing risks model to account for the fact that liquidation and dismissed are competing outcomes that prevent bankruptcy survival from occurring altogether.
The bankruptcy survival subdistribution hazard function is expressed as
Graph
where t is the length of time since entering bankruptcy, Ti, survival is a random variable for the length of time to bankruptcy survival, and Ti, survival' is a random variable for the length of time to a competing event (liquidation, dismissal) for firm i. Therefore, hi(t) gives the instantaneous rate of surviving bankruptcy for firm i at time t given that the firm has not already emerged from bankruptcy (i.e., survived) or has been liquidated or had its case dismissed. I use this subdistribution hazard function to specify the bankruptcy survival competing risks model as
Graph
where λ0(t) is an unspecified baseline survival subdistribution hazard function ([24]). Positive coefficient estimates indicate that the associated predictor increases the "hazard" of surviving bankruptcy. A positive and significant estimate for γ1 would support H1 and a positive and significant estimate for γ2 would support H2.
My objective is to identify the causal effects of advertising and R&D on bankruptcy survival. This requires that Advertisingi and R&Di are exogenous in the bankruptcy survival models. Because variables that influence bankruptcy survival might also be correlated with advertising or R&D, I include control variables that have been shown to influence bankruptcy survival in prior literature. There are other variables that are unobserved which also influence bankruptcy survival (e.g., macroeconomic conditions, industry conditions, the firm's relationships with its creditors). If these variables are correlated with advertising or R&D, omitting them may result in issues of endogeneity (e.g., [63]). Therefore, I adopt two approaches to establish the causal links for advertising and R&D with bankruptcy survival.
First, I include industry and year dummy variables to the models to capture unobserved industry and macroeconomic effects. Second, I specify the models with instrumental variables to account for the possibility that there may be unobserved firm characteristics that affect bankruptcy survival that may be correlated with advertising or R&D. Standard instrumental variables approaches for linear models, such as two-stage least squares, would provide inconsistent results for my nonlinear models ([ 1]). Therefore, I use a control function approach, which has been used in extant nonlinear marketing models (e.g., [32]).
Following recent literature ([32]; [54]; [57]), I use the competitor-average advertising (the average advertising for other firms that operate in the same industry), the competitor-average R&D (the average R&D for other firms that operate in the same industry), and their interactions with Supplier influencei to obtain four instruments. For the instruments to be valid, they must be relevant and exogenous. Competitor-average advertising and competitor-average R&D are exogenous because they should not affect other omitted variables that might be correlated with advertising or R&D (e.g., the firm's relationships with its creditors). I find that the coefficient estimates for the associated instruments in each of the first-stage regressions are significant (p <.01), indicating that the instruments are relevant.
To estimate the models, I create a data set that combines a firm's bankruptcy data with the firm's financial data. I obtain bankruptcy data from New Generation Research's BankruptcyData database, which has been used in finance ([31]), accounting ([17]), management ([30]), and law ([13]) research. I extract financial data from Standard & Poor's Capital IQ Compustat database.
I Winsorize all continuous variables at the 99% level to reduce the influence of outliers. Observations higher than the 99th percentile of each variable are set to the 99th percentile value, and observations lower than the 1st percentile of each variable are set to the 1st percentile value.[17] The data set has 1,672 bankruptcy cases for public firms that filed under Chapter 11 of the U.S. Bankruptcy Code from January 1, 1996 to November 8, 2019.[18] The data set has 934 (56%) firms that survived bankruptcy (i.e., they emerged from bankruptcy), 577 (35%) firms that did not survive bankruptcy (i.e., they were liquidated), 139 (8%) firms that had their bankruptcy case dismissed, and 22 (1%) firms that do not have an outcome because their bankruptcy case is still ongoing.[19] I present the details of the process used to assemble the data set and the procedure used to control for potential sample selection bias in the "Data Sample" section in the Web Appendix.
The correlation matrix and descriptive statistics are presented in Table 3. To diagnose multicollinearity, I compute variance inflation factors, condition indices, and correlations. The maximum variance inflation factor is 1.63, which is below the "rule of thumb" of 10 ([40]), and the maximum condition index is 10.33, which is below the "rule of thumb" of 30 ([ 6]), suggesting that multicollinearity is not a problem. I estimate the models using the 1,504 bankruptcies from 1996 to 2015 and use the 168 bankruptcies from 2016 to 2019 for out-of-sample bankruptcy survival prediction validation. The logit model is estimated on the subsample of data for which the bankruptcy cases are not dismissed or ongoing (1,371 bankruptcy cases from 1996 to 2015). Further details on the data set, including descriptive statistics by bankruptcy outcome, number of bankruptcies by year, average value of assets in bankruptcy by year, and number of bankruptcies by industry, are presented in the "Additional Data Information" section in the Web Appendix.
In Table 4, I present the estimation results for four models: a baseline logit model that does not include the interaction terms (i.e., β1 = β2 = 0), the bankruptcy survival logit model, a baseline competing risks model that does not include the interaction terms (i.e., γ1 = γ2 = 0), and the bankruptcy survival competing risks model.
Graph
Table 4. The Impact of Advertising and R&D on Bankruptcy Survival is Contingent on Supplier Influence.
| Logit Model | Competing Risks Model | |
|---|
| I | II | III | IV | Hypothesis |
|---|
| Advertising × Supplier influence | | 31.28***(8.53) | | 19.11***(7.38) | + (H1) |
| R&D × Supplier influence | | 6.67***(2.32) | | 3.23**(1.58) | + (H2) |
| Advertising | −2.97***(.88) | −11.00***(1.62) | −2.48***(.91) | −7.24***(2.28) | |
| R&D | −.24(.24) | −1.18***(.45) | −.20(.20) | −.69*(.37) | |
| Leverage | .38**(.16) | .44***(.17) | .23**(.10) | .24**(.10) | |
| Liquidity | −.54***(.09) | −.61***(.11) | −.30***(.04) | −.29***(.04) | |
| Profit | −.00(.01) | −.00(.01) | .00(.01) | .00(.01) | |
| Size | .26***(.05) | .30***(.05) | .16***(.03) | .17***(.03) | |
| Supplier influence | −.43(.60) | −4.21***(1.44) | −.92**(.36) | −3.10***(.91) | |
| (Intercept) | −2.13(1.70) | −1.94(1.67) | | | |
| Industry dummies | Yes | Yes | Yes | Yes | |
| Year dummies | Yes | Yes | Yes | Yes | |
| Inverse Mills ratio | Yes | Yes | Yes | Yes | |
| Control functions | Yes | Yes | Yes | Yes | |
| Observations | 1,371 | 1,371 | 1,504 | 1,504 | |
| AIC | 1,706 | 1,689 | 11,355 | 11,342 | |
- 2 *p <.10.
- 3 **p <.05.
- 4 ***p <.01.
- 5 Notes: Standard errors are in parentheses. AIC = Akaike information criterion.
I find that the coefficient estimates for the interaction between advertising and supplier influence are positive and significant (β1 = 31.28, p <.01; γ1 = 19.11, p <.01), providing support for H1: advertising's impact on bankruptcy survival is contingent on supplier influence. I also find that the coefficient estimates for the interaction between R&D and supplier influence are positive and significant (β2 = 6.67, p <.01; γ1 = 3.23, p <.05), providing support for H2: R&D's impact on bankruptcy survival is contingent on supplier influence.
The coefficient estimates for advertising and R&D are negative. The coefficient estimates in Table 4, Columns I and III, are for the average impact of advertising and R&D on bankruptcy survival, without controlling for their contingencies on supplier influence, and the coefficient estimates in Columns II and IV are for the impact of advertising and R&D on bankruptcy survival when Supplier influence equals zero (i.e., when there is no debt that is owed to suppliers). The negative and significant coefficient estimates in Columns II and IV provide support for my argument that when suppliers have no influence, advertising and R&D decrease bankruptcy survival.
The coefficient estimates show that the boundary between the regions of monotonicity and nonmonotonicity for advertising (i.e., the crossover point where advertising switches from increasing to decreasing bankruptcy survival) is Supplier influencei =.35 (11.00 ÷ 31.28) in the logit model and Supplier influencei =.38 (7.24 ÷ 19.11) in the competing risks model. This indicates that advertising increases bankruptcy survival for a firm that owes more than 35%−38% of its debt to suppliers, whereas it decreases bankruptcy survival for a firm that owes less than 35%−38% of its debt to suppliers, providing further support for H1: advertising increases bankruptcy survival when supplier influence is high, whereas it decreases bankruptcy survival when supplier influence is low.
The boundary between the regions of monotonicity and nonmonotonicity for R&D is Supplier influencei =.18 (1.18 ÷ 6.67) in the logit model and Supplier influencei =.21 (.69 ÷ 3.23) in the competing risks model. This indicates that R&D increases bankruptcy survival for a firm that owes more than 18%−21% of its debt to suppliers, whereas it decreases bankruptcy survival for a firm that owes less than 18%−21% of its debt to suppliers, providing further support for H2: R&D increases bankruptcy survival when supplier influence is high, whereas it decreases bankruptcy survival when supplier influence is low.
The results indicate that leverage increases bankruptcy survival, which is consistent with findings in some previous studies ([18]; [38]) but differs from the insignificant effect found in other studies ([15]). The results indicate that liquidity decreases bankruptcy survival, which is consistent with findings in prior literature ([15]). The results also indicate that profitability does not significantly influence bankruptcy survival, which is consistent with findings in some previous studies ([11]; [18]) but differs from the positive effect found in other studies ([10]; [38]; [45]). Finally, the results indicate that the bankrupt firm's size increases bankruptcy survival, which is consistent with findings in the majority of previous studies ([10]; [15]; [37]; [38]; [45]) but differs from the insignificant effect found in some previous studies ([11]; [18]).
To quantify the effect size of each of the predictor variables, I compute the difference in predicted bankruptcy survival associated with an increase in each variable from one standard deviation below its mean value to one standard deviation above its mean value. I compute the effects of advertising and R&D separately for bankrupt firms with high versus low Supplier influence, and I define high (low) Supplier influence as greater (less) than 50%. The results for the logit model, presented in Table 5, indicate that on average the increase in advertising increases the predicted probability of surviving bankruptcy 44.99% for a firm with high supplier influence and decreases the predicted probability of surviving bankruptcy 26.21% for a firm with low supplier influence. On average, the increase in R&D increases the predicted probability of surviving bankruptcy 74.76% for a firm with high supplier influence and decreases the predicted probability of surviving bankruptcy 5.20% for a firm with low supplier influence.
Graph
Table 5. Difference in Average Predicted Bankruptcy Survival Associated with an Increase in Each Predictor Variable.
| Predicted Bankruptcy Survival Probability (%) | Predicted Bankruptcy Survival Duration (Months) |
|---|
| Logit Model | Competing Risks Model |
|---|
| Mean − 1 SD | Mean + 1 SD | Difference | Mean − 1 SD | Mean + 1 SD | Difference |
|---|
| Advertising (high Supplier influence) | 23.23 | 68.22 | 44.99 | 26.15 | 7.68 | −18.47 |
| Advertising (low Supplier influence) | 73.11 | 46.90 | −26.21 | 3.30 | 10.44 | 7.14 |
| R&D (high Supplier influence) | 10.90 | 85.67 | 74.76 | 38.52 | 5.25 | −33.28 |
| R&D (low Supplier influence) | 63.73 | 58.52 | −5.20 | 4.68 | 7.37 | 2.69 |
| Leverage | 57.10 | 64.03 | 6.93 | 6.81 | 5.61 | −1.19 |
| Liquidity | 72.56 | 47.47 | −25.08 | 4.47 | 8.54 | 4.07 |
| Profit | 61.06 | 60.16 | −.90 | 6.25 | 6.11 | −.14 |
| Size | 48.33 | 71.84 | 23.51 | 8.81 | 4.33 | −4.48 |
6 Notes: High (low) Supplier influence is greater (less) than 50%.
These findings provide further support for H1 and H2: advertising and R&D increase bankruptcy survival when supplier influence is high, whereas they decrease bankruptcy survival when supplier influence is low. For comparison, the results indicate that, on average, the increase in leverage increases the predicted bankruptcy survival probability 6.93%, the increase in liquidity decreases the predicted bankruptcy survival probability 25.08%, the increase in profit decreases the predicted bankruptcy survival probability.90%, and the increase in size increases the predicted bankruptcy survival probability 23.51%.
The dependent variable in the competing risks model is the bankruptcy survival subdistribution hazard function, which is the instantaneous rate of surviving bankruptcy at time t given that the firm has not already emerged from bankruptcy (i.e., survived) or has been liquidated or had its case dismissed. To quantify the effect sizes of the predictor variables in the competing risk model, I use the reciprocal of the hazard function, which gives the predicted bankruptcy survival duration (i.e., the expected length of time from entering bankruptcy until the bankrupt firm survives). A smaller predicted bankruptcy survival duration indicates that the firm has a greater likelihood of surviving bankruptcy whereas a larger predicted duration indicates that the firm has a lower likelihood of surviving bankruptcy.
The results for the competing risks model indicate that on average the increase in advertising decreases the predicted bankruptcy survival duration 18.47 months for a firm with high supplier influence and increases the predicted bankruptcy survival duration 7.14 months for a firm with low supplier influence. On average, the increase in R&D decreases the predicted bankruptcy survival duration 33.28 months for a firm with high supplier influence and increases the predicted bankruptcy survival duration 2.69 months for a firm with low supplier influence. For comparison, I note that the results indicate that on average the increase in leverage decreases the predicted bankruptcy survival duration 1.19 months, the increase in liquidity increases the predicted bankruptcy survival duration 4.07 months, the increase in profit decreases the predicted bankruptcy survival duration.14 months, and the increase in size decreases the predicted bankruptcy survival duration 4.48 months. For reference, the duration of time spent in bankruptcy for the firms in my data set ranges from 1.02 months to 79.35 months, with an average of 15.42 months.
Although extant research has modeled the impact of advertising and R&D on shareholder value (e.g., [33]; [52]) and the risk that a firm will enter bankruptcy ([32]), this study is the first to model the impact of advertising and R&D on bankruptcy survival. Therefore, to provide insight to marketing managers who are managing a relationship with a bankrupt customer or partner, I validate the out-of-sample prediction performance of the bankruptcy survival models and compare them with the performance of a baseline model that does not include the marketing variables (advertising, R&D, and their interactions with supplier influence).
Unlike research that focuses on predicting whether a firm will enter bankruptcy, predicting bankruptcy survival has received little research attention. Some attribute this to the fact that distinguishing between bankrupt firms that survive versus bankrupt firms that are liquidated is more difficult than distinguishing between firms that are healthy and firms that are financially distressed because firms that are in bankruptcy share the characteristics of financial distress ([ 4]). Therefore, drawing on recent research that has shown that machine learning can improve the ability to predict whether a firm enters bankruptcy ([ 3]), I also consider the out-of-sample prediction performance of random forests (details of this machine learning algorithm are presented in the "Bankruptcy Survival Prediction Using Random Forests Machine Learning" section in the Web Appendix).
Because the data set I used for estimating the bankruptcy survival models ends in December 2015, I validate the models using new data from January 2016 to November 2019. This new data has 168 bankruptcy cases. I eliminate cases that are ongoing because their outcome is not yet known, which results in a validation data set of 140 bankruptcy cases. To assess the bankruptcy survival prediction performance of the models, I calculate the accuracy and discrimination for each model.
Accuracy in bankruptcy survival prediction is the percentage of cases the model correctly classifies as surviving versus not surviving. I calculate the logit model's predicted outcome for each bankruptcy case in the validation data set using the coefficient estimates from Column II of Table 4. Because the logit model provides a predicted probability that falls in the range from 0 to 1, I follow standard practice and classify a bankruptcy case that has a predicted probability greater than or equal to.50 as predicted to survive and a bankruptcy case that has a predicted probability of less than.50 as predicted to not survive. I calculate the logit model's prediction accuracy as the percentage of bankruptcy cases that it classifies correctly.
The predicted outcome from the competing risks model is the predicted subdistribution hazard of bankruptcy survival after t days in bankruptcy, which is the instantaneous risk of a bankrupt firm surviving bankruptcy given that it has not already emerged from bankruptcy or has been liquidated or had its case dismissed. Because the competing risks model provides a predicted subdistribution hazard that falls in the range from 0 to infinity, there is not a midpoint cutoff that can be used to classify whether the bankruptcy case is predicted to survive. Consequently, following standard practice, I do not calculate accuracy for the competing risks model.
For the random forests machine learning model, I first train the algorithm on the estimation data set. I then use the algorithm to classify each bankruptcy case in the validation set as predicted to survive or not survive. Finally, I calculate the random forests' prediction accuracy as the percentage of bankruptcy cases that it classifies correctly.
The out-of-sample bankruptcy survival prediction performance results are presented in Table 6. I find that random forests with the variables in the full model have the greatest accuracy (78.57%) and that the marketing variables improve accuracy for both the logit model (by 6.07%) and random forests (by 11.12%).
Graph
Table 6. Out-of-Sample Validation of Bankruptcy Survival Prediction Performance.
| BaselineModel | FullModel | PerformanceImprovement(%) |
|---|
| Finance variables | Yes | Yes | |
| Marketing variables | No | Yes | |
| Accuracy (%) | | | |
| Logit regression model | 70.71 | 75.00 | 6.07 |
| Competing risks regression model | | | |
| Random forests machine learning model | 70.71 | 78.57 | 11.12 |
| Discrimination (%) | | | |
| Logit regression model | 61.45 | 73.48 | 19.58 |
| Competing risks regression model | 59.25 | 69.93 | 18.03 |
| Random forests machine learning model | 73.42 | 79.17 | 7.83 |
In addition to calculating the accuracy of the models, which measures the percentage of bankruptcy cases that are correctly classified, I calculate the discrimination of the models. Discrimination measures how well the model's prediction separates cases that survive from those that do not survive. I calculate the area under the receiver operating characteristic curve, which is the predominant measure for discrimination, for each model. For bankruptcy survival prediction, this discrimination measure is the probability that a bankruptcy case that actually survived had a higher predicted probability than did a bankrupt case that actually did not survive. I find that random forests with the variables in the full model have the greatest discrimination (79.17%) and that the marketing variables improve discrimination for the logit model (by 19.58%), the competing risks model (by 18.03%), and random forests (by 7.83%).
I measure advertising and R&D using Koyck-type distributed lag functions with decay parameters of.60 and.80, respectively. I also ran analyses with decay parameters in the range of.40 to.85 and found that the results are robust to these alternatives. As an additional robustness check, I considered distributed lag functions that do not assume a Koyck-type decay by measuring advertising and R&D using the [21] weights of.70 and.30 for advertising in the bankruptcy filing year and one year prior, respectively, and.25,.21,.18,.14,.11,.07, and.04 for R&D in the bankruptcy filing year and one to six years prior, respectively. Estimation results using these alternative advertising and R&D measures, presented in Columns I and II of Table 7, are consistent with the coefficient estimates in Columns II and IV of Table 4 in terms of signs and significance. The difference in magnitudes indicates the importance of considering the distributed lag weights used when interpreting effect sizes.
Graph
Table 7. Bankruptcy Survival Robustness Analyses.
| Advertising and R&D Measures | Industry Factors | Macroeconomic Factors |
|---|
| Logit Model | Competing Risks Model | Logit Model | Competing Risks Model | Logit Model | Competing Risks Model |
|---|
| I | II | III | IV | V | VI |
|---|
| Advertising × Supplier influence | 14.06***(4.85) | 10.35**(4.94) | 31.11***(8.29) | 19.05**(7.47) | 26.83***(6.81) | 15.35**(7.06) |
| R&D × Supplier influence | 7.58***(.79) | 9.83**(4.86) | 6.72***(2.35) | 3.27**(1.58) | 6.22***(2.27) | 2.69**(1.29) |
| Advertising | −5.52***(1.86) | −4.16***(1.58) | −10.88***(1.39) | −7.16***(2.30) | −9.83***(1.21) | −6.15***(2.19) |
| R&D | −1.39***(.17) | −1.90*(1.12) | −1.19***(.44) | −.71*(.37) | −1.27***(.42) | −.71**(.35) |
| Leverage | .42***(.16) | .26***(.10) | .43**(.17) | .23**(.10) | .41**(.18) | .23**(.10) |
| Liquidity | −.53***(.11) | −.24***(.04) | −.60***(.11) | −.29***(.04) | −.63***(.10) | −.28***(.04) |
| Profit | −.00(.01) | .00(.01) | −.00(.01) | .00(.01) | −.01(.01) | .00(.01) |
| Size | .29***(.05) | .18***(.03) | .30***(.05) | .17***(.03) | .25***(.06) | .15***(.03) |
| Supplier influence | −1.56**(.66) | −2.24***(.56) | −4.23***(1.47) | −3.14***(.93) | −3.62***(1.40) | −2.58***(.87) |
| Industry growth | | | .29(.72) | .46(.40) | | |
| Industry turbulence | | | 1.31(3.40) | 1.81(1.62) | | |
| Industry concentration | | | −.45(.45) | −.25(.25) | | |
| Recession | | | | | −.74***(.21) | −.55***(.10) |
| Interest rate | | | | | .04(.05) | −.01(.02) |
| (Intercept) | −2.19(1.62) | | −1.88(1.63) | | −1.40(1.48) | |
| Year dummies | Yes | Yes | Yes | Yes | No | No |
| AIC | 1,693 | 11,349 | 1,693 | 11,344 | 1,705 | 11,363 |
- 7 *p <.10.
- 8 **p <.05.
- 9 ***p <.01.
- 10 Notes: Standard errors are in parentheses. All models include industry dummies, the inverse Mills ratio, and control functions. N = 1,371 (logit); 1,504 (competing risks). AIC = Akaike information criterion.
I also assessed the sensitivity of the results to setting missing advertising and R&D values to zero. First, following [56] and [51], I set missing advertising and R&D values to.0001 and reestimated my models. I found the results consistent with the coefficient estimates in Columns II and IV of Table 4. Second, I eliminated observations with missing advertising and R&D values and reestimated my models on this subsample. I found the results from this analysis were also consistent with the coefficient estimates in Columns II and IV of Table 4 in terms of sign and significance.
Following extant bankruptcy survival literature, I include industry dummy variables in the models to control for unobserved industry effects ([10]; [15]; [38]). As an additional robustness check, I added industry growth, turbulence, and concentration and reestimated the models. Estimation results with these additional industry variables, presented in Columns III and IV of Table 7, are consistent with the coefficient estimates in Columns II and IV of Table 4.
I include year dummy variables in the models to control for unobserved macroeconomic effects. As an additional robustness check, I replaced the year dummy variables in the models with the following macroeconomic variables: Recessiony, which is a binary variable that is set to 1 if the bankruptcy filing year y is a recession year and 0 otherwise, and Interest ratey, which is the lending rate in bankruptcy filing year y. Following extant marketing literature ([32]; [55]), I classify 1980, 1982, 1990, 2001, and 2008 as recession years because the majority of the year occurred during a recession as classified by the National Bureau of Economic Research. For the interest rate, I use the London Interbank Offered Rate, which is the primary benchmark used by creditors for short-term interest rates. Estimation results with these alternative macroeconomic variables, presented in Columns V and VI of Table 7, are consistent with the coefficient estimates in Columns II and IV of Table 4.
Some firms file for bankruptcy again after emerging. These bankruptcies are sometimes referred to as "Chapter 22" filings (if this is the firm's second time filing for bankruptcy), "Chapter 33" filings (if this if the firm's third time filing for bankruptcy), and so on. In my data set, 143 (8.6%) of the bankruptcy cases were for firms that had previously filed for bankruptcy (127 [7.6%] had filed for bankruptcy once before, 14 [<1%] had filed for bankruptcy twice before, and 2 [<1%] had filed for bankruptcy three times before). Therefore, as an additional robustness check, I assessed whether a firm's past bankruptcy history influences the impact of advertising and R&D on bankruptcy survival.
I created a dummy variable, Prior bankruptcyi, that I set equal to 1 if firm i had previously been in bankruptcy and 0 otherwise. I added this as a control variable to the models and found that it was not significant (p >.10) and that the other coefficient estimates remained consistent with the coefficient estimates in Columns II and IV of Table 4. I also included the interaction between Advertisingi and Prior bankruptcyi; the interaction between R&Di and Prior bankruptcyi; the interaction between Supplier influencei and Prior bankruptcyi; the three-way interaction between Advertisingi, Supplier influencei, and Prior bankruptcyi; and the three-way interaction between R&Di, Supplier influencei, and Prior bankruptcyi and found that the associated coefficient estimates for these interactions were not significant.
As previously noted, not all firms that emerge from bankruptcy survive in the long term (i.e., some firms file for bankruptcy once again after emerging). As an additional analysis, I classified bankruptcy survival into long term versus short term and reestimated the models. Because about half the firms that refile for bankruptcy survive four years or less (see the "Bankruptcy Refiling" section in the Web Appendix), I defined short-term bankruptcy survival as emerging from bankruptcy and then refiling for bankruptcy within the next four years and long-term bankruptcy survival as emerging from bankruptcy without refiling for bankruptcy within the next four years. I replaced Survivali with Long-term survivali in my models, where Long-term survivali equals 2 for long-term survival, 1 for short-term survival, 0 for did not survive (liquidated), −1 for dismissed, and −2 for ongoing. Since there are three relevant outcomes in this analysis (long-term survival, short-term survival, and did not survive), I used ordered logit and competing risks models. I found the coefficient estimates from this analysis consistent with the coefficient estimates in Columns II and IV of Table 4.
I also ran an additional analysis to assess whether advertising and R&D have an impact on the risk that a firm that survives bankruptcy will refile for bankruptcy again in the future. For this analysis, I modeled the hazard of refiling for bankruptcy using the same predictor variables as those in my main models. I estimated this model using the subsample of firms that survived bankruptcy and found that the coefficient estimates for the advertising and supplier influence interaction and the R&D and supplier influence interaction were not significant.
In summary, the empirical evidence consistently supports H1 and H2 and is robust to using alternative measures of advertising and R&D, adding additional industry control variables, using alternative macroeconomic variables, controlling for a firm's past bankruptcy history, and classifying bankruptcy survival into long-term versus short-term.
The value of assets that public firms have under bankruptcy protection is now at its highest level in at least 24 years (see the "Additional Data Information" section in the Web Appendix), making research on marketing's impact on bankruptcy survival ever more important. While one might expect advertising and R&D to increase bankruptcy survival in the same way that they increase shareholder value, I show that this is not the case. I find robust evidence that the impact of advertising and R&D on bankruptcy survival is contingent on the influence of the bankrupt firm's suppliers. Advertising and R&D increase bankruptcy survival when suppliers have a large influence, whereas they decrease bankruptcy survival when suppliers have a small influence. Out-of-sample validation shows that the ability to predict whether a firm will survive bankruptcy is substantially improved by considering advertising, R&D, and their interactions with supplier influence, in addition to the usual financial predictors.
This research has implications for managers of financially distressed firms, managers of firms that have a bankrupt customer or partner, marketing theory on the value of advertising and R&D, and marketing theory in the context of bankruptcy.
Because a financially distressed firm is unable to generate sufficient funds from its operating activities to fund its ongoing operations and invest in growth opportunities, managers of financially distressed firms often borrow funds from outside sources. This research shows that, should the firm later enter bankruptcy, these financing decisions play an important role in determining how the firm's marketing assets influence the probability the firm will survive bankruptcy. Because advertising and R&D have a differential effect on suppliers' and banks' votes on whether to accept a bankrupt firm's debt reorganization plan, a manager of a financially distressed firm that has made large investments in advertising and R&D should consider the influence that the firm's suppliers have relative to its other creditors. The results in this article indicate that managers need to be aware that their advertising will only help them in bankruptcy if at least 35%−38% of their firm's debt has been borrowed from suppliers and their R&D will only help them in bankruptcy if at least 18%−21% of their firm's debt has been borrowed from suppliers, as these are the points at which advertising and R&D cross over from decreasing to increasing the probability of surviving bankruptcy.
An understanding of marketing's impact on bankruptcy survival is critical not just for managers of the bankrupt firm but also for managers of firms that have a bankrupt customer or partner. If a supplier has a bankrupt customer that does not survive, the supplier loses the future revenue stream from that customer, loses the value of any relationship-specific investments it has made in the customer, incurs increased costs associated with trying to replace the customer, and loses any payments that were due from the customer. If a firm has a bankrupt channel or alliance partner that does not survive, it loses the value of any relationship-specific investments it has made in the partnership and incurs increased costs associated with trying to replace the partner. When a firm has a bankrupt customer or partner, the firm's managers decide either to invest in the relationship (if they expect the customer or partner to survive) or to minimize their exposure to the bankrupt customer or partner (if they expect them to not survive).
A key challenge faced by a manager who has a bankrupt customer or partner is predicting whether the customer or partner will survive. The manager likely has information on the customer's credit rating from when the manager's firm loaned money to the customer and could also obtain a partner's credit rating. However, once a firm files for bankruptcy, the credit rating agencies set the bankrupt firm's credit rating to the lowest level ("default") and do not provide any insight into whether the firm will survive. Therefore, the manager needs to use an alternative approach to assess the firm's bankruptcy survival odds. The results of the out-of-sample validation indicate that managers can improve their ability to predict whether their customer or partner will survive bankruptcy by considering the firm's advertising, R&D, and suppliers' influence, in addition to the usual financial predictors.
This research extends theory on how advertising and R&D create shareholder value by showing how they affect bankruptcy survival. Extant theory on how advertising and R&D build shareholder value suggests that they increase both survival values and liquidation values, which have opposing effects on bankruptcy survival. Therefore, I develop a conceptual framework to explain the conditions under which advertising and R&D increase versus decrease the probability of surviving bankruptcy. By shedding light on the differences in noncontractual revenue and collateral between suppliers and banks, this article extends extant marketing theory by showing that advertising and R&D increase bankruptcy survival when suppliers have a large influence, whereas they decrease bankruptcy survival when suppliers have a small influence.
An emerging stream of research has begun to develop theories about marketing's role in the context of bankruptcy ([ 2]; [32]; [48]; [49]). This stream of research has focused solely on marketing's role in the context of entering bankruptcy. Because the risk of entering bankruptcy is a function of a firm's expected near-term cash flow, marketing theory in this area has focused on how marketing influences a firm's expected near-term cash flow ([32]). The current research adds to the theory on marketing's role in the context of entering bankruptcy by developing theory on marketing's role in the context of exiting (i.e., surviving) bankruptcy. Importantly, this work shifts from contributing to theory on marketing's impact on expected near-term cash flow to contributing to theory on marketing's impact on the relative value that a creditor receives if a bankrupt firm survives versus gets liquidated.
This is the first study of how a firm's marketing investments affect its likelihood of surviving bankruptcy. Given the importance of this topic, further research on the relationship between marketing investments and bankruptcy survival is warranted. Following the predominant approach in extant literature on the impact of advertising and R&D on shareholder value, I used aggregated measures of advertising and R&D. Future research should consider the impact of more disaggregated measures of advertising and R&D on bankruptcy survival. For example, research could study whether certain types of advertising (e.g., price-oriented vs. brand-building) or certain types of R&D (e.g., process vs. product) have differential impacts on bankruptcy survival. Future research should also consider more aggregated marketing assets (e.g., brand equity) that are built through a combination of advertising and R&D that might affect bankruptcy survival. Future research could also consider whether a firm's marketing capabilities influence the extent to which advertising and R&D affect bankruptcy survival.
As the first study on marketing's influence on bankruptcy survival, the current article identifies the independent effects of advertising and R&D on bankruptcy survival. Future research could explore more nuanced effects of advertising and R&D in this context. For example, research could explore whether there are positive or negative interaction effects between advertising and R&D (i.e., whether they accentuate each other's effects or whether they are redundant) on bankruptcy survival. Future research could also consider whether the relative amount that a firm spends on advertising versus R&D influences bankruptcy survival.
Research could also study alternative mechanisms through which advertising and R&D influence creditor behavior, such as the spillover effects documented for investors (e.g., [33]). Furthermore, research could consider whether changes to a firm's advertising and R&D spending while the firm is in bankruptcy have an influence on the firm's postbankruptcy performance if it survives. Bankruptcy might also tarnish a firm's image and reputation in a similar manner as a product recall or brand scandal. Future research could study whether bankruptcy reduces the effectiveness of a firm's advertising and R&D spending in building and maintaining brand equity with customers.
The current research uses data from firms that filed for bankruptcy in the United States. It may be the case that marketing's link with bankruptcy survival might differ in countries that have different bankruptcy laws. Future research should address this question.
The conceptual arguments for how marketing influences liquidation value focused on the context of bankruptcy in this article. Future research should also consider the liquidation value of marketing assets outside of bankruptcy. For example, the liquidation value of a marketing asset determines the terms of a loan that is secured with the marketing asset as collateral. With the growth in the number of loans that are being secured with marketing assets as collateral ([28]), future research should extend the conceptual arguments in this article to consider the liquidation value of a marketing asset outside of bankruptcy and, consequently, how it influences the size and terms of the loan that a firm can obtain.
Supplemental Material, Bankruptcy_survival_web_appendix - The Impact of Advertising and R&D on Bankruptcy Survival: A Double-Edged Sword
Supplemental Material, Bankruptcy_survival_web_appendix for The Impact of Advertising and R&D on Bankruptcy Survival: A Double-Edged Sword by Niket Jindal in Journal of Marketing
Footnotes 1 Associate EditorAlina Sorescu
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 ORCID iDNiket Jindal https://orcid.org/0000-0002-1011-0057
5 Online supplement: https://doi.org/10.1177/0022242920936205
6 1Throughout the article, I use the term "advertising" to refer to the cumulative effect of a firm's past advertising and "R&D" to refer to the cumulative effect of a firm's past R&D. Prior research has also referred to these constructs as advertising and R&D assets (e.g., [32]), capital (e.g., [41]), and stock (e.g., [47]).
7 2Research on supplier debt contracts has shown that suppliers provide business customers an average of 59.2 days to make payments ([36]). This indicates that the contractual payment a supplier is due from a business customer at any point in time is, on average, only 16% of the annual noncontractual revenue it receives from that customer (59.2 days ÷ 365.25 days per year =.16). However, the noncontractual revenue from a business customer can be much lower if the business customer is not viable in the long term, which is why suppliers consider the customer's expected future cash flows when making assessments on noncontractual revenue.
8 3Examples of bankrupt firms' assets that have been sold off in liquidation include Circuit City's brand name, trademark, and website; the Sharper Image's brand name, catalog, and website; Bombay Company's brand name and website; and Nortel's intellectual property, technology licensing rights, and patents.
9 4To provide some additional evidence for this argument, I present statements regarding collateral from suppliers' annual reports and data from bankrupt firms on their largest unsecured creditors in the "Collateral Differences Between Suppliers and Banks" section in the Web Appendix.
5I follow the predominant approach used in extant bankruptcy survival research and classify firms that are acquired while in bankruptcy as having emerged from bankruptcy unless the acquisition was through the purchase of a portion of the firm's assets in liquidation ([15]; [37]; [38]).
6Prior literature has used decay parameters in the range of.40 to.85 for advertising and R&D ([16]; [19]; [29]; [32]; [41]; [46]; [47]; [55]). Additional analyses using alternative decay parameters within this range provided consistent results.
7The advertising disclosure requirement was mandated in the Securities and Exchange Commission's 1994 FRR44 accounting legislation and the R&D disclosure requirement was mandated in the FASB's 1974 "Statement of Financial Accounting Standards No. 2." Because companies started adopting the new rules for advertising disclosure in 1994 and 1995, I follow extant literature (e.g., [42]) and begin my data set in 1996.
8Early research in this area also used discriminant analysis ([45]) and two-sample t-tests ([37]).
9Results using a probit model were very similar.
10A dismissed case is one in which the bankruptcy court has terminated the bankruptcy process without either approving the debt reorganization plan or ordering liquidation. Because dismissals can be voluntary (e.g., a request by the bankrupt firm due to an improvement in its financial condition) or involuntary (e.g., the court dismisses the case due to the parties not following due process or meeting their obligations), there is not a straightforward mapping of dismissed cases into survival versus liquidation.
11Hazard models were originally developed in the medical field, in which the outcome of interest was death (thus the name "hazard" model). In the bankruptcy survival context, the outcome of interest (i.e., the "hazard") is surviving bankruptcy.
12To assess the robustness of my results to this decision, I also estimated the models using data that were not Winsorized. I found that the results were consistent.
13The number of bankruptcy cases in this data set (1,672) compares favorably with the following numbers of bankruptcy cases in data sets used in extant research on bankruptcy survival: 48 ([37]), 72 ([45]), 113 ([11]), 120 ([18]), 121 ([10]), 440 ([15]), and 604 ([38]).
14The 56% survival rate in my data set is in line with the following survival rates reported in extant bankruptcy research: 29% ([37]), 50% ([11]), 51% ([18]), 61% ([45]), 68% ([10]), and 70% ([38]).
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The Joint Effects of Ex Ante Contractual Completeness and Ex Post Governance on Compliance in Franchised Marketing Channels
This study examines the heretofore neglected joint effects of ex ante contractual completeness and ex post governance on compliance in a franchise setting. In contrast tomuch of the extant literature that views contractual completeness in the aggregate, the present research disaggregates contractual completeness into ex ante monitoring and enforcement completeness, and additionally distinguishes between ex post monitoring and enforcement, allowing for a nuanced examination of the joint effects of different types of ex ante and ex post governance on compliance. Additionally, the authors advance the concept of consummate compliance, thereby complementing the literature, which tends to view compliance solely in terms of perfunctory compliance—an important distinction because the results suggest that perfunctory compliance has a negative relationship with customer satisfaction, whereas consummate compliance has a positive one. Drawing on multiple data sources, the authors demonstrate that ex ante monitoring completeness positively moderates the relationship between ex post monitoring and both types of compliance; however, ex ante enforcement completeness negatively moderates the relationship between ex post enforcement and both types of compliance.
Contractual completeness, the extent to which relevant clauses are codified in a contract ex ante, and subsequent ex post governance efforts, such as monitoring and enforcement, play critical roles in the governance and management of interfirm relationships (e.g., Kashyap, Antia, and Frazier 2012; Mooi and Ghosh 2010; Wuyts and Geyskens 2005). Notably, however, ex ante completeness and ex post governance may differ greatly (Heide 1994; Macaulay 1963). In few places are their roles more distinct than in the $890 billion franchising industry in the United States (IHS Economics 2015). For instance, a more complete franchise contract may include an extensive number of monitoring and enforcement clauses, yet franchisors may undertake very little monitoring or enforcement in practice. Despite important insights by marketing scholars into contractual completeness (e.g., Mooi and Ghosh 2010; Wuyts and Geyskens 2005), monitoring and enforcement (e.g., Antia and Frazier 2001; Heide, Wathne, and Rokkan 2007; Mooi and Gilliland 2013), and even the direct relationships between the two (Kashyap, Antia, and Frazier 2012; Mooi and Gilliland 2013), the impact of the joint effects of types of ex ante contractual completeness (i.e., monitoring completeness and enforcement completeness) and ex post governance remains largely unexplored (see Table 1). An examination of these joint effects is worthwhile because the effects of ex post governance on important marketing variables may not be invariant to ex ante governance considerations. Thus, the present research addresses the following research question: How do the joint effects of ex ante monitoring and enforcement completeness and ex post monitoring and enforcement impact important outcomes, such as contractual compliance?
Examining these joint effects affords the opportunity to contribute to the literature in a number of ways. First, it has been customary to view contractual completeness in the aggregate by, for example, accounting for the total number of clauses in the contract (e.g., Anderson and Dekker 2005; Kashyap, Antia, and Frazier 2012; Wuyts and Geyskens 2005; for a recent exception, see Mooi and Gilliland 2013). Such a view, however, can mask potentially meaningful distinctions between types of contractual completeness (e.g., Ryall and Sampson 2009), which may have independent and different effects on important exchange variables. As such, we distinguish between two key types of ex ante contractual completeness: monitoring completeness and enforcement completeness (e.g., Heide 1994). Monitoring completeness is the extent to which clauses pertaining to the franchisor's rights to observe its franchisees are codified in the contract (Reuer and Ariño 2007); enforcement completeness is the extent to which clauses pertaining to the franchisor's rights to discipline a franchisee's contractual violations are codified in the contract (e.g., Malhotra and Lumineau 2011; Reuer and Ariño 2007). We demonstrate that increasing ex ante monitoring completeness positively affects the relationship between ex post monitoring and franchisee compliance. In contrast, increasing ex ante enforcement completeness negatively affects the relationship between ex post enforcement and franchisee compliance. Thus, our research builds on work suggesting that context can affect how ex post governance is interpreted by channel members (e.g., Bamberger 2008; Heide, Kumar, and Wathne 2014; Heide, Wathne, and Rokkan 2007), and it advances the contracting literature by demonstrating nuanced and novel joint effects between different types of contractual completeness and ex post governance (e.g., Poppo and Zhou 2014).
TABLE: TABLE 1 Relevant Empirical Research on Contracting in Interfirm Relationships
TABLE: TABLE 1 Continued
TABLE: TABLE 1 Continued
TABLE: TABLE 1 Continued
TABLE: TABLE 1 Continued
| Study | Empirical Context, Data Collection | Contractual Characteristic | Ex Post Governance | Outcome(s) Assessed | Major Findings |
| Achrol and Gundlach (1999) | Manufacturer-distributor dyads in computer industry (simulation with student subjects) | Contractual safeguards (content analysis)0 | None | Opportunism | Relational norms decrease opportunism; comparative commitment increases opportunism. Contractual safeguards have little impact on opportunism (three-way significant and negative interaction with relational norms and comparative commitment). |
| Anderson and Dekker (2005) | Survey of supplier of information technology (IT) products | Contractual extensiveness; management control structured | None | Ex post transaction problems; costs of contracting | Contractual extensiveness is positively related to transaction hazards resulting from transaction size, specificity, and complexity, as well as to contracting costs. A misalignment between transaction hazards and management control structure is associated with poor performance. |
| Antia and Frazier (2001) | Survey of top managers in franchisor organizations | None | Enforcement | Enforcement | Transaction-specific investments (TSIs), obligation criticality, interdependence magnitude, interdependence asymmetry (marginal) are positively related to contract enforcement, and network density (marginal), network centrality, and relationalism are negatively related to contract enforcement. Network density and network centrality moderate the relationships between obligation criticality and contract enforcement, while TSIs moderate the relationship between relationalism and contract enforcement. |
| Antia et al. (2006) | Survey of U.S.-based manufacturers of branded personal care products and lab experiment | None | Enforcement | Gray market incidence | Survey findings: severe enforcement deters gray market incidence when the certainty of enforcement is high and when both detection ability and the speed of enforcement are high; price differential, premium positioning, free-riding potential, and customer heterogeneity on services in a market are positively related to gray market incidence. Experiment findings: severity of response reduces gray market incidence when used together with detection ability, speed of enforcement, or both; severity reduces gray market incidence when speed is high but the likelihood of detection is low. |
| Argyres, Bercovitz, and Mayer (2007) | IT services contracts3 | Contingency planning; task description | None | Contingency planning; task description | Contingency planning and task description act as complements in contracts. Contingency planning becomes more prevalent over time and is more likely to be included in contracts between partners with longer relationship histories. |
| Barthélémy and Quélin (2006) | Survey of senior executives in charge of managing outsourced activities | Contractual complexity (Parkhe's measure)13 | None | Ex post transaction costs | High switching costs, core-related specificity, and environmental uncertainty result in more complex contracts. Greater contractual complexity leads to higher ex post transaction costs. |
| Crocker and Reynolds (1993) | Jet engine procurement contractsa | Contractual completenessb | None | Degree of contractual completeness | Environmental uncertainty decreases while past opportunism increases the use of more complete contracts. Contracts become more complete over time. |
| Deeds and Hill (1998) | Interviews of top managers in biotechnology firms | Contractual safeguards (Parkhe's measure)b,c | None | Opportunism | Congruence between the backgrounds of the partners in a research alliance decreases perceived opportunism. An increase in contact frequency decreases opportunism. An inverted U-shaped relationship occurs between the age of the relationship and perceived opportunism. The honeymoon period (when the relationship is shielded from negative outcomes) appears to lengthen as the alliance increases in importance. |
| Ghosh and John (2005) | Survey of purchasing managers in nonelectrical machinery, electrical machinery, and transportation equipment sectors | Contractual incompletenessb,c | None | Original equipment manufacturer (OEM)-specific investments; contractual incompleteness; cost reduction (CR) outcomes; end product enhancement (EPE) outcomes | More incomplete contracts increase the OEM's specific investments; OEM's specific investments lead to more incomplete contract terms. CR outcomes are lower and EPE outcomes are higher when OEM investments are aligned with more incomplete contracts. EPE benefits are reduced when OEMs possessing greater end-product market strength align their investments with more incomplete contracts. OEM investments are greater when suppliers' specific investments are greater, when suppliers are fewer, and when the component becomes more important. Contracts are more incomplete when technological uncertainty, performance ambiguity, and volume uncertainty increase and when the buyer's size decreases. CR outcomes are lower as contracts become more incomplete and greater as the relationship age increases. EPE outcomes increase as OEM investments increase, contract terms become more incomplete, technological uncertainty increases, and performance ambiguity increases. |
| Gong et al. (2007) | Survey of international joint venture (JV) CEOs | Contractual completenessb,c | None | Contractual completeness; partner cooperation; JV performance | An increase in the number of partners negatively affects contractual completeness and partner cooperation. Contractual completeness and partner cooperation mediate the impact of the number of parents on JV performance. |
| Jap and Ganesan (2000) | Survey to retailers of a chemical producer | Explicit contractsb,c | None | Supplier commitment and performance; conflict; satisfaction | A retailer's perceptions of supplier commitment are negatively related to its TSIs, while the supplier's TSIs have a positive effect on the retailer's perception of supplier commitment. Perceptions of supplier commitment are positively related to relational norms but negatively to explicit contracts. Supplier TSIs, explicit contracts, and relational norms separately moderate the negative impact of retailer investments on perceptions of supplier commitment contingent on the relationship phase. |
| Kashyap, Antia, and Frazier (2012) | Survey of automobile franchisees; archival franchise contracts | Contractual completenessb | Enforcement and monitoring | Opportunism; compliance | Contractual completeness results in reduced ex post behavior monitoring and enforcement efforts. |
| Lumineau and Malhotra (2011) | Legal disputes (cases) between firms3 | Contractual governance structureb | None | Dispute resolution costs | When disputes arise, heavy (vs. light) contractual structures lead to different approaches to resolve the dispute. In turn, these approaches result in different costs associated with dispute resolution. |
| Lusch and Brown (1996) | Survey of merchant wholesalers of durable and nondurable goods | Explicit and normative contractsb,c | None | Wholesaler performance | Suppliers' explicit (hard) contracting has no significant impact on relational norms or wholesaler performance; normative (soft) contracting increases relational norms and wholesaler performance. |
| Luo (2002) | Survey of general/deputy general managers of international joint ventures (IJVs) and archival data on IJV performance. | Term specificity; contingency adaptabilityc | None | JV performance | Contractual completeness and cooperation drive IJV performance both independently and interactively. When term specificity and contingency adaptability are higher, a stronger positive relationship occurs between cooperation and performance. The contribution of contractual completeness to IJV performance declines as completeness is enhanced. |
| Mayer and Bercovitz (2008) | IT service contractsa | Contingency planning | None | Contingency planning | An inertial effect occurs in that partners tend to use the same level of contingency planning they used in previous contracts; this effect dissipates over time. |
| Mellewigt, Madhok, and Weibel (2007) | Survey of HR executives | Contractual complexityb,c | None | Contractual complexity | The higher the strategic importance of the outsourcing relationship, the higher the contractual complexity. The relationship between asset specificity (strategic importance) and contractual complexity is weakened (strengthened) in the presence of trust between the partners. |
| Mooi and Ghosh (2010) | Survey of managers responsible for making IT transactions | Contract specificityb,c | None | Ex post transaction problems; ex ante contracting costs | Contracts that were more specific than predicted lowered ex post transaction costs. |
| Mooi and Gilliland (2013) | Survey of managers responsible for making IT transactions | Relational and transactional safeguards; service and warranties; product and pricec | Enforcement | Satisfaction | If the relationship is protected by contractual components focusing on the relationship, enforcement is less likely (ceteris paribus); if the contractual components protect the transaction itself, enforcement is more likely. Alignment of enforcement with transactional attributes and contractual components enhances satisfaction. |
| Parkhe (1993) | Mail survey of senior executives | Contractual safeguardsb,c | None | Contractual safeguards | Contractual safeguards are positively linked to opportunism and negatively to payoff from initial cooperation, behavioral transparency, and length of time horizons. |
| Poppo and Zenger (2002) | Survey of information services executives | Contractual complexityb,c | None | Relational governance; contractual complexity; performance | Contractual complexity increases as asset specificity increases. Measurement difficulty has a positive effect and technological change has a negative effect on contractual complexity. The interaction of these two hazards has a negative effect on contractual complexity. Contractual complexity and relational governance function as complements in influencing satisfaction with exchange performance. |
| Poppo and Zhou (2014) | Survey of buyers and suppliers in a manufacturing context | Contractual complexity; contractual recurrenceb,c | Monitoring; socializing | Procedural and distributive fairness; exchange performance | Procedural fairness partially mediates the relationship between contractual complexity and performance. Distributive fairness partially mediates the relationship of contractual recurrence and performance. Socializing dampens the effect of contractual complexity on procedural fairness. Contractual complexity engenders greater perceptions of procedural fairness as monitoring increases. |
| Reuer and Arino (2002) | Mail survey of key informants related to alliance formation | Contractual safeguardsb,c | None | Contract renegotiation | Extensiveness of contractual safeguards is negatively related to the likelihood of contract renegotiation. |
| Reuer and Arino (2007) | Mail survey of key informants in firms responsible for alliance formation | Contractual complexityb,c | None | Contractual complexity | Contractual complexity is positively related to alliances involving TSIs and to time-bound alliances. |
| Reuer, Arino, and Mellewigt (2006) | Mail survey of key informants in telecommunications companies | Contractual complexityb,c | None | Contractual complexity | Search costs and more strategically important alliances are positively related to the use of more complex contracts; alliances that are less strategically important are negatively related to contractual complexity. |
| Saussier (2000) | Coal transportation contracts3 | Contractual completenessb | None | Contractual completeness | Contracting parties choose the level of completeness that will be the most cost effective in minimizing transaction costs. |
| Wuyts and Geyskens (2005) | Survey of purchasing managers (industrial and commercial machinery, computer equipment, and electrical equipment components) | Detailed contract draftingb,c | None | Close partner selection; opportunism | Uncertainty avoidance and power distance increase detailed contract drafting. Detailed contract drafting and close partner selection are substitutes in hedging against partner opportunism. Network embeddedness enhances the effectiveness of detailed contract drafting in reducing opportunism. |
| This study | Survey of hotel franchisees; franchise contracts3; third-party performance measures | Ex ante monitoring completeness and ex ante enforcement completeness | Ex post monitoring and ex post enforcement | Perfunctory and consummate compliance; customer satisfaction | |
aArchival source.
bGlobal measure.
clnformant reported.
Second, our consideration of compliance allows us to contribute to the literature on an outcome of great interest to marketing scholars (e.g., Heide and Wathne 2006; Kashyap, Antia, and Frazier 2012). The dominant focus in extant research has been on perfunctory compliance, or a strict adherence to contractual obligations (e.g., Gilliland and Manning 2002; Heide and Wathne 2006). The literature, however, acknowledges another type of compliance: consummate compliance, which involves going beyond contractual obligations (e.g., Kim and Mauborgne 1993). Adopting an expanded view of compliance is important because, as we show in a franchise setting, perfunctory and consummate compliance influence franchisees' customer satisfaction scores negatively and positively, respectively. Although the counterintuitive, yet negative effects of perfunctory compliance have been variously alluded to (Bernheim and Whinston 1998; Heide and Wathne 2006; Macaulay 1963), they have not been formally tested in the research we are aware of. Thus, our research helps explain differences in intrafranchise system outcomes as evaluated by third parties (i.e., customers) in a manner not assessed in the prior literature.
We undertook two data collections, one qualitative and one quantitative, to inform and test our theoretical contentions. Our qualitative effort, involving interviews with 12 franchising experts from multiple industries, provides in-depth insights into our conceptual framework. We coupled this effort with a rigorous empirical study in conjunction with a large hotel franchisee association in the United States. We conducted a survey of its members and contacted respondents' franchisors to obtain the actual franchise contracts. In addition, we acquired third-party customer satisfaction measures for each of the respondents. This intensive data collection effort allowed us to create a unique database compiled from three data sources (survey data from 193 franchisees across 26 hotel brands, coded archival contractual data on each of these brands, and third-party customer satisfaction measures for each of the responding franchisees).
We present our conceptual framework next, followed by our hypotheses as informed by our qualitative effort and the existing literature. We then elaborate on the methodology and results of our quantitative study. Finally, we provide implications for theory and practice, recognize limitations, and suggest future research directions.
Research on interfirm contracting has emphasized the importance and implications of ex ante contractual completeness (e.g., Cao and Lumineau 2015; Kashyap, Antia, and Frazier 2012)1 and ex post monitoring and enforcement (e.g., Antia and Frazier 2001; Heide, Wathne, and Rokkan 2007), but the joint effects of the two have received less attention.2 We draw on recent research on the functions of contracts relating to coordination and control (e.g., Lumineau 2014; Mayer and Argyres 2004; Mooi and Gilliland 2013; Reuer and Ariño 2007; Ryall and Sampson 2009; Schepker et al. 2014), the literature on monitoring (e.g., Heide, Wathne, and Rokkan 2007) and enforcement (e.g., Antia and Frazier 2001), and insights from a set of field interviews to forward a contingency perspective (e.g., Kumar, Heide, and Wathne 2011; Weitz 1981) to inform our understanding of these joint effects. The resulting conceptual model (Figure 1) suggests that the impact of monitoring and enforcement on both perfunctory and consummate compliance is contingent on the context (e.g., Heide, Kumar, and Wathne 2014; Heide, Wathne, and Rokkan 2007) associated with monitoring completeness and enforcement completeness, respectively (e.g., Heide, Wathne, and Rokkan 2007). Our invocation of context (Bamberger 2008) helps resolve the complexities inherent in specifying main-effects relationships between governance mechanisms (e.g., monitoring and enforcement) and compliance because each mechanism, by itself, may induce or reduce compliance (e.g., Heide, Kumar, and Wathne 2014; Kashyap, Antia, and Frazier 2012; Mooi and Gilliland 2013).
In franchising, the setting for our research, formal contracts replicate the coordination and control features associated with franchise organizations (e.g., Gulati and Singh 1998; Puranam, Singh, and Zollo 2006), thereby providing a context for franchisees to evaluate their franchisors' ex post governance efforts. The coordinating function of contracts aims to deliberately align or adjust the partners' actions in an orderly manner to achieve jointly determined goals (Gulati, Wohlgezogen, and Zhelyazkov 2012). Contractual clauses highlighting the coordination function typically involve the specification and operation of information-sharing, decision-making, and feedback mechanisms in the relationship to unify and bring order to partners' efforts, and to combine partners' resources in productive ways (e.g., Gulati, Wohlgezogen, and Zhelyazkov 2012). In the hotel franchise setting that we examine, such clauses, for example, outline the franchisor's right to inspect the franchisee. On the other hand, the control function of contracts defines the rights and obligations of parties to minimize deviant behavior through the use of authority mechanisms (Williamson 1985) and makes potential sanctions explicit (Stinchcombe 1985). Clauses reflecting the control function of contracts provide firms with the option of sanctioning an exchange partner who is unable or unwilling to abide by agreed-upon terms (Joskow 1987). In our setting, such clauses can pertain to a hotel franchisor's right to impose penalties on the franchisee. Table 2 provides additional examples of clauses that correspond to monitoring and enforcement.
In line with our conceptualization of the coordination and control functions of contracts, we argue that monitoring clauses are informational in nature, and, therefore, greater monitoring completeness creates a context of coordination (Reuer and Ariño 2007, p. 313, 327), which positively affects the relationship between monitoring and compliance. In contrast, enforcement clauses make sanctions explicit, and therefore, greater enforcement completeness creates a context of control (Parkhe 1993; Reuer and Ariño 2007), which negatively influences the relationship between enforcement and compliance.3
In the following sections, we integrate recent theoretical developments on the functions of contracts (e.g., Lumineau 2014; Reuer and Ariño 2007; Schepker et al. 2014) with qualitative insights from 12 interviews with franchise practitioners and experts across a wide array of industries and experience levels in the United States. Table 3 provides additional information about our interview respondents. In addition to exploring issues of contractual completeness and monitoring and enforcement, we were interested in gaining a deeper understanding of compliance types and their effects on customer satisfaction. The interviews lasted between 30 minutes and 2.5 hours. We were permitted to audio record and transcribe verbatim five of the interviews and took copious notes during the other seven. We followed the interview guide approach (Patton 1990) and structured the interviews around two general questions: ( 1) How do you view franchisor monitoring and enforcement toward franchisees/your business (a) in general, and (b) relative to what is outlined in the franchise contract? ( 2) How does (a) adhering to and (b) going above and beyond the letter of the contract affect franchisees/your business?
TABLE: TABLE 2 Examples of Monitoring and Enforcement Contractual Clauses
| Monitoring Clauses | Enforcement Clauses |
| • Inspection without prior notice (e.g., mystery shoppers) | • Penalty for unpaid dues (e.g., interest) |
| • Review of guest comment cards | • Withholding consent for opening hotel |
| • CPA certification of dues | • Reevaluation/reinspection fees |
| • Written consent required to change plans and designs | • Reimbursement fees (e.g., for data breach) |
| • Inspection of books and records | • Exclusion from programs |
| • Inspection of equipment, food products, and supplies | • Suspension of privileges |
| • Prior consent to deviate from standards | |
Prior research has typically conceptualized compliance as strict adherence to formal requirements (e.g., Gilliland and Manning 2002). This conceptualization reflects perfunctory compliance, or the extent to which a franchisee adheres to what is formally required in its franchise contract (e.g., Gilliland and Manning 2002). Analogous to the "compulsory execution" of stipulated responsibilities (Kim and Mauborgne 1993, p. 15), such compliance "involves job performance of a minimally acceptable sort" (Williamson, Wachter, and Harris 1975, p. 266). In one of our interviews for this study, the owner/operator of two hotel franchises corroborated these sentiments from the literature:
Well, performing to the letter means that you do what is required of you. You open up a hotel, you operate a hotel, they tell you what to do as far as, "Here are your brand standards. You've got to follow it."
Franchisees may, however, choose to go beyond formal contractual requirements (e.g., Kim and Mauborgne 1993; Williamson, Wachter, and Harris 1975) and exhibit a willingness to take initiative (e.g., Kim and Mauborgne 1996; Wuyts 2007). Thus, "consummate compliance" indicates franchisee effort beyond contractual requirements (Kim and Mauborgne 1993). For instance, hotel franchisees may install, at their own expense, optional electronic reservations systems to streamline reservations. The incoming chairman of a hotel franchisee association provided additional examples:
In terms of hotel operators, guys do that [consummate compliance] all day long. Breakfast standards might be a certain thing and they go above and beyond. Room supplies—you put in more than you're required. That stuff happens every day.
The present research, therefore, adopts an expanded view of compliance beyond what is typically reflected in the literature.4 In the following sections, we describe how the joint effects of monitoring completeness and monitoring, and enforcement completeness and enforcement, impact both types of compliance. Subsequently, we explain how perfunctory and consummate compliance impact customer satisfaction.
Monitoring is the extent to which a franchisor observes the behaviors and outcomes of franchisees (e.g., Heide, Wathne, and Rokkan 2007). The effects of monitoring on compliance are not entirely conclusive. Research informed by institutional economic theories has suggested that monitoring is likely to increase compliance because it enables the verification of behaviors (e.g., Eisenhardt 1985; Wathne and Heide 2000; Williamson 1975). Monitoring may also increase compliance because it provides important feedback and useful guidance to franchisees (Heide, Kumar, and Wathne 2014). For instance, the owner/operator of a fast-food restaurant explained:
Those types of things they do monitor—they' re primarily to provide feedback to help me get better at running my business, so from a more consulting role.
TABLE: TABLE 3 Interviewee Characteristics
| Respondent | Title | Length of Interview | Experience (Years) | Franchise Sector | Number of Outlets |
| 1 | President, Restaurant Franchisee Association | 1 hour | 20 | Restaurant | N.A. |
| 2 | Chairman of the Board, Restaurant Franchisee | 45 minutes | 20 | Restaurant | 31 |
| Association; Owner/Operator | | | | |
| 3 | Owner/Operator | 2.5 hours | 1 | Restaurant | 7 |
| 4 | Owner/Operator | 1 hour | 30 | Fitness center | 3 |
| 5 | Owner/Operator | 45 minutes | 1 | Fitness center | 9 |
| 6 | Franchise Industry Expert/Consultant | 1 hour | 17 | Restaurant | N.A. |
| 7 | Owner/Operator | 1 hour | | Restaurant | 6 |
| 8 | Owner/Operator | 35 minutes | 14 | Restaurant | 1 |
| 9 | Board Member, Hotel Franchisee Association; | 30 minutes | 19 | Hotels | 2 |
| Owner/Operator | | | | |
| 10 | Owner/Operator | 1 hour | 25 | Hotels | 5 |
| 11 | Incoming Chairman, Hotel Franchisee Association | 30 minutes | 20+ | Hotels | 4 |
| 12 | Owner/Operator | 40 minutes | 30+ | Automobile dealership | 19 |
Notes: N.A. = not applicable.
In contrast, monitoring may signal distrust and increase psychological reactance, a motivational state directed toward the reestablishment of free behaviors that have been eliminated or threatened with elimination (Brehm 1966), thereby decreasing the likelihood of franchisee compliance. The president of a restaurant franchisee association lamented,
Franchisees are not excited when the operations person shows up. Does this guy have a "gotcha" mentality?
As such, research has advocated that context can play an important role in determining the effects of monitoring (Frey 1993; Heide, Kumar, and Wathne 2014; Heide, Wathne, and Rokkan 2007). We suggest that greater monitoring completeness provides a context grounded in information exchange and coordination, which positively influences the relationship between monitoring and both types of compliance.
Monitoring completeness is the extent to which clauses pertaining to the franchisor's rights to observe its franchisees are codified in the contract (Reuer and Ariño 2007). Examples of monitoring clauses in a hotel franchise setting include clauses pertaining to inspections of equipment, food products, and supplies, as well as obtaining prior consent to deviate from standards (see Table 2). Considerable research has suggested that by outlining monitoring rights, monitoring clauses help align expectations and behaviors, clarify roles and responsibilities, and facilitate information exchange, all of which provide a context of coordination (Argyres, Bercovitz, and Mayer 2007; Argyres and Mayer 2007; Faems et al. 2008; Lumineau 2014; Mayer and Argyres, 2004; Reuer and Ariño 2007; Schepker et al. 2014). For example, Reuer and Ariño (2007, p. 322) refer to "coordination provisions" and suggest they ( 1) relate "more directly to the monitoring and adaptation of the collaborative agreement" and ( 2) are "informational provisions concerning the monitoring and adaptation [of the alliance]" (p. 327). Similarly, Heide, Kumar, and Wathne (2014, p. 1167) suggest that "monitoring and coordination through feedback represent distinct actions, but the two are proximal constructs."
Given the coordination benefits of greater monitoring completeness, franchisees may view monitoring in the context of greater monitoring completeness as particularly helpful for the following reasons. As roles and responsibilities become clearer with greater monitoring completeness, monitoring is likely to be viewed by franchisees as more legitimate and less haphazard (Heide, Kumar, and Wathne 2014; Heide, Wathne, and Rokkan 2007). Contracts may also act as "knowledge repositories" (Mayer and Argyres 2004), which facilitate and improve coordination. Thus, franchisees are likely to view monitoring and feedback as more credible when it is conducted against the backdrop of greater monitoring completeness, which reflects firsthand, idiosyncratic franchisor experience and knowledge (e.g., Barthelemy 2008; Combs and Ketchen 1999; Heide, Kumar, and Wathne 2014; Mayer and Argyres 2004). Correspondingly, franchisees are likely to appreciate the feedback they receive from franchisor monitoring, because it can help improve their performance (Frazier 1999), and to reciprocate by fulfilling their contractual duties (e.g., Poppo and Zhou 2014).
H1: Monitoring completeness positively moderates the relationship between monitoring and perfunctory compliance.
Monitoring completeness is also likely to positively influence the relationship between monitoring and consummate compliance. We suggest that monitoring will be perceived by franchisees as fair when viewed in a context of coordination—that is, against the backdrop of an extensive set of legitimate and informed monitoring clauses that foster a common understanding of both partners' goals and the manner in which these goals should be achieved (Poppo and Zhou 2014; Ryall and Sampson 2009). Indeed, Heide, Wathne, and Rokkan (2007, p. 427) suggest that "monitoring may be perceived as fair to the extent that it is executed against the backdrop of an established agreement." Extensive prior research has suggested a positive relationship between fairness perceptions and discretionary behaviors such as organizational citizenship behaviors (LePine, Erez, and Johnson 2002; Moorman 1991; Organ and Ryan 1995; Podsakoff et al. 2000). Likewise, such perceptions should engender franchisee discretionary efforts beyond formal contractual requirements (i.e., consummate compliance).
H2: Monitoring completeness positively moderates the relationship between monitoring and consummate compliance.
Enforcement is defined as the severity of the disciplinary action undertaken by the franchisor in response to franchisee violations (Antia and Frazier 2001). Similar to monitoring, the effects of enforcement on compliance are not definitive. On the one hand, enforcement can increase compliance due to its deterrence effect by imposing nontrivial costs on franchisees, the extreme manifestation being termination of the franchise relationship (e.g., Antia et al. 2006; Kashyap, Antia, and Frazier 2012). The owner of 19 automobile franchises commented,
If they [the franchisor] came into our showroom and found out that instead of just having Toyotas here we had Toyotas and new Nissans in the showroom, then that would be a violation of our franchise agreement. And they would immediately call their boss and say, "Hey, these guys are turning this dealership into a dual dealership, Toyota, Nissan." And they would not permit it. And if we wouldn't change, they would terminate us for that.
On the other hand, enforcement can also cause reactance and, ultimately, reduce compliance under some conditions (Mooi and Gilliland 2013), which suggests that context can also play a key role in determining how enforcement affects compliance (Antia et al. 2006; Mooi and Gilliland 2013). Accordingly, we argue that enforcement completeness, by providing a context of control, negatively affects the relationship between enforcement and both types of compliance.
Enforcement completeness is the extent to which clauses pertaining to the franchisor's rights to discipline a franchisee's contractual violations are codified in the contract (e.g., Malhotra and Lumineau 2011; Reuer and Ariño 2007). Extensive research has equated enforcement clauses to control (Lumineau 2014; Parkhe 1993; Reuer and Ariño 2007; Schepker et al. 2014), as is also reflected in our interviews. For example, the owner/operator of four hotel franchises described how enforcement completeness (and correspondingly control) increases as franchisors address loopholes in their enforcement clauses:
If, for some reason, the courts rule in favor of the franchisee, the lawyers will quickly struggle to find a loophole and say, "All right, let's put this language in there so the next time we don't get burned."
Greater enforcement completeness, therefore, is likely to be associated with a context of control because enforcement clauses detail rights of the franchisor to terminate the franchise relationship, initiate lawsuits, and impose objective financial penalties on franchisees (Lumineau 2014; Schepker et al. 2014). A franchisor's increasing emphasis on such clauses, though legitimate, is likely to make franchisees view enforcement efforts juxtaposed against the greater enforcement completeness in the contract as particularly punitive and controlling (Lumineau 2014; Reuer and Ariño 2007; Schepker et al. 2014). Feeling overly restricted in the relationship, franchisees may be motivated to reestablish their autonomy with defensive behaviors such as reduced compliance (Brehm 1966; Brown, Dev, and Lee 2000; Ghoshal and Moran 1996, p. 14).
H3: Enforcement completeness negatively moderates the relationship between enforcement and perfunctory compliance.
Franchisees, who are subject to the "take-it-or-leave-it" (Lafontaine and Kaufmann 1994) enforcement clauses in their contracts, may resent the ability of the franchisor to take recourse to the contract when they have no similar avenue (Klein 1980). Unsurprisingly, therefore, franchisees are likely to view greater enforcement completeness as reflecting a context of greater control (e.g., Reuer and Ariño 2007). Similar to H2, we suggest that perceptions of fairness are likely to reside in the relationship between enforcement completeness and enforcement (e.g., Heide, Wathne, and Rokkan 2007); however, the context of control associated with greater enforcement completeness is likely to make franchisees view enforcement as less fair. Rather than deterring noncompliance, greater enforcement conducted against the backdrop of a controlling context will be regarded as especially punitive and unfair. The owner of two hotel franchises echoed these sentiments:
They're increasing costs of all the little things that keep adding up. New fees, that fee, addressing customer complaints. A fee for that. There are things that we think are unfair [emphasis added], but according to our contract we have to sort of pay that.
As a result, franchisees are less likely to take initiative and provide effort beyond what is formally prescribed in their contract, that is, to consummately comply (Kim and Mauborgne 1996; LePine, Erez, and Johnson 2002; Moorman 1991; Organ and Ryan 1995).5
H4: Enforcement completeness negatively moderates the relationship between enforcement and consummate compliance.
Customer satisfaction in the hotel franchising domain is defined as the extent to which customers perceive their hotel stays to be of high quality (e.g., accommodation quality, quality of hotel rooms) (e.g., Fornell 1992). Interest in customer satisfaction as a performance outcome in marketing has increased in recent years (e.g., Fornell, Morgeson, and Hult 2016a; Katsikeas et al. 2016; Kumar 2016), with its implications for the potential for improved economic performance of firms (e.g., Fornell, Morgeson, and Hult 2016b; Sorescu and Sorescu 2016), as well as customer-related performance outcomes such as increased loyalty and repurchase intentions (e.g., Fornell et al. 1996). Accordingly, customer satisfaction is an important performance-related variable to examine in the context of contracts (Mooi and Gilliland 2013), but so far, it has been ignored in the extant research on interorganizational compliance. Therefore, we advance novel hypotheses on the relationship between each type of compliance and customer satisfaction.
Perfunctory compliance and customer satisfaction. Recall that perfunctory compliance involves strict adherence to formal requirements (Kim and Mauborgne 1993). To the extent that behaviors conducive to customer satisfaction are articulated in the franchise contract and apply broadly to most franchisee locations, one might expect a positive relationship between perfunctory compliance and customer satisfaction. However, our interviews and prior research provide several reasons that point to a potential negative relationship.
Several interviewees bemoaned the "one size fits all" characteristic of franchise contracts that ignore important differences across locations. Adherence to such contracts, therefore, may correspond to the other-induced (rather than self-volitional) nature of perfunctory compliance (e.g., Malhotra and Galletta 2003). Consequently, franchisees may perfunctorily comply with less applicable clauses with resentment, which customers may sense. For instance, the operator of seven pizza franchisees lamented that his compliance with certain clauses can negatively impact his customers:
After 8 P.M., I might sell one pizza, but I have to be open till midnight. So, I do what I'm supposed to, but with no personality. Customers don't get that good feeling.
This is consistent with literature suggesting that perfunctory compliance involves minimally acceptable levels of effort (Williamson 1975; Williamson, Wachter, and Harris 1975), which may correspond to cursory or superficial completion of tasks that are antithetical to customer satisfaction. Adherence to contractual rules can also decrease task enjoyment and creativity, which can undermine the experiences franchisees provide to their customers (e.g., Amabile and Gitomer 1984; Deci and Ryan 1985). By having to strictly follow contractual clauses that may be less applicable to their specific situations, franchisees may even refuse to consider franchisor advice that could potentially increase customer satisfaction (e.g., Heide and Wathne 2006, p. 97).
Somewhat counterintuitively, therefore, noncompliance may restore a sense of self-determination, which increases intrinsic motivation and enthusiasm to provide better customer service. Our interviews provide several examples to this end. For instance, some respondents referred to "rule breaking" to enhance customer satisfaction, even if at the expense of the franchisee-franchisor relationship. The chairman of a restaurant franchisee association and owner/operator of 31 fast-casual restaurants recalled,
We were the ones that thought pouring room-temperature syrup on pancakes wasn' t a good idea. We got a warmer and brought it to folks. Got in trouble for it, but it became a brand standard.
Therefore, in a franchise context, our interviews and insights from the literature suggest a negative relationship between perfunctory compliance and customer satisfaction.
H5: Perfunctory compliance has a negative relationship with customer satisfaction.
Consummate compliance and satisfaction. Consummate compliance involves going beyond formal contractual requirements (Kim and Mauborgne 1996; Williamson, Wachter, and Harris 1975). Our interviewees consistently reinforced a positive relationship between consummate compliance and customer satisfaction. For example, a franchisee might also take the initiative to enhance the property's surrounding landscape, which may fall outside the confines of the formal contract. A hotel franchisee provides several additional examples:
Or you can say, "I want high-end furniture. I want more breakfast than the standard breakfast." … The brand might say, "You know, you should have a director of sales." You might say, "Well, I think I'm a big enough property to have a director of sales and an assistant to help."
Such discretionary behaviors have been shown to have beneficial outcomes, such as increased profitability (Wuyts 2007). We expect they will also enhance customers' satisfaction judgments.
H6: Consummate compliance has a positive relationship with customer satisfaction.
We test our hypotheses in the branded U.S. hotel franchising industry. Branded hotels account for over 70% of all hotels in the United States (Mayock 2011), which makes them an appropriate context in which to study governance issues (e.g., Brown, Dev, and Lee 2000). Importantly, branded hotels utilize brand-specific explicit contracts to govern their franchise relationships. The concepts of contractual completeness and compliance are integral to this industry due to the importance of maintaining consistency across franchisees.
Primary data collection. In collaboration with a large U.S. hotel franchisee association, the members of which account for more than 40% of U.S. hotels (Hotel Interactive 2015), we drew a random sample of 1,000 U.S. hotel franchisee owner-principals. Consistent with recommendations (Kumar, Stern, and Achrol 1992), we identified owner-principals as qualified respondents for each franchisee. The association's board of directors e-mailed them a customized letter requesting their participation and a link to the survey. We asked them to answer survey questions in reference to the last property/ location they had visited. In return for their participation, respondents were given the option to receive a report of the study's results. After sending two e-mail reminders, we received completed responses from 236 owner-principals representing 26 hotel brands. After we accounted for missing data from merging three data sets (see "Secondary data collection" paragraph for two additional sources), our final sample size was 193. This final response rate of 19.3% compares favorably with rates obtained in prior research in the hotel industry (e.g., Brown, Dev, and Lee 2000).
Our owner-principal respondents had an average of 5 years (range: 2-28 years) of experience with their franchisors and thus were knowledgeable about their relationship with the franchisor. Overall, 42.5% of the respondents were single-unit owners, while 57.5% owned multiple units.6 We compared our final sample with the typical membership profile of the association on the number of total properties owned and found no significant differences. We also compared early-versus late-responding franchisees across all study variables and found no significant differences (Armstrong and Overton 1977).
Secondary data collection. We collected data from two secondary sources. First, we obtained current franchise contracts from the 26 brands that surfaced in the primary data collection. The 26 brands represent more than 50% of rooms in the branded hotel industry (Miller and Washington 2013). Inspection revealed, however, that several contracts were almost identical across brands (cf. Lafontaine and Shaw 1999). For example, franchise contracts were very similar across brands within the Choice Hotels corporation, such as Quality Inn, Econo Lodge, Comfort Inn, and Rodeway Inn. Consequently, we assessed the completeness of enforcement and monitoring clauses on the basis of a content analysis of 15 unique contracts. Second, we obtained three Medallia Inc. customer service scores about hotel stays for all 26 brands in this study: scores for overall customer experience, overall accommodation quality, and quality of hotel rooms for each of the 193 hotels in our sample. The individual measures reflect the aggregate scores for each item across a hotel's customers. We then created a composite index of the three customer satisfaction scores.
Table 4 provides the correlation matrix and descriptive statistics for the study variables. The Appendix provides the items measuring each construct, with their loadings and coefficient alpha, composite reliability, and average variance extracted statistics (Fornell and Larcker 1981).
Contractual completeness. We followed a three-step procedure to assess the monitoring and enforcement completeness of each contract (see Kashyap, Antia, and Frazier 2012). The first step was to generate an overall list of clauses pertaining to monitoring and enforcement across all 15 contracts. Two independent coders created comprehensive lists of all monitoring and enforcement clauses across the contracts. Frequent discussion ensured consistency in how clauses were interpreted and categorized. The lists were then integrated to create two supersets of clauses (one for monitoring and one for enforcement clauses). Second, the coders independently revisited each contract and noted the presence or absence of each clause. The level of agreement was 92%, with the remaining differences resolved by discussion. Third, we summed the number of enforcement and monitoring clauses specified in each contract and then undertook a natural-log transformation of each count variable to reduce the skewness of the distribution (Meyers, Well, and Lorch 2010).
Monitoring and enforcement. We adapted the monitoring measures of Niehoff and Moorman (1993) and Heide, Wathne, and Rokkan (2007) for our study. Heide, Wathne, and Rokkan suggest differentiating between output and behavior monitoring; however, a factor analysis of the items in our data resulted in a single factor with five items for the monitoring construct. We adapted Antia and Frazier's (2001) seven-item enforcement scale for this study.
Compliance. We drew from the literature to adapt scales for perfunctory and consummate compliance to the present context (Gilliland and Manning 2002; Kim and Mauborgne 1993). Our final scales for both these constructs had three items each.
Control variables. We controlled for several pertinent variables. We included the norm of solidarity, which may influence franchisee compliance (e.g., Joshi and Arnold 1998), using a four-item scale (e.g., Kumar, Heide, and Wathne 2011). Dependence by both franchisees and franchisors can also influence compliance. Therefore, we controlled for dependence of the franchisee on the franchisor by including the total number of properties owned by each franchisee for that franchisor. Moreover, we controlled for franchisees' dependence on the franchisor by including franchisee transaction-specific investments (TSIs), using a five-item scale, as per Kumar, Heide, and Wathne (2011). The existence of TSIs can lock the franchisee into a relationship with the franchisor, potentially creating dependence (e.g., Heide and John 1988) and, therefore, engendering compliance (e.g., Wathne and Heide 2000). Finally, we use a three-item scale to control for franchisor dependence on a franchisee that assesses franchisees' perceptions of their franchisors' dependence on them (Lusch and Brown 1996).
Measure validation. A confirmatory factor analysis of the latent constructs in our study yields satisfactory psychometric properties. Although the chi-square value is significant (χ2 = 463.02, d.f. = 371, p < .01), other fit statistics (comparative fit index = .98; Tucker-Lewis index = .97; root mean square error of approximation = .04) meet or exceed established guidelines (Browne and Cudeck 1992). The items underlying each latent construct display statistically significant loadings; the lowest t-statistic is 6.09. The average variance extracted (AVE) and composite reliability (CR) scores computed for each construct (see the Appendix) exceed benchmark guidelines (Fornell and Larcker 1981), suggesting strong convergent and discriminant validity. Furthermore, the multicollinearity diagnostics for the explanatory variables in our model show that the variance inflation factors (VIFs) are well below 10 (highest VIF = 2.85), indicating that multicollinearity is not a problem in our model.
TABLE: TABLE 4 Descriptive Statistics and Correlation Matrix
| M (SD) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| 1. Monitoring completeness | 3.72 (.25) | | | | | | | | | | |
| 2. Enforcement completeness | 3.67 (.22) | .78** | | | | | | | | | |
| 3. Monitoring | 2.71 (1.09) | .01 | -.03 | | | | | | | | |
| 4. Enforcement | 3.66 (1.16) | .01 | -.03 | -.03 | | | | | | | |
| 5. Perfunctory compliance | 4.41 (.72) | .02 | -.05 | -.02 | .33** | | | | | | |
| 6. Consummate compliance | 4.26 (.91) | -.02 | -.03 | .02 | .25** | .34** | | | | | |
| 7. Customer satisfaction | 8.01 (1.47) | -.03 | -.09 | .18* | .07 | .01 | .17* | | | | |
| 8. Dependence | 2.85 (1.29) | .10 | .10 | .07 | -.16 | -.05 | -.01 | .00 | | | |
| 9. TSIs | 4.50 (.64) | -.16* | -.13 | .01 | .24** | .41** | .37** | .15* | -.11 | | |
| 10. Solidarity | 2.87 (1.31) | .01 | .11 | .23** | -.13 | -.08 | -.01 | .04 | .15* | -.05 | |
| 11. Total properties | 1.80 (1.20) | .04 | -.04 | -.00 | -.06 | -.15* | -.06 | .02 | -.03 | -.05 | .05 |
*p < .05.
**p < .01.
Our model specification must account for three key characteristics of the data. First, our data on 193 franchisees across 15 contracts violates the assumption of independent observations; accordingly, we must accommodate the likely correlation among observations within individual clusters. Second, we expect the two endogenous behavioral outcomes—perfunctory and consummate compliance—and franchisee customer satisfaction to be correlated as well. Ignoring the potential violation of the assumption of independent observations could result in inflated standard errors and unacceptably large Type I error, whereas accommodating correlated errors among the behavioral outcomes and franchisee customer satisfaction facilitates more efficient estimates (Cameron and Trivedi 2008). Third, our hypotheses link contractual and franchisor monitoring and enforcement to franchisee compliance, thereby requiring us to explicitly acknowledge the endogeneity of each type of compliance. We accommodate all the preceding requirements with a recursive set of three interrelated equations:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed.
where
PCOij = perfunctory compliance by franchisee j of franchisor i,
CCOij = consummate compliance by franchisee j of franchisor i,
MONij = monitoring by franchisor i of franchisee j,
ENFij = enforcement by franchisor i of franchisee j,
MCPi = monitoring completeness of franchisor i,
ECPi = enforcement completeness of franchisor i,
CSj = customer satisfaction of franchisee j,
SOLIDij = norm of solidarity between franchisor i and franchisee j,
TOTj = total properties owned by franchisee j,
DEPij = dependence of franchisor i on franchisee j, and
TSIij = transaction-specific investments made by franchisee j for franchisor i.
We mean-centered all latent regressors to aid interpretation of the hypothesized moderator effects (Cohen et al. 2003). We estimate Equations 1-3 jointly using Roodman's (2011) conditional mixed process (CMP) regression technique, as implemented in Stata 12.1. This technique is gainfully applied to recursive systems of equations and uses a seemingly unrelated regression estimator to maximize a higher-order multivariate normal generalization of the likelihood function. Since Roodman's (2011) exposition and the availability of the CMP Stata add-on module, the algorithm has been used in a wide range of applications (e.g., Antia, Zheng, and Frazier 2013; Kashyap, Antia, and Frazier 2012). The preceding specification explicitly accounts for clustered observations, the likely correlation among the compliance outcomes and between franchisee compliance and customer satisfaction, the endogeneity of each of the compliance-related "second stage" regressors, and the four different pairwise multiplicative interactions.
Research in marketing has indicated the potential endogeneity of governance, given that the alignment between governance mechanisms and transactional characteristics can result in better performance (e.g., Mooi and Gilliland 2013; Sande and Haugland 2015). Acknowledging this possibility, we explicitly test for the exogeneity of the contractual completeness and monitoring and enforcement constructs separately for each equation, with perfunctory and consummate compliance as dependent variables, using instrumental variables (IVs) (Sargan 1958), a procedure commonly used to address endogeneity concerns. We use three instruments from our survey—norm of restraint, enforcement forbearance, and opportunism8—to test for the exogeneity of enforcement (ENF). Despite our best efforts, we could not locate instruments from our survey that satisfied both the conditions of relevance and exogeneity (i.e., orthogonality) for the remaining three governance variables, namely, monitoring completeness (MCP), enforcement completeness (ECP), and monitoring (MON), in our model. This inability reflects the difficulty of finding appropriate instruments, which impedes the use of IV techniques (e.g., Ebbes et al. 2005). For these variables, we use a latent-variable method (e.g., Susarla, Oh, and Tan 2016) recently proposed by Lewbel (2012) that enables the construction of instruments as simple functions of the model's data when no external instruments are available. This approach is similar to the Arellano and Bond (1991) approach using panel data estimators, but it can be applied to cross-sectional data as well and can be implemented in Stata using the ivreg2h routine (Baum and Schaffer 2015). Identification relies on finding regressors that are uncorrelated with the product of heteroskedastic errors. Accordingly, we use mean-centered versions of variables in our models to generate instruments for testing the exogeneity of MCP, ECP, and MON. We use MCP, ECP, and MON to create instruments for MCP and ECP, and we test for the exogeneity of MON using instruments generated from MCP, ENF, and solidarity (SOLID).
We used a series of tests to check for the relevance and exogeneity of the instruments in our research (Bascle 2008). The F-statistic for each of the first-stage equations was above the benchmark value of 9.08 (lowest first-stage F = 11.11), providing evidence of our chosen instruments' relevance (Stock and Yogo 2004). We used Hansen's J-statistic of over-identifying restrictions (Hansen 1982) to test for the overall exogeneity of our instruments and were unable to reject the null hypotheses (lowest p = .23) that our instruments are uncorrelated with the error term. We used the difference of two Sargan-Hansen statistics (C-statistic) to test whether we could treat the proposed regressors as exogenous (Baum, Schaffer, and Stillman 2003) and were unable to reject the null hypothesis (all ps > .10), indicating that each of our instruments is exogenous. Overall, these results suggest that our instruments are valid and exogenous. Finally, we use the standard form of the Durbin-Wu-Hausman test (Davidson and MacKinnon 1993) to test for the endogeneity of the governance variables in our research. We were unable to reject the null hypothesis for the Durbin-Wu-Hausman test in each instance, showing that endogeneity is not an issue with the governance constructs in our model.
Table 5 displays the estimates obtained from the CMP regression. The results provide considerable support for our hypotheses. We find that monitoring completeness significantly moderates the relationships between monitoring and both perfunctory (b15 = .30, p < .05) and consummate compliance (b35 = .23, p < .05), providing support for H1 and H2, respectively. Enforcement completeness significantly moderates the relationship between enforcement and perfunctory compliance (b16 = -.18, p < .05) but has only a marginal impact on the relationship between enforcement and consummate compliance (b36 = -.23, p < .10). These results provide support for H3 and partial support for H4. We also find that perfunctory compliance has a significant negative impact on customer satisfaction (b51 = -12.01, p < .01), in support of H5. Consummate compliance is positively related to customer satisfaction (b52 = 12.50, p < .01), thereby providing support for H6.
With respect to the control variables, we find that the norm of solidarity is not associated with either perfunctory (b17 = .00, n.s.) or consummate compliance (b37 = .00, n.s.). Similarly, the total number of properties owned by the franchisee has no impact on either perfunctory (b18 = -.06, n.s.) or consummate compliance (b38 = -.05, n.s.). Dependence of the franchisor on its franchisees has no impact on either perfunctory (b19 = .02, n.s.) or consummate compliance (b39 = .02, n.s.). However, franchisee TSIs are positively related to both perfunctory (b20 = .44, p < .01) and consummate compliance (b40 = .44, p < .01).
We conduct floodlight analyses of the significant interactions using the Johnson-Neyman technique (Hayes and Matthes 2009; Johnson and Neyman 1936; Spiller et al. 2013) to identify the region in the range of the moderator variable for which the effect of the independent variable on the dependent variable is significant. We use the transformed variables in our regression model for conducting our analysis. Regressing both compliance types on monitoring (MON), monitoring completeness (MCP) (min = -.51, max = .74), and their interactions suggests that MON has a significant negative effect on perfunctory compliance for values of MCP less than -.29 (BJN = -.10, SE = .05, p = .05) but not for any value of MCP greater than -.29. This represents all values of MCP between -.29 and -.51 in our data. Similarly, MON has a significant negative effect on consummate compliance for values of MCP less than -.44 (BJN = -.10, SE = .05, p = .05) but not for any value of MCP greater than -.44. This represents all values between -.44 and -.51. These results suggest that monitoring has a significant negative effect on perfunctory and consummate compliance at relatively low levels of monitoring completeness.
Regressing compliance types on enforcement (ENF), enforcement completeness (ECP) (min = -.24, max = .59), and their interactions suggests that ENF has a significant positive effect on perfunctory compliance for all values of ECP less than .30 (BJN = .09, SE = .05, p = .05) but not for any value of ECP greater than .30. This represents all values between -.24 and .30. Similarly, ENF has a significant positive effect on consummate compliance for values of ECP less than .25 (BJN = .08, SE = .04, p = .05) but not for any value of ECP greater than .25. This represents all values of ECP between -.24 and .25. These results suggest that enforcement has a significant positive effect on perfunctory and consummate compliance at moderate and lower levels of enforcement completeness. Figure 2 shows the interactions among these variables.
TABLE: TABLE 5 Conditional Mixed Process Regression Estimates
| Dependent Variables |
| Perfunctory Compliance | Consummate Compliance | Customer Satisfaction |
| Coefficient | t-Value | Coefficient | t-Value | Coefficient | t-Value |
| Intercept | 2.50 | 4.73*** | 2.28 | 3.28*** | 7.70 | .74 |
| Monitoring (MON) | -.01 | -.43 | .00 | .08 | | |
| Enforcement (ENF) | .14 | 5.25*** | .14 | 5.56*** | | |
| Monitoring completeness (MCP) | .54 | 2.87*** | .56 | 3.45*** | | |
| Enforcement completeness (ECP) | -.38 | -1.58 | -.45 | -2.10** | | |
| MON × MCP | .30 | 1.98** | .23 | 2.13** | | |
| ENF × ECP | -.18 | -1.96** | -.23 | -1.91* | | |
| Perfunctory compliance | | | | | -12.01 | -7.34*** |
| Consummate compliance | | | | | 12.50 | 4.88*** |
| Control variables | | | | | | |
| Solidarity | -.00 | -.21 | .00 | .11 | | |
| Total properties | -.06 | -1.07 | -.05 | -1.05 | | |
| Dependence | .02 | .63 | .02 | .71 | | |
| TSIs | .44 | 4.88*** | .44 | 3.79*** | | |
*p < .10.
**p < .05.
***p < .01.
Notes: Two-tailed tests of significance. Wald χ2 = 6,139.88; log-likelihood = -749.75; AIC = 1,528.90; BIC = 1,577.84.
We also tested several alternate models. First, we tested for potential direct effects of monitoring and enforcement, and monitoring completeness and enforcement completeness, on customer satisfaction by including these as additional regressors in Equation 3. The poorer fit statistics of this alternate model (Akaike information criterion [AIC] = 1,532.85; Bayesian information criterion [BIC] = 1,581.79) relative to the proposed model (AIC = 1,528.90; BIC = 1,577.84), along with the non-significance of all the additional direct effects on customer satisfaction, provide evidence that monitoring and enforcement indirectly affect customer satisfaction. Second, in line with prior research (e.g., Kashyap, Antia, and Frazier 2012; Mooi and Gilliland 2013), we also tested for potential direct effects of monitoring completeness and enforcement completeness on monitoring and enforcement, respectively. We find no evidence of any direct effect (i.e., all direct effects were non-significant), and the model exhibits poorer fit (AIC = 2,714.03; BIC = 2,762.97) than the proposed model. Finally, we tested a model with just the main effects, without the interaction terms.9 As before, the poorer fit statistics (AIC = 1,541; BIC = 1,589.94) of this alternate model relative to the proposed model (AIC = 1,528.90; BIC = 1,577.84) lead us to retain our hypothesized model.
We estimated two additional models to ensure that the relatively high correlation between enforcement and monitoring completeness (r = .78) had no undue effect on our results. The first model excludes monitoring and monitoring completeness, and the second model excludes enforcement and enforcement completeness. The results are consistent with those obtained in Equations 1 and 2.10
Our research advances the literature on interfirm governance by demonstrating novel joint effects of different ex ante (monitoring completeness and enforcement completeness) and ex post (monitoring and enforcement) governance mechanisms on channel-member compliance. We employ a contingency perspective to theorize and find that the ex ante completeness of contracts provides the context for how franchisees interpret ex post monitoring and enforcement efforts. Our research also advances two types of compliance—perfunctory and consummate—which are negatively and positively related to customer satisfaction, respectively. Next, we expand on the major implications of our research.
Governance implications. Our findings advance the study of governance in several ways. Prior research has tended to examine ex ante and ex post governance issues either separately or in direct-effects relations to each other (see Table 1). By examining the joint effects of these mechanisms, our research contributes to the literature by demonstrating that ex ante contractual completeness can affect the ability of ex post governance to drive important channel-member behaviors. Although recent research has examined the joint effects of contractual completeness (in the aggregate) and monitoring (Poppo and Zhou 2014), to our knowledge, ours is the first to examine the joint effects of both monitoring completeness and monitoring, and enforcement completeness and enforcement, at a more granular level.
Beyond these joint effects, the present research also contributes to what we know about the "syndrome" of governance (Heide 1994; Noordewier, John, and Nevin 1990; Stinchcombe 1985). Our results suggest that franchisees may evaluate ex ante contractual completeness and ex post governance efforts as constituting an overarching governance constellation (e.g., Kumar, Heide, and Wathne 2011), rather than as individual mechanisms with the potential to influence their own behavior. Responses to monitoring and enforcement efforts in channel relationships, therefore, are not devoid of the context in which these efforts are designed (Heide, Wathne, and Rokkan 2007). Accordingly, we find that the efficacy of the franchisor' s monitoring and enforcement efforts in eliciting franchisee compliance is influenced by specific contractual clauses related to monitoring and enforcement. Thus, ex post governance efforts are assessed by channel members against the backdrop in which these efforts are designed and do not exist in a "context-free vacuum" (Noordewier, John, and Nevin 1990, p. 84).
Our research also contributes to the literature by providing a more nuanced view of governance within the ex ante and ex post phases. In particular, unlike most prior research, we disaggregate contractual completeness into its monitoring and enforcement components, which provides a more refined perspective of contracts and complements recent research that has advocated a disaggregated approach to analyzing contracts (e.g., Mooi and Gilliland 2013; Reuer and Ariño 2007; Ryall and Sampson 2009). In addition, unlike most research on ex post governance (for a recent exception, see Kashyap, Antia, and Frazier 2012), we incorporate both monitoring and enforcement in the same study. By distinguishing among aspects of governance, we are able to provide a much more detailed and interrelated account of governance mechanisms than has been offered in prior research. As such, our findings lend further credence to the call for exploring "constellations of governance" (Heide, Wathne, and Rokkan 2007, p. 432) mechanisms.
The functions of contracts. Relatedly, our efforts at disaggregating contractual completeness into its monitoring and enforcement aspects lends additional evidence to the emerging view that different aspects of contracts can serve different functions (Lumineau 2014; Mayer and Argyres 2004; Ryall and Sampson 2009; Schepker et al. 2014). Consistent with this research, we suggest that monitoring clauses can help align and clarify expectations and behaviors, thereby facilitating a context of coordination (e.g., Heide, Kumar, and Wathne 2014; Reuer and Ariño 2007). A greater number of monitoring clauses, therefore, can help offset reactance stemming from monitoring (e.g., Murry and Heide 1998). Alternatively, enforcement clauses create a context of control, and, therefore, a greater number of these clauses can dampen the effects enforcement has on compliance (e.g., Antia et al. 2006). Thus, our research contributes to what is known about the dual functions of contracts, namely, coordination and control.
Compliance. Finally, our efforts further what is known about compliance in channel relationships. Compliance has traditionally been viewed as perfunctory compliance (i.e., strict adherence to policies). To our knowledge, no studies have incorporated and empirically examined both perfunctory and consummate compliance and their effects on customer satisfaction. Notably, we find that perfunctory and consummate compliance are negatively and positively related to customer satisfaction, respectively. Although prior research (Heide and Wathne 2006, p. 97) has hinted at the potential dysfunctional nature of perfunctory compliance, to our knowledge, we are the first to empirically demonstrate it in a franchise setting.
We note that our research was conducted in a franchise setting. Although this is a large and growing sector in which the use of contracts predominates, the generalizability of our findings may be somewhat limited. From a franchise management perspective, our findings have implications for both franchisors and franchisees.
Franchisor implications. Our research provides some direction to franchisors looking to get the most mileage out of their governance efforts, which can be expensive. In particular, franchisors should consider the joint effects of the monitoring and enforcement clauses in their contracts and their monitoring and enforcement efforts. Our research indicates that monitoring and enforcement clauses can create contexts of coordination and control, respectively, within which franchisees interpret their field-based monitoring and enforcement efforts. For instance, our results suggest that monitoring has a significant negative effect on compliance at low levels of monitoring completeness; conversely, enforcement has a significant positive effect on compliance at moderate and lower levels of enforcement completeness. Thus, managers should be mindful that franchisees are likely to evaluate their monitoring and enforcement efforts in the field against a backdrop of the clauses in their contracts rather than in isolation.
An important issue for managers is that "compliance" could assume the form of either perfunctory or consummate compliance, which, in our setting, affect customer satisfaction negatively and positively, respectively. Managers need to carefully consider how these two compliance types might impact important outcomes in settings other than franchising. Franchise contracts tend to be unilaterally drafted by franchisors as "form-adhesive" and are offered to franchisees on a "take-it or leave-it" basis (e.g., Eigen 2012), leaving franchisees with little choice other than "accepting" the clauses therein without necessarily internalizing them. However, in other settings, wherein exchange partners mutually design contracts, perfunctory compliance may be less harmful. It is also possible that perfunctory compliance could have beneficial effects on other important exchange outcomes, such as effectiveness and efficiency. Keeping our results in mind, managers should weigh the benefits of consummate compliance against the potential downsides of perfunctory compliance for their particular contexts.
To the extent that the "dark side" of perfunctory compliance persists in a particular context, managers are then faced with the task of reducing its detrimental effects while fostering the beneficial effects of consummate compliance. Recent findings from the academic literature and our interviews offer some guidance on this issue. For instance, franchisors who develop better relationships with their franchisees may find that, rather than having a "check the box" mentality, their franchisees may comply more enthusiastically, even with mandated contractual obligations, which should limit the negative effects of perfunctory compliance on customer satisfaction (Brown et al. 2016).
In addition, our interviewees expressed a significant amount of resistance to and frustration with the "one size fits all" nature of franchise contracts that fail to consider location and market idiosyncrasies. For instance, in one of our interviews, an owner/ operator of seven franchised pizza locations communicated his frustration thus:
In order to change hours of operation, you need to apply for the variance. Basically, you need to do a research paper on the competition, provide six months of records, caller ID logs, etc. Pulling back is a nightmare; however, staying open later is an automatic approval.
To restore a sense of autonomy and, in turn, mitigate the negative relationship between perfunctory compliance and customer satisfaction, franchisors may want to make the process of adaptation (e.g., "applying for variance") easier for their franchisees. Our interviews suggested that such "variance" is often minor and innocuous, which ensures the maintenance of important franchise standards yet can restore a sense of autonomy to franchisees that may limit the detrimental effects of perfunctory compliance on customer satisfaction.
Franchisee implications. Our research also has important implications for prospective and current franchisees. Prospective franchisees may infer whether a franchisor emphasizes a context of either coordination or control on the basis of the completeness of monitoring and enforcement clauses in the contract. Understanding each context can help prospective franchisees choose an appropriate franchise relationship suited to their individual styles and preferences. Moreover, prospective franchisees may benefit from efforts at comprehending franchisors' monitoring and enforcement efforts vis-à-vis the monitoring and enforcement clauses in the franchise contract. Such information, which can be solicited from other franchisees, franchise associations, owners' associations, and franchise advisory councils, could possibly help franchisees to infer levels and types of compliance across the franchise system, again allowing them to make more informed decisions regarding their choice of potential franchisor.
Not surprisingly, our research also shows that going the extra mile makes good business sense for existing franchisees, as opposed to strictly adhering to mandated requirements. While delivering consistent customer experiences by following contractual standards may be important to protecting the integrity of the brand, mandated compliance may ignore local market conditions. Franchisee innovations that take local market requirements into account could very well become future system standards, as is evident from our interviews. Franchisees would do well to emphasize the potential for meaningful innovation at the outlet level to their franchisors.
Our research has its strengths but also its limitations. Although we attempted to enhance the generalizability of our results with insights from multiple interviews, our focus on a single industry for our empirical study may limit the generalizability of our results. Thus, future research should attempt to account for monitoring and enforcement clauses in other types of contracts and settings, such as information technology (Mayer 2006) and buyer-supplier contracts (e.g., Wuyts and Geyskens 2005). In addition, we obtained compliance measures from franchisees, which may be susceptible to social desirability effects. Future research within a single franchise system may be able to obtain objective measures of compliance or perceptions derived from a franchisor's field representative(s). Furthermore, although a factor analysis of monitoring resulted in a single factor, considering behavior and output monitoring as distinct dimensions (e.g., Crosno and Brown 2014) may shed additional light on the joint effects of ex ante contractual completeness and ex post governance.
Beyond addressing these limitations, there are several opportunities to advance the present research. Our work presents interesting possibilities for the conceptualization of perfunctory compliance. Perfunctory compliance could include additional nuances in that it may reflect behavior done without energy or enthusiasm simply because of habit or because it is expected. Although we follow established definitions of perfunctory compliance as forwarded in prior research (e.g., Gilliland and Manning 2002; Heide and Wathne 2006), future research could adopt a more fine-grained view of perfunctory compliance by distinguishing between adherence to contractual obligations with and without enthusiasm.11
In this article, we focus on individual franchisees. Interestingly, however, from a systemwide perspective, consummate compliance has the potential to have deleterious effects on customer satisfaction. A hotel franchisee explained this further:
[Hotel brand] doesn't want you to do that [consummate compliance] because they have a standard and they feel like if you give the guest at that one property more, then they'll expect that at the next property they visit and then it's saying, "Wait a minute, I had this at this property, now I have to settle for something lower at this other property and it's the same brand." … The guest expectation is there and that sort of tends to lead to negative reviews if you now went to a great place and now all of a sudden you're going to an average place.
Thus, important future efforts may expand the scope of the present research by integrating system-based arguments with those presented here.
Our work, overall, contributes to the literature on governance by demonstrating nuanced and novel joint effects between types of governance mechanisms that firms employ to manage inter-organizational relationships and their consequences on important relationship and firm outcomes. Specifically, our finding that ex post monitoring and enforcement is contingent on ex ante contractual completeness should encourage researchers to extend our work by considering additional possibilities of combinations of governance mechanisms beyond what we consider here and in contexts other than franchising.
Both authors contributed equally to the manuscript (order was determined by a coin flip). The authors thank Kersi Antia, Ajay Kohli, Goutam Challagalla, Kenneth Wathne, Sarah Magnotta, and Tereza Dean for their helpful suggestions and feedback on earlier versions of the manuscript. The authors are also grateful for the insightful and helpful comments of the entire review team throughout the review process. Jan Heide served as area editor for this article.
A: Monitoring and Perfunctory Compliance
B: Monitoring and Consummate Compliance
C: Enforcement and Perfunctory Compliance
D: Enforcement and Consummate Compliance
Endnotes 1 Completeness is similar to incompleteness (Ghosh and John 2005), detailed contract drafting (Wuyts and Geyskens 2005), and specificity (Mooi and Ghosh 2010). For ease of exposition, we subsequently refer to ex ante monitoring completeness and ex ante enforcement completeness simply as "monitoring completeness" and "enforcement completeness," respectively. We also refer to ex post monitoring and ex post enforcement simply as "monitoring" and "enforcement," respectively.
2 Our conceptualization complements prior research that has examined direct effects between contract design and ex post governance efforts (e.g., Agrawal and Lal 1995; Kashyap, Antia, and Frazier 2012; Mooi and Gilliland 2013) by suggesting that ex ante governance mechanisms (i.e., monitoring completeness and enforcement completeness) and ex post governance efforts (i.e., monitoring and enforcement) may interact to impact compliance. Such a contingency perspective is consistent with that of Heide, Wathne, and Rokkan (2007), who examine the interactive effects of monitoring and social contracts on opportunism, and of Poppo and Zhou (2014), who examine the interactive effects of monitoring and (aggregate) contractual complexity on fairness perceptions.
3 The following examples further illustrate the contexts of coordination and control associated with monitoring completeness and enforcement completeness, respectively. Parkhe (1993, p. 813, 829) ranks the following eight contractual safeguards in increasing order of their stringency: (1) periodic written reports of all relevant transactions, (2) prompt written notice of any departures from the agreement, (3) the right to examine and audit all relevant records through a firm of CPAs, (4) designation of certain information as proprietary and subject to confidentiality provision of the contract, (5) nonuse of proprietary information even after termination of agreement, (6) termination of agreement, (7) arbitration clauses, and (8) lawsuit provisions. Corresponding factor analysis of these eight provisions by Reuer and Ariño (2007) produces two factors, which they label "coordination provisions" (consisting of provisions 1-3) and "enforcement provisions" (consisting of provisions 4-8). Malhotra and Lumineau (2011) use a similar categorization by distinguishing between "coordination provisions" (based on provisions 1 and 2) and "control provisions" (based on provisions 4, 5, 6, and 8). Similar distinctions of contractual provisions are made in terms of "monitoring" and "penalties" (Ryall and Sampson 2009). The distinctions made and the contractual provisions in prior research are consistent with the "monitoring" and "enforcement" terminology we use, as well as the provisions we coded for, in this study (see Table 2).
4 Schepker et al. (2014) make a similar distinction between perfunctory and consummate performance. Perfunctory performance reflects "outcomes within the contract: enforceable by a court of law," whereas consummate performance reflects "outcomes outside the contract: beyond what the minimum of the contract requires" (p. 197, emphasis in original). Note that we conceptualize perfunctory and consummate compliance vis-à-vis franchise contracts. Therefore, perfunctory and consummate compliance differ slightly from the concepts of in-role and extrarole behaviors (and organization citizenship behaviors), respectively. In-role behaviors often involve aspects of performance (e.g., MacKenzie, Podsakoff, and Ahearne 1998), whereas perfunctory compliance, in our research, does not include performance. Furthermore, unlike extrarole behaviors (e.g., Wuyts 2007) and organizational citizenship behaviors that often incorporate a greater range of behaviors, such as extrarole performance (MacKenzie, Podsakoff, and Ahearne 1998), sportsmanship, civic virtue, helping, stewardship, and voice (e.g., Podsakoff and MacKenzie 1994), our conceptualization of consummate compliance is narrower in that it does not include these behaviors. We thank an anonymous reviewer for raising this issue.
5 We conceptualize enforcement in general through an ongoing relationship. Thus, enforcement can affect compliance, which is consistent with the work of Kashyap, Antia, and Frazier (2012). However, we acknowledge that enforcement may also be in response to specific violations (noncompliance) (Mooi and Gilliland 2013). We thank an anonymous reviewer for this comment.
6 To allow for the possibility that hotel owners might not be involved in the day-to-day operations of their hotels, we obtained additional archival data about passive ownership for 75 franchise systems in different industry formats from their franchise disclosure documents. The data cover 16 years (1993-2009). An analysis of these data reveals that of the 75 franchise systems, only 2 have allowed passive ownership outright, with the rest of franchisors either actively forbidding passive ownership or discouraging it. Moreover, none of the firms in these data that allow passive ownership are hotel franchisors, indicating that passive ownership is less common than might be expected. The president of the board of directors of the franchise organization from which we collected our data further validated these results by confirming that most of the members in the organization were involved in the day-to-day operations of the hotel. We thank an anonymous reviewer for raising this issue.
7 We thank an anonymous reviewer for suggestions about endogeneity.
8 The items and measurement properties for the scales for norm of restraint, enforcement forbearance, and opportunism are available from the authors on request.
9 We thank an anonymous reviewer for this suggestion.
The results of the alternate CMP regression models are available from the authors on request.
We thank an anonymous reviewer for this suggestion.
GRAPH: FIGURE 2 Graphical Presentation of Interactions
DIAGRAM: FIGURE 1 Conceptual Framework
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TABLE: TABLE A1 Archival Measures
| Measure | Source | Description |
| Monitoring completeness | Studies including Anderson and Dekker (2005); Kashyap, Antia, and Frazier (2012) | Natural logarithm of the count of contractual clauses pertaining to the franchisor's rights to meter behavior and output |
| Enforcement completeness | Studies including Anderson and Dekker (2005) | Natural logarithm of the count of clauses pertaining to the franchisor's rights to discipline contractual violations |
| Customer satisfaction | Medallia Inc. scores | Composite index of guest-reported scores on overall customer experience, overall accommodation quality, and overall quality of hotel rooms, collected through a guest satisfaction system and reported on a scale of 1 to 10 |
TABLE: TABLE A2 Measures
| Measures and Items | Standardized Loadings |
| Monitoringa | |
| Our franchisor frequently meets with us to review our business practices. | .84 |
| Our franchisor regularly inspects our facilities. | .57 |
| Our franchisor regularly monitors the way we work. | .64 |
| Our franchisor frequently meets with us to review our sales results. | .85 |
| Our franchisor closely monitors our sales growth numbers. | .66 |
| Enforcementb | |
| Our franchisor takes tough measures when its contract is violated. | .82 |
| Our franchisor responds firmly to contractual violations. | .71 |
| Our franchisor takes strict disciplinary action against franchisees that violate their contracts. | .85 |
| Our franchisor takes stern punitive actions against franchisees that violate their franchise agreements. | .85 |
| Our franchisor is uncompromising when it comes to contractual breach. | .90 |
| Our franchisor imposes the highest levels of sanctions after contractual violations. | .96 |
| Our franchisor takes severe action after its contract is violated. | .89 |
| Perfunctory Compliancec | |
| We do what is required in the franchisee contract. | .84 |
| We act in accordance with our formally prescribed role requirements. | .85 |
| We fulfill the duties outlined in the franchise contract. | .96 |
| Consummate Complianced | |
| We work beyond the minimum requirements outlined in the franchisee contract. | .92 |
| We take the initiative to go beyond the minimum standards for excellence outlined in the franchisee contract. | .96 |
| We voluntarily exert effort that is not prescribed by the franchisee contract's formal requirements. | .84 |
aα = .86; CR = .84; AVE = .52; sources: Heide, Wathne, and Rokkan (2007); Niehoff and Moorman (1993).
bα = .95; CR = .95; AVE = .74; source: Antia and Frazier (2001).
cα = .91; CR = .92; AVE = .78.
dα = .93; CR = .94; AVE = .83.
Notes: All items were measured on a five-point scale (1 = "strongly disagree," and 5 = "strongly agree").
TABLE: TABLE A3 Control Variables
| Measures and Items | Standardized Loading |
| Dependencea | |
| Our franchisor is dependent on us. | .60 |
| Our franchisor would find it difficult to replace us. | .85 |
| Our franchisor would find it costly to lose us. | .91 |
| TSIsb | |
| We have made significant investments in equipment dedicated to this franchise. | .83 |
| Training our people to deal with this franchise has involved substantial commitment of time and money. | .80 |
| Our systems have been tailored to meet the requirements of dealing with this franchise. | .60 |
| Our procedures and routines are tailored for this particular franchise. | .89 |
| If we lost our franchise, we would have to write-off substantial investments. | .52 |
| Norm of Solidarityc | |
| Both parties in this relationship do not mind owing each other favors. | .70 |
| Problems that arise in the course of this relationship are treated by both parties as joint rather than individual responsibilities. | .84 |
| Both parties are committed to improvements that may benefit the relationship as a whole, and not only the individual parties. | .95 |
| The responsibility for making sure that the relationship works for both parties is shared jointly. | .92 |
| Number of total properties of this franchisor owned by the franchisee | — |
aα = .83; CR = .84; AVE = .64; source: e.g., Lusch and Brown (1996).
bα= .84; CR = .86; AVE = .55; source: e.g., Kumar, Heide, and Wathne (2011).
cα = .92; CR = .92; AVE = .74; source: e.g., Kumar, Heide, and Wathne (2011).
Notes: All items were measured on a five-point scale (1 = "strongly disagree," and 5 = "strongly agree").
~~~~~~~~
Vishal Kashyap is Professor of Marketing, University of Graz.
Brian R. Murtha is Associate Professor of Marketing and E. Vernon & William Smith Endowed Fellow, Gatton College of Business & Economics, University of Kentucky.
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Record: 192- The Long Reach of Sponsorship: How Fan Isolation and Identification Jointly Shape Sponsorship Performance. By: Mazodier, Marc; Henderson, Conor M.; Beck, Joshua T. Journal of Marketing. Nov2018, Vol. 82 Issue 6, p28-48. 21p. 4 Color Photographs, 1 Diagram, 3 Charts, 3 Graphs. DOI: 10.1177/0022242918807673.
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The Long Reach of Sponsorship: How Fan Isolation and Identification Jointly Shape Sponsorship Performance
Globalization and technology have expanded the reach of sports teams, giving brand sponsors new opportunities to engage and build relationships in real time with fans outside a team's home market. This research investigates the role of fan isolation, or the experience of feeling separated from the team community, in shaping sponsorship effectiveness. The authors posit that such isolation increases the desire to affiliate with the team community, which can increase preferences for team-linked brands. However, the effect of isolation on sponsor performance depends on the strength of fan identification. Isolation increases strong fans' desire to affiliate with the team community, thereby enhancing sponsorship performance; by contrast, isolation causes weak fans to avoid team-linked brands. Two field studies and four quasi experiments conducted across three countries (N = 1,412) confirm these predictions. Isolated strong fans exhibit increased recall, attitudes, purchase intentions, and word of mouth for sponsors, while isolated weak fans display the opposite effects. For brand managers, the proposed framework reveals whether isolated fans provide the best or worst returns on their sponsorships.
Keywords: brand performance; identification; isolation; sponsorship
Many fans live or spend time outside their favorite team's home market ([10]; [15]). In the United States, out-of-market fans can account for the bulk of nonticket revenue ([17]). Brand sponsors are beginning to recognize these fans when drafting their sponsorship initiatives. For example, Chevrolet's $560 million sponsorship of the British soccer team Manchester United specifically targeted team fans who live in other countries ([ 3]). Indeed, as much as 80% of Premier League fans, such as those of Manchester United, live outside the United Kingdom ([48]). These examples highlight the market potential of out-of-market sponsorship. Thus, the purpose of this research is to understand how the experience of out-of-market fans shapes sponsorship performance to determine which fans provide the best return on sponsorship.
Brand sponsorship aims to leverage fans' relationship with the sponsored team to drive customers through the purchase funnel from brand awareness to consideration, attitudes, purchase, and word-of-mouth (WOM) ([12]). However, industry experts estimate that as much as one-third of sponsorship budgets are wasted ([27]). For example, only 50% of television viewers can recall a sponsor of the National Football League (NFL) ([54]), and even fans who regularly attend games are unable to recall most brand sponsors ([60]).
The present research adopts a fresh perspective on sponsorship by positing that sponsorship effectiveness depends partly on where strong (vs. weak) fans are situated relative to other fans. This perspective is increasingly relevant as advances in location-based advertising platforms coupled with a more globally distributed media environment allow managers to target audiences according to the combination of their real-time location and personal interests; this raises "the issue of getting marketing 'right' in real time" (Marketing Science [36], p. 6), especially for personal interest–based sponsorships.
Drawing on theories of identity and affiliation ([16]; [40]), we predict that fan isolation, or the experience of feeling separated from the team community, generates an affiliation motive that triggers different responses, depending on the level of fan identification. In isolating contexts, strong fans should pursue team-based affiliation, whereas weak fans should pursue alternative means of affiliation with others in their immediate social environment. These distinct affiliation strategies result in isolated strong fans being more receptive to brands that support the team ("doubling-down" effects of enhanced recall, attitudes, purchase intentions, and WOM) and isolated weak fans avoiding brands that support the team ("desertion" effects).
The objective of this research is to investigate the interplay of fan isolation and identification in the sponsorship domain using a multimethod approach across three countries and three sports. With natural variance from field surveys of actual fans, Studies 1a and 1b demonstrate that brand sponsorship performance depends on the interaction between the level of fan isolation and identification. In a survey of Premier League fans, Study 1a finds that the accurate recall rate of a team's brand sponsor is greater among more (vs. less) isolated strong fans (68% vs. 50%) but lesser among more (vs. less) isolated weak fans (31% vs. 37%).[ 6] Study 1b shows a similar pattern of effects on performance among Lakers' fans; among strong fans, isolation results in a 22% increase in both brand sponsor purchase intentions and WOM, while among weak fans, isolation results in an 8% decline in purchase intentions and a 9% decline in WOM. Thus, in natural settings, fan identification polarizes the effects of isolation on sponsorship performance.
Through quasi experiments in Studies 2 and 3, we manipulate the feeling of isolation to validate the real-world findings from Study 1. Study 2a also demonstrates a key role of sponsorship, finding no effects on performance when the brand is not presented as a team sponsor. Offering a key managerial insight, Study 2b further reveals that just one exposure to the brand sponsorship for isolated strong fans is as effective as multiple exposures to the brand sponsor for all fans. Study 3 replicates these effects and confirms the key mediating role of affiliation. That is, isolation increases affiliation motives for all fans, but the effects of affiliation on brand sponsor performance vary by fan identification, such that affiliation increases attitudes toward a brand sponsor among strong fans but decreases it among weak fans. Furthermore, this pattern of effects extends to fans' choice of gift card. Finally, in the "Managerial Implementation" section, we demonstrate how a brand manager can use the proposed framework in a targeted Facebook campaign.
Together, these investigations provide three primary contributions to marketing theory and practice. First, the findings provide insights to brand managers wanting to reach mass-market, global consumers through sports sponsorships. Sports are unique in their broad appeal to live audiences ([59]), leading to substantial increases in spending on sports sponsorships (annual growth of 4.3% vs. 2.6% for general brand advertising) and predictions that these will soon exceed $16 billion annually in North America alone ([26]). Yet investors remain skeptical; many sponsorship announcements lead to negative abnormal stock returns ([38]). Our framework reveals which fans are more receptive to brand sponsorships; that is, strong fans who are relatively isolated, either permanently or temporarily, provide the best returns on sponsorships. These findings support the sports industry shift to out-of-market sponsorship.
Second, extant theories of consumer affiliation suggest that isolation increases people's desire to connect with in-group (e.g., team-sponsoring) brands ([30]). Our research demonstrates a critical role of identification in shaping these responses to isolation. Thus, whereas prior work reveals key differences between in-group and out-group influences ([ 6]; [62]), we identify the considerable variation across in-group members in their pursuit of group-linked brands, explained by their level of identification. While this research is situated in sports, we expect the constructs and theoretical relationships to extend to any consumer community.
Third, this work complements growing research on the role of geographic proximity in determining marketing outcomes ([ 1]; [41]). Across the studies, we examine the pivotal impact of fan isolation, from geographic displacement or social experience, on sponsorship effectiveness.
This work bridges the research streams of fan isolation and fan identification. Importantly, this research aims to advance the sponsorship strategy literature by understanding the joint effects of fan isolation and identification on sponsorship performance at the individual fan level. We first briefly review sponsorship as a key marketing strategy and then discuss foundational research in each stream and the contribution of the current research.
Sponsorship is an investment, in cash or kind, in an event, person, or idea, and sponsorship marketing refers to the organization and implementation of marketing activities to build and communicate an association with a sponsored entity ([12]; [37]). A large body of sponsorship research provides a foundation for crafting an effective sponsorship strategy. By leveraging fans' relationships with sponsored entities (e.g., teams), sponsorship generates brand awareness through brand exposure, establishes a brand–team connection, and facilitates the transfer of attitudes and associations (e.g., image) from the team to the brand with little cognitive mediation ([12]; [22]; [37]). Sponsor–team fit (i.e., congruity) facilitates these processes ([11]; [53]). Variables contributing to fans' positive view of the sponsored entity, such as event involvement and self-congruity with the event, also predict enhanced sponsorship performance ([37]; [61]). In addition to fit-and-transfer models, an emerging line of research situated in attribution theory shows the importance of inference making about the sponsorship itself. Fans infer motives for the sponsorship based on observable features, such as whether a brand's headquarters are located near the team, and fans favor sponsorships that appear more authentic ([53]; [63]).
Critically, although prior sponsorship research has examined sponsorship performance in relation to situational variables (e.g., geographic location; [49]) and individual variables (e.g., fan identification; [20]; [34]), little is known about how these variables may interact, though recent work suggests an interaction; for example, [52] find that fan identification is more positively related to liking and recommending a cycling event among fans who traveled farther to attend the event. We posit that this effect likely stems from the increased affiliation motives of isolated strong fans. Next, we review these separate research streams and then develop a theoretical model for how the two constructs interact to determine sponsorship effectiveness.
Isolation refers to the psychological separation of the self from others ([ 8]), and we define "fan isolation" as a fan's feeling of separation from the team community. We use this term to capture the underlying psychological state that can emanate from social situations that cause fans to feel separated from their team communities (i.e., teams and other fans). For example, prior work has examined how geographically distant fans (e.g., Chinese residents who are National Basketball Association [NBA] fans) feel a sense of separation and also vicarious achievement when watching NBA games, despite having never lived in the United States ([49]). Relatedly, work shows how displaced fans, or those who have moved away from their team community and now reside in an out-of-market location, feel a sense of isolation that causes them to engage more in online team-based communities ([56]). Fans may also feel a sense of temporary separation from the team community when its focus is on rivals. A person can be physically close to others but psychological distant, due to differences in group membership ([18]). Previous work shows that merely thinking about rivals can evoke a sense of separation, which can result in motives such as holding more favorable attitudes toward the in-group fan community and derogating out-group (rival) brand sponsors ([ 7]; [21]). Thus, fan isolation, which occurs for a variety of reasons, can increase people's desires to connect with the fan community.
The concept of identification is rooted in [58] model of in-group membership, which explains the process of self-categorization in a group and the emotional significance of sharing in that group's experiences. In this research, we broadly define "fan identification" as the extent to which a person self-categorizes as a fan of a given team and his or her generalized sense of emotional significance and symbolic meaning derived from belonging to the team's fan community ([ 9]; [19]; [35]). Identification does not require physical presence; a team community can be imagined, similar to the concepts of "imagined community" and "imagined collective" ([51]).
Even within a particular team, some fans identify more intensely than others; this difference is captured by the level or strength of identification, which refers to the chronic sense of connection one experiences with a given social group ([ 9]; [13]; [18]). Notably, identification is not binary (fan vs. nonfan) but instead falls along a continuum (nonfan vs. fan identification levels). We examine the differences in the level of identification among people who indicate they are fans of a given team. For simplicity, we use strong and weak fans as shorthand to differentiate between fans with more and less intense levels of identification, respectively.
The variance in fan identification can explain fan behavior over time. Both strong and weak fans may enjoy the benefits of their fan identity (e.g., easy conversation starter, source of entertainment), but weak fans likely find it easier to avoid thinking about the team when they are among a nonfan out-group. Strong fans are prouder of the team's achievements and, as a consequence, are more reliable advocates and consumers of team-related experiences and memorabilia ([14]). Fan identification can be a source of personal self-esteem, as strong fans derive more personal validation from team achievements ([25]). Conversely, weak or "fair-weather" fans do not regard the team as much of a central or enduring aspect of their self-concept and are less likely to attend games when the team has a losing record ([42]).
Fan identification is a critical determinant of sponsorship performance. For example, strong (vs. weak) fans are more likely to purchase sponsor brands, especially when such purchasing is the norm in the team's fan community ([34]). Strong fans are also more likely to see their team's image in the brand sponsor, by way of image transfer ([12]; [20]), and will purchase sponsor brands as a way of demonstrating commitment to the team community ([31]). Together, this research supports the notion that strong fans use brand sponsors as a means to build a sense of connection with the team. Thus, central to our research aim, it is important to understand how strong (vs. weak) fans react to isolation.
How do fan isolation and identification jointly affect sponsorship effectiveness? In this section, we build a contingent affiliation model for sponsorship to understand the combined effects of these key constructs. The two streams of research presented previously imply that the positive effects of fan isolation and identification are additive. Conversely, we propose that the joint effects are multiplicative and that this interactive effect stems from fans' differential responses to isolation.
Fans of a given team will find themselves, at some point in time, in isolating social contexts, whether from the presence of rival fans or the absence of in-group fans when the focal fans are temporarily or permanently outside the team's home market. In such situations, belongingness theory predicts that the sense of isolation will trigger an increased desire to affiliate ([ 2]; [39]). For example, isolated fans might turn to online communities to satisfy their affiliation motives ([56]). In addition, fan isolation might encourage positive attention to brand sponsors. As prior work has demonstrated, isolation increases the pursuit of nonhuman (e.g., brand) relationships ([30]), and sponsor brands are prime relational targets because they represent a meaningful connection with the team community ([24]). Consider, for example, a strong fan of the Los Angeles Lakers who lives in Chicago. When presented with an ad for Wish (a retail brand sponsoring the Lakers), this fan may embrace the brand to satisfy a desire to affiliate with the team community. Fans living in Los Angeles, surrounded by other fans, temper one another's desire for affiliation. Thus, fan isolation should increase the desire to affiliate, which may lead to improved sponsorship performance. We argue, however, that chronic differences in fan identification determine reactions to an increased desire to affiliate.
Fan response to isolation likely varies on the level of fan identification. That is, across all fans, isolation should increase affiliation motives. Two paths are available to satisfy these enhanced affiliation motives: ( 1) bolstering fan identity (doubling-down effect), which would entail actively seeking a connection with the team community, and ( 2) suppressing fan identity (desertion effect), which would entail actively avoiding team-linked content, which in turn frees fans to affiliate with proximal others in ways unrelated to their preferred sports team. We predict that fans' level or strength of identification determines which pathway they pursue.
[44] defines strong identifiers as schematic; group membership is integral to their sense of self. Thus, bolstering identification should help them overcome social isolation ([47]). We predict that fan isolation increases the desire of strong fans to affiliate with the team community and therefore increases their receptiveness to team-linked content, as suggested by belongingness theory. By contrast, weak identifiers are aschematic; group membership is not integral to their sense of self and is merely a fact. We argue that weak fans will feel a sense of separation from their home community when isolated but will be able to suppress their fan identity and choose not to satisfy the desire to affiliate with the team. Weak fans will view the team as an obstacle to building relationships with proximal others.
In support of this theory, research in cultural identity domains indicates that the strength of the home culture identity determines whether a person bolsters or avoids activities and groups related to that home culture. Strong identifiers pursue home culture–related activities, while weak identifiers actively avoid such activities and find other means of affiliating ([16]). Similarly, we expect that strong fans in isolating contexts pursue brands linked to their team (i.e., brand sponsors), while weak fans in isolating contexts try to avoid them. This predicted fan isolation × identification interaction effect on sponsor effectiveness stems from an affiliation-based process. Thus, we hypothesize the following:
- H1: There is a fan isolation × identification interaction effect on sponsor effectiveness, such that (a) for strong fans, isolation enhances brand sponsor performance (recall, attitude, purchase intention, and WOM) while (b) for weak fans, isolation reduces brand sponsor performance.
- H2: Affiliation motives mediate the fan isolation × identification interaction effect on sponsor performance.
Three sets of studies test our hypotheses. Studies 1a and 1b examine the prediction that isolation yields differential effects on sponsorship performance as a function of fan identification, using field data from Premier League (Study 1a) and NBA (Study 1b) fans. Study 2a employs experimental methods to confirm the causal role of isolation in producing the focal interaction when a brand advertisement features the sponsorship but not when the sponsorship information is absent. Study 2b replicates these effects and tests whether multiple exposures to the advertisement with sponsorship improve performance for all fans (i.e., boundary condition). In Study 3, we test whether desire to affiliate mediates the fan isolation × identification interaction effect on sponsor brand performance. We repeat Study 3 with another sponsor and show that isolation almost doubles the percentage of strong fans who choose a gift card for the sponsor brand over its competitor. Figure 1 provides an overview of our conceptual model, as well as the operationalization of focal constructs and key performance outcomes.
Graph: Figure 1. Conceptual model and operationalization of key constructs across studies.
Study 1 tests the central prediction that fans' reception to a brand sponsor will vary as a function of fan isolation and identification. Across two contexts, we observe the level of fans' isolation and identification and test sponsor performance as their recall of their team's main sponsor (Study 1a: fans of Premier League teams) or their recall, purchase intentions, and WOM of their team's main sponsor (Study 1b: fans of the Los Angeles Lakers).
Three hundred eighty-nine fans of a Premier League team in the United Kingdom (43.4% female, average age 43 years) completed an online survey after being invited to participate by a marketing research firm that we hired. The panel company recruited only football fans. The fans were naturally dispersed geographically, such that some fans lived near many other fans of the same team and others did not. Respondents selected their favorite Premier League team and then completed measures of isolation and identification. As the focal dependent variable, respondents were asked to recall the primary brand sponsor of their team, which prominently appears on the athletes' jerseys. Finally, respondents provided demographic information.
Fans selected their favorite team, and then we assessed the level of fan identification with [ 9] scale, which we adapted to reflect fan identity. Respondents indicated their level of agreement with 12 items on a seven-point scale (1 = "strongly disagree," and 7 = "strongly agree"; α =.82): "I have a lot in common with other [team name] fans"; "I feel strong ties to other [team name] fans"; "I find it difficult to form a bond with other [team name] fans" (reversed); "I don't feel a sense of being 'connected' with other [team name] fans" (reversed); "I often think about being a [team name] fan"; "Overall, being a [team name] fan has very little to do with how I feel about myself" (reversed); "In general, being a [team name] fan is an important part of my self-image"; "The fact that I am a [team name] fan rarely enters my mind" (reversed); "In general, I am glad to be a [team name] fan"; "I often regret that I am a [team name] fan" (reversed); "I don't feel good about being a [team name] fan" (reversed); and "Generally, I feel good when I think about myself as a [team name] fan."
Respondents answered the question, "What is the jersey sponsor of the [team name] football club (i.e., the brand displayed on the football shirt like Pirelli for the Inter Milan football club)?" We coded the responses as accurate recall ( 1) or inaccurate (0). Recall followed the measurement of fan identification, to ensure that a recall failure did not influence self-reported identification.
Respondents indicated their city of residence. Next, they were instructed to "think about your local geographic community and the people you live with in [city of residence]." We measured isolation with a five-item seven-point scale (1 = "strongly disagree," and 7 = "strongly agree"; α =.78): "My community supports the [team] (reversed)," "My community supports a different team," "My community mainly ignores the [team]," "My community dislikes the [team]," and "The average citizen of [city of residence] is very different from the [team's] community of supporters." Table 1 reports the descriptive statistics and correlations.
Graph
Table 1. Study 1a and 1b Descriptive Statistics and Correlations.
| Study 1a: British Premier League |
|---|
| Variables | M | SD | α | 1. | 2. | 3. | 4. | 5. | 6. | 7. | | | |
|---|
| 1. | Sponsor Brand Recall | .61 | .49 | | 1 | | | | | | | | | |
| 2. | Identification | 4.49 | .89 | .82 | .17 | 1 | | | | | | | | |
| 3. | Isolation | 4.26 | 1.16 | .78 | .00 | −.27 | 1 | | | | | | | |
| 4. | Age | 42.66 | 13.6 | | −.04 | .02 | .03 | 1 | | | | | | |
| 5. | Gender (Female) | .43 | .50 | | −.11 | −.17 | −.07 | −.06 | 1 | | | | | |
| 6. | Education | 3.37 | 1.21 | | .05 | −.18 | .03 | −.17 | .09 | 1 | | | | |
| 7. | Length of Residence | 24.87 | 17.5 | | .05 | .10 | −.15 | .46 | .01 | −.18 | 1 | | | |
| Study 1b: Los Angeles Lakers |
| Variables | M | SD | α | 1. | 2. | 3. | 4. | 5. | 6. | 7. | 8. | 9. | 10. |
| 1. | Sponsor Brand Recall | .40 | .49 | | 1 | | | | | | | | | |
| 2. | Sponsor Brand Purchase Intentions | 4.58 | 1.66 | | .23 | 1 | | | | | | | | |
| 3. | Sponsor Brand Word of Mouth | 4.51 | 1.60 | | .23 | .96 | 1 | | | | | | | |
| 4. | Identification | 4.993 | .89 | .82 | .01 | .29 | .26 | 1 | | | | | | |
| 5. | Isolation | 3.46 | 1.28 | .82 | .14 | .02 | .01 | −.35 | 1 | | | | | |
| 6. | Age | 42.23 | 13.82 | | −.15 | −.18 | −.14 | −.11 | −.17 | 1 | | | | |
| 7. | Gender (Female) | 0.412 | 0.5 | | −.02 | −.04 | −.05 | −.11 | .11 | −.34 | 1 | | | |
| 8. | Education | 3.42 | .93 | | .06 | .00 | .00 | .01 | −.07 | .18 | −.22 | 1 | | |
| 9. | Length of Residence | 22.44 | 15.56 | | .00 | .03 | .04 | −.07 | −.14 | .43 | −.16 | −.05 | 1 | |
| 10. | Purchase Intentions Competitors | 5.189 | 1.1 | | .01 | .49 | .52 | .22 | −.07 | −.14 | .02 | .05 | −.07 | 1 |
| 11 | Word of Mouth Competitors | 5.113 | 1.13 | | .00 | .51 | .54 | .29 | −.10 | −.13 | .00 | .02 | −.08 | .92 |
10022242918807672 Notes: M is mean, SD is standard deviation, and α is Cronbach's alpha. Significant correlations (p <.05) are in bold font.
To understand how fan isolation and identification jointly shape sponsor brand performance, we submitted sponsor brand recall (1/0) to a logistic regression with isolation, identification, and their interaction as predictors (see Table 2). Beyond fan isolation and identification, many other factors related to each team, each fan, and each sponsor can affect a fan's ability to recall the current sponsor of a favorite team. We explicitly control for unobserved factors that might correlate with each team–brand combination (through team fixed effects) and personal characteristics of age, gender, education, and length of residence in the community ([61]). The results remain stable and significant whether we control for these factors or not.
Graph
Table 2. Study 1 Estimations Results: Sponsorship Performance for Premier League (Study 1a) and the Lakers (Study 1b).
| | Study 1a | | Study 1b |
|---|
| | Brand Recall | | Brand Recall | | Purchase Intentions | | Word of Mouth |
|---|
| | Estimate | p-Value | | Estimate | p-Value | | Estimate | p-Value | | Estimate | p-Value |
|---|
| Primary Predictors | | | | | | | | | | | | | | | |
| Identification | .57 | (.15) | <.001 | | .13 | (.23) | .571 | | .43 | (.15) | .005 | | .28 | (.14) | .050 |
| Isolation | .32 | (.14) | .025 | | .53 | (.21) | .013 | | .38 | (.13) | .003 | | .38 | (.12) | .002 |
| Identification × Isolation | .24 | (.11) | .020 | | .38 | (.18) | .030 | | .29 | (.10) | .006 | | .30 | (.10) | .003 |
| Controls | | | | | | | | | | | | | | | |
| Age | −.01 | (.01) | .168 | | −.04 | (.02) | .022 | | −.02 | (.01) | .031 | | −.02 | (.01) | .077 |
| Gender (Female) | −.38 | (.24) | .112 | | −.27 | (.41) | .509 | | −.23 | (.26) | .373 | | −.21 | (.25) | .388 |
| Education | .20 | (.10) | .046 | | .29 | (.21) | .169 | | .04 | (.13) | .790 | | .05 | (.13) | .708 |
| Length of Residence | .02 | (.01) | .023 | | .01 | (.01) | .269 | | .02 | (.01) | .060 | | .02 | (.01) | .052 |
| Ratings of Competitor Brands | | | | | | | | | .73 | (.12) | <.001 | | .78 | (.11) | <.001 |
| Team Fixed Effects | Included | | | | | | |
| Intercept | −.64 | (.96) | .507 | | .27 | (.97) | .782 | | 1.86 | (.89) | .039 | | 1.21 | (.83) | .149 |
| Model Fit | | | | | | | | | | | | | | | |
| R-square | | | | | | | | | .34 | | .39 |
| Deviance (−2 log likelihood) | 453.9 | | 170.0 | | 462.1 | | 446.4 |
| AIC | 507.9 | | 186.0 | | 482.14 | | 466.4 |
| BIC | 614.9 | | 209.3 | | 511.27 | | 495.6 |
| Likelihood ratio chi-square test | 65.45 (df = 26) | | 13.57 (df = 7) | | 60.33 (df = 8) | | 66.51 (df = 8) |
| | <.001 | | .059 | | <.001 | | <.001 |
In support of H1, the key interaction between isolation and identification was positive and significant (b =.24, p <.05). We probed the interaction using a floodlight analysis ([55]) of the simple effect of fan isolation at varying levels of identification. As the results indicate, fan isolation had a positive and significant (p <.05) effect on recall for fans in the top 22% of our sample in terms of identification (Johnson–Neyman [JN] point at identification scores equal to or greater than 5.09). For the weakest 1% of fans in our sample, evidence at the 90% confidence interval (CI) shows that fan isolation was associated with worse recall of the sponsor (JN point = 2.27). Figure 2 displays these estimated effects.
Graph: Figure 2. Study 1: Estimated effects of fan identification × isolation on sponsor performance. Notes: 1.8 and 1.9 were the minimum observed value for fan identification in the Study 1a and 1b samples. Shading represents regions at which effect of isolation is significant at 90% (light gray) and 95% (dark gray) confidence levels.
In Study 1b, we surveyed fans of the Los Angeles Lakers to ( 1) conceptually replicate Study 1a's results, ( 2) examine additional sponsorship performance outcomes, ( 3) hold the brand sponsor constant, and ( 4) implement a recontact method to insert temporal distance between measurement of fan identification, isolation, and our focal dependent variables. At the time of the survey, the Lakers had recently added a sponsor brand (e-commerce retailer Wish) to the team's uniforms. We examine the effectiveness of this sponsorship halfway through the 2017–2018 season, the first season in which NBA jersey sponsorship was permitted.
The marketing research firm from Study 1a conducted a two-wave online survey of Los Angeles Lakers fans. The firm aimed to recruit half the sample from within the Los Angeles metropolitan area and half from outside it. Respondents were screened using three questions: "Are you interested in the NBA?" "Please, select your favorite NBA team"; and "From the following list, who is your all-time favorite Los Angeles Lakers' player?" Respondents were invited to complete the entire survey if they selected "yes," "Los Angeles Lakers," and a player who actually played for the Lakers. In the first wave, 291 fans completed the same fan isolation (α =.84) and identification (α =.83) measures as in Study 1a. As an alternative indicator of identification, they also completed a single-item, image-based pictograph measure consisting of a series of increasingly overlapping circles representing the fan and the team ([ 5]).
One week later, the marketing research firm recontacted respondents to invite them to complete a seemingly unrelated survey on e-commerce brands. One hundred thirty-six respondents completed this second wave of the survey (41.2% women, average age 42 years, 40.4% living outside the Los Angeles metropolitan area). Respondents completed measures of purchase intention and WOM for four e-commerce brands: Wish (Lakers' sponsor), Rakuten, Amazon.com, and eBay. They rated purchase intentions and WOM using single items ("I will purchase products from [brand]" and "I will recommend [brand] to my friends to buy products online") on seven-point scales (1 = "strongly disagree," and 7 = "strongly agree"). Then, they were asked to recall the jersey sponsor for the Los Angeles Lakers, which provided a measure of sponsor brand recall, which we coded as a dichotomous outcome (1 = accurate recall, 0 = inaccurate or not recall). Finally, respondents provided demographic information. Table 1 reports the descriptive statistics and correlations.
For the sponsorship performance outcomes of sponsor brand purchase intentions and WOM, we conducted ordinary least squares regressions with isolation, identification, and their interaction as predictors. We also controlled for purchase intention and WOM for the other e-commerce companies (Rakuten, Amazon, and eBay) and gender, age, and education to account for baseline variance in consumers' attitudes toward and familiarity and comfort with e-commerce. Consistent with the pattern of results from Study 1a (for model estimates, see Table 2), the key interaction between isolation and identification was positive and significant for purchase intentions (b =.29, p <.01) and WOM (b =.30, p <.01). Floodlight analysis revealed that fan isolation had a significant, positive effect (p <.05) on purchase intention and WOM among strong fans at the top 38% of the sample (JN point = 5.25). Conversely, for relatively weak fans, fan isolation corresponded to a marginally significant, negative effect (p <.10) on sponsor purchase intention (JN point = 3.36; bottom 2% of sample) and a significant, negative effect (p <.05) on WOM (JN point = 3.22).
As in Study 1a, we used logistic regression to analyze the interactive effects of fan isolation and identification on sponsor brand recall. The interaction between fan isolation and identification was positive and significant (b =.38, p <.05). Floodlight analysis revealed that fan isolation had a significant, positive effect (p <.05) on sponsor brand recall among strong fans at the top 33% of the sample (JN point = 5.35).
We repeated each analysis using the scores for identification from the image-based pictograph measure of identification ([ 5]), which correlated with our main identification scale at.48 (p <.001). The results were stable; the key interaction between isolation and identification was positive and significant for sponsor brand recall (b =.19, p <.05), purchase intentions (b =.11, p <.05), and WOM (b =.08, p <.10).
Studies 1a and 1b provide real-world, managerially relevant evidence that isolated strong fans respond most favorably to brand sponsors. Among Premier League fans, the model estimates that unaided sponsor brand recall among the strongest fans (identification = 7) is 28 percentage points higher in more isolated than less isolated contexts (isolation ±1 standard deviation). Similarly, among the strongest Lakers fans, purchase intentions are 39% higher and WOM is 43% higher in more than less isolated contexts.
As a caveat, these results rely on naturally occurring variance, which reduces confidence in the causal role of isolation in sponsorship performance. Furthermore, the finding that isolation evokes worse recall, purchase intentions, and WOM among the weakest identifiers could be due to their fewer exposures to the sponsor's logo on team jerseys; that is, an isolated fan is less likely to encounter other fans wearing the team's jersey in public. To increase confidence in the causal role of fan isolation and explicitly vary exposures to sponsor-based advertising, we conduct quasi experiments that hold the team and brand constant while manipulating fan isolation and the number of exposures to a sponsor-linked advertisement.
With the experimental design in Study 2, our goal is to confirm the key fan isolation × identification interaction across several managerially relevant advertising conditions. In Study 2a, we examine performance as unaided recall for a brand after just one exposure to a brand advertisement in which the brand is (vs. is not) presented as a sponsor. The key interaction should hold only in the sponsorship condition. In Study 2b, we test this same single-exposure condition against an alternative condition with two exposures to the sponsorship advertisement. If the fan isolation × identification interaction operates through selective interest in the sponsor brand, emanating from affiliation motives, the interaction should be present with a single exposure but overrun in the two-exposure condition. Testing these effects also enables us to provide managerial insights into the efficacy of brand sponsorship promotions across single and repeat exposures.
Both Studies 2a and 2b followed an identical procedure. First, 288 fans of Paris Saint Germain (PSG), a professional soccer team based in France, completed an initial online screening survey after being invited to participate by the same marketing research firm we employed for Study 1. The marketing research firm contacted soccer fans who followed PSG but lived in "la province" (i.e., outside Paris).[ 7]
Second, 1 week later, the marketing research firm recontacted the same respondents to complete a seemingly unrelated online survey for marketers soliciting informants' views on the new layout of a national newspaper. This temporal separation minimizes the possibility that self-reporting interfered with the isolation manipulation. Two hundred ten respondents completed this survey (39.1% women, average age 39 years); we randomly assigned them to a 2 (fan isolation: more vs. less isolated) × 2 (sponsorship: present vs. absent) between-subjects experimental design. We manipulated fan isolation in the cover story of the second survey and manipulated the advertisement in the newspaper. After reading the newspaper, respondents provided open-ended feedback about the new layout and also reported the names of any brands that advertised therein, which we coded as an indication of sponsor brand recall. Finally, respondents described themselves and completed measures to indicate the salience of their fan identity, before providing demographic information.
As in Study 1, we assessed identification using [ 9] scale. Fans completed the 12-item scale for PSG fan identification (α =.87).
In the low- (high-) isolation condition, respondents were told that the marketing research firm was seeking evaluations of the newspaper from 500 (50) citizens of "la province" and 50 (500) citizens of Paris (adapted from [13]). At the end of the survey, as a manipulation check, respondents indicated whether they felt isolated during the study, on two items (i.e., "I felt isolated" and "I felt alone"; seven-point Likert scales; α =.87), which confirmed that they felt relatively more isolated when the majority of newspaper evaluators were described as being from "la province" rather than from Paris (MProvince = 2.51, MParis = 2.07; t(208) = 2.25, p <.05). In other words, taking a survey that was purportedly administered to people who were less (vs. more) likely to share the same team affinity enhanced feelings of isolation.
Next, respondents read the 16-page newspaper, which included six real advertisements (see the Web Appendix). One of the advertisements (on page 7) was from the French automobile brand Citroën, an actual sponsor of PSG. We manipulated sponsorship by showing either an unaltered version of a Citroën advertisement that referred to its sponsorship of PSG or an altered version that removed the PSG logo and the tagline mentioning its sponsorship of PSG (see Appendix A).
After reading the newspaper and providing open-ended feedback about the layout, respondents listed all the brands they could recall from the ads. We coded the Citroën ad as effective or not depending on brand recall (i.e., 1 = Citroën was listed as an advertiser, 0 = Citroën was not listed).
Because prior work has shown enhanced memory for identity-linked stimuli from identity salience generated by feeling distinct from others ([18]), we tested whether salience played a role in the effects observed. Respondents reported their identity salience in response to an open-ended question ("Please tell us about yourself in your own words. Please take one minute to do so") and two seven-point items ("At this moment, to what extent are you thinking about you being a fan of the PSG?" and "At this moment, to what extent are you considering yourself a fan of PSG?") anchored by "not at all" and "very much" (α =.95; adapted from [50]). Fan isolation and the interaction between fan isolation and identification did not correspond to either identity salience measure (ps >.23).
Considering the dichotomous performance outcome (brand recall) and the 2 (fan isolation: more vs. less isolated) × 2 (sponsorship: present vs. absent) × continuous(identification) design, we submitted sponsor brand recall (1/0) to a logistic regression with fan isolation, identification, sponsorship, and their interaction as predictors (see Table 3). The three-way interaction of isolation, identification, and sponsorship was significant (b = 1.82, p <.01). In support of H1, when the Citroën ad featured the sponsorship of PSG, the interaction between fan isolation and identification was significant for recall of Citroën (b = 1.51, p <.01); neither factor mattered for the Citroën ad without the sponsorship of PSG. In the sponsorship ad conditions, we decomposed the isolation × identification interaction, using spotlight analyses ([55]) of the simple effect of isolation at two levels of identification (±1 standard deviation from the mean for strong and weak fans). Isolation increased the likelihood of recalling the Citroën ad among strong fans (b = 1.72, p <.05), in line with the doubling-down effect, but decreased this likelihood among weak fans (b = –1.63, p <.05), in line with the desertion effect. In other words, identification predicted likelihood to recall Citroën among more isolated fans (b = 1.62, p <.001) but not among less isolated fans. In the less isolated condition, recall was similar with or without sponsorship.
Graph
Table 3. Studies 2a and 2b Estimation Results: Recall of Citroën Advertisement.
| Study 2a: Ad with or Without Sponsorship | Study 2b: One or Two Exposures to Ad with Sponsorship |
|---|
| Estimate | p-Value | Estimate | p-Value |
|---|
| Primary Predictors | | | | | | |
| Advertisement condition (Ad) | 2.26 | (.79) | .004 | −.21 | (.65) | .741 |
| Identification | 1.67 | (.48) | <.001 | 1.34 | (.33) | <.001 |
| Isolation | 1.72 | (.85) | .043 | 1.29 | (.59) | .029 |
| Identification × Isolation | 1.51 | (.56) | .007 | 1.12 | (.41) | .007 |
| Identification × Ad | 1.74 | (.53) | .001 | 1.20 | (.45) | .007 |
| Isolation × Ad | 2.71 | (1.04) | .009 | 1.27 | (.86) | .140 |
| Identification × Isolation × Ad | 1.82 | (.68) | .007 | 1.28 | (.57) | .030 |
| Controls | | | | | | |
| Age | .01 | (.01) | .228 | .02 | (.01) | .030 |
| Gender (Female) | −.75 | (.31) | .017 | −.48 | (.30) | .111 |
| Intercept | 1.81 | (.84) | .031 | .88 | (.62) | .155 |
| Model Fit | | | | | | |
| Deviance (−2 log likelihood) | 254.7 | 316.9 |
| AIC | 274.7 | 336.9 |
| BIC | 308.2 | 373.7 |
| Likelihood ratio chi-square test | 35.49 (df = 9) | 62.88 (df = 9) |
| <.001 | <.001 |
20022242918807670 Notes: Standard errors in parentheses. Isolation, identification, and advertisement conditions are shifted so 0 = the isolated condition, one exposure to the advertisement with sponsorship, and one standard deviation above mean levels of identification, which displays the estimated effect of each direct effect and two-way interactions at a meaningful 0 point for the other variable.
For a model-free illustration of sponsor brand performance, we grouped fans into stronger and weaker identification, using a median split (at 4.08), and then calculated the percentage of fans who recalled Citroën in each experimental condition (see Figure 3). In the sponsorship ad conditions (i.e., PSG team logo present), only 55% of strong fans recalled Citroën if they were less isolated, but recall jumped to 83% in the more isolated condition, in line with the doubling-down effect. For strong isolated fans, the recall percentage more than doubled, from 40% for the ad without sponsorship to 83% for the ad with sponsorship, exhibiting the potential value of a well-targeted sponsorship advertisement. However, for weak fans, the pattern flipped to reveal a desertion effect, such that only 17% recalled Citroën from the ad with sponsorship in the more isolated condition, compared with 52% in the less isolated condition. This desertion effect is especially stark given that 55% of weak isolated fans recalled Citroën when the ad simply omitted sponsorship information. The collapse in recall implies that weak fans actively avoid vestiges of their fan identity that interfere with their efforts to affiliate with their more proximal social environment.
Graph: Figure 3. Studies 2a and 2b: Percentage of fans who recall the sponsor brand in each isolation × identification × sponsorship advertisement condition. Notes: Grouping of fan identification is based on a median split for strong and weak fans at above or below 4.08 for Study 2a and 4.00 for Study 2b. Bar graphs represent the raw percentage of fans in each cell who correctly recalled the sponsor brand as an advertiser in an unaided recall task.
We theorize that isolation stimulates affiliation motives that differentially affect team-linked recall by increasing strong (weak) fans' desire to seek (ignore) team-based sponsorship affiliation. Thus, isolated strong fans are simply paying more attention to brand sponsors. If this is valid, multiple exposures to brand sponsors should increase recall for all fans, which we test in Study 2b. Furthermore, Study 2b formally shows that our central predictions have strong managerial implications, in terms of media planning, by providing a comparison of the efficacies of a targeted, single ad and a repeated-exposure ad. Finally, Study 2b rules out alternative accounts based on mood ([45]) or threat ([13]).
Other than these alternative accounts, the design was identical to that in Study 2a. The same market research firm recruited 386 PSG fans from "la province" (i.e., outside Paris) and measured fan identification (α =.84). A week later, 293 of these informants completed the second survey (30.4% women, average age 44.59 years). The second survey included the same cover, randomly assigned fan isolation manipulation, and newspaper from Study 2a. The 16-page newspaper displayed the Citroën ad with its sponsorship of PSG either twice in the two-exposure condition (pages 7 and 13) or once in the single-exposure condition (page 7, with an ad for another brand on page 13). Respondents rated mood on a seven-point bipolar scale ("very negative/very positive") and indicated their sense of feeling threatened ("unhappy/threatened/attacked/maligned") on seven-point scales ("not at all/very much"; α =.88).
This study was a 2 (fan isolation: more vs. less isolated) × 2 (ad exposure: one vs. two) × continuous (identification) design, and we submitted sponsor brand recall (1/0) to a logistic regression with isolation, identification, ad exposure, and their interaction as predictors (see Table 3). The three-way interaction of isolation, identification, and ad exposures was significant (b = 1.28, p <.05). In support of H1, the interaction between isolation and identification in the single-exposure condition was significant (b = 1.12, p <.01), replicating the finding from Study 2a. However, neither factor mattered for respondents who saw the Citroën ad twice. Overall recall increased from 50% in the single-exposure condition to 79% in the two-exposure condition. Fan identification in response to isolation appears most influential at the point of encoding, suggesting that selective attention explains the effect. We also tested whether mood or a sense of being threatened might explain the influence of isolation but found no significant main effects on fan isolation or on the interaction between fan isolation and identification (ps >.72).
To understand the model-free impact of isolation across advertising conditions for strong and weak fans, we grouped fans into stronger and weaker identification categories, using a median split (at 4.00), and then calculated the percentage of strong and weak fans who recalled the Citroën ad in each condition (see Figure 3). For fans exposed to the sponsor-linked ad once, the results mimic the pattern from Study 2a. Weak fans display desertion behavior (recall of Citroën falls from 49% in the less isolated condition to 28% in the more isolated condition), and strong fans exhibit doubling-down behavior (recall jumps from 44% in the less isolated condition to 82% in the more isolated condition).
Both Studies 2a and 2b provide experimental evidence of a doubling-down effect for strong fans and a desertion effect for weak fans in an isolated context. For brand managers, this doubling-down effect can even be taken literally; one exposure to the team-linked sponsorship ad performs as well as two exposures (82% and 83% recall) among strong fans in an isolated context. Thus, brand managers interested in improving awareness and recall should recognize isolated strong fans as efficient advertising targets.
In terms of understanding how fans respond to isolation, these studies support motivated encoding (or ignoring) of the sponsorship, consistent with an affiliation motive driving people's responses to isolation. We find no evidence to support alternative explanations based on identity salience, mood, or feeling threatened. Next, in Study 3 we aim to directly demonstrate the mediating role of affiliation motives and to show how a motivated response to sponsors translates into additional sponsorship performance outcomes.
In Study 3, we test the proposed process by which isolation creates an affiliation motivation, which can elicit greater interest in brand sponsors among strong fans but less interest among weak fans. Brands often engage in sponsorships in the hope of gaining favor among fans ([37]; [53]), and therefore we measure sponsor brand performance as attitude toward the brand. The same fan isolation × identification interaction pattern should emerge for attitude toward the brand if fans embrace or reject sponsor brands. Finally, Study 3 gives us the opportunity to test the viability of alternative explanations: identity salience ([18]), mood ([45]), and feelings of threat ([13]).
We recruited 578 U.S. adults from Amazon Mechanical Turk to take part in this study in exchange for a nominal fee. Respondents began the survey by indicating whether they were fans of a team in the NFL and then selected their favorite team and state of residence; only those who were fans of a team based in a state other than their state of residence were allowed to proceed.[ 8] After screening, 227 respondents (101 women, median age of 32 years) entered the final sample. We used a 2 (fan isolation: more vs. less) × continuous (fan identification) design. Respondents first completed one of two writing tasks as a manipulation of fan isolation. Then, all respondents evaluated an online article that contained an embedded sponsorship advertisement, after which they completed several measures: affiliation motives, identity salience, brand sponsor attitudes, mood, threat, identification, and manipulation checks. Finally, respondents provided demographic information, including whether they owned the sponsoring brand, and were thanked for their efforts.
To manipulate fan isolation, we told respondents that we were trying to "cultivate examples of how fans watch games in different surroundings," so they needed to complete a writing task, with their favorite team name listed for [team]. We randomly assigned respondents to one of two conditions with a similar writing prompt; the text in italics appeared only in the more isolated experience condition; the text in parentheses appeared only in the less isolated experience condition.
As a fan of the [team], think about a time when you were watching a [team] game and everyone around you was rooting for a different team (also rooting for the [team]). What was it like to be so distinct from (similar to) other fans? How did you feel? Using the space below, describe this experience.
To ensure the manipulation took effect, toward the end of the survey, respondents rated how isolated they felt ("Describe how you felt during this study") on three seven-point scales (1 = "in the majority," and 7 = "in the minority"; 1 = "same as others," and 7 = "different from others"; 1 = "part of the crowd," and 7 = "an outcast"). The averaged scores (α =.85) revealed that respondents assigned to the more isolated condition scored higher than those in the less isolated condition (MMore = 4.07, MLess = 3.11; t(225) = 5.64, p <.001).
We then asked respondents to proceed to an ostensibly separate task for which they read an article about the upcoming schedule for the 2016 NFL season and the first [divisional] game between the [team] and the [first divisional opponent of team]. We customized the team, division, and opponent names for each article. We told the respondents that we were seeking "insight into fans' experience of reading about their favorite team." The article was formatted as a Yahoo article, which allowed for several display ads to appear around it. The sponsorship ad appeared directly below the article, with a large logo for Chevrolet and the text "A proud supporter of the [team]" (see Appendix B). After reading the article, respondents provided open-ended feedback about their reading experience.
Respondents rated their affiliation motives ("When you described your experience watching a game of the [team], how did you feel?") using five items measured on seven-point agreement scales (α =.86): "I felt a strong desire to fit in with fellow [team] fans," "I felt a need to belong to a [team] group of fans," "Feeling a mutual connection with other [team] fans was important to me," "I felt a strong desire to connect with other [team] fans," and "I did not need to be a part of the group of [team] fans" (reversed).
Respondents rated their fan identity salience ("At this moment, to what extent are you thinking about your identity as a [team] fan?") on a dichotomous scale (0 = "not at all," 1 = "very much") ([50]). Then, they rated their mood on a 21-point scale (–10 = "very unpleasant," and 10 = "very pleasant") and also the extent to which they felt threatened ("unhappy/threatened/attacked/maligned/challenged/impugned") on seven-point scales ("not at all/very much"). We averaged the ratings (α =.89).
Next, respondents rated their attitudes toward seven automobile brands ("Rate your attitude toward the following brands") on the same single-item seven-point scale (1 = "dislike a great deal," 7 = "like a great deal"): the sponsor Chevrolet as well as Ford, Cadillac, Acura, Chrysler, Tesla, and Toyota.
Finally, respondents rated their level of fan identification using the same 12-item seven-point scale from the previous studies, adapted to their favorite NFL (α =.80).
We regressed sponsor brand attitude on fan isolation, identification, and their interaction, which revealed a significant interaction (b =.69, p <.01). Floodlight analysis revealed that fan isolation had a positive and significant (p <.05) effect on attitude for strong fans in the top 19% of our sample (JN point at identification scores equal to or greater than 5.42). However, isolation led to less favorable attitudes toward the sponsor for relatively weak fans in the bottom 7% of our sample (JN point = 3.72) (see Figure 4). The interaction remained significant whether we controlled for age, gender, mood, Chevrolet ownership, or team fixed effects (b =.55, p <.05).
Graph: Figure 4. Study 3: Attitudes toward sponsor brand as a function of fan identification × isolation. Notes: 2.5 was the minimum observed value for fan identification in this sample. Shading represents regions at which effect of isolation is significant at 90% (light gray) and 95% (dark gray) confidence levels.
Next, we examined whether this focal interaction between fan isolation and identification stems from how people deal with an increase in affiliation motives under an isolating social situation. We began by confirming that fan isolation had a main effect on affiliation motives. An independent sample t-test of affiliation motive ratings revealed a significant main effect of fan isolation (MMore = 3.80, MLess = 3.21; t(225) = 3.57, p <.001). Identification did not moderate the effect on affiliation motives in a regression (p =.34), nor did we find a significant main effect of isolation on identity salience, mood, threat, or any interactions with identification (ps >.15).
We conducted a moderated mediation analysis with affiliation motives using [23]) PROCESS macro and bootstrapping procedures (model 15, 10,000 bootstrapped samples). We entered affiliation motives as the mediator of the effect of fan isolation (0 = more isolated, –1 = less isolated) on attitude toward the sponsor brand, with fan identification moderating how affiliation motives translate into attitudes toward the sponsor brand. In support of H2 and consistent with the predicted process, the highest-order index of moderated mediation was significant (index =.133, standard error [SE] =.069; 95% CI =.03,.30). By decomposing this highest-order index, we found that the conditional indirect effect of fan isolation, mediated by affiliation motives, was positive when identification was strong (i.e., 90th percentile; effect =.158, SE =.099; 95% CI:.01,.40), but we observed the opposite pattern when identification was weak (i.e., 10th percentile), though this effect was only marginally significant with a 90% CI below 0 (effect = –.131, SE =.088; 90% CI: –.31, –.02). We found no statistical support for alternative models of mediation without moderation or with the key interaction on affiliation motives.
Study 3 provides experimental evidence of the proposed affiliation motive process. It also extends the isolation × identification interaction to another sponsorship performance outcome—namely, attitude toward the sponsor brand. We observe an overall lift in attitudes toward the brand, as well as a lift relative to the brand's competitive set, for isolated strong fans who embrace the sponsor brand. Study 3 also offers further evidence that isolated weak fans desert a sponsor brand affiliated with their team.
To strengthen the managerial relevance of our framework, we repeated the experimental design in Study 3 with Chipotle as the brand sponsor of the respondents' favorite NFL team, with the sponsorship displayed at the bottom of an ESPN article about the team (for stimuli, see Appendix C). At the end of the survey, as a "thank you," we entered respondents into a raffle for one of two $20 gift cards. Respondents indicated their preference for Chipotle or Panera Bread, Chipotle's closest fast-casual competitor ([43]). One hundred sixty-three U.S. adults recruited from Amazon Mechanical Turk took part in this study. Six responses originating from the same IP address were dropped, leaving a final sample of 157 respondents (76 women, median age of 33 years). We submitted choice of sponsor brand (1/0) to a logistic regression with fan isolation, identification, and their interaction as predictors. The results revealed a marginally significant interaction (b =.71, p =.066). Decomposing this interaction, we found that fan isolation had a positive, significant effect (p <.05) on choice of the sponsor brand among the strongest 54% of fans in the sample (JN point = 5.05). The estimated probability of a strong fan choosing the brand sponsor's gift card was 67% in the isolated condition but only 35% in the less isolated condition. This extension to a behavioral outcome demonstrates the potential value of sponsorship driving strong fans through the purchase funnel more effectively in isolating contexts.
Three sets of studies involving 1,412 real-life sports fans across multiple sports (soccer, basketball, and football), countries (United Kingdom, United States, and France), and methods (field surveys and quasi experiments) demonstrate that fan isolation and identification jointly determine how receptive fans are to sponsorship and, thus, sponsorship performance. This research sheds light on how contextual factors shape advertising effectiveness ([29]). Drawing on identity and affiliation theories ([16]; [40]), we find that isolation from the team community generates an affiliation motive, but this motive triggers different responses depending on the strength of fans' identification. In isolating contexts, fans with strong identities "double down" and embrace team-linked brands. We show that sponsor brand performance is much higher for strong fans in more than less isolating contexts, as indicated by the 46% better unaided recall, 38% more favorable attitudes, 39% higher purchase intentions, 43% higher WOM, and 91% greater likelihood to choose the brand sponsor instead of a competitor. Conversely, isolation hurts brand performance for weak fans. These findings extend theory and provide useful managerial insights.
Extant research on sponsor performance at the fan level has examined the importance of team proximity to brands and fans ([10]; [15]; [63]). Other research has established fan identification as a key driver of sponsor performance (e.g., [20]; [34]). The current research bridges these two strands of literature to understand the contingent nature of both fan isolation and identification.
As an initial examination of how social context interacts with fan characteristics to shape sponsorship performance, we build a theory of fan affiliation for out-of-market sponsorship. Prior work on cultural identity, mobility, and affiliation explains the cultural activities people adopt or avoid in novel social contexts ([16]). With an analogous process pertaining to fans in relation to team communities, the affiliation lens predicts how strong and weak fans will differentially respond to brand sponsors depending on the level of social isolation.
Prior research that demonstrates the impact of sources of a shared identity on consumers generally considers domains in which identity is uniformly strong ([ 6]; [62]). Our work highlights the critical role of varying levels of identification across consumers with the same shared identity. Fans differ in how enduring, chronically salient, and emotionally significant their fan identification is to their sense of self. Ostensibly, all fans of a team should favor team sponsors in all circumstances; however, we show that isolation triggers different affiliation strategies depending on the level of identification, highlighting critical differences that exist among strong and weak fans. Dichotomizing identity into members and nonmembers (fans and nonfans) would hide the opposing effects of isolation across members of varying levels of identification.
This research offers new insights for branding and advertising efforts that aim to build connections with consumers around points of passion. Managerial interest in sponsorship is growing because sponsorship provides mass-market visibility in a fragmenting media environment ([59]), and technology is improving not only the precision of sponsor targeting but also the measurement of sponsorship effectiveness ([28]). Our framework suggests that by successfully targeting strong fans in isolating contexts, managers can achieve performance outcomes with a single advertisement that are comparable to those of multiple ad exposures (Study 2b). This finding adds to the growing body of research on targeted advertising. For example, [33] suggest that greater involvement in entertainment activities significantly reduces consumers' interest in mobile in-app advertisements. Given that isolation can be a transitory experience, we show that managers can selectively target strong fans when they are isolated.
Local brands that sponsor local teams should keep in mind that weak fans are equally receptive as strong fans. Thus, local sponsors should not narrowly target strong fans or use overly nuanced promotional messaging that weak fans might fail to comprehend. Overall, our findings justify the $560 million sponsorship of Manchester United by Chevrolet, despite criticisms ([ 3]). Chevrolet had global ambitions in choosing its sponsorship, and our findings indicate that teams with many isolated fans (e.g., Manchester United, the Lakers) should be attractive to global brands even if the sponsorship falls flat in local markets.
To demonstrate how brand managers armed with the power of social media can target audiences on the basis of interest and location, we created Facebook ads for "Boon," a fictitious brand appearing as a sponsor for the two teams playing in the 2018 Super Bowl (for the ads, targeting, and further details, see Appendix D). Through Facebook's targeting settings, we delivered the ads to people residing in each team's home market, who should experience little isolation, or those in Texas, a neutral, out-of-market context. We then selected items such as "Away from hometown" for more isolation with "Demographics > Life Events." We used interest in the NFL team to identify strong fans, and we identified weak fans as those with interest in the team's city but not explicitly in the team, because identification with a city is a driver of fan identification ([10]). The performance of the ads, based on clicks per ad, was consistent with our empirical studies; strong fans outside the team's market had 8% more clicks than strong fans inside the team's market. Yet the same number of clicks was generated from all people in the team's market regardless of fan identification. Isolated weak fans, however, had 26% fewer clicks than weak fans in the team's market. Although these findings are preliminary, they illustrate how managers can implement the framework. In summary, sponsor brands should consider promoting their sponsorship to all consumers in the team's home market regardless of fandom while also promoting it to strong fans out-of-market using interest- and location-based targeting.
This research is not without limitations, several of which deserve attention. First, we only examined fans' reception to current sponsors or held the brand constant; we did not examine brand-specific factors, such as the effects of fit between the sports team and the sponsor or the degree to which a brand sponsors many teams. We show that proximity to or isolation from the fan community is relevant for fans' acceptance of sponsors; the proximity of the brand to the team is also relevant for fans' acceptance of sponsors ([63]). Several brand-related factors may alter attributions for sponsorship and thus affect how willing even strong isolated fans are to embrace the sponsor. Therefore, it is worth testing whether fans' acceptance of poorly fitting sponsors is conditional on their location or characteristics of the brand. Furthermore, a brand sponsor with a limited geographic footprint may lack relevance for distant fans.
Second, we do not examine antecedents determining variance in fan identification. Further research should take a dynamic perspective to determine how identification evolves in light of greater isolation, to provide insights to managers trying to grow the number of strong isolated fans. Understanding the evolution of identification is of wide interest in sports marketing ([32]), but managers might face challenges when trying to cultivate strong isolated fans because of factors such as competition from other teams or fan difficulties in viewing games. Once cultivated, however, isolated strong fans may become especially valuable advocates because of the need to justify their support to themselves and others.
Third, further research is required to develop techniques to assess when experiences of isolation arise (e.g., does watching a team play an away game at another team's stadium create a temporary experience of isolation?) and for whom identification is strong through observable behavior (e.g., time spent viewing team-related posts, number of hours spent on the team website, tickets bought online). [57] demonstrate the importance of measurement accuracy for behavior-based advertising targeting. Advertisement platforms such as Facebook and Google offer detailed information on online behavior and location, and connecting these data with fan identification would unlock powerful targeting opportunities.
Fourth, we adopted a broad view of fan identification as a starting point to investigate its interplay with isolation, but further research could expand on the nuances in the main object of fans' identification. For example, by construction, our focal measure of fan identification was broad and included both cognitive and affective elements ([ 9]). The focal effects were robust to an alternative, more cognitive measure of identification (Study 1b), but isolation may have different effects for cognitive versus affective identification. For example, perhaps the tendency for isolated weak fans to actively ignore brand sponsors is a form of emotionally based coping. Furthermore, strong fans usually dislike sponsors of rival teams ([ 7]; [21]). Research could test whether isolated strong fans still react positively to their team's sponsors when those brands also support a rival team. If not, sponsors could benefit from rivalry games that amplify isolation ([ 4]).
Last, we focused on professional sponsorship, but brands also sponsor local teams and grassroots sports clubs ([46]). Grassroots teams are less likely to have out-of-market fans, but fans may feel isolated in different social situations. Further research might explore whether our framework extends to these contexts. In conclusion, research on the interplay between consumers' interests and social context deserves more attention, especially as mobile technology and social media platforms empower marketers with greater information about both.
Supplemental Material, DS_10.1177_0022242918807673 - The Long Reach of Sponsorship: How Fan Isolation and Identification Jointly Shape Sponsorship Performance
Supplemental Material, DS_10.1177_0022242918807673 for The Long Reach of Sponsorship: How Fan Isolation and Identification Jointly Shape Sponsorship Performance by Marc Mazodier, Conor M. Henderson, and Joshua T. Beck in Journal of Marketing
Graph: Appendix A. Study 2: Sponsorship Advertising Stimuli.
Graph: Appendix B Example of Stimuli for Study 3.
Graph: Appendix C Example of Stimuli for Study 3's Extension to Choice as an Objective Performance Outcome.
We distributed ads for each team the day before the team appeared in the Super Bowl. We spent $60 promoting each advertisement. We targeted four audiences for each of the Eagles ad and the Patriots ad, as follows:
- Isolation
- High: Location in state of Texas (neutral market) and Demographics > Life Events > "Away from family," "Away from hometown," or "Recently moved"
- Low: In team market: 50-mile radius of Philadelphia (or Boston for Patriots)
- Fan identification
- Strong fans: Interests > Additional Interests > "Philadelphia Eagles" ("New England Patriots")
- Weak fans: Interests > Additional Interests > "Philadelphia" ("Boston") and Exclude People > Additional Interests > "Philadelphia Eagles" ("New England Patriots")
Graph: Appendix D Advertisements for Managerial Implementation Demonstration.
Footnotes 1 Author ContributionsThe authors contributed equally and appear in reverse alphabetical order.
2 Area EditorRobert Meyer served as area editor for this article.
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partially funded by the Business School of Hong Kong Baptist University and the Warsaw Sports Marketing Center at the University of Oregon.
5 Online supplement: https://doi.org/10.1177/0022242918807673
6 1These distinctions are based on model estimates at plus or minus one standard deviation from mean levels of fan isolation and identification.
7 2The research firm screened for fans of PSG by asking potential respondents "In which sports are you interested?" and then, if they answered soccer (football), asking them to list the team they were a fan of; if they identified the PSG, they were asked to complete [9] scale to assess their level of fan identification. We instructed the firm to focus on fans living outside Paris to enhance the efficacy of the isolation manipulation.
8 3We recruited fans separated from their team to facilitate a stronger manipulation of experienced isolation. We lift this restriction in the extension described in Study 3.
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Record: 193- The New Product Portfolio Innovativeness–Stock Returns Relationship: The Role of Large Individual Investors' Culture. By: Cillo, Paola; Griffith, David A.; Rubera, Gaia. Journal of Marketing. Nov2018, Vol. 82 Issue 6, p49-70. 22p. 5 Charts, 1 Graph. DOI: 10.1177/0022242918805405.
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The New Product Portfolio Innovativeness–Stock Returns Relationship: The Role of Large Individual Investors' Culture
The marketing–finance interface literature has investigated the direct link between innovativeness and stock returns. The authors extend this research by focusing on two open questions: How and under what conditions is innovativeness associated with stock returns? Answering these questions is important for managers who have to defend innovation investments to board members and time the introductions of new products. The authors investigate large individual investors and their national culture in the food and beverage industry. Combining multiple data sets, they first examine the relationship between innovativeness and large individual investors' stock holding decisions (i.e., to sell, hold onto, or buy a firm's stocks). The results indicate that national culture moderates this relationship. At the firm level, the authors show that large investors' stock holding partially mediates the innovativeness–stock returns relationship and that the culture of a firm's large investors moderates this mediated relationship. Thus, they unveil a special segment of investors, large individual investors, who influence the extent to which firms benefit from innovativeness in the stock market in the food and beverage industry.
Keywords: marketing–finance interface; new product portfolio innovativeness; stock holding; investor heterogeneity; Hofstede
In the last decade, the marketing–finance interface literature has emerged as a fertile area of research, with the goal of making marketing's contribution to firm value more visible to top managers and investors ([55]). Research in this area has particularly focused on the innovativeness of the new products that a firm introduces each period (e.g., [53]; [56]). We define this as "new product portfolio innovativeness" ("innovativeness" hereinafter). Historically, this literature has investigated the direct link between innovativeness (and marketing actions, in general) and stock market performance. While it has convincingly shown this link exists, significant research questions remain unanswered.
First, how is innovativeness associated with stock returns? Prior research ([11]; Ng and Wu 2009) and anecdotal evidence hint to a special group of investors, large individual investors ("large investors" hereinafter), who influence stock returns to firm actions. Many small investors mimic large investors' stock holding decisions through portfolio trackers, and some fund managers use large investors' decisions to inform choices about their own funds ([17]). For instance, on October 19, 2015, Oprah Winfrey announced that she had purchased a 10% stake in Weight Watchers. In 2 days, her stock holding decision generated $700 million in stock market value for Weight Watchers ([61]). In addition, when the famous Indian investor Rakesh Jhunjhunwala bought stocks in Prakash Industries, the price increased by 13%. When he sold his stocks in Design Arena, the stock lost 12.4% of its value the day after the announcement ([15]; [34]). Given their relevance, we identify large investors' stock holding as one of the possible mechanisms through which the innovativeness–stock returns relationship occurs. To understand this mechanism, we start with an investor-level perspective that analyzes each large investor's stock holding decision (i.e., to buy, hold on to, or sell stocks) associated with innovativeness. We then move to a firm-level perspective that investigates whether large investors' stock holding mediates the innovativeness–stock returns relationship.
Second, under what conditions is innovativeness associated with stock holding and stock returns? Thus far, the literature has treated these relationships as unconditional. However, investors are heterogeneous in their preferences for firm actions ([32]; [43]), which suggests that the stock returns associated with innovativeness are conditioned on the characteristics of the firm's large investors. Theoretical and empirical reasons lead us to focus on the national culture of large investors. Prior literature has shown that customers vary in their response to innovativeness depending on their national culture ([10]; [59]). If national culture ("culture" hereinafter) plays the same role for investors, then large investors from different cultures might react differently to innovativeness. It is particularly important to understand the role of culture in the context of increasing diversity in the nationality of firms' investors. Foreign holdings of U.S. stocks and bonds hit record levels in 2016: foreign investors account for 20% of the U.S. equity market and 50% of publicly traded debt ([ 6]). In the next 5 to 10 years, foreign investors may own one-third of U.S. stocks ([30]). Similarly, the investor profile of foreign stock markets is increasingly diverse ([24]). We use a national cultural, work-oriented-values approach (i.e., [23]) to study how culture moderates the relationships between innovativeness and ( 1) stock holding and ( 2) stock returns.
We contribute to the literature in three ways. First, we identify large investors' stock holding as one of the routes through which the innovativeness–stock returns relationship occurs. In this way, we advance the marketing–finance interface literature, which has previously focused on the direct link, by shedding light on one of the mechanisms through which marketing creates value in the stock market. For managers, who must defend their innovation investments to board members, it is important to understand the process wherein innovativeness creates value.
Second, we identify one key boundary condition of the innovativeness–stock returns relationship: given the same level of innovativeness, stock returns are contingent on large investors' culture. As such, we highlight how the characteristics of a firm's large investors magnify or dilute the contribution of marketing actions to firm value. We also extend the cross-cultural marketing literature, which has examined consumers' financial decisions ([37]), to the investor domain. For managers, whose compensation is tied to stock price, it is important to understand not only whether innovativeness is associated with stock returns but also under what conditions it is most lucrative.
Third, [55] note that firms spend substantial resources in communicating with the market, and they call for research to advise managers on which marketing actions to communicate to specific investors. Despite this call, the marketing literature has been largely silent about which investors matter and what to communicate to them. To researchers and managers, we delineate a relevant segment of investors (i.e., large investors) to target with customized communication. Drawing on our results, we suggest how to best position innovativeness depending on the cultural specificities of each large investor. More generally, we suggest that key marketing concepts such as segmentation, targeting, and positioning that have been traditionally adopted for customers can be applied to investors too.
Collectively, our findings provide a reframing of future research in the marketing–finance interface literature from the question of whether marketing action or assets increase firm value in the stock market to a more complex set of questions: How and under what conditions does marketing create value in the stock market, and for which investors does this occur?
The marketing–finance interface literature theorizes that marketing actions that enhance and accelerate cash flows, reduce their volatility, and increase residual firm value stimulate investors, including large investors, to increase stock holding ([57]). The introduction of innovative products accomplishes these tasks in the following ways.[ 5] First, innovative products enable firms to dominate markets while protecting the firm from competitive attacks, as it takes time for competitors to imitate innovative new products ([53]). Innovativeness signals investors that the firm will stay ahead of competition, appropriating quasimonopolistic rents that increase the value of the firm for investors ([41]). Second, more innovative products provide benefits not offered by any existing product in the market, allowing a firm to secure a unique positioning in the marketplace and command a higher premium price, thus enhancing cash flows. Third, innovative products enable a firm to address new customer segments or new needs, thereby reducing cash flow volatility ([56]). Finally, investors view innovative products as platforms for future product introductions, increasing investors' view of residual value of the firm ([56]).
Beyond this framework that applies to all investors, we identify two additional characteristics of large investors that further increase their favorable disposition to innovativeness. First, large investors own a significant block of a firm's stocks. They cannot sell their stocks without depressing the stock price and taking a substantial loss on the transaction ([25]). Given this high investment level, large investors are likely to respond positively to innovativeness strategies that have the aforementioned positive effects on firm cash flows. Second, large investors tend to have an extensive knowledge of the firm ([25]). Research has shown that, for individual investors, deeper knowledge is associated with lower risk perceptions and higher expectations about future returns ([29]). This suggests that risk concerns that might curb positive response to innovativeness ([56]) are less likely to arise in the case of large investors.
In summary, our theory leads to the prediction that individual large investors will buy more of a firm's stocks when innovativeness increases. In addition to this individual investor level, we also theorize and test this effect at the firm level to expand the development and importance of our effect. We denote these as "individual" and "firm" levels, respectively, for the remainder of the article. At the firm level, this translates into an increase of large investors' stock holding (i.e., the total percentage of stocks in the hands of large investors). Formally,
- H1: New product portfolio innovativeness positively influences the stock holding of (a) individual-level large investors and (b) firm-level large investors.
The finance literature theorizes that decision biases lead investors to respond differently to the same information (in our case, innovativeness) ([50]). Representativeness bias—that is, the tendency to attribute one characteristic to imply another—has received significant scholarly attention ([48]). This bias leads investors to buy stocks in firms with attributes that they deem desirable, regardless of the objective value of the specific attribute ([48]).
We argue that representativeness bias, founded in an investor's personal values, influences a large investor to increase her stock holding in innovative firms more when she considers innovativeness a desirable attribute than when she does not. Consistent with this theorization, prior literature has found that culture influences the extent to which innovativeness is perceived as a desirable attribute (e.g., [59]). We theorize that, given the same level of innovativeness, large investors from cultures that value innovativeness will increase their stock holding more than large investors from cultures that do not value innovativeness to the same degree. We recognize that representativeness bias may lead investors to increase stock holding in firms with attributes other than innovativeness that they may consider desirable as well (e.g., process innovation).
The role of culture (i.e., the collective programming of the mind that separates the members of one group of people from another; [22])—and, in particular, the role of Hofstede's cultural dimensions—in influencing investment decisions is well documented. For instance, [13] find a positive correlation between individualism and momentum trading strategies; [44] reports that investors from countries low in individualism and high in uncertainty avoidance rely on market sentiment to make stock holding decisions more than other investors. Consistent with this research and the argument that representativeness bias is influenced by an individual's values, we employ the work-oriented value framework of Hofstede (e.g., [23]) to study stock holding decisions because these are personal business decisions that affect an investor's personal wealth.
Hofstede's work-oriented value framework consists of six value dimensions (i.e., individualism, uncertainty avoidance, power distance, masculinity, long-term orientation, and indulgence; [23]). [21] argues that scholars should employ dimensions that are theoretically relevant to the phenomenon under study. In this work, to gain the broadest theoretical understanding of the phenomenon, we employ all six dimensions. Next, we hypothesize the specific moderating role of each of these six values for both a single large investor as well as for the overall large investors' stock holding.
Individualism refers to the strength of relations between members of a society ([22]). It is anchored by individualism and collectivism. Members of individualist cultures value independence and aim to separate themselves from others. Members of collectivist cultures value group cohesion and harmonious interdependence. Because investors typically view innovativeness as a means to distinguish a firm from its competitors ([56]), a firm introducing innovative products should be more appealing to large investors from individualist cultures than to those from collectivist cultures. This suggests that, owing to representativeness bias, a large investor from an individualist culture would buy more stock in innovative firms than a large investor from a collectivist culture. At the firm level, given the same level of innovativeness, the percentage of stock in the hands of large investors will increase as the individualism among a firm's large investors increases. Thus, we hypothesize,
- H2: The positive effect of new product portfolio innovativeness on (a) individual-level large investors' stock holding and (b) firm-level large investors' stock holding increases as large investor individualism increases.
Uncertainty avoidance refers to how a society manages future uncertainty ([23]). Low-uncertainty-avoidance cultures accept higher levels of risk; high-uncertainty-avoidance cultures work to minimize future uncertainty. When firms introduce innovative products, investors face uncertainty regarding when these products will generate cash flows ([56]). Because large investors from higher-uncertainty-avoidance cultures tend to avoid risk, they should view innovativeness as a less desirable attribute than investors from lower-uncertainty-avoidance cultures. Following the same logic adopted for individualism, we hypothesize,
- H3: The positive effect of the new product portfolio innovativeness on (a) individual-level large investors' stock holding and (b) firm-level large investors' stock holding decreases as large investor uncertainty avoidance increases.
Power distance refers to the way a society addresses differences among its members ([22]). Higher-power-distance cultures value greater social stratification and increased social hierarchy, similar to markets characterized by market leaders and market followers. Lower-power-distance cultures work to minimize inequalities. Innovativeness is typically considered a way to create inequities in the marketplace ([60]). Inequities are favored in larger-power-distance cultures, which view the world as consisting of winners and losers ([23]), but are less desirable in lower power distance cultures. Thus, stocks of a firm that introduces more innovative products are more appealing to large investors from high-power-distance cultures than to those from low-power-distance cultures, because of representativeness bias. Thus,
- H4: The positive effect of new product portfolio innovativeness on (a) individual-level large investors' stock holding and (b) firm-level large investors' stock holding increases as large investor power distance increases.
Masculinity refers to the distinction between gender roles in a society ([23]). It is anchored by masculinity and femininity. More masculine cultures value autonomy, dominance, success, and wealth ([22]). More feminine cultures are concerned with the overall welfare of the entire society. Large investors from more masculine cultures value firm actions that are aggressive and aimed to dominate the market more than large investors from feminine cultures. Because innovativeness is considered a strategy to displace existing market leaders and dominate markets ([60]), we theorize that, owing to representativeness bias, the stocks of firms that introduce more innovative products are more appealing to large investors from more masculine cultures. Thus,
- H5: The positive effect of new product portfolio innovativeness on (a) individual-level large investors' stock holding and (b) firm-level large investors' stock holding increases as large investor masculinity increases.
Long-term orientation refers to the life orientation of people in a society, reflected in virtues oriented toward future rewards. Long-term-oriented cultures value perseverance and maintaining the status quo and are suspicious of change. Short-term-oriented cultures value change and quick results ([23]). Investors typically view innovativeness as a firm's commitment to establishing a long-term competitive advantage ([56]). Innovativeness should be a more desirable quality for large investors from long-term-oriented cultures, who are more forward-looking and appreciative of a firm's long-term commitment to growth, than for investors from short-term-oriented cultures. This prediction is consistent with [37], who find that people from long-term-oriented cultures have higher saving rates than others. Thus,
- H6: The positive effect of new product portfolio innovativeness on (a) individual-level large investors' stock holding and (b) firm-level large investors' stock holding increases as large investor long-term orientation increases.
Indulgence reflects whether a society values gratification of human desires related to enjoying life and having fun ([23]). This value is anchored by indulgence and restraint. People from cultures higher in indulgence value the satisfaction of personal desires and increase spending for personal gratification ([26]). Societies that value restraint adhere to strict social norms that curb gratification. We argue that a firm's introduction of innovative new products provides excitement. Investors in more indulgent cultures value this excitement compared with investors from more restrained cultures, because it creates gratification in the acquisition of the firm's stock. Thus, innovativeness should be a more desirable quality for large investors from cultures higher in indulgence. Following this logic, we hypothesize,
- H7: The positive effect of new product portfolio innovativeness on (a) individual-level large investors' stock holding and (b) firm-level large investors' stock holding increases as large investor indulgence increases.
Thus far, we have theorized about how large investors make stock holding decisions in response to innovativeness. Because the compound of each investor's stock holding decisions determines a firm's stock market returns, we next theorize about the innovativeness–stock returns relationship.
A vast body of studies have consistently shown that the introduction of more innovative products is positively linked to stock returns (e.g., [41]; [52]; [53]; [56]). To set a baseline hypothesis, we state,
- H8 : New product portfolio innovativeness positively influences stock returns.
Higher demand (i.e., investors who buy stocks) and lower supply (i.e., investors who do not sell stocks) increases stock returns ([62]; [64]). We maintain that large investors' stock holding mediates the innovativeness–stock returns relationship by influencing demand and supply of a firm's stocks associated with innovativeness and, ultimately, stock returns. We theorize two reasons why this mediation might occur.
First, an increase in large investors' stock holding subsumes higher demand of stocks by large investors coupled with constrained supply, because large investors who own the firm's stocks are not willing to sell. Second, other investors, especially retail investors, mimic the behavior of correlated trading by large investors (i.e., they buy when large investors buy, and they sell when large investors sell; [11]; [28]; [36]). Thus, innovativeness is associated with higher demand by both large investors and, owing to imitation, other investors in the market. It is also associated with constrained supply by large investors and, owing to imitation, other investors. Higher demand and constrained supply creates a shortage that increases stock returns ([64]). We theorize that large investors' stock holding is one of the routes through which the innovativeness–stock returns relationship occurs. Because we do not expect large investors to fully influence other investors, we argue for partial mediation:
- H9: The positive effect of new product portfolio innovativeness on stock returns is partially mediated by large investors' stock holding.
The culture of large investors determines the magnitude of the effect of innovativeness on large investors' stock holding (H2b–H7b). It follows that the innovativeness–stock returns relationship is one of moderated mediation, in which culture moderates the path between innovativeness and large investors' stock holding, the latter being a mediator of the innovativeness–stock returns relationship (H9).
Given the same level of innovativeness, a firm experiences higher stock returns when large investors from cultures that value innovativeness own more of the firm's stocks. These investors increase their stock holding, thus raising demand, while holding onto the stocks that they already own, thus constraining supply. Stated differently, given the same level of innovativeness, a firm experiences lower stock returns when large investors from cultures that do not value innovativeness own more of the firm's stocks. These investors sell stocks, causing price drops. Price drops are more dramatic when these large investors own more stocks because they supply a larger quantity of stocks to the market. Thus, the route through which innovativeness influences stock returns (i.e., large investors' stock holding) strengthens or weakens depending on the culture of a firm's large investors. Because this route partially determines the innovativeness–stock returns relationship (H9), we hypothesize,
- H10: The positive effect of new product portfolio innovativeness on stock returns increases as the (a) individualism, (b) power distance, (c) masculinity, (d) long-term orientation, and (e) indulgence of the firm's large investors increases, whereas (f) it decreases as the uncertainty avoidance of the firm's large investors increases.
The sample begins with the population of firms tracked by Mintel Global New Products Database (GNPD), which introduced at least one new product in the food and beverage industries worldwide in the 2006–2014 time frame. We select these industries because product innovation is an integral component of their strategy, and they have been the focus of many other innovation studies (e.g., [35]; [53]). We use Thomson One to retain listed firms. To rule out the possibility that stock holding decisions are driven by product introductions that are observable by investors but not by us, we retain listed firms whose North American Industry Classification System codes belong to the food and beverage industries only. We exclude one company listed in two stock exchanges. This results in 56 firms on 27 stock exchanges (Web Appendix A).
For these firms, we collect quarterly data about large investors' stock holding. Thomson One reports information about individual investors who are required by national legislation to notify the stock exchange that their stock holding has reached a minimum threshold. Because different countries have different minimum thresholds (see Web Appendix A), we perform a robustness analysis on just those investors whose stock holding is higher than 10%, the highest disclosure threshold in our sample. The results do not change. We exclude officers who own stock as part of their compensation plans, their relatives, and board members because their stock holding decisions may be influenced by their managerial role or by information not fully available to investors. We also exclude firm founders or relatives because their stock holding is independent of the firm's innovation activity.
To ensure that we track stock holding decisions from the first quarter that a large investor owned a firm's stocks, we restrict our sample to large investors that bought stock in our firms for the first time either in the first quarter of 2006 or later. Our final sample comprises 458 large investors in 36 quarters, for a total of 4,057 observations in the 2006–2014 time frame.
To gain a sense of how frequently managers meet with large investors, we analyze the annual reports of firms in our sample listed in the Shenzhen stock exchange. Since 2009, these firms must disclose investors' site visits in their annual reports, and this is the only publicly available source identifying the investors with whom managers meet privately (for an example, see Web Appendix B). We found that visits by large investors represent 15% of total visits.
Consistent with previous literature (e.g., [53]; [56]), we define innovativeness from a consumer perspective. We use Mintel GNPD to collect data on new products that firms introduced. The GNPD classifies products as new to the market, line extensions, or new formulations. Products that are introduced for the first time in a country are classified as new to the market. We measure new product portfolio innovativeness, defined as the innovativeness of the new products that a firm introduces each period, as the ratio between the number of new-to-the-market products and the total number of new products that a firm introduces in a quarter. This is consistent with [56], who consider new-to-the-market products as the most innovative type of products. Web Appendix C provides examples of products from Mintel GNPD. The descriptive statistics for the number of new-to-the-markets products introduced each quarter are as follows: M =.64, SD = 1.63, Max = 20 (Parmalat in the fourth quarter of 2007). As for number of other products: M =.89, SD = 2.23, Max = 20 (Ottogi in the third quarter of 2014). We collect data on mergers and acquisitions of the firms in our sample to ensure that we assign products to the correct firm, consistent with [53].
Stock holding change is the percentage of firm f's outstanding stocks that investor i holds at the end of quarter t, minus the percentage of f's outstanding stocks that i held at the end of quarter t − 1. We collect this data from Thomson One.
We measure the six dimensions of a large investor's national culture through index scores from [23].
We control for the relevance of the firm in the investor's portfolio—that is, the extent to which the performance of an investor's portfolio depends on the performance of the firm ([20]). High firm relevance means that investors are more interested in the firm's long-term growth ([20]). We measure this as the portion of investor's portfolio (in U.S. dollars) invested in the firm. The data are from Thomson One.
Frequency of trading is the average holding period for stocks in the investor's portfolio. Investors who frequently trade their stocks are more sensitive to short-term gains than other investors ([ 7]). These data are from Thomson One, which classifies investors as high or low frequency. We use a dummy variable that takes a value of 1 when the average holding period is less than one year, suggesting a short-term investment horizon and frequent trading, and 0 otherwise.
Because innovativeness is risky, investors may limit the purchase of stocks of innovative firms if they hold a risky portfolio. We control for the beta and alpha of investor i's portfolio. We collect information from Thomson One about the percentage of i's portfolio that is invested in each firm. For all the firms in i's portfolio, including those not in our sample, we collect the daily market returns from Compustat Security Daily. We calculate our betas and alphas with a three-factor Fama–French model similar to the one described in Equation 0, daily data, and a 120-day rolling-window approach, weighted by the dollar percentage of i's portfolio invested in a firm. Quarterly betas/alphas are the average of the three-month periods.
The disposition effect in the finance literature indicates that investors may sell stocks whose price has gone up since purchase ([49]). We control for the difference between the closing stock price at the end of quarter q and the purchase price, divided by the purchase price. Stock price data are from Compustat Security Daily.
Tenure is the length of the relationship between the investor and the firm. Consistent with previous research on financial decision making, we control for tenure, measured as the number of quarters the investor has held the firm's stock ([37]).
Local investor controls for possible home bias—namely, investors' tendency to tilt their portfolios towards local stocks ([47]). We use a dummy variable that takes a value of 1 when the investor owns stock in a firm headquartered in his or her same country and 0 otherwise.
Gender is a dummy that takes a value of 1 when the investor is male and 0 otherwise. Research has shown gender differences in investment behavior (e.g., [ 1]). We use the Gender API (available at gender-api.com) to derive gender from an investor's name.
We control for the investor's country regulatory profile (i.e., the level of corruption in the investor's country), which influences financial decision making. We measure this variable with the corruption perception index by Transparency International ([37]). Finally, we use two dummies for the first or last period in which an investor holds a firm's stocks.
We control for the innovativeness of the firm's existing products with two variables: ( 1) number of new-to-the-market products and ( 2) number of other products introduced in the previous 3 years. The data are from Mintel GNPD.
The number of investors in the firm (including institutional investors) may influence the extent to which a large investor can influence the firm's activities. The mean number of investors is 44 (including institutional investors), median is 22, and maximum is 889 (Parmalat in the first quarter of 2011).[ 6] Our firms have an average of 3.1 large individual investors (SD = 2.42, Mdn = 2, Max = 12). The data are from Thomson One.
Branding strategy reflects the firm's approach to branding its products. Investors prefer firms that adopt corporate branding strategy rather than house-of-brands or mixed branding strategies ([39]). We use two dummies, with mixed branding strategy serving as the reference category. We collect this information from each firm's website.
Stock price difference refers to a firm's stock price fluctuations between quarters. Large price changes catch investors' attention, influencing their stock holding decisions ([ 2]). We compute stock price difference as closing price at the end of quarter t minus the closing price at the end of quarter t − 1. The data are from Compustat Security Daily.
Abnormal trading volume refers to variations in a firm's trading volume. Investors pay more attention to stocks that experience abnormally heavy trading volume ([ 2]). For each stock in each quarter, we calculate the ratio of the stock's trading volume to its average trading volume in the previous three quarters. The data are from Compustat Security Daily.
The number of countries variable is the number of countries in which a firm has introduced products in the current quarter. We control for this variable because the introduction of new products in more countries may be more visible than the introduction of new products in fewer countries.
Stock index growth may influence investors' stock holding decisions: investors may prefer to invest in firms listed in indices that perform well. We measure it as the index closing price at the end of quarter t minus closing price at the end of quarter t − 1. The data are from Yahoo! Finance.
We control for the institutional context (i.e., economic, regulatory and cultural system) of the countries in which a firm introduces new products. Prior research has shown that some countries are more conducive to the success of innovative products than others, causing innovativeness to generate higher cash flow and firm residual value ([58]).
The economic system of the countries in which a firm introduced innovations refers to how a country's economic institutions fulfill the material needs of its people. We consider three components of the economic system. First, we consider market size, which ensures that innovations have a potential large pool of consumers who can buy them. We measure market size as the log-transformed yearly gross domestic product (GDP; in US$ billions) at purchasing power parity ([58]), which we obtain from the Global Competitiveness Report (GCR), and the total expenditures (in US$) in the food and beverage industries, which we obtain from Passport. Because a firm may introduce products in more than one country each quarter, we measure market size as the average of country k's GDP/expenditures weighted by the percentage of all firm f's products introduced in country k at time t (i.e., countries are weighted in proportion to the number of products introduced therein):
Graph
We use the same approach for all the institutional context variables. Our data for economic and regulative system are yearly but tend to be stable over time. Thus, we measure the institutional context of each quarter with the corresponding yearly data (e.g., we measure the market size of France in the first quarter of 2011 with the French GDP in 2011).
Second, we consider market efficiency using a yearly indicator provided by the GCR, which measures the extent to which a country is characterized by healthy market competition (67%) and by quality of demand conditions (33%). Third, market infrastructure allows for faster dissemination of information about the distribution of new products, increasing the diffusion of new products ([10]). We measured this component with a yearly indicator from the GCR, assessing the quality of the transportation (50%) and of the electricity and telephone infrastructure (50%).
Rule of law is a key element of a country's regulatory system ([58]). It refers to the degree to which the behavior of individuals and organizations is guided by formal and transparent rules. A strong rule of law guarantees protection from imitation and counterfeit products, which limit the cash flow that a firm can generate from its innovations. Thus, investors may see more favorable innovations introduced in countries with stronger rule of law. We control for the rule of law with a composite indicator provided by the World Bank.
The cultural system of the countries in which a firm introduced innovations represents the customs, traditions, norms, values, and habits of a society ([58]; Steenkamp, Ter [59]). In countries where consumers are more open to innovation, new products may take off faster, accelerating cash flows. Marketing scholars have relied on the work of Schwartz and colleagues (e.g., [45]) to understand consumer attitudes toward new products (e.g., [42]; [59]). Thus, we use Schwartz's seven values ([46]) to measure the cultural system.
We use a [16] three-factor model:
Rft=αf+βfMKTRFMKTRFft+βfSMBSMBft +βfHMLHMLft+∊ft,0
where is the raw return of firm f in excess of the quarterly risk-free rate in quarter t; is the difference between the quarterly return of the stock market portfolio and risk-free rate in quarter t; and and are returns of factor-mimicking portfolios for size and book-to-market, respectively. The raw quarterly data for stock market portfolios, risk-free rates, SMB, and HML are from Kenneth French's data library, which provides data for five areas: the United States, Europe, Japan, Asia Pacific, and global (which includes the data from the previous four areas and areas they did not cover). Ideally, we would have information for each country in our sample; however, these data are not available, and Kenneth French's data library is the best available source.
The mean large investors' stock holding is 13.37%, standard deviation is 15.88%, and maximum is 75.18%. We measure the change as the percentage of firm f's outstanding stocks that large investors collectively hold at the end of quarter t minus the percentage of f's outstanding stocks that they held at the end of quarter t − 1.
For each dimension, we calculate the sum of the scores of each dimension of all N large investors i weighted by i's stock holding. For instance, the individualism of the firm's large investor base is as follows:
Graph
We control for the innovativeness of the firm's existing products, the institutional context of the countries in which new products are introduced, and branding strategies as described previously. We control for number of outstanding stocks. These data are from Compustat.
Investors react only to new, unanticipated information ([53]). This means that investors respond if innovativeness is higher or lower than expected. Thus, we estimate the unanticipated components of innovativeness as follows (see [53]):
INNOVATIVENESSft^=θ0f+θ1INNOVATIVENESSft−1^+∊UNEXPft,1
where is the firm-specific intercept that measures time-invariant firm heterogeneity, is the first-order autoregressive coefficient depicting the persistence of the time series, and is the unexpected innovativeness of firm f at time t, which we then use in our analysis.
We have a repeated cross-sectional design in which firms are monitored repeatedly every quarter for 9 years, and we observe the stock holding of each large investor within these firms. Thus, our data have a hierarchical structure: at the lowest level, our data consist of observations about large investors' stock holding change (Level 1), nested within investors' countries as well as within time (i.e., each quarter; Level 2). Unlike a traditional HLM, our observations are nested within two membership structures: investors' countries c and time t. Furthermore, because we have a cross-sectional design, time periods are nested within firms f (Level 3), which in turn are nested in their own countries k (Level 4). We depict our structure in Web Appendix D.[ 7]
To estimate the most appropriate model for our data, we start testing a random-intercept cross-classified model as follows:
ΔStock holdingi, ct, fk=Yi, ct, fk=θ00000+uc+utfk+rfk+vk+∊i, ct, fk2
Graph
where
- Yi,ct,fk is the observed stock holding change of investor i in investor's country c at the end of quarter t in firm f and firm's country k;
- θ00000 is the grand mean of an investor's stock holding change across investors, investor's country, time, firms, and firm's country;
- ∊i,ct,fk is the random investor coefficient, or the deviation in stock holding change from the mean of investor i in investors' country c at time t, in firm f and firm's country k;
- uc is the random intercept of investor's country c;
- utfk is the random intercept of time t;
- rfk is the random intercept of firm f; and
- vk is the random intercept of firm's country k.
Because a Woolridge test reveals no auto-correlation in our data, we use a regular variance-covariance structure. This model partitions the total variance of ( + into five components: Level 1 among investors, Level 2a among the investor's country, Level 2b among time within firms, Level 3 among firms within the firm's country, and Level 4 among the firm's country. The proportion of total variance (PV) attributable to each level is calculated as follows: / is the PV among investors, / is the PV between large investors' countries, / is the PV across time, / is the PV between firms, and / is the PV between firm's countries. This partitioning enables us to understand whether we can estimate a simpler model with fewer levels—an important step, because unnecessary levels inflate standard errors ([40]).
To understand the best nested structure to represent our data, we perform a model selection analysis, which we detail in Web Appendix E. We estimate the model with a Bayesian estimation approach, which provides more precise estimates than traditional approaches by producing the best linear, unbiased predictor. We use MLwiN 3.01 software and the Markov chain Monte Carlo procedure with a Bayesian estimation ([ 5]). We estimate the initial values of the parameters necessary to run this procedure using iterative generalized least squares, which are equivalent to the maximum likelihood estimators. We run MLwiN in Stata with the "runmlwin" command. The best model is a cross-classified one in which observations are nested in both the investor's country c and the firm f, and the two random coefficients are additive. We use this structure for our analysis.
We develop our model, in which stock holding change varies according to the unanticipated component of innovativeness at the firm level (i.e., ) and cultural variables at the investor's country level ( ). Our model is as follows:
L1: Yicft=β0cft+∑i=16βicfTV-INVicft +∑i=710βicfNTV−INVicf+∊icft,3a
where
- TV-INV are the following time-varying variables at the investor level: relevance of the firm in the investor's portfolio, alpha and beta of the investor's portfolio, stock purchase price difference, and dummies for the first and last periods that an investor holds stocks in the firm;
- NTV−INV are the following time-invariant variables at the investor level: frequency of trading, tenure, local, and gender; and
- ∊icft is the random investor intercept.
At Level 2, we include variables at the investor's country level and at the firm level:
L2:βocft=γ0000+γ01cINNOVft+∑s=27γ0sCULTcl +∑s=813γ0s(INNOVft ×CULTcl)+γ014REGct +∑s=1533γosTV-FIRMft+∑s=3435γosBRANDINGf +∑s=3662γosINDEXf+∑s=6373γ0sTIMEft+uc+rf,3b
where
- INNOV is the residual term from Equation 1 and represents unexpected innovativeness;
- l represents the following time-invariant variables at the investor's country level: individualism, uncertainty avoidance, power distance, masculinity, long-term orientation, and indulgence;
- REG is the regulatory profile of the investor's country, which is time-varying;
- TV−FIRM are the following time-varying variables at the firm level: number of previous new-to-the-market and incremental products, number of investors, stock price difference, abnormal trading volume, number of countries where the firm introduced new products, stock index growth, and variables about the institutional context of the countries in which a firm has introduced products (market size [GDP and consumer expenditures], market efficiency, market infrastructure, rule of law, and the seven Schwartz's values);
- BRANDING represents time-invariant dummies that refer to the branding strategy of the firm;
- INDEX represents dummy variables for the stock exchange in which a firm is listed to control for unobserved stock index effects;
- TIME represents dummies for years and quarters to control for unobserved time effects; and
- uc and are as defined previously.
The other slope coefficients from Level 1 are treated as fixed.
Consistent with [58], we mean-center the continuous Level 1 predictors within investors' countries and firms and center at the grand-mean the continuous investors' country- and firm-level predictors. This centering helps us obtain results purified of possible sources of endogeneity across firms and investors' countries. We control for stationarity because nonstationarity may produce spurious results, and inferences based on t-values can be misleading ([58]). The Fisher-augmented Dickey–Fuller panel unit root test on stock holding change reports no evidence of unit roots (P = 615.57, p <.05). We use an iterative maximum likelihood estimation, which enables us to simultaneously estimate relationships at multiple levels. We use the procedure XTMIXED in Stata 14 for estimation.
The decision related to innovativeness may be endogenous, which might cause us to underestimate standard errors and thus overestimate the significance of the results. We identify three possible sources of endogeneity in our study. First, at the firm level, there may be some unobserved variables that drive both innovativeness and a large investor's stock holding decision. For instance, the possession of more resources may help a firm introduce products that are more innovative than those of its competitors. At the same time, high resources may make a firm more visible to a large investor: for instance, the firm may invest more in advertising, which has an impact on investors' willingness to buy stock ([55]). Thus, firm resources may also influence stock holding decisions, as large investors may prefer to increase their stock holding in firms with many resources than in firms with few resources. We alleviate this concern by taking advantage of our model specification: in HLM, centering at the firm mean yields coefficients purified from all between-firm variation ([14]). Therefore, the results reported in Table 2 are purified of possible variations across firms.
Second, time effects may drive both innovativeness and stock holding change. For instance, research has shown that some firms strategically pace their new product introductions across quarters ([35]). In addition, the finance literature has shown that investors tend to sell stocks in the last quarter for tax reasons ([49]). We account for unobserved time effects by including year and quarter dummies in our main analysis.
Third, stock exchange effects may drive both innovativeness and stock holding changes. Firms may pursue a certain innovativeness to gain legitimacy in the stock market. For the same reason, large investors may prefer more innovative firms listed in countries that traditionally value innovativeness. We control for this issue with stock exchange dummies.
The only remaining source of endogeneity is both firm and time specific. The traditional instrument approach is good, as the instruments are uncorrelated with errors, but there is no way to empirically determine whether the exclusion restriction is satisfied. Thus, we use instrument-free methods: the Blundell–Blond estimator and Lewbel's instrument method. Collectively, eliminating the effect of unobserved variables at the firm level, controlling for unobserved time and stock exchange effects, and finding that our results are robust to different instrument-free methods minimize endogeneity concerns.
In the raw data, we find a positive correlation between innovativeness and stock holding change (ρ =.04, p <.05; see Table 1). Table 2 reports the results of the cross-classified HLMs and Model 1 reports the results of the direct-effects model. We find a positive relationship between innovativeness and stock holding change (b =.169, p <.05), which indicates that large investors increase their stock holding in companies that introduce more innovative products, in support of H1a. As for the other control variables, consumer hierarchy (b =.605, p <.05) and the first period in which large investors buy a firm's stocks (b = 1.055, p <.05) increase stock holding. In contrast, consumer mastery (b = −1.367, p <.01) and the last period in which a large investor holds a firm's stock (b = −.717, p <.01) decrease stock holding.
Graph
Table 1. Correlation Matrices.
| A: Investor-Level Analysis |
|---|
| Mean | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | |
|---|
| 1. Stock holding change | 2.77 | 7.04 | 1 | | | | | | | |
| 2. Innovativeness | .16 | .30 | .04* | 1 | | | | | | |
| 3. Individualism | 63.43 | 32.43 | .10* | −.05* | 1 | | | | | |
| 4. Uncertainty avoidance | 50.54 | 22.58 | .07* | .05* | −.04* | 1 | | | | |
| 5. Power distance | 64.79 | 15.84 | −.04 | .04* | −.56* | −.47* | 1 | | | |
| 6. Masculinity | 53.96 | 15.2 | −.01 | −.11* | .14* | −.13* | .23* | 1 | | |
| 7. Long-term orientation | 74.45 | 31.29 | −.08* | .04* | −.66* | −.12* | .48* | .31* | 1 | |
| 8. Indulgence | 40.19 | 15.28 | .01 | −.10* | .44* | .22* | −.74* | −.26* | −.64* | |
| B: Firm-Level Analysis |
| Mean | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| 1. Stock returns | .02 | .36 | 1 | | | | | | | |
| 2. DLISHa | .17 | 3.95 | .10* | 1 | | | | | | |
| 3. Innovativeness | .15 | .31 | .10* | .04 | 1 | | | | | |
| 4. LI individualism | 4.49 | 7.36 | .01 | .19* | .01 | 1 | | | | |
| 5. LI uncertainty avoidance | 6.14 | 8.64 | .05* | .22* | −.01 | .56* | 1 | | | |
| 6. LI power distance | 7.28 | 10.53 | .05* | .19* | −.01 | .45* | .62* | 1 | | |
| 7. LI masculinity | 6.24 | 9.41 | .01 | −.18* | −.01 | .64* | .74* | .78* | 1 | |
| 8. LI long-term orientation | 7.85 | 11.75 | .03 | .17* | .01 | .32* | .66* | .78* | .72* | 1 |
| 9. LI indulgence | 4.66 | 7.40 | .04 | .22* | -.01 | .72* | .67* | .62* | .71* | .46* |
- 10022242918805404 *p <.05.
- 20022242918805404 a Change in large investors' stock holding.
- 30022242918805404 Notes: LI = large investors. The Web Appendix reports the full correlation matrix.
Graph
Table 2. Investor-Level Analysis: Results of the HLM Analysis.
| DV: Stock Holding Change | Model 1 | Model 2 |
|---|
| Intercept | .142 (2.183) | .418 (2.178) |
| Unexpected innovativeness (INN) | .169 (.084)** | .843 (.306)*** |
| INN × Individualism (IND) | | .027 (.008)*** |
| INN × Uncertainty avoidance (UA) | | .017 (.006)*** |
| INN × Power distance (PD) | | .045 (.011)*** |
| INN × Masculinity (MASC) | | −.027 (.006)*** |
| INN × Long-term orientation (LTO) | | .013 (.005)** |
| INN × Indulgence (INDULG) | | .027 (.009)*** |
| IND | −.014 (.015) | −.014 (.015) |
| UA | .001 (.012) | .001 (.012) |
| PD | −.002 (.013) | −.001 (.013) |
| MASC | .008 (.013) | .007 (.013) |
| LTO | −.002 (.012) | −.001 (.012) |
| INDULG | .003 (.026) | .005 (.026) |
| Market GDP | −.004 (.045) | −.007 (.045) |
| Market consumer expenditures | .002 (.002) | .003 (.002) |
| Market efficiency | −.093 (.182) | −.036 (.188) |
| Market infrastructure | −.113 (.125) | −.144 (.126) |
| Rule of law | .062 (.167) | .059 (.168) |
| Consumer conservatism | 9.926 (5.117)* | 10.230 (5.158)** |
| Consumer affective autonomy | −.341 (.245) | −.266 (.255) |
| Consumer intellectual autonomy | .571 (.394) | .651 (.401) |
| Consumer hierarchy | .605 (.295)** | .551 (.298)* |
| Consumer mastery | −1.367 (.464)*** | −1.217 (.473)** |
| Consumer harmony | .252 (.301) | .275 (.306) |
| Consumer egalitarian commitment | −9.322 (5.069)* | −9.920 (5.105)* |
| Firm's relevance | .001 (.001) | .001 (.001) |
| Frequency trading | −.063 (.116) | −.067 (.115) |
| Investor's portfolio beta | .072 (.146) | .046 (.146) |
| Investor's portfolio alpha | −.001 (.001) | −.001 (.001) |
| ▵ Stock purchase price | −.013 (.034) | −.011 (.034) |
| Investor tenure | −.001 (.003) | −.001 (.003) |
| Local Investor | −.006 (.133) | .024 (.133) |
| Gender | .001 (.041) | .002 (.041) |
| First period | 1.055 (.510)** | 1.054 (.508)** |
| Last period | −.717 (.092)*** | −.713 (.092)*** |
| # new-to-the-market products in prior 3 years | .001 (.008) | .002 (.008) |
| # incremental products in prior 3 years | .001 (.004) | −.001 (.004) |
| # investors | −.001 (.001) | −.001 (.001) |
| Corporate branding | .006 (.080) | −.004 (.080) |
| House of brands | −.006 (.082) | −.001 (.081) |
| Stock price difference | .099 (.087) | .093 (.087) |
| Abnormal trading volume | .004 (.009) | .004 (.009) |
| # countries | −.048 (.042) | −.041 (.042) |
| Stock index growth | −.170 (.191) | −.164 (.191) |
| Investor country's regulatory profile | −.534 (.285)* | −.466 (.287) |
| Random Effects | | |
| Country ( σc2) | .0000006 | .00000005 |
| Firm ( σf2) | .0000004 | .000000003 |
| Errors ( σε2) | 1.27*** | 1.26** |
| −Log likelihood | 5,865.97 | 5,852.37 |
| Akaike information criterion | 11,879.93 | 11,870.74 |
- 40022242918805410 *p <.10. **p <.05. ***p <.01.
- 50022242918805410 Notes: DV = dependent variable. Quarter, year, and stock index dummies included.
Model 2 adds the moderation effects. We find that individualism positively moderates the innovativeness–stock holding change relationship (b =.027, p <.01), in support of H2a. To further understand how this relationship varies at different levels of individualism, we perform a floodlight analysis ([54]). We use the Johnson–Neyman technique ([27]) to identify the region in the range of individualism for which this relationship is significant. We plot the relationship and the confidence interval in Figure 1, Panel A. Because we mean-center the moderators in our analysis, to obtain more meaningful results in the figure, we rescale the x-axis by adding the mean back ([19]). The analysis reveals that the relationship is significant when the individualism score is higher than 36 (BJN =.18, SE =.09, p =.05).
Graph: Figure 1. The effect of innovativeness on stock holding change at different values of individualism and masculinity. Notes: The solid line represents the point estimate; the dotted lines represent the upper and lower limits of the 95% bias-corrected confidence intervals.
Uncertainty avoidance positively moderates the innovativeness–stock holding relationship (b =.017, p <.01), contradicting H3a. We find that this relationship is negative for values of uncertainty avoidance below 30 (BJN = −.30, SE =.15, p =.05), not significant for values between 31 and 54, and positive for values above 55 (BJN =.19, SE =.09, p =.05). Figures for uncertainty avoidance and all other cultural variables appear in Web Appendix F.
Power distance positively moderates the innovativeness–stock holding change relationship (b =.045, p <.01), in support of H4a. This relationship is negative for values of power distance less than or equal to 52 (BJN = −.26, SE =.13, p =.043), not significant for values between 53 and 61, and positive for values of 62 or higher (BJN =.19, SE =.09, p =.032).
Masculinity negatively moderates the innovativeness–stock holding change relationship (b = −.027, p <.01), rejecting H5a. The floodlight analysis (Figure 1, Panel B) shows that this relationship is positive for values of masculinity below 50 (BJN =.17, SE =.085, p =.05), not significant between 51 and 66, and negative for values above 67 (BJN = −.28, SE =.14, p =.05).
Long-term orientation positively moderates the innovativeness–stock holding change relationship (b =.013, p <.05), in support of H6a. This relationship is negative for values of long term orientation below 32 (BJN = −.44, SE =.22, p =.05), not significant for values between 33 and 76 (BJN =.18, SE =.09, p =.05), and positive at higher values.
Indulgence positively moderates the innovativeness–stock holding change relationship (b =.027, p <.01), in support of H7a. This relationship is negative when indulgence is 30 or below (BJN = −.29, SE =.14, p =.043), not significant between 31 and 45, and positive at values above 46 (BJN =.20, SE =.10, p =.043).
Our results are robust to different measures of innovativeness, methods to deal with endogeneity, moderation effect of the institutional context, nonlinear effect of innovativeness, foreign bias, and the exclusion of firms listed in Taiwanese stock exchanges, which have the highest minimum disclosure threshold (10%). For detailed robustness analyses, see Web Appendix G.
Our firm-level data are nested within the firm's stock exchange k. To control what percentage of the total variance of change in large investor's stock holding ( ) occurs at the stock exchange level, we estimate an unconditional means model with no predictor. At Level 1, we express the observed in firm f in stock exchange k at time t as the sum of a fixed intercept ( ) plus a random component ( ) that defines the deviation in from the mean of firm f in stock exchange k at time t:
Level 1:ΔLISHfkt=βSH0kt+rSHfkt,where rSHfkt ∼N(0,σ2).4a
At Level 2 (the stock exchange level), we express the stock exchange intercept as the sum of the grand mean of ( ) and a series of random deviations from that mean ( ) that represent the random-intercept at the stock exchange-level:
Level2:βSH0kt=γSH000+uSHk,where υSHk∼N(0,τ00).4b
The portion of variance that occurs at the stock exchange level is 24%, which supports the appropriateness of using HLM.
We estimate a similar unconditional means model for abnormal stock returns:
Rfkt=γRET000+rRETfkt+uRETk,where rRETfkt ∼N(0,σ2) and υRETk ∼N(0,τ00).4c
The portion of variance that occurs at the stock exchange level is 16%, indicating that using HLM is appropriate in this case as well.
To estimate the relationship between innovativeness and stock returns, the mediating role of change in large investor's stock holding ( ), and the moderating role of the culture of a firm's large investors, we estimate the following simultaneous system of equations (in the interest of space, we report our HLM in compact form):
ΔLISHfkt=γSH000+γSH10INNOVV+∑s=27γSHs0FIRM CULTfktl+∑s=813γSHs0(INNOVfkt ×FIRM CULTfktl)+∑s=1429γSHs0Cfkt+∑s=3031γSHs0BRANDINGfk+∑s=3242γSHs0TIMEfkt +rSHfkt+uSHk,5
where
- INNOV is the residual term from Equation 1 and represents unexpected innovativeness;
- I represents the six variables that define the national culture of the firm f's large investor base. Each of them is computed as described in the equation (FirmCULT);
- C represents the following time-varying variables: number of previous new-to-the-market and incremental products, number of countries where the firm introduced new products, variables about the institutional context of these countries, and number of outstanding stocks;
- BRANDING and are as described in Equation 3b; and
- rSHfkt and are as defined in Equations 4a and 4b.
We then employ the stock return response modeling approach, an analytical tool to evaluate whether information contained in a metric is associated with changes in stock returns (Srinivasan and Hanssens 2019). We model abnormal returns as a function of innovativeness, predicted change in large investors' stock holding from Equation 5 ( ), and a set of control variables:
Rfkt=γRET000+γRET10MKTRFfkt+γRET20SMBfkt+γRET30HMLfkt+γRET40INNOVfkt+γRET50ΔLISHfkt^+∑s=621γRETs0 Cfkt+∑s=2223γRETs0BRANDINGfk+∑s=2434γRETs0TIMEfkt+rRETfkt+uRETk,6
where , , , and are defined in Equation 0, the other variables are as defined in Equation 5; and are as defined in Equation 4c.
We present the results in Table 3. In Model 1, we begin estimating the relationship between innovativeness and large investors' stock holding change. In support of H1b, we find a positive association (b =.875, p <.05). This finding indicates that increases in innovativeness are positively associated with large investors buying more of a firm's stocks.
Graph
Table 3. Firm-Level Analysis: Results of the HLM Analysis.
| Model 1DV: ▵LISHa | Model 2DV: ▵LISHa | Model 3DV: Stock Returns | Model 4DV: Stock Returns |
|---|
| Intercept | .480 (.395) | .460 (.392) | −.001 (.039) | −.024 (.039) |
| ▵LISHa | | | | .046 (.008)*** |
| Unexpected innovativeness (INN) | .875 (.394)** | .788 (.392)** | .169 (.036)*** | .125 (.037)*** |
| Large investors' (LI) individualism | .307 (.096)*** | .232 (.098)** | | |
| LI uncertainty avoidance | −.105 (.081) | .006 (.084) | | |
| LI power distance | .274 (.090)*** | .155 (.093)* | | |
| LI masculinity | −.726 (.139)*** | −.439 (.150)*** | | |
| LI long-term orientation | .087 (.065) | .057 (.066) | | |
| LI indulgence | .533 (.126)*** | .327 (.134)** | | |
| INN × LI individualism | | .610 (.281)** | | |
| INN × LI uncertainty avoidance | | −.357 (.252) | | |
| INN × LI power distance | | .478 (.243)** | | |
| INN X LI masculinity | | −1.879 (.352)*** | | |
| INN × LI long-term orientation | | .435 (.215)** | | |
| INN × LI indulgence | | .880 (.369)** | | |
| # new-to-the-market products in prior 3 years | −.029 (.021) | −.018 (.021) | .002 (.002) | .002 (.002) |
| # incremental products in prior 3 years | .015 (.013) | .013 (.013) | .001 (.001) | .001 (.001) |
| # countries | −.065 (.169) | −.045 (.169) | .013 (.015) | .022 (.015) |
| Market GDP | .267 (.157)* | .268 (.156)* | −.017 (.014) | −.027 (.013)** |
| Market consumer expenditures | −.011 (.013) | −.011 (.013) | .001 (.001) | .001 (.001) |
| Market efficiency | −.320 (.487) | −.394 (.485) | .037 (.043) | .057 (.043) |
| Market infrastructure | .128 (.474) | .155 (.471) | .022 (.042) | −.006 (.042) |
| Rule of law | .836 (.483)* | .971 (.481)** | −.015 (.043) | −.033 (.043) |
| Consumer conservatism | −3.983 (1.590)** | −4.028 (1.582)** | .009 (.141) | .202 (.144) |
| Consumer affective autonomy | −.922 (.976) | −.910 (.969) | −.244 (.088)*** | −.220 (.087)** |
| Consumer intellectual autonomy | −1.191 (1.783) | −1.642 (1.783) | −.053 (.158) | .054 (.157) |
| Consumer hierarchy | −3.768 (1.06)*** | −3.829 (1.062)*** | −.118 (.096) | .064 (.101) |
| Consumer mastery | 6.528 (1.846)*** | 7.107 (1.852)*** | .239 (.166) | −.110 (.176) |
| Consumer harmony | 2.669 (1.696) | 2.641 (1.705) | .002 (.151) | −.108 (.151) |
| Consumer egalitarian commitment | −.513 (.277)* | −.510 (.275)* | .049 (.024)** | .066 (.024)*** |
| Corporate branding | .107 (.267) | .119 (.265) | −.039 (.028) | −.045 (.027)* |
| House of brands | .279 (.208) | .306 (.206) | −.025 (.023) | −.039 (.023)* |
| # outstanding shares | −.004 (.003) | −.003 (.003) | −.001 (.001) | −.001 (.001) |
| Market returns | | | .434 (.067)*** | .435 (.067)*** |
| SMB | | | .182 (.154) | .167 (.153) |
| HML | | | .260 (.156)* | .248 (.154) |
| Firm's country random effects | 1.61e-11 | 4.31e-12 | .072 | .071 |
| Errors | 3.75 | 3.72 | .343 | .330 |
| −Log likelihood | 4,427.567 | 4,412.834 | 53.120 | 515.080 |
| Akaike information criterion | 8,931.133 | 8,913.669 | 113.242 | 1,102.179 |
- 60022242918805410 *p <.10. **p <.05. ***p <.01.
- 70022242918805410 a Change in large investors' stock holding.
- 80022242918805400 Notes: DV = dependent variable. Quarter, year, and stock index dummies included.
In Model 2, we test for the moderation role of the culture of the firm's large investors on the innovativeness– large investors' stock holding path. We find that this relationship becomes stronger as the individualism (b =.610, p <.05), power distance (b =.478, p <.05), long-term orientation (b =.435, p <.05), and indulgence (b =.880, p <.05) of the firm's large investors increases, in support of H2b, H4b, H6b, and H7b, respectively. This relationship becomes weaker as the masculinity of the firm's large investors increases (b = −1.879, p <.01), contradicting H5b. We find no significant moderation effect for uncertainty avoidance (b = −.357, p >.05; H3b).
In Model 3, we estimate the direct innovativeness–stock returns relationship. In support of the existing literature and H8, the relationship is positive (b =.169, p <.01). In Model 4, we add our mediator. We find that it is positively associated with stock returns (b =.046, p <.01): the more stocks large investors buy, the higher the stock returns. In addition, the relationship between innovativeness and stock returns remains significant (b =.125, p <.01), indicating that large investors' stock holding only partially mediates the innovativeness–stock returns relationship ([ 4]). To test for H9, which advocates an unconditional mediating role for large investors' stock holding, we run a bootstrap analysis with 1,000 resamples ([38]). We explain this analysis in detail in Web Appendix H. The mediated effect of innovativeness on stock returns is equal to.028 (i.e.,.88 ×.032). The bias-corrected confidence interval does not contain zero, suggesting that large investors' stock holding mediates the innovativeness–stock returns relationship, in support of H9.
H10 maintains that the innovativeness–stock returns relationship is contingent on the cultural characteristics of the firm's large investors. To test for this, we run a moderated mediation analyses, which we explain in detail in Web Appendix H. We find that this relationship increases as individualism, power distance, long-term orientation, and indulgence of the firm's large investors increases, in support of H10a–b and H10d–e. This relationship becomes weaker as the masculinity of the firm's large investors increases, contradicting H10c. We find no significant moderation effect for uncertainty avoidance (H10f).
We control for possible endogeneity with Lewbel's method. We also control for change in number of outstanding stocks rather than just the number. The results, reported in Web Appendix I, are stable to the use of these alternate estimation approaches.
We check the robustness of the results using a portfolio approach similar to [31] and [64]. First, we divide the sample into two subsamples according to the unexpected innovativeness. Each quarter, we insert firms whose unexpected innovativeness is below (above) the average unexpected innovativeness of the quarter in the low (high) portfolio. We update the portfolio each quarter. We calculate the average monthly abnormal stock returns (AVMSR) in month t over Np firms in portfolio p as in [31]:
Graph
We find that the average monthly abnormal stock returns for the high-innovativeness portfolio is.09, whereas it is −.01 for the low-innovativeness portfolio. A t-test reveals that this difference is significant (t-test = −5.37, p <.001).
Next, to test for the moderating effects of cultural variables, we construct two portfolios within each innovativeness subsample. For instance, for individualism, we divided firms in each sample according to the median value of individualism of the large investors' base in each quarter. We thus create four portfolios: low/high innovativeness and low/high individualism. We then compare the stock returns of low- versus high-individualism portfolios when innovativeness is low and when innovativeness is high. We adopt the same logic for the other cultural dimensions.
We report the results of the one-tailed t-test comparisons in Web Appendix J. In the low- and high-innovativeness subsamples, stock returns are always significantly higher in portfolios of firms with high individualism, uncertainty avoidance, power distance, long-term orientation, and indulgence than in the portfolios in which the cultural dimensions are low. Low-masculinity portfolios have higher returns than high-masculinity portfolios in the high-innovativeness subsample, in support of the finding that masculinity negatively moderates the innovativeness–stock returns relationship. In the low innovativeness portfolio, the opposite effect occurs.
Adopting a similar logic, we split the sample into three subsamples: ( 1) sell portfolio, which includes all firms that experienced a negative change in the large investors' stock holding in the quarter; ( 2) no change; and ( 3) buy portfolio, which includes all firms that experienced a positive change in the large investors' stock holding in the quarter. We find that abnormal returns are lower for the sell portfolio (.002) than for the no-change portfolio (.007), but the difference is not significant (t =.22, p >.10). The stock returns of the buy portfolio (.07) are higher than both the no-change (t = −2.40, p <.01) and sell (t = −1.69, p <.05) portfolios. Thus, we find support that the changes in large investors' stock holdings are related to stock returns.
This work contributes to the marketing–finance interface literature by clarifying for which investors, how, and under what conditions innovativeness is associated with stock holding and stock returns. Historically, this literature was created to show the existence of a direct link between marketing and firm value. Having convincingly done that, the literature has advanced toward a more granular understanding of how this link operates. For instance, [62] denote the role of new investors, who increase demand for a firm's stock. We expand the literature by highlighting the critical role of large investors and their culture.
Prior marketing literature has identified increased demand for a firm's stock ([62]) and constrained supply of stock by current investors ([64]) as the drivers of stock market response to marketing actions. However, no study has investigated how each investor makes stock holding decisions in response to marketing actions. This is probably because the marketing literature has considered all investors to be homogenous and equally important. By adopting an investor-level perspective, we identify a segment of investors (i.e., large investors) influential enough to convey the effect of marketing to the stock market. Furthermore, we begin to shed light on differences across large investors in their stock holding decisions related to innovativeness. We intend this as an initial theoretical step to get at the root causes of how marketing actions create value in the stock market. Future research should identify other factors.
We extend the marketing–finance interface literature by advancing a cultural perspective. We develop a theoretical framework consistent with the reality of many firms, whose investor base is heterogeneous in terms of national culture. Our results caution against the traditional wisdom that all investors positively evaluate innovativeness and that higher innovativeness is always associated with increased firm value in the stock market. Reality is more nuanced, in the sense that some large investors sell stocks when firms increase their innovativeness. At the firm level, this means that the stock market response may be negative if the firm has many large investors from cultures that do not value innovativeness. For instance, when masculinity among large investors is too high, the innovativeness–stock returns relationship is negative. Thus, we identify the national culture of a firm's large investors as a key boundary condition in the innovativeness–stock returns relationship. Notably, we find that these moderation effects occur in North American, European, and Asian stock exchanges, even after we control for possible heterogeneity across exchanges. To the best of our knowledge, this is the first study to show how the cultural composition of a firm's large investor base magnifies or dilutes the value that marketing creates in the stock market. Because the investor base is becoming increasingly heterogeneous, in terms of nationalities, future research should account for this heterogeneity.
Collectively, our findings provide a reframing of future research in the marketing–finance interface literature from the question of whether a marketing action or asset increase firm value in the stock market to a more complex set of questions: How and under what conditions does marketing create value in the stock market, and for which investors does this occur?
Managers spend a considerable amount of time meeting with general investors ([55]) and selectively providing information to some of them in private meetings at public conferences, investors' offices, and firms' headquarters or manufacturing facilities ([ 8]; [51]). The commitment to meeting with investors is observed in the United States ([ 8]), Europe ([ 3]), and China ([12]). Our findings indicate to marketing managers that large investors are a key segment to nurture and communicate with, in relation to the innovativeness of a firm's offerings.
Our analysis reveals that communication to large investors is critical when it comes to the innovativeness of the firm's portfolio of new products. When new products are launched, there is high uncertainty about their performance, especially if they are innovative ([56]). Initially, investors react without a full understanding or knowledge of consumer response. To increase the returns from innovativeness, it is very important for firms to manage this uncertainty phase by crafting proper communications to large investors. Our findings identify the need to ( 1) segment large investors and ( 2) position innovativeness differently in each segment. Traditionally, marketing strategy has been about segmenting, targeting, and positioning products to customers. The marketing–finance interface literature has added investors to the picture. We contribute to this literature by bringing to light an important strategic role for marketing in helping firms navigate the stock market: managers should apply to investors the segmentation, targeting, and positioning concepts traditionally adopted for customers.
We find that large investors are heterogeneous in their preferences toward innovativeness. Because we find that culture is one possible explanation for these differences, we propose culture as a key criterion with which to segment large investors. This is an important implication for managers because stock markets are becoming more global, and a firm's large investor base is increasingly heterogeneous in terms of culture. Heterogeneity in large investors' culture, and thus in preferences for innovativeness, makes it difficult for firms to cater to the conflicting demands of its large investors through a one-size-fits-all communication strategy about innovativeness. Instead, managers must target large investors from different countries with ad hoc positioning of innovativeness, consistent with the cultural specificities of each investor.
We suggest that managers adopt a three-step procedure. First, they should investigate how each Hofstede dimension influences the innovativeness–stock holding relationship. To facilitate this effort, Table 4 reports the relationship by each cultural dimension for each country. The first row of the table shows when the innovation–stock holding relationship is positive and the second row shows when it is negative. Numbers in between these scores do not influence the relationship. The block following these two rows shows each country's scores. For example, firms interacting with large investors in Brazil can expect the innovativeness–stock holding relationship to be positive given the scores of individualism (38), uncertainty avoidance (76), power distance (69), masculinity (49), and indulgence (59). In all, five of the six cultural dimensions indicate that innovativeness should produce a strong stock holding response (see Table 4). In this way, managers can easily identify, for each investor, which cultural dimensions depress the positive role of innovativeness.
Graph
Table 4. Hofstede Dimensions and the Innovativeness–Stock Holding Change Relationship.
| When the innovativeness–stock holding change relationship is negative... | IND | UA | PD | MASC | LTO | INDULG | Number of cultural dimensions strengthening vs. weakening the relationship |
|---|
| <30b | ≤52b | >67b | <32b | ≤30b |
|---|
| When the innovativeness–stock holding change relationship is positive... | >36a | >55a | ≥62a | <50a | >76a | >45a |
|---|
| Country | | | | | | | |
| Angola | 18 | 60a | 83a | 20a | 15b | 83a | 4 vs. 1 |
| Argentina | 46a | 86a | 49b | 56 | 20b | 62a | 3 vs. 2 |
| Australia | 90a | 51 | 38b | 61 | 21b | 71a | 2 vs. 2 |
| Austria | 55a | 70a | 11b | 79b | 60 | 63a | 3 vs. 2 |
| Bangladesh | 20 | 60a | 80a | 55 | 47 | 20b | 2 vs. 1 |
| Belgium | 75 | 94a | 65a | 54 | 82a | 57a | 5 vs. 0 |
| Belgium (Flemish) | 78a | 97a | 61 | 43a | | | 3 vs. 0 |
| Belgium (Walloons) | 72a | 93a | 67a | 60 | | | 3 vs. 0 |
| Brazil | 38a | 76a | 69a | 49a | 44 | 59a | 5 vs. 0 |
| Bulgaria | 30 | 85a | 70a | 40a | 69 | 16b | 3 vs. 1 |
| Canada | 80a | 48 | 39b | 52 | 36 | 68a | 2 vs. 1 |
| Canada (Quebec) | 73a | 60a | 54 | 45a | | | 3 vs. 0 |
| Cape Verde | 20 | 40 | 75a | 15a | 12b | 83a | 3 vs. 1 |
| Chile | 23 | 86a | 63a | 28a | 31b | 68a | 4 vs. 1 |
| China | 20 | 30 | 80a | 66 | 87a | 24b | 2 vs. 1 |
| Colombia | 13 | 80a | 67a | 64 | 13b | 83a | 3 vs. 1 |
| Costa Rica | 15 | 86a | 35b | 21a | | | 2 vs. 1 |
| Croatia | 33 | 80a | 73a | 40a | 58 | 33 | 3 vs. 0 |
| Czech Republic | 58a | 74a | 57 | 57 | 70 | 29b | 2 vs. 1 |
| Denmark | 74a | 23b | 18b | 16a | 35 | 70a | 3 vs. 2 |
| Dominican Republic | 30 | 45 | 65a | 65 | 13b | 54 | 2 vs. 1 |
| Ecuador | 8 | 67a | 78a | 63 | | | 2 vs. 0 |
| Egypt | 25 | 80a | 70a | 45a | 7b | 4b | 3 vs. 2 |
| El Salvador | 19 | 94a | 66a | 40a | 20b | 89a | 4 vs. 1 |
| Estonia | 60a | 60a | 40b | 30a | 82a | 16b | 4 vs. 2 |
| Ethiopia | 20 | 55 | 70a | 65 | | | 1 vs. 0 |
| Finland | 63a | 59a | 33b | 26a | 38 | 57a | 4 vs. 1 |
| France | 71a | 86a | 68a | 43a | 63 | 48a | 5 vs. 0 |
| Germany | 67a | 65a | 35b | 66 | 83a | 40 | 3 vs. 1 |
| Ghana | 15 | 65a | 80a | 40a | 4b | 72a | 4 vs. 1 |
| Greece | 35 | 112a | 60 | 57 | 45 | 50a | 2 vs. 0 |
| Guatemala | 6 | 99a | 95a | 37a | | | 3 vs. 0 |
| Honduras | 20 | 50 | 80a | 40a | | | 3 vs. 0 |
| Hong Kong | 25 | 29b | 68a | 57 | 61 | 17b | 1 vs. 2 |
| Hungary | 80a | 82a | 46b | 88b | 58 | 31 | 2 vs. 2 |
| India | 48a | 40 | 77a | 56 | 51 | 26b | 2 vs. 1 |
| Indonesia | 14 | 48 | 78a | 46a | 62 | 38 | 2 vs. 0 |
| Iran | 41a | 59a | 58 | 43a | 14b | 40 | 3 vs. 1 |
| Iraq | 30 | 85a | 95a | 70b | 25b | 17b | 2 vs. 3 |
| Ireland | 70a | 35 | 28b | 68b | 24b | 65a | 2 vs. 3 |
| Israel | 54a | 81a | 13b | 47a | 38 | | 3 vs. 1 |
| Italy | 76a | 75a | 50b | 70b | 61 | 30b | 2 vs. 3 |
| Japan | 46a | 92a | 54 | 95b | 88a | 42 | 3 vs. 1 |
| Jordan | 30 | 65a | 70a | 45a | 16b | 43 | 3 vs. 1 |
| Kenya | 25 | 50 | 70a | 60 | | | 1 vs. 0 |
| Kuwait | 25 | 80a | 90a | 40a | | | 3 vs. 0 |
| Latvia | 70a | 63a | 44b | 9a | 69 | 13b | 3 vs. 2 |
| Lebanon | 40a | 50 | 75a | 65 | 14b | 25b | 2 vs. 2 |
| Libya | 38a | 68a | 80a | 52 | 23b | 34 | 3 vs. 1 |
| Lithuania | 60a | 65a | 42b | 19a | 82a | 16b | 4 vs. 2 |
| Luxembourg | 60a | 70a | 40b | 50 | 64 | 56a | 3 vs. 1 |
| Malawi | 30 | 50 | 70a | 40a | | | 2 vs. 0 |
| Malaysia | 26 | 36 | 104a | 50 | 41 | 57a | 2 vs. 0 |
| Malta | 59a | 96a | 56 | 47a | 47 | 66a | 4 vs. 0 |
| Mexico | 30 | 82a | 81a | 69b | 24b | 97a | 3 vs. 2 |
| Morocco | 46a | 68a | 70a | 53 | 14b | 25b | 3 vs. 2 |
| Mozambique | 15 | 44 | 85a | 38a | 11b | 80a | 3 vs. 1 |
| Namibia | 30 | 45 | 65a | 40a | 35 | | 2 vs. 0 |
| Netherlands | 80a | 53 | 38b | 14a | 67 | 68a | 3 vs. 1 |
| New Zealand | 79a | 49 | 22b | 58 | 33 | 75a | 2 vs. 1 |
| Nigeria | 30 | 55 | 80a | 60 | 13b | 84a | 2 vs. 1 |
| Norway | 69a | 50 | 31 | 8a | 35 | 55a | 3 vs. 0 |
| Pakistan | 14 | 70a | 55 | 50 | 50 | | 1 vs. 0 |
| Peru | 16 | 87a | 64a | 42a | 25b | 46a | 4 vs. 1 |
| Philippines | 32 | 44 | 94a | 64 | 27b | 42 | 2 vs. 1 |
| Poland | 60a | 93a | 68a | 64 | 38 | 29b | 3 vs. 1 |
| Portugal | 27 | 104a | 63a | 31a | 28b | 33 | 4 vs. 1 |
| Puerto Rico | 27 | 38 | 68a | 56 | 19b | 99a | 2 vs. 1 |
| Romania | 30 | 90a | 90a | 42a | 52 | 20b | 3 vs. 1 |
| Russia | 39a | 95a | 93a | 36a | 81a | 20b | 4 vs. 1 |
| Saudi Arabia | 25 | 80a | 95a | 60 | 36 | 52a | 3 vs. 0 |
| Senegal | 25 | 55 | 70a | 45a | 25b | | 2 vs. 1 |
| Serbia | 25 | 92a | 86a | 43a | 52 | 28b | 3 vs. 1 |
| Singapore | 20 | 8b | 74a | 48a | 72 | 46a | 3 vs. 1 |
| Slovak Republic | 52a | 51 | 104a | 110b | 77a | 28b | 3 vs. 2 |
| Slovenia | 27 | 88a | 71a | 19a | 49 | 48a | 4 vs. 0 |
| South Africa | 65a | 49 | 49b | 63 | 34 | 63a | 2 vs. 1 |
| South Korea | 18 | 85a | 60 | 39a | 100a | 29b | 3 vs. 1 |
| Spain | 51a | 86a | 57 | 42a | 48 | 44 | 3 vs. 0 |
| Sweden | 71a | 29b | 31 | 5a | 53 | 78a | 3 vs. 1 |
| Switzerland | 68a | 58a | 34b | 70b | 74 | 66a | 3 vs. 2 |
| Switzerland (French cantons) | 64a | 70a | 70a | 58 | | | 3 vs. 0 |
| Switzerland (German cantons) | 69a | 56a | 26b | 72b | | | 2 vs. 2 |
| Syria | 35 | 60a | 80a | 52 | 30b | | 2 vs. 1 |
| Taiwan | 17 | 69a | 58 | 45a | 93a | 49a | 4 vs. 0 |
| Tanzania | 25 | 50 | 70a | 40 | 34 | 38 | 2 vs. 0 |
| Thailand | 20 | 64a | 64a | 34a | 32 | 45 | 3 vs. 0 |
| Trinidad and Tobago | 16 | 55 | 47b | 58 | 13b | 80a | 1 vs. 2 |
| Turkey | 37a | 85a | 66a | 45a | 46 | 49a | 5 vs. 0 |
| Ukraine | 25 | 95a | 92a | 27a | 55 | 18b | 3 vs. 1 |
| United Arab Emirates | 25 | 80a | 90a | 50 | | | 2 vs. 0 |
| United Kingdom | 89a | 35 | 35b | 66 | 51 | 69a | 2 vs. 1 |
| United States | 91a | 46 | 40b | 62 | 26b | 68a | 2 vs. 2 |
| Uruguay | 36a | 100a | 61 | 38a | 26b | 53a | 4 vs. 1 |
| Venezuela | 12 | 76a | 81a | 73b | 16b | 100a | 3 vs. 2 |
| Vietnam | 20 | 30b | 70a | 40a | 57 | 35 | 2 vs. 1 |
| Zambia | 35 | 50 | 60 | 40a | 30b | 42 | 1 vs. 1 |
90022242918805400 a Indicates that the cultural dimension strengthens the innovativeness–stock holding change relationship. bIndicates that the cultural dimension weakens it.
Second, managers should position innovativeness to large investors in each country to reflect the impact of cultural values. In making our predictions about large investors' response to innovativeness, we relied on investors' typical perception of innovativeness as a means to stand out, create inequities in the market, and signal commitment to long-term competitive advantage. We find that this typical perception of innovativeness conflicts with some cultural values, leading some large investors to sell stocks of innovative firms. To soften the negative effects of the cultural values that work against innovativeness, we suggest that managers adapt the positioning of innovativeness according to the scores of each cultural dimension. Table 5 reflects our recommendation to managers based on our empirical analysis, which we summarize next.
Graph
Table 5. Managerial Recommendations for Positioning Innovativeness to Large Investors.
| Recommended Culture Value Position to Large Investors |
|---|
| Score Is Below Noted Level | Score Is Above Noted Level |
|---|
| Individualisma | | Stand out in the market.Value scores >36. |
| Uncertainty avoidance | Create instability in the market.Value scores <30. | Secure the prosperity of the firm.Value scores >55. |
| Power distance | Level the field by reducing the gap with competitors.Value scores ≤52. | Create inequity among firms and consumers.Value scores ≥62. |
| Masculinity | Contribute to the overall welfare of society.Value scores <50. | Reach worldwide recognition as a leader and dominant force in the market.Value scores >67. |
| Long-term orientation | Keep the status quo.Value scores <32. | Be an agent of change.Value scores >76. |
| Indulgence | Comply with a market law that the firm is forced to abide to in order to survive.Value scores ≤30. | Satisfy personal enjoyment through an arbitrary choice.Value scores >45. |
100022242918805400 a Individualism is the only dimension of Hofstede's framework that never weakens the innovativeness–stock holding change relationship.
For large investors who score higher than 36, managers should position innovativeness as a way to stand out in the market. Large investors who score below 36 should not receive any special positioning strategies.
Large investors who score below 30 tend to look for novel and less predictable situations ([59]). For these investors, managers should present innovativeness as a way to create instability in the market. For large investors who score above 55, managers should position innovativeness as a way to secure the prosperity of the firm. Large investors who score between 30 and 55 should not receive any special positioning strategies.
Large investors who score 52 or below prefer an equal distribution of power. For these investors, managers should position innovativeness as a way for the firm to level the field by reducing the gap with competitors. For large investors who 62 or above, managers should emphasize that innovativeness would help the firm stand out in the market and create inequity among firms and consumers. Large investors who score between 53 and 61 should not receive any special positioning strategies.
For large investors who score below 50, managers should explain how new products contribute to the overall welfare of society, which is what concerns these investors. Large investors who score above 67 value prestige and control, so managers should position innovativeness as a means for the firm to reach worldwide recognition as a leader. Large investors who score between 50 and 67 should receive no special positioning.
Large investors who score below 32 prefer the status quo and are suspicious toward societal changes. For these investors, managers should position innovativeness as a necessary means for the firm to keep the status quo in the market. For large investors who score above 76, innovativeness should instead be positioned as an agent of change. Large investors who score between 32 and 76 should not receive any special positioning strategies.
Large investors who score 30 or below believe that people should focus on maintaining social norms more than pursuing their own desires. For these investors, managers should position innovativeness as a market law that the firm is forced to comply with to survive in the market. For large investors who score above 45, managers should position innovativeness as an arbitrary choice to satisfy personal enjoyment. Large investors who score between 31 and 45 should not receive any special positioning strategies.
The third and final step is to position innovativeness to each large investor in a manner that is consistent with his or her scores across all six dimensions. Take the case of a Chinese investor. Her scores on individualism (20), uncertainty avoidance (30), and masculinity (66) require no special positioning; her scores on long-term orientation (87), indulgence (24), and power distance (80) indicate that innovativeness should be positioned as an agent of change and a market law that a firm is forced to comply with to create inequity among consumers. Managers can adopt a similar approach to position innovativeness to each large investor from any culture.
Our findings provide managers with a better understanding of how innovativeness plays out in the stock market, which can help managers defend the value of their innovation investments in front of board members, especially when stock performance is subpar. Previous marketing studies have shown that firms act in response to stock market reactions ([ 9]; [33]; [63]). This responsiveness to stock market feedback is one of the major concerns about public companies, which are frequently blamed for cutting innovation investments to meet investors' short-term demands. Managers who experience lower-than-expected returns from innovativeness may be tempted to reduce future investments in innovation. We warn managers that stock market performance below expectations might be due to a nonfavorable composition of the large investor base. Differences in the cultures of large investors may explain, over time, the variation in stock returns as well as cross-sectional differences with competitors that have a new product portfolio with similar innovativeness yet reap greater benefits simply because they have a more favorable large investor base.
[35] report that some firms artificially delay the introductions of innovations to satisfy stock markets, even at the expense of sales in product markets. We suggest that managers carefully assess the percentage of stocks held by large investors and the culture of these investors to identify the most appropriate moment that maximizes stock returns from innovativeness. A firm could time the innovativeness of its new product portfolio depending on the composition of its large investor base. Echoing Moorman et al., we warn managers that it is not possible to delay new product introductions for too long, as this hurts customer perceptions of firm innovativeness. In this regard, marketing plays a fundamental role to help firms find the optimal balance between the desire of maximizing stock price and the necessity of preserving customer assets, which are fundamental to long-term performance.
Although this study presents new insights, it is not without limitations. First, we examine product innovations only. Process innovations are also important. Although in the food and beverage industry some of the process innovations result in new product innovations (and thus, we indirectly account for them), we have no data about "internal" process innovations that result in cost efficiency. Future research could complement our study by analyzing process innovation.
Second, innovativeness is not the only marketing action that influences stock market performance. Future research could investigate how other marketing actions influence stock holding decisions of large investors. Third, consistent with many marketing studies (e.g., [37]), we restrict our examination of investor culture to Hofstede's framework. Future research could use alternative approaches to national culture, such as Inglehart's ([58]). In addition, we focus on the culture of large investors. Future research could investigate the interaction between large investors' and small investors' culture. In particular, it could investigate how small investors from various cultures mimic the stock holding decisions of large investors.
Fourth, future research could identify other mediators of the innovativeness–stock holding relationship, such as higher price/earnings ratio. Fifth, we study just one industry. Prior research has highlighted two industry factors that may influence our findings. Consumers are more prone to accept innovation in industries with intense competition on innovation ([18]). Thus, innovativeness may play a larger role in these contexts than in ours. In addition, [41] argue that investors are less sensitive to innovativeness in contexts in which innovations are frequently introduced (e.g., high-tech industries). Future research is needed to investigate the generalizability of our findings to other contexts. Finally, our research is limited to large individual investors. However, many large investors are institutional funds, whose culture may be more difficult to measure. Future research could investigate whether the national culture of a fund's managers influences institutional investors' response to innovativeness similarly to what we report for large individual investors.
Supplemental Material, DS_10.1177_0022242918805405 - The New Product Portfolio Innovativeness–Stock Returns Relationship: The Role of Large Individual Investors' Culture
Supplemental Material, DS_10.1177_0022242918805405 for The New Product Portfolio Innovativeness–Stock Returns Relationship: The Role of Large Individual Investors' Culture by Paola Cillo, David A. Griffith, and Gaia Rubera in Journal of Marketing
Footnotes 1 Area EditorChristine Moorman served as area editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242918805405
5 1Firms can also innovate through process innovation, which can be instrumental to product innovation and can result in cost reductions. Consistent with prior research in marketing, we focus on product innovation. We thank an anonymous reviewer for pointing out the importance of process innovation. Our focus on product innovation alone is a limitation of our research.
6 2Unlike individual investors, who must disclose their holdings only when they pass a certain minimum threshold, in the countries in our sample, institutional investors that invest more than a certain amount in equities must disclose all their holdings in every firm, even if the holding in a single firm is below the minimum threshold. For instance, in the United States, all institutional investors with at least $100 million in U.S.-listed equities must disclose all their holdings.
7 3We empirically check for multiple membership of investors within firms but find that 98% of investors own stock in just one of the firms in our sample. In addition, because we consider a firm's country k to be the country where the firm's headquarters is located, each firm belongs to just one country, eliminating concerns about membership of firms within multiple countries.
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Record: 194- The New Regulator in Town: The Effect of Walmart's Sustainability Mandate on Supplier Shareholder Value. By: Gielens, Katrijn; Geyskens, Inge; Deleersnyder, Barbara; Nohe, Max. Journal of Marketing. Mar2018, Vol. 82 Issue 2, p124-141. 18p. 6 Charts, 1 Graph. DOI: 10.1509/jm.16.0276.
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The New Regulator in Town: The Effect of Walmart's Sustainability Mandate on Supplier Shareholder Value
Suppliers are increasingly being forced by dominant retailers to clean up their supply chains. These retailers argue that their sustainability mandates may translate into profits for suppliers, but many suppliers are cynical about these mandates because the onus to undertake the required investments is on them while potential gains may be usurped by the mandating retailer. We examine whether supplier fears are justified by studying the impact of Walmart's sustainability mandate on its suppliers' (short-term) shareholder value. Although about two-thirds of suppliers are indeed financially harmed, approximately one-third benefit. To delve deeper into this variation, we relate suppliers' short-term abnormal returns to Walmart's appropriation power and explore whether and to what extent a supplier's referent and expert power sources, derived from its marketing and operational characteristics, respectively, can counteract Walmart's appropriation attempts. We find that the supplier's marketing characteristics (its environmental reputation, brand equity, and advertising) provide it with the countervailing power needed to resist Walmart's appropriation attempts. In contrast, cost-efficient suppliers and suppliers that invest heavily in R&D have more difficulty withstanding Walmart's squeeze attempts.
Manufacturers such as Clorox, Mattel, and KimberlyClark have been given requests to shrink their packaging material, cut back on their use of toxic chemicals, conserve water, decrease their greenhouse gas emissions, and so forth. These requests may sound like government mandates, aimed at propelling corporate America into a new era of sustainability, but they are not. They are mandates from some of the world's most powerful retailers, who increasingly act as the most vigorous of a new breed of private-sector regulators (Advertising Age 2011, p. 1). Suppliers—being captive given that retailers are their main gateway to consumers—have generally no choice but to comply (Dauvergne and Lister 2012). The most prominent example of such a retailer is Walmart, which issued requests on how its suppliers should transition to greener products and processes, and publicly stated its intent to tie its suppliers' sustainability to their "compensation" (Major 2012).
There is no denying that the largest retailers are getting bigger and more powerful every year, and thus they can use their scale to force mandates on their suppliers. Indeed, retailers now rank among the biggest corporations in the world, often dwarfing their largest suppliers (Dukes and Geylani 2016). These dominant retailers—not governments—are becoming the driving force behind suppliers' sustainability efforts (Hermes 2012).
While sustainability mandates from dominant retailers can take different forms across industries, three common elements stand out. First, such mandates reach further than the nature of the product itself (e.g., is the product recyclable?), and include the process by which it was produced (e.g., what resources are consumed to make the product?). Second, to increase the effectiveness of their mandates, retailers translate suppliers' sustainability performance into a simple rating for consumers. By arming consumers with transparent information on the environmental costs of products, retailers turn consumers into informed decision makers who can hold suppliers accountable for negative environmental externalities (Cutting, Cahoon, and Leggette 2006). Third, these mandates do not take place in a collaborative spirit but are implemented on a formal "Here is what we need you to do" basis (Brockhaus, Kersten, and Knemeyer 2013).[ 1]
To propitiate their suppliers, dominant retailers typically emphasize how complying with their sustainability mandates may benefit suppliers. According to these retailers, not only can suppliers reduce their operating costs (e.g., through energy savings or by using cheaper recycled raw materials) (Brockhaus, Kersten, and Knemeyer 2013) but positive consumer reactions may also result as retailers make suppliers' sustainability efforts visible within their stores (Hepler 2015). Retailer reassurance notwithstanding, many suppliers are cynical about these mandates because the onus to undertake the required investments is on them, whereas potential gains may be usurped by the mandating retailer. Hence, these suppliers see sustainability mandates as just another way for dominant retailers to squeeze them. Indeed, when interviewed about the matter (Brockhaus, Kersten, and Knemeyer 2013, pp. 175-76), several suppliers voiced their fears about unfair retailer appropriation.
Whether supplier fears, rather than retailer assurances, are justified is unclear. Against this background, we assess whether and when suppliers are affected by a dominant retailer's sustainability mandate. To test our hypotheses, we study the rollout of Walmart's sustainability mandate, with shareholder value as our performance metric. In doing so, we address Srinivasan and Hanssens's (2009, p. 308) call for more research on the "stock market impact of corporate social responsibility initiatives, such as environmental sustainability." We find that, on average, a dominant retailer's sustainability mandate reduces a supplier's short-term stock market abnormal returns. However, the effect is highly contingent on the interplay between the retailer' s power over the supplier—which increases the likelihood that the retailer will extract rents from the supplier—and the supplier' s countervailing power—which may help it to withstand the retailer's squeeze attempts.
Our findings contribute in two ways to the literature on corporate social responsibility (CSR) initiatives. First, the CSR literature has reported mixed evidence on the financial implications of CSR, partly because CSR includes a variety of facets that may affect firm performance in different ways. With the exception of Jayachandran, Kalaignanam, and Eilert (2013) and Mishra and Modi (2016), research has not distinguished different CSR types. We home in on one facet that is gaining momentum: environmental sustainability. In doing so, we respond to recent calls to study the potentially distinct link between specific types of CSR and their outcomes (e.g., Homburg, Stierl, and Bornemann 2013; Mishra and Modi 2016). Indeed, in contrast to Margolis, Elfenbein, and Walsh (2008), who report an overall small but positive meta-analytical correlation between an aggregate CSR construct and financial performance, we find that suppliers' short-term stock market abnormal returns on average are reduced by a dominant retailer's sustainability mandate.
Second, the sustainability literature has established a positive relationship between environmental CSR and stock market performance (e.g., Flammer 2013; Klassen and McLaughlin 1996), with firms benefiting similarly from complying with environmental regulations set by government agencies—a reactive strategy—and from voluntarily investing in sustainability initiatives—a proactive strategy (Dixon-Fowler et al. 2013). The literature has been silent on the effect of a second reactive strategy, compliance with suppliers' prime customer, the retailer—which is surprising, given the retailer' s central role in the supply chain as a gateway to consumers. We show that, unlike the effect of complying with government regulations, the effect of complying with a dominant retailer's mandate is negative for many suppliers due to the retailer's ability to appropriate its suppliers' gains.
A dominant retailer's sustainability mandate can improve suppliers' environmental performance. But how do these mandates affect suppliers' financial performance? Two schools of thought shape the debate (Lankoski 2009). The traditional view sees environmental sustainability as harming financial performance (e.g., Friedman 1970; Palmer, Oates, and Portney 1995), whereas the revisionist view believes it improves performance (e.g., Nidumolu, Prahalad, and Rangaswami 2009; Porter and Van der Linde 1995). The impact of these mandates may be more nuanced than either view claims and include both cost and revenue effects (Kumar and Christodoulopoulou 2014).
Cost effects. Addressing a dominant retailer's sustainability mandate requires supplier investments in time and money. Also, because natural resources such as water and air are freely available, suppliers can use them without cost and hence often use them in excess (Ambec and Lanoie 2008). A dominant retailer' s sustainability mandate confronts suppliers with their real production costs. Such a mandate may thus come with an immediate cost increase.
On the other hand, suppliers may increase sustainability and reduce costs at the same time. Indeed, "pollution … involves unnecessary or incomplete utilization of resources," and "reducing pollution is often coincident with improving the productivity with which resources are used" (Porter and Van der Linde 1995, pp. 98, 105). Increasing sustainability may thus translate into cost savings because fewer purchased inputs, such as energy and waste management services, are needed. These longer-term cost savings may at least partially offset the short-term costs incurred to comply with a dominant retailer's sustainability mandate.
Revenue effects. Complying with a dominant retailer's sustainability mandate may affect the purchasing decisions of consumers, who are suppliers' relevant stakeholder group with respect to revenues. Revenue effects can materialize through suppliers' sales volume and/or the prices obtained for their products. Suppliers may reach new consumers—the number of environmentally conscious consumers is growing (Kotler 2011)—and may increase repeat sales (Kumar, Teichman, and Timpernagel 2012). Supplier revenues may also increase when consumers are willing to pay a premium for more environmentally friendly products.
On the other hand, consumers have been shown to respond negatively when CSR initiatives are undertaken only alter external pressure is exerted (Nyilasy, Gangadharbatla, and Paladino 2014), because such actions are perceived as forced and insincere (Groza, Pronschinske, and Walker 2011). Thus, supplier revenues may also decrease in response to a sustainability mandate.
Since a dominant retailer's sustainability mandate can have both positive and negative effects for suppliers, we propose a contingency framework that pinpoints the type of supplier most likely to reap the benefits, or downplay the negative effects, from a mandate. We start from the premise that the more powerful the retailer, the more likely it is to appropriate supplier benefits (cost savings and/or revenue gains) that result from the mandate. However, power being two-sided (Emerson 1962), a mandating retailer does not only hold power over its suppliers. Suppliers may also hold (countervailing) power over the retailer, and the more they do so, the better they are able to resist the retailer's attempts to appropriate rents (Etgar 1976). A supplier's (countervailing) power over a retailer is shaped by the power sources available to the supplier (El-Ansary and Stern 1972). We study both marketing and operational characteristics as sources of supplier power.
Suppliers performing better in the marketing domain hold more sources of referent power over a retailer. Referent power is the ability of one party to confer prestige upon another (Gaski 1986). In distribution channels, referent power is especially visible when retailers pride themselves on carrying certain brands (Palmatier, Stern, and El-Ansary 2015, p. 302). We consider three sources of a supplier's referent power: ( 1) its environmental reputation, since a sustainability-promoting retailer seeks to rent that reputation (Chu and Chu 1994); ( 2) its brand equity, since a retailer tries to identify with brands that are valued by consumers (Shervani, Frazier, and Challagalla 2007); and ( 3) its advertising, since a retailer values the pull effect created by the manufacturer (Etgar 1976).
Suppliers that score better in the operational domain hold more sources of expert power. A supplier possesses expert power when it enjoys specialized knowledge of value to the retailer (Gaski 1986). We consider two sources of a supplier's expert power: ( 1) its cost efficiency, since a retailer aims to sell products at competitive prices (Hofer et al. 2012); and ( 2) its R&D, since a retailer seeks access to unique, innovative products (Dean, Griffith, and Calantone 2016).
Next, we theorize how the power of the mandating retailer over a supplier affects the supplier's shareholder value following a sustainability mandate, and how the supplier's referent and expert power sources may alter this relationship. Table 1 offers an overview of our predictions.
TABLE: TABLE 1 Summary of Predictions
TABLE 1 Summary of Predictions
| Variable | Effect on Costs | Effect on Revenues | Predictions | Corroborated |
| Retailer power over supplier | √ | √ | A mandating retailer that holds more power over a supplier can appropriate more supplier benefits (cost savings and/or revenue gains) that result from complying with the mandate. | |
| Supplier's Sources of Referent Power |
| Environmental reputation | | √ | Suppliers with a stronger environmental reputation can more readily resist a mandating retailer's appropriation attempts. | |
| Brand equity | | √ | Suppliers with a higher brand equity can more readily resist a mandating retailer's appropriation attempts. | √ |
| Advertising | | √ | Suppliers that advertise more can more readily resist a mandating retailer's appropriation attempts. | √ |
| Supplier's Sources of Expert Power |
| Cost efficiency | √ | | • Suppliers that are more cost efficient can more readily resist a mandating retailer's appropriation attempts. | √ |
| | | • Suppliers that are more cost efficient are less resistant to a mandating retailer's appropriation attempts. | √ |
| R&D | √ | | • Suppliers that invest more in R&D can more readily resist a mandating retailer's appropriation attempts. | √ |
| | | • Suppliers that invest more in R&D are less resistant to a mandating retailer's appropriation attempts. | √ |
In an interfirm relationship, "power" refers to the ability of one firm to influence another firm's behavior (El-Ansary and Stern 1972). A retailer that represents a substantial revenue stream for a supplier has power over that supplier because of its economic importance to the supplier (Pfeffer and Salancik 1978). Although power is defined as an ability rather than a behavior, the more power a retailer possesses over a supplier, the more apt it is to exercise that power (Gaski and Nevin 1985; for a similar reasoning, see Boyd, Chandy, and Cunha 2010). The mandating retailer may thus use its power to demand that a supplier's cost savings and/or revenue gains resulting from complying with a mandate are passed on. The appropriation attempts by the mandating retailer are more likely to be successful when made with more dependent suppliers,[ 2] leaving the latter worse off.
A mandating retailer' s appropriation attempts may be thwarted by a supplier's power sources (Etgar 1976). Next, we argue how a supplier's marketing characteristics may affect a retailer's attempts to appropriate the supplier's revenue gains (in the form of increased sales and/or higher prices) that result from complying with a mandate.
Environmental reputation. A supplier's environmental reputation captures whether it is already known for operating sustainably. A supplier with a better environmental reputation holds a source of referent power over a mandating retailer that wishes to play the sustainability card, since that retailer can rent the supplier' s reputation to improve the credibility of its mandate (Chu and Chu 1994). As such, a better environmental reputation increases a supplier' s countervailing power, thereby softening the negative effect of the mandating retailer's power and limiting its appropriation of the supplier's revenue gains. These gains may even be lower to begin with, since prior positive expectations (i.e., the supplier has been environmentally responsible) may generate a ceiling effect such that new information (i.e., adherence to the mandate) can hardly improve an already high positive rating (Smith 1993). For less reputable suppliers, on the other hand, the revenue gains—which are typically larger than those of more reputable suppliers—are more likely to be skimmed away by a more powerful retailer.
Brand equity. Brand equity refers to the net present value of the incremental cash flows attributable to a brand, and to the firm owning that brand, compared with an identical product without brand name or brand-building efforts (Shankar, Azar, and Fuller 2008). A supplier with higher brand equity holds more countervailing power over the mandating retailer because the retailer's own customers place value on the supplier's brands (Shervani, Frazier, and Challagalla 2007). Thus, a higher-equity supplier is more resistant to a mandating retailer's attempts to appropriate the supplier's revenue gains that result from complying with the mandate. The lower countervailing power of a lower-equity supplier, on the other hand, leaves the supplier more vulnerable to the mandating retailer' s appropriation attempts. The revenue gains of a lower-equity supplier are larger than those of a higher-equity supplier, because a brand with less equity can more readily establish new associations (Swaminathan, Fox, and Reddy 2001) and position itself as "a CSR brand" rather than as "a brand that just engages in CSR" (Du, Bhattacharya, and Sen 2010, p. 226). The prospect of these gains turns suppliers of low-equity brands into attractive appropriation targets with little defense mechanisms in place to withstand the demands of a powerful retailer (Etgar 1976).
Advertising. Consumers are often unaware of a supplier's sustainability initiatives (Bhattacharya and Sen 2004; Pomering and Dolnicar 2009). Advertising increases consumers' awareness of the advertising supplier, prompting them to become informed about not only the firm's products but also its practices, including its sustainability efforts (Servaes and Tamayo 2013).[ 3],[ 4] Ceteris paribus, this may generate a consumer pull effect, which leads to more and faster consumer trial and adoption of new products (Steenkamp and Gielens 2003), providing heavier advertisers with more countervailing power. This may mitigate the negative effect of the mandating retailer's power over the supplier.
A retailer's appropriation attempts may also be affected by a supplier's expert power sources (Etgar 1976). However, in contrast to a supplier's referent power sources, for which we argued that they translate into supplier countervailing power, opposite arguments exist for a supplier's expert power sources. Next, we discuss how a supplier's operational characteristics may affect the retailer's appropriation of the supplier's cost savings that result from complying with the sustainability mandate.
Cost efficiency. Cost efficiency refers to a firm's ability to combine various materials and activities in such a way that it can offer its final products at a lower cost (Dutta, Narasimhan, and Rajiv 1999). Cost efficiency is a source of supplier expert power because retailers need to sell products at competitive prices (Hofer et al. 2012). This need may lead to less retailer skimming of a supplier' s cost advantages that follow from complying with a sustainability mandate. These cost advantages may be realized particularly by a more cost-efficient supplier, since it developed the capability to absorb the costs of any new activity long before a mandate was imposed by a dominant retailer. In the longer term, this capability may allow a more cost-efficient supplier to comply with a dominant retailer' s sustainability mandate in a way that lowers the supplier's operating expenses (Hayes and Upton 1998).
In contrast, given that retailers work with a multitude of cost-efficient suppliers, who often also supply them with private labels, retailers always have access to products at competitive prices through sourcing alternative manufacturers (Ter Braak, Dekimpe, and Geyskens 2013). Cost efficiency as such may therefore not be a viable source of expert power. As a result, a supplier's cost efficiency may not translate into strong countervailing power, which may then increase appropriation by the mandating retailer. Thus, while suppliers with higher cost efficiency may realize more cost savings after a mandate, they may also be more vulnerable to a powerful retailer's appropriation attempts.
R&D. Suppliers differ in the degree to which they generate superior products and/or production processes through R&D (Mizik and Jacobson 2003). A supplier that invests more in R&D holds a source of expert power, since retailers tend to prefer innovative products that are unique (Dean, Griffith, and Calantone 2016) and that incorporate the latest technological developments in the category (Alpert, Kamins, and Graham 1992). This may help the supplier fight the retailer's appropriating behavior and hold on more to the cost savings it realizes from complying with the mandate. These cost savings result from the supplier' s higher inclination to try innovative green technologies (with no immediate cost savings but with the potential of future savings) rather than using off-the-shelf alternatives (with guaranteed short-term success but less long-term cost-saving potential; Berrone et al. 2013).
In contrast, given that suppliers must reveal (proprietary) insights into their activities when complying with a dominant retailer's sustainability mandate, possibly the greatest risk for an R&D-intensive supplier comes in the potential loss of tacit knowledge to the retailer who internalizes the supplier's knowledge (Dutta and Weiss 1997). As a result, a supplier's R&D may not translate into countervailing power, thereby making the supplier more susceptible to the retailer skimming away the supplier's realized cost savings. Thus, while suppliers that invest more in R&D may be able to benefit more from a mandate in terms of cost savings, they may also have a harder time holding on to these savings.
We study Walmart's 2012 sustainability mandate. After years of discussion about sustainability and numerous accusations of greenwashing (Mitchell 2012), "just when many suppliers may have imagined it was safe to put sustainability on the back burner, Walmart issued a call to action" (Major 2012). On September 13, 2012, Walmart held a milestone meeting, where it announced the rollout of its mandate in front of an audience of suppliers, news channels, and nongovernmental organizations. The mandate consisted of specific requests concerning the environmental impact of suppliers' operations, encompassing areas such as the use of recycled materials in packaging and of environmentally friendly substitutes for chemicals in cosmetics.
Logic. Two challenges in measuring the supplier performance impact of a dominant retailer's sustainability mandate are that ( 1) suppliers do not disclose information on costs and/or revenues resulting from compliance with a mandate, and ( 2) a temporal asymmetry exists between complying with a mandate and its potential payoff. Indeed, while the costs of complying occur mainly in the short term, the benefits are uncertain and may arise only in the longer term (Ambec and Lanoie 2008). To handle these issues, we follow Walley and Whitehead's (1994, p. 52) suggestion that "for all environmental issues, shareholder value … is the critical unifying metric." We rely on the event study method and use abnormal returns around the announcement date to capture the short-term financial impact of Walmart's sustainability mandate on its suppliers.
Four-factor model. We use the Fama-French-Carhart four-factor model:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 1)
where rit is the rate of return on supplier i' s stock on day t,[ 5] RMt is the rate of return on the market index, SMBt is the return differential between stock portfolios with small and large market capitalizations, HMLt is the return differential between stock portfolios with high and low book-to-market ratios, and UMDt is the momentum factor. The α and β parameters are estimated from an ordinary least squares regression for each stock separately over a period of 250 trading days, ranging from 260 to 10 trading days prior to the announcement date. We use the estimates obtained from this model to predict the daily abnormal returns for each supplier i at time t as Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. To account for possible information leakage prior to the event day and gradual dissipation of information after the event day, values of ARit are cumulated over a time window [tļ, t2], which includes the event day (t = 0), into a cumulative abnormal return (CAR):
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 2)
The extent of information leakage and dissemination of information is an empirical issue and is determined on the basis of the significance of the CARs for multiple event windows surrounding the event day. We select the window with the most significant Patell statistic (for a similar practice, see, e.g., Gielens et al. 2008; Swaminathan and Moorman 2009).
Explaining Variation in Suppliers' Short-Term Abnormal Returns
We regress the standardized CARs (SCARs) on the set of predictors:[ 6]
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 3)
where POWi is Walmart' s power over supplier i; ENVREPi is supplier i's environmental reputation, BREQi its brand equity, ADVi its advertising, COSTEFFi its cost efficiency, and R&Di its R&D; and CONTROL is a vector of control variables. To allow for the potential intercorrelation among suppliers within the same industry (at the two-digit SIC level), we use cluster-robust estimation. To account for potential selection effects and to correct for possible endogeneity of supplier advertising and R&D, we add the inverse Mills ratio (IMRi) and two endogeneity correction terms (ADVci and R&Dci) as predictors to Equation 3. The error term εi follows a normal distribution.
Correction for sample selection. Our sample contains suppliers that depend on Walmart for at least 10% of their revenues (for details, see the "Data" section). A sample selection bias may occur when the selection of Walmart suppliers is not independent of the outcome variable. To account for potential selection effects, we use a Heckman model and estimate the following binary probit regression:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 4)
where SUPPi = 1 if supplier i is included in our sample, and 0 otherwise. We include five variables that are expected to affect Walmart' s choice for a supplier but that do not affect the performance impact of a sustainability mandate. First, we include the supplier's accounts receivable relative to other suppliers in the industry (ACCRECi), since retailers tend to prefer suppliers with more credit-line opportunities (Gosman and Kelly 2002). Second, we include a set of variables that capture several social standards to which Walmart holds its suppliers:[ 7] In order to become a Walmart supplier, suppliers must ( 1) fully comply with applicable national and/or local laws and regulations (LAWCOMPLi), ( 2) provide competitive wages and benefit packages for their employees (WAGESj), ( 3) respect the right of workers to choose whether to form or join trade unions (UNIONSi), and ( 4) offer a safe and healthy work environment and take proactive measures to prevent workplace hazards (SAFETYi).[ 8] In addition, we include the same variables in the selection model as in Equation 3, unless the required information was not available for the suppliers that uniquely feature in the selection sample, in which case we replace the missing variable with a proxy (for a similar practice, see, e.g., Robinson, Tuli, and Kohli 2014; Swaminathan and Moorman 2009). As such, we use a supplier' s selling, general, and administrative expenses (SGAi) as a proxy measure for its advertising and its brand equity.[ 9] Finally, we include the squares of the supplier's referent and expert power sources to account for their potential nonlinear effect on the probability of being selected as a supplier.
We estimate Equation 4 on a sample of 1,102 U.S. suppliers operating in the same industry (at the two-digit SIC level) as the Walmart suppliers, for which data were available for all covariates. We again use cluster-robust estimation to account for industry heterogeneity. We draw on the parameters to compute IMRi, which we add as a regressor to Equation 3.
Correction for endogeneity of advertising and R&D. To correct for potential endogeneity of advertising and R&D, we implement Gaussian copulas. In contrast to classical methods to correct for endogeneity, this approach does not require instrumental variables to partial out the exogenous variation in the endogenous regressor; it directly models the joint distribution of the potentially endogenous regressor and the error term through a control function term (Park and Gupta 2012). Specifically, the endogenous regressor is treated as a random variable, consisting of an exogenous part (which is nonnormally distributed) and an endogenous part (which is normally distributed). The assumption that the endogenous regressor contains an exogenous, nonnormally distributed part is similar in spirit to the "exclusion" restriction for instrumental variables. With a normally distributed error term, an identification requirement of the Gaussian copula method is that the endogenous regressor is not normally distributed. Indeed, if the endogenous regressor is normally distributed, variation due to the endogenous regressor cannot be separated from variation due to the error term, giving rise to an identification problem (Papies, Ebbes, and Van Heerde 2017). The copula terms are obtained as
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 5)
where Ø-1 is the inverse of the cumulative normal distribution function and HADV(ADVi) and HR&D(R&Di) denote the empirical distribution functions of advertising and R&D, respectively. The nonnormal distribution of the potentially endogenous regressors, ADV¡ and R&D¡, is confirmed by a Shapiro-Wilk test (WADV = .751, p < .001; WR&D = .745, p < .001). We first estimate a model in which we include a copula for both potentially endogenous variables. Following Mathys, Burmester, and Clement (2016), we retain the copula terms that are statistically significant and then re-estimate the model.
The Securities and Exchange Commission (SEC) requires publicly listed firms to disclose which customers (such as Walmart) account for more than 10% of their annual revenues. In addition, firms sometimes voluntarily report the names of important customers below the 10% threshold. Following Gosman and Kohlbeck (2009), we identified suppliers of Walmart by searching the SEC filings of all firms with a SIC code suggestive of manufacturers that sell through retailers. This includes 70 industries in the SIC 2000-3999 range. For each of the 1,341 active firms classified in one of these industries, we inspected the 10-K report filed in the year preceding the mandate for any reference to Walmart as a retail customer. We identified 114 Walmart suppliers. To validate and/or extend this information, we searched Planet Retail reports and news articles pertaining to Walmart. We located an additional 25 suppliers that had SIC codes outside the 2000-3999 range but that also listed Walmart as one of their customers.[10] From the resulting 139 suppliers, we dropped ( 1) eleven firms that were not publicly listed during the estimation period; ( 2) five firms that did not have stock price information available during the entire estimation period, because they started trading during the estimation period (three firms) or did not trade on a daily basis (two firms); and ( 3) two firms that interacted with Walmart as an end customer rather than as an intermediary.
We extensively checked the remaining 121 firms for potential concurrent events (e.g., announcements of CEO changes, quarterly results, dividend payments, mergers and acquisitions, strategic alliances, new product introductions) during the [-3, +3] trading-day window surrounding the announcement, using Factiva, LexisNexis, and SDC Platinum. This resulted in the elimination of three suppliers that could confound the results. In addition, we searched Walmart's investors' news portal for any press releases or news reports on its U.S. suppliers. We repeated this procedure for Walmart's ten largest (in terms of banner sales in the year prior to the event) retail competitors in the United States.[11] Neither Walmart nor its competitors made announcements concerning their suppliers during the [-3, +3] window.
The final sample consists of 118 Walmart suppliers, of which 115 had complete information on all covariates. The size of our sample is similar to that of other event studies on buyer-supplier relationships (e.g., Kalaignanam et al. 2013; Liu et al. 2014) and customer power (e.g., Boyd, Chandy, and Cunha 2010; Deitz, Hansen, and Richey 2009). The suppliers in our sample are active in grocery categories, such as cereals (e.g., General Mills, Kellogg's) and meat products (e.g., Bob Evans, Tyson Foods), as well as nongrocery categories, such as apparel (e.g., Hanesbrands, VF Corporation), electronics (e.g., Emerson Radio Corporation, Koss), and entertainment (e.g., Activision Blizzard, Electronic Arts). The sample spans well-known companies such as Procter & Gamble and Mattel and also covers less-known suppliers such as OurPets and NACCO Industries. The Web Appendix summarizes the screened SIC codes and the number of suppliers per SIC code included in our sample.
We compiled data from a wide variety of sources. All independent variables are measured in the year prior to the mandate. All supplier characteristics (marketing and operational) are expressed relative to other suppliers in the same industry (at the two-digit SIC level), to convey the relative power of suppliers within an industry. Table 2 offers an overview.
TABLE: TABLE 2 Summary of Measures and Data Sources
TABLE 2 Summary of Measures and Data Sources
| Variable (Label) | Data Source | Operationalizationa |
| Retailer power over supplier (POW) | SEC filings | Percentage of supplier revenues realized through the mandating retailer if reported in the 10-K filings; 0 if not reported (for a similar practice, see, e.g., Kelly and Gosman 2000). |
| Environmental reputation (ENVREP) | KLD | Number of environmental strengths the supplier is known for, ranging from zero to seven, divided by the average number of environmental strengths of all suppliers in the same industry (at the two-digit SIC level). |
| Brand equity (BREQ) | Euromonitor | (REV — REVPL)/CATREV, where REV is supplier revenues in the category that accounts for the largest proportion of the supplier's revenues, REVPL is private-label revenues in the same category, and CATREV is total category revenues. |
| Advertising (ADV) | Kantar Media | Supplier expenditures on television advertising divided by the total expenditures on television advertising of suppliers in the same industry (at the two-digit SIC level). |
| Cost efficiency (COSTEFF) | Compustat | Ratio of supplier revenues to COGS, divided by the average cost efficiency of suppliers in the same industry (at the two-digit SIC level); items: COGS, SALE. |
| R&D (R&D) | Compustat | Supplier expenditures on R&D divided by the total R&D of suppliers in the same industry (at the twodigit SIC level); set to 0 when a supplier's R&D expenditures are not reported (for a similar practice, see, e.g., Borah and Tellis 2014); item: XRD. |
| Resource slack (SLACK) | Compustat | Ratio of supplier net cash flows from operating activities to assets; items: OANCF, AT. |
| Inventory turnover (INV) | Compustat | Ratio of supplier revenues to inventory; items: SALE, INVT. |
| Prior environmental investments (PRIOR) | Factiva, LexisNexis | Dummy variable that equals 1 when the supplier announced investments in environmental sustainability in the year prior to the mandate, 0 otherwise. |
| Semidurables supplier (SEMIDUR) | Compustat | Effect-coded variable that equals 1 when the supplier operates in semidurables industries, -1 in grocery industries, and 0 otherwise; item: SIC. |
| Durables supplier (DUR) | Compustat | Effect-coded variable that equals 1 when the supplier operates in durables industries, -1 in grocery industries, and 0 otherwise; item: SIC. |
| Entertainment supplier (ENT) | Compustat | Effect-coded variable that equals 1 when the supplier operates in entertainment industries, -1 in grocery industries, and 0 otherwise; item: SIC. |
| SGA (SGA)b | Compustat | Supplier sales and general administrative expenses (SGA) divided by the total SGA of suppliers in the same industry (at the two-digit SIC level); item: SGA. |
| Accounts receivable (ACCREC)b | Compustat | The outstanding invoices a supplier has because it has delivered products or services to its customers, divided by the average accounts receivable of suppliers in the same industry (at the twodigit SIC level); receivables represent a line of credit extended by a supplier; item: RECCH. |
| Compliance with laws (LAWCOMPL)b | KLD | A composite indicator that identifies suppliers that support social regulation, divided by the average score of suppliers in the same industry (at the two-digit SIC level). |
| Competitive wages (WAG ES)b | KLD | A composite indicator that identifies suppliers that provide strong employment benefits and performance incentives, divided by the average score of suppliers in the same industry (at the two-digit SIC level). |
| Union rights (UNIONS)b | KLD | A composite indicator that identifies suppliers with a high union density, divided by the average score of suppliers in the same industry (at the two-digit SIC level). |
| Workplace safety (SAFETY)b | KLD | A composite indicator that identifies suppliers that have strong employee health and safety programs, divided by the average score of suppliers in the same industry (at the two-digit SIC level). |
aAII variables are measured in the year prior to Walmart's sustainability mandate.
bVariable features exclusively in the selection equation.
Financial measures. We obtained daily data on suppliers' stock market returns from the Center for Research in Security Prices database and data on the four Fama-French-Carhart factors from Kenneth French's website.[12]
Retailer power over supplier. We measure the retailer's power over a supplier as the percentage of revenues the supplier realizes through the retailer, if this is reported in the supplier's 10-K filings.[13] Immaterial percentages do not have to be disclosed in the SEC filings. To avoid dropping suppliers from our sample, for the 24 cases in which the supplier identified the mandating retailer as a customer but did not report the percentage of revenues it realized through this retailer, we set this percentage to zero (following Kelly and Gosman 2000).
Environmental reputation. In line with Mishra and Modi (2016), we use the KLD index of environmental strengths in the year prior to the mandate. This index ranges from 0 to 7, adding one index point for each of seven environmental strengths a firm may possess (e.g., pollution prevention, use of renewable energy). We divide this index by the average environmental reputation of all firms belonging to the same industry (at the two-digit SIC level). The KLD database is widely used by institutional investors (Mishra and Modi 2016) and has been hailed as the "de facto research standard" in sustainability research because of its unmatched coverage of monitored sources (Waddock 2003, p. 369).
Brand equity. We use a market-based measure of brand equity, namely, revenue premium, which reflects the difference between a supplier's revenues and private-label revenues in the same category (Ailawadi, Lehmann, and Neslin 2003).[14] To ensure comparability across categories, we divide by total category revenue.
Advertising. We use a share-of-voice measure: the firm's advertising relative to the advertising in that industry (at the twodigit SIC level) (Reibstein and Wittink 2005; Steenkamp and Fang 2011). Following Sotgiu and Gielens (2015), we use national expenditures on television advertising.
Cost efficiency. A supplier's cost of goods sold (COGS)to-sales ratio captures the proportion of sales revenue used to pay for expenses that vary directly with sales. Since a firm enjoys a higher cost efficiency if its COGS/sales ratio is lower (Moatti et al. 2015), a supplier's cost efficiency can be proxied by the inverse of this ratio.[15] We divide this measure by the average cost efficiency of all firms belonging to the same industry (at the two-digit SIC level).
R&D. We include the supplier's R&D share, that is, its expenditures on R&D relative to the total R&D in the industry (at the two-digit SIC level).
Control variables. To control for the supplier's resources, we include its resource slack (SLACK) and inventory turnover (INV). Slack allows a firm to experiment (Grewal and Tansuhaj 2001), thus promoting more innovative projects that might not be approved by a more constrained firm. Inventory turnover also allows for experimentation because less of the supplier' s working capital is tied up in inventories (Ailawadi, Borin, and Farris 1995). We further add a dummy variable (PRIOR) that captures whether the supplier announced investments in environmental sustainability in the year prior to the mandate. Recent investments in environmentally friendly technologies may make it easier for a supplier to meet the standards imposed by the mandating retailer (Berrone et al. 2013), resulting in lower short-term costs. In contrast, a supplier may find it complex to change the investment path it is on. Being stuck in the middle between its own investment path and the retailer' s demands may lead to a suboptimal solution and reduce the potential for cost savings in the longer run (Hermes 2012). Finally, to control for systematic differences between suppliers of groceries (e.g., soft drinks, detergents), semidurables (e.g., apparel, household textiles), durables (e.g., household appliances, audio equipment), and entertainment (e.g., games, films), we add three effect-coded variables (SEMIDUR, DUR, and ENT), with grocery suppliers as the baseline.[16] Table 3 provides descriptives.
TABLE: TABLE 3 Descriptives
TABLE 3 Descriptives
| Variable (Label) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| 1. Retailer power over supplier (POW) | 1.00 | | | | | | | | | | | |
| 2. Environmental reputation (ENVREP) | -.06 | 1.00 | | | | | | | | | | |
| 3. Brand equity (BREQ) | -.05 | .31 | 1.00 | | | | | | | | | |
| 4. Advertising (ADV) | .12 | .47 | .19 | 1.00 | | | | | | | | |
| 5. Cost efficiency (COSTEFF) | .11 | .07 | .20 | .04 | 1.00 | | | | | | | |
| 6. R&D (R&D) | -.08 | .74 | .53 | .39 | .07 | 1.00 | | | | | | |
| 7. Resource slack (SLACK) | -.09 | .24 | .18 | .21 | .15 | .22 | 1.00 | | | | | |
| 8. Inventory turnover (INV) | .25 | -.05 | .00 | .12 | .01 | -.04 | -.11 | 1.00 | | | | |
| 9. Prior environmental. investments (PRIOR) | -.03 | .36 | .05 | .34 | .10 | .30 | .10 | -.06 | 1.00 | | | |
| 10. Semidurables supplier (DUR) | -.11 | -.06 | -.06 | -.03 | -.20 | -.05 | -.16 | .05 | -.16 | 1.00 | | |
| 11. Durables supplier (SEMIDUR) | -.02 | -.01 | -.02 | .06 | -.42 | .00 | -.10 | -.01 | -.10 | .62 | 1.00 | |
| 12. Entertainment supplier (ENT) | .01 | .05 | -.07 | .01 | -.31 | .03 | -.14 | .29 | -.16 | .67 | .60 | 1.00 |
| Meana | 15.22 | 2.73 | .00 | .27 | .53 | .04 | .08 | 42 | .13 | .14 | .36 | .17 |
| Standard deviation | 12.30 | 6.01 | .14 | .41 | .20 | .10 | .08 | 190.97 | - | - | - | - |
aFor dummy variables, we report the proportion of observations having the value of one.
Notes: N = 115.
We test whether the cumulative average abnormal returns (CAARs) over several event windows, extending up to 10 trading days before and after the event date, are different from zero (see Table 4). The [-2, +1] window is most significant according to Patell's t-test (t = -3.81, p < .001). This window, which is similar to those of other event studies on interfirm relations (e.g., Fang, Lee, and Yang 2015; Swaminathan and Moorman 2009), suggests the presence of both leakage and delayed dissemination of information. Over the [-2, +1] window, the announcement of Walmart's sustainability mandate, on average, decreases suppliers' stock returns by .57%. To demonstrate the economic significance of this finding, we calculate the average change in shareholder value of a median-sized Walmart supplier in our sample (determined according to suppliers' market capitalization on the day before our event window, i.e., t — 3) (for a similar practice, see Geyskens, Gielens, and Dekimpe 2002). With an average decrease of .57% in the CAR of a supplier, the shareholder value of a median supplier with a market capitalization of $1,052 million is reduced (adjusted for overall market movements) by $6 million in three days.
TABLE: TABLE 4 Cumulative Average Abnormal Returns Across Selected Event Windows
TABLE 4 Cumulative Average Abnormal Returns Across Selected Event Windows
| Event Window (Days) | Average CAR | Average SCAR | Patell t | Percentage Negativea |
| -10 to +10 | -.19% | -.13% | -1.37 | 57% |
| -3 to +3 | -.37% | -.20% | -2.18* | 66% |
| -2 to +2 | -.54% | -.30% | -3.28*** | 68% |
| -1 to +1 | -.77% | -.31% | -3.37*** | 64% |
| -3-0 | .08% | -.17% | -1.87 | 66% |
| -2-0 | -.20% | -.23% | -2.54** | 65% |
| -1-0 | -.39% | -.17% | -1.85 | 63% |
| 0 to +1 | -.48% | -.19% | -2.02* | 56% |
| 0 to +2 | -.45% | -.14% | -1.48 | 62% |
| 0 to +3 | -.55% | -.08% | -.82 | 61% |
| -1 to +2 | -.74% | -.26% | -2.77** | 65% |
| -2 to +1 | -.57% | -.36% | -3.81*** | 64% |
*p < .05.
**p < .01.
***p < .001.
aPercentage of suppliers with negative abnormal returns.
Notes: Two-tailed tests of significance. N = 118.
More noteworthy than the overall negative effect is the wide variation in CARs across suppliers. While 64% of the suppliers faced a negative effect, 36% experienced a positive outcome. Some of the suppliers most hurt by the mandate were Elizabeth Arden and Lance, with CARs of -6.0% and -4.8%, respectively. With a market capitalization of $1,370 million, Elizabeth Arden, for instance, lost $82.2 million in shareholder value in three days. On the other side of the spectrum, some of the best performers were Spectrum Brands and Hasbro, with CARs of 5.7% and 3.7%, respectively. Best performer Spectrum Brands, for example, increased its shareholder value by $113.3 million to $2,119 million in three days. Whereas low and high performers realize roughly the same percentage of revenues through Walmart (Elizabeth Arden: 11%; Lance: 18%; Spectrum Brands: 18%; Hasbro: 17%), they differ substantially in terms of their sources of referent and expert power. Low performers Elizabeth Arden and Lance, for example, possessed little sources of referent power to help them resist the mandating
retailer' s appropriation attempts, since their brand equities were low and they hardly invested in advertising. In contrast, high performer Hasbro advertised more than the industry average, while Spectrum Brands excelled on brand equity, with an above-average revenue premium.
Table 5 presents the result for the selection model. The likelihood ratio test shows good model fit (χ2(18) = 129.78, p < .001). The hit rate of 90.0% is significantly better than chance (80.9% = a2 + (1 — a),2 with a = 10.7%; Morrison 1969). In terms of variance inflation factors (VIFs), none of the variables exceeds the commonly used threshold of 10 (maximum VIF = 2.89) (Hair et al. 2010). Suppliers with higher accounts receivable (p < .001) that offer more competitive wages (p < .05) and a better workplace safety (p < .01) have a higher chance of realizing a large percentage of their revenues through Walmart. Suppliers lower on SGA have a lower likelihood of being selected (p < .01), up to a certain point, after which the likelihood increases again (p = .07), which suggests that Walmart prefers suppliers with either a low or a high brand equity over suppliers that are average on brand equity. Finally, suppliers with less resource slack (p < .01) and entertainment suppliers (p < .01) are less likely to have Walmart as a major customer.
TABLE: TABLE 5 Selection Model Results
TABLE 5 Selection Model Results
| Variable (Label) | Estimate | Wald x2(1) |
| Intercept | -1.687 | 53.11*** |
| Selection Variables | | |
| Accounts receivable (ACCREC) | .038 | 11.03*** |
| Compliance with laws (LAWCOMPL) | -.593 | .69 |
| Competitive wages/benefits (WAGES) | .247 | 5.57* |
| Union rights (UNIONS) | -1.050 | 3.03 |
| Workplace safety (SAFETY) | .779 | 9.95** |
| Supplier's Sources of Referent Power | | |
| Environmental reputation (ENVREP) | .037 | 3.10 |
| Environmental reputation2 | -.001 | 1.79 |
| SGA (SGA) | -.133 | 6.14* |
| SGA2 | .003 | 3.22 |
| Supplier's Sources of Expert Power | | |
| Cost efficiency (COSTEFF) | 1.124 | 2.48 |
| Cost efficiency2 | -.499 | 1.22 |
| R&D (R&D) | 4.929 | .76 |
| R&D2 | -25.082 | 1.38 |
| Control Variables | | |
| Resource slack (SLACK) | .624 | 8.60** |
| Inventory turnover (INV) | -.000 | .84 |
| Semidurables supplier (SEMIDUR) | .051 | .09 |
| Durables supplier (DUR) | -.184 | 2.73 |
| Entertainment supplier (ENT) | -.411 | 9.02** |
*p < .05.
**p < .01.
***p < .001.
Notes: Two-tailed tests of significance. N = 1,102.
Table 6 reports the results of our contingency analysis. The correlations between all variables are below the recommended threshold of .8 (Judge et al. 1988), and none of the VIFs exceeds 10 (maximum VIF = 8.9) (Hair et al. 2010). Thus, multicollinearity is not likely to be a problem. The estimate of the inverse Mills ratio is significant (γı9 = -.601, p < .05), implying that a selection correction is indeed warranted. The negative sign suggests that, on average, unobserved factors that make a firm more likely to have Walmart as a major customer tend to have an opposite effect on the shareholder value implications from the sustainability mandate issued by Walmart. We find that the copula correction term for R&D is significant, albeit only at the .09 level, underscoring the importance of controlling for endogeneity in R&D. Advertising is not endogenous (p > .10). Hence, we only retain the copula correction for R&D in our final model.
TABLE: TABLE 6 Explaining the Variation in the Effect of a Dominant Retailer's Sustainability Mandate on Suppliers' Abnormal Returns
TABLE 6 Explaining the Variation in the Effect of a Dominant Retailer's Sustainability Mandate on Suppliers' Abnormal Returns
| Variable (Label) | Estimate | t |
| Intercept | 1.387 | 2.03* |
| Retailer power over supplier (POW) | .006 | .59 |
| Supplier's Sources of Referent Power | | |
| Retailer power × Environmental reputation | .003 | 2.05* |
| Retailer power × Brand equity | .227 | 3.03** |
| Retailer power × Advertising | .041 | 2.14* |
| Environmental reputation (ENVREP) | -.105 | -2.42* |
| Brand equity (BREQ) | -5.390 | -3.96*** |
| Advertising (ADV) | -.088 | -.13 |
| Supplier's Sources of Expert Power | | |
| Retailer power × Cost efficiency | -.028 | -2.04* |
| Retailer power × R&D | -.530 | -3.88*** |
| Cost efficiency (COSTEFF) | 1.147 | 2.09* |
| R&D (R&D) | 10.812 | 3.60*** |
| Control Variables | | |
| Resource slack (SLACK) | -2.847 | -3.01** |
| Inventory turnover (INV) | -.001 | -2.97** |
| Prior environmental investments (PRIOR) | -.037 | -.15 |
| Semidurables supplier (SEMIDUR) | .053 | .35 |
| Durables supplier (DUR) | .367 | 2.94** |
| Entertainment supplier (ENT) | .320 | 1.10 |
| Inverse Mills ratio (IMR) | -.601 | -2.40* |
| Copula correction R&D (R&Dc)a | -.639 | -1.76 |
| R2 | .320 | |
*p < .05.
**p < .01.
***p < .001.
aThe copula correction for R&D is significant at p = .09. The copula correction for advertising was not significant (p = .84) and was therefore not retained in the model.
Notes: Two-tailed tests of significance. N = 115.
The main effect of Walmart' s power over the supplier is not significant (p > .10). Thus, the mandating retailer does (or can) not always use its power to demand that the supplier's cost savings and/or revenue gains due to complying with the mandate are passed on. Indeed, our results support that the retailer's attempts to exert power can be thwarted by the supplier' s power sources.
Suppliers' sources of referent power. The higher a supplier' s environmental reputation, the more negative the shareholder value implications from the mandate are (γ7 = -.105, p < .05). However, a supplier's environmental reputation also provides it with countervailing power, which limits appropriation by the mandating retailer (γ2 = .003, p < .05). Whereas suppliers that hold more brand equity benefit less in the face of a mandate (γ8 = -5.390, p < .001)—implying that they have more difficulty tapping into a new revenue stream when forced to move to more sustainable operations—they can use their countervailing power to resist the mandating retailer and prevent it from skimming the benefits (even when there are fewer benefits to begin with), as reflected in the positive interaction of the mandating retailer' s power over the supplier with the supplier's brand equity (γ3 = .227, p < .01). In addition, suppliers that advertise more are better able to withstand the dominant retailer's appropriation pressure (γ4 = .041, p < .05).
Supplier's sources of expert power. Whereas cost-efficient suppliers gain more (γ 10 = 1.147, p < .05)—implying that they are, on average, better able to absorb the costs of a mandate—their cost efficiency does not provide them with the countervailing power that prevents appropriation by the dominant retailer. Interestingly, their gains are usurped more by the mandating retailer than those of less cost-efficient suppliers, as indicated by the negative interaction effect (γ5 = -.028, p < .05). R&D also positively affects the supplier's stock market reaction following the mandate (γn = 10.812, p < .001), but, again, it does not provide the supplier with the countervailing power to resist the dominant retailer's appropriation attempts. Similar to cost-efficient suppliers, suppliers high in R&D suffer more from appropriation by the mandating retailer than suppliers low in R&D (γ6 = -.530, p < .001).
To further illustrate the nature of the interaction effects, we depict the simple slopes in Figure 1. Specifically, we portray the effect of the dominant retailer's power over its suppliers on the suppliers' SCARs for low and high values of supplier power sources (determined as one standard deviation below and above each variable' s mean). The plots clearly show that referent power sources offer countervailing power, whereas expert power sources do not. Whereas a strong environmental reputation (simple slope = .034, p < .05), sizeable brand equity (simple slope = .037, p < .05), and major advertising spending (simple slope = .033, p < .01) clearly help prevent the mandating retailer from appropriating a supplier's gains, resulting in higher abnormal returns for the supplier, the opposite holds for high cost efficiency (simple slope = -.015, p < .05) and considerable R&D investments (simple slope = -.068, p < .01). For low levels of a supplier's power sources, the effect of Walmart's power on supplier shareholder value is not significant (ps > .05), except for low-R&D suppliers, for which the effect is positive and significant (simple slope = .042, p < .01). Perhaps the retailer (rightfully or wrongfully) believes that these suppliers may not have the required skills to transform the mandate into successful solutions without incurring prohibitive additional costs and therefore reduces its appropriation attempts disproportionately.
Alternative operationalizations. We reran our model using alternative measures for ( 1) the mandating retailer's power over a supplier, ( 2) a supplier's brand equity, and ( 3) the supplier's cost efficiency. We set the mandating retailer's power over a supplier equal to 5% (rather than 0) for the 24 cases for which this value was not reported in the SEC filings. We measure brand equity using a dummy variable equal to 1 if a supplier features on
the Brand Finance Global 500 list in the year prior to the announcement and 0 otherwise. We derive an alternative measure for cost efficiency using a stochastic frontier model:
Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. ( 6)
where SALESit is supplier i's revenues in year t; COGSit, LABORit, and CAPCOSTit are its COGS, labor, and capital; vit is the error term; and eηit is supplier i's cost efficiency in year t. Using Compustat data for the ten years before Walmart's announcement, we estimate Equation 6 by industry (at the twodigit SIC level), using all the suppliers included in our selection sample. Results were very robust to these alternative operationalizations, although the significance of the interaction between retailer power and supplier cost efficiency dropped slightly (p = .06, p = .11, and p = .18 for robustness checks 1, 2, and 3, respectively).
Alternative sample compositions. We re-estimated Equation 3 after ( 1) removing the bottom 5% of the observations according the SCAR ranking; ( 2) removing the top 5% of the observations; and ( 3) following Gielens et al. (2008), adding two suppliers with more than 60 (but less than 250) days of stock price information in the estimation period. In all three instances, parameters were robust in sign. The retailer power X environmental reputation interaction became marginally significant, at .06, in robustness check 1. In addition, the significance of the retailer power X advertising interaction dropped to p = .22 in robustness check 1, while the significance of the retailer power X cost efficiency interaction dropped to p = .30 and p = .15 in robustness checks 2 and 3, respectively.
The alleged exercise of market power by dominant retailers is featuring high on the public policy agenda. Interestingly, while almost any practice of dominant retailers (e.g., slotting allowances, buying alliances) receives antitrust scrutiny, governments allow retailers to use their market muscle as a bully pulpit to push sustainability through their supply chain, since legislation "may not be able to solve the problem quickly or completely" (Nidumolu, Prahalad, and Rangaswami 2009, p. 57). Suppliers are captive because retailers are their main gateway to consumers, so they generally have no choice but to comply with retailer mandates, as they fear being replaced by another supplier. Although the majority of suppliers (64%) are harmed by a dominant retailer' s sustainability mandate, others (36%) benefit. To explain this variation, we relate the short-term changes in supplier shareholder value to the power structure of the supplier-retailer relationship.
Unlike the classical CSR studies that evaluate companies' voluntary sustainability activities (e.g., Homburg, Stierl, and Bornemann 2013; Peloza and Shang 2011), we evaluate the stock market performance impact of sustainability activities that are enforced by a dominant retailer on its suppliers. Findings regarding the impact of voluntary, broad CSR activities do not readily transfer to a context of enforced environmental sustainability. In addressing this issue, our findings contribute to prior research in several ways.
First, dominant retailers' sustainability mandates are coercive. The power literature has repeatedly established that coercive power use is detrimental to channel performance (Geyskens, Steenkamp, and Kumar 1999). Not only do suppliers perceive costs in complying with retailers' threats (Anderson and Narus 1990), these threats also decrease channel members' outcomes (Scheer and Stern 1992). Still, Deitz, Hansen, and Richey (2009) demonstrate that suppliers experienced, on average, a net gain in short-term abnormal stock returns after the announcement of Walmart' s RFID mandate. In contrast, in the context of a dominant retailer' s sustainability mandate, we find that most suppliers are harmed. This suggests that the market is reacting not only to the coercive nature of a mandate but also to the type of mandate.
Second, the environmental CSR literature implicitly suggests that dominant retailers' sustainability mandates should be beneficial for suppliers, as it has repeatedly established that CSR is good. For example, Hamilton (1995), Klassen and McLaughlin (1996), and Flammer (2013) all point toward a positive effect of environmental CSR on stock market performance, with firms benefiting similarly from pursuing reactive strategies (actions driven by compliance with environmental regulations) and proactive strategies (voluntary actions) (Dixon-Fowler et al. 2013). Our findings are more nuanced in that we find that a dominant retailer' s sustainability mandate is not inevitably bad for the supplier, as one would expect based on the power use literature, nor is it always good, as one would expect based on the environmental CSR literature.
Suppliers who have already established a strong environmental reputation prior to a dominant retailer' s sustainability mandate benefit less from that mandate. This suggests a level playing field, whereby suppliers with a weaker environmental reputation catch up with their competitors that had a head start. However, the higher countervailing power that their environmental reputation offers allows them to hold on to their revenue gains, even when these are smaller than those of less reputable suppliers. Suppliers with higher brand equity benefit less when they face a dominant retailer's sustainability mandate, presumably because stronger brands gain less from adding a sustainability dimension to their brand association mix than lower-equity brands. Still, their countervailing power allows them to overcome this setback because they are better able to defend themselves against appropriation attempts by the mandating retailer, in contrast to lower-equity suppliers, who stand to gain more from a mandate but also tend to be strongarmed more. Suppliers that advertise more benefit more from a dominant retailer's sustainability mandate, as the consumer-pull effect of advertising provides them with the countervailing power needed to withstand the retailer's appropriation attempts.
More cost-efficient suppliers can better absorb the costs of a dominant retailer's mandate in the long run. However, their cost savings do not provide them with countervailing power and are therefore (partly) skimmed away by the mandating retailer. R&D also does not provide suppliers with countervailing power. In fact, the reverse is true: suppliers that rate higher on R&D are considerably worse off when their dependence on the mandating retailer increases. Apparently, once the retailer learns from the supplier, the supplier's expert power source drops immediately (Palmatier, Stern, and El-Ansary 2015, p. 298). Collectively, our findings (summarized in Table 1) provide support for the viewpoint that referent power sources are regarded as the sources having the broadest range, in contrast to expert power sources, which may quickly dissipate (French and Raven 1959). As such, referent power sources translate into countervailing power, but expert power sources do not, at least not in the setting of a supplier dependent on a dominant retailer.
Given the often opposing direct and indirect effects of the supplier's power sources, Table 7 reports a summary of the net effect of a supplier' s power sources on its short-term shareholder value, for the four industry types studied, and evaluated at the within-industry average of the retailer' s power over the supplier (for a similar exercise, see, e.g., Dotzel, Shankar, and Berry 2013).
TABLE: TABLE 7 Supplier Power Sources and Their Net Effect on Supplier Shareholder Value by Industry
TABLE 7 Supplier Power Sources and Their Net Effect on Supplier Shareholder Value by Industry
| Industry | Environmental Reputation (ENVREP) | Brand Equity (BREQ) | Advertising (ADV) | Cost Efficiency (COSTEFF) | R&D (R&D) |
| Groceries | -.15 | -.01 | 1.64 | .44 | .09 |
| Semidurables | -.09 | .05 | .12 | .50 | .09 |
| Durables | -.15 | .00 | 1.90 | .31 | .10 |
| Entertainment | -.21 | .03 | 3.39 | .32 | .08 |
Notes: The net effect for every power source is calculated by multiplying the corresponding main and moderating effect parameter estimates in Table 6 by the average values of the power source and the retailer's power over the supplier in the industry under investigation.
Because suppliers in different industries tend to differ with respect to the level of power sources they possess, the usefulness of the various power sources varies across industries. In the face of a dominant retailer's sustainability mandate, suppliers that are higher in environmental reputation benefit less (or are hurt more) in all four industry groups, but the effect is more negative for suppliers in the entertainment industry (-.21) than for suppliers in semidurables (-.09), with suppliers in groceries and durables falling in between (-.15). Across all industry groups, suppliers in semidurables benefit most if they have strong brands (.05), whereas this power source has almost no effect for durables (.00) and groceries (-.01). This implies that in groceries and durables, potential revenue gains for high-equity suppliers are completely appropriated by the mandating retailer. To avoid this, grocery suppliers may want to steer away from the typical price-promotion trap eating into their brand equity. Advertising is most effective for suppliers from entertainment industries (3.39), followed by durables (1.90) and groceries (1.64), with almost no effect for semidurables. Semidurable suppliers may want to consider upping the ante in the advertising realm because they are currently missing out on a strong countervailing tool. The effects of cost efficiency and R&D are more or less the same across the board—in all four industry groups, suppliers that rate higher on cost efficiency and R&D, on average, benefit more (or are hurt less) from a dominant retailer' s sustainability mandate. The positive net effects imply that at average levels of retailer power, the main effects of cost efficiency and R&D exceed their interaction effects with retailer power: suppliers that are more cost efficient and that invest more in R&D realize benefits from the mandate that are partly, but not entirely, appropriated by the dominant retailer.
While Walmart has led the sustainability charge, suppliers are far from reaching the aspirational goals of carbon and water neutrality, zero waste, or 100% sustainable sourcing (Dauvergne and Lister 2012). As such, different dominant retailers can, and typically do, set different sustainability requirements (Hermes 2012), leading to a morass of different requirements for their suppliers (Advertising Age 2011; Major 2012). On October 7, 2013, Target announced that it would monitor its suppliers' environmental footprint and proclaimed adverse consequences to suppliers in case of poor environmental performance (Winston 2015). Using a similar screening procedure as we did for Walmart, we carried out an event study for 40 Target suppliers. The announcement of Target's sustainability mandate significantly decreased its suppliers' short-term stock returns (CAAR = -1.51%, p < .01). For the 33 suppliers that had already been subject to Walmart's mandate, the CAAR was also negative (CAAR = -2.50%, p < .05),[17] indicating that earlier sustainability mandates from dominant retailers do not help a supplier to absorb the downsides of a new mandate. This also signals that our findings are not merely of historical interest but can offer guidelines to what is in store for a broad set of suppliers, including grocery retailers in other countries and retailers in other industries (e.g., office products, home improvement, hardware).
It would be fruitful to explore the generalizability of our findings for retailers that have sustainability as part of their DNA (e.g., Whole Foods) and for online retailers. Indeed, our study has focused on brick-and-mortar retailers. Yet online retailers, such as Amazon, are becoming increasingly powerful and are considered the biggest disruptor in the retail industry. The shift from shopping in-store to online has left many big suppliers unable to ignore Amazon, increasing the retailer' s dominance. About 55% of all product searches now start at Amazon, compared with 28% at other search engines (Stevens 2017), allowing Amazon to lean heavily on its suppliers to help execute on its promise of low prices. Amazon's outsize retail muscle increasingly allows the retailer to threaten and terminate its relationship with any supplier that violates its supplier code of conduct.
Further, it would be interesting to explore to what extent our results generalize to retailers that are less powerful than Walmart. Interestingly, smaller retailers are joining forces to collectively impose sustainability mandates on their suppliers. For example, Alidis, a buying group combining the purchasing forces of six European retailers, announced that the combined scale of its members will be used to enhance sustainability in the supply chain.
We rely on secondary data, and thus our study is limited to the variables for which we could obtain data. Potentially interesting variables related to firm profits or factors such as top management influence and contractual details could not be tested. Further research could expand the set of moderators.
In addition, our negative average short-term abnormal return represents the initial reaction of investors to a dominant retailer's sustainability mandate, and the long-term value created for shareholders may differ from that suggested by our findings. Future studies could use, for instance, the calendartime portfolio method, which offers estimates of long-term returns and is especially relevant when suppliers face mandates from multiple retailers (see, e.g., Sorescu, Shankar, and Kushwaha 2007; Sorescu, Warren, and Ertekin 2017).
Finally, we evaluate the net effect on suppliers' financial stock market performance, but it remains unclear to what extent these net effects emerge due to shifts in costs versus changes in revenues. Future research could assess the impact on realized costs and revenues separately.
Endnotes 1 As a testament to this, one of the suppliers interviewed by Brockhaus, Kersten, and Knemeyer (2013, p. 175) indicated about the mandating retailer, "They said: 'You WILL provide us with a sustainable product.'… It wasn't a question; it was a statement."
2 Dependence is the mirror image of power. A retailer's power over a supplier increases with the supplier's dependence on that retailer (Emerson 1962).
3 This does not imply that firms need to advertise their sustainability initiatives. All that is required is that advertising leads to increased firm awareness (for a similar argument, see Servaes and Tamayo 2013).
4 Following Srivastava, Shervani, and Fahey (1998), we argue that brand equity represents a stock variable that captures a specific amount of knowledge or value possessed by a brand. It accumulates slowly over time through various marketing efforts (including advertising). Advertising, in contrast, is a flow variable and reflects the extent to which a stock of a particular asset is augmenting or decaying. Hence, brand equity and advertising capture two distinct but related constructs, as also evidenced by their low correlation of .11 in our sample.
5 The return rit measures the relative change in stock price of supplier i on two consecutive trading days and is obtained by rit = (Pit — Pit-1)/Pit-1, where Pit is the closing stock price for supplier i on day t, adjusted for dividends and splits.
6 The standardized CAR is the CAR divided by the standard deviation of the abnormal returns in the estimation period. Standardizing the CARs reduces heteroskedasticity that may arise when the estimated variances of the four-factor model residuals vary across firms (Jain 1982).
7 See http://cdn.corporate.walmart.com/67/fd/5c9b7b964883b792 bce97dd00edf/standards-for-suppliers-poster_129884072278822736.pdf.
8 We checked whether these five variables affected the performance outcome of a dominant retailer's sustainability mandate by adding them to Equation 3 and re-estimating the model. None of these variables was significant (all p > .10).
9 This is supported by our data, in which SGA is correlated significantly with both measures (advertising: r = .52, p < .001; brand equity: r = .32, p < .001).
For instance, Bob Evans Farms is listed in SIC code 5812 (Retail—Eating Places) even though a substantial part of its business consists of ready meals sold through grocery retailers, including Walmart.
These include Kroger, Walgreens, Target, Costco, Home Depot, CVS, Lowe's, Best Buy, Safeway (USA), and Supervalu.
See http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ data_library.html.
Sometimes, firms voluntarily report the names of important customers below the 10% threshold, but without indicating the actual percentage of revenues. An example found in the SEC filings for one of the Walmart suppliers is "We sell our products through a network of grocery stores, mass merchandisers and club stores, including Safeway, Stop & Shop, Ralph's, Kroger, Publix, Whole Foods Market, Target and Walmart."
We use Euromonitor's product category definitions. Euromonitor defines categories at a more refined level than the two-digit SIC level. Suppliers may be active in multiple categories. In such instances, we measure a supplier's brand equity in the category that accounts for the largest proportion of its revenues.
The ratio of sales to COGS is also known as the gross margin (see, e.g., Kesavan, Gaur, and Raman 2010).
Note that we also allow for industry heterogeneity at the twodigit SIC level by using cluster-robust estimation. This is a more parsimonious way to account for industry heterogeneity than using industry fixed effects. As such, it is particularly suitable for small samples with many clusters (Cameron, Gelbach, and Miller 2008).
Again, there was wide variation in CARs across suppliers. Unfortunately, a follow-up contingency analysis for Target was not feasible due to the small sample size for Target. Indeed, given Walmart's size and visibility, we benefited from increased data availability on Walmart's supplier base. Not surprisingly, perhaps, Walmart has been used repeatedly as the sole retailer to study the impact of a dominant retailer on its suppliers (Bloom and Perry 2001; Deitz, Hansen, and Richey 2009; Huang et al. 2012) and competitors (Ailawadi et al. 2010; Gielens et al. 2008; Jia 2008).
GRAPH: FIGURE 1 Simple Slope Analyses
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~~~~~~~~
Katrijn Gielens is Associate Professor of Marketing and Sarah Graham Kenan Scholar, University of North Carolina at Chapel Hill
Inge Geyskens is Professor of Marketing, Tilburg University
Barbara Deleersnyder is Associate Professor of Marketing, Tilburg University
Max Nohe is a doctoral candidate in marketing, Tilburg University
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Record: 195- The Perils of Category Management: The Effect of Product Assortment on Multicategory Purchase Incidence. By: Hong, Sungtak; Misra, Kanishka; Vilcassim, Naufel J. Journal of Marketing. Sep2016, Vol. 80 Issue 5, p34-10. 29p. 1 Black and White Photograph, 2 Diagrams, 17 Charts, 2 Graphs. DOI: 10.1509/jm.15.0060.
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Record: 196- The Relative Effects of Business-to-Business (vs. Business-to-Consumer) Service Innovations on Firm Value and Firm Risk: An Empirical Analysis. By: Dotzel, Thomas; Shankar, Venkatesh. Journal of Marketing. Sep2019, Vol. 83 Issue 5, p133-152. 20p. 1 Diagram, 9 Charts, 4 Graphs. DOI: 10.1177/0022242919847221.
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The Relative Effects of Business-to-Business (vs. Business-to-Consumer) Service Innovations on Firm Value and Firm Risk: An Empirical Analysis
Many firms introduce both business-to-business service innovations (B2B-SIs) and business-to-consumer service innovations (B2C-SIs) and need to better allocate their resources. However, they are unsure about B2B-SIs' effects on firm value or risk, especially relative to those of B2C-SIs. The authors address this problem by developing hypotheses that relate the number of B2B-SIs and B2C-SIs to firm value and firm risk together with the moderators (the number of product innovations and customer-focus innovations). To test the hypotheses, the authors develop and estimate a model using unique panel data of 2,263 SIs across 15 industries over eight years assembled from multiple data sources and controlling for firm- and market-specific factors, heterogeneity, and endogeneity. They analyze innovation announcements using natural language processing. The results show that B2B-SIs have a positive effect on firm value and an insignificant influence on firm risk. Importantly, the effect of a B2B-SI on firm value is significantly greater than that of a B2C-SI. Unlike B2C-SIs, the effect of B2B-SIs on firm value is greater when the firm has more product innovations. Surprisingly, unlike B2C-SIs, the effect of B2B-SIs on firm value is less positive when the SIs emphasize customers. These findings offer important insights about the relative value of B2B-SIs.
Keywords: B2B marketing; finance–marketing interface; innovation; machine learning; natural language processing; services; shareholder value; strategy
Services, which constitute the bulk of the U.S. economy, are becoming increasingly important in economic development. The contribution of services to the U.S. gross domestic product grew from 73% in 2000 to 77% in 2016 ([66]). In particular, services are becoming more important in business-to-business (B2B) markets.
Business-to-business markets constitute a lion's share of all markets ([30]; [33]; [54]), and B2B commerce accounts for the majority of commerce in the United States. ([50]). A growing number of B2B firms are becoming service-dominant (e.g., IBM, Xerox) because services enable them to build long-lasting relationships with their direct customers and their direct customers' customers ([25]; [73]).
Business-to-business firms rely on new services or service innovations (SIs) for growth ([ 2]; [69]). Formally, an SI is the exploitation of an idea for a service that is new to the firm and intended to provide its customers new benefits (adapted from [ 6]]). This conceptualization is also consistent with [ 7], who defines an SI as a new or improved service concept that satisfies the customer's unmet needs. Firms constantly seek to introduce SIs to create value and stay competitive (e.g., [10]). The need for SIs is growing for B2B firms. In a survey of 7,000 customers of predominantly B2B firms, 63% expected companies to provide new services more frequently than ever before ([57]).
For shareholders, a firm's B2B service innovations (B2B-SIs) create both value and risk that are critical to study. The Institute for the Study of Business Markets (ISBM) points out that "as firms continue to seriously 'mix' service offerings...with hard product offerings, the issue of computing value, demonstrating value, and documenting value is becoming ever more important" ([29], p. 30). Formally, a B2B-SI is the exploitation of an idea for a service that is new to the firm and intended to provide its business customers new benefits. However, because innovations are risky, firm value created from B2B-SIs may be associated with firm risk. Managers need to better understand the returns and risks arising from B2B-SIs to determine whether the returns are commensurate with the risks.
Many firms that introduce B2B-SIs also offer business-to-consumer service innovations (B2C-SIs). We define a B2C-SI as the exploitation of an idea for a service that is new to the firm and intended to provide its individual consumers new benefits. Companies such as FedEx and Dell introduce SIs in both business and consumer markets. For example, Dell introduced Premier Enterprise Services, a B2B-SI that installs and customizes business systems. However, Dell also introduced B2C-SIs, such as a service to pick up and recycle old computer equipment from customers' doorsteps. Even firms that operate predominantly in the consumer space introduce new B2B services in addition to their new B2C service offerings. For instance, after learning that its florist store owners needed help running their small businesses, 1-800-Flowers began offering marketing, accounting, and computer services to them for a flat fee. Many firms need to allocate their resources across these SI types. Therefore, it is important for all firms to better understand the impact of B2B-SIs relative to B2C-SIs on firm value and risk.
What are the value and risk created by B2B-SIs relative to those derived from B2C-SIs? Given the differences between business and consumer markets along several dimensions, including number of buyers, scalability, and heterogeneity, it is important to better understand the differences between B2B-SIs and B2C-SIs. On the one hand, the markets for B2B-SIs are less heterogeneous, offering a steady (often contractual) cash flow potential, than those for B2C-SIs. On the other hand, B2C-SIs can be scaled to a wider market to generate larger cash flows. These differences have different implications for firm value and firm risk across B2B-SIs and B2C-SIs.
Other innovation types (e.g., product innovation, or the creation and market introduction of a physical good that is new to the firm) and innovation characteristics (e.g., customer focus) may interact with and moderate the effects of SIs on firm value and risk. These interaction effects may be positive or negative, and a deeper understanding of such effects is useful from both academic and managerial standpoints. The definitions of different types of innovations used in this article appears in Table 1. All innovations except new-to-market innovations are just new to the firm.
Graph
Table 1. Formal Definitions of Major Constructs.
| Construct | Definition |
|---|
| Service innovation (SI) | The exploitation of an idea for a service that is new to the firm and intended to provide its customers new benefits. |
| Business-to-business service innovation (B2B-SI) | The exploitation of an idea for a service that is new to the firm and intended to provide its business customers new benefits. |
| Business-to-consumer service innovation (B2C-SI) | The exploitation of an idea for a service that is new to the firm and intended to provide its individual consumers new benefits. |
| Product innovation | The creation and market introduction of a physical good that is new to the firm. |
| Customer-focus service innovation | New service that emphasizes the solution to a customer problem and customer benefits over other aspects. |
| Technology-focus service innovation | New service that emphasizes the technological features of the new offering over other aspects. |
| People-enabled service innovation | New service that is delivered primarily through human interactions. |
| New-to-market service innovation | New service that creates a new benefit not witnessed by the market before. |
1 Notes: A customer-focus (technology-focus) service innovation announcement predominantly highlights customer (technology) in the description of the new service being offered. If the primary aspect highlighted is not customer (technology), the new service is not treated as a customer-focus (technology-focus) innovation.
Compared with research on product innovation, the literature on SIs is sparse (e.g., [ 9]; [21]; [38]; [41]). Research has shown that SIs significantly differ from goods innovations along dimensions such as scalability and coproduction ([47]). Among B2C-SIs, electronic-SIs (e-SIs) differ substantially from people-SIs (p-SIs) ([21]). However, despite their managerial and theoretical relevance, not much is known about any systematic differences between B2B-SIs and B2C-SIs and their effects on firm value and risk.
The purpose of this article is to fill the void in the B2B services and innovation literature streams by addressing the following research questions: ( 1) What are the effects of B2B-SIs on firm value? ( 2) What are the effects of B2B-SIs on firm risk? ( 3) How do these effects compare with those of B2C-SIs? ( 4) Are the effects of B2B-SIs on firm value and firm risk moderated by other types of innovations or SI characteristics?
We address these questions by developing and empirically testing hypotheses that relate B2B-SIs and B2C-SIs to moderators, firm value, and firm risk. We develop and estimate our model using a unique panel data set of 2,263 SIs assembled from multiple data sources across 15 industries for eight years, controlling for firm- and market- specific factors, heterogeneity, and endogeneity. We analyze innovation announcements using natural language processing (NLP) to gather insights on the quality of innovations.
The results show that B2B-SIs have a positive effect on firm value that is greater than that of B2C-SIs. Importantly, relative to B2C-SIs, B2B-SIs have a marginally lower effect on firm risk. Unlike B2C-SIs, the effect of B2B-SIs on firm value is greater when the firm has more product innovations. However, surprisingly, unlike B2C-SIs, the effect of B2B-SIs on firm value is less positive when the SIs highlight customers. We find that B2B-SIs (B2C-SIs) have a greater effect on firm value in B2B (B2C)-dominant industries. These results have significant managerial implications.
Our results make substantial contributions to both marketing theory and practice. From a theoretical perspective, to our knowledge, our research offers the first detailed explanation of how and why B2B-SIs affect firm value and risk, how these effects differ from those of B2C-SIs, and what factors moderate these effects. It also provides a better understanding of the combined effects of SIs and product innovations in firm value creation. From a practitioner viewpoint, it helps managers better understand both the returns and risks arising from B2B-SIs and make more informed decisions about the number of B2B-SIs and B2C-SIs to introduce and the focus of the innovation announcements. It is particularly helpful for managers of firms that offer both B2B-SIs and B2C-SIs to better manage their portfolio of B2B-SIs and B2C-SIs. Taken together, the theoretical explanations and new insights offer substantial value to the field.
Several measures of firm innovativeness exist in the academic and trade literatures (e.g., number of innovations, patent applications, customer assessments, expert opinions; [19]; [63]). In general, these measures assume that firm innovativeness is positively associated with the number and frequency of a firm's innovations. Therefore, we use the number of innovations announced by the firm as a proxy for firm innovativeness.
Research on goods innovations shows that such innovations may have mixed financial consequences. [23] report no significant effects on financial value. In contrast, [23], [61], [62], [63], [65], and [51] find positive effects of goods innovation on firm value. However, goods innovation may also be positively associated with systematic risk ([20]).
Services differ from goods in many ways, but there is limited research on SIs (e.g., [ 9]; [21]; [38]; [41]; [47]) relative to goods innovation. [21] compare e-SIs and p-SIs and find that while e-service innovativeness has a positive and significant direct effect on firm value, p-service innovativeness has an overall significantly positive effect on firm value only in human-dominated industries; both e- and p-service innovativeness are positively associated with idiosyncratic risk. Among SIs, B2C-SIs have been the focus of research attention (e.g., [21]). Given the importance of B2B markets and B2B-SIs, it is surprising that we do not know much about financial consequences of B2B-SIs and how they compare with those of B2C-SIs.
We propose a conceptual model delineating the relationships among B2B-SIs, B2C-SIs, moderators, firm value, and firm risk. Figure 1 presents the proposed model. The conceptual model is based on the marginal benefits (returns) and marginal costs (risk) aspects of economic theory but also draws from the resource-based view of the firm ([ 4]) and the role of industry or product markets in creating competitive advantage ([56]).
Graph: Figure 1. Conceptual model linking B2B-SIs, B2C-SIs, moderators, firm value, and firm risk.Notes: Continuous lines indicate focal relationships and dashed lines represent relationships involving control variables.
Innovations—in particular, goods innovations—have a direct effect on firm value ([ 5]; [23]; [43]). Service innovations may also affect firm value. First, SIs can offer new customer value, generating demand from new customers ([59]). The growth in demand will result in increased revenue streams and cash flows. Second, SIs will also likely enhance existing customer value and customer loyalty. Customer value derived from a service is the outcome of a coproduction process between the customer and the firm (e.g., [39]; [70]). To minimize the cost of coproduction over time, customers may avoid switching from their service provider, enhancing their loyalty to the service provider. Enhanced loyalty will likely lead to higher future cash flows, resulting in a positive effect of SIs on firm value. Indeed, B2C-SIs have a positive direct effect on firm value ([21]).
We believe that B2B-SIs in particular may have a direct effect on firm value because innovations are critical to the growth of B2B firms ([14]; [49]). The B2B buying process is often highly complex, involving multiple stakeholders such as financial analysts, engineers, and purchasing agents ([27]). Business-to-business firms invest in direct sales force and channel intermediaries, allowing them to build unique resources for privileged access and strong ties with customers ([69]). In the services context, close ties with the customers enable a firm to fine-tune its B2B-SIs and add customized value to its business customers. Service innovations that offer greater customized value have a greater potential for generating price premiums. Strong relationships with business customers allow firms to enjoy high margins through value pricing ([ 1]). Such premium services create substantial marginal benefits for the firm by significantly adding to its future cash flows.
Furthermore, B2B-SIs allow customers to coproduce, enhancing value and boosting cash flows to the innovating firms. When customers coproduce an SI, customer value is enhanced ([70]). For example, if a small- or medium-enterprise customer wants to maximize the value from a new network installation service offered by Dell, it can actively participate in the value creation process. This active coproduction of value includes the customer providing detailed information about the network needs, giving access to facilities, and ensuring that staff members are well-trained to operate the network. If a firm does not do its part in the coproduction process, customer value may significantly decrease or even diminish ([28]). Enhanced customer value can lead to greater cash flows, resulting in greater firm value. Drawing on these theoretical arguments, we advance H1a.
- H1a: B2B-SIs have a positive effect on firm value.
Next, we compare the positive effect of B2B-SIs on firm value with that of B2C-SIs. We note that B2B-SIs differ from B2C-SIs on market characteristics, service characteristics, and resource-based advantage sources. We summarize these theoretical differences in Table 2.
Graph
Table 2. Differences Between B2B and B2C and B2B-SIs and B2C-SIs.
| Characteristics of Business Markets and Consumer Markets |
|---|
| B2B | B2C |
|---|
| Number of customers | Low | High (e.g., Sears introduces new service to its 60 million credit card customers to view and pay bills online) |
| Geographic location of customersa | Generally concentrated | Generally dispersed |
| Customer contact | Direct | Indirect |
| Buying sequence | Complex | Simple (e.g., Apple starts selling movies and TV shows through iTunes) |
| Vendor evaluation | Generally formal | Generally informal |
| Value pricing | Easy to implement | Difficult to implement |
| Service design and delivery | Customized (e.g., Dell introduces new services that facilitate the planning and deployment of enterprise-ready solutions on Dell server and storage systems) | Standardized (e.g., Sears introduces an online scheduling service to schedule service and repairs on appliances and heating and cooling systems) |
| Promotion | Seller comes to buyer | Buyer comes to seller |
| Distribution channels | Short and direct | Long and indirect |
| Contracts | Formal (e.g., Xerox launches eClick services. eClick contracts include maintenance, supplies management and printing service for large organizations.) | Informal |
| Depth of relationship | Deep | Shallow |
| Vendor loyalty | High | Low |
| B2B-SI | B2C-SI |
| Services Characteristics |
| Scalability | Low | High |
| Intangibility | High | Low |
| Heterogeneity | Low | High |
| Development cost | Moderate | High |
| Bases of Resource-Based Advantage |
| Value-creating ability | High | High |
| Rarity | High | High |
| Inimitability | High | Low |
| Substitutability | Low | Low |
2 aThe categorization is generally true and relative between B2B and B2C markets. There could be exceptions.
Fundamental differences in the characteristics of B2B and B2C markets have key implications for differences between B2B-SIs and B2C-SIs. Business-to-business markets have fewer customers and customers that are generally geographically more concentrated (e.g., [45]). In B2B markets, the buying sequence is complex, and the seller's contact with the buyer is typically direct ([30]). Unlike B2C markets, B2B markets are driven by formal vendor evaluation and buyer–seller contracts ([13]). Buyers in B2B markets make fewer purchases, each of which typically offers more value than an average B2C purchase, making value pricing key for B2B-SIs. Services offered by B2B firms are often tailored to the buyer's needs, whereas B2C services are standardized. In B2B markets, the seller goes to the buyer and has short and direct distribution channels, unlike in B2C markets (e.g., [45]). Firms in B2B markets have deeper relationships with customers, and mutual buyer–seller loyalty is typically high (e.g., [73]). Firms in B2C markets offer greater scale, but the average customer has a lower lifetime value.
The differences between B2B and B2C markets create dissimilarities in service characteristics between B2B-SIs and B2C-SIs. Because of the smaller number of customers and greater complexity and customization in B2B markets, scale-based cost reductions are not as important in B2B markets as in B2C markets (e.g., [35]). Services offered in B2B markets are more intangible than B2C services because of the embeddedness of ties between the seller and the buyer resulting from deeper and more loyal relationships ([49]). Moreover, B2B markets are concentrated and have a smaller number of customers with similar problems. Consequently, even though customization needs can be high for some customers, B2B-SIs serve less diverse needs overall, face less customer heterogeneity, and have lower development costs than B2C-SIs.
Between B2B-SIs and B2C-SIs, some sources of resource-based advantages may be similar, but the degree of advantage and other sources may differ. Both B2B-SIs and B2C-SIs have high value-creating ability, high rarity, and low substitutability. However, because of larger markets and greater scalability, some B2C-SIs can create greater value for firms than B2B-SIs. In contrast, B2B-SIs are harder to imitate because they are often customized due to the close and direct relationships between the firms and the customers.
These differences between B2B-SIs and B2C-SIs suggest different sources of marginal benefits for B2B-SIs compared with B2C-SIs. On the one hand, B2B-SIs can produce positive returns from a smaller number of high-value customers with formal contracts ([27]). On the other hand, B2C-SIs can create high or very high returns if they can be scaled adequately (low costs at high volumes). Because an average SI may not be easily scalable, the extent of marginal benefits from these sources could be higher for B2B-SIs than B2C-SIs, leading to greater marginal cash flows for B2B-SIs. Therefore, we anticipate the effect to be greater for B2B-SIs than for B2C-SIs.
- H1b: The positive effect of B2B-SIs on firm value is greater than that of B2C-SIs.
Business-to-business SIs involve marginal costs to the firm that affect firm risk, marked by stock price volatility. Innovation is inherently associated with firm risk ([24]; [63]). Variability in stock prices reflects two types of underlying firm risk: systematic risk and idiosyncratic risk. Systematic risk or market risk is the extent to which the firm's stock return corresponds with the average return of all the stocks in the market ([60]). Idiosyncratic risk is the residual risk associated with the firm's abnormal returns after controlling for systematic risk and is important to multiple stakeholders, including debt holders, employees, suppliers, and customers ([26]). Indeed, SIs are associated with both systematic and idiosyncratic firm risk ([21]).
Introducing innovations or investing in research and development will lower systematic risk ([16]) by creating strategic differentiation that can protect innovative firms from market downturns ([64]). However, SIs may reduce systematic risk only in people-intensive industries but will either increase or have an insignificant effect on systematic risk in other industries ([21]). Given that services are difficult to scale, hard to protect through patents, and coproduced to create value, it may be tough for a firm to affect systematic risk through SIs in industries that are not people intensive.
Business-to-business SIs may reduce systematic risk. Because systematic risk is a market-level risk, to affect systematic risk, a firm's B2B-SIs will have to influence investors' perceptions in such a way that the firm's stock will respond differently from the market return. Such B2B-SIs could be disruptive or market-creating innovations ([ 6]). They could also be introduced during economic cycles that could have a significant negative effect on market risk. Generally, B2B firms have embedded ties with their business customers that help counter any increase in market-level risk associated with B2B-SIs, consistent with [49]. Thus, we expect B2B-SIs to lower systematic risk.
We anticipate the effect of B2B-SIs on systematic risk to significantly differ from that of B2C-SIs, as shown in Table 2. Relative to B2C-SIs, formal, high-value contracts with fewer well-known customers for B2B-SIs may mitigate some of the marginal costs or uncertainties (e.g., [48]). They will reduce the scalability disadvantage of B2B-SIs compared with B2C-SIs while increasing inimitability, rarity, and the value-creating ability of the firm. Compared with B2B-SIs, B2C-SIs appeal to a wider and more heterogeneous customer base and could potentially affect the market as a whole. These B2C characteristics imply that B2C-SIs could lead to high uncertainty in overall market demand and, thus, unsteady cash flows that investors cannot easily anticipate. Because B2C services have to scale to a much larger customer base, the market uncertainty about whether the additional sales revenue volume will offset the vagaries of satisfying more diverse customers may be greater for B2C-SIs than B2B-SIs. As a result, investors will perceive the returns from B2C-SIs as less stable, making firms that introduce B2C-SIs more sensitive to market downturns. Thus, compared with B2C-SIs, B2B-SIs are more likely to have a more negative influence on systematic risk.
- H2: B2B-SIs have (a) a negative effect on systematic risk and (b) a more negative effect on systematic risk than B2C-SIs.
We also expect B2B-SIs to be negatively related to idiosyncratic risk. Firms in B2B markets have fewer customers overall and have close relationships with these customers, and any new service is often thoroughly vetted with customers and potential clients before it is launched. In fact, customers participate in activities such as opportunity recognition, funding, and feedback provision, absorbing the vulnerability of the innovation ([18]). Many B2B-SIs are solutions to customer problems, increasing the inimitability and rarity of the innovation from resource-based view ([ 4]). Moreover, many B2B services are contractual in nature, providing not only stability but also visibility to a firm's cash flows ([74]). Furthermore, many B2B firms build execution risk assessment and mitigation capability critical to introduce new services ([69]). Therefore, they may not carry any firm-specific risk. Rather, because the SIs may replace or augment prior unsatisfactory solutions of the firms with contractual revenue streams, they may offer greater certainty in cash flows. Thus, this reduced uncertainty in customer demand and cash flows may even result in B2B-SIs helping lower idiosyncratic risk.
Because of the differences between business and consumer markets illustrated in Table 2, we anticipate B2C-SIs to be positively related to idiosyncratic risk. Usually, B2C-SIs target larger customer segments than do new business services ([27]). Although many of these new services may be pilot tested with a small sample of consumers, the acceptance of a much broader and diverse set of consumers is often uncertain. Moreover, B2C-SIs need to be scaled to a larger audience than B2B-SIs, involving greater investments, often with longer return horizons. In addition, unlike B2B-SIs, many B2C-SIs involve fewer and shallower direct contacts with customers, leading to inefficiencies in the coproduction process of the new service and heightening the uncertainty in the firm's cash flows. Finally, contracts in consumer markets are predominantly informal, and switching between providers is easier compared with business markets. This characteristic will make it difficult for investors to evaluate the size and stability of the cash flows and will expose the firm to increased residual risk. These arguments lead to H3.
- H3: B2B-SIs have (a) a negative effect on idiosyncratic risk and (b) a more negative effect on idiosyncratic risk than B2C-SIs.
Both B2B-SIs and B2C-SIs may interact with other innovation types, such as product innovations, and with innovation characteristics, such as customer focus, to have greater or smaller effects on firm value and firm risk.[ 5] We choose these moderators or interaction constructs on the basis of relevance, prior research, and managerial value. Product innovations can combine with SIs to offer customers greater benefits and customized value, potentially enhancing firm value ([59]; [69]). Customer focus can significantly influence an innovation's success ([71]). We define customer focus innovations as new services that emphasize the solution to a customer problem and customer benefits over other aspects. For example, in 2006, to promote healthy living, Whole Foods Market started offering healthy-lifestyle workshops for consumers at its stores, providing practical guidance related to nutrition and beauty topics. Innovations that focus on customers may affect investor perceptions of how B2B-SIs and B2C-SIs will shape firm value by changing their expectations of customer acceptance and future cash flows.
Product innovations may interact with SIs to affect firm value. Firms that introduce both product innovations and SIs typically market them to their customers as a way of offering them greater value (e.g., [59]). For example, in addition to continually introducing new printers and other office equipment to the market, Xerox Corp. launched its consulting services to help its business customers maximize the value they can generate from these new products. Consequently, investors may expect a synergistic effect of product and service innovations and expect accelerated cash flows. Enhanced cash flows will likely result in greater firm value. This interaction effect will probably be positive regardless of whether the SIs are B2B-SIs or B2C-SIs.
The interaction effect of product innovations and SIs may differ between B2B-SIs and B2C-SIs. As discussed previously, B2B-SIs may have a greater effect on firm value than B2C-SIs. Given the differences between B2B and B2C markets from Table 2, when a firm offers more product innovations in addition to B2B-SIs, it is likely to better satisfy the solution needs of its business customers, which may be fewer and needing combined (product and service) offerings. As a result, when product innovations interact with B2B-SIs, they are likely to contribute more to firm value than they would interacting with B2C-SIs, as the latter require greater effort to work with product innovations at scale to create higher firm value. Thus,
- H4: The interaction of B2B-SIs with product innovations has (a) a positive effect on firm value and (b) a more positive effect on firm value than that of B2C-SIs.
Customer-focus innovations may interact with B2B-SIs or moderate the effects of B2B-SIs on firm value. Relative to B2B-SIs that do not highlight customers, B2B-SIs that emphasize customers may resonate more with customers, boosting their adoption and increasing revenues and cash flows. The enhanced cash flows will reflect in greater firm value.
The interaction effect of B2B-SIs with customer-focus innovations on firm value may be greater than that for B2C-SIs. Similar to B2B-SIs, a greater number of B2C-SIs that emphasize consumers could also help boost cash flows in consumer markets, so customer-focus B2C-SIs will also result in greater firm value. However, given the differences between B2B and B2C market listed in Table 2, because B2B-SIs are typically more customized than B2C-SIs and B2B relationships are deeper than B2C relationships, B2B-SIs that highlight customers may lead to greater cash flows than B2C-SIs.
- H5: The interaction of B2B-SIs with customer-focus innovations has (a) a positive effect on firm value and (b) a more positive effect on firm value than that of B2C-SIs.
Consistent with prior research (e.g., [21]; [63]), we control for firm factors (people-enabled SIs, new-to-the-market SIs, technology-focus SIs, announcement sentiment, firm size, firm age, acquisitions, alliances, and operating margin) and market factors (competitor innovation activity, market size, and market growth) that might directly affect firm value and firm risk.[ 6] We also control for the main effect of the moderator variables, product innovations and customer-focus SIs. Details on the operationalization of control variables appear in Web Appendix A. We also control for industry fixed effects ([55]) through industry dummies and for temporal effects through year dummies.
To empirically test our hypotheses, we require panel data on firm value, firm risk, firm and market factors driving SIs, and the number and type of SIs. Because these data are not readily available from a single source, we manually assembled a unique panel data set using different sources. An advantage of this approach is that we avoid common method bias by using separate sources for key independent and dependent variables ([42]).
To obtain a mix of B2B and B2C innovations across a broad cross-section of companies from different industries, we constructed our sample from three broad lists, the American Customer Satisfaction Index (ACSI) database, the ISBM member company list, and the Forbes list of most innovative companies.[ 7] We chose these lists as our sampling frame for the following reasons. Although the ACSI database has been widely as a representative sampling frame used in previous research (e.g., [44]; [68]), it does not include pure B2B firms. The absence of pure B2B firms can introduce a sampling bias given our research focus on B2B-SIs. We avoid this bias by balancing our ACSI sample with firms from the ISBM and Forbes lists that predominantly operate in business markets, which yields a representative sample of 119 firms that include pure B2B firms and firms that introduce both B2B-SIs and B2C-SIs. In this sample, 76 firms introduced both B2B-SIs and B2C-SIs, while 14 (29) firms launched only B2B-SIs (B2C-SIs).
We collected data from a cross-section of 15 industries—namely, computers, automobiles, chemicals, metals, electrical goods, wholesale, business services, consumer goods, utilities, retailing, insurance, telecommunication, hospitality/courier services, airlines, and internet portals/online travel services. We chose the years 2000 to 2007 because they represent the period after the internet bubble and before the economic recession of 2008–2009 to avoid any confound with macroeconomic factors. We obtained firm age data from Hoover's company profiles. We collected information on 2,263 publicly announced SIs introduced by 119 firms by applying an archival method (see details in Web Appendix B).
To gather insights on the quality of innovations, we analyzed the innovation announcements using a machine learning or NLP method called topic modeling. Topic models help unearth the main themes or topics that underlie unstructured documents (innovation announcements). These include latent Dirichlet allocation (LDA) and the correlated topic model (CTM) ([12]; [11]; [67]).
Topic models treat a hidden topic as a distribution over the words in the documents. We used the LDA model that assumes that the words of each document arise from a mixture of topics, each of which is a distribution over the vocabulary. We examined word frequencies in topics for different cluster sizes. We selected topics that make sense in the given context and for which there are interesting cooccurrences of words in the topics. As part of a rigorous implementation of LDA, we dropped stop words and connecting words (e.g., and, but, if), some common words that exist in most announcements (e.g., business, company, announce, announced), and the names of the companies. We also estimated different variations of LDA as a robustness check.
Three topic clusters emerged from the LDA analysis: Cluster 1, emphasizing customer; Cluster 2, emphasizing technology; and Cluster 3, emphasizing service. We created three dummy variables for these clusters and assigned each announcement to the cluster with the highest probability of the announcement's membership. We totaled the number of innovations emphasizing each topic for each firm in each time period to compute the number of customer-focus innovations, technology-focus innovations, and service-focus innovations. We then standardized each innovation focus by dividing it by the total number of SIs introduced by each firm in each year. Because the sum of these innovation-focus measures equals one and because service emphasis is an expected base level, we retained the two variables, customer-focus innovations and technology-focus innovations, for inclusion in our model.
To capture the overall sentiment of the innovation announcements for each firm, we performed a sentiment analysis of each announcement, consistent with [72]. This sentiment analysis algorithm (tidytext) identifies and computes the number of positive and negative (toward the innovation) words in the announcement. It computes a net sentiment score for an innovation announcement that can be positive, negative, or zero. For some announcements, there were no positive or negative words, so their net sentiment score was zero. We totaled the sentiment scores across the firm's innovation announcements in a given year to develop the overall innovation announcement sentiment.
Table 3 provides a detailed list of variables, operationalization, and data sources. Some examples for each type of SI appear in Table 4.
Graph
Table 3. Variables, Measures, and Data Sources.
| Variable | Notation | Operational Measure | Data Source |
|---|
| Focal Variables |
| B2B-SIs | B2BSI | Annual firm-level count of B2B-SIs | LexisNexis |
| B2C-SIs | B2CSI | Annual firm-level count of B2C-SIs | LexisNexis |
| Firm value (Tobin's q) | FV | Tobin's q | CRSP, Compustat |
| Firm value (abnormal returns) | FV | Compounded annual abnormal returns from Carhart four-factor model | CRSP |
| Systematic risk | SRISK | Value of beta obtained from the Carhart four-factor model | CRSP |
| Idiosyncratic risk | IRISK | Standard deviation of residuals of the Carhart four-factor model | CRSP |
| Control Variables |
| Product innovations | PI | Annual firm-level count of product innovations | LexisNexis |
| Customer-focused SIs | CFSI | Percentage of innovation announcement indicating customer focus based on NLP correlated topics modeling | LexisNexis |
| People-enabled SIs | PSI | Annual firm-level count of people enabled service innovations | LexisNexis |
| New-to-market SIs | NTMSI | Annual firm-level count of service innovations that are new to the market | LexisNexis |
| Technology-focused SIs | TFSI | Percentage of innovation announcement indicating technology focus based on NLP correlated topics modeling | LexisNexis |
| Announcement sentiment | SENTI | Net sentiment score of innovation announcements | LexisNexis |
| Firm size | LFSIZE | Natural logarithm of firm's sales revenues | COMPUSTAT |
| Firm age | LFAGE | Natural logarithm of firm age in years | Hoover's Company Profiles |
| Acquisition | ACQUIS | Annual firm-level count of acquisitions | SDC Platinum |
| Alliance | ALLIANCE | Annual firm-level count of strategic alliances | SDC Platinum |
| Operating margin | OPMARGIN | Ratio of net income before depreciation to sales revenues | COMPUSTAT |
| Competitor innovation activity | COMPINA | Ratio of annual incremental cumulative competitors' sales revenues to market size | COMPUSTAT |
| Market size | LMSIZE | Natural logarithm of industry sales revenues | COMPUSTAT |
| Market growth | MGROWTH | Annual percentage growth in industry sales revenues | COMPUSTAT |
Graph
Table 4. Examples of B2B-SIs and B2C-SIs.
| Firm | Year Introduced | Type | Service Innovation Announcement |
|---|
| Yahoo! | 2000 | B2B-SI | "Yahoo!, the top Web-navigation company, is launching a business-information portal called Corporate Yahoo!; the new service will enable companies to display an internal corporate Web page integrated company information and programs with Yahoo content such as weather, stock quotes and news" (6/26/2000) |
| Dell | 2002 | B2B-SI | "Dell plans Monday to formally announce a new line of services for small and medium-sized businesses that typically do not have large technical staffs or budgets. [...] Services include network design, network installation and staff training." (12/8/2002) |
| FedEx | 2002 | B2B-SI | "On June 10 FedEx Freight East will launch a pioneering service, FedEx Freight Next Day Plus, to assist companies in reducing inventory cycle times. With the new money-back guaranteed service, FedEx Freight East will deliver shipments via truck in selected lanes up to 900-miles by the next business day, well over the regional LTL industry standard of up to 500 miles by the next day." (6/10/2002) |
| US Airways | 2002 | B2C-SI | "US Airways announces a new convenience at that enables customers to check-in for domestic flights and obtain boarding passes online. (12/20/2002) |
| Wal-Mart | 2003 | B2C-SI | "Wal-Mart is introducing basic financial services for US customers, using the same low-margin strategy that has turned it into the world's largest retailer. The entry of the discount superstore giant into financial services has always been feared by financial competitors worried that it could undercut their margins while facing a lighter regulatory burden." (1/8/2003) |
| Starbucks | 2004 | B2C-SI | "Coffee shop giant Starbucks said Thursday it was launching the first of its "music bars" where customers can listen to digital recordings and burn their own CDs." (10/14/2004) |
The distributions of B2B-SIs, B2C-SIs, and total SIs appear in Figure 2, Panels A, B, and C, respectively. Because innovations can be expensive, not every firm can introduce an SI every year. For example, it cost Anheuser-Busch $30 million to develop and launch an online entertainment service called bud.tv in 2007 ([ 3]). The distributions reflect a skew toward zero annual innovations. We find that B2B-SIs have a higher proportion of zeros than B2C-SIs as B2B-SIs may be focused on fewer customers and may take longer to implement than B2C-SIs due to the more complex buying sequence.
Graph: Figure 2. Distribution of number of SIs.
We use Tobin's q and abnormal cumulative stock returns as measures of firm value, following prior research (e.g., [ 8]; [31]; [34]; [53]; [63]). Using the Compustat/Center for Research on Security Prices (CRSP) database, we compute Tobin's q as (market value of common stock shares + book value of preferred stocks + book value of long-term debt + book value of inventories + book value of current liabilities – book value of current assets)/(book value of total assets), consistent with prior research (e.g., [17]). Tobin's q has key advantages over alternative measures. First, as [34] point out, Tobin's q is a forward-looking measure, as it is derived from stock market prices. Second, it captures the long-term performance of a firm because it compares its replacement value to its market value. Third, it is insensitive to accounting standards, which makes it apt for application across multiple industries ([15]). Our cumulative abnormal stock returns measure is consistent with [32].
Following [34] and [37], we adopt a more conservative approach to calculate Tobin's q. Rather than use year-end stock price and common shares outstanding, we use the average stock price and common shares outstanding at the end of the four quarters to calculate Tobin's q. This approach is more conservative because it overcomes the volatility problem that may be present when the year-end measure of stock price and common shares outstanding approach is used. Figure 3 shows the smoothed distribution of Tobin's q in our sample. The distribution is unimodal and exhibits some symmetry around the mode, allowing us to use a normal approximation for modeling purposes.
Graph: Figure 3. Distribution of firm value (Tobin's q).
The smoothed distributions of systematic risk and idiosyncratic risk appear in Figures 4 and 5, respectively. Both are unimodal, but idiosyncratic risk exhibits a sharper peak and is less symmetric than systematic risk.
Graph: Figure 4. Distribution of systematic risk.
Graph: Figure 5. Distribution of idiosyncratic risk.
We show the smoothed distribution of abnormal returns in Figure W1 in Web Appendix C. The distribution appears to be normal, mirroring that of Tobin's q. The distributions of B2B-SIs and B2C-SIs for the firms that introduced both B2B-SIs and B2C-SIs appear in Figures W2 and W3 in Web Appendix C. The distributions of B2B-SIs for the firms that introduced only B2B-SIs and of B2C-SIs for the firms that launched only B2C-SIs appear in Figures W4 and W5 in Web Appendix C. All the distributions are similar to those in Figure 2, Panels A–C.
The summary statistics of the key variables appear in Table 5. The average number of B2B-SIs (.85) is smaller than that of B2C-SIs (1.95). The mean Tobin's q (abnormal returns) in the sample is 1.54 (.08). The average systematic risk and idiosyncratic risk are 1.03 and.02, respectively, consistent with those reflected by the smoothed distributions in Figures 4 and 5. The mean number of product innovations is 1.55, smaller than that of B2C-SIs. The mean number of new to market SIs is small (.40) because they are hard to develop and introduce in the market. The average score of customer-focus SIs is.21, while that of technology-focus SIs is.17. The overall innovation announcement sentiment ranges from −3 to 86 with a mean of 5.09.
Graph
Table 5. Summary Statistics.
| Mean | Median | SD | Min | Max |
|---|
| Focal Variables |
| B2B-SIs | .85 | .00 | 1.92 | .00 | 18.00 |
| B2C-SIs | 1.95 | 1.00 | 4.36 | .00 | 59.00 |
| Firm value (Tobin's q) | 1.54 | 1.14 | 1.28 | −.11 | 17.02 |
| Firm value (abnormal stock returns) | .08 | .06 | .33 | −1.89 | 2.19 |
| Systematic risk | 1.03 | .97 | .39 | .14 | 3.08 |
| Idiosyncratic risk | .02 | .01 | .01 | .01 | .09 |
| Control Variables |
| Product innovations | 1.55 | .00 | 5.02 | .00 | 53.00 |
| Customer-focus SIs | .21 | .00 | .34 | .00 | 1.00 |
| People-enabled SIs | .86 | .00 | 1.70 | .00 | 18.00 |
| New-to-market SIs | .40 | .00 | .99 | .00 | 10.00 |
| Technology-focus SIs | .17 | .00 | .29 | .00 | 1.00 |
| Announcement sentiment | 5.09 | 2.00 | 9.15 | −3.00 | 86.00 |
| Firm size | 9.03 | 9.04 | 1.29 | 4.38 | 12.75 |
| Firm age | 4.03 | 4.32 | .87 | 1.10 | 5.35 |
| Acquisitions | 1.09 | .00 | 2.00 | .00 | 17.00 |
| Alliances | .41 | .00 | 1.07 | .00 | 8.00 |
| Operating margin | .04 | .05 | .13 | −2.50 | .41 |
| Competitor innovation activity | .07 | .05 | .08 | .00 | .44 |
| Market size | 11.82 | 11.80 | 1.39 | 7.20 | 14.09 |
| Market growth (%) | 7.81 | 6.99 | 26.95 | −71.50 | 578.60 |
3 Notes: Sample size/number of observations = 807. An observation refers to the combination of firm and year for which data are available.
Table 6 shows the correlation matrix for the key variables. No correlation is high, and the variance inflation factors are below five; therefore, we do not consider multicollinearity an issue in the data.
Graph
Table 6. Correlation Matrix for the Model Variables.
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |
|---|
| 1. B2B-SIs | 1.00 | | | | | | | | | | | | | | | | | | | |
| 2. B2C-SIs | .23** | 1.00 | | | | | | | | | | | | | | | | | | |
| 3. Firm value (TQ) | .09** | .21** | 1.00 | | | | | | | | | | | | | | | | | |
| 4. Firm value (ASR) | .04 | −.01 | .26** | 1.00 | | | | | | | | | | | | | | | | |
| 5. Systematic risk | .08** | .18** | .03 | −.05 | 1.00 | | | | | | | | | | | | | | | |
| 6. Idiosyncratic risk | .05 | .13** | .11** | .05 | .48** | 1.00 | | | | | | | | | | | | | | |
| 7. Product innovations | .10** | −.03 | .13** | −.02 | .02 | −.01 | 1.00 | | | | | | | | | | | | | |
| 8. Customer-focus SIs | .01 | −.01 | .00 | −.05 | −.06 | .04 | −.02 | 1.00 | | | | | | | | | | | | |
| 9. People-enabled SIs | .30** | .35** | .07** | −.07* | .08 | −.01 | .07* | .06 | 1.00 | | | | | | | | | | | |
| 10. New-to-market SIs | .39** | .51** | .15** | −.06* | .12** | .03 | .08** | .02 | .30** | 1.00 | | | | | | | | | | |
| 11. Technology-focus SIs | .09** | .16** | .03 | .00 | .12** | .06 | .02 | −.18** | .19** | .16** | 1.00 | | | | | | | | | |
| 12. Announcement sentiment | .40** | .45** | .05 | .00 | .15** | .17** | −.02 | .10** | .31** | .29** | .23** | 1.00 | | | | | | | | |
| 13. Firm size | .08** | .05 | −.24** | −.15** | −.09** | −.27** | .11** | .04 | .20** | .09** | .12** | .09** | 1.00 | | | | | | | |
| 14. Firm age | −.10** | −.24** | −.37** | −.12** | −.17** | −.37** | −.15** | .04 | −.02 | −.14** | −.05 | −.16** | .29** | 1.00 | | | | | | |
| 15. Acquisitions | .19** | .22** | .12** | .01 | .02 | .05 | .12** | .06* | .04 | .11** | .06 | .12** | .16** | −.07** | 1.00 | | | | | |
| 16. Alliances | .13** | .22** | .17** | −.01 | .19** | .18** | .17** | .00 | .05 | .17** | .06* | .17** | .01 | −.20** | .30** | 1.00 | | | | |
| 17. Operating margin | .07** | .03 | .16** | .11** | −.23** | −.37** | .00 | −.04 | −.03 | .06* | .04 | −.01 | .07* | .20** | .07** | .00 | 1.00 | | | |
| 18. Competitor innovation activity | .00 | .03 | −.07** | −.06* | −.11** | .04* | −.05 | .06 | −.02 | .00 | −.08** | −.01 | .02 | .00 | .01 | −.05 | .03 | 1.00 | | |
| 19. Market size | .05 | .00 | −.25** | −.09** | −.13** | −.08** | .06* | .04 | .06* | .01 | .00 | .05 | .45** | .14** | .04 | −.03 | −.01 | .24** | 1.00 | |
| 20. Market growth | .00 | .05 | .03* | .04 | −.06* | .07** | .00 | .00 | .00 | .05 | .08** | .08** | .00 | −.07* | .02 | −.04 | .00 | .50** | .08** | 1.00 |
- 4 *p <.10.
- 5 **p <.05.
- 6 Notes: TQ = Tobin's q; ASR = abnormal stock returns. Sample size = 807.
We develop a system of three equations with firm value, systematic risk, and idiosyncratic risk as the dependent variables. In each equation, subscript i represents the firm and subscript t represents the calendar year.
Graph
1
where FV is firm value, B2BSI is number of B2B-SIs, B2CSI is number of B2C-SIs, PSI is number of people-enabled SIs, NTMSI is the number of new to market SIs, PI is number of product innovations, B2BSI × PI and B2CSI × PI are interactions of B2B-SIs and B2C-SIs with product innovations, CFSI is customer-focus SI score, B2BSI × CFSI and B2CSI × CFSI are interaction of SIs with CFSI, TFSI is technology-focus SI score,[ 8] SENTI is overall innovation announcement sentiment score, LFSIZE is natural logarithm of the size of the firm, LFAGE is natural logarithm of the age of the firm, ACQUIS is number of acquisitions, ALLIANCE is number of alliances, OPMGIN is operating margin, COMPINA is competitor innovation activity, LMSIZE is natural log of the market size, MGROWTH is market growth rate, and IND are dummy variables representing industries other than the base industry (consumer products firms). YEARs are dummy variables that denote calendar years in the sample, with 2001 as the base year and ε is an error term. The industry and year dummy variables allow us to control for heterogeneity using the fixed-effects approach, consistent with prior research (e.g., [21]; [58]). α, φ, and ϕ represent parameters.
Graph
2
where SRISK is the systematic risk; η is an error term; β, γ, and π represent parameters; and the other terms are as defined previously.
Graph
3
where IRISK is the idiosyncratic risk; ν is an error term; δ, λ, and θ represent parameters; and the other terms are as defined previously (for returns and risk computation, see Web Appendix B).
The errors across the three equations are likely to be correlated. Therefore, we estimate the three equations by the seemingly unrelated regression estimation approach ([75]).
We control for unobserved heterogeneity through the industry and year fixed effects. Because we estimate both systematic and idiosyncratic risks from the same four-factor model in Equation 5, they may be heteroskedastic. Therefore, following [21], we estimate our model by a weighted least-squares approach. We weigh the risk observations by the inverse of the square root of the sum of one and the estimated systematic risk variance from Equation 2 in Web Appendix B.
To control for the endogeneity of B2B-SIs, B2C-SIs, and other innovation variables, we use the number of industry competitor innovations as the primary instruments, consistent with prior research that adopts the average number of innovations in the industry, excluding those of the focal firm. Industry competitor innovations may influence a focal firm's number of SIs but may not directly affect its firm value, making them good exclusion variables. The industry is a much larger entity to influence any one company's firm value. The remaining instruments include all the exogenous variables in the system. We estimate the models using the control function approach, consistent with [52].
Because B2B-SIs, B2C-SIs, and other innovations are count variables, we use a zero-inflated negative binomial regression model for the endogenous equations, consistent with [36]). To control for the endogeneity of other independent variables in Equations 1, 2, and 3, following prior research (e.g., [21]; [37]; [44]; [53]; [63]), we lag these independent variables by one time period. Using lagged variables not only helps eliminate potential reverse causality but also overcomes potential correlations of the independent variables with the error term.[ 9]
Table 7 presents the estimation results of Equations 1, 2, and 3. With regard to the firm value results, we discuss the Tobin's q results first. We subsequently discuss the abnormal returns results and the similarities/differences. Our hypotheses on the effects of SIs on firm value are partially confirmed. In line with H1a, B2B-SIs have a positive effect on firm value (p <.01). Consistent with our arguments, B2B-SIs create new revenue streams with clients with which the firm may have already established relationships, leading to incremental anticipated cash flows. These anticipated cash flows are associated with greater firm value. Moreover, B2C-SIs have a marginally positive influence on firm value (p <.10).
Graph
Table 7. Weighted Least Squares/Seemingly Unrelated Regression Estimation Results of Firm Value and Firm Risk Equations.
| Parameter/Independent Variables | Tobin's q | Systematic Risk | Idiosyncratic Risk | Abnormal Stock Returns |
|---|
| Focal Variables | |
| Intercept | 3.95 (.51)*** | 1.2497 (.0991)*** | .0412 (.0016)*** | .73 (.16)*** |
| B2B-SIs | .13 (.04)*** | −.0124 (.0101) | −.0002 (.0002) | .03 (.01)*** |
| B2C-SIs | .02 (.01)* | .0073 (.0038)* | .0001 (.0001) | .00 (.00) |
| Interactions and Additional Variables |
| Product innovations × B2B-SIs | .01 (.00)** | −.0006 (.0012) | −.0001 (.0000) | −.00 (.00) |
| Product innovations × B2C-SIs | .00 (.00) | −.0006 (.0012) | −.0001 (.0000) | −.00 (.00) |
| Customer-focus × B2B-SIs | −.14 (.08)* | −.0219 (.0488) | .0000 (.0008) | −.01 (.03) |
| Customer-focus × B2C-SIs | .04 (.05) | −.0200 (.0298) | −.0005 (.0005) | −.01 (.02) |
| Product innovations | .01 (.01) | .0049 (.0039) | .0003 (.0001)* | −.00 (.00) |
| Customer-focus SIs | −.05 (.11) | −.0514 (.0324) | .0002 (.0011) | −.05 (.03) |
| People-enabled SIs | .03 (.03) | .0050 (.0093) | .0004 (.0003) | .00 (.01) |
| New-to-market SIs | .10 (.06)* | .0688 (.0215)*** | .0016 (.0007)** | −.03 (.02) |
| Technology-focus SIs | −.07 (.13) | .0314 (.0395) | −.0012 (.0014) | −.00 (.04) |
| Announcement sentiment | −.02 (.00)*** | .0014 (.0016) | −.0000 (.0001) | −.00 (.00)* |
| Firm size | −.23 (.04)*** | .0044 (.0257) | −.0016 (.0004)*** | −.04 (.01)*** |
| Firm age | −.35 (.05)*** | .0371 (.0331) | −.0008 (.0005) | −.01 (.02) |
| Acquisitions | .07 (.02)*** | −.0065 (.0143) | .0001 (.0002) | −.01 (.01) |
| Alliances | .13 (.04)*** | .0213 (.0294) | −.0002 (.0005) | .02 (.01)* |
| Operating margin | 1.68 (.29)*** | −.6209 (.2914)** | −.0029 (.0048) | .31 (.09)*** |
| Competitor innovation activity | .37 (.54) | −.8652 (.4432)* | −.0288 (.0072)*** | −.47 (.17)*** |
| Market size | .05 (.04) | −.0924 (.0217)*** | −.0031 (.0004)*** | −.03 (.01)** |
| Market growth | .00 (.00) | −.0009 (.0016) | .0001 (.0000)** | .00 (.00) |
| Fixed Effects/Dummy Variablesa |
| Utilities | −1.42 (.15)*** | .2567 (.0453)*** | .0062 (.0007)*** | .06 (.05) |
| Retailing | −.09 (.13) | .2225 (.0401)*** | .0008 (.0007) | .10 (.04)** |
| Insurance | −1.58 (.29)*** | .3126 (.0679)*** | .0055 (.0011)*** | .18 (.08)** |
| Telecommunications | −1.06 (.22)*** | .1600 (.0624)*** | .0051 (.0010)*** | .05 (.07) |
| Hospitality/courier services | −.89 (.19)*** | .2285 (.0590)*** | −.0011 (.0010) | −.01 (.06) |
| Airlines | −1.64 (.21)*** | .8508 (.0653)*** | .0080 (.0011)*** | −.36 (.07)*** |
| Internet portals/online travel services | −.63 (.37)* | .4271 (.1097)*** | .0075 (.0018)*** | .54 (.12)*** |
| Computers | −1.33 (.19)*** | .2025 (.0579)*** | .0013 (.0009) | .07 (.06) |
| Automobiles | −1.58 (.26)*** | .2324 (.0750)*** | .0041 (.0012)*** | .18 (.08)** |
| Chemicals | −1.15 (.15)*** | .3413 (.0457) *** | .0007 (.0007) | −.06 (.05) |
| Metals | −1.48 (.19)*** | .4116 (.0575)*** | −.0021 (.0009)** | −.06 (.06) |
| Electrical goods | −.88 (.27)*** | .4656 (.0825)*** | −.0005 (.0013) | .00 (.09) |
| Wholesale | −.78 (.27)*** | .1634 (.0829)** | .0008 (.0014) | .06 (.09) |
| Business services | −.54 (.20)*** | .0533 (.0599) | −.0026 (.0010)*** | −.02 (.06) |
| Endogeneity control B2B-SI | −.12 (.04)*** | .0520 (.0329) | .0006 (.0005) | −.03 (.01)** |
| Endogeneity control B2C-SI | .00 (.00)*** | .0030 (.0009)*** | .0000 (.0000)*** | .00 (.00)*** |
| Endogeneity control new-to-market SI | −.06 (.05) | −.1241 (.0447)*** | −.0005 (.0002)* | .02 (.02) |
| Endogeneity control people-enabled SI | −.01 (.03) | .0062 (.0080) | −.0003 (.0003) | −.00 (.01) |
| Endogeneity control product innovations | .00 (.00)** | .0001 (.0000)** | .0000 (.0000) | .00 (.00) |
| R-square | .46 | .46 | .79 | .19 |
- 7 *p <.10.
- 8 **p <.05.
- 9 ***p <.01.
- 10 aBase industry is consumer products.
- 11 Notes: Standard errors in parentheses. Sample size = 807.
Importantly, the effect of B2B-SIs on firm value is significantly greater than that of B2C-SIs (p <.05), in support of H1b. The marginal return is higher for a B2B-SI than for a B2C-SI. This finding suggests that, on average, a B2B-SI is more valuable than a B2C-SI.
The effects of B2B-SIs and B2C-SIs on systematic risk differ. Contrary to H2a, B2B-SIs do not significantly raise or lower systematic risk (p >.10). This result suggests that B2B-SIs do not significantly affect anticipated volatility in cash flows. Interestingly, B2C-SIs are marginally positively associated with systematic risk (p <.10). There is greater uncertainty surrounding acceptance by a wider market and increased vagaries in cost scalability for B2C-SIs. Thus, B2C-SIs carry greater cash flow variability. The enhanced cash flow variability is associated with greater systematic risk. Importantly, B2B-SIs have a less positive effect on systematic risk than B2C-SIs (p <.05), lending support to H2b. Therefore, there is an asymmetry in the effects of B2B-SIs and B2C-SIs on systematic risk.
We find that B2B-SIs do not have a significant effect on idiosyncratic risk (p >.10); thus, H3a is not supported. However, the differences in these effects between B2B-SIs and B2C-SIs are marginally significant (p <.10), with B2B-SIs having more negative effects on idiosyncratic risk than B2C-SIs, consistent with H3b. Thus, B2B-SIs are less risky than B2C-SIs.
The interaction of B2B-SIs with product innovations has a positive effect on firm value (p <.05), consistent with H4a. Investors perceive a synergistic effect of B2B-SIs with product innovations. Most firms that introduce B2B-SIs also launch product innovations, so the market views this information in a positive light. However, the effect of the interaction of B2C-SIs with product innovations on firm value is insignificant (p >.10). Interestingly, the differences between these effects are insignificant (p >.10), contrary to H4b.
The interaction of B2B-SIs with customer-focus SIs has a marginally negative effect on firm value (p <.10), counter to H5a. One explanation for this surprising result follows. Investors typically expect B2B-SIs to be focused on customers by default because B2B markets have a smaller number of customers with which firms have deep and loyal relationships. If the innovation announcements highlight customers, they may heighten investor expectations of firm cash flows. When the actual cash flows fall short of the high levels of anticipated cash flows, firm value may drop. Furthermore, it could also be that customer focus is a given for a B2B-SI—that is, investors expect all B2B-SIs to have a customer focus. Highlighting an innovation's customer focus in the innovation announcement may cause investors to question why a B2B firm needs to emphasize it, creating doubts about the impact of that innovation on the market and firm value. The effect of interaction of B2C-SIs with customer-focus SIs is insignificant (p >.10). However, consistent with H5b, the difference between the effects of the interaction of B2B-SIs with customer-focus SIs and the interaction of B2C-SIs with customer-focus SIs is marginally significant (p <.10), with the B2B-SI interaction being more negative.
The effects of most of the control variables on firm value are in the expected directions. New-to-market SIs have a marginally positive effect on firm value (p <.10). Investors typically expect new-to-market SIs to create high cash flows, boosting firm value. Interestingly, cumulative announcement sentiment has a negative effect on firm value (p <.01). A possible explanation is that investors may perceive extremely upbeat or optimistic announcements as suspicious and unreasonable, resulting in lower firm value.
Among other firm factors, the coefficients of firm size (p <.01) and firm age (p <.01) are negative, implying that smaller and younger firms tend to have a higher firm value. Furthermore, firms growing through acquisitions and alliances tend to be more valuable (p <.01). Firms with greater operating margins have higher value (p <.01). Turning to market factors, competitor innovation activity, market growth, and market size have insignificant effects on firm value (p >.10), indicating that firm value is primarily driven by micro-level factors.
The effects of the control variables on firm risk are consistent. New-to-market SIs elevate systematic risk (p <.01) and idiosyncratic risk (p <.05). Product innovations marginally positively affect idiosyncratic risk (p <.10). Competitor innovation activity and market size lower both systematic and idiosyncratic risk, while operating margin is negatively related to systematic risk (p <.10 or better). In addition, market growth is positively associated with idiosyncratic risk (p <.05), while firm size is negatively related to idiosyncratic risk (p <.01).
The results of the model with cumulative abnormal returns as the dependent measure appear in the rightmost column of Table 7. In general, these results are substantively and directionally similar to those from our proposed firm value measure (Tobin's q), especially for B2B-SIs, innovation announcement sentiment, firm size, and operating margin. Only the effects of some interactions and control variables on firm value are no longer significant (p >.10). These insignificant effects may be reasonable due to the differences between the two measures of firm value. Tobin's q is measured for a year, while cumulative abnormal returns are computed based on an event with a short window. Because the effects of many variables such as innovations and investments accrue over a longer time horizon, we expect more effects to be significant in the Tobin's q model rather than in the abnormal returns model.
Table 8 provides a summary of our key findings. We find that B2B-SIs have a positive effect on firm value and do not exacerbate either systematic risk or idiosyncratic risk. Relative to B2C-SIs, they marginally lower systematic risk. Importantly, B2B-SIs have a greater influence than B2C-SIs on firm value. Unlike B2C-SIs, the effect of B2B-SIs on firm value is greater when the firm has more product innovations. However, surprisingly, unlike B2C-SIs, the effect of B2B-SIs on firm value is marginally less positive when the SIs emphasize customers. These results underscore the attractiveness of B2B-SIs in a firm's innovation portfolio.
Graph
Table 8. Summary of Key Findings.
| Dependent Variable |
|---|
| Independent Variable | Firm Value | Systematic Risk | Idiosyncratic Risk |
|---|
| B2BSI | +a(H1a) | n.s.(H2a) | n.s.(H3a) |
| (B2BSI − B2CSI) | +a(H1b) | −†(H2b) | −†(H3b) |
| B2BSI × PI | +a(H4a) | N.A. | N.A. |
| (B2BSI × PI − B2CSI × PI) | NS(H4b) | N.A. | N.A. |
| B2BSI × CFSI | −†(H5a) | N.A. | N.A. |
| (B2BSI × CFSI − B2CSI × CFSI) | −†(H5b) | N.A. | N.A. |
- 12 †Marginally significant (p <.10).
- 13 aThe difference between the effects of B2B-SIs and B2C-SIs on firm value is positive and significant (p <.05) in the entire sample. It is also positive and significant (p <.05 or better) in the subsample of firms that introduce both B2B-SIs and B2C-SIs.
- 14 Notes: Firm value is measured by Tobin's q. N.A. = not applicable; n.s. = not significant.
We ensured the robustness of our results by performing several additional analyses. First, we included firm-level dummy variables instead of industry-level dummy variables to determine whether the industry dummies parsimoniously capture the firm-specific fixed effects. Although the number of firms is much higher than that of industries and the coefficients differ, the effects of the main variables of interest were consistent with those from the proposed model.
Second, we estimated our model using a random-effects panel model. Because the random-effects model is much more parsimonious than the fixed-effects model, we expect the results to change. However, the effects of the main variables were substantively consistent, indicating that our model results are fairly robust to different specifications of unobserved heterogeneity.
Third, our sample includes three groups of firms: firms that introduced only B2B-SIs, firms that introduced only B2C-SIs, and firm that introduced both B2B-SIs and B2C-SIs. To ensure that our results are not skewed by the mix of these three types of firms, we estimated our model on a subsample of 510 observations from 76 firms that introduced both B2B-SIs and B2C-SIs. The results for firm value and abnormal stock returns are consistent with those in the overall sample (see Tables W2 and W3 in Web Appendix C). Thus, our results are robust to the sample mix.
Fourth, to test whether the coefficients of B2B-SIs and B2C-SIs in Equations 1, 2, and 3 changed with B2B versus B2C industries, we first estimated a model with interactions of a dummy variable representing whether the industry was primarily B2B or B2C with each of these coefficients. The results remained substantively the same.
Fifth, to check whether B2B-SIs and B2C-SIs have an interaction effect on firm value and firm risk, we attempted to estimate a model by including their interaction in all the three equations. However, the correlations between this interaction variable and each of B2B-SIs and B2C-SIs were high (.77), precluding a thorough investigation of the interaction effect.
Finally, we estimated our model using an alternative process: the conditional mixed-process framework ([55]) that accounts for correlated error terms. The results did not substantively change.
Our results have important implications for theory. The findings that B2B-SIs create firm value and enhance neither systematic risk nor idiosyncratic risk suggest the following possible underlying theoretical mechanism: B2B-SIs are targeted at fewer, geographically concentrated customers, and successful firms introducing B2B-SIs have a high degree of customer contact to understand customers' complex buying sequence. These firms also compete aggressively to emerge as favorites in the buyers' formal vendor evaluations. When introducing B2B-SIs, these firms also likely customize the innovation, practice value pricing, and promote it to their customers through direct channels ([69]). As a result, they are able to forge long-term contracts with their buyers for the new service offerings ([74]). By remaining loyal vendors, they deepen their relationships and embed ties with their business customers ([49]). All these activities improve cash flows arising from the introduction of B2B-SIs. The anticipated enhanced cash flows result in increased firm value.
The finding that a B2B-SI has a greater marginal effect on firm value than a B2C-SI reflects the underlying differences between B2B-SIs and B2C-SIs. A B2B-SI is typically highly customized, whereas a B2C-SI is standardized to serve a much larger number of customers. Furthermore, compared with B2C-SIs, B2B-SIs generally facilitate deeper customer relationships, fostering greater loyalty. Moreover, B2B-SIs are more intangible and less heterogeneous than B2C-SIs. These factors help a B2B-SI enhance firm value to a greater extent than a B2C-SI.
Firms typically create B2B-SIs based on a clear understanding of customer needs and sell them to their customers through formal contracts and deep relationships. As a result, these innovations mitigate volatility in cash flows at both the market level and the firm level. The lukewarm volatility does not significantly alter either systematic risk or idiosyncratic risk.
The results on the differences between the effects of B2B-SIs and B2C-SIs on firm risk suggest possible differences in theoretical mechanisms. Unlike B2B-SIs, B2C-SIs are more scalable, tangible, and heterogeneous in value to their consumers. Although these characteristics enable B2C-SIs to create value for the firm, they also make them more susceptible to market downturns and create uncertainty in the levels of price premiums different consumers may pay for the innovations. Such uncertainty in prices leads to fluctuations in future cash flows of firms introducing B2C-SIs. In contrast, the revenues and margins from B2B-SIs are more predictable because of the smaller and more stable customer base entering into formal contracts for the B2B-SIs.
The findings on the effects of the interaction of SIs with product innovations on firm value and firm risk suggest that product and SIs work more synergistically in B2B markets than in B2C markets. Because B2B offerings are more customized and complex than B2C offerings, the potential for customer value creation is greater when product innovations and SIs come together. Investors anticipate this value addition more for B2B innovations than B2C innovations. This reasoning also explains why there is less additional investor uncertainty about cash flows when B2B-SIs interact with product innovations. This theoretical implication is consistent with hybrid (product and service) innovations theory that hybrid innovations enhance value and mitigate risk (e.g., [40]; [69]).
The results on the effects of the interactions of B2B-SIs with customer-focus innovations imply how innovations may affect customer value and investor assessment of firm value. Customers of B2B firms interact extensively with vendors and expect any B2B-SI to be highly focused on their needs. Consequently, when B2B-SIs highlight customer focus in their announcements, customers may prefer these offerings more or pay a premium for these offerings only if the offerings exceed their expectations. The investors, in turn, infer the firm's future cash flows from the announcement signal and either raise their expectations or doubt the quality of innovations. If the B2B-SIs do not live up to the enhanced expectations or create doubts, firm value falls.
The results on the differences in the effects of B2B-SIs on firm value and firm risk across industries also raise new questions for further theoretical exploration. Why is the net firm value from B2B-SIs higher or lower in some industries? Why do the net effects of B2B-SIs on firm risk also differ across industries? Why are the effects systematically different from those of B2C-SIs in certain industries? A deeper exploration of industry differences with in-depth industry data may be able to shed light on these questions.
Our findings have critical implications for managerial practice. Table 9 summarizes the managerial implications of the key findings. The finding that B2B-SIs have a positive effect on firm value, combined with the finding that B2B-SIs do not have a significant effect on firm risk, suggests that B2B firms should consider introducing B2B-SIs whenever possible. It may take a long time to develop and coproduce B2B-SIs with the customers, but once designed and marketed, they can become valuable assets and mitigate the risks associated with growth strategies.
Graph
Table 9. Summary of Key Managerial Implications.
| Key Finding | Managerial Implication |
|---|
| B2B-SIs have a positive effect on firm value. | B2B firms should consider introducing more B2B-SIs. |
| B2B-SIs have a greater effect on firm value than B2C-SIs. | If resource requirements are similar, firms should consider launching a B2B-SI over a B2C-SI. |
| B2C-SIs increase risk marginally more than B2B-SIs. | If managers have to make a choice, a B2B-SI is a safer option than B2B-SI from a risk reduction standpoint. |
| B2B-SIs interact with product innovations to enhance firm value. | B2B firms should coordinate service and product innovations at a strategic level. Managers should leverage hybrid (product and service) innovations where possible. |
| B2B-SIs and customer-focus innovations together have a marginally negative interaction effect on firm value. | Managers should be wary of emphasizing customers in B2B-SI announcements. |
Relative to B2C-SIs, B2B-SIs have a negative effect on systematic risk, suggesting that B2C-SIs adversely affect market volatility. Firms will have to carefully evaluate the benefits and costs of introducing B2C-SIs. Firms should be more vigilant in comparing the returns of B2C-SIs against the risks. Because of this vulnerability, firms should launch B2C-SIs only after they determine that the risk-adjusted returns are acceptable or exceed their hurdle rate. If managers are risk averse and must allocate resources between B2B-SIs and B2C-SIs, they may be better off focusing on B2B-SIs. However, in some cases, the returns from B2C-SIs may be greater, so managers who might be more open to risky projects could perform a thorough risk-return analysis based on the effects of prior innovations.
The positive interaction effect of B2B-SIs with product innovations on firm value has useful implications for B2B managers. If a firm tends to have both SIs and product innovations, as many B2B firms do, managers may want to leverage their positive effects by introducing more of them and, where possible, combining them to generate hybrid innovations. In many firms, SIs and product innovations emanate from separate decision-making units, and sometimes even different business units. This finding implies that rather than treating them as outputs from silos, B2B firms should view service and product innovations at the strategic level with a view to better allocate resources and to potentially create synergies among them.
The negative effect of B2B-SIs with customer-focus innovations on firm value offers a cautionary tale for B2B managers. Managers should be wary of overemphasizing customers in B2B-SI announcements. They should plan on their customers and investors to expect any B2B-SI to be customer-centered and avoid overemphasizing customers in B2B-SI announcements.
The returns–risk balance differs across industries. A summary of the average number of B2B-SIs and B2C-SIs and their net incremental effects on firm value and risk for the 15 industries in our sample appears in Table W1 in Web Appendix C. The average number of B2B-SIs per year is highest in the business services industry (2.46), while the average annual number of B2C-SIs is highest for internet portal/online travel services (14.29). The net effect on firm value of B2B-SIs also is highest for business services at.31 change in Tobin's q. The net effect of B2C-SIs is highest for internet portals/online travel services at.35 change in Tobin's q. In general, for industrial goods and services, B2B-SIs generate higher firm value, whereas in consumer industries, B2C-SIs create higher firm value. The one exception is hospitality/courier services, for which the net effect of B2B-SIs on Tobin's q (.25) is greater than that of B2C-SIs (.09). For this industry, which has a balanced set of business and individual customers, B2B-SIs have a higher impact on firm value than B2C-SIs. The results are consistent for the abnormal returns measure of firm value.
The effects of B2B-SIs and B2C-SIs on firm risk vary across industries. In the business services industry, B2B-SIs lower systematic risk by the largest amount, while those in the consumer products industry reduce it by the least amount. In contrast, B2C-SIs increase both systematic and idiosyncratic risk most in the internet portals/online travel services industry and least in the metals industry. In all industries, B2B-SIs have negligible effect on idiosyncratic risk.
Taken together, the results from Table W1 offer concrete managerial recommendations. Executives in the business services industry should anticipate the best returns to B2B-SIs at the lowest risk. Similarly, managers of internet portals/online travel services should expect to earn the highest returns for B2C-SIs but must also be prepared for the highest risk (both systematic and idiosyncratic risk). Hospitality/courier services executives, who typically have a portfolio of B2B-SIs and B2C-SIs, may want to lean more toward B2B-SIs because, compared with B2C-SIs, B2B-SIs in this industry raise firm value more without significantly enhancing systematic or idiosyncratic risks. Therefore, firms in such industries should have a stronger focus on B2B-SIs.
The limitations of this study provide opportunities for future research. First, some of the effects in our research are marginal. In a larger sample, these effects could be stronger. Future research could address these effects with larger samples. Second, our study is limited to the variables for which we could obtain data. While we control for unobserved heterogeneity using fixed effects, other influential variables could be added to the model if data on them are available. Third, we could not investigate the interaction of B2B-SIs and B2C-SIs because they were collinear in our data. Because these variables will likely be highly correlated for most firms, a promising avenue for estimating their interaction effects is to identify and collect data on firms for whom the correlation is low. Fourth, future research could analyze B2B-SIs deeper by further classifying them as e-innovations versus p-innovations and radical versus incremental SIs if data on these dimensions are not collinear. Fifth, future research could examine nonlinear relationships of services mix in a firm's innovation portfolio on its firm value, similar to [46]. Finally, we used LDA as our topic model. A drawback of LDA is its inability to model correlations among topics. In some cases, CTM may fit better than LDA ([12]). Future research can explore CTM.
In conclusion, we have taken an important first step in studying the effects of B2B-SIs on firm value and firm risk. The results show that B2B-SIs have a positive and significant effect on firm value but an insignificant impact on firm risk. However, relative to B2C-SIs, B2B-SIs are associated with lower firm risk. We find that B2B-SIs (B2C-SIs) have a higher effect on firm value in B2B (B2C)-dominant industries. In industries with a mix of business customers and consumers, B2B-SIs have a slightly higher impact on firm value than B2C-SIs. Unlike B2C-SIs, the effect of B2B-SIs on firm value is greater when the firm has more product innovations. However, surprisingly, unlike B2C-SIs, the effect of B2B-SIs on firm value is less positive when the SIs emphasize customers. Our findings offer executives important insights about the relative value of B2B-SIs that assist in their innovation investment, allocation, and management decisions.
Supplemental Material, DS_10.1177_0022242919847221 - The Relative Effects of Business-to-Business (vs. Business-to-Consumer) Service Innovations on Firm Value and Firm Risk: An Empirical Analysis
Supplemental Material, DS_10.1177_0022242919847221 for The Relative Effects of Business-to-Business (vs. Business-to-Consumer) Service Innovations on Firm Value and Firm Risk: An Empirical Analysis by Thomas Dotzel and Venkatesh Shankar in Journal of Marketing
Supplemental Material, Web_Appendix_Apr_8_2019_Final - The Relative Effects of Business-to-Business (vs. Business-to-Consumer) Service Innovations on Firm Value and Firm Risk: An Empirical Analysis
Supplemental Material, Web_Appendix_Apr_8_2019_Final for The Relative Effects of Business-to-Business (vs. Business-to-Consumer) Service Innovations on Firm Value and Firm Risk: An Empirical Analysis by Thomas Dotzel and Venkatesh Shankar in Journal of Marketing
Footnotes 1 Associate EditorTomas Hult
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242919847221
5 1We thank anonymous reviewers for suggesting the possibility of existence of these effects and for recommending the inclusion and exploration of these effects in our analysis.
6 2For definitions of people-enabled SI and new-to-market SI, see Table 1.
7 3We considered banks but could not include them in our sample because banks have different regulations with regard to financial reporting. We also had to exclude conglomerates because the data for all SIs across all subsidiaries of the conglomerates were unavailable. These firms represent a small proportion of all firms.
8 4We could not include interactions with technology-focus service innovations due to multicollinearity.
9 5We attempted to control for reverse causality through a Granger causality test, but the insufficient number of longitudinal observations precluded us from conducting a valid Granger test ([22]).
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Record: 197- The Role of Marketer-Generated Content in Customer Engagement Marketing. By: Meire, Matthijs; Hewett, Kelly; Ballings, Michel; Kumar, V.; Van den Poel, Dirk. Journal of Marketing. Nov2019, Vol. 83 Issue 6, p21-42. 22p. 13 Charts, 1 Graph. DOI: 10.1177/0022242919873903.
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The Role of Marketer-Generated Content in Customer Engagement Marketing
Despite the demonstrated importance of customer sentiment in social media for outcomes such as purchase behavior and of firms' increasing use of customer engagement initiatives, surprisingly few studies have investigated firms' ability to influence the sentiment of customers' digital engagement. Many firms track buyers' offline interactions, design online content to coincide with customers' experiences, and face varied performance during events, enabling the modification of marketer-generated content to correspond to the event outcomes. This study examines the role of firms' social media engagement initiatives surrounding customers' experiential interaction events in influencing the sentiment of customers' digital engagement. Results indicate that marketers can influence the sentiment of customers' digital engagement beyond their performance during customers' interactions, and for unfavorable event outcomes, informational marketer-generated content, more so than emotional content, can enhance customer sentiment. This study also highlights sentiment's role as a leading indicator for customer lifetime value.
Keywords: customer engagement; customer lifetime value; customer sentiment; econometric modeling; marketer-generated content; social media
A growing trend among marketers is the use of social media to drive customer engagement ([20]; [24]; [25]; [72]). Customer engagement initiatives, defined as organizational initiatives that facilitate firm–customer interactions to foster emotional or psychological bonds between customers and firms ([19]; [36]) are increasingly used by marketers. Such engagement initiatives on social media involve marketers' posts surrounding experiential events, defined as firm–customer interactions that are finite in time ([54]). This study examines how firms should leverage such initiatives based on firm performance during experiential events. Consider, for example, an event such as a professional sports competition. Following a poor performance, such as a loss, can the team effectively engage fans and enhance the sentiment of those fans' social media contributions based on the team's posts on social media, and if so, what should those posts say? Should the team's posts appeal to fans' emotions or instead offer informational content such as contributing factors for a disappointing outcome? Similarly, after a win, should the team post on social media, and should the content be emotional in nature (e.g., images of elated fans or players) or informational (e.g., game or player statistics or details regarding upcoming events)? The goal of this study is to provide insight regarding important customer outcomes from strategically adjusting marketer-generated content (MGC) surrounding customers' experiential events based on marketers' performance during those events.
We examine how marketers should adjust both the volume and content of their social media posts based on the firm's performance during such experiential events. Our key outcome of interest is customers' digital engagement, defined as "brand-related cognitive, emotional or behavioral activity during or related to focal consumer-brand interactions" ([15], p. 102), and our focus is social media engagement activities. In particular, we are interested in the sentiment, or valence (negative or positive), of consumers' comments on brand-related social media pages in reaction to firms' social media content surrounding consumer–brand interactions. Whereas organizers of events such as concerts or sports competitions often share emotional content to drive engagement during or after performances, marketers also use social media to provide informational content surrounding such events. For example, during an extensive delay before the first-ever concert (Garth Brooks) at the Mercedes Benz stadium in Atlanta, the venue's management took to social media to keep fans informed regarding efforts to resolve sound issues in the stadium in an attempt to reduce negative sentiment expressed by attendees based on their disappointing experience ([ 5]).
We define "event outcomes"[ 6] as the observed level of firm performance during customers' experiential interactions. In formulating our conceptual arguments, we draw on customer engagement theory, which proposes that customers' brand- or firm-related experiences influence their emotional or affective states, which then influence the nature of their indirect engagement with firms, including contributing positive word of mouth on social media ([55]). We argue that MGC surrounding event outcomes will influence the sentiment of customers' digital engagement beyond firms' objective performance during those events. In testing these relationships, we address two key questions:
- Can marketers' posts (i.e., MGC) surrounding event outcomes influence the sentiment of consumers' comments above and beyond the characteristics of the event outcomes themselves?
- What type of content is most effective in moderating the relationship between experiential event outcomes and the sentiment of customers' digital engagement?
In answering these questions, this study makes several important contributions. First, it demonstrates how MGC content, whether informational or emotional, can moderate the relationship between event outcomes and the sentiment of customers' digital engagement. In doing so, it offers guidance to firms in strategically managing both the volume and content of their social media contributions based on their performance. Second, this study captures the richness of customers' digital engagement by focusing on the sentiment of such engagement as its key outcome, as opposed to metrics such as comment volume or sharing activity, which do not consider the tone of customers' social media contributions. Third, this study provides a granular view of the environment in which firms make decisions regarding their MGC strategies by focusing on the customer-event level as opposed to an aggregated series of events. Furthermore, given its emphasis on firms' objective performance during customers' experiential events, its findings may be particularly applicable for firms with the ability to adjust MGC in accordance with their own internal performance metrics, without requiring data from customers.
Although research has begun to examine the value of customers' social media contributions for firms, most studies have examined customers' digital engagement without aligning with MGC or customers' brand- or firm-related experiences. The lack of research aligning these concepts is surprising because many firms track buyers' offline interactions (e.g., presence at events), design online content to coincide with customers' experiences, and face varied performance during events, enabling the modification of MGC based on event outcomes. Studies focusing on customers' brand- or firm-related interactions have also tended to use experimental approaches, introducing such interactions through scenarios ([26]; [42]) or selecting respondents on the basis of negative experiences ([47]; [59]) rather than using objective data on firm performance during the interactions. One body of work incorporating customer perceptions of firm performance during such interactions is that focusing on customer reviews on sites such as Amazon or travel-related forums ([16]; [22]). However, most of these studies either do not examine the role of MGC ([16]) or do not consider the content of MGC ([22]; [30]).
Table 1 compares our study with relevant research focusing on empirical investigations of firms' efforts to drive customer engagement through their social media contributions, irrespective of whether customers' engagement is digital. We also identify whether studies include objective performance data regarding customers' firm- or brand-related interactions, as we do.
Graph
Table 1. Study Comparison with Relevant Literature.
| Citation | Research Focus | Context | New Insights | Accounted for in the Model |
|---|
| MGC | UGC | Objective |
|---|
| Content | Volume/Presence | By Focal Customer | By Others | Event Outcome |
|---|
| This study | Interplay between firms' MGC and objective event outcomes in influencing sentiment of customers' digital engagement | European soccer team's Facebook fan page surrounding customers' experiential events | MGC surrounding experiential events can influence sentiment of customers' digital engagement beyond firms' objective performance during those events. | X | X | X | X | X |
| Tellis et al. (2019) | Drivers of online sharing of MGC | Online video ads on YouTube for 109 brands | Informational content negatively affects sharing, except in risky contexts, whereas positive emotions positively affect sharing. | X | X | X | | |
| Grewal, Stephen, and Coleman (2019) | How posting about products on social media affect consumers' purchase intentions | Lab and MTurk experiments using FB and Pinterest, with outcomes measured for backpack/tote bag brands | Posting products on social media framed as identity-relevant can reduce purchase intentions for the same and similar products. | | X | X | | |
| John et al. (2017) | Whether "liking" a brand influences brand evaluations | Lab experiments using soda brands' FB pages | Endorsement on FB is less effective than endorsements external to FB. | | Xb | Xa | X | |
| Mochon et al. (2017) | How FB page likes affect offline customer behavior | Wellness brand's FB page | Likes on FB pages drive customers' offline behavior. | | Xb | Xa | | |
| Baker, Donthu, and Kumar (2016) | How WOM conversations about a brand relate to purchase and retransmission intentions | Survey regarding WOM conversations for 15 product categories | Positive, mixed, and negative sentiment increases intentions to retransmit WOM messages. | | | X | | |
| Kumar et al. (2016) | MGC's effect on customer behavior and profitability | Wine and spirits retailer's social media page | MGC in social media affects customer behavior beyond other communication tools. | | X | X | | |
| Saboo, Kumar, and Park (2016) | Assessing both consumer responsiveness and real-time ROI of direct-marketing efforts | Home improvement retailer's online direct marketing campaign | Influence of direct marketing on sales varies significantly over the customer life cycle. | | X | | | |
| Beukeboom, Kerkhof, and De Vries (2015) | Whether following a brand's FB updates affects brand evaluations | Paint brand's FB page | Following a brand's FB updates affects evaluations. | | X | X | | |
| Homburg, Ehm, and Artz (2015) | Consumer reactions to firms partaking in consumer-to-consumer conversations | Do-it-yourself retailer's online community | Consumers show diminishing returns to digital engagement with a firm. | | X | X | X | |
| Manchanda, Packard, and Pattabhiramaiah (2015) | Effect of customers' joining firm social media community on expenditures | Media retailer's online community | Joining an online community leads to greater expenditures. | | | X | | |
| Xie and Lee (2015) | Effects of exposures to earned and owned social media activities on purchase | Fast-moving consumer goods firm group online fan page | Exposure to brands' social media activities influences brand purchase likelihood. | | X | | X | |
| Zadeh and Sharda (2014) | Customer reactions to firms' crowd engagement activities | Twitter information streams of >120 brands | Popularity growth patterns of brand post contents can be simulated via point process models. | | X | X | X | |
| Kumar et al. (2013) | How social media can be used to generate WOM and influence performance | Ice cream brand's FB and Twitter pages | Social media campaigns affect sales, ROI, and positive WOM on social media. | | X | X | X | |
| Goh, Heng, and Lin (2013) | Impacts of both UGC and MGC on repeat purchase behaviors | Asian apparel retailer's social media fan page | Digital engagement positively impacts purchase expenditures. | Xc | | X | X | |
| Rishika et al. (2013) | Effect of participation in firm social media efforts on customer value | Wine and spirits retailer FB page | Customer participation in firm social media efforts impacts frequency of customer visits. | | | X | X | |
| Nam, Manchanda, and Chintagunta (2010) | Effect of service quality on customer acquisition, accounting for spillover effects from WOM | Video-on-demand service | Effects of negative WOM from poor performance are greater than effects of positive WOM from good performance. | | | | Xd | X |
1 a UGC captured as "Likes."
- 2 b MGC captured as invitation or incentive to join FB page.
- 3 c MGC captured as information richness.
- 4 d Not modeled directly; rather, assumed based on geographic proximity to other subscriber.
- 5 Notes: FB = Facebook; WOM = word of mouth; ROI = return on investment.
To answer our questions, we conducted two studies. For the first, we built an unprecedented longitudinal database featuring brand-related customer-level social media metrics including marketers' posts surrounding events and customers' comments on those posts. The context is a European soccer team's Facebook fan page, which provides regular chances for brand interaction and enables us to capture variance in event outcomes. We also capture objective characteristics of event outcomes, including firm performance and expectations regarding those outcomes. Attending brand-sponsored experiential events such as sports is also common in settings such as entertainment or fundraising activities. As evidence of these sectors' importance, the value of the global entertainment and media market alone is expected to top $2.2 trillion by 2021 ([82]). For the second study, we conducted a scenario-based experiment on Amazon Mechanical Turk (MTurk), modifying the levels of event outcome and MGC in the different scenarios.
We find that marketers can influence the sentiment of customers' digital engagement beyond the marketers' objective performance during experiential events—and in the case of unfavorable event outcomes, informational MGC, more so than emotional content, offers a substantial means to improve the sentiment of customers' digital engagement. Through a series of post hoc analyses, we find that with as few as two additional informational posts following a negative event outcome, marketers can increase the sentiment of customers' digital engagement by approximately 10%. Emotional content has a positive and significant influence regardless of the outcome of the event. Drawing on our ability to link social media variables to transactions, we also highlight in our implications section sentiment's role as a leading indicator of customer lifetime value (CLV). Whereas [19] describe engagement initiatives as creating value for customers but not intending to prompt sales, we explore the potential association of such initiatives with customer purchases.
Customer engagement theory ([55]) serves as an overarching theoretical perspective in which to ground our conceptual framework. We furthermore build on related research focusing on the concept of customer engagement initiatives ([19]). From a customer engagement perspective, we argue that ( 1) customers' positive (negative) brand- or firm-related experiences will be associated with positive (negative) affective states ([54]); ( 2) customers' affective states will, in turn, influence the nature of their digital engagement with the firm ([55]), which we capture through sentiment; and ( 3) by managing the information environment in which firms and customers interact, as with their customer engagement initiatives, firms can influence the sentiment of customers' digital engagement. In other words, through their social media activities, firms can reinforce positive experiences or enhance customers' knowledge about a brand when questioning poor experiences (Van Doorn et al. 2010), thereby enhancing the sentiment of their digital engagement. We also explore the role of the sentiment of customers' digital engagement as a leading indicator of CLV, consistent with research linking customer sentiment to purchases ([ 3]; [20]).
Next, we conceptualize our key variables. We then describe the expected relationships, drawing on customer engagement theory with supporting arguments from research related to firms' use of social media to influence customer mindset metrics ([11]) and drive customers' digital engagement ([19]).
We conceptualize our dependent variable, the sentiment of customers' digital engagement, as the tone or valence (negative or positive) of customers' comments on brand-related social media pages in reaction to firms' social media content surrounding particular consumer–brand interactions. We focus specifically on comments posted by individuals on brand-related social media pages in response to firms' own posts, consistent with the notion of firms' efforts to facilitate firm–customer interactions ([19]). In addition, our emphasis is on comments that are ( 1) not commercially motivated, ( 2) not incentivized by firms, and ( 3) interactive in nature ([ 3]). Outside our purview are ( 1) consumers' comments controlled by firms (e.g., firm posts of buyer testimonials; [11]), ( 2) online word of mouth not on a brand-related social media page (e.g., reviews on sites such as Amazon, consumer posts on non-brand-related issues), and ( 3) content incentivized by firms.
As mentioned previously, event outcomes refer to the observed firm performance during customers' brand- or firm-related interactions. We focus on individuals' experiential interactions with a firm or brand, consistent with research on service encounters ([46]), and postconsumption product perceptions ([ 2]). While overall satisfaction ([16]; [26]; [47]) and brand perceptions ([70]) reflect customers' experiences, these concepts are often broader, reflecting a series of interactions or experiences. These concepts are also perceptual as opposed to objective. In one of the few studies to include objective measures of firm performance during customers' firm or brand-related interactions, [18] examine the impact of successful railway connections on service quality perceptions. However, their measure is aggregated to a monthly level. In Web Appendix W1, we summarize related concepts, highlighting distinctions with that used in this study.
Drawing from [11], we define MGC as a firm or brand's communication created and shared through online social network assets. In conceptualizing MGC, we also draw on research examining message content categories in social media. Categories used in related research include information-focused, emotion-focused, or commercial content ([77]); directly informative and brand-personality related content ([40]); information-sharing, emotion-evoking, and action-inducing content ([76]); informative and persuasive/emotional content ([20]); informational, promotional, or community-building content ([67]); and entertainment and information ([85]), among others. After reviewing our data, we conclude that posts in our data set can be cleanly grouped into two types of content: informational and emotional. Moreover, we observe a great deal of overlap between our categories and those in other studies, as described previously, and with categorizations used in traditional advertising as well (e.g., informational and transformational advertising, as described by [61]]).
We define informational content as MGC in which the content is neither directly promotional in nature nor aimed at prompting audience engagement (e.g., providing updates on events without directly encouraging attendance). Emotionally oriented content is defined as messages that employ affect-laden content and are aimed at evoking sensory or emotional experiences ([14]). Details regarding the coding of marketer posts, including examples, are provided in the description of the independent variables for Study 1.
In this section, we present our conceptual arguments for the relationships in our conceptual framework, depicted in Figure 1. We also summarize the expected relationships in our framework through a series of propositions (Table 2).
Graph: Figure 1. Conceptual framework.
Graph
Table 2. Summary of Propositions and Conceptual/Theoretical Arguments.
| Number | Proposed Relationship | Conceptual/Theoretical Arguments |
|---|
| P1 | Positive event outcomes lead to positive customer sentiment.a | The relative quality of customers' brand- or firm-related experiences is associated with customers' affective states and the likelihood of positive (negative) customer engagement behaviors, including contributing positive (negative) word of mouth in social media (Van Doorn et al. 2010).
|
| P2 | Negative event outcomes lead to negative customer sentiment. |
| P3 | MGC content that is either informational or emotional leads to positive customer sentiment. | Posts by marketers on their own social media channels, as with customer engagement initiatives, can make brands more salient in consumers' minds, triggering positive brand-associated thoughts, which result in more positive customer sentiment (Colicev et al. 2018). The greater the extent to which firms are viewed as fostering interactivity, the more positive the customer sentiment that should result (Hajli et al. 2017).
|
| P4 | If a negative event outcome is observed, informational MGC content is more influential than emotional MGC content on customer sentiment. | Undesirable outcomes can lead to negative affective states, which foster consumers' use of more analytical processing strategies focusing on detailed information (Schwarz 1990). To overcome negative affect, individuals will try to change their current situation by assessing the situation based on an analysis of potential causal links among its features—informational content will be more useful than emotional content for this purpose (Cohen and Andrade 2004).
|
| P5 | If a positive event outcome is observed, emotional MGC content is more influential than informational MGC content on customer sentiment. | Positive affective states signal that an individual's personal world is a satisfactory place, and there is little need to seek information to make sense of it; therefore, informational MGC will be less influential on the sentiment of customers' digital engagement (Schwarz 1990). Message content with less of an emphasis on information tends to include affective appeals, often aimed at evoking sensory or emotional experiences (Dubé, Chatthopadhyay, and Letarte 1996).
|
6 a Note that we use "customer sentiment" here to refer to the sentiment of customers' digital engagement.
We first propose that firm performance during experiential events will influence customers' affective states, which will in turn influence the sentiment of their digital engagement with the firm (see P1 and P2 in Table 2). That is, we expect event outcomes to positively influence the sentiment of customers' digital engagement such that more positive (negative) event outcomes will lead to a positive (negative) sentiment. From an engagement theory perspective, the relative quality of customers' experiences is associated with the likelihood of positive customer engagement behaviors such as providing supportive online reviews ([79]). There is also evidence that marketers' (dis)satisfactory performance during experiential events leads to positive (negative) affective states ([54]). Also, consistent with the service-profit chain ([ 1]), customers' perceptions of their experiences are an important antecedent of positive customer engagement behaviors such as contributing positive word of mouth on social media. Indeed, research in general confirms that positive word of mouth on social media is a key outcome of positive experiences ([34]). This relationship has also been argued to be important for experiential purchases in particular ([57]).
On average (i.e., for both positive and negative event outcomes), we expect a greater MGC volume to positively impact customer sentiment (P3). From a customer engagement perspective, firms' engagement initiatives, such as posting content on social media, can lead to heightened customer interactive participation with the firm on social media ([19]). Consistent with arguments regarding the role of firms' social media investments ([11]), posts by marketers on their own social media channels (e.g., their Facebook page) can make brands more salient in consumers' minds, triggering positive brand-associated thoughts, which, in turn, should result in a more positive sentiment in terms of customers' digital engagement. Related research offers evidence that the more interactive an online community, and the more contributions by both firms and customers, the greater the relationship-building, trust, and loyalty outcomes ([23]). Greater fan interactivity on a brand's social media page has also been demonstrated to lead to a greater affective and cognitive engagement with the brand ([28]) and more positive brand attitudes ([32]). Accordingly, the greater the extent to which firms are perceived as fostering interactivity, the more positive the sentiment of customers' digital engagement should be. In support of this expectation is [35]'s finding that buyer reactions to firm social media posts can improve brand evaluations.
From a customer engagement perspective, firms' efforts to manage the information environment in which firms and customers interact, which occurs when marketers post content on social media, can enable them to influence the sentiment of customers' digital engagement. That is, firms' own social media activities can motivate customers to voice concerns or compliments, thereby reinforcing positive experiences, or to enhance their knowledge about a brand when questioning poor experiences ([79]). We propose that consumers will respond differently to different types of MGC content surrounding negative versus positive event outcomes. In particular, we expect informational MGC to be more influential on the sentiment of customers' digital engagement when a negative event outcome is observed (P4) and emotional MGC to be more influential when a positive event outcome is observed (P5).
Support for this expectation comes from the "feelings as information" theoretical perspective ([69]) used to explain the role of affective states in message processing. According to this perspective, undesirable outcomes can lead to negative affective states, which then foster the use of more analytical processing strategies focusing on detailed information ([69]). Negative affective states can prompt an individual to try to change their current situation by assessing the situation through information seeking and analyzing potential causal links among its features. Consistent with this perspective, we argue that informational content may be useful in overcoming negative affect surrounding undesirable event outcomes, akin to the notion of mood repair ([10]).
In the case of desirable event outcomes, the feelings-as-information perspective suggests that positive affective states resulting from such outcomes require less explanation than negative ones and will therefore promote less effortful heuristic strategies in processing messages. As summarized by [69], positive affective states signal that an individual's personal world is a satisfactory place, and that there is little need to seek information to make sense of it. Whether due to reduced motivation or cognitive capacity constraints, both shown to be associated with positive affective states ([84]), these customers are less likely to focus on and process informational content. Thus, following desirable event outcomes, informational MGC will be less relevant and therefore less influential on customer sentiment, whereas content focused less on information will be more influential.
Message content with less of an emphasis on information tends to include affective appeals, often aimed at evoking sensory or emotional experiences ([14]). Emotional messages are argued to be particularly impactful for experiential products ([29]). [41] argue that sports consumption is primarily for affective purposes and that emotional appeals aimed at evoking arousal are more likely to be positively received. When consumers are in positive affective states, we expect existing emotional bonds with the team and the emotional experience of the recent event to be salient. In such cases, messages employing affect-laden content should enhance the sentiment of customers' digital engagement.
The ideal experiment to study MGC effects such as those examined here would entail a randomized controlled design in a field setting in which the cells are combinations of three factors: customers' expectations regarding performance during their experiences, the marketer's actual performance, and MGC content (emotional, informational, none), where the cells in which MGC is none would constitute a control group. There are two issues that preclude this ideal experiment in our observational context: self-selection and the strategic nature of MGC. In addition, practically, it would be difficult to convince a marketer in a field setting to intentionally perform poorly during customer interactions.
Because the ideal experiment is infeasible, we conduct two studies to provide evidence of the key causal effects underlying MGC's moderation of the link between event outcomes and customer sentiment. The first study employs real-life, longitudinal, observational data and accommodates self-selection with a Heckman selection model and the endogenous nature of MGC with a control function approach. In the second study, we run a randomized controlled scenario-based experiment on MTurk. The strength of the first study is its high degree of realism, while the second study's advantage is the randomized assignment of participants, the gold standard for determining causality. We find the results of Studies 1 and 2 to be highly consistent. Next, we describe each study.
Study 1's context is a European soccer team's Facebook fan page, and we focus on the dominant social media platform: Facebook ([31]). All social media data were collected through Facebook's application programming interface with consent from users. We extracted all customers' digital engagement in the form of comments on posts on the team's official Facebook page, yielding 265,530 comments from 52,431 users between June 2011 and June 2015 (i.e., the four seasons following the team's Facebook fan page launch in November 2010). We also collected the team's posts during this period. Finally, we gathered declared match attendance using Facebook's application programming interface and matched conditions and outcomes, expectations (odds), and stakes (match attendance) using publicly available websites.
To address selection effects, we merged the Facebook data with the team's internal data, keeping customers active at any point in the study's time frame. We merged the data via names, the only personal information available for those posting comments. Names occurring twice or more were deleted to ensure matching quality. The internal data comprise transactional (e.g., purchase frequency), customer (e.g., name, gender, birthday), and privacy-related data (phone and identity card number disclosure). Of 53,794 customers, we matched 9,424 (people who made one comment or more more) who made 21,604 comments. The actual number of users and comments in our main analysis is more restricted because we focus on positive and negative comments only (discussed next).
We model the sentiment of a customer's digital engagement using comments on team posts[ 7] through each user's expressed sentiment during each of 212 potential experiential events (matches) over 48 months. We restrict user-generated content (UGC) to comments on the team's Facebook posts within a two-day window after an event (match) to ( 1) increase the chance that comments relate to a particular event and reduce "noise" (i.e., comments unrelated to firm interactions) and ( 2) reduce the chance of capturing comments regarding multiple events because matches can be as close as three days. We use a classification algorithm based on a sentiment lexicon to determine sentiment ([20]) and use only positive and negative comments, yielding 10,345 user-match records for 3,749 customers. This choice is based on evidence of a much smaller effect of neutral versus positive or negative comments ([74]) and on claims that positive and negative comments are most relevant for extracting sentiment ([78]).[ 8]
Consistent with [30] and [20], we capture MGC through the focal team's posts on its own Facebook page between the end of an experiential event and the posting time of a particular user's comment. Informational (emotional) MGC is operationalized as the number of focal team informational (emotional) posts between the end of match m and posting time of comment c by user u ([30]). We use content analysis to model informational versus emotional posts. Given the relatively low number of posts and the absence of ready-to-use dictionaries, we opt for manual labeling rather than an (un)supervised approach (e.g., [33], [40]). We follow the typical steps used in the literature ([75]). First, we define a set of coding instructions, leveraging existing literature and adapting some of these concepts to our context. With regard to informational content, we use dimensions identified by [62] and operationalized by [40] and complement them with insights identified by other content-coding research (e.g., [51]). In general, informational content is focused on the focal team, its services (matches), and other relevant information regarding these services. Similarly, for emotional content, we based our dimensions on research revealing constructs to measure emotional, persuasive, and engaging content ([12]; [40]; [75]). We continued the process until we found no new dimensions.
In the second step, two of the authors used the coding handbook to independently classify a subset of 100 posts. We first used this subset to ensure that all instructions were clear, resolve any remaining issues, and identify any dimension not identified in the literature. The coders then independently classified the remaining posts. We allowed posts to be classified as both informational and emotional; however, this occurred in a very limited set of cases (10). The Fleiss κ-index for interrater reliability was.864, which indicates a high agreement ([39]). For posts for which initially no agreement was reached, the coders discussed the content and reached congruence. Table 3 summarizes the defining characteristics of informational and emotional MGC and gives several (translated into English) examples for both types.
Graph
Table 3. Characteristics and Examples of Informational and Emotional MGC.
| Informational MGC | Emotional MGC |
|---|
| Defining characteristics of MGC | Informing customers about the product (in this case, e.g., match results, player injuries; Lee, Hosanagar, and Nair 2018; Stephen, Sciandra, and Inman 2015) Messages informing the customers about relevant events and conditions (Muntinga, Moorman, and Smit 2011) Messages providing information about the company in general (e.g., facts; Lee, Hosanagar, and Nair 2018; Stephen, Sciandra, and Inman 2015)
| Messages containing emotions (Lee, Hosanagar, and Nair 2018) Messages evoking sensory emotions (Dubé, Chatthopadhyay, and Letarte 1996) Messages high in arousal (Stephen, Sciandra, and Inman 2015) Messages containing calls to action and persuasive content (Stephen, Sciandra, and Inman 2015) and promotion and mobilization (Saxton and Waters 2014) Messages containing entertaining content (De Vries, Gensler, and Leeflang 2012) Messages focusing on community building and dialogue (Saxton and Waters 2014)
|
| Examples of MGCa | "The coach's vision on the injuries, the international football break and his strikers""Important: When you charge your card online, use the card number that is at the top right of your season ticket""Get to know the five possible opponents in the Champions League play-offs.""Player X plays his 150th official match for our team tomorrow. Player Y his 120th official game." | "Everything stays possible! Come in great numbers to the stadium on Thursday and push the team to the next round in the cup!""All Together Now!""Thanks fans for your fantastic support yesterday! We are one team!""There will be a Twinterview with player X on our Twitter account on Wednesday! Follow us, ask your question and get a chance to win a jersey!""Share and support the team!" |
7 a Comments were translated to English and anonymized.
To assess MGC's moderating effect on the relationship between the event outcome and the sentiment of customers' digital engagement, we include both the number of (informational and emotional) team posts on its Facebook page (Informational MGCu,c,m and Emotional MGCu,c,m) and the interaction between the event outcome and the MGC measures.
We include match result to reflect the firm's objective performance during each experiential event (win, loss, or draw; Resultm).
Consistent with prior research incorporating odds to account for customer expectations ([ 4]), we gather preplay betting odds for each outcome per event (win, loss, or draw, from the focal team's point of view).[ 9] We compare the actual result with expectations (odds) and identify unexpected results in case actual and expected results differ (see Web Appendix W2). We include a binary variable, Unexpected Result, and its interaction with event outcome (result) to account for implications of unexpected results. In line with prior research ([13]), unexpected results should amplify reactions (i.e., unexpected wins [losses] should be viewed more [less] positively than expected wins [losses]).
We control for event attendance (Total Event Attendancem)[10] to measure stakes. According to the uncertainty-of-outcome hypothesis ([64]), attendance will be higher with higher stakes. We include the number of focal team red and yellow cards (RedCardsm and YellowCardsm) because they are penalties and contribute to negative experiences ([ 9]). We define prior customer sentiment (Customer Sentimentu,m−1) on the basis of comments during the previous match window in which a user commented. For first-time comments, it is zero. Intentions to attend an event (yes/no; indicated on social mediaSM) (EventFacebooku, m) are a form of online team identification and should positively relate to sentiment ([ 7]).
We include previous comment volume in the thread (OtherUGCVolumeu,c,m). We assess comment context through the valence of the last comment before a focal comment (losing the first per thread; OtherUGCValenceu,c,m) and expect a positive relationship with sentiment ([30]; [49]). Comment length is captured through a word-count log (Comment Lengthu,c,m; [30]) and should negatively affect sentiment.
Finally, there is evidence that customer sentiment is highly influenced by events shortly after they occur, but this effect ebbs over time ([13]). Thus, we include a variable that measures (in hours) the time between match end and focal comment. Because this effect can vary on the basis of event outcome, we also include its interaction with the event outcome.
Because we have a binary dependent variable (positive/negative sentiment) and want to include random effects to account for both customer and match heterogeneity, we use a generalized linear mixed-effects model (with a probit link function) to model sentiment:
Graph
1
where denotes the sentiment of digital engagement for user u, expressed in comment c for match m; and represent the user and match random elements, respectively; and is the error term. The variable . represents a vector of year dummies accounting for factors that vary by year, and Result and UnexpectedResult are also captured as dummy vectors. Finally, we interact Informative MGC, Emotional MGC, UnexpectedResult, TotalEventAttendance, and CommentTime with event outcome.[11]
Because we worked with an online population and used observed behavior, it is likely that our sample suffers from self-selection bias (e.g., [20]). People commented on team Facebook posts and, as a result, became part of our study. However, these people may not be representative of the entire population under study (the team's customer base) because there may be unobserved factors influencing both the decision to comment and the sentiment of this digital engagement. For example, these people may already be more positive toward the company than others, biasing the parameter estimates upward. This self-selection potentially leads to an endogeneity issue due to omitted variables bias ([86]), which can be alleviated by implementing a binary probit choice model as a Heckman selection model ([27]).
The probit regression models the propensity to comment on the team's post and provides a correction factor for self-selection to be included in the sentiment model. The regression is defined as a linear function of three categories of variables that help identify which customers will comment on team posts ([20]; [35]). These include the following:
- Demographic variables such as age, gender, and language. We expect younger men to post more comments, given young people's high digital awareness and the relatively masculine soccer culture. Moreover, those who do not speak the language used on the team's Facebook page may have a lower propensity to comment.
- The number of online ticket purchases (in contrast to offline purchase). This is based on the expectation that online purchasers have higher technological savviness and are inclined to use social media more often ([35]).
- Variables indicating team involvement. This is based on an expectation that more highly involved customers are more likely to comment on posts, for which we use recency of the last purchase and tenure.
We need at least one (significant) independent variable in the selection equation that does not affect sentiment to satisfy the exclusion restrictions and allow identification ([60]). The online purchase–related variable helps in meeting the exclusion restriction because there is no obvious reason to believe online purchasers are a priori more positive or negative toward the company. The regression can be defined as:
Graph
We derive the inverse Mills ratio (IMR) from the probit regression as follows:
Graph
3
where as usual indicates the IMR, and and indicate the probability and cumulative density functions, respectively. The IMR is a monotone decreasing function of the probability of an individual's self-selecting into the sample. The customer sentiment model is the second step of the selection model, which depends on the selection equation. By including the IMR in the customer sentiment model as an explanatory variable, we correct for potential endogeneity issues resulting from self-selection. If the IMR coefficient is significant, self-selection is an issue.
In addition to random shocks to MGC (due to, e.g., changes in the team's communication staff, game outcomes), firms may strategically choose both their MGC volume and content on the basis of their expectations of future comment sentiment ([30]). Firms' expectations of future consumer sentiment are not observed by the researcher and reside in the error term in Equation 1. However, because firms' expectations of future comment sentiment also affect MGC content choice and volume, the error term in Equation 1 may correlate to MGC content and volume, inducing potential endogeneity. To help isolate MGC's potential endogeneity, we use a control function approach, preferred in the case of a limited dependent variable ([58]). First, we model the endogenous variables (informational and emotional MGC) as a function of exogenous and instrumental variables. We control for each event's context (result, red/yellow cards, [un]expected result, number of attendees, and whether the team is playing at home) and time elapsed between the MGC posting and the event's end.
We require instrumental variables to fulfill the exclusion restriction requirement ([58]). We use lagged differences in MGC content (difference in volume of emotional and informational posts between the first and second month preceding the focal post)[12] as an instrument because these differences can capture trends in team posting behavior indicative of the team's overall activity level for each post type. One could argue that lagged MGC values potentially reveal trends in team performance that also influence sentiment (thereby passing the test of relevance). One could also argue that lagged differences in MGC are unrelated to the sentiment of customers' digital engagement (thereby passing the exclusion restriction test). Note that the differences also include non-event-related posts and sum all MGC over a month, making it unlikely that the difference is related to the sentiment of customers' comments on team posts surrounding a finite event. If previous events influence sentiment, more weight is given to the most recent experiences, which is not the case for the instrumental variables. Finally, we note that older MGC is not very visible on Facebook (unless users scroll down extensively), and customers therefore almost exclusively react to the most recent post. Thus, it is unlikely that the posting behavior from one to two months earlier will have a direct influence on customer sentiment; rather, any possible influence will go through the latest MGC post. In summary, we conclude that our instrumental variables satisfy both relevance and exclusion criteria.[13] As robustness, we also include trends in Equation 1 as a control in the sentiment equation.
The control function dependent variables (i.e.., the volume of informational and emotional MGC before the focal post p) are counts and are zero-inflated. In other words, when there is only one post, it can be either informational or emotional; therefore, the other variable will take the value of zero. We add random effects to account for match-specific factors not captured by exogenous variables; formally, the regressions are specified as zero-inflated random-effects Poisson models:
Graph
4
Graph
5
where Z is the matrix of exogenous variables and ΔInformationalPosts (ΔEmotionalPosts) is the lagged difference in informational (emotional) MGC volume between the previous two months before the focal MGC. The regressions are the same except for the dependent variable. We include the instrumental variables in both ([56]). The final step of the endogeneity correction approach is to include residuals ( ) of the control equations as independent variables in the customer sentiment regression. This allows us to test for the presence of endogeneity using the standard z-test, after bootstrapping the standard errors ([56]). The final customer sentiment model includes correction terms for self-selection and endogeneity.
We provide all main sentiment model variables in Table 4, and the correlation matrix in Table 5. Web Appendix W3 provides descriptive figures for the selection and control equations.
Graph
Table 4. Description of Variables Used in the Main Model.
| Variable | Description | M | SD | Range |
|---|
| Dependent | Measure of Subjective Sentiment in Customers' Comments | | | |
| CustomerSentimentu,c,m | Dependent variable. Customer u sentiment, as expressed in comment c, during match m. (binary variable) | .73 | .45 | [0, 1] |
| Event Outcome | Objective Measures of Performance during Events | | | |
| Result (Lost)m | Dummy variable indicating whether the match m was lost by the focal team (in contrast to a draw) | .32 | .47 | [0, 1] |
| Result (Won)m | Dummy variable indicating whether the match m was won by the focal team (in contrast to a draw) | .46 | .50 | [0, 1] |
| MGC Variables | Measures of Marketer-Generated Content | | | |
| Informational MGCu,c,m | Number of informational posts on Facebook by the focal team between the end of match m and the time of posting of comment c by user u (comment on post p) | 2.42 | 4.43 | [0, 49] |
| Resultm × Informational MGCu,c,m | Interaction effect between Result of match m and informational MGC | | | |
| Emotional MGCu,c,m | Number of emotional posts on Facebook by the focal team between the end of match m and the time of posting of comment c by user u (comment on post p) | 2.45 | 6.09 | [0, 47] |
| Resultm × Emotional MGCu,c,m | Interaction effect between Result of match m and emotional MGC | | | |
| Control Variables | Control Variables for Customer Sentiment | | | |
| Unexpected Resultm | Dummy variable indicating whether the result of match m is in line with the expectations based on the odds | .42 | .49 | [0, 1] |
| Resultm × Unexpected Resultm | Interaction effect between actual result of match m and the dummy variable Unexpected Result | | | |
| TotalEventAttendancem | The number of spectators for game m, which is a proxy for the importance of the game and quality of the opponent | 18,156 | 9,192 | [1,819, 65,110] |
| Resultm × TotalEventAttendancem | Interaction effect between actual result of match m and the dummy variable Spectators | | | |
| RedCardsm | The number of red cards for the focal team in match m | .17 | .39 | [0, 2] |
| YellowCardsm | The number of yellow cards for the focal team in match m | 1.90 | 1.24 | [0, 6] |
| Home Matchm | Dummy indicating whether the match m is a home match | .50 | .50 | [0, 1] |
| EventFacebooku, m | Dummy indicating whether user u has declared on Facebook to attend match m | .05 | .23 | [0, 1] |
| Customer Sentiment u,m-1 | Lag of measured customer sentiment of user u (1 for positive or 0 for negative) | .30 | .46 | [0, 1] |
| Other UGC Valence u,c,m | Valence of the previous comment in the post thread of comment c by user u during match m (polarity score from −5 to +5 [with + being positive]) | .18 | .56 | [−2.9, 5] |
| Other UGC Volume u,c,m | Volume of other user's comments in the post thread of comment c by user u during match m | 110 | 141 | [1, 1,106] |
| Comment Lengthu,c,m | Length of the comment (number of characters) c by user u during match m (logarithm of length) | 4.29 | 1.07 | [1.10, 7.65] |
| Comment Timeu,c,m | Time (in hours) that has passed by (at the moment of posting comment c by user u) since the end of the match m (logarithm of time) | 2.19 | 1.43 | [.05, 9.62] |
| Result m × Comment Timeu,c,m | Interaction effect between the result of match m and the comment time | | | |
| θm | Dummy variables indicating the year in which the match m was held | | | |
Graph
Table 5. Pairwise Correlation Coefficients.
| Variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|
| 1. Informational MGCu,c,m | 1 | | | | | | | | | |
| 2. Emotional MGCu,c,m | .008 | 1 | | | | | | | | |
| 3. TotalEventAttendancem | −.027 | .005 | 1 | | | | | | | |
| 4. RedCardsm | .073 | −.020 | .027 | 1 | | | | | | |
| 5. YellowCardsm | .088 | .024 | .044 | .322 | 1 | | | | | |
| 6. Customer Sentimentu, m – 1 | .001 | .018 | .015 | −.009 | −.005 | 1 | | | | |
| 7. Other Sentiment Valenceu,c,m | −.181 | −.073 | −.078 | −.035 | −.105 | −.021 | 1 | | | |
| 8. Other Sentiment Volumeu,c,m | .013 | .017 | .023 | −.022 | −.037 | .010 | −.017 | 1 | | |
| 9. Comment Lengthu,c,m | −.018 | −.037 | −.053 | .050 | .019 | .061 | .184 | −.042 | 1 | |
| 10. Comment Timeu,c,m | .039 | .026 | .013 | .001 | −.011 | −.012 | −.023 | .009 | −.021 | 1 |
8 Notes: Only continuous variables are reported. Correlations above.019 are significant at a 5% level (N = 10,345).
First, we discuss the results of the selection and endogeneity control equations (Tables 6 and 7, respectively). The results of the selection regression indicate which customers self-select into our sample. The overall model is significant (likelihood-ratio All variables except gender are significant and have expected signs; younger people and native speakers of the language are more likely to comment ( = −.335, p <.01; =.309, p <.01). Customers who buy tickets online ( =.083, p <.01) and with longer tenure ( =.135, p <.01) and who bought tickets more recently ( = −.124, p <.01) are more likely to comment.
Graph
Table 6. Results for Selection Equation for Customer Sentiment.
| Variables | Estimate | z-score |
|---|
| Intercept | −1.288*** | −25,760 |
| Age | −.335*** | −41.842 |
| Gender | −.013 | −.553 |
| Language | .309*** | 6.896 |
| OnlinePurchase | .083*** | 13.316 |
| Tenure | .135*** | 19.500 |
| Recency | −.124*** | −16.987 |
- 9 ***p <.01.
- 10 Notes: Coefficients are standardized.
Graph
Table 7. Results for Endogeneity Control Functions.
| Informational MGC Control Equation | Emotional MGC Control Equation |
|---|
| Variables | Estimate | z-Score | Estimate | z-Score |
|---|
| Intercept | 1.020*** | 7.01 | −1.104*** | −3.48 |
| Result(Lost) | −.859*** | −3.24 | −.220 | −.42 |
| Result(Won) | −.435*** | −2.75 | .699** | 2.09 |
| Unexpected Result | −.398** | −2.03 | .290 | .69 |
| ResultLost × Unexpected | .891*** | 2.78 | −.528 | −.82 |
| ResultWon × Unexpected | .199 | .71 | .395 | .70 |
| Event Attendance | .057 | .51 | −.057 | −.25 |
| ResultLost × Event Attendance | −.061 | −.40 | .004 | .01 |
| ResultLost × Event Attendance | −.049 | −.39 | .210 | .80 |
| Red Cards | .313** | 2.27 | −.311 | −1.05 |
| Yellow Cards | .025 | .48 | .052 | .51 |
| Home | −.064 | −.62 | .872*** | 4.19 |
| Post time | .567*** | 30.83 | .428*** | 23.01 |
| ΔInformational posts | .125*** | 2.66 | −.045 | −.47 |
| ΔEmotional posts | −.083 | −1.32 | .419*** | 3.63 |
- 11 *p <.10.
- 12 **p <.05.
- 13 ***p <.01.
- 14 Notes: Coefficients are standardized.
For the first-stage endogeneity correction regressions, the instrumental variables are all significant in at least one regression. The difference in volume of informational (emotional) posts is positive and significantly related to informational (emotional) MGC volume (informational: =.125, p <.01; emotional: =.419, p <.01). However, the instrumental variables are only significant in one of the control functions; the variable related to informational posts is not significant in the control function for emotional posts, and vice versa. Thus, we have ensured that each (potentially) endogenous variable is identified by at least one unique instrumental variable. Moreover, similar to a traditional F-test, we test whether the instrumental variables significantly affect the log-likelihood of the regressions by comparing our models with models without the instrumental variables. We confirm that this is the case (likelihood-ratios of = 78.16 and = 61.9 for informational and emotional MGC respectively, both ps <.01).
Table 8 presents the Akaike information criterion (AIC) for several models, going from a model without MGC to the final model (discussed subsequently). As shown in Table 8, MGC and the different self-selection and endogeneity corrections significantly improve our model. The coefficients of the final customer sentiment model are presented in Table 9. The parameter estimates related to experiential event outcomes show that variables have expected signs but not all are significant. However, all are significant before the inclusion of event-specific intercepts (results not shown); these intercepts account for most of the variance in the parameters. Wins result in higher sentiment versus draws ( =.251 for wins, p <.01), while losses do not lead to more negative sentiment versus draws. Thus, as we suggest in P1, positive event outcomes lead to positive sentiment. Furthermore, as we suggest in P2, we find that the sentiment of customers' digital engagement in the case of a loss is lower (more negative) than in the case of a win.
Graph
Table 8. Model Comparison.
| Model 1 | Model 2 | Model 3 | Model 4 | Main Model |
|---|
| Description | Standard model (no MGC, self-selection, or endogeneity correction) | Model 1 + MGC main effects | Model 2 + MGC interactions | Model 3 + self-selection correction | Model 4 + endogeneity correction |
| Log-likelihood | −5,606.9 | −5,600.9 | −5,591.9 | −5,589.9 | −5,586.0 |
| AIC | 11,265.88 | 11,257.86 | 11,247.18 | 11,245.70 | 11,242.0 |
| -test | | = 12.02 (p <.01) | = 18.68 (p <.01) | = 3.47 (p <.06) | = 7.72 (p <.02) |
Graph
Table 9. Results for Main Customer Sentiment Model.
| Variables | Estimate | z-score |
|---|
| Intercept | .717*** | 9.393 |
| Result (Lost)m | −.100 | −.988 |
| Result (Won)m | .251*** | 3.548 |
| Informational MGCu,c,m | .057 | 1.493 |
| ResultLostm × Informational MGCu,c,m | .325*** | 2.579 |
| ResultWonm × Informational MGCu,c,m | −.044 | −1.053 |
| Emotional MGCu,c,m | .122** | 2.246 |
| ResultLostm × Emotional MGCu,c,m | −.007 | −.099 |
| ResultWonm × Emotional MGCu,c,m | −.046 | −.815 |
| Unexpected Resultm | −.099 | −1.167 |
| ResultLostm × Unexpected m | .060 | .493 |
| ResultWonm × Unexpected m | .269** | 2.207 |
| TotalEventAttendancem | −.118** | −2.429 |
| ResultLostm × TotalEventAttendance m | .095 | 1.497 |
| ResultWonm × TotalEventAttendance m | .148** | 2.567 |
| RedCardsm | −.043* | −1.891 |
| YellowCardsm | −.022 | −1.000 |
| Home Gamem | −.050 | −1.096 |
| EventFacebooku, m | .040 | .596 |
| Customer Sentimentu, c, m-1 | .087*** | 2.794 |
| Other UGC Valenceu,c,m | .046*** | 3.189 |
| Other UGC Volumeu,c,m | .042** | 2.355 |
| Comment Lengthu,c,m | −.072*** | −4.733 |
| Comment Time u,c,m | .155*** | 4.571 |
| ResultLostm × Comment Timeu,c,m | .090** | 2.003 |
| ResultWonm × Comment Timeu,c,m | −.153*** | −3.541 |
| IMRu | .028* | 1.843 |
| Endogeneity Correction Informational MGCm | −.071*** | −3.199 |
| Endogeneity Correction Emotional MGCm | −.039* | −1.793 |
| Log-likelihood | −5,586.0 |
| AIC | 11,242.0 |
- 15 *p <.10.
- 16 **p <.05.
- 17 ***p <.01.
- 18 Notes: Coefficients are standardized. The standard errors are bootstrapped.
Next, we examine the variables related to MGC content. Please note that a draw serves as the reference event outcome category in our results. Thus, the main effect of informational MGC refers to the impact of informational MGC in the case of a draw. The interaction terms show the additional effects in case of losses and wins. To facilitate interpretation of the results, we provide interaction plots for informational MGC and emotional MGC in Figure 2, Panels A and B. For informational MGC, the effect of MGC for draws is not significant ( , nor is the effect in the case of wins. Importantly, we find a positive impact of informational MGC on customer sentiment with a loss (total effect in case of losses is.382 [.057 +.325], p <.05). The effect is only significant for losses, and it is so pronounced that with more informational posts, sentiment for losses is higher than for wins. Finally, emotional MGC has a significant positive impact on sentiment ( that does not differ by outcome. Thus, as we suggest in P3, we find a positive effect of emotional content on the sentiment of customers' digital engagement. Consistent with our arguments for P4, we find a significantly greater influence of informational MGC on the sentiment of customers' digital engagement in the case of a loss. Finally, as suggested in P5, we find no significant effect for informational content and a positive significant effect for emotional content in the case of a win.
Graph: Figure 2. Effects of MGC on the sentiment of customers' digital engagement. (a) Effect of Emotional MGC on Sentiment for Different Event Outcomes, (b) Effect of Informational MGC on Sentiment for Different Event Outcomes, (c) Effect of an Unexpected Event Outcome on Sentiment for Different Event Outcomes, (d) Effect of the Total Event Attendance on Sentiment for Different Event Outcomes.
With regard to our control variables, we provide plots in Figure 2, Panel C, to facilitate interpreting our results regarding unexpected event outcomes. While the results follow expectations, we find only unexpected wins to yield a significant positive impact on sentiment ( ), with no significant negative effects of unexpected draws or losses. Figure 2, Panel D, shows the plot for event attendance (proxy for stakes and opponent quality) and match result. Results follow expectations: a win for a high-stakes match leads to more positive sentiment, whereas high-stakes draws or losses result in more negative sentiment. However, only the effect for draws is significant.[14] One potential explanation for this result is that draws may indicate close matches that do not fulfill expectations related to high-stakes events.
Regarding the other controls, most are significant and have the expected signs. Significant, expected effects are found for the number of red cards, previous sentiment, both volume and valence of others' UGC, and comment length. As for the timing of comments in the case of a draw, sentiment increases over time, and for losses, the increase in sentiment is even larger over time (total effect is.245 [=.155 +.090]). For wins, we find a neutral total effect of time (.002 [=.155 –.153]), in line with expectations, as mood drops after a draw and even more after a loss, and gradually returns to a steady state ([13]). The effects of the number of yellow cards and others' UGC volume are not significant.
The IMR is marginally significant (.028, p <.10); thus, self-selection is likely an issue. Variables to correct for endogenous informational and emotional MGC content are significant (p <.01 and p <.10, respectively). The z-statistic can also serve as a way to perform a Hausman test in the control function approach, suggesting here the presence of endogeneity.
To investigate the influence of data and modeling choices and more generally check the robustness of our findings, we estimate several model variants related to different operationalizations of the dependent variable, MGC, sampling strategy, time frame, and elements outside the two-day timeframe that may influence sentiment and customer segments. First, we included a neutral sentiment. To do so, we ran a multinomial mixed regression, which models neutral versus negative comments and positive versus negative comments (see Web Appendix W4). The results, including interaction plots, show that results related to informational MGC hold for the neutral sentiment model. Emotional MGC does not affect the sentiment of customers' digital engagement. However, the effect sizes cannot be directly compared with the main model results due to the changes in the dependent variable. To enhance understanding of the model while including neutral comments, we create one continuous fractional dependent variable (representing the probability of a positive comment), as in Homburg, Artz, and Ehm (2015). We use a machine learning approach to determine customer sentiment. We present the results in Web Appendix W5. As we expected, overall sentiment scores are lower compared with the main analysis (as more neutral comments are included); however, our main relationships and conclusions still hold.
We investigated two nonlinear relationships between MGC and the sentiment of customers' digital engagement: a squared (level) model with squared MGC terms, and a logarithmic model including the natural logarithm of informational and emotional MGC, testing for a U-shaped relationship and diminishing positive returns, respectively (see Web Appendices W6 and W7, respectively). The AIC of the models shows that the squared model performs slightly worse than the linear model, but the log model performs better. However, the relationships provide the same insights as the linear model. Thus, we repeat our conclusions and add that effects may be more prevalent at lower MGC levels and flatten at higher levels.
Third, while our approach corrects for self-selection, we limit it to include the team's customers, although many comments come from noncustomers. This may introduce another selection effect. Thus, we run the model for all comments and replicate the results (Web Appendix W8). Fourth, we reestimate the model using a one-day window and replicate the results (Web Appendix W9) with only one difference: more informational MGC after a win results in more negative customer sentiment (p <.05). Fifth, we examine the effects of other factors that may affect sentiment, including focal users' comments from the week and month before a focal event, and find no significant effect of these comments on sentiment. Next, we include trends (total number of points gained over the last month and number of wins over the last five matches), next to the implicit inclusion of prematch expectations. These trends are not significant. Finally, we test whether our results vary across segments and find no differences for segments based on loyalty or commenting behavior.
Three hundred fifty-six participants (62.5% male; Mage = 34.8 years, SD = 10.5) recruited from MTurk for the scenario experiment were randomly assigned to conditions defined by expectations, match outcomes, and MGC. We used a 2 (expected to win or lose) × 2 (actual win or loss) × 3 (MGC: emotional, informational, none) between-subjects design. Participants commented on Facebook on average every few weeks (M = 4.05, SD = 2.3; 1 = "Less than once every 2–3 months," and 7 = "Daily"). Participants' sentiment ratings were elicited immediately following the scenario descriptions.
Participants were asked to list their favorite team sport and sports team to help set the context. Then they read a scenario to which they were randomly assigned. All scenarios started as follows: "Imagine a situation in which you just watched a game of your favorite team-sport. Before the start of the game, your expectations were that your favorite team would [win/lose]. [Indeed/However], they [won/lost]." In the no-MGC condition, the scenario stopped here. In the MGC conditions, the respondents saw the following sentence: "You go to the team's Facebook page and see that the team has posted the following:" For the informational MGC condition, we used the following three sentences (because the mean MGC volume in our data set is also approximately three posts), reflecting several aspects of the game: "Our next game is against XYZ," "Did you know there were X spectators in the stadium today?," and "Player X played his 200th game today." For the emotional MGC condition, respondents read the following: "Come in great numbers to the stadium for the next game to encourage the team!," "Thanks fans for the fantastic support!," and "We are one team!." These posts reflect actual posts observed in the data.
After reading the scenarios, respondents rated how they would feel in this scenario on a seven-point Likert scale. We base our dependent variable on the Positive and Negative Affect Schedule ([83]). We asked respondents to rate how they think they would feel based on each of the 20 emotions on five-point Likert scales (1 = "Not at all," and 5 = "Extremely") given the scenario. The ten positive emotions are interested, excited, strong, enthusiastic, alert, inspired, determined, attentive, proud, and active. The ten negative emotions are upset, guilty, scared, hostile, distressed, irritable, ashamed, nervous, jittery, and afraid. Affect (M = 1.30, SD = 1.17) is then computed as the mean of the positive emotions minus the mean of the negative emotions and used as our dependent variable.
The results of our ordinary least squares regression (R2 =.433) are shown in Table 10. The results are consistent with the results of our main study in that the coefficient for emotional MGC is significantly positive, informational MGC is not significant, the interaction between informational MGC and match result is significant and negative (i.e., informational MGC has significantly less impact in the case of a win) and the interaction between emotional MGC and result is not significant. This provides further evidence of the key causal relationships in our study and confirms MGC moderation on the match outcome–customer sentiment link.
Graph
Table 10. Results of the Scenario Experiment.
| Variables | Estimate | t-Score |
|---|
| Intercept | −.22 | −.08 |
| Result Won | 2.18*** | 8.61 |
| Informational MGC | .27 | 1.20 |
| Informational MGC × Result Won | −.50* | −1.81 |
| Emotional MGC | .54** | 2.39 |
| Emotional MGC × Result Won | −.43 | −1.56 |
| Expected Win | .10 | .66 |
| Expected Win × Result Won | −.72*** | −3.65 |
| Comment Frequency Facebook | −.06*** | −2.77 |
| Age | .02*** | 3.88 |
| Gender (male) | −.02 | −.15 |
- 19 *p <.1.
- 20 **p <.05.
- 21 ***p <.01.
- 22 Notes: To avoid making the scenario experiment too complex, we included only two possible results (win and loss). We use a loss as reference category.
This article examines the potential for firms' customer engagement initiatives on social media to influence the sentiment of customers' digital engagement. Regarding our first research question, we show that marketers can use MGC surrounding experiential events to influence the sentiment of customers' digital engagement even with no change in their objective performance during events. The volume of MGC positively influences the sentiment of customers' digital engagement, consistent with prior research ([11]; [30]). However, we demonstrate these results related to customer interaction event outcomes, an important distinction from prior research, given firms' abilities to monitor their performance during such events and adapt their social media contributions in line with their performance. Furthermore, this study extends previous literature by more richly characterizing the complex nature of the social media environments in which firms and customers interact. Regarding our second research question, we find that while emotional content has a positive influence on the sentiment of digital engagement, informational content has a larger positive influence in the case of undesirable event outcomes. Thus, our findings contribute to understanding of how social media can be used effectively as a marketing tool. Next, we discuss our studies' implications, illustrating how managers can use our results and offering theoretical insights for researchers.
We review here the implications of our results within the customer engagement framework and examine implications related to the concept of customer engagement marketing.
Customer engagement theory is argued to have its roots in marketing's service-dominant logic, which proposes that important engagement-related customer outcomes are generated by their brand- or firm-related interactive experiences ([81]). We link the concept of customer engagement and brand- or firm-related customer interactive experiences and furthermore extend the customer engagement theory framework developed by [55]. Whereas Pansari and Kumar argue that marketing efforts can serve as an antecedent to customers' experiences (i.e., by creating awareness and motivating their initial purchase), we reveal the interactive role of such marketing efforts with those experiences and demonstrate their ability to influence the sentiment of customers' digital engagement.
Our research lends empirical support to the value of customer engagement marketing proposed by [25] and of formal customer engagement initiatives ([19]). [25] distinguish between task-based and experiential initiatives, with task-based initiatives involving customer actions, such as sharing brand knowledge with other customers on social media, and experiential initiatives incorporating sensory or emotional content that subsequently links to the mental representation of the brand or core offering. Our results suggest that such initiatives should be strategically adapted on the basis of firm performance during customers' interaction events. For example, with positive event outcomes, experiential initiatives leveraging multisensory and emotional content may be particularly effective at reinforcing any experience-related positive affect, further enriching customers' mental representations of a brand ([43]). For negative event outcomes, task-based engagement initiatives in which marketers share and then encourage others to share brand- or firm-related information might be more effective at enhancing customers' recall of brand-related information ([ 8]).
Our results offer important implications for the use of MGC. Using our ability to link social media data and customers' transactions, we also highlight the potential role of the sentiment of customers' digital engagement as a leading indicator of CLV, a form of direct customer engagement.
Whereas prior research has revealed how firms can track sentiment on social media at an aggregate level, tracking it at an individual level can enable the design of tailored actions based on firm performance during customer interactions. Given estimates that 40% of consumers follow their favorite brands on social media ([45]), there is sizable opportunity for firms to leverage MGC surrounding customer interactions to influence the sentiment of digital engagement.
Beyond designing tailored content, another potential implication of our research is that marketers may leverage insights from their one-to-one social media interactions with customers to enhance customers' experiences, enhancing the likelihood of customer engagement. For example, banks increasingly use social media to monitor customer sentiment and identify opportunities for service recovery, tailoring their posts to particular issues mentioned by customers. As noted by [30], feedback detected from customers' social media content may be more timely than other sources (e.g., stock price fluctuations) and may furthermore enable well-timed online interventions. Relatedly, our findings might also extend to personalized communications sent by marketers' postpurchase. For instance, the beauty products chain Sephora sends recommendations and educational content to buyers immediately after they make a purchase ([71]). As part of firms' efforts to track UGC, a mechanism for responding in a customized manner may help further enhance the sentiment of customers' digital engagement and subsequent behaviors such as purchases, discussed further next.
Although our analysis does not allow us to make claims of causality between customer sentiment and direct engagement, existing research supports a causal link between sentiment and firm performance ([ 2]; [53]). Given this link, the issue then becomes how to influence the sentiment of digital engagement—our primary goal. Purchase behaviors may not occur as frequently as social media posts. Thus, whereas monitoring purchases would potentially uncover at-risk customers, it can only do so after behaviors occur. Because, in our case, purchases only occur once per year, the firm would have limited opportunity to intervene to reverse the adverse effects of unfavorable performances. With regular access to the sentiment of customers' digital engagement, firms can intervene with marketing actions as well as learn from the success of those actions. It is also likely that many of a firm's customers are active on social media, providing regular chances to influence the sentiment of their digital engagement. To illustrate the potential influence of MGC content, we performed several post hoc analyses in which we varied informational or emotional MGC levels, comparing resulting sentiment (averaged across customers) for different event outcomes. Table 11 provides the results.
Graph
Table 11. Change in Sentiment Based on Increases in Informational and Emotional MGC.
| Informational MGC | Emotional MGC |
|---|
| Δ + 50% | Δ + 100% | Δ + 50% | Δ + 100% |
|---|
| Loss | 10.07% | 19.40% | 3.20% | 6.34% |
| Draw | 1.28% | 2.55% | 2.89% | 5.70% |
| Win | .33% | .66% | 1.15% | 2.26% |
23 Notes: Percentages indicate the difference in resulting sentiment (averaged over all customers).
We simulate two levels of MGC increases, keeping all other variables the same. Variables are standardized; thus, increases refer to the percentage of those variables' standard deviations. With 50% more informational MGC (corresponding to 2.2 additional posts), customer sentiment in case of a loss would increase by 10%. However, in the case of a draw or win, this percentage is much lower (1.28% and.33%, respectively). A 100% increase (increase of 4.4 posts) would bring a 19.40% increase in customer sentiment when incurring a loss. With regard to emotional MGC, the increase in sentiment is smaller (larger) in the case of a loss (draw and win) compared with informational MGC. The differences (resulting from different levels of emotional MGC) between wins, losses, and draws are not significantly different.
Finally, increasing MGC has diminishing returns, consistent with prior research ([30]); increases at already high levels do not yield the same proportional increases as those at low levels. This finding is not entirely clear in Table 7 because MGC levels are still relatively low (e.g., the average level of informational MGC is 2.42 posts in a match window; SD = approximately 4.4 posts). In Figure 2, Panel B, the MGC interaction shows that, at this range, the lines are fairly proportional but flatten for higher MGC levels, a result confirmed in the robustness check using log-transformed MGC variables. This shows that firms may be able to influence the sentiment of customers' digital engagement with even minor adjustments in MGC volume and content. For firms that track objective performance during interaction, we encourage the use of adaptive MGC strategies.
While our focus is on the sentiment of customers' digital engagement, an indirect form of engagement, prior research has linked customer sentiment to purchase behavior ([44]). Through our ability to link to transactions, and the common use of purchase incidence and amount in modeling CLV ([37]), we aimed to explore customer sentiment's role as a leading indicator of CLV (for details, see Web Appendix W10). First, we decompose the CLV model in two regressions, binary purchase incidence and amount. We include a variable capturing the predicted sentiment of customers' digital engagement—based on our sentiment model and aggregated per year—as an independent variable in both regressions, along with typical variables used in CLV models and the team's social media share of interest (percentage of customer's team-related page likes). Results indicate that, even when controlling for transactional variables, predicted sentiment has a significant positive relationship with purchase incidence—an indicator of direct engagement ([55])—but not with purchase amount. Thus, we reveal the potential role of the sentiment of customers' digital engagement as a leading indicator of purchase incidence, with the benefit that it is available more frequently than actual purchases. Our findings also demonstrate the value of the additional information on social media beyond information typical in firms' customer databases.
This research represents one of the few empirical demonstrations of the link between objective event outcomes, MGC, and the sentiment of customers' digital engagement. However, several limitations should be considered in evaluating our findings. First, while we argue that the sports context is ideal for the phenomena studied, other firm-specific factors, such as industry and customer involvement, vary across contexts and may be important. However, we believe our findings are relevant for marketers across contexts in which ( 1) marketers can identify individual customer interactions, whether group or individual experiences such as purchases or service experiences, and ( 2) customers are exposed to MGC on social media. Consumers regularly turn to social media to voice reactions to brand- or firm-related interactions across a variety of contexts. Firms also connect with customers on their own social media pages, posting content related to events such as performing arts (for an example in the motion pictures context, see, e.g., [50]]), sports, or promotional events. Across contexts, firms are interested in gauging customer perceptions of their experiences. Thus, MGC's importance for customer sentiment represents a valuable finding. Our approach could be extended to other settings such as purely contractual situations in which buyers have less discretion in purchase decisions.
Second, in an empirical study such as ours, it is challenging to completely address all potential endogeneity concerns. For example, it is possible that marketers use heuristics, past experience, and the context of a game when deciding which content to post to social media and how much, even when the company itself does not have a clearly defined social media posting strategy. Similarly, there may be factors that we do not account for that drive people to comment on company social media posts and drive self-selection. Despite our efforts to account for this (self-selection correction, instrumental variables, and a rigorous set of control variables), we cannot completely rule out endogeneity. However, the experiment in Study 2 verifies our results from Study 1, indicating that potential remaining endogeneity does not strongly influence the results.
Next, the use of social media data from Facebook alone may represent a limitation. While we argue that it is appropriate for our context, insights from other platforms may be valuable and supplement those from Facebook in helping firms assess and influence customer sentiment. However, whereas some researchers have argued that UGC can differ by the platform ([70]), [73] find that positive brand-related sentiment in UGC does not differ across platforms (Facebook, Twitter, and YouTube) and argue that brand interactions in particular influence comments on Facebook. Future studies might assess the ability of other social networking sites to assess the role of MGC for customer sentiment.
Our exploration of MGC's moderating role considered two types of content, namely, informational and emotional. There is evidence that other types of content (e.g., promotional messages) can result in negative customer behaviors (Scholtz et al. 2018; [80]) or in increased warnings regarding consumer skepticism toward social media advertising ([66]) and "spammy" content ([17]). Due to the limited presence of promotional messages in our sample of MGC, we were unable to investigate the potential impact of such content here. Thus, future research should examine a broader set of content, including promotional messages on social media, in extending our findings regarding MGC's moderating role on the influence of event outcomes on customer sentiment on social media.
Although we do not find evidence that MGC's influence depends on customer segments, prior research has found differential effects of, for instance, social media participation for high and low social media users ([63]). Thus, we see it as a fruitful avenue for further research to investigate possible conditions in which segmentation can become important (e.g., sector, industry, countries, social media venue).
Finally, our study is limited to four years. A longer window or more purchase occasions may yield additional insights not observable in our limited time frame.
Supplemental Material, DS_10.1177_0022242919873903 - The Role of Marketer-Generated Content in Customer Engagement Marketing
Supplemental Material, DS_10.1177_0022242919873903 for The Role of Marketer-Generated Content in Customer Engagement Marketing by Matthijs Meire, Kelly Hewett, Michel Ballings, V. Kumar and Dirk Van den Poel in Journal of Marketing
Footnotes 1 Associate EditorHari Sridhar
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by a grant from the Marketing Science Institute.
4 ORCID iDsMatthijs Meire https://orcid.org/0000-0002-3246-5668 Kelly Hewett https://orcid.org/0000-0002-9715-9629 Dirk Van den Poel https://orcid.org/0000-0002-8676-8103
5 Online supplement: https://doi.org/10.1177/0022242919873903
6 1We use the terms "events," "customer interactions," "interaction events," and "experiential events" interchangeably, with each referring to a customer's brand- or firm-related experiences during events that are finite in time.
7 2Besides possibly reacting to team posts, users have no other opportunities to post messages on this Facebook page.
8 3We note that by setting up the data frame, several forms of selection biases might occur: (1) omitting neutral sentiment, (2) including only actual customers, and (3) restricting comments to a two-day window. We evaluate each of these potential biases in the robustness checks.
9 4These odds are also called 1 × 2 odds. The odds were gathered using the website https://www.oddsportal.com/, which captures odds from different bookmakers and presents the average of all odds so that we do not rely on one specific bookmaker. The preplay odds are closed just before the start of the match, thus taking into account all information that is also available to customers.
5Source of the spectator data: https://www.transfermarkt.nl/.
6We use match result in particular, and not other aspects of the match, because it is arguably the performance outcome over which the team has the greatest control.
7To classify all 7,692 posts as informational or emotional, we build a random-forest predictive model that uses the 2,149 manually labeled posts (within a two-day time frame after the game) as a training data set.
8We also tested the robustness of our instrumental variable choice by adding the average number of all league competitors' informational and emotional posts in the previous month as instrumental variables. By averaging across competitors, and not only those from focal events, we conclude that it is unlikely that this influences customer sentiment for this event. These variables are significant, though the effect in the final customer sentiment model was minimal, and our results and conclusions replicate.
9While the parameter for Result Won × Total Event Attendance is significant, this merely indicates that it is different from the effect for draws. Using wins as reference makes it clear that the effect is not significant (p <.28).
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Record: 198- The Role of Mere Closeness: How Geographic Proximity Affects Social Influence. By: Meyners, Jannik; Barrot, Christian; Becker, Jan U.; Goldenberg, Jacob. Journal of Marketing. Sep2017, Vol. 81 Issue 5, p49-66. 18p. 2 Diagrams, 10 Charts, 1 Graph. DOI: 10.1509/jm.16.0057.
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The Role of Mere Closeness: How Geographic Proximity Affects Social Influence
Geographic proximity has become increasingly relevant due to the growing number of marketing services that use consumers’ geographic locations, thus increasing the importance of gaining insights from this information. In five studies (both field and experimental), the authors analyze the effect of geographic proximity on social influence and demonstrate that not only social proximity but also perceived homophily can trigger social influence. They find that this effect holds under alternative representations of geographic distance and is confirmed for a range of different services and even for physical goods. Furthermore, the authors show that geographic proximity has a relative effect because the social influence of a closer sender is stronger than that of a more distant sender, regardless of the absolute distances. They present managerially relevant conditions under which the influence of geographic proximity not only is comparable to other types of information such as age or gender but also provides sufficient informational value for customers to offset differences among alternatives (e.g., due to higher prices) in trade-off decisions.
Online Supplement: http://dx.doi.org/10.1509/jm.16.0057
The growing availability of consumer geographic information (Crandall et al. 2010; Takhteyev, Gruzd, and Wellman 2012) provides companies with ample opportunity to use this information for marketing purposes (Luo et al. 2013; Xu et al. 2011). Given that the likelihood of social influence has long been known to be higher with geographic proximity (Festinger, Schachter, and Black 1963; Ha¨gerstrand 1967), obvious applications of geographic information would be to stimulate word of mouth and enhance social influence among consumers. Despite the large body of research on the indirect role of geographic proximity in the likelihood of social influence (Van den Bulte 2010) and the conditions for successful word of mouth (Libai et al. 2010), insights into how geographic proximity directly affects the strength—and, therefore, the success—of social influence remain sparse. Because messages from similar others exert greater influence on receivers (Brown and Reingen 1987; Forman, Ghose, and Wiesenfeld 2008), we propose that geographic information might provide signals that lead to perceived homophily. Thus, this article focuses on the relationship between geographic proximity and perceived homophily and investigates the conditions under which information about geographic proximity is sufficient to establish levels of perceived homophily that are high enough to produce a sizable social influence.
We contribute to marketing research and practice by demonstrating, for the first time, that not only actual homophily (or social proximity) but also perceived homophily can trigger social influence in a managerial context. In the experimental studies, we find that this effect holds under different representations of geographic distance, even when alternative indicators of homophily (such as age and gender) are considered. Furthermore, we show that geographic proximity has a relative effect because the social influence of a closer sender is stronger than that of a more distant sender, regardless of the absolute distances. We can derive concrete implications because the observed effect of perceived homophily is confirmed through a range of different services and physical goods. Most intriguing and novel is our finding that perceived homophily provides sufficient informational value for customers to offset the differences among alternatives (e.g., due to higher prices) in trade-off decisions.
Our findings indicate the economic relevance of geographic data and yield important insights to help companies manage social influences among consumers. We show that geographic information could be especially relevant in the context of electronic and mobile commerce (e.g., Luo et al. 2013) and derive managerial implications for increasing the efficiency of online customer reviews (OCRs), targeted advertising in social networks, and product recommendations among peers.
Previous Research and Overview of the Current Set of Studies
The importance of geographic proximity to information dissemination—and, thus, to social contagion—has been acknowledged since the early stages of innovation adoption research (Rogers 2003), with seminal work dating to the 1960s (e.g., Ha¨gerstrand 1967). In the digital age, research on the effects of geographic proximity redirected attention as new sources of large-scale data became available, and the rise of mobile communication channels triggered increasing interest in location-based services within managerial practice. Although some scholars predicted that distance would become meaningless in the digital age (Cairncross 2001), the continued importance of geographic proximity to social contagion has been proved repeatedly in marketing research despite the advent of Internet and mobile communication channels. This finding holds not only for interactions between consumers (Bell and Song 2007) but also for information transfer between firms (Agrawal, Kapur, and McHale 2008; Angst et al. 2010; Barrot et al. 2008; Bronnenberg and Mela 2004). Table 1 provides an overview of recent studies investigating geographic proximity and social influence. Beyond the research context, the studies show differences with respect to the data (particularly the availability of social network information), the level of analysis, the analysis of moderating and mediating effects, and the operationalization of geographic proximity. The literature generally refers to an indirect effect—geographic proximity increases the likelihood of social proximity, which, in turn, increases the likelihood of social influence. In this work, we are interested in the following direct effect: Does geographic proximity affect social influence even in the absence of social proximity?
One consequence of a lack of actual social network data is that the influence on consumers of geographic proximity cannot be distinguished from that of social proximity. Because social and geographic proximity are highly interlinked concepts, geographic proximity can be used in place of social proximity if network data are absent (Manchanda, Ying, and Youn 2008; Nam, Manchanda, and Chintagunta 2010; Van den Bulte 2010). However, this interdependency also implies that a lack of network data makes it difficult to determine whether geographic proximity actually affects social influence or whether mere social proximity leads to spatial diffusion patterns (Brown and Reingen 1987; Frenzen and Nakamoto 1993). In this article, we investigate the relationship between geographic proximity (in terms of the distance between customers) and social influence when controlling for social proximity by conducting a large-scale descriptive study with customer data (Study 1; see Figure 1).
Although Study 1 provides new insights into whether the social influence of geographically close people is not only more likely to occur but also stronger (apart from social proximity), Table 1 shows that there is little understanding of which psychological construct causes the effect or of which marketing-relevant variables might influence it. In a set of four experimental studies, we contribute to the literature by exploring moderating and mediating effects on the link between geographic proximity and social influence.
When considering the factors that typically affect the strength of social influence among consumers, previous research has identified dyadic network traits (i.e., traits that describe the relationship between two people)—for instance, tie strength, communication, and mutual trust (Gilly et al. 1998)—to be most important. In Studies 2–5, we propose that consumers use their geographic proximity to senders of social influence as a cue for their level of homophily with those senders (perceived homophily), which has been repeatedly shown to affect consumer decisions about which products to adopt (Nejad, Amini, and Babakus 2015). Generally, homophily explains the tendency of people in social networks to form ties with others who are similar to themselves; therefore, homophily refers to the degree of similarity between two people (Kossinets and Watts 2009; McPherson, Smith-Lovin, and Cook 2001). In this respect, there are two reasons that high degrees of homophily can explain concurrent adoption between two consumers. First, because homophily implies similar tastes, consumers with high levels of homophily are more likely to adopt the same products (Ma, Krishnan, and Montgomery 2014). Second—and more relevant to our research question—the degree of homophily determines the amount of influence that two people can exert on each other; thus, they are likely to adopt the same products because of stronger social influences (Rogers and Bhowmik 1970). Numerous studies have shown that the degree to which a recipient considers a sender of social influence to be similar to herself or himself is associated with how likely the recipient is to change his or her attitudes and act on the sender’s recommendation (Brown and Reingen 1987; Feick and Higie 1992). Thus, we investigate the mediating role of perceived homophily on geographic proximity in Study 2.
As Table 1 shows, the existing research inconsistently operationalizes geographic proximity. Because consumer perceptions can differ with the operationalization, we use the commonly used measure colocation instead of distance in Study 3. To derive implications for marketing practice, we test the managerial strength of geographic proximity. Specifically, we compare the effect of geographic proximity with those of other strong marketing demographic characteristics, such as gender and age (see Study 4). Finally, in Study 5, we test the practically relevant moderating effect of price to assess the monetary value of the proposed dimension and to demonstrate its value to customers and importance for firms.
Overall, this article provides new insights relevant to marketing research and practice by using field data, including social network information, to investigate the importance of geographic proximity for social influence and its interdependency with social proximity. When social proximity is absent, we analyze the mediating role of perceived homophily on the relationship between geographic proximity and social influence. Unlike familiarity, a related construct, perceived homophily does not require previous interactions between individuals (Edmond and Brannon 2016; Hinds et al. 2000). In addition, we evaluate the moderating influence of geographic information relative to demographic characteristics and in trade-off decisions.
Geographic Proximity and Social Influence
Theoretical Background
One reason it is difficult to determine whether geographic proximity actually affects social influence or whether social proximity merely leads to spatial diffusion patterns is the socalled “propinquity effect,” which can be defined as the higher likelihood of the formation, maintenance, and strength of social network ties in geographic proximity (Preciado et al. 2012). Multiple studies have shown that the likelihood of the formation and maintenance of social ties between people can be expressed as an inverse logarithmic function of the geographic distance between them (Levy and Goldenberg 2014). Although this relationship holds true for large distances (such as those between cities or regions; Lambiotte et al. 2008; Mok and Wellman 2007), it also applies to very small differences, such as the distance between rooms in student dormitories (Festinger et al. 1963). Interestingly, the emergence of new communication technologies whose financial costs are independent of geographic distance has not altered this relationship (Mok, Wellman, and Carrasco 2010). For instance, distributions of Twitter links, ties on social networking sites, and e-mail traffic remain a function of geographic distance that steeply declines beyond a few miles (Lee, Scherngell, and Barber 2011; Takhteyev, Gruzd, and Wellman 2012).
TABLE: TABLE 1 Review of Research Investigating Geographic Proximity and Social Influence in the Digital Age
| Research | Study Context | Data Origin | Social Network Data | Level of Analysis | Moderators | Mediators | Geographic Proximity |
|---|
| This study | Adoption of telecommunication + review choice | Field data + experiments | Yes | Individual (customers) | Social proximity + price | Perceived homophily | Distance (in miles) + colocation |
| Agrawal, Kapur, and McHale (2008) | Citations of patents | Field data | No | Individual (patents) | Coethnicitya | €” | Colocationa |
| Angst et al. (2010) | Adoptions of technological innovation | Field data | No | Individual (hospitals) | €” | €” | Distance (in miles) |
| Baptista (2000) | Adoptions of technological innovation | Field data | No | Individual (companies) | €” | €” | Colocationa |
| Barrot et al. (2008) | Adoptions of technological innovation | Field data | No | Individual (companies) | €” | €” | Distance (in kilometers) + colocation |
| Bell and Song (2007) | Adoption of online shopping | Field data | No | Region | €” | €” | Neighboring zip code areasa |
| Bronnenberg and Mela (2004) | Market entry of brands | Field data | No | Individual (supermarkets) | €” | €” | Adjacency of marketsa |
| Choi, Hui, and Bell (2010) | Adoption of online shopping | Field data | No | Individual (customers) | €” | €” | Distance (in miles) |
| Forman, Ghose, and Wiesenfeld (2008) | Behavior in online shopping | Field data | No | State | €” | €” | Colocationa |
| Gimpel et al. (2008) | Candidate support in gubernatorial elections | Field data | No | County | €” | €” | Distance (in miles) |
| Lambiotte et al. (2008) | Diffusion of communication networks | Field data | Yes | Individual (customers) | €” | €” | Distance (in kilometers) |
| Lee, Scherngell, and Barber (2011) | Acquaintanceship in social networking site | Field data | Yes | University | €” | €” | Travel time + colocationa |
| Levy and Goldenberg (2014) | Social links in social networks | Field data | Yes | Individual (customers) | €” | €” | Distance (in kilometers) |
| Manchanda, Ying, and Youn (2008) | Adoption of pharmaceutical innovation | Field data | No | Individual (physicians) | €” | €” | Colocationa |
| Mok and Wellman (2007) | Communication behavior in social networks | Survey data | Yes | Individual (citizens) | €” | €” | Distance (in miles) |
| Mok, Wellman, and Carrasco (2010) | Communication behavior in social networks | Survey data | Yes | Individual (citizens) | Immigrant respondents | €” | Distance (in miles) |
| Nam, Manchanda, and Chintagunta 2010 | Adoption of video-on-demand service | Field data | No | Individual (customers) | Signal quality | €” | Colocation |
| Preciado et al. (2012) | Development of friendships | Survey data | Yes | Individual (citizens) | Same schoola | €” | Distance (in kilometers) |
| Takhteyev, Gruzd, and Wellman 2012 | Formation of ties on Twitter | Field data | Yes | Individual (customers) | €” | €” | Distance (in kilometers) |
Thus, although it is well established in the literature that the frequency of interaction and communication depend on geographic proximity (the propinquity effect), it remains uncertain whether social influence from geographically close people is not only more likely to occur but also stronger—apart from mere propinquity and social closeness. To address this gap, we build on previous studies on social influence in geographic proximity (Bradner and Mark 2002; Latane´ 1981; Latane´ et al. 1995; Moon 1999) and on the effect of spatial distance on psychological distance (Fujita et al. 2006; Henderson et al. 2011; Trope and Liberman 2010) that do not incorporate social closeness or the propinquity effect. Thus, we examine the effect of geographic proximity on social influence and its interplay with social closeness, controlling for the propinquity effect in the following descriptive study.
Study 1: Empirical Evidence of the Relationship Between Geographic Proximity and Social Influence
Given that whether geographic proximity is merely a surrogate for social closeness, whether it increases social influence on its own, and how the two phenomena interact remain unclear, we conduct a descriptive study and examine the role of geographic proximity in social influence in a real-life setup by controlling for the propinquity effect (for detailed information on the data, the method, and the results, see the Web Appendix). Note that for this descriptive study, as well as the following experimental studies, we define geographic proximity as the distance between people based on their predominant location (living geographically close) and not necessarily on their current location (being geographically close), because temporal locations (i.e., when traveling) are less significant because they are not associated with consumers’ identities or choices. Using comprehensive social network data, we analyze how the effect of geographic proximity changes with the strength of the relationships between consumers. Specifically, we analyze how previous adopters of a mobile phone provider influence potential adopters within the same social network by incorporating information about the geographic distance between potential and previous adopters and by controlling for their social networks.
Data. The data set comprises 509,191 customers who signed up with the provider during our observation period, spanning more than 37 months from market launch. The customer data include individual-level information on the date of adoption, age, gender, zip code, and encrypted last name. In addition, we tracked the provider’s call records for all customers using encoded phone numbers for the entire observation period. The call records include all calls and text messages made to the provider’s other customers and to noncustomers. The duration and number of calls and messages are aggregated per dyad on a monthly level, resulting in information about more than 100 million phone connections. Having tracked all outgoing calls, the call records enable us to reconstruct each customer’s social network—that is, all contacts that a customer has, including social ties (i.e., relationships or links) to existing customers and to the customers of other providers. Furthermore, these data enable us to compute network measures such as the strength of a customer’s social ties. Mobile phone data have repeatedly been shown to be valid and highly representative proxies for the social relationships between individuals (Onnela et al. 2007; Shi, Yang, and Chiang 2009). Considering not only the 509,191 customers of the provider but also their social ties, we construct a social network of more than 14 million actors (i.e., individuals) and ties. In addition to these individual-level data, we recorded the provider’s monthly advertising spending, another important influence on customer behavior. The data include total monthly spending and spending on specific media, such as newspapers, television, radio, and Internet. By matching the advertising data with the geographic reach of each print medium, we can obtain the advertising spending per month at the zip code level to control for regional heterogeneity as a potential reason for spatial contagion. For national media (the smallest share of the company’s advertising activities), we allocated spending evenly across all zip codes. To further control for regional attributes, we obtained additional data, such as the purchasing power in each zip code. A zip code refers to an area of 10,000 inhabitants on average, the smallest possible aggregation level with respect to the available spatial data.
Method. We use comprehensive data on the dates on which each customer adopted the product and multiple sources of influence on each potential customer to develop an empirical model of the effect of each driver of the adoption decision. Specifically, we examine the extent to which social contagion affects the decision to adopt the provider by analyzing geographic proximity among consumers and controlling for numerous sources of influence and social network traits. Given the dynamic nature of the data, we estimate a semiparametric Cox regression model that includes time-varying covariates. The model includes covariates and estimates the effect of each on the hazard to adopt by using both time-varying covariates and time-invariant predictors. The full model including all variables can be written as follows:
(1) hiðtjXitÞ = f ðExposureit, GeographicProximityit, NetworkOverlapit, Householdit, TieStrengthit, Agei, Genderi, NetworkDegreei, LocalPenetrationit, PurchasingPoweri, AdvertisingitÞ.
Table 2 provides an overview of the operationalization of all the variables. The descriptive measures and correlations appear in Appendix A.
Results. Table 3 presents the results from the hazard regression estimated with 509,191 cases. The hazard ratios are interpreted such that an increase in the variable of one unit increases the hazard of adoption by 1 hazard ratio percent. The model has high explanatory power, as indicated by the chisquare test, and the signs of the focal variables and the control variables are in line with both our expectations and traditional adoption theory.
TABLE: TABLE 2 Operationalization of Variables
| Variable | Operationalization |
|---|
| 1. Exposureit | 𝚺 previous adopters j in i€s social network up to month t |
| 2. GeographicProximityit | 𝚺j (1/1 + distance in km between and j) |
| 3. NetworkOverlapit | 𝚺j (# joint social ties of ij/# social ties of I + # social ties of j) |
| 4. TieStrengthit | 𝚺j (communication volume ij/communication volume i) |
| 5. Householdit | 1 = I and j share same zip code and same last name |
| 6. Agei | Prospect i€s age in years |
| 7. Genderi | Prospect i€s gender (1 = female) |
| 8. NetworkDegreei | Number of social ties in i€s social network |
| 9. LocalPenetrationit | 𝚺 previous adopters in i€s zip code per 1,000 inhabitants up to month |
| 10. PurchasingPoweri | Purchasing power (in V) in i€s zip code |
| 11. Advertisingit | Provider€s advertising spend (in V) in i€s zip code in month t |
The results show a strong effect of geographic proximity (exp(b2) = 1.057, p < .01) on adoption. The coefficients are interpreted as an increase of one standard deviation (i.e., .63) that accelerates the time to adoption by 5.7%. We use the inverse of the distance, so an increase of one unit represents approximately 1.6 km (or 1 mile). The social influence of previous adopters living close to the subsequent adopter is significantly stronger than that of those living farther away. We find this significant influence of geographic proximity even though the model incorporates three control variables for the propinquity effect (i.e., NetworkOverlapit, TieStrengthit, and Householdit), all of which are significant and explain a considerable part of adoption behavior. Yet the increase in adoption hazard of 5.7% caused by a mere 1.6 km difference shows that, over larger distances (e.g., between cities), differences in geographic proximity yield substantial differences in the strength of social influence. It is important to recall the non-linearity of the effect: whereas a difference of approximately 1.6 km will have a large impact on consumers who live closer to each other (e.g., 1 km vs. 3 km), the effect becomes smaller as distances increase (e.g., 150 km vs. 152 km). Here, an additional distance of 1 km is less relevant, but differences of, for instance, 50 km will have a strong influence on whether a recommendation by a previous adopter is followed.
TABLE: TABLE 3 Hazard Regression Results on the Strength of Adoption Drivers
| | | Hazard Ratio | SE | Z-Score |
|---|
| aVariables are standardized to have mean of 0 and standard deviation of 1. |
| Exposureit | ß1 | 1.121 | .0018 | 69.36 |
| GeographicProximityita | ß2 | 1.057 | .0017 | 35.27 |
| NetworkOverlapita | ß3 | 1.023 | .0010 | 22.70 |
| TieStrengthita | ß4 | 1.089 | .0014 | 67.97 |
| Householdit | ß5 | 1.258 | .0072 | 40.40 |
| Ageia | ß6 | 1.054 | .0015 | 37.64 |
| Genderi | ß7 | .963 | .0027 | -13.13 |
| NetworkDegreeia | ß8 | 1.061 | .0004 | 162.57 |
| LocalPenetrationita | ß9 | 1.014 | .0008 | 17.02 |
| PurchasingPoweria | ß10 | 1.016 | .0014 | 11.40 |
| Advertisingita | ß11 | 1.021 | .0032 | 6.65 |
| GeographicProximityit · TieStrengthi | ß12 | .979 | .0004 | -51.35 |
| Likelihood-ratio c2 | | | 47,006 | |
| Log-likelihood | | | -6,216,943 | |
Interestingly, the interaction between geographic proximity and tie strength indicates that the effect of geographic proximity is negatively affected by tie strength (exp(b12) = .979, p < .01). This finding is remarkable in two ways: First, the negative sign of the interaction shows that the effect of geographic proximity is not merely a result of the propinquity effect but that geographic proximity leads to a stronger social influence independent of the propinquity effect. Second, this finding shows that the effect of geographic proximity increases with decreasing tie strength to previous adopters. In particular, we find that if consumers receive word of a specific product or its features by an adopter whom they do not know well (e.g., more remote acquaintances), consumers look for cues about the adopter that they can use to assess the recommendation about the product’s suitability to their personal needs and tastes.
Discussion. The findings indicate that geographic proximity exhibits the expected effect on the strength of social influence—even after controlling for the propinquity effect. Most importantly, the results also indicate that the effect of geographic proximity becomes stronger with decreasing tie strength between the previous adopter and the subsequent adopter. This result suggests that people with high tie strength do not need to use distance as a cue for homophily to assess the credibility or helpfulness of a recommendation; however, geographic proximity may work as a cue when ties are weaker because of the absence of other information. This fact has two important practical implications. First, geographic data beyond social network information are valuable to companies, and thus, geographic proximity is not merely a proxy for social proximity but can be used as an independent construct. Second, the results indicate that geographic information is especially useful when social ties are unknown or nonexistent—for example, in electronic or mobile commerce environments such as OCRs.
Naturally, this descriptive study cannot support the hypothesized mediation of perceived homophily, and it cannot reveal the value of this information to the consumer and the firm. The next sections address these two questions. In the following, we theorize that perceived homophily indeed serves as a mediator for geographic proximity, and we test this relationship experimentally.
Relationship Between Geographic Proximity and Perceived Homophily
Theoretical Background
Because homophily is a highly multidimensional construct, there are various dimensions on which two people can either be actually similar or merely perceive themselves to be similar (Lazarsfeld and Merton 1954). These dimensions can be objective (e.g., age, gender, income) or subjective (e.g., lifestyles, values, beliefs, attitudes; McPherson, Smith-Lovin, and Cook 2001; Rogers and Bhowmik 1970). Multiple studies have demonstrated that both objective demographic homophily (Brown and Reingen 1987; Nitzan and Libai 2011; Risselada, Verhoef, and Bijmolt 2014) and subjective homophily strongly affect word of mouth and the strength of social influence (Gilly et al. 1998). A higher level of homophily increases the likelihood that a recommendation will be perceived as credible, helpful, and relevant to the receiver’s needs. This positive perception of the recommendation reduces the uncertainty that customers face before making a purchase (Gilly et al. 1998; Schmitt, Skiera, and Van den Bulte 2011). Moreover, interpersonal similarity has been shown to decrease the psychological distance such that actions from similar individuals are construed at more concrete levels, increasing individuals’ influence on one another’s decisions (Liviatan, Trope, and Liberman 2008; Zhao and Xie 2011).
However, what if people know one another only a little or not at all and are thus unable to rely on homophily? For instance, a consumer receiving a product recommendation from a remote acquaintance cannot validly assess whether the recommender is actually similar to him or her. In many contexts of online peer influence (e.g., OCRs on shopping websites), an OCR reader does not know the reviewer at all and faces uncertainty concerning whether a recommendation should be followed. In that circumstance, consumers must use cues that allow them to derive the level of (perceived) homophily to the recommendation sender. These cues can take the form of the sender’s available personal information or take more subtle forms, such as the sender’s style of communication (Berger and Iyengar 2013; Moon 1999).
We propose that consumers use geographic proximity to senders of social influence as a cue for their level of homophily with those senders. To support this proposition, different theoretical streams can be used to argue for the proposed relationship between geographic proximity and perceived homophily. First, geographic space can be considered a source of homophily because neighborhoods are often homogeneously formed with respect to social, economic, or educational attributes (Hipp, Faris, and Boessen 2012; McPherson, Smith-Lovin, and Cook 2001). As a result, deriving homophily on the basis of a shared geographic location can be reasonable—though in reality, the relationship refers only to a limited geographic area (i.e., a neighborhood). Second, according to construal level theory (Trope and Liberman 2010), both the degree of homophily and the spatial proximity determine the psychological distance (Fujita et al. 2006; Henderson et al. 2011; Liviatan, Trope, and Liberman 2008); that is, events are construed and information is processed similarly for spatial distance and for the level of homophily. Thus, we argue that in the absence of information about the actual level of homophily, consumers might use spatial proximity as a proxy for the level of homophily to construe and process recommendations. Furthermore, to increase a consumer’s feeling of similarity to another because of geographic proximity, the geographic area in question (e.g., the consumer’s neighborhood, city, region, or state) must serve as an indicator of specific traits that people are expected to share. In this respect, social identity theory (Turner and Tajfel 1986) might serve to explain more general relationships among geographic proximity, homophily, and social influence. According to social identity theory, a geographic area can be considered part of one’s social identity, which might lead consumers to perceive geographically close people to be more similar to them (Huddy and Khatib 2007; Turner and Tajfel 1986). As a result, we propose that the level of perceived homophily is higher, which is why geographic proximity increases the strength of the social influence that a recommendation has on people. In Study 2, we examine the role of geographic proximity on social influence in the absence of actual social interaction and test the mediation of perceived homophily using a discrete choice experiment.
Study 2: Effects of Geographic Proximity and Homophily
To examine the causal effect of geographic proximity on social influence and the mediation of perceived homophily, we conduct a controlled experiment. We choose online ratings, a special form of OCR, as our experimental scenario because they not only have the advantage of possessing significant managerial relevance (Chevalier and Mayzlin 2006; Godes and Mayzlin 2004; Naylor, Lamberton, and Norton 2011) but also provide a setting that can experimentally exclude potential confounds.1 Reviewers and OCR receivers typically do not know one another, which is why other social network traits, such as actual homophily or tie strength, play no role in the customer’s decision of whether to follow a review. Furthermore, there is neither direct communication between the reviewer and the OCR receiver nor a (reasonable) chance of future communication that could increase the trustworthiness of a recommendation (Bradner and Mark 2002). For this reason, geographic proximity can become important to indicate potential homophily. In this case, testing a scenario can be credible because of its high resemblance to the day-to-day experiences of the people in the study.
Procedure. To test the causal relationship between geographic proximity and social influence, we conducted a discrete choice experiment that very closely resembled a real-life setting and customer experience. Specifically, participants were presented a fictitious scenario involving a smartphone application called surprise-vacations.com, which they browsed to book hotels for their vacations. Next, we introduced blind booking as a special feature of the app in which the customer can book an unknown hotel at his or her desired destination in exchange for a large discount (analogous to Hotwire.com). The participants were shown OCRs and had to decide on a hotel. The blind-booking scenario has the advantage of excluding the qualitative, written content of a review as a confounder while maintaining a realistic scenario (we told the participants that all materials that could contain information about the exact hotel were excluded). The participants were shown a screenshot of the mock app that displayed three hotels and their OCRs in a visually similar design to other apps of this type. The participants had to choose one of these hotels for their next vacation at a destination of their choice (see the full scenario in Appendix B).
For each hotel, we displayed star ratings for multiple relevant criteria, a generic reviewer user name, and the date that the review was posted. The last element did not vary but was included to provide a more realistic setting. Most importantly, we displayed the exact distance to the (fictitious) reviewers using five distances (1.2 miles, 5.6 miles, 48 miles, 110 miles, and 890 miles). The distances were set such that the analysis covered different geographic areas, such as neighborhoods, regions, or states. Appendix C shows an example of the choice set in the fictitious app.
Among the three OCRs shown, two were equal with respect to their star ratings (dominant options) but were ostensibly written by users at different geographic distances. Among the participants, we used all possible combinations of two distances from among the five distances enumerated previously. The third OCR provided a lower star rating (dominated option) and was displayed to serve as a manipulation check and to create a more realistic setting in which the choice between the hotels is not driven by the distance. The distance for the dominated option was randomly assigned, but the option never exhibited the shortest distance. In addition, we varied the user name, the hotel name (A, B, or C), and the order in which the hotels were shown. After choosing the desired hotel, the participants were asked to indicate on a multi-item seven-point scale their perceived homophily to two of the reviewers (Gershoff, Mukherjee, and Mukhopadhyay 2007).
Participants. Our sample consisted of 606 participants2 from the United States recruited online through Amazon Mechanical Turk (MTurk). We chose the number of participants on the basis of 20 experimental conditions altering geographic proximity and order of hotel presentation, as well as our aim of obtaining 30 participants per condition. The mean age was 31 years, and 44% of our participants were female. The distribution of U.S. zip codes in our sample was well matched to the actual distribution of the population (see the descriptive statistics in Table 4).
Results. The manipulation in which the star ratings created dominant and dominated options seemed to work well, as only 1% of respondents chose the dominated option. Using only the two dominant options, we estimated a conditional logit model with hotel choice as the dependent variable and geographic proximity as the choice predictor. The results in Table 5 also reveal a significant effect of geographic proximity on hotel choice (b = .97; z = 10.59, p < .01), with a model fit of pseudoR2 = .15. Furthermore, if the choice of hotel did not depend on geographic proximity to the reviewer, we would expect a random choice of a 50% share for each of the two dominant options. However, an analysis of all of the choices independent of the specific combination of distances reveals that 72% of the participants picked the hotel reviewed by the user that was geographically closer. Thus, the actual choice is significantly different from being equally split between the close and the distant reviewers (F(1, 605) = 151.26, p < .01). A binomial test for the share of the close hotel also yields a significant difference (p < .01) from .5. In addition, we test how the choice of the geographically close review is affected by response latency by using a one-standard-deviation difference from the mean to indicate low and high outliers (Ratcliff 1993). In this respect, the results are robust to outliers because excluding outliers only slightly alters the share of those who chose the hotel with a geographically closer reviewer (excluding outliers = 74% vs. including outliers = 72%).
Analyzing the possible combinations of distances between the two dominant options separately shows that the effect is, surprisingly, independent of the actual combination of distances displayed. It is sufficient that one reviewer lives geographically closer than does the other reviewer. Thus, the effect of geographic proximity seems to be relative and is not limited to a specific area or range of miles.
Because geographic proximity has also been shown to affect the strength of social influence in OCRs, we test how the role of geographic proximity relates to perceived homophily (Judd, Kenny, and McClelland 2001). Using a seven-point scale in a postexperiment survey, we indeed find a significant difference (F(1, 605) = 96.23, p < .01) between perceived homophily with the geographically closer user (M = 4.86, SD = 1.03) and perceived homophily with the more distant reviewer (M = 4.45, SD = 1.05). This finding holds for each combination of two distances when measured separately. Estimating the conditional logit model with geographic proximity and homophily as choice predictors allows us to statistically test the mediation effect of homophily between geographic proximity and hotel choice (Sobel 1982). The analysis indicates significant mediation (z = 5.05, p < .01) of homophily between geographic proximity and hotel choice. In addition, we test the mediation by conducting a bootstrap analysis (1,000 samples), which also yields a significant indirect effect of proximity on choice through homophily (b = .15, SE = .036; 95% confidence interval = [.084, .222]).
TABLE: TABLE 4 Descriptive Statistics for the Experimental Studies
| | Study 2 | Study 2b | Study 2c | Study 2d | Study 2e | Study 3 | Study 4 | Study 5 |
|---|
| | M | SD | M | SD | M | SD | M | SD | M | SD | M | SD | M | SD | M | SD |
|---|
| aBased on a seven-point rating scale. |
| bDistance between reviewer and participant (in miles). |
| cDifference in age between reviewer and participant (in years). |
| dMatch in gender between reviewer and participant (1 = yes). |
| eDifference in price/homophily between close and far hotel. |
| Dependent Variables |
| Choice of … | Hotel (1 = close) | Hotel (1 = close) | Cloud service | Online banking (1 = close) | Smartphone (1 = close) | Hotel (1 = close) | Option (1 = close) | Hotel (1 = expensive) |
| Choice: close | 72% | 68% | 86% | 87% | 85% | 78% | €” | 78% |
| Choice: far | 27% | 28% | 12% | 11% | 13% | 21% | – | 21% |
| Choice: dominated | 1% | 2% | 2% | 2% | 2% | <1% | – | <1% |
| No choice | – | 3% | – | – | – | – | – | – |
| Independent Variables |
| Perceived homophilya |
| Close choice | 4.86 | 1.03 | 4.91 | 1.09 | 4.85 | 1.06 | 4.87 | 1.08 | 4.83 | 1.04 | 4.86 | 1.06 | | | | |
| Far choice | 4.45 | 1.05 | 4.09 | 1.09 | 4.28 | 1.10 | 4.26 | 1.20 | 4.43 | 1.07 | 4.31 | 1.09 | | | | |
| Geographic distanceb | | | | | | | | | | | | | 204.09 | 336.55 | | |
| Agec | | | | | | | | | | | | | 12.53 | 8.60 | | |
| Genderd | | | | | | | | | | | | | .49 | .50 | | |
| Price differencee | -7.14 | | | | | | | | | | | | | | | 5.92 |
| Homophily differencee | -.55 | | | | | | | | | | | | | | | 1.14 |
| Price · Homophily | -.04 | | | | | | | | | | | | | | | 5.93 |
| N | 606 | 702 | 701 | 711 | 743 | 636 | 702 | 405 |
Robustness checks. Considering that in real life, consumers can choose to abort a buying decision if they do not feel confident making a decision on the basis of the information provided, it is possible that consumers prefer not to choose any of the presented options when distances have no information value. Therefore, we replicated Study 2 with an additional option allowing the participants to choose neither of the presented hotels (for details, see Study 2b in Table 4). Interestingly, the results indicate that providing a no-choice option does not significantly alter the participants’ choices (only 3% chose not to pick any hotel option). Given that the ratios among the close (68%), far (28%), and dominated (2%) alternatives equaled those in Study 2, it is unsurprising that we also found the results concerning the relationship between geographic proximity and perceived homophily to be very similar.
TABLE: TABLE 5 Conditional Logit Model on the Influence of Geographic Proximity (Study 2)
| | Model A | Model B |
|---|
| | Coefficient | SE | Z-Score | Coefficient | SE | Z-Score |
|---|
| Geographic proximity | .97 | .09 | 10.59 | .75 | .10 | 7.58 |
| Perceived homophily | | | | .94 | .13 | 7.46 |
| Pseudo-R2 | | .15 | | | .24 | |
To assess whether the relationship found holds in other product categories, we conducted three analogous experiments using different services (i.e., cloud services and online banking) and physical products (i.e., smartphones). Unlike hotels, in which the evaluation depends on consumers’ specific tastes, these products can be assessed on the basis of objective features. Furthermore, all of these services are location-independent and, thus, avoid potential confounders that might arise in a tourism or travel context. In terms of the choices and perceived homophily, the results from Studies 2c–e (see Table 4) are very similar, if not more pronounced, to the results of the hotel setting; between 85% (smartphone) and 87% (online banking) of participants chose the closer of the two dominant options. Similarly, we find for all three additional settings a significant difference between perceived homophily with the geographically closer user (M = 4.83–4.87, SD = 1.04–1.08) and perceived homophily with the more distant reviewer (M = 4.26–4.43, SD = 1.07–1.20). In conclusion, we can show that the findings from Study 2 are generalizable across product categories and hold for both services and physical goods.
Discussion. Study 2 demonstrates that if two people are geographically closer to one another, their social influence on one another is stronger than with more geographically distant people, regardless of the geographic distances used in the analysis. Short distances, for example, can refer to a neighborhood or city, whereas longer distances can indicate a region or state; the results, however, do not change with respect to which two distances are compared. Furthermore, the results indicate that the greater influence of geographically close reviews is mediated by perceived homophily. That is, geographically close people are believed to be more similar, leading those people to have a stronger social influence on the subject. This result is supported for a range of services and physical goods. These findings corroborate and extend the results from Study 1 by replicating the role of geographic proximity on strangers and by demonstrating the mediation of perceived homophily. Next, we explore the conditions under which this effect exists.
Testing Conditions for the Influence of Geographic Proximity
Study 2 establishes the relationship between geographic proximity and perceived homophily. Although the results demonstrate a mediating effect of perceived homophily for the influence of geographic proximity on consumer choice, it is necessary to investigate the conditions under which information about geographic proximity can establish levels of perceived homophily that result in sizable social influence. Considering the multitude of conditions that can affect the influence of geographic proximity, we limit ourselves to variations that are of managerial relevance. Specifically, we investigate (1) whether the mediating effect of perceived homophily changes with the presentation of geographic proximity (location instead of distance); (2) how information on geographic proximity compares with other types of information used by companies, such as age and gender; and (3) whether and to what degree the availability of information on the geographic proximity of a reviewer affects the willingness of prospective customers to accept higher prices in trade-off decisions.
Study 3: Location Versus Distance
In the preceding studies, we use a direct measure of geographic proximity either derived from the actual distance in kilometers between consumers (Study 1) or displayed as the distance in miles to an online reviewer (Study 2). However, in cases in which information on the exact locations and distances of both parties is unavailable, most OCR-based websites (e.g., Expedia, TripAdvisor, Yelp, Amazon) provide information about the actual location of the reviewer. Thus, we test whether the findings hold when concrete locations (instead of distances) are displayed to ensure their robustness and managerial applicability. With this design, and in order to increase the validity of this phenomenon, Study 3 aims to replicate Forman, Ghose, and Wiesenfeld’s (2008) research, which has shown that colocation at the state level influences customer behavior but has simultaneously shown that this effect is not just a state-level effect but exists at all scales. Finally, the purpose of this study is to establish the link to perceived homophily as a general mechanism that works on all scales (including at the state level).
Procedure. We altered the experiment from Study 2 by showing the U.S. state in which the reviewer lived instead of the distance from the reviewer in miles (for an example, see Appendix D). After the participants were asked to provide their demographics and indicate the state in which they lived, they were shown a blind-booking scenario similar to that in Study 2. One OCR was ostensibly written by a user living in the same state as the participant, and the two other OCRs were ostensibly written by users from two (randomly chosen) different states. Again, two hotels had equal star ratings (dominant options), and one hotel had an inferior star rating.
Participants. The sample consisted of 511 participants recruited from the United States with the help of MTurk. The mean age of participants was 33 years, and 36% were female. The participants were from various U.S. regions, and the distribution of the participants’ states corresponded to the actual distribution of the population (for details on the variables, see Table 4).
TABLE: TABLE 6 Conditional Logit Model on the Influence of Geographic (Same-State) Location (Study 3)
| | Coefficient | SE | Z-Score |
|---|
| Geographic proximity | .83 | .12 | 6.71 |
| Homophily | 1.02 | .14 | 7.24 |
| | Pseudo-R2 | .37 | |
Results. Overall, the results are very similar to those found in Study 2 in which the geographically close option received a 72% share. Specifically, in this setting, 78.3% chose the hotel reviewed by a user from the same state, and only 21.1% picked the hotel reviewed by a user from a distant state. As before, we analyzed the effect of geographic proximity by estimating a conditional logit model (see Table 6). The results indicate a significant influence of geographic location (b = .83; z = 6.71, p < .01) on the choice between the same-state OCR and the distant-state OCR (again, we used only the two dominant options). With respect to perceived homophily with the reviewers, we find a positive influence (b = 1.02; z = 7.24, p < .01) that is significantly higher (F(1, 510) = 213.24, p < .01) for users from the home state (M = 4.91, SD = 1.09) than for users from a distant state (M = 4.09, SD = 1.09). The results replicate the findings from Studies 1 and 2; moreover, we find that the role of geographic proximity holds regardless of whether the distance or an actual location is displayed.
This result emphasizes not only the robustness but also the managerial applicability of our findings. Even when firms have rather nonspecific information about the geographic locations of their reviewers (such as their home state), this information can be used to improve the selection of OCRs displayed in search results.
Study 4: Geographic Versus Demographic Information
Geographic proximity is not the only type of information that influences perceived homophily. For example, information on the reviewers’ age and gender has been found to influence perceived homophily (Brown and Reingen 1987; Nitzan and Libai 2011; Risselada, Verhoef, and Bijmolt 2014) and is also frequently used by companies (e.g., TripAdvisor). If companies had information on geographic proximity, age, and gender available and wanted to present prospective customers with OCRs sorted on the basis of one type of information, they would not know which to choose. In other words, given that age and gender are typically considered most important for social influence, we test whether geographic proximity adds value beyond these strong drivers. If so, it should be added as a sorting criterion in practice. To understand the importance of this effect, we want to compare these three factors on their respective relative effects on social influence. Given the importance of default settings (e.g., Johnson, Bellman, and Lohse 2002), knowing which sorting criterion exerts the greatest influence is of high interest to companies, because OCRs that are sorted in the most influential fashion can considerably increase the conversion probability of prospective customers.
Procedure. To test the relative influence of distance on age and gender, we conducted an experimental study with a blind-booking scenario similar to those in Studies 2 and 3 in which we asked the participants to choose among three hotels. Each of the three hotel options was presented with an OCR that indicated the age and gender of the reviewer and her distance from the participant. Unlike in Studies 2 and 3, all options showed equal star ratings for the hotels. After choosing one hotel, the participants indicated their own age and gender. To analyze the relative influence of geographic proximity, age, and gender on the choice of each option shown, we estimated a conditional logit model. In line with the assumption that these variables induce perceived homophily, we used demographic similarity and distance as explanatory measures for hotel choice. For each presented hotel option, we computed a similarity score for age by subtracting the participant’s age from the age shown in the review. Analogously, for gender, we created a dichotomous variable that indicated whether the gender of reviewer and participant matched (for details, see Table 4). Note that we used standardized variables in the analysis to compare the coefficients.
TABLE: TABLE 7 Conditional Logit Model on the Relative Influence of Geographic Proximity, Age, and Gender (Study 4)
| | Coefficient | SE | Z-Score |
|---|
| Geographic distance | -.38 | .06 | -6.84 |
| Age | -.47 | .06 | -8.27 |
| Gender | .14 | .05 | 2.88 |
| Pseudo-R2 | | .09 | |
Participants. In total, 702 respondents from the United States (average age = 34 years; 34% female) were recruited with help from MTurk. Again, the distribution of participants’ states matched the actual distribution of the overall population.
Results. The results in Table 7 demonstrate significant influences of distance, age, and gender on the hotel choice. In line with our expectations, we find that the smaller the age difference or the better the gender fit between participant and reviewer, the more likely the participants are to choose the hotel. Interestingly, the influence exerted by distance (b = -.38; z = 6.84, p < .01) is comparable to that exerted by age (b = -.47; z = 8.27, p < .01) and substantially greater than that exerted by gender (b = .14; z = 2.88, p < .01). Furthermore, the perceived homophily with the reviewer of the chosen hotel (M = 4.95, SD = 1.07; p < .01) is significantly greater (t(701) = 18.56, p < .01) than with reviewers of the discarded hotel options (M = 4.24, SD = 1.02; p < .01). These results not only confirm the relationship between geographic proximity and perceived homophily but also demonstrate that distance information is as potent as demographic information. Although age is already used as a key variable for targeting and segmentation in all fields of marketing, we find that geographic location is equally powerful. This finding is of special importance for the practice of marketing because it places distance in the same league with central marketing parameters such as age and gender. Considering that companies can acquire information on location much more easily and cheaply than on demographics for anonymous website users (Crandall et al. 2010; Takhteyev, Gruzd, and Wellman 2012), this result not only implies effective default sorting mechanisms for OCR-based websites but also provides a strong, measurable marketing variable. Given the considerable effort that online retailers typically exert to increase conversion by only a few percentage points, the strong effect of a factor that is usually available and known by the company can considerably affect the business of online retailers and review websites.
Study 5: Value of Geographic Proximity
As the previous studies show, stronger social influence results from higher perceived homophily with geographically close consumers. Feeling more similar to recommenders increases the recipients’ trust that the reviewed product meets their needs and that they will be satisfied with their choice (Schmitt, Skiera, and Van den Bulte 2011). Thus, a geographically close recommendation helps consumers reduce uncertainty in their purchase-decision process (Murray 1991). In the previous studies, the uncertainty was equal for the (dominant) alternatives, and we manipulated only information concerning the reviewer (i.e., geographic proximity, age, and gender). In reality, alternatives are rarely equal, which leads customers to make trade-offs and, thus, raises the question of whether information about geographic proximity can offset differences in uncertainty concerning alternatives (e.g., uncertainty induced by different prices). This question is highly relevant from a managerial perspective because such an effect would enable managers to display the geographic information of OCRs to influence consumer choices directly. For example, a consumer might choose a more expensive hotel offering over an equally rated alternative if the author of the respective OCR is perceived to be more similar than the one reviewing a cheaper hotel based on only geographic distance. Furthermore, the availability of geographically close reviews for a specific product could even be used as an input parameter for the dynamic pricing algorithms that many online retailers use. As more close reviews become available, companies could charge that user a higher price for the product. At the same time, such trade-off decisions reveal how much value a customer assigns to the displayed review from a closer source (compared with a more distant source) and, thus, provide a measurable estimation of the value of geographic distance.
Procedure. To examine whether information about geographic proximity can offset differences in uncertainty concerning alternatives, we altered the choice experiment from Study 2 by adding prices to the hotels in the surprise-vacations.com mobile app display. We retained the previous experimental setting to make the results comparable. Considering the results from Study 2, which showed that the effect of geographic proximity was independent of the concrete combination of distances, we varied the distances on only two options (i.e., close vs. distant). Specifically, participants observed a choice set of hotels at the same destination, with one hotel ostensibly reviewed by a user living 1.2 miles away (close review) and another hotel (with the same star ratings) ostensibly reviewed by a user living 890 miles away (distant review). Again, as a manipulation check, we included a dominated option with worse ratings than the two dominant options.
To test whether participants would still choose the geographically close option despite a higher price, we added price tags to the hotels. Although we varied the price of the hotel reviewed by a geographically distant user between $79 and $99, the prices for the hotel with the OCR in geographic proximity ($99) and the dominated option ($109) were constant. Specifically, we tested prices of $79, $89, and $91–$99 (in increments of two dollars) and varied the generic username and the order of the hotels. Thus, each participant picked a hotel from the choice set and, as in Study 2, indicated the perceived similarity of the reviewers.
In addition, we measured the effect of the price in a control group in which the price difference between the two dominant options varied but no information about the location of the reviewer was displayed. This approach allowed us to compare how many participants chose the more expensive hotel in the cases of both knowing (experimental group) and not knowing (control group) the reviewer’s location. Given that participants must make trade-off lower prices against reviews from someone close to them, the experimental setup enables us to examine the extent to which consumers value the product feature of a close review.
Participants. We conducted the experiment on MTurk using 817 participants from the United States. The number of participants was again based on 27 different conditions with respect to prices and the order of hotels, and we aimed to collect at least 30 participants per condition. The mean age of our sample was 34 years, and 42% of the participants were female. Details on the variables used in the analyses appear in Table 4.
Results. The condition with equal prices in which both the hotel with a close review and the hotel with a distant review cost $99 replicates the findings from Study 2; 78% of the participants chose the hotel with the close review compared with 21% who chose the distant review. Thus, the close review received a significantly higher share than did the distant review (F(1, 51) = 25.44; p < .01). Again, less than 1% of participants chose the dominated hotel. Moreover, perceived homophily is significantly higher (F(1, 51) = 10.29, p < .01) for the geographically close user (M = 4.56, SD = 1.19) than for the distant one (M = 3.99, SD = 1.12). Most importantly, the finding of greater homophily with the geographically close user holds across all price conditions.
With respect to the share of the more expensive hotel, we predict a clear pattern if there is no information about the reviewers’ locations. Although respondents shown equal prices should be indifferent between the options, we expect that all those who experience price differences prefer the less expensive hotel. As one would intuit, the dashed line in Figure 2 declines steeply with differences of a few dollars and remains at a low level of approximately 10%, showing that respondents in the control group presented with equal prices (i.e., price difference = 0) were indeed indifferent between the options (i.e., share = .5). For respondents presented with price differences in the range of $2–$20, 80%–90% chose the less expensive option. Because the respondents in the control group acted as expected, we can exclude confounding factors.
The solid line in Figure 2, however, shows how the share for the more expensive hotel changes with the inclusion of a review written by someone who is geographically close. Specifically, the share of participants who chose the more expensive hotel with a geographically close review does not decline as steeply as that of the control group. That is, a substantial difference between the two lines shows the uplift caused by a geographically close review. The difference not only demonstrates that information on geographic proximity offsets the higher uncertainty of an alternative but can also be interpreted as the willingness to pay (WTP) for a hotel recommended by someone geographically close. Up to an 8% price difference ($8), the share of the close review remains approximately 50%. Surprisingly, even at price differences of 10%, a substantial share of participants would rather choose a hotel reviewed by someone living geographically close an equally rated, cheaper option. At a 20% difference ($20), the share received by the distant review is equivalent to the share received by the close review in the equal price condition (approximately 70%). Thus, the social influence resulting merely from geographic proximity is so strong that consumers are willing to accept higher prices to decrease their uncertainty.
To relate the findings to our theoretical argument, we also examined how this effect develops conditional on the level of perceived homophily. To do so, we estimated a logit regression of the price difference for the expensive hotel with a distant review together with the difference in homophily between the close and the distant reviewers on the choice of the more expensive hotel. The results displayed in Table 8 indicate that a price difference has a negative effect (b = -.09; z = -4.37, p < .01) and that the difference in homophily positively affects the choice of the more expensive hotel (b = .81; z = 6.34, p < .01). More interestingly, we also find a significant positive interaction between price and homophily (b = .05; z = 1.99, p < .05), which shows that the negative effect of price is weaker because perceived homophily with the close reviewer increases compared with the distant reviewer. Thus, in line with our theoretical argumentation, appreciation for a close review increases with the effect of geographic proximity on perceived homophily. This result further supports the previous results concerning the role of homophily as a mediator between geographic proximity and the strength of social influence. The results not only hold across all price levels but also show that perceived homophily exerts social influence, which also explains why consumers are willing to accept higher prices. Given that consumers accept higher prices for a product reviewed by a geographically close customer, the results from Study 5 emphasize that the social influence induced by geographic proximity and perceived homophily found in Studies 1–4 produces highly relevant managerial implications.
General Discussion
Drawing on the findings of a series of studies, this research contributes to marketing theory and practice by providing several important insights into how geographic proximity, perceived homophily, and social influence are interconnected. Specifically, the results of five studies demonstrate that not only actual homophily (or social proximity) but also perceived homophily triggers social influence. In experimental studies, we find that this effect holds under different representations of geographic distance and even when alternative indicators of homophily (such as age and gender) are presented. Furthermore, we show that geographic proximity has a relative effect because the social influence from a closer sender is stronger than that of a more distant sender regardless of the absolute distances and that geographic proximity provides sufficient informational value for customers to offset differences between alternatives (e.g., higher prices) in trade-off decisions. We can derive concrete implications because the observed effect of perceived homophily is confirmed for a range of services and physical goods.
Managerial Implications
Because the availability of consumers’ geographic information is growing (Crandall et al. 2010; Takhteyev, Gruzd, and Wellman 2012), geo-marketing is becoming increasingly important (Luo et al. 2013; Xu et al. 2011). The results of our studies yield important insights to help companies actively manage social influence between consumers using geographic information and, thus, affect customer behavior.
TABLE: TABLE 8 Logit Model on the Influence of Price and Homophily (Study 5)
| | Model 1 | Model 2 |
|---|
| | Coefficient | SE | Z-Score | Coefficient | SE | Z-Score |
|---|
| Price | -.09 | .02 | -4.37 | -.12 | .03 | -4.56 |
| Homophily | .81 | .13 | 6.34 | .49 | .19 | 2.62 |
| Price · Homophily | | | | .05 | .02 | 1.99 |
| Intercept | .89 | .18 | .49 | .26 | .20 | 1.32 |
| Pseudo-R2 | | .13 | | | .14 | |
The finding with the most basic practical relevance is likely that geographic proximity offsets obvious differences among alternative offers. Geographic proximity is not the only type of information to induce perceived homophily and, consequently, social influence (as well as age and gender; see the comparison in Study 4), but it appears to be another valuable dimension that can be used. The results from Study 5 demonstrate the importance of reducing consumers’ uncertainty in the purchase process. Increasing perceived homophily by providing information about the reviewers (e.g., their location) can outweigh obvious differences in offerings and increase the conversion rates of online shops or websites. We show that the dimension of geographic distance is stronger than gender and comparable in strength to age, a key variable for targeting and segmentation in all fields of marketing. Considering that it is much easier for companies to acquire information on the location than on the demographic characteristics of anonymous website users (Crandall et al. 2010; Takhteyev, Gruzd, and Wellman 2012), this finding provides a strong, measurable marketing variable.
Our finding that geographic proximity leads to stronger social influence has its most obvious application in the context of electronic and mobile commerce. Given that consumers seem to rely more heavily on geographically close consumers and consider their recommendations more helpful, companies could sort OCRs so that those from geographically close users are displayed first. Unlike age and gender information, which must be provided or disclosed by users, the geographic locations of online and mobile prospects can be easily and objectively tracked (e.g., through cookies or global positioning system tracking on smartphones). Therefore, customized sorting based on geographic proximity could easily be implemented on a technical level and would lead to more helpful recommendations. In this case, OCRs would not be displayed in the same order for every user (e.g., first listing those rated most helpful by other users); instead, helpfulness would be considered individually depending on geographic location. By implementing such an individually tailored order, companies can improve conversion, and customers will be better able to find products that suit their needs. If additional demographic information were available, a combination of geographic location and variables such as age or gender would result in an even more powerful sorting mechanism.
Beyond the obvious context of online shopping, the findings might be applicable in the fast-growing industry of social network advertising to increase the effectiveness of ads. In online social networks, such as Facebook, Google+, or Twitter, users’ geographic locations are typically available and can be used to target social ads (i.e., ads that show Internet users products or services that their contacts like, follow, or use). Our results imply that advertising based on contacts that live in geographic proximity to the user (e.g., “John Doe likes Company XYZ”) would be more influential than advertising based on someone who is geographically distant. In this context, that the higher influence of geographic proximity works in both the presence and absence of actual social ties is highly relevant. In other words, companies that use location-based services need not possess a large amount of information about the actual relationship or tie strength between two consumers, which is often unavailable to companies. Instead, geographic proximity can be used to induce feelings of homophily between consumers, a particularly interesting phenomenon with respect to the increasing number of location-based services and smartphone applications that extensively use geographic data but do not possess direct social network information about consumers.
Theoretical Implications
The findings in this article also yield several contributions to the marketing literature. First, this article extends previous studies on the effect of geographic proximity (e.g., Barrot et al. 2008; Bell and Song 2007; Garber et al. 2004; Strang and Tuma 1993) by explicitly incorporating social network data into the analysis (Study 1) and by conducting individual controlled experiments (Studies 2–5). In doing so, we not only show that geographic proximity actually matters to the strength of social influence but also examine the social network mechanisms that explain when and why geographic proximity matters. Specifically, this study demonstrates that the influence of geographic proximity is not merely a result of propinquity (Festinger et al. 1963; Mok, Wellman, and Carrasco 2010; Preciado et al. 2012). We contribute to the literature by showing that the effect of geographic proximity holds independent of the propinquity effect; indeed, the effect of geographic proximity increases as ties weaken.
We further contribute to the literature by showing that geographic proximity increases the strength of social influence even in the complete absence of social ties and that the reason behind this strength is a perceived homophily mechanism. Our findings experimentally demonstrate that geographic proximity leads to higher perceived homophily that, in turn, leads to stronger social influence. That is, consumers use geographic proximity as a signal of homophily to assess whether they should follow a recommendation. As we show, this effect is sufficiently strong that consumers are willing to accept higher prices for the uncertainty reduction of geographic proximity. These findings are not only relevant to the literature on geographic proximity but also extend the literature on homophily by including a geographic dimension and presenting the relationship between homophily and geographic location as part of consumers’ social identity.
Furthermore, the results show that the effect of geographic proximity can be considered a relative one. That is, social influence is stronger when a sender is geographically closer than when the sender is geographically distant―regardless of the absolute distances or geographic ranges of the two senders. This effect is an important extension of existing studies of the effect of geographic proximity on social influence and word of mouth. Thus far, research has primarily considered the importance of individuals living close to one another, which indirectly facilitates the probability of a social influence between them (because of the likelihood of communication). However, the absolute level of geographic proximity is not the only one that matters; as we show, relatively proximate consumers also have a stronger social influence (a direct effect). In absolute terms, people living very close to one another can experience a dual, reinforcing effect through the higher likelihood and strength of social influence between them.
Finally, we analyze the conditions under which the effect of geographic proximity becomes larger or smaller. Specifically, we show that decreasing tie strength leads to an increasing effect of geographic proximity, which emphasizes that geographic proximity itself is employed to evaluate and assess a recommendation and determine whether it should be followed. In addition, we find that the results hold in spite of varying measures of geographic proximity, indicating that a customer not only infers geographic proximity from distances but also develops perceived homophily from actual locations.
Limitations and Avenues for Further Research
Our research is not without limitations that yield promising avenues for further research. First, the data used in the descriptive Study 1 had limits. Similar to previous studies (e.g., Nitzan and Libai 2011; Onnela et al. 2007), we had to assume that the customers’ social networks are stable before and after adoption. In addition, we did not have information on either the valence of the signals from the social network or the location of nonadopters. Therefore, we were not able to account for them in the hazard model.
Second, we examine the effect of geographic proximity and the mediation of homophily using a set of experimental studies. Although the results of Study 1 point in the same direction, future research studies based on field data could extend our findings to even more product categories.
Third, Studies 2–5 show that online reviews written by geographically close users are more influential and that customers are willing to accept higher prices for these reviewed products. In our analyses, we use experimental designs to demonstrate the effect of geographic proximity on social influence by excluding potential confounders to ensure high internal validity. Given that the reviewer’s location is a driver of social influence on others, analyzing the importance of geographic proximity relative to other drivers of social influence (other than age and gender) and the interplay of geographic proximity with review traits, such as review content or photos, could be a worthwhile topic for future studies. Considering traits in addition to mere distance could also help rule out potential demand effects that might be created by the design of our experiments.
Fourth, in Study 5, we analyze the trade-off between prices and geographically close OCRs. Although the results show that consumers are indeed willing to accept higher prices for reduced uncertainty with the help of an OCR by someone geographically close, the experimental setup does not allow us to derive a generalizable WTP or to determine whether this WTP is an absolute or a relative amount compared with a cheaper product being rated by someone geographically distant.
Fifth, we chose an experimental design that enabled us to identify the influence of geographic information while controlling for confounding effects. Although the results demonstrate the influence of geographic proximity for a realistic case of individual-level customer ratings, a multitude of setups for OCRs exist in reality for which the results might differ. Although the validity of the results should remain unchanged, investigating the role of geographic information in different settings, for instance, with reviews containing textual information or multiple layers of sorting (aggregated ratings first, then individual-level reviews), would be worthwhile.
Finally, we study the effect of geographic proximity in two countries (a large European country and the United States) that differ with respect to the average spatial distances between cities or regions but are culturally relatively similar (Hofstede 2001). Thus, a valuable avenue for further research could be to study how the role of geographic proximity differs across countries for either geographic or cultural reasons.
TABLE: APPENDIX A Descriptive Measures and Correlations
| Variable | M | SD | Mdn | Min | Max | VIF | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|
| 1. Exposureit | .50 | .84 | 0 | 0 | 60 | 3.76 | 1.00 | | | | | | | | | |
| 2. GeographicProximityit | .31 | .63 | 0 | 0 | 15 | 3.98 | .80 | 1.00 | | | | | | | | |
| 3. NetworkOverlapit | .04 | .11 | 0 | 0 | 3.42 | 1.95 | .68 | .63 | 1.00 | | | | | | | |
| 4. TieStrengthit | .07 | .18 | 0 | 0 | 1 | 1.64 | .53 | .48 | .38 | 1.00 | | | | | | |
| 5. Householdit | .13 | .34 | 0 | 0 | 1 | 1.87 | .48 | .66 | .41 | .38 | 1.00 | | | | | |
| 6. Agei | 41.37 | 13.73 | 42 | 11 | 108 | 1.06 | -.07 | -.03 | -.02 | .02 | .04 | 1.00 | | | | |
| 7. Genderi | .42 | .49 | 0 | 0 | 1 | 1.01 | .07 | .06 | .05 | .08 | .05 | -.04 | 1.00 | | | |
| 8. NetworkDegreei | 29.45 | 32.99 | 21 | 0 | 1924 | 1.14 | .16 | .06 | .05 | -.07 | -.02 | -.20 | -.02 | 1.00 | | |
| 9. LocalPenetrationit | 3.59 | 3.12 | 3.37 | .17 | 284.77 | 1.04 | .10 | .08 | .06 | .06 | .03 | -.04 | .01 | -.12 | 1.00 | |
| 10. PurchasingPoweri | 16.74 | 2.62 | 16.68 | 8.95 | 36.47 | 1.00 | -.03 | -.03 | -.02 | -.02 | -.01 | .03 | .01 | .00 | -.01 | 1.00 |
| 11. Advertisingit | 7.20 | 5.27 | 5.48 | 1.49 | 56.99 | 1.01 | .01 | .00 | .00 | -.01 | -.01 | .00 | -.01 | .01 | -.01 | .02 |
Appendix B: Scenario Details of Studies 2–6
Imagine that you are browsing on your smartphone to book hotels for your next vacation at a destination of your choice. For this, you are using a mobile app that is called surprisevacations.com. The app offers hotels via “blind booking” (similar to hotwire.com). This means that you do not know the name of the hotel before booking, but you receive high discounts on your hotel stay.
Imagine that in the booking process you have indicated a category (e.g., 4 stars) and your desired destination. Afterward, the app shows you a set of different hotels that you can choose from. All information are hidden that would allow you to infer the name of the hotel.
In the following, you are shown the set of hotels and you are asked to indicate which hotel you would most likely choose for your next vacations.
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APPENDIX D
Example of Choice Set for Study 3
APPENDIX C
Example of Choice Set in Blind Booking Mobile App
APPENDIX A
Descriptive Measures and Correlations
Note: VIF = variance inflation factor. N = 509,191. Time-varying variables are measured at time of adoption.
TABLE 8
Logit Model on the Influence of Price and Homophily (Study 5)
FIGURE 2
Choice of More Expensive Hotel With and Without Close Review Depending on Price Difference
Notes: The control group was the choice of the more expensive hotel without reviewers’ locations.
TABLE 7
Conditional Logit Model on the Relative Influence of Geographic Proximity, Age, and Gender (Study 4)
Notes: Variables are standardized to have mean of 0 and standard deviation of 1. N = 702. Interaction effects were not significant.
TABLE 5
Conditional Logit Model on the Influence of Geographic Proximity (Study 2)
TABLE 6
Conditional Logit Model on the Influence of Geographic (Same-State) Location (Study 3)
TABLE 4
Descriptive Statistics for the Experimental Studies
aBased on a seven-point rating scale.
bDistance between reviewer and participant (in miles).
cDifference in age between reviewer and participant (in years).
dMatch in gender between reviewer and participant (1 = yes).
eDifference in price/homophily between close and far hotel.
2The deviation from the exact number of participants we aimed for is because some MTurk users took the survey but failed to confirm the completed task. For this reason, in Studies 2–3, we have a few more participants than intended.
1In previous research, both online ratings and reviews were subsumed under the term OCR. Because ratings only provide a more structured way to review products or services, we use rating/review and rater/reviewer correspondently.
TABLE 3
Hazard Regression Results on the Strength of Adoption Drivers
TABLE 2
Operationalization of Variables
TABLE 1
Review of Research Investigating Geographic Proximity and Social Influence in the Digital Age
FIGURE 1
Overview of Studies
GRAPH
GRAPH
PHOTO (COLOR)
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Record: 199- The Role of the Partner Brand’s Social Media Power in Brand Alliances. By: Kupfer, Ann-Kristin; vor der Holte, Nora Pähler; Kübler, Raoul V.; Hennig-Thurau, Thorsten. Journal of Marketing. May2018, Vol. 82 Issue 3, p25-44. 20p. 1 Diagram, 4 Charts. DOI: 10.1509/jm.15.0536.
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- Business Source Complete
The Role of the Partner Brand’s Social Media Power in Brand Alliances
Managers frequently seek strategies to profit systematically from social media to increase product sales. By forming a brand alliance, they can acquire an installed social media base from a partner brand in an attempt to boost the sales of their composite products. Drawing from power theory, this article develops a conceptual model of the influence of the social media power of partner brands on brand alliance success. The proposed framework details the partner brand’s social media power potential (size and activity of the social media network), social media power exertion (different posting behaviors and comments), and their interaction. The authors test this framework with an extensive data set from the film industry, in which films function as composite products and actors represent partner brands. The data set features 442 movies, including 1,318 actor–movie combinations and weekly social media data (including 41,547 coded Facebook posts). The authors apply a linear mixed-effects model, in which they account for endogeneity concerns. The partner brand’s social media power potential, power exertion, and their interaction can all lead to higher composite product sales. By coding different types of product-related posts, this article provides estimates of their varying monetary value.
Keywords: social media, content marketing, influencer marketing, movie stars, entertainment marketing Online Supplement: http://dx.doi.org/10.1509/jm.15.0536 The enormous growth of social media demands that managers and scholars understand how it influences the effectiveness of marketing strategies (e.g., HennigThurau, Hofacker, and Bloching 2013). Managers dedicate vast resources to building their own brand presence on social media platforms (Pitney Bowes 2012), and scholarly insights suggest that a brand’s social media–related activities can positively affect its performance (e.g., Saboo, Kumar, and Ramani 2016). However, it is unknown ( 1) whether brands can strategically harvest the social media networks of other brands, such as actors, athletes, and other types of influencers, and ( 2) which social media activities by those other brands would then be particularly promising for selling products. Obtaining richer insights into those strategy–performance links represents a key priority.
One constellation in which the strategic use of another brand’s social media resources appears particularly well-suited is a brand alliance with a partner brand, wherein two or more brands combine to develop composite offerings. Such brand alliances are common in todays’ brandscape; examples include McDonald’s McFlurry ice cream featuring Oreos and Terminator movies emphasizing the participation of Arnold Schwarzenegger as a human brand. Traditionally, when forming a brand alliance, partner brands are selected for their expertise, such that they function as quality signals for consumers (Rao, Qu, and Ruekert 1999; Rao and Ruekert 1994). However, with the widespread use of social media platforms today, the potential to profit not only from partner brands’ expertise but also from their social media presence might offer an additional reason to build brand alliances. However, the contributions of such social media effects—and thus, their valuation—are yet unclear. Whereas Disney’s president of production rejoiced at the “unexpected byproduct” when Emma Watson’s personal social media accounts triggered many trailer views of the Beauty and the Beast remake (Fleming 2017), the same social media power did not save her next movie, The Circle, from becoming a major flop (D’Alessandro 2017).
We investigate these contributions by offering a model based on power theory (e.g., Bacharach and Lawler 1980), which identifies various sources of social influence (French and Raven 1959). In addition to the power arising from expertise, it suggests the existence of a referent power base, among others. Here, power results from offering strong identification potential to others who seek a close association with that source. Social media might boost this form of power of partner brands over consumers by enabling them to interact with consumers in direct, personal, and reciprocal ways (Labrecque 2014). A brand’s social media presence can grant customers the feeling of knowing the brand intimately, which may enhance their sense of identification with the brand. We thus argue that by strengthening the referent power base, social media gives partner brands a new opportunity to encourage consumers to buy the composite product, which differs from their function as quality signals.
The contribution of the strategic use of this social connection to composite product sales cannot, however, be determined by theoretical considerations alone, something that particularly applies to its relative role compared with the many other success drivers of composite products that have been identified (e.g., Simonin and Ruth 1998). Things get even more complicated when considering potential negative effects from the partner’s social media power. Advertising-like communication in this social environment might trigger reactance and, consequently, hurt the composite product’s performance. Moreover, what about those posts by the partner that do not pertain to the composite product? A related issue of importance for managers of brand alliances is the effectiveness of different social media strategies: What kinds of partner posts work best? Which do not work at all, or might even drive customers away from the composite product? Does it pay to be authentic or offer exclusive insights? Do persuasive appeals on social media by partner brands to purchase the composite product mobilize followers or repel them?
We address these intriguing research questions and inconclusive industry examples with this research and offer empirical insights. Our power theory–inspired model links the social media power of partner brands over consumers to brand alliance performance, distinguishing between a partner brand’s social media power potential (i.e., the access of the partner brand to a large and active social media network) and its social media power exertion (i.e., different communication forms with which the partner brand actively addresses this network). We test our model in the movie industry, in which films represent composite products that combine movie brands as the host, and movie stars as the partner brands (Luo et al. 2010). Using weekly data about 442 movies featuring 1,318 actor–movie combinations, we link the actors’ social media data with films’ actual financial performance in theaters.1 We analyze this longitudinal set of social media and sales data in a linear mixed-effects model, using instruments and extensive controls to account for the possible endogenous nature of social media activities and supply-side variables.
Our results make four contributions to research and practice. First, we contribute to the emerging literature on the value of social media by showing the incremental monetary value of the externally acquired social media presence of a partner brand. This social media power denotes a conceptually unique brand resource that exists beyond the brand’s expert power and its traditional ways of promoting the composite product. Contrary to the often-expressed perception of social media’s low effectiveness (The CMO Survey 2017), we can show its sizable economic value for selling composite products, both in absolute and relative terms.
Second, we analyze what determines social media’s economic value by comparing the effectiveness of its facets. By applying established power theory to the emerging stream of social media research, we introduce the conceptual and empirical distinction between social media power potential, social media power exertion, and their interaction. We find all of them to be significantly linked to brand alliance success—namely, the sales of the composite product. The most powerful social media facet is the partner brand’s product-related social media communication, especially if sent to a sizable and active social media fan base. Interestingly, non-product-related posts are associated with a significant decrease in composite product sales, suggesting a distraction effect.
Third, we contribute to social media research by being the first to link different social media posting strategies to actual sales data to determine which posting behaviors offer the highest monetary value. Persuasive product-related posts are associated with the greatest monetary value in our data, counter to the prevalent perception that such a communication style repels followers. We find that sending exclusive and authentic product-related posts are also promising approaches for increasing the composite product’s financial performance.
Fourth, we study boundary conditions and derive actionable implications for implementing the social media power of partner brands. Concerning boundaries, we find the partner brands’ social media power to be limited to the most central partner brand (i.e., the lead actor instead of the supporting cast) and the most visible communication form (i.e., posts instead of replies). Concerning actionable recommendations for social media managers, we offer specific guidelines for how to select and manage the social media power of partner brands for brand alliances.
The emergence of social media has changed the ways consumers communicate and bond with one another and with brands (Hennig-Thurau et al. 2010). Their interactive, real-time nature enables consumer–brand relationships to evolve, marked by direct exchanges, intimate connections, and parasocial relationships (Labrecque 2014). Paralleling the rapid rise of social media, marketing research has investigated its strategy– performance link (e.g., Kumar et al. 2013). Srinivasan, Rutz, and Pauwels (2016) establish a significant link between a brand’s Facebook likes and sales. Kumar et al. (2016) affirm that firm-generated social media content affects customer behavior. Mochon et al. (2017) find firm-solicited page likes to influence customer offline behavior, thereby highlighting the need to send those followers firm-initiated promotional communications. Saboo, Kumar, and Ramani (2016) show an increase in a musician’s sales when more consumers follow him, comment on his page, or sample his products.
The economic value of firms’ activities on social media largely results from consumers’ sense of belonging to a community (Algesheimer, Dholakia, and Herrmann 2005). Manchanda, Packard, and Pattabhiramaiah (2015) attribute estimated sales increases to consumers who are more active and have more social ties in a community. Rishika et al. (2013) similarly find an economic effect from consumers who participate in firm-hosted social media sites, which increases with more social media activity. However, Algesheimer et al. (2010) show that firms’ strategic efforts to increase consumers’ participation can backfire, resulting in decreased consumer spending on the corresponding platform.
Thus, we must differentiate the various social media activities to determine their effectiveness. For example, De Vries, Gensler, and Leeflang (2012) find that vivid, interactive posts yield strong levels of consumer engagement; Stephen, Sciandra, and Inman (2015) also show that a post’s content characteristics (e.g., relevance, message clarity, tone) influence engagement. Akpinar and Berger (2017) find that emotional appeals in social media advertising are more effective for fostering shares, but informational appeals are better at increasing brand evaluations and purchase intentions. Similarly, Lee, Hosanagar, and Nair (2017) find that brand personality–related posts increase engagement, but informational posts increase clicks on referenced external websites. Despite these insights, an important question remains unanswered: How do different posting behaviors relate to actual sales?
In brand alliance contexts, consumers confront two or more brands that jointly produce a composite product (Park, Jun, and Shocker 1996). Brand alliance studies often try to understand how integration of partner brands influences consumer perceptions (e.g., Desai and Keller 2002). A strong partner brand can signal quality and improve consumer evaluations of the composite product (Rao and Ruekert 1994). Simonin and Ruth (1998) note that consumers’ preexisting attitudes toward individual brands and the level of fit between them drive their evaluations of a brand alliance. Thus, to increase their chances of success, host brand managers need to identify appropriate partner brands when building brand alliances (Venkatesh and Mahajan 1997). We propose that a partner brand’s social media power is pertinent to such selections because of its likely influence on the success of the composite product.
To address the role of social media power of partner brands in brand alliances, we draw from power theory, consistent with widespread applications of power concepts in marketing strategy and organizational theory (e.g., Gaski 1984; Homburg, Jensen, and Krohmer 2008; Mintzberg 1983), as well as political science, sociology, and social psychology (e.g., French and Raven 1959). Power is someone’s ability to prompt another person to do what (s)he would not have done otherwise (Dahl 1957).
According to French and Raven’s (1959) seminal work, such power is based on specific sources, called “power bases,” such as the expert power base and the referent power base. An expert power base implies that someone is powerful because (s)he appears particularly knowledgeable or skillful in a given area. For instance, Intel’s power is based on its ability to produce high-quality processors. In the movie industry, an actor’s great acting skills or physical attractiveness constitute forms of expertise that may lead consumers to watch a movie featuring that actor. When forming a brand alliance, partner brands traditionally have been chosen for this expert power base—an observation consistent with general brand management literature, which shows how partner brands function mainly as quality signals (Rao and Ruekert 1994).
However, social media also implies the relevance of a different power base for selecting partner brands, called referent power base. A referent power base exists if someone or something offers strong identification potential to others that desire to be closely related or intimately connected with it (French and Raven 1959). Celebrities such as Kim Kardashian are influential less because of their expertise in a specific field and more because people strongly identify with and aim to be like them. Social media is a central tool for this referent power base, giving brands such as celebrities a new platform for building relationships with fans by offering a glimpse into their lives and addressing them directly. By fostering such personal bonds with consumers, social media increases the identification potential of brands, which adds to the power of the brand.
Marketing scholars have noted the general desire of consumers for identification and closeness with brands (for human brands, see Thomson 2006; for general concept of “consumer–brand relationship quality,” see Fournier 1998). In the predigital era, consumer–brand relationships were one-sided or “parasocial” interactions that prohibited mutual exchange (Horton and Wohl 1956). Yet social media reduces the perceived distance between brands and consumers, such that relationships are (or appear to be) two-sided, intimate, and close (Labrecque 2014). This increased identification through social media strengthens a brand’s referent power base, which should result in an influence over fans’ consumption behavior, making it a relevant resource for marketers.
French and Raven (1959) stress that power is rarely limited to one source, and we assume that expert and referent power bases coexist in brand alliances. Partner brands can be recognized for their skills and talent (expert power base) but also for their social closeness, established through social media relationships (referent power base). Such social media power can thus act as a unique brand resource that partner brands might strategically leverage beyond their expert power, to the advantage of their (co)branded products.
Our conceptual model builds on power theory’s three key concepts: ( 1) power potential and ( 2) power exertion, which then determine the ( 3) power outcome (Bacharach and Lawler 1980; Frazier 1983). Power potential reflects a structural position (Wrong 1968). For example, a central position in a network represents a power potential because it grants access to and potential control over valuable resources (Brass and Burkhardt 1993). We apply the power potential concept to the context of social media, defining the social media power potential of a brand as the position that an entity (here, the partner brand) has achieved within a social media network. In turn, we distinguish two forms of social media power potential: the size and the activity level of the social media network of the partner brand. In our study context, having built a large social media network grants a partner brand access to a sizable pool of potential customers, and an active social media network grants the partner brand access to engaged promoters. For example, Vin Diesel has managed to accumulate more than 100 million prospective moviegoers who follow him on Facebook. His fan base is also highly active; his followers act as recommenders by sharing his posts, beyond his own network.
Power exertion generally refers to the actual use of power, which requires some expenditure of energy by the powerful person (Mintzberg 1983); for example, by requesting specific actions by subordinates (Brass and Burkhardt 1993). We define social media power exertion as the actual behavior by an entity (partner brand) of addressing its social media network. Forms of social media power exertion include posts on a social media wall or responsive comments to members of a social media network. In our study context, a partner brand might exert its social media power by informing the network about a new product or asking them to buy it, such as when Vin Diesel posted photos from the set of the XXX movie on his Facebook page, thereby actively sending product-related information to his social media followers.
In our conceptual brand alliance model in Figure 1, we link a partner brand’s social media power (potential and exertion) with the success of a new composite product that features both host and partner brands. The partner brand’s social media power potential and power exertion should generate power outcomes, manifested as the increased success of the brand alliance (i.e., additional sales of the composite product). Furthermore, we stress the relevance of their interaction, such that social media power potential and power exertion should amplify each other. To extend this general social media power model, we distinguish types of social media power potential and exertion. To specify the incremental impact of the partner brand’s social media power, we also consider a set of brand alliance factors, encompassing host brand factors (e.g., host brand type, its social media network size) and partner brand factors (e.g., traditional partner brand strength, traditional partner brand promotions).
Social media power potential. Greater social media power potential should be positively associated with power outcomes. Power can have an effect, even without being explicitly used (Wrong 1968). The presence of a professor, even if (s)he takes no specific action, can quiet a room of students. Students may sit down, which they anticipate will please the powerful professor, without requiring a direct request (e.g., Brass and Burkhardt 1993). This effect is well established for formal positions of power, but less certain for informal power (Mintzberg 1983).
In the context of social media, a bigger network of fans and followers enhances the focal brand’s sales (Srinivasan, Rutz, and Pauwels 2016). Furthermore, more active consumers (“posters”) within a social brand community particularly contribute to increasing the focal brand’s sales (Manchanda, Packard, and Pattabhiramaiah 2015). We investigate whether this positive effect of social media power potential, derived from the referent power base, can be harnessed by another (host) brand that offers a composite product together with the powerful partner brand. The effect might stem from two key facets of social media power potential: the size of the social media network of the partner brand (i.e., number of fans) and the activity level of the social media network of the partner brand (i.e., fans’ sharing activity). Consuming a new product (e.g., movie) that features the partner brand (e.g., star) can appease longing for the partner brand and comply with consumers’ wish to “please” this partner brand. This effect should hold, to some extent, even if the partner brand does not explicitly refer to the product on social media. We thus offer hypotheses for both facets of social media power potential:
H1: The (a) size and (b) activity levels of the social media network of the partner brand relate positively to the sales of the composite product.
Social media power exertion. Power researchers emphasize the relevance of power exertion (Mintzberg 1983), in that certain behaviors, such as assertive communication, create perceptions of power (Brass and Burkhardt 1993). In a social media context, Kumar et al. (2016) uncover a significant link between the amount of firm-generated social media communication on a brand’s official pages and the brand’s sales.
We transfer this link to brand alliances, proposing that it holds for constellations of a partner brand’s social media activities and a host brand’s financial performance, even though such an effect would require substantial spillover from the partner brand to the composite product, which must exert an influence in addition to many other factors in a brand alliance. Based on our theoretical power model, we argue that every post shared by the partner brand should strengthen its referent power, by making fans feel closer and more intimately connected to it.
Direct communications with consumers and posting regular updates, such as pictures and general news about the partner brand, can all enhance intimate perceptions of closeness with the brand, which is positively associated with consumers’ evaluations of connected products (Gong and Li 2017; Hung, Chan, and Tse 2011). When Vin Diesel posts behind-the-scenes footage from a film, replies to fans, or shares intimate photos of him with his daughter, it all likely fosters his identification potential for fans, increasing their desire for him and for products connected with him. This strengthening of his referent power base through posts that involve the partner brand should consequently encourage fans to buy composite products featuring the partner brand.
We expect this effect to result from various kinds of social media power exertion—namely, postings about the brand alliance (product-related posts), replies to fans’ comments (responsive comments), and general postings about/by the partner brand that do not pertain to the composite product (nonproduct-related posts). Even if a partner brand’s post is not linked to the composite product, it should positively affect the latter’s success by strengthening consumers’ perceptions of intimacy and connectedness with the partner brand that is part of the composite, but we concede that the effect might be weaker in this case. We thus predict that they are positively associated with brand alliance success as the ultimate power outcome:
H2: The number of (a) product-related posts, (b) responsive comments, and (c) non-product-related posts of the partner brand relate positively to the sales of the composite product.
Interaction effects. Power literature has suggested that the interplay of power potential and power exertion produces the strongest power outcomes (Mintzberg 1983). Consistent with this logic, Mochon et al. (2017) show that Facebook likes are most effective when addressed by firm-initiated promotional communication. Social media power potential and exertion thus may have an interaction effect on power outcomes, beyond their isolated effects, such that combinations of high values of both variables contribute to greater success. The effectiveness of social media power exertion efforts should be systematically higher if the social media power potential is also high, in terms of both network size and activity level. Accordingly, we offer a third hypothesis, which we limit for parsimony to product-related posts:
H3: (a) The larger the size of the social media network of the partner brand and (b) the higher the activity level of the social media network of the partner brand, the stronger the positive association of product-related posts with composite product sales.
Findings by power scholars have suggested that power outcomes vary with the “skillfulness” of power exertion (Mintzberg 1983). Transferring this into the context of partner brands’ social media power, we expect the respective effect sizes of different types of power exertion to differ. Consistent with this logic, social media research has found that different social media posting strategies result in varying levels of fan engagement (DeVries, Gensler, and Leeflang 2012; Lee, Hosanagar, and Nair 2017). Specifically, we investigate three types of a partner brand’s social media power exertion: authentic, exclusive, and persuasive product-related posts.
Authentic social media power exertion. A brand is considered authentic if consumers perceive it to be faithful to itself and true to its fans (Morhart et al. 2015). This perception can be conveyed through a brand’s communication style, by cues that express the brand’s sincere motivation and care for consumers (Morhart et al. 2015). Authentic communication enhances relationships between human brands and consumers, increasing emotional brand attachment and brand choice likelihood (Morhart et al. 2015; Thomson 2006). We are not aware of any empirical research into the value of authentic social media communication, but several scholars have claimed that authenticity is a positive characteristic of social media exchanges (e.g., Hennig-Thurau, Hofacker, and Bloching 2013) and call for research on this topic (Morhart et al. 2015). Practitioners similarly stress the concept’s importance, such as when the chief marketing officer of Paramount Pictures praised Emma Watson for her communication style: “When she speaks to her fans, it’s authentic. She is incredibly tuned in to them with honest dialogue and conversation” (Busch 2014). Such authentic communication should enable consumers to infer that the brand, as a relational partner, is sincere and real (Giles 2002), offering increased identification potential. The referent power base thus might be exercised more effectively than is the case with other product-related posts.
H4: The number of the partner brand’s authentic product-related posts has a higher positive association with sales of the composite product than the number of other product-related posts.
Exclusive social media power exertion. A resource is exclusive if it is available only to a limited audience (Barone and Roy 2010); exclusiveness is an attribute that consumers generally value (Balachander and Stock 2009). Sharing information that is unknown to others can enhance relationships, particularly if the recipient attributes the disclosure of this exclusive information to the notion that (s)he is special (e.g., especially trustworthy; Collins and Miller 1994). Such exclusiveness should influence the effectiveness of social media posts; an example is when Vin Diesel released a Fast Five trailer exclusively to his social media followers (“before everyone else gets it”) and cited this exclusiveness as evidence of “respecting the true fans.” Being among a chosen group of people who see content “first” should create a feeling of being special and appreciated by the partner brand. In turn, the social relationship with the partner brand, and thus the influence drawn from its referent power base, should be stronger than it would be for other product-related social media posts.
H5: The number of the partner brand’s exclusive product-related posts has a higher positive association with sales of the composite product than the number of other product-related posts.
Persuasive social media power exertion. Direct requests and other types of assertive behavior are influential ways to exert power (Kipnis, Schmidt, and Wilkinson 1980). In a goaldirected form of power exertion, a partner brand can explicitly ask or persuade followers to act on its wishes. Some of these strategic appeals can be disillusioning for fans (Alperstein 1991), leading to negative forms of reaction such as reactance. However, they can also be activating and result in stronger mobilization of the social media network, evoking positive outcomes. Persuasive communication is also practiced by partner brands for brand alliances on social media, generally including explicit appeals to buy the composite product. For example, movie star Channing Tatum commanded his network to watch his movie, announcing, “There’s a #MagicMikeXXL ticket with your name on it. Grab yours … NOW!” We regard such persuasive product-related posts as the most goal-directed type of the partner’s social media power exertion, directly aimed at influencing the network’s behavior toward the composite product. Therefore, we expect such posts to result in above-average goal-directed activation of the brand’s social media power potential, outweighing possible consumer reactance in terms of immediate performance outcome. Overall, we predict persuasiveness to increase the value of social media power for the financial performance of the composite product, more so than other product-related social media posts.
H6: The number of the partner brand’s persuasive product-related posts has a higher positive association with sales of the composite product than the number of other product-related posts.
We test our hypotheses in the motion picture industry, in which each new movie constitutes a brand alliance (Luo et al. 2010). A movie is a composite product that combines the movie brand (host brand) and the actors as branded human ingredients (partner brands). Actors accumulate many fans; experts recommend them as role models for other brands for social media marketing (Seetharaman 2015).
Our data set covers all movies released in North American theaters between 2012 and 2014, with pre- and postrelease observations spanning from September 2011 to June 2015. After excluding specialty releases, productions from nonEnglish-speaking countries, animated movies, and documentaries,2 we use 442 movies in our analyses, combined with the actors credited first, second, and third on Box Office Mojo as partner brands, resulting in a total of 1,318 actor–movie combinations.3
For each of those actors in our data set who had a Facebook brand page or profile during (parts of) September 2011–June 2015, we collected extensive social media data about the number and content of posts, actor comments, and fan shares, using Facebook’s official application programming interface. Facebook, as the largest social network with approximately 1.28 billion active daily users (Facebook 2017), supports open access historic data collection, which guarantees the completeness of our data set and rules out omitted variable bias due to unobserved social media behavior (Ruths and Pfeffer 2014). We aggregated the partner brand’s social media power exertion to the weekly level to match movie-related variables, such as advertising spending and distribution intensity.
TABLE: TABLE 1 Variable Operationalizations
TABLE: TABLE 1 Variable Operationalizations
| Variable | Description | Source |
|---|
| Social Media Power Potential Variables |
| Network size | Number of Facebook followers of the actor (U.S. only, three months before product release) | Page Data, Facebook |
| Network activity | Number of Facebook shares by the partner brand’s followers (U.S. only, in the fourth month before product release), residual after controlling for network size | Facebook |
| Social Media Power Exertion Variables |
| Product-related posts | Weekly number of partner brand posts mentioning the focal composite product, detected using movie-specific dictionaries, stock variable (only Model A) | Facebook |
| Acknowledging responsive comments | Weekly number of partner brand comments that acknowledge the support of fans, detected using category-specific dictionaries, stock variable | Facebook |
| Promotional responsive comments | Weekly number of partner brand comments that promote composite products, detected using category-specific dictionaries, stock variable | Facebook |
| Non-product-related posts | Weekly number of partner brand posts not mentioning the focal composite product, detected using movie-specific dictionaries, stock variable | Facebook |
| Authentic product-related posts | Weekly number of authentic partner brand posts mentioning the focal composite product, detected using LIWC, stock variable, residual corrected for the occurrence of exclusive and persuasive posts (only Model B) | Facebook |
| Exclusive product-related posts | Weekly number of exclusive partner brand posts mentioning the focal composite product, detected using two human coders, stock variable, residual corrected for the occurrence of authentic and persuasive posts (only Model B) | Facebook |
| Persuasive product-related posts | Weekly number of persuasive partner brand posts mentioning the focal composite product, detected using two human coders, stock variable, residual corrected for the occurrence of authentic and exclusive posts (only Model B) | Facebook |
| Other product-related posts | Weekly number of partner brand posts mentioning the focal composite product, detected using movie-specific dictionaries, stock variable, residual corrected for the occurrence of authentic, exclusive, and persuasive posts (only Model B) | Facebook |
| Partner Brand Variables |
| Traditional partner brand strength | Aggregation of (1) the combined revenues of the partner brand’s previous three movies in which (s)he was listed among the first four actors, with a discount of 10% for each year, and (2) a ratio of the number of inclusions in Quigley’s “Top 10 Money Making Stars” list, as polled by North American theater owners, over three years before movie release | Box Office Mojo, Quigley |
| Traditional partner brand promotions | Aggregation of (1) weekly appearances in TV shows (daytime and late night) and (2) weekly mentions in news outlets (magazines and newspapers), residual corrected for selection criteria of the media | IMDb |
| Host Brand Variables |
| Host brand type | Binary variable equal to 1 if a previous host brand is extended, such as in the case of a sequel, remake, or bestseller adaptation | IMDb |
| Network size host | Number of Facebook followers of the host (U.S. only, three months before product release) | Page Data, Box Office |
| Fit | Binary variable equal to 1 if the partner brand is known for the product type that the composite product represents, measured as a match of the focal movie’s genre with the genre of the actor’s “most known for” movie | IMDb, Box Office Mojo |
| Product type (drama, comedy, action, horror, thriller) | Binary variable equal to 1 if the composite product belongs to the respective movie genre; one movie can belong to multiple genres (drama, comedy, action, horror, thriller) | IMDb |
| Advertising | Weekly advertising spending for the product, stock variable, predicted values after performing the first-stage regression | Kantar Media |
| Distribution | Weekly number of theaters in North America offering the product, predicted values after performing the first-stage regression | Box Office Mojo |
| Social media handles | Weekly number of posts mentioning the product by the Facebook pages of IMDb, Box Office Mojo, Yahoo Movies, Entertainment on Facebook, and Movie Pilot, stock variable | Facebook |
| Reviewer judgment | Average rating of the movie by professional movie critics | Metacritic |
| Reviewer dissent | Concentration of positive, mixed, and negative critical reviews, measured with Herfindahl index | Metacritic |
| Consumer evaluation | Average rating of the movie by consumers registered on IMDb | IMDb |
| Season | Binary variables indicating four seasons (January–March, April–June, July–September, October–December) | Box Office Mojo |
| Dependent Variable |
| Sales | Weekly box office revenues of the composite product in North American theaters | Box Office Mojo |
In Table 1, we detail the operationalization of the dependent and independent variables as well as their data sources. We report descriptive statistics of our metric variables in Table 2. The dependent variable captures the success of the new composite product (i.e., movie) as the power outcome. Specifically, we employ the weekly box office revenues (US$) generated by a movie over its lifetime in North American theaters. The independent variables correspond with our conceptual model; we include extensive industry controls to avoid an omitted variable bias.
Measuring social media power potential. The stars’ number of Facebook followers and Facebook activity level provide our measures of the partner brands’ social media power potential. Because the power potential concept describes an initial position, we use the pertinent values three months before a focal movie’s release. Studios usually start their advertising campaigns at this point, so deliberately excluding these three months from both size and activity measures limits the potential for reverse causality (see Knapp, Hennig-Thurau, and Mathys 2014). Our measure of the partner brand’s network size is the lead actor’s number of Facebook fans (corrected by the U.S. percentage of fans) three months before movie release. We use the same approach for the supporting actors (credited second or third), then sum the values for model parsimony.
TABLE: TABLE 2 Descriptive Statistics
| | Min | Max | M | SD |
|---|
| Social Media Power Potential Variables of Partner Brand |
| Network size | 0 | 17399529 | 474699 | 1772777 |
| Network activity | 0 | 535026.83 | 4765.133 | 31736.891 |
| Social Media Power Exertion Variables of Partner Brand |
| Product-related posts | 0 | 34.677 | 0.37 | 1.638 |
| Acknowledging responsive comments | 0 | 18.099 | 0.048 | 0.573 |
| Promotional responsive comments | 0 | 4.126 | 0.012 | 0.131 |
| Non-product-related posts | 0 | 624.884 | 2.756 | 17.673 |
| Product-related authentic posts | 0 | 13.233 | 0.154 | 0.695 |
| Product-related exclusive posts | 0 | 3.004 | 0.016 | 0.117 |
| Product-related persuasive posts | 0 | 7.344 | 0.053 | 0.315 |
| Social Media Power Variables of Supporting Cast |
| Network size of supporting cast | 0 | 10459089 | 325261 | 1151602 |
| Network activity of supporting cast | 0 | 169210.858 | 1357.113 | 7988.252 |
| Product-related posts of supporting cast | 0 | 421.322 | 0.437 | 6.954 |
| Acknowledging responsive comments of supporting cast | 0 | 88.011 | 0.172 | 2.152 |
| Promotional responsive comments of supporting cast | 0 | 11.577 | 0.033 | 0.321 |
| Non-product-related posts of supporting cast | 0 | 370.241 | 3.99 | 15.812 |
| Partner Brand Variables |
| Traditional partner brand strength | 0 | 7.618 | 0.609 | 0.886 |
| Traditional partner brand promotions | 0 | 12.096 | 1.19 | 1.343 |
| Traditional partner brand strength of supporting cast | 0 | 6.708 | 0.943 | 1.298 |
| Traditional partner brand promotions of supporting cast | 0 | 14.275 | 1.149 | 1.339 |
| Host Brand Variables |
| Network size of host brand | 0 | 15211879 | 306452 | 1252962 |
| Advertising | 0 | 17572343 | 1099345 | 2151441 |
| Distribution | 0 | 4404 | 812 | 1100 |
| Social media handles | 0 | 12.627 | 0.213 | 0.636 |
| Reviewer judgment | 0 | 10 | 5.7 | 1.8 |
| Reviewer dissent | 0.337 | 1 | 0.562 | 0.169 |
| Consumer evaluation | 1.6 | 8.7 | 6.7 | 0.9 |
| Dependent Variable |
| Sales | 0 | 270019373 | 4203385 | 13168110 |
Notes: To avoid taking the logarithm of 0, we added a respective constant to each variable. Stock values are provided for the communication-related variables. Non-instrumented and non-residual-corrected values are provided. For time-varying variables, weekly values are provided.
For the partners’ network activity level, we use the cumulative number of shares of an actor’s posts throughout the fourth month before the release. Shares signal a high level of engagement; by sharing, consumers recommend the content to their own Facebook friends. Because the number of shares also depends on the number of followers, we follow Chatterjee and Price (1977) and use the residuals of an auxiliary regression, in which the number of shares is the dependent variable and the number of fans is the independent variable.4
Measuring social media power exertion. We measure a partner brand’s social media power exertion by the numbers of posts and responsive comments. Power exertion is a dynamic concept, spanning the weeks leading to the release of the composite product and the weeks that follow. We use weekly measures for these variables, starting 13 weeks before the release and ending when the film is no longer being shown in North American theaters (maximum weeks in theaters is 26).
In total, we collected 41,547 posts from actors. To separate product-related from non-product-related posts, we applied an automated text analysis. For each movie, we developed individual dictionaries that included the movie title (with abbreviations if necessary) and movie-related hashtags, identified by human coders from the movie’s or starring actors’ Facebook pages.5 We applied these dictionaries to categorize each of the 41,547 actor posts as product related or not. Human coders ensured the reliability of this approach by manually checking for any erroneously coded product-related posts and removing them as needed. Overall, 4,857 posts were identified as productrelated, and the remaining 36,690 were classified as nonproduct-related posts.
Our measure of responsive comments is the number of comments an actor made in reply to a prior fan comment on his or her own Facebook page. We identified 7,499 actor comments during the respective time frame. To address heterogeneity in
the responsive comments variable, we coded these comments automatically into two major categories of replies: “Acknowledging partner brand comments” covers replies in reaction to fan engagement, thanking them for their support with words like “thank” and “appreciate.” The “promotional partner brand comments” category includes responses that draw followers’ attention to composite products, using words such as “check out” and “release.” As for all social media power exertion measures, we aggregated the comments on a weekly basis and used the same approach for the supporting cast.
Measuring different types of product-related posts. We further classified each of the 4,857 product-related posts as authentic/not authentic, exclusive/not exclusive, and persuasive/not persuasive; these categories were not mutually exclusive. To code authenticity, we followed Humphreys and Wang (2017) by employing the computerized text analysis software Linguistic Inquiry and Word Count (LIWC). We used their well-established dictionary from linguistic research to rate authenticity, by identifying texts that are “honest, personal, and disclosing” (Pennebaker et al. 2015, p. 22), which corresponds to the conceptualization of perceived brand authenticity (Morhart et al. 2015). As a validation measure, two trained independent human coders rated each actor’s overall communication style as authentic/not authentic on the basis of guidelines such as disclosures of personal information or the presence of the actor’s “own” words. If both coders considered an actor’s communication as authentic, the variable took a value of 1, and 0 otherwise. A correlation of .72 (p < .01) between these actor-movie-level assessments and the number of authentic posts identified by the LIWC software affirms the successful coding of authenticity at the post level.
Because LIWC does not provide established dictionaries for exclusiveness and persuasiveness, we trained two independent human coders to code these types of product-related posts. Neither was involved in the project, and they rated each of the 4,857 product-related posts using objective guidelines. For exclusiveness, the guidelines relied on keywords such as “my fans get to see first,” “never-before-seen,” and “for your eyes only.” For persuasiveness, the guidelines required the use of imperatives (e.g., “go see the movie,” “watch me in my new movie”) or implicit activation callings (e.g., “there is a seat waiting for you,” “who is going to see my new movie?”). The intercoder reliability was very high (99% agreement for both variables), providing confidence in these classifications. In both cases, the variable took a value of 1 only if both coders agreed a post was exclusive or persuasive, and 0 in all other cases.
Again, we aggregated each type of product-related post on a weekly basis and applied the same approach to the supporting cast. Post types are not mutually exclusive (e.g., they can be authentic and exclusive at the same time), so we residual corrected each of them for the existence of the other two types to remove overlap, then used only the respective residuals in the analyses. The specific auxiliary regressions necessary for this correction can be found in Web Appendix A. In Web Appendix B, next to the variable descriptions in Table 1, we detail our operationalization of partner brand and host brand variables that we include as controls.
To rigorously test our hypotheses, we need to address four main methodological challenges: ( 1) carryover effects, ( 2) endogeneity of social media activities, ( 3) endogeneity of advertising and screens, and ( 4) the nested data structure. These four challenges guide our data preparation and model selection, which leads us to a two-stage model approach with stock variables, in which we account for endogeneity using a probit estimation to create inverse Mills ratios and instrumental variables. Then we include these variables into a hierarchically structured linear mixed-effects model that accounts for the nested structure of our data.
Carryover effects. Posts, comments, advertising spending, and promotional activities in the prerelease phase likely have lagged effects on future sales. To account for such anticipationbased forms of communication prior to product launch, we use a stock specification for weekly measures of social media power exertion, host brand variables involving any mentions of a film by social media handles and advertising, and the partner brand variable of an actor’s traditional promotional activities (see Burmester et al. 2016). To build the stocks for each lagged variable, we use the Koyck (1954) model, with the respective stock variables determined as follows:
( 1) Stockit = lStockit-1 + Xit,
where X denotes a particular variable (product-related posts, responsive comments, non-product-related posts, authentic product-related posts, exclusive product-related posts, persuasive product-related posts, promotional activities, mentions by social media handles, or advertising) for movie I in week t, for all I (= 1, …, I) and t (= –13, …, T). We set the carryover coefficient l to .5, in line with meta-analytical findings for mass media advertising (Ko¨hler et al. 2017). When we reestimate our model with a l value of .25, the results remain robust; however, higher Bayesian and Akaike information criteria (BIC and AIC) values confirm the eligibility of l = .5.
Endogeneity of social media activities. We further account for the endogeneity of whether an actor engages in activities on social media or not, as such decisions are likely correlated with unobservables. Systematic differences might exist between actors who decide to engage in such activities and those who do not. Failure to account for such differences can bias parameter estimates. For example, if studio executives take an actor’s proclivity to post into consideration when casting a role, an actor’s social media activity may be related to unobservables (also considered by a studio executive) that affect movie sales. To control for this proclivity to post online, we build on the procedure proposed by Heckman (1979) and extensions (Wooldridge 2010). We estimate a probit model, which predicts the probability that an actor engages in social media power exertion, to calculate the inverse Mills ratios needed to implement a control function approach. These inverse Mills ratios control for unobservables in the movie performance equation that may be related to actors’ proclivity to post on social media. Including them in the main model accounts for the endogenous nature of partner brands’ social media activities.
For this first-stage probit model, we classify actors as those who are more or less likely to post. The resulting binary dependent variable separates a group with high social media activities (second/third terciles) in the fourth month before release from a control group with no or limited social media activities (no activities/first tercile). We leverage the dichotomous nature of this variable to generate more efficient estimates by modeling it as a probit and obtaining relevant inverse Mills ratios. Because this potentially endogenous variable is highly correlated with several measures of the actor’s social media activity that are included in the main model, the constructed inverse Mills ratios enable us to control for any average bias that might affect the coefficients of these more detailed social media activity measures over the run of the movie. As independent variables, we exclusively include variables in our probit model that have strong impacts on the decision to engage in social media activities, but little or no impact on movie revenues. This step minimizes concerns about the collinearity of the Heckman correction factor with other variables in the equation of interest (Wooldridge 2010). Three pertinent variables appear likely to fulfill this requirement:
• Age of the partner brand: Unlike older actors, younger actors have grown up with social media. Consistent with survey data (Pew Research Center 2018), we expect them to be more prone to post on Facebook. Yet it is unlikely that consumers base their movie decision on unobservables related to actor age that are not captured by the movie-specific observables we control for in the main equation (e.g., fit).
• Gender of the partner brand: Survey data suggest that women
tend to post more content on social media than men (Pew Research Center 2018). Specifically, women overtook men in terms of social media activities by the end of 2008. Although we therefore expect female actors to be more likely to engage in social media activities, we do not expect consumers to decide in favor of or against a movie on the basis of unobservables related to the actor’s gender that are not captured by the movie-specific observables in the main equation.
• Social media account age: Actors who have been active on social media for longer (measured as days since account creation) have gained more familiarity with the platform and are thus expected to be less inhibited about posting content. At the same time, consumers are unlikely to make ticket purchases dependent on this information (or even be aware of it).
Following Wooldridge (2010), we use these three variables, along with all other independent variables from our main model, to estimate the probability that a partner brand engages in social media activities or not using a probit model:
( 2) SocialMediaEngagementij where Social Media Engagement is a binary variable indicating whether the lead actor j belonged to a group with medium/high (taking the value of 1) or no/limited social media activities (taking the value of 0) in the fourth month before the release of movie i. PBA (partner brand age), PBG (partner brand gender), and SMA (social media account age) denote the three exclusion restriction variables, pertaining to lead actor j before the release of movie i.6 Finally, å bkIVk depicts the k independent variables from the final seckond-stage model that explain composite product success.
To derive the correction terms for our final composite product success model, we subsequently determine two inverse Mills ratios (Wooldridge 2010) for actor j being active on social media before the release of movie I or not. We first determine the inverse Mills ratio for an actor’s decision to be active by IMRAij ctive in = ½fðzÞ=½FðzÞ and IMRiAj ctive out = 0. Here, z represents the z-score associated with the predicted probability of being active on social media, fðzÞ is the standard normal propensity distribution function (evaluated at z), and FðzÞ is the standard normal cumulative distribution function (also evaluated at z). Similarly, we determine the inverse Mills ratio for actor j not being active on social media before the release of movie I by IMRiAj ctive in = 0 and IMRiAj ctive out = ½fðzÞ= ½FðzÞ - 1. Both inverse Mills ratios enter the main model that explains composite product success (see Equations 3 and 4 in the model specification section).7
Endogeneity of advertising and screens. Previous movie research has suggested that the allocation of weekly screens and advertising can be endogenous with movies’ revenues (e.g., Elberse and Eliashberg 2003; Gopinath, Chintagunta, and Venkataraman 2013). To control for this effect, we apply an instrumental variable approach. We model the endogenous variables, advertising and screens, as functions of the exogenous variables and three instrumental variables, which we select on the basis of their strong associations with decisions by theater owners and studios to be relevant and lack of association with the unobserved heterogeneity component of consumers’ demand for the movie to be exogenous (Luan and Sudhir 2010). The combined budget of competing movies per week provides our first instrument (Karniouchina 2011). It likely encourages managers to assign fewer scarce resources to the focal movie (due to the strong alternatives), independent of the unobserved heterogeneity component pertaining to the focal movie. Our second and third instruments leverage the patterns of theater owners’ repeated decisions to allocate screens to movie categories. Specifically, we form movie categories according to the movies’ production budget and to their genre and age restriction affiliations. We then construct typical screen allocation patterns for the resulting movie categories over time (Lee 2013; Papies and Van Heerde 2017). These instruments can capture patterns in allocation decisions, but, by construction, are unrelated to the movie’s unobserved characteristics (Lee 2013). We rely on these three instrumental variables when applying our two-stage least squares approach to account for the endogeneity of advertising and screens. We explain the endogenous variables with the instruments and all other time-variant variables from our main model in a first-stage regression, then use the resulting predicted values in our second-stage model of composite product sales. We report a more detailed description of the instruments used, the model specifications, and the results of the first-stage regressions in Web Appendix C.
To test the instruments, we mimic a linear mixed-effects model in two-stage least squares as closely as possible (see Papies and Van Heerde 2017). The multivariate Sanderson– Windmeijer F-test confirms the sufficient strength of our instruments (F-valueadvertising = 969.91, d.f. = 3, p < .01; F-valuescreens = 1,183.01, d.f. = 3, p < .01). A nonsignificant Sargan test confirms that the exclusion restriction is satisfied (X2 = .138; d.f. = 1, p = .21). The Hausman–Wu test also shows systematic differences between the models with and without endogeneity controls (c2 = 53.91, d.f. = 2, p < .01).
Data structure. A final modeling challenge arises from the different nature and nested structure of our data. Actors as partner brands appear in specific movies as composite products. In this setting, several actor-specific variables, such as their social media power potential and most of the brand alliance variables (e.g., movie genre) do not vary over the time that the particular movie is shown. However, variables such as social media power exertion and marketing efforts vary for each movie over time. Furthermore, our conceptual model requires interactions across both sorts of variables. Such nested structures with interactions across time-varying and non-time-varying variables are common in management and marketing practice (see, e.g., Hofmann 1997 or Allenby and Rossi 1998). To account for the nested structure of our data, we follow previous work in the field (e.g., Allenby and Rossi 1998) and apply a linear mixed-effects model (often also referred to as a hierarchical linear model). It allows us to model effects on different levels and apply cross-level interactions. Interaction effects in linear mixed-effects models require centered variables (Kreft, De Leeuw, and Aiken 1995); we use a residual centering approach, which also addresses potential multicollinearity concerns (Lance 1988).
Consistent with previous movie and social media research, we adopt a log-log formulation. This formulation not only accounts for nonlinear effects but also generates elasticities (e.g., Burmester et al. 2016). We add constants where necessary to avoid taking a log of 0. We adopt Pauwels, Erguncu, and Yildirim’s (2013) notation for a two-level linear mixed-effects model, with time-invariant observations on level 1 and timevarying observations in week t, captured throughout the theatrical run of movie i, on level 2. To facilitate readability, we formulate our model in general terms, with the random component at the movie level I (see also Pauwels, Erguncu, and Yildirim 2013): where Sales it represents the logged sales of movie I in week t, and a is the main constant of the hierarchical model. The vector represents the effects of social media power exertion from actor j for movie I in week t, which equal the logged number
of product-related posts from actor j about movie I in week t. Similarly, depicts the logged number of product-related posts from supporting actors s related to movie I in week t. Vector incorporates all social media power potential variables of actor j, which reflect the logged size of j’s network three months prior to the release of movie I and the residual-corrected logged average network activity of j in the fourth month before release. Similarly, contains the logged network size and logged, residual-corrected network activity for the supporting actors s. ·
presents the cross-level interactions be
tween actor j’s social media power exertion for movie I and the two social media power potential variables of actor j before the release of movie i. Vector depicts general social media power exertion by actor j that is not specifically related to movie i, consisting of the logged number of nonproduct-related posts from actor j in week t, as well as the logged numbers of responsive comments sent by actor j in week t. contains similar variables for the supporting actors.
Next, PBPROMjðt2Þ and PBPROMsðt2Þ represent traditional promotional activities by actor j and supporting actors s in week t, operationalized as the stocked, residual-corrected, and logged number of media appearances in week t. Similarly, vector HBPROMiðt2Þ spans the promotion and distribution values for movie I in week t, containing the stocked and logged number of
instrumented advertising, and the logged number of instrumented screens for movie I in week t, as well as the stocked and logged number of mentions of movie I by other social media handles in week t. With PBSTðij1Þ and PBSTiðs1Þ, we represent the variables that measure the traditional brand strength of actor j and the supporting actors s in relation to movie i. Then HBSTið1Þ accounts for traditional host brand–related values of movie i, including the logged number of social media fans of movie I three months before release; movie i’s host brand type; a vector indicating the genre of movie i; a vector indicating the season in which movie I is released; the logged scores for reviewer judgment, reviewer dissent, and consumer evaluations for movie i; and a fit indicator for movie i. The vector IMRðij2tÞ contains the two inverse Mills ratios from the probit estimation, generated by Equation 2. Finally, ui depicts the random intercept for each movie i, the beta values incorporate moviespecific slopes, and eit accounts for the model’s error term. We estimate the models with the LME4 package in R (Bates et al. 2015). The variance inflation factors stay below 3, indicating that multicollinearity is not an issue.
Equation 4 similarly offers the linear mixed-effects model that incorporates the different types of social media power exertion by the lead partner brand:
TABLE: TABLE 3 Results from Linear Mixed-Effects Model of Social Media Power
| | Model A | Model B |
|---|
| b | p-Value | VIF | b | p-Value | VIF |
|---|
| Intercept |
| Social Media Power Potential Variables of Partner Brand | 9.117 | .000 | | 9.346 | .000 | |
| Network size | .031 | .003 | 1.849 | .039 | .000 | 1.885 |
| Network activity | .049 | .070 | 1.139 | .056 | .041 | 1.141 |
| Social Media Power Exertion Variables of Partner Brand |
| Product-related posts | .255 | .000 | 1.826 | – | – | – |
| Acknowledging responsive comments | -.041 | .775 | 1.380 | -.066 | .650 | 1.378 |
| Promotional responsive comments | -.140 | .587 | 1.343 | -.322 | .234 | 1.469 |
| Non-product-related posts | -.188 | .000 | 2.130 | -.169 | .000 | 1.935 |
| Interaction Effects Power Potential and Exertion |
| Product-related posts • network size | .018 | .089 | 1.144 | – | – | – |
| Product-related posts • network activity | .032 | .036 | 1.097 | – | – | – |
| Types of Product-Related Posts by Partner Brand |
| Authentic product-related posts | – | – | – | .601 | .000 | 2.977 |
| Exclusive product-related posts | – | – | – | .605 | .021 | 1.602 |
| Persuasive product-related posts | – | – | – | 1.014 | .000 | 2.541 |
| Other product-related posts | – | – | – | .082 | .444 | 1.096 |
| Social Media Power Variables of Supporting Cast |
| Network size of supporting cast | .006 | .550 | 1.498 | .005 | .580 | 1.495 |
| Network activity of supporting cast | .056 | .040 | 1.138 | .054 | .050 | 1.137 |
| Product-related posts of supporting cast | .016 | .749 | 1.402 | .023 | .655 | 1.396 |
| Acknowledging responsive comments of supporting cast | .094 | .400 | 1.465 | .115 | .308 | 1.467 |
| Promotional responsive comments of supporting cast | .186 | .252 | 1.430 | .188 | .247 | 1.430 |
| Non-product-related posts of supporting cast | .008 | .810 | 1.512 | .009 | .787 | 1.507 |
| Partner Brand Variables |
| Traditional partner brand strength | .395 | .003 | 1.318 | .367 | .006 | 1.317 |
| Traditional partner brand promotions | .221 | .000 | 2.048 | .220 | .000 | 2.054 |
| Traditional partner brand strength of supporting cast | .492 | .000 | 1.347 | .483 | .000 | 1.346 |
| Traditional partner brand promotions of supporting cast | .067 | .214 | 2.019 | .067 | .213 | 2.022 |
| Host Brand Variables | .310 | .006 | 1.216 | .304 | .007 | 1.216 |
| Host brand type |
| Network size of host brand | .047 | .000 | 1.285 | .047 | .000 | 1.284 |
| Fit | .287 | .012 | 1.124 | .299 | .009 | 1.122 |
| Action | .252 | .052 | 1.523 | .266 | .041 | 1.522 |
| Comedy | -.112 | .354 | 1.598 | -.117 | .335 | 1.597 |
| Horror | .547 | .004 | 1.362 | .555 | .003 | 1.364 |
| Drama | -.379 | .002 | 1.758 | -.389 | .002 | 1.757 |
| Thriller | .145 | .263 | 1.381 | .140 | .281 | 1.379 |
| Advertising | .536 | .000 | 1.898 | .519 | .000 | 1.910 |
| Distribution | .458 | .000 | 1.952 | .491 | .000 | 1.970 |
| Social media handles | .561 | .000 | 1.555 | .566 | .000 | 1.557 |
| Reviewer judgment | -.096 | .706 | 2.869 | -.081 | .751 | 2.864 |
| Reviewer dissent | -.259 | .634 | 1.318 | -.186 | .733 | 1.319 |
| Consumer evaluation | 1.447 | .005 | 2.614 | 1.348 | .010 | 2.605 |
| Season 1 | .275 | .040 | 1.571 | .270 | .045 | 1.572 |
| Season 2 | .116 | .385 | 1.499 | .115 | .393 | 1.499 |
| Season 4 | .258 | .048 | 1.577 | .254 | .053 | 1.578 |
| Inverse Mills Ratios |
| Inverse Mills ratio active in | -.243 | .147 | 1.253 | -.247 | .146 | 1.238 |
| Inverse Mills ratio active out | -.062 | .661 | 1.216 | -.031 | .827 | 1.220 |
Notes: Dependent variable = weekly box office revenues. N = 5,722. VIF = variance inflation factor.
The main difference between Equations 3 and 4 is that PEiðj2tÞ, covering general product-related social media power exertion by the lead actor, is replaced by the vector PETðij2tÞ, which spans the different types of product-related posts, as proposed in H4–H6. Specifically, PETiðj2tÞ consists of the residual-corrected, stocked, and logged numbers of authentic, exclusive, and persuasive product-related posts from actor j about movie I in week t. It also includes a measure of “other” product-related posts, which serves as the comparison standard for testing these hypotheses. It is operationalized with product-related posts that are classified as not authentic, exclusive, or persuasive (e.g., Channing Tatum’s post, “Chan is at Comic-Con in San Diego promoting ‘Haywire’ today”).8 To avoid multicollinearity issues, this model does not incorporate additional cross-level interaction effects.
Before reporting the results of our modeling efforts, we calculated bivariate correlations between our focal power concepts (i.e., the different kinds of social media power potential and exertion) with power outcome to provide some model-free evidence. We find significant positive associations between all social media variables and power outcome; correlations differ from greater than .20 (for product-related posts and power potential variables) to less than .10 (for responsive comments and non-product-related posts) (see Web Appendix D). Whereas these results provide some initial support for the proposed role of the partner brand’s social media resources and activities, they do not address the several econometric challenges noted previously.
We first evaluate the probit estimation (Heckman 1979; Wooldridge 2010). Here, we test whether our proposed characteristics significantly explain the probability that partner brands engage in social media activities. The age of the lead actor relates significantly to the probability of engaging in social media activities (b = –.84, p < .01). Consistent with our prediction, younger actors post more than older actors. The gender of the lead actor is significantly linked to the probability of posting (b = .68, p < .01); as anticipated, women post more messages than men. The age of the partner brand’s social media account is significantly associated with the partner brand’s probability of posting on social media (b = .46, p < .01). As we predicted, actors who are more familiar with the social media platform are more likely to post. Because we find strong and significant estimates for all our exclusion restriction variables, we conclude that we successfully corrected for unobservables in the main model that may be related to actors’ proclivity to engage in social media activities. We report the full table of the probit estimation in Web Appendix E.
Partner brand’s social media power. We next evaluate the results of our linear mixed-effects model on the social media power of actors as partner brands, as displayed in Table 3, Model A. The model explains the success of the movie as a composite product well (conditional R2 = .87).9 Changes in the R2, AIC, and BIC values point to a substantial impact of partner brands’ social media power (DAIC = –155.9, DBIC = –22.8, DR2 = .06). The inverse Mills ratios are nonsignificant in the model of interest, meaning that our coefficients are not biased.10
In the test for H1, we find that the partner brand’s social media power potential is positively related to success. The size of the actor’s social media network has significant associations with composite product sales (b = .03, p < .01), as does the activity level on a marginal level (b = .05, p < .10). We treat this finding as empirical support for H1.
The results also support H2a, showing a strong association between product-related posts sent by the actor on his or her own Facebook page and composite product sales (b = .26, p < .01). However, we cannot confirm H2b, because we find no significant relationship between the number of responsive comments and sales, whether in the form of acknowledging comments (b = –.04, p > .10) or promotional comments (b = –.14, p > .10). The nonsignificant effect also persists when we rerun the model with all responsive comments. For H2c, we find a significant but negative association with sales for non-product-related posts (b = –.19, p < .01), which contrasts with our expectations. Interestingly, instead of profiting from a strengthened bond, the community seems distracted from the composite product when an actor issues non-product-related posts.
In the test of H3a, we find that the interaction of productrelated posts with the partner brand’s social media network size has a marginally significant association with composite product sales in the proposed direction (b = .02, p < .10). The interaction between product-related posts and network activity reaches significance (b = .03, p < .05), in support of H3b. An actor’s social media power thus is most strongly linked to fostering composite product sales when goal-directed social media power exertion is amplified by a high level of social media power potential.
Different product-related post types. We next consider the results for the different types of product-related posts, as specified in Table 3, Model B. The model explains the success of the new composite product well (conditional R2 = .87).11 In support of H4, H5, and H6, we find strong, positive associations of authentic, exclusive, and persuasive product-related posts with composite product success (authentic: b = .60, p < .01; exclusive: b = .61, p < .05; persuasive: b = 1.01, p < .01). All three parameters are clearly larger than the small and insignificant parameter for “other” product-related posts (b = .08, p > .10), which captures the remaining product-related posts after controlling for authenticity, exclusiveness, and persuasiveness. A likelihood ratio test (LRT) further confirms that these effects are also significantly different from the base category of other product-related posts (LRTauthentic = 15.85, p < .01; LRTexclusive = 5.36, p < .05; LRTpersuasive = 23.20, p < .01). Thus, it matters how social media power is exerted.
Findings for other partner brands and controls. For movies as composite products, several partner brands generally participate in the brand alliance. Our results (Table 3) show that social media power exertion by the supporting cast has no significant impact on the composite product.12 If the supporting cast has a very active fan base, it might result in a minor advantage at the box office, but the impact remains small, indicating that social media power mainly stems from the leading partner brand in our context—the one featured most prominently in the alliance.
The results for the partner and host brand variables are as expected. The traditional brand strength of the partner and its promotional activities in traditional channels are significant, despite the simultaneous inclusion of its social media activities; the promotional effect is comparable in size with the one reported by Burmester et al. (2016) for prelaunch publicity of video games. The host brand type, the host’s network size, and fit are also associated with higher composite product sales. Different product types show varying effects; for example, horror movies outperform dramas. We find positive associations of advertising, distribution, and weekly product mentions by social media handles. Controlling for movie quality, we find significant effects of consumer evaluations (but not critic evaluations or dissent); seasonal effects also exist.
Additional analyses for industry specifics. We ran additional analyses to check for potential industry-specific effects, as displayed in Web Appendix G. A notable characteristic of our setting is the human character of the partner brand in the movie industry, such that demographic traits of this human brand might affect the results. However, adding the lead actor’s gender as an independent variable produced an insignificant interaction with product-related posts. We checked for time-varying effects of social media power exertion by testing interactions of product-related posts with the week count and the opening week; both remain insignificant.
Awards as external quality signals are important for movies as experience products. We thus tested whether social media power exertion might interact with Oscar nominations. A positive, significant interaction suggests that product-related posts amplify the positive effect of award nominations (b = .48, p < .05). By sending this quality signal to their social media networks, partner brands can enhance the effect of award nominations on composite product success.
Finally, we tested whether social media mentions by other stars (those not involved in the movie but who act as influencers) affect movie performance. We find a significant effect (b = .29, p < .01), with the previously reported findings remaining unchanged. That is, partner brands’ social media power is not limited to their own composite products but is also significantly associated with other endorsed products to which they are unrelated.
To illustrate the relevance of our findings and enhance managerial insights, we ran a simulation in which we compared box office predictions for movies featuring a lead actor who engages in product-related social media power exertion versus the same movies featuring the same actor who does not engage in it on Facebook.13 The difference in the predicted weekly box office revenues offers a descriptive estimate of the monetary value product-related partner brand posts had for the observations in the analyzed data set, from 2012 until 2014. We exclude the top and bottom 5% to avoid deriving implications based on outliers and arrive at more reliable estimates (e.g., Hawawini, Subramanian, and Verdin 2003). Table 4 displays the descriptive statistics of our estimates, generated for the average effect determined with Model A und differentiating between types of product-related posts in Model B.
For Model A, we find that product-related posts have an estimated mean value of US$107,839 and a median of US$86,099. This value is similar to the value we estimate for traditional promotional activities by the partner brand (mean = US$103,466; median = US$85,398)14; we consider this result as support of social media posts’ relevance as a marketing tool relative to more established forms of partner brand promotions (i.e., mentions in television shows or news outlets).
Drawing on Model B, we estimated the monetary values of different types of product-related posts, focusing on particularly promising communication styles with above-average expected performances. Persuasive posts are most valuable in our data (mean = US$638,824; median = US$513,662), followed by exclusive posts (mean = US$326,251; median = US$262,330) and authentic posts (mean = US$323,705; median = US$260,283). All three subsets return substantially higher values than the remaining set of other product-related posts (mean = US$36,778; median = US$29,572).
TABLE: TABLE 4 Overview of Estimations for Monetary Value of Different Types of Product-Related Social Media Posts
| | Model A | Model B |
|---|
| Type of Product-Related Post | Average | Other | Authentic | Exclusive | Persuasive |
|---|
| Mean | 107,839 | 36,778 | 323,705 | 326,251 | 638,824 |
| Median | 86,099 | 29,572 | 260,283 | 262,330 | 513,662 |
| Minimum | 30,212 | 9,997 | 87,991 | 88,683 | 173,648 |
| Maximum | 379,568 | 126,342 | 1.112,011 | 1.120,758 | 2.194,528 |
Notes: All values are estimated values in U.S. dollars. The top and bottom 5% cases were dropped to eliminate outliers.
We interpret these values as descriptive indicators of the economic relevance of social media power exertion for a composite product; they are also in the range of managerial estimates in domains similar to ours.15 The observed variations suggest that the value of a product-related post is not a fixed amount but depends on contextual factors, such as the type of post (e.g., persuasive), the type of social media power potential (e.g., large and active network), and the type of brand alliance (e.g., fit), as well as other variables in our model.
This research provides evidence of a positive link between the partner brand’s social media power and the economic success of the brand alliance. We test our theory-inspired social media power framework empirically, and the results reveal how the social media power potential of partner brands, together and in interaction with its exertion, are linked with composite product sales; a simulation exercise demonstrates the substantial size of these effects.
To achieve the greatest value, a host brand should team up with a partner brand with strong product-related social media power exertion that can be amplified by its large and active social media network. Substantial differences arise in the monetary implications, depending on the different ways a partner posts about the composite product, among other factors. Persuasive product-related posts, the most goal-directed type of social media power exertion in our study, are associated with the strongest power outcomes and highest monetary estimates. An activating, imperative communication style does not appear to repel fans, as oftentimes expected, but rather seems to help mobilize them to buy. Other effective tactics to exert social media power include the release of exclusive movie content and authentic references to the product.
Interestingly, we find a negative association between partner brand posts that do not refer to the alliance and composite product success, cautioning that some social media activities might backfire. Instead of fostering beneficial bonds with potential consumers, they seem to cause a distraction, diverting consumers’ thoughts away from the composite product (Petty and Cacioppo 1986). Although non-productrelated posts might have beneficial effects for strengthening the partner brand, they seem to hinder the success of composite products.
The findings also point to boundary conditions. First, the visibility of the communication might explain why partner brand comments, contrary to posts, do not significantly relate to composite product sales. Whereas posts are prominently featured on social media, comments instead appear in smaller font underneath a post, receiving less exposure. Although offering responsive replies to a fan might have a strong influence on the commenter’s relationship with the partner brand, it does not translate into immediate, aggregate-level sales at the box office. If the goal is to increase immediate composite product sales, the expenditure of energy should be rather directed to generating posts as the more visible communication form.
Second, the centrality of the partner brand appears to serve as an additional boundary condition, explaining why we find no significant effects of social media activities of the supporting cast. Because the leading partner brand is perceived as central and important to the composite product, information coming from and being spread about this partner brand should be processed through a central route, resulting in stronger elaboration and persuasion (Petty and Cacioppo 1986). Messages from the supporting cast are likely processed at lower levels and are thus less influential in persuading consumers to buy tickets at the box office.
Our findings offer rich insights into the strategy–performance link in the context of social media. They provide recommendations for implementing the social media power of partner brands in brand alliances, pertaining to both the selection and management of partner brands.
Partner brand selection. Practitioners have been debating whether hiring partner brands on the basis of their social media fan numbers is a good idea (Hod 2015). Our results offer an answer: Host brand managers can profit from the external social media power of a partner brand (beyond its expert-based power), as indicated by a significant increase in composite product sales. The social media power of a brand can thus act as a valid criterion for selecting a brand alliance partner, especially because “piggybacking on a star’s established social media presence can be easier than building a new online platform from scratch” (Zerbib and Verhoeven 2015).
However, managers must realize that the number of followers reflects only a potential, with limited impact on its own. Instead, the brand alliance–specific exertion of this potential power is the key for sizable social media power outcomes. Yet most managers seemingly look at only follower numbers, rather than actual posting behaviors (The Telegraph 2017). A host brand manager is recommended to use both the size and activity of a prospective partner brand’s social media network as selection criteria but is advised to put special emphasis on productrelated social media power exertion. The interaction effects stress that both potential and exertion are needed to maximize the value of a partner’s social media power.
Partner brand management. Different drivers of social media power effectiveness also inform the strategic management of social media in ongoing alliances. To leverage their social media power potential and its link to composite product sales, partner brands are recommended to actively refer to the composite product in their posts. Posts mentioning other activities detract from the alliance, so unrelated posts should be limited during the launch phase of the product.
Not only should managers encourage partner brands to address their network actively during the ongoing brand alliance, but they can also offer concrete guidance for how to do so. In leaked emails, producers allegedly debated what type of tweet they wish Kevin Hart would post, debating the effectiveness of persuasive “calls to action” (Spargo 2014). Our findings would have helped these practitioners, as we show that persuasive appeals are linked with a mobilizing effect, sharing exclusive content is linked to further increases in sales, and adopting an authentic tone is advisable. Neutral product referrals, without exclusive content or authentic product references, have no significant impact, and posts that distract fans from the product should be avoided. These insights suggest what to post and how—assuming the goal is to increase composite product sales.
We contribute to social media theory by introducing our conceptual framework. The application of well-established power theory to the modern context of social media enables us to offer a theory-inspired categorization for the unstructured occurrence of social media variables. The conceptual and empirical distinction between social media power potential and exertion, including its identified facets, provides linkages that scholars could use to systematically extend theoretical knowledge and to resolve and integrate some seemingly inconsistent observations.
Furthermore, our novel brand alliance setting offers scholars a new way to provide a clear assessment of social media effectiveness. Our use of an externally acquired social media power helps us isolate its relationship to the sales of a composite product while controlling for confounding variables. We find the partner brand’s social media power to be of high economic value—both while accounting for and compared with traditional brand- and publicity-related variables.
Concerning the generalizability of our findings, we emphasize the particularities of our empirical setting. We analyze the combination of hedonic host brands (movies) with human partner brands (talented professional actors) who represent an integral part of the composite product. What contexts offer similar structures that may facilitate (or hinder) the applicability of our general findings? Regarding the host brand, several products exist that are not purely hedonic but offer at least a certain amount of hedonic benefits and are paired with human brands (e.g., the Tiger Woods Golf collection sold by Nike). We expect the pattern of our results to be transferable to such similar contexts. Our findings may also apply to some services offering hedonic benefits that are paired with human brands, as is the case with founders such as Virgin’s Richard Branson, but also with sports clubs (paired with athletes) and political parties and their candidates. We study professional actors as human partner brands; a multitude of comparable settings also include human brands—for example, athletes and musicians as well as other types of influencers on social media. Some similarities might exist with nonhuman partner brands for hedonic brand alliances that offer anthropomorphic characteristics, but we refrain from probing other contexts, such as search goods with mainly nonemotional utilitarian attributes.
Our empirical analysis reflects the social media environment at the time represented by our data set. During this period, stars were engaged in social media activities rather unsystematically, which is beneficial for our investigation. However, the continuing changes in social media usage by partner brands might influence their general effectiveness. For example, our sample includes a limited amount of persuasive posts; increased usage might lower their effectiveness as a result of satiation effects. This caution is pertinent, especially when interpreting the value estimations derived from our simulations, which we consider descriptive rather than prescriptive.
Several exciting avenues for further research emerge from our study. Our results show that using a referent power base with social media is strongly associated with composite product sales, as are mentions by actors not involved in the movie. Future research could use these insights to further address the phenomenon of influencer marketing. For example, how can aspiring influencers build a referent power base from scratch? What are valid fit criteria for brand managers to identify good brand–influencer matches?
Another avenue would be to link our findings on the monetary value of product-related posts to those from engagement studies. Akpinar and Berger (2017) and Lee, Hosanagar, and Nair (2017) have noted differences in the effects of post types for engagement, clicks, and purchase intentions. For example, whereas persuasive appeals by partner brands are associated with a sales lift for the composite product, we might find a reversed pattern for engagement scores of the partner brand (see Stephen, Sciandra, and Inman 2015). Both outcomes are important metrics, connected to different goals (immediate product sales or strengthening long-term consumer relationships). Further research could examine their interplay and consider both short- and long-term effects.
A final avenue might be to analyze patterns of posting behaviors to develop a more holistic understanding of content marketing. How important is consistency and integration in a brand’s communication within and across social media platforms? Insights on integrated marketing communications offer a starting point for scholars to address these urgent industry questions.
Footnotes 1 By the term “actor” (or “star” or “human brand”), we refer to both male and female human beings.
2 We exclude specialty releases (with less than US$1 million domestic box office) because they follow a different business strategy. Excluding movies from non-English-speaking countries ensures a match between the language spoken by an actor and North American moviegoers. We exclude animated movies and documentaries because the role of actors in these genres differs from that of live-action scripted movies. We excluded 29 films because of missing data (i.e., number of Facebook followers and posts for the first three credited actors). The remaining 442 movies cover more than 75% of the revenues yielded in North American theaters by movies with at least US$1 million in domestic box office revenues.
3 In some very rare cases, not all three actor ranks are taken.
4 We thus capture deviations from the mean activity level predicted by the network size, with positive (negative) values indicating greater than (less than) expected network activity (lnNetworkActivity = –.104 + .472 · lnNetworkSize; adj. R2 = .713; p < .01 for all coefficients). We use the same approach for the supporting cast.
5 For example, for the movie X-Men: Days of Future Past, posts were screened for the words “XMen,” “X-Men,” “X Men,” “XMen: DOFP,” “X2,” “Days of Future Past,” “DaysOfFuturePast,” “MutantTruth,” “Wolverine,” “Professor Logan,” “Quicksilver,” “Mystique,” “Professor Xavier,” “ProfessorX,” and “Iceman.”
6 Research has shown that actor age and gender have no meaningful effect on movie performance (Hennig-Thurau et al. 2013). In addition, we estimated the probit model without those two variables and found results to be robust.
7 In addition to implementing inverse Mills ratios, we conduct additional checks for endogeneity concerns that result from actors’ posting behavior. A fixed-effects model with actor-specific fixedeffects and all the time-varying variables yields robust results. Regressions using success expectations and abnormal returns to explain the amount of product-related posts indicate insignificant results. Theoretically, this can be explained by the fact that actors are aware that their value depends on the performance of each of their movies (Luo et al. 2010). With only one or two movies to promote each year, actors sense great pressure for each of them to perform well. Thus, active posters on Facebook likely support each movie, independent of success expectations or the movie’s current popularity.
8 We determined the “other” product-related posts variable by conducting a regression in which the total number of weekly product-related posts is the dependent variable and the number of authentic, exclusive, and persuasive posts are independent variables, then we used the residuals in our main analysis (see Web Appendix A).
9 Our model uses fixed and random effects, so we use the random slope extension of Johnson (2014), implemented in R’s piecewise structural equation modeling package, to calculate the conditional R2 that accounts for the variance of the fixed effects as well as the sum of the random variance components for each level of the random factor.
We further tested an alternative coding mechanism for the dependent variable in the probit model by differentiating between actors being active on social media or not (instead of using terciles). We find the results again to be robust. For comparison purposes, we provide models without endogeneity correction (one without inverse Mills ratios and one without inverse Mills ratios and instruments) in Web Appendix F.
Model parameters, such as the intercept, remain largely the same as in Model A. The slight change results from different specifications across the two models. Whereas Model A includes a “product-related posts” variable (which captures all product-related posts) and accompanying interactions, Model B drops the interactions and exchanges the product-related posts variable for its constituting parts—namely, authentic, exclusive, and persuasive product-related posts compared to “other” product-related posts.
When testing the different posting types for the supporting cast, we again found no significant effects. For parsimony, we included only the lead partner brand in the reported estimations.
Specifically, we use the LME4 package’s predict function to predict sales for each movie I in week t that features a lead actor with at least one Facebook follower three months before release. Equations 3 and 4 serve as the basis. Setting each movie to its mean, we predict sales for the first scenario with zero posts and for the second scenario with one post for an average week. We repeat this approach for each type of product-related social media power exertion.
We applied the same procedure, this time setting posts to their mean and varying promotional activities. To ensure comparability of estimates, we used the same cases (i.e., movies with lead actors on social media).
We found business press estimates that ranged from US$6,250 to US$1 million (see, e.g., The Economist 2016; Heine 2016; Robehmed 2016).
DIAGRAM: FIGURE 1 Conceptual Model
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Record: 200- The Self-Perception Connection: Why Consumers Devalue Unattractive Produce. By: Grewal, Lauren; Hmurovic, Jillian; Lamberton, Cait; Reczek, Rebecca Walker. Journal of Marketing. Jan2019, Vol. 83 Issue 1, p89-107. 19p. 1 Diagram, 4 Charts. DOI: 10.1177/0022242918816319.
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Record: 201- The Service-Profit Chain: A Meta-Analytic Test of a Comprehensive Theoretical Framework. By: Hogreve, Jens; Iseke, Anja; Derfuss, Klaus; Eller, Tönnjes. Journal of Marketing. May2017, Vol. 81 Issue 3, p41-61. 21p. 1 Diagram, 6 Charts. DOI: 10.1509/jm.15.0395.
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The Service-Profit Chain: A Meta-Analytic Test of a Comprehensive Theoretical Framework
The service-profit chain (SPC) has served as a prominent guidepost for service managers and researchers alike. This meta-analysis provides the first comprehensive test of the SPC, showing that all the proposed links are statistically significant and substantial. However, the effect sizes vary considerably, partly according to the type of service provided. Meta-analytic structural equation models show that internal service quality translates into service performance through various mechanisms beyond employee satisfaction, and they highlight the importance of the service encounter and customer relationship characteristics for customer responses. The findings not only indicate the need to integrate complementary paths in the SPC framework but also challenge the implicit SPC rationale that firms should always maximize employee satisfaction and external service quality to optimize firm performance.
Online Supplement: http://dx.doi.org/10.1509/jm.15.0395
For decades, the service-profit chain (SPC; Heskett et al. 1994) has served as a prominent guidepost for service firms. Reflecting the basic tenets of the SPC, a Google spokesperson has noted that the company's overarching goal is "to create the happiest, most productive workplace in the world" (Stewart 2013, p. B1), and its Vice President of People Development explains, "we want our employees and future employees to love it here, because that's what's going to make us successful" (Crowley 2013). Service managers have acknowledged that sustainable corporate success demands the simultaneous satisfaction and loyalty of employees and customers. The SPC emphasizes the importance of internal and external service quality for a firm's financial performance (Heskett et al. 1994; Heskett, Sasser, and Schlesinger 1997), and with its simple, convincing rationale, the SPC has become a widely accepted framework that many successful service companies follow. In addition, the SPC also has received some critical acclaim.
Among the considerable research attention devoted to the SPC, several gaps remain. Some researchers call into question the theoretical rationale underlying the SPC. For example, Bowen and Schneider (2014, p. 7) criticize the focus on employee satisfaction in explaining service performance, noting that "such measures are neither focused on service nor on customer experience." Homburg, Wieseke, and Hoyer (2009) outline the limits of consistently improving customer satisfaction, and Kamakura et al. (2002) hint at cost effects related to service quality that may reduce firm profitability. They call for the integration of additional mechanisms in the SPC framework.
Furthermore, empirical evidence about the SPC remains fragmented and somewhat ambiguous. Most research has focused on either internal or external marketing (e.g., Koys 2001; Lariviere 2008). Few studies have examined a closeto-complete version of the SPC, and those that have offer inconclusive findings (e.g., Evanschitzky, Wangenheim, and Wunderlich 2012; Loveman 1998). Even when studies cite the SPC as their theoretical basis, they tend to analyze modified versions, in which they omit, summarize, or substitute specific elements (e.g., Homburg, Wieseke, and Hoyer 2009; Kamakura et al. 2002). This tendency also applies to recent meta-analyses that have examined modifications of the SPC (Brown and Lam 2008; Jiang et al. 2012; Zablah et al. 2012). Hong et al. (2013) provide the most comprehensive meta-analysis related to the SPC to date. They introduce service climate as an additional link and show that it partially mediates the positive effects of human resource (HR) practices and leadership on employee attitudes and service performance, which in turn enhance financial performance through customer satisfaction. However, they do not differentiate between employee satisfaction and retention and omit customer loyalty, which is a key concept in the SPC. Thus, they call for "further metaanalyzing the complete influence processes specified in the service-profit chain" (Hong et al. 2013, p. 251).
We address this call by providing the first comprehensive test of the SPC, as proposed by Heskett et al. (1994), based on 518 studies that yield 576 statistically independent data sets with 1,591 correlations. In so doing, we aim to make several contributions to marketing literature. First, we synthesize and consolidate previously fragmented and ambiguous findings about the SPC. All the core SPC links are statistically significant and substantial, yet their effect sizes vary considerably. Therefore, we extend marketing literature by investigating service characteristics (coproduction, intangibility) and contextual factors (business to business [B2B] vs. business to consumer [B2C], SPC as a theoretical basis) as moderators of core SPC links. The findings indicate that most SPC links do not vary systematically with the type of service, so the SPC generally applies to a wide range of services. However, depending on the chain relation of interest, different moderators are relevant.
Second, we address critiques regarding the theoretical foundations of the SPC and challenges to the basic tenets of the SPC, which rarely have been tested. In so doing, we help refine and revise the understanding of the SPC on the basis of metaanalytic structural equation models (Bergh et al. 2016). The conventional SPC considers employee and customer satisfaction as key mediators linking internal and external service quality with firm performance. Although our findings underscore the importance of employee and customer satisfaction, they also provide evidence for complementary mechanisms. In particular, we find that internal service quality may influence employee productivity and firm profitability by improving skills, ensuring operational excellence, and inducing customers to evaluate external service quality more favorably. Our findings underscore the need to consider service relationship mechanisms as complementary links that influence customer loyalty beyond customer satisfaction.
Third, our findings indicate the need to revise the implicit SPC rationale that firms should maximize employee satisfaction and external service quality to optimize firm performance. That is, employee satisfaction and external service quality have both positive and negative effects, and we find evidence of "toomuch-of-a-good-thing" effects within the SPC framework (Pierce and Aguinis 2013). In particular, employee satisfaction seems to be negatively associated with customer loyalty, and external service quality may have adverse effects on firm profitability, in addition to their positive effects.
Our results can help service companies manage the SPC. For example, investing in internal service quality will have multiple effects, beyond its impact on employee satisfaction. This insight is helpful when designing and evaluating internal service quality practices and policies to improve firm performance. Service firms could even suffer from reduced firm profitability when they actively attempt to maximize employee satisfaction. Therefore, our findings encourage organizations to balance employee satisfaction, operational excellence, and service orientation carefully to optimize their service performance. For their external marketing efforts, our findings imply that managers should pay attention not only to customer satisfaction with the service outcome but also to the service relationship in their effort to improve firm performance. Furthermore, the negative residual effect of external service quality on profitability suggests the need to develop specific SPCs for different customer segments.
The SPC (Figure 1) emphasizes the importance of both internal and external service quality for sustainable corporate success (Heskett, Sasser, and Schlesinger 1997). Internal service quality, defined as "support services and policies that enable employees to deliver results to customers" (Heskett et al. 1994, p. 165), enhances employee satisfaction. Satisfied employees have positive attitudes toward their jobs and engage in behaviors to support the organization and its customers (Eagly and Chaiken 1993). Thus, employee satisfaction is associated with higher employee retention and productivity (Harrison, Newman, and Roth 2006). Loyal, high-performing employees are more likely to create a positive service experience (Bowen and Schneider 2014) and thus lead to external service quality.
The external part of the SPC focuses on customers' reactions to external service quality but follows the same logic (Cronin, Brady, and Hult 2000). Service quality leads to customer satisfaction, which is a predominantly affective reaction based on the comparison between expected and perceived service quality (Rust and Oliver 1994). Customer satisfaction leads to customer loyalty because it breeds attachment to the provider (Fournier 1998) and motivates repurchase intentions and positive word of mouth (Rust and Zahorik 1993). In addition, customer loyalty drives firm performance. First, loyal customers amplify sales through frequent repurchases and customer referrals (Kamakura et al. 2002). Second, loyal customers reduce service costs and marketing expenditures because they are familiar with the service provider's processes and are reluctant to switch (Anderson 1998). We delineate previous research on the SPC before we discuss potential moderators and introduce additional effects within this framework.
Despite several very valuable research articles on the SPC, we still observe fragmented, partially ambiguous knowledge in this realm. Few studies have examined complete or nearly complete versions or have analyzed variations of the traditional chain (e.g., Homburg, Wieseke, and Hoyer 2009; Kamakura et al. 2002). First, a few studies that address the complete SPC do so through case studies of individual companies (e.g., Loveman 1998). Silvestro and Cross (2000) and Pritchard and Silvestro (2005) reproduce the complete SPC using extensive data from two retail case studies. Their findings are context specific and provide only mixed support for the SPC. Another group of publications cites the full chain to substantiate hypotheses but does not directly test it (e.g., Pugh 2001). A majority of studies have focused on selected segments of the SPC (e.g., Koys 2001; Lariviere 2008), such that some links have attracted significant attention in marketing literature, while others remain less explored. For example, only few studies have analyzed the relation between customer loyalty and profitability (e.g., Coviello, Winklhofer, and Hamilton 2006). In addition, the effect sizes in these studies tend to vary. For example, some studies provide evidence of a positive effect of employee satisfaction on employee retention (e.g., Babin and Boles 1998), but others cannot replicate this positive effect (e.g., Loveman 1998; Pritchard and Silvestro 2005).
To the best of our knowledge, four meta-analyses assess parts of the SPC, but none has provided a comprehensive test of the conventional SPC thus far (see Brown and Lam 2008; Hong et al. 2013; Jiang et al. 2012; Zablah et al. 2012; for a detailed overview of the existing studies and their contributions, see Theme 1 of the Web Appendix). Jiang et al. (2012) show that HR practices influence financial outcomes through employees' abilities and motivation and operational outcomes. However, they do not refer to the SPC, in that they omit the external marketing perspective. In a service context, customer perceptions and behaviors are vital for determining service performance. Focusing on three SPC variables, Brown and Lam (2008) consolidate empirical evidence of the bond between frontline employees and their customers and demonstrate that employee satisfaction positively affects customer satisfaction through customer perceived service quality.
Hong et al. (2013) provide the most comprehensive metaanalysis, yet with their focus on service climate, they do not analyze the complete SPC. Rather, they pool employee satisfaction and retention. By analyzing them separately in the current analysis, we aim to avoid pooling bias and address conflicts in extant research regarding the impact of these variables. Furthermore, Hong et al. (2013) omit customer loyalty, which is a key concept in the SPC. They valuably introduce service climate as a missing link; we intend to go further by uncovering additional effects among SPC variables that provide new evidence of additional mediators beyond service climate. We also expand on the database used by Hong et al., in that we include all studies that analyze at least two SPC variables, so the current article covers a wider field of research and includes more studies than do existing meta-analyses (for a list of the included studies, see Theme 3 of the Web Appendix). In addition, we complement Hong et al.'s approach by analyzing relationships mostly on an individual level. By building on and extending Hong et al.'s findings, our meta-analysis thus generates novel insights that provide a clearer understanding of the SPC as well as richer inferences and directions for marketing practice and science. It also responds to Hong et al.'s (p. 251) own call for additional meta-analyses on the complete SPC. We refer to the original SPC framework by Heskett et al. (1994) to determine whether SPC relationships vary according to specific context factors.
Services are not homogeneous but differ in their intangibility and coproduction. We argue that service characteristics determine job demands as well as service employees' needs and expectations of being supported. We predict, in turn, that service characteristics have the most influence on the impact of internal service quality on employee satisfaction.
Intangibility of service outcomes. The intangibility of services refers to the fact that service processes or outcomes cannot be seen, felt, tasted, or touched before purchase (Zeithaml, Parasuraman, and Berry 1985). The more intangible a service is, the more difficult it becomes for customers to evaluate service quality (Bowen and Schneider 1988). For highly intangible services, the service employee frames a tangible element for the customer and personifies the service. Accordingly, service firms that provide highly intangible services need to establish mechanisms to guide employee behavior (Mayer, Ehrhart, and Schneider 2009). Bebko (2000) postulates that in the case of a highly intangible service, management needs to make a strong commitment to service quality. According to the SPC, it can do so with internal service quality policies and practices. For example, internal service quality practices support service employees and reduce ambiguity with regard to expected service behavior. Such support and guidance are particularly relevant for intangible services. The positive effect of internal service quality on employee satisfaction thus should be stronger for services that are highly intangible. We propose:
H1a: The effect of internal service quality on employee satisfaction is more pronounced if the services are more intangible.
Coproduction. Coproduction is an increasingly common phenomenon in product and service settings alike (Haumann et al. 2015) and refers to a customer's active contribution to the service delivery process in the form of information, tasks, efforts, or ideas. In coproduced services, customers have an active role. Service employees must integrate customer inputs and adapt to customer behaviors during service provision. Thus, highly coproduced services appear more demanding and stressful (Chan, Yim, and Lam 2010), and employees need the ongoing support of the firm and its management to deliver quality service. Yim, Chan, and Lam (2012) show that a supportive service environment leads employees to enjoy coproducing. In particular, internal service quality may support service employees through employee development; job design; the provision of relevant information, rewards, and recognition; and the implementation of a coproduction culture. By supporting and motivating employees to coproduce, the firm leaves them more satisfied (e.g., Yim, Chan, and Lam 2012). Thus, we conclude that the design of internal service quality strengthens the link between internal service quality and employee satisfaction for coproduced services:
H1b: The effects of internal service quality on employee satisfaction are more pronounced if the services require more customer coproduction.
Industry characteristics and study-specific moderators. We explore the moderating effects of industry and study-specific variables on SPC links. First, as the importance of B2B service settings increases (Fang, Palmatier, and Steenkamp 2008), we explore whether SPC links vary, depending on the industry setting. Second, we include an indicator of whether each specific study in our meta-analysis refers to the SPC as a theoretical basis. Third, we control for the quality of the outlet, using journal quality meta-rankings (Harzing 2016).
The SPC rests on three basic assumptions that are rarely tested explicitly. First, the SPC suggests that internal service quality primarily enhances employee satisfaction and only indirectly influences employee retention and productivity, external service quality, and firm profitability. This assumption corroborates the notion that employee satisfaction is a key performance indicator (Ittner and Larcker 2003). However, in line with HR management and organizational behavior research, we argue that this interpretation of the SPC may underestimate the impact of internal service quality. Thus, we posit and test additional direct effects of internal service quality beyond its impact on employee satisfaction.
Second, the SPC implies that customer satisfaction and loyalty primarily depend on customers' perception of external service quality. Yet other research has suggested that the service encounter partly determines customer satisfaction and loyalty beyond the effect of service outcomes (Gwinner, Gremler, and Bitner 1998; Palmatier et al. 2006). We integrate this notion and test whether employee attitudes and behavior directly influence customer satisfaction and loyalty.
Third, the SPC logic implies that to optimize financial performance, firms should maximize external service quality and customer satisfaction. Yet service firms also may achieve customer loyalty by increasing customer dependence and identification (Cronin, Brady, and Hult 2000; Jones, Mothersbaugh, and Beatty 2000) or enhance profitability by improving the return on service quality (Rust, Moorman, and Dickson 2002). Therefore, we hypothesize additional direct effects of external service quality on customer loyalty and firm profitability. With this challenge to these three SPC assumptions, we empirically test whether they hold (see Figure 1).
Additional effects of internal service quality. Empirical evidence has suggested that employee satisfaction only partially mediates the effect of internal service quality on employee retention (e.g., Jiang et al. 2012) and productivity (e.g., Batt and Colvin 2011). More generally, the positive effect of employee satisfaction on employee productivity is weak or insignificant (Riketta 2008). Thus, we propose additional effects of internal service quality.
Research on employee turnover suggests that high levels of internal service quality increase employee dependence through higher costs of leaving (e.g., Mitchell, Holtom, and Lee 2001). When internal service quality is high, employees refrain from leaving, because they are not willing to forgo these benefits (Meyer and Allen 1991). According to a recent meta-analysis, employee dependence predicts employee retention beyond employee satisfaction (Jiang et al. 2012). Furthermore, internal service quality increases employee retention because it fosters reciprocal behavior. If a company provides high levels of internal service quality, employees perceive the organization as supportive and committed to long-term investments in employees (Shaw et al. 2009). With a general tendency to reciprocate (Blau 1964), employees feel more committed to and obliged to remain with the company (Meyer and Allen 1991). Research on voluntary turnover and employee commitment thus suggests that the effect of internal service quality on employee retention is only partially mediated by employee satisfaction. We predict:
H2a: The effect of internal service quality on employee retention is partially mediated by employee satisfaction, such that there is also a positive direct effect of internal service quality on employee retention.
According to research on HR management systems, internal service quality practices directly influence employee productivity by improving employees' ability to perform (e.g., Liao et al. 2009). For example, internal service quality enables service employees to acquire the skills and knowledge necessary to interact effectively with customers (Aryee et al. 2016) and tailor services to meet individual customers' needs (Liao et al. 2009). Therefore, skills and abilities are critical to service performance and increase employee productivity, regardless of the employee's satisfaction (Messersmith et al. 2011). A highly qualified service employee should be more productive than a less-well-trained one, even if both are equally satisfied. Therefore, we expect employee satisfaction to partially mediate the effect of internal service quality on employee productivity.
H2b: The effect of internal service quality on employee productivity is partially mediated by employee satisfaction, such that there is also a positive direct effect of internal service quality on employee productivity.
Internal service quality also may constitute part of the service image that directly influences customer perceptions of quality (Bitner, Booms, and Tetreault 1990; Rust and Oliver 1994). Customers tend to infer service quality from the organizational image, not just from service outcomes (Parasuraman, Zeithaml, and Berry 1988), because external service quality is often ambiguous (Bitner 1990). Achieving high internal service quality creates a professional environment and signals the company' s aspiration to provide outstanding service (Groening, Mittal, and Zhang 2016). Therefore, internal service quality contributes to a positive corporate image that may inform customers' perceptions of service quality (Brady and Cronin 2001). Brown and Dacin (1997) show that consumers use corporate ability associations when evaluating product quality. Garcia de los Salmones, Crespo, and Rodriguez del Bosque (2005) find that customers infer service quality from an organization's socially responsible behavior. Because internal service quality indicates that a company invests in employee abilities and cares about its employees, we predict that customers attribute external service quality to internal service quality. This attribution effect implies that internal service quality is directly linked to customers' perceptions of external service quality, complementing the effects of employee retention and productivity.
H2c: The effect of internal service quality on external service quality is partially mediated by employee satisfaction, retention, and productivity, such that there is also a positive direct effect of internal service quality on external service quality.
Improvements in internal service quality may also help cut costs and increase efficiency through the positive effect on employees' citizenship behavior, such that internal service quality improves firm performance beyond the effect on external service quality (Mittal et al. 2005). Previous research has suggested that internal service quality—and in particular the practices and policies that foster employee empowerment and discretion (Snape and Redman 2010)—improves service employees' prescribed task performance and organizational citizenship behavior. Citizenship behavior is discretionary and "promotes the effective functioning of the organization" (Organ 1988, p. 4). It entails interpersonal helping, preventing the occurrence of work-related problems, and taking charge in the interest of the organization. This discretionary employee behavior also helps optimize resource allocations and allows for greater organizational flexibility (Mossholder, Richardson, and Settoon 2011). Yet it often is not formally rewarded or acknowledged by supervisors (Podsakoff et al. 2009). Service employees who help colleagues and share knowledge beyond the required standards thus may increase organizational efficiency, even though their individual performance evaluations remain unaffected. Therefore, internal service quality may lead to employee citizenship behaviors that improve firm performance, without being acknowledged as productive work behavior by the individual (Podsakoff et al. 2009). Finally, internal service quality refers to well-designed internal service processes (Nishii, Lepak, and Schneider 2008). Effective internal service processes can amplify firm profitability because the firm can offer the same level of service quality with fewer resources (Mittal et al. 2005; Rust, Moorman, and Dickson 2002). Jiang et al. (2012) support the notion that HR practices directly influence financial performance, after controlling for employee motivation, performance, and operational outcomes such as customer satisfaction. Therefore, we propose:
H2d: The effect of internal service quality on profitability is partially mediated by the SPC variables, such that there is also a positive direct effect of internal service quality on profitability.
Additional service encounter effects. The SPC establishes external service quality as a key determinant of customer intentions and behaviors, with a focus on service outcomes rather than the service encounter. However, interactions between service employees and customers directly influence customer attitudes and behaviors, regardless of the perceived outcome (e.g., Gwinner, Gremler, and Bitner 1998). We propose that emotional contagion and reciprocity account for additional effects during the service encounter, which calls into question the implicit assumption that the cognitive evaluation of external service quality is the only determinant of customer satisfaction and loyalty. The satisfaction mirror of the SPC implies that employee satisfaction and customer satisfaction are highly correlated, because employee satisfaction translates into service quality, which leads to more satisfied customers (Heskett et al. 1997). Employee satisfaction also could directly enhance customer satisfaction, through emotional contagion (Hennig-Thurau et al. 2006), which is the flow of emotions from one person to another (Pugh 2001). Customers are influenced by employees' emotions because they mimic employees' emotional displays (Sutton 1991) or use employees' emotions as social information to which they adapt (Salancik and Pfeffer 1978). Barger and Grandey (2006) show that employees' positive moods enhance customer satisfaction directly, controlling for external service quality. With the assumption that employees express satisfaction through positive moods and smiling (Tsai 2001), we argue that employee satisfaction is contagious in service settings:
H3a: The effect of employee satisfaction on customer satisfaction is partially mediated by employee retention, productivity, and external service quality, such that there is also a positive direct effect of employee satisfaction on customer satisfaction.
During service encounters, customers also experience service employees' efforts and expertise. If employees' performance indicates their competence and customer orientation, customers perceive the service as reliable, benevolent, and fair. Service employees who perform well also provide a sound basis for establishing a personal bond and trust (Gremler and Gwinner 2000). Thus, employee productivity does more than enhance external service quality: it can be considered an investment in the relationship between customers and employees (Price and Arnould 1999). It induces customers to reciprocate employees' effort by demonstrating loyalty (Yim, Tse, and Chan 2008). Therefore, customer loyalty is informed by service outcomes and service employees' efforts (Mohr and Bitner 1995; Palmatier et al. 2006). Therefore, we posit:
H3b: The effect of employee productivity on customer loyalty is partially mediated by external service quality and customer satisfaction, such that there is also a positive direct effect of employee productivity on customer loyalty.
Additional effects of external service quality. The SPC rationale implies that organizations should strive to maximize external service quality and customer satisfaction to maximize firm performance. According to the SPC, customer satisfaction fully mediates the effect of external service quality on customer loyalty. However, external service quality could affect customer loyalty directly by increasing customer dependency and customer-company identification. Customers are dependent if they need to maintain a relationship to continue receiving unique and irreplaceable value (Scheer, Miao, and Palmatier 2015). If a company provides outstanding service, it might be difficult for customers to find alternatives (Jones, Mothersbaugh, and Beatty 2000), leaving them disposed to remain in the service relationship, with direct effects on customer loyalty (Scheer, Miao, and Palmatier 2015).
In terms of customer-company identification (Bhattacharya and Sen 2003), with exceptional external service quality, companies provide attractive social identities to customers that enable them to gain positive self-appraisals. As Homburg, Droll, and Totzek (2008) demonstrate, customer-company identification motivates customer loyalty beyond customer satisfaction. We propose that, analogous to the direct effect of internal service quality on employee retention, external service quality enhances customer loyalty, irrespective of customer satisfaction.
H4a: The effect of external service quality on customer loyalty is partially mediated by customer satisfaction, such that there is also a positive direct effect of external service quality on customer loyalty.
The SPC mainly focuses on the benefits of service quality, without explicitly addressing its costs (Kamakura et al. 2002). According to the return-on-quality framework (Rust, Zahorik, and Keiningham 1995), service quality requires investments. Investments in service equipment and personnel, however, relate negatively to profitability (Kamakura et al. 2002). In their study, Rust, Zahorik, and Keiningham (1995) show that investments into external service quality increase financial performance only if the improvements' benefits exceed their costs. Drawing on this idea, Mittal et al. (2005) provide evidence that to be profitable, service firms need to expand revenue and efficiency simultaneously. If a firm is not able to manage both, external service quality will negatively affect the service firm's financial performance. In addition, the costs of achieving external service quality might rise as a result of employees' opportunistic behavior to foster their own benefits at the expense of the firm. Brady, Voorhees, andBrusco (2012) argue that customer contact employees will harm a firm significantly by showing "sweethearting" behavior, such as providing free or discounted services to customers. This employee behavior increases perceived external service quality and strengthens their personal bonds to the customer but might hamper profitability. Therefore, higher external service quality that does not translate into customer loyalty to the firm will reduce profitability.
H4b: There is a negative direct effect of external service quality on profitability, in addition to the positive indirect effect of external service quality on profitability that is mediated by customer satisfaction and customer loyalty.
We conducted an elaborate literature search to identify studies related to the SPC. First, we used keyword searches in electronic full-text databases, such as Business Source Complete, ABI/ INFORM, ScienceDirect, Web of Science, ProQuest, and Social Science Research Network, to retrieve relevant articles that relate directly to the SPC. We searched all top-tier marketing, service, and management journals listed in Theme 2 of the Web Appendix for other relevant studies that include at least one relation of interest. We also contacted scholars in the field to ask for their unpublished work. In all manuscripts deemed appropriate, we consulted the reference lists to locate additional studies not yet included in our database.
The final data set comprises ( 1) all empirical studies that explicitly analyze the SPC and ( 2) all service-specific studies containing at least two different constructs of the SPC. All of these studies report correlation matrices or metrics that we could transform into correlations (e.g., standardized betas; Peterson and Brown 2005), or else their authors provided us with the missing information. To detect any duplicate studies, we used the heuristic proposed by Wood (2008) and included correlations reported for the largest (sub)sample. When we found conceptual replications, such as when one study used multiple measures of a single construct, we relied on formulas provided by Hunter and Schmidt (2004, pp. 432-39) to compute a single composite correlation and the associated reliability coefficients. If this step was impossible, we averaged the dependent correlations to derive a construct-level estimate (Geyskens et al. 2009).
The correlations refer to different levels of analysis, computed using either singleor multisource data. Both these factors might increase variation in the correlations and confound our theoretical implications (e.g., Brown and Lam 2008; Ostroff and Harrison 1999). Therefore, we controlled for the level of analysis and the data structure implied by the SPC. First, for correlations associated with the internal part of the SPC, we used all available individual-level correlations because employee satisfaction, retention, and productivity theoretically are individual-level variables. Second, we applied the same reasoning to the SPC's external paths, such that we used all available individual-level correlations for these respective relations. Third, following Brown and Lam (2008), we only considered correlations estimated from multisource data for relations that linked the employee and customer sides of the SPC because these correlations should provide more accurate estimates of the effects. Fourth, for the links between the customer variables and firm profitability, we included all studies that link customer ratings or archival measures of customer variables to firmor branch-level revenue or profitability (e.g., Homburg, Wieseke, and Hoyer 2009), as well as the few studies that focus on the revenue or profit contributions of individual employees or customers (e.g., Kumar et al. 2014). However, we decided to exclude correlations based on single-source data provided by organizational representatives (e.g., Coviello, Winklhofer, and Hamilton 2006) because multisource data are preferable for the links between the customer variables and firm profitability. Furthermore, we excluded outlying correlations, following Huffcutt and Arthur (1995). We identified 576 statistically independent data sets reported in 518 empirical studies that appeared between 1994 and 2015 (for a listing of all studies in the meta-analyses, see Theme 3 of the Web Appendix).
An extensive coding protocol (Lipsey and Wilson 2001) reflected the working definitions of the SPC variables provided by Heskett et al. (1994). We also provide definitions for the moderators (intangibility, coproduction) and additional control variables (B2B vs. B2C, journal quality, SPC mentioned; see Table 1). Using this coding protocol, two trained graduate students and one of the authors each coded all the articles separately. The initial intercoder agreement was .93. The remaining authors served as judges to clarify and resolve any inconsistencies. Ultimately, the final coding worksheet was discussed, checked, and verified by all authors; any issues were resolved by referring to the coding protocol and respective studies.
Consistent with other meta-analyses in marketing, we applied correlation coefficients as the effect-size metric (Eisend 2015). We adopted the random effects artifact-distribution meta-analysis procedures developed by Hunter and Schmidt (2004) to aggregate the correlation coefficients and correct them for sampling and measurement error (see Franke and Park 2006; Hong et al. 2013; Zablah et al. 2012). To correct for sampling error, we used the studies' sample sizes as weights. Not all studies provide reliability data, such as when they use single-item (e.g., Yim, Tse, and Chan 2008) or archival data-based (e.g., Jasmand, Blazevic, and De Ruyter 2012) construct measures. Therefore, we used the distributions of reliability coefficients to correct the mean correlations for measurement error (Hunter and Schmidt 2004). To construct these distributions, we used all available reliability coefficients from studies that we initially deemed appropriate for inclusion in our analyses, before controlling for the appropriate level of analysis and the outlier analyses. This approach guaranteed the consistency of the reliability distributions across all analyses, because studies at other levels of analysis frequently aggregated individual-level responses, such that the reliability coefficients referred to the individual level, or because studies at other levels are relevant for estimating ( 1) the relations linking the employee and customer sides of the SPC and ( 2) relations with profitability. To determine the significance of the meancorrected correlations, we constructed 95% confidence intervals (Schmidt, Oh, and Hayes 2009). Because no single test is preferable (e.g., Cortina 2003), we used the 75% rule (Hunter and Schmidt 2004) and 95% credibility intervals (Cortina 2003) to assess whether the correlations for computing the mean-corrected correlation came from a single population (Geyskens et al. 2009). To check whether a publication bias affected our results, we also calculated a fail-safe n for the correlation coefficients (Hunter and Schmidt 2004; Table 2).
TABLE: TABLE 1 Definitions of Constructs and Moderators
| Construct | Definition | Common Aliases (Perceptions/ Presence Of) | Representative Studiesa |
| Workplace design | Physical elements that shape the general working environment | Open plan office, physical design, single work station, work design, work environment, work group structure | Emery and Fredendall (2002), Montes, Fuentes, and Fernandez (2003) |
| Job design | Job assignments and job content, such as the authority to make job-related decisions | Employee involvement, empowerment, job complexity, job control, job discretion, job scope, participation, role identity, task autonomy, tolerance for selfmanagement, work standards | Ahearne, Mathieu, and Rapp (2005), Hartline and Ferrel (1996) |
| Employee selection and development | Recruitment policies and employee training and development offerings, designed to enhance employees' skills and future career prospects | Coaching support, developmental climate, employee skills, training | Babakus et al. (2003), Mathieu, Gilson, and Ruddy (2006) |
| Employee rewards and recognition | Financial and relational rewards that are contingent on an employee's work performance | Behavior-based evaluation, fairness in reward allocation, feedback from supervisors, managerial rewards, pay administration, pay level, performance feedback, performance incentives, performance monitoring | Babakusetal. (2003), Hartline, Maxham, and McKee (2000) |
| Tools for serving customers | Availability of technological resources that assist employees in delivering quality service | Information and technology, service systems, service technology, technology used by employees, work resources | Loveman (1998), Sergeant and Frenkel (2000) |
| Leadership/ management | Customerand serviceoriented leadership and management styles that enable employees to meet or exceed customer expectations | Company customer orientation, intellectual stimulation, job supervision, management commitment to service quality, organizational service orientation, service climate, service culture, service vision, supervisor support, transformational leadership | Babin and Boles (1996), Bell and Menguc (2002) |
| Internal service quality | Support services and policies that enable employees to deliver results to customers | | Heskett et al. (1994) |
| Employee satisfaction | Individual perceived satisfaction with the job in general or with distinctive job elements | Employee positive affect, job satisfaction | Babakus et al. (2003), Donovan, Brown, and Mowen (2004) |
| Employee retention | Behavioral intentions to stay, attitudes, commitment, or actual (switching) behavior of employees | Absenteeism (r), affective commitment, continuance commitment, employee-company identification, intentions to quit (r), organizational commitment, propensity to leave (r), quit rates (r), tenure, turnover (r), turnover intentions (r) | Bell and Menguc (2002), Hartline, Maxham, and McKee (2000) |
| Employee productivity | An employee's ability and performance to satisfy company and customer needs, as rated by the employee, colleagues, or superiors | Adaptability, customer need knowledge, customer service behavior, discretionary service behavior, employee effectiveness, employee productivity, extra-role customer service, group effectiveness, job performance, perceived effort, performance quality, sales performance | Ahearne, Mathieu, and Rapp (2005), Babin and Boles (1996) |
| External service quality | Customer ratings of how well the company or employees perform on various service dimensions (e.g., tangibles, reliability, responsiveness, assurance, empathy) | Outcome quality, overall quality, perceived attribute performance, perceived service quality, postpurchase quality perceptions, service excellence, service outcome, value for money, value received | Cronin, Brady, and Hult (2000), Spreng and Mackoy (1996) |
| Customer satisfaction | Individual perceived satisfaction with the service in general or with distinctive service elements | Affective satisfaction, customer positive affect, customer relationship satisfaction, transaction satisfaction | Oliver and Burke (1999), Bolton, Lemon, and Verhoef (2008) |
| Customer loyalty | Behavioral intentions to return, attitudes, commitment, or actual switching behavior of customers | Affective commitment, attitudinal loyalty, commitment, customer retention, intention to return, perceived switching costs, relationship tenure, share-ofwallet | Oliver and Burke (1999), Smith and Bolton (1998), Gustafsson, Johnson, and Roos (2005) |
| Revenue | Objective archival data and management opinions about revenue streams and revenue growth | Gross monthly sales, revenue, sales growth, sales volume, unit revenue | Batt (2002), Marinova, Ye, and Singh (2008) |
| Profitability | Objective archival data and management opinions about financial profitability indicators | Company performance, customer contribution, financial performance, margin, net profit, profit | Loveman (1998), Voss and Voss (2000) |
| Intangibility | Degree to which customers are able to evaluate a service or product using their basic senses before purchase | Immateriality (= 1 for intangible, 0 for tangible) | |
| Coproduction | Degree of customers' participation in the service creation process | Customer participation, cocreation (= 1 for high, 0 for low) | |
| Study context | Whether the study was conducted in a B2C or B2B domain | Coded 1 for B2C and 0 for B2B services | |
| Journal quality | Quality of the journal in which the paper was published | Coded 1 for high, 0 for low, following Harzing (2016) | |
aSee Theme 3 of the Web Appendix. Notes: (r) indicates reverse coded.
TABLE: TABLE 2 Meta-Analytic Results of Core SPC Relationships
| | | | | | | 95% CI | 95% Crl | | |
| Relation | k | N | r | SDr | P | SDP | Lower | Upper | Lower | Upper | % Var. Unacc. | nfs |
| Workplace design Employee satisfaction | 7 | 1,889 | .338 | .187 | .428 | .219 | .253 | .603 | -.001 | .857 | 87.14 | 68 |
| Job design Employee satisfaction | 38 | 11,955 | .310 | .172 | .385 | .199 | .317 | .453 | -.005 | .775 | 88.85 | 328 |
| Employee selection and development - | 21 | 8,002 | .339 | .171 | .426 | .201 | .334 | .518 | .032 | .820 | 89.45 | 203 |
| Employee satisfaction | | | | | | | | | | | | |
| Employee rewards and recognition Employee | 30 | 29,445 | .402 | .076 | .493 | .075 | .460 | .526 | .346 | .640 | 65.08 | 340 |
| satisfaction | | | | | | | | | | | | |
| Tools for serving customers Employee | 8 | 11,561 | .125 | .094 | .156 | .110 | .075 | .237 | -.060 | .372 | 90.87 | 23 |
| satisfaction | | | | | | | | | | | | |
| Leadership/management Employee | 61 | 29,305 | .384 | .136 | .459 | .150 | .418 | .500 | .165 | .753 | 86.71 | 639 |
| satisfaction | | | | | | | | | | | | |
| Internal service quality Employee satisfaction | 95 | 56,733 | .400 | .151 | .506 | .177 | .468 | .544 | .159 | .853 | 87.75 | 1107 |
| Employee satisfaction Employee retention | 104 | 33,679 | .468 | .192 | .580 | .224 | .534 | .626 | .141 | 1.019 | 90.71 | 1404 |
| Employee satisfaction Employee productivity | 76 | 46,860 | .210 | .128 | .258 | .146 | .223 | .293 | -.028 | .544 | 88.77 | 414 |
| Employee retention External service quality | 16 | 3,451 | .229 | .163 | .280 | .178 | .182 | .378 | -.069 | .629 | 82.15 | 96 |
| Employee productivity External service quality | 16 | 2,076 | .202 | .128 | .243 | .112 | .168 | .318 | .023 | .463 | 54.51 | 81 |
| External service quality Customer satisfaction | 126 | 61,164 | .614 | .163 | .712 | .176 | .679 | .745 | .367 | 1.057 | 87.99 | 2117 |
| Customer satisfaction Customer loyalty | 131 | 65,258 | .553 | .219 | .655 | .247 | .611 | .699 | .171 | 1.139 | 92.55 | 2014 |
| Customer loyalty Revenue | 12 | 2,147 | .186 | .239 | .227 | .274 | .062 | .392 | -.310 | .764 | 90.20 | 56 |
| Customer loyalty Profitability | 6 | 3,722 | .157 | .112 | .188 | .124 | .081 | .295 | -.055 | .431 | 86.18 | 22 |
Notes: k = the number of correlations per relation; N = the total number of respondents across k samples; r = the weighted mean correlation; SDr = the standard deviation for r; p = the weighted mean correlation corrected for artifacts; SDP = the standard deviation for the estimated p; CI = confidence interval; Crl = credibility interval; % Var. Unacc. = the percentage of unexplained variance in the correlations; nfs = the fail-safe n.
TABLE: TABLE 3 Meta-Analytic Regression Results of the Moderator Analyses
| Correlates of Employee Satisfaction | Correlate of Retention | Correlate of Productivity | Correlates of Satisfaction |
| Moderator | Job Design (k = 38) | Leadership/Management (k = 61) | Employee Selection and Development (k = 21) | Employee Rewards and Recognition (k = 31) | Employee Satisfaction (k = 104) | Employee Satisfaction (k = 76) | Employee Satisfaction (k = 22) | External Service Quality (k = 88) |
| Intercept | .266* | .423*** | .193* | .487*** | .495 | .551*** | -.025 | .839*** |
| Intangibility | .145* | -.010 | -.356*** | -.044 | -.002 | -.016 | -.312* | .013 |
| Coproduction | -.062 | .053 | .204** | .066 | -.002 | -.046 | .020 | -.041 |
| B2B (0) versus B2C (1) | .224* | .055 | -.024 | -.111 | .123* | -.143** | n.i. | -.144** |
| SPC mentioned | .214** | .059 | .136 | .052 | .101 | .087 | -.001 | .073 |
| Journal quality | .132** | .020 | .152* | .103 | -.043 | -.104** | .179 | -.016 |
R2 .265 .045 .436 .160 .051 .154 .253 .038
*p< .10.
**p < .05.
***p < .01.
Notes: One-tailed tests of significance. We did not consider the variables "workplace design" and "tools for serving customers" in the moderator analysis because of the small number of correlations, n.i. = not included owing to no variance in coding; k = the number of correlations per relation.
In using meta-regression procedures to test our proposed moderation effects (Franke and Park 2006; Zablah et al. 2012), we adopted weighted least-squares regressions to secure the correct coefficient estimates, using the inverse of the natural logarithms of the corresponding n as weights (Tully and Winer 2014). To examine the hypothesized and potential moderators, we regressed the meta-analytic correlation for the specific path on the different moderator variables.
To test the extended SPC framework (Figure 1), we used metaanalytic structural equation modeling with MPlus 6.0. The basis for our analysis was the correlation matrix that the meta-analytic process produced. We made one exception from the original SPC: to obtain a full correlation matrix, we calculated a single measure for internal service quality. Following Hunter and Schmidt (2004), we computed composite correlations for the internal service quality instruments; otherwise, it would have been impossible to produce a complete correlation matrix. We thus cannot offer predictions for the single internal service quality practices. Because the meta-analytic correlations already were corrected for measurement error, we did not need the corrections of the error terms used in other meta-analyses (see Zablah et al. 2012). Regarding the sample size for model estimation, we used the harmonic mean of the correlations' total sample sizes (n = 4,449; Viswesvaran and Ones 1995).
Table 2 summarizes the meta-analytic results for the core SPC relationships. We found positive, significant mean correlations for all core relations of the SPC, with effect sizes that were mostly moderate or large in size. Overall, these findings were in line with the conventional SPC and indicate the relevance of the core relations of the SPC and mirror the heterogeneity of prior findings (e.g., Brown and Lam 2008). The relations are blurred by nonartifactual variance, as indicated by the high standard deviations, the substantial variance that is not accounted for by statistical artifacts, and the wide credibility intervals. Therefore, we next test for service-specific moderators that might account for this heterogeneity.
Table 3 provides the results for the moderator analyses. Overall, the results provide partial support for H1a and H1b. The impact of job design on employee satisfaction is stronger for highly intangible services; however, the effect of employee selection and development practices on employee satisfaction becomes smaller. If services are highly intangible, employees appear more satisfied if job design allows for discretionary action. In contrast, for highly intangible services, employee selection and development practices are comparatively less effective for enhancing employee satisfaction, presumably because for highly intangible services, employees expect more investments in employee development. Higher expectations are more difficult to meet or exceed, and therefore, the positive effect of employee selection and development on employee satisfaction diminishes. Conversely, coproduction amplifies the effect of employee selection and development on employee satisfaction. In addition, job design shows a stronger effect in a B2C than in a B2B setting. This finding affirms the importance of service-specific mechanisms such as empowerment in producing employee satisfaction with this group of services. The moderator analysis further reveals that the effect of job design on employee satisfaction is more pronounced if the studies are clearly based on the SPC framework, presumably because these studies are more likely to measure service-oriented job designs that tend to be more effective than general HR practices (Hong et al. 2013).
We explored moderator effects for additional SPC links. Most of them do not vary systematically according to service type or industry, though there are a few exceptions. First, the positive effect of employee satisfaction on customer satisfaction is less pronounced for highly intangible services; the satisfaction mirror appears less effective in these cases. Thus, customer satisfaction might be more difficult to achieve in highly intangible services because of the higher level of risk and ambiguity (Bowen and Schneider 1988). Second, the effects of employee satisfaction on employee retention and productivity vary by industry type. The relation between employee satisfaction and retention is stronger in B2C settings, while the effect of employee satisfaction on productivity is weaker for B2C services. In B2C settings, employees may be less likely to express their level of satisfaction through changes in productivity owing to higher levels of social pressure in personal customer interactions and more standardized services. Instead, they more strongly adapt their degree of loyalty according to their satisfaction. Furthermore, the positive link between external service quality and customer satisfaction is weaker in B2C contexts perhaps because in B2C settings, the personal interaction between customers and service employees may dilute this relation. Regarding journal quality, we find significant effects for the links of employee satisfaction with job design and selection and development; these relations tend to be stronger for higher-quality journals. Journal quality also attenuates the effect of employee satisfaction on employee productivity. However, it does not moderate other SPC core relations. For all the other links, we find no significant moderator effects.
We started our analysis with the conventional SPC (Heskett et al. 1994), then added paths that correspond to the three extensions we have proposed. For the baseline model, all core SPC relationships were significant and in line with Heskett et al. (1994). However, the fit of the original SPC model was quite poor (c2(25) = 2,819.02; confirmatory fit index = .78; root mean square error of approximation = .16; standardized root mean residual = .09; Table 4; for the corresponding meta-analytic correlation matrix, see Theme 4 of the Web Appendix). That is, the meta-analytic data did not support the full mediation model implied by the conventional SPC.
TABLE: TABLE 4 Estimated Path Coefficients for the Original SPC and Its Extensions
| Path | Model 1 SPC | Extension 1 Additional Internal Service Quality Effects | Extension 2 Additional Service Encounter Effects | Extension 3 AdditionalExternal Service Quality Effects | Extension 4 Complementary Effects |
| Customer loyalty Profitability | .188 (.014)** | .179 (.015)** | .177 (.015)** | .301 (.019)** | .301 (.019)** |
| External service quality Profitability | | | | -.194 (.019)** | -.193 (.020)** |
| Internal service quality Profitability | | .101 (.015)** | .101 (.015)** | .148 (.015)** | .150 (.015)** |
| Customer loyalty Revenue | .227 (.014)** | .227 (.014)** | .225 (.014)** | .226 (.014)** | .200 (.014)** |
| Employee retention Revenue | | | | | .120 (.015)** |
| Customer satisfaction Customer loyalty | .655 (.009)** | .655 (.009)** | .589 (.009)** | .332 (.014)** | .346 (.014)** |
| External service quality Customer loyalty | | | | .365 (.014)** | .381 (.014)** |
| Employee productivity Customer loyalty | | | .290 (.011)** | .264 (.010)** | .286 (.010)** |
| Employee satisfaction Customer loyalty | | | | | -.116 (.010)** |
| External service quality Customer satisfaction | .712 (.007)** | .712 (.007)** | .691 (.008)** | .691 (.008)** | .691 (.008)** |
| Employee satisfaction Customer satisfaction | | | .081 (.011)** | .081 (.011)** | .081 (.011)** |
| Employee productivity External service quality | .205 (.014)** | .161 (.014)** | .161 (.014)** | .161 (.014)** | .161 (.014)** |
| Employee retention External service quality | .248 (.014)** | .202 (.014)** | .202 (.014)** | .202 (.014)** | .202 (.014)** |
| Internal service quality External service quality | | .185 (.015)** | .185 (.015)** | .185 (.015)** | .185 (.015)** |
| Employee satisfaction Employee productivity | .258 (.014)** | .160 (.016)** | .160 (.016)** | .160 (.016)** | .160 (.016)** |
| Internal service quality Employee productivity | | .194 (.016)** | .194 (.016)** | .194 (.016)** | .194 (.016)** |
| Employee satisfaction Employee retention | .580 (.010)** | .586 (.012)** | .586 (.012)** | .586 (.012)** | .586 (.012)** |
| Internal service quality Employee retention | | -.011 (.014) | -.011 (.014) | -.011 (.014) | -.011 (.014) |
| Internal service quality Employee satisfaction | .506 (.011)** | .506 (.011)** | .506 (.011)** | .506 (.011)** | .506 (.013)** |
| Employee retention Employee productivity | .006 (.015) | .008 (.015) | .008 (.015) | .008 (.015) | .008 (.015) |
| Profitability Revenue | .087 (.015)** | .079 (.015)** | .079 (.015)** | .048 (.015) ** | .048 (.015)** |
| X2 | 2,819.02** | 2,483.47** | 1,781.69** | 1,072.73** | 884.87** |
| d.f. | 25 | 21 | 19 | 17 | 15 |
| Ax2 | | -335.55** | -701.78** | -708.96** | -187.86** |
| CFI | .78 | .81 | .86 | .92 | .93 |
| RMSEA | .16 | .16 | .14 | .12 | .11 |
| SRMR | .09 | .07 | .05 | .05 | .04 |
**p < .01 (one-tailed).
Notes: n (harmonic mean) = 4,449. CFI = confirmatory fit index; RMSEA = root mean square error of approximation; SRMR = standardized root mean residual. All parameter estimates are standardized coefficient estimates, based on the maximum likelihood estimation method using MPLUS software. Boldface values mark the original SPC links. Standard errors are in parentheses.
TABLE: TABLE 5 Results for Model 5
| A: Results of the Mediation Analyses |
| Relationship | Type of Mediation | Total Effect | Ratio(Indirect/Total) | Mediator |
| Internal service quality → Employee retention | Full | .285** | | Employee satisfaction |
| Internal service quality → Employee productivity | Partial | .275** | 29% | Employee satisfaction |
| Employee satisfaction → Customer satisfaction | Partial | .181** | 55% | External service quality |
| External service quality → Customer loyalty | Partial | .616** | 39% | Customer satisfaction |
| B: Total and Indirect Profitability Effects Calculation |
| Relationship | Total Effect | Indirect Effect | Direct Effect |
| Internal service quality → Profitability | .158** | .008* | .150** |
| Employee satisfaction → Profitability | -.014** | -.014** | n.e. |
| Employee retention → Profitability | -.001 | -.001 | n.e. |
| Employee productivity → Profitability | .085** | .085** | n.e. |
| External service quality → Profitability | -.007 | .187** | -.194** |
| Customer satisfaction → Profitability | .105** | .105** | n.e. |
| Customer loyalty → Profitability | .304** | n.e. | .304** |
*p < .05.
**p < .01.
Notes: n.e. = not estimated.
In the first extension of the original model, we included additional effects of internal service quality by adding paths from internal service quality to employee retention, employee productivity, external service quality, and profitability. The model fit improved significantly compared with the baseline (Extension 1: Δx2( 4) = -335.55, p < .01). Not only did internal service quality increase employee satisfaction, it also enhanced employee productivity (β = .194, p < .01). Yet the direct effect ofinternal service quality on employee retention was insignificant. For the external part of the chain, internal service quality increased external service quality (β = .185, p < .01) and profitability (β = .101, p < .01). These results support H2b-H2d but fail to support H2a.
We next accounted for additional service encounter effects, which improved the model fit significantly compared with Extension 1 (Δx2( 2) = -701.78,p < .01). Employee satisfaction was directly linked, though weakly, to customer satisfaction (β = .081, p < .01), and employee productivity related directly and positively to customer loyalty (β = .290, p < .01). These results support H3a and H3b. Extension 3 fit the data better than Extension 2 (Ac2( 2) = -708.96, p < .01), so the findings support Hfe and Hb. External service quality directly increased customer loyalty, beyond its effect on customer satisfaction (β = .365, p < .01). The negative link of external service quality and profitability (β = -.194, p < .01) corroborated the notion that external service quality is costly to obtain and reduces firm profitability if it fails to enhance customer loyalty (Zeithaml 2000). In Extension 4, we tested additional paths between employee retention and revenue and employee satisfaction and customer loyalty, as suggested by high modification indices (for a similar procedure, see Hong et al. 2013; Zablah et al. 2012). Model fit improved significantly (Ac2( 2) = -187.86, p < .01). Adding a direct path between employee retention and revenue showed a positive link (β = .120, p < .01). Employee satisfaction related negatively to customer loyalty (β = -.116, p < .01), whereas the indirect effect of employee satisfaction on customer loyalty was positive (see Table 5), indicating competitive mediation (Zhao, Lynch, and Chen 2010). We also tested the mediation effects in the final model extension. We found only partial mediation, with varying strength, for the core constructs of employee satisfaction, external service quality, and customer satisfaction (for the mediation results and total effects, see Table 5, Panels A and B).
We applied several robustness checks to determine whether our results were stable with regard to changes in the analyses process. We calculated the final model on the basis of the different sample sizes (harmonic mean n vs. minimum n). Furthermore, we changed the model path structure by deleting all paths in Extension 4 apart from the core and negative relations. None of these changes affected the results of our analysis significantly. In addition, we added company size as a control factor; size could exert a significant effect on our study variables and thus lead to biased results when omitted. Again, the results did not change significantly when we included this variable. Theme 5 of the Web Appendix shows the results of all the robustness checks in detail and the correlation matrix for the SPC variables and company size.
Finally, we disaggregated employee retention and customer loyalty to differentiate between loyalty attitudes and behavior. Meta-analytic correlations between employee retention attitudes and behavior (p = .215) and customer loyalty attitudes and behavior (p = .348) appear to be moderate. We reestimated our final structural equation model including these variables. We were able to reproduce our basic findings with the following modifications: for both employees and customers, satisfaction enhanced loyalty attitudes but not loyalty behavior. In contrast, employee retention behavior but not attitude was positively associated with external service quality, and customer loyalty behavior rather than attitude enhances profitability. A detailed overview of the disentangled results for loyalty attitude and behavior appears in Theme 6 of the Web Appendix, as does the correlation matrix of this additional analysis. The findings are in line with the popular notion that affect influences behavior through attitudes (e.g., Ajzen and Fishbein 1980). Notably, customer satisfaction seems to enhance customer loyalty behavior through its positive effect on customer loyalty attitudes, but the residual direct effect is negative. This finding may indicate a nonlinear relationship between customer satisfaction and customer loyalty behavior (e.g., Anderson and Mittal 2000), the need to differentiate between loyal and nonloyal satisfied customers (e.g., Mittal and Kamakura 2001) and the potential dynamic of expectations (Mittal, Kumar, and Tsiros 1999). However, given that several meta-analytic correlations in our extended matrix are based on three or fewer studies, these findings should be interpreted with caution and certainly call for further research.
TABLE: TABLE 6 Summary of Key Findings and Implications
| Key Findings | Research and Managerial Implications |
| Core SPC Paths |
| All core relationships are supported by our data, but model fit of the full mediation model is rather poor. | The conventional SPC holds true, but additional effects need to be considered for theory development and service management. |
| Extensions of the SPC |
| Not only does internal service quality affect service performance through employee satisfaction, it also has direct positive effects on employee productivity, external service quality, and firm profitability. | The conventional SPC underestimates the direct effects of internal service quality. Managing internal service quality with a focus on maximizing employee satisfaction neglects alternative paths to improving service performance. |
| Customer responses do not depend only on external service quality: employee satisfaction directly enhances customer satisfaction and employee productivity directly improves customer loyalty. Contrary to the SPC rationale, employee satisfaction might reduce customer loyalty. | The conventional SPC neglects the impact of the service encounter on customer responses. Service employees may enhance customer satisfaction and loyalty through emotional contagion, trust, and reciprocity. Yet employee satisfaction needs to translate into customer-oriented productivity to affect customer loyalty positively. Thus, the internal and external parts of the service encounter need to be managed jointly. |
| Not only does external service quality increase customer satisfaction, it also has a direct, positive effect on customer loyalty and a direct, negative impact on firm profitability. | The conventional SPC tends to neglect the impact of the customer relationship and the costs of external service quality. Customer—company identification and positive customer relationships constitute additional levers for enhancing customer loyalty. The negative residual effect of external service quality on profitability indicates the need to differentiate between effectiveness and efficiency in marketing. For managers, this negative residual effect implies that they should target different customer segments differently. |
| Employee retention not only influences external service quality but also directly increases revenue. | The conventional SPC underestimates the effect of employee behavior on firm performance. Employee retention is associated with higher revenue, because employees who are committed to the organization serve customers well and act on behalf of the organization. Managers should balance employee satisfaction, customer-oriented behavior, and company-oriented behavior to achieve service success. |
| Moderators |
| Type of service (intangibility, coproduction) explains a significant portion of the data's heterogeneity. For some parts of internal service quality dimensions, the effects on employee satisfaction are more pronounced when the service is intangible (job design) or coproduced (employee selection and development). For high intangibility, the effects of employee selection and development on employee satisfaction and of employee satisfaction on customer satisfaction are weaker. | Intangibility of the service is an important context factor to consider when analyzing the effects of internal service quality on employee satisfaction. Coproduction shows only few moderating effects. Managers must target the right strategies according to the type of service to yield financial success. |
| In B2C settings, we find stronger relations of (1) job design with employee satisfaction and (2) employee satisfaction with employee retention. We find weaker correlations between (1) employee satisfaction and employee productivity and (2) external service quality and customer satisfaction. | Researchers need to be aware that the SPC effects change with regard to the industry characteristics, especially regarding research on the service transition. Managers of B2B services need to be aware that employee satisfaction and external service quality play an important role for service success. |
With the SPC, Heskett et al. (1994) provided a comprehensive framework that informs companies how to enhance firm performance through internal and external marketing activities. Since then, the model has been adapted by practitioners and academics as a guidepost for research initiatives and managerial discussions alike (see Heskett, Sasser, and Schlesinger 2015). We provide the first comprehensive meta-analytical test of the complete SPC and respond to recent calls for more research on the SPC's structure (Hong et al. 2013). In so doing, we advance the theoretical understanding of the SPC in several ways (for an overview of the key findings and implications, see Table 6). First, we provide evidence for all core SPC links and show that the type of service explains some portion of the heterogeneity in these data. The moderation analysis suggests that, among others, some of the internal service quality-employee satisfaction links in the SPC vary according to the type of service. Employees' needs and expectations then might vary depending on the type of service they provide. These results have gone largely unnoticed by prior research and indicate the need to adapt the SPC to specific service settings.
Second, we find effects that are closely related to the SPC rationale but not explicitly specified (Heskett et al. 1997). According to our results (see Tables 4 and 5), internal service quality influences service performance through more than just its effect on employee satisfaction. Employee satisfaction fully accounts for the effect of internal service quality on employee retention but mediates only 29% of the influence on employee productivity. Thus, internal service quality also influences employee productivity, presumably through its effect on employees' abilities (e.g., Aryee et al. 2016; Messersmith et al. 2011). In addition, the direct effect of internal service quality on firm profitability indicates that internal service quality may foster operational excellence (Hong et al. 2013). It increases firm profitability by leading to both superior service quality and greater efficiency (Kamakura et al. 2002). Considering multiple outcomes of internal service quality policies and practices, we note that the conventional SPC tends to underestimate the direct effects of internal service quality.
Our findings indicate that employee satisfaction directly elicits customer satisfaction, though this contagion effect is relatively small compared with the impact of external service quality, rendering cognitive mechanisms the primary determinant of customer satisfaction, as suggested by Heskett et al. (1994). Furthermore, our results show a significant positive effect of employee retention on revenue, after controlling for customer loyalty. Employees who are committed to the organization not only serve customers well but also act on behalf of the organization. A high degree of employee retention indicates employees' experience and tacit knowledge regarding the market and customer needs, such that employees are able and motivated to seek sales opportunities and apply appropriate selling tactics (Wieseke et al. 2009). Prior research has shown that more experienced service employees generate more revenue by selling new products (Fu et al. 2010). Furthermore, customers are less price sensitive when service employees know customer needs well (Homburg, Wieseke, and Bornemann 2009). Therefore, employee loyalty may be associated with higher revenue, regardless of the level of customer loyalty. This rationale relates closely to a SPC logic, though Heskett et al. (1994) do not propose this effect.
Third, we identify three links that signify theoretical mechanisms previously not included in but compatible with the SPC framework. Our results indicate that internal service quality directly influences customer perceptions of external service quality, suggesting an attribution effect of internal service quality. Customers appear to interpret internal service quality as an indicator of external service quality (Bowen and Schneider 1988). This association complements actual service employee performance and is as influential as employee productivity and retention. This perspective has not been widely considered by HR or marketing literature; additional research could explore how, why, and in what conditions internal service quality primes customer perceptions of external service quality. We show that customer satisfaction partially mediates the effect of external service quality on customer loyalty, with the direct effect accounting for 61% of the total effect. Switching costs and enhanced customer-company identification induced by external service quality thus may contribute to customer loyalty in addition to satisfaction (Homburg, Wieseke, and Hoyer 2009). Furthermore, customers may reward employee productivity through increased loyalty, regardless of its impact on external service quality. This effect is in line with research on service encounters (e.g., Mohr and Bitner 1995). Customers view employee productivity as an indicator of trustworthiness and effort, and they reciprocate by being loyal. The effect size is similar to that of customer satisfaction and external service quality, reinforcing the importance of the customer-employee interaction for defining customer loyalty.
Fourth, two of our findings provide evidence that contradicts implicit SPC assumptions, in that they indicate employee satisfaction and external service quality to have both positive and negative effects. Our results call into question the SPC rationale that more external service quality is always better for firm performance. In line with return-on-quality frameworks, we find that external service quality relates negatively to profitability, after controlling for customer satisfaction and loyalty; it appears to be associated with higher costs (Rust, Zahorik, and Keiningham 1995), which also may result from employee opportunistic behavior such as sweethearting (Brady, Voorhees, and Brusco 2012). The negative residual effect of external service quality on profitability indicates the need to differentiate more clearly between effectiveness and efficiency in marketing (Rust and Huang 2012).
Also contrary to the SPC rationale, we find a negative effect of employee satisfaction on customer loyalty. This adverse effect has been largely overlooked in prior studies, yet it might help explain some inconsistent findings. Previous empirical studies have suggested that the more satisfied employees are, the better the overall service performance is, an assumption questioned by Anderson and Mittal (2000) and Bowen and Schneider (2014), among others. In line with their critique, our findings imply that employee satisfaction is beneficial if it enhances employee productivity but can have adverse effects on customer loyalty if it is not associated with superior service quality or customer satisfaction, such as when employee satisfaction motivates employee behaviors that benefit coworkers or the organization instead of customers (Bowen and Schneider 2014; Harrison, Newman, and Roth 2006). In turn, customers may be less loyal if employees seem to act primarily in their coworkers' or the organization's interest. We also find a negative total effect of employee satisfaction on profitability (Table 5, Panel B). This negative effect of employee satisfaction contradicts the implicit SPC assumption that a company should maximize employee satisfaction to optimize service outcomes. To clarify this effect, we discussed our results with a focus group of 16 top-level executives of a large service organization. The group suggested that a high level of employee satisfaction may indicate a strong inward orientation, hindering the development of close customer bonds and strong customer orientation. They concluded that a company that fails to balance employee satisfaction, customer orientation, and operational efficiency will not succeed. Employee satisfaction needs to translate into employee productivity to affect customer loyalty positively.
We identify multiple implications for managers that can inform their internal and external marketing strategies—insights that are of increasing importance as more companies establish new services based on SPC arguments. For example, IBM introduced its Kenexa surveys, which promise to help companies increase their own firm performance by engaging employees and improving employee satisfaction. Data and technologies increasingly help optimize internal service quality activities with regard to their impact on financial performance. Our metaanalysis shows that internal service quality is decisive for external service quality and firm performance; a 1 SD increase in internal service quality leads to a .287 SD increase in external service quality and a .158 SD improvement in firm profitability. The effects of internal service quality practices on employee satisfaction vary in part according to the type of service. That is, managers should tailor internal service quality to ensure that the practices fit the particular jobs and meet the expectations of service employees in various service settings.
According to our results, managers should acknowledge the multiple effects of internal service quality, beyond employee satisfaction. We show that internal service quality directly affects employee productivity and firm profitability. Therefore, HR managers should implement internal service quality practices to improve employee satisfaction and enhance operational excellence. In particular, our findings indicate that managing internal service quality with an exclusive focus on maximizing employee satisfaction neglects alternative paths to improving service performance. It even may have adverse effects on customer loyalty. Thus, our meta-analysis suggests that service firms should implement internal service quality policies and practices that focus on employee satisfaction, operational excellence, and service orientation to achieve firm performance. Relying on one aspect in isolation might not lead to such positive outcomes. Furthermore, our results imply that analytics initiatives should acknowledge the multiple effects of internal service quality to account adequately for its impact on firm performance.
With regard to external marketing, our findings indicate that managers should pay attention not only to customer satisfaction with the service outcome but also to the direct impact of external service quality and the service encounter in their efforts to improve customer loyalty (Homburg, Wieseke, and Hoyer 2009). Our results suggest that customer-company identification and positive customer relationships constitute additional levers for enhancing customer loyalty. In this sense, we respond to recent calls to provide more guidance for managers on how to improve employee-customer interactions. In particular, the effort and competence displayed by service employees complement external service quality in creating strong customer relationships. Yet the negative residual effect of external service quality on profitability may imply the need to target different customer segments. Then, service firms can direct investments in external service quality toward customers who are more responsive to higher quality and exhibit increased loyalty (Mittal and Kamakura 2001).
Our findings call for closer collaboration between the marketing and HR departments. Marketing and HR managers should be aware that HR practices and employee attitudes and behavior have considerable, immediate impacts on marketing outcomes. According to our findings, internal service quality influences customer perceptions of external service quality. Marketing managers may refer to investments in internal service quality to signal a company's aspiration to provide outstanding service, as consulting companies frequently do. Human resource managers also need to consider these effects when designing and implementing HR practices, such that they can use external service quality, customer satisfaction, and customer loyalty as key performance indicators to complement employee satisfaction and retention.
We compiled many studies to meet the complexity of the SPC. However, evidence on some of its links remains scant, which exposes several avenues for further research.
Disentangling internal service quality effects. Because of an insufficient number of correlations for producing a complete correlation matrix for model estimation purposes, we only included the overall construct of internal service quality. The meta-analysis by Jiang et al. (2012) provides evidence that various HR practices have distinct effects on employee behavior and firm performance. Yet their findings are not service specific and do not analyze how HR practices affect customer attitudes and behavior. Therefore, more research is needed on the differential effects of the internal service quality dimensions.
Understanding the multidimensional nature of employee behavior. We suggest that researchers should account for the multidimensional nature of employee productivity by simultaneously considering customer-oriented employee behavior and their behavior directed toward coworkers and the organization. Such investigations might shed more light on the potentially adverse effects of employee satisfaction, given that employee satisfaction also may motivate employee behaviors that benefit coworkers or the organization but reduce customer loyalty (Bowen and Schneider 2014; Harrison, Newman, and Roth 2006). Similarly, our findings indicate that specific employee behaviors may enhance external service quality but reduce firm profitability. A more fine-grained understanding of the various facets of employee behavior may help service firms in improving their performance.
Accounting for the multifaceted nature of employee retention and customer loyalty. For employee retention and customer loyalty, our robustness checks indicate different effects of loyalty attitudes and behaviors, but small numbers of correlations limit our inferences. To further our understanding of service firms' success, future studies should analyze the differential effects of attitudinal and behavioral loyalty dimensions. For example, the distinction between loyalty attitudes and behavior also may shed more light on the potentially negative impact of employee retention on customer satisfaction.
Furthermore, future studies may provide more evidence on the various bases of customer loyalty. Our findings indicate that customer loyalty not only depends on customer satisfaction but also may be based on other customer relationship characteristics, such as customer-company identification (Homburg, Droll, and Totzek 2008), dependence, trust, and reciprocity. Integrating relational constructs in the SPC may help improve understanding of customer loyalty.
Understanding the relations between internal and external marketing. Effects that bridge the internal and external domains have received comparatively little research attention. In this sense, our synthesis highlights the need for more extensive studies that encompass the effects of internal service quality, employee attitudes, and behavior on external service quality and customer-related outcomes to extend existing knowledge. These studies may require an interdisciplinary approach as they should integrate knowledge from various fields of management research, such as marketing, HR, and organizational behavior.
Identifying boundary conditions of the SPC relations. More research is needed to clarify the contingencies of the proposed relationships. The moderators that we have specified leave some variance unexplained. Additional research should include various service industries and service organizations to gain a deeper understanding of the SPC's boundary conditions. For example, our meta-analysis includes comparatively few studies from B2B settings, so more work is needed to gain a better understanding of the success of industrial services.
Accounting for nonlinear and interdependent effects. The SPC has often been interpreted as if the implied effects are linear and independent. However, the effects could be nonlinear (e.g., Anderson and Mittal 2000; Kamakura et al. 2002). Our findings provide evidence of the curvilinear effects of employee satisfaction and external service quality, and we reveal that those variables have both positive and negative effects. Further research should explore these nonlinear effects to determine optimal levels of employee satisfaction and external service quality. The intervening variables also might influence service outcomes jointly rather than independently. For example, employee productivity may enhance customer loyalty if customer satisfaction is high but it may be insignificant if customer satisfaction is low. Researchers might apply methods that account for the causal complexity that may characterize the SPC links (e.g., Woodside 2014).
Identifying longitudinal effects. Our meta-analysis is largely based on cross-sectional studies, which limits conclusions about the direction of the mediation mechanisms. Identifying potential longitudinal effects would be worthwhile for informing managerial decision processes and understanding the causality across SPC relations. For example, Evanschitzky, Wangenheim, and Wunderlich (2012) uncover lagged effects of some of the outcomes of internal service quality investments. In particular, variations in time lags across different types of services may help in adapting the SPC to specific contexts. Furthermore, while the order of the SPC constructs is compelling, crosslagged panel models might reveal more complex patterns of causality.
We hope that our meta-analysis consolidates and extends knowledge on the SPC and encourages more research on this prominent guidepost for service management. Overall, this work may inspire marketing researchers to continue expanding our knowledge about the SPC.
The authors thank Andreas Eggert, Dwayne D. Gremler, Shashi M. Matta, and the participants of the research seminar series at the Marketing Department at Florida State University for their valuable feedback on former versions of this manuscript. They also thank the many authors who supported this project by sending in the correlation matrices and data that ultimately built the data set for the meta-analysis, as well as those colleagues who offered extensive feedback at multiple workshops and conferences. Jens Hogreve thanks the PROFOR Research Funds of the Catholic University of Eichstaett-Ingolstadt for their financial support of parts of this project. Finally, the authors thank the JM review team for their constructive comments. Robert Palmatier served as area editor for this article.
A: The Service-Profit Chain (Heskett et al. 1994)
B: Extension 1: Additional Internal Service Quality Effects
C: Extension 2: Additional Service Encounter Effects
D: Extension 3: Additional External Service Quality Effects
Notes: Gray arrows represent paths from the initial models; black arrows represent the paths that we added during the model extension.
DIAGRAM: FIGURE 1 Original and Extended SPC Model
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Jens Hogreve is Professor and Chair of Service Management, Ingolstadt School of Management, Catholic University of Eichstaett-Ingolstadt.
Anja Iseke is Professor of Human Resource Management, Hochschule Ostwestfalen-Lippe.
Klaus Derfuss is Assistant Professor ofManagement Accounting, University of Hagen.
Tönnjes Eller is a former research associate at Ingolstadt School of Management, Catholic University of Eichstaett-Ingolstadt
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Record: 202- The Sting of Social: How Emphasizing Social Consequences in Warning Messages Influences Perceptions of Risk. By: Murdock, Mitchel R.; Rajagopal, Priyali. Journal of Marketing. Mar2017, Vol. 81 Issue 2, p83-98. 16p. 1 Diagram, 2 Graphs. DOI: 10.1509/jm.15.0402.
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Section: Special Issue: Theory and Practice in MarketingThe Sting of Social: How Emphasizing Social Consequences in Warning Messages Influences Perceptions of Risk
Online Supplement: http://dx.doi.org/10.1509/jm.15.0402
With costly epidemics such as obesity and drug use sweeping the United States, government agencies Wand public policy officials are employing a variety of marketing campaigns to influence consumer risk perceptions, attitudes, and behaviors. One of the tools frequently used in such campaigns is graphic warning messages (e.g., the “Tips from Former Smokers” ad campaign from the Centers for Disease Control and Prevention [CDC]; Painter 2014). Graphic warnings usually contain images and descriptions of the health outcomes of the target behavior. For example, cigarette packages sometimes include vivid pictures of diseased lungs to visually emphasize that lung cancer is a health outcome from smoking (Andrews et al. 2014). While such warning messages can help many consumers make healthier choices, they can also backfire if not implemented correctly. For example, the billion dollars spent on the National Youth Anti-Drug Media Campaign appeared to have no effect among certain populations and, in some cases, caused a boomerang effect whereby more ad exposure predicted less intention to avoid marijuana use (Hornik et al. 2008).
One of the reasons for this mixed effectiveness of warning messages is that the health outcomes typically emphasized in such messages are perceived as so far off in the future that they fail to influence current risk perceptions (Smith and Stutts 2003). For example, while smokers acknowledge that smoking causes lung cancer, it is possible that they view cancer as likely to occur only after several decades, which lessens the urgency to change their current smoking behavior. Thus, the temporal distance between the behavior and the health outcome reduces consumers’ motivation to change their current behavior because the distance may make the health outcome seem less likely (Chandran and Menon 2004) or lead consumers to believe that they have adequate time to change their behavior and thereby avoid the outcome.
Therefore, reducing the perceived temporal distance between the target behavior and its negative health outcome should increase the perceived riskiness of the behavior by increasing its likelihood of occurrence, thereby pressuring consumers to comply with the warning message and alter their current behavior. In this context, we suggest a simple but highly effective message strategy: the addition of social consequences of the focal health outcome. That is, adding a social consequence that is caused by the focal health outcome should make the outcome seem closer in time and, therefore, more likely to occur. We suggest that because social consequences (e.g., bad breath) of a negative health outcome (e.g., mouth cancer) will be viewed as more commonplace and immediate than health consequences (e.g., mouth sores), they will make health outcomes seem closer in time. This increased proximity will make these outcomes seem more likely to occur (i.e., increase vulnerability to the outcome), thus enhancing message effectiveness. This prediction is rather counterintuitive because health consequences (e.g., mouth sores) are likely to be viewed as more severe than social consequences (e.g., bad breath). Thus, despite the greater severity of the health consequences (Pechmann and Reibling 2006), we predict that the increased proximity of the social consequences will render messages that highlight social consequences of health outcomes more effective at increasing risk perceptions than messages that focus only on health consequences.
We find support for our predictions across five studies wherein pairing social consequences with health outcomes increases the perceived temporal proximity of the health outcome and thereby increases its likelihood of occurrence. In Study 1, we demonstrate that when a negative health outcome (i.e., gingivitis) is paired with a social (vs. health) consequence, participants perceive the outcome as closer in time and feel more vulnerable to it. In Study 2, we replicate our findings from Study 1 in a different domain (i.e., excessive soda consumption leading to obesity) and find that our effect is moderated by current health status such that the advantages of social consequences are attenuated among people who are already highly vulnerable to the health effects of excessive soda consumption. In Study 3, we examine the independent effects of outcomes versus consequences of outcomes and find that the sequence of addition matters such that adding social consequences to health outcomes is more effective than adding health consequences to social outcomes. In other words, not just any combination of social and health themes is effective. In Study 4, we use ultraviolet light (UV) protection as our target behavior and find that the effects of adding social consequences are similar to the effects of temporal framing. In Study 5, using the context of UV exposure, we show that the effects of adding social consequences extend not just to intentions (intentions to protect skin) but also to actual product experience perceptions (use of sunscreen).
Our research contributes to the literature on risk perceptions (Menon, Raghubir, and Agrawal 2006), temporal perceptions (Kees 2010), and consumer experiences (Hoch and Ha 1986) in several ways. First, by documenting the significant effects of social consequences on temporal proximity and the vulnerability component of risk perceptions, we reconcile some of the inconsistent findings with respect to the relative efficacy of health versus social appeals in warning messages and thus answer the call for greater research in this area (Hoek, Hoek-Sims, and Gendall 2013; Keller and Lehmann 2008). Whereas previous research has largely focused on the relative effectiveness of using different types of outcomes (health and social) in warning messages, we focus on the combined effects of outcomes with their consequences on downstream variables of interest (intentions and attitudes). We thus delineate between outcomes and the consequences that these outcomes subsequently cause, in contrast to prior research that has focused on outcomes alone.
We also contribute to the literature on risk perceptions by documenting unique interactive effects of severity and vulnerability on message compliance. We find that increasing severity when vulnerability is high (adding health consequences to social outcomes) does not enhance compliance as much as increasing vulnerability when severity is high (adding social consequences to health outcomes), which suggests that specific combinations of severity and vulnerability may be important to consider.
Second, we add to the literature on temporal perceptions by documenting a new antecedent variable that influences perceived proximity and, thus, vulnerability: social consequences. Although prior research has considered the effects of enhancing temporal proximity of health outcomes, there is little understanding of how the use of specific outcomes and consequences can affect temporal perceptions. Third, the finding that the temporal proximity of the health outcome may be increased by the addition of social consequences contributes to the literature on psychological distance (Trope and Liberman 2010) by suggesting that the consequences of outcomes may affect perceptions of temporal distance to and likelihood of these outcomes. While prior research has examined the simple correlation between temporal distance and outcome likelihood, we suggest that this relationship is more complex and is influenced by a heretofore unexamined variable: consequence type.
Finally, a unique contribution of our work is its focus on consumer experience as a dependent variable. Prior research has focused on consumer attitudes and behavioral intentions as key indicators of the success or failure of warning messages (Andrews et al. 2004) but has also showcased the limitations of using these dependent measures (Sheeran 2002). We address this issue by considering the effects of warning messages on consumer perceptions of product experience. To the best of our knowledge, our research is the first to document the effects of warning messages on consumer perceptions of experience, thereby providing an important and alternative pathway to persuasion in the context of health warning messages.
Our work also holds important implications for marketers and public policy makers by documenting a message technique that is easy to implement and highly effective at enhancing the persuasiveness of warning messages, thereby allowing for a strong application of marketing theory to marketing practice. Given the millions of dollars spent annually on warning messages in the United States alone (e.g., $70 million on the “Tips from Former Smokers” campaign [CDC 2015a], more than $10 million on the “It Can Wait” campaign [Hall 2013]), any incremental improvement in the effectiveness of warning messages has the potential to enhance the cost effectiveness of such messaging as well as to significantly contribute to consumer well-being. Furthermore, we showcase these effects using stimuli (e.g., soda video used in New York City, CDC’s “Choose Your Cover” campaign) that have been used in warning campaigns across a variety of health domains including behaviors that promote good health (flossing and UV protection) as well as behaviors that deter bad health (soda consumption and smoking), which increases our confidence in the practical significance of our findings.
We begin by referencing the relevant literature on warning messages—in particular, research on risk perceptions, temporal distance, and social consequences—and present our research questions. We then document results from five empirical studies and conclude by summarizing the theoretical contributions, limitations, and practical implications of our research.
Risk Perceptions and Warning Messages
The aim of many warning communications is to increase consumer risk perceptions to render the behavior of interest more or less attractive. For example, increasing the perceived riskiness of smoking ought to lower its attractiveness and promote cessation or reduction in cigarettes smoked. Similarly, increasing the perceived risk of unprotected UV exposure should make sunscreen usage more attractive, promoting its use. In this regard, several health information processing models such as the health belief model (Rosenstock 1974; Rosenstock, Strecher, and Becker 1994), protection motivation theory (Rogers 1983; Tanner, Hunt, and Eppright 1991), and the extended parallel process model (Witte 1992) conceptualize risk as comprising two distinct components: perceived vulnerability and perceived severity. Perceived vulnerability is the likelihood component of risk perception and refers to the chances of experiencing a negative health outcome (Witte 1992). Perceived severity is the impact component of risk and is a person’s belief regarding the seriousness of a health outcome (Witte 1992). Perceptions of risk can vary between people (Janz and Becker 1984) and can lead to significant behavioral changes (Brewer et al. 2007).
Risk perceptions increase with increases in perceived vulnerability and perceived severity, and both severity and vulnerability need to reach adequate levels for a message to motivate attitude, intention, or behavior change (Feather 1982; Weinstein 2000). Thus, research has shown that warnings may be ineffective either because some consumers do not find the outcomes sufficiently severe or threatening (i.e., low in severity; Cox, Cox, and Mantel 2010; Hammond 2011) or because they think that the health outcome is unlikely to happen (i.e., low vulnerability; Hoek, Hoek-Sims, and Gendall 2013; Keller 1999). Indeed, often consumers suffer from a false sense of security due to the self-positivity bias (Menon, Block, and Ramanathan 2002), such that they believe that the outcome may occur for other consumers but not for them, thereby reducing their perceived vulnerability. In addition, there appear to be interactive effects of vulnerability and severity such that the joint effects of both variables are not completely clear. Thus, meta-analyses (Floyd, Prentice-Dunn, and Rogers 2000) have revealed that the combined effect of severity and vulnerability is lower than the sum of their independent effects, suggesting some negative synergies between the two. Along these lines, research has found that increases in severity, given low vulnerability, can have negative effects on intentions (Mulilis and Lippa 1990, Pechmann et al. 2003).
In summary, both vulnerability and severity are necessary to elicit message compliance; neither alone is sufficient. In this regard, most warning messages tend to focus on long-term negative health outcomes (e.g., heart disease, death, cancer) that are high in severity but low in vulnerability. Therefore, after exposure to a warning message, people may understand the seriousness of potential negative outcomes, but they do not feel susceptible to the outcome because they perceive it as temporally distant, and consequently they rarely change their behavior. Thus, an ideal warning message would be one that retains the necessary levels of perceived severity but also manages to elicit high levels of perceived vulnerability among its target audience. We propose that highlighting the social consequences of negative health outcomes may be an effective solution to increase perceptions of risk while maintaining the serious, long-term nature of the negative health outcome. This is because social consequences are likely to increase the perceived temporal proximity of the health outcome and therefore increase perceived vulnerability.
Temporal Perceptions and Likelihood Estimates
Literature on psychological distance has suggested that the temporal distance to an event can affect the estimates of likelihood of that event. For example, Chandran and Menon (2004) find that the same event was reported as being less likely to occur when it was framed as being further versus closer in time. In the context of warning messages, this suggests that perceived temporal distance may be a crucial determinant of the perceived likelihood of negative health outcomes, making it important for scholars to understand which message features alter perceptions of temporal distance, such that increasing the outcome’s proximity will increase its likelihood (i.e., vulnerability).
One way researchers have attempted to address people’s natural propensity to distance themselves from undesirable future outcomes is through the message’s temporal framing. For example, Chandran and Menon (2004) find that when health risks are presented in “day” terms, they are perceived as more threatening than those presented in “year” terms. Thus, specific attributes of the messages can make risks appear closer in time. However, the effect of message framing may have differential effects in line with consumers’ tendencies to consider the distant consequences of their behaviors. Kees (2010) demonstrates that consumers who are less future oriented benefit the most from messages displayed in a proximal (vs. distal) format. Because temporal framing cannot always be used in messages and may only benefit a segment of the audience, the current research identifies how to increase the temporal proximity of negative health outcomes in another way: by emphasizing social consequences in warning messages. We suggest that people view social consequences as more short term, more frequent, and, thus, more temporally proximate than health consequences, thereby eliciting perceptions of greater vulnerability to the health outcome.
Social Versus Health Appeals
Message content is a critical component that can amplify or undermine the effectiveness of a warning message (Pechmann and Catlin 2016). The bulk of research on warning messages has focused on the effectiveness of using negative health outcomes such as cancer (Dillard and Nabi 2006), diseases (Kees et al. 2010), and even death (Cameron, Pepper, and Brewer 2013). This is not surprising, because the vast majority of warning communications in real life tend to utilize health appeals (Keller and Lehmann 2008). There is very limited research on the effectiveness of using other types of outcomes, such as social (Agrawal and Duhachek 2010; Smith and Stutts 2006) and financial (Strahan et al. 2002) outcomes, rendering it difficult to make predictions about these types of appeals. The problem is compounded by the fact that the scant research on social appeals has offered conflicting evidence on their effectiveness (Denscombe 2001; Hoek, Hoek-Sims, and Gendall 2013; Pechmann and Goldberg 1998; Smith and Stutts 2003), with some research documenting an advantage for social appeals relative to health appeals (e.g., Schoenbachler and Whittler 1996) and other research showing the reverse effect (Pechmann et al. 2003).
In addition, most research on social-focused warning messages has been conducted with adolescents, with little research among adults (Erikson and Erikson 1998). For example, Pechmann et al. (2003) tested several types of warning messages among seventh and tenth graders and found that the most effective messages emphasized serious social disapproval. However, messages that highlighted cosmetic risks (e.g., looking unattractive)—which also have a social component—and health risks were not effective. Thus, it is difficult to determine when messages that highlight social consequences will be effective among adult populations, especially because social consequences are more relevant among young people and health consequences are generally more important among older people (Gold and Roberto 2000).
We suggest that there could be two potential explanations for the conflicting evidence on the effectiveness of social appeals. First, there is a lack of clarity in how social outcomes have been defined in warning messages, ranging from highly visible and concrete outcomes such as cosmetic risks (e.g., yellowing of teeth; Smith and Stutts 2003) to less visible, abstract risks such as social ostracism (Hoek, Hoek-Sims, and Gendall 2013) and social disapproval (Pechmann et al. 2003). Thus, certain outcomes can often be viewed as both social and health related (e.g., yellow teeth indicate tooth decay, loneliness is a mental health outcome but can also be perceived as a social problem), making it difficult to clearly define social outcomes and to distinguish whether the effects found were due to the social or health aspects of the risks.
Second, social outcomes are high in vulnerability but not severity. In other words, social outcomes are likely to be viewed as quick to occur and temporally proximate (e.g., bad breath is almost an instantaneous outcome of smoking) but are also likely to be viewed as not very severe or threatening (e.g., bad breath is not very serious). The low levels of severity lower risk perceptions of the health behavior, leading to low motivation to comply with the message and change behavior. We therefore suggest that adding social consequences to health outcomes may be an effective way to achieve high levels of both severity and vulnerability and, thus, increase message compliance. In other words, the health outcome introduces severity while the social consequences caused by this health outcome introduce temporal proximity and, thus, vulnerability to the outcome.
In this regard, an important distinction between the current research and previous research is the way that social appeals are operationalized and utilized in the warning message. Previous research has used a simple social versus health comparison whereby social outcomes of a targeted behavior (e.g., smoking) are used exclusively in one condition and health outcomes are used exclusively in the other condition. For example, Smith and Stutts (2003) showed a series of antismoking advertisements to adolescents that highlighted bad breath, stinking clothes, and stinking hair under the tagline “Smoking Stinks” for the social condition and advertisements that highlighted lung cancer, heart attack, and stroke with the tagline “Smoking Kills” for the health condition. A problem with this approach is that it confounds variables such as severity of the outcome with the outcome type. That is, the health outcomes (cancer, heart attack) are significantly more severe than the social outcomes (bad breath, stinking clothes) and, therefore, any differences between the two conditions may be attributed to either the outcome type or the difference in severity.
To eliminate this possibility, our research delineates between the outcomes of a behavior and the consequences of that outcome. We utilize the same health outcome and simply highlight a subsequent health or social consequence caused by this negative health outcome. For example, soda consumption can lead to obesity. Obesity can affect a person’s life in many different ways, including additional health consequences such as heart disease or additional social consequences such as appearing unattractive (see Figure 1). By using the same main negative health outcome in all conditions, we demonstrate how highlighting additional consequences (either social or health) can influence the perception of the same negative health outcome. This is different from prior research that has considered two separate outcomes (e.g., obesity, tooth decay) of a behavior (soda consumption) that are independent of each other. Thus, our use of the term “consequence” refers to something that is causally related to the negative outcome of a behavior.
Consequently, the dependent variables of most importance (i.e., temporal proximity, vulnerability, and severity) focus on the perceptions of the main health outcome (i.e., obesity). Furthermore, we ask respondents to generate their own social or health consequences to a health outcome in many of our studies, thereby ensuring that the consequences are perceived as being clearly either social or health.
We answer the call from Keller and Lehmann (2008) to more fully investigate the combination of health and social themes in the same warning and suggest that warning messages that emphasize the social consequences of negative health outcomes will alter the way that people perceive the temporal distance to and likelihood of those outcomes (i.e., perceived vulnerability). Furthermore, we propose that temporal proximity will mediate the relationship between consequence type and perceived vulnerability. We test these hypotheses in five studies using different health behaviors, different manipulations of consequence types, and different dependent measures including risk perceptions, behavioral intentions, and perceptions of experience.
Pretest
Prior to conducting our main studies, we conducted a pretest to investigate our contention that social consequences are viewed as more temporally proximate than health consequences of the same health outcome. Although prior research has suggested that this is likely (Smith and Stutts 2006), to date no empirical evidence in support of this claim has been reported.
We selected obesity as the negative health outcome of interest because obesity affects more than one-third of U.S. adults (Flegal et al. 2012), and obesity-related health care costs almost $200 billion per year in the United States (Cawley and Meyerhoefer 2012). Most importantly, obesity leads to a variety of well-documented health consequences (National Heart, Lung, and Blood Institute 2013) and social consequences (Brownell et al. 2005).
Procedure
Sixty U.S. adults (45% female) participated in an online survey on Amazon Mechanical Turk (MTurk) service for monetary compensation. They were informed that the study dealt with the consequences of obesity and were presented with eight health consequences (e.g., heart disease) and eight social consequences (e.g., unattractiveness) of obesity. We compiled the list of consequences from current warning messages and federal government reports on the consequences of obesity (CDC 2015b).
Our key dependent measures included perceptions of temporal proximity (e.g., “How far away do the negative health consequences of soda consumption seem to you?”), vulnerability (e.g., “How likely do you think it is that you will become obese?”), and severity (e.g., “I believe that obesity is serious”), with all measures derived from prior research (Greene et al. 1996; Ronis and Harel 1989; Witte 1992; Witte et al. 1996). Participants answered these questions for all 16 consequences (8 health and 8 social). We also collected relevant health and demographic information, including frequency of indulging in some health behaviors (e.g., soda consumption) and current and past health status. We averaged the dependent measure scores across the eight health consequences and the eight social consequences to arrive at mean results for health versus social consequences. Details of all dependent measures appear in Web Appendix A.
Although there were no differences in perceived severity of the outcome across the two experimental conditions, we conducted a second mediation analysis using severity as the mediator and found no significant effects (95% bias-corrected CI = [-.12, .12]). We also tested for the possibility that both severity and temporal proximity could be joint mediators and again found no support for severity (95% bias-corrected CI = [-.14, .12]), but only for temporal proximity (95% biascorrected CI = [-.66, -.04]). This increases our confidence that the effects on vulnerability are mediated only by temporal proximity of the outcome.
Discussion
The results of Study 1 provide support for our hypotheses and document that highlighting social consequences caused by the health outcome of not flossing increases perceived temporal proximity and vulnerability. This occurs despite the fact that the social consequence (bad breath) is marginally less severe than the health consequence (weak gum tissue). Although this did not affect the perceived severity of the health outcome, it is intriguing to consider that less severe social consequences may be more effective than more severe health consequences because of their ability to bring the health outcome closer in time, thereby increasing its likelihood.
In Study 2, we use a different health behavior—soda consumption—to generalize our results. We specifically selected soda consumption to examine whether our results would hold for warning messages that advocate the prevention of negative behaviors, in contrast to Study 1, which focused on the promotion of a positive behavior. We also examine the moderating role of current health status (body mass index [BMI]) on the effects of consequence type. Because social consequences have the effect of making the health outcome seem closer in time and thus increase perceived vulnerability to the outcome, this effect should be attenuated for consumers who already perceive themselves as being close and highly vulnerable to the outcome (i.e., consumers with high BMIs). We therefore expect that BMI will moderate the effects of consequence type, such that respondents with lower BMIs will exhibit more pronounced effects than consumers with higher BMIs. This is consistent with prior research that has documented the moderating role of BMI on risk perceptions (Kan and Tsai 2004).
A limitation of Study 1 is that we do not control for differences between the social and health consequences on dimensions unrelated to risk such as vividness, imageability, mood, and novelty. For example, it may be that social consequences are perceived as more novel in a warning message because they are not used as frequently as health consequences. Or perhaps social consequences are more vivid (and thus easier to imagine than health consequences) or elicit a more positive mood. It may be that these differences in novelty, vividness, mood, and imageability—rather than differences in temporal proximity—lead to greater elaboration of the message and, thus, enhanced vulnerability. We address this issue in Study 2 by using a more conservative manipulation of consequence type by exposing all respondents to the same warning message but allowing them to generate their own social or health consequences of a health outcome. Because all respondents viewed the same message for the same amount of time, differences in elaboration cannot explain our results. We also pretested our message to ensure that there were no differences on the other aforementioned dimensions (vividness, imageability, mood, and novelty).
Not consider social outcomes with added consequences. We have hypothesized that the severity of a health outcome, together with the proximity of a social consequence of that outcome, will enhance vulnerability perceptions. However, because prior research has suggested that social outcomes may be more effective than health outcomes for certain populations (e.g., Ho 1998), it would be worthwhile to examine whether the advantages we find for social consequences are due to the addition of social consequences to health outcomes specifically or would hold for any combination of social and health. That is, is it possible that adding health consequences (e.g., depression) to a social outcome (e.g., social rejection) of a health behavior (e.g., smoking) would enhance temporal proximity and vulnerability perceptions similar to adding social consequences (e.g., feeling ugly) to a health outcome (e.g., mouth cancer) of that same health behavior (e.g., smoking)? In other words, would enhancing severity given high vulnerability (adding health consequences to social outcomes) lead to similar compliance as enhancing vulnerability given high severity (adding social consequences to health outcomes)? Given the conflicting evidence from prior research on the interaction between vulnerability and severity (Floyd, Prentice-Dunn, and Rogers 2000; Mulilis and Lippa 1990; Pechmann et al. 2003), we treat this as an empirical question to explore in Study 3. We also expand our pool of dependent measures to include quit intentions, thus focusing on message compliance as well as vulnerability perceptions.
Study 3
The study was a 2 (outcome type: social vs. health) • 3 (consequence type: none, social, health) between-subjects design with 119 U.S. MTurk participants (43% female) who received monetary compensation for their participation. In line with previous research (Thrasher et al. 2009), we screened respondents to identify regular smokers (those who reported smoking in the past 30 days and who reported smoking at least 100 cigarettes over their lifetimes). The reason to focus on current smokers was to test whether our effects would hold among consumers who had the highest need to change their behavior. The screening also provided a strong test of our effects because our respondents were likely to have been exposed extensively to prior warning messages about smoking, likely resulting in a greater propensity to ignore such messages.
We used mouth cancer versus social rejection as our health versus social outcome and feelings of loneliness and depression versus feeling ugly and unattractive as our health versus social consequences. Thus, in a departure from our previous studies, we used mental health outcomes rather than physical health outcomes to extend the generalizability of our effects. For example, in the health (social) outcome with social (health) consequence condition, respondents were informed that “smoking can lead to mouth cancer (social rejection) which can make you feel ugly and unattractive (lead to feelings of loneliness and depression) and adversely affect your social life (health).” Thus, similar to Study 1, we controlled for consequence type within the message. All consequences were drawn from research on smoking effects (Boden, Fergusson, and Horwood 2010; Smith and Stutts 2006)
Discussion
The results of Study 3 extend our previous findings in several ways. First, we document that only a specific combination of health and social outcomes/consequences may be effective rather than any combination of these variables. Specifically, health outcomes, along with their social consequences, are more effective than social outcomes with their health consequences, and this is likely due to the optimal combination of severity (through the health outcome) and vulnerability (through the social consequence). Thus, we replicate our findings in that when the outcome is health (cancer), social consequences render the message more effective than health consequences. However, when the outcome is social in nature (social rejection), it is perceived as less severe than the health outcome, and its enhanced proximity and vulnerability do not translate to greater quit intentions. Therefore, when severity is relatively low, the increase in temporal proximity through the addition of social consequences does not seem to have a meaningful impact on message effectiveness.
Second, we expand our set of dependent measures to include an intention measure. Our results for quit intentions are particularly impressive, given that our sample only included regular smokers, a segment known to be resistant to warning messages. This increases our confidence in the generalizability and strength of our effects and suggests that there is significant benefit to adding social consequences to health warnings.
Third, we expand health consequences to include mental health consequences. This is an important contribution because it helps refute the argument that social consequences may be more visible than health consequences. Given that mental health consequences such as depression and loneliness are not readily apparent or visible, our findings help better differentiate social and health consequences from simple visibility.
Fourth, we replicate prior research (Milne, Sheeran, and Orbell 2000) in our finding that both vulnerability and severity influence quit intentions when there are significant differences in the severity of the outcomes used. In our previous studies, there were no such differences in severity of the health outcome, thereby leading to null effects for severity.
Studies 1–3 collectively provide support for our contention that the addition of social consequences of health outcomes makes these outcomes seem more temporally proximate, leading to heightened perceptions of vulnerability to these outcomes. In Study 4, we provide further process evidence of this relationship between social consequences, temporal proximity, and vulnerability by showing that our effects attenuate when the outcome is manipulated to be temporally proximate (vs. distant). That is, in Studies 1–3, we used health outcomes that were temporally distant and documented that the addition of social consequences made these outcomes seem closer in time. Instead, if the outcomes were already temporally proximate, then the addition of social consequences would not affect proximity or, thus, vulnerability, leading to the attenuation of the advantages that accrue to social consequences.
Previous research (Chandran and Menon 2004) has used temporal frames (e.g., every day vs. every year) to vary perceived closeness of health outcomes and thereby the riskiness of these outcomes. We use such temporal frames in Study 4 to
Discussion
The results of Study 4 provide additional support for our contention that the addition of social consequences increases perceived vulnerability to the outcome by making the outcome seem closer in time. The finding that the advantages of social consequences are attenuated when temporal proximity is increased through a temporal frame suggests that social consequences work by increasing temporal proximity and that if the proximity of health consequences can be increased, the differences between these two consequence types can be negated.
Similar to Study 2, Study 4 also documents that differential attention to or elaboration of messages with social consequences cannot explain our results because the time provided to respondents across all experimental conditions was held constant. Thus, our effects do not occur because social consequences are unusual or rarely found in warning communications but are rather due to the intrinsic greater proximity of such consequences.
While the effects we found on behavioral intentions in Studies 3 and 4 are encouraging, in Study 5 we examine whether the effects of social consequences could extend to attitudinal measures and actual consumption experience perceptions. Previous research has suggested that overreliance on intention measures is not desirable because the intention–behavior link is often weak or nonexistent (Scholz et al. 2008; Sutton 1998), and intentions may only explain 28% of the variance in behavior (Sheeran 2002); therefore, we decided to use a different measure to assess the effectiveness of warning messages and add to the robustness of our findings: perceptions of experience.
Consumer perceptions of product experience have been shown to have important implications for consumer attitudes and decision making (e.g., Deighton 1984; Hoch and Ha 1986). Consumer experiences are especially important to marketers because perceptions of such experiences are often malleable and easily influenced using marketing tools such as advertising (Braun 1999). We extend this stream of research and suggest that warning messages may have delayed effects on the perception of the target behaviors depicted in these warning messages. To the best of our knowledge, no prior work has considered consumer perceptions of experience as an outcome of warning messages.
Study 5
The study was a 2 (health outcome + health consequences vs. health outcome + social consequences) between-subjects design and comprised two parts. Undergraduate students who were enrolled in a marketing course participated in the first part of the study in return for course credit. They were informed that the study was an advertising evaluation study and were shown a set of six ads. The target ad was ostensibly from the CDC and was adapted from actual health communications put forth by the CDC as part of the “Choose Your Cover” campaign (Web Appendix B). Respondents were given 30 seconds to view each ad to control for exposure and elaboration time. To promote the cover story about ad evaluations, we had respondents answer different questions after each ad, and the questions about the sun protection ad constituted our dependent measures in Part I. Our manipulation of the type of consequence was similar to Studies 2 and 3, with respondents generating as many social- (vs. health-) related consequences of skin damage.
Four days after completing the first part of the study, the same students were given a chance to participate in an ostensibly unrelated study for course credit. In this part of the study, they were given a sachet containing 1.5 grams of sunscreen to try, and their opinions about the sunscreen were solicited. The sunscreen brand was Be Smart. No advertisement for the brand was provided; only a single-use sample was given to the respondents. The use of sunscreen as our target product is a very subtle and conservative measure of the effectiveness of the message because the message advocated several target behaviors including using hats, sunscreen, sunglasses, and long-sleeved clothing. Thus, respondents could easily adopt behaviors other than sunscreen use to comply with the message. Following their use of the sunscreen, respondents answered questions about their experience with the product and reported their attitudes and attitude confidence about sunscreens. Forty-three students (56% female) completed both parts of the study. to greater intentions to protect skin and, consequently, greater enjoyment of the actual sunscreen experience.
Discussion
The results of Study 5 are significant in establishing that the advantages of using social consequences of health outcomes in health warning messages extend beyond the traditional intention measures and influence attitudinal confidence as well as actual product experience. These findings also document that the favorable effects of highlighting the social consequences of a health outcome can persist even with a delay (four days) between message exposure and measurement of perceptions and can be elicited in a context that is relatively separate from the health message (our health message advocated skin protection while the target product was a specific form of skin protection [sunscreen]). This is different from our previous studies, in which the product context was identical to the advertising context, and suggests that the effects of social consequences may be generalizable to contexts associated with a general health message. The finding that the consequence type used in the warning message can affect perceptions of a target product is intriguing because it significantly expands the range of variables to consider while assessing the effectiveness of warning messages beyond attitudes and behavioral intentions.
General Discussion
Warning messages serve a critical role in informing and persuading people to understand the risks associated with certain behaviors. Because of the enormous potential for consumer well-being and the extremely high cost of warning messages, researchers have spent a significant amount of time identifying ways to improve these messages. Often, warning messages fail to be effective because the long-term health outcomes they highlight are not perceived as likely or temporally close (i.e., these messages are low in eliciting perceived vulnerability; Keller 1999). In this research, we examine how framing the consequences of a negative health outcome that is highlighted in a warning message can affect perceptions of this outcome. We find that the nature of social consequences (i.e., they develop quickly) increases the perceived likelihood of the negative health outcome without decreasing its perceived severity. Specifically, we illustrate that when the social consequences of negative health outcomes are highlighted, people perceive them as more temporally proximate and therefore feel more vulnerable to these outcomes (Studies 1–5), leading to enhanced behavioral intentions (Studies 3–5), attitudes, and experience perceptions (Study 5). However, this effect (social consequences increasing proximity) attenuates when people are already vulnerable to the negative health outcome (current health status [Study 2]), when the health outcome is not sufficiently serious (social outcomes [Study 3]), and when the health outcome is already temporally proximate (Study 4).
Our findings contribute to the research on risk perceptions, temporal perceptions, and warning messages in several ways. First, we reconcile some inconsistent findings regarding the effectiveness of health versus social appeals in warning messages by illustrating the unique attributes of social consequences of health outcomes and how they can influence perceptions of vulnerability to the health outcome. In doing so, this article answers the call for research into the effectiveness of combining health and social themes in the same message (Hoek, Hoek-Sims, and Gendall 2013; Keller and Lehmann 2008) and demonstrates that the presence of social consequences can influence the way a negative health outcome is perceived. Thus, linking a health outcome (e.g., obesity) with social consequences (e.g., social rejection) brings the health outcome closer in time and makes it appear more likely but not less severe. Our finding that temporal proximity affects vulnerability alone also enhances our understanding of the processes underlying risk perceptions.
Second, by distinguishing between the outcomes of health behaviors and the consequences of these outcomes, we extend prior research that has focused on outcomes alone, and we suggest that combinations of different types of outcomes and consequences may be an effective strategy to enhance message compliance. Our finding that every combination of health/social outcomes with health/social consequences is not equally effective provides insight into the interactions between severity and vulnerability (Floyd, Prentice-Dunn, and Rogers 2000) and suggests that increasing vulnerability to a severe outcome may elicit greater message compliance than increasing severity to a vulnerable outcome.
Third, while previous research has documented the importance of temporal framing (e.g., Chandran and Menon 2004) and temporal proximity on risk perceptions, our work is the first to link social consequences to temporal proximity and contributes to the literature on psychological distance (Trope and Liberman 2010) by suggesting that consequences of outcomes may affect perceptions of temporal distance to that outcome and the likelihood of that outcome. Although prior research has investigated the simple relationship between temporal distance and outcome likelihood, we demonstrate that this relationship is more complex and affected by the type of consequence attached to the outcome.
A unique contribution of our work is its focus on consumer experience as a dependent variable. Prior research on warning messages has focused on behavioral intentions as the key indicator of message effectiveness but has also showcased the limitations of using a dependent measure that may not translate into long-term behavioral change (Sheeran 2002). We address this issue by considering the effects of warning messages on consumer perceptions of product experience (Hoch and Ha 1986). To the best of our knowledge, our research is the first to document the effects of warning messages on consumer perceptions of experience, thereby providing an important and alternative pathway to persuasion in the context of health warning messages.
From an applied standpoint, our research provides specific recommendations to public policy makers and companies regarding the way health warning messages should be constructed for maximum effectiveness by illustrating how the consequence type highlighted in a message influences perceptions of risk. Specifically, this research identifies a critical message attribute—highlighting the social consequences of negative health outcomes—that marketers can use to alter the perceived psychological distance of the negative health outcomes, which will increase perceptions of vulnerability and ultimately enhance consumer experiences. This research echoes Keller and Lehmann’s (2008) suggestion that warning messages should include both a social and a health component to increase effectiveness. Importantly, these studies document a strategy that can be used to overcome an inherent weakness of warning messages that focus on negative health outcomes: the low perceived vulnerability of a health outcome (see Pechmann et al. 2003).
Furthermore, whereas much of the research on the effectiveness of health versus social appeals has been conducted for tobacco-related warnings, we investigate our effect across a variety of health domains including behaviors that promote good health (flossing, UV protection) as well as behaviors that deter bad health (soda consumption, smoking), thus enhancing confidence in the generalizability of our findings. Finally, whereas prior research on the effectiveness of social appeals in warning messages has focused on adolescent populations, we investigate these effects among adult populations and thereby extend the applicability of social appeals to a broader audience.
Limitations and Further Research
One limitation of our research is that we do not consider the long-term effects of using social consequences. It is important to ascertain whether the advantages of social consequences dissipate over time, and further research needs to incorporate longer delays between the message exposure and measurement of effectiveness. Future studies can also explore the effects of our “unique health outcome + additional consequence” tactic among a variety of other populations (e.g., adolescents, children) to determine its effectiveness. Furthermore, because we were not able to test all types of social consequences, additional studies could investigate different social consequences among various consumer groups. Certain groups may respond more favorably to different types of social consequences (e.g., cosmetic vs. social ostracism). Another possible avenue for further research could include consideration of other consequences beyond social and health. Thus, financial consequences may be important, given that people deal with financial consequences daily and are sensitive to the financial ramifications of their actions. In addition, people may not be aware of the potential risks of poor health. For example, medical expenses are the number-one reason for personal bankruptcy, with approximately two million people in the United States declaring bankruptcy every year as a result of medical expenses (Mangan 2013).
Previous research has shown that framing (positive vs. negative) can influence the effectiveness of warning messages (Block and Keller 1995). While the current studies address a variety of behaviors, outcomes, and consequences, the messages used in the experiments all employ a negative frame—that is, they highlight the negative social/health consequences of engaging in a behavior. Further research could vary the type of frame (positive vs. negative) to identify whether messages that use positive social consequences of positive health outcomes affect risk perceptions.
Because social consequences are more commonplace in everyday life, they may be more accessible in memory. This greater accessibility could translate into greater recall of the message, which could have delayed effects on attitudes, risk perceptions, and consumption experiences. Therefore, an important delayed effect—consumption experiences—could be included in longitudinal studies to investigate the relationship between changes in perceptions of experiences and long-term behavioral change. It would also be worthwhile to consider what other patterns of differences exist between health and social consequences (e.g., accessibility, frequency of encounters).
GRAPH: FIGURE 2 Study 2: Perceived Temporal Distance and Perceived Vulnerability
GRAPH: FIGURE 3 Study 3: Perceived Severity, Perceived Temporal Distance, Perceived Vulnerability, and Quit
DIAGRAM: FIGURE 1 Consequence Type
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Record: 203- The Temporary Marketing Organization. By: Hadida, Allègre L.; Heide, Jan B.; Bell, Simon J. Journal of Marketing. Mar2019, Vol. 83 Issue 2, p1-18. 18p. 1 Diagram, 2 Charts. DOI: 10.1177/0022242918813119.
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The Temporary Marketing Organization
Increasingly, marketing activity is carried out within temporary organizations, whereby teams are assembled to complete a specific task within a predetermined time frame. Such organizations are uniquely suited to promoting various marketing outcomes but are not well understood. From a practical standpoint, their inherent characteristics create organizational challenges that, if not appropriately managed, can compromise performance. Drawing on agency theory and research on embedded ties, the authors conceptualize these challenges in terms of particular selection and enforcement problems. They identify three forms of temporary marketing organizations that vary in their selection and enforcement qualities. Next, the authors develop a conceptual framework that shows the selection and enforcement implications of a temporary organization's task, timeline, and team composition. They also demonstrate that selection and enforcement mechanisms have portable qualities and can be "imported" to a given temporary organization, from either a prior temporary organization or a larger permanent one.
Keywords: agency theory; embeddedness; enforcement; selection; temporary organization
Marketing decisions and outcomes—for instance, those relating to new product development, service delivery, and marketing communications—depend crucially on their organizational context. Prior research has considered such decisions and outcomes within two main organizational contexts: ( 1) permanent firms ([42]; [56]; [116]) and ( 2) ongoing long-term relationships between firms ([48]; [88]). Increasingly, however, marketing activity is being carried out within the context of temporary organizations, in which the focal parties do not necessarily share a permanent structure or a long-term association. Temporary organizations are widespread in new product development ([19]; [84]), business-to-business marketing ([150]), and supply chain and channel design ([129]). They are also increasingly popular in advertising, in which campaign delivery requires the collaboration of diversely skilled specialists ([59]), and in the delivery of complex services such as health care and tourism ([146]).
Industry observers have predicted that temporary organizations will be increasingly prevalent in meeting marketing objectives, given the pressure on firms to deliver innovative outcomes while containing costs by outsourcing to external parties ([60]). New product development in the pharmaceutical industry, for example, has all but devolved into temporary coalitions of universities, private labs, research centers, and information technology firms, such that between 2000 and 2011, more than half of all new drugs approved in the United States were developed in "stand-alone entities" organized around particular drug discovery and commercialization ([118]). The use of temporary organizations can promote organizational agility, which is, according to a 2017 McKinsey Global Survey, among the top three priorities of business leaders ([ 2]).
Yet, despite their apparent attractiveness, temporary organizations pose significant challenges, many of which are not readily accounted for by standard theory ([32]). As a category, temporary organizations—described by some as the "organizational analog of a one-night stand" ([112], p. 167)—are known for the "high variance" in their outcomes ([53], p. 884). Some temporary organizations create products, services, or experiences that impress industry experts and delight customers. Others, in contrast, exceed their budgets, fall short of expectations, and are soon forgotten. As an organizational form, temporary organizations have been argued to possess unusual "liability of newness" problems ([119], p. 242). For example, upon winning the global communications business for Dell in 2007, holding company WPP built a new marketing communications temporary organization, Enfatico, from the resources of its existing agencies primarily to provide a high level of service to Dell. While enthusiastically conceived, this initiative ultimately faced several challenges and was folded back into the Y&R agency two years later ([61]).
We posit that the inherent challenges of temporary organizations, which contribute to their high variance property, follow from their discrete time dimension: namely, a lack of a distinct past and future. With respect to the past, the outcome of a temporary organization hinges on the characteristics of its "motley crew" of members ([26], p. 5), many of whom may be working together for the very first time. From an organizational standpoint, this makes team selection a crucial undertaking—even more so than in other settings. Furthermore, the outcome of a temporary organization's task depends on its members' efforts. Yet in the absence of a tangible future—many temporary organizations disband upon task completion—the inherent incentives for inducing member effort that characterize other organizational forms ([76]) are lacking. In addition, for many temporary organizations, standard mechanisms such as member socialization ([29]) and long-term rewards ([80]) are unavailable. Consequently, temporary organizations face significant enforcement problems.
We develop a conceptual framework based on three forms of temporary organizations that differ in their selection and enforcement properties. Theoretically, the framework rests in part on the notion of "portability" ([70])—namely, the idea that particular selection and enforcement mechanisms can be transferred from a given temporary organization's context and thus be supplied (exogenously) to the temporary organization in question. For instance, a "hybrid" temporary organization ([137]) can draw on organizational qualities that reside in prior ties among its members, while a "fully embedded" temporary organization benefits from the properties of the permanent organization within which it exists and which can be brought to bear on discrete tasks. [59] analysis of the U.K. advertising industry shows how temporary organizations are embedded in "organizational and social layers" (p. 243). One of our key theoretical arguments is that if a given temporary organization, owing to such "layers," benefits from portable selection and enforcement benefits, it reduces the need for task-specific selection and enforcement efforts.
We aim to make four contributions to the literature. First, we document the unique characteristics and performance outcomes of an organizational form that is increasingly important to marketing. Given that marketing capabilities have a greater impact on firm performance than operational and research-and-development (R&D) capabilities (e.g., [97]), there will be a premium on effective management of temporary marketing organizations. At the same time, this research also shows that marketing knowledge is inherently difficult to codify. This problem, we argue, is magnified in a temporary marketing organization. Unfortunately, as recent reviews show (e.g., [103]), the current knowledge about temporary organizations is both limited and represented almost exclusively by scholars in engineering, organization theory, and general management. To date, marketing has contributed little. The current research represents an initial attempt to join and inform this literature.
Second, we identify some of the specific selection and enforcement mechanisms available to temporary organizations. Organizational researchers (e.g., [ 9]; [103]) have commented on how insights into specific mechanisms are lacking in the literature, and on the tendency to view temporary organizations strictly as "tools" rather than as organizations in their own right ([126]). As such, while our main focus is on temporary organizations in a marketing context, we believe that our conceptual discussion of selection and enforcement informs the general literature on temporary organizations.
Third, we integrate economic and sociological perspectives on temporary marketing organizations. Our model draws on agency theory ([13]) and extant research on "embedded" ties ([64]; [131]; [158]) to suggest how a temporary marketing organization's selection and enforcement problems can be addressed. Both theories are uniquely suited to analyzing temporary marketing organizations, given their focus on mechanisms such as signals that do not involve permanent structures. We argue that the very logic of a temporary organization involves identifying inherent matches between members and tasks, rather than creating them through permanent structures. These theories, however, provide different perspectives on how matches come about, including the specific ways selection and enforcement take place.
Fourth, from a practical standpoint, we propose a conceptual framework that suggests how particular exogenous drivers—namely, a temporary organization's task characteristics, time frame, and team composition—give rise to particular selection and enforcement needs and, ultimately, choices among types of temporary organizations. We also show how project managers and firms decide between making organization-specific governance choices versus relying on preexisting mechanisms that are portable from prior temporary organizations. We suggest how matches between exogenous drivers and organizational choices have performance implications, with regard to ( 1) the creativity of a temporary organization's output and ( 2) its decision-making speed. We illustrate our guidelines by drawing on examples from current practice.
In the next section, we discuss the nature of the temporary marketing organization and identify the main forms that exist. Then, we introduce our conceptual framework. The final section summarizes the model, discusses managerial implications, and provides critical paths for future research.
From the original contributions of [12] and [58], a temporary organization is generally defined as a "temporally bounded group of interdependent actors formed to perform a complex task" ([21], p. 1237). "Temporally bounded" means that a temporary organization is initiated by the commitment to a certain task and terminated by its completion (i.e., attaining a particular state or reaching a predetermined timeline; [59]; [106]). In other words, a temporary organization is subject to "institutionalized termination" ([104], p. 449) rendering it effectively "disposable" ([107], p. 427). The temporary organization members are individuals who collectively possess the skills and resources required to carry out the focal task. In agency-theoretic terms (e.g., [13]; [151]), these are "agents" who are assembled and managed by a "principal"—namely, the temporary organization's project manager. The principal could be the chief technology officer on an application development initiative, the chief marketing officer (CMO) managing software developers and advertising agencies for the launch of a new brand, or a firm's chief executive officer or CMO as they assemble a temporary organization within or outside the boundaries of their permanent organization. As shown by [146], a customer may also serve as a temporary marketing organization's principal by virtue of playing the role of a "resource integrator" ([154], p. 257) who assembles a team of service providers for a given task.[ 5]
A "baseline" temporary organization has a discrete time horizon and possesses neither a past nor a future. Indeed, the notion of discreteness, which in the extant literature (e.g., [137]) is sometimes captured by the term "stand-alone," is the "constitutive property" of a temporary organization ([144], p. 200). Baseline stand-alone temporary organizations are quite common, for instance in advertising ([59]), high-technology systems selling ([115]), and supply chain design ([129]).
In Table 1, we compare a stand-alone temporary organization with three other, more commonly discussed, organizational forms: a joint venture, a start-up, and a permanent organization.[ 6] A distinctive feature of a stand-alone temporary organization is a complete absence of both ( 1) a history of interaction between members and ( 2) a possible future. In contrast, because of the employment relationship and ownership of internal divisions, a permanent organization can rely on both a history and the promise of future interaction between members on multiple tasks. Joint ventures and start-ups occupy the middle ground between temporary and permanent organizations and share certain features of each. For example, both joint ventures and start-ups are less enduring than permanent organizations, despite the general expectation of a future, because they are more prone to failure and premature termination ([72]). Start-ups also share with temporary organizations the absence of a history in part because of the novel tasks and market needs that bring together a unique group of organizational members. Neither joint ventures nor start-ups, however, exhibit the absence of both a past and a future.
Graph
Table 1. Organizational Forms.
| Stand-Alone Temporary Organization | Joint Venture | Start-Up | Permanent Organization |
|---|
| Time | | | | |
| Past | Absent | Possible | Absent | Existing |
No history of ties between organizational members; only industry-level reputations available
| History of working relationship determines joint venture partner
| Typically, no history of ties between members
| Existing members managed within employment relationship
|
| Future | Absent | Possible | Intended | Existing |
Termination implied with task completion
| Formed with multiple projects in mind
| Organizational longevity and growth are assumed
| Organizational longevity is assumed
|
| Indicative literature | Lundin and Sõderholm (1995), Engwall and Svensson (2004) | Kogut (1988), Hennart (1988), Gulati (1998) | Carter, Gartner, and Reynolds (1996), Brouthers and Brouthers (2000) | Williamson (1975), Cyert and March (1963), March and Simon (1958) |
| Example | An organization that exists solely to address a specific issue (i.e., create a new product or service), and is disbanded after the completion of the task (e.g., app developers for ) | An organization created by two or more parties, characterized by shared ownership, to combine capabilities to address one or more issues of joint interest. May dissolve (e.g., Lion and Heineken) or become permanent (e.g., Dow Corning) | A newly created organization designed to exploit a market opportunity, with a minimum viable product, service, or platform and a scalable business model (e.g., WeWork, Square, Uptake) | A limited-liability organization with (often public) established ownership arrangements and stable governance structures (e.g., independent board and management team) and ongoing employment relations (e.g., IBM, DuPont, Procter & Gamble) |
Consider next the specific organizational implications of a "stand-alone" temporary organization. As we have noted, the members of such an organization have no history of previous interactions—they are true "interdependent strangers" ([112], p. 169) who lack a collective memory ([122]). This, in turn, implies a lack of first-hand information about potential members' attributes and creates a need for explicit selection efforts on behalf of the organization's principal, who needs to identify, sometimes from scratch, the particular skills and knowledge bases the agents require to complete the focal task. As an example, [146] note the importance of systematic selection in the delivery of complex services, for which customers expect "connected experiences" and coordinated efforts from teams of suppliers or agents. Frequently the focal agent skills are unobservable or associated with information asymmetry, a condition that opportunistic agents can purposely exploit, thus creating an adverse selection problem ([ 3]; [54]).[ 7]
Agency theory's account of selection involves a principal who creates opportunities for agent self-selection ([13]; [141]), and/or agents who send signals that serve as proxies for their underlying characteristics. These signals may involve costs to the agent that create a separating equilibrium, in that only "appropriate" agents have the incentive to post the required "bond" ([85]). In general, the selection process in agency theory is task-specific and forward-looking: it focuses on the unique selection efforts that are needed for the task at hand. This perspective is of particular relevance to a stand-alone temporary organization, which does not possess inherent or preexisting organizational properties.
While selection efforts serve important organizational purposes, they are rarely sufficient on their own, in part because even the "right" agents may fail to fully use their skills ([94]; [113]). As such, explicit enforcement efforts are typically needed to mitigate ongoing moral hazard problems ([ 5]). A stand-alone temporary organization faces unique enforcement problems because of its discrete time horizon ([25]; [106]), which generates weak incentives for cooperation among its members ([ 6]; [76]).[ 8] Consider, as an example, the stand-alone temporary product development organization formed to build the True Story card game and mobile app. Its creators followed strict parameters for involvement using artificial intelligence on the Foundry online platform to assemble freelancers and communicated through messaging software. The True Story developers essentially "flash mobbed" solely to create the product and promptly disbanded after the completion of this task ([135]). The "shadow of the future" ([ 6]; [76], p. 265) was so remote in this case that the project leader even described fellow temporary organization members as "these faceless, nameless, nationless submitters" ([93]).
The lack of built-in enforcement mechanisms requires explicit or task-specific efforts. In agency theory, this involves a formal contract that specifies financial incentives and monitoring procedures ([86]). A temporary organization poses unique challenges in this regard because of the need to provide incentives in the presence of novel tasks and ambiguity with regard to individual agent contributions ([ 8]; [80]). For instance, temporary organizations developing new products in the commercial aircraft industry ([139]) often use explicit revenue-sharing contracts that are designed to align the parties' interests.
While a stand-alone temporary organization constitutes a useful baseline, research and empirical observation also point to other forms of temporary organizations. Table 2 draws on [137] to compare a stand-alone temporary organization with two other forms with respect to their past and future dimensions. The first row of the table shows a baseline stand-alone temporary organization. Next, we suggest how a temporary organization may "acquire" a past or a future, by virtue of ( 1) capitalizing on its members' prior relationships (a "hybrid" temporary organization), or ( 2) being embedded within a permanent organization in which the focal members share a larger organizational context (a "fully embedded" temporary organization).
Graph
Table 2. Temporary Organization Forms.
| Past | Temporary Organization Forms | Future |
|---|
No shared historyLack of information on member attributes Need for explicit selection
| Stand-alone (e.g., a radical innovation project, a one-off product launch event) | No shadow of the futureNo inherent incentive for member cooperation Need for explicit enforcement of member effort to complete task
|
Direct historyDirect observation and personal reputation Dyadic norms
| Hybrid (e.g., feature film production, marketing communications campaigns) | Uncertain futureWeak incentives for member cooperation Need for explicit enforcement of member effort to complete task
|
Shared organizational historyOrganization-level reputation Organization-level norms
| Fully embedded (e.g., internal consulting unit, new product development team) | Shadow of the futureOpen-ended interaction of members
Explicit enforcement mechanisms Low-powered incentives Centralized monitoring of members
|
In practice, a temporary organization's contextual influences may serve organizational purposes, with regard to how selection and enforcement are carried out. However, the specific nature of these influences, or, more generally, the way temporary organizations interact with permanent structures ([144]), remains a largely unanswered question. In contrast to agency theory and its focus on individual transactions, embeddedness theory argues that economic transactions take place within social structures ([63]) that allow for selection on the basis of direct observation of past behavior. Specifically, embedded ties facilitate the transfer of fine-grained information ([153]) that reveals parties' larger identities ([63]). These identities, in turn, may be relevant to a new temporary organization and thus serve selection purposes.[ 9]
Furthermore, the social content of embedded ties gives them inherent enforcement qualities ([99]). An agent's identity in an embedded relationship comprises more than skills and capabilities; it also includes solidarity with the other party ([47]; [73]) and thus helps align the goals of the principal and agent ([124]).[10] This larger identity, in turn, serves as an ongoing source of motivation and self-control ([ 4]).
Consider first a "hybrid" temporary organization, wherein the parties have a history gained from interacting on previous tasks. Hybrid temporary organizations are common in feature film production, in which the same parties often engage in repeated collaborations ([53]). A specific example of a hybrid is the constellation of firms and independent contractors involved with Boeing's ongoing product development of the 737 airplane, first launched in 1966 and since evolved over three generations, including nine major variants. A supplier's involvement in previous product development phases of the 737 would create a reasonable expectation of being selected for involvement in subsequent phases. In general, ongoing relationships between the same parties represent a source of stability ([147]). More specifically, as shown in Table 2, past interactions have organizational implications resulting from direct observation of a partner's behavior and the presence of relationship-level reputations and dyad-level norms that can influence a new temporary organization.
[142], p. 299) have described a hybrid temporary organization as a "latent organization" because of the parties' ability to activate prior relationships ([103]). At the same time, just like their counterparts in a stand-alone temporary organization, the members of a hybrid temporary organization do not necessarily expect to cooperate again after task completion. For instance, with more radical innovation in the Boeing product line (e.g., the 787 Dreamliner), members are far less certain of being involved in future product development tasks ([139]).
The third form is a "fully embedded" temporary organization, which exists within the boundaries of the permanent organization to which its members belong ([ 9]). This form of temporary organization may possess some of the dyadic properties of a hybrid, to the extent that the same parties may be involved in successive temporary organizations. A fully embedded temporary organization also benefits from the aggregate features of the permanent organization itself, including organizational-level reputations and norms. As we discuss subsequently, these features have particular selection benefits. Moreover, by virtue of being part of a permanent organization, a fully embedded temporary organization possesses a distinct future time horizon and a "probability of continuing association" ([44], p. 155). This time horizon provides enforcement benefits even in the absence of direct interactions between parties. [38], p. 34), for instance, notes the disciplinary effects that "the perpetual presence of tomorrow" has on organizational members. Even if discrete temporary organizations are formed and dissolved on an ongoing basis, common ownership may influence their members' actions, by allowing them to project beyond the completion of a given task.
These questions are the focus of recent research on the implications of the postproject "afterlife" for the principal–agent relationship ([121]). Consider, for example, Ford Motor Company's development of the 1992 Lincoln Continental (e.g., [132]). While organizing product development around temporary teams was becoming the norm in North American automotive manufacturing at the time, expectations of postproject interaction were limited by members' strongly held "assumptions of their job description" ([ 7], p. 66) and narrow understanding of the product development process. Yet, in reality, members of the Lincoln team remained in contact even as they "were reassigned to other projects" ([ 7], p. 68).
As Table 2 shows, in addition to its built-in future, a fully embedded temporary organization has a set of explicit enforcement mechanisms at its disposal ([30]; [156]; [160]), including low-powered incentives (e.g., promotion opportunities, nonmonetary rewards), and centralized authority and monitoring mechanisms ([36]). [66] notes how low-powered incentives help promote cooperation among specialists, a common scenario within fully embedded temporary organizations. In the following section, we discuss how different selection and enforcement mechanisms match up with particular drivers or exogenous influences to affect a temporary organization's performance.
Our conceptual framework appears in Figure 1. We first introduce its main components and linkages. Next, we discuss some of its underlying microlevel processes and develop a set of propositions.
Graph: Figure 1. Conceptual framework.
The framework's starting point is a series of drivers or exogenous influences that create governance problems of various kinds. We draw on prior research on temporary organizations ([87]; [104]) to focus on three specific manifestations of the so-called "Ts": task, time, and team. These are the novelty of the organization's focal task, its planned duration, and the composition (or heterogeneity) of its team.[11] As Figure 1 shows, the effects of the three exogenous drivers on the choice of temporary organization form are mediated by the particular selection and enforcement problems to which they give rise.
These particular theoretical linkages assume that organizational form matters. In part, this is because, as discussed, organizational forms possess inherent governance mechanisms, such as the social fabric of a hybrid and the long-term rewards of a fully embedded temporary organization. As such, the need for a certain governance mechanism (e.g., a particular enforcement mechanism) affects the choice of organizational form.
Furthermore, as represented by the backward-pointing arrow in Figure 1, we allow for the possibility that context matters, and that an existing temporary organization, in the form of either a hybrid or a fully embedded one, may make certain selection and enforcement benefits available to a new one. Under such a scenario, preexisting governance mechanisms are portable across organizational contexts and can transfer to a new temporary organization.
The last part of the conceptual framework describes a temporary organization's performance outcomes. We capture performance outcomes through output creativity and decision-making speed. Our general expectation, as expressed by the dotted line that frames the model in Figure 1, is that performance follows from matches between ( 1) the exogenous drivers and ( 2) the organizational choices made. Conversely, as we discuss subsequently, we expect that mismatches will undermine performance.
A temporary organization's task has been argued to represent its reason for being ([104]). Our particular focus is on novel tasks, in which a temporary organization is assembled for the purpose of executing a one-off, nonroutine task ([55]). Such initiatives are characterized by causal ambiguity ([149]), in that the principal does not clearly define the goals or the means to reach them at the outset ([79]; [101]). For instance, X, a highly confidential R&D facility founded in 2010, allows its parent company Alphabet to assemble temporary organizations dedicated to developing "moonshot" new products with no direct link to Google's core business of internet search. They include energy kites, salt-based energy storage, and floating internet routers (https://x.company/).
Truly novel tasks require distinctly novel human and technological resources that may not be available from prior relationships or within an existing firm and therefore require the principal to search from a new agent pool. [102] specifically describe how nonroutine activities tend to involve individuals (e.g., advertising agents, marketing consultants) who lack experience working together, even though they may share common industry or disciplinary knowledge.
For these reasons, we expect task novelty to be best addressed by a stand-alone temporary organization, albeit subject to organization-specific selection and enforcement efforts that safeguard against subsequent difficulties. In the case of True Story, the careful algorithm-based selection of contributors and the clear specification of their roles in the development of the game helped mitigate such difficulties and enabled them to discharge their responsibilities effectively. However, when the unique nature of the task led to some substandard deliverables, the principal intervened to solve the problem by hiring "another freelancer...to oversee this work" ([135], p. 4).
We expect hybrid and fully embedded temporary organizations to be more constrained with regard to novel tasks, because both involve selection from a preexisting, and thus limited, pool of agents. Familiar tasks, however, create a different scenario: For such tasks, "members know what to do, and why and by whom it should be done" ([104], p. 441). Agent requirements can be unambiguously spelled out ex ante in a way that facilitates selection, and enforcement can be carried out through standard agency devices such as monitoring, potentially against the backdrop of a formal contract ([69]). Agents' general industry roles and reputations may also serve monitoring purposes ([10]; [151]). Thus, for familiar tasks, the relatively modest selection and enforcement requirements make a hybrid or fully embedded temporary organization feasible. We propose the following:
- P1: The greater the novelty of the task, the greater the need for organization-specific selection and enforcement mechanisms, and the higher the likelihood of using a stand-alone temporary organization relative to hybrid and fully embedded forms.
Time is a second critical driver of a temporary organization's governance needs ([104]). Importantly, while all temporary organizations share the characteristics of a fixed start and end point, their planned duration varies considerably. We focus on differences in duration and their governance implications. Specifically, time limits create a sense of urgency and place a premium on selecting appropriate partners and quickly deploying enforcement mechanisms. For instance, creating a music video takes as little as two days ([10]). Such a time frame makes it virtually impossible to craft organization-specific enforcement mechanisms like social norms, and it may be necessary for the principal to draw on an existing network in which agents' reputations from direct interactions ( 1) serve informational and enforcement purposes and thus ( 2) represent functional substitutes for new or organization-specific mechanisms.
Arguably, the need for speed provides an incentive to search for agents internally and, thus, to resort to a fully embedded temporary organization. Doing so, however, may come at the expense of ( 1) acquiring truly new knowledge (as per [62]] tie strength thesis) and ( 2) identifying partners with skills that match the focal task. In essence, embedded ties from prior direct interactions—that is, the "shadow of the past" from previous exchanges—are better suited to provide portable selection and enforcement benefits that can be applied to a new task. Importantly, such benefits are not necessarily available from a fully embedded temporary organization in which the focal parties, while belonging to a common permanent organization, may not have had direct prior interactions with each other.[12] Formally,
- P2: The shorter the duration of the task, the lower the feasibility of crafting organization-specific selection and enforcement mechanisms and the higher the likelihood of using a hybrid temporary organization relative to stand-alone and fully embedded forms.
A third driver of a temporary organization's governance needs is its team composition ([104]), which, in our context, refers to the heterogeneity of its members. Heterogeneity presents several organizational challenges, perhaps the most important of which being the need to coordinate action and manage potential conflict ([75]). In these respects, we expect the fully embedded form of temporary organization to possess inherent benefits relative to the stand-alone and hybrid forms.
The high-powered incentives that characterize the stand-alone and hybrid forms serve to emphasize and "separate the interests of the agents," which may exacerbate underlying conflict within the temporary organization. The implicit and low-powered incentives characteristic of fully embedded temporary organizations, by contrast, serve to "interlock the fates of agents" through the employment relationship and, thus, reduce potential sources of conflict within the temporary organization ([30], p. 538). Furthermore, an employment relationship gives rise to member expectations of mobility within and between teams in the permanent organization ([46]). Thus, in the event of potential conflict arising from heterogeneity, the principal can substitute and redeploy temporary organization members efficiently. In practice, the inherent characteristics of a fully embedded temporary organization possess unique selection and enforcement benefits that address the problems that arise from team heterogeneity.
- P3: The greater the heterogeneity of the team, the greater the need for enforcement through low-powered incentives and the higher the likelihood of using a fully embedded temporary organization relative to hybrid and stand-alone forms.
As discussed previously, our framework is based in part on the possibility that the selection and enforcement needs of a temporary organization may be portable or supplied exogenously, either from preexisting agent relationships (in the case of a hybrid temporary organization) or from the features of a permanent organization (in the case of a fully embedded temporary organization). Portability is much less achievable (though not entirely infeasible) in the case of a stand-alone temporary organization, which benefits from neither prior agent relationships nor a common permanent organization.
Consider first the case of a hybrid. Prior research has identified patterns of repeat encounters across temporary organizations through persistent semipermanent work groups ([14]). Such interactions reduce information asymmetry because of ( 1) firsthand observation of agent attributes ([74]) and ( 2) knowledge sharing between members about agent abilities ([66]). As such, prior relationships serve potentially important selection purposes. With regard to enforcement, a preexisting tie is capable of aligning parties' goals ([124]), which in turn promotes agent effort and reduces the need for explicit incentives and monitoring. For example, for the development of subsequent generations of the 737 airplanes, Boeing, "reluctant to upset the delicate balance," has tended to draw on familiar components suppliers for model upgrades, a fact made possible by a high percentage of immutable and familiar features of the 737 product ([ 1], p. 4). This suggests that enforcement benefits may also be portable.
As noted, hybrid temporary organizations represent sources of knowledge ([66]), pertaining specifically to an individual agent and her capabilities ([97]) and motivation. Importantly, however, there are limits to the transferability of such knowledge. Stated differently, the portability that resides in prior relationships is subject to boundary conditions. As we discuss next, these boundary conditions reside in the temporary organization's task.
Consider first selection, and how novel tasks limit portability. As an example, consider how many established firms are experimenting with temporary innovation labs to leapfrog (or, at the very least, keep pace with) industry upstarts to cope with faster innovation cycles and the threat of business model disruption ([41]; [60]). The previous example of X provides an illustration of such experimentation, as does Spanish telecommunications company Telefónica, which set up a temporary innovation lab in Cambridge, United Kingdom, to develop new products for the Internet of Things and wearable devices. The lab was outside regular technology management and compliance processes at Telefónica but drew on established networks of agents, including Spanish engineers and long-time partner MediaTek, a Taiwanese semiconductor design company, which housed the lab in its R&D offices.
A principal's past interactions with individual agents reveal particular agent traits. However, given a new task, these traits are not automatically portable ([82]). Past interactions may have demonstrated "technically competent role performance" ([112]), but such information does not necessarily transfer readily to a new temporary organization with unique requirements ([137]). Theoretically, we posit that embedded ties are limited in their ability to address adverse selection problems which arise with new tasks. [59] specifically discusses how some temporary organizations face significant selection problems because of the limited signaling value of general governance mechanisms such as industry certification and professional codes.
At the margin, a preexisting tie may increase an agent's tendency to truthfully represent his or her attributes to the principal, but this in itself may have a limited effect on a temporary marketing organization's performance. In practice, this means that for selection purposes, in the context of novel tasks, the information that is naturally available to a hybrid organization must be augmented with explicit (endogenous) signals that reveal the necessary agent traits. At very high levels of novelty, the advantages of portability might diminish to the point that it makes more sense for the firm to abandon a hybrid approach, reverting to a stand-alone model of temporary organization and benefiting from searching a completely new, and larger, pool of agents.[13]
As a specific example, Boeing, in the development of the radically different 787 Dreamliner, had to adopt a new organizational model to recruit a large number of new and existing partners who were responsible for an "unprecedented portion of the design" ([139], p. 63)—design skills that Boeing lacked owing to the inclusion of new technologies that had not been used before in such large civilian aircraft. This required the development of specific incentives deployed (endogenously) for the new product development organization. In particular, Boeing asked its suppliers to self-select for a "build-to-performance" risk-and-revenue-sharing model unique to the Dreamliner development task, in which suppliers bore the upfront cost of R&D but shared in revenues from future aircraft sales. Thus, we suggest that hybrid (embedded) ties and formal (agency-based) signals are not functional substitutes for each other. Rather, they address different types of problems and may serve complementary purposes. Formally:
- P4: The greater the novelty of the task, the lower the portability of the selection benefits from a hybrid temporary organization and the greater the need for organization-specific selection mechanisms.
A preexisting tie with a particular principal also affects an agent's general motivation to support the focal relationship ([153]). As such, we posit that the enforcement properties of a hybrid tie ([99]) are portable across temporary marketing organizations, regardless of the nature of the task. Thus, perhaps counterintuitively, the enforcement effect of a hybrid tie has a broader scope than its selection effect, and inferences about likely agent effort transfer more readily across individual temporary organizations. In practice, to the extent that a hybrid tie has portable enforcement qualities, it serves as a functional substitute for formal agency mechanisms such as financial incentives and monitoring. [37] research specifically suggests that repeated interaction between parties can solve incentive problems. In formal terms, while hybrid ties possess limitations with regard to adverse selection problems, they may, because of their general effects on agent motivation, serve as solutions to moral hazard problems. We propose the following:
- P5: For a hybrid temporary organization, enforcement benefits are portable regardless of the nature of the task.
Finally, consider portability in the context of a fully embedded temporary organization. Here, the features of a permanent organization confer potentially portable selection and enforcement benefits to the temporary organization. As with hybrids, we expect portability to be task-dependent: For familiar tasks, we expect that a fully embedded temporary organization's selection and enforcement benefits will both be portable. For novel tasks, however, we expect only the enforcement benefits to be portable.
Importantly, while our expectations for hybrids and fully embedded temporary organizations parallel each other, the underlying explanatory mechanisms (and the specific sources of portability) are different. In the case of a hybrid temporary organization, the portable selection benefits (for familiar tasks) rest on direct interaction and personal reputations between principals and agents. These, in turn, make information about a partner's motivation to perform available from personal experience. This defining feature of a hybrid temporary organization is not necessarily present in a fully embedded temporary organization, where the parties, while belonging to the same permanent organization, may not necessarily have interacted directly in the past.
As a result, the selection benefits for a fully embedded temporary organization involve general agent reputations that reflect a firm's standard "experience ratings" ([156]), but which may not be based on direct interactions between the focal parties. Furthermore, the portable enforcement benefits of a hybrid are rooted in particular dyadic norms cultivated from direct contact between parties. While a fully embedded temporary organization possesses unique enforcement mechanisms such as low-powered incentives (e.g., in the form of promotion opportunities and fixed financial compensation) that serve enforcement purposes ([66]), we believe that these mechanisms, owing to their general and nonpersonal nature, are weaker than the dyadic norms that characterize a hybrid. Team members working in the Lincoln Continental (fully embedded) temporary organization, for example, contributed to a series of "process innovations" and built "communities of practice" that united them under a common mission ([ 7], p. 67). While these were highly attractive organizational features, they were less likely to survive the disbanding of the temporary organization because the "immune system of the larger company" made it more difficult for learning to permeate beyond the team at project completion ([ 7], p. 67). Accordingly, we propose the following:
- P6: The portability of a fully embedded temporary organization's selection and enforcement benefits is weaker than for those of a hybrid.
Our first six propositions are descriptive in nature, in that they illustrate the likely (as per extant theory) organizational choices made by firms. We note, however, that the theories that we relied on to derive P1–P3 (e.g., agency theory) have explicit normative underpinnings. As such, organizational choices that are consistent with these propositions represent "matches" that should manifest themselves on specific performance dimensions—including, as per the extant literature (e.g., [102]; [138]), a temporary organization's impact on the end customer. Such impact may include output creativity, as reflected in whether a temporary organization's output (e.g., a new advertising campaign or product) differs from competing alternatives in a way that is meaningful to customers ([45]). Furthermore, a match may affect decision-making speed, or the time elapsed between the initial idea generation and the deployment of the focal campaign or product launch. Next, we explore how matches between the nature of a temporary organization's task and its selection and enforcement requirements affect output creativity and decision-making speed, respectively—two outcomes that are particularly relevant to marketing.[14]
Output creativity, we argue, is inherently linked with selection efforts. To the extent that a given selection process succeeds in identifying a "motley crew" ([26], p. 5) of agents with an appropriate set of skills (a "match" scenario), it should produce an output with novel attributes, such as a new product or service that achieves a desired location in some multiattribute space ([57]). Research has shown, for instance, that groups in which experienced members are appropriately matched with new ones produce more innovative outcomes ([128]). Thus, selection efforts that match the temporary organization's novel task requirements can be expected to have performance implications in the form of output creativity.[15]
Conversely, "mismatches," or observed deviations from the stated propositions, can be expected to undermine performance. We consider two types of mismatches. First, a temporary organization's actual selection and enforcement efforts may fall short of the requirements set forth in our framework for a necessary level of task novelty. Suppose, for instance, that a principal erroneously assumes that an existing agent relationship has complete portability and, based on that assumption, fails to undertake the explicit selection efforts that the new task requires. Theoretically, this would produce an adverse selection scenario ([ 3]), in that agents who lack the necessary creative skills for the task are chosen. In practical terms, such an organization would be "underorganized" ([31]) relative to the task at hand. Ultimately, output creativity would be compromised by the insufficient selection efforts. [41] provide case evidence that suggests how customer-level outcomes such as satisfaction may be undermined by mismatches between a temporary organization's particular needs and the structures that are deployed to produce such outcomes.
In the second type of mismatch, the organizational choices that are made in a given situation may produce a temporary organization that is "overorganized," in that the mechanisms in question are redundant given the temporary organization's actual needs. This could be a result of underestimating the organizational qualities that reside in a temporary organization's larger context. For instance, unlike the logic underlying P1 that novel tasks require a stand-alone temporary organization and new (task-appropriate) skills, a firm may fall back on an existing structure and rely on a fully embedded temporary organization. For a truly novel task, this would involve a mismatch, to the extent that the necessary skills are unavailable internally. Ultimately, sought-after creativity outcomes may be compromised. This discussion suggests the following proposition:
- P7: The closer the match between the novelty of a temporary marketing organization's task and its selection efforts, the greater the creativity of its output.
P7 will hold, in principle, for matched selection efforts across all forms of temporary marketing organizations. However, absolute levels of output creativity might differ across forms. In a stand-alone temporary marketing organization, information about member motivation is indirect, but the pool of potential candidate organizations is unlimited (in terms of ability). For a hybrid, on the other hand, information about motivation is more abundant and direct, yet the principal must draw from a limited agent pool (in terms of ability). Furthermore, [109] identify the possibility of information-processing biases resulting from strong preexisting ties (as in the case of a hybrid), which cause principals to settle for suboptimal agent relationships, revealed by a tendency to attach greater weight to collaborative rather than technical skills ([24]). Taken together, hybrid temporary marketing organizations will on balance produce less creative output than stand-alone temporary marketing organizations.
Consider next the performance implications of decisions related to enforcement. Enforcement, we argue, is inherently linked with a temporary organization's decision-making speed. As an example, in the context of new product development, speed is reflected in a given product's time to market, as measured by the time elapsed between the initial idea stage of a product development process and the actual market launch ([52]). In the context of advertising, speed has to do with how quickly a campaign can be deployed that responds to key cultural events, political activities, or competitive actions ([59]).
To the extent that appropriate enforcement efforts have successfully aligned parties' objectives (e.g., through explicit incentives), the lower the friction in the organization's decision-making processes, and the more quickly the focal campaign or product can be launched. In general, aligning objectives helps develop blueprints for action ([50]), which leads to efficiency by "preventing deviation" ([152], p. 70). Where conflict does emerge between partners, it can be resolved quickly according to these predetermined protocols ([20]) that increase decision-making speed.
However, mismatches are likely to undermine decision-making speed. If a firm relies on a stand-alone temporary organization under conditions of low task novelty, they will be wasting time searching for and socializing with new organizational members when a deployment of internal staff would suffice. Furthermore, the stand-alone temporary organization's lack of built-in governance features mean that it is underorganized for the task at hand, which reduces speed.
Decision-making speed may also be compromised by relying on mechanisms that are not strictly needed given the nature of the task. Assume that a given principal chooses to rely on formal incentives and monitoring, as per agency theory. To the extent that a prior relationship (in the case of a hybrid) would have provided sufficient enforcement benefits, the overorganization represented by the new agency mechanisms imposes significant setup costs for an enforcement regime that is ultimately redundant ([133]). In fact, overorganization may have negative consequences in the form of agent reactance ([17]) which "crowds out" intrinsic motivation ([43]; [77]; [89]). Given that temporary organizations often ( 1) involve tasks that require high levels of intrinsic motivation ([25]; [123]) and/or ( 2) employ professional service providers ([67]; [155]) who value discretion and freedom from external constraints ([148]), reactance scenarios are not unlikely and, thus, will significantly slow a temporary organization's decision-making speed. Stated formally,
- P8: The closer the match between the novelty of a temporary marketing organization's task and its enforcement efforts, the greater its decision-making speed.
P7 and P8 recognize the natural linkages between ( 1) selection efforts and output creativity and ( 2) enforcement efforts and decision-making speed. However, they leave somewhat open the possibility of other linkages, namely between ( 1) selection efforts and decision-making speed and ( 2) enforcement efforts and output creativity. Consider, for example, the latter case. Enforcement fulfils the purpose of aligning goals between temporary organization members and eliminating friction in the relationships between them. As per P8, these actions result in greater decision-making speed. It might be argued, however, that matched enforcement efforts—to the extent that they get everyone "on the same page"—suppress output creativity by virtue of promoting homogeneity within the organization ([81]).
Temporary organizations are increasingly used to carry out marketing activity. However, they tend to be "high-variance" phenomena ([53], p. 884), and their outcomes are uncertain. The initial premise of this paper was that identifying ( 1) the specific sources of variance and ( 2) particular solutions requires a multitheoretical perspective on how a temporary organization's set of agent inputs translates into outcomes.
Our framework points to the risk of analyzing temporary organizations through a single theoretical lens. Agency theory's focus on individual contracts downplays the organizational qualities that may reside in prior ties and a common organizational context. At the same time, overestimating the organizational properties of the larger context may create problems in its own right, to the extent that the focal properties may not be fully portable. In general, temporary organizations face significant and partly competing demands ([136]); some of which have to do with simultaneously managing pressures toward both under- and overorganizing ([31]).
Previous research, such as [64] embeddedness thesis, has made a persuasive case for the interpenetration of economic and social action and for the need to jointly examine formal and informal organizational mechanisms ([22]; [100]). Our framework points to specific interrelationships between embedded ties and formal agency mechanisms. For selection purposes, we argue that the two may complement each other, because explicit signals that reveal agent traits may sometimes be needed to augment preexisting ties. In contrast, embedded ties may substitute for formal selection devices ([71]), but only for familiar tasks where agent traits are portable. Furthermore, embedded ties and formal agency mechanisms may substitute for each other as enforcement devices, regardless of the novelty of the temporary marketing organization's task.
In general, we aim to add precision to the embeddedness argument by suggesting that embedded ties possess particular, but to date undocumented, boundary conditions. Next, we identify some implications for managerial decision making that follow from our framework. We close with a discussion of topics for future research.
Although our current contributions are mainly theoretical, we nonetheless consider some broad prescriptions for managerial decision making. As a starting point, in an era of shrinking marketing departments and shortened average CMO tenure ([145]), marketers are more inclined to rely on temporary organizations to meet their objectives with immediate impact. Some guidance about how best to implement this organizational form is therefore essential. We consider the development of guidelines to help firms determine the most appropriate selection and enforcement mechanisms depending on the form of temporary marketing organization to be the single most important managerial implication deriving from our framework. Using task novelty, time pressures, likely team heterogeneity, and temporary organizational form as inputs, we suggest building a "playbook" to assist marketers in using temporary organizations to best effect.[16]
To begin with, firms might audit the extent to which they have access to embedded networks of partners to complete the task at hand and meet its objectives. For instance, a simple starting point might be to count the number of formal agreements a firm has with external parties (e.g., licensing, joint ventures) and the extent to which it tends to engage in single-partner alliances with multiple contractual agreements. Such assessments are likely to give a rough indication of how embedded a firm is within a network ([143]) and of the availability of preexisting agent relationships. A more analytically rigorous approach would be to capture how well an individual, team, or firm is tied to well-connected others in a supplier network ([125]). Eigenvector centrality ([16]), for example, measures the extent to which an individual, team, or firm has relationships with suppliers who are themselves connected to many others—a reflection of the density of the network in which the focal firm is embedded.
An initial, systematic assessment of task novelty, time allocated to complete the task, and the likely composition of the team are equally important to a managerial application of our framework. Task novelty might reflect the amount of change expressed, for example, as a percentage of new or different elements involved in a task between the last similar task and the current one ([68]). It is an approach similar to [34] measure of project scope as an indexed combination of the number of newly designed elements and the level of supplier-controlled input. Experienced managers will have an intuitive sense of a task's time requirement, usually as a result of track record and industry benchmarking. Even so, industry research suggests that the majority of new product launches tend to experience delays ([157]), hinting at a general lack of ability to estimate task duration. This is where more sophisticated time-tracking software, similar to the kind used in the legal industry to estimate and track project time, might assist. Finally, managers seeking to estimate in advance the optimal heterogeneity when assembling a team might borrow from some of the advances in relational demography. Salient (potential) team member characteristics might be used to calculate a Herfindahl–Hirschman coefficient ([15]) that generates a single index of team diversity. Taken together, these diagnostics of embeddedness and task novelty, duration, and team heterogeneity can guide the design of temporary marketing organizations with the explicit goal of ensuring matches that promote sought-after marketing outcomes, including output creativity and decision-making speed.
We identify four broad avenues for future research: ( 1) different forms of temporary organizations, ( 2) structural embeddedness, ( 3) multilevel relationships, and ( 4) microlevel properties and processes.
A first area of future research focuses on identifying more fine-grained temporary organization forms. Our framework treated hybrid and fully embedded temporary organizations as separate categories, and we highlighted characteristics that are uniquely associated with each one (e.g., dyadic vs. organization-level norms). Thus, in the discussion leading to P6, we focused on the unique incremental properties of a fully embedded temporary organization. We acknowledge, however, the possibility of overlap between categories, in that hybrids may take place within permanent organizations, and that the same individuals may interact with each other, either on different hybrids or on different temporary organizations within a given firm. This raises interesting theoretical questions regarding the relationships between different organizational mechanisms and whether they support or undermine each other and regarding whether the unique characteristics of a permanent organization support or undermine the particular properties of a hybrid.
Regarding the former question, while the embedded ties that underlie a hybrid are widely regarded as having self-enforcing properties ([99]), the lack of a foreseeable future may represent a source of strain. As such, hybrids that are implemented within permanent organizations that possess a "shadow of the future" are uniquely positioned to both develop and maintain strong dyadic norms. For instance, such benefits may accrue to research organizations working under the aegis of the same pharmaceutical company or components suppliers working on upgrades to the next generation of high-technology products. Furthermore, common ownership may, in some instances, serve as an important buffer. As we have noted, certain agency initiatives (e.g., monitoring) may in themselves be associated with negative consequences (e.g., agent reactance). However, to the extent that the larger organizational context legitimizes such practices, this risk may be mitigated ([156]).
There may also be inherent incompatibilities between the properties of a temporary organization and its larger context. For instance, temporary organizations frequently develop unique norms distinct from those of permanent organizations and, in some instances, may even be purposely protected from the effects of a larger permanent organization ([120]). Thus, the specific nature of the influence from a temporary marketing organization's larger context is not clear-cut. Consequently, some firms choose to colocate temporary organizations with the focal permanent firm while others intentionally put distance between them. For example, Walmart Labs works side-by-side with the Walmart Global eCommerce team to ensure seamless implementation of new ideas. By contrast, other firms intentionally set up "innovation outposts" located in distant technology clusters to enable involvement "in the tech community, without committing significant investment" ([140], p. 6). A full-fledged theory of temporary organizations will require a more comprehensive specification of how organization-level mechanisms interact with particular aspects of a temporary organization's larger context to influence performance. It will also require a more fine-grained analysis of temporary organization forms.
A second avenue for future research pertains to the interactions between a temporary organization's practices and its larger industry context. While our primary focus was on relational embeddedness between a principal and an agent, structural embeddedness—that is, connections among mutual contacts ([90]; [159])—raises interesting questions. For instance, agents' industry reputations may serve (indirect) selection purposes in a network. On the one hand, the dense networks within which many temporary organizations operate may lend themselves well to reputation effects ([10]; [60]). On the other hand, the inherent difficulty of monitoring individual agent effort undermines the disciplinary role of reputations. Conceivably, reputation effects associated with a temporary organization may be more strongly tied to relational than bespoke temporary organizational attributes. Future research could be directed toward ( 1) the key aspects of reputations in the context of temporary organizations and ( 2) the extent to which the relevant attributes serve selection and enforcement purposes.
Future research could usefully be directed toward exploring more complex relationships that may not readily fit the standard principal–agent model or that may span multiple levels involving upstream suppliers, producers, and even downstream customers. Consider first the former line of research. Some temporary organizations lack clear hierarchical structures and an independent authority to make decisions and enforce compliance. For instance, [134] note that the absence of a single executive owner (principal) of a global computing project led to lapses in problem solving. Similarly, large government marketing projects often have multiple principals, including Congressional and White House committees, specific government offices, and executive departmental units ([114]). This was apparent with the significant cost overruns that accompanied the development of the Healthcare.gov website—the portal through which U.S. citizens could compare health insurance plans under the 2010 Affordable Care Act. The coders brought in to save the product development process found themselves continually stifled by "tiers of people" across multiple government entities who, as one coder put it, were "managing developers at a distance" ([111], p. 6). Even in such temporary organizations, the agency problems of hidden information and hidden action, and thus the organizational challenges of selection and enforcement, still prevail ([49]; [134]). Differences exist, however, in the ways in which processes such as enforcement are carried out: although monitoring is a generic phenomenon, the nature of the monitoring process in temporary organizations with unclear hierarchical structures differs and calls for further enquiry.
Furthermore, a temporary organization may establish social or "communal" exchanges ([35]) with downstream customers. For instance, for certain movies, entire "brand communities" ([117]) have emerged. In [11] terminology, customers become permanent members of certain "art worlds" and come to identify with the temporary organization itself. Similarly, social ties between the relevant parties in the information technology industry blur the traditional boundaries between principals and agents ([95]). Managers of temporary marketing organizations have started to exploit these relationships, for instance, by incorporating customers' suggestions early in their product development efforts ([83]) and by hiring them as agents. By doing so, managers may be in a position to resolve both selection and enforcement problems.
Another research avenue pertains to the microlevel structural characteristics of temporary organizations. For instance, in a similar fashion to permanent organizations, characteristics such as size ([92]) may create organizational challenges with selection and enforcement implications. Furthermore, power and politics in temporary organizations provide a promising avenue of research. [91] uncover the inherently political nature of temporary organizing, and [110] build on [65] to demonstrate how the politics involving actors with different temporal orientations manifest themselves.
In general, we hope that our perspective on temporary marketing organizations will motivate further conceptual and empirical inquiry. We also hope that it will assist marketing managers in making informed choices on temporary organizations and in matching organizational forms and task, time, and team drivers. While the merging of economic and sociological perspectives that underlies our framework is "messy" ([98]), its payoffs may be considerable and add to our understanding of temporary marketing organizations.
Footnotes 1 Author ContributionsThe authors contributed equally to the article.
2 Associate EditorRebecca Slotegraaf served as associate editor for this article.
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
5 1Temporary organizations may exist that do not exhibit a standard hierarchical structure between a principal and an agent, or that exhibit more complex structures, such as multiple agency hierarchies. For instance, a temporary organization's manager may also be an agent relative to its owner (e.g., a financial investor), who then acts as principal ([151]). The principal–agent relationship within a temporary organization may also shift over time. Thus, the agency partner who initially assembles a temporary organization under the impetus of the CMO of her client organization may delegate its management to one or several senior team members, who may take turns leading it at various stages. We are thankful to an anonymous reviewer for drawing our attention to such situations.
6 2To streamline the flow of our argument and to ensure comparability in our discussion, Table 1 features only the "baseline" case for each of these alternative organizational forms. We also appreciate that our comparative organizational forms in Table 1 are not exhaustive and that partnerships and alliances (i.e., organizational forms not involving long-term contracts and equity participation) share similarities with temporary organizations. Typically, however, alliance and partnership research focuses on a "formal agreement between two partners" (emphasis added, [127], p. 1117), whereas temporary organizations are concerned with agreements between multiple members.
7 3Selection may also involve the interactions between the agents in question. [27] frames this question as the "O-Ring" property of temporary organizations, whereby each input must perform at a certain level. Creating the right matches and ensuring their continuation pose interesting problems in their own right ([33]; [40]). However, analyzing these issues goes beyond the scope of this research.
8 4Clearly, there is often a desire to continue a collaboration initiated by a temporary organization. Research shows, however, that continuity is contingent on various factors, including the focal organization's performance ([137]). Thus, a temporary organization has an uncertain future.
9 5This discussion points to one of the inherent challenges of a temporary organization: while a principal may have preexisting agent relationships, the novelty of a temporary organization's task may call for a broad search to identify new agents with the necessary skills and resources ([130]). In essence, "strong" prior ties are inherently limited with regard to information access ([62]).
6Temporary organizations have been shown to possess distinct norms and to exhibit patterns of collective behavior ([135]). As such, despite their discrete time dimensions, they are distinct from pure market transactions defined by their lack of social content, as described in [105], p. 12) famous example of "a purchase of unbranded gasoline, out-of-town, at an independent gas station, paid for with cash."
7We acknowledge that this is not an exhaustive set of drivers. [87], for instance, discuss a fourth "T": transition. Transition, however, has more to do with the transformation that takes place during the lifespan of a temporary organization (e.g., from idea to full-fledged marketed product and from temporary structure to an embedded team within a permanent organization) than with an exogenous driver per se. We also note that the drivers may be related among themselves. For instance, the nature of a temporary organization's task may determine the composition of its team. We show, however, that each driver poses unique governance challenges.
8We acknowledge that repeated ties may exist between members of the same permanent organization. If so, the temporary organization in question would be the equivalent of a hybrid as a result of the direct (rather than indirect) association between the relevant parties. We note, however, that there are qualitative differences between internal and external transactions ([4]). We return to this issue in the "Discussion" section.
9The reversion to a stand-alone form at high levels of novelty, and the selection and enforcement efforts this form requires, is captured in P1.
10For brevity, we limit ourselves to explicating the performance relationships (matches and mismatches) that involve task characteristics (i.e., novelty).
11This presumes that the relevant skills are actually deployed or applied to the focal task to ensure the conversion ([28]) from an initial idea or concept to an end product (e.g., a marketed product or a campaign).
12We concede that our "playbook" may risk oversimplifying some of the nuanced interactions between the three "Ts" as determinants of temporary organizational form. For example, some novel tasks may require more heterogeneous teams, resulting in a tension between the utility of stand-alone and fully embedded forms. Some degree of managerial judgement will be necessary to determine which project feature will dominate the choice of temporary organization form.
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Record: 204- The Unintended Consequence of Price-Based Service Recovery Incentives. By: Kanuri, Vamsi K.; Andrews, Michelle. Journal of Marketing. Sep2019, Vol. 83 Issue 5, p57-77. 21p. 3 Diagrams, 8 Charts. DOI: 10.1177/0022242919859325.
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The Unintended Consequence of Price-Based Service Recovery Incentives
Subscription-based service providers (e.g., newspapers, internet services) often issue price-based incentives to recover from service failures. However, because considerable time may pass between when providers issue a recovery incentive and when service contracts are due for renewal, it is unclear whether recovery incentives can improve customer retention in the long run. The authors investigate this question by examining 6,919 contract renewal decisions of newspaper subscribers who received varying levels of recovery incentives after newspaper delivery failures. In contrast to conventional wisdom, they find that recovery incentives are associated with lower contract renewal likelihoods. They rationalize this finding using the economic theory of reference prices and further demonstrate that firms could mitigate the unintended consequence of recovery incentives by reminding subscribers of the original price at touch points following the recovery, discounting the renewal price, and prolonging the duration between the recovery and renewal. The authors also show that the intensity of promotions in the external environment at the time of administering recovery incentives, and that acquiring subscribers by communicating the value of the subscription service, can influence the long-term effectiveness of recovery incentives. For subscription-based service providers, the authors propose a decision support model to optimize recovery and renewal incentives and demonstrate its utility within this empirical context.
Keywords: contractual relationships; customer complaints; service failure; service recovery incentives; subscription services
To err is human; to recover, divine.—[30], p. 156)
Subscription-based service providers frequently use price-based incentives to remedy service failures. For example, newspaper firms may credit subscriptions for missed newspaper deliveries, internet providers may reduce subscription prices after complaints of service interruptions, and utility companies may issue bill credits for overcharging customers ([ 5]; [ 7]; [13]; [21]). While the practice of issuing price-based recovery incentives is well-established, whether recovery incentives retain customers in the long run remains a debate. Industry experts generally advocate for price-based recovery incentives, urging firms to "be generous" and "exceed expectations" ([ 1]; [56]). Some studies endorse this recommendation, finding that discounts produce higher satisfaction in the short run ([29]; [55]). Service providers may accordingly be tempted to offer recovery incentives following a service failure, especially as customers become more willing to abandon a service after just one bad experience ([51]).
However, issuing recovery incentives may have unintended consequences in the long run. One of the key goals of recovery incentives for subscription-based service providers is to encourage subscribers to renew their service contracts when they are due for renewal. Because considerable time may pass between when providers issue a recovery incentive and when service contracts are due for renewal, the immediate boost in customer satisfaction that recovery incentives can generate may not carry over to subscription renewals. In fact, some service recovery studies warn about issuing incentives, finding that recovery incentives can have a diminishing effect on customer satisfaction and loyalty ([22]; [28]). Contractual service providers consequently face a dilemma in issuing recovery incentives, such that compensation for a service failure may boost satisfaction immediately but may ultimately fail to retain subscribers.
To resolve this dilemma, subscription-based service providers regularly deploy follow-up communications and offer additional incentives after a service recovery. Such postrecovery efforts are intended to alleviate any residual dissatisfaction caused by the service failure, remind subscribers of the resolution offered at the time of recovery, and increase retention likelihoods ([ 4]; [15]). However, which postrecovery efforts can increase the long-term effectiveness of price-based service recovery incentives remains unknown. Furthermore, while contextual factors such as the length of a subscriber's relationship with the firm can influence the immediate outcomes of a service recovery ([16]), how those factors affect the long-term outcomes of recovery incentives also remains a question.
We theorize that the influence of price-based recovery incentives on subscribers' reference price for the service may influence their renewal likelihoods. Specifically, recovery incentives may give subscribers a new price point to anchor on, which can trigger a price comparison upon contract renewal and guide the renewal decision. The saliency of the recovery incentive may be further affected by a firm's marketing efforts and contextual characteristics. That is, the actions firms take after offering a recovery incentive, as well as the conditions under which subscribers receive a recovery incentive, can affect how salient the price-based recovery incentive is to subscribers and, in turn, influence their renewal likelihood.
Our study aims to test these theoretical tenets. Specifically, we investigate how the depth of recovery incentives offered in response to service failure complaints affects the likelihood that subscribers renew their service contracts at the end of the contract period and the conditions under which these effects may vary. To do so, we use rigorous econometric techniques to examine 6,919 renewal decisions of subscribers who threatened to cancel their subscriptions following service delivery failures at a large U.S. newspaper firm. In contrast to conventional wisdom, our results reveal that price-based recovery incentives are negatively associated with renewal likelihoods. Moreover, consistent with our theory, we find that firm efforts such as reminding customers of the full service price during customer touch points, offering renewal discounts, and increasing the amount of time between the recovery and renewal can mitigate the negative effect of recovery incentives. We also find that acquiring subscribers by communicating the value of the subscription service and that the salience of promotions in the external environment at the time of administering recovery incentives influence the long-term effectiveness of price-based recovery incentives. Our results are robust to alternate model specifications, accounting for strategic incentive administration, strategic complaining behavior, unobserved heterogeneity among subscribers, and service failure severity, among other factors.
This research makes several contributions to service recovery theory and practice. First, it reveals the downside of issuing price-based recovery incentives in contractual settings. Compensation for service failures has been mostly studied in lab-based and transactional contexts ([22]; [28]). Our research investigates the long-term consequences of recovery incentives by studying actual renewal behaviors in a contractual relationship. Second, few studies have demonstrated how firms can further manage subscriber reactions to recovery incentives in the postrecovery period ([15]). Moreover, boundary conditions of recovery compensation have primarily been examined using short-term attitudes such as customer attributions of failure or customer expectations of recovery ([34]; [62]). Our research augments the literature by illustrating how firms can use various strategic levers to increase the effectiveness of price-based recovery incentives. Third, to help managers tasked with issuing price-based service recovery incentives, we propose an implementable decision support model and demonstrate its utility within our empirical context.
Price-based service recovery incentives are monetary discounts that service firms offer in response to service failures. The primary goal of these incentives is to mitigate any psychological or economic loss that subscribers may experience due to the service failure. The topic of service recovery incentives has fueled several decades of research. Next, we discuss two key areas of the service recovery incentives literature that motivate our work (for a summary, see Table 1).
Graph
Table 1. Illustrative Studies on Price-Based Service Recovery Incentives.
| Study | Sample | Method | Service Context | Compensation Amount | Outcome | Outcome Time | Moderators | Relevant Findings |
|---|
| Bitner (1990) | 145 participants | Experiment | Travel | 100% | Satisfaction | Immediate | Service failure explanation | Reimbursement increases belief of firm control, which decreases satisfaction, but reimbursement with explanations reduces belief of firm control. |
| Bitner, Booms, and Tetreault (1990) | 375 participants | Survey | Travel, hotel, restaurant | 0%–100%, upgrades | Satisfaction | Immediate | Service failure type | Compensation can increase satisfaction due to perceptions that the incident was handled properly. |
| Conlon and Murray (1996) | 143 participants | Field study | Retail | 0%–100% | Satisfaction | Immediate | None | Compensation boosts customer satisfaction with firm explanations and repurchase intent due to the positive affect compensation can generate. |
| Fang, Luo, and Jiang (2013) | >1 million customers | Field data, survey | Cellular | 0%–100% | Satisfaction | 1–39 weeks | Firm response time | Compensation has a higher and faster peak than apologies or communications, and a long decay effect on satisfaction, which may be due to equity perceptions. Simulation results suggest that more compensation is needed to boost satisfaction the longer firms take to respond to the failure. |
| Gelbrich, Gäthke, and Grégoire (2015) | 1,250 participants | Experiment | Concert, hotel, retail | 0%–200% | Satisfaction, repurchase intent | Immediate | Customer failure handling tactic | Compensation affects satisfaction nonlinearly, following an S-shaped curve, with between 60%–120% yielding the highest satisfaction return for those who reject a flawed service, and a concave shape, with the first compensation dollars yielding the highest satisfaction return for those who accept a flawed service. Customers may have reference points for expected compensation only when they reject flawed services. |
| Gelbrich and Roschk (2011) | 17 studies | Experiment, survey | Service, nonservice | 0%–100+% | Satisfaction | Immediate | Satisfaction type | Overcompensation has a lower incremental effect on satisfaction than simple compensation, especially for transaction (vs. cumulative) satisfaction, due to a greater focus on reducing loss than on receiving gains. |
| Grewal, Roggeveen, and Tsiros (2008) | 585 participants | Experiment | Travel, restaurant | 20% off the bill, 50% off coupon | Repurchase intent | Immediate | Failure frequency, firm control | Compensation boosts repurchase intent only when the failure is frequent and the firm is responsible due to perceived equity. |
| Harris et al. (2006) | 353 participants | Experiment, survey | Banking, travel | 10% off coupon, $25 credit | Satisfaction, repurchase intent | Immediate | Shopping medium | Higher compensation boosts satisfaction and purchase intentions, especially in offline mediums due to less self-blame for failures in offline mediums. Compensation is more effective for less severe failures than more severe failures. |
| Hess, Ganesan, and Klein (2003) | 346 participants | Experiment | Restaurant | Replacement, 100% discount | Satisfaction | Immediate | Customer service recovery expectation | High compensation (replacement plus 100% discount) boosts satisfaction especially for those with higher service recovery expectations due to the reference point expectations may provide. |
| Kumar et al. (2014) | 725 customers | Field data | Travel | Discounts, vouchers | Purchase frequency, revenues | 0–35 months | Economy | Compensation can decrease purchase frequency due to the persistence of negative effects. Compensation can increase service revenues when the economy is good as the recovery effort can outweigh price sensitivity. |
| Smith, Bolton, and Wagner (1999) | 977 participants | Survey | Hotel, restaurant | 20% off, 50% off, 100% off; voucher | Satisfaction | Immediate | Service failure magnitude | Compensation boosts satisfaction due to perceived equity, especially for low-magnitude failures due to nonlinear evaluations. |
| Wirtz and Mattila (2004) | 187 participants | Experiment | Restaurant | 20% off | Satisfaction, repurchase intent | Immediate | Service failure attribution | Compensation can increase satisfaction and repurchase intent except when the firm responds immediately and apologizes or when the firm delays responding and does not apologize due to its inability to substitute for other recovery outcomes. |
| This study | 6,919 subscribers | Field data | Newspaper | $0–$23 | Contract renewal | 9–13 weeks | Full price reminders, renewal incentives, temporal distance, promotional intensity, personalized acquisitions | Price-based service recovery incentives reduce renewal likelihoods at full price. The effects are mitigated when (1) the firm reminds subscribers of the full subscription price, (2) the firm offers renewal discounts, and (3) more time passes between the recovery incentive and renewal decision. Recovery incentives offered during promotion-intense times aggravate the effects, but personalized acquisitions mitigate them. |
First, prior research has largely examined transactional settings (e.g., restaurants, retail) to document the consequences of price-based recovery incentives (e.g., [26]; [54]). Contractual service settings are distinct from transactional service settings due to the inherent differences in the temporal separation between when firms administer recovery incentives and when subscribers react to those incentives (see Figure 1). Because the temporal separation could affect subscribers' memory of the service recovery incentive, the consequences of price-based recovery incentives and the theoretical mechanisms driving them may differ from those documented in transactional settings. Prior literature on transactional settings has also focused on the short-term or immediate consequences of price-based service recovery incentives. We add to this literature by examining how contractual firms can effectively manage subscriber reactions to the recovery incentive in the postrecovery period.
Graph: Figure 1. Timeline of service failure and recovery in contractual services.
Second, prior literature has tended to focus on the attitudinal effects of firm recovery attempts. For example, scholars have explored customer perceptions of fairness and justice regarding recovery attempts ([25]; [55]). Previous literature has also explored customer satisfaction with how firms handle service failures (e.g., [58]), as "artful recoveries" can generate positive consumer responses when consumers perceive that firms have good motives ([30]; [36]). In addition, a few studies have explored the behavioral consequences of recovery incentives using survey or experimental methods, possibly due to the difficulty of acquiring archival data. Those with field data, though, do not investigate price-based recovery incentives (e.g., [41]; [43]).
Our study extends prior research by using field data to demonstrate the long-term behavioral consequences of price-based recovery incentives in contractual settings. Contractual services offer an ideal context to study the long-term effects of recovery incentives due to the temporal separation between service recovery and behavioral outcomes (e.g., subscription renewal). Moreover, the lock-in nature of service contracts enables us to examine how firms can use their touch points with subscribers and marketing instruments, such as reminders of full price and follow-up incentives during the postrecovery period, to further manage subscriber reactions to recovery incentives (for a timeline of firm actions in contractual service settings, see Figure 1).
The economic theory of reference prices guides our expectation of how price-based recovery incentives will affect renewal likelihoods within contractual settings. Reference prices are internal standards of price consumers believe are fair for a given service ([63]). Consumers form beliefs of price appropriateness using a variety of information sources such as historical prices, advertised prices, competitor prices, their own or others' experience, and their knowledge of prices ([10]). Reference prices play a critical role in how consumers perceive current prices and, in turn, whether they begin or renew subscriptions. Specifically, to judge the acceptability of a current price, consumers compare the current price with their reference price ([39]). Prices higher than consumers' reference price can lead them to feel they need to pay more than what they believe is equitable ([37]). Deviations above a reference price can in turn negatively influence purchase decisions by discouraging consumers from buying.
In fact, reference prices have been shown to affect repurchase likelihoods in transactional settings involving price promotions. For example, acquisition discounts can lead first-time consumers to form reference prices much lower than the regular price, thereby decreasing their likelihood of buying again ([45]). Price promotions can also increase how sensitive consumers are to deals, leading them to purchase fewer items on future grocery trips ([ 3]). Relatedly, frequent price promotions can create a pattern of lower price points for a given good that trains consumers to lower their reference price for that good over time ([ 2]). Conditioned to promotions, consumers then perceive a return to the regular price to be too high and refrain from purchasing the good ([49]). The consequence of lowering reference prices is that price promotions can be difficult for firms to end ([59]).
Price-based recovery incentives in contractual settings are conceptually different from price promotions within the contexts of acquisition and retention. Since service failures create a deficit that subscribers expect firms to fix, price-based incentives may influence subscriber evaluations of the recovery, not the service, as in the case of regular price promotions ([41]). Moreover, because continuously provided contractual services often have a longer temporal separation between the time of purchase and renewal than noncontractual services, the factors that affect subscribers' reference prices in subscription-based services may differ from those of goods with shorter interpurchase times ([12]). We therefore discuss how deeper price-based service recovery incentives may affect the reference price of subscribers and, thus, their renewal likelihoods for contractual services. Figure 2 provides an overview of our conceptual framework.
Graph: Figure 2. Conceptual framework.
Price-based recovery incentives present subscribers with a new price point they can use to update their reference price. Specifically, to the extent that subscribers anchor their reference price on the price they were paying for the subscription service before the service failure, the price-based recovery incentive will lower that price and, thus, lower subscribers' reference price for the service. Adopting the revised price estimate as their updated reference price can consequently reveal a discrepancy at the time of the subscription renewal, when subscribers compare the full subscription price with their lowered reference price ([37]). The service failure experienced by the subscriber accentuates this discrepancy, as consumption disruptions increase the salience of the negative experience and the vividness with which subscribers recall events surrounding the disruption ([58]). Moreover, the discrepancy between a subscriber's internal reference price and the full subscription price increases with the depth of recovery incentives. Thus, we hypothesize:
- H1: Price-based recovery incentives decrease the likelihood of contract renewals at full price.
The continuity of contractual service relationships allows subscription-based service providers to manage the effectiveness of recovery incentives in the postrecovery period. We argue that firm actions such as reminders of the full service price, renewal discounts, or changes to the renewal time can affect the extent to which subscribers rely on their recovery-based reference price to inform their renewal decision. For example, recent information about price can decrease the weight that subscribers place on past prices when updating their reference price ([39]). Adjustments to the renewal price can influence whether subscribers interpret the renewal price as fair relative to their reference price ([38]). Moreover, the time between when subscribers update their reference price and when they rely on it can affect their ability to retrieve it from memory ([48]). Therefore, firm actions can likely influence how price-based recovery incentives affect renewal likelihoods, as we explain next.
Because subscribers may rely on price-based recovery incentives to guide their renewal decision, contractual service providers can influence this reliance by reminding subscribers of the full price of the service during various touch points. Full price reminders are communication mechanisms that service providers adopt to restate the original price of the service offering. Such mechanisms act as a pulse that offers subscribers the opportunity to rehearse the full price and provides a history of full price points to access for comparison ([57]). Because subscribers consider recently referenced prices to be immediate, accessible, and better sources of price information than prior prices, reminders of the full price will lead them to update their reference price ([48]). At the same time, full price reminders can weaken subscriber beliefs that the recovery price will persist into the future ([60]). Rather, reminders can reinforce subscriber beliefs that the subscription price will revert to the full price, rendering their reference price that was based on the recovery price "a thing of the past" and thus obsolete ([35]). Reminders of the full price will therefore erode the plausibility of the recovery price as a reference price and thereby increase the chance subscribers perceive a return to the full price as fair. Therefore:
- H2: Full price reminders following a recovery weaken the negative association between price-based service recovery incentives and the likelihood of contract renewals at full price.
Subscribers' likelihood of renewing their subscription is driven in part by how close the renewal price is to their reference price. A renewal price significantly higher than subscribers' reference price may discourage them more from renewing than a marginally higher renewal price. Contractual service providers may therefore offer renewal discounts to reduce the deviation above the recovery-based reference price. Service renewal discounts are monetary incentives that service providers offer at the time of subscription renewal. Such discounts shift the renewal price closer to the latitude of acceptance, or zone of indifference, that surrounds the reference price subscribers have adjusted to after the recovery. Subscribers may then perceive the reduced renewal price to be fairer than the full renewal price ([38]). By decreasing the discrepancy between the renewal price and subscribers' recovery-based reference price, renewal discounts can increase the likelihood that subscribers renew their subscriptions. Therefore:
- H3: Service renewal discounts weaken the negative association between price-based recovery incentives and the likelihood of contract renewals at full price.
The amount of time between when subscribers receive a recovery incentive and when they make a renewal decision, which we refer to as "temporal distance," can affect how much they rely on their recovery-based reference price at the time of contract renewal. Because subscribers may form and update their reference price on the basis of the "price of the last purchase as remembered" ([20], p. 188), relying on their reference price hinges on their ability to recall it. Reference prices tend to become less salient and more difficult to recall over time ([24]; [48]). For example, time deteriorates how accurately consumers can recall prices ([32]). Time thus fosters forgetting of the recovery-based price by erasing it from subscriber memory. The more time that passes between when subscribers receive a price-based recovery incentive and when they make a renewal decision can therefore increase the likelihood that they renew their subscription at full price. If so:
- H4: Temporal distance between the recovery incentive and the renewal decision weakens the negative association between price-based recovery incentives and the likelihood of contract renewals at full price.
Factors related to the conditions under which subscribers receive a price-based recovery incentive can also influence the extent to which they rely on that price to guide their renewal decision. For instance, the promotional environment in which subscribers receive a recovery incentive, how they were acquired, and how long subscribers had the service can affect the salience of the price-based recovery incentive ([46]; [47]). Understanding how contextual factors can influence the link between price-based recovery incentives and renewal likelihoods can help firms tailor their service recovery strategies. We therefore discuss each in turn.
The external environment in which subscribers encounter the recovery price can affect the salience of the recovery price. In particular, promotional intensity, defined as the extent to which other retailers offer promotions across various product and service categories, can motivate subscribers to be more selective of the price information they attend to and integrate in their reference price ([48]). Specifically, higher promotional intensity can affect subscriber perceptions of a contextually provided price more strongly by increasing its salience ([42]). Subscribers may consequently anchor more firmly on price-based recovery incentives issued during promotion intense times when updating their reference price ([ 6]). As a result, subscribers who receive a price-based recovery incentive during times of greater promotional intensity may be less likely to renew their subscription at full price. As such:
- H5: Promotional intensity in the external environment strengthens the negative association between price-based recovery incentives and the likelihood of contract renewals at full price.
Because acquisition processes can affect retention probabilities ([61]), how firms acquired subscribers may affect the extent to which subscribers rely on the recovery-based reference price in making their renewal decision. Specifically, firms may acquire subscribers though personalized and nonpersonalized channels. Personalized acquisitions refer to instances in which firms acquire subscribers through channels that permit tailored marketing communications such as personal selling or direct mail. In contrast, nonpersonalized acquisitions are instances in which firms acquire subscribers through channels that do not permit tailored marketing communications such as mass media. Personalized acquisitions can help firms customize the value of the service they convey to prospective subscribers ([27]). For instance, news service providers may emphasize their coverage of local children's events when targeting young parents, while they may highlight their coverage of local restaurants and nightlife when targeting young professionals. Personalized acquisitions may accordingly lead subscribers to associate their subscription with value-based reasons ([16]; [17]). Supporting the idea that subscribers acquired through certain marketing activities focus on the benefits of the product, [52] found a stronger association between more interpersonal acquisition modes and profitability compared with customer-initiated contacts. Subscribers acquired through personalized communications should therefore rely less on their recovery-based reference price when deciding whether to renew their subscription at full price. Thus:
- H6: Personalized acquisitions weaken the negative association between price-based recovery incentives and the likelihood of contract renewals at full price.
The amount of time a customer has subscribed to the offerings of a service provider, which we refer to as "relationship length," can also affect subscribers' reliance on the recovery-based reference price when deciding whether to renew their subscription. Subscribers who experience the firm's service for a longer duration become more accustomed to, and have stronger ties with, their service provider ([43]). The rapport and loyalty that deepen over time can in turn reduce how much subscribers rely on price when making renewal decisions ([11]). Relationship length should thus decrease the influence of the recovery-based reference price in subscribers' renewal decisions. Formally:
- H7: Relationship length weakens the negative association between price-based recovery incentives and the likelihood of contract renewals at full price.
Our data come from a U.S. newspaper firm and span 39 months (January 2010 to March 2013). The firm offers daily and weekend print subscription plans, along with custom offerings in which subscribers select delivery days. The newspaper also published free website content at the time of our data. The majority of newspaper revenue came from print advertising revenue, which accounts for almost 70% of overall revenue. Due to the prevalence of cross-market network effects in the newspaper industry, whereby readership size positively affects advertising revenue and boosts advertising profit margins, the firm is willing to subsidize print subscriptions to attract and retain subscribers.
To validate our hypotheses, we worked with the newspaper to assemble a sample of 6,919 instances of subscriber threats to defect, triggered by verified newspaper delivery failures. Customer service representatives have a small window of time to discourage subscribers who experienced a service failure from defecting at the end of their contract period. To persuade subscribers to give the firm a chance to make amends, the newspaper empowers its customer service representatives to offer an incentive at their discretion within reasonable limits. The firm issued only one recovery incentive (a single discount on the price of the subscription) in response to a subscriber's complaint about a service failure. Because subscribers are "locked in" during the contract period, all subscribers remain with the firm following their threat to quit and firm recovery in terms of newspaper delivery and payment. However, not all renew their subscription after their current contract expires. To reduce noise due to service failure type, we only obtained service failures related to newspaper deliveries.
Table 2 summarizes our variable operationalization. Our key dependent variable (Renewal) is a binary variable coded as 1 if the subscriber renews the contract at the end of the period following her threat to quit and to 0 if otherwise. We operationalize our seven key independent variables as follows. First, we measure the price-based recovery incentive (RecoveryIncentive) as the total discount offered by the customer service representative at the time of the threat for the remaining subscription period. Second, we measure the full service price reminder (FullPriceReminder) as a binary variable coded as 1 if the subscriber elected to receive a monthly bill in the mail and as 0 if the subscriber enrolled in auto billing (e.g., through a credit card charge or a bank draft from external vendors). We use payment type as a proxy for reminders of the original price because subscribers who received paper copies of their bill could see the itemized description of the original price, the recovery discount, and the final price they owed the newspaper firm. Subscribers who enrolled in auto billing could only see the amount they owed the newspaper through the automatic charge to their credit card or bank account. As such, subscribers who received mailed bills were reminded of the original price more than those who enrolled in auto billing.
Graph
Table 2. Variable Operationalization.
| Variable | Operationalization | Source |
|---|
| RecoveryIncentive | Total discount amount in USD offered by a customer service representative at the time of the threat | Collaborating firm |
| FullPriceReminder | Binary variable coded as 1 if the subscriber elects a detailed bill to be sent via mail or 0 if the subscriber elects an automatic charge to her credit card | Collaborating firm |
| RenewalIncentive | Total renewal discount in USD offered to the subscriber a month before the subscription expires | Collaborating firm |
| TemporalDistance | The amount of time (in weeks) elapsed between the recovery discount and renewal discount | Collaborating firm |
| PromotionalIntensity | Binary variable coded as 1 if the service recovery occurred during the holiday season (i.e., during the months of November or December) and 0 otherwise | Collaborating firm |
| PersonalizedAcquisition | Binary variable coded as 1 if the subscriber was acquired through personalized marketing campaigns such as salespeople, telemarketing, digital advertisements, direct mail and email, and 0 otherwise (e.g., single copy inserts, kiosks, free newspaper distribution campaigns and corporate subscriptions) | Collaborating firm |
| ServiceFreqPresent | Number of days of newspaper delivery within a week at the time of the threat | Collaborating firm |
| ServiceFreqRenewal | Number of days of newspaper delivery within a week offered at the time of renewal | Collaborating firm |
| ServiceFreqPrevious | Number of days of newspaper delivery within a week during the prior subscription contract | Collaborating firm |
| RenewalDuration | The number of weeks of subscription offered to a subscriber at the time of the renewal | Collaborating firm |
| Field | Binary variable coded as 1 if the subscriber was acquired through salespeople, and 0 otherwise | Collaborating firm |
| Telemarketing | Binary variable coded as 1 if the subscriber was acquired through telemarketing, and 0 otherwise | Collaborating firm |
| Internet | Binary variable coded as 1 if the subscriber was acquired through online targeted advertisements, and 0 otherwise | Collaborating firm |
| DirectMail | Binary variable coded as 1 if the subscriber was acquired through direct marketing, and 0 otherwise | Collaborating firm |
| LifeStage | Three binary variables coded as whether a subscriber is in a young stage (0), family stage (1), or mature stage (1) | Nielsen |
| SocialGroup | Four binary variables coded as whether a subscriber lives in a city (0), urban area (1), suburban area (1), or the country (1) | Nielsen |
| RelationshipLength | Subscriber's tenure in days at the time of administering the service recovery incentive | Collaborating firm |
| ThreatBefore | Binary variable coded as 1 if the subscriber threatened to quit in the past, and 0 otherwise | Collaborating firm |
| ValueScore | Ordinal variable representing the amount of revenue a subscriber from a certain zip code will likely contribute to the advertisers, with 1 indicating the highest contribution and 5 indicating the lowest | Collaborating firm |
| TrendScore | Popularity of the newspaper as determined by the Google trend score for the newspaper | Google Trends |
| CPI | The average change over time in the prices paid by urban consumers for a market basket of consumer goods and services (detrended and seasonally adjusted) | Bureau of Labor Statistics |
| PRIZM Segments | Distinct binary variables coded as 1 if the subscriber belongs to the corresponding PRIZM segment, and 0 if otherwise | Claritas PRIZM |
| FailureType | Distinct binary variables coded as 1 if the delivery failure reported by the subscriber belongs to the corresponding service failure category, and 0 otherwise | Collaborating firm |
| FailureSource | Distinct binary variables coded as 1 if a subscriber belongs to the corresponding delivery district (1), and 0 otherwise | Distributor of the collaborating firm |
Third, we operationalize the renewal incentive (RenewalIncentive) as the total discount offered for the next subscription period. The firm offered these incentives exactly a month before the current subscription expired. Fourth, we measure temporal distance (TemporalDistance) as the duration in weeks between the time recovery and renewal incentives were administered. Fifth, we operationalize promotional intensity in the external environment (PromotionalIntensity) as a binary variable that captures whether ( 1) or not (0) the price-based recovery incentive was offered during the year-end holiday season (i.e., during November or December). Sixth, we operationalize personalized acquisition (PersonalizedAcquisition) as a binary variable that captures whether ( 1) or not (0) a subscriber was acquired through individually targeted acquisition campaigns. Our database featured several channels through which personalized acquisition campaigns were administered, such as personal selling, email targeting, digital advertisements and telemarketing. The nonpersonalized acquisition campaigns included single copy inserts, kiosks, free newspaper distribution campaigns, and corporate subscriptions. Seventh, we operationalize relationship length as the number of days the subscriber was with the firm at the time the recovery incentive was administered (RelationshipLength).
Next, we control for a variety of factors that can influence renewal decisions. These variables serve as effective instruments to account for any latent time-varying and time-invariant heterogeneity that could offer an alternative explanation for observed renewal behaviors. First, we control for a subscriber's life stage (LifeStage) as determined by Nielsen. Life stage refers to a subscriber's phase of life as determined by his or her age. Nielsen classifies subscribers as being in a young, family, or mature stage. We also control for social group (SocialGroup) as determined by Nielsen to account for location heterogeneity. Social group refers to the life style of subscribers as determined by their location. Nielsen classifies subscribers as living in a city (City = 1), an urban area (Urban = 1), a suburban area (Suburban = 1), or a rural area (Country = 1). Demographic characteristics of subscribers could also inform retention behavior. Our collaborating firm uses Claritas PRIZM services to classify each of its subscribers into 66 demographic segmentation categories. These segments capture a host of household characteristics including a household's general purchase preferences, income, ethnicity, marital status, and lifestyle. We include these categories as fixed effects in our model (for a description of each segment, see Web Appendix A1). We also control for whether ( 1) or not (0) a subscriber threatened to quit before her current threat (ThreatBefore) to account for serial threatening behavior.
The severity of service failures could also influence retention likelihoods. For instance, a missed newspaper delivery could be perceived as more severe than a delayed newspaper and thus lead to a lower retention likelihood. Because the perceived severity of the failure is a latent individual difference variable that is unobservable to the researcher, we account for two observable characteristics of service failure that could potentially inform us about the severity of service failure. First, we account for the type of service failure (FailureType), defined as the way in which the subscription service was disrupted, by including the distinct category codes the firm uses to classify delivery failures (e.g., late delivery, wrong paper delivered, missed delivery, delivered to the wrong location) as controls. Second, we account for the source of service failure (FailureSource), defined as the cause for the disruption of the subscription service, by including newspaper delivery districts as controls. News organizations divide their territory into several delivery districts to facilitate timely delivery of newspapers. Each district has a manager who is responsible for coordinating newspaper delivery vehicles operating within his or her district as well as for training and monitoring the performance of the service personnel operating the delivery vehicles. Any operational or administrative lapses within a district could trigger a systematic pattern of service failures for the subscribers within the corresponding district. We therefore control for the delivery district to account for the unobservable source of service failure within a delivery district. We outline the list of service failures and districts in our data in Web Appendix A2.
It is possible subscribers forgo renewing contracts due to changes in newspaper popularity. Therefore, we control for the number of daily internet searches for the newspaper name (TrendScore) as identified by Google Trends. Similarly, consumers' propensity to cut spending on discretionary purchases could drive retention likelihood. We therefore control for the detrended and seasonally adjusted consumer price index (CPI), an indicator of inflation.
Next, unobserved heterogeneity in behavioral and psychological service usage characteristics could drive retention. We thus control for service frequency, which we define as the number of times a subscriber obtains the service in a given week. Specifically, we control for service frequency adopted at the time of the threat (ServiceFreqPresent), offered at the time of renewal (ServiceFreqRenewal), and adopted during the prior subscription contract (ServiceFreqPrevious) using the number of days the firm delivers its newspaper to the subscriber's location in a given week. The length of future commitment could also drive renewal decisions. Therefore, we control for the duration of the future subscription offered at the time of renewal (RenewalDuration). Finally, because newspapers are steadily losing print subscribers due to the proliferation of free news content online and increasing preferences for digital consumption, we include a continuous trend variable captured by the month number (MonthTrend) within our model. This variable should account for any exogenous factors driving attrition. Tables 3 and 4 report the distribution of key variables and correlations among nonbinary variables.
Graph
Table 3. Descriptives.
| N | Mean | SD | Min | 5th Percentile | 95th Percentile | Max |
|---|
| Renew | 7,091 | .79 | .41 | 0 | 0 | 1 | 1 |
| RecoveryIncentive | 7,091 | 8.61 | 3.84 | 0 | 7.4 | 20 | 23.53 |
| FullPriceReminder | 7,091 | .76 | .43 | 0 | 0 | 1 | 1 |
| RenewalIncentive | 7,091 | 14.79 | 17.59 | 0 | 3.25 | 38.35 | 248 |
| TemporalDistance | 7,091 | 12.49 | 1.34 | 9 | 9 | 13 | 13 |
| PromotionalIntensity | 7,091 | .10 | .30 | 0 | 0 | 1 | 1 |
| PersonalizedAcquisition | 7,091 | .73 | .45 | 0 | 0 | 1 | 1 |
| ServiceFreqPresent | 7,091 | 5.61 | 2.41 | 1 | 1 | 7 | 7 |
| ServiceFreqRenewal | 7,091 | 5.68 | 2.37 | 1 | 1 | 7 | 7 |
| ServiceFreqPrevious | 7,091 | 5.83 | 2.28 | 1 | 1 | 7 | 7 |
| RenewalDuration | 7,091 | 12.96 | 5.41 | 5 | 5 | 17 | 52 |
| RelationshipLength | 7,089 | 1,046.55 | 1,820.05 | 31 | 39 | 4,950 | 12,085 |
| Mature | 7,091 | .48 | .50 | 0 | 0 | 1 | 1 |
| Family | 7,091 | .22 | .41 | 0 | 0 | 1 | 1 |
| Urban | 7,091 | .18 | .38 | 0 | 0 | 1 | 1 |
| Suburban | 7,091 | .43 | .50 | 0 | 0 | 1 | 1 |
| Country | 7,091 | .14 | .35 | 0 | 0 | 1 | 1 |
| ThreatBefore | 7,091 | .44 | .50 | 0 | 0 | 1 | 1 |
| CPI | 7,091 | .03 | .64 | −2.73 | −.94 | .86 | 1.48 |
| TrendScore | 7,091 | 44.58 | 19.42 | 21 | 22 | 80 | 93 |
| ValueScore | 6,932 | 1.94 | 1.16 | 1 | 1 | 4 | 5 |
Graph
Table 4. Correlations.
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
|---|
| 1. RecoveryIncentive | 1.00 | | | | | | | | | | |
| 2. RenewalIncentive | −.03 | 1.00 | | | | | | | | | |
| 3. TemporalDistance | −.77 | .03 | 1.00 | | | | | | | | |
| 4. ServiceFreqPresent | −.04 | .03 | .01 | 1.00 | | | | | | | |
| 5. ServiceFreqRenewal | −.03 | .03 | .01 | .76 | 1.00 | | | | | | |
| 6. ServiceFreqPrevious | .00 | −.01 | −.04 | .50 | .46 | 1.00 | | | | | |
| 7. RenewalDuration | .00 | .42 | .01 | −.02 | −.05 | −.05 | 1.00 | | | | |
| 8. RelationshipLength | −.02 | .03 | .00 | .23 | .19 | .12 | −.06 | 1.00 | | | |
| 9. CPI | .03 | .04 | −.08 | .01 | .00 | −.01 | −.03 | .05 | 1.00 | | |
| 10. TrendScore | .19 | −.04 | −.21 | −.03 | −.04 | .09 | −.12 | −.02 | −.23 | 1.00 | |
| 11. ValueScore | .04 | −.03 | −.03 | −.29 | −.27 | −.17 | .09 | −.25 | −.02 | .04 | 1.00 |
1 Notes: |Correlations| >.02 are significant at the 95% level. Correlations are reported only for nonbinary variables.
We model the likelihood subscriber i would renew her service at time t (Yit = 1) as a binary logistic regression of the proposed firm recovery. Our model is specified as follows:
Graph
where the variables are as noted as previously and ϕZ is a vector of control variables noted previously.
Our key estimates of interest are β1, β8, β9, β10, β11, β12, and β13. The validity of these estimates hinges on the assumption that recovery and renewal promotions are exogenously determined. To the extent these variables are correlated with unobservable factors captured within the error term, the assumption of exogeneity is violated and the associations portrayed by the coefficients could be biased. In this section, we discuss various sources of time-varying and time-invariant unobservable factors that could inhibit the identification of the effects and elaborate on various strategies to mitigate them.
The first source of endogeneity bias could stem from the strategic deployment of recovery and renewal promotions. A firm could strategically vary the level of these incentives on the basis of some criteria unobservable to the researcher such that some group of subscribers receive deep versus shallow discounts. To the extent the unobservable criteria affect promotion variables and the renewal decision, the independence assumption between recovery and renewal incentive variables and the error term is violated and thereby induces an endogeneity bias.
To address such endogeneity bias resulting from the strategic administration of promotions, we adopt the control function approach ([50]). The goal of this approach is to create a control variable that can be introduced in Equation 1 such that after accounting for the influence of the control variable on the dependent variable, the endogenous independent variable is no longer correlated with the error term in the regression equation. By definition, this will restore the independence assumption between the endogenous independent variable and the error terms and thereby mitigate the endogeneity bias. The challenge then is to construct a control variable for both the endogenous variables.
Following [50], we adopt the following procedure to construct control variables. For each endogenous variable, we run an auxiliary model with the promotion variable as the dependent variable, an exogenous variable as the independent variable, and a series of control variables that likely affect the promotion variable. The exogenous variable in this auxiliary equation serves as an excluded variable that affects the endogenous variable (i.e., meets the relevance criterion) but not the expectation of the focal dependent variable (i.e., expectation of yit). The predicted residuals from the auxiliary regressions will then serve as effective controls that will restore the independence assumption between the endogenous variables and the error term in Equation 1.
To determine the excluded variable, we rely on the managerial decision-making process within our institutional setting. Newspapers typically sell ad space by demonstrating the reach of their newspaper (i.e., the number of readers) as well as the relevance of their reach in their rate cards and sales pitches to advertisers. The relevance of reach captures the extent to which each subscriber is likely to buy products offered by the newspaper's potential advertisers. Our collaborating newspaper determines the relevance of its reach using syndicated data from various external sources such as local retail scanner panel data. These data make up the newspaper's value score. The value score is thus an ordinal variable ranging from 1 ("most attractive") to 5 ("least attractive") that captures the degree of attractiveness of a subscriber to advertisers. Advertisers of our focal newspaper currently use value scores to target specific subscriber populations (e.g., using newspaper inserts). We therefore use a subscriber value score (ValueScore) as the excluded variable.
The value score meets the relevance criterion because the newspaper currently ( 1) trains its customer service representatives to use the subscriber value score to determine the depth of incentives to offer each subscriber at the time of threat and ( 2) requires the customer relationship management department to use the score to determine the incentives subscribers who threaten to quit are likely to receive at the time of renewal. The value score also satisfies the exclusion restriction because there is no theoretical reason why a subscriber's attractiveness to an advertiser should be correlated with the likelihood of renewal following a service recovery. Put differently, the value score, by definition, is independent of a subscriber's reaction to recovery incentives following a service failure (we establish the statistical validity of this intuition subsequently). Thus, we specify our auxiliary models as follows:
Graph
2a
Graph
2b
where RecoveryIncentive and RenewalIncentive are the corresponding discounts offered at the time of threat and a month before renewal. All other covariates are as defined in Equation 1. In addition, following managerial input that promotion amounts varied across years within our data, we use year dummies in Equations 2a and 2b to capture temporal variation in promotion administration. Similarly, we use individual acquisition channels as covariates to account for channel-based promotion administration. Moreover, because there is a chance the firm could have used recovery discounts, the time elapsed since the failure, and the future discount period to determine RenewalIncentive, we also include RecoveryIncentive, TemporalDistance, and FutureDiscPeriod as covariates in Equation 2b. In the second stage, we estimate the proposed model (Equation 1) along with residuals from the auxiliary regressions as control variables. The second stage equation is specified as follows:
Graph
where all terms are as previously described and β14 and β15 capture the effect of the predicted residuals from the first stage.
The second source of endogeneity could stem from subscriber-level time-variant and time-invariant unobservable factors that likely influence their renewal behavior. For instance, individuals from particular social settings (e.g., urban), acquired through certain channels or with a certain demographic profile, might be more or less likely to renew subscription plans due to latent factors that are unobservable to the researcher. Similarly, same-side network effects (e.g., social circles) and other cultural factors associated with a region might influence renewal decisions after a service failure ([53]).
To the extent renewal decisions are driven by such time-invariant, latent, unobservable factors, we mitigate endogeneity bias by including several observable customer-level time-invariant fixed effects (e.g., personalized acquisitions, location, demographic segmentation variables) in our model. To correct for unobserved time-varying factors that might differentially affect subscribers' decision to renew their subscriptions over time, we include several subscriber-level and macro-level time-varying fixed effects (e.g., life stage, whether a subscriber has threatened to leave before, popularity, CPI) in our model.
The third source of endogeneity could stem from the strategic behavior of subscribers who threaten to quit after a service failure. For instance, some subscribers may strategically threaten to quit to obtain discounts on their current subscription service or free upgrades for future service subscriptions. This unobserved intent behind complaining could create a correlated unobservables problem, as it influences subscribers' ultimate decision to renew but resides in the error term of Equation 1. To address this endogeneity concern arising from any systematic differences between subscribers who threaten to quit and those who do not, we build a selection model. The selection model allows us to estimate the likelihood a subscriber threatens to quit on the basis of her individual observable characteristics. We then derive the inverse mills ratio (IMR1), which is a ratio of the probability density function to the cumulative distribution function of the predicted probabilities. The IMR1 then serves as a control for the self-selection bias in Equation 1.
To build our selection model, we obtained additional data on 77,084 subscription decisions of subscribers who did not threaten to quit during the period captured within our data set. Using these and existing data, we build a model that predicts the likelihood of threatening to quit, which can be mathematically represented as follows:
Graph
4
where ThreatToQuit is a binary variable that represents whether (= 1) or not (= 0) subscriber i has threatened to quit at time t. To identify the selection equation, we use ValueScore as the excluded variable for the similar reasons outlined previously. All other covariates are as defined in Equation 1. In addition, because all subscribers received some promotion to subscribe to the service regardless of whether they threatened to quit, we control for the promotion amount that was administered at the beginning of the subscription period. We use the predicted values in Equation 4 to compute the IMR1 ([31]). Subsequently, we include the ratio as a control variable in Equation 3.
The last source of endogeneity results from dropping records due to incomplete information on some variables captured in our model. Of the 7,089 observations originally shared by the firm, we dropped 170 due to missing ValueScore data. Although missing data represent less than 3% of the overall sample, dropping these observations could render the sample nonrandom and lead to the correlated unobservables problem. As discussed previously, correlated unobservables could result in model misspecification and overestimation of the effect of price-based service recovery incentives.
To address this endogeneity concern, we build another selection model. The goal of this selection model is to use observable time-varying and time-invariant characteristics that are available for all of the observations in our model to predict the likelihood of an observation making it into our final sample. We then compute an inverse mills ratio (IMR2) and include it as a control in Equation 1. Our selection model can be mathematically represented as follows:
Graph
5
where ObsinFinalSample is a binary variable that represents whether (= 1) or not (= 0) an observation is included in the final sample (N = 6,919). All covariates are as defined in Equation 1. We use the predicted values in Equation 5 to compute the IMR2. Subsequently, we include the ratio as a control variable in Equation 3.
We use logistic regression to estimate our model. Given that our model has six interaction terms, each involving the RecoveryIncentive variable, multicollinearity between the interactions terms and RecoveryIncentive variable could be a concern. We adopt a sequential residual centering approach ([33]) to minimize multicollinearity issues. Specifically, for each interaction term, we run an ordinary least squares (OLS) regression with the interaction term (e.g., RecoveryIncentive × RenewalIncentive) as the dependent variable and the variables that make up the interaction (e.g., RecoveryIncentive and RenewalIncentive) as the independent variables. The residuals obtained from such an OLS regression should be void of the variance captured by the regressors and therefore should be uncorrelated with RecoveryIncentive. We use residuals from the six OLS regressions in place of the interaction terms in our econometric model. Similarly, because our collaborating firm determined renewal duration on the basis of the recovery discount, how long ago the promotion was offered, and the discount offered for the next subscription period, we adopted residual centering to minimize multicollinearity due to RenewalDuration.[ 6]
Before examining our results, we compare the goodness-of-fit of our proposed model with alternative nested models. In Model 1 (Table 5), we examine subscribers' likelihood to renew as a function of our key variable of interest featured in our hypotheses and control variables. Model 2 includes the IMRs and control function residuals, and it fits better than Model 1. Model 3 builds on Model 2 by including the interaction terms. Model 3, our proposed model, has the best fit in terms of both the Akaike information criterion ( 5,341) and −2 log likelihood ratio ( 5,121). Moreover, as we show in Table 6, ValueScore is significantly associated with RecoveryIncentive, RenewalIncentive, and ThreatToQuit at the 95% confidence level and demonstrates a low correlation (|ρ| =.01, p =.68) with the second stage disturbance terms. This provides statistical validity for our excluded variable.
Graph
Table 5. Effect of Recovery Incentives on Renewal Likelihoods.
| Model 1 | Model 2 | Model 3 |
|---|
| Estimate | Pr > χ2 | Estimate | Pr > χ2 | Estimate | Pr > χ2 |
|---|
| Intercept | 4.547 | .573 | 7.609 | .363 | 8.984 | .283 |
| RecoveryIncentive | −.039 | .026 | −.545 | .000 | −.825 | .000 |
| RecoveryIncentive × FullPriceReminder | | | | | .062 | .040 |
| RecoveryIncentive × RenewalIncentive | | | | | .005 | .000 |
| RecoveryIncentive × TemporalDistance | | | | | .062 | .000 |
| RecoveryIncentive × PromotionalIntensity | | | | | −.099 | .030 |
| RecoveryIncentive × PersonalizedAcquisition | | | | | .054 | .021 |
| RecoveryIncentive × RelationshipLength | | | | | .002 × 10−3 | .758 |
| FullPriceReminder | −1.540 | .000 | −1.075 | .000 | −1.511 | .000 |
| RenewalIncentive | .035 | .000 | .111 | .000 | .172 | .000 |
| TemporalDistance | −.194 | .000 | −.244 | .000 | −.432 | .000 |
| PromotionalIntensity | .504 | .003 | −.438 | .045 | −.742 | .001 |
| PersonalizedAcquisition | −.366 | .000 | −.314 | .001 | −.510 | .000 |
| RelationshipLength | .113 × 10−3 | .000 | .029 × 10−3 | .332 | .027 × 10−3 | .366 |
| ServiceFreqPresent | .037 | .061 | .006 | .784 | .004 | .845 |
| ServiceFreqRenewal | −.007 | .705 | −.056 | .011 | −.072 | .001 |
| ServiceFreqPrevious | .027 | .094 | .061 | .003 | .092 | .000 |
| RenewalDuration | −.250 | .000 | −.361 | .000 | −.543 | .000 |
| Mature | 3.252 | .238 | 3.894 | .160 | 5.833 | .034 |
| Family | 2.848 | .300 | 3.474 | .206 | 4.557 | .092 |
| Urban | .271 | .824 | .230 | .858 | .637 | .614 |
| Suburban | 13.521 | .943 | 15.248 | .936 | 14.554 | .939 |
| Country | −9.916 | .958 | −10.089 | .958 | −8.069 | .966 |
| ThreatBefore | .404 | .000 | .527 | .000 | .775 | .000 |
| CPI | .214 | .001 | .167 | .013 | .255 | .000 |
| TrendScore | .002 | .491 | .000 | .895 | .001 | .616 |
| MonthTrend | −.060 | .000 | −.037 | .037 | −.062 | .001 |
| PRIZM segments fixed effects | ✓ | ✓ | ✓ |
| Failure type fixed effects | ✓ | ✓ | ✓ |
| Failure source fixed effects | ✓ | ✓ | ✓ |
| Control function residuals | | ✓ | ✓ |
| IMRs | | ✓ | ✓ |
| AIC | 5,588 | 5,376 | 5,341 |
| −2 log-likelihood ratio | 5,388 | 5,168 | 5,121 |
| N | 7,089 | 6,919 | 6,919 |
Graph
Table 6. Auxiliary (First Stage) Model Estimation Results.
| Control Function Models | Selection Models |
|---|
| Model 1 | Model 2 | Model 3 | Model 4 |
|---|
| DV = RecoveryIncentive | DV = RenewalIncentive | DV = ThreatToQuit | DV = ObsinFinalSample |
|---|
| Estimate | Pr > |t| | Estimate | Pr > |t| | Estimate | Pr > χ2 | Estimate | Pr > χ2 |
|---|
| Intercept | 5.530 | .056 | −8.511 | .506 | −11.290 | .934 | .477 | .972 |
| ValueScore | .133 | .004 | −.917 | .000 | −.216 | .000 | | |
| RecoveryIncentive | | | −.059 | .495 | | | −.040 | .999 |
| RenewalIncentive | | | | | | | −.003 | .999 |
| TotalPromotionReceived | | | | | −.005 | .000 | | |
| TemporalDistance | | | .558 | .028 | | | .071 | .999 |
| FullPriceReminder | .241 | .030 | .139 | .767 | −.267 | .000 | −.386 | .975 |
| ServiceFreqPresent | −.050 | .029 | −.107 | .401 | −.003 | .642 | −.095 | .999 |
| ServiceFreqRenewal | | | .405 | .001 | | | .009 | .999 |
| ServiceFreqPrevious | .018 | .442 | −.244 | .013 | .061 | .000 | .017 | .999 |
| RenewalDuration | | | 1.537 | .000 | | | .030 | .999 |
| PromotionalIntensity | 1.799 | .000 | −.573 | .508 | −.215 | .000 | −.543 | .900 |
| RelationshipLength | −.001 × 10−3 | .968 | .353 × 10−3 | .002 | .027 × 10−3 | .000 | .207 × 10−3 | .999 |
| Field | −.102 | .632 | 2.849 | .002 | −.335 | .000 | .171 | .964 |
| Telemarketing | −.080 | .454 | .625 | .164 | −.152 | .000 | .662 | .913 |
| Internet | .140 | .667 | 1.352 | .325 | −.887 | .000 | .418 | .809 |
| DirectMail | .181 | .459 | 1.199 | .243 | −.076 | .254 | 1.839 | .060 |
| Mature | .080 | .974 | −10.877 | .284 | 9.051 | .947 | 2.090 | .801 |
| Family | −.095 | .968 | −8.167 | .414 | 9.343 | .945 | 1.128 | .832 |
| Urban | .275 | .922 | −7.456 | .531 | −.093 | .854 | −.038 | .995 |
| Suburban | −.140 | .953 | −10.975 | .274 | 10.920 | .942 | −15.084 | .086 |
| Country | −.846 | .827 | −10.018 | .539 | −.028 | .980 | 18.127 | .002 |
| MonthTrend | .028 | .128 | .350 | .000 | .052 | .000 | .065 | .999 |
| Y2011 | .399 | .002 | 5.070 | .000 | .216 | .000 | −.265 | .967 |
| Y2012 | .195 | .347 | 2.783 | .000 | .441 | .000 | −.370 | .953 |
| Y2013 | −1.556 | .000 | 7.230 | .000 | −1.919 | .000 | −.813 | .838 |
| ThreatBefore | .174 | .081 | −1.777 | .000 | | | .347 | .967 |
| PRIZM segments fixed effects | ✓ | ✓ | ✓ | ✓ |
| Failure type fixed effects | ✓ | ✓ | | ✓ |
| Failure source fixed effects | ✓ | ✓ | | ✓ |
2 Notes: We do not include failure type fixed effects in Model 3 because the variable is not relevant for the group of subscribers who did not complain. Similarly, we do not include failure source fixed effects in Model 3 because of data limitations. The distribution department at our collaborating newspaper only shared the district code information pertaining to the subscribers who complained.
As shown in Model 3 (in Table 5), the recovery discount has a significant negative association with contract renewal likelihoods (β = −.825, p <.01), in support of H1.[ 7]
Regarding the interaction between recovery incentives and the role of full service price reminders, we find a positive and significant moderating effect of reminders on the association between recovery incentives and renewal likelihoods (β =.062, p <.05), confirming H2. Frequent reminders of the original price through the mailed bill can thus lessen the negative effect of reference price that recovery incentives create. Similarly, we find a positive and significant moderating effect of renewal discounts on the association between recovery incentives and renewal likelihoods (β =.005, p <.01), confirming H3. Reducing the price of future services therefore mitigates the negative effect of recovery incentives on renewal likelihood. Next, temporal distance between the time of the threat and contract renewal has a significant and positive moderating effect on the association between recovery incentives and renewal likelihoods (β =.062, p <.01), in support of H4. The negative effect of recovery incentives on renewal likelihoods is therefore weaker among subscribers who have more time left in their service contracts after the recovery incentive was administered.
Consistent with H5, our results indicate that the negative association between recovery incentives and renewal likelihoods will be further intensified for subscribers who were offered a recovery incentive during a promotion intense time (β = −.099, p <.05). Next, we find a positive and significant moderating effect of personalized acquisitions on the association between recovery incentives and renewal likelihoods (β =.054, p <.05). In other words, subscribers acquired through personalized campaigns perceive the renewal price to be fairer at the time of renewal than their counterparts, confirming H6. The results concerning H2–H6 provide evidence of the mechanism, as downward adjustments of reference prices appear to be the strongest explanation for our results. Finally, we do not find evidence of a moderating effect of relationship length (H7). This is surprising, as one would expect firm efforts to build rapport with a subscriber to buffer the negative consequences of reference price effects induced by recovery incentives. One possibility is that the service failure could have minimized the effectiveness of rapport to a point where it can no longer counter the negative reference price effects of recovery incentives. A [51] survey, which notes that a large number of customers are willing to walk away after one bad experience, offers some support for this explanation.
To ensure the robustness of our results, we ran a series of additional models and tests. First, potential overlap between Nielsen's social group and career stage variables and Claritas PRIZM variables, as well as sparseness in the PRIZM and District categories, could make it difficult to achieve full separation among the fixed effects. Failure to achieve full separation could render the point estimates unreliable. To rule out this issue, we reestimated our model using [19] penalized likelihood method. As we show in Web Appendix A4, the key results using Firth's likelihood method are largely identical to those in Model 3 of Table 5.
Second, unobservable factors at the zip code level could influence renewal likelihoods after a service failure. For instance, an exit of a competitor from a zip code (e.g., a new TV station) could raise the barriers to exit for subscribers, thereby leading to higher retention rates. Similarly, network effects at the zip code level could motivate subscribers within the zip code to discount the intensity of service failures and continue with the newspaper ([40]). To address zip code–level issues, we reran our model presented in Equation 3 with zip code fixed effects. As we show in Web Appendix A5, the key results with zip code fixed effects are similar to those observed in Model 3 in Table 5. Third, as we show in Web Appendix A6, our results are robust to using recovery discount as a percentage of the subscription price.
Fourth, the type and source of service failure could be perceived differently by subscribers. The idiosyncratic nature of the service failure could be confounding our results, because the intensity of service failure, which is unobserved to the researcher, could be driving incentive administration and renewal decisions. However, as shown in Web Appendix A7, the type and source of service failures were not significantly associated with incentive depths (i.e., Models 1 and 2 in Table 6) or with renewal likelihoods after accounting for incentive depth (i.e., Models 1, 2, and 3 in Table 5). This provides some evidence that, on average, failure type and source may not be confounding our results. Fifth, a subscriber's choice to enroll in mailed bills and the strategic acquisition of subscribers through personalized channels could raise endogeneity issues. To address these selection issues, we build two selection models with billing methods and personalized acquisition channels as dependent variables and subsequently include the IMRs from the selection models in our second-stage model. As we show in Web Appendix A8, the results are similar to those observed in Table 5.
To enhance the managerial use of our conceptual model, we propose an optimization framework for subscription-based service providers. Our optimizer is a mathematical program that builds on the marketing-mix response model specified in Equation 1 to determine the optimum levels of recovery and renewal discounts to be administered after a service failure to maximize gross profits. The analytical formulation of the optimization model, model assumptions, and estimation technique are detailed in Web Appendix A9. The proposed model presents a multidimensional nonlinear combinatorial problem. Due to the complexity of the problem and the ease of implementing the model in an accessible software (e.g., Microsoft Excel), we adopt a genetic algorithm to solve the model.
Implementing the model on a holdout sample of 30 subscribers, we find that the firm is significantly misallocating its resources on both instruments, resulting in suboptimum gross profits. Specifically, as shown in Model 1 in Table 7, the firm underspent by about 9% on recovery incentives and overspent by about 145% on renewal incentives, resulting in a loss of approximately 20%. Model 2 in Table 7 simulates service frequency as an additional recovery instrument at the time of a subscriber threat. Offering to change the frequency or level of service subscribers receive comprises a non-price-based tactic firms may use to recover from service failures. The results of Model 2 indicate that the firm could upsell by undercutting spending on recovery incentives by about 64% and on renewal incentives by about 147%. The resulting savings would translate into an increase in gross profits by about 28%. Model 3 in Table 7 simulates service frequency at the time of renewal as an additional recovery instrument. The results of Model 3 indicate that the firm could cut back on recovery incentives by about 11% and on renewal incentives by about 175% and thereby increase gross profits by about 25%. Finally, it may be worthwhile to compare average lifts in profitability for the low and high levels of each moderator in our holdout sample. While all categories demonstrate a positive lift in profitability, we observe considerable heterogeneity within each moderator across low and high conditions (see Table 8 for details).[ 8] These results provide further takeaways for managers who want to compare the profitability implications across the various strategies discussed in our framework.
Graph
Table 7. Optimizer Results.
| Decision Variables in the Optimizer | Proposed RecoveryIncentive Spending | Proposed RenewalIncentive Spending | Expected Lift in Gross Profits |
|---|
| Recovery Incentive | Renewal Incentive | ServiceFreq | ServiceFreqRenewal |
|---|
| Model 1 | ✓ | ✓ | | | 9.38% | −145.52% | 19.82% |
| Model 2 | ✓ | ✓ | ✓ | | −63.93% | −147.42% | 27.57% |
| Model 3 | ✓ | ✓ | | ✓ | −11.00% | −175.43% | 24.90% |
3 Notes: A negative number indicates the firm is currently overspending on incentives. The values are computed as .
Graph
Table 8. Average Lift in Gross Profits across Moderators.
| Expected Lift in Gross Profits |
|---|
| Model 1 | Model 2 | Model 3 |
|---|
| FullPriceReminder | | | |
| No (= 0) | 4.68% | 36.89% | 23.49% |
| Yes (= 1) | 27.17% | 27.14% | 27.36% |
| RenewalIncentive | | | |
| Low | 28.12% | 35.89% | 29.87% |
| High | 18.60% | 24.07% | 23.75% |
| TemporalDistance | | | |
| Low | 42.21% | 42.96% | 42.23% |
| High | 19.42% | 27.39% | 23.74% |
| PersonalizedAcquisition | | | |
| No (= 0) | 13.11% | 21.32% | 15.94% |
| Yes (= 1) | 26.37% | 32.54% | 30.07% |
| RelationshipLength | | | |
| Low | 20.18% | 27.14% | 23.24% |
| High | 25.54% | 31.94% | 29.45% |
4 Notes: The low and high values for TemporalDistance, RenewalIncentive, and RelationshipLength are determined on the basis of the median values of the corresponding variables in the holdout sample. Observations with values less than the median are included in the low condition and those with values greater than the median are included in the high condition.
Can price-based service recovery incentives retain subscribers in the long run? Findings from newspaper delivery failures suggest that price-based renewal incentives offered immediately after service failures ultimately decrease the likelihood that subscribers renew their contracts. We propose that reference price effects account for these findings and demonstrate how service providers can reduce the negative impact of recovery incentives on customer repatronage. Our results have important implications for contractual service providers, marketing theory, and future research.
Our research helps inform the managerial dilemma of whether recovery incentives have long-term effects. Specifically, recovery incentives may backfire by generating the unintended consequence of discouraging subscription renewals. Discounting the cost of a subscriber's service may lead subscribers to adjust the price they believe is fair for the service. When their contract becomes due for renewal and subscribers compare the renewal price with the lower recovery-based price to which they have adjusted, they may hesitate to renew their contracts. Recovery discounts may be necessary to address extreme dissatisfaction in the short run and thus may be unavoidable. For such instances, our study proposes several ways subscription-based service providers can reduce the negative effect of deeper recovery incentives on customer repatronage. Specifically, firms could adopt the following actions.
Contractual relationships provide firms the ability to follow up with subscribers after a service recovery. By using opportunities that various touch points offer to remind subscribers of the full subscription price, service providers can help subscribers adjust their price standard back to the regular price, which in turn can increase their renewal likelihood.
Firms may reduce the price of the subscription renewal to encourage renewals from subscribers who experienced a service failure. By coordinating the depth of the renewal discount firms may offer with the depth of the recovery incentive they have already offered, firms can help align the renewal and recovery-based prices to decrease the discrepancy between the two prices and thus increase the likelihood that subscribers renew their contracts. To that end, our decision support model shows firms how to determine the optimum levels of recovery and renewal incentives simultaneously.
Extending a subscriber's service contract at the time of recovery can foster forgetting of the recovery-based price and may even help subscribers feel that they received what they paid for, thereby increasing the likelihood that subscribers renew their contracts at the full subscription price. By considering how to guide subscribers back to normal service conditions in terms of the service price, firms can help subscribers perceive that the price they pay is fair.
Understanding how subscriber characteristics and environmental factors influence customers' reliance on recovery incentives can help firms determine the optimal depth of the recovery incentive to offer. For instance, subscribers acquired through targeted communications may have a value-based reason beyond price to continue their relationship with the provider, which in turn will lead them to rely less on their recovery-based price when making their renewal decision. The intensity of the promotional environment during which firms offer recovery incentives, though, may help subscribers focus more on the recovery price and thus increase their reliance on it when renewing their subscriptions.
In addition, our research affirms that not all subscribers are alike, as some are more likely to renew after a service failure regardless of the recovery incentive. We find that subscribers in mature life stages are more likely to renew their subscription after a service failure compared with younger subscribers. This result is likely idiosyncratic to print editions of the media industry, as older subscribers typically value print subscriptions more than younger ones. Surprisingly, we find that subscribers who threatened to quit before their current threat are more likely to renew their subscription after a service failure. Our further investigation revealed that renewal likelihoods decrease with each additional threat, suggesting that such subscribers opportunistically make threats to obtain discounts. Firms may wish to balance retention desires with intervention tactics when handling subscribers who repeatedly seek redress. Subscribers with more discretionary income at their disposal also appear more likely to renew their subscription following a service failure. This result underscores the role of household budgetary constraints in evaluating service failures. In summary, our study provides evidence that treating service recovery as a continual process of repairing the relationship rather than as a one-time response can boost the long-term success of service recovery incentives in contractual settings.
Illustrating the downside of price-based recovery incentives extends the conversation on rethinking service recovery ([16]). Specifically, studies on overcompensation for service failures tend to be lab-based and involve transactional settings, which measure the immediate effects of service recovery incentives. We complement these studies by documenting how a solution that may address service failures in the short run may hurt contract renewals in the long run. Our finding implies that the attitudinal effects of service recovery incentives may not ultimately extend to behavioral ones. We thus build on the finding that deeper recovery discounts can have a diminishing effect on satisfaction ([28]) by showing the diminishing effect such recovery incentives can also have on behaviors such as renewals.
Studies have also suggested that customer suspicion of firm practices and economic viability account for the dark side of incentive depth ([22]). We provide an alternative explanation based on the theory of reference price: recovery incentives give subscribers a new price point on which to anchor, triggering a new comparison upon contract renewal. By adding the element of retention to price-based service recovery incentives, we help extend the literature on acquisition and retention effects of price promotions ([49]). Moreover, our research implies that subscribers' rational price-based reasons, in terms of reference price, may outweigh and outlast their emotion-based reasons, in terms of gratitude for the recovery incentive, when making renewal decisions. Our research also contributes to the literature on customer relationship management by underscoring how firms can manage subscriber expectations after subscribers accept a price-based recovery incentive. Specifically, we document several ways firms can influence subscribers' reference price over time, as well as contextual factors that can influence their reference price over the course of the contractual relationship.
As with any research, our study is not without caveats. For instance, our findings are limited in generalizability, as they come from a single firm in the subscription service industry. Due to data limitations, we could not document the short-term effects of price-based recovery incentives. Juxtaposing the long-term and short-term consequences of price-based recovery incentives will reveal their overall effectiveness. In addition, without understanding the ramifications of the proposed framework for advertisers, we are unable to determine whether the optimal incentive allocations prescribed by our decision support model are overall net profit maximizing for our ad-supported media platform (i.e., the newspaper). Finally, the lack of attitudinal data on service failure severity motivated us to rely on observable indicators of service failure severity (i.e., failure type and source). Although the results indicate no systematic differences between individual categories of failure types and failure sources, future research can use call transcript data from interactions between service representatives and subscribers to compute service failure severity.
Our investigation into price-based recovery incentives at subscription services revealed several opportunities for future inquiry that could spur a research subdomain within the service recovery literature. In Figure 3, we provide a research framework that classifies these opportunities into drivers, mechanisms, boundary conditions, and consequences, thus furnishing initial ideas to further the conversation we started in this research. As a starting point, future research could validate the findings of our single-firm study using multiple firms. Scholars could also document how the effectiveness of recoveries differs in other settings, such as business-to-business contexts, where supplier pools are considerably smaller and relationship-based selling is more prevalent.
Graph: Figure 3. A framework for future research on price-based service recovery incentives.
Next, in the growing subscription economy, factors that drive the issuance and magnitude of price-based recovery incentives also warrant attention. We note four key factors that originated from our interactions with marketing executives at our collaborating firm in Figure 3: organizational factors (e.g., firm reputation, discretionary resources), service failure type (e.g., operational failure, core service failure), service failure severity (e.g., magnitude of failure, frequency of failure) and notification medium (e.g., private vs. public, third-party forums such as Better Business Bureau). Future studies could also test whether other theories cited in the service recovery literature (e.g., reciprocity, expectation confirmation, attribution) explain how price-based recovery incentives affect customer attitudes and behaviors within contractual settings. Future research could also examine conditions that strengthen or weaken these mechanisms (e.g., subscriber characteristics, service characteristics, other recovery tactics). Knowledge about other likely consequences of price-based recovery incentives within the context of contractual services is also scant. For instance, more research is needed to understand the attitudinal (e.g., satisfaction, loyalty, trust) and behavioral (e.g., service usage, word of mouth, future reporting of failure) outcomes of price-based recovery incentives in both the short run and long run. These factors are not exhaustive. More qualitative research is needed to uncover the spectrum of factors constituting the drivers, mechanisms, boundary conditions, and consequences of deploying price-based service recovery incentives within contractual settings.
Understanding the timing of recovery incentive administration constitutes another important avenue for future research. For instance, while the effect of recoveries may wear off over time, the asymptotic level of decay remains unknown. By understanding when the negative effects wear out, afflicted firms may better time when to abate their recovery incentives. Next, marketers could also develop a dynamic optimizer that incorporates new data into the optimizer in real time to determine recovery and renewal incentives in real time. In addition, researchers could leverage advancements in machine learning to incorporate high-dimensional rich individual data into the model proposed in this research. Such a model could offer scalability and improve forecast accuracy.
Our research investigates the long-term impact of price-based service recovery incentives in a contractual subscription setting and prescribes ways to improve the effectiveness of service recovery incentives. We hope our work inspires scholars to continue researching the deployment and effectiveness of price-based service recovery incentives at subscription-based firms.
Supplemental Material, DS_10.1177_0022242919859325 - The Unintended Consequence of Price-Based Service Recovery Incentives
Supplemental Material, DS_10.1177_0022242919859325 for The Unintended Consequence of Price-Based Service Recovery Incentives by Vamsi K. Kanuri and Michelle Andrews in Journal of Marketing
Footnotes 1 Associate EditorRebecca Slotegraaf
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 ORCID iDVamsi K. Kanuri https://orcid.org/0000-0002-6228-8017
5 Online supplement: https://doi.org/10.1177/0022242919859325
6 1As Web Appendix A3 shows, our results are consistent without using the residual centering approach.
7 2One might expect a nonlinear association between renewal discounts and retention, consistent with prior research that demonstrates a nonlinear association between recovery incentives and customer satisfaction ([22]). However, additional analysis with the quadratic term of RecoveryIncentive did not reveal a significant nonlinear association. It is possible that customer behaviors manifest differently than attitudes over the long run, whereby a negative association between recovery incentives and renewal likelihoods exists only for behaviors. Similarly, it is possible that behaviors manifest differently in the short run than in the long run.
8 3Because newspapers are platform firms, the overall profitability lifts may depend on the impact of subscriber retention on the newspaper's print advertising revenue ([40]). Our optimizer does not account for the cross-market network effects between subscribers and advertisers.
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Record: 205- Tolerating and Managing Failure: An Organizational Perspective on Customer Reacquisition Management. By: Vomberg, Arnd; Homburg, Christian; Gwinner, Olivia. Journal of Marketing. Sep2020, Vol. 84 Issue 5, p117-136. 20p. 2 Diagrams, 7 Charts. DOI: 10.1177/0022242920916733.
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Tolerating and Managing Failure: An Organizational Perspective on Customer Reacquisition Management
Although reacquiring customers can lead to beneficial outcomes, reacquisition processes are often unpleasant for employees, who may be required to admit and address failures. Because many organizational environments reward success and punish failure, companies need to understand how to create an organizational environment that stimulates customer reacquisitions. This study investigates the impact of failure-tolerant cultures and formal reacquisition policies on successful customer reacquisition management. Drawing on organizational design theory and psychological ownership theory, the authors find that failure-tolerant cultures have an inverted U-shaped effect on reacquisition performance because moderate failure tolerance increases reacquisition attempts while not inducing more failures or increasing their severity. Formal reacquisition policies, in contrast, have a positive linear relationship. Notably, formal reacquisition policies do not conflict with failure-tolerant cultures but enhance the beneficial effects of failure tolerance on reacquisition performance; formal reacquisition policies provide guidance for reacquisition attempts that failure-tolerant cultures inspire. Finally, results show that customer reacquisition performance is positively related to overall firm financial performance, a finding that emphasizes the managerial and organizational-level importance of reacquisition management.
Keywords: culture; customer reacquisition; customer reacquisition policies; failure tolerance; marketing organization; marketing strategy; psychological ownership
Understanding how companies can reacquire customers is important in both research and practice (e.g., [82]). Although companies likely benefit from winning back lost customers (e.g., [84]), company cultures may present a hurdle to successful customer reacquisition management. More specifically, when attempting to reacquire customers, employees have to face and discuss unpleasant incidents, failures, or weaknesses. Organizational cultures often instill a tendency in employees to take failures "as indicators of poor performance, negligence, or as lack of competence" ([18], p. 665). Although companies have begun to acknowledge the value of failure ([17]), most organizations still interpret failure negatively ([44]) by rewarding success and punishing failure (e.g., [ 5]). Thus, a reasonable assumption is that in a "competitive world of business, where a mistake can mean losing a bonus, a promotion, or even a job" ([17], p. 65), employees are likely to refrain from addressing customer defection.
Successful customer reacquisition management may thus require companies to develop a failure-tolerant organizational culture that encourages a constructive treatment of failures (e.g., [ 8]). We argue that in failure-tolerant organizational cultures, employees might be willing to address failures by assuming "ownership" of the reacquisition process and going to great lengths to win customers back (e.g., [55]; [77]).
However, a failure-tolerant culture may also be counterproductive for customer reacquisitions in that employees might make decisions in other ongoing customer relationships with less due diligence ([ 8]). As a consequence, before defecting, customers may have encountered more frequent and more severe failures, substantially lowering the chance to win them back (e.g., [27]; [46]). Thus, our first research question is, How does a failure-tolerant organizational culture affect reacquisition performance (i.e., the share of lost customers the organization reacquires)?
Importantly, while a tolerance for failure might benefit customer reacquisition management, companies face difficulty in managing or controlling which employee behaviors failure tolerance inspires ([55]; [63]). Unsurprisingly, companies have started to establish formal reacquisition policies (e.g., [73]). Formal reacquisition policies are organizational specifications that guide employee behaviors during the reacquisition process ([82]). However, formal policies likely represent a different route to customer reacquisition. While failure tolerance may inspire employees to attempt reacquisition of their own accord, formal reacquisition policies prescribe and enforce employee reacquisition behaviors. Whether these two routes counterbalance or reinforce each other is unclear.
On the one hand, formal reacquisition policies may offset positive effects of failure tolerance by "crowding out" employees' intrinsic motivation for reacquisition management ([30])—a phenomenon prior literature discusses as the corruption effect of extrinsic motivation ([10]). On the other hand, formal reacquisition policies may have a "crowding-in" effect ([63]), in that they may reinforce benefits of failure tolerance by providing helpful directives for employees ([53]). Thus, our second research question is, How do formal reacquisition policies moderate the impact of failure-tolerant cultures on reacquisition performance?
Notably, customer reacquisition management creates costs—for example, directly in the form of monitoring costs or indirectly in the form of lost profits owing to price concessions—that need to be justified by revenue increases. Furthermore, reacquisition attempts may lower reference prices of not-defected customers ([42]; [56]) or provoke customers' strategic defection behaviors (i.e., customers defect to get a better offer, such as a lower price from the same company; [84]). Thus, our third research question asks, Is a company's reacquisition performance relevant to its overall firm performance?
Our study responds to the [59] call to examine organizational issues in marketing and provides three focal contributions. As our first contribution, we introduce failure tolerance as an informal success factor for customer reacquisition management and demonstrate its inverted U-shaped impact on reacquisition performance. We find that reacquisition performance becomes three times larger, increasing from low to optimal levels of failure tolerance. However, failure tolerance can also elicit negative effects: moving from optimal to high levels of failure tolerance, reacquisition performance drops by 13%.
As our second contribution, we perform the first organization-level test of the proposition that formal reacquisition policies should favorably affect reacquisition performance ([82]). We analyze the interplay between formal and informal organizational elements ([32]) of customer reacquisition management: formal reacquisition policies enhance positive effects of failure tolerance. Our results indicate that in our sample, a company with an average level of failure tolerance increases reacquisition performance by more than 1.5 times when moving to a higher level of formal reacquisition policies. For those companies, the negative effects of failure tolerance set in later.
Our third contribution is the establishment of a positive effect of reacquisition performance on overall financial performance. Our results show that positive consequences (e.g., increased revenues) more than offset costs of customer reacquisition management, such as price concessions. Overall, our findings emphasize the managerial importance of customer reacquisition management.
Table 1 reviews the scarce literature on customer reacquisition management and reveals an important research void regarding the organizational level of customer reacquisition management ([73]). Prior research has focused on the customer (e.g., [34]) and the customer relationship level (e.g., [46]). Specifically, prior literature has explored how individual actions such as price concessions help win customers back but has not investigated the role of organizational elements, thereby implying that employees are willing to address defections. However, employees are likely to avoid addressing failures ([ 5]), and organizational cultures that display low levels of failure tolerance may nurture such a tendency in employees. Thus, understanding how organizational elements contribute to reacquisition performance is important ([28]).
Graph
Table 1. Overview of Customer Reacquisition Literature and Positioning of Our Study.
| Authors (Year) | Level of Analysis | Studied Phenomena | Methodology |
|---|
| Organizational Level | Customer Level | Customer Relationship Level | Failure-Tolerant Culture | FormalReacquisition Policies | Organizational-Level Financial Performance | Number of Industries | Number of Firms | Modeling Approach |
|---|
| Homburg, Hoyer, and Stock (2007) | — | ✓ | ✓ | — | — | — | Single industry (telecommunications) | Single firm | Regression, structural equation modeling |
| Kumar, Baghwat, and Zhang (2015) | — | ✓ | ✓ | — | — | —Relationship-level financial performance | Single industry (telecommunications) | Single firm | Regression |
| Leach and Liu (2014)a | (✓) | (✓) | (✓) | — | (✓) | — | Multiple industries | Multiple firms | Qualitative interviews |
| Pick et al. (2016) | — | ✓ | ✓ | — | — | — | Single industry (romance novels) | Single firm | Regression |
| Stauss and Friege (1999)b | (✓) | (✓) | (✓) | — | ✓ | — | N.A.b | N.A.b | N.A.b |
| Thomas, Blattberg, and Fox (2004) | — | — | ✓ | — | — | — | Single industry (newspapers) | Single firm | Hazard model |
| Tokman, Davis, and Lemon (2007) | — | — | ✓ | — | — | — | Single industry (automobile maintenance) | Single firm | Analysis of variance |
| Our study | ✓ | — | — | ✓ | ✓ | ✓ | Multiple industries | Multiple firms | Regression |
1 a Qualitative study.
- 2 b Conceptual paper (no empirical analysis conducted).
- 3 Notes: ✓ = included in the study; (✓) = partially included in the study; — = not included in the study; N.A. = not applicable.
Our investigation draws on organizational design theory to study these elements. We introduce a failure-tolerant organizational culture as a focal informal organizational element and formal reacquisition policies as a focal formal organizational element for customer reacquisition management.
We couch our conceptual framework in organizational design theory, which identifies and emphasizes the importance of both informal and formal elements of organizations (e.g., [86]). Informal organizational elements largely refer to social aspects within the organization and the resulting organizational norms, values, and beliefs (e.g., [23]). Therefore, informal elements can be associated with organizational culture, which is "the pattern of shared values and beliefs that help individuals understand organizational functioning and thus provide them with the norms for behavior in the organization" ([11], p. 4; [36]).
We define a "failure-tolerant organizational culture" as organizational values, norms, and artifacts that imply that failures are constructively handled, openly addressed, and freely communicated; that the causes and underlying mechanisms of failures are analyzed for improvement; and that failures are even actively encouraged (e.g., [17]; [78]). Thus, a failure-tolerant organizational culture encompasses failure handling, failure communication, failure learning, and failure encouragement (e.g., [ 8]; [16]; [87]; [89]).
We argue that a failure-tolerant organizational culture is essential for customer reacquisition management. Employees are likely to perceive customer defection as an undesirable or unpleasant occurrence equated with failure, regardless of the reason for defection.[ 5] Usually, employees do not freely and deliberately discuss their mistakes. They may fear blame from colleagues or punishment by superiors ([ 5]; [ 7]). As reluctance to address failures would be counterproductive for reacquisition management, it renders failure tolerance an important organizational quality.
Employees typically acquire a tolerance for failure outside the reacquisition context via organizational socialization—the process by which a person acquires knowledge necessary to assume an organizational role ([88]). Organizational socialization to failure tolerance might occur in several ways. First, symbolic acts may nurture a tolerance for failure ([36]). For instance, Procter & Gamble has reportedly humorously handed out a "heroic failure award" ([58]) that employees likely find indicative of a general failure-tolerant culture. Second, group observation may implicitly contribute to employees' failure tolerance ([26]): employees may acquire a tolerance for failure through regular interactions with mentors or by observing coworkers' behaviors ([47]). Third, employees join companies with certain strengths and skills that the company values. After an employee is on board, socialization can also occur when employees discuss failures, thereby reinforcing an existing tolerance for failure as an important norm ([28]).
Once employees have internalized a failure-tolerant culture, they tend to view it as a "perfectly 'natural' response to the world of work" ([88], p. 210). Thus, a reasonable expectation is that once employees have acquired a failure-tolerant mindset, it should guide them during customer reacquisition endeavors.
"Formal reacquisition policies" refer to the extent to which companies establish and enforce strict formal rules and procedures that employees must follow when reacquiring customers. Classifying reacquisition policies as a formal element is in line with organizational design theory. Organizational design theory, for instance, lists specialization, formalization, and standardization as formal elements ([80]). In addition, organizations expect formal elements to steer employees' behavior toward support of high organizational performance ([72]). The same applies for formal reacquisition policies, further supporting their classification as formal elements.
As the reacquisition process entails "the planning, realization, and control of all processes that the company puts in place to regain customers" ([82], p. 348), formal reacquisition policies capture these three phases of the reacquisition process. Specifically, formal reacquisition policies comprise strict systematic and standardized processes for reacquisition analysis, reacquisition activities, and reacquisition monitoring ([82]).
To overview the logic, Figure 1 summarizes our predictions. We argue that a failure-tolerant organizational culture and formal reacquisition policies offer different routes to reacquisition performance (i.e., share of lost customers the organization reacquired). An internalized tolerance for failure will contribute to employees' psychological ownership of the reacquisition process, leading employees to go to great lengths to win customers back (i.e., engage in extra-role behaviors). In contrast, formal reacquisition policies likely lead employees to perform formally defined customer reacquisition tasks in expected ways (i.e., employ in-role behaviors). As these divergent routes may create tensions, we explore the interaction between failure-tolerant cultures and formal reacquisition policies.[ 6]
Graph: Figure 1. Conceptual framework.
We predict two countervailing effects of failure-tolerant cultures on reacquisition performance. We argue that reacquisition performance depends on employees' willingness to address failures, but also on the experiences in the initial relationship. Failure tolerance impacts both factors, as we briefly summarize and explain in more detail in the sections that follow.
First, failure tolerance may increase the number of failures that are addressed given employees' psychological ownership of the reacquisition process. Second, high levels of failure tolerance might, at the same time, lower the number of successful reacquisitions because more customers are likely to experience more problems due to failure frequency and severity. At low to moderate levels of failure tolerance, we expect that the benefits of the number of failures addressed will outweigh the costs of failure severity and frequency. However, as failure tolerance increases, the costs of failure severity and frequency may supersede the benefits of the number of failures addressed.
As we have noted, failure-tolerant cultures may instill in employees a feeling of psychological ownership that makes them feel responsible for customer reacquisition. Psychological ownership is a cognitive–affective construct "in which individuals feel as though the target of ownership...is theirs" ([68], p. 86). Such targets of ownership can be activities such as the reacquisition process ([67]; [77]).
Employees are likely to assume ownership of the reacquisition process in failure-tolerant companies. Failure-tolerant cultures inspire employees to voice their ideas ([13]) and discuss mistakes openly ([89]) rather than provoking fear of being blamed for failures (e.g., customer defection; [78]). Consequently, employees are more disposed to invest their skills, ideas, and effort into customer reacquisition management. According to theory, such investments stimulate feelings of ownership ([67]). In line with our rationale, research demonstrates that failure tolerance fosters employees' feelings of responsibility for their own failures as well as those of their clients ([21]).
We expect that feelings of ownership will increase the number of failures addressed by employees, meaning the number of attempts to engage in customer reacquisition. Theory predicts that once employees have assumed ownership of a target, they will be attentive to their "possessions" ([30]), and research shows various positive outcomes of psychological ownership (e.g., [40]). For instance, once employees assume psychological ownership, they engage in favorable extra-role behaviors ([77]). Thus, we predict that a sense of responsibility for the reacquisition process, which failure tolerance stimulates, spurs employees to work harder, be more creative, and act unconventionally in reacquiring customers. Thereby, they address more failures and increase reacquisition performance.
However, we expect that high levels of failure tolerance can have a boomerang effect on customer reacquisition performance. High levels of failure tolerance are likely to increase the number of lost customers experiencing higher failure frequency and failure severity in their initial customer relationships. Failure frequency refers to the number of failures customers experience in relationships. Failure severity refers to the magnitude of loss that customers experience owing to failures. Such losses can be tangible (e.g., financial loss) or intangible (e.g., annoyance, anger) ([31]). Failure severity and frequency may increase because failure tolerance can introduce laxness in companies. Once employees have internalized a tolerance for failure, they may make decisions with less due diligence and effort and can even "hide" behind a failure-tolerant culture, provoking more and increasingly severe failures in customer relationships ([ 8]).
Failure frequency and severity likely lower reacquisition performance. Reacquisition performance depends substantially on the experiences customers had in the initial relationship; employees are less likely to successfully reacquire customers who experienced multiple and severe failures ([46]). Research shows that even a few negative events in customer relationships can make customers reevaluate the complete relationship, potentially reinterpreting positive prior experiences as negative experiences ([27]). Thus, frequent and severe failures may cause "irrecoverable damage" ([25], p. 329) for reacquisition attempts, lowering reacquisition performance.
Importantly, failure tolerance may lead to an exponential increase in failure severity and frequency. In companies that are excessively failure-tolerant, coworkers are not likely to provide corrective measures when they note the occurrence of multiple and severe failures. Rather, as social learning theory predicts, dysfunctional effects of failure tolerance could spread rapidly in the organization as employees observe and imitate peers' behaviors ([ 2]; [26]).
The combination of our predictions results in an inverted U-shaped relationship between failure tolerance and reacquisition performance. An inverted U-shaped relationship arises as the result of a linear positive benefit function and a convex cost function (for a detailed discussion of theorizing U-shaped effects, see [24], [22], and [49]). The positive benefit function results from the linear relationship between psychological ownership and number of failures addressed. The convex curve stems from the exponential relationship between failure tolerance and failure severity and frequency (Figure 2, Panel A).
Graph: Figure 2. Illustration of the hypotheses on the inverted U-shaped effects.Notes: In Panel A, the inverted U-shaped effect of failure-tolerant cultures on reacquisition performances arises from a combination of benefits minus costs, where the benefits are represented by the positive effect of an increased number of addressed failures while the costs refer to nonlinearly increasing severity and frequency of failures (H1). In Panel B, the interaction effect of formal reacquisition policies and failure-tolerant cultures results in a shift of the inverted U-shaped curve to the right. This shift occurs as the aforementioned benefit function (i.e., the number of addressed failures) becomes steeper under a high degree of formal reacquisition policies (H3).
- H1: A firm's failure-tolerant culture has an inverted U-shaped effect on reacquisition performance.
Formal reacquisition policies may be another route for companies to stimulate reacquisition performance. Formal elements such as monitoring or guidelines focus on task accomplishment by directing employees to engage in in-role behaviors ([62]). They prescribe employees' behaviors, with the outcome that employees "do not feel that they can go beyond their well-defined areas of responsibility" ([69], p. 292). In line with this reasoning, formal elements lead to greater role clarity but lower engagement in extra-role behaviors ([69]).
As a formal element, formal reacquisition policies constitute clear guidelines for reacquisition analysis, providing employees with a structured framework for identifying lost customers, pinpointing reasons for defection, and evaluating reacquisition potential ([82]). Formal reacquisition policies direct employees to engage in expected behaviors. They encourage in-role behaviors and increase the number of failures addressed. Similarly, formal guidelines for reacquisition monitoring help systematically detect shortcomings in reacquisition processes and promote organizational learning processes ([54]). Overall, formal reacquisition policies encourage employees to make more reacquisition offers, increasing the firm's reacquisition performance.
- H2: Formal reacquisition policies have positive effects on reacquisition performance.
Thus far, we have predicted the individual effects of failure-tolerant cultures and formal reacquisition policies. However, informal and formal organizational elements are likely to be present simultaneously in organizations and can create tensions (e.g., [77]). In our sample, we observe a moderate correlation between failure-tolerant cultures and formal reacquisition policies (r =.34). Because the two elements represent different and potentially conflicting routes to reacquisition performance, their joint occurrence raises the question of how formal reacquisition policies moderate the effect of failure-tolerant cultures on reacquisition performance.
From a pragmatic perspective, following formal guidelines and providing reports likely ties up employees' resources, lessening the potential for discretionary behaviors ([61]). [30] even proposes (but does not test empirically) that formal elements could undermine feelings of ownership. Thus, with greater levels of formal reacquisition policies, an increase in failure tolerance may manifest in extra-role behaviors to a lesser extent ([33]; [77]).
However, in the context of customer reacquisition, we propose that formal reacquisition policies can enhance the positive effects of failure tolerance on reacquisition performance. Crowding theory suggests that formal reacquisition policies may beneficially affect outcomes of psychological ownership if employees perceive formal reacquisition policies as informative rather than controlling. In such a situation, a crowding-in effect sets in: formal management enhances intrinsic motivation for extra-role behaviors ([63]).
This effect may apply in the context of customer reacquisitions, as employees assume ownership of the reacquisition process they strive to successfully reacquire customers. However, the unstructured context of customer reacquisitions may create ambiguity for employees as to which behaviors they should engage in. In line with goal-setting theory, formal reacquisition policies may serve a directive function, allowing employees to focus on goal-relevant activities ([53]). Thus, instead of perceiving formal reacquisition policies as controlling, employees may consider them informative and respond positively ([63]). Formal reacquisition policies provide clarity, work efficiency, and guidance when the unstructured context of customer reacquisition endeavors fails to do so (e.g., [69]; [77]).
Formal reacquisition policies thus enhance the linear positive relationship between failure tolerance and failures addressed. This enhancement shifts the turning point to the right, meaning that negative effects only set in at higher levels of failure tolerance. Notably, such a shift in the turning point does not require that formal reacquisition policies also moderate the relationships between failure tolerance and failure frequency and severity ([24]).[ 7]Figure 2, Panel B, illustrates this prediction.
- H3: With increasing degrees of formal reacquisition policies, the turning point of the inverted U-shaped effect of a firm's failure-tolerant culture on reacquisition performance shifts to the right.
Several arguments suggest a positive relationship between reacquisition performance and firm performance. First, successful customer reacquisitions help maintain the customer base and thereby increase turnover and profits ([20]). Second, as reacquired customers tend to show higher purchase volumes and loyalty than in the initial relationship, they are more profitable (e.g., [85]). Third, reacquiring customers is associated with solving problems of dissatisfied customers, who tend to vent their displeasure about unsolved problems. Customer reacquisition thus prevents negative word of mouth and enhances company reputation ([71]), exerting significant positive effects on company performance ([75]). Fourth, companies that successfully reacquire customers can gain important insights into company weaknesses and eliminate them ([82]). Thus,
- H4: Reacquisition performance has positive effects on a firm's overall financial performance.
Because data on our focal construct (i.e., failure-tolerant culture) are generally not available from secondary data sources, we conducted a cross-sectional online survey to test our hypotheses ([74]). Survey research is often advantageous for investigating intraorganizational issues, because it allows for important insights that cannot be obtained from other data sources ([37]). However, as our model also includes the firm's overall financial performance, we complement our survey data with financial indicators from an objective database.
We identified potential respondents for our survey via the social business network XING, an established online career platform in Germany. We selected the contacts through filtering by position (we considered only sales positions) and work experience (we considered only respondents who had at least three years in their current position). We contacted 638 respondents via email, asking them to participate in our survey of approximately 20 minutes in length. As an incentive, we offered the choice between a €25 donation to a good cause or a €20 voucher from an online retailer. We collected 193 usable questionnaires. Our response rate of 30.25% compares favorably to the average business survey response rate of 21% ([14]). We examined the representativeness of our sample by testing the industry distribution of the effective sample against the industry distribution of people employed in Germany ([12]). Because a chi-square goodness-of-fit test indicated no significant differences, the sample is unlikely to be biased (χ2 = 9.31, p =.50).
We tested H1–H3 with this survey sample. To test H4, we used the established financial database AMADEUS to match the respective financial performance data for each participant. However, owing to less-than-comprehensive public disclosure requirements, we could not obtain financial performance data for many family-, foundation-, or state-owned companies. Therefore, the sample to test H4 consists of 131 matched cases. In our analytical procedure, we accounted for a potential selection bias of this subsample. An overview of the sample characteristics appears in Table 2.
Graph
Table 2. Sample Overview and Structural Equivalence.
| Main Survey Sample (n1 = 193) | Matched Sample (Survey and Financial Performance Data; n2 = 131) |
|---|
| Firm Industrya | % | % |
| Automotive | 4 | 5 |
| Business Services | 12 | 13 |
| Chemical and Pharmaceutical | 2 | 3 |
| Construction | 6 | 6 |
| Financial and Insurance Activities & Real Estate Activities | 1 | 1 |
| Information and Communication | 4 | 5 |
| Manufacture of Machinery and Equipment & Steel | 4 | 6 |
| Other | 15 | 16 |
| Other Service Activities | 29 | 25 |
| Trade, Transport, Accommodation and Food Services & Textile and Apparel | 23 | 20 |
| Goodness-of-fit between samplesb | χ2 = 9.75 (p =.37) | |
| Firm Annual Revenues | | |
| <€500,000 | 4 | 2 |
| €500,000–€1 million | 3 | 3 |
| >€1 million–€10 million | 7 | 6 |
| >€10 million–€100 million | 19 | 21 |
| >€100 million–€1 billion | 27 | 25 |
| >€1 billion | 39 | 43 |
| Goodness-of-fit between samplesb | χ2 = 1.91 (p =.86) | |
| Respondent Position | | |
| Head of sales/sales director | 11 | 8 |
| Sales manager | 41 | 41 |
| Key account manager | 21 | 20 |
| Sales rep | 8 | 9 |
| Other sales-related positions | 19 | 22 |
| Goodness of fit between samplesb | χ2 = 1.46 (p =.83) | |
- 4 a Industry categories based on the Federal Statistical Office of Germany ([12]).
- 5 b H0: equal distribution in both samples.
We followed standard psychometric scale development procedures, generating our measurements from a review of prior literature. All measurements and the respective items are provided in Table 3. We relied on multi-item scales unless constructs of interest are concrete enough for single-item measures. Because no widely accepted measure exists for failure tolerance, we developed a new measure along our conceptual definition. Specifically, we include failure handling, failure communication, failure learning, and failure encouragement (e.g., [ 8]; [16]; [87]; [89]). Thereby, we created a new, comprehensive scale and measured a failure-tolerant culture as a second-order construct. We conceptualize formal reacquisition policies as a second-order construct. In the initial step, we identify three first-order constructs based on conceptual work ([82])—reacquisition analysis, reacquisition activities, and reacquisition monitoring. These first-order constructs are measured reflectively and then come together to form the formative second-order construct of formal reacquisition policies ([38]).
Graph
Table 3. Survey Construct Measurements.
| Construct | ILa | IRa |
|---|
| Failure-Tolerant Culture (own development based on Danneels [2008], Edmondson [1999], Van Dyck et al. [2005], and Weinzimmer and Esken [2017]) | | |
| Failure Handling (AVE =.63; CA =.83; CR =.83) | | |
| We always try to find suitable solutions for failures in our company. | .76 | .58 |
| If employees fail, it is not considered as an indication of incompetency. | .76 | .57 |
| In our company failures are addressed in a constructive way. | .85 | .73 |
| Failure Communication (AVE =.60; CA =.75; CR =.75) | | |
| When an employee makes a mistake, her co-members in the workplace talk to her, not for the purpose of blaming her, but rather for the value of learning. | .74 | .55 |
| Employees can talk to our supervisor about things that went wrong frankly, without suspecting any negative consequences. | .80 | .65 |
| Failure Learning (AVE =.62; CA =.83; CR =.83) | | |
| Our errors point us at what we can improve. | .76 | .57 |
| A mistake is seen as an opportunity to learn. | .86 | .73 |
| People in our organization believe that errors at work can be a helpful part of the learning process. | .74 | .55 |
| Failure Encouragement (AVE =.70; CA =.86; CR =.87) | | |
| It is understood that failure is a necessary part of success. | .85 | .73 |
| Failure is accepted as an inevitable byproduct of taking a lot of initiatives. | .85 | .72 |
| For us, errors are very useful for improving the work process. | .80 | .64 |
| Formal Reacquisition Policies (own development based on Stauss and Friege [1999]) | | |
| Reacquisition Analysis (AVE =.55; CA =.78; CR =.78) | | |
| In our company, relationship terminations and reductions are traced immediately. | .57 | .33b |
| In our company, there exist clear guidelines on how to detect customer defection. | .76 | .57 |
| In our company, reacquisition potentials are systematically evaluated. | .88 | .78 |
| Reacquisition Activities (AVE =.68; CA =.88; CR =.89) | | |
| In our company, terminated and reduced relationships are reinstated with systematic customer reacquisition management. | .86 | .73 |
| In our company, customer reacquisition processes are standardized. | .89 | .79 |
| In our company, there are clear guidelines on which lost customers to target. | .87 | .76 |
| In our company, sales managers systematically address lost customers with suitable offers. | .66 | .43 |
| Reacquisition Monitoring (AVE =.75; CA =.92; CR =.92) | | |
| In our company, we document individual customer reacquisitions in detail. | .84 | .70 |
| In our company, we conduct extensive monitoring of all customer reacquisitions. | .93 | .86 |
| In our company, we have standardized methods to evaluate customer reacquisitions financially. | .79 | .63 |
| In our company, we closely observe reacquisition processes in order to improve our reacquisition management continuously. | .89 | .80 |
| Reacquisition Performance (own development); scale from 0% to 100% | | |
| On average, how many of your lost customers do you successfully reacquire (in %)? | N.A. |
| Customer Orientation (adapted from Narver and Slater [1990])(AVE =.69; CA =.87; CR =.87) | | |
| Our business objectives are mainly driven by customer satisfaction considerations. | .79 | .63 |
| Our business strategy is based on our beliefs of how to create value for our customers. | .88 | .78 |
| Our strategy to create competitive advantages is based on our understanding of customer needs. | .83 | .68 |
| Employee Autonomy (inspired by Schepers et al. [2012])(AVE =.73; CA =.82; CR =.84) | | |
| Decisions are made "close to the customer." In other words, employees often make important customer decisions without seeking management approval. | .75 | .56 |
| Employees have freedom and authority to act independently in order to provide excellent service. | .95 | .89 |
| Competition (based on Song and Parry [1997]) Seven-point Likert scale (1 = "very low," and 7 = "very high") | | |
| How high is the direct number of competitors in your market? | N.A. |
| Market Intensity (based on Jaworski and Kohli [1993]) Seven-point Likert scale (1 = "very low," and 7 = "very high") | | |
| How high is the intensity of competition-based activities in your market (e.g., price campaigns, advertising campaigns, product innovations)? | N.A. |
| Revenue Scale from 1 to 6 (1 = "<€500,000," and 6 = ">€1 billion") | | |
| How high is the yearly revenue of your company? | N.A. |
| Failures Addressedc (own development)(AVE =.77; CA =.87; CR =.87) | | |
| In our company, we try to fix all failures that lead to customer defection. | .86 | .74 |
| In our company, all failures that lead to customer defection are discussed. | .89 | .79 |
| Failure Frequencyc (adapted from Shepherd, Patzelt, and Wolfe [2011])(AVE =.64; CA =.78; CR =.78) | | |
| In our company, failures occur often. | .77 | .59 |
| Our employees make a lot of failures when interacting with customers. | .84 | .70 |
| Failure Severityc (inspired by Hess, Ganesan, and Klein 2003) | | |
| In our company, severe failures happen during customer relationships | N.A. |
- 6 a Standardized item loadings (ILs) represent the square root of indicator reliabilities (IRs) ([ 1]).
- 7 b A robustness check revealed that eliminating this item does not affect the regression estimations.
- 8 c Part of post hoc analysis to confirm the emergence of the inverted U-shaped effect (Figure 2, Panel A) but not part of the main model
- 9 Notes: Items are based on seven-point Likert scales (1 = "do not agree at all," and 7 = "totally agree") unless indicated otherwise. AVE = average variance extracted; CA = Cronbach's alpha; CR = composite reliability; N.A. = not applicable as the construct is measured with a single item.
As all existing reacquisition performance measurements refer to the relationship level, we were unable to directly use any of these measurements. Instead, we asked respondents for the average percentage of lost customers their organization is able to reacquire, which yields a so-called quasiobjective measurement.
To measure a firm's financial performance, we used data from the AMADEUS database. We chose earnings before interest and taxes (EBIT) margin as the dependent variable because it relates to the operating profit of a firm. When studying reacquisition on the organizational level, consideration of profitability rather than other indicators such as sales volume or growth is particularly important. Customer reacquisitions will naturally trigger sales number increases by raising the sales volumes of lost customers, but most reacquisitions are also associated with company costs such as costs of reacquisition policies, price discounts, or service upgrades ([82]).
In addition, we controlled for customer orientation, employee autonomy, competition, and market intensity. "Customer orientation" refers to a company's understanding of its customers and its continuing endeavors to create superior value for them. "Employee autonomy" refers to employees' degree of decision-making authority ([77]). "Competition" is the extent of direct competition in the market, and "market intensity" is the intensity of competitive actions (e.g., advertising campaigns) in a given market. Table 4 shows the correlations of all measures.
Graph
Table 4. Descriptive Statistics and Correlations.
| M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|
| 1. Reacquisition performance (%) | 19.59 | 22.92 | — | | | | | | | |
| 2. Formal reacquisition policies | 3.32 | 1.33 | .21* | — | | | | | | |
| 3. Failure-tolerant culture | 4.72 | 1.17 | .06 | .36* | — | | | | | |
| 4. Customer orientation | 5.43 | 1.25 | .15* | .27* | .61* | — | | | | |
| 5. Employee autonomy | 4.24 | 1.49 | .05 | .27* | .37* | .33 | — | | | |
| 6. Competition | 4.66 | 1.76 | −.17* | .03 | .13 | .03 | .09 | — | | |
| 7. Market intensity | 4.94 | 1.73 | −.13 | .22* | .17* | .15* | .06 | .47* | — | |
| 8. EBIT margin (%)a | 5.56 | 8.29 | .12 | .13 | .14 | .09 | .09 | −.00 | .09 | — |
- 10 *p <.05.
- 11 a Obtained from an independent financial database.
We conducted one confirmatory factor analysis that contained all reflectively measured first-order constructs to assess their reliability and validity. We found acceptable model fit (χ2/d.f. = 1.83; comparative fit index =.94; root mean square error of approximation =.06; standardized root mean square residual =.04). Overall, the analysis had satisfactory results: composite reliability, average variance extracted, and Cronbach's alpha exceeded the recommended threshold values for all constructs, and all indicator reliabilities surpassed a value of.40 except for one item from the formal reacquisition policies scale that had an indicator reliability of.33 (Table 3; [ 1]). We kept this item because one item's deviation from the.40 threshold value is still acceptable and we favored conceptual concerns over maximizing internal consistency when selecting our indicators (e.g., [ 1]; [52]). In addition, a robustness check in which we excluded this item revealed that our results remain stable. For the regression analysis, we used the mean scores for each of the constructs. However, factor scores led to similar results.
We reduced key informant concerns by preselecting only participants that had at least three years' experience in their position (e.g., [45]). In addition, key informant threats are low because most of our constructs relate to the current situation of the company and are concerned with information internal to the firm. Key informants tend to evaluate those constructs accurately ([35]).
However, key informants are less likely to be highly accurate when assessing cultural factors such as failure tolerance ([35]). Therefore, we also established key informant accuracy. For a subsample (n = 29 companies), we were able to triangulate our measures by acquiring at least one additional respondent per company. We calculated the average absolute deviation index from the mean (ADM) to evaluate interrater agreement. ADM values for our focal independent and dependent variables fell below suggested cut-off values ([ 4]), further attenuating concerns regarding a key informant bias.
Concerns regarding common method variance (CMV) are low because we rely on different data sources to test H4 ([74]) and analytical and simulation studies suggest that CMV cannot create but can only deflate quadratic (H1) and interaction effects (H3) (e.g., [79]). In addition, we further reduce CMV by separating the items for our independent and dependent variables and by eliminating common scale properties. We measured most independent variables on seven-point Likert scales, whereas we assessed our central dependent variable (reacquisition performance) in percentages. Finally, evaluating reacquisition performance requires a rather low level of abstraction as it can be verified, which further reduces CMV (e.g., [70]; [74]).
In addition, we applied [51] marker test, in which the smallest correlation of a variable that is theoretically unrelated to at least one of the constructs of the model (marker variable) is a valid indicator of CMV. With this marker variable, we built an adjusted correlation matrix and tested the new correlations for significance. Specifically, we conducted this test twice with two different marker variables: year of the company's establishment and technical turbulence, which had correlations of.01 and.06 with reacquisition performance, respectively. For the first marker variable, all prior significant correlations remained at the 5% level, and for the second marker variable, only two correlations lost significance. Thus, CMV is unlikely to affect our results. In addition, Gaussian copula terms (discussed in the next section) further reduce CMV threats ([76]).
To test our hypotheses (H1–H4), we estimated the following equations with the two dependent variables of ( 1) reacquisition performance and ( 2) EBIT margin. As EBIT margin is available for only a subset of the survey sample from Equation 1, we separately performed regression analysis on Equation 2 to avoid loss of statistical power.
Graph
1
Graph
2
where Reac_Perf is reacquisition performance; Fail_Tolerance (Fail_Tolerance2) is failure-tolerant culture (squared); Formal_RP is formal reacquisition policies, EBIT is EBIT margin; and Controls refers to a vector of control variables that comprises customer orientation, employee autonomy, competition, market intensity, revenue dummies, and industry dummies for company i; and ∊ and φ are the residual error terms. Equation 1 also contains copula terms for failure-tolerant cultures and formal reacquisition policies (specified next). Equation 2 includes an inverse Mills ratio (specified next) and copula terms for failure-tolerant cultures, formal reacquisition policies, and reacquisition performance.
We also checked for potential multicollinearity, included Gaussian copulas to account for omitted variables, and accounted for sampling-induced endogeneity. Overall, we have strong indications that these threats do not bias the results of our study.
Multicollinearity does not seem to threaten the results of our analyses. Calculated variance inflation factors and condition indices are smaller than 5 and 10, respectively, reducing potential concerns about multicollinearity.
Omitted variables such as a company's competitive strategy that may equally affect independent and dependent variables may introduce endogeneity. To model correlation between the error term and potentially endogenous regressors, [65] advise including Gaussian copulas ([15]), an instrument-free method that is increasingly popular in marketing research (e.g., [ 9]). Because measurement error (e.g., in the form of CMV) is also a form of endogeneity, Gaussian copulas serve as an additional remedy to alleviate CMV ([76]).
We include and as additional regressors in Equation 1. In Equation 2, we also include . Thereby, Φ−1 is the inverse of the cumulative distribution function, and HFail_Tolerance(•), HFormal_RP(•), and HReac_Perf(•) represent the empirical cumulative distribution functions of failure-tolerant cultures, formal reacquisition policies, and reacquisition performance, respectively. Significant copula terms represent a direct test of endogeneity, and no separate copula terms are required for interaction or quadratic terms ([64]). For identification, all variables must be nonnormally distributed. We use Kolmogorov–Smirnov and Shapiro–Wilk tests to check for nonnormal distribution. For all variables, the null hypothesis of normality can be rejected in both tests.
Our matching with archival performance data could have led to sampling-induced endogeneity. We address this possibility in two ways. First, we employed χ2 goodness-of-fit tests to compare our matched subsample (n2 = 131) with the initial survey sample (n1 = 193) in industry proportions, terms of revenues, and position of respondents. The comparison did not reveal any significant differences (all ps >.30; Table 2), indicating that availability bias does not threaten the results.
Second, we employed a Heckman selection model to account further for potential sampling-induced endogeneity ([29]). Specifically, we estimated Equation 3:
Graph
3
In Equation 3, we included the variables from Equation 2 (specified previously) and used the availability of financial performance data (Avail_FinData; 1: "financial performance data available") as the dependent variable. For identification, the set of independent variables driving the availability of financial performance data (Equation 3) should contain at least one variable that provides an exclusion restriction. That is, this variable affects the availability of financial performance data but does not directly influence financial performance. We included the legal form of the company (Legal_Form). The selection model supports the strength of our exclusion variable (Table 6, Model 7: bLegal_Form = 2.81, p <.01), and we include the inverse Mills ratio in our financial performance model (Equation 2). Notably, legal form does not perfectly predict financial performance data availability. In contrast to U.S. regulations, German regulations can also require disclosures from non-publicly-listed companies. Some private companies deliberately disclose information, and missing values in databases can emerge for various reasons ([ 3]).
Graph
Table 5. Effects of Failure-Tolerant Culture and Formal Reacquisition Policies on Reacquisition Performance.
| | Model 1 | Model 2 | Model 3 | Model 4 |
|---|
| Main Effects | Main Effects + Endogeneity Corrections | Full Model | Full Model + Endogeneity Corrections |
|---|
| Main Effects | | | | | |
| Failure-tolerant culture | H1 | −.14*** | −.15 | −.11** | −.17** |
| Failure-tolerant culture × Failure-tolerant culture | H1 | −.13*** | −.13*** | −.14*** | −.14*** |
| Formal reacquisition policies | H2 | .26*** | .34** | .35** | .42** |
| Interaction Effects | | | | | |
| Failure-tolerant culture × Formal reacquisition policies | H3 | | | −.01 | −.01 |
| Failure-tolerant culture × Failure-tolerant culture × Formal reacquisition policies | H3 | | | −.09* | −.09* |
| Gaussian Copulas | | | | | |
| Failure-tolerant culture | | | .00 | | .01 |
| Formal reacquisition policies | | | −.08 | | −.06 |
| Controls | | | | | |
| Customer orientation | | .17*** | .17*** | .16*** | .16*** |
| Employee autonomy | | .01 | .01 | .02 | .02 |
| Competition | | −.11 | −.11 | −.14 | −.14 |
| Market intensity | | −.11 | −.11 | −.11 | −.11 |
| Revenue dummies | | Included | Included | Included | Included |
| Industry dummies | | Included | Included | Included | Included |
| Observations | | 193 | 193 | 193 | 193 |
| R2 | | .19 | .19 | .20 | .20 |
- 12 *p <.10.
- 13 **p <.05.
- 14 ***p <.01.
- 15 Notes: We report standardized regression coefficients. Models 2 and 4 contain Gaussian copula terms for our focal independent variables to account for potential endogeneity. The overall pattern between the models without and with endogeneity corrections (Model 1 vs. Model 2 and Model 3 vs. Model 4) remains unaffected after correcting for potential endogeneity threats.
Graph
Table 6. Effect of Reacquisition Performance on Firm Financial Performance.
| | Financial Performance Model (EBIT Margin) | Availability of Financial Performance Data |
|---|
| Model 5 Full Model | Model 6 Full Model + Endogeneity Corrections | Model 7 Selection Model |
|---|
| Main Effects | | | | |
| Reacquisition performance | H4 | .04*** | .40** | −.02*** |
| Failure-tolerant culture | | .10** | .61* | −4.57* |
| Failure-tolerant culture × Failure-tolerant culture | | −.01 | −.01 | .47 |
| Formal reacquisition policies | | .01 | −.34 | −1.71 |
| Interaction Effects | | | | |
| Failure-tolerant culture × Formal reacquisition policies | | −.04 | −.01 | .83 |
| Failure-tolerant culture × Failure-tolerant culture × Formal reacquisition policies | | .06** | .05 | −.09 |
| Gaussian Copulas | | | | |
| Reacquisition performance | | | −.39** | |
| Failure-tolerant culture | | | −.51 | |
| Formal reacquisition policies | | | .39 | |
| Exclusion Variable | | | | |
| Legal form (1 = public company) | | | | 2.81*** |
| Controls | | | | |
| Customer orientation | | −.09 | −.11 | .28** |
| Employee autonomy | | .12 | .15* | −.08 |
| Competition | | −.00 | −.01 | −.12 |
| Market intensity | | .03 | .03 | .14 |
| Revenue dummies | | Included | Included | Included |
| Industry dummies | | Included | Included | Included |
| Inverse Mills ratio | | −.02 | .04 | |
| Observations | | 131 | 131 | 193 |
| R2/pseudo-R2 | | .21 | .23 | .50 |
- 16 *p <.10.
- 17 **p <.05.
- 18 ***p <.01.
- 19 Notes: We report standardized regression coefficients. Model 6 contains Gaussian copula terms for our focal independent variables to account for potential endogeneity. The overall pattern between Model 5 and Model 6 remains unaffected after correcting for potential endogeneity threats. For Model 7, we report the McFadden pseudo-R2 measure.
Although reacquisition performance is measured in percent (0%–100%), we use ordinary least squares regressions to estimate Equation 1 to ease the interpretation of quadratic and interactive effects ([48]; [83]).[ 8] In addition, we standardized our data before estimation. The results reveal strong support for our hypotheses. In Table 5, we report the results for H1–H3. We rely on the endogeneity-corrected models to test our hypotheses, employing Model 2 to test the main effects and Model 4 to test the interaction effects. With regard to H1—the inverted U-shaped effect—several aspects must be considered. First, the coefficient of the squared failure tolerance term is significantly negative (βFail_Tolerance2 = –.13; p <.01), which indicates the inverted U-shaped relationship. However, to validate that the inverted U-shaped effect actually exists within our data range, we tested the slope coefficients at the low end (XFail_Tolerance_low) and high end (XFail_Tolerance_high) of our data range ([24]). We demonstrate a significantly positive slope at the low end of the data range (blow = βFail_Tolerance + βFail_Tolerance2 × XFail_Tolerance_low = 14.13, p <.05) and a significantly negative slope at high end of our data range (bhigh = βFail_Tolerance + βFail_Tolerance2 × XFail_Tolerance_high = –15.34, p <.01). Furthermore, the turning point of the curve lies within the data range (turning point = 4.03 [unstandardized][ 9]). Thus, the inverted U-shaped relationship is actually in our observed data range ([24]). Appendix W1 illustrates this relationship.
In an additional analysis (not reported here), we also tested the conceptual rationale underlying the inverted U-shaped relationship. In developing H1, we noted that the inverted U-shaped relationship results from the benefits of the number of failures addressed and the costs of failure severity and frequency (Figure 2, Panel A). We measured those constructs (Table 3). In line with our hypothesis development, we observe that failure tolerance positively relates to the number of failures addressed (βFail_Tolerance =.41, p <.01). We also observe that with increasing levels of failure tolerance, failure severity (βFail_Tolerance2 =.09; p <.05) and failure frequency (βFail_Tolerance2 =.09, p <.05) increase nonlinearly, resulting in a convex relationship as we predicted.
Finally, we observe that formal reacquisition policies exert a positive influence on reacquisition performance (βFormal_RP =.34, p <.01). Thus, H2 is supported.
To test the interaction effect between failure tolerance and formal reacquisition policies (Table 5, Model 4)—H3, regarding whether a turning point shift occurs in the inverted U-shaped effect of failure tolerance on reacquisition performance—simply checking significance levels of the interaction terms in the regression model is not possible ([24]). Indeed, the interaction coefficients need not be significant. Instead, we need to perform two derivatives of Equation 1, which we then test for significance. First, we derive Equation 1 with regard to failure tolerance to determine the turning point, leading to Equation 4:
Graph
4
Second, because Equation 4 depends on the moderator formal reacquisition policies, we take its derivative to determine the direction of the turning point shift, resulting in Equation 5:
Graph
5
Because the denominator (Equation 5) can only be positive, the sign of the numerator indicates the direction in which the turning point shifts: a positive value of the numerator indicates a turning point shift to the right and a negative value a shift to the left. For our data, we observe a shift to the right ( =.01).
On the basis of Equation 5, we further test whether this shift is significant. Specifically, we observe that Equation 5 is significantly different from zero at high (p <.05) and low values (p <.05) of the moderator, providing support for the proposed turning point shift to the right. This effect can also be illustrated by calculating the (unstandardized) turning point for a low level (turning pointFormal_RP_low = 3.76; p <.01) and a high level (turning pointFormal_RP_high = 4.18; p <.01; Δ(turning pointFormal_RP_high − turning pointFormal_RP_low) =.42; p <.01) of formal reacquisition policies. We illustrate this shift in Appendix W2 for high and low levels of formal reacquisition policies. Thus, higher levels of formal reacquisition policies allow higher levels of failure tolerance until the negative effects of failure tolerance set in.
Notably, in addition to our hypothesis, we observe a significant negative interaction between the quadratic term of failure tolerance and formal reacquisition policies (βFail_Tolerance2 × Formal_RP = −.09; p <.10). Thus, the inverted U-shaped relationship steepens with increasing levels of formal reacquisition policies. Importantly, the magnitude of the moderating effect is material: the curves appear relatively distant from each other in most of the data range. Appendix W2 demonstrates that the curves cross each other within our data range at low levels of failure tolerance whereas the upper intersection point does not lie in our observed data range, which suggests that formal reacquisition policies overall enhance the returns to failure-tolerant cultures.
More formally, we also compared the slope coefficients of failure tolerance at the lower and upper bound of our observed data range. At the lower bound, failure tolerance has stronger positive effects on reacquisition performance for high levels of formal reacquisition policies as compared with low levels (Δ(bFormal_RP_high − bFormal_RP_low) = 10.99; p <.10). However, we observe no difference at the upper bound (Δ(bFormal_RP_high − bFormal_RP_low) = −5.31; n.s.). These observations imply that formal reacquisition policies have a beneficial impact on the returns of failure tolerance for most of the observed data range. In addition, at the apex of the two curves (Appendix W2), the effect of failure tolerance on reacquisition performance is almost 1.5 times larger for companies with high than with low levels of formal reacquisition policies. Overall, our results suggest that while formal reacquisition policies cannot completely offset the negative effects of failure tolerance on reacquisition performance, they enhance the positive effects of failure tolerance.
Finally, analysis of the financial performance data (Table 6) shows that the positive relationship between reacquisition performance and financial performance is significant (Model 6: βReac_Perf =.40; p <.05). Therefore, H4 is supported.
The endogeneity-corrected results in the reacquisition performance model (Table 5, Model 2 and Model 4) reveal no significant copula terms. Similarly, in the firm performance model (Table 6, Model 6) only the reacquisition performance copula term is significant (βReac_Perf_Copula = −.39; p <.05). However, in this case, endogeneity threats led only to a more conservative estimate. The estimate is even larger when accounting for endogeneity (Table 6, Model 5: βReac_Perf =.04; p <.01 vs. Model 6: βReac_Perf =.40; p <.05) while leading to the same substantive interpretation.
We further analyzed the interactions between failure tolerance and formal reacquisition policies by separately analyzing the theoretically developed dimensions of failure-tolerant cultures (failure handling, failure communication, failure learning, and failure encouragement). Appendix W3 provides the results of this post hoc study. Failure handling (Model 1: βHandling2 = −.12; p <.01), failure communication (Model 3: βComm2 = −.10; p <.01), and failure learning (Model 5: βLearning2 = −.08; p <.01) display inverted U-shaped relationships with reacquisition performance (Appendix W4). While formal reacquisition policies do not moderate the relationship of failure communication, they do affect failure handling and failure learning. Appendix W4 reveals that at low levels of formal reacquisition policies, the relationship between failure handling and reacquisition performance is rather negative; positive effects set in with higher levels of formal reacquisition policies. Specifically, at the apex of the curve, the net positive effect of failure handling on reacquisition performance is almost 1.50 times larger for companies with high versus low levels of formal reacquisition policies. The moderating effect of formal reacquisition policies becomes even more important for failure learning. While we observe an inverted U-shaped relationship between failure learning and reacquisition performance for high formal reacquisition policies, it becomes almost a null effect at low levels for formal reacquisition policies. Thus, failure learning requires formal reacquisition policies to be effective. Finally, without considering boundary conditions, failure encouragement relates linearly and negatively with reacquisition performance (Model 7: βEncourage = −.20; p <.01). However, as Appendix W4 reveals, the moderating effect of formal reacquisition policies is important: only with increasing levels of formal reacquisition policies does the effect assume an inverted U-shape, also exhibiting positive effects.
We extended our hypothesized model by including failure tolerance as a driver of formal reacquisition policies. In a comparison of the model fit statistics of the hypothesized and the alternative model, the alternative model performs worse in terms of deviance (i.e., DevianceHypo_Model = 503.38 vs. DevianceAlt_Model = 995.09), Akaike information criterion (AIC) (i.e., AICHypo_Model = 515.38 vs. AICAlt_Model = 1,007.09), and Bayesian information criterion (BIC) (i.e., BICHypo_Model = 534.95 vs. BICAlt_Model = 1,026.66). In addition, failure tolerance (βFail_Tolerance =.16; n.s.) does not relate significantly with formal reacquisition policies. Therefore, this post hoc test delivers support for our hypothesized model (Table 5).
In our models, we also controlled for customer orientation, which represents another important informal element that is central to customer reacquisition management ([34]). To check whether formal reacquisition policies also affect the relationship between customer orientation and reacquisition performance, we added the interaction of customer orientation and formal reacquisition policies and a copula term for customer orientation to our empirical model (Equation 1). We find a significant simple effect of customer orientation (β =.43; p <.01), but also find a significantly negative interaction effect with formal reacquisition policies (β = −.09; p <.01). Thus, formal reacquisition policies reduce the positive effect of customer orientation.
While formal reacquisition policies increase the positive effects of failure-tolerant cultures (Appendix W2), they weaken the positive effect of customer orientation on reacquisition performance. Thus, an important question is whether under some conditions formal reacquisition policies might be harmful. To gain such insights, we derived Equation 1 (including the added interaction between customer orientation and formal reacquisition policies) with regard to formal reacquisition policies (Equation 6):
Graph
6
We evaluated the impact of formal reacquisition policies on reacquisition performance for all possible combinations of a high versus low degree of customer orientation and failure tolerance. We observe that for high values of failure tolerance, formal reacquisition policies are always beneficial (low customer orientation: β =.46, p <.01; high customer orientation: β =.28, p <.05), suggesting their importance in failure-tolerant companies. In the situation of low failure tolerance, formal reacquisition policies are beneficial only when customer orientation is low (low customer orientation: β =.39, p <.01). However, their impact becomes insignificant when customer orientation is high (high customer orientation: β =.20, n.s.), but even in the latter situation formal reacquisition policies are not harmful for reacquisition performance.
Two decades after [82] seminal article, the field of customer reacquisition management arguably remains one of the least researched areas in customer relationship management. Prior empirical investigations of customer reacquisition management have occurred on only the customer or the customer relationship level and have implicitly assumed employees' support during reacquisition attempts (e.g., [34]). However, customer reacquisition activities can be uncomfortable, calling for employees to admit and discuss unpleasant incidents, failures, or weakness. Therefore, we took an organizational perspective ([57]) and demonstrated that formal and informal organizational elements are highly relevant for reacquisition performance. We find that a failure-tolerant culture exhibits an inverted U-shaped relationship with reacquisition performance, whereas formal reacquisition policies exert a positive relationship with reacquisition performance. In addition, we observe that formal reacquisition policies enhance the link between failure tolerance and reacquisition performance. Finally, our organizational perspective allowed us to validly demonstrate the link between reacquisition performance and financial performance. Overall, our results reveal valuable insights and have important implications.
The introduction of failure tolerance to the customer reacquisition literature has important implications. First, the inverted U-shaped relationship with reacquisition performance implies that failure tolerance affects reacquisition performance both positively and negatively. Regarding the positive effect of failure tolerance, a crucial avenue for future research is investigation of how companies can become more tolerant of failures. An understanding of how firms can increase their tolerance of failure is likely important for related research fields such as marketing agility ([41]), especially as the zero-defects mantra of total quality management, which may still dominate corporate philosophy, naturally conflicts with a culture that is open to failure. Future research should analyze, for instance, the effectiveness of various tactics (e.g., top management narratives that embrace failure and other cultural factors, [36]) to nurture a culture of failure tolerance. Regarding the negative effect of failure tolerance, our results offer a starting point for future marketing research to address the mechanisms underlying the harmful impact of failure tolerance throughout the organization.
Second, future investigators should link organization-level variables to individual reacquisition attempts. One direction would be to investigate how organization-level elements interact with a customer's reason for defection. For instance, the effectiveness of formal reacquisition policies might depend on whether the company could control the reason for defection. Similarly, future research should explore whether the roles of informal and formal elements differ between complete and partial defections. Before defecting completely, some customers first defect only partially, by lowering their transaction volumes with the company ([ 6]). In the case of partial defections, informal elements might be more pronounced, as they may cause employees to sense a threat of customer defection and initiate reacquisition processes earlier, increasing the probability of winning customers back ([84]).
Third, prior research on psychological ownership has suggested that formal elements might be "not only unnecessary but also counterproductive" ([30], p. 173) once employees have acquired psychological ownership. Relatedly, general research on the interplay between formal and informal elements has suggested that informal aspects lead employees to ignore formal management ([19]). However, the results of our study reveal that these assumptions are too categorical. Specifically, in our context, formal reacquisition policies strengthen the positive effects of failure tolerance but weaken the positive effects of customer orientation on reacquisition performance (post hoc analysis). Thus, instead of investigating whether formal elements are counterproductive, future research should focus on when negative or positive interactive effects set in.
Relatedly, future research should examine both informal and formal aspects of customer acquisition and retention management. In this regard, the theory of psychological ownership represents a valuable starting point. For instance, drawing on this theory, marketing researchers could explore how and when informal and formal elements stimulate or reduce employees' perceived psychological ownership of customers and explicitly study the outcomes of psychological ownership. While future research should investigate the impact of psychological ownership on in-role and extra-role behaviors, studies could also explore potential dysfunctional effects of psychological ownership. For instance, psychological ownership may result in a status quo bias ([40]), leading employees to focus on current customers and eliciting resistance to customer acquisition.
Fourth, our results are the first to connect a firm's reacquisition performance to its overall financial performance. How this positive financial impact originates is particularly interesting and should be explored in future studies. For instance, a positive relationship may occur because of the increased profitability of single-customer relationships ([46]) but also because of reduced negative and increased positive word of mouth, resulting in gains through overall reputation ([71]). Understanding the origin of the performance-enhancing effect has importance for customer defection management, as it would foster development of different reacquisition strategies according to how the reacquisition of those customers may contribute to performance. For example, companies could differentiate between customers who should actually be won back ("profitability customers") and others who should mainly be soothed, with reacquisition being subordinate ("reputation customers").
Our study reveals that customer reacquisition management contributes significantly to firm performance. Therefore, the central managerial implication of our study is that managers should stimulate reacquisition activities.
Given this, managers must understand the crucial role of organizational elements for successful customer reacquisition management. In this regard, our study highlights the importance of a failure-tolerant culture. Because attempting to reacquire customers likely represents an unpleasant activity for employees, companies need to create a culture in which employees have the confidence to openly address failures. Once such a culture has been established, employees may go to greater lengths—even beyond their job duties—to win customers back.
However, the results of our study suggest that managers should also be aware of potential downsides of failure tolerance. An excessively failure-tolerant culture may suffer from a "too-much-of-a-good-thing" effect: tolerating failure is beneficial only to a certain point, beyond which a boomerang effect occurs and the negative impact outweighs the positive one. In this regard, our results reveal that failure tolerance is not a substitute for the management of reacquiring customers. Instead, management through formal reacquisition policies ensures that the boomerang effect of failure tolerance sets in only at higher levels of failure tolerance.
In this regard, for instance, our post hoc analysis reveals managerially important insights. While managers are often advised to encourage failures (e.g., [58]), we observe that failure encouragement has few positive effects unless it is accompanied by formal reacquisition policies.
Finally, our analyses reveal that formal reacquisition policies offer a powerful way to increase reacquisition performance, as the unmoderated regression coefficients (Table 5, Model 2) show a strong link between formal reacquisition policies and reacquisition performance (β =.34). In addition, our post hoc analysis reveals that positive returns of formal reacquisition policies are particularly pronounced for failure-tolerant companies. However, despite these positive effects of formal reacquisition policies, we observe that on average, companies have low levels of formal reacquisition policies (Table 4: mean value = 3.32). Consequently, our study implies that managers should increase their engagement through formal reacquisition policies.
The conclusions reported here must be qualified with limitations. First, we rely on primary data. Despite our best efforts to safeguard against possible biases, such a design has limitations. Although we have addressed the issue of CMV in numerous ways, future research should employ an objective measure of company-level reacquisition performance derived from secondary data-based measurement. Second, future research might employ employee handbooks to offer more fine-grained insights into the design and effects of reacquisition policies. For instance, future investigators could examine different types of rules and how they affect the relationship between failure tolerance and reacquisition performance. Finally, our reliance on firm-level financial performance metrics is likely to have limitations. While such measures may be of interest to researchers and practitioners, they may be "causally-distant" ([43], p. 11) from the independent variables in our study. Therefore, we urge future researchers to explore potentially intervening performance variables.
Supplemental Material, jm.18.0016-File003 - Tolerating and Managing Failure: An Organizational Perspective on Customer Reacquisition Management
Supplemental Material, jm.18.0016-File003 for Tolerating and Managing Failure: An Organizational Perspective on Customer Reacquisition Management by Arnd Vomberg, Christian Homburg and Olivia Gwinner in Journal of Marketing
Footnotes 1 Associate EditorRaj Venkatesan
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242920916733
5 1Reacquisition management mainly addresses customers who have been unintentionally pushed away (e.g., customers who leave because they are dissatisfied) or who have been pulled away (e.g., customers who receive better offers from competitors or whose needs have changed over time).
6 2Building on the theory of psychological ownership, we expect interactions between formal and informal elements on reacquisition performance to occur. However, we also acknowledge that informal elements may drive formal elements, a possibility we explore further in the "Post Hoc Analyses" subsection.
7 3We focus on the moderating effect of formal reacquisition policies on the relationship between failure tolerance and number of failures addressed. However, formal reacquisition policies might also weaken the relationship between failure tolerance and failure severity and frequency (Figure 2, Panel B). For instance, formal reacquisition policies may signal threats of customer defection. Thus, they increase employees' awareness of customer defections, make employees foresee the costs of frequent and severe failures for customer relationships, and enhance employees' due diligence in decision making. As a result, the inverted U-shaped relationship between failure tolerance and reacquisition performance becomes steeper (i.e., increases in failure tolerance will more strongly affect reacquisition performance for higher levels of formal reacquisition policies as compared with lower levels; [22]; [24]; [49]).
8 4We also estimated fractional regressions (logit and probit specifications) as robustness checks, which led to the same substantive conclusions.
9 5For the analysis, we standardized our data. However, we rely on the equivalent unstandardized results for the figures and turning points because the unstandardized results are likely to be more intuitively appealing. The standardized turning point is −.56 and can be translated into the unstandardized turning point: Turning pointunstand = Turning pointstand × SDFail_Tolerance + MeanFail_Tolerance = −.56 × 1.25 + 4.72 = 4.03.
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By Arnd Vomberg; Christian Homburg and Olivia Gwinner
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Record: 206- Tournaments to Crowdsource Innovation: The Role of Moderator Feedback and Participation Intensity. By: Camacho, Nuno; Nam, Hyoryung; Kannan, P.K.; Stremersch, Stefan. Journal of Marketing. Mar2019, Vol. 83 Issue 2, p138-157. 20p. 1 Diagram, 6 Charts. DOI: 10.1177/0022242918809673.
- Database:
- Business Source Complete
Tournaments to Crowdsource Innovation: The Role of Moderator Feedback and Participation Intensity
Firms increasingly use innovation tournaments to crowdsource innovation ideas from customers. This article uncovers antecedents and consequences of customers' participation intensity over the course of a tournament. More specifically, the authors theorize on the effects that the type and timing of moderating feedback have on tournament participants' participation intensity, as well as the effect of the latter on idea quality. Through two longitudinal experiments using a commercial innovation tournament platform, the authors show that moderating feedback stimulates ideators' participation intensity. They find that negative feedback increases participation intensity, as compared to no feedback and positive feedback. Moreover, negative feedback, either provided in isolation or together with positive feedback, is more effective during the early stages than in the later stages of a tournament. Using a large-scale managerial survey, the authors show that higher participation intensity leads to higher idea quality and better business performance. The effect of participation intensity on idea quality is stronger than the effect of number of ideas and as strong as the effect of number of participants on idea quality.
Keywords: crowdsourcing; idea generation; idea maturation; innovation; innovation tournaments; online idea generation platforms; participation intensity
Firms increasingly use innovation tournaments to crowdsource innovation ideas from customers. In an innovation tournament, firms ( 1) outsource idea generation and development to a large and undefined group of people (i.e., the "crowd") in the form of an open call ([ 1]; [ 4]; [35]; [46]), and ( 2) after a prescribed time period following the idea call, select at least one winning idea from those submitted ([61]; [58]). An example of a nationwide innovation tournament is Staples' Invention Quest ([54]), hosted for the last several years by the office supply retailer to crowdsource innovative ideas for "America's next breakthrough office products."
Innovation tournaments offer three key advantages for new product development. First, sourcing ideas from a large crowd allows firms to more easily and rapidly generate ideas that require knowledge that falls outside the firm's knowledge base than sourcing ideas from internal experts or outsourcing to specialized contractors ([ 1]). Second, generating a large number of competing ideas at the onset of an innovation tournament increases the odds of discovering high-quality ideas ([25]). Third, innovation tournaments force firms to apply selection mechanisms that weed out lower-quality ideas and allow only the most promising ones to survive ([58]). Examples of successful new products generated through innovation tournaments include a line of rugged Dell laptops for marine use ([ 4]), thematic Lego sets (e.g., Back to the Future's DeLorean, Ghostbusters' Ectomobile; [49]), and Frito-Lay's "Cheesy Garlic Bread," a potato chip flavor credited with increasing Lay's sales by 8% in the three months following its launch ([ 8]).
To help idea contributors, or "ideators," revise and improve their ideas, firms hosting an innovation tournament often interact with and provide feedback to the ideators. Third-party platform providers also design features to provide moderator feedback to ideators. For instance, as part of its business model, Cognistreamer, the platform we use in our experiments, includes paid moderator feedback services for its clients. Feedback—information provided by an agent regarding aspects of another agent's performance—helps ideators assess where they are in terms of achieving their goals ([18]) and evaluate how much effort they need to invest to improve their ideas. Consequently, feedback may help participants remain actively involved in the platform by causing them to visit their own idea page and possibly update their own idea. We refer to such active involvement in the platform as "participation intensity." Prior literature recognizes the importance of number of ideas and number of participants as important drivers of idea quality in innovation tournaments (e.g., [25]; [61]). However, researchers have placed limited focus on the drivers and consequences of participation intensity. Although we focus specifically on innovation tournaments, feedback and participation intensity are important in any crowdsourcing innovation process. Thus, we complement prior literature in three main ways.
First, we examine the effect of feedback type on participation intensity in innovation tournaments. Following [18], we distinguish positive feedback (feedback focused on the accomplishments and strengths of the posted idea) from negative feedback (feedback focused on the weaknesses in the idea, the need for idea refinement, or the additional effort needed to strengthen the idea). Prior literature offers potentially competing predictions for the benefits and drawbacks of these two types of feedback. Whereas self-determination theory predicts that positive feedback on progress to date increases participation intensity ([21]; [50]), self-discrepancy theory offers the opposite prediction: negative feedback on progress to go (i.e., what the participant still needs to accomplish) increases participation intensity ([20]; [31]). Thus, the type of feedback that leads to the highest participation intensity in innovation tournaments is unclear ex ante. Our empirical findings lend support to self-discrepancy theory. For instance, in Study 1 we find that, on average, 10.43% of participants who receive negative feedback update their ideas, whereas only 2.3% of participants who receive positive feedback do the same.
Second, to the best of our knowledge, we are also the first to study the effect of feedback timing on participation intensity in innovation tournaments. We draw from the literature on goal pursuit ([19]; [22]) to argue that the way ideators respond to negative feedback depends on the motivational shifts they experience on their path to the goal. We posit that early in an innovation tournament, participants focus on how the idea still needs to develop to reach the goal, rendering negative feedback congruent with such focus. In contrast, later in the tournament, negative feedback may undermine participants' confidence in their ability to refine their ideas before the deadline. Thus, we predict and find that negative feedback is more effective during the early rather than later stages of a tournament. For example, in Study 2 we find that the percentage of participants who update their ideas when they receive negative feedback close to the end of a tournament is 20% lower than the percentage of participants who update their ideas when they receive negative feedback during the early stages of the tournament.
Third, although practitioners view participation intensity as important, the extent to which participation intensity is an important driver of idea quality and managerially relevant business outcomes is unclear. Prior literature tends to view number of ideas or ideators as the key drivers of idea quality ([ 4]; [25]; [58]). This view implicitly assumes that more ideas and participants lead to higher quality ideas, and it overlooks the potential role of ideators' participation intensity over time. We are the first to ( 1) propose an integrated theoretical framework that extends the concept of ideation quantity to include not only number of ideas and number of participants in an innovation tournament but also participation intensity, and ( 2) relate participation intensity to ideation quality and business outcomes, namely, new product performance and overall business performance. In Study 3, we find that the effect of participation intensity on idea quality is stronger than the effect of number of ideas and as strong as the effect of number of participants on idea quality.
Empirically, we examine the role of the type and timing of moderator feedback on participation intensity by conducting two longitudinal experiments (Studies 1 and 2) using a commercial innovation tournament platform, CogniStreamer. We examine the role of participation intensity (as a critical dimension of ideation quantity) on ideation quality and business outcomes using a large-scale survey of innovation managers (Study 3).
Our empirical work provides strong support for four findings: ( 1) negative feedback is effective in sustaining participation intensity but positive feedback is not, ( 2) early negative feedback increases participation intensity but late negative feedback does not, ( 3) participation intensity is a critical driver of idea quality in innovation tournaments, and ( 4) idea quality increases new product performance and overall business performance.
These findings have important implications for firms. First, firms organizing innovation tournaments and third-party platform vendors should consider training moderators to provide feedback to increase participation intensity. Second, moderators in innovation tournaments should offer negative feedback (i.e., feedback focused on the weaknesses in the idea, the need for idea refinement, or the additional effort needed to strengthen the idea) earlier rather than later in the tournament. Third, firms should go beyond measuring and incentivizing based on number of ideas and number of participants, and they should include participation intensity as a success metric in innovation tournaments.
The present inquiry complements the extant literature on crowdsourcing innovation and innovation tournaments, which we summarize in Table 1. Prior research in marketing examines the drivers of ideation quantity (i.e., number of ideators or number of ideas) and of ideation quality in crowdsourcing innovation. For instance, [ 4] finds that customers who submit a higher number of ideas to a crowdsourcing platform (serial ideators) are also more likely to generate high-quality ideas (i.e., ideas that are effectively selected and implemented by the company). [42] find that granting access to others' ideas is more beneficial for low-knowledge ideators, whereas classifying ideas into categories is more beneficial to high-knowledge ideators. [57] highlight that greater interconnectivity between customers participating in a crowdsourcing initiative reduces the innovativeness of their proposed ideas due to excessive redundancy. However, researchers have placed limited focus on the drivers and the consequences of participation intensity. Herein, we extend crowdsourcing innovation research by examining the effects of feedback type and timing on participation intensity and the consequences of participation intensity on idea quality and key business outcomes.
Graph
Table 1. Overview of Empirical Studies on Crowdsourcing Innovation and Innovation Tournaments.
| Source | Empirical Approach | Summary of Key Findings |
|---|
| Wooten and Ulrich (2017) | Six field experiments (NParticipants = 245; NIdeas = 624). | Feedback ratings stimulate participation and increase the quality of the ideas in a tournament. |
| Bockstedt, Druehl, and Mishra (2016) | Secondary data from design contests (; NContests = 1,024; NParticipants = 2,623). | Contestants who join early and remain active in a design contest are more likely to succeed. There is a curvilinear relationship between a contestant's number of submissions and success likelihood. |
| Stephen, Zubcsek, and Goldenberg (2016) | Five controlled experiments (N = 326). Participants completed ideation tasks over multiple runs (NParticipantRuns = 1,188). | Higher interconnectivity among customers participating in a crowdsourcing initiative reduces the innovativeness of customers' ideas (due to redundancy in idea generation). |
| Luo and Toubia (2015) | Two controlled experiments (NParticipants = 708; NIdeaQualityEvaluators = 4,412; NIdeas = 4,316). | Allowing customers to see others' ideas is more beneficial for low-knowledge customers. Classifying ideas into categories is more beneficial for high-knowledge ones. |
| Kornish and Ulrich (2014) | Secondary data (). Data comprises 160 products sold between Mach 2011 and March 2013. | Idea quality matters. The quality of both the raw idea and the final design predict sales outcomes. |
| Bayus (2013) | Secondary data from Dell's IdeaStorm in the period February 2007 to February 2009 (NParticipants = 4,285; NIdeas = 8,801). | Customers who submit many ideas (serial ideators) are more likely to generate an idea the organization implements. Ideators with past successes are unlikely to repeat their successful ideation. |
| Boudreau, Lacetera, and Lakhani (2011) | Secondary data (9,661 software development contests posted at TopCoder). | A higher number of participants in a contest leads to better solutions for high-uncertainty problems. For low-uncertainty problems, greater rivalry triggers effort-reducing inefficiencies and backfires. |
| Kornish and Ulrich (2011) | Classroom data (hypothetical ideas; NParticipants = 279 students; NIdeas = 1,368). | A higher number of ideas increases redundancy. However, redundancy is not detrimental. Nonredundant ideas are not generally the most valuable ones. |
| Girotra, Terwiesch, and Ulrich (2010) | One controlled experiment (NParticipants = 44 students; NIdeas = 443; NIdeaQualityEvaluators = 129). | Ideators working in a hybrid structure (in which they first work individually and then together) generate more and better ideas than those working in teams. |
We also contribute to the nascent literature stream on innovation tournaments (e.g., [25]; [39]; [40]). For instance, [61] investigate the role of in-process feedback in logo design competitions. They focus on quantitative feedback in the form of ratings and compare directed feedback (i.e., feedback correlated with idea quality) with undirected feedback (i.e., random feedback). They find that directed feedback increases idea quality, whereas undirected feedback does not. A random and uninformative rating does not offer guidance to participants on how to improve their ideas and, as such, does not constitute feedback ([18]). Considering that managers would be more interested in providing direct, high quality feedback rather than undirected feedback unrelated to idea quality, a managerially relevant issue is how a manager should effectively deliver directed feedback (i.e., how feedback type and timing influence ideation quality).
There are several key players in any innovation tournament, each of which have different roles. The hosting firm sponsors the tournament and typically determines its goals and idea selection criteria. The ideators generate and develop their ideas over a prescribed time period. Third-party innovation platform suppliers offer online platforms that facilitate the collaboration between the hosting firm and the ideators ([59]). Examples of such special-purpose online platforms include CogniStreamer, Darwinator, Hype Innovation, and Spigit.
Innovation platforms provide a means for participants to submit their ideas, receive feedback, and update their ideas ([26]). On CogniStreamer, ideators are invited via an idea call. After accepting the call and registering on the platform, participants may then submit their innovation ideas on a special submission form. As part of the submission (which has no word limit), participants can describe their proposed solution and may attach pictures or documents to offer additional documentation about their idea (see Web Appendix 1.1 for additional details and screenshots of the platform).
Once submitted, the idea becomes visible on the platform and the hosting firm or platform supplier can provide moderator feedback. To offer feedback to ideators, a firm may employ specially trained moderators who comment on the ideas in a manner similar to comments on social media platforms. When a moderator leaves a comment, a copy of the comment is also sent to the ideator's email. Ideators can log into the platform at any time to view their idea, view comments left for them, or update their idea by altering the idea's text description or any of its accompanying documents. The hosting firm may allow ideators to also view other participants' ideas. Some firms also encourage ideators to not only view but also comment and possibly even vote on other participants' ideas. On the Cognistreamer platform, social feedback is a feature that can be turned on or off depending on the hosting firm's specification requests.
Given that innovation tournaments last for a prescribed time period, some ideators may remain actively involved in developing their ideas over the course of the tournament, whereas others may not. We refer to ideators' sustained involvement in the platform as "participation intensity." Participation intensity manifests itself through repeated ideator activity on the innovation platform, such as when ideators visit their own idea page or update their own idea ([23]). These two metrics of ideator activity in the platform (viewing one's own page and updating one's idea) provide us with an objective and unobtrusive measure of whether the participant remains actively engaged with her own idea. Our key argument is that a firm's design and provision of moderator feedback during an innovation tournament can enhance participation intensity, which in turn may improve the quality of the ideas generated in the tournament by providing new insights or correcting or redirecting the focus of an idea.
Figure 1 depicts our theoretical framework in which we propose that ( 1) a firm's moderator feedback strategy (i.e., the type and timing of moderators' feedback) drives the level of participation intensity over the tenure of an innovation tournament (examined in Studies 1 and 2), ( 2) participation intensity is a key driver of the quality of the ideas generated in innovation tournaments, over and above other ideation quantity metrics such as number of ideas and number of participants (examined in Study 3), and ( 3) idea quality increases new product performance and overall business performance (also examined in Study 3).
Graph: Figure 1. Theoretical framework: Moderator feedback, ideation quantity, ideation quality and business outcomes.
We conceptualize the development and refinement of ideas posted on an innovation platform as actions driven by ideators' inherent motivations to participate in the tournament. For instance, an ideator's ultimate goal can be winning a cash prize, gaining status or reputation, or enjoying seeing the firm develop and market her ideas (see [ 7]). Feedback works by reducing the discrepancy between the current state and the desired state on the path to achieving a goal ([29]). Feedback generally focuses on three aspects of this path: how far away the goal is, the progress made toward that goal, and what to do next to reach the goal. Each of these aspects of feedback function at four levels in the ideator's pursuit of a goal: ( 1) the task level, which addresses how to perform the tasks well; ( 2) the process level, which provides an understanding of the process and steps to achieve the goal; ( 3) the self-regulation level, which helps in self-monitoring, directing, and regulating the ideator's actions; and ( 4) the self-level, which involves providing personal evaluations and positive affect about the individual ([29]). Depending on how feedback affects the individual at these four levels, the discrepancy can be reduced either by increasing effort (increased participation intensity) or by blurring, lowering, or completely abandoning the goals (reduced participation intensity). It is precisely because of these impacts that the type of feedback on the path to the goal becomes an important determinant of whether the participant reaches her goal of idea development successfully.
Following [18], we distinguish between two types of feedback: ( 1) positive feedback focused on the accomplishments and strengths of the posted idea and ( 2) negative feedback focused on the weaknesses in the idea, need for idea refinement, or the additional effort needed to strengthen the idea. Like [18], we maintain that to qualify as feedback, positive feedback must be complimentary without being needlessly flattering, and negative feedback has to offer constructive criticism (i.e., it should not be unnecessarily detrimental). Moreover, to qualify as feedback, a message needs to be "informative" in the sense that it helps participants better pursue their goals ([18]). Taken together, these requirements mean that we define feedback as "positive" when a moderator compliments the ideator's idea development to date (i.e., what the participant has already accomplished so far), and we define feedback as "negative" when the moderator challenges the idea and offers constructive criticism highlighting the idea development to go (i.e., what the participant still needs to accomplish).
To understand the impact of the type of feedback on participation intensity, we focus on two behavioral motivation theories that provide competing predictions on the nature of such impact. According to self-determination theory ([50]), positive feedback works at the self-level and at the self-regulation level and should increase participation intensity. At the self-level, positive feedback increases participants' confidence that they will be able to successfully develop and refine their ideas. At the self-regulation level, positive feedback increases participants' goal commitment. Higher confidence and goal commitment, in turn, allow participants to internalize the goal of idea development and thus stay motivated to pursue their goal going forward ([ 3]; [21]; [50]). This, in turn, increases the intensity of ideators' participation in the innovation tournament. Negative feedback, on the other hand, also works at the self-level and is likely to undermine participants' confidence in their ability to successfully develop their ideas. This demotivates the participants and causes them to abandon or reduce their goals, thereby leading to lower participation intensity.
An alternate literature stream based on cybernetic models of self-regulation provides opposite predictions ([31]; [38]). According to self-discrepancy theory ([31]), positive feedback works at the self-regulation level by providing a signal that the task/goal is being accomplished successfully and thus less effort is needed going forward. This could lead to lower participation intensity. Negative feedback, in contrast, works both at the task level and at the self-regulation level. At the task level, negative feedback highlights weaknesses in the way a task is being performed, signaling the need for corrective action. At the self-regulation level, negative feedback signals that more effort is needed to accomplish the goal of idea development. Thus, according to this viewpoint, negative feedback should encourage higher participation intensity to attain the goal.
There is no clear answer regarding the impact of positive versus negative feedback, which could depend on other factors (see [21]). Thus, we examine the effects of positive feedback and negative feedback on participation intensity. Given the conflicting views discussed previously, we propose the following competing hypotheses for empirical testing:
- H1a : Positive feedback is effective in sustaining participation intensity in an innovation tournament.
- H1b : Negative feedback is effective in sustaining participation intensity in an innovation tournament.
We also study the impact of providing different types of feedback over time throughout an innovation tournament. Recall that positive feedback focuses on the idea development to date, whereas negative feedback highlights idea development to go. Given that innovation tournaments last for a prescribed time period, the participants' focus on accomplishments to date versus efforts to go may shift over time. Thus, we posit that as a participant's tournament tenure (i.e., the amount of time a participant spent in the tournament) increases, the effect of different types of feedback on participation intensity may change. This is in line with the literature on goal pursuit, which predicts that the way individuals respond to positive and negative feedback depends on the motivational shifts experienced on their path to the goal ([19]; [22]). Thus, one may posit that during the early stage of a tournament, participants focus on idea development to go, and negative feedback is thus congruent with such focus. That is, during the early stage of a tournament, negative feedback that encourages participants to focus on idea development increases participation intensity to attain the goal. However, at the later stage of the tournament, participants may focus more on the looming deadline. At that point, negative feedback may undermine participants' confidence in their ability to successfully refine their ideas before the end of the tournament. This would render negative feedback at a later stage of a tournament less effective than negative feedback at an early stage of a tournament. Therefore, we propose the following hypothesis:
- H2 : Negative feedback is more effective in sustaining participation intensity early, rather than late, in an innovation tournament.
It should be noted that we do not hypothesize differences in the impact of positive feedback over time throughout an innovation tournament. This is because, in contrast with negative feedback, it would be difficult to form a directional hypothesis for the moderating effect of time on the impact of positive feedback on participation intensity. Specifically, it is possible that positive feedback is most effective early in an innovation tournament when participants who recently joined the tournament may need signals that reinforce their commitment to the tournament and its goal (e.g., [21]). However, it is equally possible that positive feedback is most effective late in an innovation tournament because it may restore the participant's depleted internal energy ([52]). We thus leave the impact of providing positive feedback over time throughout an innovation tournament as an empirical question.
Prior research especially emphasizes the importance of the number of ideas or participants (ideation quantity) in increasing the odds of discovering truly exceptional ideas ([25]; [58]). Consequently, researchers have focused on seeding stimulus ideas or adapting and structuring the ideation tasks with the specific objective of stimulating a high number of ideas or attracting a high number of participants ([42]; [33]; [57]). We complement this viewpoint by arguing that participation intensity is also a key driver of idea quality. That is, we argue that the higher the ideators' participation intensity throughout an innovation tournament, the higher the quality of the output ideas. We make this argument for two main reasons. First, ideators with a greater participation intensity (i.e., those who repeatedly view or update their ideas) may be better able to learn from the information generated in the tournament environment than ideators with a lower participation intensity ([ 5]). For instance, repeatedly viewing one's idea may help participants better interpret moderator feedback, thus providing new insights to participants and helping them correct or redirect the focus of their ideas. Second, participants' sustained involvement in the platform—namely, repeated updates of their ideas—helps them clarify and mature their ideas even in the absence of moderator feedback. It is well known that initial submissions to a crowdsourcing platform are typically vague and immature ([ 4]). Updating one's idea allows an ideator to gain experience with the idea maturation task. Prior literature shows that ideators with more experience in a given task are better able to retain their ideas in their memory, increasing their capacity to mature them into novel and useful ideas ([24]). Taking these two arguments into account, we hypothesize:
- H3 : The higher the ideators' participation intensity, the higher the quality of the ideas in an innovation tournament.
Prior empirical evidence suggests that idea quality is a significant predictor of market outcomes ([40]), and customers are more likely to adopt and purchase higher quality ideas ([25]). Therefore, we expect higher quality ideas to contribute to a firm's new product performance with regard to indicators such as profit, sales, and market share ([44]), which should contribute to improve the overall business performance of the firm ([37]). Thus, we hypothesize that:
- H4 : The greater the quality of the ideas in an innovation tournament, the greater (a) the firm's new product performance and (b) the firm's overall business performance.
To test our theoretical framework (see Figure 1), we conducted two longitudinal experiments (Studies 1 and 2) and one large-scale managerial survey (Study 3). Studies 1 and 2 allow us to examine the effect of feedback type and feedback timing on participation intensity (H1 and H2). Study 1 is a longitudinal classroom experiment in which student participation was required, which reduces concerns about self-selection bias but lowers realism. Study 2 is a real innovation tournament organized by one of our schools for its centennial anniversary in which participation was voluntary, which enhances the realism of our experiment. The use of these longitudinal experiments helps us establish the causality between the type and timing of moderator feedback and participation intensity by reducing the influence of unobservable factors or endogeneity ([13]). Study 3 then tests the postulated effect of participation intensity on the quality of the ideas in an innovation tournament (H3) and validates that higher idea quality enhances business outcomes (e.g., [40]), namely, new product performance (H4a) and overall business performance (H4b). To do this, we use a large-scale survey of 1,519 innovation managers across a large number of innovation tournaments and industries. We present an overview of the three studies and a summary of our findings in Table 2.
Graph
Table 2. Overview of Studies and Summary of Key Results.
| Study | Theoretical Effects Tested | Methodology | Details on Study Design | Summary of Key Results |
|---|
| 1 | Moderator feedback↓Participation intensity | Experimental | In-class experiment with students. We ran the tournament using a real innovation tournament platform (CogniStreamer). To reduce endogeneity concerns, moderator feedback type is exogenously manipulated and feedback timing is orthogonal to feedback type (i.e., we randomly assign participants to feedback treatments at each round). Student participation was required, which reduces self-selection bias concerns. | • Positive feedback has no influence either on the number of pageviews or on idea updating.• Negative feedback increases the number of pageviews and idea updating, but late negative feedback may lead participants to disengage from the tournament.• Positive plus negative feedback increases the number of pageviews and idea updating but such beneficial effects attenuate as the tournament progresses. |
| 2 | Moderator feedback↓Participation intensity | Experimental | Real innovation tournament organized by one of our schools for the centennial anniversary of the school. The experimental design and innovation tournament platform are the same used in Study 1, but participation in the tournament is voluntary to enhance the realism of the experiment. To save on degrees of freedom, we do not manipulate positive feedback. | • Negative feedback increases idea updating and the number of pageviews, but its effect on idea updating attenuates as the tournament progresses.• Nonsignificant effect of positive plus negative feedback on the number of pageviews.• Positive effect of positive plus negative feedback on idea updating, but this effect attenuates as the tournament progresses. |
| 3 | Ideation quantity (# ideas, # participants and participation intensity)↓Idea quality↓Business outcomes | Survey | Large-scale survey with managers (N = 1,519) to guarantee generalizability and test whether participation intensity drives idea quality and business outcomes in a large variety of industries. Out of the 1,519 respondents, 516 (33.97%) indicated that their firms had already organized an innovation tournament on an online platform. Econometric controls included to reduce concerns with common method bias and self-selection bias. | • Participation intensity is a significant driver of idea quality in innovation tournaments, over and above number of ideas and number of participants.• The effect of participation intensity on idea quality is stronger than the effect of number of ideas and as strong as the effect of number of participants on idea quality.• Idea quality increases new product performance and overall business performance. |
The goal of Study 1 is to test H1a, H1b, and H2 using a controlled experiment with required participation. The experiment was conducted as part of a marketing strategy course taught at Erasmus School of Economics, Erasmus University Rotterdam, the Netherlands. We organized an innovation tournament to crowdsource ideas for the future of the school from the students. Students were encouraged to find breakthrough ideas capable of innovating the school's offering to boost its impact on students, faculty, or society as a whole by 2030 across four domains: education, knowledge creation, knowledge dissemination, and internationalization. We used the following procedure, which lasted for seven weeks (see Web Appendix 1.2).
First, we issued an idea call two weeks before the start of the tournament informing all enrolled students that, to receive their course credit, they would be required to submit an idea to this tournament. We also informed students that they would mature their ideas over a period of four weeks and that they would receive feedback from selected moderators to help them develop their ideas. Note that, in this tournament, idea submission was required but participation intensity (i.e., viewing and updating one's idea) was not required for the course credit, which allowed us to investigate the impact of moderator feedback on participation intensity. Moreover, students were also informed that they had to submit a final idea proposal at the end of the innovation tournament and that the five students with the best ideas would be rewarded with a bonus on their grade.
Next, the experiment proceeded with an idea development phase that lasted for four weeks and included multiple rounds of feedback. In each of the experiment's rounds, we trained a set of six moderators who would provide feedback to participants on the following topics and in the following sequence, one per experimental round: ( 1) value creation; ( 2) strengths, weaknesses, opportunities, and threats analysis (external and internal environment); ( 3) competition; ( 4) branding; and ( 5) feasibility. Besides focusing on the effect of positive versus negative feedback, we also examined the effect of positive and negative feedback given simultaneously, as platform moderators can offer positive feedback on one aspect of the idea and negative feedback on another aspect. This is somewhat similar to what is called a "sandwich feedback strategy" in business practice ([53]). Therefore, in each experimental round, we randomly assigned participants to one of four between-subject conditions: ( 1) "no feedback," ( 2) "positive feedback," ( 3) "negative feedback," and ( 4) "positive plus negative feedback."
In the "no feedback" condition, moderators provided an uninformative message expressing appreciation for the participation but no information about the participant's idea. A message that is uninformative about the participant's idea does not help participants pursue their goals more effectively and, as such, does not constitute "feedback" ([18]). Thus, participants assigned to this "no feedback" condition constitute our control group. In the "positive feedback" condition, moderators pointed out the strengths of an idea and highlighted positive aspects of an idea (i.e., aspects in which the participant already did a good job). In the "negative feedback" condition, moderators pointed out the weaknesses of an idea and highlighted idea development to go (i.e., corrective actions that participants could implement to improve their ideas). In the "positive plus negative feedback" condition, moderators combined positive and negative feedback in the same message (see examples in Web Appendix 1.2).
Finally, one week after the last feedback was provided, students were required to submit an idea pitch, which we used to select the five best ideas and reward the winners with a grade bonus.
Table 3 summarizes the measures of key variables we obtained from this experiment. We summarize the descriptive statistics of the key variables of this study in Web Appendix 2 (Table 2.1). In total, 93 students (37 female and 56 male) submitted 93 ideas to this innovation tournament and received moderating feedback across five feedback rounds (see Figure 1.2a in Web Appendix 1). The average length of the initially submitted ideas—measured by the log of the number of characters before they receive their first feedback—is 7.30 (SD = 1.03). On average per each feedback round, participants viewed their own ideas 1.40 times (SD = 2.74), and we observed an update in 17% of the idea rounds (SD =.37).
Graph
Table 3. Measurement (Study 1 and Study 2).
| Conceptual Variable | Notation | Measured Variable | Data Source |
|---|
| Participation Intensity | | | |
| Pageviews of own idea | Pageviewsit | The number of pageviews of an idea i by the creator of the idea between feedback at t and feedback at t + 1 | Time-stamped browsing data in the platform |
| Idea updating | Idea updateit | 1 if an idea i is updated between feedback at t and feedback at t + 1, 0 otherwise |
| Feedback Type | | | |
| Positive feedbackit | 1 if positive feedback was provided to an idea i at round t, 0 otherwise | Experimentally manipulated |
| Negative feedbackit | 1 if negative feedback was provided to an idea i at round t, 0 otherwise |
| Positive plus negative feedbackit | 1 if positive plus negative feedback was provided to an idea i at round t, 0 otherwise |
| Feedback Timing | Tournament tenureit | The log of the duration of the time (measured in seconds) between feedback at t provided to an idea i and the creator of idea i's registration in the tournament | Measured |
| Social Motivations | | | |
| Agency–communion orientation | Genderi(proxy) | 1 if the creator of idea i is female (communion-oriented), –1 if he is male (agency-oriented) | Measured using self-reported data |
| Network centrality | Degreei | Degree centrality of the creator of idea i in the social network of all participants in the tournament |
| Betweennessi | Betweenness centrality of the creator of idea i in the social network of all participants in the tournament | (Study 1) and Facebook data (Study 2) |
| Clusteringi | Clustering coefficient of the creator of idea i in the social network of all participants in the tournament |
| Other Control Variables | | | |
| Length of the initial idea | Idea lengthi | The length of the initially submitted idea i (measured by the log of the number of characters before it receives its first feedback) | Time-stamped data in the platform |
| Length of feedback | Feedback lengthit | The length of the feedback provided to idea i at round t (measured by the log of the number of characters) |
| Extrinsic recruitment | Extrinsic_Reci | 1 if the creator of idea i was recruited by an extrinsic reward, –1 otherwise (Study 2 only) | Coded |
| Multiple idea submission | Multiple ideasi | 1 if the creator of idea i submitted multiple ideas, –1 otherwise (Study 2 only) |
Measures for the level of participation intensity are constructed using time stamped participant behaviors found on the tournament platform. Following [12], we use two measures of participation intensity: ( 1) the number of pageviews on an idea i made by the idea creator after receiving t-th feedback but before (t+1)-th feedback,[ 6] and ( 2) a dummy variable indicating whether a participant updated her idea after receiving t-th feedback and before (t+1)-th feedback (yes = 1; no = 0).
Prior research suggests that social motivations play two important roles in human behavior that may influence ideators' participation intensity in innovation tournaments. First, a person's agency–communion orientation (i.e., her tendency to focus on the self or others) may determine her feelings of responsibility towards others and whether or not she engages in impression management efforts ([41]). Feelings of responsibility toward others and impression management concerns, in turn, may influence ideators' participation intensity. Prior research uses gender as a proxy for agency–communion orientation (see [41]). In line with this literature, we expect communal participants (i.e., women) to show a higher level of concern for others and to have a higher need for impression management than agency-oriented participants (i.e., men; [27]). Therefore, we control for participant gender in our model, which we measured through an intake survey to all participants.
Second, a person's position in the network of participants in an innovation tournament can also increase her exposure to other participants' behavior, thereby influencing participation intensity. Prior research shows that network position is a good predictor of ideators' behavior and ideation performance because it reflects the richness and diversity of information resources ideators can access ([48]). Similarly, [43] show that in the community of open source software developers, the network centrality of innovators (which the authors capture using degree centrality and betweenness centrality in the social network of developer users) can be an important driver of project success.[ 7] More recently, [57] show that the interconnectivity of a participant in a crowdsourcing initiative, which the authors capture through the clustering coefficient,[ 8] can negatively influence the innovativeness of her idea. This is because those who are highly interconnected to others tend to come up with similar and redundant ideas due to their ideation resources being more clustered than those who are not highly interconnected to others.
We control for the effect of a participant's network centrality on participation intensity. We used dyadic friendship questions to infer each participant's friendship network and compute network centrality scores ([17]). Specifically, right after the end of the tournament, students indicated whether they knew each of their classmates in person. We say that a friendship link exists between participants i and j if either i responded "yes" to this question about j, or vice versa. We then used these self-reported friendship ties to construct each participant's social network and calculate her network metrics including degree centrality, betweenness centrality, and clustering coefficient. We only employ degree centrality and clustering coefficient to express a participant's network connectivity, because the betweenness centrality is highly correlated with degree centrality. Our results are robust to the usage of different sets of network position metrics (see Web Appendix 3.1).
We control for other variables potentially related to participation intensity, namely, the length of the raw idea (i.e., the length of the initial submission) and the length of each feedback received. We also control for carryover effects (i.e., whether prior participation intensity affects subsequent participation intensity) by including lagged terms for idea viewing and idea updating in our models.
We model pageviews on a participant's own idea page with a zero-inflated negative binomial model and idea updating with a binomial logit model.
To accommodate the excess number of zeros (54% of observations of the pageviews of a participant's own idea page) and the overdispersion in the pageview data, we employ a zero-inflated negative binomial model ([28]).[ 9] To incorporate unobserved factors associated with each participant, we allow for participant-level random effects (independent standard normal random variable). We specify the probability of the creator of idea i making yit pageviews on her own idea during the feedback round t as follows:
Pr(Yit=yit)∼{0 with probability wit, Negative Binomial (λit) with probability (1−wit) ,1
where wit is the zero-inflation parameter capturing the likelihood to observe excess zeros in pageviews, 0 < wit < 1; λit is the parameter capturing the count of pageviews, which follows a negative binomial model; and λit ≥ 0.
The zero-inflated negative binomial model is particularly well suited to our context. Specifically, by specifying the supplementary zero-inflation process for the excess zeros, which occur with probability wit, our model accommodates the fact that some participants may, at any point in time, disengage and stop participating in the tournament, thereby generating excess zeros. We predict the zero-inflation parameter wit as a function of the same set of variables that predict the number of pageviews. Specifically, we estimate wit as a function of the ( 1) type of moderator feedback, ( 2) timing of moderator feedback (i.e., tournament tenure, which is the elapsed time between the participant registering in the platform and receiving each round of feedback), ( 3) interaction effects between the type and timing of moderator feedback, ( 4) participant gender, ( 5) network centrality metrics, ( 6) carryover effect of lagged pageviews on the participant's own idea, and ( 7) remaining control variables, including round-specific fixed effects. Our final specification is as follows:
log(wit1−wit)=xit′γ+α1i,2
where captures the participant-level random effect with ∼ N (0, ) and xit captures the independent variables just described.
Likewise, we estimated the mean of the count in the negative binomial model as a function of the same set of variables described previously as follows:
log(λit)=xit′δ+α2i,3
where captures the participant-level random effect with ∼ N (0, ).
We model the probability that the creator of idea i updates her idea during the feedback round t as a binomial logit model with random effects.[10] We model the indirect utility for the creator of idea i updating her idea during feedback round t as follows (where xit captures the same independent variables described previously):
Uit=xit′β+α3i+∊it,4
where is the participant-level random effect and ∼ N (0, ), and follows an i.i.d. Type 1 extreme value distribution.
The probability that the creator of idea i updates her idea during round t is given by:
Pr(Yit=1)=exp(Uit)exp(Uit)+1.5
Table 4 presents the results of the zero-inflated negative binomial and the binomial logit models. We discuss our key findings in detail subsequently.
Graph
Table 4. Effects of Feedback on Participation Intensity in a Required-Participation Tournament (Study 1; N = 465).
| A: Pageviews Model |
|---|
| Zero-Inflation Model (Disengagement) | Negative Binomial Model(Number of Pageviews) |
|---|
| (Number of Pageviews) | Estimate | SE | Estimate | SE |
|---|
| Intercept | 1.58 | 2.31 | –1.77 | .85 |
| Idea updateit–1 | –1.44 | .90 | –.73 | .16*** |
| Pageviewsit–1 | –.07 | .09 | .03 | .01*** |
| Positive feedbackit (base: no feedback condition) | .28 | .64 | .20 | .21 |
| Negative feedbackit (base: no feedback condition) | –.43 | .76 | .49 | .21** |
| Positive plus negative feedbackit (base: no feedback condition) | .71 | .84 | .46 | .25* |
| Tournament tenureit | –.10 | .98 | .44 | .25* |
| Positive feedbackit × Tournament tenureit | .36 | .66 | –.14 | .17 |
| Negative feedbackit × Tournament tenureit | 2.93 | 1.23** | .01 | .16 |
| Positive plus negative feedbackit × Tournament tenureit | 1.29 | .88 | –.27 | .16* |
| Genderi (1 = female; –1 = male) | .14 | .24 | .06 | .11 |
| Idea lengthi | –.38 | .21* | .33 | .10*** |
| Feedback lengthit | –.33 | .32 | –.20 | .10** |
| Degreei | –.35 | .27 | .06 | .11 |
| Clusteringi | .13 | .25 | .01 | .12 |
| Overdispersion parameter | | | .04 | .02 |
| Log-likelihood | –538 | | | |
| B: Idea Updating Model | Estimate | SE | | |
| Intercept | –13.30 | 3.46*** | | |
| Idea updateit−1 | –2.98 | .85*** | | |
| Pageviewsit−1 | .24 | .09*** | | |
| Positive feedbackit (base: no feedback condition) | .12 | .80 | | |
| Negative feedbackit (base: no feedback condition) | 1.72 | .77** | | |
| Positive plus negative feedbackit (base: no feedback condition) | 1.39 | .82* | | |
| Tournament tenureit | 1.41 | .97 | | |
| Positive feedbackit × Tournament tenureit | .26 | .93 | | |
| Negative feedbackit × Tournament tenureit | –1.12 | .73 | | |
| Positive plus negative feedbackit × Tournament tenureit | –1.28 | .74* | | |
| Genderi (1 = female; –1 = male) | –.15 | .28 | | |
| Idea lengthi | 1.29 | .33*** | | |
| Feedback lengthit | .11 | .25 | | |
| Degreei | –.12 | .28 | | |
| Clusteringi | –.32 | .30 | | |
| Log-likelihood | –161 | | | |
- 10022242918809672 *p <.10; **p <.05; ***p <.01. All p-values are two-sided.
- 20022242918809670 Notes: We standardize the tournament tenure variable, which allows us to interpret the feedback parameters as the "simple effects" of different types of feedback for a participant with an average tenure in the tournament. Both pageviews and idea updating models include four dummy variables to capture the fixed effect of each feedback round. None of the dummy variables are significant.
Table 4, Panel A depicts the results from our pageviews model. We standardize the tournament tenure variable and interpret the parameter estimates of feedback type as the "simple effects" of different types of feedback for a participant with an average tenure in the tournament (see [56]).[11] We find that, for a participant with an average tournament tenure, positive feedback has no significant effect on a participant's number of own idea pageviews (δ =.20, p >.10). In contrast, we find that negative feedback is effective in encouraging participants to increase their own idea pageviews (δ =.49, p <.05). We find that positive plus negative feedback is also effective in encouraging participants to increase pageviews of their own idea page, even though the effect is only significant at the 10% level (δ =.46, p <.10). This finding suggests that positive plus negative feedback is not more effective than negative feedback in isolation. To facilitate comparison of the relative sizes of the different effects, we use the estimated parameters to compute the probability that a participant with an average tenure in the tournament views her idea at least once, conditional on the type of feedback received ([32]). For example, we find that 95.04% of participants with an average tournament tenure and who receive negative feedback view their idea at least once in a feedback round, whereas only 86.4% of participants who do not receive feedback do the same. These probabilities show that our effect sizes are meaningful, but for brevity we discuss all remaining effect sizes in Web Appendix 4.1.
In terms of feedback timing, we find a positive interaction between a participant's tenure in the tournament and negative feedback in the zero-inflation model (γ = 2.93, p <.05), indicating that negative feedback late in the tournament (i.e., at 1 SD above the mean of tournament tenure, which means close to the end of the tournament) is more likely to trigger a participant to disengage than negative feedback early in the tournament (i.e., at the mean of tournament tenure, meaning halfway through the tournament or earlier). We also find a negative interaction between a participant's tenure and positive plus negative feedback in the negative binomial model, even though the effect was significant only at the 10% level (δ = –.27, p <.10). Thus, although the effects of negative feedback and positive plus negative feedback on the number of pageviews of one's own idea page are positive early in the tournament, such beneficial effects attenuate as a participant approaches the end of the tournament.
Table 4, Panel B, depicts the results from our idea updating model. We find that whereas positive feedback does not drive idea updating (β =.12, p >.10), negative feedback and positive plus negative feedback increase the likelihood that a participant updates her idea (β = 1.72, p <.05 for negative feedback; β = 1.39, p <.10 for positive plus negative feedback), as compared to the no feedback condition. In terms of the magnitude of these effects, we find that the probability that a participant with an average tenure in the tournament who receives no feedback updates her idea is 2.04%. If moderators provide positive feedback, the updating probability is 2.3% (which is not significantly different from 2.04%). However, if moderators provide negative feedback, the updating probability is 10.4%, and if moderators provide positive plus negative feedback, the updating probability is 7.73%, showing that these effects are meaningful (for a more detailed discussion, see Web Appendix 4.1).
We find that the effect of positive plus negative feedback on idea updating decreases as participants' tenure in the tournament increases (β = –1.28, p <.10). Thus, positive plus negative feedback is less effective in enhancing idea updating late in the tournament than feedback provided earlier in the tournament.[12] In terms of negative feedback, we find an interaction effect that approaches significance at the 10% level in the predicted negative direction (β = –1.12, p =.12). In contrast, we do not find an interaction effect between tournament tenure and positive feedback (β =.26, p >.10).
Our findings across our pageviews and idea updating models suggest that positive feedback is not an effective driver of participation intensity either early or late in the tournament, leading us to reject H1a. In contrast, negative feedback is effective in enhancing participation intensity, both when provided in isolation or together with positive feedback. These results lend support to the alternative hypothesis H1b. We also find that positive plus negative feedback is not more effective than negative feedback provided in isolation, which casts doubt on the benefits of the often used "sandwich feedback" strategy. In terms of feedback timing, we find partial support for H2. Specifically, the effectiveness of negative feedback in stimulating pageviews decreases as the tournament progresses (which is in line with H2), and its effectiveness in stimulating idea updates also decreases as the tournament progresses but does so at a low p-value (p =.12). In addition, we find that positive plus negative feedback is more effective early rather than late in the tournament.
We did not find significantly higher participation intensity by women or by participants with higher network centrality. Given our sample size and number of feedback treatment conditions, this is possibly due to low degrees of freedom. Thus, this is a conservative test and these effects could possibly be captured in an experiment with higher degrees of freedom. We discuss the results of these and all other control variables in detail in Web Appendix 4.2.
Being a classroom experiment with required participation, Study 1 provided us with an opportunity to test the causal effects of the type and timing of moderating feedback on participation intensity in innovation tournaments without the threat of selection bias. At the same time, however, the classroom setting and required participation pose a threat to the external validity of our study. To alleviate these concerns, in Study 2, we ran a longitudinal experiment in a real innovation tournament conducted at Erasmus School of Economics (ESE). The tournament was branded "ESE Innovation Tournament."
We used the following procedure. First, at the occasion of its centennial celebration, the school invited students to contribute ideas for the future of the school with the same focus of boosting the school's impact by 2030 and along the same domains we used in Study 1. Given the voluntary participation, we conducted an in-campus marketing campaign to raise awareness for the tournament and recruit participants. Apart from voluntary participation, the tournament was very similar to the one in Study 1 (see Web Appendix 1.2). Specifically, we told participants they would mature their idea over a period of five (rather than four) weeks and that they would receive feedback from selected moderators to help them develop their ideas (feedback content was the same as in Study 1, but because we gave participants one additional week, they received six rather than five feedback messages, with moderators focusing the additional feedback on improving participants' pitches). We also informed participants that a jury of senior officials, including the dean, would select the top three ideas, and that we would reward the winners with a recommendation letter from the dean and a cash reward of €500.
Similar to Study 1, six selected moderators trained on the different feedback types provided the feedback. However, to conserve degrees of freedom, we excluded the positive feedback treatment in the present study. Therefore, at each experimental round, we randomly assigned participants to three different treatments: ( 1) a "no feedback" condition, ( 2) a "negative feedback" condition, and ( 3) a "positive plus negative feedback" condition.
In total, 104 participants (39 female and 65 male) submitted 142 ideas at this innovation tournament and received moderating feedback across six feedback rounds. On average, participants viewed their own ideas.87 times per feedback round (SD = 2.54), and in 15% of the idea-rounds we observed an update (SD =.36). We summarize the descriptive statistics for this study in Web Appendix 2 (Table 2.2).
We used the same measures as in Study 1, with the following exceptions. In Study 2, we obtained participants' genders and network centralities by tracking their Facebook profile pages. For those who did not disclose their gender on Facebook (N = 17, out of 106), we inferred their gender according to whether their first name is commonly used for men or women.[13] For network centrality, we inferred the position of a participant in the network of all participants from the friendship network of all participants on Facebook. Specifically, we obtained participants' lists of friends from their public Facebook profile pages.[14] Using the lists of friends elicited from Facebook profiles, we constructed the social network of participants and calculated network metrics for each participant including degree centrality, betweenness centrality, and clustering coefficient.
Our model specifications remain the same as the ones used in Study 1, with the following exceptions. First, we included a "multiple idea" control variable because, in this tournament, some participants submitted more than one idea. Second, we included a dummy to control for recruitment through extrinsic rewards because we used small gifts (e.g., Ben & Jerry's ice cream) in some of our in-campus marketing efforts to recruit students. Third, we removed the "positive feedback" treatment variable, as it was not manipulated in the present study.
Table 5 presents the estimated coefficients from pageviews and idea updating models.
Graph
Table 5. Effects of Feedback on Participation Intensity in a Voluntary Tournament (Study 2; N = 803).
| A: Pageviews Model |
|---|
| Zero-Inflation Model (Disengagement) | Negative Binomial Model (Number of Pageviews) |
| Estimate | SE | Estimate | SE |
| Intercept | 6.38 | 2.13*** | –2.97 | .78*** |
| Idea updateit–1 | –1.68 | 1.33 | –.54 | .15*** |
| Pageviewsit–1 | –.58 | .22*** | .03 | .01*** |
| Negative feedbackit (base: no feedback condition) | .20 | .90 | .52 | .29* |
| Positive plus negative feedbackit (base: no feedback condition) | –.22 | .99 | .29 | .31 |
| Tournament tenureit | –.58 | .78 | –.07 | .31 |
| Negative feedbackit × Tournament tenureit | .65 | .77 | –.12 | .30 |
| Positive plus negative feedbackit × Tournament tenureit | 1.13 | .78 | .04 | .30 |
| Genderi (1 = female; –1 = male) | –.08 | .24 | .16 | .09* |
| Extrinsic_Reci | .72 | .42* | –.35 | .36 |
| Idea lengthi | –.74 | .22*** | .38 | .09*** |
| Feedback lengthit | –.11 | .70 | .17 | .21 |
| Multiple ideasi | –.61 | .24*** | –.01 | .10 |
| Degreei | –.02 | .28 | .22 | .10** |
| Clusteringi | .32 | .31 | –.17 | .14 |
| Overdispersion parameter | | | .13 | .05*** |
| Log-likelihood | –628 | | | |
| B: Idea Updating Model | Estimate | SE | | |
| Intercept | –20.78 | 3.01*** | | |
| Idea updateit–1 | –.78 | .49 | | |
| Pageviewsit–1 | .00 | .04 | | |
| Negative feedbackit (base: no feedback condition) | 5.35 | 1.89*** | | |
| Positive plus negative feedbackit (base: no feedback condition) | 5.35 | 1.90*** | | |
| Tournament tenureit | 4.28 | 1.93** | | |
| Negative feedbackit × Tournament tenureit | –4.51 | 1.92** | | |
| Positive plus negative feedbackit × Tournament tenureit | –4.94 | 1.93** | | |
| Genderi (1 = female; –1 = male) | .89 | .20*** | | |
| Extrinsic_Reci | –.57 | .46 | | |
| Idea lengthi | 1.77 | .28*** | | |
| Feedback lengthit | –.52 | .56 | | |
| Multiple ideasi | .46 | .24* | | |
| Degreei | 1.16 | .25*** | | |
| Clusteringi | –.86 | .32*** | | |
| Log-likelihood | –189 | | | |
- 1000022242918809700 *p <.10; **p <.05; ***p <.01. All p-values are two-sided.
- 30022242918809670 Notes: We standardize the tournament tenure variable, which allows us to interpret the feedback parameters as the "simple effects" of different types of feedback for a participant with an average tenure in the tournament. Both the pageviews and idea updating models include five dummy variables to capture the fixed effect of each feedback round. Except for the dummy variable for round 2 feedback (strengths, weaknesses, opportunities, and threats) in the negative binomial component of the pageviews model, none of the dummy variables are significant. Note that we have 803 idea round observations because, unlike in Study 1, Study 2 has an unbalanced panel. This imbalance occurs because some participants joined after the first feedback round due to the voluntary nature of this tournament.
Table 5, Panel A depicts the results of our pageviews model. As in Study 1, we standardize the tournament tenure variable. Similar to our findings from Study 1, we find that negative feedback is effective in stimulating a higher number of pageviews, which is an effect that is significant at the 10% level (δ =.52, p <.10). We do not find a significant effect of positive plus negative feedback on pageviews.
In contrast with Study 1, we do not find a significant interaction between the type and timing of feedback on the number of pageviews.
Similarly to Study 1, in Study 2 we find that, when compared with participants assigned to the control condition, participants receiving negative feedback and positive plus negative feedback are more likely to update their ideas (β = 5.35, p <.01 for negative feedback; β = 5.35, p <.01 for positive plus negative feedback).
We again find that feedback timing matters. Specifically, we find that the positive effects of negative feedback and of positive plus negative feedback on idea updating decrease as participants' tenures in the tournament increase (β = –4.51, p <.01 for negative feedback; β = –4.94, p <.01 for positive plus negative feedback).
Taken together, the results from our pageviews and idea updating models reinforce the results of Study 1. Specifically, we find that negative feedback is an effective driver of participation intensity, in line with H1b. We also find that positive plus negative feedback is not more effective than negative feedback in isolation. In fact, positive plus negative feedback is not effective in driving pageviews, whereas negative feedback provided in isolation is. These results cast doubt on the effectiveness of the sandwich feedback strategy. In terms of feedback timing, the results of the idea updating model suggest that negative feedback, either provided in isolation or together with positive feedback, should be provided early rather than late in the tournament, lending support to H2.[15]
With three exceptions, our results for other variables beyond feedback type and timing reflect those of Study 1 (see Web Appendix 4.2 for a detailed discussion). First, unlike Study 1, we find a significantly higher participation intensity by female (vs. male) participants, and by participants with high (vs. low) degree or betweenness centrality. Second, we also find a significantly lower participation intensity by participants with high (vs. low) clustering coefficients. These results reinforce the possibility that the lack of significant effects of these social motivation variables in Study 1 was possibly driven by the study's low degrees of freedom. Third, in contrast with Study 1, we do not find a significant effect of the length of feedback on the number of pageviews (δ =.17; p >.10).
The purpose of Study 3 is to test H3 and H4. In other words, Study 3 examines ( 1) whether participation intensity drives the quality of the ideas emerging from innovation tournaments over and above the number of ideas and the number of participants and ( 2) whether idea quality has a positive influence on new product performance and on overall business performance. We used a large-scale managerial survey for two key reasons. First, a large-scale managerial sample allows us to observe variations in ideation quantity (i.e., number of ideas, number of participants, and participation intensity), ideation quality, and business outcomes across a large number of innovation tournaments and firms. Such data enable us to obtain direct insights into the effect of participation intensity on the quality of ideas generated in innovation tournaments and of idea quality on new product performance and overall business performance. Second, a large-scale managerial survey provides evidence of the effects examined in this study across a variety of firms and industries, thereby enhancing the external validity of our arguments.
To recruit participants to our online survey, we contracted Research Now, a leading online sampling company that manages a large executive panel and constantly monitors the quality of its panels to ensure sample representativeness and respondents' attention and motivation. In total, Research Now solicited 4,773 innovation managers from among its panel members. Respondents worked in a variety of industries, such as automotive, engineering, food/beverages, information/media, retail/wholesale, and telecommunications, to name a few (see Web Appendix 6 for the distribution). We considered respondents eligible if ( 1) they were sufficiently knowledgeable about innovation in their firm (i.e., if they had a score of six or higher on knowledge of innovation) and ( 2) they had been working at their current company for at least four years (see [34] for how these factors increase accuracy). We excluded respondents working for a firm active in finance/banking, insurance, or consulting, as firms in these industries typically position themselves as expert advisors to their customers, making it more difficult for these firms to crowdsource new products and services from their customers. Of the 4,773 solicited responses, 1,871 were eligible. Out of these, 352 exited the study early, leaving us with 1,519 eligible and complete respondents. Out of these, 516 (33.97%) indicated that their firm had already run an innovation tournament on an online platform.
Some firms may have better capabilities and resources for innovation and are thus more likely to run innovation tournaments because they expect such tournaments to deliver high quality ideas. To control for this potential endogeneity problem, we employed [30] two-step procedure. To do this, we collected information about the innovation capabilities and firm resources for all firms, including the 1,003 firms that had not run an innovation tournament on an online platform, and we used a two-step [30] correction to demonstrate that our results in the subset of firms that had already run an innovation tournament on an online platform (N = 516) are robust and not threatened by selection bias, as recommended in the extant literature ([51]). We offer a detailed description of our two-step Heckman correction in Web Appendix 5.4. In short, we first ran a probit selection model for each respondent in which we regressed the firm's decision to run an innovation tournament (firm had already run an innovation tournament = 1, firm had not yet run an innovation tournament = 0) on covariates explaining the selection decision (i.e., innovation capabilities and resources available for the firm to successfully deploy such an innovation tournament). Next, we used the probit estimates to calculate the Heckman correction factor, or inverse Mills ratio (λ). Finally, following [30], and in line with recent marketing literature ([60]), we augmented our structural equation model (SEM) by including the inverse Mills ratio as an additional predictor of idea quality.
In the first part of the questionnaire, we gathered our screening questions and two control variables: industry and firm size. Given that creating and developing high quality ideas may be easier in certain industries than in others, we control for the industry sector a firm operates in. Moreover, even though prior research has found mixed results regarding the effect of firm size on innovation performance ([11]; [15]), we also control for the effect of firm size (which we proxy using number of employees). In this part of the questionnaire, we also measured the business outcomes in our conceptual framework, namely new product performance and overall business performance.
In the second part of the questionnaire, we provided respondents with clear and simple definitions of our key terms, such as innovation tournaments and online innovation platforms. We then measured whether a respondent's firm had ever run an innovation tournament on an online innovation platform. For respondents whose firms had already run an innovation tournament, we measured participation intensity, number of ideas, and number of participants in the firm's latest innovation tournament. Finally, we measured the quality of the ideas emerging from the firm's latest innovation tournament.
We describe the measurement of all constructs, including all items, source(s), and reliability measures in Web Appendix 5.1. In Web Appendix 5.2, we show that our measures are unidimensional and reliable, that they exhibit divergent and convergent validity, and that common method variance and multicollinearity do not pose a threat in this study.
We developed three new scales for participation intensity (three items; α =.86), number of ideas (three items; α =.88), and number of participants (three items; α =.86). To develop these new scales, we domain-sampled the constructs from extant literature in marketing and innovation and examined the reliability and validity of proposed measures to guarantee the purity of our scales (as recommended by [14]).
For all other constructs, we used existing scales published in the marketing literature as the basis and inspiration for our scales. For instance, following [42], we measured idea quality by asking respondents to rate, using seven-point scales, whether the ideas generated in the latest innovation tournament at their firm were novel, insightful, valuable for customers, and well-articulated (α =.82). We also collected an alternative measure of idea quality that considers the ideas' technical feasibility, novelty, specificity, and potential market demand (see [25]; [42]). We show in Web Appendix 5.3 that our results are robust to the usage of either of the two measures of idea quality. To measure new product performance, we used a five-item scale developed by [44]; α =.93), and to measure overall business performance we used a three-item scale adapted from [37]; α =.88).
We tested our hypotheses using a Bayesian SEM estimated on the subsample of firms that had already run an innovation tournament on an online platform (N = 516). Bayesian estimation is increasingly recognized as a more flexible approach to the estimation of theory-driven structural equation models than maximum likelihood ([45]). We depict the descriptive statistics and bivariate correlations among all constructs in our model in this subsample of firms in Web Appendix 2. To compute these correlations, we averaged respondents' answers to the items in each of the scales to produce summated scales for each construct. In doing so, we followed the standard argument in psychometrics ([47]) and in marketing research textbooks ([36]) that it is both safe and useful to treat summated Likert scales as interval scales. For technical details about the econometric specification and estimation of our model, see Web Appendix 5.5.
We compared the fit of different models using the deviance information criterion (DIC), for which lower values indicate a better fit ([55]). Individual DIC values are hard to interpret in absolute terms, and the Bayesian literature recommends comparing the differences in DIC between models (ΔAvs.B = DICA – DICB; [ 9]), with a model (A) considered as having more support than an alternative model (B) if its DIC is more than 10 points below the DIC of the alternative model (i.e., ΔAvs.B < –10). We compare four models. Model 0 is a baseline model with only the control variables (i.e., number of employees and industry dummies) and where idea quality is not allowed to influence new product performance and overall business performance (DICM0 = 23,558). In Model 1, we allow idea quality to influence new product performance and overall business performance but keep only number of employees and industry dummies as drivers of idea quality, which significantly improves model fit (DICM1 = 23,463; Δ1vs.0 = –95). In Model 2, we introduce the effects of number of ideas and number of participants on idea quality, which again leads to a significant improvement in model fit (DICM2 = 23,319; Δ2vs.1 = –143). When we introduce the effect of participation intensity in Model 3, the fit of the model again improves significantly (DICM3 = 23,278; Δ3vs.2 = –42; Δ3vs.1 = –185; Δ3vs.0 = –280). Thus, the optimal model, based on minimum DIC, is Model 3. We also estimate these same models using ordinary least squares models estimated using each construct's summated scales. Again, our full model shows the best fit (see Web Appendix 5.6).
We summarize our results in Table 6. In line with H3, we find that the higher the participation intensity, the higher the quality of the ideas emerging from an innovation tournament (β. =.45; 95% CI = [.27,.63]). In addition, we find that the number of participants enrolled in the tournament also has a positive effect on the ultimate quality of the ideas emerging from an innovation tournament (β =.35; 95% CI = [.12,.58]). In contrast, controlling for participation intensity and for number of participants, the number of ideas generated in the tournament does not have a significant impact on the ultimate quality of the ideas emerging from the tournament (β =.15; 95% CI = [–.11,.43]).
Graph
Table 6. Bayesian SEM Results (Study 3; N = 516).
| Path Coefficients (Posterior Means) |
|---|
| Ideation quantity → Ideation quality | |
| Participation intensity → Idea quality | .45*** |
| Number of ideas → Idea quality | .15 |
| Number of participants → Idea quality | .35*** |
| Ideation quality → Business outcomes | |
| Idea quality → New product performance | .73*** |
| Idea quality → Overall business performance | .32*** |
| New product performance → Overall business performance | .58*** |
| Other variables | |
| Number of employees → Idea quality | .01 |
| Number of employees → New product performance | −.13*** |
| Number of employees → Overall business performance | .01 |
| Industry dummies ‡ → Idea quality | See Web Appendix 6 |
| Industry dummies ‡ → New product performance | See Web Appendix 6 |
| Industry dummies ‡ → Overall business performance | See Web Appendix 6 |
40022242918809670 *The 90% credible interval does not contain zero. **The 95% credible interval does not contain zero. ***The 99% credible interval does not contain zero. ‡The model controlled for industry dummies. See Web Appendix 6. Notes: We let all models converge and run each of our Bayesian SEM models for 35,000 draws with two chains. We then discarded the first 10,000 draws for burn-in and used the remaining 5,000 thinned draws for posterior inference (we used every 10th draw in each of the two chains to reduce autocorrelation). The parameter estimates reported in the second column are the posterior means of the path coefficients across all Markov chain Monte Carlo draws, excluding burn-in draws.
In support of H4, we find that the higher the quality of the ideas emerging from an innovation tournament, the higher a firm's new product performance (β. =.73; 95% CI = [.64,.83]) and the higher a firm's overall business performance (β. =.32; 95% CI = [.21,.42]). In addition, we find that the higher the new product performance, the higher the overall business performance (β =.58; 95% CI = [.47,.69]).
We do not find a significant effect of firm size (i.e., number of employees) on the quality of the ideas emerging from an innovation tournament (β. =.01; 95% CI = [–.05,.06]). We find 12 (out of 39) significant industry-specific effects, seven of which are on new product performance and five of which are on idea quality. None of the industry-specific dummies had a significant effect on overall business performance (see Web Appendix 6 for details).
Our work raises several important implications that are relevant to both theory and managerial practice. From a theoretical perspective, we highlight the importance of participation intensity in innovation tournaments as an important antecedent for idea quality, over and above number of ideas and number of participants, which are the more commonly used ideation quantity metrics in extant research ([ 4]; [25]; [58]). In addition, our research examines the key roles that the type of feedback and the timing of feedback play in affecting participation intensity in innovation tournaments. Whereas extant research focusing on feedback and goals provides conflicting expectations regarding the impact of type of feedback (positive versus negative) and timing of feedback (early versus late), our research provides clear confirmation and insights into why negative feedback early in an innovation tournament has a positive impact on participation intensity. These insights, although theoretically important, also have important managerial significance.
For example, for firms organizing and hosting innovation tournaments, these results document the value of feedback provision, whether internally provided or externally sourced. For third-party providers of innovation platforms, these results provide evidence they can present to their clients when they aim to sell moderator feedback services to their clients.
Moreover, our result that participation intensity is an important driver of idea quality, more so than number of ideas and number of participants, has implications regarding the measures on which firms should focus. Firms now routinely monitor only number of ideas and number of participants but do not always consistently monitor participation intensity. Our research calls for firms to pay more attention to participation intensity as a behavior to monitor, a metric to report, and an outcome to incentivize.
Practically, this may lead to many specific actions. To name just a few: Firms hosting innovation platforms may seed among ideators the expectation that ideators will view and update their idea over several rounds. Hosting firms may also consider stimulating updating behavior in ideators who show low participation intensity (i.e., who rarely visit the platform after submission) by using email campaigns, flyers, calls, or other forms of communication. Hosting firms should demand that third-party platform providers report participation intensity routinely (e.g., at the end of every day), rather than only reporting number of ideas and number of participants. Hosting firms may also use participation intensity as one of the key performance indicators for measuring the success of the tournament, and they may even negotiate with third-party platform providers to establish payment schedules that depend on participation intensity, especially if they have also secured moderator feedback services from that platform provider. When designing a request for quotation for third-party platform providers, and at these providers' pitch meetings, organizers of innovation tournaments should also push for historical evidence that a platform can generate and sustain participation intensity and make this metric a dimension on which they score or compare vendors.
Our findings also show that negative feedback beats positive feedback with regard to sustaining ideators' participation intensity in innovation tournaments. These findings enable firms organizing and hosting innovation tournaments that adopt moderator feedback to choose the right type of feedback to steer participation intensity. Innovation tournament organizers and firms supplying such services should train moderators to challenge participants' ideas and highlight the work that still needs to be done for the idea to be successful. Unambiguously signaling that more effort is needed for a participant to accomplish her goals, in turn, leads her to increase her efforts. We find in our study that positive feedback, in contrast, may not be as effective in driving participation intensity. Despite its potentially positive effects on motivation, positive feedback may have a deleterious influence on participation intensity by signaling that participants are already close to reaching their goals. We also find that negative feedback does not need to be sandwiched between positive feedback to be effective. These results demonstrate to companies that in the context of innovation tournaments, the "sandwich feedback strategy" ([16]) is not more effective than negative feedback only.
Finally, moderators should frontload their criticism of ideas to the early rather than late stages of an innovation tournament, as the effectiveness of criticism on participation intensity seems to attenuate over time. This finding has several implications. For moderator training, it would imply that moderators should be instructed to adjust the timing of the feedback according to the feedback type, providing more negative feedback as early as possible. Also, innovation tournament organizers should think about the optimal timing of feedback rounds given the timeline of the tournament. Specifically, hosting firms should allow for a sufficient number of early rounds of feedback so that moderators have sufficient opportunity to detect and raise critical shortcomings in time for participants to still be able to update their idea to its full potential. Once the initiative nears its end, we find that feedback effectiveness diminishes. Given there will always be a cost associated with providing feedback, organizers can economize feedback by providing it only earlier in the tournament.
Our study has a few limitations that offer opportunities for future research. The first issue concerns the generalizability of our results to other contexts. Randomized field experiments in a real company context, although difficult to implement given a typical firm's strategic agenda when implementing an innovation tournament, would be very valuable. Also, it would be valuable to analyze historical data from one platform vendor, for instance, across many campaigns. Future research could confirm the generalizability of our findings to other types of crowdsourcing innovation beyond innovation tournaments, such as collaboration-based crowdsourcing ([ 1]).
In our research, we did not study the impact of other participants' feedback (i.e., social feedback) on participation intensity. Thus, we cannot conjecture on the effect of social feedback and whether it is similar to the effect of moderator feedback. Research testing the generalizability of our findings to social feedback may yield valuable insights and provide new research topics to study such as feedback reciprocity, dyad (i.e., the person giving and the person receiving feedback) concordance or discordance effects, and the role of network position and other descriptors of the person giving feedback, to name just a few.
A related issue is whether the effects of internal feedback are different from those of external feedback. In reality, firms can choose to have their own staff provide feedback, which would ensure moderators with a strong firm identification, or have external staff without such firm identification provide feedback. Future research that clarifies whether moderators' identification with the firm makes a difference for the participation intensity on the platform may provide useful insights for firms as they deliberate whether to let an outside agency deliver feedback or assign internal resources to such feedback provision.
Our research did not measure the cost of feedback, nor did it estimate a model that allows trading off feedback cost and the increased idea quality one obtains thanks to such feedback. Therefore, although we can firmly say feedback can positively affect idea quality through participation intensity, we cannot infer the return on investment such feedback provision delivers. An analysis on the return on investment of different moderation strategies may provide firms with valuable insights on innovation tournament design. Beyond participation intensity, such analysis could include other dimensions of ideation quantity, such as number of participants, that also come at a cost (e.g., employee time).
We model pageviews and idea updating decisions as separate processes. Like all models, this is an abstraction of reality. In reality, it is more likely that these decisions are sequential. Participants first view their own idea and may then decide to update the idea. More generally, a participation episode in a crowdsourcing platform may follow an even richer sequence of different actions, such as a participant viewing her own idea, viewing feedback, viewing another participant's idea, returning to her own idea, viewing still another idea, updating her own idea, etc. Future research that conceptualizes the different participation patterns in a typology and models such patterns (e.g., as one would do with clickstream data) would allow studying additional research topics. For such inquiries, one would ideally have richer data than ours both in dimensionality (e.g., the number of movements throughout the platform) and in number of ideas or participants to enable sufficient statistical power to estimate more complex model structures.
One of the important findings in our research is that positive feedback is ineffective, which disconfirms what one may expect from self-determination theory. However, this finding rests mostly on the results of our Study 1, given that in Study 2 we did not manipulate positive feedback in isolation. Also note that in Study 2, we did manipulate negative plus positive feedback and did not find it to be more effective than negative feedback provided in isolation, which may reduce concerns about the possibility that positive feedback provided in isolation could be an effective driver of participation intensity in this study. Still, future research that manipulates all three types of feedback would offer a more rigorous replication and a valuable validation of the current findings. Moreover, future researchers may develop more variations in which positive feedback can be provided and examine whether some types of positive feedback are more effective than other types of positive feedback. Further validation across contexts and with different styles of positive feedback could thereby confirm, qualify, or complement the findings we report herein, thereby enriching the implications we present to firms as they consider their feedback strategy.
Finally, future research should explicitly test the mediating effect of participation intensity on the effect of feedback on idea quality. Formally examining the mechanisms through which feedback influences idea quality is a highly important theoretical question. Future experimental research could, for instance, not only experimentally manipulate feedback and measure participation intensity but also measure the quality of the output ideas in an innovation tournament. Such data would allow for a formal mediation analysis of the mechanism linking feedback to idea quality. We hope that this study will fuel the future research agenda of scholars in the crowdsourcing area, in which the managerial interest is currently still very much on the rise.
Supplemental Material, DS_10.1177_0022242918809673 - Tournaments to Crowdsource Innovation: The Role of Moderator Feedback and Participation Intensity
Supplemental Material, DS_10.1177_0022242918809673 for Tournaments to Crowdsource Innovation: The Role of Moderator Feedback and Participation Intensity by Nuno Camacho, Hyoryung Nam, P.K. Kannan, and Stefan Stremersch in Journal of Marketing
Footnotes 1 Authors' NotePart of this research was done when the second author was an Assistant Professor at Erasmus School of Economics.
2 Associate EditorMichael Haenlein served as associate editor for this article.
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We thank the Erasmus Center for Marketing and Innovation, ERIM (Erasmus Research Institute of Management), IESE Business School and University of Maryland for their financial support.
5 Online supplement: https://doi.org/10.1177/0022242918809673
6 1Note that although participants can also view other participants' ideas, pageviews on others' ideas may be driven out of curiosity and competitive concerns and do not necessarily reflect an objective measure of a participant's engagement with her own idea.
7 2Degree centrality refers to the number of ties, or neighbors, an ideator has in her network. Betweenness centrality refers to how importantly or strategically placed her ties are (i.e., an ideator with high betweenness centrality has a stronger influence in the network because a large number of ties "pass through" this ideator).
8 3Clustering refers to the density of an ideator's network of neighbors, such that a higher clustering coefficient refers to denser networks with higher interconnectivity among neighbors (see [57]).
9 4Zero-inflated count models extend standard count models by supplementing the standard count process with a secondary binary process that distinguishes "excess zeros," which occur as a realization of the binary process, from "standard zeros," which occur as a realization of the count process ([10]). We considered the following alternative models: Poisson model, negative binomial model, and zero-inflated negative binomial model. Using the goodness-of-fit indicators, we concluded that a zero-inflated negative binomial model is the most appropriate for our data (see more discussion in Web Appendix 3.2). We thank an anonymous reviewer for these suggestions.
5Given the excess zeros in our data, we considered both a logit model and a Scobit model, but a likelihood ratio test indicated that the logit model is more appropriate for our data (see Web Appendix 3.1).
6Given that we standardize the tournament tenure variable, the interaction effects can be interpreted as the effects of different types of feedback when a participant's tenure is at one standard deviation above the mean of the tournament tenure.
7Due to the nonlinearity in the idea updating models, the sign and significance of interaction coefficients between feedback type and feedback timing (i.e., tenure) may not indicate the true statistical significance of the interaction effect ([2]). Thus, following [62], we further examined the interaction effects in idea updating models by simulating the marginal effects of feedback type on the probability that a participant updates an idea across different levels of her tenure (see Web Appendix 3.3). This analysis shows that none of our effects are driven by the nonlinearity of the logit specification, even though some effects (e.g., the interaction between tournament tenure and negative feedback in Study 2) become significant only at the 10% level.
8We obtained the data about the probability of a first name to be used for boys or girls from http://www.gpeters.com/names/baby-names.php.
9For those who did not allow access to their list of Facebook friends (N = 35), we detected their friendship only when we found them in someone else's list of friends.
10Note that our key question of interest is whether different types of feedback increase or decrease participation intensity in any of its manifestations (i.e., either pageviews or idea updating). Taken together, the results of Study 2 on these different manifestations reinforce those of Study 1. We thank an anonymous reviewer for pointing this out.
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By Nuno Camacho; Hyoryung Nam; P.K. Kannan and Stefan Stremersch
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Record: 207- Toward an Optimal Donation Solicitation: Evidence from the Field of the Differential Influence of Donor-Related and Organization-Related Information on Donation Choice and Amount. By: Fajardo, Tatiana M.; Townsend, Claudia; Bolander, Willy. Journal of Marketing. Mar2018, Vol. 82 Issue 2, p142-152. 11p. 1 Chart. DOI: 10.1509/jm.15.0511.
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Toward an Optimal Donation Solicitation: Evidence from the Field of the Differential Influence of Donor-Related and Organization-Related Information on Donation Choice and Amount
The present research decomposes consumer donation behavior into two components: donation choice (i.e., whether to donate) and donation amount (i.e., how much to donate). It then considers how information related to the donor and information related to characteristics of the soliciting organization may differentially influence the two decisions. Results from four field experiments suggest that donor-related appeals have a greater effect on the donation choice decision (vs. organization-related appeals), whereas organization-related appeals have a greater effect on the donation amount decision (vs. donor-related appeals). This might lead one to conclude that presenting both types of appeals in a solicitation is ideal. However, the studies presented herein also suggest that this strategy may backfire. The simultaneous presentation of donor- and organization-related appeals can hamper both donation response rates and average contribution amounts. To address this issue, the authors identify and test an alternative solicitation strategy for maximizing solicitation effectiveness. This strategy involves a multistep request process that capitalizes on an understanding of the differential influence of donor- and organization-related information on donation choice and amount decisions.
According to the National Center for Charitable Statistics (2015), there are over 1.4 million nonprofit organizations in the United States, which collect over $358 billion in donations annually. Despite these massive numbers, nonprofits continually report that fund-raising represents a major challenge preventing them from meeting demand for their services (Nonprofit Finance Fund 2014). Furthermore, many people in charge of fund-raising for nonprofits readily admit they do not know how to stabilize their donor base or revenues (Bell and Cornelius 2013). Given this, it is unsurprising that marketing scholars are increasingly interested in the topic of charitable giving.
Nevertheless, we note three limitations in this literature. First, most existing research does not account for the fact that donation decisions are multidimensional; with the exception of Dickert, Sagara, and Slovic (2011), prior work does not differentiate between consumers' decision to make a donation (choice) and their decision of how much to give (amount). Rather, previous studies consider only one decision, leaving readers to assume, implicitly, that results apply to both (e.g., Shang, Reed, and Croson 2008; Winterich, Mittal, and Aquino 2013) or to consider the two dimensions interchangeably (e.g., Lee and Shrum 2012; Winterich, Mittal, and Ross 2009).
Second, there are two groups writing about the issue of solicitation effectiveness, with little apparent interaction. The first, composed primarily of marketing and consumer behavior academics (e.g., Shang, Reed, and Croson 2008; Winterich, Mittal, and Ross 2009), focuses on the role of donor-related factors (i.e., how appeals that influence a donor's sense of self motivate prosocial behavior). The second, composed largely of practitioners and public policy researchers (e.g., Abramson and McCarthy 2003; Gill and Wells 2014), focuses on the influence of organization-related (henceforth, "org-related") factors (i.e., how imparting information about an organization and its cause influences giving).
Third, within this domain, there has been an emphasis on establishing internal validity, using well-controlled lab experiments, at the potential expense of external validity (e.g., Winer 1999). While the insights garnered from this work have been indispensable in laying the groundwork of our theoretical knowledge of this topic, evidence of how findings might be successfully implemented by practitioners remains sparse. Such failure to balance rigor (i.e., internal validity) and relevance (i.e., external validity) in research can lead to findings that are potentially "uncoupled from the real world" (Tushman and O'Reilly 2007, p. 770).
The current research deconstructs consumer donation behavior into two dimensions—donation choice and amount—and offers a series of four field studies, which, taken together, help address the aforementioned limitations. The first three studies examine the relative influence of donor- and org-related information on donation choice and amount. In support of the idea that donation behavior is multidimensional, we find that consumers weigh donor-related information more (less) heavily than org-related information when facing a donation choice (amount) decision. An intuitive conclusion to draw from this distinction between donor- and org-related information and their differential effects on donation behavior is that nonprofits should include both types of information within a solicitation to maximize the total number and monetary value of donations. Our findings, however, indicate that this idea may be incorrect. When donor- and org-related appeals are simultaneously presented, both donation choice and amount numbers can decline. Our fourth study therefore proposes a less obvious solicitation strategy for enhancing overall solicitation effectiveness.
We contend that factors influencing donation decisions can be classified into two distinct groups: donor- and org-related. Donor-related factors involve the intrinsic characteristics of the donor and his or her self-perception. Org-related factors reflect characteristics of the charitable organization and the cause it supports and are thus extrinsic to the donor's sense of self. This conceptualization is similar to that ofWebb, Green, and Brashear (2000), who identify two distinct attitudinal measures influencing donation behavior: attitude toward helping others (an intrinsic, donor-related factor) and attitude toward charitable organizations (an extrinsic, org-related factor) (see also Sargeant, Ford, and West 2006).
Although these scholars distinguish between donor- and org-related factors, no research has systematically examined whether and how these factors might differentially relate to the specific dimensions of donation behavior. Furthermore, investigations into the influence of these factors have depended on analysis of preexisting donor- and org-related attitudes. At best, managers can only indirectly affect attitudes. Thus, to ensure more practical insights, we use solicitation content, which is under managers' precise control, to manipulate the presence of donor- and/or org-related information and examine their influence on the two donation decisions.
We also consider a specific solicitation scenario: one in which consumers are approached by an organization, presented with an unsolicited donation appeal, and asked to make an unplanned contribution. Practitioners maintain a relatively high degree of control in this context, suggesting the greatest potential for application. In this circumstance, we expected that donor-related appeals (e.g.,moral identity and self-proximity) primarily affect perceptions of donation self-relevance, whereas org-related appeals (e.g., severity of victimization and organization effectiveness) primarily affect perceptions of donation impact (for validation, see the pilot study in Web Appendix A). Importantly, this suggests that the relative influence of donor-and org-related information on donation choice and amount decisions may vary.
Choice is a behavior through which individuals overtly express self-relevant preferences and values (Kim and Drolet 2003) and through which an individual can reinforce his or her identity (Belk 1988; Tafarodi et al. 2002). As a result, people make choices that they feel reflect their actual or desired self-view and make inferences about themselves and others based on their choices (Calder and Burnkrant 1977). In addition, choice decisions encourage an egocentric frame of reference (Frith and De Vignemont 2005), increasing consideration of self-related information (Sood and Forehand 2004).
The emphasis on self-expression associated with the choice decision suggests that consumers seek information about the self relevance of a potential donation when they consider whether to contribute. Given that donor-related appeals significantly influence perceptions of donation self-relevance but org-related appeals do not (Web Appendix A), we hypothesize the following:
H1: Donor-related information has a greater effect on the donation choice decision than organization-related information.
Whereas consumers emphasize self-expression when making choice decisions, they emphasize decision outcome when making amount decisions (Lichtenstein and Slovic 1971). In the donation context, potential outcome is perhaps best reflected by the expected impact of a donation (Sargeant, Ford, and West 2006). Org-related appeals communicate the information necessary for an individual to estimate the possible impact of a donation (Web Appendix A). The emphasis on donation impact associated with the amount decision, along with the ability of org-related information to significantly influence perceptions of a donation's impact, suggests that org-related information influences donation amount decisions. Following this logic, the inability of donor-related appeals to significantly influence perceptions of donation impact (Web Appendix A) suggests that donor-related information is unlikely to be considered in the context of a donation amount decision and therefore has limited effect on donation amounts. This leads us to our second hypothesis:
H2: Organization-related information has a greater effect on the donation amount decision than donor-related information.
We offer two points of caution with these hypotheses. First, our hypotheses are specific to the contextwherein consumers are presented with an unsolicited donation appeal and asked to make an unplanned contribution. Second, note that H1 and H2 are comparative. It is not that we expect no influence of donor-related appeals on amount and no influence of org-related appeals on choice; we hypothesize only that the effect of donor-related (org-related) appeals on choice (amount) is greater than that of org-related (donor-related) appeals.
An additional factor that should be considered is the simultaneous presentation of donor- and org-related information. Given H1 and H2, one might predict that presenting donor- and org-related appeals together would result in both higher donation rates and amounts. Indeed, when presented together, both types of information may complement each other. Donor-related information emphasizes donation self-relevance and motivates potential donors to contribute, while org-related information maximizes perceptions of donation impact and motivates higher contribution amounts. There is, however, room for doubt.
First, the simultaneous presentation of donor- and org-related appeals may increase the absolute amount of information given. Research on the effect of information overload suggests that providing too much information upfront may overwhelm an individual and negatively affect donation behavior (Reuters 1996; Shenk 1997). Second, a solicitation featuring a mix of donor- and org-related appeals may be perceived as more complex than a solicitation featuring only one type of information. Consumers generally react negatively to increases in information complexity. Indeed, information complexity can increase disengagement (Pritchard, Havitz, and Howard 1999) and encourage suboptimal decision making (Bhargava and Manoli 2012; Loewenstein et al. 2013); it may also enhance inferences of manipulative intent (Diamond and Noble 2001).
Given these conflicting possibilities, we do not make a formal hypothesis about the cumulative effect of donor- and org-related information. However, in recognition of its theoretical and managerial relevance, we investigate the effect of this factor on donation behavior in the following studies.
We ran Study 1a in conjunction with a national nonprofit, Communities in Schools (CIS). We manipulated whether the mail solicitation sent out by CIS included donor- and/or org-related information and then examined subsequent donation behavior.
Participants and design. CIS allowed us to send donation appeals to a random selection of 669 members (50%) of their mailing list, including past donors, potential donors referred by current donors, and people who had shown interest through the organization's website. The design was a 2 (donor-related information: present vs. absent) × 2 (org-related information: present vs. absent) between-subjects study, resulting in four conditions. In the control condition, the solicitation included only basic information about the organization; both donor-and org-related appeals were absent. In the donor-related condition, additional appeals highlighted that people who received the solicitation were known for their generosity (moral identity) and that CIS operated in local communities. In the org-related condition, additional appeals emphasized the negative consequences of dropping out of school (severity of victimization) and the success of CIS's program (effectiveness of the organization). In the mixed-appeal condition, the solicitation included both these donor- and org-related appeals (for full language of the letters, see Web Appendix B).
Procedure. Before sending the solicitations, we randomly assigned members of the sample population to one of the four conditions described previously, balancing for the number of prior donors and the amounts of prior donations. The appeals were sent out by CIS; CIS volunteers, who were blind to our hypotheses, tracked both response rates and donation amounts.
Overall response. The overall response rate was 2.69%, which, according to CIS, compared favorably with previous attempts. Donations ranged from $20 to $10,000, with a mean donation amount of $1,652.72 ($1,161.70 without the $10,000 outlier), a median donation amount of $100.00, and a mode of $50.00. The results for donation amount were skewed (skewness of 1.88, SE = .54); thus,we log-transformed donation amounts before further analysis.
Donation choice. A binary logistic regression analysis using donation rate (i.e., whether the participant made a donation) as the dependent variable and donor-related information, org-related information, and their interaction as independent variables revealed a significant interaction between donor-related information and org-related information (β = -2.47, SE = 1.28, p = .05). First, we examined the effect of donor-related appeals. In the absence of org-related information, donor-related information led to a directional increase in donation rate (β = .98, SE = .62, p = .10). In the presence of org-related information, donor-related information had no significant effect on donation rate (β = -1.49, SE = 1.12, p = .18). Next, we examined the effect of org-related appeals. In the absence of donor-related information, there was no significant effect of org-related information on donation rate (β = .19, SE = .72, p = .79). In the presence of donor-related information, org-related information decreased donation rate (β = -2.28, SE = 1.06, p < .05).
To further test H1, we compared the donation rate achieved by the solicitation featuring only donor-related information (no org-related information) with the donation rates achieved by the other three solicitations. The solicitation featuring only donor-related appeals received the highest response (5.56%). This response rate was marginally higher than that achieved by the control (no information) solicitation (2.16%; χ2( 1) = 2.76, p < .10) and significantly higher than that of the mixed-appeal solicitation (.60%; χ2( 1) = 6.91, p < .01). However, this response rate proved only directionally higher than that achieved from the solicitation that featured only org-related information (2.60%; χ2( 1) = 1.75, p = .18).
Donation amount. Nondonors were omitted from all analyses of donation amount, across all studies. We did this to prevent donation likelihood from having a downstream influence on average donation amount and potentially confounding our results.
A linear regression analysis using log-transformed donation amounts as the dependent variable and donor-related information, org-related information, and their interaction as independent variables revealed a significant interaction between donor-related appeals and org-related appeals (β = -2.23, SE = .99, p < .05). Again, we first examined the effect of donor-related appeals. In the absence of org-related appeals, donor-related appeals did not influence average donation amount (β = .26, SE = .47, p = .59). In the presence of org-related appeals, donor-related appeals led to a decrease in average donation amount (β = -1.97, SE = .87, p < .05). Next, we examined the effect of org-related appeals. In the absence of donor-related appeals, org-related appeals increased average donation amount (β = 1.30, SE = .55, p < .05). In the presence of donor-related appeals, org-related appeals had no significant effect on average donation amount (β = -.94, SE = .82, p = .28).
To further test H2, we compared the donation amount raised by the solicitation that featured only org-related information (no donor-related information) with the amounts raised by the other three solicitations. The solicitation with only org-related appeals resulted in a significantly higher average donation amount (3.27 [log-transformed]) than did the solicitation with only donor-related appeals (2.24; F( 1, 14) = 4.89, p < .05), the control (no information) solicitation (1.98; F( 1, 14) = 5.51, p < .05), or the mixed-appeal solicitation (1.30; F( 1, 14) = 5.09, p < .05). Table 1 provides results by condition.
TABLE: TABLE 1 Study Results by Condition
TABLE 1 Study Results by Condition
| Choice Decision | Amount Decision |
| Study and Condition | N | Donation Rate | N | Mean Donation (SD) |
| Study 1aa |
| Donor-related present, org-related absent | 162 | 5.56% | 9 | 2.24 (.75)* |
| Donor-related absent, org-related present | 154 | 2.60% | 4 | 3.27 (1.06) |
| Donor-related present, org-related present | 168 | .60% | 1 | 1.30 (n.a.) |
| Donor-related absent, org-related absent | 185 | 2.16% | 4 | 1.98 (.48) |
| Study 1b |
| Donor-related | 209 | 12.92%* | 27 | $9.20 (7.50)* |
| Org-related | 217 | 6.45% | 14 | $21.00 (14.69) |
| Mixed-appeal | 150 | 6.00% | 9 | $14.67 (8.68) |
| Study 1c |
| Donor-related | 11,732 | 1.24%* | 146 | $1.09 (.81)* |
| Org-related | 10,633 | .80% | 84 | $2.26 (6.68) |
| Mixed-appeal | 12,800 | .81% | 104 | $1.30 (1.77) |
| Study 2 |
| Match | 34,679 | 1.96%* | 680 | $1.37 (4.60)* |
| Mismatch | 36,608 | 1.61% | 588 | $1.02 (.35) |
| Mixed-appeal | 41,336 | 1.57% | 648 | $1.06 (1.02) |
*Significantly greater than comparison cell at p < .05.
aMean donation amounts in Study 1a are log-transformed.
Notes: Boldface values in the same study indicate cells compared in test of H1; italic values in the same study indicate cells compared in test of H2.
Study 1a provides mixed support for our hypotheses. The solicitation featuring only donor-related information led to only a marginally higher donation rate than the control solicitation; moreover, it led to only a directionally higher donation rate than the solicitation featuring only org-related information. Thus, H1 is not supported. The solicitation featuring only org-related information led to a higher average donation amount than either the control solicitation or the solicitation featuring only donor-related information. Thus, H2 is supported. Despite these divergent findings, it is worth noting that, in accordance with both H1 and H2, the solicitation featuring only donor-related information resulted in the highest number of donations, while the solicitation that featured only org-related appeals raised the most funds ($20,050); this is the case even if we exclude an outlier who donated $10,000.
It is also worth noting that the solicitation featuring a mix of donor- and org-related appeals was, by far, the least effective. This solicitation resulted in the lowest number of donations and the smallest average contribution. Moreover, any positive effect of donor-related information on the donation choice decision occurred only in the absence of org-related information. Similarly, any positive effect of org-related information on the donation amount decision occurred only in the absence of donor-related information. This suggests that, at a minimum, the simultaneous presentation of donor- and org-related information is not a viable strategy for maximizing solicitation efficiency. In the next studies, we continue to explore the effect of mixed appeals by including solicitations featuring both donor- and org-related appeals.
Indeed, the designs of Studies 1b and 1c are almost identical to that of Study 1a, except for the following differences: First, for Studies 1b and 1c, we partnered with a different organization and used new solicitation platforms to offer a more comprehensive test of our hypotheses. Second, whereas Study 1a included some solicitation recipients who were previous donors and/or who had a preexisting relationship with the organization, the remaining studies involve campaigns targeting people unfamiliar with the organization; we expect our hypothesized effects to be strongest under these circumstances. Finally, the next two studies do not involve a condition that has neither donor-related nor org-related appeals.[ 1]
We ran Studies 1b and 1c in conjunction with Go Foster, a regional nonprofit focused on recruiting and retaining foster and adoptive families. For Study 1b, Go Foster set up a booth in an area of high foot traffic, with a large sign indicating that they were seeking donations. We varied the information content of this sign, as well as the information offered by the stand attendant.
Participants and design. The design was a 3 (solicitation information: donor-related, org-related, mixed) × 1 between-subjects study, resulting in three conditions. In the donor-related condition, the solicitation sign featured appeals pertaining to moral identity and self-proximity. In the org-related condition, the solicitation sign featured appeals pertaining to severity of victimization and effectiveness of the organization. In the mixed condition, the sign featured one donor- and one org-related appeal. Note that within the mixed condition, the specific donor- and org-related appeals used were randomized: the solicitation sign included either appeals pertaining to moral identity and severity of victimization (mixed solicitation A) or appeals pertaining to self-proximity and effectiveness of the organization (mixed solicitation B) (for full language, see Web Appendix C). This campaign had a reach (exposure) of 576.
Procedure. The study took place over the course of three nights; each night, the study ran between 5 and 8 P.M. Each condition ran one hour per night. Donor- and org-related solicitation signs were each displayed for one hour per night, and each mixed-solicitation sign was displayed for 30 minutes per night. The order of presentation was randomized across nights.
Two research assistants helped with data collection. One assistant did not interact with participants and only tracked the number of individuals who passed the booth. The second sat at the booth and recorded both the number and amounts of donations made per condition. This research assistant restricted interactions to inquiring whether participants who approached the booth were interested in donating and thanking those who made a contribution. They were extensively trained to transmit only condition-appropriate information if questioned.
Overall response. The overall response rate was 8.68%. Go Foster had never done an in-person campaign before and had no basis for evaluating the relative success of this campaign. Among participants who donated, the mean and median amount were both $13.49 (SD = $11.22).
Mixed conditions. Within the mixed condition, the specific type of donor- and org-related information presented did not have a significant effect either on donation rate (mixed A: M = 7.25%; mixed B: M = 4.94%; χ2( 1) = .35, p = .55) or average donation amount (mixed A: M = $16.40; mixed B: M= $12.51; F < 1). Therefore, when analyzing our results, we collapsed across these variations.
Donation choice. A chi-square analysis revealed that donation rates varied depending on the solicitation presented (χ2( 2) = 7.46, p < .01). Participants in the donor-related condition were more likely to make a donation (12.92%) than those in the org-related condition (6.45%; χ2( 1) = 5.12, p < .05) or those in the mixed-appeal condition (6.00%; χ2( 1) = 4.63, p < .01).
Donation amount. Among participants who donated, a one-way analysis of variance (ANOVA) on donation amount revealed a significant effect of solicitation type (F( 2, 47) = 6.27, p < .01). On average, donors in the org-related condition (M = $21.00, SD = 14.69) donated more than donors in the donor-related condition (M = $9.20, SD = 7.50; F( 1, 47) = 12.38, p < .01) but not more than donors in the mixed-appeal condition (M = $14.67, SD = 4.68; F( 1, 47) = 2.12, p = .15).
As H1 predicts, the solicitation sign emphasizing donor-related information led to a higher donation rate than the solicitation sign emphasizing org-related information. In contrast, and in line with H2, the solicitation sign emphasizing org-related information led to a higher average donation amount than the solicitation sign emphasizing donor-related information. Thus, using a different solicitation platform, in-person solicitations, and a new nonprofit organization raising money for a different charitable cause, Study 1b supports the idea that donor-related (org-related) information has a greater influence on donation choice (amount) than does org-related (donor-related) information.
Our analysis also reveals that the solicitation sign emphasizing donor-related information led to a higher donation rate than the solicitation sign with mixed appeals. In line with results from Study 1a, this suggests again that donor-related information can benefit donation choice only in the absence of org-related information. However, in contrast to the results for Study 1a, the solicitation sign emphasizing org-related information did not lead to a higher average donation amount than the solicitation sign with mixed appeals; this suggests that org-related information can positively affect donation amount regardless of whether donor-related information is simultaneously presented.
In Study 1c, we continued working with Go Foster to test our hypotheses in the field. The design of Study 1c was identical to that of Study 1b, but donations were solicited online via sponsored Facebook posts.
Participants and design. The design was a 3 (solicitation information: donor-related, org-related, mixed) × 1 between-subjects study, resulting in three conditions that varied on the type of information presented in both the solicitation post and its associated landing page. In the donor-related condition, the post and landing page emphasized appeals pertaining to moral identity and self-proximity. In the org-related condition, the post and landing page emphasized appeals pertaining to severity of victimization and effectiveness of the organization. In the mixed condition, all donor- and org-related appeals were included in the post and landing page (for sponsored post and landing page language, see Web Appendix D). This campaign ran for one week and achieved a reach of 35,165 exposures (i.e., individual Facebook users exposed to one of the three posts).
Of note, Go Foster was campaigning to raise awareness for their annual underwear drive, during which they collect new items of clothing for children in foster care. Since this campaign could be supported either through a monetary donation (made online or in person) or by dropping off items (in person), we targeted Facebook users located near drop-off locations.
Procedure. Participants were randomly exposed to one of three sponsored posts. All three posts included a "Donate Now" button. Clicking on this button served as our measure of donation choice. If participants clicked on this button, they were directed to one of three different landing pages on the Go Foster website wherein condition-specific information was re-emphasized. On the landing page, consumers were asked to pledge a specific total amount they would contribute to this campaign, accounting for any monetary donations and the approximate value of any donated items. The default donation pledge was $1, though donors could pledge any amount. The amount pledged served as our measure of donation amount.
Overall response. The overall response rate of .95% was comparable to past Facebook solicitation campaigns from Go Foster. Among participants who pledged a donation, the mean pledged amount was $1.45 (SD = $3.55); the median and mode were both $1.00.
Donation choice. A chi-square analysis revealed that donation rates varied depending on the solicitation post presented (χ2( 2) = 16.28, p < .01). Participants in the donor-related condition were more likely to pledge a donation (1.24%) than those in the org-related condition (.80%; χ2( 1) = 11.32, p < .01) or those in the mixed-appeal condition (.81%; χ2( 1) = 11.32, p < .01).
Donation amount. Among participants who pledged a donation, a one-way ANOVA on pledged donation amount revealed a significant effect of solicitation type (F( 2, 331) = 3.09, p < .05). On average, donors in the org-related condition (M = $2.26, SD = 6.68) pledged more than donors in the donor-related condition (M = $1.09, SD = .81; F( 1, 331) = 5.89, p < .05) and marginally more than those in the mixed-appeal condition (M = $1.30, SD = 1.77; F( 1, 331) = 3.47, p = .06).
The particulars of this campaign forced us to consider pledged donations rather than actual donations. However, pledged commitments to nonprofits remain extremely relevant to fundraising practitioners (Varadarajan 2003). Moreover, despite this variance in procedure, results replicated those of Study 1b: the solicitation emphasizing donor-related information led to a higher response rate than the solicitation emphasizing org-related information (H1), while the solicitation emphasizing org-related information led to a greater (expected) contribution than the solicitation emphasizing donor-related information (H2).
Also, the solicitation emphasizing donor-related information led to a higher donation rate than the solicitation with mixed appeals, suggesting that donor-related information only benefits donation choice in the absence of org-related information. Likewise, the solicitation emphasizing org-related information led to a marginally higher donation amount than the solicitation with mixed appeals, suggesting that the positive effect of org-related information on donation amount may be weakened in the presence of donor-related information. While not conclusive, these findings, along with those of Studies 1a and 1b, suggest that offering mixed appeals may hamper the effectiveness of both donor- and org-related information.
Indeed, across these studies, providing donor- and org-related information together consistently resulted in lower response rates and, at times, lower average contributions. One implication of this finding might be that solicitation campaigns should maintain only one goal—to maximize either donor base or average contribution amount—and messaging should be tailored accordingly. However, maximizing donor base and average contribution amount are both critical to the success of a nonprofit; thus, a nonprofit manager may hesitate to make such a trade-off. As such, in the next study, we test a solicitation strategy specifically designed to capitalize on the benefits of both donor- and org-related information while avoiding the potential drawbacks witnessed in mixed-appeal conditions.
Like Study 1c, Study 2 involved an online campaign by Go Foster using sponsored posts on Facebook to solicit donations for the organization's underwear drive. Participants saw a sponsored post for Go Foster's underwear drive featuring an appeal as well as a "Donate Now" button. Clicks on this button served as our measure of donation choice. Potential donors were then directed to a landing page offering additional appeal language before they were asked to give their pledge amount. The pledge amount served as our measure of donation amount.
Across three conditions, we held the information content constant but varied whether particular appeals were offered in the sponsored post, and thus in the context of the donation choice decision; and/or on the landing page, and thus in the context of the donation amount decision. We predicted that presenting only donor-related information in the initial post when decision makers are considering whether to make a donation, then presenting only org-related information on the web page when decision makers are considering how much to donate, would lead to both the highest donation rates and the highest average donation amounts. This strategy was expected to be the most successful because it would separate out the presentation of donor- and org-related appeals while still ensuring each was presented in the more appropriate decision context.
Participants and design. The design was a 3 (solicitation: match, mismatch, mixed) × 1 between-subjects study, resulting in three conditions. Information content was held constant, but the placement of the information on the sponsored post and/or its associated landing page varied across conditions. The campaign ran for three weeks and had a reach of 112,623 individuals (individual Facebook users who saw one of the three posts).
Procedure. In the match (mismatch) condition, participants saw a version of the sponsored post that featured donor-related information with appeals pertaining to moral identity and self-proximity (org-related information with appeals pertaining to severity of victimization and effectiveness of the organization). If they indicated that they would like to make a donation by pressing a "Donate Now" button on the ad, they were taken to a landing page on the Go Foster website where they were presented org-related information with appeals pertaining to severity of victimization and effectiveness of the organization (donor-related information with appeals pertaining to moral identity and self-proximity). On this page, participants could pledge a specific donation amount. In the mixed condition, participants viewed both donor- and org-related appeals on the post (order randomized; for stimuli, see Web Appendix E). If they indicated a desire to make a donation, they were directed to a landing page that re-emphasized donor- and org-related information, where they could pledge a specific donation amount. All else was the same as in Study 1c.
Overall response. The overall response rate was 1.70%, which is on par with the response seen in Study 1c and comparable to the average response rate of solicitations made via social media (M+R Strategic Services and The Nonprofit Technology Network 2013). Among participants who pledged a donation, the mean pledged amount was $1.17 (SD = $2.82); the median and mode were both $1.00.
Mixed conditions. In the mixed condition, whether donor-or org-related information appeared first did not have a significant effect on either donation rate (donor first: M = 1.50%; org first: M = 1.60%; χ2( 1) = .61, p = .43) or average donation amount (donor first: M = $1.11; org first: M = $1.03; F < 1). Therefore, we collapsed across these variations.
Donation choice. A chi-square analysis revealed that donation rates varied depending on the solicitation structure presented (χ2( 2) = 20.36, p < .01). Participants in the match conditionweremore likely to pledge a donation than those in the mismatch condition (1.96% vs. 1.61%, respectively; χ2( 1) = 12.82, p < .01) or those in the mixed condition (1.57%; χ2( 1) = 16.99, p < .01).
Donation amount. Among thosewho pledged a donation, a one-way ANOVA on pledged donation amount revealed a significant effect of solicitation type (F( 2, 1,913) = 3.04, p < .05). Donors in the match condition (M = $1.37, SD = 4.61) pledged, on average, more than donors in the mismatch condition (M = $1.02, SD = .35; F( 1, 1,913) = 4.85, p < .05) and those in the mixed condition (M = $1.06, SD = 1.02; F( 1, 1,913) = 4.08, p < .05).
Across the three solicitation strategies, tested information contentwas held constant. The only variation was in the order of presentation and, thus, the context in which decision makers considered donor- and org-related appeals. Nevertheless, there were significant differences in both the number of donations made and their average amount. As expected, the solicitation strategy wherein donor-related information was offered only in the context of the choice decision and org-related information only in the context of the amount decision led to the highest response rate and the greatest average pledge amount. These results not only substantiate our hypotheses but also help support the idea that presenting donor- and org-related appeals together does not optimize solicitation effectiveness. Indeed, across conditions, the mixed-appeal solicitation led to the lowest response rate and the smallest average pledge amount.
To test the overall validity of H1 andH2,we performed a series of single-paper meta-analyses (SPMs; McShane and Bockenholt 2017). The first SPM was conducted to determine whether donor-related information had a greater effect of on donation choice than did org-related information. In support of H1, a significant difference was found between conditions including only donor-related information (donor-related conditions of Studies 1a—c and match condition of Study 2) and conditions including only org-related information prior to choice implementation (org-related conditions of Studies 1a—c and mismatch condition of Study 2) (estimate = .39%, SE = .001; z = 4.88, p < .01). The second SPM was conducted to determine whether org-related information had a greater effect on donation amount than did donor-related information. In support of H2, a significant difference was found between conditions including only org-related information (org-related conditions of Studies 1a—c and match condition of Study 2) and conditions including only donor-related information postchoice and prior to amount commitment (donor-related conditions of Studies 1a—c and mismatch condition of Study 2) (estimate = .70, SE = .33; z = 2.12, p < .05).
Here, we must acknowledge that, managerially, the SPM effect size estimates are difficult to interpret. This is due largely to differences in the solicitation platform (mail in Study 1a, in-person in Study 1b, and online in Studies 1c and 2) and dependent measures (log-transformed donation amount in Study 1a and absolute donation amount in Studies 1b–2) used across studies. Thus, while these meta-analyses offer strong evidence for the idea that donor-related information has a stronger influence on choice (H1) whereas org-related information has a stronger influence on amount (H2), they do not offer a clear managerial takeaway about how to interpret the size of each effect. We do not necessarily interpret this as a significant failure, given that effect sizes are also likely to vary across soliciting organization, target population, and solicitation language used. We discuss this as well as other practical implications of our findings next.
Fund-raising professionals face the ever more difficult job of generating the resources their nonprofits need to sustainably provide services, part of which requires maintaining and growing a significant donor base. It is not surprising, then, that top development positions are notoriously difficult to fill, with many remaining vacant for years (Bell andCornelius 2013). The present research utilizes extensive field experimentation to provide key insights that we hope will contribute to the survivability of such positions by helping practitioners work smarter.
Across four studies conducted using varied solicitation media (i.e., mail, in-person, and online), we show that donor-related appeals have a stronger influence on the donation choice decision than do org-related appeals (H1), whereas org-related appeals have a stronger influence on the donation amount decision than do donor-related appeals (H2). Our studies also suggest a negative effect on solicitation effectiveness of offering both donor- and org-related information simultaneously. Taken together, all this implies the following: to maximize donation choice decisions, one should focus on donor-related information; to maximize amount decisions, one should focus on org-related information; and, importantly, one must be cautious of trying to get "the best of both worlds" by throwing both types of information together in a single solicitation.
In response to our findings regarding mixed-information solicitations, we identify the split-match solicitation strategy as an effective solution (Study 2). This strategy involves separating the presentation of donor- and org-related information such that donor-related information is considered prior to donation choice and org-related information prior to donation amount. This allows each dimension of donation behavior (i.e., choice and amount decisions) to be treated separately and, critically, aligns the presentation of donor- and org-related appeals to the dimension of donation behavior over which each has the greatest influence. As demonstrated, this split-match solicitation strategy optimizes both choice and amount outcomes.
Our theoretical implications are straightforward but important. At the most basic level, we validate the concept of multidimensional donation behavior, confirming prior work that suggests donation choice and amount decisions may be conceptually distinct and differentially motivated (Dickert, Sagara, and Slovic 2011). Moreover, although research has shown that consumers give for donor-related reasons (e.g., Shang, Reed, and Croson 2008; Winterich, Mittal, and Aquino 2013), our work is the first to suggest that considerations of the self may primarily influence donation likelihood. Similarly, while non-profit managers seem to understand that org-related information can affect donation behavior, ours is the first work to imply that its influence may be primarily on the donation amount decision. Given these differential effects, and the counterintuitive results of combining donor- and org-related appeals, our findings should encourage scholars to explore issues related to donation behavior in far greater theoretical specificity. Admittedly, our work may prompt more theoretical questions than it answers—a fact that we address in more detail in our discussion of future research opportunities.
At the broadest level, our research findings should prompt two critical considerations for fund-raising professionals. First, they must resist the temptation to manage their solicitation efforts by intuition alone. If a cause is important enough to prompt the formation of a nonprofit organization and the development of an inevitably complex solution to a social problem, then the organization's fund-raising efforts deserve just as much deliberate consideration. Ultimately, donors will not engage with a nonprofit simply because the organization's mission appears to be good; the way a solicitation request is communicated makes a big difference.
Second, and related, fund-raising professionals must avoid the trap of being satisfied with campaign results merely because they achieve a positive number of donations. The approach to managing fund-raising under a satisficing strategy—where positive results are deemed "good enough"—does a great disservice to the nonprofit community. Indeed, given that we observe variance in choice and amount decisions that is based on rather subtle differences in solicitation content, we would encourage fund-raising professionals to adopt an optimizing approach to their solicitation efforts. Specifically, we would encourage them to engage in the type of active experimentation demonstrated herein. If one's cause is important enough to ask others to donate to, then it should be important enough to treat in a systematic, evidence-based manner that enables practitioners to continuously improve their efforts. From these broad insights, we offer the following specific recommendations.
When to emphasize donor-related information. When soliciting potential donors, nonprofit organizations generally have multiple goals. One goal may be to maximize donation participation. Having a large number of donors is advantageous for several reasons: it minimizes the overall risk associated with each fund-raising attempt, increases awareness of the organization, and can be leveraged to identify new donors (Kurre 2010). For example, for universities, alumni donor participation rate is of critical importance because it is used a measurement of satisfaction in rankings and can also be used to garner additional funding opportunities (Golden 2007). If the goal of a particular fund-raising attempt is primarily to engage new donors, donor-related appeals should be emphasized.
There are other contexts besides maximizing solicitation response rates in which our results suggest that emphasizing donor-related information would be ideal. For instance, organizations hosting events (a major source of engagement and fund-raising for many groups; Hughes 2013) may be able to drive choice decisions (e.g., event attendance) by presenting donor-related information in their marketing materials. Relatedly, organizations attempting to influence individuals to sign up for a mailing list or to follow them on social media may want to focus on donor-related appeals because these decisions emphasize choice rather than valuation.
When to emphasize org-related information. In contrast, if the goal is to increase average donation amounts, even at the potential expense of participation rate, org-related appeals should be emphasized. Inmany cases, nonprofits collect data on their prospective donor base (Weir and Hibbert 2000), which may be leveraged to more effectively target donors. If the targets of a particular appeal have been identified as having high levels of disposable income, org-related information may be worthwhile if high donation amounts can make up for lower response rates. Likewise, if a particular audience already feels connected to an organization and has consistently given in the past, participation may be assumed and the goal should be to motivate larger donation amounts. In this instance, org-related information should be emphasized. Indeed, generally speaking, if a nonprofit suspects that a group of donors has already made some type of choice decision or commitment, org-related information should be utilized.
How to leverage both donor- and org-related information in solicitations. Of course, the preceding discussion is not intended to suggest that donor- and org-related information must be presented in complete isolation. While our results suggest that the simultaneous presentation of donor- and org-related information may have a negative effect on overall solicitation effectiveness, this can be mitigated using our recommended split-match solicitation strategy.
Admittedly, the success of such a two-wave campaign may depend on whether the campaign is run online (e.g., email, banner ad) or offline (e.g., paper, telephone). When soliciting donations online, it is easy for the nonprofit to match the initial donation request with donor-related information (e.g., in the initial email/banner ad/sponsored post/web page) and then subsequently present org-related information before donors indicate a donation amount (e.g., on the subsequent landing page). Offline, it is plausible that one could split the decisions across multiple solicitations (e.g., seek donation pledge from first solicitation, then solicit actual contribution in follow-up letter or call) or even within the same solicitation (e.g., across two pages within the same letter). In fact, such a strategy mimics the well-known foot-in-the-door persuasion technique (Cialdini and Schroeder 1976). However, these strategies may be less effective than their online counterparts if they lead to donor confusion. Moreover, if multiple letters/calls are planned, the risk of annoying donors (Van Diepen, Donkers, and Franses 2009) and the associated increase in fund-raising costs must be considered.
Even for organizations unable to utilize a two-stage request process within a single solicitation attempt, our findings regarding mixed appeals and the effectiveness of a split-match solicitation strategy have important implications for donor engagement in the long term. Donors, like all consumers, have a lifetime value; an organization will solicit the same donor numerous times (Reinartz and Kumar 2000; Venkatesan and Kumar 2004). In that sense, there should be a distinction between first-time donors and repeat donors. For first-time donors, the primary aim should be to elicit some contribution, regardless of amount, in order to maximize donor base. Thus, solicitations to first-time donors should emphasize donor-related information. Once an initial contribution has been made, the donor will be more likely to make additional contributions in the future. Indeed, repeat donors are about three times more likely than nondonors to respond to a solicitation attempt (Fundraising Effectiveness Project 2015). Given this, a solicitation to repeat donors should emphasize org-related information in an effort to maximize donation amount. Although not explicitly examined in our studies, this strategy of matching appeal type to donor type (nondonor versus repeat donor) mimics the split-match solicitation strategy advocated in Study 2 and is likely to maximize the effectiveness of an organization's fund-raising attempts, regardless of the solicitation platform used.
Overall, the studies reported herein present a robust case for our predictions; we have shown similar results in a variety of field experiments. Nevertheless, we note some limitations and opportunities for future research. Importantly, additional work is needed to test the processes underlining our effects. We suggest that choice decisions are a form of self-expression and that donor-related information has a stronger influence on perceptions of self-relevance than org-related information. As a result, donor-related information should have a stronger influence on donation choice than org-related information. We also suggest that amount decisions are concerned with decision outcome and that, in a donation context, decision outcome is most closely associated with donation impact. Since org-related information is perceived to have a stronger influence on perceptions of donation impact than donor-related information, org-related information should have a stronger influence on donation amount than donor-related information. We offer preliminary support for the first part of each of these assumptions in our pilot study: that donor-related (org-related) information influences perceptions of self-relevance (donation impact) whereas org-related (donor-related) information does not. However, future work could corroborate this as well as examine why donor-related (org-related) information has a significant influence on perceived donation self-relevance (impact) but not on donation impact (self-relevance). Doing so will help identify boundary conditions not considered herein.
One important consideration in this regard may be the sequential nature of the choice and amount decisions. It has been argued that donation choice and amount decisions are generally made sequentially, with choice occurring first and amount considered afterward (Dickert, Sagara, and Slovic 2011). This implies that donors typically decide how much to give only after confirming their sense of self through choice. If consumer's need for self-expression can be satisfied early in the donation decision-making process through donation choice, it is unsurprising that information related to the perceived self-relevance of a donation will be less relevant in later stages of this decision-making process when donation amount is determined.
Within the context examined herein, in which consumers were approached by an organization, presented with an unsolicited donation appeal, and asked to make an unplanned contribution, we believe it is likely that consumers followed this sequential decision process, first determining whether a contribution should be made and then considering how much should be given. However, the donation decision process may not always strictly follow this sequence. Consider, for example, someone seeking to donate a predetermined portion of their annual income at the end of the year or someone receiving an unexpected bonus and deciding to actively seek out an organization with which to share this windfall. In these circumstances, where a donation amount decision might precede or be otherwise decoupled from the donation choice decision, it is possible our results do not hold. Organizations can also disrupt the natural order of the decision-making process by asking for a particular amount upfront (e.g., "Five dollars a day saves a life") or highlighting a particular amount (e.g., "Platinum donors give $25,000 or more). Again, in these circumstances, our results may not hold.
Across studies, we consider two types of information shown to be donor-related but not org-related (e.g., moral identity and self-proximity) and two types shown to be org-related but not donor-related (e.g., severity of victimization and effectiveness of organization). We believe these are good examples of each type, but there are alternative ways to operationalize these concepts. This raises the question of whether our results would hold across all operationalizations or how the strength of the effects from each might compare to one another. Indeed, some differences across operationalizations of donor- and org-related information are to be expected. However, it is likely that the existence and/or strength of these differences depend on the soliciting organization, cause, and/or donor-specific traits not considered herein. As such, we leave this exploration to future researchers.
Notably, our studies focus on donation behavior cross-sectionally, as if a single donor can only be approached once. As alluded to in our discussion of managerial implications, taking a lifetime value perspective on consumer donation behavior forces one to think about the interactions between nonprofits and donors in a longitudinal way. Future research should test how the effectiveness of solicitations changes over repeated exposures, over stages of the donor—organization relationship, and with the emergence of rival nonprofits. This investigation in particular would be worthwhile given that the sequential nature of the donation decision-making process discussed earlier may change as a consumer becomes more engaged with an organization. Recent work in marketing using longitudinal growth modeling (e.g., Ahearne et al. 2010; Bolander, Dugan, and Jones 2017) may be helpful in this regard.
Finally, future research should more closely examine the effect of mixed appeals on solicitation effectiveness. While our results are not consistent across all studies, there is enough evidence to suggest that a mixed-appeal strategy might not optimize donation behavior. Nevertheless, future research is needed to validate our conclusion about the negative effect of mixed appeals on solicitation effectiveness and to more definitively identify the process underlining this effect, whether it be information overload, information complexity, inferences of manipulative intent, or something else.
In conclusion, we believe our findings may help several different audiences. In particular, we hope they are useful to nonprofit managers interested in improving solicitation efforts and marketing researchers trying to understand and further investigate fundamental mechanisms responsible for people's decision-making and donation behavior.
Endnotes 1 The organization we partnered with for Studies 1b and 1c requested that we omit a control condition in order to conserve resources.
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Tatiana Fajardo is Assistant Professor of Marketing, Florida State University (email: tfajardo@business.fsu.edu). Claudia Townsend is Associate Professor of Marketing, University of Miami (email: ctownsend@bus.miami.edu). Willy Bolander is Carl DeSantis Associate Professor of Marketing, Florida State University (email: wbolander@business.fsu.edu). The authors wish to thank Communities in Schools of Miami, Go Foster! Inc., and research assistant Daniel Bradbury for assistance in data collection. The authors also thank Michael Tsiros for his helpful comments on a previous draft of this manuscript. Robert Meyer served as area editor for this article.
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Tatiana Fajardo is Assistant Professor of Marketing, Florida State University
Claudia Townsend is Associate Professor of Marketing, University of Miami
Willy Bolander is Carl DeSantis Associate Professor of Marketing, Florida State University
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Transformative Marketing: The Next 20 Years
In my interactions within the academic community, I am often posed with the following question: What are some of the topics we should be thinking about so that we can contribute to advancing the marketing discipline? As I reflect on how to respond, I recognize the transformation that is occurring within the marketing discipline. I view this transformation as an important phenomenon occurring around us, and I present my thoughts on the same in this editorial.
The continuous change around us, also a hallmark of the marketing discipline, is informed by academic research pursuits and real-world business developments. Over the years, the developments in marketing thought, as identified by marketing streams and their associated contributions, have traversed varying academic viewpoints (e.g., Day 1996; Day and Montgomery 1999; Kerin 1996; Lutz 2011). Past editors of Journal of Marketing have also presented their emergent views on the state of the marketing discipline and their thoughts on its future directions (Bolton 2003; Lusch 1999; Rust 2006; Stewart 2002). Further, my earlier editorial posits that the future of marketing thought and practice are constantly shaped by three constituents: academic research, marketplace actions, and societal developments (Kumar 2015).
As I reconcile the scholarly viewpoints on the marketing discipline and my independent observations from my interactions in the marketing thought leadership community, I am now more convinced than ever about the interaction of these three constituents in shaping marketing thought and practice. Indeed, I believe this interaction has resulted in an overall state of transformation in the field of marketing, wherein consumers constantly see changes in how they interact with firms through various firm offerings. I refer to this phenomenon as “transformative marketing,” and I foresee the proposed elements in this phenomenon to be particularly pertinent during the next two decades.
In this editorial, I discuss the concept of transformative marketing, propose an early definition of this phenomenon, and identify its potential triggers. Further, I offer an explicative framework to describe and discuss this important topic. Specifically, I offer my thoughts and rationale on ( 1) the various forces that are transforming the marketing discipline, ( 2) the likely outcome of such a change, and ( 3) how the academic and practitioner communities can better prepare for the change. I also identify potential research areas based on this emerging development in the field of marketing.
Consider Mars Petcare, a pet food company that owns brands such as Pedigree and Whiskas. Although the firm originally sold products primarily through the retail channel, it has made important business investments over the years into pet health care solutions, veterinary clinics, and, more recently, a Fitbitlike device for dogs. These strategic changes have now enabled the firm to know more about not only their clients (i.e., pets) but also the pet owners, which translates into more opportunities to precisely design and deliver marketing messages, in addition to offering better products. In other words, Mars Petcare finds itself in a much-changed business environment wherein it can now offer better value proposition through multiple channels (online and offline). Beyond the immediate benefits of this environment (e.g., better financial performance), Mars Petcare is seeking to bolster its brands in the long run. This is no ordinary position to be in. Moreover, Mars Petcare is not alone. Major corporations, including S&P Global 500 companies such as Amazon, Adobe, and Microsoft, have embarked on significant transformations that are beginning to yield promising results (Anthony and Schwartz 2017). In relation to these examples, we can understand transformative marketing in two ways, as detailed in the following paragraphs.
First, transformations in the marketing function reflect changes in the immediate business environment. While the impact of transformations can be identified through companies’ financial results, a deeper meaning for such a transformation exists in societal changes. In other words, ongoing changes among consumers, markets, and marketing departments feature prominently in the need for business transformations.
Consider the changing consumer landscape. Companies must operate in a market in which populations are becoming more demographically diverse, customer needs and expectations are becoming more niche, and the requirements of ethnic consumer groups are becoming more distinct. This means that individual user constraints such as convenience, personalization, experiences, environmental sustainability, and social connections have come to the forefront. In this regard, examples such as Target teaming up with Shipt to offer same-day grocery deliveries to offer more convenience (National Retail Foundation 2018), Spotify’s personalized playlist recommendations (Pasick 2015), Tesla’s direct-to-consumer sales model to make the car-buying experience enjoyable (Straw 2016), Lego’s use of sugarcane as a sustainable alternative to plastic (Locker 2018), and Wendy’s social media savviness in connecting with users (Leadem 2017) can be viewed as these firms’ direct responses to address evolving consumer needs.
With regard to the changing nature of markets, geographical boundaries are increasingly blurred, and technology has emerged as a powerful integrator of markets. Consequently, the location advantage that many companies have enjoyed is gradually eroding, and they now must compete in an increasingly global world. Further, an explosion in the varied number of offerings to satisfy niche and specific user needs have made product differentiation more difficult. These changes have made companies rethink their portfolios of offerings.
Marketing departments too are under increasing pressure to perform. In fact, 80% of chief executive officers do not trust or are unimpressed with their chief marketing officers (CMOs), and CMOs have a high turnover rate (Whitler and Morgan 2017). Calls to demonstrate the efficiency of the marketing function have brought the focus on creating value and managing customer relationships in a personalized manner. Moreover, with the multiplication of media outlets, marketing managers and CMOs are now faced with increasing expectations and opportunities to offer personalized and experiential offerings, as opposed to just developing offerings that address specific user needs. This has made marketers look to newer tools such as big data and artificial intelligence (AI) to understand and develop better offerings, in addition to managing omnichannel communications.
Second, transformations in the marketing function can produce changes in the immediate business environment, such as through data, technology, and privacy factors. Advances in big data and cloud computing have brought tremendous changes in terms of need for and affordability of data. The adaptability and customization capabilities of these applications have enabled marketers to establish personalized means of communication with their user base. Employees are now able to access all their customer data and information on a point-and-click interface, connect and interact with other stakeholders involved in the marketing process, and deliver meaningful content and offerings. The network-wide interactions now made possible by the power of data are continuously evolving, and they are expected to bring more changes in the business environment.
Technology too has changed how business functions operate. Newer technologies such as intelligent agent technologies, virtual reality, facial recognition, and geofencing are increasingly reshaping how companies interact with their customers and their stakeholders. With social networks being a core element in a company’s customer-focused efforts, technological developments now encourage and sustain active customer participation online, both with the companies and among other customers. In addition, related software applications can now track social data, design company messages, initiate actions, deliver responses, and track the outcome, all in real time. This ability has enabled companies to offer relevant content and personalized messages to specific customers and to improve the customer experience at each touchpoint along the customer’s journey. Moreover, the ability to combine data across various social media platforms allows companies to not only create value for their customers but also generate benefits for all stakeholders by way of increased business activities.
As the world becomes increasingly connected, privacy issues arise. For instance, the International Telecommunication Union (ITU) recently revealed that 70% of the world’s youth (15–24 years) was online in 2017 and that mobile broadband is more affordable than fixed broadband (ITU 2017). This trend can be seen in this demographic’s heightened level of comfort in using smartphones to engage in a variety of activities such as online shopping, news consumption, information sharing via social media, and mobile gaming. Many applications also use GPS tracking to offer location-specific services. Such developments create privacy concerns and the capacity for misuse of data. The importance of ensuring privacy perhaps rests on the argument that privacy is an essential ingredient for forming relationships (Rachels 1975). Consequently, it can be deduced that a very intimate relationship can be formed when little to no personal information is revealed. This is important for companies as they go about forging relationships with their customers and stakeholders. Because of the increasing prominence of privacy concerns, governments have now become involved in ensuring that user data are not misused. For instance, the U.S. Federal Trade Commission, the agency in charge of protecting customer privacy, has outlined the Fair Information Practices Principles, a series of reports, guidelines, and model codes that represent widely accepted principles concerning fair information practices.
Therefore, it has become clear that the marketing function is not only influenced by but also influences the immediate business environment. Although this may seem self-evident, the pace of such changes, when viewed alongside technological advancements and data-processing capabilities, make the marketing function ripe to enter a phase of transformative marketing.
In light of the preceding discussion, the transformative marketing phenomenon can be better captured by defining it formally. Building on the definition of marketing provided by the American Marketing Association, I define transformative marketing as follows:
Transformative marketing is the confluence of a firm’s marketing activities, concepts, metrics, strategies, and programs that are in response to marketplace changes and future trends to leapfrog customers with superior value offerings over competition in exchange for profits for the firm and benefits to all stakeholders.
The following subsections break down the key components of this definition.
The creation. Transformative marketing strategy is born from an interesting amalgamation of rapidly evolving marketplace trends and the overall strategic process that comprises foundational concepts, metrics, and strategies. However, there is no set method for this amalgamation. While reexamining and redirecting the conversation among consumers regarding the offering category forms the core of a marketing transformation, securing the “organizational buy-in” (from top management as well as employees) to do so is equally critical in proceeding ahead with the transformation. This is because, when set into motion, the process does not belong to one single department; rather it is a unified and coordinated effort that brings together all business functions into a singular motion. For instance, Disney has realized the importance of leadership and organizational unison in delivering highly immersive and memorable experiences for its customers (Meek 2015). Further, the company shares insights through the Disney Institute to help organizations create impressionable corporate changes by integrating areas such as leadership, customer experience, brand loyalty, and creativity and innovation. Specifically, they have identified that organizational buy-in can be maximized when all levels of organization are aligned with the corporate culture.1
The purpose. Transformative marketing is specifically designed for companies to reach the intended audience with a compelling offering that far exceeds their competition. As a result, the company is able to overcome the potential trap of siloed departments and firmly places a two-pronged focus on customers and competitive value propositions. For instance, when most airlines began charging for bags, Southwest did not follow its competition; instead, the firm identified that its customers derived most value when they were not charged any extra fees (Steimer 2018).
The procedure. In embarking on a transformation, the means is as important as the end. In other words, generating profits for the firm and benefits for all its stakeholders is how the transformation ought to transpire. Changes that do not benefit either the firm or its stakeholders are likely to be short-lived and could potentially harm the firm’s continued existence. For instance, Tata Motors’ Nano, an affordable car for drivers in India and billed as the cheapest car in the world, is now facing a possible phaseout. When launched, the car did not have amenities such as radio, electric windows or locks, antilock brakes, power steering, or airbags. When crash test results awarded zero stars for Nano, the recommendation was to add airbags and simple adjustments to the frame to improve the safety, all the while keeping the cost affordable. However, the carmaker ignored these recommendations but included air conditioning and power steering in all its 2017 models (Schilling 2017). The lack of safety features has made the car noncompliant to the regulatory framework under the Bharat New Vehicle Safety Assessment Program, a proposed new car assessment program by the Indian government. As a result, Tata Motors’ managing director recently commented that complying with new regulatory framework would mean significant investments into the car, and therefore, the company is considering a possible phaseout in 2019 (Dovall 2018). Thus, regulatory changes made the Nano at odds with value for at least one of Tata’s stakeholders (i.e., the government); this has led to a situation in which the company may have to reconsider their changes.
Although the progression from relationship marketing to engagement marketing is occurring in select instances, transformative marketing amounts to leapfrogging firms’ previous attempts to reach customers more precisely. Overall, transformative marketing is an advancement over the engagement marketing concept whereby the established concepts, metrics and strategies, and evolving marketplace trends are commingled to create value to and from customers. It is important to note that technological advancements are not the sole driver of transformative marketing; rather, they constitute a key distinguishing feature in such a change. In this regard, transformative marketing uniquely blends the operational and strategic elements of an organizational change, and firms model the conversation around all their stakeholders. In other words, marketing is the vehicle that brings transformation to all the stakeholders, and consumers realize this transformation through renewed firm offerings. So, what can trigger such a transformation?
While it is evident that companies worldwide are continually transforming themselves through marketing, it is important to understand what specifically leads to transformative marketing. In other words, can all forms of change be considered transformative marketing? Of course not. However, for transformative marketing to occur, I believe three triggers discussed in the following subsections play a major role.
A state of tension. For any meaningful change to materialize, the presence of tension is an important requirement. Specifically, a tension between the “what is” and “what ought to be” aspects of the issue under consideration is essential to drive forward discussion about it and action responding to it. In other words, a change arising from conditions that are devoid of any tension is not likely to be transformative but can be viewed simply as a new way of doing things. Companies routinely implement changes such as hiring a new advertising company, updating technology standards, and expanding their legal department, and so on. Such changes can hardly be considered transformative, however beneficial and necessary they may be for the organization. When companies are faced with truly discrete and credible forces, the chosen path forward within such an environment can alone be viewed as transformative. Companies such as GE have even taken the state of tension further to a state of existential nature to drive meaningful and successful transformation. According to Jeffrey Immelt, former CEO and chairman of GE, “Every time we drove a big change, I treated it as if it were life and death. If you can instill that psychology in your management group, you can get transformation” (Immelt 2017). For the purposes of marketing productivity, I view this tension as the drive to generate value for the firm and its stakeholders. Addressing this tension is critical, because a relationship in which the firms or the stakeholders do not acquire value from being associated is not likely to be sustainable in the long run (Kumar and Rajan 2017).
Context independence. Business contexts play an important role in understanding routine changes within companies. For instance, it is important to distinguish between a change in the vendor management system at a small business (e.g., the local grocery store) and that at a national grocery chain: changes at the local grocery store may translate to only a few modifications to the billing system, but these changes for a national grocery chain could significantly affect several business functions (e.g., accounting, distribution, procurement, merchandising, retail selling). As another example, consider the case of connected cars. Cars are increasingly connected to technology and regularly receive and transmit information, which has created an entire ecosystem wherein software platforms (e.g., Android Auto, Apple CarPlay) play the role of technology integrators in bringing together service partners (e.g., GPS services, automakers, software system providers) and stakeholders (e.g., apps, insurance companies, auto service providers, government regulators) to deliver valuable offerings to consumers (Iansiti and Lakhani 2017). For companies operating in this business environment, size and scope of operations cannot be a limiting factor in delivering superior value to consumers. Further, the power and influence of business partnerships (e.g., a GM–Lyft partnership to develop selfdriving cars) and federated consortiums (e.g., the German car maker consortium of Volkswagen, BMW, and Mercedes) are further enhancing the relevance of transformative marketing for all business formats to ensure value generation.
Constrained to a specific time period. Unlike change, which is constant, transformative marketing does not extend into an indefinite period. In the past, the marketing approach of companies has changed (roughly) every two decades. Now, I believe that we are primed for the era of transformative marketing, and it should last for the next two decades. I elaborate my reasoning using how advertising in the United States has evolved over the past decades.
In the 1960s, marketers used a mass marketing approach that focused on selling. Since the United States was making an economic rebound after the Cold War, this strategy was designed to bring in more sales. This approach meant that firms marketed their offerings to everyone, and were keen on selling as much as they could. This approach changed at the turn of the 1980s. Specifically, this time saw the expansion of TV channels by way of cable and satellite TV over the traditional three network channels. As a result, marketers moved from a mass marketing approach to a targeted marketing approach. This move gave them the ability to address specific user groups with relevant marketing messages. Practices such as TV shows featuring products and celebrity endorsements became mainstays to attract specific audiences. The next change occurred in the late 1990s and early 2000s. This period saw the migration of TV technology from analog to digital. This change gave rise to the introduction of digital video recorders (e.g., TiVo), videoon-demand from cable companies, interactive TV, and online advertisements. To make use of such changes, marketers focused on personalized marketing content that could work for a user even at a personal level. In recent years, we have witnessed another change in marketing approach: recent developments in programming technology have brought in subscription videoon-demand (e.g., Netflix, Hulu), and advanced DVR services that can provide users commercial-free programming. In such an environment, TV networks are identifying new ways to compete with streaming service providers to provide better value to viewers and to increase their ad revenues (Lynch 2018). As we head to 2020 and the following years, we find ourselves in a mode of selective ad consumption in which companies actively seek to capture the attention of users through various advertising methods (Wu 2017). I refer to this phase as engaged customization, because the viewers get to decide what messages they want to see, how they want to receive it, and in what amounts.
Therefore, roughly every two decades there has been a transformation in the way marketers have communicated with their audiences. While I have used advertising as an example to explain the transformations over time, I believe these transformations to be reflective of the overall marketing experience of firms and consumers. More importantly, I believe that we are on the cusp of experiencing the transformative marketing approach that would extend for (roughly) the next two decades, rather than remain a permanent feature.
While transformative marketing is slowly but steadily happening around us, what makes businesses embrace them? I believe that market forces are pushing companies to adopt transformative marketing. In this regard, identifying and addressing these forces can help companies plan their transformative marketing strategies more effectively. To better understand why market forces influence companies toward adopting transformative marketing, let us consider a business function that is only beginning to see changes despite its existence for more than two centuries: railroads. Whereas other forms of distribution that became popular more recently and underwent periodic changes (e.g., air distribution, pipeline distribution), railroads are only beginning to see changes since their first use. Why is this so; what are the forces that are enabling such a change; and why now?
Although U.S. railroads began operations in the early eighteenth century, it was not until the late nineteenth century that they expanded and became an established form of distribution. The industrial revolution had a significant impact on this change, and this period also saw the completion of the First Transcontinental Railroad in 1869, which stretched over 1,900 miles between Nebraska and the San Francisco Bay area. With the expansion of railroads, many markets opened that made access to many areas of the United States possible. As the railroad expansion continued though the twentieth and twentyfirst centuries, it has made a discernible impact on the economy in terms of ( 1) the creation of a network of freight distribution, ( 2) an associated financial system to fund construction efforts, ( 3) establishment of systems and procedures to manage daily operations (e.g., accident prevention, detection of mechanical failures), and ( 4) a viable career option for blue- and white-collar workers. In fact, Rostow’s Stages of Economic Growth proposes that railroads were responsible for the takeoff of U.S. economic growth (Rostow 1959). More recently, research by the Association of American Railroads (AAR) has shown that spending by the seven largest U.S. railroads in 2014 created nearly $274 billion in economic activity, generated nearly $33 billion in state and federal tax revenues, and supported nearly 1.5 million jobs nationally. Furthermore, the association reports that one freight rail job has a positive impact in supporting nine jobs in allied industries such as retail, manufacturing, and warehousing (AAR 2016).
Despite being sensitive to the economic factors operating in the business environment, several attributes about railroads have not changed over the years. For instance, regarding serving specific business needs, railroads have been called out for being slow to change. They have been known to focus more on simple fuel efficiency techniques, rather than on more fundamental issues such as locomotive design and digital technologies (Case 2018; Kuehn et al. 2017). As a result, among the benefits presented by railroads to businesses, the most are in terms of moving goods over specific routes (i.e., operational efficiency), larger shipment sizes (due to economies of scale), and the ability to transport goods under controlled conditions such as refrigeration. However, trucks and water transportation are doing better than railroads in these three areas. In other areas such as timely delivery of goods and the ability to track shipments, railroads have only performed moderately well. Here too, other transport options such as air and pipelines have performed far better. Finally, from the time of initial pickup to final delivery, railroads have possession of the goods for a long period. Here again, other modes such as air and truck are known for much speedier delivery patterns (Lamb et al. 2015). This manner of operations has not changed much for a long time, which may have perhaps led to the popularity of newer modes of freight transportation.
However, recent noticeable changes in the railroad operations have occurred by way of containerization, intermodal transportation, and technological advancements (e.g., positive train control, drones, accident detection devices), to name a few. Specifically, two aspects have made the most impact on railroad management in recent times: data and technological innovations. For instance, big data continues to provide the power of information to enhance the safety, security, and effectiveness of railroads. Using big data, AskRail (a mobile app) helps firefighters and other first responders get instant access to critical and real-time scene assessment data to save lives in the event of accidents. Using GPS technology, the app also enables responders to search the contents of every rail car involved in a derailment for potentially hazardous, explosive, or flammable materials, as well as access updated maps with community assets such as hospitals, schools, and rivers within a half-mile of the accident. Railinc developed this app in response to feedback from the emergency management community (Violino 2018). Railinc, a subsidiary of AAR, also works on other ongoing datapowered initiatives such as monitoring and management of asset health for improving yard and shop efficiency, analyzing equipment failure, developing a metrics scorecard for terminal personnel to optimize resource allocation, and enhancing the safety documentation repository and exchange for timely usage, among others (Railinc 2017).
Concerning technological innovations, BNSF recently became the first railroad company to join the blockchain in Transport Alliance, a consortium of over 200 freight transportation companies, to develop blockchain standards for the logistics industry. This consortium aims to define what data go into the freight transportation blockchain, how these data are formatted, how they are structured, and when blockchain would be used (FreightWaves 2018). Similarly, using RailVision—a computer vision technology solution—BNSF was able to automatically process images collected by drones during supplemental railway inspection flights and generate actionable reports in a fraction of the time required by traditional methods. The company now plans to expand the use of drones in 2018 (Lillian 2018). Such ongoing efforts can be viewed as a direct response to market forces about technology, resource management, and regulatory framework, and they are expected to change the way railroads have been functioning.
In light of this discussion, and in conjunction with the proposed definition and the potential triggers of transformative marketing, I offer an illustration of the transformative marketing landscape to provide a better understanding of this phenomenon (see Figure 1). As the figure shows, certain forces act on the various functions of the organization that are (largely) external. These forces often create an environment that challenges the organization’s status quo and creates a sense of urgency for the organization to respond. In other words, these forces are typically too powerful for companies to ignore. When companies respond to such forces, their response is expressed through their (renewed) ideation, and the deployment of personalized processes to deliver offerings. This organizational response forms the core of transformative marketing. It determines how effectively and efficiently the firms have addressed the forces that act on them. The outcome of the firm’s response is manifest in how closely their offerings can personalize the marketing content and messages for their customers while improving the firm’s efficiency and effectiveness through the whole process.
The proposed framework operates in an environment rooted in data and innovation. The importance of data in the current business landscape is self-evident. Companies routinely use data to design and deploy their strategies and tactics. Further, with the rapid growth of digital data (e.g., social media, search queries) and the digitization of traditional data (e.g., focus groups, interviews, surveys), companies are operating in a datarich environment (Kumar et al. 2013). As a result, companies are actively working to find newer ways of exploring the data to uncover valuable insights. For instance, the health care industry is now sitting on a goldmine of data, powered jointly by the digitization of medical records and the accumulation of new digital data (e.g., health care wearables). Therefore, the data are critical in the transformative marketing process ultimately pursued by companies.
Innovation, as illustrated in this framework, can be viewed as firms’ response outcome as a result of forces acting on them. The innovation may not necessarily be in the form of breakthrough, cutting-edge creations. Even solutions resulting from uncovering inefficiencies, ineffectiveness, and growth opportunities by unlocking the full potential of a company’s data can be considered innovations. For instance, the augmented reality feature in IKEA’s app lets users design their home/work spaces better by allowing them to place the items to see what items from their online catalog worked best. While this is not a breakthrough innovation from IKEA, the user experience provided by this app is highly positive, as it is designed to facilitate shoppers’ decision making. Therefore, innovation works in the background of the proposed framework and can actively contribute to the transformative marketing process.
In the reminder of this section, I discuss the various forces influencing companies that can lead to a transformation in marketing and potential research questions for future research. I also identify the potential outcome of such a transformation in marketing approach.
The following subsections discuss forces that exert influence on businesses and serve as the instigators for a transformation in the marketing approach.
Technology. Due to advancements in technology, the pace of business functions, including marketing, has not only quickened but also changed dramatically. Companies have been using technology to design and deliver superlative offerings to meet consumer needs and aid in brand building efforts. This is not sufficient anymore. Consumers now need memorable experiences that can engage them with the brand, and sustain the conversation with the firm. For instance, drones, once used largely for recreational purposes among enthusiasts, now feature prominently in a company’s distribution system. Amazon, Google, UPS, and Walmart are actively using drones in addressing the last mile delivery challenges. Similarly, the omnichannel model, a blend of several technologies in which customers can seamlessly transition across them, is being used by companies to engage with customers and deliver superlative experiences in the service industries (Kumar et al. 2017). Technology continues to bolster the growth of new developments such as ( 1) the proliferation of the access (sharing) economy, which features companies such as Uber, Lyft, and Airbnb (Eckhardt and Bardhi 2015); ( 2) AI tools, which are being used across several marketing applications such as customer service, sales, customer relationship management, and e-commerce; ( 3) machine learning algorithms used in areas such as data security, health care, natural language processing, marketing personalization, and online recommendations; ( 4) the Internet of Things, which uses the ability to connect physical and virtual things to shape the exchange of information across people and devices (ITU 2012); and ( 5) blockchain applications such as cryptocurrency, online advertising metrics, customer profile generation, and brand building, which can help companies create and implement strategies more effectively (Newman 2017). Further, with technology playing a key role in powering management systems across several business functions such as media, workforce, legal, and knowledge, businesses will continue adopting multiple forms of technology in designing customer offerings. Given these effects of technology, I offer the following research questions:
RQ1: What are the future implications for businesses regarding (a) the technical design of devices concerning interconnectivity and information collection, (b) the shared level of control among firms and their stakeholders concerning technology and processes, and (c) combining machine learning with predictive analytics to create better forecasting?
RQ2: What are the implications for companies regarding knowledge management systems in terms of value creation for all the involved stakeholders, especially for companies operating across multiple country markets?
Environmental resources. Companies are facing increasing pressure from an environmental resource standpoint. With issues such as climate change, hydro-fracking, depleting oil reserves, reducing forest cover, use of animals in product testing and development, and so on, companies are hard-pressed to reevaluate their alternatives. While the cost of using environmental resources is negatively impacting firms, the pressure from consumer groups on firms to be environmentally responsible is also mounting. As things evolve, a more prudent use of valuable resources through marketing actions is emerging as the way forward for firms. For instance, firms can plan and optimize the printing and mailing of marketing mailers such that only those customers for whom they are intended receive them, thus avoiding sending mass mailers. In addition to enhancing the effectiveness of the firm’s marketing efforts, this would also prevent the wasteful use of valuable environmental and infrastructural resources and help firms embrace sustainability as part of their core mission (Kumar and Christodoulopoulou 2014).
Further, sustainability has the potential to address stakeholder concerns and equip managers with tools to meet their business requirements and the sustainability challenges that make our societies and ecosystems vulnerable. Among the companies that have prominently displayed their commitment to environmental concerns is Patagonia, the U.S. outdoor clothing and gear company. In 2011, the firm launched The Common Threads Initiative, which encourages customers to repair and reuse as much of their clothing as possible, instead of buying new clothes. Similarly, in 2016 Patagonia introduced beer to the menu of Patagonia Provisions, its food line, with the aim of demonstrating the commercial viability of using the wild grain kernza in brewing beer, as this grain has been shown to improve rather than deplete topsoil (Beer 2018). Other companies that are committed to environmental issues include Method Products, which sells biodegradable, nontoxic household cleaners; TerraCycle, a recycling management company that works with items that are typically difficult to recycle; and Warby Parker, an online eyewear company that is also 100% carbon neutral. The following questions are worth considering:
RQ3: What research and managerial tools can be developed to equip firms and stakeholders to tackle their changing business needs while addressing environmental concerns?
RQ4: What recommendations regarding communication formats and media preferences can be generated to address the environmental challenges?
Economic forces. The marketing environment continues to be impacted by the economic forces such as household income levels, price levels, and consumer discretionary spending levels. Concerning income levels, luxury brands tend to perform very well in markets that are characterized by very low–very high income levels, as opposed to country markets with mostly low income levels; mostly medium income levels; or even low, medium, and high income levels. For instance, luxury automakers such as BMW, Mercedes, and Audi are experiencing record year-on-year growths in emerging markets such as Asia (e.g., India, China) and the Middle East. At the same time, competition is intense among automakers in India to make affordable electric cars, as carmakers have realized that car buyers in emerging markets are not willing to pay more for environmentally friendly options (Bellman 2018). Even in developed markets such as the United States, income inequality is stark, and upward mobility (the ability to rise to a higher income level) is not equal throughout the country (Chetty et al. 2014). Given these wide variations in economic factors, marketers are increasingly facing the pressure to create offerings that offer value to consumers across all income and price spectrums. As a result, the following research questions provide thought for research:
RQ5: As we pass through inflationary and recessionary conditions in the future, what can companies adopting the transformative marketing approach do to (a) better navigate the economic fluctuations and (b) continue creating value to all stakeholders?
RQ6: Should companies adopting a transformative marketing approach have separate country market strategies for developed and emerging markets, or is it possible to create a unified strategy to accommodate all market types?
Customer preferences. Marketers are witnessing the changing face of customers, the reason for which is a blend of diverse factors that includes the demographic changes, prominence of mobile technology, changes in disposable income, need for authenticity, environmental consciousness, social connectedness, preference for experiences rather than products, the rise of technology-savvy consumers, personalized content, and an openness to change, among others. This varied set of factors is much different from the traditional marketing approach. This change has materialized in the preferences customers exhibit to marketers when consuming the offerings.
For instance, consider customer preferences concerning fulfillment and delivery. Free shipping is now a competitive necessity, and two-day delivery is becoming the norm. A recent study by the National Retail Federation found that nearly 47% of online shoppers said they would typically either back out of purchases or add additional items to their carts to meet the shipping minimum. Further, consumers expect free shipping regardless of price point, with most consumers saying they expect free shipping even for orders under $50. In response to this change in customer preference, major retailers are responding with new solutions. For example, Target recently acquired delivery service Shipt to offer same day delivery to half of its stores by the summer of 2018, and Best Buy has extended the service to 40 cities in December 2018 through its crowdsourcing service Deliv (National Retail Federation 2018). This leads to the following research questions:
RQ7: What strategies can firms implement to find, retain, and invest the appropriate amount of human and technical resources in managing the changing customer preferences better?
RQ8: From an overall firm strategy standpoint, how can firms strike a balance between the level of firm’s investment, stakeholder synergies and complementarities, and value creation opportunities when adopting a transformative marketing approach?
Government regulations. Businesses routinely must contend with government regulations. Typically, such regulations are designed to protect consumers from unfair trade practices, protect companies from unfair competition, and prevent companies from causing harm to the society. However, with every new regulation, businesses must revisit their strategies as the regulations may hamper their growth. For instance, ever since ridesharing companies such as Uber and Lyft became popular, regulations from the local governments where they operated in were constantly impacting them. However, many local governments have now devised new regulatory frameworks to accommodate these services. Similarly, marketers promoting products related to children will have to comply with the Children’s Online Privacy Protection Act, which places parents in control of what information commercial websites collect from their children online.
Since government regulations cover all aspects of regular business activities (e.g., financial system, trade, employment), they are significant in their impact on firm value. Further, the complexities involved in government regulations pose high human and time costs for firms to understand and implement (The Economist 2012). This could put firms at a disadvantage, especially when competing at a global level. In this regard, a recent study conducted by the U.S. Chamber of Commerce Foundation found that government regulations have a disproportionate impact on businesses and significantly hurt small businesses (Hendrix 2017). Therefore, it is important to investigate the following research questions:
RQ9: The involvement of cyberspace will be prominent in a transformative marketing approach. In such a case, what role will ethics play, in addition to the governmental regulations that may come up?
RQ10: Privacy and identity theft will continue to be critical issues for consumers to contend with. What changes, if any, are required to counter these issues in a transformative marketing approach?
Competitive forces. Ongoing changes such as globalization, the growth of services economies, and technological advancements have enhanced the effect competition on businesses. With the increasing fading away of country borders and the prominence of e-commerce, access to markets is no longer localized. As a result, distribution and logistics are critical competitive forces existing in the marketplace, and have witnessed innovative ideas. For instance, Hindustan Unilever’s Project Shakti, which involves underprivileged rural women as distributors, and P&G, which stocks its products in various mom-and-pop stores are examples of companies adopting innovative means to manage their distribution needs in the emerging markets (Reinartz et al. 2011). At the same time, with the increasing fragmentation of markets, companies are identifying numerous smaller segments of customers that require personalized marketing.
In addition, companies are finding it difficult to differentiate their offerings from their competitors. For instance, in the U.S. retail industry store brands and national brands are competing hard to marketplace dominance. A recently conducted survey finds that almost 81% of U.S. shoppers buy private brands on every, or almost every, shopping trip; 85% say they trust private brands as much as national ones; and private brand sales are up 4% more than national brand sales (Siegner 2018). In other words, as the differentials between competing products diminish, companies look to gain competitive advantages through closer, service- and experience-focused relationships. As a result, we can consider the following research questions:
RQ11: When competing in fragmented markets, what strategies can firms adopt that can offer genuine value to customers through personalized offerings?
RQ12: What marketing communication plans can be developed that can help firms integrate the varied communication mediums and technologies?
Based on the aforementioned forces and firms’ transformative marketing approach, the following outcomes for the companies can be observed.
Ability to personalize marketing content. The proliferation of channels, especially new electronic channels, in business and daily life has had a tremendous impact on firms’ channel options. Further, an openness on the part of customers to interact through a multitude of channels has almost forced companies to expand the number of channels through which they interact with their customers. In this regard, the combination of social media and mobile technology has provided an important access line for companies to understand the patterns of communication of customers. As a result, companies are now more equipped to test the omnichannel model, which focuses on the interplay between channels and brands (Verhoef, Kannan, and Inman 2015). In other words, the omnichannel extends beyond the typical channel management strategies to include a seamless transition between channels, and superlative user experience (Brynjolfsson, Hu, and Mohammad 2013).
All these developments point to the increasing volume of information that companies have, and this provides an opportunity for companies to personalize all content to their customers. As data collection has become more sophisticated—and technological functions such as attribution, automated optimization, and tagging are more widely available—personalizing content has become easier in some ways and more complicated in others. And this personalizing content extends beyond the “Recommendations for You” feature offered by Amazon and Netflix; companies such as Walgreens and Cracker Barrel are keen on changing their current strategy to developing one-to-one relationships with their customers. Further, using the power of real-time data, technology, and various media platforms, these companies are looking to personalize customer experiences and deliver value (Rund 2018). The transformative marketing approach can provide marketers with the right guidance in designing personalized content that will resonate with its audiences.
Ability to personalize offerings. Across the world, household structures are constantly changing. Changes such as more women in the workforce, single parenting, and dual-career households are becoming common. Additionally, social connections and community interactions are operating on a much more varied scale than a few years ago. These changes have made consumers crave a more personalized space, filled with personally identifiable and preferable lifestyle and behaviors, even within family units. As a result, households are no longer as homogeneous as they were in previous generations.
Consider the pattern of mobile phone usage. Until a decade ago, mobile phones were a luxury that only adults owned. Now, even eight- and nine-year-old children own them. No surprisingly, the usage and consumption patterns of each member of the household are different, and therefore, the current monthly household mobile phone bill has increased significantly. People now use mobile phones not just for its original purpose of communication, but also for daily needs such as shopping, information, media consumption, entertainment, and socializing, among others. For mobile companies, such changing household preferences and dynamics mean increasing opportunities for personalization. For example, in 2017 nearly 76% of Yahoo Sports’ video views were on the phone, a 13-fold increase from 2016. Further, TV agreements for professional sporting events such as the National Football League and the National Basketball Association are set to expire in 2022 and 2025, respectively; with the expectation that digital platforms (e.g., Google, Facebook) and mobile platforms (e.g., Verizon, AT&T) will be competing with TV networks for viewership deals. In response to these changes, Yahoo Sports is already planning a mobile-centric approach for its viewers, with the programming to be designed based on individual preferences, lifestyles, and time zones (Carmody 2018). In this situation, a transformative marketing approach could enable companies to develop business capabilities that can deliver superior value that are aligned with individual preferences before the competition does.
Higher efficiency. As organizations continually review their business performance, invariably they focus on their efficiency. In simple terms, efficiency underlines the success of the processes instated by an organization. This success is often understood as minimizing spending, reducing organizational inefficiencies, and accomplishing more for the same amount of resources. In this regard, an issue many organizations are facing is the dilution of media in the wake of channel proliferation. Customer needs and wants have simply become too diverse for marketers to satisfy them with a one-size-fits-all approach. In addition, the consumption of media outlets is steadily moving away from radio and print to TV and online channels, which has significant consequences for companies’ marketing strategies. Furthermore, online streaming is increasingly replacing TV viewership through services such as Netflix and Amazon Prime that allow users to watch programs at their convenience and on the mobile device of choice. A key feature in many online streaming services is the ad-free content. Even in the case of websites, ad blockers have given power to the users to control when they want to see the ads. The changing face of media consumption has created the difficulty of communicating meaningfully with customers, and companies are looking for new ways to interact with customers.
For instance, Diageo, a global producer of spirits that owns brands such as Smirnoff, Guinness, and Johnnie Walker, uses a business tool known as Catalyst for its media planning activities. The tool advises managers of the company on the budget for each brand based on potential profit, the performance of previous marketing activities, and the potential impact of the planned campaigns. In India, for example, marketers used Catalyst for its Royal Challenge and McDowell’s No. 1 brands during 2015 and gained more than US$2.1 million of value compared with the original plan (Joseph 2018). In this regard, transformative marketing can be a game-changer by helping companies identify areas of inefficiencies within their business functions, and improve their overall value creation.
Higher effectiveness. From an organization-wide standpoint, effectiveness can refer to the medium- to long-term value consequences for all stakeholders involved, realized through the development of better knowledge about customer preferences. The transformative marketing approach better serves the organization to gather information about individuals, their behavior, and their preferences and then to derive knowledge from this information. This process of learning allows the firm to improve its knowledge of customer preferences and to offer increasingly better-tailored value propositions to various customers, through the involvement of all stakeholders. The improvement in the value proposition comes through personalized content and offerings. Ultimately, an improvement in effectiveness is likely to generate sustainable competitive advantages and yield the highest value for the company in the long run.
For instance, geofencing is becoming a popular tool to ensure marketing’s effectiveness. This technology creates a virtual boundary in a certain geographical location, which then enables software to trigger a response when a mobile device enters or leaves a certain area. Home Depot has integrated this technology into its app such that shoppers can walk into a store, switch the app to “in-store” mode, and do many tasks such as locating products, scan barcodes, compare prices, search product images, find similar products, engage in social media conversations, access store-specific coupons, pick up items ordered online, and so on. The benefits of such a measure directly reflect on ensuring that marketing resources are being effectively employed for the betterment of value creation and on improving the brand in the long run. In this regard, transformative marketing can enable marketers to integrate technology, marketing, and resources to drive more value for all the stakeholders.
The transformative marketing approach can serve as an integrator, facilitator, and enhancer of marketing functions between a firm and its stakeholders. In this regard, the proposed framework has highlighted the forces that act on firms to bring about transformative marketing and how that transformation materializes. Given this is what the future holds for us, marketing academia should take steps to prepare itself better. I provide here some directions for the teaching community and the researcher community.
Owing to transformative marketing’s nascence, much preparation is needed in the instructional stages within classrooms. Some of the key areas that can help us are as follows.
Curriculum. A significant change in the marketing curriculum would be required to prepare the student better for such a change in the marketing discipline. This would involve a reevaluation of marketing topics covered in the class to reflect the knowledge and understanding needed to adapt to a changing marketplace. Further, the integration of knowledge from other relevant disciplines across both arts and sciences streams will equip the students with a wide-ranging knowledge toolkit to create and practice cutting-edge knowledge transfer. In addition, an immersive approach to marketing education would be highly beneficial for students to learn about the changing marketplace. The inclusion of virtual technology as part of the immersive education would mean that students can learn by doing and experiencing alongside their external environments. When such a mode of immersive education is also accompanied by faculty-guided learning opportunities via reflection, observation, conceptualization, and execution, the learning can become more meaningful and directly reflective of the workplace environment.
Continuing education. Continuing education is an important way to for marketing students to regularly update their knowledge base. All major U.S. universities offer some form of continuing education that offers short courses, certificate programs, digital badges, and so on that cater to a large audience. Such courses, owing to their short duration, can be very effective in updating skill sets in a short period and help them prepare for the marketplace. In addition, business schools can explore the mass open online course (MOOC) format (e.g., Coursera, edX, Stanford Online) further to offer courses that can address specific needs of this changing market environment. When employees of companies enroll in MOOCs, it can also be an opportunity for the companies to channel the employees’ efforts toward furthering the goals of the organization (Hamori 2018).
Self-learning. Educational institutions typically require considerable time and effort to arrive at a structured and welldesigned course. However, everyday informational and learning needs will continually arise. In such a situation, relevant tools and resources for self-learning can be an interim solution worth considering. For instance, for many topics that we need answers to on a daily basis, a simple Google search has become a great starting point. However, for topics that are evolving, a simple online search is not sufficient. For example, the recent popularity of blockchain technology has created an urgent need for qualified instruction for interested learners. While some MOOCs have begun offering courses on blockchain technology, they are not comprehensive in their offerings. As a result, interested learners must rely on online user groups and practitioner forums to learn about this technology, some of which are not open for all. With blockchain technology wielding a major influence on marketing, it is important that the marketing discipline address this situation. To better handle any such immediate learning needs that may arise in the future, I believe that the academic marketing community should create a ready reference–like resource for interested learners. I call on the marketing community to offer ideas and suggestions that can help us better handle immediate educational needs that may arise.
Transformative marketing opens a new stream of research. This study has identified several research areas that can be explored, and many more will be identified through continuous research. In fact, with this approach still constantly evolving, active involvement from the researcher community can ensure that all the relevant points of knowledge are uncovered. In this regard, the following aspects are worth considering by the research community.
Data. The research community routinely works with data that exist in multiple forms (e.g., structured vs. unstructured, individual vs. aggregate, single vs. network). However, in the current big data era, not all data can and will be used for research purposes. Further, when firms are unable to glean the relevant insights promptly, despite having large amounts of data, they not only miss out on growth opportunities, but also place them under intense competitive pressure. Additionally, with technology accelerating the collection and storage of data, privacy issues are now more prominent. For instance, the proposed General Data Protection Regulation legislation in Europe is aimed to regulate (as opposed to direct, according to current legislation) the nature and type of data governed by law, and penalize any offenders. Since this proposal is applicable to all companies dealing with personal data of European individuals, all global companies must pay attention to this. Researchers will have to keep track of such developments that are likely to impact any data-related activities (Thorne 2018). Therefore, detailed data collection rules and opportunities should be identified to help researchers proceed in the right direction.
Tools. The research tools hold the key to unlocking the potential lying within the data. For instance, new-age technologies have developed solutions to aid researchers and marketers for data collection (e.g., AI is now an integral part in enterprise applications of Salesforce, Oracle, and SAP), data retrieval (e.g., Trifacta uses machine learning to efficiently explore and prepare data for analysis), and data analysis (e.g., Amazon QuickSight is an AI-powered data visualization and analysis tool) (Baer 2018). These are early days for the new-age technology tools, and researchers will have to be on the lookout for solutions that can help them bring out insights effectively and efficiently.
Methods. Determining the right research method(s) for data analysis is crucial. Much of the data analysis software available now can perform advanced computing processes. Further, technological advancements have brought in techniques such as machine learning and AI to make sense of the data. For instance, IBM’s Watson is increasingly used in the health care industry to help in activities such as patient care management, drug discovery, clinical trial matching, and imaging review, due to its proficiency with the AI technique.
Concepts. As transformative marketing evolves, I expect new concepts to be uncovered and existing concepts to be reevaluated. This calls for close attention from the researcher community to track and direct the birth of the relevant concepts. For instance, blockchain has been identified to have several marketing implications such as media planning, customer-tocustomer connectivity, channel management, among others (Khan 2018). However, such applications exist now in the technology rather than the marketing domain. The formalization of such developments through the creation of marketing concepts and codifying marketing operations would constitute an impressive service to the academic community, as it would ensure that new knowledge is passed on to all its future members.
Metrics. As the old management adage goes, “You can manage only what you can measure”; metrics are important to assess and evaluate marketing actions. As shown herein, the impact of the transformative marketing approach lies in its ability to leverage data and communication technologies so that customers can be effectively served and value created for all the stakeholders. In doing so, developing metrics that can keep track of the progress of the transformation will aid the practitioner community to stay on the stated course.
Strategies. Serving as a roadmap, strategies help managers achieve the aforementioned goals. Designing strategies will be critical in the transformative marketing environment, as a careful management of goals will determine the ultimate success of organizations. As a result, researchers will have ample scope to develop strategies on transformative marketing topics for companies.
Implementation. A clear understanding of the environmental forces in which firms are operating is critical. As mentioned earlier, companies must realize that technology and technological advancements alone drive transformative marketing, and that there is no “software fix” available for firms to transform. However, I expect that a few firms will fail to acquire this realization, only to sustain failures. This is why a sound implementation plan is critical for companies to adopt a transformative marketing approach. I believe the following four principles are necessary for embracing transformative marketing: ( 1) a customer-focused viewpoint (e.g., Amazon); ( 2) value generation for all stakeholders (e.g., Dollar Shave Club); ( 3) agile processes to capture, retrieve, and use all relevant information (e.g., Netflix); and ( 4) streamlining and syncing of organizational processes that can contribute to overall value creation (e.g., Southwest Airlines). As more research is conducted in this area, I am confident that precise and finer implementation plans will emerge.
Transformative marketing has the power to significantly strengthen the interactions between firms and its stakeholders, all the while placing customers at the center of the organization to create value for all parties involved. While the adoption of this approach may seem like an organizational decision, I foresee that as more companies transform, competitive pressure will drive companies across all industries to adopt this approach.
Regarding readiness for this phase, I contend we are primed for such a change, as evidenced by the present-day data-rich and innovation-driven culture in which companies operate. Further, the impressive strides posted in the field of technology will catalyze this approach to become mainstream. As we live through these changing times, the academic research community has the responsibility to study carefully, document, and direct the course of transformation, to expand our knowledge base for the future generations to come.
Footnotes 1 More information on the Disney Institute’s approach to managing organizational change is available at https://disneyinstitute.com/ approach/.
DIAGRAM: FIGURE 1 An Illustration of the Transformative Marketing Landscape
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Record: 210- Turning Complaining Customers into Loyal Customers: Moderators of the Complaint Handling–Customer Loyalty Relationship. By: Morgeson III, Forrest V.; Hult, G. Tomas M.; Mithas, Sunil; Keiningham, Timothy; Fornell, Claes. Journal of Marketing. Sep2020, Vol. 84 Issue 5, p79-99. 21p. 1 Diagram, 6 Charts, 1 Graph. DOI: 10.1177/0022242920929029.
- Database:
- Business Source Complete
Turning Complaining Customers into Loyal Customers: Moderators of the Complaint Handling–Customer Loyalty Relationship
Firms spend substantial resources responding to customer complaints, and the marketing profession has a long history of supporting that enterprise to promote customer loyalty. The authors question whether this response is always warranted or whether its effectiveness instead depends on economic, industry, customer–firm, product/service, and customer segment factors that may alter the firm's incentives to compete on complaint management. To consider this question, they integrate economic and marketing theories and investigate factors that influence the complaint recovery–customer loyalty relationship via a sample of 35,597 complaining customers spanning a ten-year period across economic sectors, industries, and firms. Overall, the authors find that the recovery–loyalty relationship is stronger in faster-growing economies, for industries with more competition, for luxury products, and for customers with higher satisfaction and higher expectations of customization. Conversely, the recovery–loyalty relationship is weaker when customers' expectations of product/service reliability are higher, for manufactured goods, and for men compared with women. The authors discuss implications of these results for managers, policy makers, and researchers for more effective management of customer complaints.
Keywords: customer complaint behavior; complaint recovery; customer loyalty; complaint management incentives; exit-voice-loyalty theory; customer satisfaction
Although customer complaints and the consequences of a firm's poor complaint handling are as old as business itself,[ 5] most marketers agree that the financial stakes are higher in today's competitive marketing ecosystem. The speed and flexibility with which information and communications technologies can be used increase the negative risks of customer complaints and the importance of effective firm recovery of complaints. For example, social media (e.g., Facebook, Instagram, LinkedIn, Pinterest, Reddit, Snapchat, Twitter) has created an environment in which a customer's negative word of mouth is often dramatically amplified. A displeased customer can complain to a firm and simultaneously to potentially millions of other stakeholders.
In severe cases, the amplified complaint environment can create "online firestorms" of negative publicity with immense financial consequences ([25]; [64]). For instance, the negative publicity that was shaped via social media in regard to service failures by Chipotle (foodborne illness in 2015) and United Airlines (passenger boarding issues in 2017) illustrate the consequences of poor service and the heightened criticality of complaint recovery. The result was costs of billions of U.S. dollars to Chipotle and United Airlines in market value.
However, there are loyalty payoffs for firms from effective complaint management. Importantly, studies show that a customer who experiences a failure and lodges a complaint can still be satisfied and retained if the firm's recovery is acceptable (e.g., [20], [21]; [72]). Because the economic benefits of customer loyalty are sizeable in terms of a firm's cash flow and market value (e.g., [69]), especially when considering customer acquisition costs, maintaining a complaint management system that helps retain potentially disloyal customers is an economic imperative for most firms (e.g., [20]). Practically, this means that firms can turn dissatisfied customers into future loyal customers, although the cost of doing so is often high and requires considerable effort (e.g., [18]).
Despite important strides made by prior work, significant gaps remain concerning what we know about the complaint recovery–customer loyalty relationship and what we need to know in an increasingly dynamic marketing ecosystem. First, given cost and effort implications, the differing importance of recovery efforts in driving postcomplaint satisfaction and loyalty across diverse consumer industries is largely unclear and needs to be better understood by firms to optimize their complaint handling. The literature does not tell us much about cross-industry and cross-sector differences in the importance of complaint recovery to customer loyalty. Rather, the extant literature has tended to focus on only a small set of consumer industries (e.g., [50]), thereby limiting the generalizability of conclusions.
Second, research on complaint recovery has largely failed to account for the potentially dynamic nature of the recovery–loyalty relationship as it evolves in complex economic environments. Many studies imply that complaint recovery has a constant effect on customer loyalty (e.g., [23]). Yet complaint recovery may increase or decrease in importance to consumers as a determinant of their customer loyalty as macroeconomic and other exogenous factors change. Given that many consumer perceptions evolve in response to economic factors—as evidenced by measures such as consumer confidence and consumer sentiment—the relative importance of complaint behavior and a firm's responses to complaints is likely to vary over time as well. These interrelated issues (i.e., the differing importance of recovery across industries and the dynamic exogenous effects that influence customers) illustrate gaps in our knowledge of the complaint recovery–customer loyalty relationship.
Against this backdrop, we aim to answer the following overarching research question: How does the relationship between a firm's customer complaint recovery (i.e., the customer's perception of how well the firm handled a complaint) and customer loyalty vary depending on influences from economic, industry, customer–firm, product/service, and customer segment factors? We extend theorizing of the complaint recovery–customer loyalty relationship by integrating two streams: exit-voice-loyalty (EVL) theory based in economics (e.g., [27]) and the complaint handling literature grounded in expectations-disconfirmation theory (e.g., [20], [21], [22]). From these literature bases, we derive a set of factors and mechanisms that influence customers to be more or less responsive to complaint handling. These factors and mechanisms are, in turn, likely to affect firms' incentives to manage complaints, as they alter the expected loyalty payoffs from recovery efforts. We then analyze a large and rich sample of consumer data from the American Customer Satisfaction Index (ACSI), including a sample of 35,597 complaining customers spanning ten years across economic sectors, industries, and firms.
The remainder of the article is organized as follows. First, we review the complaint management literature. Second, we outline a contingency model of loyalty returns to complaint management. From this contingency model, we delineate the factors and mechanisms that both drive and influence customers' disposition to firms' complaint management efforts and firms' incentives to manage complaints. Third, we describe the ACSI data and methods utilized to analyze the data. Fourth, we present the results from our analyses. Finally, we offer implications for managers, policy makers, and researchers, and recommend directions for future research.
The literature on customer complaints, firms' complaint management, and customer loyalty is diverse, emerging nearly a half-century ago (e.g., [11]; [41]). More importantly, the idea of complaint handling as an important strategic marketing phenomenon with tangible financial impact for firms has gained significant momentum over the last two decades. Table 1 summarizes findings from studies on customer complaints, complaint management, and customer loyalty over this period.
Graph
Table 1. Sample Research on Customer Complaints, Complaint Management, and Customer Loyalty Relationships.
| Study | Methods and Sample | Economic Factors | Industry Factors | Customer–Firm Factors | Product/Service Factors | Customer Segment Factors |
|---|
| Hoffman, Kelley, and Rotalsky (1995) | Survey; n = 373 | None | Restaurants | None | None | Gender, education, age (unmodeled as moderators) |
| Spreng, Harrell, and Mackoy (1995) | Survey; n = 410 | None | Moving company | Customer satisfaction | None | None |
| Tax, Brown, and Chandrashekaran (1998) | Survey; n = 239 | None | Service encounters across multiple industries | Justice | None | None |
| Smith, Bolton, and Wagner (1999) | Mixed design; n = 375 and n = 602 | None | Restaurant and hotel | Prior experience, justice | Restaurant cost, hotel location | Gender, age (unmodeled as moderators) |
| McCollough, Berry, and Yadav (2000) | Field experiment; n = 615 | None | Airline | Expectations, satisfaction, justice | None | None |
| Mattila (2001) | Lab experiment; n = 441 | None | Restaurant, hair stylist, dry cleaner | Justice | None | Gender, age (unmodeled as moderators) |
| Smith and Bolton (2002) | Lab experiment; n = 355 and n = 549 | None | Restaurant and hotel | Expectations, satisfaction, justice | None | Age (unmodeled as moderator) |
| Maxham and Netemeyer (2002) | Survey; n = 1,356 | Multiperiod study (economy unmodeled as moderator) | Bank | Expectations, satisfaction | None | Gender, age, education (unmodeled as moderators) |
| Hess, Ganesan, and Klein (2003) | Lab experiment; n = 346 | None | Restaurant | Prior experience, expectations, satisfaction | None | Gender (unmodeled as moderator) |
| Wirtz and Mattila (2004) | Mixed design; n = 187 | None | Restaurant | Satisfaction, justice | None | Gender, age (unmodeled as moderators) |
| Homburg and Furst (2005) | Survey; n = 550 | None | Services and manufacturing industries | Satisfaction, justice | Business to business versus business to customer, services versus manufacturing | None |
| Kau and Loh (2006) | Survey; n = 153 | None | Wireless service | Satisfaction; Justice | None | Gender, age, education, income, occupation (unmodeled as moderators) |
| Michel and Meuter (2008) | Survey; n = 1,189 | None | Bank | Satisfaction, relationship strength | None | Gender, age (unmodeled as moderators) |
| Dewitt, Nguyen, and Marshall (2008) | Field experiment; n = 459 | None | Restaurant and hotel | Justice, emotion | None | Gender, age, education, ethnicity (unmodeled as moderators) |
| Evanschitzky, Brock, and Blut (2011) | Mixed design; n = 146 and n = 233 | None | Restaurant | Affective commitment; satisfaction | None | Gender, age, income, marital status (unmodeled as moderators) |
| Knox and Van Oest (2014) | Observational; n = 922 | Multiperiod study (unmodeled as moderator) | Internet retailer | None | None | None |
| Simon, Tossan and Guesquiere (2015) | Survey; n = 144 | None | Multiple sectors and industries | Brand attitude, gratitude, satisfaction | Products and services (unmodeled as moderators) | Gender, age (unmodeled as moderators) |
| Umashankar, Ward and Dahl (2017) | Mixed design; 6 studies and samples | None | Multiple industries | Relationship strength | "Strong tie" versus "weak tie" goods | Gender (unmodeled as moderator) |
| Current study | Ten-year survey data; n = 35,597 | GDP growth | 41 industries, 7 economic sectors | Customer satisfaction, expectations of customization and reliability | Necessity versus luxury,service versus manufacturing | Income, gender, age, region |
1 Notes: For the sake of parsimony, we only summarize the contents of these studies for the different factors included or excluded. For example, we include only primary customer–firm factors examined within each article—"justice" rather than distributive justice (and its subfactors), procedural justice (and its subfactors), and interactional justice (and its subfactors). The sample sizes for each study relate to complaining customers.
Previous research has focused in one of three ways on understanding the conditions under which customers who experience a failure, or are dissatisfied, and complain remain loyal. First, the literature has observed intervening consumer-psychological variables that moderate or mediate the failure, complaint, recovery, and/or loyalty perceptions of customers ([ 7]; [12]; [26]; [52]; [71]; [75]; [76]). Second, studies have examined complaint management strategies employed by firms ([30]; [73]). Third, research has investigated the "service recovery paradox" under unique circumstances (e.g., across complaints, relative to complaint frequency, longitudinally) ([44]; [51]; [54]).
Despite progress, significant gaps remain when it comes to understanding the relationship between complaint handling (recovery) and customer loyalty. The literature has tended to focus on a small cross-section of consumer industries. Of the studies in Table 1, a plurality focus either exclusively or partially on failure, complaint, and recovery with restaurants. A handful focus on hotel and commercial bank customers. A few are "multi-industry" studies of aggregate samples of consumers spread across contexts. The first two industries (restaurants and hotels) fall into a single, unique, and service-intensive economic sector (Accommodation and Food Services), while the multi-industry studies and the studies of bank customers provide a measure of diversity and exposure to a different kind of service (Finance and Insurance). Nevertheless, research on complaint management has thus far examined a narrow cohort of industries compared with the diverse consumer landscape (e.g., [12]). Given that industries differ and are characterized by variations that may affect both customers' loyalty and firms' incentives to manage complaints, this narrow focus on a small cross-section of consumer experiences results in gaps in our knowledge and potentially faulty complaint-recovery efforts by firms.
Likewise, while the research methods used so far have been somewhat eclectic, most studies adopt experimental or mixed-design methods with relatively small samples. Of the studies in Table 1, eight adopt either only experimental methods or a mixed design incorporating experimental and consumer survey data. Only one is observational ([44]), tracking complaints and actual future purchase behavior with an online retailer. Virtually all of the remaining studies focus on some type of surveying (of managers or customers) but use comparatively small, single-point-in-time cross-sectional sampling techniques. In turn, such studies fail to fully capture the recovery–loyalty relationship as it evolves in complex environments marked by variations that influence both customers' loyalty and firms' strategies.
From previous research, we draw two conclusions. First, much of the prior complaint literature focuses on a narrow set of consumer industries, such as restaurants, hotels, and banks. Already noticing this trend about two decades ago, [50], p. 583) suggested that this focus "on a single service type...or a specific service industry" has precluded a complete understanding of the recovery–loyalty relationship, and "consequently, little is known about the underlying assumptions that cover the entire spectrum." Second, a large portion of prior studies use experimental or quasi-experimental methods, and/or analyze small samples of single-point-in-time cross-sectional data (rather than repeated cross-sectional or longitudinal data). Although these studies have enriched our understanding, they are not able to effectively inform us about the influence that the broader, evolving, and dynamic marketing ecosystem has on the relationship between customer complaint behavior, complaint recovery, and customer loyalty.
Thus, in answering our research question and determining if and how the relationship between a firm's complaint recovery and a customer's loyalty vary due to influences from various factors (i.e., economic, industry, customer–firm, product/service, and customer segment factors), we aim to close significant knowledge gaps in the complaint recovery literature. The core focus is on understanding the factors that are stronger/weaker moderators of the relationship between complaint recovery and customer loyalty, as guided by our contingency model of loyalty returns to complaint management which follows.
To develop a contingency model of firm-anticipated payoffs from complaint management efforts, we synthesize two theories: EVL theory from economics ([27]) and expectations-disconfirmation theory from marketing (e.g., [15]; [60]). These theories illuminate incentives and disincentives that ( 1) dissatisfied customers have when making loyalty decisions and ( 2) firms have to convert complaint recovery to customer loyalty in their complaint management efforts.
Beginning with EVL theory ([27]), a customer who experiences dissatisfaction with a firm and its products or services has three basic options: ( 1) exhibit disloyalty and defect from the firm (i.e., "exit") to an alternative supplier; ( 2) complain and express displeasure to the firm (i.e., "voice"); or ( 3) do neither, accept the issues causing the dissatisfaction, and remain "silently loyal" (cf. [ 9]). The consumer's decision about which alternative to pursue is informed by several factors that are related to the firm (e.g., the firm's response to "quality deteriorations") but also external to the dissatisfying experience. Exit-voice-loyalty theory focuses primarily on the latter; that is, on industry conditions and the economic environment surrounding the exchange. These include the degree of market competition and the availability of alternatives; the level of investment in or price paid for the good by the consumer (i.e., the sunk cost); switching costs, the tangible and intangible costs associated with defecting from one supplier to a competitor; and the individual customer's economic situation (and perceived power) at the time of the complaint ([14]; [22]; [46]; [80]).
In addition, much like customers have choices when displeased and making loyalty decisions, EVL theory specifies that firms have both economic incentives and disincentives to convert the complaint recovery efforts to customer loyalty outcomes. Take the two extremes of monopolists and highly competitive markets. On the one hand, monopolistic firms that market necessity products during a time of slow economic growth may need to be prepared for greater complaint volume when quality deterioration results in dissatisfaction. However, because these relatively "weak" displeased customers are unable to defect and require the good, these monopolistic firms do not necessarily need to focus on complaint recovery. On the other hand, luxury goods firms in highly competitive industries with low switching costs during a period of stronger economic growth have a greater incentive to convert customer complaints to customer loyalty outcomes due to the reality of relatively frictionless customer defection.
Augmenting EVL theory, we also draw from expectations-disconfirmation theory ([15]; [52]; [72]; [73]; [75]). Expectations-disconfirmation theory and the customer satisfaction perspective focus on the customer–firm relationship and view loyalty as a function of ( 1) pre-experience consumer expectations (positively related to satisfaction and loyalty, unless negatively disconfirmed), ( 2) the customer's expected versus experienced quality (positively related to satisfaction and loyalty, if a positive gap), and ( 3) customers' overall satisfaction (or fulfillment) with the consumption experience (strongly and positively related to loyalty). Through the lens of expectations-disconfirmation theory, confirmed (high) expectations or a positive expectations gap predict stronger customer perceptions of quality and satisfaction and, thus, a stronger customer loyalty likelihood. However, when the customer has experienced negative expectations disconfirmation, poor quality, and dissatisfaction, and has chosen to voice this discontent to the firm, customers may be more likely to defect. A firm's complaint handling and system to manage the recovery–loyalty relationship is a reaction to a higher probability of customer disloyalty designed to minimize defection.
Drawing from EVL theory, expectations-disconfirmation theory, and the literature on customer satisfaction, we next identify factors likely to influence the recovery–loyalty relationship. Specifically, we argue that this relationship is likely to vary due to a set of characteristics associated with ( 1) economic factors, such as economic growth, surrounding the complaint and recovery; ( 2) industry factors, such as industry competitiveness, which affects consumers' switching costs and the availability of alternative suppliers; ( 3) customer–firm factors, that is, customer satisfaction and expectations, both of which frame the complaint and recovery experience; ( 4) product/service factors, such as whether the good consumed is a lower-priced necessity good versus a higher-priced luxury good, or a service versus a manufactured good; and ( 5) customer segment factors (e.g., income, gender, age cohort, and region of residence) related to the group that is served.
Each of these five factors is observable, but we argue that they effect the recovery–loyalty relationship through a set of unobserved mechanisms, as shown in Figure 1. The mechanisms are ( 1) consumer power; ( 2) alternatives, switching costs, and barriers; ( 3) a negative expectation-disconfirmation gap; ( 4) a reservoir of consumer goodwill, and ( 5) latent segment membership. These mechanisms arise from EVL theory, expectations-disconfirmation theory, and the literature on customer satisfaction as we highlight in the sections that follow. Table 2 summarizes the variables that affect the recovery–loyalty relationship and the influential mechanisms and factors involved.
Graph: Figure 1. Factors and mechanisms for the study of the complaint recovery–customer loyalty relationship.
Graph
Table 2. How Economic, Industry, Customer–Firm, Product/Service, and Customer Segment Factors Moderate the Complaint Recovery–Customer Loyalty Relationship.
| Factor | Moderating Effect | Primary Mechanisma |
|---|
| Economic Factors | | |
GDP growth
| Positive moderation | Consumer power: Economic growth is typically accompanied by a variety of features that result in more powerful consumers (e.g., lower unemployment, stronger income growth, more consumer spending, stronger consumer confidence). |
| Industry Factors | | |
Hirschman–Herfindahl index
| Negative moderation | Alternatives, switching costs and barriers: In competitive industries, customers recognize their ability to easily switch to alternative suppliers and also recognize their greater power relative to the firm. |
| Customer–Firm Factors | | |
Customer satisfaction
| Positive moderation | Reservoir of consumer goodwill: Cumulative customer satisfaction represents the customer's reservoir of goodwill toward the firm and product/service based in buyer habit and brand but mandates additional firm attention after failures. |
Expectations of customization
| Positive moderation | Negative expectation-disconfirmation gap: Customers with higher customization expectations anticipate more individualized service from the firm in all areas, including during a failure and recovery. |
Expectations of reliability
| Negative moderation | Negative expectation-disconfirmation gap: The unexpected failure resulting in the complaint and recovery attempt is, from the customer's perspective, reflective of either a fundamental disruption of a long problem-free relationship or an indication that the firm's promises are hollow. |
| Product/Service Factors | | |
Necessity versus luxury good
| Positive moderation | Alternatives, switching costs and barriers: Luxury goods customers will typically have greater financial resources and thus the ability to switch to alternative luxury providers or less expensive replacement goods more easily. |
Service versus manufactured good
| Negative moderation | Consumer power: For a significant proportion of manufactured goods, such as frequently purchased and inexpensive nondurable goods, complaints are less likely, with customers choosing to either remain silently loyal or defect without complaint. |
| Customer Segment Factors | | |
Customer income
| Negative moderation | Latent segment membership: Satisfaction is less influential as a determinant of loyalty for wealthier consumers due to a more expansive choice set, and so too might dissatisfaction and complaint recovery matter less to loyalty. |
Customer gender
| Positive moderation | Latent segment membership: Research has shown a stronger satisfaction-loyalty relationship among women, which suggests a stronger recovery–customer loyalty relationship as well. |
Customer age
| Positive moderation | Latent segment membership: Research has shown that the impact of satisfaction on loyalty increases with age, and complaint recovery may likewise more strongly affect customer loyalty for older generational cohorts. |
Customer region
| Moderation but unclear direction | Latent segment membership: Customer region is anticipated to have a moderating effect given the prevalence of geography-specific marketing strategies ("geomarketing," "geofencing"). |
2 a Factors often adhere to multiple mechanisms.
We predict that the recovery–loyalty relationship is influenced by economic factors (e.g., [19]; [45]). Specifically, we expect that a faster-growing economy will positively moderate the link between complaint recovery and customer loyalty. This positive moderation is due to the fact that economic growth is typically accompanied by a variety of features that result in more powerful consumers (e.g., lower unemployment, stronger income growth, more consumer spending, stronger consumer confidence). This increased consumer power (e.g., [10]; [43]) leads consumers to perceive the market as having lower switching costs and more viable alternative suppliers, easing defection and disloyalty. As such, during these faster-growing economic periods firms will be even more determined to overcome customer complaints effectively and keep customers loyal. Stronger economic growth will thus positively moderate the link between complaint recovery and customer loyalty, and firms may have a stronger incentive to convert complaining customers into enduringly loyal customers via complaint management during these periods.
We predict that the importance of complaint recovery to customer loyalty is not constant but varies across industries and economic sectors (e.g., [ 1]; [70]). This variation is due to the diversity of the competitive economic contexts experienced by consumers. We predict that the most fundamental factor influencing variance in the recovery–loyalty relationship across industries and sectors is the degree of competition. We argue that complaint recovery will exhibit a weaker (stronger) effect on customer loyalty in less (more) competitive industries. This is because in more competitive industry contexts, customers will recognize their ability to more easily switch to alternative suppliers and also recognize their greater power relative to the firm. As such, in more competitive industries, the expectation is that a stronger relationship will exist between complaint management efforts by the firm and customers' future loyalty. Consequently, due to this competitive industry dynamic, firms will have stronger incentives to manage complaints given the increased importance customers place on the recovery–loyalty relationship in more competitive industries.
Three customer–firm factors are expected to be important influencers of the recovery–loyalty relationship (e.g., [18]). We predict that customers' satisfaction and their pre-experience expectations of both the customizability and the reliability of the products/services consumed will moderate the recovery–loyalty relationship. Beginning with customer satisfaction, which is defined as the customer's overall fulfillment response to a consumption experience (e.g., [13]; [18]; [61]), we anticipate positive moderation of the recovery–loyalty relationship. As measured in this study, customer satisfaction is a cumulative phenomenon reflecting the totality of the consumers' experiences with the firm. Effectively, this form of satisfaction can be viewed to represent (a proxy for) the consumer's reservoir of goodwill toward the firm and the product/service based in buyer habit and brand identification developed (in many cases) over a lengthy and deeper customer–firm relationship. Part and parcel to this relationship, however, is the consumer's demand that the trusted firm will "go the extra mile" to resolve a problem when it occurs, as a way to reaffirm the relationship and ensure future loyalty.
Regarding the effect of expectations of customizability, defined as the customer's pre-experience perceptions of the product/service's abilities to meet personal requirements, we predict a positive moderating effect. Customers with higher customization expectations anticipate more individualized service from the firm in all areas, including during a failure and recovery. Higher expectations of customization are likely to lead the customer to demand personalized service during the recovery and, by design, a heightened positive relationship between recovery efforts and loyalty. In effect, firms have a greater incentive to manage complaints to secure loyalty due to higher expectations of customizability.
Regarding expectations of reliability, which we define as the customer's pre-experience perceptions of the probability of a lack of failure with the product/service, we predict a negative moderating effect on the recovery–loyalty relationship. Customers' stronger expectations of reliability with a firm are generally created through either multiple problem-free consumption experiences or through advertising or other marketing communications promising problem-free experiences. In the event of a failure, however, the result will be a large negative expectations-disconfirmation gap. Consequently, theoretically we predict that this disconfirmation gap will negatively frame (and weaken) the consumer's response to a firm's complaint recovery efforts relative to their loyalty intentions. This is because the unexpected failure resulting in the complaint and recovery attempt is, from the customer's perspective, reflective of either a fundamental disruption of a long problem-free relationship or an indication that the firm's promises are hollow.
We predict that the categorization of the product or service as a necessity good or a discretionary luxury good is a factor that moderates the recovery–loyalty relationship (e.g., [ 2]). We define necessity goods as basic products and services customers often require and therefore must purchase (even when, for example, income is low or declining), and/or as lower-cost goods for which more expensive substitute goods exist. Discretionary luxury goods, in contrast, are defined as superior (and typically more expensive) products and services sought out by the customer (often as income is high or rising), even though less expensive substitute alternative goods are available. Our expectation is that luxury goods customers will typically have greater financial resources and thus the ability to switch to alternative luxury providers or less expensive replacement goods more easily. Specifically, luxury goods customers tend to be financially better off (e.g., [48]), and thus they are anticipated to be less affected by the loyalty-inducing constraints of sunk costs from earlier purchases as a barrier to switching. However, if the product or service is considered a necessity the situation is often reversed, and this—combined with the fact that this category of goods typically has lower profit margins—decrease demands on firms to manage customer complaints. Thus, we expect positive moderation of the recovery–loyalty relationship among luxury goods consumers, and larger loyalty payoffs via recovery efforts for firms selling luxury goods.
Likewise, we anticipate that the importance of complaint recovery to customer loyalty varies between customers of services and manufactured goods. In particular, complaint recovery will have a weaker effect on loyalty for customers of manufactured goods relative to customers of services (i.e., negative moderation). For a significant proportion of manufactured goods, such as frequently purchased and inexpensive nondurable goods, customer complaint behavior is itself far less likely following a dissatisfying experience. That is, customers are less likely to seek recovery when displeased with this class of nondurable goods, choosing to either remain silently loyal or to defect without complaint ([18]). This suggests that, in the aggregate, complaint recovery is relatively less important to loyalty decisions for these necessity goods (often price-based commodities), and possibly also among the smaller group of customers who do complain. Moreover, prior research has confirmed that complaint recovery after a failure, as a type of interactional justice, has a stronger effect on loyalty in personal services contexts relative to less personal nonservices goods ([23]; [47]), supporting the negative moderation of the recovery–loyalty relationship for manufactured goods.
We examine four customer segment factors (i.e., customer age, gender, income, and region of residence) that will potentially influence the complaint recovery–customer loyalty relationship. Given the context and focus of our study, these customer segment factors are important inclusions in the analyses to holistically understand the recovery–loyalty link. However, limited theoretical and empirical evidence exists regarding the nature of the potential moderation for these factors within the complaint management literature. Consequently, we draw on research and seek guidance from the related literature regarding influencers of the customer satisfaction and customer loyalty relationship (e.g., [ 6]). We also draw broadly on the consumer behavior literature related to age, gender, income, and region.
Beginning with income ([36]), research indicates that customer satisfaction is less influential as a determinant of loyalty for wealthier consumers ([48]), possibly due to a more expansive choice set and lower barriers to switching, and thus so too might dissatisfaction and ratings of complaint recovery matter less to loyalty. This suggests a negative moderating effect for income ([77]) on the recovery–loyalty relationship.
However, research has shown that generally a stronger customer satisfaction–customer loyalty relationship exist among women than among men, in particular as it relates to individual providers, brands, and exchanges (e.g., [13]; [53]). As a result, we predict a stronger complaint recovery–customer loyalty relationship among women ([31]).
Considering age and generational cohort, research has shown that the impact of satisfaction on loyalty increases with age, possibly due to these customers' stronger reliance on their own evaluative abilities developed through lengthy personal experience. For this reason, complaint recovery may likewise more strongly influence customer loyalty for the older generational cohorts ([31]; [77]).
Finally, while it is reasonable to anticipate an effect on the recovery–loyalty relationship across regions within the United States ([43]), given the prevalence of geography-specific marketing strategies ("geomarketing") deployed by national firms such as mobile-service providers ("geofencing"), no theory or research offers strong predictions for moderation of the recovery–loyalty relationship based on customers' regions of residence.
To test how the factors in Figure 1 affect the complaint recovery–customer loyalty relationship, we analyze a ten-year period of data drawn from the large-scale samples included in the ACSI, which has annually interviewed customers of the largest firms in the U.S. economy since 1994. The ACSI measures customer satisfaction as its central focus but includes additional variables on customer complaint behavior, complaint recovery, and postcomplaint repurchase intention, among others (e.g., [15]; [17]; [32]; [33]; [40]; [59]). Only the most economically significant firms with the largest market shares in an industry are included in the ACSI sample each year, resulting in a data set that primarily include customers of Fortune 1000 consumer products and services companies.
The ACSI sample analyzed covers a recent ten-year period (2005–2014). We began with a sample that includes 41 distinct industry categories which span seven of the ten North American Industry Classification System (NAICS) economic sectors (Manufacturing, Retail Trade, Transportation and Warehousing, Information, Finance and Insurance, Health Care and Social Assistance, and Accommodation and Food Service; for more detail on the sectors and industries, see the Appendix). After excluding noncomplaining respondents and ensuring availability of at least 25 nonmissing firm-year observations for firms/brands, we have a sample of n = 35,597 complaining customers across firms, industries, and economic sectors with data available on all relevant variables. The volume of responses in our data set is significantly larger than what has been studied in prior customer complaint studies (see Table 1) and provides an opportunity to more deeply understand the roles of the factors and mechanisms in Figure 1 as they pertain to the recovery–loyalty relationship. Specifically, this rich ACSI sample enables us to rigorously assess how the relationship between recovery and loyalty varies across the factors (i.e., economic, industry, customer–firm, product/service, and customer segment factors).[ 6]
Table 3 details the variables used to operationalize the core factors (customer loyalty and customer complaint handling) and moderating factors (i.e., economic, industry, customer–firm, product/service, and customer segment factors), obtained from the ACSI data set as well as several secondary data sources. The core variables of loyalty and complaint handling were measured via survey variables as a part of the data collection efforts by the American Customer Satisfaction Index. Customer loyalty is operationalized via a variable measuring the customer's stated likelihood to repurchase from the same firm in the future (REPUR). Complaint handling (recovery) is measured as a variable that assesses how well, or poorly, a customer's most recent complaint was handled (HANDLE).
Graph
Table 3. Summary of Variables and Operationalization.
| Variable | Operationalization |
|---|
| Repurchase intention(REPUR) | ACSI Survey Questiona: "The next time you are going to purchase the same product or service, how likely is it that it will be with (COMPANY) again? Using a 10-point scale on which '1' means 'very unlikely' and '10' means 'very likely,' how likely is it that it will be with (COMPANY) again?" |
| Complaint handling(HANDLE) | ACSI Survey Question: "How well, or poorly, was your most recent complaint handled? Using a 10-point scale on which '1' means 'handled very poorly' and '10' means 'handled very well,' how would you rate the handling of your complaint?" |
| GDP growth(GDPGR) | Annual GDP growth data obtained via the U.S. Bureau of Economic Analysis website (). |
| Hirschman–Herfindahl index(HHI) | Annual Herfindahl–Hirschman index at the subsector (industry) level, calculated as sum of the squared company-level market share percentages of the largest firms measured in the industry. The data are from Compustat, obtained via the Wharton Research Data Services. |
| Customer satisfaction(SATIS) | ACSI Survey Question: "Please consider all your experiences to date with (COMPANY). Using a 10-point scale on which '1' means 'very dissatisfied' and '10' means 'very satisfied,' how satisfied are you with (COMPANY)?" |
| Expectations of customization(CUSTOMX) | ACSI Survey Question: "At the same time, you probably thought about things you personally require from (COMPANY). Using a 10-point scale on which '1' now means 'not very well' and '10' means 'very well,' how well did you expect (COMPANY) to meet your personal requirements?" |
| Expectations of reliability(RELYX) | ACSI Survey Question: "Thinking about your expectations before you purchased from (COMPANY), you probably thought about how often things could go wrong. Using a 10-point scale, on which '1' now means 'very often' and '10' means 'not very often,' how often did you expect that things could go wrong with (COMPANY)?" |
| Necessity versus luxury (LUXURY)b | ACSI Survey Question: "Thinking about (COMPANY), do you think of it more as a supplier of basic necessity goods and services or a supplier of exclusive luxury goods and services? On a scale from 1 to 10, where 1 = 'necessity goods and services provider' and 10 = 'luxury goods and services provider,' how would you rate (COMPANY)?" |
| Manufacturing versus service(MFG) | Manufacturing (services = 0, manufacturing = 1) based on NAICS codes. The data are from the U.S. Census Bureau website (). |
| Customer income(INCDUM) | Annual household income from the prior year (0 = $60,000 or below, 1 = Above $60,000). Data on income came from the ACSI database. |
| Customer gender(FEMALE) | Female (0 = male, 1 = female). Data on gender came from the ACSI database. |
| Customer cohort(MILLDUM)(GENXDUM)(BOOMDUM) | Indicator variables for whether consumer-respondent is part of the Millennial, Generation X, Baby Boomer, or Silent Generations (reference category). Generational cohorts were determined uniquely for each sample year based on accepted categorizations (Millennials, 1980–2000 [MILLDUM]; Generation X, 1965–1979 [GENXDUM]; Baby Boomers, 1946–1964 [BOOMDUM]; and Silent Generation pre-1946). Data on customer cohort (age) came from the ACSI database. |
| Customer region(NEDUM)(MIDWDUM)(SOTHDUM) | Indicator variables for residence of the respondent in the Northeast (NEDUM), Midwest (MIDWDUM), Southeast (SOTHDUM), or West of the United States, with the West as the reference category in our models. Regions were defined following the U.S. Census' "Regions and Divisions of the United States" (). |
- 3 a Most questions in the ACSI survey are asked on 1–10 scales and then transformed to 0–100 index scores for official reporting purposes. In this study, we analyze the variables on their original 1–10 scales.
- 4 b The luxury variable data were collected for each firm using "expert raters" (e.g., [ 4]; [ 5]; [78]) affiliated with the ACSI (n = 15). Each expert rater was asked to assess each ACSI-measured brand/company on a ten-point scale, as a firm which supplies basic necessity goods ( 1) to a supplier of high-end luxury goods (10). The average rating for each firm among the expert raters was then associated with each respondent for that firm in the sample.
The moderators were assessed via a combination of survey data from ACSI and objective data from the U.S. Bureau of Economic Analysis, Compustat (obtained via the Wharton Research Data Services), NAICS codes, and the U.S. Census's Regions and Divisions of the United States (see Table 3). To represent the economic factors, we use quarterly changes in annualized U.S. gross domestic product growth (GDPGR). Industry factors are represented by the degree of competition in an industry, operationalized with the Herfindahl–Hirschman Index (HHI). The customer–firm factors are operationalized as the respondents' overall, cumulative customer satisfaction with the purchase and consumption experience (SATIS), and the customers' pre-experience expectations regarding both the customizability (CUSTOMX) and reliability (RELYX) of the good. Product and service factors—necessity versus luxury goods and services versus manufactured goods—are measured via the LUXURY and MFG variables described in Table 3.
The customer segment factors are operationalized through latent membership in various demographic groups. These include income (INCDUM), measured categorically as the respondent's total annual household income and transformed (based on the sample median) to a low-high dummy variable; the respondent's gender, self-identified as male or female (FEMALE); customer age, measured as membership in one of four generational cohorts (Silent Generation, Baby Boomers, Generation X, and Millennials) and operationalized as three dummy variables (BOOMDUM, GENXDUM, and MILLDUM); and region of residence in the United States, measured as the West, Northeast, Midwest, or Southeast regions and operationalized via three dummy variables (NEDUM, MIDWDUM, and SOUTHDUM).
Table 4 reports descriptive statistics and correlations, including summary statistics, for all of the variables included in the model. For the core variables (REPUR and HANDLE), the mean score for repurchase intention across all ACSI respondents (n = 319,330) during the study period, including complainant and noncomplainant customers, is 8.05 (1 = "very unlikely," and 10 = "very likely"). The score drops significantly (p <.01) to 6.19 among complaining customers. The mean complaint rate across all sectors and years in the full sample of customers is 11.1%, meaning that over the ten-year study period roughly one in nine respondents had a product or service failure or other source of dissatisfaction about which they complained. The average complaint recovery (i.e., complaint handling) score is 6.31 (1 = "very poor," and 10 = "very well"), slightly higher than the customer loyalty score. None of the correlations in Table 4 are unusually high. Regarding potential concerns about multicollinearity, the final model has average variance inflation factors (VIFs) of < 10.[ 7]
Graph
Table 4. Descriptive Statistics and Correlations.
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 |
|---|
| 1. REPUR (1–10) | 1.00 | | | | | | | | | | | | | | | | |
| 2. HANDLE (1–10) | .58 | 1.00 | | | | | | | | | | | | | | | |
| 3. CUSTOMX (1–10) | .27 | .27 | 1.00 | | | | | | | | | | | | | | |
| 4. RELYX (1–10) | .17 | .18 | .44 | 1.00 | | | | | | | | | | | | | |
| 5. SATIS (1–10) | .72 | .59 | .39 | .27 | 1.00 | | | | | | | | | | | | |
| 6. GDPGR | .00 | .02 | −.01 | .00 | .00 | 1.00 | | | | | | | | | | | |
| 7. HHI | .09 | .05 | .04 | .01 | .11 | −.06 | 1.00 | | | | | | | | | | |
| 8. LUXURY (1–10) | .03 | .05 | .16 | .15 | .16 | −.01 | .17 | 1.00 | | | | | | | | | |
| 9. FEMALE (0–1) | .02 | .03 | .07 | .03 | .02 | −.01 | .00 | −.05 | 1.00 | | | | | | | | |
| 10. INCDUM (0–1) | .00 | −.01 | .03 | .04 | .02 | −.04 | .00 | .19 | −.11 | 1.00 | | | | | | | |
| 11. NEDUM (0–1) | .01 | .02 | .00 | .00 | .01 | .02 | .01 | .06 | −.01 | .06 | 1.00 | | | | | | |
| 12. MIDWDUM (0–1) | −.02 | −.01 | .01 | .01 | −.02 | −.01 | .00 | .00 | .01 | −.03 | −.29 | 1.00 | | | | | |
| 13. SOTHDUM (0–1) | .00 | .00 | .03 | .00 | .02 | −.01 | .02 | .01 | .01 | −.03 | −.34 | −.45 | 1.00 | | | | |
| 14. MILLDUM (0–1) | −.04 | −.02 | −.02 | −.06 | −.02 | .07 | .04 | .04 | −.01 | −.05 | .03 | −.02 | −.01 | 1.00 | | | |
| 15. GENXDUM (0–1) | −.04 | −.04 | −.01 | −.01 | −.05 | −.04 | −.02 | −.01 | .06 | .14 | .00 | .02 | −.02 | −.56 | 1.00 | | |
| 16. BOOMDUM (0–1) | .06 | .05 | .03 | .06 | .06 | −.02 | −.02 | −.02 | −.05 | −.08 | −.03 | −.01 | .02 | −.34 | −.53 | 1.00 | |
| 17. MFG (0–1) | .01 | .06 | .21 | .19 | .18 | −.01 | .18 | .61 | −.03 | .12 | .02 | .03 | .03 | .04 | −.01 | −.02 | 1.00 |
| Mean | 6.19 | 6.31 | 8.00 | 7.18 | 6.67 | 1.54 | .03 | 4.14 | .57 | .51 | .18 | .27 | .35 | .27 | .46 | .25 | .28 |
| SD | 3.19 | 3.15 | 2.07 | 2.55 | 2.61 | 1.77 | .01 | 1.60 | .49 | .50 | .38 | .44 | .48 | .44 | .50 | .43 | .45 |
5 Notes: All correlations ≥.02 are statistically significant at p <.05.
We examine our research question and the theoretically developed contingency model of loyalty returns to complaint management by examining the effects of customer complaint handling (HANDLE) on customer repurchase intention (REPUR) while simultaneously examining how this relationship is moderated by economic (GDPGR), industry (HHI), customer–firm (SATIS, CUSTOMX, and RELYX), product/service (LUXURY, MFG), and customer segment factors (INCDUM, FEMALE, BOOMDUM, GENXDUM, MILLDUM, NEDUM, MIDWDUM, and SOUTHDUM). Given that the nesting of customers in the same firm/brand within an industry/sector across multiple years creates a multilevel structure, we use hierarchical linear modeling (HLM) to analyze the complaint handling/recovery–repurchase intention/loyalty relationships (e.g., [29]; [65]).[ 8]
Analysis of multilevel data poses three types of potential estimation difficulties relevant to our study: aggregation bias, misestimated errors, and heterogeneity of regression. First, aggregation bias occurs when a variable takes different meanings at different levels of analysis. For example, by aggregating individual customer ratings for complaint recovery data across firms, we can conceptualize how firms vary in their ability to handle customer complaints. Recovery can be assessed at both the customer and firm levels by aggregating customer-level data.[ 9] Hierarchical linear modeling addresses these potential confounding effects on variable interpretation by decomposing the effects of variables at separate levels. Second, misestimated standard errors may arise as a result of failure to account for the dependence of observations, in this case within a firm in an economic sector or for a particular year. However, HLM avoids this problem by incorporating a unique random effect for each firm-year. Third, heterogeneity of regression could arise when relationships between complaint recovery and loyalty vary across sectors or years. By utilizing industry or economic characteristics, such as HHI or GDPGR, as Level 2 variables, HLM permits the modeling of variation in the intercepts and slopes of loyalty across firm-years.
The HLM analyses are conducted incrementally in three steps. In Step 1, we partition the total variance in customer loyalty into levels ("within" variance at the customer level and "between" variance across firm-years) through a fully unconditional model (FUM). This model specifies no predictors at the customer (Level 1) or firm/year (Level 2) levels. In Step 2 of the HLM analysis, we fit a random coefficients (RC) regression model by allowing predictors at the customer level only (Level 1). The RC regression model provides Level 1 coefficients that can subsequently be modeled with Level 2 variables. In Step 3 of the HLM analysis, we model the randomly varying intercepts and slope coefficients (obtained in Step 2) through Level 2 predictors. Thus, we estimate the following equations at the customer and firm/year levels.
The Level 1 model is as follows:
Graph
where Y represents the individual customer's repurchase intention (customer loyalty) rating (REPUR) as an outcome variable, and subscript i indexes customers, subscript j indexes firms (nested in sectors), and subscript t indexes years. Explanatory variables at Level 1 include the customer complaint handling rating (HANDLE); customer satisfaction (SATIS); expectations of customization (CUSTOMX); expectations of reliability (RELYX); a gender dummy variable (FEMALE); an income dummy variable (INCDUM); the respondent age cohort represented by the dummy variables MILLDUM, GENXDUM, and BOOMDUM; and geographical regions represented by the dummy variables NEDUM, MIDWDUM, and SOTHDUM. The Level 1 model also includes interaction terms involving HANDLE and the individual-level variables SATIS, CUSTOMX, RELYX, FEMALE, INCDUM, region dummies (e.g., NEDUM, MIDWDUM, SOTHDUM), and age/generational cohort dummies (MILLDUM, GENXDUM, BOOMDUM). We centered all variables at Level 1 before creating the interaction terms, as explained below. Finally, we include an inverse Mills ratio (IMR) in the Level 1 model to account for any potentially nonrandom selection in that the sample of complaining customers may be different from those who did not complain. We used a probit model for calculating the IMR, and in that model we included a variable representing the fraction of complaints in a particular year for a particular firm as an instrumental variable. We verified the relevance of this variable and it was positive and significant in the first-stage equation. This instrumental variable also satisfies the exclusion restriction conceptually because a particular customer's loyalty to a firm is unlikely to be related to what fraction of customers of that firm choose to voice their complaints.
At Level 2, we model the intercept and slope of the recovery–loyalty relationship by the four economic, industry, and product/service moderators: necessity versus luxury goods (LUXURY), services versus manufactured goods (MFG), GDP growth (GDPGR), and the HHI. We fixed all other slopes. Thus, the Level 2 models are
Graph
and
Graph
To estimate the coefficients, we account for differential precision of the information provided by each firm-year using the generalized least squares procedure. In addition, because the customers and Level 1 parameters vary across firm-years, we employ an iterative technique using an expectation maximization algorithm and Fisher scoring to obtain maximum likelihood estimates of the Level 1 and Level 2 variance components ([66]).
We centered the variables as suggested by [65]. In the Level 1 model, because our primary interest is in modeling the recovery–loyalty relationship, and while we use LUXURY, MFG, GDPGR and HHI at Level 2, we do not expect these variables to explain the entire variance in the slope. Thus, we allow the slope of HANDLE to vary across firm-years, and we group-mean-center the HANDLE variable across firm-years ([65]). For the remaining predictors at Level 1 (i.e., SATIS, CUSTOMX, RELYX, FEMALE, INCDUM, MILLDUM, GENXDUM, BOOMDUM, NEDUM, MIDWDUM, and SOTHDUM), we constrain the variances of their slope to be zero at Level 2 across firm-years and we grand-mean-centered these variables. For the interaction terms involving HANDLE at Level 1, we used group-mean-centered HANDLE, grand-mean-centered satisfaction and expectations variables (SATIS, CUSTOMX, and RELYX), and uncentered INCDUM and FEMALE variables. Use of such centering decisions at Level 1 implies that the intercept at Level 1 represents loyalty for a customer with an average rating of HANDLE within a firm-year and at the average values of all other variables in our sample. At Level 2, one can either grand-mean-center variables or leave them uncentered (see [65], pp. 32–35); we use uncentered variables for Level 2 (LUXURY, MFG, HHI, and GDPGR) for easier interpretation of results.[10]
From the model specifications, we first assess the model fit improvement by comparing the FUM, which specifies no predictors at either the customer (Level 1) or firm-year (Level 2) levels; the RC model, which allows predictors at the customer level (Level 1) only; and the "full" model with randomly varying intercepts and slope coefficients. Drawing on the Akaike information criterion, Bayesian information criterion, and deviance values, we find the "full" model reported in Table 5, Panels A–E, to be significantly better than the FUM and RC models. We next discuss the results for the complaint recovery–customer loyalty relationship (slope) and the moderating effects of the various factors we examine on this relationship (economic, industry, customer–firm, product/service, and customer segment factors), followed by the intercept results. To complement Table 5, Figure 2 summarizes the economic significance of the various moderators of the recovery–loyalty relationship.
Graph: Figure 2. Significant moderating effects on the complaint recovery–customer loyalty relationship (as a percentage of Mean HANDLE Slope in Table 5).Notes: Figure 2 shows the effects as percentage increases of the continuous variables (all variables except FEMALE and MFG) when the value of that variable increases from one standard deviation below the mean to one standard deviation above the mean. For the FEMALE and MFG variables, the effect is when the value of the variable changes from 0 to 1.
Graph
Table 5. HLM Estimation of Fixed Effects with Robust Standard Errors.
| Coefficient | SE |
|---|
| A: Level 1 Main Effects | | |
| Customer Satisfaction (SATIS), β2 | .664** | .012 |
| Expectations of Customizability (CUSTOMX), β3 | .049** | .008 |
| Expectations of Reliability (RELYX), β4 | −.011* | .005 |
| Gender (FEMALE), β5 | −.018 | .023 |
| Income (INCDUM), β6 | .062* | .022 |
| Millennial (MILLDUM), β7 | −.234** | .080 |
| Generation X (GENXDUM), β8 | −.179* | .079 |
| Baby Boomers (BOOMDUM), β9 | −.109 | .078 |
| Northeast (NEDUM), β10 | −.005 | .035 |
| Midwest (MIDWDUM), β11 | −.096* | .034 |
| South (SOTHDUM), β12 | −.087* | .032 |
| Inverse Mills ratio (IMR), β13 | .510** | .059 |
| B: Level 2 Modeling of Intercept β0 | | |
| INTRCPT, γ00 | 6.184** | .076 |
| LUXURY, γ01 | −.004 | .016 |
| GDP GROWTH, γ02 | .006 | .012 |
| HHI, γ03 | 8.680** | 1.454 |
| MFG, γ04 | −.989** | .049 |
| C: Level 1 Interaction Effects | | |
| SATIS × HANDLE, β14 | .015** | .002 |
| CUSTOMX × HANDLE, β15 | .009** | .002 |
| RELYX × HANDLE, β16 | −.003* | .002 |
| FEMALE × HANDLE, β17 | .019** | .007 |
| INCDUM × HANDLE, β18 | .003 | .007 |
| NEDUM × HANDLE β19 | −.007 | .012 |
| MIDWDUM × HANDLE, β20 | .008 | .011 |
| SOTHDUM × HANDLE, β21 | .008 | .011 |
| MILLDUM × HANDLE, β22 | .033 | .027 |
| GENXDUM × HANDLE, β23 | .012 | .027 |
| BOOMDUM × HANDLE, β24 | .014 | .027 |
| D: Level 2 Modeling of Complaint Handling (HANDLE), β1 | | |
| INTRCPT, γ10 | .229** | .017 |
| LUXURY, γ11 | .010* | .004 |
| GDP GROWTH, γ12 | .006* | .003 |
| HHI, γ13 | −1.413* | .442 |
| MFG, γ14 | −.037** | .012 |
| E: Variance Explained | | |
| Proportion of Variance Explained by Level 1 model | 57.5% | |
| Proportion of Variance Explained by Level 2 model for HANDLE | 9.84% | |
| Deviance (−2 log-likelihood) | 152,516.22 | |
- 6 *p <.05.
- 7 **p <.01.
- 8 Notes: The dependent variable is customer loyalty. Moderating effects on the complaint handling–loyalty relationships are in boldface.
Before discussing the moderating effects, we report on the main effects of key study variables on customer loyalty (repurchase intention) at the group-mean value of complaint recovery in Table 5 (see Panel A). Among the customer–firm factors, customer satisfaction (coefficient =.664, p <.001) and expectations of customization (coefficient =.049, p <.001) positively and significantly influence customer loyalty, while customer expectations of product/service reliability negatively influence loyalty (coefficient = −.011, p <.05). These findings suggest that customers who are more demanding of customizability are also more loyal, whereas those who anticipate stronger reliability are relatively more fickle. The results are consistent with theoretical reasoning and the related literature that highlights the importance of competing based on differentiation and customization rather than on cost or reliability (e.g., [67]).
Among the results for the customer segment factors, at the mean value of complaint handling, higher-income households tend to have higher customer loyalty (coefficient =.062, p <.01) compared with those with lower income. However, at the mean value of complaint handling, there are no differences in loyalty across men and women (coefficient = −.018, n.s.). We find that Millennial and Generation X customers have lower loyalty compared with those from the reference group of the Silent Generation, and that those residing in the Midwest and Southeast have lower loyalty than those from the West. Because there is no strong theory for predicting differences in loyalty across regions (cf. [43]), we avoid overinterpretation of these results but document them here for further research and theorizing. Finally, the instrumental inverse Mills ratio, added to our models to control for potential nonselection bias between complaining and noncomplaining customers, shows a positive and significant effect on loyalty, as expected (coefficient =.510, p <.01).
Although our principal interest in this study is to understand the factors that moderate the recovery–loyalty relationship, we also provide complementary results from modeling of the intercept in Table 5 (see Panel B). First, there is no statistically significant difference in mean repurchase intention (customer loyalty) related to GDP growth (coefficient =.006, n.s.) and being a provider of luxury goods (coefficient = −.004, n.s.). Second, we find that mean repurchase intention is higher (coefficient = 8.680, p <.001) for industries with higher market concentration (HHI), as one would expect when customers have few or no viable product or service alternatives and higher barriers to switching. Third, manufacturing firms have on average lower customer repurchase intention than service firms (coefficient = −.989, p <.001).
Table 5 (Panel D) indicates that gross domestic product growth (coefficient =.006, p <.05) has a positive and statistically significant moderating effect on the recovery–loyalty relationship, meaning that complaint recovery is positively enhanced under these conditions. In terms of economic significance, changing the score on the GDPGR variable by 3.6 (from one standard deviation below the mean to one standard deviation above the mean) is associated with a change in slope of the recovery–loyalty relationship by a 9.4% increase in the mean HANDLE slope to.022 (i.e., γ40 in Table 5). This finding suggests that loyalty payoffs from customer complaint handling are stronger when the economy is doing relatively better, and that managers should not underinvest in complaint handling when market conditions are otherwise favorable.
Turning to the industry factors and cross-sectoral differences, HHI negatively and significantly moderates the recovery–loyalty relationship (coefficient = −1.413, p <.01). This means that firms in more concentrated industries derive fewer benefits from complaint handling to drive future customer loyalty than those in more competitive industries. In terms of economic significance, changing the score on the HHI variable by.02 (from one standard deviation below the mean to one standard deviation above the mean) is associated with a change in slope of the recovery–loyalty relationship of.028, which is a 12.3% decrease in the mean HANDLE slope of.229 in Table 5 (Panel D). Put differently, this finding indicates that complaint handling is more important for loyalty in more competitive sectors where consumers have a larger number of viable alternative suppliers to choose from (and potentially defect to) and thus have lower switching barriers than in the opposite. These results are consistent with theory and the mechanisms affecting the recovery–loyalty relationship. That is, while customers of monopolists or firms in more concentrated industries may indeed care about effective complaint recovery, they also understand that defection due to poor complaint handling may not be an option (e.g., [20], [21]; [27]). Although dissatisfied enough to complain, the customers' narrow (or nonexistent) alternative choice set (i.e., fewer/no alternative supplier options) delimits the importance of complaint recovery to their customer loyalty intentions. As such, firms in more competitive industries should anticipate higher payoffs from complaint recovery.
Among the examined customer–firm relationship variables, findings indicate that while expectations of customization positively and significantly moderate the recovery–loyalty relationship (coefficient =.009, p <.01), customer expectations of product/service reliability negatively moderate the relationship (coefficient = −.003, p <.05). In terms of economic significance, changing the score on the CUSTOMX variable by 4.14 (from one standard deviation below the mean to one standard deviation above the mean) is associated with a change in slope of the recovery–loyalty relationship of.037, which is a 16.7% increase in the mean HANDLE slope of.229 in Table 5. In contrast, changing the score on the RELYX variable by 5.10 (from one standard deviation below the mean to one standard deviation above the mean) is associated with a change in slope of the recovery–loyalty relationship of.015, which is approximately a 6.7% decrease in the mean HANDLE slope of.229 in Table 5 (Panel C). These findings indicate a stronger effect of complaint handling on customer loyalty for firms with customers who are, on average, more demanding of goods customizable to their personal use, though the opposite is true of firms whose customers have higher expectations of reliability.
Our findings also indicate a positive moderating effect of customer satisfaction on the recovery–loyalty relationship (coefficient =.015, p <.001). In terms of economic significance, changing the score on the SATIS variable by 5.22 (from one standard deviation below the mean to one standard deviation above the mean) is associated with a change in slope of the recovery–loyalty relationship by.078, which is about a 34.2% increase in the mean HANDLE slope of.229 in Table 5 (Panel C). This finding extends prior work in that it shows the relatively high returns on customer satisfaction (cf. [63]) through its moderating effect on the recovery–loyalty relationship. These results are interesting given that customer satisfaction theory also suggests that firms with higher customer satisfaction are relatively insulated from occasionally less-exceptional complaint handling to secure future customer loyalty ([16]). Taken together, these findings indicate that the customer–firm relationship provides important information about variance in the recovery–loyalty relationship and thus, in firms' expected payoffs as increased loyalty through complaint management.
Regarding the moderating effect of the product/service factors on the recovery–loyalty relationship, we find that the variable for necessity versus luxury goods positively moderates the relationship (coefficient =.010, p <.05). In terms of economic significance, changing the score on the LUXURY variable by 3.2 (from one standard deviation below the mean to one standard deviation above the mean) is associated with a change in slope of the recovery–loyalty relationship of.032, which is a 14% increase in the mean HANDLE slope of.229 in Table 5 (Panel D). This finding is consistent with our theory and mechanisms and suggests that firms providing goods tending toward luxuries get a bigger return in customer loyalty from strong complaint handling, and vice versa for basic, necessity goods providers. For firms predominantly marketing to consumers of necessity goods, however, the incentive to manage complaints were anticipated to be weaker and the results confirm these expectations.
In addition, we find that the manufacturing versus services variable (MFG) negatively moderates the recovery–loyalty relationship (coefficient = −.037, p <.01). In terms of economic significance, changing the score on the MFG variable by 1 (from zero for service firms to 1 for manufacturing firms) is associated with a change in slope of the recovery–loyalty relationship of.037, which is a 16.2% decrease in the mean HANDLE slope of.229 in Table 5 (Panel D). This finding confirms that the loyalty of consumers of more personal services is more strongly affected by complaint recovery than is the case with consumers of manufactured goods, indicating that manufacturing firms have a lower payoff in customer loyalty from strong complaint handling when compared with service-delivering firms.
Turning to the customer segment results, we find a steeper recovery–loyalty slope among women (coefficient =.019, p <.01) when compared with men, consistent with prior research that has observed positive moderation in the satisfaction–loyalty relationship among women (e.g., [31]). In terms of economic significance, changing the score on the FEMALE variable from 0 to 1 (male to female) is associated with a change in slope of the recovery–loyalty relationship by.019, which is about an 8.3% increase in the mean HANDLE slope of.229 in Table 5 (Panel C). For the other customer segment factors, we fail to find a statistically significant moderating effect of income, age, or region on the recovery–loyalty relationship. While difficult to interpret within theory (or the scant record of empirical studies), we find these null results interesting in their own right.
Turning complaining customers into loyal customers was the central focus in this research. We captured the dynamics of this focus via an overarching research question: How does the relationship between a firm's customer complaint recovery (i.e., the customer's perception of how well the firm handled a complaint) and customer loyalty vary depending on influences from economic, industry, customer–firm, product/service, and customer segment factors? To address the nuances in this question, we developed a contingency model of loyalty returns to complaint management based on EVL theory ([27]), expectations-disconfirmation theory (e.g., [60]), and conceptualizations related to the mechanisms connecting complaint handling to customer loyalty (e.g., [15]). We conducted a moderated multilevel analysis of the complaint handling (recovery)–customer loyalty relationship by utilizing an ASCI data set of 35,597 complaining customers over a ten-year period across firms, industries, and economic sectors. Within the contingency modeling, we set out to better understand the implications of the recovery–loyalty relationship as moderated by the economic, industry, customer–firm, product/service, and customer segment factors. The implications of these influences (moderating effects) are next discussed for managers, policy makers, and researchers. We conclude with directions for future research.
Without a deeper understanding of the boundaries of the complaint handling–customer loyalty relationship—via the practical incorporation of the implications stemming from the moderators of economic, industry, customer–firm, product/service, and customer segment factors—firms will likely allocate cost estimates to complaint management that are too low for the required recovery actions or customer loyalty estimates that are too high, or both, instead of achieving an optimal point of recovery–loyalty yield. First, managers should recognize not only that industries vary widely in terms of the percentage of customers who complain but also that the characteristics of the economic sectors and industries can dictate the importance of complaint recovery to their customers. In an industry (i.e., market research) in which "best practices" from "leading service providers" are often recommended for adoption without regard to industry distinctiveness (e.g., [24]; [34]), our findings indicate that merely transposing a firm's complaint management from one industry to another is ill-advised and can be detrimental to a firm's performance. While this may sound self-evident, many managers are obsessed with seeking out cross-industry leaders to emulate toward improving their own customers' experiences (e.g., [ 3]; [55], [56]). There are clear differences across sectors and industries in customer experience management, customers as strategic assets, and the accompanying complaint management that should be undertaken.
Our findings also indicate that the financial ramifications of firms' complaint management efforts will likely differ significantly. Because complaint management matters more or less to customer loyalty across sectors in variant economic contexts and firms offering different classes of goods, the expected economic benefit to the firm aiming to reaffirm customer loyalty via aggressive complaint recovery will vary as well. Efforts that would produce a positive return on investment for firms in some industries offering certain goods may, at times, produce a negligible or even negative return on investment for firms in other industries. For example, we find that the recovery–loyalty relationship is stronger for customers with higher expectations of customization but weaker when the customers' expectations of product/service reliability are higher. Combine these findings with the sector and industry differences, and it is relatively easy to grasp that developing complaint management systems cannot be undertaken solely through cross-industry, best-practice benchmarking but instead must incorporate a more refined approach (i.e., based on the relevance of the economic, industry, customer–firm, product/service, and customer segment factors). Succinctly, sensitivity to economic sectors and industry contexts can save a firm from focusing too much or too little on complaint management. To be clear, this is not a call for industries with weaker recovery–loyalty relationships to ignore customer complaints. Rather, it is a call for managers to assess the most cost-effective means of soliciting and responding to customer complaints and having the dexterity to adjust those efforts when conditions warrant it.
Without context, the implications suggest that a profit-maximizing strategy simply requires that managers understand the impact of complaint recovery on customer loyalty in their industry. Added to this complexity, however, is the reality that profitability is not evenly distributed throughout the customer base. Profitability is driven by a small percentage of customers, with most customers not producing an adequate level of return ([37]). Consequently, complaint management systems designed to maximize financial performance are complex. They are likely too complex for frontline customer service representatives to handle unaided, particularly as they relate to the level of remuneration used to compensate complaining customers. Decision support systems need to be implemented that consider economic factors (and affect the expectations of customers), industry factors, and the relative profitability of customers. This will make it easier for customer service personnel to respond to complaining customers in ways that optimize customer satisfaction, customer loyalty, and the economic contribution of customers (while, importantly, also being mindful of customers' potential social media amplification of their dissatisfaction).
As possible solutions, some complaint management channels are less expensive to operate for firms than others. Often these channels vary in terms of personal contact. Interestingly, contemporary alternatives to the traditional channels of direct in-person or personalized telephone support may enhance customers' perceptions of complaint handing. For example, online customer service options, such as self-service and agent-assisted digital communication channels are on the rise and preferred by many consumers to more personal channels because of their speed of response. Customized and personalized web-based systems are clearly on the rise, and these systems appear to offer a preferred balance of customization and attentiveness and a less personalized approach. Of course, the effectiveness of different recovery strategies will be influenced by the environment in which the business operates.
For policy makers, our findings reposition previous thinking about customer complaints, complaint handling, and customer loyalty and, by extension, the health of the overall economy. Although variations in intensity across political administrations should be considered, many governments take active roles in monitoring both customers' complaints against firms and firms' responses to these complaints (e.g., in the United States via the Federal Trade Commission and the Better Business Bureau). Our findings suggest not only that varying complaint levels should be expected across industries and firms but also that customers' perceptions of how well firms have resolved their complaint issues should be expected to vary. These variations are due at least in part to the industry context within which the complaint was filed. Thus, striking a balance between overreaching in regulations (i.e., too many/overly constraining regulations) and underreaching in regulations (i.e., too few/overly flexible regulations) needs to be considered in policy.
Our study offers important implications for customer relationship researchers, in particular those focused on firm- and brand-related strategic issues and customer asset management. First, while complaint recovery is positively linked with customer loyalty across all economic sectors studied (which included seven of ten economic sectors in the marketplace), the strength of the relationship varies. We find that the recovery–loyalty relationship is stronger in faster-growing economies, for industries with more competition, for luxury products, and for customers with higher satisfaction and higher expectations of customization. Equally important, the recovery–loyalty relationship is weaker when the customers' expectations of product/service reliability are higher, for manufactured goods, and for men compared with women. Given the richness of the data, these findings raise important questions about the limitations of existing theory and empirical research to adequately explain the effectiveness of complaint recovery in securing customer loyalty. Consequently, we advance both theory and empirical understanding of the link between complaint handling and customer loyalty, including the theoretical and empirical boundaries captured by the economic, industry, customer–firm, product/service, and customer segment factors. Our contribution, as guidance for future research, is critical in that virtually all of the previous findings in this literature are derived from lab experiments (largely with student subjects) and/or single-point-in-time cross-sectional survey research, neither of which are designed to capture variance in these factors.
Second, consider existing meta-analyses from within the recovery–loyalty literature ([23]; [49]; [62]). These meta-analyses examining and aggregating recovery–loyalty effects across studies have often mentioned, but almost universally have failed to test, the possibly confounding effects of industry and economic contexts (e.g., [49]; [62]). The few studies that have included such examinations have focused on and tested macro effects at only the highest, aggregate levels (e.g., "service" industries vs. "nonservice" industries) ([23]). Such limited tests are understandable given the nature of the data that are aggregated for the meta-analyses, yet our results suggest the need for taking these effects into account at a disaggregated level for richer insights. We captured 7 of the 10 NAICS economic sectors and 41 industries within these sectors, and modeled a set of comprehensive moderators involving economic, industry, customer–firm, product/service, and customer segment factors. This modeling helped create a better understanding of the boundaries of the recovery–loyalty link. At the very least, our findings should spur further research to developing theories of customer complaint management and interpreting and comparing the effects observed across prior studies.
Third, we aimed to expand the theoretical foundations of the recovery–loyalty literature by blending theories from economics (EVL theory; [27]) with traditional marketing theory (expectancy-disconfirmation theory and the customer satisfaction literature). While most marketing studies that have examined the recovery–loyalty relationship have focused almost exclusively on marketing theory, with some complementary underpinnings in consumer psychology (e.g., justice theory), our blended economics–marketing approach provides a different and advantageous theoretical lens to expand knowledge. Through this broader and deeper lens, micro- and macroeconomic factors moderating the recovery–loyalty relationship are clearer and will contribute to the continued development and refinement of the contingency theory of loyalty returns to complaint management we offered in this research.
While we have aimed to close some of the enduring gaps in the marketing literature on customer complaints, complaint recovery, and customer loyalty, additional work remains. First, and as suggested previously, future research should work to systematically reassess existing findings from the marketing literature on the complaint handling–customer loyalty relationship based on the results of this study. For instance, a meta-analysis that more systematically integrates economic, industry, customer–firm, product/service, and customer segment factors as influencers of the recovery–loyalty relationship across the many studies produced over the last two decades could both reinforce our findings and substantially alter accepted conclusions. It is clear that previous findings have significant limitations and continually having a state-of-the-art understanding of the recovery–loyalty relationship is critically important to well-functioning firms' operational performance ([38]).
Second, future research should integrate our findings into models for determining the value of customer retention initiatives and customer loyalty, such as customer lifetime value (CLV). Models of CLV aim to illustrate the economic value of long-term customer loyalty and the financial benefits of customer retention for firms. Results are generally referenced to show that efforts that reduce churn often produce more valuable long-term customer relationships that increase profitable firm growth. Customer complaint recovery is, of course, just one of many customer retention tools. Like virtually any customer loyalty initiative that is examined through the lens of a CLV model, complaint management comes at a cost that can influence the profitability and margins of the customer segment being targeted. For example, firms may need to invest in and deploy relevant information technology and customer relationship management tools to handle complaints ([57]), including deciding on appropriate levels of human touch versus technology in dealing with complaint recovery interactions. Increasingly, advances in tools such as artificial intelligence and machine learning can facilitate managing many aspects of customer relationships in a cost-effective manner. However, the key for success will be to align deployment of such tools with a firm's strategy (e.g., [58]; [68]) and not to lose sight of the revenue impacts of marketing and technology decisions.
Ultimately, the value of a loyalty-building initiative can be deemed advisable or inadvisable on the basis of its impact on a customer segment's CLV. Some customer retention strategies can be predicted to pay off in the long run via higher CLV while others will not. Determining the difference between profitable and unprofitable loyalty efforts is important as it relates to customer complaints and complaint handling. As our results show, an essential element in gauging the true effect of customer complaint management on customer loyalty is understanding the moderators of this relationship. In particular, having a clear understanding of how the macro and micro moderators affect the strength of the relationship between complaint recovery and customer loyalty is vital to achieving superior firm performance. We examined a set of critical moderating factors on this relationship, but the dynamics of the marketplace keep evolving and so will the influencers of the recovery–loyalty relationship. For example, some indications exist that political ideology and partisanship may influence customers' complaining behavior, consumption experience, and loyalty (e.g., [35]; [43]).
Finally, we recommend two avenues for theory development in the complaint recovery–customer loyalty relationship literature. While our study has made strides in providing theoretical support for the relationship between economic, industry, customer–firm, product/service, and customer segment factors as moderators of this relationship, additional theorizing that more fully clarifies the varied and complex connections between these factors and the mechanisms driving consumer behavior could help inspire future empirical research and valuable practical insights. Relatedly, theorizing that helps explain the moderating effects of customer segment factors and demographic characteristics—such as gender and region of residence, where little theoretical or empirical work now exists—is needed. While significant in their own right, our findings regarding customer segment factors would have more robust practical implications if founded in a guiding theory. For example, most national firms tailor marketing and product offerings to men and women and variably across geographic regions, but would also likely do so for complaint management if given a compelling explanation for the moderating effects of these and other customer segment factors on the recovery–loyalty relationship.
Graph
Appendix. Economic Sectors and Consumer Industries in the Sample.
| Economic Sector | Consumer Industry |
|---|
| Manufacturing (MFG) | Beverages—Beer |
| Beverages—Soft Drinks |
| Tobacco—Cigarettes |
| Apparel |
| Athletic Shoes |
| Personal Care Products |
| Pet Foods |
| Personal Computers |
| Household Appliances |
| Consumer Electronics |
| Automobiles and Light Trucks |
| Cellular Phone Manufacturers |
| Retail Trade (RETAIL) | Department and Discount Stores |
| Specialty Retail Stores |
| Supermarkets |
| Gasoline Service Stations |
| Health and Personal Care Stores |
| E-Commerce Retail Websites |
| E-Commerce Travel Websites |
| Transportation and Warehousing (TRSPRT) | Parcel Delivery—Express Mail |
| U.S. Postal Service |
| Commercial Airlines |
| Information (INFM) | Telecommunications—Local and Long-Distance Telephone |
| Broadcasting Television |
| Publishing—Newspapers |
| Telecommunications—Cable Television |
| Cellular Telephone Service Providers |
| Telecommunications—Internet Service Providers |
| Computer Software |
| Motion Pictures |
| Finance and Insurance (FIN) | Banks |
| Life Insurance |
| Personal Property Insurance |
| Healthcare Insurance |
| Credit Unions |
| E-Commerce Financial Services Websites |
| Health Care and Social Assistance (HLTH) | Hospitals |
| Ambulatory Care |
| Accommodation and Food Services (ACCO) | Restaurants—Limited Service |
| Restaurants—Full Service |
| Hotels |
Footnotes 1 EditorsChristine Moorman and Harald van Heerde
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 ORCID iDG. Tomas M. Hult https://orcid.org/0000-0003-1728-7856
5 1The oldest known written customer complaint, which was inscribed about 3,800 years ago (c. 1750 BC) on the ancient Babylonian "Complaint Tablet of Ea-Nasir," illustrates that customers have long used threats of defection to express their dissatisfaction and seek recovery ([42]).
6 2We removed data from two economic sectors prior to analysis—Energy Utilities (gas and electric power) and Public Administration. Data for these sectors differ from the remaining sectors in the ACSI. Energy Utilities includes a far larger number of companies (nearly 30) than the average ACSI industry, due to regional monopolies in the industry, and thus includes far more completed interviews, a fact that could bias our aggregate model results. Regarding public administration, the study parameters for this sector were changed by ACSI in 2007, and samples before and after that date have only limited comparability. Pretesting confirmed suspicions, and thus, the data from the two sectors were eliminated so as not to confound our findings.
7 3The model contains a few variables for which the maximum VIF is greater than 10. However, with the exception of the interaction involving Generation X and the HANDLE variable, all other variables are statistically significant despite the high VIF. As [8] note, multicollinearity should be of less concern when high VIFs are due to product terms in interactions. Nonetheless, we further verified that results for the moderators are stable when we enter them sequentially in blocks.
8 4The authors gratefully acknowledge the input of Stephen Raudenbush on the HLM modeling.
9 5Indeed, we make use of this property in an exploratory analysis when we add the mean of the complaint handling variable in the model for the intercept at Level 2. Our key findings for the moderating effects of HHI, GDPGR, and LUXURY remain unchanged when we do so, and we find that the mean complaint recovery variable in Level 2 for the intercept is positive and statistically significant. This suggests that firms with better complaint management have higher customer loyalty, even after controlling for an individual customer's assessment of complaint handling.
6Our results for the key moderators of the recovery–loyalty relationship are qualitatively similar and robust even if we use different centering choices such as group-mean-centering of customer expectations and customer satisfaction variables at Level 1, and interaction of group-mean-centered HANDLE with such group-mean-centered variables.
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Understanding the Impact of Relationship Disruptions
Personal relationships between salespeople and customers are essential for the success of business-to-business relationships, and research has shown that a change of the salesperson can severely harm financial performance. However, such interpersonal relationship disruptions may also have positive effects by encouraging vitalizing reexplorations of the relationship. Using multilevel loyalty theory and relationship life cycle theory, the authors offer a comprehensive conceptualization of potentially countervailing consequences of relationship disruptions. In particular, disruptions may have different effects on resale revenue (from previously sold products) versus new sale revenue (from newly sold products), contingent on both the history and expected future development of the relationship. Therefore, this study examines moderators on the firm-level relationship prior to disruption and salesperson relationship management afterward. Longitudinal data from 2,040 customers of an international business-to-business firm reveal that a disruption can increase overall performance by more than 29%, depending on the firm-level relationship before disruption and the new salesperson's relationship management. Managers can use these findings proactively to evaluate and manage the risks and opportunities involved in relationship disruptions.
Keywords: business-to-business marketing; relationship disruption; relationship marketing; sales management; salesperson replacement
Customer relationships are among a company's most valuable assets, accounting for up to 20% of a firm's overall financial value ([12]). Accordingly, U.S. companies spend more than $12 billion annually to manage customer relationships and increase customer retention in business-to-business (B2B) settings ([ 6]; [85]); improving retention rates by 5% can raise profits by 25%–95% ([26]). The interpersonal relationship between a supplier's salesperson and the buying customer representative in particular is critical to establishing beneficial, long-term B2B exchanges ([29]; [35]). Therefore, scholars suggest that preserving interpersonal relationships is key to ensure the growth of B2B firms (e.g., [ 1]; [61]).
However, such interpersonal relationships can be easily disrupted ([13]; [64]; [75]), whether by managerial fiat (e.g., sales organization restructurings, account strategies, territory alignments) or through individual decisions (e.g., career path, retirement, turnover). A relationship disruption[ 5] refers to a change of salesperson in the relationship with the customer, that is, from one salesperson to another employed by the selling firm. Data suggest that 76% of U.S. consumer goods sales organizations have undergone restructurings in the past three years ([ 2]), and researchers estimate that salesperson turnover rates reach 20%–30% annually ([14]; [20]). According to Forbes, today's salespeople leave faster than ever, and 51% of them seek new jobs at other organizations ([17]).
Because relationship disruptions are so ubiquitous ([14]) and deeply impactful, they have relevant implications for B2B firms' performance ([10]; [20]). In particular, relationship disruptions may create a loss of customer knowledge, diminish interpersonal trust, and increase uncertainty ([10]), and accordingly, extant marketing research and practice tends theoretically to assume their negative effects ([56]; Table 1). [75] provide first empirical evidence that relationship disruptions lead to losses of up to 17.6% of total customer revenue, and [53] identify protecting disrupted customer relationships and their revenues as a top-three management priority after major organizational restructurings.
Graph
Table 1. Literature Review on Relationship Disruption Research.
| Authors | Research Setting | Key Dependent Variables Under Investigation | Empirical Strategy | Effects Conceptually Discussed | Effects Empirically Tested | Objective Financial Outcomes | Number of Moderators Empirically Tested |
|---|
| Bendapudi and Leone (2002) | Cross-industry B2B companies | Relationship vulnerability Customer satisfaction with the vendor firm and the transition procedures Customer's acceptance of a replacement employee Employee's willingness and likelihood to share information with vendor firm
| Qualitative study comprisingfocus groups with 72 participants (managers and key contacts) depth interviews with 47 managers survey of 100 managers
| Negative | None | No | 0 |
| Darmon (1990, 2008) | Conceptual studies including case analyses | Different types of costs Different types of benefits
| Case studies of a pharmaceutical products sales force and a maintenance product sales force | Negative and positive | None | No | 0 |
| Harrison-Walker and Coppett (2003) | Conceptual study including examples from pharmaceutical industry | Customer defection Company performance
| None | Negative | None | No | 0 |
| Lovett, Harrison, and Virick (1997) | Conceptual study | Risk of customer turnover
| None | Negative | None | No | 0 |
| Palmatier (2008) | Cross-industry B2B sales representatives | Customer value
| Hierarchal linear modeling on survey data from446 customers, nested in 427 sales representatives
| Negative | None | Yes | 1 |
| Shi et al. (2017) | U.S.-based distributor of electrical component products | Total revenue
| Difference-in-differences regression on archival data from830 customers with relationship disruption (nested in 129 sales reps) 31,615 customers without relationship disruption (nested in 550 sales reps)
| Negative and positive | Negative | Yes | 2 |
| Our study | International B2B logistics company that sells standardized postal products and services, as well as transport solutions | Total revenue Resale revenue New sale revenue
| 680 customers with relationship disruption 1,360 customers without relationship disruption
| Negative and positive | Negative and positive | Yes | 8 |
However, we posit that relationship disruptions also have positive effects. First, after a disruption, a new incoming salesperson and the customer become newly acquainted and can reexplore mutual opportunities for value creation (e.g., [77]). In this scenario, a relationship disruption may improve the accuracy of customer need identification, because an incoming salesperson is less likely to exhibit complacency and more likely to ask unbiased questions ([ 4]; [27]; [49]). The resulting offerings may better fit customer needs and increase the probability of sales. Second, a relationship disruption might create an opportunity for customers to learn about other available products. By interacting with a new salesperson with different sales experience, industry knowledge, and product focus, the customer might learn about other products, which may increase cross-buying. Third, a new salesperson needs to devote enhanced effort to obtain in-depth customer knowledge and build customers' trust after a disruption. This enhanced effort may elevate customers' perception of the value of the relationship ([31]; [78]) and motivate them to expand the relationship.
These potential positive and negative effects of a relationship disruption do not exist in a vacuum but instead should be contingent on the prior relationship between the selling firm and the customer, as well as the salesperson's relationship management after the disruption. For example, if a customer has a strong business relationship with the selling firm, a relationship disruption at the interpersonal level may not infringe as much on the overall business relationship. In some relationship contexts, changing the salesperson could initiate a reexploration and revitalization of the relationship, such that the positive effects may outweigh the negative effects. In a preliminary survey of 273 U.S. B2B purchasing managers, we gained initial support for this premise: among those who expected any impact of a disruption, only 22% predicted that future revenues would decline, whereas 78% of the managers anticipated future relationship revenue growth, and 31% indicated that they had proactively prompted relationship disruptions in the past, to revitalize B2B relationships (for details, see the Web Appendix). In response to this practical need and the scarcity of existing research, we ( 1) explore the potential positive effects of relationship disruptions and ( 2) propose a comprehensive contingency framework to understand when and how relationship disruptions (negatively or positively) affect the future performance of a customer relationship.
To the best of our knowledge, no prior research has conceptualized or hypothesized positive performance effects of an interpersonal relationship disruption, or empirically investigated such effects. Some authors suggest negative effects of long-term interpersonal relationships (e.g., [ 4]; [27]; [49]), but few studies specifically raise (and none empirically test) the possibility of beneficial effects of disruptions ([20]; [41]; [75]). We propose that a relationship disruption may allow firms to seize new, unexplored business opportunities, with benefits for the relationship's financial performance. We integrate multilevel loyalty theory (MLT; [61]) with relationship life cycle theory (RLT; [24]) to theorize these performance effects, differentiated into a loyalty and relationship development path. On the loyalty path, the loss of a salesperson as a contact leads to losses in revenue earned from previously sold products ("resale revenue" hereinafter). The relationship development path instead emphasizes the development of a new relationship with the incoming salesperson and the resulting gains in revenue earned from newly sold products ("new sale revenue" hereinafter). Data pertaining to 2,040 B2B customers of a leading European logistics company over a four-year period, implemented in a series of difference-in-differences models, reveal that relationship disruptions can decrease resale revenue by 28.8% but can also increase new sale revenues by 52.2%. These novel findings indicate that a disruption can simultaneously harm and benefit total revenue through two opposing paths.
We posit that a relationship's future development is fundamentally contingent on the relationship prior to the disruption and how it is managed afterward. Our conceptual framework, based on MLT and RLT, aims to explain the extent to which customers are motivated to maintain (through resale revenue) and expand (through new sale revenue) the relationship with the selling firm after a disruption. This motivation stems from customers' evaluations of the past value they have received from the relationship, their anticipation of future value creation potential, and value-creating activities by the selling firm after the disruption ([41]; [61]; [76]; [85]). Accordingly, we consider three categories of moderators: firm-level relationship strength and dynamics prior to disruption (in particular, the value received in the past and potential for value creation; [57], [58]) and the incoming salesperson's relationship management after the disruption (value-creating activities; [59]). Our empirical analysis indicates that, in favorable conditions, a relationship disruption can increase total revenue with a customer. For example, when the firm-level relationship was strong and prior dynamics suggest value creation potential and minimal risks for value losses, a disruption leads to substantially lower losses in resale revenue and higher gains in new sale revenue, leading to total revenue increases of 10.7%–22.6%. In these circumstances, effective relationship management activities by the incoming salesperson even can increase total revenue by 28.9%–41.1%. These findings offer meaningful, actionable managerial recommendations for coping with relationship disruptions, which we discuss and summarize in a decision framework in the managerial implications section.
Most prior research has assumed the negative performance effect of relationship disruptions, so to establish an initial sense of their potential positive effects and contingencies, we conducted semi-structured, qualitative interviews with 11 experienced managers from different industries (see the Appendix). The preliminary study explores likely contexts or reasons for beneficial effects, as well as how experienced practitioners evaluate disruptions. The questionnaire covers three main topics. We asked interviewees to explain their understanding of an interpersonal relationship and its importance to their business, whether relationships should be protected or can be disrupted, and their predictions of the effects of relationship disruptions. The semistructured interviews offer deep insights through follow-up queries, additional responses, and practical examples.
The practitioners perceive ambivalent effects of relationship disruptions, as succinctly summarized by SM5: "When a relationship experiences radical change, there are always opportunities and risks. Risks of losing things, and opportunities to freshly look upon the customer and recognize new potentials." As PM2 confirms, "Usually I was able to learn something from a changing salesperson. He conveyed knowledge to me that I hadn't had before. That can be an opportunity. But such a change can also backfire. For example, if my contact person changes and I now have someone in front of me who is not as deeply involved in the matter as I am." These practitioners also note several contingencies that may determine the extent to which the opportunities and risks materialize. A quote from the sales director of the firm with which we cooperated for the main study exemplifies this insight:
When the customer values our relationship and our mutual business history, assigning a new sales rep will not throw him off the track. Quite the opposite, I think that especially then, our sales reps may get the chance of freshly looking upon our settled old-timer relationships and give them a fresh start....Yes, I truly see benefits.
We elaborate on these contingencies in subsequent sections, thereby blending the results of our preliminary study with our theoretical framework and hypotheses.
From a customer perspective, relationship disruptions inherently comprise two facets: the loss of the customer's key contact with the selling firm and the initiation of a new interpersonal relationship with an incoming salesperson. We argue that these two facets evoke distinct effects of a relationship disruption on the firm's resale revenue (earned from previously sold products to customer) and new sale revenues (earned from newly sold products to customer) with a customer. The effects of the exit of a salesperson may be explained best by MLT ([60]; [61]); the potential initiation of a new relationship with the incoming salesperson may be explained best by RLT ([24]; [85]). Therefore, we situate our research in accordance with both MLT and RLT and posit that two distinct paths explain how relationship disruptions differentially influence resale revenue and new sale revenue. By combining both theories, we derive three contingency categories for both paths, which account for relevant determinants of customers' value perceptions, to predict their motivation to maintain or expand a relationship after disruption (see "Theoretical Rationale for a Contingency Perspective"). Figure 1 depicts our framework and hypotheses.
Graph: Figure 1. Conceptual framework and hypotheses.aBefore disruption.bAfter disruption.cRefers to a change of salesperson in the relationship with the customer (i.e., from one salesperson to another employed by the selling firm).
Multilevel loyalty theory (MLT) differentiates salesperson-owned and firm-owned loyalty; it has been tested and applied in many studies (e.g., [61]; [76]; [84]). This theory posits that customers' loyalty might accrue to the salesperson and to the selling firm ([60]). Through successful interactions and exchanges, the customer grows to trust an individual salesperson ([23]; [52]), increasing loyalty to that person. Because a salesperson often represents a selling firm's "face to the customer," loyalty to the salesperson constitutes a key driver of a B2B relationship's financial performance ([ 8]). Through successful transactions, the customer also might develop trust in the selling firm, its capabilities, and its products, increasing loyalty to that firm and the probability of future, repeated purchases ([25]; [61]).
An interpersonal relationship disruption may cause salesperson-owned loyalty and its beneficial effects to disappear ([10]; [75]), thereby hampering the customer's loyalty to the selling firm ([61]) and reducing resale revenues ([ 8]; [11]). We label this mechanism the "loyalty path."
Companies such as AT&T and Merck have deliberately cut sales jobs recently ([ 7]; [65]), which means severing personal connections and related interpersonal benefits between customers and affected salespeople. Such experiences can be frustrating for customers, as PM2 put it in our preliminary study: "It's always sad if the know-how that you expect from a salesperson suddenly vanishes and I have to explain to the salesperson again what was agreed upon." According to MLT, this loss should, on average, weaken customers' loyalty to the selling firm ([61]), which might induce them to reconsider their repeat purchases ([57]; [66]), with potentially detrimental results for resale revenue. We hypothesize the following:
- H1: An interpersonal relationship disruption decreases a selling firm's resale revenue with a customer.
Relationship life cycle theory (RLT) explains how relationships develop along distinctive relationship stages ([24]; [41]; [68]). If relational actors explore and fortify mutual communication and interaction options and receive sufficient value from the relationship, they likely enter advanced, productive relationship stages, characterized by high mutual commitment, investments, and dependence ([24]). For relationship formation, RLT puts particular emphasis on the exploration stage, when the salesperson and customer get acquainted, identify the customer's needs, and explore value creation potential ([24]); it constitutes a key precondition for developing and maintaining relationships ([24]; [85]).
After a relationship disruption, the new, incoming salesperson likely tries to initiate a new interpersonal relationship with the customer.[ 6] Both partners become acquainted, exchange new information, and form expectations about how to interact in the future ([24]; [41]). According to RLT, this likely involves a joint need exploration phase ([24]; [58]) marked by mutual scanning and learning ([42]; [74]; [85]), which can foster the generation of new sale revenue through cross-selling ([73]). As a practitioner in our preliminary study acknowledges, after a disruption, "you go through another phase of learning, testing and trial, to prove the customer that he or she can rely on you" (SM4). We label this mechanism the "relationship development path."
While a disruption also might encourage the salesperson to offer substitute products for known customer needs,[ 7] our reasoning of truly new need identification is in line with evidence from our interviews, as PM1 asserts, "If we haven't found a solution for a problem while collaborating with the previous salesperson, a new salesperson may provide an opportunity. Maybe he knows the problem from another company and has experience that we can use to solve the problem." Thus, we hypothesize the following:
- H2: An interpersonal relationship disruption increases a selling firm's new sale revenue with a customer.
The effects of relationship disruptions on revenue depend on customers' motivation to maintain or expand the relationship. If customers are highly motivated to maintain the relationship with a selling firm, it may compensate for the loss of a salesperson and thereby safeguard resale revenues. Regarding new sale revenue, an incoming salesperson can effectively identify new needs for customers only if the customers are motivated to reexplore and expand the relationship and cooperate in the newly initiated exploration process ([85]).
Both MLT and RLT posit that customers' motivation to maintain or expand a relationship depends on their overall value perception of that relationship ([24]; [61]). This value perception reflects their evaluations of past value received from the relationship, anticipation of future value creation potential, and experience of value-creating activities by the selling firm ([41]; [61]; [76]; [85]). Accordingly, we include ( 1) the strength and ( 2) dynamics of the firm-level relationship before disruption as well as ( 3) salespeople's relationship management after disruption as moderators in the contingency framework. We provide an illustrative overview on our contingency reasoning in the Web Appendix.
First, the strength of the firm-level relationship prior to disruption indicates a customer's bonds to the selling firm, primarily established because the relationship created value for the customer in the past ([57]; [83]). If strong enough, these bonds may lead the customer to find ways to overcome negative effects of disrupted interpersonal ties ([38]) and foster relationship expansion. Second, recent dynamics in the firm-level relationship may signal potentials for value creation after disruption ([58]). If highly routinized buying in the past limits need identification processes ([28]), a disruption may reveal greater potential for future value creation. Third, relationship management efforts by the new salesperson shape the customer's perception of value-creating activities after disruption ([59]). Increased personal communication might quickly build trust in the new salesperson ([85]). In the following, we hypothesize on all three classes of moderators in our conceptual model (Figure 1).
The strength of the interfirm relationship, as reflected by customers' perceptions of value and commitment to the relationship, is a core characteristic of B2B customer relationships (De [22]; [57]; [85]). Customers may perceive an interfirm relationship as particularly strong if they receive benefits from the selling firm, such as financial incentives or customized solutions that provide functional value ([22]; [67]). That is, for B2B customers, beneficial prices constitute a major part of the value they receive ([ 3]), but as a chief executive officer (CEO) interviewed for the [40] puts it, they also "want personalization of services and products. It is all about the market of one." Both financial and functional benefits thus might motivate customers to reexplore and expand the relationship with the selling firm, along the relationship development path ([22]; [24]; [41]; [61]). A positive evaluation of value received identifies the relationship as attractive and worthwhile ([24]) and fosters expectations of future benefits and value creation ([33]; [82]).
The effectiveness of the need exploration process in turn may depend fundamentally on the customer's motivation to expand and reexplore value creation potential. If customers are highly motivated to explore value creation potential, they are likely to cooperate with the incoming salesperson in the need exploration process and exchange substantial new or hidden information ([24]; [85]). This joint need exploration process effectively can uncover novel customer needs and facilitate cross-selling ([73]).
- H3a: The positive effect of an interpersonal relationship disruption on new sale revenue is stronger if prior financial benefits are high and weaker if prior financial benefits are low.
- H3b: The positive effect of an interpersonal relationship disruption on new sale revenue is stronger if prior functional benefits are high and weaker if prior functional benefits are low.
We have argued that the loss of the leaving salesperson may undermine customers' loyalty to the selling firm and reduce resale revenue (H1), on the loyalty path. However firm-level relationship strength can buffer the negative effects of disruptive relationship events ([38]). If customers receive substantial benefits from the selling firm prior to disruption, whether financial or functional, they should be more motivated to maintain the firm relationship, despite the change of salespeople, so their resale purchases would be less likely to decrease. Our reasoning is supported by MLT and our preliminary study, in which SM8 predicts, "If you supply top products and customers are satisfied, then the interpersonal relationship plays only a minor role," and PM1 confirms, "Of course you may be dependent on the supplier.... Then you have to patch things up, even if the chemistry is not right." Thus,
- H4a: The negative effect of an interpersonal relationship disruption on resale revenue is weaker if prior financial benefits are high and stronger if prior financial benefits are low.
- H4b: The negative effect of an interpersonal relationship disruption on resale revenue is weaker if prior functional benefits are high and stronger if prior functional benefits are low.
Relationship strength also might result from another common firm-level connection: contractual bonds. These bonds do not pertain to customer benefits directly but instead reflect contractual obligations ([16]; [42]; [71]), so they offer another potential moderator. Contractual obligations likely increase customers' motivation to maintain the B2B relationship after a disruption, because deviating from those obligations would be costly ([42]). Prematurely ending contracts can incur contractual penalties, switching costs, and transaction costs to form contracts with new vendors ([50]). To avoid them, customers might hesitate to change their resale purchases, even after disruption. In our preliminary interviews, SM5 elaborates on contracts that fix firm-level relationships for a period of three years, which "objectif[y] relationships and make them less dependent on interpersonal aspects." Thus,
- H4c: The negative effect of an interpersonal relationship disruption on resale revenue is weaker if prior contractual bonds are high and stronger if prior contractual bonds are low.
We do not predict that contractual bonds influence the relationship development path. According to RLT, a customer's motivation to expand a relationship depends on expectations of future benefits. The presence of contractual bonds entails switching costs, reinforcing the loyalty path, but does not necessarily entail future benefits ([42]).
Relationship dynamics are no less important than relationship strength for understanding financial performance ([58]). The developmental trajectory reflected in these variables contains information about future trends, business potential, and risks ([33]; [58]), beyond the mean level of prior relationship strength. In this sense, RLT and MLT converge on the idea that prior relationship dynamics determine future relationship developments ([41]; [61]). For example, relationship dynamics prior to a disruption should shape customer expectations about the prospective future value of the relationship and influence motivations to maintain or expand it ([24]; [33]; [58]). In accordance with purchasing research ([ 5]; [54]), we account for what customers recently purchased (the purchase object), by including product line growth ([66]) and complex growth ([54]), and we account for how customers recently purchased (the purchase process; [43]) by including variability of the purchase process ([ 5]; [28]).
Regarding dynamics related to the purchase object, a relationship can develop if the customer purchases more complex products from a selling firm as opposed to rather simple products ([80]). Purchasing complex products requires close coordination between the customer and the selling firm as well as a proficient need identification process ([51]; [77]; [81]). According to a McKinsey report, "high-value transactions are becoming increasingly complex, often including risk-sharing and service-level agreements as customers ask vendors to 'put more skin in the game' to ensure that they stay committed to providing real value" ([21]). When such complex business purchases increase rapidly, prior to a relationship disruption, the customer and selling firm likely have engaged in close exchanges to explore the customer's needs and thus exploited the customer's business potential. This customer is unlikely to be motivated to reexplore value creation potential further or purchase new products ([42]; [85]). Thus, for the incoming salesperson, identifying novel, unaddressed needs may be more difficult, and the potential to cross-sell new products is limited. Conversely, if complex purchases have not grown notably prior to the disruption, customer needs might not have been explored recently ([42]; [85]), so the incoming salesperson has more opportunity to uncover unmet customer needs, with implications for the customer's motives to expand the relationship. In our preliminary study, SM4 explains, "Opportunities arise particularly if customers' potential is untapped. You may then improve results when exchanging the colleague."
- H5a: The positive effect of an interpersonal relationship disruption on new sale revenue is stronger if recent growth in complex purchases is low and weaker if recent growth in complex purchases is high.
In addition, a relationship progresses if the customer buys from more distinct product lines. This reasoning is similar to our previous argument: If customers expand their relationship with the selling firm prior to disruption by purchasing a more distinct product lines, their needs likely have been explored recently ([73]; [85]), so they have little motivation to reexplore and expand the relationship. As SM2 notes, "It could be that the customer does not purchase more because he's finished building up this business."
- H5b: The positive effect of an interpersonal relationship disruption on new sale revenue is stronger if recent growth in the number of purchased product lines is low and weaker if recent growth in the number of purchased product lines is high.
Regarding dynamics in the purchase process, purchasing might be highly variable or strongly routinized ([81]). Routinized processes involve standardized buying procedures, with little effort or deliberation ([43]). The likelihood of continuous joint need exploration or up-to-date need identification before the relationship disruption thus seems low. As SM3 puts it, "It frequently happens that salespeople 'fall asleep' in a relationship and no longer expend effort." But if the customer's purchasing has been strongly variable, the responsible salesperson likely has engaged more and extensively explored value creation potentials with the customer. Therefore, after disruption, the new incoming salesperson may be less likely to uncover new needs or generate additional new sale revenue with this customer ([24]; [42]).
- H5c: The positive effect of an interpersonal relationship disruption on new sale revenue is stronger if the recent variability of the purchase process is low and weaker if the recent variability of the purchase process is high.
Regarding dynamics related to the purchase object, prior sales research has established that salespeople take on increasingly important roles at higher levels of complexity ([77]; [79]; [80]). For example, in complex solution selling contexts, salespeople must be involved to deploy offerings effectively for customers ([47]; [63]). If complex sales have grown prior to disruption, the customer–salesperson bond likely has grown stronger too. When this strong bond is disrupted, customers may feel neglected by the salesperson or selling firm and experience a sense of disappointment or lost trust ([33]). According to SM2, "When replacing a sales rep, I take away the entire history. Whatever we invested into the relationship, we have to prove to the customer once again." Thus, customers should be considerably less motivated to maintain the relationship with the firm after disruption in this case.
- H6a: The negative effect of an interpersonal relationship disruption on resale revenue is weaker if recent growth in complex purchases is low and stronger if recent growth in complex purchases is high.
However, growth in the number of different purchased product lines does not necessarily hinge on interpersonal ties and individual salespeople's involvement. Rather, this measure might imply increasing commitment or dependence of the customer on the selling firm ([66]). The breadth of the purchased product portfolio often serves as an important indicator of customers' motivation to maintain a business relationship ([44]; [46]; [66]). Consequently, we expect that a growing number of purchased product lines prior to the disruption buffers the negative effect of a relationship disruption on resale revenue with the selling firm.
- H6b: The negative effect of an interpersonal relationship disruption on resale revenue is weaker if recent growth in the number of purchased product lines is high and stronger if recent growth in the number of purchased product lines is low.
Finally, regarding dynamics in the purchase process, we argue that the variability of the purchase process prior to a disruption buffers the negative effect of a relationship disruption on resale revenue. High purchase process variability implies that the relationship is dynamic and changes frequently, so customers may be accustomed to devoting substantial efforts to interact, coordinate, and negotiate with the firm. They also may be used to changes in the relationship, so they can cope more readily with a relationship disruption ([33]). Conversely, if the selling process is strongly routinized, customers may cherish and expect continuity in the sales procedures, and a relationship disruption that severely disconfirms these expectations may induce customers to search for alternative suppliers, reducing their resale revenue.
- H6c: The negative effect of an interpersonal relationship disruption on resale revenue is weaker if recent variability in the purchase process is high and stronger if recent variability in the purchase process is low.
Both MLT and RLT contend that relationship development and customers' motivation to maintain or expand a relationship depend on individual salesperson activities ([24]; [41]; [61]). Extant research emphasizes a key role of personal communication in particular ([85]). Thus, we posit that the incoming salesperson's personal communication intensity—that is, the extent to which (s)he personally communicates with a customer—affects customers' motivation to maintain the relationship and retain resale revenue (loyalty path). Salespeople also can motivate customers to expand and reexplore the relationship by offering them a broader range of products ([85]) and tap new sources of value ([73]). Accordingly, the incoming salesperson's cross-selling intensity, defined as the propensity to offer a broader product portfolio, may affect the generation of new sale revenue after disruption.
Salespeople vary the breadth of the product portfolio they offer, in their efforts to cross-sell and expand the relationship ([72]). Some salespeople focus on specific products; others adopt a more general approach and offer a wider product range. We argue that a wider product range makes the positive effect of relationship disruption on new sale revenue more pronounced. If the incoming salesperson engages in greater cross-selling intensity than the leaving salesperson, it implies a greater capacity to offer and effectively tailor new products to customers' needs. For customers, this broad portfolio of products that address their needs and create value offers a key motivation to reexplore and expand the relationship ([85]), increasing the likelihood of new sale revenue. Our preliminary study corroborates this notion; as SM1 notes: "A new assignment makes sense if there's a lack of capabilities....If you need special expertise, then you replace the salesperson."
- H7a: The positive effect of an interpersonal relationship disruption on new sale revenue is stronger if the incoming salesperson exhibits greater cross-selling intensity than the leaving salesperson and weaker if the incoming salesperson exhibits lower cross-selling intensity.
We have proposed that a relationship disruption may reduce a customer's resale revenue because the leaving salesperson no longer provides a relational tie with the selling firm (H1). However, if the incoming salesperson pursues relationship maintenance through personal communication, such as personal visits ([78]), a personal bond with the customer might result more quickly, increasing customers' motivation to maintain the relationship with the firm. As [45], p. 24) note, "Communication builds trust." Thus, incoming salespeople's personal communication intensity may compensate for the loss of the exiting salesperson, buffering the harmful effects of relationship disruptions on resale revenue. As PM2 explains, "A changing salesperson has something positive if this increases personal service.... Today our contact checks in with us regularly. We hadn't heard much from her predecessor."
- H7b: The negative effect of an interpersonal relationship disruption on resale revenue is weaker if the incoming salesperson exhibits greater personal communication intensity than the exiting salesperson and stronger if the incoming salesperson exhibits lower personal communication intensity.
We employ a quasi-experimental design with difference-in-differences (DiD) models to estimate the causal effects of a relationship disruption ([75]). We collected data from a central European B2B logistics company (public limited company), one of the five largest logistics providers in its country; this company generates approximately $1.45 billion annually in total revenue. It provides a broad portfolio of logistics and warehousing services, ranging from standardized postal products and services (e.g., courier, express, parcel services) to complex transport services (e.g., logistic systems, special transport or warehousing) to customized logistics solutions (e.g., outsourcing of logistics and business processes, e-commerce solutions).[ 8] Its 5,400 employees serve small (average $36,800 annual total revenue) to large ($1.39 million annual total revenue) B2B customers across six sales regions. Customers come from various industries, such as wholesale and retail trade, manufacturing, transport and logistics, information and communication, finance, insurance, real estate, and technical services. For its field-based sales approach, the firm relies primarily on direct sales and applies a "one face to the customer" philosophy, such that each salesperson is solely responsible for her or his accounts and promotes all product categories offered by the company, with the support of specialists from marketing or logistics. Salespeople all are subject to the same compensation and incentive scheme, with a commission added to a fixed yearly salary (maximum of 25% of the fixed salary). For the variable compensation, 40% can be achieved by generating additional new sale revenues (cross-selling), whereas 60% can be achieved by generating additional resale revenues (upselling).
To explore the effects of relationship disruptions, we gathered nationwide performance records for B2B customers in all major sales regions from the company. For every customer, we obtained monthly product-level transaction records for 2012–2015, including sales volume, prices, and quantities. The data cover 7,102 B2B customers, 240 product categories, and 48 observation points, which produced a data set of more than 3.46 million transactions. In addition, we gathered key product characteristics (e.g., complexity, degree of customization) from the company. Then, we gathered and matched information on relationship disruptions from the company's customer relationship management system. Before 2016, no structural reorganization or reassignment program that might have prompted systematic relationship disruptions took place; that is, relationship disruptions occurred randomly, generally due to individual salesperson factors (e.g., job rotation, career decisions, relocation) or exogenous reasons (e.g., retirement).
The sample includes customers directly linked to and served by a specific salesperson. We define the disruption group by three conditions. First, customers must have experienced a relationship disruption in one of the four quarters of 2014. This time frame ensures that we have sufficient data before and after the disruption. These four quarters also provide the main reference for sampling the control group and calculating the time frames for the measures. The measures pertain to data before or after the respective quarter. We include quarter dummies in a subsequent analysis to control for seasonal heterogeneity (see the "Model Specification and Results" subsection). Second, customers may react differently to repeated relationship disruptions, so we include only customers that experienced exactly one disruption during 2012–2015, which ensures comparability among the disruption group. Third, none of the relationship disruptions experienced by the customers resulted from a demotion or promotion of the customer's status.
For the control group, we randomly sampled customers that did not experience any relationship disruption. Following [75], we sampled twice as many treated customers for each relevant quarter (n = 1,360). This stratified random sampling spans each customer class (i.e., categorized by customer total revenue volume, as assigned by the company) and industry, to support a comparable distribution in every quarter.
We aggregated the longitudinal data into three periods. Period T1 reflects combined data from 12 months before, and Period T2 is the 12 months after, relationship disruption. Period T0 comprises data from 12 months before Period T1, to predict the outcome in Period T1. The dependent variables thus reflect aggregated data from periods T1 and T2, whereas the independent variables rely on data from periods T0 and T1, which helps prevent reverse causality concerns when predicting the outcomes. Table 2 contains a detailed explanation of the variables.
Graph
Table 2. Constructs' Description, Representative Studies, and Operationalization.
| Constructs | Description and Conceptual Meaning | Representative Studies | Operationalization in this Study |
|---|
| Performance Indicators (DVs) |
| Total revenue (log) | Total sales revenue generated with customer | | Log-transformed total revenue generated with the customer in periods T1 and T2 |
| Resale revenue (log) | Sales revenue generated by products previously sold (customer repurchases) | Anderson, Chu, and Weitz (1987); Robinson, Faris, and Wind (1967) | Log-transformed revenue from previously sold products or services to customer in periods T1 and T2 |
| New sale revenue (log) | Sales revenue generated by newly sold products (customer cross-purchases) | Anderson, Chu, and Weitz (1987); Schmitz (2013) | Log-transformed revenue from newly sold products or services to customer in periods T1 and T2 |
| Firm-Level Relationship Strength Moderators |
| Financial benefits | Customers' financial benefits expressed by higher discounts compared to other customers indicating past value received by customer | Hennig-Thurau, Gwinner, and Gremler (2002); Ulaga and Eggert (2006) | Mean price paid by other customers for focal customer's product portfolio, divided by price paid by focal customer in periods T0 and T1 |
| Functional benefits | Customers' functional benefits expressed by customized offerings indicating past value received by customer | Gwinner, Gremler, and Bitner (1989); Palmatier et al. (2006) | Sales revenues generated with customized products and solutions divided by total revenue generated with customer in periods T0 and T1 |
| Contractual bonds | Contractual obligation between customer and selling firm through contractually fixed transactions indicates formal (non-value-creating) bonds | Jap and Ganesan (2000) | Sales revenues generated with contractually fixed transactions divided by total revenue generated with customer in periods T0 and T1 |
| Firm-Level Relationship Dynamics Moderators |
| Complex growth | Recent growth in revenues from complex products through intense coordination of relational partners implies value loss potential and reduced value creation potential after disruption | Selnes and Sallis (2003) | Mean growth rate in share of revenues generated with complex products between quarters of the periods T0 and T1 |
| Product line growth | Recent growth in distinct product lines bought implies no value loss potential but reduced value creation potential after disruption | Reinartz and Kumar (2003) | Mean growth rate in count of distinct product lines purchased by focal customer between quarters of periods T0 and T1 |
| Variability in purchase process | Variable purchase process from adaptions in paid price, type, and quantity of repeatedly purchased products implies reduced value creation potential after disruption | Anderson, Chu, and Weitz (1987); Grewal et al. (2015) | Comparative measure as percentage share (%) of revenue generated with reconfigured repurchases by the customer compared with last purchase (T0, T1) |
| Salesperson-Level Relationship Management Moderators |
| Cross-selling intensity | Incoming salesperson offering a broader product portfolio implies higher value creation for the customer | Schmitz, Lee, and Lilien (2014) | Incoming salesperson shows higher sales dispersiona among available product categories from the selling firm than leaving salesperson (1, 0 otherwise) (T2) |
| Personal communication intensity | Incoming salesperson who prefers personal over nonpersonal communication with customers implies higher value maintenance for the customer | Palmatier et al. (2008); Reynolds and Beatty (1999) | Incoming salesperson shows higher mean ratio of personal visits divided by all contacts at customers than leaving salesperson (1, 0 otherwise) (T2) |
1 a Calculated using a Herfindahl index ([73]).
2 Notes: Resale revenue and new sale revenue add up to total revenue. Period T0 is 24 to 12 months before the relationship disruption, period T1 is 12 months before the relationship disruption, and T2 is 12 months after the relationship disruption.
We measure total revenue as sales revenue generated with a customer during the respective period. Resale revenue is the volume of sales revenue in a period generated by products or services that the customer previously bought; new sale revenue is sales revenue generated from products bought for the first time in that period (cross-purchase). We use log-transformations of all three dependent variables in our subsequent analysis.
Financial benefits capture the relative price advantage granted to the customer (i.e., firm-controlled discounts), relative to other customers ([37]). Functional benefits ([57]) reflect the share of sales revenue generated by selling highly customized products or services to the customer (e.g., outsourcing logistics operations). Contractual bonds refer to the share of sales revenue from contractually fixed transactions with the customer (e.g., periodic cargo logistics).
Complex growth is the growth rate in the share of revenue generated with complex products in each period (e.g., information technology logistics services), indicating relationship intensification ([74]). Product line growth reflects the change in the number of distinct product lines purchased by the customer in each period, signaling its increasing dependence and commitment ([66]). Variability in the purchase process is measured as the percentage share of sales revenue generated from reconfigured transactions, compared with the previous transaction (e.g., different quantity, price change) ([28]).
Cross-selling intensity indicates whether sales by an incoming salesperson are more strongly dispersed across different product categories of the selling firm than sales by the exiting salesperson ([73]), which would imply a stronger cross-selling affinity (= 1, 0 otherwise). Personal communication intensity reflects whether the incoming salesperson communicates more personally with a customer (e.g., visits rather than phone calls), compared with the leaving salesperson (= 1, 0 otherwise).
We control for customer interactivity with the selling firm using the number of prior personal visits to the customer ([62]), the customer's relative importance to the selling firm in the sales region (i.e., share of selling firm's revenue in region generated with this customer; [75]), absolute customer portfolio breadth (i.e., count of distinct product lines), and the customer's portfolio complexity (i.e., revenue share of complex products) in each period. We consider three customer sales trend variables ([75]) to measure sales growth rates prior to the disruption. Dummy variables account for the customer's industry, the sales region, and the sampled quarter.
The descriptive statistics for the data set are in Table 3. Table 4 illustrates the comparison of the disruption and control groups ([15]; [39]; [75]); it indicates an appropriate balance. In addition, customers in both groups are comparably distributed across industries; 46.3% of the disruption group and 47% of the control group operate in wholesale and retail trade industries, and 38.1% and 37.5%, respectively, operate in manufacturing industries. We provide some model-free evidence and illustrate common trends for the outcome developments prior to disruptions in the Web Appendix.
Graph
Table 3. Descriptive Statistics and Correlations.
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
|---|
| 1: Total revenue (log) | | | | | | | | | | | |
| 2: Resale revenue (log) | .89** | | | | | | | | | | |
| 3: New sale revenue (log) | .38** | .21** | | | | | | | | | |
| 4: Financial benefit | −.14** | −.14** | −.06** | | | | | | | | |
| 5: Functional benefit | −.03 | −.01 | .001 | −.01 | | | | | | | |
| 6: Contractual bonds | .22** | .09** | .02 | .08** | .003 | | | | | | |
| 7: Complex growth | .10** | .09** | .05** | .01 | .004 | .05** | | | | | |
| 8: Product line growth | −.08** | −.26** | .12** | .03 | −.04* | .11** | −.02 | | | | |
| 9: Variability in purchase process | −.09** | −.04* | −.14** | .08** | .01 | .12** | .11** | −.03* | | | |
| 10: Cross-selling intensity | −.02 | −.004 | −.07** | .03 | −.02 | −.02 | −.02 | −.02 | .02 | | |
| 11: Personal communication intensity | −.07** | −.04* | −.11** | .02 | −.02 | −.11** | .01 | −.02 | .05** | .32** | |
| Mean | 10.34 | 10.13 | 5.22 | 1.02 | .02 | .42 | .13 | 1.20 | .17 | .09 | .07 |
| SD | 1.80 | 2.32 | 2.62 | .14 | .04 | .36 | .75 | 1.03 | .25 | .29 | .25 |
| Min | .63 | 0 | 0 | .67 | 0 | 0 | 0 | .10 | 0 | 0 | 0 |
| Max | 17.02 | 17.02 | 14.96 | 1.84 | 1 | 1 | 33.99 | 25 | 1 | 1 | 1 |
- 3 *p <.05.
- 4 **p <.01.
- 5 Notes: Two-tailed tests of significance. Variables 1, 2, and 3 are log-transformed.
Graph
Table 4. Differences Between Control and Disruption Groups in Period T1.
| Sample Distribution per Variable in Period T1 (Prior to Disruption) | Control Group | Disruption Group | Std. Mean Diff.a | Balancea |
|---|
| Mean | SD | Mean | SD |
|---|
| 1: Total revenue (log) | 10.35 | 1.92 | 10.36 | 1.55 | −.01 | ✓ |
| 2: Resale revenue (log) | 9.90 | 2.93 | 10.18 | 1.97 | −.14 | ✓ |
| 3: New sale revenue (log) | 6.02 | 2.35 | 5.68 | 2.31 | .15 | ✓ |
| 4: Financial benefit | 1.01 | .15 | 1.02 | .14 | .07 | ✓ |
| 5: Functional benefit | .02 | .04 | .02 | .04 | .00 | ✓ |
| 6: Contractual bonds | .44 | .36 | .40 | .36 | .15 | ✓ |
| 7: Complex growth | .15 | .65 | .13 | .48 | .05 | ✓ |
| 8: Product line growth | 1.23 | 1.16 | 1.15 | .68 | .11 | ✓ |
| 9: Variability in purchase process | .14 | .24 | .13 | .22 | .05 | ✓ |
- 6 a We find no standardized mean difference between our randomly sampled control group and the disruption group greater than.25 ([15]; [39]; [75]), indicating an appropriate balance between control and disruption groups for further analysis. As an additional robustness check, we conducted propensity score matching to account for potential differences in the control group and disruption group (see the Web Appendix).
- 7 Notes: SD = standard deviation. Freq. = frequency. Std. Mean Diff. = standardized mean difference.
We specified a series of two-period DiD models to test our hypotheses, for which we generated three variables: ( 1) a dummy indicator of whether a customer belongs to the disruption group (coded as 1) or control group (coded as 0); ( 2) a dummy indicator reflecting the focal observation time period, T1 (period before disruption, coded as 0) or T2 (period after disruption, coded as 1); and ( 3) the interaction between these dummies, to account for the individual DiD. The DiD regression coefficient reflects the average treatment effect ([15]; [75]), that is, the effect of a disruption on resale revenue (H1) or new sale revenue (H2).[ 9]
Replicating prior empirical research ([75]), we find that customers who experience a relationship disruption generate −6.8% less total revenue on average (Table 5, Model 0; breplication = −.07, p <.05). In line with H1, customers who experience a relationship disruption generate −28.8% less resale revenue (b1 = −.34, p <.01; log-transformation of estimated coefficient, see Table 5, Model 1). In support of H2, customers who experience a relationship disruption also generate 52.2% more new sale revenue (b2 =.42, p <.01). Thus, our results affirm that the effects of a relationship disruption can be positive or negative, depending on the type of revenue.[10]
Graph
Table 5. Main Effects of Relationship Disruption.
| Independent Variables | Hyp. | Model 0:Total Revenue (log) | Model 1:Resale Revenue (log) | Model 2:New Sale Revenue (log) |
|---|
| (a) Without Controls | (b) With Controls | (a) Without Controls | (b) With Controls | (a) Without Controls | (b) With Controls |
|---|
| Est. (SE) | Est. (SE) | Est. (SE) | Est. (SE) | Est. (SE) | Est. (SE) |
|---|
| Treatment Effect of Disruption | | | | | | | |
| Postperiod dummy × Rel. disruption dummy (DiD) | H1 (−),H2 (+) | −.08*** (.03) | −.07** (.03) | −.35*** (.07) | −.34*** (.07) | .40*** (.15) | .42*** (.15) |
| DiD Specification | | | | | | | |
| Relationship disruption dummy | | n.s. | .15** (.06) | .27** (.11) | .44*** (.09) | −.33*** (.11) | −.25** (.10) |
| Postdisruption period | | n.s. | n.s. | .38*** (.05) | .35*** (.05) | −1.51*** (.08) | −1.55*** (.09) |
| Control Variables | | | | | | | |
| Customer's interactivity with firm | | | .30*** (.08) | | .26*** (.09) | | .39*** (.07) |
| Customer's relative importance | | | 1.86*** (.39) | | 1.91*** (.42) | | .77*** (.25) |
| Customer's portfolio breadth | | | .11*** (.01) | | .13*** (.01) | | .06*** (.01) |
| Customer's portfolio complexity | | | .71*** (.22) | | .87*** (.29) | | n.s. |
| Sales growth rate 1 | | | n.s. | | n.s. | | .07*** (.02) |
| Sales growth rate 2 | | | −.01* (.01) | | −.02* (.01) | | n.s. |
| Sales growth rate 3 | | | n.s. | | n.s. | | .05** (.02) |
| Dummies for industry, sales region, and quarter | | Quarter only | Included | Quarter only | Included | Quarter only | Included |
| Constant | | 10.43*** (.06) | 8.56*** (.16) | 10.01*** (.08) | 7.83*** (.24) | 6.27*** (.07) | 5.05*** (.19) |
| R-square | | .020 | .459 | .019 | .378 | .085 | .161 |
- 8 *p <.10.
- 9 **p <.05.
- 10 ***p <.01.
- 11 Notes: n.s. = not significant (p >.10). Two-tailed tests of significance. We report unstandardized coefficients (robust standard errors in brackets are clustered on individual customers) and use log-transformed dependent variables. Similar to [75], the average treatment effect (DiD) can be interpreted using the transformation of e(coefficient) − 1 = percentage change. For example, after relationship disruption, resale revenue decreases by e(−.34) − 1 = −28.8%. Note that our model shows a significant effect of the relationship disruption dummy, indicating a mean difference in the outcome between disruption and control group before disruption. The DiD specification accounts for this mean difference. We check for nonviolation of the common trend assumption before disruption in the Web Appendix. In addition, our model shows a significant effect for the postperiod dummy, indicating a mean difference in the outcome variable before-to-after disruption for the control group only. This result is not surprising; developments in a customer's purchasing (independent of relationship disruption) are possible and likely given the time frame of our analysis. Again, the DiD specification accounts for the mean difference.
The results for the moderated effects of a relationship disruption on new sale revenue and resale revenue are illustrated in Table 6 and Figure 2, Panels A1–A6 and B1–B6. First, we find support for H3a and H3b: the effect of a relationship disruption on new sale revenue is positively moderated by the financial benefits (b3a = 1.66, p <.05) and functional benefits (b3b = 9.20, p <.10) that the customer reaps from the relationship prior to the disruption. At a high level of customer financial benefits, for example (+5% above average), a disruption increases new sale revenue by 21.3%. As expected, we find no significant moderation effect of contractual bonds on the disruption–new sale revenue linkage. Second, in support of H4a–H4c, the effect of a relationship disruption on resale revenue is positively moderated by customers' financial benefits (b4a = 1.08, p <.05), functional benefits (b4b = 4.79, p <.10), and contractual bonds (b4c = 1.03, p <.01).
Graph: Figure 2. Interaction plots of moderating influence of firm-level relationship strength and dynamics and relationship disruption.aInteraction not significant.Notes: Low/high value of the moderator reflects ±1 SD from the mean value.
Graph
Table 6. Moderating Effects of Relationship Disruption on New Sale Revenue and Resale Revenue.
| Independent Variables | Hyp. | Model 3: Moderation ModelNew Sale Revenue (Log) | Model 4: Moderation ModelResale Revenue (Log) |
|---|
| (a) Main Effects | (b) Moderation Effects I | (c) Moderation Effects II | (d) Moderation Effects III | (a) Main Effects | (b) Moderation Effects I | (c) Moderation Effects II | (d) Moderation Effects III |
|---|
| Est. (SE) | Est. (SE) | Est. (SE) | Est. (SE) | Est. (SE) | Est. (SE) | Est. (SE) | Est. (SE) |
|---|
| Treatment Effect of Disruption | | | | |
| Postperiod dummy × Rel. disruption dummy (DiD) | | .41*** (.15) | .42*** (.15) | .39*** (.15) | n.s. | −.33*** (.07) | −.29*** (.07) | −.24*** (.06) | −.42*** (.10) |
| Main Effects Relationship Strength | | | | |
| Financial benefit | | −.53* (.30) | −.83*** (.31) | −.81*** (.31) | −.81*** (.31) | −1.35*** (.29) | −1.57*** (.33) | −1.55*** (.33) | −1.54*** (.33) |
| Functional benefit | | n.s. | n.s. | n.s. | n.s. | n.s. | n.s. | n.s. | n.s. |
| Contractual bonds | | n.s. | n.s. | n.s. | n.s. | .71** (.13) | .58*** (.14) | .58*** (.14) | .58*** (.14) |
| Main Effects Relationship Dynamics | | | | |
| Complex growth | | n.s. | n.s. | .18** (.07) | .18** (.07) | n.s. | n.s. | .15*** (.04) | .14*** (.04) |
| Product line growth | | .36*** (.08) | .36*** (.08) | .36*** (.09) | .36*** (.09) | −.44*** (.06) | −.44*** (.06) | −.48*** (.07) | −.48*** (.07) |
| Purchase process variability | | .80*** (.17) | .80*** (.17) | .94*** (.19) | .94*** (.19) | .43*** (.16) | .43*** (.16) | .34* (.18) | .34* (.18) |
| Moderation Effects Relationship Strength | | | | |
| Financial benefit | H3a(+) H4a(+) | | 1.77** (.78) | 1.70** (.79) | 1.66** (.79) | | 1.12** (.53) | 1.11** (.51) | 1.08** (.51) |
| Functional benefit | H3b(+) H4b(+) | | 9.34* (4.96) | 9.18* (5.04) | 9.20* (4.93) | | n.s. | n.s. | 4.79* (2.79) |
| Contractual bonds | H4c(+) | | n.s. | n.s. | n.s. | | .86*** (.20) | .93*** (.19) | 1.03*** (.20) |
| Moderation Effects Relationship Dynamics | | | | |
| Complex growth | H5a(-) H6a(-) | | | −.20*** (.07) | −.20*** (.07) | | | −.13*** (.05) | −.13*** (.05) |
| Product line growth | H5b(-) H6b(+) | | | n.s. | n.s. | | | .66*** (.10) | .67*** (.10) |
| Purchase process variability | H5c(-) H6c(+) | | | −.92** (.42) | −.99** (.42) | | | .63** (.30) | .68** (.30) |
| Moderation Effects Relationship Management | | | | |
| Cross-selling intensity | H7a(+) | | | | .48** (.21) | | | | n.s. |
| Personal communication intensity | H7b(+) | | | | n.s. | | | | .33*** (.11) |
| Control Variables | | | | | | | | | |
| Disruption dummy, postdisruption period, control terms and variables | | Included | Included | Included | Included | Included | Included | Included | Included |
| Constant | | 4.43*** (.42) | 4.76*** (.43) | 4.61*** (.44) | 4.58*** (.44) | 9.21*** (.39) | 9.50*** (.42) | 9.59*** (.42) | 9.59*** (.43) |
| R-square | | .186 | .189 | .190 | .192 | .429 | .433 | .439 | .440 |
- 12 *p <.10.
- 13 **p <.05.
- 14 ***p <.01.
- 15 Notes: n.s. = not significant (p >.10). Two-tailed tests of significance. We report unstandardized coefficients (robust standard errors in brackets are clustered on individual customers) and use log-transformed dependent variables. Similar to [75], the moderating effect can be interpreted using the transformation of e(coefficient) − 1 = percentage change. For example, customers that received 10% higher financial benefits than other customers prior to the disruption generate increased new sale revenue of [e(1.66) − 1] × 10 = +42.6% after the relationship disruption.
In support of H5a and H5c, the effect of a disruption on new sale revenue is negatively moderated by the degree of prior complex growth (b5a = −.20, p <.01) and prior variability in the purchase process (b5c = −.99, p <.05). The moderation effect of product line growth is negative, as expected, but not significant (b5b = −.11, p >.10), so we cannot confirm H5b (Figure 2, Panels A4–A6). We discuss this result subsequently. The effect of a relationship disruption on resale revenue is negatively moderated by the degree of prior complex growth (b6a = −.13, p <.01) but positively moderated by the degree of prior product line growth (b6b =.67, p <.01) and the variability of the purchase process prior to disruption (b6c =.68, p <.05). These findings support H6a–H6c (Figure 2, Panels B4–B6).
With regard to new salespeople's relationship management, we find that the positive effect of a relationship disruption on new sale revenue is stronger when the incoming salesperson offers a broader product portfolio (higher cross-selling intensity) (b7a =.48, p <.05), in support of H7a. As we predicted in H7b, the negative effect of a relationship disruption on resale revenue is weaker when the incoming salesperson has a higher propensity for personally communicating with customers (higher personal communication intensity) (b7b =.33, p <.01).
To assess the robustness of our findings, we check for the nonrandomness of relationship disruptions and potential differences between the disruption and control groups.
One intuitive potential reason for nonrandomness may lie in the salesperson's prior performance and turnover. However, an initial analysis of variances reveals no significant difference in the performance of salespeople with or without disruption (b =.06, p >.10). Furthermore, the performance effects of a disruption do not differ significantly across salespeople who left the firm versus those who remain. Thus, the prior performance of the salesperson is unlikely to affect the randomness of relationship disruptions in our study context.
In addition, we conducted a two-stage Heckman selection correction ([36]), with three variables that were not part of our main analysis, which serve as proxies for higher disruption probability: ( 1) regional rate of prior relationship disruptions (for similar instruments, see [15]; [70]; [75]), ( 2) overall performance of the leaving salesperson, and ( 3) the performance growth rate of the leaving salesperson. The regional disruption rate satisfies the condition for relevance because it relates to the occurrence of a relationship disruption for the focal customer (e.g., salesperson turnover), and it satisfies the exclusion restriction, because relationship disruptions with other customers in the same sales region are unlikely to affect resale revenue and new sale revenue earned from a focal customer. We conduct a probit regression with all the variables from our core analysis and the three newly calculated variables to predict the occurrence of a relationship disruption. Next, we compute and integrate an inverse Mills ratio in our DiD analysis and repeat the hypothesis tests. The findings are robust to this measure of potential selection bias (see the Web Appendix).
To account for potential differences between the disruption and control groups, we estimate an average treatment effect (ATE) with propensity score matching to ensure good fit between groups ([75]); we compare customers with similar probabilities of experiencing a relationship disruption. For the robustness tests, we separately calculate the ATE for subsamples with high and low levels of each moderator variable, according to mean splits. The estimated ATEs support the findings for all our hypotheses (see the Web Appendix).
To enhance the managerial relevance of our findings, we consider relationship contexts in which a relationship disruption may cause more harm or benefit to total revenues. For this analysis, we rerun our moderation model to predict the contingent development of total revenue. Table 7 illustrates the impact of a relationship disruption for selected combinations of the moderators, reflecting favorable and unfavorable relationship contexts. A context is favorable if the moderators in our framework support positive effects on new sale and/or reduced negative effects on resale revenue (and unfavorable otherwise). In the Web Appendix, we identify the specific moderator conditions in which relationship disruption lowers or increases total revenue.
Graph
Table 7. Relationship Disruption and Total Revenue in Favorable and Unfavorable Relationship Contexts.
| Relationship Conditions Before Disruption | Disruption Effect on Total Revenue |
|---|
| Average Effect | Effect at Higher Cross-Selling After Disruption | Effect at Higher Personal Communication After Disruption |
|---|
| Positive Total Effects Expected If... |
| Context 1FavorableFirm-level strength | Financial benefits | High | None | None | +21.8% |
| Functional benefits | High |
| Contractual bonds | High |
| Complex growth | Ø |
| Product line growth | Ø |
| Context 2FavorableFirm-level dynamics | Financial benefits | Ø | +10.7% | +16.9% | +28.9% |
| Functional benefits | Ø |
| Contractual bonds | Ø |
| Complex growth | Low |
| Product line growth | High |
| Context 3FavorableFirm-level strength and dynamics | Financial benefits | High | +22.6% | +29.7% | +41.1% |
| Functional benefits | High |
| Contractual bonds | High |
| Complex growth | Low |
| Product line growth | High |
| Negative Total Effects Expected If... |
| Context 4UnfavorableFirm-level strength | Financial benefits | Low | −13.2% | None | None |
| Functional benefits | Low |
| Contractual bonds | Low |
| Complex growth | Ø |
| Product line growth | Ø |
| Context 5UnfavorableFirm-level dynamics | Financial benefits | Ø | −16.7% | −11.9% | None |
| Functional benefits | Ø |
| Contractual bonds | Ø |
| Complex growth | High |
| Product line growth | Low |
| Context 6UnfavorableFirm-level strength and dynamics | Financial benefits | Low | −24.7% | −20.5% | None |
| Functional benefits | Low |
| Contractual bonds | Low |
| Complex growth | High |
| Product line growth | Low |
16 Notes: The high value of each moderator is +1/2 SD, and the low value is −1/2 SD; Ø is the mean value. We did not alter the variability values for the purchase process, due to countervailing effects on resale revenue and new sale revenue. Favorable/unfavorable context refers to extreme conditions of the prior relationship, where combinations of all mentioned moderator conditions are simultaneously true. For example, total revenue increases by 22.6% on average after disruption only if, prior to the disruption, financial benefits for the customer were high, functional benefits were high, contractual bonds were high, complex growth was low, and product line growth was high.
Notably, a relationship disruption can increase total sales revenue generated with a customer if the relationship context is favorable; this effect is especially pronounced if the incoming salesperson exhibits greater personal communication intensity or cross-selling intensity compared with the leaving salesperson. A relationship disruption in an unfavorable relationship context generally harms total revenue, but an effective salesperson relationship management design can effectively prevent losses in total revenue.
Disruptions of customer–salesperson relationships are a widespread, relevant phenomenon that threaten to reduce the performance of customer relationships ([75]). We offer novel insights into these effects by proposing and showing that although a relationship disruption negatively affects future resale revenue, it can have a positive effect on future new sale revenue. Our contingency framework predicts conditions in which each path outweighs the other, leading to overall performance losses or gains. Notably, the overall effects of relationship disruptions on total revenue can be positive or negative, depending on the context (Table 7).
This study is anchored in relationship marketing research, which has substantially advanced understanding of how customer–salesperson and firm-to-firm relationships unfold and determine firm performance. We contribute in four main ways. First, we derive a conceptual framework that includes both positive and negative effects of relationship disruptions and provide empirical evidence that this ambivalence is inherent to disruptions. Relationship marketing scholars agree that building and maintaining interpersonal relationships is vital to the stability and prosperity of customer–firm interactions ([29]; [18]; [61]), so they caution against disrupting relationships, for fear of undermining hard-earned advantages and diminishing business revenue ([10]; [32]; [75]). By accounting for distinct effects of relationship disruptions on new sale and resale revenues, we show for the first time that a disruption does not necessarily harm a customer relationship; instead, it even may stimulate new sale revenue through a relationship development path, emerging from joint need exploration by salespeople and customers in the early stages of the new relationship life cycle. Recent studies have cited joint need exploration as a key process for advancing relationship development ([85]), and 82% of CEOs expect customers to demand that sellers exhibit better understanding of their needs in the future ([40]). However, specific strategies for enhancing need identification in the wake of a relationship disruption are not well understood. Additional research should find which strategies and skills enable salespeople to identify customer needs after a relationship disruption, especially if customers are not fully cognizant of their own needs ([77]).
Second, we theoretically predict and test several relevant relationship contingencies, derived from both MLT and RLT. Prior research on relationship disruptions has cited the salesperson's prior performance and industry expertise as contingencies ([75]), without considering the condition of the affected relationship as a likely moderator. According to relationship marketing research, relationship contingency factors influence relationship development ([57]), so we address firm-level relationship strength and dynamics prior to the disruption, as well as salesperson relationship management after it, as pertinent contingency factors. As our theoretical rationale, we acknowledge that these moderators shape customers' motivation to maintain or expand the relationship with the selling firm after the disruption, in accordance with the core tenets of relationship marketing research ([ 9]). Note that a customer's motivation to maintain or expand the relationship with the selling firm might hinge on other factors, such as the presence or strength of competitors. Exploring these additional contingencies would be a valuable endeavor for further research.
Third, our results provide additional support for RLT; a relationship's history (strength and dynamics) strongly informs its prospective development ([41]). Also in line with RLT, our findings underline the importance of the joint exploration phase to expand the relationship ([85]). By including relationship disruptions in the relationship life cycle, we show that a disruption may change the velocity of a relationship, which reenters a phase of new exploration, mutual scanning, and learning ([24]; [41]; [85]), leading to new sale revenue. This finding sheds new light on Jap and Anderson's (2007, p. 261) conclusion that a deteriorating relationship pattern is often difficult to reverse, because "movement through regressive patterns is negatively related to performance, and these relationships do not enjoy a fresh start." Complementing recent research on transformational relationship events ([33]), our study indicates that relationship disruptions may avert the course and give relationships new directions.
Fourth, we integrate MLT and RLT, such that our conceptual model and findings suggest the possibility of a "multilevel relationship life cycle theory" to predict how customer–firm and customer–salesperson relationships evolve in combination over time. A host of research questions arise from this notion. It would be particularly interesting to identify situations in which the synchronization of both life cycles is optimal and those in which desynchronized life cycles (e.g., after relationship disruption) can be beneficial.
Several limitations of this study suggest avenues for further research. First, the positive disruption effects might seem to imply that firms should disrupt relationships purposefully and repeatedly, to gain continuous new business. We do not find any difference in the effect of disruptions on shorter or longer tenured relationships, but we cannot ignore the possibility that repeatedly disrupting interpersonal ties would frustrate customers and create what [41], p. 272) call "psychological scars," damaging the relationship. Further research should explore the potentially diminishing returns of repeated disruptions.
Second, future research could explore the effects of disruptions in other contexts. For instance, we did not find an anticipated significant interaction of growth in distinct product lines on new sale revenue. Perhaps the broad product portfolio of our study firm limits saturation effects, even if a customer has recently purchased new products. Furthermore, following [75] we study relationship disruptions in a context with established relationships between customer firms, the selling firm, and salespeople. This approach was necessary because, for a relationship disruption to occur, by definition a prior relationship needs to exist. Yet it is unlikely that our results replicate in fully transactional contexts.
Third, research that explores the dark side of customer relationships notes potential detriments of strong bonds between salespeople and customers (e.g., [ 4]; [27]; [55]). We focus on relationship contingency factors that reflect the strength and dynamics of firm-level relationships; exploring how a relationship disruption affects future performance when the relationship features negative psychological mechanisms (e.g., opportunism, complacency; [49]) could further advance our understanding of the risks and opportunities of disruptions.
Fourth, we explicitly focus on relationship disruptions induced by changing the salesperson. A B2B relationship disruption also might occur if the customer firm's representative changes. We expect our core theoretical reasoning (loyalty and relationship development paths) to hold in these cases too, though some differences may exist (e.g., diminished loyalty losses). Purchasing research could shift the perspective and thereby complement our findings.
Fifth, we focused on selected moderators reflecting relationship strength, dynamics, and management. Further factors (e.g., salesperson's product knowledge, tenure) also could moderate the loyalty and relationship development paths, so we encourage continued research to conceptualize and empirically examine such moderators.
The ubiquity of relationship disruptions ([13]; Boles et al. 2012; [20]), their potentially detrimental effects on customer revenue ([75]), and their potential for creating new business opportunities require companies to manage their risks and pursue their opportunities carefully. To that end, our study provides three main sets of actionable implications to help managers ( 1) prioritize their efforts among customers subject to a relationship disruption, ( 2) select which activities to undertake to retain or expand business with prioritized customers, and ( 3) capitalize on the revitalization of customer relationships.
First, when a relationship disruption is impending (e.g., salesperson's resignation, retirement, or promotion), managers can use our findings to assess their exposure to financial risks and opportunities and prioritize customers accordingly. A qualitative assessment demands a thorough understanding of the theoretical mechanisms, namely, that a relationship disruption can have simultaneously negative effects on resale and positive effects on new sale revenue. Then managers could analyze a leaving salesperson's customer relationships according to their characteristics (firm-level strength, recent dynamics) to identify financial risks and opportunities and prioritize which customers to target with retention or expansion efforts. In addition, to understand the financial impacts of a relationship disruption, managers can apply quantitative predictive analytics derived from our research. The models we propose can estimate the effects of a relationship disruption on customers' resale, new sale, and total revenues, according to the favorability of the relationship context (Table 7). We implemented predictive analytics for the focal company in this study; for each salesperson's customers, we estimated the expected effects of a disruption on resale, new sale, and total revenues. The results revealed when the company should emphasize customer retention or expansion efforts. To ensure accurate predictions, companies would need to train our model with their own sales and revenue data.
Second, our findings provide guidance for managing prioritized customer relationships and preparing them for an impending disruption. If our model predicts a significant loss of resale revenue, managers should focus on managing the retention by fortifying this relationship against the disruption in advance; they could work to foster stronger firm-level ties by offering more benefits to customers (e.g., customization, discounts)[11] or attempt to renew contracts. Managers also should sensitize incoming salespeople to the risks of resale losses and the importance of relationship building. If instead the model predicts a potential rise of new sale revenue, managers should focus on managing the expansion, including training salespeople to generate new sale revenues by reexploring needs and offering corresponding and novel products to customers.
Third, to benefit from revitalization and growth in new business, managers might—very carefully—select customer relationships for proactive disruption, even if a disruption would not normally be impending. We strongly urge managers to avoid the conclusion that proactively disrupting an interpersonal relationship is a certain route to increased revenue. Beneficial effects for total revenues accrue only if the specific relationship context is favorable, such as when the firm-level relationship is particularly strong, and if appropriate replacements are available (i.e., incoming salesperson has strong personal communication and cross-selling affinity; Table 7). Even then, managers must consider potential unintended effects, such as reactance and demotivation among the sales staff. So extreme caution is warranted here.
Figure 3 summarizes these implications. If a relationship disruption is impending, the decision tree leads managers to focus on the most important activities required to manage risks or seize opportunities. If no disruption is impending, the decision tree provokes thoughts about whether a proactive relationship disruption may benefit the future development of a relationship.
Graph: Figure 3. Decision guide for managerial practice.a[56].bCarefully decide on proactive disruption if cross-seller is available and potential resale losses are negligible.c[75].
Ultimately, our study serves as a reminder that existing customers' revenue potential may not be fully realized. New salespeople's reexploration of customer needs results, on average, in increased new sale revenue by 52.2%. Managers may instruct salespeople to reexplore customer needs, even in the absence of disruptions, allowing the firm to seize new opportunities.
Supplemental Material, Interpersonal_RD_(JM_Accepted)_-_Web_Appendix_updated - Understanding the Impact of Relationship Disruptions
Supplemental Material, Interpersonal_RD_(JM_Accepted)_-_Web_Appendix_updated for Understanding the Impact of Relationship Disruptions by Christian Schmitz, Maximilian Friess, Sascha Alavi and Johannes Habel in Journal of Marketing
Graph
| Position | Age (Years) | Length of Interview (Minutes) | Type of Interview | Industry |
|---|
| Sales Manager (SM1) | 35–40 | 30 | Telephone | Wholesale |
| Sales Manager (SM2) | 35–45 | 46 | In person | Pharmaceutical industry |
| Director Sales (SM3) | 50–60 | 45 | In person | Engineering |
| Sales Manager (SM4) | 40–45 | 62 | In person | Pharmaceutical industry |
| Sales Manager (SM5) | 50–60 | 72 | In person | Manufacturing |
| Sales Manager (SM6) | 45–55 | 28 | Telephone | Manufacturing |
| Sales Manager (SM7) | 40–50 | 29 | Telephone | Manufacturing |
| Sales Manager (SM8) | 50–60 | 20 | Telephone | Manufacturing |
| Purchasing Manager (PM1) | 40–55 | 34 | In person | Manufacturing |
| Purchasing Manager (PM2) | 45–55 | 52 | In person | Professional services |
| Purchasing Manager (PM3) | 45–55 | 28 | Telephone | Manufacturing |
17 Notes: To acquire the sample of interview partners, we approached existing company contacts and asked for referrals to their sales and purchasing functions. Interviews were audiotaped and subsequently transcribed verbatim. Following common practice in qualitative research, we analyzed the transcripts by coding statements according to their underlying themes (e.g., [51]).
Footnotes 1 Associate EditorVikas Mittal
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242919882630
5 1We focus on disruptions on the interpersonal relationship level and use the terms "relationship disruption" and "interpersonal relationship disruption" interchangeably. For a detailed discussion of terms and concepts, see the "Conceptual Framework" section.
6 2Initiating a new interpersonal relationship after a disruption is similar in some ways to new customer acquisition, except that in the former case, the firm has valuable knowledge about the customer (e.g., business model, needs). Thus, the initiation of a new relationship and need exploration should be more efficient than a situation in which no prior customer–firm relationship exists.
7 3We address this alternative explanation in a supplemental analysis in the Web Appendix. It shows that the results are independent of substitution effects, in support of our argument for H2.
8 4We provide supplemental information about the firm research context, products, and services in the Web Appendix.
9 5We provide further details about the model specification in the Web Appendix.
6The effects are not driven by substitution effects among new sale revenue increases and resale losses, and they are stable across subsamples that we derive according to the type of relationship disruption (salesperson leaves vs. remains with the selling firm), disruption timing (first vs. last six months of the year), the duration of the relationship of the customer with the leaving salesperson, the sales region, or the customer's industry (see the Web Appendix).
7To identify appropriate investment volumes, managers might estimate the return on investment using their activities' revenue impacts, derived from predictive analytics, as previously described.
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Uniting the Tribes: Using Text for Marketing Insight
Words are part of almost every marketplace interaction. Online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data. But how can marketers best use such data? This article provides an overview of automated textual analysis and details how it can be used to generate marketing insights. The authors discuss how text reflects qualities of the text producer (and the context in which the text was produced) and impacts the audience or text recipient. Next, they discuss how text can be a powerful tool both for prediction and for understanding (i.e., insights). Then, the authors overview methodologies and metrics used in text analysis, providing a set of guidelines and procedures. Finally, they further highlight some common metrics and challenges and discuss how researchers can address issues of internal and external validity. They conclude with a discussion of potential areas for future work. Along the way, the authors note how textual analysis can unite the tribes of marketing. While most marketing problems are interdisciplinary, the field is often fragmented. By involving skills and ideas from each of the subareas of marketing, text analysis has the potential to help unite the field with a common set of tools and approaches.
Keywords: computational linguistics; machine learning; marketing insight; interdisciplinary; natural language processing; text analysis; text mining
The digitization of information has made a wealth of textual data readily available. Consumers write online reviews, answer open-ended survey questions, and call customer service representatives (the content of which can be transcribed). Firms write ads, email frequently, publish annual reports, and issue press releases. Newspapers contain articles, movies have scripts, and songs have lyrics. By some estimates, 80%–95% of all business data is unstructured, and most of that unstructured data is text ([41]).
Such data has the potential to shed light on consumer, firm, and market behavior, as well as society more generally. But, by itself, all this data is just that—data. For data to be useful, researchers must be able to extract underlying insight—to measure, track, understand, and interpret the causes and consequences of marketplace behavior.
This is where the value of automated textual analysis comes in. Automated textual analysis[ 5] is a computer-assisted methodology that allows researchers to rid themselves of measurement straitjackets, such as scales and scripted questions, and to quantify the information contained in textual data as it naturally occurs. Given these benefits, the question is no longer whether to use automated text analysis but how these tools can best be used to answer a range of interesting questions.
This article provides an overview of the use of automated text analysis for marketing insight. Methodologically, text analysis approaches can describe "what" is being said and "how" it is said, using both qualitative and quantitative inquiries with various degrees of human involvement. These approaches consider individual words and expressions, their linguistic relationships within a document (within-text interdependencies) and across documents (across-text interdependencies), and the more general topics discussed in the text. Techniques range from computerized word counting and applying dictionaries to supervised or automated machine learning that helps deduce psychometric and substantive properties of text.
Within this emerging domain, we aim to make four main contributions. First, we illustrate how text data can be used for both prediction and understanding, to gain insight into who produced that text, as well as how that text may impact the people and organizations that consume it. Second, we provide a how-to guide for those new to text analysis, detailing the main tools, pitfalls, and challenges that researchers may encounter. Third, we offer a set of expansive research propositions pertaining to using text as a means to understand meaning making in markets with a focus on how customers, firms, and societies construe or comprehend marketplace interactions, relationships, and themselves. Whereas previous treatments of text analysis have looked specifically at consumer text ([62]), social media communication ([68]), or psychological processes ([137]), we aim to provide a framework for incorporating text into marketing research at the individual, firm, market, and societal levels. By necessity, our approach includes a wide-ranging set of textual data sources (e.g., user-generated content, annual reports, cultural artifacts, government text).
Fourth, and most importantly, we discuss how text analysis can help "unite the tribes." As a field, part of marketing's value is its interdisciplinary nature. Unlike core disciplines such as psychology, sociology, or economics, the marketing discipline is a big tent that allows researchers from different traditions and research philosophies (e.g., quantitative modeling, consumer behavior, strategy, consumer culture theory) to come together to study related questions ([98], [99]). In reality, however, the field often seems fragmented. Rather than different rowers all simultaneously pulling together, it often feels more like separate tribes, each independently going off in separate directions. Although everyone is theoretically working toward similar goals, there tends to be more communication within groups than between them. Different groups often speak different "languages" (e.g., psychology, sociology, anthropology, statistics, economics, organizational behavior) and use different tools, making it increasingly difficult to have a common conversation. However, text analysis can unite the tribes. Not only does it involve skills and ideas from each of these areas, doing it well requires such integration because it borrows ideas, concepts, approaches, and methods from each tribe and incorporates them to achieve insight. In so doing, the approach also adds value to each of the tribes in ways that might not otherwise be possible.
We start by discussing two distinctions that are useful when thinking about how text can be used: ( 1) whether text reflects or impacts (i.e., says something about the producer or has a downstream impact on something else) and ( 2) whether text is used for prediction or understanding (i.e., predicting something or understanding what caused something). Next, we explain how text may be used to unite the tribes of marketing. Then we provide an overview of text analysis tools and methodology and discuss key questions and measures of validity. Finally, we close with a future research agenda.
Communication is an integral part of marketing. Not only do firms communicate with customers, but customers communicate with firms and one another. Moreover, firms communicate with investors and society communicates ideas and values to the public (through newspapers and movies). These communications generate text or can be transcribed into text.
A simple way to organize the world of textual data is to think about producers and receivers—the person or organization that creates the text and the person or organization who consumes the text (Table 1). While there are certainly other parties that could be listed, some of the main producers and receivers are consumers, firms, investors, and society at large. Consumers write online reviews that are read by other consumers, firms create annual reports that are read by investors, and cultural producers represent societal meanings through the creation of books, movies, and other digital or physical artifacts that are consumed by individuals or organizations.
Graph
Table 1. Text Producers and Receivers.
| Text Producers | Text Receivers |
|---|
| Consumers | Firms | Investors | Institutions/Society |
|---|
| Consumers | Online reviews (Anderson and Simester 2014; Chen and Lurie 2013; Fazio and Rockledge 2015a; Kronrod and Danziger 2013a; Lee and Bradlow 2011; Liu, Lee, and Srinivasan 2019a; Melumad, Inman, and Pham 2019; Moon and Kamakura 2017; Puranam, Narayan, and Kadiyali 2017) Social media (Hamilton, Schlosser, and Chen 2017a; Netzer et al. 2012; Villarroel Ordenes et al. 2017) Offline word of mouth (Berger and Schwartz 2011a; Mehl and Pennebaker 2003a)
| Forms and applications (Netzer, Lemaire, and Herzenstein 2019) Idea-generation contexts (Bayus 2013a; Toubia and Netzer 2017) Social media/brand communities (Herhausen et al. 2019) Consumer complaints (Ma, Baohung, and Kekre 2015) Customer language on service calls Tweeting at companies (Liu, Singh, and Srinivasan 2016a)
| Stock market reactions to consumer text (Bollen, Mao, and Zeng 2011; Tirunillai and Tellis 2012) Protests Petitions
| Crowdsourcing knowledge Letters to the editor Online comments section Activism (e.g., organizing political movements and marches)
|
| Firms | Owned media (e.g., company website and social media; Villarroel Ordenes et al. 2018) Advertisements (Fossen and Schweidel 2017a, 2019; Liaukonyte, Teixeira, and Wilbur 2015a; Rosa et al. 1999; Stewart and Furse 1986) Customer service agents (Packard and Berger 2019a; Packard, Moore, and McFerran 2018) Packaging, including labels Text used in instructions
| Trade publications (Weber, Heinze, and DeSoucey 2008a) Interfirm communication emails (Ludwig et al. 2016) White papers
| Financial reports (Loughran and McDonald 2016) Corporate communications (Hobson, Mayhew, and Venkatachalam 2012) Chief executive officer letters to shareholders (Yadav, Prabhu, and Chandy 2007
| Editorials by firm stakeholders Interviews with business leaders
|
| Investors | | Letters to shareholders (Yadav, Prabhu, and Chandy 2007) Shareholder feedback (Wies et al. 2019)
| Sector reports
| |
| Institutions/society | News content (Berger, Kim, and Meyer 2019; Berger and Milkman 2012; Humphreys 2010) Movies (Berger, Moe, and Schweidel 2019; Eliashberg, Hui, and Zhang 2007, 2014; Toubia et al. 2019) Songs (Berger and Packard 2018; Packard and Berger 2019a) Books (Akpinar and Berger 2015; Sorescu et al. 2018a)
| Business section Specialty magazines (e.g., Wired, Harvard Business Review)
| Wall Street Journal Fortune Various forms of investment advice that come from media
| Government documents, hearings, and memoranda (Chappell et al. 1997a) Forms of public dialogue or debate
|
1 a Reference appears in the Web Appendix.
Consistent with this distinction between text producer and text receiver, researchers may choose to study how text reflects or impacts. Specifically, text reflects information about, and thus can be used to gain insight into, the text producer or one can study how text impacts the text receiver.
Text reflects and indicates something about the text producer (i.e., the person, organization, or context that created it). Customers, firms, and organizations use language to express themselves or achieve desired goals, and as a result, text signals information about the actors, organization, or society that created it and the contexts in which it was created. Like an anthropologist piecing together pottery shards to learn about a distant civilization, text provides a window into its producers.
Take, for example, a social media post in which someone talks about what they did that weekend. The text that person produces provides insight into several facets. First, it provides insight into the individual themselves. Are they introverted or extraverted? Neurotic or conscientious? It sheds light on who they are in general (i.e., stable traits or customer segments; [97]) as well as how they may be feeling or what they may be thinking at the moment (i.e., states). In a sense, language can be viewed as a fingerprint or signature ([114]). Just like brush strokes or painting style can be used to determine who painted a particular painting, researchers use words and linguistic style to infer whether a play was written by Shakespeare, or if a person is depressed ([128]) or being deceitful ([82]). The same is true for groups, organizations, or institutions. Language reflects something about who they are and thus provides insight into what they might do in the future.
Second, text can provide insight into a person's attitudes toward or relationships with other attitude objects—whether that person liked a movie or hated a hotel stay, for example, or whether they are friends or enemies with someone. Language used in loan applications provides insight into whether people will default ([103]), language used in reviews can provide insight into whether they are fake ([ 3]; [49]; [107]), and language used by political candidates could be used to study how they might govern in the future.
These same approaches can also be used to understand leaders, organizations, or cultural elites through the text they produce. For example, the words a leader uses reflect who they are as an individual, their leadership style, and their attitudes toward various stakeholders. The language used in ads, on websites, or by customer service agents reflects information about the company those pieces of text represent. Aspects such as brand personality ([106]), how much a firm is thinking about its customers ([109]), or managers' orientation toward end users ([95]) can be understood through text. Annual reports provide insight into how well a firm is likely to perform in the future ([79]).
Yet beyond single individuals or organizations, text can also be aggregated across creators to study larger social groups or institutions. Given that texts reflect information about the people or organizations that created them, grouping people or organizations together on the basis of shared characteristics can provide insight into the nature of such groups and differences between them. Analyzing blog posts, for example, can shed light on how older and younger people view happiness differently (e.g., as excitement vs. peacefulness; [94]). In a comparison of newspaper articles and press releases about different business sectors, text can be used to understand the creation and spread of globalization discourse ([39]). Customers' language use further gives insight into the consumer sentiment in online brand communities ([58]).
More broadly, because texts are shaped by the contexts (e.g., devices, cultures, time periods) in which they were produced, they also reflect information about these contexts. In the case of culture, U.S. culture values high-arousal positive affective states more than East Asian cultures ([145]), and these differences may show up in the language these different groups use. Similarly, whereas members of individualist cultures tend to use first-person pronouns (e.g., "I"), members of collectivist cultures tend to use a greater proportion of third-person pronouns (e.g., "we").
Across time, researchers were able to examine whether the national mood changed after the September 11 attacks by studying linguistic markers of psychological change in online diaries ([28]). The language used in news articles, songs, and public discourse reflects societal attitudes and norms, and thus analyzing changes over time can provide insight into aspects such as attitudes toward women and minorities ([20]; [42]) or certain industries ([60]). Journal articles provide a window into the evolution of topics within academia ([54]). Books and movies serve as similar cultural barometers and could be used to shed light on everything from cultural differences in customs to changes in values over time.
Consequently, text analysis can provide insights that may not be easily (or cost-effectively) obtainable through other methods. Companies and organizations can use social listening (e.g., online reviews and blog posts) to understand whether consumers like a new product, how customers feel about their brand, what attributes are relevant for decision making, or what other brands fall in the same consideration set ([72]; [102]). Regulatory agencies can determine adverse reactions to pharmaceutical drugs ([38]; [102]), public health officials can gauge how bad the flu will be this year and where it will hit the hardest ([ 2]), and investors can try to predict the performance of the stock market ([21]; [141]).
In addition to reflecting information about the people, organizations, or society that created it, text also impacts or shapes the attitudes, behavior, and choices of the audience that consumes it. For example, take the language used by a customer service agent. While that language certainly reflects something about that agent (e.g., their personality, how they are feeling that day), how they feel toward the customer, and what type of brand they represent, that language also impacts the customer who receives it ([109]; [111]). It can change customer attitudes toward the brand, influence future purchase, or affect whether customers talk about the interaction with their friends. In that sense, language has a meaningful and measurable impact on the world. It has consequences.
This can be seen in a myriad of different contexts. Ad copy shapes customers' purchase behavior ([136]), newspaper language changes customers' attitudes ([61]), trade publications and consumer magazines shift product category perceptions (e.g., [127]), movie scripts shape audience reactions ([12]; Eliashberg, Hui, and Zhang 2014; [122]), and song lyrics shape song market success ([15]; [110]). The language used in political debates shapes which topics get attention ([16]), the language used in conversation shapes interpersonal attitudes ([59]), and the language used in news articles shapes whether people read ([99]) or share ([13]) them.
Firms' language choice has impact as well. For example, nuances in language choices by firms when responding to customer criticism online directly impacts consumers and, thus, the firms' success in containing social media firestorms ([53]). Language used in YouTube ads is correlated with their virality ([138]). Shareholder complaints about nonfinancial concerns and topics that receive high media attention substantially increase firms' advertising investments ([154]).
Note that while the distinction between text reflecting and impacting is a useful one, it is not an either/or. Text almost always simultaneously reflects and impacts. Text always reflects information about the actor or actors that created it, and as long as some audience consumes that text, it also impacts that audience.
Despite this relationship, researchers studying reflection versus impact tend to use text differently. Research that examines what the text reflects often treats it as a dependent variable and investigates how it relates to the text creator's personality, the social groups they belong to, or the time period or culture in which it was created.
Research that examines how text impacts others often treats it as an independent variable, examining if and how text shapes outcomes such as purchase, sharing, or engagement. In this framework, textual elements are linked with outcomes that are believed to be theoretical consequences of the textual components or some latent variable that they are thought to represent.
Importantly, text is also shaped by contextual factors; thus, to better understand its meaning and impact, it is important to understand the broader situation in which it was produced. Context can affect content in three ways: through technical constraints and social norms of the genre, through shared knowledge specific to the speaker and receiver, and through prior history.
First, different types of texts are influenced by formal and informal rules and norms that shape the content and expectations about the message. For example, newspaper genres such as opinion pieces or feature stories will contain a less "objective" point of view than traditional reporting ([77]). Hotel comment cards and other feedback are usually dominated by more extreme opinions. On Snapchat and other social media platforms, messages are relatively recent, short, and often ephemeral. In contrast, online reviews can be longer and are often archived dating back several years. Synchronic text exchanges, in which two individuals interactively communicate in real time may be more informal and contain dialogue of short statements and phatic responses (i.e., communication such as "Hi," which serves a social function) that indicate affiliation rather than semantic content ([70]). Some genres (e.g., social media) are explicitly public, whereas on others, such as blogs, information that is more private may be conveyed.
Text is also shaped by technological constraints (e.g., the ability to like or share) and physical constraints (e.g., character length limitations). Tweets, for example, necessarily have 280 characters or fewer, which may shape the ways in which they are used to communicate. Mobile phones have constraints on typing and may shape the text that people produce on them ([90]; [121]).
Second, the relationship between the text producer and consumer may affect what is said (or, more often, unsaid). If the producer and consumer know each other well, text may be relatively informal ([47]) and lack explicit information that a third party would need to make sense of the conversation (e.g., past events, known likes/dislikes). If both have an understanding of the goal of the communication (e.g., that the speaker wants to persuade the receiver), this may shape the content but be less explicit.
These factors are important to understand when interpreting the content of the text itself. Content has been shown to be shaped by the creator's intended audience ([150]) and anticipated effects on the receiver ([ 7]). Similarly, what consumers share with their best friend may be different (e.g., less impacted by self-presentational motivations) than what they post online for everyone to see.[ 6] Firms' annual reports may be shaped by the goals of appearing favorably to the market. What people say on a customer service call may be driven by the goal of getting monetary compensation. Consumer protests online are meant to inspire change, not merely inform others.
Finally, history may affect the content of the text. In message boards, prior posts may shape future posts; if someone raised a point in a previous post, the respondent will most likely refer to the point in future posts. If retweets are included in an analysis, this will bias content toward most circulated posts. More broadly, media frames such as #metoo or #blacklivesmatter might make some concepts or facts more accessible to speakers and therefore more likely to emerge in text, even if seemingly unrelated ([87]; [156]).
Beyond reflecting information about the text creator and shaping outcomes for the text recipient, another useful distinction is whether text is used for prediction or understanding.
Some text research is predominantly interested in prediction. Which customer is most likely to default on their loan ([103])? Which movie will sell the most tickets (Eliashberg et al. 2014)? How will the stock market perform ([21]; [141])? Whether focusing on individual-, firm-, or market-level outcomes, the goal is to predict with the highest degree of accuracy. Such work often takes many textual features and uses machine learning or other methods to combine these features in a way that achieves the best prediction. The authors care less about any individual feature and more about how the set of observable features can be combined to predict an outcome.
The main difficulty involved with using text for predictions is that text can generate hundreds and often thousands of features (words) that are all potential predictors for the outcome of interest. In some cases, the number of predictors is larger than the number of observations, making traditional statistical predictive models largely impractical. To address this issue, researchers often resort to machine learning–type methods, but overfitting needs to be carefully considered. In addition, inference with respect to the role of each word in the prediction can be difficult. Methods such as feature importance weighing can help extract some inference from these predictive models.
Other research is predominantly interested in using text for understanding. How does the language consumers use shape word of mouth's impact ([108])? Why do some online posts get shared, songs become popular, or brands engender greater loyalty? How do cultural attitudes or business practices change? Whether focusing on individual-, firm-, or market-level outcomes, the goal is to understand why or how something occurred. Such work often involves examining only one or a small number of textual features or aspects that link to underlying psychological or sociological processes and aims to understand which features are driving outcomes and why.
One challenge with using textual data for understanding is drawing causal inferences from observational data. Consequently, work in this area may augment field data with experiments to allow key independent variables to be manipulated. Another challenge is interpreting relationships with textual features (we discuss this further in the closing section). Songs that use more second-person pronouns are more popular ([110]), for example, but this relationship alone does not necessarily explain why this is the case; second-person pronouns may indicate several things. Consequently, deeper theorizing, examination of links observed in prior research, or further empirical work is often needed.
Note that research that can use either a prediction or understanding lens to study either what text reflects or what it impacts. On the prediction side, researchers interested in what text reflects could use it to predict states or traits of the text creator such as customer satisfaction, likelihood of churn, or brand personality. Researchers interested in the impact of text could predict how text will shape outcomes such as reading behavior, sharing, or purchase among consumers of that text.
On the understanding side, someone interested in what text reflects could use it to shed light on why people might use certain personal pronouns when they are depressed or why customers might use certain types of emotional language when they are talking to customer service. Someone interested in the impact of text could use it to understand why text that evokes different emotions might be more likely to be read or shared.
Furthermore, while most research tends to focus on either prediction or understanding, some work integrates both aspects. [103], for example, both use a range of available textual features to predict whether a given person will default on a loan and analyze the specific language used by people who tend to default (e.g., language used by liars).
Regardless of whether the focus is on text reflection versus impact, or prediction versus understanding, doing text analysis well requires integrating skills, techniques, and substantive knowledge from different areas of marketing. Furthermore, textual analysis opens up a wealth of opportunity for each of these areas as well.
Take consumer behavior. While hypothetical scenarios can be useful, behavioral economics has recently gotten credit for many applications of social or cognitive psychology because these researchers have demonstrated phenomena in the field. Given concerns about replication, researchers have started to look for new tools that enable them to ensure validity and increase relevance to external audiences. Previously, use of secondary data was often limited because it addressed the "what" but not the "why" (i.e., what people bought or did, but not why they did so). But text can provide a window into the underlying process. Online reviews, for example, can be used to understand why someone bought one thing rather than another. Blog posts can help marketers understand consideration sets ([72]; [102]) and the customer journey ([73]). Text even helps address the age-old issue of telling more than we can know ([105]). While people may not always know why they did something, their language often provides traces of explanation ([114]), even beyond what they can consciously articulate.
This richness is attractive to more than just behavioral researchers. Text opens a large-scale window into the world of "why" in the field and does so in a scalable manner. Quantitative modelers are always looking for new data sources and tools to explain and predict behavior. Unstructured data provides a rich set of predictors that are often readily available, at large scale, and able to be combined with structured measures as either dependent variables or independent variables. Text, through product reviews, user-driven social media activity, and firm-driven marketing efforts, provides data in real time that can shed light on consumer needs/preferences. This offers an alternative or supplement to traditional marketing research tools. In many cases, text can be retraced to an individual, allowing distinction between individual differences and dynamics. It also offers a playground where new methodologies from other disciplines can be applied (e.g., deep learning; [71]; [121]).
Marketing strategy researchers want logic by which business can achieve its marketing objectives and to better understand what affects organizational success. A primary challenge to these researchers is to obtain reliable and generalizable survey or field data about factors that lie deep in the firm's culture and structure or that are housed in the mental models and beliefs of marketing leaders and employees. Text analysis offers an objective and systematic solution to assess constructs in naturally occurring data (e.g., letters to shareholders, press releases, patent text, marketing messages, conference calls with analysts) that may be more valid. Likewise, marketing strategy scholars often struggle with valid measures of a firm's marketing assets, and text may be a useful tool to understand the nature of customer, partner, and employee relationships and the strength of brand sentiments. For example, [69] use dictionaries and support vector machine methods to extract sentiment and relate it to consumer mindset metrics.
Scholars who draw from anthropology and sociology have long examined text through qualitative interpretation and content analysis. Consumer culture theory–oriented marketing researchers are primarily interested in understanding underlying meanings, norms, and values of consumers, firms, and markets in the marketplace. Text analysis provides a tool for quantifying qualitative information to measure changes over time or make comparisons between groups. Sociological and anthropological researchers can use automated text analysis to identify important words, locate themes, link them to text segments, and examine common expressions in their context. For example, to understand consumer taste practices, [ 4] use text analysis to first identify how consumers talk about different taste objects, doings, and meanings in their textual data set (comments on a website/blog) before analyzing the relationship between these elements using interview data.
For marketing practitioners, textual analysis unlocks the value of unstructured data and offers a hybrid between qualitative and quantitative marketing research. Like qualitative research, it is rich, exploratory, and can answer the "why," but like quantitative research, it benefits from scalability, which often permits modeling and statistical testing. Textual analysis enables researchers to explore open-ended questions for which they do not know the range of possible answers a priori. With text, scholars can answer questions that they did not ask or for which they did not know the right outcome measure. Rather than forcing on participants a certain scale or set of outcomes from which to select, for example, marketing researchers can instead ask participants broad questions, such as why they like or dislike something, and then use topic modeling tools such as latent Dirichlet allocation (LDA; explained in detail subsequently) to discover the key underlying themes.
Importantly, while text analysis offers opportunities for a variety of research traditions, such opportunities are more likely to be realized when researchers work across traditional subgroups. That is, the benefits of computer-aided text analysis are best realized if we include both quantitative, positivist analyses of content and qualitative, interpretive analyses of discourse. Quantitative researchers, for example, have the skills to build the right statistical models, but they can benefit from behavioral and qualitative researchers' ability to link words to underlying psychological or social processes as well as marketing strategy researchers' understanding of organizational and marketing activities driving firm performance. This is true across all of the groups.
Thus, to really extract insights from textual data, research teams must have the interpretative skills to understand the meaning of words, the behavioral skills to link them to underlying psychological processes, the quantitative skills to build the right statistical models, and the strategy skills to understand what these findings mean for firm actions and outcomes. We outline some potential areas for fruitful collaboration in "Future Research Agenda" section.
Given the recent work using text analysis to derive marketing insight, some researchers may wonder where to start. This section reviews methodologies often used in text-based research. These include techniques needed to convert text into constructs in the research process as well as procedures needed to incorporate extracted textual information into subsequent modeling and analyses. The objective of this section is not to provide a comprehensive tutorial but, rather, to expose the reader to available techniques, discuss when different methods are appropriate, and highlight some of the key considerations in applying each method.
The process of text analysis involves several steps: ( 1) data preprocessing, ( 2) performing a text analysis of the resulting data, ( 3) converting the text into quantifiable measures, and ( 4) assessing the validity of the extracted text and measures. Each of these steps may vary depending on the research objective. Table 2 provides a summary of the different steps involved in the text analysis process from preprocessing to commonly used tools and measures and validation approaches. Table 2 can serve as a starter kit for those taking their first steps with text analysis.
Graph
Table 2. The Text Analysis Workflow.
| Data Preprocessing | Common Tools | Measurement | Validity |
|---|
Data acquisition: Obtain or download (often in an HTML format) text. Tokenization: Break text into units (often words and sentences) using delimiters (e.g., periods). Cleaning: Remove nonmeaningful text (e.g., HTML tags) and nontextual information. Removing stop words: Eliminate common words such as "a" or "the" that appear in most documents. Spelling: Correct spelling mistakes using common spellers. Stemming and lemmatization: Reduce words into their common stem or lemma.
| Entity extraction: Tools used to extract the meaning of one word at a time or simple cooccurrence of words. These tools include dictionaries; part-of-speech classifiers; many sentiment analysis tools; and, for complex entities, machine learning tools. Topic modeling: Topic modeling can identify the general topics (described as a combination of words) that are discussed in a body of text. Common tools include LDA and PF. Relation extraction: Going beyond entity extraction, the researcher may be interested in identifying textual relationships among extracted entities. Relation extraction often requires the use of supervised machine learning approaches.
| Count measures: The set of measures used to represent the text as count measures. The tf-idf measure allows the researcher to control for the popularity of the word and the length of the document. Similarity measures: Cosine similarity and the Jaccard index are often used to measure the similarity of the text between documents. Accuracy measures: Often used relative to human-coded or externally validated documents. The measures of recall, precision, F1, and the area under the curve of the receiver operating characteristic curve are often used. Readability measures: Measures such as the simple measure of gobbledygook (SMOG) are used to assess the readability level of the text.
| Internal Validity – Construct: Dictionary validation and sampling-and-saturation procedures ensure that constructs are correctly operationalized in text. – Concurrent: Compare operationalizations with prior literature. – Convergent: Multiple operationalizations of key constructs. – Causal: Control for factors related to alternative hypotheses.
External Validity – Predictive: Use conclusions to predict key outcome variable (e.g., sales, stock price). – Generalizability: Replicate effects in other domains. – Robustness: Test conclusions on holdout samples (k-fold); compare different categories within the data set.
|
2 Note: PF = Poisson factoring.
Text is often unstructured and "messy," so before any formal analyses can take place, researchers must first preprocess the text itself. This step provides structure and consistency so that the text can be used systematically in the scientific process. Common software tools for text analysis include Python (https://www.nltk.org/) and R (https://cran.r-project.org/web/packages/quanteda/quanteda.pdf, https://quanteda.io/). For both software platforms, a set of relatively easy-to-use tools has been developed to perform most of the data preprocessing steps. Some programs, such as Linguistic Inquiry and Word Count (LIWC; [137]) and WordStat ([113]), require minimal preprocessing. We detail the data preprocessing steps next (for a summary of the steps, see Table 3).
Graph
Table 3. Data Preprocessing Steps.
| Data Processing Step | Issues to Consider | Illustration |
|---|
| Data acquisition | Is the data readily available in textual format or does the research needs to use a web scraper to find the data? What are the legal guidelines for using the data (particularly relevant for web-scraped data)?
| Tweets mentioning different brands from the same category during a particular time frame are downloaded from Twitter. |
| Tokenization | What is the unit of analysis (word, sentence, thread, paragraph)? Use smart tokenization for delimiters and adjust to specific unique delimiters found in the corpora.
| The unit of analysis is the individual tweet. The words in the tweet are the tokens of the document. |
| Cleaning | Web-scraped data often requires cleaning of HTML tags and other symbols. Depending on the research objective, certain textual features (e.g., advertising on the page) may or may not be cleaned. Expansion of contractions such as "isn't" to "is not."
| URLs are removed and emojis/emoticons are converted to words. |
| Removing stop word | Use a stop word list available by the text-mining software, but adapt it to a specific application by adding/removing relevant stop words. If the goal of the analysis is to extract writing style, it is advisable to keep all/some of the stop words.
| Common words are removed. The remaining text contains brand names, nouns, verbs, adjectives, and adverbs. |
| Spelling | Can use commonly used spellers in text-mining packages (e.g., the Enchant speller). Language that is specific to the domain may be erroneously coded as a spelling mistake. May wish to record the number of spelling mistakes as an additional textual measure.
| Spelling mistakes are removed, enabling analysis into consumer perceptions (manifest through word choice) of different brands. |
| Stemming and lemmatization | Can use commonly used stemmers in text-mining packages (e.g., Porter stemmer). If the goal of the analysis is to extract writing style, stemming can mask the tense used.
| Verbs and nouns are "standardized" by reducing to their stem or lemma. |
Data acquisition can be well defined if the researcher is provided with a set of documents (e.g., emails, quarterly reports, a data set of product reviews) or more open-ended if the researcher is using a web scraper (e.g., Beautiful Soup) that searches the web for instances of a particular topic or a specific product. When scraping text from public sources, researchers should abide by the legal guidelines for using the data for academic or commercial purposes.
Tokenization is the process of breaking the text into units (often words and sentences). When tokenizing, the researcher needs to determine the delimiters that define a token (space, period, semicolon, etc.). If, for example, a space or a period is used to determine a word, it may produce some nonsensical tokens. For example, "the U.S." may be broken to the tokens "the," "U," and "S." Most text-mining software has smart tokenization procedures to alleviate such common problems, but the researcher should pay close attention to instances that are specific to the textual corpora. For cases that include paragraphs or threads, depending on the research objective, the researcher may wish to tokenize these larger units of text as well.
HTML tags and nontextual information, such as images, are cleaned or removed from the data set. The cleaning needs may depend on the format in which the data was provided/extracted. Data extracted from the web often requires heavier cleaning due to the presence of HTML tags. Depending on the purpose of the analysis, images and other nontextual information may be retained. Contractions such as "isn't" and "can't" need to be expanded at this step. In this step, researchers should also be mindful of and remove phrases automatically generated by computers that may occur within the text (e.g., "html").
Stop words are common words such as "a" and "the" that appear in most documents but often provide no significant meaning. Common text-mining tools (e.g., the tm, quanteda, tidytext, and tokenizers package in R; the Natural Language Toolkit package in Python; exclusion words in WordStat) have a predefined list of such stop words that can be amended by the researcher. It is advisable to add common words that are specific to the domain (e.g., "Amazon" in a corpora of Amazon reviews) to this list. Depending on the research objective, stop words can sometimes be very meaningful, and researchers may wish to retain them for their analysis. For example, if the researcher is interested in extracting not only the content of the text but also writing style (e.g., [111]), stop words can be very informative ([114]).
Most text-mining packages have prepackaged spellers that can help correct spelling mistakes (e.g., the Enchant speller). In using these spellers, the researcher should be aware of language that is specific to the domain and may not appear in the speller—or even worse, that the speller may incorrectly "fix." Moreover, for some analyses the researcher may want to record the number of spelling mistakes as an additional textual measure reflecting important states or traits of the communicator (e.g., [103]).
Stemming is the process of reducing the words into their word stem. Lemmatization is similar to stemming, but it returns the proper lemma as opposed to the word's root, which may not be a meaningful word. For example, with stemming, the entities "car" and "cars" are stemmed to "car," but "automobile" is not. In lemmatization, the words "car," "cars," and "automobile" are all reduced to the lemma "automobile." Several prepackaged stemmers exist in most text-mining tools (e.g., the Porter stemmer). Similar to stop words, if the goal of the analysis is to extract the writing style, one may wish to skip the stemming step, because stemming often masks the tense used.
Once the data has been preprocessed, the researcher can start analyzing the data. One can distinguish between the extraction of individual words or phrases (entity extraction), the extraction of themes or topics from the collective set of words or phrases in the text (topic extraction), and the extraction of relationships between words or phrases (relation extraction). Table 4 highlights these three types of analysis, the typical research questions investigated with each approach, and some commonly used tools.
Graph
Table 4. Taxonomy of Text Analysis Tools.
| Approach | Common Tools | Research Questions | Benefits | Limitations and Complexities | Marketing Examples |
|---|
| Entity (word) extraction: Extracting and identifying a single word/n-gram | Named entity extraction (NER) tools (e.g., Stanford NER) Dictionaries and lexicons (e.g., LIWC, EL 2.0, SentiStrength, VADER) Rule-based classification Linguistic-based NLP tools Machine learning classification tools (conditional random fields, hidden Markov models, deep learning)
| Brand buzz monitoring Predictive models where text is an input Extracting psychological states and traits Sentiment analysis Consumer and market trends Product recommendations
| Can extract a large number of entities Can uncover known entities (people, brands, locations) Can be combined with dictionaries to extract sentiment or linguistic styles Relatively simple to use
| Can be unwieldy due to the large number of entities extracted Some entities have multiple meanings that are difficult to extract (e.g., the laundry detergent brand "All") Slang and abbreviations make entity extraction more difficult in social media Machine learning tools may require large human-coded training data Can be limited for sentiment analysis
| Lee and Bradlow (2011) Berger and Milkman (2012) Ghose et al. (2012)a Tirunillai and Tellis (2012) Humphreys and Thompson (2014)a Berger, Moe, and Schweidel (2019) Packard, Moore, and McFerran (2018)
|
| Topic extraction: Extracting the topic discussed in the text | LSA LDA PF LDA2vec word embedding
| Summarizing the discussion Identifying consumer and market trends Identifying customer needs
| Topics often provide useful summarization of the data Data reduction permits the use of traditional statistical methods in subsequent analysis Easy-to-assess dynamics
| The interpretation of the topics can be challenging No clear guidance on the selection of the number of topics Can be difficult with short text (e.g., tweets)
| Tirunillai and Tellis (2014) Büschken and Allenby (2016) Puranam, Narayan, and Kadiyali (2017) Berger and Packard (2018) Liu and Toubia (2018) Toubia et al. (2019) Zhong and Schweidel (2019) Ansari, Li, and Yang (2018)a Timoshenko and Hauser (2019) Liu, Singh, and Srinivasan (2016)a Liu, Lee, and Srinivasan (2019)a
|
| Relation extraction: Extracting and identifying relationships among words | Co-occurrence of entities Handwritten rule Supervised machine learning Deep learning Word2vec word embedding Stanford Sentence and Grammatical Dependency Parser
| Market mapping Identifying problems mentioned with specific product features Identifying sentiment for a focal entity Identifying which product attributes are mentioned positively/negatively Identifying events and consequences (e.g., crisis) from consumer- or firm-generated text Managing service relationships
| Relaxes the bag-of-words assumption of most text-mining methods Relates the text to a particular focal entity Advances in text-mining methods will offer new opportunities in marketing
| Accuracy of current approaches is limited Complex relationships may be difficult to extract It is advised to develop domain-specific sentiment tools as sentiment signals can vary from one domain to another
| Netzer et al. (2012) Toubia and Netzer (2017) Boghrati and Berger (2019)
|
3 a Reference appears in the Web Appendix.
At the most basic level, text mining has been used in marketing to extract individual entities (i.e., count words) such as person, location, brands, product attributes, emotions, and adjectives. Entity extraction is probably the most commonly used text analysis approach in marketing academia and practice, partly due to its relative simplicity. It allows the researcher to explore both what was written (the content of the words) as well as how it was written (the writing style). Entity extraction can be used ( 1) to monitor discussions on social media (e.g., numerous commercial companies offer buzz monitoring services and use entity extraction to track how frequently a brand is being mentioned across alternative social media), ( 2) to generate a rich set of entities (words) to be used in a predictive model (e.g., which words or entities are associated with fake or fraudulent statements), and ( 3) as input to be used with dictionaries to extract more complex forms of textual expressions, such as a particular concept, sentiment, emotion, or writing style.
In addition to programming languages such as Python and R's tm tool kits, software packages such as WordStat make it possible to extract entities without coding. Entity extraction can also serve as input in commonly used dictionaries or lexicons. Dictionaries (i.e., a predefined list of words, such as a list of brand names) are often used to classify entities into the categories (e.g., concepts, brands, people, categories, locations). In more formal text, capitalization can be used to help extract known entities such as brands. However, in more casual text, such as social media, such signals are less useful. Common dictionaries include LIWC ([115]), EL 2.0 ([124]), Diction 5.0, or General Inquirer for psychological states and traits (for example applications, see [13]]; [80]]; [103]]).
Sentiment dictionaries such as Hedonometer ([34]), VADER ([63]), and LIWC can be used to extract the sentiment of the text. One of the major limitations of the lexical approaches for sentiment analysis commonly used in marketing is that they apply a "bag of words" approach—meaning that word order does not matter—and rely solely on the cooccurrence of a word of interest (e.g., "brand") with positive or negative words (e.g., "great," "bad") in the same textual unit (e.g., a review). While dictionary approaches may be an easy way to measure constructs and comparability across data sets, machine learning approaches trained by human-coded data (e.g., [22]; [51]; [52]) tend to be the most accurate way of measuring such constructs ([50]), particularly if the construct is complex or the domain is uncommon. For this reason, researchers should carefully weigh the trade-off between empirical fit and theoretical commensurability, taking care to validate any dictionaries used in the analysis (discussed in the next section).
A specific type of entity extraction includes linguistic-type entities such as part-of-speech tagging, which assigns a linguistic tag (e.g., verb, noun, adjective) to each entity. Most text analysis tools (e.g., the tm package in R, the Natural Language Toolkit package in Python) have a built-in part-of-speech tagging tool. If no predefined dictionary exists, or the dictionary is not sufficient for the extraction needed, one could add handcrafted rules to help define entities. However, the list of rules can become long, and the task of identifying and writing the rules can be tedious. If the entity extraction by dictionaries or rules is difficult or if the entities are less defined, machine learning–supervised classification approaches (e.g., conditional random fields [[102]], hidden Markov models) or deep learning ([140]) can be used to extract entities. The limitation of this approach is that often a relatively large hand-coded training data set needs to be generated.
To allow for a combination of words, entities can be defined as a set of consecutive words, often referred to as n-grams, without attempting to extract the relationship between these entities (e.g., the consecutive words "credit card" can create the unigram entities "credit" and "card" as well as the bigram "credit card"). This can be useful if the researcher is interested in using the text as input for a predictive model.
If the researcher wishes to extract entities while understanding the context in which the entities were mentioned in the text (thus avoiding the limitation of the bag-of-words approach), the emerging set of tools of word2vec or word embedding ([91]) can be employed. Word2vec maps each word or entity to a vector of latent dimensions called embedding vector based on the words with which each focal word appears. This approach allows the researcher not only to extract words but also to understand the similarity between words based on the similarities between the embedding vectors (or the similarities between the sentences in which each word appears). Thus, unlike the previous approaches discussed thus far, word2vec preserves the context in which the word appeared. While word embedding statistically captures the context in which a word appears, it does not directly linguistically "understand" the relationships among words.
Entity extraction has two major limitations: ( 1) the dimensionality of the problem (often thousands of unique entities are extracted) and ( 2) the interpretation of many entities. Several topic modeling approaches have been suggested to overcome these limitations. Similar to how factor analysis identifies underlying themes among different survey items, topic modeling can identify the general topics (described as a combination of words) that are discussed in a body of text. This text summarization approach increases understanding of document content and is particularly useful when the objective is insight generation and interpretation rather than prediction (e.g., [15]; [142]). In addition, monitoring topics, as opposed to words, makes it easier to assess how discussion changes over time (e.g., [159]).
Methodologically, topic modeling mimics the data-generating process in which the writer chooses the topic she wants to write about and then chooses the words to express these topics. Topics are defined as word distributions that commonly co-occur and thus have a certain probability of appearing in a topic. A document is then described as a probabilistic mixture of topics.
The two most commonly used tools for topic modeling are LDA ([18]) and Poisson factorization (PF; [48]). The predominant approach prior to LDA and PF was the support-vector-machine latent semantic analysis (LSA) approach. While LSA is simpler and faster to implement than LDA and PF, it requires larger textual corpora and often achieves lower accuracy levels. Other approaches include building an ontology of topics using a combination of human classification of documents as seeding for a machine learning classification (e.g., [97]). Whereas LDA is often simpler to apply than PF, PF has the advantage of not assuming that the topic probabilities must sum to one. That is, some documents may have more topic presences than others, and a document can have multiple topics with high likelihood of occurrence. In addition, PF tends to be more stable with shorter text. [24] relax the common bag-of-words assumption underlying the traditional LDA model and leverage the within-sentence dependencies of online reviews. LDA2vec is another approach to assess topics while accounting for the sequence context in which the word appears ([96]). In the context of search queries, [74] further extend the LDA approach to hierarchical LDA for cases in which related documents (queries and search results) are used to extract the topics. Furthermore, the researcher can use an unsupervised or seeded LDA approach to incorporate prior knowledge in the construction and interpretation of the topics (e.g., [120]; [143]).
While topic modeling methods often produce very sensible topics, because topics are selected solely based on a statistical approach, the selection of the number of topics and the interpretation of some topics can be challenging. It is recommended to combine statistical approaches (e.g., the perplexity measure, which is a model fit–based measure) and researcher judgment when selecting the number of topics.
At the most basic level, relationships between entities can be captured by the mere co-occurrence of entities (e.g., [20]; [102]; [144]). However, marketing researchers are often more interested in identifying textual relationships among extracted entities, such as the relationships between products, attributes, and sentiments. Such relationships are often more relevant for the firm than merely measuring the volume of brand mentions or even the overall brand sentiment. For example, researchers may want to identify whether consumers mentioned a particular problem with a specific product feature. [38] and [102] provide such examples by identifying the textual relationships between drugs and adverse drug reactions that imply that a certain drug may cause a particular adverse reaction.
Relation extraction also offers a more advanced route to capture sentiment by providing the link between an entity of interest (e.g., a brand) and the sentiment expressed, beyond their mere cooccurrence. Relation extraction based on the bag-of-words approach, which treats the sentence as a bag of unsorted words and searches for word cooccurrence, is limited because the cooccurrence of words may not imply a relationship. For example, the cooccurrence of a drug (e.g., Advil) with a symptom (e.g., headache) may refer to the symptom as a side effect of the drug or as the effect the drug is aiming to alleviate. Addressing such relationships requires identifying the sequence of words and the linguistic relationship among them. There have been only limited applications of such relation extraction in marketing, primarily due to the computational and linguistic complexities involved in accurately making such relational inferences from unstructured data (see, e.g., the diabetes drugs application in [102]]). However, as the methodologies used to extract entity relations evolve, we expect this to be a promising direction for marketers to take.
The most commonly used approaches for relation extraction are handwritten relationship rules, supervised machine learning approaches, and a combination of these approaches. At the most basic level, the researcher could write a set of rules that describe the required relationship. An example of such a rule may be the co-occurrence of product (e.g., "Ford"), attribute (e.g., "oil consumption"), and problem (e.g., "excessive"). However, such approaches tend to require many handwritten rules and have low recall (they miss many relations) and thus are becoming less popular.
A more common approach is to train a supervised machine learning tool. This could be linguistic agnostic approaches (e.g., deep learning) or natural language processing (NLP) approaches that aim to understand the linguistic relationship in the sentence. Such an approach requires a relatively large training data set provided by human coders in which various relationships (e.g., sentiment) are observed. One readily available tool for NLP-based relationship extraction is the Stanford Sentence and Grammatical Dependency Parser (http://nlp.stanford.edu:8080/parser/). The tool identifies the grammatical role of different words in the sentence to identify their relationship. For example, to assign a sentiment to a particular attribute, the parser first identifies the presence of an emotion word and then, in cases where a subject is present, automatically assesses if there is a grammatical relationship (e.g., in the sentence "the hotel was very nice," the adjective "nice" relates to the subject "hotel"). As with many off-the-shelf tools, the validity of the tool for a specific relation extraction needs to be tested.
Finally, beyond the relations between words/entities within one document, text can also be investigated across documents (e.g., online reviews, academic articles). For example, a temporal sequence of documents or a portfolio of documents across a group or community of communicators can be examined for interdependencies ([80], [81]).
Early work in marketing has tended to summarize unstructured text with structured proxies for this data. For example, in online reviews, researchers have used volume (e.g., [45]; [93]); valence, often captured by numeric ratings that supplement the text (e.g., [46]; [92]; [158]); and variance, often captured using entropy-type measures (e.g., [45]). However, these quantifiable metrics often mask the richness of the text. Several common metrics are often used to quantify the text itself, as we explain next.
Count measures have been used to measure the frequency of each entity's occurrence, entities' co-occurrence, or entities' relations. For example, when using dictionaries to evaluate sentiment or other categories, researchers often use the proportion of negative and/or positive words in the document, or the difference between the two ([13]; [22]; [115]; [132]; [142]). The problem with simple counts is that longer documents are likely to include more occurrences of every entity. For that reason, researchers often focus on the proportions of words in the document that belong to a particular category (e.g., positive sentiment). The limitation of this simple measure is that some words are more likely to appear than others. For example, the word "laptop" is likely to appear in almost every review in corpora that is composed of laptop reviews.
When evaluating the accuracy of text measures relative to human-coded or externally validated documents, measures of recall and precision are often used. Recall is the proportion of entities in the original text that the text-mining algorithm was able to successfully identify (it is defined by the ratio of true positives to the sum of true positives and false negatives). Precision is the proportion of correctly identified entities from all entities identified (it is defined by the ratio of true positives to the sum of true positives and false positives). On their own, recall and precision measures are difficult to assess because an improvement in one often comes at the expense of the other. For example, if one defines that every entity in the corpora is a brand, recall for brands will be perfect (you will never miss a brand if it exists in the text), but precision will be very low (there will be many false positive identifications of a brand entity).
To create the balance between recall and precision, one can use the F1 measure—a harmonic mean of the levels of recall and precision. If the researcher is more concerned with false positives than false negatives (e.g., it is more important to identify positives than negatives), recall and precision can be weighted differently. Alternatively, for unbalanced data with high proportions of true or false in the populations, a receiver operating characteristics curve can be used to reflect the relationship between true positives and false positives, and the area under the curve is often used as a measure of accuracy.
In some cases, the researcher is interested in measuring the similarity between documents (e.g., [80]). How similar is the language used in two advertisements? How different is a song from its genre? In such cases, measures such as linguistic style matching, similarity in topic use ([15]), cosine similarity, and the Jaccard index (e.g., [144]) can be used to assess the similarity between the text of two documents.
In some cases, the researcher is interested in evaluating the readability of the text. Readability can reflect the sophistication of the writer and/or the ability of the reader to comprehend the text (e.g., [44]). Common readability measures include the Flesch–Kincaid reading ease and the simple measure of gobbledygook (SMOG) measures. These measures often use metrics such as average number of syllables and average number of words per sentence to evaluate the readability of the text. Readability measures often grade the text on a 1–12 scale reflecting the U.S. school grade-level needed to comprehend the text. Common text-mining packages have built-in readability tools.
While the availability of text has opened up a range of research questions, for textual data to provide value, one must be able to establish its validity. Both internal validity (i.e., does text accurately measure the constructs and the relationship between them?) and external validity (i.e., do the test-based findings apply to phenomena outside the study?) can be established in various ways ([62]). Table 5 describes how the text analysis can be evaluated to improve different types of validity ([29]).
Graph
Table 5. Text Analysis Validation Techniques.
| Type of Validity | Validation Technique | Description of Method for Validation | References |
|---|
| Internal Validity | | | |
| Construct validity | Dictionary validation | After draft dictionary is created, pull 10% of the sample and calculate the hit rate. Measures such as hit rates, precision, and recall can be used to measure accuracy. | Weber (2005) |
| Have survey participants rate words included in the dictionary. Based on this data, the dictionary can also be weighted to reflect the survey data. | Brysbaert, Warriner, and Kuperman (2014)a |
| Have three coders evaluate the dictionary categories. If two of the three coders agree that the word is part of the category, include; if not, exclude. Calculate overall agreement. | Humphreys (2010); Pennebaker, Francis, and Booth (2001)a |
| Saturation | Pull 10% of instances coded from the data and calculate the hit rate. Adjust word list until saturation reaches 80% hit rate. | Weber (2005) |
| Concurrent validity | Multiple dictionaries | Calculate and compare multiple textual measures of the same construct (e.g., multiple sentiment measures) | Hartmann et al. (2018) |
| Comparison of topics | Compare with other topic models of similar data sets in other research (e.g., hotel reviews) | Mankad et al. (2016)a |
| Convergent validity | Triangulation | Look within text data for converging patterns (e.g., positive/e emotion correlates with known-positive attributes); apply Principle Components Analysis to show convergent groupings of words | Humphreys (2010); Kern et al. (2016) |
| Multiple operationalizations | Operationalize constructs with textual and nontextual data (e.g., sentiment, star rating) | Ghose et al. (2012)a; Mudambi, Schuff, and Zhang (2014)a |
| Causal validity | Control variables | Include variables in the model that address rival hypotheses to control for these effects | Ludwig et al. (2013) |
| Laboratory study | Replicate focal relationship between the independent variable and dependent variable in a laboratory setting | Spiller and Belogolova (2016)a; Van Laer et al. (2018) |
| External Validity | | | |
| Generalizability | Replication with different data sets | Compare the results from the text analysis with the results obtained other (possibly non-text-related) data sets | Netzer et al. (2012) |
| Predict key performance measure | Include results from text analysis in regression or other model to predict a key outcome (e.g., sales, engagement) | Fossen and Schweidel (2019) |
| Predictive validity | Holdout sample | Train model on approximately 80%–90% of the data and validate the model with the remaining data. Validation can be done using k-fold validation, which trains the mode on k-1 subsets of the data and predicts for the remaining subset of testing. | Jurafsky et al. (2014) |
| Robustness | Different statistical measures, unitizations | Use different, but comparable, statistical measures or algorithms (e.g., lift, cosine similarity, Jaccard similarity), aggregate at different levels (e.g., day, month) | Netzer et al. (2012) |
4 a Reference appears in the Web Appendix.
Internal validity is often a major threat in the context of text analysis because the mapping between words and the underlying dimension the research aims to measure (e.g., psychological state and traits) is rarely straightforward and can vary across contexts and textual outlets (e.g., formal news vs. social media). In addition, given the relatively young field of automated text analysis, validation of many of the methods and constructs is still ongoing.
Accordingly, it is important to confirm the internal validity of the approach used. A range of methods can be adopted to ensure construct, concurrent, convergent, discriminant, and causal validity. In general, the approach for ensuring internal validity is to ensure that the text studied accurately reflects the theoretical concept or topic being studied, does so in a way that is congruent with prior literature, is discriminant from other related constructs, and provides ample and careful evidence for the claims of the research.
Construct validity (i.e., does the text represent the theoretical concept?) is perhaps the most important to address when studying text. Threats to construct validity occur when the text provides improper or misleading evidence of the construct. For instance, researchers often rely on existing standardized dictionaries to extract constructs to ensure that their work is comparable with other work. However, these dictionaries may not always fit the particular context. For example, extracting sentiment from financial reports using sentiment tools developed for day-to-day language may not be appropriate. Particularly when attempting to extract complex constructs (e.g., psychological states and traits, relationships between consumers and products, and even sentiment), researchers should attempt to validate the constructs on the specific application to ensure that what is being extracted from the text is indeed what they intended to extract. Construct validity can also be challenged when homonyms or other words do not accurately reflect what researchers think they do.
Strategies for addressing threats to construct validity require that researchers examine how the instances counted in the data connect to the theoretical concept(s) ([62]). Dictionaries can also be validated using a saturation approach, pulling a subsample of coded entries and verifying with a hit rate of approximately 80% ([153]). Another method is to use input from human coders, as is done to support machine learning applications (as previously discussed). For example, one can use Amazon Mechanical Turk workers to label phrases on a scale from "very negative" to "very positive" for sentiment analysis and then use these words to create a weighted dictionary. In many cases, multiple methods for dictionary validation are advisable to ensure that one is achieving both theoretical and empirical fit. For topic modeling, researchers infer topics from a list of cooccurring words. However, these are theoretical inferences made by researchers. As such, construct validity is equally important and can be ascertained using some of the same methods of validation, through saturation and calculating a hit rate through manual analysis of a subset of the data. When using a classification approach, confusion matrices can be produced to provide details on accuracy, false positives, and false negatives ([31]).
Concurrent validity concerns the way that the researcher's operationalization of the construct relates to prior operationalizations. Threats to concurrent validity often come when researchers create text-based measures inductively from the text. For instance, if one develops a topic model from the text, it will be based on the data set and may not therefore produce topics that are comparable with previous research. To address these threats, one should compare the operationalization with other research and other data sources. For example, [132] propose a measure of brand sentiment based on social media text data and validate it by comparing it with brand measures obtained through a traditional marketing research survey. Similarly, [102] compare the market structure maps derived from textual information with those derived from product switching and surveys, and [142] compare the topics they identify with those found in Consumer Reports. When studying linguistic style ([116]), for example, it is beneficial to use robust measures from prior literature where factor analysis and other methods have already been employed to create the construct.
Convergent validity ensures that multiple measurements of the construct (i.e., words) all converge to the same concept. Convergent validity can be threatened when the measures of the construct do not align or have different effects. Convergent validity can be enhanced by using several substantively different measures (e.g., dictionaries) of the same construct to look for converging patterns. For example, when studying posts about the stock market, [31] compare five classifiers for measuring sentiment, comparing them in a confusion matrix to examine false positives. Convergent evidence can also come from creating a correlation or similarity matrix of words or concepts and checking for patterns that have face validity. For instance, [60] looks for patterns between the concept of crime and negative sentiment to provide convergent evidence that crime is negatively valenced in the data.
Discriminant validity, the degree to which the construct measures are sufficiently different from measures of other constructs, can be threatened when the measurement of the construct is very similar to that of another construct. For instance, measurements of sentiment and emotion in many cases may not seem different because they are measured using similar word lists or, when using classification, return the same group of words as predictors. Strategies for ensuring discriminant validity entail looking for discriminant rather than convergent patterns and boundary conditions (i.e., when and how is sentiment different from emotion?). Furthermore, theoretical refinements can be helpful in drawing finer distinctions. For example, anxiety, anger, and sadness are different kinds of emotion (and can be measured via psychometrically different scales), whereas sentiment is usually measured as positive, negative, or neutral ([115]).
Causal validity is the degree to which the construct, as operationalized in the data set, is actually the cause of another construct or outcome, and it is best ascertained through random assignment in controlled lab conditions. Any number of external factors can threaten causal validity. However, steps can be taken to enhance causal validity in naturally occurring textual data. In particular, rival hypotheses and other explanatory factors for the proposed causal relationship can be statistically controlled for in the model. For example, [80] include price discount in the model when studying the relationship between product reviews and conversion rate to control for this factor.
To achieve external validity, researchers should attempt to ensure that the effects found in the text apply outside of the research framework. Because text analysis often uses naturally occurring data that is often of large magnitude, it tends have a relatively high degree of external validity relative to, for example, lab experiments. However, establishing external validity is still necessary due to threats to validity from sampling bias, overfitting, and single-method bias. For example, online reviews may be biased due to self-selection among those who elected to review a product ([131]).
Predictive validity is threatened when the construct, though perhaps properly measured, does not have the expected effects on a meaningful second variable. For example, if consumer sentiment falls but customer satisfaction remains high, predictive validity could be called into question. To ensure predictive validity, text-based constructs can be linked to key performance measures such as sales (e.g., Fossen and Schweidel 2019) or consumer engagement ([ 6]). If a particular construct has been theoretically linked to a performance metric, then any text-based measure of that construct should also be linked to that performance metric. [141] show that the volume of Twitter activity affects stock price, but they find mixed results for the predictive validity of sentiment, with negative sentiment being predictive but positive sentiment having no effect.
Generalizability can be threatened when researchers base results on a single data set because it is unknown whether the findings, model, or algorithm would apply in the same way to other texts or outside of textual measurements. Generalizability of the results can be established by viewing the results of text analysis along with other measures of attitude and behavioral outcomes. For example, [102] test their substantive conclusions and methodology on message boards of both automobile discussions and drug discussions from WebMD. Evaluating the external validity and generalizability of the findings is key, because the analysis of text drawn from a particular source may not reflect consumers more broadly (e.g., [132]).
Robustness can be limited when there is only one metric or method used in the model. Researchers can ensure robustness by using different measures for relationships (e.g., Pearson correlation, cosine similarity, lift) and probing results by relaxing different assumptions. The use of holdout samples and k-fold cross-validation methods can prevent researchers from overfitting their models and ensure that relationships found in the data set will hold with other data as well ([66]; see also [62]). Probing on different "cuts" of the data can also help. [15], for example, compare lyrics from different genres, and [80] include reviews of both fiction and nonfiction books.
Finally, researchers should bear in mind the limitations of text itself. There are thoughts and feelings that consumers, managers, or other stakeholders may not express in text. The form of communication (e.g., tweets, annual reports) may also shape the message; some constructs may not be explicit enough to be measured with automated text analysis. Furthermore, while textual information can often involve large samples, these samples may not be representative. Twitter users, for example, tend to be younger and more educated ([117]). Those who contribute textual information, particularly in social media, may represent polarized points of view. When evaluating cultural products or social media, one should consider the system in which they are generated. Often viewpoints are themselves filtered through a cultural system ([55]; [88]) or elevated by an algorithm, and the products make it through this process may share certain characteristics. For this reason, researchers and firms should use caution when making attributions on the basis of a cultural text. It is not necessarily a reflection of reality ([65]) but rather may represent ideals, extremes, or institutionalized perceptions, depending on the context.
We hope this article encourages more researchers and practitioners to think about how they can incorporate textual data into their research. Communication and linguistics are at the core of studying text in marketing. Automated text analysis opens the black box of interactions, allowing researchers to directly access what is being said and how it is said in marketplace communication. The notion of text as indicative of meaning-making processes creates fascinating and truly novel research questions and challenges. There are many methods and approaches available, and there is no space to do all of them justice. While we have discussed several research streams, given the novelty of text analysis, there are still ample opportunities for future research, which we discuss next.
Returning to how text analysis can unite the tribes of marketing, it is worth highlighting a few areas that have mostly been examined by one research tradition in marketing where fruitful cross-pollination between tribes is possible through text analysis. Brand communities were first identified and studied by researchers coming from a sociology perspective ([100]). Later, qualitative and quantitative researchers further refined the concepts, identifying a distinct set of roles and status in the community (e.g., Mathwick, Wiertz, and De [85]). Automated text analysis allows researchers to study how consumers in these communities interact at scale and in a more quantifiable manner—for instance, examining how people with different degrees of power use language and predict group outcomes based on quantifiably different dynamics (e.g., [84]). Researchers can track influence, for example, by investigating which types of users initiate certain words or phrases and which others pick up on them. Research could examine whether people begin to enculturate to the language of the community over time and predict which individuals may be more likely to stay or leave on the basis of how well they adapt to the group's language ([30]; [135]). Quantitative or machine learning researchers might capture the most commonly discussed topics and how these dynamically change over the evolution of the community. Interpretive researchers might examine how these terms link conceptually, to find underlying community norms that lead members to stay. Marketing strategy researchers might then use or develop dictionaries to connect these communities to firm performance and to offer directions for firms regarding how to keep members participating across different brand communities (or contexts).
The progression can flow the other way as well. Outside of a few early investigations (e.g., [33]), word of mouth was originally studied by quantitative researchers interested in whether interpersonal communication actually drove individual and market behavior (e.g., [27]; Iyengar, Van den [64]). More recently, however, behavioral researchers have begun to study the underlying drivers of word of mouth, looking at why people talk about and share some stories, news, and information rather than others ([13]; [32]; for a review, see [10]]). Marketing strategy researchers might track the text of word-of-mouth interactions to predict the emergence of brand crises or social media firestorms (e.g., [159]) as well as when, if, and how to respond ([53]).
Consumer–firm interaction is also a rich area to examine. Behavioral researchers could use the data from call centers to better understand interpersonal communication between consumers and firms and record what drives customer satisfaction (e.g., [109]; [111]). The back-and-forth between customers and agents could be used to understand conversational dynamics. More quantitative researchers should use the textual features of call centers to predict outcomes such as churn and even go beyond text to examine vocal features such as tone, volume, and speed of speech. Marketing strategy researchers could use calls to understand how customer-centric a company is or assess the quality, style, and impact of its sales personnel.
Finally, it is worth noting that different tribes not only have different skill sets but also often study substantively different types of textual communication. Consumer-to-consumer communication is often studied by researchers in consumer behavior, whereas marketing strategy researchers more often tend to study firm-to-consumer and firm-to-firm communication. Collaboration among researchers from the different subfields may allow them to combine these different sources of textual data. There is ample opportunity to apply theory developed in one domain to enhance another. Marketing strategy researchers, for example, often use transaction economics to study business-to-business relationships through agency theory, but these approaches may be equally beneficial when studying consumer-to-consumer communications.
As noted in Table 1, certain text flows have been studied more than others. A large portion of existing work has focused on consumers communicating to one another through social media and online reviews. The relative availability of such data has made it a rich area of study and an opportunity to apply text analysis to marketing problems.[ 7] Furthermore, for this area to grow, researchers need to branch out. This includes expanding ( 1) data sources, ( 2) actors examined, and ( 3) research topics.
Offline word of mouth, for example, can be examined to study what people talk about and conversational dynamics. Doctor–patient interactions can be studied to understand what drives medical adherence. Text items such as yearbook entries, notes passed between students, or the text of speed dating conversations can be used to examine relationship formation, maintenance, and dissolution. Using offline data requires carefully transcribing content, which increases the amount of effort required but opens up a range of interesting avenues of study. For example, we know very little about the differences between online recommendations and face-to-face recommendations, where the latter also include the interplay between verbal and nonverbal information. Moreover, in the new era of "perpetual contact" our understanding of cross-message and cross-channel implications is limited. Research by [ 9] and [148] suggests that appropriate sequencing of messages matters; it might similarly matter across channels and modality. Given the rise of technology-enabled realities (e.g., augmented reality, virtual reality, mixed reality), assistive robotics, and smart speakers, understanding the roles and potential differences between language and nonverbal cues could be achieved using these novel data sources.
There are numerous dyads relevant to marketing in which text plays a crucial role. We discuss just a few of the areas that deserve additional research.
Considering consumer–firm interactions, we expect to see more research leveraging the rich information exchanged between consumers and firms through call centers and chats (e.g., [109]; [111]). These interactions often reflect inbound communication between customers and the firm, which can have important implications for the relationship between parties. In addition, how might the language used on packaging or in brand mission statements reflect the nature of organizations and their relationship to their consumers? How might the language that is most impactful in sales interactions differ from the language that is most useful in customer service interactions? Research could also probe how the impact of such language varies across contexts. The characteristics of language used by consumer packaged goods brands and pharmaceuticals brands in direct-to-consumer advertising likely differ. Similarly, the way in which consumers process the language used in disclosures in advertisements for pharmaceuticals (e.g., [101]) and political candidates (e.g., [152]) may vary.
Turning to firm-to-firm interactions, most conceptual frameworks on business-to-business (B2B) exchange relations emphasize the critical role of communication (e.g., [112]). Communicational aspects have been linked to important B2B relational measures such as commitment, trust, dependence, relationship satisfaction, and relationship quality. Yet research on actual, word-level B2B communication is very limited. For example, very little research has examined the types of information exchanged between salespeople and customers in offline settings. The ability to gather and transcribe data at scale points to important opportunities to do so. As for within-firm communication, researchers could study informal communications such as marketing-related emails, memos, and agendas generated by firms and consumed by their employees.
Similarly, while a great deal of work in accounting and finance has begun to use annual reports as a data source (for a review, see [79]]), marketing researchers have paid less attention to this area to study communication with investors. Most research has used this data to predict outcomes such as stock performance and other measures of firm valuation. Given recent interest in linking marketing-related activities to firm valuation (e.g., [86]), this may be an area to pursue further. All firm communication, including required documents such as annual reports or discretionary forms of communication such as advertising and sales interactions, can be used to measure variables such as market orientation, marketing capabilities, marketing leadership styles, and even a firm's brand personality.
There are also ample research opportunities in the interactions between consumers, firms, and society. Data about the broader cultural and normative environment of firms, such as news media and government reports, may be useful to shed light on the forces that shape markets. To understand how a company such as Uber navigates resistance to market change, for example, one might study transcripts of town hall meetings and other government documents in which citizen input is heard and answered. Exogenous shocks in the forms of social movements such as #metoo and #blacklivesmatter have affected marketing communication and brand image. One potential avenue for future research is to take a cultural branding approach ([57]) to study how different publics define, shape, and advocate for certain meanings in the marketplace. Firms and their brands do not exist in a vacuum, independent of the society in which they operate. Yet limited research in marketing has considered how text can be used to derive firms' intentions and actions at the societal level. For example, scholars have shown how groups of consumers such as locavores (i.e., people who eat locally grown food; [139]), fashionistas ([130]), and bloggers ([89]) shape markets. Through text analysis, the effect of the intentions of these social groups on the market can then be measured and better understood.
Another opportunity for future research is the use of textual data to study culture and cultural success. Topics such as cultural propagation, artistic change, and the diffusion of innovations have been examined across disciplines with the goal of understanding why certain products succeed while others fail ([ 8]; [23]; [25]; [126]; [129]; [133]). While success may be random ([17]; [56]), another possibility is that cultural items succeed or fail on the basis of their fit with consumers ([11]). By quantifying aspects of books, movies, or other cultural items quickly and at scale, researchers can measure whether concrete narratives are more engaging, whether more emotionally volatile movies are more successful, whether songs that use certain linguistic features are more likely to top the Billboard charts, and whether books that evoke particular emotions sell more copies. While not as widely available as social media data, more and more data on cultural items has recently become available. Data sets such as the Google Books corpus ([ 1]), song lyric websites, or movie script databases provide a wealth of information. Such data could enable analyses of narrative structure to identify "basic plots" (e.g., [122]; [53]).
Beginning with previously developed ways of representing marketing constructs can help some researchers address validity concerns. This section details a few of these constructs to aid researchers who are beginning to use text analysis in their work (see the Web Appendix). Using prior operationalization of a construct can ensure concurrent validity—helping build the literature in a particular domain—but researchers should take steps to ensure that the prior operationalization has construct validity with their data set.
At the individual level, sentiment and satisfaction are perhaps some of the most common measurements (e.g., [24]; [58]; [53]; [83]; [132]) and have been validated in numerous contexts. Other aspects that may be extracted from text include the authenticity and emotionality of language, which have also been explored through robust surveys and scales or by combining multiple existing measurements (e.g., [94]; [53]). There are also psychological constructs, such as personality type and construal level ([68]; [134]), that are potentially useful for marketing researchers and could also be inferred from the language used by consumers.
Future work in marketing studying individuals might consider measurements of social identification and engagement. That is, researchers currently have an idea of positive or negative consumer sentiment, but they are only beginning to explore emphasis (e.g., [123]), trust, commitment, and other modal properties. To this end, harnessing linguistic theory of pragmatics and examining phatics over semantics could be useful (see, e.g., [149]). Once such work is developed, we recommend that researchers carefully validate approaches proposed to measure such constructs along the lines described previously.
At the firm level, constructs have been identified in firm-produced text such as annual reports and press releases. Market orientation, advertising goals, future orientation, deceitful intentions, firm focus, and innovation orientation have all been measured and validated using this material (see Web Appendix Table 1). Work in organizational studies has a history of using text analysis in this area and might provide some inspiration and validation in the study of the existence of managerial frames for sensemaking and the effect of activists on firm activities.
Future work in marketing at the firm level could further refine and diversify measurements of strategic orientation (e.g., innovation orientation, market-driving vs. market-driven orientations). Difficult-to-measure factors deep in the organizational culture, structure, or capabilities may be revealed in the words the firm, its employees, and external stakeholders use to describe it (see [95]]). Likewise, the mindsets and management style of marketing leaders may be discerned from the text they use (see [157]]). Firm attributes that are important outcomes of firm action (e.g., brand value) could also be explored using text (e.g., [53]). In this case, there is an opportunity to use new kinds of data. For instance, internal, employee-based brand value could be measured with text on LinkedIn or Glassdoor. Finally, more subtle attributes of firm language, including conflict, ambiguity, or openness, might provide some insight into the effects of managerial language on firm success. For this, it may be useful to examine less formal textual data of interactions such as employee emails, salesperson calls, or customer service center calls.
Less work in marketing has measured constructs on the social or cultural level, but work in this vein tends to focus on how firms fit into the cultural fabric of existing meanings and norms. For instance, institutional logics and legitimacy have been measured by analyzing media text, as has the rise of brand publics that increase discussion of brands within a culture ([ 5]).
At the cultural level, marketing research is likely to maintain a focus on how firms fit into the cultural environment, but it may also look to how the cultural environment affects consumers. For instance, measurement of cultural uncertainty, risk, hostility, and change could benefit researchers interested in the effects of culture on both consumer and firm effects as well as the effects of culture and society on government and investor relationships. Measuring openness and diversity through text are also timely topics to explore and might inspire innovations in measurement, focusing on, for example, language diversity rather than the specific content of language. Important cultural discourses such as language around debt and credit could also be better understood through text analysis. Measurement of gender- and race- related language could be useful in exploring diversity and inclusion in the way firms and consumers react to text from a diverse set of writers.
As the development of text analysis tools advances, we expect to see new and improved use of these tools in marketing, which can enable scholars to answer questions we could not previously address or have addressed only in a limited manner. Here are a few specific method-driven directions that seem promising.
First, the vast majority of the approaches used for text analysis in marketing (and elsewhere) rely on bag-of-words approaches, and thus, the ability to capture true linguistic relationships among words beyond their cooccurrence was limited. However, in marketing we are often interested in capturing the relationship among entities. For example, what problems or benefits did the customer mention about a particular feature of a particular product? Such approaches require capturing a deeper textual relationship among entities than is commonly used in marketing. We expect to see future development in these areas as deep learning and NLP-based approaches enable researchers to better capture semantic relationships.
Second, in marketing we are often interested in the latent intention or latent states of writers when creating text, such as their emotions, personality, and motivations. Most of the research in this area has relied on a limited set of dictionaries (primarily the LIWC dictionary) developed and validated to capture such constructs. However, these dictionaries are often limited in capturing nuanced latent states or latent states that may manifest differently across contexts. Similar to advances made in areas such as image recognition, with the availability of a large number of human-coded training data (often in the millions) combined with deep learning tools, we hope to see similar approaches being taken in marketing to capture more complex behavioral states from text. This would require an effort to human-code a large and diverse set of textual corpora for a wide range of behavioral states. Transfer learning methods commonly used in deep learning tools such as conventional neural nets can then be used to apply the learning from the more general training data to any specific application.
Third, there is also the possibility of using text analysis to personalize customer–firm interactions. Using machine learning, text analysis can also help personalize the customer interaction by detecting consumer traits (e.g., personality) and states (e.g., urgency, irritation) and perhaps eventually predicting traits associated with value to the firm (e.g., customer lifetime value). After analysis, firms can then tailor customer communication to match linguistic style and perhaps funnel consumers to the appropriate firm representative. The stakes of making such predictions may be high, mistakes costly, and there are clearly contexts in which using artificial intelligence impedes constructing meaningful customer–firm relationships (e.g., health care; [78]).
Fourth, while our discussion has focused on textual content, text is just one example of unstructured data, with audio, video, and image being others. Social media posts often marry text with images or videos. Print advertising usually overlays text on a carefully constructed visual. Although television advertising may not include text on the screen, it may have an audio track that contains text that progresses simultaneously with the video.
Until recently, text data has received the most attention, mainly due to the presence of tools to extract meaningful features. That said, tools such as Praat ([19]) allow researchers to extract information from audio (e.g., [147]). One of the advantages of audio data over text data is that it provides richness in the form of tone and voice markers that can add to the actual words expressed (e.g., [155]). This enables researchers to study not just what was said, but how it was said, examining how pitch, tone, and other vocal or paralinguistic features shape behavior.
Similarly, recent research has developed approaches to analyze images (e.g., [76]), either characterizing the content of the image or identifying features within an image. Research into the impact of the combination of text and images is sparse (e.g., [50]). For example, images can be described in terms of their colors. In the context of print advertising, textual content may be less persuasive when used in conjunction with images of a particular color palette, whereas other color palettes may enhance the persuasiveness of text. Used in conjunction with simple images, the importance of text may be quite pronounced. But, when text is paired with complex imagery, viewers may attend primarily to the image, diminishing the impact of the text. If this is the case, legal disclosures that are part of an advertisement's fine print may not attract the audience's attention.
Analogous questions arise as to the role that text plays when incorporated into videos. Research has proposed approaches to characterize video content (e.g., [76]). In addition to comprising the script of the video, text may also appear visually. In addition to the audio context in which text appears, its impact may depend on the visuals that appear simultaneously. It may also be the case that its position within a video, relative to the start of the video, may moderate its effectiveness. For example, emotional text content that is spoken later in a video may be less persuasive for several reasons (e.g., the audience may have ceased paying attention by the time the text is spoken). Alternatively, the visuals with which the audio is paired may be more compelling to viewers, or the previous content of the video may have depleted a viewer's attentional resources. As our discussion of both images and videos suggests, text is but one component of marketing communications. Future research must investigate its interplay with other characteristics, including not only the content in which it appears but also when it appears (e.g., [67]), and in what media.
While there are a range of opportunities, textual data also brings with it various challenges. First is the interpretation challenge. In some ways, text analysis seems to provide more objective ways of measuring behavioral processes. Rather than asking people how much they focused on themselves versus others when sharing word of mouth, for example, one can count the number of first-person (e.g., "I") and second-person (e.g., "you"; [ 7]) pronouns, providing what seems more like ground truth. But while part of this process is certainly more objective (e.g., the number of different types of pronouns), the link between such measures and underlying processes (i.e., what it says about the word-of-mouth transmitter) still requires some degree of interpretation. Other latent modes of behavior are even more difficult to count. While some words (e.g., "love") are generally positive, for example, how positive they are may depend heavily on idiosyncratic individual differences as well as the context.
More generally, there is challenge and opportunity in understanding the context in which textual information appears. While early work in this space, particularly research using entity extraction, asked questions such as how much emotion is in a passage of text, more accurate answers to that question take must take context into account. A restaurant review may contain lots of negative words, for example, but does that mean the person hates the food, the service, or the restaurant more generally? Songs that contain more second person-pronouns (e.g., "you") may be more successful ([110]), but to understand why, it helps to know whether the lyrics use "you" as the subject or object of the sentence. Context provides meaning, and the more one understands not just which words are being used but also how they are being used, the easier it will be to extract insight. Dictionary-based tools are particularly susceptible to variation in the context in which the text appears, as dictionaries are often created in a context-free environment to match multiple contexts. Whenever possible, it is advised to use a dictionary that was created for the specific context of study (e.g., the financial sentiment tool developed by [79]]).
As mentioned previously, there are also numerous methodological challenges. Particularly when exploring the "why," hundreds of features can be extracted, making it important to think about multiple hypothesis testing (and use of Bonferroni and other corrections). Only the text used by the text creator is available, so in some sense there is self-selection. Both the individuals who decide to contribute and the topics people decide to raise in their writing may suffer from self-selection. Particularly when text is used to measure (complex) behavioral constructs, validity of the constructs needs to be considered. In addition, for most researchers, analyzing textual information requires retooling and learning a whole new set of skills.
Data privacy challenges represent a significant concern. Research often uses online product reviews and sales ranking data scraped from websites (e.g., [151]) or consumers' social media activity scraped from the platform (e.g., [45]; [141]). Although such approaches are common, legal questions have started to arise. LinkedIn was unsuccessful in its attempt to block a startup company from scraping data that was posted on users' public profiles ([125]). While scraping public data may be permissible under the law, it may conflict with the terms of service of those platforms that have data of interest to researchers. For example, Facebook deleted accounts of companies that violated its data-scraping policies ([104]).[ 8] Such decisions raise important questions about the extent to which digital platforms can control access to content that users have chosen to make publicly available.
As interest in extracting insights from digitized text and other forms of digitized content (e.g., images, videos) grows, researchers should ensure that they have secured the appropriate permissions to conduct their work. Failure to do so may result in it becoming more difficult to conduct such projects. One potential solution is the creation of an academic data set, such as that made available by Yelp (https://www.yelp.com/dataset), which may contain outdated or scrubbed data to ensure that it does not pose any risk to the company's operations or user privacy.
The collection and analysis of digitized text, as well as other user-created content, also raises questions around users' expectations for privacy. In the wake of the European Union's General Data Protection Regulation and revelations about Cambridge Analytica's ability to collect user data from Facebook, researchers must be mindful of the potential abuses of their work. We should also consider the extent to which we are overstepping the intended use of user-generated content. For example, while a user may understand that actions taken on Facebook may result in their being targeted with specific advertisements for brands with which they have interacted, they may not anticipate the totality of their Facebook and Instagram activity being used to construct psychographic profiles that may be used by other brands. Understanding consumers' privacy preferences with regard to their online behaviors and the text they make available could provide important guidance for practitioners and researchers alike. Another rich area for future research is the advancement of the precision with which marketing can be implemented while minimizing intrusions of privacy (e.g., [119]).
Communication is an important facet of marketing that encompasses communication between organizations and their partners, between businesses and their consumers, and among consumers. Textual data holds details of these communications, and through automated textual analysis, researchers are poised to convert this raw material into valuable insights. Many of the recent advances in the use of textual data were developed in fields outside of marketing. As we look toward the future and the role of marketers, these recent advancements should serve as exemplars. Marketers are well positioned at the interface between consumers, firms, and organizations to leverage and advance tools to extract textual information to address some of the key issues faced by business and society today, such as the proliferation of misinformation, the pervasiveness of technology in our lives, and the role of marketing in society. Marketing offers an invaluable perspective that is vital to this conversation, but it will only be by taking a broader perspective, breaking theoretical and methodological silos, and engaging with other disciplines that our research can reach its largest possible audience to affect the public discourse. We hope this framework encourages a reflection on the boundaries that have come to define marketing and opens avenues for future groundbreaking insights.
Supplemental Material, DS_10.1177_0022242919873106 - Uniting the Tribes: Using Text for Marketing Insight
Supplemental Material, DS_10.1177_0022242919873106 for Uniting the Tribes: Using Text for Marketing Insight by Jonah Berger, Ashlee Humphreys, Stephan Ludwig, Wendy W. Moe, Oded Netzer and David A. Schweidel in Journal of Marketing
Footnotes 1 EditorsChristine Moorman and Harald van Heerde
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242919873106
5 1Computer-aided approaches to text analysis in marketing research are generally interchangeably referred to as computer-aided text analysis ([118]), text mining ([102]), automated text analysis ([62]), or computer-aided content analysis ([35]).
6 2Note that intermediaries can amplify (e.g., retweet) an original message and may have different motivations than the text producer.
7 3While readily available data facilitates research, there are downsides to be recognized, including the representatives of such data and the terms of service that govern the use of this data.
8 4Facebook's terms of service with regard to automated data collection can be found at https://www.facebook.com/apps/site%5fscraping%5ftos%5fterms.php.
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Record: 217- Unpacking the Relationship Between Sales Control and Salesperson Performance: A Regulatory Fit Perspective. By: Katsikeas, Constantine S.; Auh, Seigyoung; Spyropoulou, Stavroula; Menguc, Bulent. Journal of Marketing. May2018, Vol. 82 Issue 3, p45-69. 25p. 1 Diagram, 7 Charts. DOI: 10.1509/jm.16.0346.
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Unpacking the Relationship Between Sales Control and Salesperson Performance: A Regulatory Fit Perspective
The literature examining the effect of sales control on salesperson performance is, at best, equivocal. To reconcile inconsistencies in empirical findings, this research introduces two new types of salesperson learning: exploratory and exploitative learning. Drawing on regulatory focus theory, the authors conceptualize exploratory learning as promotion focused and exploitative learning as prevention focused and find that salespeople exhibit both exploratory and exploitative learning, though one is used more than the other depending on the type of sales control employed. The results also suggest that the fit between salesperson learning type, customer characteristics (i.e., purchase-decisionmaking complexity), and salesperson characteristics (i.e., preference for sales predictability) is critical to salesperson performance and that salesperson learning mediates the relationship between sales control and salesperson performance (Study 1). Study 2 corroborates the findings using new panel data collected over two waves. The results of this research have important implications for integrating sales control, salesperson learning, and salesperson performance.
Online Supplement: http://dx.doi.org/10.1509/jm.16.0346
An effective sales force is an indispensable asset, because salespeople play a fundamental role in marketing strategy implementation (Kumar, Sunder, and Leone 2014). A competent sales force is vital for firms attempting to outperform competitors through enhanced customer service and satisfaction. However, salespeople are some of the costliest resources to acquire, develop, and manage (Zoltners and Sinha 2005). According to a survey conducted by the Association for Talent Development, “U.S.-based companies spend approximately $20 billion per year on sales training. Yet, many sales organizations get low ROIs from their sales training initiatives” (Behar 2014). Not surprisingly, the literature has focused on sales control systems as a reflection of firms’ efforts to productively utilize the knowledge, experiences, and skills of their salespeople; to motivate them to perform; and to help them maximize work outcomes (e.g., Ahearne et al. 2010).
As the sales job often involves independent, entrepreneurial, and autonomous tasks and responsibilities, building an effective sales control system is an important means to successfully manage salespeople. A sales control system is defined as “the organization’s set of procedures for monitoring, directing, evaluating, and providing feedback to its employees” (Anderson and Oliver 1987, p. 76). It has been suggested that different types of sales control systems (e.g., outcomes, activities) can be conducive or restrictive to salesperson performance (e.g., Miao and Evans 2013; Oliver and Anderson 1994). However, the literature offers conflicting evidence (see Table 1), and therefore no clear guidelines, about the link between various types of sales control systems and salesperson performance (e.g., Challagalla and Shervani 1996).1
TABLE: TABLE 1 Empirical Research on Salesforce Control Systems and Salesperson Performance
TABLE: TABLE 1 Empirical Research on Salesforce Control Systems and Salesperson Performance
TABLE: TABLE 1 Empirical Research on Salesforce Control Systems and Salesperson Performance
| | Type of Control and Its Link to Performance Outcome(s) |
|---|
| Average | Other | Authentic | Exclusive | Persuasive |
|---|
| Cravens et al. (1993) • Sales units | 144 field sales managers from diverse U.S. sales organizations | Field sales managers’ ratings of performance • Selling behavioral performance (technical knowledge, sales presentations) • Nonselling behavioral performance (providing information, controlling expenses) • Outcome performance (achieving sales objectives) | Sales force characteristics • Professional competence • Team orientation • Risk taking • Intrinsic motivation • Recognition motivation • Planning orientation • Sales support orientation • Customer orientation | • Technical knowledge (0) • Making sales presentations (0) • Providing information (+) • Controlling expenses (0) • Achieving sales objectives (+) | • Technical knowledge (0) • Making sales presentations (+) • Providing information (0) • Controlling expenses (0) • Achieving sales objectives (0) | |
| Oliver and Anderson (1994) • Sales units | 347 salespeople of independently owned/operated sales agencies in the electronics industry | Self-report performance (e.g., sales goals, overall performance, annual sales) | | | • Relative performance (–) • Sales expense control (+) • Sales presentation/ planning (+) | |
| Babakus et al. (1996) • Sales units | 58 chief sales executives and 146 sales managers from 58 companies | • Behavioral performance (e.g., technical knowledge, adaptive selling, teamwork, sales presentation, sales planning, and sales support) • Outcome performance (e.g., achieving sales objectives) | • Territory design | | Chief executives’ sample • Behavioral performance (+) • Outcome performance (+) Salesmanagers’ sample • Behavioral performance (0) • Outcome performance (0) | |
| Challagalla and Shervani (1996) • Salesperson | 270 salespeople from five industrial product divisions of two Fortune 500 companies | Self-reported performance (achieving sales targets) | • Supervisor role ambiguity • Customer role ambiguity | • Information/ punishment (0) • Rewards (–) | • Performance (0) | • Information/ rewards (–) • Punishment (0) |
| Ramaswami, Srinivasan, and Gorton (1997) • Salesperson | 165 salespeople of a Fortune 100 organization in the agriculture industry | Supervisor ratings (e.g., performance, business growth, professional growth, overall evaluation) | • Information asymmetry • Dysfunctional behavior | • Performance (0) | • Performance (0) | |
| Atuahene-Gima and Li (2002) • Salesperson | Chinese sample: 215 salespeople in high-tech firms U.S. sample: 190 salespeople in nonmanufacturing firms | Self-reported performance (e.g., contributing to market share, generating a high level of sales, quickly generating sales from the new product) | • Supervisee trust | Chinese sample • Performance (0) U.S. sample • Performance (+) | Chinese sample • Performance (0) U.S. sample • Performance (+) | |
| Menguc and Barker (2003) • Sales units | 102 field sales managers from 47 Canadian organizations | Sales unit performance (e.g., sales volume, profitability, customer satisfaction) | | • Sales volume (0) • Profitability (0) • Customer satisfaction (+) | • Sales volume (0) • Profitability (0) • Customer satisfaction (–) | |
| Fang, Evans, and Landry (2005) • Salesperson | Chinese sample: 308 salespeople from 30 companies U.S. sample: 247 salespeople from 152 sales units | Performance expectations | • Attributional ascriptions (e.g., effort, strategy, ability) • Attributional dimensions (e.g., stable, internal) | Chinese sample • Performance (–) U.S. sample • Performance (0) | Chinese sample • Performance (–) U.S. sample • Performance (0) | Chinese sample • Performance (–) U.S. sample • Performance (0) |
| Piercy et al. (2006) • Salesperson | 214 salespeople in a large, commercial directory publisher. | Self-reported performance • Outcome • Behavioral | • Organizational support • Organizational citizenship behaviors | | • Outcome performance (0) • Behavior performance (+) | |
| Evans et al. (2007) • Salesperson | 310 salespeople from 82 manufacturing, wholesaling, and services firms | Self-rated outcome performance | • Customer orientation • Sales supportiveness • Sales innovativeness | • Performance (+) | • Performance (0) | • Performance (0) |
| Ahearne et al. (2010) • Salesperson | 226 sales representatives of a pharmaceutical firm | Objective performance (e.g., new product sales) | | | • Performance (0) | |
| Schepers et al. (2012) • Service employees | 262 customer contact employees of a medical equipment manufacturer’s European customer contact center | Supervisor ratings of salesperson in-role performance | | • Performance (+) | • Performance (+) | |
| Miao and Evans (2013) • Salesperson | 223 salespeople from manufacturing firms | Self-rated performance (e.g., contributions to market share and dollar sales) | • Job engagement • Job stress | • Performance (0) | • Performance (0) | • Performance (0) |
Sales scholars have raised concerns about the ability of sales control systems to have a direct effect on salesperson performance (e.g., Evans et al. 2007; Kohli, Shervani, and Challagalla 1998). The elusive and contentious notion of a direct relationship has been voiced in the literature, suggesting that direct effect results “either did not support or provided contradictory support for the hypotheses” (Lusch and Jaworski 1991, p. 412). Although some studies have found a positive link between outcome control and performance (e.g., Evans et al. 2007), others report no relationship (e.g., Kohli, Shervani, and Challagalla 1998; Miao and Evans 2013), and still others reveal a negative link (e.g., Fang, Evans, and Landry 2005). This study helps clarify the path from sales control to salesperson performance by offering new empirical evidence on the underlying mechanism and contingencies in this relationship.
In this article, we apply the concepts of exploratory and exploitative learning from the organizational learning literature (e.g., Levinthal and March 1993; March 1991) to the salesperson context, which has received neither conceptual nor empirical attention in the extant literature. We define exploratory learning as a salesperson’s opportunity-seeking learning behavior that is based on entrepreneurial actions focused on experimenting with, searching for, and discovering novel, creative, and innovative selling techniques. We define exploitative learning as a salesperson’s advantage-seeking learning behavior that enhances productivity and efficiency by adhering to proven methods of selling and leveraging existing knowledge and experience, resulting in minimal deviation from routine selling (Tuncdogan, Van Den Bosch, and Volberda 2015). We ground these two types of learning in regulatory focus theory (RFT; see Higgins 1997, 2002) and propose that the two learning behaviors represent contrasting approaches to addressing customer problems. Specifically, exploratory learning is promotion focused and involves the renewal and reconfiguration of existing selling skills to develop novel solutions, while exploitative learning is prevention focused and involves the adherence to current selling skills and practices that play to the salesperson’s strengths, thus resulting in a safer, more established, and proven approach (Tuncdogan, Van Den Bosch, and Volberda 2015).
In developing our conceptual model, we draw on RFT (Higgins 1997, 2002) and regulatory fit to ( 1) investigate how salespeople adopt the two learning behaviors to varying degrees in response to different types of control systems, ( 2) examine the indirect effect of controls on salesperson performance as mediated by exploratory and exploitative learning, and ( 3) explore how these learning behaviors differentially affect salesperson performance under the conditioning roles of salesperson and customer characteristics. We test the conceptual model using primary data from salespeople and their supervisors within pharmaceutical firms.
The pharmaceutical sector is undergoing a sweeping transformation, as the critical decision makers about drugs are changing from doctors to hospital administrators. The shift in the decision-making unit from a doctor to a team of administrators and doctors (Bonoma 2006) makes the sales of pharmaceutical products much more complex and thus offers a fertile context in which to test our model. The sales function in the pharmaceutical industry is based on effectively managing the requirements of unique customer groups: ( 1) physicians, the most important customer segment because they have the authority and expertise to make decisions about prescribing a drug; ( 2) hospitals, which are high-volume customers that buy directly from pharmaceutical companies and wholesale drug distributors; and ( 3) patients, who use and buy the medicines (though physicians must still decide on the selection of drugs). Doctors, who are charged with caring for their patients, prescribe certain drugs (vs. other drugs) for their healing attributes, but they must do so within constraints set by insurance companies and governmental regulations.
The sales function within pharmaceutical companies is typically organized as different units that are constructed to meet the requirements of diverse market segments and individual customers (e.g., diabetes consultants, hospitals). Sales reps focus their attention on developing and managing close relationships with doctors, who are often confronted with betterinformed and more demanding patients, growing health cost pressures, and limited time to meet and interact with medical reps (e.g., Ahearne et al. 2010; Kappe and Stremersch 2016).
Our study contributes to the literature in three important ways. First, we integrate the sales control and learning literature and show that different sales control systems influence distinct salesperson learning approaches in different ways. Thus, consistent with RFT, we conceptualize exploratory and exploitative learning as malleable states (i.e., situationally induced) in response to different types of sales controls, not as stable and fixed traits or dispositions (Higgins 2002).
Second, this study helps reconcile discordant findings on the link between sales controls and performance. At the core of this unresolved issue lies the theoretical and practical dilemma that companies experience when using sales controls. Firms often deploy controls in an effort to change a salesperson’s behavior, ultimately hoping to improve his or her performance. Although cognitive and attitudinal change can lead to performance change, without change in action, the change may be modest or short lived at best. Thus, to address these mixed results, we use a dual mediating mechanism of exploratory and exploitative learning to show that different controls affect salesperson performance via increasing or decreasing the two learning behaviors. Prior research has attempted to show the performance impact of sales control indirectly through changes in cognition (e.g., psychological climate; Evans et al. 2007) and job engagement (e.g., adaptive selling, sales effort), but these efforts have had limited success (Miao and Evans 2013). Our findings reveal that, rather than changes in cognition or attitude, behavioral change (i.e., salesperson learning) effectively mediates the relationship between sales control and performance.
Third, we contribute to the sales literature by articulating the conditions under which the strength of the salesperson learning–performance link varies. We introduce a salesperson characteristic (i.e., preference for sales predictability) and a customer characteristic (i.e., purchase-decision-making complexity) as moderators that have received limited attention despite their theoretical and practical relevance. These factors reflect the changing landscape of how purchase decisions are made in the pharmaceutical context. Preference for sales predictability is a dispositional concept that constitutes a key element of the sales task in this setting; specifically, it captures a salesperson’s desire to convince doctors of a drug’s efficacy and superiority in the hope of boosting prescriptions and closing sales transactions. Customers’ purchase-decision-making complexity refers to the time, amount of information, and number of parties involved in a purchase decision. Because decision making about health care products is increasingly shifting from a single source (i.e., a doctor) to strategic procurement teams that include administrators and doctors (Rockoff 2014), it is important to consider purchase-decision-making complexity to delineate boundary conditions of the performance impact of salesperson learning.
We test our model across two studies and conclude with a discussion of the theoretical implications for integrating the sales control, salesperson learning, and salesperson performance literature streams. We offer practical suggestions for effectively aligning control systems with learning and leveraging learning according to salesperson and customer characteristics.
We ground our conceptual model (see Figure 1) in the overarching theoretical framework of RFT and argue that salespeople engage in exploratory and exploitative learning to different degrees depending on the type of sales control system deployed. We adopt a tripartite conceptualization of sales control (i.e., outcome, activity, and capability), consistent with the works of Challagalla and Shervani (1996) and Kohli, Shervani, and Challagalla (1998). In an attempt to reconcile conflicting findings in the literature on the sales control– performance link, our conceptual model posits that exploratory and exploitative learning are mediators. Consistent with regulatory fit, we also argue that performance will improve when salesperson learning “fits” with the preference for sales predictability and purchase-decision-making complexity.
Exploratory learning refers to the “pursuit of new knowledge” (Levinthal and March 1993, p. 105) and is characterized by “search, variation, risk taking, experimentation, play, flexibility, discovery, and innovation” (March 1991, p. 71). Exploitative learning involves “the use and development of things already known” (Levinthal and March 1993, p. 105) and is characterized by “refinement, choice, production, efficiency, selection, implementation, and execution” (March 1991, p. 71).
We build on this strong theoretical foundation and propose that salesperson exploratory learning is a self-regulated promotion-focused behavior that involves searching for, experimenting with, and discovering new selling techniques and skill sets that help improve sales performance. In contrast, exploitative learning is a self-regulated prevention-focused behavior in which the salesperson adheres to proven existing selling techniques and skill sets that leverage known knowledge and capabilities to enhance performance. Regardless of which learning style a salesperson adopts, consistent with the RFT explanation of goal pursuit, both strategies strive to achieve the common goal of improved performance.
TABLE: TABLE 2 Select Studies on Exploratory and Exploitative Learning in Marketing
TABLE: TABLE 2 Select Studies on Exploratory and Exploitative Learning in Marketing
TABLE: TABLE 2 Select Studies on Exploratory and Exploitative Learning in Marketing
| Study | Sample | Unit of Analysis | Research Context | Position of Exploratory/Exploitative Learning in Conceptual Model | Major Findings |
|---|
| Kyriakopoulos and Moorman (2004) | 96 Dutch firms in the food industry | Firm | New product development and marketing strategy | Independent variable moderated by market orientation | • The interaction between exploration and exploitation marketing strategies has a positive (negative) effect on new product financial performance only when market orientation is high (low). |
| Auh and Menguc (2005) | 260 Australian manufacturing firms | Firm | Marketing strategy | Independent variable as antecedents to effective and efficient firm performance | • For both prospectors and defenders, exploration is more positively related to effective firm performance than exploitation. • Exploration (exploitation) has a greater effect than exploitation (exploration) on firm performance for prospectors (defenders). • For defenders (prospectors) at high competitive intensity, exploitation is negatively (positively) related to firm efficiency. |
| Atuahene-Gima (2005) | 227 Chinese electronics firms | Firm | New product innovation (radical and incremental) | Mediator between customer and competitor orientations and radical and incremental product innovations | • Competence exploration has a positive (negative) effect on radical (incremental) product innovation. • Competence exploitation has a positive (negative) effect on incremental (radical) product innovation. • Exploration and exploitation mediate the relationships of customer and competitor orientations with radical innovation. |
| Atuahene-Gima and Murray (2007) | 179 Chinese technology new ventures | Firm | New product development | Mediator between structural, relational, and cognitive dimensions of social capital and new product performance | • Intraindustry managerial ties positively affect exploratory and exploitative learning. • Extraindustry managerial ties positively affect exploratory and negatively affect exploitative learning. • Exploratory (exploitative) learning positively (negatively) affects new product performance. • The interaction between exploratory and exploitation learning negatively affects new product performance. |
| Li, Chu, and Lin (2010) | 253 Taiwanese firms mostly in the electronic information industry | Firm | New product development | Independent variable moderated by several moderators (e.g., reward systems, encouragement to take risks, project development formalization) | • Exploratory learning has a positive effect on new product performance when the process reward system is high (vs. low). • Exploitative learning has a positive effect on new product performance when the output reward system is high (vs. low). • Exploratory learning has a positive effect on new product performance when encouragement to take risks is high (vs. low). • Exploitative learning has a positive effect on new product performance when project formalization is high (vs. low). |
| Vorhies, Orr, and Bush (2011) | 169 U.S. firms in the goods and services industry | Firm | Marketing strategy | Mediator between market knowledge development and customer-focused marketing capabilities | • Market knowledge development positively affects marketing exploration and exploitation capabilities. • The interaction between marketing exploration and exploitation capabilities negatively affects customerfocused marketing capabilities. |
| Yannopoulos, Auh, and Menguc (2012) | 216 Canadian high-tech firms | Firm | New product performance | Independent variable moderated by proactive and responsive market orientation | • New product performance suffers when exploratory (exploitative) learning is complemented by responsive (proactive) market orientation. • New product performance improves when exploratory learning is complemented by proactive market orientation. |
| Mu (2015) | U.S. sample: 324 firms Chinese sample: 569 high-tech firms | Firm | New product development | Mediator between marketing capability and new product development performance | • Exploration and exploitation mediate the relationship between marketing capability and new product development performance (United States and China). |
In marketing, exploratory and exploitative learning has been studied primarily at the firm level in the contexts of innovation (e.g., Atuahene-Gima 2005; Atuahene-Gima and Murray 2007; Jin, Zhou, and Wang 2016) and strategy (e.g., Kyriakopoulos and Moorman 2004; Vorhies, Orr, and Bush 2011). However, it is important to distinguish learning at different units of analysis because exploratory learning at the individual level may be considered exploitative learning at the firm level. Consider, for example, the case in which a salesperson experiments and discovers a new and unconventional approach to selling products, but then the sales organization capitalizes on this opportunity by exploiting it for scalability. What one salesperson may consider exploratory learning, another may perceive as exploitative learning, and vice versa. Thus, at the individual level, there can be considerable variation in terms of how people view what constitutes exploratory and exploitative learning.
The literature on organizational learning as a mediator between different types of strategic orientation and firm performance is inconclusive. For example, Noble, Sinha, and Kumar (2002) find that exploitative learning mediates the relationship between competitor orientation and return on assets. AtuaheneGima (2005) shows that competence exploration fully mediates the effect of competitor orientation (but not customer orientation) on radical innovation performance, while competence exploitation partially mediates the effects of customer and competitor orientations on incremental innovation performance. Notwithstanding the contribution that organizational learning has made to the marketing literature, there is a dearth of research on exploratory and exploitative learning at the individual level (see Table 2), as echoed by Gupta, Smith, and Shalley (2006, p. 703), who note that “studies that examine exploration and exploitation at a micro level are relatively scarce.”
The few studies that have investigated salesperson learning tend to focus specifically on learning effort (Wang and Netemeyer 2002) and its link to organizational learning (Bell, Menguc, and Widing 2010). Yet two important issues merit further refinement and development. First, salesperson learning lacks a more nuanced articulation of the exploratory and exploitative learning approaches that salespeople pursue. Such learning occurs not only by acquiring new sales skills and techniques but also by refining, tweaking, and perfecting existing sales techniques to improve efficiency.
In the pharmaceutical context, for example, medical reps sell products to doctors and hospitals on the basis of information about drug efficacy, dosing, and side effects; drug and food interactions; and drug costs (see Kappe and Stremersch 2016). They search for novel ideas, skills, and knowledge and seek new selling techniques to promote drugs and build close relationships with customers (e.g., physicians, hospitals). For example, sales reps may research the hobbies and interests of a given doctor (e.g., wine, art, golf, travel, gastronomy) so that they can engage in an intellectual and personal conversation that goes beyond the mere recitation of drug facts. This approach describes exploratory learning. That said, given the complexity involved in health care product sales and the myriad constraints that doctors face, medical reps also need to deploy selling techniques that have worked well for them—reliable tactics that help them perform tasks productively and manage customer relationships efficiently. An example of such exploitative learning would be when a sales rep relies on predefined scripts that compare the pros and cons of his or her drug with those of competitors (i.e., strictly a product-centered approach). To provide some additional deeper context to these different approaches to learning, we conducted interviews with pharmaceutical sales reps to provide a better understanding and more specific examples of exploratory and exploitative learning (see Web Appendix A).
Second, the operationalization of salesperson learning suffers from an overlap with learning orientation. The items that make up the individual learning effort dimension of salesperson learning in Bell, Menguc, and Widing (2010) mirror those of the learning orientation construct (Kohli, Shervani, and Challagalla 1998). Thus, there is a need to create a more nuanced salesperson learning construct that is distinct from learning goal orientation and embodies learning through exploration and exploitation.
Finally, it is important that we distinguish the two learning approaches from learning orientation (also known as mastery orientation), which pertains to the intrinsic desire to learn and improve (Ames and Archer 1988). As Kohli, Shervani, and Challagalla (1998, p. 263) assert, “Salespeople with a learning orientation have a strong desire to improve and master their selling skills and abilities continually and view achievement situations as opportunities to improve their competence.” In this study, we focus on salesperson exploratory and exploitative learning, but not on learning orientation, which we include as a control in our model (see Figure 1).
Regulatory focus theory proposes two types of regulatory focus: ( 1) chronic regulatory focus describes a trait or disposition that is chronic and stable in nature, whereas ( 2) situational regulatory focus, which we adopt in this article, is evoked and malleable and is affected by leadership style, organizational climate, and certain situational tasks and demands. Because of these characteristics of situational regulatory focus, it is typically hypothesized to be a mediator in many conceptual models (e.g., Neubert et al. 2008; Wallace and Chen 2006).
Regulatory focus theory explains how goals are achieved using two self-regulatory behaviors: promotion-focused and prevention-focused behaviors (Higgins 1997). Regulatory fit occurs when people pursue promotion- or prevention-focused strategies that are appropriately aligned with their regulatory orientation, with the task, or with situational demands (Higgins 2000). Regulatory fit suggests that people are more likely to achieve goals and perform better because fit increases motivation and engagement (Avnet and Higgins 2006). As Higgins (2000, p. 1219) notes, “people experience a regulatory fit when they use goal pursuit means that fit their regulatory orientations, and this regulatory fit increases the value of what they are doing.”
Drawing on the situational (vs. chronic) perspective of regulatory focus, we define exploratory learning as opportunity seeking, entrepreneurial, innovative, experimental, and risk taking, and we categorize this type of learning as promotion focused (Liberman et al. 1999). Because exploratory learning is concerned with growth, the focal issue tends to be avoiding errors of omission (i.e., missing an opportunity that can lead to growth), resulting in a greater motivation to push boundaries and try new selling techniques (DeCarlo and Lam 2016). In contrast, exploitative learning, when viewed as advantage seeking, attempts to avoid deviations from proven tactics and enhance protection; as such, the primary motivation is to avoid errors of commission (i.e., making mistakes). Drawing on the situational perspective of regulatory focus, we categorize this type of learning as prevention focused because prevention-focused people prefer stability and show a strong endowment effect (Liberman et al. 1999).2
Consistent with the tenets of regulatory fit, Wallace and Chen (2006, p. 533) argue that “different situations require different strategies, and, thus, a different regulatory focus. Hence, employees’ levels of work-specific promotion focus and prevention focus may be more likely to change as situational stimuli change, such as when employees are exposed to changes in leadership, work climate, or task demands.” The authors further maintain that “the choice for engaging in promotion or prevention strategies may depend at least in part on situational and task demands (Brockner and Higgins 1997).” (p. 533). Our preceding arguments are further justified by Anderson and Oliver (1987, p. 86), who state that “a salesperson’s selling strategies also should be a function of the type of control system.” Here, we focus on three primary types of control systems: outcome control, activity control, and capability control. We discuss each in turn in the following subsections.
Outcome control and exploratory and exploitative learning. The focus of outcome control is to monitor, evaluate, and provide feedback on a salesperson’s results, including sales volume, sales revenue, and quota achievement (Kohli, Shervani, and Challagalla 1998). Outcome control underscores short-term results (Oliver and Anderson 1994).
Salespeople are not rewarded for learning new sales techniques and approaches but instead are compensated for attaining objective and quantifiable results. Thus, there is little motivation for salespeople to learn novel skill sets that might be risky, uncertain, and difficult to master quickly. Because salespeople are often compensated to some extent with monetary incentives as opposed to a more traditional set salary, time and effort invested in learning, experimenting with, and discovering creative and innovative selling techniques entail risk and ambiguity and can jeopardize their income.
It follows, then, that under outcome control, salespeople will adhere to proven and well-rehearsed selling techniques that are closely aligned with and reinforce their existing strengths and experience. Such salespeople tend to focus on preventing mistakes and minimizing variation in outcomes by refining their existing sales approaches to realize greater efficiency and productivity. As Oliver and Anderson (1994, p. 56) note, “outcomecontrol salespeople view time to train and learn as time out of the field (with a high opportunity cost) and are relatively unwilling to experiment with new products and approaches because their reliance on commission income pressures them to gain quick results.” Thus, we predict that outcome control encourages exploitative learning, which is prevention focused, and discourages exploratory learning, which is promotion focused. Formally,
H1: Outcome control results in (a) less exploratory learning and (b) more exploitative learning.
Activity control and exploratory and exploitative learning. The purpose of activity control is to monitor and evaluate salespeople on the basis of certain processes and activities and reward them for how well they follow a prescribed formula (Anderson and Oliver 1987). Activity control entails following day-to-day rules and procedures and complying with expectations. Empirical evidence (Oliver and Anderson 1994) suggests that activity control is most effective when salespeople are risk averse. Supervisors monitor activities that are mechanical and routine and do not deviate from standard practice (Kohli, Shervani, and Challagalla 1998).
Consistent with regulatory fit, salespeople engage in behaviors that are in line with the work environment or situation (Neubert et al. 2008; Wallace and Chen 2006). Because activity control emphasizes prevention-focused behavior through nonrisk-seeking, routine, mechanical, and standardized activities (e.g., number of sales calls made, number of samples distributed), salespeople are likely to engage in more exploitative and less exploratory learning because it is a safer and more standardized type of learning and is a better overall fit with this type of working environment (Avnet and Higgins 2006).
H2: More activity control results in (a) less exploratory learning and (b) more exploitative learning.
Capability control and exploratory and exploitative learning. The purpose of capability control is to develop salespeople’s competencies so that they can perform better in their tasks and responsibilities. Capability control involves setting goals to develop sales techniques and customer relationship management abilities, monitoring and evaluating how salespeople are performing in relation to these goals, and providing feedback on areas that need improvement. By its nature, developing capabilities (e.g., the ability to close a sale without pressuring customers, managing customers’ expectations and emotions) takes time and patience. Capabilities are typically tacit and thus require a long-term perspective to learn, develop, and master.
In the context of pharmaceutical sales, capability control is used to educate and train salespeople to understand the unique needs of doctors and hospitals so that they can tailor their sales pitch to different recipients. Role playing and contingency scenarios are developed so that salespeople can make the most out of their short meeting time with doctors. Capability control pushes salespeople to go beyond what the firm provides them with in terms of knowledge and resources and to use their individual strengths to connect and build rapport with doctors either through technical knowledge or personal affinity. Capability control also encourages salespeople to educate themselves so that they take risks and move beyond their comfort zones to experiment with bold and novel approaches to selling (e.g., talking about wine, arts, sports, or other hospitals’ best practices)—whatever it takes to forge a connection with doctors.
When it is understood that supervisors are interested in investing in and evaluating their salespeople’s capabilities, the message is that salespeople should be directing their behaviors more toward searching for and experimenting with innovative sales techniques rather than seeking to refine status quo approaches. Mistakes, deviations from routine selling, and trial and error are inevitable consequences of capability control, and such miscues are often viewed as the natural consequences of progression toward discovering novel solutions to customers’ problems. Thus, capability control encourages exploratory learning that is promotion focused.
H3: Capability control results in (a) more exploratory learning and (b) less exploitative learning.
The discordant findings regarding the effects of sales control on performance prompted us to examine the complexity underpinning this relationship and, in turn, to propose a set of mediation hypotheses in an attempt to unpack this contentious issue. We reason that sales control is too distal to have a direct impact on performance and instead propose a new mechanism—namely, that sales control influences performance through a more proximal path of salesperson learning. Specifically, we argue that sales control will enhance performance when salespeople self-regulate their behaviors (in either a prevention- or a promotion-focused manner) in ways that display regulatory fit with the type of control being used.
Using a distal–proximal framework, Lanaj, Chang, and Johnson (2012) show through meta-analysis that distal personality traits have an impact on work behaviors (e.g., task performance, organizational citizenship behavior, innovative performance) through more proximal regulatory focus. As the authors argue, “because regulatory foci represent proximal motivational constructs (Scholer and Higgins 2008), they may operate as channels through which more distal individual differences affect work behaviors” (p. 999). Research has shown that regulatory-focused behaviors function as mediators between distal personal and situational antecedents and performance. For example, Wallace and Chen (2006) show that promotion and prevention regulatory foci mediate the relationships between conscientiousness and group safety climate as well as those between production and safety performance. Research has also reported that prevention focus mediates the relationship of initiating structure with in-role performance and deviant behavior, whereas promotion focus mediates the relationship of servant leadership with helping and creative behavior (Neubert et al. 2008).
Given the strong theoretical and empirical support of the mediating role of regulatory foci, we posit that the two types of salesperson learning mediate the relationship between sales control and performance. However, because each type of sales control affects exploratory and exploitative learning in different directions, we expect different signs for the indirect effect depending on the relationship between sales control and learning.
For outcome and activity control, we predict that there will be a negative (positive) indirect effect on salesperson performance when mediated by exploratory (exploitative) learning. This reasoning is based on our prediction that outcome and activity controls discourage (encourage) exploratory (exploitative) learning. For capability control, we posit that there will be a positive (negative) indirect effect on salesperson performance when it is mediated by exploratory (exploitative) learning because capability control encourages (discourages) exploratory (exploitative) learning. The positive performance effects of exploratory and exploitative learning are in line with RFT; irrespective of whether a promotion- or prevention-focused behavior is used, both share the goal of improving performance. Formally, we propose the following hypotheses:
H4a: Outcome control has a negative indirect effect on salesperson performance when it is mediated by exploratory learning.
H4b: Outcome control has a positive indirect effect on salesperson performance when it is mediated by exploitative learning.
H5a: Activity control has a negative indirect effect on salesperson performance when it is mediated by exploratory learning.
H5b: Activity control has a positive indirect effect on salesperson performance when it is mediated by exploitative learning.
H6a: Capability control has a positive indirect effect on salesperson performance when it is mediated by exploratory learning.
H6b: Capability control has a negative indirect effect on salesperson performance when it is mediated by exploitative learning.
We chose two moderators—( 1) preference for sales predictability and ( 2) customers’ purchase-decision-making complexity—based on theoretical grounds that either can strengthen or weaken regulatory fit and ultimately influence performance by accentuating or attenuating the impact of regulatory-focused behavior on performance. On a practical level, it is also well known that salespeople are conscious of the need to close sales transactions and feel the pressure to do so. However, there is little academic research on this topic. Therefore, the construct of preference for sales predictability taps into this characteristic of a salesperson, and our model captures this construct as a moderator. Furthermore, given the pharmaceutical context of this study, it is appropriate to examine customers’ purchase-decisionmaking complexity as a moderator because the number of parties involved in making purchase decisions about drugs is changing from a single source (e.g., doctors) to multiple parties (e.g., doctors and hospital administrators), and we expect such complexities to condition the impact of the two learning behaviors on performance (Bonoma 2006).
Preference for sales predictability. The literature on need for closure suggests that salespeople who have a high preference for predictability desire prompt, firm, and transparent answers (Webster and Kruglanski 1994). They are less tolerant of uncertainty and thus tend to avoid situations that are unpredictable and less straightforward. Therefore, salespeople with a high preference for sales predictability will prefer prevention-focused behaviors (Cesario, Grant, and Higgins 2004). The combination of exploitative learning and a high preference for sales predictability is compatible because both evoke a prevention focus, thus strengthening regulatory fit and, in turn, increasing performance. Conversely, the combination of exploratory learning and a high preference for sales predictability is incompatible because exploratory learning is associated with promotionfocused behavior, thus weakening regulatory fit and, in turn, mitigating performance. Thus, we propose the following:
H7a: The effect of exploitative learning on salesperson performance increases as a salesperson’s preference for sales predictability increases.
H7b: The effect of exploratory learning on salesperson performance decreases as a salesperson’s preference for sales predictability increases.
Customers’ purchase-decision-making complexity. Purchase decision making becomes more complex when customers ( 1) take longer to make a purchase decision, ( 2) require more information to arrive at a purchase decision, ( 3) involve multiple parties rather than a single person, and ( 4) perform a purchase task that is new rather than routine or standard (e.g., Schmitz and Ganesan 2014). Therefore, high customer purchase-decisionmaking complexity creates a risky and uncertain situation in which prevention-focused behaviors are more likely to pay off and promotion-focused behaviors can be costly. Consistent with Jaworski’s (1988) argument that fit between sales control and the environment is critical to realize performance, we posit that the impact of exploitative learning on salesperson performance will be elevated under high customer purchase-decision-making complexity.
As March (1991, p. 85) argues, “the distance in time and space between the locus of learning and the locus for the realization of returns is generally greater in the case of exploration than in the case of exploitation, as is the uncertainty.” Therefore, the performance of a salesperson who relies on exploratory learning will suffer when dealing with customers whose purchase decision making accentuates, compounds, and acutely raises the risks associated with exploratory learning. This suggests that there is poor regulatory fit when a promotion-focused behavior such as exploratory learning is used in a situation that demands prevention-focused actions, as in high customer purchasedecision-making complexity. The overall effect, therefore, is to weaken the impact of exploratory learning on salesperson performance. Formally, we hypothesize the following:
H8a: The effect of exploitative learning on salesperson performance increases as customer purchase decision making becomes more complex.
H8b: The effect of exploratory learning on salesperson performance decreases as customer purchase decision making becomes more complex.
We tested our conceptual model across two studies using data collected from South Korea, one of the largest pharmaceutical markets in the world and the third largest in Asia, with sales expected to grow from $15.1 billion in 2015 to $18.3 billion by 2020. There is considerable government regulation on pricing and advertising to patients in the Korean pharmaceutical industry. All selling, marketing, and advertising activities are targeted toward physicians and hospital administrators rather than patients. The Korean pharmaceutical industry has one of the highest selling, general, and administrative expenses, which account for 30.5% of total sales, higher than the average 20% typically found in Korean manufacturing firms (Kim 2017). Therefore, the Korean pharmaceutical market can be characterized as an industry that competes mostly through sales promotion versus price differentiation. Doctors occupy an important position (although the decision-making unit becomes more complex for larger university hospitals) in deciding which prescription drugs to use. This implies that salespeople have a window of opportunity in influencing a doctor to use their drugs. Thus, the pressure to be creative and leave a lasting impression and to stand out from the crowd is key to influencing doctors to choose their drugs.
Furthermore, the Korean government regulates rebates (i.e., gifts and monetary incentives) and kickbacks that pharmaceutical firms use to persuade doctors to prescribe their drugs, although such practices have yet to be firmly rooted out. Such an environment pushes salespeople to experiment with new selling techniques and forces them to step outside of their comfort zones. For example, they understand that they must try to learn foreign selling approaches, which may not necessarily play to their strengths. Thus, the competency of sales representatives is a critical asset that can determine the fate of pharmaceutical firms in this industry. The two companies chosen for this study are global pharmaceutical companies operating in South Korea. The first company markets more than 80 products and has annual sales exceeding $300 million, while the second firm sells more than 100 products and has sales exceeding $350 million.
In Study 1, we collected salesperson data on control systems (Wave 1) and, after two months, data on salesperson learning and customer and salesperson characteristics (Wave 2). Then, we matched salesperson data with sales managers’ evaluations of salesperson performance, which we gathered three weeks after Wave 2. However, the model does not fully capture the change in salesperson learning and performance over time. Thus, in line with recent research (Kumar et al. 2011; Kumar and Pansari 2016), we conducted Study 2 to assess the robustness of our model using panel data collected from salespeople and sales managers at two points in time. There is a dearth of studies that offer insights into how salesperson learning unfolds over time (Mathieu et al. 2008), and our two studies are designed to fill this gap.
TABLE: TABLE 3 Definition, Operationalization, and Reference of Key Constructs
| Construct | Definition | Operationalization | Source (Reference) |
|---|
| Exploratory learning | A salesperson’s self-regulated promotion-focused behavior that searches for, experiments with, and discovers new and innovative selling techniques and skill sets. | A five-item scale (1 = “never,” and 5 = “always”) | New scale |
| Exploitative learning | A salesperson’s self-regulated prevention-focused behavior that adheres to proven and well-established selling techniques and skill sets that leverage known knowledge and capabilities. | A five-item scale (1 = “never,” and 5 = “always”) | New scale |
| Outcome control | A laissez-faire control method in which the salesperson is given the freedom to use whichever method (s)he prefers as long as certain outcomes (e.g., sales volume, quota) are achieved. | Salesperson’s incentive rate as the ratio of total variable compensation (i.e., total compensation minus base salary) to sales revenue in the last financial year | Lo, Ghosh, and LaFontaine (2011) |
| Activity control | A more involved control method from management in which a salesperson is not responsible for outcomes as long as certain procedures and routines (number of sales calls made, number of samples distributed) are followed. | A five-item Likert scale (1 = “strongly disagree,” and 5 = “strongly agree”) | Kohli, Shervani, and Challagalla (1998) |
| Capability control | A developmental and nurturing control method in which management provides training and guidance to develop and improve a salesperson’s skill sets and abilities (presentation skills, client interaction and negotiation skills, relationship management skills). | A five-item Likert scale (1 = “strongly disagree,” and 5 = “strongly agree”) | Kohli, Shervani, and Challagalla (1998) |
| Preference for sales predictability | A salesperson’s trait-like disposition that favors prompt, transparent, expected, and results over opaque, surprising, and delayed outcomes. | A four-item scale (1 = “never,” and 5 = “always”) using one of the dimensions of Webster and Kruglanski’s (1994) need-forclosure scale | Choi et al. (2008); Houghton and Grewal (2000) |
| Customers’ purchase-decisionmaking complexity | The extent to which customers’ purchase decision making involves time, information, multiple parties, and new processes. | A five-item scale Likert scale (1 = “strongly disagree,” and 5 = “strongly agree”) | John and Weitz (1989) |
| Salesperson performance | The degree to which salespeople meet sales objectives. | A seven-item formative scale (1 = “needs improvement,” and 5 = “outstanding”) | Behrman and Perreault (1982) |
We designed our study and took all necessary procedural measures to minimize common method bias (Podsakoff et al. 2003). To reduce evaluation apprehension and protect anonymity, respondents were assured that there were no right or wrong answers and that responses would remain strictly confidential. We randomized the order of the measures to reduce respondents’ tendency to rate items similarly (e.g., rating control systems and exploratory and exploitative learning consistently high or low). To limit potential common method bias effects, we obtained data on salesperson performance from sales managers and data on all other constructs from salespeople at two points in time. Because the unit of analysis is the individual salesperson, we measured all variables at the individual level. Unless otherwise stated, we used a five-point scale to assess responses (see Table 3).
Exploratory and exploitative learning. Because there are no established scales that measure exploratory and exploitative learning in the sales context, we developed the scales according to the following steps3 (Churchill 1979). First, we generated items to tap exploratory and exploitative learning. Following existing firm- and/or unit-level scales (e.g., Atuahene-Gima and Murray 2007), we used buzzwords such as “explore,” “search,” “discovery,” “experimentation,” “risk taking,” and “novelty” for exploratory learning and “implementation,” “proven approaches,” “adherence,” “efficiency,” and “productivity” for exploitative learning (March 1991, p. 71). We were careful to create scale items in such a way as to create two distinct measures of learning so that they would not overlap with existing measures, such as adaptive selling. Second, we conducted in-depth interviews with 20 salespeople, instructing them to assess the scale items in terms of relevance, clarity, and thoroughness. We made necessary revisions in line with their feedback. Third, we assessed the revised scales using data collected from a new batch of 78 salespeople. Test results indicated that the scales were reliable, valid, and unidimensional, so it was not necessary to drop any scale items to improve reliability or validity.
Control systems. We measured activity control (five items) and capability control (five items) with scales borrowed from Kohli, Shervani, and Challagalla (1998). We operationalized outcome control in terms of incentive rate using Lo, Ghosh, and LaFontaine’s (2011) formula. Specifically, we calculated incentive rate for each salesperson as the ratio of total variable compensation (i.e., total compensation minus base salary) to sales revenue in the last financial year. We chose this measure over alternatives (e.g., variable-to-total compensation) because it is “consistent with the notion of ex ante incentives per agency theoretic models and thus is not susceptible to distortions arising from ex post realizations of outcomes” (Lo, Ghosh, and LaFontaine 2011, p. 788).
Moderating variables. We measured preference for sales predictability using a four-item scale.4 Preference for predictability is one of the dimensions of Webster and Kruglanski’s (1994) higher-order need-for-closure scale, which has been adapted to various contexts such as consumer information search and shopping behavior (e.g., Choi et al. 2008; Houghton and Grewal 2000). We adapted previously validated items to the sales context. We measured customer purchase-decision-making complexity using a five-item scale (John and Weitz 1989).
Salesperson performance. We asked sales managers to rate the extent to which salespeople met sales objectives. We measured salesperson performance with a seven-item formative scale (1 = “needs improvement,” and 5 = “outstanding”; Behrman and Perreault 1982).
Control variables. We detail the control variables in Web Appendix B.
We used a two-wave, multirespondent approach to collect data from two large pharmaceutical firms with the endorsement of their human resources managers.5 We collected the salesperson data in two waves. In the first wave, we sent the questionnaire to 616 salespeople through a link in the firms’ intranet system. Salespeople were informed about the purpose of the study and the confidentiality of responses and were asked to respond to questions about demographics, learning goal orientation, sales volatility, activity control, and capability control. After two reminders, we obtained 414 usable salesperson responses. Two months later, we conducted the second wave of the study with the initial 414 responding salespeople, who were then asked to respond to questions pertaining to exploratory and exploitative learning, preference for sales predictability, and customers’ purchase-decision-making complexity. After two reminders, we received 378 usable responses (Company A = 142, Company B = 236), for a response rate of 61% (Company A = 61%, Company B = 64%).
Three weeks later, we collected data from sales managers. We received responses from 42 managers, who, on average, provided information on the performance of nine salespeople. We found no significant differences between early and late respondents with regard to the model constructs, demographics, and matched performance data. Salespeople were mostly male (91.5%), were an average of 34.9 years of age, served an average of 62 customers, and received an average of 40.6 hours of training. In addition, 54% held graduate degrees, and they averaged 7.4 years of territory experience, 4.8 years of firm experience, and 7.4 years of career experience.
TABLE: TABLE 4 Scales and Confirmatory Factor Analyses Results (Study 1)
TABLE: TABLE 4 Scales and Confirmatory Factor Analyses Results (Study 1)
| | Loadings |
|---|
| Exploratory learning | Salesperson Responses |
| (c2 = 944.97, d.f. = 499; GFI = .838; TLI = .916; CFI = .925; RMSEA = .049) |
| Activity Control |
| My immediate manager … |
| … informs me about the sales activities I am expected to perform. | .630 |
| … informs me on whether I meet his/her expectations on sales activities. | .690 |
| … evaluates my sales activities. | .792 |
| … monitors my sales activities. | .758 |
| If my immediate manager feels I need to adjust my sales activities, s/he tells me about it. | Deleted |
| Capability Control |
| My immediate manager … |
| …evaluates how I make sales presentations and communicate with customers. | .743 |
| … provides guidance on ways to improve selling skills and abilities. | .848 |
| … assists by suggesting why using a particular sales approach may be useful. | .774 |
| … periodically evaluates the selling skills I use to accomplish a task. | .701 |
| My immediate manager has standards by which my selling skills are evaluated. | Deleted |
| Sales Volatility The amount I sell is largely beyond my control. | .782 |
| I have a difficult time in predicting my sales from year to year. | .879 |
| I do not really know how much more I could sell if I worked harder. | .841 |
| Learning Goal Orientation |
| An important part of being a good salesperson is continually improving my skills. | .702 |
| It is important for me to learn from each selling experience I have. | .705 |
| There really are not a lot of new things to learn about selling. (R) Deleted It is worth spending a great deal of time learning new approaches for dealing with my customers. | .769 |
| Learning how to be a better salesperson is of fundamental importance to me. | .697 |
| I put in a great deal of effort in order to learn something new about serving my customers. | .765 |
| Exploratory Learning |
| I search for novel information and ideas that enable me to learn new sales techniques. | .708 |
| I discover new selling techniques that take me beyond my current knowledge, skills, and abilities in improving my performance. | .735 |
| I engage in learning new selling skills and knowledge that help me look at existing customers’ problems in a different light. | .706 |
| I explore novel and useful approaches that I can use to respond to customers’ needs and wants in the future. | .772 |
| I focus on learning new knowledge of selling techniques that involve experimentation and the potential risk of failure. | .689 |
| Exploitative Learning |
| I adhere to sales techniques that I can implement well to ensure productivity rather than those that could lead me to implementation mistakes. | .577 |
| I implement my proven approaches to leverage my existing knowledge and experience in selling to customers. | .755 |
| I adopt sales techniques that suit well to my current knowledge and experience. | .739 |
| I execute those sales techniques that are aligned well with my selling routines. | .802 |
| I prefer undertaking sales tasks with little variation in my performance compared to sales tasks with handsome rewards but with risks involved. | .677 |
| Preference for Sales Predictability |
| I feel uncomfortable going into sales situations without knowing what might happen. | .713 |
| I dislike unpredictable sales situations. | .706 |
| I don’t like to do business with customers who are capable of unexpected actions. | .766 |
| I don’t like to go into sales situations without knowing what I can expect from it. | .724 |
| Customers’ Purchase-Decision-Making Complexity |
| My customers usually make their purchase decision quickly. (R) | .694 |
| Several people are usually involved in the purchase decision. | .611 |
| My customers usually need a lot of information before purchasing. | .770 |
| My customers usually consider the purchase decision to be routine. (R) | .737 |
| My customers’ purchase decision usually evolves over a long period of time. | |
| Deleted Sales Manager Responses | |
| Salesperson Performance (Formative Scale) This salesperson … | |
| …produces a high market share for the company in his/her territory. | |
| … produce sales or blanket contracts with longterm profitability. | |
| …makes sales of those products with the highest profit margin. | |
| … generates a high level of dollar sales. | |
| … quickly generates sales of new company products. | |
| …identifies and sells to major accounts in his/her territory. | |
| …exceeds all sales targets and objectives for his/ her territory during the year. |
Measure validation. We conducted a confirmatory factor analysis to assess the reliability and validity of the measures to which salespeople had responded. The confirmatory factor analysis shows good fit to the data, after we deleted items with a low factor loading (see Table 4). The composite reliability and average variance extracted values were above .70 and .50, respectively. Standard testing procedures (Anderson andGerbing 1988; Bagozzi andYi 1988; Fornell and Larcker 1981) supported both convergent and discriminant validity of the measures (Table 5).6
TABLE: TABLE 5 Descriptive Statistics, Intercorrelations, and Reliabilities (Study 1)
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |
|---|
| 1. Companya | | | | | | | | | | | | | | | | |
| 2. Sales experience (ln) | –.017 | | | | | | | | | | | | | | | |
| 3. Training (ln hours) | –.574** | 0.05 | | | | | | | | | | | | | | |
| 4. Number of customers (ln) | –.322** | .132** | .183** | | | | | | | | | | | | | |
| 5. Sales effort (ln) | –.136** | –.102* | .191** | .159** | | | | | | | | | | | | |
| 6. Past performance | 0.075 | .163** | –.065 | .142** | 0.068 | | | | | | | | | | | |
| 7. Learning orientation | –.068 | 0.044 | 0.081 | –.035 | .127* | 0.04 | | | | | | | | | | |
| 8. Sales volatility | .175** | –.230** | –.227** | 0.014 | –.009 | –.129* | –.166** | | | | | | | | | |
| 9. Preference for predictability | –.046 | –.147** | –.117* | 0.058 | 0.065 | –.109* | .121* | .477** | | | | | | | | |
| 10. Customers’ PDMC | –.040 | –.001 | –.040 | 0.058 | 0.01 | –.150** | .118* | .347** | .302** | | | | | | | |
| 11. Activity control | –.020 | –.090 | –.090 | 0.09 | 0.013 | –.091 | 0.016 | .500** | .544** | .342** | | | | | | |
| 12. Capability control | –.087 | –.011 | 0.084 | –.144** | 0.032 | 0.084 | .495** | –.050 | .171** | .231** | .329* | | | | | |
| 13. Outcome control | –.079 | –.193** | 0.074 | 0.015 | 0.004 | –.030 | –.076 | .176** | .125* | .111* | .135** | 0.038 | | | | |
| 14. Exploratory learning | –.051 | 0.062 | .102* | –.057 | 0.088 | 0.037 | .569** | –.075 | .187** | .147** | 0.053 | .448** | –.167** | | | |
| 15. Exploitative learning | .117* | –.118* | –.070 | –.019 | 0.018 | –.072 | 0.004 | .538** | .310** | .315** | .457** | –.006 | .233** | –.023 | | |
| 16. Salesperson performance | –.246** | .235** | .128* | .198** | .145** | .128* | .405** | 0.032 | 0.095 | .399** | .158** | .323** | .149** | .382** | .168** | |
| M | – | 2.82 | 3.11 | 3.87 | 2.84 | 3.78 | 4.05 | 2.73 | 3.26 | 3.28 | 3.05 | 3.8 | 2.84 | 4.08 | 3.09 | 3.53 |
| SD | – | 0.28 | 1.18 | 0.71 | 1.4 | 1.04 | 0.56 | 0.95 | 0.75 | 0.61 | 0.85 | 0.68 | 0.94 | 0.53 | 0.84 | 0.63 |
| Cronbach’s a | – | – | – | – | – | – | 0.83 | 0.87 | 0.83 | 0.79 | 0.84 | 0.85 | – | 0.83 | 0.83 | – |
| Composite reliability | – | – | – | – | – | – | 0.85 | 0.87 | 0.82 | 0.8 | 0.81 | 0.85 | – | 0.84 | 0.84 | – |
| Average variance extracted | – | – | – | – | – | – | 0.53 | 0.7 | 0.53 | 0.5 | 0.52 | 0.59 | – | 0.52 | 0.51 | – |
*p < .05 (two-tailed test).
**p < .01 (two-tailed test).
aDummy variable (Company A = 1; Company B = 2).
Notes: PDMC = purchase-decision-making complexity. Outcome control (i.e., incentive rate) is natural log–transformed.
Common method bias. We assessed the extent of common method bias in salesperson-rated measures using the marker variable technique (Lindell and Whitney 2001). A three-item scale of firm dependence on the key supplier (Jap and Ganesan 2000) served as a marker variable because it is not theoretically related to the study’s core variables and has good reliability (M = 3.47, SD = .82, Cronbach’s a = .78). Common method bias was not a major threat, as the pattern and magnitude of covariances did not change significantly before and after the marker variable’s inclusion in the measurement model.
We estimate the model by taking into consideration ( 1) measurement error, ( 2) alternative models, and ( 3) endogeneity of exploratory and exploitative learning. We review each of these in Web Appendix C.
Main effects. As Table 6 reports, outcome control is negatively related to exploratory learning (b = –.072, p < .01) and positively related to exploitative learning (b = .107, p < .01), in support of H1a and H1b. Activity control is not related to exploratory learning (b = .020, n.s.) but is positively related to exploitative learning (b = .257, p < .01), in support of H2b but not H2a. Capability control is positively related to exploratory learning (b = .174, p < .01) and negatively related to exploitative learning (b = –.106, p < .05), in support of both H3a and H3b.
Mediation effects. Our conceptual model hypothesizes the mediating role of salesperson learning. We estimate the indirect effects of control systems on salesperson performance through exploratory and exploitative learning by bootstrapping ( 1,000 samples) at the 95% confidence level (Zhao, Lynch, and Chen 2010). None of the control systems has a significant direct effect on salesperson performance. However, outcome control has a negative, significant indirect effect on performance through exploratory learning (b = –.015, 95% confidence interval [CI] = [–.031, –.005], p < .01) and a positive, significant indirect effect on performance through exploitative learning (b = .020, CI = [.006, .043], p < .05), in support of H4a and H4b. For activity control, the indirect effect through exploitative learning is positive and significant (b = .049, CI = [.018, .100], p < .01), while the indirect effect through exploratory learning is not (b = .004, CI = [–.007, .022], n.s.). These findings support H5b but not H5a. Finally, capability control reveals a positive, significant indirect effect on performance through exploratory learning (b = .037, CI = [.015, .065], p < .01) but not through exploitative learning (b = –.020, CI = [–.056, .001], n.s.), in support of H6b but not H6a.
Interaction effects. In line with H7a, the effect of exploitative learning on performance increases as a salesperson’s preference for sales predictability increases (b = .095, p < .01). Exploitative learning has a stronger positive effect on performance at high levels of preference for predictability (b = .279, p < .01) than at low levels of preference for predictability (b = .137, p < .05), in support of H7a. However, the interaction effect of exploratory learning and preference for sales predictability is not significant (b = .026, n.s.). Thus, the results do not support H7b.
The effect of exploitative learning on salesperson performance increases as customers’ purchase-decision-making becomes more complex (b = .166, p < .01). Exploitative learning is related to performance at low levels of purchase-decisionmaking complexity (b = .107, p < .05), but the effect becomes stronger at high levels of purchase-decision-making complexity (b = .308, p < .01), in support of H8a. The effect of exploratory learning on salesperson performance decreases as purchase-decision-making complexity becomes more complex (b = –.148, p < .05). Exploratory learning is related significantly to performance at low levels of purchase-decision-making complexity (b = .263, p < .01) but not at high levels of purchase-decision-making complexity (b = .083, n.s.), in support of H8b.
Post hoc test. We conducted a post hoc analysis to test the direct, indirect, and total effects on performance and the effect of exploratory and exploitative learning on performance. We detail the test results in Web Appendix D.
Study 1 reinforces the notion that sales control systems are of crucial importance for the effectiveness and efficiency of salespeople and sales organizations. However, Study 1 examines the performance impact of sales control systems by taking a static approach. We still do not know how changes in sales control systems over time influence salesperson performance. Therefore, a dynamic model of sales control systems is needed. As stated previously, the literature offers mixed results on the performance effect of sales control systems. We speculate that these conflicting findings may partly be due to the static approach taken in studying sales control systems. Examining the sales control systems– performance relationship by taking a dynamic approach might shed light on the contradictory findings in the literature. Thus, the purpose of Study 2 is to examine the relationship between changes in the degree of sales control systems, exploratory/exploitative learning, and performance over time.
TABLE: TABLE 6 Results (Study 1)
| | Main-Effects Model | Full Model |
|---|
| Exploratory Learning (R2 = .372) | Exploitative Learning (R2 = .368) | Salesperson Performance (R2 = .468) | Exploratory Learning (R2 = .388) | Exploitative Learning (R2 = .377) | Salesperson Performance (R2 = .520) |
|---|
| Paths | b | SE | b | SE | b | SE | b | SE | b | SE | b | SE |
|---|
| Direct Effects |
| Outcome control | –.072** | .020 | .107** | .031 | | | –.072** | .020 | .107** | .031 | | |
| Activity control | .020 | .030 | .257** | .048 | | | .020 | .030 | .257** | .048 | | |
| Capability control | .174** | .038 | –.106* | .070 | | | .174** | .038 | –.106* | .070 | | |
| Exploitative learning | | | | | .224** | .062 | | | | | | |
| Exploratory learning | | | | | .236** | .056 | | | | | | |
| Moderating Variables |
| Preference for predictability | | | | | –.094** | .039 | | | | | –.089* | .038 |
| Customers’ PDMC | | | | | .351** | .045 | | | | | .350** | .044 |
| Interaction Effects |
| Exploitative learning • Preference for predictability | | | | | | | | | | | .095** | .039 |
| Exploratory learning • Preference for predictability | | | | | | | | | | | .026 | .064 |
| Exploitative learning • Customers’ PDMC | | | | | | | | | | | .166** | .047 |
| Exploratory learning • Customers’ PDMC | | | | | | | | | | | –.148* | .075 |
| Controls |
| Company (Company A = 1; Company B = 2) | .008 | .021 | .066* | .033 | –.108** | .024 | .008 | .021 | .066* | .033 | –.122** | .023 |
| Sales experience | .057 | .083 | .073 | .131 | .413** | .092 | .057 | .083 | .073 | .131 | .366** | .088 |
| Training (ln hours) | .038 | .023 | .072* | .037 | –.023 | .026 | .038 | .023 | .072* | .037 | –.035 | .025 |
| Number of customers (ln) | –.021 | .034 | –.059 | .054 | .065 | .038 | –.021 | .034 | –.059 | .054 | .077* | .036 |
| Sales effort (ln) | .007 | .016 | .006 | .026 | .035* | .018 | .007 | .016 | .006 | .026 | .032 | .017 |
| Past performance | .003 | .022 | .011 | .035 | .077** | .024 | .003 | .022 | .011 | .035 | .081** | .023 |
| Learning goal orientation | .426** | .046 | .175* | .073 | .270** | .054 | .426** | .046 | .175* | .073 | .228** | .053 |
| Sales volatility | .025 | .028 | .362** | .045 | .034 | .035 | .025 | .028 | .362** | .045 | –.025 | .036 |
| Endogeneity Correction |
| Exploratory learningresidual | | | | | –.087 | .062 | | | | | –.079 | .065 |
| Exploitative learningresidual | | | | | -.087* | .037 | | | | | –.090* | .037 |
*p < .05 (one-tailed test for hypothesized, directional relationships; two-tailed test for control variables).
**p < .01 (one-tailed test for hypothesized, directional relationships; two-tailed test for control variables).
Notes: Unstandardized coefficients and standard errors with Monte Carlo integration ( 1,000 bootstraps) are reported. Fit statistics: The main-effects model: (c2 = 3.304, d.f. = 3; GFI = .999; TLI = .993; CFI = 1.0; RMSEA = .016); The interaction model: (c2 = 4.110, d.f. = 3; GFI = .999; TLI = .969; CFI = 1.0; RMSEA = .031). PDMC = purchase-decision-making complexity. GFI = goodness-of-fit index, TLI = Tucker–Lewis index, CFI = comparative fit index, and RMSEA = root mean square error of approximation.
Study 2 makes two important contributions. First, we provide empirical evidence as to whether the findings of the conceptual model (Figure 1) tested in Study 1 can be replicated when changes in sales control systems and salesperson performance are taken into consideration. Second, we test whether change in exploratory/ exploitative learning is a key mechanism by which change in sales control systems can lead to change in performance.
For Study 2, we collected new data from a large pharmaceutical firm at two points in time to capture matched salesperson and supervisor responses to the model constructs. We targeted 352 salespeople and 24 supervisors to complete the questionnaire at Time 1. We received 253 and 24 usable responses from salespeople and supervisors, respectively. One year later, we asked all Time 1 respondents to complete the questionnaire again. This yielded usable responses from 214 salespeople and 24 supervisors at Time 2. Salespeople were mostly male (88.8%), with an average age of 34.8 years. A total of 88% held a graduate degree, and they averaged 7 years of territory experience, 6.6 years of firm experience, and 7 years of career experience. Salespeople served an average of 65 customers and received an average of 53.8 hours of training.
The analytical approach involved two steps. First, we performed measure validation for the scales based on the salespeople’s responses at Time 1 and Time 2. Second, similar to previous studies (e.g., Kumar and Pansari 2016), we tested the proposed links in Figure 1 by considering changes in variables over time by using the growth modeling approach. We provide the details of the analytic approach in Web Appendix E. Next, we present the results.
Main effects. As Table 7 shows, change in outcome control is negatively related to change in exploratory learning (b = –.162, p < .01) and positively related to change in exploitative learning (b = .166, p < .01). Change in activity control is not related to change in exploratory learning (b = .113, n.s.) but is positively related to change in exploitative learning (b = .162, p < .01). Change in capability control is positively related to change in exploratory learning (b = .187, p < .01) but is not related to change in exploitative learning (b = .053, n.s.). Changes in exploratory learning (b = .252, p < .01) and exploitative learning (b = .478, p < .01) are both positively associated with change in performance.
Mediation effects. Change in outcome control directly affects change in performance (b = .236, p < .01). While outcome control’s indirect effect through change in exploitative learning is positive (b = .039, p < .05), this effect is negative through change in exploratory learning (b = –.032, p < .05), suggesting partial mediation through an increased change in exploitative learning and a decreased change in exploratory learning. Change in activity control has no direct effect on change in performance (b = .163, n.s.); however, the indirect effect through change in exploitative learning is significant (b = .038, p < .05), while the same effect through change in exploratory learning is not (b = .022, n.s.), suggesting full mediation only through change in exploitative learning. Finally, the direct effect of change in capability control on change in performance is significant (b = .215, p < .01), as is the indirect effect through change in exploratory learning (b = .038, p < .05), but not through change in exploitative learning (b = .013, n.s.), in support of partial mediation only through change in exploratory learning.
Interaction effects. Change in preference for sales predictability positively moderates change in the exploitative learning–performance link (b = .469, p < .01) but negatively moderates change in the exploratory learning–performance link (b = –.315, p < .05). Change in customers’ purchase-decisionmaking complexity positively moderates change in the exploitative learning–performance relationship (b = .452, p < .01) and negatively moderates the exploratory learning–performance link (b = –.204, p < .05).
Using RFT and regulatory fit as the overarching theoretical framework, this study integrates how different research streams, such as sales control systems and salesperson learning, which have evolved independently despite room for cross-fertilization, can come together to explain the influence of sales control on performance. First, our research introduces two novel constructs to the sales literature: salesperson exploratory and exploitative learning. We demonstrate that exploitative learning and exploratory learning can be encouraged or discouraged, depending on the type of sales control used. Second, we find that each type of control has a dual indirect effect on performance through either exploratory or exploitative learning, with the dual mediation pathways revealing opposite effects (one positive and the other negative). Third, we employ moderators that tap into salesperson and customer characteristics to delineate boundary conditions that shape the salesperson learning–performance linkage.
Integrating the literature on sales control and salesperson learning. The sales control and learning literature streams have advanced in parallel without much integration. We attempt to reverse this trend by theorizing and empirically showing that there is an intricate link between the two. Results suggest that ( 1) when outcome control is used, more exploitative and less exploratory learning occurs; ( 2) when activity control is used, more exploitative learning occurs; and ( 3) when capability control is used, more exploratory and less exploitative learning occurs.
If the objective is to have salespeople engage in experimental, creative, risk-taking, and bold endeavors to address customers’ needs in different and novel ways, capability control is optimal. Conversely, if the goal is to encourage salespeople to use safe and proven methods with little ambiguity and risk, outcome or activity control would be more effective. These results extend the regulatory fit literature to the sales context by showing that there is greater alignment between sales control and salesperson learning if a salesperson adopts a more promotion-focused (preventionfocused) learning approach when management is more (less) tolerant of mistakes, uncertainty, and risks and takes a longer-term (shorter-term) perspective. Our research shows that salespeople engage in both types of learning but gravitate toward one more than the other in response to the type of sales control adopted (Jaworski 1988).
TABLE: TABLE 7 Results (Study 2)
| | Main-Effects Model | Full Model |
|---|
| Exploratory Learning | Exploratory Learning | Sales Performance | Exploratory Learning | Exploratory Learning | Sales Performance |
|---|
| b | SE | b | SE | b | SE | b | SE | b | SE | b | SE |
|---|
| Direct Effects |
| Outcome control | –.162** | .058 | .166** | .052 | | | –.162** | .058 | .166** | .052 | | |
| Activity control | .113 | .074 | .162** | .066 | | | .113 | .074 | .162** | .066 | | |
| Capability control | .187** | .053 | .053 | .047 | | | .187** | .053 | .053 | .047 | | |
| Exploitative learning | | | | | .252** | .107 | | | | | .478** | .111 |
| Exploratory learning | | | | | .195* | .094 | | | | | .252** | .090 |
| Additional Paths |
| Outcome control | | | | | .274** | .076 | | | | | .239** | .072 |
| Capability control | | | | | .190** | .070 | | | | | .167** | .067 |
| Moderating Variables |
| Preference for predictability | | | | | .069 | .072 | | | | | .151* | .072 |
| Customers’ PDMC | | | | | –.105 | .089 | | | | | .051 | .089 |
| Interaction Effects |
| Exploitative learning • Preference for predictability | | | | | | | | | | | .469** | .107 |
| Exploratory learning • Preference for predictability | | | | | | | | | | | –.315* | .142 |
| Exploitative learning • DCustomers’ PDMC | | | | | | | | | | | .452** | .160 |
| Exploratory learning • DCustomers’ PDMC | | | | | | | | | | | –.204* | .123 |
| Controls |
| Sales volatility | .075* | 0.038 | –.014 | 0.033 | 0.043 | 0.089 | .075* | 0.038 | –.014 | 0.033 | 0.059 | 0.05 |
| Learning goal orientation | 0.006 | 0.06 | –.144** | 0.053 | 0.095 | 0.052 | 0.006 | 0.06 | –.144** | 0.053 | 0.075 | 0.086 |
| Adjusted R2 | .234 | | .253 | | .159 | | .234 | | .253 | | .248 | |
*p < .05 (one-tailed test for directional relationships, two-tailed test for control variables).
**p < .01 (one-tailed test for directional relationships, two-tailed test for control variables).
Notes: Model fit (main-effects model: c2 = 2.292, d.f. = 1; GFI = .998; TLI = .938; CFI = .996; RMSEA = .068; full model: c2 = 1.792, d.f. = 1; GFI = .999; TLI = .934; CFI = .999; RMSEA = .052).
Unstandardized coefficients and standard errors with Monte Carlo integration ( 1,000 bootstraps) are reported. PDMC = purchase-decision-making complexity. GFI = goodness-of-fit index, TLI = Tucker–Lewis index, CFI = comparative fit index, and RMSEA = root mean square error of approximation.
Contribution to the link between sales control and salesperson performance. Our study articulates a clear but complicated mediation process between sales control and performance through salesperson learning. The results reveal that outcome and activity controls have negative (positive) indirect effects on performance when mediated by exploratory (exploitative) learning, while capability control has a positive (negative) indirect effect on performance when mediated by exploratory (exploitative) learning. These results show how the dual mediation paths can lead in opposite directions and often result in equivocal and conflicting results depending on the type of learning. Because each control system can have two pathways to performance, through either exploratory or exploitative learning, whereby one is positive and the other is negative, the two paths may cancel each other out and, in turn, nullify the direct impact of control on performance. Given this new insight, our findings can partially explain the mixed results in the literature pertaining to control systems and performance.
Contribution to the contingency effect of salesperson learning. Performance effects related to the two types of learning we examine depend on salesperson and customer characteristics. Although research has shown that learning efforts lead to greater self-efficacy, the literature is silent on when salesperson learning, let alone different types of learning, results in different levels of performance (Wang and Netemeyer 2002). Building on the reasoning of regulatory fit and in line with the results from Studies 1 and 2, we find that at high (low) levels of preference for predictability, the effect of exploitative learning on performance increases (decreases), whereas the effect of exploratory learning on performance decreases (increases). At high (low) levels of purchase-decision-making complexity, the effect of exploitative learning on performance also increases (decreases), whereas the effect of exploratory learning on performance decreases (increased). Collectively, these interaction effects support our theorizing that performance benefits (suffers) from salesperson learning when there is regulatory fit (misfit) between learning and salesperson and customer characteristics.
Contribution to salesperson learning. The marketing literature has emphasized learning at the firm level (e.g., Hurley and Hult 1998). This focus might be responsible for the limited theoretical and practical advancement pertaining to learning at the individual level, despite repeated calls for such research (Tuncdogan, Van Den Bosch, and Volberda 2015). This study is one of the few to examine exploratory and exploitative learning at the salesperson level. Given that individual exploratory and exploitative learning are the microfoundations for organizational and team-level learning, our study enhances the understanding of the role that a salesperson’s learning plays in higher-level learning within firms. As Argyris and Schon (1978, p. 20) note, “there is no organizational learning without individual learning.”
In the pharmaceutical industry, salespeople are getting less face time with physicians. Instead, they are finding themselves in a position of having to convince hospital administrators, who are increasingly acting as gatekeepers of purchase approvals (Rockoff 2014). This paradigm shift is rewriting the rulebooks for salespeople, who must adapt to the turbulent health care environment.
When to use salesperson exploratory or exploitative learning. When a salesperson can sell to a doctor (i.e., a single decision-making unit) rather than to a group of hospital administrators (i.e., a group decision-making unit), using exploratory learning is more likely to pay off. However, in complex buying situations, such as new purchases involving multiple people with different roles (e.g., purchaser, influencer), or when the salesperson has a low tolerance for closing sales transactions, exploitative learning will be the preferable mode of learning to enhance salesperson performance.
It is necessary to understand salesperson and customer characteristics to determine which control system should be used to maximize impact on performance. Given the dual mediating route from sales control to performance, it is important to identify the combination of salesperson and customer characteristics that will produce the greatest impact from each type of sales control on performance and what the dominant salesperson learning is that accounts for how this occurs (see Web Appendix D). For example, we find that outcome and activity controls maximize performance when both preference for sales predictability and purchase-decision-making complexity are high, while capability control benefits performance the most when both preference for sales predictability and purchase-decision-making complexity are low. Furthermore, it is critical to understand that exploitative learning, rather than exploratory learning, is the dominant path through which the impact of outcome and activity control on performance is maximized when both moderators are high. Conversely, exploratory learning is the dominant route through which capability control’s effect on salesperson performance is maximized when both moderators are low. Managers need to be cognizant of these difference effects and ensure that the appropriate learning style is aligned with the given type of sales control that is being employed.
The empirical assessment of our model should be interpreted in light of certain limitations, due in part to trade-off decisions in our research design. We tested our model in the pharmaceutical industry in South Korea, but it is important to conduct studies beyond this context to assess the generalizability of our findings. We test the proposed model with data collected at the salesperson level. Thus, our findings reflect the variation in the level of exploratory and exploitative learning across salespeople. Yet salespeople perform a variety of tasks. Accordingly, the extent to which salespeople emphasize exploratory and exploitative learning may well depend on the nature (or type) of the task they perform. In this case, the appropriate unit of analysis would be at the task level rather than at the salesperson level, and data collected at the task level may capture variation in the level of exploratory and exploitative learning across tasks more appropriately. Moreover, although not examined in this research, a change in learning over time may be needed, such as from exploitative to exploratory, or vice versa, even for the same doctor.
In addition, we have suggested that because prior studies using cognition or attitude as mediators between sales control and salesperson performance have provided limited and inconclusive results, behaviors such as salesperson learning, which is more proximal to salesperson performance, may be the more appropriate mediator. However, our model does not include cognition- or attitude-related mediators, and therefore, a more robust and rigorous test would be to include cognition-, attitude-, and behavior-related mediators all in one model.
Furthermore, although it is likely that firms deploy combinations of sales controls (Jaworski, Stathakopoulos, and Krishnan 1993), this study does not focus on interactions between control systems (Miao and Evans 2013) and their impact on salesperson learning. Moreover, our research focuses on formal, as opposed to informal (i.e., self, social, and cultural), controls (Jaworski 1988). It would be enlightening to examine the effects of informal sales controls, as well as combinations of control systems, on salesperson learning.
Finally, because we were not able to obtain objective performance measures, we use a single, subjective generic scale (Behrman and Perreault 1982) to measure salesperson performance. This scale has been used extensively in previous research (e.g., Cravens et al. 1993; Evans et al. 2007; Sujan, Weitz, and Kumar 1994) and is one of the most reliable measures of salesperson (outcome) performance. That said, an objective performance measure (e.g., quota) and/or a measure that is more related to learning would have been ideal.
Notes: Sales managers’ evaluations of salesperson performance were gathered three weeks after the completion of Wave 2.
Footnotes 1 A systematic review identifies two streams of research: one stream focuses on performance outcomes at the sales unit level (e.g., Cravens et al. 1993; Oliver and Anderson 1994), and the other investigates performance outcomes at the salesperson level (e.g., Challagalla and Shervani 1996; Miao and Evans 2013). The current study focuses on the individual salesperson and examines the effects of sales control systems on a salesperson’s performance as evaluated by the sales manager, consistent with recent research (e.g., Evans et al. 2007; Miao and Evans 2013).
2 We substantiated our theoretical framework by collecting data in a pilot study of 78 salespeople in a midsized pharmaceutical firm. We measured promotion focus and prevention focus with a six-item, five-point (1 = “never,” and 5 = “constantly”) scale (Wallace and Chen 2006). We used the scales of exploratory and exploitative learning developed specifically for this study (see the “Instruments and Measures” section in Study 1). The model estimating exploratory (exploitative) learning as a function of promotion (prevention) focus suggests that (1) promotion focus is related positively to exploratory learning (b = .285, p < .05) but not to exploitative learning (b = .085, n.s.) and (2) prevention focus is related positively to exploitative learning (b = .309, p < .01) but not to explorative learning (b = –.174, n.s.). These findings support our argument that promotion-focused salespeople tend to engage in more exploratory learning, while prevention-focused salespeople adopt exploitative learning. These results are consistent with Tuncdogan, Van Den Bosch, and Volberda’s (2015) predictions that a promotion (prevention) focus is more strongly related to exploration (exploitation) than a prevention (promotion) focus.
3 In-depth interviews with managers and sales representatives clearly indicated salespeople’s involvement in exploratory and exploitative learning in an effort to improve their sales tasks.
4 Our scale differs from Lo, Ghosh, and LaFontaine’s (2011) salesperson risk aversion scale. These authors measure “the manager’s perceptions of the focal salesperson’s preference for income stability and aversion to variations in outcomes and pay” (p. 789), whereas our measure captures a salespeople’s perceptions of preference for predictability in sales situations and aversion to variations in customers’ expectations.
5 We dummy-coded the two firms to control for their fixed effects on learning and performance using the weighted dummyvariable approach due to an unequal distribution of responses from each company.
6 The exploratory and exploitative learning measures must also be distinct from those of related constructs, such as adaptive selling (Spiro and Weitz 1990) and learning goal orientation (Sujan, Weitz, and Kumar 1994). We compared the unconstrained and constrained (i.e., the correlation between constructs was set to 1) models (Anderson and Gerbing 1988) for each type of learning and adaptive selling as well as learning goal orientation. In all cases, the chisquare difference between the two models for each pair was significant (Dc2 > 3.84, Dd.f. = 1, p < .01), which suggests that the two types of learning are distinct from other similar constructs. We also tested the proposed model by controlling for the effect of adaptive selling on performance. The model with adaptive selling explained an additional 3% of the variance in performance, with no change in the significance of direct and interaction effects.
DIAGRAM: FIGURE 1 Conceptual Model
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Record: 218- Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data. By: McCarthy, Daniel M.; Fader, Peter S.; Hardie, Bruce G. S. Journal of Marketing. Jan2017, Vol. 81 Issue 1, p17-35. 19p. 1 Diagram, 8 Charts, 4 Graphs. DOI: 10.1509/jm.15.0519.
- Database:
- Business Source Complete
Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data
The growth of subscription-based commerce has changed the types of data that firms report to external shareholders. More than ever, companies are discussing and disclosing information on the number of customers acquired and lost, customer lifetime value, and other data. This has fueled an increasing interest in linking the value of a firm’s customers to the overall value of the firm, with the term “customer-based corporate valuation” being used to describe such efforts. Although several researchers in the fields of marketing and accounting have explored this idea, their underlyingmodels of customer acquisition and retention do not adequately reflect the empirical realities associated with these behaviors, and the associated valuation models do not meet the standards of finance professionals. The authors develop a framework for valuing subscription-based firms that addresses both issues, and they apply it to data from DISH Network and Sirius XM Holdings.
The pioneering work of Gupta, Lehmann, and Stuart (2004; hereinafter GLS) was the first to explicitly link firm value to CLV for public companies. However, their treatment of the valuation problem suffers from two major issues. First, they performed their CLV calculations assuming a constant retention rate, which can result in an undervaluing of existing customers (Fader and Hardie 2010). Second, their valuation framework does not incorporate key financial/accounting issues such as firm capital structure and nonoperating assets. While other researchers–most notably Schulze, Skiera, and Wiesel (2012; hereinafter SSW)–have built on GLS’s seminal work, the underlying models of customer behavior and the associated valuation frameworks are not up to the standards expected by marketers and financial professionals, respectively.
Our objective is to present a framework for valuing subscription-based business. The parameters of the underlying model of customer behavior can be estimated using only publicly disclosed customer data, making it suitable for passive investors valuing a going concern. We present models of the firm’s acquisition and retention processes that accommodate factors such as customer heterogeneity, duration dependence, seasonality, and changes in population size. We explicitly account for the fact that publicly reported data are typically aggregated (temporally and across customers) and suffer from “missingness” (i.e., the reported data are not available in all periods).
Journal of Marketing
The rest of this article is organized as follows. In the next section, we discuss the principles of customer-based corporate valuation, first reviewing the basic concepts of firm valuation and then exploring the nature of the customer data typically released by subscription-based businesses. Following a review of the literature, we present our model of customer behavior for such settings, presenting models for customer acquisition, retention, and spend. We then provide an empirical analysis that explores how such a model can be fit to real public company data; the two firms considered in our analysis are DISH Network and Sirius XM. After demonstrating the validity of our model, we present our valuations of the firms and explore other insights that can be derived using our model. We conclude with a discussion of the results and propose future avenues of research.
The Logic of Customer-Based Corporate Valuation
Before reviewing the literature and then developing our framework for valuing subscription-based businesses using publicly disclosed data, let us first review a standard approach to firm valuation (identifying the key information requirements) and then identify the data that are typically available within the firm (in contrast to the data that firms with a subscription-based business model tend to report to the public).
Valuation 101
According to standard corporate valuation theory (Damodaran 2012; Greenwald et al. 2004; Holthausen and Zmijewski 2014; Koller, Goedhart, and Wessels 2015), the value of a firm equals the value of the operating assets (OA) plus the nonoperating assets (NOA), minus the net debt (ND) of the firm. Denoting the value of the firm at time T by SHVT (for shareholder value),1 we have
The value of a firm’s operating assets is equal to the sum of all future free cash flows (FCFs) the firm will generate,2 discounted at the weighted average cost of capital (WACC): minus the difference between capital expenditures (CAPEX)
1 nonfinancial working capital (DNFWC):
( 3) FCFt = NOPATt - ðCAPEXt - D&AtÞ - DNFWCt.
The most important ingredient of FCF is NOPAT, which is a measure of the underlying profitability of the operating assets of the firm. NOPAT is equal to revenues (REV) times the contri-
( 4) NOPATt = ½REVt • ð1 - VCtÞ - FCt • ð1 - TRtÞ.
The other elements of Equation 3 make adjustments for balance sheet-related cash flow effects and are generally of secondary importance to the value of the firm.
The framework we have summarized is a discounted cash flow (DCF) model, which is the de facto industry standard way in which operating assets are valued within the financial community.3 At the heart of any such valuation exercise is the estimation of period-by-period FCF, central to which are estimates of period-by-period revenue (Equations 3 and 4). The task of generating accurate revenue projections has received surprisingly little attention in the finance community (Damodaran 2005).
A Data Structure for Subscription-Based Businesses
Let us assume that the firm has a monthly internal reporting period. The key numbers of interest are monthly revenues, which we denote by R(m) (where m = 1 corresponds to the firm’s first month of commercial operations).
Although some researchers would be tempted to approach the task of forecasting revenue by using a time-series model, it makes more sense to first decompose these aggregate revenue numbers, separately model the constituent components, and then combine the forecasts of these components to arrive at the desired revenue forecasts.4 First, we should recognize that revenue comes from customers, so decomposing revenue into its “number of customers” and “average revenue per customer” components would be a good start. Second, as we think about the number of customers the firm has in a month, it makes sense to decompose this quantity into the number of new customers acquired that month and the number of customers acquired in previous months who still have a relationship with the firm. Knowing the number of new customers acquired each month is a critical input to any valuation exercise, especially for firms with high subscriber acquisition costs. It is important to note that such information will be overlooked if we simply apply a time
series model to the revenue numbers.
As a starting point, let us think about what lies behind the
“total number of customers” number. It is helpful to think of a
“number of customers” matrix, Cð$, $Þ, which tracks customer
behavior by time of acquisition. With reference to Figure 1 (in
which the columns correspond to (calendar) time since the start
of the firm’s commercial operations and the rows correspond to
acquisition cohorts), let Cðm, m9Þ be the number of customers
acquired by the firm in month m who are still active in month
m9. It follows that the total number of customers the firm has at
the end of month m9 is given by the column total Cð., m9Þ =
åm9 m=
1
Cðm,
m9Þ.
The
number
of
customers
in
any
cohort
must be nonincreasing over time (i.e., Cðm, m9Þ Cðm, m0Þ
for m9 < m0).
The Cð$, $Þ matrix, along with Rð$Þ, lies at the heart of
several customer metrics reported both internally and externally.
• A sophisticated subscription-based firm will report the
Cð$, $Þ matrix internally, either in its raw form or as cohortby-cohort survival percentages (Cðm, m9Þ=Cðm, mÞ • 100%) (e.g., Mart´inez-Jerez et al. 2013).
• The number of customers acquired each month by the firm is
• The number of customers “lost” each month by the firm is given by
It follows that an aggregate monthly churn rate can be computed as LðfmÞ=Cð., m - 1Þ.
• For most firms with a subscription-based business model, the average revenue per customer is relatively constant across customers during a given period of time.5 Let us denote this quantity by ARPU(m) and compute it in the following manner:
has during month m. Publicly disclosed customer data are typically reported
quarterly, with the associated unit of time being the quarter; as such, they represent a temporal aggregation of the true underlying process. Commonly reported measures include the number of customers active at the end of each quarter (ENDq) and the number of customers added and lost each quarter (ADDq and LOSSq, respectively). Assume that the firm started operations at the beginning of a reporting quarter (i.e., q = 1 comprises m = {1, 2, 3}; equivalently, the first month of each quarter is January, April, July, or October),
5In contrast, average revenue per customer for firms with a non-
Figure 1 illustrates this mapping from the internal “number of customers” matrix to ADD and END.
Quarterly revenues (REVq) are given by
The challenge we face is how to make projections of R(m) and A(m) far into the future (as required for calculating for the FCF numbers) using the publicly reported ADD, LOSS, END, and REV numbers. We pursue this important task in our “Model Development” section, but first we discuss how other researchers have utilized the valuation concepts and data structures discussed here.
The idea of value-based management (i.e., the notion that maximizing shareholder value should be the guiding principle
when making strategic decisions) gained popularity in the 1980s, and the associated writings–especially Rappaport (1986)– brought the basic principles of firm valuation (as reviewed in the “Valuation 101” subsection) to a broader, nonfinance audience.
In his review of the valuation literature, Damodaran (2005, p. 1) writes, “Given the centrality of its role, you would think that the question of how best to value a business, private or public, would have been well researched…. The research into valuation models and metrics in finance is surprisingly spotty, with some aspects of valuation, such as risk assessment, being deeply analyzed and others, such as how best to estimate cash flows … not receiving the attention that they deserve.”
Kim, Mahajan, and Srivastava (1995) were the first marketing academics to recognize the potential for using some of the models of customer behavior developed by marketing scientists to generate the key inputs for estimating cash flows. They used the logistic internal-influence model for the diffusion of an innovation (which is equivalent to Fisher and Pry’s (1971) model of technology substitution) to characterize (and then project) the market penetration of mobile phones (and therefore the associated revenues of a cellular communication company), resulting in an estimate of the market value of a business explicitly based on a model of customer behavior.6 Pioneering as it was, the biggest shortcoming in their analysis was that they did not consider the reality of customer churn (i.e., it is assumed that once the customer has adopted the service, [s]he remains as a customer forever).
Driven in part by the interest in moving from transactionoriented/product-centric marketing strategies to relationshiporiented/customer-centric marketing strategies (with their emphasis on customer acquisition, retention, and development), the 1990s saw the notion of customer lifetime value (CLV)– defined as “the present value of the future cash flows attributed to the customer relationship” (Pfeifer, Haskins, and Conroy 2005, p. 17)–emerge from the confines of specialized direct/database marketing firms and become what is now a fundamental concept for most marketers. Blattberg and Deighton (1996) introduced the concept of “customer equity” (CE), which is the sum of the lifetime values of the firm’s customers, both current and future. Kumar and Shah (2015) provide a comprehensive guide to the literature on CE.
The pioneering work of GLS was the first to explicitly link CLV and firm value. Underpinning their work was the logistic internal-influence model to characterize customer acquisitions and a simple model for the CLV of acquired customers. After calibrating the models using publicly available data (along with expert judgment), they arrived at estimates of market value for five listed companies. However, their treatment of the valuation problem suffers from two major issues. First, their CLV calculations are performed assuming a constant retention rate. Second, their valuation framework does not incorporate key financial/accounting issues such as firm capital structure and nonoperating assets.
Several researchers have built on GLS’s seminal work. Most notably, SSW provide a thorough treatment of how CE relates to firm value using financial valuation theory, addressing many of the financial/accounting issues associated with the valuation aspect of GLS’s work. Several researchers have explored several technical issues associated with any valuation exercise. Pfeifer (2011) shows how one must be careful with the timing of cash flows when estimating retention rates and the value of the firm’s existing customers (which he calls currentcustomer equity [CCE]) using publicly disclosed company data. However, he stops short of providing his own estimate of CCE for any firm, let alone an estimate of the value of the firm as a
6It is important to note that they applied the model at the level of whole. Fader and Hardie (2010) show how assuming a constant retention rate (i.e., ignoring the phenomenon of increasing retention rates at the level of the cohort) results in downwardbiased estimates of the future or residual lifetime value (RLV) of the firm’s current customers. Other key works include Kumar and Shah (2009), Libai, Muller, and Peres (2009), and Wiesel, Skiera, and Villanueva (2008).
These ideas have been gaining attention and respect outside of marketing. Within the accounting literature, for instance, Bonacchi, Kolev, and Lev (2015) provide a systematic analysis across multiple companies linking CCE to shareholder value. Other accounting-related work includes Andon, Baxter, and Bradley (2001), Bonacchi, Ferrari, and Pellegrini (2008), Bonacchi and Perego (2012), Boyce (2000), Gourio and Rudanko (2014), and Hand (2015).
We summarize the literature and our contributions to it in Table 1. Because we use the models proposed by GLS and SSW as benchmarks in our empirical analysis, let us briefly comment on the structure of these models. Both rely on the logistic internal-influence model to model customer acquisition and both assume a homogeneous retention rate in their CLV calculations. However, GLS and SSW did not incorporate the impacts of seasonality and macroeconomic conditions into their models as well as the issues of (temporal) aggregation and missing data associated with the information released by companies. These are issues that we address in the valuation framework we develop in this research.
Although many of the articles we have reviewed discuss DCF methods for firm valuation (often anchoring on Rappaport’s (1986) expression for SHV), they do not explicitly make use of such a framework when generating an estimate of firm value. Rather, they take what Skiera and Schulze (2014) call a customer-based valuation approach. Skiera and Schulze (2014, p. 123) first state that “customer-based valuation first uses information about the customer base (for example, number of customers, contribution margin per customer, retention rate) to determine the value of the firm,” and then describe an approach based on the (residual) lifetime value of existing customers and the lifetime value of as-yet-to-be-acquired customers, multiplying these two quantities by the number of current and expected future customers (respectively) and adjusting for various financial considerations. Their position is that DCF and “customerbased valuation” methods are fundamentally different.
We believe that this is more polarizing than it needs to be. With reference to Figure 1, previous “customer-based valuation” methods are performing on a row-by-row basis what are effectively net present value calculations across columns and then summing across rows. “DCF methods” (as outlined in the “Valuation 101” subsection) are summing the columns and then effectively performing a net present value calculation across these column totals. Given the same customer matrix Cð$, $Þ, and with the assumption that all the various accounting issues are handled correctly, both approaches should yield the same estimate of firm value.
Whereas marketers may naturally be comfortable with discussions of firm valuation that are explicitly based on CLV
TABLE:
| | Setting | Customer Dynamics | Valuation Elements |
|---|
| Research | Data Source | Missing Data (Left-Censoring) | Heterogeneity (Churn) | Duration Dependence | Covariates | DCF Model | WACC Estimated | Debt/Other Assets | Customers Valued | Rolling Validation |
|---|
| Kim, Mahajan, and Srivastava (1995) | Public + experts | No | No | No | No | Yes | No | No | Current + future | No |
| GLS | Public + experts | No | No | No | No | No | No | No | Current + future | Yes |
| Wiesel, Skiera, and Villanueva (2008) | Public | No | No | No | No | No | No | No | Current | No |
| Kumar and Shah (2009) | Private | No | No | No | Yes | No | No | No | Current | Yes |
| Libai, Muller, and Peres (2009) | Public + experts | No | No | No | Yes | No | No | No | Current + future | Yes |
| Fader and Hardie (2010) | Simulated | No | Yes | No | No | No | No | No | Current | No |
| Pfeifer (2011) | None | No | No | No | No | No | No | No | Current | No |
| SSW | Public | No | No | No | No | No | Yes | Yes | Current + future | Yes |
| Bonacchi, Kolev, and Lev (2015) | Public | No | No | No | No | No | Yes | No | Current | No |
| Proposed | Public | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Current + future | Yes |
TABLE 1
and CE calculations, finance professionals (including the chief financial officer and company directors) are more comfortable with a valuation model based on estimates of period-by-period
FCF. What marketers can bring to the table are customer-based methods for estimating these period-by-period cash flows. As developed in a previous subsection, we feel that the Cð$, $Þ matrix lies at the heart of any such effort, and we next turn our attention to the development of a model that can be calibrated on publicly available data used to project Cð$, $Þ far into the future, thereby giving us the required inputs for computing future period-by-period cash flow.
Model Development
Our goal is to develop a model of customer behavior that can be used to generate long-term projections of R(m) and A(m), one whose parameters can be estimated using only publicly reported ADD, LOSS, END, and REV numbers. It is important that our approach to parameter estimation accounts for the “missingness” and aggregation associated with the data reported by companies. This missingness typically takes on one of two forms. First, there is the issue of left-censoring. For example, although Sirius XM (one of the companies we consider in our empirical analysis) began commercial operations in 2001/2002,7 it began disclosing paying customer data in Q3 2008. Second, there is the issue that some measures are reported for some period of time (e.g., END) before being complemented by other measures (e.g., ADD, LOSS). With respect to aggregation, the publicly disclosed customer data are typically reported quarterly with the associated unit of time being the quarter, whereas the firm is operating on a finer time interval (which, for the purposes of our analysis, we assume to be the month).8
At the heart of this work are models for the customer acquisition and retention processes that enable us to project Cð$, $Þ into the future. Coupled with a model for ARPU(m), we can then generate our projections of R(m).
We begin by describing our model for the retention process. This assumes that we know how many customers the firm acquires each month. We then describe our model for the customer acquisition process, one that takes into consideration the possibility of reacquiring customers who have previously churned, and then examine how to jointly estimate the parameters of these two models. Finally, we present a simple model for the evolution of ARPU(m) and then outline how to combine all of these submodels to generate the desired projections of R(m).
The Retention Process
Let the survival function SRðm9 - mjmÞ denote the probability that a customer acquired in month m remains a customer for at least m9 - m months. Having acquired AðmÞ = Cðm, mÞ customers in month m, it follows that
(12) Cðm, m9Þ = Cðm, mÞ • SRðm9 - mjmÞ, m9 m .
model for the duration of customers’ relationships with the firm. 7The two companies that later merged to form Sirius XM began
In addition to capturing the effects of (cross-sectional) heterogeneity and duration dependence, we want to accommodate time-varying covariates to control for the effects of seasonality and macroeconomic conditions.
We use a proportional hazards model with a Weibull baseline and capture cross-sectional heterogeneity in the baseline churn propensity using a gamma distribution. This is a well-accepted model for duration data and has proved effective and robust in several different application settings (Moe and Fader 2002; Morrison and Schmittlein 1980; Schweidel, Fader, and Bradlow 2008b).
Given a customer’s individual-specific baseline propensity to churn (lR), a homogeneous retention process shape parameter (cR), time-varying retention covariates (XRðm + 1, m9Þ = ½xRðm + 1Þ, xRðm + 2Þ, …, xRðm9Þ), and the coefficients as-
SR½m9 - m
i = m+1
Following Schweidel, Fader, and Bradlow (2008b) and Jamal and Bucklin (2006), we expect cR 1. When cR = 1, it reduces to an exponential baseline proportional hazards model.
If we assume that lR is distributed gamma (rR, aR) across the population, the unconditional probability that a customer acquired in month m survives at least m9 - m months is
Plugging this survival function into Equation 12 enables us to predict the number of active customers in future months for the month m cohort. Therefore, if we know Cðm, mÞ over all months, we can predict the remainder of the upper triangular matrix in Figure 1 as well as the number of customers lost each month (Equation 6). Estimates of LOSSq and ENDq follow from Equations 8 and 10 for all q = 1, 2, ….
In theory, we could estimate the model parameters ðrR, aR, cR, bRÞ by minimizing the sum of squared differences between our model-based estimates of LOSSq and the reported numbers. However this assumes that we know the monthly customer acquisition numbers, A(m), which is not the case. We only have quarterly customer additions ADDq, and some of these observations are probably missing. We therefore need to develop a model of the acquisition process whose parameters can be estimated using the reported ADDq data. These two models will be estimated simultaneously to give us the required A(m) and L(m) numbers.
The Acquisition Process
At first glance, specifying a model for the acquisition of customers over time seems to be a relatively simple exercise. The
Bass (1969) model or a simplified variant such as the logistic internal-influence model (as used by GLS and SSW) would appear to be the obvious choice. However, for the following four reasons, this is not the case:
- It assumes that all churning customers disappear forever– once an acquired customer has churned, (s)he cannot reenter the pool of “potential adopters.” SSW attempt to overcome this problem by using the logistic internal-influence model to characterize the number of net total customers (i.e., the number of customers after churn).9
- It assumes that the population size is fixed, when we know that the number of potential customers typically increases over time due to population growth.
- The Bass model and its simplified variants have several unfavorable properties, most notably the fact that the resulting adoption curve is symmetric about the period of peak acquisition. In real data sets, skewness about the peak is almost always present.
- It ignores the effects of seasonality and macroeconomic events.
We therefore develop a model from first principles that addresses each of these issues.
Let POP(m) denote the size of the population in month m, with POP(0) being the population size when the firm first commences operations. We assume POP(m) is nondecreasing over time.
Each month sees the formation of a new prospect pool of size M(m). We set M(0), the size of firm’s prospect pool when it commences operations, to the size of the population at that time. The size of the prospect pool in the company’s second month of operation is simply the increase in the size of the population over the preceding month. Thereafter, the size of the prospect pool is equal to the growth in the population during the preceding month plus the number of customers who churned during the month:
We assume that population growth is the only source of potential adopter growth over time, aside from previously churned customers.
Once a prospect pool has formed, some time will elapse before people within that pool are acquired as customers.10 Let FAðm9 - mjmÞ denote the probability that a member of prospect pool m is acquired by the end of month m9. It follows that the total number of new customers in month m is m-1
NA pool will never be acquired. For those who are potential
customers, we characterize the time to acquisition using a
proportional hazards model with a Weibull baseline and capture
cross-sectional heterogeneity in the baseline acquisition pro
pensity using a gamma distribution. Given a prospect’s individual-specific baseline propensity
to be acquired (lA), a homogeneous acquisition shape parameter (cA), time-varying acquisition covariates (XAðm + 1, m9Þ =
i = m+1
If we assume that lA is distributed gamma (rA, aA) across the population distribution, the unconditional probability that a customer from prospect pool m will be acquired by the end
This acquisition model is flexible yet parsimonious. Parsimony is especially important in limited data settings (such as those considered here) because, as Van den Bulte and Lilien (1997) show, ill-conditioning is a serious enough problem with small sample sizes that adding new predictors to alleviate model misspecification concerns may make the resulting model fit (and forecast) worse than it had been prior to the introduction of those covariates.
Parameter Estimation for the Acquisition and Retention Processes
We estimate the parameters of the acquisition and retention process models jointly using nonlinear least squares, minimizing the sum of squared differences between the actual and model-based estimates of quarterly acquisitions and losses. Let y denote the acquisition and retention process model parameters collectively, y ” ðrA, aA, cA, pNA, bA, rR, aR, cR, bRÞ, and let Q be the number of quarters from the commencement of the firm’s commercial operations to the end of the model calibration period.
If the firm reports the quarterly numbers from the very start of its operations, our parameters are those that minimize the following sum-of-squared errors (SSE):
Q nÀ d Á2
where AdDDq, LbOSSq, and EdNDq are the model-based estimates of these quantities computed using yb.11 Note that we optimize over all parameters jointly because of the dependence of the
retention process on the acquisition process (i.e., customers
cannot churn until they have been acquired) and vice versa (i.e.,
churning customers enter future prospect pools).
Equation 20 assumes that there is no missing data. How
ever, this is rarely, if ever, the case. Most companies start dis
closing ending customer count data (END) some number of quarters into the company’s operations, then begin disclosing customer ADD and LOSS data at a later date. Let qA be the first quarter for which the firm reports END data, and qB the first quarter for which it reports ADD and LOSS data (qB = qA = 1). In
QQ
This accounts for the shortened contiguous customer addition and loss data and the missingness present at the beginning of the time series.
Average Revenue per User
We make use of a simple time-series model to capture (and project) the evolution of ARPU(m). Assuming linear growth in ARPU,12 we can use a simple time-trend regression:
The mean of many economic and financial time series is nonstationary (Zivot and Wang 2006). When this is so, the fitted residuals of the regression given in Equation 22 will fail tests for nonstationarity, the most popular of which is the augmented Dickey-Fuller test (Dickey and Fuller 1979; Elliott, Rothenberg, and Stock 1996). If this is the case, the parameter estimates from Equation 22 are invalid, and we should instead
use an ARIMA(0,1,0) model: À 2Á
likelihood.13 ARPU(m) is a standard internally reported measure for a
subscription-based firm. Some firms do report quarterly ARPU publicly, but this data cannot be used in general because there
11This assumes that the firm started operation at the beginning of a are no well-accepted standards for calculating it. As DISH (2015) stated in its 2014 annual filing, “We are not aware of any uniform standards for calculating ARPU and believe pre
sentations of ARPU may not be calculated consistently by other companies in the same or similar businesses.” Because there is no standard definition of ARPU, different firms may have different definitions for it, picking and choosing what sources of revenue to include in the numerator. As such, the reported ARPU numbers may not be representative of all revenue derived from the customer base.
Revenue numbers are more reliable. However, they are only provided quarterly, so we need to impute monthly revenues. For m2f3q - 2, 3q - 1, 3qg, the revenue in month m is equal to
= REVq • Cð.,3q - 3Þ + 2Cð.,3q - 2Þ + 2Cð.,3q - 1Þ + Cð.,3qÞ.
Strictly speaking, we are using Cbð$, $Þ, computed using the estimated parameters of the acquisition and retention processes. Having imputed R(m), our estimates of ARPU(m) follow from Equation 7.
Bringing It All Together
Recall that our goal has been to generate long-term projections of R(m) and A(m), from which we can compute estimates of period-by-period FCF and the value of the firm. Next, we outline the process by which we compute these revenue numbers using the models described previously.
1. We estimate the parameters of the acquisition and retention processes (see the “Parameter Estimation for the Acquisition and Retention Processes” subsection). Assuming that the firm has been in operation for Q quarters, we then compute our estimate of the 3Q • 3Q matrix Cð$, $Þ, the diagonal of which is our estimate of the number of customers acquired each month over this time period, and the rows of which are estimates of the number of customers in each cohort that survive each of the subsequent months.
- 2. As outlined in the “Average Revenue per User” subsection, we use Cbð$, $Þ and the reported quarterly revenue numbers to impute the corresponding monthly revenue numbers, from which we estimate the parameters of our model for average revenue per user.
- 3. To project Cð$, $Þ into the future, we need estimates of POP (m) over the time horizon of interest. In some cases, such data may be available from a secondary source. In the absence of such a source, we can use a simple model for forecasting POP (m). For example, we could use the long-term compound growth rate in POP(m) and assume that it holds into the future.
- 4. Having projected Cð$, $Þ far into the future (i.e., to a point in time when the present value of any associated cash flows is effectively zero), we compute the column totals Cð., mÞ to give us the total number of customers for each month.
- 5. We compute expected ARPU(m) across this time horizon using Equation 22 or 23. Rearranging Equation 7, it follows that
(25) RðmÞ = ARPUðmÞ • Cð., m - 1Þ + Cð., mÞ .
professional would do when building a DCF valuation model. In the next section, we bring this valuation model to life from start to finish using data for two public companies.
TABLE:
| | Acquisition | Retention |
|---|
| | Parameter | SE | Parameter | SE |
|---|
| r | 11.440 | 5.123 | 1.648 | .232 |
| a | 269,053.452 | 105,966.200 | 387.578 | 19.666 |
| c | 2.001 | .011 | 1.423 | .056 |
| bQ1 | -.052 | .008 | -.079 | .007 |
| bQ2 | -.057 | .007 | .036 | .009 |
| bQ3 | .036 | .008 | .107 | .008 |
| bRec | -.099 | .011 | .129 | .010 |
| pNA | .525 | .006 | | |
Empirical Analyses
We first apply our model of customer behavior to data from DISH Network Corporation (Nasdaq: DISH), a large pay-TV service provider. We estimate the parameters of the model, evaluate its in-sample fit, evaluate the predictive validity of the model by performing rolling two-year-ahead forecasts over all possible calibration periods, and compare its performance with that of the models of customer behavior proposed by GLS and SSW. After demonstrating the validity of the model, we then use its revenue projections (along with the associated estimates of customer acquisition) to arrive at our estimate of the value of DISH’s shareholder’s equity. Next, to further establish the robustness of our proposed model, we apply it to a second publicly traded company, Sirius XM Holdings (Nasdaq: SIRI), a satellite radio service provider. We conclude by exploring some other insights into customer behavior that can be derived using the model.
DISH Network
DISH commenced operations in March 1996 (DISH 2015),14 and end-of-period customer counts were first disclosed that quarter. However, the gross customer acquisition data are left-censored–gross customer additions were first disclosed seven quarters later, in Q1 1998 (i.e., with reference to Equation 21, qA = 1 while qB = 9). All historical customer data (ADDq, LOSSq, ENDq, and REVq) come from DISH’s quarterly and annual reports, Forms 10-Q and 10-K, respectively. We model these customer data up to and including Q1 2015 (i.e., Q = 77). The vast majority of DISH’s revenues come from its subscriber base.15
We use the same four time-varying covariates in our models of the acquisition and retention processes: three quarterly dummy variables to capture seasonal fluctuations in the propensity to sign up to and churn from the service and a “Great Recession” dummy variable to account for the diminished propensity to sign up and the increased propensity to churn during that recession.16 Given the nature of the DISH’s service offering, our unit of population is the household. We use data on U.S. household growth provided in the U.S. Census Bureau’s Current Population Survey/Housing Vacancy Survey data tables.
Parameter estimates and evaluation of fit. We first estimate the parameters of the acquisition and retention models
14 using all the available data. The parameters are reported in Table 2; the associated model SSE is 310,777. The story told by these parameters is consistent with what DISH has disclosed in its public filings. With reference to the coefficients of the quarterly dummies, consider DISH’s comments on the seasonality of its operations in its 2015 annual report: “Historically, the first half of the year generally produces fewer gross new subscriber activations than the second half of the year, as is typical in the pay-TV industry. In addition, the first and fourth quarters generally produce a lower churn rate than the second and third quarters.”
The negative effect of the 2008 recession on DISH’s financials is unmistakable; its effect on acquisition and retention propensities was greater than all of the respective seasonal terms. The coefficient in the acquisition model is negative because customers have a lower propensity to acquire services during a recession, whereas the coefficient in the retention model is positive because customers have a higher propensity to churn during a recession.
In Figure 2, we plot model estimates for gross customer additions, losses, and end-of-period total customer counts against what we actually observed. (The gray area indicates the duration of the Great Recession.) We must back-cast gross customer additions and losses because DISH did not disclose ADD and LOSS data prior to Q1 1998. Our resulting fits are good; we see a clear seasonal pattern within acquisitions and losses, and lower acquisitions and higher losses during the recession of 2008. DISH appears to be past the point of peak adoption, a sentiment echoed by DISH chief executive officer Charlie Ergen in DISH’s Q1 2015 conference call: “My general sense is that the linear pay television business probably peaked a couple of years ago and that it’s in a very slight decline.”
Average revenue per user is modeled as described previously. We assume linear growth, which is consistent with comments made in DISH’s annual financial reports. First fitting the simple time-trend regression given in Equation 22, we find that the model residuals fail the augmented Dickey-Fuller unit root test (t = -2.6, p = .31). We therefore model ARPU using the ARIMA(0,1,0) model specified in Equation 23, with bb0 = :246 (SE = .091) and an associated R2 of 93%.
Parameter Estimates: DISH Network
Acquisition
Retention
analysis shows that our in-sample fit is very good, it does not give us any real insight into the predictive validity of our model native models (i.e., those presented in GLS and SSW). These are important questions, because the quality of our estimate of firm value is a direct function of the quality of the projections of revenue (and customer acquisitions) from our model.
To shed light on these questions, we perform a rolling validation in which we vary the model calibration period and compare the model predictions of ADD, LOSS, and END with the actual numbers reported by DISH. Letting Q = 10, 11, …, 69 (corresponding to all possible calibration periods ending from Q2 1998 to Q1 2013), we calibrate our model on all data up to and including quarter Q and then predict ADD, LOSS, and END for the next two years (i.e.,
ADDQ+q*, LOSSQ+q*, and ENDQ+q* for q = 1, 2, …, 8). Because of missing data, only two quarters of ADD and LOSS data are available when Q = 10, making it a reasonable starting point to the rolling analysis. As a result, our evaluation of model performance is based on predictions made using 60 different calibration periods.
In Figure 3, we plot all resulting predictions over all cali-
bration periods for ADD (first column), LOSS (second column), and END (third column) using GLS (first row), SSW (second row), and our proposed model (third row). While the general patterns of over- and underestimation are similar for SSW and
GLS, the overall predictive validity of SSW is generally better than that of GLS. We find that the GLS model underestimates future ADD, LOSS, and END figures, often severely so. This is primarily because the logistic internal-influence model for ADD and constant retention rate model for LOSS are unable to capture the underlying dynamics in customer behavior over time. Because SSW model END (rather than ADD) using the logistic internal-influence model, their resulting predictions for END are generally quite accurate. Both methods have the most difficulty forecasting ADD, as evidenced by the large deviations between the predictions in gray and the actual data in black. This is important because ADD is an important input for these models’ respective valuation models.
In contrast, our proposed model forecasts ADD, LOSS, and END very accurately, as evidenced by the tight correspondence between the gray and black lines in the bottom row of Figure 3. In contrast to the forecasts associated with the GLS and SSW models, this correspondence remains tight even for short calibration periods, which is further proof of the robustness of the model’s predictions.
To summarize the relative performance of these three models, we compute the absolute percentage error in the ADD, LOSS, and END forecasts for each of the (rolling) eight holdout quarters and take the average across the 60 different calibration periods. Table 3 reports the resulting mean absolute percentage error (MAPE) numbers. We observe that the MAPE figures associated with the SSW model are generally half those of the GLS model, while the MAPE figures for our proposed method are generally one-third smaller than those of SSW.
These conclusions are not affected by the fact that our model incorporates the effects of covariates, whereas the models of GLS and SSW do not. We created variants of the GLS and SSW models that include the quarterly seasonality and Great Recession effects (through a logit formulation for retention and a proportional hazards specification for acquisition [GLS] and ending customers [SSW]), and do not observe any significant changes to our conclusions regarding the relative performance of the three models.
TABLE:
| Quantity | Horizon | GLS | SSW | Proposed |
|---|
| ADD | Q + 1 | 26.0 | 14.7 | 8.2 |
| Q + 2 | 29.2 | 16.4 | 9.5 |
| Q + 3 | 32.5 | 16.4 | 10.4 |
| Q + 4 | 36.5 | 16.3 | 11.3 |
| Q + 5 | 41.1 | 17.7 | 13.2 |
| Q + 6 | 45.5 | 19.8 | 14.5 |
| Q + 7 | 50.6 | 21.5 | 15.6 |
| Q + 8 | 55.8 | 22.9 | 16.0 |
| LOSS | Q + 1 | 26.0 | 14.7 | 8.2 |
| Q + 2 | 29.2 | 16.4 | 9.5 |
| Q + 3 | 32.5 | 16.4 | 10.4 |
| Q + 4 | 36.5 | 16.3 | 11.3 |
| Q + 5 | 41.1 | 17.7 | 13.2 |
| Q + 6 | 45.5 | 19.8 | 14.5 |
| Q + 7 | 50.6 | 21.5 | 15.6 |
| Q + 8 | 55.8 | 22.9 | 16.0 |
| END | Q + 1 | 26.0 | 14.7 | 8.2 |
| Q + 2 | 29.2 | 16.4 | 9.5 |
| Q + 3 | 32.5 | 16.4 | 10.4 |
| Q + 4 | 36.5 | 16.3 | 11.3 |
| Q + 5 | 41.1 | 17.7 | 13.2 |
| Q + 6 | 45.5 | 19.8 | 14.5 |
| Q + 7 | 50.6 | 21.5 | 15.6 |
| Q + 8 | 55.8 | 22.9 | 16.0 |
Valuation results. Having demonstrated the performance of our proposed model, we next turn to the primary reason it was developed in the first place: to compute an estimate of the value of the firm. We first project revenues (see the “Bringing It All Together” subsection) far enough into the future so that all subsequent profits/losses have no effect on our valuation; we choose 50 years. We forecast that POP will continue to grow at a per month rate of .06% into the future; this is equal to the historical monthly U.S. household growth rate over the period from March 1996 to March 2015.
Our revenue projections drive detailed financial projections that are used to estimate future FCFs, WACC, the value of nonoperating assets, and net debt. We then add the value of the operating assets to the nonoperating assets and subtract the net debt to arrive at our best estimate of shareholder value using Equation 1 (see Table 4).
TABLE:
| Value of operating assets | $15.7 billion |
| Nonoperating assets – Net debt | $14.1 billion |
| Shareholder value | $29.9 billion |
| Shares outstanding | 462.1 million |
| Implied stock price | $64.62 |
| Actual stock price | $66.38 |
| Over-/(under-)estimation | (2.7%) |
We estimate a stock price of $64.62 based on Q1 2015 results, which were disclosed on May 11, 2015. The end-of-day stock price that day was $66.38, implying that we are within 3% of the then-current stock price. Holding all else constant, the DISH Network stock price estimates computed using the GLS
TABLE 4 and SSW models for customer acquisition and retention were $48.84 and $63.72, respectively.
Our valuation and the corresponding implied stock price are point estimates. To get a sense of the uncertainty in these estimates, we undertake the following sensitivity analysis. First, holding the parameters of the retention and ARPU processes constant, we draw a new set of parameter values for the acquisition process model through bootstrap resampling of the model residuals (Efron and Tibshirani 1993, Chapter 9). Given this set of parameters, we compute the resulting revenue numbers, the corresponding estimate of the value of the firm, and the implied stock price. We do this for 500 draws and compute a 95% interval for our implied stock price. We repeat this for the retention process (holding the parameters of the acquisition and ARPU processes constant) and the ARPU process (holding the parameters of the acquisition and retention processes constant). The interval associated with the acquisition process is [$64.48, $64.77] (-.2%). The equivalent intervals for the retention and ARPU processes are [$62.47, $66.78] (-3.4%) and [$62.76, $66.49] (-3.0%), respectively. This suggests that it would be most beneficial to investors if DISH were to provide more or better data regarding customer retention
Value of operating assets
$15.7 billio
Sirius XM
To test the robustness of our framework, we repeat our valuation exercise for a second company, Sirius XM, which is a broadcasting company that provides satellite radio services in the United States. Sirius XM is a good complementary example to that of DISH for five reasons:
- Sirius XM is a relatively high-growth business, while DISH is a mature business. We note that ADD, LOSS, and END are all past their peak for DISH (Figure 2), whereas they are increasing for Sirius XM.
- Sirius XM suffers from more severe missingness than DISH. Sirius XM was formed by the merger of Sirius Satellite and XM Satellite, which began commercial operations in February 2002 and November 2001, respectively. Neither Sirius nor XM disclosed ADD, LOSS, or END data for paying customers. It was not until after the merger that the company first publicly disclosed these data (Q3 2008). As a result, almost half of Sirius XM’s customer data are missing.
- Sirius XM is a high fixed-cost business because its satellite radios are preinstalled in most new vehicles, while DISH Network is a high variable-cost business. Most of Sirius XM’s operating expenses, net of subscriber acquisition costs (SAC), are fixed in nature, whereas most of DISH’s operating expenses are variable. All else being equal, this substantially increases the marginal profitability of new Sirius XM users.
- Sirius XM has a very different customer base and customer profile than DISH. Sirius XM has a larger number of customers, each of whom generates less revenue but is much cheaper to acquire.
- Sirius XM sells almost entirely into cars, whereas DISH sells almost entirely into homes. All else being equal, this makes Sirius XM a more cyclical business than DISH.
For a comparison of the two companies on the basis of some basic measures, see Table 5.
Despite these differences, we proceed with virtually the same model. The main change is that the population unit for
Sirius XM is cars (as opposed to households for DISH). The market size for Sirius XM is equal to the number of vehicles on the road, as provided by the Bureau of Transportation Statistics. Correspondingly, we use vehicle sales, as defined/provided by Federal Reserve Bank of St. Louis, as our macroeconomic covariate. We denote the coefficient associated with the vehicle sales covariate by bVS.
TABLE:
| | DISH | Sirius XM |
|---|
| Total paying customers (millions) | 13.8 | 22.9 |
| Monthly ARPU | $88.72 | $16.72 |
| WACC | 7.2% | 6.9% |
| SAC/customer | $716.46 | $82.06 |
| ARPU growth per year | $2.95 | $.49 |
The parameter estimates of the acquisition and retention process models appear in Table 6; the associated model SSE is 146,799.17 Again, we assume linear growth when modeling ARPU. Fitting a simple time-trend regression (Equation 22) to the data, we find that the residuals do not fail the augmented Dickey-Fuller unit root test (test statistic: t = -3.56, p = .04); the associated parameter estimates are bb0 = 9:643 (SE = .218) and bb1 = :041 (SE = .002), with R2 = 88%.
In Figure 4, we plot model estimates for ADD, LOSS, and END against what we actually observed. We overlay a set of two-year rolling predictions corresponding to all possible calibration periods ending from Q3 2010 to Q1 2013, as we had done for DISH in the “Predictive Validation and Comparison” subsection. As was the case with DISH, the in-sample and outof-sample fits for Sirius XM, in terms of all three customer metrics, are good.
As with DISH, we project revenues 50 years into the future. We project both future vehicles on the road and vehicle sales assuming that monthly growth rates are equal to their respective historical cumulative average growth rates from 1980 until 2015. We perform a detailed margin and cash flow analysis to turn the revenue projections into monthly free cash flow projections. Table 7 presents the resulting valuation. We estimate Sirius XM’s operating assets to be worth $27.1 billion. After adding nonoperating assets (Sirius XM has approximately $1.1 billion in net operating loss carryforward) and subtracting net debt, we estimate shareholder value to be $23.4 billion using Equation 1. This implies a stock price of $4.24, based on Sirius XM’s Q1 2015 results, which were released on April 28, 2015. The end-of-day stock price that day was $3.90. Holding all else constant, the Sirius XM stock price estimates computed using GLS’s and SSW’s models for customer acquisition and retention were $.41 and $6.55, respectively.
TABLE:
| | Acquisition | Retention |
|---|
| | Parameter | SE | Parameter | SE |
| r | .208 | .239 | 153.206 | 35.129 |
| a | 92,277.702 | 127,791.501 | 8,620.306 | 2,138.741 |
| c | 2.228 | .154 | 1.066 | .085 |
| bQ1 | -.106 | .012 | .029 | .014 |
| bQ2 | -.049 | .014 | –.039 | .016 |
| bQ3 | -.047 | .013 | .003 | .015 |
| bVS | .077 | .003 | -.013 | .003 |
| pNA | .011 | .075 | | |
Additional Insights
Confident that our model provides sensible valuation estimates, we return to DISH to study other insights that we are able to draw from the model beyond stock price estimates. We look at the remaining/residual lifetime and lifetime value of DISH customers as a function of the length of their relationship (i.e., tenure) with the firm. We then decompose DISH’s CCE by tenure. Although these seem like fairly ordinary applications of a customer-level model, it is important to keep in mind that we are doing these analyses with no customer-level data; all we have are the aggregated summaries that companies disclose to the public.
Comparison of residual value by tenure. Let us consider a DISH customer acquired at the end of Q1 2015 whom we call “Recent Robin,” and another DISH customer acquired ten years earlier at the end of Q1 2005 whom we call “Longtime Larry.” One quantity of managerial interest is the expected remaining (or residual) lifetime of Recent Robin and how it compares with the expected residual lifetime of Longtime Larry. The GLS and SSW models both assume that all customers are equal and thus would predict Recent Robin and Longtime Larry to share the same expected future lifetime. However, we intuitively expect that Longtime Larry is likely to remain a customer for a longer period of time because his long history with DISH thus far suggests that he has a lower churn propensity. By definition, the expected residual lifetime of a customer acquired in month m who is still a customer in month M is
‘
Appendix.) The expected residual lifetime of Recent Robin and Longtime Larry are 5.5 years and 9.4 years, respectively. This difference is in line with our intuition.
Investors should be interested in the expected lifetime of customers. Longer expected customer lifetimes imply more stable future cash flows, all else being equal, because future cash flows are less reliant on the acquisition of new customers. At DISH, we see not only that older customers have longer residual lifetimes but also that all customers live for a relatively long time, which should be heartening to investors. Reducing investors’ perceived risk of future cash flows reduces the cost of capital, raising firm valuation.
Another quantity of interest is customers’ RLV.18 Calculating RLVs using nothing but the information provided in a firm’s financial statements requires careful consideration of what expenses are fixed versus variable as well as a proper handling of subscriber acquisition costs. (For details of how we perform this associated with Recent Robin to be $1,426, excluding average initial acquisition costs of $854, while Longtime Larry is worth $1,932. Although it is not possible to provide predictive validation of these customer insights because of the aggregated nature of the data, the predictive validation analysis that we performed earlier provides general validity for these results.
The following information is useful to many stakeholders:
• Investors may track CLV relative to SAC per customer,
viewing these metrics as financial barometers of customer health. Unfavorable trends in these figures (as has been evident at DISH, for example) could be indicative of decreasing customer (and, thus, firm) profitability.
• Competitors, comparable companies, and investors will be interested in the absolute level of CLV and RLV for benchmarking purposes. If a competitor estimates its own CLV to be less than DISH’s, there may be opportunities to “close the gap,” identifying what it could be that is causing the gap in average customer profitability. Investors may ask the same question and demand that changes be made to improve CLV and RLV.
TABLE:
| Value of operating assets | $27.1 billion |
| Nonoperating assets – Net debt | –$3.7 billion |
| Shareholder value | $23.4 billion |
| Shares outstanding | 5,513.7 million |
| Implied stock price | $4.24 |
| Actual stock price | $3.90 |
| Over-/(under-)estimation | 7.4% |
Although the preceding analysis has focused on expected RLV and CLV, we can examine the distribution of these quantities across all possible Recent Robins and Longtime Larrys. In Figure 5 we present their respective RLV distributions. This provides us with additional information regarding the riskiness of future cash flows associated with new and existing customers. For example, we estimate that there is a 41% chance that the company will incur a loss on a Recent Robin (i.e., 41% of Recent Robin’s RLV samples [Figure 5] lie to the left of $854, the SAC per customer for DISH). We infer a long right tail to Longtime Larry’s pretax RLV, which drives up Longtime Larry’s expected pretax RLV but also implies a much higher variance about that expectation. Longtime Larry is more valuable but is also more risky (McCarthy, Fader, and Hardie 2016).
Customer-base decomposition. The raw data available from virtually any public source reveals nothing about the tenure of existing customers or how these “lifetimes” vary across the customer base. As the examples of Recent Robin and Longtime Larry suggest, this can be important information to outside investors. Fortunately, as we have demonstrated, our proposed model makes it easy for analysts to infer these lifetimes. We can go further and segment the customer base on this basis.
åi=1Cbði, 3QÞ
Although knowing the count of customers within each segment is helpful, the value of those customers is probably of greater interest to investors and managers. Recall that the sum of RLV across all the firm’s current customers is CCE. It follows that the proportion of total CCE, as at the end of quarter Q, coming from customers who were born in month m is the RLV-weighted where RLVm,3Q is the residual lifetime value of a customer acquired in month m who is still active in month 3Q. The resulting decomposition of DISH’s customer base appears in Table 8. We estimate, for example, that approximately one-eighth of DISH’s customer base is comprised of highly loyal/inertial customers who have been DISH subscribers for more than ten years. We also infer that longer-lived segments make up proportionally more of the total value of the customer base because they are inferred to have higher RLVs, as is evident from our comparison of Recent Robin and Longtime Larry.
TABLE:
| Tenure (Years) | % Customer Base | % CCE |
|---|
| 0–2 | 31 | 28 |
| 2–5 | 31 | 29 |
| 5–10 | 25 | 27 |
| 10+ | 12 | 16 |
This decomposition, and other granular inferences that can be drawn from our model, can provide useful insights for investors. In some sense, the overall corporate valuation shown previously is not necessarily very insightful by itself; it merely captures the “voice” of the financial market. It could be argued that the real value of our proposed approach is the ability to go beyond the macrovaluation estimate to offer useful, operational diagnostics to better understand where that value is coming from and what it might mean to the firm, its competitors, suppliers, investors, and possibly even public policy makers. In the case of DISH, the considerable amount of value from long-lived customers is indicative of a very mature business and implies that the valuation of the business as a whole will be much more dependent on and sensitive to changes in the company’s ability to retain existing companies, rather than acquire new ones. In contrast, Sirius XM’s valuation is far more dependent on the firm’s ability to acquire new users and to earn a high rate of return on the firm’s investment in those new users. This is important information for investors and managers alike.
General Discussion and Future Work
As we noted at the outset of the article, our objective has been to create a realistic model for customer acquisition and retention (which can be estimated using data publicly reported by firms with a subscription-based business model) and embed it within a standard financial framework for corporate valuation. Beyond the methods developed here, we hope this article will serve as a call to action for firms, analysts, and investors to perform these kinds of analyses on a more regular and rigorous basis. We have provided several use cases for the insights that can be derived from our analyses, including but not limited to comparing CLV across comparable/competing firms, performing customer value segmentation, and providing investors with improved forward-looking sales visibility. All of this is possible because we have performed our valuation using a flexible, general-purpose model of customer behavior in contractual/subscription settings.
Although our model is particularly suited to third parties analyzing publicly traded companies using their public disclosures, we contend that this same exercise can–and should–be undertaken internally as well. Firms can easily implement an equivalent version of this model using internal company data, which would enhance its overall validity. While the estimation procedures differ slightly (given access to more granular data), the models for customer acquisition, retention, and spending, along with the proposed valuation framework, would remain essentially the same. Measuring and tracking CLV and RLV can improve the return on investment of a company’s acquisition and retention spending, and our valuation framework gives company executives the ability to estimate how much value this improvement in return on investment has created for the overall value of the firm. This provides executives with an important key performance indicator to which they can hold themselves and their marketing managers accountable.
Although our model is more flexible than previously published customer-based corporate valuation models (e.g., in terms of the dynamics that it can accommodate), it has nevertheless remained parametrically parsimonious because the available data are limited and will likely stay that way for the foreseeable future. For example, it is highly unlikely that firms will begin to disclose the kinds of data required to properly account for other sources of customer value, such as the referral value of a customer (Kemper 2010; Kumar et al. 2010; Kumar, Petersen, and Leone 2007), the impact of social media (Luo, Zhang, and Duan 2013; Yu, Duan, and Cao 2013), customer satisfaction (Anderson, Fornell, and Mazvancheryl 2004; Homburg, Koschate, and Hoyer 2005; Luo and Bhattacharya 2006), or heterogeneity in the spend per customer (McCarthy, Fader, and Hardie 2016). At the same time, indirect proxies for these factors may be obtainable in some cases through external data sources for a small subset of companies.
Furthermore, it may seem tempting to add in other “bells and whistles” to further enrich the model specification used here. We should be open to such possibilities but are cautious about our ability to do so. For instance, it may be the case that individuallevel acquisition and retention propensities are correlated (i.e., customers who take longer to acquire may have a lower propensity to churn after they have been acquired or vice versa; see Schweidel, Fader, and Bradlow 2008a), but our ability to empirically identify such a correlation is very limited, which increases the risk that we overburden the limited data we have available. Many other theoretically reasonable extensions (e.g., allowing for cross-cohort effects [Gopalakrishnan, Bradlow, and Fader 2016]), specifying a more complicated market potential model) will likely suffer from similar issues. Bodapati and Gupta (2004) warn that when data are highly aggregated, even identifying heterogeneity (in their setting, using a finite mixture model) can be challenging. Model parsimony is a good thing.
Another limitation we readily acknowledge is that our framework is appropriate only for subscription-based businesses, for whom attrition is observed (Fader and Hardie 2009). For noncontractual settings (where attrition is unobserved), we can specify an alternative model for the number of customers matrix. However, it is not clear what kinds of metrics a company operating in such a setting would have to disclose for an outside analyst to be able to estimate Cð$, $Þ and perform the valuation task in a valid manner. This is an important area of further research.
We have focused our attention strictly on conducting the valuation process for one company at a time, but our predictive accuracy may be improved if we were to develop a hierarchical
Bayesian model, estimating the parameters for many companies at the same time. This may alleviate some of our data inadequacy issues by “borrowing strength” across firms but would require considerable methodological advancements to properly “share” information across firms and handle aggregated missing data.
Beyond the methodological improvements, our valuation framework could provide perspective to the ongoing discussion among marketing scholars regarding the accounting of CE and advertising spending. Consistent with Srinivasan (2015), the vast majority of DISH’s SAC is expensed and not capitalized (82% in Q1 2015); the primary component of SAC that is capitalized is spending to purchase satellite receivers, which are then owned by DISH and depreciate over a useful life of approximately four years. In contrast, just-acquired customers have, on average, a longer “useful life” of 5.5 years (see the “Additional Insights” subsection) and yet are not considered assets (Wiesel, Skiera, and Villanueva 2008). As a result, subscriber acquisition activities create costs that are incurred immediately but whose benefits are received in the future; as such, the income statement is not reflective of the underlying economic condition of the business. It is no surprise, then, that DISH was generally unprofitable earlier in its history and only became profitable in recent years.
As companies increasingly recognize the importance and merit of customer-centric business strategies (Fader 2012) and disclose customer data on a more regular and thorough basis, there will be a growing opportunity for marketing scholars to study the behavior of large, publicly traded companies through their customer data in conjunction with their financial statements. We hope that this article lays a sound foundation for how future analyses will incorporate and shed further light on company valuation.
Appendix
Residual lifetime value can be expressed mathematically as
or her RLV is computed, E½VðtÞ is the expected net cash flow of the customer at time t (assuming that [s]he is alive at that time), Sðtjt > t9Þ is the probability that the customer has remained alive to at least time t (given that [s]he was alive at t9), and dðt - t9Þ is a discount factor that reflects the present value of money received at time t (Fader and Hardie 2015).
This is acceptable as a mathematical representation of the definition of (expected) RLV but is of limited use in practice because it ignores the accounting issues identified in the “Comparison of Residual Value by Tenure” subsection. However, we considered these accounting issues when performing our valuation, and an intermediate result from these calculations can be used to compute RLV. The intermediate result of interest is EBITDASAC, earnings before interest, taxes, depreciation, amortization, and subscriber acquisition costs.
Let tmðk,ÞM be the kth sampled residual lifetime of a customer
t = 1,2, ….
The expected residual lifetime of a customer acquired in month m who is still active in month M can be computed as the average of many samples drawn from this
k=1
We set K = 1,000,000 in our analysis. Given monthly EBITDASAC numbers, the value of an
average customer in month M + m* is
Therefore, the pretax RLV of a customer with sampled residual lifetime tmðk,ÞM is
Figure 5 plots the empirical distribution of these draws. Averaging over these sampled realizations of residual lifetime gives us the expected RLV of a customer acquired in month m who is still active in month M,
K
Footnotes 1 In this work, we take the perspective of a passive investor valuing a going concern. This same basic valuation framework can also be used by owners and managers who can influence the firm’s decision making to evaluate alternative investment decisions (Damodaran 2012; Koller, Goedhart, and Wessels 2015). The idea of value-based management (i.e., the notion that maximizing shareholder value should be the guiding principle when making strategic decisions) was popularized by Rappaport (1986) and has influenced thinking in the marketing strategy literature (e.g., Day and Fahey 1988; Doyle 2000; Srivastava, Shervani, and Fahey 1997, 1998). As such, most of the shareholder value-related discussions in the marketing literature pertain to strategic decision making, as opposed to firm valuation.
2 Strictly speaking, we are referring to expected FCFs.
3 As Damodaran (2012, p. 925) notes, “The problem in valuation is not that there are not enough models to value an asset, it is that there are too many. Choosing the right model to use in valuation is as critical to arriving at a reasonable value as understanding how to use the model.” Using the decision framework developed in Damodaran (2012, Chapter 34), it is clear that the DCF approach used in this research is the most appropriate for the task at hand.
4 There is an obvious parallel with the new product sales forecasting literature (e.g., Fourt and Woodlock 1960; Eskin 1973; Fader, Hardie, and Huang 2004), in which researchers first decompose total sales into trial and repeat components, develop separate models for trial and repeat sales, and then combine the associated forecasts of these components to arrive at a forecast of total sales.
5 In contrast, average revenue per customer for firms with a nonsubscription- based business model tends to vary considerably across customers in any given period of time.
6 It is important to note that they applied the model at the level of the industry, not the firm, in their empirical analysis.
7 The two companies that later merged to form Sirius XM began commercial operations in September 2001 and February 2002, respectively.
8 Our model can also be used in situations in which customer data are only reported annually with minimal modification.
9 Libai, Muller, and Peres (2009) extend the basic Bass model to allow for lost customers reentering the pool of potential adopters, but they assume a constant retention rate.
In line with most of the work on modeling the diffusion of innovations, we ignore the intermediate role of awareness because we do not have sufficient data to account for it.
This assumes that the firm started operation at the beginning of a reporting quarter, as discussed previously. If this is not the case, minor modifications to Equations 20 and 21 are needed.
If we assume that ARPU grows at a constant growth rate over time, we would use log.
As in GLS and SSW, we assume that spend and churn are uncorrelated. The data are too limited to identify such a correlation, and the lack of heterogeneity in spend limits the practical benefit of allowing for it.
Although DISH Network was technically incorporated in 1980, the relevant starting date for our analysis is when DISH actually commenced commercial operations and could thus begin acquiring customers.
In Q1 2015, .9% of DISH’s revenue was derived from equipment sales, which are not core to the business and have not been increasing over time. DISH has made investments in wireless spectrum over the past three years–wireless spectrum is a non– operating asset–but earns no revenue from it, and the core operations of the business do not depend on it.
According to the National Bureau of Economic Research, the “Great Recession” began in December 2007 and ended June 2009 (http://www.nber.org/cycles.html).
Unlike DISH, the Weibull-gamma baseline retention process for Sirius XM is not significantly different from a Weibull baseline. We retain the more general formulation for consistency with DISH.
GRAPH: FIGURE 5 Histogram of One Million Sampled RLVs: Recent Robin and Longtime Larry
GRAPH: Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data
GRAPH: Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data
GRAPH: Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data
DIAGRAM: Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data
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Record: 219- Video Content Marketing: The Making of Clips. By: Liu, Xuan; Wei Shi, Savannah; Teixeira, Thales; Wedel, Michel. Journal of Marketing. Jul2018, Vol. 82 Issue 4, p86-101. 16p. 1 Diagram, 6 Charts, 2 Graphs. DOI: 10.1509/jm.16.0048.
- Database:
- Business Source Complete
Video Content Marketing: The Making of Clips
Consumers have an increasingly wide variety of options available to entertain themselves. This poses a challenge for content aggregators who want to effectively promote their video content online through original trailers of movies, sitcoms, and video games. Marketers are now trying to produce much shorter video clips to promote their content on a variety of digital channels. This research is the first to propose an approach to produce such clips and to study their effectiveness, focusing on comedy movies as an application. Web-based facial-expression tracking is used to study viewers’ real-time emotional responses when watching comedy movie trailers online. These data are used to predict both viewers’ intentions to watch the movie and the movie’s box office success. The authors then propose an optimization procedure for cutting scenes from trailers to produce clips and test it in an online experiment and in a field experiment. The results provide evidence that the production of short clips using the proposed methodology can be an effective tool to market movies and other online content.
Online Supplement: http://dx.doi.org/10.1509/jm.16.0048
The Internet has drastically reduced barriers to the distribution of video content. This has caused an unprecedented proliferation of sitcoms, scripted series, documentaries, and long- and short-format movies. Online content aggregators are making this vast array of video material readily available to consumers for on-demand streaming. For short-format user-generated video, there is YouTube. For video games, there is Twitch. For broadcast and cable shows, there is Hulu. And for movies and web series, there are Netflix and Amazon.
Given that consumers have such a wide variety of options available to entertain themselves, determining how to effectively promote video content is a challenge for online content aggregators. Synopses, critics’ reviews, and viewer ratings are important, but the best way for consumers to evaluate the quality of video content and to determine whether they want to see it is for them to watch a sample. For that reason, video content producers have historically used trailers as their main marketing tool. This started around 1920, when movie theaters produced snippets of upcoming films with simple text overlays to “trail” a feature film to entice viewers to return to the theater. The National Screen Service, a company that wrote scripts and produced trailers on behalf of movie studios, was founded soon thereafter. It developed a template for trailer design that included a montage and music and held a monopoly over the creation and distribution of movie trailers that lasted into the 1950s, when more competitors entered the market. Movie trailer production has evolved into an industry with dozens of independent production houses charging upwards of $500,000 for a trailer (Last 2004). Today, there are trailers not only for movies but also for sitcoms, video games, and even books. These trailers are typically two- to three-minute videos created by selecting and editing scenes from the original video content and adding music and other sound effects. Their purpose is to elicit a sample of the emotions that viewers will experience when watching the full content (Kerman 2004). At least a dozen websites are uniquely devoted to showing trailers (e.g., traileraddict.com, booktrailersforreaders.com, comingsoon.net), and trailers themselves have become some of the most popular forms of entertainment on the web.
Yet, given consumers’ increasingly short attention spans, the original trailers for movies, sitcoms, and video games are becoming less effective marketing tools on some digital channels, particularly those that do not support sound (e.g., email, social media). Therefore, online aggregators have cut the trailers obtained from production studios down to “clips” of 30, 20, or sometimes 10 seconds. The newfound marketing problem for content aggregators has become not one of creating promotional trailers but, rather, one of editing down the trailer content provided by trailer production studios into formats that are suitable for digital marketing channels. However, according to a manager at Netflix, “The current approach of providing just the first few seconds of a trailer for online viewing is not always effective.” Thus, marketers need better tools to produce these short clips from original trailers. However, despite its importance, there is no academic research that assists in the development of these tools or that even helps marketers understand whether and how these short clips can induce consumers to experience a sample of emotions and, ultimately, watch the full content.
This article is the first, to our knowledge, to examine how marketers of video-based content should edit trailers to produce (shorter) clips that help consumers decide whether to watch the content. While marketing departments at content aggregators often have limited control over the specific content of the trailer, they do have control over which scenes to select from the trailer in producing a shorter clip. Our conceptual framework and methodology are therefore founded on the notion that the scene is the basic semantic building block of video content as well as the elementary unit for the production of trailers and clips.
To illustrate our method, we focus on the creation of clips for comedy movies as an application. We collected and analyzed four different types of data and propose an optimization algorithm for clip production. First, in an online facial-expressiontracking experiment, viewers were shown movie trailers and their intentions to watch the full movie were recorded. We used these data to calibrate a model that explains viewing preferences on the basis of the audio-visual scene structure of trailers, as well as the real-time emotional responses evoked from each viewer. Second, we collected information on various ratings and box office sales of the movies in question. This enables us to validate in-market the role of the scene structure (which is under the marketer’s control) and assess the intermediary role of the emotions evoked. We account for content-specific effects by controlling for movie ratings and variations of trailers for the same movie (we do not code and insert content variables because the online aggregator marketer has no control over them; instead, we measure consumers’ emotional response to that content). Third, after understanding how the scene structure is associated with the emotional response, intention to watch, and box office sales, we optimized the editing of trailers to produce short film clips for use on digital channels in which sound is (e.g., Internet Movie Database [IMDb]) or is not (e.g., Facebook) supported. Fourth, we validated the proposed approach in an online experiment as well as in a large-scale field experiment with one of the world’s largest video content aggregators. We show that our approach ( 1) is superior to the currently used heuristic approach for producing clips and ( 2) can be automated and, thus, is scalable.
Online content aggregators can apply our methodology to design clips that deliver an optimal emotional experience and, consequently, induce higher watching intentions and sales for their video content. The proposed framework applies not only to the comedy movies, trailers, and clips that we examined as a prototypical case but also to all movie genres and, more generally, to other types of digital content (e.g., news, books, TV shows) that are marketed with clips. Apart from contributing to the literature on content marketing, and movie marketing in particular (Eliashberg, Hui, and Zhang 2007; Faber and O’Guinn 1984; Litman 1983), this article also aims to contribute to the literature on online advertising (Teixeira, Wedel, and Pieters 2012) and fits the recent trend, in practice, toward shorter advertising messages (Maheshwari 2017).
This article is structured as follows. First, we review the relevant literature and provide a general conceptual framework for how the scene structure of trailers affects consumers’ viewing experience and watching intentions. Then, we develop an empirical model of watching intentions and propose an optimization tool to produce clips. Subsequently, we describe the empirical results of the model estimation for data on comedy movies and the prediction of their box office success. We then show the managerial implications of optimal clip production by testing the optimal clips through simulation, an online experiment, and a large-scale field experiment. Finally, we discuss the insights obtained and the potential use of our tools by marketers of online content and reflect on future developments regarding automation and personalization.
The movie industry and its box office performance have been the subject of ample research in marketing. Litman (1983) showed the relationships between box office success and determinants such as time of release, distributor, movie genre, production costs, and Academy Awards. Faber and O’Guinn (1984) then confirmed that the effect of movie previews and movie excerpts (such as trailers) on moviegoing behavior is stronger than the effects of word of mouth and critics’ reviews, though Eliashberg and Shugan (1997) showed that reviews play a role. Sharda and Delen (2006) revealed that a movie’s success is determined by the number of screens on which it is shown during the initial launch as well as its featured stars. Eliashberg, Hui, and Zhang (2007) demonstrated further that the scripts of trailers could be used to forecast a movie’s return on investment. More recently, Boksem and Smidts (2015) showed, using electroencephalography measures, that emotions are important predictors of movie preferences and box office success. Although significant research has been conducted to explain the causes and drivers of successful movies, less work has been done on movie marketing per se. Because approximately 50% of a major Hollywood studio’s movie budget is spent on marketing (the other half is spent on production), this gap in the literature is rather puzzling.
Movie marketing is a big business today. According to a report by Statista (2015), global cinema advertising spending was $2.7 billion in 2015 and is expected to total $3.3 billion in 2020. The main tool for movie and other video-based content marketers is currently the trailer. By including scenes from the movie that elicit a sample of the emotions that viewers will experience while watching the full movie, a trailer allows viewers to form expectations of the experience of watching the entire film (Kerman 2004). Trailer music is used to support the emotional experience elicited by the montage, but the majority of trailers use “library” music (Shannon-Jones 2011) that is not from the movie’s soundtrack itself. Indeed, movie trailers have been shown to be the most influential factor on consumers’ intentions to watch a movie (Faber and O’Guinn 1984).
Unfortunately, trailers for movies and other video content are no longer always directly useful in their original format as marketing tools for online content aggregators and distributors, given concerns over consumers’ increasingly short attention spans. The present research focuses on the scene as the elementary building block of trailers and as the basis for creating short “clips” for online marketing. Figure 1 visualizes our conceptual framework. It reflects the fact that scenes are the basic audiovisual building blocks of video content and that the creative design of a trailer involves a montage of selected scenes from that content. Marketers at online streaming services have no influence on the trailer’s plot, narrative, or script but, rather, need to edit and cut scenes to produce an effective promotional clip.
Our framework in Figure 1, which applies not only to movies but generally to other categories of video content as well, is therefore based on the recognition that the problem of modern-day online content marketers is one of editing content, as opposed to producing it from scratch. The cutting of scenes from the trailer produces a clip that retains important elements of the movie’s emotional experience, with the aim of generating in viewers a positive intention to watch the full content. Practitioners tend to view trailer “cutting” more as an art than a science and tend to use ad hoc methods in trailer design by, for example, applying “a lot more cutting” or adding “an unexpected jolt of some kind or a wonderful piece of music” (Hart 2014). Ultimately, to produce clips, marketers often simply use the first few scenes of the trailer. We call for a more rigorous approach.
Emotions play an essential role in experiencing movies and TV shows and, thus, in the trailers and clips created to promote this content (Boksem and Smidts 2015). Movies draw audiences in because they provide a concentrated emotional experience (Hewig et al. 2005; McGraw and Warren 2010). Each movie genre is characterized by a prototypical narrative that is designed to elicit a central emotion; for example, horror movies evoke fear, tragedies evoke sadness, and comedies evoke happiness (Grodal 1997). In the present study, we focus on comedy movies, with happiness as their central emotion. Our conceptual framework in Figure 1 shows that the key problem that marketers must address to create effective clips is to identify which scenes of the original trailer evoke the highest level of that central emotion among viewers. Only after determining the intensity and timing of the central emotion can content marketers edit down long-form trailers to shorter clips that are potentially even more effective than the originals.
The psychology literature on events in film has focused on the scene as a unit of analysis (Zacks, Speer, and Reynolds 2009) and has shown that viewers parse a film into events on the basis of the perceptual information that defines and delineates the scenes in the film (Cutting, Brunick, and Candan 2012). Our framework therefore revolves around the audiovisual scene structure of trailers (Figure 1). Marketers control the way consumers experience a trailer—and a clip in particular—through the pacing and length of scene cuts. Consumer behavior research has shown that pacing and sequencing (Galak, Kruger, and Loewenstein 2011, 2013; Ratner, Kahn, and Kahneman 1999; Zauberman, Diehl, and Ariely 2006)—in the present context, the number and length of scenes—and delays and interruptions (Nelson and Meyvis 2008; Nowlis, Mandel, and McCabe 2004)—in the present context, scene transitions and cuts—are prime components that affect consumption experiences. Fast-paced consumption leads to a decrease in enjoyment as a result of overly fast satiation (Galak, Kruger, and Loewenstein 2013), whereas a slower consumption, sometimes with an interruption, slows down satiation and leads to a more enjoyable experience (Nelson and Meyvis 2008). We therefore predict that the pacing of the scenes in a comedy movie trailer will exert a similar impact: happiness levels will generally improve across the sequence of scenes in a trailer, but a fast-paced trailer with a larger number of scenes results in a lower level of happiness and, consequently, a lower watching intention. Prior research has also addressed the impact of the consumption sequence. As Nowlis, Mandel, and McCabe (2004) demonstrate, a delay in consumption will lead to greater consumption enjoyment because the utility of anticipating a pleasant consumption outweighs the utility of waiting. Loewenstein and Prelec (1993) also show that people prefer to anticipate the best outcome at the end of a consumption experience. We therefore predict that if the key scene (typically the longest) is placed later in a comedy trailer, happiness levels and watching intention will be improved.
Sound is particularly important in producing clips, and this includes voice, special effects, and music. Prior literature has shown that sound plays a dual role in experiences: it orients attention and intensifies emotions. Research has shown that the intensity (volume) of the sound particularly amplifies the emotional experience if it occurs in a synchronized manner (Bradley and Lang 2000; Lang 1995; Lang, Bradley, and Cuthbert 1997). Therefore, we expect a positive impact of moment-to-moment overall volume and music volume in a comedy trailer on happiness and, consequently, on watching intention. Moreover, because some of the digital media in which clips are commonly placed (e.g., email, social media) do not support sound, it is important to be able to predict consumers’ reaction to a clip that does not include any audio or music. In our framework, we therefore allow for the possibility that several aspects of audio and music volume (including start, peak, trend, and end volumes) can have affect emotions and viewing intentions. The aspects that exert an influence on the experience depend on the context (Zauberman, Diehl, and Ariely 2006), and given the lack of prior literature, we do not formulate specific predictions on the effects of these specific measures on happiness or watching intentions.
TABLE: TABLE 1 Theoretical Predictions for the Effects of Video and Audio Characteristics on Happiness and Watching Intentions
| Primitives | References | Variables Affecting Happiness | Variables Affecting Watching Intentions |
|---|
| Video |
Pacing and sequence | Galak, Kruger, and Loewenstein (2011, 2013); Ratner, Kahn, and Kahneman (1999) | Number of scenes (—) Scene sequence (+) | Number of scenes (—) Scene sequence (+) |
| Delays and interruptions | Nelson and Meyvis (2008); Nowlis, Mandel, and McCabe (2004); Shiv and Nowlis (2004) | Average scene length (—) Longest scene index number (+) | Average scene length (—) Longest scene index number (+) |
| Audio |
| Moment-to-moment level | Bradley and Lang (2000); Lang (1995); Lang, Bradley, and Cuthbert (1997) | Volume level (+) Music level (+) | Volume level (+) Music level (+) |
| Start, peak, end, and trend | Zauberman, Diehl, and Ariely (2006) | Volume start, peak, end, and trend (+/-) Music start, peak, end and trend (+/-) | Volume start, peak, end, and trend (+/-) Music start, peak, end and trend (+/-) |
| Emotions Happiness | Baumgartner et al. (1997); Elpers, Wedel, and Pieters (2003); Fredrickson and Kahneman (1993) | | Happiness start (+/-), peak (+), end (+), and trend (+) |
Notes: Expected direction (+, -, or +/-) of the effects are in parentheses.
Because the goal of trailer and clip design is to produce a representative emotional experience, it is necessary to characterize the emotional content of trailer scenes. While the entire emotional experience throughout trailer consumption may be significant, research has shown that the peak and end points of the emotional experience are disproportionately more important in consumers’ overall evaluation of the experience (Baumgartner, Sujan, and Padgett 1997; Fredrickson and Kahneman 1993). Thus, we predict that scenes with high peak and end happiness result in higher watching intentions. In addition, research has shown that the general trend of the emotional experience—whether it is increasing, stable, or decreasing—also affects overall enjoyment (Elpers, Wedel, and Pieters 2003). We therefore predict that a positive trend in happiness results in higher watching intentions.
In Table 1, we summarize the predicted relationships between the scene-level factors of the trailers we examine as well as their downstream impact on emotions and watching intentions in the context of comedies and happiness as their central emotion, as guided by the literature. With respect to the visual scene structure, the number, length, and sequencing of scenes should have a measurable impact on the viewer’s emotions. With respect to audio, total sound volume as well as the music-only volume of scenes should have a direct impact on watching intentions as well as an indirect impact, because they evoke or intensify the central emotion of the genre. Not all moments in the trailer are expected to have a significant impact, but rather the start, peak, end, and trajectory of audio and emotions should matter.
In the next section, we explain the methodology we employed to collect emotional reactions to trailers to understand their role in consumers’ intentions to watch movies. We focus on trailers for comedy movies as a prototypical case. Comedy has been the leading genre in the past two decades, with a little more than 2,000 comedies produced and a market share of over 20%. The average gross revenue was approximately $20 million per movie. The main goal of comedy movies is to elicit joy and laughter among the audience (McGraw and Warren 2010), and effective trailers for comedy movies are designed to induce happiness as the central emotion (Grodal 1997). We thus focus on happiness as the key emotion in the illustration of our methodology but also study the roles of surprise and disgust as secondary emotions. In the next section, we explain the method we used to parse out trailers into scenes and to measure emotions moment to moment from a large sample of viewers’ reactions.
An online experiment was conducted in collaboration with nViso,1 in which facial expressions and watching intentions were collected for participants watching 100 comedy movie trailers. Each participant was asked to view a web page that contained 12 comedy movie trailers in a setup that mimicked what (s)he would encounter on trailer websites such as IMDb, iTunes, or YouTube. Participants watched the trailer in their natural environment (e.g., home, work), which increases the external validity of data collection. Facial expressions were recorded remotely through the webcams on participants’ computers. At the end of the experiment, the participants were asked to answer questions regarding their evaluations of the trailers and the corresponding movies, as well as their intentions to watch the movies. To make the study incentive-compatible, participants entered a lottery to win the DVD of the movie that they most wanted to watch. Each participant also received $5 in the form of an Amazon gift card if they completed the experiment.
A total of 122 paid participants were recruited online. The participants had a mean age of 24 years and an age range from 18 to 68 years, with 28% being men. Participants had to have access to a personal computer with a webcam and high-speed Internet connection and have near-perfect vision without glasses or contact lenses. Male participants with a full mustache or beard were excluded.
A total of 100 comedy movie trailers were taken from public access video channels. Thirteen comedy subgenres were selected, including nine drama comedies, eight animation comedies, seven action comedies, seven romantic comedies, four horror comedies, four indie comedies, four parodies, two dark comedies, and one each from political comedy, scifi comedy, slapstick, sports comedy, and late-night comedy. The trailer for a movie typically comes in multiple versions with varied lengths developed for different viewing situations and/or audiences. Two versions of the trailer for each movie were included in the present study to separately identify trailer-specific features (e.g., scene use, sound, volume) from movie-specific features (e.g., stars, casting, plot). Taking the movie Project X as an example, one trailer is one minute and 37 seconds long and contains 29 scenes, while the second trailer is two minutes and 26 seconds long, contains 32 scenes, and has louder sound, on average. Each participant was exposed to only one version of the trailer for the same movie. Overall, we used 100 trailers for 50 comedies in the study; we selected them from a pool of 100 comedy trailers through a balanced incomplete block design. Data collection generated a massive data set of more than 1.5 million (participant • time • movie) emotional reactions to audio and video content scenes. The design minimized spillover effects by randomizing the order of the trailers shown to each participant. One randomly selected comedy trailer was used as a control stimulus to form an individual-specific emotional baseline and was shown to all participants at the beginning of the experiment.
The participants were asked for consent to participate in the experiment and to be recorded by their webcam. Participants needed to be in a well-lit environment and, at most, 60 centimeters (2 ft.) away from their webcams. Participants were requested to refrain from eating, chewing, drinking, or talking. Although this request may have had some impact on the external validity of the study, it was necessary to ensure accurate recording of facial expressions, and recordings were checked afterward for compliance.
Each participant was shown a random series of 12 trailers. The length of each trailer was between one and three minutes. After each trailer, participants were asked five questions about their previous exposure to and evaluation of the trailer and the movie. Watching intention (WatchMovie) was measured on a scale ranging from one to seven, with seven being the highest intention, indicating how much participants would like to watch the movie after they had been exposed to the trailer. After all trailers were shown, participants were asked to answer questions about their demographics and their general moviegoing behavior. At the end of the experiment, participants were entered in a raffle in which they had a one in ten chance to win a free DVD. They were asked to choose one or more movies from any of the movie trailers they had just watched in the experiment. If they won, one movie was selected from the choices they made. The whole experiment took up to 45 minutes.
The facial expressions of emotions were collected, calculated, and provided to the researchers in raw data form by the company nViso, which provides real-time cloud computing to measure consumers’ emotion reactions in online experiments. For each second that a participant watched a trailer, a probability was calculated indicating the intensity of the emotion. An emotional profile was created for each participant containing the moment-to-moment measures of happiness and other emotions. The original videos of participants’ expressions were not retained because of privacy concerns, as outlined in internal review board regulations for the study. There were 122 participants in the online questionnaire data, and 104 participants provided valid emotion data. Ninety participants completed the entire questionnaire and had a valid emotion profile, indicating full compliance with the instructions. Five participants did not provide valid control data for the calibration trailer, and therefore, the final sample consisted of 85 participants from whom we obtained complete data, which is comparable to the sample sizes commonly used by nViso in its online tests. For each of the 100 trailers, the data from participants who had seen the movie previously were removed. Therefore, the number of participants per trailer varied from 3 to 19, with an average of 8.43 (SD = 3.14) participants per trailer.
Measuring emotions had been a long-standing problem (Mauss and Robinson 2009) until Ekman and Friesen developed the Facial Action Coding System (FACS) to systematically categorize emotions by coding instant facial muscular changes (Ekman and Friesen 1978). The FACS decomposes facial movements into anatomically based “action units” reflecting the muscular activity that produces the facial appearance. For example, happiness is characterized by two primary and three secondary action units. Recently, the Expression Descriptive Units, which measure the interactions among facial muscular movements (Antonini et al. 2006), as well as Appearance Parameters, which consider global facial features (Sorci et al. 2010), have been used to augment emotion recognition. Although, initially, emotions had to be assessed by trained coders, today several off-the-shelf software solutions are available to provide automatic and accurate momentto-moment emotion identification (Fasel and Luettin 2003).
This software has been used in previous marketing studies (Teixeira, Picard, and El Kaliouby 2014; Teixeira, Wedel, and Pieters 2012). It has been proven to outperform the work of nonexpert coders and to be approximately as accurate as that of expert coders (Bartlett et al. 1999). The automated algorithm used by nViso splits the video recording of the user’s face into separate frames and then uses the facial expression in each static frame to identify the probability of the occurrence of six basic emotions (happiness, surprise, fear, disgust, sadness, and anger) based on a multinomial logit model. The explanatory variables include the measurements from Ekman’s FACS, the Expression Descriptive Units, and the Appearance Parameters (MacCallum and Gordon 2011; Sorci et al. 2010; more details appear in Web Appendix I).
The algorithm has been validated using 11-fold cross validation on a database of 1,271 images of facial expressions and manually coded for the expression of emotions by 33 human coders. This cross-validation yielded a (normed) correlation of .76 (Sorci et al. 2010, Table 4, p. 800). This result is comparable to those for similar automated algorithms, for which classification accuracies ranging from .78 to .88 have been reported (Brodny et al. 2016; McDuff et al. 2013). Sorci et al. (2010) also report other supportive evidence on the performance of the nViso algorithm and compare it to neural networks based on Histogram Intersection and Kullback–Leibler measures.
The movie trailer video and audio content of all 100 trailers was analyzed using image and audio processing software, which yielded the following variables.
Scene cuts. Scene cuts in the movie trailers were detected automatically using the “Scene Detector” (http://www.scenedetector.com), which is software that detects the scene boundaries solely using the frame image data. From the scene cuts, we calculated the following variables for use in the analysis: the total number of scenes, the average length of scenes, and the location of the longest scene in the trailer.
Audio volume. We extracted two types of volume data. One is total volume: amplitude data were extracted every millisecond from MP3 audio files using the sound processing software SoX (http://sox.sourceforge.net). The absolute values were averaged on a second-by-second basis to match the video data. The other is music volume: by removing vocals utilizing SoX, the music was separated from the audio files, and its volume was calculated as described previously. For both the total volume data and total music volume data, based on our conceptual framework, we calculated the following variables to be used in the analysis: the moment-to-moment volume across the trailer, the trend of volume over the course of the trailer, the average volume in the start scene, the average volume in the end scene, and the scene with peak volume. Figure 2 shows an example of total volume and music volume from one movie trailer.
The trailers are composed of 23 scenes (Min = 1, Max = 56, SD = 14.23), on average, with an average length of 11 seconds (SD = 17.6). The total volume is .053 dB (SD = .025), while the peak volume is .097 dB (SD = .046). The music volume is substantially lower, .017 dB (SD = .016), with an average peak volume of .041 dB (SD = .038).
We measured intensity of happiness for each participant on a second-by-second basis. In line with our conceptual framework, we calculated aggregate measures of this emotion for each trailer as follows: Start, the total emotional intensity during the first scene; the Trend, calculated using a linear fit to each emotion curve; Peak, the average happiness of the scene with the highest average emotion level; PeakIndex, the location of that scene in the trailer; and End, the total emotional intensity during the last scene. Figure 3 shows an example of moment-to-moment happiness and its summary measures for the movie trailer for Men in Black 3. Several comedy subgenres may rely on other, concomitant emotions of happiness—most importantly, surprise in spoof and action comedies and disgust in dark, satire, and horror comedies (McGraw and Warren 2010). Therefore, we retained moment-to-moment surprise and disgust as secondary emotions in comedy trailers and calculated the start, peak, trend, and end measures for these two emotions as well. Tests based on a random effects linear model show that there is no significant trend in any of the emotion variables across the sequence in which the trailers are shown.
While there are countless variables that one could incorporate into the model, we chose to incorporate the ones readily available to marketers of online video content aggregators. In that spirit, we obtain several control variables for movies extracted from the online databases IMDb (owned by Amazon), Rotten Tomatoes, and The Numbers, including Motion Picture Association of America (MPAA) ratings, user ratings (from IMDb), number of critics’ ratings above 3.5 out of 5 (from Rotten Tomatoes, log-transformed), and release time (whether the movie is released during the summer or Christmas holiday season).
In our conceptual framework (see Figure 1 and Table 1): ( 1) audio and video features affect the moment-to-moment emotional experience; ( 2) aggregate measures of emotions, including start, peak, and end levels, affect intentions to watch the movie; ( 3) in addition to their indirect effects through emotions, some aggregate audio and video measures may affect watching intentions directly; and ( 4) finally, watching intentions, together with aggregate emotion measures and highlevel movie characteristics (e.g., reviews, ratings) influence box office revenue.
The statistical methodology reflects the postulated theoretical relations. Whereas prior research has mostly used emotions as explanatory variables, here we model the momentto-moment emotional response jointly with the end-point behaviors of prime interest (watching intention and box office revenues). There are three submodels combined in this joint model, one for the (longitudinal) happiness data, one for watching intention data, and one for box office revenue data. The happiness and watching intention submodels are connected through individual-specific random effects (Tsiatis and Davidian 2004). We account for unobserved individual differences though a hierarchical formulation. Given that there are over 40 predictor variables, we simultaneously apply Bayesian variable selection to each of the three model components to select the specific measures that predict watching intention.
First, the logit-transformed happiness probabilities for individual i watching trailer j at time t are denoted as hijt, and are modeled as
Here, qijt is the underlying true emotion trajectory (modeled as described subsequently). Whereas previous research has treated moment-to-moment measurements of emotions as fixed exogeneous variables (e.g., Teixeira, Wedel, and Pieters 2012), here measurement/classification error, denoted as xijt, is accommodated. It is obvious that xijt is not separately identified from other sources of error (say, §ijt) and therefore is subsumed in the model’s error term: eijt = xijt + §ijt. The error terms eijt are assumed to be independently Normally distributed, and because the emotions are classified independently on a frame-by-frame basis, it is not unreasonable to assume that they are uncorrelated over time. In Equation 1, the underlying emotion trajectory qijt is expressed as
Here, W1iðtÞ are subject-specific random effects (see the following paragraph). As for the moment-to-moment audio and video features, Sjt represents the index of the scene at time t; Vjt represents the total audio volume of trailer j at time t; and Mjt represents the volume of music of trailer j at time t. The matrix Xj1 contains the trailer-specific aggregate video and audio variables (start, peak, end, and trend) described in the previous section.
Second, an ordered logit model is developed for the watching intentions, with yij representing individual i’s intention to watch the movie j, modeled as a function of the latent variable yi*j as follows:
Here, D = 7 and W2i contains subject-specific effects, similar to W1iðtÞ. (The threshold parameters satisfy the order constraint: t1 < t2 < … < tD, and the first and last thresholds are fixed for identification (Lenk, Wedel, and Bo¨ckenholt 2006). The matrix X2i, j contains the predictor variables, including the aggregate measures of emotions, and the aggregate video and audio variables extracted from the movie trailers, which are our main explanatory variables. This model is linked to the model for happiness through the dependence between W1iðtÞ and W2i. These random effects capture unobserved individual-specific effects in the intercept and the trend of happiness, respectively. Specifically, W1iðtÞ and W2i are expressed as
The random effects for the intercept and the slope are denoted as u1i and u2i, and together with u3i they are assumed to follow Normal distributions. The parameters n1 and n2 capture the association between the happiness and watching intention models.
Third, we model log–box office revenues at the movie level as a function of predicted watching intentions and, as such:
Here, y*$j = å y*ij=N, and Xj3 contains the aggregate emotion measures anid control variables described in the previous section. Note that Equations 1–6 constitute a system of simultaneous equations that are jointly estimated. We obtained gross box office revenue for each of the movies corresponding to the trailers used in the study from IMDb for the year in which the study was conducted.
To identify a parsimonious model that has fewer explanatory variables, we apply the Gibbs variable selection procedure developed by Dellaportas, Forster, and Ntzoufras (2000) to efficiently search for the best subset of predictor variables in Xi1j, Xi2j, and Xj3 for each of the three model components, respectively. We use a variable selection approach because this allows us to use a limited subset of predictor variables in holdout data collection and model validation. In the Gibbs variable selection approach, the coefficients of the regression model are assumed to have spike-and-slab prior distributions with a mixture of a point mass at 0 and a diffuse distribution elsewhere. Specifically, an auxiliary indicator variable Ik is introduced for each covariate in Equations 2, 3, and 6, with Ik = 0 indicating the absence of the covariate k in the model and Ik = 1 indicating its presence. A (generic) regression coefficient bk in any of these equations is then specified as
The joint density PðIk, bkÞ = PðbkjIkÞPðIkÞ. The effect size parameter hk is assumed to have a mixture prior: PðhkjIkÞ = ð1 - IkÞ • Nðm~, ~t2Þ + Ik • Nð0, s~2Þ, where ðm~, ~t2Þ requires tuning. The parameter, s~2, is the fixed prior variance of hk. A Bernoulli prior distribution is assumed for the indicator: Ik ~ Bernð:5Þ. The model is estimated with Markov chain Monte Carlo using the JAGS software, with the code provided in Web Appendix II.
The integrated model described in the previous section is used to produce optimal short movie clips that can be inserted in emails, messages, and social media as well as in the apps, landing pages, and user interfaces of content providers. Online advertising channels are idiosyncratic in the video formats they accept, specifically regarding whether they support audio. For example, YouTube plays videos with sound by default, whereas on Facebook, 85% of videos are watched without sound (Patel 2016), and Netflix allows only GIF-format clips without audio and subtitles in its promotional emails. We produce clips of approximately 30 seconds in length (but, in principle, any desired length is possible) and design optimal trailers both with and without audio. For the former, we use the full model described previously; for the latter, we recalibrate the model while excluding the audio variables.
Let be the sequence of scene indicators across Tj, the length of trailer j, and let there be scenes for trailer j. The criterion optimized is the mean of the posterior distribution of the predicted watching intention of
where F contains all model parameters, denotes the posterior distribution of the parameters, and g denotes across the length of the clip, the Tj*. sequence of scene indicators The algorithm we propose to find an optimal trailer j of approximately 30 seconds (or any other length) is a backward elimination algorithm that one by one, eliminates scenes that reduce least. The algorithm works as follows:
- Start with the complete trailer,
- Eliminate all, and in turn, delete the corresponding elements from Vj, Mj, and hij, and calculate with draw from the Gibbs sampler;
- Retain the clip without scene l all time periods t for which and stop, and if not, return to step 2. In the application, we use e = 1 second, and D is set to be 30 seconds. The proposed backward elimination algorithm is part of a class of “greedy” algorithms that make a locally optimal decision at each stage s of the algorithm. For example, at step 1 for trailer j, it eliminates the most redundant scene from the trailer to produce an optimal clip with Kj - 1 scenes. This simple backward selection algorithm is computationally attractive because, for a trailer j, it will provide a solution in fewer than Kj steps. It avoids the need to enumerate all possible configurations of scenes, which would be required to find the globally optimal solution. In some cases, the proposed backward elimination strategy may thus not produce a globally optimal solution, but it will yield a locally optimal approximation of that solution (Couvreur and Bresler 2000). For online content aggregators, this approach has two benefits. It allows the movie marketer to optimally create clips of any length shorter than the original trailer, and it can be done very quickly.
We first test alternative specifications of the joint model with the purpose of investigating the contribution of the happiness and secondary emotion measures, the video characteristics, the audio characteristics, and the intention as a predictor of box office revenue. We calculate several measures of model fit, including Akaike information criterion (AIC), the deviance information criterion (DIC; Spiegelhalter et al. 2002), and the Watanabe–Akaike information criterion (WAIC). The last criterion was proposed by Gelman, Hwang, and Vehtari (2014) as a computationally convenient predictive measure that is based on the entire posterior distribution rather than a point estimate (as is the case for the AIC and DIC statistics). A smaller value for these statistics indicates a better fitting or predicting model.
TABLE: TABLE 2 Model Comparison Statistics for the Full Model and Five Models That Arise by Removing Variables from the Full Model
| Model | AIC | DIC | WAIC |
|---|
| 1. Full model | 233,519.2 | 233,459.6 | 253,212.9 |
| 2. Without surprise and disgust | 233,505.3 | 233,444.4 | 253,009.8 |
| 3. Without emotion variables | 233,592.9 | 233,524.1 | 253,406.2 |
| 4. Without video variables | 233,709.6 | 233,640.5 | 254,027.4 |
| 5. Without audio variables | 233,760.5 | 233,675.9 | 257,211.0 |
| 6. Without intention | 233,603.9 | 233,543.6 | 253,536.8 |
We examine six models: ( 1) the full model, and five models for which we remove the following sets of predictor variables from the full model: ( 2) a model without the measures of the secondary emotions surprise and disgust; ( 3) a model without the measures of all emotion variables of happiness, surprise, and disgust; ( 4) a model without all video variables; ( 5) a model without all audio variables; and ( 6) a model without intention as a predictor of box office sales. Table 2 shows the AIC, BIC, and WAIC statistics for each of the models. The model without the audio variables shows the largest reduction in (predictive) fit relative to the full model, followed by the model without the video variables. This model reveals the importance of audio, which has ramifications for the design of clips for media that do not support audio. Dropping watching intention as a predictor of box office success significantly reduces the (predictive) fit of the model. However, although dropping all emotion measures simultaneously reduces model fit significantly, the model without the (start, trend, peak, and end) measures of surprise and disgust fits better than the full model. Apparently, for our sample of movie trailers, these two secondary emotions do not play a significant role in the formation of watching intentions. We therefore report the results of the model without the secondary emotions (model 2, main model) next.
TABLE: TABLE 3 Inclusion Probabilities of Variables in the Joint Model, Estimated with Bayesian Variable Selection
| Happiness Model |
|---|
| Variables | Prob. |
|---|
| Scene | 1.000 |
| SceneNum | 1.000 |
| SceneLengthAvg | .395 |
| SceneLongestInd | 1.000 |
| Volume | 1.000 |
| Volume Peak | 1.000 |
| Volume End | 1.000 |
| Volume Start | 1.000 |
| Volume Trend | 1.000 |
| Music | 1.000 |
| Music Peak | .046 |
| Music End | .346 |
| Music Start | .352 |
| Music Trend | .546 |
| Watch Intention Model |
|---|
| Variables | Prob. |
|---|
| SceneNum | .030 |
| SceneLengthAvg | .053 |
| SceneLongestInd | .452 |
| Volume Peak | .016 |
| Volume End | .028 |
| Volume Start | .416 |
| Volume Trend | .714 |
| Music Peak | .029 |
| Music End | .029 |
| Music Start | .271 |
| Music Trend | .142 |
| Happiness Avg | .196 |
| Happiness Peak | .984 |
| Happiness End | .223 |
| Happiness Start | .898 |
| Happiness Trend | .625 |
| Box Office Model |
|---|
| Variables | Prob. |
|---|
| Holiday | .481 |
| MPAA = PG | .637 |
| MPAA = PG-13 | .622 |
| MPAA = R | 1.000 |
| Ratings (from IMDb) | .055 |
| NumRatingAbove3.5 | 1.000 |
| Happiness Avg | .265 |
| Happiness Peak | .226 |
| Happiness End | .209 |
| Happiness Start | .225 |
| Happiness Trend | .230 |
Notes: The posterior means of inclusion in the model are reported. Boldfaced cells represent estimates that are greater than the cutoff of .2.
Table 3 displays the posterior means of the inclusion probabilities obtained from the Bayesian variable selection. The table shows that for the happiness model, only the music peak has a very low inclusion probability (.046). For the watching intention model, variables that have very low inclusion probabilities are the sequence number of the scenes (.030); the average scene length (.053); the peak volume (.016); the end volume (.028); the music peak (.029); the end volume (.029); and, to a lesser extent, the trend in music volume (.142). Thus, while most of the audio and video variables affect moment-tomoment happiness, only a few (longest scene, total and music volume in the first scene, and the trend in volume) affect watching intention directly. Almost all emotion measures affect watching intention, but the inclusion probability of average happiness in the watching intention model is relatively low (.196). For the box office model, only the IMDb ratings have a low inclusion probability (.055).
TABLE: TABLE 4 Parameter Estimates Capturing the Effects of Variables on Happiness, Watching Intention, and Box Office Performance in the Joint Model
| Model Component | Variables | Mean | SD | Posterior 2.50% | Posterior 97.50% |
|---|
| Happiness | Video | Scene | .002 | .000 | .001 | .003 |
| SceneNum | 2.023 | .004 | -.030 | -.016 |
| SceneLengthAvg | -.005 | .002 | -.010 | .000 |
| SceneLongestInd | .027 | .002 | .022 | .031 |
| Audio | Volume | .210 | .066 | .078 | .341 |
| Volume Peak | 2.032 | .003 | -.037 | -.027 |
| Volume End | .010 | .003 | .003 | .017 |
| Volume Start | -.005 | .003 | -.012 | .001 |
| Volume Trend | 2.008 | .002 | -.013 | -.004 |
| Music | Music | 2.351 | .104 | -.553 | -.144 |
| Music End | -.007 | .004 | -.015 | .002 |
| Music Start | .029 | .005 | .019 | .038 |
| Music Trend | .006 | .004 | -.002 | .014 |
| Linkage | Random Intercept | 2.743 | .188 | -1.097 | -.381 |
| Random Slope | .619 | 9.864 | -18.660 | 19.650 |
| Watch intention | Video | SceneLongestInd | .165 | .065 | .037 | .293 |
| Audio | Volume Start | -.126 | .083 | -.286 | .035 |
| Volume Trend | 2.221 | .065 | -.351 | -.093 |
| Music | Music Start | 2.161 | .082 | -.321 | -.003 |
| Happiness | Happiness Peak | .514 | .124 | .273 | .762 |
| Happiness End | .358 | .139 | .083 | .631 |
| Happiness Start | -.111 | .122 | -.354 | .126 |
| Happiness Trend | .237 | .090 | .064 | .414 |
| Threshold parameters | t1 | -.249 | .383 | -.999 | .502 |
| t2 | .692 | .383 | -.059 | 1.442 |
| t3 | 1.236 | .384 | .483 | 1.989 |
| t4 | 1.944 | .388 | 1.183 | 2.705 |
| t5 | 2.794 | .396 | 2.017 | 3.571 |
| t6 | 4.155 | .416 | 3.340 | 4.970 |
| Box office | | Intercept | -2.951 | 1.745 | -6.407 | .442 |
| WatchIntention | .562 | .270 | .033 | 1.089 |
| Holiday | .930 | 1.068 | -1.205 | 2.953 |
| MPAA = PG | 22.666 | 1.052 | -4.726 | -.562 |
| MPAA = PG-13 | 22.914 | 1.049 | -5.009 | -.888 |
| MPAA = R | 23.792 | .928 | -5.615 | -1.974 |
| NumRatingAbov3.5 | 1.986 | .097 | 1.802 | 2.186 |
| Happiness | Happiness Avg | 1.645 | 1.550 | -1.450 | 4.684 |
| Happiness Peak | -.589 | .852 | -2.265 | 1.099 |
| Happiness End | .397 | 1.184 | -1.884 | 2.751 |
| Happiness Start | -1.004 | .996 | -2.958 | .925 |
| Happiness Trend | -1.375 | .746 | -2.847 | .077 |
Notes: Boldfaced cells reflect parameters for which the 95% credible interval does not include zero.
In our application, we search over a very large number of models, and even the set of most promising models may be large. We therefore use a heuristic cutoff on the inclusion probabilities to select the “best” model and, in line with Table 3, employ a standard cutoff of .2. We investigate the sensitivity to the cutoff by varying it from .10 to .35 in steps of .05 and reestimating the model, including the variables, on the basis of that cutoff. All coefficients of predictor variables in the emotion and box office models that are significant (have a credible interval that does not cover zero) stay the same, regardless of the cutoff used, but for the
watching intention model, as the cutoff changes there are some relatively minor variations in the significance of coefficients.2 We discuss the estimates of the final model next.
TABLE: TABLE 5 Results of Optimal Clips Resulting from Stepwise Removal of Scenes from the Trailer, Compared with a Benchmark with the First Scenes of the Trailer
| Optimal Clip | | Benchmark Clip |
|---|
| Movie Clips with Audio |
| | Average number of scenes | 3.57 (2.07) | | 4.76 (3.20) |
| Predicted watching Intention | 3.83 (.92) | | 2.91 (.68) |
| Difference in watching intentions | .92 (.77) | | |
| Percentage of clips with positive improvement | | 90.91% | |
| Improvement in Box office revenue | | 3.17% | |
| Movie Clips Without Audio |
| | Average number of scenes | 3.56 (2.07) | | 4.76 (3.20) |
| Predicted watching Intention | 3.80 (.68) | | 3.28 (.55) |
| Difference in watching intentions | .49 (.43) | | |
| Percentage of clips with positive improvement | | 90.91% | |
| Improvement in box office revenue | | 1.75% | |
| Average difference between clips with and without audio | .04 (.53) | | |
| Percentage of clips with audio better than those without | | 57.58% | |
Notes: Standard deviations appear in parentheses. One trailer only had one scene, so no optimization is performed. The optimization of movie clips without audio is based on the model without sound variables.
In Table 4, we present the estimates of the final model. The table shows that video features directly affect momentary feelings of happiness. The level of happiness increases with increasing scene sequences (Scene). The number of scenes in a trailer (SceneNum) has a negative effect on happiness, confirming that fast-paced comedy trailers tend to result in a lower level of happiness. We find that longer scenes placed later in the trailers (SceneLongestInd) increase happiness significantly. These findings confirm our predictions (see Table 1). As for audio, we find that its moment-to-moment volume has a significant positive instantaneous effect on happiness, as predicted (Table 1). We did not make specific predictions on the effects of the sound volume measures, but peak volume (VolumePeak) and increasing trend in volume (VolumeTrend) decrease happiness. End volume (VolumeEnd) has a positive effect. Music volume (Music) has a negative moment-to-moment effect on happiness, but louder music at the start of the trailer improves happiness (MusicStart).
The moment-to-moment experience of happiness throughout the trailer positively affects watching intentions. As predicted by peak-end theory (Fredrickson and Kahneman 1993), both the peak happiness (HappinessPeak) and the happiness experienced at the end of the trailer (HappinessEnd) have a positive effect on watching intentions (Table 1). In line with our prediction, an increasing trend in happiness (HappinessTrend) also affects watching intentions positively. Finally, the association between the random intercepts in the happiness and watching intention models is significant, with a higher variation in the level of happiness being associated with lower watching intentions. These findings confirm our predictions (Table 1).
In addition to the indirect effects of video and audio variables on watching intentions through the happiness experienced, there are also direct effects. Longer scenes placed later in the trailers (SceneLongestInd) increase watching intentions significantly, beyond their effect on happiness, as predicted (Table 1). Furthermore, increasing volume (VolumeTrend) decreases not only happiness but also watching intentions directly. However, louder music at the start of the trailer (MusicStart), while improving happiness, has a negative direct effect on watching intentions.
In the box office revenue model, several of the control variables have a significant impact, including ratings from Rotten Tomatoes (NumRatingabove3.5; positive effect) and MPAA reviews (MPAA = PG, PG-13, and R; negative effect). Finally, and importantly, watching intentions (WatchIntention), as predicted by the watching intention model, have a significant positive impact on box office revenues.
The parameter estimates in Table 4 were used as inputs to the stepwise scene selection algorithm to produce an optimal movie clip of approximately 30 seconds in length for each of the 50 pairs of trailers. Because some media for which these clips are intended do not allow for sound, clips were produced both with and without sound. For this purpose, we use two models with and without the sound variables. As a benchmark for comparison, we use the current practice to produce clips by selecting the first 30 seconds of the trailer. The results appear in Table 5.
Optimal movie clips with audio. Table 5 shows that the optimal clips consist on average of 3.6 scenes, while the benchmark has more (and thus, shorter) scenes, 4.8 on average. The predicted average watching intention of the optimal clips (seven-point scale) is considerably higher (3.83) than that of the benchmark clips (2.91). The predicted watching intention of the original trailer is 3.32 (SD = .55), and thus, the shorter optimal clip results in an even higher intention to watch the movie than does the original trailer. The average difference in watching intention between the optimal and benchmark clips is almost a full point (.92) on the seven-point scale, and over 90% of the optimal clips have higher watching intentions than the corresponding benchmark clips. These watching intentions translate to a predicted 3.17% improvement in expected box office revenue for the optimal clips.
Recall that the data contain two versions of the trailer for each movie. For each of the two versions of a trailer, we produced a clip using our algorithm. On average, the difference in predicted watching intention between these two clips for the same movie was 1.29 (SD = .76). From each pair of clips, we selected the one with the highest predicted watching intention. These clips had an average predicted watching intention across all movies of 4.21 (SD = .92), which translates to a 4.80% predicted increase in box office revenue. Thus, because the two different trailers have a wider range of scenes from the movie, selecting the best clip from the pair results in considerably higher watching intentions and predicted box office success.
Optimal silent movie clips. For clips produced without audio (“silent clips”), the optimal clip contains 3.6 scenes, on average, while the benchmark contains 4.8 scenes. These results are not noticeably different from those for clips with audio. The predicted watching intentions for the optimal silent clips are 3.80 (seven-point scale), which is only somewhat lower than those for the optimal clips with audio (Table 5). Yet 42% of the silent clips result in a higher watching intention than their counterparts with audio. Note that this does not reflect a lack of contribution of audio to watching intentions but reveals that it is possible to eliminate scenes from the trailer in such a way that even the resulting silent clips still induce a high watching intention.
The optimal silent clips result in higher watching intentions than the original trailer (3.32) and the benchmark silent clips (3.28). The average predicted difference in watching intention between the optimal and benchmark silent clips is approximately a half point (.5), and over 90% of the optimal silent clips have higher watching intentions than the benchmark silent clips. These higher watching intentions result in a predicted 1.75% improvement in expected box office revenue. We conducted a similar analysis using the best of the two versions of the clip for each movie. On average, the difference in predicted watching intention between the two silent clips for the same movie was .79 (SD = .45). For the best silent clip in each pair, the average predicted watching intention is 4.07 (SD = .66), which translates to a 2.45% increase in box office revenue.
We thus demonstrate the beneficial effects of optimizing movie clips through simulation. To investigate consumers’ response to the actual clips in holdout validations, we conducted two experiments. The first is an online experiment and the second is a large-scale field experiment.
We selected the five best-performing clips with audio and the five best-performing silent clips from the simulation analyses. The movie titles included Dark Shadows, Mirror Mirror, The Odd Life of Timothy Green, Project X, Rock of Ages, Some Guy Who Kills People, Wanderlust, and What to Expect When You’re Expecting. Two clips overlapped between the two sets of five: Project X and Mirror Mirror. For each clip, we produced the actual benchmark and optimal movie clips by editing the digital video file of the trailers in line with the proposed procedure. The clips were produced in GIF format and were approximately 30 seconds long.
One hundred seventy-five undergraduate and graduate students were recruited for the experiment and participated for extra course credit. To make the study incentive-compatible, participants were entered in a lottery for the chance to win a $50 gift card to be used to see the movie they liked the most. Some platforms that do not allow for sound (e.g., Facebook) do allow clips to show subtitles. We therefore also added subtitles to the optimized silent clips, as this may increase comprehension of the narrative. We showed each participant five clips in a randomized order. To avoid spillover effects, we showed only one (randomly selected) version of a clip (optimized or benchmark) to each participant. After watching each clip online, the participants were asked to answer three questions on seven-point scales to assess their evaluation of the clip (“How much do you like this movie clip?”), the movie (“How would you rate the movie based on this trailer?”), and their intention to watch the movie (“Would you like to watch this movie?”).
We obtained usable data from 169 respondents. We used multivariate analysis of variance to analyze the average of the three evaluation measures and found strong evidence of the performance of the optimized clips over the benchmark clips with audio (p < .001, partial eta-squared h2 = .08) and without audio (p < .001, h2 = .08) and of the effect of adding subtitles to silent clips (p < .001, h2 = .13). Relative to the benchmark, the optimization procedure significantly improves the measures for each type of movie clip, with moderate effect sizes. Table 6 presents the results for the average of the three evaluation measures for each of the five movie clips separately. For all three types of clips, improvement over the benchmark is among the largest for Mirror Mirror: for the clip with audio, evaluations increase by 21.9% (p = .003, h2 = .16); for silent clips without subtitles, they increase by 17.1% (p = .056, h2 = .16); and for silent clips with subtitles, they increase by 32.3% (p = .002, h2 = .21). The benefits of the proposed procedure are smallest for silent clips; thus, when autoplay videos are muted, it may be important to add subtitles, which is not currently a widespread practice. This holdout study, in which actual clips were produced and presented to a new sample of respondents, provides evidence of the effectiveness of the proposed model and optimization procedure.
To test whether optimized clips indeed improve the actual viewing behavior of actual customers, we worked with Netflix’s messaging team and conducted a field experiment to test our approach on an email campaign for one of Netflix’s original romantic comedy movies just before its launch in August 2017. First, we collected data in a facial-tracking experiment (through nViso) with a sample of 41 participants who viewed the trailer of this movie, using the procedure described in the methodology section. From our model estimates reported in Table 4, usable facial-expression data from 40 participants, and scene and other characteristics of the trailer, we produced silent optimized benchmark clips of 19 seconds in GIF format (without subtitles), according to Netflix’s requirements. In addition to comparing the optimized clip with the benchmark clip, we also compared it with a static image, as these are still frequently used in Netflix’s email campaigns.
TABLE: TABLE 6 Averages of the Three Evaluation Measures for the Optimized and Benchmark Clips in the Online Validation Experiment
| Clips With Sound |
| | The Odd Life of Timothy Green | Dark Shadows | Mirror Mirror | Project X | Rock of Ages |
| Benchmark | Average | 3.86 | 3.61 | 4.16 | 3.58 | 3.24 |
| Optimized | Average | 4.42 | 3.97 | 5.07 | 4.38 | 3.74 |
| % difference | 14.70 | 9.94 | 21.92 | 22.55 | 15.48 |
| Clips Without Sound |
| | What to Expect When You’re Expecting | Some Guy Who Kills People | Mirror Mirror | Project X | Wanderlust |
| Benchmark | Average | 3.66 | 2.96 | 3.86 | 3.52 | 3.78 |
| Optimized (no subtitles) | Average | 4.02 | 3.33 | 4.52 | 4.00 | 4.03 |
| % difference | 9.75 | 12.49 | 17.10 | 13.65 | 6.38 |
| Optimized (no subtitles) | Average | 4.52 | 3.82 | 5.11 | 4.35 | 4.58 |
| % difference | 23.40 | 29.16 | 32.27 | 23.67 | 20.98 |
Using a stratified sampling procedure, Netflix users were allocated to strata on the basis of a unique combination of their region, device type (e.g., iPhone, Android, Apple TV), payment type (e.g., debit, credit), tenure (e.g., one year), and plan (basic, standard, premium). Then, using machine-generated random numbers, the participants in each stratum were randomly assigned to one of three conditions: ( 1) the baseline with a static image, ( 2) the benchmark clip, and ( 3) the optimized clip. Each condition has an equal number of participants from each of the strata. In total, 40,000 Netflix customers from non-U.S., English-speaking countries were involved. Each participant received a promotional email from Netflix with the optimized clip, benchmark clip, or static image embedded. The emails had the same subject line and supporting text, and the clips looped. Next, we report Netflix’s standard statistics on the variables directly related to streaming behavior, as well as effect sizes.3
First, compared with the static image, the optimized and benchmark clips performed significantly better in terms of the average number of streaming hours with a moderate effect size (an average lift of 1.60%, p .001, Cohen’s h = .253), showing that customers are more receptive to clips than to static images. In addition, while a .30% higher watching percentage (customers who watched at least 70% of the movie) for the optimized clip relative to the benchmark is not significant, the optimized clip reduces the percentage of short viewers (customers who viewed less than six minutes of the movie) by 10.5% (p = .058, odds ratio = 1.121) and reduces the bad player ratio (percentage of short viewers divided by watching percentage) by 12.5% (p < .000, odds ratio = 1.170) compared with the benchmark clip. Although the effect sizes are relatively small, the optimized clip enhanced streaming behavior compared with the benchmark clip.
The results of the field test, while preliminary, are encouraging. This is especially the case because the results rely only on a single silent movie clip, the Netflix original comedy movie was not part of the model calibration data, the samples came from very different populations, and streaming behaviors occur far downstream from exposure to the clips. Nevertheless, the field test showed that optimizing the clip with the proposed approach meaningfully influenced streaming behavior.
Movie trailers have long been regarded as the movie industry’s most effective marketing tool (Faber and O’Guinn 1984), but original two- to three-minute trailers, not only for movies but also for sitcoms and video games, are becoming less effective in new digital media. Marketers are therefore aiming to produce much shorter video clips to promote their content in these media. Film clips are ads for movies, akin to those for video games and TV shows, but they differ from food, car, and electronics commercials in that they are made up of samples of the product that is being promoted. Viewers of clips thus experience a sample of the emotions that they will experience when they go see the movie. The challenge for marketers resides in identifying how many and which scenes of the trailer to show in a short video clip. The goal of this research is to support movie marketers in this effort by investigating how to cut the trailers provided by trailer production houses down to short clips that are suitable for today’s electronic media while eliciting an emotional experience that is representative of the movie and stimulates people to go and watch it.
Yet how to optimally sample the trailer content remains an open question. The contribution of this research lies in the development of a theoretical and methodological framework for moment-to-moment emotions, watching intentions, and box office success (Figure 1) to support this goal. The framework centers on the scene as the basic building block of movies, trailers, and clips. The proposed method helps marketers select those scenes from a trailer that render short clips the most effective. The findings of our analyses, simulations, and online and field tests show that our approach enables the design of short clips that not only increase consumers’ intentions to watch the movie but also improve predicted box office success and streaming behavior.
This research marks a first attempt to investigate the effectiveness of clips, with the application focusing on clips for the comedy movie genre. Although happiness, as the central emotion of the genre, has strong effects, we do not find an effect of concomitant emotions (e.g., surprise, disgust) that might be significant in spoof, action, dark, satire, and horror comedies. This result might be caused by the sample of participants and movies in the present study, and future research should further examine the role of such concomitant emotions. We do expect the proposed approach to be directly applicable to other movie genres that elicit a different central emotion (Grodal 1997). Further refinement of the approach for that purpose may be useful.
In a broader context, our approach involves content marketing. In content marketing, a sample of the product is marketed, and applications arise not only for movies but also for immersive games, for TV shows on HBO and Netflix, for news items shown on news sites and news aggregators (e.g., Flipboard), and even for books (Arons 2013). Determining how our approach can be extended to support the marketing of these other types of products requires further study. In such future research, face tracking could be combined with measures such as those derived from electroencephalography, which have recently been shown to be predictive of movie preferences and box office success (Boksem and Smidts 2015).
Online ads have a short “shelf life” compared with traditional forms of advertising, such as TV commercials. As such, online marketers constantly need to create new content for these ads to attract and retain consumers’ attention online. The traditional production process of ads is expensive and often slow, and therefore marketers are increasingly considering automation to produce variations of ads as quickly and cheaply as possible. Our approach to advertising online content through short clips can be automated, scaled up, and personalized. Once representative calibration data are available on which the models in question have been trained, film clips can be automatically produced using the proposed algorithm. Taking this one step further, using customer-level data, our procedure could be utilized to customize the selection of scenes to produce personalized clips that maximize the elicited response from each individual customer. The pursuit of the automation and personalization of the content of movie clips holds promise to greatly enhance marketing effectiveness (Wedel and Kannan 2016). We hope the present study provides a starting point for these future research avenues and consequently improves the effectiveness of marketing for movies and the marketing of content.
2The Random Intercept, Index of the Longest Scene, the Audio Trend, Happiness Peak, and Happiness Trend variables are “significant” for all cutoffs. The signs of the coefficients are stable, but Average Happiness (cutoff .2), Happiness End (cutoff = .2), Music Start (cutoff = .2), and Volume Start (cutoff .3) are significant only for some of the cutoffs.
3We calculate the p-value from tests on the relationship between proportions of two groups (Newcombe 1998; Wilson 1927) using the stats package in R (prop.test) and set the alternative hypothesis as greater or less. We calculate the odds ratio as an effect-size measure (e.g., Cornfield 1951) and calculate Cohen’s h for the lift measure of streaming hours because the odds ratio cannot be calculated in this case.
PHOTO (BLACK & WHITE): Notes: Scenes are the basic building blocks of video content. The audiovisual scene structure of movies elicits an intended emotional response. Scenes from the movie are selected for the trailer, and scenes from the trailer are selected to produce a clip. The clip provides a representative emotional experience and results in intentions to watch the full movie.
PHOTO (BLACK & WHITE): Notes: The solid line indicates the total sound volume (in dB). The dotted line indicates the music volume (in dB) with vocals removed. Vertical dashed lines indicate scene cuts.
PHOTO (BLACK & WHITE): Notes: The happiness measure ranges from 0 to 1 (the algorithm assigns a probability on the basis of three sets of facial expression measurements; details about the measurement are provided in Web Appendix I). Vertical dashed lines indicate scene cuts. The middle shaded area is the region of the scene with the happiness peak; left and right shaded areas are start and end scenes. The horizontal dashed line indicates 75% of the peak value; the dotted line is a linear fit used to represent the happiness trend.
REFERENCES 1 Arons, Rachel (2013), “The Awkward Art of Book Trailers,” The New Yorker (December 19), http://www.newyorker.com/books/ page-turner/the-awkward-art-of-book-trailers.
2 Antonini, Gianluca, Matteo Sorci, Michel Bierlaire, and Jean-Philippe Thiran (2006), “Discrete Choice Models for Static Facial Expression Recognition,” in International Conference on Advanced Concepts for Intelligent Vision Systems. Berlin, Heidelberg: Springer, 710–21.
3 Bartlett, Marian Stewart, Joseph C. Hager, Paul Ekman, and Terrence J. Sejnowski (1999), “Measuring Facial Expressions by Computer Image Analysis,” Psychophysiology, 36 (2), 253–63.
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What Drives Herding Behavior in Online Ratings? The Role of Rater Experience, Product Portfolio, and Diverging Opinions
Consumers' postpurchase evaluations have received much attention due to the strong link between ratings and sales. However, less is known about how herding effects from reference groups (i.e., crowd and friends) unfold in online ratings. This research examines the role of divergent opinions, rater experience, and firm product portfolio in attenuating/amplifying herding influences in online rating environments. Applying robust econometric techniques on data from a community of board gamers, we find that herding effects are significant and recommend a more nuanced view of herding. Highlighting the role of rater experience, the positive influence of the crowd is weakened and friend influences are amplified as the rater gains experience. Furthermore, divergent opinions between reference groups create herding and differentiation depending on the reference group and the rater's experience level. Finally, firms can influence online opinion through their product portfolio in profound ways. A broad and deep product portfolio not only leads to favorable quality inferences but also attenuates social influence. Implications for online reputation management, rating system design, and firm product strategy are discussed.
Keywords: diverging opinions; disagreement; herding effects; online ratings; product scope; rater experience; reflection problem
User-generated online ratings and reviews play an important role in the consumer decision process. They act as a key source of quality information for consumers and have profound downstream market impact. With easy access to online reviews and ratings, consumers rely more and more on the opinions of others when making purchase decisions ([ 8]; [42]). Academic work points to significant positive effects of online ratings on market outcomes, reputation, and purchase behavior across various business contexts ([ 2]; [13]; [14]; [37]; [59]). Given the positive consequences of online ratings, a great deal of recent interest has emerged among academics and practitioners in understanding the antecedents of ratings ([17]; [21]; [22]; [36]).
When generating an online rating or review, raters create an evaluation of a product that reflects their personal perception of the product's quality and qualities. However, most review sites (e.g., Yelp, TripAdvisor, Amazon) also expose raters to others' ratings. These prior ratings by others often influence current raters' evaluations, a process generally known as "herding."[ 6] In the recent past, several popular ratings platforms have begun to display friends' and crowd ratings distinctly on their websites. For example, Facebook Local allows individuals to rate and review places such as restaurants and other service establishments and share this information with their friend network ([30]). Yelp displays the ratings from the crowd and allows individuals to "connect with friends" to easily access their individual ratings. Other services, such as Netflix, Foursquare, and TripAdvisor, allow their accounts to be connected to popular social networks such as Facebook. This provides raters with multiple, often conflicting, sources of social information that can cause herding effects.
Typically, research on herding in online ratings has focused on social influence arising from the online community as an aggregate whole ([17]; [21]; [22]; [32]; [38]; [51]; [55]). Less work has focused on parsing out herding effects from multiple sources. A rater's friend network may exert a different kind of influence relative to that of the general public. For instance, a friend's rating may prove to be more salient than the rating of the crowd, especially when the friend's interests overlap with those of the rater. More recent research has stressed the importance of identifying and separating multiple sources of herding ([31]; [57]). Still, scant research exists that explores the contingencies under which herding may or may not occur for these multiple sources. Our research relates to, and in many ways extends, the recent empirical work by [31] and [57] highlighting differences in the herding effects across multiple reference groups. Lee, Hosanagar, and Tan uncovered the differences between crowd and friend herding effects. They found that although friends' ratings always induced herding effects, crowd ratings caused herding for popular products and differentiation for unpopular ones. We build on Lee, Hosanagar, and Tan by controlling for their moderators and covariates while breaking new ground by establishing the influential role that individual-level and firm-controllable factors play in moderating herding behavior. Furthermore, our research complements Zhang and Godes, who examined herding from the perspective of an individual's number of social ties and whether these ties are strong or weak. We control for the number of ties and instead study the important roles that the valence and consistency of opinions play in herding. A further notable difference from Zhang and Godes has to do with the operationalization of social ties. In their work, "weak ties" are represented by people a rater follows, whereas "strong ties" are bidirectional (i.e., exist when the rater and their peer follow each other). We expand this view to consider influences arising from self-declared friend networks and the overall community, not just people who a rater might choose to follow.
Our work aims to be the first to focus not only on the distinct herding effects produced by crowd and friend networks but also on understanding the contingencies that govern the herding influences. In addition to decomposing herding effects into influences exerted by in-group (i.e., friends) and out-group (i.e., crowd) networks on a rater's subsequent rating, we aim to ( 1) uncover key rater-level and firm-level factors that attenuate/amplify herding and ( 2) examine the role of "mixed" opinions (i.e., disagreement between crowd and friends) on the herding effect. Using the theory of herding ([ 6]; [ 9]) as our theoretical lens, we develop a conceptual framework to understand and evaluate the herding effects. We examine the aforementioned research objectives and test the proposed conceptual framework on unique data from an online community in the board gaming industry comprising 44,108 board gamers rating 5,138 games from 2,206 publishing firms spanning over ten years. Our data and modeling strategy allow us to exploit the timing of tie formation and exogenous variation created through partially overlapping network pairs ([11]) to credibly identify the herding effects. In the spirit of building on and extending previous work, we ensure that our findings are interpretable over and above what extant research has accomplished. Wherever appropriate, we include covariates, moderators, and other control variables that prior work has highlighted and then demonstrate our additional contributions. In Table 1, we juxtapose our research with prior work across dimensions including research scope, conceptualization, and application, placing our research in the context of extant literature on social influence in online ratings.
Graph
Table 1. Representative Empirical Research on Herding Effects in Online Ratings.
| Study | Main Effects | Moderating Effects | Level of Analysis | Data and Empirical Context |
|---|
| Crowd | Friends/Peers | Divergence of Opinion | Rater Experience | Firm's Product Scope |
|---|
| Moe and Trusov (2011) | Yes | No | Main effect onlya | No | No | Aggregate | Online retail (500 products, one year) |
| Moe and Schweidel (2012) | Yes | No | Main effect onlya | Yes | No | Individual | Online retail (4,974 raters, 1,811 products, six months) |
| Sridhar and Srinivasan (2012) | Yes | No | No | Main effect only | No | Individual | Hospitality (7,499 raters, 114 products, four years) |
| Muchnik, Aral, and Taylor (2013) | Yes | No | No | No | No | Individual | News (3,600 raters, 163 days) |
| Lee, Hosanagar, and Tan (2015) | Yes | Yes | No | Main effect only | No | Individual | Movies (28,160 raters, 149 products, 16 weeks) |
| Zhang and Godes (2018) | Only number of ties | Only number of ties | No | Yes | No | Individual | Books (5,389 raters, 16,595 products, 50 days) |
| This study | Yes | Yes | Divergence between reference groups + moderation effects | Main + moderation effects | Main + moderation effects | Individual | Board games (44,108 raters, 5,138 products, ten years) |
1 a Considers only aggregate distribution (variance) of prior ratings, not divergence between specific reference groups.
This article adds to a growing stream of academic work aimed at assessing social effects in online rating environments and highlighting the importance of distinguishing the herding influences exerted by weak (i.e., crowd) and strong (i.e., friend) ties. We find that crowd and friend effects are distinct, significant, and positive; there is indeed wisdom to be found in crowds and friends. We offer a more nuanced view of herding by making three important contributions. First, we find that the herding effect is not universal but depends on an individual's own experience level. All herding effects should not be treated equally, because rater experience attenuates the herding effect of the crowd but amplifies the herding effect of friends. Second, we demonstrate how firms can leverage their category-level experience in online rating environments through their product line strategy. A firm's product scope serves as an additional source of diagnostic information for raters and influences rater quality judgments both directly and indirectly. Ceteris paribus, not only do firms with greater product scope receive more favorable ratings, greater product scope also attenuates the herding effect. Third, we show that, in general, raters rely more on the crowd's opinions than on those of friends when the two reference groups disagree with each other. However, more experienced raters will rely more heavily on friends' ratings.
We believe this research has important implications for online reputation management, online rating platform design, and product strategy. First, our study suggests that firms can and should take advantage of herding in rating environments. Firms can manage their online reputations by strategically targeting their review solicitations. Specifically, firms seeking more objective ratings should target experienced raters because they are less influenced by the herding effect. Firms that want to ride a positive bandwagon effect should solicit ratings from new users. Of course, managers must be careful, as we demonstrate that herding influences are a double-edged sword. Positive word of mouth results in more word of mouth that is positive, but the reverse is also true. Second, our research has implications for online rating platform design. Depending on prior product ratings and the goals of the website, friend and crowd information can be made more or less accessible. Third, this research provides guidance for portfolio planning, as a firm's product strategy has profound positive effects on online opinion, both directly and indirectly. Firms with broader and deeper product portfolios are viewed more favorably, and this higher product scope attenuates herding influences from both crowds and friends.
The rest of the article is organized as follows. We first introduce the theoretical lens of herding, which forms the basis of our conceptual framework and hypotheses describing the herding effects and key moderators on online rating behavior. Following this, we describe the empirical context and develop our measures. We then discuss the modeling framework that forms the basis of our hypothesis testing. After estimating the model, we report the empirical findings, test our hypotheses, and conduct a series of robustness analyses. Finally, we discuss the theoretical and managerial implications of this research before concluding with limitations and avenues for future research.
The theory of herding ([ 6]; [ 9]) guides how social influence manifests in online rating environments. Our application joins a long list of applied empirical work studying herding in purchase, adoption, and consumption decisions across various contexts in economics, finance, information systems, and marketing.[ 7] Herding can be explained as a response to an individual's perceived uncertainty about an action. That is, in an effort to minimize uncertainty about their own decisions, people tend to utilize information provided by others and converge on similar behaviors. An individual's decision is a reflection of two main sources of information: imperfect individual information and the sequential actions of others. The first source is the individual's own signal or preference that encompasses all the information that one is able to gather about the decision. The second source is the history of actions taken by other individuals who also were faced with the same decision. To reconcile these two sources, individuals weigh their own information against the sequential actions of others. When people are more certain about their preferences and quality assessment, their information dominates the herding effect.[ 8] On the other hand, in the presence of uncertainty (e.g., when individuals are unsure about their preferences), the theory of herding predicts that the observed sequential actions of others will play an enhanced role in forming a preference.
Our research is well suited to the theory of herding because it satisfies the two main criteria for rational herding to exist. First, ratings are based on preferences and quality judgments; two sources that are notoriously uncertain ([56]; [58]). Second, ratings arrive in a sequential order and are clearly visible to the individual. In our context, as in most online rating platforms, the ratings provided by the community are salient to the individual at the time of rating. Our framework enables us to extend this theory by allowing for multiple sources of sequential actions from friends and crowds in addition to demonstrating how firms can influence herding through their product strategy.
Individuals adjust their ratings of products in line with the online rating information of the crowd ([32]; [38]). That is, to conform to the crowd's opinion, rating behavior is positively related to prior crowd rating behavior. Conformance does not just exist with crowd behavior—herding has been demonstrated among friend networks as well ([31]; [57]). The theory of herding supports these conclusions. Raters make use of the observed behaviors of the reference groups (i.e., crowd and friends) to adjust their own quality judgments in an effort to reduce uncertainty. In addition, there is often a social cost to having opinions that diverge from the community and reference groups. Although we do not formally hypothesize an effect, given the theoretical motivations along with prior literature, we expect that the rating valence of the reference groups, both crowd and friends, will be positively related to the subsequent valence of an individual's rating, resulting in a positive herding effect. As raters in an online community are exposed to more diagnostic information when rating a specific product online, their susceptibility to herding may shift. We focus on three factors that may govern the herding effect: rater experience, the firm's competencies as inferred through product portfolio scope, and the role that divergent opinions play in online ratings. In the following subsections, we offer theoretical arguments for each moderation effect and develop hypotheses for the same.
As individuals develop their own preferences and gain knowledge of and expertise with a product category, they become more confident in their own evaluations and opinions ([18]). Highly experienced raters view themselves as opinion leaders and tend to be less affected by the crowd. Empirical work in the medical domain and physician prescription behavior has found asymmetric peer effects among physicians according to their perceived opinion leadership ([27]; [40]). We expect similar effects to exist among highly experienced raters. According to the theory of herding, experienced individuals gain confidence in their own preferences and information. As such, highly experienced raters should rely relatively less on others' ratings and more on their own experiences. In addition, experienced raters may find value in differentiation. In an online community context, where there are fewer individual markers to establish identity, experts try to signal divergence from the crowd ([ 7]) in an effort to appear knowledgeable ([49]) and display "good taste" ([25]). Thus, we expect that as raters gain more experience by spending more time and rating more products, their reliance on the crowd will decrease.
Although diverging from the majority may be of social value, the same cannot be said for having separate opinions from friends. The potential cost of divergence of opinion is amplified when members belong to in-group networks ([ 9]). As a result, raters tend to conform strongly within self-selected friend networks. The stronger the in-group network grows over time, the more opinions coalesce, and the more attitudes shift to be consistent with the in-group ([34]). From the perspective of social identity theory ([ 5]), herding from known in-group networks such as friends is likely to be stronger among embedded individuals. That is, raters who are strongly embedded within a friend network share a common identity with the network and feel the need to signal this common shared identity. In the case of online social networks, it is expected that experienced raters, who have spent more time on the social network and carefully select their reference groups, are more embedded within their friend networks. This sharing of identity and norms within friend networks causes experienced raters to coalesce their opinions to signal common tastes and mutual respect with the friend groups ([ 5]). Furthermore, these shared group norms create barriers to dissenting opinions ([19]; [23]). Therefore, although we expect experienced raters to diverge from the "common" others (i.e., out-group networks), we expect them to coalesce with friends (i.e., in-group networks). A rater's prior experience will amplify the effect of self-declared friends' ratings on subsequent evaluations. In summary, we expect that the moderating influence of rater experience on crowd and friend influences acts in opposing directions.
- H1: As an individual's prior rating experience increases, the crowd's influence on subsequent ratings decreases.
- H2: As an individual's prior rating experience increases, the friends' influence on subsequent ratings increases.
Although friends and crowds both exert herding influences on the individual, these two sources may not always agree with each other. One of our main objectives is to investigate the role that disagreements play on herding effects. As predicted by the theory of herding ([ 6]; [ 9]), when faced with mixed signals, the average rater calculates the cost of diverging from each reference group. The cost of divergence is not the same for the crowd and friends. The average rater shares some of their identity with their friend groups, but this shared identity is not strong enough to offset the cost of diverging from the majority opinion of the crowd. Notably, the average rater, by definition, is less motivated (than experienced raters) to signal strong self or shared identities and therefore has less incentive to diverge ([ 7]; [49]). Given this, the average rater would perceive a higher cost of divergence with the crowd (i.e., the majority opinion) than among friends (i.e., the minority opinion).
- H3: When the divergence between crowd and friend ratings is high (vs. low), raters favor the crowd over friends such that (a) the crowd's herding influence increases and (b) friends' herding influence decreases.
Although, on average, we expect raters to coalesce with the crowd more than with friends, when faced with mixed opinions, the role of rater experience cannot be ignored. As raters gain experience, their relationships with their friend networks and their mutually held attitudes strengthen ([34]). This makes disagreements with the strong in-group ties more costly, lending relatively more weight to the opinions of friends. Stronger in-group ties encourage the formation of behaviors and attitudes that minimize in-group differences while maximizing out-group differences ([53]). As we posit in H2, experienced raters conform to their self-selected reference groups, thereby avoiding sanctions from the strong in-group network ([23]) and preventing the erosion of that group's shared identity ([ 5]). Conversely, experienced raters gain less value from following the crowd, as it is through divergence from the crowd that raters can signal expertise and opinion leadership ([27]; [40]; [49]). The combined result of these two competing influences is that experienced raters are less influenced by the crowd and are increasingly influenced by their friends, especially when these sources disagree. Formally,
- H4: When the divergence between crowd and friend ratings is high (vs. low), as rater experience increases, raters favor friends over the crowd such that (a) the crowd's herding influence decreases and (b) friends' herding influence increases.
A firm's historical product launches and product strategy can play an important role in building its reputation ([28]; [46]). One factor of success is past product production experience, where advantages come from both breadth and depth of the product line ([50]). Specifically, having several products concentrated within one category (i.e., depth) enables a firm to learn through repetition of tasks and processes. Offering a wide range of products across multiple categories (i.e., breadth) enables a firm to learn through variation ([ 4]; [41]; [48]). The depth and breadth of a firm's product portfolio, its product scope, helps build its category-level experience. Raters interpret this scope as a diagnostic measure of competence and use this information when making quality judgments. Therefore, as firm product scope increases, raters see this as a direct cue of product success. One may also view the firm product scope effect through the lens of assemblage theory ([15]; [16]), wherein "assemblages" refer to emergent wholes made up of heterogeneous components. Applying assemblage theory to the current context, a firm's product portfolio could be viewed as an assemblage of its various offerings that form its identity in the marketplace ([12]; [33]; [47]). A firm with a broad and deep portfolio of offerings, when compared with a firm with a narrow and shallow product portfolio, is able to achieve a clearer, well-integrated assemblage, thus resulting in a stronger and clearer identity in the minds of raters. As a result, firms are able to signal their competencies through their assemblages, leading to more favorable inferences from consumers. Given these theoretical arguments, we hypothesize the following:
- H5: Firm product scope is positively related to an individual's subsequent rating of the product.
Firm product scope provides another source of diagnostic information, albeit an external rather than an internal source, that acts in conjunction with herding. Following the theory of herding, firm product scope contributes to a rater's private information and weighs against herding effects ([18]). As firm product scope increases, the firm's identity is more clearly signaled and the relative diagnosticity of this information improves ([33]; [47]). The rater, thus, has more confidence in their judgment about a product and relies less on the herd's opinion. Formally,
- H6: As firm product scope increases, the crowd's influence on individuals' subsequent ratings decreases.
- H7: As firm product scope increases, the friends' influence on individuals' subsequent ratings decreases.
The empirical context of our research is the board gaming (i.e., tabletop gaming) industry. Despite operating alongside an increasingly digitized entertainment industry, board gaming has continued to grow. According to NPD Group, the board gaming industry grew 28% in 2016 and is currently valued at $9.6 billion ([10]). The industry consists of very large publishing firms, such as Hasbro (which publishes Monopoly), as well as smaller competitors, such as the recently popular Cards Against Humanity. Our data come from BoardGameGeek (BGG), the largest online discussion community for the board gaming industry. Raters log into the free website to research and rate board games. Often, individuals form communities by self-declaring "GeekBuddies," which is a proxy for friend networks.[ 9] On the game page, individuals can see the average rating (i.e., crowd effects) that the game has received as well as view the GeekBuddy ratings (i.e., friends' effects). In addition, they can learn about the publishers and genres under which a game is classified. Games are classified into eight genres, and similarly to the movie context, a specific game can be classified into multiple genres. For example, the game Catan is classified as a "family" game as well as a "strategy" game. In addition, multiple publishers can collaborate and launch a game in various regions with multiple publisher names appearing on the game box (e.g., Catan is published by KOSMOS, Mayfair Games, and 999 Games, among others[10]). In our data, 60.8% of games are published by multiple firms.
Given our research focus, we restrict our sample to those who have declared online friends. This produces a sample size of 44,108 individuals with 2,218,574 ratings. Furthermore, the data include 2,206 firms publishing over 5,138 games. The structure of the data is as follows: We observe multiple publishers offering multiple games, which are subsequently rated by multiple raters. Our data are at the individual level, in conjunction with publisher- and game-level descriptive information. Each rater can leave only one rating for each game, and our data have a panel structure with a time stamp for each rating.
The key dependent variable in our analysis is an individual's rating of a game. We define the dependent variable as individual i's rating of product j at time t. Individuals rate games on a continuous scale of 1–10, unlike conventional rating scales that are typically discrete. Although most individuals do stick to a discrete rating value, close to 20% of the data consists of decimals. This informs our modeling approach as we elaborate in the "Methodology" section.
The independent variables of interest in this research include information regarding the social effects in the perceived quality information (i.e., crowd and friend rating), rater-level factors that describe relative experience (i.e., number of prior ratings), and publisher-level factors that describe the breadth and depth of a firm's product portfolio (i.e., product scope). To capture the overall valence of opinion within the community, we measure as the average rating of all N individuals on the website who have rated game j prior to individual i's rating. Similarly, to capture the overall valence of opinion among friends, denotes the average rating level of game j by an individual's online friend network ( ) computed just prior to individual i rating the product. is the size of individual i's network and can change over time. On the website, individuals declare their friend networks, which we capture separately. This information allows us to reliably identify an individual's friend network without using behaviors or location-based measures. This method of determining reference groups has advantages in terms of identification ([35]) which we discuss in detail in the "Methodology" section.
Following [44], we measure a rater's cumulative experience ( ) as the number of ratings that the individual has submitted prior to time t. The rationale behind this measure of experience stems from the ideas that learning stems from consumption, and experience is known to be a particularly good proxy for consumer knowledge ([44]). Measuring rater experience in this way is also supported by the literature on experiential learning ([43]). That is, consumers, especially in online environments, gather knowledge and experience through repeated activity and learning by doing. In Web Appendix B, we test the robustness of the results using alternative definitions of rater experience—namely, average number of prior ratings and time since joining the site.
Our measure of a firm's product scope ( ) is derived from organizational learning literature, which posits that a firm's accumulated knowledge bases need to capture the breadth and depth of its product portfolio ([ 4]; [41]; [48]). Following [50] and [54], we measure the product scope ( ) as the product of the entropy of product offerings (breadth) and the number of products in the firm's product portfolio (depth) as follows[11]:
Graph
1
where
- f = publishing firm;
- c = product category;
- t = time;
- = proportion of the firm f's products in category relative to its overall product portfolio at time t; and
- = overall number of products offered by firm f at time t.
Our final covariate of interest is the measure of divergence of opinion between crowd and friends which will be used to test H3 and H4. We begin by defining the absolute value of divergence between crowd and friend ratings as . We define as a categorical variable that takes the value of 1 if is greater than its median and 0 otherwise.[12] Thus, denotes relatively high divergence between friend and crowd while describes relatively low divergence of opinion.
We include a rich set of control variables at the rater and game level as well as temporal and sequential/volume effects that may influence the relationships. Following [21], we include the time (elapsed) since the first rating to capture temporal effects and volume of crowd ratings to capture sequence effects. In addition, we control for the volume of ratings from the friend networks for game j until time t to control for any volume effects arising specifically from the friend reference groups. Volume of crowd and volume of friend ratings allow us to control for some of the "popularity" effects described in [31]. We also tested robustness of our results after including several two-way and three-way interactions between volume and valence that prior research has highlighted (Web Appendix B). As some raters may exhibit loyalty toward specific game publishers, we control rater–publisher loyalty by including publisher loyalty as the number of previous games from the publisher that the individual has previously rated. We include the number of friends the individual has in the online community at the time of rating to control for the size of one's social network.
In Table 2, we report the basic descriptive statistics for key variables in the data at the individual-product level, the individual level, and the product level. In our sample, an average individual has approximately 14.1 declared buddies/friends on the website. Given that the data span a period of over 10 years, we find that the average membership period is also quite high (≅7 years). However, the variation in membership length is also quite high, suggesting that there are several newer hobbyists interspersed within the older ones. Looking at the product-level descriptives, we find that the average number of publishers per game is 3.4, with a maximum of 141. Finally, we see that games are often classified into multiple categories (mean = 1.2), supporting our decision not to aggregate firm-level variables but to instead match a firm's accumulated experience with the product at the category level.
Graph
Table 2. Descriptive Statistics.
| Level of Analysis | Variable | M | SD | Min | Max |
|---|
| Individual level (44,108 users) | Number of ratings | 50.3 | 100.8 | 1 | 2,216 |
| Number of friends | 14.1 | 37.0 | 1 | 972 |
| Membership length (in months) | 80.4 | 41.5 | 0 | 190 |
| Product level (5,138 games) | Number of publishers per game | 3.4 | 4.7 | 1 | 141 |
| Number of genres per game | 1.2 | 0.4 | 1 | 3 |
| Individual-product level (2,218,574 ratings) | Rating | 6.9 | 1.5 | 1 | 10 |
| Crowd rating | 7.2 | 0.7 | 1 | 10 |
| Friends' rating | 7.1 | 1.3 | 1 | 10 |
| Product scope | 46.0 | 36.1 | 0 | 227 |
| Rater experience | 216.7 | 257.2 | 0 | 2,215 |
Figure 1 presents the distribution of the dependent variable and highlights the ratings of two popular games: Catan and Monopoly. Catan (average rating = 6.95) was much better received by the board gaming community than Monopoly (average rating = 4). As we can see from Figure 1, there is significant variation across games in terms of how individuals rate them. The distribution of ratings in our sample is consistent with previous work in online ratings ([13]; [31]; [32]; [51]).[13] The median individual rates 16 games (mean = 50.3 games), which may seem high but is realistic given the long time series span of the data (seven years per rater, on average). There is a large variation in the number of ratings provided (Figure 2).
Graph: Figure 1. Distribution of ratings.
Graph: Figure 2. Distribution of rating frequency (per individual).
Table 3 presents the full correlation matrix for all the variables used in the model. In a model-free sense, Table 3 suggests a positive correlation between crowd and friend ratings with the focal rating. In addition, product scope ( ) is also positively correlated with rating valence. In the following subsection, we present more model-free evidence of patterns in the data that speak to the effects that we describe in this article.
Graph
Table 3. Correlation Matrix.
| Variable | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) |
|---|
| 1. Rating | 1 | | | | | | | | | |
| 2. Crowd rating | .444* | 1 | | | | | | | | |
| 3. Friends' rating | .381* | .363* | 1 | | | | | | | |
| 4. Product scope | .134* | .103* | .085* | 1 | | | | | | |
| 5. Rater experience | −.086* | −.064* | −.073* | .072* | 1 | | | | | |
| 6. Rating order | −.101* | −.255* | −.132* | −.029* | −.148* | 1 | | | | |
| 7. Volume of friends' ratings | .021* | .020* | −.013* | −.037* | .314* | .088* | 1 | | | |
| 8. Time since the first rating | .071* | .255* | .131* | .265* | −.063* | .366* | .124* | 1 | | |
| 9. Publisher loyalty | .006* | .015* | .001* | .207* | .373* | −.098* | .079* | −.064* | 1 | |
| 10. Number of friends | −.082* | −.075* | −.079* | −.036* | .487* | −.146* | .387* | −.056* | .196* | 1 |
2 *p <.05.
In Figure 3, we plot an individual's rating of games at time t against the lagged average ratings of the two herding groups: the crowd and friends. In general, there is a positive correlation between crowd and friends' ratings with the focal rater's rating, suggesting that herding may indeed be prevalent in the data.
Graph: Figure 3. Notes: The solid line in both panels denotes the ordinary least squares regression line.
However, the rater does not always agree with the crowd or friends. To illustrate this, Figure 4 plots the individual correlations: rater versus friend and rater versus the crowd. The y-axis denotes the rater–friend correlation coefficient and the x-axis denotes the rater–crowd correlation coefficient, each computed at the individual level. On average, herding appears to exist in the data. However, there are raters who conform strongly with the crowd and others who conform strongly with friends. The question then becomes, What factor might drive this behavior? We posit that one such factor may be rater experience.
Graph: Figure 4. Correlations between rater and reference group at the individual level.
To explore this, we computed the mean absolute deviation (MAD) between the rater and the crowd/friends since the first month that the rater joined the website. If our hypotheses hold, as raters become more familiar and gain experience in rating products, they should deviate more from the crowd and less from friends' ratings. To visualize this in a model-free sense, we plotted the MAD between rater and the crowd/friends over time in Figure 5. As time progresses, raters deviate more from the crowd and less from friends, suggesting model-free support for our conceptual framework.
Graph: Figure 5. Deviation from reference group (crowd/friend).
Of course, this model-free evidence is correlational at best, not causal. We need a robust methodology to causally identify the proposed herding effects. In the following section, we present our empirical strategy, the data variation that allows for identification, and the modeling approach used to estimate our hypothesized effects.
We model rating behavior using a linear specification that approximates it as a reduced form of the underlying data-generating process. A linear specification is more appropriate than an ordinal logit- or probit-style model because ratings in our data are not strictly discrete. Raters occasionally enter decimal values for their rating of games, thus violating the typical discrete nature of the dependent variable.[14] Furthermore, our objective is to explain ratings behavior and not to predict specific game ratings. As such, a linear regression is an approximation of the conditional expectation function even when the distribution of ratings may be discrete ([ 3]). Finally, our conceptual framework and corresponding hypotheses require the use of interaction variables. Interpretation in nonlinear models is not straightforward and is especially complicated in the presence of interaction effects ([ 1]). Given this, in the spirit of parsimony, we adopt a linear model to explain rating behavior. Thus, indexing the rater by i, product by j, and time with t, we can model rating valence ( ) as follows:
Graph
2
where
- and = the average rating for the crowd and the rater's friend network for product j up until time t,
- = product scope of firm f at time t,
- = rater i's cumulative experience in rating products (number of ratings provided) until time t,
- = indicator variable describing the level of divergence between crowd and friend ratings, and
- = unobserved factors that shift an individual's rating.
The model is specified at the rater-game level; when a rater rates a product j, the covariates used are friend rating valence and crowd rating valence for the game. In Equation 2, β1–β5 capture the direction and magnitude of the main effects, while δ1–δ8 describe the effects of the moderators (product scope, rater experience, and divergence of opinions). δ1 and δ2 capture the moderating influences of rater experience on herding (H1 and H2), while δ3–δ6 assess the role of divergence of opinions on rating behaviors. The two-way interactions inform how the herding effect may be amplified or attenuated when there is divergence between friend and crowd (δ3 and δ4). Specifically, this shows whether raters rely more heavily on friends or on the crowd when divergence exists (H3). Similarly, three-way interactions between and provide insight into who experienced raters favor in the event of divergence between friends and the crowd (δ5 and δ6) (H4). β3, δ7, and δ8 capture the main and moderating effects of product scope (H5, H6, and H7). In the following section, we discuss key identification challenges that must be addressed to causally infer the herding effects described in Equation 2.
To establish causality, we need to address key endogeneity concerns that arise in studying friend effects due to the reflection problem ([35]). Specifically, we need to address three main issues that confound the identification of causal peer effects: endogenous group formation, simultaneity, and other correlated unobservables. Before introducing and addressing these, we first comment on how we determine reference groups.
The first important challenge that a researcher faces in identifying a peer effect in nonexperimental data is the ability to clearly determine reference groups. That is, we need to identify the proper friends' networks for each agent exogenously, without using the behavior itself as a measure of reference groups ([35]). Using behaviors to group users introduces an upward bias in the peer effects, while using geographic or location based grouping methods confounds the peer effects with other correlated unobservables. We make use of "sociometrics" in identifying reference groups to overcome these issues ([27]; [40]). Fortunately, in our data, raters self-identify their website friend networks, thus allowing us to determine reference groups without having to make any assumptions on friendships through geographic or behavioral similarities. Next, we discuss the identification issues that arise when studying herding effects using observational secondary data.
Although the exogenous measure of reference groups resolves the issue of group determination, it does not address the endogeneity that can exist due to group formation. Endogenous group formation arises if raters self-select into reference groups due to similarities in tastes with friends. It is possible that the observed rating behavior is correlated with the friend behavior simply because the friends and the rater share common tastes that led to the formation of the friendship in the first place. As such, the unobserved part of a rater's behavior ( ) may be correlated with the peer effect ( ), resulting in bias due to endogeneity. Following guidance from [24], we address the endogenous group formation issue by exploiting the panel structure of the data and specifying fixed effects at the individual level ( ). The individual fixed effect controls for the part of the random error that is related to the common tastes that a rater shares with their reference group ([40]). In essence, the variation caused by common tastes among raters within the same reference group is removed through the individual fixed effect.
Social effects cannot be identified if the focal individual's ratings both influence and reflect peer ratings. This simultaneity problem makes it difficult for us to distinguish between the individual's effect on their peers versus the peers' effect on the individual. We adopt a three-pronged approach to address simultaneity. First, we leverage the panel nature of the data and impose temporal ordering between the peer covariate ( ) and the rater's rating ( ). In Equation 2, we ensure that is computed until time t (not including the current time period), thus ensuring that this relationship is not reversible. Second, we exploit the network formation timing and force temporal ordering such that a friend's influence is included only after tie formation. This is distinct from [31], who were unable to observe time of friendship formation and acknowledge this as a limitation. Although temporal ordering of friend rating and tie formation controls for observable sources of simultaneity between peers and the individual, it does not account for the potential unobservable cues between individuals that may still cause simultaneity. Temporal ordering alone does not solve the simultaneity issue. Therefore, we use instruments with clear exclusion restrictions to address endogeneity arising from the peer variables.
To be a valid instrument, the proposed instrument candidate should satisfy criteria of exclusion and relevance. The exclusion criterion requires that the instrument be uncorrelated with the error term in Equation 2. Given the rich network information in the data, we exploit the availability of partially overlapping pairs or intransitive triads ([11]) to create exclusion restrictions that allow reliable identification of the peer effect. The intuition behind our instrument strategy is that the characteristics of friends' friends who are not also friends of the focal individual act as instruments for controlling endogeneity in the reflection problem. We illustrate this using a hypothetical five-person network ( ) in Figure 6. An intransitive triad exists when individual A is friends with B and C, but not D and E. In addition, B and D are friends and B and E are friends. We refer to as second-degree peers of A, and the networks A, B, D and A, B, E form intransitive triads. These intransitive triads create an identifying condition whereby characteristics of individuals affect A only through individual B, thus satisfying the exogeneity condition of the instruments. As long as individuals D and E are not friends of individual A, the exogeneity condition is satisfied.[15] Web Appendix C provides a visual representation of the network for two users in the data and highlights second-degree peers and overlapping peers.
Graph: Figure 6. Illustration of intransitive triads (or) partially overlapping groups.
The relevance criterion requires that the instrument be correlated with the peer covariate's rating behavior ( ). We use four characteristics of friends' friends (e.g., individuals D and E in Figure 6) as instruments for : ( 1) second-degree friends' volume of ratings, ( 2) second-degree friends' membership length, ( 3) second-degree friends' average network size, and ( 4) second-degree friends' declared groups/guilds. From a relevance standpoint, these variables are relevant to because they influence valence of ratings. We empirically verify this using an instrument relevance test as well. To elaborate how the instruments are calculated, we refer to the example network described in Figure 6. Ignoring the t subscript for this static example, let denote the vector of instruments (describing characteristics of nodes ) used in the regression. As we explained in the previous paragraph, because nodes D and E are second-degree peers of A, their characteristics can be used as instruments for A's behavior. That is, the average characteristics of friends' friends ( ) is used in instrument variable regression for node A. In addition, following [11], also enters the main regression equation for node A as a control variable. In the estimation, we adopt the control function approach to incorporate the instruments within the model framework. Specifically, the endogenous variable ( ) is expressed as a function of the instruments (denoted by ) and the residuals from this regression ( ) are then introduced into Equation 2 as covariates for endogeneity correction.
Next, we address the data variation that allows identification of the hypothesized friend effects. Identification comes from data variation in the network size. Figure 7 presents evidence of significant data variation that allows reliable identification. Panel A in Figure 7 describes the distribution of friend network size across individuals and suggests that there is sufficient cross-sectional variation to aid identification. Raters have, on average, 14.1 friends, with a standard deviation of 37. Panel B in Figure 7 plots the friend network growth over the tenure of the individual. As we see, most people form the majority of their friend networks within the first few months of joining; the network size after initial formation is relatively stable. Taken together, these patterns suggest sufficient cross-sectional variation in network size to identify the peer effects through exclusion restrictions arising from second-degree peers.
Graph: Figure 7. Variation in friend networks.
A final concern is that there could be correlated, unobservable variables that influence the rater and peers simultaneously. A common approach to address correlated unobservables is to include a rich set of fixed effects. By including rater-level fixed effects ( ), we control for the across-rater variation and use only within-rater variation for identification. Furthermore, as we elaborated previously, the individual fixed effects also help account for unobserved common tastes shared among raters within a reference group. Moreover, there could be correlated unobservables arising from game-level factors that influence the rater and reference groups simultaneously. These factors can reveal themselves as peer effects. By including game-level fixed effects ( ), we control for any across-game variations that may be causing endogeneity. We include year fixed effects ( ) to control for any macro-level trends such as website popularity that could influence the effects within the model. In addition, as we elaborate in the "Measures" subsection, we control for several game-level, rater-level, friend network–level, and temporal factors highlighted in prior work that may confound the findings. This not only controls for confounds but also demonstrates evidence of our proposed effects over and above what prior research has established.
We decompose the unobserved factors ( ) in Equation 4 into individual fixed effects ( ); game-level fixed effects ( ); year fixed effects ( ); control variables (a row vector of control variables) and θ (a vector of parameters), where K is the number of control variables in the sample; and endogeneity correction ( ) as follows:
Graph
3
Notably, consists of all the control variables described in the "Measures" subsection as well as first-degree friend network–level controls as suggested in [11]. Substituting Equation 4 into Equation 3, we get the model equation for rating behavior.
Graph
4
Equation 4 needs a few modifications to avoid estimation of an intractable number of parameters. As described previously, we difference out the game-level fixed effects rather than estimate the parameters. That is, we subtract the game-level means from each variable in the right-hand and left-hand side of Equation 4. After we subtract the game-level means, the game-level fixed effects ( ) drop out of the equation. Denoting the covariates on the right-hand side of Equation 5 as , we can write the differenced equation as
Graph
5
Equation 5 uses fixed effects linear panel data specifications, and the year fixed effects are included after differencing. Next, we describe the estimation results and demonstrate the robustness of the results to alternative variable operationalizations, model specifications, and data considerations.
The main findings are organized as follows. We begin by presenting the results describing the main effects (i.e., crowd, friends, and product scope) and the moderating effects (i.e., product scope, rater experience, and divergence of opinions) on the focal rater's behavior. We estimate a series of regressions progressively adding complexity through observed and unobserved heterogeneity and show the consistency of the results throughout. Finally, we conduct a series of robustness analyses to ensure that the findings are robust to alternative specifications such as the inclusion of a rating incidence model and an ordered probit estimation.
Table 4 presents the results from the estimation of the fixed-effects linear specification outlined previously. We estimate four nested models for consistency: Model 1 (main + moderation effects only), Model 2 (main + moderation effects with observed heterogeneity), Model 3 (main + moderation effects with observed and unobserved heterogeneity [individual fixed effects]) and Model 4 (main + moderation effects with observed and unobserved heterogeneity [individual + game-level fixed effects]). We include year fixed effects in all the models. Notably, Model 1 ignores the endogeneity problem because it does not include the control function or any fixed effects, which helps us isolate the friend effect.
Graph
Table 4. Main Estimation Results.
| Variable | Hypotheses | Model 1 | Model 2 | Model 3 | Model 4 |
|---|
| Estimate | SE | Estimate | SE | Estimate | SE | Estimate | SE |
|---|
| Main Variables of Interest | | | | | | | | | |
| Crowd rating | | .688*** | .007 | .734*** | .007 | .845*** | .006 | .431*** | .013 |
| Friends' rating | | .322*** | .007 | .400*** | .005 | .264*** | .005 | .188*** | .004 |
| Rater experience | | −.0005*** | .00002 | −.0006*** | .00003 | −.0007*** | .00003 | −.0005*** | .00001 |
| Divergence between friends and crowd | | −.126*** | .011 | −.164*** | .020 | −.125*** | .019 | −.023*** | .002 |
| Crowd rating × Rater experiencea | H1 (–) | −.149*** | .018 | −.132*** | .015 | −.212*** | .015 | −1.008*** | .052 |
| Friends' rating × Rater experiencea | H2 (+) | .049*** | .015 | .028*** | .006 | .096*** | .013 | .163*** | .021 |
| Crowd rating × Divergence between friends and crowd | H3a (+) | .146*** | .007 | .129*** | .007 | .071*** | .006 | .048** | .014 |
| Friends' rating × Divergence between friends and crowd | H3b (–) | −.127*** | .007 | −.107*** | .006 | −.056*** | .005 | −.031*** | .004 |
| Crowd rating × Rater experience × Divergence between friends and crowda | H4a (–) | −.064*** | .017 | −.034** | .012 | −.074*** | .012 | −.042** | .015 |
| Friends' rating × Rater experience × Divergence between friends and crowda | H4b (+) | .052*** | .016 | .042*** | .011 | .090*** | .012 | .047*** | .009 |
| Product scope | H5 (+) | .008*** | .0003 | .009*** | .0003 | .008*** | .0003 | .079*** | .001 |
| Crowd rating × Product scope | H6 (–) | −.001*** | .00004 | −.001*** | .00004 | −.001*** | .00004 | −.015*** | .003 |
| Friends' rating × Product scope | H7 (–) | −.0003*** | .00002 | −.0003*** | .00002 | −.0003*** | .00002 | −.005*** | .0005 |
| Control Variables | | | | | | | | |
| Rating order/volume of crowd ratingsa | | | −.009*** | .0003 | −.015*** | .0002 | −.007*** | .0003 |
| Volume of friends' ratings | | | .0002** | .0001 | .002*** | .0001 | .002*** | .0001 |
| Time since the first ratinga | | | .019*** | .001 | .033*** | .001 | .039*** | .002 |
| Publisher loyalty | | | .003*** | .0002 | .004*** | .0002 | .005*** | .0002 |
| Number of friendsa | | | .478*** | .020 | .238*** | .047 | .157** | .046 |
| Average network size of friendsa | | | .040* | .016 | .019* | .009 | .037* | .017 |
| Number of groups friends are part of | | | −.003** | .001 | −.002* | .001 | .001 | .001 |
| Friends' average length of membershipa | | | −.020*** | .001 | .005*** | .001 | .002 | .001 |
| Endogeneity correction | | | −.111*** | .002 | −.054*** | .003 | −.022*** | .001 |
| Intercept | −.294*** | .016 | −1.114*** | .030 | −.835*** | .028 | −.070*** | .013 |
| Individual-level fixed effect | No | No | Yes | Yes |
| Game-level fixed effect | No | No | No | Yes |
| Year fixed effect | Yes | Yes | Yes | Yes |
- 3 *p <.05.
- 4 **p <.01.
- 5 ***p <.001.
- 6 a The coefficients and standard errors are rescaled (i.e., multiplied by 1,000) to improve readability.
- 7 Notes: All standard errors are bootstrapped and clustered at the individual level.
We first comment on the validity of the instruments. Table 4 shows that the endogeneity correction term is significant and negative across all the models. This provides some evidence that endogeneity is most likely an important concern that needs to be addressed in this context. We also conduct two tests to assess instrument strength and validity. First, we conduct an instrument relevance test using the results from the first-stage instrument regression (reported in Web Appendix D). The F-statistic is highly significant (F-statistic = 5,144.93, p <.001), which suggests that that the chosen instruments do not suffer from the weak instruments problem. Second, because we have more than two instruments to address one endogenous covariate, we also test for overidentifying restrictions. The Hansen J statistic of 1.69 is not significant (p >.10), thus indicating that overidentification is not a serious issue with our instruments.
We find that significant herding from the crowd exists across all the estimated models. That is, an increase in the crowd's rating of a product leads to an increase in the focal rater's rating of the same product ( =.431, p <.001). Similarly, herding is evident among friends. The rating patterns of the friends' network has a positive, significant effect on the individual ( =.188, p <.001).[16] Taken together, the results provide evidence of herding influence in online rating behavior; individuals perceive wisdom in both the crowd and friends. An increase in crowd (friend) rating by 1 point leads to a.431- (.188-) unit increase in the subsequent rating. Interestingly, the results suggest that the friend effect is smaller than the crowd influence (also indicated in Figure 3). To explore this in more detail, we conducted a Wald test. The null hypothesis for the Wald test is that the coefficients for and are equal. The F-statistic of the Wald test is significant (F-statistic = 288.98, p <.01), thus rejecting the null hypothesis that the coefficients are equal. This suggests that the crowd effect is significantly greater than the friend effect after we control for volume of ratings (among other factors). One possible explanation is that this result is driven by the specific website design. As in many rating platforms (e.g., Yelp), BGG's crowd rating is more predominantly displayed and, thus, more salient than friend rating information.
In support of H5, we find that a firm's product scope has a positive effect on the rater's own evaluation of the product ( =.079, p <.001); the greater the firm's scope of product portfolio, the higher the rater's evaluation of its products. Thus, the firm's own product line strategy can act as a signal of its competence and achieve favorable quality ratings online. It is important to note that the diagnostic information here is not a firm's promotion strategy (e.g., advertising) but simply its product line strategy. Finally, although we did not hypothesize an effect, rater's own experience has a negative main effect on the subsequent evaluation ( = −.0005, p <.001), in line with prior research ([36]; [49]). One potential explanation is that raters who consider themselves "experts" try to signal their identity by posting more negative opinions. These highly experienced gamers are much more confident of their own quality inferences about games and are thus likely to be more critical and strict in their evaluations. Finally, although not hypothesized, we find that raters adjust their evaluations of products downward when disagreement between the crowd and friends is high (vs. low; = −.023, p <.001), suggesting that mixed opinions lead to stricter ratings.
Turning to the interaction effects presented in Table 4, some interesting patterns emerge. We theorized that the diagnosticity of herding would be influenced by rater- and firm-level factors. We find that the rater's own experience and the firm's relevant product scope significantly moderate the herding effect from the crowd. In support of H1, we find that the individual's own rating experience negatively moderates the relationship between crowd rating and the individual's rating ( = −.001, p <.001). As raters rely more on their own experience, they rely less on the wisdom of the crowd, and the herding effect is attenuated. However, greater rater experience amplifies the social influence of friends ( =.0002, p <.001), confirming H2. As raters gain more experience, they not only know their own preferences better but also learn to listen more to like-minded friends and less to the crowd.
We investigate how divergence of opinion between key reference groups influences rating behavior. Furthermore, we examine whether the rater's experience again plays a moderating role. In support of H3, we find that raters increasingly favor crowd ratings and decreasingly favor friends' ratings when disagreement exists between the two ( =.048, p <.01 = −.031, p <.001). This is consistent with the greater herding influence of the crowd and may be driven by the "cost of divergence" ([ 6]; [ 9]), as raters do not want to appear to contradict the crowd. Turning to the three-way interaction with rater experience, we find support for H4. That is, more experienced raters tend to decreasingly favor the crowd's rating ( = −.00004, p <.01) but increasingly favor friend ratings ( =.00005, p <.001) when disagreement exists between the two. This result further bolsters our initial finding that as raters gain experience in rating products, they coalesce with their friend group more easily than with the crowd, in an effort to indicate identity ([ 7]) and conform to group norms within strong ties ([19]; [23]). Taken together, this analysis provides a unique insight into how raters behave when faced with mixed signals from reference groups. We demonstrate that diverging opinions can create herding and differentiation depending on the reference group and the experience level of the rater.
We find support for H6, as the firm's product scope negatively moderates the crowd effect ( = −.015, p <.001). Raters view the firm's product scope as diagnostic information and are inclined to discount the wisdom of the crowd. The firm's product scope also negatively moderates the relationship between friends' rating behavior and own rating behavior ( = −.005, p <.001), thus confirming H7. With the main effect of product scope, these results underscore the importance of product line management, not just for bottom line growth but for improving quality perceptions as well. From the manager's perspective, a firm's product portfolio acts as strong diagnostic information and helps attenuate herding effects. While, historically, firms have viewed herding as beyond their control, we demonstrate that the effectiveness of herding can be influenced by firm actions through product portfolio decisions.
Overall, the results demonstrate that although herding effects exist in online opinion formation, the source matters. We find that the crowd effect on an average rater is greater than that of the friend effect and that herding effects are not always consistent. We identify key rater-level and firm-level factors that govern the role of herding in online opinion formation. On the rater's side, the results show that a rater's experience positively moderates the friend effect and negatively moderates the crowd effect. As raters gain more experience, they find value in conforming with like-minded friends and diverging from the more general crowd. The herding and differentiation effects are even more apparent when there is disagreement between reference groups. That is, we find that an average rater coalesces with the crowd's opinion more than with friends when faced with mixed signals. However, experienced raters differentiate from the crowd and conform to friends' opinions. On the firm's side, we show that the effect of herding is influenced by firm actions. Specifically, the results highlight the value of a firm's product portfolio in online rating environments. Even in the absence of promotional messaging or advertising, a firm can influence the perceived quality of its product through its product strategy both directly and indirectly. We find that, in addition to a main effect on online ratings, the firm's scope of expertise attenuates the herding influences from crowds and friends.
Consistent with prior work, we find a negative influence of rating order/volume of crowd ratings ( = −.00001, p <.001) but a positive influence of temporal effect on user ratings (time since first rating; =.039, p <.001) ([21]; [32]; [55]). That is, the average rating level of a game is shifted downward as more ratings arrive and upward as more time passes, suggesting that the results are not simply driven by rating distributions among the crowd and friends or solely by contextual factors. We find that the volume of ratings from the friend networks has a positive relationship with the user ratings, which indicates that when there are many friends who have rated the game, the rater is more positive toward a specific game ( =.002, p <.001). Furthermore, we find that rater–publisher loyalty has a positive effect on rating behavior, suggesting that firms should work toward building loyalty among the user base. The positive loyalty effect also suggests that raters are quite aware of and have clear preferences about publishers ( =.005, p <.001). Rating valence is not only influenced by the volume of friends' ratings; the size of the social network (i.e., the number of friends) also has a significant positive influence on subsequent rating ( =.0002, p <.05; [57]). In summary, the results strongly support the hypotheses proposed in the conceptual framework.
In view of the multiple modeling, data considerations, and variable operationalization decisions in our analysis, we conduct several robustness checks to ensure that our findings are not an artifact of the choices we made. We discuss these checks briefly here and report the full analyses in the Web Appendix. The robustness checks can be broadly classified into ( 1) alternative variable operationalizations, ( 2) alternative modeling considerations, and ( 3) sample considerations.
Our first set of robustness checks deals with variable operationalization. Specifically, we aim to demonstrate robustness of the results to alternative measures of rater experience, product scope, and the variable. In the main results, the friend rating information was included within the computation. We rerun the model after removing the friend information from the variable and find that the results remain virtually unchanged. Next, we rerun the model considering two alternate measures of rater experience: the average number of prior ratings and time since joining the website. The results continue to be qualitatively consistent. Finally, we estimate the model using three additional measures of a firm's product portfolio: relevant product scope to capture the within-category product relevance to the game being evaluated, overall entropy to capture the breadth of a firm's product portfolio, and depth of a firm's product portfolio. Again, the estimation results remain robust. We present all model results of alternative operationalization in Web Appendix B.
Our second set of robustness checks concerns model specifications. The results may be influenced by selection bias because rating valence could be correlated with an individual's propensity to rate (i.e., rating incidence). To address this concern, we employ a Tobit II estimation in which the first stage is a rating incidence model that precedes the rating valence model. Next, to examine the robustness of our results to functional form, we replicate the results using an ordinal model specification. Ignoring the continuous nature of the dependent variable in our context, we round the individual ratings to the nearest discrete value and then estimate an ordered probit model. Finally, to account for the possibility that firm-level heterogeneity is influencing the results, we reestimate the model with firm-level fixed effects. All models replicate the main findings, and we report full estimation results in Web Appendix E.
Our final set of robustness analyses involve relaxing sample considerations and including additional control variables. First, we reestimate the model after including raters with no friends in the estimation sample. Although, this does not allow us to test the friend effect, it provides a robustness check for the crowd effect. The results remain qualitatively unchanged (see Web Appendix F). Next, we include an additional source of observed heterogeneity: friends' experience level. Though not hypothesized, we find that the interaction of friends' rating with average experience of friends is significant and positive. Raters are more likely to be influenced by friends when the friend network is more experienced. We also conduct a series of additional analyses investigating whether the effect of rater experience varies by the volume of ratings and synergies between friend and crowd herding effects. Throughout these analyses, we replicate our main findings. Due to page restrictions, we report and discuss these additional analyses in Web Appendix G. We also test for the possibility that a rater may not be aware of a firm's product portfolio through the website.[17] We rerun the main regression model considering only raters who had rated a publishers' game prior to the focal game rating, thus ensuring that the raters did indeed have some prior knowledge about the publisher. Although not perfect, this condition gives us more confidence that only raters who are aware of the publisher are included in the model. The results (presented in Web Appendix F) remain robust. Finally, we explore the possibility that, given the skewness in rating frequency, the results may be driven by outliers in the data. We reestimate the model after dropping the top 1% of raters in terms of rating frequency. The results, reported in Web Appendix F, remain qualitatively unchanged.
As an avenue for consumers to express their opinions and evaluations about products, online ratings have become a staple component of the customer experience. In this article, we carefully unpack herding into friend and crowd effects using a rich data set of board gamers' ratings and offer a nuanced view of herding in online rating environments. Our identification strategy exploits the timing of tie formation and exogenous variation created through partially overlapping network pairs ([11]) to parse out the friend effect. We put the findings through a battery of tests and demonstrate that our results are robust to alternative variable measurement, modeling choices, and sample considerations. Next, we summarize the key takeaways from this research and place our study in context within extant literature.
- There is wisdom in the crowd and among friends, but source matters: While not strictly a contribution, our results add to growing evidence (e.g., [31]; [57]; [56]) highlighting the importance of separating the herding influences of the crowd and friends, thus underscoring the role that in-groups and out-groups play in online ratings. We find multiple herding effects on online ratings that are positive and significant; there is indeed wisdom to be found in both the crowd and friends. However, the source matters. On average, crowds exert a stronger herding influence on the average rater.
- Brands/firms influence online opinion through their product portfolio in profound ways: We uncover key boundary conditions under which herding effects may be attenuated or amplified. Notably, we contribute to the product strategy literature ([28]; [45]; [50]) by demonstrating how a firm's product line strategy can used as a positive signal in product evaluations. The depth and breadth of a firm's product portfolio acts as a strong proxy for firm competence. We apply this stream of thought to online ratings and demonstrate that consumers take note of product scope and this directly influences rating behavior. We show that a firm's product strategy creates advantages even in online rating forums and can significantly attenuate herding.
- Social influence varies by expertise/experience: Although prior research on opinion leadership suggests that experienced users tend to differentiate from existing opinions ([27]; [40]; [49]), we find that a more nuanced view is required when studying the role of rater experience in social contexts. Specifically, we find that rater experience creates herding and differentiation depending on the strength of the social bond. Experienced raters discount the crowd but continue to herd toward their friends.
- Diverging opinions between reference groups create herding and differentiation: Much extant work studying dispersion or disagreement in online opinions has focused on aggregate measures of "variation" in word of mouth (e.g., [20]; [39]; [52]). Less is known about how diverging opinions between specific herding groups influence opinion. Our research finds that divergence between friend and crowd ties can create herding and differentiation, depending on the experience level of the rater. More specifically, we show that when the two reference groups disagree with each other, experienced raters coalesce more on friends' rating more than with the crowd. We believe this is the first research to show this phenomenon in a large-scale empirical analysis.
To visualize the effects presented in the article, we plot the marginal effects of herding at different levels of rater experience and firm product scope (see Figure 8). On average, we find that a 1-unit increase in crowd (friend) ratings will lead to a.43- (.19-) unit increase in the focal rater's rating represented by the dotted line in Figure 8, Panels A, B, C, and D. Panels A and B present the unit change in the crowd and friend effects for an increase in rater experience. As rater experience increases, the crowd effect clearly decreases (Panel A) but the friend effect increases (Panel B). Furthermore, the effects are most pronounced at the extremes. When a rater is highly experienced, the positive influence of the crowd on rating behavior weakens and the positive influence of the friend strengthens. That is, for a rater in the 95th percentile of experience level, a 1-unit increase in crowd rating is expected to result only a.13 rating points increase in rating valence (as opposed to a.43 rating point for the average rater). In fact, the confidence interval at the 95th percentile includes zero. Turning to the friend effect (Panel B), for a rater in the 95th percentile of experience level, a 1-unit increase in friend rating valence is expected to increase the focal rater's rating by.26 rating points (as opposed to a.19 rating point increase for the average rater). In contrast, for novice raters who have low rater experience (5th percentile), a 1-unit increase in crowd (friend) rating leads to approximately.59- (.16-) unit change in rating. Clearly, novice raters value the crowd much more than experienced raters.
Graph: Figure 8. Notes: The y-axis is the change in the herding effect measured as unit change in rating points. The dotted lines denote the marginal effect of the crowd (Panels A and C) and friends (Panels B and D) holding all other variables at their respective means. For a 1-unit change in crowd (friend) ratings, the corresponding change in the focal user's rating is.43 (.19) rating points. The points on the figure can be interpreted as the effect of the herding source at the corresponding moderating variable value. For instance, in Panel A, for a rater in the 95th percentile of rater experience, a 1-unit increase in crowd rating valence is expected to increase a focal rater's evaluation by only.13 rating points, whereas for a rater in the 5th percentile of rater experience, a 1-unit increase in crowd rating valence is expected lead to a.59 rating points increase in the focal rater's rating. Panels B, C, and D are interpreted similarly.
Turning to the effects of firm product line scope (Panels C and D in Figure 8), we see the negative relationship between herding and a firm's product scope. When a firm cannot provide cues of domain competence to compete with herding (i.e., low product scope in Panels C and D), the positive influence of the crowd on an individual's rating behavior is strengthened compared with the average effect. In comparison, when a firm demonstrates domain competence through product scope (i.e., high product scope), the herding influence of the crowd on an individual's rating behavior weakens significantly. We find a similar pattern for the herding influence of friends.
This research provides actionable guidance to managers concerned with online reputation management, product strategy and planning, and the design of online rating platforms. We expand on these topics in the following subsections.
Given the increased recent interest in review solicitation in online rating systems ([29]), our results suggest that even in the absence of conventional advertising strategies, firms can leverage reputation effects in ratings by strategically targeting their review solicitations. For instance, firms can get ratings that are more objective by targeting experienced raters as they are less influenced by the herding effect. Alternately, if managers are trying to ride a positive bandwagon effect, then new or less experienced reviewers should be targeted. The findings also have implications for reputation management when combatting negative online word of mouth as herding effects can be a double-edged sword. When faced with predominantly negative ratings, firms can try to identify and solicit favorable highly networked reviews to offset the effects of the average consumer. Finally, this research provides managers with guidance on where word of mouth is likely to be more impactful. A firm is more likely to be affected in product categories where their product lines are less deep, suggesting that when venturing into noncore products, firms should more closely monitor for online herding effects.
We demonstrate that a firm's product scope is critical in influencing online rating behavior. Firms with greater product scope gain both directly and indirectly in terms of product evaluations. This provides firms with another example of the advantages of a product line that is both broad and deep. It also presents a dilemma for firms to consider: when one goes too broad, it becomes increasingly difficult to develop depth across multiple categories. Still, this work demonstrates the value of a "branded house"—firm scope not only increases the possibility of favorable online reviews but also can act as a counterbalance to the herding effect.
This research could have significant impact on online rating system designs. We find evidence of herding in online ratings, thus reducing the "objectivity" of online product evaluations. That is, herding from the crowd and friends can introduce some amount of bias in a user's online rating level. If the goal of an online rating system (e.g., Yelp, the Internet Movie Database) is to ensure independent, nonbiased reviews, then our results add to extant work that suggests the contrary often manifests ([31]; [38]). From a generalizability standpoint, we expect that the findings would be consistent for most medium- to high-involvement product categories (e.g., movies, restaurants, books, hotels, travel, health care; [26]). In a highly engaged and connected marketplace, consumers are increasingly turning to these online rating platforms to judge the "quality" of various services. This research adds to a growing body of work on herding effects in rating behavior by investigating contingent factors, both firm-controllable and rater-level, that may actually govern the herding effect. Finally, advertisers can use these results to decide what kind of social and product information to display to users when they rate products.
Although the models and data utilized are rich and robust in many ways, our analysis does have its limitations. First, although we use various reduced-form econometric techniques to control for network formation, we do not formally model it. A structural model of network formation combined with the rating model presented here could create opportunities for interesting counterfactuals that we do not examine here. Second, there could be some underreporting of friend networks in our data. Although we expect that the inclusion of these data would only strengthen the results, we acknowledge this as a data limitation. Third, our measures of rater experience consider only online experience and are agnostic to offline knowledge gathering. As such, our results need to be interpreted in relative terms. Fourth, like many others before us, this research was conducted using data from one firm, in one industry. As such, future investigations could very easily adapt the proposed framework to different contexts and perhaps conduct a cross-industry study of herding effects. In addition, the data context, which is primarily offline consumption, does not include information on offline friendship networks that may also influence behaviors. A promising avenue for future research would be to compare the role of offline and online reference groups in online opinion formation. Finally, this study used only ratings and did not consider review text. While we expect that factors such as the complexity of ideas or the length of written reviews may affect subsequent ratings and provide alternative measures of rater experience, our data do not allow us to measure this. We leave such opportunities to future research.
Supplemental Material, DS_10.1177_0022242919875688 - What Drives Herding Behavior in Online Ratings? The Role of Rater Experience, Product Portfolio, and Diverging Opinions
Supplemental Material, DS_10.1177_0022242919875688 for What Drives Herding Behavior in Online Ratings? The Role of Rater Experience, Product Portfolio, and Diverging Opinions by Sarang Sunder, Kihyun Hannah Kim and Eric A. Yorkston in Journal of Marketing
Footnotes 1 Associate EditorWendy Moe
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 ORCID iDSarang Sunder https://orcid.org/0000-0002-2875-9209
5 Online supplement: https://doi.org/10.1177/0022242919875688
6 1A note on terminology. We use the general term "herding" to describe people's behaviors that follow the observed actions of others ([18]). The specific herding behavior that we uncover in this research has been referred to as "information-motivated herding" in prior literature ([32]). Researchers have used the terms "social dynamics" (e.g., [36]; [37]), "peer effects" and "social multipliers" (e.g., [40]), and "information cascades" (e.g., [31]) to describe the same behavior.
7 2[18] provide an excellent review of research applying herding theory in various contexts.
8 3Note that individuals' information can be gained either through direct experience or by gathering information from external sources (e.g., the firm's reputation).
9 4A screenshot of the typical interface (using the game Catan as an illustration) that users see on BGG is available in Web Appendix A.
5This is also similar to the movie industry, in which multiple production houses collaborate to produce a final movie. According to the Internet Movie Database http://(www.imdb.com), the movie Dunkirk was produced by seven production houses, including Warner Bros., Syncopy, and Dombey Street Productions. Similar examples exist in the video gaming industry as well.
6In a previous version of this manuscript, we used a measure of relevant product scope. In the spirit of parsimony, we use the more standard product scope measure for our main results and report the relevant product scope results in Web Appendix B.
7We present the categorical variable results here for ease of interpretability. The results remain robust even when considering the continuous variable, ABS_DIVijt (Web Appendix B).
8Web Appendix C describes temporal patterns in rating behavior for two games (Catan and Monopoly).
9Of the observed ratings, 19.8% contain decimal values. In the "Robustness Analyses" subsection, we show that our results are qualitatively consistent in an ordinal probit model even if we ignore the continuous nature of the data.
10[11] show that the exogeneity condition is met as long as some intransitive triads exist in the data. In our data, because we observe a large number of raters over a significant period, we are able to leverage this condition for identification.
11It is noteworthy that, although directionally similar, the effect size of the friend effect is overestimated when endogeneity is ignored. For Model 1, which ignores endogeneity as well as observed and unobserved heterogeneity, the estimated friend effect is.322, but after accounting for these factors in Model 4, we can see that the effect is much smaller.
12Notably, raters have easy access to a firm's entire publishing history, categories, and so on through the website. Anecdotally, we confirmed with the data provider as well as several users on the website that raters do often click through to the firm's profile page (which details its publishing history).
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Record: 225- What Drives Virality (Sharing) of Online Digital Content? The Critical Role of Information, Emotion, and Brand Prominence. By: Tellis, Gerard J.; MacInnis, Deborah J.; Tirunillai, Seshadri; Zhang, Yanwei. Journal of Marketing. Jul2019, Vol. 83 Issue 4, p1-20. 20p. 1 Diagram, 7 Charts. DOI: 10.1177/0022242919841034.
- Database:
- Business Source Complete
What Drives Virality (Sharing) of Online Digital Content? The Critical Role of Information, Emotion, and Brand Prominence
The authors test five theoretically derived hypotheses about what drives video ad sharing across multiple social media platforms. Two independent field studies test these hypotheses using 11 emotions and over 60 ad characteristics. The results are consistent with theory and robust across studies. Information-focused content has a significantly negative effect on sharing, except in risky contexts. Positive emotions of amusement, excitement, inspiration, and warmth positively affect sharing. Various drama elements such as surprise, plot, and characters, including babies, animals, and celebrities arouse emotions. Prominent (early vs. late, long vs. short duration, persistent vs. pulsing) placement of brand names hurts sharing. Emotional ads are shared more on general platforms (Facebook, Google+, Twitter) than on LinkedIn, and the reverse holds for informational ads. Sharing is also greatest when ad length is moderate (1.2 to 1.7 minutes). Contrary to these findings, ads use information more than emotions, celebrities more than babies or animals, prominent brand placement, little surprise, and very short or very long ads. A third study shows that the identified drivers predict sharing accurately in an entirely independent sample.
Keywords: virality; shares; ad cues; emotion; brand prominence
The goal of this article is to enhance our understanding of ad-related characteristics that drive virality (sharing) of online ads. We investigate this issue in the context of online video ads that advertisers have uploaded to YouTube. We emphasize this context because YouTube ads can benefit marketers in myriad ways. First, such ads have high potential to enhance exposure and sharing, perhaps leading to virality. Unlike traditional ads, sharing online ads creates new exposures, as the video reaches new viewers across social media such as Facebook, Twitter, LinkedIn, and Google+. Second, YouTube ads are highly cost-efficient. Aside from the cost of making and optionally promoting the video, YouTube advertising exposure is free. Moreover, advertising through YouTube is unlimited. Advertisers can upload as many videos as they wish at minimum cost. Third, there is almost no length restriction on YouTube ads. Long ads can tell a story or portray a drama that can arouse strong emotions. Fourth, unlike some other advertising methods, viewership is voluntary. The ad is viewed only if a viewer chooses to watch it. Finally, YouTube complements TV advertising in new and important ways. Marketers can publish ads on YouTube as a pretest before placing them in paid TV channels. Conversely, marketers can use paid TV channels as a seed to influence the sharing of ads that are uploaded to YouTube.
A unique feature of online digital content is that consumers can easily and readily share what they like with others. Such sharing can exponentially impact the total number of views of digital content and the extent to which it goes viral. We define virality as achieving a large number of views in a short time period due to sharing. Virality is maximized to the extent that content viewed by one consumer is shared with others. The degree of virality is intrinsically dependent on the degree of content sharing ([55]). Sharing has become vitally important in the current environment because shared content can reach vast audiences in a short period of time at low cost. Thus, a primary motive for posting online content is to have it shared. However, what drives the online sharing of marketer-generated content?
The current study advances prior research on sharing online content in several important ways, as suggested by Table 1. First, we examine the real-world behavior of people who have actually shared ads across multiple media. We measure actual sharing as opposed to intentions to share (which prior studies have examined). In contrast to some prior work that has focused only on ads that have already gone viral (e.g., [15]; [13]), we examine ads that are highly shared versus those that are not and the extent of sharing. We also emphasize ads (as opposed to other types of communications such as WOM or online reviews) because marketers have a strong interest in consumers' involvement with and sharing of their ads. Moreover, factors that influence ad sharing might differ when one examines sharing of ads versus sharing of noncommercial content. We also examine sharing behavior across four major social media (Facebook, LinkedIn, Twitter, and Google+[ 5]) and ask if certain ad-characteristics that influence sharing vary by media. Prior studies (e.g., [47]; [40]) have examined shares on only one social medium (Facebook).
Graph
Table 1. How the Current Article Advances Prior Research on Sharing of Online Content.
| Focus (DV) | Study | Actual Shares Online | Sharing of Video Ads | Shared by Four Major Media | Number of IVs | Effect of Brand on Sharing | Effect of Ad Length on Sharing | Drivers of Emotions | Out-of-Sample Prediction |
|---|
| Sharing of video ads | Current Article | Yes | Yes | Yes | 60+ | Yes | Yes | Yes | Yes |
| Akpinar and Berger 2017 | Yes | Yes | Yes | 14 | Yes | Yes | No | No |
| Nelson et al. 2013 | Yes | Yes | No | 16 | No | No | No | No |
| Dafonte-Gómez 2014 | Yes | Yes | No | 9 | No | No | No | No |
| Intention to share (self-report) | Berger and Milkman 2012 | No | No | No | 10 | No | No | No | No |
| Chen and Lee 2014 | No | No | No | 2 | No | No | No | No |
| Eckler and Bolls 2011 | No | No | No | 3 | No | No | No | No |
| Hagerstrom, Alhabash, and Kononova 2014 | No | No | No | 2 | No | No | No | No |
| Hsieh, Hsieh, and Tang 2012 | No | No | No | 4 | No | No | No | No |
| Lee, Ham, and Kim 2013 | No | No | No | 6 | No | No | No | No |
| Shehu, Bijmolt, and Clement 2016 | No | No | No | 7 | No | No | No | No |
| Berger 2011 | No | No | No | 4 | No | No | No | No |
| Chiang and Hsiao 2015 | No | No | No | 10 | No | No | No | No |
| Yang and Wang 2015 | No | No | No | 15 | No | No | No | No |
| Baker, Donthu, and Kumar 2016 | No | No | No | 3 | No | No | No | No |
| Motivation to share (self-report) | Henning-Thurau et al. 2004 | No | No | No | 8 | No | No | No | No |
| Syn and Oh 2015 | No | No | No | 10 | No | No | No | No |
| Phelps et al. 2004 | No | No | No | 23 | No | No | No | No |
| Views | Southgate, Westoby, and Page 2010 | No | No | No | 7 | Yes | No | No | No |
| WOM of brands | Lovett, Peres, and Shachar 2013 | No | No | No | 24 | Yes | No | No | No |
| WOM messages | Dubois, Bonezzi, and de Angelis 2016 | No | No | No | 4 | No | No | No | No |
| Viral messages | Dobele et al. 2007 | Yes | No | No | 6 | No | No | No | No |
| Retweet of ads | Stieglitz and Dang-Xuan 2013 | Yes | No | No | 6 | No | No | No | No |
Second, our findings provide advertisers with concrete insight into how to design ads to influence sharing (see the "Implications" section). Taking prior advertising theory and motivations for sharing into account, we predict which ad characteristics should influence sharing and why. We also code over 60 ad characteristics that might influence sharing, which is a considerably higher number of characteristics than is considered in other studies (see Table 1).
Third, we study the impact of discrete emotions on sharing. This focus provides advertisers with concrete information about which specific emotional states evoked from ads induce sharing. In addition, we examine which ad content characteristics evoke emotions. Consistent with our theory, highly shared ads are not merely emotional; rather, they unfold as dramas, with highly likable characters and a plot. Although drama ads have been invoked theoretically as a potential predictor of sharing (e.g., [ 2]), ours is the first study to examine the impact of drama on sharing. Ad sharing is also maximized when ads do not make the brand prominent. Ironically, advertisers rarely use factors that enhance sharing. Thus, our findings have the potential to influence how marketers develop ads so that they have greater potential to be shared and become viral.
Fourth, our field findings are replicated across ads, raters, rating scales, and time, and they show accurate out-of-sample predictability. In two independent empirical studies, we tracked a large number of online video ads from YouTube between November 25, 2013, and March 4, 2014, and between January 2014 and December 2016. From these video ads, we drew stratified random samples of 345 ads in Study 1 and 512 ads in Study 2. The two studies used different ads, coders, and time periods and moderately different brands and rating scales. Only 42 brands and one ad are common to the two studies. Despite these differences, we find consistent results across the studies. A third study further validates our findings with out-of-sample prediction of sharing of videos in Study 2 from the estimated influence of drivers of sharing from Study 1. Those characteristics found to be significant in the hypotheses also have relatively high predictive power. No prior study has shown the out-of-sample predictive power of drivers of sharing (see Table 1).
We propose a conceptual framework involving the ad characteristics that influence online ad sharing (see Figure 1). This model is grounded both in prior research on the executional elements of advertising and content sharing, as well as in theory. We briefly review this work to highlight our framework and our contributions in relation to prior research (see also Table 1, which further distinguishes our work from prior studies).
Graph: Figure 1. Conceptual framework.Notes: Circles and ellipses are constructs. Rectangles are measures. Information-Focused Content, Ad Content: Risk, Discrete Positive Emotions, and Commercial Content are independent constructs. Number of Shares is the dependent construct.
Our conceptual framework has several parts: motivation for sharing, informational ad content, emotional ad content, and commercial ad content. The motivation for sharing explains the fundamental reasons why people share. Although we do not measure these motivations, we use these motivations to formulate our hypotheses. We do explicitly measure sharing, information content, emotional content, and commercial content.
To understand when and why information-focused, emotion-focused, and commercial-focused content influences sharing of real-world online ads, we briefly review factors that motivate sharing given their instrumentality to our hypotheses. These motives fall into three broad categories: ( 1) self-serving, ( 2) social, and ( 3) altruistic motivations.
First, individuals share content for self-serving motivations; that is, they share content that benefits themselves without directly considering the benefit to others. One often-studied self-serving motivation is the motivation for self-enhancement (e.g., [ 5]; [16]; [34]). We define self-enhancement as the basic human need to feel good about oneself in the eyes of others. Sharing valuable or impactful content can enhance one's status by making one seem knowledgeable or expert about the marketplace ([30]). Individuals also share content to foster information sharing by others (i.e., reciprocity) and to learn from others in the future ([34]; [50]). They also share information to express or signal uniqueness ([27]; [34]). Finally, individuals share information because they find the act of sharing to be enjoyable ([50]).
Beyond these self-serving motivations, individuals also share online content for purposes of social engagement. That is, individuals share information to engage with a community ([50]), learn about community interests ([50]), socialize with particular community members ([30]; [34]), and/or feel that they belong to or are part of a group ([27]). Finally, altruistic motivations drive sharing. Individuals share content to show concern for others ([26]), show empathy for others ([50]), and to try to help others ([34]). We rely on these sharing motivations to develop our hypotheses about which ad characteristics affect sharing. The left side of Figure 1 identifies three broad ad content domains that are under the control of advertisers and for which theoretical and empirical work on advertising has previously been conducted (informational content, emotional content, and commercial content).
Prior integrative models of advertising (e.g., [35]) have proposed two routes by which advertising can influence consumers: an informational route and an emotional route. Researchers have used informational versus emotional ad content to study the effectiveness of ads on dependent variables such as ad and brand attitudes, brand recall (see reviews by [25]), ad viewing behavior ([45]), purchase intentions (e.g., [31]), sales (e.g., [ 9]; [36]), sharing intentions (e.g., [ 5]), and, as with our study, actual sharing behavior (e.g., [ 2]). It has also been used in the study of both online and offline ads. In addition, researchers have used the informational and emotional framework in Figure 1 to study sharing or sharing intentions of non-advertising content (e.g., eWOM, Facebook posts, tweets, and sharing of news articles; see, e.g., [16]; [34]; [43]; [49]).
We define "commercial content" as content that has a goal of influencing behavior in favor of a branded product or service. Marketing communications such as ads are different from nonmarketer-generated content such as eWOM and news articles because they are commercial in nature. Specifically, marketers develop ads with the goal of influencing or persuading consumers (and inducing actions such as purchase and sharing). Prior theory ([21]) indicates that the activation of such "persuasion knowledge" can cause consumers to discount or counterargue persuasive messages. Noncommercial material should not activate such knowledge. For these reasons, prior work on content characteristics that cause consumers to share or intend to share news articles (e.g., [ 5]), tweets ([49]), stories ([ 5]), WOM ([ 3]; [16]; [34]), or other noncommercial content may not generalize to commercial content such as advertising.
Consumers today live in a content-rich and time-poor environment. Consequently, people need to be discerning about what content they consume and share. Millions of pieces of online (ad- and non-ad-related) content are generated each day. Every person who encounters such content must judge whether to consume it or not and whether to share it or not. The repetition of this process ultimately leads to outstanding material going "viral." Our hypotheses deal with how emotional, informational, or commercial content of ads affect sharing.
Information-focused content is verbally rich. It typically involves a narrator or a voice-over delivering arguments or factual descriptions about products, attributes, people, behaviors, and events. Because of its argumentative or factual focus, however, information-focused content can be dry and uninteresting, particularly when the brand is familiar and well-known. Rather than being shared, information-focused content may irritate consumers and be avoided. So, sharing such ads should be limited. People risk reputational harm and lower prospects for self-enhancement when they share content that others do not find relevant, interesting, or compelling. Sharing information about known and familiar brands is also inconsistent with other self-serving motivations such as the motivation to demonstrate one's uniqueness. In addition, altruistic motives for sharing may be limited when ads focus on factual features of the product or brand as opposed to the higher order (emotional) goals that consumers can achieve from product use. Finally, individuals are unlikely to encourage reciprocity from other consumers if they share dry and factual information. Considering these factors, we predict that:
- H1: Information-focused content negatively influences sharing, except under high-risk conditions.
Although we predict that information-focused content is generally negatively related to sharing, we also anticipate that risk moderates this effect (see Figure 1) such that consumers share ads with information-focused content only when risk is high. We consider two types of risk: ( 1) product risk, which may be high when products are new and unknown, and ( 2) purchase risk, which is high when products are expensive.
Product risk is high when buying a new product or service, as the consequences of product usage are unknown. Consumers often search for information that reduces usage uncertainties when risk is high ([33]). In these cases, information-focused content can provide compelling facts and arguments that describe new benefits and/or reduce risk perceptions associated with the new product. Prior research of offline ads finds that ads that use information-focused appeals are most likely to impact behavior when markets are new (vs. mature; [ 9]; [36]). However, that work did not examine sharing as a dependent variable. In an online sharing context, [ 2] found that information-focused ads were less likely to be shared (which is consistent with H1). However, they did not examine the moderating role of product newness to the market (or risk in general). As such, our examination of the moderating role of risk on sharing behavior is novel.
New products may trigger several motivations for sharing. Consumers appear to be more knowledgeable about the marketplace when sharing information about new products, perhaps enhancing their reputation as being an opinion leader or a market maven. Burnishing one's reputation in this manner is consistent with a motivation for self-enhancement. Sharing information about new products also enhances the potential for future reciprocity and personal gain, as others might subsequently help the sharer by sharing information about new products they have discovered. Sharing information about new products may endear the sharer to members of a community of like-minded others, as is consistent with a motivation to socialize. Individuals may also share information about new products to express their uniqueness by showing what new products they find to be of personal interest. Finally, sharing information about new products is consistent with altruistic motivations for sharing, as consumers may aim to help others for whom the new product is also relevant. We expect that the extent of positive sharing is directly related to the newness of the information. This reasoning suggests the following hypothesis:
- H2a: The effect of information-focused ad content on sharing is positive when products or services are new (vs. not new) to the market.
Consumers might also share information-focused content when purchase risk is high, as would be the case with high-priced products. There are several reasons for this prediction. First, a high-priced product or service triggers greater financial risk for consumers because a bad choice can create a significant economic loss. Second, a high-priced product or service enhances consumers' involvement in the choice process. In such contexts, consumers are generally attentive and receptive to information about the product or service, and they process this information deeply to minimize purchase risk ([42]). Therefore, consumers may more carefully process information-focused content for high-priced (vs. low-priced) products and services.
Consumers may share information with others to minimize the recipient's purchase risk, as is consistent with an altruistic motivation for sharing. Sharing such information also places the sharer in the position of one who is concerned about the welfare of the recipient, thus enhancing the self. Individuals may also share information about high-priced products in the hopes that recipients will reciprocate by sharing their own information about high priced products, which is an effect consistent with self-serving motivations. To the best of our knowledge, no prior research has examined the moderating role of purchase risk on the relationship between information-focused ad content and ad sharing. We expect that:
- H2b: The effect of information-focused ad content on sharing is positive when products or services are high (vs. low) in price.
Emotion-focused content can arouse either positive or negative emotions. Most research in advertising and on content sharing emphasizes emotions evoked by (vs. those depicted in) ads. The reason is that evoked emotions influence important advertising outcomes such as attitudes toward the ad and brand (e.g., [18]), purchase intentions (e.g., [31]), recall (e.g., [46]), viewing time (e.g., [52]), sales (e.g., [ 9]; [36]), ad sharing ([ 2]), and retweeting ([49]). Within the literature on advertising and on content sharing, work has examined the effect of ( 1) emotional versus informational content, ( 2) the general emotionality of content (high vs. low), ( 3) the role of specific discrete emotions, and ( 4) the dimensions to describe emotions. In particular, these dimensions include whether emotions are positive versus negative in valence and high versus low in arousal.
Pertinent to the first point, work on advertising outside the sharing context finds that compared with information-focused ads, emotion-focused ads generally have more impact ([31]), except under conditions of risk, as when markets or products are new ([ 9]; [36]). Only [ 2] studied the effect of information- versus emotion-focused ads on sharing, finding that the latter induce more sharing/sharing intentions. This finding is generally consistent with H1; however, H1 emphasizes the degree of information in ads as opposed to the effect of informational versus emotionally focused ads. Given the limited number of studies on the relative impact of these two types of ad content, replicating the effects of emotional ads on sharing is warranted. Relevant to the second point, [43] found that the extent of emotionality of content positively influenced sharing; however, they examined sharing of emails, not ads. Again, assessing whether the extent of emotion in ads affects sharing is warranted.
Consistent with the third point, some prior work on sharing/sharing intentions has examined content that evokes discrete emotions such as amusement, awe, inspiration, surprise, joy, affection, anger, disgust, sadness, and fear (see [ 5]; [13]; [15]; [28]; [41]; [43]). However, none of these studies examined whether the extent to which ads evoked these discrete emotions created variation in sharing of real-world ads. Instead, prior research has used experimentally created ads ([ 5]) or non-ads ([43]) or has examined sharing of ads that had already gone viral ([13]; [15]). Therefore, it is important to examine whether the degree to which ads create specific discrete emotions influences sharing.
Finally, some work on content sharing examines the valence and/or arousal dimensions of evoked emotions on sharing/sharing intentions, as previously suggested by the fourth point. Prior work suggests that content that evokes high (vs. low) arousal emotions evokes greater sharing or sharing intentions ([ 4]; [ 5]; [24]; [40]). However, only [40] study on sharing of real-world ads found that arousal is related to video ad sharing. In terms of valence, [17] found that consumers had stronger intentions to forward ads that were positive (vs. negative or mixed in affective tone), though they did not study actual shares. Several other studies suggest that both high arousal and positive valence are related to sharing ([24]; [40]). However, only [40] studied actual shares.
Whereas prior research examines many different aspects of emotional ad content (as previously noted in points 1–4), our research emphasizes the role of discrete emotions on sharing (point 3) for several reasons. First, understanding which discrete emotions prompt sharing provides specific guidance to marketers on exactly what type of emotional content is most shared. This knowledge can help advertisers design ads that maximize sharing. Second, studying discrete emotions allows us to determine whether sharing is influenced by the emotionality of ads in general (as previously suggested by point 2). Third, the study of discrete emotions allows for the subsequent categorization of these emotions into their arousal and valence components, as previously suggested by point 4. This allows us to determine if we replicate prior work on arousal and valence when examining the sharing of real-world ads.
We predict that people are more likely to share ad content that arouses discrete positive (vs. negative) emotions. Sharing negative ad content might be consistent with an altruistic motive; that is, individuals might share content that warns others of fear, shame, or sadness-inducing outcomes that may befall them due to lack of product use. However, such content is likely to be disconcerting to the receiver, negatively impacting the sharer's potential for self-enhancement, limiting the potential for reciprocity, and mitigating opportunities for socializing.
Instead, ads that create discrete positive emotions, such as amusement and excitement, love, joy, warmth, inspiration, and pride, make viewers feel good, which induces a positive mood. Sharing content that evokes these emotional states should make the receiver feel positively toward the sharer, enhancing the sharer's opportunities for self-enhancement in the present and reciprocity by the recipient in the future. We base this prediction on the well-known finding that a positive mood (evoked by the positive emotions just mentioned) creates feelings that generalize toward other entities (i.e., in this case, the sharer; e.g., [29]). Finally, discrete positive emotions are conducive to socializing motivations for sharing. Receivers are likely to feel more positively inclined toward socializing with those individuals who make them feel good. This reasoning suggests that:
- H3: Content that arouses discrete positive emotions such as warmth, love, pride, and joy has a positive effect on sharing compared to ads that evoke negative emotions such as fear, sadness, and shame.
Emotion-focused content can be created by using drama rather than a third-party narrator or "voice-over" ([14]). Drama ads include three critical characteristics: plot, characters, and surprise. Dramatization increases to the extent that any piece of content uses these elements.
A plot is a sequence of events that creates increasing suspense or tension until it reaches a climax, followed by a surprising resolution ([53]; [54]). The plot is effective when the sequence of events flows smoothly, is captivating, and has an unexpected solution (surprise). This solution should flow from the internal nature of characters rather than an external event. The more the plot evokes surprise, the higher the interest, engagement, and emotional arousal of the viewer. If the ad's resolution lacks surprise, the plot becomes trivial and uninteresting. Thus, drama, plot, and surprise are positive triggers of the emotions that can, in turn, lead to sharing.
Characters are individuals portrayed in the plot. They captivate the audience when they are ( 1) appealing (attractive), ( 2) similar to the audience, and ( 3) endearing or likable ([42]). The relationships among characters create tension in the plot, which draws the audience in and immerses them into the unfolding events of the plot ([14]). Characters can be everyday people, celebrities, animals, babies, or cartoons. We expect more positive emotions, views, and shares as characters become increasingly attractive, likable, or similar to the customer. Characters depicted as everyday people evoke positive emotions because of their similarity to the audience. Celebrities do the same because of their attractiveness and ability to grab the attention of the audience. Babies, animals, and cartoons do so because of their likeability or cuteness ([42]). Characters enrich drama, make ads (and brands) more likable ([42]), and enhance positive emotions.
In contrast to these three elements of drama (plot, character, and surprise), ads could also include a narrator or voice-over. This element involves a third party between the ad and the receiver. It distracts from the characters and plot and hinders engagement and emotional arousal ([14]). So, the more an ad includes drama and plot and the less it uses a narrator, the higher the dramatization, arousal of emotion, and engagement.
Drama ads are likely to arouse more intense emotions than information-focused content, for three reasons ([53]; [54]). First, ads with drama are easier to process than are ads with information. Transportation theory ([23]) suggests that individuals naturally gravitate to stories as relayed through drama. Moreover, processing drama-based messages involving characters and a plot requires limited effort.
Second, ads containing drama are engaging. Transportation theory ([23]; [22]) suggests that characters can transport the reader into the plot by evoking empathy and mental imagery with the characters. The characters draw the viewer into the plot, transporting them into their lives and experiences ([23]). The viewer can become immersed in the plot and the experiences of the characters ([14]).
Third, drama ads are also enjoyable because the storyline keeps the viewer glued to the unfolding plot until the surprising resolution. The greater the surprise, the greater the enjoyment and thus the more likely the content will arouse emotions and engagement. By virtue of their plot, such ads have been found to evoke strong positive emotions like pride, warmth, joy, amusement, and love ([ 1]; [ 8]; [20]). Viewers likely assume that others will find such ads emotionally evocative as well and will therefore share the content. [10] suggest that ads rated as high in transportation (i.e., those that use plot to engage the viewer) enhance sharing intentions. However, their study did not examine actual shares. This reasoning suggests the following hypothesis:
- H4: Content that uses drama and drama-supporting elements (e.g., a plot, characters, surprise) positively impacts engagement and the arousal of emotions.
As noted earlier, an important factor differentiating ads from non-marketer-generated content (e.g., WOM) is that they are commercial in nature and designed to persuade. Viewers' awareness that content is commercial can create resistance to persuasion ([21]). One important ad characteristic that might enhance an ad's commercial appearance is the extent to which the brand is prominent in the ad. Although advertisers often want their brand name to be prominent to encourage recall at the time of purchase, a prominent brand name can also activate persuasion knowledge by triggering thoughts about the advertiser and their commercial motives. Such a process can make consumers resistant to the message ([51]).
Beyond inducing ad avoidance, we predict that brand prominence reduces sharing. Prominent brand names can interfere with those ad characteristics that support the use of drama, thereby limiting ( 1) consumers' abilities to engage with the plot, ( 2) the ad's ability to arouse emotions, and ( 3) the ad's ability to induce sharing (H3, H4). Moreover, if consumers resist ads with strong commercial content because they activate persuasion knowledge, they are unlikely to share such ads with others. Doing so would be inconsistent with the self-serving motivation of self-enhancement. Sharing ads with a strong commercial message is also inconsistent with socializing and altruistic motives.
[ 2]; Studies 1 and 2) found that consumers were more likely to share ads when the brand was integral to the narrative. However, the extent to which the brand is integral to the narrative can be independent of the extent to which it is featured prominently. Moreover, that study involved a fictitious ad/brand as opposed to real-world ads and brands. Their field study, which looked at sharing of actual real-world ads, found that emotional (vs. informational) ads affected actual sharing even when controlling for the presence of the brand in the ad. However, they did not report the independent effect of brand prominence on sharing.
Considering the logic we have presented, we expect that:
- H5: High levels of brand prominence (longer duration, earlier placement) in ads negatively impact sharing.
Next, we describe the data, sampling, and coding to test the hypotheses. We test these hypotheses in two independent empirical studies that cover substantially different time periods, videos, and raters, and moderately different scales and brands.
The context of the study is online video ads freely uploaded on YouTube in branded channels, which viewers voluntarily view and share. It does not include paid ads. We chose YouTube because it is highly relevant to marketers in ways described in the introduction. Moreover, advertising on YouTube offers the potential to reach a large audience. From 2009 through 2013, more than 6,000 brands released more than 11,500 advertising campaigns and 179,900 video ads on YouTube. These ads generated more than 19 billion video views (Visible Measures 2013). YouTube now has more than one billion unique users who watch over one billion hours of video daily.[ 6] Thousands of advertisers have established branded channels on YouTube. A branded channel is an account on YouTube through which a brand (a) uploads video ads, (b) communicates with users, and (c) manages video information.
The number of branded channels and video ads on YouTube is enormous. We had to sample judiciously by using several criteria to select target brands. First, we identified the top 100 U.S. advertisers in 2012 by expenditure.[ 7] Not all of them owned YouTube channels. Second, if there was a channel that closely matched the target brand, we recorded that brand's channel name on YouTube and used it in our sample. Third, we included additional brands that were historically active on YouTube. These brands had uploaded at least one video ad per month and had released at least one popular ad (more than 1 million views) in the last 12 months. This step helped us capture as many shared ads as possible because most ads were barely shared (see section on Data Sampling). Our sampling process resulted in a sample of 109 brands. Out of these, we used 79 brands (see the Data Sampling section for details) for the final analysis, 50 of which were in the top 100 brands and the rest were the additional brands. The list of brands is in Web Appendix Table A1.
We tracked these brands and recorded all video ads that these brands uploaded between November 25, 2013 and March 4, 2014. The brands uploaded 1,962 video ads over approximately 100 days. Each channel uploaded one video ad per 5.6 days during this period. We collected the number of shares of the video ads across various social media and the number of brand channel subscribers.
We relied on the application programming interfaces (APIs) provided by major social media to extract the number of shares of the video ads on these media. Requests for these APIs returned the number of times the URL of a given video had been shared on these media. The major social media are Facebook, Twitter, Google+, and LinkedIn. For example, to obtain the number of shares of the ad "Amazon Prime Air," we first looked up its URL on YouTube, which is: https://www.youtube.com/watch?v=98BIu9dpwHU. We then constructed a query to the social networks' APIs to retrieve the number of times users shared this URL. The query request depends on the target social network.[ 8] We sent such requests every hour to retrieve and store the sharing information, beginning with the time the video ad was uploaded to YouTube. This step was critical because the APIs usually limit the query to only look for recent data (of the last weeks or months). Queries sent many months after the ad was uploaded would not get the complete sharing information. We also took care not to exceed the daily limit set by the APIs for the maximum number of requests.
Although we also tracked other social media (StumbleUpon, Pinterest, etc.), shares on these media were considerably smaller than shares on the four major media. We thus defined the total number of shares for each ad as the sum of the shares across the four major social media. According to this definition, one viewer of a video can share the video on multiple occasions. We observed no substantive changes in shares of the video after it was 30 days old. As such, we used the total number of shares observed during the first 30 days in the following analysis.
We extracted the number of channel subscribers from the YouTube API[ 9] at the time the video ad was uploaded to measure channel popularity. It is important to collect the subscriber information at the time the video ad is uploaded rather than at some time post publication. This is because viewers of the video ad may subscribe to the channel, thus making post-publication channel subscribers endogenous to social shares. Indeed, a viral ad may substantially affect the number of channel subscribers.
Given the challenges in coding all video ads, we selected a sample with which to work. Our random sampling procedure was based on stratified sampling because the distribution of the shares across the video ads was highly skewed. Web Appendix Table A2 shows the sample quantiles of the observed shares. Approximately 10% of the ads are not shared at all, and more than 50% are shared less than 158 times. Using a simple random sampling procedure would result in a sample that contains a large portion of nonshared ads, making it less informative for identifying the drivers of sharing. In the stratified sampling, we divided all video ads into four groups and sampled from each group randomly. The breakpoints for the four strata are based on the 50%, 75%, and 90% quantiles of the shares.[10] We then drew 90 video ads from each group randomly, resulting in 360 video ads. We excluded 15 duplicates in which the advertiser had uploaded the same ad multiple times, resulting in a final sample of 345 English-language video ads belonging to 79 brands. The top panel of Web Appendix Figure A3 depicts the frequency distribution of the brands in these videos.
We adapted the scales from [ 9] to code the content of the video ads. Next, we describe the video ad characteristics for which coders provided ratings, how coders were trained, and intercoder reliabilities. Summaries of these characteristics are in Table 2.
Graph
Table 2. Important Ad Characteristics that Drive Social Shares.
| Video Characteristics | Type of Measure or Cue |
|---|
| Information and Risk Characteristics | |
| Argument | Six-point scale (0 = "very weak," and 5 = "very strong")"To what extent does the ad use logical reasoning, factual claims, or offers?" |
| New product | Binary scale (0 indicates absence; 1 indicates presence)"Is the ad about the introduction of a new product/service?" |
| Price | Categorical: 1 = "low" (e.g., consumer packaged goods), 2 = "intermediate" (e.g., consumer electronic goods), and 3 = "high" (e.g., automobiles)"Is the product price low (more like a consumer packaged good), moderate (more like a consumer electronic good) or high (more like an automobile)?" |
| Emotional Characteristics | |
| Love, pride, courage, joy, triumph, warmth, excitement, sadness, shame, fear, humor, anger, disgust, hatred, deprivation, failure | Six-point scale (0 = "very weak," and 5 = "very strong")"To what extent does the ad arouse the specified emotion?" |
| Drivers of Emotions | |
| Surprise, suspense, drama, narrative, character, plot, sex | Six-point scale (1 = "very weak," and 5 = "very strong")"To what extent does the ad have the specified driver of emotion?" |
| Surprise location | Categorical: no element, at beginning, at middle, at end, throughout"Where in the ad does the surprising outcome occur?" |
| Baby, animal, cartoon, celebrity | Binary scale (0 indicates absence; 1 indicates presence)"Does the ad use the specified ad element?" |
| Commercial Features | |
| Brand timing: early, end, intermittent, none | Binary scale (0 indicates absence; 1 indicates presence of the brand in the ad) |
| Brand duration | Duration of a brand's appearance in the ad (in seconds) |
| Control Characteristics | |
| Ad length | Total duration of the video ad (in seconds) |
| Number of subscribers | Total number of subscribers to the channel |
| Timeliness | Binary scale (0 indicates absence; 1 indicates presence)"Is the ad related to a contemporary event?" |
We defined information-focused content (H1) by the following criteria. First, the ad uses logical reasoning. For example, the ad could compare the target brand with a competitive brand. Second, the ad makes factual claims of a product. Third, the ad identifies certain functional benefits to users. Coders considered these aspects together when rating the information-focused content of the ad. Coders used a six-point scale to rate the extent to which the ad used these characteristics of information (0 = "not at all," and 5 = "very strong"). Furthermore, coders recorded whether the ad concerns the launch of a new product or service (0 = "no," and 1 = "yes"; H2a). In addition, another two coders rated whether the product's price was low (= 1; e.g., a consumer packaged good), moderate (= 2; e.g., consumer electronics or retail store purchases), or high (= 3; e.g., automobiles; H2b).
For characteristics that trigger discrete emotions (H3), coders rated the extent to which the ad arouses emotions (0 = "not at all," and 5 = "very strong"). The coders also rated the degree to which specific discrete emotions were aroused by the ad. The individual emotions included both discrete positive emotions (e.g., love, pride, joy, warmth, excitement), and negative emotions (e.g., sadness, shame, anger, fear). For the negative emotions, we also included ratings for anger, disgust, and hatred; however, these were not present in the sampled video ads and were thus dropped from the analysis. Coders rated humor by how funny the ad was to them (0 = "not at all funny," and 5 = "very funny"). The intensity of humor was measured on a six-point scale (0 = "not at all," and 5 = "very strong").
We hypothesize that ads with the elements of drama and characteristics that facilitate the use of drama will enhance positive emotions and sharing (H4). The use of character and plot in the ad served as indicators of the dramatization scale ([14]). Coders rated the presence of both character and plot on a six-point scale (0 = "not at all," and 5 = "very strong"). We defined the dramatization scale as the average of character and plot. Coders also identified the specific type of characters in the ad. We focused on celebrities, babies, animals, and cartoons. We created a binary indicator for each type of character, with 1 indicating its use and 0 indicating otherwise. Multiple characters can appear in the same ad. We originally used separate indicators for babies and animals but combined them given their potential to create similar feelings of endearment and given their limited occurrence in our sample. We coded the use of a narrator, defined as a third-party voice or text that describes what is going on in the ad (this definition follows [14]). We assessed surprise by the extent to which the ad is inconsistent with a common viewer's prior belief. The intensity of surprise was measured on a six-point scale (0 = "not at all," and 5 = "very strong"). Coders also rated the extent of suspense in the ad (0 = "not at all," and 5 = "very strong").
The coders counted in seconds the total time ( 1) the brand name/logo was present or ( 2) the brand was mentioned in the ad. We operationalized brand prominence (H5) as a function of brand name exposure frequency and ad length. In other words, we normalized the duration of brand name appearance by the length of the ad to account for differences in ad length. The location where the brand name appeared was recorded as "none," "early," "end," or "intermittent." It was coded as "intermittent" if the brand name appeared in multiple places in the middle of the ad.
We also coded factors that were not part of our theoretical model but could affect sharing. Ad length refers to the duration of the ad in seconds, which was collected directly from YouTube. Ads can also contain content pertinent to a contemporary event, such as the Olympics, the World Cup, and the Super Bowl. Coders indicated whether the ad was (= 1) or was not (= 0) relevant to a contemporary event. Coders also used a 0/1 scale to indicate whether the ad contained (= 1) or did not contain (= 0) sexual appeals.
Except for price (which was coded by two coders), three paid coders who were blind to the purpose of this research coded the data independently. We explained the rating scales and engaged in extensive coder training using test video ads unrelated to the selected sample. Coders discussed the results of the test cases. We reviewed discrepancies and clarified the definitions to minimize future discrepancies in the coding of the actual ads used in the study. We then gave coders copies of each of the video ads that comprised our sample. We asked them to base their ratings on only the information provided (not on further search or additional information). Beyond the video ad itself, coders saw only the title of each ad and the brand channel that published it. Following these instructions, the three coders rated the sampled video ads independently.
The overall interrater agreement percentage was.76, and the kappa and tau correlations were.67 and.63, respectively. Since brand duration is a continuous variable, it had the lowest agreement percentage. If we exclude this variable, the interrater agreement percentage was.81, and the kappa and tau correlations were.70 and.60, respectively. According to [38], a 70% level of agreement and a kappa of.50 are generally regarded as adequate. The level of agreement we observed is high considering that many characteristics of content were rated on continuous (vs. binary) scales. To determine the final scale for each variable in the analysis, we set the scale of the variable to reflect the agreed-upon value when at least two coders gave the same rating on a characteristic. Otherwise, we used the mean of the three ratings. The percentage of scales on which all three coders disagreed is approximately 4% (excluding brand duration).
Web Appendix Table A3 shows that we coded over 60 ad characteristics. However, not all characteristics were sufficiently frequent in our sample to include in the analysis. Web Appendix Table A4 shows those characteristics for which there was sufficient frequency and variability to warrant inclusion. Web Appendix Table A4 shows the frequency of each scale for all characteristics of content (except for those that are numeric). The median ad length is approximately 60 seconds, with first and third quantiles being approximately 30 and 120 seconds. The last column of Table A4 shows the result of a simple regression of the logarithmic shares over the rated scale of each ad characteristic.
Recall that we measured the extent to which the ad aroused 11 emotions. Recall as well that our focus is on discrete emotions as opposed to the dimensions of arousal and valence studied in prior work on sharing. At the same time, we aimed to develop as parsimonious a model as possible by reducing collinearity among the set of discrete emotions. We use PCA with Varimax rotation to extract the underlying emotional components from the 11 measured emotions. Web Appendix Figure A2 shows the scree plot of eigenvalues for the extracted components. Table A5, Panel 1 shows the loadings for the first six components, which are sufficient to explain the variances in the originally measured emotions. We label the positive components as "inspiration," "warmth," "amusement," and "excitement," and the negative components as "fear" and "shame" on the basis of the components on which the emotions load. We then use these derived components in the empirical analysis.
Our empirical analysis follows the structure laid out in Figure 1. We first describe the model, then the results.
We investigate the effect of ad characteristics on social shares by estimating a mixed-effects model as follows:
Graph
where α and βi are coefficients to be estimated and ε are error terms initially assumed to be independently and identically distributed. The subscripts for individual ads are suppressed for ease of reading. We include the level of information, the four emotion dimensions, and the six characteristics used in emotion-focused content (see Figure 1). We also include the interaction terms of information x new product/services and information x price levels (low, medium, and high) to test H2a and H2b. We also test hypotheses regarding brand prominence. We treat ad length as a control variable and examine both the linear and curvilinear effect of length. A positive coefficient on the former and negative on the latter would suggest that longer ads increase sharing up to a point, after which longer ads reduce sharing.
There may be substantial differences regarding the brand and product category that influence the sharing of video ads. When estimating the impact of ad characteristics, we control for brand effects in two ways. First, we include the observed channel followers to account for the observed heterogeneity in brand popularity. Some brands may have more followers on their channels, leading to higher views and shares. Second, we include a brand-level random intercept (αbrand) to account for any additional unobserved heterogeneity in brand or product characteristics that may influence sharing. We use the logarithmic shares as the response variable to account for skewness in the sharing data. We standardize the response and the numeric scales so that the magnitude of the parameter estimates can be compared. No scaling is applied to binary or categorical covariates. There are no multicollinearity concerns. The ad length and the square of the ad length have variance inflation values that are higher than others (7.17 and 6.64, respectively) due to their definition. However, they are well below the conventional limits expected in the models.
Table 3 reports the estimated effects of the ad characteristics on shares. We discuss the results in terms of information-focused content, emotion-focused content, and attribute-focused content, following Figure 1. We note several results. First, the use of information (argument and factual descriptions) is significant (–.39, p =.002) and negatively related to social shares. This result implies that information-focused content is less likely to be shared, which supports H1. This result is likely due to the dryness of arguments and facts that constitute information. However, the main effect of new products is positive and significant (.46, p =.002) as hypothesized. Ads introducing new products are generally shared more often because they contain novel and interesting facts, the sharing of which may make sharers look like they are knowledgeable about the marketplace. Our model is log-linear, and the measures are standardized. Thus, we can interpret the effect sizes as the change in the (log of) shares due to one standard deviation change in the independent variable.
Graph
Table 3. Estimated Effects of Ad Characteristics on Social Shares from Mixed-Effects Model (Study 1. Dependent Variable is Log of Shares).
| Beta Coefficient | Effect Size (%) | Standard Error | p-Value |
|---|
| Information-Focused Content |
| Extent of argument | –.39 | –32.56 | .13 | .002** |
| New product | .46 | 57.78 | .13 | .002** |
| Argument × new product | .25 | 27.76 | .12 | .042* |
| Price (moderate) | –.12 | –11.22 | .15 | .43 |
| Price (high) | .01 | 1.11 | .18 | .94 |
| Argument × moderate | .28 | 31.92 | .13 | .030* |
| Argument × high | .33 | 39.38 | .15 | .028* |
| Emotion-Focused Content | | | | |
| Extent of inspiration | .11 | 11.52 | .05 | .018** |
| Extent of warmth | .13 | 14.00 | .05 | .002** |
| Extent of amusement | .20 | 21.53 | .04 | .001** |
| Extent of fear | –.05 | –5.26 | .04 | .19 |
| Extent of shame | .07 | 7.36 | .04 | .06 |
| Extent of excitement | .12 | 13.09 | .04 | .008** |
| Commercial Content | | | | |
| Brand duration | .01 | .50 | .14 | .46 |
| Brand none | –.67 | –48.73 | .43 | .10 |
| Brand early | –.36 | –29.88 | .12 | .002** |
| Brand intermittent | –.31 | –26.51 | .11 | .008** |
| Ad length | .12 | 12.98 | .05 | .024* |
| Ad length sq | –.10 | –9.06 | .03 | .004** |
| log(subscribers) | .39 | 48.14 | .06 | .001** |
| Timeliness | –.11 | –10.06 | .14 | .46 |
1 The parameter in the first row is the effect of argument when used for old and low-priced products (when new product = 0 and price = low). Effect sizes are in percentage terms, as they are estimates of a log linear model. They represent the percent change in shares due to unit change in the dependent variable. For small values, they are close to the coefficient value expressed as a percentage. Significance levels: ***.001, **.01, and *.05.
H2 proposes that consumers might share information-focused ads when the product or purchase context involves risk (see Figure 1). As Table 3 shows, the interaction between the information and new product is positive and significant (.25, p =.042). This result indicates that greater use of information in ads could indeed facilitate social sharing for new products, possibly because the information about new products is likely to be novel and valuable to recipients, making the sharer look good. These results are consistent with H2a.
The interaction effect of information and price level is also positive and significant. We use three price levels: low, medium, and high. In the model, we exclude the low level and use dummy variables for medium and high. As such, the coefficients of these two variables must be interpreted against the low reference level. Both coefficients are significant and positive (.28, p =.03 for argument interacted with moderate pricing and.33, p =.028 for argument interacted with high pricing). The high price brands have a greater impact on sharing than moderately priced brands. All these results support H2b.
Table 3 shows that ads evoking discrete positive emotions generate more shares, which supports H3. Among the different types of emotions, ads that evoke inspiration (.11, p =.018), warmth (.13, p =.002), amusement (.20, p =.001), and excitement (.12, p =.008) are most likely to be shared. None of the coefficients for the negative emotions are significant in the analysis, which also supports H3. However, in our data, few ads evoke negative emotions (see Web Appendix Table A4). This pattern may exist because advertisers have already anticipated the negative effects of ads that evoke negative emotions. Our findings confirm the results of studies that have examined the effect of ads with positive emotional valence on sharing intentions (e.g., [24]). [40] show similar effects, with discrete positive emotions influencing sharing. Note that we find no evidence that high arousal emotions (e.g., inspiration, excitement, fear) affect sharing more than low arousal emotions (e.g., warmth, amusement, shame). This finding contrasts with prior work on the sharing implications of highly arousing non-ad content and users' ad-sharing intentions (e.g., [ 5]). In contrast with such work, our findings more clearly support a valence account, with discrete positive emotions resulting in sharing. As noted earlier, [43] found that the emotionality of content positively influences the sharing of emails. Our results suggest that when it comes to advertising, the extent to which the ad evokes greater emotionality is restricted to discrete positive emotions.
We hypothesized (H4) that the ad characteristics that arouse emotions include dramatization, surprise, suspense, and the type of characters in the ad (celebrity, babies, or animals). We regressed each of the four significant (latent) components of emotions over these characteristics. Table 4 reports the estimated effects of these characteristics on emotions. Several results are noteworthy. First, greater use of dramatization has a significant positive effect on emotions, as hypothesized in H4. In particular, the use of drama increases the emotions of inspiration (.17, p =.004), warmth (.13, p =.024), and amusement (.54, p =.00). Second, surprise plays an important role in arousing emotions, as hypothesized. Specifically, the use of surprise leads to significantly higher amusement (.18, p <.0001). Third, the use of a celebrity is important in arousing positive emotions, as hypothesized. In particular, celebrities significantly increase the emotions of excitement (.26, p =.035) and inspiration (.36, p =.003). Fourth, the use of endearing sources like babies and animals is also effective, as hypothesized. They are especially effective at stimulating the emotions of inspiration, warmth, and amusement. Notably, the effect of suspense is not significant in arousing emotions. The probable reason may be that its effect is already captured by dramatization.
Graph
Table 4. Estimated Effects of Drama-Based Elements on Emotions (Study 1).
| Characteristics | Extent of Inspiration | Extent of Warmth | Extent of Amusement | Extent of Excitement |
|---|
| Mean | p-Value | Mean | p-Value | Mean | p-Value | Mean | p-Value |
|---|
| Dramatization | .17 | .004** | .13 | .024** | .54 | .000** | .02 | .699 |
| Extent of surprise | –.13 | .015** | .04 | .433 | .18 | .000** | .1 | .073* |
| Use of celebrity | .36 | .003** | –.14 | .242 | –.10 | .283 | .26 | .035** |
| Use of baby/animal | .59 | .035** | 1.24 | .000** | .45 | .043** | .04 | .876 |
| Use of cartoon | –.23 | .233 | –.24 | .211 | .54 | .001** | .04 | .862 |
| Use of sex appeal | –.25 | .355 | –.23 | .396 | .08 | .732 | –.26 | .349 |
| Extent of suspense | .04 | .505 | –.09 | .108 | –.04 | .409 | .10 | .074* |
2 Significance levels: ***.001, **.01, and *.05.
The location of brand appearance has an influence on sharing, as predicted in H5 ("brand end" is set to be the reference level). The estimates in Table 3 for "brand none," "brand early," and "brand intermittent" represent the differential effect from that of "brand end." According to these estimates, showing the brand at the end of the ad is significantly better than placing it at the beginning (–.36, p =.002) and intermittently (–.31, p =.008) for the purpose of promoting social shares, which supports H5. Ads that show later brand placement may allow the viewer to become absorbed in the ad as a form of drama-based entertainment, rather than as a commercial message. When ad exposure is voluntary, as it is with our choice of freely uploaded YouTube ads, the prominence of brand names in the ad can increase the likelihood of ad avoidance, as hypothesized. When brand names become prominent, they look less like entertaining stories and more like traditional ads. Consumers are unlikely to feel that others will look at them favorably by sharing a traditional, marketer-driven message. [51] suggest that pulsing is the best strategy for placing brand names in TV ads, but our results suggest that end placement of brand names is best for YouTube ads.
We controlled for use of sexual appeals and cartoons. Sexual appeals had no significant effect. Cartoons only affected amusement. Notably, though, these characteristics are infrequently used in our sample. We also controlled for length. Whereas TV ads are historically short (15 or 30 seconds), in marked contrast, almost no restrictions are placed on the length of video ads on YouTube. Longer ads allow for more development of characters and unfolding of the plot, which is necessary for drama ads. That said, viewers (and ad receivers) live in a time-constrained environment and generally lack the patience to stay with a very long ad. Thus, although longer ads facilitate an unfolding drama, there are limits on the ad lengths consumers will tolerate. For this reason, we expected an inverted U-shape between social shares and ad length, as hypothesized in H6. We operationalized length on a logarithmic scale and included a quadratic term to test the potential nonlinearity. Both the linear (.12, p =.024) and quadratic (–.10, p =.004) terms were significantly different from zero. The coefficient of the quadratic term was negative, which determines an inverted U-shape between social shares and ad length.
One disadvantage of the quadratic polynomial is that it implies a symmetric relationship that may be too strict. For this reason, we replaced the quadratic polynomial of ad length by a penalized spline term ([19]), keeping other aspects of the model unchanged. Using a nonparametric spline function on ad length allows flexible patterns between shares and ad length to be estimated from the data. Penalty on the spline coefficients was imposed to avoid over-fitting. Web Appendix Figure A1a shows the estimated relationship between social shares and ad length from the penalized spline model. It still displays an inverted U-shape, and it peaks at 1.2 minutes. The asymmetry of the curve indicates that compared with very short ads, consumers are more likely to share longer ads. For example, according to the estimates, a two-minute ad is three times more likely to be shared than a 15-second ad. The 15-second ads are the least shared among various ad lengths.
We investigated the relationship between social shares and ad length separately for information-focused and emotion-focused ads. Web Appendix Figure A1b shows these relationships. Consistent with our analysis, and using all ads, we observe an inverted U-shape relationship between social shares and ad length. The optimal length is around 1.5 minutes. However, after the peak, the effect of ad length decays much faster for informational ads than for emotional ads. Thus, consumers appear to more easily tire of and are less likely to share long ads that resort to providing information than long ads that arouse emotions.
Although Study 1 supports our hypotheses and observes interesting effects by medium and for ad length, it is limited in several ways. First, the sample size is restricted to 345 ads, warranting replication with an entirely new sample of ads. Second, the brands in Study 1 were relatively limited. Replication over a wider sample of brands would help generalizability. Third, one might wonder if the effects are restricted to the time period in which the data was collected. Because various environmental forces (e.g., economic uncertainty, political forces) might influence sharing, replication across time is useful. Fourth, one might also wonder whether the results are specific to the coders who coded the ads, warranting replication across coders. Fifth, Study 1 uses a six-point rating scale to evaluate emotions and their predicted drivers. Replicating the effects using a five-point scale in which 3 represents the scale midpoint would provide greater confidence that the results are not specific to the rating scale used.
For exploratory purposes, we also examined whether sharing of information-focused ads varies by medium. The four major media for sharing are Facebook, Twitter, Google+, and LinkedIn. For this analysis, we ran a sequence of mixed-effect models, which link the number of ad shares on each of these four major social media to the rated characteristics of content. The important effects of characteristics across social media are in Table 5. The detailed estimates of the effects of characteristics for each platform are in Web Appendix Table A6.
Graph
Table 5. Estimated Effect of Key Ad Characteristics (Information and Emotions) on Sharing by Platform (Study 1).
| Facebook | Twitter | Google+ | LinkedIn |
|---|
| Estimate | p-Value | Estimate | p-Value | Estimate | p-Value | Estimate | p-Value |
|---|
| Extent of argument | –.23 (.11) | .039** | –.22 (.08) | .007** | –.12 (.086) | .162 | –.044 (.08) | .569 |
| Extent of amusement | .43 (.15) | .006** | .238 (.12) | .039** | .265 (.12) | .029** | –.04 (.11) | .711 |
| Use of celebrity | .85 (.329) | .010** | .817 (.25) | .001** | .591 (.26) | .023** | .298 (.23) | .186 |
| Use of baby/animal | 2.38 (.79) | .003** | 1.561 (.60) | .010** | 1.522 (.62) | .015** | .851 (.54) | .114 |
3 Significance levels: ***.001, **.01, and *.05.
Across the four social media for which we tracked sharing, LinkedIn is the most distinct regarding the ad characteristics that drive sharing. This result is likely because LinkedIn is a business-oriented social networking environment where the users are mostly professionals. The other three media are social networking sites that can connect a variety of people. In particular, information-focused ads do not have a significant negative effect on shares for LinkedIn compared to the other social media. We surmise that individuals on the other social media are more motivated to watch video ads for entertainment, whereas entertainment is less likely to be the primary motivation on LinkedIn. Moreover, the use of amusement, celebrity, and babies or animals, which may be a source of entertainment, do not significantly affect shares on LinkedIn but generally do affect sharing on the other three media.
Study 2 is a replication of Study 1 using an entirely different time period, different raters, and a different set of YouTube video ads, as well as a moderately different rating scale. Only the rating instrument items and the analytical model are common to the two studies. The purpose of Study 2 is to test the robustness or generalizability of the results of Study 1. If the results replicate despite the multiple differences in the studies, we may consider the results robust and potentially generalizable. If not, further research is required before reaching firm conclusions.
We randomly sampled approximately 7,700 video ads uploaded on YouTube between January 2014 and February 2017. We retained only English-language videos and products that targeted customers in the United States. We did this by first using automated language detection on the titles, followed by manual verification of the sampled videos. We filtered the videos to eliminate copies of old video ads or adaptations of prior video ads (e.g., funny commercial compilations, bloopers). As in Study 1, we adopted a stratified sampling on shares due to the skewed nature of the social media shares. We divided the videos into groups based on the share counts and randomly sampled the video ads from each of these groups. This process yielded 512 videos across 228 brands in the sample for the analysis.
Web Appendix Figure A3 depicts the frequency distribution of the ads by brands in Study 2. Note that Study 2 has a wider sampling of brands than Study 1. Whereas Study 1 has 79 brands in the sample of 345 videos, Study 2 has 228 brands spanning the sample of 512 videos. Only 42 brands overlap, and only one ad overlaps.
As in Study 1, we used the YouTube API to capture the characteristics of the video ads. We captured video characteristics including the unique YouTube video ID (used later to track the shares on social media platforms), public availability of the video, title, date and time of upload, length (duration), total views, number of likes, and number of dislikes. We also collected information about the corresponding channel, including name and subscriber count.
We counted total shares across the four major social media platforms: Facebook, Twitter, Google+, and LinkedIn. We used the same procedure as described in Study 1, using each platform's API to collect shares. For example, we counted Facebook shares using the Facebook Graph API. We used the updated URL[11] to retrieve the number of shares through the API calls. A similar procedure was adopted for the rest of the platforms (LinkedIn, Twitter, and Google+) using their respective APIs.
We trained coders in a similar manner to Study 1. We asked them to rate the content of the videos after undergoing the same training and using the same instruments as in Study 1, with minor variation between the two studies. First, we used an odd-numbered, five-point scale for the items in Study 2 instead of the even numbered, six-point scale in the previous study. Web Appendix Table A3 presents the scales used in Study 2. Second, we used two coders (vs. the three used in Study 1) given resource constraints. Any disagreements between the coders were decided using the evaluation of a third coder. To ensure consistency between the studies, we benchmarked the coders with a subset of Study 1 videos. The interrater agreement of Study 2 raters on this benchmark is high (kappa =.62). This suggests consistency among raters between the two studies.
We used the same mixed-effects regression model (Equation 1) as in Study 1 to analyze the drivers of sharing. We summarize the results of the replication study subsequently. We highlight the main difference between the two studies before proceeding to the results.
All the results of Study 1 were replicated in Study 2, with one exception. Unlike the first analysis in which we find that information content of the ad influences the sharing for both moderately and high-priced products, in Study 2 we find that the information content of the ad influences sharing only for the high-priced products and not for moderately priced products. Note, however, that this result still accords with H2b.
As in Study 1, we reduced the 11 measured emotions to their components using PCA with Varimax rotation. The results of the PCA analysis are in Web Appendix Table A5, Panel 2. Web Appendix Figure A2 shows the scree plot of the principal components extracted. The PCA yielded six components that capture 85% of the variance in emotions: four positive emotions (inspiration, warmth, amusement, and excitement) and two negative emotions (fear and shame). The results are similar to those of Study 1 (see Web Appendix Table A5, Panel 1). The estimated value of these six components was used in the mixed-effect model.
We tested multiple models and compared fits using various statistics. We benchmarked the models using the mixed-effects model-specific R2 following the [39] approach, as has been done in some of the prior studies in marketing (e.g., [ 7]). Mixed-effects models account for both within-individual variance and between-individual variance. Thus, conventional R2s for linear models are not appropriate for capturing the model fit. [39] suggest using modified R-square values for mixed-effects model comparison. We use this approach to compare the models and demonstrate the relative importance of the three major content domains of the video ads: information-focused content, emotion-focused content, and commercial content, as discussed in Figure 1. The conditional R2 captures the variance explained by the fixed effects (e.g., informational, emotional, and commercial content) and the random effects (brands) in the model. We also report the marginal R2 measures to reflect the variance due to the fixed effects for comparison. In addition, we present the Akaike information criterion statistic of these models. The model fit statistics are at the bottom of Web Appendix Table A7.
Model 4 is the full model that includes all three major content domains: informational, emotional, and commercial content. Each of the first three models excludes one major domain to show the decrease in R2 due to that domain. Model 1 excludes information content and includes the other two. Model 2 excludes commercial content (i.e., brand prominence) but includes the other two ad content domains. Model 3 excludes emotional content but includes the other two. As can be seen, the full model has a better fit as reflected by the conditional R-square. Excluding the emotional domain reduces the explained variation by 7%. Informational and commercial focused content each account for 3% of the variance. We observe a similar fit when we compare the model fit with the Akaike information criteria.
Web Appendix Table A7 summarizes the estimated effects of video ad content on social shares. The results show that the effect of informational content (argument and factual descriptions) is significant (–.20, p =.01) and negatively related to social shares, which supports H1. The main effect of new product status is significant and positive, indicating the propensity of viewers to share videos of new products. The interaction between informational content and new product status is again positive and significant (.25, p =.05), which supports H2a. Thus, informational ads facilitate social sharing for new (vs. old) products. As with the results of Study 1, the interaction between high price and informational content is also positive and significant (.26, p =.03). Similar to Study 1, we exclude the low level and use dummy variables for moderately priced and high-priced products. Therefore, the coefficients must be interpreted against low-priced products. We replicate H2b, finding that the coefficient for high-priced products is significant and positive. As mentioned previously (and unlike Study 1), moderately priced products do not significantly influence video sharing. However, this does not affect H2b. Information-focused content is more likely to be shared for high-priced products.
We again find that ads that evoke discrete positive emotions generate more shares. Ads evoking inspiration, warmth, amusement, and excitement have significantly higher shares. Negative emotions are not significant in the analysis, perhaps due to inadequate variation in the sample. There is no evidence that sharing is restricted to high arousal content. These results support H3. We do not find significant impact of different emotions under the conditions of risk (i.e., of price or new product information), with the exception of the emotion of inspiration. The interaction between the extent of inspiration and moderately or high- priced products is negatively related to sharing. Replicating Study 1, we find that placing brands at the end of the video has a significant positive influence on sharing compared to placing ads early (–.44, p <.001) or using intermittent placement (–.38, p =.01), which supports H5.
Although we treat ad length as a control variable in Study 2, we also find that the linear (.43, p <.001) and quadratic (–.33, p <.001) terms of the video ad's length are significant. While ad length is positively related to sharing, the quadratic term of the ad length is negative. These results suggest an inverted U-shape relationship between shares and length. To further examine this effect, we followed Study 1's procedure. We used a penalized spline term for the ad length in the model to avoid overfitting. Web Appendix Figure A1c depicts the estimated relationship. The results replicate Study 1. The relationship between social shares and ad length is indeed an inverted U-shape, with a peak at 1.7 minutes. As in Study 1, the response curve is asymmetric, which indicates that relatively longer ads have higher shares than shorter ones.
To assess the strength of the factors that were significant in the mixed-effects regression, we developed an out-of-sample predictive model. Specifically, we used a logit regression to test if the factors that were significant in the results of Studies 1 and 2 would indeed predict the likelihood of video sharing among consumers across various platforms. We divided the videos into high and low shares on the basis of the median of the distribution of shares. We used the information-focused content (argument, price, and new product), the emotion-focused content (emotions of inspiration, warmth, amusement, and excitement), and the commercial-focused content (timing of brand appearance) that were significant in the prior studies as the predictor of shares. To simplify the analysis, we grouped the continuous predictor variables as high or low on the basis of their averages, and we controlled for the duration.
We performed out-of-sample predictive testing of the model within each study, as well as across the two studies. For the first case (within-study prediction), we used 80% of the data in Study 1 as the calibration data and the rest of the sample as the holdout sample for prediction. We used a fivefold cross validation to ensure that the results were not obtained by chance due to one-shot sampling. We repeated this procedure for the sample of videos in Study 2. For the second case (cross-study prediction), we used the sample from Study 1 as the calibration data to construct the predictive model. We used the sample from Study 2 as the holdout sample to check the predictive power of the resulting model.
We used three commonly used metrics to evaluate goodness of prediction in the computational sciences literature: precision, recall, and the F1 score. (e.g., [ 6]; [12]). "Precision" measures the proportion of highly shared videos that are correctly classified in the predicted sample. "Recall" measures the proportion of the videos correctly classified in the actual sample. The F1 score is the harmonic mean of the precision and recall, capturing the overall predictive accuracy. The calculation is as follows:
Graph
2
Graph
3
Graph
4
The results are in Table 6. Note that all the prediction rates are around 70%. These results indicate that the significant and hypothesized drivers of sharing have high predictive power. The important and notable result is the high accuracy of the cross-sample prediction: the prediction of shared videos in Study 2 based on the estimates of the coefficients of the model in Study 1. Note that the Study 1 and Study 2 samples were collected in substantially different time periods, rated by different coders, and included substantially different videos and moderately different brands and rating scales. The high accuracy in out-of-sample prediction suggests that the drivers of sharing are potentially generalizable and not idiosyncratic.
Graph
Table 6. Performance of the Predictive Analysis Including All Variables.
| Method | Calibration Sample | Holdout Sample | Precisiona | Recallb | F1 Scorec |
|---|
| Cross sample | Study 1 | Study 2 | 72.7 | 70.5 | 71.6 |
| Within sample 1 | 80% Study 1 | 20% Study 1 | 70.6 | 69.3 | 68.6 |
| Within sample 2 | 80% Study 2 | 20% Study 2 | 70.4 | 67.4 | 67.6 |
- 4 a"Precision" measures the proportion of highly shared videos that were correctly classified in the predicted sample.
- 5 b"Recall" measures the sensitivity of the classification by measuring the fraction of the videos classified correctly in the actual sample.
- 6 cThe F1 score is the harmonic mean of the precision and recall, thus measuring the overall predictive accuracy.
In addition, we tested the robustness of the model prediction with and without the emotion variables to ascertain the predictive value of emotions in the ads (Table 7). Without emotions, cross-sample precision decreases by approximately 7%, recall decreases by approximately 50%, and the F1 score decreases 35%. These results imply that emotions play a vital role in the sharing of online video ads.
Graph
Table 7. Performance of the Predictive Analysis Without Emotions.
| Method | Calibration Sample | Holdout Sample | Precisiona | Recallb | F1 Score c |
|---|
| Cross sample | Study 1 | Study 2 | 88 | 22 | 35 |
| Within sample 1 | 80% Study 1 | 20% Study 1 | 62 | 78 | 66 |
| Within sample 2 | 80% Study 2 | 20% Study 2 | 67 | 59 | 62 |
- 7 a"Precision" measures the proportion of highly shared videos that were correctly classified in the predicted sample.
- 8 b"Recall" measures the sensitivity of the classification by measuring the fraction of the videos classified correctly in the actual sample.
- 9 cThe F1 score is the harmonic mean of the precision and recall, thus measuring the overall predictive accuracy.
Content that goes viral gets a great deal of exposure at minimal cost. Thus, getting content to go viral is important for marketers. We ascertain what ad characteristics affect sharing of real-world ads on YouTube. Understanding the drivers of sharing might help marketers develop ads that get high shares and go viral. The primary distinction from traditional TV advertising is that the exposure to video ads is generally voluntary and driven largely by social sharing. Understanding ad sharing on YouTube has gained increasing interest over the years, given this medium's potential to create effective ad campaigns at relatively low cost. Subsequent sharing of such ads increases exposure to content at no further cost to marketers.
We developed five hypotheses regarding the characteristics of video ad content that drive sharing on the basis of a conceptual framework of online sharing of video content. We collected social sharing of ads on four platforms using two independent samples covering substantially different time periods, video ads, and coders, and moderately different scales and brands. We had coders rate the video ads on over 60 measures, including over 30 executional characteristics.
Two studies using different ads, time periods, and coders show consistent support for the hypotheses. First, the use of information appeals generally has a significant negative effect on social sharing. Second, two variables moderate the extent to which users share information-focused ads. Specifically, information-focused ads positively affect sharing only when product or purchase risk is high: the product or service is new or its price is high. Third, ads that evoke positive emotions of inspiration, warmth, amusement, and excitement stimulate significantly positive social sharing. Fourth, ads that use elements of a drama, such as surprise, likable characters, and a plot, significantly affect positive uplifting emotions and induce sharing. Fifth, a prominent brand name impedes sharing. Early or intermittent display of the brand name drives significantly less sharing than late placement of the brand name. Finally, an asymmetric inverted U-curve characterizes the relationship between social shares and ad length, with ads between 1.2 to 1.7 minutes being most likely to be shared. These effects are significant and robust. Effects for other variables are either not significant or not robust.
These results have important implications for advertising with video ads. First, approximately 55% of our sample's ads (see Web Appendix Table A4) used information-focused (vs. emotion-focused) content. This number would likely have been even higher had we not used a stratified sample to eliminate ads that were not shared. However, our results imply that the effectiveness of such content is limited to conditions in which consumers perceive risk. Content that evokes positive emotions is generally more effective than information-focused content in driving social sharing. However, in the current sample, only 45% of the ads were rated as emotion-focused, and only 7% of them were rated as evoking strong positive emotions (rated emotional scale ≥ 4). These results suggest that marketers are underutilizing or failing to maximize the positive impact of uplifting emotions to encourage sharing.
We find that strong drama, surprise, and the use of celebrities, babies, and animals are effective in arousing emotions and creating social shares. However, only 11% of ads used strong drama (≥ 4), only 10% elicited surprise, and less than 3% used babies or animals as characters in the ad. Because of the low cost and lack of length restriction, YouTube gives marketers great opportunities to design ads that tell a good story or portray strong drama. Consumers are more likely to react positively to such ads and share them. In the data, more than 26% of the ads use celebrities. Although the use of celebrities can arouse emotions and generate shares, it can be costly. In comparison, babies and animals are much less expensive. Appropriate use of these sources can help achieve a higher return for the campaign, and their use may be viable for companies with high budget constraints.
Our results suggest it is better to place the brand name at the end of the ad. However, only 30% of the ads in the sample used late placement. In addition, ad length is easy to control. Our results suggest that the optimum length of ads for sharing is generally between 1.2 and 1.7 minutes. In contrast, only approximately 25% of ads were between 1 and 1.5 minutes; 50% of the ads were shorter than one minute and approximately 25% were longer than two minutes in the sample. Although the length of the ad can improve storytelling, it can also detract from the viewing experience if length exceeds interest value. Viewers are often impatient with lengthy content and disengage from it. In contrast, content that is too short may be insufficient to arouse strong emotions. Advertisers should manage the length of the ad to both attract and sustain viewers' interest while not exceeding their levels of patience.
Finally, advertisers may want to use different ads for different social media. Although the use of amusement, celebrities, and babies/animals may be effective on Facebook, Twitter, and Google+, they may not be as effective when the goal is to communicate one's professional profile on LinkedIn.
Readers may wonder whether drivers of ad sharing in Figure 1 differ from those of ad likability, also known as attitude toward the ad. Likable ads may be shared. Moreover, emotional content, informational content, and brand prominence may affect ad likability (e.g., [18]; [42]; [48]). However, although some of the same factors that drive ad likability might also enhance sharing, sharing depends greatly on social motivations, whereas likability does not. Individuals might share even disliked ads if they believe that these ads might help others (altruistic motivation), burnish their reputation (self-serving motivation), or help to connect them with others (social motivation). Moreover, likability is a soft measure obtained from self-reporting, whereas number of shares is a hard measure from field data.
In addition, liked ads need not induce sharing if they do not foster or activate sharing motives. For example, consumers may not share humorous ads (which are often liked) if they believe that others regard the humor as offensive. Furthermore, many factors known to affect ad likability in a traditional advertising context are not present in video ad contexts in which sharing is possible. For example, likeability of traditional ads varies as a function of competitive clutter, the program or editorial context in which the ad is embedded, and ad repetition (see [37]). YouTube ads are not placed in a competitive or editorial/program context, and the viewer controls repetition, not the advertiser. That said, future research might explore the conditions under which the same factors that induce ad likability also induce sharing.
Supplemental Material, DS_10.1177_0022242919841034 - What Drives Virality (Sharing) of Online Digital Content? The Critical Role of Information, Emotion, and Brand Prominence
Supplemental Material, DS_10.1177_0022242919841034 for What Drives Virality (Sharing) of Online Digital Content? The Critical Role of Information, Emotion, and Brand Prominence by Gerard J. Tellis, Deborah J. MacInnis, Seshadri Tirunillai and Yanwei Zhang in Journal of Marketing
Footnotes 1 Associate EditorP.K. Kannan served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study benefited from a grant of Marketing Science Institute and a grant of Don Murray to the USC Marshall Center for Global Innovation.
4 Online supplement: https://doi.org/10.1177/0022242919841034
5 1Google+ has now been shut down, but it was one of the top social media platforms at the time this research started.
6 2http://www.youtube.com/yt/press/statistics.html
7 3http://www.adbrands.net/us/top%5fus%5fadvertisers.htm
8 4For example, the following returned the shares of the ad on Facebook: https://graph.facebook.com/fql?q=SELECTshare_count,like_countFROMlink_statWHEREurl=https://www.youtube.com/watch?v=98BIu9dpwHU.
9 5https://developers.google.com/youtube/v3/
6These correspond to points close to 100, 1,000, and 10,000 shares.
7"http://graph.facebook.com/?fields=og%5fobject{likes.summary(true).limit(0)},share&id=https://www.youtube.com/watch?v={Videoid}," where the "Videoid" refers to the YouTube-specific ID given to the video.
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By Gerard J. Tellis; Deborah J. MacInnis; Seshadri Tirunillai and Yanwei Zhang
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Record: 226- When 1 + 1 > 2: How Investors React to New Product Releases Announced Concurrently with Other Corporate News. By: Warren, Nooshin L.; Sorescu, Alina. Journal of Marketing. Mar2017, Vol. 81 Issue 2, p64-82. 19p. 3 Charts. DOI: 10.1509/jm.15.0275.
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Section: Special Issue: Theory and Practice in MarketingWhen 1 + 1 > 2: How Investors React to New Product Releases Announced Concurrently with Other Corporate News
Online Supplement: http://dx.doi.org/10.1509/jm.15.0275 ew product announcements are important events for firms. Managers issue these announcements in the hope Nthat investors will notice, recognize, and reward their new product development efforts. As such, most new product announcements are carefully choreographed, stand-alone press releases. However, in a nontrivial number of cases, firms announce new products concurrently with other corporate news. For example, on October 26, 2010, Juniper Networks announced the introduction of Junos Pulse Mobile Security Suite while also announcing the opening of the Juniper Global Threat Center, a facility based in Columbus, Ohio. Similarly, on March 18, 2003, findwhat.com announced both the launch of AdAnalyzer and the hiring of Ernst & Young as its new independent auditor.
These examples raise two important questions: Under which conditions are firms more likely to make concurrent announcements, and when are firms more likely to gain from announcing new products concurrently with other positively valenced corporate news, rather than separately?1 At first glance, the theory of efficient markets suggests that, all else being equal, the effect of concurrent announcements should equal the sum of the effects of the two announcements issued separately; thus, managers should be indifferent between the two options (Fama 1970). Why, then, do such events occur, and are these actions beneficial to firms?
Despite significant research in the area of new product announcements, the economic implications of concurrent announcements are not well understood. Previous event studies have opted to exclude concurrent announcements from analysis rather than study them in isolation, because of their confounding effects on stock returns (e.g., Borah and Tellis 2014; Lee et al. 2000; Woolridge and Snow 1990). As a result, the financial consequences of concurrent announcements, despite being of potential substantive interest to managers, remain unknown. Insights are also lacking into their prevalence and the conditions that make them more desirable. If at least some concurrent announcements elicit more positive stock market reactions than the sum of two comparable, separate announcements, managers should try to understand the circumstances under which this communication tool can benefit their shareholders. Conversely, this strategy should be avoided if concurrent announcements have a negative effect on shareholder value. To the best of our knowledge, our study is the first to investigate the determinants and consequences of new product announcements made concurrently with other positively valenced corporate news.
We begin by proposing that concurrent new product announcements meet the characteristics of events that Barber and Odean (2008) identify as “attention grabbing.” As a result, concurrent announcements may disproportionately increase the firm’s visibility and attract the attention of additional investors who want to buy the firm’s stock. If so, this would increase the firm’s investor base, reduce the firm’s cost of capital, and increase the firm’s market value. The positive relationship between firm value and investor base is documented in Robert Merton’s (1987) model of capital market equilibrium, which provides the theoretical foundation for our research. We use Merton’s model to identify three conditions that are conducive to concurrent announcements—that is, the conditions under which firms benefit the most from an increase in their investor base. We then verify that concurrent announcements made under these conditions are associated with significant increases in firm value.
We use propensity score matching (PSM) to empirically document the stock market gains from concurrent new product announcements relative to those obtained by issuing two separate but similar announcements. We empirically test our hypotheses on a sample of more than 80,000 corporate announcements, which include concurrent and stand-alone new product releases and other positively valenced corporate announcements made by U.S. publicly traded firms from January 2003 to December 2013. We find that the stock market reaction to concurrent announcements is greater than the sum of the reactions to similar stand-alone announcements.
This study makes two main contributions to marketing theory and practice. First, from a theory standpoint, it contributes to an emerging stream of research on firm communications with investors. Much has been written about how firms can improve their communications with consumers; researchers are now examining how effective firms are at capturing investor attention (e.g., Chemmanur and Yan 2009; Grullon, Kanatas, and Weston 2004; Kaniel, Starks, and Vasudevan 2007). We theorize and provide empirical support for the assertion that issuing a new product release on the same day with another positively valenced announcement increases the likelihood that these announcements will stand out in the stock market and be noticed by investors. This finding opens the door for additional research on how other types of marketing announcements can be sequenced and leveraged to maximize their stock market impact.
Second, concurrent announcements are routinely eliminated from event studies in the marketing literature, but we argue that they should not be. Eliminating them yields an incomplete picture of the phenomenon being studied and could lead to inaccurate results. We first examine these announcements and then provide descriptive evidence of their financial value contingent on the characteristics of the firms that issue them. A manager considering a concurrent announcement is faced with a cost–benefit analysis. For example, there may be costs associated with unduly delaying a product announcement to issue it concurrently with other good news possibly happening later (Moorman et al. 2012). Guided by our findings and the contingencies that characterize them, managers can better assess the benefits of issuing concurrent new product announcements and, potentially, of other types of concurrent announcements.
Theoretical Framework
It is not obvious whether combining positively valenced announcements benefits firms more than issuing the same announcements separately. The efficient market hypothesis (EMH) suggests that the total gains from the two announcements should be the same whether they are combined or issued separately. Specifically, EMH posits that investors have access to all publicly available information needed to estimate the expected returns of all the stocks in the market (Fama 1970; Sharpe 1964). When new information becomes available, EMH assumes that investors react only to its content, regardless of the timing or the source of this information (assuming that these factors do not in themselves carry additional informational content). Thus, all else being equal, the stock market returns from announcing new product introductions concurrently with other news should be equal to the combined stock returns from announcing the news items separately; that is, the two options should be financially equivalent.
An alternative paradigm is that of rational expectations with incomplete information. Researchers in this area argue that it is unrealistic to expect investors to access and process all publicly available information. Kaniel, Starks, and Vasudevan (2007) note that publicly available news is incorporated into investment decisions only when investors start paying attention to it. Indeed, evidence has shown that investors can ignore good news if the manner in which it is made publicly available is not particularly salient, even if the news is a promising cancer treatment (Huberman and Regev 2001). Prominent in this domain is Robert Merton’s (1987) model of capital market equilibrium with incomplete information. Merton relaxes the complete information assumption of EMH, while retaining the more fundamental assumption of rational expectations, and mathematically derives a negative relationship between the firm’s investor base and its cost of equity. Specifically, Merton shows that the more investors know about the stock, the higher the demand for the stock, and the lower the cost of equity. The more a company moves to being “universally known,” the closer its cost of equity approaches that dictated by the capital asset pricing model (i.e., the cost of equity that would obtain if all EMH assumptions were verified).
The main takeaway from Merton’s (1987) model is that the required rate of return for stocks (also called “equity discount rate” or “cost of equity”) depends on investor recognition, and this rate decreases as investor recognition increases. This means that companies with higher investor recognition are able to raise money through the stock market at lower costs. Merton demonstrates this argument mathematically in Equation 28.d (p. 495). This equation shows that the relationship between investor recognition and discount rates is negative. Thus, an increase in investor recognition (e.g., one that stems from a concurrent announcement) corresponds to a decrease in the cost of equity. In turn, this translates into an immediate one-time increase in the stock price.2 (We follow Merton in using the terms “investor recognition” and “investor base” interchangeably.)
We propose that concurrent announcements are one mechanism that firms can use to increase investor recognition. The intuition behind this increase in awareness is similar to the one that supports the findings of the S-shaped curve in advertising literature (e.g., Johansson 1979; Vakratsas et al. 2004). Too little advertising is likely to have no impact on sales; a threshold must be reached for advertising to begin being effective. Likewise, we argue that a concurrent announcement has more “punch” than two stand-alone, positively valenced announcements and therefore is more likely to be noticed by investors. In the following subsection, we draw from research on “attention-grabbing stocks” to explain why concurrent announcements cause a disproportionate increase in visibility, higher than the sum of the separate effects resulting from standalone announcements (Barber and Odean 2008).
Concurrent Announcements and Investor Recognition
Researchers have linked investor attention and breadth of stock ownership to advertising, news coverage, and media coverage of the chief executive officer (CEO) (e.g., Barber and Odean 2008; Grullon, Kanatas, and Weston 2004; Nguyen 2015). For example, Barber and Odean (2008) show that investors are more likely to buy stocks that are in the news than stocks that are not. They also show that investors are more likely to pay attention to and buy stocks that exhibited extreme one-day returns on the previous day, regardless of their performance on the focal day, in part because of their performance and in part because these stocks are more likely to be highlighted in various news outlets, such as the Wall Street Journal’s previous day’s big gainers column. We argue that concurrent announcements share the characteristics of the events highlighted by Barber and Odean because they have a higher chance of moving the stock of the firm in an attention-grabbing territory than a stand-alone, positively valenced announcement. For example, if a standalone new product announcement can increase the price of a stock by 2% and another positively valenced announcement can increase it by 1%, if announced together, they should yield a 3% increase (if not more), and that change in returns has a higher chance of attracting investor attention than the increase produced by either one of the stand-alone announcements.
Thus, we propose and show empirically that issuing a new product announcement concurrently with another positively valenced announcement increases investor recognition more than separately issuing two comparable announcements. However, firms do not equally benefit from the increase in investor recognition, and Merton (1987) captures these differences in his model. Specifically, after establishing that increases in investor recognition are associated with decreases in the cost of equity, Merton presents a set of determinants of the marginal increase in firm value that are due to the increase in investor recognition for that firm’s stock.
In the next subsection, we formally hypothesize three of these determinants as antecedents of concurrent announcements (we include firm size, the fourth and last determinant theorized by Merton [1987], as a control in the empirical analysis). Specifically, drawing from Equation 33 (p. 500) in Merton’s article, we focus on the value of the firm, investor recognition, and idiosyncratic volatility. According to Merton, firms with higher value (i.e., firms facing higher expectations of cash flows), firms with lower investor recognition, and firms with higher idiosyncratic volatility will benefit more from an increase in investor recognition, and their value will also increase to a greater extent. We provide more details on Merton’s model in Web Appendix A.
Conditions Under Which Concurrent New Product Announcements Are More Likely to Occur, and Their Financial Consequences
Firms with high values. Several studies have suggested that high-value and high-performing firms take actions geared toward maintaining their momentum (e.g., Markovitch, Steckel, and Yeung 2005). For example, they may reduce discretionary expenses such as marketing and research-and-development budgets to smooth out their earnings (Chakravarty and Grewal 2011). Alternatively, Markovitch, Steckel, and Yeung (2005) find that pharmaceutical firms that have performed well in the past spend more on direct sales efforts to extract greater value from their existing products. We focus on another avenue that high-value firms can pursue to deliver on the high expectations embedded in their valuation: increasing their investor base.
The stock of high-value firms is likely to trade at a price that is a higher multiple of the firm’s earnings. This stock is expensive, and the average shareholder of these firms, aiming to diversify, is not likely to buy additional shares. These firms would benefit from expanding their investor base and adding new potential buyers who have not previously considered the stock.
We select two proxies that correspond to firm value and to expectations of future cash flows. First, we use Tobin’s q, measured as the ratio of firm value to the book value of assets. Second, we use the news sentiment—the investor sentiment associated with past corporate announcements—as a determinant of investors’ expectations of future firm performance.3
Specifically, when a firm consistently makes announcements over time that are positively perceived by investors, investors gradually begin to expect that this firm will continue to do well in the future. If investors’ expectations are high, the price of that firm’s stock is also likely to be high (relative to its current earnings) as it incorporates higher expectations of future earnings (Warren and Sorescu 2016). This argument applies equally to firms with high news sentiment and to firms with a high Tobin’s q. Therefore, these firms are more likely to take an action, such as issuing concurrent new product announcements, to attract new investors who may be willing to buy a relatively expensive stock. Thus,
H1a: Firms that elicit a higher news sentiment from their recent corporate announcements are more likely to issue concurrent new product announcements than firms whose recent announcements elicit a lower news sentiment.
H1b: Firms with a higher Tobin’s q are more likely to issue concurrent new product announcements than firms with a lower Tobin’s q.
Firms with low investor recognition. The second condition identified from Merton’s (1987) model proposes that the lower the investor recognition, the more the firm benefits from increasing it. Intuitively, a firm that is known to the majority of investors has limited upside potential from further expanding its investor base: most investors have had the opportunity to evaluate the stock and decide whether it is worth owning. In contrast, the potential to find new interested buyers is relatively higher for a firm that has a smaller base of current investors: many potential buyers have simply not yet considered that stock.
Therefore, we propose that firms are more likely to make concurrent new product announcements when they are relatively unknown (research has called the stocks of these firms “neglected” or “generic” stocks; Arbel 1985; Arbel, Carvell, and Strebel 1983). These firms have the strongest incentive to leverage a communication strategy that can place them on the radar of investors unaware of their stock. We include two commonly used proxies for the size of the investor base: institutional investor holdings and the number of analysts following a firm (e.g., Arbel 1985; Baker, Powell, and Weaver 1999). Thus,
H2a: Firms with a lower percentage of shares held by institutional investors are more likely to issue concurrent new product announcements than firms with a higher percentage of shares held by institutional investors.
H2b: Firms followed by a smaller number of analysts are more likely to issue concurrent new product announcements than firms followed by a larger number of analysts.
We note that high firm value and a large investor base are not equivalent concepts. As an illustration, consider a young biotech firm that has a small following of investors who believe that the firm will produce a blockbuster drug at some point in the future. This expectation would result in a relatively high firm value despite the low investor base.
Firms with high idiosyncratic volatility. Firms with high idiosyncratic volatility suffer from high levels of information opacity and a high dispersion of investor opinions (Harris and
Raviv 1993; Moeller, Schlingemann, and Stulz 2007; Shalen 1993). Idiosyncratic volatility is also inversely related to the market power of a firm in its product markets; therefore, firms with high idiosyncratic volatility are more likely to operate in competitive industries with low margins, in which it is more difficult to stand out and attract investor attention (Gaspar and Massa 2006).
These arguments suggest that high idiosyncratic volatility amplifies the effect of investor neglect. For a stock that has few investors, this characteristic induces an additional risk premium because these investors might find it difficult to diversify idiosyncratic risk away. Consequently, the marginal effect of adding new investors on the cost of equity is stronger for firms with high idiosyncratic volatility, and these firms will have a stronger incentive to use communications that can make them more visible to investors, such as concurrent announcements. Thus,
H3: Firms with high idiosyncratic volatility are more likely to issue concurrent new product announcements than firms with low idiosyncratic volatility.
Finally, our last hypothesis follows directly from Merton (1987). As we argued previously, concurrent announcements have the potential to increase investor recognition more than their stand-alone counterparts. In turn, increased investor recognition leads to an increase in the stock liquidity, a decrease in the cost of capital of the firm, and an increase in firm value (Gervais, Kaniel, and Mingelgrin 2001; Grullon, Kanatas, and Weston 2004; Merton 1987). Thus, when firms are in a position to benefit more from investor recognition, concurrent announcements will be associated with a larger increase in firm value than their stand-alone counterparts. Specifically,
H4: The stock market reaction to a concurrent new product announcement made under the conditions described in H1–H3 is greater, on average, than the sum of the reactions to a similar stand-alone new product announcement and a similar standalone positively valenced corporate news item.
Method
Data and Sample
Testing our hypotheses requires a comprehensive sample of positively valenced corporate announcements, including new product announcements. Most event studies of corporate announcements have used archival searches in Dow Jones, LexisNexis, or the Wall Street Journal index to identify the news (e.g., Chaney, Devinney, and Winer 1991; Sood and Tellis 2009; Sorescu, Shankar, and Kushwaha 2007; Wang, Chen, and Chang 2011). However, archival searches typically rely on keywords and cannot guarantee that all corporate announcements are retrieved, which may result in selection bias.
Thus, we compile our sample from RavenPack News Analytics. RavenPack is a news provider that collects all major real-time news wires and news from other Internet sources, including financial and business websites, such as the Wall Street Journal, Dow Jones, Barron’s, blogs, and local and regional newspapers. Although researchers in finance and accounting have increasingly used RavenPack (e.g., Akbas et al. 2016; Kelley and Tetlock 2013; Samadi 2016; Shroff et al. 2013), marketing researchers have not yet widely adopted it.
A useful feature in RavenPack is the classification of corporate news into categories such as product releases, acquisitions, award announcements, and executive appointments. The most relevant category for our study is product releases, which include announcements of new products, new services, or upgrades of existing products or services. To test our hypotheses, we need to compile, in addition to product releases, all other types of corporate news announced on the same day as the product releases, as well as control samples of stand-alone corporate announcements. Again, we define concurrent announcements as two separate announcements issued on the same calendar day by the same firm. We choose a one-day window for concurrent announcements so that we can leverage the visibility arguments implicit in Merton’s (1987) theory.
We use the RavenPack database to obtain all new product releases and other types of positively valenced announcements by U.S. publicly traded companies from January 2003 to December 2013. For each announcement in the sample, the database provides the date when it was issued, the name of the parent company, and an event-specific sentiment score (ESS). The ESS measures the valence of the news; a computer algorithm sets the strength of the score using a coding system established by experts who classify entity-specific events according to whether they convey generally positive or negative sentiment to investors and the degree to which they do so. The ESS ranges from 0 to 100, whereby values above 50 indicate a more positive sentiment and values below 50 denote a more negative sentiment. A score of 50 indicates that the news is neutral, in the sense that the experts believe that it will not influence firm value.4
To obtain a clean, usable sample, we employed two steps: First, our focus is on events whose timing the firm can control; as a result, the sample is limited to announcements made directly by the firm through a press release. Second, we use the RavenPack variable called “relevance score,” which enables us to reliably ascribe each announcement to its parent firm. To confirm that the press release is generated by the specific parent company (and not by another entity that may tangentially refer to the parent firm in one of its press releases), we use only announcements with a relevance score of 100. A relevance score of 100 is always ascribed to the firm issuing the announcement, whereas a lower relevance score may be assigned to a competitor marginally referenced in the announcement.
We assemble two subsamples of firm announcements. The first subsample includes all product releases, and the second subsample includes all other positively valenced corporate announcements whose ESS is greater than 50.5 We collect the stock return data from the Center for Research in Security Prices (CRSP), financial data from Compustat, and investor recognition data from Thomson Reuters (institutional holdings) and I/B/E/S (analyst following).
We eliminate announcements for which we could not collect financial data from Compustat, CRSP, or Thomson Reuters. Finally, to cleanly measure the stock market reaction to the concurrent announcements, we eliminate other press releases issued by firms in our sample a day before or a day after the concurrent announcement. This is consistent with our definition and in line with previous research (e.g., Chen, Ganesan, and Liu 2009; Geyskens, Gielens, and Dekimpe 2002; Homburg, Vollmayr, and Hahn 2014).
The final sample contains two subsamples. The first subsample includes 28,753 new product announcements. Of these, 1,162 were released on the same day as one other positively valenced corporate announcement (other than new product releases). The remaining 27,591 announcements are standalone new product announcements. The second subsample consists of 53,597 positively valenced corporate news that are not new product announcements and belong to categories such as partnerships, acquisitions, awards, facility upgrades, executive appointments, and so on. This subsample includes 1,162 corporate announcements issued on the same day as the 1,162 new product announcements included in the first subsample and 52,435 positively valenced stand-alone corporate news.
Announcements included in the two subsamples are press releases from 2,873 firms. We use the complete set of firms tracked in the RavenPack database that made at least one new product announcement across the sample period, whether they used concurrent announcements or not.
Variables and Models
To test H1a–H3, we use two logistic regressions. The first regression models the probability that a new product announcement is made concurrently with another corporate announcement, as opposed to being stand-alone (we conduct the estimation over the sample of new product announcements, both concurrent and stand-alone). The second regression models the probability that a corporate announcement other than a new product announcement is made concurrently with a new product announcement, as opposed to being stand-alone (we conduct the estimation over the sample of corporate announcements excluding new product announcements, both concurrent and stand-alone). In each model, the dependent variable is a categorical dummy variable, as follows:
• In the first model, Concurrent_NPA equals 1 if the new product
was announced on the same day as one other positively valenced corporate news item (other than product releases) and 0 if the new product announcement is stand-alone.
• In the second model, Concurrent_News equals 1 if the corporate news (other than products releases) was announced on the same day as a new product release and 0 if the corporate announcement is stand-alone.
We next define our independent variables. We construct variables based on RavenPack or CRSP data (available at the daily level) using data collected up to the day preceding the announcement window. We construct variables based on financial data available at the quarterly or annual level using the most recent fiscal quarter (or fiscal year) preceding the announcement window: the announcement window is the threeday window centered on the announcement day, which, as we describe shortly, we use to assess the stock market reaction to the announcement.
News sentiment (News_Sentiment). We use firms’ average news sentiment before the announcements as a proxy for firm value and for investors’ expectations of the firm’s future cash flows. If a firm has recently undertaken a string of positively received actions, investors expect the firm to continue to do so and to generate high cash flow in the future. We measure news sentiment by averaging the ESSs for all announcements of all types made by a firm six months before each announcement under consideration. For robustness, we subsequently report the results obtained with the same variable computed over rolling windows of 1, 2, 3, and 12 months before the event.
Tobin’s q (Tobin_q). We measure firms’ Tobin’s q before each announcement as a proxy for the value of the firm (e.g., Morgan and Rego 2009; Rubera and Kirca 2012; Sorescu and Spanjol 2008). Tobin’s q is the ratio of a firm’s market value of assets to its replacement value assets for the year before the announcement and is calculated as
where AT is the book value of the total assets, Price is the price of the stock, CSQ is the number of common shares outstanding, and CEQ is the book value of common stocks. We obtain data used to compute Tobin’s q from Compustat. This operationalization of Tobin’s q differs slightly from one proposed by Chung and Pruitt (1994), which has been frequently used in the marketing literature. In Web Appendix B, we explain the difference between our approach and Chung and Pruitt’s and we report results obtained with it.
Investor recognition. In line with previous research, we use two proxies for investor recognition: percentage of shares held by institutions (Institutional_Holdings) and number of analysts following the firm (Analysts) (Arbel 1985; Arbel, Carvell, and Strebel 1983; Jain and Kim 2006; King and Segal 2009).
Stock return volatility (Volatility). Firms’ idiosyncratic volatility is the standard deviation of the market-adjusted residuals of daily stock returns recorded during the quarter preceding the measurement date (e.g., Gaspar and Massa 2006; Moeller, Schlingemann, and Stulz 2007).
Control Variables
Frequency of firms’ recent news. While we use Merton’s (1987) model to identify theory-driven conditions in which concurrent announcements are more likely to occur, we also need to control for the cost of making these announcements. That is, if firms have difficulty making concurrent announcements, they may be more likely not to use this communication strategy, even if Merton’s model suggests that they stand to benefit from doing so. For example, firms that seldom make new product announcements or any other types of positively valenced corporate announcements may need to delay their press releases to be able to issue them concurrently, which may not be optimal from a competitive standpoint (Moorman et al. 2012). We use the following two variables to account for firms’ ability to make concurrent new product announcements:
• New product announcements (Firm_NPA): We control for
the frequency of firms’ new product announcements. To capture this frequency, we compute rolling window measurements of the number of new product announcements that firms made in the six months preceding each new product announcement included in the sample.
• Other corporate news (Corporate_News): Similarly, we con
trol for the frequency of firms’ recent corporate news (other than product releases) and compute this variable as a rollingwindow measurement of the number of all the firms’ corporate announcements (other than new product announcements) made in the six months preceding the announcement of each corporate news item included in the sample.
Past concurrent announcements. Firms that have used concurrent announcements in the past might be in a better position to understand the conditions under which these announcements succeed in increasing investor recognition and might be more likely to identify when these conditions occur. We use the relative number of concurrent announcements to total announcements of the firm as a proxy for such factors, and we use two rolling-window variables, Relative_Concurrent_ NPA and Relative_Concurrent_News, computed as the ratio of the counts of the concurrent new product announcements (other corporate announcements) to the counts of stand-alone new product announcements (other corporate announcements) made by the firm in the six months preceding each new product announcement. We use a relative measure because the absolute number of concurrent announcements is correlated with the total number of announcements, which we also include in the model estimated.
Firm-specific factors. The propensity to combine announcements may differ for firms of different sizes, performance, and capital structure. We control for firm size (Firm_Size) using the logarithm of firm assets (e.g., Lin and Chang 2012), for firm performance using return on assets (ROA) (Luo, Homburg, and Wieseke 2010; Rego, Billett, and Morgan 2009), and for firms’ capital structure using financial leverage (Luo, Homburg, and Wieseke 2010).
Industry-specific factors. To control for the effects of industry-specific factors on the occurrence of concurrent announcements, we include three control variables corresponding to the industry’s innovativeness, communication baseline with the investors, and competitiveness:
• Competitors’ recent new product announcements (Com
petitors_NPA): We control for the industry’s innovativeness by computing rolling-window measurements of the counts of new product announcements made by the firm’s competitors in the six months preceding each new product announcement; we define competitors as all firms operating in the same threedigit Standard Industrial Classification code.
• Competitors’ communication baseline with investors
(Competitors_News): To control for the industry’s communication baseline with investors, we compute rollingwindow measurements of the counts of competitors’ corporate announcements (other than new product announcements) in the six months preceding each corporate announcement.
• Industry concentration (Industry_Concentration): We con
trol for industry concentration using the Herfindahl index (e.g., Lee and Grewal 2004; Lin and Chang 2012). Similar to previous studies, we calculate the Herfindahl index as the sum of the squared percentage of sales of firms in the same threedigit Standard Industrial Classification code.
Models
Testing H1a–H3. To test H1a–H3, we run two separate logit models that estimate the probability of new product announcements being made concurrently with other types of corporate news. The first equation estimates the probability that a new product announcement is made concurrently with another positively valenced corporate announcement, as opposed to being stand-alone. The second equation estimates the probability that a corporate announcement (other than a new product announcement) is made concurrently with a new product announcement, as opposed to being stand-alone. Thus, the first model focuses on product releases, and the second model
In both models, the subscripts i and t denote the firm and the announcement date, respectively, while the subscripts p and c denote new product announcements and other types of cor
porate announcements, respectively. The terms epit and ecit are random firm- and time-specific effects, and the other variables are as previously defined. In addition, Investor_Recognition includes both Institutional_Holdings and Analysts, which we add to the models one at a time because of their high correlation. Year is a linear time trend included to control for the possibility that concurrent announcements have increased in recent years. In both models, we adjust the standard errors for possible simultaneous correlations across firms and time in the residuals using two-dimensional clustered standard errors (Petersen 2009).6
Testing H4. To test H4, we verify that the stock market reaction to a concurrent new product announcement made under the conditions described in H1a–H3 is greater, on average, than the sum of the reactions to two similar, separately issued announcements. We use the short-term event study methodology to compute the stock market reaction to concurrent announcements. Short-term event studies are commonly used to measure the stock market reaction to a firm’s financial, strategic, or marketing announcements. In particular, research has used such studies to investigate new product announcements (e.g., Chaney, Devinney, and Winer 1991; Sood and Tellis 2005), partnerships (e.g., Kale, Dyer, and Singh 2002), channel additions (e.g., Homburg, Vollmayr, and Hahn 2014), and brand acquisitions (e.g., Wiles, Morgan, and Rego 2012). The methodology is well specified over short-term horizons (see Brown and Warner 1985).
Specifically, we estimate abnormal returns (AR) for the firms in our sample as
where Rit is the realized rate of return of stock i on day t and E(Rit) is the estimated return of stock i on day t in the absence of the event. We compute E(Rit) in three ways and provide results obtained with all three measures: where Rmt is the average rate of return of all stocks trading on the U.S. stock market, Rft is the risk-free rate of return on a U.S. Treasury bond on day t, SMB is the difference between the rate
of returns of small and large stocks, HML is the difference in
returns between high and low book-to-market stocks, and UMD
is the momentum factor, all during day t. Equation 5 corre
sponds to the market-adjusted model, Equation 6 to the market model, and Equation 7 to the Fama–French–Carhart model (Brown and Warner 1985; Carhart 1997).
We calculate ARit as the abnormal return of firm i on day t using each of the three models. We then calculate the daily
cumulative abnormal returns (CARs) over a time window (t1, t2) around the announcements day, as follows:
We use a three-day window around the event date (t1 = t – 1, t2 = t + 1) to account for possible information leakage during the day before the announcement and for possible delays in the dissemination of news during the day after the announcement. We
compute the CARs for each of the different methods used to
estimated expected returns: MAR_CAR are the CARs for the
market-adjusted model, MM_CAR are the CARs for the market model, and FFC_CAR are the CARs for the Fama–French– Carhart model.
To test H4, we need to show that the CARs to the concurrent announcements are significantly higher than the sum of the CARs to stand-alone announcements:
If firms randomly made some announcements concurrently and some separately, we could have tested Inequality 9 using t-tests of the mean differences between CAR_Concurrentit and (CAR_NPA[stand-alone]it + CAR_Corporate_News[stand-alone]it). However, concurrent announcements do not occur randomly, so we need to account for the circumstances under which they do so. To eliminate selection bias, we would ideally need to observe the counterfactual stand-alone new product announcement and the counterfactual stand-alone corporate announcement that correspond to each concurrent announcement. Because these counterfactual announcements are not available, we employ an appropriate statistical matching method, PSM, to control for the potential endogeneity resulting from the non-randomly-assigned treatments (Angrist, Imbens, and Rubin 1996; Verbeek 2008). Propensity score matching has been widely used in economics (e.g., Dehejia and Wahba 1999), information systems (e.g., Rishika et al. 2013), strategic management (e.g., Chang, Chung, and Moon 2013), and marketing research (e.g., Garnefeld et al. 2013; Wangenheim and Bayo´n 2007). Furthermore, several studies in finance have applied PSM to compute matched differences in short-term CARs to various events, including acquisition announcements and announcements of sovereign wealth fund equity investments (e.g., Bortolotti, Fotak, and Megginson 2015; IskandarDatta and Jia 2012; Masulis and Nahata 2011). This method leverages matching techniques that identify the “statistical twin” of each treated observation in the pool of untreated ones (Guo and Fraser 2010; Rosenbaum and Rubin 1983).
Several matching techniques have been proposed in the literature (for a review, see Caliendo and Kopeinig 2008). We present our main results using matching conducted with the nearest neighbor with caliper. In the “Robustness Checks and Additional Analysis” subsection, we report results using an alternative method, Kernel matching. The nearest-neighbor method is a one-on-one matching method that helps reduce bias (the difference between the treated and untreated group) and is preferred when researchers have large samples of untreated observations relative to the treated group (Caliendo and Kopeinig 2008).
Propensity score matching uses a propensity score as the criterion to find the most similar match to the treated observation. We use the estimated probability obtained from Equations 2 and 3 as the propensity score (i.e., as the metric used announcements). Nearest-neighbor PSM matches each treated announcement (concurrent) with the untreated one (stand-alone) that has the closest propensity score to the treated announcement (e.g., Bronnenberg, Dube´, and Mela 2010; Rosenbaum and Rubin 1983). To increase the quality of matching and to ensure that the propensity scores in the control samples are reasonably close to those in the treated samples, nearest neighbor with caliper requires that the absolute distance between the two propensity scores be less than a predetermined caliper (e), calculated as e = .25sP, where sP is the standard deviation of the propensity score (Guo and Fraser 2010).
To increase the similarity between announcements in the treated and control groups and to account for the unobservable industry- and firm-specific factors not included in the logit models, we conduct two separate types of matching. In the first matching method, we require that the matched announcements belong to the same industry. In the second matching method, we require that the announcements belong to the same firm. This second type of matching is more conservative and may lead to a loss of data, but it offers a cleaner comparison between treated and control groups because it helps control for unobserved firm heterogeneity.
For each matching method (within the same industry or within the same firm), we estimate two matching procedures. The first PSM procedure runs through the sample of new product announcements. Using the estimated probability from Equation 2, we identify, for each concurrent announcement, the stand-alone new product announcement that is from the same industry (respectively the same firm) and has the closest propensity score to the concurrent new product announcement within the appropriate caliper. We label this stand-alone matched new product announcement as Matched_NPA. The second PSM procedure runs through the sample of other positively valenced corporate news and identifies, for each concurrent announcement, the stand-alone corporate announcement that meets the two criteria described previously. We label this stand-alone matched corporate announcement as Matched_News.
After obtaining the Matched_NPA and Matched_News for each treated observation, we align them with the corresponding concurrent announcements to calculate the treatment effect as follows:
The average of these differences for all the announcements in the treated group is the average treatment effect (ATE):
To test H4, we run t-tests on the ATE_CAR defined in Equation 11.
Results
Panel A of Table 1 shows descriptive statistics for the subsample of new product announcements, and Panel B shows descriptive statistics for the other positively valenced corporate news. The first column in each panel defines the variables of interest, and the next four columns provide means and standard deviations for all variables, for concurrent and stand-alone announcements. The remaining columns provide correlations within variables.
TABLE:
| | New Product Announcements (N 5 28,753) | |
|---|
| | Concurrent (N 5 1,162) | Stand-Alone (N 5 27,591) | Correlationsa |
|---|
| Variables | M | SD | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
|---|
| 1. MAR_CAR (%) | .423 | 5.41 | .308 | 5.04 | 1 | | | | | | | | | | | | | | |
| 2. MM_CAR (%) | .505 | 5.36 | .354 | 5.00 | .95 | 1 | | | | | | | | | | | | | |
| 3. FFC_CAR (%) | .421 | 5.37 | .302 | 5.01 | .93 | .95 | 1 | | | | | | | | | | | | |
| 4. News_Sentiment | 59.25 | 3.82 | 57.04 | 4.15 | -.03 | -.03 | -.03 | 1 | | | | | | | | | | | |
| 5. Tobin_q | 2.07 | 1.34 | 2.11 | 1.49 | -.00 | -.01 | -.00 | .09 | 1 | | | | | | | | | | |
| 6. Institutional_Holdings | .43 | .49 | .55 | .46 | -.03 | -.03 | -.03 | .03 | .04 | 1 | | | | | | | | | |
| 7. Analyst | 12.08 | 12.07 | 8.58 | 10.98 | -.04 | -.03 | -.04 | .23 | .11 | .60 | 1 | | | | | | | | |
| 8. Volatility | .021 | .018 | .025 | .019 | .08 | .08 | .08 | -.13 | .03 | -.19 | -.27 | 1 | | | | | | | |
| 9. Firm_NPA | 8.55 | 10.07 | 3.61 | 5.65 | -.03 | -.02 | -.03 | .45 | -.01 | .04 | .35 | -.18 | 1 | | | | | | |
| 10. Relative_Concurrent_NPA | .14 | .19 | .06 | .15 | -.01 | -.01 | -.01 | .21 | .01 | .01 | .16 | -.10 | .33 | 1 | | | | | |
| 11. Competitors_NPA | 176.6 | 176.8 | 162.6 | 172.93 | .00 | .00 | .00 | .20 | .19 | -.00 | .07 | .04 | .08 | .06 | 1 | | | | |
| 12. Industry_Concentration | .21 | .18 | .18 | .16 | -.01 | -.01 | -.01 | -.03 | -.13 | -.00 | .00 | -.06 | .07 | .03 | -.45 | 1 | | | |
| 13. Firm_Size (in billion dollars) | 107.2 | 297.7 | 29.3 | 142.3 | -.08 | -.08 | -.08 | .23 | -.19 | .11 | .48 | -.45 | .45 | .25 | -.19 | .20 | 1 | | |
| 14. Leverage | .16 | .18 | .15 | .20 | -.01 | -.01 | -.01 | -.05 | -.11 | -.06 | -.04 | -.02 | .03 | .01 | -.21 | .13 | .21 | 1 | |
| 15. ROA | .024 | .22 | -.041 | 1.38 | .00 | -.00 | .00 | .01 | .01 | .05 | .05 | -.16 | .03 | .02 | .01 | .02 | .11 | .01 | 1 |
The average CARs are positive and significant for all three subsamples: concurrent new product announcements, stand-alone new product announcements, and stand-alone other announcements (all ps < .01). For the new product announcements, our findings are in line with prior research that shows that investors reward, on average, new product introductions. For the other types of corporate announcements, the significantly positive average CARs provide face validity for the ESS we used to determine the valence of these announcements.
Table 1 also reports the correlation matrix for each subsample. The correlations between the three CAR variables are high (.93 or higher), indicating that the abnormal returns measured with the three alternative methods are similar. The correlation between institutional holdings and number of analysts is also high in both subsamples (.60 in Panel A and .69 in Panel B). This result is consistent with research in accounting that documents a strong link between analysts’ decisions to follow firms and institutional investors’ decisions to hold the same firms in their portfolios (e.g., O’Brien and Bhushan 1990). Therefore, we use these as alternative measures of investor recognition in separate models.
Test of Hypotheses
TABLE:
| | New Product Announcements (N 5 28,753) | |
|---|
| | Concurrent (N 5 1,162) | Stand-Alone (N 5 27,591) | Correlationsa |
|---|
| Variables | M | SD | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
|---|
| aCorrelations are based, in each case, on the subsample described in the heading of the panel (stand-alone + concurrent announcements). |
| bWe include the means and standard deviations for concurrent announcements in both Panels A and B to maintain the symmetry of the table. |
| 1. MAR_CAR (%) | .423 | 5.41 | .500 | 6.92 | 1 | | | | | | | | | | | | | | |
| 2. MM_CAR (%) | .505 | 5.36 | .508 | 6.91 | .97 | 1 | | | | | | | | | | | | | |
| 3. FFC_CAR (%) | .421 | 5.37 | .522 | 6.93 | .95 | .96 | 1 | | | | | | | | | | | | |
| 4. News_Sentiment | 59.25 | 3.82 | 56.11 | 4.15 | -.02 | -.02 | -.02 | 1 | | | | | | | | | | | |
| 5. Tobin_q | 2.07 | 1.34 | 1.97 | 1.43 | -.00 | -.01 | -.00 | .09 | 1 | | | | | | | | | | |
| 6. Institutional_Holdings | .43 | .49 | .53 | .49 | -.02 | -.02 | -.02 | .01 | .03 | 1 | | | | | | | | | |
| 7. Analyst | 12.08 | 12.07 | 6.99 | 9.25 | -.03 | -.03 | -.03 | .11 | .05 | .69 | 1 | | | | | | | | |
| 8. Volatility | .021 | .018 | .025 | .020 | .08 | .08 | .09 | -.09 | .05 | -.19 | -.23 | 1 | | | | | | | |
| 9. Corporate_News | 15.87 | 12.43 | 8.25 | 6.02 | -.03 | -.03 | -.03 | .30 | -.01 | .05 | .21 | -.16 | 1 | | | | | | |
| 10. Relative_Concurrent_News | .19 | .19 | .08 | .14 | -.02 | -.02 | -.03 | .11 | -.04 | .04 | .14 | -10 | .40 | 1 | | | | | |
| 11. Competitors_News | 643.7 | 667.4 | 488.5 | 616.2 | .00 | -.01 | .00 | .19 | .19 | -.00 | -.01 | .09 | -.01 | -.01 | 1 | | | | |
| 12. Industry_Concentration | .21 | .18 | .20 | .18 | -.00 | .00 | -.01 | -.04 | -.11 | .02 | .01 | -.07 | .1 | .06 | -.45 | 1 | | | |
| 13. Firm_Size (in billion dollars) | 107.2 | 297.7 | 35.7 | 178.4 | -.07 | -.06 | -.07 | .02 | -.01 | -.02 | .07 | -.06 | .43 | .17 | -.06 | .07 | 1 | | |
| 14. Leverage | .16 | .18 | .17 | .20 | -.01 | -.01 | -.00 | -.01 | -.10 | .00 | -.00 | -.05 | .07 | .04 | -.18 | .11 | -.00 | 1 | |
| 15. ROA | .024 | .22 | -.056 | 1.48 | .01 | .00 | .01 | -.00 | -.00 | .05 | .05 | -14 | .02 | .01 | -.02 | .10 | .01 | .02 | 1 |
Results for H1a–H3. Table 2 shows the results for the logit models. Panel A shows the results for Equation 2. In Model A-1 (A-2), we measure investor recognition using institutional holdings (the number of analysts following the firm). H1a and H1b propose a positive effect of news sentiment and Tobin’s q on the likelihood that the new product will be announced concurrently with other news. The coefficients for the firm’s news sentiment and Tobin’s q are positive and significant in both models (news sentiment: bp2 = .058 [Model A-1], bp2 = .063 [Model A-2]; ps < .01; Tobin’s q: bp3 = .057 [Model A-1], bp3 = .062 [Model A-2]; ps < .05). Thus, the results provide support for H1a and H1b: firms are more likely to issue concurrent announcements when their firm value is high.7
TABLE:
| DV: Concurrent_NPA (N 5 28,753) | Model A-1 | Model A-2 |
|---|
| News_Sentiment | .058*** (.011) | .063*** (.011) |
| Tobin_q | .057** (.028) | .062** (.027) |
| Institutional_Holdings | -1.16*** (.23) | – |
| Analyst | – | -.022*** (.0064) |
| Volatility | 3.36* (1.72) | 4.64*** (1.42) |
| Firm_NPA | .0098 (.0088) | .015* (.0089) |
| Relative_Concurrent_NPA | .30 (.19) | .43** (.19) |
| Competitors_NPA | .0011*** (.00029) | .0011*** (.00026) |
| Industry_Concentration | .79*** (.30) | .71*** (.26) |
| Firm_Size | .26*** (.024) | .28*** (.025) |
| Leverage | -.00075 (.205) | .032 (.018) |
| ROA | .11 (.20) | .0032 (.018) |
| Year | -.0030 (.015) | -.012 (.014) |
| Wald c2 2 | 42.44 | 285.92 |
| Log-likelihood | -4,323.62 | -4,389.86 |
| Pseudo-R2 | .19 | .14 |
| AIC | 8,675.25 | 8,807.71 |
| DV: Concurrent_NPA (N 5 28,753) | Model A-1 | Model A-2 |
|---|
| *p < .10. |
| **p < .05. |
| ***p < .01. |
| News_Sentiment | .079*** (.010) | .086*** (.0098) |
| Tobin_q | .093*** (.031) | .084*** (.030) |
| Institutional_Holdings | -1.44*** (.11) | – |
| Analyst | – | -.013** (.0047) |
| Volatility | 3.66* (2.18) | 5.93*** (2.04) |
| Corporate_News | .017*** (.0057) | .018*** (.0055) |
| Relative_Concurrent_News | .60** (.24) | .73*** (.23) |
| Competitors_News | .00055*** (.00010) | .00050*** (.00009) |
| Industry_Concentration | .63* (.36) | .52 (.32) |
| Firm_Size | .29*** (.029) | .26*** (.027) |
| Leverage | -.40 (.29) | -.34 (.25) |
| ROA | .22 (.22) | .022 (.11) |
| Year | .032** (.013) | .0062 (.012) |
| Wald c2 | 408.38 | 304.14 |
| Log-likelihood | -4,535.57 | -4,635.79 |
| Pseudo-R2 | .23 | .14 |
| AIC | 9,099.14 | 9,299.59 |
H2a and H2b focus on the effect of investor recognition on the likelihood that the new product will be announced concurrently with other news. The coefficient for institutional holdings is negative and significant in Model A-1 (bp4 = –1.16, p < .01). Similarly, the coefficient for the number of analysts following is negative and significant in Model A-2 (bp4 = –.022, p < .01). These results are consistent with H2a and H2b, respectively: firms with low investor recognition are more likely to make concurrent announcements in the hope of increasing such recognition and, subsequently, their firm value.
H3 focuses on the positive effect of idiosyncratic volatility for volatility is positive and marginally significant in Model A-1 (bp5 = 3.36, p < .10) and significant in Model A-2 (bp5 = 4.64, p < .01).
Panel B of Table 2 provides the results for Equation 3. The coefficients for firm value and investor recognition are consistent with those presented in Panel A (news sentiment: bc2 = .079 [Model B-1], bc2 = .086 [Model B-2]; ps < .01; Tobin’s q: bc3 = .093 [Model B-1], bc3 = .084 [Model B-2]; ps < .01; institutional holdings: bc4 = –1.44, p < .01 [Model B-1]; analysts: bc4 = –.013, p < .05 [Model B-2]). Therefore, H1a–H2b are also supported in the subsample of other corporate news.
The coefficient for volatility is positive and marginally significant in Model B-1 (bc5 = 3.66, p < .10) and significant in Model B-2 (bc5 = 5.93, p < .01). Thus, we obtain partial support for H3 in the subsample of other corporate news. We find that firms with high idiosyncratic volatility are more likely to make concurrent announcements if analyst following is used as a measure of investor recognition.
We also find that competitors’ propensity to announce new products and other types of corporate announcements is positively associated with the likelihood of concurrent announcements (p < .01), as is industry concentration (Models A-1–A-2: p < .01; Model B-1: p < .10). Larger firms are also more likely to make concurrent announcements (p < .01), consistent with Merton’s (1987) predictions. Finally, we find that ROA and leverage do not significantly affect the dependent variables in any of the models (A-1–B-2).
Results for H4. We examine the impact of concurrent announcements on abnormal returns by verifying that ATE_CAR > 0. To carry out the matching necessary to compute ATE_CAR, we extract estimated probabilities from
the logit models presented in the previous section. The estimated probabilities can come from Models A-1 and B-1 or, alternatively, from Models A-2 and B-2. A comparison of the explanatory power and goodness of fit of these models indicates that the pseudo-R-square statistics are higher for Model A-1 (.19) than for Model A-2 (.14) and for Model B-1 (.23) than for Model B-2 (.14). Models A-1 and B-1 also have lower Akaike information criterion (AIC) rates than Models A-2 and B-2, respectively. Therefore, we use estimated probabilities obtained from Models A-1 and B-1 as propensity scores for the matching procedure. The results obtained from Models A-2 and B-2 (not
reported) are substantively similar to those presented here. After we identify the two matched groups of stand-alone
announcements, we test the quality of matching by calculating the percentage reduction in bias (PRB) statistic (e.g., Garnefeld et al. 2013; Wangenheim and Bayo´n 2007).8 The PRB statistic shows the reduction in bias (i.e., the difference in the mean of covariates between the treated and untreated groups) obtained from using the matched group rather than a random group of stand-alone announcements. The average PRB for matching within the same firm is 88.37% for the sample of new product announcements and 80.00% for the sample of the other positively valenced news. Thus, the matching procedure helped us generate a control group that is more similar to the treated group, in that the mean differences between the treated and the matched group on relevant observable covariates are, on average, 80% smaller than the mean differences between the treated and untreated announcements on the same covariates. The average
PRB for our matching procedures compares favorably with the PRB reported in previous research (e.g., Garnefeld et al. 2013; Wangenheim and Bayo´n 2007). Finally, the average PRB for within-industry matching is 78.3% for matching of new product announcements and 66.84% for matching of other corporate news. This indicates that within-firm matching, which accounts for firm-specific unobservable factors, provides a higher-quality matching than the within-industry procedure.
We also conduct a Kolmogorov–Smirnov test to compare the distribution of the propensity score in the treated and untreated groups with that in the treated and matched groups. The p-value of the Kolmogorov–Smirnov test that compares the concurrent announcements with the untreated stand-alone announcements is less than .001, while the p-value of the test that compares the concurrent announcements and matched
stand-alone announcements is equal to 1 (for both matching within firm and within industry). These statistics provide evidence of similarity of propensity score distributions in the treated and matched groups.
Finally, we proceed with the matched samples to calculate the ATE_CAR defined in Equation 11 and to test whether ATE_CAR > 0. Panel A of Table 3 shows the test of ATEs for all three CAR models. The ATEs for all three CAR models are positive and significant (matched within the same firms: ATE_MAR_CAR = .430%, ATE_MM_CAR = .386%, ATE_ FFC_CAR = .397%; matched within the same industry: ATE_MAR_CAR = .343%, MM_CAR = .398%, FFC_CAR = .386%; all ps < .05). These results indicate that the average CARs for the concurrent announcements are, on average, approximately.4% higher than the sum of the CARs for stand-alone new product announcements and stand-alone positively valenced corporate news. Thus, concurrent announcements are associated with increases to firm value when made under the conditions previously described and incorporated in the logit model.
Process Check: Are Concurrent Announcements Associated with Changes in Investor Recognition?
For each concurrent announcement and its matched stand-alone counterparts, we estimate changes in investor recognition by calculating changes in institutional holdings and the number of analysts surrounding the announcements. These two metrics are available on a quarterly basis. Therefore, we compare the values of these variables in the quarter immediately preceding the announcement with those in the quarter immediately following it. If concurrent new product announcements increase firm value because of an increase in investor recognition, as Merton (1987) indicates, we should observe higher growth in institutional holdings and number of analysts after concurrent announcements than the sum of changes after the matched stand-alone announcements.
As a robustness check, we also calculate changes in trading volume. Previous research has used trading volume, defined as the total number of shares that changes hands during each trading day, as a proxy for visibility of the firm’s stock (Barber and Odean 2008; Gervais, Kaniel, and Mingelgrin 2001). Trading volume captures not only awareness but also expectations of higher firm value (Gervais, Kaniel, and Mingelgrin 2001) and therefore is a weaker proxy than institutional holdings and analyst coverage. However, it is the only proxy for investor recognition for which data are available on a daily basis; thus, it enables us to calculate the changes in investor recognition inside the window of the focal event and to document a more precise association between the concurrent announcement and the increase in investor recognition. We compute abnormal trading volume by comparing the mean volume of trade during the period of interest with the mean volume of trade during an earlier reference period for the same stock (Akbas 2016; Bailey et al. 2003; Brav et al. 2008; Graham, Michaely, and Roberts 2003; Loughran and McDonald 2011). To capture the change of investor recognition occurring during the event window, we compute a short-term measure by taking the difference in the average trading volume measured during the three-day event window and the average trading volume measured during the three-day window immediately before the event window. In addition, we compute a long-term measure by taking the average trading volume during the quarter that immediately follows the event window and subtract from it the average trading volume during the quarter that immediately precedes the announcement window. Similar to the short-term measure, we divide this difference by the average trading volume from the preceding quarter, which results in a percentage change in trading volume.
In a manner similar to our previous tests based on CARs (Table 3, Panel A), we perform a t-test on the ATE for changes in investor recognition for each of the four alternative proxies described previously. The results appear in Panel B of Table 3. The ATEs for all four variables are positive and significant (p < .05 or better when matched within the same firm, and p < .10 or better when matched within the same industry), suggesting that after making concurrent new product announcements, firms experience, on average, an increase in their institutional holdings, in the number of analysts following their firm, and in the volume of trade of their stock. This process check provides empirical evidence for the theoretical investor recognition mechanism derived from Merton (1987). The positive ATE for trading volume measured in the announcement window also confirms our assertion derived from Barber and Odean (2008): concurrent new product announcements are indeed more likely to turn the firms’ stocks into attention-grabbing stocks than their stand-alone counterparts. We note that other factors could also affect changes to institutional holdings, analyst coverage, or trading volume and that our data and empirical tests do not allow us to establish causality; however, our empirical evidence highlights one channel through which firms can leverage their corporate communications to create shareholder value. The Appendix provides a summary of the significant effects obtained across all analyses.
TABLE:
| A: On Stock Market Returns |
|---|
| ATEs | PSM Variables (Nearest Neighbor with Caliper) |
|---|
| CARconcurrent – (CARmatched NPA 1 CARmatched corporate news) | Match Within the Same Industrya | Match Within the Same Firmb |
|---|
| ATE_MAR_CAR (%) | .343** | .430** |
| ATE_MM_CAR (%) | .398** | .386** |
| ATE_FFC_CAR (%) | .386** | .397** |
| A: On Stock Market Returns |
|---|
| ATEs | PSM Variables (Nearest Neighbor with Caliper) |
|---|
| CARconcurrent – (CARmatched NPA 1 CARmatched corporate news) | Match Within the Same Industrya | Match Within the Same Firmb |
|---|
| *p < .10. |
| **p < .05. |
| ***p < .01. |
| aN = 1,060 treated versus 1,060 matched. We are able to match 91.2% of the concurrent announcements. We lose 102 announcements from the restrictions of the matching method: when the matching is within the same industry and it uses a caliper, not all treated observations have a match that meets all imposed matching conditions. bN = 917 treated versus 917 matched. The restr |
| bN = 917 treated versus 917 matched. The restrictions are even more stringent in matching within the same firm, in which we are able to match 917 (78.9%) concurrent announcements. |
| ATE_Change_Institutional_Holding (%) | .89** | 1.50*** |
| CIHconcurrent – (CIHmatched NPA + CIHcorporate news) ATE_Change_Analysts | .053* | .073** |
| CAconcurrent – (CAmatched NPA + CAcorporate news) ATE_Change_TradingVolumeevent window (%) CTVEconcurrent – (CTVEmatched NPA + CTVEcorporate news) | 4.92** | 4.21** |
| ATE_Change_TradingVolumequarterly (%) CTVQconcurrent – (CTVQmatched NPA + CTVQcorporate news) | 2.82* | 3.53** |
Are Merton’s (1987) Model Prescriptions Verified for Concurrent Announcements?
The analyses so far provide evidence in support of Merton’s (1987) model: concurrent new product announcements can, under specific conditions, be beneficial to firms by increasing investor recognition. Next, we investigate whether the same factors that are conducive to an increase in investor recognition are also associated with a corresponding increase in firm value (measured by CARs). We examine differences in CARs for concurrent new product announcements made by firms in the highest and lowest decile of the five factors previously used in H1a–H3: ( 1) Tobin’s q, ( 2) news sentiment, ( 3) idiosyncratic volatility, ( 4) institutional holdings, and ( 5) number of analysts following the firm. The results corroborate our main finding: CARs to concurrent new product announcements are significantly higher for firms with high Tobin’s q (p < .05), high volatility (p < .05), and the small number of analysts following the firm (p < .05). In addition, CARs to concurrent new product announcements are marginally higher for firms with high expectations (high past news sentiment) (p = .058) and for firms with low institutional holdings (p = .083).
Robustness Tests and Additional Analysis
Alternative PSM technique. To establish the robustness of our results, we repeat our empirical analysis using an alternative PSM technique. Specifically, we use a weighted matching method, Kernel matching, to match within the same firm and the same industry. This method pairs each treated observation with each untreated observation, one at a time, and assigns a weight that reflects the closeness of the propensity scores for the two observations to each pair. It then matches each treated observation with a weighted combination of all untreated observations, reducing the possibility that treated observations remained unmatched. Finally, we obtain the ATE by subtracting the stock return effect of the treated observation from the weighted average of the stock return effect of the untreated group. This method leads to a smaller reduction in bias but is useful when researchers have small samples of untreated observations and risk having a significant portion of the treated observations being left unmatched. Although this is not likely to be the case in our context, we nevertheless check the robustness of our results by using the Kernel method. We find that the results obtained with the two matching methods are similar for our sample.
Controlling for time and sequence of announcements. In addition to calculating the ATE for the overall sample, to control for the effect of the distance in time between announcements, we test H4 on three smaller subsamples: ( 1) a subsample in which we match announcements within the same firm and the same year, ( 2) a subsample in which we match announcements within the same year only, and ( 3) a subsample for which announcements are issued at most three months apart. Although these three subsamples constitute only a fraction of our original sample, we find that the ATEs for CARs in all three subsamples are also positive and significant (p < .10 or better).
Furthermore, we repeat the analysis controlling for the time sequencing of the two announcements that make up the concurrent pair. Recall that the concurrent announcements are separate press releases made by the same firm on the same day, but RavenPack provides the exact time of the day when the press release is issued. We divide concurrent announcements into two groups on the basis of whether the new product announcement occurs before or after the other announcement. We find no difference in average CARs between the two groups.
Controlling for announcement-specific factors. The PSM technique used in our empirical analysis ensures that the concurrent announcements are comparable to their stand-alone counterparts on all observable variables included in the two logit models. Furthermore, we matched announcements within firm and year, which controls for firm and time heterogeneity. However, the PSM method does not control for the unobservable announcement-specific heterogeneity, and the size of our sample does not allow us to manually code announcements to extract their characteristics. To address this limitation, we conduct a subsample analysis focused on detecting differences in announcement characteristics between new product announcements made concurrently and those that are stand-alone. Specifically, we randomly select 200 concurrent new product announcements, 200 stand-alone new product announcements selected from the matched group (Matched_NPAs) of the PSM analysis, and 200 stand-alone new product announcements not selected as part of the matching sample (untreated group). We content-analyze these announcements to determine whether they differ in content. We code each announcement on the following six dimensions: ( 1) the innovativeness of the product announced, coded using a dictionary of words from prior research (Sorescu, Shankar, and Kushwaha 2007); ( 2) whether the announcement is a preannouncement or the announcement of an actual introduction; ( 3) whether the announcement was made by a CEO versus another firm representative; ( 4) whether the announcement was made by any top-level executive versus a public relations representative9; ( 5) the number of words in each announcement; and ( 6) whether the announcement is about a product developed in-house or through an alliance. The latter four dimensions may provide some indication of the significance of the product to the firm. We then compare the subsamples on each dimension.
We find that approximately 14% of concurrent new product announcements are for radically innovative products, compared with 11% for matched stand-alone new product announcements. In addition, 26% of concurrent new product announcements are preannouncements, compared with 20% for the matched group. Approximately 14% of concurrent announcements are announced by CEOs and 22% by top executives, while 15% of matched stand-alone new product announcements are announced by CEOs and 24% by top executives. The average number of words in the press releases is 769.7 for the concurrent group and 746.5 for the matched group. Finally, 15.5% of the concurrent and 17.0% of the matched new product announcements are developed through an alliance. All differences are statistically nonsignificant.
In addition, we use the RavenPack categorization to define each news item as finance, legal, human resources, marketing, or other. We compute the ATE for a subsample in which the matched corporate news belongs to the same firm and to the same category as the concurrently announced corporate news (n = 340). The ATE_CARs for marketadjusted and market models are positive and marginally significant (.48%, p = .05; .59%, p = .09, respectively). The ATE_CAR for the Fama–French model is positive (.45%) but not significant (p = .10). Although matching on this announcement characteristic reduces, by two-thirds, the sample of concurrent announcements on which the ATE_ CAR can be computed, even in the presence of a more stringent matching rule, our results remain in the hypothesized direction.
Alternative specifications of the independent variables. We check whether the results are robust to the length of the period used to compute the backward-looking, rolling-window variables that measure past corporate activities of firms in our sample. We recompute all rolling-window variables over 1-, 2-, 3-, and 12-month windows, in addition to the 6-month time frame used to report the main results, and estimate the logit models and the ATEs using each of these alternative measures. The results remain consistent with those obtained with the six-month window and are reported in Web Appendix B.
Finally, we estimate our models on a subsample of new product announcements that are concurrently announced with positively valenced corporate news that have highly positive investor sentiments. We report our main results using a cutoff of ESS > 50 to define positively valenced corporate news, but we also estimate our models on nested subsamples of new product announcements that are concurrently announced with corporate news with ESS 60 and, respectively, ESS 65. The ATE_CARs for these two subsamples are positive and significant (p < .05 or better), and the average of these ATE_CARs ranges from .521% to .932%. These results also appear in Web Appendix B.
Discussion and Conclusion
Our analysis of a large sample of new product announcements shows that almost 7% of new product introductions are announced concurrently with another corporate news item. However, extant research has provided no guidance to managers to either avoid or make concurrent new product announcements. The financial consequences of concurrent announcements are unexplored in the marketing literature because all event studies of new product announcements eliminate them to avoid confounding effects. The current research is a first attempt to explain when these announcements occur and to measure their effect on stock prices.
The nature of our data does not allow us to determine whether the concurrent announcements were intentionally issued on the same day or were simply the product of chance. However, we interviewed several executives who confirmed that most firms closely control the release of all announcements by ensuring that they are issued from a unique public relations office.10 This may be particularly true for good news, the type of announcements we focus on in this article. Firms do not have an unlimited supply of good news; thus, concurrent announcements are unlikely to be just a by-product of a high volume of routine corporate news.
Our data indicate that concurrent new product announcements are more likely to occur when firms have high stock market values, a history of corporate announcements that have been positively received by investors, high idiosyncratic volatility, and low investor recognition. We argue that issuing announcements concurrently makes their content stand out from the informational flow that investors face. We verify that firms are successful in this endeavor, evidenced by a higher increase in investor recognition after concurrent than stand-alone announcements. The stock market reaction to concurrent new product announcements made under these conditions is also higher, on average, than the sum of the stock market reactions to similar stand-alone announcements.
This research contributes to the marketing literature by identifying conditions that are conducive to concurrent corporate announcements. We focused on new product announcements to keep the theory simple and the data analysis manageable. However, new product announcements are not the only marketing actions that firms announce concurrently. Other types of concurrent corporate announcements have likewise been discarded in empirical research. The method we propose here can also be used to investigate the financial consequences of other types of concurrent announcements, such as announcements of brand extensions, partnerships, business contracts, acquisitions, and market expansions. We also contribute to the finance literature by providing an empirical test of Merton’s (1987) model of capital market equilibrium with incomplete information in a unique context that has not been previously studied. Using Merton’s model to link marketing actions to firm stock performance opens up an exciting area of research in marketing because it provides a theoretical basis for how marketing actions geared to increasing investor recognition can increase firm value through a reduction in the equity discount rate. To our knowledge, most of the research on the marketing–finance interface has focused on how marketing efforts directed to consumers can increase firm value through an increase in expected future cash flows.
The average firm in our sample made approximately three new product announcements and eight other positively valenced announcements in a six-month period. This indicates that concurrent new product announcements are not difficult to implement given the flow of corporate communications we have documented. Our findings suggest that managers could use concurrent announcements as a tool to increase the visibility of their stocks within the investor community. This tool is more valuable for managers of niche firms with a low investor base, for high-value firms striving to meet high investor expectations, and for firms experiencing high idiosyncratic volatility. Note that our results do not suggest that the mere action of combining corporate announcements will increase the stock market value of firms. Because we are unable to control for heterogeneity in announcement content, our results should be viewed as primarily descriptive in nature. A thorough analysis of causality is left as an open topic for further research.
This study provides novel insights into concurrent new product announcements, but many other aspects of these announcements could be investigated further. First, a more detailed content analysis of two announcements made concurrently could help further qualify the financial gains of these announcements. Second, in this article we examine separate announcements made on the same day, but future studies could explore whether visibility gains accrue to announcements made on consecutive days or further investigate ways in which announcements can be sequenced to attract investors’ attention. Third, we explore the consequences of combining two positively valenced announcements, mainly because few new product announcements are paired with bad news. However, other types of positively valenced corporate announcements could be announced alongside bad news. If so, it would be fruitful to determine the financial consequences of these types of concurrent announcements.
Appendix: Summary of Results
Conditions Under Which Concurrent Announcements Are More Likely to Occur
Theory Driven
High expectations of future cash flows:
• Positive news sentiment associated with recent corporate announcements
• High Tobin’s q
High idiosyncratic volatility:
• High standard deviation of returns preceding the
announcements
Low investor recognition:
• Small percentage of shares held by institutional investors • Small number of analysts following the stock
Empirical: Control Variables with Significant Coefficients
Firm level:
• Large firms • Firms that have more frequently made concurrent announce
ments in the past
• Firms with a large volume of corporate announcements
Industry level:
• Low-concentration industries • High volume of corporate announcements made by
competitors
Consequences of Concurrent Announcements Made Under the Conditions Identified in the Article
Concurrent Announcements Increase Firm Value
• Higher CARs than the sum of CARs from stand-alone similar
announcements
Concurrent Announcements Increase Investor Recognition
• Higher increase in volume of trade during the event window
than the sum of increases associated with stand-alone similar announcements
• Higher increase in the percentage of shares held by institutional investors in the postannouncement quarter than the previous quarter relative to the sum of increases associated with stand-alone similar announcements
• Higher increase in the number of analysts following the stock in the postannouncement quarter than the previous quarter relative to the sum of increases associated with stand-alone similar announcements
1Positively valenced announcements are those that convey good news to the external stakeholders of firms. We focus on the combination of two positively valenced news items because concurrently announcing new products with negatively valenced announcements (i.e., those that convey bad news) is a rare occurrence. We find that in our extensive, multi-industry sample, which spans 11 years of announcements, less than .05% of new product introductions are announced concurrently with a negatively valenced news item. Throughout the article, when we refer to concurrent new product announcements, we mean one new product announcement and one other, distinct, positively valenced corporate announcement made on the same day by the same firm, but in separate press releases.
2Investor recognition in Merton’s (1987) model is a long-term driver of firm value, while the stock market reaction to a change in investor recognition is a short-term effect, resulting from a change in this long-term variable. The theory of rational expectations posits that if investors acquire new information that leads to a change in a fundamental variable—in this case, the discount rate of a stock—they will immediately incorporate this expectation into the stock price. This happens even though the new discount rate might not materialize for some time.
3We formally define news sentiment at time t as the cumulative strength of the valence of the totality of news that the firm has released over a specified period that immediately precedes time t.
4According to details provided by RavenPack, the ESS is based on ratings obtained from a panel of experts with extensive professional backgrounds in finance and economics. These experts evaluate more than 2,000 types of corporate events for their content.
The ratings obtained from the training sample are encapsulated in an
algorithm that then evaluates the content of each news event and
generates a score for it. The algorithm uses additional information such as ratings scales from all major brokerage firms, investment banks, and credit rating agencies. Research in finance has used RavenPack’s ESS in a manner similar to ours (e.g., Akbas et al. 2016; Kelley and Tetlock 2013).
5We only include news with ESSs above 50. This follows directly from the RavenPack’s definition of the news sentiment variable that adding a neutral-sentiment news item to a new product introduction is not likely to significantly change the stock market reaction to the new product announcement. We also eliminate concurrent news with a negative news sentiment (ESS < 50). These are extremely rare occurrences, representing less than .05% of the announcements in the sample.
6Following Petersen (2009), we assume that epit = gpi + lpt + upit and ecit = gci + lct + ucit, where gpi and gci are firm-specific components, lpt and lct are time-specific components, and upit and ucit are idiosyncratic components unique to each observation. We estimate the variance–covariance matrix as V^ arðb^Þ = V^ ðfirmÞ + V^ ðtimeÞ - V^ ðfirm-timeÞ.
7For robustness, we use an alternative proxy for the value of the firm, the P/E ratio, instead of Tobin’s q. Results obtained with the P/E ratio remain substantively similar and appear in Web Appendix B.
8Rosenbaum and Rubin (1985) define PRB as
where XA = the mean for the treatment group after matching (treated observations that have a match), XA9 = the mean for the nontreatment group after matching (matched observations), XB = the mean for the treatment group before matching (all treated observations), and XB9 = the mean for the nontreatment group before matching (untreated observations).
9Top-level executives identified in the sample announcements are CEOs, chief marketing officers, chief finance officers, chief information officers, chief technology officers, and chief operation officers.
10Specifically, at the 2015 Theory + Practice in Marketing
Conference in Atlanta, we interviewed six upper-management individuals employed by firms in industries ranging from the financial sector to gaming and entertainment.
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Record: 227- When and How Board Members with Marketing Experience Facilitate Firm Growth. By: Whitler, Kimberly A.; Krause, Ryan; Lehmann, Donald R. Journal of Marketing. Sep2018, Vol. 82 Issue 5, p86-105. 20p. 1 Diagram, 9 Charts. DOI: 10.1509/jm.17.0195.
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When and How Board Members with Marketing Experience Facilitate Firm Growth
Scholars have expressed concern that marketing’s influence at the strategic levels of the firm is waning. Consistent with this view, only 2.6% of firms’ board members have marketing experience. The authors suggest that this is short-sighted and that including more marketing-experienced board members (MEBMs) will increase firm growth by (1) helping firms prioritize growth as a strategic objective and (2) contributing their expertise to improve the effectiveness of revenue growth strategies. Drawing on the behavioral model of corporate governance, the authors develop a theoretical framework explicating the situational, dispositional, and structural influence moderators that alter the impact of MEBMs on firm growth. Using 64,086 director biographies from S&P 1500 firms, the authors find that MEBMs positively affect firm-level revenue growth and that this relationship is strengthened or weakened by important contingencies that occur in the firm. The findings suggest that the common practice of not including experienced marketers on boards of directors puts firms at a competitive disadvantage.
marketing-experienced board member; chief of marketing; board of directors; revenue growth; board diversity; upper echelons
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Record: 228- When and Why Saying "Thank You" Is Better Than Saying "Sorry" in Redressing Service Failures: The Role of Self-Esteem. By: You, Yanfen; Yang, Xiaojing; Wang, Lili; Deng, Xiaoyan. Journal of Marketing. Mar2020, Vol. 84 Issue 2, p133-150. 18p. 6 Graphs. DOI: 10.1177/0022242919889894.
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When and Why Saying "Thank You" Is Better Than Saying "Sorry" in Redressing Service Failures: The Role of Self-Esteem
In their initial recovery efforts after a service failure, service providers need to decide what to communicate to consumers to restore their satisfaction. Prior work has primarily examined apology (saying "sorry") as a symbolic recovery strategy; the current research suggests appreciation (saying "thank you") as an alternative, often more effective strategy. Drawing from research on linguistic framing and self-view, the authors reason that the shift of focus in the service provider–consumer interaction, from emphasizing service providers' fault and accountability (apology) to spotlighting consumers' merits and contributions (appreciation), can increase consumers' self-esteem and, in turn, postrecovery satisfaction. Across multiple service failure contexts, Studies 1a–1e establish the superiority of appreciation in redressing service failures. By measuring and manipulating self-esteem and examining the moderating role of narcissism and recovery timing, Studies 2–5 provide converging evidence for consumers' state self-esteem as the underlying mechanism. Studies 6 and 7 go beyond examining appreciation in isolation and show that it is as effective as recovery messages that combine appreciation and apology (Study 6) and that its superiority over apology holds when service providers combine symbolic and utilitarian recovery (Study 7).
Keywords: apology; appreciation; linguistic framing; self-esteem; service failure; service recovery
Business leaders worldwide report that consumers' expectations of service quality are higher than ever ([22]). It is therefore not surprising that consumers' interactions with service providers are often rife with service failures, or negative service encounters that do not live up to their expectations ([58]; [60]). Consider, for example, restaurant service. A high proportion of consumers are dissatisfied with various aspects of their dining experience, with 60.8% complaining about slow services, 29.4% about inadequate food and beverage quality, and 21.6% about inefficient staff ([21]). Meanwhile, service failures engender formidable consequences to businesses, such as considerable financial loss and negative word of mouth (WOM). For example, U.S. companies lost $1.6 trillion in 2016 from customer switching caused by poor service, and 44% of unsatisfied customers vented their frustrations on social channels ([ 1]).
Given the prevalence and dire consequences of service failures, identifying effective recovery methods that are cost efficient and easy to implement is critical to restore consumer satisfaction and increase retention. Our research thus focuses on symbolic (efforts to make amends to consumers socially or psychologically; [60]) rather than utilitarian (efforts involving material compensation) recovery. Going beyond prior service recovery research, which has focused on apology (e.g., [24]; [71]), our research examines appreciation as an alternative symbolic recovery strategy. Consider the following service failure scenario: A plumber promised to arrive at the client's house by 2 p.m. but showed up at 3 p.m. On arrival, the plumber could redress the delay by saying either "Sorry for the wait" (apology) or "Thank you for the wait" (appreciation). Of the two recovery strategies, what are their distinct psychological impacts on consumers, and which is more effective, when used in isolation, at redressing the service failure?
Recent work on linguistic framing (e.g., [ 8]; [43]; [44]) suggests that subtle changes in expressions can elicit distinct psychological feedback from consumers. For example, [ 8] illustrate that framing the same coupon redemption window as expansive ("anytime between") or restrictive ("only between") affects consumers' evaluations of the sales promotion and their coupon redemption behavior. In light of these findings, we propose that apology and appreciation, though both appropriate in redressing service failures ([12]), can have distinct psychological influences on consumers. Specifically, while apology (e.g., "Sorry for the wait") acknowledges the service failure through the admission of service providers' fault and accountability, appreciation (e.g., "Thank you for your wait") acknowledges the service failure by honoring consumers as a benefactor and highlighting their merits and contributions. Drawing from research on consumer self-view (e.g., [27]; [31]; [51]), we expect that this shift of focus in the service provider–consumer interaction (from "sorry" to "thank you") can significantly increase consumer self-esteem. As a result, appreciation should be superior to apology in boosting consumer postrecovery satisfaction.
Service failures often trigger a domino effect, in which the initial customer dissatisfaction escalates into a plethora of severe downstream consequences, including customer complaints, product returns, negative WOM, customer switching, and even boycotts of the company ([ 3]; [40]). In response to a service failure, regardless of the presence of utilitarian recovery ([60]) or its type (tangible vs. intangible [[57]]), service providers almost always need to first engage in some kind of symbolic recovery (e.g., apology) to verbally acknowledge the transgression and provide consumers with social and psychological compensation ([ 4]). In this sense, symbolic recovery constitutes an initial and indispensable part of companies' recovery effort ([24]; [49]; [71]). Moreover, unlike utilitarian recovery, which incurs economic expenditures such as goods, discounts, and reimbursements and thus imposes a substantial cost on companies ([17]; [60]), symbolic recovery is costless and therefore represents a highly cost-efficient recovery method in restoring consumer satisfaction (e.g., [24]).
According to speech acts research, apology and appreciation can be used interchangeably in situations involving indebtedness (e.g., asking for a favor; [12]). That is, both apology and appreciation are equally appropriate responses in the case of service failures. However, prior work on service recovery has examined only apology as a symbolic recovery strategy (e.g., [24]; [71]). Drawing from the linguistic framing and self-esteem literature, we hypothesize and demonstrate that appreciation is an alternative, often more effective symbolic recovery strategy. We next turn to the linguistic framing literature to show that appreciation and apology can have different effects on consumers' self-esteem and, thus, their postrecovery satisfaction.
Research on linguistic framing provides examples of how minor changes in wording in communication messages may influence people's judgments, decisions, and behaviors ([72]). Although previous research has not particularly focused on the interaction between service providers and consumers, in the context of self-talks, studies have documented the effects of linguistic framing on consumers' self-views and subsequent behaviors ([56]). This line of work suggests that subtle changes in the way consumers speak to themselves provide distinct psychological feedback, which in turn influences their subsequent behavior. For example, [55] show that introspective self-talks using an interrogative ("Will I...") versus a declarative ("I will...") phrase signal intrinsic motives to oneself and thus lead to better performance on subsequent tasks. [44] reveal that self-talks starting with "I don't" versus "I can't" empower consumers more, which makes them less susceptible to temptations.
More germane to our theorizing, accumulating evidence documents the linguistic framing effects of marketing messages, showing how structurally and semantically similar marketing messages make a difference in consumer judgments and evaluations. For example, [39] demonstrate that persuasion messages starting with "I think" versus "I feel" are more convincing for cognitively versus affectively oriented recipients, respectively. Similarly, [25] shows that framing promotional messages as questions ("The pen for you?") instead of statements ("The pen for you") conveys a greater level of interestingness but less clarity to consumers.
Building on these findings, in the service recovery context, we posit that framing the recovery messages as an apology versus an appreciation orients consumers to either the service provider's mistakes or their own merits, resulting in different levels of elevation in consumers' self-view (self-esteem) and postrecovery satisfaction. Next, we explain why and when appreciation is superior to apology in increasing consumer self-esteem and satisfaction.
As a form of symbolic recovery, apology can help restore consumer satisfaction ([24]), especially when it is timely, earnest, and elaborate ([49]; [71]). Research on interpersonal transgression also acknowledges the effectiveness of apology in eliciting the recipients' conciliatory responses (e.g., forgiveness) to the transgressor. For example, apology improves the recipient's impression of the transgressor and leads to forgiveness ([64]). Other research also indicates that apology reduces the recipient's anger, aggression ([13]; [42]), and reproach ([28]).
In the context of service recovery, an apology (vs. no apology) conveys that the service providers have recognized their mistakes and acknowledged their responsibility for the service failure. It thus serves as a form of psychological compensation, the realization of which can boost consumers' postrecovery self-esteem. Indeed, consistent findings in research on the self show that people constantly strive to feel good about themselves ([35]) and that they can gain self-esteem through external sources, such as others' respect, admiration, and acceptance ([36]; [38]). As such, consumers' state self-esteem (i.e., self-esteem experienced in a particular situation) is sensitive to the way other people treat them. For example, social exclusion or rejection can significantly reduce consumers' state self-esteem ([36]). In the context of service failure, a lack of apology signals the service provider's refusal to acknowledge and redress the transgression, and this rejection inflicts on consumers' self-esteem ([36]). Because unfairness perceptions, anger, and aggression are often manifestations of feelings of being disrespected or rejected ([18]; [54]), apology's ability to restore perceived fairness, elicit forgiveness, and alleviate anger suggests that it can elevate consumers' thwarted self-esteem to some extent ([68]; [67]).
Despite its usefulness in restoring customers' fairness perceptions and self-esteem, apology as a symbolic recovery strategy can also have potentially negative effects on the service provider. Some researchers assert that apologies express not only the apologizers' regret about the offense ([33]) but also their admission of fault ([13]), which could name them as the responsible party. In the service failure context, apologies emphasize the service provider's fault and underscore their accountability, a situation that may carry over to lead consumers to harbor negative thoughts about the service providers as the at-fault party, boycott them, and even file lawsuits against them. Consistent with our view, research indicates that though a seemingly successful apology can induce positive responses immediately after the negative service encounter, certain characteristics of the initial failure (e.g., responsibility) still linger to negatively influence future service provider–consumer interactions (e.g., repatronage intentions; [65]).
Unlike apology, appreciation redresses the service transgression by highlighting consumers' merits and contributions in the service encounter. It thus instigates a shift in consumers' attention from the service provider's mistakes and responsibilities to their own merits and contributions, further boosting their postrecovery self-esteem. According to self-verification theory, people readily accept and positively respond to statements that converge with their desired beliefs about themselves ([15]; [69]); this is because people are generally motivated to pursue a positive self-view ([27]; [31]; [51]). More germane to our research is work on speech acts that suggests that when saying "thank you," the appreciating party (beneficiary) shows his or her appreciation for an act performed by the appreciated party (benefactor); as such, appreciation demonstrates the appreciating party's approval of the appreciated party's merits by recognizing his or her contributions ([52]; [66]). In line with sociometer theory ([34]), such positive approval and acceptance from others elevates consumers' self-esteem.
Meanwhile, though focusing on praising consumers' merits and contributions, appreciation uttered by service providers following a service failure (e.g., "Thank you for your wait!") also implies their recognition of the transgression and acknowledgment of consumers' sacrifice. Therefore, as a symbolic recovery strategy, similar to apology, appreciation to some extent signals the admission of fault. Research on conversation comprehension suggests that the understanding of an expression involves the comprehension of not only its literal meaning but also its pragmatic meaning based on the inferences of the speakers' intention ([10]). Thus, understanding of an appreciation utterance ("Thank you") in the context of a service failure should include its acknowledgment of the disequilibrium in the social exchange (e.g., [12]; [32]; [66]). We thus posit that appreciation is superior to apology in boosting consumers' self-esteem. This is because, in addition to implicitly acknowledging the service provider's responsibilities, appreciation focuses on placing consumers in the benefactor position and highlights their merits and contributions. In other words, whereas an apology conveys no positive information about the consumers, appreciation emphasizes the service provider's approval of the consumers' positive qualities and thereby increases their self-esteem even more.
In turn, the elevation in consumers' self-esteem prompted by the service provider's appreciation as a recovery message increases their postrecovery satisfaction. Research in both interpersonal and marketing contexts has consistently shown that people typically evaluate others who approve of their virtues positively (e.g., [47]). Indeed, research on self-enhancement theory indicates that people are motivated to evaluate themselves favorably and therefore respond positively to those who offer approval of their positive self-views ([11]). In other words, receiving others' positive feedback is a comforting indicator of one's self-worth and thus induces more favorable evaluations of the feedback provider ([30]). These positive effects manifest even when consumers view the positive feedback as driven by ulterior motives ([69]). Therefore,
- H1: Appreciation, as a symbolic recovery strategy, is more effective than apology in restoring consumers' postrecovery satisfaction (e.g., satisfaction with the recovery effort, satisfaction with the store, recommendation intention).
- H2: The elevation in consumers' self-esteem mediates the superior effect of appreciation to apology on postrecovery satisfaction.
Although people have a universal motive to pursue self-esteem, the strength of this self-image-oriented motive varies from person to person. Specifically, narcissism, a concept denoting a grandiose and inflated sense of self ([ 7]; [23]), captures the strength of people's desire to pursue self-esteem ([19]; [53]). The motive to obtain others' approval and be well regarded by others is especially strong among narcissists, whose consumption activities often revolve around the pursuit of self-esteem ([ 5]; [37]). As such, self-presentation and self-enhancement behaviors, such as displaying their material possessions ([ 9]) and purchasing customized and exclusive products signaling personal uniqueness ([37]), often occur among people high in narcissism but not among people low in narcissism.
Accordingly, narcissism often moderates the effects of self-esteem related variables. For example, because obtaining scarce resources can improve self-esteem, narcissism moderates the effect of product scarcity on product evaluations and willingness to pay, such that scarce products lead to more favorable evaluations and willingness to pay among consumers high in narcissism but had no impact on those low in narcissism ([37]). If self-esteem is the driving force behind the superiority of appreciation to apology as a recovery strategy, we expect the following:
- H3: Consumers' level of narcissism moderates the superior effect of appreciation to apology on postrecovery satisfaction, such that the effect exists among consumers high in narcissism but disappears among those low in narcissism.
Studies 1a–1e establish the basic effect, in both field and lab settings, that appreciation is more effective than apology in redressing service failure. Studies 2–5 provide converging evidence for consumers' state self-esteem as the proposed mechanism underlying the superior effect of appreciation. Specifically, Study 2 measures self-esteem, Study 3 manipulates self-esteem, and Study 4 adopts the process-by-moderation approach by assessing the moderating role of narcissism. Finally, Study 5 examines a situation in which consumers' self-esteem is intact (i.e., when they are forewarned about a service failure) and tests the prediction that when appreciation or apology is offered before the service failure, the advantageous effect of appreciation disappears.
While Studies 1–5 focus on examining appreciation in isolation by comparing it to apology (as the control symbolic recovery), the next two studies assess the robustness of the superior effect of appreciation in more complex situations by examining recovery communications that combine appreciation and apology (Study 6) and that combine symbolic and utilitarian recovery (Study 7). Based on our theorizing that, similar to apology, appreciation uttered by service providers following a service failure also acknowledges (though less explicitly) the disequilibrium in a social exchange, we predict and show (in Study 6) that recovery communication combining both appreciation and apology will not have an advantage over the appreciation-only recovery. Finally, Study 7 demonstrates that the superior effects of appreciation still hold in the presence of utilitarian recovery. Table 1 in Web Appendix A provides a summary of all studies.
In Study 1, we present three real-behavior (Studies 1a–1c) and two scenario-based (Studies 1d–1e) studies to examine the relative effectiveness of appreciation versus apology in an array of service failure contexts. Although our real-behavior studies use undergraduate students as participants, the contexts investigated in these studies are direct implementations of service failure situations. In Study 1a, we provide participants with gifts that are "inferior" to what was previously described. To ensure that the superior effect of appreciation (vs. apology) is not driven by the negative effect of apology, we also include a control condition that provides no recovery. Study 1b attempts to replicate the superior effect of appreciation in a field setting by first having participants complete a "wrong" survey and then measuring whether they agree to take the "correct" survey later. In Study 1c, participants received their compensation one day later than the preannounced time. We designed Studies 1d–1e to further examine the superior effect of appreciation in other service failure situations (airline overbooking and service provider unavailability) using more consequential measures of recovery satisfaction (e.g., consumers' likelihood to file a complaint or accept the new arrangement).
One hundred thirty-nine undergraduate students from a large Midwestern university in the United States (56.83% female; Mage = 20.31 years, SD = 1.38) participated in a three-cell, one-way (recovery strategy: appreciation vs. apology vs. none) between-subjects design for course credit. They were randomly assigned to one of the three conditions.
Participants took part in a lab session that consisted of three studies unrelated to one another. The first study involved asking them to choose between two products as a token of appreciation for their participation. After the lab session, participants were approached by a lab assistant to complete a "Lab Satisfaction Survey" as part of the debriefing process, in which they read a short description of each of the three studies in the session and reported their satisfaction with the way the researchers handled it.
Our focal manipulation of recovery strategy was embedded in the description of the first study. Specifically, participants were first reminded that this study asked them to choose between two gifts. To induce a service failure situation, they were then told that the actual gift they would receive did not look as high quality as in the pictures they saw previously. In the appreciation condition, participants then read, "Thank you for your understanding. We appreciated it!" In the apology condition, participants read, "Sorry about this situation. We apologize!" In the control condition, participants received neither appreciation nor apology. Afterward, participants reported their satisfaction with the way the researchers handled this study (1 = "very negative/frustrated/bad/dissatisfied," and 7 = "very positive/content/good/satisfied"; α =.94; averaged to form a postrecovery satisfaction index; adapted from [63]]). Participants then received similar debriefing and reported their satisfaction in the remaining two studies in the lab session. Finally, they provided their demographic information and were given the gift they had chosen.
A one-way analysis of variance (ANOVA) (recovery strategy: appreciation vs. apology vs. none) run on the postrecovery satisfaction index revealed that the effect was significant (F( 2, 136) = 16.48, p <.01, =.20). Planned contrasts indicated that participants in the apology condition (M = 5.47, SD = 1.30) were more satisfied than those in the no-recovery control condition (M = 4.72, SD = 1.52; F( 1, 136) = 8.33, p <.01, =.06). More importantly, participants in the appreciation condition (M = 6.31, SD =.96) were more satisfied than those in the apology condition (M = 5.47, SD = 1.30; F( 1, 136) = 9.21, p <.01, =.06), providing support for H1. In addition, gender did not have any effect (for the detailed statistics and a summary of gender's effects across all studies, see Web Appendix B).
Having established that apology is better than no recovery in increasing consumer satisfaction, in Studies 1b–1e we focus on demonstrating that appreciation is better than apology in this regard. Study 1b used a two-cell, one-way (recovery strategy: appreciation vs. apology) between-subjects design. One hundred five students (60.95% female; Mage = 22.00 years, SD = 3.54) from a large Eastern university in China participated in the study for a small gift (candy bar). They were randomly assigned to one of the two conditions.
On the day of the study, a research assistant approached students, one at a time, on campus. She introduced herself as a member of the behavioral research team from the university's School of Management and invited the students to participate in a short survey about students' self-perceptions. After receiving the consent to participate, the research assistant handed them a tablet to complete the survey. She then stepped aside and told them to notify her once they completed the survey. When participants finished the survey, the research assistant pretended to realize that she had provided the link to the "wrong" survey. She then used either the appreciation or the apology recovery strategy. In the appreciation condition, she said "Thank you for your participation! I will fix the link ASAP." In the apology condition, she said "Sorry for the issue! I will fix the link ASAP." Then, she asked the participants to wait before returning with a card with a QR code printed on it, which she presented to them and said, "This is our research team's QR code. We will provide the correct link in our chat group. If you would like to help us complete the correct survey, please scan the QR with your cell phone to join our research group. Thank you again! [Sorry again!]" Whether participants scanned the QR code (0 = no, 1 = yes) served as the measure of the effectiveness of the recovery strategy. Providing different QR codes in the appreciation and apology conditions enabled us to count the number of participants who scanned the code in each condition. The "correct" survey that participants eventually completed was unrelated to our research.
A chi-square test showed that a greater proportion of participants in the appreciation condition (75.47%) scanned the QR code than that in the apology condition (57.69%; χ2( 1) = 3.73, p =.05; odds ratio = 2.26). This result provides additional support for H1.
Study 1c employed the same two-cell, one-way (recovery strategy: appreciation vs. apology) between-subjects design as in Study 1b. One hundred seven undergraduate students from a large Eastern university in China participated in the study and were randomly assigned to one of the two conditions.
The study took place at the end of the semester. Participants were told that as a token of appreciation for their support during the semester, they would be entered into a lucky draw to win a small amount of money at the end of the lab session. We set up the lucky draw in such a way that every participant won a fixed amount of money (approximately $1.60). After completing some other unrelated tasks, participants were entered into the lucky draw. The research assistant promised participants that they would receive their compensation (the money received from the lucky draw) at around 8 p.m. on the same day of the experiment via WeChat (a mobile communication and payment app). However, the research assistant did not distribute the compensation until 8 p.m. the next day. The research assistant tried to redress the service failure with a short message. In the appreciation condition, the message read, "Thank you for waiting for such a long time!" In the apology condition, it said, "Sorry for keeping you waiting for such a long time!" Afterward, the research assistant asked participants to help the research team by taking a short survey about the lab. The survey link was provided in the same WeChat conversation. Whether participants completed the survey served as a measure of the effectiveness of the recovery strategy (85% of the participants completed the survey). To more directly measure postrecovery satisfaction, in this follow-up survey, we also had participants report their satisfaction with the way the compensation was handled using the same measures as in Study 1a (α =.91; averaged to form a postrecovery satisfaction index).
A chi-square test revealed that participants in the appreciation condition were more likely to complete the survey than those in the apology condition (91.07% vs. 78.43%; χ2( 1) = 3.35; p =.07; odds ratio = 2.81). An ANOVA run on the postrecovery satisfaction index also showed that participants in the appreciation condition were more satisfied (M = 5.52, SD =.95) than those in the apology condition (M = 5.11, SD = 1.06; F( 1, 89) = 3.90, p =.05, =.04). These results also provide additional support for H1.
Study 1d featured the same two-cell, one-way (recovery strategy: appreciation vs. apology) between-subjects design as in the previous studies. One hundred sixteen U.S. Amazon Mechanical Turk (MTurk) workers (57.76% female; Mage = 37.96 years, SD = 12.20) completed the study for monetary compensation and were randomly assigned to one of the two conditions.
Participants were asked to imagine that they were at the airport waiting to board their flight. Their ticket showed that they had a window seat in the middle section of the plane. At the time of boarding, however, the gate agent informed them that their seat had been double booked and they were being moved to a different seat in the rear section of the plane. The gate agent then expressed either appreciation ("Thank you for your understanding. We appreciate it!") or apology ("Sorry for the issue. We apologize!"). Afterward, participants took the same measures assessing postrecovery satisfaction as in Studies 1a and 1c (α =.98; averaged to form a postrecovery satisfaction index). They also indicated their intention to file a complaint against the airline (1 = "not at all," and 7 = "very much"), which served as a more consequential measure of recovery effectiveness.
Two ANOVAs showed that, compared with apology, appreciation led to greater postrecovery satisfaction (Mappreciation = 4.10, SD = 1.62 vs. Mapology = 3.48, SD = 1.64; F( 1, 114) = 4.17, p =.04, =.04) and a lower likelihood of filing a complaint against the company (M = 3.88, SD = 2.04 vs. M = 4.59, SD = 1.93; F( 1, 114) = 3.67, p =.06, =.03). These results provide additional support for H1.
Study 1e featured the same two-cell, one-way (recovery strategy: appreciation vs. apology) between-subjects design as in the previous studies. One hundred ninety-eight U.S. MTurk workers (44.44% female; Mage = 37.62 years, SD = 11.53) completed the study for monetary compensation and were randomly assigned to one of the two conditions.
Participants were asked to imagine that they planned to have their hair done for a friend's upcoming wedding. On a friend's recommendation, they decided to try David, a hairstylist working at a local hair salon. They had contacted the salon and made an appointment with David. However, when they showed up at the scheduled time, the receptionist informed them that David was not available, recommended another hairstylist to them, and ended the conversation by saying either "Thank you for your understanding. We appreciate it!" or "Sorry for the situation. We apologize!" After reading the scenario, participants reported their postrecovery satisfaction by responding to the same measures as in Studies 1a, 1c, and 1d (α =.98; averaged to form a postrecovery satisfaction index). In addition, we assessed participants' likelihood to accept the new service arrangement, file a complaint, and ask for compensation (1 = "not at all," and 7 = "very much") as more consequential measures of recovery effectiveness.
The ANOVA results showed that appreciation led to greater postrecovery satisfaction than apology (Mappreciation = 4.61, SD = 1.46 vs. Mapology = 3.97, SD = 1.35; F( 1, 196) = 10.26, p <.01, =.05). Furthermore, participants in the appreciation condition were less likely to file a complaint (Mappreciation = 3.19, SD = 1.80 vs. Mapology = 3.85, SD = 1.90; F( 1, 196) = 6.23, p =.01, =.03), more likely to accept the new arrangement (Mappreciation = 5.10, SD = 1.49 vs. Mapology = 4.65, SD = 1.39; F( 1, 196) = 4.81, p =.03, =.02), and less likely to ask for compensation (Mappreciation = 2.78, SD = 1.60 vs. Mapology = 3.34, SD = 1.78; F( 1, 196) = 5.40, p =.02, =.03) than those in the apology condition. These results strongly supported H1.
Across five studies, using an array of service failure contexts (inferior gift, wrong survey, delayed compensation, airline overbooking, and service provider unavailability) and various measures of recovery effectiveness (e.g., postrecovery satisfaction, real behaviors or behavioral intentions indicating satisfaction, intention to file a complaint, intention to ask for compensation, likelihood to accept the new service arrangement), we consistently demonstrate the superior effects of the appreciation (vs. apology) as a symbolic recovery strategy. In the following studies, we test the underlying mechanism of and boundary conditions for this effect. We also attempt to examine alternative explanations that might account for the observed effect.
In Study 2, we examine the proposed mediation role of self-esteem in explaining the superiority of appreciation to apology in boosting postrecovery satisfaction. In addition, we measure repatronizing and recommendation intentions as behavioral consequences of postrecovery satisfaction. Finally, to further assess the robustness of our effect, we use a different service context: plumbing service.
Study 2 employed a three-cell, one-way (recovery strategy: appreciation vs. apology vs. none) between-subjects design. We included a control condition, which provided no recovery, to ensure that the superior effect of appreciation (vs. apology) in elevating self-esteem is not driven by the negative influence of apology. One hundred ninety-four U.S. MTurk workers (57.22% female; Mage = 38.74 years, SD = 13.60) completed the study for monetary compensation and were randomly assigned to one of the three conditions.
Participants were asked to imagine that the plumbing in their house had backed up and they called a plumber, who promised to stop by around 11 a.m.; however, the plumber did not show up until noon, an hour after the scheduled time. In the appreciation condition, the plumber said, "Thank you for your patience. I appreciate it!" In the apology condition, the plumber said, "Sorry for keeping you waiting. I apologize!" In the control condition, participants received no recovery message. Then, the plumber began working on fixing the plumbing issues.
Afterward, participants indicated their satisfaction with the way the plumber redressed the service failure using the same measures as in the previous studies (α =.96; averaged to form a postrecovery satisfaction index). In addition, they reported their repatronizing intentions: "To what extent will you be using this plumber's service again if you encounter plumbing problems in the future?" and "To what extent would you like to use this plumber's service again?" (1 = "not at all," and 7 = "very much"; r =.86, p <.001; averaged to form a repatronizing intention index). Moreover, we measured participants' willingness to recommend this plumber to their friends (1 = "very unlikely," and 7 = "very likely"). After that, we assessed participants' state self-esteem with three items adapted from [50]: "The way the plumber handled the delay made me feel that I'm a person of worth/take a positive attitude toward myself/believe I am a valuable person" (1 = "strongly disagree," and 7 = "strongly agree"; α =.90; averaged to form a state self-esteem index). Finally, participants provided their demographic information.
We ran ANOVAs on the postrecovery satisfaction index, repatronizing intention index, and recommendation intention, and they consistently revealed a significant effect of recovery strategy (satisfaction: F( 2, 191) = 34.31, p <.01, =.26; repatronizing intention: F( 2, 191) = 10.11, p <.01, =.10; recommendation intention: F( 2, 191) = 14.10, p <.01, =.13). Planned contrasts indicated that compared with participants in the no-recovery control condition, those in the apology condition showed greater satisfaction (M = 4.50, SD = 1.20 vs. M = 3.30, SD = 1.20; F( 1, 191) = 28.45, p <.01, =.13), repatronizing intentions (M = 4.19, SD = 1.39 vs. M = 3.71, SD = 1.60; F( 1, 191) = 3.30, p =.07, =.02), and recommendation intentions (M = 4.00, SD = 1.59 vs. M = 3.17, SD = 1.58; F( 1, 191) = 8.67, p <.01, =.04). More importantly, consistent with H1, participants in the appreciation condition exhibited greater satisfaction (M = 5.10, SD = 1.37; F( 1, 191) = 7.20, p <.01, =.04), repatronizing intentions (M = 4.86, SD = 1.43; F( 1, 191) = 6.71, p =.01, =.03), and recommendation intentions (M = 4.62, SD = 1.54; F( 1, 191) = 5.16, p =.02, =.03) than those in the apology condition (see Figure 1).
Graph: Figure 1. The effect of symbolic recovery on postrecovery responses (Study 2).†p <.10.*p <.05.**p <.01.
The ANOVA run on the state self-esteem index also showed a significant effect of recovery strategy (F( 2, 191) = 20.86, p <.01, =.18). Participants in the apology condition (M = 3.92, SD = 1.34) reported higher self-esteem than those in the control condition (M = 3.03, SD = 1.41; F( 1, 191) = 12.89, p <.01, =.06). More importantly, participants in the appreciation condition (M = 4.59, SD = 1.41) reported higher self-esteem than those in the apology condition (F( 1, 191) = 7.57, p <.01, =.04) (see Figure 1). These results confirm our theorizing that apology (vs. no apology) increases consumer self-esteem and that appreciation (vs. apology) elevates consumer self-esteem even more.
To demonstrate that self-esteem mediates the effect of recovery strategy on postrecovery satisfaction, we performed a mediation analysis (PROCESS Model 4; [26]) with 5000 bootstrapping iterations. We dummy-coded recovery strategy and assigned apology as the reference group (dummy 1: apology = 0, control = 1; dummy 2: apology = 0, appreciation = 1). The results showed that the relative indirect effect of recovery strategy on postrecovery satisfaction through self-esteem was significant (dummy 1: b = –.54, SE =.16, 95% confidence interval [CI] = [–.88, –.24]; dummy 2: b =.41, SE =.14, 95% CI = [.13,.69]). These results confirm H2 that elevated self-esteem mediates the superior effect of appreciation to apology in boosting postrecovery satisfaction (represented by dummy 2: appreciation vs. apology).
We ran the same analysis on the other two dependent measures (intention to repatronize and intention to recommend). The results consistently showed that the relative indirect effects of recovery strategy through self-esteem on intention to repatronize (dummy 1: b = –.57, SE =.17, 95% CI = [–.92, –.24]; dummy 2: b =.43, SE =.15, 95% CI = [.12,.73]) and intention to recommend (dummy 1: b = –.65, SE =.19, 95% CI = [–1.02, –.29]; dummy 2: b =.49, SE =.18, 95% CI = [.14,.84]) were significant.
Study 2 again documented that the advantageous effect of appreciation versus apology applies not only to postrecovery satisfaction but also to behavioral tendencies such as repatronizing and recommendation intentions. It also confirmed the role of self-esteem in mediating these effects. However, one could argue that the state self-esteem measures we used in Study 2 are different from the [50] items because of the situational modifiers. To provide additional evidence for self-esteem as the underlying mechanism, we manipulated it in Study 3.
To ensure that the observed effect is not caused by the possible differences in the wording of recovery strategy, in a follow-up study (for details, see Web Appendix C), we improved the manipulation by keeping the wording as similar as possible across the apology and appreciation conditions (i.e., "Thank you for the wait. We appreciate it!" vs. "Sorry about the wait. We apologize!"). In this study, we again found support for the proposed underlying mechanism that elevated self-esteem mediates the effect of recovery strategy on recovery satisfaction, in a different context: airline service failure.
As mentioned, consumers may draw inferences from the wording of the recovery message. As such, phrasing the message as apology or appreciation may lead to different inferences, confounding the effect of recovery strategy. In this research, we attempt to examine five alternative accounts related to "phrasing as information": ( 1) cause attribution, where apology (vs. appreciation) might imply that the cause of the service failure is internal to the company, or that the failure is stable and under the control of the company; ( 2) severity inference, where apology (vs. appreciation) might imply that the service failure is more severe; ( 3) frequency inference, where apology (vs. appreciation) might imply that the service failure occurs on a more regular basis; ( 4) relationship perception, where appreciation (vs. apology) might lead consumers to perceive their relationship with the service provider as more communal (vs. exchange) oriented; and ( 5) valence of the recovery message, where appreciation (vs. apology) might set a positive tone and imply the service provider's expectations of the customers' patience, inducing them to behave consistently with their expected "merits." In the aforementioned follow-up study, we measured cause attribution and severity inference and found that they could not account for our findings. For a detailed report of all alternative accounts (in which studies they were measured), see Web Appendix D.
The primary objective of Study 3 is to provide further evidence for self-esteem elevation as the mechanism underlying appreciation's superiority to apology by manipulating (rather than measuring) participants' state self-esteem. If appreciation (vs. apology) increases consumers' postrecovery satisfaction because it enhances their self-esteem, this advantageous effect should attenuate or even disappear if consumers' need for high self-esteem has been met before receiving the recovery strategy manipulation. Furthermore, Study 3 aims to examine two other alternative accounts related to "phrasing as information": frequency inference and relationship perception. Moreover, we measure an additional behavioral consequence of postrecovery satisfaction—consumers' tipping behavior. Finally, we examine the effect in a different service context—restaurant experience.
Study 3 featured a 2 (prerecovery self-esteem: low vs. high) × 2 (recovery strategy: appreciation vs. apology) between-subjects design. Two hundred fifty-nine undergraduate students (55.98% female; Mage = 20.32 years, SD = 1.20) from a large Midwestern university in the United States participated in this study for course credit and were randomly assigned to one of the four conditions.
Participants were informed at the outset that the study consisted of several unrelated tasks. In the first task, we manipulated participants' prerecovery self-esteem with a procedure widely used in literature (e.g., [41]; [46]). Specifically, participants were given ten letters (ARCBOENTML) and asked to generate as many English words as possible using any number and any combination of the letters. Participants were told that this word generation task was designed to provide an accurate and reliable measure of their verbal command (adopted from [14]]) and that their performance would be determined by the number of words generated as well as the quality (complexity) of those words. After the task, participants waited a short time for an ostensible algorithm to calculate their score. Participants in the low- (high-) self-esteem condition were told that their performance was in the 10th (90th) percentile. A separate posttest (for details, see Web Appendix E) showed that our manipulation of self-esteem (high vs. low) was successful.
Afterward, participants proceeded to a seemingly unrelated task that asked them to vividly imagine a service failure scenario in which they went out for dinner with some friends but, after being seated at the table, had to wait 30 minutes before the server came back to take their orders. The scenario differed in how the waiter handled the delay. In the apology condition, the waiter came back and apologized for the delay, saying "I'm sorry for keeping you waiting. I apologize!" In the appreciation condition, the waiter came back and noted appreciation for their patience, saying "Thank you for your patience. I appreciated it!" The scenario then described that the waiter refilled everyone's glasses with water and took their orders.
Participants then indicated their tipping likelihood (1 = "not at all likely," and 7 = "very likely") and tip amount ("What percentage of the bill would you tip your waiter?"; options ranged between 0% and 30%), which served as a behavioral proxy of postrecovery satisfaction. As confound checks, we also measured frequency inference ("To what extent do you feel that the service delay happens frequently in this restaurant?"; 1 = "not at all," and 7 = "very much so") and relationship perception ("How do you feel about your relationship with the server?"; 1 = "more like friendship," and 7 = "more like business relationship"). Finally, participants reported basic demographic information.
We conducted two 2 (prerecovery self-esteem) × 2 (recovery strategy) ANOVAs on tipping likelihood and tip amount (0%–30% of the bill), respectively. The main effects of prerecovery self-esteem and recovery strategy did not approach significance (ps >.12). The results revealed only a significant interaction effect (tipping likelihood: F( 1, 255) = 6.75, p =.010, =.03; tip amount: F( 1, 255) = 3.63, p =.06, =.01; see Figure 2). Planned contrasts showed that in the low prerecovery self-esteem condition, appreciation (vs. apology) led to a higher tipping likelihood (Mappreciation = 4.87, SD = 1.60 vs. Mapology = 3.94, SD = 1.86; F( 1, 255) = 8.20, p <.01, =.03) and a higher tip amount (Mappreciation = 12.13%, SD = 4.90% vs. Mapology = 9.88%, SD = 5.60%; F( 1, 255) = 5.11, p =.03, =.02). However, this effect disappeared in the high prerecovery self-esteem condition for both tipping likelihood (Mappreciation = 4.25, SD = 1.80 vs. Mapology = 4.49, SD = 1.95; F( 1, 255) =.59, p =.44, =.002) and tip amount (Mappreciation = 10.66%, SD = 6.00% vs. Mapology = 11.04%, SD = 5.55%; F( 1, 255) =.16, p =.69, =.001). Additional analysis showed that recovery strategy had no effect on frequency inference or relationship perception (see Web Appendix D).
Graph: Figure 2. The interaction effect of prerecovery self-esteem and symbolic recovery on tipping likelihood and tip amount (Study 3).*p <.05.**p <.01.
Whereas Study 2 assessed the mediating role of self-esteem by measuring participants' state self-esteem, Study 3 provided additional evidence for this underlying mechanism by manipulating participants' state self-esteem—a process-by-moderation approach ([61]). Specifically, we found that when participants' self-esteem was elevated before receiving recovery strategy, the advantage of appreciation over apology in redressing service failure disappeared. In addition, Study 3 showed that two of the "phrasing-as-information" accounts, participants' frequency inference of the service failure and perceived relationship with the service provider, could not account for our findings because our manipulation of recovery strategy did not influence them.
Study 4 aims to provide additional support for our theorizing by examining the moderation effect of narcissism (H3). If self-esteem is indeed the driving force behind appreciation's superiority to apology in boosting favorable consumer responses, participants' level of narcissism should moderate this effect, such that it attenuates among consumers relatively low in narcissism (i.e., those who are not motivated to pursue high self-esteem).
Study 4 employed a 2 (recovery strategy: appreciation vs. apology) × continuous (narcissism) between-subjects design. Two hundred fifty-three U.S. MTurk workers (50.59% female; Mage = 39.52 years, SD = 12.41) completed the study for monetary compensation and were randomly assigned to one of the two conditions.
We again used the restaurant service failure scenario from Study 3. Participants were asked to imagine the scenario as vividly as possible, received the recovery strategy manipulation (appreciation vs. apology), and indicated their tip amount as in Study 3. Afterward, participants were presented with 16 pairs of statements that assessed their level of narcissism ([ 2]) and asked to indicate which statement in each pair was more applicable to them. Each pair contained a narcissism-consistent statement (e.g., "I know that I am good because everybody keeps telling me so") and a narcissism-inconsistent statement (e.g., "When people compliment me, I sometimes get embarrassed"). Finally, participants reported demographic information.
Consistent with the work of [ 2], we coded participants' narcissism-consistent responses as 1 and narcissism-inconsistent responses as 0 and then summed their responses across the 16 pairs of statements (α =.78) to form a narcissism index (M = 2.96, SD = 2.95, range = 0–15), with higher numbers indicating higher levels of narcissism. We conducted moderation analysis (PROCESS Model 1; [26]) on participants' tip amount, with recovery strategy (0 = apology, 1 = appreciation), participants' narcissism index (mean centered), and their interaction as the predictors. The analysis revealed a main effect of recovery strategy (b = 1.77, SE =.72, t(249) = 2.46, p =.01) and an interaction effect between recovery strategy and narcissism (b =.47, SE =.25, t(249) = 1.86, p =.06).[ 6] To decompose this interaction effect, we conducted a floodlight analysis ([62]) using the Johnson–Neyman technique to identify the regions of the narcissism index for which the simple effect of recovery strategy on tip amount was significant. This analysis revealed a significant, positive effect of appreciation (vs. apology) among participants whose narcissism index was higher than 2.27 (BJN = 1.44, SE =.73, p =.05) but not among those whose narcissism index was lower than 2.27 (45.45% of the participants; see Figure 3). Therefore, H3 was confirmed.
Graph: Figure 3. Narcissism moderates the effect of symbolic recovery on tip amount (Study 4).
Consistent with prior research ([ 5]) specifying that people high in narcissism have a stronger desire to pursue self-esteem, we found that the advantageous effect of appreciation over apology disappeared for participants low in narcissism, who did not have a strong desire to pursue self-esteem. Thus, Study 4 establishes narcissism as a boundary condition for our core effects and provides triangulating support for self-esteem as the process explanation.
So far we have examined situations in which symbolic recovery is delivered after the occurrence of a service failure, a scenario most service recovery research has focused on. However, under many circumstances symbolic recovery can be communicated to the consumers before the service failure ([45]). For example, a server can inform the consumers on arrival that they may need to wait longer than usual because the restaurant is busy. The server could then (before the wait) use the appreciation or the apology strategy in their communication. Therefore, it is managerially relevant to examine the effects of the precursory appreciation and apology communications (e.g., apology and appreciation before a service failure). Indeed, service failures are often unpredictable ([58]; [60]) and create disconfirmations when they actually happen. Because forewarning consumers of a service failure removes consumers' disconfirmations, we thus anticipate that a precursory apology will be as effective as a precursory appreciation in helping assuage dissatisfaction.
Study 5 aims to examine whether the timing of the recovery (before or after the service failure) moderates the superior effect of appreciation. Doing this also allows us to examine the valence of the recovery strategy as another rival account. If valence indeed plays a role, we should observe a superior effect of appreciation to apology regardless of whether the recovery message comes before or after the service failure. In addition to the valence of the recovery strategy, Study 5 attempts to replicate the results from the previous studies that examined three alternative accounts related to "phrasing as information": severity inference (Study 2 follow-up), frequency inferences (Study 3), and relationship perception (Study 3). Finally, in this study, we use similar wording across the apology and appreciation recovery messages (as we did in Study 1c and the Study 2 follow-up) to control for wording as a potential confound.
Study 5 employed a 2 (recovery strategy: appreciation vs. apology) × 2 (recovery timing: prefailure vs. postfailure) between-subjects design. One hundred seventy undergraduate students (55.29% female; Mage = 20.17 years, SD =1.31) from a large Midwestern university in the United States completed the study for course credit.
Participants were randomly assigned to read one of four versions of the restaurant service failure scenario. All versions began with the same basic background introduction and asked participants to imagine that they went out for dinner with friends and that the server gave them a few minutes to make their food decisions. Our manipulations of recovery strategy and recovery timing were embedded in the remaining descriptions of the scenario. Participants in the prefailure recovery condition read that after collecting their order, the server told them to expect a 30-minute wait before the courses would be served. Depending on the recovery strategy condition, the server proceeded to either apologize for the delay, saying "I'm sorry for keeping you waiting. I apologize!" or appreciate their patience, saying "Thank you for waiting. I appreciate it!" Afterward, participants went on to read that while they waited for their meal to arrive, they checked their watch from time to time to make sure they had enough time to eat before the start of the movie they planned to watch later and that, about 30 minutes later, the server came back with the meal, refilled their glasses with water, and left.
Participants in the postfailure recovery condition read that after the server took their orders and while waiting for the server to come back with their meal, they checked their watch from time to time to make sure that they had enough time to eat before the start of the movie they planned to watch later. They went on to read that when the meal was served, they noticed that 30 minutes had passed, and the server either apologized for the delay, saying "I'm sorry for keeping you waiting. I apologize!" or appreciated their patience, saying "Thank you for waiting. I appreciate it!" The server then refilled their water glasses and left.
Afterward, participants indicated their tip amount with the same measure as in Studies 3 and 4. In addition, they responded to the previously used measures of severity inference (α =.83; averaged to form a severity inference index), frequency inference, and relationship perception. Finally, participants provided basic demographic information.
The ANOVA conducted on tip amount revealed a significant main effect of recovery timing (F( 1, 166) = 11.83, p <.01, =.07), such that the prefailure recovery (M = 16.33%, SD = 4.27%) led to a higher tip amount than the postfailure recovery (M = 13.93%, SD = 4.90%). The main effect of recovery strategy was not significant (F( 1, 166) =.39, p =.53, =.002). More importantly, the interaction effect was significant (F( 1, 166) = 3.92, p <.05, =.02). Planned contrasts showed that, replicating our previous findings, when the recovery message was delivered after the service failure, appreciation (M = 14.83%, SD = 4.53%) led to a higher tip amount than apology (M = 13.00%, SD = 5.14%; F( 1, 166) = 3.47, p =.06, =.02); however, this effect disappeared when the recovery message was offered before the service failure (M = 15.85%, SD = 4.58% vs. M = 16.80%, SD = 3.93%; F( 1, 166) =.90, p =.34, =.005; see Figure 4). Additional analysis showed that these results were not confounded by severity inference, frequency inference, or relationship perception (see Web Appendix D).
Graph: Figure 4. The interaction effect of recovery timing and symbolic recovery on tip amount (Study 5).†p <.10.
By manipulating the timing of recovery and showing that the superior effect of appreciation to apology went away when the recovery was provided before the service failure, Study 5 established a boundary condition for our core effect. The results further ruled out valence of the recovery message as an alternative account because, for this account to stand, the advantage of appreciation over apology needs to hold regardless of recovery timing. In addition, this study again suggested that three "phrasing-as-information" accounts could not explain our findings by showing that the two symbolic recovery strategies did not cause any change in consumers' severity inference, frequency inference, or perceived relationship with the service provider.
Study 6 aims to examine the effectiveness of a recovery message that combines both apology and appreciation. According to our theorizing, appreciation implies the service provider's recognition and acknowledgment of the service failure and their responsibility. Because this acknowledgment is essentially why apology (vs. no apology) is effective in redressing service failure, we expect that the combined use of apology and appreciation will not have an advantage over appreciation alone.
Study 6 employed a three-cell, one-way (recovery strategy: appreciation vs. apology vs. apology + appreciation) between-subjects design. Two hundred seven undergraduate students (66.18% female; Mage = 25.01 years, SD = 7.59) from a large Southwestern university in the United States completed the study for course credit and were randomly assigned to one of the three conditions.
Participants were asked to imagine that they placed an order with an online store; despite the store's "two-day delivery" guarantee, they received the otherwise fine product on the third day (one day after the promised delivery time). On receiving the product, they discovered a note from the store. In the appreciation condition, it read, "Thank you for your patience. We appreciate it!" In the apology condition, it read, "Sorry for the delayed delivery. We apologize!" In the apology + appreciation condition, the note read, "Sorry for the delayed delivery. We appreciate your patience!" Afterward, participants indicated their satisfaction with the way the store handled the shipping delay (same measures as used previously; α =.98; averaged to form a postrecovery satisfaction index) and their overall satisfaction with the store (adapted from the measures used in the Study 2 follow-up; see Web Appendix C; α =.92; averaged to form an overall satisfaction index).
In addition, as confound checks, participants reported their frequency inference and relationship perception. We adapted the frequency inference and relationship perception measures from Study 3. Finally, participants reported their basic demographic information.
We ran ANOVAs on the postrecovery satisfaction index and overall satisfaction index, respectively, which revealed a significant main effect of recovery strategy (recovery satisfaction: F( 2, 204) = 3.40, p =.035, =.03; overall satisfaction: F( 2, 204) = 3.97, p =.02, =.04). Planned contrasts showed that appreciation (M = 5.24, SD = 1.40) led to greater recovery satisfaction (M = 4.80, SD = 1.43; F( 1, 204) = 3.67, p =.06, =.02) and greater overall satisfaction (M = 5.00, SD = 1.21 vs. M = 4.53, SD = 1.48; F( 1, 204) = 4.23, p =.04, =.02) than apology. Furthermore, the combination of both strategies (M = 5.36, SD = 1.14) led to greater recovery satisfaction (F( 1, 204) = 6.26, p =.01, =.03) and greater overall satisfaction (M = 5.13, SD = 1.27; F( 1, 204) = 7.32, p <.01, =.04) than apology. However, consistent with our expectation, there was no significant difference between appreciation and the combined strategy on recovery satisfaction (F( 1, 204) =.32, p =.58, =.002) or overall satisfaction (F( 1, 204) =.39, p =.53, =.002; see Figure 5). Finally, we did not find any significant difference in frequency inference and relationship perception among the three conditions (ps >.27) (see Web Appendix D).
Graph: Figure 5. The effect of symbolic recovery on consumer responses (Study 6).†p <.10.*p <.05.
Study 6 not only replicated the superior effect of appreciation to apology but also provided evidence to suggest that the effect of appreciation overshadows the effect of apology when both strategies are used. As a result, the recovery message combining both apology and appreciation did not show an advantage over appreciation alone. Study 6 also showed that the alternative accounts of frequency inference and relationship perception could not explain our findings.
The goal of Study 7 is to examine the relative effectiveness of appreciation versus apology in the presence of utilitarian recovery. To this end, we manipulated whether a utilitarian recovery (a free drink) is present or absent in a restaurant service delay context. Because previous research suggests that utilitarian recovery or material compensation is necessary to restore customer satisfaction when the failure is severe ([48]), in this study we also manipulated the severity of the delay (30 minutes vs. 60 minutes).
We anticipate that when service failure is severe, symbolic recovery alone is not enough to reach a complete recovery; thus, in the absence of utilitarian recovery, the superior effect of appreciation to apology should attenuate or even disappear. In the presence of utilitarian recovery though, we should still observe the advantage of appreciation over apology. In other words, for severe service failures, whether a utilitarian recovery is provided or not should moderate the effect of symbolic recovery.
When service failures are less severe, prior work has suggested that consumers do not necessarily expect a utilitarian recovery ([48]). Therefore, we predict that the effect of symbolic recovery (apology vs. appreciation) does not hinge on the presence or absence of utilitarian recovery. Instead, we expect to find a main effect such that appreciation outperforms apology in redressing the less severe service failure.
Study 7 employed a 2 (symbolic recovery: appreciation vs. apology) × 2 (utilitarian recovery: present vs. absent) × 2 (delay severity: 30 minutes vs. 60 minutes) between-subjects design. Five hundred thirty-nine U.S. MTurk workers (54.35% female; Mage = 39.34 years, SD = 11.92) completed the study for monetary compensation and were randomly assigned to one of the eight conditions.
In this study, we used the same restaurant scenario as in Studies 3–5 with two modifications related to our new manipulations. First, to manipulate the severity of service failure, half the participants received the 30-minute wait manipulation as used in Studies 3–5, while the other half received the 60-minute wait manipulation. Second, to manipulate the presence or absence of utilitarian recovery, half the participants imagined that the server offered each of the guests a free drink as a token of apology or appreciation, while the other half did not receive such information.
After that, participants indicated their satisfaction with the way the server handled the service failure using the same measure as used previously (α =.95; averaged to form a postrecovery satisfaction index). Finally, participants reported their demographic information.
A 2 × 2 × 2 ANOVA run on the postrecovery satisfaction index revealed three significant main effects: symbolic recovery (F( 1, 531) = 33.55, p <.01, =.06), utilitarian recovery (F( 1, 531) = 149.38, p <.001, =.22), and delay severity (F( 1, 531) = 544.55, p <.01, =.51). Two of the two-way interactions were significant: symbolic recovery × utilitarian recovery (F( 1, 531) = 8.97, p <.01, =.02) and delay severity × utilitarian recovery (F( 1, 531) = 28.32, p <.01, =.05). Importantly, the three-way interaction was also significant (F( 1, 531) = 8.90, p <.01, =.02, see Figure 6).
Graph: Figure 6. The effect of symbolic recovery, utilitarian recovery, and severity of service failure on postrecovery satisfaction (Study 7).*p <.05.**p <.01.
To decompose this three-way interaction, we examine the interaction between utilitarian recovery and symbolic recovery at different levels of delay severity. In the 30-minute delay condition (Figure 6, Panel A), not surprisingly the main effect of utilitarian recovery was significant (F( 1, 531) = 156.95, p <.01, =.23), such that the postrecovery satisfaction was greater when the utilitarian recovery was present (M = 4.97, SD =.70) than when it was absent (M = 3.88, SD =.79). More importantly, and replicating our previous findings, the main effect of symbolic recovery (F( 1, 531) = 14.33, p <.01, =.03) was significant, such that appreciation (M = 4.53, SD =.95) led to greater postrecovery satisfaction than apology (M = 4.17, SD =.85). Consistent with our expectation, the interaction effect was not significant (F( 1, 531) =.008, p =.994, <.001).
In the 60-minute delay condition (Figure 6, Panel B), we found a main effect of symbolic recovery (F( 1, 531) = 19.37, p <.01, =.04), such that appreciation (M = 3.14, SD =.79) led to greater postrecovery satisfaction than apology (M = 2.78, SD =.66), and a main effect of utilitarian recovery (F( 1, 531) = 23.35, p <.01, =.04), such that the presence of utilitarian recovery (M = 3.22, SD =.72) led to greater postrecovery satisfaction than its absence (M = 2.77, SD =.71). More importantly and as we expected, the interaction between utilitarian recovery and symbolic recovery was significant (F( 1, 531) = 17.53, p <.01, =.03). When utilitarian recovery (a free drink) was provided, appreciation (M = 3.57, SD =.63) led to greater postrecovery satisfaction than apology (M = 2.82, SD =.61; F( 1, 531) = 32.34, p <.01, =.06); when a free drink was not provided, the advantageous effect of appreciation over apology disappeared (Mappreciation = 2.78, SD =.74; Mapology = 2.76, SD =.69; F( 1, 531) =.03, p =.87, <.001).
Study 7 suggests that when the service failure is outside the normal zone of acceptance (one-hour wait in a restaurant), utilitarian recovery (e.g., a free drink) is necessary to restore customer satisfaction, such that in its absence, symbolic recovery strategies (apology vs. appreciation) do not make a difference; in its presence, however, the results replicated the superior effect of appreciation to apology, demonstrating the robustness of our core finding (i.e., it holds relative to utilitarian recovery and is not dominated by it). The presence or absence of utilitarian recovery does not moderate the effect of symbolic recovery, however, when the service failure is within the normal zone of acceptance. Regardless of whether utilitarian recovery is offered, appreciation is always better than apology in recovering the service failure.
In a series of 12 real-behavior and lab studies, across diverse samples (eastern, western, student, and MTurk samples), service failure situations (e.g., service delay, error, unavailability), and industries (e.g., restaurant, airline, plumbing), we examine when and why appreciation (saying "thank you") might work better than apology (saying "sorry") as a symbolic recovery in redressing service failures. Our work contributes to research on service recovery, linguistic framing, and self-view.
Our research findings contribute to the literature on service recovery. Prior service recovery research has focused primarily on two types of service recovery: compensation and apology (e.g., [71]). The former is a utilitarian tactic that offers tangible material benefits while the latter is a symbolic tactic in which acknowledgment of the service failure is offered without material compensation ([60]). Our research represents the first attempt to identify appreciation as an additional viable symbolic recovery strategy that is often superior to the apology strategy, broadening the scope of symbolic recovery. Furthermore, by identifying self-esteem as the mechanism underlying the advantageous effect of appreciation over apology and by establishing the moderating role of factors affecting self-esteem such as consumer narcissism and recovery timing, our research delineates when and why the appreciation recovery strategy is more effective than apology in enhancing consumer satisfaction.
Our research also adds to the emerging research on linguistic framing by examining the effects of saying "sorry" versus "thank you," two statements often made by service providers after service failures without much thought for how they affect consumer satisfaction. Focusing on pairs of logically or semantically similar expressions, work on linguistic framing has demonstrated that, though these similar expressions can be used somewhat interchangeably, the change in wording conveys distinct psychological feedback that renders different behavioral outcomes ([ 8]; [39]; [44]). Our work contributes to this research by showing that a mere shift from saying "sorry" to "thank you" in service providers' verbal response to service failure can alter consumers' state self-esteem, which in turn influences their postrecovery satisfaction.
Our findings also provide insights into research on self-view by identifying previously unexplored antecedents and consequences of self-esteem. Prior research has shown that the pursuit of self-esteem is prevalent in consumer behavior ([20]), as consumers routinely engage in consumption activities that facilitate positive self-perceptions ([ 9]; [20]). For example, consumers can boost self-esteem by resorting to conspicuous and status-signaling consumption ([59]) or by sharing only positive information with socially distant others in WOM communications ([16]). However, extant research on self-esteem centers primarily on consumers' self-directed consumption activities. Our research suggests that marketer-initiated behavior or marketing strategy, rather than consumers' own behavior, can also increase consumers' self-esteem. Specifically, we demonstrate that service providers' verbal appreciation (vs. apology) after service failures enhances consumer self-esteem, which induces favorable marketing outcomes such as satisfaction and WOM.
Our findings have substantial implications for service providers regarding how to effectively recover service failures. As an initial step after service failures, service providers need to decide what to say to consumers to redress the failure and restore satisfaction. Despite abundant guidance on whether and when to redress a service failure, researchers have offered little advice on what service providers should say, except for recommending that they apologize for the service failure. Our work suggests that rather than saying "sorry," service providers should say "thank you," which is often more effective in enhancing consumer satisfaction. In addition, our studies identify the effectiveness of various symbolic recovery strategies for service failures and thus contribute to better managing business operations.
Moreover, this research emphasizes that what service providers ultimately say ("thank you" or "sorry") should be tailored to certain situational (i.e., timing of the recovery, severity of failure, and presence of utilitarian recovery) and individual (e.g., consumers' trait narcissism) factors. For example, our results suggest that service providers could monitor the service delivery and redress potential failures in advance. Furthermore, we show that when the service failure is severe, utilitarian recovery or material compensation is a prerequisite for the superior effect of appreciation. We also alert service providers to the importance of consumers' personality traits, especially their level of narcissism. Our research suggests that service providers should use appreciation in their service recovery for consumers with a higher narcissistic tendency (e.g., those who use social networks more, are younger) but should be aware that appreciation is not necessarily better than apology for those low in narcissism.
Further research could extend our findings in different directions. First, although we examined the moderating roles of consumer narcissism, recovery timing, severity of failure, and presence of utilitarian recovery in the superiority of appreciation to apology, future research could explore other moderating variables or boundary conditions. One such variable is lay rationalism (i.e., the individual tendency to rely on reasons instead of emotions in decision making; [29]). Because elevated self-esteem drives the advantageous effect of appreciation, this effect may disappear among people predisposed to focus on reasons (vs. emotions) when making decisions. Likewise, future research could explore situations (e.g., more severe failures than those examined in our research) in which apology outperforms appreciation in redressing service failures. For example, if a valet crashes a consumer's car, a simple "thank you for your understanding" might make consumers feel even angrier.
Throughout our studies, we examined five alternative mechanisms such as cause attribution, severity inference, and relationship perception and found that they could not account for our findings. Despite the "null" effects for these alternative mechanisms in our studies, it did not mean that they were not operative in our studies or that there are no contexts in which they could be operative. The null effects suggest that they were less likely to be explanations in the contexts we studied, or that the measures we used were not sensitive enough in those contexts. Future research could further examine situations in which these alternative accounts play a role in affecting consumer responses to the symbolic recovery strategies.
Finally, we focused on appreciation as a symbolic recovery strategy that can enhance consumers' self-esteem and promote their postrecovery satisfaction. One unanswered question is whether marketing strategies that appreciate customers and enhance their self-esteem are beneficial in the long run. Research on entitlement ([ 6]; [70]) has shown that consumers often feel entitled to receive treatment that matches their perceived status. A possible outcome is that enhancing consumer self-esteem through appreciation may make consumers feel entitled to preferential treatment from service providers. Further research could examine this intriguing possibility.
To sum up, across multiple industries, diverse participant samples, and various service failure contexts, our research finds that in most service failure situations, the appreciation recovery communication strategy (e.g., "Thank you for the wait") is more effective than the apology strategy (e.g., "Sorry for the wait") in redressing service failures, creating positive effects on a wide array of consumer response variables (e.g., consumer satisfaction, repurchase intentions, WOM, tip amount). Therefore, in redressing service failures, rather than apologizing, service providers should consider appreciating consumers.
Supplemental Material, web_appendix_updated - When and Why Saying "Thank You" Is Better Than Saying "Sorry" in Redressing Service Failures: The Role of Self-Esteem
Supplemental Material, web_appendix_updated for When and Why Saying "Thank You" Is Better Than Saying "Sorry" in Redressing Service Failures: The Role of Self-Esteem by Yanfen You, Xiaojing Yang, Lili Wang and Xiaoyan Deng in Journal of Marketing
Footnotes 1 Associate EditorRobert Meyer
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partially supported by the Grant of National Natural Science of China (No. 71572179 and 71972169) given to the third author. Correspondence concerning this article should be addressed to Lili Wang.
4 ORCID iDXiaojing Yang https://orcid.org/0000-0001-9337-907X
5 Online supplement:https://doi.org/10.1177/0022242919889894
6 1We noted that the recovery strategy manipulation unexpectedly influenced participants' trait narcissism scores (F(1, 251) = 4.27, p =.04, =.02) such that the appreciation strategy led participants to be more narcissistic (M = 3.33, SD = 3.20) than the apology strategy (M = 2.57, SD = 2.61). We suspect that participants' responses to the trait narcissism measures might have been affected by their prior responses to the other dependent variable measures. To be more rigorous, we contacted the MTurk workers who took part in the original study and invited them to participate in a brief survey that measured their trait narcissism for a payment of $2, seven months after the original study. One hundred forty-one original participants responded (55.32% female; Mage = 42.49 years, SD = 12.52) and completed the trait narcissism measures once again (α =.82). This time, we did not find a difference in narcissism scores between participants who were originally assigned to the appreciation and apology conditions (F(1, 139) =.11, p =.74, =.001). After matching these participants' narcissism scores (collected seven months later) with their tip amounts reported in the original study, we conducted the same moderation analysis as we used in the original study (PROCESS Model 1; [26]). Replicating the original results, this analysis again revealed a main effect of recovery strategy (b = 2.23, SE =.94; t(137) = 2.37, p =.02) and an interaction effect between recovery strategy and narcissism (b =.53, SE =.31; t(137) = 1.74, p =.08). The same floodlight analysis again revealed a significant, positive effect of appreciation (vs. apology) among participants whose narcissism index was higher than 2.18 (BJN = 1.90, SE =.96, p =.05) but not among those whose narcissism index was lower than 2.18 (39.01%). Our follow-up data collection and analysis further confirmed H3.
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When Does Corporate Social Irresponsibility Become News? Evidence from More Than 1,000 Brand Transgressions Across Five Countries
Companies are increasingly held accountable for their corporate social irresponsibility (CSI). However, the extent to which a CSI event damages the firm largely depends on the coverage of this event in high-reach news media. Using the theory of news value developed in communications research, the authors explain the amount of media coverage by introducing a set of variables related to the event, the involved brand, and media outlet. The authors analyze a sample of 1,054 CSI events that were reported in 77 leading media outlets in five countries in the period 2008–2014. Estimation results reveal many drivers. For example, the number of media covering the story may be 39% higher for salient and strong brands. 80% more media report the event if a foreign brand is involved in a domestic CSI event. When a brand advertises heavily or exclusively in a news medium, this reduces the likelihood of the news medium to cover negative stories about the brand. The average financial loss at the U.S. stock market due to a CSI event amounts to US$321 million. However, the market reacts to the event only if 4 or more U.S. high-reach media outlets report on the event.
Keywords: brand management; corporate social irresponsibility; event study; media; mixed binary logit model; theory of news value
Companies are increasingly held accountable for their social irresponsibility. External stakeholders, including investors, no longer tolerate unethical firm behavior and are demanding proactive social responsibility (e.g., [39]; [43]). Indeed, events of corporate misbehavior may propel a firm into a severe if not existential crisis. For example, Enron's accounting fraud in 2001 led to not only its bankruptcy but also the dissolution of its then-auditor, Arthur Andersen. A Wall Street journalist played a major role in the discovery of the Enron scandal and won several media awards for his investigation. The case demonstrates the enormous power of media. By construction, unethical behavior has no consequences until it is revealed and reported in the media. In fact, extensive research on corporate crises suggests that media coverage is one of the most important (if not the most important) accelerators of a brand/firm crisis (e.g., [ 4]; [43]; [45]). [ 4] show that the immediate loss in brand strength deteriorates from −13% to −21% if 12 instead rather than 6 German media outlets cover a crisis event. In addition, the brand then needs two months longer to recover from the crisis. [43] study how media coverage of corporate social irresponsibility (CSI) increases financial risk. They find that one additional article may cost a firm up to US$140 million per year. Thus, media coverage is a key factor that shapes the depth and length of a crisis and its consequences for a company. Media coverage of a CSI event, however, can be very heterogeneous. The recent Volkswagen emission scandal reached broad worldwide press coverage, and it was covered by all the leading U.S. newspapers. In contrast, only a few outlets reported on the accused misappropriation of funds by Banorte, a leading bank in Mexico in 2012. In another example, both Goldman Sachs and J.P. Morgan were accused of fraud in 2012 and 2010, respectively. Whereas 9 of the 15 leading U.S. media outlets reported on Goldman Sachs, only 3 outlets covered J.P. Morgan. From the firm's perspective, it is therefore of utmost relevance to understand and anticipate media coverage in a crisis situation, and scholars have recently called for research in this area ([16]).
Studying the drivers of media coverage of CSI events is the key objective of this study. We develop a model of how journalists and their media outlets decide whether to report on a CSI event. This model includes theory-based drivers of news selection such as the evidence on the event, brand strength, brand origin, and exclusivity of advertising partnership. We acknowledge that there are many sorts of negative publicity about a firm (e.g., product recalls, scandals of sponsored celebrities, CSI incidents); however, it is beyond our scope to cover all of them, and we chose to focus on CSI behavior that relates to environmental, social, and governance issues because although their relevance has risen consistently in recent years, they are less studied than other events such as product recalls.
This research makes several contributions. We develop a conceptual model for understanding when a CSI event becomes news. We draw on the theory of news value ([29]), an established paradigm of news generation in communication research, and significantly extend previous work in journalism, particularly by introducing managerially relevant news selection variables.
We apply the model to a large multicountry data set and quantify the impact of news selection variables on the probability of reporting a CSI event. Specifically, we collect data on CSI events and their coverage during 2008–2014 in the leading online and offline media in five countries: the United States, Mexico, the United Kingdom, Germany, and France. The final sample includes a set of 1,054 CSI events involving 324 brands across diverse industries. Unlike product recalls, which must be reported to authorities, we can only identify CSI events from their coverage in media channels. An important benefit of the multicountry design is that we observe events that were not reported in one country through their media coverage in other countries. Thus, the multicountry data set significantly contributes to the power of the analysis. Furthermore, we uncover important differences between media coverage of a domestic versus a foreign event as well as for a domestic versus a foreign brand. Only in a multicountry data set is there a quasiexperimental variation across domestic and foreign for the same CSI event and the same brand, which supports identification of the related effects. The unique data set also yields substantive and generalizable results that covers both developed and developing countries.
Finally, we investigate the economic consequences of media coverage of a CSI event in a financial event study. Although it is well-known that media attention may influence investor behavior (e.g., [22]; [24]; [58]), we need to demonstrate this for the domain of CSI news to establish practical relevance, which has not been done so far. Together with the analysis of the drivers of media coverage, this analysis sheds light on the financial risks of a CSI event dependent on brand and event characteristics.
This study offers important insights into the world of media and how outlets report on corporate issues. Consistent with the theory of news value, we find that media prefer reporting CSI events for strong and well-established brands. For example, 53% of the leading U.S. media covered a story when Google violated privacy concerns by collecting personal information in Europe in 2010. In contrast, Citibank, a much weaker and less salient brand than Google, was accused of fraud in India in the same year, and no leading U.S. media outlet reported on this event. Media also show a preference for events that happen in the home market, particularly involving foreign brands; however, they tend to cover CSI news less often for their advertising partners. Furthermore, we find that "right-oriented" media are less likely to cover a CSI event compared with "left-oriented" media. This effect becomes weaker when the brand is more present because of recent overall advertising pressure.
Moreover, the event study produces an unexpected finding. If we do not control for media coverage, the analysis suggests that investors do not care about unethical firm behavior. However, we do find evidence for a negative stock market effect when 4 or more media outlets out of the 15 largest U.S. outlets cover the CSI event.
The remainder of this article is structured as follows. The next section gives an overview of related literature. Following that, we introduce the model of CSI news selection. We then develop expectations about news selection drivers. The following sections present the data, the empirical model of news selection, and estimation results. We then introduce the event study to measure the economic impact of media coverage. The final section concludes with a discussion of implications and limitations.
This study extends the marketing literature on brand/firm crises in a new direction. Prior research has studied the effects of negative corporate news on various performance metrics and conditions—for example, sales ([15]), advertising effectiveness (e.g., [45]), and shareholder value (e.g., [25]). In addition, researchers have analyzed consequences for customer mindset variables such as attitude toward the brand (e.g., [ 1]), brand equity (e.g., [17]), brand attention and brand strength (e.g., [ 4]), and online word of mouth (WOM; e.g., [11]).
Another related stream of research in marketing and economics studies the behavior and outcomes in media markets. Researchers have suggested both theoretical models (e.g., [28]; [60]) and empirical models (e.g., [31]; [34]; [51]). The interdependency of media and their advertising partners and how it results in various forms of media biases have been of particular interest in these studies (e.g., [ 7]; [34]; [51]). As a result, the studies typically focus on advertising but do not pay much attention to other variables that also might significantly influence media coverage.
We build on this stream and also study the role of advertising. But advertising is only one among several other variables in our model of media coverage. Specifically, we adapt an established paradigm—the theory of news value ([29])—to derive a list of important drivers of the coverage of CSI events. Empirical research in this domain has studied a wide range of news topics including international crises ([29]), celebrity news ([49]), and protest events ([50]). Although the theory of news value is widely accepted in the field of journalism and covered in standard textbooks (e.g., [57]), empirical research appears to be limited to case studies and qualitative research (e.g., [12]; [18]; [29]; [35], [36]; [49]). A rare exception is [50], who study the coverage of 382 protest events by two local newspapers in a small U.S. city. Their context with protests on social and political issues in a small city, however, is very different from our nationwide coverage of unethical firm behavior events. The set of driver variables also appears to be limited with a focus on the number of protesters, the type of supporting organization, the location and time of the protest, and the type of protest. Our set of drivers is larger and covers all relevant news factors suggested by the theory of news value. Importantly, we also consider news selection variables that are actionable for managers because they can influence them.
We add to the media literature in marketing by building on the theory of news value. Studying the coverage of CSI events has not been the focus before. Our quantitative study includes more than 1,000 events over a period of 6.4 years, 77 media outlets, and five countries. To the best of our knowledge, it is the first study that focuses on specific drivers of media coverage of news that is relevant to marketing managers and other company executives. We acknowledge that recent work in marketing has already addressed the dynamic interplay between news media coverage and other communication channels as well as economic outcomes ([14]; [37]; [58]). A key message from these studies is that media coverage plays a significant role that should not be neglected by management. However, these studies do not investigate CSI events, which is our focus here.
In this section, we develop a model that describes the process and drivers of selecting negative corporate news due to an event of unethical behavior. We introduce variables that influence editors' choice to cover the CSI news and discuss their direction of influence.
In a seminal article, [29] suggest a theory of news value to answer the question "How does an 'event' become 'news'?" This theory has gained wide acceptance to explain the selection process of news across various fields (international politics, entertainment, sports, etc.), though not corporate news. Although researchers have suggested extensions and refinements of the theory (e.g., [35]; [57]), it has not lost its relevance and still applies to today's digital media world ([36]).
Galtung and Ruge begin with the basic premise that journalists follow ground rules to evaluate an event and put forward a taxonomy of 12 factors describing the newsworthiness of the event. The theory contends that the more factors an event satisfies, the more likely it is to be reported on. Moreover, news factors may compensate each other and altogether have to pass a certain threshold to qualify as news.
We adopt the theory of news value to explain how media outlets assess the newsworthiness of a CSI event. The news factors proposed by the theory describe the reasons for newsworthiness at an abstract level. As a theoretical contribution, we derive explicit news selection variables from the news factors that apply to the specific context of unethical firm behavior. These news selection variables are observable and thus suitable for empirical validation. In the following section, we briefly describe this process before we set up the conceptual model.
We proceeded in three steps (Figure 1). We began with the original news factors (12) proposed by [29] and their major extension (+6 news factors) by [35]. The description of the news factors provides the basis for our understanding of their meaning that we transferred to the context of firms and brands. For example, when Galtung and Ruge refer to power elite as a news factor they describe this as stories involving powerful and well-known individuals, organizations, or institutions. In the corporate world, this can be transferred to brands that are strong (powerful) and salient (well-known). As a result, we consider the two variables brand salience and brand strength as specific news selection variables for our model. Another news factor is unambiguity, which suggests that an event is more likely to be selected if it can be understood more clearly and interpreted without multiple meanings ([29]). To reflect unambiguity, we introduce a news selection variable that measures the evidence base of a CSI event. We ended up with a catalog of 28 potential news selection variables (for a detailed list, see Web Appendix Table WA.1) that represent factors proposed by Galtung and Ruge and Harcup and O'Neill. In addition to the logical generation of news selection variables, the relevant academic media literature in politics (e.g., [29]), journalism (e.g., [35]), sociology (e.g., [50]), economics (e.g., [21]), and management (e.g., [26]) helped us identify five sets of potentially relevant control variables.
Graph: Figure 1. Process of deriving CSI news selection variables.
In a second step, we evaluated these variables according to their relevance in the context of our study and their measurability, by which we mean that the criterion must be objective and specific enough. Criteria such as positive news do not apply to the context of CSI events. Others, such as a surprising character of the news, are too vague to be clearly operationalized. The second step reduced the set of variables to 14 news selection variables and 3 sets of controls. This set of news selection variables is large and captures a great portion of the heterogeneity of CSI events.
In the third step, we conducted in-depth interviews with four newspaper editors to validate our selection.[ 5] The editors considered all news selection variables highly relevant on a rating scale from 1 to 5, except for political orientation and advertising relationship. We still kept these variables because the prior literature has documented their influence on editorial decisions (e.g., [34]; [44]). We also used the interviews to identify new variables that met the aforementioned requirements, but no new variables came up.
Drawing on the theory of news value and the interviews with the editors, we suggest a model of how the media cover CSI events and how this affects the stock market (see Figure 2). We assume that a CSI event comes to the attention of a media outlet through various sources (e.g., own research, news agencies, press releases, social media). We do not investigate these sources further. The editorial team evaluates each event in terms of its newsworthiness. To be reported, the news must pass a certain threshold. The news selection variables determine the extent to which this threshold is crossed. We have derived these variables from the theory of news value as explained previously and shown in Figure 2. We distinguish three groups of variables that are specific to the brand, the CSI event, and the media outlet. Table 1 summarizes each news selection variable and its impact on media coverage in the first two columns. Columns 3 and 4 show the connection to the related higher-level news factor and its abstract meaning from which the variable was derived. We discuss details on measurement of the news selection variables subsequently.
Graph: Figure 2. Media coverage of CSI news and its impact on the stock market.Notes: We include and test for interactions between brand-related variables and CSI-related and media outlet–related variables (denoted by the dashed arrows).
Graph
Table 1. News Selection Variables for CSI Events and Their Correspondence to Original News Factors.
| News Selection Variable | Impact on Media Coverage | Related News Factor | Description of News Factor |
|---|
| Brand-Related Variables | | |
| Brand salienceBrand strengthBrand presence: Total advertisingBrand presence: Online interest in brand | The higher the level of brand salience, strength and presence, the more likely a media outlet is to report on a related CSI event. | Power elite | Stories involving powerful and well-known individuals, organizations, or institutions. |
| Celebritya | Stories involving people who are already famous. |
| Negative WOM | The higher the level of negative WOM for a brand, the more likely a media outlet is to report on a related CSI event. | Consonance | The news selector may predict (or, indeed, want) something to happen, thus forming a mental "preimage" of an event. |
| Domestic brand | If a CSI event involves a domestic brand, it is more likely that a media outlet reports on it. | Relevance | Stories about issues, groups, and nations perceived to be relevant to the audience. Things that are culturally similar are likely to be selected because they fit into the news selector's frame of reference. |
| Brand CSI history | The more CSI events have been reported for a brand in the recent past, the more likely a media outlet is to report on a new CSI event. | Continuity | Once a topic/issue has become headline news, it remains in the media spotlight for some time—even if its amplitude has been greatly reduced—because it has become familiar and easier to interpret. |
| CSI Event–Related Variables | | |
| Domestic CSI event | If a CSI event occurs in the home country a media outlet is more likely to report on it. | Relevance | Stories about issues, groups, and nations perceived to be relevant to the audience. Things that are culturally similar are likely to be selected because they fit into the news selector's frame of reference. |
| Evidence-based CSI event | If a CSI event is based on evidence, it is more likely that a media outlet reports on it. | Unambiguity | The less ambiguity, the more likely the event is to become news. The more clearly an event can be understood, and interpreted without multiple meanings, the greater the chance of it being selected. |
| Other brand news | If other potential brand news is available at the time of a CSI event, it is less likely that a media outlet reports on the CSI event. | Balance | An event may be included as news less because of its intrinsic news value than because it fits into the overall composition or balance of a newspaper. If there are already many news items on a subject, the threshold value for a new item will be increased. |
| Media Outlet–Related Variables | |
| Frequency of publication | The higher the frequency of publication of the media outlet, the more likely the media outlet is to report on the CSI event. | Frequency | An event that unfolds at the same or similar frequency as the news medium (e.g., murder) is more likely to be selected as news than is a social trend that takes place over a long period of time. |
| Political orientation | The more left oriented the media outlet, the more likely it is to report on a CSI event. | Newspaper's own agendaa | Stories that set or fit the news organization's own agenda. |
| Advertising relationshipSelective advertising partnership | If the media outlet has an advertising relationship with a brand, it is less likely that the media outlet reports on a CSI event the brand is involved in. |
1 a News factor added by [35].
2 Notes: News factors are based on [29] and [35].
The main effects of the news selection variables appear in column 2 of Table 1. In line with insights from theoretical models ([28]), we also consider a special condition in which the advertiser concentrates its effort exclusively on one media outlet. This is called a selective advertising partnership. Finally, we allow for the possibility that the impact of CSI-related and media outlet–related variables depends on brand characteristics. However, the theory of news value is not sufficiently developed to advance a comprehensive set of moderators and a priori expectations on their influence. We therefore test for potential interaction effects and follow [55] by using inductive reasoning to explain these effects. This inductive approach is also philosophically supported by [ 6].
We consider three dimensions of a brand's role and perception in the population: brand salience, brand strength, and brand presence. The first two constructs result from long-term processes of information processing, learning, and evaluation. They constitute the two key dimensions of brand knowledge according to [40] and cover both volume and valence. Brand salience reflects the prominence or level of activation of a brand in long-term memory ([ 2]). Brand strength builds on these knowledge structures but adds a directional meaning by integrating cognition, emotions, and behavioral intentions. Strong and salient brands should lead to higher media coverage when involved in a CSI event.
Brand presence refers to the fact that a brand may also be more or less present in short-term memory at a specific point in time. This presence fluctuates over time, as it is driven by short-term influences such as advertising pressure, rumors, viral activities, and so on. We identified two news selection variables that reflect brand presence in terms of recent brand advertising expenditures and relative online interest in the brand (Google Trends). The more present the brand is, the higher the press's likelihood of reporting on the brand.
Negative WOM on a brand leads to more news articles on that brand. [37] show this phenomenon for banks in a complex multimedia system of communication. We expect the same for our context of CSI events.
The interviewed editors emphasized that stories on a domestic brand are more likely to be published, simply because domestic brands are more relevant to the average citizen. Readers will pay more attention to culturally similar items and take less notice of culturally distant items.
If a topic has already been covered in the media, it is likely that it will continue to be defined as news for the near future. The reason is that the topic has become familiar and easy to interpret by the potential reader.
Events characterized by CSI occur in every corner of the world. An event that happens in the home country is closer to the people than an event outside the country, in line with the previous argument that cultural proximity increases the relevance of an event.
Ambiguity about the facts and consequences of an event hinders a clear interpretation of the event and also may undermine the credibility of newspapers. Thus, media outlets have a strong preference for clear and unambiguous stories ([29]).
Editors strive for a balanced composition of news to meet the demand of their readers for variety ([29]). Therefore, the threshold for reporting on the CSI event of a brand will be higher if other brand news competes for space at the same time.
Frequency of publication is an important variable that corresponds to the news factor frequency. The occurrence of a CSI event is much more in sync with a daily frequency and may be outdated to report on in a weekly or monthly magazine.
Newspapers have their own editorial line, competitive strategies, and relationship with advertising partners that influences their business decisions (e.g., [28]; [44]). [35] summarize such considerations under the newspaper's own agenda. We argue that the general political orientation of a media outlet shapes the editorial line and therefore may lead to more or less coverage of CSI stories. The left versus right contrast is the only scheme of political orientation that applies across countries. Profit-oriented companies and their representatives are the natural enemy of left-oriented ideologies. Their power and focus on profit maximization are considered the key source for exploitation of the workforce and unequal distribution of wealth ([47]). News about corporate misbehavior therefore is most welcome in the fight against the power of these companies, which fits into the frame of reference for readers of left-oriented newspapers. In contrast, right-oriented media outlets tend to be more sympathetic toward private enterprises and capitalism as a whole. Consequently, we expect that left-oriented media are more likely to report on a CSI event.
Another potentially influential factor on editorial decisions is the relationship to advertising partners. Outlets rely on advertising money and want to maintain good relationships with their advertisers. News selection decisions are vulnerable to the interests of advertising partners (e.g., [34]; [51]; [60]). Thus, we expect a negative effect of advertising on media coverage.
In a theoretical analysis under the assumption of heterogeneous customer preferences and differentiated products, [28] show that it may be rational for firms to place their advertisements exclusively in one media outlet. Moreover, their analysis implies that the outlet introduces even more positive reporting in favor of the advertiser compared with a situation in which the advertiser targets more than one outlet. This result suggests a negative effect of advertising investment on media coverage that is even stronger than for a nonselective partnership.
Finally, we consider the economic consequences of a CSI event in terms of its stock market response (see Figure 2). Prior research has produced mixed results. Whereas [25] reports a negative stock market effect of environmental issues, other authors do not find a significant stock market response to CSI events (e.g., [33]; [48]). None of these studies, however, analyzes the role of media coverage. We consider media coverage in our framework as a potentially important driver of the impact of a CSI event on stock returns. Because the news selection variables may also influence the investors' reaction to a CSI event, we include both their potential direct effect and the indirect effect (mediation via media coverage).
We apply our research framework (Figure 2) to five countries: the United States, Mexico, the United Kingdom, France, and Germany. We wanted to investigate countries that are relevant to the global economy, that represent different continents, and for which we have sufficient linguistic expertise to understand and rate news articles. The countries account for 38% of the world's gross domestic product and even include an emerging economy (Mexico). The observation period covers 6.4 years from 2008 to mid-2014.
We define the brands of YouGov's BrandIndex database as our population. This database offers representative attitudinal brand information for a wide range of brands on a daily basis and has been used in prior research (e.g., [37]; [46]). Across the five countries, we cover 2,300 brands.[ 6]
Using published data for 2012, we identify the media outlets with the largest print circulation and the leading online newspapers (based on website traffic; see https://www.alexa.com/siteinfo) in each country. Given that reach is the main driver of impact on society, consumers, and investors, the focus on leading newspapers should not be a critical limitation ([37]; [43]). Articles for most of the outlets were searchable in the LexisNexis database; if not, we looked for other publicly accessible archives. As a result, we analyze 77 outlets that include between 13 and 18 outlets per country (for the list of outlets, see Web Appendix 2).
Unlike for product recalls, there is no requirement to report CSI events, and thus, no publicly accessible database is available. Therefore, it was necessary to uncover CSI events ex post on our own. We searched for potential CSI events within the sample of YouGov brands and media outlets country by country (for a similar strategy, see [25]). We are aware that this strategy comes at a cost: we might overlook a few CSI events that were considered by all outlets in the selection stage but not reported by any of them in any of the five countries. This limitation should be less of an issue for the international brands that are part of YouGov's brand list. At the country level, we repeatedly observe that media in one country report on a CSI event for an international brand (e.g., McDonald's in the United States) but do not do so in other countries (e.g., McDonald's in France). Through this mechanism, we also effectively uncover events that were not reported at all in a specific country.
We proceeded as follows to identify CSI events (see also [25]). We searched country by country for potentially relevant media reports on unethical behavior in all outlets using LexisNexis and online archives. We submitted the brand or company name together with up to 500 keywords per language on typical environmental (e.g., pollution, animal mistreatment), social (e.g., child labor, discrimination), and governance (e.g., fraud, corruption) issues. We identified more than 50,000 articles including a huge set of articles that were not related to CSI events. Therefore, one coauthor and six graduate students (among them native speakers in English, Spanish, French, and German) read and content-analyzed all articles. Using a set of criteria to identify a CSI event (for more details, see Web Appendix 3), we ultimately identified 1,054 CSI events. We assigned each event to one of the three categories of environmental, social, and governance issues. There was no disagreement for the majority of assignments (95%), and the few cases of disagreement were resolved by discussion.
We required that a media report must have occurred within 14 days after the first published report to be counted as coverage (see also [21]; [50]). This time frame is more than sufficient to identify all reporting media outlets (see also Web Appendix 4 for a related robustness check). Note that we do not count the number of articles per outlet but only whether the outlet has reported on the CSI event.
In this section, we describe how we measured the selection variables. We combined various databases to build the data set for estimation. We provide further details on the operationalization of news selection variables in Table 2 and correlations in Web Appendix 5. Variance inflation factors are less than the critical value of 10. Thus, we find no indication of collinearity issues.
Graph
Table 2. Details on the Operationalization of the Variables.
| Variable | Detailed Description |
|---|
| Brand-Related News Selection Variables (Country-Specific) |
| Brand strength[−100 to 100]Source: YouGov | Brand strength is measured along six dimensions and corresponds to YouGov's BrandIndex: brand quality, brand value, brand satisfaction, brand recommendation, brand identification, and brand overall impression. For each dimension, respondents independently evaluate brands out of a competitive range of up to 20 brands. We take the country-specific mean of the 30 days preceding the first media report on a CSI event.a |
| Brand salience[0 to 100]Source: YouGov | Brand salience measures the retrieval behavior that is observed when respondents evaluate brand strength dimensions. We count the relative number of negative and positive responses for a brand across all six dimensions and respondents. A score of 100 means that all respondents evaluate the brand on all dimensions (either positively or negatively). We take the country-specific mean of the 30 days preceding the first media report on a CSI event.a |
| Brand presence:Total advertising[000 EUR]Source: Ebiquity | Total advertising is measured by a stock variable. Let St denote advertising stock in week t and xt be total advertising expenditures across media channels. We compute the stock by , where ρ measures the carryover rate. We consider total advertising expenditure of a brand by country in the 24 months preceding the month of the CSI event. We convert the generalized monthly mass media carryover of.523 reported by Köhler et al. (2017) into its weekly equivalent. |
| Brand presence:Online interest[continuous]Source: Google Trends | Online interest in the brand is measured by using Google Trends data (Stephen and Galak 2012). Google Trends is a normalized index for search volume data. Even though these data do not reveal the absolute amount of search requests for a specific brand, they inform about interest for brands in a country relative to each other, which is sufficient for our purpose. Measurement requires a baseline against which searches for all other brands are indexed. Coca-Cola is a well-searched brand in all countries and serves as our baseline. We take the country-specific mean of the 30 days preceding the first media report on a CSI event. |
| Negative WOM[−100 to 100]Source: YouGov | Negative WOM corresponds to YouGov's buzz metric that calculates the relative number of respondents who heard something negative or positive about a brand in the last two weeks. The buzz metric runs from −100 (all respondents report negative buzz) to +100 (all respondents report positive buzz). We reverse the metric for our purpose to measure negative WOM and take the country-specific mean of the 30 days preceding the first media report on a CSI event.a |
| Domestic brand[dummy]Source: Online search | Domestic brand is a dummy variable that changes across countries. It is based on company headquarter information and indicates whether a brand originates from the focal country or is considered a foreign brand. We take note of the fact that companies may have acquired brands originating from different countries over the years and treat them appropriately as a domestic brand in their original home country. For example, Beck's is coded as German and Budweiser as American beer, though both brands belong to Anheuser Busch Inbev, today's largest brewery group with headquarters in Brussels, Belgium. |
| Brand CSI history[continuous]Source: Press search | Brand CSI history measures the number of CSI events for the focal brand in the 12 months preceding the current CSI event. We apply a linear time weight to the accumulation to account for the process of forgetting. This weighting also alleviates the censoring issue associated with this variable at the beginning of our time series. |
| CSI Event–Related News Selection Variables (Country-Specific) |
| Domestic CSI event[dummy]Source: Press search | Domestic CSI event refers to the origin of the CSI event and is a dummy variable. For the majority of events, this is unambiguous. Any disagreement among coders was solved by discussion. Very few events are truly global events (e.g., the manipulation of interest rates [the LIBOR scandal]); we coded such events as domestic in all countries. |
| Evidence-based CSI event (across countries)[dummy]Source: Press search | Evidence-based CSI event is a dummy variable measuring whether a CSI event is based on rumor or evidence. We code an event as evidence based if accusations in the media report are confirmed by the company or supported by legal institutions (e.g., court decisions). |
| Other brand news[dummy]Source: Press search | Other brand news is a dummy variable that explains whether other brand-related news was announced in a time window around the CSI event. We followed the same search strategy as for CSI events and considered financial news (e.g., earnings announcements, mergers and acquisitions, large investments), customer-related news (e.g., new product releases, changes in price strategies, product recalls), and other potentially relevant brand-related news (e.g., health of chief executive officer, external industry shocks). The time window starts three days before the CSI event and ends seven days after the event date. |
| Media Outlet–Related News Selection Variables (Media-Specific) |
| Frequency of publication[dummies]Source: Press search | Dummy variables for weekly and daily online/offline newspapers |
| Political orientation[−2 = left, – 2 = right]Source: Worldpress, interviews | Political orientation is measured with a left–right scheme on a five-point rating scale (Fuchs and Klingemann 1990). Public sources such as provide political classifications (e.g., conservative, liberal) for a wide range of outlets. We converted these classifications in a first step into ratings on a five-point scale. To validate these ratings, we then randomly selected 20 postdoctoral researchers and professors in the field of politics and journalism in all five countries and asked them to rate the political leaning of the media outlets of their countries on a three-point scale. In case of consistent ratings across raters and our own first rating of public information, we rated the outlet as left, center, or right. In all other cases, we rated an outlet as in between (i.e., either center-left or center-right; see also Web Appendix 2). |
| Advertising relationship with media outlet[000 EUR]Source: Ebiquity | We measure advertising relationship with media outlet as advertising stock (see definition for brand presence: total advertising), similar to brand presence. The difference from brand presence (total advertising) is that we only consider advertising expenditures in the focal outlet. The stock measure is a good proxy as it incorporates both the depth and length (in months) of investment. |
| Selective (advertising) partnership[000 EUR]Source: Ebiquity | Selective advertising partnership measures the investment in a single outlet in terms of advertising stock (same definition as for brand presence: total advertising). The focal brand must have advertised in the focal outlet exclusively for the previous six months before the CSI event. The stock measure is a good proxy as it incorporates both the depth and length (in months) of selective investment. |
3 a For more details on data collection and the exact items, see Web Appendix 6.
YouGov, a global market research company specializing in online panels, provided us with access to its BrandIndex database (for details, see Web Appendix 6). This unique database offers a representative measurement of brand attitudinal variables at the daily level. The brand variables have been used in prior research, albeit with different labels (e.g., [ 4]; [37]; [46]).
Brand strength (YouGov's BrandIndex) is a multidimensional index that runs from −100 to +100 ([ 4]; [46]). Brand salience measures the depth of brand knowledge and runs from 0 to 100. We used YouGov's buzz metric and reverse-coded it to measure negative WOM ([37]). The metric computes the number of respondents who heard something positive about the brand minus the number who heard something negative in the last two weeks relative to the total number. Using Google Trends data ([56]), we measured relative online interest in the brand to capture brand presence. To avoid reverse causality issues, we measured all variables before the CSI event. This ensures, for example, that negative WOM is not confounded by WOM created by the event itself. We measured recent advertising pressure, our second brand presence variable, with a stock variable. Ebiquity, an international market research company, provided us with advertising data across offline and online media (note, however, that advertising data were only available to us for the United Kingdom, France, and Germany). Brand CSI history of the recent past measures the number of CSI events for the focal brand in the 12 months preceding the current CSI event. Domestic brand is a dummy variable. It varies by country because McDonald's is a domestic brand for U.S. outlets but a foreign brand for all other outlets in the sample.
Domestic CSI event refers to the origin of the crisis event and is a dummy variable. Evidence is a dummy variable measuring whether a CSI event is based on rumor or on evidence. We measured other brand news using a dummy variable that explains whether other brand-related news was announced within a time window of three days before and seven days after the CSI event date (for a detailed list of events, see Web Appendix 7).
We measured political orientation of media outlets using a left–right scheme on a five-point rating scale ([27]). Public sources such as worldpress.org provide political classifications (e.g., conservative, liberal) for a wide range of outlets. We converted these classifications in a first step into ratings on a five-point scale and asked 20 experts in politics to validate the ratings in a second step. We represented a brand's advertising relationship with the media outlet using advertising stock, as with brand presence; the difference here is that we only considered advertising in the focal outlet. We identified a selective advertising relationship as one in which the focal brand has advertised in the focal outlet exclusively for the six months previous to the CSI event. Frequency of publication refers to weekly or daily online and offline issues.
Table 3 presents descriptive statistics of the news selection variables. In Table 4, we show a summary of our search for CSI events. Note that our observation period differs somewhat across countries because YouGov started collecting its BrandIndex data at different points in time. From 2013 on, YouGov introduced a change in its methodology across markets. Even though the change was modest, we ended our observation period by country with this change to ensure a consistent measurement of the brand data.
Graph
Table 3. Summary Statistics of News Selection Variables.
| Variables | Scale | Mean | Max | Min | SD |
|---|
| Brand-Related News Selection Variables (Country-Specific) |
| Brand salience | [0 to 100] | 41.28 | 81.64 | 1.18 | 15.54 |
| Brand strength | [−100 to 100] | 19.14 | 75.51 | −36.90 | 20.54 |
| Brand presence: Total Advertising | [000 EUR] | 3,763 | 67,536 | 0 | 5,627 |
| Brand presence: Online Interest | [continuous] | 488 | 10,100 | 1 | 1,085 |
| Negative WOM | [−100 to 100] | −10.51 | 53.74 | −74.79 | 16.51 |
| Domestic brand | [dummy] | .37 | — | — | — |
| Brand CSI history | [continuous] | 1.30 | 8.42 | 0 | 1.73 |
| CSI Event–Related News Selection Variables (Country-Specific) |
| Domestic CSI event | [dummy] | .35 | — | — | — |
| Evidence-based CSI event(across countries) | [dummy] | .43 | | | |
| Other brand news | [dummy] | .49 | — | — | — |
| Media Outlet–Related News Selection Variables (Media-Specific) |
| Frequency of publication | | | | | |
| Daily offline newspapers | [dummy] | .48 | — | — | — |
| Daily online newspaper | [dummy] | .36 | — | — | — |
| Weekly newspaper | [dummy] | .16 | — | — | — |
| Political orientation | [−2 = left, 2 = right] | .16 | 2 | −2 | 1.44 |
| Advertising relationship | [000 EUR] | 70 | 7,397 | 0 | 274 |
| Selective advertising partnership | | .17a | — | — | — |
| [000 EUR] | .58 | 2,457 | 0 | 24.45 |
| Control Variables | | | | | |
| CSI event types (across countries) |
| Governance issue | [dummy] | .51 | — | — | — |
| Social issues | [dummy] | .40 | — | — | — |
| Environmental issues | [dummy] | .09 | — | — | — |
| Product type (across countries) | | |
| Durables | [dummy] | .18 | — | — | — |
| Nondurables | [dummy] | .04 | — | — | — |
| Retail | [dummy] | .14 | — | — | — |
| Services | [dummy] | .64 | — | — | — |
| Country of outlet | | | | | |
| Germany | [dummy] | .21 | — | — | — |
| United Kingdom | [dummy] | .31 | — | — | — |
| France | [dummy] | .16 | — | — | — |
| United States | [dummy] | .24 | — | — | — |
| Mexico | [dummy] | .08 | — | — | — |
4 a We report the frequency of selective partnerships here. Of 296 brands, 49 advertised in only one media outlet for the last six months before the brand was involved in a CSI event. For model estimation, we use the advertising stock invested into the specific outlet.
Graph
Table 4. Summary Statistics for CSI Events by Country.
| Germany | United States | United Kingdom | Mexico | France | Total |
|---|
| Brands and Media Outlets | | | | | |
| Observation period | Jan. 2008–Dec. 2012 | Jan. 2009–Nov. 2012 | Jan. 2009–Jul. 2013 | May 2011–May 2014 | Sep. 2011–May 2013 | Feb. 2008–May 2014 |
| Total # brands covered | 600 | 1,200 | 925 | 325 | 300 | 2,300 |
| Total # media outlets analyzed | 15 | 15 | 16 | 13 | 18 | 77 |
| CSI Events | | | | |
| # CSI events | 450 | 530 | 629 | 213 | 298 | 1,054 |
| # brands with CSI event | 100 | 152 | 168 | 40 | 82 | 324 |
| CSI type of issue | | | | | | |
| Governance issues | .45 | .57 | .48 | .60 | .55 | .51 |
| Social issues | .45 | .35 | .43 | .33 | .38 | .40 |
| Environmental issues | .10 | .09 | .09 | .06 | .08 | .09 |
| Total # instances | 6,750 | 7,950 | 10,064 | 2,756 | 5,364 | 32,884 |
| Likelihood of reporting on a CSI event | .21 (.41) | .15 (.36) | .16 (.37) | .17 (.37) | .17 (.38) | .17 (.38) |
| Most Criticized Brands | | | | | |
| Rank 1 | Google/Apple | Google | Google | Telcel | Apple | Google |
| Rank 2 | Facebook | Apple | Apple | Apple | Google | Apple |
| Rank 3 | BP | Facebook | Facebook | Walmart | Samsung | Facebook |
| Rank 4 | Shell | Walmart/BP | Tesco | Google | Société Générale | BP |
| Rank 5 | Microsoft/IKEA/Samsung | UBS | BP | Samsung/Telefonica | EDF/Total | Samsung |
5 Notes: Standard deviations in parentheses. The ranking of criticized brands is based on the number of CSI events that are reported for the focal brand.
In total, we identified 1,054 CSI events within the 6.4 years. Given the unbalanced data set, this leads to 32,884 instances in which a media outlet could have reported on a CSI event. They chose to do so in only 5,685 instances, which results in an average reporting rate of 17.29%. Of the 2,300 brands covered in our analysis, 324 were involved in these events, which represents a share of 12% (in total), or 1.9% per year. Note that the total number of events and brands is smaller than the sum across countries because of the significant overlap in brands and, thus, crisis events. As Table 4 shows, Google and Apple are among the top five most criticized brands in each country.
According to the theory of news value, news factors of an event add up in a compensatory, additive manner and collectively need to pass a threshold to become news. We do not observe the difference between the perceived newsworthiness and the threshold but only whether the threshold was passed and the news reported. Assuming that the error term of the latent evaluation of newsworthiness is independently, identically distributed extreme value gives rise to the binary logit model that we apply to the data.
Specifically, let xijkl denote a vector that includes the news selection and control variables, where i ∈ I is an index for CSI event, j ∈ J is an index for media outlet, k ∈ K is an index for brand, and l ∈ L is an index for country. Pijkl measures the probability that media outlet j in country l is reporting on CSI event i of brand k:
Graph
1
with
where .
By specifying an event-specific constant αi, we control for event-specific news factors that are unobservable to us. Specifically, we capture their joint influence in the unobserved term μi that is assumed to be normally distributed with zero mean and variance . Technically, this results in a mixed binary logit model. We estimate the overall mean α0 and variance .
Equation 1 also includes two random error terms, vj and wk, which we assume to be normally distributed with zero mean and a variance to be estimated. By incorporating these error components, we account for unobserved effects that are specific to the outlet and the brand. Note that this specification allows the errors to be correlated within outlets and within brands.
Vector x summarizes our brand-related, CSI event–related, and media outlet–related news selection variables. In addition, it includes several control variables, which are dummy variables to measure the type of CSI event (environmental, social, and governance), the country, and the type of product (services, durables, nondurables, and retail), and a trend variable that counts calendar weeks. We always excluded the dummy for the reference category for identification purposes. The β parameters are to be estimated.
We also considered including prior coverage of the CSI event by other outlets as a control. Although it seems plausible to assume that editors use competitors as sources of information, it also implies that editors reiterate the news of others, which goes against their principles of exclusivity and timeliness. The analysis of the diffusion of media reports in Web Appendix 4 suggests that there is no strong direct dependency among outlets. Media report the event either immediately or not at all.
We estimated the model with simulated maximum likelihood. We used the estimator implemented in LIMDEP 10.0, which approximates the integral to obtain the unconditional likelihood function by Monte Carlo simulation (see also [32], pp. 629–33, 733f).
The large number of CSI events and media outlets creates an effective sample size of more than 32,000 observations. We exploit the rich variation of our focal variables across and within CSI events, brands, and media outlets (see Table 3) to identify the effects of interest. A CSI event is a rare and exogenous shock and occurs unexpectedly. Equation 1 models the media outlet's endogenous decision process of whether to report on the event. We subsequently discuss potential endogeneity issues that involve advertising variables and other brand news.
Research on product recalls (e.g., [52]) suggests that firms may change their advertising expenditures ex ante in expectation of lower economic performance after a recall announcement. Because a recall is predictable and is certain to happen, firms have an incentive to do so. In contrast, it is not a given that CSI-related firm behavior will be revealed. Indeed, senior management might not even be aware of a CSI issue. Therefore, a proactive change in advertising prior to the disclosed CSI event is not very likely. Employing Granger causality tests, we do not find evidence that CSI events Granger-cause advertising, which does not prove exogeneity but is consistent with this assumption (see Web Appendix 8).
Endogeneity concerns might also be related to the variable other brand news, which can be interpreted as similar to a "confounding" event known from event studies. In these studies, observations with confounding events are simply removed from the sample so that they do not interfere with the event of interest. Our estimation results and conclusions are robust when we follow this procedure and exclude observations with other brand news (see Web Appendix 8).
However, we also have an interest in estimating the impact of other brand news on the reporting likelihood. Therefore, we must determine the extent to which a potential simultaneity between our dependent variable and the coverage of other brand news affects estimation. For this purpose, we adopted both an instrumental variables approach and a structural approach. The instruments are strong according to the incremental F-statistic ([ 3]) and valid according to the overidentification Sargan–Hansen J-test ([59]). The Hausman–Wu test ([59]), however, does not support the assumption that other brand news is endogenous. Because instrumental variables estimation produces less efficient estimates, we do not focus on these results here, but we do report them in full detail, including statistics on the strength and validity of instruments, in Web Appendix 8.
We present the results of model estimation in Table 5. In the first column of data, we report estimation results by using the full data set across all five countries. The second column of data shows the analysis for those countries for which we also have advertising data available. Following our conceptual model (see Figure 2), we also tested for possible interactions of brand-related variables with CSI event-related and media outlet-related variables. To be included, the interaction variable had to be statistically relevant (likelihood ratio test: p <.05), meet standards for collinearity statistics (variance inflation factor < 10), and not affect the stability of other estimated coefficients in the model (for similar approaches, see [ 8]] and [20]]). Specifically, we first tested for the significance of each interaction effect separately. In the following steps, we sorted out those interactions that did not pass the likelihood ratio test or caused severe collinearity issues after we included all interaction variables together. From this procedure, we identified and added four additional interactions to the model. Web Appendix 9 describes the four-step selection process in detail.
Graph
Table 5. Drivers of Media Coverage of CSI Events (Estimation Results for Equation 1).
| DV: Media Outlet Reports on a CSI Event (Yes/No) | Sample I(U.S., France, Germany, Mexico, U.K.) | Sample II(France, Germany, U.K.) |
|---|
| Expected Sign | Estimated Coefficient | SE | Estimated Coefficient | SE |
|---|
| Intercept (average across CSI events) | | −3.3833*** | (.08879) | −3.88838*** | (.13458) |
| Standard deviation of intercept (across CSI events) | | .25948*** | (.01668) | 1.25650*** | (.02994) |
| Brand-Related News Selection Variables | | | | | |
| Brand salience | + | .01328*** | (.00102) | .01722*** | (.00174) |
| Brand strength | + | .00553*** | (.00118) | .00512*** | (.00180) |
| Brand presence: Total advertising | + | — | — | .9 × 10−5** | (.3 × 10−5) |
| Brand presence: Online interest | + | .00004*** | (.00001) | −.00003 | (.00002) |
| Negative WOM | + | .00913*** | (.00145) | .00839*** | (.00238) |
| Domestic brand (base) | | — | — | — | — |
| Foreign brand | − | −.54641*** | (.03854) | −.33992*** | (.06466) |
| Brand CSI history | + | .07947*** | (.00703) | .05138*** | (.01047) |
| CSI Event-Related News Selection Variables | | | | | |
| Domestic CSI event | + | .71806*** | (.03370) | .89924*** | (.06330) |
| Domestic CSI event × Foreign brand | | .57537*** | (.04445) | .44768*** | (.07380) |
| Evidence-based CSI event | + | .30241*** | (.02403) | .32555*** | (.03501) |
| Other brand news | − | −.37316*** | (.03457) | −.41038*** | (.06268) |
| Other brand news × Foreign brand | | .09509** | (.04189) | .13856** | (.07060) |
| Media Outlet–Related News Selection Variables | | | | | |
| Frequency | | | | | |
| Weekly offline (base) | | — | — | — | — |
| Daily online | + | 1.50057*** | (.05461) | 1.84193*** | (.07653) |
| Daily offline | + | 1.5166*** | (.05550) | 1.59226*** | (.07831) |
| Political orientation | − | .00846 | (.00805) | −.03009** | (.01300) |
| Political orientation × Total advertising | | — | — | .5 × 10−5*** | (.2 × 10−5) |
| Advertising relationship | − | — | — | −.00012** | (.00006) |
| Adv. relationship × Foreign brand | | — | — | .00062*** | (.00014) |
| Selective advertising partnership | − | — | — | −.01407*** | (.00578) |
| Control Variables | | | | | |
| CSI event type | | | | | |
| Governance issues (base) | | — | — | — | — |
| Social issues | | −.22104*** | (.02511) | −.26004*** | (.03626) |
| Environmental issues | | −.09278** | (.04575) | −.14195** | (.07135) |
| Product type | | | | | |
| Services (base) | | — | — | — | — |
| Durables | | .03936 | (.03337) | −.02397 | (.04779) |
| Nondurables | | −.25561*** | (.06728) | −.47445*** | (.10952) |
| Retailer | | −.05615 | (.03450) | −.05035 | (.05225) |
| Country of outlet | | | | | |
| United Kingdom (base) | | — | — | — | — |
| Germany | | .43735*** | (.02687) | .37821*** | (.03608) |
| United States | | −.66094*** | (.03345) | — | — |
| France | | −.04790 | (.03059) | .15481*** | (.04994) |
| Mexico | | −.35493*** | (.05116) | — | — |
| Time | | .00080*** | (.00020) | .00054 | (.00028) |
| Error Components | | | | | |
| Standard deviation of media outlet–specific error | | .01356 | (.01175) | .17580*** | (.01713) |
| Standard deviation of brand-specific error | | .71623*** | (.01293) | .16151*** | (.01689) |
| Sample I: | N (obs) = 32,884 | N (outlets) = 77 | N (brands) = 324 | N (CSI events) = 1,054 | log-likelihood = −12,424 |
| Sample II: | N (obs) = 16,438 | N (outlets) = 41 | N (brands) = 209 | N (CSI events) = 749 | log-likelihood = −6,189 |
- 6 **p <.05.
- 7 ***p <.01.
- 8 Notes: One-sided t-test only for expectations, two-sided t-test otherwise.
We find support for the relevance of all brand-related news selection variables. Estimated parameters associated with these variables are significant. Brands that show a higher salience level (β1 =.0133, p <.01) and have greater brand strength (β2 =.0055, p <.01) are more likely to be reported when they are involved in a CSI event. This probably explains the difference in coverage of the U.S. fraud case in our introductory example. Goldman Sachs is by far the stronger and more salient brand. Therefore, three times more media reported on Goldman Sachs than on J.P. Morgan. We also find evidence for a higher likelihood for brands that are more present, as reflected in their recent advertising pressure (β3 =.9 × 10−5, p <.05) and online interest (β4 =.4 × 10−4, p <.01). In 2012, 60% of the leading German news outlets reported on L'Oréal, which was accused of illegal campaign donation in the presidential elections. Being a strong and salient brand, the unusual high media coverage for a foreign brand involved in a foreign governance issue event might be also explained by the strong brand presence of L'Oréal due to the extraordinary high advertising expenditures around the event.
The results suggest that the level of negative WOM about a brand before the CSI event increases the chance that a media outlet reports on that event (β5 =.0091, p <.01). The chance is also greater if the brand is a domestic brand and if the brand has had more reported CSI events in the past (β6 =.0795, p <.01). Note that we use domestic brand as a reference category to allow for the identification of the interaction effect of foreign brand with domestic event and other variables. Therefore, we report a negative parameter estimate for foreign brand in Table 5 (β7 = −.5464, p <.01).
All expected relationships are supported with respect to our CSI event–related variables. A media outlet is more likely to cover a story on a domestic CSI event (β8 =.7181, p <.01). Conversely, this means that foreign events are less reported, which could be the reason for the low coverage of Apple's use of underage interns in India. Despite the high popularity of the brand, only 13% of U.S. media covered the story. The results also show that a CSI event is more likely to be covered if it is based on evidence (β9 =.3024, p <.01). The existence of other brand news around the event date, however, reduces the chance that the CSI event will be reported (β10 = −.3732, p <.01).
Media outlet characteristics also have an influence on the chance that a CSI event is reported. If the media outlet is issued at a higher frequency (e.g., daily versus weekly: β11 = 1.5006, p <.01), the likelihood of reporting the CSI event is greater. The last column of Table 5 shows that the parameter estimate for political orientation of the outlet is consistent with our expectation for the subsample of countries where we also control for advertising (β12 = −.0301, p <.05). Thus, left-oriented media are more likely to report on a CSI event. A deeper recent advertising relationship of an outlet with a brand involved in a CSI event reduces the likelihood of being reported in that outlet (β13 = −.0001, p <.05). As expected, the effect is even stronger when the advertising partnership with the brand is selective (β14 = −.0141, p <.01).
Being a foreign brand appears to make a difference for the role of news selection variables. We detect three significant interactions of variables with this brand characteristic and one more interaction between total advertising and political orientation. We discuss the results next.
A domestic CSI event increases the likelihood of reporting. This likelihood is even greater if a foreign brand is involved in the event (β15 =.5754, p <.01). From a theoretical perspective, this finding is consistent with the idea of ethnocentrism ([53]). According to this idea, consumers tend to behave patriotically and want to protect their domestic economy. Catering to the preferences of patriotic consumers, media outlets are more critical toward foreign brands when these brands are involved in a potential scandal in their home country. A prominent recent example is the Volkswagen pollution scandal, which was covered in 100% of the leading U.S. media.
We find that the negative effect of other brand news is weaker for foreign brands (β16 =.0951, p <.05). We draw on the notion of consumer patriotism to explain this result. Editors seem to be less willing to substitute the CSI event for other news on the foreign brand because a negative event associated with the foreign brand reinforces beliefs about the strength of the home economy that needs to be protected.
The attenuation effect of advertising appears to be weaker for foreign brands (β17 =.0006, p <.01). For an explanation, we again follow the line of argument that consumers tend to favor domestic brands over foreign brands and want to protect their domestic economy. Catering to the preferences of patriotic consumers conflicts with outlets' strategic interest to protect their advertising relationship. Compared with domestic brands, this strategic interest weighs less for foreign brands.
Left-oriented media outlets are more likely to report on a CSI event, but this effect becomes weaker for brands with higher advertising pressure (β18 =.5 × 10−5, p <.01). Conversely, although right-oriented media might be less prone to report on a CSI event, their editors cannot ignore characteristics that cater to the preferences of their readers. Particularly, when a brand is more present among readers because of high advertising expenditures in the recent past, not reporting the news may backfire and threaten the credibility of the outlet.
We also find that several of our control variables influence the likelihood of covering a CSI event in the media. Unsurprisingly, the likelihood has increased over time (β19 =.0008, p <.01), confirming the view that companies are increasingly held accountable for their social and environmental footprint. Social issues tend to be less covered in the media (β20 = −.2210, p <.01) relative to governance and environmental issues. We also observed differences in the level of CSI news coverage across countries (e.g., Germany vs. United Kingdom, β21 =.4374, p <.01).
Table 6 shows three classification statistics computed for different thresholds of classifying an event as being reported. The model user needs to decide about the threshold level for the predicted probability on which an event is classified as being reported. Note that we face a highly unbalanced sample, with only 17.3% of "positive" reporting events. This makes it difficult for any model to beat the maximum chance criterion where each event is classified as unreported or "negative," yielding a classification rate of 82.7%. Therefore, measures such as precision, recall, and the F1 score are more informative about the classification performance of the model.
Graph
Table 6. Model Classification Statistics.
| Threshold | Recall | Precision | F1 Score |
|---|
| .10 | .4285 | .3817 | .4037 |
| .15 | .1777 | .4903 | .2608 |
| .20 | .0730 | .6112 | .1304 |
| .25 | .0278 | .6475 | .0533 |
9 Notes: Model statistics are based on Sample I (the United States, France, Germany, Mexico, and the United Kingdom). Threshold defines the predicted probability from which on an event is classified as being reported. Recall computes the number of correctly identified reporting events relative to all reporting events in the sample. Precision computes the number of correctly identified reporting events relative to all predicted reporting events. The F1 Score measures the harmonic mean between recall and precision.
Precision focuses on the power of the model to correctly classify events as being reported. For example, if our model predicts 20 reporting events and 15 of these predictions are correct, the precision is 75% = 15/20. Assuming that the total sample included 40 reporting events, the model would have identified 37.5% = 15/40 in total, which is the recall rate. Recall, therefore, is a measure of completeness. It depends on the context and objectives of the user of the model, which type of performance is more important. Table 6 shows that the model performance changes with the threshold in different directions for the two metrics. The F1 score is a compromise because it considers both precision and recall (harmonic mean of both statistics). For our model, it is largest at a threshold of.10 and achieves a reasonable classification score of.404, whereas 1.0 represents perfect prediction.
We performed several additional analyses to determine whether our estimation results are robust. For the sake of brevity, we cannot report all results here but refer to Web Appendix 10.
First, we tested alternative model specifications. We substituted a count variable of the number of media outlets reporting on the CSI event for our dependent variable and estimated a linear regression model and a zero-inflated Poisson model. Results are highly consistent with our focal model's results (see columns 2 and 3 in Table WA10.2). However, we do not use these models, because they restrict us in investigating the media-specific variables. In another specification, we included fixed effects for brand and outlet in Model 1 instead of their respective error components. The results do not change substantially.
Second, we used alternative ways to operationalize news selection variables. We measured the variables brand salience, brand strength, advertising stock, selective advertising partnership, and brand CSI history differently. The results in Table WA10.3 do not suggest anything different.
Third, we added new, potentially relevant variables—specifically, brand strength dispersion ([46]), square of brand strength, a dummy for business newspapers as outlet (e.g., Financial Times), and several financial variables for listed companies from Compustat. Likelihood-ratio tests indicate that none of these variables are relevant (p >.05).
Fourth, we performed three analyses to check for the robustness of our sampling strategy for CSI events and brands. We deleted the 50 weakest brands (15%) on the basis of their brand strength rating from the sample and reestimated our model. By this analysis, we simulate a possible sampling effect arising from a concentration on well-established, larger brands. Table WA10.5 (column 2) shows that results are consistent with our focal sample.
In another analysis, we randomly deleted one outlet that reported on a CSI event for each event and reestimated the model. By this procedure, we simulate that the information about the event has been uncovered by an exogenous source and not by an outlet included in the model estimation. Results are again stable (see Table WA10.5, column 3).
In our final analysis, we restrict our sample to include only those brands that are covered by YouGov in more than one country. By this restriction, we minimize the possibility that we exclude important events, because they have not been reported by any outlet in one country. The proportion of these nonreported events in a country is fairly high in this subsample with 39%. The results based on this subsample in Table WA10.5 (column 4), however, do not suggest a different conclusion.
In this section, we investigate the capital market impact of a CSI event. We adopt the established event study methodology to test the effects in a subsample of 97 brands/firms and 347 CSI events for which we have U.S. stock market data available.
We use the Fama–French–Carhart four-factor model to measure the abnormal returns ([13]; [23]). This model accounts for four risk factors to explain daily stock returns and has been extensively applied in marketing research (e.g., [11]). The premise is that daily abnormal returns are due to the unanticipated CSI event and driven by its media coverage and news selection variables (see Figure 2). Abnormal returns are given as follows:
Graph
2
where Rkt is the stock return of a firm that owns brand k on day t and E(Rkt) denotes the expected return from a regression of Rkt on the risk factors. These risk factors are Rmt for the stock return of the benchmark market portfolio, SMBt for excess return of small over big stocks, HMLt for the difference in returns between high and low book-to-market ratio stocks, and UMDt for the momentum factor. The terms represent the parameter estimates.
To estimate the Fama–French–Carhart model, we need data from the U.S. capital market. This restricts our sample to firms that are primarily listed at U.S. stock exchanges. It applies to 97 brands and 347 CSI events, which creates a healthy sample size that is comparable with prior event studies. We obtain data on the risk factors from the Kenneth R. French online data library. The benchmark portfolio includes all NYSE, AMEX, and NASDAQ stocks (for details, see https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/). Stock return data are obtained from Thomson Reuters.
Following previous research (e.g., [38]), we use an estimation window of 250 days until 15 days prior to the CSI event to estimate the return equation. We select a three-day event window [−1 to 1] to calculate cumulative abnormal returns (CARs). This window has been used in prior studies to account for lead and lagged effects of stock market response (e.g., [10]; [19]). The results are fully robust to other windows [−1 to 0], [−1 to 2], and [−1 to 3] (see Web Appendix 11).
Estimated CARs [−1, 1] are the dependent variable in a second step, in which we regress this variable on media coverage (measured as a count variable: 0–15), our news selection variables, aforementioned control variables, and additional financial control variables that have been used in previous research (e.g., [19]). Specifically, we estimate the following equation:
Graph
3
where ∊ik is an i.i.d. error term, and the δ parameters are to be estimated. Note that we cannot include the publication frequency dummies. By definition, media coverage is the sum of the reporting frequencies across daily and weekly outlets scaled by the total number of outlets. We test for the inclusion of firm-specific effects to control for unobserved brand/firm heterogeneity. The Baltagi–Li Lagrange multiplier test ([ 5]), however, rejects this assumption (χ2( 1) =.1323, p >.70).
The fact that media outlets differ in their decision to report on the CSI event does not create a selection bias because these decisions are aggregated in our focal media coverage variable. But it could be that unobserved variables influence both investors' reaction and media coverage. This would introduce a potential correlation of predictors with the error term and thus bias the results. By including the news selection variables in the event model, we reduce the danger of an omitted variable bias, but we cannot rule it out. We therefore test for the exogeneity of media coverage by using the Hausman–Wu test ([59]). By employing two alternative sets of outside instruments, the test does not suggest endogeneity. Consequently, we estimate the model with ordinary least squares. For the sake of brevity, we report details on instruments and tests in Web Appendix 12.
The analysis reveals a nonsignificant main effect of the average cumulative abnormal returns ( = −.08%, p =.62). Other frequently used event test statistics (see [54]) do not lead us to other conclusions (e.g., Patell Z, p =.15; Boehmer et al. p =.18 [[ 9]]; Corrado rank, p =.94). To further investigate the potential role of media coverage in driving the stock market effect, we differentiate the level of coverage. Specifically, we consider events with zero or one reporting outlets, two or three outlets, and four or more outlets. Table 7 reveals that there is a negative stock market reaction to CSI events in our sample but only if four or more media report on it ( = −1.12%, p <.01). Thus, we have evidence for the existence of a threshold effect.
Graph
Table 7. Threshold Analysis: Impact of CSI Event on Stock Returns.
| DV: Cumulative Abnormal Returns [−1,1]a | Estimated Coefficient | SE |
|---|
| Intercept | .00208 | (.00253) |
| Events with a media coverage of zero or one outlet (base) | — | — |
| Events with a media coverage of two or three outlets | .00135 | (.00405) |
| Events with a media coverage of at least four outlets | −.01211*** | (.00409) |
| N (CSI events) = 347 | N (brands) = 97 |
- 10 ***p <.01 (two-sided t-test).
- 11 a Based on an estimation window of 250 days until 14 days before the event.
Table 8 shows the results from estimating Equation 3. We include the events with other brand news in the first results column but exclude them as confounded events from the sample in the second results column. We find a significant negative effect ( = −.00198, p <.05) for our focal variable media coverage that is robust to both specifications. Other variables do not appear to show a robust effect across the two specifications. Thus, media coverage aggravates the loss in stock return. Interestingly, we find no evidence for a direct (shield) effect of brand strength and brand salience.
Graph
Table 8. Estimation Results: Drivers of Cumulative Abnormal Returns.
| DV: Cumulative Abnormal Returns [−1, 1]a | Expected Sign | Estimated Coefficient | SE | Estimated Coefficient | SE |
|---|
| Intercept | | .00614 | (.01448) | .00289 | (.02065) |
| Focal variable: Media coverage of CSI eventb | − | −.00198** | (.00081) | −.00228** | (.00112) |
| News Selection Variables | | | | | |
| Brand salience | +/− | .00011 | (.00017) | .00032 | (.00024) |
| Brand strength | +/− | −.00014 | (.00023) | .00047 | (.00039) |
| Brand presence: Online interest | +/− | −.00001*** | (.3 × 10−5) | −.6 × 10−5 | (.6 × 10−5) |
| Negative WOM | +/− | −.00008 | (.00032) | .00065 | (.00056) |
| Brand CSI history | +/− | .00029 | (.00123) | .00039 | (.00243) |
| Foreign brandc | | — | — | — | — |
| Domestic CSI event | +/− | .00284 | (.00436) | .00657 | (.00673) |
| Evidence-based CSI event | +/− | .00074 | (.00342) | −.00180 | (.00530) |
| Other brand news | +/− | .00012 | (.00388) | — | — |
| Frequencyd | | | | | |
| Weekly offline | +/− | — | — | — | — |
| Daily online | +/− | — | — | — | — |
| Daily offline | +/− | — | — | — | — |
| Political orientation | +/− | −.00223 | (.00187) | −.00474 | (.00312) |
| Control Variables | | | | | |
| CSI event type | | | | | |
| Governance issues (base) | | — | — | — | — |
| Social issues | | −.00183 | (.00397) | −.00727 | (.00667) |
| Environmental issues | | .00222 | (.00755) | .00385 | (.01071) |
| Product type | | | | | |
| Services (base) | | — | — | — | — |
| Durables | | −.00242 | (.00531) | −.01648* | (.00911) |
| Nondurables | | −.01521* | (.00861) | −.02737** | (.01230) |
| Retailer | | .00728 | (.00503) | .00263 | (.00792) |
| Time | | −.00006* | (.00003) | −.00009* | (.00005) |
| Financial Control Variables | | | | | |
| Firm size | | .00097 | (.00267) | .00046 | (.00394) |
| Return on assets | | .02239 | (.02876) | .04939 | (.04794) |
| Financial leverage | | .00012* | (.00007) | .00007 | (.00014) |
| | N (CSI events) = 347 | N (CSI events) = 179 |
| | N (brands) = 97 | N (brands) = 87 |
| | R2 =.123 | R2 =.130 |
- 12 * p <.10.
- 13 ** p <.05.
- 14 *** p <.01.
- 15 a Based on an estimation window of 250 days until 14 days before the event.
- 16 b We did not implement a threshold effect for media coverage here. Results are therefore rather conservative, with the size of coefficient being smaller than in Table 7. Results do not change when we specify the threshold effect as in Table 7.
- 17 c No sufficient variation, because sample is based on firms that are primarily listed in the United States.
- 18 d Not applicable, because the focal variable, media coverage, is a scaled linear combination of these variables.
- 19 Notes: Two-sided tests of significance.
The empirical findings lend strong support for the relevance of the theory of news value to explain the coverage of unethical firm behavior. In this section, we discuss managerial implications and contributions to marketing theory. Before discussing implications, however, we must first examine the practical size of the effects.
Because estimated parameters cannot be directly compared to evaluate the relative importance of news selection variables, we use Models 1 and 3 to simulate the effect of a change in a news selection variable on media coverage and stock return. The stock return effects reflect the impact of media coverage because we find no significant direct effects for the news selection variables. As a baseline (base scenario), we define a situation in which all variables are set to their sample average. The reporting likelihood is 17.3% in the base scenario. For simulation, we either increase a metric variable by two standard deviations or set a dummy variable to 1. We always apply parameter estimates of the larger sample when available (see Table 5). Changes are expressed in percentage points (unless stated otherwise) in the following subsections and in Figure 3.
Graph: Figure 3. Impact of news selection variables on likelihood of reporting (in percentage points) and on stock returns.aIn millions.Notes: The analysis simulates the likelihood of CSI coverage. A news selection variable is either set at 1 for the focal category or increases by two standard deviations for metric variables. The base scenario is the sample mean for the likelihood of a CSI being covered (all variables in the model are set at their sample mean). The financial effect of frequency of outlet and political orientation assumes that outlets change their frequency from weekly to daily, as an example, and their political orientation (e.g., due to a change in ownership).
Figure 3 shows the results of the simulation. Overall, the impact of news selection variables is quite substantial. Several variables produce a relative change in the likelihood of reporting of 30% and more. Considering brand-related variables, four variables stand out in their capacity to increase the likelihood of reporting. First, the level of brand salience increases the likelihood by 6.7%, the level of brand strength by 3.5%, and the level of negative WOM by 4.7%. For the simulated higher brand salience, this means that 39% more media cover the CSI event (rise of base likelihood from 17.3% to 24%). When the involved brand is a domestic brand, the reporting likelihood rises by 5.5%. It also increases by 4.3% for brands with more CSI events in the recent past. Salient, strong national brands appear to be at a substantial disadvantage when it comes to media coverage of a CSI event.
Among CSI event–related drivers of media coverage, two essential drivers stand out: domestic event and its interaction with a foreign brand. The likelihood of reporting rises by 7.7% for a domestic event. The increase is even larger for a foreign brand (13.9%), implying that 80% more media report on the event (rise of base likelihood from 17.3% to 31.1%).
Considering media outlet-related variables, the frequency of publication plays an important role. Unsurprisingly, a daily frequency raises the likelihood by 3.6% (online) and 3.9% (offline). The effects of political orientation and advertising relationship are relatively small. However, we find a strong impact of a selective advertising partnership. Such a partnership lowers the reporting likelihood by −7.8%, to a level as low as 9.5%.
The second column of Figure 3 shows the simulated effects on stock return. On average, a firm loses US$321 million as a result of a CSI event when four or more media outlets in the United States report on the event. This is a substantial financial loss. The analysis of the news selection variables suggests that this loss is significantly larger under several conditions. For salient and strong brands, the loss increases by US$216 million and US$114 million, respectively. A higher negative WOM also expands the loss by US$154 million. The largest impact, however, arises from a domestic CSI event and one that involves a foreign brand. Here, the financial damage rises by US$246 million for a U.S. brand involved in an event in the U.S. and US$426 million for a foreign brand involved in an event in the U.S., respectively, to −US$567 million and −US$747 million, respectively. This represents a substantial burden for any company. Note that evaluating the financial effect of a selective advertising partnership is not meaningful, because the lower reporting likelihood applies by definition to only one outlet.
This study makes important theoretical contributions to research on corporate misbehavior. While it is well known that media coverage aggravates the negative effects of CSI and product recalls on various firm outcomes (e.g., [ 4]; [43]; [45]), we do not know much about when and why the media cover such corporate news. This study is a first step toward answering these questions and extends the literature on CSI and firm crisis events.
Although prior research in marketing points to the power of the media to drive stock market effects (e.g., [58]; [30]) it has not been established for the coverage of CSI events. The results of our study, however, suggest that coverage in a single high-reach outlet is insufficient to drive stock market response. Rather, there is a critical mass of at least four high-reach outlets in the U.S. that need to report the CSI event. Adding to this result, we find that, on average, a CSI event does not provoke a stock market reaction ( ). One could, therefore, erroneously conclude that investors do not care about unethical firm behavior. But they do, as our threshold analysis reveals.
It has been long argued that strong brands may shield the company from the negative impact of a CSI event. Researchers used both experimental data ([ 1]; [41]) and observational data ([ 4]) to demonstrate the shield effect with respect to brand-related consumer metrics. The effect, however, is less studied in terms of stock market reactions.
We do not find evidence for a direct effect of brand salience and brand strength on stock market response. However, because they significantly drive media coverage, their indirect effect via media coverage is negative. Consequently, companies with strong brands suffer more from a CSI event. This finding challenges the view that a strong brand generally protects the company from the negative impact of a crisis event.
We also extend the literature on international marketing. This study shows that the extent of media coverage largely depends on whether the brand is a foreign or a domestic brand and whether the CSI event occurs in the home market or a foreign market. As a result, the potential harm effects on brand equity are not uniform across countries, which adds to the complexity of building and maintaining international brands. Being a foreign brand backfires in various ways. Initially, it appears to be an advantage in terms of the main effect on media coverage. The likelihood of reporting is higher by 5.5% for a domestic brand (see Figure 3). Yet our analysis also points to several interaction effects with respect to foreign brands. According to the results, it hurts more to be a foreign brand that is involved in a domestic CSI event. In addition, foreign brands do not benefit as much as domestic brands from the attenuation effects of other brand news and advertising investments.
Although the focus of our study is generally on the various drivers of media coverage and not on media bias, we consider two variables, political orientation and advertising, that have been studied in the media bias literature (e.g., [34]; [44]). Both variables have not been considered in the context of reporting on events of CSI, yet. For political orientation, we find evidence in support of our expectation that left-oriented media are more inclined to report on unethical firm behavior. However, we also show that this difference reduces the larger the total advertising spending of the brand. The higher the current presence of the brand due to its total advertising spending across all media channels, the higher the likelihood that a right-oriented media outlet covers a CSI story.
We also add to the literature on advertising and media relationships. Prior research has suggested that advertisers have a strong influence on media outlets to cover their products more often and in a favorable manner (e.g., [31]; [51]). We extend this knowledge and document that advertisers may also have the power to deter media from covering negative stories about their brands. While the effect is comparatively modest in general, it becomes larger provided that the advertiser and media outlet are in an exclusive relationship.
What are the implications for firms and managers? First, our study educates managers that CSI events are not equally covered in the media. While many of them might have real-world examples available, suggesting differences in coverage of CSI events, it remains difficult to predict when an event will be broadly covered in the press. This study can help managers predict and anticipate media attention so they can prepare their organizations to better handle the risks. It would be particularly helpful to predict whether a CSI event has the potential to exceed the threshold of four media outlets for the impact on the U.S. stock market. Managers of investor relations might then think about useful measures to deal with the expected increased attention of investors and their likely need for more information. Further research could answer the question of what kind of information and measures this would be.
Companies might also consider the strategic launch of other neutral or positive brand news when being confronted with the possible reporting of a CSI event. Other brand news has the power to crowd out negative CSI event news, as the analysis suggests. International brand managers should be aware of the higher likelihood of media coverage if their brand is involved in a CSI event in a foreign market. While this alone can hardly be the main reason to adopt a local brand-name strategy, it should be considered in these often-difficult strategic decisions.
Furthermore, the results show that advertising investment—and in particular, investments in selective partnerships—helps shield against broader media coverage of a CSI event. This finding points to an overlooked role of advertising investment in traditional, high-reach newspapers, which have come under increasing pressure from digital media channels. The rather tiny marginal effect of advertising in Figure 3 may create the impression that it is of only marginal relevance to the firm. However, even a tiny effect could cause a CSI event to cross the threshold of coverage in four media outlets, which differentiates between no financial market impact and an average loss of US$321 million.
This study is not without limitations that may stimulate future research. Although we cover several important Western economies, we do not know the extent to which our results extend to other important economies such as China, India, or Japan. Because of cultural differences, not all or possibly additional news selection factors may play a role, which would be interesting to test for in a further study. We also focus on CSI issues, but we acknowledge that there are many other potentially negative firm events (e.g., celebrity scandals). These events would be worthwhile to study. Finally, we consider the coverage of single CSI events. Future work could study the dynamics and evolution of a broader CSI issue that often encompasses a string of several single CSI events and related media stories.
Supplemental Material, Media_Coverage_CSI_Events_Web_Appendix - When Does Corporate Social Irresponsibility Become News? Evidence from More Than 1,000 Brand Transgressions Across Five Countries
Supplemental Material, Media_Coverage_CSI_Events_Web_Appendix for When Does Corporate Social Irresponsibility Become News? Evidence from More Than 1,000 Brand Transgressions Across Five Countries by Samuel Stäbler and Marc Fischer in Journal of Marketing
Footnotes 1 Associate EditorJan-Benedict Steenkamp
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242920911907
5 1We interviewed three business editors and one vice editor-in-chief of three newspapers in Germany, taz, Rheinische Post, and Bild Zeitung. The interviews lasted 60 to 90 minutes and were recorded and transcribed verbatim. We chose these three newspapers because they reflect the German media landscape well in terms of size (small and large circulation), geographic coverage (regional and national), and political orientation (left, neutral, and right).
6 2Strictly speaking, our results hold only for this selection of brands and associated firms. However, given that small and lesser-known brands have a lower chance of being monitored by YouGov, we believe that the results are rather conservative (i.e., Type 1 error should be higher).
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~~~~~~~~
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When Less Is More: How Mindset Influences Consumers' Responses to Products with Reduced Negative Attributes
Marketing communications often describe a reduction in a product's negative attributes (e.g., "our mineral water now uses 34% less plastic"). This claim may be interpreted as a trend of improving relative to previous state. However, such a claim may also call attention to a negative product feature that might have otherwise been overlooked. The authors suggest that whether consumers are positively or negatively influenced by such claims depends on whether the claims are interpreted through an incremental or entity mindset. When a reduction in negative attributes is viewed through an incremental mindset—the tendency to think of attributes as malleable—a trend-based interpretation results in improved product evaluations. In contrast, an entity mindset that emphasizes attributes are unlikely to change produces a negative effect for the claim. Four experiments and a field survey (N = 2,543) across food, pharmaceuticals, and plastic bottle products confirm the effects and indicate that the effects diminish when consumers believe the attribute is easy to eliminate or when the attribute has extremely threatening consequences. The opposite is observed for claims of reduced positive attributes, such that an entity mindset produces more positive evaluations. The findings offer marketers consumer insights to guide the communication of negatively framed attributes.
Keywords: attribute framing; incremental versus entity mindset; marketing communications; reduced negative attribute
Many products contain what consumers might characterize as "negative attributes," such as negative nutrients, impurities, excessive packaging, and other features that may cause consumers harm. Firms often aim to reduce these attributes and to communicate this status as a way to acquire and retain customers. As an example, in 2010 Coca-Cola introduced the Bonaqua lightweight bottle with environmentally friendly packaging, which was "specially engineered to use 34% less plastic." Products with reduced negative attributes are common across various product categories (e.g., the ASUS X540 laptop, which decreases electromagnetic radiation by 30%; Tulip luncheon meat with 50% less sodium; a Gree Electric air conditioner model with a reduced amount of Freon). Despite the use of this strategy, there is the risk that firms will draw attention to or remind consumers of negative attributes that they might have otherwise overlooked. This research aims to offer firms insight into how they can frame such reductions without fear of these negative repercussions.
The present research suggests that claims of reduced negative attributes may be interpreted in one of two ways—as a trend of improvement relative to its previous state or as an end state that focuses on the remaining level of the attribute. We identify consumers' incremental versus entity mindset ([12]; [13]) as a critical factor in influencing their interpretations of changes in negative attributes. A growing body of literature supports the notion that people tend to have either an incremental mindset or an entity mindset about the malleability of their own traits and that this mindset further affects inferences of other people's or objects' attributes (e.g., [13]). Specifically, while an entity mindset is characterized by the belief that traits are dispositional, an incremental mindset is associated with the belief in continual changes of traits with time and effort ([13]). Drawing on the view that these mindsets lead to selective interpretation in information processing ([ 3]; [27]; [45]), we argue that an incremental mindset focuses on the change in the observed activity, which leads consumers to emphasize the trend of improvement (i.e., the gains due to the reductions in the negative nutrient). In contrast, an entity mindset, which focuses on the state of an observed activity, leads consumers to interpret the communications based on the end state (i.e., what is left of the negative attribute). The results of four experiments and a survey in the field offer support to these propositions.
This research provides firms with strategies to effectively frame a reduction in negative attributes—a unique challenge that is prevalent in marketing practice but has not been investigated in prior research. The attribute framing literature indicates that consumers generally evaluate products with negative attributes less favorably, showing a "valence-consistent evaluation shift" ([50], p. 456; see also [37]; [66]). Building on this work, in the current study we use marketing activities to induce an incremental versus entity mindset as a critical moderator of this negative effect. In addition, we also examine the mechanisms underlying this mindset effect while also identifying several additional factors that indicate when this strategy will be effective.
Finally, our findings offer guidance to marketers in designing more effective marketing communications to induce an incremental mindset when focusing on reduced negative attributes. Our studies point to strategies marketers can use to induce more positive evaluations and to the conditions when these investments are likely to be more effective. Our findings also generalize across product categories as broad as food, pharmaceuticals, and plastic bottles.
The following sections outline the theoretical framework and present five empirical studies that support our predictions. We conclude with a discussion of the academic and marketing significance of our findings and offer directions for future research.
Advertising a reduction in negative attributes is common in marketing communication, especially given today's competitive business environment and the salience of negative attributes relative to positive ones ([49]). According to the attribute framing literature ([37]; [41]), labeling an attribute as negative encourages the retrieval of negatively valenced information from memory, leading to more negative product evaluations. For example, ground beef described as 75% lean is more appealing compared with beef described as 25% fat ([37]). Subsequent research, however, has identified important boundary conditions for the attribute framing effect, such that negative labeling can exert positive (or less negative) effects.
These boundary conditions include ( 1) factors related to the nature of decisions, such as trust versus choice evaluation ([31]), or whether the decision is of personal relevance ([42]); ( 2) situational/communicational factors, such as the psychological distance from the issue ([15]), decision makers' positive affect ([48]), as well as the effectiveness of communicating the seller's reputation ([62]); and ( 3) individual-level differences, such as the perception of having the capability to respond to the issue (i.e., issue capability, [50]), high involvement and need for cognition ([59]; [66]), and expertise ([44]). Table 1 offers a detailed summary of the attribute framing effects and their moderators.
Graph
Table 1. Attribute-Framing Effects: Summary of Empirical Findings.
| Article | Topic | Moderator | Findings |
|---|
| No Moderators | | | |
| Peterson and Wilson (1992) | Consumption decision | | Valence-consistent shift: judgments become more positive or negative depending on the framing of the attribute information.
|
| Dunegan (1993) | Managerial decision | |
| Levin, Jasper, and Gaeth (1996) | Automobile evaluation | |
| Krishnamurthy, Carter, and Blair (2001) | Health decision | |
| Chakravarti et al. (2002) | Product bundle evaluation | |
| Sanford et al. (2002) | Food consumption | |
| Isaac and Poor (2016) | Consumption decision | |
| Situational and Communication Moderators |
| Levin et al. (1986) | Gambling | Availability of probability information | Valence-consistent shift did not occur when probability information was missing.
|
| Levin and Gaeth (1988) | Consumption decision | Order of framing versus experience | The framing effect was reduced when consumers sampled the product as compared with when they did not.
|
| Mittal and Ross (1998) | Strategic decision | Affect state | The framing effect on issue interpretation and risk taking was stronger for negative- (vs. positive-) affect participants.
|
| Buda and Zhang (2000) | Consumption decision | Order presentation × source credibility | Responses of negatively (vs. positively) framed message participants were influenced more by order presentation and source credibility.
|
| Janiszewski, Silk, and Cooke (2003) | Consumption decision | Range and level of reference values | When the range of reference values was narrower for a positive frame rather than a negative frame, attribute values above expected performance levels favor the positively framed information and attribute values below expected performance levels favored the negatively framed information. When the range of reference values was wider for a positive frame compared with a negative frame, the opposite was true.
|
| Zhang and Mittal (2005) | Consumption decision | Procedure vs. outcome accountability | The effect of better (vs. worse)-than-reference decision framing on choice deferral was larger for outcome accountability than for procedure accountability.
|
| Roggeveen, Grewal, and Gotlieb (2006) | Consumption decision | Retailer's reputation | Consumers perceive greater performance risk for negatively framed options when retailer's reputation is weak (vs. strong).
|
| Freling, Vincent, and Henard (2014) | Meta-analysis,social decision | Construal level of the framed event | Attribute framing was the most effective when there was congruence between the construal level evoked in a frame and the evaluator's psychological distance from the framed event.
|
| Individual Difference Moderators |
| Marteau (1989) | Health decision | Knowledge on the issue | No framing effects emerged for health-related predicaments judged by medical students.
|
| Mittal, Ross, and Tsiros (2002) | Managerial decision | Capability on the issue | With low capability, decision makers were more likely to invest effort in an issue that was positively (vs. negatively) framed.
|
| Levin et al. (2002) | Meta-analysis | Conscientiousness, agreeableness | Conscientiousness is negatively related to the attribute framing effect and agreeableness is positively related.
|
| Putrevu (2010) | Advertising effect | Gender, need-for-cognition, involvement | Participants with low involvement or need-for-cognition exhibited larger frame effects; women responded less favorably to negatively framed appeals compared with men.
|
| Task and Decision Moderators |
| Levin, Schnittjer, and Thee (1988) | Social decision | Personal relevance | No framing effect emerged for self-evaluations of cheating.
|
| Kuvaasa and Selart (2004) | Business decision | Task nature: recall versus evaluation | Decision makers receiving negatively (vs. positively) framed information had significantly better recall.
|
| Keren (2007) | Consumption decision | Task nature: trust versus choice | Negative framing weighed more in trust assessments, and positive framing weighed more in choice.
|
Extending the research on the moderators of attribute framing effects, the present study focuses on the framing and communication of reduced negative attributes. On the one hand, such claims imply a positive trend, which signals the process of a dynamic and directional improvement in a negative feature and is expected to provide incremental value to consumers. On the other hand, the negative attribute claims can also be interpreted on the basis of the end state, which reminds consumers of the continued existence of a negative nature of the attribute despite the manufacturer's effort. As this component evokes unfavorable associations in memory ([42]), the salience of negative information may prevail ([29]), and thus it may lead to consumers' unfavorable responses. Because these two interpretations have directionally opposite effects, the question is which interpretation consumers would primarily rely on. The next section introduces the concept of incremental versus entity mindset and poses an answer to this question.
Building on [12] studies of learned helplessness, previous research has documented two types of mindsets regarding the mutability of traits and abilities ([13]). Activating an incremental mindset leads people to believe that a person's qualities are contextual and malleable, given sufficient time and effort. In contrast, inducing an entity mindset leads to the belief that attributes are dispositional and unlikely to change. Importantly, these two mindsets may be differentially activated by situational factors ([ 7]; [52]). For example, advertisements with slogans featuring changeable versus fixed brand personalities can effectively activate an incremental versus entity mindset ([74]). These mindsets can also result from social learning accumulated over time and manifest as chronic individual differences ([22]).
The activation of an incremental versus entity mindset may affect information processing in consumption contexts, including the perception of product performance ([27]), brand extension acceptance ([74]), and attributions of product wrongdoings ([60]). For instance, [74] demonstrated that consumers with an incremental mindset are more accepting of brand extensions than are those holding an entity mindset. [27] suggested that product advertisements conveyed in approach or avoidance terms influence consumers with an incremental mindset more than those with an entity mindset. Theoretically, this suggests that consumers with an activated incremental mindset should be more accepting of reduced negative attributes because such a mindset emphasizes the process or the change in an observed activity. We explain our reasoning in the next section.
The notion that an incremental versus entity mindset may generalize from the interpretation of human traits to that of product attributes is built on the literature on knowledge accessibility. Previous research has suggested that cognitive processes (e.g., using an incremental vs. fixed way of interpreting human traits) vary in terms of their accessibility in memory (e.g., [72]). Once activated, a mindset can increase the accessibility of the more general process that the specific process exemplifies (i.e., applying an incremental vs. entity mindset in making interpretation in general), which can be used to process another stimulus (in our case, interpreting product attributes).
Prior research has suggested that an incremental versus entity mindset affects individuals' interpretations of the message they receive ([51]). Specifically, entity and incremental mindsets differ in the focus on outcome versus process, respectively, in interpreting the observed activity ([ 3]; [27]; [45]; [58]). While consumers with an incremental mindset perceive the changing process in performance over time as more diagnostic in making judgments ([14]; [16]), those with an entity mindset perceive the outcome-related information as more indicative ([ 3]). In consumer research, for example, [53] suggested that an incremental mindset emphasizes the process of "how" a product delivers benefits to the consumers, whereas an entity mindset focuses on the outcome or the "what" of the product. In the context of product message interpretation, [27] found that the manner in which a message is conveyed (i.e., the process) influences persuasion when consumers have an incremental mindset but not when they have an entity mindset.
We propose that when interpreting communication about reduced negative attributes, an incremental mindset (with a process focus) leads customers to rely more on a trend-based interpretation in making judgments. A trend-based interpretation highlights product improvement and should lead to more favorable evaluations of the product with reduced negative attribute. In contrast, an entity mindset (with an outcome focus) increases reliance on an "all or nothing" (i.e., presence vs. absence) perspective on product attributes ([22]; [54]). Thus, an entity mindset should lead to the end-state-based interpretation, resulting in less favorable responses.
We propose that marketing communications across different contexts and attributes can moderate the proposed relationship between consumer mindset and evaluation of products with reduced negative attributes. We discuss these next.
Negative attributes may differ in terms of the perceived ease/difficulty of achieving zero performance, which implies that negative performance has been totally eliminated. When negative performance on an attribute is perceived as easy to eliminate, customers will perceive merely reducing the negative performance as inadequate. This should result in a negative response, regardless of consumers' mindsets. Stated differently, the effect of consumer mindset on evaluation of reduce negative attributes should be attenuated when consumers perceive that eliminating the negative attribute is relatively easy. Such a prediction is theoretically consistent with previous research on the disconfirmation of expectations. Specifically, negative disconfirmation of expectations leads to more unfavorable responses than positive disconfirmation does on an attribute ([10]; [49]).
Negative attributes are also likely to differ in the degree to which consumers perceive them as a threat, defined as having harmful consequences and frightening to consumers ([19]; [67]). The more threatening an attribute is perceived to be, the higher the likelihood that it will undermine consumers' feeling of control. Theoretically, this occurs because negative attributes represent a hurdle for consumers to attain consumption goals ([56]). The more extreme the negative attribute, the higher the hurdle positioned to consumers' consumption goals, and the higher the motivation and attention for consumers to reduce the negative attributes. This supposition is conceptually consistent with the literature on goal framing (e.g., [41]), which suggests that presenting the negative consequences of an attribute will direct consumers' attention to any means that potentially helps them avoid such consequences. Thus, the salient threat emanating from examining extremely negative attributes may lead to highlighted attentional focus on possible solutions to relieve the negative consequences and regain self-control over the consumption goal ([30]; [56]). Subsequently, consumers with either an entity or an incremental mindset may evaluate the reduction of the extremely harmful negative attribute as an improvement.
Marketers often frame the changes in product attribute in various ways ([48]; [50]; [75]), such as a reduction of positive attributes, or a total elimination of a negative attribute ([55]). Although our research focuses on the framing of reduced negative attributes, we predict that it will have implications for other attribute framing changes, given that an activated incremental versus entity mindset directly concerns consumers' beliefs about changes. For example, when communicating a "reduced positive attribute," a company typically states that the positive quality contained in a product attribute has been reduced. In this situation, such a framing implies a positive end state accompanied by a negative trend. Following from our theory, we predict a more favorable response from consumers with an entity mindset (who rely on end-state-based interpretation) compared with those with an incremental mindset (who rely on trend-based interpretation). We explored the differential mindset effects in various attribute framing changes in one of our studies (i.e., Study 4).
We conducted four experiments (Studies 1–4) and one field survey (Study 5) to test our hypotheses. Table 2 provides an overview of our five studies and main findings. Study 1 examines the proposed interaction effect of incremental versus entity mindset and attribute framing (negative vs. neutral) as well as the mediating role of the trend-based versus end-state-based interpretation. Studies 2 and 3 identify two boundary conditions for the proposed effect. Specifically, the proposed effect disappears when eliminating the negative attribute is perceived as rather easy (Study 2) or when the attribute is perceived as extremely threatening (Study 3). Study 4 involves a more rigorous test to enhance the managerial relevance of our hypothesis by investigating the effect of mindset on attribute framing changes in a broader scope. We incorporate a full 2 (valence: negative vs. positive attribute) × 3 (change: reduce vs. increase vs. elimination) design to address changes in two attribute valences across different marketing situations. In addition, Study 4 adopts a two-stage design to improve internal validity. Finally, Study 5, a field survey, increases external validity of the effect by showing its robustness outside the lab, using real products as stimuli and actual purchase behavior as the dependent variable.
Graph
Table 2. Summary of Results by Study Condition.
| Study | Sample Size | Design | Product, Attribute | Mindset | DV | Results |
|---|
| Mindset |
|---|
| Incremental | Entity | Passive control | Active control |
|---|
| 1 | 325 (undergraduate students) | 2 (attribute framing: reduced negative vs. neutral) × 2 (mindset: entity vs. incremental) | Luncheon meat, sodium nitrite | Manipulated with product description | Product evaluation | Framing = reduced negative | 5.03 (1.50)a | 3.61 (1.49)c | — | — |
| Framing = control | 4.31 (1.45)b | 4.19 (1.39)b | — | — |
| 2 | 650 (Prolific) | 4 (mindset: entity vs. incremental vs. active control vs. passive control) × 2 (difficulty: moderate vs. easy) | Mussels, microplastic content | Manipulated with promotional direct mail | Product evaluation | Difficulty = moderate | 5.14a | 3.54c | 4.18b | 4.25b |
| Difficulty = easy | 3.41c | 3.16c | 3.24c | 3.33c |
| 3 | 210 (MTurk) | 2 (mindset: entity vs. incremental) × 2 (threat: moderate vs. high) | Antidiabetic drug, excessive lactic acid | Manipulated with quotes from spokesperson | Product evaluation | Threat = moderate | 5.57 (1.24)a | 4.62 (1.82)b | — | — |
| Threat = high | 5.44 (1.61)a | 5.38 (1.41)a | — | — |
| 4 | 884 (MTurk) | 7 (attribute framing: [2 (valence: positive vs. negative) × 3 (change: reduce vs. increase vs. elimination) + 1 (neutral)]) × 2 (mindset: entity vs. incremental) | Stereo speaker, nonrecyclable materials | Measured | Product evaluation | Framing = reduced negative | .71a | −.35b | — | — |
| Framing = reduced positive | −.67a | .48b | — | — |
| Framing = increased negative | −.86 | −.77 | — | — |
| Framing = increased positive | .66 | .47 | — | — |
| Framing = total elimination (i.e., eliminated negative) | 1.00 | .91 | — | — |
| Framing = negative only(i.e., eliminated positive) | −.32 | −.45 | — | — |
| Framing = neutral | .22 | .12 | — | — |
| 5 | 474 (shoppers) | 2 (mindset: entity vs. incremental) | Bottled water, plastic; yogurt, excessive sugar | Measured | Real purchase | Stimuli: bottled water | 74.92%a | 42.35%b | — | — |
| Stimuli: yogurt | 74.88%a | 59.99%b | — | — |
1 Notes: The cells present means per condition, with standard deviations in parentheses. Different superscripts (e.g., a, b, c) in Studies 1–3 indicate significant differences at p <.05. Different superscripts in Studies 4 and 5 indicate significant differences across mindsets in the same row at p <.05.
Study 1 had two objectives. First, we tested the interaction effect of attribute framing and mindset on the evaluation of a product (i.e., luncheon meat) containing a negative attribute. We followed [74] and induced an entity versus incremental mindset by an advertisement. Second, we investigated the underlying mechanism of the trend-based versus end-state-based interpretation using direct measures of these constructs.
Overall, 325 undergraduate students at a large public university in Hong Kong (223 women; Mage = 21.57 years, SD = 3.64) participated in the experiment in return for HK$20 (equivalent to US$2.55). In Study 1, we adopted a 2 (attribute framing: reduced negative attribute vs. neutral) × 2 (mindset: entity vs. incremental) between-subjects design. The design manipulates both factors simultaneously.
We selected sodium nitrite in luncheon meat (with a fictitious brand name, COPO) as a negative attribute based on its broad recognition among consumers, as validated by a pretest (N = 50) (M = 5.68; t(49) = 8.92, p <.001, compared with the neutral point; 1 = "good for health," and 7 = "bad for health"). In the main study, all participants were shown a print advertisement for the luncheon meat (see Web Appendix W1).
Following [74], we varied the advertising slogan to induce an incremental or entity mindset by highlighting either people's changeability (in the incremental mindset condition) or their commitment to consistency (in the entity mindset condition). An independent between-design pretest (N = 100) showed that the two versions of mindset manipulation materials did not differ in terms of persuasiveness, informativeness, believability, likeability, or the quality of writing ([47]; ps >.246) (see Web Appendix W2). Participants in the main study completed an eight-item scale as a mindset manipulation check (α =.94; [21]; [43]; see Web Appendix W3), which formed an index with a higher (lower) score indicating an incremental (entity) mindset.
Participants in the reduced negative attribute condition were told that the luncheon meat "has had its sodium nitrite reduced by 30%," whereas those in the neutral condition were told the luncheon meat "is pre-cooked for your convenience." The time each participant spent viewing the advertisement page was tracked to indicate their cognitive involvement in the product information.
Subsequently, all participants evaluated the product on a four-item scale (1 = "bad/dislikable/negative/unfavorable," and 7 = "good/likeable/positive/favorable"; α =.96, [20]).
Participants then rated the extent to which they interpreted the product information based on the trend of improvement or the negative end state of the attribute by completing a four-item scale (i.e., "I perceive the product has a trend of improvement in its attribute than before," "I perceive the product is in the process of being improved in its attribute," "I perceive the product has an attribute with an undesirable end state" [reversed], and "I perceive the product contains a negative attribute that has not been completely removed" [reversed]; 1 = "strongly disagree," and 7 = "strongly agree"; α =.83). Participants' responses were averaged to form a trend interpretation index, with a higher (lower) score indicating participants' higher reliance on trend- (end-state-) based interpretation.
We assessed participants' emotional state using the 20-item PANAS (Positive and Negative Affect Schedule) scale (αs >.92; [70]). In addition to the time spent viewing the product information page, the participants reported their situational need for cognition using an 18-item seven-point scale (α =.87) adopted from [ 4].
A 2 (attribute framing) × 2 (mindset) full-factor analysis of variance (ANOVA) on the mindset index revealed only a significant main effect of mindset condition (Mentity = 3.92, SD = 1.24 vs. Mincremental = 4.71, SD = 1.34; F( 1, 321) = 30.07, p <.001), with all other effects nonsignificant (Fs < 1.72, ps >.191). Thus, the manipulation was successful and unconfounded. In this and each subsequent study, adding gender to the analyses (including manipulation checks, dependent variables, and mediation analyses) did not change any of the results reported subsequently.
A 2 × 2 ANOVA indicated that the interaction between attribute framing and mindset was significant (F( 1, 321) = 16.18, p <.001), as shown in Table 2. Specifically, in the incremental mindset condition, the presence of reduced negative attribute led to more favorable evaluation (M = 5.03, SD = 1.50) compared with its absence (M = 4.31, SD = 1.45; F( 1, 321) = 9.84, p =.002). However, in the entity mindset condition, the opposite effect emerged: the presence of reduced negative attribute resulted in less favorable evaluations (M = 3.61, SD = 1.49) compared with its absence (M = 4.19, SD = 1.39; F( 1, 321) = 6.49, p =.011). In other words, an incremental mindset and an entity mindset did not lead to different evaluations of the product when it was described without the reduced negative attribute (4.31 vs. 4.19; F( 1, 321) =.30, p =.582). However, participants with an incremental mindset evaluated the product more favorably than those with an entity mindset when the reduced negative attribute was present (5.03 vs. 3.61; F( 1, 321) = 38.78, p <.001).
A 2 × 2 ANOVA on the trend-based versus end-state-based interpretation index revealed a similar interaction effect (F( 1, 321) = 20.05, p <.001). Specifically, while an incremental mindset resulted in greater reliance on a trend interpretation (Mreduced neg. att. = 4.92, SD = 1.20 vs. Mneutral = 4.16, SD = 1.18; F( 1, 321) = 15.91, p <.001), an end-state interpretation was dominant in the entity mindset condition (Mreduced neg. att. = 3.49, SD = 1.30 vs. Mneutral = 4.04, SD = 1.12; F( 1, 321) = 8.65, p =.004).
Based on our theorizing, trend-based versus end-state-based interpretation mediates the interaction effect between attribute framing and mindset on product evaluation. We tested this moderated mediation model using bootstrapping procedures (PROCESS Model 8 with 10,000 bootstrapping samples; [18]). Consistent with our expectation, results revealed that the indirect effect of attribute framing on evaluation via a trend-based versus end-state-based interpretation was conditional on mindset (index of moderated mediation =.29, SE =.14; 95% confidence interval [CI] = [.2133,.7779]). For incremental mindset, the indirect effect was positive and significant (95% CI = [.1189,.4627]), whereas for entity mindset, the indirect effect was negative and significant (95% CI = [−.3958, −.0589]).
First, previous literature has shown that an entity mindset is associated with the use of heuristic cues when making judgments (e.g., [35]); thus, those with an incremental mindset may more effortfully process information in advertisement. Second, people may have negative emotional responses toward information that is inconsistent with their worldviews (e.g., [63]), leading to an unfavorable evaluation of products with a reduced negative attribute (i.e., somewhat improved) by consumers with an entity mindset. Results from a series of ANOVAs revealed that mindset did not significantly affect participants' emotional reactions (for positive affect in PANAS: p =.762; for negative affect in PANAS: p =.409), situational need for cognition (need for cognition scale: p =.203), and viewing time for the product information page (p =.216). Thus, such alternative accounts do not explain the effects observed.
A third alternative account is that an incremental (vs. entity) mindset is associated with additional capability to properly process complex information—in our case, the reduced negative attribute message, a kind of "double negation" ([73]). To evaluate this possibility, we conducted a supplementary study (similar to Study 1 but with different stimuli; see Web Appendix W4) using participants' grade point average, a proxy for their ability to process complex information, as a covariate. Results were consistent with those of Study 1.
Study 1 shows that incremental versus entity mindset influences consumers' response to reduced negative attribute framing, and validates our trend-based versus end-state-based interpretation as an underlying mediating mechanism.
Study 1 has several limitations. First, Study 1 manipulated mindset and attribute framing within the same advertisement and measured responses immediately after exposing participants to the advertisement, which could have led to a demand effect. Study 2 incorporated more robust tests by inducing mindset prior to and independent of the description of the focal product. Second, one might argue that the reduced negative attribute used in Study 1 (i.e., sodium nitrite in luncheon meat) is to some extent equivocal, as the attribute can contribute to product benefits as well (i.e., sodium nitrite helps increase the shelf life of luncheon meat; [61]). To address this issue, we used a less equivocal negative attribute in Study 2. Finally, although we added a neutral control condition of attribute framing in Study 1, there was no control condition for mindset manipulation. In Study 2, we focused only on reduced negative attribute framing and added control conditions regarding mindset manipulation to further localize causation.
In addition to testing the moderating effect of the perceived difficulty of a product to eliminate a negative attribute, Study 2 had several additional objectives. First, we developed a new method (a "direct promotional mail" by a shopping mall) to induce mindset independent of the product stimuli. Second, to localize causation, we added two control conditions of mindset manipulation: a "passive" control lacking mindset-related text and an "active" control with neutral filler material. Third, we used a less equivocal negative attribute (i.e., microplastic content in seafood). When describing its reduction in the product, we specified the volumes of microplastic contained in the previous and current versions of the product, which is a different expression from the percentage reduction in Study 1. We also included multiple attributes (both positive and negative ones) in the product description to increase ecological and external validity of our findings.
We recruited 650 U.K. consumers via the Prolific online research pool as respondents (406 women; Mage = 34.63 years, SD = 14.30) in return for GBP.50. Study 2 utilized a 4 (mindset: entity vs. incremental vs. passive control vs. active control) × 2 (measured perceived difficulty to eliminate the negative attribute: easy vs. moderate) between-subjects design.
We first exposed participants to an anniversary sale direct mail sent from a fictitious shopping mall named "MAGO" and induced mindset by varying part of the contents in the mail (see Web Appendix W5 for all conditions). In the entity mindset condition, they read, "We understand that customers are always committed to consistency over the course of their lives. Just like you, at MAGO we remain, we persist, and we keep steady. We always stay the same with you." In contrast, those in the incremental mindset condition read, "We understand that customers always change over the course of their lives. Just like you, at MAGO we evolve, we develop, and we are flexible. We always change with you." Those in the active control condition read a mindset-neutral paragraph of comparable length: "We understand that customers need high quality products and services. And we are glad to welcome you to come to our anniversary sale. And you will enjoy your shopping here." Those in the passive control condition did not read a corresponding paragraph. An independent between-design pretest (N = 100) showed that the versions of advertisement involving mindset activation did not differ in terms of persuasiveness, informativeness, believability, likeability, and quality of writing level (ps >.368) (see Web Appendix W6). As a manipulation check for mindset, participants completed the same eight-item scale used in Study 1 (α =.87), which formed an index with a higher (lower) score indicating an incremental (entity) mindset.
All participants were then shown the same description of a frozen mussel product, which they "may consider purchasing during the anniversary sale from the shopping mall" (see Web Appendix W7). Mussels have always been a popular kind of seafood among U.K. consumers, representing a business of tens of millions of GBP in the United Kingdom ([ 5]). The description read "Abbondante Mussel is now available! With its portable packaging, you can enjoy the wonderful taste whenever you want. The new Abbondante now reduces microplastic content to 0.9 ppm, as compared with the previous version (1.5 ppm). Care about your health and buy Abbondante Mussel!" An independent pretest (N = 50) showed a general recognition that microplastic contained in seafood is perceived negatively (M = 6.58; t(49) = 21.84, p <.001, compared with the neutral point; 1 = "good for health," and 7 = "bad for health").
Participants then evaluated the product using the same four-item scale used in Study 1 (α =.97).
Participants then rated their perception of the difficulty of achieving zero microplastic in frozen mussels on a two-item scale, "To what extent do you think it is easy/difficult for frozen mussel manufacturers to totally eliminate the microplastic content?" (1 = "very easy," and 7 = "very difficult") and "To what extent do you think totally eliminating the microplastic content for frozen mussel is achievable?" (1 = "not at all," and 7 = "very much") (reversed). Their responses were recoded to form an index of perceived difficulty, with a higher (lower) score indicating more difficulty (ease) (r =.87, p <.001).
A one-way ANOVA of the mindset index revealed a significant main effect of the mindset manipulation; participants in the incremental mindset condition scored higher (Mincremental = 5.13, SD = 1.06) compared with those in the two control conditions (Mactive = 4.52, SD = 1.17; F( 1, 646) = 27.85, p <.001; Mpassive = 4.60, SD = 1.19; F( 1, 646) = 20.98, p <.001), who, in turn, scored higher than did those in the entity mindset condition (Mentity = 4.09, SD = 1.26; compared with active control: F( 1, 646) = 10.05, p =.002; compared with passive control: F( 1, 646) = 13.66, p =.001). The two control conditions, however, did not differ from each other in the index (F( 1, 646) =.32, p =.570). Importantly, the mindset manipulation did not influence the difficulty index (F( 1, 646) = 1.63, p =.182), indicating that the manipulation of mindset was unconfounded.
We then conducted regression analysis with product evaluation as the dependent variable, with mindset (dummy coded), the mean-centered perceived difficulty index, and their interaction as the independent variables. As we predicted, the overall interaction between mindset and the difficulty index was significant (B =.10, SE =.03, t = 3.23, p =.001). To reveal the nature of the significant interaction, we displayed product evaluation by mindset conditions at moderate-level difficulty (+1 SD) and low-level difficulty (−1 SD) using a spotlight analysis ([68]). As shown in Table 2, when eliminating the negative attribute was perceived as moderately difficult, participants in the incremental mindset condition evaluated the product more favorably (Mincremental = 5.14) compared with those in the control conditions (Mactive = 4.18, B = 1.04, SE =.26, t = 3.93, p <.001; Mpassive = 4.25, B =.50, SE =.13, t = 3.82, p <.001), who, in turn, evaluated the product more favorably than did those in the entity mindset condition (Mentity = 3.54; compared with active control: B =.19, SE =.09, t = 1.99, p =.047; compared with passive control: B =.26, SE =.14, t = 1.83, p =.057). The two control conditions, however, did not differ from each other (B =.07, SE =.27, t =.24, p =.806). In contrast, when eliminating the negative attribute was perceived as easy, participants in the four mindset conditions did not differ in product evaluation (Mincremental = 3.41 vs. Mactive = 3.24 vs. Mpassive = 3.33 vs. Mentity = 3.16; ts <.80, ps >.425).
We further performed floodlight analysis and used the Johnson–Neyman technique to identify the range of the perceived difficulty index in which the effect of mindset on product evaluation was significant ([68]). The Johnson–Neyman point was 3.26 for the effect between incremental and entity mindsets (p <.05), which is below the mean of perceived difficulty index (4.58). These results indicated that the effect of mindset on product evaluation diminished under low level of difficulty. Note that the results of an ANOVA (without including difficulty index as an independent variable) provided consistent results, such that an incremental mindset led to more favorable evaluation (M = 4.18, SD = 1.85) than did an entity mindset (M = 3.42, SD = 1.85; F( 1, 646) = 13.81, p <.001).
The findings of Study 2 indicated that activating an incremental (vs. entity) mindset increases the evaluation of a product with a reduced negative attribute when total elimination is perceived as at least moderately difficult. However, the effect diminishes when consumers believe that total elimination is rather easy, in which case merely reducing the negative attribute might be interpreted as meaningless. Finally, incorporating two control conditions provided further evidence that the effect is driven by different mindsets, rather than confounding factors such as the length/presence of manipulation materials.
While the findings of Studies 1 and 2 provide consistent evidence for our conceptualization, there are some unresolved questions. For example, the negative attributes in both studies did not arouse extreme threats, such as those are life-threatening, and thus one may wonder whether the proposed effect can still apply in situations where the negative attributes are extremely harmful or threatening. Furthermore, the product category used in both studies was food and findings may not generalize. Thus, in the next study, we used chronic disease drugs with a life-threatening negative side effect as the stimuli. Such a research context of chronic diseases constitutes a worldwide concern and thus provides more practical implications.
Study 3 had three objectives. First, it tested the moderating role of the threat of attribute for the proposed effect. Specifically, extremely harmful negative attributes that frighten consumers might mitigate the end-state effect, leading to an overwhelming positive product evaluation by consumers with either an incremental or entity mindset. In contrast, when the attribute was perceived as moderately harmful, an incremental (vs. entity) mindset should lead to a more favorable evaluation. Second, Study 3 extended the proposed effect to another important consumption situation, namely, medical decisions for chronic diseases. We asked diabetic patients to evaluate antidiabetic drugs with reduced negative ingredients, which were framed as either moderately or highly threatening, after inducing an entity versus incremental mindset. As of 2015, an estimated 415 million people had diabetes worldwide ([25]), representing 8.3% of the adult population. Thus, investigating these patients' responses to reduced negative attribute communication has important marketing and social policy implications. Finally, we developed a new method to activate consumers' mindset by using product spokespersons' quotes in advertising.
A total of 218 diabetic patients (128 women; Mage = 42.77 years, SD = 14.32) were recruited using Turkprime, an Amazon Mechanical Turk (MTurk)-based crowdsourcing data acquisition platform that helps identify participants based on selected variables. At the end of the survey, we included questions that validated they were diabetic patients, and eight participants reported that they had never had diabetes. We thus included only 210 participants in all analyses that follow. The participants were randomly assigned to conditions of a 2 (mindset: incremental vs. entity) × 2 (threat of the negative attribute: moderate vs. high) between-subjects design. The design manipulates both factors simultaneously.
We selected Metformin, the top medication used for the treatment of diabetes as our product. The negative product attribute selected was lactic acidosis based on the broad awareness as a side effect among diabetic patients, as qualified by an independent pretest (N = 50) (M = 5.68; t(49) = 8.72, p <.001, compared with the neutral point; 1 = "good for health," and 7 = "bad for health"). More importantly, diabetic patients perceived the lactic acidosis side effect as a moderate threat (M = 4.12; 1 = "not fatal at all," and 7 = "very fatal").
In the main study, each participant was presented with a print advertisement for Metformin with a fictitious brand name (Salutis) and a fictitious spokesperson (Dr. Gerald Anderson) (see Web Appendix W8). We varied the quotes from the spokesperson to induce mindset by highlighting either people's changeability or their commitment to consistency. Specifically, in the incremental mindset condition, participants read, "People always change through the life time, just like you and me"; in the entity mindset condition, participants read "People are always committed to consistency through the life time, just like you and me." As a manipulation check, participants' mindsets were assessed using the same eight-item scale as in previous studies (α =.92).
All participants received the same reduced negative attribute description of Metformin. In the high-threat condition, we included a small printed note in the lower right corner of the ad, stating that "lactic acidosis can be fatal, causing kidney disorders, lung/liver disease, and heart failure." In the moderate-threat condition, no such note was added. An independent between-design pretest (N = 200) indicated that the versions of advertisement were not perceived as different in persuasiveness, informativeness, believability, likeability, or the quality of writing (ps >.437) (see Web Appendix W9). At the end of the main study, the participants rated on a five-item version of an established scale that measured perceived threat as a manipulation check ([56]; [71]). Participants' responses to these items were averaged to form a threat index (α =.97), with a higher score indicating a greater perceived threat.
All the participants evaluated the Metformin product by completing the same items as in the previous studies (α =.96).
The results of a 2 (mindset) × 2 (threat) full-factor ANOVA on the mindset index revealed a significant main effect of mindset manipulation (Mincremental = 4.64, SD = 1.28 vs. Mentity = 3.98, SD = 1.29; F( 1, 206) = 13.80, p <.001). No other effect on the index was significant (Fs < 1.32, ps >.252). The results of another 2 × 2 ANOVA on the threat index revealed a significant main effect of perceived threat condition (Mmoderate = 3.77, SD = 1.84 vs. Mhigh = 4.48, SD = 1.75; F( 1, 206) = 7.98, p =.005), with no other significant effect (Fs <.27, ps >.602). Thus, the manipulations for mindset and threat were successful and unconfounded.
Consistent with our previous findings, the results of a 2 × 2 ANOVA on evaluation revealed a significant main effect of mindset manipulation (Mincremental = 5.50, SD = 1.43 vs. Mentity = 5.01, SD = 1.66; F( 1, 206) = 5.58, p =.019), as qualified by a significant interaction between mindset and threat (F( 1, 206) = 4.09, p =.037; see Table 2). Further simple contrasts indicated that incremental mindset led to more favorable product evaluation compared with entity mindset when the threat of the negative attribute was perceived as moderate (Mincremental = 5.57, SD = 1.24 vs. Mentity = 4.62, SD = 1.82; F( 1, 206) = 9.93, p =.002); however, participants' evaluations did not differ when the threat was perceived as high (Mincremental = 5.44, SD = 1.61 vs. Mentity = 5.38, SD = 1.41; F( 1, 206) =.04, p =.850). In other words, the observed interaction arose from the high threat framing increasing product evaluation in the entity mindset condition (F( 1, 206) = 6.42, p =.012) but not in the incremental mindset condition (F( 1, 206) =.20, p =.654). We suspected that the null effect of threat in the incremental mindset condition was due to a ceiling effect—that is, exposing participants with incremental mindset to a reduced negative attribute communication without additional reminders of the threat led to sufficiently high evaluation. In addition, these results were consistent with prior research suggesting people with an entity mindset react more explicitly and strongly to emotion-laden activities ([13]).
Study 3 identified a second boundary condition of the proposed effect. Specifically, when the consequences of the negative attribute are seen as more threatening, consumers with either an incremental or an entity mindset appreciate any improvement in the attribute.
Across Studies 1–3, we used three different methods to situationally activate an entity versus an incremental mindset (i.e., product description, direct mail, and spokesperson's slogan, respectively), providing companies with practical tools to apply our conceptualization to marketing practices. However, these mindsets can also be chronic ([13]; [74]). Across Studies 4 and 5, we tested our hypotheses by relying on trait indicators. Furthermore, although the current research focused on the reduction of negative attributes, the proposed effects should also have implications for other attribute framing changes. Study 4 explored these situations.
Study 4 aimed to expand our research scope in three ways to broaden the managerial implication of our findings. First, while the current research focuses on the moderating effect of mindset on the association between attribute framing (negative vs. neutral) and product preference, it is unclear how mindset influences other types of attribute framings. We thus incorporated a full 2 (valence: negative vs. positive attribute) × 3 (change: reduce vs. increase vs. elimination) set of attribute framing situations. Second, we examined the proposed effect with a chronic—not situationally induced—operationalization of the incremental versus entity mindset. Third, to enhance internal validity and reduce demand effects, we measured participants' changes in responses to marketing communications three weeks apart.
Participants were recruited from MTurk and informed that this study was two-staged and would be conducted three weeks apart. In stage one, 1,702 MTurk workers (967 women; Mage = 39.55 years, SD = 15.77) completed a survey in return for US$.20. In stage two, of those completing the original survey, 884 MTurk workers (51.94%; 495 women; Mage = 40.06 years, SD = 12.68) completed the second survey in return for US$1.60. Study 4 included six attribute framings: 2 (valence: negative vs. positive attribute) × 3 (change: reduce vs. increase vs. elimination) (for all manipulations, see Web Appendix W10). To control for possible influencing factors due to the time lapse between stages one and two, a neutral control condition without additional information was used in stage two (i.e., no-communication condition). Therefore, Study 4 had a 7 (framing: [2 (valence) × 3 (change) + 1 (neutral)]) × 2 (measured implicit theories: incremental vs. entity) design.
We selected nonrecyclable materials in a stereo speaker as the negative product attribute, as validated by a pretest (N = 50) showing a broad recognition among consumers that such materials are nonenvironmental friendly (M = 6.20, t(49) = 11.06, p <.001, compared with the neutral point; 1 = "environmentally friendly," and 7 = "not environmentally friendly").
Implicit theories were assessed in the stage one survey, using the same scale employed in previous studies (α =.87).
In stage one, the participants were first presented with a picture of a fictitious stereo speaker brand (i.e., Aequitas) and a general product description (see Web Appendix W10).
In stage two, participants were randomly assigned to one of the [2 × 3 + 1] framing conditions. Whereas those in the neutral condition were presented with the same general product information as in the stage one survey, additional product information was added in the remaining 2 (valence: negative vs. positive attribute) × 3 (change: reduce vs. increase vs. elimination) conditions. Those in the reduced negative attribute condition were presented with additional information stating that "The newly introduced Aequitas stereo speaker has reduced 50% nonrecyclable materials compared to previous versions." Those in the reduced positive attribute condition were presented with information stating that "The newly introduced Aequitas stereo speaker contains 50% less recyclable materials compared to previous versions." Those in the increased negative attribute condition were presented with information stating that "The newly introduced Aequitas stereo speaker contains 50% more non-recyclable materials compared to previous versions." Those in the increased positive attribute condition were presented with information stating that "The newly introduced Aequitas stereo speaker has increased 50% recyclable materials compared to previous versions." Referring to real marketing practice using such negative attribute framing and prior research ([69]), those in the negative-only condition (i.e., positive attribute elimination) were presented with what they were told was a warning added in the product description by the company, which said, "Note: the newly introduced Aequitas stereo speaker contains a certain percentage of non-recyclable materials." A general description of "a certain percentage" was used to be consistent with an ambiguous initial negative value in other conditions. Those in the full elimination condition (i.e., negative attribute elimination) were presented with additional information stating that "The newly introduced Aequitas stereo speaker has totally removed non-recyclable materials compared to previous versions."
Product evaluation was accessed by letting participants complete the same set of questions as in previous studies, in both stage one (α =.95) and stage two (α =.94).
As the manipulation of attribute framing was administered in stage two, there was no selection issue caused by attrition. Additionally, participants' product evaluation in stage one did not differ across framing × measured implicit theory conditions (ps >.442). Thus, we calculated evaluation change by subtracting each participant's evaluation score in stage one from that in stage two. In the neutral condition, evaluation change was minimum (M =.17, SD =.73) and did not differ across incremental and entity theorists (p =.412). Thus, we focused on the 2 (valence: negative vs. positive attribute) × 3 (change: reduce vs. increase vs. elimination) conditions in the following analyses (N = 746; 413 women; Mage = 40.08 years, SD = 12.81).
The eight implicit theory items were recoded and averaged into a single index, with a higher (lower) score indicating an incremental (entity) theory (M = 4.41, SD = 1.51). The results of PROCESS Model 3 ([18]) revealed that the overall three-way interaction of 2 (valence) × 3 (change; dummy coded) × the continuous mean-centered implicit theory index on product evaluation was significant (B =.33, SE =.09, t = 3.68, p <.001). We performed a spotlight analysis to examine the influence of 2 (valence) × 3 (change) attribute framings on product evaluation across the entire range of implicit theories, as recommended by [68]. Specifically, we decomposed the interaction by performing regression analyses for each of the three conditions of change (see Table 2). The results of PROCESS Model 1 ([18]) performed within the reduced condition indicate a significant main effect of valence (B = 2.71, SE =.62, t = 4.39, p <.001), and mindset (B = 1.01, SE =.19, t = 5.27, p <.001), and a significant interaction effect (B = −.68, SE =.13, t = −5.17, p =.002). Consistent with our expectation, incremental theorists evaluated the reduced negative attribute framing more favorably than did entity theorists (M =.71 vs. M = −.35; B =.33, SE =.08, t = 4.09, p <.001); in contrast, incremental theorists evaluated the reduced positive attribute framing less favorably than did entity theorists (M = −.67 vs. M =.48; B = −.35, SE =.11, t = −3.38, p =.001).
Similar analysis performed in increase conditions revealed only a significant main effect of valence (B = 1.69, SE =.81, t = −2.10, p =.037). Incremental and entity theorists did not offer different evaluations in either their (overwhelmingly negative) responses toward increased negative attribute framing (M = −.86 vs. M = −.77; B = −.03, SE =.07, t = −.38, p =.704) or their (overwhelmingly positive) evaluations for increased positive attribute framing (M =.66 vs. M =.47; B =.06, SE =.08, t =.78, p =.436). Finally, in the elimination condition, we observed only a significant main effect of valence (B = 1.40, SE =.54, t = 2.61, p =.010). Incremental and entity theorists did not differ in their (overwhelmingly negative) responses toward negative-only framing (M = −.32 vs. M = −.45; B =.04, SE =.08, t =.49, p =.622), and both of them had overwhelmingly positive evaluations for the full elimination framing (M = 1.00 vs. M =.91; B =.03, SE =.07, t =.37, p =.710).
As a direct support for our main proposition, the results of PROCESS Model 1 ([18]) performed in the reduced negative attribute framing and the neutral conditions revealed that incremental theorists evaluated the product more favorably when the reduced negative attribute was present as opposed to the neutral condition (B = 1.72, SE =.65, t = 2.95, p =.008). In contrast, entity theorists evaluated the product less favorably when the reduced negative attribute was present compared with the neutral condition (B = −.83, SE =.35, t = −2.38, p =.018).
A reversed pattern was found from a PROCESS Model 1 performed in the reduced positive attribute framing and the neutral conditions: incremental theorists evaluated the product less favorably when the reduced positive attribute was present compared with the neutral condition (B = −2.02, SE =.75, t = −2.71, p =.007), whereas entity theorists evaluated the product more favorably when the reduced positive attribute was present compared with the neutral condition (B =.85, SE =.40, t = 2.13, p =.034).
By measuring chronic incremental versus entity mindset and evaluation in various attribute framings at two time points with a long-enough lapse (i.e., three weeks), Study 4 demonstrated the proposed effect with high internal validity. More importantly, by expanding the research scope to various attribute framing situations, Study 4 provided richer marketing implications. For example, the results indicated that an incremental mindset and an entity mindset showed contrasting effects for reduced negative framing and reduced positive framing. These results suggest that marketers should consider or find ways to strategically intervene the consumers' incremental versus entity mindset when introducing products with such attributes. Whereas Studies 1–4 showed the robustness of the effects in lab settings, in Study 5 we aim to generalize our findings using actual purchase behavior in a real market setting.
Study 5 used a field survey to examine whether our proposed effects could be replicated with actual product choice in real market situations. For this survey, two pairs of product options (i.e., either two bottled waters or two yogurts) with and without a reduced negative attribute framing were juxtaposed for shoppers to choose. We assessed consumers' chronic incremental versus entity mindset using a short survey administered during their shopping trips.
Two research assistants carried out the field study at a convenience store located in a downtown area of Beijing, China. The study was conducted between 10:00 a.m. and 6:00 p.m. for two consecutive weeks (June 20–July 3, 2018), with a total of 474 shoppers. Among them, 218 were presented with two water bottle brands, and 205 of them completed the transaction (89 women; Mage = 31.32 years, SD = 8.66, Min = 17 years, Max = 66 years). The other 256 shoppers were presented with two yogurt brands, and 240 of them completed the transaction (135 women; Mage = 28.91 years, SD = 7.45, Min = 19 years, Max = 64 years). Because implicit theories did not affect whether shoppers completed the transaction (p =.774 for water bottle; p =.974 for yogurt), in later data analyses, we focused on only those who completed product transactions.
In China, BingLu and ChunYue are two leading brands of bottled water, and both are produced by the Coca-Cola Company. The two brands have similar market shares and positionings. Both sell 550 mL bottles (see Web Appendix W11) for exactly the same price (about US$.30 per bottle). However, the 550 mL BingLu bottle claims to have reduced its usage of plastic materials by 35%, whereas the 550 mL ChunYue bottle makes no such claim. The reduced negative attribute claim is printed on the BingLu packaging and announced in its advertising. A pretest (N = 50) indicated that the consumers generally recognized that the plastic used in the packaging of bottled water is not environmentally friendly (M = 6.38; t(49) = 14.99, p <.001, compared with the neutral point; 1 = "environmentally friendly," and 7 = "not environmentally friendly"). In addition, we selected two local brands of yogurt with comparable prices and market shares as product options: WeiQuan yogurt, with 50% reduced sugar (a product with a reduced negative attribute claim), and YiXiao yogurt (a product without such a claim). A pretest (N = 31) indicated a general recognition that the sugar added in the yogurt is not healthy (M = 5.42; t(30) = 8.92, p <.001, compared with the neutral point; 1 = "very healthy," and 7 = "very unhealthy").
The shoppers in the convenience store were informed that they were invited to participate in a promotional event sponsored by a local consumer research company. Participants filled out the same questionnaire of implicit theories that was used in previous studies ([16]; α =.72).
Subsequently, some shoppers were presented with two bottled water brands about US$.08 per bottle, and they were asked to make their purchase decisions regarding one of the two options. Other shoppers were presented with two yogurt brands about US$.30 per cup, and then they were asked to make their purchase decision.
Afterward, all participants completed another short survey that included their demographic information, and consumption habits of bottled water and yogurt (i.e., purchase frequency, familiarity for each of the two brands) assessed on seven-point scales.
The results of a Z-test indicated that the proportion of shoppers choosing either BingLu or ChunYue was different beyond chance (overall 64.88% choosing BingLu; z = 4.26, p <.001). We then examined the influence of the mean-centered implicit theory index (SD =.84, min = −3.23, max = 2.77) on the choice of water by conducting a logistic regression. The choice of BingLu (i.e., the option with a reduced negative attribute) was coded as 1, whereas the choice of ChunYue (i.e., the option without such a claim) was coded as 0. Consistent with our prediction, implicit theories had a significant effect on the choice of water brand (β =.46, likelihood-ratio χ2( 1) = 6.59, p =.010), with incremental (entity) theorists being more (less) likely to purchase BingLu over ChunYue. Adding consumption habit-related variables (i.e., purchase frequency and familiarity for each of the two brands) and their interaction terms with implicit theories did not change the model, with the focal results remaining significant.
The proportion of shoppers choosing either WeiQuan or YiXiao was different beyond chance (overall 67.50% choosing WeiQuan, z = 5.42, p <.001). We examined the influence of the mean-centered implicit theory index (SD =.87, min = −2.45, max = 3.05) on the choice of yogurt by conducting a logistic regression. The choice of WeiQuan (i.e., the option with a reduced negative attribute) was coded as 1, whereas the choice of YiXiao (i.e., the option without such a claim) was coded as 0. Consistent with our prediction, implicit theories had a significant effect on the choice of yogurt brand (β =.39, likelihood ratio χ2( 1) = 5.92, p =.015), with incremental (entity) theorists being more (less) likely to purchase WeiQuan over YiXiao. Adding consumption habit-related variables (i.e., purchase frequency, familiarity for each of the two brands) and their interaction terms with implicit theories did not change the model significantly.
This field survey provided additional behavioral support for our conceptualization using real choice data. Incremental theorists preferred the option with a reduced negative attribute claim over the option without such a claim to a greater extent compared with the entity theorists, when both options were available simultaneously. We controlled for the selected brands (i.e., both water brands are from the same international company [Coca-Cola] and both yogurt brands are from local companies), which have the same price and similar market shares. In addition, adding variables related to consumption habits as control variables did not change the pattern of results. Note that prior research shows that incremental theorists tend to be more politically liberal ([32]; [36]), which results in greater environmental concern ([ 1]). Although in previous studies we obtained similar results in nonenvironmental attributes and thus ruled out such an alternative account, we acknowledge this potential limitation for the field survey.
Our findings offer novel evidence and provide plausible explanations for how activating consumers' incremental versus entity mindset influences their interpretations of and responses to reduced negative attribute framings. Across five studies, we presented evidence that communicating reduced negative product attributes leads to more (less) favorable product evaluations for consumers with an incremental (entity) mindset. This effect held when the information was presented using percentages (Study 1 and Studies 3–5) or specific integer values (Study 2), and when the incremental versus entity mindset was induced with the marketing mix (advertising copy in Study 1, direct promotional mail in Study 2, spokespersons' quotes in Study 3) or assessed as chronic implicit theories of the self (Studies 4 and 5). Our findings indicate that the distinct trend-based versus end-state-based interpretation underlies the effect as evidenced by a direct mediation test in Study 1 and by ruling out several alternative accounts in subsequent studies. Furthermore, we found that the proposed effect diminished when eliminating the negative attribute was perceived as rather easy (Study 2) or when the negative attribute was perceived as extremely threatening (Study 3). Moreover, our findings have implications for various other framings of attribute changes (Study 4). Finally, we generalized our findings from the product evaluations of a fictitious product to behavioral choice concerning real products in a real market setting (Study 5). Across all studies with activated mindsets and continuous dependent variables (Studies 1–3), we conducted a single-paper meta-analysis to examine the robustness and generalizability of our findings ([46]) (see Web Appendix W12).
Our findings contribute to the attribute framing research (e.g., [37]). The existing literature suggests that labeling an attribute as negative encourages the recruitment of negatively valenced information from memory, producing more negative evaluations ([37]). More recent research, however, identifies important boundary conditions for the attribute framing effect, such that a negative label will exert nonnegative or even positive effects ([15]; [50]). Extending this line of research, the present study focuses on contexts that involve the reduction of a negative attribute and identifies consumers' incremental versus entity mindset as a moderator of valence-consistent evaluation shifts. Specifically, communicating a negatively framed attribute leads to a positive effect when consumers are prompted to focus on the trend of attribute improvement rather than its negative nature. We focus on the role of incremental mindset as one reason consumers will focus on the positive trend.
This research also contributes to the marketing communication literature by examining the communication of reduced negative product attributes—a pervasively used yet underresearched marketing strategy. Many studies have focused on communicating the complete removal or addition of attributes (e.g., [24]; [55]). Taking a new perspective, our study explored whether the reduction of a negative attribute influences consumers' responses through a novel mechanism ([17]; [65]). We hope our conceptualization will encourage future research to further investigate the consequences of communicating the quantitative change in a product's attribute.
Our research also contributes to the literature on the incremental versus entity mindset. Previous research has used these mindsets to interpret interpersonal evaluations (e.g., [23]) and extended them to consumer research domains, such as brand extension ([74]), the framing of persuasive messages ([27]), and brand anthropomorphism ([60]). The present research is among the first to introduce these mindsets to research on product attribute changes. Therefore, this research enriches the repertoire of the cognitive consequences of activating an incremental versus an entity mindset.
By manipulating and assessing incremental versus entity mindset using various approaches and by showing the robust interaction effect of mindset and attribute framing on consumers' responses to reduced negative attribute communication in different paradigms, our findings provide managerially actionable implications for marketers. First, our research provides new techniques for marketers' toolboxes to more easily activate consumers' mindsets as a controllable variable. Unlike previous work, Studies 1–3 activated these mindsets using the three different marketing-mix tactics—product description, direct promotional mail, and spokespersons' quotes.
Second, the rapid improvement in consumer literacy has motivated marketers to minimize the negative attributes of products ([55]). In doing so, companies intuitively expect that promoting a reduction in negative attributes should benefit sales as opposed to doing nothing. Our findings imply, however, that communicating a reduced negative attribute might have unintended consequences if consumers approach it with the wrong mindset. Bearing this in mind, marketers should estimate the potential risks of such communications in advance and work to activate an incremental mindset at the point of sale.
Importantly, our findings further suggest that, ironically, when negative attributes are only partially reduced, communication efforts may not always lead to a better outcome compared with simply doing nothing. Although consumers with an incremental mindset are likely to embrace a product with a reduced negative attribute, those with an entity mindset may view the communication as a reminder of that negative attribute that they might otherwise overlook. To solve this problem, the current research suggests that marketers should carry out such communication strategically (e.g., along with properly activating consumers' mindset).
However, these investments only appear necessary when consumers believe the attribute is to some extent difficult to eliminate and when the attribute does not have extremely threatening consequences. Consumers who perceive that manufacturers can quite easily eliminate the negative attribute would interpret its mere reduction as insufficient. Thus, marketers need to appropriately remind consumers of the difficulty of completely removing the negative attribute. In addition, extremely harmful negative attributes that frighten consumers might diminish the mindset × attribute framing effects. Thus, marketers should investigate consumers' perception of threat for the negative attributes before implementing the strategy.
Third, an important implication of our findings is the possibility that marketers may also strategically induce these mindsets to fight back against the competitors. For companies that want to entice consumers from a competitor's product that claims a reduction of a negative attribute, marketers might activate an entity mindset using advertising slogans, such as BMW's "Safety is always in the driver's seat" or De Beers's "A diamond is forever." Moreover, slogans for significant social events can also temporarily prime different mindsets. For example, Barack Obama's "change" campaign ("Change we can believe in") likely activated an incremental mindset, whereas the "Tea Party movement" and its emphasis on end state was more likely to elicit an entity mindset. Marketers can strategically leverage such social events as opportunities to promote products containing reduced negative attributes.
Fourth, research has suggested that individuals in Western countries (e.g., Americans) typically hold entity beliefs, whereas those in Eastern countries (e.g., Chinese) typically hold incremental beliefs ([ 9], [ 8]). Thus, another important managerial implication of our findings is that the promotion strategies for products with reduced negative attributes need to be customized across cultures. Similarly, more recent research has suggested that consumers' incremental versus entity mindset can be traced along with their demographic, geographic, or political ideology information; for example, an entity (incremental) mindset is associated with conservative (liberal) political views ([36]). Thus, marketers need to consider these factors in promoting products with a reduced negative attribute.
Finally, marketers may also benefit from applying our findings to promote products with other framings of attribute changes. For example, our finding in Study 4 suggests that marketers could strategically activate consumers' entity mindset when communicating a reduced positive attribute to achieve desirable responses.
Supplemental Material, jm.17.0556-File003 - When Less Is More: How Mindset Influences Consumers' Responses to Products with Reduced Negative Attributes
Supplemental Material, jm.17.0556-File003 for When Less Is More: How Mindset Influences Consumers' Responses to Products with Reduced Negative Attributes by Vincent Chi Wong, Lei Su and Howard Pong-Yuen Lam in Journal of Marketing
Footnotes 1 Author ContributionsThe first two authors contributed equally and correspondence concerning this article should be addressed to either Lei Su or Vincent Chi Wong.
2 Associate EditorVikas Mittal
3 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
4 FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is supported by a Hong Kong SAR Research Grant (GRF: HKBU12522916) awarded to the second author.
5 ORCID iDVincent Chi Wong https://orcid.org/0000-0001-9546-5596
6 Online supplement: https://doi.org/10.1177/0022242920920859
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By Vincent Chi Wong; Lei Su and Howard Pong-Yuen Lam
Reported by Author; Author; Author
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Record: 236- Who Is Wary of User Design? The Role of Power-Distance Beliefs in Preference for User-Designed Products. By: Paharia, Neeru; Swaminathan, Vanitha. Journal of Marketing. May2019, Vol. 83 Issue 3, p91-107. 17p. 2 Diagrams, 2 Charts, 3 Graphs. DOI: 10.1177/0022242919830412.
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Who Is Wary of User Design? The Role of Power-Distance Beliefs in Preference for User-Designed Products
This article evaluates when a user-design approach is and is not effective in strengthening brand preference. It specifically delves into the role of power-distance beliefs in influencing preferences for user-designed products and brands. The authors demonstrate that low-power-distance consumers prefer user-designed products to company-designed products, whereas this effect is attenuated or reversed for high-power-distance consumers. The authors find process evidence that both feelings of empowerment and values of expertise differentially mediate brand preferences depending on power-distance beliefs, thus extending prior research findings. Field experiments conducted in the United States and cross-culturally (Austria and Guatemala) with Facebook's advertising platform provide convergent evidence using country and political orientation as managerially accessible proxies. This research sheds light on when and why firms should be wary of user-design approaches, based on how power-distance beliefs drive consumers' preferences.
Keywords: brand preferences; cocreation; cross-cultural; political orientation; power-distance beliefs
What do Threadless and Linux have in common? These brands rely on a user-design philosophy to crowdsource product designs and develop software code with input from user communities. More traditional brands such as Starbucks also employ user-design philosophies as a way to elicit new product ideas (e.g., via ideas.starbucks.com [formerly mystarbucksidea.com], through which more than 320 crowdsourced ideas have been implemented in stores over an eight-year period; [20]). "Crowdsourcing," a more popular term to describe what is defined in the literature as "user-driven design," is a successful innovation approach in which users create value alongside the firm ([ 8]; [38]; Von [43]). Research has found that nonparticipating users, or those who only observe and learn about a firm's design philosophy (user design vs. company design), view user-design approaches as more innovative and customer oriented, leading to positive evaluations ([ 2]; [14]; [31]; [38]). Other research has shown that user-design approaches increase identification with the firm through feelings of vicarious empowerment ([ 8]).
In light of these generally positive findings, this research addresses an important managerial question: Should corporations such as Starbucks be selective about when and how they communicate their use of user-designed approaches, based on variations in cultural contexts? Stated differently, will the benefits seen in employing user-design strategies in a U.S. context be just as successful when employed in high-power-distance cultures such as India, Guatemala, or China? We demonstrate (through both experiments and field studies) that the positive observer user-design effect found in previous research is either attenuated or reversed for those who have high-power-distance beliefs (via country, political orientation, individual difference, and manipulation). Power-distance beliefs are the extent to which one accepts that power in society is distributed unequally ([19]), including the expectation of power disparity ([46]). We propose that the empowerment offered by user-design approaches will resonate more with low-power-distance consumers because of their general preference for equality. By contrast, we propose that high-power-distance consumers value company expertise, leading to higher quality inferences, which will contribute to their relative preference for company-designed products. Because those high in power-distance beliefs respect authority more, they may consider a company's expertise more valid. Importantly, we also examine the role of political orientation as an important proxy for power-distance beliefs. Political orientation is a managerially accessible variable that can capture power-distance beliefs in the U.S. market ([16]; [23]). A key variable that distinguishes liberals from conservatives is their differential views of equality versus authority, an attribute that aligns closely with power-distance beliefs.
In the case of Starbucks, our research suggests that the firm will benefit from highlighting the use of ideas.starbucks.com in low-power-distance countries such as Austria (and for U.S. liberals) but not in high-power-distance countries such as Guatemala (and for U.S. conservatives), an important managerial insight. We test our hypotheses across a variety of product categories (software, furniture, T-shirts, and academic research), with an emphasis on using real data (Facebook advertising and Google Trends) and managerially relevant proxies (country and political orientation) to confirm the generalizability of the results. We offer several contributions to extant literature. First, we demonstrate that the value of publicizing a user-design approach may be attenuated or even possibly backfire for consumers with high-power-distance beliefs. Second, we uncover the mediating processes and show that feelings of empowerment and valuing company expertise vary on the basis of power-distance beliefs. Third, we demonstrate the effects of political orientation as a managerially accessible segmentation variable. Recent research has demonstrated differential effects on consumer preferences depending on political orientation ([11]; [24]; [25]; [26]). Our research contributes to this important line of research while also demonstrating the relationship between power-distance beliefs and political orientation and revealing their downstream consequences.
The literature describes user design as an innovation approach whereby companies draw from their user communities to generate new product ideas ([ 8]). Such approaches have been shown to be effective across industries, from apparel (Threadless.com) to software (Apache), to household items (Muji), consisting of various levels of consumer contributions. Accordingly, we use the term "user design" (and "user driven") to describe a variety of contexts in which users create value with the firm (Von [43]).
Traditionally, products are designed by professionals who work for the firm and have expertise that allows them to perform design tasks at a high level of quality ([38]; [42]). However, several studies have shown evidence that under certain conditions, consumers prefer user-designed products ([ 2]; [ 8]; [14]; [31]; [38]). This line of research finds user-design approaches to be more innovative and customer oriented, and these approaches also increase consumers' feelings of identification and empowerment. However, user-design approaches are not always valued. In cases where sophisticated technical abilities are required (for highly complex products and luxury products), user-designed approaches are viewed as inferior because users are not assumed to possess the necessary design skills to create high-quality products ([13]; [38]). For example, given the sophisticated hardware engineering required to make a television, it seems unlikely that a user would have the requisite skills required to perform the task. Beyond these product-related variables, the current research proposes that a preference for user-designed products/approaches may also be contingent on consumer segmentation variables. In particular, we explore consumers' power-distance beliefs, a segmentation variable that can be used to differentiate consumers. In the context of user design, prior published work has referred to segmentation variables in limited ways ([13]).
Power-distance beliefs are an important part of cultural values, and they refer to the extent to which people accept unequally distributed power in a society or in an organization ([18]; [33]). Variations in power-distance beliefs have been documented across cultures, with some countries exhibiting high power-distance beliefs (e.g., Guatemala) and some countries exhibiting low power-distance beliefs (e.g., Austria). For those high in power-distance beliefs, inequality is viewed as a natural (and even desirable) aspect of the social order, even by those who are lower in power. [27] argue that those with high-power-distance beliefs tend to follow authority figures (e.g., leaders of an organization) and believe that such entities are superior and elite, with the ability to make more reliable decisions ([21]). Accordingly, the norm in high-power-distance cultures legitimizes differences in decision-making power between those in high positions of power and those in low positions of power. Conversely, the norms in low-power-distance cultures aim to reduce power differences among people in positions of decision-making authority. More broadly, we can conceptualize low power-distance beliefs as a preference for egalitarianism and high power-distance beliefs as a preference for hierarchy (and respect for authority), regardless of one's own position in the hierarchy. A recent stream of research has applied power-distance beliefs in consumer behavior contexts pertaining to self-control, charitable giving, and price–quality inferences ([29]; [44]; [46]).
The theoretical principle underlying our proposition that power-distance beliefs moderate the user-design effect is that, in product domains, user-design approaches increase the power of consumers compared with traditional company-designed approaches wherein firms retain power. First, to confirm that companies (by default) are viewed as having greater power and authority and are higher on a hierarchy compared with consumers, we conducted a pretest (N = 155) with six items using nine-point scales (1 = "strongly disagree," and 9 = "strongly agree") (α =.89; for exact items, see the Web Appendix). For example, participants were asked, "How much do you agree or disagree with the following: Companies typically have more authority than consumers in product domains." Compared with the midpoint of the scale of 5, participants indicated significantly higher levels of agreement with these statements (M = 6.48, SD = 1.5; t(154) = 12.26). Accordingly, by default, companies are viewed as having more authority than consumers. Given this context of perceived inequality, as consumers increasingly become involved in various aspects of product design, the relationship between them and the company becomes more egalitarian, and there is a growing empowerment of customers in relation to the firm ([35]). Conversely, if a product is company designed, the company retains control and therefore is less egalitarian. This decision to follow a user- or company-design philosophy may have differential consequences for low- versus high-power-distance consumers depending on their feelings of empowerment and value of firm expertise.
Low-power-distance consumers may value the egalitarian approach that constitutes a user-design philosophy, which resonates with their greater preference for and value of having input into a decision. When low-power-distance consumers consider a user-design philosophy, a feeling of vicarious empowerment may emerge, as they are predisposed to desire situations in which they can imagine themselves participating in the decision. Recent research has shown that feelings of vicarious empowerment drive greater feelings of identification with companies that adopt user-design philosophies than with firms that adopt company-design philosophies ([ 8]). High-power-distance consumers may experience relatively weaker feelings of empowerment because they believe that everyone should have a "defined" place in the social order regardless of position ([44]). Feelings of empowerment may be less fluent and accessible to high-power-distance consumers because empowerment is a feeling that is inconsistent with maintaining the social order. Thus, we propose that feelings of empowerment will be contingent on power-distance beliefs.
Across a variety of categories, [38] find that participants perceive firm designers as having more expertise than user designers (i.e., in the context of T-shirts, cereals, household products, outdoor sports equipment, consumer electronics, gardening products, and robotic toys). Firm employees often have a significant advantage over consumers, either real or perceived, in terms of knowledge, training, and experience ([30]). In the case of self-customized products, consumers make upward comparisons between themselves and professionals at higher levels on the ability scale ([30]).
Despite these perceptions of expertise, research has found that under certain conditions, people favor user-designed over company-designed products. Consumers believe that more innovative ideas come from diverse and unconstrained users who understand their own needs than from a team of designers removed from the consumer ([31]; [38]). This counterintuitive finding that consumers prefer user-designed to company-designed products (despite the firm's holding greater expertise) may be contingent on consumers' power-distance beliefs. High-power-distance consumers accept and even prefer inequality and regard those higher up in the hierarchy as superior ([ 6]). As such, high-power-distance consumers may then value a firm's expertise more than low-power-distance consumers. Because high-power-distance consumers have greater respect for a firm's expertise, they may view company-designed products as higher in quality than user-designed products. Building on the preceding discussion, we propose the following:
- H1: Power-distance beliefs moderate the effects of design source on brand preference. When power-distance beliefs are low, brand preference is higher for user-designed products than for company-designed products. However, when power-distance beliefs are high, this effect is attenuated or reversed.
Furthermore, in line with our discussion on empowerment and expertise, we predict that for low-power-distance consumers, a preference for user-designed products will be driven by experiencing greater feelings of empowerment. However, these effects of empowerment will be weaker for high-power-distance consumers. By contrast, we propose that because high-power-distance consumers respect authority more, they will find company-designed products relatively more attractive given their greater value for company expertise and higher quality inferences. Accordingly, the effects proposed in H1 will be differentially mediated by empowerment and quality/expertise as described in the following hypotheses:
- H2: The mediating effects of empowerment vary on the basis of power-distance beliefs. They are stronger for those low in power-distance beliefs and weaker for those high in power-distance beliefs.
- H3: The mediating effects of quality/expertise vary on the basis of power-distance beliefs. They are stronger for those high in power-distance beliefs and weaker for those low in power-distance beliefs.
Because prior research has documented a strong user-design effect, we are careful to predict that power-distance beliefs only moderate this effect—whether the effect is attenuated or fully reversed may depend on how these and various other competing inferences and psychological processes may be operating in tandem.
While power distance has typically been investigated within cross-cultural contexts ([18]), we propose that political orientation is a managerially accessible proxy for power-distance beliefs in the U.S. market. Research has found that liberal and conservative Americans have different belief systems. [23] conclude that two primary characteristics of a conservative ideology are resistance to change and acceptance of inequality, views that are more consistent with high-power-distance beliefs. [16] show that conservatives also respect authority more while liberals value equality more and are more sensitive to fairness considerations, which are views more consistent with low-power-distance beliefs. These findings in the [16] article were found to hold even after controlling for covariates such as age, gender, household income, and education.
While multiple psychological variables may distinguish political liberals from conservatives, evidence has shown that the adoption of conservatism in political orientation is strongly linked to power-distance beliefs. According to [15], p. 40), "One major criterion continually reappears in distinguishing left from right: attitudes toward equality. The left favors greater equality, while the right sees society as inevitably hierarchical." [22] demonstrate that the desire for group-based dominance and opposition to equality (social dominance orientation) varies across groups and individuals, and this orientation also correlates significantly with political conservatism across of range of studies ([36]; [37]; [39]).
In discussing the relationship between power-distance beliefs and political orientation, it is important to consider individual levels of power. Beyond power-distance beliefs, experienced levels of power (such as income and level of education) could also vary with political orientation ([34]), which could alternatively affect preferences for hierarchy (vs. egalitarianism) and for user-designed (vs. company designed) products. To account for these possibilities, we control for income and education in studies in which we measure individual differences (power-distance beliefs and political orientation).
In summary, the tolerance for inequality is one of the key pillars that has traditionally distinguished conservatives from liberals. Accordingly, we propose that political orientation can serve as a managerially accessible proxy for power-distance beliefs.
We operationalize power-distance beliefs in several ways. First, in Study 1 we use country as a proxy for power-distance beliefs and present real field evidence using Google Trends data (Study 1a). We also present converging data from Facebook's advertising platform targeting high- and low-power-distance countries (Guatemala vs. Austria; Study 1b). In Study 2 we again use Facebook's advertising platform and use political orientation as a proxy for power-distance beliefs. In Study 3, we measure individual differences of power-distance beliefs and political orientation (liberal vs. conservative). We also test the mediating process of empowerment as well as the mediating process of quality. In Study 4, we induce power-distance beliefs through a priming task. In Study 5, we demonstrate additional process evidence by manipulating empowerment in the user-design condition. In summary, we provide converging evidence from real-world and controlled experiments that power-distance beliefs moderate the positive effect of a user-design approach.
Consistent with our hypotheses on power-distance beliefs, in Study 1 our aim was to demonstrate that user-designed products would be favored more in low-power-distance countries than in high-power-distance countries. To demonstrate managerial relevance, we provide evidence with real-world data through an analysis of Google Trends (Study 1a) and a field study using Facebook's advertising platform (Study 1b).
In Study 1a we used Google Trends data and measured search volume for Threadless. Threadless is a user-design T-shirt company that describes its model as soliciting designs from artists around the world. Although the data collected in this study is correlational, our goal was to determine whether the predicted effects of power-distance beliefs would emerge in a real-world context. If H1 is supported, we would expect greater search volume for Threadless in low-power-distance countries than in high-power-distance countries.
We used Google Trends data and specifically examined whether there were differences in search volume for the term "Threadless" across countries depending on differences in power distance. We used an online source (https://geerthofstede.com/research-and-vsm/) that provides power-distance index (PDI) ratings across countries, updated in 2015. Our use of Google Trends data follows recent research (e.g., [ 9]) that applies a similar approach to supplement experimental findings. We used Google Trends to identify the number of searches for Threadless from 2011 through mid-2018. To account for the role of population and internet usage in search, we constructed a normalized dependent variable that included the amount of search divided by the number of internet users in a given country (to obtain the internet users, we multiplied the total population by the percentage of internet users). We also controlled for Hofstede's ([18]) other dimensions—namely, individualism, uncertainty avoidance, masculinity, and long-term orientation. Search can also be influenced by per capita gross domestic product (GDP) and GDP growth, which we included as controls in the model, based on an eight-year average corresponding to the period of the Google search (2011–2018). We also collected data on educational attainment and happiness and included these as control variables (for data sources, see the Web Appendix). Overall, Google Trends provides data on countries that produce a certain search volume for Threadless. Because we did not observe searches for Threadless across many of the countries, our sample size consists of observations from 25 countries.[ 6]
We conducted a regression analysis using ordinary least squares, and we discuss the results of three separate models that include and exclude different sets of control variables. Model 1 consists only of power distance. The overall model was significant (F( 1, 23) = 24.03, p <.01), and the adjusted R-square was 49%. Importantly, the power distance index was negative and significant (Std β = −.715, p <.01), which provides support for our hypothesis that high-power-distance cultures demonstrate lower interest in user-designed products.
To examine whether these results hold in the presence of various control variables, we reestimated the model including controls for the effects of other cultural dimensions, including individualism, masculinity, uncertainty avoidance, and long-term orientation. Model 2 also controls for a variety of factors that could drive search volume, including per capita GDP, GDP growth, education, and happiness. The variance inflation factors (VIFs) were within the acceptable range (<10), indicating that multicollinearity was not a significant factor. The overall model was significant (F( 9, 15) = 2.68, p <.05), and the adjusted R-square was 39%. More importantly, the results reveal that even in the presence of these control variables, the power-distance index was still negative and significant (Std β = –.771, p <.05). Model 3 included a control for number of internet users per country. We note that Model 3 did not include the controls found in Model 2 (e.g., GDP per capita, GDP growth, education) because of concerns about multicollinearity. Model 3 was significant (F( 7, 17) = 6.11, p <.01), and the adjusted R-square was high (60%). The VIFs were within acceptable range (<10). Importantly, the power-distance index was negative and significant (Std β = −.498, p <.05). As these results show, the effect of power distance is robust to the inclusion and exclusion of different control variables. Table 1 summarizes these results.
Graph
Table 1. Study 1a: Google Search Term Results for Brand (Threadless) by Country.
| Variable | Model 1: Google Search (Normalized, Model with No Controls, N = 25) | Model 2: Google Search (Normalized, Controlling for GDP, Educational Attainment, and Country Happiness, N = 25) | Model 3: Google Search (Normalized, Controlling for Number of Internet Users, N = 25) |
|---|
| Standardized Parameter Estimate | t-Value | Sig. | Standardized Parameter Estimate | t-Value | Sig. | Standardized Parameter Estimate | t-Value | Sig. |
|---|
| Intercept | 0 | 6.81 | .001 | 0 | .81 | n.s. | 0 | .84 | n.s. |
| Power distance | −.715 | −4.90 | .001 | −.771 | −2.46 | <.05 | −.498 | −2.16 | <.05 |
| Individualism | | | | −.054 | −.19 | n.s. | −.023 | −.130 | n.s. |
| Masculinity | | | | −.196 | −.71 | n.s. | −.051 | −.31 | n.s. |
| Uncertainty avoidance | | | | .247 | .58 | n.s. | −.110 | −.33 | n.s. |
| Long-term orientation | | | | −.129 | −.46 | n.s. | −.174 | −1.04 | n.s. |
| GDP per capita | | | | −.049 | −.16 | n.s. | | | |
| GDP growth rate | | | | .153 | .63 | n.s. | | | |
| Education | | | | −.008 | .03 | n.s. | | | |
| Happiness | | | | .231 | .50 | n.s. | .231 | .50 | n.s. |
| Number of internet users | | | | | | | .382 | 2.58 | <.05 |
| Model F | 24.03 (p <.01) | 2.68 (p <.05) | 6.11 (p <.01) |
| Adjusted R2 | 48.9% | 39% | 60% |
1 Notes: n.s. = not significant. The dependent variable is normalized search; we divided the amount of search volume from Google trends by the number of internet users in a given country. To calculate the number of internet users, we multiplied population and percentage of internet users. Model 1 shows the effect of PDI without controls, and Model 2 includes several control variables. Both Models 1 and 2 have VIFs within the acceptable range (<10). We also reestimated the second model including number of internet users (see Model 3). We excluded GDP variables and education in Model 3 because of high correlations among number of internet users and GDP growth, GDP per capita, and happiness.
To confirm the generalizability of the power-distance results with a different source of real data, in Study 1b, we conducted a field experiment using Facebook's advertising platform. We targeted high-power-distance and low-power-distance consumers using country as a proxy for power-distance beliefs (Guatemala vs. Austria), and tracked click-through rates (CTRs) on advertisements. Facebook allows advertisers to target users by country. We set up our ads to be optimized for clicks versus impressions. As the context implies, an advertiser would pay only for the number of clicks (cost per click [CPC]) rather than the number of impressions. For instance, if 10,000 people viewed our ad and 30 people clicked on it, we would pay the same amount as if only 5,000 people viewed an ad and 30 people clicked on it (assuming the same CPC). This is a recommended and commonly used way of setting up digital advertising ([32]). As we budgeted for a certain number of clicks, the number of impressions was likely to vary considerably depending on the impressions required to generate a certain number of clicks. Conceptually, we wanted to know how many impressions had to be made to attain a certain number of clicks. If one condition required significantly fewer impressions, we could determine that this condition operated better. To account for both clicks and impressions, we calculate the CTR, which is a function of clicks and impressions and a common metric in digital advertising. Advertisers frequently pay using clicks (CPC) and use "A/B" testing evaluating the CTR to determine advertising effectiveness ([32]; [45]).
We set up our advertising campaigns using automatic bidding, in which Facebook would determine the optimal bid for each click. The actual CPC is based on Facebook's algorithm, which uses auctions for ad space at any given time, such that an advertiser may pay $.80 for one click and $1.00 for another click for the same advertisement depending on other competing ads and their bids. Facebook reports the average CPC. Although Facebook's algorithm is opaque, we have no reason to believe it would operate differently across the conditions we set. However, to control for variance attributed to device or platform, we restricted the ads to be shown only on the Facebook desktop application (vs. mobile, Instagram, or Audience Network).
We examined the appeal of a user-design versus company-design positioning in an ad across cultures with varying levels of power-distance beliefs. We use the same online source from Study 1a (https://geerthofstede.com/research-and-vsm/) that provides PDI ratings across countries. Of the countries ranked, the country with the third-highest power distance was Guatemala (PDI = 95). Malaysia had the highest PDI, but we chose Guatemala so that we could focus on one accessible primary language (Spanish). The country with the lowest PDI in the list was Austria (PDI = 11); accordingly, we had the ads translated into German. Thus, we used Guatemala and Austria as examples of high- and low-PDI countries, respectively. Research in marketing has also used two countries that vary on one attribute (e.g., social mobility beliefs) as a way to demonstrate the effect of that attribute on consumers ([ 1]; [ 4]). Although many attributes are likely to vary across cultures, if H1 is supported, we would expect to find that Austrians have a stronger preference for a user-design appeal, whereas in Guatemala, this effect would be attenuated or reversed. Furthermore, although we are unable to fully control for potential confounds, our aim is to demonstrate the effect in a real-world context usable by marketing managers.
We created four separate Facebook ad campaigns in a 2 (country: Austria vs. Guatemala) × 2 (user-design vs. company-design) between-subjects design, using Facebook's ad targeting system. Each campaign had a budget of $40 and was set up on a CPC basis, as described previously. In all conditions, participants viewed an ad for the website acawiki.org. AcaWiki is a "Wikipedia for academic research," whose purpose is to host freely available summaries of academic papers for better dissemination to the public. Similar to Wikipedia, anyone can contribute summaries, though frequently the authors themselves do so. Given that anyone can create these summaries, we positioned AcaWiki's content as "created by users" or as "created by experts." We chose the term "experts" because it would seem unusual to advertise that content was produced by firm employees, in addition to making expertise more explicit. We created two versions of an ad that emphasized that the brand content was either crowdsourced or expert created. In the user-design condition, the headline of the English translation of the ad was "Free summaries of academic papers summarized and crowdsourced by our users!" In the expert condition, the English translation of the headline read, "Free summaries of academic papers summarized by a team of experts!" (for samples of the ad, see Appendix A). We pretested the ads (in English) to confirm that the user-design ad would be associated more with the user community and the company ad would be more associated with AcaWiki, the organization. Thirty participants, recruited from Amazon Mechanical Turk (MTurk), viewed screenshots of both ads in English. For each ad, participants were asked, "Based on the ad below, are summaries more likely to have been made by the user community (i.e., anyone) or by AcaWiki, the organization?" (1 = "user community," and 7 = "AcaWiki the organization"). The user-design ad was associated with users and the company-design ad was associated with AcaWiki (Muser = 3.17 vs. Mcompany = 4.87; F( 1, 29) = 13.08, p =.001).
Using Facebook's system, we targeted the ads to either Guatemalan or Austrian Facebook users according to the demographic category used when they set up their Facebook accounts. We ran four different campaigns targeting each group with the user-design or the company-design ad. The ads ran for a four-day period. If a Facebook user clicked on the ad, the campaign was charged for a click and the user was taken directly to acawiki.org.
Facebook records the number of impressions and clicks for each campaign. Across the four ad conditions, the total number of impressions was 532,066 (see Table 2). As noted, because we budgeted for a certain number of clicks and the advertising model is based on CTRs, the number of impressions was likely to vary considerably depending on how many impressions were required to generate a certain number of clicks (which is indicative of the impact and relevance of a given ad). Furthermore, the number of impressions is likely to vary across countries because of variations in marketplace conditions (e.g., ad competition differences). However, we were interested in how design source differences cause variations in CTRs for a given country and how power-distance beliefs can account for these variations. Accordingly, the CTR in the Guatemalan company-design condition was.25% and.16% in the Guatemalan user-design condition. For Austrian Facebook users, the CTR was.07% for the company-designed ad and.12% for the user-designed ad. These percentages are not unusual, given that the average CTR for Facebook ads across all industries is.9% ([10]) and the average CTR in countries such as Austria is even lower at.11% ([ 7]).
Graph
Table 2. Study 1b: Facebook Ad Campaign–Based Field Experiment with Country as Proxy.
| Impressions | Clicks | CTR | Average CPC |
|---|
| Austrian user-design condition | 52,806 | 66 | .12% | $.61 |
| Guatemalan user-design condition | 240,266 | 378 | .16% | $.11 |
| Austrian company-design condition | 91,531 | 64 | .07% | $.62 |
| Guatemalan company-design condition | 147,463 | 374 | .25% | $.11 |
| Study 2: Facebook Ad Campaign-Based Field Experiments with Political Orientation as Proxy |
| Very liberal user-design condition | 5,210 | 31 | .60% | $.99 |
| Very conservative user-design condition | 22,424 | 41 | .18% | $.72 |
| Very liberal company-design condition | 23,003 | 37 | .16% | $.86 |
| Very conservative company-design condition | 17,578 | 27 | .15% | $1.16 |
We used these data to conduct a logistic regression based on the number of users who viewed the ad (impressions) and specifying whether they clicked on the ad as the binary choice. Among those who saw the ad, we coded those who did not click on it as 0 and those who clicked on it as 1. We conducted a logistic regression with two factors (Guatemalan vs. Austrian and user-design vs. company-design) and an interaction term. With ad click-through as the dependent variable, we found a main effect of ad type (β = −.48, SE =.07, Wald χ2 = 42.95, p <.001), a main effect of country (β = −1.29, SE =.14, Wald χ2 = 90.9, p <.001), and a significant interaction between country and ad type (β = 1.06, SE =.19, Wald χ2 = 31.09, p <.001). Guatemalan Facebook users preferred the company-designed ad to the user-designed ad (β = −.48, SE =.07, Wald χ2 = 42.95, p <.001). However, the Austrian Facebook users preferred the user-designed ad to the company-designed ad (β =.58, SE =.18, Wald χ2 = 10.97, p =.001). These results provide support for H1. We also found that the absolute number of impressions and clicks was higher for Guatemala than Austria, as the CPC was considerably lower for Guatemala, suggesting less ad competition in the country.
Our approach followed standard practice in the digital marketing context, in which impressions vary but clicks do not. As an additional robustness check, we tracked the number of clicks per condition for the first 52,806 impressions (the lowest number of impressions in any condition). Accordingly, we measured the number of clicks for the first 52,806 impressions in each condition. The Guatemalan participants clicked significantly more in the company-design condition (131 clicks) than the user-design condition (93 clicks; χ2 = 6.46, p =.01), while the Austrian consumers clicked significantly more in the user-design condition (66 clicks) than the company-design condition (28 clicks; χ2 = 15.38, p <.001).
The results of Study 1a, the Google Trends analysis, provides evidence for H1. Using Threadless as an example, we demonstrate that the popularity of user-designed brands is lower in high-power-distance countries (or more popular in low-power-distance countries), even after controlling for differences in GDP, education, happiness, and other cultural difference indicators. Although this study offers important insights into the role of power-distance beliefs in influencing interest in user-design philosophies, it does not shed light on company-design philosophies. Furthermore, this analysis does not provide direct causal evidence for the role of power-distance beliefs in influencing preferences. In Study 1b, we aimed to bolster these findings by conducting a field experiment using Facebook's advertising platform. We found that people from Guatemala, a high-power-distance culture, preferred the company-design positioning while Austrian consumers preferred the user-design positioning. While many differences between Guatemala and Austria other than power-distance beliefs could be driving the results, these findings are consistent with H1 and are usable by managers as a way to target countries that vary in power-distance beliefs. We acknowledge that in field experiments, there are likely to be confounding factors such that a third variable (unrelated to power-distance beliefs) may also explain our findings. However, our experiment uses real applications and real segmentation variables accessible to managers, a significant contribution of this study.
Rather than using country as a proxy for power-distance beliefs, in Study 2 we used political orientation. We conducted another Facebook study, again using AcaWiki, targeting either liberal or conservative U.S. consumers with user-design or company-design ads following a similar set of procedures from Study 1b. Consistent with our predictions regarding power-distance beliefs specified in H1, we predicted that liberals would prefer user-designed products more than company-design products and that this effect would be attenuated or reversed for conservatives. Our dependent variable was again advertising CTR for each ad.
We created four separate Facebook ad campaigns in a 2 (liberal vs. conservative) × 2 (user-design vs. company-design) between-subjects design. Each campaign had a budget of $30 and was set up on a CPC basis. In all conditions, participants viewed an ad for acawiki.org using the same manipulation as in Study 1b; however, as the ads were targeted at U.S. users, they were presented in English (see Appendix A). In addition, using Facebook's system, we targeted the ads to either "very liberal" or "very conservative" Facebook users. These characteristics were based on how people specify their own political orientation as a demographic category when setting up their Facebook user account. Accordingly, we ran four different campaigns targeting each group with the user-design or the company-design ad. The ads ran for a three-day period.
Similar to Study 1b, we conducted a logistic regression with two factors (conservative vs. liberal and user design vs. company design). We found no main effect of ad type (β =.18, SE =.25, Wald χ2 =.5, n.s.) or political orientation (β =.05, SE =.25, Wald χ2 =.03, n.s.); however, we found a significant interaction between political orientation and ad type (β = 1.14, SE =.35, Wald χ2 = 10.7, p =.001). For very conservative Facebook users, we found no significant difference in CTRs between the user-design ad and company-design ad (β =.18, SE =.25, Wald χ2 =.5, n.s.). However, liberals preferred the user-design ad to the company-design ad (β = 1.31, SE =.24, Wald χ2 = 28.94, p <.001) (see Table 2).
Similar to Study 1b, using political orientation as a proxy for power-distance beliefs, we found differing effects between liberal and conservative Facebook users. In this case, liberals preferred the user-design product whereas conservatives showed no difference in preference. Studies 1 and 2 demonstrate our hypothesized effects in field settings. To enhance internal validity, in Study 3 we test these effects in a more controlled setting.
In Study 3, we used software as the focal product category and varied the extent to which the product was an open-source software product allowing the code to be altered by the user community. Although companies can permit users to participate in myriad ways, open-source software is a context in which a user-design philosophy is visible to everyone, as users interact with one another and the company to modify the software code and functionality. We measure power-distance beliefs both as an individual difference measure of power-distance beliefs and through U.S. political orientation. Accordingly, we propose that political orientation can serve as a managerially accessible proxy for power-distance beliefs. Furthermore, individual difference measures provide utility to managers who can target consumers differentially depending on accessible demographic characteristics (e.g., political orientation).
We recruited 406 U.S. participants (47% female, Mage = 36 years) from MTurk to take part in this study. Participants were randomly assigned to one of two between-subjects conditions in which they read about a software brand that focused on user design or company design. Because software requires some technical skills for the user community to participate, we explicitly stated that the user-designed and company-designed products were high in quality. In the user-design philosophy condition, participants read, "Imagine that Company A is a technology company. The company delivers an extremely high-quality product. To ensure the greatest quality, the company has an open platform where changes and new developments to its software are able to be made by its community." In the company-design condition, participants read, "Imagine that Company A is a technology company. The company delivers an extremely high-quality product. To ensure the greatest quality, the company maintains complete control over the software, with changes and developments made by the company's software developers."
Participants were then asked items on brand preference (α =.92), quality, empowerment (α =.97), and identification (α =.96). The empowerment and identification items were taken from [ 8]. Participants reported their power-distance beliefs on a four-item scale adapted from [ 6] (α =.73; M = 4.17, SD = 1.11). An example item was "People at lower levels in organizations should carry out the requests of people at higher levels without question" (for measures, see Appendix B). Participants then answered one item measuring their political orientation: "What is your political orientation?" (1 = "extremely liberal," and 5 = "extremely conservative"). The mean was 2.78, and the standard deviation was 1.07. As predicted, the power-distance scale and political orientation were positively correlated (r =.34, p <.001). Beyond age and gender, we collected demographic variables on income and education. These variables do not substantially change the results when included as covariates in the model and thus are not discussed further in the analysis. The Web Appendix reports a brief discussion of the results with identification.
To confirm that our multi-item constructs were distinct from one another, we first tested for discriminant validity between the empowerment construct and the brand preference measure. The average variance extracted (AVE) for each item exceeded their squared correlation (AVE three-item empowerment =.92; AVE three-item brand preference =.86; squared correlation =.14) ([12]). Furthermore, the 95% confidence interval (CI) around the correlation between the two factors excluded 1 (CI = [.3,.47]) ([ 3]). Thus, these two tests provide evidence for the discriminant validity of our measures.
We first conducted a multiple regression with design source (user design vs. company design), power-distance beliefs, and the interaction of the two independent variables (mean-centered) with brand preference as the dependent variables. We found a significant main effect of design source (β = –.26, t(402) = 2.35, p <.05), a significant main effect of power-distance beliefs (β =.12, t(402) = 2.4, p <.05), and a significant interaction between power-distance beliefs and design source (β =.48, t(402) = 4.79, p <.001), in support of H1. To examine the simple effects of design source (user design vs. company design) on brand preference across different levels of power-distance beliefs, we applied the Johnson–Neyman procedure to identify regions of significance. We found a significant effect of design source on brand preference, in that participants low in power-distance beliefs favored the user-design brand (4.26 and below on the seven-point scale; β = −.22, t(402) = 1.97, p =.05). At values between 4.27 and 5.39 on the power-distance scale, we found no significant differences in preference for the user-designed or company-designed software. At high levels of power-distance beliefs, we found a preference for company-designed software (5.39 and above: β =.32, t(402) = 1.97, p =.05; see Figure 1).
Graph: Figure 1. Study 3: Effects on brand preferences.Notes: Shaded areas indicate regions of significance (Panel A: below 4.26 and above 5.39; Panel B: below 2.82 and above 4.8).
We conducted Model 8 with empowerment and quality as simultaneous mediators and power-distance beliefs as the moderator (see Figure 2). A Model 8 relationship suggests that the mediator is moderated—that is, feelings of empowerment and perceptions of quality depend on power-distance beliefs. An index of moderated mediation was significant for both empowerment (.07; CI = [.001,.15]) and quality (.16; CI = [.07,.28]) in support of H2 and H3. Greater feelings of empowerment mediated purchase intention for participants low in power-distance beliefs (−1 SD from the mean) in favor of the user-designed software (−.6; CI = [−.8, −.44]) but less so for those high in power-distance beliefs (+1 SD from the mean: –.44, CI: –.62, –.31). Perceptions of higher quality mediated purchase intention in favor of the company-designed software for those high in power-distance beliefs (.26; CI = [.11,.46]) but not for those low in power-distance beliefs (−.09; CI = [−.26,.06]). These results support H2 and H3.
Graph: Figure 2. Mediation path diagram with power-distance beliefs (Study 3).*p <.05.**p <.01.nsNot significant.Notes: Two-tailed tests of significance. a1 is the effect of the independent variable (IV) on the mediator; a3 is the interaction of the IV and the moderator on the mediator; b1 is the effect of the mediator on the dependent variable (DV).
We briefly report the preceding analyses using political orientation (rather than power-distance beliefs) as the moderator. We found a significant interaction between political orientation and design source on brand preference (β =.35, t(395) = 3.32, p =.001). For those more liberal in their political orientation, we found a preference for the user-designed software (2.82 and below on the five-point scale; β = −.22, t(395) = 1.97, p =.05). For those more conservative in their political orientation, we found a preference for the company-designed software (4.8 and above: β =.47, t(395) = 1.97, p =.05). We conducted Model 8 with empowerment and quality as simultaneous mediators and political orientation as the moderator. An index of moderated mediation was significant for empowerment (index:.11; CI = [.05,.2]) and quality (index:.11; CI = [.02,.23]), following a similar pattern of results as power-distance beliefs.
In Study 3, we found that low-power-distance consumers prefer user-designed software, whereas high-power-distance consumers prefer company-designed software. We found a similar pattern when using political orientation as a proxy for power-distance beliefs. These effects were differentially mediated by feelings of empowerment and perceptions of quality.
It could be that those low in power-distance beliefs felt more similar to the user community, thus driving preferences for the user-designed software ([ 8]). We conducted a follow up posttest (N = 101) to confirm that feelings of similarity to the user community were not correlated with power-distance beliefs (r = −.01, p >.5) or political orientation (r = −.13, p =.2) (similarity α =.93; for specific items, see Appendix B).
Study 3 tests moderation through an individual difference measure. To provide converging evidence, in Study 4, we manipulate power-distance beliefs through a priming task. We also examine a different product category (i.e., furniture).
The purpose of Study 4 was to examine the impact of power-distance beliefs (through a priming task) on how consumers perceive user-design versus company-design philosophies. Participants read about a furniture company that sold either user-designed or company-designed products.
We recruited 214 U.S. participants (48% female, Mage = 36 years) from MTurk, using a 2 × 2 design with design source (user-design vs. company-design) and power-distance manipulation (high vs. low) as between-subjects factors. Twenty-six participants did not complete the sentence priming task correctly (e.g., left it blank, wrote "NA"), which left a usable sample of 188 participants. We manipulated power-distance beliefs using a priming task that required participants to unscramble sentences ([44]; [46]). To determine whether this power-distance manipulation would be successful, we pretested it with a different sample (N = 260) and found that the priming task was effective. Specifically, those in the low-power-distance condition had higher ratings on the manipulation check index, indicating greater preference for social equality (Mlow = 5.61, SD = 1.43; Mhigh = 5.02, SD = 1.76; F( 1, 258) = 8.92, p =.003; for the priming task and power-distance beliefs manipulation check items, see the Web Appendix).
After the priming task, participants read about a wood furniture company that employed either a user- or company-design approach (procedures adapted from [38]]). In both conditions, participants read the following: "Company A is a company that specializes in wood furniture. As with many firms nowadays, this company has an online user community." Following [38], this cue regarding the user community was added to avoid any confounding effects from having a community. Participants then read, "Below is a picture of products that have recently been marketed by the company. New products of Company A are regularly and exclusively designed by [professional designers who work for Company A]." The text in brackets was replace by [members of its user community] in the user-design condition. We asked participants two items on brand preference (liking and purchase intention; α =.77), three items on feelings of empowerment (α =.96), and one item on quality perceptions (similar to Study 3). We conducted a discriminant validity analysis similar to Study 3 (reported in the Web Appendix).
We conducted an analysis of variance with two factors (user design vs. company design × low power-distance beliefs vs. high power-distance beliefs) and their two-way interaction on the brand preference measure. We found no significant main effect of power-distance beliefs, a significant main effect of design source (F( 1, 184) = 8.88, p <.005), and a significant interaction between power-distance beliefs and design source (F( 1, 184) = 5.63, p <.02), in support of H1. Participants in the low-power-distance condition showed a higher preference for user-designed than company-designed furniture (Muser = 5.49, SD = 1.13; Mcompany = 4.61, SD = 1.07; F( 1, 184) = 13.62, p <.001). However, we found no significant difference in the high-power-distance condition (Muser = 5.03, SD = 1.09; Mcompany = 4.93, SD = 1.18; F( 1, 184) =.19, n.s.; see Figure 3).
Graph: Figure 3. Power-distance prime and brand preferences (Study 4).
We conducted a similar two-factor analysis of variance with empowerment as the dependent variable. We found a significant interaction between design source and power-distance prime for feelings of empowerment (F( 1, 184) = 7.17, p =.008). Participants in the low-power-distance condition reported higher feelings of empowerment for the user-designed furniture than the company-designed furniture (Muser = 5.32, SD = 1.10; Mcompany = 3.01, SD = 1.55; F( 1, 184) = 56.05, p <.001). These effects were weaker in the high-power-distance condition (Muser = 4.56, SD = 1.46; Mcompany = 3.39, SD = 1.61; F( 1, 184) = 15.98, p <.05). We conducted a similar analysis with quality as the dependent variable and found no significant interaction (F( 1, 184) =.73, n.s.). Accordingly, H3 was not supported.
In line with H2, we would expect the mediating effect of empowerment to influence brand preference and to vary depending on power-distance manipulation. We again conducted a moderated mediation analysis ([17], Model 8). An index of moderated mediation including empowerment was significant with the confidence interval excluding zero (index:.33; CI = [.11,.66]); thus, H2 was supported. Specifically, in the low-power-distance condition, the impact of empowerment led participants to more strongly favor the user-designed furniture (−.67; CI = [−1.0, −.4]) compared with those in the high-power-distance condition (−.34; CI = [−.59, −.16]).
The results of Study 4 provide support for H1 and H2. Using previously validated stimuli, we found that power-distance beliefs moderate design source, in support of H1. While participants primed with low power-distance beliefs preferred a user-design approach to a company-design approach, this effect was attenuated for those primed with high power-distance beliefs. Furthermore, those primed with low power-distance beliefs experienced greater feelings of empowerment for the user-designed product, and this served to mediate purchase intentions (in support of H2). Unlike Study 3, we did not find a preference for company-designed products, or mediation through quality, when power-distance beliefs were primed to be higher. There were several differences between Studies 3 and 4 that could account for these differential effects. First, given that U.S. consumers are lower in power-distance beliefs, it may be difficult to prime them to a level that is high enough to reverse the user-design effect. Second, it could also be that quality perceptions are less amenable to priming manipulations compared with individual differences. Third, the differences in results could be related to the category (software vs. furniture) or the stimuli. In Study 3, participants did not view a picture whereas in Study 4 they did. The presence of a picture could have been more diagnostic, making it difficult to manipulate quality inferences.
Still, across both studies we find that the user-design effect is moderated by power-distance beliefs, with stronger feelings of empowerment for those low in power-distance beliefs. By explicitly manipulating power-distance beliefs, this moderation effect cannot be plausibly explained by previously documented accounts, such as familiarity with a user-design approach, similarity to other users, category, or customer orientation ([ 8]; [14]; [38]). We have no reason to believe that priming power-distance beliefs would make participants feel more or less similar to the user community, be more familiar with design innovation, view category differently, or care about customer orientation in any differential way.
In Studies 1–4, we explored the moderating effects of power-distance beliefs through country, political orientation, individual difference measures, and a priming task. The purpose of Study 5 was to manipulate empowerment to provide stronger process evidence ([41]). More specifically, we manipulated empowerment in the user-design condition by specifying that the company would allow only certain selected users to participate ([ 8]). Although the products in this condition were still designed by users, the fact that only a limited number of users could participate should reduce feelings of empowerment. In this case, consumers may feel less empowered because they personally would feel unable to influence the product's design. If empowerment is driving preferences for low- and high-power-distance consumers, we would expect that limiting user participation would create a condition that is more similar to the company-design condition. Accordingly, we added a third condition in which we manipulated feelings of empowerment (user-design limited) by allowing only selected user participation. We expected the effects of power-distance beliefs to reverse in this condition.
We again measured power-distance beliefs as an individual difference measure. We also collected a measure of valuing expertise in addition to quality perceptions to test the mediating process more directly. Finally we prescreened participants and used quota sampling to recruit large numbers of very liberal and very conservative consumers. In Study 3 we found that few participants were at the extreme ends of the political orientation scale (on the five-point scale, only 12% of participants were "very liberal" and 5% of participants were "very conservative"). In this study, by recruiting only participants at the extreme ends of the scale, we would more concretely solidify the relationship between power-distance beliefs and political orientation by testing differences between these two groups.
We prescreened participants and used quota sampling to recruit large numbers of participants who were more liberal or more conservative, which we predicted would also create more variance in power-distance beliefs. In an initial survey, participants were first prescreened for their political orientation using the same five-item scale from Study 3. If participants indicated that they were "very liberal" or "very conservative," they were allowed to continue to the main survey. We aimed to recruit at least 150 "very liberal" and 150 "very conservative" participants. Others were disqualified from taking the main survey and were paid for their participation in the prescreen task. This resulted in 366 U.S. participants (61% female, Mage = 37 years) from MTurk who participated in the main study. To confirm the effectiveness of the quota-sampling procedure, political orientation was again measured at the end of the main study (1 = "very liberal," and 5 = "very conservative"). The quota sampling yielded 182 participants who identified as "very liberal," 15 participants who identified as "liberal," 1 participant who identified as "middle of the road," 11 participants who identified as "conservative," and 157 participants who identified as "very conservative."
Participants noted their power-distance beliefs on the four-item scale from Study 3 (α =.80; M = 3.95, SD = 1.4). To confirm that power-distance beliefs and political orientation are related, we tested the correlation between political orientation and power-distance beliefs. They were significantly correlated (r =.52, p <.001). To provide further evidence, we conducted a t-test between only the "very liberal" and "very conservative" participants, with power-distance beliefs as the dependent variable. We found that very liberal participants were significantly lower in power-distance beliefs compared with very conservative participants (Mliberal = 3.24, SD = 1.25 vs. Mconservative = 4.72, SD = 1.14; t(337) = 11.31, p <.001), yielding a large effect size (Cohen's d = 1.24). Thus, this quota sampling provides more robust evidence that political orientation can be used as a proxy for power-distance beliefs.
Participants were randomly assigned to one of three between-subjects conditions in which they read about a software brand focused on user-design open, user-design limited, or company design (similar to Study 3), with stimuli adapted from ([ 8]). In the user-design-open condition, participants read the following:
Imagine that Company A is a technology company. To ensure the greatest quality, the company has an open platform where changes and new developments to its software are able to be made by its community. Everyone can co-develop the software and participate in its further development. This means Company A gives any user who is interested an opportunity to advance and improve the software.
The text in the user-design-limited condition read,
Imagine that Company A is a technology company. To ensure the greatest quality, the company has an open platform where changes and new developments to its software are able to be made by its community. Only selected people from its community can co-develop the software and participate in its further development. That means that Company A only allows selected users from its community to advance and improve the software.
Finally, the company-design condition read,
Imagine that Company A is a technology company. To ensure the greatest quality, the company maintains complete control over the software, with changes and developments made by the company's software developers.
Participants responded to similar measures of brand preference (α =.92), empowerment (α =.96), and quality as in Study 4. In addition, they responded to a question about the value of expertise ("I value Company A's expertise"). Participants were also asked about their familiarity with user-design approaches as a potential competing process. Income and education levels were also collected. Controlling for these demographic variables (in addition to age and gender) does not substantially change the results, and accordingly are not discussed further. As in previous studies, we conducted discriminant validity analysis (reported in the Web Appendix).
We constructed two dummy variables to signify the three experimental conditions. After mean-centering the continuous measure of power-distance beliefs, we constructed interactions of power-distance beliefs with the user-design limited and company design conditions (with user-design open serving as the reference condition). We found a significant main effect of power-distance beliefs (Std β = −.31; p <.001), a significant main effect of the company-design dummy variable (Std β = −.14, p =.01), and a significant main effect of the user-limited dummy variable (Std β = −.29; p <.001). Importantly, we found a significant interaction between power-distance beliefs and company design (Std β =.39, p <.001), and a significant interaction between power-distance beliefs and user-design limited (Std β =.38, p <.001). Simple effects showed a significant positive effect of power-distance beliefs in the user-design-limited condition (Std β =.31, p <.01) and a significant positive effect of power-distance beliefs in the company-design condition (Std β =.45, p <.01). In contrast, simple effects showed a significant negative effect of power-distance beliefs in the user-design open condition (Std β = −.31, p <.01). Figure 4 visually depicts these results. As predicted, by reducing empowerment in the user-design limited condition, we reversed the effects of power-distance beliefs. We found a similar pattern of results when we used political orientation as a moderator (see the Web Appendix).
Graph: Figure 4. User-designed products and brand preference: the role of empowerment (Study 5).
Because the effects of power-distance beliefs were similar in the user-design-limited and company-design conditions, we collapsed the two and then conducted a moderated mediation analysis with empowerment and expertise[ 7] as simultaneous mediators. We found a similar pattern of results to that in Study 3, with the index of moderated mediation being significant for both empowerment (.07, CI:.02,.14) and expertise (.39, CI:.23,.57). Greater feelings of empowerment mediated purchase intention for participants low in power-distance beliefs in favor of the user-designed software (one standard deviation below the mean: −.47, CI: −.68, −.3) but less so for those high in power-distance beliefs (−.26, CI: −.4, −.15). Valuing the firm's expertise mediated purchase intentions in favor of the company-designed software for those high in power-distance beliefs (.38, CI:.08,.7) but mediated purchase intentions in favor of the user-designed software for those low in power-distance beliefs (−.7, CI: −.99, −.45). These results remained consistent when we included familiarity as a simultaneous mediator in the model and also when we did not collapse the data (comparing user-design open with company-design and user-design open with user-design limited). Therefore, as expected, when empowerment was reduced in the user-design-limited condition, the effects of power-distance beliefs reversed.
Our findings highlight the important role of power-distance beliefs as a moderator of the positive user contribution effect found in prior research. In Study 1, we provide real-world evidence using Google Trends and Facebook's advertising platform, segmenting consumers by country. In Study 2, we provide additional real-world evidence segmenting consumers by political orientation. We measured power-distance beliefs and political orientation in Study 3 and manipulated power-distance beliefs in Study 4. Finally, we manipulated empowerment in Study 5 to test the mechanism through moderation.
Taken together, the studies' results reveal that some consumers are enthusiastic about user-driven approaches while others are ambivalent to the use of this strategy. Consumers with low-power-distance beliefs prefer user-designed products because they experience greater feelings of empowerment, whereas those with high-power-distance beliefs favor company-designed products because they view such products as higher in quality and value their expertise more.
This research makes several significant contributions to the literature. First, we expand research on user-designed products ([ 8]; [30]; [38]) by uncovering a novel moderator of the impact of user contributions on brand preference. We identify power-distance beliefs as a moderator of reactions to user-designed versus company-designed products and thereby uncover a segmentation variable that rests within the consumer.
Second, we used two managerially accessible proxies for power-distance beliefs: country and political orientation. Our findings suggest that beyond its role in shaping social and religious beliefs ([16]), political orientation also shapes attitudes toward cocreated products and brands. Prior research has also linked political conservatism to various psychological traits, such as self-esteem ([23]). However, to our knowledge, our study is the first exploration of how political orientation influences the propensity to favor user-designed products; this is a key contribution of this research. Similarly, while power-distance beliefs have appeared in consumer behavior contexts such as impulse buying, price inferences, and donations (e.g., [29]; [44]; [46]), our work demonstrates its role in user design.
Third, we explored how feelings of empowerment, previously demonstrated to mediate the relationship between design source and brand preference ([ 8]), may vary depending on power-distance beliefs. Our findings suggest that feelings of empowerment are more fluent and consistent with low-power-distance beliefs than high-power-distance beliefs. Furthermore, we find evidence that those with high-power-distance beliefs may be predisposed to value a firm's expertise, which influences quality perceptions. Thus, we find that cultural beliefs (i.e., power-distance beliefs) influence brand perceptions.
Although we found that consumers low in power-distance beliefs consistently preferred user-designed products, the effects for high-power-distance (and conservative) consumers were either attenuated or fully reversed. It could be that power-distance beliefs as an individual difference measure (Studies 3 and 5) and at the country level (Study 1b) are more effective at capturing high-power-distance beliefs than power-distance primes (Study 3) or political orientation as a binary variable (Study 2). It is worthy to note that those high in power-distance beliefs experienced more empowerment from user-designed products (compared with company-designed products), albeit to a lesser extent than low power-distant consumers. Given competing processes, this may contribute to an attenuation rather than a reversal of the user-design effect.
Firm size and firm power are other moderators that might interact with power-distance beliefs, such that low-power-distance consumers may exhibit an even stronger preference for user-designed than company-designed products, when the firm is powerful. Examining whether these results translate to other types of experts (e.g., using professional designers vs. company personnel), and the role of other mediating processes such as trust and identification with a company is a worthy direction for future research ([40]). The role of empowerment and its downstream consequences also offers promise for further research. In line with [28], future research could examine the role of empowering consumers in various contexts (e.g., donation behavior) and investigate related outcomes (e.g., willingness to pay a price premium, brand loyalty).
We urge marketing managers to proceed cautiously when implementing user-design initiatives. Such initiatives are a wise strategy for consumers with low-power-distance beliefs but not for those with high-power-distance beliefs. In line with our findings, we suggest that publicizing user-designed products would be effective for liberals in blue states and in low-power-distance countries such as Austria, Israel, and Denmark. Thus, publicizing user-design approaches may have better outcomes in certain regions or countries.
Our insights into the moderating role of power-distance beliefs is meaningful to managers who could highlight the important role of user design for segments of consumers with low-power-distance beliefs. Managers targeting those with high-power-distance beliefs might instead be advised to avoid a user-design approach altogether or to minimize the visibility of its role. If managers can provide alternative advertisements that direct consumers to different landing pages featuring either crowdsourced product designs or company-based product designs, they could appeal to consumers with both low- and high-power-distance beliefs. This ability is increasingly possible with digital and social media marketing. For example, both Facebook and Google have ad-targeting capabilities that enable companies to offer varying products or alternative ads (with different features) to appeal to different target audiences (as demonstrated with Studies 1b and 2).
While we investigated user design (vs. company design) in particular, other types of cocreation might exhibit similar effects. For example, in a follow-up study, again using Facebook's advertising platform, we found that liberals were more likely to click on an ad from the company 80sTees.com when we highlighted that its shirts were crowdfunded. However, we found no such effects for conservatives. Accordingly, the effects of power-distance beliefs and political orientation could be extended to other forms of cocreation, including crowdfunding and contests, thereby building on recent findings regarding crowdfunding ([ 5]). We also encourage researchers to examine related constructs such as beliefs about economic mobility, and cultural dimensions such as uncertainty avoidance, masculinity, and so forth, to determine their impact on responses to user-designed products.
Supplemental Material, DS_10.1177_0022242919830412 - Who Is Wary of User Design? The Role of Power-Distance Beliefs in Preference for User-Designed Products
Supplemental Material, DS_10.1177_0022242919830412 for Who Is Wary of User Design? The Role of Power-Distance Beliefs in Preference for User-Designed Products by Neeru Paharia and Vanitha Swaminathan in Journal of Marketing
Graph: 10.1177_0022242919830412-fig5.tif
Graph: 10.1177_0022242919830412-fig6.tif
Brand Preference:
- How much do you like technology company A? (1 = "not at all," and 7 = "very much")
- How likely is it that you would buy a product from company A? (1 = "very unlikely," and 7 = "very likely")
- How would you rate technology company A? (1 = "unfavorable," and 7 = "favorable")
Please indicate how much you agree or disagree with the following statements regarding company A (1 = "strongly disagree," and 7 = "strongly agree"):
Empowerment:
- Company A makes me feel that I can make a difference.
- Company A makes me feel like I have power on the firm's product offerings.
- Company A makes me feel that I have been empowered.
Quality:
- Company A's products are high in quality.
Identification:
- I identify with Company A.
- I feel close to Company A.
- I feel a strong bond with Company A.
- I feel connected to Company A.
Power-distance beliefs individual difference scale:
- People at lower levels in organizations should carry out the requests of people at higher levels without question.
- An organization is most effective if it is clear who is the leader and who is the follower.
- If followers trust their leaders wholeheartedly, the group will be most successful.
- People at lower levels in the organization should not have much power in the organization.
Posttest similarity items:
- There are many similarities between me and members of the community.
- I feel similar to members of the community.
- I feel very close to the members of the community.
- I can identify with the community members.
Footnotes 1 Associate EditorPage Moreau served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 ORCID iDVanitha Swaminathan https://orcid.org/0000-0002-8752-8881
5 Online supplement: https://doi.org/10.1177/0022242919830412
6 1The list of countries included in the data set is as follows: Australia, Austria, Belgium, Brazil, Canada, Chile, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Mexico, New Zealand, Norway, Poland, Portugal, Spain, Switzerland, Turkey, and United States.
7 2.Expertise and quality were highly correlated (r =.72). The results are consistent when we use quality instead of expertise or if we collapse the two.
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By Neeru Paharia and Vanitha Swaminathan
Reported by Author; Author
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Record: 237- Who Receives Credit or Blame? The Effects of Made-to-Order Production on Responses to Unethical and Ethical Company Production Practices. By: Paharia, Neeru. Journal of Marketing. Jan2020, Vol. 84 Issue 1, p88-104. 17p. 1 Diagram, 4 Graphs. DOI: 10.1177/0022242919887161.
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Who Receives Credit or Blame? The Effects of Made-to-Order Production on Responses to Unethical and Ethical Company Production Practices
While prior research has found that consumer-influenced production improves purchase intentions, the author proposes that it can counterintuitively backfire. This work demonstrates that when consumers have some control over production (e.g., ordering products on demand, customization, preordering), they have lower purchase intentions for products made with unethical processes (e.g., pollution, underpaid labor) than if they had no role in production (i.e., buying what is already in inventory). This effect reverses, however, with positive ethical production (e.g., recycled materials). Because consumers have direct responsibility for whether a product is made, feelings of anticipated guilt or gratification result depending on the ethicality of the production process. This work also proposes a novel threefold conceptualization of responsibility that can be used as managerial levers: direct responsibility, diffusion of responsibility, and broad responsibility. Field studies using Facebook's advertising platform demonstrate positioning strategies for fair-trade brands and advocacy groups.
Keywords: agency; cocreation; customization; ethics; operations; supply chain; sustainability
Imagine that a consumer goes to a well-known shoe store and finds a nice pair of shoes she would like to own. This same consumer also recently read an article accusing the shoe brand of using poor labor practices to make its shoes ([ 4]). Although this consumer does not endorse the use of poor labor practices, she buys the shoes feeling little guilt, thinking that the damage has already been done. Now instead, imagine that the same shoe brand uses on-demand production. In this case, the brand produces the shoes only after the consumer orders the item from a catalog. Rationally, in both cases, the consumer is choosing whether to purchase a product made with poor labor practices. However, in the case of on-demand production, would this consumer still make the same purchase?
Many companies have recently found success using various types of on-demand business models. For example, Nike offers consumers the ability to customize their own shoes with Nike by You (https://www.nike.com/us/en%5fus/c/nikeid/origins). Custom Ink allows customers to design their own T-shirts. On-demand printing companies allow customers to order out-of-print and rare titles. More recently, Amazon announced "The Drop," clothing made on-demand and designed by prominent influencers. The dominant wisdom of these made-to-order business models is that they can improve operational efficiency and/or consumer preference fit. However, giving consumers a role in production also has the consequence of giving them direct responsibility over whether a product will be produced. For example, in the case of on-demand books, a book will be produced only if a consumer orders it. In their pursuit of made-to-order business models, marketing managers may have overlooked the consequences of giving consumers control over this aspect of production.
In this research, I argue that giving consumers control of production can prompt them to weigh ethical production attributes (e.g., recycled materials, pollution, underpaid labor) more heavily than when they simply choose premade products from inventory already in stores. While it is most often the firm that decides the ethical parameters of production (e.g., labor, pollution), when products are made-to-order, it is the consumer who ultimately pulls the trigger. Overall, I demonstrate that when consumers are given responsibility for whether a product is produced (e.g., made-to-order, on-demand production, customization, preorders), a stronger link between consumers and production leads to anticipated feelings of guilt or gratification depending on the ethicality of the production process, which then mediates purchase intentions. This link is weaker when consumers do not participate in production (choosing premade products from inventory). Although firms typically choose their operational strategy based on economies of scale, inventory management, and fit with consumer preferences in mind (Cachon and Terwiesch 2013), this work is the first to demonstrate that the interaction of operational strategy and production ethicality can have emotional consequences that then impact consumer behavior.
Specifically, considering ethical production attributes (e.g., environmental impact, labor) ([48]), I find that when consumers participate in production (operationalized as made-to-order production), they disfavor products made with negative ethical attributes more than if they had considered these same products from inventory (made-to-stock production). Study 1 demonstrates these effects using Facebook's advertising platform to test the real-world applicability of the effect. Study 2 finds an interaction between participation in production and the ethicality of production (negative vs. neutral), while also demonstrating the mediating process of guilt using an incentive-compatible design. Importantly, I presented a version of Study 2 to a group of marketing managers, where the majority were unable to predict the study's results (see Study 2's "Results and Discussion" section). Study 3 explores whether the effects reverse with more positive ethical production (e.g., recycled paper) and finds that participants are willing to pay a premium for recycled products when they have control over production because they anticipate feeling more gratification. Studies 4 and 5 explore two other dimensions of responsibility, diffusion of responsibility and broad responsibility, which can help strengthen the link between consumers and production even when products are premade (made-to-stock), thereby acting as moderators. Study 4 varies the size of the consumer group to reduce diffusion of responsibility and social loafing. Study 5 cues participants to think beyond their direct level of responsibility and to consider a broader societal type of responsibility. Figure 1 presents the conceptual model.
Graph: Figure 1. Conceptual model.
In this growing era of "prosumerism," in which consumers are also becoming producers ([54]; [84]), this research examines how consumer participation in production (e.g., made-to-order) interacts with a company's ethical and unethical business practices to moderate purchase intention, a previously unexplored interaction that has important implications for both business and society. Prior research has found that cocreation of design (e.g., choosing customized personal styles, colors, artwork) generally improves purchase intentions ([25]; [30]; [31]; [32]; [64]; [70]; [87]). Alternatively, I examine how the notion of "cocreation of production," giving consumers the ability to determine whether a product will be made, can interact with negative production ethicality and lead to a backfiring effect, thus reducing purchase intention. While managers often focus on the benefits of made-to-order production (e.g., operational efficiency, customization), they seem to overlook what it means to connect consumers with the physical production of their products. Second, I show that the opposite effect can occur as well. Whereas ethical efforts such as fair-trade practices can increase product appeal ([89]), giving consumers some influence over production can lead them to take credit for improving production conditions, thus increasing purchase intentions. This research highlights that the ability to cocreate production is an important dimension that can trigger independent psychological processes beyond ones triggered by the ability to cocreate designs.
I document the dynamics underlying these effects, which rely on a novel threefold conceptualization of responsibility: direct responsibility, diffusion of responsibility, and broad responsibility. To my knowledge, these three dimensions and their consequences have not been examined in the context of consumers and production ethicality. I further demonstrate that these dimensions of responsibility can lead to feelings of guilt or gratification depending on the ethicality of the production process. More broadly, I show that responsibility in a consumer setting is malleable and can vary depending on the decision context.
This work contributes to an emerging stream of research on the paradox of high-stated interest but low market share for ethically made products ([48]; [59]; [86]), an attitude–behavior gap that is arguably the biggest challenge for marketers promoting ethical consumption ([88]). This research demonstrates that whether consumers do or do not participate in production can help explain this contradiction, a factor that managers can use to increase consumers' interest in more ethical products. Furthermore, this work answers recent calls in the Journal of Marketing for research on how agency and collective responsibility can encourage sustainable consumption ([88]).
Importantly, this work contributes to the operations research literature. Choosing an operational strategy with made-to-order or made-to-stock production is a decision that virtually all operations managers face, and it is also a fundamental topic for operations researchers ([71]; [73]). In the context of a recent movement toward greater operational and ethical transparency with brands such as Everlane and Patagonia, this work makes important contributions to both research and practice ([17]). While marketing managers have typically used communications regarding corporate social responsibility (CSR) initiatives to explicitly strengthen the link between consumers and production (e.g., highlighting living wage and environmental initiatives; [78]; [88]), I find that this link can also be strengthened using an operational variable.
In the case of production, control has typically resided on the supplier side, with companies deciding what, when, and how to produce products ([34]; [75]). In this context, consumers only have the power to choose between alternatives in the market ([82]). This refers to "made-to-stock" production, in which production occurs before actual consumer demand, and products are sold from a predetermined inventory of items. Other models give consumers the ability to order products, which are then made on-demand, or to customize products, which are then made-to-order. In this case, consumers have the power to choose whether products will be produced (or not). Made-to-order business models give consumers the ability to cocreate production, in which consumers choose whether to have products made. A subset of these business models also gives consumers the ability to cocreate designs in which they can customize their own product designs (e.g., colors, styles), whereas others may not allow for design customization (e.g., on-demand books, preorders).
From the consumers' perspective, product attributes such as price and quality tend to be the most salient and affect their utility for using a product. Process attributes (e.g., how a product is made) tend to be less observable to consumers and, in most cases, do not affect their use of a product directly ([ 1]). Operations research has discussed how process characteristics can generate negative externalities that may have no direct impact on consumers' use of a product but can have a myriad of effects on the environment and society (e.g., pollution, poor working conditions) ([40]). For example, in 2013, the Rana Plaza building in Bangladesh collapsed, killing 1,129 garment workers, due to inadequate safety protocols. Recent research in sustainable operations has focused on ways to induce suppliers to behave responsibly, using devices such as contracts, monitoring, and regulation ([ 2]; [22]; [23]; [52]; [55]) or by increasing consumer demand through operational transparency ([18]).
The marketing literature commonly describes these specific process attributes as "ethical attributes" that reflect a person's conscience and activate their moral value ([ 6]; [21]; [27]; [47]; [72]). Prior research has examined consumers' reactions to the consequences of more negative ethical production (e.g., pollution, rain forest depletion, unfair wages, animal testing) and more favorable ethical production (e.g., fair-trade production, use of recycled materials) ([27]; [48]; [89]).
Although a firm may intend to increase efficiency, variety, or preference fit, made-to-order production also strengthens the link between the consumer and the production process itself. Given that common ethical attributes are often tied to the production process, giving consumers direct responsibility for production may influence how they weigh these ethical attributes by increasing a feeling of causal agency. Agency, also called autonomy, choice freedom, and locus of control ([10]; [11]; [33]), refers to the belief that one can control external events through one's actions. When people perceive themselves as meaningful agents, they attribute outcomes to their own actions, thus having a sense of responsibility and ownership for the outcome ([13], [14]). By contrast, when a company has full responsibility for outcomes, consumers will likely perceive these as causal forces beyond their control. This weakened causal link may be vulnerable to motivated processes in which consumers can more easily justify that their choice will make no difference to society ([27]; [68]).
When consumers have direct responsibility for production, I theorize that anticipated feelings of guilt or gratification will result depending on the relative ethicality of the production process. Guilt is a negative, self-directed emotion ([28]) and is often associated with some degree of self-attribution of responsibility for a negative outcome ([15]; [42]; [58]; [90]). Although guilt can be experienced, it can also be anticipated, having the power to influence people's behavior in the present so they will not feel guilty later ([ 8]; [38]; [45]).
At the other end of the spectrum are feelings of gratification, or a "warm glow," which can arise from being responsible for a favorable event. Such feelings can occur by supporting companies that engage in CSR or company actions that advance social good (e.g., favorable labor practices, green production) beyond what is required by law ([50]; [61]). Consumers may feel more gratification if they are part of a causal force toward positive production that is not required by law ([28]). From a rational perspective, given similar product and ethical attributes, participating in production should lead to no differences in consumers' preferences. However, I predict that consumers will have a stronger reaction to the ethicality of production when they have direct responsibility for whether a product is made. This responsibility will lead them to anticipate feeling guilt or gratification depending on the ethicality of production, which in turn will mediate purchase intentions. Thus:
- H1: Participating in production (a) reduces purchase interests under negative ethical production and (b) increases purchase interests under positive ethical production.
- H2: Anticipated feelings of guilt mediate the effect specified in H1a, while anticipated feelings of gratification mediate the effects specified in H1b.
Given that most products are made-to-stock and changing operational strategies is not always viable, increasing feelings of responsibility in other ways could help companies be more successful in selling ethical products. Beyond increasing direct responsibility, I theorize that made-to-order production also limits diffusion of responsibility, because typically one product is being made for one consumer. However, when products are made-to-stock, a consumer's choice will be diffused across a large assortment of products intended for a large group of anonymous consumers. In group contexts, the presence of others triggers a form of "social loafing," diffusing responsibility for the outcomes of group decisions, particularly in cases of negative consequences ([29]; [51]; [65]). Given that large groups of consumers (which are typical in mass consumption) can trigger social loafing and diffusion-of-responsibility effects, I predict that being part of a smaller group of consumers will create a decision context that is more similar to made-to-order: production, even when there is no direct responsibility. Accordingly, I predict that group size will moderate the effect
- H3: When consumers feel as though they are part of a small (vs. large) group of consumers in the made-to-stock condition, they weigh ethical production more heavily.
Feelings of responsibility and guilt may arise from directly causing something to occur (culpability) or be more broad and indirect in nature ([ 7]; [28]). For example, people may feel guilty for not helping a homeless person, even though they were not directly responsible for this person's situation. This broad type of responsibility is based more on a group's collective role in a causal chain ([60]) and may be less spontaneous than more direct forms of responsibility where consequences are immediately apparent. Although large groups can also elicit feelings of diffusion of responsibility (as discussed previously), considering the group from a broad collective standpoint can make the group feel more like an individual unit (vs. an assortment of individuals), which can strengthen the sense of collective responsibility and efficacy ([ 5]; [88]). Accordingly, for those considering made-to-stock products, I propose that cueing broad responsibility will nudge consumers to consider their high-level societal role in production ([85]) despite their lack of direct responsibility. By contrast, for those considering made-to-order production, thinking about their broad responsibility can also weaken the influence of direct responsibility. Because direct responsibility is high for made-to-order production, thinking about broad responsibility can act to diffuse responsibility across the overall system. Accordingly:
- H4: The effects specified in H1 are attenuated when participants are cued to think about their broad level of responsibility.
Next, I present a series of studies that test the hypothesized effects in a variety of different contexts, across varied categories and participant populations.
Study 1 is a field study using Facebook's advertising platform. I test whether varying participation in production will change consumers' responses to more negative ethical production (H1a). As an initial exploration, I focused on negative ethicality because it may elicit stronger reactions than positive ethicality ([ 3]; [12]; [16]; [78]). I tested two distinct contexts (fair-trade support and brand protest) to confirm the generalizability of the effect and used Facebook's advertising platform to collect real data.
The dependent variable was advertising click-through rates (CTRs), a function of impressions and clicks, for ads created on Facebook's advertising platform. Advertisers frequently pay by cost per click (CPC) and use A/B testing, evaluating CTRs, to determine advertising effectiveness ([44]; [66]). I set up the ads to be optimized for clicks versus impressions using CPC. For example, if 10,000 people viewed an ad and 30 people clicked on it, I would pay the same amount as if only 5,000 people viewed the ad and 30 people clicked on it (assuming the same CPC). This is a commonly used way to set up digital advertising ([66]). Because I budgeted for a certain number of clicks, the number of impressions was likely to vary depending on the impressions required to generate a certain number of clicks. If one condition required significantly fewer impressions, I would determine that this condition operated better. I created ads in the contexts of fair-trade support and brand protest[ 5] and varied participation in production (made-to-order vs. made-to-stock).
I pretested the ads for realism. Participants (from Amazon's Mechanical Turk [MTurk]; N = 202) viewed one of the four ads between subjects and indicated their agreement with the item "This ad is realistic" (1 = "strongly disagree," and 7 = "strongly agree"). All ads were rated significantly above the midpoint of the scale (collapsed: M = 4.92, SD = 1.53; t(201) = 8.46, p <.001).
Because advertising negative ethical production related to a specific brand would be unrealistic (e.g., Nike would not advertise negative labor practices), I created ads that referred to unethical practices at the industry level and then measured participants' interest in fair-trade products. I created two Facebook advertising campaigns sponsored by Fairtrade America, a Facebook page created to sponsor the ad (see Figure W1 in Web Appendix A). In the made-to-stock condition, participants read, "Many clothes bought online have already been made in sweatshops before you order them. Learn about affordable fair-trade clothing options today!" In the made-to-order condition, participants read, "Many clothes bought online will only then be made in sweatshops after you order them. Learn about affordable fair-trade clothing options today!" After clicking on the ads, participants were taken to thegoodtrade.com, described as an "online publication featuring brands, products, and ideas creating positive social change." I predicted that greater responsibility in the made-to-order (vs. made-to-stock) condition would drive higher CTRs, thus indicating greater interest in fair-trade products.
In the brand protest context, rather than considering interest in a product, I measured motivation to sign a petition to improve labor conditions for Apple iPhones ([26]), to demonstrate that the effects generalize to contexts (e.g., civic action) other than only purchase intentions. I also used "preorders" as the context; preorders are an emerging strategy used by firms such as Apple and Pebble to forecast demand ([56]). Preorders can coincide with made-to-order production, with the firm only producing once an order is received, or made-to-stock production, with consumers reserving already-produced products before their official release. I again created two Facebook ad campaigns sponsored by Fairtrade America (see Figure W2 in Web Appendix A). In the made-to-order condition, participants read, "After you preorder your new iPhone, it will only then be made in a sweatshop. We can't live without our iPhones so sign a petition forcing Apple to improve labor conditions!" In the made-to-stock condition, participants read, "Before you preorder your new iPhone it would have already been made in a sweatshop. We can't live without our iPhones so sign a petition forcing Apple to improve labor conditions!" After clicking on the ad, participants were taken to a petition from Change.org regarding Apple's labor practices. I predicted that higher responsibility in the made-to-order (vs. made-to-stock) condition would drive higher CTRs.
Each context (fair trade and brand protest) and each ad (made-to-order vs. made-to-stock) had a budget of $30–$50 (total spend can vary slightly from the specified budget). I restricted the ads to appear only on the Facebook mobile application for U.S. users to control for any variance being attributed to nationality or the use of a specific device. To better direct the ads to consumers who would naturally be more interested in them, I also targeted the ads to Facebook users who listed "Fair Trade" or "Fair Trade Certification" as an interest. I reasoned that the experiment would be more effective if the audience understood the meaning of fair trade and had some interest in the topic rather than the broader Facebook audience, given the low involvement and interaction with Facebook ads ([57]). For the brand protest context, I restricted the ads to mobile devices running iOS (presumably Apple products).
Facebook records the number of impressions and the number of clicks for each campaign. Across the two contexts, the ads generated 17,215 impressions, 149 of which were clicked on (.87%). This CTR is within normal range for Facebook advertisements ([46]), thus confirming that the ads were similar in interest and targeting to other ads on Facebook. I used these data to conduct a log-linear regression based on the number of users who viewed the ad (impressions) and specifying whether they clicked on the ad as the binary choice (1 = click, 0 = no click). A log-linear model for made-to-order versus made-to-stock by context revealed the predicted main effect; made-to-order production significantly increased CTRs (1.16%) over made-to-stock production (.68%; χ2( 1) = 20.39, p <.001). The model also revealed a significant context effect (χ2( 1) = 7.58, p <.01), in which clicks varied between the brand protest (.74%) and fair-trade support (1.13%) contexts. The effect received further support from the nonsignificant interaction (p >.4); the effect was comparable in size, indicating generalizability across diverse settings.
Despite the nonsignificant interaction, I examined each context separately to illustrate potential managerial implications. In the fair-trade support context, participants were more likely to click on an ad for fair-trade products when made-to-order production was the focus (1.68%) than when made-to-stock production was the focus (.84%; χ2( 1) = 7.88, p =.005). In the brand protest context, participants were more likely to click on an ad to sign a petition when the ad focused on made-to-order production (.94%) rather than made-to-stock production (.60%; χ2( 1) = 4.42, p <.04; see Table W1 in Web Appendix B).
My approach followed standard practice in the digital marketing context, in which impressions vary but clicks do not. As an additional robustness check, I tracked the number of clicks for the first 6,809 impressions in the made-to-order and made-to-stock conditions (the lowest number of impressions in either condition). For the first 6,809 impressions per condition, Facebook users clicked significantly more in the made-to-order condition (78 clicks) than in the made-to-stock condition (38 clicks; χ2 = 13.91, p <.001).
The purpose of Study 1 was to test the effects of participation in production in real-world contexts with two distinct dependent variables. I found that varying control over production affected CTRs in a real advertising context, thus showing how managers can frame appeals more effectively. In the fair-trade context, I found that a brand can frame the competition to increase interest in a brand's more ethical products. A limitation of this study is that I required participants to have an interest in fair trade. Study 2 addresses this concern and builds on Study 1 by demonstrating the predicted effects in a more controlled setting, explicitly testing negative and neutral ethical production, and testing the mediating process of guilt using an incentive-compatible design.
In Study 2, I aimed to provide more controlled evidence for my theorizing. Consistent with H1a, I predicted that with unethical production, consumers would prefer made-to-stock production to made-to-order production and that anticipated guilt would mediate this effect (H2). However, I posited that these effects would not emerge with neutral ethicality.
I chose Nike as the stimuli in this study because it sells both off-the-shelf and made-to-order shoes (e.g., Nike by You), and the dependent variable was a real choice. To provide a realistic and subtle manipulation of ethicality, I used country of production (rather than explicitly mentioning sweatshop labor as in Study 1). At least in the United States, the law requires that companies disclose where products were made with a label (e.g., "Made in Vietnam"). Accordingly, country of origin is a variable that consumers are exposed to often. I surmised that participants would infer that products made in developing countries might have unfavorable production ethicality, without the need to mention anything explicitly.
In a pretest, participants viewed the study stimuli (MTurk, N = 205; see Web Appendix C) and then indicated their agreement with the following item: "This product was made with ethical production practices" (1 = "strongly disagree," and 7 = "strongly agree"). Participants viewed shoes produced in Bangladesh to be significantly less ethical than when no country of production was mentioned (MBangladesh = 3.11, SD = 1.55 vs. Mno mention = 3.9, SD = 1.67; F( 1, 199) = 12.16; p <.001; other ps >.2). Furthermore, participants viewed shoes produced in Bangladesh as unethical (t(102) = 5.72, p <.001; significantly below the midpoint of the scale of 4); however, when country was not mentioned, they viewed the shoes as neutral in ethicality (t(101) =.59, n.s.; did not differ from the midpoint of the scale). This pretest confirms that production ethicality is perceived as negative when Nike shoes are produced in Bangladesh but neutral when no country of production is mentioned.
Two hundred eighty-four U.S. participants were recruited from MTurk (Mage = 38 years; 46% female). They were randomly assigned to a participation in production condition (made-to-order vs. made-to-stock) and an ethicality condition (Bangladesh vs. no mention) in a 2 × 2 between-subjects design. All participants received details about the shoes, which were identical across conditions (e.g., picture, price, description) (for stimuli, see Web Appendix C).
In the made-to-stock condition, participants were told that the shoes would be shipped from inventory, and in the made-to-order condition, they were told that the shoes would be custom-made, sewn, and shipped. In the Bangladesh conditions, participants were told that the shoes were (would be) produced in Bangladesh, and in the no-mention conditions, they were not given any information about where the shoes were (would be) made. To make the study incentive compatible, participants were informed that there would be a lottery, in which, if won, they would receive either cash or a Nike gift card. Because cash is often considered more valuable than noncash rewards ([49]), I offered participants the choice between $40 in cash or a $100 Nike gift card to buy the shoes they read about. Participants then indicated their choice in the event they won the lottery. In the case of anticipated guilt, because guilt can arise for many reasons (e.g., purchase indulgence), I included a prompt referring to the context of wages. More specifically, participants were asked: "Consider how buying these shoes would make you feel about the wages paid to produce these shoes. How would you feel?" with items guilty and bad (r =.94; 1 = "not at all," and 9 = "very much"). Participants also answered two manipulation check items on direct responsibility and agency: "Think about the production of the shoes. How much agency did you have in the shoes being produced?" (1 = "no agency," and 7 = "a lot of agency") and "Think about the production of the shoes. How responsible would you have been for having the shoes made?" (1 = "not responsible," and 7 = "very responsible"; r =.88). Participants were debriefed after the study.
I conducted a 2 × 2 analysis of variance (ANOVA) with participation in production and ethicality as the independent variables and the agency/responsibility manipulation check as the dependent variable. I found a main effect of participation in production (F( 1, 280) = 111.15, p <.001, =.28; other ps >.2), in which agency/responsibility was higher in the made-to-order condition than in the made-to-stock condition (Mmade-to-order = 5.31, SD = 1.52 vs. Mmade-to-stock = 3.21, SD = 1.82).
I conducted a 2 × 2 binary logistic regression with participation in production (made-to-order = −1, made-to-stock = 1), ethicality (Bangladesh = −1, no mention = 1), and their interaction as the independent variables and choice of the gift card or cash (cash = 0, gift card = 1) as the dependent variable. I found a significant main effect of participation in production (b =.34, SE =.13, p <.01), a significant main effect of ethicality (b =.41, SE =.13, p =.001), and a significant interaction between participation in production and ethicality (b = −.33, SE =.13, p =.01). When production was in Bangladesh, only 18% of participants chose the Nike gift card in the made-to-order condition, while 46% of participants chose the gift card in the made-to-stock condition (b = −66, SE =.19, p =.001). In the no-mention conditions, 49% of participants chose the Nike gift card in the made-to-order condition, while 50% of participants chose it in the made-to-stock condition (b = −.01, SE =.17, n.s.; see Figure 2).
Graph: Figure 2. Study 2: choice of gift card or cash for shoes.
I conducted 2 × 2 ANOVA with guilt as the dependent variable and found a significant main effect of participation in production (F( 1, 280) = 7.23, p =.008, =.03), a significant main effect of ethicality (F( 1, 280) = 11.57, p =.001, =.04), and a significant interaction between participation in production and ethicality (F( 1, 280) = 8.74, p =.003, =.03). Regarding the interaction, when production was in Bangladesh, participants anticipated feeling more guilt in the made-to-order condition than in the made-to-stock condition (Mmade-to-order = 5.23, SD = 2.69 vs. Mmade-to-stock = 3.49, SD = 2.55; F( 1, 280) = 16.27, p <.001, =.06); In the no-mention conditions, there were no effects of participation in production on guilt (no mention: Mmade-to-order = 3.27, SD = 2.79 vs. Mmade-to-stock = 3.36, SD = 2.22; F( 1, 280) =.035, n.s).
I conducted a bootstrap moderated mediation (Model 7 in PROCESS; [41]) using 5,000 resamples, with participation in production as the independent variable (made-to-order = −1, made-to-stock = 1), ethicality as the moderator (Bangladesh = −1, no mention = 1), anticipated guilt as the mediator, and choice of the gift card as the dependent variable (for the path diagram, see Figure W3 in Web Appendix D). Guilt mediated the effect of participation in production on choice in the Bangladesh condition (b = −.08, 95% confidence interval [CI] = [−.2, −.007]) but not in the no-mention condition (b =.004, CI = [−.03,.06]).
In Study 2, I found an interaction between ethicality and participation in production. Under negative ethicality (made in Bangladesh), participants were less likely to choose a Nike gift card over $40 in cash if the shoes were made-to-order than if they were made-to-stock, in support of H1a. However, when there was no mention of where the shoes were produced, there was no difference based on participation in production. This result emerged even when I used "made in Bangladesh," a more subtle but common informational cue of negative ethicality. Furthermore, anticipated guilt mediated these effects, in support of H2.
While using a real brand (i.e., Nike) and varying ethicality by only mentioning country of origin is ecologically valid, other factors could be affecting the results, such as specific inferences about the brand or country of production. To address these concerns and prove the generalizability of the effects, I conducted an additional study using a different participant population (Prolific, N = 349; for the full study, see Web Appendix E). Participants read about a (fictitious) furniture company that varied on ethicality (did not pay a living wage vs. no mention of wages), with purchase intention as the dependent variable. Again, I varied whether production was made-to-order or made-to-stock. Consistent with Study 2, the interaction between participation in production and ethicality was significant on purchase intention (F( 1, 345) = 6.9, p <.01, =.02). I again found that guilt mediated the effect of participation in production on purchase intention in the no living wage conditions (b = −.35, CI = [.12,.58]) but not in the no-mention conditions (b = −.07, CI = [−.21,.06]).
As mentioned previously, 42 marketing managers (recruited through Qualtrics) read about both the made-to-order and the made-to-stock Nike shoes in which production was in Bangladesh (for stimuli, see Web Appendix C). I altered the stimuli to be from the perspective of a marketing manager. In addition, to ensure that managers would perceive the labor conditions as negative, they read that Bangladesh is a country known for underpaying labor. The scenario also explicitly mentioned that the shoes were similar in price, quality, variety, and delivery time. Controlling for these factors creates a stronger test, as managers would rely less on these factors to predict the study results. Participants were asked, "As a marketing manager, which option do you think will be more popular with customers?" Only 24% of marketing managers checked the made-to-stock option. The remaining either indicated that the made-to-order option would be more popular (50%) or that both would be equally popular (26%). When explaining their answers further, of the 24% who predicted correctly, only one manager noted the link between consumers and production in Bangladesh. Thus, most marketing managers may be overlooking the consequences of connecting consumers with the actual physical production of their products.
Importantly, the results of this study have implications for brands that use offshore production (which, at least in the apparel domain, is the majority). In the made-to-stock conditions, I found that roughly half the participants chose the Nike gift card regardless of whether Bangladesh was mentioned. However, when made-to-order production was used, participants avoided purchasing shoes that would be made in Bangladesh. As companies increasingly try to reap the benefits of customization and made-to-order production, they should also realize that they may face a significant backlash depending on the ethicality of production.
In Study 2, I found that preference for a made-to-order versus made-to-stock product varied under negative ethicality versus neutral ethicality. Study 3 aims to extend these findings by focusing on positive ethicality. Given that greater responsibility leads to an avoidance of unethical made-to-order products, I predicted that it would also drive an attraction to made-to-order products under positive ethicality (H1b). To more effectively compare positive and neutral ethicality, in this study I employed a mixed design used in previous research on positive ethicality ([59]; [69]). That research indicates that ethical (e.g., fair trade, recycled) products often come with a price premium. Therefore, to make the price–ethicality trade-off salient, a mixed design better enables participants to compare a cheaper standard product with a more expensive ethical product ([69]). Use of a mixed design should also help make clear that one option is more ethical than the other, thus increasing evaluability ([43]; [48]). Importantly, this study tests whether willingness to pay for ethical production increases when production is made-to-order. In this study, no specific brand name is mentioned to avoid brand effects.
In a pretest using repeated measures (MTurk, N = 50; for measures, see Web Appendix J), results showed that recycled paper was perceived as more ethical than regular paper (Mrecycled = 5.72, SD = 1.13 vs. Mregular = 3.92, SD = 1.34; F( 1, 49 = 52.92, _I_p_i_ <.001; 1 = "strongly disagree," and 7 = "strongly agree"). Furthermore, recycled paper was positive in ethicality (t(49) = 10.8, p <.001; significantly above the midpoint of the scale of 4); however, regular paper was viewed as neutral in ethicality (t(49) =.42, n.s.; did not differ from the midpoint of the scale). Moreover, recycled paper was viewed to be more expensive than regular paper (Mrecycled = 4.46, SD = 1.58 vs. Mregular = 3.67, SD = 1.29; F( 1, 47 = 6.87, _I_p_i_ <.02) but equal in quality (Mrecycled = 5.04, SD = 1.14 vs. Mregular = 5.28, SD = 1.14; F( 1, 49) = 1.09, n.s.). This pretest confirms that regular paper is neutral in ethicality and recycled paper is positive in ethicality.
In Study 3, I modeled the stimuli after the company PsPrint (part of Deluxe), which gives customers the option to print on regular paper or recycled paper for a 25% premium. Participants considered using regular paper or recycled paper with a price premium, for a book from inventory or one that would be produced on-demand. Three hundred sixty-one student participants were recruited from a U.S. university (Mage = 20 years; 44% female). In a mixed design, participants were randomly assigned to condition with participation in production (made-to-order vs. made-to-stock) and type of store (offline vs. online) as the between-subjects factors and paper type (regular vs. recycled) as the within-subject factor. The online versus offline store factor was included as a replicate designed to test the generalizability of the effect.
For the offline store, in the made-to-stock condition, participants read, "Imagine you found a book that you want to buy. A bookstore sells two versions of the book. You can buy the book that had been printed on regular paper for $11.00 or you can buy the book that had been printed on recycled paper for $14." In the made-to-order condition, participants read, "Imagine you found a book that you want to buy. A bookstore prints books on demand. That is, they will only print the book after you order it. You can choose to print the book on regular paper for $11 or on recycled paper for $14." In the online store conditions, the only difference was that participants were told that it was an online store and the book would be mailed in two days. Participants were then asked one bipolar item and two individual items to measure purchase intentions. More specifically, participants were asked, "Which version of the book are you more likely to buy?" (1 = "more likely regular paper for $11," and 9 = "more likely recycled paper for $14"). Participants were also asked two individual measures: "How likely would you buy the regular paper version of the book for $11?" and "How likely would you buy the recycled paper version of the book for $14?" (1 = "very unlikely," and 9 = "very likely").
Given that I am comparing regular items to ethical items (no unethical items) I focused on anticipated feelings of gratification (rather than guilt). I used bipolar items modeled from past research on ethicality ([59]) and cocreation ([24]) that explicitly compares feelings toward regular and recycled products. More specifically, participants were asked, "Think about your level of agency and responsibility in the process of causing the book to be made. How would you feel about choosing either option?" (1 = "more true for the regular version," and 9 = "more true for the recycled version"). This was followed by two items on gratification ("gratification" and "happy"; r =.91). It could also be that people are more willing to pay for recycled paper when the book is made-to-order because they would identify more with the book. Accordingly, participants were asked, "How much do you identify with the book?" (1 = "not at all," and 5 = "a lot"). Participants were asked the same manipulation checks items on agency and responsibility as in Study 2 (r =.79). Across the dependent variables, there were no significant main effects or interaction differences between the online and offline store condition (ps >.1), so I collapsed the data.
Participants felt they had more agency/responsibility in the made-to-order condition than in the made-to-stock condition (Mmade-to-order = 4.54, SD = 1.87 vs. Mmade-to-stock = 2.44, SD = 1.37; t(359) = 12.16, p <.001).
On the bipolar purchase intention measure, participants indicated that they were significantly more likely to buy the recycled book for $14 (vs. the regular book for $11) in the made-to-order condition than in the made-to-stock condition (Mmade-to-order = 3.22, SD = 2.49 vs. Mmade-to-stock = 2.49, SD = 2.05; t(359) = 3.04, p <.003, =.03). I conducted an ANOVA on the two independent purchase intention measures, with participation in production (made-to-order vs. made-to-stock) as the between-subjects factor and ethicality as the within-subject factor (regular vs. recycled book). I found a significant interaction (F( 1, 356) = 9.32, p =.002, =.03). Participants were less likely to buy the regular version of the book in the made-to-order condition than in the made-to-stock condition (Mmade-to-order = 6.69, SD = 2.11 vs. Mmade-to-stock = 7.24, SD = 2.02; t(357) = 2.53, p =.01, =.02) but significantly more likely to buy the recycled version of the book (Mmade-to-order = 3.94, SD = 2.28 vs. Mmade-to-stock = 3.31, SD = 2.21; t(358) = 2.67, p =.008, =.02; see Figure 3). There was no significant effect of participation in production on feelings of identification with the book (Mmade-to-order = 1.69, SD =.89 vs. Mmade-to-stock = 1.56, SD =.76; t(358) = 1.54, n.s.).
Graph: Figure 3. Study 3: purchase intention for books.
Participants indicated they would feel significantly more gratification with the recycled option in the made-to-order condition than in the made-to-stock condition (Mmade-to-order = 6.03, SD = 1.9 vs. Mmade-to-stock = 5.54, SD = 1.66; t(359) = 2.61, p =.009, =.02).
I collapsed the three purchase intention measures (bipolar measure, regular book [reverse coded], and recycled book) to create one measure, with higher values indicating a stronger interest in the recycled book than the regular book (α =.82). I conducted a bootstrap mediation analyses using 5,000 resamples, with participation in production as the independent variable (made-to-order = −1, made-to-stock = 1), gratification as the mediator, and the three-item purchase intention measure as the dependent variable. I found that increased feelings of gratification led to higher purchase intentions for the recycled book (vs. the regular book) in the made-to-order condition than in the made-to-stock condition (gratification: b = −.06, CI: −.13, −.02; for the path diagram, see Figure W4 in Web Appendix F).
In Study 3, I found that participants indicated a higher purchase intention for a book printed on recycled paper, a product that is positive in ethicality, when it is made-to-order compared with when it is made-to-stock, in support of H1b. Confirming H2, this effect was driven by anticipated gratification and occurred even though the product was more expensive. In the pretest, I also found that participants did not view the recycled paper as higher in quality. Thus, it seems unlikely that quality in the made-to-order condition is driving this effect. One weakness of this study is that participants were cued through a prefatory question to think about responsibility/agency driving feelings of gratification, which could contribute to demand effects for the mediating process. In the next study, I employ a similar design without any prefatory questions before the mediator.
In Studies 1–3, I explored how participation in production can increase direct responsibility, thereby influencing CTRs, real choice, and purchase intentions. In Studies 4 and 5, the goal is to identify diffusion of responsibility and broad responsibility as dimensions of responsibility that can be elicited even when products are made-to-stock. More specifically, I aim to identify methods to manipulate feelings of responsibility independent of direct causal agency to strengthen the link between consumers and production in the made-to-stock condition. Study 4 manipulates diffusion of responsibility using group size. I reason that in typical mass-consumption contexts, diffusion of responsibility is high and feelings of efficacy are low because consumer groups are so large. However, by reducing group size, diffusion of responsibility could be reduced and feelings of efficacy could be increased. Accordingly, I predicted that reducing group size in the made-to-stock condition could increase interest in ethicality, consistent with H3.
Six hundred thirty-one participants were recruited from Prolific (Mage = 36 years; 51% female). I restricted the sample to the United Kingdom, United States, and Canada and required that English was spoken as a first language. This resulted in a distribution of 65% U.K., 30% U.S., and 5% Canadian participants. There were no effects of nationality on the results, so I do not discuss them further. Participants were assigned to one of three between-subjects conditions in a one-factor, three-cell design (made-to-order, small group made-to-stock, and large group made-to-stock). Participants considered a similar choice between a regular book and a recycled book as in Study 3. More specifically, in the made-to-order condition, participants were told, "Imagine that a new bookstore company prints books on demand. That is, they will only print the book after you order it, which then gets printed immediately. You can choose to print a book on regular paper for $11 or on recycled paper for $14." In the small group made-to-stock condition, participants were told, "Imagine that a new bookstore company is initially launching its products to a small group of 5 customers that you are a part of. You can choose to purchase a book that was printed on regular paper for $11 or on recycled paper for $14." In the large group made-to-stock condition, the text "a small group of 5 customers" was replaced by "a large group of 10,000 customers." Participants then answered the same two individual purchase intention measures for the regular book and the recycled book as in Study 3. In this study, participants were asked a more general prompt on feelings of gratification: "How would you feel about choosing either option?" again followed by items gratification and happy (r =.83). Participants were also asked one manipulation check item on feelings of agency: "Think about the production of the book. How much agency did you have in the book being produced?" (1 = "no agency," and 7 = "a lot of agency").
To determine whether the small group made-to-stock manipulation created more efficacy and less diffusion than the other conditions, participants indicated their agreement with the following items: "My choice makes a difference" and "My role in this system feels diffused." In addition, because I varied group size, I asked one question to control for visibility: "This choice will be visible to others" (1 = "strongly disagree," and 7 = "strongly agree").
I conducted a one-way ANOVA with three conditions and agency as the dependent measure. There was a significant effect of condition (F( 2, 626) = 226.35, p <.001, =.42), in that planned contrasts revealed more agency in the made-to-order condition than in the small group made-to-stock condition (Mmade-to-order = 5.26, SD = 1.7 vs. Msmall = 2.31, SD = 1.58; t(626) = 18.6, p <.001, =.45) or the large group made-to-stock condition (Mlarge = 2.39, SD = 1.58; t(626) = 18.29, p <.001, =.44). There were no significant differences between the small group and the large group conditions in agency (t(626) =.54, n.s.). There were also no significant effects of the visibility measure in the three conditions (Mmade-to-order = 3.92, SD = 1.71 vs. Msmall = 3.95, SD = 1.61 vs. Mlarge = 3.88, SD = 1.71; F( 2, 555) =.07, n.s.).
On feeling as if one's choice could make a difference, there was a significant effect of condition (F( 2, 627) = 11.14, p <.001, =.03). Compared with those in the large group made-to-stock condition, participants felt like they could make more of a difference in the small group made-to-stock condition (Msmall = 5.19, SD = 1.4 vs. Mlarge = 4.6, SD = 1.52; t(627) = 3.93, p <.001, =.04) and the made-to-order condition (Mmade-to-order = 5.15, SD = 1.41; t(627) = 4.21, p <.001, =.03). There was no significant difference between the made-to-order condition and the small group made-to-stock condition (t(627) =.28, n.s.). On feeling a diffusion of responsibility, there was a significant effect of condition (F( 2, 628) = 16.68, p <.001, =.05). Compared with the large group made-to-stock condition, participants felt less diffusion in the small group made-to-stock condition (Msmall = 3.6, SD = 1.35 vs. Mlarge = 4.32, SD = 1.27; t(628) = 5.77, p <.001, =.07) and the made-to-order condition (Mmade-to-order = 4.00, SD = 1.23; t(628) = 2.64, p <.01, =.02). Participants also felt significantly less diffusion of responsibility in the small group condition than in the made-to-order condition (t(628) = 3.09, p <.005, =.02). These manipulation checks indicate that the small group does not affect feelings of direct agency; however, it reduces feelings of diffusion of responsibility, suggesting that these are different types of responsibility.
I conducted a repeated measures ANOVA with the three conditions as the between-subjects factor and ethicality as the within-subject factor (regular vs. recycled book), and purchase intention as the dependent variable. I found a significant interaction (F( 2, 615) = 6.73, p =.001, =.02). As planned contrasts showed, participants indicated a higher purchase intention for the regular book in the large group made-to-stock condition than in the small group made-to-stock condition (Mlarge = 6.78, SD = 2.26 vs. Msmall = 6.3, SD = 2.32; t(620) = 2.14, p =.03, =.01) and the made-to-order condition (Mmade-to-order = 5.98, SD = 2.4; t(620) = 3.55, p =.001, =.03). For the recycled book, the opposite pattern occurred; purchase intention was lower in the large group made-to-stock condition than in the small group made-to-stock condition (Mlarge = 4.12, SD = 2.54 vs. Msmall = 4.78, SD = 2.59; t(623) = 2.6, p =.009, =.02) and the made-to-order conditions (Mmade-to-order = 4.95, SD = 2.66; t(623) = 3.25, p =.001, =.03). There were no significant differences between the made-to-order and the small group made-to-stock conditions on purchase intentions for the regular book (t(620) = 1.4, n.s.) or the recycled book (t(623) =.65, n.s.; see Figure 4).
Graph: Figure 4. Study 4: diffusion of responsibility.
There was a significant effect of condition on gratification (F( 2, 625) = 6.7, p =.001, =.02). Participants anticipated feeling significantly more gratification in the made-to-order condition than in the large group made-to-stock condition (Mmade-to-order = 5.93, SD = 2.24 vs. Mlarge = 5.17, SD = 2.27; t(625) = 3.48, p =.001, =.03). Participants also anticipated feeling significantly more gratification in the small group made-to-stock condition than in the large group made-to-stock condition (Msmall = 5.76, SD = 2.22; t(625) = 2.59, p =.007, =.02). There was no significant difference between the made-to-order and the small group made-to-stock conditions on gratification (t(625) =.78, n.s.).
I collapsed the two purchase intention measures (regular book [reverse coded] and recycled book) to create one measure, with higher values indicating a stronger interest in the recycled book than the regular book (r =.73). Similar to Study 3, anticipated gratification significantly mediated the relationship between the made-to-order condition (coded as −1) and the large group made-to-stock condition (coded as 1; b = −.25, CI = [−.4, −.11]). Gratification also significantly mediated the relationship between the small group made-to-stock condition (coded as −1) and the large group made-to-stock condition (coded as 1; b = −.19, CI = [−.33, −.05]; for path models, see Figure W5 in Web Appendix G).
In Study 4, I demonstrate that reducing group size (vs. a typical mass-consumption context of a large group size) can reduce feelings of diffusion of responsibility and increase feelings of efficacy, leading consumers to weigh ethical production more heavily, consistent with H3. These effects are driven by gratification, in support of H2. Even when products are made-to-stock, I find that reducing diffusion of responsibility can increase purchase intentions for more ethical products.
In Study 5, I aimed to further extend Study 4's findings by identifying a second method to manipulate feelings of responsibility independent of direct causal agency. In this study, I cued participants to consider a broad type of responsibility (rather than direct responsibility) that is more systemic in nature. Because feelings of broad responsibility may be less spontaneous than feelings of direct responsibility, I predicted that cuing participants to think about broad responsibility would moderate the effect (consistent with H4). I reasoned that broad responsibility (vs. direct responsibility) would act to strengthen guilt (weakening purchase intention) in the made-to-stock condition and weaken guilt (strengthening purchase intention) in the made-to-order condition. In this study, I revert back to negative ethicality (unfair wages) and use jeans as the product category.
I first conducted a pretest of the manipulation of broad responsibility to confirm that it would elicit a different response than a more direct and spontaneous form of responsibility. Participants (MTurk, N = 168) viewed the study stimuli (see Web Appendix H), which were either made-to-order or made-to-stock jeans, and were told, "Consider that there are two types of responsibility. There is direct responsibility, such as when you personally cause something to happen. There is also a type of responsibility that is more indirect and broad. This type of responsibility is participating in a system along with other actors that causes something to happen." Participants were asked about their direct and broad levels of responsibility: "Imagine you purchased the jeans. How directly responsible would you have been for having the jeans made?" and "Imagine you purchased the jeans. In a more broad sense, how responsible would you have been for having the jeans made?" (1 = "not at all responsible," and 7 = "very responsible").
I conducted a repeated measures analysis with participation in production (made-to-order vs. made-to-stock) as the between-subjects factor and direct versus broad responsibility as the within-subject repeated measure. I found a significant main effect of participation in production (F( 1, 166) = 90.66, p <.001), a significant main effect of responsibility type (F( 1, 166) = 15.89, p <.001), and a significant interaction between participation in production and responsibility type (F( 1, 166) = 43.88, p <.001). As expected, participants felt more direct responsibility in the made-to-order condition than in the made-to-stock condition (Mmade-to-order = 5.81, SD = 1.45 vs. Mmade-to-stock = 2.9, SD = 1.88; F( 1, 166) = 123.15, p <.001, =.42). However, this effect was significantly weakened when participants were cued to consider broad responsibility (Mmade-to-order = 5.49, SD = 1.23 vs. Mmade-to-stock = 4.23, SD = 1.8; F( 1, 171) = 27.79, p <.001, =.14). The results of this pretest suggest that broad responsibility and direct responsibility are different constructs, which can be differentially elicited depending on whether production is made-to-order or made-to-stock.
In the main study, 461 participants were recruited from a student pool at a U.S. East Coast university (Mage = 20 years; 45% female). In a 2 × 2 between-subjects design, participants were randomly assigned to a participation in production condition (made-to-order vs. made-to-stock) and a responsibility cue condition (broad responsibility vs. no mention). To increase realism, the stimuli were based on a popular brand of jeans, with pictures and attributes of the jeans (wash, cut, and material) taken from their websites (see Web Appendix H). All participants were told that the jeans were made in Vietnam in a factory that does not pay a living wage and that the jeans were either made-to-order or made-to-stock. After reading about the jeans, participants in the broad responsibility condition were cued to think about their broad responsibility, through the pretested manipulation. Participants then answered the question, "Would you buy these jeans?" ("yes/no"). Participants responded to the same two items on guilt as in Study 2 (r =.96). Because broad responsibility was explicitly cued, I asked participants to complete the Perceived Awareness of the Research Hypothesis scale ([74]) used in previous marketing research ([37]). An example item on the scale is "I knew what the researchers were investigating in this research." Adding this variable as a covariate can help control for participants' hypothesis awareness.
I conducted a 2 × 2 binary logistic regression with participation in production (made-to-order vs. made-to-stock) and responsibility cue (broad responsibility vs. no mention) as the independent variables and choice as the dependent variable. I found a significant main effect of participation in production (b =.45, SE =.1, p <.001), no significant main effect of responsibility cue (b =.04, SE =.1, n.s.), and a significant interaction between participation in production and responsibility cue (b = −.27, p =.006). Regarding the interaction, in the no-mention conditions, only 23% of participants chose to buy the jeans in the made-to-order condition, while 56% chose to buy the jeans in the made-to-stock condition (b = −.46, SE =.15, p <.001). In the broad responsibility conditions, 37% of participants chose to buy the jeans in the made-to-order condition, while 45% chose to buy the jeans in the made-to-stock condition (b =.17, SE =.13, n.s.). These interactive effects held when including the Perceived Awareness of the Research Hypothesis scale in the model (b = −.27, SE =.93, p =.007; see Figure 5).
Graph: Figure 5. Study 5: broad responsibility.
One would expect that if broad responsibility was cued, feelings of guilt would also be affected. I conducted a 2 × 2 ANOVA with guilt as the dependent variable. I found a significant main effect of participation in production (F( 1, 457) = 12.71, p <.001, =.03), no significant main effect of responsibility cue (F( 1, 457) =.04, n.s.), and a significant interaction between participation in production and responsibility cue (F( 1, 457) = 5.98, p =.015, =.01). Regarding the interaction, in the no-mention conditions, participants anticipated feeling more guilt in the made-to-order condition than in the made-to-stock condition (Mmade-to-order = 6.37, SD = 2.67 vs. Mmade-to-stock = 4.85, SD = 2.82; F( 1, 457) = 17.86, p <.001, =.04). In the broad responsibility condition, there were no effects of participation in production on feelings of guilt (Mmade-to-order = 5.7, SD = 2.77 vs. Mmade-to-stock = 5.42, SD = 2.65; F( 1, 457) =.64, n.s).
I conducted a bootstrap moderated mediation (Model 7 in PROCESS) using 5,000 resamples, with participation in production as the independent variable (made-to-order = −1, made-to-stock = 1), responsibility cue as the moderator (no mention = −1 vs. broad responsibility = 1), anticipated guilt as the mediator, and choice intention as the dependent variable. I found that guilt mediated choice intention in the no-mention conditions (b =.2, CI = [.1,.33]) but not in the responsibility cue conditions (b =.04, CI = [−.06,.14]) (for a path model, see Figure W6 in Web Appendix I).
Cuing participants to think beyond their direct level of responsibility and consider their broad collective level of responsibility attenuated the previously found effects, in support of H4. That is, thinking about a broad collective type of responsibility weakened purchase intentions in the made-to-stock condition and strengthened purchase intentions in the made-to-order condition. While direct responsibility appears to be more spontaneous and results from personally causing or not causing something to happen, broad responsibility can be cued to focus consumers on a more collective type of responsibility, attenuating the effect.
This research demonstrates how participation in production can influence consumer behavior with respect to ethical production. Using a diverse set of scenarios across categories (technology devices, clothing, shoes, and books), ethical production practices (labor and recycled products), and participant populations (Facebook, MTurk, Prolific, and students), I found that participation in production increases the importance of ethical attributes. From a rational perspective, given similar product attributes and levels of ethical production, consumers should be indifferent between whether they have control of production or not. In both cases, they are considering a product made in a factory with underpaid labor. However, I found that having responsibility for production increases feelings of guilt and gratification, thereby mediating purchase intentions, depending on the ethicality of the production process. Furthermore, I explore two additional dimensions of responsibility that can act as moderators. I find that strengthening the link between consumers and production by reducing the group size in the made-to-stock condition or inducing broad responsibility through a cue attenuates the effect.
This research makes several important contributions. My theoretical framework and findings shed light on how participation in production can influence purchase intentions depending on ethical attributes. More broadly, this research contributes to an emerging stream of work that details the various ways consumers increasingly enact their political and moral views in the marketplace (e.g., boycotts and "buycotts"; [ 9]; [53]; [62]; [63]; [67]; [76]; [79]). The findings suggest that willingness to act on ethical considerations is to some extent contingent on the link between consumers and production, in addition to the various ways responsibility can be direct, diffused, or viewed more broadly. While research on CSR has focused on how explicit marketing communications can strengthen the link between consumers and production ([78]), the current article demonstrates that participation in production alone can also strengthen (or weaken) this link.
Importantly, I contribute to the cocreation literature ([32]; [34]; [36]; [64]) by showing how these efforts can backfire. Although this literature has largely examined how consumers react positively to having the ability to cocreate designs, it has not thoroughly examined how consumers react to the ability to cocreate production (i.e., controlling whether a product will be made). I demonstrate that participation in production can have positive or negative effects depending on the ethicality of the production process.
This work also contributes to the literature on responsibility and agency ([13]). I propose a novel threefold conceptualization of responsibility and demonstrate how these dimensions interact with ethical production. To my knowledge, no work has explored the interaction of these three dimensions of responsibility and ethicality in consumer contexts. Furthermore, while direct responsibility (or lack thereof) is more immediately apparent, diffusion of responsibility can be weakened by reducing group size, and broad responsibility can be cued by encouraging consumers to think at a higher level. Given that responsibility can be manipulated, this research identifies important managerial strategies.
Because most consumption occurs within the context of choosing products from inventory, this work could also explain why consumers' stated preferences and market realities are inconsistent ([48]; [59]; [86]; [88]). It could be that when in the store, the link between the ethicality of production is weak and diffused and thus weighted less heavily. I demonstrate that giving consumers control over production can increase their interest in products with more rather than less favorable ethical attributes, a consequence marketing managers appear to be unaware of.
Finally, I contribute to operations management literature by identifying how operational strategy (made-to-stock or made-to-order) affects consumers' emotions and their subsequent behavior. A fundamental operational decision is whether a firm should use made-to-stock or made-to-order production ([19]). Another recent stream of literature in operations assesses whether and how firms should offer products with ethical or sustainable attributes (for a review, see [ 1]]). That research simply assumes that consumers are willing to pay a premium for sustainable products (e.g., [ 2]; [40]) and does not investigate how operational strategy in itself can influence consumers' emotions and behaviors.
One limitation of this research is that I treated responsibility as a manipulation check because I reasoned it to be a more objective manipulation of responsibility. However, feelings of responsibility could also be more subjective and nuanced. For example, though some may feel direct responsibility when production is made-to-order, others may not feel the same way, because the company still chooses the parameters of production. Given this possibility, responsibility could alternatively be operationalized as a mediator in the model that then sequentially influences feelings of guilt and gratification. Made-to-order (vs. made-to-stock) production could also be manipulating a variety of factors beyond responsibility that could influence the results.
Although this study was limited to ethical production as defined by marketing literature (e.g., labor and environmental), further research should consider other ethical contexts in which control over production could affect consumer choice. I found in a follow-up study that participants were less likely to purchase meat that was "processed on demand" because the causal link between their choice and the outcome was stronger. Given that most meat consumption in the United States is purchased from inventory, such findings illuminate a structural factor that influences demand of a category. If meat production were made-to-order, overall demand might be lower. More broadly, any negative company action beyond production (e.g., chief executive officer misconduct, tax evasion) might interact with operational strategy. Here, research could more thoroughly examine the role of identification (rather than responsibility) as a driving factor. Although I controlled for identification, I surmise that these constructs are related in some way (causal attributions and identification can be intertwined). My work focuses primarily on agency and responsibility as the theoretical drivers.
In this research, I investigated participation in production at the individual level. Research has also explored how consumers cocreate production at the market level, through crowdfunding and consumer voting initiatives ([34]). In this case, consumers are exercising control over which products a company will bring to market. In another follow-up study, I found that participants indicated higher purchase intentions for a more expensive fair-trade shirt if they were purchasing it within a crowdfunding context than if they were choosing it from inventory. More broadly, other operational factors beyond made-to-order that link consumers to production could also find similar effects. For example, ethical attributes could be weighted more heavily for products made locally than those made nonlocally.
Recent industry and consumer behavior research suggests that made-to-order production is becoming an increasingly important product strategy for firms ([32]; [35]; [39]; [77]; [80]). For example, prominent companies across various categories, including Levi's, Mars, LensCrafters, Dell, Hallmark, Keds, Ford, Nike, and Kraft, have jumped on the customization bandwagon ([39]; [80]; [83]), and the use of preorders is common even for large companies, such as Apple ([20]). At the same time, companies that focus on responsible supply chains (e.g., Everlane, Patagonia) and certification standards (e.g., fair trade, B-Corp, Energy Star, Marine Stewardship Council) are gaining traction. Major investors are also putting pressure on companies to increase social accountability ([81]).
In the context of these two emerging trends, this work has clear and actionable forward-looking recommendations for managers, suggesting that marrying these two strategies could be particularly effective. When deciding whether to give consumers control over production, managers should consider how this might influence how consumers weigh ethical attributes. If a company invests in more ethical production, it should try to strengthen the link (use made-to-order production), cue customers to think about their broad level of responsibility, and/or reduce diffusion of responsibility. Conversely, if a company has more negative ethical production, it should try to diminish a causal link (use made-to-stock production).
Firms may also want to be more strategic about how they organize their supply chains across a portfolio of products. For example, Timbuk2 produces its off-the-shelf bags abroad (in Indonesia and Vietnam) and its customized bags in the United States (where the labor conditions are presumably more favorable). Although the company may have a variety of economic reasons for setting up operations in this way, it might also have learned that customers feel a stronger connection with production in one case versus the other. As such, managers should think carefully about how control over production and ethical attributes interact in order to identify opportunities and avoid risks.
Supplemental Material, jm.16.0077-File003 - Who Receives Credit or Blame? The Effects of Made-to-Order Production on Responses to Unethical and Ethical Company Production Practices
Supplemental Material, jm.16.0077-File003 for Who Receives Credit or Blame? The Effects of Made-to-Order Production on Responses to Unethical and Ethical Company Production Practices by Neeru Paharia in Journal of Marketing
Footnotes 1 Associate EditorC. Page Moreau
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242919887161
5 1I originally ran the two contexts as separate studies with data collected at different times. The effects were significant across both studies (ps <.05). I collapsed the studies for brevity in reporting and to demonstrate the generalizability of the effects across different contexts.
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By Neeru Paharia
Reported by Author
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Record: 238- Why Consumers Don’t See the Benefits of Genetically Modified Foods, and What Marketers Can Do About It. By: Hingston, Sean T.; Noseworthy, Theodore J. Journal of Marketing. Sep2018, Vol. 82 Issue 5, p125-140. 16p. 3 Diagrams, 1 Chart, 2 Graphs. DOI: 10.1509/jm.17.0100.
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Why Consumers Don’t See the Benefits of Genetically Modified Foods, and What Marketers Can Do About It
Evidence from four studies suggests that the moral opposition toward genetically modified (GM) foods impedes the perception of their benefits, and critically, marketers can circumvent this moral opposition by employing subtle cues to position these products as being “man-made.” Specifically, if consumers view the GM food as man-made, and if they understand why it was created, moral opposition to the product diminishes, and the GM food’s perceived benefits increase, which subsequently increases purchase intentions for the product. This effect is replicated in the field (in both controlled and naturalistic settings), in a laboratory experiment, and with an online consumer panel. The results suggest that marketers can help consumers better consider all information when assessing the merits of GM foods by using packaging and promotion strategies to cue consumers to view the GM food for what it is (i.e., a man-made object created with intent). The findings have implications for the recent GM food labeling debate.
genetically modified organisms; moral opposition; food and health; utilitarian benefits; field studies
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By Sean T. Hingston and Theodore J. Noseworthy
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Record: 239- Why the Dynamics of Competition Matter for Category Profitability. By: Voleti, Sudhir; Gangwar, Manish; Kopalle, Praveen K. Journal of Marketing. Jan2017, Vol. 81 Issue 1, p1-16. 16p. 1 Diagram, 8 Charts, 3 Graphs. DOI: 10.1509/jm.15.0304.
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Why the Dynamics of Competition Matter for Category Profitability
Category management (CM) has become a widespread trade practice in recent years. A category manager’s decision problem is complex and multifaceted owing to demand dependencies across products and across time. Extant research on CM has typically focused on one or the other of these dependencies, but seldom both. The authors address this research gap by presenting a competition framework that reconciles cross-sectional breadth (large numbers of stockkeeping units in any given period) with longitudinal depth (demand effects across time). The endeavor is to offer retailers a general, realistic, and practical CM approach by comprehensively accounting for competitive effects. The authors demonstrate their approach using real-world data in the beer category for a midsize grocery chain in the northeastern United States. After determining the optimal weekly prices for the entire assortment over 23 weeks, the authors report a profit yield that is 3.30% more than in the benchmark logit model and substantially more than in the retailer’s current everyday low price policy.
Organized retail is an important business sector in mature economies, and it is growing rapidly in emerging
Oeconomies. For instance, with $1.13 trillion of busi
ness, retail trade contributed 7.9% to the U.S. economy in 2009 (eMarketer 2014). Category management (CM) has emerged as a popular, rapidly expanding trade practice (e.g., ACNielsen 2004) in the past two decades. The premise of CM is that category managers will endeavor to coordinate marketing decisions (on prices, promotions, assortments, etc.) across manufacturers to maximize outcomes (e.g., sales, profits) at a category level rather than a brand level. In contrast, the traditional brand-centered management role independently manages the marketing mix of individual brands in the category. Almost every major U.S. retailer has adopted CM in some form (Gajanan, Basuroy, and Beldona 2007, p. 135), and vast majorities of both manufacturers (89%) and retailers (98%) agree that CM is the most critical issue they face (ACNielsen 2004). Consequently, the topic of CM has garnered much research and trade attention over the past two decades (ACNielsen 2004; Basuroy, Mantrala, and Walters 2001; Chintagunta 2002; Gooner, Morgan, and Perreault 2011; Zenor 1994).
The retail category manager’s problem of maximizing outcomes over the entire category–and not necessarily within any particular firm, brand, or stockkeeping unit (SKU)–is complex and multifaceted. Solving the CM problem facilitates answers to several downstream questions of managerial interest. For instance, category managers might want to know which among an “everyday low pricing” (EDLP) strategy, a hi-lo price promotions strategy, or a strategy between the two is best for a particular category at a particular retailer. Which SKUs and brands would do better under EDLP than under a hi-lo strategy? How long should a promotion be run (for insight into how best to end promotions, see, e.g., Tsiros and Hardesty [2010])? Which brands and SKUs exert the highest competitive impact on each other and thus can be viewed as close substitutes? How much incremental gain in outcomes (e.g., sales, profits), measured in dollar terms, does the proposed approach deliver as opposed to the benchmark? These questions motivate our work in this article. We find a systematic way to answer these questions and demonstrate our approach using real-world data.
Extant empirical research that has taken a prescriptive view has largely considered either dependencies across products (following classical competition modeling literature such as Eliashberg and Chatterjee 1985; Sudhir 2001; Wedel and Zhang 2004) or dependencies across time (classical dynamic programming literature starting with Rust 1987). Thus, there exists an opportunity to unite these two types of dependencies in the context of CM and arrive at an optimal solution. In this article, we address this gap in the literature. A large body of work on CM is descriptive (e.g., Besanko, Dube´, and Gupta 2005; Dhar, Hoch, and Kumar 2001; Gooner, Morgan, and Perrault 2011), is analytical (e.g., Du, Lee and Staelin 2005; Raju, Sethuraman, and Dhar 2005), is cross-category in scope (e.g., Chen et al. 1999), or involves interactions across channel members (e.g., Gruen and Shah 2000; Moorthy 2005). Table 1 displays a subset of empirical work in marketing that either implicitly or
Journal of Marketing explicitly aims to solve the category maximization problem. The studies are assessed on four dimensions (the four columns) to better annotate the positioning and contribution of the current research.
In summary, we offer retailers a general, realistic, and practical CM approach by comprehensively accounting for competitive effects. Our competition framework reconciles cross-sectional breadth (large numbers of SKUs in any given period) with longitudinal depth (demand effects across time). In addition, our approach is prescriptive, predicated on a descriptive analysis, in that we compute the optimal set of prices that maximizes category profits over a longer time horizon. We implement our approach on real-world data in the beer category from a midsize retailer in the northeastern United States. We address the retailer’s multiperiod, category profit maximization problem for a reasonably large number (96) of SKUs. Our results show that our proposed approach yields realistic substitution patterns and cross-SKU effects. Consistent with expectations, we find asymmetric competitive effects (i.e., some SKUs exert greater competitive pressure on others). We demonstrate a case in which incorporating crossproduct demand dependencies (through interproduct similarity and competition variables) in a log-linear framework over a multiperiod horizon results in an optimal pricing strategy that is hi-lo in nature.
TABLE:
| Studies Focused on Profit Maximization Under Category Management | Accounts for Interproduct Dependence at the SKU level | Accounts for Intertemporal Demand Dependence | Yields Optimal Category Profits | Applies to Stable Assortments in Established Categories |
|---|
| Zenor (1994) | ✖ | ✖ | ✓ | ✓ |
| Basuroy, Mantrala, and Walters (2001) | ✓ | ✖ | ✖ | ✓ |
| Chintagunta (2002) | ✖ | ✖ | ✓ | ✓ |
| Kumar et al. (2009) | ✖ | ✖ | ✓ | ✓ |
| Mantrala et al. (2006) | ✓ | ✖ | ✓ | ✖ |
| Hall, Kopalle, and Krishna (2010) | ✖ | ✓ | ✓ | ✓ |
| Shah, Kumar, and Zhao (2015) | ✓ | ✓ | ✖ | ✖ |
| Voleti, Kopalle, and Ghosh (2015) | ✓ | ✖ | ✖ | ✓ |
| The current research | ✓ | ✓ | ✓ | ✓ |
Conceptual Background
Consider a scenario in which a category manager for the beer category wants to set prices for each beer SKU for each week over a specified period (e.g., six months, after which some SKUs might be added or dropped and the planograms reset) to maximize an outcome of interest (e.g., longer-term profitability) for the category as a whole. One approach is to rely on intuition and past experience to identify sets of products with strong demand interconnections (e.g., Budweiser and Coors at the brand level, cans and bottles of Budweiser at the SKU level) and thereafter set prices such that total sales are smoothed over and profits are higher than under a less active pricing regime (e.g., markup pricing, competitor-benchmarked pricing). Some research suggests that such an approach is not uncommon in that many retailers are unable to deal with the complexity of cross-price effects (McAlister 2005, 2007). Although this approach is useful and fast when managing a small number of SKUs, it rapidly becomes suboptimal when the category hosts a large assortment. A more scientific approach would require that category demand, comprising individual product demands, be formally studied and modeled. Suppose that in pursuit of category profit maximization, our focal manager fits a traditional demand model to her sales data, computes own- and cross-price elasticities, and develops a picture of how one product affects another’s demand. After all, effective CM requires knowledge of where to allocate scarce marketing resources (shelf space, promotion spends, price discounts and couponing, feature and display, etc.) to get the biggest bang for the buck (Dhar, Hoch, and Kumar 2001). As long as any two products are viewed as substitutes to some extent, one product’s characteristics and marketing activities affect the other product’s demand. A typical product category in a U.S. grocery store contains approximately 50-100 SKUs, and the retail category manager needs to set prices at a weekly level for each of these SKUs while keeping in mind that each SKU’s marketing mix affects demand for all the other SKUs. Furthermore, these effects may be asymmetric. Thus, changing Budweiser’s sixpack 12 oz bottle prices by 5% might affect Coors’s six-pack 12 oz can sales much more than the opposite. Compared with the literature available on interbrand competition (e.g., Hall, Kopalle, and Krishna 2010; Wedel and Zhang 2004), there is little research on the intensity and effects of inter-SKU competition, either across brands or within a brand. To determine the competitive nature across all (J) SKUs simultaneously, the category manager needs to estimate an asymmetric (J • J) matrix of own- and cross- (or competitive) price effects. This can be quite a challenge given the number of coefficients involved, including many with wrong signs, among other issues.
Moreover, even if our focal category manager were to fit a demand model to her sales data, conventional demand models assume independence across time periods (unless dynamic effects are explicitly considered). Such an assumption risks overlooking instances in which the marketing activities of a SKU in one period affect its demand in future periods. For example, forward-looking consumers may either stockpile goods given low current prices or postpone purchases in anticipation of low prices in the next period (e.g., Kumar and
Leone 1988; Sun, Neslin, and Srinivasan 2003). In the beer example, price-promoting Budweiser for a two-week period not only affects the sales of Coors in that two-week period but also might reduce sales of Budweiser (and Coors) in the weeks following the promotion period as a result of consumers stockpiling and forward-buying. It may be that the net profit from increased sales at promotional prices (low margins) does not cover the next period’s forgone profits from lost sales at full prices (see, e.g., Raju 1992). Other examples from the literature that suggest that past purchases may influence the probability of future purchases include instances of state dependence in brand choice (e.g., Seetharaman, Ainslie, and Chintagunta 1999), or changes in the salience of reference prices to consumers (e.g., Kopalle, Rao, and Assunção 1996). Thus, if demand effects from past periods and/or those on future-period sales are not considered, then either optimal actions (from a multiperiod perspective) are not taken and money is left on the table or sales fall below their status-quo level as a result of competition effects from past periods. Thus, category managers must also take into consideration the dynamics of product competition–the effect of current prices on future period profitability–and then set each SKU’s price by jointly maximizing the category profit. As a result of both these factors, CM in general and category price optimization in particular present a complex problem.
Theoretical Support for Dynamic Effects
A rich body of theoretical work in economics and marketing explains why a single-price, EDLP strategy (pure strategy) may not be optimal for firms selling frequently purchased categories (e.g., Narasimhan 1988; Raju, Srinivasan, and Lal 1990; Rao 1991; Varian 1980). Intuitively, firms in competitive markets have an incentive to lower prices to attract price-sensitive consumers. Because competitors also follow the same logic, this can lead to an unnecessary price war or suppressed prices for the long run. To circumvent this predicament, the firm occasionally provides discounts (mixed strategy) to avoid aggressive competitive response and price wars in the market. This enables a firm that is mainly content with the demand from its loyal base (who are willing to buy at regular prices) to occasionally gain additional demand from the price-sensitive segment. In a recent article, Gangwar, Kumar, and Rao (2014) find in an IRI Marketing Science data set that a majority of the SKUs exhibit temporal price dispersion, which they argue is a result of intertemporal shifts in demand due to consumer stockpiling.
From a consumer standpoint, one may ask why past prices should affect demand at all, given that lagged price does not enter into the consumer’s budget constraint or utility directly. One plausible explanation for lagged-price effects on current demand is the existence of some type of state dependence (e.g., inventory, reference price, habit formation). Beginning with Guadagni and Little (1983), there has been considerable effort and a rich body of literature in marketing devoted to understanding the various types of state dependence.
Factors Affecting Optimal Prices
To demonstrate our profitability analysis, we consider SKU prices over time, which in retailing have a strong impact on customer behavior and retailer profitability (Levy et al. 2004; Ma et al. 2011). To price SKUs optimally, retailers need to consider at least four factors in their pricing decisions:
- Product attributes, which are essentially SKU characteristics such as brand, size, type, and so on;
- Price sensitivity (i.e., the change in a SKU’s demand with changes in its price);
- Substitution or competitive effects (i.e., the change in a SKU’s demand changes due to changes in other SKUs’ characteristics [both measured and latent] and marketing mix). The retailer would thus need to evaluate the relative demand effects and differential SKU margins before changing the price of any focal SKU; and
- Dynamic effects of the marketing mix, which comprise the competitive effects on demand over time (both on self and on other SKUs). For instance, would changing a SKU’s price today affect its own demand tomorrow? Would it affect other SKUs’ demands in future periods?
We note that these factors are not limited to consumer packaged goods in brick-and-mortar stores but extend also to electronic channels and information products (e.g., Kannan, Pope, and Jain 2009).
One of the difficulties in producing a useful demand model that appropriately captures all the competitive effects is the sheer number of SKUs within a category. Prior research has typically captured interproduct competition with variants of the logit model (Ben-Akiva and Lerman 1985; Guadagni and Little 1983; Sriram, Balachander, and Kalwani 2007) that model the probability of purchasing a product. The logit formulation of demand relates purchase probability (or market share) to a set of covariates (that includes the marketing mix of all the products and their respective characteristics) in the denominator, thereby implicitly accounting for competition effects. Thus, the logit serves as a logical baseline against which candidate approaches to incorporating competition can be evaluated. In this regard, although Berry, Levinsohn, and Pakes’s (1995) framework (see also Sudhir 2001) can incorporate both heterogeneity and competitive effects, it ( 1) does not consider all SKU attributes in the estimation (because of collinearity across attributes), ( 2) does not explicitly account for similarities across SKUs (either in observed or in latent attributes), and ( 3) faces estimation challenges in the presence of a large number of SKUs or the inclusion of dynamic effects. In this article, our approach addresses these shortcomings and provides a more profitable pricing path for the various brands in the category.
One article that is similar in spirit to ours is Shah, Kumar, and Zhao (2014), which takes a CM perspective in modeling assortment decisions among heterogeneous retailers that do not set prices and operate in a limited SKU setting and in an underpenetrated (i.e., potentially nonstationary) market in the developing world. Our work complements theirs in that ours is relevant in a stable, large fixed-assortment context in which we study aggregate the demand impact of dynamic SKU pricing decisions. We focus on a single retailer that centralizes price setting in a developed market. In this context, we consider a simple but intuitive way of capturing competition that is dynamic in nature and is based on inter-SKU similarities. We then conduct our estimation and optimization accordingly to arrive at prescriptive results.
Dynamics of Competition / 3
A Framework for Competition
Recall our previous illustrative example wherein the beer category manager, to maximize category profits, needs to resolve two types of complexities: dependence across products and dependence across time. Competition lies at the core of both these types of demand dependencies. Much research attention has focused on analyzing competition to disentangle its components (see, e.g., Moorthy 2005). Two products can have innate similarity in fixed attributes (e.g., Miller Lite and Bud Light share the “light beer” attribute). Alternatively, perceived similarity between two products can be affected by changes in their marketing mix (e.g., Budweiser Select is priced much higher than regular Budweiser or Miller and is typically not viewed as competing with them). Next, we link fixed attributes of competition (both in the space of observed data and latent parameters) and the time-varying marketing-mix attributes into a unified framework of competition that accommodates ( 1) both the inter-SKU and the dynamic aspects of competition, ( 2) the category’s brand-SKU hierarchy, and ( 3) a mechanism to facilitate price optimization downstream.
In their work on time-varying brand equity, Voleti and Ghosh (2013) consider static inter-SKU competition for linear demand models. However, their model has limitations. First, although it accounts for cross-product competition, it rules out dynamic effects and, therefore, own-product competition. This is limiting. Second, their optimal price paths would imply an independent application of the inverse elasticity rule in each period for each product, and thus they are restrictive. We extend Voleti and Ghosh’s general framework by dynamically connecting changes in the marketing mix (MMIX) of any product to changes in demand both across SKUs and across time.
Let COMPTN(jt, it) be the competitive impact on the sales in period t of focal SKU j from rival SKU i. At least two main factors influence COMPTN(jt, it): inter-SKU attribute similarity and marketing-mix effects. Let SIMIL(i, j) denote the degree of attribute similarity between i and j, and let MMIXit denote the endogeneity-corrected marketing-mix activity of SKU i in period t.1 Although MMIX effects can be contemporaneous (e.g., Eliashberg and Chatterjee 1985; Kumar et al. 2009; Kannan and Yim 2001), consumer behavior such as stockpiling, consumption, and purchase postponement (e.g., Sun, Neslin, and Srinivasan 2003) may result in intertemporal MMIX effects. Let k periods of MMIX lags–denoted by MMIXi,t-1, MMIXi,t-2, …, MMIXi,t-k–affect j’s demand. Let the term MMIX.EFFECTi,t,k denote the combined effect of the rival product i’s marketing-mix activity in period t from 0 to k lagged periods. We model a multiplicative relation between SIMIL(i, j) and the MMIX.EFFECT, effectively implying that the degree of similarity between i and j scales up or down the MMIX effect of i on j. Thus,
( 1) COMPTNðjt, itÞ = SIMILði, jÞ • MMIX. EFFECTði, t, kÞ.
1Some variables, such as the time-varying MMIX elements, may
Because SIMIL represents the degree of attribute similarity between products, it can be inversely related to an interproduct “distance” in attribute space (e.g., for preference structures, see Carpenter and Nakamoto [1989]). Let l = 1, 2, …, L represent observed, discrete attributes of products j = 1, 2, …, J at the SKU level. Let indicator function I(.) assign a product pair (i, j) a value of 1 if it shares attribute l, and 0 otherwise. Because not all product attributes are created equal in their impact on product similarity, we use a set of parameters to differentially
å( 2) SIMILij = dl • I li = lj. l =1
The attribute importance weights are assumed to be proportional to the marginal sales impact of the corresponding attributes drawn from an auxiliary analysis sample. Voleti, Kopalle, and Ghosh (2015), in their static two-stage demand model based on a nested Dirichlet process, extend the COMPTN variable in Equation 1 to include as arguments the effect of unobserved attributes in latent parameter space. We propose to extend the COMTPN approach to include the effects of current and past MMIX activity as well. We provide a detailed description of the development of SIMIL, as well as other aspects of competition, in Web Appendix A. MMIX. EFFECTi,t,k captures i’s current and past MMIX activity as a
Per Equations 1-3, the term COMPTN(jt, it) in Equation 1 captures the competitive impact–both contemporaneous and lagged up to k periods–of rival SKU i on focal SKU j in period t. Next, we aggregate COMPTN(jt, it) to arrive at the overall competitive impact due to all rival SKUs i, j on focal SKU j in period t:
Thus, we incorporate competition in a dynamic fashion through inter-SKU attribute similarity and past and current MMIX levels and yield, for every SKU j in every period t, the net competitive impact on j’s sales due to the MMIX actions of all other SKUs in time t to (t - k). Furthermore, consistent with findings in the marketing literature (e.g., Russell and Kamakura 1994), we find the interproduct competition is asymmetric. That is,
( 5) COMPTNðjt, itÞ, COMPTNðit, jtÞ, lagged MMIX levels. Overall, the COMPTN variable, as constructed, primarily provides a mechanism to transmit the effects of changes in any product’s marketing mix to all other products’ sales in both current and future periods.
Demand Specification
We begin with a basic specification of the popular log-log demand model (e.g., Hoch et al. 1995; Wedel and Zhang 2004) in which the log of sales quantity is regressed over a set of demand determinants. The logarithmic function offers a natural diminishing-returns pattern, accommodation of various response shapes and rates (e.g., Lilien, Kotler, and Moorthy 1992), and a
Here, Zt is a vector of control variables (e.g., seasonality), log is taken for continuous variables, and d1 is the corresponding coefficient vector. Ln(MMIXjt) is a vector of SKU-specific log price, log promotion (or advertising), and log distribution variables in period t, corrected for endogeneity. a1,j is a vector of MMIX elasticities specific to SKU j. Ln (MMIXj,t-k) represents the k-period lagged MMIX effects and a2+k represents the corresponding MMIX elasticities. Parameters a2+k are not SKU-specific to avoid making excessive demands on the data and to preserve degrees of freedom. This formulation
is general and based on our knowledge of the lag-length
k. Furthermore, because the log-log model corresponds
to a multiplicative model, we log-transform the variables distri
bution (DISTBN) and promotion (PROMO), which may take zero values, using the transformation logDISTBN = Ln(1 + DISTBN). This avoids computational traps. We did a robust
ness check regarding our use of the constant 1.0 with other
values (.1, .01, etc.) and found qualitatively similar model
results. COMPTNjt summarizes the dynamic effects of competition on SKU j due to all relevant SKUs in period t and a3+k, j, the average sales impact of competition on SKU j. Similarly,
SIMILj represents the average extent of attributes shared between SKU j and the rest of the assortment, weighted by attribute importance with (homogeneous) coefficient a3+k (because SIMIL does not vary with time). Note that both
COMPTN and SIMIL variables in Equation 6 have been con
structed prior to estimating the demand model using parameter
weights from an auxiliary data set (which does not feature
in the subsequent analyses). The aggregate sales impact of all
time-invariant product attributes is captured directly in the SKUspecific random-effects term, hj. The measurement error ejt is drawn i.i.d. from a mean-zero normal distribution.
One implication of the model in Equation 6 is that the competitors’ MMIX has long-term effects on future focal brand sales (SALESjt). We note that there are primarily two possible ways in which a competitor’s (say, i) MMIX in past periods (say, t - k for k > 0; let k = 1 for a one-period lag) can affect a focal SKU’s (say, j) current sales SALESjt: ( 1) a direct effect in which SALESjt is a direct function of MMIXi,t-1 and ( 2) an indirect effect in which MMIXi,t-k affects MMIXi,t, which in turn affects SALESjt. There is no strong theoretical or intuitive support for the first path in the absence of some type of state dependence. In contrast, the second path is clearly a
state-dependent approach but raises the question of how to
model SALESjt without explicitly bringing in MMIXi into the right-hand side of Equation 6 (owing to model parsimony con
siderations). This is where the COMPTN term, as formulated, helps. We model SALESjt as a function of MMIXjt, MMIXj,t-1, and COMPTNjt, in which COMPTNjt summarizes the indirect sales effects on SALESjt of all MMIXi,t and MMIXi,t-1.
The set of SKU-specific random parameters ½a2,j a3,j hj, denoted by qj, are drawn from a general distribution F(.). Different specifications for F(.) yield different models for
quantities), we implement a Bayesian semiparametric ap
proach based on a Dirichlet process prior (e.g., Antoniak 1974). We express the Dirichlet process prior-based model, parameterized by a concentration parameter a and a base
Note that our unit of analysis consists of two distinct product levels–namely, brand and SKU–which have a clear hierarchical relationship in that brands deploy SKUs. Modeling interrelated product demand would require that similarities and dissimilarities at these two distinct product levels be accommodated. The literature has recognized importance of doing so (e.g., Fader and Hardie 1996; Wedel and Zhang 2004). Brand hierarchies imply a tiered structure that reflects firms’ branding strategies (e.g., Rao, Agarwal, and Dalhoff 2004) and have implications for category structure (e.g., Fader and Lodish 1990) and, at a more abstract level, the market structure (e.g., Erdem 1996). Both manufacturers and retailers work with product lines that typically have brand hierarchies embedded in them. In the unlikely scenario that demand dependencies across products are known a priori, the nested logit model (Kannan and Wright 1991; Sudhir 2001) can account for a tiered product assortment. Kumar et al. (2009) use a weighted randomcoefficient model to explicitly model competition across adjacent price and brand tiers. We take the approach of Voleti, Kopalle, and Ghosh (2015), who use a nested Dirichlet process (Rodr´iguez, Dunson, and Gelfand 2008) to uncover demand dependencies with minimal a priori assumptions about competitive structure.
To justify their use of a more complex demand model (the nested Dirichlet process [nDP-based approach]) as against a simpler alternative (the normal parametric), Voleti, Kopalle, and Ghosh (2015) perform a model assessment and validation exercise. We replicated their exercise and found that the nDP significantly outperforms the normal parametric on several measures including model fit, predictive ability, and accommodation of category branding constraints (see Web Appendix B). More generally, our modeling approach accounts for brand influences that act through SKUs in at least three ways. First, the nDP demand specification explicitly acknowledges and accounts for the brand-SKU hierarchy by nesting similar SKU groups (”child”-level clustering) within a “parent”-level clustering of similar brands. The groupings here are endogeneously derived and represent an improvement over those supplied ex ante in the nested logit case. Second, in our competition submodel, brand membership of SKUs is among the time-invariant product attributes considered that make up SIMIL(i, j), which is one of two arguments required to specify the COMPTN(it, jt) term. Third, in our actual optimization routine, we optimize the brand-level price vectors directly (described in the following section).
Enhancing Category Profitability
Given demand model parameters, improving profitability would require a comprehensive price optimization exercise. The issue is inherently dynamic because of intertemporal effects (i.e., price changes from past periods affecting current period demand). We next illustrate how the interrelated product demand helps improve category profitability in the context of a retailer’s multiperiod profit maximization problem.
Maximizing Profitability
The retailer maximizes category profit (i.e., maximizes the sum of discounted profits over a finite time horizon t = 1, 2, …, T) by selecting a sequence of SKU prices over time. Thus,
Here, Q is the discount rate, bSjt is the expected sales for SKU j in period t using the revised demand model specified in Equation 6, which includes the dynamic competition variable COMPTN(nDP), the marketing mix (which comprises price, promotion and distribution variables in our empirical application) of SKU j in period t.
The retailer’s decision problem can now be reformulated as follows. Given the initial values (at time t = 0) of the marketingmix variables, select SKU prices in each period that maximizes the objective function in Equation 8. The retailer’s category profit-maximization problem is thus formulated as an optimal control problem in discrete time, with past marketing-mix variables as the state variables and current period prices of the SKUs as the control variables. We use dynamic programming to solve this. We provide a more detailed description of the normative dynamic programming model in Web Appendix C. Note that we use expected sales in our optimization model. In theory, the error in sales (Equation 6) should be a state variable. However, our model is already cursed with the dimensionality problem, and making the error a state variable will only exacerbate the issue. Prior approaches to price optimization have relied on expected sales (Ailawadi, Kopalle, and Neslin 2005; Basuroy, Mantrala, and Walters 2001) and we follow this literature in this article.
Benchmark Model
The traditional multinomial logit formulation implicitly accounts for competitive responses by including rival products’
MMIX variables in its denominator. As such, it is an appropriate benchmark model both for the efficacy of the competition function in aiding demand estimation and for dynamic price optimization. To maintain comparability with the proposed linear demand specification, we create a mixed logit demand model with SKU-specific parameter heterogeneity for fixed attributes and price. The lagged MMIX variables, however,
are modeled as homogeneous parameters, in keeping with the proposed linear demand specification. We find that the most important MMIX variable–price per unit volume (or size normalized price)–shows limited intrabrand variation across SKUs, for every brand in the sample, relative to interbrand
variation. Thus, we use normalized price at the brand level as
our price variable in all the models we estimate. This enables
us to create an apples-to-apples comparison across models. Let j = 1, 2, …, J index SKUs in the sample. Let MMIXjt and MMIXj,t -1 be contemporaneous and lagged marketing-mix vectors for SKU j in time t, with corresponding coefficients bj and gk, respectively. Let aj denote the random intercept for
The benchmark mixed logit model in Equation 9 connects price changes made in the course of the backward induction algorithm to own- and cross-product sales impact through the contemporaneous and lagged marketing-mix variables. To arrive at predicted sales in the logit model, we need an estimate of the total market size, Mt, for the product category at the focal retailer. Previous studies that have used the logit (e.g., Sudhir 2001) do so in different ways. We fixed the market size to ten times the maximum category sales recorded in the data set. We tested for the sensitivity of estimated mixed logit parameters by changing market size to 20 and 50 times the maximum category sales in the data set and found very similar results. Thus, the predicted sales figure is given by and the estimation steps in our proposed approach. This completes our model specification. In the next section, we implement the model on real-world data in the beer category and analyze the results.
Empirical Application
Data Description
Because our approach takes the perspective of the retail category manager’s profit-maximization problem, for relevance and consistency, we use data from a single retail chain. We take beer category data from 56 stores of a midsize grocery chain in the northeastern United States. This retailer has centralized pricing and assortment planning in all its stores. We have 23 weeks of sales and marketing-mix data for 96 SKUs from 15 brands, yielding a total of 2,171 usable observations. The timevarying MMIX variables available are DISTRIBN (% stores in which the SKU was available in a particular week), PROMO (% SKU sales made on any promotion in that week), PRICE (per fluid ounce of product for that SKU in that week), and COST (unit cost per fluid ounce [as price instrument] for that SKU in that week). The national ADSPEND of each brand for that year is also available and is modeled as a fixed effect because only cross-sectional variation across brands for this variable is available in our data. Control variables were month dummies (MONTH; January to May) for seasonality control. The time-invariant product attributes available in the data are BRAND, CONTAINER (bottle/can), TYPE (ale, light, craft, and regular), COLOR (light, amber, dark, and golden), PACKAGING (6-pack 12 oz, 12-pack 12 oz, 18-pack, and 24-pack). We use this fixed product attribute information in two ways–as price instruments in a hedonic regression and as an input to computing SIMIL, the product attribute-based inter-SKU similarity measure.
Table 2 summarizes the variables used in the analysis. Considerable variation is present in both the dependent and the MMIX variables. The instrumental variables for the three time-varying MMIX elements consist of exogenous and upstream variables (season dummies, Bureau of Labor Standards data on Purchasers’ Price Indices for material inputs to beer [namely, malt and barley as well as aluminum] all used as price instruments, and regional food inflation indices from the Bureau of Labor Statistics) in addition to the fixed product attributes. The R-squares of the instrumental variables regressions are well above 85%, and the respective correlations between the actual and predicted MMIX variables are also high (above .75).
Model Details
In the interest of brevity, we move the descriptive detail of the choice of priors for the Bayesian model parameters, the modeling choices, convergence diagnostics, and related robustness checks to Web Appendix D. Furthermore, in Web Appendix E, we provide the code used to estimate the linear demand model. Next, we elaborate on the choice of lag length and the form of the price input to the dynamic programming algorithm.
To find the appropriate lag length k for marketing-mix effects in Equation 3, we varied k from 1 to 4 and computed the competition variable for the sample each time. We found that the competition terms for k = 1-4 are highly correlated (well above the .90 level). Therefore, for simplicity and tractability, we use k = 1 as the appropriate lag length. Likewise, we found that coefficients of lagged own-MMIX elasticities in a mixedeffects regression rapidly decayed for k > 1. Thus, we chose k = 1 in Equation 6.
There may be environmental and institutional constraints that we as researchers may not directly observe in the data. For instance, prices above certain latent thresholds may induce consumers to switch retailers entirely. Consequently, to ensure that the results are within reasonable bounds, in determining the profitability, we constrained the prescribed prices to remain within the range of the currently set prices. Similarly, we constrained unit profit margins to not vary far (more than 1.5 SD) beyond the observed range of profit margins. This helps rule out instances of unrealistic model predictions. We set the normalized prices of all SKUs within a brand at the mean value for the brand for ease of computation and tractability in the dynamic programming stage. Because the price per fluid ounce changes very little across SKUs within a brand, we expect minimal impact. By the interpolation of the state space, we expect to obtain prescribed policy paths of the prices of the 15 brands in the sample over corresponding managerial insights.
TABLE:
| | Description | Mean (SD) |
|---|
| Dependent Variables |
| SALES | Beer volume in fluid ounces | 39,204 (61,531) |
| Price |
| PRICE | Retail price per fluid ounce | .07 (.02) |
| COST | Wholesale price per ounce | .06 (.01) |
| DISTBN | % stores that sold the SKU | .70 (.29) |
| PROMO |
| UNITSP | % units sold on promotion | .22 (.36) |
| ADSPEND | National ad spend ($ million per year) | 13.97 (20.38) |
| Control Variables |
| MONTH |
| Jan | Sales week is in January | .170 |
| Feb | Sales week is in February | .173 |
| March | Sales week is in March | .173 |
| April | Sales week is in April | .175 |
| May | Sales week is in May | .175 |
| June | Sales week is in June | .134 |
| | Proportion |
|---|
a Some brands’ unit sizes are 11.5 oz (e.g., Labatt); for simplicity, we consider these 12 oz units.
|
| Beer Type |
| Ale | .011 |
| Lite | .386 |
| Craft | .095 |
| Regular | .519 |
| Beer Color |
| Light | .679 |
| Amber | .094 |
| Dark | .042 |
| Golden | .185 |
| Packaginga |
| Bottle | .711 |
| 6-pack, 12 oz | .299 |
| 12-pack, 12 oz | .454 |
| 18-pack, 12 oz | .013 |
| 24-pack, 12 oz | .021 |
Results
Competition Results
First, we summarize the competition variable. The mean and standard deviation of nonnormalized COMPTN is 444 and 205, respectively. For better interpretability, in the demand model, we normalized COMPTN to have a mean of 1. Likewise, the average SIMIL across all SKU pairs is normalized to 1. The primary advantages of using this term are that it is intuitive, it is easy to compute cross-product marginal effects, and substitution patterns are implied. To demonstrate this, we present an example in Table 3. We chose to compare three SKUs, each with high, medium, and low SIMIL for illustrative purposes. The aim is to show (in a 3 • 3 table) how cross-price elasticity, SIMIL, and COMPTN vary across them.
• SKU #56 is the best-selling SKU in our data for the focal
retail chain: A light-colored Canadian import called “Labatt Blue Light” 12-pack 11.5 oz bottle (priced in the low tier).
• SKU #49 reports the highest attribute similarity with SKU
#56. It is the Labatt Blue Light 6-pack 11.5 oz bottle.
• SKU #42, which shows the lowest SIMIL score with SKU
#56, is Heineken 6-pack 12 oz can–a golden-colored regular (i.e., not light or low-calorie) brew (priced in the super premium tier).
TABLE:
| | | SKU i (Rival SKU) |
|---|
| | | SKU #56 | SKU #49 | SKU #42 |
|---|
| SKU j (Focal SKU) | SKU #56 | 24.112 [SIMIL (j, i)] {COMPTN(j, i)} | 2.401 [1.866] {10.498} | 0 [0] {0} |
| SKU #49 | 3.052 [1.866] {9.711} | -4.026 [SIMIL (j, i)] {COMPTN(j, i)} | .679 [.081] {.423} |
| SKU #42 | 0 [0] {0} | .404 [.081] {.350} | -4.572 [SIMIL (j, i)] {COMPTN(j, i)} |
Table 3 shows, for each focal SKU (in the rows), the crossprice elasticity with the rival SKU (in the columns), the SIMIL (focal, rival) score in [brackets], and the average COMPTN impact of the rival on the focal SKU in {braces}. The diagonal cells show only the own-price elasticities because a SKU cannot have defined SIMIL and COMPTN with itself. As we expected, among the off-diagonal cells, the cross-price elasticities are positive, implying that a rise in the price of substitutes causes focal SKU sales to rise. The magnitudes indicate that substitution effects correlate strongly with interproduct similarity. Other notable details emerge. For instance, ( 1) attribute similarity between SKUs #56 and #49 is very high (SIMIL = 1.8660 against an average of 1), whereas similarity between #56 and #42 is the minimum possible value (SIMIL = 0). ( 2) The interproduct COMPTN effects demonstrate asymmetry. For example, the competitive effect of SKU #49 on SKU #56 is 9.711, whereas that of #56 on #42 is 10.4977. Because SIMIL between dissimilar SKU pairs is low, the COMPTN effect is as well. Thus, for example, SIMIL between #56 and #42
COMPT impact of the rival SKU on the focal SKU. Diagonal cel is 0 and, thus, both COMPTN and cross-price elasticity are 0. Similarly, SIMIL between #42 and #49 is very small (.08 when the average is 1) and, consequently, so are the corresponding COMPTN and cross-price elasticity values.
Demand Model Results
One may ask whether and to what extent the proposed additions to the log-linear demand specification (i.e., SIMIL and COMPTN) affect model performance. We assess the value of including the SIMIL and COMPTN terms in the linear demand model by estimating four models as part of a 2 • 2 grid–with and without SIMIL versus with and without COMPTN–and comparing the complexity penalized fit in the holdout prediction sample across them.
TABLE:
| | Calibration Sample (Model Fit) | Holdout Sample (Predictive Fit) |
|---|
| Model Specification | RMSE | Log-Marginal Density | RMSE | Log-Marginal Density |
|---|
| Both SIMIL and COMPTN | .1661 | 566.378 | .2170 | 81.480 |
| SIMIL only | .1804 | 533.405 | .2371 | -113.121 |
| COMPTN only | .1792 | 559.906 | .2331 | -60.673 |
| Neither SIMIL nor COMPTN | .1840 | 518.696 | .2386 | -110.127 |
Table 4 displays these results. We find that the “full” model specification (with both SIMIL and COMPTN) yields the best predictive fit to the data compared with alternative specifications. Even on calibration sample fit, the proposed model specification does best. Hereinafter, for results and discussions, we use the full model specification as our proposed demand model.
To assess the relative importance of the constructed measures of interproduct similarity and competition, following Silber, Rosenbaum, and Ross (1995), we compute the contribution of explaining variance in sales of different groups of regressors–namely, contemporaneous own-price effect on sales, own-lagged price effect on sales, nonprice MMIX effects, cross-SKU sales effects (measured by SIMIL and COMPTN), and other independent and control variables. The results appear in Figure 2. We find that although variation
TAB n boldface show own-price elasticities.
in products’ own price dominates variance explained in sales (close to two-thirds of the modeled effects), lagged prices play an important part (explaining ~18% of the modeled variation in Y), emphasizing the need to account for dynamics. This is followed by nonprice MMIX variables (distribution and promotion, in our data), at ~11%. Finally, because cross-product competitive effects contribute to more than 5% of the modeled variance in sales, the need to account for these effects is elucidated. Table 5 displays summaries of the posterior draws of the full log-linear demand specification’s main effects–both from SKU-specific random parameters (Panel A) and from homogeneous parameters (Panel B). In Panel A, the parameter estimates appear to bear face validity. For instance, we find that the price parameter is negative in sign, whereas the promotion and distribution parameters are positive. We find that, as we expected, the marginal effect of competitive intensity on sales is negative. The demand model’s main strength in the present application context is that it parsimoniously captures the dynamic impact of competition while accommodating category branding structures.
TABLE:
| Variables | Estimates a |
|---|
| SKU-specific rando effect | -5.62 [-6.34, -5.08] |
| PRICE parameter | -3.94 [-5.01, -2.87] |
| DISTBN parameter | .61 [-.10, 1.23] |
| PROMO parameter | .08 [-.09, .15] |
| COMPTN response | -1.29 [-1.93, -.68] |
| Variables | Estimatesb |
|---|
| a In Panel A, estimates are the average of posterior means [range of posterior means]. |
| b In Panel B, estimates are the posterior mean [95% credibility interval]. |
| Ln ADSPEND | .037 [.027, .045] |
| SIMIL | 2.192 [1.919, 2.451] |
| Lagged Ln PRICE | -2.054 [-2.154, -1.958] |
| Lagged Ln DISTBN | .007 [-.041, .055] |
| Lagged Ln PROMO | -.005 [-.011, .002] |
In Table 5, Panel B, we find that advertising spend positively correlates with sales, that high prices for a product depress demand not only in the current period but also in the next time period (lagged price coefficient is negative and significant),2 and that the distributional reach or promotional intensity of a product in the past week has no significant effect on sales in the current period. We find that a product’s overall SIMIL score correlates positively with sales. Recall that the variable SIMIL for each (focal) SKU is simply the average number of shared attributes with every other SKU weighted by attribute importance. Thus, SIMIL’s operationalization in this context can be interpreted as a measure of attribute
popularity. For instance, ceterus paribus, bottles sell better
than cans and light beer outsells regular beer (based on
auxiliary sample regression results). Thus, high-selling SKUs are likely share these “popular” attributes with one another. In turn, this implies that a high SIMIL score may correlate
strongly with sales.
Finally, Table 6 displays the benchmark model (mixed logit) results. The logit model’s parameters do not have a “direct” or marginal-effects interpretation. However, the sign of the effects remain indicative of the direction of impact. Thus, we find that the price parameter exerts a negative influence on market share (aggregation of purchase probabilities), whereas that of promotion or distribution is positive on the average. To compare model fit between the logit and the competition-based models, we calculated the root mean square error from the prediction error in log(sales). We find that both models seem comparable in terms of fit in both the estimation and the holdout samples. Thus, both the logit and
the linear models appear to approximate the true demand model in a manner consistent with each other’s fit and results. However, relative model performance in the optimal pricing
algorithm remains to be seen. We used the parameter esti
mates both from the log-linear model and the mixed logit models as inputs to the backward-induction, profit-enhancing algorithm, whose results we discuss next.
In summary, we find that accounting for both crosssectional and longitudinal competition improves outcomes. In the demand model, both model fit and prediction in a holdout sample are improved with SIMIL and COMPTN than without them. Furthermore, we find that SIMIL and COMPTN together explain 5.5% of variation in sales–the third most impactful group of regressors after price and
nonprice marketing mix variables. Introducing the dynamics of competition also hugely affects outcomes. We find that lagged price, as a determinant of demand alone, explains 18.2% of the variation in sales.
Optimal Price Paths
TABLE:
| | Metric | Estimates |
|---|
| Variables |
| SKU-specific random effect | Average of posterior means [range of the posterior means] | -5.43 [-9.17, -.29] |
| PRICE parameter | | -34.82 [-79.36, -16.04] |
| DISTBN parameter | | 1.07 [-.06, 2.96] |
| PROMO parameter | | .02 [-.11, .09] |
| One-Period Lagged MMIX Variables |
| Lagged PRICE parameter | Posterior mean [95% credibility interval] | -.718 [-1.23, .13] |
| Lagged DISTBN parameter | | .182 [.09, .43] |
| Lagged PROMO parameter | | .004 [.0016, .0068] |
| Model Fit and Inference |
| Calculated nonparametric | In-sample Ln Sales RMSE | .159 |
| Measure of fit | Holdout sample Ln Sales RMSE | .223 |
The backward-induction-based dynamic programming algorithm provides a set of category-profit maximizing “optimal” prices for each brand in each time period. Panels A-C of Figure 3 show the observed price paths, the optimal price paths under the log-linear model, and those under the mixed logit specification, respectively, for five of the best-known and highestselling brands at the retailer–namely, Labatt, Budweiser, Miller, Corona, and Heineken. The y-axes are the same scale in both plots to provide a better comparison.
We note the following five salient points. First, the price paths under the current pricing policy (Figure 3, Panel A) seem to follow an EDLP policy–that is, stable over several weeks, followed by changes in small increments. The average percentage difference between the highest and lowest prices for each brand in the sample period is 4.4% under the current price path. The corresponding figures for the linear and logit models are 27% and 11%, respectively.
Second, the log-linear model (Figure 3, Panel B) prescribes some variation in pricing levels across weeks and resembles a hi-lo pattern more than an EDLP one. In line with prior literature in marketing, which suggests that hi-lo pricing policies are generally more profitable relative to a constant price strategy (e.g., Hoch, Dre‘ze, and Purk 1994; Kopalle, Rao, and Assunção 1996; Mazumdar, Raj, and Sinha 2005), ex ante, our category profit expectations would be higher from a hi-lo pricing path than from an EDLP one.
Third, the logit model prescribes a price path that resembles an EDLP policy in that price volatility appears to be relatively low. Thus, ex ante, we would expect the logit’s category profit to be closer to the current price path (and lower than under the linear price path).
At this stage, one may ask why the proposed and baseline models differ in their optimal strategies. The proposed loglinear COMPTN model and the baseline mixed logit model both account for interproduct demand dependencies or competition effects, but they do so in different ways. The logit approach models the focal SKU’s share of sales as a function of the ratio of the demand effects of the focal SKU’s attributes to the aggregate demand effect of the attributes of all the SKUs in the sample. This latter quantity–the aggregate demand effect of the all the SKUs’ attributes–appears in the denominator in the right-hand-side term in the model. Note that even though parameter heterogeneity for attribute effects is allowed in the mixed logit model, this denominator term is identical across SKUs (e.g., taking logs on both sides of the logit model yields the denominator as the inclusive value–a constant term–on the right-hand side).
In contrast, the log-linear model constructs a COMPTN term to capture inter-SKU demand dependencies as a variable of interest in the data set. Furthermore, the effect of this COMPTN term on focal SKU sales is modeled as heterogeneous parameters (under a robust, flexible nested Dirichlet density). Thus, the proposed model is able to account for SKU-specific heterogeneous competition effects, whereas the mixed logit model assumes a homogeneous competition effect across SKUs. We include the exploitation of this property of the proposed model as part of the article’s incremental contribution. Because of the difference in the way competition effects are captured in the two model specifications, we see very different optimal outcomes– hi-lo versus EDLP–and thus, the corresponding managerial implications emerge.
TABLE:
| | Current Price Path | Optimal Path from COMPTN Model | Optimal Path from Logit Model | Price Summary ($/oz) |
|---|
a Spearman correlation coefficients.
|
b Pearson correlation coefficients.
|
| Current price path | 1.00 | .26a | -.05a | .076 (.0149) |
| Optimal path from COMPTN model | .30b | 1.00 | -.10a | .074 (.018) |
| Optimal path from logit model | .38b | .47b | 1.00 | .076 (.016) |
Fourth, to better assess the magnitude of the similarities and differences between the prescribed price paths and the current one, we analyze the statistical correlation between the current and the prescribed price paths as well as the rank correlation between brands using the average current and prescribed prices. Table 7 shows the correlation coefficients. The lower triangular cells contain the Pearson correlation between price paths and the upper triangular cells contain the Spearman rank correlation coefficients. Table 7 indicates that although the logit model prescribes a price path that is correlated with the current one, it changes the order of the brands within price tiers much more than the proposed COMPTN model does. The log-linear COMPTN model thus appears to yield a realistic optimal price path; although it varies, it does so within the same price band as the product’s current pricing. For example, Labatt is the lowest-priced brand and Corona is the highest-priced brand in Figure 3, Panels A and B. However, the order changes in Figure 3, Panel C (logit), in which Heineken moves to the top slot.
Fifth, Table 7’s right-most column shows the mean and standard deviation of the observed prices under the status quo as well as the optimal prices under the COMPTN and logit models. The proposed COMPTN demand model recommends lowering the average prices slightly to take advantage of the volume expansion that would follow. This is unsurprising given that the own-price elasticities are close to -4. Finally, there is no significant time trend in any of the three sets of price paths. Thus, the prescribed price paths do not recommend what may amount to an unrealistic shift in pricing over time. We find a qualitatively similar pattern in the optimal price paths of the remaining ten brands (see the Appendix).
Profit Simulations
If we assume that our demand specifications (COMPTN and logit, respectively), represent the “true” underlying demand system, or at least capture the true demand system more accurately than does the firm’s current approach, then it should be possible to “simulate” the profit that would be realized from the analysis sample and “predict” the profit in the holdout sample under each demand specification. Using classical prediction under statistical control to simulate profits in the holdout period would only produce hypothetical profit numbers that are not observed or realized in the data. Thus, we look to Zhang and Krishnamurthi (2004) who devise a test-based on observed data in the holdout sample alone to validate their optimal brand promotion strategies. Their basic idea is simple: identify brands in the holdout sample that, through random chance, closely follow the optimal strategy and compare their profits against those of all other brands. If the optimal strategy indeed enhances
TABL profits, then the same should be borne out in the holdout sample profit comparison.
Zhang and Krishnamurthi (2004) take a manufacturer’s perspective to customize promotions for the target brand, and their optimization routine maximizes each individual brand’s profit independently given the promotion strategies of all other brands. We note that such a strategy would not apply to our case, which necessarily views different demand functions as interdependent (thus, we solved for all brands simultaneously). However on closer inspection, we find that we can adapt Zhang and Krishnamurthi’s proposed validation test for our interdependent product demand context while retaining their core idea that brands that happen to follow (or deviate minimally from) the proposed optimal path should show higher holdout sample profitability than the rest of the sample.
We identify time periods (weeks) wherein the set of brand price vectors in the holdout sample deviates minimally from those in the optimal solution. We then compare profits in the identified weeks with those in the other weeks. To ensure that week-to-week variation from other unforeseen factors (e.g., seasonality) does not confound our results, we normalize our profits by volume. Furthermore, we check for any systematic differences in nonprice marketing-mix variables (primarily promotion and distribution) between the chosen 8 weeks and the remaining 15 weeks that might systematically affect outcomes, and we found no such differences. The exact steps we followed were to identify approximately one-third of the holdout sample’s time periods (roughly eight weeks) that most closely follow the optimal strategy’s price pattern. Next, compare the observed average profits across these 8 weeks with those for the remaining 15 weeks. Expectedly, we obtain a different set of 8 weeks for the proposed nDP model compared with the benchmark logit model. Table 8 displays the results of this validation test. They show that ( 1) following the proposed nDP-based linear demand model’s optimal price path yields, on average, a higher profit (~$51,088 a week) compared with the retailer’s current pricing strategy in the remaining 15 weeks; ( 2) following the benchmark logit demand model’s optimal price path yields, on average, a higher profit (~$49,543 a week) compared with the retailer’s current pricing strategy in the remaining 15 weeks; and ( 3) the proposed nDP-based demand model does better than the benchmark logit model in the holdout optimal strategy per week profits.
E7 optimal path Remaining 15 weeks
In summary, we show that our demand model performs better than the firm’s current approach, and a retailer’s profit can significantly improve by adopting the suggested pricing strategy. The recommended price path further shows strong patterns of similarity to current pricing in terms of price tiers, thereby ensuring that the price paths are realistic and practical. We acknowledge that we have not modeled manufacturer reactions to retailer price changes in this article. However, because there is no change in the price tiers or in the brand rankings by average price in the optimal price vectors prescribed by the proposed demand model, concerns about manufacturer and consumer backlash are mitigated. Consumers (and manufacturers) are not treated to unexpected or unusual changes in retail price. This ensures that the prescribed price paths remain practical and implementable from a retailer’s perspective.
TABLE:
| | Average Profit Per Week ($) |
|---|
| Holdout Subsamples | nDP Optimal Path | Logit-Based Optimal Path |
|---|
| 8 weeks closest to optimal path | 51,088 | 49,543 |
| Remaining 15 weeks | 32,942 | 33,767 |
Conclusion
In conclusion, we contribute to the marketing literature on category management in substantive ways. First, we address a gap in the extant empirical literature by accounting for both interproduct and intertemporal demand dependencies. We do so by incorporating the dynamic elements of competition in a robust, flexible, and scalable fashion in demand models that meet practical considerations and bear face validity. Second, we develop a parsimonious, flexible, and scalable competition construct within a linear demand model framework that allows for SKU-specific heterogeneity in interproduct and interperiod competition effects (in contrast to the baseline mixed logit). Third, we find that a hi-lo pricing strategy is optimal in a multiproduct, multiperiod setting in a real-world category. Finally, conditional on our COMPTN demand model’s ability to better capture true demand than the focal firm’s current approach, our results show that optimizing prices by considering the competitive dynamic effects yields a significant rise in profitability compared with the focal retailer’s current EDLP policy. The mixed logit demand model also performs well and yields a higher category profit than the current pricing policy.
Our research has limitations that further research could address. First, we acknowledge that the model currently does not include manufacturer reactions to retailer price changes. An opportunity to model the game involving manufacturer, retailer, and consumer incentives would be an interesting extension that provides a fruitful avenue for further research. Moreover, we assume consumer reactions in the aggregate to be in line with those predicted by the proposed demand model. Second, although we have optimized SKU prices, one area for further research is to conduct a field experiment to establish the improvement in profitability at a retail chain (Kumar, Petersen, and Leone 2010). Third, we consider data from one category only, whereas data from multiple categories would help generalize the results presented in this research. We chose the beer category to demonstrate our model and approach primarily because it has the advantage that the retailer does not have a store brand in the category and therefore avoids potential biases (store brands being treated systematically differently by the retailer than other brands) and confounds (the objective function could be market-share maximization with store brands rather than profit maximization). Furthermore, we analyzed data from one retailer only. Although this helps maintain consistency in the retailer’s core clientele and the product assortment, for more generality, using data from more categories or retailers from different geographic regions would be another avenue for further research. Fourth, among the different marketing-mix elements, we optimized price alone. In principle, we could extend our approach to optimize other marketing-mix variables, such as promotions and distribution. Finally, we used a simplification in our approach, in that whereas our competition function readily computed the SKU-level sales impact of every change in any SKU’s prices, we constrained all SKUs belonging to a brand to have a common, optimal normalized unit price. A more comprehensive model might relax this constraint. Finally, further research could extend our results using a stochastic dynamic programming approach in which the error term is incorporated as a state variable in the optimization phase. Thus, although work remains to be done, the concept of using a dynamic, parsimonious description of competitive effects in a categorymanagement setting seems viable and worthy of the effort required to understand it more fully.
of MMIX.
[0]
[.081]
[SIMIL (j, i)]
{0}
{.350}
{COMPTN(j, i)}
Notes: For each focal SKU, this table shows cross-price elasticity with the rival SKU. Brackets indicate the SIMIL score; braces indicate the average
Comparative Fit and Prediction Due to SIMIL and COMPTN
Calibration Sample (Model Fit)
Holdout Sample
TABLE 5 Demand Model Estimates for the Log-Linear
COMPTN Specification a
Estimatesb
In Panel B, estimates are the posterior mean [95% credibility interval].
Correlation Between Different Price Paths
Current
Optimal Path from
Optim
Footnotes 1 Some variables, such as the time-varying MMIX elements, may be strategically set by manufacturers and retailers. We treat these potentially endogenous variables using a set of instruments comprising exogenous or predetermined variables (detailed in the “Data Description” section) to obtain a set of endogeneity-corrected values of MMIX.
GRAPH: FIGURE 2 Variance Explained Attributable to Groups of Regressors
GRAPH: FIGURE 3 Current and Optimal Price Paths by Brand ($ Price per Ounce vs. Week)
GRAPH: APPENDIX Price Paths for the Remaining Ten Brands
DIAGRAM: FIGURE 1 Flowchart of the Proposed Approach (Numbered by Sequence of Implementation)
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Record: 240- Why Unhappy Customers Are Unlikely to Share Their Opinions with Brands. By: Hydock, Chris; Chen, Zoey; Carlson, Kurt. Journal of Marketing. Nov2020, Vol. 84 Issue 6, p95-112. 18p. 2 Diagrams, 3 Charts, 1 Graph. DOI: 10.1177/0022242920920295.
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Why Unhappy Customers Are Unlikely to Share Their Opinions with Brands
For brands to thrive, they must understand consumer sentiment; if consumers' likelihood of sharing their opinion is a function of their attitude toward a brand, then brands' perception of consumer sentiment may be systematically biased. While research in consumer-to-consumer sharing (i.e., word of mouth) suggests that those with extreme attitude are more likely to share than those with neutral attitude (a U-shaped relationship), the relationship between consumers' attitude toward a brand and their propensity to share with a brand is unknown. In contrast to the U-shaped pattern observed in word of mouth, the authors find a hockey stick–shaped relationship between attitude and sharing with brands ("__/"). Those with positive attitude (vs. neutral attitude) are more likely to share their opinion, but those with negative attitude do not show a similar increase in sharing. The authors show that this pattern emerges because, among consumers with positive (vs. neutral) attitude toward a brand, reciprocity norms drive increased sharing, but among consumers with negative (vs. neutral) attitude, competing mechanisms drive behavior: the desire to vent increases sharing, but at the same time an aversion to criticize others directly deters sharing. The authors test these ideas using a series of studies, including a field study.
Keywords: attitude; aversion to criticize; brand feedback; consumer-to-brand sharing; reciprocity; surveys; venting; word of mouth
For brands to succeed, it is imperative that they understand consumer sentiment; for example, what do customers think about their brand and products? Being "in-the-know" distinguishes successful from unsuccessful brands ([31]). Brands derive much of this understanding from the information and opinions consumers share with them. While prior work in word of mouth (WOM) has examined how attitude toward a brand affects consumers' willingness to share their opinion about the brand with other consumers, research on the relationship between consumers' attitude toward a brand (from very negative to very positive) and their likelihood of sharing their opinion with said brand is limited. In this research, we ( 1) examine how consumers' attitude toward a brand affects their willingness to share verbal or written opinions directly with the brand (e.g., relative to completing brand surveys or comment forms), ( 2) elucidate the psychological drivers of this relationship, and ( 3) examine contextual moderators of this relationship (e.g., ability to exit the brand relationship). Altogether, we deepen the understanding of consumer-to-brand sharing and uncover distortions in the information brands receive from consumers, a topic of considerable practical importance.
Extrapolating from consumer WOM, one might intuit that direct consumer-to-brand opinion sharing is most likely to occur among those with very positive or very negative attitude toward a brand (i.e., a U-shaped relationship; [ 3]). This intuition is echoed by managers: In a survey of 27 business professionals (average of more than five years of work experience, enrolled in a Master of Business Administration program at an East Coast university in the United States), which asked about the relationship between customers' attitude toward a brand and their likelihood of sharing with the brand, the majority expected a U-shaped relationship (63%; for details, see Web Appendix A).[ 5]
Counter to this intuition, we demonstrate that consumers' likelihood of sharing their opinion directly with a brand depends on a confluence of psychological mechanisms that result in a hockey stick–shaped relationship between attitude level and sharing ("__/"). Among those with positive attitude toward the brand, we show that the more positive a consumer's attitude, the more likely they are to share their opinions with the brand due to reciprocity norms ([22]), explaining the right side of the "__/" attitude-sharing relationship. Among those with negative attitude toward a brand, we show two counteracting factors that result in a null relationship between attitude and sharing. First, as attitude toward the brand becomes more negative, consumers are more likely to share due to their desire to vent. Importantly though, a second factor is relevant when sharing with brands: the attitude object and audience are one and the same (the brand). People tend to avoid sharing negative information with the attitude object ([29]) because doing so induces social discomfort and guilt ([51]). Given that people often anthropomorphize brands ([ 1]), it follows then that unhappy consumers may be averse to sharing their attitude with the brand. This aversion to criticize counteracts the desire to vent (explaining the left side of the "__/").
We examine these ideas in a series of studies using diverse methods, including two studies that measure real sharing behavior. In so doing, this article makes several contributions for theory and practice. First, we empirically explore the relationship between consumer attitude toward a brand and their likelihood of sharing their opinion directly with the brand. We find that counter to the lay belief of a U-shaped relationship, which is found between attitude and sharing with other consumers (WOM), the results show a hockey stick–shaped ("__/") relationship between attitude and sharing with the brand. Second, we identify aversion to criticize as an important determinant of consumer (non)sharing with brands and show that unhappy consumers may be unlikely to share their discontent with a brand because doing so is uncomfortable. Furthermore, while prior work suggests that consumers often ascribe human-like traits to brands, our work provides empirical support for this within the context of opinion sharing and shows one interpersonal norm—the aversion to criticize someone directly—even applies when consumers communicate information to brands.
Third, the current research sheds light into the drivers of consumer sharing (desire to reciprocate, desire to vent, and aversion to criticize) and suggests that activation of these motives is context dependent. For example, while reciprocity norms and the need for emotion regulation may be active when sharing with both other consumers and brands, we show that the aversion to criticize uniquely suppresses sharing with brands. As such, the current work extends research on motivations behind social sharing ([ 7]; [13]; [14]; [15]; [32], [33]; [42]).
Furthermore, this work extends prior research in consumer complaint behavior (CCB; [46]) and suggestion sharing ([11]). Building on [25] voice/exit/loyalty model, research in CCB has focused primarily on providing a taxonomy of complaint behaviors (e.g., complaining to companies, friends, third parties; exiting; [45]) as well as understanding how individual differences (e.g., sophistication, belief in efficacy) and context (e.g., industry) moderate complaining behavior ([45], [46]) after a negative incident ([47]). And so while the context of our exploration differs from that of the CCB literature in several ways (e.g., we look at general attitude rather than reaction to a specific incident and we examine the full attitude spectrum rather than focus just on dissatisfaction), we extend the research in CCB by shedding light on the underlying psychological mechanisms that link attitude to complaining (and other forms of sharing) directly with the brand.
Finally, for practitioners who are interested in understanding consumer sentiment, this work suggests that the information consumers share with brands may be nonrepresentative and positive-leaning. By documenting this bias, our work highlights the need for firms to adopt opinion-seeking strategies that minimize consumers' anticipated discomfort (i.e. their aversion to criticize) or provide sufficient incentives to overcome this psychological barrier.
Research in WOM suggests a U-shaped relationship between attitude and consumers' likelihood of sharing information about a brand with other consumers ([ 3]). A separate stream of research has provided some insight into the motivations behind WOM sharing (e.g., [ 7]; [17]; [24]). Two psychological motives identified by this latter stream may help explain the effect of attitude on sharing with brands: the desires to reciprocate and vent.
Prior research has suggested that consumers are likely to talk about a brand they like as a form of reciprocating for previous benefits the brand provided ([ 7]; [24]). This line of thinking is consistent with research in social interactions where balance is derived from a reciprocal relationship ([ 4]; [22]). For example, by buying a coworker lunch one day, an employee is more likely to receive help on request at a later date. In a marketing domain, prior research has suggested that the more a brand provides a consumer, the more the consumer will feel that they owe the brand and are thus more likely to comply with the brand's requests ([34]; [37]). The reciprocal exchange ([26]) does not require actions to be of a similar nature ([10]). Within our context, this suggests that as consumers' positive attitude toward a brand increases, they will feel an increased desire to reciprocate and, in turn, share information with the brand.
On the flip side, a second motive that might drive sharing as a function of attitude is the desire to vent. When unhappy, people often try to regulate their negative emotions ([ 6]; [18]) by engaging in acts such as finding a distraction (e.g., watch movies, visit a spa, exercise, go hunting; [43]), suppressing negative emotions ([12]), or reappraising their thoughts ([44]). In a consumer context, as consumers' negative attitude toward a brand becomes more negative, their desire to vent (which produces a cathartic release; [ 2]; [ 8]) is likely to increase. Consistent with this mechanism, previous work has found that negative attitude corresponds to complaining behavior ([47]; also referred to as "voicing"; [45]) or negative WOM ([ 7]; [24]). Within our context, we posit that as consumers' attitude toward a brand becomes more negative, they will have a greater desire to vent (to regulate their emotions), which in turn leads them to share their opinion with the brand.
Together, the desire to reciprocate and the desire to vent suggest that the relationship between consumers' attitude and their propensity to share with brands is U-shaped. As attitude becomes increasingly positive (i.e., going from neutral to very positive), the desire to reciprocate will increase sharing (right side of the U). As attitude becomes increasingly negative (i.e., going from neutral to very negative), the desire to vent will increase sharing (left side of the U).
While the aforementioned mechanisms would lead to a U-shaped relationship between attitude and sharing with brands, one unique feature of sharing with brands is that the recipient of the information is also the attitude object itself. Prior research has shown that sharing negative information about a person with that person induces discomfort, even when the sharing is anonymous ([36]; [50]). For example, students dislike telling others that they (the others) did poorly on tests ([50]), and people are less likely to mail postcards that notify the receiver of a fine ([36]). This discomfort stems from multiple causes, including adherence to social norms, guilt from transmission, and avoidance of negative responses or retribution (thus the adage "don't shoot the messenger"; [39]). Because of the discomfort, people often delay, sugarcoat, or outright avoid bearing the bad news ([28]; [29]; [51]). While these findings are rooted in interpersonal communication contexts, prior research has suggested that consumers often ascribe human characteristics to brands ([ 1]), and so one possibility then is that the aversion to sharing negative information with the target of that negativity might extend to consumer-to-brand sharing.
Therefore, while consumers with an increasingly negative attitude might be motivated to share their opinion with a brand out of the desire to vent, they weigh the costs and benefits of doing so ([25]; [46]). Therefore, even though the desire to vent might compel people to share their opinions (to reap a cathartic release), the discomfort (psychological cost) associated with sharing negative information with the attitude object is likely to deter consumers from doing so. This psychological cost is likely to be effective in deterring sharing because consumers typically have other methods of emotional regulation available to them, such as leaving the brand ([25]; [30]; [46]).
Taken all together, while prior research and intuition might suggest that sharing with the brand is characterized by a U-shaped relationship, we hypothesize that sharing with a brand will be better described by a hockey stick–shaped relationship. On the right side of the attitude spectrum (going from neutral to positive), the desire to reciprocate will drive consumers with increasingly positive attitude to share their opinion. On the left side of the attitude spectrum (going from negative to neutral), the desire to vent might drive consumers with increasingly negative attitude to share; however, the aversion to criticize deters those from sharing. Please note that while we focus on these mechanisms in the current article given their theoretical backing as drivers of sharing, this does not mean that we reject the possibility of other mechanisms, such as effort, desire to help oneself, reluctance to help the brand, perceived futility of sharing, and so on. We test some of these alternative mechanisms and acknowledge that other mechanisms may be relevant in the current or other sharing contexts ([11]).[ 6] For an overview of our proposed framework, see Figure 1.
Graph: Figure 1. Conceptual model for the effect attitude on sharing with brands.Notes: Studies 1–7 examine the attitude-sharing relationship. Studies 3–5 examine the suppressing role of aversion to criticize on sharing among those with negative attitude. Studies 5 and 6 examine the mediating role of the desire to reciprocate and desire to vent, with Study 5 examining alternative mechanisms. Studies 6 and 7 examine the moderating role of the aversion to criticize (in relation to ability to exit in Study 6 and audience composition in Study 7).
In a series of seven studies (Studies 1–7), we test and demonstrate a hockey stick–shaped relationship between attitude and sharing with the brand. Studies 3–7 provide insight into the mechanisms behind this relationship, and Studies 3, 6, and 7 examine managerially important contextual variables (brand anthropomorphism [Study 3], the ability to exit [Study 6], and audience composition [Study 7]). Note that in studies where attitude is measured (Studies 1– 3, 5, and 7), we use segmented regression analysis to test our proposed theory; in studies where attitude was manipulated (Studies 4 and 6), we use dummy coding.
In Study 1, we collaborated with an on-campus retailer (that operates convenience stores, coffee shops, and a salad shop) and tested the relationship between participants' attitude toward the retailer and their completion of a real customer survey sent by the retailer.
In this study, data were collected from 1,285 participants (47% female, Mage = 21.2 years) from a U.S. East Coast university research pool; participants received partial course credit for participating. Following lab norms, we recruited all students who are enrolled in the participant pool. For the independent variable, participants indicated their attitude toward a well-known campus retailer that operates convenience stores around campus: "How satisfied are you with [retailer]?" (1 = "very dissatisfied," and 7 = "very satisfied"; for manipulations and measures for all studies, see Web Appendix B). Approximately two weeks later, participants received a survey request from the campus retailer. Participants received customized links so that we could connect their survey completion data with the attitude data they provided in the lab. The email subject line read, "Give [retailer] Feedback" and the email message read: "[Retailer] is conducting a survey. Please take a few minutes and fill out the survey at the link below. Thank you for your time!" This was followed by a hyperlink to the survey. No incentives were offered. To measure sharing with the brand, we recorded whether or not participants completed the customer survey. Overall, 146 out of 1,285 (11.3%) participants completed the survey.
A visual inspection of the data provides initial evidence for a nonlinear, hockey stick–shaped relationship between attitude and sharing (see Figure 2, Panel A; for cell means for all studies, see Table 1). To test the hockey stick–shaped relationship, we used a segmented regression analysis ([19]). The segmented regression tests for structural break in the regression coefficient by examining whether the independent variable (attitude in our case) has a differential effect on the dependent variable (sharing) in different segments of the regression. We used the segmented package in R ([35]), in which a suggested breakpoint is entered initially to guide the regression; for all studies that measured attitude, four was entered as the breakpoint as it is the neutral point for attitude on the scale. A Davies test ([16]) revealed that the effect of attitude on sharing (coefficients for attitude) was different above and below the breakpoint (p <.05). Below the breakpoint (negative side of the attitude spectrum), there was no effect of attitude on sharing (b = −.0002, z = −.01, p >.1). Above the breakpoint (positive side of the attitude spectrum), there was a positive (albeit marginal) effect of attitude on sharing (b =.04, z = 1.78, p =.075).
Graph: Figure 2. Sharing as a function of attitude.
Graph
Table 1. Summary Results (Completion Count/Likelihood, N, and SE) by Satisfaction Level for Each Study and Text Coding for Study 5.
| | Attitude |
|---|
| Study 1 | | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|
| % | 9% | 8% | 9% | 8% | 13% | 15% | 20% |
| n | 99 | 150 | 176 | 301 | 241 | 249 | 69 |
| se | .03 | .02 | .02 | .02 | .02 | .02 | .03 |
| Study 2 | | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| Comment | M | 2.82 | 2.85 | 2.76 | 2.74 | 3.12 | 3.45 | 4.12 |
| n | 11 | 33 | 46 | 139 | 311 | 340 | 220 |
| SD | 1.54 | 1.44 | 1.23 | 1.58 | 1.58 | 1.57 | 1.85 |
| Survey | M | 3.33 | 3.44 | 3.40 | 3.50 | 4.01 | 4.67 | 5.16 |
| n | 15 | 27 | 57 | 103 | 278 | 287 | 237 |
| SD | 1.99 | 1.97 | 1.79 | 1.53 | 1.72 | 1.54 | 1.78 |
| Study 3 | | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| % | 27% | 30% | 28% | 28% | 34% | 39% | 43% |
| n | 274 | 191 | 252 | 258 | 414 | 315 | 260 |
| se | .03 | .03 | .03 | .03 | .02 | .03 | .03 |
| Study 4 | | Negative | Neutral | Positive | | | | |
| M | 3.61 | 3.63 | 4.83 | | | | |
| n | 109 | 109 | 109 | | | | |
| SD | 2.04 | 1.86 | 1.89 | | | | |
| Study 5 | | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| % | 38% | 32% | 42% | 34% | 44% | 56% | 65% |
| n | 24 | 22 | 71 | 133 | 262 | 324 | 242 |
| SE | .10 | .10 | .06 | .04 | .03 | .03 | .03 |
| Text Coding: | n | 9 | 7 | 30 | 45 | 116 | 180 | 157 |
| Sentiment | M | 2.0 | 2.1 | 2.4 | 3.2 | 3.7 | 4.5 | 5.1 |
| SD | .55 | .97 | .95 | .89 | .94 | 1.08 | 1.00 |
| Praise/compliment | M | 1.1 | 1.4 | 1.3 | 1.7 | 2.1 | 3.2 | 3.9 |
| SD | .16 | .94 | .54 | 1.12 | 1.28 | 1.69 | 1.64 |
| Anger/frustration | M | 4.7 | 4.7 | 3.7 | 2.8 | 2.3 | 1.7 | 1.3 |
| SD | 1.25 | 1.43 | 1.61 | 1.23 | 1.21 | .87 | .53 |
| Helpful/constructive | M | 3.8 | 3.6 | 3.3 | 3.2 | 2.9 | 2.9 | 2.8 |
| SD | 1.14 | .99 | 1.04 | 1.31 | 1.22 | 1.31 | 1.16 |
| Study 6 | | Negative | Neutral | Positive | | | | |
| Exit | M | 3.96 | 3.96 | 5.07 | | | | |
| n | 52 | 53 | 54 | | | | |
| SD | 1.96 | 1.74 | 1.44 | | | | |
| No exit | M | 5.79 | 3.80 | 5.39 | | | | |
| n | 52 | 55 | 54 | | | | |
| SD | 1.42 | 1.95 | 1.49 | | | | |
| Study 7 | | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| Brand | M | 3.38 | 3.47 | 3.46 | 3.51 | 3.89 | 4.60 | 5.57 |
| n | 48 | 62 | 99 | 163 | 213 | 178 | 91 |
| SD | 2.39 | 2.07 | 1.75 | 1.72 | 1.58 | 1.61 | 1.65 |
| Consumers | M | 5.69 | 5.16 | 4.76 | 4.68 | 5.35 | 5.71 | 6.14 |
| n | 29 | 62 | 106 | 145 | 227 | 188 | 101 |
| SD | 1.49 | 1.61 | 1.69 | 1.48 | 1.02 | .98 | 1.15 |
As a complementary analysis, we ran a logistic regression that modeled sharing as a function of negative attitude (negative attitude dummy = 1 for attitude levels of 1–3, 0 otherwise) and positive attitude (positive attitude dummy = 1 for attitude levels of 5–7, 0 otherwise), with neutral (attitude = 4) serving as the baseline.[ 7] Results revealed a null effect of the negative attitude dummy on sharing (b =.05, z =.19, p >.1), but a significant effect of positive attitude dummy on sharing (b =.66, z = 2.79, p <.01). These results mean that consumers with negative attitude (8.7% shared) were no more or less likely to share as those with neutral attitude (8.3%), but those with positive attitude (15.0%) were more likely to share (8.3%) compared with neutral.
The data from Study 1 also enable us to speak to the managerial question of how sentiment gathered by a firm might be biased. Specifically, we compared the average attitude of those answering the survey (vs. not) and show that mean attitude for respondents (M = 4.57) is significantly higher than the mean attitude for nonrespondents (M = 4.07; t( 1,283) = −3.32, p <.01). Altogether, Study 1 provides initial support for our theory in the field: using real attitude toward a brand and real sharing behavior, we show a hockey stick–shaped relationship between attitude and sharing. One might wonder, however, if there is something unique about the sharing context (feedback survey) or specific product category used (campus retailer). Study 2 addresses both of these issues by introducing a new context (an online comment form) and by examining a different product category (wireless providers).
In Study 2 we test the robustness of our effect in two different consumer-to-brand sharing contexts: a survey and a comment form found on the brand's website. Namely, we measure consumers' attitude toward their wireless provider and their likelihood of sharing with the brand in each context. If the effect is robust and generalizable, then we should observe a hockey stick–shaped relationship between attitude and sharing for both sharing contexts.
For the study, 2,126 workers (55% female; Mage = 35.47 years) from Amazon Mechanical Turk (MTurk) participated for a small payment ($.25). Existing research suggests that the distribution of satisfaction data skews left ([20]) where there are often more consumers with positive than negative attitude. To approximate the rule-of-thumb recommendation of 30 observations per cell, we estimated that approximately five times more participants would be needed than the 210 that would be recruited if attitude was assumed to be equally distributed. Thus, we posted the study for 2,100 participants and 2,126 participants completed it.
Participants first identified their wireless service provider from a dropdown menu. Participants then provided their likelihood (1 = "very unlikely," and 7 = "very likely") of sharing directly with the brand by responding to one of two randomly assigned questions: "Imagine your wireless service provider sent you a survey, how likely would you be to complete it?" (survey condition) or "Imagine you are on your wireless service provider's website. You happen to see a comment box that requires you to enter your email. How likely are you to leave a comment?" (comment form condition; modeled after online comment forms from major brands' websites). Participants also indicated their attitude toward their service provider, "How satisfied are you with your wireless service provider overall?" (1 = "very dissatisfied," 4 = "neither satisfied nor dissatisfied," and 7 = "very satisfied") The sharing and attitude questions were randomized to avoid question order effect ([17]). Twenty-two recruits who listed their company as "not applicable" were excluded from the analysis.
We followed the same approach used in Study 1 to test for the hockey stick–shaped relationship between attitude and sharing, but we also included context as a moderating variable (coded as 0 = comment form, 1 = survey). A Davies test showed that the effect of attitude on sharing was different above and below the breakpoint (p <.001). Below the breakpoint (i.e., negative side of the attitude spectrum), attitude did not have a significant effect on sharing (b = −.05, t( 2,098) = −.60, p >.1). Above the breakpoint (positive side of the attitude spectrum), there was a positive effect of attitude on sharing (b =.53, t( 2,098) = 5.29, p <.001). There was also a marginal effect of sharing context, where participants were more likely to share via the survey than the comment form (b =.45, t( 2,098) = 1.83, p =.07), and a significant interaction of attitude and sharing context (b =.12, t( 2,098) = 2.24, p <.05), where the slope between attitude and sharing is more positive for participants sharing via a survey than comment form (see Figure 2, Panel B). Regardless of context, we see a hockey stick–shaped relationship.
As a supplementary analysis, we again ran a regression to examine the effect of attitude on sharing by using negative and positive dummies (where neutral served as the baseline) and their interactions with context (0 = comment, 1 = survey). The results revealed a null effect of the negative dummy on sharing (b =.06, t( 2,098) =.25, p >.1) but a significant positive effect of the positive dummy on sharing (b =.75, t( 2,098) = 4.91, p <.001). There was a significant effect of sharing context (b =.75, t( 2,098) = 3.44, p <.001) but no interaction of context and attitude level (ps >.1). Taken together, regardless of the sharing context (survey or online comment form), consumers were more no more likely to share if they had negative (Msurvey = 3.40, Mcomment = 2.80) than neutral (Msurvey = 3.50, Mcomment = 2.74) attitude, but they were more likely to share if they had positive (Msurvey = 4.58, Mcomment = 3.50) than neutral attitude.
Study 2 provides additional evidence for the hockey stick–shaped relationship between attitude and sharing in a different product context (wireless provider) and across different forms of sharing (completing a solicited survey and offering an unsolicited online comment). In the subsequent studies, we focus on the underlying mechanisms. Specifically, in Studies 3 and 4 we focus on the role of aversion to criticize given its novelty in the consumer sharing context, and then in Study 5, we examine its role in conjunction with the desire to reciprocate and the desire to vent.
In Study 3 we examine the relationship between consumers' attitude toward a brand and real sharing (via survey completion) behavior while digging deeper into our aversion-to-criticize mechanism. Importantly, our theory presumes that consumers often anthropomorphize brands and thus are averse to delivering bad news about the brand to the brand itself. This suggests that individual differences in the extent to which consumers anthropomorphize a brand should moderate their likelihood of sharing with the brand such that greater (lesser) anthropomorphization should lead to decreased (increased) sharing among those with negative attitude toward the brand.
Study 3 was conducted in two parts: we first measured attitude and anthropomorphism and then actual sharing behavior (via survey completion). In part one, 2,030 workers (54% female; Mage = 36.98 years) from MTurk participated for a small payment ($.50). The goal of this first part was to identify a suitable focal brand to use for part two and to measure participants' attitude toward and anthropomorphization of the brand. In randomized order, participants provided their attitude toward five different tech companies (e.g., Facebook, Google; 1 = "very dissatisfied," 4 = "neither satisfied nor dissatisfied," and 7 = "very satisfied") and the extent to which they anthropomorphized them. Anthropomorphism was measured using items adapted from [27]; "It seems almost as if [company] has its own beliefs and desires," "It seems almost as if [company] has its own consciousness," "It seems almost as if [company] has a mind of its own," "It seems almost as if [company] has feelings and emotions," and "It seems almost as if [company] should be treated like a human"; 1 = "strongly agree," and 7 = "strongly disagree"; α =.92). An opt-out option was available for each company ("I have never used this company"). Facebook was chosen as the focal brand given high usage rate (97%) and high variation of attitude toward the company compared with the other brands (Mattitude = 4.28, SD = 1.97[ 8]).
Two weeks later, participants who indicated Facebook usage in part one (N = 1,964) received an email for a seemingly unrelated survey: "Dear Facebook User, MGU Market Research Group is surveying Facebook users to learn about their experiences on the platform and perceptions of the company. The results of the survey will be shared with Facebook. You are invited to complete the survey by following the link below, participation is completely voluntary" (see survey in Web Appendix C). Sharing was measured as survey completion (0 = not completed, 1 = completed). Participants were debriefed following the study.
We followed the same approach used in Study 1; the results again show a hockey stick–shaped relationship (see Figure 2, Panel C). To test the predicted attitude-sharing relationship, along with the moderating effect of anthropomorphism, we ran segmented analysis with anthropomorphism (mean-centered) as a moderating variable; the Davies test showed that the effect of attitude on sharing was marginally different above and below the breakpoint of four (p <.08). Below the breakpoint (i.e., negative side of the attitude spectrum), attitude did not have a significant effect on sharing (b =.01, z =.59, p >.1). Above the breakpoint (positive side of the attitude spectrum), there was a positive effect of attitude on sharing (b =.05, z = 2.35 p <.05). Importantly, the segmented regression also revealed a marginal interaction of attitude and anthropomorphism (b =.01, z = 1.85 p =.06). The interactive effect of attitude and anthropomorphism, coupled with the differential effect of attitude above and below the breakpoint, is captured in Figure 2, Panel C: the data reveal that among those with negative attitude toward the brand, greater anthropomorphization of the brand led to decreased sharing.
As a supplementary analysis, we again ran a logistic regression to examine the effect of attitude on sharing by using negative and positive dummies (where neutral served as the baseline) and interacting each dummy with anthropomorphism. The results revealed a positive effect of the positive dummy on sharing (b =.09, z = 2.87, p <.01) and that the positive dummy did not interact with anthropomorphism (p >.1). There was no main effect of negative attitude (b = −.01, z = −.18, p >. 1), but there was a marginal interaction between negative dummy and anthropomorphism (b = −.04, z = −1.71, p =.09), meaning that as anthropomorphism increased, unhappy consumers were less likely to share.
Study 3 provides additional evidence for the hockey stick–shaped relationship between attitude and sharing using real behavior, and provides additional evidence for our underlying theory. Specifically, we see that among consumers with a negative attitude toward a brand, the extent to which they anthropomorphized the brand affected their likelihood of sharing with the brand: the more (less) consumers anthropomorphized the brand, and thus felt greater (less) aversion to criticize, the less (more) likely they were to share. Showing the moderating effect of anthropomorphism further buttresses our underlying framework.
Study 4 tests the role aversion to criticize plays in generating the hockey stick–shaped effect more directly. To do this, we measure individual differences in aversion to criticize and test its moderating effects on sharing among those with negative attitude toward the brand.
For this study, we recruited 327 workers from MTurk (46% female; Mage = 37.3 years) for a 3 (attitude: negative, neutral, positive) × 1 (continuous; measured aversion to criticize) between-subjects design. Participants imagined an experience with a cable company and were assigned to one of three attitude levels: negative (e.g., outages are common, agents are very curt), neutral (e.g., outages are occasional, agents are nondescript), or positive (e.g., outages are rare, agents are polite; for full stimuli, see Web Appendix B). Then, participants were asked how likely they would be to complete a 20-minute customer feedback survey (1 = "very unlikely," and 7 = "very likely"). As a manipulation check, participants were asked to indicate their attitude toward the brand (1 = "very dissatisfied," 6 = "neither satisfied nor dissatisfied," and 11 = "very satisfied"). We then measured individual differences in the aversion to criticize using four items that were designed to capture anticipated conflict and discomfort stemming from delivering negative information to the source of that negativity: "I feel uncomfortable expressing dissatisfaction to companies," "I don't like to tell companies what they are doing wrong," "I like to avoid the discomfort of confronting brands that have left me dissatisfied," and "I like to avoid the conflict of telling companies what I don't like" (1 = "strongly disagree," and 7 = "strongly agree"). The items were averaged to form a single index (α =.92), where higher scores reflect a greater aversion to criticize others directly.
To check our manipulation, we used dummy coding (positive dummy = 1 if in the positive condition, 0 otherwise; negative dummy = 1 if in the negative condition, 0 otherwise), with the neutral condition serving as the baseline. Results showed that consumers' attitude toward the brand was more negative in the negative condition (M = 4.19) than the neutral condition (M = 6.91; b = −2.72, t(324) = −10.71, p <.001); those in the positive condition (M = 9.04) had more positive attitude than those in the neutral condition (b = 2.13, t(324) = 7.89, p <.001).
To test the proposed hockey stick–shaped relationship, we modeled sharing as a function of the positive and negative attitude dummies, with the neutral condition as the baseline. Consistent with the hockey stick–shaped relationship, the model revealed a significant effect of the positive attitude (b = 1.20, t(324) = 4.59, p <.001), indicating that consumers in the positive attitude condition were more likely to share than those in the neutral condition (M = 3.63); there was no effect of negative (3.61) versus neutral attitude (b = −.02, t(324) = −.11, p >.1).
To test the moderating effect of aversion to criticize, we then modeled sharing as a function of the positive and negative attitude dummies, as well as their respective interactions with aversion to criticize. The model again revealed a significant effect of the positive attitude (b = 1.56, t(321) = 2.86, p <.01); there was no interaction between positive attitude and aversion to criticize (b = −.13, t(321) = −.75, p >.1). Importantly, and consistent with our theory, the model revealed a significant interaction of negative attitude and the aversion to criticize (b = −.61, t(321) = −3.29, p <.01), meaning that as aversion to criticize increases (decreases), those in the negative attitude condition become less (more) likely to share. Figure 2, Panel D, provides a visual representation of sharing as a function of attitude and aversion to criticize. Among those low in aversion to criticize, we see a U-shaped relationship; among those high in aversion to criticize, we see that as attitude becomes more negative, they are less likely to share. These results are consistent with our theory that aversion to criticize deters those with negative attitude toward a brand from sharing.
In Study 5, we test the proposed underlying mechanisms driving the relationship between attitude and sharing simultaneously. To do so, we measure participants' attitude toward their wireless provider; their likelihood of sharing with their wireless provider; and their desire to reciprocate, desire to vent, and aversion to criticize. We predict a mediating effect of the desire to reciprocate as consumers move from neutral to positive attitude, and a mediating effect of the desire to vent as consumers move from neutral to negative attitude. Importantly, our theory predicts that the aversion to criticize should moderate the mediating effect of the desire to vent, where the mediating effect of the desire to vent should weaken as aversion to criticize increases. Furthermore, while we are primarily interested in whether consumers share, one might also be curious about what consumers share. As such, we collect open-ended text data in the study to provide insight into this question. Finally, it is possible other motivations drive consumer-to-brand sharing. One might argue that our effect is driven by effort, where those with positive attitudes might be more willing to bear the effort of providing feedback. Along the same vein, it is also possible that our effect is driven by those with negative attitudes being reluctant to help the firm (in relation to sharing their opinion). Finally, one might argue that rather than being driven by the desire to vent, those with increasingly negative attitudes are motivated to share due to the desire to benefit themselves. We test all three explanations in the current study.
One thousand ninety-one participants (54% female; Mage = 34.60 years) from MTurk completed the study for a small payment ($.50). Following the logic from Study 2, we posted for 1,050 participants; 1,091 ended up completing the study. Participants first identified their wireless service provider; as in Study 2, participants (13) who responded "not applicable" were excluded from the analysis. Participants were then asked whether they would answer a survey sent by their wireless provider (1 = "yes," 0 = "no") and were asked to indicate their attitude toward their wireless service provider: "How satisfied are you with your wireless service provider overall?" (1 = "very dissatisfied," 4 = "neither satisfied nor dissatisfied," and 7 = "very satisfied"); order of the sharing and attitude questions was randomized.
Participants who said that they would complete the survey were then asked to write their comments/feedback in an open-ended question. All participants then indicated their desire to vent, desire to reciprocate, and aversion to criticize (order randomized). The desire to vent was measured using three items: "I want to vent about my experiences with the company," "I want to express my frustrations about the company," and "I want to let out my feelings about the company" (α =.93). The desire to reciprocate was also measured using three items: "I feel like I owe the company some favor(s)," "I am happy to comply with request(s) from the company," and "I feel like I should reciprocate for what the company has done for me" (α =.77). The aversion to criticize was measured using four items: "I tend to feel uncomfortable expressing my opinions to those who have made me feel unhappy (companies, people, etc.)," "I don't like to tell companies and people what they are doing wrong," "I like to avoid the discomfort of confronting companies and others who have wronged me," and "I like to avoid the conflict of telling companies and other people my issues with them" (α =.91).
One might wonder if the hockey stick–shaped relationship might be better explained by a cost–benefit analysis. As such, we measured perceived effort ("It would be effortful to share information with this company") as well as perceived benefit (two items: "It would benefit me to share my opinion of the company" and "I would be better off by giving feedback to the company"; α =.89). Furthermore, one might argue that the shape is driven by the desire to (not) benefit the company itself. To examine this, we measured people's propensity to help the company ("I want to help the company" and "Sharing information with the company would help them"; α =.75; all measures taken on a scale of 1 = "strongly disagree," and 7 = "strongly agree"). For correlations between measures, see Table 2.[ 9]
Graph
Table 2. Correlations Between Variables in Study 5.
| Attitude | Sharing | Reciprocate | Vent | Aversion | Help Self | Help Brand |
|---|
| All Participants | | | | | | |
| Sharing | .19*** | | | | | | |
| Reciprocate | .38*** | .38*** | | | | | |
| Vent | −.39*** | .16*** | .07* | | | | |
| Aversion to criticize | .02 | −.09** | .15*** | −.05 | | | |
| Help self | .10** | .38*** | .41*** | .33*** | −.04 | | |
| Help brand | .32*** | .35*** | .50*** | .04 | −.01 | .54*** | |
| Effort | .05 | −.11*** | .04 | .06 | .09** | .06* | .11*** |
| Participants with Negative Attitude (1–4) | | | | | |
| Sharing | .03 | | | | | | |
| Reciprocate | .23*** | .11 | | | | | |
| Vent | −.30*** | .39*** | .07 | | | | |
| Aversion to criticize | .00 | −.19** | .10 | −.29*** | | | |
| Help self | .02 | .24*** | .20** | .38*** | −.11 | | |
| Help brand | .09 | .26*** | .33*** | .24*** | −.06 | .47*** | |
| Effort | .09 | −.04 | .01 | .02 | .07 | .13* | .18** |
| Participants with Positive Attitude (5–7) | | | | | |
| Sharing | .16*** | | | | | | |
| Reciprocate | .26*** | .41*** | | | | | |
| Vent | −.19*** | .18*** | .21*** | | | | |
| Aversion to criticize | .01 | −.06 | .17*** | .03 | | | |
| Help self | .09** | .41*** | .46*** | .37*** | −.30 | | |
| Help brand | .17*** | .35*** | .49*** | .12*** | .01 | .57*** | |
| Effort | .05 | −.14*** | .04 | .08* | .10** | .04 | .09* |
1 *p <.05.
Following previous studies, we first ran a segmented regression. A Davies test again showed that the effect of attitude on sharing was different above and below the midpoint (p <.01). Consistent with a hockey stick shape, below the breakpoint there was no effect of attitude on sharing (b = −.01, z = −.40, p >.1), but above there was a positive effect of attitude on sharing (b =.10, z = 3.02, p <.01; see Figure 2, Panel E). As in Studies 1 and 2, we ran a supplemental analysis in which we regressed negative attitude and positive attitude dummies on sharing (with neutral attitude serving as baseline). The results show a nonsignificant effect of negative attitude (39%) on sharing (vs. neutral [34%]; b =.23, z =.90, p >.1) but a significant positive effect of positive attitude (55%) on sharing (b =.86, z = 4.38, p <.001).
To test the proposed psychological processes that lead to the hockey stick–shaped relationship, we examine whether the desire to reciprocate drives sharing among those with increasingly positive attitude, whether the desire to vent drives sharing among those with increasingly negative attitude, and whether the latter process weakens as aversion to criticize increases. Given differential predictions for those with positive negative attitude, we used PROCESS Model 58 ([23]) and treated attitude as a categorical independent variable, which enables us to observe differences as attitude goes from neutral to negative and neutral to positive (X1: 1 = attitude 1–3, 0 = attitude 4–7; X2: 1 = attitude 5–7, 0 = attitude 1–4); sharing is the dependent variable (Y: 1 = yes, 0 = no), the desire the vent and the desire to reciprocate are mediators (Ms; we also included effort, the desire to help the brand, and the desire to help oneself as mediators to test alternative mechanisms), and aversion to criticize is the moderator (W). For brevity, our reporting focuses on our central hypotheses: that the desire to vent drives sharing among those with negative attitude (which is affected by aversion to criticize) and that the desire to reciprocate drives sharing among those with positive attitude (the full mediation output appears in Web Appendix D).
We first examine the mediating effect of the desire to vent for those with negative attitude. For consumers with negative attitude (relative to neutral), we see a mediating effect of the desire to vent (at mean level of aversion to criticize, IE =.15, 95% confidence interval [CI]: [.06,.28]).[10] Importantly, this result stands at low levels of aversion to criticize (one standard deviation below the mean; IE =.18, 95% CI: [.04,.37]), but at high levels of aversion to criticize (one standard deviation above the mean), desire to vent no longer drives sharing as the 95% confidence interval crosses 0 (IE =.12, 95% CI: [−.02,.31]). We next examine the mediating effect of the desire to reciprocate among those with positive attitude. For consumers with positive attitude (relative to neutral), we see that the desire to reciprocate drives sharing across all levels of aversion to criticize (IE−1 SD =.34, 95% CI: [.16,.57]; IEmean =.36, 95% CI: [.27,.54]; IE+1 SD =.39, 95% CI: [.19,.65]). Note that PROCESS does not provide an index of moderated mediation or pairwise contrasts for this analysis because the moderator is continuous, so we assess our hypotheses by examining confidence intervals for individual indirect effects ([23]). In light of this, we ran an alternative analysis using PROCESS Model 76, which provides relevant pairwise contrasts. Results of this model support our hypotheses and show that the desire to vent drives sharing among those with negative attitude (and this is moderated by aversion to criticize) and the desire to reciprocate drives sharing among those with positive attitude. Given the complexity and granularity of the output, we report the details of the model in Web Appendix E.
To assess the potential alternative explanations, we also consider the mediating effects of perceived effort, desire to help oneself, and desire/reluctance to help the brand. The analysis did not find process evidence for effort or the desire to help oneself as the confidence intervals for these mediators crossed 0 for both positive and negative attitudes. The desire/reluctance to help the brand did not drive sharing for those with negative attitudes but showed significant mediation for those with positive attitude (b−1 SD =.19, 95% CI: [.04,.41]; bmean =.24, 95% CI: [.11,.41]; b+1 SD =.33, 95% CI: [.11,.62]). While we did not predict this a priori, we see this result as unsurprising because reciprocal norms are likely to encompass several related submotives, such as wanting to helping the brand. Importantly, our focal results stand while we control for the motives of effort, help the self, and help the brand, which speak to the robustness of the venting, reciprocity, and aversion-to-criticize mechanisms.
Next, we looked at the open-ended text data (from those who indicated they would share with the company) to provide insights into what people share. While imperfect and subject to self-selection (because only those who indicated they would share provided text data), we attempted to extract some insights by using human coders and automated text analysis (Linguistic Inquiry and Word Count; [38]). Unsurprisingly, the results show that increasingly positive attitude led to more positive content and evidence of reciprocity (e.g., via compliments), whereas increasingly negative attitude led to more negative content and evidence of venting (e.g., via anger and swear words; for details of these analyses and extended discussion, see Web Appendix F).
Study 5 provides support for the three proposed psychological mechanisms that drive the hockey stick–shaped relationship between attitude and sharing. Specifically, we show that the desire to reciprocate drives sharing for consumers with positive attitude toward a brand, and that the desire to vent drives sharing for consumers with a negative attitude. Importantly, the latter process is moderated by the aversion to criticize, which counteracts the mediating effect of the desire to vent. Furthermore, the current study sheds light into what people share with brands and provides some corroboration that the desire to reciprocate and the desire to vent are psychological factors involved in sharing with brands.
As we have mentioned, sharing with a brand is just one way to regulate one's negative emotion toward a brand. People normally have the option to forgo this route because they have other methods of emotion regulation, such as leaving the brand (e.g., returning a product, switching to a competitor, buying a different product; [25]; [45]). However, if this typical form of emotion regulation is restricted, then consumers should be more likely to be driven by the desire to vent (another form of emotional regulation), and thus increase sharing. Accordingly, in Study 6, we restrict consumers' ability to exit a brand to test whether, in such a scenario, consumers are more likely to share their opinion with a brand, resulting in a U-shaped rather than the hockey stick–shaped attitude-sharing relationship.
For this study, we recruited 320 workers from MTurk (46.3% female; Mage = 34.03 years) for a 3 (attitude: negative, neutral, positive) × 2 (exit: able to exit, unable to exit) between-subjects design (for full stimuli, see Web Appendix B). Participants read a scenario about a TV they recently purchased from an electronics retailer that described either a negative (e.g., the image quality was lacking, mounting was a hassle), neutral (e.g., the image quality was average, mounting required some effort), or positive (e.g., the image quality was superb, mounting was easy) experience. Half of the participants were told that they could return the TV to the retailer (i.e., can exit) whereas the other half was told that they could not return the TV (i.e., cannot exit). They were then asked how likely they would be to fill out a survey sent by the company that makes the TV, "In the weeks after purchasing the TV, the company that made the TV you purchased asks you to complete a 10-minute unpaid survey. How likely are you to complete it?" (1 = "very unlikely," and 7 = "very likely"). As a manipulation check, participants were asked to indicate their attitude toward the TV in the scenario (1 = "very dissatisfied," 4 = "neither satisfied nor dissatisfied," and 7 = "very satisfied"). Next, participants responded to the two mediators, "Completing a survey for the company would allow me to reciprocate for the benefits they have provided me" and "Completing a survey for the company would allow me to vent frustrations" on a seven-point scale (1 = "strongly disagree," and 7 = "strongly agree"). In line with our theory, the desire to reciprocate should drive those with positive attitude to share regardless of the ability to exit. In contrast, the mediating effect of the desire to vent should be a stronger driver of sharing among those who are unable (vs. able) to exit a brand relationship.
A manipulation check using dummy variables representing positive and negative conditions revealed that those in the negative condition had more negative attitude (M = 1.92) than those in the neutral condition (M = 3.35; b = −1.53, t(314) = −5.91, p <.001); those in the positive condition (M = 5.25) had more positive attitude than those in the neutral condition (b = 1.93, t(314) = 7.52, p <.001). There were no interactions with ability to exit.
We regressed sharing on negative attitude (negative dummy: 1 = negative condition, 0 = otherwise), positive attitude (positive dummy: 1 = positive, 0 = otherwise), and their respective interactions with ability to exit (able to exit = 0, unable to exit = 1); neutral again serves as the baseline. The regression revealed an interaction between negative attitude and exit (b = 1.99, t(314) = 4.31, p <.001). When participants were able to exit, there is no effect of negative attitude (M = 3.96) compared with neutral (M = 3.96, b = −.001 t(314) =.02, p >.1), but when unable to exit, people with negative attitude were more likely to share (M = 5.79) relative to neutral (M = 3.80; b = 1.99 t(314) = 6.11, p <.001). There was no interaction between positive attitude and ability to exit (b =.48 t(314) = 1.04, p >.1), but there was a positive main effect of positive attitude (M = 5.23; b = 1.11 t(314) = 3.42, p <.001). In other words, when consumers are able to exit, we replicate our hockey stick shape, but when they are unable to exit, we observe a U-shaped relationship (see Figure 2, Panel F).
To test that inability to exit increased sharing among those with negative attitude due to the desire to vent, we used PROCESS Model 58 ([23]), with attitude as a categorical variable to allow for testing differential effect of moving from neutral to positive versus neutral to negative (X1: 1 = negative, 0 = neutral and positive; X2: 1 = positive, 0 = neutral and negative), sharing as the dependent variable (Y: 1 = very unlikely, 7 = very likely), the desire to vent and the desire to reciprocate as mediators (Ms), and ability to exit (W: 0 = unable, 1 = able) as a moderator. As in Study 5, our reporting focuses on our central hypotheses: that the desire to vent drives sharing among those with negative attitude (which is moderated by the ability to exit) and that the desire to reciprocate drives sharing among those with positive attitude; the full mediation output appears in Web Appendix F.
Bias-corrected bootstrap confidence intervals show that consistent with our predictions, the mediating effect of the desire to vent was moderated by the ability to exit as consumers moved from neutral to negative (index moderated mediation = −.60; 95% CI: [−.95, −.29]; see Figure 3). Consistent with our theory, the desire to vent was a stronger mediator for those who are unable to exit (b =.59, 95% CI: [.29,.93]) versus able to exit (b = −.01, 95% CI: [−.09,.04]). This is consistent with the idea that the desire to vent should be a stronger driver of sharing when people are prevented from regulating their emotion some other way (i.e., exiting the brand). On the flip side, the bias-corrected bootstrap confidence intervals show that as consumers moved from neutral to positive, the mediating effect of the desire to reciprocate was not moderated by the ability to exit (index moderated mediation =.01; 95% CI: [−.55,.55]) because sharing in both conditions was mediated by the desire to reciprocate (bunable to exit =.71, 95% CI: [.37, 1.10]; bable to exit =.71, 95% CI: [.31, 1.15]).
Graph: Figure 3. Process model for Study 6 highlighting the moderating effect of the ability to exit on the mediating effect of the aversion to criticize.**p <.01.***p <.001.Notes: This figure depicts the mediating effect of the desire to reciprocate (IV = positive attitude dummy) and the mediating effect of the desire to vent (IV = negative attitude dummy). For the full output, see Web Appendix G.
In Study 6, we show that ability to exit moderates the hockey stick–shaped relationship between attitude and sharing. When consumers are able to exit the brand (i.e., have alternative ways of regulating their negative emotions), we replicate our basic hockey stick–shaped effect; when consumers are unable to exit the relationship, those with negative attitude become more likely to share with a brand and we see a U-shaped relationship between attitude and sharing that is due to a stronger mediating effect of the desire to vent.
To further test our theory that the aversion to criticize uniquely affects sharing with brands, and to show that mechanisms identified in the WOM literature alone might not fully explain sharing with brands, we compare consumer-to-brand and consumer-to-consumer sharing. Namely, consumer-to-consumer sharing (i.e., WOM) should be less affected by the aversion to criticize relative to consumer-to-brand sharing because the attitude object is different from the audience. Accordingly, we predict a hockey stick–shaped relationship between attitude and sharing when the audience is the brand, but a U-shaped relationship when the audience is other consumers.
For the study, 2,120 workers (51% female, Mage = 34.57 years) from MTurk participated in a 2 (consumer audience vs. brand audience) × 1 (attitude; measured, seven-point scale) between-subjects design for a small payment ($.25). We determined sample size following the logic from Study 2. Consumers first identified their cable/internet company from a drop-down menu. Participants then reported their willingness to share their opinions (regarding the company) with the company itself or other consumers: "Imagine (company/a friend) sent email you asking about your experience with <insert cable/internet company> how likely are you to respond to the email?" (1 = "very unlikely," and 7 = "very likely"). They also indicated their attitude (1 = "very dissatisfied," 4 = "neither satisfied nor dissatisfied," and 7 = "very satisfied") toward the company (sharing and attitude measurement was randomized). We also measured aversion to criticize, the desire to reciprocate, and the desire to vent (for all measures, see Web Appendix B). Of the 2,120 recruits, 408 identified their company as "not applicable" and were excluded from the analysis.
Our hypotheses predict a different effect of attitude on sharing for those sharing with brands (hockey stick–shaped) versus consumers (U-shaped). A visual inspection of the data provides initial support (see Figure 2, Panel F). We used a moderated segmented regression (moderated by audience, where 0 = brand audience, 1 = consumer audience) to test for a hockey stick–shaped (U-shaped) relationship between attitude and sharing with brands (consumers). A Davies test revealed there was a significantly different effect of sharing on attitude above relative to below the breakpoint (p <.001). Importantly, there was an interaction of attitude and audience (b = −.18, t(1706) = −3.86, p <.001). When sharing with brands, there was no effect of attitude on sharing below the breakpoint (b = −.10, t( 1,706) = −1.02, p >.1), but above the breakpoint, there was a positive effect of attitude on sharing (b =.66, t( 1,706) = 13.57, p <.001); together, this produced the hockey stick relationship. When sharing with other consumers, there was a negative relationship between attitude and sharing (b = −.27, t( 1,706) = −2. 69, p <.01) below the breakpoint; above the breakpoint, there was a positive effect of attitude on sharing with other consumers (b =.47, t( 1,706) = 10.00, p <.001). Together, this produces a U-shaped relationship between attitude and sharing. Finally, there was a positive effect of audience (b = 2.09, t( 1,706) = 9.24, p <.001), indicating that consumers were more likely to share with other consumers than with brands.
As a supplemental analysis, we ran a regression in which we modeled sharing as a function of positive and negative attitude dummy variables and their interactions with audience (0 = consumer, 1 = brand). The analysis revealed a marginal interaction of the negative attitude dummy and audience (b =.41, t( 1,706) = 1.75, p =.08). This interaction is characterized by a nonsignificant effect of negative (vs. neutral) attitude (Mnegative = 3.45, Mneutral = 3.51; b = −.06, t( 1,706) = −.39, p >.1) when sharing with brands but a positive effect of negative (vs. neutral) attitude (Mnegative = 5.02, Mneutral = 4.68; b =.34, t( 1,706) = 2.04, p <.05) when sharing with consumers. The regression did not reveal an interaction of positive attitude and audience (b = −.00, t( 1,706) = −.01, p >.1) but did reveal a main effect of the positive dummy (b =.96, t( 1,706) = 6.77, p <.001), suggesting a positive effect of the positive attitude (vs. neutral) across both audiences (consumer: Mpositive = 5.63 vs. Mneutral = 4.68; brands: Mpositive = 4.46 vs. Mneutral = 3.51). Finally, the analysis also revealed a significant effect of audience (b = 1.16, t( 1,706) = 6.53, p <.001), suggesting that consumers were more likely to share with consumers than brands.
In Study 7, we manipulate audience to test the underlying effect of aversion to criticize, which should be stronger (weaker) when the audience is brand (consumers). Consistent with this idea, we observe a hockey stick–shaped (U-shaped) relationship between attitude and sharing when the audience is a brand (consumers). These results are consistent with the notion that the aversion to criticize is less likely to deter sharing to consumers among those with negative attitude because the attitude object (the brand) and audience (consumers) are different.
Through a series of studies, we show a hockey stick–shaped relationship between attitude toward a brand and consumers' likelihood of sharing with the brand. We show that this is jointly caused by three underlying mechanisms. On the negative side of the attitude spectrum, countervailing effects of the desire to vent and an aversion to criticize result in a null relationship between attitude and sharing; on the positive side of the attitude spectrum, the desire to reciprocate results in a positive relationship between attitude and sharing.
In a field study (Study 1), we first demonstrate the hockey stick–shaped relationship between consumers' attitude toward a brand and their real-life sharing with a brand (as measured as survey completion). Study 2 replicates these effects in a different product domain and in both survey and online comment form contexts. Studies 3 and 4 focus on the aversion-to-criticize mechanism: Study 3 examines real sharing behavior and shows that greater anthropomorphization of a brand deters unhappy customers from sharing with the brand (which aligns with the aversion-to-criticize mechanism), and Study 4 shows that individual differences in aversion to criticize moderate sharing among those with negative attitude. In Study 5, we provide support for the overall framework by simultaneously examining the three process mechanisms: desire to reciprocate, desire to vent, and aversion to criticize. This study also sheds light on what people share: using a combination of human coders and automated text analysis, we see that unhappy consumers share anger, anxiety and swear words (consistent with venting), whereas happy consumers share compliments/gratitude (consistent with reciprocity). Finally, Studies 6 and 7 examine important context variables and show that (in)ability to exit the brand (Study 6) and composition of the audience (Study 7) moderate our effect.
The current research contributes to a better understanding of the relationship between attitude and sharing with brands, identifies aversion to criticize as a key suppressor of sharing with brands (which differentiates consumer-to-brand sharing from consumer-to-consumer sharing), sheds light on the psychological processes that drive the relationship between attitude and sharing, extends the research in consumer complaint behavior, and provides important implications for managers. We expand on each of these contributions next.
First, we find a hockey stick–shaped relationship between attitude and sharing with brands, which contrasts with the U-shaped relationship documented between attitude and sharing with consumers ([ 3]). This finding extends our understanding of how attitude drives sharing to a new audience (brands) and extends prior work that has focused on documenting the frequency of positive versus negative WOM (East, Hammond, and Wright 2007; [40]; Wangenheim and Bayón 2007).
Second, we show that aversion to criticize suppresses unhappy customers from sharing with brands. Because consumers' interaction with brands and other people do not always follow the same patterns ([ 5]; [49]), the activation of aversion to criticize—which applies to human-to-human interactions—as a suppressor of sharing with a brand is not a foregone conclusion. By showing that aversion to criticize affects human-to-brand relationships, we identify a previously unexplored mechanism in consumer–brand interactions ([21]).
Third, we shed light on the trio of mechanisms that drive the relationship between attitude and sharing with brands and extend recent literature on motives behind social sharing (for a review, see [ 7]]). Specifically, the desire to reciprocate drives consumers who have increasingly positive attitude toward a brand to share, providing evidence for previous suppositions of this mechanism ([24]). At the same time, we show that while the desire to vent drives consumers with increasingly negative attitude to share, the aversion to criticize simultaneously deters them from sharing. These results suggest that consumers' decision to share with a brand as a function of their attitude is multiplicatively determined by anticipated psychological costs (discomfort from aversion to criticize) and benefits (emotion regulation from sharing negative attitude, fulfillment of a reciprocal relationship). That said, we do not expect these to be the only motives affecting sharing, because oftentimes, it is likely that multiple motives (some in addition to what we examined) are simultaneously activated. For example, in Study 5 we find evidence that the desire to help oneself may drive sharing among some consumers.
Fourth, we build on literature in CCB ([45], [46]), which has focused on providing a taxonomy of consumer complaint behaviors (e.g., complain to the firm, complain to third parties; [45]) and identifying consumer individual differences in complaint behavior ([ 9]). This work usually uses the "critical incidence" approach ([47]) where consumers are asked to recall a bad experience/complaint and are then asked questions about the incident ([47]; [48]). In contrast, we consider overall attitude (as opposed to attitude toward a particular incident) as the a priori and look at likelihood of sharing with the brand as a function of that attitude; thus, we capture not only data regarding those who have shared their opinions but also information regarding consumers who did not share. Importantly, we believe the finding that aversion to criticize prevents consumers from sharing negative information with brands might be of particular interest to the CCB literature, as it offers a potential explanation for why dissatisfaction level has been only weakly linked to complaining behavior ([47]). More broadly, while CCB literature has provided important insights into how dissatisfaction is linked to downstream behavior ([25]; Singh 1998, [46]), we examine the entire attitude spectrum (including neutral and positive) to learn how attitude affects sharing across a range of consumer sentiments.
Importantly, this research yields critical implications for brands' understanding of consumers. Specifically, it suggests that the data brands gather from customer feedback surveys might be rose tinted. Study 1 speaks specifically to this point and shows that while the average attitude of those who responded to the firm's survey was 4.57 (on a seven-point scale), those who did not fill out the customer survey had an average attitude of 4.07. Expressed alternatively, we found that approximately 8% of those with negative attitude shared, but 16% of those with positive attitude shared, which suggests that responses from those with negative attitude might need to be double-weighted to get a more balanced picture of sentiment. Of course, this multiplication factor of two is based on the combination of consumer segment (students) and product category (convenient store) and may be different in other contexts. That said, our studies, in which we used different product categories and participants, consistently provided a hockey stick–shaped attitude-sharing relationship, which suggests that the factor is typically greater than one.
While we showed that the sentiment collected by firms may be positively biased and displayed how certain moderators influence this bias, one especially important question to explore is how to increase sharing among unhappy customers. To this end, we conducted an additional study that varied monetary incentive (no incentive vs. $1 vs. $25; for details, see the Appendix). The results show that while a small incentive ($1) increased sharing, it did so among consumers of all attitude levels, and the hockey stick–shaped effect remained. When the incentive is made sufficiently large ($25), however, the hockey stick–shaped pattern disappeared as respondents were equally likely to share across attitude levels (reaching a near ceiling; for study details, see the Appendix). These results suggest that large monetary incentives may overpower psychological catalysts and deterrents of sharing identified herein. In light of this finding, firms could seek to experiment with more cost-effective approaches to increase sharing (e.g., lottery-based incentives). Alternatively, brands might be able to tie feedback to product rebates, which could simultaneously increase purchase rates as a sales promotion and increase the representativeness of opinions shared with the brand.
Outside of monetary incentives, firms might also explore nonmonetary ways to increase sharing of negative opinions by following the principles outlined herein. For instance, if consumers are averse to sharing negative opinions because they do not want to offend the brand, then explicit encouragement of critiques (e.g., "Critiques are vital and encouraged") might boost sharing from unhappy customers. These requests for critiques might be especially beneficial in driving sharing because the fear of retaliation is one driver of the aversion to criticize, and thus assuaging concerns of retribution is likely to increase sharing of negative attitude. Alternatively, brands might also increase sharing of negative opinions by guaranteeing that they (the brand) will analyze all data at the aggregate level and explicating that no individual consumer will be singled out due their feedback.
While we have identified a confluence of variables that influence sharing with brand, future work is needed to elucidate additional motives that drive sharing with brands as well as other audiences. For instance, research could examine how channel affects the attitude-sharing relationship. One possibility is that when sharing with brands through channels that involve more personal interaction (such as talking to an agent face-to-face or on the phone), the discouraging effect of aversion to criticize will be amplified, resulting in even less sharing from consumers with negative attitude. This possibility may be further exacerbated in service industries (relative to a product industries), where brand reps hold more agency. Along these lines, future work might also consider more broadly the cases in which consumers treat brands more like inanimate entities rather than anthropomorphized beings. It is possible, for example, that brands for which consumers tend to have very limited and cursory interactions (e.g., infrequent or one-time purchases), consumers might view the brand as less human-like and thus would be less likely to show conflict aversion when interacting with these brands. Also possible is the notion that short-term or one-time interactions would limit the possibility of a fear of retaliation, such that unhappy customers might be willing to speak out because there is little at stake and few opportunities for the brand to retaliate.
Take perceived effort, for example. It would be interesting to determine when effort concerns might be a greater or smaller deterrent for sharing. One possibility is that perceived effort is a smaller deterrent of sharing for high- (vs. low-) priced items. Another possibility is that effort is a greater deterrent of sharing when the audience is composed of other consumers rather than the brand (because the potential sharer believes WOM requires higher communication standards and is thus more effortful). Future research might also examine when prosocial goals and motives, such as helping other consumers, might influence sharing with brands. While it is easy to imagine that prosocial motives dictate WOM, might there be situations where it drives sharing with the brand?
While the hockey stick–shaped relationship replicated across two modes of sharing that differ in consumer involvement (survey [passive] or comment form [active]), it would be worthwhile to examine if there are mechanisms that uniquely moderate or mediate sharing in active versus passive sharing contexts. For example, it could be that self-serving considerations are more salient in active sharing contexts (because sharing is more voluntary [rather than solicited]), whereas effort might be more important in passive sharing contexts. Relatedly, future research could also study consumer-to-brand sharing when there are multiple audiences. For instance, when a consumer posts on a brand's social media, the post can be seen by both the brand and other consumers. In these contexts, it is likely that multiple (and potentially competing) motives are elicited; one set driven by the fact that the consumer knows the brand will see the message, and a second set that is driven by the fact that the consumer knows that other consumers will see the message. In such scenarios, research might examine which motives will prevail and under which conditions. Research is also needed to test whether the relationship between attitude and sharing is better determined by the consumers' most recent experience with a brand or their overall experience, as inconsistent information can alter the predictive value of attitude ([41]).
Finally, future work could explore the robustness of these effects in a wider range of product categories and in contexts where consumers' expectations may vary. One can imagine that in product categories where consumer expectations are very low, consumers may feel more comfortable sharing their negative opinion because they may believe that brands in this category expect negative feedback and thus anticipate little discomfort from being the bearer of bad news. Conversely, in product categories with high expectations, people might be less likely to vent. Industry-specific switching costs might also play a part. Compared with industries with low switching costs, in industries with high switching costs (e.g., high-end cars, college enrollment), in which consumers have low ability to exit, consumers might be more willing to share their negative emotions because it is difficult for them to regulate emotions by switching brands.
Supplemental Material, jm.18.0390-File003 - Why Unhappy Customers Are Unlikely to Share Their Opinions with Brands
Supplemental Material, jm.18.0390-File003 for Why Unhappy Customers Are Unlikely to Share Their Opinions with Brands by Chris Hydock, Zoey Chen and Kurt Carlson in Journal of Marketing
In this study, we examine a managerial solution to motivating consumer sharing: economic incentives. Specifically, we compare two levels of monetary incentive, moderate ($1) and large ($25), with a no incentive control.
Participants ( 2,361) recruited from MTurk (52% female; Mage = 31.37 years) for a 3 (no incentive, moderate, large) × 1 (attitude; measured [11 levels]) between-subjects design. Participants first identified their cable/internet company. They then indicated their likelihood of sharing with the brand ("Imagine [company] sent you a 15-minute survey about your experiences with them. [There would be no compensation for completing the survey/ For completing the survey you would be paid $1/For completing the survey you would be paid $25.] How likely would you be to complete it?"; 1 = "very unlikely," and 7 = "very likely"). Participants also provided their attitude toward their wireless service provider ("How satisfied are you with your wireless service provider overall?"; 1 = "very dissatisfied," 6 = "neither satisfied nor dissatisfied," and 11 = "very satisfied"). Four hundred fourteen participants specified "not applicable" for their cable/internet company and were excluded from analysis.
We report the segmented regression (6 was input as the breakpoint given that the study used an 11-point scale) to test for a hockey stick–shaped relationship between attitude and sharing with brands. A Davies test revealed there was a significantly greater effect of sharing on attitude above relative to below the midpoint (p <.001). Below the breakpoint, there was a small main effect of attitude on sharing (b =.10, t( 2,355) = 2.87, p <.01), but above the breakpoint there was a more positive effect of attitude on sharing (b =.20, t( 2,355) = 4.47, p <.001). There was also an effect of moderate incentive (b = 1.48, t( 2,355) = 7.14, p <.001) and large incentive (b = 4.39, t( 2,355) = 21.89, p <.001), indicating consumers in the moderate- and large-incentive conditions were more likely to share than those in the no-incentive condition. There was no interaction of attitude and moderate incentive (b =.02, t( 2,355) =.71, p >.1), indicating that there was a hockey stick–shaped relationship between attitude and sharing in both conditions. However, there was an interaction of attitude and large incentive (b = −.15, t( 2,355) = −5.15, p <.001), indicating that likelihood of sharing did not increase as rapidly for those above the break point in the large-incentive condition (for cell means, see Table A1).
Graph
Table A1. Likelihood of Sharing and Subject Count by Attitude Level.
| | Attitude (1 = "Very Dissatisfied," and 11 = "Very Satisfied") |
|---|
| Condition | | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
|---|
| No payment | M | 2.6 | 2.3 | 2.3 | 2.6 | 2.5 | 2.7 | 3.2 | 3.0 | 3.7 | 4.1 | 4.0 |
| n | 33 | 22 | 44 | 61 | 56 | 122 | 102 | 126 | 102 | 53 | 66 |
| $1 payment | M | 3.8 | 3.9 | 4.0 | 4.2 | 4.4 | 4.4 | 4.3 | 4.9 | 5.2 | 5.6 | 5.9 |
| n | 22 | 15 | 37 | 40 | 52 | 115 | 93 | 149 | 121 | 68 | 74 |
| $25 payment | M | 6.4 | 6.5 | 6.5 | 6.6 | 6.6 | 6.4 | 6.4 | 6.5 | 6.7 | 6.6 | 6.8 |
| | n | 32 | 23 | 31 | 39 | 49 | 121 | 89 | 128 | 135 | 59 | 82 |
4 Notes: The results indicate that moderate incentives increased sharing across all attitude levels, but did not moderate the hockey stick–shaped relationship between attitude and sharing. This suggests that economic incentives can be additive with the psychological forces that drive sharing. However, large incentives increased sharing to near ceiling points for consumers of all attitude levels. This suggests that sufficient incentives can overcome the aversion to criticize, which typically deters sharing among those with negative attitude toward a brand.
Footnotes 1 Associate Editor Don Lehmann
2 Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242920920295
5 1 ∼30% expected a negative relationship (i.e., unhappy customers are more likely to respond), ∼4% expected a positive relationship, and ∼4% expected no relationship.
6 2 We test effort, desire to help oneself, and desire to help the brand in Study 5. These and other alternative mechanisms are discussed further in the "General Discussion" section.
7 3 We conducted this supplemental analysis (partition attitude into three levels) to facilitate comparison across all studies (Studies 4 and 6 use three attitude levels), which provides a better overview of our empirical results.
8 4 Usage and attitude data for Google (%users > 99%, Mattitude = 6.05, SD = 1.33), Uber (%users = 76%, Mattitude = 5.33, SD = 1.47), Airbnb (%users = 63%, Mattitude = 5.41, SD = 1.35), and Amazon (%users = 99%, Mattitude = 5.83, SD = 1.26).
9 5 We also measured self–brand connection, brand love, psychological ownership, and regret (see Web Appendix B). Because these are not focal to our hypotheses, we do not discuss them further. However, information regarding these measures can be obtained directly from the authors.
6 Note that PROCESS provides all indirect effects as a function of the moderator (aversion to criticize). Thus, we report all results as broken down by aversion to criticize, even in cases where we do not predict moderation (such as for the reciprocation mediator, etc.).
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By Chris Hydock; Zoey Chen and Kurt Carlson
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Record: 241- With Power Comes Responsibility: How Powerful Marketing Departments Can Help Prevent Myopic Management. By: Srinivasan, Raji; Ramani, Nandini. Journal of Marketing. May2019, Vol. 83 Issue 3, p108-125. 18p. 1 Diagram, 7 Charts. DOI: 10.1177/0022242919831993.
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With Power Comes Responsibility: How Powerful Marketing Departments Can Help Prevent Myopic Management
Firms sometimes engage in myopic management (e.g., cutting marketing spending, providing lenient credit to customers to improve short-term results). Although marketing is at the center of such myopic management, there are few insights on whether a marketing department could prevent it. To address this gap, the authors examine the role of powerful marketing departments in preventing myopic marketing spending and revenue management. They hypothesize that there are internal and external enablers of marketing department power (i.e., a chief executive officer with marketing experience, the firm's power over its customers, analyst coverage, and institutional stock ownership) that help a powerful marketing department prevent myopic management. They test the hypotheses using a panel of 781 publicly listed U.S. firms between 2000 and 2015. As hypothesized, when the firm has ( 1) a chief executive officer with a marketing background and ( 2) power over its customers, increasing marketing department power decreases the likelihood of both myopic marketing spending and myopic revenue management; increasing marketing department power and analyst coverage decreases the likelihood of myopic marketing spending. The findings highlight powerful marketing leadership as a hitherto overlooked way to prevent myopic management and improve firm performance.
Keywords: marketing department power; myopic management; myopic marketing spending; myopic revenue management; power over customers
The motivation to satisfy Wall Street earnings expectations may be overriding common sense business practices...where managers cut corners....We have got to break this pattern where short-term estimates rather than long-term results drive a company's stock.
—Arthur Levitt, U.S. Securities and Exchange Commission, November 16, 1998
As U.S. Securities and Exchange Commission (SEC) Chairman Arthur Levitt noted more than 20 years ago, managers engage in real earnings management, changing their business practices to emphasize short-term results at the expense of long-term performance. For example, to increase current-period earnings, managers may cut marketing spending or provide lenient credit terms to customers to accelerate sales from the next fiscal year into the current year. Although marketing activities (e.g., spending on new products, building brands and customer relationships) are at the center of myopic management, there are few insights on the role of the marketing department in preventing myopic management.
There is a large body of work on myopic management focused on myopic marketing spending—that is, the cutting back of advertising and research-and-development (R&D) spending. In Table 1, we provide an overview of the literature, much of which has focused on the negative effects of myopic marketing spending on long-term performance. Myopic marketing spending ([15]; [45]; [53]) decreases firm value ([ 2]; [ 4]; [10]; [31]; [36]; [37]; [40]; [45]) and profits ([13]).
Graph
Table 1. Literature Review: Marketing Leadership and Myopic Management.
| Reference | Effects | Role of Marketing Leadership | Financial Drivers | Organizational Drivers | Myopic Marketing Spending | Myopic Revenue Management | Role of Other Functions in TMT | CEOs' Role | Context/Method |
|---|
| Mizik and Jacobson (2007) | Yes | No | Yes | No | Yes | No | No | No | Seasoned equity offerings |
| Cohen and Zarowin (2010) | Yes | No | No | No | Yes | Yes | No | No | Seasoned equity offerings |
| Mizik (2010) | Yes | No | No | No | Yes | No | No | No | Publicly listed U.S. firms |
| Chakravarty and Grewal (2011) | No | No | Yes | Yes | Yes | No | No | No | Publicly listed U.S. high-tech firms |
| Chapman and Steenburgh (2011) | Yes | Yes | Yes | Yes | Yes | Yes | No | No | Soup data in grocery stores |
| Currim, Lim, and Kim (2012) | No | No | No | Yes | Yes | No | No | Yes | Publicly listed U.S. firms |
| Moorman et al. (2012) | Yes | No | Yes | No | Yes (innovations) | No | No | No | Publicly listed and private U.S. firms |
| Wies and Moorman (2015) | Yes | No | Yes | No | Yes (innovations) | No | No | No | Initial public offering |
| Kothari, Mizik, and Roychowdhury (2015) | Yes | No | No | No | Yes | No | No | No | Seasoned equity offerings |
| Ahearne et al. (2016) | Yes | No | Yes | Yes | Yes | Yes | No | No | Cross-country (40 countries) survey: sales executives |
| Chakravarty and Grewal (2016) | Yes | No | Yes | Yes | Yes | No | No | Yes | Publicly listed U.S. firms |
| Saboo, Chakravarty, and Grewal (2016) | Yes | No | No | No | Yes | No | No | No | Initial public offering |
| Bendig et al. (2018) | Yes | No | Yes | Yes | Yes | No | No | No | Share repurchases in publicly listed U.S. firms |
| The current study | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Publicly listed U.S. firms |
Prior research has largely overlooked myopic revenue management, or the pulling of future revenues into the current fiscal period by offering lenient credit terms or excessive price promotions to customers. [11] and [ 2] represent two notable exceptions. Using retail data, [11] find that myopic revenue management through price promotions decreases stock returns. Using a large-scale cross-country survey of firms, [ 2], p. 1240) document negative effects of firms' real earnings management propensities on their performance.
We know much less about the factors that affect myopic management, of which there are two types: financial and organizational. As might be expected, stock market pressure ([45]; [53]), share repurchases ([ 4]), past stock returns, and volatility ([ 8]) increase myopic management. Organizational factors that affect myopic management include chief executive officer (CEO) experience and compensation, firms' marketing and R&D spending ([10]; [15]), and finance leadership ([ 2]; [11]). Prior research has primarily focused on the factors that influence myopic marketing spending, overlooking those of myopic revenue management (two exceptions are [ 2]] and [11]]).
Marketing leadership—manifest as chief marketing officers (CMOs) ([ 6]; [23]; [42]), powerful marketing departments ([20]), board members with marketing experience ([52]), and a customer on the boards of business-to-business (B2B) firms ([ 5])—improves firm performance. However, the literature has overlooked whether marketing leadership can improve firm performance by preventing myopic management. We consider this a notable omission, as marketing strategy and activities are at the core of myopic management. To address this gap, we examine whether powerful marketing departments ([20]) can prevent myopic management. In doing so, we heed the research call issued by [ 9], p. 289) for "a thorough study of the practice [of myopic management] to understand when and why it occurs."
We present three features of our hypothesis development. First, because myopic management also involves senior finance leadership and the CEO ([ 2]; [30]), we conceptualize the power of the marketing department relative to the finance department. Second, we propose that although a powerful marketing department may intend to prevent myopic management, it will not, by itself, be able to do so, because spending and revenue decisions are under the purview of the CEO and the finance department. Thus, we do not anticipate a main effect of a powerful marketing department in preventing myopic management. Third, we theorize that other internal and external enablers of power will help the marketing department prevent myopic management. We hypothesize that two internal enablers of marketing department power (the presence of a CEO with marketing experience ["marketing CEO" hereinafter] and the firm's power over its customers) and two external enablers of power (analyst coverage [[54]; i.e., the number of financial analysts following the firm] and institutional stock ownership [[ 1]]) will help a powerful marketing department's advocate against myopic management.
We test the hypotheses using a panel of 5,907 firm-years of 781 publicly listed firms between 2000 and 2015. We collect data from Standard and Poor's Compustat database, ExecuComp database, I/B/E/S analyst files, and Thomson Reuters 13F Institutional Holdings Filings database. Following [36], we measure myopic marketing spending and myopic revenue management respectively by noting whether there is an unanticipated decrease (increase) in a firm's advertising and R&D spending (accounts receivables) when the unanticipated change in profit is positive. Following [20], we measure marketing department power using data on firms' top management teams (TMTs) in the ExecuComp database.
The results, which are robust, indicate that, as expected, marketing department power does not, by itself, affect myopic management. However, as we hypothesize, when a firm has both a marketing CEO and power over its customers, increasing marketing department power decreases the likelihood of both myopic marketing spending and myopic revenue management, while increasing marketing department power and increasing analyst coverage decrease the likelihood of myopic marketing spending.
This article makes several findings that extend marketing theory. First, we contribute to the literature on myopic management by establishing a key role for marketing leadership advocacy in helping prevent both myopic marketing spending and myopic revenue management. In doing so, we show that preventing myopic behaviors is an additional mechanism by which marketing departments can improve firm performance. Second, this research's findings extend the theory on subunit power (i.e., marketing department power) by demonstrating how marketing department power can influence firms' strategies, depending on other internal (a marketing CEO and the firm's power over its customers) and external (analyst coverage) enablers of power.
Our findings also have high managerial relevance, because myopic management hurts long-term performance. Moreover, whereas myopic marketing spending is a firm's strategic choice, myopic revenue management is not. When the SEC detects myopic revenue management, it considers it a violation of generally accepted accounting principles (GAAP), resulting in significant penalties for firms and their managers. By implementing the guidelines from this research's findings, firms can prevent myopic management, improve firm performance, and reduce their risk exposure.
We organize the rest of the article as follows. We first provide an overview of myopic marketing spending and myopic revenue management and develop the hypotheses. We then describe the data and method used for hypothesis testing and present the results of the hypothesis tests. We conclude with a discussion of the article's theoretical contributions, managerial implications, and limitations and opportunities for further research.
Under pressure from investors to improve the firm's current-period performance, senior managers may manipulate its performance through accounting-based earnings management (discretionary accruals) and/or myopic management (real earnings management). Discretionary accruals manipulations involve accounting changes in financial reports ([18]) including, for example, capitalizing versus expensing costs and delaying write-offs. Real earnings management involves changes in firms' strategies including, for example, reducing advertising and R&D spending and increasing trade stuffing ([36]).
To examine the prevalence of myopic management practices, we interviewed Executive Master of Business Administration students enrolled in a public business school in the southern United States (n = 60; average work experience = 8.1 years; male = 70%). We found that 30% and 39% of the managers reported that their firm had engaged in myopic marketing spending and myopic revenue management, respectively, when the firm's current performance was worse than expected. This evidence provides support for the prevalence of myopic marketing spending and myopic revenue management.
Per GAAP regulations, brands, new technology, new products, and customer loyalty created by advertising and R&D spending are not recognized as tangible assets on firms' balance sheets. Under current accounting rules, firms must expense R&D spending in the fiscal period in which it occurs because its future benefits are not certain ([21]). Managers may expect that, by engaging in myopic marketing spending (i.e., decreasing advertising, R&D, and travel to customers), their firms' current revenues will not decrease but their current profits may increase and their future performance will not suffer. Nonetheless, evidence suggests that myopic marketing spending does decrease firm value and profits (see Table 1).
Firms sometimes engage in early revenue recognition of sales that belong to a future period, if they occur at all ([ 2]; [11]). Myopic revenue management can occur through multiple mechanisms, including increased price promotions to customers and trade stuffing (i.e., offering customers lenient credit terms to pull forward into the current fiscal period). Myopic revenue management through the premature sales of products is a central marketing issue because marketing and sales departments are entrusted with meeting sales targets. Moreover, early sales through trade stuffing, over time, may annoy channel members and lead to increased dissatisfaction ([29]).
The SEC considers such improper revenue recognition, through trade stuffing, as the primary issue contributing to financial restatements and the most frequent cause of large, negative stock market reactions ([50]). Consistent with the SEC's position that myopic revenue management decreases firm performance, [11] report that myopic revenue management decreases firms' stock returns. Yet the extant literature has offered little guidance on how firms can prevent it.
Specifically, the SEC considers myopic management through trade stuffing, the focus of this research, as either negligence or intentional manipulation and a violation of GAAP, because it misleads investors about the firm's financial health. When the SEC detects myopic revenue management through trade stuffing, there are large financial penalties for both firms and their managers. In Table WA1 in Web Appendix 1, we provide a few high-profile examples of such myopic revenue management found by the SEC.
Next, we discuss differences between myopic marketing spending and myopic revenue management. First, the anticipated positive effects of myopic marketing spending and myopic revenue management on firm performance in the current fiscal period occur at different points in financial statements. Myopic marketing spending enters the income statement in its expenses, whereas myopic revenue management enters the income statement in its revenue and enters the balance sheet in its accounts receivables. Second, managers may expect myopic marketing spending to increase profits in the current period, without any decreases in their current-period revenues or in future performance. In contrast, managers may expect myopic revenue management to increase both their firm's revenues and profits in the current period, without any corresponding decreases in its future performance. Third, because marketing spending is discretionary, myopic marketing spending is a firm's strategic choice, with little, if any, scrutiny from regulators. However, as we have noted, when the SEC detects myopic revenue management, it is considered a fraudulent practice.
We develop hypotheses relating marketing department power in a firm to the likelihood of myopic management. We extend ideas from resource dependence theory positing that the power of a department depends on the extent to which its resources ([46]) are crucial for the firm's competitive advantage. Departments with more valuable resources have more power and greater influence on the firm's strategy ([27]). As [16], p. 89) notes, "Some functions will be relatively more powerful than others, that is, they will control more resources and have more influence in the strategy dialogue." Thus, a powerful marketing department in a firm reflects the key role of marketing resources (i.e., brands, channels, and customers) in its strategy and performance ([20]). Furthermore, a powerful finance department reflects the firm's strong commitment to meet investors' expectations, as it is the finance department, including the chief financial officer (CFO), that manages the interface with investors ([30]).
Extending this logic on the roles of the marketing and finance departments to myopic management, we present three features of our hypothesis development. First, because myopic management involves senior finance leadership and the CEO ([ 2]; [30]), we conceptualize the power of the marketing department relative to the finance department. Second, because myopic management is a complex strategic management issue involving all departments and the CEO, we do not believe that a powerful marketing department, by itself, can prevent myopic management. That is, we do not anticipate a main effect of a firm's powerful marketing department on the likelihood of myopic management. Third, we theorize that two internal enablers of power (the presence of a marketing CEO and the firm's power over its customers) and two external enablers of power (analyst coverage [[54]; i.e., the number of financial analysts following the firm] and institutional stock ownership [[ 1]]) will help a powerful marketing department advocate against and prevent myopic management. We provide the theoretical framework in Figure 1. We note that, given the limited research relating marketing leadership to myopic management, a priori, we hypothesize similar effects of marketing department power on both myopic marketing spending and myopic revenue management.
Graph: Figure 1. Marketing department power and myopic management.Notes: For ease of presentation, we do not present the unhypothesized main effects of the interaction effect variables on myopic marketing spending and myopic revenue management, which are included in the model for hypothesis testing.
By virtue of their rank and experience, CEOs influence the decision making of senior executives in different functions in their firms ([43]). We define a marketing CEO as one who had prior marketing experience before being appointed as a CEO. By definition, a marketing CEO has had considerable experience building brands, channels, and customers and will be likely to consider the possible long-term negative effects of myopic management, especially on the firm's customers and brands. However, the marketing CEO is responsible for delivering on revenues and earnings to investors and may feel pressure to engage in real earnings management. Given these opposing effects, we do not anticipate a main effect of a marketing CEO on myopic management. Our interest is in the effect of marketing department power on myopic management when the firm has a marketing CEO.
Will the redundancy of perspectives (protecting brands, channel partners, and customers) of a powerful marketing department and a marketing CEO have no effect on the firm's myopic management? We suggest not, in light of the common knowledge effect in the group decision-making literature ([24]), which argues that the influence of an item of information in group decision making increases as the number of group members with knowledge of it increases. Furthermore, the power of the marketing department is higher when there is a marketing CEO ([51]). Thus, a marketing CEO represents an important internal enabler of power, resulting in a powerful marketing department's advocacy against myopic management.
Accordingly, we propose that a powerful marketing department will advocate for the need to build and protect brands, channel partners, and customer relationships, common knowledge that it shares with the marketing CEO. In such a firm, the powerful marketing department, empowered by a marketing CEO, can effectively emphasize to their senior colleagues, the negative effects of myopic marketing spending and revenue management on the firm's future performance. Accordingly, we anticipate that increasing marketing department power when there is a marketing CEO in a firm will decrease the likelihood of myopic management. Thus, we propose H1:
- H1: In a firm with a marketing CEO, the higher the marketing department power, the less likely it is to engage in (a) myopic marketing spending and (b) myopic revenue management.
Following the definition of power in the literature ([22]), we define the firm's power over its customers as its ability to influence its customers' behaviors. According to resource dependence theory ([46]), a firm's customers and their sales represent crucial resources, essential for its performance. We are interested in the interaction effect between marketing department power and the firm's power over its customers on the likelihood of its myopic management.
When customers are powerful, they may impose their short-term demands on the firm, including on investments in new technologies that may benefit the firm but not directly benefit the customers in the short run ([12]). In contrast, when a firm is powerful relative to its customers, a powerful marketing department's long-term time horizons on resource allocations may be more influential in arguments with senior leaders in the firm, including in decisions related to marketing spending and revenue management. Moreover, when a firm has power over its customers, the greater the value of the marketing department and the greater the marketing department's influence on the firm's strategy and operations ([39]).
In line with these arguments, we expect that when the firm has power over its customers, increasing marketing department power will result in more effective marketing leadership advocacy against myopic management, reducing the likelihood of its occurrence. Thus, we propose H2:
- H2: In a firm that has power over its customers, the higher the marketing department power, the less likely it is to engage in (a) myopic marketing spending and (b) myopic revenue management.
Analyst coverage refers to the number of financial analysts who follow a firm and monitor its performance on behalf of the firm's shareholders ([32]). Financial analysts, trained in finance and accounting, have specialized skills to analyze firms' financial statements and are domain experts in the industries of the firms that they follow. These distinctive characteristics of financial analysts qualify them to be effective monitors of firms' reporting and real earnings management ([54]). This suggests that analysts' private information production and monitoring uncovers fraud ([19]) and reduces real earnings management ([54]). However, other developments in career concerns theory (Christensen and Bower) suggest the opposite. A lack of analysts' forecasts results in lower bonuses for CEOs ([34]). Thus, increasing analyst coverage may increase performance pressure on the firm's managers, leading managers to engage in myopic behavior ([28]) to improve their firm's current performance and their future compensation and career prospects.
We are interested in the effect of a powerful marketing department on myopic management when there is increasing analyst coverage. We propose that greater monitoring from increasing analyst coverage will increase the vigilance of the firm's senior management in ensuring that the firm's actions are not myopic. Furthermore, this increased vigilance may make the senior management more receptive to their powerful marketing department's advocacy against myopic management. Therefore, the power wielded by analysts over the firm may enable a powerful marketing department to advocate effectively against myopic management. Accordingly, we anticipate that increased marketing department power, in conjunction with increasing analyst coverage, will reduce the likelihood of myopic management. Thus, we propose H3:
- H3: The higher a firm's analyst coverage and higher its marketing department power, the less likely it is to engage in (a) myopic marketing spending and (b) myopic revenue management.
A key corporate governance mechanism in U.S. publicly listed firms is institutional ownership of a firm's stocks, with attendant governance rights for these large institutional owners of stock ([17]). Although institutions owned only 10% of publicly listed firms' stocks in 1970, by 2006, they owned more than 60% ([ 1]), suggesting their increasing role in the corporate governance of publicly listed firms.
While institutional investors may have different investment horizons, they demand greater information production and transparency to ensure lower their monitoring costs and maximize portfolio returns ([ 3]). Managers try to attract institutional investors who are likely to have more power and a longer-term investment horizon in the firm. As a result, institutional stock ownership leads to greater public information production and transparency by the firm. Furthermore, the low-cost monitoring resulting from such increased information production can result in the firm's leadership behaving in ways consistent with the needs of institutional investors. This suggests a negative main effect of institutional stock investors on the likelihood of myopic management ([ 7]). However, as with the main effect of analyst coverage (see H3), there may be an opposite effect of institutional stock ownership on myopic management. Specifically, consistent with career concerns theory, there may be increased performance monitoring by institutional investors, which will raise managers' career concerns and increase myopic management.
We are interested in the effect of a powerful marketing department on myopic management when there is increasing institutional stock ownership in the firm. The greater transparency from enhanced information production, resulting from increased institutional stock ownership, will increase the vigilance of the firm's senior leadership ([ 2]). In such a firm, a powerful marketing department may effectively advocate against myopic management. Furthermore, the increased transparency and monitoring resulting from increased institutional stock ownership may also make the firm's senior leadership (e.g., the finance leadership, the CEO) more receptive to the advocacy of the powerful marketing department against myopic management. Therefore, increasing institutional stock ownership of a firm along with increasing marketing department power may decrease the likelihood of myopic management. Thus, we propose H4:
- H4: The higher a firm's institutional stock ownership and the higher its marketing department power, the less likely it is to engage in (a) myopic marketing spending and (b) myopic revenue management.
We test the hypotheses using data on publicly listed firms in the United States between 2000 and 2015. We exclude financial firms because their accounting-based performance measures do not lend themselves to the same interpretation as those of other firms ([44]). Furthermore, we exclude retail, utilities, agriculture, international affairs, and nonoperating establishments because the practices of myopic marketing spending and myopic revenue management do not apply to them. We develop measures of myopic marketing spending and revenue management using data from Compustat annual and quarterly databases.
We collect data on explanatory variables from multiple secondary sources: marketing and other department powers and executive compensation from ExecuComp database, various firm characteristics from Compustat annual database, analyst coverage from I/B/E/S analyst details and summary files, and institutional stock ownership data from Thomson Reuters 13F Institutional Holdings Filings. We also collect data from multiple sources on the functional background of CEOs. We provide the constructs, measures, and data sources in Table 2.
Graph
Table 2. Constructs and Measures: Main Variables.
| Construct | Measure | Data Source |
|---|
| Myopic marketing spending (MY_MS) | Indicator variable: 1 if firm's unanticipated profit is greater than 0 and firm's unanticipated annual advertising and R&D spending is less than 0, and 0 otherwise | Compustat annual data |
| Myopic revenue management (MY_RM) | Indicator variable: 1 if firm's unanticipated profit is greater than 0 and firm's unanticipated accounts receivable in the fourth quarter is greater than 0, and 0 otherwise | Compustat quarterly data |
| Marketing department power (MKT_DP) | As per Feng, Morgan, and Rego (2015) using the (1) proportion of marketing executives in the TMT, (2) marketing executives' compensation relative to the total TMT executives' compensation, (3) hierarchical level of the highest-level marketing TMT executive's job title, (4) the cumulative hierarchical level of all the marketing executives in the TMT, and (5) the number of responsibilities reflected in the marketing TMT executives' job titles, divided by finance department power calculated in the same manner | ExecuComp data |
| Marketing CEO (MKT_CEO) | Indicator variable: 1 if CEO has a marketing background, 0 otherwise | Collected from various data sources |
| Power over customers (PWR_CUST) | Indicator variable: 1 if firm has no major customer, 0 otherwise | Firm 10-Ks, Compustat segments data |
| Analyst coverage (AN_COV) | Natural logarithm of number of analysts covering the firm in the year | I/B/E/S summary files |
| Firm size (SIZE) | Natural logarithm of lagged total assets (annual) | Compustat annual data |
| Firm profit (PRF) | Lagged profit as measured by EBITDA/total assets | Compustat annual data |
| Revenue growth (RG) | Lagged revenue growth as measured by increase in revenue over the previous year | Compustat annual data |
| Just beat earnings (JBE) | Calculated as per Gunny (2010), based on whether a firm's profit or change in profit (net Income/total assets) is greater or equal to 0, but less than.01 | Compustat annual data |
| Auditor quality (AU) | Indicator variable: 1 if a firm's auditor is one of the top four auditing firms, 0 otherwise | Compustat annual data |
| Operations CEO (OPS_CEO) | Indicator variable: 1 if CEO has an operations background, 0 otherwise | Collected from various data sources |
| Engineering CEO (ENG_CEO) | Indicator variable: 1 if CEO has an engineering background, 0 otherwise | Collected from various data sources |
| Other CEO (OTH_CEO) | Indicator variable: 1 if CEO does not have marketing, finance, operation, or engineering backgrounds, 0 otherwise | Collected from various data sources |
| CEO option pay (CEO_SOP) | Proportion of CEO pay from options, lagged | ExecuComp data |
| CEO compensation (CEO_COMP) | Natural logarithm of CEO's total compensation/TMT's total compensation, lagged | ExecuComp data |
| Operations department power (OPS_DP) | Natural logarithm of operations department power, calculated as per Feng, Morgan, and Rego (2015) using (1) proportion of operations executives in the TMT, (2) operations executives' compensation relative to the total TMT executives' compensation, (3) the hierarchical level of the highest-level operations TMT executive's job title, (4) the cumulative hierarchical level of all the operations executives in the TMT, and (5) the number of responsibilities reflected in the operations TMT executives' job titles, lagged | ExecuComp data |
| Engineering department power (ENG_DP) | Natural logarithm of engineering department power, calculated as per Feng, Morgan, and Rego (2015) using (1) proportion of engineering executives in the TMT, (2) engineering executives' compensation relative to the total TMT executives' compensation, (3) the hierarchical level of the highest-level engineering TMT executive's job title, (4) the cumulative hierarchical level of all the engineering executives in the TMT, and (5) the number of responsibilities reflected in the engineering TMT executives' job titles, lagged | ExecuComp data |
| Other department power (OTH_DP) | Natural logarithm of 100 − (marketing department power + finance department power + operations department power + engineering department power), lagged | ExecuComp data |
| Marketing option pay (MKT_SOP) | Natural logarithm of average proportion of option pay in the compensation of marketing executives in the TMT, lagged. Following prior research (Sanders and Hambrick 2007), we calculate all option pay as a two-year average (t − 1 and t − 2) to reflect the fact that options are meant to motivate behavior over multiple years. | ExecuComp data |
| Finance option pay (FIN_SOP) | Natural logarithm of average proportion of option pay in the compensation of finance executives in the TMT, lagged | ExecuComp data |
| Operations option pay (OPS_SOP) | Natural logarithm of average proportion of option pay in the compensation of operations executives in the TMT, lagged | ExecuComp data |
| Engineering option pay (ENG_SOP) | Natural logarithm of average proportion of option pay in the compensation of engineering executives in the TMT, lagged | ExecuComp data |
1 Notes: EBITDA = earnings before interest, tax, depreciation, and amortization.
Following [36], we operationalize whether a firm engages in myopic marketing spending (MY_MS) using an indicator variable. We measure marketing spending using advertising and R&D spending reported in Compustat. Following prior research ([10]; [36]; [45]), we compute the unanticipated profit (ROAit − ) and unanticipated marketing spending (MKTit − ) after scaling the firm's marketing spending by its sales ([36]). The unanticipated profit, unanticipated R&D, and unanticipated advertising reflect the portion of the current budget that cannot be explained by prior period budgets. Following prior literature, we calculate these unanticipated values as residuals of the equation in which we regress current-period values (profits, R&D, and advertising) on previous-period values. We provide details of the estimation approach and the results of this estimation in Web Appendix 2.
Both R&D and advertising are the firm's annual spending values scaled by sales ([36]). The indicator variable takes on a value of 1 (0 otherwise) if the firm has higher-than-expected profit (unanticipated profit greater than 0) and lower-than-expected marketing spending (unanticipated advertising spending less than 0 and unanticipated R&D spending less than 0).[ 5] We found that 15% of firm-years in the sample engaged in myopic marketing spending, which is consistent with [36] finding of 20.7% of firm-years engaging in myopic marketing spending.
We operationalize whether a firm engages in myopic revenue management (MY_RM) through trade stuffing on the basis of the extent to which it offers abnormally lenient credit terms to customers and/or channel partners at the end of the fiscal year. Because firms that engage in trade stuffing ship products to their trade partners without receiving cash, myopic revenue management will increase the number of days of accounts receivables. Our examination of the business press reveals that financial analysts use the metric of days of accounts receivables to detect trade stuffing. As financial analyst Jason Wittes of Brean Capital noted while commenting on trade stuffing at Osiris Therapeutics, "While we have been cautious on the name as a result of elevated DSO's and extended payment terms, indicating the potential for channel stuffing, the belated release of the recent 10-Q gives us more concern, as it shows evidence of aggressive accounting, particularly in regard to revenue recognition. Specifically, there are 3 restatements from distributor relationships" ([14]).
Thus, we operationalize a firm's trade stuffing by the number of days of its accounts receivables in the fourth quarter of the fiscal year scaled by fourth quarter sales, multiplied by 91 days (AC). Again, following prior research, we compute the firm's unanticipated annual profit (ROAit − ) and its unanticipated days of accounts receivables (ACit − ). The unanticipated profit and unanticipated accounts receivable reflect the portion of the current budget that prior period budgets cannot explain. We calculate these unanticipated values as residuals of the equation in which we regress current-period values (profits and accounts receivable) on previous-period values. Using the accounts receivable variable calculated as shown, myopic revenue management takes on a value of 1 (0 otherwise) if the firm has unanticipated profit greater than 0 and unanticipated accounts receivables greater than 0. More firms (34%) in the sample engaged in myopic revenue management than in myopic marketing spending (15%).
The ranges of the myopic marketing spending, advertising, R&D, and profits variables are comparable in size to those reported in the literature ([10]; [36]).
Following [20], we operationalize a firm's marketing department power using five items: ( 1) number of marketing executives on the TMT (job title keywords: customer, marketing, sales, brand, and advertising, etc.) divided by the total number of TMT executives, ( 2) marketing TMT executives' compensation relative to the compensation of all TMT executives, ( 3) hierarchical level of the highest-level marketing TMT executive's job title, ( 4) the cumulative hierarchical levels of all marketing executives in the TMT, and ( 5) the number of responsibilities in the marketing TMT executives' job titles. We scale all five items relative to the industry average (the firm's primary Standard Industrial Classification code). We then combine them using principal components analysis and scale this score between 1 and 100.
In Web Appendix 3 (Tables WA3a and WA3b), we report details of the measures of marketing department power. As we discussed in our theoretical development, the finance department is primarily responsible for and actively engaged in the firm's real earnings management ([ 2]; [30]), and we operationalize marketing department power relative to finance department power (job title keywords: financial, finance, controller, administrative, legal, investor, and accounting; MKT_DP) (M =.48, SD =.61).
We obtained the names of firms' CEOs from the ExecuComp database to construct the marketing CEO (MKT_CEO) variable. We collected data on CEOs' functional backgrounds from LinkedIn, Bloomberg, Equilar, and corporate websites. We used the same job title keywords as listed previously to ascertain evidence of the CEO's marketing experience. We classify a CEO as having a marketing background using a dummy variable of 1 (0 otherwise) if the CEO had marketing experience (23% of CEO-years in the sample).
Firms report major customers that account for more than 10% of their revenues. Our measure of the firm's power over customers (PWR_CUST) is the inverse of the measure of the customer's power over the firm in prior literature ([ 6]). This indicator variable takes on a value of 1 if the firm has no major customers (0 otherwise) (M =.42, SD =.49).
We measure the firm's analyst coverage (AN_COV) by the natural logarithm of the mean number of analysts following the firm in a fiscal year, obtained from the I/B/E/S analyst details and summary files (M = 2.12, SD =.73).
We extract the firm's institutional stock ownership (IN_OW) from Thomson Reuters 13F Institutional Holdings Filings, which contain information on institutional shareholding positions. We measure institutional stock ownership as the proportion of the firm's shares held by institutional investors at the end of the previous fiscal year (M =.76, SD =.18).
We include several control variables in the models for hypothesis testing. We provide details of the control variables in Table 2. We control for the firm's size (SIZE) measured by the logarithm of total assets and profit (PRF) measured by the ratio of earnings before income taxes, depreciation, and amortization (EBITDA) to total assets, and the firm's revenue growth (RG), the change in the firm's sales over its sales in the previous year.
We also control for whether the firm beat analysts' earnings expectations (JBE) on the basis of whether the change in the net income scaled by assets lies between 0 and.01 ([26]); auditor quality on the basis of whether the firm's auditor is a top-four auditing firm (AU); a CEO with an operations (OPS_CEO), engineering (ENG_CEO) or other (OTH_CEO) background; and the CEO's stock option pay (CEO_SOP). We also control for the proportion of the CEO's compensation in the TMT total compensation (CEO_COMP), operations department power (OPS_DP; job title keywords of manufacturing, plant, operations, planning, supply chain, etc.), engineering department power (ENG_DP; job title keywords of research, technology, R&D, engineering, etc.), other department power (OTH_DP; obtained by subtracting the sum of all other department powers from 100), marketing executives' stock option pay (MKT_SOP), finance executives' stock option pay (FIN_SOP), operations executives' stock option pay (OPS_SOP), and engineering executives' stock option pay (ENG_SOP). We use the natural logarithm of department power, proportion of CEO compensation, and department executives' option pay.
The final panel with data on all these variables consists of 5,907 firm-years of 781 unique firms. We provide the descriptive statistics of the key variables, winsorized at 1%, in Table 3. The correlations among the variables are low, as are the variance inflation factors (all of which are below 5), assuaging concern about threats from multicollinearity.
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Table 3. Descriptive Statistics and Correlation Matrix of Key Variables.
| Mean | SD | MY_MS | MY_RM | MKT_DP | MKT_CEO | PWR_CUST | AN_COV | IN_OW |
|---|
| Myopic marketing spending (MY_MS) | .15 | .36 | 1.00 | | | | | | |
| Myopic revenue management (MY_RM) | .34 | .48 | .07*** | 1.00 | | | | | |
| Marketing department power (MKT_DP) | .48 | .61 | .01 | .02* | 1.00 | | | | |
| Marketing CEO (MKT_CEO) | .23 | .42 | .01 | .02 | .00 | 1.00 | | | |
| Power over customers (PWR_CUST) | .42 | .49 | −.04*** | .12*** | .03*** | .01 | 1.00 | | |
| Analyst coverage (AN_COV) | 2.12 | .73 | .01 | −.05*** | .03*** | .03** | .05*** | 1.00 | |
| Institutional stock ownership (IN_OW) | .76 | .18 | .01 | −.10*** | −.04*** | .00 | −.06*** | .21*** | 1.00 |
- 2 *p <.10.
- 3 **p <.05.
- 4 ***p <.01.
Next, we present model-free evidence of the relationship between myopic marketing spending and myopic revenue management and firm value, an outcome important to marketers and C-suite executives. We expect a firm's myopic management behaviors to be negatively associated with its shareholder value, which we measure through its market capitalization. Because market capitalization may represent firm size, merely capturing scale effects, we take its natural logarithm and subtract it from the natural logarithm of the prior period's market capitalization.
Firms that engage in myopic marketing spending have lower firm value compared with those that do not (.033 vs..087, respectively; p <.01), as do firms that engage in myopic revenue management compared with those that do not (.037 vs..100, respectively; p <.01). This evidence, which is consistent with the vast empirical evidence in the literature on the harmful effects of myopic management on firm performance, suggests that the organizational leadership antecedents of myopic marketing spending and myopic revenue management merit scholarly attention.
To test the hypotheses, we estimate the following equations for firm i in year t, with the inclusion of firm-level controls and firm fixed effects to account for unobserved firm heterogeneity and year fixed effects to control for changes in the economic environment. We implement the Hausman test comparing the model estimated using a firm–fixed effects specification with one using a random-effects specification. The Hausman test is significant for both the myopic marketing spending (χ2 = 70.01, p <.01) and myopic revenue management (χ2 = 74.96, p <.01) models suggesting the appropriateness of using firm fixed effects. We estimate the following logit models:
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β0i and γ0i refer to the firm fixed effects, and τt refer to the year fixed effects, β6–9 and γ6–9 are the coefficients of interest, and β10 and γ10 refer to the coefficients of the control variables. A key concern for testing the effects of a firm's marketing department power on its myopic marketing spending and myopic revenue management is endogeneity (i.e., that the explanatory independent variables of interest are correlated with the error term).
Despite including firm-level controls and firm fixed effects, we may not be capturing all factors that affect both a firm's marketing department power and its myopic management. To overcome this endogeneity concern, we use the control function approach. We derive controls for the dependence between the endogenous independent variables and the error term. By including these controls in Equations 1 and 2, we ensure that the endogenous independent variables do not correlate with the error terms, mitigating endogeneity concerns. We do this in two steps. First, we perform an auxiliary estimation with the endogenous variable as the dependent variable and identify a variable that satisfies the exclusion restriction that it correlates with the endogenous independent variable but does correlate with (unobserved) drivers of a firm's myopic management. Second, the predicted residual from the auxiliary estimation provides a control function correction in the model used to test the hypotheses.
We estimate five auxiliary regressions for the five independent variables—the firm's marketing department power, marketing CEO, power over its customers, analyst coverage, and institutional stock ownership—using as the excluded variables, the average of all other firms (except the focal firm) in the same four-digit Standard Industrial Classification industry for each endogenous independent variable. There is empirical precedent for using the industry average of an independent variable as an excluded variable, including for marketing department power ([20]). The identifying assumption is that industry levels of these independent variables are not affected by firm-level idiosyncratic shocks and do not correlate strongly with the residuals in Equations 1 and 2 ([33]).
Theoretical developments suggest both positive and negative relationships between the key independent variables and their respective industry averages. Neoinstitutional theory posits that environments pressure firms to imitate other firms to gain legitimacy ([35]), suggesting a positive relationship. Yet other developments in the strategy literature ([25]) suggest that firms may try to differentiate themselves from industry peers, in which case, the behaviors of the focal firm may be negatively related to the behaviors of peer firms.
Finally, we note that industry averages of explanatory variables meet the exclusion restriction, as it is unlikely that peer firms' decisions relating to these variables would relate to the focal firm's omitted variables affecting myopic management. The auxiliary estimations for the first stage of the control function estimation are as follows:
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3
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4
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5
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6
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In Equation 3 for firm i in period t, the fixed effect represents the firm-specific heterogeneity in marketing department power, represents the effect of industry-average marketing department power (excluding the focal firm) on a firm's marketing department power, represents the year fixed effects, the coefficient vector represents the effects of the control variables, and is a random error term. The same logic applies to Equations 4, 5, 6, and 7 but for the auxiliary regressions for marketing CEO, the firm's power over its customers, analyst coverage, and institutional stock ownership, respectively.
We present the results for the auxiliary regressions for marketing department power, marketing CEO, the firm's power over its customers, analyst coverage, and institutional stock ownership in Columns 1–5 of Table A1 in Appendix A. As we expected, industry-average variables are significant predictors of the focal firm's marketing department power (negative, p <.01), marketing CEO (positive, p <.01), power over its customers (negative, p <.01), analyst coverage (positive, p <.01), and institutional stock ownership (positive, p <.05).
To test the hypotheses, we estimate the second-stage model with the predicted residuals from Equations 3–7. Because we model two types of myopic management that are binary variables correlated with each other (ρ =.07, p <.01), we simultaneously estimate ([41]) the following two logit models using a seemingly unrelated estimation with robust standard errors:
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9
where β0i and γ0i represent firm fixed effects, τt and represent year fixed effects, β6–9 and γ6–9 are the coefficients of interest, β10 and γ10 are the coefficients of the control variables, and and are the effects of the five residuals in the vector from the auxiliary regressions (marketing department power, marketing CEO, the firm's power over its customers, analyst coverage, and institutional stock ownership) on myopic marketing spending and myopic revenue management. To preclude reverse causality, we lag all control variables by one year, excluding whether the firm just beat analysts' earnings expectations, auditor quality, and the CEO's functional background, because they may contemporaneously affect myopic management.
We present the results for the models for myopic marketing spending and myopic revenue management with only the main effects in Columns 1 and 2 of Table 4, respectively. The results in Column 1 indicate that, in isolation, marketing department power has no effect on myopic marketing spending (b = −.233, n.s.). The results in Column 2 indicate that, in isolation, marketing department power has a marginal, negative effect on myopic revenue management (b = −1.042, p <.10).
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Table 4. Marketing Department Power and Myopic Management.
| (1) | (2) | (3) | (4) |
|---|
| Myopic Marketing Spending | Myopic Revenue Management | Myopic Marketing Spending | Myopic Revenue Management |
|---|
| Marketing department power × Marketing CEO (H1a–b) | | | −.555 (.277)** | −.499 (.232)** |
| Marketing department power × Power over customers(H2a–b) | | | −.384 (.171)** | −.305 (.144)** |
| Marketing department power × Analyst coverage (H3a–b) | | | −.252 (.127)** | −.003 (.116) |
| Marketing department power × Institutional stock ownership (H4a–b) | | | −.276 (.428) | −.297 (.425) |
| Main Effects | | | | |
| Marketing department power | −.233 (.590) | −1.042 (.600)* | .917 (.721) | −.566 (.669) |
| Marketing CEO | .095 (.159) | .107 (.148) | 1.278 (3.335) | 3.983 (3.121) |
| Power over customers | −.127 (.174) | −.147 (.143) | −5.240 (2.552)** | −2.501 (1.954) |
| Analyst coverage | .185 (.162) | .504 (.141)*** | −.301 (1.123) | −.950 (1.013) |
| Institutional stock ownership | .938 (.450)** | .974 (.411)** | 2.080 (13.083) | .136 (11.939) |
| Controls | | | | |
| Firm size | −.510 (.151)*** | −.584 (.126)*** | −.272 (.465) | −.171 (.376) |
| Firm profit | .655 (.578) | .910 (.449)** | 1.125 (2.068) | 1.944 (1.738) |
| Firm revenue growth | −.259 (.192) | .215 (.179) | −.207 (.243) | .241 (.230) |
| Just beat earnings | −.259 (.119)** | .110 (.090) | −.320 (.143)** | .055 (.112) |
| Auditor quality | −.763 (.233)*** | .193 (.178) | −.956 (.369)** | −.088 (.295) |
| Operations CEO | .306 (.182)* | −.052 (.146) | .099 (.307) | .047 (.287) |
| Engineering CEO | .095 (.200) | .282 (.183) | .152 (.460) | .720 (.438) |
| Other CEO | −.006 (.186) | −.013 (.163) | .109 (.408) | .380 (.383) |
| CEO option pay | .444 (.279) | .191 (.236) | .402 (.357) | .132 (.327) |
| CEO compensation | .563 (.529) | .477 (.408) | .456 (.767) | .266 (.643) |
| Operations department power | .015 (.071) | −.051 (.054) | .053 (.079) | −.039 (.060) |
| Engineering department power | −.114 (.074) | −.076 (.063) | −.075 (.085) | −.038 (.074) |
| Other department power | −.024 (.120) | −.076 (.115) | .041 (.159) | .072 (.155) |
| Marketing option pay | 1.610 (2.179) | .826 (1.880) | 2.447 (3.147) | 3.384 (2.692) |
| Finance option pay | −.417 (1.408) | −2.652 (1.235)** | −.325 (1.529) | −1.836 (1.428) |
| Operations option pay | −1.167 (2.323) | −.893 (1.876) | −.612 (2.803) | −1.574 (2.412) |
| Engineering option pay | −2.053 (2.278) | −3.107 (2.010) | −1.441 (3.759) | −4.715 (3.259) |
| Residuals from First Stage |
| Marketing department power | .108 (.588) | 1.036 (.599)* | −.029 (.594) | .974 (.590)* |
| Marketing CEO | | | −.893 (3.314) | −3.626 (3.121) |
| Power over customers | | | 5.359 (2.550)** | 2.548 (1.952) |
| Analyst coverage | | | .633 (1.163) | 1.487 (1.020) |
| Institutional stock ownership | | | −.971 (13.109) | .991 (11.952) |
| Firm fixed effects | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes |
| Observations | 5,907 | 5,907 | 5,907 | 5,907 |
| Number of firms | 781 | 781 | 781 | 781 |
| LR chi-square | 730.88 | 126.07 | 753.26 | 142.41 |
| Prob > chi-square | .000 | .000 | .000 | .000 |
- 5 *p <.10.
- 6 **p <.05.
- 7 ***p <.01.
- 8 Notes: Unstandardized parameter estimates and robust standard errors in parentheses.
In Column 3 of Table 4, we present the results for the hypothesized model of myopic marketing spending. The model fit improves with a higher likelihood ratio (LR) chi-square statistic (LR chi-square = 753.26, p <.01) over the model with only the main effects (LR chi-square = 730.88, p <.01). In support of H1a, when there is a marketing CEO in a firm, increasing marketing department power decreases the likelihood of myopic marketing spending (b = −.555, p <.05). As hypothesized in H2a, when the firm has power over its customers, increasing marketing department power decreases the likelihood of myopic marketing spending (b = −.384, p <.05). The results further support H3a, indicating that increasing marketing department power in a firm and increasing analyst coverage decrease the likelihood of myopic marketing spending (b = −.252, p <.05). However, we do not find support for H4a's prediction that increasing marketing department power and increasing institutional stock ownership will decrease the likelihood of myopic marketing spending (b = −.276, n.s.). In terms of unhypothesized main effects of the moderator variables, only the firm's power over its customers decreases the likelihood of myopic marketing spending (b = −5.240, p <.05), which suggests that powerful marketing departments are responsible custodians of customer relationships and do not engage in myopic management when they have power over their customers.
In Column 4 of Table 4, we present the results for the hypothesized model of myopic revenue management. Again, the LR chi-square statistic improves (LR chi-square = 142.41, p <.01) from the model with only the main effects (LR chi-square = 126.07, p <.01). In support of H1b, when the firm has a marketing CEO, increasing marketing department power decreases the likelihood of myopic revenue management (b = −.499, p <.05). As hypothesized in H2b, when the firm has power over its customers, increasing marketing department power decreases the likelihood of myopic revenue management (b = −.305, p <.05). However, the results do not support the predictions of H3b and H4b, respectively, that increasing marketing department power decreases the likelihood of myopic revenue management as either analyst coverage (b = −.003, n.s.) or institutional stock ownership (b = −.297, n.s.) increases.[ 6] Next, we next report additional analyses that examine the robustness of the results.
We first examine whether the firm's absolute marketing department power, without regard to finance department power, affects its myopic marketing spending and myopic revenue management. We reestimate the models using the absolute, rather than relative (to finance), measure of marketing department power in a firm. The results reported in Columns 1 and 2 of Table 5 support only H1.
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Table 5. Falsification Checks for Measure of Marketing Department Power.
| Absolute Marketing Department Power | CMO Presence |
|---|
| (1) | (2) | (1) | (2) |
|---|
| Myopic Marketing Spending | Myopic Revenue Management | Myopic Marketing Spending | Myopic Revenue Management |
|---|
| Marketing department power × Marketing CEO (H1a–b) | −.038 (.016)** | −.024 (.014)* | | |
| Marketing department power × Power over customers (H2a–b) | −.012 (.014) | −.013 (.012) | | |
| Marketing department power × Analyst coverage (H3a–b) | −.007 (.009) | .010 (.009) | | |
| Marketing department power × Institutional stock ownership (H4a–b) | −.016 (.034) | −.037 (.030) | | |
| CMO presence × Marketing CEO | | | 1.065 (.610)* | .685 (.649) |
| CMO presence × Power over customers | | | .794 (.493) | .605 (.486) |
| CMO presence × Analyst coverage | | | −.248 (.372) | −.353 (.319) |
| CMO presence × Institutional stock ownership | | | −.111 (1.501) | .242 (1.367) |
| Firm fixed effects | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes |
| Observations | 5,944 | 5,944 | 5,944 | 5,944 |
| Number of firms | 787 | 787 | 787 | 787 |
| LR chi-square | 753.42 | 138.14 | 752.6 | 131.73 |
| Prob > chi-square | .000 | .000 | .000 | .000 |
- 9 *p <.10.
- 10 **p <.05.
- 11 ***p <.01.
- 12 Notes: Unstandardized parameter estimates and robust standard errors in parentheses. The models include variables of the main effects, control variables, and the errors from the control function estimation, as in Table 4.
We next examine whether the results hold with CMO presence in the firm ([42]) instead of marketing department power in the model. We measure CMO presence in a firm as 1 if at least one marketing member is on the firm's TMT that year, and 0 otherwise. We note that the correlation between marketing department power and the presence of a CMO is low (ρ =.190, p <.01). We report these results using the CMO instead of marketing department power in Columns 3 and 4 of Table 5, which do not replicate the results in Table 4.
In results not reported here (available upon request from the authors), the estimation results support H1–H3a in a sample with the subset of firms for which advertising and R&D spending data are reported. Also in results not reported here, we estimate the model for hypothesis testing on a sample of only B2B firms ([49]). Consistent with the results in Columns 3 and 4 of Table 4, the results support H1–H3a. Next, we estimate the model using a sample that includes retail firms. The results support H1 and H2 but do not support H3a. Finally, we report results using a sample excluding observations from 2008 (during the Great Recession). Again, consistent with results reported in Table 4, the results support H1–H3a. Overall, the results are robust and support the hypotheses.
Myopic practices can occur at all levels of the organizations—at the very top, where resource allocation and investment decisions are made, and at the very end of the channel, where consumer interactions occur. ([36], p. 596)
Myopic management occurs frequently in business practice, negatively affects firm performance, and sometimes may be in violation of GAAP rules. Yet prior research has overlooked the organizational leadership drivers of myopic management and, specifically, the role of senior marketing leadership, the focus of this work. We conclude with a discussion of the article's theoretical contributions, managerial implications, and opportunities for future research.
First, our finding that powerful marketing departments are effective in preventing myopic management is consistent with [ 2], who find that sales managers are responsible in protecting customer accounts. Taken together, these findings identify myopic management as a mechanism by which powerful marketing leadership can improve firm performance.
In a review article, [38], p. 16) argue that while there is agreement that a strong marketing function contributes to firm value, it would be useful to "offer a deeper analysis of how the aforementioned mechanisms [i.e., marketing departments] interrelate to influence firm performance....Such an analysis could help marketing departments identify the most promising ways to contribute to firms." Our research addresses their call for research and identifies contingencies by which marketing department power can improve firm performance by preventing myopic management.
Second, our findings on the contingent ability of marketing department power to prevent myopic management highlights the top-down effect of marketing leadership on the firms' TMTs in improving firm performance. This finding, which highlights the importance of marketing leadership in corporate governance, is consistent with recent developments on the role of marketing-experienced board members ([52]) and the presence of a customer on B2B firms' boards of directors ([ 5]) in improving firm performance. Further research examining the effects of senior marketing leadership (TMTs, boards of directors, CEOs) on other firm strategies and outcomes will be useful extensions to this work.
Third, the lack of support for the main effect of marketing department power, absolute marketing department power, and the presence of a CMO in the firm on preventing myopic management suggests that neither marketing department power nor CMO presence, by themselves, do not necessarily empower marketing department's voice in the firm with respect to preventing myopic management. There appear to be other factors at play. We conjecture that this may be because myopic management directly affects firms' financial performance, which is under the purview of the CEO and the finance department.
Fourth, support for the negative interaction effects between increasing marketing department power and ( 1) a marketing CEO, ( 2) the firm's power over its customers, and ( 3) increasing analyst coverage indicate that internal and external enablers of power can influence the power of subunits (marketing department). These findings on the role of powerful marketing departments extend the emergent literature on marketing leadership ([20]; [23]). The findings do not support an interaction effect between marketing department power and institutional stock ownership on preventing myopic management. We conjecture that this may be because marketing departments are unable to advocate to institutional investors, who primarily liaise with CFOs and CEOs ([ 7]).
Fifth, the incidence of myopic revenue management in our sample (34%) is higher than myopic marketing spending (15%), suggesting its widespread prevalence in practice. However, much previous research has focused on myopic marketing spending. Against this background, our study is the first large-scale simultaneous investigation of myopic marketing spending and myopic revenue management, providing a complete picture of the drivers of firms' myopic management practices.
Finally, by providing evidence on the leadership drivers of myopic revenue management, we contribute to the literature on the theory of myopic management (e.g., [ 8]; [36]) which has hitherto primarily focused on the effects and drivers of myopic marketing spending, overlooking myopic revenue management, a source of risk exposure for firms and their managers.
We next examine the size of the effects of marketing department power on myopic management behaviors to assess the findings' practical significance. Drawing on our parameter estimates (Table 4), we compute the decrease in the likelihood of myopic marketing spending and myopic revenue management as marketing department power increases. These estimations (presented in Table 6) suggest that, when the firm has a marketing CEO, a unit increase in marketing department power decreases the likelihood of myopic marketing spending by 7.13% and decreases the likelihood of myopic revenue management by 8.64%. Similarly, when the firm has power over its customers, a unit increase in marketing department power decreases the likelihood of myopic marketing spending by 5.50% and decreases the likelihood of myopic revenue management by 6.03%. Finally, a unit increase in marketing department power combined with a unit increase in analyst coverage decreases the likelihood of myopic marketing spending by 3.94%.
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Table 6. Effect of Marketing Department Power on Myopic Marketing Behaviors.
| Drivers of Myopic Management | Percentage Change in Probability |
|---|
| Marketing CEO → Myopic marketing spending | −7.13% |
| Marketing CEO → Myopic revenue management | −8.64% |
| Power over customers → Myopic marketing spending | −5.50% |
| Power over customers → Myopic revenue management | −6.03% |
| Analyst coverage → Myopic marketing spending | −3.94% |
13 Notes: We compute the decrease in probability of myopic marketing spending and myopic revenue management for a unit increase in each variable, at the mean value of all other variables.
This study's findings generate actionable guidelines for senior managers aiming to prevent myopic management. First, the findings that highlight that the responsible nature of powerful marketing departments may be useful to senior marketing executives in strengthening the case for providing marketing managers a seat at the C-suite table.
Second, the findings suggest that a marketing CEO, the firm's power over its customers, and high analyst coverage can enable powerful marketing departments to avoid myopic management. In doing so, senior marketing executives, whom some analysts ([48]) consider profligate spenders, can actually help prevent myopic management.
Third, these findings present guidance on TMT appointments. Although TMT appointments are driven by corporate governance considerations and not just by the need to prevent myopic management, boards of directors can consider the benefits of appointing powerful marketing executives on firms' TMTs in helping prevent myopic management (under the contingencies in the supported interactions).
Finally, myopic revenue management is in violation of GAAP rules aimed to prevent managers from engaging in behaviors that distort their firms' fiscal health and mislead investors. The SEC's detection of trade stuffing by a firm results in significant financial and legal penalties for the firm and its managers. Increasing the power of the marketing department when ( 1) there is a marketing CEO, ( 2) the firm has power over its customers, and ( 3) there is high analyst coverage will prevent myopic management, reducing the firm's risk exposure.
First, in this research on marketing leadership advocacy against myopic management, we focused on the effects of marketing department power in conjunction with other firm characteristics. Can marketing department advocacy increase marketing spending to the point of diminishing returns and result in more caution than warranted in revenue management? Future research examining this issue would generate insights on potential downsides of powerful marketing departments. Given the paucity of work on the organizational leadership drivers of myopic management, we hypothesized similar effects of marketing department power on both myopic marketing spending and myopic revenue management. However, because myopic marketing spending and myopic revenue management are conceptually different, it would be useful to develop grounded theory (e.g., interviews of senior executives) followed by empirical testing of different drivers of myopic marketing spending and myopic revenue management. Moreover, in measuring the firm's power over its customers, following empirical precedent in the literature ([ 6]), we used the inverse of having a powerful customer. Future research using more fine-grained measures of the firm's power over its customers would constitute a useful extension to this work.
Second, a hybrid myopic management practice to increase sales in the current period (myopic revenue management) is to offer price discounts/sales promotions (myopic marketing spending) ([11]). However, information on product-level price discounts/sales promotion spending is not readily available in public databases. Future research combining surveys of senior managers on the drivers of myopic price discounts/sales promotion spending would be a useful extension to this work.
Third, to test the hypotheses, we used secondary data that precluded consideration of organizational factors (e.g., differentiators vs. cost leaders). Future research relating firms' marketing strategy characteristics to myopic management, using primary data (including surveys of senior executives) and other downstream outcomes (e.g., patents) would be beneficial.
In conclusion, this article provides insights on the organizational contingencies of when powerful marketing leadership can help prevent myopic management. In doing so, this research highlights yet another mechanism—myopic management prevention—by which marketing departments can improve firm performance.
Supplemental Material, DS_10.1177_0022242919831993 - With Power Comes Responsibility: How Powerful Marketing Departments Can Help Prevent Myopic Management
Supplemental Material, DS_10.1177_0022242919831993 for With Power Comes Responsibility: How Powerful Marketing Departments Can Help Prevent Myopic Management by Raji Srinivasan and Nandini Ramani in Journal of Marketing
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Table A1. Estimates from First Stage Control Function Regression.
| Construct | Marketing Department Power | Marketing CEO | Power over Customers | Analyst Coverage | Institutional Stock Ownership |
|---|
| Industry average excluding focal firm | −.275 (.025)***a | .077 (.019)*** | −.100 (.020)*** | .159 (.014)*** | .044 (.018)** |
| Controls | | | | | |
| Firm size | −.076 (.016)*** | −.003 (.009) | .026 (.009)*** | .224 (.010)*** | .026 (.004)*** |
| Firm profit | .091 (.067) | −.041 (.040) | .069 (.039)* | .407 (.042)*** | .143 (.015)*** |
| Firm revenue growth | .035 (.027) | .003 (.016) | .009 (.016) | .018 (.017) | −.012 (.006)** |
| Just beat earnings | .026 (.015)* | −.001 (.009) | −.007 (.009) | −.018 (.009)* | −.006 (.003) |
| Auditor quality | −.045 (.035) | −.048 (.021)** | −.047 (.020)** | .046 (.022)** | −.012 (.008) |
| Operations CEO | .009 (.023) | −.075 (.014)*** | −.049 (.013)*** | −.023 (.014) | −.004 (.005) |
| Engineering CEO | .012 (.025) | −.125 (.015)*** | −.012 (.015) | −.013 (.016) | .005 (.006) |
| Other CEO | −.014 (.021) | −.110 (.013)*** | .002 (.013) | −.016 (.014) | .001 (.005) |
| CEO option pay | .011 (.039) | .023 (.024) | −.005 (.023) | .020 (.025) | .018 (.009)** |
| CEO compensation | .050 (.065) | .072 (.039)* | −.018 (.038) | .047 (.041) | .037 (.015)** |
| Operations department power | −.009 (.009) | .001 (.005) | .008 (.005) | −.003 (.006) | .002 (.002) |
| Engineering department power | −.008 (.009) | −.008 (.006) | .009 (.006) | −.005 (.006) | .002 (.002) |
| Other department power | −.052 (.015)*** | −.031 (.009)*** | .003 (.009) | .021 (.010)** | .000 (.003) |
| Marketing option pay | 2.006 (.231)*** | −.583 (.140)*** | .077 (.137) | .138 (.146) | .026 (.053) |
| Finance option pay | −1.133 (.161)*** | −.034 (.098) | −.049 (.095) | .492 (.102)*** | .035 (.037) |
| Operations option pay | −.203 (.324) | .434 (.196)** | .168 (.192) | .384 (.204)* | −.025 (.074) |
| Engineering option pay | .033 (.331) | .722 (.201)*** | .218 (.196) | .400 (.209)* | .113 (.075) |
| Intercept | 1.633 (.135)*** | .469 (.081)*** | .377 (.080)*** | .249 (.090)*** | .380 (.033)*** |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| Observations | 6,962 | 6,996 | 6,996 | 6,996 | 6,996 |
| Number of firms | 1,081 | 1,083 | 1,083 | 1,083 | 1,083 |
| F-statistic | 21.72 | 7.64 | 4.67 | 47.05 | 53.75 |
| Prob < F | .000 | .000 | .000 | .000 | .000 |
| Overall R-square | .102 | .022 | .004 | .533 | .060 |
- 14 *p <.10.
- 15 **p <.05.
- 16 ***p <.01.
- 17 Notes: Unstandardized parameter estimates and standard errors in parentheses.
Footnotes 1 Associate EditorNeil Morgan served as associate editor for this article.
2 Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
3 FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.
4 Online supplement: https://doi.org/10.1177/0022242919831993
5 1To avoid a large drop in sample size because of missing values of advertising and R&D expenditure, we set a value of.0001 for these missing values. In our sample, several firms that report R&D expenditures do not report advertising expenditures. Because these firms can use their R&D expenditures to engage in myopic management, we include them in our sample by setting missing values to.0001. The results are robust when we set missing values of advertising and R&D expenditures to 0, as we subsequently report.
6 2Following the suggestion of an anonymous reviewer, we examined and found that the tests of the hypotheses are robust to the exclusion of profit and revenue growth as control variables in the regression models.
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Record: 242- Within-Seller and Buyer–Seller Network Structures and Key Account Profitability. By: Gupta, Aditya; Kumar, Alok; Grewal, Rajdeep; Lilien, Gary L. Journal of Marketing. Jan2019, Vol. 83 Issue 1, p108-132. 25p. 2 Diagrams, 4 Charts, 1 Graph. DOI: 10.1177/0022242918812056.
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